diff --git "a/5227.jsonl" "b/5227.jsonl" new file mode 100644--- /dev/null +++ "b/5227.jsonl" @@ -0,0 +1,1414 @@ +{"seq_id":"19892297491","text":"from django.contrib.auth.forms import UserCreationForm\nfrom django import forms\nfrom django.contrib.auth.models import User, Permission\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.apps import apps\n\n\nclass RegistrationForm(UserCreationForm):\n \"\"\"\n This class will create a form for registration\n \"\"\"\n email = forms.EmailField()\n\n class Meta:\n \"\"\"\n This class will define the meta data for RegistrationForm\n \"\"\"\n model = User\n fields = [\"username\", \"email\", \"password1\", \"password2\"]\n\n def save(self, commit=True):\n \"\"\"\n While saving the user we need to make sure that the registered user should have viewing access\n of our models and it should be a staff user so that it can access admin page.\n Arguments:\n commit {bool} -- True\n Returns:\n object -- User object\n \"\"\"\n user = super(RegistrationForm, self).save(commit=False)\n user.is_staff = True\n if commit:\n user.save()\n app_models = apps.get_app_config(\"email_scheduler\").get_models()\n for model in app_models:\n permission_codename = \"view_\" + model._meta.model_name\n content_type = ContentType.objects.get_for_model(model)\n permission = Permission.objects.get(\n codename=permission_codename, content_type=content_type)\n user.user_permissions.add(permission)\n return user\n","repo_name":"divyang02/MailerOwl","sub_path":"apps/registration/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"8316896543","text":"import logging\n\nfrom .components import Individual\n\nlog = logging.getLogger(__name__)\n\n\nclass OperatorSet:\n \"\"\" Provides a thin layer for ea operators for logging, callbacks and safety. \"\"\"\n\n def __init__(\n self,\n mutate,\n mate,\n create_from_population,\n create_new,\n compile_,\n eliminate,\n evaluate_callback,\n max_retry=50,\n completed_evaluations=None,\n ):\n \"\"\"\n\n :param mutate:\n :param mate:\n :param create:\n :param create_new:\n \"\"\"\n\n self._mutate = mutate\n self._mate = mate\n self._create_from_population = create_from_population\n self._create_new = create_new\n self._compile = compile_\n self._safe_compile = None\n self._eliminate = eliminate\n self._max_retry = max_retry\n self._evaluate = None\n self._evaluate_callback = evaluate_callback\n self._completed_evaluations = completed_evaluations\n\n def evaluate(self, *args, **kwargs):\n evaluation = self._evaluate(*args, **kwargs)\n if self._evaluate_callback:\n #print(\"call callback from ops\")\n self._evaluate_callback(evaluation)\n return evaluation\n\n def wait_next(self, async_evaluator):\n future = async_evaluator.wait_next()\n if future.result is not None:\n evaluation = future.result\n if self._evaluate_callback is not None:\n self._evaluate_callback(evaluation)\n\n elif future.exception is not None:\n log.warning(f\"Error raised during evaluation: {str(future.exception)}.\")\n return future\n\n def try_until_new(self, operator, *args, **kwargs):\n for _ in range(self._max_retry):\n individual = operator(*args, **kwargs)\n if str(individual.main_node) not in self._completed_evaluations:\n return individual\n else:\n log.debug(f\"50 iterations of {operator.__name__} did not yield new ind.\")\n # For progress on solving this, see #11\n return individual\n\n def mate(self, ind1: Individual, ind2: Individual, *args, **kwargs):\n def mate_with_log():\n new_individual1, new_individual2 = ind1.copy_as_new(), ind2.copy_as_new()\n self._mate(new_individual1, new_individual2, *args, **kwargs)\n new_individual1.meta = dict(parents=[ind1._id, ind2._id], origin=\"cx\")\n return new_individual1\n\n individual = self.try_until_new(mate_with_log)\n return individual\n\n def mutate(self, ind: Individual, *args, **kwargs):\n def mutate_with_log():\n new_individual = ind.copy_as_new()\n mutator = self._mutate(new_individual, *args, **kwargs)\n new_individual.meta = dict(parents=[ind._id], origin=mutator.__name__)\n return new_individual\n\n ind = self.try_until_new(mutate_with_log)\n return ind\n\n def individual(self, *args, **kwargs):\n expression = self._create_new(*args, **kwargs)\n if self._safe_compile is not None:\n compile_ = self._safe_compile\n else:\n compile_ = self._compile\n ind = Individual(expression, to_pipeline=compile_)\n ind.meta[\"origin\"] = \"new\"\n # print(\"Voy a imprimir el individuo en operator_set.py\")\n # print(ind)\n return ind\n\n def create(self, *args, **kwargs):\n return self._create_from_population(self, *args, **kwargs)\n\n def eliminate(self, *args, **kwargs):\n return self._eliminate(*args, **kwargs)\n","repo_name":"israelCamperoJurado/GAMA_generalized_island_model_AutoML","sub_path":"gama/genetic_programming/operator_set.py","file_name":"operator_set.py","file_ext":"py","file_size_in_byte":3592,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"21580498370","text":"import argparse\nimport sys\nsys.path.append('/mnt/efs/project/') \nimport random\nfrom datetime import datetime\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\n\n#BartBase\nfrom transformers import BartTokenizer, BartForConditionalGeneration\n\n#Dataloader\nfrom loaders.dataloader_pt import BartBatcher\ntorch_device = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n# smoother\nfrom scipy.ndimage import gaussian_filter\n\n# peaks\nfrom scipy.signal import find_peaks\n\n# numpy\nimport numpy as np\n\n\ndef train(args):\n tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-xsum')\n model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-xsum')\n\n # load from checkpoint\n if args.checkpoint != None:\n model.load_state_dict(torch.load(args.checkpoint)[\"model\"])\n print(\"Loaded model from {} checkpoint\".format(args.checkpoint))\n\n model_config = model.config\n #print(model_config)\n if torch_device == 'cuda': model.cuda()\n print(\"#parameters:\", sum(p.numel() for p in model.parameters() if p.requires_grad))\n\n # Training Data\n batcher = BartBatcher(tokenizer, model.config, args.train_path, torch_device, filtered=True)\n\n maxpd = []\n smoothpd = []\n mepd = []\n anotpd = []\n percents = []\n percentsa = []\n while batcher.epoch_counter < 1:\n # get a batch\n input_ids, attention_mask, target_ids, target_attention_mask, idns, scores = batcher.get_a_batch(batch_size=1, idn=True)\n shifted_target_ids, shifted_target_attention_mask = batcher.shifted_target_left(target_ids, target_attention_mask)\n\n # BART forward\n with torch.no_grad():\n x = model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n decoder_input_ids=target_ids,\n decoder_attention_mask=target_attention_mask,\n output_attentions=True, # --> can add this in to get attention weights, can then use cross attention weights in loss function\n )\n\n toplot = torch.mean(x.cross_attentions[-1].squeeze(),dim=0).detach().cpu().numpy()\n toplot = toplot[:,1:-2] # get rid of first and last columns\n supressed = np.copy(toplot)\n supressed[supressed<0.01] = 0\n maxs = np.amax(toplot, axis=0)\n smoothed_maxs = gaussian_filter(maxs, sigma = 2) # can also take maxs, smooth maxs, logged, smooth logged of the supressed toplot\n logged = [ np.log(x+1) for x in maxs]\n smooth_logged = gaussian_filter(logged, sigma = 2)\n nm = np.linalg.norm(maxs)\n norm_max = maxs/nm\n ns = np.linalg.norm(smoothed_maxs)\n norm_smooth = smoothed_maxs / ns\n\n #import pdb; pdb.set_trace()\n # IPD OF MAXS\n peakl, _ = find_peaks(logged, width=1)\n if sum(peakl) > 0:\n peaksl = peakDif(peakl)\n if len(peaksl) !=0:\n #pl = sum(peaksl)/len(peaksl)\n pl = max(peaksl)\n #pl = np.median(peaksl)\n else:\n pl = 0\n else:\n pl = len(logged)\n \n peaksmoothl, _ = find_peaks(smooth_logged, width=1)\n if sum(peaksmoothl) > 0:\n peaksmoothsl = peakDif(peaksmoothl)\n if len(peaksmoothsl) !=0:\n #psl = sum(peaksmoothsl)/len(peaksmoothsl)\n psl = max(peaksmoothsl)\n #psl = np.median(peaksmoothsl)\n else:\n psl = 0\n else:\n psl = len(smooth_logged)\n\n # GET EXAMPLE\n example = scores[0]\n me = example[\"me\"]\n wanot = example[\"with_anot\"]\n\n # IPD OF EXAMPLE\n nme = np.linalg.norm(me)\n norm_me = me / nme\n na = np.linalg.norm(wanot)\n norm_wanot = wanot / na\n peakme, _ = find_peaks(norm_me)\n if sum(peakme) > 0:\n peakmes = peakDif(peakme)\n if len(peakmes) !=0:\n pm = sum(peakmes)/len(peakmes)\n else:\n pm = 0\n else:\n pm = len(norm_me)\n peakanot, _ = find_peaks(norm_wanot)\n if sum(peakanot) > 0:\n peakanots = peakDif(peakanot)\n if len(peakanots) != 0:\n pa = sum(peakanots)/len(peakanots)\n else:\n pa = 0\n else:\n pa = len(norm_wanot)\n\n # PERCENTAGE OF EXAMPLE\n percent = sum(me) / len(me)\n percent_wanot = np.count_nonzero(wanot) / len(wanot)\n\n # ADD TO ARRAYS\n maxpd.append(pl)\n smoothpd.append(psl)\n mepd.append(pm)\n anotpd.append(pa)\n percents.append(percent)\n percentsa.append(percent_wanot)\n\n return maxpd, smoothpd, mepd, anotpd, percents, percentsa\n \n \ndef peakDif(arr):\n result = []\n for i in range(len(arr) - 1) :\n diff = abs(arr[i] - arr[i + 1])\n result.append(diff)\n return result\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-train_path\", default=\"Data/\", type=str, nargs='+')\n parser.add_argument(\"-checkpoint\", default=None, type=str)\n \n args = parser.parse_args()\n \n # GET CORRELATIONS\n MPD, SPD, MEPD, APD, P, PA = train(args)\n #print(MV, SV, MVN, SVN, MEV, AV)\n\n # me pct\n mp = np.corrcoef(MPD, P)\n sp = np.corrcoef(SPD, P)\n\n # anot pct\n mpa = np.corrcoef(MPD, PA)\n spa = np.corrcoef(SPD, PA)\n\n print( mp, sp, mpa, spa)\n #print(mpn, mpna)\n","repo_name":"amazon-science/aws-dialogue-qa-pair-generation","sub_path":"coverage/IPD.py","file_name":"IPD.py","file_ext":"py","file_size_in_byte":5538,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"74144582760","text":"# -*- coding: utf-8 -*-\n\nimport datetime\n\ndef exercice():\n\n#----------affectation de variables--------\n# sous Python le typage est dynamique voir ci-dessous\n librairie = \"Montlhéry\" #type string\n hote = \"localhost\"\n heure_ouverture = datetime.time(9, 00) #type date\n heure_fermeture = datetime.time(16, 30)\n nbre_livres = 120 #type int\n port = 12800\n prix_adherent = 52.68 #type float\n reduction = 20.68\n\n#affectation multiple et de type different\n init_cpt, ouverture = 0, True #type int et Boolean\n prix_etudiant = prix_adherent - reduction\n\n\n#affichage d'une donnée avec la fonction print()\n print(\"affiche le prix étudiant : \", prix_etudiant, \"€\")\n\n return librairie, heure_fermeture, nbre_livres, prix_etudiant, ouverture\n\n","repo_name":"nido91/cours_python","sub_path":"cours/introduction/variables.py","file_name":"variables.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"33858339108","text":"from tkinter import *\nfrom tkinter import ttk, messagebox\n\nclass Iva:\n def __init__(self):\n ventana = Tk()\n ventana.geometry('300x280')\n ventana.configure(bg='white')\n ventana.title('Ejercicio 2 Unidad4')\n self.__option = IntVar()\n self.__iva = StringVar()\n self.__precioIva = StringVar()\n opts = { 'padx': 10, 'pady': 10 }\n ttk.Label(ventana, text='Cálculo de IVA',background='lightblue',anchor='center').grid(row=0,column=0,columnspan=2,sticky='nswe',ipady=15,ipadx=110)\n ttk.Label(ventana,text='Precio sin IVA',background='white').grid(row=1,column=0,**opts)\n ttk.Label(ventana,text='IVA',background='white').grid(row=4,column=0,**opts)\n ttk.Label(ventana,text='Precio con IVA',background='white').grid(row=5,column=0,**opts)\n \n Radiobutton(ventana,background='white',highlightbackground='white',text='IVA 21 %',value=0,variable=self.__option).grid(row=2,column=0,padx=5,pady=5)\n Radiobutton(ventana,background='white',highlightbackground='white',text='IVA 10.5 %',value=1,variable=self.__option).grid(row=3,column=0,padx=5,pady=5)\n self.__precio = ttk.Entry(ventana,textvariable='')\n self.__precio.grid(row=1,column=1,padx=5)\n ttk.Entry(ventana,textvariable=self.__iva).grid(row=4,column=1,pady=5,padx=5)\n ttk.Entry(ventana,textvariable=self.__precioIva).grid(row=5,column=1,pady=5,padx=5)\n\n Button(ventana, text='Calcular IVA',command=self.calcular,background='lightgreen',\n highlightbackground='black',highlightthickness=1).grid(row=6,column=0,padx=5)\n Button(ventana, text='Salir',command=ventana.destroy,background='red',highlightbackground='black',\n highlightthickness=1,foreground='white').grid(row=6,column=1,padx=5,pady=15)\n ventana.mainloop()\n\n def calcular(self):\n try:\n precio=int(self.__precio.get())\n if self.__option.get() == 0:\n self.__precioIva.set(precio+(precio * 0.21))\n self.__iva.set(precio * 0.21)\n else:\n self.__precioIva.set(precio+(precio * 0.105))\n self.__iva.set(precio * 0.105)\n return\n except:\n messagebox.showerror(title='Error de Formulario',message='Completa precio sin Iva con algun número')\n return","repo_name":"nicoescudero/PythonOOP_UNSJ","sub_path":"Unidad4/Ejercicio2/iva.py","file_name":"iva.py","file_ext":"py","file_size_in_byte":2366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35922887041","text":"class Solution:\n def containsDuplicate(self, nums: List[int]) -> bool:\n hmap = {}\n \n for i,n in enumerate(nums): #i=index,n=nums\n if n in hmap:\n return True\n hmap[n] = i\n return False\n \n \n\n #Time complexity: O(n). We do search() and insert() for nnn times and each operation takes constant time.\n\n #Space complexity: O(n). The space used by a hash table is linear with the number of elements in it.\n","repo_name":"SanketRevadigar/LeetCode","sub_path":"217-contains-duplicate/217-contains-duplicate.py","file_name":"217-contains-duplicate.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"33615926978","text":"import time\nfrom machine import PWM, Pin, UART\nimport json\n\nMIN_DUTY = 1700\nMAX_DUTY = 8200\n\npinoServo = 15\nservoPwm = PWM(Pin(pinoServo))\nservoPwm.freq(50)\n\npinoMotor = 14\nmotorPwm = PWM(Pin(pinoMotor))\nmotorPwm.freq(50)\n\nbluetoothModule = UART(0, 9600)\n\ndef definirValoresIniciais():\n ledImbutido = 25\n\n Pin(ledImbutido).low()\n servoPwm.duty_u16(obterDutyDirecao(90))\n\ndef obterDutyDirecao(direcao):\n intervaloPorGrau = (MAX_DUTY - MIN_DUTY) / 180\n return int(intervaloPorGrau * direcao) + MIN_DUTY\n\ndef obterDutyMotor(velocidade):\n intervaloPorGrau = (MAX_DUTY - MIN_DUTY) / 180\n return int(intervaloPorGrau * velocidade) + MIN_DUTY\n\ndef tratarDadosBluetooth(dados):\n return str(dados).replace('\\\\', '').replace(\"b'{\", \"{\").replace(\"}'\", '}')\n\ndef obterDadoBluetooth():\n dadosObtidos = bluetoothModule.read()\n dadosObtidos = tratarDadosBluetooth(dadosObtidos)\n return json.loads(dadosObtidos)\n\ndefinirValoresIniciais()\nwhile True:\n if bluetoothModule.any():\n try:\n command = obterDadoBluetooth()\n servoPwm.duty_u16(obterDutyDirecao(command['direcao']))\n motorPwm.duty_u16(obterDutyMotor(command['velocidade']))\n except:\n pass\n","repo_name":"JeanZap/BluetoothRC","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9871267316","text":"from __future__ import annotations\n\nfrom flightSim.model import compute_performance\n\n\ndef reset_status_with_fpl(pdata, fpl):\n pdata.alt = fpl.min_alt\n compute_performance(pdata.acType, fpl.min_alt, pdata.performance)\n performance = pdata.performance\n pdata.hSpd = performance.normCruiseTAS\n pdata.vSpd = 0\n\n [wpt0, wpt1] = fpl.routing.waypointList[:2]\n pdata.location.set(wpt0.location)\n pdata.heading = wpt0.bearing(wpt1)\n pdata.phase = 'EnRoute'\n\n\ndef update_status(phsyData, guidance):\n move_horizontal(phsyData, guidance)\n move_vertical(phsyData, guidance)\n update_performance(phsyData)\n\n\ndef move_horizontal(pdata, guidance):\n preHSpd = pdata.hSpd\n if pdata.hSpd > guidance.targetHSpd:\n dec = pdata.acType.normDeceleration\n pdata.hSpd = max(preHSpd - dec * 1, guidance.targetHSpd)\n elif pdata.hSpd < guidance.targetHSpd:\n acc = pdata.acType.normAcceleration\n pdata.hSpd = min(preHSpd + acc * 1, guidance.targetHSpd)\n \n performance = pdata.performance\n diff = (guidance.targetCourse - pdata.heading) % 360\n diff = diff-360 if diff > 180 else diff\n if abs(diff) > 90:\n turn = performance.maxTurnRate * 1\n else:\n turn = performance.normTurnRate * 1\n diff = min(max(-turn, diff), turn)\n pdata.heading = (pdata.heading + diff) % 360\n\n pdata.location.move(pdata.heading, (preHSpd + pdata.hSpd) * 1 / 2)\n\n\ndef move_vertical(pdata, guidance):\n diff = guidance.targetAlt - pdata.alt\n\n if diff < 0:\n v_spd = max(-pdata.performance.normDescentRate * 1, diff)\n elif diff > 0:\n v_spd = min(pdata.performance.normClimbRate * 1, diff)\n else:\n v_spd = 0\n\n pdata.alt += v_spd\n pdata.vSpd = v_spd\n\n\ndef update_performance(pdata):\n if pdata.performance.altitude != pdata.alt:\n compute_performance(pdata.acType, pdata.alt, pdata.performance)\n","repo_name":"Lydia-Yahuhe/macr_maddpg","sub_path":"flightSim/aircraft/flight_mechanics.py","file_name":"flight_mechanics.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"11562685585","text":"from collections import OrderedDict\nimport copy\n\nfrom astropy import constants as const\nfrom astropy.time import Time\nimport astropy.units as u\nimport numpy as np\nfrom numpy.testing import assert_allclose\n\nfrom ..pystrometry import semimajor_axis_barycentre_linear, pjGet_m2, get_ephemeris, semimajor_axis_relative_linear, semimajor_axis_relative_angular\nfrom ..pystrometry import convert_from_linear_to_angular, convert_from_angular_to_linear\nfrom ..pystrometry import thiele_innes_constants, geometric_elements, OrbitSystem, get_cpsi_spsi_for_2Dastrometry\n\n\ndef test_angular_to_linear():\n \"\"\"Test conversion between milliarcsecond and meter.\"\"\"\n\n a_mas = 3.\n absolute_parallax_mas = 100.\n\n a_recovered_mas = convert_from_linear_to_angular(convert_from_angular_to_linear(a_mas, absolute_parallax_mas), absolute_parallax_mas)\n # print(np.abs(a_mas - a_recovered_mas))\n assert np.abs(a_mas - a_recovered_mas) < 1e-14\n\n\ndef test_get_ephemeris():\n \"\"\"Test retrieval of ephemeris information from JPL Horizons.\"\"\"\n\n start_time = Time(2018., format='jyear')\n stop_time = Time(2019., format='jyear')\n\n xyzdata = get_ephemeris(start_time=start_time, stop_time=stop_time, step_size='5d',\n verbose=False, overwrite=True)\n\n assert len(xyzdata) == 74\n\n\ndef test_keplerian_equations(verbose=False):\n \"\"\"Test semimajor axis and mass consistency when applying Kepler's equations.\"\"\"\n\n MS_kg = const.M_sun.value\n MJ_kg = const.M_jup.value # jupiter mass in kg\n\n m1_kg = copy.deepcopy(MS_kg)\n m2_kg = copy.deepcopy(MJ_kg)\n P_day = 400.\n\n a_m = semimajor_axis_barycentre_linear(m1_kg / MS_kg, m2_kg / MJ_kg, P_day)\n\n # m2_kg_recovered = keplerian_secondary_mass(m1_kg, a_m, P_day)\n m2_kg_recovered = pjGet_m2(m1_kg, a_m, P_day)\n\n if verbose:\n print('')\n print(m2_kg/MJ_kg)\n print(m2_kg_recovered/MJ_kg)\n print(m2_kg/m2_kg_recovered - 1 )\n\n assert m2_kg/m2_kg_recovered - 1 < 1e-14\n\n\ndef test_thiele_innes():\n \"\"\"Test conversion between Thiele Innes constants and geometric elements.\"\"\"\n\n n_grid = 20\n a_mas = np.linspace(0.1, 100, n_grid)\n omega_deg = np.linspace(0.1, 90, n_grid, endpoint=False)\n OMEGA_deg = np.linspace(0.1, 90, n_grid, endpoint=False)\n i_deg = np.linspace(0.5, 180, n_grid, endpoint=False)\n\n a_mesh, omega_mesh, OMEGA_mesh, i_mesh = np.meshgrid(a_mas, omega_deg, OMEGA_deg, i_deg)\n a = a_mesh.flatten()\n omega = omega_mesh.flatten()\n OMEGA = OMEGA_mesh.flatten()\n i = i_mesh.flatten()\n\n # input_array = np.array([a_mas, omega_deg, OMEGA_deg, i_deg])\n input_array = np.array([a, omega, OMEGA, i])\n\n thiele_innes_parameters = thiele_innes_constants(input_array)\n geometric_parameters = geometric_elements(thiele_innes_parameters)\n\n absolute_tolerance = 1e-6\n assert_allclose(a, geometric_parameters[0], atol=absolute_tolerance)\n assert_allclose(omega, geometric_parameters[1], atol=absolute_tolerance)\n assert_allclose(OMEGA, geometric_parameters[2], atol=absolute_tolerance)\n assert_allclose(i, geometric_parameters[3], atol=absolute_tolerance)\n\n geometric_parameters = geometric_elements(thiele_innes_parameters, post_process=True)\n assert_allclose(a, geometric_parameters[0], atol=absolute_tolerance)\n assert_allclose(omega, geometric_parameters[1], atol=absolute_tolerance)\n assert_allclose(OMEGA, geometric_parameters[2], atol=absolute_tolerance)\n assert_allclose(i, geometric_parameters[3], atol=absolute_tolerance)\n\ndef test_geometric_elements():\n \"\"\"Test conversion between Thiele Innes constants and geometric elements.\"\"\"\n\n n_grid = 20\n a_mas = np.linspace(0.1, 100, n_grid)\n omega_deg = np.linspace(0.1, 360, n_grid, endpoint=False)\n OMEGA_deg = np.linspace(0.1, 179, n_grid, endpoint=False)\n i_deg = np.linspace(0.5, 180, n_grid, endpoint=False)\n\n a_mesh, omega_mesh, OMEGA_mesh, i_mesh = np.meshgrid(a_mas, omega_deg, OMEGA_deg, i_deg)\n a = a_mesh.flatten()\n omega = omega_mesh.flatten()\n OMEGA = OMEGA_mesh.flatten()\n i = i_mesh.flatten()\n\n # input_array = np.array([a_mas, omega_deg, OMEGA_deg, i_deg])\n input_array = np.array([a, omega, OMEGA, i])\n\n thiele_innes_parameters = thiele_innes_constants(input_array)\n\n absolute_tolerance = 1e-6\n\n # assert that Thiele-Innes are the same for the degenerate orbit configuration\n thiele_innes_parameters_alternative = thiele_innes_constants(np.array([a, omega-180, OMEGA-180, i]))\n assert_allclose(thiele_innes_parameters, thiele_innes_parameters_alternative, atol=absolute_tolerance)\n\n # use with post_process=True to have omega in [0, 180) and OMEGA in [0, 360)\n geometric_parameters = geometric_elements(thiele_innes_parameters, post_process=True)\n assert_allclose(a, geometric_parameters[0], atol=absolute_tolerance)\n assert_allclose(omega, geometric_parameters[1], atol=absolute_tolerance)\n assert_allclose(OMEGA, geometric_parameters[2], atol=absolute_tolerance)\n assert_allclose(i, geometric_parameters[3], atol=absolute_tolerance)\n\n\n\n\ndef test_orbit_computation(verbose=False):\n \"\"\"Perform basic checks on single Keplerian systems.\n\n The first check is to verify that two orbits that defer by +180deg in omega_deg\n and OMEGA_deg are equivalent in terms of astrometry.\n\n The effect of that change on the radial velocity alone is the same as replacing i\n by 180deg - i (but the astrometry changes in that case).\n\n \"\"\"\n\n # example orbit system\n attribute_dict = OrderedDict([('RA_deg', 164.9642810928918), ('DE_deg', -21.2190511382063),\n ('offset_alphastar_mas', 154.35507953), ('offset_delta_mas', -206.09777548),\n ('absolute_plx_mas', 27.0817358), ('muRA_mas', 105.77971602),\n ('muDE_mas', -162.05280475), ('rho_mas', -27.13228264),\n ('Tp_day', 57678.474567094345), ('omega_deg', -23.772188428200618),\n ('P_day', 687.96689982), ('ecc', 0.0807917), ('OMEGA_deg', 114.45626491875018),\n ('i_deg', 31.976978902085566), ('delta_mag', 5.74), ('m1_MS', 0.08),\n ('m2_MJ', 69.38476024572319), ('Tref_MJD', 57622.37084552435),\n ('scan_angle_definition', 'hipparcos'),\n ('parallax_correction_mas', 0.8182385292016127)])\n\n\n n_grid = 5\n omega_deg = np.linspace(0.1, 180, n_grid, endpoint=False)\n OMEGA_deg = np.linspace(0.1, 180, n_grid, endpoint=False)\n i_deg = np.linspace(0.5, 180, n_grid, endpoint=False)\n\n omega_mesh, OMEGA_mesh, i_mesh = np.meshgrid(omega_deg, OMEGA_deg, i_deg)\n omega_array = omega_mesh.flatten()\n OMEGA_array = OMEGA_mesh.flatten()\n i_array = i_mesh.flatten()\n\n for k in range(len(omega_array)):\n attribute_dict['omega_deg'] = omega_array[k]\n attribute_dict['OMEGA_deg'] = OMEGA_array[k]\n attribute_dict['i_deg'] = i_array[k]\n\n systems = OrderedDict()\n\n # first orbit\n orbit = OrbitSystem(attribute_dict=attribute_dict)\n if verbose:\n print(orbit)\n\n # second modified orbit\n attribute_dict2 = copy.deepcopy(attribute_dict)\n attribute_dict3 = copy.deepcopy(attribute_dict)\n attribute_dict2['OMEGA_deg'] += 180.\n attribute_dict2['omega_deg'] += 180.\n orbit2 = OrbitSystem(attribute_dict=attribute_dict2)\n if verbose:\n print(orbit2)\n\n attribute_dict3['i_deg'] = 180. - attribute_dict3['i_deg']\n orbit3 = OrbitSystem(attribute_dict=attribute_dict3)\n if verbose:\n print(orbit3)\n\n systems[0] = {'orbit_system': orbit}\n systems[1] = {'orbit_system': orbit2}\n systems[2] = {'orbit_system': orbit3}\n\n n_orbit = 2\n n_curve = 100\n\n # compute timeseries for both systems\n for i, system in systems.items():\n orbit_system = system['orbit_system']\n\n timestamps_curve_2D = np.linspace(orbit_system.Tp_day - orbit_system.P_day,\n orbit_system.Tp_day + n_orbit + orbit_system.P_day,\n n_curve)\n\n\n timestamps_curve_1D, cpsi_curve, spsi_curve, xi_curve, yi_curve = get_cpsi_spsi_for_2Dastrometry(timestamps_curve_2D)\n\n # relative orbit\n phi0_curve_relative = orbit_system.relative_orbit_fast(timestamps_curve_1D, spsi_curve, cpsi_curve,\n shift_omega_by_pi=True)\n\n systems[i]['relative_orbit'] = phi0_curve_relative\n systems[i]['rv_orbit'] = orbit_system.compute_radial_velocity(timestamps_curve_2D)\n systems[i]['barycentric_orbit'] = orbit_system.pjGetBarycentricAstrometricOrbitFast(timestamps_curve_1D,\n spsi_curve, cpsi_curve)\n\n\n absolute_tolerance = 1e-6\n\n # check that relative orbits are the same\n assert_allclose(systems[0]['relative_orbit'], systems[1]['relative_orbit'], atol=absolute_tolerance)\n\n # check that barycentric orbits are the same\n assert_allclose(systems[0]['barycentric_orbit'], systems[1]['barycentric_orbit'], atol=absolute_tolerance)\n\n # check that RV orbits are the same except for the sign\n if (systems[0]['orbit_system'].gamma_ms ==0) and (systems[1]['orbit_system'].gamma_ms ==0):\n assert_allclose(systems[0]['rv_orbit'], -1*systems[1]['rv_orbit'], atol=absolute_tolerance)\n assert_allclose(systems[0]['rv_orbit'], systems[2]['rv_orbit'], atol=absolute_tolerance)\n\n\ndef test_default_orbit(verbose=False):\n \"\"\"Perform basic checks on single Keplerian systems.\"\"\"\n\n orb = OrbitSystem()\n times_mjd = np.array([40672.5])\n orb.ppm(times_mjd)\n\n # test photocentre orbit\n timestamps_curve_1d, cpsi_curve, spsi_curve, xi_curve, yi_curve = get_cpsi_spsi_for_2Dastrometry(times_mjd)\n assert_allclose(orb.photocenter_orbit(timestamps_curve_1d, cpsi_curve, spsi_curve),\n [-1.57307485e-03, -6.08159828e-19], rtol=1e-9)\n\n\ndef test_semimajor_axes():\n m1_mjup = (const.M_earth / const.M_jup).value\n p_day = u.year.to(u.day)\n d_pc = 10.\n a_relative_m = semimajor_axis_relative_linear(1.0, m1_mjup, p_day)\n assert_allclose(a_relative_m*u.m.to(u.AU), 1, atol=1e-4)\n\n assert_allclose(semimajor_axis_relative_angular(1.0, m1_mjup, p_day, d_pc), 10, atol=1e-3)","repo_name":"Johannes-Sahlmann/pystrometry","sub_path":"pystrometry/tests/test_pystrometry.py","file_name":"test_pystrometry.py","file_ext":"py","file_size_in_byte":10418,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"18"} +{"seq_id":"18626569194","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#name: Yusen Wu\n#Email: wuyusen@bu.edu\n#Assigment: 6\n#File Description: This file contains conde for a user defined class Matrix, \n#and a set of functions to to print out and process related statistics. The\n#user can also perform a series of basica matrix operation including addtion,\n#subtraction, dot product\n\nimport random\n\nclass Matrix:\n \"\"\"\n A user defined class object of a 2-dimensional matrix.\n The matrix will support a wide-variety of operations, \n including printing itself out, row-based operations, \n and some linear-algebra operations.\n \"\"\"\n def __init__(self,matrix):\n \"\"\"\n initiate a matrix object requires a 2D list containing\n all data of the matrix\n \"\"\"\n self.__matrix = matrix\n\n def __repr__(self):\n \"\"\"\n A nicely fomatted textual representation of the matrix\n \"\"\"\n repr = '['\n\n #Iterate over and print each row \n for i,row in enumerate(self.__matrix):\n if i > 0:\n repr += ' '\n\n #sum up elements\n repr += '[' + ', '.join([f'{num:.2f}' for num in row]) + ']'\n if i != len(self.__matrix) - 1:\n repr += '\\n'\n\n repr += ']'\n return repr\n\n def describe(self):\n \"\"\"\n returns a string containing some useful descriptive information about \n the values in a Matrix\n \"\"\"\n repr = self.__repr__()\n repr += '\\n'\n\n #print dimensions\n repr += f'dimensions: {len(self.__matrix)} X {len(self.__matrix[0])}'\n repr += '\\n'\n\n #print sum of elements\n repr += f'sum of elements: {sum([num for row in self.__matrix for num in row])}'\n repr += '\\n'\n\n #print mean of elements\n repr += f'mean of elements: {sum([num for row in self.__matrix for num in row])/(len(self.__matrix)*len(self.__matrix[0]))}'\n repr += '\\n'\n\n #print column sums\n repr += f'column sums: {[sum(col) for col in self.transpose().__matrix]}'\n repr += '\\n'\n\n #print column means\n repr += f'column means: {[sum(col)/len(col) for col in self.transpose().__matrix]}'\n repr += '\\n'\n\n return repr\n\n def __eq__(self, other):\n \"\"\"\n Self defined euqality test bewteen different matrix object\n \"\"\"\n #check data type\n if type(self) != type(other):\n return False\n\n #check dimensions\n if (len(self.__matrix) != len(other.__matrix)) or (len(self.__matrix[0]) != len(other.__matrix[0])):\n return False\n\n #Iterate over each element to check if euqal in both Matrixs\n for row in range(len(self.__matrix)):\n for col in range(len(self.__matrix[0])):\n if self.__matrix[row][col] != other.__matrix[row][col]:\n return False\n return True\n\n def add_row_into(self, src, dest):\n \"\"\"\n performs the elementary-row operation to add the src \n row into the dest row\n \"\"\"\n assert src <= len(self.__matrix) - 1, f\"original index outbound\"\n assert dest <= len(self.__matrix) - 1, f\"destination index outbound\"\n\n self.__matrix[dest] = [self.__matrix[src][col] + self.__matrix[dest][col] for col in range(len(self.__matrix[0]))]\n\n def add_mult_row_into(self, scalar, src, dest):\n \"\"\"\n performs the elementary-row operation to add the a scalar times \n src row into the dest row\n \"\"\"\n assert src <= len(self.__matrix) - 1, f\"original index outbound\"\n assert dest <= len(self.__matrix) - 1, f\"destination index outbound\"\n\n self.__matrix[dest] = [scalar*self.__matrix[src][col] + self.__matrix[dest][col] for col in range(len(self.__matrix[0]))]\n\n def swap_rows(self, src, dest):\n \"\"\"\n perform the elementary row operation that exchanges \n two rows within the matrix\n \"\"\"\n assert src <= len(self.__matrix) - 1, f\"original index outbound\"\n assert dest <= len(self.__matrix) - 1, f\"destination index outbound\"\n\n self.__matrix[src], self.__matrix[dest] = self.__matrix[dest], self.__matrix[src]\n\n def add_scalar(self, scalar):\n \"\"\"\n return a new matrix object that is poinwisely scalar \n added on the orignal matrix\n \"\"\"\n assert(isinstance(scalar,(int,float))), f'wrong data type'\n return Matrix([[scalar + col for col in row] for row in self.__matrix])\n\n def mult_scalar(self, scalar):\n \"\"\"\n return a new matrix object that is poinwisely scalar \n multiplied on the orignal matrix\n \"\"\"\n assert(isinstance(scalar,(int,float))), f'wrong data type'\n return Matrix([[scalar*col for col in row] for row in self.__matrix])\n\n def add_matrices(self, other):\n \"\"\"\n takes as parameters 2 matrices (2d lists) and returns \n a new matrix which is the element-wise sum of the matrices \n A and B\n \"\"\"\n assert len(self.__matrix) == len(other.__matrix), f'incompatible dimensions: cannot add ({len(self.__matrix)},{len(self.__matrix[0])}) with ({len(other.__matrix)},{len(other.__matrix[0])})!'\n assert len(self.__matrix[0]) == len(other.__matrix[0]), f'incompatible dimensions: cannot add ({len(self.__matrix)},{len(self.__matrix[0])}) with ({len(other.__matrix)},{len(other.__matrix[0])})!'\n \n return Matrix([[self.__matrix[row][col] + other.__matrix[row][col] for col in range(len(self.__matrix[0]))] for row in range(len(other.__matrix))])\n\n def __add__(self, scalar):\n \"\"\"\n return a new matrix object that is poinwisely scalar \n added on the orignal matrix or matrix addition\n \"\"\"\n assert(isinstance(scalar,(int,float,Matrix))), f'only matrix, int, float data type is supported'\n\n #check for matrix addtion or scalar addtion\n if isinstance(scalar, Matrix):\n return self.add_matrices(scalar)\n else:\n return self.add_scalar(scalar)\n\n def __sub__(self, scalar):\n \"\"\"\n return a new matrix object that is poinwisely scalar \n minused on the orignal matrix or matrix subtraction\n \"\"\"\n assert(isinstance(scalar,(int,float,Matrix))), f'only matrix, int, float data type is supported'\n\n #check for matrix subtraction or scalar subtraction\n if isinstance(scalar, Matrix):\n return self.add_matrices(scalar.mult_scalar(-1))\n else:\n return self.add_scalar(-scalar)\n\n def __mul__(self, scalar):\n \"\"\"\n return a new matrix object that is poinwisely scalar \n multiplied on the orignal matrix or matrix dot product\n \"\"\"\n assert(isinstance(scalar,(int,float,Matrix))), f'only matrix, int, float data type is supported'\n\n #check for matrix multiplication or scalar multiplication\n if isinstance(scalar,Matrix):\n return self.dot_product(scalar)\n else:\n return self.mult_scalar(scalar)\n \n def __truediv__(self, scalar):\n \"\"\"\n return a new matrix object that is poinwisely scalar \n divided on the orignal matrix\n \"\"\"\n assert isinstance(scalar,(int,float)),f'unsupported data type'\n return self.mult_scalar(1/scalar)\n\n def transpose(self):\n \"\"\"\n creates and returns the transpose of itself\n \"\"\"\n rows = len(self.__matrix[0])\n cols = len(self.__matrix)\n\n #switch over col and row number\n return Matrix([[self.__matrix[col][row] for col in range(cols)] for row in range(rows)])\n\n def dot_product(self, other):\n \"\"\"\n takes as parameters two matrices M and N, and \n returns a new matrix containing dot product \n of these matrices\n \"\"\"\n assert len(self.__matrix[0]) == len(other.__matrix), f'incompatible dimensions: cannot dot product ({len(self.__matrix)},{len(self.__matrix[0])}) with ({len(other.__matrix)},{len(other.__matrix[0])})!'\n \n #Iniatiate a data container\n matrix = [[0 for _ in other.__matrix[0]] for _ in self.__matrix]\n other_T = other.transpose()\n\n #Loop over each row and col of final matrix: matrix[row][col] is dot product of A[row]*B_T[col]\n for row in range(len(matrix)):\n for col in range(len(matrix[0])):\n matrix[row][col] = sum([self.__matrix[row][i] * other_T.__matrix[col][i] for i in range(len(self.__matrix[0]))])\n return Matrix(matrix)\n\n @classmethod\n def zeros(Matrix, row, col = None):\n \"\"\"\n return a matrix that is filled by zeros\n \"\"\"\n #check for column specification\n if col == None:\n return Matrix([[0 for _ in range(row)] for _ in range(row)])\n else:\n return Matrix([[0 for _ in range(col)] for _ in range(row)])\n\n @classmethod\n def ones(Matrix, row, col = None):\n \"\"\"\n return a matrix that is filled by ones\n \"\"\"\n #check for column specification\n if col == None:\n return Matrix([[1 for _ in range(row)] for _ in range(row)])\n else:\n return Matrix([[1 for _ in range(col)] for _ in range(row)])\n \n @classmethod\n def identity(Matrix, n):\n \"\"\"\n return an identity matrix accoding to the specification\n \"\"\"\n return Matrix([[1 if col == row else 0 for col in range(n)] for row in range(n)])\n\n @classmethod\n def random_int_matrix(Matrix, row, col = None, low = 1, high = 10):\n \"\"\"\n draw a row*col matrix of random integers in the range of low to high\n \"\"\"\n #check for column specification and draw random numbers\n if col == None:\n return Matrix([[random.randint(low, high) for _ in range(row)] for _ in range(row)])\n else:\n return Matrix([[random.randint(low, high) for _ in range(col)] for _ in range(row)])\n \n @classmethod\n def random_float_matrix(Matrix, row, col = None, low = 0, high = 1):\n \"\"\"\n draw a row*col matrix of random floats in the range of low to high\n \"\"\"\n #check for column specification and draw random numbers\n if col == None:\n return Matrix([[random.uniform(low, high) for _ in range(row)] for _ in range(row)])\n else:\n return Matrix([[random.uniform(low, high) for _ in range(col)] for _ in range(row)])","repo_name":"RayNG123/FinancialTrading_Toolbox","sub_path":"Bond Utils/Matrix for Bond Object/a6task1.py","file_name":"a6task1.py","file_ext":"py","file_size_in_byte":9561,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"21935094338","text":"from itertools import product\n\nwith open(\"input\") as f:\n commands = f.read().strip().split(\"\\n\")\n\nprint(len(commands))\n\ndef prepare_command(c):\n a, b = c.split(\" = \")\n if a == \"mask\":\n return (\"mask\", (b,))\n m = int(a[4:-1])\n b = int(b)\n return (\"mem\", (m, b))\ncommands = [prepare_command(command) for command in commands]\n\nmem = {}\nfor op, par in commands:\n if op==\"mask\":\n m, = par\n or_mask = int(m.replace('X', \"0\"), base=2)\n and_mask = int(m.replace(\"X\", \"1\"), base=2)\n elif op==\"mem\":\n add, val = par\n val = val & and_mask | or_mask\n mem[add] = val\n else:\n print(\"error\")\n\nprint(sum(mem.values()))\n\nmem = {}\nfor op, par in commands:\n if op==\"mask\":\n mask, = par\n elif op==\"mem\":\n add, val = par\n for comb in product([\"0\", \"1\"], repeat=mask.count(\"X\")):\n or_mask = int(mask.replace(\"X\", \"0\"), base=2)\n and_mask = int(mask.replace(\"0\", \"1\").replace(\"X\", \"0\"), base=2)\n _mask = int(mask.replace(\"X\", \"{}\").format(*comb), base=2)\n _add = (add | or_mask) & and_mask | _mask\n mem[_add] = val\n else:\n print(\"error\")\nprint(sum(mem.values()))\n","repo_name":"Fedor-Lyanguzov/aoc_2020_python","sub_path":"14/14.py","file_name":"14.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40202513125","text":"# Date: 2018-07-10\n# Author: Lucas Nadalete\n#\n# License: GPL v3\n\n\n\"\"\"\n Controller class used to get a individual CV or a lot of CVs, consuming directly of the SOAP\n service provided by CNPq.\n\"\"\"\n\nfrom json import dumps\n\nfrom flask import request\nfrom flask_restful import Resource, abort\n\nfrom basic_auth import requires_auth\nfrom service.cnpq_soap_service import CnpqSoapService\nfrom webapp import app\n\n\nclass CnpqCvsController(Resource):\n\n def __init__(self):\n super().__init__()\n self.service = CnpqSoapService()\n\n @requires_auth\n def post(self):\n response = None\n curriculos = []\n cpfs = list(set(request.json['cpfs']))\n if len(cpfs) > 5:\n cpfs = [cpf for cpf in cpfs[:5] if cpf is not None]\n for cpf in cpfs:\n json_content = self.service.get_json_cv(cpf)\n if json_content is not None:\n curriculos.append(json_content)\n else:\n curriculos.append(dict(cpf=cpf, message='No curriculum found to the CPF informed'))\n if len(curriculos) > 0:\n response = app.response_class(\n response=dumps({\"curriculos\": curriculos}),\n status=201,\n mimetype='application/json'\n )\n else:\n abort(404, message='No curriculum found to the CPFs informed')\n return response\n\n\nclass CnpqCvController(Resource):\n\n def __init__(self):\n super().__init__()\n self.service = CnpqSoapService()\n\n @requires_auth\n def get(self, cpf=None):\n content_type = request.mimetype\n response = None\n if cpf is not None:\n if content_type == 'application/xml':\n xml_content = self.service.get_xml_cv(cpf)\n else:\n xml_content = self.service.get_json_cv(cpf)\n if xml_content is not None:\n xml_content = dumps(xml_content)\n # Verify if the header content-type is JSON or XML\n if xml_content is not None:\n response = app.response_class(\n response=xml_content,\n status=200,\n mimetype=content_type\n )\n else:\n abort(404, message='No curriculum found to the CPF informed or invalid mimetype')\n\n else:\n abort(412, message='No valid CPF value informed')\n return response\n\n\nclass CnpqUpdateDateController(Resource):\n\n def __init__(self):\n super().__init__()\n self.service = CnpqSoapService()\n\n @requires_auth\n def get(self, cpf=None):\n response = {'cpf': cpf}\n if cpf is not None:\n identificador = self.service.get_identificador(cpf)\n if identificador is not None:\n response['identificador'] = identificador\n date = self.service.get_data_atualizacao_cv(identificador)\n if date is not None:\n response['data_atualizacao'] = date\n response = app.response_class(\n response=dumps(response),\n status=200,\n mimetype='application/json'\n )\n else:\n abort(404, message='No update date found to the CPF informed')\n\n else:\n abort(412, message='No valid CPF value informed')\n return response\n","repo_name":"InovaCPS/inova-proxy","sub_path":"src/main/python/controller/proxy_controller.py","file_name":"proxy_controller.py","file_ext":"py","file_size_in_byte":3443,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"16173687315","text":"from typing import List, Type\n\nfrom sqlalchemy.orm import Session\nfrom src.repository.users import get_user_by_user_id\n\nfrom src.database.models import Doc\nfrom src.schemas.doc import DocResponse\n\n\nasync def delete_user_documents(user_id: int, db: Session) -> bool:\n user = await get_user_by_user_id(user_id, db)\n docs = db.query(Doc).filter_by(user_id=user.id).all()\n if docs:\n for doc in docs:\n db.delete(doc)\n db.commit()\n return True\n else:\n return False\n\n\nasync def get_user_documents(user_id: int, db: Session) -> List[DocResponse]:\n user = await get_user_by_user_id(user_id, db)\n docs = db.query(Doc).filter_by(user_id=user.id).limit(5).all()\n result = []\n for doc in docs:\n doc_resp = DocResponse(\n id=doc.id,\n user_id=doc.user_id,\n name=doc.name\n )\n result.append(doc_resp)\n\n return result\n\n\nasync def get_all_doc(db: Session) -> List[DocResponse]:\n docs = db.query(Doc).all()\n result = []\n for doc in docs:\n doc_resp = DocResponse(\n id=doc.id,\n user_id=doc.user_id,\n name=doc.name\n )\n result.append(doc_resp)\n\n return result\n\n\nasync def get_doc_by_id(doc_id: int, db: Session) -> Doc | None:\n return db.query(Doc).filter_by(id=doc_id).first()\n\n\nasync def create_doc(user_id, file_name, text, db: Session) -> Doc:\n new_doc = Doc()\n new_doc.user = await get_user_by_user_id(user_id, db)\n new_doc.name = file_name\n new_doc.description = text\n db.add(new_doc)\n db.commit()\n db.refresh(new_doc)\n return new_doc\n","repo_name":"last-war/ColumBOT","sub_path":"src/repository/doc.py","file_name":"doc.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"29096917888","text":"\n# coding: utf-8\n\n# # Rule inference on multi-omics networks - Data processing and Figure 1\n\n# In[121]:\n\n\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport pandas as pd\nimport numpy as np\nfrom upsetplot import from_contents, UpSet, plot\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport gseapy as gp\nfrom math import ceil\n\n\n# # Phosphoproteomics data analysis\n\n# In[122]:\n\n\ncombined_clean = pd.read_excel(\n 'raw_data/202007Quantitaive results_Cleaned.xlsx',\n sheet_name=\"Quan_Combined\").fillna(0)\ncombined_clean.head()\n\n\n# ## Log2 normalize data\n\n# In[123]:\n\n\ncombined_clean.iloc[:, 4:51] = combined_clean.iloc[:, 4:51].apply(\n lambda x: np.log2(x + 1), raw=False)\ncombined_clean.iloc[0:4, 4:51]\n\n\n# ## Make conditions matrix\n\n# In[124]:\n\n\nconditions = pd.read_excel('raw_data/202007Quantitaive results_Cleaned.xlsx',\n sheet_name=\"condition_matrix\").fillna(0)\nconditions.head()\n\n\n# ## Add gene names to expression matrix\n\n# In[125]:\n\n\ngene_mapping = pd.read_excel('raw_data/202007Quantitaive results_Cleaned.xlsx',\n sheet_name=\"gene mapping\").fillna(0)\ngene_mapping.head()\ncombined_clean.loc[:, \"Gene\"] = [\n gene_mapping.To[gene_mapping.From == combined_clean.loc[\n i, \"Protein AC\"]].tolist()[0] if\n len(gene_mapping.To[gene_mapping.From == combined_clean.loc[i,\n \"Protein AC\"]])\n > 0 else combined_clean.loc[i, \"Protein AC\"]\n for i in range(len(combined_clean))\n]\n\n\n# ## For rows corresponding to the same protein/gene - Retain only those rows with the highest abundance.\n\n# In[126]:\n\n\ncombined_clean.select_dtypes(include=np.number).mean(axis=1)\n\n\n# In[127]:\n\n\ncombined_clean.loc[:, \"MeanAbundance\"] = combined_clean.select_dtypes(\n include=np.number).mean(axis=1)\ncombined_clean.loc[:, \"RowSums\"] = combined_clean.select_dtypes(\n include=np.number).sum(axis=1)\n#combined_clean = combined_clean.set_index(\"Gene\", drop=False)\ncombined_clean = combined_clean.sort_values(\"MeanAbundance\", ascending=False)\ncombined_clean = combined_clean.groupby(\"Gene\", sort=False)\ncombined_clean = combined_clean.first()\ncombined_clean = combined_clean.reset_index()\ncombined_clean = combined_clean.drop(columns=[\"MeanAbundance\", \"RowSums\"])\nprint(combined_clean.shape)\n\n\n# # Process proteomics dataset\n\n# In[128]:\n\n\nproteomics = pd.read_csv(\"raw_data/ramos_genes.csv\").fillna(0)\nproteomics.loc[:, [\n \"norm_19-A\", \"norm_19-B\", \"norm_19-C\", \"norm_1-A\", \"norm_1-B\", \"norm_1-C\",\n \"norm_1-1ug-A\", \"norm_1-1ug-B\", \"norm_1-1ug-C\"\n]] = proteomics.loc[:, [\n \"norm_19-A\", \"norm_19-B\", \"norm_19-C\", \"norm_1-A\", \"norm_1-B\", \"norm_1-C\",\n \"norm_1-1ug-A\", \"norm_1-1ug-B\", \"norm_1-1ug-C\"\n]].apply(lambda x: np.log2(x + 1), raw=False)\nproteomics.loc[:, [\n \"norm_19-A\", \"norm_19-B\", \"norm_19-C\", \"norm_1-A\", \"norm_1-B\", \"norm_1-C\",\n \"norm_1-1ug-A\", \"norm_1-1ug-B\", \"norm_1-1ug-C\"\n]] = proteomics.loc[:, [\n \"norm_19-A\", \"norm_19-B\", \"norm_19-C\", \"norm_1-A\", \"norm_1-B\", \"norm_1-C\",\n \"norm_1-1ug-A\", \"norm_1-1ug-B\", \"norm_1-1ug-C\"\n]].loc[~(proteomics.loc[:, [\n \"norm_19-A\", \"norm_19-B\", \"norm_19-C\", \"norm_1-A\", \"norm_1-B\", \"norm_1-C\",\n \"norm_1-1ug-A\", \"norm_1-1ug-B\", \"norm_1-1ug-C\"\n]] == 0).all(axis=1)]\nproteomics = proteomics.fillna(0)\nprot_common_conditions = [\n \"Gene\", \"norm_19-A\", \"norm_19-B\", \"norm_19-C\", \"norm_1-A\", \"norm_1-B\",\n \"norm_1-C\", \"norm_1-1ug-A\", \"norm_1-1ug-B\", \"norm_1-1ug-C\"\n]\n\n\n# # Process transcriptomics dataset\n\n# In[129]:\n\n\ntranscriptomics = pd.read_csv(\"raw_data/unfiltered_rpm_counts.txt\",\n sep=\"\\t\").fillna(0)\ntranscriptomics.loc[:, [\n \"Ramos_19O2_NoCyclo_1\", \"Ramos_19O2_NoCyclo_2\", \"Ramos_19O2_NoCyclo_3\",\n \"Ramos_19O2_PlusCyclo_1\", \"Ramos_19O2_PlusCyclo_2\",\n \"Ramos_19O2_PlusCyclo_3\", \"Ramos_1O2_NoCyclo_1\", \"Ramos_1O2_NoCyclo_2\",\n \"Ramos_1O2_NoCyclo_3\", \"Ramos_1O2_PlusCyclo_1\", \"Ramos_1O2_PlusCyclo_2\",\n \"Ramos_1O2_PlusCyclo_3\"\n]] = transcriptomics.loc[:, [\n \"Ramos_19O2_NoCyclo_1\", \"Ramos_19O2_NoCyclo_2\", \"Ramos_19O2_NoCyclo_3\",\n \"Ramos_19O2_PlusCyclo_1\", \"Ramos_19O2_PlusCyclo_2\",\n \"Ramos_19O2_PlusCyclo_3\", \"Ramos_1O2_NoCyclo_1\", \"Ramos_1O2_NoCyclo_2\",\n \"Ramos_1O2_NoCyclo_3\", \"Ramos_1O2_PlusCyclo_1\", \"Ramos_1O2_PlusCyclo_2\",\n \"Ramos_1O2_PlusCyclo_3\"\n]].apply(lambda x: np.log2(x + 1), raw=False)\ntranscriptomics.loc[:, [\n \"Ramos_19O2_NoCyclo_1\", \"Ramos_19O2_NoCyclo_2\", \"Ramos_19O2_NoCyclo_3\",\n \"Ramos_19O2_PlusCyclo_1\", \"Ramos_19O2_PlusCyclo_2\",\n \"Ramos_19O2_PlusCyclo_3\", \"Ramos_1O2_NoCyclo_1\", \"Ramos_1O2_NoCyclo_2\",\n \"Ramos_1O2_NoCyclo_3\", \"Ramos_1O2_PlusCyclo_1\", \"Ramos_1O2_PlusCyclo_2\",\n \"Ramos_1O2_PlusCyclo_3\"\n]] = transcriptomics.loc[:, [\n \"Ramos_19O2_NoCyclo_1\", \"Ramos_19O2_NoCyclo_2\", \"Ramos_19O2_NoCyclo_3\",\n \"Ramos_19O2_PlusCyclo_1\", \"Ramos_19O2_PlusCyclo_2\",\n \"Ramos_19O2_PlusCyclo_3\", \"Ramos_1O2_NoCyclo_1\", \"Ramos_1O2_NoCyclo_2\",\n \"Ramos_1O2_NoCyclo_3\", \"Ramos_1O2_PlusCyclo_1\", \"Ramos_1O2_PlusCyclo_2\",\n \"Ramos_1O2_PlusCyclo_3\"\n]].loc[~(transcriptomics.loc[:, [\n \"Ramos_19O2_NoCyclo_1\", \"Ramos_19O2_NoCyclo_2\", \"Ramos_19O2_NoCyclo_3\",\n \"Ramos_19O2_PlusCyclo_1\", \"Ramos_19O2_PlusCyclo_2\",\n \"Ramos_19O2_PlusCyclo_3\", \"Ramos_1O2_NoCyclo_1\", \"Ramos_1O2_NoCyclo_2\",\n \"Ramos_1O2_NoCyclo_3\", \"Ramos_1O2_PlusCyclo_1\", \"Ramos_1O2_PlusCyclo_2\",\n \"Ramos_1O2_PlusCyclo_3\"\n]] == 0).all(axis=1)]\ntranscriptomics = transcriptomics.fillna(0)\n\n\n# # Find common genes between all three datasets - all conditions, all measured genes included\n\n# In[130]:\n\n\ncommonGenes = from_contents({\n 'Proteomics': set(proteomics.index),\n 'Transcriptomics': set(transcriptomics.index),\n 'Phosphoproteomics': set(combined_clean.index)\n})\ncommonGenes\n\n\n# In[131]:\n\n\nwith plt.style.context('tableau-colorblind10'):\n plt.figure(figsize=(2, 5))\n plot(commonGenes,\n show_counts=True,\n min_subset_size=1,\n facecolor=\"midnightblue\",\n orientation='horizontal')\n plt.savefig(\"figure1a_unfiltered.pdf\")\n plt.savefig(\"figure1a_unfiltered.png\", dpi=600)\n\n\n# # Make datasets that just have the common genes and common conditions/samples\n\n# ## Common conditions\n\n# ### Phosphoproteomics\n\n# In[132]:\n\n\ngroup1 = conditions['TMT.group'] == 1\nlowO2 = conditions.O2 == \"Low\"\nnoCXCL12 = conditions.CXCL12 == \"No\"\ngroup2 = conditions['TMT.group'] == 2\nhighO2 = conditions.O2 == \"High\"\nnoCyA = conditions.CyA == \"No\"\nphosph_common_conditions = conditions.loc[\n (group2 & highO2 & noCXCL12 & noCyA) | (group1 & lowO2 & noCXCL12),\n 'Column labels - cleaned dataset'].tolist()[\n 0:9] #ignore last three samples - repeat\nphosph_common_conditions.insert(0, \"Gene\")\nprint(phosph_common_conditions)\n\n\n# ### Proteomics\n\n# In[133]:\n\n\nprot_common_conditions = [\n \"Gene\", \"norm_19-A\", \"norm_19-B\", \"norm_19-C\", \"norm_1-A\", \"norm_1-B\",\n \"norm_1-C\", \"norm_1-1ug-A\", \"norm_1-1ug-B\", \"norm_1-1ug-C\"\n]\n\n\n# ### Transcriptomics\n\n# In[134]:\n\n\ntranscript_common_conditions = [\n 'Gene', \"Ramos_19O2_NoCyclo_1\", \"Ramos_19O2_NoCyclo_2\",\n \"Ramos_19O2_NoCyclo_3\", \"Ramos_1O2_NoCyclo_1\", \"Ramos_1O2_NoCyclo_2\",\n \"Ramos_1O2_NoCyclo_3\", \"Ramos_1O2_PlusCyclo_1\", \"Ramos_1O2_PlusCyclo_2\",\n \"Ramos_1O2_PlusCyclo_3\"\n]\n\n\n# ## Create datasets\n\n# In[135]:\n\n\n# transcriptomics\ntranscript_common = transcriptomics[set(\n transcript_common_conditions).intersection(set(transcriptomics.columns))]\ntranscript_common.set_index(\"Gene\", inplace=True)\n#transcript_common.sort_index(inplace=True)\ntranscript_common = transcript_common[transcript_common.median(axis=1) > 0]\n\n# phosphoproteomics\nphospho_common = combined_clean[set(phosph_common_conditions).intersection(\n set(combined_clean))]\nphospho_common.set_index(\"Gene\", inplace=True)\n#phospho_common.sort_index(inplace=True)\nphospho_common = phospho_common[phospho_common.median(axis=1) > 0]\n\n# proteomics\nprot_common = proteomics[set(prot_common_conditions).intersection(\n set(proteomics.columns))]\nprot_common.set_index(\"Gene\", inplace=True)\n#prot_common.sort_index(inplace=True)\nprot_common = prot_common[prot_common.median(axis=1) > 0]\n\n\n# In[136]:\n\n\nphospho_common.to_csv(\"bonita_phosphoproteomics.csv\")\nprot_common.to_csv(\"bonita_proteomics.csv\")\ntranscript_common.to_csv(\"bonita_transcriptomics.csv\")\nprint(phospho_common.shape, transcript_common.shape, prot_common.shape)\n\n\n# # Make figures showing overlap in genes, using filtered datasets\n\n# In[137]:\n\n\ncommonGenes = from_contents({\n 'Proteomics': set(prot_common.index),\n 'Transcriptomics': set(transcript_common.index),\n 'Phosphoproteomics': set(phospho_common.index)\n})\ncommonGenes\nwith plt.style.context('tableau-colorblind10'):\n plt.figure(figsize=(2, 5))\n plot(commonGenes,\n show_counts=True,\n min_subset_size=1,\n facecolor=\"green\",\n orientation='horizontal')\n plt.savefig(\"figure1a_filtered.pdf\")\n plt.savefig(\"figure1a_filtered.png\", dpi=600)\n\n\n# In[138]:\n\n\ncommonGene = list(\n set(\n set(prot_common.index) & set(transcript_common.index)\n & set(phospho_common.index)))\n\ntranscript_common = transcript_common.loc[transcript_common.index.isin(\n commonGene)]\nphospho_common = phospho_common.loc[phospho_common.index.isin(commonGene)]\nprot_common = prot_common.loc[prot_common.index.isin(commonGene)]\n\nprint(transcript_common.shape, phospho_common.shape, prot_common.shape)\n\n\n# # Calculate pairwise correlations between datasets\n\n# In[139]:\n\n\ntemp = transcript_common.reset_index(drop=False).melt(id_vars='Gene')\ntemp['Condition'] = [i[:-2] for i in temp.variable]\nfor i in range(len(temp['Condition'])):\n if temp.loc[i, 'Condition'] == 'Ramos_19O2_NoCyclo':\n temp.loc[i, 'Condition'] = \"19% O2, CyA-\"\n elif temp.loc[i, 'Condition'] == 'Ramos_1O2_NoCyclo':\n temp.loc[i, 'Condition'] = \"1% O2, CyA-\"\n elif temp.loc[i, 'Condition'] == 'Ramos_1O2_PlusCyclo':\n temp.loc[i, 'Condition'] = \"1% O2, CyA+\"\na = pd.DataFrame(temp.groupby(['Condition', 'Gene']).median()) #.reset_index()\na\n\n\n# In[140]:\n\n\ntemp2 = temp[['variable', 'Condition']].drop_duplicates().reset_index(drop=True)\ntemp2['O2'] = [i[:-6] for i in temp2.Condition]\ntemp2['CyA'] = [i[-4:] for i in temp2.Condition]\nfor i in set(temp2.Condition):\n temp2[i] = np.nan\n for j in range(len(temp2)):\n if (temp2.loc[j, 'Condition'] == i):\n temp2.loc[j, i] = 1\n else:\n temp2.loc[j, i] = 0\ntemp2[[\"1% O2, CyA+\",\"1% O2, CyA-\",\"19% O2, CyA-\"]].to_csv(\"transcriptomics_conditions.csv\")\ntemp2\n\n\n# In[141]:\n\n\ntemp = phospho_common.reset_index(drop=False).melt(id_vars='Gene')\ntemp.columns = ['Gene', 'Condition', 'value']\nfor i in range(len(temp['Condition'])):\n oldcond = temp.loc[i, 'Condition']\n newcond = ''\n if conditions.loc[conditions['Column labels - cleaned dataset'] == oldcond,\n \"O2\"].tolist()[0] == \"Low\":\n newcond = newcond + \"1% O2, \"\n else:\n newcond = newcond + \"19% O2, \"\n if conditions.loc[conditions['Column labels - cleaned dataset'] == oldcond,\n \"CyA\"].tolist()[0] == \"No\":\n newcond = newcond + \"CyA-\"\n else:\n newcond = newcond + \"CyA+\"\n temp.loc[i, 'Condition'] = newcond\n temp.loc[i, 'variable'] = oldcond\nb = pd.DataFrame(temp.groupby(['Condition', 'Gene']).median()) #.reset_index()\nb\n\n\n# In[142]:\n\n\nb\n\n\n# In[143]:\n\n\ntemp2 = temp[['variable', 'Condition']].drop_duplicates().reset_index(drop=True)\ntemp2['O2'] = [i[:-6] for i in temp2.Condition]\ntemp2['CyA'] = [i[-4:] for i in temp2.Condition]\nfor i in set(temp2.Condition):\n temp2[i] = np.nan\n for j in range(len(temp2)):\n if (temp2.loc[j, 'Condition'] == i):\n temp2.loc[j, i] = 1\n else:\n temp2.loc[j, i] = 0\ntemp2[[\"1% O2, CyA+\",\"1% O2, CyA-\",\"19% O2, CyA-\"]].to_csv(\"phosphoproteomics_conditions.csv\")\ntemp2\n\n\n# In[144]:\n\n\ntemp = prot_common.reset_index(drop=False).melt(id_vars='Gene')\ntemp['Condition'] = [i[:-2] for i in temp.variable]\n\nfor i in range(len(temp['Condition'])):\n if temp.loc[i, 'Condition'] == 'norm_19':\n temp.loc[i, 'Condition'] = \"19% O2, CyA-\"\n elif temp.loc[i, 'Condition'] == 'norm_1':\n temp.loc[i, 'Condition'] = \"1% O2, CyA-\"\n elif temp.loc[i, 'Condition'] == 'norm_1-1ug':\n temp.loc[i, 'Condition'] = \"1% O2, CyA+\"\n\nc = pd.DataFrame(temp.groupby(['Condition', 'Gene']).median())\nc\n\n\n# In[153]:\n\n\ntemp2 = temp[['variable', 'Condition']].drop_duplicates().reset_index(drop=True)\ntemp2['O2'] = [i[:-6] for i in temp2.Condition]\ntemp2['CyA'] = [i[-4:] for i in temp2.Condition]\nfor i in set(temp2.Condition):\n temp2[i] = np.nan\n for j in range(len(temp2)):\n if (temp2.loc[j, 'Condition'] == i):\n temp2.loc[j, i] = 1\n else:\n temp2.loc[j, i] = 0\ntemp2[[\"1% O2, CyA+\",\"1% O2, CyA-\",\"19% O2, CyA-\"]].to_csv(\"proteomics_conditions.csv\", index=False)\ntemp2\n\n\n# In[146]:\n\n\nmedianDF = pd.DataFrame({\n \"Transcriptomics\": a.value,\n \"Proteomics\": c.value,\n \"Phosphoproteomics\": b.value\n}).reset_index()\nmedianDF\n\n\n# In[147]:\n\n\ng = sns.pairplot(medianDF,\n diag_kind='hist',\n kind='reg',\n plot_kws={'scatter_kws': {\n 'alpha': 0.25\n }},\n markers=[\"o\", \"s\", \"D\"],\n corner=False,\n hue='Condition',\n palette='colorblind')\ng.savefig(\"figure1b.pdf\")\ng.savefig(\"figure1b.png\", dpi=600)\n\n\n# In[148]:\n\n\na = prot_common.T.reset_index(drop=True).corrwith(\n transcript_common.T.reset_index(drop=True), axis=0)\nsns.histplot(a, color=\"blue\")\nb = phospho_common.T.reset_index(drop=True).corrwith(\n transcript_common.T.reset_index(drop=True), axis=0)\nsns.histplot(b, color=\"red\")\nc = phospho_common.T.reset_index(drop=True).corrwith(\n prot_common.T.reset_index(drop=True), axis=0)\nsns.histplot(c, color=\"brown\")\n\n\n# In[149]:\n\n\na = transcript_common.reset_index(drop=False).melt(\n id_vars=\"Gene\").sort_values(\"Gene\")\nb = prot_common.reset_index(drop=False).melt(\n id_vars=\"Gene\").sort_values(\"Gene\")\nc = phospho_common.reset_index(drop=False).melt(\n id_vars=\"Gene\").sort_values(\"Gene\")\n\n\n# In[150]:\n\n\nfrom scipy.stats import spearmanr, pearsonr\nprint(spearmanr(a.value, b.value), pearsonr(a.value, b.value))\n\n\n# In[151]:\n\n\nprint(spearmanr(a.value, c.value), pearsonr(a.value, c.value))\n\n\n# In[152]:\n\n\nprint(spearmanr(b.value, c.value), pearsonr(b.value, c.value))\n\n\"\"\"\n# # Make proteomics coexpression network\n\n# In[ ]:\n\n\nproteomics_coexp = proteomics[set(\n prot_common_conditions).intersection(set(proteomics.columns))]\nproteomics_coexp.set_index(\"Gene\", inplace=True)\nproteomics_coexp = proteomics_coexp.T\nproteomics_coexp = proteomics_coexp.corr(method=\"spearman\")\nproteomics_coexp.head\nproteomics_coexp.to_csv(\"proteomics_corr.csv\")\n\n\n# In[ ]:\n\n\nproteomics_coexp = pd.read_csv(\"proteomics_corr.csv\", index_col=0)\nproteomics_coexp['Gene'] = list(proteomics_coexp.index)\nproteomics_coexp = proteomics_coexp.melt(id_vars=['Gene']).dropna()\nproteomics_coexp.value = proteomics_coexp.value.abs()\nproteomics_coexp = proteomics_coexp[proteomics_coexp.value >= 0.75]\nproteomics_coexp = proteomics_coexp[proteomics_coexp.value != 1]\n#proteomics_coexp.head\nproteomics_net = nx.from_pandas_edgelist(proteomics_coexp,\n source=\"Gene\",\n target=\"variable\",\n edge_attr=\"value\")\nnx.set_node_attributes(proteomics_net,\n nx.betweenness_centrality(proteomics_net),\n \"prot_betweenness\")\n#Gcc = sorted(nx.connected_components(proteomics_net), key=len, reverse=True)\n#proteomics_net = proteomics_net.subgraph(Gcc[0])\nnx.write_graphml_lxml(proteomics_net, \"proteomics_net.graphml\")\nlen(proteomics_net)\n\"\"\"\nproteomics_net = nx.read_graphml(\"proteomics_net.graphml\")\nlen(proteomics_net)\n\n\n# # Make transcriptomics coexpression network\n\n# In[ ]:\n\n\"\"\"\ntranscriptomics_coexp = transcriptomics[set(\n transcript_common_conditions).intersection(set(transcriptomics.columns))]\ntranscriptomics_coexp = transcriptomics_coexp.T\ntranscriptomics_coexp = transcriptomics_coexp.corr(method=\"spearman\")\ntranscriptomics_coexp.head\ntranscriptomics_coexp.to_csv(\"transcriptomics_corr.csv\")\n\n\n# In[67]:\n\n\ntranscriptomics_coexp = pd.read_csv(\"transcriptomics_corr.csv\", index_col=0)\ntranscriptomics_coexp['Gene'] = list(transcriptomics_coexp.index)\ntranscriptomics_coexp = transcriptomics_coexp.melt(id_vars=['Gene']).dropna()\ntranscriptomics_coexp.value = transcriptomics_coexp.value.abs()\ntranscriptomics_coexp = transcriptomics_coexp[\n transcriptomics_coexp.value >= 0.75]\ntranscriptomics_coexp = transcriptomics_coexp[transcriptomics_coexp.value != 1]\n\n\ntranscriptomics_net = nx.from_pandas_edgelist(transcriptomics_coexp,\n source=\"Gene\",\n target=\"variable\",\n edge_attr=\"value\")\nnx.set_node_attributes(transcriptomics_net,\n nx.betweenness_centrality(transcriptomics_net),\n \"trans_betweenness\")\n#Gcc = sorted(nx.connected_components(transcriptomics_net), key=len, reverse=True)\n#transcriptomics_net = transcriptomics_net.subgraph(Gcc[0])\nnx.write_graphml_lxml(transcriptomics_net, \"transcriptomics_net.graphml\")\nlen(transcriptomics_net)\n\"\"\"\n\ntranscriptomics_net = nx.read_graphml(\"transcriptomics_net.graphml\")\n\n# # Make phosphoproteomics coexpression network\n\n# In[ ]:\n\n\nphospho_coexp = combined_clean[set(\n phosph_common_conditions).intersection(set(combined_clean.columns))]\nphospho_coexp = phospho_coexp.T\nphospho_coexp = phospho_coexp.corr(method=\"spearman\")\nphospho_coexp.head\nphospho_coexp.to_csv(\"phospho_corr.csv\")\n\n\n# In[ ]:\n\n\nphospho_coexp = pd.read_csv(\"phospho_corr.csv\", index_col=0)\nphospho_coexp['Gene'] = list(phospho_coexp.index)\nphospho_coexp = phospho_coexp.melt(id_vars=['Gene']).dropna()\nphospho_coexp.value = phospho_coexp.value.abs()\nphospho_coexp = phospho_coexp[phospho_coexp.value >= 0.75]\nphospho_coexp = phospho_coexp[phospho_coexp.value != 1]\n\nphospho_net = nx.from_pandas_edgelist(phospho_coexp,\n source=\"Gene\",\n target=\"variable\",\n edge_attr=\"value\")\nnx.set_node_attributes(phospho_net, nx.betweenness_centrality(phospho_net),\n \"phospho_betweenness\")\n#Gcc = sorted(nx.connected_components(phospho_net), key=len, reverse=True)\n#phospho_net = phospho_net.subgraph(Gcc[0])\nnx.write_graphml_lxml(phospho_net, \"phospho_net.graphml\")\n\n\n# # Find edges overlapping between coexpression networks\n\n# In[ ]:\n\n\ndef getOverlapGraph(netList=[], returnGiantComponent=True):\n overlapNet = nx.intersection_all(netList)\n if returnGiantComponent:\n Gcc = sorted(nx.connected_components(overlapNet), key=len, reverse=True)\n G0 = overlapNet.subgraph(Gcc[0])\n return G0\n else:\n return overlapNet\n\n\n# In[ ]:\n\n\n# transcriptomics - proteomics \ntrans_prot = getOverlapGraph(netList=[transcriptomics_net, proteomics_net])\nprint(len(trans_prot))\n\n\n# In[ ]:\n\n\n# transcriptomics - phosphoproteomics \ntrans_phospho = getOverlapGraph(netList=[transcriptomics_net, phospho_net])\nprint(len(trans_phospho))\n\n\n# In[ ]:\n\n\n# phosphoproteomics - proteomics\nprot_phospho = getOverlapGraph(netList=[proteomics_net, phospho_net])\nprint(len(prot_phospho))\n\n\n# In[ ]:\n\n\n# make consensus network\n\nconsensus_net = getOverlapGraph(netList=[transcriptomics_net, proteomics_net, phospho_net], returnGiantComponent=False)\n\nnx.set_node_attributes(consensus_net, nx.betweenness_centrality(consensus_net), \"consensus_betweenness\")\n\nnx.write_graphml_lxml(consensus_net, \"consensus_net.graphml\")\n\nconsensus_net_giant = getOverlapGraph(netList=[transcriptomics_net, proteomics_net, phospho_net], returnGiantComponent=True)\n\nnx.write_graphml_lxml(consensus_net_giant, \"consensus_net_largest_connected_component.graphml\")\n\nprint(len(consensus_net_giant))\n\n\n# In[ ]:\n\n\n# make upset plot showing intersections between coexpression network edges\n\ncommonNodes = from_contents({\n 'Proteomics': set(proteomics_net.edges),\n 'Transcriptomics': set(transcriptomics_net.edges),\n 'Phosphoproteomics': set(phospho_net.edges)\n})\nwith plt.style.context('tableau-colorblind10'):\n plt.figure(figsize=(2, 5))\n plot(commonNodes,\n show_counts=True,\n min_subset_size=1,\n facecolor=\"navy\",\n orientation='horizontal')\n plt.savefig(\"figure2c.pdf\")\n plt.savefig(\"figure2c.png\", dpi=600)\n\n\n# In[ ]:\n\n\ndef enrichmentNodes(networkNodes):\n consensusNodesEnrich = gp.enrichr(gene_list=list(networkNodes),\n gene_sets=['KEGG_2021_Human'],\n organism='Human',\n cutoff=0.05)\n consensusNodesEnrich.results.Genes = [\n temp.split(';') for temp in consensusNodesEnrich.results.Genes.tolist()\n ]\n enrichr_common_nodes = consensusNodesEnrich.results[\n consensusNodesEnrich.results['Adjusted P-value'] < 0.01]\n enrichr_common_nodes = enrichr_common_nodes.assign(\n log10_adjusted_p_value=[(-1) * np.log10(i)\n for i in enrichr_common_nodes['Adjusted P-value']])\n return enrichr_common_nodes\n\ndef makeEnrichBubblePlot(enrichResult):\n with plt.style.context('tableau-colorblind10'):\n sns.set_context(\n \"paper\",\n rc={\n \"font.size\": 16,\n \"axes.labelsize\": 'medium',\n 'ytick.labelsize': 'medium',\n 'xtick.labelsize': 'medium',\n 'axes.titlesize': 'medium',\n 'legend.fontsize': 'medium',\n })\n sns.scatterplot(data=enrichResult,\n y=\"Term\",\n x=\"log10_adjusted_p_value\",\n palette=\"Blues\", s = 75)\n plt.xlabel(\"-log10 (adjusted p-value)\")\n plt.ylabel(\"\")\n axes = plt.gca()\n axes.yaxis.grid(color='grey',\n linestyle=(0, (5, 10)),\n linewidth=0.5)\n axes.xaxis.grid(color='grey',\n linestyle=(0, (5, 10)),\n linewidth=0.5)\n plt.xticks(range(0,ceil(max(enrichResult[\"log10_adjusted_p_value\"])+1)))\n\n\n# In[ ]:\n\n\n# consensus network\nconsensusNodesEnrich = enrichmentNodes(consensus_net_giant.nodes)\nplt.figure(figsize=(3,4))\nmakeEnrichBubblePlot(consensusNodesEnrich)\n\n\n# In[ ]:\n\n\n# proteomics only\nlen(set(transcriptomics_net.nodes))# - set(transcriptomics_net.nodes)) #.difference(set(phospho_net.nodes)))\n#proteomics_net.subgraph(proteomics_net.nodes.in)\n#proteomicsNodesEnrich = enrichmentNodes(G0.nodes)\n#plt.figure(figsize=(5,10))\n#makeEnrichBubblePlot(proteomicsNodesEnrich)\n\n\n# In[ ]:\n\n\n# transcriptomics only\noverlapNet = nx.difference(transcriptomics_net, consensus_net.subgraph(transcriptomics_net.nodes))\nGcc = sorted(nx.connected_components(overlapNet), key=len, reverse=True)\nG0 = overlapNet.subgraph(Gcc[0])\ntranscriptomicsNodesEnrich = enrichmentNodes(G0.nodes)\nplt.figure(figsize=(5,10))\nmakeEnrichBubblePlot(transcriptomicsNodesEnrich)\n\n\n# In[ ]:\n\n\n# phosphoproteomics only\nphosphoproteomicsNodesEnrich = enrichmentNodes(set(phospho_net.nodes) - set(consensus_net_giant.nodes))\nplt.figure(figsize=(3,10))\nmakeEnrichBubblePlot(phosphoproteomicsNodesEnrich)\n\n\n# In[ ]:\n\n\nlen(set(phospho_net.nodes) - set(consensus_net_giant.nodes))\nlen(set(proteomics_net.nodes) - set(consensus_net_giant.nodes))\n\n","repo_name":"mgp13/mBONITA","sub_path":"code used to generate figures in manuscript/Figure 1/Figure1.py","file_name":"Figure1.py","file_ext":"py","file_size_in_byte":23606,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"28730456582","text":"#!/usr/bin/python\n\n# Importing Numpy (math, arrays, etc...)\nimport numpy as np\n# Importing Matplotlib (plotting)\nimport matplotlib.pyplot as plt\n# Importing plot utilities\nfrom plotutils import loadFile, getPoint\n\ndata = loadFile(\"../envelope.dat\")\nTc, Pc = getPoint(\"../envelope.dat\",\"#Critical\")\nTcb, Pcb = getPoint(\"../envelope.dat\",\"#Cricondenbar\")\nTct, Pct = getPoint(\"../envelope.dat\",\"#Cricondentherm\")\n\nenv, = plt.plot(data[:,0]-273.15,data[:,1],label=\"Envelope\")\nif Tc > 1.0:\n crit, = plt.plot(Tc-273.15,Pc,'ko',label=\"Crit\")\nif Tcb > 1.0:\n criconBar, = plt.plot(Tcb-273.15,Pcb,'ro',label=\"Cricondenbar\")\nif Tct > 1.0:\n criconTherm, = plt.plot(Tct-273.15,Pct,'go',label=\"Cricondentherm\")\n\n\nplt.xlabel(u\"$T$ (\\N{DEGREE SIGN}C)\")\nplt.ylabel(r\"$P$ (bar)\")\nplt.grid(b=True, which='major', color='k', linestyle='--')\nleg = plt.legend(loc=\"lower right\",numpoints=1)\nleg.get_frame().set_linewidth(0.0)\n#plt.xlim((-50,40))\nplt.savefig(\"envelope.pdf\")\nplt.show()\n","repo_name":"SINTEF/thermopack","sub_path":"pyplot/envelope.py","file_name":"envelope.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"18"} +{"seq_id":"15981570112","text":"import numpy as np\nimport multiflap as mf\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\nx = [1., 1.]\nmy_model = mf.ForcedVanDerPol(lam=1.8)\n\nms_obj = mf.MultipleShooting(x, M=2, period=10.4719755, t_steps=4000,\n model=my_model, option_jacobian='numerical')\n\nmysolution = mf.Solver(tolerance=1e-6, ms_obj = ms_obj).lma()\n\nsol_array = mysolution[3].space\nsol_time = mysolution[3].time\nperiod = sol_time[-1]\n\nx0 = sol_array[0,:]\njac = mysolution[4]\neigenvalues, eigenvectors = np.linalg.eig(jac)\n\nprint(eigenvalues)\ntarray = np.linspace(0, 10, 40000)\nsolution_odeint = odeint(my_model.dynamics, x0, tarray)\n\nplt.plot( sol_array[:,0], sol_array[:,1])\nplt.plot(solution_odeint[:,0], solution_odeint[:,1], color='red')\nplt.scatter(sol_array[0,0], sol_array[0,1])\nplt.show()\n","repo_name":"vortexlab-uclouvain/multiflap","sub_path":"examples/forced_van_der_pol.py","file_name":"forced_van_der_pol.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"18"} +{"seq_id":"23841440680","text":"import discord\nfrom discord.ext import commands, tasks\nfrom is_ppt_near import is_ppt_now\nimport asyncio\n\nchannel_id = 883965511422058516\n\n\nclass Ppt(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @commands.Cog.listener()\n async def on_ready(self):\n self.get_ppt.start(self.bot)\n\n @tasks.loop(hours=6)\n async def get_ppt(self, bot):\n if (is_ppt_now()):\n txt_msg = f\"Boiis today is PPT!!!\"\n msg = discord.Embed(\n title=f\"Click Here to Regiter.\",\n url=\"https://ppt.pes.edu\",\n colour=0xd73422,\n )\n msg.set_author(name=\"PES UNIVERSITY\",\n icon_url=\"https://edushineglobal.com/edushine_api/public/col_logos/col-14270.png\")\n msg.set_image(url=\"https://ppt.pes.edu/static/images/logos/PESUPPTLogo2-01.png\")\n channel = bot.get_channel(channel_id)\n await channel.send(txt_msg, embed=msg)\n await asyncio.sleep(86400)\n await channel.purge(limit=1)\n else:\n pass\n\n\ndef setup(bot):\n bot.add_cog(Ppt(bot))","repo_name":"sushantpeace10/PROJ-DISCORD_BOT","sub_path":"ppt.py","file_name":"ppt.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"18977468839","text":"import requests\n\nclass HtmlDownloader(object):\n\n def download(self, url):\n if url is None:\n return None\n\n print(url)\n user_agent = 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36'\n headers = {'User_Agent': user_agent}\n sessions = requests.session()\n sessions.headers = headers\n response = sessions.get(url, allow_redirects=True)\n\n if response.status_code != 200:\n return None\n\n return response.text","repo_name":"wzy1990/pythonSpider","sub_path":"baike_spider/html_downloader.py","file_name":"html_downloader.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"33072186444","text":"#!/usr/bin/env python3\nfrom typing import Set, List\nimport re\nimport sys\nimport json\nimport traceback\nimport urllib.parse\nimport datetime\nfrom bs4 import BeautifulSoup # type: ignore\nimport logging\nimport discord # type: ignore\nimport asyncio\nfrom oil import oil # type: ignore\n\nlogging.basicConfig(\n\t\tlevel=logging.INFO,\n\t\thandlers=[\n\t\t\tlogging.FileHandler(\"minerva.log\"),\n\t\t\tlogging.StreamHandler(),\n\t\t],\n\t)\n\nclient = discord.Client()\n\nETYPE_COLORS = {\n\t\t'epub': discord.Colour.blue(),\n\t\t'html': discord.Colour.gold(),\n\t\t'mobi': discord.Colour.green(),\n\t\t'pdf': discord.Colour.red(),\n\t}\n\nAPI_PREFIX = 'https://fichub.net/api/v0/epub?q='\nAPI_AUTO_PREFIX = 'https://fichub.net/api/v0/epub?automated=true&q='\n\ndef plog(msg: str) -> None:\n\tlogging.info(msg)\n\ndef lookup(query: str):\n\timport requests\n\ttry:\n\t\treq = requests.get(API_PREFIX + query)\n\t\tres = req.json()\n\t\treturn res\n\texcept:\n\t\treturn {'error':-1,'msg':'lookup failed :('}\n\nasync def automatedLookup(query: str):\n\ttry:\n\t\timport aiohttp\n\t\turl = API_AUTO_PREFIX + query\n\t\tasync with aiohttp.request(method=\"GET\", url=url) as resp:\n\t\t\tresp.raise_for_status()\n\t\t\tbody = await resp.text()\n\t\t\treturn json.loads(body)\n\texcept:\n\t\treturn {'error':-1,'msg':'lookup failed :('}\n\nclass RequestSource:\n\tdef __init__(self, id_, created_, isAutomated_, route_, description_):\n\t\tself.id = id_\n\t\tself.created = created_\n\t\tself.isAutomated = isAutomated_\n\t\tself.route = route_\n\t\tself.description = description_\n\n\t@staticmethod\n\tdef select(id_: int) -> 'RequestSource':\n\t\twith oil.open() as db, db.cursor() as curs:\n\t\t\tcurs.execute('''\n\t\t\t\tselect rs.id, rs.created, rs.isAutomated, rs.route, rs.description\n\t\t\t\tfrom requestSource rs\n\t\t\t\twhere rs.id = %s\n\t\t\t''', (id_,))\n\t\t\tr = curs.fetchone()\n\t\t\treturn None if r is None else RequestSource(*r)\n\nclass RequestLog:\n\tdef __init__(self, id_, created_, sourceId_, etype_, query_, infoRequestMs_,\n\t\t\turlId_, ficInfo_, exportMs_, exportFileName_, exportFileHash_, url_):\n\t\tself.id = id_\n\t\tself.created = created_\n\t\tself.sourceId = sourceId_\n\t\tself.etype = etype_\n\t\tself.query = query_\n\t\tself.infoRequestMs = infoRequestMs_\n\t\tself.urlId = urlId_\n\t\tself.ficInfo = ficInfo_\n\t\tself.exportMs = exportMs_\n\t\tself.exportFileName = exportFileName_\n\t\tself.exportFileHash = exportFileHash_\n\t\tself.url = url_\n\n\t@staticmethod\n\tdef maxId() -> int:\n\t\twith oil.open() as db, db.cursor() as curs:\n\t\t\tcurs.execute('select max(id) from requestLog')\n\t\t\tr = curs.fetchone()\n\t\t\treturn r[0]\n\t\treturn -1\n\n\t@staticmethod\n\tdef fetchAfter(after):\n\t\twith oil.open() as db, db.cursor() as curs:\n\t\t\tcurs.execute('''\n\t\t\t\tselect r.id, r.created, r.sourceId, r.etype, r.query, r.infoRequestMs,\n\t\t\t\t\tr.urlId, r.ficInfo, r.exportMs, r.exportFileName, r.exportFileHash,\n\t\t\t\t\tr.url\n\t\t\t\tfrom requestLog r\n\t\t\t\twhere id > %s and (r.exportFileHash is null or not exists (\n\t\t\t\t\tselect 1\n\t\t\t\t\tfrom requestLog r2\n\t\t\t\t\twhere r2.exportFileHash = r.exportFileHash\n\t\t\t\t\t\tand r2.id < r.id\n\t\t\t\t))\n\t\t\t\t''', (after,))\n\t\t\tls = [RequestLog(*r) for r in curs.fetchall()]\n\t\t\treturn ls\n\t\treturn []\n\n\t@staticmethod\n\tdef mostRecentByUrlId(urlId):\n\t\twith oil.open() as db, db.cursor() as curs:\n\t\t\tcurs.execute('''\n\t\t\tselect r.id, r.created, r.sourceId, r.etype, r.query, r.infoRequestMs,\n\t\t\t\tr.urlId, r.ficInfo, r.exportMs, r.exportFileName, r.exportFileHash,\n\t\t\t\tr.url\n\t\t\tfrom requestLog r\n\t\t\twhere urlId = %s\n\t\t\torder by created desc limit 1''', (urlId,))\n\t\t\tr = curs.fetchone()\n\t\t\tif r is None:\n\t\t\t\treturn None\n\t\t\treturn RequestLog(*r)\n\n@client.event\nasync def on_ready():\n\tplog(f'We have logged in as {client.user}')\n\ndef escape_msg(msg: str) -> str:\n\treturn discord.utils.escape_mentions(discord.utils.escape_markdown(msg))\n\nasync def sendFicInfo(channel, l: RequestLog):\n\ttry:\n\t\turl = urllib.parse.urljoin('https://fichub.net/', l.url)\n\t\tinfo = json.loads(l.ficInfo)\n\t\tdescSoup = BeautifulSoup(info['desc'], 'lxml')\n\t\tinfoTime = f'{l.infoRequestMs/1000.0:.3f}s'\n\t\texportTime = f'{l.exportMs/1000.0:.3f}s'\n\t\tmsg = f'request for <{info[\"source\"]}> => `{l.urlId}` ({infoTime})'\n\t\tmsg += f', generated {l.etype} in {exportTime}'\n\t\ttitle = escape_msg(f'{info[\"title\"]} by {info[\"author\"]}')\n\t\t# description cannot exceed 2048 bytes\n\t\tdesc = f'\\n{info[\"words\"]} words in {info[\"chapters\"]} chapters'\n\t\tdesc2 = escape_msg(descSoup.get_text())\n\t\tif len(desc2) >= 2040 - len(desc):\n\t\t\tdesc = desc2[:2040 - len(desc)] + '...' + desc\n\t\telse:\n\t\t\tdesc = desc2 + desc\n\t\te = discord.Embed(title=title, description=desc, url=url)\n\t\tif l.etype in ETYPE_COLORS:\n\t\t\te.colour = ETYPE_COLORS[l.etype]\n\t\tawait channel.send(msg, embed=e)\n\t\treturn True\n\texcept Exception as e:\n\t\tplog(f'sendFicInfo: error: {e}\\n{traceback.format_exc()}')\n\treturn False\n\nasync def sendDevFicInfo(channel, l: RequestLog):\n\turl = urllib.parse.urljoin('https://fichub.net/', l.url)\n\tm1 = f'request for {l.etype} of <{l.query}> => `{l.urlId}` ({l.infoRequestMs}ms)'\n\tm2 = f'`````` ({l.exportMs}ms)'\n\tm3 = f'<{url}> (`{l.exportFileHash}`)'\n\tmsg = '\\n'.join([m1, m2, m3])\n\n\tleftover = 1800 - len(msg) - 16\n\tlfi = l.ficInfo[0:leftover]\n\tm2 = f'```{lfi}``` ({l.exportMs}ms)'\n\tmsg = '\\n'.join([m1, m2, m3])\n\n\ttry:\n\t\tawait channel.send(msg)\n\texcept Exception as e:\n\t\tplog(f'sendDevFicInfo: error: {e}\\n{traceback.format_exc()}')\n\nasync def sendErrorLog(channel, l: RequestLog):\n\tplog(f'failed request {l.id}')\n\ttry:\n\t\tmsg = f'failed request {l.id}: ```' + str(l.__dict__)\n\t\twhile len(msg) > 1800:\n\t\t\tawait channel.send(msg[:1800] + '```')\n\t\t\tmsg = '```' + msg[1800:]\n\t\tawait channel.send(msg + '```')\n\texcept Exception as e:\n\t\tplog(f'sendErrorLog: error: unable to report error: {e}\\n{traceback.format_exc()}')\n\nasync def delerr_q(chan, errq) -> int:\n\tplog(f'delerr_q({errq})')\n\tcnt=0\n\tasync for pm in chan.history(limit=500):\n\t\tif pm.author == client.user and pm.content.find(errq) >= 0:\n\t\t\tawait pm.delete()\n\t\t\tcnt += 1\n\treturn cnt\n\nasync def delerr(msg) -> None:\n\tprefix = '!delerr '\n\tif not msg.content.startswith(prefix):\n\t\treturn\n\terrq = msg.content[len(prefix):].strip()\n\tcnt = await delerr_q(msg.channel, errq)\n\tawait msg.channel.send(f'deleted {cnt} matching messages')\n\treturn\n\nasync def cleanup_retry(chan, q) -> int:\n\tplog(f'cleanup_retry({q})')\n\ttry:\n\t\tlr = await automatedLookup(q)\n\t\tif 'err' in lr and int(lr['err']) != 0:\n\t\t\treturn 0\n\t\tif 'error' in lr and int(lr['error']) != 0:\n\t\t\treturn 0\n\texcept:\n\t\treturn 0\n\tplog(f'cleanup_retry({q}): now successful, deleting old errors')\n\tcnt=await delerr_q(chan, f\", 'query': '{q}', \")\n\tplog(f'cleanup_retry({q}): now successful, deleted {cnt} old errors')\n\treturn cnt\n\nasync def cleanup(msg) -> None:\n\tprefix = '!cleanup'\n\tif not msg.content.startswith(prefix):\n\t\treturn\n\tvalidPrefixes = [\n\t\t\t'www.fanfiction.net/s/',\n\t\t\t'm.fanfiction.net/s/',\n\t\t\t'fanfiction.net/s/',\n\t\t\t'www.fictionpress.com/s/',\n\t\t\t'm.fictionpress.com/s/',\n\t\t\t'archiveofourown.org/works/',\n\t\t\t'forums.sufficientvelocity.com/threads',\n\t\t\t'forums.spacebattles.com/threads',\n\t\t\t'forum.questionablequesting.com/threads',\n\t\t\t'www.royalroad.com/fiction/',\n\t\t\t'royalroad.com/fiction/',\n\t\t\t'harrypotterfanfiction.com/',\n\t\t\t'(?:[^\\.]*).adult-fanfiction.org/story.php?'\n\t\t]\n\tif msg.content.startswith(prefix + ' '):\n\t\targ = msg.content[len(prefix) + 1:]\n\t\tvalidPrefixes = [p for p in validPrefixes if p.find(arg) >= 0]\n\t\tplog(f'cleanup: limited validPrefixes to: {validPrefixes}')\n\n\ttoRecheck: Set[str] = set()\n\ttoRecheckList: List[str] = []\n\tmsgCount = 0\n\tasync for pm in msg.channel.history(limit=500):\n\t\tif pm.author != client.user:\n\t\t\tcontinue\n\t\tfor vp in validPrefixes:\n\t\t\tfor proto in ['', 'https://', 'http://']:\n\t\t\t\trr = re.match(f\".*'epub', 'query': '({proto}{vp}[^']*)', .*\", pm.content)\n\t\t\t\tif rr is None:\n\t\t\t\t\tcontinue\n\t\t\t\tquery = rr.group(1)\n\t\t\t\tmsgCount += 1\n\t\t\t\tif query in toRecheck:\n\t\t\t\t\tcontinue\n\t\t\t\ttoRecheck |= { query }\n\t\t\t\ttoRecheckList += [ query ]\n\tplog(f'cleanup: going to recheck {len(toRecheck)} queries in {msgCount} messages: {toRecheck})')\n\tcnt=0\n\tfor i in range(len(toRecheckList)):\n\t\tq = toRecheckList[i]\n\t\tplog(f'cleanup: going to recheck query {i + 1}/{len(toRecheck)}: {q})')\n\t\tcnt += await cleanup_retry(msg.channel, q)\n\t\tawait asyncio.sleep(1)\n\tawait msg.channel.send(f'finished cleanup: removed {cnt} now-successful')\n\n@client.event\nasync def on_message(message):\n\tif message.author == client.user:\n\t\treturn\n\n\tmdict = {\"message\":{\n\t\t\"author\":str(message.author),\n\t\t\"content\":str(message.clean_content),\n\t\t\"id\":str(message.id),\n\t\t\"created_at\":str(message.created_at),\n\t}}\n\n\tif isinstance(message.channel, discord.DMChannel):\n\t\tmdict[\"channel\"] = {\"id\":message.channel.id,\"dm\":True}\n\telse:\n\t\tmdict[\"channel\"] = {\n\t\t\t\t\"id\":message.channel.id, \"dm\":False,\n\t\t\t\t\"name\":message.channel.name\n\t\t\t}\n\tplog(json.dumps(mdict))\n\n\tif message.content.startswith('!test'):\n\t\tawait message.channel.send('Hello!')\n\tif message.content.startswith('!delerr '):\n\t\tawait delerr(message)\n\t\treturn\n\tif message.content.startswith('!cleanup'):\n\t\tawait cleanup(message)\n\t\treturn\n\n\tinfoCommandPrefixes = ['lookup', 'info', 'epub', 'link']\n\tfor pre in infoCommandPrefixes:\n\t\tif message.content.startswith(f\"!{pre}\"):\n\t\t\tquery = message.content[len(f\"!{pre}\"):].strip()\n\t\t\tbreak\n\telse:\n\t\treturn\n\tquery = query.strip('<>| \\t').strip()\n\tawait message.add_reaction('👍')\n\n\ttry:\n\t\tfut = asyncio.get_event_loop().run_in_executor(None, lookup, query)\n\t\tres = await fut\n\texcept Exception as e:\n\t\tawait message.channel.send(f\"unable to lookup '{query}'\")\n\t\tawait message.add_reaction('❌')\n\t\tplog(f'on_message: error: {e}\\n{traceback.format_exc()}')\n\n\t# FIXME why do we need this client param?\n\tawait message.remove_reaction('👍', client.user)\n\tif res is None or 'urlId' not in res \\\n\t\t\tor ('error' in res and int(res['error']) != 0):\n\t\tawait message.channel.send(f\"unable to lookup '{query}'\")\n\t\tawait message.add_reaction('❌')\n\t\tplog(res)\n\t\treturn\n\n\ttry:\n\t\tl = RequestLog.mostRecentByUrlId(res['urlId'])\n\t\tawait sendFicInfo(message.channel, l)\n\t\tawait message.add_reaction('✅')\n\texcept Exception as e:\n\t\tawait message.channel.send(f\"unable to find lookup result for '{query}'\")\n\t\tawait message.add_reaction('❌')\n\t\tplog(f'on_message: error: {e}\\n{traceback.format_exc()}')\n\nasync def watch_requests():\n\tawait client.wait_until_ready()\n\tbotspam_priv = client.get_channel(754481638695501866) # #botspam-priv\n\tbotspam_err = client.get_channel(785868128096747540) # #botspam-err\n\trequest_feed = client.get_channel(754779814740754492) # #request-feed\n\n\tmaxId = RequestLog.maxId()\n\tif len(sys.argv) > 1 and sys.argv[1].isnumeric():\n\t\tmaxId = int(sys.argv[1])\n\t\tplog(f'set maxId to {maxId} from cli')\n\n\tawait botspam_priv.send('started up')\n\twhile not client.is_closed():\n\t\tawait asyncio.sleep(3)\n\t\tls = RequestLog.fetchAfter(maxId)\n\t\tfor l in ls:\n\t\t\tmaxId = max(maxId, l.id)\n\n\t\t\tif l.exportFileHash is None:\n\t\t\t\tawait sendErrorLog(botspam_err, l)\n\t\t\t\tcontinue\n\n\t\t\t#await sendDevFicInfo(botspam_priv, l)\n\n\t\t\trs = RequestSource.select(l.sourceId)\n\t\t\tif not rs.isAutomated:\n\t\t\t\tawait sendFicInfo(request_feed, l)\n\n\t\t\tawait asyncio.sleep(1)\n\n\tplog('watch_requests: ending')\n\nif __name__ == \"__main__\":\n\tclient.loop.create_task(watch_requests())\n\timport secret\n\tclient.run(secret.BOT_TOKEN)\n\n","repo_name":"FicHub/minerva","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":11087,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"5711725769","text":"\nimport os\nimport glob\nimport pandas as pd\n\nif __name__ == \"__main__\":\n\n\n\n os.chdir('./data/')\n extension = 'parquet'\n all_filenames = [i for i in glob.glob('*.{}'.format(extension))]\n\n\n df = pd.concat([pd.read_parquet(f) for f in all_filenames ])\n\n print(df.isna().any())\n\n\n df.reset_index(inplace=True, drop = True)\n\n os.chdir('../')\n df.to_csv('Data.csv')\n\n\n print('Saving data to csv...')\n","repo_name":"Khalleud/URL_Classification","sub_path":"prepareData.py","file_name":"prepareData.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3855652596","text":"import scrapy \n\nfrom scrapy.selector import HtmlXPathSelector\n\nfrom scrapy.spiders.crawl import Rule\nfrom scrapy.linkextractors.sgml import SgmlLinkExtractor\n\n\n\nclass MySpider(CrawlSpider):\n name = \"craigs\"\n allowed_domains = [\"sfbay.craigslist.org\"]\n start_urls = [\"http://sfbay.craigslist.org/search/npo\"]\n\n rules = (\n Rule(SgmlLinkExtractor(allow=(), restrict_xpaths=('//a[@class=\"button next\"]',)), callback=\"parse_items\", follow= True),\n )\n\n def parse_items(self, response):\n hxs = HtmlXPathSelector(response)\n titles = hxs.xpath('//span[@class=\"pl\"]')\n items = []\n for title in titles:\n item = items.CraigslistSampleItem()\n item[\"title\"] = title.select('a/span[@id=\"titletextonly\"]/text()').extract()\n item[\"link\"] = title.xpath(\"a/@href\").extract()\n items.append(item)\n return(items)\n","repo_name":"Purushisolve/Scrapy-Samples","sub_path":"crawlspider/craigslist_sample/spiders/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"33237764086","text":"import discord\nimport pickle\nimport config\nimport datetime\nimport random\nfrom src.bot_work import languages\n\n\ndef storeSettings():\n lottery_settings = config.lottery_settings\n\n try:\n\n with open(config.directory + \"//pickle-db//lotterySettings.pickle\", \"wb\") as handler:\n pickle.dump(lottery_settings, handler, protocol=pickle.HIGHEST_PROTOCOL)\n\n # FOR DEBUGGING\n # print(\"LOTTERY SETTINGS SAVED: \" + str(ads_e))\n\n except Exception as e:\n print(\"!!!COULDN'T SAVE LOTTERY SETTINGS!!!\\n\" + str(e))\n\n\ndef storeLottery():\n lottery_queue = config.lottery_queue\n lottery_excludes = config.lottery_excludes\n\n try:\n\n with open(config.directory + \"//pickle-db//lotteryQueue.pickle\", \"wb\") as handler:\n pickle.dump(lottery_queue, handler, protocol=pickle.HIGHEST_PROTOCOL)\n\n # FOR DEBUGGING\n # print(\"LOTTERY QUEUE SAVED: \" + str(ads_e))\n\n except Exception as e:\n print(\"!!!COULDN'T SAVE LOTTERY QUEUE!!!\\n\" + str(e))\n\n try:\n\n with open(config.directory + \"//pickle-db//lotteryExcluded.pickle\", \"wb\") as handler:\n pickle.dump(lottery_excludes, handler, protocol=pickle.HIGHEST_PROTOCOL)\n\n # FOR DEBUGGING\n # print(\"LOTTERY EXCLUDED SAVED: \" + str(ads_e))\n\n except Exception as e:\n print(\"!!!COULDN'T SAVE LOTTERY EXCLUDED!!!\\n\" + str(e))\n\n\ndef checkUser(user_id):\n \"\"\"See if a user win's the lottery.\n\n :parameter user_id: User's discord id.\n :returns: True, user has won the lottery; False, user has not won the lottery.\n\n \"\"\"\n\n try:\n\n if config.lottery_settings[\"on\"] == False:\n return False\n\n if user_id in config.lottery_excludes:\n return False\n\n else:\n if user_id in config.lottery_queue:\n # Get the time stamp for now\n now = datetime.datetime.now()\n\n if ((now - config.lottery_queue[user_id]).seconds / 60) > config.lottery_settings[\"timeOut\"]:\n\n config.lottery_queue[user_id] = now\n\n storeLottery()\n\n if round(random.randrange(1, config.lottery_settings[\"iceRate\"])) == 1:\n return True\n\n else:\n return False\n\n else:\n return False\n\n else:\n # Get the time stamp for now\n now = datetime.datetime.now()\n\n config.lottery_queue[user_id] = now\n\n storeLottery()\n\n if round(random.randrange(1, config.lottery_settings[\"iceRate\"])) == 1:\n return True\n\n else:\n return False\n\n except Exception as e:\n print(\"ERROR IN CHECK USER LOTTERY: \" + str(e))\n\n return False\n\n\ndef lotteryEmbed(language, name):\n \"\"\"Creates and returns a lottery embed.\n\n :parameter language: Language embed should be shown in.\n :parameter name: Name of the winner.\n :returns: Discord embed object.\n\n \"\"\"\n\n try:\n\n # FOR DEBUGGING\n # print(\"AD ID: \" + str(ad_id))\n\n embed = discord.Embed(title=languages.lotteryTitle(language, name), colour=discord.Colour.gold(), url=str(config.bot_server), description=languages.lotteryDescription(language))\n\n embed.set_author(name=str(config.bot_name), url=str(config.bot_server), icon_url=str(config.bot_icon))\n\n embed.set_image(url=\"https://vaingloryhack.com/wp-content/uploads/2017/03/download.png\")\n\n embed.set_footer(text=\"Thank you for supporting us :3 | Contact us!\")\n\n return embed\n\n except Exception as e:\n print(\"LOTTERY EMBED ERROR: \" + str(e))\n","repo_name":"ClarkThyLord/Computer-BOT","sub_path":"src/bot_work/lottery.py","file_name":"lottery.py","file_ext":"py","file_size_in_byte":3706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14392094931","text":"\"\"\"\nThis program receives an integer and returns the divisors of this integer.\n\n\nhttps://www.cyberciti.biz/faq/python-command-line-arguments-argv-example/\n\n\"\"\"\n\nimport sys\n\n# print(sys.argv)\n# ['find_divisors.py', '10']\n\ndef main():\n\n if len(sys.argv) > 2:\n raise Exception(\"Incorrect number of arguments. Include just one argument which should be an integer number.\")\n\n number = sys.argv[1]\n number = int(number)\n print(\"\\n===============================\")\n print(f\" The divisors of {number} are:\")\n for divisor in range(1, number):\n if number % divisor == 0:\n print(f\" {divisor}\")\n\n print(\"===============================\")\n\n\n\nif __name__ == \"__main__\":\n main()\n\n","repo_name":"williamszk/python_study","sub_path":"various/220427_01_cmd_program_for_divisors_of_integer/find_divisors.py","file_name":"find_divisors.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72457486439","text":"class Solution:\n def isValid(self, s: str) -> bool:\n match = {\n ')': '(', ']': '[', '}': '{'\n }\n \n stack = []\n for item in s:\n if item in match:\n if not stack or stack.pop() != match[item]:\n return False\n else:\n stack.append(item)\n return not stack","repo_name":"Dulou/leetcode-python","sub_path":"Python/easy/20. Valid Parentheses.py","file_name":"20. Valid Parentheses.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8358938632","text":"\"\"\"\nPrzygotuj funkcję, która dla dowolnej liczby policzy jej silnię. Skorzystaj z pętli while, np. get_factorial(5)\npowinno zwrócić wynik 5 * 4 * 3 * 2 * 1\n\"\"\"\n\n\ndef get_factorial(number: int):\n count = number\n factorial = number\n while count > 1:\n count -= 1\n factorial *= count\n return factorial\n\n\nprint(get_factorial(7))\n","repo_name":"Ryuuken-dev/Python-Zadania","sub_path":"Moduł III-praca domowa/3.3.py","file_name":"3.3.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9816130551","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Dec 1 22:14:16 2021\r\n\r\n@author: Hafsa Sheikh\r\n\"\"\"\r\nimport requests\r\nimport json\r\nAPI_KEY = \"qyNC12c3YyY0bhfyfYq0VVErI3i_iTxexfk059pJOhrq\"\r\ntoken_response = requests.post('https://iam.cloud.ibm.com/identity/token', data={\"apikey\": API_KEY, \"grant_type\": 'urn:ibm:params:oauth:grant-type:apikey'})\r\nmltoken = token_response.json()[\"access_token\"]\r\nprint(\"mltoken\", mltoken)\r\n\r\nheader = {'Content-Type': 'application/json', 'Authorization': 'Bearer ' + mltoken}\r\n\r\n# NOTE: manually define and pass the array(s) of values to be scored in the next line\r\npayload_scoring = {\"input_data\": [{\"field\": [\"id\", \"week\", \"center_id\", \"meal_id\", \"checkout_price\", \"base_price\", \"emailer_for_promotion\", \"homepage_featured\"], \"values\": [[ 2.0, 3, 647, 56, 3 ,250.0, 0, 0]]}]}\r\n\r\nresponse_scoring = requests.post('https://eu-gb.ml.cloud.ibm.com/ml/v4/deployments/346f6e2b-7733-496c-a352-abd243e8e46a/predictions?version=2021-11-30', json=payload_scoring, headers={'Authorization': 'Bearer ' + mltoken})\r\nprint(\"Scoring response\")\r\nprint(response_scoring.json())\r\npredictions = response_scoring.json()\r\nprint(predictions['predictions'][0]['values'][0][0])","repo_name":"smartinternz02/SBSPS-Challenge-8190-Food-Demand-Forecasting-for-Food-Delivery-Company-using-IBM-Cloud","sub_path":"prediction.py","file_name":"prediction.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35918914078","text":"from django.conf.urls import patterns, include, url\n\nfrom django.contrib import admin\nfrom question.views import QuestionView, ComponentView\n\nfrom project.views import ProjectListView, ProjectDetailView, NewProjectView,\\\n ProjectAnswerView, ProjectChangeTitleView, DeactivateUserView,\\\n ProjectDetailCSVView, ProjectDeleteView, ProjectAnswerViewPDF\nfrom django.views.generic.base import TemplateView\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.generic.simple import direct_to_template\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n url(r'^$', TemplateView.as_view(template_name='home.html'), name='home'),\n url(r'^admin/', include(admin.site.urls)),\n # authentication\n url(r'^accounts/login/$', 'django.contrib.auth.views.login', name='login'),\n url(r'^openid/login/$', 'django_openid_auth.views.login_begin', name='openid-login'),\n url(r'^login-complete/$', 'django_openid_auth.views.login_complete', name='openid-complete'),\n url(r'^logout/$', 'django.contrib.auth.views.logout', {'next_page': '/',}, name='logout'),\n url(r'^deactivate/$', login_required(DeactivateUserView.as_view()), name='deactivate_user'),\n url(r'^project/$', login_required(ProjectListView.as_view()), name='project_list'),\n url(r'^project/new/$', login_required(NewProjectView.as_view()), name='project_new'),\n url(r'^project/(?P\\d+)/$', login_required(ProjectDetailView.as_view()),name='project_detail'),\n url(r'^project/(?P\\d+)/delete/$', login_required(ProjectDeleteView.as_view()), name='project_delete'),\n url(r'^project/(?P\\d+)/rename/$', login_required(ProjectChangeTitleView.as_view()),name='project_change_title'),\n url(r'^project/(?P\\d+)/csv/$', login_required(ProjectDetailCSVView.as_view()),name='project_csv'),\n url(r'^project/(?P\\d+)/pdf/$', login_required(ProjectAnswerViewPDF.as_view()),name='project_pdf'),\n url(r'^project/(?P\\d+)/answers/$', login_required(ProjectAnswerView.as_view()),name='project_answers'),\n url(r'^project/(?P\\d+)/answers/(?P\\d+)/$', login_required(ProjectAnswerView.as_view()),name='project_answers'),\n url(r'^project/(?P\\d+)/hardware-software/$', login_required(ComponentView.as_view()), name='hardware-software'),\n url(r'^project/(?P\\d+)/(?P\\d+)/$', login_required(QuestionView.as_view()), name='answer_question'),\n url(r'^features/$',direct_to_template, {'template': 'features.html'},name='features'),\n url(r'^create/$',direct_to_template, {'template': 'create.html'},name='create'),\n url(r'^support/$',direct_to_template, {'template': 'support.html'},name='support'),\n url(r'^resources/$',direct_to_template, {'template': 'resources.html'},name='resources'),\n)\n","repo_name":"ircwash/2012-WASHCost-calculator-prototype","sub_path":"washcost/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2772,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"71285136359","text":"import os\nimport time\nfrom operator import itemgetter\n\nimport pandas as pd\nfrom sklearn.tree import DecisionTreeRegressor\nfrom tqdm import tqdm\n\nfrom lib.load_dataset import load\nfrom lib.classifier import NeuralNetwork, LogisticRegression, SVM\nfrom lib.utils import *\nfrom lib.metrics import * # include fairness and corresponding derivatives\n\nfrom bokeh.io import curdoc\nfrom bokeh.layouts import column, row\nfrom bokeh.models import ColumnDataSource, Select, Slider, Panel, PreText, Button, DataTable, TableColumn, Tabs, Div\nfrom bokeh.plotting import figure\nfrom bokeh.core.properties import Color\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn import metrics\nimport copy\n\ndataset = Select(title=\"Dataset\", value=\"german\", options=['german', 'adult', 'sqf'])\nclf = Select(title=\"Classifier\", value=\"Logistic Regression\", options=['Logistic Regression', 'Support Vector Machine',\n 'Neural Network'])\nlvl = Select(title='Level', value='3', options=['2', '3', '4', '5'])\nsup_lb = Select(title='Support Lower Bound (%)', value='5', options=['1', '2.5', '5', '10', '20'])\nsup_ub = Select(title='Support Upper Bound (%)', value='15', options=['10', '15', '20', '25', '30'])\nmetric_sel = Select(title='Fairness Metric', value='statistical parity',\n options=['statistical parity', 'equal opportunity', 'predictive parity'])\ncontainment_th = Slider(title='Containment Filtering Threshold', start=0.0, end=1.0, value=0.2, step=0.1)\n\nupdate_attr_1 = Select(title='attribute 1:', value='None', options=['None'])\nupdate_attr_2 = Select(title='attribute 2:', value='None', options=['None'])\nupdate_attr_3 = Select(title='attribute 3:', value='None', options=['None'])\nupdate_attr_4 = Select(title='attribute 4:', value='None', options=['None'])\n\nupdate_val_1 = Select(title='value:', value='None', options=['None'])\nupdate_val_2 = Select(title='value:', value='None', options=['None'])\nupdate_val_3 = Select(title='value:', value='None', options=['None'])\nupdate_val_4 = Select(title='value:', value='None', options=['None'])\n\nupdated_val_1 = Select(title='updated value:', value='None', options=['None'])\nupdated_val_2 = Select(title='updated value:', value='None', options=['None'])\nupdated_val_3 = Select(title='updated value:', value='None', options=['None'])\nupdated_val_4 = Select(title='updated value:', value='None', options=['None'])\n\nupdate_attrs = [update_attr_1, update_attr_2, update_attr_3, update_attr_4]\nupdate_vals = [update_val_1, update_val_2, update_val_3, update_val_4]\nupdated_vals = [updated_val_1, updated_val_2, updated_val_3, updated_val_4]\n\nfor v in updated_vals:\n v.disabled = True\n\nfor i in range(1, 4):\n update_attrs[i].visible = False\n update_attrs[i].height_policy = 'fixed'\n update_attrs[i].height = 0\n\n update_vals[i].visible = False\n update_vals[i].height_policy = 'fixed'\n update_vals[i].height = 0\n\n updated_vals[i].visible = False\n updated_vals[i].height_policy = 'fixed'\n updated_vals[i].height = 0\n\nacc = PreText(text='')\nspd = PreText(text='')\ntpr = PreText(text='')\nppr = PreText(text='')\npre_compute_percent = PreText(text='')\n\ntrain = Button(label='Train', button_type='success')\npre_compute = Button(label='Start Precomputation', button_type='success')\npre_compute.disabled = True\nremoval_explain = Button(label='Generate Removal-based Explanation', button_type='success')\nremoval_explain.disabled = True\nupdate_data_view = Button(label='Update data preview', button_type='success')\nupdate_explain = Button(label='Generate Update-based Explanation', button_type='success')\nupdate_explain.disabled = True\nadd_attr = Button(label='Add Predicate', button_type='success')\nremove_attr = Button(label='Remove Predicate', button_type='danger')\n\nload_expl_1 = Button(label='Load', button_type='success', width=100, align='start')\nload_expl_1.disabled = True\nexpl_1_txt = Div(text='', name='expl_1_txt')\nload_expl_col_1 = column([Div(text='Pattern 1:', style={'font-weight': 'bold', 'font-size': '13px'}), expl_1_txt,\n load_expl_1], name='load_rmv_1')\n\nload_expl_2 = Button(label='Load', button_type='success', width=100, align='start')\nload_expl_2.disabled = True\nexpl_2_txt = Div(text='', name='expl_2_txt')\nload_expl_col_2 = column([Div(text='Pattern 2:', style={'font-weight': 'bold', 'font-size': '13px'}), expl_2_txt,\n load_expl_2], name='load_rmv_2')\n# curdoc().add_root(load_expl_col_2)\n\nload_expl_3 = Button(label='Load', button_type='success', width=100, align='start', sizing_mode='fixed')\nload_expl_3.disabled = True\nexpl_3_txt = Div(text='', name='expl_3_txt')\nload_expl_col_3 = column([Div(text='Pattern 3:', style={'font-weight': 'bold', 'font-size': '13px'}), expl_3_txt,\n load_expl_3], name='load_rmv_3', sizing_mode='scale_width')\n# curdoc().add_root(load_expl_col_3)\n\nload_expls = [load_expl_1, load_expl_2, load_expl_3]\ncurrent_attr_idx = 0\n\nX_train, X_test, y_train, y_test = load(dataset='german', sample=False)\nX_train_orig, X_test_orig = copy.deepcopy(X_train), copy.deepcopy(X_test)\nX_train_show_upd = X_train_orig.copy()\nsource = ColumnDataSource(data=dict())\ncols = [TableColumn(field=col, title=col) for col in X_train.columns]\ntable = DataTable(source=source, columns=cols, autosize_mode='fit_columns', align='center')\n\nsource_metric = ColumnDataSource(data=dict())\ncols_metric = [TableColumn(title='Metric', field='metric'),\n TableColumn(title='Score', field='val')]\ntable_metric = DataTable(source=source_metric, columns=cols_metric, autosize_mode='fit_columns',\n align='center', index_position=None)\n\nsource_rmv = ColumnDataSource(data=dict())\ncols_rmv = [TableColumn(title='Explanations', field='explanations'),\n TableColumn(title='Support (%)', field='support'),\n TableColumn(title='Δ bias (%)', field='second_infs'),\n TableColumn(title='Interestingness', field='score')]\ntable_rmv = DataTable(source=source_rmv, columns=cols_rmv, autosize_mode='fit_columns', align='center')\n\nsource_fot = ColumnDataSource(data=dict())\ncols_fot = [TableColumn(title='Explanations', field='explanations'),\n TableColumn(title='Support (%)', field='support'),\n TableColumn(title='Δ bias (%)', field='second_infs'),\n TableColumn(title='Interestingness', field='score')]\ntable_fot = DataTable(source=source_fot, columns=cols_fot, autosize_mode='fit_columns', align='center')\n\nmodel = LogisticRegression(input_size=X_train.shape[-1])\nnum_params = len(convert_grad_to_ndarray(list(model.parameters())))\nloss_func = logistic_loss_torch\nhessian_all_points = []\ndel_L_del_theta = []\nmetric = 0\nmetric_val = 0\nmetric_vals = []\nv1 = None\ninfs_1 = []\nhinv = None\nhinv_v = None\ncandidates = None\nexplanations = None\nsc = StandardScaler()\n\ncol_sens_1 = Div(text=' Sensitive Attribute: ', sizing_mode='stretch_width',\n style={'font-weight': 'bold', 'font-size': '13px'})\ncol_sens_2 = Div(text=' Privileged/Protected: ', sizing_mode='stretch_width',\n style={'font-weight': 'bold', 'font-size': '13px'})\ncurdoc().add_root(column(col_sens_1, col_sens_2, sizing_mode='stretch_width', name='tab1_sens_attr'))\n\nspd_source = ColumnDataSource(data=dict(y=[0], y_text=[' ']))\nspd_fig = figure(height=350, toolbar_location=None, outline_line_color=None,\n sizing_mode=\"scale_both\", name=\"spd_fig\", x_range=(-1, 1), y_range=(-30, 30))\nspd_fig.vbar(x=0, bottom=0, top='y', color=\"grey\", alpha=0.5, source=spd_source)\nspd_fig.text(x=0, y='y', text='y_text', angle=0, x_offset=0, y_offset=-10, text_align='center', text_baseline='bottom',\n text_font_size={'value': '15px'}, source=spd_source)\nspd_fig.xgrid.grid_line_color = None\nspd_fig.xaxis.major_label_text_color = None\nspd_fig.xaxis.major_tick_line_color = None\nspd_fig.axis.minor_tick_line_color = None\nspd_fig.yaxis.axis_label = 'Probability Difference (%)'\ncurdoc().add_root(spd_fig)\n\ntpr_source = ColumnDataSource(data=dict(y=[0], y_text=[' ']))\ntpr_fig = figure(height=350, toolbar_location=None, outline_line_color=None,\n sizing_mode=\"scale_both\", name=\"tpr_fig\", x_range=(-1, 1), y_range=(-30, 30))\ntpr_fig.vbar(x=0, bottom=0, top='y', color=\"grey\", alpha=0.5, source=tpr_source)\ntpr_fig.text(x=0, y='y', text='y_text', angle=0, x_offset=0, y_offset=-10, text_align='center', text_baseline='bottom',\n text_font_size={'value': '15px'}, source=tpr_source)\ntpr_fig.xgrid.grid_line_color = None\ntpr_fig.xaxis.major_label_text_color = None\ntpr_fig.xaxis.major_tick_line_color = None\ntpr_fig.axis.minor_tick_line_color = None\ntpr_fig.yaxis.axis_label = 'Probability Difference (%)'\ncurdoc().add_root(tpr_fig)\n\nppr_source = ColumnDataSource(data=dict(y=[0], y_text=[' ']))\nppr_fig = figure(height=350, toolbar_location=None, outline_line_color=None,\n sizing_mode=\"scale_both\", name=\"ppr_fig\", x_range=(-1, 1), y_range=(-30, 30))\nppr_fig.vbar(x=0, bottom=0, top='y', color=\"grey\", alpha=0.5, source=ppr_source)\nppr_fig.text(x=0, y='y', text='y_text', angle=0, x_offset=0, y_offset=-10, text_align='center', text_baseline='bottom',\n text_font_size={'value': '15px'}, source=ppr_source)\nppr_fig.xgrid.grid_line_color = None\nppr_fig.xaxis.major_label_text_color = None\nppr_fig.xaxis.major_tick_line_color = None\nppr_fig.axis.minor_tick_line_color = None\nppr_fig.yaxis.axis_label = 'Probability Difference (%)'\ncurdoc().add_root(ppr_fig)\n\nupdate_fairness_source = ColumnDataSource(data=dict(y0=[0], y0_text=[' '], y1=[0], y1_text=[' ']))\nupdate_fairness_fig = figure(height=450, toolbar_location=None, outline_line_color=None,\n sizing_mode=\"scale_both\", name=\"tab3_fairness_fig\", x_range=(-1, 2), y_range=(-30, 30))\nupdate_fairness_fig.vbar(x=0, bottom=0, top='y0', color=\"grey\", alpha=0.5, source=update_fairness_source,\n legend_label='original')\nupdate_fairness_fig.text(x=0, y='y0', text='y0_text', angle=0, x_offset=0, y_offset=-10,\n text_align='center', text_baseline='bottom', text_font_size={'value': '15px'},\n source=update_fairness_source)\nupdate_fairness_fig.vbar(x=1, bottom=0, top='y1', color=\"blue\", alpha=0.5, source=update_fairness_source,\n legend_label='updated')\nupdate_fairness_fig.text(x=1, y='y1', text='y1_text', angle=0, x_offset=0, y_offset=-10,\n text_align='center', text_baseline='bottom', text_font_size={'value': '15px'},\n source=update_fairness_source)\nupdate_fairness_fig.xgrid.grid_line_color = None\nupdate_fairness_fig.xaxis.major_label_text_color = None\nupdate_fairness_fig.xaxis.major_tick_line_color = None\nupdate_fairness_fig.axis.minor_tick_line_color = None\nupdate_fairness_fig.yaxis.axis_label = 'Probability Difference (%)'\nupdate_fairness_fig.xaxis.axis_label = 'Update-based Explanation:\\n'\n\nupdate_acc_source = ColumnDataSource(data=dict(y0=[0], y0_text=[' '], y1=[0], y1_text=[' ']))\nupdate_acc_fig = figure(height=450, toolbar_location=None, outline_line_color=None,\n sizing_mode=\"scale_both\", name=\"tab3_acc_fig\", x_range=(-1, 2), y_range=(0, 100))\nupdate_acc_fig.vbar(x=0, bottom=0, top='y0', color=\"grey\", alpha=0.5, source=update_acc_source,\n legend_label='original')\nupdate_acc_fig.text(x=0, y='y0', text='y0_text', angle=0, x_offset=0, y_offset=-10,\n text_align='center', text_baseline='bottom', text_font_size={'value': '15px'},\n source=update_acc_source)\nupdate_acc_fig.vbar(x=1, bottom=0, top='y1', color=\"blue\", alpha=0.5, source=update_acc_source,\n legend_label='updated')\nupdate_acc_fig.text(x=1, y='y1', text='y1_text', angle=0, x_offset=0, y_offset=-10,\n text_align='center', text_baseline='bottom', text_font_size={'value': '15px'},\n source=update_acc_source)\nupdate_acc_fig.xgrid.grid_line_color = None\nupdate_acc_fig.xaxis.major_label_text_color = None\nupdate_acc_fig.xaxis.major_tick_line_color = None\nupdate_acc_fig.axis.minor_tick_line_color = None\nupdate_acc_fig.yaxis.axis_label = 'Accuracy (%)'\nupdate_acc_fig.xaxis.axis_label = 'Update-based Explanation:\\n'\n\nTOOLTIPS_RMV = [\n (\"Pattern\", \"@pattern\"),\n]\n\nrmv_gopher_source = ColumnDataSource(data=dict(y0=[0], y0_text=[' ']))\nrmv_gopher_fig = figure(height=300, toolbar_location=None, outline_line_color=None, sizing_mode=\"stretch_width\",\n y_range=(-30, 30), tooltips=TOOLTIPS_RMV, name='tab2_comp_fig')\nrmv_gopher_fig.vbar(x='x0', bottom=0, top='y0', color=\"c\", alpha=0.5, source=rmv_gopher_source)\nrmv_gopher_fig.text(x='x0', y='y0', text='y0_text', angle=0, x_offset=0, y_offset='y0_off', text_align='center',\n text_baseline='bottom', text_font_size={'value': '15px'}, source=rmv_gopher_source)\nrmv_gopher_fig.xgrid.grid_line_color = None\nrmv_gopher_fig.xaxis.major_label_text_font_size = \"15px\"\nrmv_gopher_fig.xaxis.major_tick_line_color = None\nrmv_gopher_fig.axis.minor_tick_line_color = None\nrmv_gopher_fig.yaxis.axis_label = 'Probability Difference (%)'\ntab_gopher = Panel(child=column(rmv_gopher_fig, sizing_mode='stretch_both'), title=\"Gopher Fairness Comparison\")\n\nrmv_fot_source = ColumnDataSource(data=dict(y0=[0], y0_text=[' ']))\nrmv_fot_fig = figure(height=300, toolbar_location=None, outline_line_color=None, sizing_mode=\"stretch_width\",\n y_range=(-30, 30), tooltips=TOOLTIPS_RMV, name='tab2_comp_fig')\nrmv_fot_fig.vbar(x='x0', bottom=0, top='y0', color=\"c\", alpha=0.5, source=rmv_fot_source)\nrmv_fot_fig.text(x='x0', y='y0', text='y0_text', angle=0, x_offset=0, y_offset='y0_off', text_align='center',\n text_baseline='bottom', text_font_size={'value': '15px'}, source=rmv_fot_source)\nrmv_fot_fig.xgrid.grid_line_color = None\nrmv_fot_fig.xaxis.major_label_text_font_size = \"15px\"\nrmv_fot_fig.xaxis.major_tick_line_color = None\nrmv_fot_fig.axis.minor_tick_line_color = None\nrmv_fot_fig.yaxis.axis_label = 'Probability Difference (%)'\ntab_fot = Panel(child=column(rmv_fot_fig, sizing_mode='stretch_both'), title=\"FO-Tree Fairness Comparison\")\n\nTOOLTIPS_CLASS = [\n (\"Negative\", \"@y0_text\"),\n (\"Positive\", \"@y1_text\")\n]\n\nclass_distrib_source = ColumnDataSource(data=dict(y0=[0 for _ in range(6)], y1=[0 for _ in range(6)],\n x0=list(np.arange(6) * 3), x1=list(np.arange(6) * 3 + 1),\n y0_text=['0' for _ in range(6)], y1_text=['0' for _ in range(6)]))\nclass_distrib_fig = figure(height=300, toolbar_location=None, outline_line_color=None, sizing_mode=\"stretch_width\",\n y_range=(0, 100), tooltips=TOOLTIPS_CLASS)\nclass_distrib_fig.vbar(x='x0', bottom=0, top='y0', color=\"green\", alpha=0.5, legend_label='Negative Label',\n source=class_distrib_source)\nclass_distrib_fig.vbar(x='x1', bottom=0, top='y1', color=\"orange\", alpha=0.5, legend_label='Positive Label',\n source=class_distrib_source)\nclass_distrib_fig.xgrid.grid_line_color = None\nclass_distrib_fig.xaxis.major_label_text_font_size = \"15px\"\nclass_distrib_fig.xaxis.major_tick_line_color = None\nclass_distrib_fig.axis.minor_tick_line_color = None\nclass_distrib_fig.yaxis.axis_label = 'Percentage within subset (%)'\nclass_distrib_fig.xaxis.ticker = list(np.arange(6) * 3 + 0.5)\nclass_distrib_xticks_dict = dict()\nfor i in range(3):\n class_distrib_xticks_dict[3 * i + 0.5] = f'Gopher Pattern {i + 1}'\n class_distrib_xticks_dict[3 * i + 9.5] = f'FO-Tree Pattern {i + 1}'\nclass_distrib_fig.xaxis.major_label_overrides = class_distrib_xticks_dict\ntab_class_distrib = Panel(child=column(class_distrib_fig, sizing_mode='stretch_both'),\n title=\"Class Distribution Comparison\")\n\ncurdoc().add_root(Tabs(tabs=[tab_gopher, tab_fot, tab_class_distrib], name='tab2_comp_fig'))\n\nTOOLTIPS_UPD = [\n (\"Attribute\", \"@attr\"),\n (\"Original avg. meaning\", \"@orig_meaning\"),\n (\"New avg. meaning\", \"@new_meaning\")\n]\n\nupdate_val_source = ColumnDataSource(data=dict(y0=[0], y0_text=[' '], y1=[0], y1_text=[' ']))\nupdate_val_fig = figure(height=300, toolbar_location=None, outline_line_color=None, sizing_mode=\"stretch_width\",\n name=\"tab3_val_fig\", y_range=(0, 5), tooltips=TOOLTIPS_UPD)\nupdate_val_fig.vbar(x='x0', bottom=0, top='y0', color=\"grey\", alpha=0.5, source=update_val_source)\nupdate_val_fig.text(x='x0', y='y0', text='y0_text', angle=0, x_offset=0, y_offset='y0_off', text_align='center',\n text_baseline='bottom', text_font_size={'value': '15px'}, source=update_val_source)\nupdate_val_fig.vbar(x='x1', bottom=0, top='y1', color=\"blue\", alpha=0.5, source=update_val_source)\nupdate_val_fig.text(x='x1', y='y1', text='y1_text', angle=0, x_offset=0, y_offset='y1_off', text_align='center',\n text_baseline='bottom', text_font_size={'value': '15px'}, source=update_val_source)\n\nupdate_val_fig.xgrid.grid_line_color = None\nupdate_val_fig.xaxis.major_label_text_font_size = \"15px\"\nupdate_val_fig.xaxis.major_tick_line_color = None\nupdate_val_fig.axis.minor_tick_line_color = None\nupdate_val_fig.yaxis.axis_label = 'Attribute Values'\ncurdoc().add_root(update_val_fig)\n\n\ndef update_fairness(spd, tpr, ppr):\n spd_source.data['y'] = [round(-spd * 100, 2)]\n spd_source.data['y_text'] = [str(round(-spd * 100, 2))]\n tpr_source.data['y'] = [round(-tpr * 100, 2)]\n tpr_source.data['y_text'] = [str(round(-tpr * 100, 2))]\n ppr_source.data['y'] = [round(-ppr * 100, 2)]\n ppr_source.data['y_text'] = [str(round(-ppr * 100, 2))]\n adp_range(spd_fig, [-v * 100 for v in metric_vals])\n adp_range(tpr_fig, [-v * 100 for v in metric_vals])\n adp_range(ppr_fig, [-v * 100 for v in metric_vals])\n\n\ndef update_comparison_fig():\n global num_params, loss_func, metric_vals, model, v1, sc\n\n update_explain.disabled = True\n model.fit(X_train, y_train)\n metric = ['statistical parity', 'equal opportunity', 'predictive parity'].index(metric_sel.value)\n metric_val = metric_vals[metric] * 100\n update_fairness_source.data['y0'] = [-round(metric_val, 2)]\n update_fairness_source.data['y0_text'] = [str(-round(metric_val, 2))]\n acc = computeAccuracy(y_test, model.predict_proba(X_test)) * 100\n update_acc_source.data['y0'] = [round(acc, 2)]\n update_acc_source.data['y0_text'] = [str(round(acc, 2))]\n\n ns = [50, 30, 10]\n expl = []\n for attr_id in range(current_attr_idx + 1):\n attr = update_attrs[attr_id].value\n val = update_vals[attr_id].value\n if val != 'None':\n expl.append(f'{attr}={attr_val_mapping(attr, val, dataset.value, inv=True)}')\n idx = get_subset(expl)\n if len(idx) > 0:\n S = torch.Tensor(X_train[idx])\n S.requires_grad = True\n delta = torch.zeros(1, X_train.shape[1])\n delta.requires_grad = True\n S_new = S + delta\n part_1 = torch.FloatTensor(v1).repeat(len(S_new), 1).reshape(len(S_new), 1, -1)\n part_2 = []\n for i in range(len(S_new)):\n inner_lst = []\n del_L_del_theta_i_t = convert_grad_to_tensor(\n del_L_del_theta_i(model, S_new[i], y_train[i], retain_graph=True))\n for j in range(len(del_L_del_theta_i_t)):\n inner_grad = convert_grad_to_ndarray(grad(del_L_del_theta_i_t[j], delta, retain_graph=True))\n inner_lst.append(inner_grad)\n part_2.append(np.array(inner_lst))\n\n part_2 = np.array(part_2)\n part_2 = torch.FloatTensor(part_2)\n part_2 = part_2.mean(dim=0).unsqueeze(0).repeat(len(S_new), 1, 1)\n final = torch.bmm(part_1, part_2).reshape((len(S_new), -1))\n delta_bak = delta.detach().clone()\n new_metric_vals = []\n X_train_news = []\n for n in ns:\n delta = delta_bak - n * final\n X_train_new = X_train.copy()\n X_train_new[idx] = X_train_new[idx] + delta.detach().numpy()\n from scipy.optimize import Bounds, minimize\n mins = []\n maxs = []\n numCols = len(X_train[0])\n for i in range(numCols):\n mins.insert(i, min(X_train[:, i]))\n maxs.insert(i, max(X_train[:, i]))\n\n bounds = Bounds(mins, maxs)\n tbar = tqdm(total=len(idx))\n for i in idx:\n X_train_pert_pt = X_train_new[i]\n f = lambda x: np.linalg.norm(x - X_train_pert_pt)\n\n x0 = X_train[i]\n res = minimize(f, x0, method='trust-constr', options={'verbose': 0}, bounds=bounds)\n X_train_new[i] = res.x\n tbar.update()\n\n if clf.value == 'Logistic Regression':\n model = LogisticRegression(input_size=X_train.shape[-1])\n elif clf.value == 'Support Vector Machine':\n model = SVM(input_size=X_train.shape[-1])\n else:\n model = NeuralNetwork(input_size=X_train.shape[-1])\n model.fit(X_train_new, y_train)\n y_pred_test = model.predict_proba(X_test)\n metric_id = ['statistical parity', 'equal opportunity', 'predictive parity'].index(metric_sel.value)\n new_metric_vals.append(computeFairness(y_pred_test, X_test_orig, y_test, metric_id, dataset.value))\n X_train_news.append(X_train_new)\n\n n_idx = np.argmin(np.abs(new_metric_vals))\n new_metric_val = new_metric_vals[n_idx]\n X_train_new = X_train_news[n_idx]\n update_fairness_source.data['y1'] = [round(-new_metric_val * 100, 2)]\n update_fairness_source.data['y1_text'] = [str(round(-new_metric_val * 100, 2))]\n adp_range(update_fairness_fig, update_fairness_source.data['y0'] + update_fairness_source.data['y1'])\n\n acc = computeAccuracy(y_test, y_pred_test)\n update_acc_source.data['y1'] = [round(acc * 100, 2)]\n update_acc_source.data['y1_text'] = [str(round(acc * 100, 2))]\n\n k = 5\n X_train_new_inv = sc.inverse_transform(X_train_new)\n orig_vals = np.mean(X_train_orig.loc[idx], axis=0)\n upd_vals = np.mean(X_train_new_inv[idx], axis=0)\n val_diff = upd_vals - orig_vals\n joint_list = list(zip(list(X_train_orig.columns), list(val_diff), list(orig_vals), list(upd_vals)))\n sorted_joint_list = sorted(joint_list, key=lambda x: abs(x[1]), reverse=True)[:k]\n update_val_source.data['attr'] = [p[0] for p in sorted_joint_list]\n update_val_source.data['orig_meaning'] = [attr_val_mapping(p[0], int(p[2] + 0.5), dataset.value) \\\n for p in sorted_joint_list]\n update_val_source.data['new_meaning'] = [attr_val_mapping(p[0], int(p[3] + 0.5), dataset.value) \\\n for p in sorted_joint_list]\n update_val_source.data['x0'] = 3 * np.arange(k)\n update_val_source.data['x1'] = 3 * np.arange(k) + 1\n update_val_source.data['y0'] = [round(p[2], 2) for p in sorted_joint_list]\n update_val_source.data['y1'] = [round(p[3], 2) for p in sorted_joint_list]\n update_val_source.data['y0_text'] = [str(round(p[2], 2)) for p in sorted_joint_list]\n update_val_source.data['y1_text'] = [str(round(p[3], 2)) for p in sorted_joint_list]\n update_val_source.data['y0_off'] = np.where(np.array(update_val_source.data['y0']) < 0, 1, -0.25) * 20\n update_val_source.data['y1_off'] = np.where(np.array(update_val_source.data['y1']) < 0, 1, -0.25) * 20\n update_val_fig.xaxis.ticker = 3 * np.arange(k) + 0.5\n update_val_fig.xaxis.major_label_overrides = {3 * i + 0.5: update_val_source.data['attr'][i] for i in\n np.arange(k)}\n\n update_expl = []\n for i in range(0, current_attr_idx + 1):\n attr = update_attrs[i].value\n attr_id = list(X_train_orig.columns).index(attr)\n val = int(float(upd_vals[attr_id]) + 0.5)\n update_expl.append(f'{attr}: {attr_val_mapping(attr, val, dataset.value)}')\n\n update_expl_txt = join_predicates_with_linebreak(update_expl, break_len=60)\n\n update_acc_fig.xaxis.axis_label = 'Update-based Explanation:\\n' + update_expl_txt\n update_fairness_fig.xaxis.axis_label = 'Update-based Explanation:\\n' + update_expl_txt\n\n update_explain.disabled = False\n\n\ndef join_predicates_with_linebreak(predicates, break_len=60):\n expl_txt = ''\n line_char_cnt = 0\n for e_idx, e in enumerate(predicates):\n if line_char_cnt != 0:\n if line_char_cnt + len(e) + 3 > break_len:\n expl_txt += ('\\n' + e + ' ∧ ')\n line_char_cnt = len(e) + 3\n else:\n expl_txt += (e + ' ∧ ')\n line_char_cnt += (len(e) + 3)\n else:\n expl_txt += (e + ' ∧ ')\n line_char_cnt += (len(e) + 3)\n if e_idx == len(predicates) - 1:\n expl_txt = expl_txt[:-3]\n return expl_txt\n\n\ndef del_L_del_theta_i(model, x, y_true, retain_graph=False):\n loss = loss_func(model, x, y_true)\n w = [p for p in model.parameters() if p.requires_grad]\n return grad(loss, w, create_graph=True, retain_graph=retain_graph)\n\n\ndef del_f_del_theta_i(model, x, retain_graph=False):\n w = [p for p in model.parameters() if p.requires_grad]\n return grad(model(torch.FloatTensor(x)), w, retain_graph=retain_graph)\n\n\ndef hvp(y, w, v):\n \"\"\" Multiply the Hessians of y and w by v.\"\"\"\n # First backprop\n first_grads = grad(y, w, retain_graph=True, create_graph=True)\n\n # Elementwise products\n elemwise_products = 0\n for grad_elem, v_elem in zip(convert_grad_to_tensor(first_grads), v):\n elemwise_products += torch.sum(grad_elem * v_elem)\n\n # Second backprop\n return_grads = grad(elemwise_products, w, create_graph=True)\n\n return return_grads\n\n\ndef hessian_one_point(model, x, y):\n x, y = torch.FloatTensor(x), torch.FloatTensor([y])\n loss = loss_func(model, x, y)\n params = [p for p in model.parameters() if p.requires_grad]\n first_grads = convert_grad_to_tensor(grad(loss, params, retain_graph=True, create_graph=True))\n hv = np.zeros((len(first_grads), len(first_grads)))\n for i in range(len(first_grads)):\n hv[i, :] = convert_grad_to_ndarray(grad(first_grads[i], params, create_graph=True)).ravel()\n return hv\n\n\n# Compute multiplication of inverse hessian matrix and vector v\ndef s_test(model, xs, ys, v, hinv=None, damp=0.01, scale=25.0, r=-1, batch_size=-1, recursive=False):\n xs, ys = torch.FloatTensor(xs.copy()), torch.FloatTensor(ys.copy())\n n = len(xs)\n if recursive:\n hinv_v = copy.deepcopy(v)\n if batch_size == -1: # default\n batch_size = 10\n if r == -1:\n r = n // batch_size + 1\n sample = np.random.choice(range(n), r * batch_size, replace=True)\n for i in range(r):\n sample_idx = sample[i * batch_size:(i + 1) * batch_size]\n x, y = xs[sample_idx], ys[sample_idx]\n loss = loss_func(model, x, y)\n params = [p for p in model.parameters() if p.requires_grad]\n hv = convert_grad_to_ndarray(hvp(loss, params, torch.FloatTensor(hinv_v)))\n # Recursively caclulate h_estimate\n hinv_v = v + (1 - damp) * hinv_v - hv / scale\n else:\n if hinv is None:\n hinv = np.linalg.pinv(np.sum(hessian_all_points, axis=0))\n scale = 1.0\n hinv_v = np.matmul(hinv, v)\n\n return hinv_v / scale\n\n\ndef proc_expls(expl_line, df_name=\"german\"):\n if df_name == \"german\":\n status_d = {v: k for k, v in {'< 0DM': 0, '0 ~ 200DM': 1, '≥ 200DM / salary assignment for at least 1 year': 2,\n 'no checking account': 3}.items()}\n credit_hist_d = {v: k for k, v in {'critical account/ other credits existing (not at this bank)': 0,\n 'delay in paying off in the past': 1,\n 'existing credits paid back duly till now': 2,\n 'all credits at this bank paid back duly': 3,\n 'no credits taken/ all credits paid back duly': 4}.items()}\n savings_d = {v: k for k, v in {'< 100DM': 0, '100DM ~ 500DM': 1, '500DM ~ 1000DM': 2, '≥ 1000DM': 3,\n 'unknown/ no savings account': 4}.items()}\n employment_d = {v: k for k, v in {'unemployed': 0, '< 1 year': 1, '1 year ~ 4 years': 2, '4 years ~ 7 years': 3,\n '≥ 7 years': 4}.items()}\n gender_d = {v: k for k, v in {'male': 1, 'female': 0}.items()}\n debtors_d = {v: k for k, v in {'none': 0, 'co-applicant': 1, 'guarantor': 2}.items()}\n property_d = {v: k for k, v in\n {'real estate': 3, 'building society savings agreement/ life insurance': 2, 'car or other': 1,\n 'unknown / no property': 0}.items()}\n job_d = {v: k for k, v in {'unemployed/ unskilled - non-resident': 0, 'unskilled - resident': 1,\n 'skilled employee / official': 2,\n 'management/ self-employed/ highly qualified employee/ officer': 3}.items()}\n install_plans_d = {v: k for k, v in {'bank': 1, 'stores': 1, 'none': 0}.items()}\n telephone_d = {v: k for k, v in {'none': 0, 'yes, registered under the customers name': 1}.items()}\n foreign_worker_d = {v: k for k, v in {'yes': 1, 'no': 0}.items()}\n age_d = {1: '≥ 45', 0: '< 45'}\n credit_amt_d = {0: '≤ 2000', 1: '2000 ~ 5000', 2: ' > 5000'}\n duration_d = {0: '≤ 12', 1: '12 ~ 24', 2: '24 ~ 36', 3: '> 36'}\n install_transfer = lambda x: str(x) + '%'\n install_rate_d = dict()\n for i in range(5):\n install_rate_d[i] = install_transfer(i)\n\n residence_transfer = lambda x: str(x) + ' year(s)'\n residence_d = dict()\n for i in range(5):\n residence_d[i] = residence_transfer(i)\n\n elif df_name == \"adult\":\n income_d = {v: k for k, v in {'≤ 50K': 0, '> 50K': 1}.items()}\n age_d = {v: k for k, v in {'< 45': 0, '≥ 45': 1}.items()}\n workclass_d = {v: k for k, v in\n {'Never-worked': 0, 'Without-pay': 1, 'State-gov': 2, 'Local-gov': 3, 'Federal-gov': 4,\n 'Self-emp-inc': 5, 'Self-emp-not-inc': 6, 'Private': 7}.items()}\n education_d = {v: k for k, v in\n {'Preschool': 0, '1st-4th': 1, '5th-6th': 2, '7th-8th': 3, '9th': 4, '10th': 5, '11th': 6,\n '12th': 7, 'HS-grad': 8, 'Some-college': 9, 'Bachelors': 10, 'Prof-school': 11,\n 'Assoc-acdm': 12, 'Assoc-voc': 13, 'Masters': 14, 'Doctorate': 15}.items()}\n marital_d = {v: k for k, v in\n {'Married-civ-spouse': 2, 'Divorced': 1, 'Never-married': 0, 'Separated': 1, 'Widowed': 1,\n 'Married-spouse-absent': 2, 'Married-AF-spouse': 2}.items()}\n relationship_d = {v: k for k, v in\n {'Wife': 1, 'Own-child': 0, 'Husband': 1, 'Not-in-family': 0, 'Other-relative': 0,\n 'Unmarried': 0}.items()}\n race_d = {v: k for k, v in\n {'White': 1, 'Asian-Pac-Islander': 0, 'Amer-Indian-Eskimo': 0, 'Other': 0, 'Black': 0}.items()}\n gender_d = {v: k for k, v in {'Male': 1, 'Female': 0}.items()}\n hours_d = {v: k for k, v in {'> 40': 1, '≤ 40': 0}.items()}\n\n elif df_name == \"sqf\":\n # inout_I_d = {v: k for k, v in {'O': 0, 'I': 1}.items()}\n # inout_O_d = {v: k for k, v in {'I': 0, 'O': 1}.items()}\n # sex_M_d = {v: k for k, v in {'F': 0, 'M': 1}.items()}\n # sex_F_d = {v: k for k, v in {'M': 0, 'F': 1}.items()}\n race_d = {v: k for k, v in {'B': 0, 'W': 1, 'P': 0, 'Q': 1}.items()}\n build_d = {v: k for k, v in {'H': 2, 'U': 2, 'M': 1, 'T': 0}.items()}\n age_d = {v: k for k, v in {'< 25': 0, '25 ~ 45': 1, '≥ 45': 2}.items()}\n ht_feet_d = {v: k for k, v in {'< 5': 0, '5': 1, '> 5': 2}.items()}\n weight_d = {v: k for k, v in {'< 135': 0, '135 ~ 250': 1, '≥ 250': 2}.items()}\n perobs_d = {v: k for k, v in {'< 2': 0, '≥ 2': 1}.items()}\n lst = ['cs_objcs',\n 'cs_descr',\n 'cs_casng',\n 'cs_lkout',\n 'cs_drgtr',\n 'cs_vcrim',\n 'ac_proxm',\n 'inout_I',\n 'inout_O',\n 'sex_M',\n 'sex_F',\n 'frisked']\n for n in lst:\n exec(n + '_d = {v: k for k, v in {\\'N\\': 0, \\'Y\\': 1}.items()}')\n # frisked_d = {v: k for k, v in {'Y': 0, 'N': 1}.items()}\n # sqf_col_d = {v: k for k, v in {\n # 'STOP_LOCATION_PRECINCT': 'pct',\n # 'LOCATION_IN_OUT_CODE': 'inout',\n # 'SUSPECT_SEX': 'sex',\n # 'SUSPECT_RACE_DESCRIPTION': 'race',\n # 'SUSPECT_BODY_BUILD_TYPE': 'build',\n # 'SUSPECT_REPORTED_AGE': 'age',\n # 'SUSPECT_HEIGHT': 'ht_feet',\n # 'SUSPECT_WEIGHT': 'weight',\n # 'OBSERVED_DURATION_MINUTES': 'perobs',\n # 'SUSPECTED_CRIME_DESCRIPTION': 'crimsusp',\n # 'SUSPECTS_ACTIONS_CONCEALED_POSSESSION_WEAPON_FLAG': 'cs_objcs',\n # 'SUSPECTS_ACTIONS_DECRIPTION_FLAG': 'cs_descr',\n # 'SUSPECTS_ACTIONS_CASING_FLAG': 'cs_casng',\n # 'SUSPECTS_ACTIONS_LOOKOUT_FLAG': 'cs_lkout',\n # 'SUSPECTS_ACTIONS_DRUG_TRANSACTIONS_FLAG': 'cs_drgtr',\n # 'BACKROUND_CIRCUMSTANCES_VIOLENT_CRIME_FLAG': 'cs_vcrim',\n # 'SUSPECTS_ACTIONS_PROXIMITY_TO_SCENE_FLAG': 'ac_proxm',\n # 'FRISKED_FLAG': 'frisked'}.items()}\n\n out = \"\"\n expl_lst = eval(expl_line)\n for expl in expl_lst:\n expl_type = expl.split(\"=\")[0]\n expl_value = int(expl.split(\"=\")[-1])\n d = expl_type + \"_d\"\n try:\n expl_d = eval(d)\n except NameError:\n # print()\n print(df_name, d, expl_value)\n out += expl_type + \": \" + str(expl_value) + \" ∧ \"\n continue\n\n meaning = expl_d[expl_value]\n out += expl_type + \": \" + meaning + \" ∧ \"\n\n out = out[:-3]\n return out\n\n\ndef attr_val_mapping(attr, val, df_name=\"german\", inv=False):\n if df_name == \"german\":\n status_d = {v: k for k, v in {'< 0DM': 0, '0 ~ 200DM': 1, '≥ 200DM / salary assignment for at least 1 yaer': 2,\n 'no checking account': 3}.items()}\n credit_hist_d = {v: k for k, v in {'critical account/ other credits existing (not at this bank)': 0,\n 'delay in paying off in the past': 1,\n 'existing credits paid back duly till now': 2,\n 'all credits at this bank paid back duly': 3,\n 'no credits taken/ all credits paid back duly': 4}.items()}\n savings_d = {v: k for k, v in {'< 100DM': 0, '100DM ~ 500DM': 1, '500DM ~ 1000DM': 2, '≥ 1000DM': 3,\n 'unknown/ no savings account': 4}.items()}\n employment_d = {v: k for k, v in {'unemployed': 0, '< 1 year': 1, '1 year ~ 4 years': 2, '4 years ~ 7 years': 3,\n '≥ 7 years': 4}.items()}\n gender_d = {v: k for k, v in {'male': 1, 'female': 0}.items()}\n debtors_d = {v: k for k, v in {'none': 0, 'co-applicant': 1, 'guarantor': 2}.items()}\n property_d = {v: k for k, v in\n {'real estate': 3, 'building society savings agreement/ life insurance': 2, 'car or other': 1,\n 'unknown / no property': 0}.items()}\n job_d = {v: k for k, v in {'unemployed/ unskilled - non-resident': 0, 'unskilled - resident': 1,\n 'skilled employee / official': 2,\n 'management/ self-employed/ highly qualified employee/ officer': 3}.items()}\n install_plans_d = {v: k for k, v in {'bank': 1, 'stores': 1, 'none': 0}.items()}\n telephone_d = {v: k for k, v in {'none': 0, 'yes, registered under the customers name': 1}.items()}\n foreign_worker_d = {v: k for k, v in {'yes': 1, 'no': 0}.items()}\n age_d = {1: '≥ 45', 0: '< 45'}\n credit_amt_d = {0: '≤ 2000', 1: '2000 ~ 5000', 2: ' > 5000'}\n duration_d = {0: '≤ 12', 1: '12 ~ 24', 2: '24 ~ 36', 3: '> 36'}\n install_transfer = lambda x: str(x) + '%'\n install_rate_d = dict()\n for i in range(5):\n install_rate_d[i] = install_transfer(i)\n\n residence_transfer = lambda x: str(x) + ' year(s)'\n residence_d = dict()\n for i in range(5):\n residence_d[i] = residence_transfer(i)\n\n elif df_name == \"adult\":\n income_d = {v: k for k, v in {'≤ 50K': 0, '> 50K': 1}.items()}\n age_d = {v: k for k, v in {'< 45': 0, '≥ 45': 1}.items()}\n workclass_d = {v: k for k, v in\n {'Never-worked': 0, 'Without-pay': 1, 'State-gov': 2, 'Local-gov': 3, 'Federal-gov': 4,\n 'Self-emp-inc': 5, 'Self-emp-not-inc': 6, 'Private': 7}.items()}\n education_d = {v: k for k, v in\n {'Preschool': 0, '1st-4th': 1, '5th-6th': 2, '7th-8th': 3, '9th': 4, '10th': 5, '11th': 6,\n '12th': 7, 'HS-grad': 8, 'Some-college': 9, 'Bachelors': 10, 'Prof-school': 11,\n 'Assoc-acdm': 12, 'Assoc-voc': 13, 'Masters': 14, 'Doctorate': 15}.items()}\n marital_d = {v: k for k, v in\n {'Married-civ-spouse': 2, 'Divorced': 1, 'Never-married': 0, 'Separated': 1, 'Widowed': 1,\n 'Married-spouse-absent': 2, 'Married-AF-spouse': 2}.items()}\n relationship_d = {v: k for k, v in\n {'Wife': 1, 'Own-child': 0, 'Husband': 1, 'Not-in-family': 0, 'Other-relative': 0,\n 'Unmarried': 0}.items()}\n race_d = {v: k for k, v in\n {'White': 1, 'Asian-Pac-Islander': 0, 'Amer-Indian-Eskimo': 0, 'Other': 0, 'Black': 0}.items()}\n gender_d = {v: k for k, v in {'Male': 1, 'Female': 0}.items()}\n hours_d = {v: k for k, v in {'> 40': 1, '≤ 40': 0}.items()}\n\n elif df_name == \"sqf\":\n race_d = {v: k for k, v in {'B': 0, 'W': 1, 'P': 0, 'Q': 1}.items()}\n build_d = {v: k for k, v in {'H': 2, 'U': 2, 'M': 1, 'T': 0}.items()}\n age_d = {v: k for k, v in {'< 25': 0, '25 ~ 45': 1, '≥ 45': 2}.items()}\n ht_feet_d = {v: k for k, v in {'< 5': 0, '5': 1, '> 5': 2}.items()}\n weight_d = {v: k for k, v in {'< 135': 0, '135 ~ 250': 1, '≥ 250': 2}.items()}\n perobs_d = {v: k for k, v in {'< 2': 0, '≥ 2': 1}.items()}\n lst = ['cs_objcs',\n 'cs_descr',\n 'cs_casng',\n 'cs_lkout',\n 'cs_drgtr',\n 'cs_vcrim',\n 'ac_proxm',\n 'inout_I',\n 'inout_O',\n 'sex_M',\n 'sex_F',\n 'frisked']\n for n in lst:\n exec(n + '_d = {v: k for k, v in {\\'N\\': 0, \\'Y\\': 1}.items()}')\n\n d = attr + \"_d\"\n if not inv:\n try:\n expl_d = eval(d)\n except NameError:\n return str(val)\n return expl_d[val]\n else:\n try:\n expl_d = eval(d)\n expl_d_inv = {v: k for k, v in expl_d.items()}\n except NameError:\n return str(val)\n\n return expl_d_inv[str(val)]\n\n\ndef range_conversion(attr, val, direction='right'):\n uniq_vals = X_train_orig.loc[:, attr].unique()\n if direction == 'right':\n uniq_vals = uniq_vals[uniq_vals >= int(attr_val_mapping(attr, val, dataset.value, inv=True))]\n else:\n uniq_vals = uniq_vals[uniq_vals <= int(attr_val_mapping(attr, val, dataset.value, inv=True))]\n if len(uniq_vals) <= 1:\n return val\n if val[0] not in ['≥', '>', '<', '≤']:\n prefix = 'at least ' if direction == 'right' else 'at most '\n if '~' in val:\n val = val.split('~')[0].strip(' ') if direction == 'right' else val.split('~')[1].strip(' ')\n return prefix + val\n else:\n return val\n\n\ndef get_fot_pattern_idx(predicates):\n df = X_train_orig.copy()\n for predicate in predicates:\n if '≤' in predicate:\n attr = predicate.split('≤')[0]\n val = float(predicate.split('≤')[1])\n df = df[df[attr] <= val]\n else: # >=\n attr = predicate.split('≥')[0]\n val = float(predicate.split('≥')[1])\n df = df[df[attr] >= val]\n return df.index\n\n\ndef convert_fot_pattern(expls):\n processed = []\n # print(predicates)\n for expl in expls:\n prcessed_predicate = []\n for predicate in expl:\n # print(predicate)\n if '≤' in predicate:\n attr = predicate.split('≤')[0]\n val = range_conversion(attr, attr_val_mapping(attr, int(float(predicate.split('≤')[1])), dataset.value),\n direction='left')\n\n else: # >\n attr = predicate.split('≥')[0]\n val = range_conversion(attr, attr_val_mapping(attr, int(float(predicate.split('≥')[1])), dataset.value),\n direction='right')\n prcessed_predicate.append(f'{attr}: {val}')\n processed.append(' ∧ '.join(prcessed_predicate))\n return processed\n\n\ndef get_fot_explanations(tree, max_lvl=3, sup_upper=0.15, sup_lower=0.05):\n features = tree.tree_.feature\n threshold = tree.tree_.threshold\n qualified_expl = []\n\n def get_qualified_nodes(root, lvl, expl):\n nonlocal qualified_expl\n idx = get_fot_pattern_idx(expl)\n if (len(idx) / len(X_train_orig) <= sup_upper) and (\n len(idx) / len(X_train_orig) >= sup_lower) and lvl <= max_lvl:\n qualified_expl.append(expl)\n if (lvl < max_lvl) and (tree.tree_.children_left[root] != tree.tree_.children_right[root]):\n attr = X_train_orig.columns[features[root]]\n split_val = threshold[root]\n get_qualified_nodes(tree.tree_.children_left[root], lvl + 1, expl + [f'{attr}≤{np.floor(split_val)}'])\n get_qualified_nodes(tree.tree_.children_right[root], lvl + 1, expl + [f'{attr}≥{np.ceil(split_val)}'])\n\n get_qualified_nodes(0, 0, [])\n # print(qualified_expl)\n return qualified_expl\n\n\ndef update_dataset_preview():\n global X_train, X_test, y_train, y_test, X_train_orig, X_test_orig, table, sc, X_train_show_upd\n # print(1111)\n X_train, X_test, y_train, y_test = load(dataset=dataset.value, sample=False)\n X_train_show, _, _, _ = load(dataset=dataset.value, sample=False)\n X_train_show_upd = X_train_show.copy()\n source.data = dict()\n # print(1211111)\n table.columns = [TableColumn(field=col, title=col) for col in X_train_show.columns]\n for col in X_train_show.columns:\n # print(1111111)\n source.data[col] = X_train_show[col][:1000].apply(lambda x: attr_val_mapping(col, x, dataset.value))\n X_train_orig = copy.deepcopy(X_train)\n X_test_orig = copy.deepcopy(X_test)\n sc = StandardScaler()\n X_train = sc.fit_transform(X_train)\n X_test = sc.transform(X_test)\n for upd_attr in update_attrs:\n upd_attr.options = ['None'] + list(X_train_orig.columns)\n if dataset.value == 'german':\n col_sens_1.text = ' Sensitive Attribute: Age'\n col_sens_2.text = ' Privileged/Protected: Old/Young'\n elif dataset.value == 'sqf':\n col_sens_1.text = ' Sensitive Attribute: Race'\n col_sens_2.text = ' Privileged/Protected: White/Non-white'\n else:\n col_sens_1.text = ' Sensitive Attribute: Gender'\n col_sens_2.text = ' Privileged/Protected: Male/Female'\n\n\ndef apply_along_array(f, arr):\n return [f(i) for i in arr]\n\n\ndef update_val1_option():\n update_val_1.options = ['None'] + apply_along_array(\n lambda x: attr_val_mapping(update_attr_1.value, x, dataset.value), X_train_orig[update_attr_1.value].unique())\n\n\ndef update_val2_option():\n update_val_2.options = ['None'] + apply_along_array(\n lambda x: attr_val_mapping(update_attr_2.value, x, dataset.value), X_train_orig[update_attr_2.value].unique())\n\n\ndef update_val3_option():\n update_val_3.options = ['None'] + apply_along_array(\n lambda x: attr_val_mapping(update_attr_3.value, x, dataset.value), X_train_orig[update_attr_3.value].unique())\n\n\ndef update_val4_option():\n update_val_4.options = ['None'] + apply_along_array(\n lambda x: attr_val_mapping(update_attr_4.value, x, dataset.value), X_train_orig[update_attr_4.value].unique())\n\n\ndef load_expl_1_handler():\n expl = source_rmv.data['explanations'][0].split('∧')\n lvl = min(len(expl), 4)\n while current_attr_idx > 0:\n remove_attr_handler()\n while current_attr_idx < lvl - 1:\n add_attr_handler()\n for i in range(lvl):\n predicate = expl[i].strip(' ')\n pair = predicate.split(':')\n attr = pair[0].strip(' ')\n val = pair[1].strip(' ')\n assert attr in update_attrs[i].options\n update_attrs[i].value = attr\n assert val in update_vals[i].options\n update_vals[i].value = val\n\n\ndef load_expl_2_handler():\n expl = source_rmv.data['explanations'][1].split('∧')\n lvl = min(len(expl), 4)\n while current_attr_idx < lvl - 1:\n add_attr_handler()\n for i in range(lvl):\n predicate = expl[i].strip(' ')\n pair = predicate.split(':')\n attr = pair[0].strip(' ')\n val = pair[1].strip(' ')\n assert attr in update_attrs[i].options\n update_attrs[i].value = attr\n try:\n assert val in update_vals[i].options\n except:\n print(attr, val, update_vals[i].options)\n exit()\n update_vals[i].value = val\n\n\ndef load_expl_3_handler():\n expl = source_rmv.data['explanations'][2].split('∧')\n lvl = min(len(expl), 4)\n while current_attr_idx < lvl - 1:\n add_attr_handler()\n for i in range(lvl):\n predicate = expl[i].strip(' ')\n pair = predicate.split(':')\n attr = pair[0].strip(' ')\n val = pair[1].strip(' ')\n assert attr in update_attrs[i].options\n update_attrs[i].value = attr\n assert val in update_vals[i].options\n update_vals[i].value = val\n\n\ndef add_attr_handler():\n global current_attr_idx\n if current_attr_idx < 3:\n current_attr_idx += 1\n update_attrs[current_attr_idx].visible = True\n update_attrs[current_attr_idx].height_policy = 'auto'\n update_attrs[current_attr_idx].height = None\n\n update_vals[current_attr_idx].visible = True\n update_vals[current_attr_idx].height_policy = 'auto'\n update_vals[current_attr_idx].height = None\n\n updated_vals[current_attr_idx].visible = True\n updated_vals[current_attr_idx].height_policy = 'auto'\n updated_vals[current_attr_idx].height = None\n\n\ndef remove_attr_handler():\n global current_attr_idx\n if current_attr_idx >= 0:\n update_attrs[current_attr_idx].visible = False\n update_attrs[current_attr_idx].height_policy = 'fixed'\n update_attrs[current_attr_idx].height = 0\n\n update_vals[current_attr_idx].visible = False\n update_vals[current_attr_idx].height_policy = 'fixed'\n update_vals[current_attr_idx].height = 0\n\n updated_vals[current_attr_idx].visible = False\n updated_vals[current_attr_idx].height_policy = 'fixed'\n updated_vals[current_attr_idx].height = 0\n current_attr_idx -= 1\n\n\ndef update_pre():\n global num_params, loss_func, model, metric_vals\n if clf.value == 'Logistic Regression':\n model = LogisticRegression(input_size=X_train.shape[-1])\n elif clf.value == 'Support Vector Machine':\n model = SVM(input_size=X_train.shape[-1])\n else:\n model = NeuralNetwork(input_size=X_train.shape[-1])\n model.fit(X_train, y_train)\n torch.save(model.state_dict(), 'gopher-demo-dev/lib/model.pth')\n metric_vals = []\n y_pred_test = model.predict_proba(X_test)\n accuracy = computeAccuracy(y_test, y_pred_test)\n acc.text = f'Acc. of classifier {clf.value} on dataset {dataset.value}:' + str(round(accuracy * 100, 4)) + '%'\n spd_val = computeFairness(y_pred_test, X_test_orig, y_test, 0, dataset.value)\n spd.text = \"Initial statistical parity: \" + str(round(spd_val, 6))\n metric_vals.append(spd_val)\n tpr_val = computeFairness(y_pred_test, X_test_orig, y_test, 1, dataset.value)\n tpr.text = \"Initial equal opportunity: \" + str(round(tpr_val, 6))\n metric_vals.append(tpr_val)\n ppr_val = computeFairness(y_pred_test, X_test_orig, y_test, 2, dataset.value)\n ppr.text = \"Initial predictive parity: \" + str(round(ppr_val, 6))\n metric_vals.append(ppr_val)\n update_fairness(*metric_vals)\n\n num_params = len(convert_grad_to_ndarray(list(model.parameters())))\n if isinstance(model, LogisticRegression) or isinstance(model, NeuralNetwork):\n loss_func = logistic_loss_torch\n elif isinstance(model, SVM):\n loss_func = svm_loss_torch\n\n pre_compute.disabled = False\n\n metric_df = calculate_metrics(y_test.to_numpy(), np.where(y_pred_test > 0.5, 1, 0), y_pred_test,\n return_type='dataframe')\n source_metric.data['metric'] = metric_df.metric.to_list()\n source_metric.data['val'] = list(np.round(metric_df.val.to_numpy(), 2))\n\n\ndef pre_computation():\n global hessian_all_points, del_L_del_theta, model\n hessian_all_points = []\n pre_compute_percent.text = 'Pre-computation in progress: 0.0%'\n t0 = time.time()\n hname = dataset.value + '_' + clf.value + '_h.npy'\n dname = dataset.value + '_' + clf.value + '_d.npy'\n if os.path.exists(hname):\n hessian_all_points = np.load(hname)\n del_L_del_theta = np.load(dname)\n os.system('pwd')\n else:\n tbar = tqdm(total=len(X_train) // 20)\n for i in range(len(X_train)):\n hessian_all_points.append(hessian_one_point(model, X_train[i], y_train[i]) / len(X_train))\n if i % 20 == 0:\n tbar.update()\n # percent = round(100 * i / len(X_train), 4)\n # pre_compute_percent.text = 'Pre-computation in progress: ' + str(percent) + '%'\n hessian_all_points = np.array(hessian_all_points)\n\n del_L_del_theta = []\n for i in range(int(len(X_train))):\n model.zero_grad()\n gradient = convert_grad_to_ndarray(del_L_del_theta_i(model, X_train[i], int(y_train[i])))\n while np.sum(np.isnan(gradient)) > 0:\n model.zero_grad()\n gradient = convert_grad_to_ndarray(del_L_del_theta_i(model, X_train[i], int(y_train[i])))\n del_L_del_theta.append(gradient)\n del_L_del_theta = np.array(del_L_del_theta)\n\n np.save(hname, hessian_all_points)\n np.save(dname, del_L_del_theta)\n # os.system('pwd')\n # print(del_L_del_theta)\n total_time = time.time() - t0\n pre_compute_percent.text = f'Pre-computation Done in {round(total_time, 4)} seconds.'\n pre_compute.disabled = True\n fairness_specific_precompute(metric_sel.value)\n # tab2.disabled = False\n removal_explain.disabled = False\n\n\nclass Topk:\n \"\"\"\n top explanations: explanation -> (minhash, set_index, score)\n \"\"\"\n\n def __init__(self, method='containment', threshold=0.75, k=5):\n self.method = method\n if method != 'containment':\n raise NotImplementedError\n\n self.top_explanations = dict()\n self.k = k\n self.threshold = threshold\n self.min_score = -100\n self.min_score_explanation = None\n self.containment_hist = []\n\n def _update_min(self, new_explanation, new_score):\n if len(self.top_explanations) > 0:\n for explanation, t in self.top_explanations.items():\n if t[1] < new_score:\n new_score = t[1]\n new_explanation = explanation\n self.min_score = new_score\n self.min_score_explanation = new_explanation\n\n def _containment(self, x, q):\n c = len(x & q) / len(q)\n self.containment_hist.append(c)\n return c\n\n def update(self, explanation, score):\n if (len(self.top_explanations) < self.k) or (score > self.min_score):\n s = get_subset(explanation)\n explanation = json.dumps(explanation)\n\n if self.method == 'lshensemble':\n raise NotImplementedError\n elif self.method == 'lsh':\n raise NotImplementedError\n elif self.method == 'containment':\n q_result = set()\n for k, v in self.top_explanations.items():\n if self._containment(v[0], s) > self.threshold:\n q_result.add(k)\n\n if len(q_result) == 0:\n if len(self.top_explanations) <= self.k - 1:\n self._update_min(explanation, score)\n self.top_explanations[explanation] = (s, score)\n return 0\n return -1\n\n\ndef get_subset(explanation):\n subset = X_train_orig.copy()\n for predicate in explanation:\n attr = predicate.split(\"=\")[0].strip(' ')\n val = int(predicate.split(\"=\")[1].strip(' '))\n subset = subset[subset[attr] == val]\n return subset.index\n\n\ndef first_order_group_influence(U, del_L_del_theta):\n n = len(X_train)\n return 1 / n * np.sum(np.dot(del_L_del_theta[U, :], hinv), axis=0)\n\n\ndef second_order_group_influence(U, del_L_del_theta):\n u = len(U)\n s = len(X_train)\n p = u / s\n c1 = (1 - 2 * p) / (s * (1 - p) ** 2)\n c2 = 1 / ((s * (1 - p)) ** 2)\n del_L_del_theta_sum = np.sum(del_L_del_theta[U, :], axis=0)\n hinv_del_L_del_theta = np.matmul(hinv, del_L_del_theta_sum)\n hessian_U_hinv_del_L_del_theta = np.sum(np.matmul(hessian_all_points[U, :], hinv_del_L_del_theta), axis=0)\n term1 = c1 * hinv_del_L_del_theta\n term2 = c2 * np.matmul(hinv, hessian_U_hinv_del_L_del_theta)\n sum_term = (term1 + term2 * len(X_train))\n return sum_term\n\n\ndef fairness_specific_precompute(new):\n global metric, v1, hinv, hinv_v, model, candidates, metric_val, infs_1\n metric = ['statistical parity', 'equal opportunity', 'predictive parity'].index(new)\n metric_val = metric_vals[metric]\n if metric == 0:\n v1 = del_spd_del_theta(model, X_test_orig, X_test, dataset.value)\n elif metric == 1:\n v1 = del_tpr_parity_del_theta(model, X_test_orig, X_test, y_test, dataset.value)\n elif metric == 2:\n v1 = del_predictive_parity_del_theta(model, X_test_orig, X_test, y_test, dataset.value)\n hinv = np.linalg.pinv(np.sum(hessian_all_points, axis=0))\n hinv_v = s_test(model, X_train, y_train, v1, hinv=hinv)\n\n model.fit(X_train, y_train)\n\n attributes = []\n attributeValues = []\n first_order_influences = []\n second_order_influences = []\n fractionRows = []\n\n for col in X_train_orig.columns:\n if dataset.value == 'german':\n if \"purpose\" in col or \"housing\" in col: # dummy variables purpose=0 doesn't make sense\n vals = [1]\n else:\n vals = X_train_orig[col].unique()\n elif dataset.value == 'adult':\n vals = X_train_orig[col].unique()\n elif dataset.value == 'sqf':\n vals = X_train_orig[col].unique()\n else:\n raise NotImplementedError\n for val in vals:\n idx = X_train_orig[X_train_orig[col] == val].index\n if len(idx) / len(X_train) > float(sup_lb.value) / 100:\n y = y_train.drop(index=idx, inplace=False)\n if len(y.unique()) > 1:\n idx = X_train_orig[X_train_orig[col] == val].index\n\n # First-order subset influence\n params_f_1 = first_order_group_influence(idx, del_L_del_theta)\n del_f_1 = np.dot(v1.transpose(), params_f_1)\n\n # Second-order subset influence\n params_f_2 = second_order_group_influence(idx, del_L_del_theta)\n del_f_2 = np.dot(v1.transpose(), params_f_2)\n\n attributes.append(col)\n attributeValues.append(val)\n first_order_influences.append(del_f_1)\n second_order_influences.append(del_f_2)\n fractionRows.append(len(idx) / len(X_train) * 100)\n expl = [attributes, attributeValues, first_order_influences, second_order_influences, fractionRows]\n expl = np.array(expl).T.tolist()\n\n explanations = pd.DataFrame(expl, columns=[\"attributes\", \"attributeValues\", \"first_order_influences\",\n \"second_order_influences\", \"fractionRows\"])\n explanations['second_order_influences'] = explanations['second_order_influences'].astype(float)\n explanations['first_order_influences'] = explanations['first_order_influences'].astype(float)\n explanations['fractionRows'] = explanations['fractionRows'].astype(float)\n candidates = copy.deepcopy(explanations)\n candidates.loc[:, 'score'] = candidates.loc[:, 'second_order_influences'] * 100 / candidates.loc[:, 'fractionRows']\n\n infs_1 = [np.dot(v1.transpose(), first_order_group_influence([i], del_L_del_theta)) for i in\n range(len(X_train_orig))]\n\n removal_explain.disabled = False\n update_explain.disabled = False\n\n\ndef removal_based_explanation():\n global explanations\n candidates_all = []\n total_rows = len(X_train_orig)\n support_small = 0.3\n support = float(sup_lb.value) / 100\n del_f_threshold = 0.1 * metric_val\n\n # Generating 1-candidates\n candidates_1 = []\n for i in range(len(candidates)):\n candidate_i = candidates.iloc[i]\n if ((candidate_i[\"fractionRows\"] >= support_small) or\n ((candidate_i[\"fractionRows\"] >= support) & (candidate_i[\"second_order_influences\"] > del_f_threshold))\n ):\n attr_i = candidate_i[\"attributes\"]\n val_i = int(float(candidate_i[\"attributeValues\"]))\n idx = X_train_orig[X_train_orig[attr_i] == val_i].index\n predicates = [attr_i + '=' + str(val_i)]\n candidate = [predicates, candidate_i[\"fractionRows\"],\n candidate_i[\"score\"], candidate_i[\"second_order_influences\"], idx]\n candidates_1.append(candidate)\n\n print(\"Generated: \", len(candidates_1), \" 1-candidates\")\n candidates_1.sort()\n\n for i in range(len(candidates_1)):\n if float(candidates_1[i][2]) >= support: # if score > top-k, keep in candidates, not otherwise\n candidates_all.insert(len(candidates_all), candidates_1[i])\n\n # Generating 2-candidates\n candidates_2 = []\n for i in range(len(candidates_1)):\n predicate_i = candidates_1[i][0][0]\n attr_i = predicate_i.split(\"=\")[0]\n val_i = int(float(predicate_i.split(\"=\")[1]))\n sup_i = candidates_1[i][1]\n idx_i = candidates_1[i][-1]\n for j in range(i):\n predicate_j = candidates_1[j][0][0]\n attr_j = predicate_j.split(\"=\")[0]\n val_j = int(float(predicate_j.split(\"=\")[1]))\n sup_j = candidates_1[j][1]\n idx_j = candidates_1[j][-1]\n if attr_i != attr_j:\n idx = idx_i.intersection(idx_j)\n fractionRows = len(idx) / total_rows * 100\n isCompact = True\n # pattern is not compact if intersection equals one of its parents\n if fractionRows == min(sup_i, sup_j):\n isCompact = False\n if fractionRows / 100 >= support:\n params_f_2 = second_order_group_influence(idx, del_L_del_theta)\n del_f_2 = np.dot(v1.transpose(), params_f_2)\n score = del_f_2 * 100 / fractionRows\n if ((fractionRows / 100 >= support_small) or\n ((score > candidates_1[i][2]) & (score > candidates_1[j][2]))):\n predicates = [attr_i + '=' + str(val_i), attr_j + '=' + str(val_j)]\n candidate = [sorted(predicates, key=itemgetter(0)), len(idx) * 100 / total_rows,\n score, del_f_2, idx]\n candidates_2.append(candidate)\n if isCompact:\n candidates_all.append(candidate)\n print(\"Generated: \", len(candidates_2), \" 2-candidates\")\n\n # Recursively generating the rest\n candidates_L_1 = copy.deepcopy(candidates_2)\n set_L_1 = set()\n iteration = 2\n while (len(candidates_L_1) > 0) & (iteration < int(lvl.value)):\n print(\"Generated: \", iteration)\n candidates_L = []\n for i in range(len(candidates_L_1)):\n candidate_i = set(candidates_L_1[i][0])\n sup_i = candidates_L_1[i][1]\n idx_i = candidates_L_1[i][-1]\n for j in range(i):\n candidate_j = set(candidates_L_1[j][0])\n sup_j = candidates_L_1[j][1]\n idx_j = candidates_L_1[j][-1]\n merged_candidate = sorted(candidate_i.union(candidate_j), key=itemgetter(0))\n if json.dumps(merged_candidate) in set_L_1:\n continue\n if len(merged_candidate) == iteration + 1:\n intersect_candidates = candidate_i.intersection(candidate_j)\n setminus_i = list(candidate_i - intersect_candidates)[0].split(\"=\")\n setminus_j = list(candidate_j - intersect_candidates)[0].split(\"=\")\n attr_i = setminus_i[0]\n attr_j = setminus_j[0]\n if attr_i != attr_j:\n # merge to get L list\n idx = idx_i.intersection(idx_j)\n fractionRows = len(idx) / len(X_train) * 100\n isCompact = True\n # pattern is not compact if intersection equals one of its parents\n if fractionRows == min(sup_i, sup_j):\n isCompact = False\n if fractionRows / 100 >= support:\n params_f_2 = second_order_group_influence(idx, del_L_del_theta)\n del_f_2 = np.dot(v1.transpose(), params_f_2)\n\n score = del_f_2 * 100 / fractionRows\n if (((score > candidates_L_1[i][2]) & (score > candidates_L_1[j][2])) or\n (fractionRows >= support_small)):\n candidate = [merged_candidate, fractionRows,\n del_f_2 * len(X_train) / len(idx), del_f_2, idx]\n candidates_L.append(candidate)\n set_L_1.add(json.dumps(merged_candidate))\n if isCompact:\n candidates_all.insert(len(candidates_all), candidate)\n set_L_1 = set()\n print(\"Generated:\", len(candidates_L), \" \", str(iteration + 1), \"-candidates\")\n candidates_L_1 = copy.deepcopy(candidates_L)\n candidates_L_1.sort()\n iteration += 1\n\n candidates_support_3_compact = copy.deepcopy(candidates_all)\n print(len(candidates_support_3_compact))\n candidates_df_3_compact = pd.DataFrame(candidates_support_3_compact,\n columns=[\"predicates\", \"support\", \"score\", \"2nd-inf\", 'idx'])\n\n candidates_df_3_compact = candidates_df_3_compact[\n candidates_df_3_compact['support'] < float(sup_ub.value)].sort_values(by=['score'], ascending=False)\n containment_df = candidates_df_3_compact.sort_values(by=['score'], ascending=False).copy()\n\n topk = Topk(method='containment', threshold=float(containment_th.value) / 100, k=3)\n for row_idx in range(len(containment_df)):\n row = containment_df.iloc[row_idx]\n explanation, score = row[0], row[2]\n topk.update(explanation, score)\n if len(topk.top_explanations) == topk.k:\n break\n\n explanations = list(topk.top_explanations.keys())\n metric_idx = ['statistical parity', 'equal opportunity', 'predictive parity'].index(metric_sel.value)\n supports = list()\n scores = list()\n gt_scores = list()\n infs = list()\n gts = list()\n new_accs = list()\n for e in explanations:\n idx = get_subset(json.loads(e))\n X = np.delete(X_train, idx, 0)\n y = y_train.drop(index=idx, inplace=False)\n model.fit(np.array(X_train), np.array(y_train))\n model.fit(np.array(X), np.array(y))\n y_pred = model.predict_proba(np.array(X_test))\n new_acc = computeAccuracy(y_test, y_pred)\n inf_gt = computeFairness(y_pred, X_test_orig, y_test, metric_idx, dataset.value) - metric_val\n\n condition = candidates_df_3_compact.predicates.apply(lambda x: x == json.loads(e))\n supports.append(float(candidates_df_3_compact[condition]['support']))\n scores.append(float(candidates_df_3_compact[condition]['score']))\n infs.append(float(candidates_df_3_compact[condition]['2nd-inf']))\n gts.append(inf_gt / (-metric_val))\n gt_scores.append(inf_gt * 100 / float(candidates_df_3_compact[condition]['support']))\n new_accs.append(new_acc)\n\n expl = [explanations, supports, scores, gt_scores, infs, gts, new_accs]\n expl = np.array(expl).T.tolist()\n\n explanations = pd.DataFrame(expl, columns=[\"explanations\", \"support\", \"score\", \"gt-score\",\n \"2nd-inf(%)\", \"gt-inf(%)\", \"new-acc\"])\n explanations['score'] = explanations['score'].astype(float)\n explanations['gt-score'] = explanations['gt-score'].astype(float)\n explanations['support'] = explanations['support'].astype(float)\n explanations['2nd-inf(%)'] = explanations['2nd-inf(%)'].astype(float) / (-metric_val)\n explanations['gt-inf(%)'] = explanations['gt-inf(%)'].astype(float)\n explanations['new-acc'] = explanations['new-acc'].astype(float)\n\n pd.set_option('max_colwidth', 100)\n explanations.sort_values(by=['score'], ascending=False)\n top_explanations = explanations.copy().reset_index(drop=True)\n print(top_explanations)\n source_rmv.data = dict()\n # print(dataset.value)\n source_rmv.data['explanations'] = top_explanations[\"explanations\"].apply(\n lambda x: proc_expls(x, dataset.value)).tolist()\n source_rmv.data['support'] = top_explanations[\"support\"].apply(lambda x: str(f'{round(x, 2)}')).tolist()\n source_rmv.data['score'] = [f'{round(value, 2)}' for value in top_explanations[\"gt-score\"]]\n source_rmv.data['second_infs'] = [f'{round(value * 100, 2)}' for value in top_explanations[\"gt-inf(%)\"]]\n\n expl_1_txt.text = join_predicates_with_linebreak(source_rmv.data['explanations'][0].split(' ∧ '), 30)\n expl_2_txt.text = join_predicates_with_linebreak(source_rmv.data['explanations'][1].split(' ∧ '), 30)\n expl_3_txt.text = join_predicates_with_linebreak(source_rmv.data['explanations'][2].split(' ∧ '), 30)\n\n load_expl_1.disabled = False\n load_expl_2.disabled = False\n load_expl_3.disabled = False\n\n # from sklearn.tree import export_text\n tree = DecisionTreeRegressor(random_state=0, max_depth=int(lvl.value),\n min_samples_leaf=int(float(sup_lb.value) * total_rows / 100))\n tree.fit(X_train_orig, np.array(infs_1))\n # print(export_text(tree, feature_names=list(X_train_orig.columns)))\n fot_expls = get_fot_explanations(tree, max_lvl=int(lvl.value), sup_lower=float(sup_lb.value) / 100,\n sup_upper=float(sup_ub.value) / 100)\n # print(fot_expls)\n fot_expls_info = []\n for e in fot_expls:\n idx = get_fot_pattern_idx(e)\n X = np.delete(X_train, idx, 0)\n y = y_train.drop(index=idx, inplace=False)\n model.fit(np.array(X_train), np.array(y_train))\n model.fit(np.array(X), np.array(y))\n y_pred_test = model.predict_proba(X_test)\n gt_inf = metric_val - computeFairness(y_pred_test, X_test_orig, y_test, metric_idx, dataset.value)\n new_acc = computeAccuracy(y_test, y_pred_test)\n support = len(idx) / total_rows\n fot_expls_info.append((gt_inf / metric_val * 100, new_acc, -gt_inf / support, support, e))\n fot_expls_info = sorted(fot_expls_info, key=lambda x: abs(x[2] + metric_val / support), reverse=False)[:3]\n source_fot.data = dict()\n source_fot.data['explanations'] = convert_fot_pattern([p[-1] for p in fot_expls_info])\n source_fot.data['support'] = [str(round(p[-2] * 100, 2)) for p in fot_expls_info]\n source_fot.data['score'] = [str(round(p[2], 2)) for p in fot_expls_info]\n source_fot.data['second_infs'] = [str(round(p[0], 2)) for p in fot_expls_info]\n\n rmv_gopher_source.data['x0'] = list(np.arange(4) * 2)\n rmv_gopher_source.data['y0'] = [-np.round(metric_val * 100, 2)] + \\\n list(np.round((top_explanations[\"gt-inf(%)\"] * metric_val - metric_val) * 100, 2))\n rmv_gopher_source.data['c'] = ['grey', 'blue', 'blue', 'blue']\n rmv_gopher_source.data['y0_text'] = [str(round(p, 2)) for p in rmv_gopher_source.data['y0']]\n rmv_gopher_source.data['y0_off'] = np.where(np.array(rmv_gopher_source.data['y0']) < 0, 1, -0.25) * 20\n rmv_gopher_source.data['pattern'] = ['-'] + source_rmv.data['explanations']\n rmv_gopher_fig.xaxis.ticker = 2 * np.arange(4)\n rmv_gopher_xticks_dict = {2 * i: f'Gopher Pattern {i}' for i in range(1, 4)}\n rmv_gopher_xticks_dict[0] = 'Original'\n rmv_gopher_fig.xaxis.major_label_overrides = rmv_gopher_xticks_dict\n adp_range(rmv_gopher_fig, rmv_gopher_source.data['y0'])\n\n rmv_fot_source.data['x0'] = list(np.arange(4) * 2)\n rmv_fot_source.data['y0'] = [-np.round(metric_val * 100, 2)] + list(\n np.round([p[0] * metric_val - metric_val * 100 for p in fot_expls_info], 2))\n rmv_fot_source.data['c'] = ['grey', 'blue', 'blue', 'blue']\n rmv_fot_source.data['y0_text'] = [str(round(p, 2)) for p in rmv_fot_source.data['y0']]\n rmv_fot_source.data['y0_off'] = np.where(np.array(rmv_fot_source.data['y0']) < 0, 1, -0.25) * 20\n rmv_fot_source.data['pattern'] = ['-'] + source_fot.data['explanations']\n rmv_fot_fig.xaxis.ticker = 2 * np.arange(4)\n rmv_fot_xticks_dict = {2 * i: f'FO-Tree Pattern {i}' for i in range(1, 4)}\n rmv_fot_xticks_dict[0] = 'Original'\n rmv_fot_fig.xaxis.major_label_overrides = rmv_fot_xticks_dict\n adp_range(rmv_fot_fig, rmv_fot_source.data['y0'])\n\n fot_idxs = [get_fot_pattern_idx(p[-1]) for p in fot_expls_info]\n fot_pos_pct = [np.round(np.sum(y_train.loc[idx]) / len(idx) * 100, 2) for idx in fot_idxs]\n gopher_idxs = [get_subset(json.loads(e)) for e in list(topk.top_explanations.keys())]\n gopher_pos_pct = [np.round(np.sum(y_train.loc[idx]) / len(idx) * 100, 2) for idx in gopher_idxs]\n class_distrib_source.data['y1'] = gopher_pos_pct + fot_pos_pct\n class_distrib_source.data['y0'] = [np.round(100 - x, 2) for x in (gopher_pos_pct + fot_pos_pct)]\n class_distrib_source.data['y1_text'] = [str(x) + '%' for x in class_distrib_source.data['y1']]\n class_distrib_source.data['y0_text'] = [str(x) + '%' for x in class_distrib_source.data['y0']]\n\n\ndef adp_range(fig, bias_list, step=5, space=5):\n lb, ub = fig.y_range.start, fig.y_range.end\n min_bias, max_bias = min(min(bias_list), 0), max(max(bias_list), 0)\n while ub >= max_bias + step + space:\n ub -= step\n while lb <= min_bias - step - space:\n lb += step\n fig.y_range.start, fig.y_range.end = lb, ub\n\n\ndef calculate_metrics(y_true, y_pred, y_score, return_type=\"dict\"):\n \"\"\"\n y_score: The predicted probability of the testing data.\n return_type: \"dict\": return a dictionary,\n \"dataframe\": return a Pandas DataFrame\n \"\"\"\n accs = metrics.accuracy_score(y_true, y_pred)\n prec = metrics.precision_score(y_true, y_pred)\n # jaccard = metrics.jaccard_score(y_true, y_pred)\n f1_score = metrics.f1_score(y_true, y_pred)\n roc_auc = metrics.roc_auc_score(y_true, y_score)\n\n average_prec = metrics.average_precision_score(y_true, y_score)\n precision, recall, thresholds = metrics.precision_recall_curve(y_test, y_score)\n pr_auc = metrics.auc(recall, precision)\n recall = metrics.recall_score(y_true, y_pred)\n\n if return_type == \"dict\":\n return {\"Accuracy\": accs,\n \"Precision\": prec,\n \"Recall\": recall,\n # \"Jaccard\": jaccard,\n \"F1\": f1_score,\n \"PR AUC\": pr_auc,\n \"ROC AUC\": roc_auc}\n\n else:\n return pd.DataFrame({\"metric\": [\"Accuracy\", \"Precision\", \"Recall\", \"F1\", \"PR AUC\", \"ROC AUC\"],\n \"val\": [accs, prec, recall, f1_score, pr_auc, roc_auc]})\n\n\nupdate_dataset_preview()\n\ncontrols = [dataset, clf, train, pre_compute]\ncol11 = column(*controls, name=\"tab1_inp\", sizing_mode='stretch_width')\n\n# metrics = [acc, spd, tpr, ppr, pre_compute_percent]\n# col12 = column(*metrics, sizing_mode='stretch_width')\n\ntrain.on_click(handler=update_pre)\npre_compute.on_click(handler=pre_computation)\ndataset.on_change('value', lambda attr, old, new: update_dataset_preview())\ntab1_inp = col11\ntab1_table1 = column(table, sizing_mode='stretch_both', name=\"tab1_table\")\ntab1_table2 = column(table_metric, sizing_mode='stretch_width', name=\"tab1_metric_table\", height=170)\ncurdoc().add_root(tab1_inp)\ncurdoc().add_root(tab1_table1)\ncurdoc().add_root(tab1_table2)\n\nsettings = [lvl, sup_lb, sup_ub, containment_th, metric_sel, removal_explain]\nremoval_explain.on_click(handler=removal_based_explanation)\nmetric_sel.on_change('value', lambda attr, old, new: fairness_specific_precompute(new))\ncol21 = column(*settings, name='tab2_inp', sizing_mode='stretch_width')\ncurdoc().add_root(col21)\ncurdoc().add_root(column(table_rmv, sizing_mode='stretch_width', name='tab2_table1'))\ncurdoc().add_root(column(table_fot, sizing_mode='stretch_width', name='tab2_table2'))\n\nrow30 = row(add_attr, remove_attr, sizing_mode='stretch_width')\n# row301 = row(load_expl_2, load_expl_3, sizing_mode='stretch_width')\nrow31 = row(update_attr_1, update_val_1, sizing_mode='stretch_width')\nupdate_attr_1.on_change('value', lambda attr, old, new: update_val1_option())\nrow32 = row(update_attr_2, update_val_2, sizing_mode='stretch_width')\nupdate_attr_2.on_change('value', lambda attr, old, new: update_val2_option())\nrow33 = row(update_attr_3, update_val_3, sizing_mode='stretch_width')\nupdate_attr_3.on_change('value', lambda attr, old, new: update_val3_option())\nrow34 = row(update_attr_4, update_val_4, sizing_mode='stretch_width')\nupdate_attr_4.on_change('value', lambda attr, old, new: update_val4_option())\n# update_data_view.on_click(handler=update_dataset_upd_preview)\n\nadd_attr.on_click(handler=add_attr_handler)\nremove_attr.on_click(handler=remove_attr_handler)\nupdate_explain.on_click(handler=update_comparison_fig)\ncol31 = column(row30, row31, row32, row33, row34, update_explain, name='tab3_inp', sizing_mode='stretch_width')\ncurdoc().add_root(col31)\n# section3 = row(col31, table_upd)\n# tab3 = Panel(child=section3, title=\"Update-based Explanation\")\n\n# curdoc().add_root(Tabs(tabs=[tab1, tab2, tab3], height=100))\ncurdoc().title = \"Gopher Demo\"\n# curdoc().theme = 'night_sky'\n\ntab1 = Panel(child=column(update_fairness_fig, sizing_mode='stretch_both'), title=\"Fairness Comparison\")\ntab2 = Panel(child=column(update_acc_fig, sizing_mode='stretch_both'), title=\"Accuracy Comparison\")\ncomp_tab = Tabs(tabs=[tab1, tab2], name='comp_tab')\ncurdoc().add_root(comp_tab)\n\n# update_pre()\npre_compute.disabled = True\nremoval_explain.disabled = True\n# tab2.disabled = True\nload_expl_1.on_click(handler=load_expl_1_handler)\nload_expl_2.on_click(handler=load_expl_2_handler)\nload_expl_3.on_click(handler=load_expl_3_handler)\ncurdoc().add_root(load_expl_col_1)\ncurdoc().add_root(load_expl_col_2)\ncurdoc().add_root(load_expl_col_3)\n","repo_name":"lodino/gopher-demo","sub_path":"gopher-demo-dev/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":76614,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"24915662666","text":"# Poisson equation \n# LZ: 03/27 I can see the CR is h^r with mixed variational form\n# Poisson equation with particular boundary conditions reads:\n# Be careful: if change exact solution, we also need to change g. \n# I don't know how to write general sym.diff for g, so this program is not suitable for change exact solution to test CR \n# .. math::\n# - \\nabla^{2} u &= f \\quad {\\rm in} \\ \\Omega, \\\\\n# u &= 0 \\quad {\\rm on} \\ \\Gamma_{D}, \\\\\n# \\nabla u \\cdot n &= g \\quad {\\rm on} \\ \\Gamma_{N}. \\\\\nfrom dolfin import *\nimport sympy as sym\nfrom sympy import sin, cos, exp\n# import matplotlib.pyplot as plt\n\nr=2\n# uNum= File('u_h.pvd')\n# uex = File('uex.pvd')\nx, y = sym.symbols('x[0], x[1]')\nu_fcn = x(1-x)*y*(1-y)\nf = -u_fcn.diff(x,2) - u_fcn.diff(y,2)\nu_fcn = sym.printing.ccode(u_fcn)\nf_fcn = sym.printing.ccode(f)\nfor i in range(1,6): \n\n N = int(pow(2,i-1)*4)\n # Create mesh\n mesh = UnitSquareMesh(N, N)\n\n # Define finite elements spaces and build mixed space\n BDM = FiniteElement(\"BDM\", mesh.ufl_cell(), r)\n DG = FiniteElement(\"DG\", mesh.ufl_cell(), r-1)\n \n W = FunctionSpace(mesh, BDM * DG)\n # Vs = FunctionSpace(mesh, BDM)\n # Us = FunctionSpace(mesh, DG)\n\n # Define trial and test functions\n (sigma, u) = TrialFunctions(W)\n (tau, v) = TestFunctions(W)\n # sigma = TrialFunctions(Vs)\n # u = TrialFunctions(Us)\n # tau = TestFunctions(Vs)\n # v = TestFunctions(Us)\n\n # Define function G such that G \\cdot n = g\n class BoundarySource(UserExpression):\n def __init__(self, mesh, **kwargs):\n self.mesh = mesh\n if has_pybind11():\n super().__init__(**kwargs)\n def eval_cell(self, values, x, ufc_cell):\n cell = Cell(self.mesh, ufc_cell.index)\n n = cell.normal(ufc_cell.local_facet)\n g = x[0]*(x[0]-1)\n values[0] = g*n[0]\n values[1] = g*n[1]\n def value_shape(self):\n return (2,)\n\n G = BoundarySource(mesh, degree=2)\n\n # Specifying the relevant part of the boundary can be done as for the\n # Poisson demo (but now the top and bottom of the unit square is the\n # essential boundary): ::\n\n # Define essential boundary on y=0, y=1\n def Nboundary(x):\n return x[1] < DOLFIN_EPS or x[1] > 1.0 - DOLFIN_EPS\n bc = DirichletBC(W.sub(0), G, Nboundary)\n\n u_ex = Expression(u_fcn,degree=4)\n u_ex_int = interpolate(u_ex, W.sub(1).collapse())\n \n \n # proj_error = errornorm(u_ex_int, u_ex)\n # print('interpolation error=', proj_error) \n\n # Now, all the pieces are in place for the construction of the essential\n # boundary condition: ::\n\n # Define source function\n # f = Expression('2*x[1]*(1-x[1])+2*x[0]*(1-x[0])', degree=4)\n f = Expression(f_fcn,degree=4)\n # Define variational form\n a = (dot(sigma, tau) + div(tau)*u + div(sigma)*v)*dx\n L = - f*v*dx \n # Compute solution\n # sigma = Function(Vs)\n # u = Function(Vs)\n w = Function(W)\n solve(a == L, w, bc)\n (sigma, u) = w.split(deepcopy=True)\n # uNum << u\n\n # solve(a == L, w, bc)\n # (sigma, u) = w.split(deepcopy=True)\n # uNum << u\n\n L2_error = errornorm(u, u_ex, 'L2')\n print('L2_error=', L2_error)\n\n # u_exN = interpolate(u_ex, W.sub(1).collapse())\n # uex << u_exN\n","repo_name":"Julieusa/FeniCSTest","sub_path":"Test CR/demo_poisson_mixedCR.py","file_name":"demo_poisson_mixedCR.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25633169148","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n#\n# Created by WW on Jan. 26, 2020\n# All rights reserved.\n#\n\nimport h5py\nimport numpy as np\n\ndef main():\n\t#=======================================================================================\n\t# Create a HDF5 file.\n\tf = h5py.File(\"h5py_example.hdf5\", \"w\") \t# mode = {'w', 'r', 'a'}\n\n\t# Create two groups under root '/'.\n\tg1 = f.create_group(\"bar1\")\n\tg2 = f.create_group(\"bar2\")\n\n\t# Create a dataset under root '/'.\n\td = f.create_dataset(\"dset\", data=np.arange(16).reshape([4, 4]))\n\n\t# Add two attributes to dataset 'dset'\n\td.attrs[\"myAttr1\"] = [100, 200]\n\td.attrs[\"myAttr2\"] = \"Hello, world!\"\n\n\t# Create a group and a dataset under group \"bar1\".\n\tc1 = g1.create_group(\"car1\")\n\td1 = g1.create_dataset(\"dset1\", data=np.arange(10))\n\n\t# Create a group and a dataset under group \"bar2\".\n\tc2 = g2.create_group(\"car2\")\n\td2 = g2.create_dataset(\"dset2\", data=np.arange(10))\n\n\t# Save and exit the file.\n\tf.close()\n\n\t''' h5py_example.hdf5 file structure\n\t+-- '/'\n\t| +--\tgroup \"bar1\"\n\t| | +-- group \"car1\"\n\t| | | +-- None\n\t| | | \n\t| | +-- dataset \"dset1\"\n\t| |\n\t| +-- group \"bar2\"\n\t| | +-- group \"car2\"\n\t| | | +-- None\n\t| | |\n\t| | +-- dataset \"dset2\"\n\t| | \n\t| +-- dataset \"dset\"\n\t| | +-- attribute \"myAttr1\"\n\t| | +-- attribute \"myAttr2\"\n\t| | \n\t| \n\t'''\n\n\t#=======================================================================================\n\t# Read HDF5 file.\n\tf = h5py.File(\"h5py_example.hdf5\", \"r\") \t# mode = {'w', 'r', 'a'}\n\n\t# Print the keys of groups and datasets under '/'.\n\tprint(f.filename, \":\")\n\tprint([key for key in f.keys()], \"\\n\") \n\n\t#===================================================\n\t# Read dataset 'dset' under '/'.\n\td = f[\"dset\"]\n\n\t# Print the data of 'dset'.\n\tprint(d.name, \":\")\n\tprint(d[:])\n\n\t# Print the attributes of dataset 'dset'.\n\tfor key in d.attrs.keys():\n\t\tprint(key, \":\", d.attrs[key])\n\n\tprint()\n\n\t#===================================================\n\t# Read group 'bar1'.\n\tg = f[\"bar1\"]\n\n\t# Print the keys of groups and datasets under group 'bar1'.\n\tprint([key for key in g.keys()])\n\n\t# Three methods to print the data of 'dset1'.\n\tprint(f[\"/bar1/dset1\"][:])\t\t# 1. absolute path\n\n\tprint(f[\"bar1\"][\"dset1\"][:])\t# 2. relative path: file[][]\n\n\tprint(g['dset1'][:])\t\t# 3. relative path: group[]\n\n\n\n\t# Delete a database.\n\t# Notice: the mode should be 'a' when you read a file.\n\t'''\n\tdel g[\"dset1\"]\n\t'''\n\n\t# Save and exit the file\n\tf.close()\n\nif __name__ == \"__main__\":\n main()\n\n\n\n","repo_name":"NoNo721/Python-Examples","sub_path":"HDF5/h5py_example.py","file_name":"h5py_example.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"} +{"seq_id":"8509124114","text":"def perfect_number(number):\n divisors = list()\n for num in range(1, number):\n if number % num == 0:\n divisors.append(num)\n return divisors\n\n\ninput_number = int(input())\n\nif sum(perfect_number(input_number)) == input_number:\n print(\"We have a perfect number!\")\nelse:\n print(\"It's not so perfect.\")\n","repo_name":"dinocom33/Programming-Fundamentals-with-Python-September-2022","sub_path":"4-Functions/2-Exercises/perfect_number.py","file_name":"perfect_number.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"31631500623","text":"# -*- coding: utf-8 -*-\ndef data_mod():\n import pandas as pd\n # import numpy as np\n data = pd.read_csv(\"olym_data.csv\")\n data = data.fillna(data.median())\n return data\n\ndef years(Season):\n data = data_mod()\n unique_years = data.Year[data[\"Season\"]==Season].unique()\n return unique_years\n\ndef result(S, y):\n data = data_mod()\n result = data[(data[\"Season\"]==S) &(data[\"Year\"]==y)]\n result = result.groupby(\"NOC\")[\"Medal\"].value_counts().unstack()\n result = result.iloc[:[1,2,0]]\n result = result.fillna(0)\n result[\"Total\"] = result.apply(lambda x: sum(x), axis=1)\n result = result.sort_values(\"Gold\")\n return result","repo_name":"piyush546/Machine-Learning-Bootcamp","sub_path":"Olympic project/GUI part/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"42161860527","text":"# For this project, we will create a machine learning model to help us predict the stock market. With the extreme\r\n# volatility of the market these days (thanks to the rate hiking environment created by the Federal Open Market\r\n# Committee) stocks have plummeted, rallied, and plummeted and rallied again. Any sort of insight into the trends of\r\n# the market this year are invaluable. As such, let's see if we can help an investor's portfolio.\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.metrics import mean_squared_error\r\nfrom datetime import datetime\r\nfrom sklearn.linear_model import LinearRegression\r\nimport matplotlib.pyplot as plt\r\n\r\n# Adjust our display settings\r\npd.set_option('display.max_columns', 12) # This allows us to view all the column within our PyCharm IDE\r\npd.set_option('display.width', 1000)\r\n\r\n# First, we specify the url of the dataset that we will be working with\r\npath = r'https://raw.githubusercontent.com/nntrongnghia/Dataquest-Guided-Project-Predicting-the-stock-market/master/sphist.csv'\r\n\r\n# We then create a dataframe for our dataset\r\nmarkets = pd.read_csv(path)\r\n\r\n# We then do some initial preprocessing and cleaning. We first convert the time column to a datetime object.\r\nmarkets[\"Date\"] = pd.to_datetime(markets[\"Date\"])\r\n\r\n# Before we apply our mask, let's sort the initial dataframe.\r\nmarkets.sort_values(ascending=True, by=['Date'], inplace=True)\r\nmarkets = markets.reset_index(drop=True)\r\n\r\n# However, before we split our dataframe into the train and test sets, it's crucial to note the time series nature of\r\n# our dataset. Each row is not independent. As such, it would be better to create a few rolling-average columns. We will\r\n# create columns that contain data for the 5 and 30 day rolling average along with a 5-day standard deviation. I later\r\n# added rolling volume averages to try and improve our accuracy\r\nmarkets[\"5_day_avg\"] = markets[\"Close\"].rolling(5).mean()\r\nmarkets[\"30_day_avg\"] = markets[\"Close\"].rolling(30).mean()\r\nmarkets[\"5_day_std\"] = markets[\"Close\"].rolling(5).std()\r\nmarkets[\"5_day_vol\"] = markets[\"Volume\"].rolling(5).mean()\r\nmarkets[\"253_day_vol\"] = markets[\"Volume\"].rolling(253).mean() # The stock market is only open 253 days per year\r\n\r\n# Note that we have to consider a very important contingency. When computing rolling statistics for our model, we must\r\n# shift all of our results forward by 1. This is because the rolling mean uses the current day's price. This imputes\r\n# future knowledge into our model and will make it less accurate when executed in the real-world.\r\nmarkets[\"5_day_avg\"] = markets[\"5_day_avg\"].shift(1, axis=0)\r\nmarkets[\"30_day_avg\"] = markets[\"30_day_avg\"].shift(1, axis=0)\r\nmarkets[\"5_day_std\"] = markets[\"5_day_std\"].shift(1, axis=0)\r\nmarkets[\"5_day_vol\"] = markets[\"5_day_vol\"].shift(1, axis=0)\r\nmarkets[\"253_day_vol\"] = markets[\"253_day_vol\"].shift(1, axis=0)\r\n\r\n# Let's display our dataframe to understand what our data looks like\r\nprint(markets.tail())\r\nprint(\"\\n\")\r\n\r\n# The first few rows in our dataframe have NaN values since there is no data prior to 1950. Let's just drop all data\r\n# from 1950 and begin our analysis/model construction from 1951 and onward.\r\nmask3 = markets[\"Date\"] >= datetime(year=1951, month=1, day=3)\r\nbetter_markets = markets[mask3]\r\nbetter_markets = better_markets.dropna(axis=0) # Drop rows with NaN values\r\n\r\n# We wish to split our dataset on the time frame. Our data has historical information from 1950 to 2015.\r\n# We will use the data from 1950-2012 to train our model and test it on the data from 2013 to 2015.\r\nmask1 = better_markets[\"Date\"] >= datetime(year=2013, month=1, day=1)\r\nmask2 = better_markets[\"Date\"] < datetime(year=2013, month=1, day=1)\r\n\r\n# We are now ready to split our dataframe into a train and test set\r\ntrain = better_markets[mask2]\r\ntest = better_markets[mask1]\r\n\r\n# =========================================== MODEL CONSTRUCTION ===================================================== #\r\nlin_reg = LinearRegression() # Creates an instance of the Linear Regression class\r\nX_train = train[['5_day_avg', '30_day_avg', '5_day_std', '5_day_vol', '253_day_vol']] # Define training feature matrix\r\ny_train = train[\"Close\"] # Define our training target\r\nX_test = test[['5_day_avg', '30_day_avg', '5_day_std', '5_day_vol', '253_day_vol']] # Define our test feature matrix\r\ny_test = test[\"Close\"] # Define our test target\r\n\r\n# We now fit our model to the training data and test it on our test set to calculate the RMSE score\r\nlin_reg.fit(X_train, y_train)\r\npredictions = lin_reg.predict(X_test)\r\nrmse = np.sqrt(mean_squared_error(y_test, predictions))\r\nprint(\"Our model's rmse score:\", rmse)\r\nprint(\"\\n\")\r\n\r\n# ========================================== VISUALIZATION OF MODEL ================================================== #\r\nplt.plot(test[\"Date\"], test[\"Close\"], label='Actual')\r\nplt.plot(test[\"Date\"], predictions, label='Predicted')\r\nplt.title(\"Accuracy of the Stock Market Model\")\r\nplt.xlabel(\"Dates\")\r\nplt.ylabel(\"S&P500 Index\")\r\nplt.legend()\r\nplt.show()\r\n\r\n# =========================================== CONCLUSION ============================================================= #\r\n# For this project, we were able to utilize Linear Regression to help predict the S&P500 Index from 2013 to the end of\r\n# 2016. We used historical data from 1951 to the end of 2012 to train our model. Our final RMSE score was just over 22,\r\n# implying that our model was, on average, 22 points off in its predictions each day. Considering the fact that the\r\n# markets swings hundreds of points in a day, sometimes thousands, this score is very accurate.\r\n","repo_name":"musicmaster81/Stock_Market_Project","sub_path":"Stock Market Project.py","file_name":"Stock Market Project.py","file_ext":"py","file_size_in_byte":5615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34907909687","text":"import os\n\nimport pandas as pd\nfrom time import gmtime, strftime\nimport lexrank_config\nfrom utils.logger import Logger\n\n\nclass RemoveDuplication:\n\n def __init__(self):\n pass\n\n @staticmethod\n def remove_duplication(merge_df_path, log_file_name, level='info'):\n \"\"\"\n add columns to DF with the POS taggers\n :param merge_df_path: df contain all descriptions\n :return:\n \"\"\"\n\n # update logger properties\n Logger.set_handlers('RemoveDuplication', log_file_name, level=level)\n\n Logger.info('start to remove duplication in DF')\n\n # items and their descriptions\n merge_df = pd.read_csv(merge_df_path)\n\n df_without_dup = merge_df.drop_duplicates(subset=None, keep='first', inplace=False)\n\n Logger.info('Number of row before detecting duplication: {}'.format(merge_df.shape[0]))\n Logger.info('Number of row after detecting duplication: {}'.format(df_without_dup.shape[0]))\n Logger.info('Removed rows: {}'.format(merge_df.shape[0]-df_without_dup.shape[0]))\n\n # '../data/descriptions_data/1425 users input/clean_balance_{}_{}.csv'\n dir_path = '../results/data/RemoveDuplication/'\n csv_file_name = '{}_{}.csv'.format(\n str(df_without_dup.shape[0]),\n str(strftime(\"%Y-%m-%d %H:%M:%S\", gmtime()))\n )\n\n file_path = RemoveDuplication._create_folder_and_save(\n df_without_dup,\n dir_path,\n csv_file_name,\n 'save file without duplications')\n\n return file_path\n\n @staticmethod\n def _create_folder_and_save(df, dir_path, file_name, log_title):\n\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n file_path = '{}{}'.format(dir_path, file_name)\n df.to_csv(file_path, index=False)\n Logger.info('{}: {}'.format(log_title, str(file_name)))\n\n return file_path\n","repo_name":"guyelad88/Personality-based-commerce","sub_path":"processing/remove_duplication.py","file_name":"remove_duplication.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"112778912","text":"\"\"\"\nInputs:\n - data: Source data in the form of dataframe\n - column: Name of the column with respect to which sorting will be done\n - ascending: Sorting order(True(default value)/False)\nOutput:\n - Data in the form of dataframe with the sorted rows\n\"\"\"\nimport pandas as pd\nfrom dft.base_execution_handler import BaseExecutionHandler\n\n\nclass ExecutionHandler(BaseExecutionHandler):\n def execute(self, data: pd.DataFrame, column, ascending=True):\n\n try:\n if not ascending:\n data = data.sort_values(column, ascending=False)\n else:\n data = data.sort_values(column, ascending=True)\n\n data = data.reset_index().drop('index', axis=1)\n\n except Exception as e:\n raise type(e)(e)\n\n return data\n","repo_name":"sb-datafacade/df-lib","sub_path":"old/applications/DataFacadeOOB/code/ActionDefinition/sort_row/python/ExecutionHandler.py","file_name":"ExecutionHandler.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30476662320","text":"from lista import LinkedList\nfrom random import randint\nfrom time import sleep\nclass Jogo:\n def __init__(self, quantity:int, winners:int):\n self.__quantity = quantity\n self.__winners = winners\n self.__players = LinkedList()\n def playersAut(self, arq):\n arqOpen =open(arq, 'r')\n string_jogadores = arqOpen.readlines()\n string_jogadores = string_jogadores[0]\n print(string_jogadores)\n arr_jogadores = string_jogadores.split(', ')\n for i in range(self.__quantity):\n player = arr_jogadores[i].title()\n if self.__players.isEmpty() == False:\n self.__players.verifyElement(player)\n self.__players.insert(player,1)\n def playersManual(self):\n for i in range(self.__quantity):\n player = input(\"Nome do jogador: \").title()\n if self.__players.isEmpty() == False:\n self.__players.verifyElement(player)\n position = int(input(\"Posição do jogador: \"))\n self.__players.insert(player,position)\n def passarJogador(self,quantity):\n for i in range(quantity):\n print(self.__players.advance())\n sleep(1)\n def definirPrimeiro(self, quantity):\n if quantity==1:\n return\n for i in range(quantity):\n self.__players.advance()\n def selecionarJogador(self,start,quantity):\n eliminado = self.__players.goTo(start, quantity)\n posicaoEliminado = self.__players.index(eliminado)+1\n return posicaoEliminado\n def eliminarJogador(self, position):\n removido =self.__players.remove(position)\n return removido\n def mostrarJogador(self, quantity):\n jogador = self.__players.element(quantity)\n return jogador\n def posicaoJogador(self, elemen):\n posicao = self.__players.index(elemen)+1\n return posicao\n def __str__(self) -> str:\n str = '[ '\n for i in range(len(self.__players)):\n str += f'{self.__players.element(i)}, '\n str = str[:-2] + \" ]\"\n return str\n def __len__(self):\n return len(self.__players)\n def iniciarJogo(self,start:int):\n round = 1\n while self.__winners < len(self):\n music = randint(4,15)\n print(\"--------------------COMEÇO DO ROUND------------------\")\n print(f\"participantes: {self}\")\n print(f\"Rodada: {round}\")\n print(f\"Start: {self.mostrarJogador(start-1)} K={music}\")\n for i in range(music):\n proximoEliminado =self.__players.advance()\n print(proximoEliminado)\n sleep(1)\n eliminado =self.posicaoJogador(proximoEliminado)-1\n start = eliminado\n print(f\"Jogador eliminado: {self.eliminarJogador(eliminado)}\")\n print(\"--------------------FIM DO ROUND------------------\")\n print()\n round +=1\n sleep(2)\n print()\n print(f\"O(s) participante(s) vencedor(es): {self}\")\n ","repo_name":"steph4nn/circuito-bomba","sub_path":"jogo.py","file_name":"jogo.py","file_ext":"py","file_size_in_byte":3063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"36406950242","text":"from models.ingredient import Ingredient # noqa: F401, E261, E501\nfrom models.dish import Dish # noqa: F401, E261, E501\n\n\n# Req 3\nclass MenuData:\n def __init__(self, source_path: str) -> None:\n self.dishes = set()\n\n with open(source_path, 'r') as file:\n data = file.readlines()\n\n new_dish = None\n for line in range(1, len(data)):\n line_data = data[line].split(',')\n previos_line_data = data[line - 1].split(',')\n\n if line_data[0] != previos_line_data[0] or new_dish is None:\n if new_dish is not None:\n self.dishes.add(new_dish)\n new_dish = Dish(line_data[0], float(line_data[1]))\n\n quantity = line_data[3].rstrip('\\n')\n ingredient = Ingredient(line_data[2])\n new_dish.add_ingredient_dependency(ingredient, int(quantity))\n\n if line == len(data) - 1:\n self.dishes.add(new_dish)\n","repo_name":"WillianDutra/python-restaurant-orders","sub_path":"src/services/menu_data.py","file_name":"menu_data.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"40703657738","text":"import torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom .attention_gate import AttentionGate\nfrom .bottom_up import BottomUp\nfrom ..anet.FPANet import FPNCNet\nfrom ..anet.sca_attention2 import ExternaAtt\nfrom ..anet import sca_attention2, sca_attention\nfrom ..anet.BiFPN import BiFPN\nfrom ..anet.efficientnet.model import BiFPN1\n\n\nclass FPN(nn.Module):\n \"\"\"\n Module that adds FPN on top of a list of feature maps.\n The feature maps are currently supposed to be in increasing depth\n order, and must be consecutive\n \"\"\"\n\n def __init__(\n self, in_channels_list, out_channels, conv_block, top_blocks=None\n ):\n \"\"\"\n Arguments:\n in_channels_list (list[int]): number of channels for each feature map that\n will be fed\n out_channels (int): number of channels of the FPN representation\n top_blocks (nn.Module or None): if provided, an extra operation will\n be performed on the output of the last (smallest resolution)\n FPN output, and the result will extend the result list\n \"\"\"\n \n\n super(FPN, self).__init__()\n\n self.inner_blocks = []\n self.layer_blocks = []\n\n for idx, in_channels in enumerate(in_channels_list, 1): \n \n inner_block = \"fpn_inner{}\".format(idx)\n\n \n layer_block = \"fpn_layer{}\".format(idx)\n\n \n inner_block_module = conv_block(in_channels, out_channels, 1)\n\n \n layer_block_module = conv_block(out_channels, out_channels, 3, 1)\n\n \n self.add_module(inner_block, inner_block_module)\n self.add_module(layer_block, layer_block_module)\n\n \n self.inner_blocks.append(inner_block)\n self.layer_blocks.append(layer_block)\n\n \n self.top_blocks = top_blocks\n # self.bifpn = nn.Sequential(\n # *[BiFPN(256,\n # in_channels_list,\n # True if _ == 0 else False,\n # attention=True,\n # )\n # for _ in range(2)])\n self.bifpn = nn.Sequential(\n *[BiFPN(256,\n in_channels_list,\n True if _ == 0 else False,\n attention=True ,\n )\n for _ in range(2)])\n\n def forward(self, x):\n \"\"\"\n Arguments:\n x (list[Tensor]): feature maps for each feature level.\n Returns:\n results (tuple[Tensor]): feature maps after FPN layers.\n They are ordered from highest resolution first.\n \"\"\"\n\n last_inner = getattr(self, self.inner_blocks[-1])(x[-1])\n # print('-----------count----------', count)\n # print('layer_last shape: ', last_inner.shape)\n results = []\n\n \n c5 = getattr(self, self.layer_blocks[-1])(last_inner)\n results.append(c5)\n cx_results = []\n cx_results.append(c5)\n\n results=self.bifpn(x)\n \n if self.top_blocks is not None:\n last_results = self.top_blocks(results[-1])\n results.extend(last_results)\n\n return tuple(results)\n\n\n\nclass LastLevelMaxPool(nn.Module):\n def forward(self, x):\n return [F.max_pool2d(x, 1, 2, 0)]\n","repo_name":"chaibosong/SAFPN","sub_path":"maskrcnn_benchmark/modeling/backbone/fpn.py","file_name":"fpn.py","file_ext":"py","file_size_in_byte":3315,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"34"} +{"seq_id":"2765646239","text":"\"\"\"In this file, we define the set-up procedure of the model\"\"\"\n\nimport random\nimport itertools\nfrom stockmarket import firms, stock, valuationfunctions, switchingstrategies, buysellfunctions\nfrom stockmarket.agent import Trader\n\n\ndef setup_agents(init_money, init_bid_ask_spread, init_ma_s, init_ma_l, init_propensity_to_switch,\n init_price_to_earnings_window, trader_volume_risk_aversion, momentum_traders=3, reversion_traders=3):\n \"\"\"This returns an initialized agent set\"\"\"\n agent_set = []\n init_agent = lambda x, y: agent_set.append(\n Trader(name=x,\n money=randomize_init_variable(init_money[0], init_money[1]),\n bid_ask_spread=randomize_init_variable(init_bid_ask_spread[0], init_bid_ask_spread[1]),\n ma_short=randomize_init_variable(init_ma_s[0], init_ma_s[1]),\n ma_long=randomize_init_variable(init_ma_l[0], init_ma_l[1]),\n valuation_function=y, propensity_to_switch=init_propensity_to_switch,\n price_to_earnings_window=(randomize_init_variable(init_price_to_earnings_window[0][0],\n init_price_to_earnings_window[0][1]),\n randomize_init_variable(init_price_to_earnings_window[1][0],\n init_price_to_earnings_window[1][1])),\n trader_volume_risk_aversion=trader_volume_risk_aversion,\n switching_strategy=switchingstrategies.adaptive_switching))\n for agent in range(momentum_traders):\n init_agent(agent, buysellfunctions.momentum)\n for agent in range(momentum_traders, reversion_traders+momentum_traders):\n init_agent(agent, buysellfunctions.mean_reversion)\n return agent_set\n\n\ndef setup_agents_with_noise_traders(init_money, init_bid_ask_spread, init_ma_s, init_ma_l,\n init_propensity_to_switch, init_price_to_earnings_window,\n trader_volume_risk_aversion,\n momentum_traders=3, reversion_traders=3, noise_traders=3):\n \"\"\"This returns an initialized agent set\"\"\"\n agent_set = []\n init_agent = lambda x, y: agent_set.append(\n Trader(name=x,\n money=randomize_init_variable(init_money[0], init_money[1]),\n bid_ask_spread=randomize_init_variable(init_bid_ask_spread[0], init_bid_ask_spread[1]),\n ma_short=randomize_init_variable(init_ma_s[0], init_ma_s[1]),\n ma_long=randomize_init_variable(init_ma_l[0], init_ma_l[1]),\n valuation_function=y, propensity_to_switch=init_propensity_to_switch,\n price_to_earnings_window=(randomize_init_variable(init_price_to_earnings_window[0][0],\n init_price_to_earnings_window[0][1]),\n randomize_init_variable(init_price_to_earnings_window[1][0],\n init_price_to_earnings_window[1][1])),\n trader_volume_risk_aversion=trader_volume_risk_aversion,\n switching_strategy=switchingstrategies.adaptive_switching))\n for agent in range(momentum_traders):\n init_agent(agent, buysellfunctions.momentum)\n for agent in range(momentum_traders, reversion_traders + momentum_traders):\n init_agent(agent, buysellfunctions.mean_reversion)\n for agent in range(reversion_traders + momentum_traders, reversion_traders + momentum_traders + noise_traders):\n init_agent(agent, buysellfunctions.noise_trading)\n return agent_set\n\n\ndef setup_firms(init_book_value,\n init_profit,\n firm_profit_mu,\n firm_profit_delta,\n firm_profit_sigma,\n backward_simulated_time,\n amount_of_firms=1):\n \"\"\"This returns an initialized firm set\"\"\"\n firm_set = []\n for firm in range(amount_of_firms):\n firm_set.append(firms.Firm(name=firm,\n book_value=randomize_init_variable(init_book_value[0], init_book_value[1]),\n profits=[randomize_init_variable(init_profit[0], init_profit[1])],\n mu=firm_profit_mu,\n brownian_delta=firm_profit_delta,\n brownian_sigma=firm_profit_sigma,\n dividend_rate=1))\n for firm in firm_set:\n # creates a profit history for the last 6 periods.\n for _ in itertools.repeat(None, backward_simulated_time):\n firm.update_profits(firm.determine_profit())\n\n return firm_set\n\n\ndef setup_stocks(set_of_firms, amount):\n stock_set = []\n for firm in set_of_firms:\n stock_set.append(stock.Stock(firm, amount))\n return stock_set\n\n\ndef randomize_init_variable(min_amount, max_amount):\n return random.randint(min_amount, max_amount)\n\n\ndef distribute_initial_stocks(stocks, agents):\n for stock in stocks:\n agent_number = len(agents)\n amount_each = stock.amount // agent_number\n rest = int(stock.amount % agent_number)\n for x in range(0, rest):\n agents[x].stocks[stock] += amount_each + 1\n agents[x].portfolio_value_history[0] += agents[x].stocks[stock] * stock.price_history[-1]\n for x in range(rest, agent_number):\n agents[x].stocks[stock] += amount_each\n agents[x].portfolio_value_history[0] += agents[x].stocks[stock] * stock.price_history[-1]\n # initialize the agents portfolio value history\n\n","repo_name":"lbolla/Agent-Based-Stock-Market-Model","sub_path":"stockmarket/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":5993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"19652764150","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('consommation/', views.consommation, name='consommation'),\n path('login/', views.login, name='login'),\n path('rapport/', views.rapport, name='rapport'),\n]","repo_name":"NathanMwape/electtrical_with_Django","sub_path":"consommation/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"25621446438","text":"# Name : Anshu Kumar Singh\n# Date : 05/11/23\n# Title : Exercise 2 - \n\n'''\nCheck to see if a string of parentheses is balanced or not.\n\nBy \"balanced,\" we mean that for every open parenthesis, there is a matching closing \nparenthesis in the correct order. For example, the string \"((()))\" has three pairs \nof balanced parentheses, so it is a balanced string. On the other hand, the string \n\"(()))\" has an imbalance, as the last two parentheses do not match, so it is not \nbalanced. Also, the string \")(\" is not balanced because the close parenthesis needs \nto follow the open parenthesis.\n\nYour program should take a string of parentheses as input and return True if it is \nbalanced, or False if it is not. In order to solve this problem, use a Stack data \nstructure.\n\nFunction name:\nis_balanced_parentheses\n\nRemember: this is not a method within the Stack class, this is a separate function.\n'''\n\nfrom stack_main import Stack\n\ndef is_balanced_parentheses(parens):\n stack = Stack()\n for paren in parens:\n if paren == '(':\n stack.push(paren)\n elif paren == ')':\n if stack.is_empty() or stack.pop().value != '(':\n return False\n return stack.is_empty()\n\n\nprint(\"Enter a set of balanced parenthesis: \", end=\"\")\nparenthesis = input()\n\nif is_balanced_parentheses(parenthesis):\n print(\"Good\")\nelse:\n print(\"No these are not balanced!\")\n\n\n\n'''\nI couldn't solve this one. I just kept thinking how it could be solved using stacks. \nAll of my solutions were less efficient than if I hadn't used stacks. \n\nWhen I checked the solutions, I thought of this solution as a piece of art. Beautifully \nsolved.\n'''\n\n\n\n\n","repo_name":"ThisIsSidam/PythonDSA","sub_path":"3-StacksnQueues/Exercise-2.py","file_name":"Exercise-2.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"10038112260","text":"import numpy as np\n\ndef influence_theta(agent, history):\n hessian = agent.hessian(history)\n inv = np.linalg.pinv(hessian)\n delta_thetas = np.array([\n -inv @ agent.grad_single_loss(trans) for trans in history\n ])\n\n return delta_thetas\n","repo_name":"chmnchiang/CS234-project","sub_path":"influence.py","file_name":"influence.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"27183448737","text":"import json\nfrom pathlib import Path\nimport re\nimport requests\nimport jwt\nfrom urllib.parse import urljoin\nimport subprocess\nimport re\nimport os\n\n# URL = \"http://localhost:8000/\"\nURL = \"http://samehadaku.intechfest.cc/\"\n\nEXPLOIT_FILE_NAME = \"exploit.conf.yaml\"\n\n\ndef execute_slipit(text, filepath):\n tmpdir = \"exploit.tar\"\n if Path(tmpdir).exists():\n os.unlink(tmpdir)\n dirname = os.path.dirname(filepath)\n filename = os.path.basename(filepath)\n command = [\n 'slipit',\n '--archive-type', 'tar',\n tmpdir,\n filename,\n '--prefix', dirname,\n '--static', text,\n '--separator', '/',\n '--depth', '0',\n '--overwrite'\n ]\n subprocess.check_output(command)\n return tmpdir\n\n\ndef make_zip_symlink(filename, linkto):\n zipname = \"exploit.zip\"\n if Path(zipname).exists():\n os.unlink(zipname)\n subprocess.check_output(['ln', '-s', linkto, filename])\n command = [\n 'zip', '--symlinks', '-r', zipname, filename\n ]\n subprocess.check_output(command)\n os.remove(filename)\n return zipname\n\n\ndef extract_secret_key(environ_string):\n pattern = b'SECRET_KEY=(.*?)\\x00'\n secret_key_match = re.search(pattern, environ_string)\n if secret_key_match:\n return secret_key_match.group(1)\n return None\n\n\ndef forge_token(username, is_admin, configfile, secret_key):\n creds = json.dumps({\n \"username\": username,\n \"isAdmin\": is_admin,\n \"configfile\": configfile\n })\n jwt_cookie_dat = {\n \"sub\": creds,\n \"type\": \"access\",\n }\n token = jwt.encode(jwt_cookie_dat, secret_key, algorithm='HS256')\n return token\n\n\nclass ExploitApi:\n def __init__(self, url=URL) -> None:\n self.url = url\n self.session = requests.Session()\n\n def path(s, path):\n return urljoin(s.url, path)\n\n def uploadtar(s, tarfile):\n \"\"\"\n require login as admin\n \"\"\"\n res = s.session.post(s.path(\"/uploadtar\"), files={\n \"file\": open(tarfile)\n })\n return res\n\n def uploadzip(s, zipfile):\n res = s.session.post(s.path(\"/uploadzip\"), files={\n \"file\": open(zipfile, \"rb\")\n })\n return res\n\n def forge_and_set_cookie(s, username, is_admin, configfile, secret_key):\n token = forge_token(username, is_admin, configfile, secret_key)\n s.session.cookies.set(\n name='access_token_cookie',\n value=token.decode(),\n )\n\n def read_upload(s, filename):\n return s.session.get(s.path(str(filename)))\n\n def get_secret(s):\n \"\"\"\n get SECRET_KEY from environtment variable\n \"\"\"\n filename = \"foobarfoo\"\n zipfilename = make_zip_symlink(filename, \"/proc/self/environ\")\n uploaded_filename = Path(s.uploadzip(zipfilename).json()['filename'])\n environ = s.read_upload(uploaded_filename/filename).content\n return extract_secret_key(environ)\n\n def upload_exploit(s):\n \"\"\"\n upload exploit.conf.yaml into the config folder using arbitary file write\n and bypass the waf using symlink to create a link to parent directory\n \"\"\"\n os.environ['payload'] = open(\"./\"+EXPLOIT_FILE_NAME).read()\n # path transversal using symlink, because the blacklist only\n # blacklisted the path name, we can usign symlink to path transfersal to parent directory.\n os.system('slipit exploit.tar link --symlink ../ --overwrite --depth 0')\n s.uploadtar(\"exploit.tar\")\n os.system(f'slipit exploit.tar {EXPLOIT_FILE_NAME} --prefix \"link/config\" --separator / --static \"$payload\" --depth 0 --overwrite')\n return s.uploadtar(\"exploit.tar\")\n\n def triger_rce(s):\n return s.session.get(s.path(\"/\"))\n\n\nif __name__ == \"__main__\":\n api = ExploitApi()\n secret_key = api.get_secret()\n print(\"secret:\", secret_key)\n api.forge_and_set_cookie(\n username=\"dimas\",\n is_admin=True,\n configfile=EXPLOIT_FILE_NAME,\n secret_key=secret_key\n )\n res = api.upload_exploit()\n print(res.text)\n api.triger_rce()\n","repo_name":"TCP1P/INTECHFEST-CTF-2023-Challenges","sub_path":"web/Samehadaku KW/solution/solver/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":4123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"27432862893","text":"import copy\nimport math\nimport os\nfrom urllib.request import Request, urlopen\n\nfrom PyQt6 import QtCore, QtGui\nfrom PyQt6.QtCore import *\nfrom PyQt6.QtGui import *\nfrom PyQt6.QtGui import QPixmap, QFont, QPainterPath, QPainter\nfrom PyQt6.QtWidgets import *\nfrom hearthstone.enums import Zone as HS_Zone\nfrom PyQt6.QtCore import Qt\n\nfrom fireplace.card import Spell, Hero, HeroPower\nfrom ui.animations import *\n\n# cards_path = os.path.join(os.path.abspath(os.getcwd()), \"ui\", \"cards\")\ncards_path = os.path.join(\"ui\", \"cards\")\nprint(f\"PATH TO CARDS = {cards_path}\")\n\n\"\"\"\n (known card zones): INVALID, PLAY, DECK, HAND, GRAVEYARD, REMOVEDFROMGAME, SETASIDE, SECRET\n\"\"\"\n\nFONT = \"Belwe Bd BT Alt Style [Rus by m\"\n\n\nclass Zone:\n def __init__(self, x, y):\n self.x = x\n self.y = y\n self.cards = []\n\n @property\n def count(self):\n return len(self.cards)\n\n\nclass OutlinedLabel(QLabel):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.textColor = Qt.GlobalColor.white\n\n def setColor(self, color):\n self.textColor = color\n # self.render()\n\n def paintEvent(self, a0: QtGui.QPaintEvent) -> None:\n off = 10\n painter = QPainter(self)\n path = QPainterPath()\n draw_font = self.font()\n path.addText(off, draw_font.pointSize() + off, draw_font, self.text())\n painter.setRenderHint(QtGui.QPainter.RenderHint.Antialiasing)\n painter.strokePath(path, QPen(QColor(Qt.GlobalColor.black), 2))\n painter.fillPath(path, QBrush(self.textColor))\n size = path.boundingRect().size().toSize()\n self.resize(size.width() + off * 2, size.height() + off * 2)\n\n\nclass QCard(QLabel):\n # TODO:\n FONT_SIZE = 21\n\n def __init__(self, qwindow, width, height, entity):\n super().__init__(qwindow)\n\n self.entity = entity\n\n self.width = width\n self.height = height\n\n self.card_overlay = QLabel(self)\n self.cost = OutlinedLabel(self)\n\n self.cost.setText(str(entity.cost))\n self.cost.move(int(self.width * 0.031), int(self.height * 0.067))\n self.cost.setFont(QFont(FONT, self.FONT_SIZE - 1))\n self.cost.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n overlay_path = \"ui/images/spell.png\"\n\n if type(entity) != Spell and type(entity) != HeroPower:\n self.at = OutlinedLabel(self)\n self.hp = OutlinedLabel(self)\n self.hp.setText(str(entity.health))\n self.at.setText(str(entity.atk))\n\n self.at.setFont(QFont(FONT, self.FONT_SIZE - 2))\n self.at.setGeometry(QtCore.QRect(int(self.width * 0.04), int(self.height * 0.715), 500, 500))\n\n self.hp.move(int(self.width * 0.70), int(self.height * 0.715))\n self.hp.setFont(QFont(FONT, self.FONT_SIZE - 2))\n self.hp.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n overlay_path = \"ui/images/minion.png\"\n\n self.card_overlay.setPixmap(QPixmap(overlay_path))\n self.card_overlay.move(int(self.width * 0.045), int(self.height * 0.07))\n self.card_overlay.setScaledContents(True)\n self.card_overlay.resize(int(self.width * 0.9), int(self.height * 0.85))\n\n self.resize(self.width, self.height)\n\n def rerender(self, entity=None):\n if entity is None:\n entity = self.entity\n self.cost.setText(str(entity.cost))\n if type(entity) != Spell:\n if entity.damage > 0 :\n self.hp.setColor(Qt.GlobalColor.red)\n\n self.hp.setText(str(entity.health))\n self.at.setText(str(entity.atk))\n # print(f\"All set, {entity.cost} {entity.health} {entity.atk}\")\n\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n super(MainWindow, self).__init__()\n self.setWindowTitle(\"Game Ep\")\n\n # self.setStyleSheet('background: rgb(255, 64, 64);')\n\n self.void_size = 20\n self.card_width = 128\n self.card_height = 194\n\n self.entities = {'Deck1': Zone(1100, 600), 'Deck2': Zone(1100, 50),\n 'Hand1': Zone(200, 600), 'Hand2': Zone(200, 50),\n 'Face1': Zone(12, 600), 'Face2': Zone(12, 50),\n 'Power1': Zone(100, 590), 'Power2': Zone(100, 40),\n 'Field1': Zone(200, 430), 'Field2': Zone(200, 240)}\n\n self.turn_label = QLabel(self)\n self.turn_label.resize(400, 50)\n self.turn_label.setFont(QFont(FONT, 20))\n self.turn_label.setText(\"Game starting\")\n self.turn_label.move(1100, 400)\n self.turn_label.show()\n\n self.deck1_amount_label = OutlinedLabel(self)\n self.deck1_amount_label.resize(400, 50)\n self.deck1_amount_label.setFont(QFont(FONT, 20))\n self.deck1_amount_label.setText(\"30\")\n self.deck1_amount_label.move(self.entities[\"Deck1\"].x - 25 + self.card_width // 2,\n self.entities[\"Deck1\"].y - 20 + self.card_height // 2)\n self.deck1_amount_label.show()\n\n self.deck2_amount_label = OutlinedLabel(self)\n self.deck2_amount_label.resize(400, 50)\n self.deck2_amount_label.setFont(QFont(FONT, 20))\n self.deck2_amount_label.setText(\"30\")\n self.deck2_amount_label.move(self.entities[\"Deck2\"].x - 25 + self.card_width // 2,\n self.entities[\"Deck2\"].y - 20 + self.card_height // 2)\n self.deck2_amount_label.show()\n\n self.card_back1_label = QLabel(self)\n self.card_back1_label.resize(self.card_width + 10, self.card_height + 10)\n self.card_back1_label.move(self.entities[\"Deck1\"].x - 5, self.entities[\"Deck1\"].y - 5)\n self.card_back1_label.setScaledContents(True)\n self.card_back1_label.setPixmap(QPixmap(\"ui/images/cardback.png\"))\n\n self.card_back2_label = QLabel(self)\n self.card_back2_label.resize(self.card_width + 10, self.card_height + 10)\n self.card_back2_label.move(self.entities[\"Deck2\"].x - 5, self.entities[\"Deck2\"].y - 5)\n self.card_back2_label.setScaledContents(True)\n self.card_back2_label.setPixmap(QPixmap(\"ui/images/cardback.png\"))\n\n self.mana_label1 = QLabel(self)\n self.mana_label1.resize(400, 50)\n self.mana_label1.setFont(QFont(FONT, 20))\n self.mana_label1.setText(\"mana: 0/0\")\n self.mana_label1.move(self.entities[\"Deck1\"].x,\n self.entities[\"Deck1\"].y - 50)\n self.mana_label1.show()\n\n self.mana_label2 = QLabel(self)\n self.mana_label2.resize(400, 50)\n self.mana_label2.setFont(QFont(FONT, 20))\n self.mana_label2.setText(\"mana: 0/0\")\n self.mana_label2.move(self.entities[\"Deck2\"].x,\n self.entities[\"Deck2\"].y + 25 + self.card_height)\n self.mana_label2.show()\n\n self.id_list = []\n self.anims = []\n self.start_timer()\n self.resize(1300, 800)\n self.show()\n\n def add_animation(self, animation):\n self.anims.append(animation)\n\n def change_state(self, text):\n self.add_animation(ChangeTextAnim(self.turn_label, text))\n\n def add_entity_to_hand(self, entity):\n player_name = entity.controller.name\n hand = \"Hand\" + player_name[-1]\n card_position = len(self.entities[hand].cards) - 1\n cardID = entity.id\n entity_zone_pos = entity.zone_position - 1\n\n if not os.path.exists(os.path.join(cards_path, cardID + \".png\")):\n req = Request(\n 'https://art.hearthstonejson.com/v1/render/latest/enUS/256x/{}.png'.format(\n cardID),\n headers={'User-Agent': 'Mozilla/5.0'})\n webpage = urlopen(req).read()\n with open(\"ui/cards/{}.png\".format(cardID), \"wb\") as file:\n file.write(webpage)\n\n self.entities[hand].cards.insert(entity_zone_pos, QCard(self, self.card_width, self.card_height, entity))\n # self.entity[hand].cards.append(QLabel(self))\n self.entities[hand].cards[entity_zone_pos].setObjectName(str(entity.uuid))\n self.entities[hand].cards[entity_zone_pos].setScaledContents(True)\n self.entities[hand].cards[entity_zone_pos].setPixmap(QPixmap(os.path.join(cards_path,\n cardID + \".png\")))\n self.reorganise(hand)\n self.entities[entity.uuid] = self.entities[hand].cards[entity_zone_pos]\n zone = self.entities[self.get_zone(HS_Zone.DECK, entity.controller.name)]\n self.entities[entity.uuid].move(zone.x, zone.y)\n self.entities[hand].cards[entity_zone_pos].show()\n\n self.card_back1_label.raise_()\n self.card_back2_label.raise_()\n\n self.deck1_amount_label.raise_()\n self.deck2_amount_label.raise_()\n\n def change_card(self, entity):\n self.anims.append(ChangeAnim(self.entities[entity.uuid]))\n # self.entities[entity.uuid].rerender(entity)\n\n def change_deck_amount(self, p_name, cards_amount):\n if p_name[-1] == '1':\n cur = self.deck1_amount_label\n else:\n cur = self.deck2_amount_label\n self.add_animation(ChangeTextAnim(cur, str(cards_amount)))\n\n def change_mana(self, p_name, max_mana, mana):\n if p_name[-1] == '1':\n cur = self.mana_label1\n else:\n cur = self.mana_label2\n self.add_animation(ChangeTextAnim(cur, \"mana: \" + str(mana) + \"/\" + str(max_mana)))\n\n def add_hero(self, hero):\n zone = 'Face' + hero.controller.name[-1]\n entity_zone_pos = hero.zone_position - 1\n cardID = hero.id\n self.entities[zone].cards.append(QCard(self, self.card_width, self.card_height, hero))\n self.entities[zone].cards[entity_zone_pos].setObjectName(str(hero.uuid))\n self.entities[zone].cards[entity_zone_pos].setScaledContents(True)\n\n if not os.path.exists(os.path.join(cards_path, cardID + \".png\")):\n req = Request(\n 'https://art.hearthstonejson.com/v1/render/latest/enUS/256x/{}.png'.format(\n cardID),\n headers={'User-Agent': 'Mozilla/5.0'})\n webpage = urlopen(req).read()\n with open(\"ui/cards/{}.png\".format(cardID), \"wb\") as file:\n file.write(webpage)\n self.entities[zone].cards[entity_zone_pos].setPixmap(QPixmap(os.path.join(cards_path,\n cardID + \".png\")))\n self.entities[zone].cards[entity_zone_pos].show()\n self.entities[hero.uuid] = self.entities[zone].cards[entity_zone_pos]\n\n self.entities[hero.uuid].move(self.entities[zone].x, self.entities[zone].y)\n # self.reorganise(zone)\n\n def add_hero_power(self, heropower):\n zone = 'Power' + heropower.controller.name[-1]\n entity_zone_pos = heropower.zone_position - 1\n cardID = heropower.id\n self.entities[zone].cards.append(QCard(self, self.card_width, self.card_height, heropower))\n self.entities[zone].cards[entity_zone_pos].setObjectName(str(heropower.uuid))\n self.entities[zone].cards[entity_zone_pos].setScaledContents(True)\n\n if not os.path.exists(os.path.join(cards_path, cardID + \".png\")):\n req = Request(\n 'https://art.hearthstonejson.com/v1/render/latest/enUS/256x/{}.png'.format(\n cardID),\n headers={'User-Agent': 'Mozilla/5.0'})\n webpage = urlopen(req).read()\n with open(\"ui/cards/{}.png\".format(cardID), \"wb\") as file:\n file.write(webpage)\n self.entities[zone].cards[entity_zone_pos].setPixmap(QPixmap(os.path.join(cards_path,\n cardID + \".png\")))\n self.entities[zone].cards[entity_zone_pos].show()\n self.entities[heropower.uuid] = self.entities[zone].cards[entity_zone_pos]\n\n self.entities[heropower.uuid].move(self.entities[zone].x, self.entities[zone].y)\n self.entities[heropower.uuid].raise_()\n\n def change_zone(self, entity, zoneID_from, zoneID_to): # cardID: CardID, zoneID: ZoneID,\n zone_to_remove = zoneID_to\n player_name = entity.controller.name\n zoneID_from = self.get_zone(zoneID_from, player_name)\n zoneID_to = self.get_zone(zoneID_to, player_name)\n\n card = self.entities[entity.uuid] # cardPos: original position\n self.entities[zoneID_from].cards.remove(card)\n self.reorganise(zoneID_from)\n position = entity.zone_position - 1\n\n # moving to another position\n self.entities[zoneID_to].cards.insert(position, card)\n cord_x = self.entities[zoneID_to].x + (position * (self.card_width + self.get_void_size(zoneID_to)))\n self.add_animation(MoveCardAnim(card, cord_x, self.entities[zoneID_to].y))\n if zoneID_to[0:5] == \"Field\" and type(entity) == Spell:\n self.remove_entity(entity, zone_to_remove)\n self.reorganise(zoneID_to)\n\n # print(\"card started moving\")\n\n @staticmethod\n def get_zone(zone, player_name):\n _zone = \"\"\n if zone == HS_Zone.HAND:\n _zone = \"Hand\" + player_name[-1]\n elif zone == HS_Zone.PLAY:\n _zone = \"Field\" + player_name[-1]\n elif zone == HS_Zone.DECK:\n _zone = \"Deck\" + player_name[-1]\n elif zone == \"Face\":\n _zone = \"Face\" + player_name[-1]\n return _zone\n\n def remove_entity(self, entity, prev_zone):\n player_name = entity.controller.name\n if type(entity) == Hero:\n zone = self.get_zone(\"Face\", player_name)\n else:\n zone = self.get_zone(prev_zone, player_name)\n # self.entity[hand].cards\n label = self.entities[entity.uuid]\n # label.clear()\n # sip.delete(label)\n self.entities[zone].cards.remove(label)\n del self.entities[entity.uuid]\n\n # ql = QLabel(self)\n # ql.layout().removeWidget(ql)\n self.add_animation(DeleteWidgetAnim(label))\n # label.clear()\n self.reorganise(zone)\n # self.entities[hand].cards[entity.zone_position - 1].clear()\n # del self.entities[hand].cards[entity.zone_position - 1]\n\n def reorganise(self, zoneID):\n superAnim = SuperAnim()\n for i in range(self.entities[zoneID].count):\n card = self.entities[zoneID].cards[i]\n # if \"Hand\" in zoneID:\n # i += 1\n vs = self.get_void_size(zoneID)\n if card.x != self.entities[zoneID].x + (i * (self.card_width + vs)):\n superAnim.anims.append(MoveCardAnim(card, self.entities[zoneID].x + (\n i * (self.card_width + vs)), self.entities[zoneID].y))\n self.add_animation(superAnim)\n\n def render_hand(self, hand):\n for card_position, card in enumerate(self.entities[hand].cards):\n card.move(self.entities[hand].x + 140 * (card_position - 1), self.entities[hand].y)\n\n def start_timer(self):\n timer = QtCore.QTimer(self)\n timer.timeout.connect(self.animation)\n timer.start(10) # 50 for debugging\n\n def animation(self):\n if 0 >= len(self.anims):\n return\n if self.anims[0].steps >= 20:\n self.anims[0].last_step()\n self.anims.pop(0)\n return\n self.anims[0].step()\n\n self.update()\n\n def get_void_size(self, zoneID):\n return 1000 // (self.entities[zoneID].count + 1) - self.card_width\n\n def attack(self, source, target):\n zoneID_from = self.get_zone(HS_Zone.PLAY, source.controller.name)\n zoneID_to = self.get_zone(HS_Zone.PLAY, target.controller.name)\n target_pos = target.zone_position - 1\n source_pos = source.zone_position - 1\n if type(source) == Hero:\n source_pos += 1\n zoneID_from = self.get_zone(\"Face\", source.controller.name)\n if type(target) == Hero:\n target_pos += 1\n zoneID_to = self.get_zone(\"Face\", target.controller.name)\n\n card1 = self.entities[source.uuid]\n cord_x_to = self.entities[zoneID_to].x + (target_pos * (self.card_width + self.get_void_size(zoneID_from)))\n cord_x_from = self.entities[zoneID_from].x + (source_pos * (self.card_width + self.get_void_size(zoneID_from)))\n self.add_animation(MoveCardAnim(card1, cord_x_to, self.entities[zoneID_to].y, True))\n self.add_animation(MoveCardAnim(card1, cord_x_from, self.entities[zoneID_from].y))\n\n def activate_hero_power(self, entity, target):\n # This method place red square on target\n opacity_effect = QGraphicsOpacityEffect()\n opacity_effect.setOpacity(0.5)\n self.add_animation(SetGraphicsEffectAnim(self.entities[entity.uuid], opacity_effect))\n red_square = None\n if target is not None:\n red_square = QWidget(self)\n red_square.setStyleSheet(\"background-color: red\")\n red_square.move(self.entities[target.uuid].x() - 30 + self.card_width // 2,\n self.entities[target.uuid].y() - 30 + self.card_height // 2)\n red_square.resize(60, 60)\n self.add_animation(AddWidgetAnim(red_square))\n self.add_animation(WaitAnim(3))\n clear_effect = QGraphicsOpacityEffect()\n clear_effect.setOpacity(1)\n self.add_animation(SetGraphicsEffectAnim(self.entities[entity.uuid], clear_effect))\n if red_square is not None:\n self.add_animation(DeleteWidgetAnim(red_square))\n\n def card_mulligan(self, cards, args):\n background = QWidget(self)\n background.setObjectName(\"background\")\n background.resize(1300, 800)\n backLayout = QVBoxLayout(background)\n container = QWidget(self)\n container.setObjectName(\"Container\")\n backLayout.addWidget(container, alignment=QtCore.Qt.AlignmentFlag.AlignCenter)\n layout = QHBoxLayout(container)\n layout.setContentsMargins(10, 10, 10, 10)\n layout.setSpacing(20)\n\n choice_cards = {}\n for entity in cards:\n cardID = entity.id\n\n if not os.path.exists(os.path.join(cards_path, cardID + \".png\")):\n req = Request(\n 'https://art.hearthstonejson.com/v1/render/latest/enUS/256x/{}.png'.format(\n cardID),\n headers={'User-Agent': 'Mozilla/5.0'})\n webpage = urlopen(req).read()\n with open(\"ui/cards/{}.png\".format(cardID), \"wb\") as file:\n file.write(webpage)\n\n card = QCard(self, self.card_width, self.card_height, entity)\n choice_cards[entity.uuid] = card\n card.setScaledContents(True)\n card.setPixmap(QPixmap(os.path.join(cards_path, cardID + \".png\")))\n self.add_animation(AddCardMulliganAnim(layout, card))\n\n for arg in args:\n opacity_effect = QGraphicsOpacityEffect()\n opacity_effect.setOpacity(0.5)\n self.add_animation(WaitAnim(1))\n self.add_animation(SetGraphicsEffectAnim(choice_cards[arg.uuid], opacity_effect))\n background.show()\n\n self.add_animation(WaitAnim(1))\n self.add_animation(DeleteWidgetAnim(background))\n\n # def send_to_graveyard(self, entity, prev_zone):\n # player_name = entity.controller.name\n # zone = self.get_zone(prev_zone, player_name)\n # self.change_zone(entity, zone, \"Graveyard\" + player_name[-1])\n # label = self.entities[entity.uuid]\n # label.setPixmap(QtGui.QPixmap(os.path.join(cards_path, \"card_back.png\")))\n","repo_name":"PotatoHDs/neural-hearthstone","sub_path":"ui/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":19715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"5965931085","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n#Ricardos.geral@gmail.com\n\nimport pygsheets\nimport pandas as pd\n\n# get signed credentials\ntry:\n gc = pygsheets.authorize(service_file=\"service_creds.json\") # the file must be updated by user\n initial_rows = 1000 # default number of rows of the worksheet\n initial_colmn = 15 # default number of columns\n google_creds = True\n print('Google signed credentials are OK')\n\nexcept:\n print('Problem: Google signed credentials')\n google_creds = False\n\ndef spreadsheet_worksheet(ssheet_title, wsheet_title, share_email):\n # get spredsheet instance\n list_of_ssheets = gc.spreadsheet_titles() # gets all the available sheets titles in the account of the service_creds.jon\n ssheet_exists = False\n for ssheet in list_of_ssheets:\n if ssheet == ssheet_title:\n ssheet_exists = True\n if ssheet_exists == True: # if sheet exists\n sh = gc.open(ssheet_title) # open it\n else: # if sheet does not exists\n sh = gc.create(ssheet_title) # create it\n\n sh.share(share_email, role='writer') # share it to the provided email\n\n wsheet_exists = False\n try:\n sh.worksheets(sheet_property='title',value= wsheet_title)\n wsheet_exists = True\n except:\n pass\n\n if wsheet_exists == True:\n wks = sh.worksheet(property='title', value= wsheet_title)\n wks.clear()\n else:\n wks = sh.add_worksheet(title=wsheet_title, rows=str(initial_rows), cols=str(initial_colmn))\n\n #cell instance\n a1 = wks.cell('A1')\n a1.text_format['bold'] = True # set headers to bold\n a1.update()\n # Getting a Range object\n rng = wks.get_values('A1', 'I1', returnas='range')\n rng.apply_format(a1) # set format of a1 to all cells in the range rng\n return wks\n\ndef write_gsh(data, row, wks):\n global initial_rows\n fieldnames = ['date', 'time',\n # 'v_up', 'v_int', 'v_down',\n # 'bar_up', 'bar_int', 'bar_down',\n 'mmH2O_up', 'mmH2O_int', 'mmH2O_down',\n 'ana_turb', 'turb', 'flow', 'liters',\n 'water_temp' #, 'air_temp', 'air_pressure', 'air_humidity'\n ]\n\n df = pd.DataFrame(columns=fieldnames)\n # Create a row\n df.loc[-1]=[data['date'], data['time'],\n #data['v_up'], data['v_int'], data['v_down'],\n #data['bar_up'], data['bar_int'], data['bar_down'],\n data['mmH2O_up'], data['mmH2O_int'], data['mmH2O_down'],\n data['ana_turb'], data['turb'], data['flow'], data['liters'],\n data['water_temp'] #, data['air_temp'], data['air_pressure'], data['air_humidity]'\n ]\n df.index = df.index + 1\n\n #update the first sheet with df\n if row == 1:\n wks.set_dataframe(df,(row,1),copy_head=True)\n else:\n wks.set_dataframe(df, (row + 1, 1), copy_head=False)\n if row == initial_rows-3: # when max number of rows is being reached\n wks.add_rows(1000) #adds another 1000 rows\n initial_rows += 1000 #new number of rows","repo_name":"pibico/relier","sub_path":"google_sheets.py","file_name":"google_sheets.py","file_ext":"py","file_size_in_byte":3186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"24988688412","text":"# importing opencv\nimport cv2\n\n# reading the images\nstart = cv2.imread('task2.png')\ndilation = cv2.imread('dimadilation.jpg')\n\n# subtract the images\nsubtracted = cv2.subtract(dilation, start)\n\n# TO show the output\ncv2.imshow('image', subtracted)\n\n# To close the window\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"pavelrudenia/OpenCVLab_7sem","sub_path":"Lab2/task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"5213411650","text":"# Radio Buttons\n\nfrom tkinter import *\nfrom PIL import ImageTk,Image\n\nroot = Tk()\nroot.title('Learn To code at Codemy.com')\nroot.iconbitmap('/Users/baldez/Documents/GitHub/TkInter/images/Balde3.png')\n\n#r = IntVar()\n#r.set(\"2\")\n\nTOPPINGS = [\n (\"Pepperoni\", \"Pepperoni\"),\n (\"Cheese\", \"Cheese\"),\n (\"Mushroon\", \"Mushroon\"),\n (\"Onion\", \"Onion\")\n]\n\npizz = StringVar()\npizz.set(\"Pepperoni\")\n\nfor text, topping in TOPPINGS:\n Radiobutton(root, text=text, variable=pizz, value=topping).pack(anchor=W)\n\ndef clicked(value):\n myLabel = Label(root, text=value)\n myLabel.pack()\n\n#Radiobutton(root, text=\"Option 1\", variable=r, value=1, command=lambda: clicked(r.get())).pack()\n#Radiobutton(root, text=\"Option 2\", variable=r, value=2, command=lambda: clicked(r.get())).pack()\n\n#myLabel = Label(root, text=pizz.get())\n#myLabel.pack()\n\nmyButton = Button(root, text=\"Click Me!\", command=lambda: clicked(pizz.get()))\nmyButton.pack()\n\nroot.mainloop()","repo_name":"Balde641/TkInter","sub_path":"Exam0--24/exam9.py","file_name":"exam9.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"34332228646","text":"\"\"\"\n Selenium的文档见有道云笔记\n 此节介绍具体使用：\n 实现目标：\n 1.模拟126邮箱登录\n 2.获取相应的联系人信息并保存\n 3.获取相应邮件清单[部分]信息并保存\n\"\"\"\nimport json\nimport os\nimport pickle\nfrom urllib.request import Request\nfrom urllib.parse import urlencode\nfrom fake_useragent import UserAgent\nfrom urllib.request import build_opener, HTTPCookieProcessor\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom bs4 import BeautifulSoup\nimport time\nimport requests\nimport re\n\nusername = 'xlys_000'\npwd = '80105202xlys'\n# 构造请求和w无头浏览器\nlogin_page_url = 'https://mail.126.com/'\n\n# headers = {\n# 'User-Agent': UserAgent().chrome\n# }\n#\n# request = Request(login_page_url, headers=headers)\noptions = webdriver.ChromeOptions().add_argument('--headless')\nchrome = webdriver.Chrome(chrome_options=options)\n\n# -------------------------------------------------发送请求进行登录-------------------------------------------------------------------------------------\n# 发送请求\nchrome.get(login_page_url)\ntime.sleep(5)\n# chrome.save_screenshot(r'E:\\Pythonlearning\\PythonProjects\\Spider_learning\\Spider\\testdata\\shot.jpg')\n# print(chrome.page_source)\n\n# 拿到响应后定位元素并填充值\n# 获取iframe嵌套页面src地址:此嵌套页面是真正的登录输入表单\n# iframe_url = chrome.find_element(By.XPATH, '//div[@class=\"loginForm\"]/div[1]/div[1]/iframe').get_attribute('src')\n# print(iframe_url)\niframe = chrome.find_element(By.XPATH, '//div[@class=\"loginForm\"]/div[1]/div[1]/iframe')\nchrome.switch_to.frame(iframe)\ntime.sleep(2)\n# 获取iframe中的内容\n# 定位账户名输入框并输入账户名\nusername_input_element = chrome.find_element(By.XPATH, '//div[@id=\"account-box\"]/div[2]/input')\nusername_input_element.send_keys(username)\n# 定位密码输入框并输入密码\npwd_input_element = chrome.find_element(By.XPATH, '//input[@name=\"password\"]')\npwd_input_element.send_keys(pwd)\n# 点击进行登录\nlogin_btn_element = chrome.find_element(By.XPATH, '//a[@id=\"dologin\"]') # 点击登录\nlogin_btn_element.click()\ntime.sleep(5)\n# 切换到新的跳转窗口\nchrome.switch_to.window(chrome.window_handles[-1])\n# print(chrome.window_handles)\n# print(chrome.page_source)\n# 在这里打印登录后的sid,用于拼接获取邮件信息的url\nprint(chrome.current_url)\nafter_login_url = chrome.current_url\n\n# ------------------------------------------------------获取cookie后访问其他需登录才能访问的网页--------------------------------------------------------------------------------\n# 获取登录后的页面cookie\ncookies_for_126email = chrome.get_cookies()\ncookies = {}\nfor cookie in cookies_for_126email:\n # 这里我们仅仅保存cookie重要的name和value两个属性\n cookies[cookie['name']] = cookie['value']\n\n# 利用 pickle 存储相关的cookies信息，下次可以直接调用。\n# pickle 是Python特有的序列化工具，能够快速高效存储Python数据类型，反序列化读取后返回的仍是原先的python数据类型。\n# 而.txt 等都是字符串类型，需要转换。\nfilepath = '../testdata/'\nif not os.path.exists(filepath):\n os.makedirs(filepath)\ncookies_output = open(filepath + 'cookies_126.pickle', 'wb')\npickle.dump(cookies, cookies_output)\ncookies_output.close()\n\n# -------------------------------------------------获取联系人信息并保存-------------------------------------------------------------------------------------\n# 这个url地址是浏览器中获取到的全字段提取联系人信息的url\ncontact_info_url = 'https://mail.126.com/contacts/call.do?uid=xlys_000@126.com&sid=PApvyrPQrVjkUvUtfMQQlWFEAQSVsEnr&from=webmail&cmd=newapi.getContacts&vcardver=3.0&ctype=all&attachinfos=yellowpage,frequentContacts&freContLim=20'\n# 这个是简化测试过的提取联系人的url【可能只能提取部分数据，不是全部的~~待测试】。sid参数不能为空，可以任意！且必须是账号登录状态才可以访问返回数据。\nbase_contact_info_url = 'https://mail.126.com/contacts/call.do?uid=xlys_000@126.com&from=webmail&cmd=newapi.getContacts&sid=xxx'\n# 添加cookie前要先打开一个页面。同时我们先删除所有cookie看看访问效果：应该是不可以访问的！\nchrome.delete_all_cookies()\nchrome.get(base_contact_info_url)\nloaded_cookies = pickle.load(open('../testdata/cookies_126.pickle', 'rb'))\n# 再把所有登录后保存的cookie加上去访问，看看效果：应该可以访问！\nfor cookie_key in loaded_cookies:\n chrome.add_cookie({\n 'domain': 'mail.126.com',\n 'httpOnly': False,\n 'name': cookie_key,\n 'path': '/',\n 'secure': False,\n 'value': loaded_cookies[cookie_key]\n })\ntime.sleep(5)\nchrome.refresh()\n\nresult_info_json = chrome.find_element(By.XPATH, '//pre').text # 这个联系人列表是json字符串\n# 通过json.loads(json_data)将json字符串转成python对象\nresult_info_dict = dict(json.loads(result_info_json))\n# 获得联系人信息列表并保存为文件\ncontacts_info = result_info_dict['data']['contacts']\ncontact_filepath = '../testdata/'\nif not os.path.exists(contact_filepath):\n os.makedirs(contact_filepath)\nopen(contact_filepath + 'contacts.txt', 'wb').write(bytes(json.dumps(contacts_info), encoding='utf-8'))\ntime.sleep(5)\n\n# -------------------------------------------------获取邮件清单[部分]信息并保存-------------------------------------------------------------------------------------\n# 利用正则获取sid,生成最终的邮件列表链接\nsid = re.search(re.compile(r'.*sid=(\\w+)'), after_login_url).group(1)\nbase_emails_info_url = 'https://mail.126.com/js6/s?sid={}&func=mbox:listMessages'.format(sid)\n# 此时的Chrome已经携带了登录过的cookies，直接使用即可\nchrome.get(base_emails_info_url)\nif not os.path.exists('../testdata/'):\n os.makedirs('../testdata/')\nopen('../testdata/raw_emails_info.html', 'wb').write(bytes(chrome.page_source, encoding='utf-8'))\ntime.sleep(5)\n# new_cookies = chrome.get_cookies()\n# for cookie in new_cookies:\n# chrome.add_cookie(cookie)\n# chrome.get(base_emails_info_url)\n# for cookie_key in loaded_cookies:\n# chrome.add_cookie({\n# 'domain': 'mail.126.com',\n# 'httpOnly': False,\n# 'name': cookie_key,\n# 'path': '/',\n# 'secure': False,\n# 'value': loaded_cookies[cookie_key]\n# })\n# chrome.refresh()\n\nchrome.close()\n","repo_name":"xljun801052/Spider","sub_path":"course03/19_selenium的使用.py","file_name":"19_selenium的使用.py","file_ext":"py","file_size_in_byte":6536,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"15631972771","text":"\r\n\r\ndef main():\r\n\r\n try:\r\n num1 = int(input(\"ENTER DIVIDENT ->>> \"))\r\n num2 = int(input(\"ENTER DIVISOR ->>> \"))\r\n num3 = num1/num2\r\n num4 = num1 % num2\r\n print(f\"THE QUOTIENT IS {num3:.3f}\")\r\n if num4 > 0:\r\n print(f\"THE REMAINDER IS {num4}.\")\r\n\r\n except ZeroDivisionError:\r\n print(\"DIVISOR MUST BE GREATER THAN ZERO!\")\r\n\r\n except ValueError:\r\n print(\"PLEASE, ENTER VALID NUMBERS!\")\r\n\r\n finally:\r\n ask = input(\"Type 'R' To Play Again! ->>> \").upper()\r\n if ask == 'R':\r\n main()\r\n\r\n\r\nmain()\r\n","repo_name":"iBasnet/LEARN-PYTHON","sub_path":"DivsionProMax.py","file_name":"DivsionProMax.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"28675804248","text":"\nimport pickle\nimport numpy as np\n\nclass ProcessData:\n def __init__(self):\n pass\n\n #to use: look at main function, how i used it.\n\n def loadFaceImages(self, fileName):\n fileName = 'Data/facedata/' + fileName\n f = open(fileName)\n lines = f.readlines()\n f.close()\n\n # list of images\n images = []\n\n # each image is 70x60, currently each element is 0\n image = np.zeros((70, 60))\n k = 0\n i = 0\n #for each line\n while k < len(lines):\n if (i == 70):\n i = 0\n images.append(image)\n image = np.zeros((70, 60))\n line = lines[k]\n for c, j in zip(line, range(0, 60)):\n if c == '\\n':\n continue\n elif c == '#':\n image[i][j] = 1\n elif c == '+':\n image[i][j] = .5\n k += 1\n i += 1\n\n features = self.extractFeatures(images)\n\n #returns a list of vectors (70*60)x1, each vector represents 1 image\n return features\n\n def makeFaceLabels(self, fileName):\n fileName = 'Data/facedata/'+fileName\n f = open(fileName)\n lines = f.readlines()\n f.close()\n\n #convert list of each line into integer list\n integers = list(map(int, lines))\n\n #initialize 1x1 numpy vector to represent labels\n labels = [np.zeros((1, 1)) for i in range(len(lines))]\n\n #make element at index of label to 1 if face, or 0 if not\n #0 = [[0]] (not face)\n #1 = [[1]] (face)\n for i, vector in zip(integers, labels):\n vector[0][0] = i\n\n return labels\n\n def loadDigitImages(self, fileName):\n #make a list of each line from txt file\n fileName = 'Data/digitdata/'+fileName\n f = open(fileName)\n lines = f.readlines()\n f.close()\n\n #initialize 28x28 matrices for each image in the txt file (number of lines // 28 = number of images)\n images = []\n\n image = np.zeros((28, 28))\n k=0\n i=0\n while k < len(lines):\n if (i == 28):\n i = 0\n images.append(image)\n image = np.zeros((28, 28))\n line = lines[k]\n for c, j in zip(line, range(0, 28)):\n if c == '\\n':\n continue\n elif c == '#':\n image[i][j] = 1\n elif c == '+':\n image[i][j] = .5\n k += 1\n i += 1\n\n features = self.extractFeatures(images)\n\n #returns a list of vectors (28*28)x1, each vector represents 1 image\n return features\n\n def makeDigitLabels(self, fileName):\n fileName = 'Data/digitdata/'+fileName\n f = open(fileName)\n lines = f.readlines()\n f.close()\n\n #take the character at each line, make it an int, convert it to list\n integers = list(map(int, lines))\n\n #initialize listof 10x1 numpy vectors for each label\n #each label is a 10x1 vector of zeros, with one value as 1, the index where 1\n # is the number represented by label\n # 5 = [ [0]\n # [0]\n # [0]\n # [0]\n # [0]\n # [1]\n # [0]\n # [0]\n # [0]\n # [0] ]\n labels = [np.zeros((10, 1)) for i in range(len(lines))]\n\n #if number is 5, i=5, and [5][0] will be set to 1\n for i, vector in zip(integers, labels):\n vector[i][0] = 1\n\n return labels\n\n def extractFeatures(self, images):\n # no averaging take the values for each change, make it into a vector, instead of matrix\n # for each image in images, take the size and make it a vector, add to a list called features.\n features = [img.reshape(img.size,1) for img in images]\n return features\n\n def originalExtractFeatures(self, images):\n # initialize numpy vector to represent the features for each image, divide the number of elements by 4\n # since we are taking average over 4 elemnts\n features = [np.zeros( (img.size//4, 1) ) for img in images]\n\n # iterate thru each image in the data to extract its feature\n for image,ft in zip(images,features):\n #iterate thru each element of feature for first image\n x = np.nditer(ft, op_flags=['writeonly'])\n for i in range(0, 28, 2):\n for j in range(0, 28, 2):\n #create a 4x4 submatrix of the image\n submatrix = image[i:i+2, j:j+2]\n #get average for all 4 of these elements and set to feature\n x[0] = np.mean(submatrix)\n x.iternext()\n if (x.finished): break\n if (x.finished): break\n return features\n\n def unpickleFile(self, fileName):\n fileName = 'Data/ProcessedData/'+fileName\n f = open(fileName, \"rb\")\n data = pickle.load(f)\n return data\n\n\n","repo_name":"AmanyElgarf/AI-image-classification","sub_path":"ProcessData.py","file_name":"ProcessData.py","file_ext":"py","file_size_in_byte":5131,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"24653630082","text":"from __future__ import division\n\nimport statistics\nimport itertools\nimport numpy as np\nimport random\nimport copy\nfrom collections import defaultdict\n\nimport time\nimport math\nimport random\n\nclass Nim():\n\n def __init__(self, n_rows: int = 4):\n self.initial_state = tuple([int((x + 1)) for x in range(0, n_rows * 2, 2)])\n self.possible_values_in_rows = []\n\n for idx in self.initial_state:\n temp_list = []\n for ldx in range(idx+1):\n temp_list.append(ldx)\n self.possible_values_in_rows.append(temp_list)\n\n self.states = (tuple(itertools.product(*self.possible_values_in_rows)))\n self.n_states = len(self.states)\n self.current_state = copy.deepcopy(self.initial_state)\n\n self.win_states = list()\n for idx in range(n_rows):\n list_of_zeros = [0] * n_rows\n list_of_zeros[idx] = 1\n self.win_states.append(tuple(list_of_zeros))\n self.win_states = tuple(self.win_states)\n\n def reset(self):\n self.current_state = self.initial_state\n return self.current_state\n\n def get_all_states(self):\n return self.states\n\n def is_terminal(self, state):\n if not any(state): return True\n return False\n\n def get_possible_actions(self, state):\n possible_actions = []\n\n if self.is_terminal(state):\n possible_actions.append(state)\n return tuple(possible_actions)\n\n for row_idx, number_in_row in enumerate(state):\n for number in range(1, number_in_row + 1):\n single_action = [0 for _ in range(len(state))]\n single_action[row_idx] = number\n single_action = tuple(single_action)\n possible_actions.append(single_action)\n\n return tuple(possible_actions)\n\n def get_next_states(self, state, action):\n assert action in self.get_possible_actions(\n state), \"cannot do action %s from state %s\" % (action, state)\n # return self.transition_probs[state][action]\n next_state = tuple([idx_1 - idx_2 for idx_1, idx_2 in zip(state, action)])\n return next_state\n\n def get_number_of_states(self):\n return self.n_states\n\n def get_reward(self):\n\n reward = 0\n\n if self.current_state in self.win_states:\n reward = 1\n\n return reward\n\n def step(self, action):\n self.current_state = tuple([idx_1 - idx_2 for idx_1, idx_2 in zip(self.current_state, action)])\n return self.current_state\n\n\ndef randomPolicy(node):\n while not node.is_terminal():\n try:\n action = random.choice(node.get_possible_actions())\n except IndexError:\n raise Exception(\"Non-terminal state has no possible actions: \" + str(node))\n node = node.step(action)\n return node.get_reward()\n\n\nclass treeNode():\n def __init__(self, state, parent):\n\n self.initial_state = tuple([int((x + 1)) for x in range(0, 4 * 2, 2)])\n self.possible_values_in_rows = []\n for idx in self.initial_state:\n temp_list = []\n for ldx in range(idx + 1):\n temp_list.append(ldx)\n self.possible_values_in_rows.append(temp_list)\n\n self.states = (tuple(itertools.product(*self.possible_values_in_rows)))\n\n self.state = state\n self.isTerminal = self.is_terminal()\n self.isFullyExpanded = self.isTerminal\n self.parent = parent\n self.numVisits = 0\n self.totalReward = 0\n self.children = {}\n self.current_player = 1\n\n def __str__(self):\n s=[]\n s.append(\"totalReward: %s\"%(self.totalReward))\n s.append(\"numVisits: %d\"%(self.numVisits))\n s.append(\"isTerminal: %s\"%(self.isTerminal))\n s.append(\"possibleActions: %s\"%(self.children.keys()))\n return \"%s: {%s}\"%(self.__class__.__name__, ', '.join(s))\n\n def getCurrentPlayer(self):\n return self.current_player\n\n def reset(self):\n self.state = self.initial_state\n return self.state\n\n def get_all_states(self):\n return self.states\n\n def is_terminal(self):\n if not any(self.state): return True\n return False\n\n def get_possible_actions(self):\n possible_actions = []\n\n if self.is_terminal():\n possible_actions.append(self.state)\n return tuple(possible_actions)\n\n for row_idx, number_in_row in enumerate(self.state):\n for number in range(1, number_in_row + 1):\n single_action = [0 for _ in range(len(self.state))]\n single_action[row_idx] = number\n single_action = tuple(single_action)\n possible_actions.append(single_action)\n\n return tuple(possible_actions)\n\n def get_reward(self):\n\n reward = 0\n\n if self.is_terminal():\n reward = -1\n\n return reward\n\n def step(self, action):\n new_state = tuple([idx_1 - idx_2 for idx_1, idx_2 in zip(self.state, action)])\n new_node = treeNode(new_state, self)\n if self.current_player == 1:\n new_node.current_player = -1\n elif self.current_player == -1:\n new_node.current_player = 1\n return new_node\n\nclass mcts():\n def __init__(self, iterationLimit=None, explorationConstant = (1 / math.sqrt(2)),\n rolloutPolicy = randomPolicy):\n\n self.root = None\n self.searchLimit = iterationLimit\n self.explorationConstant = explorationConstant\n self.rollout = rolloutPolicy\n\n def search(self, initialState):\n\n self.root = treeNode(initialState, None)\n for i in range(self.searchLimit):\n self.executeRound()\n\n bestChild = self.getBestChild(self.root, 0.1)\n action=(action for action, node in self.root.children.items() if node is bestChild).__next__()\n\n return action\n\n def perform_simulation(self, initialState):\n self.root = treeNode(initialState, None)\n for i in range(self.searchLimit):\n self.executeRound()\n\n def get_best_action(self):\n bestChild = self.getBestChild(self.root, 0.1)\n action = ((action, node) for action, node in self.root.children.items() if node is bestChild).__next__()\n return action\n\n def executeRound(self):\n \"\"\"\n execute a selection-expansion-simulation-backpropagation round\n \"\"\"\n node = self.selectNode(self.root)\n reward = self.rollout(node)\n self.backpropogate(node, reward)\n\n def selectNode(self, node):\n while not node.isTerminal:\n if node.isFullyExpanded:\n node = self.getBestChild(node, self.explorationConstant)\n else:\n return self.expand(node)\n return node\n\n def expand(self, node):\n actions = node.get_possible_actions()\n for action in actions:\n if action not in node.children:\n newNode = node.step(action)\n # newNode = treeNode(new_state, node)\n node.children[action] = newNode\n if len(actions) == len(node.children):\n node.isFullyExpanded = True\n return newNode\n\n raise Exception(\"Should never reach here\")\n\n def backpropogate(self, node, reward):\n while node is not None:\n node.numVisits += 1\n node.totalReward += reward\n node = node.parent\n\n def getBestChild(self, node, explorationValue):\n bestValue = float(\"-inf\")\n bestNodes = []\n for child in node.children.values():\n nodeValue = node.getCurrentPlayer() * child.totalReward / child.numVisits + explorationValue * math.sqrt(\n 2 * math.log(node.numVisits) / child.numVisits)\n if nodeValue > bestValue:\n bestValue = nodeValue\n bestNodes = [child]\n elif nodeValue == bestValue:\n bestNodes.append(child)\n return random.choice(bestNodes)\n\nnim = Nim()\n# play sarsa lambda vs random\nplayer_1_wins = 0\nplayer_2_wins = 0\n\n# searcher = mcts(iterationLimit=1000)\n# searcher.search(initialState=nim.current_state, current_player=1)\n# next_action, next_node = searcher.get_best_action()\n# print(next_node.current_player)\n# nim.step(next_action)\n# searcher.search(initialState=nim.current_state)\n# next_action, next_node = searcher.get_best_action()\n# print(next_node.current_player)\n# print(next_action, next_node)\n# searcher.root = next_node\n# next_action, next_node = searcher.get_best_action()\n# print(next_action, next_node)\n\n\nfor epoch in range(100):\n searcher = mcts(iterationLimit=100)\n nim.reset()\n turn = 0\n while not nim.is_terminal(nim.current_state):\n # print(nim.current_state)\n if not turn % 2:\n action = searcher.search(initialState=nim.current_state)\n next_action, next_node = searcher.get_best_action()\n print(next_node)\n # print(action)\n # searcher.root = node\n # print('\\n')\n # print(nim.current_state, action)\n # print([child.parent_action for child in node.children])\n # print([child._results for child in node.children])\n # print(node.q())\n else:\n action = random.choice(nim.get_possible_actions(nim.current_state))\n\n nim.step(action)\n\n if nim.is_terminal(nim.current_state):\n # print('---------------------------------')\n if turn % 2:\n player_1_wins += 1\n else:\n player_2_wins += 1\n\n turn += 1\n if not epoch % 10: print(epoch)\n\nprint(f'Algorithm winrate: {player_1_wins * 100 / (player_1_wins + player_2_wins)}%')","repo_name":"Norceis/UW","sub_path":"mcts_new.py","file_name":"mcts_new.py","file_ext":"py","file_size_in_byte":9767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"24837341034","text":"from phyltr.commands.base import PhyltrCommand\n\n\nclass Scale(PhyltrCommand):\n \"\"\"\n Scale the branch lengths in a treestream by a constant factor.\n \"\"\"\n __options__ = [\n (\n ('-s', '--scale'),\n dict(\n dest='scalefactor', type=float, default=1.0,\n help='The factor to scale by, expressed in floating point notation.')),\n ]\n\n def process_tree(self, t, _):\n for node in t.traverse():\n node.dist *= self.opts.scalefactor\n return t\n","repo_name":"lmaurits/phyltr","sub_path":"src/phyltr/commands/scale.py","file_name":"scale.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"34"} +{"seq_id":"30128369324","text":"#!/usr/bin/env python\n\nfrom __future__ import nested_scopes, absolute_import, with_statement, print_function\n#from future import builtins, standard_library\n\nimport os, sys, stat, errno, traceback\nimport multiprocessing, time, signal\nimport datetime, argparse, logging, json\n\nimport platform, subprocess, email.encoders\n\nfrom subprocess import PIPE\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\nfrom time import sleep\n\n__DESCRIPTION__=\"NetSapiens Apache/MySQL health watchdog\"\n\nclass Email(object):\n def __init__(self, args=None):\n self.log = logging.getLogger(__name__)\n self.config = self.__initConfig(args)\n self.testEmail()\n self.log.info(':%s Email services initialized sucesefully...' % (__name__))\n\n\n def __initConfig(self, args=None):\n try:\n args.email_from\n if args.email_from is None:\n raise ValueError\n except:\n args.email_from = 'checker.app@%s' % platform.node()\n\n __config = {\n 'server': {\n 'host': args.email_host,\n 'port': args.email_port,\n 'timeout': 3\n },\n 'header': {\n 'from': args.email_from,\n 'to': args.email_to,\n 'cc': args.email_cc,\n 'subject': args.email_subject\n },\n 'debug': args.debug,\n 'pretest': args.email_pretest\n }\n try:\n if args.email_host != None: raise NotImplementedError\n except Exception as e:\n _msg = 'SMTP protocol as not been not implemented yet'\n self.log.critical(':%s.__initConfig %s' % (__name__, _msg))\n raise NotImplementedError(_msg)\n return __config\n\n\n def testEmail(self):\n if self.config['pretest'] == False:\n return True\n\n try:\n self.sendMail(_test=True)\n self.log.debug(':%s.testEmail Test was sucesefull...' \n % (__name__))\n except Exception as e:\n self.log.error(':%s.testEmail Test was failed...' \n % (__name__))\n self.log.debug(':%s.testEmail Library returned %s' \n % (__name__, e.message))\n raise\n return True\n\n\n def __buildHeader(self, _mime, _test=False, mailto=None):\n if type(_mime) is None:\n raise TypeError('message must be in Mime format')\n \n _mime['From'] = self.config['header']['from']\n if mailto is not None:\n _mime['To'] = mailto\n else:\n _mime['To'] = self.config['header']['to']\n _mime['Cc'] = self.config['header']['cc']\n if _test:\n _s = 'pre-flight email test for'\n else:\n _s = 'caught a failure on server'\n _mime['Subject'] = 'checker.app %s %s at %s' % (_s, platform.node(),time.asctime())\n return _mime\n\n\n def __buildBody(self, _mime, _test=None, **msg):\n _b = []\n _p = \"\"\n _m = None\n if _test:\n _p = \"{ 'message': 'Test email for %s script' }\" % __name__\n else:\n for _k in msg:\n _message = None\n try:\n _message = str(msg[_k]).strip()\n except:\n _message = \"NO DATA TO DELIVER\"\n else:\n if _message is None or len(_message) == 0:\n _message = \"NO DATA TO DELIVER\"\n\n try:\n if _message[:6] == \"\":\n _t = 'html'\n _m = MIMEText(_message, _t, 'utf-8')\n _file = \"%s.%s\" % (_k, _t)\n elif _message[0] == '{' and _message[-1] == '}':\n _t = 'json'\n _m = MIMEText(_message, 'plain', 'utf-8')\n _file = \"%s.%s\" % (_k, _t)\n else:\n _t = 'plain'\n _m = MIMEText(str(_message), 'plain', 'utf-8')\n _file = \"%s.%s\" % (_k, 'txt')\n except Exception as e:\n self.log.debug(\"%s.__buildBody Error loading mime for entry %s... Data is bellow:\\n%r\\n%r\" %\n (__name__, _k, e.message, _message))\n _m = MIMEBase('application', 'octet-stream')\n _m.set_payload(_message)\n email.encoders.encode_base64(_m)\n _file = \"%s.%s\" % (_k, 'bin')\n _m.add_header('Content-Disposition', 'attachment', filename=_file)\n _mime.attach(_m)\n if _m == None:\n _p = \"{ 'message': 'No data to deliver' }\"\n else:\n _p = \"{ 'message': 'Web, DB and Error messages attached' }\"\n _mime.preamble = _p\n\n\n\n #XXX: This function could use a lot of work utilizing MIMEs and attachmenets\n def sendMail(self, _test=False, mailto=None, **msg):\n _mime = MIMEMultipart()\n self.__buildHeader(_mime, _test, mailto=mailto)\n self.__buildBody(_mime, _test, **msg)\n\n if self.config['debug'] is True and self.config['pretest'] is False:\n sendmail_location = ['/usr/sbin/sendmail', '-t', '-oi'] # sendmail location\n #sendmail_location = ['/bin/sh', '-c', 'cat>/dev/stderr']\n else:\n sendmail_location = ['/usr/sbin/sendmail', '-t', '-oi'] # sendmail location\n #self.log.debug(':%s.sendMail : Attempting to send email using local sendmail at %r' %\n # (__name__, sendmail_location))\n try:\n p = subprocess.Popen(args=sendmail_location, stdin=PIPE)\n p.communicate(_mime.as_string())\n _status = p.pipe_cloexec()\n except Exception as e:\n self.log.critical(':%s.sendMail Failed to send email...' % (__name__))\n self.log.debug(':%s.sendMail Library returned %r' % (__name__, e))\n raise\n else:\n self.log.info(':%s.sendMail EMail sent sucesefully' %\n (__name__))\n return True\n\nif __name__ == \"__main__\":\n \"\"\"We are being loaded as a program???\"\"\"\n pass\n\n","repo_name":"modelli/netsapiens-devops-appchecker","sub_path":"checker.app/NetSapiens/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":5472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"14391893494","text":"import random\n\ndef play():\n user = input(\"What's your choice? 'r' for rock 'p' for paper, 's' for scissors: \")\n computer = random.choice(['r','p','s'])\n\n if user == computer:\n return 'tie'\n \n if is_win(user,computer):\n return(\"You won!\")\n\n return(\"You lost\")\n\n\n\ndef is_win(player, oppponent):\n if(player=='r'and oppponent=='s' or player=='s' and oppponent=='p' or player=='p' and oppponent=='r'):\n return True\n\nprint(play())\n","repo_name":"ashmaghimire8/beginner-python-projects","sub_path":"rockpaperscissors.py","file_name":"rockpaperscissors.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"13476015737","text":"from sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\n\nimport datetime\n\nfrom main.models.transaction import Transaction\n\nclass DataProviderService:\n def __init__(self, engine):\n \"\"\"\n :param engine: The engine route and login details\n :return: a new instance of DAL class\n :type engine: string\n \"\"\"\n if not engine:\n raise ValueError('The values specified in engine parameter has to be supported by SQLAlchemy')\n self.engine = engine\n db_engine = create_engine(engine)\n db_session = sessionmaker(bind=db_engine)\n self.session = db_session()\n\n def init_database(self):\n \"\"\"\n Initializes the database tables and relationships\n :return: None\n \"\"\"\n init_database(self.engine)\n\n \n def init_database(engine):\n db_engine = create_engine(engine, echo=True)\n Transaction.metadata.create_all(db_engine)\n\n def add_transaction(self, bitfinex_id, bitfinex_currency, bitfinex_timestamp, bitfinex_price, bitfinex_amount):\n \"\"\"\n Creates and saves a new transaction to the database.\n\n :param bitfinex_id: ID from bitfinex api\n :param bitfinex_currency: Currency from bitfinex api\n :param bitfinex_timestamp: Timestamp from bitfinex api\n :param bitfinex_price: Price from bitfinex api\n :param bitfinex_amount: Amount from bitfinex api\n :return: The id of the new transaction\n \"\"\"\n\n new_transaction = Transaction(bitfinex_id=bitfinex_id,\n bitfinex_currency=bitfinex_currency,\n bitfinex_timestamp=bitfinex_timestamp,\n bitfinex_price=bitfinex_price,\n bitfinex_amount=bitfinex_amount,\n languages=languages,\n skills=skills)\n\n self.session.add(new_transaction)\n self.session.commit()\n\n return new_transaction.id\n\n def get_transaction(self, id=None, serialize=False):\n \"\"\"\n If the id parameter is defined then it looks up the transaction with the given id,\n otherwise it loads all the transactions\n\n :param id: The id of the transaction which needs to be loaded (default value is None)\n :return: The transaction or transactions.\n \"\"\"\n\n all_transactions = []\n\n if id is None:\n all_transactions = self.session.query(transaction).order_by(transaction.bitfinex_currency).all()\n else:\n all_transactions = self.session.query(transaction).filter(transaction.id == id).all()\n\n if serialize:\n return [transact.serialize() for transact in all_transactions]\n else:\n return all_transactions\n\n def get_last_transactions(self, bitfinex_currency=None, nr_of_transactions=20, serialize=false):\n \"\"\"\n If the id parameter is defined then it looks up the transaction with the given id,\n otherwise it loads all the transactions\n\n :param id: The id of the transaction which needs to be loaded (default value is None)\n :return: The transaction or transactions.\n \"\"\"\n\n all_transactions = []\n\n if id is None:\n all_transactions = self.session.query(transaction).order_by(transaction.created_date).limit(nr_of_transactions)\n else:\n all_transactions = self.session.query(transaction).filter(transaction.bitfinex_currency == bitfinex_currency).all()\n\n if serialize:\n return [transact.as_dict() for transact in all_transactions]\n else:\n return all_transactions","repo_name":"vrenko007/Vrenko-Bitfinex","sub_path":"App_Old/server/main/services/data_provider_service.py","file_name":"data_provider_service.py","file_ext":"py","file_size_in_byte":3734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"22605009470","text":"import datetime\nimport logging\nfrom datetime import timedelta\nfrom enum import Enum, auto\n\nfrom telegram import InlineKeyboardButton, InlineKeyboardMarkup, Update\nfrom telegram.ext import (CallbackContext, CallbackQueryHandler,\n ConversationHandler)\n\nfrom bot.models import Student, TimeSlot\nfrom bot.utils.timeslots_utils import CALL_TIME_MINUTES, make_timeslots\n\nlogger = logging.getLogger(\"student\")\n\n\nclass States(Enum):\n SELECT_TIME = auto()\n\n\nclass Consts(Enum):\n SELECT_TIME = \"select_date\"\n END_SELECTING = \"end_selecting\"\n CHOSE_NOTHING = \"cancel all\"\n\n\nCALLBACK_NAME = \"CHOOSE_TIME\"\nSEPARATOR = \"::\"\nLIKE_ICON = \"\\u2705\"\n\n\ndef create_callback_time(time):\n \"\"\"Create the callback time associated to each button\"\"\"\n return f\"{CALLBACK_NAME}{SEPARATOR}{time}\"\n\n\ndef separate_callback_data(data: str):\n return data.split(SEPARATOR)\n\n\ndef keyboard_row_divider(full_list, row_width=2):\n \"\"\"Divide list into rows for keyboard\"\"\"\n for i in range(0, len(full_list), row_width):\n yield full_list[i : i + row_width]\n\n\ndef keyboard_generator(context, keys=None):\n context.user_data[\"time_keys\"] = keys\n buttons = [\n InlineKeyboardButton(text=time_el, callback_data=create_callback_time(time_el))\n for time_el in keys\n ]\n key_buttons = list(keyboard_row_divider(buttons, 3))\n key_buttons.append(\n [\n InlineKeyboardButton(\n text=\"Отменить все\", callback_data=Consts.CHOSE_NOTHING.value\n )\n ]\n )\n key_buttons.append(\n [\n InlineKeyboardButton(\n text=\"Закончить\", callback_data=Consts.END_SELECTING.value\n )\n ]\n )\n\n return InlineKeyboardMarkup(key_buttons)\n\n\ndef select_time(update: Update, context: CallbackContext):\n empty_slots = (\n TimeSlot.objects.filter(\n product_manager__isnull=False, student__isnull=True, status=TimeSlot.FREE\n )\n .values(\"time_slot\")\n .distinct()\n )\n user_id = update.callback_query.from_user.id\n student_time = []\n try:\n student = Student.objects.get(tg_id=user_id)\n logger.info(student)\n student_slots = student.timeslots.values(\"time_slot\").distinct()\n student_time = [slot[\"time_slot\"].strftime(\"%H:%M\") for slot in student_slots]\n except Student.DoesNotExist:\n pass\n\n empty_time = [slot[\"time_slot\"].strftime(\"%H:%M\") for slot in empty_slots]\n prepare_time = []\n for time in empty_time:\n new_time = time\n if time in student_time:\n new_time = f\"{time}{LIKE_ICON}\"\n prepare_time.append(new_time)\n\n keyboard = keyboard_generator(context, prepare_time)\n\n update.callback_query.answer()\n update.callback_query.edit_message_text(\n text=\"Выберите удобное для Вас время\", reply_markup=keyboard\n )\n\n return States.SELECT_TIME\n\n\ndef time_handler(update, context):\n query = update.callback_query\n _, choosing_time = separate_callback_data(query.data)\n keys = context.user_data.get(\"time_keys\")\n new_keys = []\n for key in keys:\n if key == choosing_time:\n if key.endswith(f\"{LIKE_ICON}\"):\n new_keys.append(key.replace(f\"{LIKE_ICON}\", \"\"))\n else:\n new_keys.append(f\"{key}{LIKE_ICON}\")\n else:\n new_keys.append(key)\n\n update.callback_query.answer()\n context.bot.edit_message_text(\n text=query.message.text,\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n reply_markup=keyboard_generator(context, new_keys),\n )\n\n return States.SELECT_TIME\n\n\ndef cancel_all(update, context):\n query = update.callback_query\n keys = context.user_data.get(\"time_keys\")\n new_keys = []\n for key in keys:\n if key.endswith(f\"{LIKE_ICON}\"):\n new_keys.append(key.replace(f\"{LIKE_ICON}\", \"\"))\n else:\n new_keys.append(key)\n\n update.callback_query.answer()\n if keys != new_keys:\n context.bot.edit_message_text(\n text=query.message.text,\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n reply_markup=keyboard_generator(context, new_keys),\n )\n\n return States.SELECT_TIME\n\n\ndef collect_time(context: CallbackContext):\n keys = context.user_data.get(\"time_keys\")\n result_keys = [\n key.replace(f\"{LIKE_ICON}\", \"\") for key in keys if key.endswith(f\"{LIKE_ICON}\")\n ]\n return result_keys\n\n\ndef clear_time(user_id):\n student = Student.objects.get(tg_id=user_id)\n student.timeslots.all().delete()\n\n\ndef save_time(user_id, time_keys):\n clear_time(user_id)\n time_delta = timedelta(minutes=CALL_TIME_MINUTES - 1)\n for key in time_keys:\n start_time = datetime.datetime.strptime(key, \"%H:%M\")\n fin_time = start_time + time_delta\n make_timeslots(start_time, fin_time, user_id)\n\n\ndef finer(update: Update, context: CallbackContext):\n update.callback_query.answer()\n user_id = update.callback_query.from_user.id\n student_time = collect_time(context)\n save_time(user_id, student_time)\n text = \"Вы отменили свое участие в проекте\"\n if student_time:\n text = f\"Вы выбрали время: {', '.join(student_time)}\"\n update.callback_query.edit_message_text(text=text)\n\n return ConversationHandler.END\n\n\nstudent_conv = ConversationHandler(\n entry_points=[\n CallbackQueryHandler(\n select_time, pattern=\"^\" + str(Consts.SELECT_TIME.value) + \"$\"\n )\n ],\n states={\n States.SELECT_TIME: [\n CallbackQueryHandler(time_handler, pattern=f\"^{CALLBACK_NAME}\"),\n CallbackQueryHandler(cancel_all, pattern=f\"^{Consts.CHOSE_NOTHING.value}\"),\n ],\n },\n fallbacks=[\n CallbackQueryHandler(\n finer, pattern=\"^\" + str(Consts.END_SELECTING.value) + \"$\"\n ),\n ],\n map_to_parent={\n # End everything!\n ConversationHandler.END: ConversationHandler.END,\n },\n)\n","repo_name":"tarodo/ProjectsAutomation","sub_path":"bot/management/commands/_student_conversation.py","file_name":"_student_conversation.py","file_ext":"py","file_size_in_byte":6123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"26447771403","text":"from django.contrib import admin\nfrom painel.models import User\n\nfrom instituicao.models import Instituicao,Conhecimento,Turma\n\nclass InstituicaoAdmin(admin.ModelAdmin):\n list_display=('nome','descricao',)\n fields=('nome','descricao',)\nadmin.site.register(Instituicao,InstituicaoAdmin)\n\nclass ConhecimentoAdmin(admin.ModelAdmin):\n list_display=('id','usuario','nome','ativo','descricao','inicio','fim','instituicao',)\n fields=('usuario','nome','ativo','descricao','inicio','fim','instituicao',)\nadmin.site.register(Conhecimento,ConhecimentoAdmin)\n\nclass TurmaAdmin(admin.ModelAdmin):\n list_display=('ativo','conhecimento','nome','questao','descricao','inicio','fim','alunos_mm',)\n fields=('ativo','conhecimento','nome','descricao','inicio','fim','alunos',)\nadmin.site.register(Turma,TurmaAdmin)\n","repo_name":"catalunha/cata_django","sub_path":"instituicao/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"33313947891","text":"import cv2\nimport numpy as np\nfrom question_2 import read_image\n\n\ndef main():\n log_transform(read_image('../res/img.png'))\n\n\ndef log_transform(image):\n c = 255 / np.log(1 + np.max(image))\n log_image = c * (np.log(image + 1))\n log_image = np.array(log_image, dtype=np.uint8)\n cv2.imshow('log img', log_image)\n cv2.imwrite('../outputs/Assignment1/question_8.png', log_image)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Rohitrajak1807/image_processing_lab","sub_path":"Assignment1/question_8.py","file_name":"question_8.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"38039957780","text":"import re\n\npath = 'C:/Users/hp/Desktop/coding/python/assests'\n\ndef names():\n simple_string = \"\"\"Amy is 5 years old, and her sister Mary is 2 years old. \n Ruth and Peter, their parents, have 3 kids.\"\"\"\n\n # YOUR CODE HERE\n name = re.findall('[A-Z][\\w]{1,4}',simple_string)\n print(name)\n return name\n\ndef grades():\n with open (\"assets/grades.txt\", \"r\") as file:\n grades = file.read()\n\n # YOUR CODE HERE\n names = []\n for item in re.finditer(\"([A-Z][\\w]* [A-Z][\\w]*)[\\w]*: B\", grades):\n names.append(item.groups(2)[0])\n return names\n\ndef logs():\n with open(\"assets/logdata.txt\", \"r\") as file:\n logdata = file.read()\n \n # YOUR CODE HERE\n pattern=\"\"\"(?P[0-9]+\\.[0-9]+\\.[0-9]+\\.[0-9]+)\n (\\ - \\ )\n (?P(\\w*)(\\S))\n (\\ \\S)\n (?P