None:\n dist, delta_angle, timestamp = self.vision.get_vision_data()\n # collect data only once per loop\n if timestamp is None:\n # self.next_state(\"searching\")\n # print(f\"tracking -> searching {self.vision.get_vision_data()}\")\n self.state = self.searching\n else:\n if abs(delta_angle) > self.find_allowable_angle(dist):\n # print(f\"Telling turret to slew by {delta_angle}\")\n self.turret.slew(delta_angle)\n if self.ready_to_spin():\n # self.next_state(\"firing\")\n # print(f\"tracking -> spining_up {self.vision.get_vision_data()}\")\n self.distance = dist\n self.state = self.spining_up","def trace(self):\n\n print(f\"TRACE: %02X | %02X %02X %02X |\" % (\n self.pc,\n # self.flag,\n #self.ie,\n self.ram_read(self.pc),\n self.ram_read(self.pc + 1),\n self.ram_read(self.pc + 2)\n ), end='')\n\n for i in range(8):\n print(\" %02X\" % self.reg[i], end='')\n\n print()","def update_state(self, state):\n txt = state.text()\n attr = 'state'\n if state == SoylaState.RECORDING:\n attr = 'recording'\n self._state_text.set_text((attr, txt))\n self._instructions_text.set_text(('instructions', state.instruction()))","def print_state(self):\n print(\"n\\tg\\to\\ta\\tc\\ts\\ttau\\td\\tN\")\n for p in self.persons:\n p.print_state()\n print(\"type\\tpersons\")\n for ps in self.partnerships:\n ps.print_state()","def _get_state(self):","def update(self):\n self._state = 23","def output_debug_info(self):","def on_x(self):\r\n self.log()","def write_to_file(self):\n\t\tfile = open(\"states.txt\", \"w\")\n\t\t\n\t\tpointer = self.head\n\t\twhile pointer != None:\n\t\t\tfile.write(pointer.state + \"\\t\" + pointer.info)\t\n\t\t\tpointer = pointer.next\n\n\t\tfile.close()","def _report_iteration(self):\n # Call report_iteration_items for a subclass-friendly function\n self._report_iteration_items()\n self._reporter.write_timestamp(self._iteration)\n self._reporter.write_last_iteration(self._iteration)","def turn_on(self):\n self.write(\"OUT1\\n\")","def state(self):\n return str(self)","def _print_target_reached(self, history):\n print_target_reached(history)","def state(self):\n\n\t\treturn str(self)","def mark_inspection(self, passed):\n if passed is True:\n self.passed_inspection = True"],"string":"[\n \"def print_state(self):\\n raise AIError(\\\"Must be implemented in child class!\\\")\",\n \"def Report(self):\\n return True\",\n \"def report():\\n pass\",\n \"def log_state(self):\\n rospy.loginfo(\\\"STATE: %s [%s]\\\" %(self.__class__.__name__, 15 - self.ros_node.get_time()))\",\n \"def save_state(self):\\n pass\",\n \"def report_state(self):\\n text = \\\"Status: %d\\\"%self.state.num;\\n if self.state.msg !=\\\"\\\":\\n text += \\\", Msg: %s\\\"%self.state.msg;\\n return text;\",\n \"def report_trial(self):\\n pass\",\n \"def show_state(self):\\n print \\\"I don't know how to show_state.\\\"\",\n \"def save_state(self):\\n\\t\\tf = open('output.csv', 'a')\\n\\t\\tstate = ';'.join([str(datetime.now()), str(self.thin._actuation_value), str(self.thin.temperature), str(self.thin.presence), str(self.outside.temperature)])\\n\\t\\tprint(state)\\n\\t\\tf.write(state + '\\\\n')\\n\\t\\tf.close()\",\n \"def state(self):\\n pass\",\n \"def show_state(self):\\n print(\\\"I don't know how to show_state.\\\")\",\n \"def status(self):\",\n \"def enter_state(self):\\r\\n self.__log__(logging.debug)\\r\\n return\",\n \"def notify(self) :\\n newOutState = self.evaluate()\\n if newOutState != self.outState.getState() :\\n self.outState.setState(newOutState)\",\n \"def log_state(self):\\n\\n log('-' * 50)\\n log('.level=%d' % self.level)\\n log('.view_llon=%.3f, .view_rlon=%.3f'\\n % (self.view_llon, self.view_rlon))\\n log('.view_tlat=%.3f, .view_blat=%.3f'\\n % (self.view_tlat, self.view_blat))\\n log('.ppd_x=%.2f, .ppd_y=%.2f' % (self.ppd_x, self.ppd_y))\\n log('.view_offset_x=%d, .view_offset_y=%d'\\n % (self.view_offset_x, self.view_offset_y))\\n log('.view_width=%d, .view_height=%d'\\n % (self.view_width, self.view_height))\\n log('-' * 50)\\n log('')\",\n \"def saveState(self) -> None:\\n # TODO: Saves State\\n pass\",\n \"def status(self):\\n pass\",\n \"def status(self):\\n pass\",\n \"def report(self) -> Any:\",\n \"def state_print_do(cfg, app, win, events):\",\n \"def render(self):\\n logging.info(self._get_status())\\n pass\",\n \"def on(self):\\n print(f\\\"RF {self.name} on\\\")\\n self.status(True)\",\n \"def sitOut(self):\\n self.sit_out = True\",\n \"def state(self) -> str:\",\n \"def save_state(self) -> None:\\n raise NotImplementedError(\\\"Save state is is not implemented.\\\")\",\n \"def on_b(self):\\r\\n self.log()\",\n \"def writeState(self, saveState: ghidra.framework.options.SaveState) -> None:\\n ...\",\n \"def _PrintStatus(self):\\n comment = 'iter %i' % (self.n_iter)\\n self.gfile.write(fileio.GetPrintCoordsXyzString(\\n self.mol.atoms, comment, 14, 8))\\n self.PrintEnergy(self.n_iter)\\n self.gfile.flush()\\n self.efile.flush()\",\n \"def on(self):\\n if self._hidden:\\n self.off()\\n return\\n if self._state != True:\\n self.log_state_change('+')\\n self._state = True\",\n \"def enter_state(self):\\r\\n self._logger_.enter_state()\\r\\n return\",\n \"def output_status(self):\\n raise NotImplementedError\",\n \"def reportProgress(self):\\n \\n pass\",\n \"def _print(self):\\n self.i += 1\\n np.set_printoptions(precision=3, suppress=True)\\n if self.i%40 == 0:\\n self.i = 0\\n print self.ekf.current_state_estimate[4:7]\",\n \"def on_all(self):\\n self._set_status(\\\"on\\\", '11111111')\",\n \"def _print_status(self):\",\n \"def state(self):\\n raise NotImplementedError\",\n \"def log(self):\\n\\n\\t\\t# Only every 1/10 second (or so) to avoid flooding networktables\\n\\t\\tif not self.log_timer.running or not self.log_timer.hasPeriodPassed(self.log_timer_delay):\\n\\t\\t\\treturn\\n\\n\\t\\twpilib.SmartDashboard.putString('Pressure', '{0:.2f}'.format(self.get_pressure()))\\n\\t\\twpilib.SmartDashboard.putBoolean(\\\"Garbo?\\\", self.is_pbot)\\n\\n\\t\\tself.drive.log()\\n\\t\\tself.elevator.log()\\n\\t\\tself.intake.log()\",\n \"def print_state(self):\\n print(self.identifier, \\n self.gender, \\n self.age,\\n self.sexual_activity,\\n self.disease_status,\\n self.time_since_infection,\\n self.number_of_partners,\\n self.current_partners)\",\n \"def StatusChanged(self, state, info):\\n pass\",\n \"def report():\\n Robot.report()\",\n \"def state(self):\\n raise NotImplementedError()\",\n \"def savestate(self, state):\\n pass\",\n \"def print_state(self):\\n print(self.type,\\n self.persons[0].identifier,\\n self.persons[1].identifier)\",\n \"def state_info(self):\\n raise NotImplementedError()\",\n \"def report(self, individuals):\\n print(f'Iteration: {self.current}')\\n self.current += 1\",\n \"def render (self, mode=\\\"human\\\"):\\n print(\\\"location:\\\", self.state)\",\n \"def simulation_step(self):\\n if self.data_valid.get():\\n print(\\\"Output pin %s writing %s\\\" % (self.name, self.debug_data.get()))\",\n \"def print_info(self):\\r\\n self.system.print_to_log(\\r\\n f\\\"{self.__class__.__name__} model: Infection probability: {self.p}, Infectious period: {self.i}, Recovery period: {self.r}.\\\")\",\n \"def set_state( self ):\",\n \"def print_state(self):\\n print('\\\\nthe current state is: ' + str(self.state) + '\\\\n')\",\n \"def on_L2(self):\\r\\n self.log()\",\n \"def storeState(self):\\n\\n self.action_history[self.trial] = self.action\\n self.ball_history[self.trial] = self.ballcolor\",\n \"def display_state(self):\\n # self.__display(self.state)\\n self.__draw(self.state)\",\n \"def send_states(self):\\n\\n teleop_enabled_msg = Bool()\\n teleop_enabled_msg.data = self.teleop_enabled\\n\\n assisted_driving_enabled_msg = Bool()\\n assisted_driving_enabled_msg.data = self.assisted_driving_enabled\\n\\n self.teleop_enabled_pub.publish(teleop_enabled_msg)\\n self.assisted_driving_enabled_pub.publish(assisted_driving_enabled_msg)\",\n \"def status():\\n pass\",\n \"def report(self, trade, is_entry):\\n pass\",\n \"def on_L1(self):\\r\\n self.log()\",\n \"def print_state():\\n global simulator\\n if simulator is None:\\n print \\\"program is not started\\\"\\n return\\n print simulator.state()\",\n \"def report(self):\\r\\n print(\\\"\\\".join(self.memory), self.error, self.steps)\",\n \"def report_feature_completed(self):\\n pass\",\n \"def on_R2(self):\\r\\n self.log()\",\n \"def get_state(self):\\n pass\",\n \"def on(self):\\n self._set_state(on=True)\",\n \"def printState(self,board):\\n self.printBoard(board.getBoard())\\n self.printScore(board,board.getScore())\",\n \"def state_print_exit(cfg, app, win):\",\n \"def report(self):\\n self.last_contacted = time.time()\",\n \"def dump_pre_iteration(self,status):\\n pass\",\n \"def log_all(self):\\n self.save_raw()\\n self.log()\",\n \"def sit(self):\\n self.sit_out = False\",\n \"def mark_inspection(self, passed):\\n self.passed_inspection = passed\",\n \"def state(self) -> TState:\\n pass\",\n \"def __report(self):\\n # Count the number of sons in the current MST\\n count = 0\\n for _in in range(self.num_neighbors):\\n edge = self.edges[_in]\\n if edge.status == EdgeStatus.branch and _in != self.father:\\n count += 1\\n\\n if self.rec == count and self.test_edge == -1:\\n # Received report from all kids\\n self.__change_state(State.found)\\n self.__edge_stub(self.father, Message.report, [self.best_weight])\",\n \"def check_state(self):\\n pass\",\n \"def set_attenuator_output(self, state):\\n try:\\n self.attenuator.write('O {0}'.format(state))\\n except visa.VisaIOError as err:\\n raise IOError('Error: %s' % err)\",\n \"def _transit_state(self) -> None:\\n pass\",\n \"def on_R1(self):\\r\\n self.log()\",\n \"def state(self):\\r\\n return str(self)\",\n \"def status(self):\\r\\n return STATUS.FINE\",\n \"def report(self, result):\\n raise NotImplementedError\",\n \"def output(self, state):\\n h, t = state\\n\\n return h\",\n \"def final(self, state):\\n \\\"*** YOUR CODE HERE ***\\\"\\n return\\n util.raiseNotDefined()\",\n \"def report():\\n _get_cached_tracker().report()\",\n \"def status(self):\\n raise NotImplementedError()\",\n \"def water_now(self):\\n if DEBUG:\\n print(\\\"Debug mode, not turning on relay\\\")\\n else:\\n self.state['last_water_time'] = time.time()\\n with open(self.state_file_name, 'r+') as sf:\\n Pickle.dump(self.state, sf)\\n self.relay.set_state(self.relay.ON)\\n time.sleep(self.watering_duration)\\n self.relay.set_state(self.relay.OFF)\",\n \"def mark_inspection(self, passed):\\n\\n self.passed_inspection = passed\",\n \"def mark_inspection(self, passed):\\n\\n self.passed_inspection = passed\",\n \"def tracking(self) -> None:\\n dist, delta_angle, timestamp = self.vision.get_vision_data()\\n # collect data only once per loop\\n if timestamp is None:\\n # self.next_state(\\\"searching\\\")\\n # print(f\\\"tracking -> searching {self.vision.get_vision_data()}\\\")\\n self.state = self.searching\\n else:\\n if abs(delta_angle) > self.find_allowable_angle(dist):\\n # print(f\\\"Telling turret to slew by {delta_angle}\\\")\\n self.turret.slew(delta_angle)\\n if self.ready_to_spin():\\n # self.next_state(\\\"firing\\\")\\n # print(f\\\"tracking -> spining_up {self.vision.get_vision_data()}\\\")\\n self.distance = dist\\n self.state = self.spining_up\",\n \"def trace(self):\\n\\n print(f\\\"TRACE: %02X | %02X %02X %02X |\\\" % (\\n self.pc,\\n # self.flag,\\n #self.ie,\\n self.ram_read(self.pc),\\n self.ram_read(self.pc + 1),\\n self.ram_read(self.pc + 2)\\n ), end='')\\n\\n for i in range(8):\\n print(\\\" %02X\\\" % self.reg[i], end='')\\n\\n print()\",\n \"def update_state(self, state):\\n txt = state.text()\\n attr = 'state'\\n if state == SoylaState.RECORDING:\\n attr = 'recording'\\n self._state_text.set_text((attr, txt))\\n self._instructions_text.set_text(('instructions', state.instruction()))\",\n \"def print_state(self):\\n print(\\\"n\\\\tg\\\\to\\\\ta\\\\tc\\\\ts\\\\ttau\\\\td\\\\tN\\\")\\n for p in self.persons:\\n p.print_state()\\n print(\\\"type\\\\tpersons\\\")\\n for ps in self.partnerships:\\n ps.print_state()\",\n \"def _get_state(self):\",\n \"def update(self):\\n self._state = 23\",\n \"def output_debug_info(self):\",\n \"def on_x(self):\\r\\n self.log()\",\n \"def write_to_file(self):\\n\\t\\tfile = open(\\\"states.txt\\\", \\\"w\\\")\\n\\t\\t\\n\\t\\tpointer = self.head\\n\\t\\twhile pointer != None:\\n\\t\\t\\tfile.write(pointer.state + \\\"\\\\t\\\" + pointer.info)\\t\\n\\t\\t\\tpointer = pointer.next\\n\\n\\t\\tfile.close()\",\n \"def _report_iteration(self):\\n # Call report_iteration_items for a subclass-friendly function\\n self._report_iteration_items()\\n self._reporter.write_timestamp(self._iteration)\\n self._reporter.write_last_iteration(self._iteration)\",\n \"def turn_on(self):\\n self.write(\\\"OUT1\\\\n\\\")\",\n \"def state(self):\\n return str(self)\",\n \"def _print_target_reached(self, history):\\n print_target_reached(history)\",\n \"def state(self):\\n\\n\\t\\treturn str(self)\",\n \"def mark_inspection(self, passed):\\n if passed is True:\\n self.passed_inspection = True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6147335","0.6102503","0.6033684","0.59858537","0.5905538","0.5904048","0.5902007","0.5894178","0.5862637","0.5854155","0.5839881","0.57337976","0.5700883","0.56285226","0.56130916","0.56054235","0.55884707","0.55884707","0.5560808","0.5552423","0.55473894","0.55093735","0.55046874","0.5502149","0.54962224","0.54866624","0.54831994","0.54735416","0.547143","0.5468116","0.5467691","0.54421043","0.5435009","0.5428659","0.5410192","0.5406544","0.5340456","0.5336078","0.533556","0.5330127","0.5309096","0.52915204","0.5276095","0.5268891","0.5267062","0.52632326","0.5256008","0.5235187","0.52337945","0.5224024","0.5220978","0.5215319","0.521002","0.51994556","0.5195796","0.51869977","0.5162562","0.5159918","0.5158654","0.5148658","0.5144908","0.5141166","0.5139714","0.5135102","0.5129489","0.5124745","0.5124304","0.5115814","0.51152617","0.5112292","0.51051223","0.51016176","0.50991136","0.50984484","0.50894314","0.50846726","0.50749856","0.50736517","0.507223","0.5063088","0.50603694","0.5060019","0.5052823","0.5044813","0.5032467","0.5032467","0.5025051","0.50178844","0.50178444","0.5017504","0.5016986","0.5016298","0.5003381","0.50005436","0.49961585","0.4994484","0.4985501","0.49812654","0.49810472","0.49806213","0.49797726"],"string":"[\n \"0.6147335\",\n \"0.6102503\",\n \"0.6033684\",\n \"0.59858537\",\n \"0.5905538\",\n \"0.5904048\",\n \"0.5902007\",\n \"0.5894178\",\n \"0.5862637\",\n \"0.5854155\",\n \"0.5839881\",\n \"0.57337976\",\n \"0.5700883\",\n \"0.56285226\",\n \"0.56130916\",\n \"0.56054235\",\n \"0.55884707\",\n \"0.55884707\",\n \"0.5560808\",\n \"0.5552423\",\n \"0.55473894\",\n \"0.55093735\",\n \"0.55046874\",\n \"0.5502149\",\n \"0.54962224\",\n \"0.54866624\",\n \"0.54831994\",\n \"0.54735416\",\n \"0.547143\",\n \"0.5468116\",\n \"0.5467691\",\n \"0.54421043\",\n \"0.5435009\",\n \"0.5428659\",\n \"0.5410192\",\n \"0.5406544\",\n \"0.5340456\",\n \"0.5336078\",\n \"0.533556\",\n \"0.5330127\",\n \"0.5309096\",\n \"0.52915204\",\n \"0.5276095\",\n \"0.5268891\",\n \"0.5267062\",\n \"0.52632326\",\n \"0.5256008\",\n \"0.5235187\",\n \"0.52337945\",\n \"0.5224024\",\n \"0.5220978\",\n \"0.5215319\",\n \"0.521002\",\n \"0.51994556\",\n \"0.5195796\",\n \"0.51869977\",\n \"0.5162562\",\n \"0.5159918\",\n \"0.5158654\",\n \"0.5148658\",\n \"0.5144908\",\n \"0.5141166\",\n \"0.5139714\",\n \"0.5135102\",\n \"0.5129489\",\n \"0.5124745\",\n \"0.5124304\",\n \"0.5115814\",\n \"0.51152617\",\n \"0.5112292\",\n \"0.51051223\",\n \"0.51016176\",\n \"0.50991136\",\n \"0.50984484\",\n \"0.50894314\",\n \"0.50846726\",\n \"0.50749856\",\n \"0.50736517\",\n \"0.507223\",\n \"0.5063088\",\n \"0.50603694\",\n \"0.5060019\",\n \"0.5052823\",\n \"0.5044813\",\n \"0.5032467\",\n \"0.5032467\",\n \"0.5025051\",\n \"0.50178844\",\n \"0.50178444\",\n \"0.5017504\",\n \"0.5016986\",\n \"0.5016298\",\n \"0.5003381\",\n \"0.50005436\",\n \"0.49961585\",\n \"0.4994484\",\n \"0.4985501\",\n \"0.49812654\",\n \"0.49810472\",\n \"0.49806213\",\n \"0.49797726\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":12387,"cells":{"query":{"kind":"string","value":"lambda function handler for getting trash day"},"document":{"kind":"string","value":"def lambda_handler(event, context) -> dict:\n logging.info('Starting function with context=%s and event=%s', context, event)\n date = event['date']\n\n holiday_schedule = trash_schedule_service.get_schedule()\n trash_day = trash.next_trash_day(date, holiday_schedule)\n logging.info('Completed function with response=%s', trash_day)\n return trash_day"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def lambda_handler(event, context):\n logging.info('Starting function with context=%s and event=%s', context, event)\n holiday_schedule = trash.holidayschedule()\n old_holiday_schedule = trash_service.list()['data']\n old_holidays = [old_holiday['name'] for old_holiday in old_holiday_schedule]\n logging.info('Updating holiday schedule with schedule=%s', holiday_schedule)\n update_schedule(old_holidays, holiday_schedule)","def delete_UI_transaction_day(account):\n\t_day = read_day()\n\tdeleted = delete_transaction_day(account, _day)\n\tif (not deleted):\n\t\tprint('Nu s-a efectuat nici o stergere.')\n\telse:\n\t\tprint('Stergere finalizata.')","def on_delete(self, req, resp):\n try:\n days_to_retain = int(req.params[\"days\"])\n except Exception:\n days_to_retain = 90\n\n try:\n retention_status = self.state_manager.task_retention(\n retain_days=str(days_to_retain))\n if not retention_status:\n resp.status = falcon.HTTP_404\n return\n resp.text = \"Tables purged successfully.\"\n except Exception as e:\n self.error(req.context, \"Unknown error: %s\" % (str(e)))\n resp.text = \"Unexpected error.\"\n resp.status = falcon.HTTP_500\n return\n resp.status = falcon.HTTP_200","def do_rt(self, arg):\n self.do_timesheet('report today')","def do_upt(self, arg):\n self.do_timesheet('update today')","def reminders_soon(request):\n now = timezone.now()\n soon = now + timedelta(days=2)\n return Task.objects.filter(\n user=request.user, reminder__lt=soon, reminder_seen=False, done=False).exclude(folder='trash')","def next_regular_trash_day(date: str) -> str:\n parsed_date = parser.parse(date)\n day_of_week = parsed_date.weekday()\n\n if day_of_week < TRASH_DAY:\n delta = TRASH_DAY - day_of_week\n elif day_of_week == TRASH_DAY:\n delta = 0\n else:\n delta = 7 - (day_of_week - TRASH_DAY)\n\n next_trash_date = parsed_date + datetime.timedelta(days=delta)\n return next_trash_date.strftime('%Y-%m-%d')","def test_calendar_query_todo_alarm(self):\n raise SkipTest(\"test unimplemented\")","def weekday(self, *args, **kwargs): # real signature unknown\r\n pass","def delete_events(usrservice,calservice):\r\n print(args.action, args.inuser, 'celendar events')","def lambda_handler(event, context):\n try:\n day = datetime.datetime.now().weekday()\n\n fetcher = assume_role()\n config = fetch_config_from_s3(fetcher)\n print config\n\n if is_weekday(day, config['schedule']['halfDay']):\n client = assume_role()\n for role_arn in config['role_arns']:\n account_number = role_arn.split(\":\")[4]\n ec2_user = create_temp_user(client, role_arn)\n\n start_up_time, stop_time, now, tz = convert_to_datetime(config['times'])\n logger.info(\"Lambda started for account : {}\".format(config['account_names'][account_number]))\n start_stop(now, start_up_time, stop_time, ec2_user, config, tz)\n else:\n logger.info(\"I do not operate on weekends.\")\n except Exception as error:\n logger.info(\"Lambda failed to run with the following error : {}\".format(error))","def next_trash_day(date: str, holidays: list) -> dict:\n next_regular = next_regular_trash_day(date)\n weekdays = get_weekdays(next_regular)\n default_trash_day = {'type': 'default', 'schedule': calendar.day_name[TRASH_DAY]}\n if holiday.contains_holiday(weekdays):\n holiday_name = holiday.get_holiday(weekdays)\n find_holiday = list(filter(lambda holiday_delays: holiday_delays['name'] == holiday_name, holidays))\n if len(find_holiday) > 0:\n trash_day = {'type': 'holiday', 'holiday': holiday_name, 'schedule': find_holiday[0]['routeDelays']}\n else:\n trash_day = default_trash_day\n else:\n trash_day = default_trash_day\n\n return trash_day","def get_day():\n return handle_invalid_inputs(question_4, days)","def del_calender_event():\n return jsonify(None)","def call_fut(self, node):\r\n fut = duedate.get_extended_due_date\r\n return fut(node)","def day_of_the_week(arg):","def lambda_handler(event, context):\n if event.get('zipcode') and event.get('country') and event.get('job'):\n data = get_current_temperature(event['zipcode'], event['country'])\n send_to_badash(event['job'], data)\n else:\n print('Error: no zipcode and/or country and/or job supplied!')\n exit(-1)","def one_day(status, after):\n return woo.fetch_all_orders(status, after)","def schedule_handler(userdata, *args):\n\t\tfor event in database.devschedule(userdata[\"cursor\"], args[0]):\n\t\t\tprint(str(event))\n\t\t\n\t\tprint(\"\")","def ticket_deleted(self, ticket):","def fnight(var, wrapper, message):\n if var.PHASE != \"day\":\n wrapper.pm(messages[\"not_daytime\"])\n else:\n hurry_up(0, True)","def todo(self):\n # sort events with eventid using datetime string\n pass","def for_day_request_handler(handler_input: HandlerInput) -> Response:\n log.info(\n f\"forDay: session_attributes={handler_input.attributes_manager.session_attributes}\"\n )\n intent = handler_input.request_envelope.request.intent\n when = intent.slots.get(\"WHEN\")\n log.info(f\"forDay: when.value={when.value}\")\n if when.value:\n day = date_parser.parse(when.value)\n day = datetime(day.year, day.month, day.day)\n else:\n day = events.get_date()\n text = events.for_day(day)\n log.info(f\"forDay events for {day} = {text}\")\n\n return (\n handler_input.response_builder.speak(text)\n .set_card(SimpleCard(f\"Hillbrook events for {day.strftime('%A')}:\\n{text}\"))\n .set_should_end_session(True)\n .response\n )","def getTrash(self):\n return self.trash","def list_1day_renu(self,fday,tday):\n dayList = self._list_day(fday, tday)\n return zip(dayList,[self._get_ndays_renu(d,1) for d in dayList])","def extra_tasks_for_today(self):\n localtz = tzlocal()\n datetime_today = datetime.fromtimestamp(rospy.get_rostime().to_sec(), tz=localtz)\n day_today = datetime_today.strftime(\"%A\")\n date_today = datetime_today.date()\n rospy.loginfo('Looking for daily tasks for %s, %s' % (day_today, date_today))\n \n eight_forty_five= time(8,45, tzinfo=localtz)\n eleven_thirty= time(11,30, tzinfo=localtz)\n fourteen_thirty=time(14,30, tzinfo=localtz)\n seventeen_fifteen= time(17,15, tzinfo=localtz)\n past_bedtime = time(23,59, tzinfo=localtz)\n \n # day_end = seventeen_fifteen\n day_end = past_bedtime\n\n\n\n metric_wps=['WayPoint13', 'WayPoint18', 'WayPoint9','WayPoint11','WayPoint5','WayPoint3'] \n object_learn_wps=['WayPoint13', 'WayPoint18', 'WayPoint9', 'WayPoint11'] \n object_search_wps=['WayPoint1', 'WayPoint2', 'WayPoint3']\n door_wps=['WayPoint7', 'WayPoint4']\n \n morning_start = eight_forty_five\n morning_duration = delta_between(eleven_thirty, morning_start)\n \n lunch_start = eleven_thirty\n lunch_duration = delta_between(fourteen_thirty, lunch_start)\n\n afternoon_start = fourteen_thirty\n afternoon_duration = delta_between(day_end, afternoon_start)\n\n tasks = []\n \n #door checks at fixed times (to evaluate system ability to do stuff at corret times)\n task=create_door_check_task(door_wps[0])\n start_time=datetime.combine(date_today, time(10,30, tzinfo=localtz))\n end_time = start_time+timedelta(seconds=30)\n task.start_after=rospy.Time(unix_time(start_time))\n task.end_before=rospy.Time(unix_time(end_time))\n tasks.append(task)\n \n task=create_door_check_task(door_wps[0])\n start_time=datetime.combine(date_today, time(13,30, tzinfo=localtz))\n end_time = start_time+timedelta(seconds=30)\n task.start_after=rospy.Time(unix_time(start_time))\n task.end_before=rospy.Time(unix_time(end_time))\n tasks.append(task)\n \n task=create_door_check_task(door_wps[0])\n start_time=datetime.combine(date_today, time(16,30, tzinfo=localtz))\n end_time = start_time+timedelta(seconds=30)\n task.start_after=rospy.Time(unix_time(start_time))\n task.end_before=rospy.Time(unix_time(end_time))\n tasks.append(task)\n \n \n #random tasks\n for i in range(4):\n #morning\n task=create_metric_map_task(random.choice(metric_wps))\n self.set_random_task_time(task, date_today, morning_start, morning_duration)\n tasks.append(task)\n \n task=create_door_check_task(random.choice(door_wps))\n self.set_random_task_time(task, date_today, morning_start, morning_duration)\n tasks.append(task)\n \n if i<3:\n task=create_object_learn_task(random.choice(object_learn_wps))\n self.set_random_task_time(task, date_today, morning_start, morning_duration)\n tasks.append(task)\n \n task=create_object_search_task(random.choice(object_search_wps))\n self.set_random_task_time(task, date_today, morning_start, morning_duration)\n tasks.append(task)\n \n #lunch (less tasks because we want the robot mostly learning people tracks)\n if i<1:\n task=create_metric_map_task(random.choice(metric_wps))\n self.set_random_task_time(task, date_today, lunch_start, lunch_duration)\n tasks.append(task)\n \n task=create_door_check_task(random.choice(door_wps))\n self.set_random_task_time(task, date_today, lunch_start, lunch_duration)\n tasks.append(task)\n \n task=create_object_learn_task(random.choice(object_learn_wps))\n self.set_random_task_time(task, date_today, lunch_start, lunch_duration)\n tasks.append(task)\n \n task=create_object_search_task(random.choice(object_search_wps))\n self.set_random_task_time(task, date_today, lunch_start, lunch_duration)\n tasks.append(task)\n \n \n #afternoon\n task=create_metric_map_task(random.choice(metric_wps))\n self.set_random_task_time(task, date_today, afternoon_start, afternoon_duration)\n tasks.append(task)\n \n task=create_door_check_task(random.choice(door_wps))\n self.set_random_task_time(task, date_today, afternoon_start, afternoon_duration)\n tasks.append(task)\n \n if i<3:\n task=create_object_learn_task(random.choice(object_learn_wps))\n self.set_random_task_time(task, date_today, afternoon_start, afternoon_duration)\n tasks.append(task)\n \n task=create_object_search_task(random.choice(object_search_wps))\n self.set_random_task_time(task, date_today, afternoon_start, afternoon_duration)\n tasks.append(task)\n return tasks","def lambda_handler(event, content):\n imap = email_startup()\n status, messages = imap.select('Inbox')\n days_old = input('Enter many days ago do you want to use as the cutoff?: ')\n new_date = get_days_old(days_old)\n messages = apply_filter(imap, new_date)\n initial_unread = get_unread_count(imap)\n print(f'Initial unread emails: {initial_unread}')\n print(f'Emails to be filter: {len(messages)}')\n a_pause = input('Continue by pressing enter.')\n\n print(f'Processing {len(messages)} unread emails from before {new_date}')\n print(\"=\"*100)\n process_messages(imap, messages)\n print(\"=\"*100)\n\n # Determine results from script\n post_unread = get_unread_count(imap)\n print(f'Processed Emails: {initial_unread - post_unread}')\n print(f'After processing, there are {post_unread} unread emails.')\n\n # close the connection and logout\n imap.close()\n imap.logout()","def day(d):\n\t\tx = db.cquery(\"day\",d)\n\t\tprint \"Total:\", x[0]\n\t\tf = raw_input(\"[L]ist [N]ew overview or [B]ack to home \").lower()\n\t\tif f == \"l\":\n\t\t\tfor i in x[1]:\n\t\t\t\tprint ui.statsid(), i[0], i[1], \" \", ui.statstimein(), i[2], ui.statstimeout(), i[3]\n\t\t\traw_input(\"[Enter] to go back to search\")\n\t\t\thome_stats()\n\t\telif f == \"n\":\n\t\t\thome_stats()\n\t\telif f == \"b\":\n\t\t\thome()\n\t\telse:\n\t\t\tpass","def ENTRY(entry_code):\n\tif check_user(entry_code) ==True:\n\t\t###workday = Workday.objects.filter(date=get_time()).get()\n\t\tenter_workday(entry_code)","def lambda_handler(event, context):\n # By default, treat the user request as coming from the America/New_York time zone.\n os.environ['TZ'] = 'America/New_York'\n time.tzset()\n #logger.debug('event.bot.name={}'.format(event['bot']['name']))\n\n return dispatch(event)","def function_to_execute(event, context):\r\n action = base64.b64decode(event['data']).decode('utf-8')\r\n\r\n if (action == \"calculate_exceedances_for_last_28_days\"):\r\n calculate_exceedances_for_last_28_days()","async def dailytomorrow(self, ctx):\n if ctx.invoked_subcommand is None:\n await ctx.send_help(ctx.command)","def tapahtumat_tanaan():\n\n events = get_events()\n tanaan = datetime.datetime.today().isoformat()[:10]\n out = []\n for _, calendar_events in events.items():\n out += [event for event in calendar_events if event[0] == tanaan]\n\n return out","def fday(var, wrapper, message):\n if var.PHASE != \"night\":\n wrapper.pm(messages[\"not_nighttime\"])\n else:\n transition_day()","def _DeleteEvent(self, event):\n\n self.cal_client.Delete(event.GetEditLink().href)","async def afterHoursAutoPurge(self, ctx: Context):","def send_today_reminder(self, request, pk=None):\n try:\n today_menu = Menu.objects.get(\n menu_date=timezone.localtime(timezone.now()).date()\n )\n today_menu.send_today_menu_slack_each_user()\n except Menu.DoesNotExist:\n return Response({\"detail\": \"Not found.\"}, status=status.HTTP_404_NOT_FOUND)\n return Response(\n {\"detail\": \"Reminder sent successfully.\"}, status=status.HTTP_200_OK\n )","def lambda_handler(event, context):\n # By default, treat the user request as coming from the Europe/Paris time zone.\n os.environ['TZ'] = 'Europe/Paris'\n time.tzset()\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\n\n return dispatch(event)","def formatday(self, day, weekday):\n\t\tif day == 0:\n\t\t\treturn self.day_cell('noday',' ') # day outside month\n\t\ttext = get_days_activity(self.pilot_id, self.year, self.month, day)\n\t\tif date.today() == date(self.year, self.month, day): # mark today\n\t\t\tbody = '<a href=\"/FlightLog/add_sorties.html\">%s</a>' %day\n\t\t\treturn self.day_cell('today',body)\n\t\tif date(self.year,self.month,day) in self.done_sorties: # mark a day with a done flight\n\t\t\tbody = '<a href=\"/FlightLog/delete_from_db?date=%d-%02d-%02d\">%s</a>' % (self.year,self.month,day,day)\n\t\t\treturn self.day_cell('done',body)\n\t\tif date(self.year,self.month,day) in self.planned_sorties: # mark a day with a planned flight\n\t\t\tbody = '<a href=\"/FlightLog/delete_from_db?date=%d-%02d-%02d\">%s</a>' % (self.year,self.month,day,text)\n\t\t\treturn self.day_cell('planned',body)\n\t\telse: # a non-special month day\n\t\t\tbody = '<a href=\"/FlightLog/calendar_add?date=%d-%02d-%02d\">%s</a>' % (self.year,self.month,day,day)\n\t\t\treturn self.day_cell('free',body)","def test_return_day(self):\n self.assertEqual(functions.return_day(1), \"Sunday\")\n self.assertEqual(functions.return_day(2), \"Monday\")\n self.assertEqual(functions.return_day(3), \"Tuesday\")\n self.assertEqual(functions.return_day(4), \"Wednesday\")\n self.assertEqual(functions.return_day(5), \"Thursday\")\n self.assertEqual(functions.return_day(6), \"Friday\")\n self.assertEqual(functions.return_day(41), None)","def day(self):\n try:\n return self.schedule.day\n except Schedule.DoesNotExist:\n return None","def test_remove_recurring_schedule(self):\n pass","def _(event):\n # TODO","def list_7day_renu(self,fday,tday):\n dayList = self._list_day(fday, tday)\n return zip(dayList,[self._get_ndays_renu(d,7) for d in dayList])","def lambda_handler(event, context):\n return dispatch(event)","def event_remove(event_name:str):#maybe make a add events function\n global removed_manually\n global notification_index #always incremented so notifications can be removed\n for y in range(len(Events_list)): # sorted in ascending order.\n if removed_manually is False:#if the alrm has expired by its own ,check if it has extras selected and if so; display them:\n if Events_list[y][5] == \"true\": #news\n temp_notifi_list = news().copy()\n for w in temp_notifi_list:\n a = w[0]#news title\n b = w[1]#link\n notification_list.append(['\"{}\"'.format(a), \"not_alarm\",[str(notification_index),\"newslink\",'{}'.format(b)]])\n notification_index+=1\n temp_notifi_list = covid_data().copy()\n for w in temp_notifi_list:\n notification_list.append(['\"{}\"'.format(w), \"not_alarm\",[str(notification_index)]])\n notification_index+=1\n if Events_list[y][4] == \"true\":#weather\n notification_list.append(['\"{}\"'.format(\"Todays Weather: \" + weather()), \"not_alarm\",[str(notification_index)]])\n notification_index+=1\n if Events_list[y][0] == event_name:\n event_log(\"rescheduling alarm for next day....\",\"\")\n if Events_list[y][3] == \"true\" and removed_manually is False:#if this is true the the event is set to be repeated\n new_date = time.strftime('%Y-%m-%d %H:%M', time.localtime(convert_to_epoch(Events_list[y][1]) + 86400))\n set_alarm(new_date, event_name, Events_list[y][2], Events_list[y][3],Events_list[y][4],Events_list[y][5])\n notification_list.append(['\"{}\"'.format(event_name) + \"Has been rescheduled for next day\", \"alarm\", Events_list[y]])\n speak_output(event_name + \"has been repeated for next day\")\n else:#event is going to be removed\n event_log(\"removing alarm....\",\"\")\n removed_manually = False#changes the boolean so the function can go back to deafult state as this is global varibale\n notification_list.append(['\"{}\"'.format(event_name) + \" is expired/deleted\", \"alarm\", Events_list[y]])\n speak_output(event_name + \"has expired or been removed\")\n Events_list.pop(y)#removes from list\n temp_notifi_list = []\n write()\n break","def lambda_handler(event, context):\n return","def lambda_handler(event, context):\n return","def lambda_method(self,t): \n return 5*math.sin(2*math.pi*1*t) # I don't see the value of 1 here but this is how lamda is defined in the exercise.","def tickets(number, day, premium_seating):\n #fill in your code here. \n return 0.0","def get_rest_days(self, cr, uid, employee_id, dt, context=None):\n\n day = dt.strftime(OE_DTFORMAT)\n ids = self.search(\n cr, uid, [\n ('employee_id', '=', employee_id),\n ('date_start', '<=', day),\n ('date_end', '>=', day),\n ], context=context)\n if len(ids) == 0:\n return None\n elif len(ids) > 1:\n raise orm.except_orm(_('Programming Error'), _(\n 'Employee has a scheduled date in more than one schedule.'))\n\n # If the day is in the middle of the week get the start of the week\n if dt.weekday() == 0:\n week_start = dt.strftime(OE_DFORMAT)\n else:\n week_start = (\n dt + relativedelta(days=-dt.weekday())).strftime(OE_DFORMAT)\n\n return self.get_rest_days_by_id(\n cr, uid, ids[0], week_start, context=context\n )","def reschedule():\n if not schedule.empty():\n purge_events() \n\n today_s = tuple_to_str(time.localtime()[:3])\n\n # first check if exception entry exist for today in datemap\n if today_s in datemap:\n \tschedule_day(datemap[today_s])\n else:\n # otherwise schedule it as normal weekday\n schedule_day(days[time.strftime(\"%A\")])","def _list_day(self, fday=None, tday=None):\n date_fday = fday or date.today()\n date_tday = tday or date.today()\n days = (date_tday - date_fday).days+1\n dayList = [date_fday+timedelta(v) for v in range(days)] \n return dayList","def day_to_day(self):\n while True:\n yield 0","def lambda_handler(event, context):\n # By default, treat the user request as coming from the America/New_York time zone.\n os.environ['TZ'] = 'America/New_York'\n time.tzset()\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\n\n return dispatch(event)","def lambda_handler(event, context):\n # By default, treat the user request as coming from the America/New_York time zone.\n os.environ['TZ'] = 'America/New_York'\n time.tzset()\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\n\n return dispatch(event)","def lambda_handler(event, context):\n # By default, treat the user request as coming from the America/New_York time zone.\n os.environ['TZ'] = 'America/New_York'\n time.tzset()\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\n\n return dispatch(event)","def remove_bday_callback(id: int) -> None:\n\n BOT_GLOBAL.loop.create_task(remove_bday(id))","def test_handle_weather_message_calls_tomorrow(self):\n pass","def run_daily_hygienist(self):\n self.remove_priorities_from_all_not_due_today()","def cron_update_yesterday_stats(_request):\n today = datetime.datetime.utcnow().date()\n day = str(today - datetime.timedelta(days=1))\n tasks = [day, '30', 'monthly']\n taskqueue.add(\n url=reverse(task_update_stats),\n params={'tasks': json.dumps(tasks), 'date': str(today)},\n queue_name='update-stats')\n out = 'Triggered tasks for day %s: %s' % (day, ', '.join(tasks))\n logging.info(out)\n return HttpTextResponse(out)","def timed_handler(userdata, *args):\n\t\tfuzzy = False\n\t\t\n\t\tif args[3] == \"exact\":\n\t\t\tfuzzy = False\n\t\telif args[3] == \"fuzzy\":\n\t\t\tfuzzy = True\n\t\telse:\n\t\t\traise FormatError(\"Expected 'fuzzy' or 'exact' but found '\" + args[3] + \"' instead.\")\n\t\t\n\t\tevent = Event.create_once(args[4], fuzzy, args[2])\n\t\t\n\t\tif args[0] == \"add\":\n\t\t\tdevice.schedule(userdata, args[1], event)\n\t\telif args[0] == \"del\":\n\t\t\tdevice.unschedule(userdata, args[1], event)\n\t\telse:\n\t\t\traise FormatError(\"Expected 'add' or 'del' but found '\" + args[0] + \"' instead.\")","def lambda_handler(event, context):\r\n # By default, treat the user request as coming from the America/New_York time zone.\r\n os.environ['TZ'] = 'America/New_York'\r\n time.tzset()\r\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\r\n\r\n return dispatch(event)","def yesterday():\n return datetime.today() - timedelta(1)","def logDayDetails(self):\n console().info(\"Today is {}.\".format(self.today.strftime(DATE_FMT)))\n hours = self.contractDetails.tradingHours.split(\";\")[0].split(\":\")[1]\n console().info(\"Today's Trading Hours Are: {}\".format(hours))\n if self.normalDay:\n console().info(\"Today is a Valid Day for Trading\")\n else:\n console().info(\"Today is not a Valid Trading Day. Sleeping Until Tomorrow\")","def _(event):\n pass","def _(event):\n pass","def _(event):\n pass","def trash(request):\n return Task.objects.select_related('project').filter(user=request.user, folder='trash')","def cron_on_off(key, item, dst):\n\n action = \"off\"\n active = \"\"\n days = item['days']\n nexaid = item['nexaid']\n\n if re.match(r'^on', key):\n action = \"on\"\n\n if item['_active'] is False:\n active = \"# \"\n\n if days == \"1,2,3,4,5\":\n days = \"1-5\"\n\n if days == \"0,1,2,3,4,5,6\":\n days = \"*\"\n\n # Any clock defined ?\n\n if item[key]:\n clock = item[key].split(':')\n\n line = \"{0}{1:0>2} {2:0>2} * * {3} pi /home/pi/rfx-commands/commands/cmd-to-nexa.sh {4} {5}\".format(\n active,\n clock[1],\n clock[0],\n days,\n nexaid,\n action\n )\n\n add_item(item['name'], {'line': line, 'hour': int(clock[0]), 'minute': int(clock[1])}, dst)\n\n # Any sunset defined ?\n\n if item['sunset'] is not None:\n sunset = item['_sunriseset']['sunset']\n offset = sunset + datetime.timedelta(minutes=item['sunset'])\n\n comment = \"Sunset {0:0>2}:{1:0>2} {2} min\".format(sunset.hour, sunset.minute, item['sunset'])\n line = \"{0}{1:0>2} {2:0>2} * * {3} pi /home/pi/rfx-commands/commands/cmd-to-nexa.sh {4} {5} # {6}\".format(\n active,\n offset.minute,\n offset.hour,\n days,\n nexaid,\n \"on\",\n comment\n )\n\n add_item(item['name'], {'line': line, 'hour': int(offset.hour), 'minute': int(offset.minute)}, dst)\n\n # Reset this, We only want one per row\n\n item['sunset'] = None","def getOneDay(self,day_number=0):\n return self.event_time_sequence[day_number]","def recurrent_handler(userdata, *args):\n\t\tfuzzy = False\n\t\t\n\t\tif args[5] == \"exact\":\n\t\t\tfuzzy = False\n\t\telif args[5] == \"fuzzy\":\n\t\t\tfuzzy = True\n\t\telse:\n\t\t\traise FormatError(\"Expected 'fuzzy' or 'exact' but found '\" + args[5] + \"' instead.\")\n\t\t\n\t\tevent = Event.create_recurrent(args[6], fuzzy, args[2], args[3], args[4])\n\t\t\n\t\tif args[0] == \"add\":\n\t\t\tdevice.schedule(userdata, args[1], event)\n\t\telif args[0] == \"del\":\n\t\t\tdevice.unschedule(userdata, args[1], event)\n\t\telse:\n\t\t\traise FormatError(\"Expected 'add' or 'del' but found '\" + args[0] + \"' instead.\")","def todo_added(name, description):","def TODAY():\n return datetime.date.today()","def delete_haikus_unposted_timedelta(cls, db_session, days: float = None) -> list:\n if days is not None:\n ts_end = datetime.now(tz=timezone.utc) - timedelta(days=days)\n try:\n logger.info(f\"Deleting unposted haikus older than {days} days\")\n delete_q = (\n cls.__table__.delete()\n .where(cls.created_at < ts_end)\n .where(cls.date_posted == None) # noqa: E711\n )\n\n db_session.execute(delete_q)\n db_session.commit()\n except Exception as e:\n logger.warning(f\"Exception when deleting old unposted haikus: {e}\")\n db_session.rollback()","def _get_current_week_entries(today, user):\n some_day = today + timedelta(days=1)\n monday_of_week = some_day - timedelta(days=(some_day.isocalendar()[2] - 1))\n sunday_of_week = monday_of_week + timedelta(days=6)\n weekevents = TimeEntry.objects.filter(\n booking_date__gte=monday_of_week, booking_date__lt=sunday_of_week, user=user\n )\n return weekevents","def _on_articles_trash(self, evt=None):\n \n # get selected articles\n articles = self._articles_view.GetSelectedArticles()\n if not articles:\n return\n \n # update library\n self._library.trash(articles, True)\n \n # refresh collections view\n self._collections_view.UpdateCounts()\n \n # refresh articles view\n self._articles_view.ShowArticles()","def weekly():","def update_timesheet(item):\n\tj=json.loads(item)\n\tprint(\"-----------------------garffff---------------------\")\n\tnew_employee=None;\n\ttimesheet=frappe.get_doc(\"Time Sheet\",j[\"name\"])\n\tjarray=[]\n\tfor passed_employee in j['employees']:\n\t\tif 'new' in passed_employee.keys():\n\t\t\t#create employee\n\t\t\tnew_employee=frappe.get_doc({\n\t\t\t\t\"doctype\":\"employee_link_with_time\",\n\t\t\t\t\"employee\":passed_employee['employee']\n\t\t\t});\n\n\t\tjarray.append(passed_employee['employee']);\n\t\tfor employee in timesheet.employees:\n\t\t\tif passed_employee[\"employee\"]==employee.employee:\n\t\t\t\tif \"start\" in passed_employee:\n\t\t\t\t\temployee.start=passed_employee[\"start\"]\n\t\t\t\tif \"end\" in passed_employee:\n\t\t\t\t\temployee.end=passed_employee[\"end\"];\n\tforRemove=[]\n\tfor employee_container in timesheet.employees:\n\t\tif employee_container.employee not in jarray:\n\t\t\tforRemove.append(employee_container)\n\tprint(\"___________REMOVE______________\")\n\tprint(forRemove);\n\tif forRemove:\n\t\tfor remove in forRemove:\n\t\t\ttimesheet.employees.remove(remove)\n\n\tif new_employee is not None:\n\t\ttimesheet.append(\"employees\",new_employee)\n\n\t#handel status\n\ttimesheet.status=j[\"status\"]\n\ttimesheet.save()\n\treturn frappe.get_doc(\"Time Sheet\",j[\"name\"])","def insert_day():\n analytics.insert_day(6)","def trayagent(forwho,trayclickedfunction):\r\n\r\n tray = QSystemTrayIcon(forwho.windowIcon(), forwho)\r\n forwho.connect(tray,SIGNAL(\"activated(QSystemTrayIcon::ActivationReason)\"),trayclickedfunction)\r\n tray.show()\r\n return tray","def weekday(day):\n return (day % 7) - 1","def refilter_previous_rts():\n rtlist = get_list_of_rts()\n if rtlist == None or len(rtlist) == 0:\n return\n for tweet in rtlist:\n if not filter_tweet(tweet['retweeted_status']):\n print(\"Previous tweet does not pass the filter anymore !\")\n print(\"@%s (%s) : %s\" % (\n tweet['retweeted_status']['user']['screen_name'],\n tweet['retweeted_status']['source'],\n tweet['retweeted_status']['text']))\n answer = raw_input(\"Delete ? [y/N] \")\n if answer == 'y':\n delete_tweet(tweet['id_str'])","def runs_today(self,s_id,day):\n if self.schedule_keys[s_id][day]==1:\n return True\n else:\n return False","def unscheduleFirst(self, runnable):\n for evt in self.store.query(TimedEvent, TimedEvent.runnable == runnable, sort=TimedEvent.time.ascending):\n evt.deleteFromStore()\n break","def do(self):\n self.message += 'Searching for unpayed loans \\n'\n today = dt.now()\n tomorrow = today + timedelta(days=1)\n\n expired = Loan.objects.filter(due_date=today)\n almost = Loan.objects.filter(due_date=tomorrow)\n\n self.charge(expired)\n self.notify(almost)\n\n self.log_success()","def menu_python_daily(self, event=None):\n self.link('http://www.pythonware.com/daily/')","def to_be_deleted(self):\n return self.filter(start__lte=timezone.now() - datetime.timedelta(days=1))","def test_extraction(self):\n thing = ThingThatMovesAround(store=self.accountStore,\n superValue=self.IMPORTANT_VALUE)\n self.ss.schedule(thing, Time() + datetime.timedelta(days=1))\n self.test_noTimedEventsExtraction()","def day_from_moment(cls, tee):\n return quotient(cls.lunar_phase(tee), 12) + 1","def returnEvent(self):\r\n item = self.__agenda.pop(1)\r\n return item[1]","def get_rest_days(self, cr, uid, t_id, context=None):\n\n tpl = self.browse(cr, uid, t_id, context=context)\n if tpl.restday_ids:\n res = [rd.sequence for rd in tpl.restday_ids]\n else:\n weekdays = ['0', '1', '2', '3', '4', '5', '6']\n scheddays = []\n scheddays = [\n wt.dayofweek\n for wt in tpl.worktime_ids\n if wt.dayofweek not in scheddays\n ]\n res = [int(d) for d in weekdays if d not in scheddays]\n # If there are no work days return nothing instead of *ALL* the\n # days in the week\n if len(res) == 7:\n res = []\n\n return res","def lambda_handler(event, context):\n try:\n aq = Aquifer()\n aq.run()\n\n return \"Completed\"\n\n except (Exception, KeyboardInterrupt) as e:\n return \"Error occurred\"","def do_rrt(self, arg):\n self.do_timesheet('report extend track today')","def clickedCell(self, row, col) ->QDate:\n itemWidget = self.table.cellWidget(row, col)\n data = itemWidget.data\n dataMonth = data.month() - 1\n dataYear = data.year()\n self.currentDate = data\n # print('cell clicked flags: ', itemWidget.dictFlags)\n # print('cell clicked flags currentDate: ', data)\n # print('cell clicked flags item date: ', itemWidget.data)\n if data not in self.daysInTheMonth:\n if dataYear > self.currentYear:\n self.bot_next.click()\n elif dataYear < self.currentYear:\n self.bot_prev.click()\n elif dataMonth > self.indexMonth:\n self.bot_next.click()\n else:\n self.bot_prev.click()\n self.singleClicked.emit(data)\n return data","def process_IN_DELETE_SELF(self, event):","def schedule_callback(request):\n print \"In schedule function\"\n print request\n\n context = request['context']\n try:\n context['pincode'] = first_entity_value(request['context']['entities'], 'phone_number')\n except:\n context['pincode'] = None\n\n schedule_details = ['Pincode Network Issues']","def check_market_status():\n # today = datetime.datetime.now(pytz.timezone('America/New_York')).date()\n today_utc = pd.to_datetime('now').date()\n ndq = mcal.get_calendar('NASDAQ')\n open_days = ndq.schedule(start_date=today_utc - pd.Timedelta('10 days'), end_date=today_utc)\n if today_utc in open_days.index:\n return open_days\n else:\n return None","def clean_expired_task():\n day_ago = datetime.datetime.now(pytz.timezone(\"UTC\")) - datetime.timedelta(days=ASYNC_EXPORT_FILE_EXPIRED_DAYS)\n # 获取过期的内网下载文件\n expired_task_list = AsyncTask.objects.filter(created_at__lt=day_ago, is_clean=False)\n # nfs文件需要进行定期清理操作\n storage_type = FeatureToggleObject.toggle(FEATURE_ASYNC_EXPORT_COMMON).feature_config.get(\n FEATURE_ASYNC_EXPORT_STORAGE_TYPE\n )\n\n if storage_type or storage_type == RemoteStorageType.NFS.value:\n # 删除NFS文件\n for expired_task in expired_task_list:\n target_file_dir = os.path.join(settings.EXTRACT_SAAS_STORE_DIR, expired_task.file_name)\n if os.path.isfile(target_file_dir):\n os.remove(os.path.abspath(target_file_dir))\n expired_task.is_clean = True\n expired_task.save()","def _remove_direct_alerts(self, cr, uid, ids, context=None):\n\n if isinstance(ids, (int, long)):\n ids = [ids]\n\n alert_obj = self.pool.get('hr.schedule.alert')\n\n # Remove alerts directly attached to these schedule details\n #\n alert_ids = []\n scheds = []\n sched_keys = []\n for sched_detail in self.browse(cr, uid, ids, context=context):\n\n [alert_ids.append(alert.id) for alert in sched_detail.alert_ids]\n\n # Hmm, creation of this record triggers a workflow action that\n # tries to write to it. But it seems that computed fields aren't\n # available at this stage. So, use a fallback and compute the day\n # ourselves.\n day = sched_detail.day\n if not sched_detail.day:\n day = time.strftime('%Y-%m-%d', time.strptime(\n sched_detail.date_start, '%Y-%m-%d %H:%M:%S'))\n key = str(sched_detail.schedule_id.employee_id.id) + day\n if key not in sched_keys:\n scheds.append((sched_detail.schedule_id.employee_id.id, day))\n sched_keys.append(key)\n\n if len(alert_ids) > 0:\n alert_obj.unlink(cr, uid, alert_ids, context=context)\n\n return scheds"],"string":"[\n \"def lambda_handler(event, context):\\n logging.info('Starting function with context=%s and event=%s', context, event)\\n holiday_schedule = trash.holidayschedule()\\n old_holiday_schedule = trash_service.list()['data']\\n old_holidays = [old_holiday['name'] for old_holiday in old_holiday_schedule]\\n logging.info('Updating holiday schedule with schedule=%s', holiday_schedule)\\n update_schedule(old_holidays, holiday_schedule)\",\n \"def delete_UI_transaction_day(account):\\n\\t_day = read_day()\\n\\tdeleted = delete_transaction_day(account, _day)\\n\\tif (not deleted):\\n\\t\\tprint('Nu s-a efectuat nici o stergere.')\\n\\telse:\\n\\t\\tprint('Stergere finalizata.')\",\n \"def on_delete(self, req, resp):\\n try:\\n days_to_retain = int(req.params[\\\"days\\\"])\\n except Exception:\\n days_to_retain = 90\\n\\n try:\\n retention_status = self.state_manager.task_retention(\\n retain_days=str(days_to_retain))\\n if not retention_status:\\n resp.status = falcon.HTTP_404\\n return\\n resp.text = \\\"Tables purged successfully.\\\"\\n except Exception as e:\\n self.error(req.context, \\\"Unknown error: %s\\\" % (str(e)))\\n resp.text = \\\"Unexpected error.\\\"\\n resp.status = falcon.HTTP_500\\n return\\n resp.status = falcon.HTTP_200\",\n \"def do_rt(self, arg):\\n self.do_timesheet('report today')\",\n \"def do_upt(self, arg):\\n self.do_timesheet('update today')\",\n \"def reminders_soon(request):\\n now = timezone.now()\\n soon = now + timedelta(days=2)\\n return Task.objects.filter(\\n user=request.user, reminder__lt=soon, reminder_seen=False, done=False).exclude(folder='trash')\",\n \"def next_regular_trash_day(date: str) -> str:\\n parsed_date = parser.parse(date)\\n day_of_week = parsed_date.weekday()\\n\\n if day_of_week < TRASH_DAY:\\n delta = TRASH_DAY - day_of_week\\n elif day_of_week == TRASH_DAY:\\n delta = 0\\n else:\\n delta = 7 - (day_of_week - TRASH_DAY)\\n\\n next_trash_date = parsed_date + datetime.timedelta(days=delta)\\n return next_trash_date.strftime('%Y-%m-%d')\",\n \"def test_calendar_query_todo_alarm(self):\\n raise SkipTest(\\\"test unimplemented\\\")\",\n \"def weekday(self, *args, **kwargs): # real signature unknown\\r\\n pass\",\n \"def delete_events(usrservice,calservice):\\r\\n print(args.action, args.inuser, 'celendar events')\",\n \"def lambda_handler(event, context):\\n try:\\n day = datetime.datetime.now().weekday()\\n\\n fetcher = assume_role()\\n config = fetch_config_from_s3(fetcher)\\n print config\\n\\n if is_weekday(day, config['schedule']['halfDay']):\\n client = assume_role()\\n for role_arn in config['role_arns']:\\n account_number = role_arn.split(\\\":\\\")[4]\\n ec2_user = create_temp_user(client, role_arn)\\n\\n start_up_time, stop_time, now, tz = convert_to_datetime(config['times'])\\n logger.info(\\\"Lambda started for account : {}\\\".format(config['account_names'][account_number]))\\n start_stop(now, start_up_time, stop_time, ec2_user, config, tz)\\n else:\\n logger.info(\\\"I do not operate on weekends.\\\")\\n except Exception as error:\\n logger.info(\\\"Lambda failed to run with the following error : {}\\\".format(error))\",\n \"def next_trash_day(date: str, holidays: list) -> dict:\\n next_regular = next_regular_trash_day(date)\\n weekdays = get_weekdays(next_regular)\\n default_trash_day = {'type': 'default', 'schedule': calendar.day_name[TRASH_DAY]}\\n if holiday.contains_holiday(weekdays):\\n holiday_name = holiday.get_holiday(weekdays)\\n find_holiday = list(filter(lambda holiday_delays: holiday_delays['name'] == holiday_name, holidays))\\n if len(find_holiday) > 0:\\n trash_day = {'type': 'holiday', 'holiday': holiday_name, 'schedule': find_holiday[0]['routeDelays']}\\n else:\\n trash_day = default_trash_day\\n else:\\n trash_day = default_trash_day\\n\\n return trash_day\",\n \"def get_day():\\n return handle_invalid_inputs(question_4, days)\",\n \"def del_calender_event():\\n return jsonify(None)\",\n \"def call_fut(self, node):\\r\\n fut = duedate.get_extended_due_date\\r\\n return fut(node)\",\n \"def day_of_the_week(arg):\",\n \"def lambda_handler(event, context):\\n if event.get('zipcode') and event.get('country') and event.get('job'):\\n data = get_current_temperature(event['zipcode'], event['country'])\\n send_to_badash(event['job'], data)\\n else:\\n print('Error: no zipcode and/or country and/or job supplied!')\\n exit(-1)\",\n \"def one_day(status, after):\\n return woo.fetch_all_orders(status, after)\",\n \"def schedule_handler(userdata, *args):\\n\\t\\tfor event in database.devschedule(userdata[\\\"cursor\\\"], args[0]):\\n\\t\\t\\tprint(str(event))\\n\\t\\t\\n\\t\\tprint(\\\"\\\")\",\n \"def ticket_deleted(self, ticket):\",\n \"def fnight(var, wrapper, message):\\n if var.PHASE != \\\"day\\\":\\n wrapper.pm(messages[\\\"not_daytime\\\"])\\n else:\\n hurry_up(0, True)\",\n \"def todo(self):\\n # sort events with eventid using datetime string\\n pass\",\n \"def for_day_request_handler(handler_input: HandlerInput) -> Response:\\n log.info(\\n f\\\"forDay: session_attributes={handler_input.attributes_manager.session_attributes}\\\"\\n )\\n intent = handler_input.request_envelope.request.intent\\n when = intent.slots.get(\\\"WHEN\\\")\\n log.info(f\\\"forDay: when.value={when.value}\\\")\\n if when.value:\\n day = date_parser.parse(when.value)\\n day = datetime(day.year, day.month, day.day)\\n else:\\n day = events.get_date()\\n text = events.for_day(day)\\n log.info(f\\\"forDay events for {day} = {text}\\\")\\n\\n return (\\n handler_input.response_builder.speak(text)\\n .set_card(SimpleCard(f\\\"Hillbrook events for {day.strftime('%A')}:\\\\n{text}\\\"))\\n .set_should_end_session(True)\\n .response\\n )\",\n \"def getTrash(self):\\n return self.trash\",\n \"def list_1day_renu(self,fday,tday):\\n dayList = self._list_day(fday, tday)\\n return zip(dayList,[self._get_ndays_renu(d,1) for d in dayList])\",\n \"def extra_tasks_for_today(self):\\n localtz = tzlocal()\\n datetime_today = datetime.fromtimestamp(rospy.get_rostime().to_sec(), tz=localtz)\\n day_today = datetime_today.strftime(\\\"%A\\\")\\n date_today = datetime_today.date()\\n rospy.loginfo('Looking for daily tasks for %s, %s' % (day_today, date_today))\\n \\n eight_forty_five= time(8,45, tzinfo=localtz)\\n eleven_thirty= time(11,30, tzinfo=localtz)\\n fourteen_thirty=time(14,30, tzinfo=localtz)\\n seventeen_fifteen= time(17,15, tzinfo=localtz)\\n past_bedtime = time(23,59, tzinfo=localtz)\\n \\n # day_end = seventeen_fifteen\\n day_end = past_bedtime\\n\\n\\n\\n metric_wps=['WayPoint13', 'WayPoint18', 'WayPoint9','WayPoint11','WayPoint5','WayPoint3'] \\n object_learn_wps=['WayPoint13', 'WayPoint18', 'WayPoint9', 'WayPoint11'] \\n object_search_wps=['WayPoint1', 'WayPoint2', 'WayPoint3']\\n door_wps=['WayPoint7', 'WayPoint4']\\n \\n morning_start = eight_forty_five\\n morning_duration = delta_between(eleven_thirty, morning_start)\\n \\n lunch_start = eleven_thirty\\n lunch_duration = delta_between(fourteen_thirty, lunch_start)\\n\\n afternoon_start = fourteen_thirty\\n afternoon_duration = delta_between(day_end, afternoon_start)\\n\\n tasks = []\\n \\n #door checks at fixed times (to evaluate system ability to do stuff at corret times)\\n task=create_door_check_task(door_wps[0])\\n start_time=datetime.combine(date_today, time(10,30, tzinfo=localtz))\\n end_time = start_time+timedelta(seconds=30)\\n task.start_after=rospy.Time(unix_time(start_time))\\n task.end_before=rospy.Time(unix_time(end_time))\\n tasks.append(task)\\n \\n task=create_door_check_task(door_wps[0])\\n start_time=datetime.combine(date_today, time(13,30, tzinfo=localtz))\\n end_time = start_time+timedelta(seconds=30)\\n task.start_after=rospy.Time(unix_time(start_time))\\n task.end_before=rospy.Time(unix_time(end_time))\\n tasks.append(task)\\n \\n task=create_door_check_task(door_wps[0])\\n start_time=datetime.combine(date_today, time(16,30, tzinfo=localtz))\\n end_time = start_time+timedelta(seconds=30)\\n task.start_after=rospy.Time(unix_time(start_time))\\n task.end_before=rospy.Time(unix_time(end_time))\\n tasks.append(task)\\n \\n \\n #random tasks\\n for i in range(4):\\n #morning\\n task=create_metric_map_task(random.choice(metric_wps))\\n self.set_random_task_time(task, date_today, morning_start, morning_duration)\\n tasks.append(task)\\n \\n task=create_door_check_task(random.choice(door_wps))\\n self.set_random_task_time(task, date_today, morning_start, morning_duration)\\n tasks.append(task)\\n \\n if i<3:\\n task=create_object_learn_task(random.choice(object_learn_wps))\\n self.set_random_task_time(task, date_today, morning_start, morning_duration)\\n tasks.append(task)\\n \\n task=create_object_search_task(random.choice(object_search_wps))\\n self.set_random_task_time(task, date_today, morning_start, morning_duration)\\n tasks.append(task)\\n \\n #lunch (less tasks because we want the robot mostly learning people tracks)\\n if i<1:\\n task=create_metric_map_task(random.choice(metric_wps))\\n self.set_random_task_time(task, date_today, lunch_start, lunch_duration)\\n tasks.append(task)\\n \\n task=create_door_check_task(random.choice(door_wps))\\n self.set_random_task_time(task, date_today, lunch_start, lunch_duration)\\n tasks.append(task)\\n \\n task=create_object_learn_task(random.choice(object_learn_wps))\\n self.set_random_task_time(task, date_today, lunch_start, lunch_duration)\\n tasks.append(task)\\n \\n task=create_object_search_task(random.choice(object_search_wps))\\n self.set_random_task_time(task, date_today, lunch_start, lunch_duration)\\n tasks.append(task)\\n \\n \\n #afternoon\\n task=create_metric_map_task(random.choice(metric_wps))\\n self.set_random_task_time(task, date_today, afternoon_start, afternoon_duration)\\n tasks.append(task)\\n \\n task=create_door_check_task(random.choice(door_wps))\\n self.set_random_task_time(task, date_today, afternoon_start, afternoon_duration)\\n tasks.append(task)\\n \\n if i<3:\\n task=create_object_learn_task(random.choice(object_learn_wps))\\n self.set_random_task_time(task, date_today, afternoon_start, afternoon_duration)\\n tasks.append(task)\\n \\n task=create_object_search_task(random.choice(object_search_wps))\\n self.set_random_task_time(task, date_today, afternoon_start, afternoon_duration)\\n tasks.append(task)\\n return tasks\",\n \"def lambda_handler(event, content):\\n imap = email_startup()\\n status, messages = imap.select('Inbox')\\n days_old = input('Enter many days ago do you want to use as the cutoff?: ')\\n new_date = get_days_old(days_old)\\n messages = apply_filter(imap, new_date)\\n initial_unread = get_unread_count(imap)\\n print(f'Initial unread emails: {initial_unread}')\\n print(f'Emails to be filter: {len(messages)}')\\n a_pause = input('Continue by pressing enter.')\\n\\n print(f'Processing {len(messages)} unread emails from before {new_date}')\\n print(\\\"=\\\"*100)\\n process_messages(imap, messages)\\n print(\\\"=\\\"*100)\\n\\n # Determine results from script\\n post_unread = get_unread_count(imap)\\n print(f'Processed Emails: {initial_unread - post_unread}')\\n print(f'After processing, there are {post_unread} unread emails.')\\n\\n # close the connection and logout\\n imap.close()\\n imap.logout()\",\n \"def day(d):\\n\\t\\tx = db.cquery(\\\"day\\\",d)\\n\\t\\tprint \\\"Total:\\\", x[0]\\n\\t\\tf = raw_input(\\\"[L]ist [N]ew overview or [B]ack to home \\\").lower()\\n\\t\\tif f == \\\"l\\\":\\n\\t\\t\\tfor i in x[1]:\\n\\t\\t\\t\\tprint ui.statsid(), i[0], i[1], \\\" \\\", ui.statstimein(), i[2], ui.statstimeout(), i[3]\\n\\t\\t\\traw_input(\\\"[Enter] to go back to search\\\")\\n\\t\\t\\thome_stats()\\n\\t\\telif f == \\\"n\\\":\\n\\t\\t\\thome_stats()\\n\\t\\telif f == \\\"b\\\":\\n\\t\\t\\thome()\\n\\t\\telse:\\n\\t\\t\\tpass\",\n \"def ENTRY(entry_code):\\n\\tif check_user(entry_code) ==True:\\n\\t\\t###workday = Workday.objects.filter(date=get_time()).get()\\n\\t\\tenter_workday(entry_code)\",\n \"def lambda_handler(event, context):\\n # By default, treat the user request as coming from the America/New_York time zone.\\n os.environ['TZ'] = 'America/New_York'\\n time.tzset()\\n #logger.debug('event.bot.name={}'.format(event['bot']['name']))\\n\\n return dispatch(event)\",\n \"def function_to_execute(event, context):\\r\\n action = base64.b64decode(event['data']).decode('utf-8')\\r\\n\\r\\n if (action == \\\"calculate_exceedances_for_last_28_days\\\"):\\r\\n calculate_exceedances_for_last_28_days()\",\n \"async def dailytomorrow(self, ctx):\\n if ctx.invoked_subcommand is None:\\n await ctx.send_help(ctx.command)\",\n \"def tapahtumat_tanaan():\\n\\n events = get_events()\\n tanaan = datetime.datetime.today().isoformat()[:10]\\n out = []\\n for _, calendar_events in events.items():\\n out += [event for event in calendar_events if event[0] == tanaan]\\n\\n return out\",\n \"def fday(var, wrapper, message):\\n if var.PHASE != \\\"night\\\":\\n wrapper.pm(messages[\\\"not_nighttime\\\"])\\n else:\\n transition_day()\",\n \"def _DeleteEvent(self, event):\\n\\n self.cal_client.Delete(event.GetEditLink().href)\",\n \"async def afterHoursAutoPurge(self, ctx: Context):\",\n \"def send_today_reminder(self, request, pk=None):\\n try:\\n today_menu = Menu.objects.get(\\n menu_date=timezone.localtime(timezone.now()).date()\\n )\\n today_menu.send_today_menu_slack_each_user()\\n except Menu.DoesNotExist:\\n return Response({\\\"detail\\\": \\\"Not found.\\\"}, status=status.HTTP_404_NOT_FOUND)\\n return Response(\\n {\\\"detail\\\": \\\"Reminder sent successfully.\\\"}, status=status.HTTP_200_OK\\n )\",\n \"def lambda_handler(event, context):\\n # By default, treat the user request as coming from the Europe/Paris time zone.\\n os.environ['TZ'] = 'Europe/Paris'\\n time.tzset()\\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\\n\\n return dispatch(event)\",\n \"def formatday(self, day, weekday):\\n\\t\\tif day == 0:\\n\\t\\t\\treturn self.day_cell('noday',' ') # day outside month\\n\\t\\ttext = get_days_activity(self.pilot_id, self.year, self.month, day)\\n\\t\\tif date.today() == date(self.year, self.month, day): # mark today\\n\\t\\t\\tbody = '<a href=\\\"/FlightLog/add_sorties.html\\\">%s</a>' %day\\n\\t\\t\\treturn self.day_cell('today',body)\\n\\t\\tif date(self.year,self.month,day) in self.done_sorties: # mark a day with a done flight\\n\\t\\t\\tbody = '<a href=\\\"/FlightLog/delete_from_db?date=%d-%02d-%02d\\\">%s</a>' % (self.year,self.month,day,day)\\n\\t\\t\\treturn self.day_cell('done',body)\\n\\t\\tif date(self.year,self.month,day) in self.planned_sorties: # mark a day with a planned flight\\n\\t\\t\\tbody = '<a href=\\\"/FlightLog/delete_from_db?date=%d-%02d-%02d\\\">%s</a>' % (self.year,self.month,day,text)\\n\\t\\t\\treturn self.day_cell('planned',body)\\n\\t\\telse: # a non-special month day\\n\\t\\t\\tbody = '<a href=\\\"/FlightLog/calendar_add?date=%d-%02d-%02d\\\">%s</a>' % (self.year,self.month,day,day)\\n\\t\\t\\treturn self.day_cell('free',body)\",\n \"def test_return_day(self):\\n self.assertEqual(functions.return_day(1), \\\"Sunday\\\")\\n self.assertEqual(functions.return_day(2), \\\"Monday\\\")\\n self.assertEqual(functions.return_day(3), \\\"Tuesday\\\")\\n self.assertEqual(functions.return_day(4), \\\"Wednesday\\\")\\n self.assertEqual(functions.return_day(5), \\\"Thursday\\\")\\n self.assertEqual(functions.return_day(6), \\\"Friday\\\")\\n self.assertEqual(functions.return_day(41), None)\",\n \"def day(self):\\n try:\\n return self.schedule.day\\n except Schedule.DoesNotExist:\\n return None\",\n \"def test_remove_recurring_schedule(self):\\n pass\",\n \"def _(event):\\n # TODO\",\n \"def list_7day_renu(self,fday,tday):\\n dayList = self._list_day(fday, tday)\\n return zip(dayList,[self._get_ndays_renu(d,7) for d in dayList])\",\n \"def lambda_handler(event, context):\\n return dispatch(event)\",\n \"def event_remove(event_name:str):#maybe make a add events function\\n global removed_manually\\n global notification_index #always incremented so notifications can be removed\\n for y in range(len(Events_list)): # sorted in ascending order.\\n if removed_manually is False:#if the alrm has expired by its own ,check if it has extras selected and if so; display them:\\n if Events_list[y][5] == \\\"true\\\": #news\\n temp_notifi_list = news().copy()\\n for w in temp_notifi_list:\\n a = w[0]#news title\\n b = w[1]#link\\n notification_list.append(['\\\"{}\\\"'.format(a), \\\"not_alarm\\\",[str(notification_index),\\\"newslink\\\",'{}'.format(b)]])\\n notification_index+=1\\n temp_notifi_list = covid_data().copy()\\n for w in temp_notifi_list:\\n notification_list.append(['\\\"{}\\\"'.format(w), \\\"not_alarm\\\",[str(notification_index)]])\\n notification_index+=1\\n if Events_list[y][4] == \\\"true\\\":#weather\\n notification_list.append(['\\\"{}\\\"'.format(\\\"Todays Weather: \\\" + weather()), \\\"not_alarm\\\",[str(notification_index)]])\\n notification_index+=1\\n if Events_list[y][0] == event_name:\\n event_log(\\\"rescheduling alarm for next day....\\\",\\\"\\\")\\n if Events_list[y][3] == \\\"true\\\" and removed_manually is False:#if this is true the the event is set to be repeated\\n new_date = time.strftime('%Y-%m-%d %H:%M', time.localtime(convert_to_epoch(Events_list[y][1]) + 86400))\\n set_alarm(new_date, event_name, Events_list[y][2], Events_list[y][3],Events_list[y][4],Events_list[y][5])\\n notification_list.append(['\\\"{}\\\"'.format(event_name) + \\\"Has been rescheduled for next day\\\", \\\"alarm\\\", Events_list[y]])\\n speak_output(event_name + \\\"has been repeated for next day\\\")\\n else:#event is going to be removed\\n event_log(\\\"removing alarm....\\\",\\\"\\\")\\n removed_manually = False#changes the boolean so the function can go back to deafult state as this is global varibale\\n notification_list.append(['\\\"{}\\\"'.format(event_name) + \\\" is expired/deleted\\\", \\\"alarm\\\", Events_list[y]])\\n speak_output(event_name + \\\"has expired or been removed\\\")\\n Events_list.pop(y)#removes from list\\n temp_notifi_list = []\\n write()\\n break\",\n \"def lambda_handler(event, context):\\n return\",\n \"def lambda_handler(event, context):\\n return\",\n \"def lambda_method(self,t): \\n return 5*math.sin(2*math.pi*1*t) # I don't see the value of 1 here but this is how lamda is defined in the exercise.\",\n \"def tickets(number, day, premium_seating):\\n #fill in your code here. \\n return 0.0\",\n \"def get_rest_days(self, cr, uid, employee_id, dt, context=None):\\n\\n day = dt.strftime(OE_DTFORMAT)\\n ids = self.search(\\n cr, uid, [\\n ('employee_id', '=', employee_id),\\n ('date_start', '<=', day),\\n ('date_end', '>=', day),\\n ], context=context)\\n if len(ids) == 0:\\n return None\\n elif len(ids) > 1:\\n raise orm.except_orm(_('Programming Error'), _(\\n 'Employee has a scheduled date in more than one schedule.'))\\n\\n # If the day is in the middle of the week get the start of the week\\n if dt.weekday() == 0:\\n week_start = dt.strftime(OE_DFORMAT)\\n else:\\n week_start = (\\n dt + relativedelta(days=-dt.weekday())).strftime(OE_DFORMAT)\\n\\n return self.get_rest_days_by_id(\\n cr, uid, ids[0], week_start, context=context\\n )\",\n \"def reschedule():\\n if not schedule.empty():\\n purge_events() \\n\\n today_s = tuple_to_str(time.localtime()[:3])\\n\\n # first check if exception entry exist for today in datemap\\n if today_s in datemap:\\n \\tschedule_day(datemap[today_s])\\n else:\\n # otherwise schedule it as normal weekday\\n schedule_day(days[time.strftime(\\\"%A\\\")])\",\n \"def _list_day(self, fday=None, tday=None):\\n date_fday = fday or date.today()\\n date_tday = tday or date.today()\\n days = (date_tday - date_fday).days+1\\n dayList = [date_fday+timedelta(v) for v in range(days)] \\n return dayList\",\n \"def day_to_day(self):\\n while True:\\n yield 0\",\n \"def lambda_handler(event, context):\\n # By default, treat the user request as coming from the America/New_York time zone.\\n os.environ['TZ'] = 'America/New_York'\\n time.tzset()\\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\\n\\n return dispatch(event)\",\n \"def lambda_handler(event, context):\\n # By default, treat the user request as coming from the America/New_York time zone.\\n os.environ['TZ'] = 'America/New_York'\\n time.tzset()\\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\\n\\n return dispatch(event)\",\n \"def lambda_handler(event, context):\\n # By default, treat the user request as coming from the America/New_York time zone.\\n os.environ['TZ'] = 'America/New_York'\\n time.tzset()\\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\\n\\n return dispatch(event)\",\n \"def remove_bday_callback(id: int) -> None:\\n\\n BOT_GLOBAL.loop.create_task(remove_bday(id))\",\n \"def test_handle_weather_message_calls_tomorrow(self):\\n pass\",\n \"def run_daily_hygienist(self):\\n self.remove_priorities_from_all_not_due_today()\",\n \"def cron_update_yesterday_stats(_request):\\n today = datetime.datetime.utcnow().date()\\n day = str(today - datetime.timedelta(days=1))\\n tasks = [day, '30', 'monthly']\\n taskqueue.add(\\n url=reverse(task_update_stats),\\n params={'tasks': json.dumps(tasks), 'date': str(today)},\\n queue_name='update-stats')\\n out = 'Triggered tasks for day %s: %s' % (day, ', '.join(tasks))\\n logging.info(out)\\n return HttpTextResponse(out)\",\n \"def timed_handler(userdata, *args):\\n\\t\\tfuzzy = False\\n\\t\\t\\n\\t\\tif args[3] == \\\"exact\\\":\\n\\t\\t\\tfuzzy = False\\n\\t\\telif args[3] == \\\"fuzzy\\\":\\n\\t\\t\\tfuzzy = True\\n\\t\\telse:\\n\\t\\t\\traise FormatError(\\\"Expected 'fuzzy' or 'exact' but found '\\\" + args[3] + \\\"' instead.\\\")\\n\\t\\t\\n\\t\\tevent = Event.create_once(args[4], fuzzy, args[2])\\n\\t\\t\\n\\t\\tif args[0] == \\\"add\\\":\\n\\t\\t\\tdevice.schedule(userdata, args[1], event)\\n\\t\\telif args[0] == \\\"del\\\":\\n\\t\\t\\tdevice.unschedule(userdata, args[1], event)\\n\\t\\telse:\\n\\t\\t\\traise FormatError(\\\"Expected 'add' or 'del' but found '\\\" + args[0] + \\\"' instead.\\\")\",\n \"def lambda_handler(event, context):\\r\\n # By default, treat the user request as coming from the America/New_York time zone.\\r\\n os.environ['TZ'] = 'America/New_York'\\r\\n time.tzset()\\r\\n logger.debug('event.bot.name={}'.format(event['bot']['name']))\\r\\n\\r\\n return dispatch(event)\",\n \"def yesterday():\\n return datetime.today() - timedelta(1)\",\n \"def logDayDetails(self):\\n console().info(\\\"Today is {}.\\\".format(self.today.strftime(DATE_FMT)))\\n hours = self.contractDetails.tradingHours.split(\\\";\\\")[0].split(\\\":\\\")[1]\\n console().info(\\\"Today's Trading Hours Are: {}\\\".format(hours))\\n if self.normalDay:\\n console().info(\\\"Today is a Valid Day for Trading\\\")\\n else:\\n console().info(\\\"Today is not a Valid Trading Day. Sleeping Until Tomorrow\\\")\",\n \"def _(event):\\n pass\",\n \"def _(event):\\n pass\",\n \"def _(event):\\n pass\",\n \"def trash(request):\\n return Task.objects.select_related('project').filter(user=request.user, folder='trash')\",\n \"def cron_on_off(key, item, dst):\\n\\n action = \\\"off\\\"\\n active = \\\"\\\"\\n days = item['days']\\n nexaid = item['nexaid']\\n\\n if re.match(r'^on', key):\\n action = \\\"on\\\"\\n\\n if item['_active'] is False:\\n active = \\\"# \\\"\\n\\n if days == \\\"1,2,3,4,5\\\":\\n days = \\\"1-5\\\"\\n\\n if days == \\\"0,1,2,3,4,5,6\\\":\\n days = \\\"*\\\"\\n\\n # Any clock defined ?\\n\\n if item[key]:\\n clock = item[key].split(':')\\n\\n line = \\\"{0}{1:0>2} {2:0>2} * * {3} pi /home/pi/rfx-commands/commands/cmd-to-nexa.sh {4} {5}\\\".format(\\n active,\\n clock[1],\\n clock[0],\\n days,\\n nexaid,\\n action\\n )\\n\\n add_item(item['name'], {'line': line, 'hour': int(clock[0]), 'minute': int(clock[1])}, dst)\\n\\n # Any sunset defined ?\\n\\n if item['sunset'] is not None:\\n sunset = item['_sunriseset']['sunset']\\n offset = sunset + datetime.timedelta(minutes=item['sunset'])\\n\\n comment = \\\"Sunset {0:0>2}:{1:0>2} {2} min\\\".format(sunset.hour, sunset.minute, item['sunset'])\\n line = \\\"{0}{1:0>2} {2:0>2} * * {3} pi /home/pi/rfx-commands/commands/cmd-to-nexa.sh {4} {5} # {6}\\\".format(\\n active,\\n offset.minute,\\n offset.hour,\\n days,\\n nexaid,\\n \\\"on\\\",\\n comment\\n )\\n\\n add_item(item['name'], {'line': line, 'hour': int(offset.hour), 'minute': int(offset.minute)}, dst)\\n\\n # Reset this, We only want one per row\\n\\n item['sunset'] = None\",\n \"def getOneDay(self,day_number=0):\\n return self.event_time_sequence[day_number]\",\n \"def recurrent_handler(userdata, *args):\\n\\t\\tfuzzy = False\\n\\t\\t\\n\\t\\tif args[5] == \\\"exact\\\":\\n\\t\\t\\tfuzzy = False\\n\\t\\telif args[5] == \\\"fuzzy\\\":\\n\\t\\t\\tfuzzy = True\\n\\t\\telse:\\n\\t\\t\\traise FormatError(\\\"Expected 'fuzzy' or 'exact' but found '\\\" + args[5] + \\\"' instead.\\\")\\n\\t\\t\\n\\t\\tevent = Event.create_recurrent(args[6], fuzzy, args[2], args[3], args[4])\\n\\t\\t\\n\\t\\tif args[0] == \\\"add\\\":\\n\\t\\t\\tdevice.schedule(userdata, args[1], event)\\n\\t\\telif args[0] == \\\"del\\\":\\n\\t\\t\\tdevice.unschedule(userdata, args[1], event)\\n\\t\\telse:\\n\\t\\t\\traise FormatError(\\\"Expected 'add' or 'del' but found '\\\" + args[0] + \\\"' instead.\\\")\",\n \"def todo_added(name, description):\",\n \"def TODAY():\\n return datetime.date.today()\",\n \"def delete_haikus_unposted_timedelta(cls, db_session, days: float = None) -> list:\\n if days is not None:\\n ts_end = datetime.now(tz=timezone.utc) - timedelta(days=days)\\n try:\\n logger.info(f\\\"Deleting unposted haikus older than {days} days\\\")\\n delete_q = (\\n cls.__table__.delete()\\n .where(cls.created_at < ts_end)\\n .where(cls.date_posted == None) # noqa: E711\\n )\\n\\n db_session.execute(delete_q)\\n db_session.commit()\\n except Exception as e:\\n logger.warning(f\\\"Exception when deleting old unposted haikus: {e}\\\")\\n db_session.rollback()\",\n \"def _get_current_week_entries(today, user):\\n some_day = today + timedelta(days=1)\\n monday_of_week = some_day - timedelta(days=(some_day.isocalendar()[2] - 1))\\n sunday_of_week = monday_of_week + timedelta(days=6)\\n weekevents = TimeEntry.objects.filter(\\n booking_date__gte=monday_of_week, booking_date__lt=sunday_of_week, user=user\\n )\\n return weekevents\",\n \"def _on_articles_trash(self, evt=None):\\n \\n # get selected articles\\n articles = self._articles_view.GetSelectedArticles()\\n if not articles:\\n return\\n \\n # update library\\n self._library.trash(articles, True)\\n \\n # refresh collections view\\n self._collections_view.UpdateCounts()\\n \\n # refresh articles view\\n self._articles_view.ShowArticles()\",\n \"def weekly():\",\n \"def update_timesheet(item):\\n\\tj=json.loads(item)\\n\\tprint(\\\"-----------------------garffff---------------------\\\")\\n\\tnew_employee=None;\\n\\ttimesheet=frappe.get_doc(\\\"Time Sheet\\\",j[\\\"name\\\"])\\n\\tjarray=[]\\n\\tfor passed_employee in j['employees']:\\n\\t\\tif 'new' in passed_employee.keys():\\n\\t\\t\\t#create employee\\n\\t\\t\\tnew_employee=frappe.get_doc({\\n\\t\\t\\t\\t\\\"doctype\\\":\\\"employee_link_with_time\\\",\\n\\t\\t\\t\\t\\\"employee\\\":passed_employee['employee']\\n\\t\\t\\t});\\n\\n\\t\\tjarray.append(passed_employee['employee']);\\n\\t\\tfor employee in timesheet.employees:\\n\\t\\t\\tif passed_employee[\\\"employee\\\"]==employee.employee:\\n\\t\\t\\t\\tif \\\"start\\\" in passed_employee:\\n\\t\\t\\t\\t\\temployee.start=passed_employee[\\\"start\\\"]\\n\\t\\t\\t\\tif \\\"end\\\" in passed_employee:\\n\\t\\t\\t\\t\\temployee.end=passed_employee[\\\"end\\\"];\\n\\tforRemove=[]\\n\\tfor employee_container in timesheet.employees:\\n\\t\\tif employee_container.employee not in jarray:\\n\\t\\t\\tforRemove.append(employee_container)\\n\\tprint(\\\"___________REMOVE______________\\\")\\n\\tprint(forRemove);\\n\\tif forRemove:\\n\\t\\tfor remove in forRemove:\\n\\t\\t\\ttimesheet.employees.remove(remove)\\n\\n\\tif new_employee is not None:\\n\\t\\ttimesheet.append(\\\"employees\\\",new_employee)\\n\\n\\t#handel status\\n\\ttimesheet.status=j[\\\"status\\\"]\\n\\ttimesheet.save()\\n\\treturn frappe.get_doc(\\\"Time Sheet\\\",j[\\\"name\\\"])\",\n \"def insert_day():\\n analytics.insert_day(6)\",\n \"def trayagent(forwho,trayclickedfunction):\\r\\n\\r\\n tray = QSystemTrayIcon(forwho.windowIcon(), forwho)\\r\\n forwho.connect(tray,SIGNAL(\\\"activated(QSystemTrayIcon::ActivationReason)\\\"),trayclickedfunction)\\r\\n tray.show()\\r\\n return tray\",\n \"def weekday(day):\\n return (day % 7) - 1\",\n \"def refilter_previous_rts():\\n rtlist = get_list_of_rts()\\n if rtlist == None or len(rtlist) == 0:\\n return\\n for tweet in rtlist:\\n if not filter_tweet(tweet['retweeted_status']):\\n print(\\\"Previous tweet does not pass the filter anymore !\\\")\\n print(\\\"@%s (%s) : %s\\\" % (\\n tweet['retweeted_status']['user']['screen_name'],\\n tweet['retweeted_status']['source'],\\n tweet['retweeted_status']['text']))\\n answer = raw_input(\\\"Delete ? [y/N] \\\")\\n if answer == 'y':\\n delete_tweet(tweet['id_str'])\",\n \"def runs_today(self,s_id,day):\\n if self.schedule_keys[s_id][day]==1:\\n return True\\n else:\\n return False\",\n \"def unscheduleFirst(self, runnable):\\n for evt in self.store.query(TimedEvent, TimedEvent.runnable == runnable, sort=TimedEvent.time.ascending):\\n evt.deleteFromStore()\\n break\",\n \"def do(self):\\n self.message += 'Searching for unpayed loans \\\\n'\\n today = dt.now()\\n tomorrow = today + timedelta(days=1)\\n\\n expired = Loan.objects.filter(due_date=today)\\n almost = Loan.objects.filter(due_date=tomorrow)\\n\\n self.charge(expired)\\n self.notify(almost)\\n\\n self.log_success()\",\n \"def menu_python_daily(self, event=None):\\n self.link('http://www.pythonware.com/daily/')\",\n \"def to_be_deleted(self):\\n return self.filter(start__lte=timezone.now() - datetime.timedelta(days=1))\",\n \"def test_extraction(self):\\n thing = ThingThatMovesAround(store=self.accountStore,\\n superValue=self.IMPORTANT_VALUE)\\n self.ss.schedule(thing, Time() + datetime.timedelta(days=1))\\n self.test_noTimedEventsExtraction()\",\n \"def day_from_moment(cls, tee):\\n return quotient(cls.lunar_phase(tee), 12) + 1\",\n \"def returnEvent(self):\\r\\n item = self.__agenda.pop(1)\\r\\n return item[1]\",\n \"def get_rest_days(self, cr, uid, t_id, context=None):\\n\\n tpl = self.browse(cr, uid, t_id, context=context)\\n if tpl.restday_ids:\\n res = [rd.sequence for rd in tpl.restday_ids]\\n else:\\n weekdays = ['0', '1', '2', '3', '4', '5', '6']\\n scheddays = []\\n scheddays = [\\n wt.dayofweek\\n for wt in tpl.worktime_ids\\n if wt.dayofweek not in scheddays\\n ]\\n res = [int(d) for d in weekdays if d not in scheddays]\\n # If there are no work days return nothing instead of *ALL* the\\n # days in the week\\n if len(res) == 7:\\n res = []\\n\\n return res\",\n \"def lambda_handler(event, context):\\n try:\\n aq = Aquifer()\\n aq.run()\\n\\n return \\\"Completed\\\"\\n\\n except (Exception, KeyboardInterrupt) as e:\\n return \\\"Error occurred\\\"\",\n \"def do_rrt(self, arg):\\n self.do_timesheet('report extend track today')\",\n \"def clickedCell(self, row, col) ->QDate:\\n itemWidget = self.table.cellWidget(row, col)\\n data = itemWidget.data\\n dataMonth = data.month() - 1\\n dataYear = data.year()\\n self.currentDate = data\\n # print('cell clicked flags: ', itemWidget.dictFlags)\\n # print('cell clicked flags currentDate: ', data)\\n # print('cell clicked flags item date: ', itemWidget.data)\\n if data not in self.daysInTheMonth:\\n if dataYear > self.currentYear:\\n self.bot_next.click()\\n elif dataYear < self.currentYear:\\n self.bot_prev.click()\\n elif dataMonth > self.indexMonth:\\n self.bot_next.click()\\n else:\\n self.bot_prev.click()\\n self.singleClicked.emit(data)\\n return data\",\n \"def process_IN_DELETE_SELF(self, event):\",\n \"def schedule_callback(request):\\n print \\\"In schedule function\\\"\\n print request\\n\\n context = request['context']\\n try:\\n context['pincode'] = first_entity_value(request['context']['entities'], 'phone_number')\\n except:\\n context['pincode'] = None\\n\\n schedule_details = ['Pincode Network Issues']\",\n \"def check_market_status():\\n # today = datetime.datetime.now(pytz.timezone('America/New_York')).date()\\n today_utc = pd.to_datetime('now').date()\\n ndq = mcal.get_calendar('NASDAQ')\\n open_days = ndq.schedule(start_date=today_utc - pd.Timedelta('10 days'), end_date=today_utc)\\n if today_utc in open_days.index:\\n return open_days\\n else:\\n return None\",\n \"def clean_expired_task():\\n day_ago = datetime.datetime.now(pytz.timezone(\\\"UTC\\\")) - datetime.timedelta(days=ASYNC_EXPORT_FILE_EXPIRED_DAYS)\\n # 获取过期的内网下载文件\\n expired_task_list = AsyncTask.objects.filter(created_at__lt=day_ago, is_clean=False)\\n # nfs文件需要进行定期清理操作\\n storage_type = FeatureToggleObject.toggle(FEATURE_ASYNC_EXPORT_COMMON).feature_config.get(\\n FEATURE_ASYNC_EXPORT_STORAGE_TYPE\\n )\\n\\n if storage_type or storage_type == RemoteStorageType.NFS.value:\\n # 删除NFS文件\\n for expired_task in expired_task_list:\\n target_file_dir = os.path.join(settings.EXTRACT_SAAS_STORE_DIR, expired_task.file_name)\\n if os.path.isfile(target_file_dir):\\n os.remove(os.path.abspath(target_file_dir))\\n expired_task.is_clean = True\\n expired_task.save()\",\n \"def _remove_direct_alerts(self, cr, uid, ids, context=None):\\n\\n if isinstance(ids, (int, long)):\\n ids = [ids]\\n\\n alert_obj = self.pool.get('hr.schedule.alert')\\n\\n # Remove alerts directly attached to these schedule details\\n #\\n alert_ids = []\\n scheds = []\\n sched_keys = []\\n for sched_detail in self.browse(cr, uid, ids, context=context):\\n\\n [alert_ids.append(alert.id) for alert in sched_detail.alert_ids]\\n\\n # Hmm, creation of this record triggers a workflow action that\\n # tries to write to it. But it seems that computed fields aren't\\n # available at this stage. So, use a fallback and compute the day\\n # ourselves.\\n day = sched_detail.day\\n if not sched_detail.day:\\n day = time.strftime('%Y-%m-%d', time.strptime(\\n sched_detail.date_start, '%Y-%m-%d %H:%M:%S'))\\n key = str(sched_detail.schedule_id.employee_id.id) + day\\n if key not in sched_keys:\\n scheds.append((sched_detail.schedule_id.employee_id.id, day))\\n sched_keys.append(key)\\n\\n if len(alert_ids) > 0:\\n alert_obj.unlink(cr, uid, alert_ids, context=context)\\n\\n return scheds\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6090012","0.54774374","0.5461324","0.5409436","0.53761524","0.53742063","0.5331051","0.5287995","0.51901144","0.5178555","0.5161938","0.5114322","0.5079114","0.50751776","0.5070596","0.50560266","0.50401527","0.50214356","0.49688548","0.49537805","0.49516058","0.4899427","0.48943612","0.48820135","0.48690954","0.48539737","0.48517373","0.48436627","0.48372668","0.48182443","0.48042843","0.48013556","0.47969347","0.47822627","0.4771371","0.47447702","0.47368485","0.47328302","0.4722177","0.47047505","0.4704028","0.46834898","0.46632865","0.46589568","0.46296474","0.46277222","0.46277204","0.46277204","0.46231496","0.4622089","0.46183175","0.46094167","0.46051827","0.45955887","0.4594397","0.4594397","0.4594397","0.45915627","0.4587376","0.45805058","0.45752862","0.45723793","0.45557705","0.45430756","0.45381805","0.45345157","0.45345157","0.45345157","0.45338464","0.4526484","0.45260155","0.45240182","0.45239833","0.45191807","0.4513612","0.45128986","0.4512447","0.4508267","0.45040292","0.4491627","0.44893488","0.4478578","0.44709873","0.44683683","0.4466551","0.4465711","0.44640672","0.44605175","0.44552332","0.4454348","0.44500124","0.44452533","0.4439769","0.44354004","0.4433531","0.44315767","0.44304907","0.4425423","0.44253436","0.44225514"],"string":"[\n \"0.6090012\",\n \"0.54774374\",\n \"0.5461324\",\n \"0.5409436\",\n \"0.53761524\",\n \"0.53742063\",\n \"0.5331051\",\n \"0.5287995\",\n \"0.51901144\",\n \"0.5178555\",\n \"0.5161938\",\n \"0.5114322\",\n \"0.5079114\",\n \"0.50751776\",\n \"0.5070596\",\n \"0.50560266\",\n \"0.50401527\",\n \"0.50214356\",\n \"0.49688548\",\n \"0.49537805\",\n \"0.49516058\",\n \"0.4899427\",\n \"0.48943612\",\n \"0.48820135\",\n \"0.48690954\",\n \"0.48539737\",\n \"0.48517373\",\n \"0.48436627\",\n \"0.48372668\",\n \"0.48182443\",\n \"0.48042843\",\n \"0.48013556\",\n \"0.47969347\",\n \"0.47822627\",\n \"0.4771371\",\n \"0.47447702\",\n \"0.47368485\",\n \"0.47328302\",\n \"0.4722177\",\n \"0.47047505\",\n \"0.4704028\",\n \"0.46834898\",\n \"0.46632865\",\n \"0.46589568\",\n \"0.46296474\",\n \"0.46277222\",\n \"0.46277204\",\n \"0.46277204\",\n \"0.46231496\",\n \"0.4622089\",\n \"0.46183175\",\n \"0.46094167\",\n \"0.46051827\",\n \"0.45955887\",\n \"0.4594397\",\n \"0.4594397\",\n \"0.4594397\",\n \"0.45915627\",\n \"0.4587376\",\n \"0.45805058\",\n \"0.45752862\",\n \"0.45723793\",\n \"0.45557705\",\n \"0.45430756\",\n \"0.45381805\",\n \"0.45345157\",\n \"0.45345157\",\n \"0.45345157\",\n \"0.45338464\",\n \"0.4526484\",\n \"0.45260155\",\n \"0.45240182\",\n \"0.45239833\",\n \"0.45191807\",\n \"0.4513612\",\n \"0.45128986\",\n \"0.4512447\",\n \"0.4508267\",\n \"0.45040292\",\n \"0.4491627\",\n \"0.44893488\",\n \"0.4478578\",\n \"0.44709873\",\n \"0.44683683\",\n \"0.4466551\",\n \"0.4465711\",\n \"0.44640672\",\n \"0.44605175\",\n \"0.44552332\",\n \"0.4454348\",\n \"0.44500124\",\n \"0.44452533\",\n \"0.4439769\",\n \"0.44354004\",\n \"0.4433531\",\n \"0.44315767\",\n \"0.44304907\",\n \"0.4425423\",\n \"0.44253436\",\n \"0.44225514\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74920136"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12388,"cells":{"query":{"kind":"string","value":"Convert an image from LAB color space to XYZ color space"},"document":{"kind":"string","value":"def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\n l, a, b = tf.unstack(image, axis=-1)\n\n var_y = (l + 16) / 116\n var_x = a / 500 + var_y\n var_z = var_y - b / 200\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\n (var_x - 16 / 116) / 7.787)\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\n (var_y - 16 / 116) / 7.787)\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\n (var_z - 16 / 116) / 7.787)\n\n refx = 95.047\n refy = 100.00\n ref_z = 108.883\n\n x = var_x * refx\n y = var_y * refy\n z = var_z * ref_z\n xyz_image = tf.stack([x, y, z], axis=-1)\n return xyz_image"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def Lab_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n xyz_y = (cobj.lab_l + 16.0) / 116.0\r\n xyz_x = cobj.lab_a / 500.0 + xyz_y\r\n xyz_z = xyz_y - cobj.lab_b / 200.0\r\n \r\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\r\n xyz_y = math.pow(xyz_y, 3)\r\n else:\r\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\r\n\r\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\r\n xyz_x = math.pow(xyz_x, 3)\r\n else:\r\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\r\n \r\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\r\n xyz_z = math.pow(xyz_z, 3)\r\n else:\r\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\r\n \r\n xyz_x = (illum[\"X\"] * xyz_x)\r\n xyz_y = (illum[\"Y\"] * xyz_y)\r\n xyz_z = (illum[\"Z\"] * xyz_z)\r\n \r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)","def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\n r, g, b = tf.unstack(image, axis=-1)\n var_r = r / 255\n var_g = g / 255\n var_b = b / 255\n\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\n var_r / 12.92)\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\n var_g / 12.92)\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\n var_b / 12.92)\n var_r = var_r * 100\n var_g = var_g * 100\n var_b = var_b * 100\n\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\n\n image_xyz = tf.stack([x, y, z], axis=-1)\n return image_xyz","def example_rgb_to_xyz():\r\n\r\n print(\"=== RGB Example: RGB->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n rgb = sRGBColor(120, 130, 140)\r\n # Show a string representation.\r\n print(rgb)\r\n # Convert RGB to XYZ using a D50 illuminant.\r\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def XYZ_to_Lab(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n temp_x = cobj.xyz_x / illum[\"X\"]\r\n temp_y = cobj.xyz_y / illum[\"Y\"]\r\n temp_z = cobj.xyz_z / illum[\"Z\"]\r\n \r\n if temp_x > color_constants.CIE_E:\r\n temp_x = math.pow(temp_x, (1.0 / 3.0))\r\n else:\r\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \r\n\r\n if temp_y > color_constants.CIE_E:\r\n temp_y = math.pow(temp_y, (1.0 / 3.0))\r\n else:\r\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\r\n \r\n if temp_z > color_constants.CIE_E:\r\n temp_z = math.pow(temp_z, (1.0 / 3.0))\r\n else:\r\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\r\n \r\n lab_l = (116.0 * temp_y) - 16.0\r\n lab_a = 500.0 * (temp_x - temp_y)\r\n lab_b = 200.0 * (temp_y - temp_z)\r\n return LabColor(\r\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)","def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\n xyz = rgb_to_xyz(image)\n lab_image = xyz_to_lab(xyz)\n return lab_image","def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\n xyz = lab_to_xyz(image)\n rgb_image = xyz_to_rgb(xyz)\n return rgb_image","def example_lab_to_xyz():\r\n\r\n print(\"=== Simple Example: Lab->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.22)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n xyz = convert_color(lab, XYZColor)\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\n\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)","def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n\n refx = 95.047\n refy = 100.00\n refz = 108.883\n\n var_x = x / refx\n var_y = y / refy\n var_z = z / refz\n\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\n (7.787 * var_x) + (16 / 116))\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\n (7.787 * var_y) + (16 / 116))\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\n (7.787 * var_z) + (16 / 116))\n\n l = (116 * var_y) - 16\n a = 500 * (var_x - var_y)\n b = 200 * (var_y - var_z)\n lab_image = tf.stack([l, a, b], axis=-1)\n return lab_image","def rgb_to_xyz(rgb_color):\n\n r = (rgb_color[0] / 255)\n g = (rgb_color[1] / 255)\n b = (rgb_color[2] / 255)\n\n if r > 0.04045:\n r = ((r + 0.055) / 1.055) ** 2.4\n else:\n r = r / 12.92\n\n if g > 0.04045:\n g = ((g + 0.055) / 1.055) ** 2.4\n else:\n g = g / 12.92\n\n if b > 0.04045:\n b = ((b + 0.055) / 1.055) ** 2.4\n else:\n b = b / 12.92\n\n r = r * 100\n g = g * 100\n b = b * 100\n x = (r * 0.4124) + (g * 0.3576) + (b * 0.1805)\n y = (r * 0.2126) + (g * 0.7152) + (b * 0.0722)\n z = (r * 0.0193) + (g * 0.1192) + (b * 0.9505)\n\n return x, y, z","def RGB_to_XYZ(RGB,\n illuminant_RGB,\n illuminant_XYZ,\n RGB_to_XYZ_matrix,\n chromatic_adaptation_transform='CAT02',\n decoding_cctf=None):\n\n if decoding_cctf is not None:\n RGB = decoding_cctf(RGB)\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n transform=chromatic_adaptation_transform)\n\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\n\n XYZ_a = dot_vector(M, XYZ)\n\n return XYZ_a","def RGB_to_XYZ_matrix(self):\n\n if not self._use_derived_RGB_to_XYZ_matrix:\n return self._RGB_to_XYZ_matrix\n else:\n return self._derived_RGB_to_XYZ_matrix","def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n var_x = x / 100\n var_y = y / 100\n var_z = z / 100\n\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\n\n var_r = tf.where(var_r > 0.0031308,\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\n 12.92 * var_r)\n var_g = tf.where(var_g > 0.0031308,\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\n 12.92 * var_g)\n var_b = tf.where(var_b > 0.0031308,\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\n 12.92 * var_b)\n r = var_r * 255\n g = var_g * 255\n b = var_b * 255\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\n return rgb_image","def hlab_to_xyz(hlab: Vector, white: VectorLike) -> Vector:\n\n xn, yn, zn = alg.multiply(util.xy_to_xyz(white), 100, dims=alg.D1_SC)\n ka = CKA * alg.nth_root(xn / CXN, 2)\n kb = CKB * alg.nth_root(zn / CZN, 2)\n l, a, b = hlab\n l /= 100\n y = (l ** 2) * yn\n x = (((a * l) / ka) + (y / yn)) * xn\n z = (((b * l) / kb) - (y / yn)) * -zn\n return alg.divide([x, y, z], 100, dims=alg.D1_SC)","def lab_to_rgb(img):\n new_img = np.zeros((256, 256, 3))\n for i in range(len(img)):\n for j in range(len(img[i])):\n pix = img[i, j]\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\n new_img = color.lab2rgb(new_img) * 255\n new_img = new_img.astype('uint8')\n return new_img","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def rgb2alpha(img):\n\t### First of all we need the size of our picture to make the transforms\n\n\tx = len(img) ; y = len(img[0])\n\talpha = np.full((x, y, 3), 0, dtype = float)\t\t## This will be the transformed image\n\n\t### Now we gotta access each pixel of the picture\n\n\tfor i, vi in enumerate(img):\n\t\tfor j, px in enumerate(vi):\n\t\t\t### There we are\n\n\t\t\t# Step 1 : LMS transform, for that we use r_l\n\n\t\t\talpha[i][j] = np.matmul(r_l, px)\n\n\t\t\t# Step 2 : log em all (decimal log)\n\n\t\t\talpha[i][j][0] = log(alpha[i][j][0])\n\t\t\talpha[i][j][1] = log(alpha[i][j][1])\n\t\t\talpha[i][j][2] = log(alpha[i][j][2])\n\n\t\t\t# Step 3 : l alpha beta transform, by using l_a\n\n\t\t\talpha[i][j] = np.matmul(l_a, alpha[i][j])\n\n\treturn alpha","def Luv_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n # Without Light, there is no color. Short-circuit this and avoid some\r\n # zero division errors in the var_a_frac calculation.\r\n if cobj.luv_l <= 0.0:\r\n xyz_x = 0.0\r\n xyz_y = 0.0\r\n xyz_z = 0.0\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\r\n\r\n # Various variables used throughout the conversion.\r\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\r\n u_sub_0 = (4.0 * illum[\"X\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n v_sub_0 = (9.0 * illum[\"Y\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\r\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\r\n\r\n # Y-coordinate calculations.\r\n if cobj.luv_l > cie_k_times_e:\r\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\r\n else:\r\n xyz_y = cobj.luv_l / color_constants.CIE_K\r\n\r\n # X-coordinate calculation.\r\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\r\n # Z-coordinate calculation.\r\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\r\n\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)","def linear_rgb2xyz(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(xyz_from_rgb, arr)","def rgb_to_xy(red, green, blue):\n\n # gamma correction\n red = pow((red + 0.055) / (1.0 + 0.055), 2.4) if red > 0.04045 else (red / 12.92)\n green = pow((green + 0.055) / (1.0 + 0.055), 2.4) if green > 0.04045 else (green / 12.92)\n blue = pow((blue + 0.055) / (1.0 + 0.055), 2.4) if blue > 0.04045 else (blue / 12.92)\n\n # convert rgb to xyz\n x = red * 0.649926 + green * 0.103455 + blue * 0.197109\n y = red * 0.234327 + green * 0.743075 + blue * 0.022598\n z = green * 0.053077 + blue * 1.035763\n\n # convert xyz to xy\n x = x / (x + y + z)\n y = y / (x + y + z)\n\n # TODO check color gamut if known\n \n return [x, y]","def RGB_to_XYZ_matrix(self, value):\n\n if value is not None:\n value = np.asarray(value)\n self._RGB_to_XYZ_matrix = value","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def sRGBFromLab(lab):\n return xyzTosRGB(labToXYZ(lab, [0.9504559, 1, 1.089058]))","def convert_color(image, color_space):\n out_image = None\n if color_space != 'RGB':\n if color_space == 'HSV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\n elif color_space == 'LUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\n elif color_space == 'HLS':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\n elif color_space == 'YUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\n elif color_space == 'YCrCb':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\n else:\n out_image = np.copy(image)\n return out_image","def _convert_to_yolo_img(self, img):\n\n img = img / 255.0\n h, w, c = img.shape\n img = img.transpose(2, 0, 1)\n outimg = make_image(w, h, c)\n img = img.reshape((w*h*c))\n data = c_array(c_float, img)\n outimg.data = data\n rgbgr_image(outimg)\n return outimg","def lab_to_rgb(img_l, img_ab):\n lab = np.empty([*img_l.shape[0:2], 3])\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\n lab[:, :, 1:] = img_ab * 127\n return color.lab2rgb(lab)","def test_srgb_conversion_to_xyz_d65(self):\r\n\r\n xyz = convert_color(self.color, XYZColor)\r\n self.assertColorMatch(xyz, XYZColor(0.294, 0.457, 0.103))","def test_srgb_conversion_to_xyz_d50(self):\r\n\r\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))","def rgb2Lab(rgbvalue):\r\n RGB2Lab_Matrix = np.array([[0.412453, 0.357580, 0.180423],\r\n [0.212671, 0.715160, 0.072169],\r\n [0.019334, 0.119193, 0.950227]])\r\n R = rgbvalue[0]\r\n G = rgbvalue[1]\r\n B = rgbvalue[2]\r\n gammaR = gamma(R / 255.0)\r\n gammaG = gamma(G / 255.0)\r\n gammaB = gamma(B / 255.0)\r\n RGBvalue = np.array([gammaR, gammaG, gammaB])\r\n RGBvalue = RGBvalue.reshape(3, 1)\r\n XYZvalue = np.dot(RGB2Lab_Matrix, RGBvalue)\r\n assert XYZvalue.shape == (3, 1)\r\n correction = np.array([[1.0 / 0.950456, 1.0, 1.0 / 1.088754]]).T\r\n assert correction.shape == (3, 1)\r\n XYZ = XYZvalue * correction\r\n assert XYZ.shape == (3, 1)\r\n YYn = ft(XYZ[1])\r\n XXn = ft(XYZ[0])\r\n ZZn = ft(XYZ[2])\r\n L = 116 * YYn - 16\r\n a = 500 * (XXn - YYn)\r\n b = 200 * (YYn - ZZn)\r\n return [int(L), int(a), int(b)]","def test_adobe_conversion_to_xyz_d50(self):\r\n\r\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\r\n xyz = convert_color(adobe, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.247, 0.431, 0.060))","def test_adobe_conversion_to_xyz_d65(self):\r\n\r\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\r\n xyz = convert_color(adobe, XYZColor)\r\n self.assertColorMatch(xyz, XYZColor(0.230, 0.429, 0.074))","def cie_xyz(self):\n return tuple(matrix_mult(\n ((0.4124564, 0.3575761, 0.1804375),\n (0.2126729, 0.7151522, 0.0721750),\n (0.0193339, 0.1191920, 0.9503041),\n ),\n (from_srgb(self.red),\n from_srgb(self.green),\n from_srgb(self.blue)\n )\n ))","def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\n\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)","def applyJET(img):\n\treturn applyColorMap(img, \"jet\")","def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):\r\n\r\n # Will contain linearized RGB channels (removed the gamma func).\r\n linear_channels = {}\r\n\r\n if isinstance(cobj, sRGBColor):\r\n for channel in ['r', 'g', 'b']:\r\n V = getattr(cobj, 'rgb_' + channel)\r\n if V <= 0.04045:\r\n linear_channels[channel] = V / 12.92\r\n else:\r\n linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)\r\n else:\r\n # If it's not sRGB...\r\n gamma = cobj.rgb_gamma\r\n\r\n for channel in ['r', 'g', 'b']:\r\n V = getattr(cobj, 'rgb_' + channel)\r\n linear_channels[channel] = math.pow(V, gamma)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n xyz_x, xyz_y, xyz_z = apply_RGB_matrix(\r\n linear_channels['r'], linear_channels['g'], linear_channels['b'],\r\n rgb_type=cobj, convtype=\"rgb_to_xyz\")\r\n\r\n if target_illuminant is None:\r\n target_illuminant = cobj.native_illuminant\r\n \r\n # The illuminant of the original RGB object. This will always match\r\n # the RGB colorspace's native illuminant.\r\n illuminant = cobj.native_illuminant\r\n xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)\r\n # This will take care of any illuminant changes for us (if source\r\n # illuminant != target illuminant).\r\n xyzcolor.apply_adaptation(target_illuminant)\r\n\r\n return xyzcolor","def rgb2lab(r, g, b):\n r, g, b = r / 255.0, g / 255.0, b / 255.0\n\n # http://www.brucelindbloom.com/index.html?Math.html\n # Inverse sRGB Companding\n r = r / 12.92 if r <= 0.04045 else ((r + 0.055) / 1.055) ** 2.4\n g = g / 12.92 if g <= 0.04045 else ((g + 0.055) / 1.055) ** 2.4\n b = b / 12.92 if b <= 0.04045 else ((b + 0.055) / 1.055) ** 2.4\n\n # http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html\n # sRGB, D65\n x = r * 0.4124564 + g * 0.3575761 + b * 0.1804375\n y = r * 0.2126729 + g * 0.7151522 + b * 0.0721750\n z = r * 0.0193339 + g * 0.1191920 + b * 0.9503041\n\n # http://www.brucelindbloom.com/index.html?Eqn_XYZ_to_Lab.html\n kappa, epsilon = 903.3, 0.008856\n\n # http://brucelindbloom.com/index.html?Eqn_ChromAdapt.html\n # White point for D65\n xr, yr, zr = x / 0.95047, y / 1.00000, z / 1.08883\n\n fx = xr ** (1 / 3.0) if xr > epsilon else (kappa * xr + 16) / 116.0\n fy = yr ** (1 / 3.0) if yr > epsilon else (kappa * yr + 16) / 116.0\n fz = zr ** (1 / 3.0) if zr > epsilon else (kappa * zr + 16) / 116.0\n\n l = 166.0 * fy - 16.0\n a = 500.0 * (fx - fy)\n b = 200.0 * (fy - fz)\n\n return l, a, b","def img_jet(img):\n if len(img.shape) == 2:\n normalized_img = img / 255.0\n jet = np.array(cm.jet(1 - normalized_img)[:, :, :3] * 255, np.uint8)\n else:\n jet = img\n return jet","def rgb_to_lab(img, l=False, ab=False):\n img = img / 255\n l_chan = color.rgb2lab(img)[:, :, 0]\n l_chan = l_chan / 50 - 1\n l_chan = l_chan[..., np.newaxis]\n\n ab_chan = color.rgb2lab(img)[:, :, 1:]\n ab_chan = (ab_chan + 128) / 255 * 2 - 1\n if l:\n return l_chan\n else:\n return ab_chan","def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\n y: torch.Tensor = image[..., 0, :, :]\n cb: torch.Tensor = image[..., 1, :, :]\n cr: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n cb_shifted: torch.Tensor = cb - delta\n cr_shifted: torch.Tensor = cr - delta\n\n r: torch.Tensor = y + 1.403 * cr_shifted\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\n b: torch.Tensor = y + 1.773 * cb_shifted\n return torch.stack([r, g, b], -3)","def sRGBToLab(rgb):\n return xyzToLab(xyzFromsRGB(rgb), [0.9504559, 1, 1.089058])","def load_color_image_features(img_path):\n ac = scipy.misc.imread(img_path, mode='RGB')\n ac = ac / (255.0 / 2) - 1.0\n return np.array(ac)","def XYZ_to_Luv(cobj, *args, **kwargs):\r\n \r\n temp_x = cobj.xyz_x\r\n temp_y = cobj.xyz_y\r\n temp_z = cobj.xyz_z\r\n \r\n luv_u = (4.0 * temp_x) / (temp_x + (15.0 * temp_y) + (3.0 * temp_z))\r\n luv_v = (9.0 * temp_y) / (temp_x + (15.0 * temp_y) + (3.0 * temp_z))\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n temp_y = temp_y / illum[\"Y\"]\r\n if temp_y > color_constants.CIE_E:\r\n temp_y = math.pow(temp_y, (1.0 / 3.0))\r\n else:\r\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\r\n \r\n ref_U = (4.0 * illum[\"X\"]) / (illum[\"X\"] + (15.0 * illum[\"Y\"]) + (3.0 * illum[\"Z\"]))\r\n ref_V = (9.0 * illum[\"Y\"]) / (illum[\"X\"] + (15.0 * illum[\"Y\"]) + (3.0 * illum[\"Z\"]))\r\n \r\n luv_l = (116.0 * temp_y) - 16.0\r\n luv_u = 13.0 * luv_l * (luv_u - ref_U)\r\n luv_v = 13.0 * luv_l * (luv_v - ref_V)\r\n \r\n return LuvColor(\r\n luv_l, luv_u, luv_v, observer=cobj.observer, illuminant=cobj.illuminant)","def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\r\n\r\n temp_X = cobj.xyz_x\r\n temp_Y = cobj.xyz_y\r\n temp_Z = cobj.xyz_z\r\n\r\n logger.debug(\" \\- Target RGB space: %s\", target_rgb)\r\n target_illum = target_rgb.native_illuminant\r\n logger.debug(\" \\- Target native illuminant: %s\", target_illum)\r\n logger.debug(\" \\- XYZ color's illuminant: %s\", cobj.illuminant)\r\n \r\n # If the XYZ values were taken with a different reference white than the\r\n # native reference white of the target RGB space, a transformation matrix\r\n # must be applied.\r\n if cobj.illuminant != target_illum:\r\n logger.debug(\" \\* Applying transformation from %s to %s \",\r\n cobj.illuminant, target_illum)\r\n # Get the adjusted XYZ values, adapted for the target illuminant.\r\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\r\n temp_X, temp_Y, temp_Z,\r\n orig_illum=cobj.illuminant, targ_illum=target_illum)\r\n logger.debug(\" \\* New values: %.3f, %.3f, %.3f\",\r\n temp_X, temp_Y, temp_Z)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\r\n temp_X, temp_Y, temp_Z,\r\n rgb_type=target_rgb, convtype=\"xyz_to_rgb\")\r\n\r\n # v\r\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\r\n # V\r\n nonlinear_channels = {}\r\n if target_rgb == sRGBColor:\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n if v <= 0.0031308:\r\n nonlinear_channels[channel] = v * 12.92\r\n else:\r\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\r\n else:\r\n # If it's not sRGB...\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\r\n\r\n return target_rgb(\r\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])","def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n L: torch.Tensor = image[..., 0, :, :]\n u: torch.Tensor = image[..., 1, :, :]\n v: torch.Tensor = image[..., 2, :, :]\n\n # Convert from Luv to XYZ\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\n\n # Compute white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n a: torch.Tensor = u_w + u / (13 * L + eps)\n d: torch.Tensor = v_w + v / (13 * L + eps)\n c: torch.Tensor = 3 * y * (5 * d - 3)\n\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\n\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\n\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\n\n # Convert from RGB Linear to sRGB\n rgb_im = linear_rgb_to_rgb(rgbs_im)\n\n return rgb_im","def sRGBFromLabD50(lab):\n return xyzTosRGBD50(labToXYZ(lab, [0.9642957, 1, 0.8251046]))","def XYZ_to_sRGB(XYZ):\n\n rgb = XYZ_to_sRGB_linear(XYZ)\n rgb = sRGB_linear_to_sRGB(rgb)\n\n return rgb","def pixel_to_xyz(self, pixels, max_depth=25):\n assert pixels.shape[0] == 4\n d = np.abs(pixels[0] - pixels[2]) # disparity U_L - U_R\n z = self.fsub / d\n z[z > max_depth] = max_depth\n \n # calcualte xy\n u_L = pixels[0] # take first row\n v_L = pixels[1] # take 2nd row\n x = (u_L - self.cu) / self.fsu * z\n y = (v_L - self.cv) / self.fsv * z\n \n return np.vstack((x,y,z))","def XYZ_to_RGB(XYZ,\n illuminant_XYZ,\n illuminant_RGB,\n XYZ_to_RGB_matrix,\n chromatic_adaptation_transform='CAT02',\n encoding_cctf=None):\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n transform=chromatic_adaptation_transform)\n\n XYZ_a = dot_vector(M, XYZ)\n\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\n\n if encoding_cctf is not None:\n RGB = encoding_cctf(RGB)\n\n return RGB","def example_lab_to_rgb():\r\n\r\n print(\"=== RGB Example: Lab->RGB ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.217)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n rgb = convert_color(lab, sRGBColor)\r\n print(rgb)\r\n print(\"=== End Example ===\\n\")","def xyY_to_XYZ(cobj, *args, **kwargs):\r\n \r\n xyz_x = (cobj.xyy_x * cobj.xyy_Y) / cobj.xyy_y\r\n xyz_y = cobj.xyy_Y\r\n xyz_z = ((1.0 - cobj.xyy_x - cobj.xyy_y) * xyz_y) / cobj.xyy_y\r\n \r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)","def matplotlib_image(image):\n if image.ndim == 2:\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\n else:\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return rgb","def XYZ_to_RGB_matrix(self):\n\n if not self._use_derived_XYZ_to_RGB_matrix:\n return self._XYZ_to_RGB_matrix\n else:\n return self._derived_XYZ_to_RGB_matrix","def convert_color(image, color_space='RGB'):\n color_space = color_space.lower()\n if color_space != 'rgb':\n if color_space == 'hsv':\n color_transformation = cv2.COLOR_BGR2HSV\n elif color_space == 'luv':\n color_transformation = cv2.COLOR_BGR2LUV\n elif color_space == 'hls':\n color_transformation = cv2.COLOR_BGR2HLS\n elif color_space == 'yuv':\n color_transformation = cv2.COLOR_BGR2YUV\n elif color_space == 'ycrcb':\n color_transformation = cv2.COLOR_BGR2YCrCb\n else:\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\n 'HLS, YUV, YCrCb' % color_space)\n\n return cv2.cvtColor(image, color_transformation)\n else:\n return image","def rgbToCIE1931(self, red, green, blue):\n point = self.color.getXYPointFromRGB(red, green, blue)\n return [point.x, point.y]","def use_derived_RGB_to_XYZ_matrix(self):\n\n return self._use_derived_RGB_to_XYZ_matrix","def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:\n r: torch.Tensor = image[..., 0, :, :]\n g: torch.Tensor = image[..., 1, :, :]\n b: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b\n cb: torch.Tensor = (b - y) * 0.564 + delta\n cr: torch.Tensor = (r - y) * 0.713 + delta\n return torch.stack([y, cb, cr], -3)","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())","def example_spectral_to_xyz():\r\n\r\n print(\"=== Example: Spectral->XYZ ===\")\r\n spc = SpectralColor(\r\n observer='2', illuminant='d50',\r\n spec_380nm=0.0600, spec_390nm=0.0600, spec_400nm=0.0641,\r\n spec_410nm=0.0654, spec_420nm=0.0645, spec_430nm=0.0605,\r\n spec_440nm=0.0562, spec_450nm=0.0543, spec_460nm=0.0537,\r\n spec_470nm=0.0541, spec_480nm=0.0559, spec_490nm=0.0603,\r\n spec_500nm=0.0651, spec_510nm=0.0680, spec_520nm=0.0705,\r\n spec_530nm=0.0736, spec_540nm=0.0772, spec_550nm=0.0809,\r\n spec_560nm=0.0870, spec_570nm=0.0990, spec_580nm=0.1128,\r\n spec_590nm=0.1251, spec_600nm=0.1360, spec_610nm=0.1439,\r\n spec_620nm=0.1511, spec_630nm=0.1590, spec_640nm=0.1688,\r\n spec_650nm=0.1828, spec_660nm=0.1996, spec_670nm=0.2187,\r\n spec_680nm=0.2397, spec_690nm=0.2618, spec_700nm=0.2852,\r\n spec_710nm=0.2500, spec_720nm=0.2400, spec_730nm=0.2300)\r\n xyz = convert_color(spc, XYZColor)\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def XYZ_to_sRGB_linear(XYZ):\n\n rgb = np.tensordot(T, XYZ, axes=([1, 0]))\n\n if clip_method == CLIP_CLAMP_TO_ZERO:\n # set negative rgb values to zero\n rgb = np.where(rgb < 0, 0, rgb)\n return rgb\n\n if clip_method == CLIP_ADD_WHITE:\n # add enough white to make all rgb values nonnegative\n # find max negative rgb (or 0.0 if all non-negative), we need that much white\n rgb_min = np.amin(rgb, axis=0)\n # get max positive component\n rgb_max = np.amax(rgb, axis=0)\n\n # get scaling factor to maintain max rgb after adding white\n scaling = np.where(rgb_max > 0.0, rgb_max / (rgb_max - rgb_min + 0.00001), 1.0)\n\n # add enough white to cancel this out, maintaining the maximum of rgb\n rgb = np.where(rgb_min < 0.0, scaling * (rgb - rgb_min), rgb)\n return rgb","def getRGBFromXYAndBrightness(self, x, y, bri=1):\n # The xy to color conversion is almost the same, but in reverse order.\n # Check if the xy value is within the color gamut of the lamp.\n # If not continue with step 2, otherwise step 3.\n # We do this to calculate the most accurate color the given light can actually do.\n xyPoint = XYPoint(x, y)\n\n if not self.checkPointInLampsReach(xyPoint):\n # Calculate the closest point on the color gamut triangle\n # and use that as xy value See step 6 of color to xy.\n xyPoint = self.getClosestPointToPoint(xyPoint)\n\n # Calculate XYZ values Convert using the following formulas:\n Y = bri\n X = (Y / xyPoint.y) * xyPoint.x\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\n\n # Convert to RGB using Wide RGB D65 conversion\n r = X * 1.612 - Y * 0.203 - Z * 0.302\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\n b = X * 0.026 - Y * 0.072 + Z * 0.962\n\n # Apply reverse gamma correction\n r, g, b = map(\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\n [r, g, b]\n )\n\n # Bring all negative components to zero\n r, g, b = map(lambda x: max(0, x), [r, g, b])\n\n # If one component is greater than 1, weight components by that value.\n max_component = max(r, g, b)\n if max_component > 1:\n r, g, b = map(lambda x: x / max_component, [r, g, b])\n\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\n\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\n return (r, g, b)","def carla_cityscapes_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\n image.convert(carla.ColorConverter.CityScapesPalette) # pylint: disable=no-member\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = array.astype(np.float32) / 255\n array = np.reshape(array, (image.height, image.width, 4))\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n return array","def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):\n\n with domain_range_scale(1):\n values = None\n if model == 'CIE XYZ':\n values = XYZ\n elif model == 'CIE xyY':\n values = XYZ_to_xyY(XYZ, illuminant)\n elif model == 'CIE xy':\n values = XYZ_to_xy(XYZ, illuminant)\n elif model == 'CIE Lab':\n values = XYZ_to_Lab(XYZ, illuminant)\n elif model == 'CIE LCHab':\n values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))\n elif model == 'CIE Luv':\n values = XYZ_to_Luv(XYZ, illuminant)\n elif model == 'CIE Luv uv':\n values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)\n elif model == 'CIE LCHuv':\n values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))\n elif model == 'CIE UCS':\n values = XYZ_to_UCS(XYZ)\n elif model == 'CIE UCS uv':\n values = UCS_to_uv(XYZ_to_UCS(XYZ))\n elif model == 'CIE UVW':\n values = XYZ_to_UVW(XYZ, illuminant)\n elif model == 'DIN 99':\n values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))\n elif model == 'Hunter Lab':\n values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))\n elif model == 'Hunter Rdab':\n values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))\n elif model == 'IPT':\n values = XYZ_to_IPT(XYZ)\n elif model == 'JzAzBz':\n values = XYZ_to_JzAzBz(XYZ)\n elif model == 'OSA UCS':\n values = XYZ_to_OSA_UCS(XYZ)\n elif model == 'hdr-CIELAB':\n values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)\n elif model == 'hdr-IPT':\n values = XYZ_to_hdr_IPT(XYZ, **kwargs)\n\n if values is None:\n raise ValueError(\n '\"{0}\" not found in colourspace models: \"{1}\".'.format(\n model, ', '.join(COLOURSPACE_MODELS)))\n\n return values","def loadDeth(ficXYZ):\n data=np.array(Image.open(ficXYZ))\n cloud=data.reshape((-1)).view(dtype=np.uint16).reshape((data.shape[0],data.shape[1]/2,data.shape[2]))-0x7FFF\n # change of the type of the data to correspond to the encoding of the data in the imageXYZ + data refocusing\n cloud=cloud.astype('int16')\n cloud=cloud.astype('float32')/1000.0\n return cloud","def rgb_processing(rgb_img, center, scale, rot=0):\n rgb_img = crop(rgb_img, center, scale, \n [constants.IMG_RES, constants.IMG_RES], rot=rot)\n # (3,224,224),float,[0,1]\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\n return rgb_img","def convert_3d(points_2d, depth_image, image):\n fx = 525.0 # focal length x\n fy = 525.0 # focal length y\n cx = 319.5 # optical center x\n cy = 239.5 # optical center y\n factor = 5000 # for the 16-bit PNG files\n points_3d = []\n cols = []\n colors = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n for v in range(depth_image.shape[0]):\n for u in range(depth_image.shape[1]):\n Z = depth_image[v,u] / factor\n X = (u - cx) * Z / fx\n Y = (v - cy) * Z / fy\n points_3d.append([X,Y,Z])\n cols.append(colors[v,u])\n points = []\n for i in range(len(points_2d)):\n x = int(points_2d[i,0])\n y = int(points_2d[i,1])\n # print(y)\n Z = depth_image[y,x] / factor\n X = (x - cx) * Z / fx\n Y = (y - cy) * Z / fy\n points.append([X,Y,Z])\n points_3d = np.array(points_3d)\n cols = np.array(cols)\n points = np.array(points)\n \n return points, points_3d, cols","def read_colormap(name):\n\n path = get_demo_file(name + '.c3g')\n\n out = []\n with open(path, 'r') as file:\n for line in file:\n if 'rgb(' not in line:\n continue\n line = line.split('(')[-1].split(')')[0]\n out.append([float(n) for n in line.split(',')])\n\n return np.asarray(out).astype(float) / 256.","def xyz2linear_rgb(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(rgb_from_xyz, arr)","def get_rgb(self, img, r, g, b):\r\n\r\n # Get specific bands of hyperspectral image\r\n red_channel = img[:, :, r]\r\n green_channel = img[:, :, g]\r\n blue_channel = img[:, :, b]\r\n\r\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\r\n img = img.astype('float32')\r\n return img","def r2n2_xyz_images(self):\n if not hasattr(self, '_r2n2_xyz_images'):\n xyz_images = []\n for i in range(24):\n im_i = geom_util_np.apply_4x4(\n self.r2n2_cam_images[i, ...],\n self.r2n2_cam2world[i, ...],\n are_points=True)\n mask = np_util.make_mask(self.r2n2_depth_images[i, ...])\n xyz_images.append(np_util.zero_by_mask(mask, im_i).astype(np.float32))\n self._r2n2_xyz_images = np.stack(xyz_images)\n return self._r2n2_xyz_images","def to_image(x):\n x = denorm(x.data.cpu())\n ndarr = x.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()\n im = ndarr\n return im","def colorize_images(self, img):\n self.load_model()\n self.mdn.eval()\n self.vae.eval()\n n, _, _ = img.shape\n img = img.astype(np.float32) / 255\n img = torch.tensor(img, dtype=torch.float, device=self.device).unsqueeze(1)\n with torch.no_grad():\n z = self.mdn(img)\n ab_out = self.vae.decode(z)\n lab_out = torch.cat((img, ab_out), dim=1)\n lab_out = self.unnormalize(lab_out).cpu().numpy()\n lab_out = np.transpose(lab_out, (0, 2, 3, 1)).astype(np.uint8)\n for i in range(n):\n color_out = cv2.cvtColor(lab_out[i], cv2.COLOR_LAB2BGR)\n color_out = cv2.resize(color_out, (96, 96), interpolation=cv2.INTER_AREA)\n cv2.imwrite(\"../datasets/stl10/divcolor/{}.png\".format(str(i)), color_out)\n return","def imcast(img, dtype, color_space=\"default\"):\n if img.dtype == dtype:\n return img\n if color_space == \"default\":\n if dtype == np.uint8:\n if img.dtype == np.uint16:\n return np.asarray(img / 257, np.uint8)\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img * 255., np.uint8)\n elif dtype == np.uint16:\n if img.dtype == np.uint8:\n return np.asarray(img, np.uint16) * 257\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img * 65535., np.uint16)\n elif dtype == np.float32 or dtype == np.float64:\n if img.dtype == np.uint8:\n return np.asarray(img, dtype) / 255.\n elif img.dtype == np.uint16:\n return np.asarray(img, dtype) / 65535.\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img, dtype)\n elif color_space == \"CIE-L*a*b*\":\n if dtype == np.uint8:\n if img.dtype == np.float32 or img.dtype == np.float64:\n dst = np.empty(img.shape, np.uint8)\n dst[:,:,0] = img[:,:,0] * 255. / 100.\n dst[:,:,1] = img[:,:,1] + 128.\n dst[:,:,2] = img[:,:,2] + 128.\n return dst\n elif dtype == np.float32 or dtype == np.float64:\n if img.dtype == np.uint8:\n dst = np.empty(img.shape, dtype)\n dst[:,:,0] = np.asarray(img[:,:,0], dtype) / 255. * 100.\n dst[:,:,1] = np.asarray(img[:,:,1], dtype) - 128.\n dst[:,:,2] = np.asarray(img[:,:,2], dtype) - 128.\n return dst\n raise Exception(\n \"Unexpected conversion from '%s' to '%s' with '%s' color space\" % \\\n (img.dtype, dtype, color_space))","def pos2im_coordinates(x, z):\n # x_lim = [-0.85, 0.86]\n # z_lim = [-1.22, 0.47]\n x_lim = [-0.365, 0.365]\n z_lim = [-0.95, -0.24]\n\n pix_x = int(127 * (x_lim[1] - x) / (x_lim[1] - x_lim[0]))\n pix_z = int(127 * (z_lim[1] - z) / (z_lim[1] - z_lim[0]))\n return pix_x, pix_z","def CAM16_to_XYZ(\n specification: CAM_Specification_CAM16,\n XYZ_w: ArrayLike,\n L_A: ArrayLike,\n Y_b: ArrayLike,\n surround: InductionFactors_CIECAM02\n | InductionFactors_CAM16 = VIEWING_CONDITIONS_CAM16[\"Average\"],\n discount_illuminant: bool = False,\n) -> NDArrayFloat:\n\n J, C, h, _s, _Q, M, _H, _HC = astuple(specification)\n\n J = to_domain_100(J)\n C = to_domain_100(C)\n h = to_domain_degrees(h)\n M = to_domain_100(M)\n L_A = as_float_array(L_A)\n XYZ_w = to_domain_100(XYZ_w)\n _X_w, Y_w, _Z_w = tsplit(XYZ_w)\n\n # Step 0\n # Converting *CIE XYZ* tristimulus values to sharpened *RGB* values.\n RGB_w = vector_dot(MATRIX_16, XYZ_w)\n\n # Computing degree of adaptation :math:`D`.\n D = (\n np.clip(degree_of_adaptation(surround.F, L_A), 0, 1)\n if not discount_illuminant\n else ones(L_A.shape)\n )\n\n n, F_L, N_bb, N_cb, z = viewing_conditions_dependent_parameters(\n Y_b, Y_w, L_A\n )\n\n D_RGB = D[..., None] * Y_w[..., None] / RGB_w + 1 - D[..., None]\n RGB_wc = D_RGB * RGB_w\n\n # Applying forward post-adaptation non-linear response compression.\n RGB_aw = post_adaptation_non_linear_response_compression_forward(\n RGB_wc, F_L\n )\n\n # Computing achromatic responses for the whitepoint.\n A_w = achromatic_response_forward(RGB_aw, N_bb)\n\n # Step 1\n if has_only_nan(C) and not has_only_nan(M):\n C = M / spow(F_L, 0.25)\n elif has_only_nan(C):\n raise ValueError(\n 'Either \"C\" or \"M\" correlate must be defined in '\n 'the \"CAM_Specification_CAM16\" argument!'\n )\n\n # Step 2\n # Computing temporary magnitude quantity :math:`t`.\n t = temporary_magnitude_quantity_inverse(C, J, n)\n\n # Computing eccentricity factor *e_t*.\n e_t = eccentricity_factor(h)\n\n # Computing achromatic response :math:`A` for the stimulus.\n A = achromatic_response_inverse(A_w, J, surround.c, z)\n\n # Computing *P_1* to *P_3*.\n P_n = P(surround.N_c, N_cb, e_t, t, A, N_bb)\n _P_1, P_2, _P_3 = tsplit(P_n)\n\n # Step 3\n # Computing opponent colour dimensions :math:`a` and :math:`b`.\n ab = opponent_colour_dimensions_inverse(P_n, h)\n a, b = tsplit(ab) * np.where(t == 0, 0, 1)\n\n # Step 4\n # Applying post-adaptation non-linear response compression matrix.\n RGB_a = matrix_post_adaptation_non_linear_response_compression(P_2, a, b)\n\n # Step 5\n # Applying inverse post-adaptation non-linear response compression.\n RGB_c = post_adaptation_non_linear_response_compression_inverse(RGB_a, F_L)\n\n # Step 6\n RGB = RGB_c / D_RGB\n\n # Step 7\n XYZ = vector_dot(MATRIX_INVERSE_16, RGB)\n\n return from_range_100(XYZ)","def rowcol2XY(row,col,CCD):\n pixscale = 0.015 #mm/pix\n X = CCD[1]+1024*pixscale-(col*pixscale+pixscale/2.)\n Y = CCD[2]+2048*pixscale-(row*pixscale+pixscale/2.)\n return X,Y","def rgb_to_ycbcr(image: np.ndarray) -> np.ndarray:\n\n \"\"\" from RGB (0-1).\n \"\"\"\n\n if not is_rgb(image):\n raise ValueError(\"Input needs to be an array of RGB values\")\n\n m = np.array(\n [\n [+065.481, +128.553, +024.966],\n [-037.797, -074.203, +112.000],\n [+112.000, -093.786, -018.214],\n ]\n )\n a = np.array([16, 128, 128])\n\n return np.dot(image, m.T) + a","def rgb(self, xyz: Union[ndarray, Iterable[float]]) -> ndarray:\n return self.value.xyz_to_rgb_matrix @ xyz","def XYZ_to_optimization_colour_model(XYZ: ArrayLike) -> NDArrayFloat:\n\n return XYZ_to_Lab(XYZ, RGB_COLOURSPACE_ACES2065_1.whitepoint)","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))","def scale_to_01(color: C3I) -> C3F:\n r, g, b = color\n return r / 255, g / 255, b / 255","def imageprepare():\r\n file_name = 'temp_image.png'\r\n im = Image.open(file_name).convert('L')\r\n im = im.resize((20, 20))\r\n p = Image.new('L', (28,28), (255))\r\n p.paste(im,(4,4,24,24))\r\n p.save(\"last_image.png\")\r\n\r\n tv = list(p.getdata()) # get pixel values\r\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\r\n tva = np.reshape(tva, (28, 28))\r\n\r\n return tva","def generate_lut(self):\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n for i in Numeric.arange(256):\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\n return r,g,b","def sRGBLuminance(x):\n lin=linearFromsRGB3(x)\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722","def camera_to_pixel(self, X):\n raise NotImplementedError","def sRGBToLabD50(rgb):\n return xyzToLab(xyzFromsRGBD50(rgb), [0.9642957, 1, 0.8251046])","def _colored_img_to_arr(image, verbose=False):\n height, width = image.size\n arr = np.array(image.getdata())\n arr = arr.reshape(3, height, width)\n r = arr[0]\n g = arr[1]\n b = arr[2]\n return r, g, b","def create_cloud_xyz32rgb(header, points):\n fields = [PointField('x', 0, PointField.FLOAT32, 1),\n PointField('y', 4, PointField.FLOAT32, 1),\n PointField('z', 8, PointField.FLOAT32, 1),\n PointField('rgb', 12, PointField.UINT32, 1)]\n return pcl2.create_cloud(header, fields, points)","def XYZ_to_xy(XYZ: ArrayLike) -> NDArrayFloat:\n\n return xyY_to_xy(XYZ_to_xyY(XYZ))","def XYZ_to_RGB_matrix(self, value):\n\n if value is not None:\n value = np.asarray(value)\n self._XYZ_to_RGB_matrix = value","def colorspace_to_image(cspace: DataFrame, m: int, n: int) -> np.array:\n \n assert isinstance(cspace, DataFrame), \"Colorspace must be a dataframe\"\n assert len(cspace) == m * n, 'Image dimensions must match'\n assert all(np.isin(['R', 'G', 'B'], cspace.columns)), \"Colorspace must contain RGB columns\"\n \n result = np.empty([m,n,3])\n result[:,:, 0] = cspace['R'].values.reshape(m, n)\n result[:,:, 1] = cspace['G'].values.reshape(m, n)\n result[:,:, 2] = cspace['B'].values.reshape(m, n)\n return result","def read_img(filename) -> Tuple[np.ndarray, np.ndarray]:\n img = cv2.imread(filename, 3)\n labimg = cv2.cvtColor(cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE)), cv2.COLOR_BGR2Lab)\n return np.reshape(labimg[:, :, 0], (config.IMAGE_SIZE, config.IMAGE_SIZE, 1)), labimg[:, :, 1:]","def red_channel(img):\n\n red = np.zeros(img.shape,dtype=float)\n\n red[:,:,2] = np.copy(img[:,:,2])\n\n return red","def convertColorSpace(\n self,\n img, # Image in some color space\n srcColorSpace = 'BGR', # Source color space\n tgtColorSpace = 'RGB', # Traget color space\n ):\n\n if srcColorSpace == tgtColorSpace:\n return img\n\n if srcColorSpace == 'BGR':\n img_bgr = img\n elif srcColorSpace == 'RGB':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n elif srcColorSpace == 'HSV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n elif srcColorSpace == 'HLS':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\n elif srcColorSpace == 'LUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\n elif srcColorSpace == 'YUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\n elif srcColorSpace == 'YCrCb':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\n else:\n raise Exception(\"Incorrect color space: {}\".format(srcColorSpace))\n\n if tgtColorSpace == 'BGR':\n img_tgt = img_bgr\n elif tgtColorSpace == 'RGB':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\n elif tgtColorSpace == 'HSV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\n elif tgtColorSpace == 'HLS':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\n elif tgtColorSpace == 'LUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\n elif tgtColorSpace == 'YUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\n elif tgtColorSpace == 'YCrCb':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\n else:\n raise Exception(\"Incorrect color space: {}\".format(tgtColorSpace))\n\n return img_tgt","def chromatic_transform(im, label=None, d_h=None, d_s=None, d_l=None):\n # Set random hue, luminosity and saturation which ranges from -0.1 to 0.1\n if d_h is None:\n d_h = (np.random.rand(1) - 0.5) * 0.2 * 180\n if d_l is None:\n d_l = (np.random.rand(1) - 0.5) * 0.2 * 256\n if d_s is None:\n d_s = (np.random.rand(1) - 0.5) * 0.2 * 256\n # Convert the BGR to HLS\n hls = cv2.cvtColor(im, cv2.COLOR_BGR2HLS)\n h, l, s = cv2.split(hls)\n # Add the values to the image H, L, S\n new_h = (h + d_h) % 180\n new_l = np.clip(l + d_l, 0, 255)\n new_s = np.clip(s + d_s, 0, 255)\n # Convert the HLS to BGR\n new_hls = cv2.merge((new_h, new_l, new_s)).astype('uint8')\n new_im = cv2.cvtColor(new_hls, cv2.COLOR_HLS2BGR)\n\n if label is not None:\n I = np.where(label > 0)\n new_im[I[0], I[1], :] = im[I[0], I[1], :]\n return new_im","def flatten_pixcoord(image):\n hdr = pyfits.getheader(image)\n naxis1 = hdr['naxis1']\n naxis2 = hdr['naxis2']\n pixcoord = np.mgrid[1:naxis1+1,1:naxis2+1]\n pixcoord = pixcoord.swapaxes(0,1).swapaxes(1,2)\n pixcoord = pixcoord.ravel().reshape(naxis1*naxis2, 2)\n return pixcoord","def data_to_bytescale_rgb(data): # used to create the SOURCE PNGs (MRI, FA, MD)\n im = bytescale(data)\n w, h = im.shape\n ret = np.empty((w,h,3), dtype=np.uint8)\n ret[:,:,0] = im\n ret[:,:,1] = im\n ret[:,:,2] = im\n return ret","def negative(img): \n for pixel in img:\n x, y, col = pixel \n r, g, b = col\n \n new_color = create_color(255 - r, 255 - g, 255 - b)\n set_color(img, x, y, new_color)","def depth_to_xyz(self,u,v,depth_val):\n '''\n u - x image coordinate\n v - y image coodrinate\n depth_val - depth value at that (u,v) from depth_image\n '''\n\n fx=self.cam_intrin[0]\n fy=self.cam_intrin[4]\n cx=self.cam_intrin[2]\n cy=self.cam_intrin[5]\n\n z = float(depth_val)\n x = float((u - cx)/fx)*z\n y = float((v - cy)/fy)*z\n\n result = [x, y, z]\n return result","def test_conversion_to_xyz_with_negatives(self):\r\n\r\n self.color.spec_530nm = -0.0736\r\n # TODO: Convert here.\r","def im2pos_coordinates(pix_x, pix_z):\n # x_lim = [-0.85, 0.86]\n # z_lim = [-1.22, 0.47]\n x_lim = [-0.365, 0.365]\n z_lim = [-0.95, -0.24]\n x = x_lim[1] - (x_lim[1] - x_lim[0]) * pix_x/127\n z = z_lim[1] - (z_lim[1] - z_lim[0]) * pix_z/127\n return x, z"],"string":"[\n \"def Lab_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n xyz_y = (cobj.lab_l + 16.0) / 116.0\\r\\n xyz_x = cobj.lab_a / 500.0 + xyz_y\\r\\n xyz_z = xyz_y - cobj.lab_b / 200.0\\r\\n \\r\\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\\r\\n xyz_y = math.pow(xyz_y, 3)\\r\\n else:\\r\\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\\r\\n\\r\\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\\r\\n xyz_x = math.pow(xyz_x, 3)\\r\\n else:\\r\\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\\r\\n \\r\\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\\r\\n xyz_z = math.pow(xyz_z, 3)\\r\\n else:\\r\\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\\r\\n \\r\\n xyz_x = (illum[\\\"X\\\"] * xyz_x)\\r\\n xyz_y = (illum[\\\"Y\\\"] * xyz_y)\\r\\n xyz_z = (illum[\\\"Z\\\"] * xyz_z)\\r\\n \\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n r, g, b = tf.unstack(image, axis=-1)\\n var_r = r / 255\\n var_g = g / 255\\n var_b = b / 255\\n\\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\\n var_r / 12.92)\\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\\n var_g / 12.92)\\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\\n var_b / 12.92)\\n var_r = var_r * 100\\n var_g = var_g * 100\\n var_b = var_b * 100\\n\\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\\n\\n image_xyz = tf.stack([x, y, z], axis=-1)\\n return image_xyz\",\n \"def example_rgb_to_xyz():\\r\\n\\r\\n print(\\\"=== RGB Example: RGB->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n rgb = sRGBColor(120, 130, 140)\\r\\n # Show a string representation.\\r\\n print(rgb)\\r\\n # Convert RGB to XYZ using a D50 illuminant.\\r\\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def XYZ_to_Lab(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n temp_x = cobj.xyz_x / illum[\\\"X\\\"]\\r\\n temp_y = cobj.xyz_y / illum[\\\"Y\\\"]\\r\\n temp_z = cobj.xyz_z / illum[\\\"Z\\\"]\\r\\n \\r\\n if temp_x > color_constants.CIE_E:\\r\\n temp_x = math.pow(temp_x, (1.0 / 3.0))\\r\\n else:\\r\\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \\r\\n\\r\\n if temp_y > color_constants.CIE_E:\\r\\n temp_y = math.pow(temp_y, (1.0 / 3.0))\\r\\n else:\\r\\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\\r\\n \\r\\n if temp_z > color_constants.CIE_E:\\r\\n temp_z = math.pow(temp_z, (1.0 / 3.0))\\r\\n else:\\r\\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\\r\\n \\r\\n lab_l = (116.0 * temp_y) - 16.0\\r\\n lab_a = 500.0 * (temp_x - temp_y)\\r\\n lab_b = 200.0 * (temp_y - temp_z)\\r\\n return LabColor(\\r\\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\\n xyz = rgb_to_xyz(image)\\n lab_image = xyz_to_lab(xyz)\\n return lab_image\",\n \"def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n xyz = lab_to_xyz(image)\\n rgb_image = xyz_to_rgb(xyz)\\n return rgb_image\",\n \"def example_lab_to_xyz():\\r\\n\\r\\n print(\\\"=== Simple Example: Lab->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.22)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n xyz = convert_color(lab, XYZColor)\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\\n\\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)\",\n \"def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n\\n refx = 95.047\\n refy = 100.00\\n refz = 108.883\\n\\n var_x = x / refx\\n var_y = y / refy\\n var_z = z / refz\\n\\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\\n (7.787 * var_x) + (16 / 116))\\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\\n (7.787 * var_y) + (16 / 116))\\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\\n (7.787 * var_z) + (16 / 116))\\n\\n l = (116 * var_y) - 16\\n a = 500 * (var_x - var_y)\\n b = 200 * (var_y - var_z)\\n lab_image = tf.stack([l, a, b], axis=-1)\\n return lab_image\",\n \"def rgb_to_xyz(rgb_color):\\n\\n r = (rgb_color[0] / 255)\\n g = (rgb_color[1] / 255)\\n b = (rgb_color[2] / 255)\\n\\n if r > 0.04045:\\n r = ((r + 0.055) / 1.055) ** 2.4\\n else:\\n r = r / 12.92\\n\\n if g > 0.04045:\\n g = ((g + 0.055) / 1.055) ** 2.4\\n else:\\n g = g / 12.92\\n\\n if b > 0.04045:\\n b = ((b + 0.055) / 1.055) ** 2.4\\n else:\\n b = b / 12.92\\n\\n r = r * 100\\n g = g * 100\\n b = b * 100\\n x = (r * 0.4124) + (g * 0.3576) + (b * 0.1805)\\n y = (r * 0.2126) + (g * 0.7152) + (b * 0.0722)\\n z = (r * 0.0193) + (g * 0.1192) + (b * 0.9505)\\n\\n return x, y, z\",\n \"def RGB_to_XYZ(RGB,\\n illuminant_RGB,\\n illuminant_XYZ,\\n RGB_to_XYZ_matrix,\\n chromatic_adaptation_transform='CAT02',\\n decoding_cctf=None):\\n\\n if decoding_cctf is not None:\\n RGB = decoding_cctf(RGB)\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n return XYZ_a\",\n \"def RGB_to_XYZ_matrix(self):\\n\\n if not self._use_derived_RGB_to_XYZ_matrix:\\n return self._RGB_to_XYZ_matrix\\n else:\\n return self._derived_RGB_to_XYZ_matrix\",\n \"def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n var_x = x / 100\\n var_y = y / 100\\n var_z = z / 100\\n\\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\\n\\n var_r = tf.where(var_r > 0.0031308,\\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\\n 12.92 * var_r)\\n var_g = tf.where(var_g > 0.0031308,\\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\\n 12.92 * var_g)\\n var_b = tf.where(var_b > 0.0031308,\\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\\n 12.92 * var_b)\\n r = var_r * 255\\n g = var_g * 255\\n b = var_b * 255\\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\\n return rgb_image\",\n \"def hlab_to_xyz(hlab: Vector, white: VectorLike) -> Vector:\\n\\n xn, yn, zn = alg.multiply(util.xy_to_xyz(white), 100, dims=alg.D1_SC)\\n ka = CKA * alg.nth_root(xn / CXN, 2)\\n kb = CKB * alg.nth_root(zn / CZN, 2)\\n l, a, b = hlab\\n l /= 100\\n y = (l ** 2) * yn\\n x = (((a * l) / ka) + (y / yn)) * xn\\n z = (((b * l) / kb) - (y / yn)) * -zn\\n return alg.divide([x, y, z], 100, dims=alg.D1_SC)\",\n \"def lab_to_rgb(img):\\n new_img = np.zeros((256, 256, 3))\\n for i in range(len(img)):\\n for j in range(len(img[i])):\\n pix = img[i, j]\\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\\n new_img = color.lab2rgb(new_img) * 255\\n new_img = new_img.astype('uint8')\\n return new_img\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def rgb2alpha(img):\\n\\t### First of all we need the size of our picture to make the transforms\\n\\n\\tx = len(img) ; y = len(img[0])\\n\\talpha = np.full((x, y, 3), 0, dtype = float)\\t\\t## This will be the transformed image\\n\\n\\t### Now we gotta access each pixel of the picture\\n\\n\\tfor i, vi in enumerate(img):\\n\\t\\tfor j, px in enumerate(vi):\\n\\t\\t\\t### There we are\\n\\n\\t\\t\\t# Step 1 : LMS transform, for that we use r_l\\n\\n\\t\\t\\talpha[i][j] = np.matmul(r_l, px)\\n\\n\\t\\t\\t# Step 2 : log em all (decimal log)\\n\\n\\t\\t\\talpha[i][j][0] = log(alpha[i][j][0])\\n\\t\\t\\talpha[i][j][1] = log(alpha[i][j][1])\\n\\t\\t\\talpha[i][j][2] = log(alpha[i][j][2])\\n\\n\\t\\t\\t# Step 3 : l alpha beta transform, by using l_a\\n\\n\\t\\t\\talpha[i][j] = np.matmul(l_a, alpha[i][j])\\n\\n\\treturn alpha\",\n \"def Luv_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n # Without Light, there is no color. Short-circuit this and avoid some\\r\\n # zero division errors in the var_a_frac calculation.\\r\\n if cobj.luv_l <= 0.0:\\r\\n xyz_x = 0.0\\r\\n xyz_y = 0.0\\r\\n xyz_z = 0.0\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\\r\\n\\r\\n # Various variables used throughout the conversion.\\r\\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\\r\\n u_sub_0 = (4.0 * illum[\\\"X\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n v_sub_0 = (9.0 * illum[\\\"Y\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\\r\\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\\r\\n\\r\\n # Y-coordinate calculations.\\r\\n if cobj.luv_l > cie_k_times_e:\\r\\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\\r\\n else:\\r\\n xyz_y = cobj.luv_l / color_constants.CIE_K\\r\\n\\r\\n # X-coordinate calculation.\\r\\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\\r\\n # Z-coordinate calculation.\\r\\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\\r\\n\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)\",\n \"def linear_rgb2xyz(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(xyz_from_rgb, arr)\",\n \"def rgb_to_xy(red, green, blue):\\n\\n # gamma correction\\n red = pow((red + 0.055) / (1.0 + 0.055), 2.4) if red > 0.04045 else (red / 12.92)\\n green = pow((green + 0.055) / (1.0 + 0.055), 2.4) if green > 0.04045 else (green / 12.92)\\n blue = pow((blue + 0.055) / (1.0 + 0.055), 2.4) if blue > 0.04045 else (blue / 12.92)\\n\\n # convert rgb to xyz\\n x = red * 0.649926 + green * 0.103455 + blue * 0.197109\\n y = red * 0.234327 + green * 0.743075 + blue * 0.022598\\n z = green * 0.053077 + blue * 1.035763\\n\\n # convert xyz to xy\\n x = x / (x + y + z)\\n y = y / (x + y + z)\\n\\n # TODO check color gamut if known\\n \\n return [x, y]\",\n \"def RGB_to_XYZ_matrix(self, value):\\n\\n if value is not None:\\n value = np.asarray(value)\\n self._RGB_to_XYZ_matrix = value\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def sRGBFromLab(lab):\\n return xyzTosRGB(labToXYZ(lab, [0.9504559, 1, 1.089058]))\",\n \"def convert_color(image, color_space):\\n out_image = None\\n if color_space != 'RGB':\\n if color_space == 'HSV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\\n elif color_space == 'LUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\\n elif color_space == 'HLS':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\\n elif color_space == 'YUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\\n elif color_space == 'YCrCb':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\\n else:\\n out_image = np.copy(image)\\n return out_image\",\n \"def _convert_to_yolo_img(self, img):\\n\\n img = img / 255.0\\n h, w, c = img.shape\\n img = img.transpose(2, 0, 1)\\n outimg = make_image(w, h, c)\\n img = img.reshape((w*h*c))\\n data = c_array(c_float, img)\\n outimg.data = data\\n rgbgr_image(outimg)\\n return outimg\",\n \"def lab_to_rgb(img_l, img_ab):\\n lab = np.empty([*img_l.shape[0:2], 3])\\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\\n lab[:, :, 1:] = img_ab * 127\\n return color.lab2rgb(lab)\",\n \"def test_srgb_conversion_to_xyz_d65(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor)\\r\\n self.assertColorMatch(xyz, XYZColor(0.294, 0.457, 0.103))\",\n \"def test_srgb_conversion_to_xyz_d50(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))\",\n \"def rgb2Lab(rgbvalue):\\r\\n RGB2Lab_Matrix = np.array([[0.412453, 0.357580, 0.180423],\\r\\n [0.212671, 0.715160, 0.072169],\\r\\n [0.019334, 0.119193, 0.950227]])\\r\\n R = rgbvalue[0]\\r\\n G = rgbvalue[1]\\r\\n B = rgbvalue[2]\\r\\n gammaR = gamma(R / 255.0)\\r\\n gammaG = gamma(G / 255.0)\\r\\n gammaB = gamma(B / 255.0)\\r\\n RGBvalue = np.array([gammaR, gammaG, gammaB])\\r\\n RGBvalue = RGBvalue.reshape(3, 1)\\r\\n XYZvalue = np.dot(RGB2Lab_Matrix, RGBvalue)\\r\\n assert XYZvalue.shape == (3, 1)\\r\\n correction = np.array([[1.0 / 0.950456, 1.0, 1.0 / 1.088754]]).T\\r\\n assert correction.shape == (3, 1)\\r\\n XYZ = XYZvalue * correction\\r\\n assert XYZ.shape == (3, 1)\\r\\n YYn = ft(XYZ[1])\\r\\n XXn = ft(XYZ[0])\\r\\n ZZn = ft(XYZ[2])\\r\\n L = 116 * YYn - 16\\r\\n a = 500 * (XXn - YYn)\\r\\n b = 200 * (YYn - ZZn)\\r\\n return [int(L), int(a), int(b)]\",\n \"def test_adobe_conversion_to_xyz_d50(self):\\r\\n\\r\\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\\r\\n xyz = convert_color(adobe, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.247, 0.431, 0.060))\",\n \"def test_adobe_conversion_to_xyz_d65(self):\\r\\n\\r\\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\\r\\n xyz = convert_color(adobe, XYZColor)\\r\\n self.assertColorMatch(xyz, XYZColor(0.230, 0.429, 0.074))\",\n \"def cie_xyz(self):\\n return tuple(matrix_mult(\\n ((0.4124564, 0.3575761, 0.1804375),\\n (0.2126729, 0.7151522, 0.0721750),\\n (0.0193339, 0.1191920, 0.9503041),\\n ),\\n (from_srgb(self.red),\\n from_srgb(self.green),\\n from_srgb(self.blue)\\n )\\n ))\",\n \"def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\\n\\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)\",\n \"def applyJET(img):\\n\\treturn applyColorMap(img, \\\"jet\\\")\",\n \"def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):\\r\\n\\r\\n # Will contain linearized RGB channels (removed the gamma func).\\r\\n linear_channels = {}\\r\\n\\r\\n if isinstance(cobj, sRGBColor):\\r\\n for channel in ['r', 'g', 'b']:\\r\\n V = getattr(cobj, 'rgb_' + channel)\\r\\n if V <= 0.04045:\\r\\n linear_channels[channel] = V / 12.92\\r\\n else:\\r\\n linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n gamma = cobj.rgb_gamma\\r\\n\\r\\n for channel in ['r', 'g', 'b']:\\r\\n V = getattr(cobj, 'rgb_' + channel)\\r\\n linear_channels[channel] = math.pow(V, gamma)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n xyz_x, xyz_y, xyz_z = apply_RGB_matrix(\\r\\n linear_channels['r'], linear_channels['g'], linear_channels['b'],\\r\\n rgb_type=cobj, convtype=\\\"rgb_to_xyz\\\")\\r\\n\\r\\n if target_illuminant is None:\\r\\n target_illuminant = cobj.native_illuminant\\r\\n \\r\\n # The illuminant of the original RGB object. This will always match\\r\\n # the RGB colorspace's native illuminant.\\r\\n illuminant = cobj.native_illuminant\\r\\n xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)\\r\\n # This will take care of any illuminant changes for us (if source\\r\\n # illuminant != target illuminant).\\r\\n xyzcolor.apply_adaptation(target_illuminant)\\r\\n\\r\\n return xyzcolor\",\n \"def rgb2lab(r, g, b):\\n r, g, b = r / 255.0, g / 255.0, b / 255.0\\n\\n # http://www.brucelindbloom.com/index.html?Math.html\\n # Inverse sRGB Companding\\n r = r / 12.92 if r <= 0.04045 else ((r + 0.055) / 1.055) ** 2.4\\n g = g / 12.92 if g <= 0.04045 else ((g + 0.055) / 1.055) ** 2.4\\n b = b / 12.92 if b <= 0.04045 else ((b + 0.055) / 1.055) ** 2.4\\n\\n # http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html\\n # sRGB, D65\\n x = r * 0.4124564 + g * 0.3575761 + b * 0.1804375\\n y = r * 0.2126729 + g * 0.7151522 + b * 0.0721750\\n z = r * 0.0193339 + g * 0.1191920 + b * 0.9503041\\n\\n # http://www.brucelindbloom.com/index.html?Eqn_XYZ_to_Lab.html\\n kappa, epsilon = 903.3, 0.008856\\n\\n # http://brucelindbloom.com/index.html?Eqn_ChromAdapt.html\\n # White point for D65\\n xr, yr, zr = x / 0.95047, y / 1.00000, z / 1.08883\\n\\n fx = xr ** (1 / 3.0) if xr > epsilon else (kappa * xr + 16) / 116.0\\n fy = yr ** (1 / 3.0) if yr > epsilon else (kappa * yr + 16) / 116.0\\n fz = zr ** (1 / 3.0) if zr > epsilon else (kappa * zr + 16) / 116.0\\n\\n l = 166.0 * fy - 16.0\\n a = 500.0 * (fx - fy)\\n b = 200.0 * (fy - fz)\\n\\n return l, a, b\",\n \"def img_jet(img):\\n if len(img.shape) == 2:\\n normalized_img = img / 255.0\\n jet = np.array(cm.jet(1 - normalized_img)[:, :, :3] * 255, np.uint8)\\n else:\\n jet = img\\n return jet\",\n \"def rgb_to_lab(img, l=False, ab=False):\\n img = img / 255\\n l_chan = color.rgb2lab(img)[:, :, 0]\\n l_chan = l_chan / 50 - 1\\n l_chan = l_chan[..., np.newaxis]\\n\\n ab_chan = color.rgb2lab(img)[:, :, 1:]\\n ab_chan = (ab_chan + 128) / 255 * 2 - 1\\n if l:\\n return l_chan\\n else:\\n return ab_chan\",\n \"def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\\n y: torch.Tensor = image[..., 0, :, :]\\n cb: torch.Tensor = image[..., 1, :, :]\\n cr: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n cb_shifted: torch.Tensor = cb - delta\\n cr_shifted: torch.Tensor = cr - delta\\n\\n r: torch.Tensor = y + 1.403 * cr_shifted\\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\\n b: torch.Tensor = y + 1.773 * cb_shifted\\n return torch.stack([r, g, b], -3)\",\n \"def sRGBToLab(rgb):\\n return xyzToLab(xyzFromsRGB(rgb), [0.9504559, 1, 1.089058])\",\n \"def load_color_image_features(img_path):\\n ac = scipy.misc.imread(img_path, mode='RGB')\\n ac = ac / (255.0 / 2) - 1.0\\n return np.array(ac)\",\n \"def XYZ_to_Luv(cobj, *args, **kwargs):\\r\\n \\r\\n temp_x = cobj.xyz_x\\r\\n temp_y = cobj.xyz_y\\r\\n temp_z = cobj.xyz_z\\r\\n \\r\\n luv_u = (4.0 * temp_x) / (temp_x + (15.0 * temp_y) + (3.0 * temp_z))\\r\\n luv_v = (9.0 * temp_y) / (temp_x + (15.0 * temp_y) + (3.0 * temp_z))\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n temp_y = temp_y / illum[\\\"Y\\\"]\\r\\n if temp_y > color_constants.CIE_E:\\r\\n temp_y = math.pow(temp_y, (1.0 / 3.0))\\r\\n else:\\r\\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\\r\\n \\r\\n ref_U = (4.0 * illum[\\\"X\\\"]) / (illum[\\\"X\\\"] + (15.0 * illum[\\\"Y\\\"]) + (3.0 * illum[\\\"Z\\\"]))\\r\\n ref_V = (9.0 * illum[\\\"Y\\\"]) / (illum[\\\"X\\\"] + (15.0 * illum[\\\"Y\\\"]) + (3.0 * illum[\\\"Z\\\"]))\\r\\n \\r\\n luv_l = (116.0 * temp_y) - 16.0\\r\\n luv_u = 13.0 * luv_l * (luv_u - ref_U)\\r\\n luv_v = 13.0 * luv_l * (luv_v - ref_V)\\r\\n \\r\\n return LuvColor(\\r\\n luv_l, luv_u, luv_v, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\\r\\n\\r\\n temp_X = cobj.xyz_x\\r\\n temp_Y = cobj.xyz_y\\r\\n temp_Z = cobj.xyz_z\\r\\n\\r\\n logger.debug(\\\" \\\\- Target RGB space: %s\\\", target_rgb)\\r\\n target_illum = target_rgb.native_illuminant\\r\\n logger.debug(\\\" \\\\- Target native illuminant: %s\\\", target_illum)\\r\\n logger.debug(\\\" \\\\- XYZ color's illuminant: %s\\\", cobj.illuminant)\\r\\n \\r\\n # If the XYZ values were taken with a different reference white than the\\r\\n # native reference white of the target RGB space, a transformation matrix\\r\\n # must be applied.\\r\\n if cobj.illuminant != target_illum:\\r\\n logger.debug(\\\" \\\\* Applying transformation from %s to %s \\\",\\r\\n cobj.illuminant, target_illum)\\r\\n # Get the adjusted XYZ values, adapted for the target illuminant.\\r\\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n orig_illum=cobj.illuminant, targ_illum=target_illum)\\r\\n logger.debug(\\\" \\\\* New values: %.3f, %.3f, %.3f\\\",\\r\\n temp_X, temp_Y, temp_Z)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n rgb_type=target_rgb, convtype=\\\"xyz_to_rgb\\\")\\r\\n\\r\\n # v\\r\\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\\r\\n # V\\r\\n nonlinear_channels = {}\\r\\n if target_rgb == sRGBColor:\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n if v <= 0.0031308:\\r\\n nonlinear_channels[channel] = v * 12.92\\r\\n else:\\r\\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\\r\\n\\r\\n return target_rgb(\\r\\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])\",\n \"def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n L: torch.Tensor = image[..., 0, :, :]\\n u: torch.Tensor = image[..., 1, :, :]\\n v: torch.Tensor = image[..., 2, :, :]\\n\\n # Convert from Luv to XYZ\\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\\n\\n # Compute white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n a: torch.Tensor = u_w + u / (13 * L + eps)\\n d: torch.Tensor = v_w + v / (13 * L + eps)\\n c: torch.Tensor = 3 * y * (5 * d - 3)\\n\\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\\n\\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\\n\\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\\n\\n # Convert from RGB Linear to sRGB\\n rgb_im = linear_rgb_to_rgb(rgbs_im)\\n\\n return rgb_im\",\n \"def sRGBFromLabD50(lab):\\n return xyzTosRGBD50(labToXYZ(lab, [0.9642957, 1, 0.8251046]))\",\n \"def XYZ_to_sRGB(XYZ):\\n\\n rgb = XYZ_to_sRGB_linear(XYZ)\\n rgb = sRGB_linear_to_sRGB(rgb)\\n\\n return rgb\",\n \"def pixel_to_xyz(self, pixels, max_depth=25):\\n assert pixels.shape[0] == 4\\n d = np.abs(pixels[0] - pixels[2]) # disparity U_L - U_R\\n z = self.fsub / d\\n z[z > max_depth] = max_depth\\n \\n # calcualte xy\\n u_L = pixels[0] # take first row\\n v_L = pixels[1] # take 2nd row\\n x = (u_L - self.cu) / self.fsu * z\\n y = (v_L - self.cv) / self.fsv * z\\n \\n return np.vstack((x,y,z))\",\n \"def XYZ_to_RGB(XYZ,\\n illuminant_XYZ,\\n illuminant_RGB,\\n XYZ_to_RGB_matrix,\\n chromatic_adaptation_transform='CAT02',\\n encoding_cctf=None):\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\\n\\n if encoding_cctf is not None:\\n RGB = encoding_cctf(RGB)\\n\\n return RGB\",\n \"def example_lab_to_rgb():\\r\\n\\r\\n print(\\\"=== RGB Example: Lab->RGB ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.217)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n rgb = convert_color(lab, sRGBColor)\\r\\n print(rgb)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def xyY_to_XYZ(cobj, *args, **kwargs):\\r\\n \\r\\n xyz_x = (cobj.xyy_x * cobj.xyy_Y) / cobj.xyy_y\\r\\n xyz_y = cobj.xyy_Y\\r\\n xyz_z = ((1.0 - cobj.xyy_x - cobj.xyy_y) * xyz_y) / cobj.xyy_y\\r\\n \\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)\",\n \"def matplotlib_image(image):\\n if image.ndim == 2:\\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\\n else:\\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n return rgb\",\n \"def XYZ_to_RGB_matrix(self):\\n\\n if not self._use_derived_XYZ_to_RGB_matrix:\\n return self._XYZ_to_RGB_matrix\\n else:\\n return self._derived_XYZ_to_RGB_matrix\",\n \"def convert_color(image, color_space='RGB'):\\n color_space = color_space.lower()\\n if color_space != 'rgb':\\n if color_space == 'hsv':\\n color_transformation = cv2.COLOR_BGR2HSV\\n elif color_space == 'luv':\\n color_transformation = cv2.COLOR_BGR2LUV\\n elif color_space == 'hls':\\n color_transformation = cv2.COLOR_BGR2HLS\\n elif color_space == 'yuv':\\n color_transformation = cv2.COLOR_BGR2YUV\\n elif color_space == 'ycrcb':\\n color_transformation = cv2.COLOR_BGR2YCrCb\\n else:\\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\\n 'HLS, YUV, YCrCb' % color_space)\\n\\n return cv2.cvtColor(image, color_transformation)\\n else:\\n return image\",\n \"def rgbToCIE1931(self, red, green, blue):\\n point = self.color.getXYPointFromRGB(red, green, blue)\\n return [point.x, point.y]\",\n \"def use_derived_RGB_to_XYZ_matrix(self):\\n\\n return self._use_derived_RGB_to_XYZ_matrix\",\n \"def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:\\n r: torch.Tensor = image[..., 0, :, :]\\n g: torch.Tensor = image[..., 1, :, :]\\n b: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b\\n cb: torch.Tensor = (b - y) * 0.564 + delta\\n cr: torch.Tensor = (r - y) * 0.713 + delta\\n return torch.stack([y, cb, cr], -3)\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())\",\n \"def example_spectral_to_xyz():\\r\\n\\r\\n print(\\\"=== Example: Spectral->XYZ ===\\\")\\r\\n spc = SpectralColor(\\r\\n observer='2', illuminant='d50',\\r\\n spec_380nm=0.0600, spec_390nm=0.0600, spec_400nm=0.0641,\\r\\n spec_410nm=0.0654, spec_420nm=0.0645, spec_430nm=0.0605,\\r\\n spec_440nm=0.0562, spec_450nm=0.0543, spec_460nm=0.0537,\\r\\n spec_470nm=0.0541, spec_480nm=0.0559, spec_490nm=0.0603,\\r\\n spec_500nm=0.0651, spec_510nm=0.0680, spec_520nm=0.0705,\\r\\n spec_530nm=0.0736, spec_540nm=0.0772, spec_550nm=0.0809,\\r\\n spec_560nm=0.0870, spec_570nm=0.0990, spec_580nm=0.1128,\\r\\n spec_590nm=0.1251, spec_600nm=0.1360, spec_610nm=0.1439,\\r\\n spec_620nm=0.1511, spec_630nm=0.1590, spec_640nm=0.1688,\\r\\n spec_650nm=0.1828, spec_660nm=0.1996, spec_670nm=0.2187,\\r\\n spec_680nm=0.2397, spec_690nm=0.2618, spec_700nm=0.2852,\\r\\n spec_710nm=0.2500, spec_720nm=0.2400, spec_730nm=0.2300)\\r\\n xyz = convert_color(spc, XYZColor)\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def XYZ_to_sRGB_linear(XYZ):\\n\\n rgb = np.tensordot(T, XYZ, axes=([1, 0]))\\n\\n if clip_method == CLIP_CLAMP_TO_ZERO:\\n # set negative rgb values to zero\\n rgb = np.where(rgb < 0, 0, rgb)\\n return rgb\\n\\n if clip_method == CLIP_ADD_WHITE:\\n # add enough white to make all rgb values nonnegative\\n # find max negative rgb (or 0.0 if all non-negative), we need that much white\\n rgb_min = np.amin(rgb, axis=0)\\n # get max positive component\\n rgb_max = np.amax(rgb, axis=0)\\n\\n # get scaling factor to maintain max rgb after adding white\\n scaling = np.where(rgb_max > 0.0, rgb_max / (rgb_max - rgb_min + 0.00001), 1.0)\\n\\n # add enough white to cancel this out, maintaining the maximum of rgb\\n rgb = np.where(rgb_min < 0.0, scaling * (rgb - rgb_min), rgb)\\n return rgb\",\n \"def getRGBFromXYAndBrightness(self, x, y, bri=1):\\n # The xy to color conversion is almost the same, but in reverse order.\\n # Check if the xy value is within the color gamut of the lamp.\\n # If not continue with step 2, otherwise step 3.\\n # We do this to calculate the most accurate color the given light can actually do.\\n xyPoint = XYPoint(x, y)\\n\\n if not self.checkPointInLampsReach(xyPoint):\\n # Calculate the closest point on the color gamut triangle\\n # and use that as xy value See step 6 of color to xy.\\n xyPoint = self.getClosestPointToPoint(xyPoint)\\n\\n # Calculate XYZ values Convert using the following formulas:\\n Y = bri\\n X = (Y / xyPoint.y) * xyPoint.x\\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\\n\\n # Convert to RGB using Wide RGB D65 conversion\\n r = X * 1.612 - Y * 0.203 - Z * 0.302\\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\\n b = X * 0.026 - Y * 0.072 + Z * 0.962\\n\\n # Apply reverse gamma correction\\n r, g, b = map(\\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\\n [r, g, b]\\n )\\n\\n # Bring all negative components to zero\\n r, g, b = map(lambda x: max(0, x), [r, g, b])\\n\\n # If one component is greater than 1, weight components by that value.\\n max_component = max(r, g, b)\\n if max_component > 1:\\n r, g, b = map(lambda x: x / max_component, [r, g, b])\\n\\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\\n\\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\\n return (r, g, b)\",\n \"def carla_cityscapes_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\\n image.convert(carla.ColorConverter.CityScapesPalette) # pylint: disable=no-member\\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\\\"uint8\\\"))\\n array = array.astype(np.float32) / 255\\n array = np.reshape(array, (image.height, image.width, 4))\\n array = array[:, :, :3]\\n array = array[:, :, ::-1]\\n return array\",\n \"def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):\\n\\n with domain_range_scale(1):\\n values = None\\n if model == 'CIE XYZ':\\n values = XYZ\\n elif model == 'CIE xyY':\\n values = XYZ_to_xyY(XYZ, illuminant)\\n elif model == 'CIE xy':\\n values = XYZ_to_xy(XYZ, illuminant)\\n elif model == 'CIE Lab':\\n values = XYZ_to_Lab(XYZ, illuminant)\\n elif model == 'CIE LCHab':\\n values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))\\n elif model == 'CIE Luv':\\n values = XYZ_to_Luv(XYZ, illuminant)\\n elif model == 'CIE Luv uv':\\n values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)\\n elif model == 'CIE LCHuv':\\n values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))\\n elif model == 'CIE UCS':\\n values = XYZ_to_UCS(XYZ)\\n elif model == 'CIE UCS uv':\\n values = UCS_to_uv(XYZ_to_UCS(XYZ))\\n elif model == 'CIE UVW':\\n values = XYZ_to_UVW(XYZ, illuminant)\\n elif model == 'DIN 99':\\n values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))\\n elif model == 'Hunter Lab':\\n values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))\\n elif model == 'Hunter Rdab':\\n values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))\\n elif model == 'IPT':\\n values = XYZ_to_IPT(XYZ)\\n elif model == 'JzAzBz':\\n values = XYZ_to_JzAzBz(XYZ)\\n elif model == 'OSA UCS':\\n values = XYZ_to_OSA_UCS(XYZ)\\n elif model == 'hdr-CIELAB':\\n values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)\\n elif model == 'hdr-IPT':\\n values = XYZ_to_hdr_IPT(XYZ, **kwargs)\\n\\n if values is None:\\n raise ValueError(\\n '\\\"{0}\\\" not found in colourspace models: \\\"{1}\\\".'.format(\\n model, ', '.join(COLOURSPACE_MODELS)))\\n\\n return values\",\n \"def loadDeth(ficXYZ):\\n data=np.array(Image.open(ficXYZ))\\n cloud=data.reshape((-1)).view(dtype=np.uint16).reshape((data.shape[0],data.shape[1]/2,data.shape[2]))-0x7FFF\\n # change of the type of the data to correspond to the encoding of the data in the imageXYZ + data refocusing\\n cloud=cloud.astype('int16')\\n cloud=cloud.astype('float32')/1000.0\\n return cloud\",\n \"def rgb_processing(rgb_img, center, scale, rot=0):\\n rgb_img = crop(rgb_img, center, scale, \\n [constants.IMG_RES, constants.IMG_RES], rot=rot)\\n # (3,224,224),float,[0,1]\\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\\n return rgb_img\",\n \"def convert_3d(points_2d, depth_image, image):\\n fx = 525.0 # focal length x\\n fy = 525.0 # focal length y\\n cx = 319.5 # optical center x\\n cy = 239.5 # optical center y\\n factor = 5000 # for the 16-bit PNG files\\n points_3d = []\\n cols = []\\n colors = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n for v in range(depth_image.shape[0]):\\n for u in range(depth_image.shape[1]):\\n Z = depth_image[v,u] / factor\\n X = (u - cx) * Z / fx\\n Y = (v - cy) * Z / fy\\n points_3d.append([X,Y,Z])\\n cols.append(colors[v,u])\\n points = []\\n for i in range(len(points_2d)):\\n x = int(points_2d[i,0])\\n y = int(points_2d[i,1])\\n # print(y)\\n Z = depth_image[y,x] / factor\\n X = (x - cx) * Z / fx\\n Y = (y - cy) * Z / fy\\n points.append([X,Y,Z])\\n points_3d = np.array(points_3d)\\n cols = np.array(cols)\\n points = np.array(points)\\n \\n return points, points_3d, cols\",\n \"def read_colormap(name):\\n\\n path = get_demo_file(name + '.c3g')\\n\\n out = []\\n with open(path, 'r') as file:\\n for line in file:\\n if 'rgb(' not in line:\\n continue\\n line = line.split('(')[-1].split(')')[0]\\n out.append([float(n) for n in line.split(',')])\\n\\n return np.asarray(out).astype(float) / 256.\",\n \"def xyz2linear_rgb(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(rgb_from_xyz, arr)\",\n \"def get_rgb(self, img, r, g, b):\\r\\n\\r\\n # Get specific bands of hyperspectral image\\r\\n red_channel = img[:, :, r]\\r\\n green_channel = img[:, :, g]\\r\\n blue_channel = img[:, :, b]\\r\\n\\r\\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\\r\\n img = img.astype('float32')\\r\\n return img\",\n \"def r2n2_xyz_images(self):\\n if not hasattr(self, '_r2n2_xyz_images'):\\n xyz_images = []\\n for i in range(24):\\n im_i = geom_util_np.apply_4x4(\\n self.r2n2_cam_images[i, ...],\\n self.r2n2_cam2world[i, ...],\\n are_points=True)\\n mask = np_util.make_mask(self.r2n2_depth_images[i, ...])\\n xyz_images.append(np_util.zero_by_mask(mask, im_i).astype(np.float32))\\n self._r2n2_xyz_images = np.stack(xyz_images)\\n return self._r2n2_xyz_images\",\n \"def to_image(x):\\n x = denorm(x.data.cpu())\\n ndarr = x.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()\\n im = ndarr\\n return im\",\n \"def colorize_images(self, img):\\n self.load_model()\\n self.mdn.eval()\\n self.vae.eval()\\n n, _, _ = img.shape\\n img = img.astype(np.float32) / 255\\n img = torch.tensor(img, dtype=torch.float, device=self.device).unsqueeze(1)\\n with torch.no_grad():\\n z = self.mdn(img)\\n ab_out = self.vae.decode(z)\\n lab_out = torch.cat((img, ab_out), dim=1)\\n lab_out = self.unnormalize(lab_out).cpu().numpy()\\n lab_out = np.transpose(lab_out, (0, 2, 3, 1)).astype(np.uint8)\\n for i in range(n):\\n color_out = cv2.cvtColor(lab_out[i], cv2.COLOR_LAB2BGR)\\n color_out = cv2.resize(color_out, (96, 96), interpolation=cv2.INTER_AREA)\\n cv2.imwrite(\\\"../datasets/stl10/divcolor/{}.png\\\".format(str(i)), color_out)\\n return\",\n \"def imcast(img, dtype, color_space=\\\"default\\\"):\\n if img.dtype == dtype:\\n return img\\n if color_space == \\\"default\\\":\\n if dtype == np.uint8:\\n if img.dtype == np.uint16:\\n return np.asarray(img / 257, np.uint8)\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img * 255., np.uint8)\\n elif dtype == np.uint16:\\n if img.dtype == np.uint8:\\n return np.asarray(img, np.uint16) * 257\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img * 65535., np.uint16)\\n elif dtype == np.float32 or dtype == np.float64:\\n if img.dtype == np.uint8:\\n return np.asarray(img, dtype) / 255.\\n elif img.dtype == np.uint16:\\n return np.asarray(img, dtype) / 65535.\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img, dtype)\\n elif color_space == \\\"CIE-L*a*b*\\\":\\n if dtype == np.uint8:\\n if img.dtype == np.float32 or img.dtype == np.float64:\\n dst = np.empty(img.shape, np.uint8)\\n dst[:,:,0] = img[:,:,0] * 255. / 100.\\n dst[:,:,1] = img[:,:,1] + 128.\\n dst[:,:,2] = img[:,:,2] + 128.\\n return dst\\n elif dtype == np.float32 or dtype == np.float64:\\n if img.dtype == np.uint8:\\n dst = np.empty(img.shape, dtype)\\n dst[:,:,0] = np.asarray(img[:,:,0], dtype) / 255. * 100.\\n dst[:,:,1] = np.asarray(img[:,:,1], dtype) - 128.\\n dst[:,:,2] = np.asarray(img[:,:,2], dtype) - 128.\\n return dst\\n raise Exception(\\n \\\"Unexpected conversion from '%s' to '%s' with '%s' color space\\\" % \\\\\\n (img.dtype, dtype, color_space))\",\n \"def pos2im_coordinates(x, z):\\n # x_lim = [-0.85, 0.86]\\n # z_lim = [-1.22, 0.47]\\n x_lim = [-0.365, 0.365]\\n z_lim = [-0.95, -0.24]\\n\\n pix_x = int(127 * (x_lim[1] - x) / (x_lim[1] - x_lim[0]))\\n pix_z = int(127 * (z_lim[1] - z) / (z_lim[1] - z_lim[0]))\\n return pix_x, pix_z\",\n \"def CAM16_to_XYZ(\\n specification: CAM_Specification_CAM16,\\n XYZ_w: ArrayLike,\\n L_A: ArrayLike,\\n Y_b: ArrayLike,\\n surround: InductionFactors_CIECAM02\\n | InductionFactors_CAM16 = VIEWING_CONDITIONS_CAM16[\\\"Average\\\"],\\n discount_illuminant: bool = False,\\n) -> NDArrayFloat:\\n\\n J, C, h, _s, _Q, M, _H, _HC = astuple(specification)\\n\\n J = to_domain_100(J)\\n C = to_domain_100(C)\\n h = to_domain_degrees(h)\\n M = to_domain_100(M)\\n L_A = as_float_array(L_A)\\n XYZ_w = to_domain_100(XYZ_w)\\n _X_w, Y_w, _Z_w = tsplit(XYZ_w)\\n\\n # Step 0\\n # Converting *CIE XYZ* tristimulus values to sharpened *RGB* values.\\n RGB_w = vector_dot(MATRIX_16, XYZ_w)\\n\\n # Computing degree of adaptation :math:`D`.\\n D = (\\n np.clip(degree_of_adaptation(surround.F, L_A), 0, 1)\\n if not discount_illuminant\\n else ones(L_A.shape)\\n )\\n\\n n, F_L, N_bb, N_cb, z = viewing_conditions_dependent_parameters(\\n Y_b, Y_w, L_A\\n )\\n\\n D_RGB = D[..., None] * Y_w[..., None] / RGB_w + 1 - D[..., None]\\n RGB_wc = D_RGB * RGB_w\\n\\n # Applying forward post-adaptation non-linear response compression.\\n RGB_aw = post_adaptation_non_linear_response_compression_forward(\\n RGB_wc, F_L\\n )\\n\\n # Computing achromatic responses for the whitepoint.\\n A_w = achromatic_response_forward(RGB_aw, N_bb)\\n\\n # Step 1\\n if has_only_nan(C) and not has_only_nan(M):\\n C = M / spow(F_L, 0.25)\\n elif has_only_nan(C):\\n raise ValueError(\\n 'Either \\\"C\\\" or \\\"M\\\" correlate must be defined in '\\n 'the \\\"CAM_Specification_CAM16\\\" argument!'\\n )\\n\\n # Step 2\\n # Computing temporary magnitude quantity :math:`t`.\\n t = temporary_magnitude_quantity_inverse(C, J, n)\\n\\n # Computing eccentricity factor *e_t*.\\n e_t = eccentricity_factor(h)\\n\\n # Computing achromatic response :math:`A` for the stimulus.\\n A = achromatic_response_inverse(A_w, J, surround.c, z)\\n\\n # Computing *P_1* to *P_3*.\\n P_n = P(surround.N_c, N_cb, e_t, t, A, N_bb)\\n _P_1, P_2, _P_3 = tsplit(P_n)\\n\\n # Step 3\\n # Computing opponent colour dimensions :math:`a` and :math:`b`.\\n ab = opponent_colour_dimensions_inverse(P_n, h)\\n a, b = tsplit(ab) * np.where(t == 0, 0, 1)\\n\\n # Step 4\\n # Applying post-adaptation non-linear response compression matrix.\\n RGB_a = matrix_post_adaptation_non_linear_response_compression(P_2, a, b)\\n\\n # Step 5\\n # Applying inverse post-adaptation non-linear response compression.\\n RGB_c = post_adaptation_non_linear_response_compression_inverse(RGB_a, F_L)\\n\\n # Step 6\\n RGB = RGB_c / D_RGB\\n\\n # Step 7\\n XYZ = vector_dot(MATRIX_INVERSE_16, RGB)\\n\\n return from_range_100(XYZ)\",\n \"def rowcol2XY(row,col,CCD):\\n pixscale = 0.015 #mm/pix\\n X = CCD[1]+1024*pixscale-(col*pixscale+pixscale/2.)\\n Y = CCD[2]+2048*pixscale-(row*pixscale+pixscale/2.)\\n return X,Y\",\n \"def rgb_to_ycbcr(image: np.ndarray) -> np.ndarray:\\n\\n \\\"\\\"\\\" from RGB (0-1).\\n \\\"\\\"\\\"\\n\\n if not is_rgb(image):\\n raise ValueError(\\\"Input needs to be an array of RGB values\\\")\\n\\n m = np.array(\\n [\\n [+065.481, +128.553, +024.966],\\n [-037.797, -074.203, +112.000],\\n [+112.000, -093.786, -018.214],\\n ]\\n )\\n a = np.array([16, 128, 128])\\n\\n return np.dot(image, m.T) + a\",\n \"def rgb(self, xyz: Union[ndarray, Iterable[float]]) -> ndarray:\\n return self.value.xyz_to_rgb_matrix @ xyz\",\n \"def XYZ_to_optimization_colour_model(XYZ: ArrayLike) -> NDArrayFloat:\\n\\n return XYZ_to_Lab(XYZ, RGB_COLOURSPACE_ACES2065_1.whitepoint)\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))\",\n \"def scale_to_01(color: C3I) -> C3F:\\n r, g, b = color\\n return r / 255, g / 255, b / 255\",\n \"def imageprepare():\\r\\n file_name = 'temp_image.png'\\r\\n im = Image.open(file_name).convert('L')\\r\\n im = im.resize((20, 20))\\r\\n p = Image.new('L', (28,28), (255))\\r\\n p.paste(im,(4,4,24,24))\\r\\n p.save(\\\"last_image.png\\\")\\r\\n\\r\\n tv = list(p.getdata()) # get pixel values\\r\\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\r\\n tva = np.reshape(tva, (28, 28))\\r\\n\\r\\n return tva\",\n \"def generate_lut(self):\\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\\n for i in Numeric.arange(256):\\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\\n return r,g,b\",\n \"def sRGBLuminance(x):\\n lin=linearFromsRGB3(x)\\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722\",\n \"def camera_to_pixel(self, X):\\n raise NotImplementedError\",\n \"def sRGBToLabD50(rgb):\\n return xyzToLab(xyzFromsRGBD50(rgb), [0.9642957, 1, 0.8251046])\",\n \"def _colored_img_to_arr(image, verbose=False):\\n height, width = image.size\\n arr = np.array(image.getdata())\\n arr = arr.reshape(3, height, width)\\n r = arr[0]\\n g = arr[1]\\n b = arr[2]\\n return r, g, b\",\n \"def create_cloud_xyz32rgb(header, points):\\n fields = [PointField('x', 0, PointField.FLOAT32, 1),\\n PointField('y', 4, PointField.FLOAT32, 1),\\n PointField('z', 8, PointField.FLOAT32, 1),\\n PointField('rgb', 12, PointField.UINT32, 1)]\\n return pcl2.create_cloud(header, fields, points)\",\n \"def XYZ_to_xy(XYZ: ArrayLike) -> NDArrayFloat:\\n\\n return xyY_to_xy(XYZ_to_xyY(XYZ))\",\n \"def XYZ_to_RGB_matrix(self, value):\\n\\n if value is not None:\\n value = np.asarray(value)\\n self._XYZ_to_RGB_matrix = value\",\n \"def colorspace_to_image(cspace: DataFrame, m: int, n: int) -> np.array:\\n \\n assert isinstance(cspace, DataFrame), \\\"Colorspace must be a dataframe\\\"\\n assert len(cspace) == m * n, 'Image dimensions must match'\\n assert all(np.isin(['R', 'G', 'B'], cspace.columns)), \\\"Colorspace must contain RGB columns\\\"\\n \\n result = np.empty([m,n,3])\\n result[:,:, 0] = cspace['R'].values.reshape(m, n)\\n result[:,:, 1] = cspace['G'].values.reshape(m, n)\\n result[:,:, 2] = cspace['B'].values.reshape(m, n)\\n return result\",\n \"def read_img(filename) -> Tuple[np.ndarray, np.ndarray]:\\n img = cv2.imread(filename, 3)\\n labimg = cv2.cvtColor(cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE)), cv2.COLOR_BGR2Lab)\\n return np.reshape(labimg[:, :, 0], (config.IMAGE_SIZE, config.IMAGE_SIZE, 1)), labimg[:, :, 1:]\",\n \"def red_channel(img):\\n\\n red = np.zeros(img.shape,dtype=float)\\n\\n red[:,:,2] = np.copy(img[:,:,2])\\n\\n return red\",\n \"def convertColorSpace(\\n self,\\n img, # Image in some color space\\n srcColorSpace = 'BGR', # Source color space\\n tgtColorSpace = 'RGB', # Traget color space\\n ):\\n\\n if srcColorSpace == tgtColorSpace:\\n return img\\n\\n if srcColorSpace == 'BGR':\\n img_bgr = img\\n elif srcColorSpace == 'RGB':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\\n elif srcColorSpace == 'HSV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\\n elif srcColorSpace == 'HLS':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\\n elif srcColorSpace == 'LUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\\n elif srcColorSpace == 'YUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\\n elif srcColorSpace == 'YCrCb':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(srcColorSpace))\\n\\n if tgtColorSpace == 'BGR':\\n img_tgt = img_bgr\\n elif tgtColorSpace == 'RGB':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\\n elif tgtColorSpace == 'HSV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\\n elif tgtColorSpace == 'HLS':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\\n elif tgtColorSpace == 'LUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\\n elif tgtColorSpace == 'YUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\\n elif tgtColorSpace == 'YCrCb':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(tgtColorSpace))\\n\\n return img_tgt\",\n \"def chromatic_transform(im, label=None, d_h=None, d_s=None, d_l=None):\\n # Set random hue, luminosity and saturation which ranges from -0.1 to 0.1\\n if d_h is None:\\n d_h = (np.random.rand(1) - 0.5) * 0.2 * 180\\n if d_l is None:\\n d_l = (np.random.rand(1) - 0.5) * 0.2 * 256\\n if d_s is None:\\n d_s = (np.random.rand(1) - 0.5) * 0.2 * 256\\n # Convert the BGR to HLS\\n hls = cv2.cvtColor(im, cv2.COLOR_BGR2HLS)\\n h, l, s = cv2.split(hls)\\n # Add the values to the image H, L, S\\n new_h = (h + d_h) % 180\\n new_l = np.clip(l + d_l, 0, 255)\\n new_s = np.clip(s + d_s, 0, 255)\\n # Convert the HLS to BGR\\n new_hls = cv2.merge((new_h, new_l, new_s)).astype('uint8')\\n new_im = cv2.cvtColor(new_hls, cv2.COLOR_HLS2BGR)\\n\\n if label is not None:\\n I = np.where(label > 0)\\n new_im[I[0], I[1], :] = im[I[0], I[1], :]\\n return new_im\",\n \"def flatten_pixcoord(image):\\n hdr = pyfits.getheader(image)\\n naxis1 = hdr['naxis1']\\n naxis2 = hdr['naxis2']\\n pixcoord = np.mgrid[1:naxis1+1,1:naxis2+1]\\n pixcoord = pixcoord.swapaxes(0,1).swapaxes(1,2)\\n pixcoord = pixcoord.ravel().reshape(naxis1*naxis2, 2)\\n return pixcoord\",\n \"def data_to_bytescale_rgb(data): # used to create the SOURCE PNGs (MRI, FA, MD)\\n im = bytescale(data)\\n w, h = im.shape\\n ret = np.empty((w,h,3), dtype=np.uint8)\\n ret[:,:,0] = im\\n ret[:,:,1] = im\\n ret[:,:,2] = im\\n return ret\",\n \"def negative(img): \\n for pixel in img:\\n x, y, col = pixel \\n r, g, b = col\\n \\n new_color = create_color(255 - r, 255 - g, 255 - b)\\n set_color(img, x, y, new_color)\",\n \"def depth_to_xyz(self,u,v,depth_val):\\n '''\\n u - x image coordinate\\n v - y image coodrinate\\n depth_val - depth value at that (u,v) from depth_image\\n '''\\n\\n fx=self.cam_intrin[0]\\n fy=self.cam_intrin[4]\\n cx=self.cam_intrin[2]\\n cy=self.cam_intrin[5]\\n\\n z = float(depth_val)\\n x = float((u - cx)/fx)*z\\n y = float((v - cy)/fy)*z\\n\\n result = [x, y, z]\\n return result\",\n \"def test_conversion_to_xyz_with_negatives(self):\\r\\n\\r\\n self.color.spec_530nm = -0.0736\\r\\n # TODO: Convert here.\\r\",\n \"def im2pos_coordinates(pix_x, pix_z):\\n # x_lim = [-0.85, 0.86]\\n # z_lim = [-1.22, 0.47]\\n x_lim = [-0.365, 0.365]\\n z_lim = [-0.95, -0.24]\\n x = x_lim[1] - (x_lim[1] - x_lim[0]) * pix_x/127\\n z = z_lim[1] - (z_lim[1] - z_lim[0]) * pix_z/127\\n return x, z\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72538626","0.6935359","0.6835545","0.6804529","0.6711759","0.66599464","0.6521106","0.64231324","0.6351376","0.63161516","0.62939584","0.62429935","0.6191677","0.6155172","0.6133433","0.60679656","0.605217","0.600619","0.6003839","0.5994653","0.5972754","0.59319156","0.590566","0.5903485","0.59027094","0.5897333","0.5878697","0.5873358","0.5862743","0.5847499","0.5812417","0.5792483","0.5784269","0.57708067","0.5768425","0.5737487","0.571416","0.56845105","0.5660157","0.5654942","0.5647606","0.5643041","0.5642408","0.5641842","0.5638644","0.5561191","0.55464965","0.5546161","0.5528929","0.55286396","0.5527009","0.55182886","0.55066055","0.55048656","0.54864675","0.5485127","0.54798186","0.5475872","0.5456458","0.54377395","0.5436156","0.5422636","0.5415524","0.5413205","0.54101187","0.5406529","0.538891","0.5387667","0.5381756","0.5377735","0.53755945","0.53386563","0.5331905","0.53290564","0.532595","0.5318409","0.53142244","0.5312182","0.5311445","0.5310173","0.5299141","0.5293935","0.5293562","0.52900416","0.5288166","0.52861136","0.52796435","0.52756166","0.5275605","0.52746934","0.52667254","0.5254755","0.52312344","0.52287036","0.521744","0.5216842","0.52140945","0.5210013","0.5209715","0.5203404"],"string":"[\n \"0.72538626\",\n \"0.6935359\",\n \"0.6835545\",\n \"0.6804529\",\n \"0.6711759\",\n \"0.66599464\",\n \"0.6521106\",\n \"0.64231324\",\n \"0.6351376\",\n \"0.63161516\",\n \"0.62939584\",\n \"0.62429935\",\n \"0.6191677\",\n \"0.6155172\",\n \"0.6133433\",\n \"0.60679656\",\n \"0.605217\",\n \"0.600619\",\n \"0.6003839\",\n \"0.5994653\",\n \"0.5972754\",\n \"0.59319156\",\n \"0.590566\",\n \"0.5903485\",\n \"0.59027094\",\n \"0.5897333\",\n \"0.5878697\",\n \"0.5873358\",\n \"0.5862743\",\n \"0.5847499\",\n \"0.5812417\",\n \"0.5792483\",\n \"0.5784269\",\n \"0.57708067\",\n \"0.5768425\",\n \"0.5737487\",\n \"0.571416\",\n \"0.56845105\",\n \"0.5660157\",\n \"0.5654942\",\n \"0.5647606\",\n \"0.5643041\",\n \"0.5642408\",\n \"0.5641842\",\n \"0.5638644\",\n \"0.5561191\",\n \"0.55464965\",\n \"0.5546161\",\n \"0.5528929\",\n \"0.55286396\",\n \"0.5527009\",\n \"0.55182886\",\n \"0.55066055\",\n \"0.55048656\",\n \"0.54864675\",\n \"0.5485127\",\n \"0.54798186\",\n \"0.5475872\",\n \"0.5456458\",\n \"0.54377395\",\n \"0.5436156\",\n \"0.5422636\",\n \"0.5415524\",\n \"0.5413205\",\n \"0.54101187\",\n \"0.5406529\",\n \"0.538891\",\n \"0.5387667\",\n \"0.5381756\",\n \"0.5377735\",\n \"0.53755945\",\n \"0.53386563\",\n \"0.5331905\",\n \"0.53290564\",\n \"0.532595\",\n \"0.5318409\",\n \"0.53142244\",\n \"0.5312182\",\n \"0.5311445\",\n \"0.5310173\",\n \"0.5299141\",\n \"0.5293935\",\n \"0.5293562\",\n \"0.52900416\",\n \"0.5288166\",\n \"0.52861136\",\n \"0.52796435\",\n \"0.52756166\",\n \"0.5275605\",\n \"0.52746934\",\n \"0.52667254\",\n \"0.5254755\",\n \"0.52312344\",\n \"0.52287036\",\n \"0.521744\",\n \"0.5216842\",\n \"0.52140945\",\n \"0.5210013\",\n \"0.5209715\",\n \"0.5203404\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72095025"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":12389,"cells":{"query":{"kind":"string","value":"Convert an image from XYZ color space to LAB color space"},"document":{"kind":"string","value":"def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n\n refx = 95.047\n refy = 100.00\n refz = 108.883\n\n var_x = x / refx\n var_y = y / refy\n var_z = z / refz\n\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\n (7.787 * var_x) + (16 / 116))\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\n (7.787 * var_y) + (16 / 116))\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\n (7.787 * var_z) + (16 / 116))\n\n l = (116 * var_y) - 16\n a = 500 * (var_x - var_y)\n b = 200 * (var_y - var_z)\n lab_image = tf.stack([l, a, b], axis=-1)\n return lab_image"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\n xyz = rgb_to_xyz(image)\n lab_image = xyz_to_lab(xyz)\n return lab_image","def XYZ_to_Lab(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n temp_x = cobj.xyz_x / illum[\"X\"]\r\n temp_y = cobj.xyz_y / illum[\"Y\"]\r\n temp_z = cobj.xyz_z / illum[\"Z\"]\r\n \r\n if temp_x > color_constants.CIE_E:\r\n temp_x = math.pow(temp_x, (1.0 / 3.0))\r\n else:\r\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \r\n\r\n if temp_y > color_constants.CIE_E:\r\n temp_y = math.pow(temp_y, (1.0 / 3.0))\r\n else:\r\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\r\n \r\n if temp_z > color_constants.CIE_E:\r\n temp_z = math.pow(temp_z, (1.0 / 3.0))\r\n else:\r\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\r\n \r\n lab_l = (116.0 * temp_y) - 16.0\r\n lab_a = 500.0 * (temp_x - temp_y)\r\n lab_b = 200.0 * (temp_y - temp_z)\r\n return LabColor(\r\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)","def Lab_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n xyz_y = (cobj.lab_l + 16.0) / 116.0\r\n xyz_x = cobj.lab_a / 500.0 + xyz_y\r\n xyz_z = xyz_y - cobj.lab_b / 200.0\r\n \r\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\r\n xyz_y = math.pow(xyz_y, 3)\r\n else:\r\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\r\n\r\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\r\n xyz_x = math.pow(xyz_x, 3)\r\n else:\r\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\r\n \r\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\r\n xyz_z = math.pow(xyz_z, 3)\r\n else:\r\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\r\n \r\n xyz_x = (illum[\"X\"] * xyz_x)\r\n xyz_y = (illum[\"Y\"] * xyz_y)\r\n xyz_z = (illum[\"Z\"] * xyz_z)\r\n \r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)","def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\n xyz = lab_to_xyz(image)\n rgb_image = xyz_to_rgb(xyz)\n return rgb_image","def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\n l, a, b = tf.unstack(image, axis=-1)\n\n var_y = (l + 16) / 116\n var_x = a / 500 + var_y\n var_z = var_y - b / 200\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\n (var_x - 16 / 116) / 7.787)\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\n (var_y - 16 / 116) / 7.787)\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\n (var_z - 16 / 116) / 7.787)\n\n refx = 95.047\n refy = 100.00\n ref_z = 108.883\n\n x = var_x * refx\n y = var_y * refy\n z = var_z * ref_z\n xyz_image = tf.stack([x, y, z], axis=-1)\n return xyz_image","def lab_to_rgb(img):\n new_img = np.zeros((256, 256, 3))\n for i in range(len(img)):\n for j in range(len(img[i])):\n pix = img[i, j]\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\n new_img = color.lab2rgb(new_img) * 255\n new_img = new_img.astype('uint8')\n return new_img","def example_rgb_to_xyz():\r\n\r\n print(\"=== RGB Example: RGB->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n rgb = sRGBColor(120, 130, 140)\r\n # Show a string representation.\r\n print(rgb)\r\n # Convert RGB to XYZ using a D50 illuminant.\r\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def rgb_to_lab(img, l=False, ab=False):\n img = img / 255\n l_chan = color.rgb2lab(img)[:, :, 0]\n l_chan = l_chan / 50 - 1\n l_chan = l_chan[..., np.newaxis]\n\n ab_chan = color.rgb2lab(img)[:, :, 1:]\n ab_chan = (ab_chan + 128) / 255 * 2 - 1\n if l:\n return l_chan\n else:\n return ab_chan","def example_lab_to_xyz():\r\n\r\n print(\"=== Simple Example: Lab->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.22)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n xyz = convert_color(lab, XYZColor)\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def lab_to_rgb(img_l, img_ab):\n lab = np.empty([*img_l.shape[0:2], 3])\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\n lab[:, :, 1:] = img_ab * 127\n return color.lab2rgb(lab)","def sRGBToLab(rgb):\n return xyzToLab(xyzFromsRGB(rgb), [0.9504559, 1, 1.089058])","def rgb2Lab(rgbvalue):\r\n RGB2Lab_Matrix = np.array([[0.412453, 0.357580, 0.180423],\r\n [0.212671, 0.715160, 0.072169],\r\n [0.019334, 0.119193, 0.950227]])\r\n R = rgbvalue[0]\r\n G = rgbvalue[1]\r\n B = rgbvalue[2]\r\n gammaR = gamma(R / 255.0)\r\n gammaG = gamma(G / 255.0)\r\n gammaB = gamma(B / 255.0)\r\n RGBvalue = np.array([gammaR, gammaG, gammaB])\r\n RGBvalue = RGBvalue.reshape(3, 1)\r\n XYZvalue = np.dot(RGB2Lab_Matrix, RGBvalue)\r\n assert XYZvalue.shape == (3, 1)\r\n correction = np.array([[1.0 / 0.950456, 1.0, 1.0 / 1.088754]]).T\r\n assert correction.shape == (3, 1)\r\n XYZ = XYZvalue * correction\r\n assert XYZ.shape == (3, 1)\r\n YYn = ft(XYZ[1])\r\n XXn = ft(XYZ[0])\r\n ZZn = ft(XYZ[2])\r\n L = 116 * YYn - 16\r\n a = 500 * (XXn - YYn)\r\n b = 200 * (YYn - ZZn)\r\n return [int(L), int(a), int(b)]","def rgb2lab(r, g, b):\n r, g, b = r / 255.0, g / 255.0, b / 255.0\n\n # http://www.brucelindbloom.com/index.html?Math.html\n # Inverse sRGB Companding\n r = r / 12.92 if r <= 0.04045 else ((r + 0.055) / 1.055) ** 2.4\n g = g / 12.92 if g <= 0.04045 else ((g + 0.055) / 1.055) ** 2.4\n b = b / 12.92 if b <= 0.04045 else ((b + 0.055) / 1.055) ** 2.4\n\n # http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html\n # sRGB, D65\n x = r * 0.4124564 + g * 0.3575761 + b * 0.1804375\n y = r * 0.2126729 + g * 0.7151522 + b * 0.0721750\n z = r * 0.0193339 + g * 0.1191920 + b * 0.9503041\n\n # http://www.brucelindbloom.com/index.html?Eqn_XYZ_to_Lab.html\n kappa, epsilon = 903.3, 0.008856\n\n # http://brucelindbloom.com/index.html?Eqn_ChromAdapt.html\n # White point for D65\n xr, yr, zr = x / 0.95047, y / 1.00000, z / 1.08883\n\n fx = xr ** (1 / 3.0) if xr > epsilon else (kappa * xr + 16) / 116.0\n fy = yr ** (1 / 3.0) if yr > epsilon else (kappa * yr + 16) / 116.0\n fz = zr ** (1 / 3.0) if zr > epsilon else (kappa * zr + 16) / 116.0\n\n l = 166.0 * fy - 16.0\n a = 500.0 * (fx - fy)\n b = 200.0 * (fy - fz)\n\n return l, a, b","def sRGBFromLab(lab):\n return xyzTosRGB(labToXYZ(lab, [0.9504559, 1, 1.089058]))","def rgb2alpha(img):\n\t### First of all we need the size of our picture to make the transforms\n\n\tx = len(img) ; y = len(img[0])\n\talpha = np.full((x, y, 3), 0, dtype = float)\t\t## This will be the transformed image\n\n\t### Now we gotta access each pixel of the picture\n\n\tfor i, vi in enumerate(img):\n\t\tfor j, px in enumerate(vi):\n\t\t\t### There we are\n\n\t\t\t# Step 1 : LMS transform, for that we use r_l\n\n\t\t\talpha[i][j] = np.matmul(r_l, px)\n\n\t\t\t# Step 2 : log em all (decimal log)\n\n\t\t\talpha[i][j][0] = log(alpha[i][j][0])\n\t\t\talpha[i][j][1] = log(alpha[i][j][1])\n\t\t\talpha[i][j][2] = log(alpha[i][j][2])\n\n\t\t\t# Step 3 : l alpha beta transform, by using l_a\n\n\t\t\talpha[i][j] = np.matmul(l_a, alpha[i][j])\n\n\treturn alpha","def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\n\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def sRGBToLabD50(rgb):\n return xyzToLab(xyzFromsRGBD50(rgb), [0.9642957, 1, 0.8251046])","def Luv_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n # Without Light, there is no color. Short-circuit this and avoid some\r\n # zero division errors in the var_a_frac calculation.\r\n if cobj.luv_l <= 0.0:\r\n xyz_x = 0.0\r\n xyz_y = 0.0\r\n xyz_z = 0.0\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\r\n\r\n # Various variables used throughout the conversion.\r\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\r\n u_sub_0 = (4.0 * illum[\"X\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n v_sub_0 = (9.0 * illum[\"Y\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\r\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\r\n\r\n # Y-coordinate calculations.\r\n if cobj.luv_l > cie_k_times_e:\r\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\r\n else:\r\n xyz_y = cobj.luv_l / color_constants.CIE_K\r\n\r\n # X-coordinate calculation.\r\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\r\n # Z-coordinate calculation.\r\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\r\n\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)","def XYZ_to_Luv(cobj, *args, **kwargs):\r\n \r\n temp_x = cobj.xyz_x\r\n temp_y = cobj.xyz_y\r\n temp_z = cobj.xyz_z\r\n \r\n luv_u = (4.0 * temp_x) / (temp_x + (15.0 * temp_y) + (3.0 * temp_z))\r\n luv_v = (9.0 * temp_y) / (temp_x + (15.0 * temp_y) + (3.0 * temp_z))\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n temp_y = temp_y / illum[\"Y\"]\r\n if temp_y > color_constants.CIE_E:\r\n temp_y = math.pow(temp_y, (1.0 / 3.0))\r\n else:\r\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\r\n \r\n ref_U = (4.0 * illum[\"X\"]) / (illum[\"X\"] + (15.0 * illum[\"Y\"]) + (3.0 * illum[\"Z\"]))\r\n ref_V = (9.0 * illum[\"Y\"]) / (illum[\"X\"] + (15.0 * illum[\"Y\"]) + (3.0 * illum[\"Z\"]))\r\n \r\n luv_l = (116.0 * temp_y) - 16.0\r\n luv_u = 13.0 * luv_l * (luv_u - ref_U)\r\n luv_v = 13.0 * luv_l * (luv_v - ref_V)\r\n \r\n return LuvColor(\r\n luv_l, luv_u, luv_v, observer=cobj.observer, illuminant=cobj.illuminant)","def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\n r, g, b = tf.unstack(image, axis=-1)\n var_r = r / 255\n var_g = g / 255\n var_b = b / 255\n\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\n var_r / 12.92)\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\n var_g / 12.92)\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\n var_b / 12.92)\n var_r = var_r * 100\n var_g = var_g * 100\n var_b = var_b * 100\n\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\n\n image_xyz = tf.stack([x, y, z], axis=-1)\n return image_xyz","def example_lab_to_rgb():\r\n\r\n print(\"=== RGB Example: Lab->RGB ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.217)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n rgb = convert_color(lab, sRGBColor)\r\n print(rgb)\r\n print(\"=== End Example ===\\n\")","def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n L: torch.Tensor = image[..., 0, :, :]\n u: torch.Tensor = image[..., 1, :, :]\n v: torch.Tensor = image[..., 2, :, :]\n\n # Convert from Luv to XYZ\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\n\n # Compute white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n a: torch.Tensor = u_w + u / (13 * L + eps)\n d: torch.Tensor = v_w + v / (13 * L + eps)\n c: torch.Tensor = 3 * y * (5 * d - 3)\n\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\n\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\n\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\n\n # Convert from RGB Linear to sRGB\n rgb_im = linear_rgb_to_rgb(rgbs_im)\n\n return rgb_im","def sRGBFromLabD50(lab):\n return xyzTosRGBD50(labToXYZ(lab, [0.9642957, 1, 0.8251046]))","def sRGBLuminance(x):\n lin=linearFromsRGB3(x)\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722","def linear_rgb2lab(rgb, illuminant=\"D65\", observer=\"2\"):\n return xyz2lab(linear_rgb2xyz(rgb), illuminant, observer)","def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n var_x = x / 100\n var_y = y / 100\n var_z = z / 100\n\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\n\n var_r = tf.where(var_r > 0.0031308,\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\n 12.92 * var_r)\n var_g = tf.where(var_g > 0.0031308,\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\n 12.92 * var_g)\n var_b = tf.where(var_b > 0.0031308,\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\n 12.92 * var_b)\n r = var_r * 255\n g = var_g * 255\n b = var_b * 255\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\n return rgb_image","def _convert_to_yolo_img(self, img):\n\n img = img / 255.0\n h, w, c = img.shape\n img = img.transpose(2, 0, 1)\n outimg = make_image(w, h, c)\n img = img.reshape((w*h*c))\n data = c_array(c_float, img)\n outimg.data = data\n rgbgr_image(outimg)\n return outimg","def applyJET(img):\n\treturn applyColorMap(img, \"jet\")","def hlab_to_xyz(hlab: Vector, white: VectorLike) -> Vector:\n\n xn, yn, zn = alg.multiply(util.xy_to_xyz(white), 100, dims=alg.D1_SC)\n ka = CKA * alg.nth_root(xn / CXN, 2)\n kb = CKB * alg.nth_root(zn / CZN, 2)\n l, a, b = hlab\n l /= 100\n y = (l ** 2) * yn\n x = (((a * l) / ka) + (y / yn)) * xn\n z = (((b * l) / kb) - (y / yn)) * -zn\n return alg.divide([x, y, z], 100, dims=alg.D1_SC)","def convert_color(image, color_space):\n out_image = None\n if color_space != 'RGB':\n if color_space == 'HSV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\n elif color_space == 'LUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\n elif color_space == 'HLS':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\n elif color_space == 'YUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\n elif color_space == 'YCrCb':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\n else:\n out_image = np.copy(image)\n return out_image","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def XYZ_to_sRGB_linear(XYZ):\n\n rgb = np.tensordot(T, XYZ, axes=([1, 0]))\n\n if clip_method == CLIP_CLAMP_TO_ZERO:\n # set negative rgb values to zero\n rgb = np.where(rgb < 0, 0, rgb)\n return rgb\n\n if clip_method == CLIP_ADD_WHITE:\n # add enough white to make all rgb values nonnegative\n # find max negative rgb (or 0.0 if all non-negative), we need that much white\n rgb_min = np.amin(rgb, axis=0)\n # get max positive component\n rgb_max = np.amax(rgb, axis=0)\n\n # get scaling factor to maintain max rgb after adding white\n scaling = np.where(rgb_max > 0.0, rgb_max / (rgb_max - rgb_min + 0.00001), 1.0)\n\n # add enough white to cancel this out, maintaining the maximum of rgb\n rgb = np.where(rgb_min < 0.0, scaling * (rgb - rgb_min), rgb)\n return rgb","def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\n\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)","def RGB_to_XYZ(RGB,\n illuminant_RGB,\n illuminant_XYZ,\n RGB_to_XYZ_matrix,\n chromatic_adaptation_transform='CAT02',\n decoding_cctf=None):\n\n if decoding_cctf is not None:\n RGB = decoding_cctf(RGB)\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n transform=chromatic_adaptation_transform)\n\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\n\n XYZ_a = dot_vector(M, XYZ)\n\n return XYZ_a","def XYZ_to_sRGB(XYZ):\n\n rgb = XYZ_to_sRGB_linear(XYZ)\n rgb = sRGB_linear_to_sRGB(rgb)\n\n return rgb","def matplotlib_image(image):\n if image.ndim == 2:\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\n else:\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return rgb","def test_srgb_conversion_to_xyz_d50(self):\r\n\r\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))","def XYZ_to_RGB(XYZ,\n illuminant_XYZ,\n illuminant_RGB,\n XYZ_to_RGB_matrix,\n chromatic_adaptation_transform='CAT02',\n encoding_cctf=None):\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n transform=chromatic_adaptation_transform)\n\n XYZ_a = dot_vector(M, XYZ)\n\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\n\n if encoding_cctf is not None:\n RGB = encoding_cctf(RGB)\n\n return RGB","def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\r\n\r\n temp_X = cobj.xyz_x\r\n temp_Y = cobj.xyz_y\r\n temp_Z = cobj.xyz_z\r\n\r\n logger.debug(\" \\- Target RGB space: %s\", target_rgb)\r\n target_illum = target_rgb.native_illuminant\r\n logger.debug(\" \\- Target native illuminant: %s\", target_illum)\r\n logger.debug(\" \\- XYZ color's illuminant: %s\", cobj.illuminant)\r\n \r\n # If the XYZ values were taken with a different reference white than the\r\n # native reference white of the target RGB space, a transformation matrix\r\n # must be applied.\r\n if cobj.illuminant != target_illum:\r\n logger.debug(\" \\* Applying transformation from %s to %s \",\r\n cobj.illuminant, target_illum)\r\n # Get the adjusted XYZ values, adapted for the target illuminant.\r\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\r\n temp_X, temp_Y, temp_Z,\r\n orig_illum=cobj.illuminant, targ_illum=target_illum)\r\n logger.debug(\" \\* New values: %.3f, %.3f, %.3f\",\r\n temp_X, temp_Y, temp_Z)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\r\n temp_X, temp_Y, temp_Z,\r\n rgb_type=target_rgb, convtype=\"xyz_to_rgb\")\r\n\r\n # v\r\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\r\n # V\r\n nonlinear_channels = {}\r\n if target_rgb == sRGBColor:\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n if v <= 0.0031308:\r\n nonlinear_channels[channel] = v * 12.92\r\n else:\r\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\r\n else:\r\n # If it's not sRGB...\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\r\n\r\n return target_rgb(\r\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])","def xyz_to_hlab(xyz: Vector, white: VectorLike) -> Vector:\n\n xn, yn, zn = alg.multiply(util.xy_to_xyz(white), 100, dims=alg.D1_SC)\n ka = CKA * alg.nth_root(xn / CXN, 2)\n kb = CKB * alg.nth_root(zn / CZN, 2)\n x, y, z = alg.multiply(xyz, 100, dims=alg.D1_SC)\n l = alg.nth_root(y / yn, 2)\n a = b = 0.0\n if l != 0:\n a = ka * (x / xn - y / yn) / l\n b = kb * (y / yn - z / zn) / l\n return [l * 100, a, b]","def XYZ_to_optimization_colour_model(XYZ: ArrayLike) -> NDArrayFloat:\n\n return XYZ_to_Lab(XYZ, RGB_COLOURSPACE_ACES2065_1.whitepoint)","def getRGBFromXYAndBrightness(self, x, y, bri=1):\n # The xy to color conversion is almost the same, but in reverse order.\n # Check if the xy value is within the color gamut of the lamp.\n # If not continue with step 2, otherwise step 3.\n # We do this to calculate the most accurate color the given light can actually do.\n xyPoint = XYPoint(x, y)\n\n if not self.checkPointInLampsReach(xyPoint):\n # Calculate the closest point on the color gamut triangle\n # and use that as xy value See step 6 of color to xy.\n xyPoint = self.getClosestPointToPoint(xyPoint)\n\n # Calculate XYZ values Convert using the following formulas:\n Y = bri\n X = (Y / xyPoint.y) * xyPoint.x\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\n\n # Convert to RGB using Wide RGB D65 conversion\n r = X * 1.612 - Y * 0.203 - Z * 0.302\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\n b = X * 0.026 - Y * 0.072 + Z * 0.962\n\n # Apply reverse gamma correction\n r, g, b = map(\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\n [r, g, b]\n )\n\n # Bring all negative components to zero\n r, g, b = map(lambda x: max(0, x), [r, g, b])\n\n # If one component is greater than 1, weight components by that value.\n max_component = max(r, g, b)\n if max_component > 1:\n r, g, b = map(lambda x: x / max_component, [r, g, b])\n\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\n\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\n return (r, g, b)","def rgb_to_hunterlab(rgb_color, illuminant):\n\n return xyz_to_hunterlab(rgb_to_xyz(rgb_color), illuminant)","def chromatic_transform(im, label=None, d_h=None, d_s=None, d_l=None):\n # Set random hue, luminosity and saturation which ranges from -0.1 to 0.1\n if d_h is None:\n d_h = (np.random.rand(1) - 0.5) * 0.2 * 180\n if d_l is None:\n d_l = (np.random.rand(1) - 0.5) * 0.2 * 256\n if d_s is None:\n d_s = (np.random.rand(1) - 0.5) * 0.2 * 256\n # Convert the BGR to HLS\n hls = cv2.cvtColor(im, cv2.COLOR_BGR2HLS)\n h, l, s = cv2.split(hls)\n # Add the values to the image H, L, S\n new_h = (h + d_h) % 180\n new_l = np.clip(l + d_l, 0, 255)\n new_s = np.clip(s + d_s, 0, 255)\n # Convert the HLS to BGR\n new_hls = cv2.merge((new_h, new_l, new_s)).astype('uint8')\n new_im = cv2.cvtColor(new_hls, cv2.COLOR_HLS2BGR)\n\n if label is not None:\n I = np.where(label > 0)\n new_im[I[0], I[1], :] = im[I[0], I[1], :]\n return new_im","def yuv2lab(tens: Tensor) -> Tensor:\n if not _is_yuv_image(tens):\n raise ValueError(\n f\"Tensor of shape 3 expected. Found shape {len(tens.shape)}. \"\n \"This function converts an YUV Tensor to its LAB counterpart\"\n )\n\n img = yuv2bgr(tens)\n img = bgr2lab(tens)\n return to_tensor(img, cspace=\"lab\")","def LCHab_to_Lab(cobj, *args, **kwargs):\r\n \r\n lab_l = cobj.lch_l\r\n lab_a = math.cos(math.radians(cobj.lch_h)) * cobj.lch_c\r\n lab_b = math.sin(math.radians(cobj.lch_h)) * cobj.lch_c\r\n return LabColor(\r\n lab_l, lab_a, lab_b, illuminant=cobj.illuminant, observer=cobj.observer)","def l_to_rgb(img_l):\n lab = np.squeeze(255 * (img_l + 1) / 2)\n return color.gray2rgb(lab) / 255","def test_srgb_conversion_to_xyz_d65(self):\r\n\r\n xyz = convert_color(self.color, XYZColor)\r\n self.assertColorMatch(xyz, XYZColor(0.294, 0.457, 0.103))","def colorize_images(self, img):\n self.load_model()\n self.mdn.eval()\n self.vae.eval()\n n, _, _ = img.shape\n img = img.astype(np.float32) / 255\n img = torch.tensor(img, dtype=torch.float, device=self.device).unsqueeze(1)\n with torch.no_grad():\n z = self.mdn(img)\n ab_out = self.vae.decode(z)\n lab_out = torch.cat((img, ab_out), dim=1)\n lab_out = self.unnormalize(lab_out).cpu().numpy()\n lab_out = np.transpose(lab_out, (0, 2, 3, 1)).astype(np.uint8)\n for i in range(n):\n color_out = cv2.cvtColor(lab_out[i], cv2.COLOR_LAB2BGR)\n color_out = cv2.resize(color_out, (96, 96), interpolation=cv2.INTER_AREA)\n cv2.imwrite(\"../datasets/stl10/divcolor/{}.png\".format(str(i)), color_out)\n return","def generate_lut(self):\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n for i in Numeric.arange(256):\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\n return r,g,b","def rgb_to_luv(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n # Convert from sRGB to Linear RGB\n lin_rgb = rgb_to_linear_rgb(image)\n\n xyz_im: torch.Tensor = rgb_to_xyz(lin_rgb)\n\n x: torch.Tensor = xyz_im[..., 0, :, :]\n y: torch.Tensor = xyz_im[..., 1, :, :]\n z: torch.Tensor = xyz_im[..., 2, :, :]\n\n threshold = 0.008856\n L: torch.Tensor = torch.where(y > threshold, 116.0 * torch.pow(y.clamp(min=threshold), 1.0 / 3.0) - 16.0, 903.3 * y)\n\n # Compute reference white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n u_p: torch.Tensor = (4 * x) / (x + 15 * y + 3 * z + eps)\n v_p: torch.Tensor = (9 * y) / (x + 15 * y + 3 * z + eps)\n\n u: torch.Tensor = 13 * L * (u_p - u_w)\n v: torch.Tensor = 13 * L * (v_p - v_w)\n\n out = torch.stack([L, u, v], dim=-3)\n\n return out","def RGB_to_XYZ_matrix(self):\n\n if not self._use_derived_RGB_to_XYZ_matrix:\n return self._RGB_to_XYZ_matrix\n else:\n return self._derived_RGB_to_XYZ_matrix","def _get_lab_color1_vector(color):\r\n\r\n if not color.__class__.__name__ == 'LabColor':\r\n raise ValueError(\r\n \"Delta E functions can only be used with two LabColor objects.\")\r\n return numpy.array([color.lab_l, color.lab_a, color.lab_b])","def labelimg2pixelcluster(self,labelimg):\n if not hasattr(self,'translator'):\n self.translator = {self.labeltools.labels.index(\"electron\"):larcv.kROIEminus,\n self.labeltools.labels.index(\"muon\"):larcv.kROIMuminus,\n self.labeltools.labels.index(\"pion\"):larcv.kROIPiminus,\n self.labeltools.labels.index(\"proton\"):larcv.kROIProton,\n self.labeltools.labels.index(\"gamma\"):larcv.kROIGamma}\n \n clusters = {}\n for iplane in xrange(0,3):\n clusters[iplane] = larcv.Pixel2DCluster()\n if iplane >= len(labelimg):\n continue\n labelmat = labelimg[iplane]\n idx_bg = self.labeltools.labels.index(\"background\")\n idxlabels = (labelmat != idx_bg).nonzero()\n ivertmax = self.images.img_v[iplane].shape[1]-1\n for ivert in xrange(0,len(idxlabels[0])):\n vert = larcv.Pixel2D( idxlabels[0][ivert], ivertmax - idxlabels[1][ivert] )\n label = labelmat[ idxlabels[0][ivert], idxlabels[1][ivert] ]\n print \"storing (\",vert.X(),\",\",vert.Y(),\") label=\", self.labeltools.labels[label]\n vert.Intensity( float(self.translator[ label ] ) )\n clusters[iplane] += vert\n\n return ( clusters[0], clusters[1], clusters[2] )","def luminance(self):\n \n return (self.r + self.g + self.b) // 3","def test_adobe_conversion_to_xyz_d50(self):\r\n\r\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\r\n xyz = convert_color(adobe, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.247, 0.431, 0.060))","def load_color_image_features(img_path):\n ac = scipy.misc.imread(img_path, mode='RGB')\n ac = ac / (255.0 / 2) - 1.0\n return np.array(ac)","def _get_lab_color2_matrix(color):\r\n if not color.__class__.__name__ == 'LabColor':\r\n raise ValueError(\r\n \"Delta E functions can only be used with two LabColor objects.\")\r\n return numpy.array([(color.lab_l, color.lab_a, color.lab_b)])","def read_img(filename) -> Tuple[np.ndarray, np.ndarray]:\n img = cv2.imread(filename, 3)\n labimg = cv2.cvtColor(cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE)), cv2.COLOR_BGR2Lab)\n return np.reshape(labimg[:, :, 0], (config.IMAGE_SIZE, config.IMAGE_SIZE, 1)), labimg[:, :, 1:]","def img_jet(img):\n if len(img.shape) == 2:\n normalized_img = img / 255.0\n jet = np.array(cm.jet(1 - normalized_img)[:, :, :3] * 255, np.uint8)\n else:\n jet = img\n return jet","def linear_rgb2xyz(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(xyz_from_rgb, arr)","def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):\n\n with domain_range_scale(1):\n values = None\n if model == 'CIE XYZ':\n values = XYZ\n elif model == 'CIE xyY':\n values = XYZ_to_xyY(XYZ, illuminant)\n elif model == 'CIE xy':\n values = XYZ_to_xy(XYZ, illuminant)\n elif model == 'CIE Lab':\n values = XYZ_to_Lab(XYZ, illuminant)\n elif model == 'CIE LCHab':\n values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))\n elif model == 'CIE Luv':\n values = XYZ_to_Luv(XYZ, illuminant)\n elif model == 'CIE Luv uv':\n values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)\n elif model == 'CIE LCHuv':\n values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))\n elif model == 'CIE UCS':\n values = XYZ_to_UCS(XYZ)\n elif model == 'CIE UCS uv':\n values = UCS_to_uv(XYZ_to_UCS(XYZ))\n elif model == 'CIE UVW':\n values = XYZ_to_UVW(XYZ, illuminant)\n elif model == 'DIN 99':\n values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))\n elif model == 'Hunter Lab':\n values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))\n elif model == 'Hunter Rdab':\n values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))\n elif model == 'IPT':\n values = XYZ_to_IPT(XYZ)\n elif model == 'JzAzBz':\n values = XYZ_to_JzAzBz(XYZ)\n elif model == 'OSA UCS':\n values = XYZ_to_OSA_UCS(XYZ)\n elif model == 'hdr-CIELAB':\n values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)\n elif model == 'hdr-IPT':\n values = XYZ_to_hdr_IPT(XYZ, **kwargs)\n\n if values is None:\n raise ValueError(\n '\"{0}\" not found in colourspace models: \"{1}\".'.format(\n model, ', '.join(COLOURSPACE_MODELS)))\n\n return values","def xyz_to_hunterlab(xyz_color, illuminant):\n\n if illuminant == 'D65':\n ka = 172.10\n kb = 66.70\n xr = 94.83\n yr = 100.0\n zr = 107.38\n elif illuminant == 'D60':\n ka = 172.45\n kb = 64.28\n xr = 95.21\n yr = 100.0\n zr = 99.60\n elif illuminant == 'D75':\n ka = 171.76\n kb = 70.76\n xr = 94.45\n yr = 100.0\n zr = 120.70\n else:\n raise InvalidIlluminant(\"Wrong Illuminant. Value must be: 'D60',\"\n \" 'D65' or 'D75'.\")\n\n l = 100.0 * np.sqrt(xyz_color[1] / yr)\n a = ka * (((xyz_color[0] / xr) - (xyz_color[1] / yr)) / np.sqrt(\n xyz_color[1] / yr))\n b = kb * (((xyz_color[1] / yr) - (xyz_color[2] / zr)) / np.sqrt(\n xyz_color[1] / yr))\n return l, a, b","def XYZ_to_RGB_matrix(self):\n\n if not self._use_derived_XYZ_to_RGB_matrix:\n return self._XYZ_to_RGB_matrix\n else:\n return self._derived_XYZ_to_RGB_matrix","def rgb_to_xyz(rgb_color):\n\n r = (rgb_color[0] / 255)\n g = (rgb_color[1] / 255)\n b = (rgb_color[2] / 255)\n\n if r > 0.04045:\n r = ((r + 0.055) / 1.055) ** 2.4\n else:\n r = r / 12.92\n\n if g > 0.04045:\n g = ((g + 0.055) / 1.055) ** 2.4\n else:\n g = g / 12.92\n\n if b > 0.04045:\n b = ((b + 0.055) / 1.055) ** 2.4\n else:\n b = b / 12.92\n\n r = r * 100\n g = g * 100\n b = b * 100\n x = (r * 0.4124) + (g * 0.3576) + (b * 0.1805)\n y = (r * 0.2126) + (g * 0.7152) + (b * 0.0722)\n z = (r * 0.0193) + (g * 0.1192) + (b * 0.9505)\n\n return x, y, z","def prepare_images(images):\n images = color.rgb2lab(images)\n\n l = images[:,:,:,:1]/100.\n ab = images[:,:,:,1:]/200. + 0.5\n\n return l, ab","def get_rgb(self, img, r, g, b):\r\n\r\n # Get specific bands of hyperspectral image\r\n red_channel = img[:, :, r]\r\n green_channel = img[:, :, g]\r\n blue_channel = img[:, :, b]\r\n\r\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\r\n img = img.astype('float32')\r\n return img","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())","def xyz2linear_rgb(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(rgb_from_xyz, arr)","def convert_color(image, color_space='RGB'):\n color_space = color_space.lower()\n if color_space != 'rgb':\n if color_space == 'hsv':\n color_transformation = cv2.COLOR_BGR2HSV\n elif color_space == 'luv':\n color_transformation = cv2.COLOR_BGR2LUV\n elif color_space == 'hls':\n color_transformation = cv2.COLOR_BGR2HLS\n elif color_space == 'yuv':\n color_transformation = cv2.COLOR_BGR2YUV\n elif color_space == 'ycrcb':\n color_transformation = cv2.COLOR_BGR2YCrCb\n else:\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\n 'HLS, YUV, YCrCb' % color_space)\n\n return cv2.cvtColor(image, color_transformation)\n else:\n return image","def red_channel(img):\n\n red = np.zeros(img.shape,dtype=float)\n\n red[:,:,2] = np.copy(img[:,:,2])\n\n return red","def Lab_to_LCHab(cobj, *args, **kwargs):\r\n \r\n lch_l = cobj.lab_l\r\n lch_c = math.sqrt(math.pow(float(cobj.lab_a), 2) + math.pow(float(cobj.lab_b), 2))\r\n lch_h = math.atan2(float(cobj.lab_b), float(cobj.lab_a))\r\n \r\n if lch_h > 0:\r\n lch_h = (lch_h / math.pi) * 180\r\n else:\r\n lch_h = 360 - (math.fabs(lch_h) / math.pi) * 180\r\n \r\n return LCHabColor(\r\n lch_l, lch_c, lch_h, observer=cobj.observer, illuminant=cobj.illuminant)","def XYZ_to_optimization_colour_model(XYZ: ArrayLike) -> NDArrayFloat:\n\n return XYZ_to_Oklab(XYZ)","def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\n y: torch.Tensor = image[..., 0, :, :]\n cb: torch.Tensor = image[..., 1, :, :]\n cr: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n cb_shifted: torch.Tensor = cb - delta\n cr_shifted: torch.Tensor = cr - delta\n\n r: torch.Tensor = y + 1.403 * cr_shifted\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\n b: torch.Tensor = y + 1.773 * cb_shifted\n return torch.stack([r, g, b], -3)","def test_adobe_conversion_to_xyz_d65(self):\r\n\r\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\r\n xyz = convert_color(adobe, XYZColor)\r\n self.assertColorMatch(xyz, XYZColor(0.230, 0.429, 0.074))","def RudermanLABFwd(I):\n\n # get input image dimensions\n m = I.shape[0]\n n = I.shape[1]\n\n # define conversion matrices\n RGB2LMS = np.array([[0.3811, 0.5783, 0.0402],\n [0.1967, 0.7244, 0.0782],\n [0.0241, 0.1288, 0.8444]])\n LMS2LAB = np.array([[1 / (3**0.5), 0, 0],\n [0, 1 / (6**0.5), 0],\n [0, 0, 1 / (2**0.5)]]).dot(np.array([[1, 1, 1],\n [1, 1, -2],\n [1, -1, 0]]))\n\n # calculate LMS values from RGB\n I = np.reshape(I, (m * n, 3))\n LMS = np.dot(RGB2LMS, np.transpose(I))\n LMS[LMS == 0] = np.spacing(1)\n logLMS = np.log(LMS)\n\n # calculate LAB values from LMS\n I_LAB = LMS2LAB.dot(logLMS)\n\n # reshape to 3-channel image\n I_LAB = np.reshape(I_LAB.transpose(), (m, n, 3))\n\n return I_LAB","def luminance(rgb):\n \n (r, g, b) = rgb\n return (r + g + b) // 3","def luma(cspace: np.array) -> np.array:\n \n assert isinstance(cspace, DataFrame), \"Colorspace must be a dataframe\"\n assert all(np.isin(['R', 'G', 'B'], cspace.columns)), \"Colorspace must contain RGB columns\"\n return 0.2989 * cspace['R'] + 0.5870 * cspace['G'] + 0.1140 * cspace['B']","def enhance_contrast(img):\n # CLAHE (Contrast Limited Adaptive Histogram Equalization)\n clahe = cv2.createCLAHE(clipLimit=3., tileGridSize=(8, 8))\n\n lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) # convert from BGR to LAB color space\n l, a, b = cv2.split(lab) # split on 3 different channels\n\n l2 = clahe.apply(l) # apply CLAHE to the L-channel\n\n lab = cv2.merge((l2, a, b)) # merge channels\n img2 = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # convert from LAB to BGR\n\n return img2","def read_colormap(name):\n\n path = get_demo_file(name + '.c3g')\n\n out = []\n with open(path, 'r') as file:\n for line in file:\n if 'rgb(' not in line:\n continue\n line = line.split('(')[-1].split(')')[0]\n out.append([float(n) for n in line.split(',')])\n\n return np.asarray(out).astype(float) / 256.","def example_lchab_to_lchuv():\r\n\r\n print(\"=== Complex Example: LCHab->LCHuv ===\")\r\n # Instantiate an LCHab color object with the given values.\r\n lchab = LCHabColor(0.903, 16.447, 352.252)\r\n # Show a string representation.\r\n print(lchab)\r\n # Convert to LCHuv.\r\n lchuv = convert_color(lchab, LCHuvColor)\r\n print(lchuv)\r\n print(\"=== End Example ===\\n\")","def rgb(self, xyz: Union[ndarray, Iterable[float]]) -> ndarray:\n return self.value.xyz_to_rgb_matrix @ xyz","def use_derived_RGB_to_XYZ_matrix(self):\n\n return self._use_derived_RGB_to_XYZ_matrix","def imagetopalette(image, palcolors):\n assert image.mode == 'L', \"Only grayscale images supported !\"\n pal = [(palcolors[i],palcolors[i+1]) for i in range(len(palcolors)-1)]\n image.putdata([colortopalette(c,pal) for c in list(image.getdata())])","def loadDeth(ficXYZ):\n data=np.array(Image.open(ficXYZ))\n cloud=data.reshape((-1)).view(dtype=np.uint16).reshape((data.shape[0],data.shape[1]/2,data.shape[2]))-0x7FFF\n # change of the type of the data to correspond to the encoding of the data in the imageXYZ + data refocusing\n cloud=cloud.astype('int16')\n cloud=cloud.astype('float32')/1000.0\n return cloud","def green_channel(input_image):\n return input_image[:, :, 1]","def read_img(img): #X\n im = plt.imread(img)\n im = im[:, :, :3]\n if im.max()>200:\n im = im/255.\n return rgb_to_hsv(im)-0.5","def __enhance_image(self, img):\n\n blue = self.g.clahe.apply(img[:,:,0])\n green = self.g.clahe.apply(img[:,:,1])\n red = self.g.clahe.apply(img[:,:,2])\n img[:,:,0] = blue\n img[:,:,1] = green\n img[:,:,2] = red\n return img","def negative(img): \n for pixel in img:\n x, y, col = pixel \n r, g, b = col\n \n new_color = create_color(255 - r, 255 - g, 255 - b)\n set_color(img, x, y, new_color)","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))","def scale_to_01(color: C3I) -> C3F:\n r, g, b = color\n return r / 255, g / 255, b / 255","def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):\r\n\r\n # Will contain linearized RGB channels (removed the gamma func).\r\n linear_channels = {}\r\n\r\n if isinstance(cobj, sRGBColor):\r\n for channel in ['r', 'g', 'b']:\r\n V = getattr(cobj, 'rgb_' + channel)\r\n if V <= 0.04045:\r\n linear_channels[channel] = V / 12.92\r\n else:\r\n linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)\r\n else:\r\n # If it's not sRGB...\r\n gamma = cobj.rgb_gamma\r\n\r\n for channel in ['r', 'g', 'b']:\r\n V = getattr(cobj, 'rgb_' + channel)\r\n linear_channels[channel] = math.pow(V, gamma)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n xyz_x, xyz_y, xyz_z = apply_RGB_matrix(\r\n linear_channels['r'], linear_channels['g'], linear_channels['b'],\r\n rgb_type=cobj, convtype=\"rgb_to_xyz\")\r\n\r\n if target_illuminant is None:\r\n target_illuminant = cobj.native_illuminant\r\n \r\n # The illuminant of the original RGB object. This will always match\r\n # the RGB colorspace's native illuminant.\r\n illuminant = cobj.native_illuminant\r\n xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)\r\n # This will take care of any illuminant changes for us (if source\r\n # illuminant != target illuminant).\r\n xyzcolor.apply_adaptation(target_illuminant)\r\n\r\n return xyzcolor","def cie_xyz(self):\n return tuple(matrix_mult(\n ((0.4124564, 0.3575761, 0.1804375),\n (0.2126729, 0.7151522, 0.0721750),\n (0.0193339, 0.1191920, 0.9503041),\n ),\n (from_srgb(self.red),\n from_srgb(self.green),\n from_srgb(self.blue)\n )\n ))","def linearTosRGB3(c):\n return [linearTosRGB(c[0]), linearTosRGB(c[1]), linearTosRGB(c[2])]","def compute_luminosity(red, green, blue):\r\n return (0.299 * red) + (0.587 * green) + (0.114 * blue)","def RGB_to_XYZ_matrix(self, value):\n\n if value is not None:\n value = np.asarray(value)\n self._RGB_to_XYZ_matrix = value","def example_spectral_to_xyz():\r\n\r\n print(\"=== Example: Spectral->XYZ ===\")\r\n spc = SpectralColor(\r\n observer='2', illuminant='d50',\r\n spec_380nm=0.0600, spec_390nm=0.0600, spec_400nm=0.0641,\r\n spec_410nm=0.0654, spec_420nm=0.0645, spec_430nm=0.0605,\r\n spec_440nm=0.0562, spec_450nm=0.0543, spec_460nm=0.0537,\r\n spec_470nm=0.0541, spec_480nm=0.0559, spec_490nm=0.0603,\r\n spec_500nm=0.0651, spec_510nm=0.0680, spec_520nm=0.0705,\r\n spec_530nm=0.0736, spec_540nm=0.0772, spec_550nm=0.0809,\r\n spec_560nm=0.0870, spec_570nm=0.0990, spec_580nm=0.1128,\r\n spec_590nm=0.1251, spec_600nm=0.1360, spec_610nm=0.1439,\r\n spec_620nm=0.1511, spec_630nm=0.1590, spec_640nm=0.1688,\r\n spec_650nm=0.1828, spec_660nm=0.1996, spec_670nm=0.2187,\r\n spec_680nm=0.2397, spec_690nm=0.2618, spec_700nm=0.2852,\r\n spec_710nm=0.2500, spec_720nm=0.2400, spec_730nm=0.2300)\r\n xyz = convert_color(spc, XYZColor)\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def __add_color(self, intensity):\n val = color.rgb2lab(\n (self.image[self.lip_y, self.lip_x] / 255.)\n .reshape(len(self.lip_y), 1, 3)\n ).reshape(len(self.lip_y), 3)\n l_val, a_val, b_val = np.mean(val[:, 0]), np.mean(val[:, 1]), np.mean(val[:, 2])\n l1_val, a1_val, b1_val = color.rgb2lab(\n np.array(\n (self.red_l / 255., self.green_l / 255., self.blue_l / 255.)\n ).reshape(1, 1, 3)\n ).reshape(3,)\n l_final, a_final, b_final = (l1_val - l_val) * \\\n intensity, (a1_val - a_val) * \\\n intensity, (b1_val - b_val) * intensity\n val[:, 0] = np.clip(val[:, 0] + l_final, 0, 100)\n val[:, 1] = np.clip(val[:, 1] + a_final, -127, 128)\n val[:, 2] = np.clip(val[:, 2] + b_final, -127, 128)\n self.image[self.lip_y, self.lip_x] = color.lab2rgb(val.reshape(\n len(self.lip_y), 1, 3)).reshape(len(self.lip_y), 3) * 255","def convertColorSpace(\n self,\n img, # Image in some color space\n srcColorSpace = 'BGR', # Source color space\n tgtColorSpace = 'RGB', # Traget color space\n ):\n\n if srcColorSpace == tgtColorSpace:\n return img\n\n if srcColorSpace == 'BGR':\n img_bgr = img\n elif srcColorSpace == 'RGB':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n elif srcColorSpace == 'HSV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n elif srcColorSpace == 'HLS':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\n elif srcColorSpace == 'LUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\n elif srcColorSpace == 'YUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\n elif srcColorSpace == 'YCrCb':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\n else:\n raise Exception(\"Incorrect color space: {}\".format(srcColorSpace))\n\n if tgtColorSpace == 'BGR':\n img_tgt = img_bgr\n elif tgtColorSpace == 'RGB':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\n elif tgtColorSpace == 'HSV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\n elif tgtColorSpace == 'HLS':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\n elif tgtColorSpace == 'LUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\n elif tgtColorSpace == 'YUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\n elif tgtColorSpace == 'YCrCb':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\n else:\n raise Exception(\"Incorrect color space: {}\".format(tgtColorSpace))\n\n return img_tgt"],"string":"[\n \"def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\\n xyz = rgb_to_xyz(image)\\n lab_image = xyz_to_lab(xyz)\\n return lab_image\",\n \"def XYZ_to_Lab(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n temp_x = cobj.xyz_x / illum[\\\"X\\\"]\\r\\n temp_y = cobj.xyz_y / illum[\\\"Y\\\"]\\r\\n temp_z = cobj.xyz_z / illum[\\\"Z\\\"]\\r\\n \\r\\n if temp_x > color_constants.CIE_E:\\r\\n temp_x = math.pow(temp_x, (1.0 / 3.0))\\r\\n else:\\r\\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \\r\\n\\r\\n if temp_y > color_constants.CIE_E:\\r\\n temp_y = math.pow(temp_y, (1.0 / 3.0))\\r\\n else:\\r\\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\\r\\n \\r\\n if temp_z > color_constants.CIE_E:\\r\\n temp_z = math.pow(temp_z, (1.0 / 3.0))\\r\\n else:\\r\\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\\r\\n \\r\\n lab_l = (116.0 * temp_y) - 16.0\\r\\n lab_a = 500.0 * (temp_x - temp_y)\\r\\n lab_b = 200.0 * (temp_y - temp_z)\\r\\n return LabColor(\\r\\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def Lab_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n xyz_y = (cobj.lab_l + 16.0) / 116.0\\r\\n xyz_x = cobj.lab_a / 500.0 + xyz_y\\r\\n xyz_z = xyz_y - cobj.lab_b / 200.0\\r\\n \\r\\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\\r\\n xyz_y = math.pow(xyz_y, 3)\\r\\n else:\\r\\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\\r\\n\\r\\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\\r\\n xyz_x = math.pow(xyz_x, 3)\\r\\n else:\\r\\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\\r\\n \\r\\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\\r\\n xyz_z = math.pow(xyz_z, 3)\\r\\n else:\\r\\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\\r\\n \\r\\n xyz_x = (illum[\\\"X\\\"] * xyz_x)\\r\\n xyz_y = (illum[\\\"Y\\\"] * xyz_y)\\r\\n xyz_z = (illum[\\\"Z\\\"] * xyz_z)\\r\\n \\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n xyz = lab_to_xyz(image)\\n rgb_image = xyz_to_rgb(xyz)\\n return rgb_image\",\n \"def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n l, a, b = tf.unstack(image, axis=-1)\\n\\n var_y = (l + 16) / 116\\n var_x = a / 500 + var_y\\n var_z = var_y - b / 200\\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\\n (var_x - 16 / 116) / 7.787)\\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\\n (var_y - 16 / 116) / 7.787)\\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\\n (var_z - 16 / 116) / 7.787)\\n\\n refx = 95.047\\n refy = 100.00\\n ref_z = 108.883\\n\\n x = var_x * refx\\n y = var_y * refy\\n z = var_z * ref_z\\n xyz_image = tf.stack([x, y, z], axis=-1)\\n return xyz_image\",\n \"def lab_to_rgb(img):\\n new_img = np.zeros((256, 256, 3))\\n for i in range(len(img)):\\n for j in range(len(img[i])):\\n pix = img[i, j]\\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\\n new_img = color.lab2rgb(new_img) * 255\\n new_img = new_img.astype('uint8')\\n return new_img\",\n \"def example_rgb_to_xyz():\\r\\n\\r\\n print(\\\"=== RGB Example: RGB->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n rgb = sRGBColor(120, 130, 140)\\r\\n # Show a string representation.\\r\\n print(rgb)\\r\\n # Convert RGB to XYZ using a D50 illuminant.\\r\\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def rgb_to_lab(img, l=False, ab=False):\\n img = img / 255\\n l_chan = color.rgb2lab(img)[:, :, 0]\\n l_chan = l_chan / 50 - 1\\n l_chan = l_chan[..., np.newaxis]\\n\\n ab_chan = color.rgb2lab(img)[:, :, 1:]\\n ab_chan = (ab_chan + 128) / 255 * 2 - 1\\n if l:\\n return l_chan\\n else:\\n return ab_chan\",\n \"def example_lab_to_xyz():\\r\\n\\r\\n print(\\\"=== Simple Example: Lab->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.22)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n xyz = convert_color(lab, XYZColor)\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def lab_to_rgb(img_l, img_ab):\\n lab = np.empty([*img_l.shape[0:2], 3])\\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\\n lab[:, :, 1:] = img_ab * 127\\n return color.lab2rgb(lab)\",\n \"def sRGBToLab(rgb):\\n return xyzToLab(xyzFromsRGB(rgb), [0.9504559, 1, 1.089058])\",\n \"def rgb2Lab(rgbvalue):\\r\\n RGB2Lab_Matrix = np.array([[0.412453, 0.357580, 0.180423],\\r\\n [0.212671, 0.715160, 0.072169],\\r\\n [0.019334, 0.119193, 0.950227]])\\r\\n R = rgbvalue[0]\\r\\n G = rgbvalue[1]\\r\\n B = rgbvalue[2]\\r\\n gammaR = gamma(R / 255.0)\\r\\n gammaG = gamma(G / 255.0)\\r\\n gammaB = gamma(B / 255.0)\\r\\n RGBvalue = np.array([gammaR, gammaG, gammaB])\\r\\n RGBvalue = RGBvalue.reshape(3, 1)\\r\\n XYZvalue = np.dot(RGB2Lab_Matrix, RGBvalue)\\r\\n assert XYZvalue.shape == (3, 1)\\r\\n correction = np.array([[1.0 / 0.950456, 1.0, 1.0 / 1.088754]]).T\\r\\n assert correction.shape == (3, 1)\\r\\n XYZ = XYZvalue * correction\\r\\n assert XYZ.shape == (3, 1)\\r\\n YYn = ft(XYZ[1])\\r\\n XXn = ft(XYZ[0])\\r\\n ZZn = ft(XYZ[2])\\r\\n L = 116 * YYn - 16\\r\\n a = 500 * (XXn - YYn)\\r\\n b = 200 * (YYn - ZZn)\\r\\n return [int(L), int(a), int(b)]\",\n \"def rgb2lab(r, g, b):\\n r, g, b = r / 255.0, g / 255.0, b / 255.0\\n\\n # http://www.brucelindbloom.com/index.html?Math.html\\n # Inverse sRGB Companding\\n r = r / 12.92 if r <= 0.04045 else ((r + 0.055) / 1.055) ** 2.4\\n g = g / 12.92 if g <= 0.04045 else ((g + 0.055) / 1.055) ** 2.4\\n b = b / 12.92 if b <= 0.04045 else ((b + 0.055) / 1.055) ** 2.4\\n\\n # http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html\\n # sRGB, D65\\n x = r * 0.4124564 + g * 0.3575761 + b * 0.1804375\\n y = r * 0.2126729 + g * 0.7151522 + b * 0.0721750\\n z = r * 0.0193339 + g * 0.1191920 + b * 0.9503041\\n\\n # http://www.brucelindbloom.com/index.html?Eqn_XYZ_to_Lab.html\\n kappa, epsilon = 903.3, 0.008856\\n\\n # http://brucelindbloom.com/index.html?Eqn_ChromAdapt.html\\n # White point for D65\\n xr, yr, zr = x / 0.95047, y / 1.00000, z / 1.08883\\n\\n fx = xr ** (1 / 3.0) if xr > epsilon else (kappa * xr + 16) / 116.0\\n fy = yr ** (1 / 3.0) if yr > epsilon else (kappa * yr + 16) / 116.0\\n fz = zr ** (1 / 3.0) if zr > epsilon else (kappa * zr + 16) / 116.0\\n\\n l = 166.0 * fy - 16.0\\n a = 500.0 * (fx - fy)\\n b = 200.0 * (fy - fz)\\n\\n return l, a, b\",\n \"def sRGBFromLab(lab):\\n return xyzTosRGB(labToXYZ(lab, [0.9504559, 1, 1.089058]))\",\n \"def rgb2alpha(img):\\n\\t### First of all we need the size of our picture to make the transforms\\n\\n\\tx = len(img) ; y = len(img[0])\\n\\talpha = np.full((x, y, 3), 0, dtype = float)\\t\\t## This will be the transformed image\\n\\n\\t### Now we gotta access each pixel of the picture\\n\\n\\tfor i, vi in enumerate(img):\\n\\t\\tfor j, px in enumerate(vi):\\n\\t\\t\\t### There we are\\n\\n\\t\\t\\t# Step 1 : LMS transform, for that we use r_l\\n\\n\\t\\t\\talpha[i][j] = np.matmul(r_l, px)\\n\\n\\t\\t\\t# Step 2 : log em all (decimal log)\\n\\n\\t\\t\\talpha[i][j][0] = log(alpha[i][j][0])\\n\\t\\t\\talpha[i][j][1] = log(alpha[i][j][1])\\n\\t\\t\\talpha[i][j][2] = log(alpha[i][j][2])\\n\\n\\t\\t\\t# Step 3 : l alpha beta transform, by using l_a\\n\\n\\t\\t\\talpha[i][j] = np.matmul(l_a, alpha[i][j])\\n\\n\\treturn alpha\",\n \"def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\\n\\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def sRGBToLabD50(rgb):\\n return xyzToLab(xyzFromsRGBD50(rgb), [0.9642957, 1, 0.8251046])\",\n \"def Luv_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n # Without Light, there is no color. Short-circuit this and avoid some\\r\\n # zero division errors in the var_a_frac calculation.\\r\\n if cobj.luv_l <= 0.0:\\r\\n xyz_x = 0.0\\r\\n xyz_y = 0.0\\r\\n xyz_z = 0.0\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\\r\\n\\r\\n # Various variables used throughout the conversion.\\r\\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\\r\\n u_sub_0 = (4.0 * illum[\\\"X\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n v_sub_0 = (9.0 * illum[\\\"Y\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\\r\\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\\r\\n\\r\\n # Y-coordinate calculations.\\r\\n if cobj.luv_l > cie_k_times_e:\\r\\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\\r\\n else:\\r\\n xyz_y = cobj.luv_l / color_constants.CIE_K\\r\\n\\r\\n # X-coordinate calculation.\\r\\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\\r\\n # Z-coordinate calculation.\\r\\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\\r\\n\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)\",\n \"def XYZ_to_Luv(cobj, *args, **kwargs):\\r\\n \\r\\n temp_x = cobj.xyz_x\\r\\n temp_y = cobj.xyz_y\\r\\n temp_z = cobj.xyz_z\\r\\n \\r\\n luv_u = (4.0 * temp_x) / (temp_x + (15.0 * temp_y) + (3.0 * temp_z))\\r\\n luv_v = (9.0 * temp_y) / (temp_x + (15.0 * temp_y) + (3.0 * temp_z))\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n temp_y = temp_y / illum[\\\"Y\\\"]\\r\\n if temp_y > color_constants.CIE_E:\\r\\n temp_y = math.pow(temp_y, (1.0 / 3.0))\\r\\n else:\\r\\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\\r\\n \\r\\n ref_U = (4.0 * illum[\\\"X\\\"]) / (illum[\\\"X\\\"] + (15.0 * illum[\\\"Y\\\"]) + (3.0 * illum[\\\"Z\\\"]))\\r\\n ref_V = (9.0 * illum[\\\"Y\\\"]) / (illum[\\\"X\\\"] + (15.0 * illum[\\\"Y\\\"]) + (3.0 * illum[\\\"Z\\\"]))\\r\\n \\r\\n luv_l = (116.0 * temp_y) - 16.0\\r\\n luv_u = 13.0 * luv_l * (luv_u - ref_U)\\r\\n luv_v = 13.0 * luv_l * (luv_v - ref_V)\\r\\n \\r\\n return LuvColor(\\r\\n luv_l, luv_u, luv_v, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n r, g, b = tf.unstack(image, axis=-1)\\n var_r = r / 255\\n var_g = g / 255\\n var_b = b / 255\\n\\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\\n var_r / 12.92)\\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\\n var_g / 12.92)\\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\\n var_b / 12.92)\\n var_r = var_r * 100\\n var_g = var_g * 100\\n var_b = var_b * 100\\n\\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\\n\\n image_xyz = tf.stack([x, y, z], axis=-1)\\n return image_xyz\",\n \"def example_lab_to_rgb():\\r\\n\\r\\n print(\\\"=== RGB Example: Lab->RGB ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.217)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n rgb = convert_color(lab, sRGBColor)\\r\\n print(rgb)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n L: torch.Tensor = image[..., 0, :, :]\\n u: torch.Tensor = image[..., 1, :, :]\\n v: torch.Tensor = image[..., 2, :, :]\\n\\n # Convert from Luv to XYZ\\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\\n\\n # Compute white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n a: torch.Tensor = u_w + u / (13 * L + eps)\\n d: torch.Tensor = v_w + v / (13 * L + eps)\\n c: torch.Tensor = 3 * y * (5 * d - 3)\\n\\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\\n\\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\\n\\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\\n\\n # Convert from RGB Linear to sRGB\\n rgb_im = linear_rgb_to_rgb(rgbs_im)\\n\\n return rgb_im\",\n \"def sRGBFromLabD50(lab):\\n return xyzTosRGBD50(labToXYZ(lab, [0.9642957, 1, 0.8251046]))\",\n \"def sRGBLuminance(x):\\n lin=linearFromsRGB3(x)\\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722\",\n \"def linear_rgb2lab(rgb, illuminant=\\\"D65\\\", observer=\\\"2\\\"):\\n return xyz2lab(linear_rgb2xyz(rgb), illuminant, observer)\",\n \"def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n var_x = x / 100\\n var_y = y / 100\\n var_z = z / 100\\n\\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\\n\\n var_r = tf.where(var_r > 0.0031308,\\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\\n 12.92 * var_r)\\n var_g = tf.where(var_g > 0.0031308,\\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\\n 12.92 * var_g)\\n var_b = tf.where(var_b > 0.0031308,\\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\\n 12.92 * var_b)\\n r = var_r * 255\\n g = var_g * 255\\n b = var_b * 255\\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\\n return rgb_image\",\n \"def _convert_to_yolo_img(self, img):\\n\\n img = img / 255.0\\n h, w, c = img.shape\\n img = img.transpose(2, 0, 1)\\n outimg = make_image(w, h, c)\\n img = img.reshape((w*h*c))\\n data = c_array(c_float, img)\\n outimg.data = data\\n rgbgr_image(outimg)\\n return outimg\",\n \"def applyJET(img):\\n\\treturn applyColorMap(img, \\\"jet\\\")\",\n \"def hlab_to_xyz(hlab: Vector, white: VectorLike) -> Vector:\\n\\n xn, yn, zn = alg.multiply(util.xy_to_xyz(white), 100, dims=alg.D1_SC)\\n ka = CKA * alg.nth_root(xn / CXN, 2)\\n kb = CKB * alg.nth_root(zn / CZN, 2)\\n l, a, b = hlab\\n l /= 100\\n y = (l ** 2) * yn\\n x = (((a * l) / ka) + (y / yn)) * xn\\n z = (((b * l) / kb) - (y / yn)) * -zn\\n return alg.divide([x, y, z], 100, dims=alg.D1_SC)\",\n \"def convert_color(image, color_space):\\n out_image = None\\n if color_space != 'RGB':\\n if color_space == 'HSV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\\n elif color_space == 'LUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\\n elif color_space == 'HLS':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\\n elif color_space == 'YUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\\n elif color_space == 'YCrCb':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\\n else:\\n out_image = np.copy(image)\\n return out_image\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def XYZ_to_sRGB_linear(XYZ):\\n\\n rgb = np.tensordot(T, XYZ, axes=([1, 0]))\\n\\n if clip_method == CLIP_CLAMP_TO_ZERO:\\n # set negative rgb values to zero\\n rgb = np.where(rgb < 0, 0, rgb)\\n return rgb\\n\\n if clip_method == CLIP_ADD_WHITE:\\n # add enough white to make all rgb values nonnegative\\n # find max negative rgb (or 0.0 if all non-negative), we need that much white\\n rgb_min = np.amin(rgb, axis=0)\\n # get max positive component\\n rgb_max = np.amax(rgb, axis=0)\\n\\n # get scaling factor to maintain max rgb after adding white\\n scaling = np.where(rgb_max > 0.0, rgb_max / (rgb_max - rgb_min + 0.00001), 1.0)\\n\\n # add enough white to cancel this out, maintaining the maximum of rgb\\n rgb = np.where(rgb_min < 0.0, scaling * (rgb - rgb_min), rgb)\\n return rgb\",\n \"def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\\n\\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)\",\n \"def RGB_to_XYZ(RGB,\\n illuminant_RGB,\\n illuminant_XYZ,\\n RGB_to_XYZ_matrix,\\n chromatic_adaptation_transform='CAT02',\\n decoding_cctf=None):\\n\\n if decoding_cctf is not None:\\n RGB = decoding_cctf(RGB)\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n return XYZ_a\",\n \"def XYZ_to_sRGB(XYZ):\\n\\n rgb = XYZ_to_sRGB_linear(XYZ)\\n rgb = sRGB_linear_to_sRGB(rgb)\\n\\n return rgb\",\n \"def matplotlib_image(image):\\n if image.ndim == 2:\\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\\n else:\\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n return rgb\",\n \"def test_srgb_conversion_to_xyz_d50(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))\",\n \"def XYZ_to_RGB(XYZ,\\n illuminant_XYZ,\\n illuminant_RGB,\\n XYZ_to_RGB_matrix,\\n chromatic_adaptation_transform='CAT02',\\n encoding_cctf=None):\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\\n\\n if encoding_cctf is not None:\\n RGB = encoding_cctf(RGB)\\n\\n return RGB\",\n \"def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\\r\\n\\r\\n temp_X = cobj.xyz_x\\r\\n temp_Y = cobj.xyz_y\\r\\n temp_Z = cobj.xyz_z\\r\\n\\r\\n logger.debug(\\\" \\\\- Target RGB space: %s\\\", target_rgb)\\r\\n target_illum = target_rgb.native_illuminant\\r\\n logger.debug(\\\" \\\\- Target native illuminant: %s\\\", target_illum)\\r\\n logger.debug(\\\" \\\\- XYZ color's illuminant: %s\\\", cobj.illuminant)\\r\\n \\r\\n # If the XYZ values were taken with a different reference white than the\\r\\n # native reference white of the target RGB space, a transformation matrix\\r\\n # must be applied.\\r\\n if cobj.illuminant != target_illum:\\r\\n logger.debug(\\\" \\\\* Applying transformation from %s to %s \\\",\\r\\n cobj.illuminant, target_illum)\\r\\n # Get the adjusted XYZ values, adapted for the target illuminant.\\r\\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n orig_illum=cobj.illuminant, targ_illum=target_illum)\\r\\n logger.debug(\\\" \\\\* New values: %.3f, %.3f, %.3f\\\",\\r\\n temp_X, temp_Y, temp_Z)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n rgb_type=target_rgb, convtype=\\\"xyz_to_rgb\\\")\\r\\n\\r\\n # v\\r\\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\\r\\n # V\\r\\n nonlinear_channels = {}\\r\\n if target_rgb == sRGBColor:\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n if v <= 0.0031308:\\r\\n nonlinear_channels[channel] = v * 12.92\\r\\n else:\\r\\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\\r\\n\\r\\n return target_rgb(\\r\\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])\",\n \"def xyz_to_hlab(xyz: Vector, white: VectorLike) -> Vector:\\n\\n xn, yn, zn = alg.multiply(util.xy_to_xyz(white), 100, dims=alg.D1_SC)\\n ka = CKA * alg.nth_root(xn / CXN, 2)\\n kb = CKB * alg.nth_root(zn / CZN, 2)\\n x, y, z = alg.multiply(xyz, 100, dims=alg.D1_SC)\\n l = alg.nth_root(y / yn, 2)\\n a = b = 0.0\\n if l != 0:\\n a = ka * (x / xn - y / yn) / l\\n b = kb * (y / yn - z / zn) / l\\n return [l * 100, a, b]\",\n \"def XYZ_to_optimization_colour_model(XYZ: ArrayLike) -> NDArrayFloat:\\n\\n return XYZ_to_Lab(XYZ, RGB_COLOURSPACE_ACES2065_1.whitepoint)\",\n \"def getRGBFromXYAndBrightness(self, x, y, bri=1):\\n # The xy to color conversion is almost the same, but in reverse order.\\n # Check if the xy value is within the color gamut of the lamp.\\n # If not continue with step 2, otherwise step 3.\\n # We do this to calculate the most accurate color the given light can actually do.\\n xyPoint = XYPoint(x, y)\\n\\n if not self.checkPointInLampsReach(xyPoint):\\n # Calculate the closest point on the color gamut triangle\\n # and use that as xy value See step 6 of color to xy.\\n xyPoint = self.getClosestPointToPoint(xyPoint)\\n\\n # Calculate XYZ values Convert using the following formulas:\\n Y = bri\\n X = (Y / xyPoint.y) * xyPoint.x\\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\\n\\n # Convert to RGB using Wide RGB D65 conversion\\n r = X * 1.612 - Y * 0.203 - Z * 0.302\\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\\n b = X * 0.026 - Y * 0.072 + Z * 0.962\\n\\n # Apply reverse gamma correction\\n r, g, b = map(\\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\\n [r, g, b]\\n )\\n\\n # Bring all negative components to zero\\n r, g, b = map(lambda x: max(0, x), [r, g, b])\\n\\n # If one component is greater than 1, weight components by that value.\\n max_component = max(r, g, b)\\n if max_component > 1:\\n r, g, b = map(lambda x: x / max_component, [r, g, b])\\n\\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\\n\\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\\n return (r, g, b)\",\n \"def rgb_to_hunterlab(rgb_color, illuminant):\\n\\n return xyz_to_hunterlab(rgb_to_xyz(rgb_color), illuminant)\",\n \"def chromatic_transform(im, label=None, d_h=None, d_s=None, d_l=None):\\n # Set random hue, luminosity and saturation which ranges from -0.1 to 0.1\\n if d_h is None:\\n d_h = (np.random.rand(1) - 0.5) * 0.2 * 180\\n if d_l is None:\\n d_l = (np.random.rand(1) - 0.5) * 0.2 * 256\\n if d_s is None:\\n d_s = (np.random.rand(1) - 0.5) * 0.2 * 256\\n # Convert the BGR to HLS\\n hls = cv2.cvtColor(im, cv2.COLOR_BGR2HLS)\\n h, l, s = cv2.split(hls)\\n # Add the values to the image H, L, S\\n new_h = (h + d_h) % 180\\n new_l = np.clip(l + d_l, 0, 255)\\n new_s = np.clip(s + d_s, 0, 255)\\n # Convert the HLS to BGR\\n new_hls = cv2.merge((new_h, new_l, new_s)).astype('uint8')\\n new_im = cv2.cvtColor(new_hls, cv2.COLOR_HLS2BGR)\\n\\n if label is not None:\\n I = np.where(label > 0)\\n new_im[I[0], I[1], :] = im[I[0], I[1], :]\\n return new_im\",\n \"def yuv2lab(tens: Tensor) -> Tensor:\\n if not _is_yuv_image(tens):\\n raise ValueError(\\n f\\\"Tensor of shape 3 expected. Found shape {len(tens.shape)}. \\\"\\n \\\"This function converts an YUV Tensor to its LAB counterpart\\\"\\n )\\n\\n img = yuv2bgr(tens)\\n img = bgr2lab(tens)\\n return to_tensor(img, cspace=\\\"lab\\\")\",\n \"def LCHab_to_Lab(cobj, *args, **kwargs):\\r\\n \\r\\n lab_l = cobj.lch_l\\r\\n lab_a = math.cos(math.radians(cobj.lch_h)) * cobj.lch_c\\r\\n lab_b = math.sin(math.radians(cobj.lch_h)) * cobj.lch_c\\r\\n return LabColor(\\r\\n lab_l, lab_a, lab_b, illuminant=cobj.illuminant, observer=cobj.observer)\",\n \"def l_to_rgb(img_l):\\n lab = np.squeeze(255 * (img_l + 1) / 2)\\n return color.gray2rgb(lab) / 255\",\n \"def test_srgb_conversion_to_xyz_d65(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor)\\r\\n self.assertColorMatch(xyz, XYZColor(0.294, 0.457, 0.103))\",\n \"def colorize_images(self, img):\\n self.load_model()\\n self.mdn.eval()\\n self.vae.eval()\\n n, _, _ = img.shape\\n img = img.astype(np.float32) / 255\\n img = torch.tensor(img, dtype=torch.float, device=self.device).unsqueeze(1)\\n with torch.no_grad():\\n z = self.mdn(img)\\n ab_out = self.vae.decode(z)\\n lab_out = torch.cat((img, ab_out), dim=1)\\n lab_out = self.unnormalize(lab_out).cpu().numpy()\\n lab_out = np.transpose(lab_out, (0, 2, 3, 1)).astype(np.uint8)\\n for i in range(n):\\n color_out = cv2.cvtColor(lab_out[i], cv2.COLOR_LAB2BGR)\\n color_out = cv2.resize(color_out, (96, 96), interpolation=cv2.INTER_AREA)\\n cv2.imwrite(\\\"../datasets/stl10/divcolor/{}.png\\\".format(str(i)), color_out)\\n return\",\n \"def generate_lut(self):\\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\\n for i in Numeric.arange(256):\\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\\n return r,g,b\",\n \"def rgb_to_luv(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n # Convert from sRGB to Linear RGB\\n lin_rgb = rgb_to_linear_rgb(image)\\n\\n xyz_im: torch.Tensor = rgb_to_xyz(lin_rgb)\\n\\n x: torch.Tensor = xyz_im[..., 0, :, :]\\n y: torch.Tensor = xyz_im[..., 1, :, :]\\n z: torch.Tensor = xyz_im[..., 2, :, :]\\n\\n threshold = 0.008856\\n L: torch.Tensor = torch.where(y > threshold, 116.0 * torch.pow(y.clamp(min=threshold), 1.0 / 3.0) - 16.0, 903.3 * y)\\n\\n # Compute reference white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n u_p: torch.Tensor = (4 * x) / (x + 15 * y + 3 * z + eps)\\n v_p: torch.Tensor = (9 * y) / (x + 15 * y + 3 * z + eps)\\n\\n u: torch.Tensor = 13 * L * (u_p - u_w)\\n v: torch.Tensor = 13 * L * (v_p - v_w)\\n\\n out = torch.stack([L, u, v], dim=-3)\\n\\n return out\",\n \"def RGB_to_XYZ_matrix(self):\\n\\n if not self._use_derived_RGB_to_XYZ_matrix:\\n return self._RGB_to_XYZ_matrix\\n else:\\n return self._derived_RGB_to_XYZ_matrix\",\n \"def _get_lab_color1_vector(color):\\r\\n\\r\\n if not color.__class__.__name__ == 'LabColor':\\r\\n raise ValueError(\\r\\n \\\"Delta E functions can only be used with two LabColor objects.\\\")\\r\\n return numpy.array([color.lab_l, color.lab_a, color.lab_b])\",\n \"def labelimg2pixelcluster(self,labelimg):\\n if not hasattr(self,'translator'):\\n self.translator = {self.labeltools.labels.index(\\\"electron\\\"):larcv.kROIEminus,\\n self.labeltools.labels.index(\\\"muon\\\"):larcv.kROIMuminus,\\n self.labeltools.labels.index(\\\"pion\\\"):larcv.kROIPiminus,\\n self.labeltools.labels.index(\\\"proton\\\"):larcv.kROIProton,\\n self.labeltools.labels.index(\\\"gamma\\\"):larcv.kROIGamma}\\n \\n clusters = {}\\n for iplane in xrange(0,3):\\n clusters[iplane] = larcv.Pixel2DCluster()\\n if iplane >= len(labelimg):\\n continue\\n labelmat = labelimg[iplane]\\n idx_bg = self.labeltools.labels.index(\\\"background\\\")\\n idxlabels = (labelmat != idx_bg).nonzero()\\n ivertmax = self.images.img_v[iplane].shape[1]-1\\n for ivert in xrange(0,len(idxlabels[0])):\\n vert = larcv.Pixel2D( idxlabels[0][ivert], ivertmax - idxlabels[1][ivert] )\\n label = labelmat[ idxlabels[0][ivert], idxlabels[1][ivert] ]\\n print \\\"storing (\\\",vert.X(),\\\",\\\",vert.Y(),\\\") label=\\\", self.labeltools.labels[label]\\n vert.Intensity( float(self.translator[ label ] ) )\\n clusters[iplane] += vert\\n\\n return ( clusters[0], clusters[1], clusters[2] )\",\n \"def luminance(self):\\n \\n return (self.r + self.g + self.b) // 3\",\n \"def test_adobe_conversion_to_xyz_d50(self):\\r\\n\\r\\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\\r\\n xyz = convert_color(adobe, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.247, 0.431, 0.060))\",\n \"def load_color_image_features(img_path):\\n ac = scipy.misc.imread(img_path, mode='RGB')\\n ac = ac / (255.0 / 2) - 1.0\\n return np.array(ac)\",\n \"def _get_lab_color2_matrix(color):\\r\\n if not color.__class__.__name__ == 'LabColor':\\r\\n raise ValueError(\\r\\n \\\"Delta E functions can only be used with two LabColor objects.\\\")\\r\\n return numpy.array([(color.lab_l, color.lab_a, color.lab_b)])\",\n \"def read_img(filename) -> Tuple[np.ndarray, np.ndarray]:\\n img = cv2.imread(filename, 3)\\n labimg = cv2.cvtColor(cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE)), cv2.COLOR_BGR2Lab)\\n return np.reshape(labimg[:, :, 0], (config.IMAGE_SIZE, config.IMAGE_SIZE, 1)), labimg[:, :, 1:]\",\n \"def img_jet(img):\\n if len(img.shape) == 2:\\n normalized_img = img / 255.0\\n jet = np.array(cm.jet(1 - normalized_img)[:, :, :3] * 255, np.uint8)\\n else:\\n jet = img\\n return jet\",\n \"def linear_rgb2xyz(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(xyz_from_rgb, arr)\",\n \"def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):\\n\\n with domain_range_scale(1):\\n values = None\\n if model == 'CIE XYZ':\\n values = XYZ\\n elif model == 'CIE xyY':\\n values = XYZ_to_xyY(XYZ, illuminant)\\n elif model == 'CIE xy':\\n values = XYZ_to_xy(XYZ, illuminant)\\n elif model == 'CIE Lab':\\n values = XYZ_to_Lab(XYZ, illuminant)\\n elif model == 'CIE LCHab':\\n values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))\\n elif model == 'CIE Luv':\\n values = XYZ_to_Luv(XYZ, illuminant)\\n elif model == 'CIE Luv uv':\\n values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)\\n elif model == 'CIE LCHuv':\\n values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))\\n elif model == 'CIE UCS':\\n values = XYZ_to_UCS(XYZ)\\n elif model == 'CIE UCS uv':\\n values = UCS_to_uv(XYZ_to_UCS(XYZ))\\n elif model == 'CIE UVW':\\n values = XYZ_to_UVW(XYZ, illuminant)\\n elif model == 'DIN 99':\\n values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))\\n elif model == 'Hunter Lab':\\n values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))\\n elif model == 'Hunter Rdab':\\n values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))\\n elif model == 'IPT':\\n values = XYZ_to_IPT(XYZ)\\n elif model == 'JzAzBz':\\n values = XYZ_to_JzAzBz(XYZ)\\n elif model == 'OSA UCS':\\n values = XYZ_to_OSA_UCS(XYZ)\\n elif model == 'hdr-CIELAB':\\n values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)\\n elif model == 'hdr-IPT':\\n values = XYZ_to_hdr_IPT(XYZ, **kwargs)\\n\\n if values is None:\\n raise ValueError(\\n '\\\"{0}\\\" not found in colourspace models: \\\"{1}\\\".'.format(\\n model, ', '.join(COLOURSPACE_MODELS)))\\n\\n return values\",\n \"def xyz_to_hunterlab(xyz_color, illuminant):\\n\\n if illuminant == 'D65':\\n ka = 172.10\\n kb = 66.70\\n xr = 94.83\\n yr = 100.0\\n zr = 107.38\\n elif illuminant == 'D60':\\n ka = 172.45\\n kb = 64.28\\n xr = 95.21\\n yr = 100.0\\n zr = 99.60\\n elif illuminant == 'D75':\\n ka = 171.76\\n kb = 70.76\\n xr = 94.45\\n yr = 100.0\\n zr = 120.70\\n else:\\n raise InvalidIlluminant(\\\"Wrong Illuminant. Value must be: 'D60',\\\"\\n \\\" 'D65' or 'D75'.\\\")\\n\\n l = 100.0 * np.sqrt(xyz_color[1] / yr)\\n a = ka * (((xyz_color[0] / xr) - (xyz_color[1] / yr)) / np.sqrt(\\n xyz_color[1] / yr))\\n b = kb * (((xyz_color[1] / yr) - (xyz_color[2] / zr)) / np.sqrt(\\n xyz_color[1] / yr))\\n return l, a, b\",\n \"def XYZ_to_RGB_matrix(self):\\n\\n if not self._use_derived_XYZ_to_RGB_matrix:\\n return self._XYZ_to_RGB_matrix\\n else:\\n return self._derived_XYZ_to_RGB_matrix\",\n \"def rgb_to_xyz(rgb_color):\\n\\n r = (rgb_color[0] / 255)\\n g = (rgb_color[1] / 255)\\n b = (rgb_color[2] / 255)\\n\\n if r > 0.04045:\\n r = ((r + 0.055) / 1.055) ** 2.4\\n else:\\n r = r / 12.92\\n\\n if g > 0.04045:\\n g = ((g + 0.055) / 1.055) ** 2.4\\n else:\\n g = g / 12.92\\n\\n if b > 0.04045:\\n b = ((b + 0.055) / 1.055) ** 2.4\\n else:\\n b = b / 12.92\\n\\n r = r * 100\\n g = g * 100\\n b = b * 100\\n x = (r * 0.4124) + (g * 0.3576) + (b * 0.1805)\\n y = (r * 0.2126) + (g * 0.7152) + (b * 0.0722)\\n z = (r * 0.0193) + (g * 0.1192) + (b * 0.9505)\\n\\n return x, y, z\",\n \"def prepare_images(images):\\n images = color.rgb2lab(images)\\n\\n l = images[:,:,:,:1]/100.\\n ab = images[:,:,:,1:]/200. + 0.5\\n\\n return l, ab\",\n \"def get_rgb(self, img, r, g, b):\\r\\n\\r\\n # Get specific bands of hyperspectral image\\r\\n red_channel = img[:, :, r]\\r\\n green_channel = img[:, :, g]\\r\\n blue_channel = img[:, :, b]\\r\\n\\r\\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\\r\\n img = img.astype('float32')\\r\\n return img\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())\",\n \"def xyz2linear_rgb(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(rgb_from_xyz, arr)\",\n \"def convert_color(image, color_space='RGB'):\\n color_space = color_space.lower()\\n if color_space != 'rgb':\\n if color_space == 'hsv':\\n color_transformation = cv2.COLOR_BGR2HSV\\n elif color_space == 'luv':\\n color_transformation = cv2.COLOR_BGR2LUV\\n elif color_space == 'hls':\\n color_transformation = cv2.COLOR_BGR2HLS\\n elif color_space == 'yuv':\\n color_transformation = cv2.COLOR_BGR2YUV\\n elif color_space == 'ycrcb':\\n color_transformation = cv2.COLOR_BGR2YCrCb\\n else:\\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\\n 'HLS, YUV, YCrCb' % color_space)\\n\\n return cv2.cvtColor(image, color_transformation)\\n else:\\n return image\",\n \"def red_channel(img):\\n\\n red = np.zeros(img.shape,dtype=float)\\n\\n red[:,:,2] = np.copy(img[:,:,2])\\n\\n return red\",\n \"def Lab_to_LCHab(cobj, *args, **kwargs):\\r\\n \\r\\n lch_l = cobj.lab_l\\r\\n lch_c = math.sqrt(math.pow(float(cobj.lab_a), 2) + math.pow(float(cobj.lab_b), 2))\\r\\n lch_h = math.atan2(float(cobj.lab_b), float(cobj.lab_a))\\r\\n \\r\\n if lch_h > 0:\\r\\n lch_h = (lch_h / math.pi) * 180\\r\\n else:\\r\\n lch_h = 360 - (math.fabs(lch_h) / math.pi) * 180\\r\\n \\r\\n return LCHabColor(\\r\\n lch_l, lch_c, lch_h, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def XYZ_to_optimization_colour_model(XYZ: ArrayLike) -> NDArrayFloat:\\n\\n return XYZ_to_Oklab(XYZ)\",\n \"def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\\n y: torch.Tensor = image[..., 0, :, :]\\n cb: torch.Tensor = image[..., 1, :, :]\\n cr: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n cb_shifted: torch.Tensor = cb - delta\\n cr_shifted: torch.Tensor = cr - delta\\n\\n r: torch.Tensor = y + 1.403 * cr_shifted\\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\\n b: torch.Tensor = y + 1.773 * cb_shifted\\n return torch.stack([r, g, b], -3)\",\n \"def test_adobe_conversion_to_xyz_d65(self):\\r\\n\\r\\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\\r\\n xyz = convert_color(adobe, XYZColor)\\r\\n self.assertColorMatch(xyz, XYZColor(0.230, 0.429, 0.074))\",\n \"def RudermanLABFwd(I):\\n\\n # get input image dimensions\\n m = I.shape[0]\\n n = I.shape[1]\\n\\n # define conversion matrices\\n RGB2LMS = np.array([[0.3811, 0.5783, 0.0402],\\n [0.1967, 0.7244, 0.0782],\\n [0.0241, 0.1288, 0.8444]])\\n LMS2LAB = np.array([[1 / (3**0.5), 0, 0],\\n [0, 1 / (6**0.5), 0],\\n [0, 0, 1 / (2**0.5)]]).dot(np.array([[1, 1, 1],\\n [1, 1, -2],\\n [1, -1, 0]]))\\n\\n # calculate LMS values from RGB\\n I = np.reshape(I, (m * n, 3))\\n LMS = np.dot(RGB2LMS, np.transpose(I))\\n LMS[LMS == 0] = np.spacing(1)\\n logLMS = np.log(LMS)\\n\\n # calculate LAB values from LMS\\n I_LAB = LMS2LAB.dot(logLMS)\\n\\n # reshape to 3-channel image\\n I_LAB = np.reshape(I_LAB.transpose(), (m, n, 3))\\n\\n return I_LAB\",\n \"def luminance(rgb):\\n \\n (r, g, b) = rgb\\n return (r + g + b) // 3\",\n \"def luma(cspace: np.array) -> np.array:\\n \\n assert isinstance(cspace, DataFrame), \\\"Colorspace must be a dataframe\\\"\\n assert all(np.isin(['R', 'G', 'B'], cspace.columns)), \\\"Colorspace must contain RGB columns\\\"\\n return 0.2989 * cspace['R'] + 0.5870 * cspace['G'] + 0.1140 * cspace['B']\",\n \"def enhance_contrast(img):\\n # CLAHE (Contrast Limited Adaptive Histogram Equalization)\\n clahe = cv2.createCLAHE(clipLimit=3., tileGridSize=(8, 8))\\n\\n lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) # convert from BGR to LAB color space\\n l, a, b = cv2.split(lab) # split on 3 different channels\\n\\n l2 = clahe.apply(l) # apply CLAHE to the L-channel\\n\\n lab = cv2.merge((l2, a, b)) # merge channels\\n img2 = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # convert from LAB to BGR\\n\\n return img2\",\n \"def read_colormap(name):\\n\\n path = get_demo_file(name + '.c3g')\\n\\n out = []\\n with open(path, 'r') as file:\\n for line in file:\\n if 'rgb(' not in line:\\n continue\\n line = line.split('(')[-1].split(')')[0]\\n out.append([float(n) for n in line.split(',')])\\n\\n return np.asarray(out).astype(float) / 256.\",\n \"def example_lchab_to_lchuv():\\r\\n\\r\\n print(\\\"=== Complex Example: LCHab->LCHuv ===\\\")\\r\\n # Instantiate an LCHab color object with the given values.\\r\\n lchab = LCHabColor(0.903, 16.447, 352.252)\\r\\n # Show a string representation.\\r\\n print(lchab)\\r\\n # Convert to LCHuv.\\r\\n lchuv = convert_color(lchab, LCHuvColor)\\r\\n print(lchuv)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def rgb(self, xyz: Union[ndarray, Iterable[float]]) -> ndarray:\\n return self.value.xyz_to_rgb_matrix @ xyz\",\n \"def use_derived_RGB_to_XYZ_matrix(self):\\n\\n return self._use_derived_RGB_to_XYZ_matrix\",\n \"def imagetopalette(image, palcolors):\\n assert image.mode == 'L', \\\"Only grayscale images supported !\\\"\\n pal = [(palcolors[i],palcolors[i+1]) for i in range(len(palcolors)-1)]\\n image.putdata([colortopalette(c,pal) for c in list(image.getdata())])\",\n \"def loadDeth(ficXYZ):\\n data=np.array(Image.open(ficXYZ))\\n cloud=data.reshape((-1)).view(dtype=np.uint16).reshape((data.shape[0],data.shape[1]/2,data.shape[2]))-0x7FFF\\n # change of the type of the data to correspond to the encoding of the data in the imageXYZ + data refocusing\\n cloud=cloud.astype('int16')\\n cloud=cloud.astype('float32')/1000.0\\n return cloud\",\n \"def green_channel(input_image):\\n return input_image[:, :, 1]\",\n \"def read_img(img): #X\\n im = plt.imread(img)\\n im = im[:, :, :3]\\n if im.max()>200:\\n im = im/255.\\n return rgb_to_hsv(im)-0.5\",\n \"def __enhance_image(self, img):\\n\\n blue = self.g.clahe.apply(img[:,:,0])\\n green = self.g.clahe.apply(img[:,:,1])\\n red = self.g.clahe.apply(img[:,:,2])\\n img[:,:,0] = blue\\n img[:,:,1] = green\\n img[:,:,2] = red\\n return img\",\n \"def negative(img): \\n for pixel in img:\\n x, y, col = pixel \\n r, g, b = col\\n \\n new_color = create_color(255 - r, 255 - g, 255 - b)\\n set_color(img, x, y, new_color)\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))\",\n \"def scale_to_01(color: C3I) -> C3F:\\n r, g, b = color\\n return r / 255, g / 255, b / 255\",\n \"def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):\\r\\n\\r\\n # Will contain linearized RGB channels (removed the gamma func).\\r\\n linear_channels = {}\\r\\n\\r\\n if isinstance(cobj, sRGBColor):\\r\\n for channel in ['r', 'g', 'b']:\\r\\n V = getattr(cobj, 'rgb_' + channel)\\r\\n if V <= 0.04045:\\r\\n linear_channels[channel] = V / 12.92\\r\\n else:\\r\\n linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n gamma = cobj.rgb_gamma\\r\\n\\r\\n for channel in ['r', 'g', 'b']:\\r\\n V = getattr(cobj, 'rgb_' + channel)\\r\\n linear_channels[channel] = math.pow(V, gamma)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n xyz_x, xyz_y, xyz_z = apply_RGB_matrix(\\r\\n linear_channels['r'], linear_channels['g'], linear_channels['b'],\\r\\n rgb_type=cobj, convtype=\\\"rgb_to_xyz\\\")\\r\\n\\r\\n if target_illuminant is None:\\r\\n target_illuminant = cobj.native_illuminant\\r\\n \\r\\n # The illuminant of the original RGB object. This will always match\\r\\n # the RGB colorspace's native illuminant.\\r\\n illuminant = cobj.native_illuminant\\r\\n xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)\\r\\n # This will take care of any illuminant changes for us (if source\\r\\n # illuminant != target illuminant).\\r\\n xyzcolor.apply_adaptation(target_illuminant)\\r\\n\\r\\n return xyzcolor\",\n \"def cie_xyz(self):\\n return tuple(matrix_mult(\\n ((0.4124564, 0.3575761, 0.1804375),\\n (0.2126729, 0.7151522, 0.0721750),\\n (0.0193339, 0.1191920, 0.9503041),\\n ),\\n (from_srgb(self.red),\\n from_srgb(self.green),\\n from_srgb(self.blue)\\n )\\n ))\",\n \"def linearTosRGB3(c):\\n return [linearTosRGB(c[0]), linearTosRGB(c[1]), linearTosRGB(c[2])]\",\n \"def compute_luminosity(red, green, blue):\\r\\n return (0.299 * red) + (0.587 * green) + (0.114 * blue)\",\n \"def RGB_to_XYZ_matrix(self, value):\\n\\n if value is not None:\\n value = np.asarray(value)\\n self._RGB_to_XYZ_matrix = value\",\n \"def example_spectral_to_xyz():\\r\\n\\r\\n print(\\\"=== Example: Spectral->XYZ ===\\\")\\r\\n spc = SpectralColor(\\r\\n observer='2', illuminant='d50',\\r\\n spec_380nm=0.0600, spec_390nm=0.0600, spec_400nm=0.0641,\\r\\n spec_410nm=0.0654, spec_420nm=0.0645, spec_430nm=0.0605,\\r\\n spec_440nm=0.0562, spec_450nm=0.0543, spec_460nm=0.0537,\\r\\n spec_470nm=0.0541, spec_480nm=0.0559, spec_490nm=0.0603,\\r\\n spec_500nm=0.0651, spec_510nm=0.0680, spec_520nm=0.0705,\\r\\n spec_530nm=0.0736, spec_540nm=0.0772, spec_550nm=0.0809,\\r\\n spec_560nm=0.0870, spec_570nm=0.0990, spec_580nm=0.1128,\\r\\n spec_590nm=0.1251, spec_600nm=0.1360, spec_610nm=0.1439,\\r\\n spec_620nm=0.1511, spec_630nm=0.1590, spec_640nm=0.1688,\\r\\n spec_650nm=0.1828, spec_660nm=0.1996, spec_670nm=0.2187,\\r\\n spec_680nm=0.2397, spec_690nm=0.2618, spec_700nm=0.2852,\\r\\n spec_710nm=0.2500, spec_720nm=0.2400, spec_730nm=0.2300)\\r\\n xyz = convert_color(spc, XYZColor)\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def __add_color(self, intensity):\\n val = color.rgb2lab(\\n (self.image[self.lip_y, self.lip_x] / 255.)\\n .reshape(len(self.lip_y), 1, 3)\\n ).reshape(len(self.lip_y), 3)\\n l_val, a_val, b_val = np.mean(val[:, 0]), np.mean(val[:, 1]), np.mean(val[:, 2])\\n l1_val, a1_val, b1_val = color.rgb2lab(\\n np.array(\\n (self.red_l / 255., self.green_l / 255., self.blue_l / 255.)\\n ).reshape(1, 1, 3)\\n ).reshape(3,)\\n l_final, a_final, b_final = (l1_val - l_val) * \\\\\\n intensity, (a1_val - a_val) * \\\\\\n intensity, (b1_val - b_val) * intensity\\n val[:, 0] = np.clip(val[:, 0] + l_final, 0, 100)\\n val[:, 1] = np.clip(val[:, 1] + a_final, -127, 128)\\n val[:, 2] = np.clip(val[:, 2] + b_final, -127, 128)\\n self.image[self.lip_y, self.lip_x] = color.lab2rgb(val.reshape(\\n len(self.lip_y), 1, 3)).reshape(len(self.lip_y), 3) * 255\",\n \"def convertColorSpace(\\n self,\\n img, # Image in some color space\\n srcColorSpace = 'BGR', # Source color space\\n tgtColorSpace = 'RGB', # Traget color space\\n ):\\n\\n if srcColorSpace == tgtColorSpace:\\n return img\\n\\n if srcColorSpace == 'BGR':\\n img_bgr = img\\n elif srcColorSpace == 'RGB':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\\n elif srcColorSpace == 'HSV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\\n elif srcColorSpace == 'HLS':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\\n elif srcColorSpace == 'LUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\\n elif srcColorSpace == 'YUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\\n elif srcColorSpace == 'YCrCb':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(srcColorSpace))\\n\\n if tgtColorSpace == 'BGR':\\n img_tgt = img_bgr\\n elif tgtColorSpace == 'RGB':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\\n elif tgtColorSpace == 'HSV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\\n elif tgtColorSpace == 'HLS':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\\n elif tgtColorSpace == 'LUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\\n elif tgtColorSpace == 'YUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\\n elif tgtColorSpace == 'YCrCb':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(tgtColorSpace))\\n\\n return img_tgt\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7460319","0.7458232","0.72022015","0.7077035","0.6824317","0.6632177","0.65915054","0.65851694","0.65589637","0.65200937","0.6511321","0.6508878","0.63595843","0.62574404","0.6182268","0.61593455","0.61016095","0.6056708","0.6038471","0.6021243","0.5998962","0.59938616","0.59123904","0.5899039","0.5799198","0.5791629","0.57569957","0.572488","0.57222295","0.5721425","0.57126707","0.5707092","0.5702422","0.56995595","0.56292975","0.56208616","0.56012017","0.559684","0.55938804","0.5592717","0.55885196","0.55792445","0.55760837","0.5572165","0.55716115","0.556402","0.55362576","0.553331","0.5527506","0.5513192","0.5507845","0.5499371","0.5489791","0.5480122","0.5468796","0.54622585","0.54567397","0.5453196","0.5452388","0.5450557","0.54463226","0.54447186","0.5435252","0.5430531","0.5419798","0.54053676","0.5401315","0.5397503","0.53891516","0.53877103","0.53850466","0.5378802","0.53458345","0.53436565","0.5335449","0.5320676","0.53187454","0.5298133","0.52844244","0.52818614","0.5273745","0.5272557","0.52602744","0.5258809","0.52575487","0.52552","0.5251569","0.5251429","0.52446675","0.5242042","0.52362853","0.52329","0.5232254","0.52286536","0.52220964","0.52201843","0.52118164","0.52112323","0.52030784","0.51863563"],"string":"[\n \"0.7460319\",\n \"0.7458232\",\n \"0.72022015\",\n \"0.7077035\",\n \"0.6824317\",\n \"0.6632177\",\n \"0.65915054\",\n \"0.65851694\",\n \"0.65589637\",\n \"0.65200937\",\n \"0.6511321\",\n \"0.6508878\",\n \"0.63595843\",\n \"0.62574404\",\n \"0.6182268\",\n \"0.61593455\",\n \"0.61016095\",\n \"0.6056708\",\n \"0.6038471\",\n \"0.6021243\",\n \"0.5998962\",\n \"0.59938616\",\n \"0.59123904\",\n \"0.5899039\",\n \"0.5799198\",\n \"0.5791629\",\n \"0.57569957\",\n \"0.572488\",\n \"0.57222295\",\n \"0.5721425\",\n \"0.57126707\",\n \"0.5707092\",\n \"0.5702422\",\n \"0.56995595\",\n \"0.56292975\",\n \"0.56208616\",\n \"0.56012017\",\n \"0.559684\",\n \"0.55938804\",\n \"0.5592717\",\n \"0.55885196\",\n \"0.55792445\",\n \"0.55760837\",\n \"0.5572165\",\n \"0.55716115\",\n \"0.556402\",\n \"0.55362576\",\n \"0.553331\",\n \"0.5527506\",\n \"0.5513192\",\n \"0.5507845\",\n \"0.5499371\",\n \"0.5489791\",\n \"0.5480122\",\n \"0.5468796\",\n \"0.54622585\",\n \"0.54567397\",\n \"0.5453196\",\n \"0.5452388\",\n \"0.5450557\",\n \"0.54463226\",\n \"0.54447186\",\n \"0.5435252\",\n \"0.5430531\",\n \"0.5419798\",\n \"0.54053676\",\n \"0.5401315\",\n \"0.5397503\",\n \"0.53891516\",\n \"0.53877103\",\n \"0.53850466\",\n \"0.5378802\",\n \"0.53458345\",\n \"0.53436565\",\n \"0.5335449\",\n \"0.5320676\",\n \"0.53187454\",\n \"0.5298133\",\n \"0.52844244\",\n \"0.52818614\",\n \"0.5273745\",\n \"0.5272557\",\n \"0.52602744\",\n \"0.5258809\",\n \"0.52575487\",\n \"0.52552\",\n \"0.5251569\",\n \"0.5251429\",\n \"0.52446675\",\n \"0.5242042\",\n \"0.52362853\",\n \"0.52329\",\n \"0.5232254\",\n \"0.52286536\",\n \"0.52220964\",\n \"0.52201843\",\n \"0.52118164\",\n \"0.52112323\",\n \"0.52030784\",\n \"0.51863563\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6739732"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":12390,"cells":{"query":{"kind":"string","value":"Convert an image from XYZ color space to RGB color space"},"document":{"kind":"string","value":"def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n var_x = x / 100\n var_y = y / 100\n var_z = z / 100\n\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\n\n var_r = tf.where(var_r > 0.0031308,\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\n 12.92 * var_r)\n var_g = tf.where(var_g > 0.0031308,\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\n 12.92 * var_g)\n var_b = tf.where(var_b > 0.0031308,\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\n 12.92 * var_b)\n r = var_r * 255\n g = var_g * 255\n b = var_b * 255\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\n return rgb_image"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def XYZ_to_RGB(XYZ,\n illuminant_XYZ,\n illuminant_RGB,\n XYZ_to_RGB_matrix,\n chromatic_adaptation_transform='CAT02',\n encoding_cctf=None):\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n transform=chromatic_adaptation_transform)\n\n XYZ_a = dot_vector(M, XYZ)\n\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\n\n if encoding_cctf is not None:\n RGB = encoding_cctf(RGB)\n\n return RGB","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\n xyz = lab_to_xyz(image)\n rgb_image = xyz_to_rgb(xyz)\n return rgb_image","def get_rgb(self, img, r, g, b):\r\n\r\n # Get specific bands of hyperspectral image\r\n red_channel = img[:, :, r]\r\n green_channel = img[:, :, g]\r\n blue_channel = img[:, :, b]\r\n\r\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\r\n img = img.astype('float32')\r\n return img","def rgb_processing(rgb_img, center, scale, rot=0):\n rgb_img = crop(rgb_img, center, scale, \n [constants.IMG_RES, constants.IMG_RES], rot=rot)\n # (3,224,224),float,[0,1]\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\n return rgb_img","def convert_color(image, color_space):\n out_image = None\n if color_space != 'RGB':\n if color_space == 'HSV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\n elif color_space == 'LUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\n elif color_space == 'HLS':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\n elif color_space == 'YUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\n elif color_space == 'YCrCb':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\n else:\n out_image = np.copy(image)\n return out_image","def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\n r, g, b = tf.unstack(image, axis=-1)\n var_r = r / 255\n var_g = g / 255\n var_b = b / 255\n\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\n var_r / 12.92)\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\n var_g / 12.92)\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\n var_b / 12.92)\n var_r = var_r * 100\n var_g = var_g * 100\n var_b = var_b * 100\n\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\n\n image_xyz = tf.stack([x, y, z], axis=-1)\n return image_xyz","def example_rgb_to_xyz():\r\n\r\n print(\"=== RGB Example: RGB->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n rgb = sRGBColor(120, 130, 140)\r\n # Show a string representation.\r\n print(rgb)\r\n # Convert RGB to XYZ using a D50 illuminant.\r\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\n y: torch.Tensor = image[..., 0, :, :]\n cb: torch.Tensor = image[..., 1, :, :]\n cr: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n cb_shifted: torch.Tensor = cb - delta\n cr_shifted: torch.Tensor = cr - delta\n\n r: torch.Tensor = y + 1.403 * cr_shifted\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\n b: torch.Tensor = y + 1.773 * cb_shifted\n return torch.stack([r, g, b], -3)","def XYZ_to_sRGB(XYZ):\n\n rgb = XYZ_to_sRGB_linear(XYZ)\n rgb = sRGB_linear_to_sRGB(rgb)\n\n return rgb","def rgb(self, xyz: Union[ndarray, Iterable[float]]) -> ndarray:\n return self.value.xyz_to_rgb_matrix @ xyz","def grey_to_rgb_imitation(img):\n return np.repeat(img[...,np.newaxis], 3, -1)","def img_to_rgb(img):\r\n if len(img.shape) < 3 or img.shape[2] == 1:\r\n return np.repeat(img, 3).reshape(img.shape[0], img.shape[1], 3)\r\n else:\r\n return img","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def grey_to_rgb(im):\n assert im.n_channels in [1, 3]\n\n if im.n_channels == 3:\n return im\n\n im.pixels = np.vstack([im.pixels] * 3)\n return im","def matplotlib_image(image):\n if image.ndim == 2:\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\n else:\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return rgb","def _colored_img_to_arr(image, verbose=False):\n height, width = image.size\n arr = np.array(image.getdata())\n arr = arr.reshape(3, height, width)\n r = arr[0]\n g = arr[1]\n b = arr[2]\n return r, g, b","def convert_color(image, color_space='RGB'):\n color_space = color_space.lower()\n if color_space != 'rgb':\n if color_space == 'hsv':\n color_transformation = cv2.COLOR_BGR2HSV\n elif color_space == 'luv':\n color_transformation = cv2.COLOR_BGR2LUV\n elif color_space == 'hls':\n color_transformation = cv2.COLOR_BGR2HLS\n elif color_space == 'yuv':\n color_transformation = cv2.COLOR_BGR2YUV\n elif color_space == 'ycrcb':\n color_transformation = cv2.COLOR_BGR2YCrCb\n else:\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\n 'HLS, YUV, YCrCb' % color_space)\n\n return cv2.cvtColor(image, color_transformation)\n else:\n return image","def XYZ_to_RGB_matrix(self):\n\n if not self._use_derived_XYZ_to_RGB_matrix:\n return self._XYZ_to_RGB_matrix\n else:\n return self._derived_XYZ_to_RGB_matrix","def red_channel(img):\n\n red = np.zeros(img.shape,dtype=float)\n\n red[:,:,2] = np.copy(img[:,:,2])\n\n return red","def yuv_to_rgb(img_yuv):\n\n y = img_yuv[..., 0]\n u = img_yuv[..., 1]\n v = img_yuv[..., 2]\n\n r = y + 1.14 * v\n g = y - 0.396 * u - 0.581 * v\n b = y + 2.029 * u\n\n img_rgb = np.stack((r, g, b), axis=2)\n img_rgb = np.clip(img_rgb, 0, 1)\n return img_rgb","def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\r\n\r\n temp_X = cobj.xyz_x\r\n temp_Y = cobj.xyz_y\r\n temp_Z = cobj.xyz_z\r\n\r\n logger.debug(\" \\- Target RGB space: %s\", target_rgb)\r\n target_illum = target_rgb.native_illuminant\r\n logger.debug(\" \\- Target native illuminant: %s\", target_illum)\r\n logger.debug(\" \\- XYZ color's illuminant: %s\", cobj.illuminant)\r\n \r\n # If the XYZ values were taken with a different reference white than the\r\n # native reference white of the target RGB space, a transformation matrix\r\n # must be applied.\r\n if cobj.illuminant != target_illum:\r\n logger.debug(\" \\* Applying transformation from %s to %s \",\r\n cobj.illuminant, target_illum)\r\n # Get the adjusted XYZ values, adapted for the target illuminant.\r\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\r\n temp_X, temp_Y, temp_Z,\r\n orig_illum=cobj.illuminant, targ_illum=target_illum)\r\n logger.debug(\" \\* New values: %.3f, %.3f, %.3f\",\r\n temp_X, temp_Y, temp_Z)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\r\n temp_X, temp_Y, temp_Z,\r\n rgb_type=target_rgb, convtype=\"xyz_to_rgb\")\r\n\r\n # v\r\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\r\n # V\r\n nonlinear_channels = {}\r\n if target_rgb == sRGBColor:\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n if v <= 0.0031308:\r\n nonlinear_channels[channel] = v * 12.92\r\n else:\r\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\r\n else:\r\n # If it's not sRGB...\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\r\n\r\n return target_rgb(\r\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])","def get_color(im_obj):\n #im = Image.open(path, 'r')\n x, y = im_obj.size\n\n r, g, b = 0, 0, 0\n for i in xrange(x):\n for j in xrange(y):\n color_px = im_obj.getpixel((i, j))\n #print color_px\n r += color_px[0]\n g += color_px[1]\n b += color_px[2]\n\n r = r / (x * y)\n g = g / (x * y)\n b = b / (x * y)\n return (r, g, b)","def lab_to_rgb(img):\n new_img = np.zeros((256, 256, 3))\n for i in range(len(img)):\n for j in range(len(img[i])):\n pix = img[i, j]\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\n new_img = color.lab2rgb(new_img) * 255\n new_img = new_img.astype('uint8')\n return new_img","def imcast(img, dtype, color_space=\"default\"):\n if img.dtype == dtype:\n return img\n if color_space == \"default\":\n if dtype == np.uint8:\n if img.dtype == np.uint16:\n return np.asarray(img / 257, np.uint8)\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img * 255., np.uint8)\n elif dtype == np.uint16:\n if img.dtype == np.uint8:\n return np.asarray(img, np.uint16) * 257\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img * 65535., np.uint16)\n elif dtype == np.float32 or dtype == np.float64:\n if img.dtype == np.uint8:\n return np.asarray(img, dtype) / 255.\n elif img.dtype == np.uint16:\n return np.asarray(img, dtype) / 65535.\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img, dtype)\n elif color_space == \"CIE-L*a*b*\":\n if dtype == np.uint8:\n if img.dtype == np.float32 or img.dtype == np.float64:\n dst = np.empty(img.shape, np.uint8)\n dst[:,:,0] = img[:,:,0] * 255. / 100.\n dst[:,:,1] = img[:,:,1] + 128.\n dst[:,:,2] = img[:,:,2] + 128.\n return dst\n elif dtype == np.float32 or dtype == np.float64:\n if img.dtype == np.uint8:\n dst = np.empty(img.shape, dtype)\n dst[:,:,0] = np.asarray(img[:,:,0], dtype) / 255. * 100.\n dst[:,:,1] = np.asarray(img[:,:,1], dtype) - 128.\n dst[:,:,2] = np.asarray(img[:,:,2], dtype) - 128.\n return dst\n raise Exception(\n \"Unexpected conversion from '%s' to '%s' with '%s' color space\" % \\\n (img.dtype, dtype, color_space))","def yuv2rgb(im):\n ## conflicting definitions exist depending on whether you use the full range\n ## of YCbCr or clamp out to the valid range. see here\n ## http://www.equasys.de/colorconversion.html\n ## http://www.fourcc.org/fccyvrgb.php\n from numpy import dot, ndarray, array\n # if not im.dtype == 'uint8':\n # raise ImageUtilsError('yuv2rgb only implemented for uint8 arrays')\n\n ## better clip input to the valid range just to be on the safe side\n yuv = ndarray(im.shape) ## float64\n yuv[:, :, 0] = im[:, :, 0].clip(16, 235).astype(yuv.dtype) - 16\n yuv[:, :, 1:] = im[:, :, 1:].clip(16, 240).astype(yuv.dtype) - 128\n\n ## ITU-R BT.601 version (SDTV)\n A = array([[1., 0., 0.701],\n [1., -0.886 * 0.114 / 0.587, -0.701 * 0.299 / 0.587],\n [1., 0.886, 0.]])\n A[:, 0] *= 255. / 219.\n A[:, 1:] *= 255. / 112.\n\n ## ITU-R BT.709 version (HDTV)\n # A = array([[1.164, 0., 1.793],\n # [1.164, -0.213, -0.533],\n # [1.164, 2.112, 0.]])\n\n rgb = dot(yuv, A.T)\n return rgb.clip(0, 255).astype('uint8')","def _process_img_rgb(self, sensor_data):\n img = np.array(sensor_data.raw_data).reshape((self.img_y, self.img_x, 4))\n img = img[:, :, :3] # sensor is actualy rgba, we dont need alpha values\n self.rgb = img # need to scale rgb values to be {0,1}","def RGB_to_XYZ(RGB,\n illuminant_RGB,\n illuminant_XYZ,\n RGB_to_XYZ_matrix,\n chromatic_adaptation_transform='CAT02',\n decoding_cctf=None):\n\n if decoding_cctf is not None:\n RGB = decoding_cctf(RGB)\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n transform=chromatic_adaptation_transform)\n\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\n\n XYZ_a = dot_vector(M, XYZ)\n\n return XYZ_a","def rgb_to_xy(red, green, blue):\n\n # gamma correction\n red = pow((red + 0.055) / (1.0 + 0.055), 2.4) if red > 0.04045 else (red / 12.92)\n green = pow((green + 0.055) / (1.0 + 0.055), 2.4) if green > 0.04045 else (green / 12.92)\n blue = pow((blue + 0.055) / (1.0 + 0.055), 2.4) if blue > 0.04045 else (blue / 12.92)\n\n # convert rgb to xyz\n x = red * 0.649926 + green * 0.103455 + blue * 0.197109\n y = red * 0.234327 + green * 0.743075 + blue * 0.022598\n z = green * 0.053077 + blue * 1.035763\n\n # convert xyz to xy\n x = x / (x + y + z)\n y = y / (x + y + z)\n\n # TODO check color gamut if known\n \n return [x, y]","def to_rgb(im):\n w, h = im.shape\n ret = np.empty((w, h, 3), dtype=np.uint8)\n ret[:, :, 2] = ret[:, :, 1] = ret[:, :, 0] = im\n return ret","def to_color(self):\n if self.channels == 4:\n color = opencv.cvtColor(self.img, opencv.COLOR_BGRA2BGR)\n return Image(color)\n elif self.channels == 1:\n color = opencv.cvtColor(self.img, opencv.COLOR_GRAY2BGR)\n return Image(color)\n else:\n return Image(self.img)","def test_srgb_conversion_to_xyz_d65(self):\r\n\r\n xyz = convert_color(self.color, XYZColor)\r\n self.assertColorMatch(xyz, XYZColor(0.294, 0.457, 0.103))","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())","def rgb_to_xyz(rgb_color):\n\n r = (rgb_color[0] / 255)\n g = (rgb_color[1] / 255)\n b = (rgb_color[2] / 255)\n\n if r > 0.04045:\n r = ((r + 0.055) / 1.055) ** 2.4\n else:\n r = r / 12.92\n\n if g > 0.04045:\n g = ((g + 0.055) / 1.055) ** 2.4\n else:\n g = g / 12.92\n\n if b > 0.04045:\n b = ((b + 0.055) / 1.055) ** 2.4\n else:\n b = b / 12.92\n\n r = r * 100\n g = g * 100\n b = b * 100\n x = (r * 0.4124) + (g * 0.3576) + (b * 0.1805)\n y = (r * 0.2126) + (g * 0.7152) + (b * 0.0722)\n z = (r * 0.0193) + (g * 0.1192) + (b * 0.9505)\n\n return x, y, z","def read_colormap(name):\n\n path = get_demo_file(name + '.c3g')\n\n out = []\n with open(path, 'r') as file:\n for line in file:\n if 'rgb(' not in line:\n continue\n line = line.split('(')[-1].split(')')[0]\n out.append([float(n) for n in line.split(',')])\n\n return np.asarray(out).astype(float) / 256.","def load_image_as_rgb(image_path):\n im = imageio.imread(image_path)\n y_size = im.shape[0]\n x_size = im.shape[1]\n logging.info(\"Image has dimensions X:%d Y:%d\" % (x_size, y_size))\n arr = np.zeros((im.shape[0],im.shape[1]), dtype=int)\n i = 0\n for im_row in im:\n j = 0\n for vec in im_row:\n arr[i,j] = rgb_vec_to_num(vec)\n j = j + 1\n i = i + 1\n return arr","def yiq2rgb(imYIQ):\n trans = np.array([[1, 0.956, 0.62], [1, -0.272, -0.647], [1, -1.108, 1.705]])\n return np.dot(imYIQ, trans)","def yiq2rgb(im_yiq):\n return multiply_by_left_matrix(np.linalg.inv(YIQ_MATRIX), im_yiq)","def RGB_to_RGB(RGB,\n input_colourspace,\n output_colourspace,\n chromatic_adaptation_transform='CAT02',\n apply_decoding_cctf=False,\n apply_encoding_cctf=False):\n\n if apply_decoding_cctf:\n RGB = input_colourspace.decoding_cctf(RGB)\n\n M = RGB_to_RGB_matrix(input_colourspace, output_colourspace,\n chromatic_adaptation_transform)\n\n RGB = dot_vector(M, RGB)\n\n if apply_encoding_cctf:\n RGB = output_colourspace.encoding_cctf(RGB)\n\n return RGB","def convert_grayscale_to_rgb(x: np.ndarray) -> np.ndarray:\n return np.stack((x, ) * 3, axis=-1)","def load_color_image_features(img_path):\n ac = scipy.misc.imread(img_path, mode='RGB')\n ac = ac / (255.0 / 2) - 1.0\n return np.array(ac)","def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):\r\n\r\n # Will contain linearized RGB channels (removed the gamma func).\r\n linear_channels = {}\r\n\r\n if isinstance(cobj, sRGBColor):\r\n for channel in ['r', 'g', 'b']:\r\n V = getattr(cobj, 'rgb_' + channel)\r\n if V <= 0.04045:\r\n linear_channels[channel] = V / 12.92\r\n else:\r\n linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)\r\n else:\r\n # If it's not sRGB...\r\n gamma = cobj.rgb_gamma\r\n\r\n for channel in ['r', 'g', 'b']:\r\n V = getattr(cobj, 'rgb_' + channel)\r\n linear_channels[channel] = math.pow(V, gamma)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n xyz_x, xyz_y, xyz_z = apply_RGB_matrix(\r\n linear_channels['r'], linear_channels['g'], linear_channels['b'],\r\n rgb_type=cobj, convtype=\"rgb_to_xyz\")\r\n\r\n if target_illuminant is None:\r\n target_illuminant = cobj.native_illuminant\r\n \r\n # The illuminant of the original RGB object. This will always match\r\n # the RGB colorspace's native illuminant.\r\n illuminant = cobj.native_illuminant\r\n xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)\r\n # This will take care of any illuminant changes for us (if source\r\n # illuminant != target illuminant).\r\n xyzcolor.apply_adaptation(target_illuminant)\r\n\r\n return xyzcolor","def test_srgb_conversion_to_xyz_d50(self):\r\n\r\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))","def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n L: torch.Tensor = image[..., 0, :, :]\n u: torch.Tensor = image[..., 1, :, :]\n v: torch.Tensor = image[..., 2, :, :]\n\n # Convert from Luv to XYZ\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\n\n # Compute white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n a: torch.Tensor = u_w + u / (13 * L + eps)\n d: torch.Tensor = v_w + v / (13 * L + eps)\n c: torch.Tensor = 3 * y * (5 * d - 3)\n\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\n\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\n\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\n\n # Convert from RGB Linear to sRGB\n rgb_im = linear_rgb_to_rgb(rgbs_im)\n\n return rgb_im","def getRGBFromXYAndBrightness(self, x, y, bri=1):\n # The xy to color conversion is almost the same, but in reverse order.\n # Check if the xy value is within the color gamut of the lamp.\n # If not continue with step 2, otherwise step 3.\n # We do this to calculate the most accurate color the given light can actually do.\n xyPoint = XYPoint(x, y)\n\n if not self.checkPointInLampsReach(xyPoint):\n # Calculate the closest point on the color gamut triangle\n # and use that as xy value See step 6 of color to xy.\n xyPoint = self.getClosestPointToPoint(xyPoint)\n\n # Calculate XYZ values Convert using the following formulas:\n Y = bri\n X = (Y / xyPoint.y) * xyPoint.x\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\n\n # Convert to RGB using Wide RGB D65 conversion\n r = X * 1.612 - Y * 0.203 - Z * 0.302\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\n b = X * 0.026 - Y * 0.072 + Z * 0.962\n\n # Apply reverse gamma correction\n r, g, b = map(\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\n [r, g, b]\n )\n\n # Bring all negative components to zero\n r, g, b = map(lambda x: max(0, x), [r, g, b])\n\n # If one component is greater than 1, weight components by that value.\n max_component = max(r, g, b)\n if max_component > 1:\n r, g, b = map(lambda x: x / max_component, [r, g, b])\n\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\n\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\n return (r, g, b)","def _rgb(x, y, z):\n rgb = np.array([x, y, z]).T\n rgb -= rgb.min(0)\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\n return rgb","def carla_rgb_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\n image.convert(carla.ColorConverter.Raw) # pylint: disable=no-member\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = array.astype(np.float32) / 255\n array = np.reshape(array, (image.height, image.width, 4))\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n return array","def convert_to_grayscale(image_rgb):\n red, green, blue = image_rgb[:, :, 0], image_rgb[:, :, 1], image_rgb[:, :, 2]\n image_gray = 0.2989 * red + 0.5870 * green + 0.1140 * blue\n return image_gray","def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\n\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)","def colorspace_to_image(cspace: DataFrame, m: int, n: int) -> np.array:\n \n assert isinstance(cspace, DataFrame), \"Colorspace must be a dataframe\"\n assert len(cspace) == m * n, 'Image dimensions must match'\n assert all(np.isin(['R', 'G', 'B'], cspace.columns)), \"Colorspace must contain RGB columns\"\n \n result = np.empty([m,n,3])\n result[:,:, 0] = cspace['R'].values.reshape(m, n)\n result[:,:, 1] = cspace['G'].values.reshape(m, n)\n result[:,:, 2] = cspace['B'].values.reshape(m, n)\n return result","def RGB_to_XYZ_matrix(self):\n\n if not self._use_derived_RGB_to_XYZ_matrix:\n return self._RGB_to_XYZ_matrix\n else:\n return self._derived_RGB_to_XYZ_matrix","def toRGB(self):\r\n return mage_hsv_tuple_to_rgb(self.Coords)","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))","def hyper2rgb(img, bands):\n rgb = spectral.get_rgb(img, bands)\n rgb /= np.max(rgb)\n rgb = np.asarray(255 * rgb, dtype='uint8')\n return rgb","def bgr_to_rgb(image_bgr):\n b, g, r = cv2.split(image_bgr)\n image_rgb = cv2.merge([r, g, b])\n return image_rgb","def yiq2rgb(imYIQ):\n return __image_color_conversion(imYIQ, YIQ_TO_RGB_MATRIX)","def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:\n r: torch.Tensor = image[..., 0, :, :]\n g: torch.Tensor = image[..., 1, :, :]\n b: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b\n cb: torch.Tensor = (b - y) * 0.564 + delta\n cr: torch.Tensor = (r - y) * 0.713 + delta\n return torch.stack([y, cb, cr], -3)","def Lab_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n xyz_y = (cobj.lab_l + 16.0) / 116.0\r\n xyz_x = cobj.lab_a / 500.0 + xyz_y\r\n xyz_z = xyz_y - cobj.lab_b / 200.0\r\n \r\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\r\n xyz_y = math.pow(xyz_y, 3)\r\n else:\r\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\r\n\r\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\r\n xyz_x = math.pow(xyz_x, 3)\r\n else:\r\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\r\n \r\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\r\n xyz_z = math.pow(xyz_z, 3)\r\n else:\r\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\r\n \r\n xyz_x = (illum[\"X\"] * xyz_x)\r\n xyz_y = (illum[\"Y\"] * xyz_y)\r\n xyz_z = (illum[\"Z\"] * xyz_z)\r\n \r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)","def get_rgb_bands(image, bands):\n if bands is not MONOCHROME:\n red = image[:, :, bands['red']]\n green = image[:, :, bands['green']]\n blue = image[:, :, bands['blue']]\n\n img = np.rollaxis(np.array([red, green, blue]), 0, 3)\n else:\n img = color.grey2rgb(image)\n\n return img","def transformYIQ2RGB(imgYIQ: np.ndarray) -> np.ndarray:\r\n yiq_from_rgb = np.array([[0.299, 0.587, 0.114],\r\n [0.59590059, -0.27455667, -0.32134392],\r\n [0.21153661, -0.52273617, 0.31119955]])\r\n rgb_from_yiq = np.linalg.inv(yiq_from_rgb)\r\n\r\n RGBImg = np.ndarray(imgYIQ.shape)\r\n\r\n RGBImg[:, :, 0] = rgb_from_yiq[0,0] * imgYIQ[:, :, 0] + rgb_from_yiq[0,1] * imgYIQ[:, :, 1] + rgb_from_yiq[0,2] * imgYIQ[:, :, 2]\r\n RGBImg[:, :, 1] = rgb_from_yiq[1,0] * imgYIQ[:, :, 0] + rgb_from_yiq[1,1] * imgYIQ[:, :, 1] + rgb_from_yiq[1,2] * imgYIQ[:, :, 2]\r\n RGBImg[:, :, 2] = rgb_from_yiq[2,0] * imgYIQ[:, :, 0] + rgb_from_yiq[2,1] * imgYIQ[:, :, 1] + rgb_from_yiq[2,2] * imgYIQ[:, :, 2]\r\n\r\n return RGBImg","def bin2RGB(img):\n dtype = img.dtype\n if dtype == numpy.uint8:\n out_dtype = numpy.int32\n else:\n out_dtype = dtype\n shape = img.shape\n if len(shape) == 3:\n new_shape = shape[0] // 2, shape[1] // 2, shape[2]\n new_img = img\n else:\n new_shape = shape[0] // 2, shape[1] // 2, 1\n new_img = img.reshape((shape[0], shape[1], 1))\n out = numpy.zeros(new_shape, dtype=out_dtype)\n out += new_img[::2, ::2, :]\n out += new_img[1::2, ::2, :]\n out += new_img[1::2, 1::2, :]\n out += new_img[::2, 1::2, :]\n out /= 4\n if len(shape) != 3:\n out.shape = new_shape[0], new_shape[1]\n if dtype == numpy.uint8:\n return out.astype(dtype)\n else:\n return out","def XYZ_to_sRGB_linear(XYZ):\n\n rgb = np.tensordot(T, XYZ, axes=([1, 0]))\n\n if clip_method == CLIP_CLAMP_TO_ZERO:\n # set negative rgb values to zero\n rgb = np.where(rgb < 0, 0, rgb)\n return rgb\n\n if clip_method == CLIP_ADD_WHITE:\n # add enough white to make all rgb values nonnegative\n # find max negative rgb (or 0.0 if all non-negative), we need that much white\n rgb_min = np.amin(rgb, axis=0)\n # get max positive component\n rgb_max = np.amax(rgb, axis=0)\n\n # get scaling factor to maintain max rgb after adding white\n scaling = np.where(rgb_max > 0.0, rgb_max / (rgb_max - rgb_min + 0.00001), 1.0)\n\n # add enough white to cancel this out, maintaining the maximum of rgb\n rgb = np.where(rgb_min < 0.0, scaling * (rgb - rgb_min), rgb)\n return rgb","def rgb2gray(img):\r\n return 0.2989 * img[..., 0] + 0.587 * img[..., 1] + 0.114 * img[..., 2]","def camera_to_pixel(self, X):\n raise NotImplementedError","def generate_normalized_rgb(self):\n \n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n for i in Numeric.arange(256):\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\n return r/256.0,g/256.0,b/256.0","def _convert_to_yolo_img(self, img):\n\n img = img / 255.0\n h, w, c = img.shape\n img = img.transpose(2, 0, 1)\n outimg = make_image(w, h, c)\n img = img.reshape((w*h*c))\n data = c_array(c_float, img)\n outimg.data = data\n rgbgr_image(outimg)\n return outimg","def bgr_to_rgb(ims):\n out = []\n for im in ims:\n out.append(im[:,:,::-1])\n return out","def lab_to_rgb(img_l, img_ab):\n lab = np.empty([*img_l.shape[0:2], 3])\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\n lab[:, :, 1:] = img_ab * 127\n return color.lab2rgb(lab)","def _rgb2y(self, im):\n if len(im.shape) < 3:\n return im\n return np.sum(im * [0.299, 0.587, 0.114], axis=2)","def to_image(x):\n x = denorm(x.data.cpu())\n ndarr = x.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()\n im = ndarr\n return im","def img_recolor(self, args, input_image_path):\n \n ec = encoder.Encoder(output_path=args.intermediate_representation, method=args.method,\n size=args.size, p=args.p, grid_size=args.grid_size, plot=args.plot, quantize=args.quantize)\n dc = decoder.Decoder(output_path=args.output_path, method=args.method, size=args.size, p=args.p, gpu_id=args.gpu_id, plot=args.plot)\n\n ec.encode(input_image_path)\n img_gray_name = ar_utils.gen_new_gray_filename(input_image_path)\n img_gray_path = os.path.join(args.intermediate_representation, img_gray_name)\n dc.decode(img_gray_path)\n\n if args.delete_gray and os.path.exists(img_gray_path):\n os.remove(img_gray_path)","def XYZ_to_RGB_matrix(self, value):\n\n if value is not None:\n value = np.asarray(value)\n self._XYZ_to_RGB_matrix = value","def convertColorSpace(\n self,\n img, # Image in some color space\n srcColorSpace = 'BGR', # Source color space\n tgtColorSpace = 'RGB', # Traget color space\n ):\n\n if srcColorSpace == tgtColorSpace:\n return img\n\n if srcColorSpace == 'BGR':\n img_bgr = img\n elif srcColorSpace == 'RGB':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n elif srcColorSpace == 'HSV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n elif srcColorSpace == 'HLS':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\n elif srcColorSpace == 'LUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\n elif srcColorSpace == 'YUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\n elif srcColorSpace == 'YCrCb':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\n else:\n raise Exception(\"Incorrect color space: {}\".format(srcColorSpace))\n\n if tgtColorSpace == 'BGR':\n img_tgt = img_bgr\n elif tgtColorSpace == 'RGB':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\n elif tgtColorSpace == 'HSV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\n elif tgtColorSpace == 'HLS':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\n elif tgtColorSpace == 'LUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\n elif tgtColorSpace == 'YUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\n elif tgtColorSpace == 'YCrCb':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\n else:\n raise Exception(\"Incorrect color space: {}\".format(tgtColorSpace))\n\n return img_tgt","def _toRgbImage(self, fractal, colors, color_offset):\n soln_real = adjustRange(fractal[0], 0, 127)\n soln_imag = adjustRange(fractal[1], 0, 127)\n iters = adjustRange(fractal[2], 0, 128)\n\n rgb_image = np.array([\n soln_real + iters,\n soln_imag + iters,\n iters\n ]\n ).astype(dtype=np.uint8)\n\n return rgb_image.T","def convert_color(img, conv='RGB2YCrCb'):\n if conv == 'RGB2YCrCb':\n return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\n if conv == 'BGR2YCrCb':\n return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)\n if conv == 'RGB2LUV':\n return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)","def GetRGB(self, *args):\n return _XCAFDoc.XCAFDoc_Color_GetRGB(self, *args)","def l_to_rgb(img_l):\n lab = np.squeeze(255 * (img_l + 1) / 2)\n return color.gray2rgb(lab) / 255","def green_channel(input_image):\n return input_image[:, :, 1]","def read_color_image(path):\n with open(path, 'rb') as f:\n img = Image.fromarray(read_ppm(f), mode='RGB')\n img = tf.keras.preprocessing.image.img_to_array(img, dtype=int)\n img = tf.convert_to_tensor(img)\n return img","def grayscale2rgb(gray_image):\r\n\r\n\tassert gray_image.ndim == 2\r\n\r\n\timage = np.stack([gray_image, gray_image, gray_image], axis = -1).astype(np.uint8)\r\n\r\n\treturn image","def greyscale(img):\n grey_img = np.zeros([210, 160])\n for i in range(3):\n grey_img =np.sum([grey_img, img[:, :, i]], 0)\n grey_img /= 3\n grey_img = grey_img.astype(np.uint8)\n return grey_img","def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\n\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)","def recreate_image(x):\n reverse_mean = [-0.485, -0.456, -0.406]\n reverse_std = [1/0.229, 1/0.224, 1/0.225]\n in_channel = x.shape[-1]\n recreated_im = copy.copy(x) # C, H, W\n if in_channel == 3:\n for c in range(in_channel):\n recreated_im[:, :, c] /= reverse_std[c]\n recreated_im[:, :, c] -= reverse_mean[c]\n elif in_channel == 1:\n recreated_im[:, :, 0] /= reverse_std[1]\n recreated_im[:, :, 0] -= reverse_mean[1]\n recreated_im[recreated_im > 1] = 1\n recreated_im[recreated_im < 0] = 0\n recreated_im = np.round(recreated_im * 255)\n\n recreated_im = np.uint8(recreated_im) # H, W, C\n return recreated_im","def RGB2BGR(x):\n out = cv2.cvtColor(x, cv2.COLOR_RGB2BGR)\n return out","def xyz2linear_rgb(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(rgb_from_xyz, arr)","def get_BGR_img(self):\n img = self.img.copy()\n # Convert BGR to HSV\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n # define range of BGR color in HSV\n threshold_blue = np.array([[100,43,46], [124,255,255]])\n threshold_green = np.array([[35,43,46], [77,255,255]])\n threshold_red1 = np.array([[0,43,46], [10,255,255]])\n threshold_red2 = np.array([[156,43,46], [180,255,255]])\n # Threshold the HSV image to get only BGR colors\n mask_blue = cv2.inRange(hsv, threshold_blue[0], threshold_blue[1])\n mask_green = cv2.inRange(hsv, threshold_green[0], threshold_green[1])\n mask_red1 = cv2.inRange(hsv, threshold_red1[0], threshold_red1[1])\n mask_red2 = cv2.inRange(hsv, threshold_red2[0], threshold_red2[1])\n mask_red = mask_red1 | mask_red2\n # Bitwise-AND mask and original image\n self.blue = cv2.bitwise_and(img, img, mask=mask_blue)\n self.green = cv2.bitwise_and(img, img, mask=mask_green)\n self.red = cv2.bitwise_and(img, img, mask=mask_red)\n # 返回 bgr 三通道的分量合成的图片\n return np.stack((self.blue[:, :, 0], self.green[:, :, 1], self.red[:, :, 2]), axis=2)","def GetRGB(*args):\n return _gdi_.Colour_GetRGB(*args)","def test_conversion_through_rgb(self):\r\n\r\n xyz = convert_color(self.color, XYZColor)\r\n hsl = convert_color(xyz, HSLColor, through_rgb_type=AdobeRGBColor)\r\n # Notice how we don't have to pass through_rgb_type explicitly.\r\n xyz2 = convert_color(hsl, XYZColor)\r\n self.assertColorMatch(xyz, xyz2)","def red_filter(img):\r\n #with Image.open(filename) as img:\r\n w = img.width\r\n h = img.height\r\n\r\n newimg = Image.new('RGB', (w,h))\r\n for y in range(h):\r\n for x in range(w):\r\n r, g, b = img.getpixel((x,y))\r\n \r\n newimg.putpixel((x, y), (r, 0, 0))\r\n \r\n return newimg","def greyscale(c):\n return desaturate(c, 1)","def _toRgbImage(self, fractal, colors, color_offset):\n hsv_img = np.array(\n [\n # Cycle through color wheel.\n (fractal * colors + color_offset) % 1,\n\n # Saturation = 1 where fractal values > 0,\n # Saturation = 0 otherwise.\n fractal.astype(dtype=bool).astype(dtype=float),\n\n # Invert colours\n 1 - fractal\n ]\n ).astype(dtype=float).T\n\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\n return rgb_img","def xyToRGB(self, x, y, bri=1):\n r, g, b = self.color.getRGBFromXYAndBrightness(x, y, bri)\n return (r, g, b)","def example_lab_to_rgb():\r\n\r\n print(\"=== RGB Example: Lab->RGB ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.217)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n rgb = convert_color(lab, sRGBColor)\r\n print(rgb)\r\n print(\"=== End Example ===\\n\")","def grayscale(img):\n for pixel in img:\n x, y, col = pixel\n r, g, b = col\n \n r = (r + g + b)/3\n r = g = b\n \n new_color = create_color(r, g, b)\n set_color(img, x, y, new_color)","def _preprocess(self, image):\n\n # Scale from [0, 255] to [0, 1] and BGR to RGB \n return (image / 255.0)[:, :, ::-1]","def toGreyscale(img):\n # Get color arrays\n red = img[...,2]\n green = img[...,1]\n blue = img[...,0]\n \n # Fill array with shades of grey\n outImg = np.zeros((img.shape[0], img.shape[1]))\n outImg[...] = 0.299 * red + 0.587 * green + 0.114 * blue\n \n # Round result shades\n outImg = np.round(outImg)\n \n # Update image\n return outImg","def sRGBLuminance(x):\n lin=linearFromsRGB3(x)\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722","def read_colored_image(data_path):\n return cv2.imread(data_path, cv2.IMREAD_COLOR)","def get_colors(self, image: np.ndarray, coordinates: np.ndarray) -> np.ndarray:\r\n x = coordinates.squeeze(1)\r\n return np.flip(image[x[:, 1], x[:, 0]].astype(np.float64) / 255.0, axis=1)","def convert_img(self):\r\n self.img = self.img.convert('RGB')"],"string":"[\n \"def XYZ_to_RGB(XYZ,\\n illuminant_XYZ,\\n illuminant_RGB,\\n XYZ_to_RGB_matrix,\\n chromatic_adaptation_transform='CAT02',\\n encoding_cctf=None):\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\\n\\n if encoding_cctf is not None:\\n RGB = encoding_cctf(RGB)\\n\\n return RGB\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n xyz = lab_to_xyz(image)\\n rgb_image = xyz_to_rgb(xyz)\\n return rgb_image\",\n \"def get_rgb(self, img, r, g, b):\\r\\n\\r\\n # Get specific bands of hyperspectral image\\r\\n red_channel = img[:, :, r]\\r\\n green_channel = img[:, :, g]\\r\\n blue_channel = img[:, :, b]\\r\\n\\r\\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\\r\\n img = img.astype('float32')\\r\\n return img\",\n \"def rgb_processing(rgb_img, center, scale, rot=0):\\n rgb_img = crop(rgb_img, center, scale, \\n [constants.IMG_RES, constants.IMG_RES], rot=rot)\\n # (3,224,224),float,[0,1]\\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\\n return rgb_img\",\n \"def convert_color(image, color_space):\\n out_image = None\\n if color_space != 'RGB':\\n if color_space == 'HSV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\\n elif color_space == 'LUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\\n elif color_space == 'HLS':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\\n elif color_space == 'YUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\\n elif color_space == 'YCrCb':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\\n else:\\n out_image = np.copy(image)\\n return out_image\",\n \"def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n r, g, b = tf.unstack(image, axis=-1)\\n var_r = r / 255\\n var_g = g / 255\\n var_b = b / 255\\n\\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\\n var_r / 12.92)\\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\\n var_g / 12.92)\\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\\n var_b / 12.92)\\n var_r = var_r * 100\\n var_g = var_g * 100\\n var_b = var_b * 100\\n\\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\\n\\n image_xyz = tf.stack([x, y, z], axis=-1)\\n return image_xyz\",\n \"def example_rgb_to_xyz():\\r\\n\\r\\n print(\\\"=== RGB Example: RGB->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n rgb = sRGBColor(120, 130, 140)\\r\\n # Show a string representation.\\r\\n print(rgb)\\r\\n # Convert RGB to XYZ using a D50 illuminant.\\r\\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\\n y: torch.Tensor = image[..., 0, :, :]\\n cb: torch.Tensor = image[..., 1, :, :]\\n cr: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n cb_shifted: torch.Tensor = cb - delta\\n cr_shifted: torch.Tensor = cr - delta\\n\\n r: torch.Tensor = y + 1.403 * cr_shifted\\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\\n b: torch.Tensor = y + 1.773 * cb_shifted\\n return torch.stack([r, g, b], -3)\",\n \"def XYZ_to_sRGB(XYZ):\\n\\n rgb = XYZ_to_sRGB_linear(XYZ)\\n rgb = sRGB_linear_to_sRGB(rgb)\\n\\n return rgb\",\n \"def rgb(self, xyz: Union[ndarray, Iterable[float]]) -> ndarray:\\n return self.value.xyz_to_rgb_matrix @ xyz\",\n \"def grey_to_rgb_imitation(img):\\n return np.repeat(img[...,np.newaxis], 3, -1)\",\n \"def img_to_rgb(img):\\r\\n if len(img.shape) < 3 or img.shape[2] == 1:\\r\\n return np.repeat(img, 3).reshape(img.shape[0], img.shape[1], 3)\\r\\n else:\\r\\n return img\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def grey_to_rgb(im):\\n assert im.n_channels in [1, 3]\\n\\n if im.n_channels == 3:\\n return im\\n\\n im.pixels = np.vstack([im.pixels] * 3)\\n return im\",\n \"def matplotlib_image(image):\\n if image.ndim == 2:\\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\\n else:\\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n return rgb\",\n \"def _colored_img_to_arr(image, verbose=False):\\n height, width = image.size\\n arr = np.array(image.getdata())\\n arr = arr.reshape(3, height, width)\\n r = arr[0]\\n g = arr[1]\\n b = arr[2]\\n return r, g, b\",\n \"def convert_color(image, color_space='RGB'):\\n color_space = color_space.lower()\\n if color_space != 'rgb':\\n if color_space == 'hsv':\\n color_transformation = cv2.COLOR_BGR2HSV\\n elif color_space == 'luv':\\n color_transformation = cv2.COLOR_BGR2LUV\\n elif color_space == 'hls':\\n color_transformation = cv2.COLOR_BGR2HLS\\n elif color_space == 'yuv':\\n color_transformation = cv2.COLOR_BGR2YUV\\n elif color_space == 'ycrcb':\\n color_transformation = cv2.COLOR_BGR2YCrCb\\n else:\\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\\n 'HLS, YUV, YCrCb' % color_space)\\n\\n return cv2.cvtColor(image, color_transformation)\\n else:\\n return image\",\n \"def XYZ_to_RGB_matrix(self):\\n\\n if not self._use_derived_XYZ_to_RGB_matrix:\\n return self._XYZ_to_RGB_matrix\\n else:\\n return self._derived_XYZ_to_RGB_matrix\",\n \"def red_channel(img):\\n\\n red = np.zeros(img.shape,dtype=float)\\n\\n red[:,:,2] = np.copy(img[:,:,2])\\n\\n return red\",\n \"def yuv_to_rgb(img_yuv):\\n\\n y = img_yuv[..., 0]\\n u = img_yuv[..., 1]\\n v = img_yuv[..., 2]\\n\\n r = y + 1.14 * v\\n g = y - 0.396 * u - 0.581 * v\\n b = y + 2.029 * u\\n\\n img_rgb = np.stack((r, g, b), axis=2)\\n img_rgb = np.clip(img_rgb, 0, 1)\\n return img_rgb\",\n \"def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\\r\\n\\r\\n temp_X = cobj.xyz_x\\r\\n temp_Y = cobj.xyz_y\\r\\n temp_Z = cobj.xyz_z\\r\\n\\r\\n logger.debug(\\\" \\\\- Target RGB space: %s\\\", target_rgb)\\r\\n target_illum = target_rgb.native_illuminant\\r\\n logger.debug(\\\" \\\\- Target native illuminant: %s\\\", target_illum)\\r\\n logger.debug(\\\" \\\\- XYZ color's illuminant: %s\\\", cobj.illuminant)\\r\\n \\r\\n # If the XYZ values were taken with a different reference white than the\\r\\n # native reference white of the target RGB space, a transformation matrix\\r\\n # must be applied.\\r\\n if cobj.illuminant != target_illum:\\r\\n logger.debug(\\\" \\\\* Applying transformation from %s to %s \\\",\\r\\n cobj.illuminant, target_illum)\\r\\n # Get the adjusted XYZ values, adapted for the target illuminant.\\r\\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n orig_illum=cobj.illuminant, targ_illum=target_illum)\\r\\n logger.debug(\\\" \\\\* New values: %.3f, %.3f, %.3f\\\",\\r\\n temp_X, temp_Y, temp_Z)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n rgb_type=target_rgb, convtype=\\\"xyz_to_rgb\\\")\\r\\n\\r\\n # v\\r\\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\\r\\n # V\\r\\n nonlinear_channels = {}\\r\\n if target_rgb == sRGBColor:\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n if v <= 0.0031308:\\r\\n nonlinear_channels[channel] = v * 12.92\\r\\n else:\\r\\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\\r\\n\\r\\n return target_rgb(\\r\\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])\",\n \"def get_color(im_obj):\\n #im = Image.open(path, 'r')\\n x, y = im_obj.size\\n\\n r, g, b = 0, 0, 0\\n for i in xrange(x):\\n for j in xrange(y):\\n color_px = im_obj.getpixel((i, j))\\n #print color_px\\n r += color_px[0]\\n g += color_px[1]\\n b += color_px[2]\\n\\n r = r / (x * y)\\n g = g / (x * y)\\n b = b / (x * y)\\n return (r, g, b)\",\n \"def lab_to_rgb(img):\\n new_img = np.zeros((256, 256, 3))\\n for i in range(len(img)):\\n for j in range(len(img[i])):\\n pix = img[i, j]\\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\\n new_img = color.lab2rgb(new_img) * 255\\n new_img = new_img.astype('uint8')\\n return new_img\",\n \"def imcast(img, dtype, color_space=\\\"default\\\"):\\n if img.dtype == dtype:\\n return img\\n if color_space == \\\"default\\\":\\n if dtype == np.uint8:\\n if img.dtype == np.uint16:\\n return np.asarray(img / 257, np.uint8)\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img * 255., np.uint8)\\n elif dtype == np.uint16:\\n if img.dtype == np.uint8:\\n return np.asarray(img, np.uint16) * 257\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img * 65535., np.uint16)\\n elif dtype == np.float32 or dtype == np.float64:\\n if img.dtype == np.uint8:\\n return np.asarray(img, dtype) / 255.\\n elif img.dtype == np.uint16:\\n return np.asarray(img, dtype) / 65535.\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img, dtype)\\n elif color_space == \\\"CIE-L*a*b*\\\":\\n if dtype == np.uint8:\\n if img.dtype == np.float32 or img.dtype == np.float64:\\n dst = np.empty(img.shape, np.uint8)\\n dst[:,:,0] = img[:,:,0] * 255. / 100.\\n dst[:,:,1] = img[:,:,1] + 128.\\n dst[:,:,2] = img[:,:,2] + 128.\\n return dst\\n elif dtype == np.float32 or dtype == np.float64:\\n if img.dtype == np.uint8:\\n dst = np.empty(img.shape, dtype)\\n dst[:,:,0] = np.asarray(img[:,:,0], dtype) / 255. * 100.\\n dst[:,:,1] = np.asarray(img[:,:,1], dtype) - 128.\\n dst[:,:,2] = np.asarray(img[:,:,2], dtype) - 128.\\n return dst\\n raise Exception(\\n \\\"Unexpected conversion from '%s' to '%s' with '%s' color space\\\" % \\\\\\n (img.dtype, dtype, color_space))\",\n \"def yuv2rgb(im):\\n ## conflicting definitions exist depending on whether you use the full range\\n ## of YCbCr or clamp out to the valid range. see here\\n ## http://www.equasys.de/colorconversion.html\\n ## http://www.fourcc.org/fccyvrgb.php\\n from numpy import dot, ndarray, array\\n # if not im.dtype == 'uint8':\\n # raise ImageUtilsError('yuv2rgb only implemented for uint8 arrays')\\n\\n ## better clip input to the valid range just to be on the safe side\\n yuv = ndarray(im.shape) ## float64\\n yuv[:, :, 0] = im[:, :, 0].clip(16, 235).astype(yuv.dtype) - 16\\n yuv[:, :, 1:] = im[:, :, 1:].clip(16, 240).astype(yuv.dtype) - 128\\n\\n ## ITU-R BT.601 version (SDTV)\\n A = array([[1., 0., 0.701],\\n [1., -0.886 * 0.114 / 0.587, -0.701 * 0.299 / 0.587],\\n [1., 0.886, 0.]])\\n A[:, 0] *= 255. / 219.\\n A[:, 1:] *= 255. / 112.\\n\\n ## ITU-R BT.709 version (HDTV)\\n # A = array([[1.164, 0., 1.793],\\n # [1.164, -0.213, -0.533],\\n # [1.164, 2.112, 0.]])\\n\\n rgb = dot(yuv, A.T)\\n return rgb.clip(0, 255).astype('uint8')\",\n \"def _process_img_rgb(self, sensor_data):\\n img = np.array(sensor_data.raw_data).reshape((self.img_y, self.img_x, 4))\\n img = img[:, :, :3] # sensor is actualy rgba, we dont need alpha values\\n self.rgb = img # need to scale rgb values to be {0,1}\",\n \"def RGB_to_XYZ(RGB,\\n illuminant_RGB,\\n illuminant_XYZ,\\n RGB_to_XYZ_matrix,\\n chromatic_adaptation_transform='CAT02',\\n decoding_cctf=None):\\n\\n if decoding_cctf is not None:\\n RGB = decoding_cctf(RGB)\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n return XYZ_a\",\n \"def rgb_to_xy(red, green, blue):\\n\\n # gamma correction\\n red = pow((red + 0.055) / (1.0 + 0.055), 2.4) if red > 0.04045 else (red / 12.92)\\n green = pow((green + 0.055) / (1.0 + 0.055), 2.4) if green > 0.04045 else (green / 12.92)\\n blue = pow((blue + 0.055) / (1.0 + 0.055), 2.4) if blue > 0.04045 else (blue / 12.92)\\n\\n # convert rgb to xyz\\n x = red * 0.649926 + green * 0.103455 + blue * 0.197109\\n y = red * 0.234327 + green * 0.743075 + blue * 0.022598\\n z = green * 0.053077 + blue * 1.035763\\n\\n # convert xyz to xy\\n x = x / (x + y + z)\\n y = y / (x + y + z)\\n\\n # TODO check color gamut if known\\n \\n return [x, y]\",\n \"def to_rgb(im):\\n w, h = im.shape\\n ret = np.empty((w, h, 3), dtype=np.uint8)\\n ret[:, :, 2] = ret[:, :, 1] = ret[:, :, 0] = im\\n return ret\",\n \"def to_color(self):\\n if self.channels == 4:\\n color = opencv.cvtColor(self.img, opencv.COLOR_BGRA2BGR)\\n return Image(color)\\n elif self.channels == 1:\\n color = opencv.cvtColor(self.img, opencv.COLOR_GRAY2BGR)\\n return Image(color)\\n else:\\n return Image(self.img)\",\n \"def test_srgb_conversion_to_xyz_d65(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor)\\r\\n self.assertColorMatch(xyz, XYZColor(0.294, 0.457, 0.103))\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())\",\n \"def rgb_to_xyz(rgb_color):\\n\\n r = (rgb_color[0] / 255)\\n g = (rgb_color[1] / 255)\\n b = (rgb_color[2] / 255)\\n\\n if r > 0.04045:\\n r = ((r + 0.055) / 1.055) ** 2.4\\n else:\\n r = r / 12.92\\n\\n if g > 0.04045:\\n g = ((g + 0.055) / 1.055) ** 2.4\\n else:\\n g = g / 12.92\\n\\n if b > 0.04045:\\n b = ((b + 0.055) / 1.055) ** 2.4\\n else:\\n b = b / 12.92\\n\\n r = r * 100\\n g = g * 100\\n b = b * 100\\n x = (r * 0.4124) + (g * 0.3576) + (b * 0.1805)\\n y = (r * 0.2126) + (g * 0.7152) + (b * 0.0722)\\n z = (r * 0.0193) + (g * 0.1192) + (b * 0.9505)\\n\\n return x, y, z\",\n \"def read_colormap(name):\\n\\n path = get_demo_file(name + '.c3g')\\n\\n out = []\\n with open(path, 'r') as file:\\n for line in file:\\n if 'rgb(' not in line:\\n continue\\n line = line.split('(')[-1].split(')')[0]\\n out.append([float(n) for n in line.split(',')])\\n\\n return np.asarray(out).astype(float) / 256.\",\n \"def load_image_as_rgb(image_path):\\n im = imageio.imread(image_path)\\n y_size = im.shape[0]\\n x_size = im.shape[1]\\n logging.info(\\\"Image has dimensions X:%d Y:%d\\\" % (x_size, y_size))\\n arr = np.zeros((im.shape[0],im.shape[1]), dtype=int)\\n i = 0\\n for im_row in im:\\n j = 0\\n for vec in im_row:\\n arr[i,j] = rgb_vec_to_num(vec)\\n j = j + 1\\n i = i + 1\\n return arr\",\n \"def yiq2rgb(imYIQ):\\n trans = np.array([[1, 0.956, 0.62], [1, -0.272, -0.647], [1, -1.108, 1.705]])\\n return np.dot(imYIQ, trans)\",\n \"def yiq2rgb(im_yiq):\\n return multiply_by_left_matrix(np.linalg.inv(YIQ_MATRIX), im_yiq)\",\n \"def RGB_to_RGB(RGB,\\n input_colourspace,\\n output_colourspace,\\n chromatic_adaptation_transform='CAT02',\\n apply_decoding_cctf=False,\\n apply_encoding_cctf=False):\\n\\n if apply_decoding_cctf:\\n RGB = input_colourspace.decoding_cctf(RGB)\\n\\n M = RGB_to_RGB_matrix(input_colourspace, output_colourspace,\\n chromatic_adaptation_transform)\\n\\n RGB = dot_vector(M, RGB)\\n\\n if apply_encoding_cctf:\\n RGB = output_colourspace.encoding_cctf(RGB)\\n\\n return RGB\",\n \"def convert_grayscale_to_rgb(x: np.ndarray) -> np.ndarray:\\n return np.stack((x, ) * 3, axis=-1)\",\n \"def load_color_image_features(img_path):\\n ac = scipy.misc.imread(img_path, mode='RGB')\\n ac = ac / (255.0 / 2) - 1.0\\n return np.array(ac)\",\n \"def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):\\r\\n\\r\\n # Will contain linearized RGB channels (removed the gamma func).\\r\\n linear_channels = {}\\r\\n\\r\\n if isinstance(cobj, sRGBColor):\\r\\n for channel in ['r', 'g', 'b']:\\r\\n V = getattr(cobj, 'rgb_' + channel)\\r\\n if V <= 0.04045:\\r\\n linear_channels[channel] = V / 12.92\\r\\n else:\\r\\n linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n gamma = cobj.rgb_gamma\\r\\n\\r\\n for channel in ['r', 'g', 'b']:\\r\\n V = getattr(cobj, 'rgb_' + channel)\\r\\n linear_channels[channel] = math.pow(V, gamma)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n xyz_x, xyz_y, xyz_z = apply_RGB_matrix(\\r\\n linear_channels['r'], linear_channels['g'], linear_channels['b'],\\r\\n rgb_type=cobj, convtype=\\\"rgb_to_xyz\\\")\\r\\n\\r\\n if target_illuminant is None:\\r\\n target_illuminant = cobj.native_illuminant\\r\\n \\r\\n # The illuminant of the original RGB object. This will always match\\r\\n # the RGB colorspace's native illuminant.\\r\\n illuminant = cobj.native_illuminant\\r\\n xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)\\r\\n # This will take care of any illuminant changes for us (if source\\r\\n # illuminant != target illuminant).\\r\\n xyzcolor.apply_adaptation(target_illuminant)\\r\\n\\r\\n return xyzcolor\",\n \"def test_srgb_conversion_to_xyz_d50(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))\",\n \"def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n L: torch.Tensor = image[..., 0, :, :]\\n u: torch.Tensor = image[..., 1, :, :]\\n v: torch.Tensor = image[..., 2, :, :]\\n\\n # Convert from Luv to XYZ\\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\\n\\n # Compute white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n a: torch.Tensor = u_w + u / (13 * L + eps)\\n d: torch.Tensor = v_w + v / (13 * L + eps)\\n c: torch.Tensor = 3 * y * (5 * d - 3)\\n\\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\\n\\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\\n\\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\\n\\n # Convert from RGB Linear to sRGB\\n rgb_im = linear_rgb_to_rgb(rgbs_im)\\n\\n return rgb_im\",\n \"def getRGBFromXYAndBrightness(self, x, y, bri=1):\\n # The xy to color conversion is almost the same, but in reverse order.\\n # Check if the xy value is within the color gamut of the lamp.\\n # If not continue with step 2, otherwise step 3.\\n # We do this to calculate the most accurate color the given light can actually do.\\n xyPoint = XYPoint(x, y)\\n\\n if not self.checkPointInLampsReach(xyPoint):\\n # Calculate the closest point on the color gamut triangle\\n # and use that as xy value See step 6 of color to xy.\\n xyPoint = self.getClosestPointToPoint(xyPoint)\\n\\n # Calculate XYZ values Convert using the following formulas:\\n Y = bri\\n X = (Y / xyPoint.y) * xyPoint.x\\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\\n\\n # Convert to RGB using Wide RGB D65 conversion\\n r = X * 1.612 - Y * 0.203 - Z * 0.302\\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\\n b = X * 0.026 - Y * 0.072 + Z * 0.962\\n\\n # Apply reverse gamma correction\\n r, g, b = map(\\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\\n [r, g, b]\\n )\\n\\n # Bring all negative components to zero\\n r, g, b = map(lambda x: max(0, x), [r, g, b])\\n\\n # If one component is greater than 1, weight components by that value.\\n max_component = max(r, g, b)\\n if max_component > 1:\\n r, g, b = map(lambda x: x / max_component, [r, g, b])\\n\\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\\n\\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\\n return (r, g, b)\",\n \"def _rgb(x, y, z):\\n rgb = np.array([x, y, z]).T\\n rgb -= rgb.min(0)\\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\\n return rgb\",\n \"def carla_rgb_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\\n image.convert(carla.ColorConverter.Raw) # pylint: disable=no-member\\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\\\"uint8\\\"))\\n array = array.astype(np.float32) / 255\\n array = np.reshape(array, (image.height, image.width, 4))\\n array = array[:, :, :3]\\n array = array[:, :, ::-1]\\n return array\",\n \"def convert_to_grayscale(image_rgb):\\n red, green, blue = image_rgb[:, :, 0], image_rgb[:, :, 1], image_rgb[:, :, 2]\\n image_gray = 0.2989 * red + 0.5870 * green + 0.1140 * blue\\n return image_gray\",\n \"def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\\n\\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)\",\n \"def colorspace_to_image(cspace: DataFrame, m: int, n: int) -> np.array:\\n \\n assert isinstance(cspace, DataFrame), \\\"Colorspace must be a dataframe\\\"\\n assert len(cspace) == m * n, 'Image dimensions must match'\\n assert all(np.isin(['R', 'G', 'B'], cspace.columns)), \\\"Colorspace must contain RGB columns\\\"\\n \\n result = np.empty([m,n,3])\\n result[:,:, 0] = cspace['R'].values.reshape(m, n)\\n result[:,:, 1] = cspace['G'].values.reshape(m, n)\\n result[:,:, 2] = cspace['B'].values.reshape(m, n)\\n return result\",\n \"def RGB_to_XYZ_matrix(self):\\n\\n if not self._use_derived_RGB_to_XYZ_matrix:\\n return self._RGB_to_XYZ_matrix\\n else:\\n return self._derived_RGB_to_XYZ_matrix\",\n \"def toRGB(self):\\r\\n return mage_hsv_tuple_to_rgb(self.Coords)\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))\",\n \"def hyper2rgb(img, bands):\\n rgb = spectral.get_rgb(img, bands)\\n rgb /= np.max(rgb)\\n rgb = np.asarray(255 * rgb, dtype='uint8')\\n return rgb\",\n \"def bgr_to_rgb(image_bgr):\\n b, g, r = cv2.split(image_bgr)\\n image_rgb = cv2.merge([r, g, b])\\n return image_rgb\",\n \"def yiq2rgb(imYIQ):\\n return __image_color_conversion(imYIQ, YIQ_TO_RGB_MATRIX)\",\n \"def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:\\n r: torch.Tensor = image[..., 0, :, :]\\n g: torch.Tensor = image[..., 1, :, :]\\n b: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b\\n cb: torch.Tensor = (b - y) * 0.564 + delta\\n cr: torch.Tensor = (r - y) * 0.713 + delta\\n return torch.stack([y, cb, cr], -3)\",\n \"def Lab_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n xyz_y = (cobj.lab_l + 16.0) / 116.0\\r\\n xyz_x = cobj.lab_a / 500.0 + xyz_y\\r\\n xyz_z = xyz_y - cobj.lab_b / 200.0\\r\\n \\r\\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\\r\\n xyz_y = math.pow(xyz_y, 3)\\r\\n else:\\r\\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\\r\\n\\r\\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\\r\\n xyz_x = math.pow(xyz_x, 3)\\r\\n else:\\r\\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\\r\\n \\r\\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\\r\\n xyz_z = math.pow(xyz_z, 3)\\r\\n else:\\r\\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\\r\\n \\r\\n xyz_x = (illum[\\\"X\\\"] * xyz_x)\\r\\n xyz_y = (illum[\\\"Y\\\"] * xyz_y)\\r\\n xyz_z = (illum[\\\"Z\\\"] * xyz_z)\\r\\n \\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def get_rgb_bands(image, bands):\\n if bands is not MONOCHROME:\\n red = image[:, :, bands['red']]\\n green = image[:, :, bands['green']]\\n blue = image[:, :, bands['blue']]\\n\\n img = np.rollaxis(np.array([red, green, blue]), 0, 3)\\n else:\\n img = color.grey2rgb(image)\\n\\n return img\",\n \"def transformYIQ2RGB(imgYIQ: np.ndarray) -> np.ndarray:\\r\\n yiq_from_rgb = np.array([[0.299, 0.587, 0.114],\\r\\n [0.59590059, -0.27455667, -0.32134392],\\r\\n [0.21153661, -0.52273617, 0.31119955]])\\r\\n rgb_from_yiq = np.linalg.inv(yiq_from_rgb)\\r\\n\\r\\n RGBImg = np.ndarray(imgYIQ.shape)\\r\\n\\r\\n RGBImg[:, :, 0] = rgb_from_yiq[0,0] * imgYIQ[:, :, 0] + rgb_from_yiq[0,1] * imgYIQ[:, :, 1] + rgb_from_yiq[0,2] * imgYIQ[:, :, 2]\\r\\n RGBImg[:, :, 1] = rgb_from_yiq[1,0] * imgYIQ[:, :, 0] + rgb_from_yiq[1,1] * imgYIQ[:, :, 1] + rgb_from_yiq[1,2] * imgYIQ[:, :, 2]\\r\\n RGBImg[:, :, 2] = rgb_from_yiq[2,0] * imgYIQ[:, :, 0] + rgb_from_yiq[2,1] * imgYIQ[:, :, 1] + rgb_from_yiq[2,2] * imgYIQ[:, :, 2]\\r\\n\\r\\n return RGBImg\",\n \"def bin2RGB(img):\\n dtype = img.dtype\\n if dtype == numpy.uint8:\\n out_dtype = numpy.int32\\n else:\\n out_dtype = dtype\\n shape = img.shape\\n if len(shape) == 3:\\n new_shape = shape[0] // 2, shape[1] // 2, shape[2]\\n new_img = img\\n else:\\n new_shape = shape[0] // 2, shape[1] // 2, 1\\n new_img = img.reshape((shape[0], shape[1], 1))\\n out = numpy.zeros(new_shape, dtype=out_dtype)\\n out += new_img[::2, ::2, :]\\n out += new_img[1::2, ::2, :]\\n out += new_img[1::2, 1::2, :]\\n out += new_img[::2, 1::2, :]\\n out /= 4\\n if len(shape) != 3:\\n out.shape = new_shape[0], new_shape[1]\\n if dtype == numpy.uint8:\\n return out.astype(dtype)\\n else:\\n return out\",\n \"def XYZ_to_sRGB_linear(XYZ):\\n\\n rgb = np.tensordot(T, XYZ, axes=([1, 0]))\\n\\n if clip_method == CLIP_CLAMP_TO_ZERO:\\n # set negative rgb values to zero\\n rgb = np.where(rgb < 0, 0, rgb)\\n return rgb\\n\\n if clip_method == CLIP_ADD_WHITE:\\n # add enough white to make all rgb values nonnegative\\n # find max negative rgb (or 0.0 if all non-negative), we need that much white\\n rgb_min = np.amin(rgb, axis=0)\\n # get max positive component\\n rgb_max = np.amax(rgb, axis=0)\\n\\n # get scaling factor to maintain max rgb after adding white\\n scaling = np.where(rgb_max > 0.0, rgb_max / (rgb_max - rgb_min + 0.00001), 1.0)\\n\\n # add enough white to cancel this out, maintaining the maximum of rgb\\n rgb = np.where(rgb_min < 0.0, scaling * (rgb - rgb_min), rgb)\\n return rgb\",\n \"def rgb2gray(img):\\r\\n return 0.2989 * img[..., 0] + 0.587 * img[..., 1] + 0.114 * img[..., 2]\",\n \"def camera_to_pixel(self, X):\\n raise NotImplementedError\",\n \"def generate_normalized_rgb(self):\\n \\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\\n for i in Numeric.arange(256):\\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\\n return r/256.0,g/256.0,b/256.0\",\n \"def _convert_to_yolo_img(self, img):\\n\\n img = img / 255.0\\n h, w, c = img.shape\\n img = img.transpose(2, 0, 1)\\n outimg = make_image(w, h, c)\\n img = img.reshape((w*h*c))\\n data = c_array(c_float, img)\\n outimg.data = data\\n rgbgr_image(outimg)\\n return outimg\",\n \"def bgr_to_rgb(ims):\\n out = []\\n for im in ims:\\n out.append(im[:,:,::-1])\\n return out\",\n \"def lab_to_rgb(img_l, img_ab):\\n lab = np.empty([*img_l.shape[0:2], 3])\\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\\n lab[:, :, 1:] = img_ab * 127\\n return color.lab2rgb(lab)\",\n \"def _rgb2y(self, im):\\n if len(im.shape) < 3:\\n return im\\n return np.sum(im * [0.299, 0.587, 0.114], axis=2)\",\n \"def to_image(x):\\n x = denorm(x.data.cpu())\\n ndarr = x.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()\\n im = ndarr\\n return im\",\n \"def img_recolor(self, args, input_image_path):\\n \\n ec = encoder.Encoder(output_path=args.intermediate_representation, method=args.method,\\n size=args.size, p=args.p, grid_size=args.grid_size, plot=args.plot, quantize=args.quantize)\\n dc = decoder.Decoder(output_path=args.output_path, method=args.method, size=args.size, p=args.p, gpu_id=args.gpu_id, plot=args.plot)\\n\\n ec.encode(input_image_path)\\n img_gray_name = ar_utils.gen_new_gray_filename(input_image_path)\\n img_gray_path = os.path.join(args.intermediate_representation, img_gray_name)\\n dc.decode(img_gray_path)\\n\\n if args.delete_gray and os.path.exists(img_gray_path):\\n os.remove(img_gray_path)\",\n \"def XYZ_to_RGB_matrix(self, value):\\n\\n if value is not None:\\n value = np.asarray(value)\\n self._XYZ_to_RGB_matrix = value\",\n \"def convertColorSpace(\\n self,\\n img, # Image in some color space\\n srcColorSpace = 'BGR', # Source color space\\n tgtColorSpace = 'RGB', # Traget color space\\n ):\\n\\n if srcColorSpace == tgtColorSpace:\\n return img\\n\\n if srcColorSpace == 'BGR':\\n img_bgr = img\\n elif srcColorSpace == 'RGB':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\\n elif srcColorSpace == 'HSV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\\n elif srcColorSpace == 'HLS':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\\n elif srcColorSpace == 'LUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\\n elif srcColorSpace == 'YUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\\n elif srcColorSpace == 'YCrCb':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(srcColorSpace))\\n\\n if tgtColorSpace == 'BGR':\\n img_tgt = img_bgr\\n elif tgtColorSpace == 'RGB':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\\n elif tgtColorSpace == 'HSV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\\n elif tgtColorSpace == 'HLS':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\\n elif tgtColorSpace == 'LUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\\n elif tgtColorSpace == 'YUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\\n elif tgtColorSpace == 'YCrCb':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(tgtColorSpace))\\n\\n return img_tgt\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n soln_real = adjustRange(fractal[0], 0, 127)\\n soln_imag = adjustRange(fractal[1], 0, 127)\\n iters = adjustRange(fractal[2], 0, 128)\\n\\n rgb_image = np.array([\\n soln_real + iters,\\n soln_imag + iters,\\n iters\\n ]\\n ).astype(dtype=np.uint8)\\n\\n return rgb_image.T\",\n \"def convert_color(img, conv='RGB2YCrCb'):\\n if conv == 'RGB2YCrCb':\\n return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\\n if conv == 'BGR2YCrCb':\\n return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)\\n if conv == 'RGB2LUV':\\n return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)\",\n \"def GetRGB(self, *args):\\n return _XCAFDoc.XCAFDoc_Color_GetRGB(self, *args)\",\n \"def l_to_rgb(img_l):\\n lab = np.squeeze(255 * (img_l + 1) / 2)\\n return color.gray2rgb(lab) / 255\",\n \"def green_channel(input_image):\\n return input_image[:, :, 1]\",\n \"def read_color_image(path):\\n with open(path, 'rb') as f:\\n img = Image.fromarray(read_ppm(f), mode='RGB')\\n img = tf.keras.preprocessing.image.img_to_array(img, dtype=int)\\n img = tf.convert_to_tensor(img)\\n return img\",\n \"def grayscale2rgb(gray_image):\\r\\n\\r\\n\\tassert gray_image.ndim == 2\\r\\n\\r\\n\\timage = np.stack([gray_image, gray_image, gray_image], axis = -1).astype(np.uint8)\\r\\n\\r\\n\\treturn image\",\n \"def greyscale(img):\\n grey_img = np.zeros([210, 160])\\n for i in range(3):\\n grey_img =np.sum([grey_img, img[:, :, i]], 0)\\n grey_img /= 3\\n grey_img = grey_img.astype(np.uint8)\\n return grey_img\",\n \"def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\\n\\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)\",\n \"def recreate_image(x):\\n reverse_mean = [-0.485, -0.456, -0.406]\\n reverse_std = [1/0.229, 1/0.224, 1/0.225]\\n in_channel = x.shape[-1]\\n recreated_im = copy.copy(x) # C, H, W\\n if in_channel == 3:\\n for c in range(in_channel):\\n recreated_im[:, :, c] /= reverse_std[c]\\n recreated_im[:, :, c] -= reverse_mean[c]\\n elif in_channel == 1:\\n recreated_im[:, :, 0] /= reverse_std[1]\\n recreated_im[:, :, 0] -= reverse_mean[1]\\n recreated_im[recreated_im > 1] = 1\\n recreated_im[recreated_im < 0] = 0\\n recreated_im = np.round(recreated_im * 255)\\n\\n recreated_im = np.uint8(recreated_im) # H, W, C\\n return recreated_im\",\n \"def RGB2BGR(x):\\n out = cv2.cvtColor(x, cv2.COLOR_RGB2BGR)\\n return out\",\n \"def xyz2linear_rgb(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(rgb_from_xyz, arr)\",\n \"def get_BGR_img(self):\\n img = self.img.copy()\\n # Convert BGR to HSV\\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\\n # define range of BGR color in HSV\\n threshold_blue = np.array([[100,43,46], [124,255,255]])\\n threshold_green = np.array([[35,43,46], [77,255,255]])\\n threshold_red1 = np.array([[0,43,46], [10,255,255]])\\n threshold_red2 = np.array([[156,43,46], [180,255,255]])\\n # Threshold the HSV image to get only BGR colors\\n mask_blue = cv2.inRange(hsv, threshold_blue[0], threshold_blue[1])\\n mask_green = cv2.inRange(hsv, threshold_green[0], threshold_green[1])\\n mask_red1 = cv2.inRange(hsv, threshold_red1[0], threshold_red1[1])\\n mask_red2 = cv2.inRange(hsv, threshold_red2[0], threshold_red2[1])\\n mask_red = mask_red1 | mask_red2\\n # Bitwise-AND mask and original image\\n self.blue = cv2.bitwise_and(img, img, mask=mask_blue)\\n self.green = cv2.bitwise_and(img, img, mask=mask_green)\\n self.red = cv2.bitwise_and(img, img, mask=mask_red)\\n # 返回 bgr 三通道的分量合成的图片\\n return np.stack((self.blue[:, :, 0], self.green[:, :, 1], self.red[:, :, 2]), axis=2)\",\n \"def GetRGB(*args):\\n return _gdi_.Colour_GetRGB(*args)\",\n \"def test_conversion_through_rgb(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor)\\r\\n hsl = convert_color(xyz, HSLColor, through_rgb_type=AdobeRGBColor)\\r\\n # Notice how we don't have to pass through_rgb_type explicitly.\\r\\n xyz2 = convert_color(hsl, XYZColor)\\r\\n self.assertColorMatch(xyz, xyz2)\",\n \"def red_filter(img):\\r\\n #with Image.open(filename) as img:\\r\\n w = img.width\\r\\n h = img.height\\r\\n\\r\\n newimg = Image.new('RGB', (w,h))\\r\\n for y in range(h):\\r\\n for x in range(w):\\r\\n r, g, b = img.getpixel((x,y))\\r\\n \\r\\n newimg.putpixel((x, y), (r, 0, 0))\\r\\n \\r\\n return newimg\",\n \"def greyscale(c):\\n return desaturate(c, 1)\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n hsv_img = np.array(\\n [\\n # Cycle through color wheel.\\n (fractal * colors + color_offset) % 1,\\n\\n # Saturation = 1 where fractal values > 0,\\n # Saturation = 0 otherwise.\\n fractal.astype(dtype=bool).astype(dtype=float),\\n\\n # Invert colours\\n 1 - fractal\\n ]\\n ).astype(dtype=float).T\\n\\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\\n return rgb_img\",\n \"def xyToRGB(self, x, y, bri=1):\\n r, g, b = self.color.getRGBFromXYAndBrightness(x, y, bri)\\n return (r, g, b)\",\n \"def example_lab_to_rgb():\\r\\n\\r\\n print(\\\"=== RGB Example: Lab->RGB ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.217)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n rgb = convert_color(lab, sRGBColor)\\r\\n print(rgb)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def grayscale(img):\\n for pixel in img:\\n x, y, col = pixel\\n r, g, b = col\\n \\n r = (r + g + b)/3\\n r = g = b\\n \\n new_color = create_color(r, g, b)\\n set_color(img, x, y, new_color)\",\n \"def _preprocess(self, image):\\n\\n # Scale from [0, 255] to [0, 1] and BGR to RGB \\n return (image / 255.0)[:, :, ::-1]\",\n \"def toGreyscale(img):\\n # Get color arrays\\n red = img[...,2]\\n green = img[...,1]\\n blue = img[...,0]\\n \\n # Fill array with shades of grey\\n outImg = np.zeros((img.shape[0], img.shape[1]))\\n outImg[...] = 0.299 * red + 0.587 * green + 0.114 * blue\\n \\n # Round result shades\\n outImg = np.round(outImg)\\n \\n # Update image\\n return outImg\",\n \"def sRGBLuminance(x):\\n lin=linearFromsRGB3(x)\\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722\",\n \"def read_colored_image(data_path):\\n return cv2.imread(data_path, cv2.IMREAD_COLOR)\",\n \"def get_colors(self, image: np.ndarray, coordinates: np.ndarray) -> np.ndarray:\\r\\n x = coordinates.squeeze(1)\\r\\n return np.flip(image[x[:, 1], x[:, 0]].astype(np.float64) / 255.0, axis=1)\",\n \"def convert_img(self):\\r\\n self.img = self.img.convert('RGB')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6828028","0.68103015","0.68066865","0.6763418","0.66843706","0.66742265","0.66261303","0.65840024","0.6561155","0.6552539","0.64690375","0.645088","0.63681024","0.6365761","0.63621247","0.6326144","0.6306346","0.6306079","0.6283632","0.6256421","0.62433654","0.62383455","0.62226963","0.6117911","0.6095532","0.6088051","0.60821766","0.60728455","0.60607404","0.6055855","0.6048131","0.6048042","0.60375625","0.60349584","0.6025372","0.6014628","0.6007638","0.6000251","0.5981813","0.5977883","0.59428304","0.593842","0.59242284","0.59218425","0.5908843","0.5896996","0.5896824","0.5889424","0.588515","0.5879707","0.5874953","0.58737224","0.58619905","0.5859301","0.58586246","0.58469677","0.5846777","0.5840282","0.583042","0.58299696","0.5828725","0.5823826","0.582189","0.58035195","0.5802021","0.5795793","0.57955855","0.5793794","0.5793278","0.5789499","0.57878286","0.5778776","0.5771592","0.57610536","0.5749198","0.57483554","0.5736792","0.57269645","0.5726296","0.57036453","0.5702941","0.5678666","0.56625545","0.56619644","0.5661905","0.5651188","0.5646833","0.56446505","0.5641619","0.5640471","0.56321776","0.56317925","0.56316704","0.5619051","0.5610693","0.56105715","0.5605793","0.5598296","0.5590711","0.5588196"],"string":"[\n \"0.6828028\",\n \"0.68103015\",\n \"0.68066865\",\n \"0.6763418\",\n \"0.66843706\",\n \"0.66742265\",\n \"0.66261303\",\n \"0.65840024\",\n \"0.6561155\",\n \"0.6552539\",\n \"0.64690375\",\n \"0.645088\",\n \"0.63681024\",\n \"0.6365761\",\n \"0.63621247\",\n \"0.6326144\",\n \"0.6306346\",\n \"0.6306079\",\n \"0.6283632\",\n \"0.6256421\",\n \"0.62433654\",\n \"0.62383455\",\n \"0.62226963\",\n \"0.6117911\",\n \"0.6095532\",\n \"0.6088051\",\n \"0.60821766\",\n \"0.60728455\",\n \"0.60607404\",\n \"0.6055855\",\n \"0.6048131\",\n \"0.6048042\",\n \"0.60375625\",\n \"0.60349584\",\n \"0.6025372\",\n \"0.6014628\",\n \"0.6007638\",\n \"0.6000251\",\n \"0.5981813\",\n \"0.5977883\",\n \"0.59428304\",\n \"0.593842\",\n \"0.59242284\",\n \"0.59218425\",\n \"0.5908843\",\n \"0.5896996\",\n \"0.5896824\",\n \"0.5889424\",\n \"0.588515\",\n \"0.5879707\",\n \"0.5874953\",\n \"0.58737224\",\n \"0.58619905\",\n \"0.5859301\",\n \"0.58586246\",\n \"0.58469677\",\n \"0.5846777\",\n \"0.5840282\",\n \"0.583042\",\n \"0.58299696\",\n \"0.5828725\",\n \"0.5823826\",\n \"0.582189\",\n \"0.58035195\",\n \"0.5802021\",\n \"0.5795793\",\n \"0.57955855\",\n \"0.5793794\",\n \"0.5793278\",\n \"0.5789499\",\n \"0.57878286\",\n \"0.5778776\",\n \"0.5771592\",\n \"0.57610536\",\n \"0.5749198\",\n \"0.57483554\",\n \"0.5736792\",\n \"0.57269645\",\n \"0.5726296\",\n \"0.57036453\",\n \"0.5702941\",\n \"0.5678666\",\n \"0.56625545\",\n \"0.56619644\",\n \"0.5661905\",\n \"0.5651188\",\n \"0.5646833\",\n \"0.56446505\",\n \"0.5641619\",\n \"0.5640471\",\n \"0.56321776\",\n \"0.56317925\",\n \"0.56316704\",\n \"0.5619051\",\n \"0.5610693\",\n \"0.56105715\",\n \"0.5605793\",\n \"0.5598296\",\n \"0.5590711\",\n \"0.5588196\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7146014"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12391,"cells":{"query":{"kind":"string","value":"Convert an image from RGB color space to XYZ color space"},"document":{"kind":"string","value":"def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\n r, g, b = tf.unstack(image, axis=-1)\n var_r = r / 255\n var_g = g / 255\n var_b = b / 255\n\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\n var_r / 12.92)\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\n var_g / 12.92)\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\n var_b / 12.92)\n var_r = var_r * 100\n var_g = var_g * 100\n var_b = var_b * 100\n\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\n\n image_xyz = tf.stack([x, y, z], axis=-1)\n return image_xyz"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def rgb_to_xyz(rgb_color):\n\n r = (rgb_color[0] / 255)\n g = (rgb_color[1] / 255)\n b = (rgb_color[2] / 255)\n\n if r > 0.04045:\n r = ((r + 0.055) / 1.055) ** 2.4\n else:\n r = r / 12.92\n\n if g > 0.04045:\n g = ((g + 0.055) / 1.055) ** 2.4\n else:\n g = g / 12.92\n\n if b > 0.04045:\n b = ((b + 0.055) / 1.055) ** 2.4\n else:\n b = b / 12.92\n\n r = r * 100\n g = g * 100\n b = b * 100\n x = (r * 0.4124) + (g * 0.3576) + (b * 0.1805)\n y = (r * 0.2126) + (g * 0.7152) + (b * 0.0722)\n z = (r * 0.0193) + (g * 0.1192) + (b * 0.9505)\n\n return x, y, z","def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n var_x = x / 100\n var_y = y / 100\n var_z = z / 100\n\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\n\n var_r = tf.where(var_r > 0.0031308,\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\n 12.92 * var_r)\n var_g = tf.where(var_g > 0.0031308,\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\n 12.92 * var_g)\n var_b = tf.where(var_b > 0.0031308,\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\n 12.92 * var_b)\n r = var_r * 255\n g = var_g * 255\n b = var_b * 255\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\n return rgb_image","def example_rgb_to_xyz():\r\n\r\n print(\"=== RGB Example: RGB->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n rgb = sRGBColor(120, 130, 140)\r\n # Show a string representation.\r\n print(rgb)\r\n # Convert RGB to XYZ using a D50 illuminant.\r\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def RGB_to_XYZ(RGB,\n illuminant_RGB,\n illuminant_XYZ,\n RGB_to_XYZ_matrix,\n chromatic_adaptation_transform='CAT02',\n decoding_cctf=None):\n\n if decoding_cctf is not None:\n RGB = decoding_cctf(RGB)\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n transform=chromatic_adaptation_transform)\n\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\n\n XYZ_a = dot_vector(M, XYZ)\n\n return XYZ_a","def RGB_to_XYZ_matrix(self):\n\n if not self._use_derived_RGB_to_XYZ_matrix:\n return self._RGB_to_XYZ_matrix\n else:\n return self._derived_RGB_to_XYZ_matrix","def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\n\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)","def rgb_to_xy(red, green, blue):\n\n # gamma correction\n red = pow((red + 0.055) / (1.0 + 0.055), 2.4) if red > 0.04045 else (red / 12.92)\n green = pow((green + 0.055) / (1.0 + 0.055), 2.4) if green > 0.04045 else (green / 12.92)\n blue = pow((blue + 0.055) / (1.0 + 0.055), 2.4) if blue > 0.04045 else (blue / 12.92)\n\n # convert rgb to xyz\n x = red * 0.649926 + green * 0.103455 + blue * 0.197109\n y = red * 0.234327 + green * 0.743075 + blue * 0.022598\n z = green * 0.053077 + blue * 1.035763\n\n # convert xyz to xy\n x = x / (x + y + z)\n y = y / (x + y + z)\n\n # TODO check color gamut if known\n \n return [x, y]","def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\n l, a, b = tf.unstack(image, axis=-1)\n\n var_y = (l + 16) / 116\n var_x = a / 500 + var_y\n var_z = var_y - b / 200\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\n (var_x - 16 / 116) / 7.787)\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\n (var_y - 16 / 116) / 7.787)\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\n (var_z - 16 / 116) / 7.787)\n\n refx = 95.047\n refy = 100.00\n ref_z = 108.883\n\n x = var_x * refx\n y = var_y * refy\n z = var_z * ref_z\n xyz_image = tf.stack([x, y, z], axis=-1)\n return xyz_image","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def RGB_to_XYZ_matrix(self, value):\n\n if value is not None:\n value = np.asarray(value)\n self._RGB_to_XYZ_matrix = value","def Lab_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n xyz_y = (cobj.lab_l + 16.0) / 116.0\r\n xyz_x = cobj.lab_a / 500.0 + xyz_y\r\n xyz_z = xyz_y - cobj.lab_b / 200.0\r\n \r\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\r\n xyz_y = math.pow(xyz_y, 3)\r\n else:\r\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\r\n\r\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\r\n xyz_x = math.pow(xyz_x, 3)\r\n else:\r\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\r\n \r\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\r\n xyz_z = math.pow(xyz_z, 3)\r\n else:\r\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\r\n \r\n xyz_x = (illum[\"X\"] * xyz_x)\r\n xyz_y = (illum[\"Y\"] * xyz_y)\r\n xyz_z = (illum[\"Z\"] * xyz_z)\r\n \r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)","def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):\r\n\r\n # Will contain linearized RGB channels (removed the gamma func).\r\n linear_channels = {}\r\n\r\n if isinstance(cobj, sRGBColor):\r\n for channel in ['r', 'g', 'b']:\r\n V = getattr(cobj, 'rgb_' + channel)\r\n if V <= 0.04045:\r\n linear_channels[channel] = V / 12.92\r\n else:\r\n linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)\r\n else:\r\n # If it's not sRGB...\r\n gamma = cobj.rgb_gamma\r\n\r\n for channel in ['r', 'g', 'b']:\r\n V = getattr(cobj, 'rgb_' + channel)\r\n linear_channels[channel] = math.pow(V, gamma)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n xyz_x, xyz_y, xyz_z = apply_RGB_matrix(\r\n linear_channels['r'], linear_channels['g'], linear_channels['b'],\r\n rgb_type=cobj, convtype=\"rgb_to_xyz\")\r\n\r\n if target_illuminant is None:\r\n target_illuminant = cobj.native_illuminant\r\n \r\n # The illuminant of the original RGB object. This will always match\r\n # the RGB colorspace's native illuminant.\r\n illuminant = cobj.native_illuminant\r\n xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)\r\n # This will take care of any illuminant changes for us (if source\r\n # illuminant != target illuminant).\r\n xyzcolor.apply_adaptation(target_illuminant)\r\n\r\n return xyzcolor","def test_srgb_conversion_to_xyz_d65(self):\r\n\r\n xyz = convert_color(self.color, XYZColor)\r\n self.assertColorMatch(xyz, XYZColor(0.294, 0.457, 0.103))","def convert_color(image, color_space):\n out_image = None\n if color_space != 'RGB':\n if color_space == 'HSV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\n elif color_space == 'LUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\n elif color_space == 'HLS':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\n elif color_space == 'YUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\n elif color_space == 'YCrCb':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\n else:\n out_image = np.copy(image)\n return out_image","def linear_rgb2xyz(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(xyz_from_rgb, arr)","def test_srgb_conversion_to_xyz_d50(self):\r\n\r\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))","def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\n y: torch.Tensor = image[..., 0, :, :]\n cb: torch.Tensor = image[..., 1, :, :]\n cr: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n cb_shifted: torch.Tensor = cb - delta\n cr_shifted: torch.Tensor = cr - delta\n\n r: torch.Tensor = y + 1.403 * cr_shifted\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\n b: torch.Tensor = y + 1.773 * cb_shifted\n return torch.stack([r, g, b], -3)","def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\n xyz = lab_to_xyz(image)\n rgb_image = xyz_to_rgb(xyz)\n return rgb_image","def rgb_processing(rgb_img, center, scale, rot=0):\n rgb_img = crop(rgb_img, center, scale, \n [constants.IMG_RES, constants.IMG_RES], rot=rot)\n # (3,224,224),float,[0,1]\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\n return rgb_img","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def _convert_to_yolo_img(self, img):\n\n img = img / 255.0\n h, w, c = img.shape\n img = img.transpose(2, 0, 1)\n outimg = make_image(w, h, c)\n img = img.reshape((w*h*c))\n data = c_array(c_float, img)\n outimg.data = data\n rgbgr_image(outimg)\n return outimg","def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\r\n\r\n temp_X = cobj.xyz_x\r\n temp_Y = cobj.xyz_y\r\n temp_Z = cobj.xyz_z\r\n\r\n logger.debug(\" \\- Target RGB space: %s\", target_rgb)\r\n target_illum = target_rgb.native_illuminant\r\n logger.debug(\" \\- Target native illuminant: %s\", target_illum)\r\n logger.debug(\" \\- XYZ color's illuminant: %s\", cobj.illuminant)\r\n \r\n # If the XYZ values were taken with a different reference white than the\r\n # native reference white of the target RGB space, a transformation matrix\r\n # must be applied.\r\n if cobj.illuminant != target_illum:\r\n logger.debug(\" \\* Applying transformation from %s to %s \",\r\n cobj.illuminant, target_illum)\r\n # Get the adjusted XYZ values, adapted for the target illuminant.\r\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\r\n temp_X, temp_Y, temp_Z,\r\n orig_illum=cobj.illuminant, targ_illum=target_illum)\r\n logger.debug(\" \\* New values: %.3f, %.3f, %.3f\",\r\n temp_X, temp_Y, temp_Z)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\r\n temp_X, temp_Y, temp_Z,\r\n rgb_type=target_rgb, convtype=\"xyz_to_rgb\")\r\n\r\n # v\r\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\r\n # V\r\n nonlinear_channels = {}\r\n if target_rgb == sRGBColor:\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n if v <= 0.0031308:\r\n nonlinear_channels[channel] = v * 12.92\r\n else:\r\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\r\n else:\r\n # If it's not sRGB...\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\r\n\r\n return target_rgb(\r\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])","def get_rgb(self, img, r, g, b):\r\n\r\n # Get specific bands of hyperspectral image\r\n red_channel = img[:, :, r]\r\n green_channel = img[:, :, g]\r\n blue_channel = img[:, :, b]\r\n\r\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\r\n img = img.astype('float32')\r\n return img","def pos2im_coordinates(x, z):\n # x_lim = [-0.85, 0.86]\n # z_lim = [-1.22, 0.47]\n x_lim = [-0.365, 0.365]\n z_lim = [-0.95, -0.24]\n\n pix_x = int(127 * (x_lim[1] - x) / (x_lim[1] - x_lim[0]))\n pix_z = int(127 * (z_lim[1] - z) / (z_lim[1] - z_lim[0]))\n return pix_x, pix_z","def XYZ_to_RGB(XYZ,\n illuminant_XYZ,\n illuminant_RGB,\n XYZ_to_RGB_matrix,\n chromatic_adaptation_transform='CAT02',\n encoding_cctf=None):\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n transform=chromatic_adaptation_transform)\n\n XYZ_a = dot_vector(M, XYZ)\n\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\n\n if encoding_cctf is not None:\n RGB = encoding_cctf(RGB)\n\n return RGB","def XYZ_to_RGB_matrix(self):\n\n if not self._use_derived_XYZ_to_RGB_matrix:\n return self._XYZ_to_RGB_matrix\n else:\n return self._derived_XYZ_to_RGB_matrix","def test_adobe_conversion_to_xyz_d65(self):\r\n\r\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\r\n xyz = convert_color(adobe, XYZColor)\r\n self.assertColorMatch(xyz, XYZColor(0.230, 0.429, 0.074))","def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:\n r: torch.Tensor = image[..., 0, :, :]\n g: torch.Tensor = image[..., 1, :, :]\n b: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b\n cb: torch.Tensor = (b - y) * 0.564 + delta\n cr: torch.Tensor = (r - y) * 0.713 + delta\n return torch.stack([y, cb, cr], -3)","def to_image(x):\n x = denorm(x.data.cpu())\n ndarr = x.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()\n im = ndarr\n return im","def camera_to_pixel(self, X):\n raise NotImplementedError","def imcast(img, dtype, color_space=\"default\"):\n if img.dtype == dtype:\n return img\n if color_space == \"default\":\n if dtype == np.uint8:\n if img.dtype == np.uint16:\n return np.asarray(img / 257, np.uint8)\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img * 255., np.uint8)\n elif dtype == np.uint16:\n if img.dtype == np.uint8:\n return np.asarray(img, np.uint16) * 257\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img * 65535., np.uint16)\n elif dtype == np.float32 or dtype == np.float64:\n if img.dtype == np.uint8:\n return np.asarray(img, dtype) / 255.\n elif img.dtype == np.uint16:\n return np.asarray(img, dtype) / 65535.\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img, dtype)\n elif color_space == \"CIE-L*a*b*\":\n if dtype == np.uint8:\n if img.dtype == np.float32 or img.dtype == np.float64:\n dst = np.empty(img.shape, np.uint8)\n dst[:,:,0] = img[:,:,0] * 255. / 100.\n dst[:,:,1] = img[:,:,1] + 128.\n dst[:,:,2] = img[:,:,2] + 128.\n return dst\n elif dtype == np.float32 or dtype == np.float64:\n if img.dtype == np.uint8:\n dst = np.empty(img.shape, dtype)\n dst[:,:,0] = np.asarray(img[:,:,0], dtype) / 255. * 100.\n dst[:,:,1] = np.asarray(img[:,:,1], dtype) - 128.\n dst[:,:,2] = np.asarray(img[:,:,2], dtype) - 128.\n return dst\n raise Exception(\n \"Unexpected conversion from '%s' to '%s' with '%s' color space\" % \\\n (img.dtype, dtype, color_space))","def test_adobe_conversion_to_xyz_d50(self):\r\n\r\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\r\n xyz = convert_color(adobe, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.247, 0.431, 0.060))","def convert_color(image, color_space='RGB'):\n color_space = color_space.lower()\n if color_space != 'rgb':\n if color_space == 'hsv':\n color_transformation = cv2.COLOR_BGR2HSV\n elif color_space == 'luv':\n color_transformation = cv2.COLOR_BGR2LUV\n elif color_space == 'hls':\n color_transformation = cv2.COLOR_BGR2HLS\n elif color_space == 'yuv':\n color_transformation = cv2.COLOR_BGR2YUV\n elif color_space == 'ycrcb':\n color_transformation = cv2.COLOR_BGR2YCrCb\n else:\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\n 'HLS, YUV, YCrCb' % color_space)\n\n return cv2.cvtColor(image, color_transformation)\n else:\n return image","def _colored_img_to_arr(image, verbose=False):\n height, width = image.size\n arr = np.array(image.getdata())\n arr = arr.reshape(3, height, width)\n r = arr[0]\n g = arr[1]\n b = arr[2]\n return r, g, b","def XYZ_to_sRGB(XYZ):\n\n rgb = XYZ_to_sRGB_linear(XYZ)\n rgb = sRGB_linear_to_sRGB(rgb)\n\n return rgb","def rgb(self, xyz: Union[ndarray, Iterable[float]]) -> ndarray:\n return self.value.xyz_to_rgb_matrix @ xyz","def rgb2alpha(img):\n\t### First of all we need the size of our picture to make the transforms\n\n\tx = len(img) ; y = len(img[0])\n\talpha = np.full((x, y, 3), 0, dtype = float)\t\t## This will be the transformed image\n\n\t### Now we gotta access each pixel of the picture\n\n\tfor i, vi in enumerate(img):\n\t\tfor j, px in enumerate(vi):\n\t\t\t### There we are\n\n\t\t\t# Step 1 : LMS transform, for that we use r_l\n\n\t\t\talpha[i][j] = np.matmul(r_l, px)\n\n\t\t\t# Step 2 : log em all (decimal log)\n\n\t\t\talpha[i][j][0] = log(alpha[i][j][0])\n\t\t\talpha[i][j][1] = log(alpha[i][j][1])\n\t\t\talpha[i][j][2] = log(alpha[i][j][2])\n\n\t\t\t# Step 3 : l alpha beta transform, by using l_a\n\n\t\t\talpha[i][j] = np.matmul(l_a, alpha[i][j])\n\n\treturn alpha","def xyY_to_XYZ(cobj, *args, **kwargs):\r\n \r\n xyz_x = (cobj.xyy_x * cobj.xyy_Y) / cobj.xyy_y\r\n xyz_y = cobj.xyy_Y\r\n xyz_z = ((1.0 - cobj.xyy_x - cobj.xyy_y) * xyz_y) / cobj.xyy_y\r\n \r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)","def matplotlib_image(image):\n if image.ndim == 2:\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\n else:\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return rgb","def load_color_image_features(img_path):\n ac = scipy.misc.imread(img_path, mode='RGB')\n ac = ac / (255.0 / 2) - 1.0\n return np.array(ac)","def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\n xyz = rgb_to_xyz(image)\n lab_image = xyz_to_lab(xyz)\n return lab_image","def colorspace_to_image(cspace: DataFrame, m: int, n: int) -> np.array:\n \n assert isinstance(cspace, DataFrame), \"Colorspace must be a dataframe\"\n assert len(cspace) == m * n, 'Image dimensions must match'\n assert all(np.isin(['R', 'G', 'B'], cspace.columns)), \"Colorspace must contain RGB columns\"\n \n result = np.empty([m,n,3])\n result[:,:, 0] = cspace['R'].values.reshape(m, n)\n result[:,:, 1] = cspace['G'].values.reshape(m, n)\n result[:,:, 2] = cspace['B'].values.reshape(m, n)\n return result","def cie_xyz(self):\n return tuple(matrix_mult(\n ((0.4124564, 0.3575761, 0.1804375),\n (0.2126729, 0.7151522, 0.0721750),\n (0.0193339, 0.1191920, 0.9503041),\n ),\n (from_srgb(self.red),\n from_srgb(self.green),\n from_srgb(self.blue)\n )\n ))","def grey_to_rgb_imitation(img):\n return np.repeat(img[...,np.newaxis], 3, -1)","def pixel_to_xyz(self, pixels, max_depth=25):\n assert pixels.shape[0] == 4\n d = np.abs(pixels[0] - pixels[2]) # disparity U_L - U_R\n z = self.fsub / d\n z[z > max_depth] = max_depth\n \n # calcualte xy\n u_L = pixels[0] # take first row\n v_L = pixels[1] # take 2nd row\n x = (u_L - self.cu) / self.fsu * z\n y = (v_L - self.cv) / self.fsv * z\n \n return np.vstack((x,y,z))","def _process_img_rgb(self, sensor_data):\n img = np.array(sensor_data.raw_data).reshape((self.img_y, self.img_x, 4))\n img = img[:, :, :3] # sensor is actualy rgba, we dont need alpha values\n self.rgb = img # need to scale rgb values to be {0,1}","def recreate_image(x):\n reverse_mean = [-0.485, -0.456, -0.406]\n reverse_std = [1/0.229, 1/0.224, 1/0.225]\n in_channel = x.shape[-1]\n recreated_im = copy.copy(x) # C, H, W\n if in_channel == 3:\n for c in range(in_channel):\n recreated_im[:, :, c] /= reverse_std[c]\n recreated_im[:, :, c] -= reverse_mean[c]\n elif in_channel == 1:\n recreated_im[:, :, 0] /= reverse_std[1]\n recreated_im[:, :, 0] -= reverse_mean[1]\n recreated_im[recreated_im > 1] = 1\n recreated_im[recreated_im < 0] = 0\n recreated_im = np.round(recreated_im * 255)\n\n recreated_im = np.uint8(recreated_im) # H, W, C\n return recreated_im","def example_lab_to_xyz():\r\n\r\n print(\"=== Simple Example: Lab->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.22)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n xyz = convert_color(lab, XYZColor)\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def img_jet(img):\n if len(img.shape) == 2:\n normalized_img = img / 255.0\n jet = np.array(cm.jet(1 - normalized_img)[:, :, :3] * 255, np.uint8)\n else:\n jet = img\n return jet","def rgb_to_ycbcr(image: np.ndarray) -> np.ndarray:\n\n \"\"\" from RGB (0-1).\n \"\"\"\n\n if not is_rgb(image):\n raise ValueError(\"Input needs to be an array of RGB values\")\n\n m = np.array(\n [\n [+065.481, +128.553, +024.966],\n [-037.797, -074.203, +112.000],\n [+112.000, -093.786, -018.214],\n ]\n )\n a = np.array([16, 128, 128])\n\n return np.dot(image, m.T) + a","def im2pos_coordinates(pix_x, pix_z):\n # x_lim = [-0.85, 0.86]\n # z_lim = [-1.22, 0.47]\n x_lim = [-0.365, 0.365]\n z_lim = [-0.95, -0.24]\n x = x_lim[1] - (x_lim[1] - x_lim[0]) * pix_x/127\n z = z_lim[1] - (z_lim[1] - z_lim[0]) * pix_z/127\n return x, z","def r2n2_xyz_images(self):\n if not hasattr(self, '_r2n2_xyz_images'):\n xyz_images = []\n for i in range(24):\n im_i = geom_util_np.apply_4x4(\n self.r2n2_cam_images[i, ...],\n self.r2n2_cam2world[i, ...],\n are_points=True)\n mask = np_util.make_mask(self.r2n2_depth_images[i, ...])\n xyz_images.append(np_util.zero_by_mask(mask, im_i).astype(np.float32))\n self._r2n2_xyz_images = np.stack(xyz_images)\n return self._r2n2_xyz_images","def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n L: torch.Tensor = image[..., 0, :, :]\n u: torch.Tensor = image[..., 1, :, :]\n v: torch.Tensor = image[..., 2, :, :]\n\n # Convert from Luv to XYZ\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\n\n # Compute white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n a: torch.Tensor = u_w + u / (13 * L + eps)\n d: torch.Tensor = v_w + v / (13 * L + eps)\n c: torch.Tensor = 3 * y * (5 * d - 3)\n\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\n\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\n\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\n\n # Convert from RGB Linear to sRGB\n rgb_im = linear_rgb_to_rgb(rgbs_im)\n\n return rgb_im","def use_derived_RGB_to_XYZ_matrix(self):\n\n return self._use_derived_RGB_to_XYZ_matrix","def to_color(self):\n if self.channels == 4:\n color = opencv.cvtColor(self.img, opencv.COLOR_BGRA2BGR)\n return Image(color)\n elif self.channels == 1:\n color = opencv.cvtColor(self.img, opencv.COLOR_GRAY2BGR)\n return Image(color)\n else:\n return Image(self.img)","def Luv_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n # Without Light, there is no color. Short-circuit this and avoid some\r\n # zero division errors in the var_a_frac calculation.\r\n if cobj.luv_l <= 0.0:\r\n xyz_x = 0.0\r\n xyz_y = 0.0\r\n xyz_z = 0.0\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\r\n\r\n # Various variables used throughout the conversion.\r\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\r\n u_sub_0 = (4.0 * illum[\"X\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n v_sub_0 = (9.0 * illum[\"Y\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\r\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\r\n\r\n # Y-coordinate calculations.\r\n if cobj.luv_l > cie_k_times_e:\r\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\r\n else:\r\n xyz_y = cobj.luv_l / color_constants.CIE_K\r\n\r\n # X-coordinate calculation.\r\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\r\n # Z-coordinate calculation.\r\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\r\n\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())","def rgbToCIE1931(self, red, green, blue):\n point = self.color.getXYPointFromRGB(red, green, blue)\n return [point.x, point.y]","def XYZ_to_Lab(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n temp_x = cobj.xyz_x / illum[\"X\"]\r\n temp_y = cobj.xyz_y / illum[\"Y\"]\r\n temp_z = cobj.xyz_z / illum[\"Z\"]\r\n \r\n if temp_x > color_constants.CIE_E:\r\n temp_x = math.pow(temp_x, (1.0 / 3.0))\r\n else:\r\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \r\n\r\n if temp_y > color_constants.CIE_E:\r\n temp_y = math.pow(temp_y, (1.0 / 3.0))\r\n else:\r\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\r\n \r\n if temp_z > color_constants.CIE_E:\r\n temp_z = math.pow(temp_z, (1.0 / 3.0))\r\n else:\r\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\r\n \r\n lab_l = (116.0 * temp_y) - 16.0\r\n lab_a = 500.0 * (temp_x - temp_y)\r\n lab_b = 200.0 * (temp_y - temp_z)\r\n return LabColor(\r\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)","def XYZ_to_RGB_matrix(self, value):\n\n if value is not None:\n value = np.asarray(value)\n self._XYZ_to_RGB_matrix = value","def XYZ_to_xy(XYZ: ArrayLike) -> NDArrayFloat:\n\n return xyY_to_xy(XYZ_to_xyY(XYZ))","def convert_img(self):\r\n self.img = self.img.convert('RGB')","def read_colormap(name):\n\n path = get_demo_file(name + '.c3g')\n\n out = []\n with open(path, 'r') as file:\n for line in file:\n if 'rgb(' not in line:\n continue\n line = line.split('(')[-1].split(')')[0]\n out.append([float(n) for n in line.split(',')])\n\n return np.asarray(out).astype(float) / 256.","def example_spectral_to_xyz():\r\n\r\n print(\"=== Example: Spectral->XYZ ===\")\r\n spc = SpectralColor(\r\n observer='2', illuminant='d50',\r\n spec_380nm=0.0600, spec_390nm=0.0600, spec_400nm=0.0641,\r\n spec_410nm=0.0654, spec_420nm=0.0645, spec_430nm=0.0605,\r\n spec_440nm=0.0562, spec_450nm=0.0543, spec_460nm=0.0537,\r\n spec_470nm=0.0541, spec_480nm=0.0559, spec_490nm=0.0603,\r\n spec_500nm=0.0651, spec_510nm=0.0680, spec_520nm=0.0705,\r\n spec_530nm=0.0736, spec_540nm=0.0772, spec_550nm=0.0809,\r\n spec_560nm=0.0870, spec_570nm=0.0990, spec_580nm=0.1128,\r\n spec_590nm=0.1251, spec_600nm=0.1360, spec_610nm=0.1439,\r\n spec_620nm=0.1511, spec_630nm=0.1590, spec_640nm=0.1688,\r\n spec_650nm=0.1828, spec_660nm=0.1996, spec_670nm=0.2187,\r\n spec_680nm=0.2397, spec_690nm=0.2618, spec_700nm=0.2852,\r\n spec_710nm=0.2500, spec_720nm=0.2400, spec_730nm=0.2300)\r\n xyz = convert_color(spc, XYZColor)\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def convertColorSpace(\n self,\n img, # Image in some color space\n srcColorSpace = 'BGR', # Source color space\n tgtColorSpace = 'RGB', # Traget color space\n ):\n\n if srcColorSpace == tgtColorSpace:\n return img\n\n if srcColorSpace == 'BGR':\n img_bgr = img\n elif srcColorSpace == 'RGB':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n elif srcColorSpace == 'HSV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n elif srcColorSpace == 'HLS':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\n elif srcColorSpace == 'LUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\n elif srcColorSpace == 'YUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\n elif srcColorSpace == 'YCrCb':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\n else:\n raise Exception(\"Incorrect color space: {}\".format(srcColorSpace))\n\n if tgtColorSpace == 'BGR':\n img_tgt = img_bgr\n elif tgtColorSpace == 'RGB':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\n elif tgtColorSpace == 'HSV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\n elif tgtColorSpace == 'HLS':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\n elif tgtColorSpace == 'LUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\n elif tgtColorSpace == 'YUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\n elif tgtColorSpace == 'YCrCb':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\n else:\n raise Exception(\"Incorrect color space: {}\".format(tgtColorSpace))\n\n return img_tgt","def yiq2rgb(im_yiq):\n return multiply_by_left_matrix(np.linalg.inv(YIQ_MATRIX), im_yiq)","def xyz2linear_rgb(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(rgb_from_xyz, arr)","def carla_rgb_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\n image.convert(carla.ColorConverter.Raw) # pylint: disable=no-member\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = array.astype(np.float32) / 255\n array = np.reshape(array, (image.height, image.width, 4))\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n return array","def _rgb2y(self, im):\n if len(im.shape) < 3:\n return im\n return np.sum(im * [0.299, 0.587, 0.114], axis=2)","def red_channel(img):\n\n red = np.zeros(img.shape,dtype=float)\n\n red[:,:,2] = np.copy(img[:,:,2])\n\n return red","def yuv2rgb(im):\n ## conflicting definitions exist depending on whether you use the full range\n ## of YCbCr or clamp out to the valid range. see here\n ## http://www.equasys.de/colorconversion.html\n ## http://www.fourcc.org/fccyvrgb.php\n from numpy import dot, ndarray, array\n # if not im.dtype == 'uint8':\n # raise ImageUtilsError('yuv2rgb only implemented for uint8 arrays')\n\n ## better clip input to the valid range just to be on the safe side\n yuv = ndarray(im.shape) ## float64\n yuv[:, :, 0] = im[:, :, 0].clip(16, 235).astype(yuv.dtype) - 16\n yuv[:, :, 1:] = im[:, :, 1:].clip(16, 240).astype(yuv.dtype) - 128\n\n ## ITU-R BT.601 version (SDTV)\n A = array([[1., 0., 0.701],\n [1., -0.886 * 0.114 / 0.587, -0.701 * 0.299 / 0.587],\n [1., 0.886, 0.]])\n A[:, 0] *= 255. / 219.\n A[:, 1:] *= 255. / 112.\n\n ## ITU-R BT.709 version (HDTV)\n # A = array([[1.164, 0., 1.793],\n # [1.164, -0.213, -0.533],\n # [1.164, 2.112, 0.]])\n\n rgb = dot(yuv, A.T)\n return rgb.clip(0, 255).astype('uint8')","def carla_cityscapes_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\n image.convert(carla.ColorConverter.CityScapesPalette) # pylint: disable=no-member\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = array.astype(np.float32) / 255\n array = np.reshape(array, (image.height, image.width, 4))\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n return array","def applyJET(img):\n\treturn applyColorMap(img, \"jet\")","def get_colors(self, image: np.ndarray, coordinates: np.ndarray) -> np.ndarray:\r\n x = coordinates.squeeze(1)\r\n return np.flip(image[x[:, 1], x[:, 0]].astype(np.float64) / 255.0, axis=1)","def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\n\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)","def create_colorful_test_image(self):\n ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)\n ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)\n ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)\n imr = np.concatenate((ch255, ch128, ch128), axis=2)\n img = np.concatenate((ch255, ch255, ch0), axis=2)\n imb = np.concatenate((ch255, ch0, ch255), axis=2)\n imw = np.concatenate((ch128, ch128, ch128), axis=2)\n imu = np.concatenate((imr, img), axis=1)\n imd = np.concatenate((imb, imw), axis=1)\n image = np.concatenate((imu, imd), axis=0)\n return image","def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):\n\n with domain_range_scale(1):\n values = None\n if model == 'CIE XYZ':\n values = XYZ\n elif model == 'CIE xyY':\n values = XYZ_to_xyY(XYZ, illuminant)\n elif model == 'CIE xy':\n values = XYZ_to_xy(XYZ, illuminant)\n elif model == 'CIE Lab':\n values = XYZ_to_Lab(XYZ, illuminant)\n elif model == 'CIE LCHab':\n values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))\n elif model == 'CIE Luv':\n values = XYZ_to_Luv(XYZ, illuminant)\n elif model == 'CIE Luv uv':\n values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)\n elif model == 'CIE LCHuv':\n values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))\n elif model == 'CIE UCS':\n values = XYZ_to_UCS(XYZ)\n elif model == 'CIE UCS uv':\n values = UCS_to_uv(XYZ_to_UCS(XYZ))\n elif model == 'CIE UVW':\n values = XYZ_to_UVW(XYZ, illuminant)\n elif model == 'DIN 99':\n values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))\n elif model == 'Hunter Lab':\n values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))\n elif model == 'Hunter Rdab':\n values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))\n elif model == 'IPT':\n values = XYZ_to_IPT(XYZ)\n elif model == 'JzAzBz':\n values = XYZ_to_JzAzBz(XYZ)\n elif model == 'OSA UCS':\n values = XYZ_to_OSA_UCS(XYZ)\n elif model == 'hdr-CIELAB':\n values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)\n elif model == 'hdr-IPT':\n values = XYZ_to_hdr_IPT(XYZ, **kwargs)\n\n if values is None:\n raise ValueError(\n '\"{0}\" not found in colourspace models: \"{1}\".'.format(\n model, ', '.join(COLOURSPACE_MODELS)))\n\n return values","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))","def depth_to_xyz(self,u,v,depth_val):\n '''\n u - x image coordinate\n v - y image coodrinate\n depth_val - depth value at that (u,v) from depth_image\n '''\n\n fx=self.cam_intrin[0]\n fy=self.cam_intrin[4]\n cx=self.cam_intrin[2]\n cy=self.cam_intrin[5]\n\n z = float(depth_val)\n x = float((u - cx)/fx)*z\n y = float((v - cy)/fy)*z\n\n result = [x, y, z]\n return result","def yuv_to_rgb(img_yuv):\n\n y = img_yuv[..., 0]\n u = img_yuv[..., 1]\n v = img_yuv[..., 2]\n\n r = y + 1.14 * v\n g = y - 0.396 * u - 0.581 * v\n b = y + 2.029 * u\n\n img_rgb = np.stack((r, g, b), axis=2)\n img_rgb = np.clip(img_rgb, 0, 1)\n return img_rgb","def create_cloud_xyz32rgb(header, points):\n fields = [PointField('x', 0, PointField.FLOAT32, 1),\n PointField('y', 4, PointField.FLOAT32, 1),\n PointField('z', 8, PointField.FLOAT32, 1),\n PointField('rgb', 12, PointField.UINT32, 1)]\n return pcl2.create_cloud(header, fields, points)","def data_to_bytescale_rgb(data): # used to create the SOURCE PNGs (MRI, FA, MD)\n im = bytescale(data)\n w, h = im.shape\n ret = np.empty((w,h,3), dtype=np.uint8)\n ret[:,:,0] = im\n ret[:,:,1] = im\n ret[:,:,2] = im\n return ret","def get_color(im_obj):\n #im = Image.open(path, 'r')\n x, y = im_obj.size\n\n r, g, b = 0, 0, 0\n for i in xrange(x):\n for j in xrange(y):\n color_px = im_obj.getpixel((i, j))\n #print color_px\n r += color_px[0]\n g += color_px[1]\n b += color_px[2]\n\n r = r / (x * y)\n g = g / (x * y)\n b = b / (x * y)\n return (r, g, b)","def rgb_to_yuv(img_rgb):\n\n r = img_rgb[..., 0]\n g = img_rgb[..., 1]\n b = img_rgb[..., 2]\n\n y = 0.299 * r + 0.587 * g + 0.114 * b\n u = 0.493 * (b - y)\n v = 0.877 * (r - y)\n\n img_yuv = np.stack((y, u, v), axis=2)\n return img_yuv","def load_image_as_rgb(image_path):\n im = imageio.imread(image_path)\n y_size = im.shape[0]\n x_size = im.shape[1]\n logging.info(\"Image has dimensions X:%d Y:%d\" % (x_size, y_size))\n arr = np.zeros((im.shape[0],im.shape[1]), dtype=int)\n i = 0\n for im_row in im:\n j = 0\n for vec in im_row:\n arr[i,j] = rgb_vec_to_num(vec)\n j = j + 1\n i = i + 1\n return arr","def rgb_to_matplot_lib(rgb):\n r, g, b = rgb\n return (r / 255., g / 255., b / 255.)","def convert_3d(points_2d, depth_image, image):\n fx = 525.0 # focal length x\n fy = 525.0 # focal length y\n cx = 319.5 # optical center x\n cy = 239.5 # optical center y\n factor = 5000 # for the 16-bit PNG files\n points_3d = []\n cols = []\n colors = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n for v in range(depth_image.shape[0]):\n for u in range(depth_image.shape[1]):\n Z = depth_image[v,u] / factor\n X = (u - cx) * Z / fx\n Y = (v - cy) * Z / fy\n points_3d.append([X,Y,Z])\n cols.append(colors[v,u])\n points = []\n for i in range(len(points_2d)):\n x = int(points_2d[i,0])\n y = int(points_2d[i,1])\n # print(y)\n Z = depth_image[y,x] / factor\n X = (x - cx) * Z / fx\n Y = (y - cy) * Z / fy\n points.append([X,Y,Z])\n points_3d = np.array(points_3d)\n cols = np.array(cols)\n points = np.array(points)\n \n return points, points_3d, cols","def XYZ_to_sRGB_linear(XYZ):\n\n rgb = np.tensordot(T, XYZ, axes=([1, 0]))\n\n if clip_method == CLIP_CLAMP_TO_ZERO:\n # set negative rgb values to zero\n rgb = np.where(rgb < 0, 0, rgb)\n return rgb\n\n if clip_method == CLIP_ADD_WHITE:\n # add enough white to make all rgb values nonnegative\n # find max negative rgb (or 0.0 if all non-negative), we need that much white\n rgb_min = np.amin(rgb, axis=0)\n # get max positive component\n rgb_max = np.amax(rgb, axis=0)\n\n # get scaling factor to maintain max rgb after adding white\n scaling = np.where(rgb_max > 0.0, rgb_max / (rgb_max - rgb_min + 0.00001), 1.0)\n\n # add enough white to cancel this out, maintaining the maximum of rgb\n rgb = np.where(rgb_min < 0.0, scaling * (rgb - rgb_min), rgb)\n return rgb","def imageprepare():\r\n file_name = 'temp_image.png'\r\n im = Image.open(file_name).convert('L')\r\n im = im.resize((20, 20))\r\n p = Image.new('L', (28,28), (255))\r\n p.paste(im,(4,4,24,24))\r\n p.save(\"last_image.png\")\r\n\r\n tv = list(p.getdata()) # get pixel values\r\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\r\n tva = np.reshape(tva, (28, 28))\r\n\r\n return tva","def cam2pixel(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):\n b, _, h, w = cam_coords.size()\n cam_coords_flat = cam_coords.view(b, 3, -1) # [B, 3, H*W]\n if proj_c2p_rot is not None:\n pcoords = proj_c2p_rot.bmm(cam_coords_flat)\n else:\n pcoords = cam_coords_flat\n\n if proj_c2p_tr is not None:\n pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]\n X = pcoords[:, 0]\n Y = pcoords[:, 1]\n Z = pcoords[:, 2].clamp(min=1e-8)\n\n X_norm = 2 * (X / Z) / (w - 1) - 1 # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]\n Y_norm = 2 * (Y / Z) / (h - 1) - 1 # Idem [B, H*W]\n if padding_mode == 'zeros':\n X_mask = ((X_norm > 1) + (X_norm < -1)).detach()\n X_norm[X_mask] = 2 # make sure that no point in warped image is a combinaison of im and gray\n Y_mask = ((Y_norm > 1) + (Y_norm < -1)).detach()\n Y_norm[Y_mask] = 2\n\n pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]\n return pixel_coords.view(b, h, w, 2)","def getRGBFromXYAndBrightness(self, x, y, bri=1):\n # The xy to color conversion is almost the same, but in reverse order.\n # Check if the xy value is within the color gamut of the lamp.\n # If not continue with step 2, otherwise step 3.\n # We do this to calculate the most accurate color the given light can actually do.\n xyPoint = XYPoint(x, y)\n\n if not self.checkPointInLampsReach(xyPoint):\n # Calculate the closest point on the color gamut triangle\n # and use that as xy value See step 6 of color to xy.\n xyPoint = self.getClosestPointToPoint(xyPoint)\n\n # Calculate XYZ values Convert using the following formulas:\n Y = bri\n X = (Y / xyPoint.y) * xyPoint.x\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\n\n # Convert to RGB using Wide RGB D65 conversion\n r = X * 1.612 - Y * 0.203 - Z * 0.302\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\n b = X * 0.026 - Y * 0.072 + Z * 0.962\n\n # Apply reverse gamma correction\n r, g, b = map(\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\n [r, g, b]\n )\n\n # Bring all negative components to zero\n r, g, b = map(lambda x: max(0, x), [r, g, b])\n\n # If one component is greater than 1, weight components by that value.\n max_component = max(r, g, b)\n if max_component > 1:\n r, g, b = map(lambda x: x / max_component, [r, g, b])\n\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\n\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\n return (r, g, b)","def features_colorspace(X, colorspace):\n if colorspace == \"BGR\":\n return X\n\n X = np.array(X)\n\n if colorspace == \"HLS\":\n cs = cv2.COLOR_BGR2HLS\n elif colorspace == \"HSV\":\n cs = cv2.COLOR_BGR2HSV\n elif colorspace == \"LUV\":\n cs = cv2.COLOR_BGR2LUV\n elif colorspace == \"YUV\":\n cs = cv2.COLOR_BGR2YUV\n elif colorspace == \"YCrCb\":\n cs = cv2.COLOR_BGR2YCrCb\n else:\n raise ValueError(colorspace)\n\n for i in range(X.shape[0]):\n X[i,...] = cv2.cvtColor(X[i,...], cs)\n\n return X","def test_dx10_r8g8b8a8_unorm_srgb():\n\n with Image.open(TEST_FILE_DX10_R8G8B8A8_UNORM_SRGB) as im:\n im.load()\n\n assert im.format == \"DDS\"\n assert im.mode == \"RGBA\"\n assert im.size == (16, 16)\n assert im.info[\"gamma\"] == 1 / 2.2\n\n assert_image_equal_tofile(\n im, TEST_FILE_DX10_R8G8B8A8_UNORM_SRGB.replace(\".dds\", \".png\")\n )","def yiq2rgb(imYIQ):\n trans = np.array([[1, 0.956, 0.62], [1, -0.272, -0.647], [1, -1.108, 1.705]])\n return np.dot(imYIQ, trans)","def process_image(img):\n img[0] = img[0] * 0.229\n img[1] = img[1] * 0.224\n img[2] = img[2] * 0.225\n img[0] += 0.485\n img[1] += 0.456\n img[2] += 0.406\n\n return img.cpu().numpy().transpose((1, 2, 0))","def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n\n refx = 95.047\n refy = 100.00\n refz = 108.883\n\n var_x = x / refx\n var_y = y / refy\n var_z = z / refz\n\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\n (7.787 * var_x) + (16 / 116))\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\n (7.787 * var_y) + (16 / 116))\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\n (7.787 * var_z) + (16 / 116))\n\n l = (116 * var_y) - 16\n a = 500 * (var_x - var_y)\n b = 200 * (var_y - var_z)\n lab_image = tf.stack([l, a, b], axis=-1)\n return lab_image","def load_raster_xyz(self, filename):\n with rasterio.open(filename, 'r') as src:\n ## Alias 'affine' no longer works for 'transform'\n ##matrix = src.affine\n matrix = src.transform\n self.size = (src.width, src.height)\n # read per scan line\n for row in range(0, src.height):\n window = ((row, row+1), (0, src.width))\n data = src.read(window=window)\n this_row = data[0][0]\n for column in range(0, src.width):\n x, y = matrix * (column, row)\n yield x, y, this_row[column]","def flatten_pixcoord(image):\n hdr = pyfits.getheader(image)\n naxis1 = hdr['naxis1']\n naxis2 = hdr['naxis2']\n pixcoord = np.mgrid[1:naxis1+1,1:naxis2+1]\n pixcoord = pixcoord.swapaxes(0,1).swapaxes(1,2)\n pixcoord = pixcoord.ravel().reshape(naxis1*naxis2, 2)\n return pixcoord","def _pillow2array(img, flag='color', channel_order='bgr'):\n channel_order = channel_order.lower()\n if channel_order not in ['rgb', 'bgr']:\n raise ValueError('channel order must be either \"rgb\" or \"bgr\"')\n\n if flag == 'unchanged':\n array = np.array(img)\n if array.ndim >= 3 and array.shape[2] >= 3: # color image\n array[:, :, :3] = array[:, :, (2, 1, 0)] # RGB to BGR\n else:\n # If the image mode is not 'RGB', convert it to 'RGB' first.\n if img.mode != 'RGB':\n if img.mode != 'LA':\n # Most formats except 'LA' can be directly converted to RGB\n img = img.convert('RGB')\n else:\n # When the mode is 'LA', the default conversion will fill in\n # the canvas with black, which sometimes shadows black objects\n # in the foreground.\n #\n # Therefore, a random color (124, 117, 104) is used for canvas\n img_rgba = img.convert('RGBA')\n img = Image.new('RGB', img_rgba.size, (124, 117, 104))\n img.paste(img_rgba, mask=img_rgba.split()[3]) # 3 is alpha\n if flag == 'color':\n array = np.array(img)\n if channel_order != 'rgb':\n array = array[:, :, ::-1] # RGB to BGR\n elif flag == 'grayscale':\n img = img.convert('L')\n array = np.array(img)\n else:\n raise ValueError(\n 'flag must be \"color\", \"grayscale\" or \"unchanged\", '\n f'but got {flag}')\n return array","def read_color_image(path):\n with open(path, 'rb') as f:\n img = Image.fromarray(read_ppm(f), mode='RGB')\n img = tf.keras.preprocessing.image.img_to_array(img, dtype=int)\n img = tf.convert_to_tensor(img)\n return img"],"string":"[\n \"def rgb_to_xyz(rgb_color):\\n\\n r = (rgb_color[0] / 255)\\n g = (rgb_color[1] / 255)\\n b = (rgb_color[2] / 255)\\n\\n if r > 0.04045:\\n r = ((r + 0.055) / 1.055) ** 2.4\\n else:\\n r = r / 12.92\\n\\n if g > 0.04045:\\n g = ((g + 0.055) / 1.055) ** 2.4\\n else:\\n g = g / 12.92\\n\\n if b > 0.04045:\\n b = ((b + 0.055) / 1.055) ** 2.4\\n else:\\n b = b / 12.92\\n\\n r = r * 100\\n g = g * 100\\n b = b * 100\\n x = (r * 0.4124) + (g * 0.3576) + (b * 0.1805)\\n y = (r * 0.2126) + (g * 0.7152) + (b * 0.0722)\\n z = (r * 0.0193) + (g * 0.1192) + (b * 0.9505)\\n\\n return x, y, z\",\n \"def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n var_x = x / 100\\n var_y = y / 100\\n var_z = z / 100\\n\\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\\n\\n var_r = tf.where(var_r > 0.0031308,\\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\\n 12.92 * var_r)\\n var_g = tf.where(var_g > 0.0031308,\\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\\n 12.92 * var_g)\\n var_b = tf.where(var_b > 0.0031308,\\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\\n 12.92 * var_b)\\n r = var_r * 255\\n g = var_g * 255\\n b = var_b * 255\\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\\n return rgb_image\",\n \"def example_rgb_to_xyz():\\r\\n\\r\\n print(\\\"=== RGB Example: RGB->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n rgb = sRGBColor(120, 130, 140)\\r\\n # Show a string representation.\\r\\n print(rgb)\\r\\n # Convert RGB to XYZ using a D50 illuminant.\\r\\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def RGB_to_XYZ(RGB,\\n illuminant_RGB,\\n illuminant_XYZ,\\n RGB_to_XYZ_matrix,\\n chromatic_adaptation_transform='CAT02',\\n decoding_cctf=None):\\n\\n if decoding_cctf is not None:\\n RGB = decoding_cctf(RGB)\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n return XYZ_a\",\n \"def RGB_to_XYZ_matrix(self):\\n\\n if not self._use_derived_RGB_to_XYZ_matrix:\\n return self._RGB_to_XYZ_matrix\\n else:\\n return self._derived_RGB_to_XYZ_matrix\",\n \"def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\\n\\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)\",\n \"def rgb_to_xy(red, green, blue):\\n\\n # gamma correction\\n red = pow((red + 0.055) / (1.0 + 0.055), 2.4) if red > 0.04045 else (red / 12.92)\\n green = pow((green + 0.055) / (1.0 + 0.055), 2.4) if green > 0.04045 else (green / 12.92)\\n blue = pow((blue + 0.055) / (1.0 + 0.055), 2.4) if blue > 0.04045 else (blue / 12.92)\\n\\n # convert rgb to xyz\\n x = red * 0.649926 + green * 0.103455 + blue * 0.197109\\n y = red * 0.234327 + green * 0.743075 + blue * 0.022598\\n z = green * 0.053077 + blue * 1.035763\\n\\n # convert xyz to xy\\n x = x / (x + y + z)\\n y = y / (x + y + z)\\n\\n # TODO check color gamut if known\\n \\n return [x, y]\",\n \"def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n l, a, b = tf.unstack(image, axis=-1)\\n\\n var_y = (l + 16) / 116\\n var_x = a / 500 + var_y\\n var_z = var_y - b / 200\\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\\n (var_x - 16 / 116) / 7.787)\\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\\n (var_y - 16 / 116) / 7.787)\\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\\n (var_z - 16 / 116) / 7.787)\\n\\n refx = 95.047\\n refy = 100.00\\n ref_z = 108.883\\n\\n x = var_x * refx\\n y = var_y * refy\\n z = var_z * ref_z\\n xyz_image = tf.stack([x, y, z], axis=-1)\\n return xyz_image\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def RGB_to_XYZ_matrix(self, value):\\n\\n if value is not None:\\n value = np.asarray(value)\\n self._RGB_to_XYZ_matrix = value\",\n \"def Lab_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n xyz_y = (cobj.lab_l + 16.0) / 116.0\\r\\n xyz_x = cobj.lab_a / 500.0 + xyz_y\\r\\n xyz_z = xyz_y - cobj.lab_b / 200.0\\r\\n \\r\\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\\r\\n xyz_y = math.pow(xyz_y, 3)\\r\\n else:\\r\\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\\r\\n\\r\\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\\r\\n xyz_x = math.pow(xyz_x, 3)\\r\\n else:\\r\\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\\r\\n \\r\\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\\r\\n xyz_z = math.pow(xyz_z, 3)\\r\\n else:\\r\\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\\r\\n \\r\\n xyz_x = (illum[\\\"X\\\"] * xyz_x)\\r\\n xyz_y = (illum[\\\"Y\\\"] * xyz_y)\\r\\n xyz_z = (illum[\\\"Z\\\"] * xyz_z)\\r\\n \\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def RGB_to_XYZ(cobj, target_illuminant=None, *args, **kwargs):\\r\\n\\r\\n # Will contain linearized RGB channels (removed the gamma func).\\r\\n linear_channels = {}\\r\\n\\r\\n if isinstance(cobj, sRGBColor):\\r\\n for channel in ['r', 'g', 'b']:\\r\\n V = getattr(cobj, 'rgb_' + channel)\\r\\n if V <= 0.04045:\\r\\n linear_channels[channel] = V / 12.92\\r\\n else:\\r\\n linear_channels[channel] = math.pow((V + 0.055) / 1.055, 2.4)\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n gamma = cobj.rgb_gamma\\r\\n\\r\\n for channel in ['r', 'g', 'b']:\\r\\n V = getattr(cobj, 'rgb_' + channel)\\r\\n linear_channels[channel] = math.pow(V, gamma)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n xyz_x, xyz_y, xyz_z = apply_RGB_matrix(\\r\\n linear_channels['r'], linear_channels['g'], linear_channels['b'],\\r\\n rgb_type=cobj, convtype=\\\"rgb_to_xyz\\\")\\r\\n\\r\\n if target_illuminant is None:\\r\\n target_illuminant = cobj.native_illuminant\\r\\n \\r\\n # The illuminant of the original RGB object. This will always match\\r\\n # the RGB colorspace's native illuminant.\\r\\n illuminant = cobj.native_illuminant\\r\\n xyzcolor = XYZColor(xyz_x, xyz_y, xyz_z, illuminant=illuminant)\\r\\n # This will take care of any illuminant changes for us (if source\\r\\n # illuminant != target illuminant).\\r\\n xyzcolor.apply_adaptation(target_illuminant)\\r\\n\\r\\n return xyzcolor\",\n \"def test_srgb_conversion_to_xyz_d65(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor)\\r\\n self.assertColorMatch(xyz, XYZColor(0.294, 0.457, 0.103))\",\n \"def convert_color(image, color_space):\\n out_image = None\\n if color_space != 'RGB':\\n if color_space == 'HSV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\\n elif color_space == 'LUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\\n elif color_space == 'HLS':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\\n elif color_space == 'YUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\\n elif color_space == 'YCrCb':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\\n else:\\n out_image = np.copy(image)\\n return out_image\",\n \"def linear_rgb2xyz(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(xyz_from_rgb, arr)\",\n \"def test_srgb_conversion_to_xyz_d50(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))\",\n \"def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\\n y: torch.Tensor = image[..., 0, :, :]\\n cb: torch.Tensor = image[..., 1, :, :]\\n cr: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n cb_shifted: torch.Tensor = cb - delta\\n cr_shifted: torch.Tensor = cr - delta\\n\\n r: torch.Tensor = y + 1.403 * cr_shifted\\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\\n b: torch.Tensor = y + 1.773 * cb_shifted\\n return torch.stack([r, g, b], -3)\",\n \"def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n xyz = lab_to_xyz(image)\\n rgb_image = xyz_to_rgb(xyz)\\n return rgb_image\",\n \"def rgb_processing(rgb_img, center, scale, rot=0):\\n rgb_img = crop(rgb_img, center, scale, \\n [constants.IMG_RES, constants.IMG_RES], rot=rot)\\n # (3,224,224),float,[0,1]\\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\\n return rgb_img\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def _convert_to_yolo_img(self, img):\\n\\n img = img / 255.0\\n h, w, c = img.shape\\n img = img.transpose(2, 0, 1)\\n outimg = make_image(w, h, c)\\n img = img.reshape((w*h*c))\\n data = c_array(c_float, img)\\n outimg.data = data\\n rgbgr_image(outimg)\\n return outimg\",\n \"def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\\r\\n\\r\\n temp_X = cobj.xyz_x\\r\\n temp_Y = cobj.xyz_y\\r\\n temp_Z = cobj.xyz_z\\r\\n\\r\\n logger.debug(\\\" \\\\- Target RGB space: %s\\\", target_rgb)\\r\\n target_illum = target_rgb.native_illuminant\\r\\n logger.debug(\\\" \\\\- Target native illuminant: %s\\\", target_illum)\\r\\n logger.debug(\\\" \\\\- XYZ color's illuminant: %s\\\", cobj.illuminant)\\r\\n \\r\\n # If the XYZ values were taken with a different reference white than the\\r\\n # native reference white of the target RGB space, a transformation matrix\\r\\n # must be applied.\\r\\n if cobj.illuminant != target_illum:\\r\\n logger.debug(\\\" \\\\* Applying transformation from %s to %s \\\",\\r\\n cobj.illuminant, target_illum)\\r\\n # Get the adjusted XYZ values, adapted for the target illuminant.\\r\\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n orig_illum=cobj.illuminant, targ_illum=target_illum)\\r\\n logger.debug(\\\" \\\\* New values: %.3f, %.3f, %.3f\\\",\\r\\n temp_X, temp_Y, temp_Z)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n rgb_type=target_rgb, convtype=\\\"xyz_to_rgb\\\")\\r\\n\\r\\n # v\\r\\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\\r\\n # V\\r\\n nonlinear_channels = {}\\r\\n if target_rgb == sRGBColor:\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n if v <= 0.0031308:\\r\\n nonlinear_channels[channel] = v * 12.92\\r\\n else:\\r\\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\\r\\n\\r\\n return target_rgb(\\r\\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])\",\n \"def get_rgb(self, img, r, g, b):\\r\\n\\r\\n # Get specific bands of hyperspectral image\\r\\n red_channel = img[:, :, r]\\r\\n green_channel = img[:, :, g]\\r\\n blue_channel = img[:, :, b]\\r\\n\\r\\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\\r\\n img = img.astype('float32')\\r\\n return img\",\n \"def pos2im_coordinates(x, z):\\n # x_lim = [-0.85, 0.86]\\n # z_lim = [-1.22, 0.47]\\n x_lim = [-0.365, 0.365]\\n z_lim = [-0.95, -0.24]\\n\\n pix_x = int(127 * (x_lim[1] - x) / (x_lim[1] - x_lim[0]))\\n pix_z = int(127 * (z_lim[1] - z) / (z_lim[1] - z_lim[0]))\\n return pix_x, pix_z\",\n \"def XYZ_to_RGB(XYZ,\\n illuminant_XYZ,\\n illuminant_RGB,\\n XYZ_to_RGB_matrix,\\n chromatic_adaptation_transform='CAT02',\\n encoding_cctf=None):\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\\n\\n if encoding_cctf is not None:\\n RGB = encoding_cctf(RGB)\\n\\n return RGB\",\n \"def XYZ_to_RGB_matrix(self):\\n\\n if not self._use_derived_XYZ_to_RGB_matrix:\\n return self._XYZ_to_RGB_matrix\\n else:\\n return self._derived_XYZ_to_RGB_matrix\",\n \"def test_adobe_conversion_to_xyz_d65(self):\\r\\n\\r\\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\\r\\n xyz = convert_color(adobe, XYZColor)\\r\\n self.assertColorMatch(xyz, XYZColor(0.230, 0.429, 0.074))\",\n \"def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:\\n r: torch.Tensor = image[..., 0, :, :]\\n g: torch.Tensor = image[..., 1, :, :]\\n b: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b\\n cb: torch.Tensor = (b - y) * 0.564 + delta\\n cr: torch.Tensor = (r - y) * 0.713 + delta\\n return torch.stack([y, cb, cr], -3)\",\n \"def to_image(x):\\n x = denorm(x.data.cpu())\\n ndarr = x.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()\\n im = ndarr\\n return im\",\n \"def camera_to_pixel(self, X):\\n raise NotImplementedError\",\n \"def imcast(img, dtype, color_space=\\\"default\\\"):\\n if img.dtype == dtype:\\n return img\\n if color_space == \\\"default\\\":\\n if dtype == np.uint8:\\n if img.dtype == np.uint16:\\n return np.asarray(img / 257, np.uint8)\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img * 255., np.uint8)\\n elif dtype == np.uint16:\\n if img.dtype == np.uint8:\\n return np.asarray(img, np.uint16) * 257\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img * 65535., np.uint16)\\n elif dtype == np.float32 or dtype == np.float64:\\n if img.dtype == np.uint8:\\n return np.asarray(img, dtype) / 255.\\n elif img.dtype == np.uint16:\\n return np.asarray(img, dtype) / 65535.\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img, dtype)\\n elif color_space == \\\"CIE-L*a*b*\\\":\\n if dtype == np.uint8:\\n if img.dtype == np.float32 or img.dtype == np.float64:\\n dst = np.empty(img.shape, np.uint8)\\n dst[:,:,0] = img[:,:,0] * 255. / 100.\\n dst[:,:,1] = img[:,:,1] + 128.\\n dst[:,:,2] = img[:,:,2] + 128.\\n return dst\\n elif dtype == np.float32 or dtype == np.float64:\\n if img.dtype == np.uint8:\\n dst = np.empty(img.shape, dtype)\\n dst[:,:,0] = np.asarray(img[:,:,0], dtype) / 255. * 100.\\n dst[:,:,1] = np.asarray(img[:,:,1], dtype) - 128.\\n dst[:,:,2] = np.asarray(img[:,:,2], dtype) - 128.\\n return dst\\n raise Exception(\\n \\\"Unexpected conversion from '%s' to '%s' with '%s' color space\\\" % \\\\\\n (img.dtype, dtype, color_space))\",\n \"def test_adobe_conversion_to_xyz_d50(self):\\r\\n\\r\\n adobe = AdobeRGBColor(0.482, 0.784, 0.196)\\r\\n xyz = convert_color(adobe, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.247, 0.431, 0.060))\",\n \"def convert_color(image, color_space='RGB'):\\n color_space = color_space.lower()\\n if color_space != 'rgb':\\n if color_space == 'hsv':\\n color_transformation = cv2.COLOR_BGR2HSV\\n elif color_space == 'luv':\\n color_transformation = cv2.COLOR_BGR2LUV\\n elif color_space == 'hls':\\n color_transformation = cv2.COLOR_BGR2HLS\\n elif color_space == 'yuv':\\n color_transformation = cv2.COLOR_BGR2YUV\\n elif color_space == 'ycrcb':\\n color_transformation = cv2.COLOR_BGR2YCrCb\\n else:\\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\\n 'HLS, YUV, YCrCb' % color_space)\\n\\n return cv2.cvtColor(image, color_transformation)\\n else:\\n return image\",\n \"def _colored_img_to_arr(image, verbose=False):\\n height, width = image.size\\n arr = np.array(image.getdata())\\n arr = arr.reshape(3, height, width)\\n r = arr[0]\\n g = arr[1]\\n b = arr[2]\\n return r, g, b\",\n \"def XYZ_to_sRGB(XYZ):\\n\\n rgb = XYZ_to_sRGB_linear(XYZ)\\n rgb = sRGB_linear_to_sRGB(rgb)\\n\\n return rgb\",\n \"def rgb(self, xyz: Union[ndarray, Iterable[float]]) -> ndarray:\\n return self.value.xyz_to_rgb_matrix @ xyz\",\n \"def rgb2alpha(img):\\n\\t### First of all we need the size of our picture to make the transforms\\n\\n\\tx = len(img) ; y = len(img[0])\\n\\talpha = np.full((x, y, 3), 0, dtype = float)\\t\\t## This will be the transformed image\\n\\n\\t### Now we gotta access each pixel of the picture\\n\\n\\tfor i, vi in enumerate(img):\\n\\t\\tfor j, px in enumerate(vi):\\n\\t\\t\\t### There we are\\n\\n\\t\\t\\t# Step 1 : LMS transform, for that we use r_l\\n\\n\\t\\t\\talpha[i][j] = np.matmul(r_l, px)\\n\\n\\t\\t\\t# Step 2 : log em all (decimal log)\\n\\n\\t\\t\\talpha[i][j][0] = log(alpha[i][j][0])\\n\\t\\t\\talpha[i][j][1] = log(alpha[i][j][1])\\n\\t\\t\\talpha[i][j][2] = log(alpha[i][j][2])\\n\\n\\t\\t\\t# Step 3 : l alpha beta transform, by using l_a\\n\\n\\t\\t\\talpha[i][j] = np.matmul(l_a, alpha[i][j])\\n\\n\\treturn alpha\",\n \"def xyY_to_XYZ(cobj, *args, **kwargs):\\r\\n \\r\\n xyz_x = (cobj.xyy_x * cobj.xyy_Y) / cobj.xyy_y\\r\\n xyz_y = cobj.xyy_Y\\r\\n xyz_z = ((1.0 - cobj.xyy_x - cobj.xyy_y) * xyz_y) / cobj.xyy_y\\r\\n \\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)\",\n \"def matplotlib_image(image):\\n if image.ndim == 2:\\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\\n else:\\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n return rgb\",\n \"def load_color_image_features(img_path):\\n ac = scipy.misc.imread(img_path, mode='RGB')\\n ac = ac / (255.0 / 2) - 1.0\\n return np.array(ac)\",\n \"def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\\n xyz = rgb_to_xyz(image)\\n lab_image = xyz_to_lab(xyz)\\n return lab_image\",\n \"def colorspace_to_image(cspace: DataFrame, m: int, n: int) -> np.array:\\n \\n assert isinstance(cspace, DataFrame), \\\"Colorspace must be a dataframe\\\"\\n assert len(cspace) == m * n, 'Image dimensions must match'\\n assert all(np.isin(['R', 'G', 'B'], cspace.columns)), \\\"Colorspace must contain RGB columns\\\"\\n \\n result = np.empty([m,n,3])\\n result[:,:, 0] = cspace['R'].values.reshape(m, n)\\n result[:,:, 1] = cspace['G'].values.reshape(m, n)\\n result[:,:, 2] = cspace['B'].values.reshape(m, n)\\n return result\",\n \"def cie_xyz(self):\\n return tuple(matrix_mult(\\n ((0.4124564, 0.3575761, 0.1804375),\\n (0.2126729, 0.7151522, 0.0721750),\\n (0.0193339, 0.1191920, 0.9503041),\\n ),\\n (from_srgb(self.red),\\n from_srgb(self.green),\\n from_srgb(self.blue)\\n )\\n ))\",\n \"def grey_to_rgb_imitation(img):\\n return np.repeat(img[...,np.newaxis], 3, -1)\",\n \"def pixel_to_xyz(self, pixels, max_depth=25):\\n assert pixels.shape[0] == 4\\n d = np.abs(pixels[0] - pixels[2]) # disparity U_L - U_R\\n z = self.fsub / d\\n z[z > max_depth] = max_depth\\n \\n # calcualte xy\\n u_L = pixels[0] # take first row\\n v_L = pixels[1] # take 2nd row\\n x = (u_L - self.cu) / self.fsu * z\\n y = (v_L - self.cv) / self.fsv * z\\n \\n return np.vstack((x,y,z))\",\n \"def _process_img_rgb(self, sensor_data):\\n img = np.array(sensor_data.raw_data).reshape((self.img_y, self.img_x, 4))\\n img = img[:, :, :3] # sensor is actualy rgba, we dont need alpha values\\n self.rgb = img # need to scale rgb values to be {0,1}\",\n \"def recreate_image(x):\\n reverse_mean = [-0.485, -0.456, -0.406]\\n reverse_std = [1/0.229, 1/0.224, 1/0.225]\\n in_channel = x.shape[-1]\\n recreated_im = copy.copy(x) # C, H, W\\n if in_channel == 3:\\n for c in range(in_channel):\\n recreated_im[:, :, c] /= reverse_std[c]\\n recreated_im[:, :, c] -= reverse_mean[c]\\n elif in_channel == 1:\\n recreated_im[:, :, 0] /= reverse_std[1]\\n recreated_im[:, :, 0] -= reverse_mean[1]\\n recreated_im[recreated_im > 1] = 1\\n recreated_im[recreated_im < 0] = 0\\n recreated_im = np.round(recreated_im * 255)\\n\\n recreated_im = np.uint8(recreated_im) # H, W, C\\n return recreated_im\",\n \"def example_lab_to_xyz():\\r\\n\\r\\n print(\\\"=== Simple Example: Lab->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.22)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n xyz = convert_color(lab, XYZColor)\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def img_jet(img):\\n if len(img.shape) == 2:\\n normalized_img = img / 255.0\\n jet = np.array(cm.jet(1 - normalized_img)[:, :, :3] * 255, np.uint8)\\n else:\\n jet = img\\n return jet\",\n \"def rgb_to_ycbcr(image: np.ndarray) -> np.ndarray:\\n\\n \\\"\\\"\\\" from RGB (0-1).\\n \\\"\\\"\\\"\\n\\n if not is_rgb(image):\\n raise ValueError(\\\"Input needs to be an array of RGB values\\\")\\n\\n m = np.array(\\n [\\n [+065.481, +128.553, +024.966],\\n [-037.797, -074.203, +112.000],\\n [+112.000, -093.786, -018.214],\\n ]\\n )\\n a = np.array([16, 128, 128])\\n\\n return np.dot(image, m.T) + a\",\n \"def im2pos_coordinates(pix_x, pix_z):\\n # x_lim = [-0.85, 0.86]\\n # z_lim = [-1.22, 0.47]\\n x_lim = [-0.365, 0.365]\\n z_lim = [-0.95, -0.24]\\n x = x_lim[1] - (x_lim[1] - x_lim[0]) * pix_x/127\\n z = z_lim[1] - (z_lim[1] - z_lim[0]) * pix_z/127\\n return x, z\",\n \"def r2n2_xyz_images(self):\\n if not hasattr(self, '_r2n2_xyz_images'):\\n xyz_images = []\\n for i in range(24):\\n im_i = geom_util_np.apply_4x4(\\n self.r2n2_cam_images[i, ...],\\n self.r2n2_cam2world[i, ...],\\n are_points=True)\\n mask = np_util.make_mask(self.r2n2_depth_images[i, ...])\\n xyz_images.append(np_util.zero_by_mask(mask, im_i).astype(np.float32))\\n self._r2n2_xyz_images = np.stack(xyz_images)\\n return self._r2n2_xyz_images\",\n \"def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n L: torch.Tensor = image[..., 0, :, :]\\n u: torch.Tensor = image[..., 1, :, :]\\n v: torch.Tensor = image[..., 2, :, :]\\n\\n # Convert from Luv to XYZ\\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\\n\\n # Compute white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n a: torch.Tensor = u_w + u / (13 * L + eps)\\n d: torch.Tensor = v_w + v / (13 * L + eps)\\n c: torch.Tensor = 3 * y * (5 * d - 3)\\n\\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\\n\\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\\n\\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\\n\\n # Convert from RGB Linear to sRGB\\n rgb_im = linear_rgb_to_rgb(rgbs_im)\\n\\n return rgb_im\",\n \"def use_derived_RGB_to_XYZ_matrix(self):\\n\\n return self._use_derived_RGB_to_XYZ_matrix\",\n \"def to_color(self):\\n if self.channels == 4:\\n color = opencv.cvtColor(self.img, opencv.COLOR_BGRA2BGR)\\n return Image(color)\\n elif self.channels == 1:\\n color = opencv.cvtColor(self.img, opencv.COLOR_GRAY2BGR)\\n return Image(color)\\n else:\\n return Image(self.img)\",\n \"def Luv_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n # Without Light, there is no color. Short-circuit this and avoid some\\r\\n # zero division errors in the var_a_frac calculation.\\r\\n if cobj.luv_l <= 0.0:\\r\\n xyz_x = 0.0\\r\\n xyz_y = 0.0\\r\\n xyz_z = 0.0\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\\r\\n\\r\\n # Various variables used throughout the conversion.\\r\\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\\r\\n u_sub_0 = (4.0 * illum[\\\"X\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n v_sub_0 = (9.0 * illum[\\\"Y\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\\r\\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\\r\\n\\r\\n # Y-coordinate calculations.\\r\\n if cobj.luv_l > cie_k_times_e:\\r\\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\\r\\n else:\\r\\n xyz_y = cobj.luv_l / color_constants.CIE_K\\r\\n\\r\\n # X-coordinate calculation.\\r\\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\\r\\n # Z-coordinate calculation.\\r\\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\\r\\n\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())\",\n \"def rgbToCIE1931(self, red, green, blue):\\n point = self.color.getXYPointFromRGB(red, green, blue)\\n return [point.x, point.y]\",\n \"def XYZ_to_Lab(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n temp_x = cobj.xyz_x / illum[\\\"X\\\"]\\r\\n temp_y = cobj.xyz_y / illum[\\\"Y\\\"]\\r\\n temp_z = cobj.xyz_z / illum[\\\"Z\\\"]\\r\\n \\r\\n if temp_x > color_constants.CIE_E:\\r\\n temp_x = math.pow(temp_x, (1.0 / 3.0))\\r\\n else:\\r\\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \\r\\n\\r\\n if temp_y > color_constants.CIE_E:\\r\\n temp_y = math.pow(temp_y, (1.0 / 3.0))\\r\\n else:\\r\\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\\r\\n \\r\\n if temp_z > color_constants.CIE_E:\\r\\n temp_z = math.pow(temp_z, (1.0 / 3.0))\\r\\n else:\\r\\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\\r\\n \\r\\n lab_l = (116.0 * temp_y) - 16.0\\r\\n lab_a = 500.0 * (temp_x - temp_y)\\r\\n lab_b = 200.0 * (temp_y - temp_z)\\r\\n return LabColor(\\r\\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def XYZ_to_RGB_matrix(self, value):\\n\\n if value is not None:\\n value = np.asarray(value)\\n self._XYZ_to_RGB_matrix = value\",\n \"def XYZ_to_xy(XYZ: ArrayLike) -> NDArrayFloat:\\n\\n return xyY_to_xy(XYZ_to_xyY(XYZ))\",\n \"def convert_img(self):\\r\\n self.img = self.img.convert('RGB')\",\n \"def read_colormap(name):\\n\\n path = get_demo_file(name + '.c3g')\\n\\n out = []\\n with open(path, 'r') as file:\\n for line in file:\\n if 'rgb(' not in line:\\n continue\\n line = line.split('(')[-1].split(')')[0]\\n out.append([float(n) for n in line.split(',')])\\n\\n return np.asarray(out).astype(float) / 256.\",\n \"def example_spectral_to_xyz():\\r\\n\\r\\n print(\\\"=== Example: Spectral->XYZ ===\\\")\\r\\n spc = SpectralColor(\\r\\n observer='2', illuminant='d50',\\r\\n spec_380nm=0.0600, spec_390nm=0.0600, spec_400nm=0.0641,\\r\\n spec_410nm=0.0654, spec_420nm=0.0645, spec_430nm=0.0605,\\r\\n spec_440nm=0.0562, spec_450nm=0.0543, spec_460nm=0.0537,\\r\\n spec_470nm=0.0541, spec_480nm=0.0559, spec_490nm=0.0603,\\r\\n spec_500nm=0.0651, spec_510nm=0.0680, spec_520nm=0.0705,\\r\\n spec_530nm=0.0736, spec_540nm=0.0772, spec_550nm=0.0809,\\r\\n spec_560nm=0.0870, spec_570nm=0.0990, spec_580nm=0.1128,\\r\\n spec_590nm=0.1251, spec_600nm=0.1360, spec_610nm=0.1439,\\r\\n spec_620nm=0.1511, spec_630nm=0.1590, spec_640nm=0.1688,\\r\\n spec_650nm=0.1828, spec_660nm=0.1996, spec_670nm=0.2187,\\r\\n spec_680nm=0.2397, spec_690nm=0.2618, spec_700nm=0.2852,\\r\\n spec_710nm=0.2500, spec_720nm=0.2400, spec_730nm=0.2300)\\r\\n xyz = convert_color(spc, XYZColor)\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def convertColorSpace(\\n self,\\n img, # Image in some color space\\n srcColorSpace = 'BGR', # Source color space\\n tgtColorSpace = 'RGB', # Traget color space\\n ):\\n\\n if srcColorSpace == tgtColorSpace:\\n return img\\n\\n if srcColorSpace == 'BGR':\\n img_bgr = img\\n elif srcColorSpace == 'RGB':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\\n elif srcColorSpace == 'HSV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\\n elif srcColorSpace == 'HLS':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\\n elif srcColorSpace == 'LUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\\n elif srcColorSpace == 'YUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\\n elif srcColorSpace == 'YCrCb':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(srcColorSpace))\\n\\n if tgtColorSpace == 'BGR':\\n img_tgt = img_bgr\\n elif tgtColorSpace == 'RGB':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\\n elif tgtColorSpace == 'HSV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\\n elif tgtColorSpace == 'HLS':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\\n elif tgtColorSpace == 'LUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\\n elif tgtColorSpace == 'YUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\\n elif tgtColorSpace == 'YCrCb':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(tgtColorSpace))\\n\\n return img_tgt\",\n \"def yiq2rgb(im_yiq):\\n return multiply_by_left_matrix(np.linalg.inv(YIQ_MATRIX), im_yiq)\",\n \"def xyz2linear_rgb(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(rgb_from_xyz, arr)\",\n \"def carla_rgb_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\\n image.convert(carla.ColorConverter.Raw) # pylint: disable=no-member\\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\\\"uint8\\\"))\\n array = array.astype(np.float32) / 255\\n array = np.reshape(array, (image.height, image.width, 4))\\n array = array[:, :, :3]\\n array = array[:, :, ::-1]\\n return array\",\n \"def _rgb2y(self, im):\\n if len(im.shape) < 3:\\n return im\\n return np.sum(im * [0.299, 0.587, 0.114], axis=2)\",\n \"def red_channel(img):\\n\\n red = np.zeros(img.shape,dtype=float)\\n\\n red[:,:,2] = np.copy(img[:,:,2])\\n\\n return red\",\n \"def yuv2rgb(im):\\n ## conflicting definitions exist depending on whether you use the full range\\n ## of YCbCr or clamp out to the valid range. see here\\n ## http://www.equasys.de/colorconversion.html\\n ## http://www.fourcc.org/fccyvrgb.php\\n from numpy import dot, ndarray, array\\n # if not im.dtype == 'uint8':\\n # raise ImageUtilsError('yuv2rgb only implemented for uint8 arrays')\\n\\n ## better clip input to the valid range just to be on the safe side\\n yuv = ndarray(im.shape) ## float64\\n yuv[:, :, 0] = im[:, :, 0].clip(16, 235).astype(yuv.dtype) - 16\\n yuv[:, :, 1:] = im[:, :, 1:].clip(16, 240).astype(yuv.dtype) - 128\\n\\n ## ITU-R BT.601 version (SDTV)\\n A = array([[1., 0., 0.701],\\n [1., -0.886 * 0.114 / 0.587, -0.701 * 0.299 / 0.587],\\n [1., 0.886, 0.]])\\n A[:, 0] *= 255. / 219.\\n A[:, 1:] *= 255. / 112.\\n\\n ## ITU-R BT.709 version (HDTV)\\n # A = array([[1.164, 0., 1.793],\\n # [1.164, -0.213, -0.533],\\n # [1.164, 2.112, 0.]])\\n\\n rgb = dot(yuv, A.T)\\n return rgb.clip(0, 255).astype('uint8')\",\n \"def carla_cityscapes_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\\n image.convert(carla.ColorConverter.CityScapesPalette) # pylint: disable=no-member\\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\\\"uint8\\\"))\\n array = array.astype(np.float32) / 255\\n array = np.reshape(array, (image.height, image.width, 4))\\n array = array[:, :, :3]\\n array = array[:, :, ::-1]\\n return array\",\n \"def applyJET(img):\\n\\treturn applyColorMap(img, \\\"jet\\\")\",\n \"def get_colors(self, image: np.ndarray, coordinates: np.ndarray) -> np.ndarray:\\r\\n x = coordinates.squeeze(1)\\r\\n return np.flip(image[x[:, 1], x[:, 0]].astype(np.float64) / 255.0, axis=1)\",\n \"def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\\n\\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)\",\n \"def create_colorful_test_image(self):\\n ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)\\n ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)\\n ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)\\n imr = np.concatenate((ch255, ch128, ch128), axis=2)\\n img = np.concatenate((ch255, ch255, ch0), axis=2)\\n imb = np.concatenate((ch255, ch0, ch255), axis=2)\\n imw = np.concatenate((ch128, ch128, ch128), axis=2)\\n imu = np.concatenate((imr, img), axis=1)\\n imd = np.concatenate((imb, imw), axis=1)\\n image = np.concatenate((imu, imd), axis=0)\\n return image\",\n \"def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):\\n\\n with domain_range_scale(1):\\n values = None\\n if model == 'CIE XYZ':\\n values = XYZ\\n elif model == 'CIE xyY':\\n values = XYZ_to_xyY(XYZ, illuminant)\\n elif model == 'CIE xy':\\n values = XYZ_to_xy(XYZ, illuminant)\\n elif model == 'CIE Lab':\\n values = XYZ_to_Lab(XYZ, illuminant)\\n elif model == 'CIE LCHab':\\n values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))\\n elif model == 'CIE Luv':\\n values = XYZ_to_Luv(XYZ, illuminant)\\n elif model == 'CIE Luv uv':\\n values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)\\n elif model == 'CIE LCHuv':\\n values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))\\n elif model == 'CIE UCS':\\n values = XYZ_to_UCS(XYZ)\\n elif model == 'CIE UCS uv':\\n values = UCS_to_uv(XYZ_to_UCS(XYZ))\\n elif model == 'CIE UVW':\\n values = XYZ_to_UVW(XYZ, illuminant)\\n elif model == 'DIN 99':\\n values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))\\n elif model == 'Hunter Lab':\\n values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))\\n elif model == 'Hunter Rdab':\\n values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))\\n elif model == 'IPT':\\n values = XYZ_to_IPT(XYZ)\\n elif model == 'JzAzBz':\\n values = XYZ_to_JzAzBz(XYZ)\\n elif model == 'OSA UCS':\\n values = XYZ_to_OSA_UCS(XYZ)\\n elif model == 'hdr-CIELAB':\\n values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)\\n elif model == 'hdr-IPT':\\n values = XYZ_to_hdr_IPT(XYZ, **kwargs)\\n\\n if values is None:\\n raise ValueError(\\n '\\\"{0}\\\" not found in colourspace models: \\\"{1}\\\".'.format(\\n model, ', '.join(COLOURSPACE_MODELS)))\\n\\n return values\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))\",\n \"def depth_to_xyz(self,u,v,depth_val):\\n '''\\n u - x image coordinate\\n v - y image coodrinate\\n depth_val - depth value at that (u,v) from depth_image\\n '''\\n\\n fx=self.cam_intrin[0]\\n fy=self.cam_intrin[4]\\n cx=self.cam_intrin[2]\\n cy=self.cam_intrin[5]\\n\\n z = float(depth_val)\\n x = float((u - cx)/fx)*z\\n y = float((v - cy)/fy)*z\\n\\n result = [x, y, z]\\n return result\",\n \"def yuv_to_rgb(img_yuv):\\n\\n y = img_yuv[..., 0]\\n u = img_yuv[..., 1]\\n v = img_yuv[..., 2]\\n\\n r = y + 1.14 * v\\n g = y - 0.396 * u - 0.581 * v\\n b = y + 2.029 * u\\n\\n img_rgb = np.stack((r, g, b), axis=2)\\n img_rgb = np.clip(img_rgb, 0, 1)\\n return img_rgb\",\n \"def create_cloud_xyz32rgb(header, points):\\n fields = [PointField('x', 0, PointField.FLOAT32, 1),\\n PointField('y', 4, PointField.FLOAT32, 1),\\n PointField('z', 8, PointField.FLOAT32, 1),\\n PointField('rgb', 12, PointField.UINT32, 1)]\\n return pcl2.create_cloud(header, fields, points)\",\n \"def data_to_bytescale_rgb(data): # used to create the SOURCE PNGs (MRI, FA, MD)\\n im = bytescale(data)\\n w, h = im.shape\\n ret = np.empty((w,h,3), dtype=np.uint8)\\n ret[:,:,0] = im\\n ret[:,:,1] = im\\n ret[:,:,2] = im\\n return ret\",\n \"def get_color(im_obj):\\n #im = Image.open(path, 'r')\\n x, y = im_obj.size\\n\\n r, g, b = 0, 0, 0\\n for i in xrange(x):\\n for j in xrange(y):\\n color_px = im_obj.getpixel((i, j))\\n #print color_px\\n r += color_px[0]\\n g += color_px[1]\\n b += color_px[2]\\n\\n r = r / (x * y)\\n g = g / (x * y)\\n b = b / (x * y)\\n return (r, g, b)\",\n \"def rgb_to_yuv(img_rgb):\\n\\n r = img_rgb[..., 0]\\n g = img_rgb[..., 1]\\n b = img_rgb[..., 2]\\n\\n y = 0.299 * r + 0.587 * g + 0.114 * b\\n u = 0.493 * (b - y)\\n v = 0.877 * (r - y)\\n\\n img_yuv = np.stack((y, u, v), axis=2)\\n return img_yuv\",\n \"def load_image_as_rgb(image_path):\\n im = imageio.imread(image_path)\\n y_size = im.shape[0]\\n x_size = im.shape[1]\\n logging.info(\\\"Image has dimensions X:%d Y:%d\\\" % (x_size, y_size))\\n arr = np.zeros((im.shape[0],im.shape[1]), dtype=int)\\n i = 0\\n for im_row in im:\\n j = 0\\n for vec in im_row:\\n arr[i,j] = rgb_vec_to_num(vec)\\n j = j + 1\\n i = i + 1\\n return arr\",\n \"def rgb_to_matplot_lib(rgb):\\n r, g, b = rgb\\n return (r / 255., g / 255., b / 255.)\",\n \"def convert_3d(points_2d, depth_image, image):\\n fx = 525.0 # focal length x\\n fy = 525.0 # focal length y\\n cx = 319.5 # optical center x\\n cy = 239.5 # optical center y\\n factor = 5000 # for the 16-bit PNG files\\n points_3d = []\\n cols = []\\n colors = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n for v in range(depth_image.shape[0]):\\n for u in range(depth_image.shape[1]):\\n Z = depth_image[v,u] / factor\\n X = (u - cx) * Z / fx\\n Y = (v - cy) * Z / fy\\n points_3d.append([X,Y,Z])\\n cols.append(colors[v,u])\\n points = []\\n for i in range(len(points_2d)):\\n x = int(points_2d[i,0])\\n y = int(points_2d[i,1])\\n # print(y)\\n Z = depth_image[y,x] / factor\\n X = (x - cx) * Z / fx\\n Y = (y - cy) * Z / fy\\n points.append([X,Y,Z])\\n points_3d = np.array(points_3d)\\n cols = np.array(cols)\\n points = np.array(points)\\n \\n return points, points_3d, cols\",\n \"def XYZ_to_sRGB_linear(XYZ):\\n\\n rgb = np.tensordot(T, XYZ, axes=([1, 0]))\\n\\n if clip_method == CLIP_CLAMP_TO_ZERO:\\n # set negative rgb values to zero\\n rgb = np.where(rgb < 0, 0, rgb)\\n return rgb\\n\\n if clip_method == CLIP_ADD_WHITE:\\n # add enough white to make all rgb values nonnegative\\n # find max negative rgb (or 0.0 if all non-negative), we need that much white\\n rgb_min = np.amin(rgb, axis=0)\\n # get max positive component\\n rgb_max = np.amax(rgb, axis=0)\\n\\n # get scaling factor to maintain max rgb after adding white\\n scaling = np.where(rgb_max > 0.0, rgb_max / (rgb_max - rgb_min + 0.00001), 1.0)\\n\\n # add enough white to cancel this out, maintaining the maximum of rgb\\n rgb = np.where(rgb_min < 0.0, scaling * (rgb - rgb_min), rgb)\\n return rgb\",\n \"def imageprepare():\\r\\n file_name = 'temp_image.png'\\r\\n im = Image.open(file_name).convert('L')\\r\\n im = im.resize((20, 20))\\r\\n p = Image.new('L', (28,28), (255))\\r\\n p.paste(im,(4,4,24,24))\\r\\n p.save(\\\"last_image.png\\\")\\r\\n\\r\\n tv = list(p.getdata()) # get pixel values\\r\\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\r\\n tva = np.reshape(tva, (28, 28))\\r\\n\\r\\n return tva\",\n \"def cam2pixel(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):\\n b, _, h, w = cam_coords.size()\\n cam_coords_flat = cam_coords.view(b, 3, -1) # [B, 3, H*W]\\n if proj_c2p_rot is not None:\\n pcoords = proj_c2p_rot.bmm(cam_coords_flat)\\n else:\\n pcoords = cam_coords_flat\\n\\n if proj_c2p_tr is not None:\\n pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]\\n X = pcoords[:, 0]\\n Y = pcoords[:, 1]\\n Z = pcoords[:, 2].clamp(min=1e-8)\\n\\n X_norm = 2 * (X / Z) / (w - 1) - 1 # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]\\n Y_norm = 2 * (Y / Z) / (h - 1) - 1 # Idem [B, H*W]\\n if padding_mode == 'zeros':\\n X_mask = ((X_norm > 1) + (X_norm < -1)).detach()\\n X_norm[X_mask] = 2 # make sure that no point in warped image is a combinaison of im and gray\\n Y_mask = ((Y_norm > 1) + (Y_norm < -1)).detach()\\n Y_norm[Y_mask] = 2\\n\\n pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]\\n return pixel_coords.view(b, h, w, 2)\",\n \"def getRGBFromXYAndBrightness(self, x, y, bri=1):\\n # The xy to color conversion is almost the same, but in reverse order.\\n # Check if the xy value is within the color gamut of the lamp.\\n # If not continue with step 2, otherwise step 3.\\n # We do this to calculate the most accurate color the given light can actually do.\\n xyPoint = XYPoint(x, y)\\n\\n if not self.checkPointInLampsReach(xyPoint):\\n # Calculate the closest point on the color gamut triangle\\n # and use that as xy value See step 6 of color to xy.\\n xyPoint = self.getClosestPointToPoint(xyPoint)\\n\\n # Calculate XYZ values Convert using the following formulas:\\n Y = bri\\n X = (Y / xyPoint.y) * xyPoint.x\\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\\n\\n # Convert to RGB using Wide RGB D65 conversion\\n r = X * 1.612 - Y * 0.203 - Z * 0.302\\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\\n b = X * 0.026 - Y * 0.072 + Z * 0.962\\n\\n # Apply reverse gamma correction\\n r, g, b = map(\\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\\n [r, g, b]\\n )\\n\\n # Bring all negative components to zero\\n r, g, b = map(lambda x: max(0, x), [r, g, b])\\n\\n # If one component is greater than 1, weight components by that value.\\n max_component = max(r, g, b)\\n if max_component > 1:\\n r, g, b = map(lambda x: x / max_component, [r, g, b])\\n\\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\\n\\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\\n return (r, g, b)\",\n \"def features_colorspace(X, colorspace):\\n if colorspace == \\\"BGR\\\":\\n return X\\n\\n X = np.array(X)\\n\\n if colorspace == \\\"HLS\\\":\\n cs = cv2.COLOR_BGR2HLS\\n elif colorspace == \\\"HSV\\\":\\n cs = cv2.COLOR_BGR2HSV\\n elif colorspace == \\\"LUV\\\":\\n cs = cv2.COLOR_BGR2LUV\\n elif colorspace == \\\"YUV\\\":\\n cs = cv2.COLOR_BGR2YUV\\n elif colorspace == \\\"YCrCb\\\":\\n cs = cv2.COLOR_BGR2YCrCb\\n else:\\n raise ValueError(colorspace)\\n\\n for i in range(X.shape[0]):\\n X[i,...] = cv2.cvtColor(X[i,...], cs)\\n\\n return X\",\n \"def test_dx10_r8g8b8a8_unorm_srgb():\\n\\n with Image.open(TEST_FILE_DX10_R8G8B8A8_UNORM_SRGB) as im:\\n im.load()\\n\\n assert im.format == \\\"DDS\\\"\\n assert im.mode == \\\"RGBA\\\"\\n assert im.size == (16, 16)\\n assert im.info[\\\"gamma\\\"] == 1 / 2.2\\n\\n assert_image_equal_tofile(\\n im, TEST_FILE_DX10_R8G8B8A8_UNORM_SRGB.replace(\\\".dds\\\", \\\".png\\\")\\n )\",\n \"def yiq2rgb(imYIQ):\\n trans = np.array([[1, 0.956, 0.62], [1, -0.272, -0.647], [1, -1.108, 1.705]])\\n return np.dot(imYIQ, trans)\",\n \"def process_image(img):\\n img[0] = img[0] * 0.229\\n img[1] = img[1] * 0.224\\n img[2] = img[2] * 0.225\\n img[0] += 0.485\\n img[1] += 0.456\\n img[2] += 0.406\\n\\n return img.cpu().numpy().transpose((1, 2, 0))\",\n \"def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n\\n refx = 95.047\\n refy = 100.00\\n refz = 108.883\\n\\n var_x = x / refx\\n var_y = y / refy\\n var_z = z / refz\\n\\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\\n (7.787 * var_x) + (16 / 116))\\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\\n (7.787 * var_y) + (16 / 116))\\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\\n (7.787 * var_z) + (16 / 116))\\n\\n l = (116 * var_y) - 16\\n a = 500 * (var_x - var_y)\\n b = 200 * (var_y - var_z)\\n lab_image = tf.stack([l, a, b], axis=-1)\\n return lab_image\",\n \"def load_raster_xyz(self, filename):\\n with rasterio.open(filename, 'r') as src:\\n ## Alias 'affine' no longer works for 'transform'\\n ##matrix = src.affine\\n matrix = src.transform\\n self.size = (src.width, src.height)\\n # read per scan line\\n for row in range(0, src.height):\\n window = ((row, row+1), (0, src.width))\\n data = src.read(window=window)\\n this_row = data[0][0]\\n for column in range(0, src.width):\\n x, y = matrix * (column, row)\\n yield x, y, this_row[column]\",\n \"def flatten_pixcoord(image):\\n hdr = pyfits.getheader(image)\\n naxis1 = hdr['naxis1']\\n naxis2 = hdr['naxis2']\\n pixcoord = np.mgrid[1:naxis1+1,1:naxis2+1]\\n pixcoord = pixcoord.swapaxes(0,1).swapaxes(1,2)\\n pixcoord = pixcoord.ravel().reshape(naxis1*naxis2, 2)\\n return pixcoord\",\n \"def _pillow2array(img, flag='color', channel_order='bgr'):\\n channel_order = channel_order.lower()\\n if channel_order not in ['rgb', 'bgr']:\\n raise ValueError('channel order must be either \\\"rgb\\\" or \\\"bgr\\\"')\\n\\n if flag == 'unchanged':\\n array = np.array(img)\\n if array.ndim >= 3 and array.shape[2] >= 3: # color image\\n array[:, :, :3] = array[:, :, (2, 1, 0)] # RGB to BGR\\n else:\\n # If the image mode is not 'RGB', convert it to 'RGB' first.\\n if img.mode != 'RGB':\\n if img.mode != 'LA':\\n # Most formats except 'LA' can be directly converted to RGB\\n img = img.convert('RGB')\\n else:\\n # When the mode is 'LA', the default conversion will fill in\\n # the canvas with black, which sometimes shadows black objects\\n # in the foreground.\\n #\\n # Therefore, a random color (124, 117, 104) is used for canvas\\n img_rgba = img.convert('RGBA')\\n img = Image.new('RGB', img_rgba.size, (124, 117, 104))\\n img.paste(img_rgba, mask=img_rgba.split()[3]) # 3 is alpha\\n if flag == 'color':\\n array = np.array(img)\\n if channel_order != 'rgb':\\n array = array[:, :, ::-1] # RGB to BGR\\n elif flag == 'grayscale':\\n img = img.convert('L')\\n array = np.array(img)\\n else:\\n raise ValueError(\\n 'flag must be \\\"color\\\", \\\"grayscale\\\" or \\\"unchanged\\\", '\\n f'but got {flag}')\\n return array\",\n \"def read_color_image(path):\\n with open(path, 'rb') as f:\\n img = Image.fromarray(read_ppm(f), mode='RGB')\\n img = tf.keras.preprocessing.image.img_to_array(img, dtype=int)\\n img = tf.convert_to_tensor(img)\\n return img\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6907906","0.6867097","0.68299574","0.66412497","0.66398394","0.65136164","0.6503859","0.6343428","0.6280779","0.6266033","0.62629265","0.620036","0.61658543","0.6152105","0.60904413","0.6024829","0.5986624","0.59797454","0.597226","0.5968314","0.59129363","0.58743936","0.58658713","0.58486485","0.5839945","0.58384067","0.58263016","0.58202267","0.5802494","0.57842463","0.5780034","0.5746885","0.5746512","0.57443994","0.5734911","0.5726437","0.57164264","0.5703005","0.5700834","0.5686129","0.56698585","0.56618816","0.5631606","0.56177884","0.56121683","0.55960405","0.55849034","0.55800086","0.55646306","0.55504155","0.55460286","0.5538073","0.5504926","0.54972297","0.54915696","0.54899406","0.54728985","0.54555655","0.5452597","0.5449073","0.5439262","0.54211384","0.5405506","0.54023457","0.5390802","0.53833646","0.5381999","0.5372788","0.537111","0.5354005","0.5349049","0.5344242","0.53409314","0.5339757","0.53390783","0.5338857","0.53371006","0.5326929","0.5318615","0.5307042","0.5299247","0.52988017","0.5295347","0.52951103","0.52822894","0.5282117","0.52795154","0.52702194","0.52659893","0.52637583","0.52612805","0.52609724","0.52600056","0.52593976","0.5237371","0.52357185","0.52345526","0.52336824","0.5227816","0.5225825"],"string":"[\n \"0.6907906\",\n \"0.6867097\",\n \"0.68299574\",\n \"0.66412497\",\n \"0.66398394\",\n \"0.65136164\",\n \"0.6503859\",\n \"0.6343428\",\n \"0.6280779\",\n \"0.6266033\",\n \"0.62629265\",\n \"0.620036\",\n \"0.61658543\",\n \"0.6152105\",\n \"0.60904413\",\n \"0.6024829\",\n \"0.5986624\",\n \"0.59797454\",\n \"0.597226\",\n \"0.5968314\",\n \"0.59129363\",\n \"0.58743936\",\n \"0.58658713\",\n \"0.58486485\",\n \"0.5839945\",\n \"0.58384067\",\n \"0.58263016\",\n \"0.58202267\",\n \"0.5802494\",\n \"0.57842463\",\n \"0.5780034\",\n \"0.5746885\",\n \"0.5746512\",\n \"0.57443994\",\n \"0.5734911\",\n \"0.5726437\",\n \"0.57164264\",\n \"0.5703005\",\n \"0.5700834\",\n \"0.5686129\",\n \"0.56698585\",\n \"0.56618816\",\n \"0.5631606\",\n \"0.56177884\",\n \"0.56121683\",\n \"0.55960405\",\n \"0.55849034\",\n \"0.55800086\",\n \"0.55646306\",\n \"0.55504155\",\n \"0.55460286\",\n \"0.5538073\",\n \"0.5504926\",\n \"0.54972297\",\n \"0.54915696\",\n \"0.54899406\",\n \"0.54728985\",\n \"0.54555655\",\n \"0.5452597\",\n \"0.5449073\",\n \"0.5439262\",\n \"0.54211384\",\n \"0.5405506\",\n \"0.54023457\",\n \"0.5390802\",\n \"0.53833646\",\n \"0.5381999\",\n \"0.5372788\",\n \"0.537111\",\n \"0.5354005\",\n \"0.5349049\",\n \"0.5344242\",\n \"0.53409314\",\n \"0.5339757\",\n \"0.53390783\",\n \"0.5338857\",\n \"0.53371006\",\n \"0.5326929\",\n \"0.5318615\",\n \"0.5307042\",\n \"0.5299247\",\n \"0.52988017\",\n \"0.5295347\",\n \"0.52951103\",\n \"0.52822894\",\n \"0.5282117\",\n \"0.52795154\",\n \"0.52702194\",\n \"0.52659893\",\n \"0.52637583\",\n \"0.52612805\",\n \"0.52609724\",\n \"0.52600056\",\n \"0.52593976\",\n \"0.5237371\",\n \"0.52357185\",\n \"0.52345526\",\n \"0.52336824\",\n \"0.5227816\",\n \"0.5225825\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7601516"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12392,"cells":{"query":{"kind":"string","value":"Convert an image from RGB color space to LAB color space RGB > XYZ > LAB"},"document":{"kind":"string","value":"def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\n xyz = rgb_to_xyz(image)\n lab_image = xyz_to_lab(xyz)\n return lab_image"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\n xyz = lab_to_xyz(image)\n rgb_image = xyz_to_rgb(xyz)\n return rgb_image","def lab_to_rgb(img):\n new_img = np.zeros((256, 256, 3))\n for i in range(len(img)):\n for j in range(len(img[i])):\n pix = img[i, j]\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\n new_img = color.lab2rgb(new_img) * 255\n new_img = new_img.astype('uint8')\n return new_img","def lab_to_rgb(img_l, img_ab):\n lab = np.empty([*img_l.shape[0:2], 3])\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\n lab[:, :, 1:] = img_ab * 127\n return color.lab2rgb(lab)","def rgb_to_lab(img, l=False, ab=False):\n img = img / 255\n l_chan = color.rgb2lab(img)[:, :, 0]\n l_chan = l_chan / 50 - 1\n l_chan = l_chan[..., np.newaxis]\n\n ab_chan = color.rgb2lab(img)[:, :, 1:]\n ab_chan = (ab_chan + 128) / 255 * 2 - 1\n if l:\n return l_chan\n else:\n return ab_chan","def rgb2lab(r, g, b):\n r, g, b = r / 255.0, g / 255.0, b / 255.0\n\n # http://www.brucelindbloom.com/index.html?Math.html\n # Inverse sRGB Companding\n r = r / 12.92 if r <= 0.04045 else ((r + 0.055) / 1.055) ** 2.4\n g = g / 12.92 if g <= 0.04045 else ((g + 0.055) / 1.055) ** 2.4\n b = b / 12.92 if b <= 0.04045 else ((b + 0.055) / 1.055) ** 2.4\n\n # http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html\n # sRGB, D65\n x = r * 0.4124564 + g * 0.3575761 + b * 0.1804375\n y = r * 0.2126729 + g * 0.7151522 + b * 0.0721750\n z = r * 0.0193339 + g * 0.1191920 + b * 0.9503041\n\n # http://www.brucelindbloom.com/index.html?Eqn_XYZ_to_Lab.html\n kappa, epsilon = 903.3, 0.008856\n\n # http://brucelindbloom.com/index.html?Eqn_ChromAdapt.html\n # White point for D65\n xr, yr, zr = x / 0.95047, y / 1.00000, z / 1.08883\n\n fx = xr ** (1 / 3.0) if xr > epsilon else (kappa * xr + 16) / 116.0\n fy = yr ** (1 / 3.0) if yr > epsilon else (kappa * yr + 16) / 116.0\n fz = zr ** (1 / 3.0) if zr > epsilon else (kappa * zr + 16) / 116.0\n\n l = 166.0 * fy - 16.0\n a = 500.0 * (fx - fy)\n b = 200.0 * (fy - fz)\n\n return l, a, b","def XYZ_to_Lab(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n temp_x = cobj.xyz_x / illum[\"X\"]\r\n temp_y = cobj.xyz_y / illum[\"Y\"]\r\n temp_z = cobj.xyz_z / illum[\"Z\"]\r\n \r\n if temp_x > color_constants.CIE_E:\r\n temp_x = math.pow(temp_x, (1.0 / 3.0))\r\n else:\r\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \r\n\r\n if temp_y > color_constants.CIE_E:\r\n temp_y = math.pow(temp_y, (1.0 / 3.0))\r\n else:\r\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\r\n \r\n if temp_z > color_constants.CIE_E:\r\n temp_z = math.pow(temp_z, (1.0 / 3.0))\r\n else:\r\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\r\n \r\n lab_l = (116.0 * temp_y) - 16.0\r\n lab_a = 500.0 * (temp_x - temp_y)\r\n lab_b = 200.0 * (temp_y - temp_z)\r\n return LabColor(\r\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)","def rgb2Lab(rgbvalue):\r\n RGB2Lab_Matrix = np.array([[0.412453, 0.357580, 0.180423],\r\n [0.212671, 0.715160, 0.072169],\r\n [0.019334, 0.119193, 0.950227]])\r\n R = rgbvalue[0]\r\n G = rgbvalue[1]\r\n B = rgbvalue[2]\r\n gammaR = gamma(R / 255.0)\r\n gammaG = gamma(G / 255.0)\r\n gammaB = gamma(B / 255.0)\r\n RGBvalue = np.array([gammaR, gammaG, gammaB])\r\n RGBvalue = RGBvalue.reshape(3, 1)\r\n XYZvalue = np.dot(RGB2Lab_Matrix, RGBvalue)\r\n assert XYZvalue.shape == (3, 1)\r\n correction = np.array([[1.0 / 0.950456, 1.0, 1.0 / 1.088754]]).T\r\n assert correction.shape == (3, 1)\r\n XYZ = XYZvalue * correction\r\n assert XYZ.shape == (3, 1)\r\n YYn = ft(XYZ[1])\r\n XXn = ft(XYZ[0])\r\n ZZn = ft(XYZ[2])\r\n L = 116 * YYn - 16\r\n a = 500 * (XXn - YYn)\r\n b = 200 * (YYn - ZZn)\r\n return [int(L), int(a), int(b)]","def sRGBToLab(rgb):\n return xyzToLab(xyzFromsRGB(rgb), [0.9504559, 1, 1.089058])","def Lab_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n xyz_y = (cobj.lab_l + 16.0) / 116.0\r\n xyz_x = cobj.lab_a / 500.0 + xyz_y\r\n xyz_z = xyz_y - cobj.lab_b / 200.0\r\n \r\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\r\n xyz_y = math.pow(xyz_y, 3)\r\n else:\r\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\r\n\r\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\r\n xyz_x = math.pow(xyz_x, 3)\r\n else:\r\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\r\n \r\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\r\n xyz_z = math.pow(xyz_z, 3)\r\n else:\r\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\r\n \r\n xyz_x = (illum[\"X\"] * xyz_x)\r\n xyz_y = (illum[\"Y\"] * xyz_y)\r\n xyz_z = (illum[\"Z\"] * xyz_z)\r\n \r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)","def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n L: torch.Tensor = image[..., 0, :, :]\n u: torch.Tensor = image[..., 1, :, :]\n v: torch.Tensor = image[..., 2, :, :]\n\n # Convert from Luv to XYZ\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\n\n # Compute white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n a: torch.Tensor = u_w + u / (13 * L + eps)\n d: torch.Tensor = v_w + v / (13 * L + eps)\n c: torch.Tensor = 3 * y * (5 * d - 3)\n\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\n\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\n\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\n\n # Convert from RGB Linear to sRGB\n rgb_im = linear_rgb_to_rgb(rgbs_im)\n\n return rgb_im","def rgb2alpha(img):\n\t### First of all we need the size of our picture to make the transforms\n\n\tx = len(img) ; y = len(img[0])\n\talpha = np.full((x, y, 3), 0, dtype = float)\t\t## This will be the transformed image\n\n\t### Now we gotta access each pixel of the picture\n\n\tfor i, vi in enumerate(img):\n\t\tfor j, px in enumerate(vi):\n\t\t\t### There we are\n\n\t\t\t# Step 1 : LMS transform, for that we use r_l\n\n\t\t\talpha[i][j] = np.matmul(r_l, px)\n\n\t\t\t# Step 2 : log em all (decimal log)\n\n\t\t\talpha[i][j][0] = log(alpha[i][j][0])\n\t\t\talpha[i][j][1] = log(alpha[i][j][1])\n\t\t\talpha[i][j][2] = log(alpha[i][j][2])\n\n\t\t\t# Step 3 : l alpha beta transform, by using l_a\n\n\t\t\talpha[i][j] = np.matmul(l_a, alpha[i][j])\n\n\treturn alpha","def sRGBFromLab(lab):\n return xyzTosRGB(labToXYZ(lab, [0.9504559, 1, 1.089058]))","def example_rgb_to_xyz():\r\n\r\n print(\"=== RGB Example: RGB->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n rgb = sRGBColor(120, 130, 140)\r\n # Show a string representation.\r\n print(rgb)\r\n # Convert RGB to XYZ using a D50 illuminant.\r\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\n l, a, b = tf.unstack(image, axis=-1)\n\n var_y = (l + 16) / 116\n var_x = a / 500 + var_y\n var_z = var_y - b / 200\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\n (var_x - 16 / 116) / 7.787)\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\n (var_y - 16 / 116) / 7.787)\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\n (var_z - 16 / 116) / 7.787)\n\n refx = 95.047\n refy = 100.00\n ref_z = 108.883\n\n x = var_x * refx\n y = var_y * refy\n z = var_z * ref_z\n xyz_image = tf.stack([x, y, z], axis=-1)\n return xyz_image","def sRGBToLabD50(rgb):\n return xyzToLab(xyzFromsRGBD50(rgb), [0.9642957, 1, 0.8251046])","def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n\n refx = 95.047\n refy = 100.00\n refz = 108.883\n\n var_x = x / refx\n var_y = y / refy\n var_z = z / refz\n\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\n (7.787 * var_x) + (16 / 116))\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\n (7.787 * var_y) + (16 / 116))\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\n (7.787 * var_z) + (16 / 116))\n\n l = (116 * var_y) - 16\n a = 500 * (var_x - var_y)\n b = 200 * (var_y - var_z)\n lab_image = tf.stack([l, a, b], axis=-1)\n return lab_image","def example_lab_to_rgb():\r\n\r\n print(\"=== RGB Example: Lab->RGB ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.217)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n rgb = convert_color(lab, sRGBColor)\r\n print(rgb)\r\n print(\"=== End Example ===\\n\")","def sRGBFromLabD50(lab):\n return xyzTosRGBD50(labToXYZ(lab, [0.9642957, 1, 0.8251046]))","def _convert_to_yolo_img(self, img):\n\n img = img / 255.0\n h, w, c = img.shape\n img = img.transpose(2, 0, 1)\n outimg = make_image(w, h, c)\n img = img.reshape((w*h*c))\n data = c_array(c_float, img)\n outimg.data = data\n rgbgr_image(outimg)\n return outimg","def l_to_rgb(img_l):\n lab = np.squeeze(255 * (img_l + 1) / 2)\n return color.gray2rgb(lab) / 255","def get_rgb(self, img, r, g, b):\r\n\r\n # Get specific bands of hyperspectral image\r\n red_channel = img[:, :, r]\r\n green_channel = img[:, :, g]\r\n blue_channel = img[:, :, b]\r\n\r\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\r\n img = img.astype('float32')\r\n return img","def example_lab_to_xyz():\r\n\r\n print(\"=== Simple Example: Lab->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.22)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n xyz = convert_color(lab, XYZColor)\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\n r, g, b = tf.unstack(image, axis=-1)\n var_r = r / 255\n var_g = g / 255\n var_b = b / 255\n\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\n var_r / 12.92)\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\n var_g / 12.92)\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\n var_b / 12.92)\n var_r = var_r * 100\n var_g = var_g * 100\n var_b = var_b * 100\n\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\n\n image_xyz = tf.stack([x, y, z], axis=-1)\n return image_xyz","def matplotlib_image(image):\n if image.ndim == 2:\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\n else:\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return rgb","def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n var_x = x / 100\n var_y = y / 100\n var_z = z / 100\n\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\n\n var_r = tf.where(var_r > 0.0031308,\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\n 12.92 * var_r)\n var_g = tf.where(var_g > 0.0031308,\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\n 12.92 * var_g)\n var_b = tf.where(var_b > 0.0031308,\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\n 12.92 * var_b)\n r = var_r * 255\n g = var_g * 255\n b = var_b * 255\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\n return rgb_image","def convert_color(image, color_space):\n out_image = None\n if color_space != 'RGB':\n if color_space == 'HSV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\n elif color_space == 'LUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\n elif color_space == 'HLS':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\n elif color_space == 'YUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\n elif color_space == 'YCrCb':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\n else:\n out_image = np.copy(image)\n return out_image","def sRGBLuminance(x):\n lin=linearFromsRGB3(x)\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722","def rgb_to_luv(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n # Convert from sRGB to Linear RGB\n lin_rgb = rgb_to_linear_rgb(image)\n\n xyz_im: torch.Tensor = rgb_to_xyz(lin_rgb)\n\n x: torch.Tensor = xyz_im[..., 0, :, :]\n y: torch.Tensor = xyz_im[..., 1, :, :]\n z: torch.Tensor = xyz_im[..., 2, :, :]\n\n threshold = 0.008856\n L: torch.Tensor = torch.where(y > threshold, 116.0 * torch.pow(y.clamp(min=threshold), 1.0 / 3.0) - 16.0, 903.3 * y)\n\n # Compute reference white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n u_p: torch.Tensor = (4 * x) / (x + 15 * y + 3 * z + eps)\n v_p: torch.Tensor = (9 * y) / (x + 15 * y + 3 * z + eps)\n\n u: torch.Tensor = 13 * L * (u_p - u_w)\n v: torch.Tensor = 13 * L * (v_p - v_w)\n\n out = torch.stack([L, u, v], dim=-3)\n\n return out","def rgb_processing(rgb_img, center, scale, rot=0):\n rgb_img = crop(rgb_img, center, scale, \n [constants.IMG_RES, constants.IMG_RES], rot=rot)\n # (3,224,224),float,[0,1]\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\n return rgb_img","def colorize_images(self, img):\n self.load_model()\n self.mdn.eval()\n self.vae.eval()\n n, _, _ = img.shape\n img = img.astype(np.float32) / 255\n img = torch.tensor(img, dtype=torch.float, device=self.device).unsqueeze(1)\n with torch.no_grad():\n z = self.mdn(img)\n ab_out = self.vae.decode(z)\n lab_out = torch.cat((img, ab_out), dim=1)\n lab_out = self.unnormalize(lab_out).cpu().numpy()\n lab_out = np.transpose(lab_out, (0, 2, 3, 1)).astype(np.uint8)\n for i in range(n):\n color_out = cv2.cvtColor(lab_out[i], cv2.COLOR_LAB2BGR)\n color_out = cv2.resize(color_out, (96, 96), interpolation=cv2.INTER_AREA)\n cv2.imwrite(\"../datasets/stl10/divcolor/{}.png\".format(str(i)), color_out)\n return","def red_channel(img):\n\n red = np.zeros(img.shape,dtype=float)\n\n red[:,:,2] = np.copy(img[:,:,2])\n\n return red","def read_img(filename) -> Tuple[np.ndarray, np.ndarray]:\n img = cv2.imread(filename, 3)\n labimg = cv2.cvtColor(cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE)), cv2.COLOR_BGR2Lab)\n return np.reshape(labimg[:, :, 0], (config.IMAGE_SIZE, config.IMAGE_SIZE, 1)), labimg[:, :, 1:]","def linear_rgb2lab(rgb, illuminant=\"D65\", observer=\"2\"):\n return xyz2lab(linear_rgb2xyz(rgb), illuminant, observer)","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def luminance(rgb):\n \n (r, g, b) = rgb\n return (r + g + b) // 3","def chromatic_transform(im, label=None, d_h=None, d_s=None, d_l=None):\n # Set random hue, luminosity and saturation which ranges from -0.1 to 0.1\n if d_h is None:\n d_h = (np.random.rand(1) - 0.5) * 0.2 * 180\n if d_l is None:\n d_l = (np.random.rand(1) - 0.5) * 0.2 * 256\n if d_s is None:\n d_s = (np.random.rand(1) - 0.5) * 0.2 * 256\n # Convert the BGR to HLS\n hls = cv2.cvtColor(im, cv2.COLOR_BGR2HLS)\n h, l, s = cv2.split(hls)\n # Add the values to the image H, L, S\n new_h = (h + d_h) % 180\n new_l = np.clip(l + d_l, 0, 255)\n new_s = np.clip(s + d_s, 0, 255)\n # Convert the HLS to BGR\n new_hls = cv2.merge((new_h, new_l, new_s)).astype('uint8')\n new_im = cv2.cvtColor(new_hls, cv2.COLOR_HLS2BGR)\n\n if label is not None:\n I = np.where(label > 0)\n new_im[I[0], I[1], :] = im[I[0], I[1], :]\n return new_im","def applyJET(img):\n\treturn applyColorMap(img, \"jet\")","def generate_lut(self):\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n for i in Numeric.arange(256):\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\n return r,g,b","def grey_to_rgb_imitation(img):\n return np.repeat(img[...,np.newaxis], 3, -1)","def read_img(img): #X\n im = plt.imread(img)\n im = im[:, :, :3]\n if im.max()>200:\n im = im/255.\n return rgb_to_hsv(im)-0.5","def prepare_images(images):\n images = color.rgb2lab(images)\n\n l = images[:,:,:,:1]/100.\n ab = images[:,:,:,1:]/200. + 0.5\n\n return l, ab","def luminance(self):\n \n return (self.r + self.g + self.b) // 3","def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\n y: torch.Tensor = image[..., 0, :, :]\n cb: torch.Tensor = image[..., 1, :, :]\n cr: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n cb_shifted: torch.Tensor = cb - delta\n cr_shifted: torch.Tensor = cr - delta\n\n r: torch.Tensor = y + 1.403 * cr_shifted\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\n b: torch.Tensor = y + 1.773 * cb_shifted\n return torch.stack([r, g, b], -3)","def getRGBFromXYAndBrightness(self, x, y, bri=1):\n # The xy to color conversion is almost the same, but in reverse order.\n # Check if the xy value is within the color gamut of the lamp.\n # If not continue with step 2, otherwise step 3.\n # We do this to calculate the most accurate color the given light can actually do.\n xyPoint = XYPoint(x, y)\n\n if not self.checkPointInLampsReach(xyPoint):\n # Calculate the closest point on the color gamut triangle\n # and use that as xy value See step 6 of color to xy.\n xyPoint = self.getClosestPointToPoint(xyPoint)\n\n # Calculate XYZ values Convert using the following formulas:\n Y = bri\n X = (Y / xyPoint.y) * xyPoint.x\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\n\n # Convert to RGB using Wide RGB D65 conversion\n r = X * 1.612 - Y * 0.203 - Z * 0.302\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\n b = X * 0.026 - Y * 0.072 + Z * 0.962\n\n # Apply reverse gamma correction\n r, g, b = map(\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\n [r, g, b]\n )\n\n # Bring all negative components to zero\n r, g, b = map(lambda x: max(0, x), [r, g, b])\n\n # If one component is greater than 1, weight components by that value.\n max_component = max(r, g, b)\n if max_component > 1:\n r, g, b = map(lambda x: x / max_component, [r, g, b])\n\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\n\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\n return (r, g, b)","def yuv2lab(tens: Tensor) -> Tensor:\n if not _is_yuv_image(tens):\n raise ValueError(\n f\"Tensor of shape 3 expected. Found shape {len(tens.shape)}. \"\n \"This function converts an YUV Tensor to its LAB counterpart\"\n )\n\n img = yuv2bgr(tens)\n img = bgr2lab(tens)\n return to_tensor(img, cspace=\"lab\")","def convertColorSpace(\n self,\n img, # Image in some color space\n srcColorSpace = 'BGR', # Source color space\n tgtColorSpace = 'RGB', # Traget color space\n ):\n\n if srcColorSpace == tgtColorSpace:\n return img\n\n if srcColorSpace == 'BGR':\n img_bgr = img\n elif srcColorSpace == 'RGB':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n elif srcColorSpace == 'HSV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n elif srcColorSpace == 'HLS':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\n elif srcColorSpace == 'LUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\n elif srcColorSpace == 'YUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\n elif srcColorSpace == 'YCrCb':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\n else:\n raise Exception(\"Incorrect color space: {}\".format(srcColorSpace))\n\n if tgtColorSpace == 'BGR':\n img_tgt = img_bgr\n elif tgtColorSpace == 'RGB':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\n elif tgtColorSpace == 'HSV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\n elif tgtColorSpace == 'HLS':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\n elif tgtColorSpace == 'LUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\n elif tgtColorSpace == 'YUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\n elif tgtColorSpace == 'YCrCb':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\n else:\n raise Exception(\"Incorrect color space: {}\".format(tgtColorSpace))\n\n return img_tgt","def rgb2hsl_img(rgb):\r\n \r\n def core(_rgb, _hsl):\r\n\r\n irgb = _rgb.astype(np.uint16)\r\n ir, ig, ib = irgb[:, :, 0], irgb[:, :, 1], irgb[:, :, 2]\r\n h, s, l = _hsl[:, :, 0], _hsl[:, :, 1], _hsl[:, :, 2]\r\n\r\n imin, imax = irgb.min(2), irgb.max(2)\r\n iadd, isub = imax + imin, imax - imin\r\n\r\n ltop = (iadd != 510) * (iadd > 255)\r\n lbot = (iadd != 0) * (ltop == False)\r\n\r\n l[:] = iadd.astype(np.float) / 510\r\n\r\n fsub = isub.astype(np.float)\r\n s[ltop] = fsub[ltop] / (510 - iadd[ltop])\r\n s[lbot] = fsub[lbot] / iadd[lbot]\r\n\r\n not_same = imax != imin\r\n is_b_max = not_same * (imax == ib)\r\n not_same_not_b_max = not_same * (is_b_max == False)\r\n is_g_max = not_same_not_b_max * (imax == ig)\r\n is_r_max = not_same_not_b_max * (is_g_max == False) * (imax == ir)\r\n\r\n h[is_r_max] = ((0. + ig[is_r_max] - ib[is_r_max]) / isub[is_r_max])\r\n h[is_g_max] = ((0. + ib[is_g_max] - ir[is_g_max]) / isub[is_g_max]) + 2\r\n h[is_b_max] = ((0. + ir[is_b_max] - ig[is_b_max]) / isub[is_b_max]) + 4\r\n h[h < 0] += 6\r\n h[:] /= 6\r\n\r\n hsl = np.zeros(rgb.shape, dtype=np.float)\r\n cpus = multiprocessing.cpu_count()\r\n length = int(math.ceil(float(hsl.shape[0]) / cpus))\r\n line = 0\r\n threads = []\r\n while line < hsl.shape[0]:\r\n line_next = line + length\r\n thread = threading.Thread(target=core, args=(rgb[line:line_next], hsl[line:line_next]))\r\n thread.start()\r\n threads.append(thread)\r\n line = line_next\r\n\r\n for thread in threads:\r\n thread.join()\r\n\r\n return hsl","def __enhance_image(self, img):\n\n blue = self.g.clahe.apply(img[:,:,0])\n green = self.g.clahe.apply(img[:,:,1])\n red = self.g.clahe.apply(img[:,:,2])\n img[:,:,0] = blue\n img[:,:,1] = green\n img[:,:,2] = red\n return img","def make_grayscale(img: Image.Image) -> Image.Image:\n orig_mode = img.mode\n\n if orig_mode in [\"RGB\", \"CMYK\", \"YCbCr\", \"LAB\", \"HSV\"]:\n return img.convert(\"L\")\n elif orig_mode == \"RGBA\":\n return img.convert(\"LA\").convert(\"RGBA\")\n elif orig_mode == \"P\":\n # Using ITU-R 601-2 luma transform: L = R * 299/1000 + G * 587/1000 + B * 114/1000\n pal = img.getpalette()\n for i in range(len(pal) // 3):\n # Using ITU-R 601-2 luma transform\n gray = (pal[3 * i] * 299 + pal[3 * i + 1] * 587 + pal[3 * i + 2] * 114)\n gray = gray // 1000\n pal[3 * i: 3 * i + 3] = [gray, gray, gray]\n img.putpalette(pal)\n return img\n else:\n return img","def convert_color(image, color_space='RGB'):\n color_space = color_space.lower()\n if color_space != 'rgb':\n if color_space == 'hsv':\n color_transformation = cv2.COLOR_BGR2HSV\n elif color_space == 'luv':\n color_transformation = cv2.COLOR_BGR2LUV\n elif color_space == 'hls':\n color_transformation = cv2.COLOR_BGR2HLS\n elif color_space == 'yuv':\n color_transformation = cv2.COLOR_BGR2YUV\n elif color_space == 'ycrcb':\n color_transformation = cv2.COLOR_BGR2YCrCb\n else:\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\n 'HLS, YUV, YCrCb' % color_space)\n\n return cv2.cvtColor(image, color_transformation)\n else:\n return image","def load_color_image_features(img_path):\n ac = scipy.misc.imread(img_path, mode='RGB')\n ac = ac / (255.0 / 2) - 1.0\n return np.array(ac)","def rgb_to_hunterlab(rgb_color, illuminant):\n\n return xyz_to_hunterlab(rgb_to_xyz(rgb_color), illuminant)","def yuv2rgb(im):\n ## conflicting definitions exist depending on whether you use the full range\n ## of YCbCr or clamp out to the valid range. see here\n ## http://www.equasys.de/colorconversion.html\n ## http://www.fourcc.org/fccyvrgb.php\n from numpy import dot, ndarray, array\n # if not im.dtype == 'uint8':\n # raise ImageUtilsError('yuv2rgb only implemented for uint8 arrays')\n\n ## better clip input to the valid range just to be on the safe side\n yuv = ndarray(im.shape) ## float64\n yuv[:, :, 0] = im[:, :, 0].clip(16, 235).astype(yuv.dtype) - 16\n yuv[:, :, 1:] = im[:, :, 1:].clip(16, 240).astype(yuv.dtype) - 128\n\n ## ITU-R BT.601 version (SDTV)\n A = array([[1., 0., 0.701],\n [1., -0.886 * 0.114 / 0.587, -0.701 * 0.299 / 0.587],\n [1., 0.886, 0.]])\n A[:, 0] *= 255. / 219.\n A[:, 1:] *= 255. / 112.\n\n ## ITU-R BT.709 version (HDTV)\n # A = array([[1.164, 0., 1.793],\n # [1.164, -0.213, -0.533],\n # [1.164, 2.112, 0.]])\n\n rgb = dot(yuv, A.T)\n return rgb.clip(0, 255).astype('uint8')","def Array2PIL(a,lut=None,minvalue=None,maxvalue=None,width=None,height=None,\n flip=None):\n import Image # we only need it here ...\n\n if flip==\"ud\": #up-down exchange\n a=a[::-1,:]\n h,w=Numeric.shape(a)\n## a_min=Numeric.minimum.reduce((Numeric.ravel(a)))\n## a_max=Numeric.maximum.reduce((Numeric.ravel(a)))\n a_min=min(Numeric.ravel(a))\n a_max=max(Numeric.ravel(a))\n\n # allow for an user-specified maximal value:\n if maxvalue!=None and maxvalue>a_max:\n a_max=maxvalue\n # allows for an user-specified minimal value:\n if minvalue!=None and minvalue<a_min:\n a_min=minvalue\n\n if lut is not None:\n if len(lut[0]) == 256:\n \n a=(Numeric.ravel(255.0*(a-a_min)/\n (a_max-a_min))).astype(Numeric.UInt8)\n\n rgb=Numeric.zeros( (len(a),3),typecode=Numeric.UInt8)\n\n\n lut_=Numeric.zeros( (3,len(lut[0])),Numeric.UInt8)\n lut_[0]=lut[0].astype(Numeric.UInt8)\n lut_[1]=lut[1].astype(Numeric.UInt8)\n lut_[2]=lut[2].astype(Numeric.UInt8)\n\n # This is much faster than the original zip/ravel variant ...\n rgb[:,0]=Numeric.take(lut_[0],a)\n #print \"rtake\"\n rgb[:,1]=Numeric.take(lut_[1],a)\n #print \"gtake\"\n rgb[:,2]=Numeric.take(lut_[2],a)\n #print \"btake\"\n #rgb=Numeric.ravel(((Numeric.array(zip(r,g,b),\n # typecode=Numeric.UInt8))))\n\n #print \"rgb done\"\n else:\n N = len(lut[0])\n print \"LUT with N=%d entries\" % N\n if N>=256*256:\n print \"UUPS, more than uint16 colors??\", N\n raise ValueError(\"N too large\")\n \n a = (Numeric.ravel((N-1)*(a-a_min)/\n (a_max-a_min))).astype(Numeric.UInt16)\n\n rgb = Numeric.zeros( (len(a), 3), typecode=Numeric.UInt16)\n\n lut_ = Numeric.zeros( (3,len(lut[0])), Numeric.UInt16)\n lut_[0] = lut[0].astype(Numeric.UInt16)\n lut_[1] = lut[1].astype(Numeric.UInt16)\n lut_[2] = lut[2].astype(Numeric.UInt16)\n\n # This is much faster than the original zip/ravel variant ...\n rgb[:,0] = Numeric.take(lut_[0],a)\n rgb[:,1] = Numeric.take(lut_[1],a)\n rgb[:,2] = Numeric.take(lut_[2],a)\n\n rgb = (rgb*256.0/N).astype(Numeric.UInt8)\n\n else: # simple grey scale ramp...\n a=(Numeric.ravel(255.0*(a-a_min)/\n (a_max-a_min))).astype(Numeric.UInt8)\n # convert to (r_0,g_0,b_0,r_1,g_1,b_1,....)\n rgb=Numeric.ravel(Numeric.array(zip(a,a,a)))\n\n # create a PIL RGB image\n #print \"w/h\",w,h\n im=Image.new(\"RGB\",(w,h))\n #print \"imfromstring:\"\n im.fromstring(rgb.tostring())\n #print \"done ...\"\n \n # scale image ?\n if height!=None and width==None:\n im=im.resize(w/h*height,height)\n elif height==None and width!=None:\n im=im.resize(width,h/w*width)\n elif height!=None and width!=None:\n im=im.resize(width,height)\n\n return(im)","def data_to_bytescale_rgb(data): # used to create the SOURCE PNGs (MRI, FA, MD)\n im = bytescale(data)\n w, h = im.shape\n ret = np.empty((w,h,3), dtype=np.uint8)\n ret[:,:,0] = im\n ret[:,:,1] = im\n ret[:,:,2] = im\n return ret","def RudermanLABFwd(I):\n\n # get input image dimensions\n m = I.shape[0]\n n = I.shape[1]\n\n # define conversion matrices\n RGB2LMS = np.array([[0.3811, 0.5783, 0.0402],\n [0.1967, 0.7244, 0.0782],\n [0.0241, 0.1288, 0.8444]])\n LMS2LAB = np.array([[1 / (3**0.5), 0, 0],\n [0, 1 / (6**0.5), 0],\n [0, 0, 1 / (2**0.5)]]).dot(np.array([[1, 1, 1],\n [1, 1, -2],\n [1, -1, 0]]))\n\n # calculate LMS values from RGB\n I = np.reshape(I, (m * n, 3))\n LMS = np.dot(RGB2LMS, np.transpose(I))\n LMS[LMS == 0] = np.spacing(1)\n logLMS = np.log(LMS)\n\n # calculate LAB values from LMS\n I_LAB = LMS2LAB.dot(logLMS)\n\n # reshape to 3-channel image\n I_LAB = np.reshape(I_LAB.transpose(), (m, n, 3))\n\n return I_LAB","def convert_image(rel_path_in, rel_path_out):\n #Lade Bild mit Originalmaske im Grayscale-Modus\n img = cv2.imread(rel_path_in, cv2.IMREAD_GRAYSCALE)\n #Jetzt steht in img ein 2D-Array/Matrix mit jedem Graufstufen-Wert der Pixel\n #Skaliere Pixelwerte runter\n for zeilen_index in range(0,img.__len__()):\n for spalten_index in range(0, img[zeilen_index].__len__()):\n #Hole Pixel-Wert an aktueller Stelle\n wert = img[zeilen_index][spalten_index]\n #Falls Wert != 0 (also Pixel gehoert nicht zum Hintergrund)\n if wert != 0: # != 0 statt == 255, da auch z.B. 253er Werte in den Masken existieren... (vielleicht durch Konvertierung in anderes Format?)\n #Markiere den Pixel mit 1 statt 255\n img[zeilen_index][spalten_index]=1\n #print(img)\n #*NACHDEM* alle Pixel skaliert wurden, zeichne Umrandung der Objekte\n umrandung_zeichnen(img)\n #change_color(img, 0, 255)\n #change_color(img, 1, 0)\n #print(img)\n #Schreibe Ergebnis-Bild in uebergebene Datei\n cv2.imwrite(rel_path_out, img)","def convert_color(img, conv='RGB2YCrCb'):\n if conv == 'RGB2YCrCb':\n return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\n if conv == 'BGR2YCrCb':\n return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)\n if conv == 'RGB2LUV':\n return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)","def imageprepare():\r\n file_name = 'temp_image.png'\r\n im = Image.open(file_name).convert('L')\r\n im = im.resize((20, 20))\r\n p = Image.new('L', (28,28), (255))\r\n p.paste(im,(4,4,24,24))\r\n p.save(\"last_image.png\")\r\n\r\n tv = list(p.getdata()) # get pixel values\r\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\r\n tva = np.reshape(tva, (28, 28))\r\n\r\n return tva","def hyper2rgb(img, bands):\n rgb = spectral.get_rgb(img, bands)\n rgb /= np.max(rgb)\n rgb = np.asarray(255 * rgb, dtype='uint8')\n return rgb","def _colored_img_to_arr(image, verbose=False):\n height, width = image.size\n arr = np.array(image.getdata())\n arr = arr.reshape(3, height, width)\n r = arr[0]\n g = arr[1]\n b = arr[2]\n return r, g, b","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())","def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\n\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)","def enhance_contrast(img):\n # CLAHE (Contrast Limited Adaptive Histogram Equalization)\n clahe = cv2.createCLAHE(clipLimit=3., tileGridSize=(8, 8))\n\n lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) # convert from BGR to LAB color space\n l, a, b = cv2.split(lab) # split on 3 different channels\n\n l2 = clahe.apply(l) # apply CLAHE to the L-channel\n\n lab = cv2.merge((l2, a, b)) # merge channels\n img2 = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # convert from LAB to BGR\n\n return img2","def labelimg2pixelcluster(self,labelimg):\n if not hasattr(self,'translator'):\n self.translator = {self.labeltools.labels.index(\"electron\"):larcv.kROIEminus,\n self.labeltools.labels.index(\"muon\"):larcv.kROIMuminus,\n self.labeltools.labels.index(\"pion\"):larcv.kROIPiminus,\n self.labeltools.labels.index(\"proton\"):larcv.kROIProton,\n self.labeltools.labels.index(\"gamma\"):larcv.kROIGamma}\n \n clusters = {}\n for iplane in xrange(0,3):\n clusters[iplane] = larcv.Pixel2DCluster()\n if iplane >= len(labelimg):\n continue\n labelmat = labelimg[iplane]\n idx_bg = self.labeltools.labels.index(\"background\")\n idxlabels = (labelmat != idx_bg).nonzero()\n ivertmax = self.images.img_v[iplane].shape[1]-1\n for ivert in xrange(0,len(idxlabels[0])):\n vert = larcv.Pixel2D( idxlabels[0][ivert], ivertmax - idxlabels[1][ivert] )\n label = labelmat[ idxlabels[0][ivert], idxlabels[1][ivert] ]\n print \"storing (\",vert.X(),\",\",vert.Y(),\") label=\", self.labeltools.labels[label]\n vert.Intensity( float(self.translator[ label ] ) )\n clusters[iplane] += vert\n\n return ( clusters[0], clusters[1], clusters[2] )","def Luv_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n # Without Light, there is no color. Short-circuit this and avoid some\r\n # zero division errors in the var_a_frac calculation.\r\n if cobj.luv_l <= 0.0:\r\n xyz_x = 0.0\r\n xyz_y = 0.0\r\n xyz_z = 0.0\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\r\n\r\n # Various variables used throughout the conversion.\r\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\r\n u_sub_0 = (4.0 * illum[\"X\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n v_sub_0 = (9.0 * illum[\"Y\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\r\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\r\n\r\n # Y-coordinate calculations.\r\n if cobj.luv_l > cie_k_times_e:\r\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\r\n else:\r\n xyz_y = cobj.luv_l / color_constants.CIE_K\r\n\r\n # X-coordinate calculation.\r\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\r\n # Z-coordinate calculation.\r\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\r\n\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)","def img_to_rgb(img):\r\n if len(img.shape) < 3 or img.shape[2] == 1:\r\n return np.repeat(img, 3).reshape(img.shape[0], img.shape[1], 3)\r\n else:\r\n return img","def green_channel(input_image):\n return input_image[:, :, 1]","def XYZ_to_RGB(XYZ,\n illuminant_XYZ,\n illuminant_RGB,\n XYZ_to_RGB_matrix,\n chromatic_adaptation_transform='CAT02',\n encoding_cctf=None):\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n transform=chromatic_adaptation_transform)\n\n XYZ_a = dot_vector(M, XYZ)\n\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\n\n if encoding_cctf is not None:\n RGB = encoding_cctf(RGB)\n\n return RGB","def grayscale(img):\n for pixel in img:\n x, y, col = pixel\n r, g, b = col\n \n r = (r + g + b)/3\n r = g = b\n \n new_color = create_color(r, g, b)\n set_color(img, x, y, new_color)","def _hsv_to_rgb(img):\n h, s, v = img.unbind(axis=-3)\n f = h * 6.0\n i = paddle.floor(f)\n f = f - i\n i = i.astype(paddle.int32) % 6\n\n p = paddle.clip(v * (1.0 - s), 0.0, 1.0)\n q = paddle.clip(v * (1.0 - s * f), 0.0, 1.0)\n t = paddle.clip(v * (1.0 - s * (1.0 - f)), 0.0, 1.0)\n\n mask = paddle.equal(\n i.unsqueeze(axis=-3),\n paddle.arange(6, dtype=i.dtype).reshape((-1, 1, 1)),\n ).astype(img.dtype)\n matrix = paddle.stack(\n [\n paddle.stack([v, q, p, p, t, v], axis=-3),\n paddle.stack([t, v, v, q, p, p], axis=-3),\n paddle.stack([p, p, t, v, v, q], axis=-3),\n ],\n axis=-4,\n )\n return paddle.einsum(\"...ijk, ...xijk -> ...xjk\", mask, matrix)","def preprocess(image):\n return (image / 255) * 2 - 1","def color_transfer(source, target):\n\t# convert the images from the RGB to L*ab* color space, being\n\t# sure to utilizing the floating point data type (note: OpenCV\n\t# expects floats to be 32-bit, so use that instead of 64-bit)\n\tsource = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype(\"float32\")\n\ttarget = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype(\"float32\")\n\n\t# compute color statistics for the source and target images\n\t(lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = image_stats(source)\n\t(lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = image_stats(target)\n\n\t# subtract the means from the target image\n\t(l, a, b) = cv2.split(target)\n\tl -= lMeanTar\n\ta -= aMeanTar\n\tb -= bMeanTar\n\n\t# scale by the standard deviations\n\tl = (lStdTar / lStdSrc) * l\n\ta = (aStdTar / aStdSrc) * a\n\tb = (bStdTar / bStdSrc) * b\n\n\t# add in the source mean\n\tl += lMeanSrc\n\ta += aMeanSrc\n\tb += bMeanSrc\n\n\t# clip the pixel intensities to [0, 255] if they fall outside\n\t# this range\n\tl = np.clip(l, 0, 255)\n\ta = np.clip(a, 0, 255)\n\tb = np.clip(b, 0, 255)\n\n\t# merge the channels together and convert back to the RGB color\n\t# space, being sure to utilize the 8-bit unsigned integer data\n\t# type\n\ttransfer = cv2.merge([l, a, b])\n\ttransfer = cv2.cvtColor(transfer.astype(\"uint8\"), cv2.COLOR_LAB2BGR)\n\t\n\t# return the color transferred image\n\treturn transfer","def split_image_into_channels(image):\n red_channel = image[:, :, 0]\n green_channel = image[:, :, 1]\n blue_channel = image[:, :, 2]\n return red_channel, green_channel, blue_channel","def scale_to_01(color: C3I) -> C3F:\n r, g, b = color\n return r / 255, g / 255, b / 255","def test_srgb_conversion_to_xyz_d50(self):\r\n\r\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))","def greyscale(img):\n grey_img = np.zeros([210, 160])\n for i in range(3):\n grey_img =np.sum([grey_img, img[:, :, i]], 0)\n grey_img /= 3\n grey_img = grey_img.astype(np.uint8)\n return grey_img","def show_rgb_img(img):\n return plt.imshow(cv2.cvtColor(img, cv2.CV_32S))","def test_RGB_mode():\n\n model = Instafilter(\"Lo-Fi\")\n\n f_image = __local__ / \"Normal.jpg\"\n\n img1 = model(f_image)\n img2 = model(f_image, is_RGB=True)\n\n diff = (img1 - img2).sum()\n\n assert abs(diff) > 0","def convertgrey(image: Image):\n matrice = np.asarray(image)\n matrice = np.mean(matrice, axis=2, dtype=np.uint8)\n return Image.fromarray(matrice)","def _rgb_to_hsv(img):\n maxc = img.max(axis=-3)\n minc = img.min(axis=-3)\n\n is_equal = paddle.equal(maxc, minc)\n one_divisor = paddle.ones_like(maxc)\n c_delta = maxc - minc\n # s is 0 when maxc == minc, set the divisor to 1 to avoid zero divide.\n s = c_delta / paddle.where(is_equal, one_divisor, maxc)\n\n r, g, b = img.unbind(axis=-3)\n c_delta_divisor = paddle.where(is_equal, one_divisor, c_delta)\n # when maxc == minc, there is r == g == b, set the divisor to 1 to avoid zero divide.\n rc = (maxc - r) / c_delta_divisor\n gc = (maxc - g) / c_delta_divisor\n bc = (maxc - b) / c_delta_divisor\n\n hr = (maxc == r).astype(maxc.dtype) * (bc - gc)\n hg = ((maxc == g) & (maxc != r)).astype(maxc.dtype) * (rc - bc + 2.0)\n hb = ((maxc != r) & (maxc != g)).astype(maxc.dtype) * (gc - rc + 4.0)\n h = (hr + hg + hb) / 6.0 + 1.0\n h = h - h.trunc()\n return paddle.stack([h, s, maxc], axis=-3)","def compute_luminosity(red, green, blue):\r\n return (0.299 * red) + (0.587 * green) + (0.114 * blue)","def yiq2rgb(im_yiq):\n return multiply_by_left_matrix(np.linalg.inv(YIQ_MATRIX), im_yiq)","def green_channel(img):\n\n green = np.zeros(img.shape,dtype=float)\n\n green[:,:,1] = np.copy(img[:,:,1])\n\n return green","def transform_image(self):\n im = cv2.imread(\"result.png\", 0)\n im2 = cv2.resize(im, (28, 28))\n im = im2.reshape(28, 28, -1)\n im = im.reshape(1, 1, 28, 28)\n im = cv2.bitwise_not(im)\n im = im.reshape(28,28)\n \n with out:\n clear_output()\n \n # resize\n img = np.array(im)\n img = img.reshape(28*28,)\n \n #img = img/255.0\n \n return img","def create_colorful_test_image(self):\n ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)\n ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)\n ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)\n imr = np.concatenate((ch255, ch128, ch128), axis=2)\n img = np.concatenate((ch255, ch255, ch0), axis=2)\n imb = np.concatenate((ch255, ch0, ch255), axis=2)\n imw = np.concatenate((ch128, ch128, ch128), axis=2)\n imu = np.concatenate((imr, img), axis=1)\n imd = np.concatenate((imb, imw), axis=1)\n image = np.concatenate((imu, imd), axis=0)\n return image","def _process_img_rgb(self, sensor_data):\n img = np.array(sensor_data.raw_data).reshape((self.img_y, self.img_x, 4))\n img = img[:, :, :3] # sensor is actualy rgba, we dont need alpha values\n self.rgb = img # need to scale rgb values to be {0,1}","def read_image(img):\n out = Image.open(img)\n return Technicolor(out)","def _preprocess(self, image):\n\n # Scale from [0, 255] to [0, 1] and BGR to RGB \n return (image / 255.0)[:, :, ::-1]","def yiq2rgb(imYIQ):\n trans = np.array([[1, 0.956, 0.62], [1, -0.272, -0.647], [1, -1.108, 1.705]])\n return np.dot(imYIQ, trans)","def process_image(img):\n img[0] = img[0] * 0.229\n img[1] = img[1] * 0.224\n img[2] = img[2] * 0.225\n img[0] += 0.485\n img[1] += 0.456\n img[2] += 0.406\n\n return img.cpu().numpy().transpose((1, 2, 0))","def linearTosRGB3(c):\n return [linearTosRGB(c[0]), linearTosRGB(c[1]), linearTosRGB(c[2])]","def imageprepare():\r\n file_name = '9-test.png'\r\n im = Image.open(file_name).convert('L')\r\n\r\n im.save(\"9-t.png\")\r\n plt.imshow(im)\r\n plt.show()\r\n tv = list(im.getdata())\r\n\r\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\r\n return tva","def greyscale(im):\n\t\n # YOUR CODE HERE\n rgb_weights= np.array([0.212, 0.7152, 0.0722])\n x,y,_ = np.shape(im)\n img = np.zeros([x,y])\n\t\n for i in range(x):\n for j in range(y):\n img[i,j] = im[i,j].dot(rgb_weights)\n\t\t\t\n return img","def testImageProcessing():\n Im_pix = getRGB( 'in.png' ) # read in the in.png image\n print \"The first two pixels of the first row are\",\n print Im_pix[0][0:2]\n # remember that Im_pix is a list (the image)\n # of lists (each row) of lists (each pixel is [R,G,B])\n New_pix = [ [ [255 - num for num in p] for p in row ] for row in Im_pix ]\n # now, save to the file 'out.png'\n saveRGB( New_pix, 'out.png' )","def grey_to_rgb(im):\n assert im.n_channels in [1, 3]\n\n if im.n_channels == 3:\n return im\n\n im.pixels = np.vstack([im.pixels] * 3)\n return im","def linearTosRGB(c):\n if c <= 0.0031308:\n return 12.92 * c\n return math.pow(c, 1.0 / 2.4) * 1.055 - 0.055","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))","def RGB_to_XYZ(RGB,\n illuminant_RGB,\n illuminant_XYZ,\n RGB_to_XYZ_matrix,\n chromatic_adaptation_transform='CAT02',\n decoding_cctf=None):\n\n if decoding_cctf is not None:\n RGB = decoding_cctf(RGB)\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n transform=chromatic_adaptation_transform)\n\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\n\n XYZ_a = dot_vector(M, XYZ)\n\n return XYZ_a","def read_colormap(name):\n\n path = get_demo_file(name + '.c3g')\n\n out = []\n with open(path, 'r') as file:\n for line in file:\n if 'rgb(' not in line:\n continue\n line = line.split('(')[-1].split(')')[0]\n out.append([float(n) for n in line.split(',')])\n\n return np.asarray(out).astype(float) / 256."],"string":"[\n \"def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n xyz = lab_to_xyz(image)\\n rgb_image = xyz_to_rgb(xyz)\\n return rgb_image\",\n \"def lab_to_rgb(img):\\n new_img = np.zeros((256, 256, 3))\\n for i in range(len(img)):\\n for j in range(len(img[i])):\\n pix = img[i, j]\\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\\n new_img = color.lab2rgb(new_img) * 255\\n new_img = new_img.astype('uint8')\\n return new_img\",\n \"def lab_to_rgb(img_l, img_ab):\\n lab = np.empty([*img_l.shape[0:2], 3])\\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\\n lab[:, :, 1:] = img_ab * 127\\n return color.lab2rgb(lab)\",\n \"def rgb_to_lab(img, l=False, ab=False):\\n img = img / 255\\n l_chan = color.rgb2lab(img)[:, :, 0]\\n l_chan = l_chan / 50 - 1\\n l_chan = l_chan[..., np.newaxis]\\n\\n ab_chan = color.rgb2lab(img)[:, :, 1:]\\n ab_chan = (ab_chan + 128) / 255 * 2 - 1\\n if l:\\n return l_chan\\n else:\\n return ab_chan\",\n \"def rgb2lab(r, g, b):\\n r, g, b = r / 255.0, g / 255.0, b / 255.0\\n\\n # http://www.brucelindbloom.com/index.html?Math.html\\n # Inverse sRGB Companding\\n r = r / 12.92 if r <= 0.04045 else ((r + 0.055) / 1.055) ** 2.4\\n g = g / 12.92 if g <= 0.04045 else ((g + 0.055) / 1.055) ** 2.4\\n b = b / 12.92 if b <= 0.04045 else ((b + 0.055) / 1.055) ** 2.4\\n\\n # http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html\\n # sRGB, D65\\n x = r * 0.4124564 + g * 0.3575761 + b * 0.1804375\\n y = r * 0.2126729 + g * 0.7151522 + b * 0.0721750\\n z = r * 0.0193339 + g * 0.1191920 + b * 0.9503041\\n\\n # http://www.brucelindbloom.com/index.html?Eqn_XYZ_to_Lab.html\\n kappa, epsilon = 903.3, 0.008856\\n\\n # http://brucelindbloom.com/index.html?Eqn_ChromAdapt.html\\n # White point for D65\\n xr, yr, zr = x / 0.95047, y / 1.00000, z / 1.08883\\n\\n fx = xr ** (1 / 3.0) if xr > epsilon else (kappa * xr + 16) / 116.0\\n fy = yr ** (1 / 3.0) if yr > epsilon else (kappa * yr + 16) / 116.0\\n fz = zr ** (1 / 3.0) if zr > epsilon else (kappa * zr + 16) / 116.0\\n\\n l = 166.0 * fy - 16.0\\n a = 500.0 * (fx - fy)\\n b = 200.0 * (fy - fz)\\n\\n return l, a, b\",\n \"def XYZ_to_Lab(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n temp_x = cobj.xyz_x / illum[\\\"X\\\"]\\r\\n temp_y = cobj.xyz_y / illum[\\\"Y\\\"]\\r\\n temp_z = cobj.xyz_z / illum[\\\"Z\\\"]\\r\\n \\r\\n if temp_x > color_constants.CIE_E:\\r\\n temp_x = math.pow(temp_x, (1.0 / 3.0))\\r\\n else:\\r\\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \\r\\n\\r\\n if temp_y > color_constants.CIE_E:\\r\\n temp_y = math.pow(temp_y, (1.0 / 3.0))\\r\\n else:\\r\\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\\r\\n \\r\\n if temp_z > color_constants.CIE_E:\\r\\n temp_z = math.pow(temp_z, (1.0 / 3.0))\\r\\n else:\\r\\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\\r\\n \\r\\n lab_l = (116.0 * temp_y) - 16.0\\r\\n lab_a = 500.0 * (temp_x - temp_y)\\r\\n lab_b = 200.0 * (temp_y - temp_z)\\r\\n return LabColor(\\r\\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def rgb2Lab(rgbvalue):\\r\\n RGB2Lab_Matrix = np.array([[0.412453, 0.357580, 0.180423],\\r\\n [0.212671, 0.715160, 0.072169],\\r\\n [0.019334, 0.119193, 0.950227]])\\r\\n R = rgbvalue[0]\\r\\n G = rgbvalue[1]\\r\\n B = rgbvalue[2]\\r\\n gammaR = gamma(R / 255.0)\\r\\n gammaG = gamma(G / 255.0)\\r\\n gammaB = gamma(B / 255.0)\\r\\n RGBvalue = np.array([gammaR, gammaG, gammaB])\\r\\n RGBvalue = RGBvalue.reshape(3, 1)\\r\\n XYZvalue = np.dot(RGB2Lab_Matrix, RGBvalue)\\r\\n assert XYZvalue.shape == (3, 1)\\r\\n correction = np.array([[1.0 / 0.950456, 1.0, 1.0 / 1.088754]]).T\\r\\n assert correction.shape == (3, 1)\\r\\n XYZ = XYZvalue * correction\\r\\n assert XYZ.shape == (3, 1)\\r\\n YYn = ft(XYZ[1])\\r\\n XXn = ft(XYZ[0])\\r\\n ZZn = ft(XYZ[2])\\r\\n L = 116 * YYn - 16\\r\\n a = 500 * (XXn - YYn)\\r\\n b = 200 * (YYn - ZZn)\\r\\n return [int(L), int(a), int(b)]\",\n \"def sRGBToLab(rgb):\\n return xyzToLab(xyzFromsRGB(rgb), [0.9504559, 1, 1.089058])\",\n \"def Lab_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n xyz_y = (cobj.lab_l + 16.0) / 116.0\\r\\n xyz_x = cobj.lab_a / 500.0 + xyz_y\\r\\n xyz_z = xyz_y - cobj.lab_b / 200.0\\r\\n \\r\\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\\r\\n xyz_y = math.pow(xyz_y, 3)\\r\\n else:\\r\\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\\r\\n\\r\\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\\r\\n xyz_x = math.pow(xyz_x, 3)\\r\\n else:\\r\\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\\r\\n \\r\\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\\r\\n xyz_z = math.pow(xyz_z, 3)\\r\\n else:\\r\\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\\r\\n \\r\\n xyz_x = (illum[\\\"X\\\"] * xyz_x)\\r\\n xyz_y = (illum[\\\"Y\\\"] * xyz_y)\\r\\n xyz_z = (illum[\\\"Z\\\"] * xyz_z)\\r\\n \\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n L: torch.Tensor = image[..., 0, :, :]\\n u: torch.Tensor = image[..., 1, :, :]\\n v: torch.Tensor = image[..., 2, :, :]\\n\\n # Convert from Luv to XYZ\\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\\n\\n # Compute white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n a: torch.Tensor = u_w + u / (13 * L + eps)\\n d: torch.Tensor = v_w + v / (13 * L + eps)\\n c: torch.Tensor = 3 * y * (5 * d - 3)\\n\\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\\n\\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\\n\\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\\n\\n # Convert from RGB Linear to sRGB\\n rgb_im = linear_rgb_to_rgb(rgbs_im)\\n\\n return rgb_im\",\n \"def rgb2alpha(img):\\n\\t### First of all we need the size of our picture to make the transforms\\n\\n\\tx = len(img) ; y = len(img[0])\\n\\talpha = np.full((x, y, 3), 0, dtype = float)\\t\\t## This will be the transformed image\\n\\n\\t### Now we gotta access each pixel of the picture\\n\\n\\tfor i, vi in enumerate(img):\\n\\t\\tfor j, px in enumerate(vi):\\n\\t\\t\\t### There we are\\n\\n\\t\\t\\t# Step 1 : LMS transform, for that we use r_l\\n\\n\\t\\t\\talpha[i][j] = np.matmul(r_l, px)\\n\\n\\t\\t\\t# Step 2 : log em all (decimal log)\\n\\n\\t\\t\\talpha[i][j][0] = log(alpha[i][j][0])\\n\\t\\t\\talpha[i][j][1] = log(alpha[i][j][1])\\n\\t\\t\\talpha[i][j][2] = log(alpha[i][j][2])\\n\\n\\t\\t\\t# Step 3 : l alpha beta transform, by using l_a\\n\\n\\t\\t\\talpha[i][j] = np.matmul(l_a, alpha[i][j])\\n\\n\\treturn alpha\",\n \"def sRGBFromLab(lab):\\n return xyzTosRGB(labToXYZ(lab, [0.9504559, 1, 1.089058]))\",\n \"def example_rgb_to_xyz():\\r\\n\\r\\n print(\\\"=== RGB Example: RGB->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n rgb = sRGBColor(120, 130, 140)\\r\\n # Show a string representation.\\r\\n print(rgb)\\r\\n # Convert RGB to XYZ using a D50 illuminant.\\r\\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n l, a, b = tf.unstack(image, axis=-1)\\n\\n var_y = (l + 16) / 116\\n var_x = a / 500 + var_y\\n var_z = var_y - b / 200\\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\\n (var_x - 16 / 116) / 7.787)\\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\\n (var_y - 16 / 116) / 7.787)\\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\\n (var_z - 16 / 116) / 7.787)\\n\\n refx = 95.047\\n refy = 100.00\\n ref_z = 108.883\\n\\n x = var_x * refx\\n y = var_y * refy\\n z = var_z * ref_z\\n xyz_image = tf.stack([x, y, z], axis=-1)\\n return xyz_image\",\n \"def sRGBToLabD50(rgb):\\n return xyzToLab(xyzFromsRGBD50(rgb), [0.9642957, 1, 0.8251046])\",\n \"def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n\\n refx = 95.047\\n refy = 100.00\\n refz = 108.883\\n\\n var_x = x / refx\\n var_y = y / refy\\n var_z = z / refz\\n\\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\\n (7.787 * var_x) + (16 / 116))\\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\\n (7.787 * var_y) + (16 / 116))\\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\\n (7.787 * var_z) + (16 / 116))\\n\\n l = (116 * var_y) - 16\\n a = 500 * (var_x - var_y)\\n b = 200 * (var_y - var_z)\\n lab_image = tf.stack([l, a, b], axis=-1)\\n return lab_image\",\n \"def example_lab_to_rgb():\\r\\n\\r\\n print(\\\"=== RGB Example: Lab->RGB ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.217)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n rgb = convert_color(lab, sRGBColor)\\r\\n print(rgb)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def sRGBFromLabD50(lab):\\n return xyzTosRGBD50(labToXYZ(lab, [0.9642957, 1, 0.8251046]))\",\n \"def _convert_to_yolo_img(self, img):\\n\\n img = img / 255.0\\n h, w, c = img.shape\\n img = img.transpose(2, 0, 1)\\n outimg = make_image(w, h, c)\\n img = img.reshape((w*h*c))\\n data = c_array(c_float, img)\\n outimg.data = data\\n rgbgr_image(outimg)\\n return outimg\",\n \"def l_to_rgb(img_l):\\n lab = np.squeeze(255 * (img_l + 1) / 2)\\n return color.gray2rgb(lab) / 255\",\n \"def get_rgb(self, img, r, g, b):\\r\\n\\r\\n # Get specific bands of hyperspectral image\\r\\n red_channel = img[:, :, r]\\r\\n green_channel = img[:, :, g]\\r\\n blue_channel = img[:, :, b]\\r\\n\\r\\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\\r\\n img = img.astype('float32')\\r\\n return img\",\n \"def example_lab_to_xyz():\\r\\n\\r\\n print(\\\"=== Simple Example: Lab->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.22)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n xyz = convert_color(lab, XYZColor)\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n r, g, b = tf.unstack(image, axis=-1)\\n var_r = r / 255\\n var_g = g / 255\\n var_b = b / 255\\n\\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\\n var_r / 12.92)\\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\\n var_g / 12.92)\\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\\n var_b / 12.92)\\n var_r = var_r * 100\\n var_g = var_g * 100\\n var_b = var_b * 100\\n\\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\\n\\n image_xyz = tf.stack([x, y, z], axis=-1)\\n return image_xyz\",\n \"def matplotlib_image(image):\\n if image.ndim == 2:\\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\\n else:\\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n return rgb\",\n \"def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n var_x = x / 100\\n var_y = y / 100\\n var_z = z / 100\\n\\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\\n\\n var_r = tf.where(var_r > 0.0031308,\\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\\n 12.92 * var_r)\\n var_g = tf.where(var_g > 0.0031308,\\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\\n 12.92 * var_g)\\n var_b = tf.where(var_b > 0.0031308,\\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\\n 12.92 * var_b)\\n r = var_r * 255\\n g = var_g * 255\\n b = var_b * 255\\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\\n return rgb_image\",\n \"def convert_color(image, color_space):\\n out_image = None\\n if color_space != 'RGB':\\n if color_space == 'HSV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\\n elif color_space == 'LUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\\n elif color_space == 'HLS':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\\n elif color_space == 'YUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\\n elif color_space == 'YCrCb':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\\n else:\\n out_image = np.copy(image)\\n return out_image\",\n \"def sRGBLuminance(x):\\n lin=linearFromsRGB3(x)\\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722\",\n \"def rgb_to_luv(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n # Convert from sRGB to Linear RGB\\n lin_rgb = rgb_to_linear_rgb(image)\\n\\n xyz_im: torch.Tensor = rgb_to_xyz(lin_rgb)\\n\\n x: torch.Tensor = xyz_im[..., 0, :, :]\\n y: torch.Tensor = xyz_im[..., 1, :, :]\\n z: torch.Tensor = xyz_im[..., 2, :, :]\\n\\n threshold = 0.008856\\n L: torch.Tensor = torch.where(y > threshold, 116.0 * torch.pow(y.clamp(min=threshold), 1.0 / 3.0) - 16.0, 903.3 * y)\\n\\n # Compute reference white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n u_p: torch.Tensor = (4 * x) / (x + 15 * y + 3 * z + eps)\\n v_p: torch.Tensor = (9 * y) / (x + 15 * y + 3 * z + eps)\\n\\n u: torch.Tensor = 13 * L * (u_p - u_w)\\n v: torch.Tensor = 13 * L * (v_p - v_w)\\n\\n out = torch.stack([L, u, v], dim=-3)\\n\\n return out\",\n \"def rgb_processing(rgb_img, center, scale, rot=0):\\n rgb_img = crop(rgb_img, center, scale, \\n [constants.IMG_RES, constants.IMG_RES], rot=rot)\\n # (3,224,224),float,[0,1]\\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\\n return rgb_img\",\n \"def colorize_images(self, img):\\n self.load_model()\\n self.mdn.eval()\\n self.vae.eval()\\n n, _, _ = img.shape\\n img = img.astype(np.float32) / 255\\n img = torch.tensor(img, dtype=torch.float, device=self.device).unsqueeze(1)\\n with torch.no_grad():\\n z = self.mdn(img)\\n ab_out = self.vae.decode(z)\\n lab_out = torch.cat((img, ab_out), dim=1)\\n lab_out = self.unnormalize(lab_out).cpu().numpy()\\n lab_out = np.transpose(lab_out, (0, 2, 3, 1)).astype(np.uint8)\\n for i in range(n):\\n color_out = cv2.cvtColor(lab_out[i], cv2.COLOR_LAB2BGR)\\n color_out = cv2.resize(color_out, (96, 96), interpolation=cv2.INTER_AREA)\\n cv2.imwrite(\\\"../datasets/stl10/divcolor/{}.png\\\".format(str(i)), color_out)\\n return\",\n \"def red_channel(img):\\n\\n red = np.zeros(img.shape,dtype=float)\\n\\n red[:,:,2] = np.copy(img[:,:,2])\\n\\n return red\",\n \"def read_img(filename) -> Tuple[np.ndarray, np.ndarray]:\\n img = cv2.imread(filename, 3)\\n labimg = cv2.cvtColor(cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE)), cv2.COLOR_BGR2Lab)\\n return np.reshape(labimg[:, :, 0], (config.IMAGE_SIZE, config.IMAGE_SIZE, 1)), labimg[:, :, 1:]\",\n \"def linear_rgb2lab(rgb, illuminant=\\\"D65\\\", observer=\\\"2\\\"):\\n return xyz2lab(linear_rgb2xyz(rgb), illuminant, observer)\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def luminance(rgb):\\n \\n (r, g, b) = rgb\\n return (r + g + b) // 3\",\n \"def chromatic_transform(im, label=None, d_h=None, d_s=None, d_l=None):\\n # Set random hue, luminosity and saturation which ranges from -0.1 to 0.1\\n if d_h is None:\\n d_h = (np.random.rand(1) - 0.5) * 0.2 * 180\\n if d_l is None:\\n d_l = (np.random.rand(1) - 0.5) * 0.2 * 256\\n if d_s is None:\\n d_s = (np.random.rand(1) - 0.5) * 0.2 * 256\\n # Convert the BGR to HLS\\n hls = cv2.cvtColor(im, cv2.COLOR_BGR2HLS)\\n h, l, s = cv2.split(hls)\\n # Add the values to the image H, L, S\\n new_h = (h + d_h) % 180\\n new_l = np.clip(l + d_l, 0, 255)\\n new_s = np.clip(s + d_s, 0, 255)\\n # Convert the HLS to BGR\\n new_hls = cv2.merge((new_h, new_l, new_s)).astype('uint8')\\n new_im = cv2.cvtColor(new_hls, cv2.COLOR_HLS2BGR)\\n\\n if label is not None:\\n I = np.where(label > 0)\\n new_im[I[0], I[1], :] = im[I[0], I[1], :]\\n return new_im\",\n \"def applyJET(img):\\n\\treturn applyColorMap(img, \\\"jet\\\")\",\n \"def generate_lut(self):\\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\\n for i in Numeric.arange(256):\\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\\n return r,g,b\",\n \"def grey_to_rgb_imitation(img):\\n return np.repeat(img[...,np.newaxis], 3, -1)\",\n \"def read_img(img): #X\\n im = plt.imread(img)\\n im = im[:, :, :3]\\n if im.max()>200:\\n im = im/255.\\n return rgb_to_hsv(im)-0.5\",\n \"def prepare_images(images):\\n images = color.rgb2lab(images)\\n\\n l = images[:,:,:,:1]/100.\\n ab = images[:,:,:,1:]/200. + 0.5\\n\\n return l, ab\",\n \"def luminance(self):\\n \\n return (self.r + self.g + self.b) // 3\",\n \"def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\\n y: torch.Tensor = image[..., 0, :, :]\\n cb: torch.Tensor = image[..., 1, :, :]\\n cr: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n cb_shifted: torch.Tensor = cb - delta\\n cr_shifted: torch.Tensor = cr - delta\\n\\n r: torch.Tensor = y + 1.403 * cr_shifted\\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\\n b: torch.Tensor = y + 1.773 * cb_shifted\\n return torch.stack([r, g, b], -3)\",\n \"def getRGBFromXYAndBrightness(self, x, y, bri=1):\\n # The xy to color conversion is almost the same, but in reverse order.\\n # Check if the xy value is within the color gamut of the lamp.\\n # If not continue with step 2, otherwise step 3.\\n # We do this to calculate the most accurate color the given light can actually do.\\n xyPoint = XYPoint(x, y)\\n\\n if not self.checkPointInLampsReach(xyPoint):\\n # Calculate the closest point on the color gamut triangle\\n # and use that as xy value See step 6 of color to xy.\\n xyPoint = self.getClosestPointToPoint(xyPoint)\\n\\n # Calculate XYZ values Convert using the following formulas:\\n Y = bri\\n X = (Y / xyPoint.y) * xyPoint.x\\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\\n\\n # Convert to RGB using Wide RGB D65 conversion\\n r = X * 1.612 - Y * 0.203 - Z * 0.302\\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\\n b = X * 0.026 - Y * 0.072 + Z * 0.962\\n\\n # Apply reverse gamma correction\\n r, g, b = map(\\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\\n [r, g, b]\\n )\\n\\n # Bring all negative components to zero\\n r, g, b = map(lambda x: max(0, x), [r, g, b])\\n\\n # If one component is greater than 1, weight components by that value.\\n max_component = max(r, g, b)\\n if max_component > 1:\\n r, g, b = map(lambda x: x / max_component, [r, g, b])\\n\\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\\n\\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\\n return (r, g, b)\",\n \"def yuv2lab(tens: Tensor) -> Tensor:\\n if not _is_yuv_image(tens):\\n raise ValueError(\\n f\\\"Tensor of shape 3 expected. Found shape {len(tens.shape)}. \\\"\\n \\\"This function converts an YUV Tensor to its LAB counterpart\\\"\\n )\\n\\n img = yuv2bgr(tens)\\n img = bgr2lab(tens)\\n return to_tensor(img, cspace=\\\"lab\\\")\",\n \"def convertColorSpace(\\n self,\\n img, # Image in some color space\\n srcColorSpace = 'BGR', # Source color space\\n tgtColorSpace = 'RGB', # Traget color space\\n ):\\n\\n if srcColorSpace == tgtColorSpace:\\n return img\\n\\n if srcColorSpace == 'BGR':\\n img_bgr = img\\n elif srcColorSpace == 'RGB':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\\n elif srcColorSpace == 'HSV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\\n elif srcColorSpace == 'HLS':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\\n elif srcColorSpace == 'LUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\\n elif srcColorSpace == 'YUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\\n elif srcColorSpace == 'YCrCb':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(srcColorSpace))\\n\\n if tgtColorSpace == 'BGR':\\n img_tgt = img_bgr\\n elif tgtColorSpace == 'RGB':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\\n elif tgtColorSpace == 'HSV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\\n elif tgtColorSpace == 'HLS':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\\n elif tgtColorSpace == 'LUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\\n elif tgtColorSpace == 'YUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\\n elif tgtColorSpace == 'YCrCb':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(tgtColorSpace))\\n\\n return img_tgt\",\n \"def rgb2hsl_img(rgb):\\r\\n \\r\\n def core(_rgb, _hsl):\\r\\n\\r\\n irgb = _rgb.astype(np.uint16)\\r\\n ir, ig, ib = irgb[:, :, 0], irgb[:, :, 1], irgb[:, :, 2]\\r\\n h, s, l = _hsl[:, :, 0], _hsl[:, :, 1], _hsl[:, :, 2]\\r\\n\\r\\n imin, imax = irgb.min(2), irgb.max(2)\\r\\n iadd, isub = imax + imin, imax - imin\\r\\n\\r\\n ltop = (iadd != 510) * (iadd > 255)\\r\\n lbot = (iadd != 0) * (ltop == False)\\r\\n\\r\\n l[:] = iadd.astype(np.float) / 510\\r\\n\\r\\n fsub = isub.astype(np.float)\\r\\n s[ltop] = fsub[ltop] / (510 - iadd[ltop])\\r\\n s[lbot] = fsub[lbot] / iadd[lbot]\\r\\n\\r\\n not_same = imax != imin\\r\\n is_b_max = not_same * (imax == ib)\\r\\n not_same_not_b_max = not_same * (is_b_max == False)\\r\\n is_g_max = not_same_not_b_max * (imax == ig)\\r\\n is_r_max = not_same_not_b_max * (is_g_max == False) * (imax == ir)\\r\\n\\r\\n h[is_r_max] = ((0. + ig[is_r_max] - ib[is_r_max]) / isub[is_r_max])\\r\\n h[is_g_max] = ((0. + ib[is_g_max] - ir[is_g_max]) / isub[is_g_max]) + 2\\r\\n h[is_b_max] = ((0. + ir[is_b_max] - ig[is_b_max]) / isub[is_b_max]) + 4\\r\\n h[h < 0] += 6\\r\\n h[:] /= 6\\r\\n\\r\\n hsl = np.zeros(rgb.shape, dtype=np.float)\\r\\n cpus = multiprocessing.cpu_count()\\r\\n length = int(math.ceil(float(hsl.shape[0]) / cpus))\\r\\n line = 0\\r\\n threads = []\\r\\n while line < hsl.shape[0]:\\r\\n line_next = line + length\\r\\n thread = threading.Thread(target=core, args=(rgb[line:line_next], hsl[line:line_next]))\\r\\n thread.start()\\r\\n threads.append(thread)\\r\\n line = line_next\\r\\n\\r\\n for thread in threads:\\r\\n thread.join()\\r\\n\\r\\n return hsl\",\n \"def __enhance_image(self, img):\\n\\n blue = self.g.clahe.apply(img[:,:,0])\\n green = self.g.clahe.apply(img[:,:,1])\\n red = self.g.clahe.apply(img[:,:,2])\\n img[:,:,0] = blue\\n img[:,:,1] = green\\n img[:,:,2] = red\\n return img\",\n \"def make_grayscale(img: Image.Image) -> Image.Image:\\n orig_mode = img.mode\\n\\n if orig_mode in [\\\"RGB\\\", \\\"CMYK\\\", \\\"YCbCr\\\", \\\"LAB\\\", \\\"HSV\\\"]:\\n return img.convert(\\\"L\\\")\\n elif orig_mode == \\\"RGBA\\\":\\n return img.convert(\\\"LA\\\").convert(\\\"RGBA\\\")\\n elif orig_mode == \\\"P\\\":\\n # Using ITU-R 601-2 luma transform: L = R * 299/1000 + G * 587/1000 + B * 114/1000\\n pal = img.getpalette()\\n for i in range(len(pal) // 3):\\n # Using ITU-R 601-2 luma transform\\n gray = (pal[3 * i] * 299 + pal[3 * i + 1] * 587 + pal[3 * i + 2] * 114)\\n gray = gray // 1000\\n pal[3 * i: 3 * i + 3] = [gray, gray, gray]\\n img.putpalette(pal)\\n return img\\n else:\\n return img\",\n \"def convert_color(image, color_space='RGB'):\\n color_space = color_space.lower()\\n if color_space != 'rgb':\\n if color_space == 'hsv':\\n color_transformation = cv2.COLOR_BGR2HSV\\n elif color_space == 'luv':\\n color_transformation = cv2.COLOR_BGR2LUV\\n elif color_space == 'hls':\\n color_transformation = cv2.COLOR_BGR2HLS\\n elif color_space == 'yuv':\\n color_transformation = cv2.COLOR_BGR2YUV\\n elif color_space == 'ycrcb':\\n color_transformation = cv2.COLOR_BGR2YCrCb\\n else:\\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\\n 'HLS, YUV, YCrCb' % color_space)\\n\\n return cv2.cvtColor(image, color_transformation)\\n else:\\n return image\",\n \"def load_color_image_features(img_path):\\n ac = scipy.misc.imread(img_path, mode='RGB')\\n ac = ac / (255.0 / 2) - 1.0\\n return np.array(ac)\",\n \"def rgb_to_hunterlab(rgb_color, illuminant):\\n\\n return xyz_to_hunterlab(rgb_to_xyz(rgb_color), illuminant)\",\n \"def yuv2rgb(im):\\n ## conflicting definitions exist depending on whether you use the full range\\n ## of YCbCr or clamp out to the valid range. see here\\n ## http://www.equasys.de/colorconversion.html\\n ## http://www.fourcc.org/fccyvrgb.php\\n from numpy import dot, ndarray, array\\n # if not im.dtype == 'uint8':\\n # raise ImageUtilsError('yuv2rgb only implemented for uint8 arrays')\\n\\n ## better clip input to the valid range just to be on the safe side\\n yuv = ndarray(im.shape) ## float64\\n yuv[:, :, 0] = im[:, :, 0].clip(16, 235).astype(yuv.dtype) - 16\\n yuv[:, :, 1:] = im[:, :, 1:].clip(16, 240).astype(yuv.dtype) - 128\\n\\n ## ITU-R BT.601 version (SDTV)\\n A = array([[1., 0., 0.701],\\n [1., -0.886 * 0.114 / 0.587, -0.701 * 0.299 / 0.587],\\n [1., 0.886, 0.]])\\n A[:, 0] *= 255. / 219.\\n A[:, 1:] *= 255. / 112.\\n\\n ## ITU-R BT.709 version (HDTV)\\n # A = array([[1.164, 0., 1.793],\\n # [1.164, -0.213, -0.533],\\n # [1.164, 2.112, 0.]])\\n\\n rgb = dot(yuv, A.T)\\n return rgb.clip(0, 255).astype('uint8')\",\n \"def Array2PIL(a,lut=None,minvalue=None,maxvalue=None,width=None,height=None,\\n flip=None):\\n import Image # we only need it here ...\\n\\n if flip==\\\"ud\\\": #up-down exchange\\n a=a[::-1,:]\\n h,w=Numeric.shape(a)\\n## a_min=Numeric.minimum.reduce((Numeric.ravel(a)))\\n## a_max=Numeric.maximum.reduce((Numeric.ravel(a)))\\n a_min=min(Numeric.ravel(a))\\n a_max=max(Numeric.ravel(a))\\n\\n # allow for an user-specified maximal value:\\n if maxvalue!=None and maxvalue>a_max:\\n a_max=maxvalue\\n # allows for an user-specified minimal value:\\n if minvalue!=None and minvalue<a_min:\\n a_min=minvalue\\n\\n if lut is not None:\\n if len(lut[0]) == 256:\\n \\n a=(Numeric.ravel(255.0*(a-a_min)/\\n (a_max-a_min))).astype(Numeric.UInt8)\\n\\n rgb=Numeric.zeros( (len(a),3),typecode=Numeric.UInt8)\\n\\n\\n lut_=Numeric.zeros( (3,len(lut[0])),Numeric.UInt8)\\n lut_[0]=lut[0].astype(Numeric.UInt8)\\n lut_[1]=lut[1].astype(Numeric.UInt8)\\n lut_[2]=lut[2].astype(Numeric.UInt8)\\n\\n # This is much faster than the original zip/ravel variant ...\\n rgb[:,0]=Numeric.take(lut_[0],a)\\n #print \\\"rtake\\\"\\n rgb[:,1]=Numeric.take(lut_[1],a)\\n #print \\\"gtake\\\"\\n rgb[:,2]=Numeric.take(lut_[2],a)\\n #print \\\"btake\\\"\\n #rgb=Numeric.ravel(((Numeric.array(zip(r,g,b),\\n # typecode=Numeric.UInt8))))\\n\\n #print \\\"rgb done\\\"\\n else:\\n N = len(lut[0])\\n print \\\"LUT with N=%d entries\\\" % N\\n if N>=256*256:\\n print \\\"UUPS, more than uint16 colors??\\\", N\\n raise ValueError(\\\"N too large\\\")\\n \\n a = (Numeric.ravel((N-1)*(a-a_min)/\\n (a_max-a_min))).astype(Numeric.UInt16)\\n\\n rgb = Numeric.zeros( (len(a), 3), typecode=Numeric.UInt16)\\n\\n lut_ = Numeric.zeros( (3,len(lut[0])), Numeric.UInt16)\\n lut_[0] = lut[0].astype(Numeric.UInt16)\\n lut_[1] = lut[1].astype(Numeric.UInt16)\\n lut_[2] = lut[2].astype(Numeric.UInt16)\\n\\n # This is much faster than the original zip/ravel variant ...\\n rgb[:,0] = Numeric.take(lut_[0],a)\\n rgb[:,1] = Numeric.take(lut_[1],a)\\n rgb[:,2] = Numeric.take(lut_[2],a)\\n\\n rgb = (rgb*256.0/N).astype(Numeric.UInt8)\\n\\n else: # simple grey scale ramp...\\n a=(Numeric.ravel(255.0*(a-a_min)/\\n (a_max-a_min))).astype(Numeric.UInt8)\\n # convert to (r_0,g_0,b_0,r_1,g_1,b_1,....)\\n rgb=Numeric.ravel(Numeric.array(zip(a,a,a)))\\n\\n # create a PIL RGB image\\n #print \\\"w/h\\\",w,h\\n im=Image.new(\\\"RGB\\\",(w,h))\\n #print \\\"imfromstring:\\\"\\n im.fromstring(rgb.tostring())\\n #print \\\"done ...\\\"\\n \\n # scale image ?\\n if height!=None and width==None:\\n im=im.resize(w/h*height,height)\\n elif height==None and width!=None:\\n im=im.resize(width,h/w*width)\\n elif height!=None and width!=None:\\n im=im.resize(width,height)\\n\\n return(im)\",\n \"def data_to_bytescale_rgb(data): # used to create the SOURCE PNGs (MRI, FA, MD)\\n im = bytescale(data)\\n w, h = im.shape\\n ret = np.empty((w,h,3), dtype=np.uint8)\\n ret[:,:,0] = im\\n ret[:,:,1] = im\\n ret[:,:,2] = im\\n return ret\",\n \"def RudermanLABFwd(I):\\n\\n # get input image dimensions\\n m = I.shape[0]\\n n = I.shape[1]\\n\\n # define conversion matrices\\n RGB2LMS = np.array([[0.3811, 0.5783, 0.0402],\\n [0.1967, 0.7244, 0.0782],\\n [0.0241, 0.1288, 0.8444]])\\n LMS2LAB = np.array([[1 / (3**0.5), 0, 0],\\n [0, 1 / (6**0.5), 0],\\n [0, 0, 1 / (2**0.5)]]).dot(np.array([[1, 1, 1],\\n [1, 1, -2],\\n [1, -1, 0]]))\\n\\n # calculate LMS values from RGB\\n I = np.reshape(I, (m * n, 3))\\n LMS = np.dot(RGB2LMS, np.transpose(I))\\n LMS[LMS == 0] = np.spacing(1)\\n logLMS = np.log(LMS)\\n\\n # calculate LAB values from LMS\\n I_LAB = LMS2LAB.dot(logLMS)\\n\\n # reshape to 3-channel image\\n I_LAB = np.reshape(I_LAB.transpose(), (m, n, 3))\\n\\n return I_LAB\",\n \"def convert_image(rel_path_in, rel_path_out):\\n #Lade Bild mit Originalmaske im Grayscale-Modus\\n img = cv2.imread(rel_path_in, cv2.IMREAD_GRAYSCALE)\\n #Jetzt steht in img ein 2D-Array/Matrix mit jedem Graufstufen-Wert der Pixel\\n #Skaliere Pixelwerte runter\\n for zeilen_index in range(0,img.__len__()):\\n for spalten_index in range(0, img[zeilen_index].__len__()):\\n #Hole Pixel-Wert an aktueller Stelle\\n wert = img[zeilen_index][spalten_index]\\n #Falls Wert != 0 (also Pixel gehoert nicht zum Hintergrund)\\n if wert != 0: # != 0 statt == 255, da auch z.B. 253er Werte in den Masken existieren... (vielleicht durch Konvertierung in anderes Format?)\\n #Markiere den Pixel mit 1 statt 255\\n img[zeilen_index][spalten_index]=1\\n #print(img)\\n #*NACHDEM* alle Pixel skaliert wurden, zeichne Umrandung der Objekte\\n umrandung_zeichnen(img)\\n #change_color(img, 0, 255)\\n #change_color(img, 1, 0)\\n #print(img)\\n #Schreibe Ergebnis-Bild in uebergebene Datei\\n cv2.imwrite(rel_path_out, img)\",\n \"def convert_color(img, conv='RGB2YCrCb'):\\n if conv == 'RGB2YCrCb':\\n return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\\n if conv == 'BGR2YCrCb':\\n return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)\\n if conv == 'RGB2LUV':\\n return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)\",\n \"def imageprepare():\\r\\n file_name = 'temp_image.png'\\r\\n im = Image.open(file_name).convert('L')\\r\\n im = im.resize((20, 20))\\r\\n p = Image.new('L', (28,28), (255))\\r\\n p.paste(im,(4,4,24,24))\\r\\n p.save(\\\"last_image.png\\\")\\r\\n\\r\\n tv = list(p.getdata()) # get pixel values\\r\\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\r\\n tva = np.reshape(tva, (28, 28))\\r\\n\\r\\n return tva\",\n \"def hyper2rgb(img, bands):\\n rgb = spectral.get_rgb(img, bands)\\n rgb /= np.max(rgb)\\n rgb = np.asarray(255 * rgb, dtype='uint8')\\n return rgb\",\n \"def _colored_img_to_arr(image, verbose=False):\\n height, width = image.size\\n arr = np.array(image.getdata())\\n arr = arr.reshape(3, height, width)\\n r = arr[0]\\n g = arr[1]\\n b = arr[2]\\n return r, g, b\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())\",\n \"def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\\n\\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)\",\n \"def enhance_contrast(img):\\n # CLAHE (Contrast Limited Adaptive Histogram Equalization)\\n clahe = cv2.createCLAHE(clipLimit=3., tileGridSize=(8, 8))\\n\\n lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) # convert from BGR to LAB color space\\n l, a, b = cv2.split(lab) # split on 3 different channels\\n\\n l2 = clahe.apply(l) # apply CLAHE to the L-channel\\n\\n lab = cv2.merge((l2, a, b)) # merge channels\\n img2 = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # convert from LAB to BGR\\n\\n return img2\",\n \"def labelimg2pixelcluster(self,labelimg):\\n if not hasattr(self,'translator'):\\n self.translator = {self.labeltools.labels.index(\\\"electron\\\"):larcv.kROIEminus,\\n self.labeltools.labels.index(\\\"muon\\\"):larcv.kROIMuminus,\\n self.labeltools.labels.index(\\\"pion\\\"):larcv.kROIPiminus,\\n self.labeltools.labels.index(\\\"proton\\\"):larcv.kROIProton,\\n self.labeltools.labels.index(\\\"gamma\\\"):larcv.kROIGamma}\\n \\n clusters = {}\\n for iplane in xrange(0,3):\\n clusters[iplane] = larcv.Pixel2DCluster()\\n if iplane >= len(labelimg):\\n continue\\n labelmat = labelimg[iplane]\\n idx_bg = self.labeltools.labels.index(\\\"background\\\")\\n idxlabels = (labelmat != idx_bg).nonzero()\\n ivertmax = self.images.img_v[iplane].shape[1]-1\\n for ivert in xrange(0,len(idxlabels[0])):\\n vert = larcv.Pixel2D( idxlabels[0][ivert], ivertmax - idxlabels[1][ivert] )\\n label = labelmat[ idxlabels[0][ivert], idxlabels[1][ivert] ]\\n print \\\"storing (\\\",vert.X(),\\\",\\\",vert.Y(),\\\") label=\\\", self.labeltools.labels[label]\\n vert.Intensity( float(self.translator[ label ] ) )\\n clusters[iplane] += vert\\n\\n return ( clusters[0], clusters[1], clusters[2] )\",\n \"def Luv_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n # Without Light, there is no color. Short-circuit this and avoid some\\r\\n # zero division errors in the var_a_frac calculation.\\r\\n if cobj.luv_l <= 0.0:\\r\\n xyz_x = 0.0\\r\\n xyz_y = 0.0\\r\\n xyz_z = 0.0\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\\r\\n\\r\\n # Various variables used throughout the conversion.\\r\\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\\r\\n u_sub_0 = (4.0 * illum[\\\"X\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n v_sub_0 = (9.0 * illum[\\\"Y\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\\r\\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\\r\\n\\r\\n # Y-coordinate calculations.\\r\\n if cobj.luv_l > cie_k_times_e:\\r\\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\\r\\n else:\\r\\n xyz_y = cobj.luv_l / color_constants.CIE_K\\r\\n\\r\\n # X-coordinate calculation.\\r\\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\\r\\n # Z-coordinate calculation.\\r\\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\\r\\n\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)\",\n \"def img_to_rgb(img):\\r\\n if len(img.shape) < 3 or img.shape[2] == 1:\\r\\n return np.repeat(img, 3).reshape(img.shape[0], img.shape[1], 3)\\r\\n else:\\r\\n return img\",\n \"def green_channel(input_image):\\n return input_image[:, :, 1]\",\n \"def XYZ_to_RGB(XYZ,\\n illuminant_XYZ,\\n illuminant_RGB,\\n XYZ_to_RGB_matrix,\\n chromatic_adaptation_transform='CAT02',\\n encoding_cctf=None):\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\\n\\n if encoding_cctf is not None:\\n RGB = encoding_cctf(RGB)\\n\\n return RGB\",\n \"def grayscale(img):\\n for pixel in img:\\n x, y, col = pixel\\n r, g, b = col\\n \\n r = (r + g + b)/3\\n r = g = b\\n \\n new_color = create_color(r, g, b)\\n set_color(img, x, y, new_color)\",\n \"def _hsv_to_rgb(img):\\n h, s, v = img.unbind(axis=-3)\\n f = h * 6.0\\n i = paddle.floor(f)\\n f = f - i\\n i = i.astype(paddle.int32) % 6\\n\\n p = paddle.clip(v * (1.0 - s), 0.0, 1.0)\\n q = paddle.clip(v * (1.0 - s * f), 0.0, 1.0)\\n t = paddle.clip(v * (1.0 - s * (1.0 - f)), 0.0, 1.0)\\n\\n mask = paddle.equal(\\n i.unsqueeze(axis=-3),\\n paddle.arange(6, dtype=i.dtype).reshape((-1, 1, 1)),\\n ).astype(img.dtype)\\n matrix = paddle.stack(\\n [\\n paddle.stack([v, q, p, p, t, v], axis=-3),\\n paddle.stack([t, v, v, q, p, p], axis=-3),\\n paddle.stack([p, p, t, v, v, q], axis=-3),\\n ],\\n axis=-4,\\n )\\n return paddle.einsum(\\\"...ijk, ...xijk -> ...xjk\\\", mask, matrix)\",\n \"def preprocess(image):\\n return (image / 255) * 2 - 1\",\n \"def color_transfer(source, target):\\n\\t# convert the images from the RGB to L*ab* color space, being\\n\\t# sure to utilizing the floating point data type (note: OpenCV\\n\\t# expects floats to be 32-bit, so use that instead of 64-bit)\\n\\tsource = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype(\\\"float32\\\")\\n\\ttarget = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype(\\\"float32\\\")\\n\\n\\t# compute color statistics for the source and target images\\n\\t(lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = image_stats(source)\\n\\t(lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = image_stats(target)\\n\\n\\t# subtract the means from the target image\\n\\t(l, a, b) = cv2.split(target)\\n\\tl -= lMeanTar\\n\\ta -= aMeanTar\\n\\tb -= bMeanTar\\n\\n\\t# scale by the standard deviations\\n\\tl = (lStdTar / lStdSrc) * l\\n\\ta = (aStdTar / aStdSrc) * a\\n\\tb = (bStdTar / bStdSrc) * b\\n\\n\\t# add in the source mean\\n\\tl += lMeanSrc\\n\\ta += aMeanSrc\\n\\tb += bMeanSrc\\n\\n\\t# clip the pixel intensities to [0, 255] if they fall outside\\n\\t# this range\\n\\tl = np.clip(l, 0, 255)\\n\\ta = np.clip(a, 0, 255)\\n\\tb = np.clip(b, 0, 255)\\n\\n\\t# merge the channels together and convert back to the RGB color\\n\\t# space, being sure to utilize the 8-bit unsigned integer data\\n\\t# type\\n\\ttransfer = cv2.merge([l, a, b])\\n\\ttransfer = cv2.cvtColor(transfer.astype(\\\"uint8\\\"), cv2.COLOR_LAB2BGR)\\n\\t\\n\\t# return the color transferred image\\n\\treturn transfer\",\n \"def split_image_into_channels(image):\\n red_channel = image[:, :, 0]\\n green_channel = image[:, :, 1]\\n blue_channel = image[:, :, 2]\\n return red_channel, green_channel, blue_channel\",\n \"def scale_to_01(color: C3I) -> C3F:\\n r, g, b = color\\n return r / 255, g / 255, b / 255\",\n \"def test_srgb_conversion_to_xyz_d50(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))\",\n \"def greyscale(img):\\n grey_img = np.zeros([210, 160])\\n for i in range(3):\\n grey_img =np.sum([grey_img, img[:, :, i]], 0)\\n grey_img /= 3\\n grey_img = grey_img.astype(np.uint8)\\n return grey_img\",\n \"def show_rgb_img(img):\\n return plt.imshow(cv2.cvtColor(img, cv2.CV_32S))\",\n \"def test_RGB_mode():\\n\\n model = Instafilter(\\\"Lo-Fi\\\")\\n\\n f_image = __local__ / \\\"Normal.jpg\\\"\\n\\n img1 = model(f_image)\\n img2 = model(f_image, is_RGB=True)\\n\\n diff = (img1 - img2).sum()\\n\\n assert abs(diff) > 0\",\n \"def convertgrey(image: Image):\\n matrice = np.asarray(image)\\n matrice = np.mean(matrice, axis=2, dtype=np.uint8)\\n return Image.fromarray(matrice)\",\n \"def _rgb_to_hsv(img):\\n maxc = img.max(axis=-3)\\n minc = img.min(axis=-3)\\n\\n is_equal = paddle.equal(maxc, minc)\\n one_divisor = paddle.ones_like(maxc)\\n c_delta = maxc - minc\\n # s is 0 when maxc == minc, set the divisor to 1 to avoid zero divide.\\n s = c_delta / paddle.where(is_equal, one_divisor, maxc)\\n\\n r, g, b = img.unbind(axis=-3)\\n c_delta_divisor = paddle.where(is_equal, one_divisor, c_delta)\\n # when maxc == minc, there is r == g == b, set the divisor to 1 to avoid zero divide.\\n rc = (maxc - r) / c_delta_divisor\\n gc = (maxc - g) / c_delta_divisor\\n bc = (maxc - b) / c_delta_divisor\\n\\n hr = (maxc == r).astype(maxc.dtype) * (bc - gc)\\n hg = ((maxc == g) & (maxc != r)).astype(maxc.dtype) * (rc - bc + 2.0)\\n hb = ((maxc != r) & (maxc != g)).astype(maxc.dtype) * (gc - rc + 4.0)\\n h = (hr + hg + hb) / 6.0 + 1.0\\n h = h - h.trunc()\\n return paddle.stack([h, s, maxc], axis=-3)\",\n \"def compute_luminosity(red, green, blue):\\r\\n return (0.299 * red) + (0.587 * green) + (0.114 * blue)\",\n \"def yiq2rgb(im_yiq):\\n return multiply_by_left_matrix(np.linalg.inv(YIQ_MATRIX), im_yiq)\",\n \"def green_channel(img):\\n\\n green = np.zeros(img.shape,dtype=float)\\n\\n green[:,:,1] = np.copy(img[:,:,1])\\n\\n return green\",\n \"def transform_image(self):\\n im = cv2.imread(\\\"result.png\\\", 0)\\n im2 = cv2.resize(im, (28, 28))\\n im = im2.reshape(28, 28, -1)\\n im = im.reshape(1, 1, 28, 28)\\n im = cv2.bitwise_not(im)\\n im = im.reshape(28,28)\\n \\n with out:\\n clear_output()\\n \\n # resize\\n img = np.array(im)\\n img = img.reshape(28*28,)\\n \\n #img = img/255.0\\n \\n return img\",\n \"def create_colorful_test_image(self):\\n ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)\\n ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)\\n ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)\\n imr = np.concatenate((ch255, ch128, ch128), axis=2)\\n img = np.concatenate((ch255, ch255, ch0), axis=2)\\n imb = np.concatenate((ch255, ch0, ch255), axis=2)\\n imw = np.concatenate((ch128, ch128, ch128), axis=2)\\n imu = np.concatenate((imr, img), axis=1)\\n imd = np.concatenate((imb, imw), axis=1)\\n image = np.concatenate((imu, imd), axis=0)\\n return image\",\n \"def _process_img_rgb(self, sensor_data):\\n img = np.array(sensor_data.raw_data).reshape((self.img_y, self.img_x, 4))\\n img = img[:, :, :3] # sensor is actualy rgba, we dont need alpha values\\n self.rgb = img # need to scale rgb values to be {0,1}\",\n \"def read_image(img):\\n out = Image.open(img)\\n return Technicolor(out)\",\n \"def _preprocess(self, image):\\n\\n # Scale from [0, 255] to [0, 1] and BGR to RGB \\n return (image / 255.0)[:, :, ::-1]\",\n \"def yiq2rgb(imYIQ):\\n trans = np.array([[1, 0.956, 0.62], [1, -0.272, -0.647], [1, -1.108, 1.705]])\\n return np.dot(imYIQ, trans)\",\n \"def process_image(img):\\n img[0] = img[0] * 0.229\\n img[1] = img[1] * 0.224\\n img[2] = img[2] * 0.225\\n img[0] += 0.485\\n img[1] += 0.456\\n img[2] += 0.406\\n\\n return img.cpu().numpy().transpose((1, 2, 0))\",\n \"def linearTosRGB3(c):\\n return [linearTosRGB(c[0]), linearTosRGB(c[1]), linearTosRGB(c[2])]\",\n \"def imageprepare():\\r\\n file_name = '9-test.png'\\r\\n im = Image.open(file_name).convert('L')\\r\\n\\r\\n im.save(\\\"9-t.png\\\")\\r\\n plt.imshow(im)\\r\\n plt.show()\\r\\n tv = list(im.getdata())\\r\\n\\r\\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\r\\n return tva\",\n \"def greyscale(im):\\n\\t\\n # YOUR CODE HERE\\n rgb_weights= np.array([0.212, 0.7152, 0.0722])\\n x,y,_ = np.shape(im)\\n img = np.zeros([x,y])\\n\\t\\n for i in range(x):\\n for j in range(y):\\n img[i,j] = im[i,j].dot(rgb_weights)\\n\\t\\t\\t\\n return img\",\n \"def testImageProcessing():\\n Im_pix = getRGB( 'in.png' ) # read in the in.png image\\n print \\\"The first two pixels of the first row are\\\",\\n print Im_pix[0][0:2]\\n # remember that Im_pix is a list (the image)\\n # of lists (each row) of lists (each pixel is [R,G,B])\\n New_pix = [ [ [255 - num for num in p] for p in row ] for row in Im_pix ]\\n # now, save to the file 'out.png'\\n saveRGB( New_pix, 'out.png' )\",\n \"def grey_to_rgb(im):\\n assert im.n_channels in [1, 3]\\n\\n if im.n_channels == 3:\\n return im\\n\\n im.pixels = np.vstack([im.pixels] * 3)\\n return im\",\n \"def linearTosRGB(c):\\n if c <= 0.0031308:\\n return 12.92 * c\\n return math.pow(c, 1.0 / 2.4) * 1.055 - 0.055\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))\",\n \"def RGB_to_XYZ(RGB,\\n illuminant_RGB,\\n illuminant_XYZ,\\n RGB_to_XYZ_matrix,\\n chromatic_adaptation_transform='CAT02',\\n decoding_cctf=None):\\n\\n if decoding_cctf is not None:\\n RGB = decoding_cctf(RGB)\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n return XYZ_a\",\n \"def read_colormap(name):\\n\\n path = get_demo_file(name + '.c3g')\\n\\n out = []\\n with open(path, 'r') as file:\\n for line in file:\\n if 'rgb(' not in line:\\n continue\\n line = line.split('(')[-1].split(')')[0]\\n out.append([float(n) for n in line.split(',')])\\n\\n return np.asarray(out).astype(float) / 256.\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76128566","0.736024","0.7093746","0.70733243","0.688894","0.67929274","0.67809963","0.6703727","0.66217625","0.6567671","0.6539482","0.6528808","0.6519189","0.64457124","0.64197767","0.63783777","0.637468","0.630608","0.6248861","0.6204791","0.618432","0.6093022","0.60678786","0.6041543","0.6036307","0.6030892","0.59891975","0.59683686","0.5943061","0.5936455","0.59234446","0.59206516","0.5916004","0.59111595","0.5904206","0.5894495","0.5877756","0.5863029","0.58598846","0.5858743","0.58474076","0.58236355","0.58210707","0.579976","0.5796978","0.5793608","0.57919204","0.5791724","0.5763083","0.5748142","0.57329094","0.57250553","0.5717014","0.57107395","0.5700194","0.5693241","0.56814736","0.56493896","0.5645311","0.5643696","0.5625392","0.5610727","0.5603417","0.55971736","0.55938846","0.5593551","0.558494","0.55741566","0.5569321","0.55634207","0.5556594","0.5547061","0.553646","0.5535061","0.5532215","0.5531503","0.55140024","0.55107373","0.5510503","0.5504814","0.5502567","0.55002284","0.5499796","0.5499647","0.549536","0.5495257","0.54882103","0.5484257","0.54817224","0.5481194","0.5479311","0.5476412","0.5475013","0.54747564","0.5470721","0.5470543","0.54605526","0.5458414","0.54524714","0.54499364"],"string":"[\n \"0.76128566\",\n \"0.736024\",\n \"0.7093746\",\n \"0.70733243\",\n \"0.688894\",\n \"0.67929274\",\n \"0.67809963\",\n \"0.6703727\",\n \"0.66217625\",\n \"0.6567671\",\n \"0.6539482\",\n \"0.6528808\",\n \"0.6519189\",\n \"0.64457124\",\n \"0.64197767\",\n \"0.63783777\",\n \"0.637468\",\n \"0.630608\",\n \"0.6248861\",\n \"0.6204791\",\n \"0.618432\",\n \"0.6093022\",\n \"0.60678786\",\n \"0.6041543\",\n \"0.6036307\",\n \"0.6030892\",\n \"0.59891975\",\n \"0.59683686\",\n \"0.5943061\",\n \"0.5936455\",\n \"0.59234446\",\n \"0.59206516\",\n \"0.5916004\",\n \"0.59111595\",\n \"0.5904206\",\n \"0.5894495\",\n \"0.5877756\",\n \"0.5863029\",\n \"0.58598846\",\n \"0.5858743\",\n \"0.58474076\",\n \"0.58236355\",\n \"0.58210707\",\n \"0.579976\",\n \"0.5796978\",\n \"0.5793608\",\n \"0.57919204\",\n \"0.5791724\",\n \"0.5763083\",\n \"0.5748142\",\n \"0.57329094\",\n \"0.57250553\",\n \"0.5717014\",\n \"0.57107395\",\n \"0.5700194\",\n \"0.5693241\",\n \"0.56814736\",\n \"0.56493896\",\n \"0.5645311\",\n \"0.5643696\",\n \"0.5625392\",\n \"0.5610727\",\n \"0.5603417\",\n \"0.55971736\",\n \"0.55938846\",\n \"0.5593551\",\n \"0.558494\",\n \"0.55741566\",\n \"0.5569321\",\n \"0.55634207\",\n \"0.5556594\",\n \"0.5547061\",\n \"0.553646\",\n \"0.5535061\",\n \"0.5532215\",\n \"0.5531503\",\n \"0.55140024\",\n \"0.55107373\",\n \"0.5510503\",\n \"0.5504814\",\n \"0.5502567\",\n \"0.55002284\",\n \"0.5499796\",\n \"0.5499647\",\n \"0.549536\",\n \"0.5495257\",\n \"0.54882103\",\n \"0.5484257\",\n \"0.54817224\",\n \"0.5481194\",\n \"0.5479311\",\n \"0.5476412\",\n \"0.5475013\",\n \"0.54747564\",\n \"0.5470721\",\n \"0.5470543\",\n \"0.54605526\",\n \"0.5458414\",\n \"0.54524714\",\n \"0.54499364\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7743007"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12393,"cells":{"query":{"kind":"string","value":"Convert an image from LAB color space to RGB color space LAB > XYZ > RGB"},"document":{"kind":"string","value":"def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\n xyz = lab_to_xyz(image)\n rgb_image = xyz_to_rgb(xyz)\n return rgb_image"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def lab_to_rgb(img):\n new_img = np.zeros((256, 256, 3))\n for i in range(len(img)):\n for j in range(len(img[i])):\n pix = img[i, j]\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\n new_img = color.lab2rgb(new_img) * 255\n new_img = new_img.astype('uint8')\n return new_img","def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\n xyz = rgb_to_xyz(image)\n lab_image = xyz_to_lab(xyz)\n return lab_image","def lab_to_rgb(img_l, img_ab):\n lab = np.empty([*img_l.shape[0:2], 3])\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\n lab[:, :, 1:] = img_ab * 127\n return color.lab2rgb(lab)","def example_rgb_to_xyz():\r\n\r\n print(\"=== RGB Example: RGB->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n rgb = sRGBColor(120, 130, 140)\r\n # Show a string representation.\r\n print(rgb)\r\n # Convert RGB to XYZ using a D50 illuminant.\r\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def sRGBFromLab(lab):\n return xyzTosRGB(labToXYZ(lab, [0.9504559, 1, 1.089058]))","def Lab_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n xyz_y = (cobj.lab_l + 16.0) / 116.0\r\n xyz_x = cobj.lab_a / 500.0 + xyz_y\r\n xyz_z = xyz_y - cobj.lab_b / 200.0\r\n \r\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\r\n xyz_y = math.pow(xyz_y, 3)\r\n else:\r\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\r\n\r\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\r\n xyz_x = math.pow(xyz_x, 3)\r\n else:\r\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\r\n \r\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\r\n xyz_z = math.pow(xyz_z, 3)\r\n else:\r\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\r\n \r\n xyz_x = (illum[\"X\"] * xyz_x)\r\n xyz_y = (illum[\"Y\"] * xyz_y)\r\n xyz_z = (illum[\"Z\"] * xyz_z)\r\n \r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)","def example_lab_to_rgb():\r\n\r\n print(\"=== RGB Example: Lab->RGB ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.217)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n rgb = convert_color(lab, sRGBColor)\r\n print(rgb)\r\n print(\"=== End Example ===\\n\")","def rgb2Lab(rgbvalue):\r\n RGB2Lab_Matrix = np.array([[0.412453, 0.357580, 0.180423],\r\n [0.212671, 0.715160, 0.072169],\r\n [0.019334, 0.119193, 0.950227]])\r\n R = rgbvalue[0]\r\n G = rgbvalue[1]\r\n B = rgbvalue[2]\r\n gammaR = gamma(R / 255.0)\r\n gammaG = gamma(G / 255.0)\r\n gammaB = gamma(B / 255.0)\r\n RGBvalue = np.array([gammaR, gammaG, gammaB])\r\n RGBvalue = RGBvalue.reshape(3, 1)\r\n XYZvalue = np.dot(RGB2Lab_Matrix, RGBvalue)\r\n assert XYZvalue.shape == (3, 1)\r\n correction = np.array([[1.0 / 0.950456, 1.0, 1.0 / 1.088754]]).T\r\n assert correction.shape == (3, 1)\r\n XYZ = XYZvalue * correction\r\n assert XYZ.shape == (3, 1)\r\n YYn = ft(XYZ[1])\r\n XXn = ft(XYZ[0])\r\n ZZn = ft(XYZ[2])\r\n L = 116 * YYn - 16\r\n a = 500 * (XXn - YYn)\r\n b = 200 * (YYn - ZZn)\r\n return [int(L), int(a), int(b)]","def XYZ_to_Lab(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n temp_x = cobj.xyz_x / illum[\"X\"]\r\n temp_y = cobj.xyz_y / illum[\"Y\"]\r\n temp_z = cobj.xyz_z / illum[\"Z\"]\r\n \r\n if temp_x > color_constants.CIE_E:\r\n temp_x = math.pow(temp_x, (1.0 / 3.0))\r\n else:\r\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \r\n\r\n if temp_y > color_constants.CIE_E:\r\n temp_y = math.pow(temp_y, (1.0 / 3.0))\r\n else:\r\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\r\n \r\n if temp_z > color_constants.CIE_E:\r\n temp_z = math.pow(temp_z, (1.0 / 3.0))\r\n else:\r\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\r\n \r\n lab_l = (116.0 * temp_y) - 16.0\r\n lab_a = 500.0 * (temp_x - temp_y)\r\n lab_b = 200.0 * (temp_y - temp_z)\r\n return LabColor(\r\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)","def rgb_to_lab(img, l=False, ab=False):\n img = img / 255\n l_chan = color.rgb2lab(img)[:, :, 0]\n l_chan = l_chan / 50 - 1\n l_chan = l_chan[..., np.newaxis]\n\n ab_chan = color.rgb2lab(img)[:, :, 1:]\n ab_chan = (ab_chan + 128) / 255 * 2 - 1\n if l:\n return l_chan\n else:\n return ab_chan","def rgb2lab(r, g, b):\n r, g, b = r / 255.0, g / 255.0, b / 255.0\n\n # http://www.brucelindbloom.com/index.html?Math.html\n # Inverse sRGB Companding\n r = r / 12.92 if r <= 0.04045 else ((r + 0.055) / 1.055) ** 2.4\n g = g / 12.92 if g <= 0.04045 else ((g + 0.055) / 1.055) ** 2.4\n b = b / 12.92 if b <= 0.04045 else ((b + 0.055) / 1.055) ** 2.4\n\n # http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html\n # sRGB, D65\n x = r * 0.4124564 + g * 0.3575761 + b * 0.1804375\n y = r * 0.2126729 + g * 0.7151522 + b * 0.0721750\n z = r * 0.0193339 + g * 0.1191920 + b * 0.9503041\n\n # http://www.brucelindbloom.com/index.html?Eqn_XYZ_to_Lab.html\n kappa, epsilon = 903.3, 0.008856\n\n # http://brucelindbloom.com/index.html?Eqn_ChromAdapt.html\n # White point for D65\n xr, yr, zr = x / 0.95047, y / 1.00000, z / 1.08883\n\n fx = xr ** (1 / 3.0) if xr > epsilon else (kappa * xr + 16) / 116.0\n fy = yr ** (1 / 3.0) if yr > epsilon else (kappa * yr + 16) / 116.0\n fz = zr ** (1 / 3.0) if zr > epsilon else (kappa * zr + 16) / 116.0\n\n l = 166.0 * fy - 16.0\n a = 500.0 * (fx - fy)\n b = 200.0 * (fy - fz)\n\n return l, a, b","def get_rgb(self, img, r, g, b):\r\n\r\n # Get specific bands of hyperspectral image\r\n red_channel = img[:, :, r]\r\n green_channel = img[:, :, g]\r\n blue_channel = img[:, :, b]\r\n\r\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\r\n img = img.astype('float32')\r\n return img","def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n L: torch.Tensor = image[..., 0, :, :]\n u: torch.Tensor = image[..., 1, :, :]\n v: torch.Tensor = image[..., 2, :, :]\n\n # Convert from Luv to XYZ\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\n\n # Compute white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n a: torch.Tensor = u_w + u / (13 * L + eps)\n d: torch.Tensor = v_w + v / (13 * L + eps)\n c: torch.Tensor = 3 * y * (5 * d - 3)\n\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\n\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\n\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\n\n # Convert from RGB Linear to sRGB\n rgb_im = linear_rgb_to_rgb(rgbs_im)\n\n return rgb_im","def sRGBToLab(rgb):\n return xyzToLab(xyzFromsRGB(rgb), [0.9504559, 1, 1.089058])","def sRGBFromLabD50(lab):\n return xyzTosRGBD50(labToXYZ(lab, [0.9642957, 1, 0.8251046]))","def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n var_x = x / 100\n var_y = y / 100\n var_z = z / 100\n\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\n\n var_r = tf.where(var_r > 0.0031308,\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\n 12.92 * var_r)\n var_g = tf.where(var_g > 0.0031308,\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\n 12.92 * var_g)\n var_b = tf.where(var_b > 0.0031308,\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\n 12.92 * var_b)\n r = var_r * 255\n g = var_g * 255\n b = var_b * 255\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\n return rgb_image","def l_to_rgb(img_l):\n lab = np.squeeze(255 * (img_l + 1) / 2)\n return color.gray2rgb(lab) / 255","def rgb2alpha(img):\n\t### First of all we need the size of our picture to make the transforms\n\n\tx = len(img) ; y = len(img[0])\n\talpha = np.full((x, y, 3), 0, dtype = float)\t\t## This will be the transformed image\n\n\t### Now we gotta access each pixel of the picture\n\n\tfor i, vi in enumerate(img):\n\t\tfor j, px in enumerate(vi):\n\t\t\t### There we are\n\n\t\t\t# Step 1 : LMS transform, for that we use r_l\n\n\t\t\talpha[i][j] = np.matmul(r_l, px)\n\n\t\t\t# Step 2 : log em all (decimal log)\n\n\t\t\talpha[i][j][0] = log(alpha[i][j][0])\n\t\t\talpha[i][j][1] = log(alpha[i][j][1])\n\t\t\talpha[i][j][2] = log(alpha[i][j][2])\n\n\t\t\t# Step 3 : l alpha beta transform, by using l_a\n\n\t\t\talpha[i][j] = np.matmul(l_a, alpha[i][j])\n\n\treturn alpha","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\n l, a, b = tf.unstack(image, axis=-1)\n\n var_y = (l + 16) / 116\n var_x = a / 500 + var_y\n var_z = var_y - b / 200\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\n (var_x - 16 / 116) / 7.787)\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\n (var_y - 16 / 116) / 7.787)\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\n (var_z - 16 / 116) / 7.787)\n\n refx = 95.047\n refy = 100.00\n ref_z = 108.883\n\n x = var_x * refx\n y = var_y * refy\n z = var_z * ref_z\n xyz_image = tf.stack([x, y, z], axis=-1)\n return xyz_image","def convert_color(image, color_space):\n out_image = None\n if color_space != 'RGB':\n if color_space == 'HSV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\n elif color_space == 'LUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\n elif color_space == 'HLS':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\n elif color_space == 'YUV':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\n elif color_space == 'YCrCb':\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\n else:\n out_image = np.copy(image)\n return out_image","def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\n r, g, b = tf.unstack(image, axis=-1)\n var_r = r / 255\n var_g = g / 255\n var_b = b / 255\n\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\n var_r / 12.92)\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\n var_g / 12.92)\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\n var_b / 12.92)\n var_r = var_r * 100\n var_g = var_g * 100\n var_b = var_b * 100\n\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\n\n image_xyz = tf.stack([x, y, z], axis=-1)\n return image_xyz","def rgb_processing(rgb_img, center, scale, rot=0):\n rgb_img = crop(rgb_img, center, scale, \n [constants.IMG_RES, constants.IMG_RES], rot=rot)\n # (3,224,224),float,[0,1]\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\n return rgb_img","def _convert_to_yolo_img(self, img):\n\n img = img / 255.0\n h, w, c = img.shape\n img = img.transpose(2, 0, 1)\n outimg = make_image(w, h, c)\n img = img.reshape((w*h*c))\n data = c_array(c_float, img)\n outimg.data = data\n rgbgr_image(outimg)\n return outimg","def matplotlib_image(image):\n if image.ndim == 2:\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\n else:\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return rgb","def red_channel(img):\n\n red = np.zeros(img.shape,dtype=float)\n\n red[:,:,2] = np.copy(img[:,:,2])\n\n return red","def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\n y: torch.Tensor = image[..., 0, :, :]\n cb: torch.Tensor = image[..., 1, :, :]\n cr: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n cb_shifted: torch.Tensor = cb - delta\n cr_shifted: torch.Tensor = cr - delta\n\n r: torch.Tensor = y + 1.403 * cr_shifted\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\n b: torch.Tensor = y + 1.773 * cb_shifted\n return torch.stack([r, g, b], -3)","def yuv2rgb(im):\n ## conflicting definitions exist depending on whether you use the full range\n ## of YCbCr or clamp out to the valid range. see here\n ## http://www.equasys.de/colorconversion.html\n ## http://www.fourcc.org/fccyvrgb.php\n from numpy import dot, ndarray, array\n # if not im.dtype == 'uint8':\n # raise ImageUtilsError('yuv2rgb only implemented for uint8 arrays')\n\n ## better clip input to the valid range just to be on the safe side\n yuv = ndarray(im.shape) ## float64\n yuv[:, :, 0] = im[:, :, 0].clip(16, 235).astype(yuv.dtype) - 16\n yuv[:, :, 1:] = im[:, :, 1:].clip(16, 240).astype(yuv.dtype) - 128\n\n ## ITU-R BT.601 version (SDTV)\n A = array([[1., 0., 0.701],\n [1., -0.886 * 0.114 / 0.587, -0.701 * 0.299 / 0.587],\n [1., 0.886, 0.]])\n A[:, 0] *= 255. / 219.\n A[:, 1:] *= 255. / 112.\n\n ## ITU-R BT.709 version (HDTV)\n # A = array([[1.164, 0., 1.793],\n # [1.164, -0.213, -0.533],\n # [1.164, 2.112, 0.]])\n\n rgb = dot(yuv, A.T)\n return rgb.clip(0, 255).astype('uint8')","def example_lab_to_xyz():\r\n\r\n print(\"=== Simple Example: Lab->XYZ ===\")\r\n # Instantiate an Lab color object with the given values.\r\n lab = LabColor(0.903, 16.296, -2.22)\r\n # Show a string representation.\r\n print(lab)\r\n # Convert to XYZ.\r\n xyz = convert_color(lab, XYZColor)\r\n print(xyz)\r\n print(\"=== End Example ===\\n\")","def grey_to_rgb_imitation(img):\n return np.repeat(img[...,np.newaxis], 3, -1)","def sRGBToLabD50(rgb):\n return xyzToLab(xyzFromsRGBD50(rgb), [0.9642957, 1, 0.8251046])","def XYZ_to_RGB(XYZ,\n illuminant_XYZ,\n illuminant_RGB,\n XYZ_to_RGB_matrix,\n chromatic_adaptation_transform='CAT02',\n encoding_cctf=None):\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n transform=chromatic_adaptation_transform)\n\n XYZ_a = dot_vector(M, XYZ)\n\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\n\n if encoding_cctf is not None:\n RGB = encoding_cctf(RGB)\n\n return RGB","def getRGBFromXYAndBrightness(self, x, y, bri=1):\n # The xy to color conversion is almost the same, but in reverse order.\n # Check if the xy value is within the color gamut of the lamp.\n # If not continue with step 2, otherwise step 3.\n # We do this to calculate the most accurate color the given light can actually do.\n xyPoint = XYPoint(x, y)\n\n if not self.checkPointInLampsReach(xyPoint):\n # Calculate the closest point on the color gamut triangle\n # and use that as xy value See step 6 of color to xy.\n xyPoint = self.getClosestPointToPoint(xyPoint)\n\n # Calculate XYZ values Convert using the following formulas:\n Y = bri\n X = (Y / xyPoint.y) * xyPoint.x\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\n\n # Convert to RGB using Wide RGB D65 conversion\n r = X * 1.612 - Y * 0.203 - Z * 0.302\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\n b = X * 0.026 - Y * 0.072 + Z * 0.962\n\n # Apply reverse gamma correction\n r, g, b = map(\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\n [r, g, b]\n )\n\n # Bring all negative components to zero\n r, g, b = map(lambda x: max(0, x), [r, g, b])\n\n # If one component is greater than 1, weight components by that value.\n max_component = max(r, g, b)\n if max_component > 1:\n r, g, b = map(lambda x: x / max_component, [r, g, b])\n\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\n\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\n return (r, g, b)","def sRGBLuminance(x):\n lin=linearFromsRGB3(x)\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722","def _colored_img_to_arr(image, verbose=False):\n height, width = image.size\n arr = np.array(image.getdata())\n arr = arr.reshape(3, height, width)\n r = arr[0]\n g = arr[1]\n b = arr[2]\n return r, g, b","def read_img(filename) -> Tuple[np.ndarray, np.ndarray]:\n img = cv2.imread(filename, 3)\n labimg = cv2.cvtColor(cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE)), cv2.COLOR_BGR2Lab)\n return np.reshape(labimg[:, :, 0], (config.IMAGE_SIZE, config.IMAGE_SIZE, 1)), labimg[:, :, 1:]","def convert_color(image, color_space='RGB'):\n color_space = color_space.lower()\n if color_space != 'rgb':\n if color_space == 'hsv':\n color_transformation = cv2.COLOR_BGR2HSV\n elif color_space == 'luv':\n color_transformation = cv2.COLOR_BGR2LUV\n elif color_space == 'hls':\n color_transformation = cv2.COLOR_BGR2HLS\n elif color_space == 'yuv':\n color_transformation = cv2.COLOR_BGR2YUV\n elif color_space == 'ycrcb':\n color_transformation = cv2.COLOR_BGR2YCrCb\n else:\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\n 'HLS, YUV, YCrCb' % color_space)\n\n return cv2.cvtColor(image, color_transformation)\n else:\n return image","def img_to_rgb(img):\r\n if len(img.shape) < 3 or img.shape[2] == 1:\r\n return np.repeat(img, 3).reshape(img.shape[0], img.shape[1], 3)\r\n else:\r\n return img","def luminance(rgb):\n \n (r, g, b) = rgb\n return (r + g + b) // 3","def load_color_image_features(img_path):\n ac = scipy.misc.imread(img_path, mode='RGB')\n ac = ac / (255.0 / 2) - 1.0\n return np.array(ac)","def hyper2rgb(img, bands):\n rgb = spectral.get_rgb(img, bands)\n rgb /= np.max(rgb)\n rgb = np.asarray(255 * rgb, dtype='uint8')\n return rgb","def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\n x, y, z = tf.unstack(image, axis=-1)\n\n refx = 95.047\n refy = 100.00\n refz = 108.883\n\n var_x = x / refx\n var_y = y / refy\n var_z = z / refz\n\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\n (7.787 * var_x) + (16 / 116))\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\n (7.787 * var_y) + (16 / 116))\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\n (7.787 * var_z) + (16 / 116))\n\n l = (116 * var_y) - 16\n a = 500 * (var_x - var_y)\n b = 200 * (var_y - var_z)\n lab_image = tf.stack([l, a, b], axis=-1)\n return lab_image","def convert_color(img, conv='RGB2YCrCb'):\n if conv == 'RGB2YCrCb':\n return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\n if conv == 'BGR2YCrCb':\n return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)\n if conv == 'RGB2LUV':\n return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)","def yuv_to_rgb(img_yuv):\n\n y = img_yuv[..., 0]\n u = img_yuv[..., 1]\n v = img_yuv[..., 2]\n\n r = y + 1.14 * v\n g = y - 0.396 * u - 0.581 * v\n b = y + 2.029 * u\n\n img_rgb = np.stack((r, g, b), axis=2)\n img_rgb = np.clip(img_rgb, 0, 1)\n return img_rgb","def yiq2rgb(imYIQ):\n trans = np.array([[1, 0.956, 0.62], [1, -0.272, -0.647], [1, -1.108, 1.705]])\n return np.dot(imYIQ, trans)","def grey_to_rgb(im):\n assert im.n_channels in [1, 3]\n\n if im.n_channels == 3:\n return im\n\n im.pixels = np.vstack([im.pixels] * 3)\n return im","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())","def luminance(self):\n \n return (self.r + self.g + self.b) // 3","def RGB_to_XYZ(RGB,\n illuminant_RGB,\n illuminant_XYZ,\n RGB_to_XYZ_matrix,\n chromatic_adaptation_transform='CAT02',\n decoding_cctf=None):\n\n if decoding_cctf is not None:\n RGB = decoding_cctf(RGB)\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n transform=chromatic_adaptation_transform)\n\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\n\n XYZ_a = dot_vector(M, XYZ)\n\n return XYZ_a","def read_img(img): #X\n im = plt.imread(img)\n im = im[:, :, :3]\n if im.max()>200:\n im = im/255.\n return rgb_to_hsv(im)-0.5","def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\n\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)","def data_to_bytescale_rgb(data): # used to create the SOURCE PNGs (MRI, FA, MD)\n im = bytescale(data)\n w, h = im.shape\n ret = np.empty((w,h,3), dtype=np.uint8)\n ret[:,:,0] = im\n ret[:,:,1] = im\n ret[:,:,2] = im\n return ret","def convertColorSpace(\n self,\n img, # Image in some color space\n srcColorSpace = 'BGR', # Source color space\n tgtColorSpace = 'RGB', # Traget color space\n ):\n\n if srcColorSpace == tgtColorSpace:\n return img\n\n if srcColorSpace == 'BGR':\n img_bgr = img\n elif srcColorSpace == 'RGB':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n elif srcColorSpace == 'HSV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n elif srcColorSpace == 'HLS':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\n elif srcColorSpace == 'LUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\n elif srcColorSpace == 'YUV':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\n elif srcColorSpace == 'YCrCb':\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\n else:\n raise Exception(\"Incorrect color space: {}\".format(srcColorSpace))\n\n if tgtColorSpace == 'BGR':\n img_tgt = img_bgr\n elif tgtColorSpace == 'RGB':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\n elif tgtColorSpace == 'HSV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\n elif tgtColorSpace == 'HLS':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\n elif tgtColorSpace == 'LUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\n elif tgtColorSpace == 'YUV':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\n elif tgtColorSpace == 'YCrCb':\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\n else:\n raise Exception(\"Incorrect color space: {}\".format(tgtColorSpace))\n\n return img_tgt","def read_colormap(name):\n\n path = get_demo_file(name + '.c3g')\n\n out = []\n with open(path, 'r') as file:\n for line in file:\n if 'rgb(' not in line:\n continue\n line = line.split('(')[-1].split(')')[0]\n out.append([float(n) for n in line.split(',')])\n\n return np.asarray(out).astype(float) / 256.","def carla_rgb_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\n image.convert(carla.ColorConverter.Raw) # pylint: disable=no-member\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = array.astype(np.float32) / 255\n array = np.reshape(array, (image.height, image.width, 4))\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n return array","def colorize_images(self, img):\n self.load_model()\n self.mdn.eval()\n self.vae.eval()\n n, _, _ = img.shape\n img = img.astype(np.float32) / 255\n img = torch.tensor(img, dtype=torch.float, device=self.device).unsqueeze(1)\n with torch.no_grad():\n z = self.mdn(img)\n ab_out = self.vae.decode(z)\n lab_out = torch.cat((img, ab_out), dim=1)\n lab_out = self.unnormalize(lab_out).cpu().numpy()\n lab_out = np.transpose(lab_out, (0, 2, 3, 1)).astype(np.uint8)\n for i in range(n):\n color_out = cv2.cvtColor(lab_out[i], cv2.COLOR_LAB2BGR)\n color_out = cv2.resize(color_out, (96, 96), interpolation=cv2.INTER_AREA)\n cv2.imwrite(\"../datasets/stl10/divcolor/{}.png\".format(str(i)), color_out)\n return","def XYZ_to_sRGB(XYZ):\n\n rgb = XYZ_to_sRGB_linear(XYZ)\n rgb = sRGB_linear_to_sRGB(rgb)\n\n return rgb","def generate_lut(self):\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n for i in Numeric.arange(256):\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\n return r,g,b","def RGB_to_RGB(RGB,\n input_colourspace,\n output_colourspace,\n chromatic_adaptation_transform='CAT02',\n apply_decoding_cctf=False,\n apply_encoding_cctf=False):\n\n if apply_decoding_cctf:\n RGB = input_colourspace.decoding_cctf(RGB)\n\n M = RGB_to_RGB_matrix(input_colourspace, output_colourspace,\n chromatic_adaptation_transform)\n\n RGB = dot_vector(M, RGB)\n\n if apply_encoding_cctf:\n RGB = output_colourspace.encoding_cctf(RGB)\n\n return RGB","def toRGB(self):\r\n return mage_hsv_tuple_to_rgb(self.Coords)","def to_rgb(im):\n w, h = im.shape\n ret = np.empty((w, h, 3), dtype=np.uint8)\n ret[:, :, 2] = ret[:, :, 1] = ret[:, :, 0] = im\n return ret","def get_color(im_obj):\n #im = Image.open(path, 'r')\n x, y = im_obj.size\n\n r, g, b = 0, 0, 0\n for i in xrange(x):\n for j in xrange(y):\n color_px = im_obj.getpixel((i, j))\n #print color_px\n r += color_px[0]\n g += color_px[1]\n b += color_px[2]\n\n r = r / (x * y)\n g = g / (x * y)\n b = b / (x * y)\n return (r, g, b)","def _process_img_rgb(self, sensor_data):\n img = np.array(sensor_data.raw_data).reshape((self.img_y, self.img_x, 4))\n img = img[:, :, :3] # sensor is actualy rgba, we dont need alpha values\n self.rgb = img # need to scale rgb values to be {0,1}","def rgb2hsl_img(rgb):\r\n \r\n def core(_rgb, _hsl):\r\n\r\n irgb = _rgb.astype(np.uint16)\r\n ir, ig, ib = irgb[:, :, 0], irgb[:, :, 1], irgb[:, :, 2]\r\n h, s, l = _hsl[:, :, 0], _hsl[:, :, 1], _hsl[:, :, 2]\r\n\r\n imin, imax = irgb.min(2), irgb.max(2)\r\n iadd, isub = imax + imin, imax - imin\r\n\r\n ltop = (iadd != 510) * (iadd > 255)\r\n lbot = (iadd != 0) * (ltop == False)\r\n\r\n l[:] = iadd.astype(np.float) / 510\r\n\r\n fsub = isub.astype(np.float)\r\n s[ltop] = fsub[ltop] / (510 - iadd[ltop])\r\n s[lbot] = fsub[lbot] / iadd[lbot]\r\n\r\n not_same = imax != imin\r\n is_b_max = not_same * (imax == ib)\r\n not_same_not_b_max = not_same * (is_b_max == False)\r\n is_g_max = not_same_not_b_max * (imax == ig)\r\n is_r_max = not_same_not_b_max * (is_g_max == False) * (imax == ir)\r\n\r\n h[is_r_max] = ((0. + ig[is_r_max] - ib[is_r_max]) / isub[is_r_max])\r\n h[is_g_max] = ((0. + ib[is_g_max] - ir[is_g_max]) / isub[is_g_max]) + 2\r\n h[is_b_max] = ((0. + ir[is_b_max] - ig[is_b_max]) / isub[is_b_max]) + 4\r\n h[h < 0] += 6\r\n h[:] /= 6\r\n\r\n hsl = np.zeros(rgb.shape, dtype=np.float)\r\n cpus = multiprocessing.cpu_count()\r\n length = int(math.ceil(float(hsl.shape[0]) / cpus))\r\n line = 0\r\n threads = []\r\n while line < hsl.shape[0]:\r\n line_next = line + length\r\n thread = threading.Thread(target=core, args=(rgb[line:line_next], hsl[line:line_next]))\r\n thread.start()\r\n threads.append(thread)\r\n line = line_next\r\n\r\n for thread in threads:\r\n thread.join()\r\n\r\n return hsl","def Array2PIL(a,lut=None,minvalue=None,maxvalue=None,width=None,height=None,\n flip=None):\n import Image # we only need it here ...\n\n if flip==\"ud\": #up-down exchange\n a=a[::-1,:]\n h,w=Numeric.shape(a)\n## a_min=Numeric.minimum.reduce((Numeric.ravel(a)))\n## a_max=Numeric.maximum.reduce((Numeric.ravel(a)))\n a_min=min(Numeric.ravel(a))\n a_max=max(Numeric.ravel(a))\n\n # allow for an user-specified maximal value:\n if maxvalue!=None and maxvalue>a_max:\n a_max=maxvalue\n # allows for an user-specified minimal value:\n if minvalue!=None and minvalue<a_min:\n a_min=minvalue\n\n if lut is not None:\n if len(lut[0]) == 256:\n \n a=(Numeric.ravel(255.0*(a-a_min)/\n (a_max-a_min))).astype(Numeric.UInt8)\n\n rgb=Numeric.zeros( (len(a),3),typecode=Numeric.UInt8)\n\n\n lut_=Numeric.zeros( (3,len(lut[0])),Numeric.UInt8)\n lut_[0]=lut[0].astype(Numeric.UInt8)\n lut_[1]=lut[1].astype(Numeric.UInt8)\n lut_[2]=lut[2].astype(Numeric.UInt8)\n\n # This is much faster than the original zip/ravel variant ...\n rgb[:,0]=Numeric.take(lut_[0],a)\n #print \"rtake\"\n rgb[:,1]=Numeric.take(lut_[1],a)\n #print \"gtake\"\n rgb[:,2]=Numeric.take(lut_[2],a)\n #print \"btake\"\n #rgb=Numeric.ravel(((Numeric.array(zip(r,g,b),\n # typecode=Numeric.UInt8))))\n\n #print \"rgb done\"\n else:\n N = len(lut[0])\n print \"LUT with N=%d entries\" % N\n if N>=256*256:\n print \"UUPS, more than uint16 colors??\", N\n raise ValueError(\"N too large\")\n \n a = (Numeric.ravel((N-1)*(a-a_min)/\n (a_max-a_min))).astype(Numeric.UInt16)\n\n rgb = Numeric.zeros( (len(a), 3), typecode=Numeric.UInt16)\n\n lut_ = Numeric.zeros( (3,len(lut[0])), Numeric.UInt16)\n lut_[0] = lut[0].astype(Numeric.UInt16)\n lut_[1] = lut[1].astype(Numeric.UInt16)\n lut_[2] = lut[2].astype(Numeric.UInt16)\n\n # This is much faster than the original zip/ravel variant ...\n rgb[:,0] = Numeric.take(lut_[0],a)\n rgb[:,1] = Numeric.take(lut_[1],a)\n rgb[:,2] = Numeric.take(lut_[2],a)\n\n rgb = (rgb*256.0/N).astype(Numeric.UInt8)\n\n else: # simple grey scale ramp...\n a=(Numeric.ravel(255.0*(a-a_min)/\n (a_max-a_min))).astype(Numeric.UInt8)\n # convert to (r_0,g_0,b_0,r_1,g_1,b_1,....)\n rgb=Numeric.ravel(Numeric.array(zip(a,a,a)))\n\n # create a PIL RGB image\n #print \"w/h\",w,h\n im=Image.new(\"RGB\",(w,h))\n #print \"imfromstring:\"\n im.fromstring(rgb.tostring())\n #print \"done ...\"\n \n # scale image ?\n if height!=None and width==None:\n im=im.resize(w/h*height,height)\n elif height==None and width!=None:\n im=im.resize(width,h/w*width)\n elif height!=None and width!=None:\n im=im.resize(width,height)\n\n return(im)","def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\n\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)","def yiq2rgb(im_yiq):\n return multiply_by_left_matrix(np.linalg.inv(YIQ_MATRIX), im_yiq)","def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\r\n\r\n temp_X = cobj.xyz_x\r\n temp_Y = cobj.xyz_y\r\n temp_Z = cobj.xyz_z\r\n\r\n logger.debug(\" \\- Target RGB space: %s\", target_rgb)\r\n target_illum = target_rgb.native_illuminant\r\n logger.debug(\" \\- Target native illuminant: %s\", target_illum)\r\n logger.debug(\" \\- XYZ color's illuminant: %s\", cobj.illuminant)\r\n \r\n # If the XYZ values were taken with a different reference white than the\r\n # native reference white of the target RGB space, a transformation matrix\r\n # must be applied.\r\n if cobj.illuminant != target_illum:\r\n logger.debug(\" \\* Applying transformation from %s to %s \",\r\n cobj.illuminant, target_illum)\r\n # Get the adjusted XYZ values, adapted for the target illuminant.\r\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\r\n temp_X, temp_Y, temp_Z,\r\n orig_illum=cobj.illuminant, targ_illum=target_illum)\r\n logger.debug(\" \\* New values: %.3f, %.3f, %.3f\",\r\n temp_X, temp_Y, temp_Z)\r\n \r\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\r\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\r\n temp_X, temp_Y, temp_Z,\r\n rgb_type=target_rgb, convtype=\"xyz_to_rgb\")\r\n\r\n # v\r\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\r\n # V\r\n nonlinear_channels = {}\r\n if target_rgb == sRGBColor:\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n if v <= 0.0031308:\r\n nonlinear_channels[channel] = v * 12.92\r\n else:\r\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\r\n else:\r\n # If it's not sRGB...\r\n for channel in ['r', 'g', 'b']:\r\n v = linear_channels[channel]\r\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\r\n\r\n return target_rgb(\r\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])","def load_image_as_rgb(image_path):\n im = imageio.imread(image_path)\n y_size = im.shape[0]\n x_size = im.shape[1]\n logging.info(\"Image has dimensions X:%d Y:%d\" % (x_size, y_size))\n arr = np.zeros((im.shape[0],im.shape[1]), dtype=int)\n i = 0\n for im_row in im:\n j = 0\n for vec in im_row:\n arr[i,j] = rgb_vec_to_num(vec)\n j = j + 1\n i = i + 1\n return arr","def _rgb2y(self, im):\n if len(im.shape) < 3:\n return im\n return np.sum(im * [0.299, 0.587, 0.114], axis=2)","def applyJET(img):\n\treturn applyColorMap(img, \"jet\")","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def bgr_to_rgb(ims):\n out = []\n for im in ims:\n out.append(im[:,:,::-1])\n return out","def test_srgb_conversion_to_xyz_d50(self):\r\n\r\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\r\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))","def yiq2rgb(imYIQ):\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))","def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:\n r: torch.Tensor = image[..., 0, :, :]\n g: torch.Tensor = image[..., 1, :, :]\n b: torch.Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b\n cb: torch.Tensor = (b - y) * 0.564 + delta\n cr: torch.Tensor = (r - y) * 0.713 + delta\n return torch.stack([y, cb, cr], -3)","def _hsv_to_rgb(img):\n h, s, v = img.unbind(axis=-3)\n f = h * 6.0\n i = paddle.floor(f)\n f = f - i\n i = i.astype(paddle.int32) % 6\n\n p = paddle.clip(v * (1.0 - s), 0.0, 1.0)\n q = paddle.clip(v * (1.0 - s * f), 0.0, 1.0)\n t = paddle.clip(v * (1.0 - s * (1.0 - f)), 0.0, 1.0)\n\n mask = paddle.equal(\n i.unsqueeze(axis=-3),\n paddle.arange(6, dtype=i.dtype).reshape((-1, 1, 1)),\n ).astype(img.dtype)\n matrix = paddle.stack(\n [\n paddle.stack([v, q, p, p, t, v], axis=-3),\n paddle.stack([t, v, v, q, p, p], axis=-3),\n paddle.stack([p, p, t, v, v, q], axis=-3),\n ],\n axis=-4,\n )\n return paddle.einsum(\"...ijk, ...xijk -> ...xjk\", mask, matrix)","def rgb_to_xy(red, green, blue):\n\n # gamma correction\n red = pow((red + 0.055) / (1.0 + 0.055), 2.4) if red > 0.04045 else (red / 12.92)\n green = pow((green + 0.055) / (1.0 + 0.055), 2.4) if green > 0.04045 else (green / 12.92)\n blue = pow((blue + 0.055) / (1.0 + 0.055), 2.4) if blue > 0.04045 else (blue / 12.92)\n\n # convert rgb to xyz\n x = red * 0.649926 + green * 0.103455 + blue * 0.197109\n y = red * 0.234327 + green * 0.743075 + blue * 0.022598\n z = green * 0.053077 + blue * 1.035763\n\n # convert xyz to xy\n x = x / (x + y + z)\n y = y / (x + y + z)\n\n # TODO check color gamut if known\n \n return [x, y]","def rgbToCIE1931(self, red, green, blue):\n point = self.color.getXYPointFromRGB(red, green, blue)\n return [point.x, point.y]","def load_rgb(path):\n bands = band_list['rgb']\n img = None\n fmt = \"_{}.tif\"\n for b in bands:\n band_ds = rasterio.open(path + fmt.format(b))\n aux = band_ds.read(1)\n aux = norm_band(aux)\n aux = np.expand_dims(aux, axis=-1)\n if img is None:\n img = aux\n else:\n img = np.concatenate((img, aux), axis=-1)\n return img","def to_color(self):\n if self.channels == 4:\n color = opencv.cvtColor(self.img, opencv.COLOR_BGRA2BGR)\n return Image(color)\n elif self.channels == 1:\n color = opencv.cvtColor(self.img, opencv.COLOR_GRAY2BGR)\n return Image(color)\n else:\n return Image(self.img)","def xyz2linear_rgb(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(rgb_from_xyz, arr)","def linear_rgb2lab(rgb, illuminant=\"D65\", observer=\"2\"):\n return xyz2lab(linear_rgb2xyz(rgb), illuminant, observer)","def Luv_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n # Without Light, there is no color. Short-circuit this and avoid some\r\n # zero division errors in the var_a_frac calculation.\r\n if cobj.luv_l <= 0.0:\r\n xyz_x = 0.0\r\n xyz_y = 0.0\r\n xyz_z = 0.0\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\r\n\r\n # Various variables used throughout the conversion.\r\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\r\n u_sub_0 = (4.0 * illum[\"X\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n v_sub_0 = (9.0 * illum[\"Y\"]) / (illum[\"X\"] + 15.0 * illum[\"Y\"] + 3.0 * illum[\"Z\"])\r\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\r\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\r\n\r\n # Y-coordinate calculations.\r\n if cobj.luv_l > cie_k_times_e:\r\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\r\n else:\r\n xyz_y = cobj.luv_l / color_constants.CIE_K\r\n\r\n # X-coordinate calculation.\r\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\r\n # Z-coordinate calculation.\r\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\r\n\r\n return XYZColor(\r\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)","def green_channel(input_image):\n return input_image[:, :, 1]","def get_rgb_bands(image, bands):\n if bands is not MONOCHROME:\n red = image[:, :, bands['red']]\n green = image[:, :, bands['green']]\n blue = image[:, :, bands['blue']]\n\n img = np.rollaxis(np.array([red, green, blue]), 0, 3)\n else:\n img = color.grey2rgb(image)\n\n return img","def linear_rgb2xyz(rgb):\n arr = _prepare_colorarray(rgb).copy()\n return _convert(xyz_from_rgb, arr)","def convert_yuv_to_rgb(img_arr): \n rgb = cv2.cvtColor(img_arr, cv2.COLOR_YUV2BGR_I420)\n rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)\n return Image.fromarray(rgb)","def get_BGR_img(self):\n img = self.img.copy()\n # Convert BGR to HSV\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n # define range of BGR color in HSV\n threshold_blue = np.array([[100,43,46], [124,255,255]])\n threshold_green = np.array([[35,43,46], [77,255,255]])\n threshold_red1 = np.array([[0,43,46], [10,255,255]])\n threshold_red2 = np.array([[156,43,46], [180,255,255]])\n # Threshold the HSV image to get only BGR colors\n mask_blue = cv2.inRange(hsv, threshold_blue[0], threshold_blue[1])\n mask_green = cv2.inRange(hsv, threshold_green[0], threshold_green[1])\n mask_red1 = cv2.inRange(hsv, threshold_red1[0], threshold_red1[1])\n mask_red2 = cv2.inRange(hsv, threshold_red2[0], threshold_red2[1])\n mask_red = mask_red1 | mask_red2\n # Bitwise-AND mask and original image\n self.blue = cv2.bitwise_and(img, img, mask=mask_blue)\n self.green = cv2.bitwise_and(img, img, mask=mask_green)\n self.red = cv2.bitwise_and(img, img, mask=mask_red)\n # 返回 bgr 三通道的分量合成的图片\n return np.stack((self.blue[:, :, 0], self.green[:, :, 1], self.red[:, :, 2]), axis=2)","def __enhance_image(self, img):\n\n blue = self.g.clahe.apply(img[:,:,0])\n green = self.g.clahe.apply(img[:,:,1])\n red = self.g.clahe.apply(img[:,:,2])\n img[:,:,0] = blue\n img[:,:,1] = green\n img[:,:,2] = red\n return img","def testImageProcessing():\n Im_pix = getRGB( 'in.png' ) # read in the in.png image\n print \"The first two pixels of the first row are\",\n print Im_pix[0][0:2]\n # remember that Im_pix is a list (the image)\n # of lists (each row) of lists (each pixel is [R,G,B])\n New_pix = [ [ [255 - num for num in p] for p in row ] for row in Im_pix ]\n # now, save to the file 'out.png'\n saveRGB( New_pix, 'out.png' )","def imcast(img, dtype, color_space=\"default\"):\n if img.dtype == dtype:\n return img\n if color_space == \"default\":\n if dtype == np.uint8:\n if img.dtype == np.uint16:\n return np.asarray(img / 257, np.uint8)\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img * 255., np.uint8)\n elif dtype == np.uint16:\n if img.dtype == np.uint8:\n return np.asarray(img, np.uint16) * 257\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img * 65535., np.uint16)\n elif dtype == np.float32 or dtype == np.float64:\n if img.dtype == np.uint8:\n return np.asarray(img, dtype) / 255.\n elif img.dtype == np.uint16:\n return np.asarray(img, dtype) / 65535.\n elif img.dtype == np.float32 or img.dtype == np.float64:\n return np.asarray(img, dtype)\n elif color_space == \"CIE-L*a*b*\":\n if dtype == np.uint8:\n if img.dtype == np.float32 or img.dtype == np.float64:\n dst = np.empty(img.shape, np.uint8)\n dst[:,:,0] = img[:,:,0] * 255. / 100.\n dst[:,:,1] = img[:,:,1] + 128.\n dst[:,:,2] = img[:,:,2] + 128.\n return dst\n elif dtype == np.float32 or dtype == np.float64:\n if img.dtype == np.uint8:\n dst = np.empty(img.shape, dtype)\n dst[:,:,0] = np.asarray(img[:,:,0], dtype) / 255. * 100.\n dst[:,:,1] = np.asarray(img[:,:,1], dtype) - 128.\n dst[:,:,2] = np.asarray(img[:,:,2], dtype) - 128.\n return dst\n raise Exception(\n \"Unexpected conversion from '%s' to '%s' with '%s' color space\" % \\\n (img.dtype, dtype, color_space))","def rgb_to_luv(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\n if not isinstance(image, torch.Tensor):\n raise TypeError(f\"Input type is not a torch.Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n # Convert from sRGB to Linear RGB\n lin_rgb = rgb_to_linear_rgb(image)\n\n xyz_im: torch.Tensor = rgb_to_xyz(lin_rgb)\n\n x: torch.Tensor = xyz_im[..., 0, :, :]\n y: torch.Tensor = xyz_im[..., 1, :, :]\n z: torch.Tensor = xyz_im[..., 2, :, :]\n\n threshold = 0.008856\n L: torch.Tensor = torch.where(y > threshold, 116.0 * torch.pow(y.clamp(min=threshold), 1.0 / 3.0) - 16.0, 903.3 * y)\n\n # Compute reference white point\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\n\n u_p: torch.Tensor = (4 * x) / (x + 15 * y + 3 * z + eps)\n v_p: torch.Tensor = (9 * y) / (x + 15 * y + 3 * z + eps)\n\n u: torch.Tensor = 13 * L * (u_p - u_w)\n v: torch.Tensor = 13 * L * (v_p - v_w)\n\n out = torch.stack([L, u, v], dim=-3)\n\n return out","def imageprepare():\r\n file_name = 'temp_image.png'\r\n im = Image.open(file_name).convert('L')\r\n im = im.resize((20, 20))\r\n p = Image.new('L', (28,28), (255))\r\n p.paste(im,(4,4,24,24))\r\n p.save(\"last_image.png\")\r\n\r\n tv = list(p.getdata()) # get pixel values\r\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\r\n tva = np.reshape(tva, (28, 28))\r\n\r\n return tva","def convert_img(self):\r\n self.img = self.img.convert('RGB')","def greyscale(img):\n grey_img = np.zeros([210, 160])\n for i in range(3):\n grey_img =np.sum([grey_img, img[:, :, i]], 0)\n grey_img /= 3\n grey_img = grey_img.astype(np.uint8)\n return grey_img","def transformYIQ2RGB(imgYIQ: np.ndarray) -> np.ndarray:\r\n yiq_from_rgb = np.array([[0.299, 0.587, 0.114],\r\n [0.59590059, -0.27455667, -0.32134392],\r\n [0.21153661, -0.52273617, 0.31119955]])\r\n rgb_from_yiq = np.linalg.inv(yiq_from_rgb)\r\n\r\n RGBImg = np.ndarray(imgYIQ.shape)\r\n\r\n RGBImg[:, :, 0] = rgb_from_yiq[0,0] * imgYIQ[:, :, 0] + rgb_from_yiq[0,1] * imgYIQ[:, :, 1] + rgb_from_yiq[0,2] * imgYIQ[:, :, 2]\r\n RGBImg[:, :, 1] = rgb_from_yiq[1,0] * imgYIQ[:, :, 0] + rgb_from_yiq[1,1] * imgYIQ[:, :, 1] + rgb_from_yiq[1,2] * imgYIQ[:, :, 2]\r\n RGBImg[:, :, 2] = rgb_from_yiq[2,0] * imgYIQ[:, :, 0] + rgb_from_yiq[2,1] * imgYIQ[:, :, 1] + rgb_from_yiq[2,2] * imgYIQ[:, :, 2]\r\n\r\n return RGBImg","def make_grayscale(img: Image.Image) -> Image.Image:\n orig_mode = img.mode\n\n if orig_mode in [\"RGB\", \"CMYK\", \"YCbCr\", \"LAB\", \"HSV\"]:\n return img.convert(\"L\")\n elif orig_mode == \"RGBA\":\n return img.convert(\"LA\").convert(\"RGBA\")\n elif orig_mode == \"P\":\n # Using ITU-R 601-2 luma transform: L = R * 299/1000 + G * 587/1000 + B * 114/1000\n pal = img.getpalette()\n for i in range(len(pal) // 3):\n # Using ITU-R 601-2 luma transform\n gray = (pal[3 * i] * 299 + pal[3 * i + 1] * 587 + pal[3 * i + 2] * 114)\n gray = gray // 1000\n pal[3 * i: 3 * i + 3] = [gray, gray, gray]\n img.putpalette(pal)\n return img\n else:\n return img","def test_conversion_through_rgb(self):\r\n\r\n xyz = convert_color(self.color, XYZColor)\r\n hsl = convert_color(xyz, HSLColor, through_rgb_type=AdobeRGBColor)\r\n # Notice how we don't have to pass through_rgb_type explicitly.\r\n xyz2 = convert_color(hsl, XYZColor)\r\n self.assertColorMatch(xyz, xyz2)","def get_rgb_from_bits(self, bits):\n bits.shape = (self._dest_height,self._dest_width,4)\n self._rgb[0] = bits[:,:,2]\n self._rgb[1] = bits[:,:,1]\n self._rgb[2] = bits[:,:,0]\n return self._rgb"],"string":"[\n \"def lab_to_rgb(img):\\n new_img = np.zeros((256, 256, 3))\\n for i in range(len(img)):\\n for j in range(len(img[i])):\\n pix = img[i, j]\\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\\n new_img = color.lab2rgb(new_img) * 255\\n new_img = new_img.astype('uint8')\\n return new_img\",\n \"def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\\n xyz = rgb_to_xyz(image)\\n lab_image = xyz_to_lab(xyz)\\n return lab_image\",\n \"def lab_to_rgb(img_l, img_ab):\\n lab = np.empty([*img_l.shape[0:2], 3])\\n lab[:, :, 0] = np.squeeze(((img_l + 1) * 50))\\n lab[:, :, 1:] = img_ab * 127\\n return color.lab2rgb(lab)\",\n \"def example_rgb_to_xyz():\\r\\n\\r\\n print(\\\"=== RGB Example: RGB->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n rgb = sRGBColor(120, 130, 140)\\r\\n # Show a string representation.\\r\\n print(rgb)\\r\\n # Convert RGB to XYZ using a D50 illuminant.\\r\\n xyz = convert_color(rgb, XYZColor, target_illuminant='D50')\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def sRGBFromLab(lab):\\n return xyzTosRGB(labToXYZ(lab, [0.9504559, 1, 1.089058]))\",\n \"def Lab_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n xyz_y = (cobj.lab_l + 16.0) / 116.0\\r\\n xyz_x = cobj.lab_a / 500.0 + xyz_y\\r\\n xyz_z = xyz_y - cobj.lab_b / 200.0\\r\\n \\r\\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\\r\\n xyz_y = math.pow(xyz_y, 3)\\r\\n else:\\r\\n xyz_y = (xyz_y - 16.0 / 116.0) / 7.787\\r\\n\\r\\n if math.pow(xyz_x, 3) > color_constants.CIE_E:\\r\\n xyz_x = math.pow(xyz_x, 3)\\r\\n else:\\r\\n xyz_x = (xyz_x - 16.0 / 116.0) / 7.787\\r\\n \\r\\n if math.pow(xyz_z, 3) > color_constants.CIE_E:\\r\\n xyz_z = math.pow(xyz_z, 3)\\r\\n else:\\r\\n xyz_z = (xyz_z - 16.0 / 116.0) / 7.787\\r\\n \\r\\n xyz_x = (illum[\\\"X\\\"] * xyz_x)\\r\\n xyz_y = (illum[\\\"Y\\\"] * xyz_y)\\r\\n xyz_z = (illum[\\\"Z\\\"] * xyz_z)\\r\\n \\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def example_lab_to_rgb():\\r\\n\\r\\n print(\\\"=== RGB Example: Lab->RGB ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.217)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n rgb = convert_color(lab, sRGBColor)\\r\\n print(rgb)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def rgb2Lab(rgbvalue):\\r\\n RGB2Lab_Matrix = np.array([[0.412453, 0.357580, 0.180423],\\r\\n [0.212671, 0.715160, 0.072169],\\r\\n [0.019334, 0.119193, 0.950227]])\\r\\n R = rgbvalue[0]\\r\\n G = rgbvalue[1]\\r\\n B = rgbvalue[2]\\r\\n gammaR = gamma(R / 255.0)\\r\\n gammaG = gamma(G / 255.0)\\r\\n gammaB = gamma(B / 255.0)\\r\\n RGBvalue = np.array([gammaR, gammaG, gammaB])\\r\\n RGBvalue = RGBvalue.reshape(3, 1)\\r\\n XYZvalue = np.dot(RGB2Lab_Matrix, RGBvalue)\\r\\n assert XYZvalue.shape == (3, 1)\\r\\n correction = np.array([[1.0 / 0.950456, 1.0, 1.0 / 1.088754]]).T\\r\\n assert correction.shape == (3, 1)\\r\\n XYZ = XYZvalue * correction\\r\\n assert XYZ.shape == (3, 1)\\r\\n YYn = ft(XYZ[1])\\r\\n XXn = ft(XYZ[0])\\r\\n ZZn = ft(XYZ[2])\\r\\n L = 116 * YYn - 16\\r\\n a = 500 * (XXn - YYn)\\r\\n b = 200 * (YYn - ZZn)\\r\\n return [int(L), int(a), int(b)]\",\n \"def XYZ_to_Lab(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n temp_x = cobj.xyz_x / illum[\\\"X\\\"]\\r\\n temp_y = cobj.xyz_y / illum[\\\"Y\\\"]\\r\\n temp_z = cobj.xyz_z / illum[\\\"Z\\\"]\\r\\n \\r\\n if temp_x > color_constants.CIE_E:\\r\\n temp_x = math.pow(temp_x, (1.0 / 3.0))\\r\\n else:\\r\\n temp_x = (7.787 * temp_x) + (16.0 / 116.0) \\r\\n\\r\\n if temp_y > color_constants.CIE_E:\\r\\n temp_y = math.pow(temp_y, (1.0 / 3.0))\\r\\n else:\\r\\n temp_y = (7.787 * temp_y) + (16.0 / 116.0)\\r\\n \\r\\n if temp_z > color_constants.CIE_E:\\r\\n temp_z = math.pow(temp_z, (1.0 / 3.0))\\r\\n else:\\r\\n temp_z = (7.787 * temp_z) + (16.0 / 116.0)\\r\\n \\r\\n lab_l = (116.0 * temp_y) - 16.0\\r\\n lab_a = 500.0 * (temp_x - temp_y)\\r\\n lab_b = 200.0 * (temp_y - temp_z)\\r\\n return LabColor(\\r\\n lab_l, lab_a, lab_b, observer=cobj.observer, illuminant=cobj.illuminant)\",\n \"def rgb_to_lab(img, l=False, ab=False):\\n img = img / 255\\n l_chan = color.rgb2lab(img)[:, :, 0]\\n l_chan = l_chan / 50 - 1\\n l_chan = l_chan[..., np.newaxis]\\n\\n ab_chan = color.rgb2lab(img)[:, :, 1:]\\n ab_chan = (ab_chan + 128) / 255 * 2 - 1\\n if l:\\n return l_chan\\n else:\\n return ab_chan\",\n \"def rgb2lab(r, g, b):\\n r, g, b = r / 255.0, g / 255.0, b / 255.0\\n\\n # http://www.brucelindbloom.com/index.html?Math.html\\n # Inverse sRGB Companding\\n r = r / 12.92 if r <= 0.04045 else ((r + 0.055) / 1.055) ** 2.4\\n g = g / 12.92 if g <= 0.04045 else ((g + 0.055) / 1.055) ** 2.4\\n b = b / 12.92 if b <= 0.04045 else ((b + 0.055) / 1.055) ** 2.4\\n\\n # http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html\\n # sRGB, D65\\n x = r * 0.4124564 + g * 0.3575761 + b * 0.1804375\\n y = r * 0.2126729 + g * 0.7151522 + b * 0.0721750\\n z = r * 0.0193339 + g * 0.1191920 + b * 0.9503041\\n\\n # http://www.brucelindbloom.com/index.html?Eqn_XYZ_to_Lab.html\\n kappa, epsilon = 903.3, 0.008856\\n\\n # http://brucelindbloom.com/index.html?Eqn_ChromAdapt.html\\n # White point for D65\\n xr, yr, zr = x / 0.95047, y / 1.00000, z / 1.08883\\n\\n fx = xr ** (1 / 3.0) if xr > epsilon else (kappa * xr + 16) / 116.0\\n fy = yr ** (1 / 3.0) if yr > epsilon else (kappa * yr + 16) / 116.0\\n fz = zr ** (1 / 3.0) if zr > epsilon else (kappa * zr + 16) / 116.0\\n\\n l = 166.0 * fy - 16.0\\n a = 500.0 * (fx - fy)\\n b = 200.0 * (fy - fz)\\n\\n return l, a, b\",\n \"def get_rgb(self, img, r, g, b):\\r\\n\\r\\n # Get specific bands of hyperspectral image\\r\\n red_channel = img[:, :, r]\\r\\n green_channel = img[:, :, g]\\r\\n blue_channel = img[:, :, b]\\r\\n\\r\\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\\r\\n img = img.astype('float32')\\r\\n return img\",\n \"def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n L: torch.Tensor = image[..., 0, :, :]\\n u: torch.Tensor = image[..., 1, :, :]\\n v: torch.Tensor = image[..., 2, :, :]\\n\\n # Convert from Luv to XYZ\\n y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3)\\n\\n # Compute white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n a: torch.Tensor = u_w + u / (13 * L + eps)\\n d: torch.Tensor = v_w + v / (13 * L + eps)\\n c: torch.Tensor = 3 * y * (5 * d - 3)\\n\\n z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps)\\n x: torch.Tensor = -(c / (d + eps) + 3.0 * z)\\n\\n xyz_im: torch.Tensor = torch.stack([x, y, z], -3)\\n\\n rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)\\n\\n # Convert from RGB Linear to sRGB\\n rgb_im = linear_rgb_to_rgb(rgbs_im)\\n\\n return rgb_im\",\n \"def sRGBToLab(rgb):\\n return xyzToLab(xyzFromsRGB(rgb), [0.9504559, 1, 1.089058])\",\n \"def sRGBFromLabD50(lab):\\n return xyzTosRGBD50(labToXYZ(lab, [0.9642957, 1, 0.8251046]))\",\n \"def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n var_x = x / 100\\n var_y = y / 100\\n var_z = z / 100\\n\\n var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986\\n var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415\\n var_b = var_x * 0.0557 + var_y * -0.2040 + var_z * 1.0570\\n\\n var_r = tf.where(var_r > 0.0031308,\\n 1.055 * tf.pow(var_r, (1 / 2.4)) - 0.055,\\n 12.92 * var_r)\\n var_g = tf.where(var_g > 0.0031308,\\n 1.055 * tf.pow(var_g, (1 / 2.4)) - 0.055,\\n 12.92 * var_g)\\n var_b = tf.where(var_b > 0.0031308,\\n 1.055 * tf.pow(var_b, (1 / 2.4)) - 0.055,\\n 12.92 * var_b)\\n r = var_r * 255\\n g = var_g * 255\\n b = var_b * 255\\n rgb_image = tf.cast(tf.stack([r, g, b], axis=-1), tf.uint8)\\n return rgb_image\",\n \"def l_to_rgb(img_l):\\n lab = np.squeeze(255 * (img_l + 1) / 2)\\n return color.gray2rgb(lab) / 255\",\n \"def rgb2alpha(img):\\n\\t### First of all we need the size of our picture to make the transforms\\n\\n\\tx = len(img) ; y = len(img[0])\\n\\talpha = np.full((x, y, 3), 0, dtype = float)\\t\\t## This will be the transformed image\\n\\n\\t### Now we gotta access each pixel of the picture\\n\\n\\tfor i, vi in enumerate(img):\\n\\t\\tfor j, px in enumerate(vi):\\n\\t\\t\\t### There we are\\n\\n\\t\\t\\t# Step 1 : LMS transform, for that we use r_l\\n\\n\\t\\t\\talpha[i][j] = np.matmul(r_l, px)\\n\\n\\t\\t\\t# Step 2 : log em all (decimal log)\\n\\n\\t\\t\\talpha[i][j][0] = log(alpha[i][j][0])\\n\\t\\t\\talpha[i][j][1] = log(alpha[i][j][1])\\n\\t\\t\\talpha[i][j][2] = log(alpha[i][j][2])\\n\\n\\t\\t\\t# Step 3 : l alpha beta transform, by using l_a\\n\\n\\t\\t\\talpha[i][j] = np.matmul(l_a, alpha[i][j])\\n\\n\\treturn alpha\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n l, a, b = tf.unstack(image, axis=-1)\\n\\n var_y = (l + 16) / 116\\n var_x = a / 500 + var_y\\n var_z = var_y - b / 200\\n var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),\\n (var_x - 16 / 116) / 7.787)\\n var_y = tf.where(tf.pow(var_y, 3) > 0.008856, tf.pow(var_y, 3),\\n (var_y - 16 / 116) / 7.787)\\n var_z = tf.where(tf.pow(var_z, 3) > 0.008856, tf.pow(var_z, 3),\\n (var_z - 16 / 116) / 7.787)\\n\\n refx = 95.047\\n refy = 100.00\\n ref_z = 108.883\\n\\n x = var_x * refx\\n y = var_y * refy\\n z = var_z * ref_z\\n xyz_image = tf.stack([x, y, z], axis=-1)\\n return xyz_image\",\n \"def convert_color(image, color_space):\\n out_image = None\\n if color_space != 'RGB':\\n if color_space == 'HSV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HSV)\\n elif color_space == 'LUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2LUV)\\n elif color_space == 'HLS':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2HLS)\\n elif color_space == 'YUV':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YUV)\\n elif color_space == 'YCrCb':\\n out_image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_RGB2YCrCb)\\n else:\\n out_image = np.copy(image)\\n return out_image\",\n \"def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:\\n r, g, b = tf.unstack(image, axis=-1)\\n var_r = r / 255\\n var_g = g / 255\\n var_b = b / 255\\n\\n var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),\\n var_r / 12.92)\\n var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055) / 1.055, 2.4),\\n var_g / 12.92)\\n var_b = tf.where(var_b > 0.04045, tf.pow((var_b + 0.055) / 1.055, 2.4),\\n var_b / 12.92)\\n var_r = var_r * 100\\n var_g = var_g * 100\\n var_b = var_b * 100\\n\\n x = var_r * 0.4124 + var_g * 0.3576 + var_b * 0.1805\\n y = var_r * 0.2126 + var_g * 0.7152 + var_b * 0.0722\\n z = var_r * 0.0193 + var_g * 0.1192 + var_b * 0.9505\\n\\n image_xyz = tf.stack([x, y, z], axis=-1)\\n return image_xyz\",\n \"def rgb_processing(rgb_img, center, scale, rot=0):\\n rgb_img = crop(rgb_img, center, scale, \\n [constants.IMG_RES, constants.IMG_RES], rot=rot)\\n # (3,224,224),float,[0,1]\\n rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0\\n return rgb_img\",\n \"def _convert_to_yolo_img(self, img):\\n\\n img = img / 255.0\\n h, w, c = img.shape\\n img = img.transpose(2, 0, 1)\\n outimg = make_image(w, h, c)\\n img = img.reshape((w*h*c))\\n data = c_array(c_float, img)\\n outimg.data = data\\n rgbgr_image(outimg)\\n return outimg\",\n \"def matplotlib_image(image):\\n if image.ndim == 2:\\n rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\\n else:\\n rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n return rgb\",\n \"def red_channel(img):\\n\\n red = np.zeros(img.shape,dtype=float)\\n\\n red[:,:,2] = np.copy(img[:,:,2])\\n\\n return red\",\n \"def ycbcr_to_rgb(image: torch.Tensor) -> torch.Tensor:\\n y: torch.Tensor = image[..., 0, :, :]\\n cb: torch.Tensor = image[..., 1, :, :]\\n cr: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n cb_shifted: torch.Tensor = cb - delta\\n cr_shifted: torch.Tensor = cr - delta\\n\\n r: torch.Tensor = y + 1.403 * cr_shifted\\n g: torch.Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\\n b: torch.Tensor = y + 1.773 * cb_shifted\\n return torch.stack([r, g, b], -3)\",\n \"def yuv2rgb(im):\\n ## conflicting definitions exist depending on whether you use the full range\\n ## of YCbCr or clamp out to the valid range. see here\\n ## http://www.equasys.de/colorconversion.html\\n ## http://www.fourcc.org/fccyvrgb.php\\n from numpy import dot, ndarray, array\\n # if not im.dtype == 'uint8':\\n # raise ImageUtilsError('yuv2rgb only implemented for uint8 arrays')\\n\\n ## better clip input to the valid range just to be on the safe side\\n yuv = ndarray(im.shape) ## float64\\n yuv[:, :, 0] = im[:, :, 0].clip(16, 235).astype(yuv.dtype) - 16\\n yuv[:, :, 1:] = im[:, :, 1:].clip(16, 240).astype(yuv.dtype) - 128\\n\\n ## ITU-R BT.601 version (SDTV)\\n A = array([[1., 0., 0.701],\\n [1., -0.886 * 0.114 / 0.587, -0.701 * 0.299 / 0.587],\\n [1., 0.886, 0.]])\\n A[:, 0] *= 255. / 219.\\n A[:, 1:] *= 255. / 112.\\n\\n ## ITU-R BT.709 version (HDTV)\\n # A = array([[1.164, 0., 1.793],\\n # [1.164, -0.213, -0.533],\\n # [1.164, 2.112, 0.]])\\n\\n rgb = dot(yuv, A.T)\\n return rgb.clip(0, 255).astype('uint8')\",\n \"def example_lab_to_xyz():\\r\\n\\r\\n print(\\\"=== Simple Example: Lab->XYZ ===\\\")\\r\\n # Instantiate an Lab color object with the given values.\\r\\n lab = LabColor(0.903, 16.296, -2.22)\\r\\n # Show a string representation.\\r\\n print(lab)\\r\\n # Convert to XYZ.\\r\\n xyz = convert_color(lab, XYZColor)\\r\\n print(xyz)\\r\\n print(\\\"=== End Example ===\\\\n\\\")\",\n \"def grey_to_rgb_imitation(img):\\n return np.repeat(img[...,np.newaxis], 3, -1)\",\n \"def sRGBToLabD50(rgb):\\n return xyzToLab(xyzFromsRGBD50(rgb), [0.9642957, 1, 0.8251046])\",\n \"def XYZ_to_RGB(XYZ,\\n illuminant_XYZ,\\n illuminant_RGB,\\n XYZ_to_RGB_matrix,\\n chromatic_adaptation_transform='CAT02',\\n encoding_cctf=None):\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)\\n\\n if encoding_cctf is not None:\\n RGB = encoding_cctf(RGB)\\n\\n return RGB\",\n \"def getRGBFromXYAndBrightness(self, x, y, bri=1):\\n # The xy to color conversion is almost the same, but in reverse order.\\n # Check if the xy value is within the color gamut of the lamp.\\n # If not continue with step 2, otherwise step 3.\\n # We do this to calculate the most accurate color the given light can actually do.\\n xyPoint = XYPoint(x, y)\\n\\n if not self.checkPointInLampsReach(xyPoint):\\n # Calculate the closest point on the color gamut triangle\\n # and use that as xy value See step 6 of color to xy.\\n xyPoint = self.getClosestPointToPoint(xyPoint)\\n\\n # Calculate XYZ values Convert using the following formulas:\\n Y = bri\\n X = (Y / xyPoint.y) * xyPoint.x\\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\\n\\n # Convert to RGB using Wide RGB D65 conversion\\n r = X * 1.612 - Y * 0.203 - Z * 0.302\\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\\n b = X * 0.026 - Y * 0.072 + Z * 0.962\\n\\n # Apply reverse gamma correction\\n r, g, b = map(\\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\\n [r, g, b]\\n )\\n\\n # Bring all negative components to zero\\n r, g, b = map(lambda x: max(0, x), [r, g, b])\\n\\n # If one component is greater than 1, weight components by that value.\\n max_component = max(r, g, b)\\n if max_component > 1:\\n r, g, b = map(lambda x: x / max_component, [r, g, b])\\n\\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\\n\\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\\n return (r, g, b)\",\n \"def sRGBLuminance(x):\\n lin=linearFromsRGB3(x)\\n return lin[0]*0.2126+lin[1]*0.7152+lin[2]*0.0722\",\n \"def _colored_img_to_arr(image, verbose=False):\\n height, width = image.size\\n arr = np.array(image.getdata())\\n arr = arr.reshape(3, height, width)\\n r = arr[0]\\n g = arr[1]\\n b = arr[2]\\n return r, g, b\",\n \"def read_img(filename) -> Tuple[np.ndarray, np.ndarray]:\\n img = cv2.imread(filename, 3)\\n labimg = cv2.cvtColor(cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE)), cv2.COLOR_BGR2Lab)\\n return np.reshape(labimg[:, :, 0], (config.IMAGE_SIZE, config.IMAGE_SIZE, 1)), labimg[:, :, 1:]\",\n \"def convert_color(image, color_space='RGB'):\\n color_space = color_space.lower()\\n if color_space != 'rgb':\\n if color_space == 'hsv':\\n color_transformation = cv2.COLOR_BGR2HSV\\n elif color_space == 'luv':\\n color_transformation = cv2.COLOR_BGR2LUV\\n elif color_space == 'hls':\\n color_transformation = cv2.COLOR_BGR2HLS\\n elif color_space == 'yuv':\\n color_transformation = cv2.COLOR_BGR2YUV\\n elif color_space == 'ycrcb':\\n color_transformation = cv2.COLOR_BGR2YCrCb\\n else:\\n raise ValueError('Invalid value %s for color_space parameters. Valid color spaces are: RGB, HSV, LUV, '\\n 'HLS, YUV, YCrCb' % color_space)\\n\\n return cv2.cvtColor(image, color_transformation)\\n else:\\n return image\",\n \"def img_to_rgb(img):\\r\\n if len(img.shape) < 3 or img.shape[2] == 1:\\r\\n return np.repeat(img, 3).reshape(img.shape[0], img.shape[1], 3)\\r\\n else:\\r\\n return img\",\n \"def luminance(rgb):\\n \\n (r, g, b) = rgb\\n return (r + g + b) // 3\",\n \"def load_color_image_features(img_path):\\n ac = scipy.misc.imread(img_path, mode='RGB')\\n ac = ac / (255.0 / 2) - 1.0\\n return np.array(ac)\",\n \"def hyper2rgb(img, bands):\\n rgb = spectral.get_rgb(img, bands)\\n rgb /= np.max(rgb)\\n rgb = np.asarray(255 * rgb, dtype='uint8')\\n return rgb\",\n \"def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:\\n x, y, z = tf.unstack(image, axis=-1)\\n\\n refx = 95.047\\n refy = 100.00\\n refz = 108.883\\n\\n var_x = x / refx\\n var_y = y / refy\\n var_z = z / refz\\n\\n var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),\\n (7.787 * var_x) + (16 / 116))\\n var_y = tf.where(var_y > 0.008856, tf.pow(var_y, 1 / 3),\\n (7.787 * var_y) + (16 / 116))\\n var_z = tf.where(var_z > 0.008856, tf.pow(var_z, 1 / 3),\\n (7.787 * var_z) + (16 / 116))\\n\\n l = (116 * var_y) - 16\\n a = 500 * (var_x - var_y)\\n b = 200 * (var_y - var_z)\\n lab_image = tf.stack([l, a, b], axis=-1)\\n return lab_image\",\n \"def convert_color(img, conv='RGB2YCrCb'):\\n if conv == 'RGB2YCrCb':\\n return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\\n if conv == 'BGR2YCrCb':\\n return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)\\n if conv == 'RGB2LUV':\\n return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)\",\n \"def yuv_to_rgb(img_yuv):\\n\\n y = img_yuv[..., 0]\\n u = img_yuv[..., 1]\\n v = img_yuv[..., 2]\\n\\n r = y + 1.14 * v\\n g = y - 0.396 * u - 0.581 * v\\n b = y + 2.029 * u\\n\\n img_rgb = np.stack((r, g, b), axis=2)\\n img_rgb = np.clip(img_rgb, 0, 1)\\n return img_rgb\",\n \"def yiq2rgb(imYIQ):\\n trans = np.array([[1, 0.956, 0.62], [1, -0.272, -0.647], [1, -1.108, 1.705]])\\n return np.dot(imYIQ, trans)\",\n \"def grey_to_rgb(im):\\n assert im.n_channels in [1, 3]\\n\\n if im.n_channels == 3:\\n return im\\n\\n im.pixels = np.vstack([im.pixels] * 3)\\n return im\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(TRANSFORM).T.copy())\",\n \"def luminance(self):\\n \\n return (self.r + self.g + self.b) // 3\",\n \"def RGB_to_XYZ(RGB,\\n illuminant_RGB,\\n illuminant_XYZ,\\n RGB_to_XYZ_matrix,\\n chromatic_adaptation_transform='CAT02',\\n decoding_cctf=None):\\n\\n if decoding_cctf is not None:\\n RGB = decoding_cctf(RGB)\\n\\n M = chromatic_adaptation_matrix_VonKries(\\n xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),\\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\\n transform=chromatic_adaptation_transform)\\n\\n XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)\\n\\n XYZ_a = dot_vector(M, XYZ)\\n\\n return XYZ_a\",\n \"def read_img(img): #X\\n im = plt.imread(img)\\n im = im[:, :, :3]\\n if im.max()>200:\\n im = im/255.\\n return rgb_to_hsv(im)-0.5\",\n \"def xyz_to_lin_a98rgb(xyz: Vector) -> Vector:\\n\\n return alg.dot(XYZ_TO_RGB, xyz, dims=alg.D2_D1)\",\n \"def data_to_bytescale_rgb(data): # used to create the SOURCE PNGs (MRI, FA, MD)\\n im = bytescale(data)\\n w, h = im.shape\\n ret = np.empty((w,h,3), dtype=np.uint8)\\n ret[:,:,0] = im\\n ret[:,:,1] = im\\n ret[:,:,2] = im\\n return ret\",\n \"def convertColorSpace(\\n self,\\n img, # Image in some color space\\n srcColorSpace = 'BGR', # Source color space\\n tgtColorSpace = 'RGB', # Traget color space\\n ):\\n\\n if srcColorSpace == tgtColorSpace:\\n return img\\n\\n if srcColorSpace == 'BGR':\\n img_bgr = img\\n elif srcColorSpace == 'RGB':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\\n elif srcColorSpace == 'HSV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\\n elif srcColorSpace == 'HLS':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_HLS2BGR)\\n elif srcColorSpace == 'LUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_LUV2BGR)\\n elif srcColorSpace == 'YUV':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\\n elif srcColorSpace == 'YCrCb':\\n img_bgr = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(srcColorSpace))\\n\\n if tgtColorSpace == 'BGR':\\n img_tgt = img_bgr\\n elif tgtColorSpace == 'RGB':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\\n elif tgtColorSpace == 'HSV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\\n elif tgtColorSpace == 'HLS':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS)\\n elif tgtColorSpace == 'LUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LUV)\\n elif tgtColorSpace == 'YUV':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YUV)\\n elif tgtColorSpace == 'YCrCb':\\n img_tgt = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)\\n else:\\n raise Exception(\\\"Incorrect color space: {}\\\".format(tgtColorSpace))\\n\\n return img_tgt\",\n \"def read_colormap(name):\\n\\n path = get_demo_file(name + '.c3g')\\n\\n out = []\\n with open(path, 'r') as file:\\n for line in file:\\n if 'rgb(' not in line:\\n continue\\n line = line.split('(')[-1].split(')')[0]\\n out.append([float(n) for n in line.split(',')])\\n\\n return np.asarray(out).astype(float) / 256.\",\n \"def carla_rgb_image_to_ndarray(image: carla.Image) -> np.ndarray: # pylint: disable=no-member\\n image.convert(carla.ColorConverter.Raw) # pylint: disable=no-member\\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\\\"uint8\\\"))\\n array = array.astype(np.float32) / 255\\n array = np.reshape(array, (image.height, image.width, 4))\\n array = array[:, :, :3]\\n array = array[:, :, ::-1]\\n return array\",\n \"def colorize_images(self, img):\\n self.load_model()\\n self.mdn.eval()\\n self.vae.eval()\\n n, _, _ = img.shape\\n img = img.astype(np.float32) / 255\\n img = torch.tensor(img, dtype=torch.float, device=self.device).unsqueeze(1)\\n with torch.no_grad():\\n z = self.mdn(img)\\n ab_out = self.vae.decode(z)\\n lab_out = torch.cat((img, ab_out), dim=1)\\n lab_out = self.unnormalize(lab_out).cpu().numpy()\\n lab_out = np.transpose(lab_out, (0, 2, 3, 1)).astype(np.uint8)\\n for i in range(n):\\n color_out = cv2.cvtColor(lab_out[i], cv2.COLOR_LAB2BGR)\\n color_out = cv2.resize(color_out, (96, 96), interpolation=cv2.INTER_AREA)\\n cv2.imwrite(\\\"../datasets/stl10/divcolor/{}.png\\\".format(str(i)), color_out)\\n return\",\n \"def XYZ_to_sRGB(XYZ):\\n\\n rgb = XYZ_to_sRGB_linear(XYZ)\\n rgb = sRGB_linear_to_sRGB(rgb)\\n\\n return rgb\",\n \"def generate_lut(self):\\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\\n for i in Numeric.arange(256):\\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\\n return r,g,b\",\n \"def RGB_to_RGB(RGB,\\n input_colourspace,\\n output_colourspace,\\n chromatic_adaptation_transform='CAT02',\\n apply_decoding_cctf=False,\\n apply_encoding_cctf=False):\\n\\n if apply_decoding_cctf:\\n RGB = input_colourspace.decoding_cctf(RGB)\\n\\n M = RGB_to_RGB_matrix(input_colourspace, output_colourspace,\\n chromatic_adaptation_transform)\\n\\n RGB = dot_vector(M, RGB)\\n\\n if apply_encoding_cctf:\\n RGB = output_colourspace.encoding_cctf(RGB)\\n\\n return RGB\",\n \"def toRGB(self):\\r\\n return mage_hsv_tuple_to_rgb(self.Coords)\",\n \"def to_rgb(im):\\n w, h = im.shape\\n ret = np.empty((w, h, 3), dtype=np.uint8)\\n ret[:, :, 2] = ret[:, :, 1] = ret[:, :, 0] = im\\n return ret\",\n \"def get_color(im_obj):\\n #im = Image.open(path, 'r')\\n x, y = im_obj.size\\n\\n r, g, b = 0, 0, 0\\n for i in xrange(x):\\n for j in xrange(y):\\n color_px = im_obj.getpixel((i, j))\\n #print color_px\\n r += color_px[0]\\n g += color_px[1]\\n b += color_px[2]\\n\\n r = r / (x * y)\\n g = g / (x * y)\\n b = b / (x * y)\\n return (r, g, b)\",\n \"def _process_img_rgb(self, sensor_data):\\n img = np.array(sensor_data.raw_data).reshape((self.img_y, self.img_x, 4))\\n img = img[:, :, :3] # sensor is actualy rgba, we dont need alpha values\\n self.rgb = img # need to scale rgb values to be {0,1}\",\n \"def rgb2hsl_img(rgb):\\r\\n \\r\\n def core(_rgb, _hsl):\\r\\n\\r\\n irgb = _rgb.astype(np.uint16)\\r\\n ir, ig, ib = irgb[:, :, 0], irgb[:, :, 1], irgb[:, :, 2]\\r\\n h, s, l = _hsl[:, :, 0], _hsl[:, :, 1], _hsl[:, :, 2]\\r\\n\\r\\n imin, imax = irgb.min(2), irgb.max(2)\\r\\n iadd, isub = imax + imin, imax - imin\\r\\n\\r\\n ltop = (iadd != 510) * (iadd > 255)\\r\\n lbot = (iadd != 0) * (ltop == False)\\r\\n\\r\\n l[:] = iadd.astype(np.float) / 510\\r\\n\\r\\n fsub = isub.astype(np.float)\\r\\n s[ltop] = fsub[ltop] / (510 - iadd[ltop])\\r\\n s[lbot] = fsub[lbot] / iadd[lbot]\\r\\n\\r\\n not_same = imax != imin\\r\\n is_b_max = not_same * (imax == ib)\\r\\n not_same_not_b_max = not_same * (is_b_max == False)\\r\\n is_g_max = not_same_not_b_max * (imax == ig)\\r\\n is_r_max = not_same_not_b_max * (is_g_max == False) * (imax == ir)\\r\\n\\r\\n h[is_r_max] = ((0. + ig[is_r_max] - ib[is_r_max]) / isub[is_r_max])\\r\\n h[is_g_max] = ((0. + ib[is_g_max] - ir[is_g_max]) / isub[is_g_max]) + 2\\r\\n h[is_b_max] = ((0. + ir[is_b_max] - ig[is_b_max]) / isub[is_b_max]) + 4\\r\\n h[h < 0] += 6\\r\\n h[:] /= 6\\r\\n\\r\\n hsl = np.zeros(rgb.shape, dtype=np.float)\\r\\n cpus = multiprocessing.cpu_count()\\r\\n length = int(math.ceil(float(hsl.shape[0]) / cpus))\\r\\n line = 0\\r\\n threads = []\\r\\n while line < hsl.shape[0]:\\r\\n line_next = line + length\\r\\n thread = threading.Thread(target=core, args=(rgb[line:line_next], hsl[line:line_next]))\\r\\n thread.start()\\r\\n threads.append(thread)\\r\\n line = line_next\\r\\n\\r\\n for thread in threads:\\r\\n thread.join()\\r\\n\\r\\n return hsl\",\n \"def Array2PIL(a,lut=None,minvalue=None,maxvalue=None,width=None,height=None,\\n flip=None):\\n import Image # we only need it here ...\\n\\n if flip==\\\"ud\\\": #up-down exchange\\n a=a[::-1,:]\\n h,w=Numeric.shape(a)\\n## a_min=Numeric.minimum.reduce((Numeric.ravel(a)))\\n## a_max=Numeric.maximum.reduce((Numeric.ravel(a)))\\n a_min=min(Numeric.ravel(a))\\n a_max=max(Numeric.ravel(a))\\n\\n # allow for an user-specified maximal value:\\n if maxvalue!=None and maxvalue>a_max:\\n a_max=maxvalue\\n # allows for an user-specified minimal value:\\n if minvalue!=None and minvalue<a_min:\\n a_min=minvalue\\n\\n if lut is not None:\\n if len(lut[0]) == 256:\\n \\n a=(Numeric.ravel(255.0*(a-a_min)/\\n (a_max-a_min))).astype(Numeric.UInt8)\\n\\n rgb=Numeric.zeros( (len(a),3),typecode=Numeric.UInt8)\\n\\n\\n lut_=Numeric.zeros( (3,len(lut[0])),Numeric.UInt8)\\n lut_[0]=lut[0].astype(Numeric.UInt8)\\n lut_[1]=lut[1].astype(Numeric.UInt8)\\n lut_[2]=lut[2].astype(Numeric.UInt8)\\n\\n # This is much faster than the original zip/ravel variant ...\\n rgb[:,0]=Numeric.take(lut_[0],a)\\n #print \\\"rtake\\\"\\n rgb[:,1]=Numeric.take(lut_[1],a)\\n #print \\\"gtake\\\"\\n rgb[:,2]=Numeric.take(lut_[2],a)\\n #print \\\"btake\\\"\\n #rgb=Numeric.ravel(((Numeric.array(zip(r,g,b),\\n # typecode=Numeric.UInt8))))\\n\\n #print \\\"rgb done\\\"\\n else:\\n N = len(lut[0])\\n print \\\"LUT with N=%d entries\\\" % N\\n if N>=256*256:\\n print \\\"UUPS, more than uint16 colors??\\\", N\\n raise ValueError(\\\"N too large\\\")\\n \\n a = (Numeric.ravel((N-1)*(a-a_min)/\\n (a_max-a_min))).astype(Numeric.UInt16)\\n\\n rgb = Numeric.zeros( (len(a), 3), typecode=Numeric.UInt16)\\n\\n lut_ = Numeric.zeros( (3,len(lut[0])), Numeric.UInt16)\\n lut_[0] = lut[0].astype(Numeric.UInt16)\\n lut_[1] = lut[1].astype(Numeric.UInt16)\\n lut_[2] = lut[2].astype(Numeric.UInt16)\\n\\n # This is much faster than the original zip/ravel variant ...\\n rgb[:,0] = Numeric.take(lut_[0],a)\\n rgb[:,1] = Numeric.take(lut_[1],a)\\n rgb[:,2] = Numeric.take(lut_[2],a)\\n\\n rgb = (rgb*256.0/N).astype(Numeric.UInt8)\\n\\n else: # simple grey scale ramp...\\n a=(Numeric.ravel(255.0*(a-a_min)/\\n (a_max-a_min))).astype(Numeric.UInt8)\\n # convert to (r_0,g_0,b_0,r_1,g_1,b_1,....)\\n rgb=Numeric.ravel(Numeric.array(zip(a,a,a)))\\n\\n # create a PIL RGB image\\n #print \\\"w/h\\\",w,h\\n im=Image.new(\\\"RGB\\\",(w,h))\\n #print \\\"imfromstring:\\\"\\n im.fromstring(rgb.tostring())\\n #print \\\"done ...\\\"\\n \\n # scale image ?\\n if height!=None and width==None:\\n im=im.resize(w/h*height,height)\\n elif height==None and width!=None:\\n im=im.resize(width,h/w*width)\\n elif height!=None and width!=None:\\n im=im.resize(width,height)\\n\\n return(im)\",\n \"def lin_a98rgb_to_xyz(rgb: Vector) -> Vector:\\n\\n return alg.dot(RGB_TO_XYZ, rgb, dims=alg.D2_D1)\",\n \"def yiq2rgb(im_yiq):\\n return multiply_by_left_matrix(np.linalg.inv(YIQ_MATRIX), im_yiq)\",\n \"def XYZ_to_RGB(cobj, target_rgb, *args, **kwargs):\\r\\n\\r\\n temp_X = cobj.xyz_x\\r\\n temp_Y = cobj.xyz_y\\r\\n temp_Z = cobj.xyz_z\\r\\n\\r\\n logger.debug(\\\" \\\\- Target RGB space: %s\\\", target_rgb)\\r\\n target_illum = target_rgb.native_illuminant\\r\\n logger.debug(\\\" \\\\- Target native illuminant: %s\\\", target_illum)\\r\\n logger.debug(\\\" \\\\- XYZ color's illuminant: %s\\\", cobj.illuminant)\\r\\n \\r\\n # If the XYZ values were taken with a different reference white than the\\r\\n # native reference white of the target RGB space, a transformation matrix\\r\\n # must be applied.\\r\\n if cobj.illuminant != target_illum:\\r\\n logger.debug(\\\" \\\\* Applying transformation from %s to %s \\\",\\r\\n cobj.illuminant, target_illum)\\r\\n # Get the adjusted XYZ values, adapted for the target illuminant.\\r\\n temp_X, temp_Y, temp_Z = apply_chromatic_adaptation(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n orig_illum=cobj.illuminant, targ_illum=target_illum)\\r\\n logger.debug(\\\" \\\\* New values: %.3f, %.3f, %.3f\\\",\\r\\n temp_X, temp_Y, temp_Z)\\r\\n \\r\\n # Apply an RGB working space matrix to the XYZ values (matrix mul).\\r\\n rgb_r, rgb_g, rgb_b = apply_RGB_matrix(\\r\\n temp_X, temp_Y, temp_Z,\\r\\n rgb_type=target_rgb, convtype=\\\"xyz_to_rgb\\\")\\r\\n\\r\\n # v\\r\\n linear_channels = dict(r=rgb_r, g=rgb_g, b=rgb_b)\\r\\n # V\\r\\n nonlinear_channels = {}\\r\\n if target_rgb == sRGBColor:\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n if v <= 0.0031308:\\r\\n nonlinear_channels[channel] = v * 12.92\\r\\n else:\\r\\n nonlinear_channels[channel] = 1.055 * math.pow(v, 1 / 2.4) - 0.055\\r\\n else:\\r\\n # If it's not sRGB...\\r\\n for channel in ['r', 'g', 'b']:\\r\\n v = linear_channels[channel]\\r\\n nonlinear_channels[channel] = math.pow(v, 1 / target_rgb.rgb_gamma)\\r\\n\\r\\n return target_rgb(\\r\\n nonlinear_channels['r'], nonlinear_channels['g'], nonlinear_channels['b'])\",\n \"def load_image_as_rgb(image_path):\\n im = imageio.imread(image_path)\\n y_size = im.shape[0]\\n x_size = im.shape[1]\\n logging.info(\\\"Image has dimensions X:%d Y:%d\\\" % (x_size, y_size))\\n arr = np.zeros((im.shape[0],im.shape[1]), dtype=int)\\n i = 0\\n for im_row in im:\\n j = 0\\n for vec in im_row:\\n arr[i,j] = rgb_vec_to_num(vec)\\n j = j + 1\\n i = i + 1\\n return arr\",\n \"def _rgb2y(self, im):\\n if len(im.shape) < 3:\\n return im\\n return np.sum(im * [0.299, 0.587, 0.114], axis=2)\",\n \"def applyJET(img):\\n\\treturn applyColorMap(img, \\\"jet\\\")\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def bgr_to_rgb(ims):\\n out = []\\n for im in ims:\\n out.append(im[:,:,::-1])\\n return out\",\n \"def test_srgb_conversion_to_xyz_d50(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor, target_illuminant='D50')\\r\\n self.assertColorMatch(xyz, XYZColor(0.313, 0.460, 0.082))\",\n \"def yiq2rgb(imYIQ):\\n return np.dot(imYIQ, np.linalg.inv(np.array(MATRIX).T))\",\n \"def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:\\n r: torch.Tensor = image[..., 0, :, :]\\n g: torch.Tensor = image[..., 1, :, :]\\n b: torch.Tensor = image[..., 2, :, :]\\n\\n delta: float = 0.5\\n y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b\\n cb: torch.Tensor = (b - y) * 0.564 + delta\\n cr: torch.Tensor = (r - y) * 0.713 + delta\\n return torch.stack([y, cb, cr], -3)\",\n \"def _hsv_to_rgb(img):\\n h, s, v = img.unbind(axis=-3)\\n f = h * 6.0\\n i = paddle.floor(f)\\n f = f - i\\n i = i.astype(paddle.int32) % 6\\n\\n p = paddle.clip(v * (1.0 - s), 0.0, 1.0)\\n q = paddle.clip(v * (1.0 - s * f), 0.0, 1.0)\\n t = paddle.clip(v * (1.0 - s * (1.0 - f)), 0.0, 1.0)\\n\\n mask = paddle.equal(\\n i.unsqueeze(axis=-3),\\n paddle.arange(6, dtype=i.dtype).reshape((-1, 1, 1)),\\n ).astype(img.dtype)\\n matrix = paddle.stack(\\n [\\n paddle.stack([v, q, p, p, t, v], axis=-3),\\n paddle.stack([t, v, v, q, p, p], axis=-3),\\n paddle.stack([p, p, t, v, v, q], axis=-3),\\n ],\\n axis=-4,\\n )\\n return paddle.einsum(\\\"...ijk, ...xijk -> ...xjk\\\", mask, matrix)\",\n \"def rgb_to_xy(red, green, blue):\\n\\n # gamma correction\\n red = pow((red + 0.055) / (1.0 + 0.055), 2.4) if red > 0.04045 else (red / 12.92)\\n green = pow((green + 0.055) / (1.0 + 0.055), 2.4) if green > 0.04045 else (green / 12.92)\\n blue = pow((blue + 0.055) / (1.0 + 0.055), 2.4) if blue > 0.04045 else (blue / 12.92)\\n\\n # convert rgb to xyz\\n x = red * 0.649926 + green * 0.103455 + blue * 0.197109\\n y = red * 0.234327 + green * 0.743075 + blue * 0.022598\\n z = green * 0.053077 + blue * 1.035763\\n\\n # convert xyz to xy\\n x = x / (x + y + z)\\n y = y / (x + y + z)\\n\\n # TODO check color gamut if known\\n \\n return [x, y]\",\n \"def rgbToCIE1931(self, red, green, blue):\\n point = self.color.getXYPointFromRGB(red, green, blue)\\n return [point.x, point.y]\",\n \"def load_rgb(path):\\n bands = band_list['rgb']\\n img = None\\n fmt = \\\"_{}.tif\\\"\\n for b in bands:\\n band_ds = rasterio.open(path + fmt.format(b))\\n aux = band_ds.read(1)\\n aux = norm_band(aux)\\n aux = np.expand_dims(aux, axis=-1)\\n if img is None:\\n img = aux\\n else:\\n img = np.concatenate((img, aux), axis=-1)\\n return img\",\n \"def to_color(self):\\n if self.channels == 4:\\n color = opencv.cvtColor(self.img, opencv.COLOR_BGRA2BGR)\\n return Image(color)\\n elif self.channels == 1:\\n color = opencv.cvtColor(self.img, opencv.COLOR_GRAY2BGR)\\n return Image(color)\\n else:\\n return Image(self.img)\",\n \"def xyz2linear_rgb(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(rgb_from_xyz, arr)\",\n \"def linear_rgb2lab(rgb, illuminant=\\\"D65\\\", observer=\\\"2\\\"):\\n return xyz2lab(linear_rgb2xyz(rgb), illuminant, observer)\",\n \"def Luv_to_XYZ(cobj, *args, **kwargs):\\r\\n\\r\\n illum = cobj.get_illuminant_xyz()\\r\\n # Without Light, there is no color. Short-circuit this and avoid some\\r\\n # zero division errors in the var_a_frac calculation.\\r\\n if cobj.luv_l <= 0.0:\\r\\n xyz_x = 0.0\\r\\n xyz_y = 0.0\\r\\n xyz_z = 0.0\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, observer=cobj.observer, illuminant=cobj.illuminant)\\r\\n\\r\\n # Various variables used throughout the conversion.\\r\\n cie_k_times_e = color_constants.CIE_K * color_constants.CIE_E\\r\\n u_sub_0 = (4.0 * illum[\\\"X\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n v_sub_0 = (9.0 * illum[\\\"Y\\\"]) / (illum[\\\"X\\\"] + 15.0 * illum[\\\"Y\\\"] + 3.0 * illum[\\\"Z\\\"])\\r\\n var_u = cobj.luv_u / (13.0 * cobj.luv_l) + u_sub_0\\r\\n var_v = cobj.luv_v / (13.0 * cobj.luv_l) + v_sub_0\\r\\n\\r\\n # Y-coordinate calculations.\\r\\n if cobj.luv_l > cie_k_times_e:\\r\\n xyz_y = math.pow((cobj.luv_l + 16.0) / 116.0, 3.0)\\r\\n else:\\r\\n xyz_y = cobj.luv_l / color_constants.CIE_K\\r\\n\\r\\n # X-coordinate calculation.\\r\\n xyz_x = xyz_y * 9.0 * var_u / (4.0 * var_v)\\r\\n # Z-coordinate calculation.\\r\\n xyz_z = xyz_y * (12.0 - 3.0 * var_u - 20.0 * var_v) / (4.0 * var_v)\\r\\n\\r\\n return XYZColor(\\r\\n xyz_x, xyz_y, xyz_z, illuminant=cobj.illuminant, observer=cobj.observer)\",\n \"def green_channel(input_image):\\n return input_image[:, :, 1]\",\n \"def get_rgb_bands(image, bands):\\n if bands is not MONOCHROME:\\n red = image[:, :, bands['red']]\\n green = image[:, :, bands['green']]\\n blue = image[:, :, bands['blue']]\\n\\n img = np.rollaxis(np.array([red, green, blue]), 0, 3)\\n else:\\n img = color.grey2rgb(image)\\n\\n return img\",\n \"def linear_rgb2xyz(rgb):\\n arr = _prepare_colorarray(rgb).copy()\\n return _convert(xyz_from_rgb, arr)\",\n \"def convert_yuv_to_rgb(img_arr): \\n rgb = cv2.cvtColor(img_arr, cv2.COLOR_YUV2BGR_I420)\\n rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)\\n return Image.fromarray(rgb)\",\n \"def get_BGR_img(self):\\n img = self.img.copy()\\n # Convert BGR to HSV\\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\\n # define range of BGR color in HSV\\n threshold_blue = np.array([[100,43,46], [124,255,255]])\\n threshold_green = np.array([[35,43,46], [77,255,255]])\\n threshold_red1 = np.array([[0,43,46], [10,255,255]])\\n threshold_red2 = np.array([[156,43,46], [180,255,255]])\\n # Threshold the HSV image to get only BGR colors\\n mask_blue = cv2.inRange(hsv, threshold_blue[0], threshold_blue[1])\\n mask_green = cv2.inRange(hsv, threshold_green[0], threshold_green[1])\\n mask_red1 = cv2.inRange(hsv, threshold_red1[0], threshold_red1[1])\\n mask_red2 = cv2.inRange(hsv, threshold_red2[0], threshold_red2[1])\\n mask_red = mask_red1 | mask_red2\\n # Bitwise-AND mask and original image\\n self.blue = cv2.bitwise_and(img, img, mask=mask_blue)\\n self.green = cv2.bitwise_and(img, img, mask=mask_green)\\n self.red = cv2.bitwise_and(img, img, mask=mask_red)\\n # 返回 bgr 三通道的分量合成的图片\\n return np.stack((self.blue[:, :, 0], self.green[:, :, 1], self.red[:, :, 2]), axis=2)\",\n \"def __enhance_image(self, img):\\n\\n blue = self.g.clahe.apply(img[:,:,0])\\n green = self.g.clahe.apply(img[:,:,1])\\n red = self.g.clahe.apply(img[:,:,2])\\n img[:,:,0] = blue\\n img[:,:,1] = green\\n img[:,:,2] = red\\n return img\",\n \"def testImageProcessing():\\n Im_pix = getRGB( 'in.png' ) # read in the in.png image\\n print \\\"The first two pixels of the first row are\\\",\\n print Im_pix[0][0:2]\\n # remember that Im_pix is a list (the image)\\n # of lists (each row) of lists (each pixel is [R,G,B])\\n New_pix = [ [ [255 - num for num in p] for p in row ] for row in Im_pix ]\\n # now, save to the file 'out.png'\\n saveRGB( New_pix, 'out.png' )\",\n \"def imcast(img, dtype, color_space=\\\"default\\\"):\\n if img.dtype == dtype:\\n return img\\n if color_space == \\\"default\\\":\\n if dtype == np.uint8:\\n if img.dtype == np.uint16:\\n return np.asarray(img / 257, np.uint8)\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img * 255., np.uint8)\\n elif dtype == np.uint16:\\n if img.dtype == np.uint8:\\n return np.asarray(img, np.uint16) * 257\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img * 65535., np.uint16)\\n elif dtype == np.float32 or dtype == np.float64:\\n if img.dtype == np.uint8:\\n return np.asarray(img, dtype) / 255.\\n elif img.dtype == np.uint16:\\n return np.asarray(img, dtype) / 65535.\\n elif img.dtype == np.float32 or img.dtype == np.float64:\\n return np.asarray(img, dtype)\\n elif color_space == \\\"CIE-L*a*b*\\\":\\n if dtype == np.uint8:\\n if img.dtype == np.float32 or img.dtype == np.float64:\\n dst = np.empty(img.shape, np.uint8)\\n dst[:,:,0] = img[:,:,0] * 255. / 100.\\n dst[:,:,1] = img[:,:,1] + 128.\\n dst[:,:,2] = img[:,:,2] + 128.\\n return dst\\n elif dtype == np.float32 or dtype == np.float64:\\n if img.dtype == np.uint8:\\n dst = np.empty(img.shape, dtype)\\n dst[:,:,0] = np.asarray(img[:,:,0], dtype) / 255. * 100.\\n dst[:,:,1] = np.asarray(img[:,:,1], dtype) - 128.\\n dst[:,:,2] = np.asarray(img[:,:,2], dtype) - 128.\\n return dst\\n raise Exception(\\n \\\"Unexpected conversion from '%s' to '%s' with '%s' color space\\\" % \\\\\\n (img.dtype, dtype, color_space))\",\n \"def rgb_to_luv(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:\\n if not isinstance(image, torch.Tensor):\\n raise TypeError(f\\\"Input type is not a torch.Tensor. Got {type(image)}\\\")\\n\\n if len(image.shape) < 3 or image.shape[-3] != 3:\\n raise ValueError(f\\\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\\\")\\n\\n # Convert from sRGB to Linear RGB\\n lin_rgb = rgb_to_linear_rgb(image)\\n\\n xyz_im: torch.Tensor = rgb_to_xyz(lin_rgb)\\n\\n x: torch.Tensor = xyz_im[..., 0, :, :]\\n y: torch.Tensor = xyz_im[..., 1, :, :]\\n z: torch.Tensor = xyz_im[..., 2, :, :]\\n\\n threshold = 0.008856\\n L: torch.Tensor = torch.where(y > threshold, 116.0 * torch.pow(y.clamp(min=threshold), 1.0 / 3.0) - 16.0, 903.3 * y)\\n\\n # Compute reference white point\\n xyz_ref_white: Tuple[float, float, float] = (0.95047, 1.0, 1.08883)\\n u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2])\\n\\n u_p: torch.Tensor = (4 * x) / (x + 15 * y + 3 * z + eps)\\n v_p: torch.Tensor = (9 * y) / (x + 15 * y + 3 * z + eps)\\n\\n u: torch.Tensor = 13 * L * (u_p - u_w)\\n v: torch.Tensor = 13 * L * (v_p - v_w)\\n\\n out = torch.stack([L, u, v], dim=-3)\\n\\n return out\",\n \"def imageprepare():\\r\\n file_name = 'temp_image.png'\\r\\n im = Image.open(file_name).convert('L')\\r\\n im = im.resize((20, 20))\\r\\n p = Image.new('L', (28,28), (255))\\r\\n p.paste(im,(4,4,24,24))\\r\\n p.save(\\\"last_image.png\\\")\\r\\n\\r\\n tv = list(p.getdata()) # get pixel values\\r\\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\r\\n tva = np.reshape(tva, (28, 28))\\r\\n\\r\\n return tva\",\n \"def convert_img(self):\\r\\n self.img = self.img.convert('RGB')\",\n \"def greyscale(img):\\n grey_img = np.zeros([210, 160])\\n for i in range(3):\\n grey_img =np.sum([grey_img, img[:, :, i]], 0)\\n grey_img /= 3\\n grey_img = grey_img.astype(np.uint8)\\n return grey_img\",\n \"def transformYIQ2RGB(imgYIQ: np.ndarray) -> np.ndarray:\\r\\n yiq_from_rgb = np.array([[0.299, 0.587, 0.114],\\r\\n [0.59590059, -0.27455667, -0.32134392],\\r\\n [0.21153661, -0.52273617, 0.31119955]])\\r\\n rgb_from_yiq = np.linalg.inv(yiq_from_rgb)\\r\\n\\r\\n RGBImg = np.ndarray(imgYIQ.shape)\\r\\n\\r\\n RGBImg[:, :, 0] = rgb_from_yiq[0,0] * imgYIQ[:, :, 0] + rgb_from_yiq[0,1] * imgYIQ[:, :, 1] + rgb_from_yiq[0,2] * imgYIQ[:, :, 2]\\r\\n RGBImg[:, :, 1] = rgb_from_yiq[1,0] * imgYIQ[:, :, 0] + rgb_from_yiq[1,1] * imgYIQ[:, :, 1] + rgb_from_yiq[1,2] * imgYIQ[:, :, 2]\\r\\n RGBImg[:, :, 2] = rgb_from_yiq[2,0] * imgYIQ[:, :, 0] + rgb_from_yiq[2,1] * imgYIQ[:, :, 1] + rgb_from_yiq[2,2] * imgYIQ[:, :, 2]\\r\\n\\r\\n return RGBImg\",\n \"def make_grayscale(img: Image.Image) -> Image.Image:\\n orig_mode = img.mode\\n\\n if orig_mode in [\\\"RGB\\\", \\\"CMYK\\\", \\\"YCbCr\\\", \\\"LAB\\\", \\\"HSV\\\"]:\\n return img.convert(\\\"L\\\")\\n elif orig_mode == \\\"RGBA\\\":\\n return img.convert(\\\"LA\\\").convert(\\\"RGBA\\\")\\n elif orig_mode == \\\"P\\\":\\n # Using ITU-R 601-2 luma transform: L = R * 299/1000 + G * 587/1000 + B * 114/1000\\n pal = img.getpalette()\\n for i in range(len(pal) // 3):\\n # Using ITU-R 601-2 luma transform\\n gray = (pal[3 * i] * 299 + pal[3 * i + 1] * 587 + pal[3 * i + 2] * 114)\\n gray = gray // 1000\\n pal[3 * i: 3 * i + 3] = [gray, gray, gray]\\n img.putpalette(pal)\\n return img\\n else:\\n return img\",\n \"def test_conversion_through_rgb(self):\\r\\n\\r\\n xyz = convert_color(self.color, XYZColor)\\r\\n hsl = convert_color(xyz, HSLColor, through_rgb_type=AdobeRGBColor)\\r\\n # Notice how we don't have to pass through_rgb_type explicitly.\\r\\n xyz2 = convert_color(hsl, XYZColor)\\r\\n self.assertColorMatch(xyz, xyz2)\",\n \"def get_rgb_from_bits(self, bits):\\n bits.shape = (self._dest_height,self._dest_width,4)\\n self._rgb[0] = bits[:,:,2]\\n self._rgb[1] = bits[:,:,1]\\n self._rgb[2] = bits[:,:,0]\\n return self._rgb\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7471666","0.7259688","0.7216436","0.6839209","0.6759264","0.67525685","0.67007047","0.6603421","0.6600922","0.6595562","0.6580605","0.6568346","0.6499439","0.64541537","0.64526325","0.6431422","0.63929","0.6372881","0.63197166","0.6308842","0.63075536","0.62842566","0.6284198","0.62655497","0.6263118","0.6215577","0.6208403","0.6144289","0.6131233","0.6115102","0.6107035","0.610672","0.6057251","0.6055549","0.6047312","0.60019904","0.598576","0.5980257","0.59642786","0.5943939","0.59438294","0.58939093","0.58926076","0.5869205","0.5866192","0.5864539","0.5863709","0.5849715","0.58433527","0.5841697","0.5824051","0.58226717","0.58170515","0.5811686","0.5794661","0.57919365","0.57805675","0.57785785","0.57732886","0.5772517","0.5768151","0.5765976","0.5760312","0.57519966","0.5745192","0.574427","0.5742001","0.57380056","0.5731747","0.5720074","0.57157886","0.570951","0.5708859","0.569794","0.56901073","0.56883615","0.5682716","0.5680048","0.5679553","0.56738925","0.5667869","0.5660641","0.5656349","0.5652683","0.5649135","0.5643758","0.5642686","0.5641807","0.56378865","0.5631773","0.5623322","0.5622579","0.56224924","0.5620768","0.5608292","0.5606627","0.5605725","0.56019133","0.55993444","0.55946696"],"string":"[\n \"0.7471666\",\n \"0.7259688\",\n \"0.7216436\",\n \"0.6839209\",\n \"0.6759264\",\n \"0.67525685\",\n \"0.67007047\",\n \"0.6603421\",\n \"0.6600922\",\n \"0.6595562\",\n \"0.6580605\",\n \"0.6568346\",\n \"0.6499439\",\n \"0.64541537\",\n \"0.64526325\",\n \"0.6431422\",\n \"0.63929\",\n \"0.6372881\",\n \"0.63197166\",\n \"0.6308842\",\n \"0.63075536\",\n \"0.62842566\",\n \"0.6284198\",\n \"0.62655497\",\n \"0.6263118\",\n \"0.6215577\",\n \"0.6208403\",\n \"0.6144289\",\n \"0.6131233\",\n \"0.6115102\",\n \"0.6107035\",\n \"0.610672\",\n \"0.6057251\",\n \"0.6055549\",\n \"0.6047312\",\n \"0.60019904\",\n \"0.598576\",\n \"0.5980257\",\n \"0.59642786\",\n \"0.5943939\",\n \"0.59438294\",\n \"0.58939093\",\n \"0.58926076\",\n \"0.5869205\",\n \"0.5866192\",\n \"0.5864539\",\n \"0.5863709\",\n \"0.5849715\",\n \"0.58433527\",\n \"0.5841697\",\n \"0.5824051\",\n \"0.58226717\",\n \"0.58170515\",\n \"0.5811686\",\n \"0.5794661\",\n \"0.57919365\",\n \"0.57805675\",\n \"0.57785785\",\n \"0.57732886\",\n \"0.5772517\",\n \"0.5768151\",\n \"0.5765976\",\n \"0.5760312\",\n \"0.57519966\",\n \"0.5745192\",\n \"0.574427\",\n \"0.5742001\",\n \"0.57380056\",\n \"0.5731747\",\n \"0.5720074\",\n \"0.57157886\",\n \"0.570951\",\n \"0.5708859\",\n \"0.569794\",\n \"0.56901073\",\n \"0.56883615\",\n \"0.5682716\",\n \"0.5680048\",\n \"0.5679553\",\n \"0.56738925\",\n \"0.5667869\",\n \"0.5660641\",\n \"0.5656349\",\n \"0.5652683\",\n \"0.5649135\",\n \"0.5643758\",\n \"0.5642686\",\n \"0.5641807\",\n \"0.56378865\",\n \"0.5631773\",\n \"0.5623322\",\n \"0.5622579\",\n \"0.56224924\",\n \"0.5620768\",\n \"0.5608292\",\n \"0.5606627\",\n \"0.5605725\",\n \"0.56019133\",\n \"0.55993444\",\n \"0.55946696\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7765698"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12394,"cells":{"query":{"kind":"string","value":"Checks if the given character is a letter."},"document":{"kind":"string","value":"def is_letter(c):\n return 'A' <= c <= 'Z' or 'a' <= c <= 'z'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_letter(string_):\n if string_ not in string.ascii_letters or len(string_) > 1:\n return False\n return True","def isLetter(c):\n ret = libxml2mod.xmlIsLetter(c)\n return ret","def is_letter(user_input):\n # If any characters is letter -> return boolean True else False\n if any(char.isalpha() for char in user_input):\n return True\n return False","def has_letter(word):\r\n for char in word:\r\n if char.isalpha():\r\n return True\r\n return False","def __valid_char(self, char: str) -> bool:\r\n if char.isdigit():\r\n raise ValueError('Characters can\\'t be numbers')\r\n\r\n return char.isalpha() or char.isspace()","def character(x):\n if (x==\"a\"or x==\"A\"or x==\"e\"or x==\"E\"or x==\"i\"or x==\"I\"or x==\"o\"or x==\"O\"or x==\"u\"or x==\"U\"):\n return('True')\n else:\n return('False')","def isSingleLetter(self, word):\n return (re.match('^\\w$', word)) != None","def is_lowercase(character):\n return 'a' <= character <= 'z'","def isalpha(self) -> bool:\n pass","def is_uppercase(character):\n return 'A' <= character <= 'Z'","def isChar(ch):\n ret = libxml2mod.xmlIsChar(ch)\n return ret","def is_allowed_char(ch):\n\treturn ch.isalnum() or ch in \"#.>+*:$-_!@\"","def isAlpha(string):\n return (True)","def is_valid_input(guess_letter):\r\n length = len(guess_letter)\r\n\r\n if length > 1 and not guess_letter.isalpha():\r\n return False\r\n elif not guess_letter.isalpha():\r\n return False\r\n elif length > 1:\r\n return False\r\n else:\r\n return True","def test_starts_letter(x):\n return x[0].isalpha()","def is_valid_char(src):\n\n return src.isalnum()","def must_contain_letter(cell):\n # Check if it's nan first\n if check_empty(cell):\n return True\n return not bool(re.search(\"[a-zA-Z]\", str(cell)))","def isAlphanum(c):\r\n return ((c >= 'a' and c <= 'z') or (c >= '0' and c <= '9') or\r\n (c >= 'A' and c <= 'Z') or c == '_' or c == '$' or c == '\\\\' or (c is not None and ord(c) > 126));","def ishex(char: chr) -> bool:\n return char.isdigit() or char in \"abcdef\"","def _is_alpha(argument):\n\n if not isinstance(argument, str):\n return False\n\n if argument.lower() == 'alpha':\n is_alpha = True\n else:\n argument, Z = _extract_charge_state(argument)\n\n if Z != 2:\n is_alpha = False\n elif argument[-2:] != '-4':\n is_alpha = False\n else:\n\n dash_position = argument.find('-')\n argument = argument[:dash_position]\n\n if argument.lower() == 'helium' or argument == 'He':\n is_alpha = True\n else:\n is_alpha = False\n\n return is_alpha","def if_letter(letter):\r\n str_abc= ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\r\n answer= False\r\n for i in range(len(str_abc)):\r\n if letter==str_abc[i]:\r\n answer=True\r\n return answer","def isalpha(self):\n return isalpha(self)","def is_word_character(ch):\n if (ch >= 'a' and ch <= 'z'): return True\n if (ch >= 'A' and ch <= 'Z'): return True\n if (ch >= '0' and ch <= '9'): return True\n if (ch >= 'À' and ch < 'ˀ'): return True\n if (ch == '-' or ch == '0xAD'): return True # hyphen or soft hyphen\n if (ch >= 'Ά' and ch <= 'ԓ'): return True\n return False","def check_type(character: str):\n if character.isupper():\n return 'upper'\n elif character.islower():\n return 'lower'\n elif character.isspace():\n return 'space'\n elif character in string.punctuation:\n return 'punc'\n else:\n return 'digit'","def check_type(character: str):\n if character.isupper():\n return 'upper'\n elif character.islower():\n return 'lower'\n elif character.isspace():\n return 'space'\n elif character in string.punctuation:\n return 'punc'\n else:\n return 'digit'","def is_valid_alphabetical_string(string_object: str):\n return string_object.isalpha()","def checkLetter():\n\tguess = False\n\twhile guess != True:\n\t\tguess = str(raw_input(\"Guess a letter: \"))\n\t\tif guess.isalpha() and len(guess) == 1 :\n\t\t\treturn guess\n\t\telif not guess.isalpha() or len(guess) > 1:\n\t\t\tprint \"The input may be one letter only!\"\n\t\telse:\n\t\t\tprint \"Error in checkLetter\"","def test_ends_letter(x):\n return x[-1].isalpha()","def contains_only_char(s, char):\n for c in s:\n if c != char:\n return False\n return True","def name_valid(name):\n return name.isalpha()","def is_char(user_input):\n # Check lenght of input and if equal to zero return True\n if len(user_input) == 0:\n return True\n return False","def is_valid_input(letter_guessed):\n\n #calulate the lengh of the letters\n NumberOfCharacters = (len(letter_guessed))\n #convert input letters to Underscore\n NumberOfUnderscore = (NumberOfCharacters * \"_\")\n\n\n # All the letters in English\n EnglishLetter = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMOPQRSTUVWXYZ\"\n\n\n if NumberOfCharacters > 1:\n print(\"false\")\n\n # If the user entered English character the string will print the character a non-English character (for example, a sign such as: &, *), the string will print \"E2\n elif letter_guessed in EnglishLetter:\n print(\"true\")\n else:\n print(\"false\")","def alphanumeric(s: str) -> bool:\n return len(re.findall(r'[^A-Za-z0-9]', s)) == 0","def is_char_token(c: str) -> bool:\n return c in [\"+\", \"-\", \"*\", \"/\", \"(\", \")\"]","def isAlphaNumeric(string):\n return (True)","def ask_letter(self):\n letter = ' '\n while letter not in string.ascii_lowercase:\n letter = input('Write a letter:\\n')\n letter.lower()\n\n return letter","def is_input_valid(char):\n\n # is there a char at all?\n if char is None:\n return False\n\n # check for embedded 0 byte\n if char == \"\\0\":\n return False\n\n return True","def sanitize_guess(self, letter): # helper function to incorrect_guess()\n self.guess = letter.lower().strip()\n if not self.guess.isalpha():\n Donatello.turtle_text(\n \"No special characters or numbers\") # no numbers, special characters or multiple words allowed\n return False\n # TODO if type != str raise assert\n return self.guess","def is_asian(char):\r\n\r\n # 0x3000 is ideographic space (i.e. double-byte space)\r\n # Anything over is an Asian character\r\n return ord(char) > IDEOGRAPHIC_SPACE","def isalpha(a):\n return _vec_string(a, bool_, 'isalpha')","def is_symbol(p):\n return len(p) == 1 and p.isalpha()","def checkChar(self, char):\n return char not in self.guessedChars","def count_letter(content, letter):\n if (not isinstance(letter, str)) or len(letter) != 1:\n raise ValueError('`letter` must be a single character string.')\n return len([char for char in content if char == letter])","def is_ascii_chars(text):\n is_ascii = True\n try:\n text.encode(encoding='utf-8').decode('ascii')\n except UnicodeDecodeError:\n is_ascii = False\n return is_ascii","def count_letter(content, letter):\n if (not isinstance(letter, str)) or len(letter) != 1:\n raise ValueError('`letter` must be a single character string.')\n return len([char for char in content if char == letter])","def count_letter(content, letter):\n if (not isinstance(letter, str)) or len(letter) != 1:\n raise ValueError('`letter` must be a single character string.')\n return len([char for char in content if char == letter])","def count_letter(content, letter):\n if (not isinstance(letter, str)) or len(letter) != 1:\n raise ValueError('`letter` must be a single character string.')\n return len([char for char in content if char == letter])","def count_letter(content, letter):\n if (not isinstance(letter, str)) or len(letter) != 1:\n raise ValueError('`letter` must be a single character string.')\n return len([char for char in content if char == letter])","def ascii_alphanumeric(s: str) -> bool:\n return frozenset(s).issubset(_ascii_an)","def is_punct_char(char):\n\treturn char in string.punctuation #1 is punctuation, 0 is not punctuation","def is_num_char(x):\n return ord('0') <= ord(x) <= ord('9')","def validateNamePart(self, passed_name):\n ## Declaring a Flag to control a while loop\n name_ok = False\n ## While loop to have user retry their input if they enter incorrectly\n while not name_ok:\n if passed_name.isalpha():\n name_ok = True\n return True\n\n else:\n print(\"You have entered an invalid character. Please try again.\")\n return False","def is_valid_char(t_char):\r\n eax = 1 # mi preparo il flag \"invalido\" per il carattere\r\n \r\n # se il carattere e' un operatore, un operando o uno spazio\r\n # il carattere e' valido\r\n if is_operator(t_char) == 0:\r\n # e' operatore\r\n eax = 0\r\n \r\n if is_operand(t_char) == 0:\r\n # e' operando\r\n eax = 0\r\n \r\n if ord(t_char) == 32:\r\n # e' uno spazio\r\n eax = 0\r\n\r\n return eax","def is_vowel(self, letter):\n\n if letter in (\"a\", \"e\", \"i\", \"o\", \"u\", \"A\", \"E\", \"I\", \"O\", \"U\"):\n return True\n return False","def is_character_key(self, p_event):\n p = rffi.cast(RSDL.KeyboardEventPtr, p_event)\n keycode = rffi.getintfield(p.c_keysym, 'c_sym')\n return RSDL.K_BACKSPACE <= keycode <= RSDL.K_z \\\n or RSDL.K_WORLD_0 <= keycode <= RSDL.K_KP_EQUALS \\\n or keycode == RSDL.K_EURO # whoever came up with this being beyond the modifier keys etc...","def beginning_is_letter(document_id):\n logger.debug('Function Successful: % s',\n 'beginning_is_letter: beginning_is_letter successfully called from process_doc', extra=d)\n logger.info('Ensuring that document ID begins with a letter...')\n letter = document_id[0]\n\n result = letter.isalpha()\n if result is True:\n logger.debug('Returning: %s',\n 'beginning_is_letter: returning True', extra=d)\n logger.info('Docuemnt ID begins with a letter')\n return True\n else:\n logger.debug('Returning: %s',\n 'beginning_is_letter: returning False', extra=d)\n logger.warning('Document ID does not begin with a letter')\n return False","def check_char(self, char: str) -> None:\n while len(char) > 1:\n char = input(\"Enter only a char -> \")\n char = char.lower()\n if char in self.word:\n indexes = []\n tmp_word = list(self.word)\n while char in tmp_word:\n index = tmp_word.index(char)\n indexes.append(index)\n tmp_word[index] = \"\"\n for i in indexes:\n self.word2[i] = char\n else:\n self.errors += 1\n print(f\"This char isn't in the word -> +1 Error {self.errors}/{self.attempts}\")","def is_ascii(token):\n\n printable = set(string.printable)\n\n for char in token:\n if char not in printable:\n return False\n\n return True","def _validate_word(self, word):\n return type(word) == type('a') and set(self._letters) == set(list(word))","def is_number_char(c: str) -> bool:\n return c.isdigit() or c == \".\"","def alphabet_position(char):\n if type(char) != type(''):\n return -1\n if len(char) != 1:\n return -1\n if char.isalpha():\n return lowerLetters.find(char.lower())\n return -1","def isalnum(self) -> bool:\n pass","def validate_name_input(name):\n if all(letter.isalpha() or letter.isspace()\n for letter in name) and len(name) != 0:\n clear()\n return True\n\n else:\n clear()\n print('** Please enter a name of alphabetic characters and spaces **')\n return False","def product_isalpha(product):\n if product.isalpha(): #This verifies if the product that has inserted, has a valid name\n return True\n else:\n return False","def guess_input(self):\n try:\n self.player_guess = input('Guess a letter: ').lower()\n Character(self.player_guess, self.selected_phrase)\n except ValueError:\n print(\"That was not a valid input. Please pick a number between 1 and 10\")\n if self.player_guess == \"\":\n print (\"Please enter a letter,try again.\")\n if not self.player_guess.isalpha():\n print (\"Please only enter a letter(a-z),try again.\")\n if len(self.player_guess) > 1:\n print(\"Please enter only one letter at a time.\")","def invalid_name(name):\n if any(not item.isalpha() for item in str(name)):\n return True\n return False","def is_apostrophe(ch):\n if (ch == '\\'' or ch == '\\u2019' or ch == '\\u02bc'): return True\n return False","def count_letters(word, letter):\n assert isinstance(word, str) == True, \"Please input a string!\"\n count = 0\n for char in word:\n if char == letter:\n count += 1\n\n return count","def com_adobe_fonts_check_find_empty_letters(ttFont):\n cmap = ttFont.getBestCmap()\n passed = True\n\n # http://unicode.org/reports/tr44/#General_Category_Values\n letter_categories = {\n 'Ll', 'Lm', 'Lo', 'Lt', 'Lu',\n }\n invisible_letters = {\n 0x115F, 0x1160, 0x3164, 0xFFA0, # Hangul filler chars (category='Lo')\n }\n for unicode_val, glyph_name in cmap.items():\n category = unicodedata.category(chr(unicode_val))\n if (_quick_and_dirty_glyph_is_empty(ttFont, glyph_name)) \\\n and (category in letter_categories) \\\n and (unicode_val not in invisible_letters):\n yield FAIL, \\\n Message(\"empty-letter\",\n \"U+%04X should be visible, but its glyph ('%s') is empty.\"\n % (unicode_val, glyph_name))\n passed = False\n if passed:\n yield PASS, \"No empty glyphs for letters found.\"","def _is_control(char):\n # These are technically control characters but we count them as whitespace\n # characters.\n if char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n return False\n cat = unicodedata.category(char)\n if cat.startswith(\"C\"):\n return True\n return False","def toChar(s):\n s = s.lower()\n ans = \"\"\n for c in s:\n if c in \"abcdefghijklmnopqrstuvwxyz\":\n ans+=c\n return ans","def char(self, aIndex, char):\n o = ord(char)\n c = -1\n # space\n if o == 32:\n c = 16\n # dash\n if o == 45:\n c = 17\n # uppercase A-F\n if 65 <= o <= 70:\n c = o - 55\n # lowercase a-f\n if 97 <= o <= 102:\n c = o - 87\n # 0-9\n if 48 <= o <= 57:\n c = o - 48\n\n self.digit(aIndex, c)","def isAlphabet(self, seqstr):\n mystr = seqstr\n if type(seqstr) is Sequence:\n mystr = seqstr.getString()\n return self.getAlphabet().isValidString(mystr)","def _is_control(char):\n # These are technically control characters but we count them as whitespace\n # characters.\n if char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n return False\n cat = unicodedata.category(char)\n if cat.startswith(\"C\"):\n return True\n return False","def _is_control(char):\n # These are technically control characters but we count them as whitespace\n # characters.\n if char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n return False\n cat = unicodedata.category(char)\n if cat.startswith(\"C\"):\n return True\n return False","def isascii(s):\n return len(s) == len(s.encode())","def isascii(s):\n return len(s) == len(s.encode())","def ValidateEntry(Secret_Word_Masked, Secret_Word_Masked_Unspaced, Used_Char):\n Guess = input(\"\\nGuess A Letter: \")\n Guess = Guess.lower()\n\n while (len(Guess) > 1 or not Guess.isalpha()):\n print(\"\\nInvalid Entry: \\'%s\\'\" %Guess)\n print(\"Alphabetic Character(s) Already Used: %s\" %Used_Char)\n print(\"So Far The Secret Word is:\\n%s\" %Secret_Word_Masked)\n Guess = input(\"\\nPlease Enter Only A Single Alphabetic Character: \")\n Guess = Guess.lower()\n\n print(\"\\nValid Entry: \\'%c\\'\" %Guess)\n\n return Guess","def has_lower_letter(string):\n if string.isupper(): # This is checking if all the letters \n print(\"Your password doesn't contain any lower letter\")\n return False # in the string are upper cased\n return True","def _is_control(char):\n if char == '\\t' or char == '\\n' or char == '\\r':\n return False\n cat = unicodedata.category(char)\n if cat.startswith('C'):\n return True\n return False","def _isascii(string):\n try:\n return len(string) == len(string.encode())\n except UnicodeDecodeError:\n return False\n except UnicodeEncodeError:\n return False","def in_alphabet(variable_name):\n # for loop to traverse sentence\n for character in variable_name:\n # check value not in alphabet dictionary\n if character not in alphabet:\n return False\n return True","def check_character(char, name, parameters):\r\n if char in name:\r\n raise NameError('Invalid character in the variable name: ' + name)\r\n\r\n # Make sure people don't include # within the name of parameters\r\n for item in parameters.keys():\r\n if char in item:\r\n raise NameError('Invalid character in the variable parameters: ' + item)","def guess(self, char: str) -> bool:\r\n equal_char = self.char.lower() == char.lower()\r\n\r\n if equal_char and self.was_guessed:\r\n raise ValueError(f'{char} har already been guessed')\r\n\r\n if equal_char:\r\n self.was_guessed = True\r\n return True\r\n\r\n return False","def _is_control(char):\n # These are technically control characters but we count them as whitespace\n # characters.\n if char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n return False # pragma: no cover\n cat = unicodedata.category(char)\n if cat in (\"Cc\", \"Cf\"):\n return True # pragma: no cover\n return False","def only_letters(value):\n regex = re.compile(r\"[A-Za-z\\sñÑáéíóúÁÉÍÓÚ]+\")\n\n if not regex.fullmatch(value):\n raise ValidationError(\"reingresar nombre, solo letras y espacios \")","def has_special_chars(word):\n for ch in constants.SPECIAL_CHARS:\n if ch in word:\n return True\n if not any(c.isalpha() for c in word):\n return True\n return False","def is_abecedarian(word):\n order = ord('a')\n for letter in word:\n if ord(letter) >= order:\n order = ord(letter)\n else:\n return False\n return True","def letter(self):\n return self._letter","def test_has_letter(row):\n assert not sudoku.no_letters(row)","def accept_letter(self, key):\n letter = key.text()\n key.setEnabled(False)\n self.keyboard.set_focus('Space')\n return letter.lower()","def has_word(self, word):\n try:\n iterator = iter(str(word))\n for symbol in iterator:\n if symbol not in self:\n raise Exception(symbol + \" is not in alphabet\")\n except Exception as error:\n print('Alphabet Error:', error)\n return False\n else:\n return True","def ascii_hexchar(s: str) -> bool:\n return frozenset(s).issubset(_ascii_h)","def all_caps(s):\n alphas = [c for c in s if c.isalpha()]\n if alphas == []:\n return False\n for x in alphas:\n if not x.isupper():\n return False\n return True","def _is_punctuation(char):\n cp = ord(char)\n if cp >= 33 and cp <= 47 or cp >= 58 and cp <= 64 or cp >= 91 and cp <= 96 or cp >= 123 and cp <= 126:\n return True\n cat = unicodedata.category(char)\n if cat.startswith('P'):\n return True\n return False","def say_letter(self, keyboard, keycode, char, modifiers):\n\n if keycode[1] in ('shift', 'rshift'):\n return # ignore.. shifted keys will have their Shift modifier set\n elif keycode[1] == 'tab':\n self.play_sound('tab')\n elif keycode[1] == 'delete':\n self.play_sound('delete')\n elif keycode[1] == 'backspace':\n self.textbox.text = self.textbox.text[:-1]\n self.play_sound('backspace')\n elif keycode[1] == 'enter':\n self.textbox.text += '\\n'\n self.play_sound('enter')\n elif char == ' ':\n self.textbox.text += ' '\n self.play_sound('space') \n elif char is None:\n self.play_sound('error')\n else:\n if 'shift' in modifiers or 'rshift' in modifiers:\n self.textbox.text += char.upper()\n else:\n self.textbox.text += char\n if RENAMED_CHAR.get(char):\n self.play_sound(RENAMED_CHAR[char])\n else: \n self.play_sound(char)","def checkForUppercase(alphabet):\n\n for char in alphabet:\n if char.upper().isupper():\n return True\n\n return False","def random_char(alph):\n char = alph[rand_generator.randrange(len(alph))]\n return char","def test_message_letter(Message, letter):\n assert get_message_letter(Message) == letter","def valid_ring_character(self, character):\n if character in self._charset:\n return character\n else:\n raise RotorRingCharacterError(character)"],"string":"[\n \"def is_letter(string_):\\n if string_ not in string.ascii_letters or len(string_) > 1:\\n return False\\n return True\",\n \"def isLetter(c):\\n ret = libxml2mod.xmlIsLetter(c)\\n return ret\",\n \"def is_letter(user_input):\\n # If any characters is letter -> return boolean True else False\\n if any(char.isalpha() for char in user_input):\\n return True\\n return False\",\n \"def has_letter(word):\\r\\n for char in word:\\r\\n if char.isalpha():\\r\\n return True\\r\\n return False\",\n \"def __valid_char(self, char: str) -> bool:\\r\\n if char.isdigit():\\r\\n raise ValueError('Characters can\\\\'t be numbers')\\r\\n\\r\\n return char.isalpha() or char.isspace()\",\n \"def character(x):\\n if (x==\\\"a\\\"or x==\\\"A\\\"or x==\\\"e\\\"or x==\\\"E\\\"or x==\\\"i\\\"or x==\\\"I\\\"or x==\\\"o\\\"or x==\\\"O\\\"or x==\\\"u\\\"or x==\\\"U\\\"):\\n return('True')\\n else:\\n return('False')\",\n \"def isSingleLetter(self, word):\\n return (re.match('^\\\\w$', word)) != None\",\n \"def is_lowercase(character):\\n return 'a' <= character <= 'z'\",\n \"def isalpha(self) -> bool:\\n pass\",\n \"def is_uppercase(character):\\n return 'A' <= character <= 'Z'\",\n \"def isChar(ch):\\n ret = libxml2mod.xmlIsChar(ch)\\n return ret\",\n \"def is_allowed_char(ch):\\n\\treturn ch.isalnum() or ch in \\\"#.>+*:$-_!@\\\"\",\n \"def isAlpha(string):\\n return (True)\",\n \"def is_valid_input(guess_letter):\\r\\n length = len(guess_letter)\\r\\n\\r\\n if length > 1 and not guess_letter.isalpha():\\r\\n return False\\r\\n elif not guess_letter.isalpha():\\r\\n return False\\r\\n elif length > 1:\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def test_starts_letter(x):\\n return x[0].isalpha()\",\n \"def is_valid_char(src):\\n\\n return src.isalnum()\",\n \"def must_contain_letter(cell):\\n # Check if it's nan first\\n if check_empty(cell):\\n return True\\n return not bool(re.search(\\\"[a-zA-Z]\\\", str(cell)))\",\n \"def isAlphanum(c):\\r\\n return ((c >= 'a' and c <= 'z') or (c >= '0' and c <= '9') or\\r\\n (c >= 'A' and c <= 'Z') or c == '_' or c == '$' or c == '\\\\\\\\' or (c is not None and ord(c) > 126));\",\n \"def ishex(char: chr) -> bool:\\n return char.isdigit() or char in \\\"abcdef\\\"\",\n \"def _is_alpha(argument):\\n\\n if not isinstance(argument, str):\\n return False\\n\\n if argument.lower() == 'alpha':\\n is_alpha = True\\n else:\\n argument, Z = _extract_charge_state(argument)\\n\\n if Z != 2:\\n is_alpha = False\\n elif argument[-2:] != '-4':\\n is_alpha = False\\n else:\\n\\n dash_position = argument.find('-')\\n argument = argument[:dash_position]\\n\\n if argument.lower() == 'helium' or argument == 'He':\\n is_alpha = True\\n else:\\n is_alpha = False\\n\\n return is_alpha\",\n \"def if_letter(letter):\\r\\n str_abc= ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\\r\\n answer= False\\r\\n for i in range(len(str_abc)):\\r\\n if letter==str_abc[i]:\\r\\n answer=True\\r\\n return answer\",\n \"def isalpha(self):\\n return isalpha(self)\",\n \"def is_word_character(ch):\\n if (ch >= 'a' and ch <= 'z'): return True\\n if (ch >= 'A' and ch <= 'Z'): return True\\n if (ch >= '0' and ch <= '9'): return True\\n if (ch >= 'À' and ch < 'ˀ'): return True\\n if (ch == '-' or ch == '0xAD'): return True # hyphen or soft hyphen\\n if (ch >= 'Ά' and ch <= 'ԓ'): return True\\n return False\",\n \"def check_type(character: str):\\n if character.isupper():\\n return 'upper'\\n elif character.islower():\\n return 'lower'\\n elif character.isspace():\\n return 'space'\\n elif character in string.punctuation:\\n return 'punc'\\n else:\\n return 'digit'\",\n \"def check_type(character: str):\\n if character.isupper():\\n return 'upper'\\n elif character.islower():\\n return 'lower'\\n elif character.isspace():\\n return 'space'\\n elif character in string.punctuation:\\n return 'punc'\\n else:\\n return 'digit'\",\n \"def is_valid_alphabetical_string(string_object: str):\\n return string_object.isalpha()\",\n \"def checkLetter():\\n\\tguess = False\\n\\twhile guess != True:\\n\\t\\tguess = str(raw_input(\\\"Guess a letter: \\\"))\\n\\t\\tif guess.isalpha() and len(guess) == 1 :\\n\\t\\t\\treturn guess\\n\\t\\telif not guess.isalpha() or len(guess) > 1:\\n\\t\\t\\tprint \\\"The input may be one letter only!\\\"\\n\\t\\telse:\\n\\t\\t\\tprint \\\"Error in checkLetter\\\"\",\n \"def test_ends_letter(x):\\n return x[-1].isalpha()\",\n \"def contains_only_char(s, char):\\n for c in s:\\n if c != char:\\n return False\\n return True\",\n \"def name_valid(name):\\n return name.isalpha()\",\n \"def is_char(user_input):\\n # Check lenght of input and if equal to zero return True\\n if len(user_input) == 0:\\n return True\\n return False\",\n \"def is_valid_input(letter_guessed):\\n\\n #calulate the lengh of the letters\\n NumberOfCharacters = (len(letter_guessed))\\n #convert input letters to Underscore\\n NumberOfUnderscore = (NumberOfCharacters * \\\"_\\\")\\n\\n\\n # All the letters in English\\n EnglishLetter = \\\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMOPQRSTUVWXYZ\\\"\\n\\n\\n if NumberOfCharacters > 1:\\n print(\\\"false\\\")\\n\\n # If the user entered English character the string will print the character a non-English character (for example, a sign such as: &, *), the string will print \\\"E2\\n elif letter_guessed in EnglishLetter:\\n print(\\\"true\\\")\\n else:\\n print(\\\"false\\\")\",\n \"def alphanumeric(s: str) -> bool:\\n return len(re.findall(r'[^A-Za-z0-9]', s)) == 0\",\n \"def is_char_token(c: str) -> bool:\\n return c in [\\\"+\\\", \\\"-\\\", \\\"*\\\", \\\"/\\\", \\\"(\\\", \\\")\\\"]\",\n \"def isAlphaNumeric(string):\\n return (True)\",\n \"def ask_letter(self):\\n letter = ' '\\n while letter not in string.ascii_lowercase:\\n letter = input('Write a letter:\\\\n')\\n letter.lower()\\n\\n return letter\",\n \"def is_input_valid(char):\\n\\n # is there a char at all?\\n if char is None:\\n return False\\n\\n # check for embedded 0 byte\\n if char == \\\"\\\\0\\\":\\n return False\\n\\n return True\",\n \"def sanitize_guess(self, letter): # helper function to incorrect_guess()\\n self.guess = letter.lower().strip()\\n if not self.guess.isalpha():\\n Donatello.turtle_text(\\n \\\"No special characters or numbers\\\") # no numbers, special characters or multiple words allowed\\n return False\\n # TODO if type != str raise assert\\n return self.guess\",\n \"def is_asian(char):\\r\\n\\r\\n # 0x3000 is ideographic space (i.e. double-byte space)\\r\\n # Anything over is an Asian character\\r\\n return ord(char) > IDEOGRAPHIC_SPACE\",\n \"def isalpha(a):\\n return _vec_string(a, bool_, 'isalpha')\",\n \"def is_symbol(p):\\n return len(p) == 1 and p.isalpha()\",\n \"def checkChar(self, char):\\n return char not in self.guessedChars\",\n \"def count_letter(content, letter):\\n if (not isinstance(letter, str)) or len(letter) != 1:\\n raise ValueError('`letter` must be a single character string.')\\n return len([char for char in content if char == letter])\",\n \"def is_ascii_chars(text):\\n is_ascii = True\\n try:\\n text.encode(encoding='utf-8').decode('ascii')\\n except UnicodeDecodeError:\\n is_ascii = False\\n return is_ascii\",\n \"def count_letter(content, letter):\\n if (not isinstance(letter, str)) or len(letter) != 1:\\n raise ValueError('`letter` must be a single character string.')\\n return len([char for char in content if char == letter])\",\n \"def count_letter(content, letter):\\n if (not isinstance(letter, str)) or len(letter) != 1:\\n raise ValueError('`letter` must be a single character string.')\\n return len([char for char in content if char == letter])\",\n \"def count_letter(content, letter):\\n if (not isinstance(letter, str)) or len(letter) != 1:\\n raise ValueError('`letter` must be a single character string.')\\n return len([char for char in content if char == letter])\",\n \"def count_letter(content, letter):\\n if (not isinstance(letter, str)) or len(letter) != 1:\\n raise ValueError('`letter` must be a single character string.')\\n return len([char for char in content if char == letter])\",\n \"def ascii_alphanumeric(s: str) -> bool:\\n return frozenset(s).issubset(_ascii_an)\",\n \"def is_punct_char(char):\\n\\treturn char in string.punctuation #1 is punctuation, 0 is not punctuation\",\n \"def is_num_char(x):\\n return ord('0') <= ord(x) <= ord('9')\",\n \"def validateNamePart(self, passed_name):\\n ## Declaring a Flag to control a while loop\\n name_ok = False\\n ## While loop to have user retry their input if they enter incorrectly\\n while not name_ok:\\n if passed_name.isalpha():\\n name_ok = True\\n return True\\n\\n else:\\n print(\\\"You have entered an invalid character. Please try again.\\\")\\n return False\",\n \"def is_valid_char(t_char):\\r\\n eax = 1 # mi preparo il flag \\\"invalido\\\" per il carattere\\r\\n \\r\\n # se il carattere e' un operatore, un operando o uno spazio\\r\\n # il carattere e' valido\\r\\n if is_operator(t_char) == 0:\\r\\n # e' operatore\\r\\n eax = 0\\r\\n \\r\\n if is_operand(t_char) == 0:\\r\\n # e' operando\\r\\n eax = 0\\r\\n \\r\\n if ord(t_char) == 32:\\r\\n # e' uno spazio\\r\\n eax = 0\\r\\n\\r\\n return eax\",\n \"def is_vowel(self, letter):\\n\\n if letter in (\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\", \\\"A\\\", \\\"E\\\", \\\"I\\\", \\\"O\\\", \\\"U\\\"):\\n return True\\n return False\",\n \"def is_character_key(self, p_event):\\n p = rffi.cast(RSDL.KeyboardEventPtr, p_event)\\n keycode = rffi.getintfield(p.c_keysym, 'c_sym')\\n return RSDL.K_BACKSPACE <= keycode <= RSDL.K_z \\\\\\n or RSDL.K_WORLD_0 <= keycode <= RSDL.K_KP_EQUALS \\\\\\n or keycode == RSDL.K_EURO # whoever came up with this being beyond the modifier keys etc...\",\n \"def beginning_is_letter(document_id):\\n logger.debug('Function Successful: % s',\\n 'beginning_is_letter: beginning_is_letter successfully called from process_doc', extra=d)\\n logger.info('Ensuring that document ID begins with a letter...')\\n letter = document_id[0]\\n\\n result = letter.isalpha()\\n if result is True:\\n logger.debug('Returning: %s',\\n 'beginning_is_letter: returning True', extra=d)\\n logger.info('Docuemnt ID begins with a letter')\\n return True\\n else:\\n logger.debug('Returning: %s',\\n 'beginning_is_letter: returning False', extra=d)\\n logger.warning('Document ID does not begin with a letter')\\n return False\",\n \"def check_char(self, char: str) -> None:\\n while len(char) > 1:\\n char = input(\\\"Enter only a char -> \\\")\\n char = char.lower()\\n if char in self.word:\\n indexes = []\\n tmp_word = list(self.word)\\n while char in tmp_word:\\n index = tmp_word.index(char)\\n indexes.append(index)\\n tmp_word[index] = \\\"\\\"\\n for i in indexes:\\n self.word2[i] = char\\n else:\\n self.errors += 1\\n print(f\\\"This char isn't in the word -> +1 Error {self.errors}/{self.attempts}\\\")\",\n \"def is_ascii(token):\\n\\n printable = set(string.printable)\\n\\n for char in token:\\n if char not in printable:\\n return False\\n\\n return True\",\n \"def _validate_word(self, word):\\n return type(word) == type('a') and set(self._letters) == set(list(word))\",\n \"def is_number_char(c: str) -> bool:\\n return c.isdigit() or c == \\\".\\\"\",\n \"def alphabet_position(char):\\n if type(char) != type(''):\\n return -1\\n if len(char) != 1:\\n return -1\\n if char.isalpha():\\n return lowerLetters.find(char.lower())\\n return -1\",\n \"def isalnum(self) -> bool:\\n pass\",\n \"def validate_name_input(name):\\n if all(letter.isalpha() or letter.isspace()\\n for letter in name) and len(name) != 0:\\n clear()\\n return True\\n\\n else:\\n clear()\\n print('** Please enter a name of alphabetic characters and spaces **')\\n return False\",\n \"def product_isalpha(product):\\n if product.isalpha(): #This verifies if the product that has inserted, has a valid name\\n return True\\n else:\\n return False\",\n \"def guess_input(self):\\n try:\\n self.player_guess = input('Guess a letter: ').lower()\\n Character(self.player_guess, self.selected_phrase)\\n except ValueError:\\n print(\\\"That was not a valid input. Please pick a number between 1 and 10\\\")\\n if self.player_guess == \\\"\\\":\\n print (\\\"Please enter a letter,try again.\\\")\\n if not self.player_guess.isalpha():\\n print (\\\"Please only enter a letter(a-z),try again.\\\")\\n if len(self.player_guess) > 1:\\n print(\\\"Please enter only one letter at a time.\\\")\",\n \"def invalid_name(name):\\n if any(not item.isalpha() for item in str(name)):\\n return True\\n return False\",\n \"def is_apostrophe(ch):\\n if (ch == '\\\\'' or ch == '\\\\u2019' or ch == '\\\\u02bc'): return True\\n return False\",\n \"def count_letters(word, letter):\\n assert isinstance(word, str) == True, \\\"Please input a string!\\\"\\n count = 0\\n for char in word:\\n if char == letter:\\n count += 1\\n\\n return count\",\n \"def com_adobe_fonts_check_find_empty_letters(ttFont):\\n cmap = ttFont.getBestCmap()\\n passed = True\\n\\n # http://unicode.org/reports/tr44/#General_Category_Values\\n letter_categories = {\\n 'Ll', 'Lm', 'Lo', 'Lt', 'Lu',\\n }\\n invisible_letters = {\\n 0x115F, 0x1160, 0x3164, 0xFFA0, # Hangul filler chars (category='Lo')\\n }\\n for unicode_val, glyph_name in cmap.items():\\n category = unicodedata.category(chr(unicode_val))\\n if (_quick_and_dirty_glyph_is_empty(ttFont, glyph_name)) \\\\\\n and (category in letter_categories) \\\\\\n and (unicode_val not in invisible_letters):\\n yield FAIL, \\\\\\n Message(\\\"empty-letter\\\",\\n \\\"U+%04X should be visible, but its glyph ('%s') is empty.\\\"\\n % (unicode_val, glyph_name))\\n passed = False\\n if passed:\\n yield PASS, \\\"No empty glyphs for letters found.\\\"\",\n \"def _is_control(char):\\n # These are technically control characters but we count them as whitespace\\n # characters.\\n if char == \\\"\\\\t\\\" or char == \\\"\\\\n\\\" or char == \\\"\\\\r\\\":\\n return False\\n cat = unicodedata.category(char)\\n if cat.startswith(\\\"C\\\"):\\n return True\\n return False\",\n \"def toChar(s):\\n s = s.lower()\\n ans = \\\"\\\"\\n for c in s:\\n if c in \\\"abcdefghijklmnopqrstuvwxyz\\\":\\n ans+=c\\n return ans\",\n \"def char(self, aIndex, char):\\n o = ord(char)\\n c = -1\\n # space\\n if o == 32:\\n c = 16\\n # dash\\n if o == 45:\\n c = 17\\n # uppercase A-F\\n if 65 <= o <= 70:\\n c = o - 55\\n # lowercase a-f\\n if 97 <= o <= 102:\\n c = o - 87\\n # 0-9\\n if 48 <= o <= 57:\\n c = o - 48\\n\\n self.digit(aIndex, c)\",\n \"def isAlphabet(self, seqstr):\\n mystr = seqstr\\n if type(seqstr) is Sequence:\\n mystr = seqstr.getString()\\n return self.getAlphabet().isValidString(mystr)\",\n \"def _is_control(char):\\n # These are technically control characters but we count them as whitespace\\n # characters.\\n if char == \\\"\\\\t\\\" or char == \\\"\\\\n\\\" or char == \\\"\\\\r\\\":\\n return False\\n cat = unicodedata.category(char)\\n if cat.startswith(\\\"C\\\"):\\n return True\\n return False\",\n \"def _is_control(char):\\n # These are technically control characters but we count them as whitespace\\n # characters.\\n if char == \\\"\\\\t\\\" or char == \\\"\\\\n\\\" or char == \\\"\\\\r\\\":\\n return False\\n cat = unicodedata.category(char)\\n if cat.startswith(\\\"C\\\"):\\n return True\\n return False\",\n \"def isascii(s):\\n return len(s) == len(s.encode())\",\n \"def isascii(s):\\n return len(s) == len(s.encode())\",\n \"def ValidateEntry(Secret_Word_Masked, Secret_Word_Masked_Unspaced, Used_Char):\\n Guess = input(\\\"\\\\nGuess A Letter: \\\")\\n Guess = Guess.lower()\\n\\n while (len(Guess) > 1 or not Guess.isalpha()):\\n print(\\\"\\\\nInvalid Entry: \\\\'%s\\\\'\\\" %Guess)\\n print(\\\"Alphabetic Character(s) Already Used: %s\\\" %Used_Char)\\n print(\\\"So Far The Secret Word is:\\\\n%s\\\" %Secret_Word_Masked)\\n Guess = input(\\\"\\\\nPlease Enter Only A Single Alphabetic Character: \\\")\\n Guess = Guess.lower()\\n\\n print(\\\"\\\\nValid Entry: \\\\'%c\\\\'\\\" %Guess)\\n\\n return Guess\",\n \"def has_lower_letter(string):\\n if string.isupper(): # This is checking if all the letters \\n print(\\\"Your password doesn't contain any lower letter\\\")\\n return False # in the string are upper cased\\n return True\",\n \"def _is_control(char):\\n if char == '\\\\t' or char == '\\\\n' or char == '\\\\r':\\n return False\\n cat = unicodedata.category(char)\\n if cat.startswith('C'):\\n return True\\n return False\",\n \"def _isascii(string):\\n try:\\n return len(string) == len(string.encode())\\n except UnicodeDecodeError:\\n return False\\n except UnicodeEncodeError:\\n return False\",\n \"def in_alphabet(variable_name):\\n # for loop to traverse sentence\\n for character in variable_name:\\n # check value not in alphabet dictionary\\n if character not in alphabet:\\n return False\\n return True\",\n \"def check_character(char, name, parameters):\\r\\n if char in name:\\r\\n raise NameError('Invalid character in the variable name: ' + name)\\r\\n\\r\\n # Make sure people don't include # within the name of parameters\\r\\n for item in parameters.keys():\\r\\n if char in item:\\r\\n raise NameError('Invalid character in the variable parameters: ' + item)\",\n \"def guess(self, char: str) -> bool:\\r\\n equal_char = self.char.lower() == char.lower()\\r\\n\\r\\n if equal_char and self.was_guessed:\\r\\n raise ValueError(f'{char} har already been guessed')\\r\\n\\r\\n if equal_char:\\r\\n self.was_guessed = True\\r\\n return True\\r\\n\\r\\n return False\",\n \"def _is_control(char):\\n # These are technically control characters but we count them as whitespace\\n # characters.\\n if char == \\\"\\\\t\\\" or char == \\\"\\\\n\\\" or char == \\\"\\\\r\\\":\\n return False # pragma: no cover\\n cat = unicodedata.category(char)\\n if cat in (\\\"Cc\\\", \\\"Cf\\\"):\\n return True # pragma: no cover\\n return False\",\n \"def only_letters(value):\\n regex = re.compile(r\\\"[A-Za-z\\\\sñÑáéíóúÁÉÍÓÚ]+\\\")\\n\\n if not regex.fullmatch(value):\\n raise ValidationError(\\\"reingresar nombre, solo letras y espacios \\\")\",\n \"def has_special_chars(word):\\n for ch in constants.SPECIAL_CHARS:\\n if ch in word:\\n return True\\n if not any(c.isalpha() for c in word):\\n return True\\n return False\",\n \"def is_abecedarian(word):\\n order = ord('a')\\n for letter in word:\\n if ord(letter) >= order:\\n order = ord(letter)\\n else:\\n return False\\n return True\",\n \"def letter(self):\\n return self._letter\",\n \"def test_has_letter(row):\\n assert not sudoku.no_letters(row)\",\n \"def accept_letter(self, key):\\n letter = key.text()\\n key.setEnabled(False)\\n self.keyboard.set_focus('Space')\\n return letter.lower()\",\n \"def has_word(self, word):\\n try:\\n iterator = iter(str(word))\\n for symbol in iterator:\\n if symbol not in self:\\n raise Exception(symbol + \\\" is not in alphabet\\\")\\n except Exception as error:\\n print('Alphabet Error:', error)\\n return False\\n else:\\n return True\",\n \"def ascii_hexchar(s: str) -> bool:\\n return frozenset(s).issubset(_ascii_h)\",\n \"def all_caps(s):\\n alphas = [c for c in s if c.isalpha()]\\n if alphas == []:\\n return False\\n for x in alphas:\\n if not x.isupper():\\n return False\\n return True\",\n \"def _is_punctuation(char):\\n cp = ord(char)\\n if cp >= 33 and cp <= 47 or cp >= 58 and cp <= 64 or cp >= 91 and cp <= 96 or cp >= 123 and cp <= 126:\\n return True\\n cat = unicodedata.category(char)\\n if cat.startswith('P'):\\n return True\\n return False\",\n \"def say_letter(self, keyboard, keycode, char, modifiers):\\n\\n if keycode[1] in ('shift', 'rshift'):\\n return # ignore.. shifted keys will have their Shift modifier set\\n elif keycode[1] == 'tab':\\n self.play_sound('tab')\\n elif keycode[1] == 'delete':\\n self.play_sound('delete')\\n elif keycode[1] == 'backspace':\\n self.textbox.text = self.textbox.text[:-1]\\n self.play_sound('backspace')\\n elif keycode[1] == 'enter':\\n self.textbox.text += '\\\\n'\\n self.play_sound('enter')\\n elif char == ' ':\\n self.textbox.text += ' '\\n self.play_sound('space') \\n elif char is None:\\n self.play_sound('error')\\n else:\\n if 'shift' in modifiers or 'rshift' in modifiers:\\n self.textbox.text += char.upper()\\n else:\\n self.textbox.text += char\\n if RENAMED_CHAR.get(char):\\n self.play_sound(RENAMED_CHAR[char])\\n else: \\n self.play_sound(char)\",\n \"def checkForUppercase(alphabet):\\n\\n for char in alphabet:\\n if char.upper().isupper():\\n return True\\n\\n return False\",\n \"def random_char(alph):\\n char = alph[rand_generator.randrange(len(alph))]\\n return char\",\n \"def test_message_letter(Message, letter):\\n assert get_message_letter(Message) == letter\",\n \"def valid_ring_character(self, character):\\n if character in self._charset:\\n return character\\n else:\\n raise RotorRingCharacterError(character)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8101875","0.7942589","0.77772945","0.7760421","0.7488684","0.7349609","0.7162389","0.7131679","0.7119435","0.71050584","0.7094554","0.70823413","0.70609343","0.7013201","0.69936603","0.69796395","0.6963873","0.6928611","0.6889942","0.68720996","0.68349135","0.68182826","0.6767468","0.67518854","0.6746451","0.6743348","0.6732877","0.66436744","0.66269696","0.6549778","0.6541035","0.65401924","0.65270483","0.6491198","0.6451369","0.641273","0.6408517","0.63804626","0.63804317","0.63163525","0.6285745","0.6272326","0.6220141","0.618846","0.6183953","0.6183953","0.6183953","0.6183953","0.6183902","0.617748","0.61692464","0.61474687","0.6143986","0.61265504","0.61233264","0.6115535","0.609223","0.6073407","0.60718626","0.60623914","0.60555845","0.6050294","0.60325927","0.6012333","0.5999067","0.599469","0.59830946","0.5980729","0.5972658","0.59696656","0.59694827","0.59674907","0.59599745","0.5954885","0.5954885","0.5945015","0.5945015","0.5941033","0.5940569","0.5938186","0.5913865","0.59057426","0.59056115","0.58846134","0.58824617","0.5873327","0.58709085","0.5867659","0.58307487","0.5826806","0.582491","0.58223134","0.579918","0.57990766","0.577843","0.5776947","0.57741886","0.5764168","0.5758772","0.57545525"],"string":"[\n \"0.8101875\",\n \"0.7942589\",\n \"0.77772945\",\n \"0.7760421\",\n \"0.7488684\",\n \"0.7349609\",\n \"0.7162389\",\n \"0.7131679\",\n \"0.7119435\",\n \"0.71050584\",\n \"0.7094554\",\n \"0.70823413\",\n \"0.70609343\",\n \"0.7013201\",\n \"0.69936603\",\n \"0.69796395\",\n \"0.6963873\",\n \"0.6928611\",\n \"0.6889942\",\n \"0.68720996\",\n \"0.68349135\",\n \"0.68182826\",\n \"0.6767468\",\n \"0.67518854\",\n \"0.6746451\",\n \"0.6743348\",\n \"0.6732877\",\n \"0.66436744\",\n \"0.66269696\",\n \"0.6549778\",\n \"0.6541035\",\n \"0.65401924\",\n \"0.65270483\",\n \"0.6491198\",\n \"0.6451369\",\n \"0.641273\",\n \"0.6408517\",\n \"0.63804626\",\n \"0.63804317\",\n \"0.63163525\",\n \"0.6285745\",\n \"0.6272326\",\n \"0.6220141\",\n \"0.618846\",\n \"0.6183953\",\n \"0.6183953\",\n \"0.6183953\",\n \"0.6183953\",\n \"0.6183902\",\n \"0.617748\",\n \"0.61692464\",\n \"0.61474687\",\n \"0.6143986\",\n \"0.61265504\",\n \"0.61233264\",\n \"0.6115535\",\n \"0.609223\",\n \"0.6073407\",\n \"0.60718626\",\n \"0.60623914\",\n \"0.60555845\",\n \"0.6050294\",\n \"0.60325927\",\n \"0.6012333\",\n \"0.5999067\",\n \"0.599469\",\n \"0.59830946\",\n \"0.5980729\",\n \"0.5972658\",\n \"0.59696656\",\n \"0.59694827\",\n \"0.59674907\",\n \"0.59599745\",\n \"0.5954885\",\n \"0.5954885\",\n \"0.5945015\",\n \"0.5945015\",\n \"0.5941033\",\n \"0.5940569\",\n \"0.5938186\",\n \"0.5913865\",\n \"0.59057426\",\n \"0.59056115\",\n \"0.58846134\",\n \"0.58824617\",\n \"0.5873327\",\n \"0.58709085\",\n \"0.5867659\",\n \"0.58307487\",\n \"0.5826806\",\n \"0.582491\",\n \"0.58223134\",\n \"0.579918\",\n \"0.57990766\",\n \"0.577843\",\n \"0.5776947\",\n \"0.57741886\",\n \"0.5764168\",\n \"0.5758772\",\n \"0.57545525\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.84225947"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12395,"cells":{"query":{"kind":"string","value":"Checks if the given character is a number."},"document":{"kind":"string","value":"def is_number(c):\n return '0' <= c <= '9'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_num_char(x):\n return ord('0') <= ord(x) <= ord('9')","def is_number_char(c: str) -> bool:\n return c.isdigit() or c == \".\"","def is_number(s):\r\n try:\r\n int(s)\r\n return True\r\n except ValueError:\r\n return False","def is_number(s):\n try:\n int(s)\n return True\n except ValueError:\n return False","def isnum(self, x):\n\n return x in '1234567890.-'","def isnumber(n):\r\n N = str(n)\r\n if N.isdigit():\r\n return True\r\n else:\r\n return False","def isNumber(string):\r\n for char in string:\r\n charNum = ord(char)\r\n if (charNum < 48 or charNum > 57):\r\n return False\r\n return True","def _is_number(self, symbol):\n if symbol.type == self.scanner.NUMBER:\n return True\n else:\n return False","def is_valid_numeric(inString):\r\n return is_int(inString) or is_float(inString)","def is_number_repl_isnumeric(s):\n return s.replace('.', '', 1).isnumeric()","def is_num(var):\n try:\n int(var)\n return True\n except ValueError:\n return False","def isnumeric(number):\n try:\n float(number)\n return True\n except (TypeError, ValueError):\n return False","def is_number(value):\n try:\n int(value)\n return True\n except (ValueError, TypeError):\n return False","def isdigit(self):\n return isdigit(self)","def isDigit(ch):\n ret = libxml2mod.xmlIsDigit(ch)\n return ret","def string_is_digit(string):\n valids = set([46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57])\n plus_minus = set([43, 45])\n characters = list(string)\n\n #First character can be number or +/-\n if ord(characters[0]) not in valids.union(plus_minus):\n return False\n \n #Iterate to check all other characters\n for character in string[1:]:\n value = ord(character)\n if value not in valids:\n return False\n elif value == 46: # 46 = '.'\n valids.remove(46) # Only one period allowed\n return True","def isdigit(self) -> bool:\n pass","def IsNumeric(text):\n try:\n _ = float(text)\n except ValueError:\n return 0\n else:\n return 1","def is_numeric(val):\n if \\\n isinstance(val, int) or \\\n isinstance(val, float):\n return True\n elif \\\n isinstance(val, str) and \\\n val.isdigit():\n return True\n else:\n return False","def __has_numbers(self, input_string):\n return bool(re.search(r'\\d', input_string))","def is_number_repl_isdigit(s):\n return s.replace('.', '', 1).isdigit()","def is_number(s):\r\n try:\r\n float(s)\r\n return True\r\n except ValueError:\r\n return False","def is_number(string):\r\n try:\r\n float(string)\r\n return True\r\n except ValueError: return False","def is_number(str):\n try:\n float(str)\n return True\n except ValueError as e:\n print(e)\n try:\n unicodedata.numeric(str)\n return True\n except (TypeError, ValueError) as e:\n print(e)\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(string):\n try:\n float(string)\n return True\n except ValueError:\n return False","def isNumber(word):\n try:\n int(word)\n return True\n except ValueError:\n return False","def _is_number(s) -> bool:\n try:\n float(s)\n except ValueError:\n return False\n else:\n return True","def isnumber(x):\n try:\n float(x)\n return True\n except ValueError:\n return False","def is_number(s: Any) -> bool:\n try:\n int(s)\n return True\n except ValueError:\n pass\n\n try:\n float(s)\n return True\n except ValueError:\n pass\n\n return False","def only_int(p):\r\n if p.isdigit():\r\n return True\r\n return False","def is_number(str):\n\n # Local constants\n\n # Local variabes\n\n #****** start is_number() ******#\n\n try:\n float(str)\n return True\n except ValueError:\n return False","def has_num(text):\n return any(str.isdigit(c) for c in text)","def is_valid_number(self, text, widget):\n if len(text) > 2:\n return False\n for char in text:\n if not char.isdigit():\n return False\n if text != '' and int(text) == 0:\n return False\n return True","def IsNumber(s):\n try:\n v = float(s)\n return True\n except ValueError:\n return False","def is_number(self) -> bool:\n return False","def isNumeric(string, needHexPrefix):\n return (True)","def is_num(n):\n return '{} is a number'.format(n)","def is_digit_regex(s: str) -> bool:\n if re.match(\"^\\d+?\\.\\d+?$\", s) is None:\n return s.isdigit()\n return True","def is_digit(user_input):\n # If any characters is digit return boolean True else False\n if any(char.isdigit() for char in user_input):\n return True\n return False","def is_number(num):\n try:\n float(num)\n return True\n except ValueError:\n return False","def is_number(G):\n return True","def is_number(x):\n if isinstance(x, (int, float)):\n return True\n else:\n return False","def number_only(number):\n number = number.replace(' ', '')\n result = re.match(r\"^[0-9]+$\", number)\n if not result:\n return True\n return False","def is_number(n):\n\ttry:\n\t\tfloat(n)\n\t\treturn True\n\texcept ValueError:\n\t\treturn False","def is_digit(self) -> bool:\n if only_numerals_and_punctuation(self.user_agent):\n return True\n\n return mostly_numerals(self.user_agent)","def check_int(s):\n if s[0] in ('-', '+'):\n return s[1:].isdigit()\n return s.isdigit()","def is_number(self, cell): \n for token in self._cell_tokenizer.tokenize(cell.get_text()):\n if self._get_token_type(token) == 'NAME':\n return False \n return True","def is_numeric(value):\n return any([\n type(value) is str and value.isnumeric(),\n hasattr(value, 'is_integer') and value.is_integer(),\n type(value) is int,\n ])","def is_number(self,s):\n try:\n float(s.replace(\" \", \"\"))\n return True\n except ValueError:\n return False","def _is_number(value):\n try:\n float(value)\n return True\n except (TypeError, ValueError):\n return False","def is_number_regex(s):\n if re_match('^\\d+?\\.\\d+?$', s) is None:\n return s.isdigit()\n return True","def validate_number(input_data):\n if input_data.startswith('-'):\n return input_data.i\n else:\n return False","def must_contain_digit(cell):\n # Check if it's nan first\n if check_empty(cell):\n return True\n return not bool(re.search(\"\\d\", str(cell)))","def isnumeric(self):\n return isnumeric(self)","def is_number_regex(s):\n if re.match(\"^\\d+?\\.\\d+?$\", s) is None:\n return s.isdigit()\n return True","def is_number_regex(s):\n if re.match(\"^\\d+?\\.\\d+?$\", s) is None:\n return s.isdigit()\n return True","def is_number(text):\n return text.lower() in AVRO_NUMBERS","def hasNumbers(inputString):\n return any(char.isdigit() for char in inputString)","def is_numeric(value):\n return isinstance(value, int) or isinstance(value, float)","def is_digit(value):\n # for loop to traverse line\n for character in value:\n # check value is a number using isdigit() method and return boolean value\n if character.isdigit() == False:\n return False\n return True","def has_number(any_string):\n return any(char.isdigit() for char in any_string)","def isdigit(a):\n return _vec_string(a, bool_, 'isdigit')","def is_number(self,val):\n try:\n float(val)\n return True\n except ValueError:\n return False","def isNumber(s):\n\ttry:\n\t\tfloat(s)\n\t\treturn True\n\texcept ValueError:\n\t\treturn False","def checknum(val):\n\n if len(val) == 0:\n return False\n\n for i in range(len(val)):\n if not val[i].isdigit():\n return False\n\n return True","def only_numbers(string):\n for character in string[:-1]:\n if not (character.isdigit() or character in (',', ' ')): \n return False\n return True","def is_numberish(G):\n return True","def is_numeric(self) -> bool:\n return False","def is_numeral(self, symbol: str) -> bool:\n return symbol in self.numerals","def could_be_number(val):\n if val == None:\n return False\n\n if isinstance(val, (float, int, long)):\n return True\n\n # allow coercion from str\n if isinstance(val, (str, unicode)):\n try:\n n = float(val)\n if not isinstance(n, float):\n raise ValueError\n else:\n return True\n except:\n return False\n\n #otherwise\n return False","def isNumber(st):\n\treturn st.replace('.','',1).isdigit()","def is_some_number(mystring):\n # print(Bcolors.cyan + re.findall(r\".*\\\\(.*)\", inspect.stack()[0][1])[0] + \" --- \"\n # + inspect.stack()[0][3] + \"()\" + Bcolors.ENDC)\n mystring = str(mystring)\n mystring = re.sub(\",\", \".\", mystring)\n try:\n if float(mystring):\n return True\n except ValueError:\n return False","def is_number(value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def isNumber(self, s):\n try:\n tmp = float(s)\n return True\n except:\n return False","def __valid_char(self, char: str) -> bool:\r\n if char.isdigit():\r\n raise ValueError('Characters can\\'t be numbers')\r\n\r\n return char.isalpha() or char.isspace()","def slug_is_numerical(slug):\r\n try:\r\n float(slug)\r\n except ValueError:\r\n return False\r\n\r\n return True","def isNumber(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def isnum(value):\n\n try:\n return bool(isinstance(value, (float, int)))\n except RuntimeError:\n return False","def is_number(number):\n try:\n float(number)\n return True\n except ValueError:\n return False","def is_number(s: Union[str, int, float]):\n if isinstance(s, str) and s.lower() == \"nan\":\n return True\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(value):\n\n return isinstance(value, (int, long, float))","def is_number(n):\n return isinstance(n, (int, float))","def _is_num(w):\n symbols = list(w)\n for s in symbols:\n if s in string.digits:\n return '<NUM>'\n return w","def check_for_integer(number):\r\n \r\n try:\r\n int(number) \r\n return True\r\n except ValueError:\r\n return False","def is_integer(self, string):\n try:\n return int(string)\n except:\n return False","def ISNUMBER(value):\n return isinstance(value, numbers.Number)","def test_is_number(self):\n \n self.assertEqual(self.var.is_number(None), False)\n self.assertEqual(self.var.is_number(\"5\"), True)\n self.assertEqual(self.var.is_number(\"a\"), False)","def is_numeric(number):\n\n if isinstance(number, bool):\n return False\n elif isinstance(number, int) or isinstance(number, float):\n return True\n else:\n return False","def is_number(value):\n try:\n float(value.replace(',', ''))\n except ValueError:\n return False\n return True","def isNumber(number):\n try:\n # Try to cast the string\n int(number)\n # The cast was successful\n return True\n # The cast was unsuccessful, the string is not a number\n except ValueError as err:\n # Write the exception in logging\n logging.exception(str(err))\n return False","def _isDecimalNumber(strWord):\n return NumberFormula.DECIMALNUMBERREGEX.match(strWord) != None","def __is_int(self,string):\r\n try: \r\n int(string)\r\n return True\r\n except ValueError:\r\n return False","def isAlphaNumeric(string):\n return (True)","def is_int(string):\n try:\n int(string)\n return True\n except ValueError:\n return False","def is_numeric (self) :\n\n return self.__isnumeric__","def isint(s):\n try:\n x = int(s)\n return True\n except:\n return False"],"string":"[\n \"def is_num_char(x):\\n return ord('0') <= ord(x) <= ord('9')\",\n \"def is_number_char(c: str) -> bool:\\n return c.isdigit() or c == \\\".\\\"\",\n \"def is_number(s):\\r\\n try:\\r\\n int(s)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def is_number(s):\\n try:\\n int(s)\\n return True\\n except ValueError:\\n return False\",\n \"def isnum(self, x):\\n\\n return x in '1234567890.-'\",\n \"def isnumber(n):\\r\\n N = str(n)\\r\\n if N.isdigit():\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def isNumber(string):\\r\\n for char in string:\\r\\n charNum = ord(char)\\r\\n if (charNum < 48 or charNum > 57):\\r\\n return False\\r\\n return True\",\n \"def _is_number(self, symbol):\\n if symbol.type == self.scanner.NUMBER:\\n return True\\n else:\\n return False\",\n \"def is_valid_numeric(inString):\\r\\n return is_int(inString) or is_float(inString)\",\n \"def is_number_repl_isnumeric(s):\\n return s.replace('.', '', 1).isnumeric()\",\n \"def is_num(var):\\n try:\\n int(var)\\n return True\\n except ValueError:\\n return False\",\n \"def isnumeric(number):\\n try:\\n float(number)\\n return True\\n except (TypeError, ValueError):\\n return False\",\n \"def is_number(value):\\n try:\\n int(value)\\n return True\\n except (ValueError, TypeError):\\n return False\",\n \"def isdigit(self):\\n return isdigit(self)\",\n \"def isDigit(ch):\\n ret = libxml2mod.xmlIsDigit(ch)\\n return ret\",\n \"def string_is_digit(string):\\n valids = set([46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57])\\n plus_minus = set([43, 45])\\n characters = list(string)\\n\\n #First character can be number or +/-\\n if ord(characters[0]) not in valids.union(plus_minus):\\n return False\\n \\n #Iterate to check all other characters\\n for character in string[1:]:\\n value = ord(character)\\n if value not in valids:\\n return False\\n elif value == 46: # 46 = '.'\\n valids.remove(46) # Only one period allowed\\n return True\",\n \"def isdigit(self) -> bool:\\n pass\",\n \"def IsNumeric(text):\\n try:\\n _ = float(text)\\n except ValueError:\\n return 0\\n else:\\n return 1\",\n \"def is_numeric(val):\\n if \\\\\\n isinstance(val, int) or \\\\\\n isinstance(val, float):\\n return True\\n elif \\\\\\n isinstance(val, str) and \\\\\\n val.isdigit():\\n return True\\n else:\\n return False\",\n \"def __has_numbers(self, input_string):\\n return bool(re.search(r'\\\\d', input_string))\",\n \"def is_number_repl_isdigit(s):\\n return s.replace('.', '', 1).isdigit()\",\n \"def is_number(s):\\r\\n try:\\r\\n float(s)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def is_number(string):\\r\\n try:\\r\\n float(string)\\r\\n return True\\r\\n except ValueError: return False\",\n \"def is_number(str):\\n try:\\n float(str)\\n return True\\n except ValueError as e:\\n print(e)\\n try:\\n unicodedata.numeric(str)\\n return True\\n except (TypeError, ValueError) as e:\\n print(e)\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(string):\\n try:\\n float(string)\\n return True\\n except ValueError:\\n return False\",\n \"def isNumber(word):\\n try:\\n int(word)\\n return True\\n except ValueError:\\n return False\",\n \"def _is_number(s) -> bool:\\n try:\\n float(s)\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def isnumber(x):\\n try:\\n float(x)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s: Any) -> bool:\\n try:\\n int(s)\\n return True\\n except ValueError:\\n pass\\n\\n try:\\n float(s)\\n return True\\n except ValueError:\\n pass\\n\\n return False\",\n \"def only_int(p):\\r\\n if p.isdigit():\\r\\n return True\\r\\n return False\",\n \"def is_number(str):\\n\\n # Local constants\\n\\n # Local variabes\\n\\n #****** start is_number() ******#\\n\\n try:\\n float(str)\\n return True\\n except ValueError:\\n return False\",\n \"def has_num(text):\\n return any(str.isdigit(c) for c in text)\",\n \"def is_valid_number(self, text, widget):\\n if len(text) > 2:\\n return False\\n for char in text:\\n if not char.isdigit():\\n return False\\n if text != '' and int(text) == 0:\\n return False\\n return True\",\n \"def IsNumber(s):\\n try:\\n v = float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(self) -> bool:\\n return False\",\n \"def isNumeric(string, needHexPrefix):\\n return (True)\",\n \"def is_num(n):\\n return '{} is a number'.format(n)\",\n \"def is_digit_regex(s: str) -> bool:\\n if re.match(\\\"^\\\\d+?\\\\.\\\\d+?$\\\", s) is None:\\n return s.isdigit()\\n return True\",\n \"def is_digit(user_input):\\n # If any characters is digit return boolean True else False\\n if any(char.isdigit() for char in user_input):\\n return True\\n return False\",\n \"def is_number(num):\\n try:\\n float(num)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(G):\\n return True\",\n \"def is_number(x):\\n if isinstance(x, (int, float)):\\n return True\\n else:\\n return False\",\n \"def number_only(number):\\n number = number.replace(' ', '')\\n result = re.match(r\\\"^[0-9]+$\\\", number)\\n if not result:\\n return True\\n return False\",\n \"def is_number(n):\\n\\ttry:\\n\\t\\tfloat(n)\\n\\t\\treturn True\\n\\texcept ValueError:\\n\\t\\treturn False\",\n \"def is_digit(self) -> bool:\\n if only_numerals_and_punctuation(self.user_agent):\\n return True\\n\\n return mostly_numerals(self.user_agent)\",\n \"def check_int(s):\\n if s[0] in ('-', '+'):\\n return s[1:].isdigit()\\n return s.isdigit()\",\n \"def is_number(self, cell): \\n for token in self._cell_tokenizer.tokenize(cell.get_text()):\\n if self._get_token_type(token) == 'NAME':\\n return False \\n return True\",\n \"def is_numeric(value):\\n return any([\\n type(value) is str and value.isnumeric(),\\n hasattr(value, 'is_integer') and value.is_integer(),\\n type(value) is int,\\n ])\",\n \"def is_number(self,s):\\n try:\\n float(s.replace(\\\" \\\", \\\"\\\"))\\n return True\\n except ValueError:\\n return False\",\n \"def _is_number(value):\\n try:\\n float(value)\\n return True\\n except (TypeError, ValueError):\\n return False\",\n \"def is_number_regex(s):\\n if re_match('^\\\\d+?\\\\.\\\\d+?$', s) is None:\\n return s.isdigit()\\n return True\",\n \"def validate_number(input_data):\\n if input_data.startswith('-'):\\n return input_data.i\\n else:\\n return False\",\n \"def must_contain_digit(cell):\\n # Check if it's nan first\\n if check_empty(cell):\\n return True\\n return not bool(re.search(\\\"\\\\d\\\", str(cell)))\",\n \"def isnumeric(self):\\n return isnumeric(self)\",\n \"def is_number_regex(s):\\n if re.match(\\\"^\\\\d+?\\\\.\\\\d+?$\\\", s) is None:\\n return s.isdigit()\\n return True\",\n \"def is_number_regex(s):\\n if re.match(\\\"^\\\\d+?\\\\.\\\\d+?$\\\", s) is None:\\n return s.isdigit()\\n return True\",\n \"def is_number(text):\\n return text.lower() in AVRO_NUMBERS\",\n \"def hasNumbers(inputString):\\n return any(char.isdigit() for char in inputString)\",\n \"def is_numeric(value):\\n return isinstance(value, int) or isinstance(value, float)\",\n \"def is_digit(value):\\n # for loop to traverse line\\n for character in value:\\n # check value is a number using isdigit() method and return boolean value\\n if character.isdigit() == False:\\n return False\\n return True\",\n \"def has_number(any_string):\\n return any(char.isdigit() for char in any_string)\",\n \"def isdigit(a):\\n return _vec_string(a, bool_, 'isdigit')\",\n \"def is_number(self,val):\\n try:\\n float(val)\\n return True\\n except ValueError:\\n return False\",\n \"def isNumber(s):\\n\\ttry:\\n\\t\\tfloat(s)\\n\\t\\treturn True\\n\\texcept ValueError:\\n\\t\\treturn False\",\n \"def checknum(val):\\n\\n if len(val) == 0:\\n return False\\n\\n for i in range(len(val)):\\n if not val[i].isdigit():\\n return False\\n\\n return True\",\n \"def only_numbers(string):\\n for character in string[:-1]:\\n if not (character.isdigit() or character in (',', ' ')): \\n return False\\n return True\",\n \"def is_numberish(G):\\n return True\",\n \"def is_numeric(self) -> bool:\\n return False\",\n \"def is_numeral(self, symbol: str) -> bool:\\n return symbol in self.numerals\",\n \"def could_be_number(val):\\n if val == None:\\n return False\\n\\n if isinstance(val, (float, int, long)):\\n return True\\n\\n # allow coercion from str\\n if isinstance(val, (str, unicode)):\\n try:\\n n = float(val)\\n if not isinstance(n, float):\\n raise ValueError\\n else:\\n return True\\n except:\\n return False\\n\\n #otherwise\\n return False\",\n \"def isNumber(st):\\n\\treturn st.replace('.','',1).isdigit()\",\n \"def is_some_number(mystring):\\n # print(Bcolors.cyan + re.findall(r\\\".*\\\\\\\\(.*)\\\", inspect.stack()[0][1])[0] + \\\" --- \\\"\\n # + inspect.stack()[0][3] + \\\"()\\\" + Bcolors.ENDC)\\n mystring = str(mystring)\\n mystring = re.sub(\\\",\\\", \\\".\\\", mystring)\\n try:\\n if float(mystring):\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def isNumber(self, s):\\n try:\\n tmp = float(s)\\n return True\\n except:\\n return False\",\n \"def __valid_char(self, char: str) -> bool:\\r\\n if char.isdigit():\\r\\n raise ValueError('Characters can\\\\'t be numbers')\\r\\n\\r\\n return char.isalpha() or char.isspace()\",\n \"def slug_is_numerical(slug):\\r\\n try:\\r\\n float(slug)\\r\\n except ValueError:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def isNumber(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def isnum(value):\\n\\n try:\\n return bool(isinstance(value, (float, int)))\\n except RuntimeError:\\n return False\",\n \"def is_number(number):\\n try:\\n float(number)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s: Union[str, int, float]):\\n if isinstance(s, str) and s.lower() == \\\"nan\\\":\\n return True\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(value):\\n\\n return isinstance(value, (int, long, float))\",\n \"def is_number(n):\\n return isinstance(n, (int, float))\",\n \"def _is_num(w):\\n symbols = list(w)\\n for s in symbols:\\n if s in string.digits:\\n return '<NUM>'\\n return w\",\n \"def check_for_integer(number):\\r\\n \\r\\n try:\\r\\n int(number) \\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def is_integer(self, string):\\n try:\\n return int(string)\\n except:\\n return False\",\n \"def ISNUMBER(value):\\n return isinstance(value, numbers.Number)\",\n \"def test_is_number(self):\\n \\n self.assertEqual(self.var.is_number(None), False)\\n self.assertEqual(self.var.is_number(\\\"5\\\"), True)\\n self.assertEqual(self.var.is_number(\\\"a\\\"), False)\",\n \"def is_numeric(number):\\n\\n if isinstance(number, bool):\\n return False\\n elif isinstance(number, int) or isinstance(number, float):\\n return True\\n else:\\n return False\",\n \"def is_number(value):\\n try:\\n float(value.replace(',', ''))\\n except ValueError:\\n return False\\n return True\",\n \"def isNumber(number):\\n try:\\n # Try to cast the string\\n int(number)\\n # The cast was successful\\n return True\\n # The cast was unsuccessful, the string is not a number\\n except ValueError as err:\\n # Write the exception in logging\\n logging.exception(str(err))\\n return False\",\n \"def _isDecimalNumber(strWord):\\n return NumberFormula.DECIMALNUMBERREGEX.match(strWord) != None\",\n \"def __is_int(self,string):\\r\\n try: \\r\\n int(string)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def isAlphaNumeric(string):\\n return (True)\",\n \"def is_int(string):\\n try:\\n int(string)\\n return True\\n except ValueError:\\n return False\",\n \"def is_numeric (self) :\\n\\n return self.__isnumeric__\",\n \"def isint(s):\\n try:\\n x = int(s)\\n return True\\n except:\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8297935","0.80840826","0.7704204","0.76546836","0.75744826","0.7568717","0.7393754","0.73098946","0.7302559","0.7258665","0.72420657","0.7230359","0.7195663","0.71815383","0.7179277","0.7152408","0.71477836","0.71332264","0.71325856","0.71297145","0.7120026","0.7101","0.71002775","0.7066229","0.70618486","0.703624","0.703624","0.703624","0.7029543","0.69642216","0.6948539","0.6932388","0.69238526","0.6905044","0.6903769","0.6857604","0.68489224","0.6834978","0.6833231","0.68260014","0.6821667","0.68034804","0.68015754","0.678993","0.67771596","0.67683643","0.67525995","0.6732744","0.6706928","0.67068774","0.6693286","0.6691228","0.6689119","0.6681921","0.6680213","0.6675907","0.66700447","0.6665641","0.6662748","0.6662748","0.66618025","0.66617143","0.66570675","0.6650491","0.664358","0.6637848","0.6622688","0.6620923","0.66197705","0.6618014","0.66099435","0.6606397","0.6600118","0.658219","0.6568505","0.6566271","0.6564588","0.6561964","0.6558055","0.651595","0.6506764","0.6497324","0.6491998","0.6488173","0.64491886","0.64439213","0.6418735","0.6416631","0.64139616","0.6399613","0.63913333","0.6390303","0.6387984","0.6374343","0.6370144","0.6369898","0.6369375","0.63688827","0.6354739","0.63341326"],"string":"[\n \"0.8297935\",\n \"0.80840826\",\n \"0.7704204\",\n \"0.76546836\",\n \"0.75744826\",\n \"0.7568717\",\n \"0.7393754\",\n \"0.73098946\",\n \"0.7302559\",\n \"0.7258665\",\n \"0.72420657\",\n \"0.7230359\",\n \"0.7195663\",\n \"0.71815383\",\n \"0.7179277\",\n \"0.7152408\",\n \"0.71477836\",\n \"0.71332264\",\n \"0.71325856\",\n \"0.71297145\",\n \"0.7120026\",\n \"0.7101\",\n \"0.71002775\",\n \"0.7066229\",\n \"0.70618486\",\n \"0.703624\",\n \"0.703624\",\n \"0.703624\",\n \"0.7029543\",\n \"0.69642216\",\n \"0.6948539\",\n \"0.6932388\",\n \"0.69238526\",\n \"0.6905044\",\n \"0.6903769\",\n \"0.6857604\",\n \"0.68489224\",\n \"0.6834978\",\n \"0.6833231\",\n \"0.68260014\",\n \"0.6821667\",\n \"0.68034804\",\n \"0.68015754\",\n \"0.678993\",\n \"0.67771596\",\n \"0.67683643\",\n \"0.67525995\",\n \"0.6732744\",\n \"0.6706928\",\n \"0.67068774\",\n \"0.6693286\",\n \"0.6691228\",\n \"0.6689119\",\n \"0.6681921\",\n \"0.6680213\",\n \"0.6675907\",\n \"0.66700447\",\n \"0.6665641\",\n \"0.6662748\",\n \"0.6662748\",\n \"0.66618025\",\n \"0.66617143\",\n \"0.66570675\",\n \"0.6650491\",\n \"0.664358\",\n \"0.6637848\",\n \"0.6622688\",\n \"0.6620923\",\n \"0.66197705\",\n \"0.6618014\",\n \"0.66099435\",\n \"0.6606397\",\n \"0.6600118\",\n \"0.658219\",\n \"0.6568505\",\n \"0.6566271\",\n \"0.6564588\",\n \"0.6561964\",\n \"0.6558055\",\n \"0.651595\",\n \"0.6506764\",\n \"0.6497324\",\n \"0.6491998\",\n \"0.6488173\",\n \"0.64491886\",\n \"0.64439213\",\n \"0.6418735\",\n \"0.6416631\",\n \"0.64139616\",\n \"0.6399613\",\n \"0.63913333\",\n \"0.6390303\",\n \"0.6387984\",\n \"0.6374343\",\n \"0.6370144\",\n \"0.6369898\",\n \"0.6369375\",\n \"0.63688827\",\n \"0.6354739\",\n \"0.63341326\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.82398206"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":12396,"cells":{"query":{"kind":"string","value":"Checks if the given nametag is valid, that it only contains letters, numbers, dashes, underscores and apostrophes. It must also start with the given tags in `Tags.py `. And returns the nametag if it is valid."},"document":{"kind":"string","value":"def get_nametag(nametag):\n # start must be valid\n if not nametag.startswith(Tags.NAMETAG_START.value):\n return None\n\n # removes the start of the tag\n nametag = nametag[len(Tags.NAMETAG_START.value):]\n\n # end must be valid\n if not nametag.endswith(Tags.NAMETAG_END.value):\n return None\n\n # removes the end of the tag\n nametag = nametag[:(len(nametag) - len(Tags.NAMETAG_END.value))]\n\n # no empty nametags\n if nametag == \"\":\n return None\n\n # checks that every single character is valid\n for c in nametag:\n if (not is_letter(c) and\n not is_number(c) and\n c != \"-\" and c != \"_\" and c != \"'\"):\n return None\n return nametag"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def isValidTagName(s):\n if s.lower().startswith(\"xml\"):\n return False\n return re.match(\"[^\\W\\d][\\w\\-_.]*\", s)","def name_valid(name):\n return name.isalpha()","def validname(name):\r\n return len(name)>0 and (\r\n Context.__invalid_character.search(name) is None)","def validName(varname):\r\n if (len(varname[0])>32):\r\n return False\r\n if not(varname[0][0].isalpha()):\r\n return False \r\n for ch in varname[0][1:]:\r\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\r\n return False\r\n \r\n return True","def invalid_name(name):\n if any(not item.isalpha() for item in str(name)):\n return True\n return False","def validate_name(name:str) -> bool:\r\n return name.isalpha() and name.count(\" \") == 0 and len(name) >= 2","def validate_names(name):\n return isinstance(name, str) and not re.search(r'[\\s]', name)","def _is_valid_varname(self, name):\n if name in RESERVED or re.match(r'^str([0-9]+|L)$', name): return False\n return True if VALID_NAME_RE.match(name) else False","def _is_valid_varname(self, name):\n if name in RESERVED or re.match(r'^str[0-9]+$', name): return False\n return True if VALID_NAME_RE.match(name) else False","def validate_name(self, name):\n import re\n\n if not re.findall(\"^[\\w',]+$\", name):\n self.msg(\"That category name contains invalid characters.\")\n return False\n return True","def validateName(name):\r\n if not name:\r\n raise IllegalName('Name can not be an empty string.')\r\n\r\n m = _NAME_RE.match(name)\r\n\r\n if m is None or m.group(0) != name:\r\n raise IllegalName('Name has to start with a letter followed by an '\r\n 'arbitrary number of alphanumeric characters or '\r\n 'underscores.')","def _clean_tag(name):\n # In the past, the first argument to summary ops was a tag, which allowed\n # arbitrary characters. Now we are changing the first argument to be the node\n # name. This has a number of advantages (users of summary ops now can\n # take advantage of the tf name scope system) but risks breaking existing\n # usage, because a much smaller set of characters are allowed in node names.\n # This function replaces all illegal characters with _s, and logs a warning.\n # It also strips leading slashes from the name.\n if name is not None:\n new_name = _INVALID_TAG_CHARACTERS.sub('_', name)\n new_name = new_name.lstrip('/') # Remove leading slashes\n if new_name != name:\n logging.warning('Summary name %s is illegal; using %s instead.', name, new_name)\n name = new_name\n return name","def _check_is_name_valid(self, name):\n if name in self.forbidden_names or name.endswith(\n self.forbidden_extensions) or self.__check_is_match_regex(name):\n return False\n return True","def IsVPCNameValid(vpc):\n if len(vpc) < 1 or len(vpc) > 63:\n return False\n return bool(re.match('^[a-z]$|^[a-z][a-z0-9-]*[a-z0-9]$', vpc))","def validate_name(name):\n name = name.strip()\n m = re.search('^[a-zA-Z0-9 ]{3,30}$', name)\n if m is None:\n return False\n else:\n return True","def CHECK_NAME(name):\n if WORDPAT.match(name):\n return name\n return None","def _filter_name(name):\n # Remove if length 3 or less\n if len(name) <= 3:\n return False\n # Remove if starts with IL-\n if name.startswith('IL-'):\n return False\n lowname = name.lower()\n # Remove if contains certain sequences\n if any(c in lowname for c in STOP_SUB):\n return False\n # Remove if (case-insensitive) exact match to stoplist\n if lowname in STOPLIST:\n return False\n comps = re.split('[ -]', lowname)\n # Remove if just single character + digits separated by spaces or hyphens (or the word compound)\n if all(c.isdigit() or len(c) == 1 or c == 'compound' for c in comps):\n return False\n # Remove if 3 or fewer letters with 2 or fewer digits\n if len(comps) == 2 and len(comps[0]) <= 3 and comps[0].isalpha() and len(comps[1]) <= 3 and comps[1].isdigit():\n return False\n # Remove if just greek characters and numbrs\n if re.match('^[Α-Ωα-ω0-9]+$', name):\n return False\n # Filter registry numbers? No real size benefit in DAWG.\n # if REG_RE.search(name):\n # keep = False\n # Handle this at the token level\n # if name.endswith(' derivative') or name.endswith(' analog') or name.endswith(' solution'):\n # keep = False\n # Filter this after matching and expanding boundaries\n # if name.startswith('-') or name.endswith('-'):\n # keep = False\n # Filter this after matching and expanding boundaries\n # if not bracket_level(name) == 0:\n # print(name)\n return True","def test_invalid_as_name(self):\n\n def make_bad_tag():\n class BadTag(ttag.helpers.AsTag):\n as_ = ttag.Arg(named=True)\n\n self.assertRaises(template.TemplateSyntaxError, make_bad_tag)","def MakeValidName(name):\n if name:\n goodName = []\n if not xml.is_name_start_char(name[0]):\n goodName.append(u'_')\n for c in name:\n if xml.is_name_char(c):\n goodName.append(c)\n else:\n goodName.append(u'_')\n return string.join(goodName, u'')\n else:\n return u'_'","def check_valid_key_name(name):\n if type(name) not in [str]:\n return False\n bad_chars = [\"*\", \".\", \"&&&&\"]\n for k in bad_chars:\n if k in name:\n return False\n return True","def match(self, name, tags):\n return name.lower() in tags","def validated_name(cls, name):\n if (name[:5] == 'hive-'\n and name[5] in ['1', '2', '3']\n and re.match(r'^hive-[123]\\d{4,6}$', name)):\n return name\n return None","def isValidPart(name):\n\tfor n in name_forms:\n\t\tif re.match(n, name.lower()) is not None:\n\t\t\treturn True\n\treturn False","def legal_name(name, is_param_name=False):\n if name.startswith('_'):\n return False\n\n if name in ('self',):\n return False\n\n if keyword.iskeyword(name):\n return False\n\n regex = r'^[a-zA-Z][a-zA-Z0-9_]*$' if is_param_name else (\n r'^[a-zA-Z][.\\w-]*$')\n return bool(re.match(regex, name))","def validate_team_name(name):\n if not re.match('^[A-Za-z0-9_]*$', name):\n print('INVALID NAME. LETTERS, NUMBERS AND UNDERSCORES ONLY')\n return False\n elif len(name) > 10:\n print('INVALID NAME. 10 CHARACTERS MAX')\n return False\n elif len(name) == 0:\n print('INVALID NAME. NOT LONG ENOUGH')\n else:\n return True","def check_name(name):\n name = sanitize_name(name)\n for letter in name:\n if letter not in all_letters:\n # print(f\"Bad letter = {letter}\")\n return False\n role = extract_role(name)\n # remove group\n name = name.replace(f' - {role}', '')\n try:\n parts = name.split(' ')\n firstname = parts[0].title()\n if firstname[0] not in letters:\n return False\n for letter in firstname[1:]:\n if letter not in LETTERS:\n return False\n familynames = parts[1:]\n for familyname in familynames:\n if familyname[0] not in letters:\n return False\n for letter in familyname[1:]:\n if letter not in LETTERS:\n return False\n return True\n except:\n return False","def clean_name(self):\n name = self.cleaned_data['name']\n if not re.match(r'[\\w{4}\\s*]+', name) or len(name) < 4:\n v_err('no_name')\n return name","def validate_tag(tag=None):\n if not tag:\n raise AttributeError('Tag cannot be empty')\n\n if tag not in TAGS:\n raise ValueError('{0} tag is not supported')","def isValidName(theString, minimum, maximum) :\n\n return theString.isalpha() == True \\\n and len(theString) >= minimum \\\n and len(theString) <= maximum","def is_real_name(name):\n return name.strip(\"<> \") in names_set","def is_real_name(name):\n return name.strip(\"<> \") in names_set","def test_parse_quotes_alphanumerics_only_in_tags(self):\n with self.assertRaisesRegexp(click.ClickException, \"invalid tag 'tag3!': only numbers, letters, and commas are allowed in tags\"):\n api.parse_quote(\"This is a quote. | Author | Publication | tag1, tag2 , tag3! \",\n simple_format=False)","def check_name(name, is_name_ok):\n try:\n name = unicode(name, 'utf-8')\n except:\n pass\n name = name[max(string.rfind(name,'/'),\n string.rfind(name,'\\\\'),\n string.rfind(name,':')\n )+1:]\n name = string.replace(name, u\"'\", u'_')\n name = string.replace(name, u'ä', u'ae')\n name = string.replace(name, u'ö', u'oe')\n name = string.replace(name, u'ü', u'ue')\n name = string.replace(name, u'Ä', u'Ae')\n name = string.replace(name, u'Ö', u'Oe')\n name = string.replace(name, u'Ü', u'Ue')\n name = string.replace(name, u'ß', u'ss')\n bad_chars = ' ,;()[]{}*\"#%+~!'\n good_chars = '________________'\n TRANSMAP = string.maketrans(bad_chars, good_chars)\n name = name.encode('iso-8859-1')\n name = string.translate(name, TRANSMAP)\n if is_name_ok:\n return name\n html = '.html'\n if name[-5:] != html :\n name += html\n return name","def validate_name(name: str) -> None:\n\n # Disallow empty.\n if not name:\n raise CleanError('Feature set name cannot be empty.')\n\n # Require starting with a letter.\n if not name[0].isalpha():\n raise CleanError(\n f\"Invalid feature set name '{name}'\"\n ' - names must start with a letter.'\n )\n\n # Require only letters, numbers, and underscores.\n if not name.replace('_', '').isalnum():\n raise CleanError(\n f\"Invalid feature set name '{name}'\"\n ' - only letters, numbers, and underscores are allowed.'\n )\n\n # Require all lowercase.\n if not name.islower():\n raise CleanError(\n f\"Invalid feature set name '{name}'\"\n ' - only lowercase letters are allowed.'\n )\n\n # Disallow leading, trailing, or consecutive underscores.\n # (these will result in a '' in the split results which evals to False)\n if not all(name.split('_')):\n raise CleanError(\n f\"Invalid feature set name '{name}'\"\n ' - leading, trailing, and consecutive underscores are'\n ' not allowed.'\n )","def check_name(name, allow_services=False):","def validate_custom_name(self, name):\n if not re.match( r'(/?[a-zA-Z_][a-zA-Z0-9_]*)+$', name):\n raise ValueError('Invalid name for node (%s)' % name)\n return","def valid_identifier_name(name):\n remove_characters_regex = '[^a-zA-Z0-9_]'\n name = re.sub(remove_characters_regex, '', name)\n # Remove beginning characters that are numbers\n name = re.sub('^[0-9]*', '', name)\n return name","def validUsername(name):\n if validCard(name):\n return False\n if ',' in name or \"?\" in name or \"=\" in name or \";\" in name or \"/\" in name or \"^\" in name or '\"' in name or '@' in name:\n return False\n if len(name) < 3:\n return False\n if \" \" in name:\n return False\n \n return True","def is_valid_string_name(src):\n\n return src[0].isupper()","def validate_name(self, username: str) -> bool:\n\t\treturn not self.registry.name_taken(username)","def validateTags(self, tags):\n\t\treturn tags.replace(', ',' ')","def validVarConstructName(self,varname):\r\n if (len(varname[0])>32):\r\n return False, ''\r\n if not(varname[0][0].isalpha()):\r\n return False, '' \r\n for ch in varname[0][1:]:\r\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\r\n return False, ''\r\n \r\n return True, varname","def test_bad_names(self):\n self.do_test_bad_name('', 'tmp/frog')\n self.do_test_bad_name('.b', 'tmp/frog')\n self.do_test_bad_name('a b', 'tmp/frog') # FAILS\n self.do_test_bad_name('a-b', 'tmp/frog') # FAILS","def hasname(self, tag: str) -> bool:\n for key in self.formal_names:\n if key in tag.lower():\n return True\n\n # Exit case if key -> value not in mapping \n return False","def invalid_username(username):\n word_letters = re.sub('[^a-zA-Z-0-9]+', '', str(username))\n if any(item.isalpha() for item in word_letters):\n return False\n return True","def clean_tag(tag):\n tmp0 = tag.strip()\n tmp1 = tmp0.lower()\n return tmp1","def valid_filename(strname):\n import string\n import os\n\n fname_only, fext_only = os.path.splitext(strname)\n valid_chars = \"-_() %s%s\" % (string.ascii_letters, string.digits)\n\n # valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n valid_fname = ''.join(c for c in fname_only if c in valid_chars)\n return valid_fname + fext_only","def match(self, name, tags):\n name, tags = self.get_compiled(name, tags)\n \n def index_of_letter(l):\n return ord(l) - ord('a')\n \n true_val, false_val = name\n \n if true_val:\n return index_of_letter(true_val) in tags\n else:\n return index_of_letter(false_val) not in tags","def username_is_valid(username: str) -> bool:\n pattern = re.compile(r'^[A-Za-z]+[A-Za-z0-9]*$')\n return pattern.match(username)","def is_valid(name):\n return bool(name)","def validateName(value, space):\n ret = libxml2mod.xmlValidateName(value, space)\n return ret","def valid_routine_name(routine):\n\treturn re.match('^[a-z_]([a-z0-9_]*)', routine) is not None","def FilterName(namefilter, safechar='P', reserved_names=None):\n # remove non accepted characters\n for c in r' -[]/\\;,><&*:%=+@!#^|?^':\n namefilter = namefilter.replace(c, '')\n\n # remove non english characters\n char_list = (c for c in namefilter if 0 < ord(c) < 127)\n namefilter = ''.join(char_list)\n\n # Make sure we have a non empty string\n if len(namefilter) <= 0:\n namefilter = safechar\n\n # Make sure we don't start with a number\n if namefilter[0].isdigit():\n print(namefilter)\n namefilter = safechar + namefilter\n\n # Make sure we are not using a reserved name\n if reserved_names is not None:\n while namefilter.lower() in reserved_names:\n namefilter = safechar + namefilter\n\n # Add the name to reserved names\n reserved_names.append(namefilter)\n\n return namefilter","def validate_username(username):\n if re.match(r\"^[a-zA-Z0-9åäöÅÄÖ]{3,20}$\", username):\n return True\n return False","def verify_name(name):\n try:\n if name.index(' '):\n return False\n except ValueError:\n return True","def is_invalid_proj_exp_runnames(name):\n return re.search(f'[^{settings.ALLOWED_PROJEXPRUN_CHARS}]', name)","def verify_name(name):\n if name and not name.isspace(): # if it's not empty/NULL and it's not whitespace\n return True\n else:\n return False","def check_funny_chars_in_names(names, is_full_qualified_name=True):\n if names and len(names) > 0:\n for name in names:\n if ('\\t' in name or '\\n' in name or '!' in name or ',' in name or\n (is_full_qualified_name and name.count('.') > 1) or (not is_full_qualified_name and name.count('.') > 0)):\n raise Exception('Name has an invalid character \"\\\\t\" \"\\\\n\" \"!\" \",\" \".\": \"%s\"' % name)","def is_dev_name_valid(self):\n return self._name_re.match(self.dev_name) is not None","def validateNamePart(self, passed_name):\n ## Declaring a Flag to control a while loop\n name_ok = False\n ## While loop to have user retry their input if they enter incorrectly\n while not name_ok:\n if passed_name.isalpha():\n name_ok = True\n return True\n\n else:\n print(\"You have entered an invalid character. Please try again.\")\n return False","def _is_file_valid(name: str) -> bool:\n return not name.startswith(\".\")","def test_sanitize_name(self):\n assert scrape_category.sanitize_name(\" foo \") == \"foo\"\n assert scrape_category.sanitize_name(' foo\" ') == \"foo\"\n assert scrape_category.sanitize_name(' foo...bar ') == \"foo.bar\"","def is_valid_tag(tag):\n if not tag or ':' not in tag or len(tag) > TAG_MAX_LEN:\n return False\n # Care only about the key. Value can be anything (including empty string).\n return bool(TAG_KEY_RE.match(tag.split(':', 1)[0]))","def check_dog_name(dog):\n if not isinstance(dog.name, str):\n raise NotStringError(\"Dog name entered is not a string\")","def wantsNametag(self):\n return 0","def ValidateName(args):\n account = properties.VALUES.core.account.Get(required=True)\n if account.find('@') == -1:\n username = account\n else:\n username = account[0:account.find('@')]\n\n args.name = args.name or username","def is_real_name(name):\n name = \"<\" + name.strip() + \">\"\n for real_name in names:\n if name == real_name:\n return True\n return False","def get_valid_name(self, name):\n return get_valid_filename(name)","def raiseNameError(text):\n pattern = re.compile(\"[a-zA-Z]\")\n if not pattern.match(text):\n raise Exception(\"Invalid Name Entered\")","def test_parse_tags_invalid(self):\n tagstring = \"tag1, tag2, tag3!\"\n\n with self.assertRaisesRegexp(Exception, \"invalid tag 'tag3!': only numbers, letters, and commas are \"\n \"allowed in tags\"):\n api.parse_tags(tagstring)","def is_attname(name):\n test = re.search(\"^@[a-z]+\", name)\n if test:\n return True\n else:\n return False","def is_tag(value):\r\n tag_names = parse_tag_input(value)\r\n if len(tag_names) > 1:\r\n raise ValidationError(_('Multiple tags were given.'))\r\n elif len(tag_names[0]) > settings.MAX_TAG_LENGTH:\r\n raise forms.ValidationError(\r\n _('A tag may be no more than %s characters long.') % settings.MAX_TAG_LENGTH)\r\n return value","def discard_name(self) -> bool:\n\n if not self.is_name_length_valid():\n return True\n\n if self.app_name_no_punc().lower() in self.discard:\n return True\n\n if self.is_substring_unwanted():\n return True\n\n if self.unwanted_regex_match():\n return True\n\n return self.is_name_mostly_numeric()","def _validate_name(name):\r\n\tif HOST_NAME != name and len(name) > 0 and ZOOM_PHRASES[0] not in name and name not in WAITING_ROOM:\r\n\t\treturn True\r\n\treturn False","def name_is_otel_safe(prefix: str, name: str) -> bool:\n return bool(stat_name_otel_handler(prefix, name, max_length=OTEL_NAME_MAX_LENGTH))","def validate_name_input(name):\n if all(letter.isalpha() or letter.isspace()\n for letter in name) and len(name) != 0:\n clear()\n return True\n\n else:\n clear()\n print('** Please enter a name of alphabetic characters and spaces **')\n return False","def _sanitizeName(name):\n\n name = name.lower() # lower.\n name = name.replace('.','') # remove periods.\n name = name.replace('-','') # remove dashes.\n name = name.replace(\"'\",'') # remove apostrophies.\n # return it.\n return name","def is_valid_username(self, username):\n rex = \"^[a-zA-Z]{3,}$\"\n return re.match(rex, username)","def validName(configsetname):\n for c in configsetname:\n if not c in string.letters+string.digits+\"$_-\":\n return False\n return configsetname != \"\"","def is_valid_package_module_name(name):\n if \".\" in name:\n for part in name.split(\".\"):\n if not is_valid_package_module_name(part):\n return False\n elif len(name):\n if name[0] not in _first_letter_for_valid_name:\n return False\n\n if len(set(name).difference(_char_set_for_valid_name)):\n return False\n else:\n return False\n return True","def looks_like_fallback(url_name):\r\n return (url_name is not None\r\n and url_name.startswith(tag)\r\n and re.search('[0-9a-fA-F]{12}$', url_name))","def clean_name(name: str) -> str:\n if not re.match(\"[a-zA-Z_]\", name[0]):\n name = \"_\" + name\n name = re.sub(\"[^0-9a-zA-Z_]+\", \"_\", name)\n if all(c == \"_\" for c in name):\n name = \"v\"\n return name","def _process_name(name):\n\n # Unescape HTML entities\n name = unescape(name)\n\n # Remove bracketed stuff on the end\n name = NG_RE.sub('', name).strip() # Nomenclature groups\n name = END_RE.sub('', name).strip(', ') # Words\n name = RATIO_RE.sub('', name).strip(', ') # Ratios\n\n # Remove stuff off start\n name = START_RE.sub('', name).strip()\n\n # Remove balanced start and end brackets if none in between\n name = BRACKET_RE.sub('\\g<1>', name)\n\n # Un-invert CAS style names\n comps = name.split(', ')\n if len(comps) == 2:\n if comps[1].endswith('-'):\n name = comps[0]\n name = '%s%s' % (comps[1], name)\n elif len(comps) > 2:\n name = comps[0]\n for i in range(1, len(comps)):\n if comps[i].endswith('-'):\n name = '%s%s' % (comps[i], name)\n else:\n name = '%s %s' % (name, comps[i])\n return name","def validate_tag(self, tag_field):\n if not tag_field.data or tag_field.data == '':\n raise ValidationError('All users must be tagged')\n return True","def legal_variable_name(name):\n var_name = name.replace(' ', '_')\n for char in r'''[]{}\\/^%$#@!+-<>?|'\"=~`,.;:''':\n if char in var_name:\n var_name = var_name.replace(char, '')\n for char in var_name:\n if ord(char) > 127: # remove non-ascii characters\n var_name = var_name.replace(char, '')\n return var_name","def validate_rss_name(rss_name):\n regex = r'^[\\w]+$'\n if not match(regex, str(rss_name)):\n raise ValueError('invalid rss name ({})'.format(rss_name))","def _assert_valid_name(name, container):\n container.file.name_validation(container.directory, name)","def verify_username(username):\n name_reg_exp = re.compile(r\"^[a-zA-Z0-9_-]{3,20}$\")\n return username and name_reg_exp.match(username)","def clean_alias(self):\n alias = self.cleaned_data.get('alias')\n if not bool(re.compile(r'^[a-zA-Z0-9-]+$').match(alias)):\n raise forms.ValidationError(\"Alias contains invalid characters\")\n return alias","def is_valid_attribute_name(self, name):\n try:\n self.validate_attribute_name(name)\n return True\n except etal.LabelsSchemaError:\n return False","def is_name_length_valid(self) -> bool:\n return 2 < len(self.app_name) <= 35","def validate(name, bracket, bracket_side, bfr):\n\n return bfr[bracket.begin:bracket.end].islower()","def check_username(username):\n if username:\n if not re.match('[a-z]', username[0]):\n return ['username_error_badfirstchar']\n # Technically both these conditions might hold. However, the common\n # case seems to be that somebody starts typing their name beginning\n # with an upper-case letter, and it's probably sufficient to just\n # issue the first error in that case.\n elif not re.match('^[-a-z0-9_]+$', username):\n return ['username_error_badchar']\n return []","def safe_formal_name(name):\n return re.sub(r\"\\s+\", \" \", re.sub(r'[!/\\\\:<>\"\\?\\*\\|]', \"\", name)).strip()","def is_simple_name(s):\n\n assert utils.is_string_type(s)\n assert len(s) > 0\n\n def valid_first(c):\n return c.isalpha() or c == \"_\"\n def valid_later(c):\n return c.isalnum() or c == \"_\"\n return valid_first(s[0]) and all(valid_later(c) for c in s)","def IsValidHotlistName(s):\n return (RE_HOTLIST_NAME.match(s) and\n len(s) <= framework_constants.MAX_HOTLIST_NAME_LENGTH)","def nameIsValid(self, name):\n self.notify.debug('nameIsValid')\n if (name in self.usedNames):\n return OTPLocalizer.ToonAlreadyExists % (name)\n\n problem = NameCheck.checkName(name, font=self.nameEntry.getFont())\n if problem:\n return problem\n\n # name has passed local checks\n return None","def clean_name(name, allowed_chars):\n ok = identifier_chars + allowed_chars\n newname = \"\".join(c if c in ok else \"-\" for c in name)\n newname = newname.lstrip(\"-\")\n if not newname:\n raise RuntimeError(f\"No valid chars in name '{name}'.\")\n return newname","def validate_person_name(value):\n disallowed_names = [\n \"anon\",\n \"anonymous\",\n \"admin\",\n \"administrator\",\n \"guest\",\n \"help\",\n \"moderator\",\n \"owner\",\n \"postmaster\",\n \"root\",\n \"superuser\",\n \"support\",\n \"sysadmin\",\n \"systemadministrator\",\n \"systemsadministrator\",\n \"user\",\n \"webadmin\",\n \"samuelpepys\",\n \"pepys\",\n \"sampepys\",\n \"keithwright\",\n \"warrenkeithwright\",\n ]\n\n # Remove all punctuation and space from the name before comparing it to the\n # disallowed names.\n exclude = list(string.punctuation)\n exclude.append(\" \")\n test_value = \"\".join(ch for ch in value if ch not in exclude).lower()\n\n if test_value in disallowed_names:\n raise ValidationError(\"%s is not an available name\" % value)\n\n # We allow one or more characters.\n # There can be one or more spaces after that sequence, with other\n # characters (including spaces) following.\n if re.match(r\"^[\\w.-_]+(?:\\s+[\\w\\s.-_]+)?$\", value) is None:\n raise ValidationError(\"%s contains invalid characters or formatting\" % value)","def _validate_content_name(content_name: str, performative: str) -> Tuple[bool, str]:\n # check content name's format\n if not _is_valid_regex(CONTENT_NAME_REGEX_PATTERN, content_name):\n return (\n False,\n \"Invalid name for content '{}' of performative '{}'. Content names must match the following regular expression: {} \".format(\n content_name, performative, CONTENT_NAME_REGEX_PATTERN\n ),\n )\n\n # check content name is not a reserved name\n if _is_reserved_name(content_name):\n return (\n False,\n \"Invalid name for content '{}' of performative '{}'. This name is reserved.\".format(\n content_name,\n performative,\n ),\n )\n\n return (\n True,\n \"Content name '{}' of performative '{}' is valid.\".format(\n content_name, performative\n ),\n )"],"string":"[\n \"def isValidTagName(s):\\n if s.lower().startswith(\\\"xml\\\"):\\n return False\\n return re.match(\\\"[^\\\\W\\\\d][\\\\w\\\\-_.]*\\\", s)\",\n \"def name_valid(name):\\n return name.isalpha()\",\n \"def validname(name):\\r\\n return len(name)>0 and (\\r\\n Context.__invalid_character.search(name) is None)\",\n \"def validName(varname):\\r\\n if (len(varname[0])>32):\\r\\n return False\\r\\n if not(varname[0][0].isalpha()):\\r\\n return False \\r\\n for ch in varname[0][1:]:\\r\\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\\r\\n return False\\r\\n \\r\\n return True\",\n \"def invalid_name(name):\\n if any(not item.isalpha() for item in str(name)):\\n return True\\n return False\",\n \"def validate_name(name:str) -> bool:\\r\\n return name.isalpha() and name.count(\\\" \\\") == 0 and len(name) >= 2\",\n \"def validate_names(name):\\n return isinstance(name, str) and not re.search(r'[\\\\s]', name)\",\n \"def _is_valid_varname(self, name):\\n if name in RESERVED or re.match(r'^str([0-9]+|L)$', name): return False\\n return True if VALID_NAME_RE.match(name) else False\",\n \"def _is_valid_varname(self, name):\\n if name in RESERVED or re.match(r'^str[0-9]+$', name): return False\\n return True if VALID_NAME_RE.match(name) else False\",\n \"def validate_name(self, name):\\n import re\\n\\n if not re.findall(\\\"^[\\\\w',]+$\\\", name):\\n self.msg(\\\"That category name contains invalid characters.\\\")\\n return False\\n return True\",\n \"def validateName(name):\\r\\n if not name:\\r\\n raise IllegalName('Name can not be an empty string.')\\r\\n\\r\\n m = _NAME_RE.match(name)\\r\\n\\r\\n if m is None or m.group(0) != name:\\r\\n raise IllegalName('Name has to start with a letter followed by an '\\r\\n 'arbitrary number of alphanumeric characters or '\\r\\n 'underscores.')\",\n \"def _clean_tag(name):\\n # In the past, the first argument to summary ops was a tag, which allowed\\n # arbitrary characters. Now we are changing the first argument to be the node\\n # name. This has a number of advantages (users of summary ops now can\\n # take advantage of the tf name scope system) but risks breaking existing\\n # usage, because a much smaller set of characters are allowed in node names.\\n # This function replaces all illegal characters with _s, and logs a warning.\\n # It also strips leading slashes from the name.\\n if name is not None:\\n new_name = _INVALID_TAG_CHARACTERS.sub('_', name)\\n new_name = new_name.lstrip('/') # Remove leading slashes\\n if new_name != name:\\n logging.warning('Summary name %s is illegal; using %s instead.', name, new_name)\\n name = new_name\\n return name\",\n \"def _check_is_name_valid(self, name):\\n if name in self.forbidden_names or name.endswith(\\n self.forbidden_extensions) or self.__check_is_match_regex(name):\\n return False\\n return True\",\n \"def IsVPCNameValid(vpc):\\n if len(vpc) < 1 or len(vpc) > 63:\\n return False\\n return bool(re.match('^[a-z]$|^[a-z][a-z0-9-]*[a-z0-9]$', vpc))\",\n \"def validate_name(name):\\n name = name.strip()\\n m = re.search('^[a-zA-Z0-9 ]{3,30}$', name)\\n if m is None:\\n return False\\n else:\\n return True\",\n \"def CHECK_NAME(name):\\n if WORDPAT.match(name):\\n return name\\n return None\",\n \"def _filter_name(name):\\n # Remove if length 3 or less\\n if len(name) <= 3:\\n return False\\n # Remove if starts with IL-\\n if name.startswith('IL-'):\\n return False\\n lowname = name.lower()\\n # Remove if contains certain sequences\\n if any(c in lowname for c in STOP_SUB):\\n return False\\n # Remove if (case-insensitive) exact match to stoplist\\n if lowname in STOPLIST:\\n return False\\n comps = re.split('[ -]', lowname)\\n # Remove if just single character + digits separated by spaces or hyphens (or the word compound)\\n if all(c.isdigit() or len(c) == 1 or c == 'compound' for c in comps):\\n return False\\n # Remove if 3 or fewer letters with 2 or fewer digits\\n if len(comps) == 2 and len(comps[0]) <= 3 and comps[0].isalpha() and len(comps[1]) <= 3 and comps[1].isdigit():\\n return False\\n # Remove if just greek characters and numbrs\\n if re.match('^[Α-Ωα-ω0-9]+$', name):\\n return False\\n # Filter registry numbers? No real size benefit in DAWG.\\n # if REG_RE.search(name):\\n # keep = False\\n # Handle this at the token level\\n # if name.endswith(' derivative') or name.endswith(' analog') or name.endswith(' solution'):\\n # keep = False\\n # Filter this after matching and expanding boundaries\\n # if name.startswith('-') or name.endswith('-'):\\n # keep = False\\n # Filter this after matching and expanding boundaries\\n # if not bracket_level(name) == 0:\\n # print(name)\\n return True\",\n \"def test_invalid_as_name(self):\\n\\n def make_bad_tag():\\n class BadTag(ttag.helpers.AsTag):\\n as_ = ttag.Arg(named=True)\\n\\n self.assertRaises(template.TemplateSyntaxError, make_bad_tag)\",\n \"def MakeValidName(name):\\n if name:\\n goodName = []\\n if not xml.is_name_start_char(name[0]):\\n goodName.append(u'_')\\n for c in name:\\n if xml.is_name_char(c):\\n goodName.append(c)\\n else:\\n goodName.append(u'_')\\n return string.join(goodName, u'')\\n else:\\n return u'_'\",\n \"def check_valid_key_name(name):\\n if type(name) not in [str]:\\n return False\\n bad_chars = [\\\"*\\\", \\\".\\\", \\\"&&&&\\\"]\\n for k in bad_chars:\\n if k in name:\\n return False\\n return True\",\n \"def match(self, name, tags):\\n return name.lower() in tags\",\n \"def validated_name(cls, name):\\n if (name[:5] == 'hive-'\\n and name[5] in ['1', '2', '3']\\n and re.match(r'^hive-[123]\\\\d{4,6}$', name)):\\n return name\\n return None\",\n \"def isValidPart(name):\\n\\tfor n in name_forms:\\n\\t\\tif re.match(n, name.lower()) is not None:\\n\\t\\t\\treturn True\\n\\treturn False\",\n \"def legal_name(name, is_param_name=False):\\n if name.startswith('_'):\\n return False\\n\\n if name in ('self',):\\n return False\\n\\n if keyword.iskeyword(name):\\n return False\\n\\n regex = r'^[a-zA-Z][a-zA-Z0-9_]*$' if is_param_name else (\\n r'^[a-zA-Z][.\\\\w-]*$')\\n return bool(re.match(regex, name))\",\n \"def validate_team_name(name):\\n if not re.match('^[A-Za-z0-9_]*$', name):\\n print('INVALID NAME. LETTERS, NUMBERS AND UNDERSCORES ONLY')\\n return False\\n elif len(name) > 10:\\n print('INVALID NAME. 10 CHARACTERS MAX')\\n return False\\n elif len(name) == 0:\\n print('INVALID NAME. NOT LONG ENOUGH')\\n else:\\n return True\",\n \"def check_name(name):\\n name = sanitize_name(name)\\n for letter in name:\\n if letter not in all_letters:\\n # print(f\\\"Bad letter = {letter}\\\")\\n return False\\n role = extract_role(name)\\n # remove group\\n name = name.replace(f' - {role}', '')\\n try:\\n parts = name.split(' ')\\n firstname = parts[0].title()\\n if firstname[0] not in letters:\\n return False\\n for letter in firstname[1:]:\\n if letter not in LETTERS:\\n return False\\n familynames = parts[1:]\\n for familyname in familynames:\\n if familyname[0] not in letters:\\n return False\\n for letter in familyname[1:]:\\n if letter not in LETTERS:\\n return False\\n return True\\n except:\\n return False\",\n \"def clean_name(self):\\n name = self.cleaned_data['name']\\n if not re.match(r'[\\\\w{4}\\\\s*]+', name) or len(name) < 4:\\n v_err('no_name')\\n return name\",\n \"def validate_tag(tag=None):\\n if not tag:\\n raise AttributeError('Tag cannot be empty')\\n\\n if tag not in TAGS:\\n raise ValueError('{0} tag is not supported')\",\n \"def isValidName(theString, minimum, maximum) :\\n\\n return theString.isalpha() == True \\\\\\n and len(theString) >= minimum \\\\\\n and len(theString) <= maximum\",\n \"def is_real_name(name):\\n return name.strip(\\\"<> \\\") in names_set\",\n \"def is_real_name(name):\\n return name.strip(\\\"<> \\\") in names_set\",\n \"def test_parse_quotes_alphanumerics_only_in_tags(self):\\n with self.assertRaisesRegexp(click.ClickException, \\\"invalid tag 'tag3!': only numbers, letters, and commas are allowed in tags\\\"):\\n api.parse_quote(\\\"This is a quote. | Author | Publication | tag1, tag2 , tag3! \\\",\\n simple_format=False)\",\n \"def check_name(name, is_name_ok):\\n try:\\n name = unicode(name, 'utf-8')\\n except:\\n pass\\n name = name[max(string.rfind(name,'/'),\\n string.rfind(name,'\\\\\\\\'),\\n string.rfind(name,':')\\n )+1:]\\n name = string.replace(name, u\\\"'\\\", u'_')\\n name = string.replace(name, u'ä', u'ae')\\n name = string.replace(name, u'ö', u'oe')\\n name = string.replace(name, u'ü', u'ue')\\n name = string.replace(name, u'Ä', u'Ae')\\n name = string.replace(name, u'Ö', u'Oe')\\n name = string.replace(name, u'Ü', u'Ue')\\n name = string.replace(name, u'ß', u'ss')\\n bad_chars = ' ,;()[]{}*\\\"#%+~!'\\n good_chars = '________________'\\n TRANSMAP = string.maketrans(bad_chars, good_chars)\\n name = name.encode('iso-8859-1')\\n name = string.translate(name, TRANSMAP)\\n if is_name_ok:\\n return name\\n html = '.html'\\n if name[-5:] != html :\\n name += html\\n return name\",\n \"def validate_name(name: str) -> None:\\n\\n # Disallow empty.\\n if not name:\\n raise CleanError('Feature set name cannot be empty.')\\n\\n # Require starting with a letter.\\n if not name[0].isalpha():\\n raise CleanError(\\n f\\\"Invalid feature set name '{name}'\\\"\\n ' - names must start with a letter.'\\n )\\n\\n # Require only letters, numbers, and underscores.\\n if not name.replace('_', '').isalnum():\\n raise CleanError(\\n f\\\"Invalid feature set name '{name}'\\\"\\n ' - only letters, numbers, and underscores are allowed.'\\n )\\n\\n # Require all lowercase.\\n if not name.islower():\\n raise CleanError(\\n f\\\"Invalid feature set name '{name}'\\\"\\n ' - only lowercase letters are allowed.'\\n )\\n\\n # Disallow leading, trailing, or consecutive underscores.\\n # (these will result in a '' in the split results which evals to False)\\n if not all(name.split('_')):\\n raise CleanError(\\n f\\\"Invalid feature set name '{name}'\\\"\\n ' - leading, trailing, and consecutive underscores are'\\n ' not allowed.'\\n )\",\n \"def check_name(name, allow_services=False):\",\n \"def validate_custom_name(self, name):\\n if not re.match( r'(/?[a-zA-Z_][a-zA-Z0-9_]*)+$', name):\\n raise ValueError('Invalid name for node (%s)' % name)\\n return\",\n \"def valid_identifier_name(name):\\n remove_characters_regex = '[^a-zA-Z0-9_]'\\n name = re.sub(remove_characters_regex, '', name)\\n # Remove beginning characters that are numbers\\n name = re.sub('^[0-9]*', '', name)\\n return name\",\n \"def validUsername(name):\\n if validCard(name):\\n return False\\n if ',' in name or \\\"?\\\" in name or \\\"=\\\" in name or \\\";\\\" in name or \\\"/\\\" in name or \\\"^\\\" in name or '\\\"' in name or '@' in name:\\n return False\\n if len(name) < 3:\\n return False\\n if \\\" \\\" in name:\\n return False\\n \\n return True\",\n \"def is_valid_string_name(src):\\n\\n return src[0].isupper()\",\n \"def validate_name(self, username: str) -> bool:\\n\\t\\treturn not self.registry.name_taken(username)\",\n \"def validateTags(self, tags):\\n\\t\\treturn tags.replace(', ',' ')\",\n \"def validVarConstructName(self,varname):\\r\\n if (len(varname[0])>32):\\r\\n return False, ''\\r\\n if not(varname[0][0].isalpha()):\\r\\n return False, '' \\r\\n for ch in varname[0][1:]:\\r\\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\\r\\n return False, ''\\r\\n \\r\\n return True, varname\",\n \"def test_bad_names(self):\\n self.do_test_bad_name('', 'tmp/frog')\\n self.do_test_bad_name('.b', 'tmp/frog')\\n self.do_test_bad_name('a b', 'tmp/frog') # FAILS\\n self.do_test_bad_name('a-b', 'tmp/frog') # FAILS\",\n \"def hasname(self, tag: str) -> bool:\\n for key in self.formal_names:\\n if key in tag.lower():\\n return True\\n\\n # Exit case if key -> value not in mapping \\n return False\",\n \"def invalid_username(username):\\n word_letters = re.sub('[^a-zA-Z-0-9]+', '', str(username))\\n if any(item.isalpha() for item in word_letters):\\n return False\\n return True\",\n \"def clean_tag(tag):\\n tmp0 = tag.strip()\\n tmp1 = tmp0.lower()\\n return tmp1\",\n \"def valid_filename(strname):\\n import string\\n import os\\n\\n fname_only, fext_only = os.path.splitext(strname)\\n valid_chars = \\\"-_() %s%s\\\" % (string.ascii_letters, string.digits)\\n\\n # valid_chars = \\\"-_.() %s%s\\\" % (string.ascii_letters, string.digits)\\n valid_fname = ''.join(c for c in fname_only if c in valid_chars)\\n return valid_fname + fext_only\",\n \"def match(self, name, tags):\\n name, tags = self.get_compiled(name, tags)\\n \\n def index_of_letter(l):\\n return ord(l) - ord('a')\\n \\n true_val, false_val = name\\n \\n if true_val:\\n return index_of_letter(true_val) in tags\\n else:\\n return index_of_letter(false_val) not in tags\",\n \"def username_is_valid(username: str) -> bool:\\n pattern = re.compile(r'^[A-Za-z]+[A-Za-z0-9]*$')\\n return pattern.match(username)\",\n \"def is_valid(name):\\n return bool(name)\",\n \"def validateName(value, space):\\n ret = libxml2mod.xmlValidateName(value, space)\\n return ret\",\n \"def valid_routine_name(routine):\\n\\treturn re.match('^[a-z_]([a-z0-9_]*)', routine) is not None\",\n \"def FilterName(namefilter, safechar='P', reserved_names=None):\\n # remove non accepted characters\\n for c in r' -[]/\\\\;,><&*:%=+@!#^|?^':\\n namefilter = namefilter.replace(c, '')\\n\\n # remove non english characters\\n char_list = (c for c in namefilter if 0 < ord(c) < 127)\\n namefilter = ''.join(char_list)\\n\\n # Make sure we have a non empty string\\n if len(namefilter) <= 0:\\n namefilter = safechar\\n\\n # Make sure we don't start with a number\\n if namefilter[0].isdigit():\\n print(namefilter)\\n namefilter = safechar + namefilter\\n\\n # Make sure we are not using a reserved name\\n if reserved_names is not None:\\n while namefilter.lower() in reserved_names:\\n namefilter = safechar + namefilter\\n\\n # Add the name to reserved names\\n reserved_names.append(namefilter)\\n\\n return namefilter\",\n \"def validate_username(username):\\n if re.match(r\\\"^[a-zA-Z0-9åäöÅÄÖ]{3,20}$\\\", username):\\n return True\\n return False\",\n \"def verify_name(name):\\n try:\\n if name.index(' '):\\n return False\\n except ValueError:\\n return True\",\n \"def is_invalid_proj_exp_runnames(name):\\n return re.search(f'[^{settings.ALLOWED_PROJEXPRUN_CHARS}]', name)\",\n \"def verify_name(name):\\n if name and not name.isspace(): # if it's not empty/NULL and it's not whitespace\\n return True\\n else:\\n return False\",\n \"def check_funny_chars_in_names(names, is_full_qualified_name=True):\\n if names and len(names) > 0:\\n for name in names:\\n if ('\\\\t' in name or '\\\\n' in name or '!' in name or ',' in name or\\n (is_full_qualified_name and name.count('.') > 1) or (not is_full_qualified_name and name.count('.') > 0)):\\n raise Exception('Name has an invalid character \\\"\\\\\\\\t\\\" \\\"\\\\\\\\n\\\" \\\"!\\\" \\\",\\\" \\\".\\\": \\\"%s\\\"' % name)\",\n \"def is_dev_name_valid(self):\\n return self._name_re.match(self.dev_name) is not None\",\n \"def validateNamePart(self, passed_name):\\n ## Declaring a Flag to control a while loop\\n name_ok = False\\n ## While loop to have user retry their input if they enter incorrectly\\n while not name_ok:\\n if passed_name.isalpha():\\n name_ok = True\\n return True\\n\\n else:\\n print(\\\"You have entered an invalid character. Please try again.\\\")\\n return False\",\n \"def _is_file_valid(name: str) -> bool:\\n return not name.startswith(\\\".\\\")\",\n \"def test_sanitize_name(self):\\n assert scrape_category.sanitize_name(\\\" foo \\\") == \\\"foo\\\"\\n assert scrape_category.sanitize_name(' foo\\\" ') == \\\"foo\\\"\\n assert scrape_category.sanitize_name(' foo...bar ') == \\\"foo.bar\\\"\",\n \"def is_valid_tag(tag):\\n if not tag or ':' not in tag or len(tag) > TAG_MAX_LEN:\\n return False\\n # Care only about the key. Value can be anything (including empty string).\\n return bool(TAG_KEY_RE.match(tag.split(':', 1)[0]))\",\n \"def check_dog_name(dog):\\n if not isinstance(dog.name, str):\\n raise NotStringError(\\\"Dog name entered is not a string\\\")\",\n \"def wantsNametag(self):\\n return 0\",\n \"def ValidateName(args):\\n account = properties.VALUES.core.account.Get(required=True)\\n if account.find('@') == -1:\\n username = account\\n else:\\n username = account[0:account.find('@')]\\n\\n args.name = args.name or username\",\n \"def is_real_name(name):\\n name = \\\"<\\\" + name.strip() + \\\">\\\"\\n for real_name in names:\\n if name == real_name:\\n return True\\n return False\",\n \"def get_valid_name(self, name):\\n return get_valid_filename(name)\",\n \"def raiseNameError(text):\\n pattern = re.compile(\\\"[a-zA-Z]\\\")\\n if not pattern.match(text):\\n raise Exception(\\\"Invalid Name Entered\\\")\",\n \"def test_parse_tags_invalid(self):\\n tagstring = \\\"tag1, tag2, tag3!\\\"\\n\\n with self.assertRaisesRegexp(Exception, \\\"invalid tag 'tag3!': only numbers, letters, and commas are \\\"\\n \\\"allowed in tags\\\"):\\n api.parse_tags(tagstring)\",\n \"def is_attname(name):\\n test = re.search(\\\"^@[a-z]+\\\", name)\\n if test:\\n return True\\n else:\\n return False\",\n \"def is_tag(value):\\r\\n tag_names = parse_tag_input(value)\\r\\n if len(tag_names) > 1:\\r\\n raise ValidationError(_('Multiple tags were given.'))\\r\\n elif len(tag_names[0]) > settings.MAX_TAG_LENGTH:\\r\\n raise forms.ValidationError(\\r\\n _('A tag may be no more than %s characters long.') % settings.MAX_TAG_LENGTH)\\r\\n return value\",\n \"def discard_name(self) -> bool:\\n\\n if not self.is_name_length_valid():\\n return True\\n\\n if self.app_name_no_punc().lower() in self.discard:\\n return True\\n\\n if self.is_substring_unwanted():\\n return True\\n\\n if self.unwanted_regex_match():\\n return True\\n\\n return self.is_name_mostly_numeric()\",\n \"def _validate_name(name):\\r\\n\\tif HOST_NAME != name and len(name) > 0 and ZOOM_PHRASES[0] not in name and name not in WAITING_ROOM:\\r\\n\\t\\treturn True\\r\\n\\treturn False\",\n \"def name_is_otel_safe(prefix: str, name: str) -> bool:\\n return bool(stat_name_otel_handler(prefix, name, max_length=OTEL_NAME_MAX_LENGTH))\",\n \"def validate_name_input(name):\\n if all(letter.isalpha() or letter.isspace()\\n for letter in name) and len(name) != 0:\\n clear()\\n return True\\n\\n else:\\n clear()\\n print('** Please enter a name of alphabetic characters and spaces **')\\n return False\",\n \"def _sanitizeName(name):\\n\\n name = name.lower() # lower.\\n name = name.replace('.','') # remove periods.\\n name = name.replace('-','') # remove dashes.\\n name = name.replace(\\\"'\\\",'') # remove apostrophies.\\n # return it.\\n return name\",\n \"def is_valid_username(self, username):\\n rex = \\\"^[a-zA-Z]{3,}$\\\"\\n return re.match(rex, username)\",\n \"def validName(configsetname):\\n for c in configsetname:\\n if not c in string.letters+string.digits+\\\"$_-\\\":\\n return False\\n return configsetname != \\\"\\\"\",\n \"def is_valid_package_module_name(name):\\n if \\\".\\\" in name:\\n for part in name.split(\\\".\\\"):\\n if not is_valid_package_module_name(part):\\n return False\\n elif len(name):\\n if name[0] not in _first_letter_for_valid_name:\\n return False\\n\\n if len(set(name).difference(_char_set_for_valid_name)):\\n return False\\n else:\\n return False\\n return True\",\n \"def looks_like_fallback(url_name):\\r\\n return (url_name is not None\\r\\n and url_name.startswith(tag)\\r\\n and re.search('[0-9a-fA-F]{12}$', url_name))\",\n \"def clean_name(name: str) -> str:\\n if not re.match(\\\"[a-zA-Z_]\\\", name[0]):\\n name = \\\"_\\\" + name\\n name = re.sub(\\\"[^0-9a-zA-Z_]+\\\", \\\"_\\\", name)\\n if all(c == \\\"_\\\" for c in name):\\n name = \\\"v\\\"\\n return name\",\n \"def _process_name(name):\\n\\n # Unescape HTML entities\\n name = unescape(name)\\n\\n # Remove bracketed stuff on the end\\n name = NG_RE.sub('', name).strip() # Nomenclature groups\\n name = END_RE.sub('', name).strip(', ') # Words\\n name = RATIO_RE.sub('', name).strip(', ') # Ratios\\n\\n # Remove stuff off start\\n name = START_RE.sub('', name).strip()\\n\\n # Remove balanced start and end brackets if none in between\\n name = BRACKET_RE.sub('\\\\g<1>', name)\\n\\n # Un-invert CAS style names\\n comps = name.split(', ')\\n if len(comps) == 2:\\n if comps[1].endswith('-'):\\n name = comps[0]\\n name = '%s%s' % (comps[1], name)\\n elif len(comps) > 2:\\n name = comps[0]\\n for i in range(1, len(comps)):\\n if comps[i].endswith('-'):\\n name = '%s%s' % (comps[i], name)\\n else:\\n name = '%s %s' % (name, comps[i])\\n return name\",\n \"def validate_tag(self, tag_field):\\n if not tag_field.data or tag_field.data == '':\\n raise ValidationError('All users must be tagged')\\n return True\",\n \"def legal_variable_name(name):\\n var_name = name.replace(' ', '_')\\n for char in r'''[]{}\\\\/^%$#@!+-<>?|'\\\"=~`,.;:''':\\n if char in var_name:\\n var_name = var_name.replace(char, '')\\n for char in var_name:\\n if ord(char) > 127: # remove non-ascii characters\\n var_name = var_name.replace(char, '')\\n return var_name\",\n \"def validate_rss_name(rss_name):\\n regex = r'^[\\\\w]+$'\\n if not match(regex, str(rss_name)):\\n raise ValueError('invalid rss name ({})'.format(rss_name))\",\n \"def _assert_valid_name(name, container):\\n container.file.name_validation(container.directory, name)\",\n \"def verify_username(username):\\n name_reg_exp = re.compile(r\\\"^[a-zA-Z0-9_-]{3,20}$\\\")\\n return username and name_reg_exp.match(username)\",\n \"def clean_alias(self):\\n alias = self.cleaned_data.get('alias')\\n if not bool(re.compile(r'^[a-zA-Z0-9-]+$').match(alias)):\\n raise forms.ValidationError(\\\"Alias contains invalid characters\\\")\\n return alias\",\n \"def is_valid_attribute_name(self, name):\\n try:\\n self.validate_attribute_name(name)\\n return True\\n except etal.LabelsSchemaError:\\n return False\",\n \"def is_name_length_valid(self) -> bool:\\n return 2 < len(self.app_name) <= 35\",\n \"def validate(name, bracket, bracket_side, bfr):\\n\\n return bfr[bracket.begin:bracket.end].islower()\",\n \"def check_username(username):\\n if username:\\n if not re.match('[a-z]', username[0]):\\n return ['username_error_badfirstchar']\\n # Technically both these conditions might hold. However, the common\\n # case seems to be that somebody starts typing their name beginning\\n # with an upper-case letter, and it's probably sufficient to just\\n # issue the first error in that case.\\n elif not re.match('^[-a-z0-9_]+$', username):\\n return ['username_error_badchar']\\n return []\",\n \"def safe_formal_name(name):\\n return re.sub(r\\\"\\\\s+\\\", \\\" \\\", re.sub(r'[!/\\\\\\\\:<>\\\"\\\\?\\\\*\\\\|]', \\\"\\\", name)).strip()\",\n \"def is_simple_name(s):\\n\\n assert utils.is_string_type(s)\\n assert len(s) > 0\\n\\n def valid_first(c):\\n return c.isalpha() or c == \\\"_\\\"\\n def valid_later(c):\\n return c.isalnum() or c == \\\"_\\\"\\n return valid_first(s[0]) and all(valid_later(c) for c in s)\",\n \"def IsValidHotlistName(s):\\n return (RE_HOTLIST_NAME.match(s) and\\n len(s) <= framework_constants.MAX_HOTLIST_NAME_LENGTH)\",\n \"def nameIsValid(self, name):\\n self.notify.debug('nameIsValid')\\n if (name in self.usedNames):\\n return OTPLocalizer.ToonAlreadyExists % (name)\\n\\n problem = NameCheck.checkName(name, font=self.nameEntry.getFont())\\n if problem:\\n return problem\\n\\n # name has passed local checks\\n return None\",\n \"def clean_name(name, allowed_chars):\\n ok = identifier_chars + allowed_chars\\n newname = \\\"\\\".join(c if c in ok else \\\"-\\\" for c in name)\\n newname = newname.lstrip(\\\"-\\\")\\n if not newname:\\n raise RuntimeError(f\\\"No valid chars in name '{name}'.\\\")\\n return newname\",\n \"def validate_person_name(value):\\n disallowed_names = [\\n \\\"anon\\\",\\n \\\"anonymous\\\",\\n \\\"admin\\\",\\n \\\"administrator\\\",\\n \\\"guest\\\",\\n \\\"help\\\",\\n \\\"moderator\\\",\\n \\\"owner\\\",\\n \\\"postmaster\\\",\\n \\\"root\\\",\\n \\\"superuser\\\",\\n \\\"support\\\",\\n \\\"sysadmin\\\",\\n \\\"systemadministrator\\\",\\n \\\"systemsadministrator\\\",\\n \\\"user\\\",\\n \\\"webadmin\\\",\\n \\\"samuelpepys\\\",\\n \\\"pepys\\\",\\n \\\"sampepys\\\",\\n \\\"keithwright\\\",\\n \\\"warrenkeithwright\\\",\\n ]\\n\\n # Remove all punctuation and space from the name before comparing it to the\\n # disallowed names.\\n exclude = list(string.punctuation)\\n exclude.append(\\\" \\\")\\n test_value = \\\"\\\".join(ch for ch in value if ch not in exclude).lower()\\n\\n if test_value in disallowed_names:\\n raise ValidationError(\\\"%s is not an available name\\\" % value)\\n\\n # We allow one or more characters.\\n # There can be one or more spaces after that sequence, with other\\n # characters (including spaces) following.\\n if re.match(r\\\"^[\\\\w.-_]+(?:\\\\s+[\\\\w\\\\s.-_]+)?$\\\", value) is None:\\n raise ValidationError(\\\"%s contains invalid characters or formatting\\\" % value)\",\n \"def _validate_content_name(content_name: str, performative: str) -> Tuple[bool, str]:\\n # check content name's format\\n if not _is_valid_regex(CONTENT_NAME_REGEX_PATTERN, content_name):\\n return (\\n False,\\n \\\"Invalid name for content '{}' of performative '{}'. Content names must match the following regular expression: {} \\\".format(\\n content_name, performative, CONTENT_NAME_REGEX_PATTERN\\n ),\\n )\\n\\n # check content name is not a reserved name\\n if _is_reserved_name(content_name):\\n return (\\n False,\\n \\\"Invalid name for content '{}' of performative '{}'. This name is reserved.\\\".format(\\n content_name,\\n performative,\\n ),\\n )\\n\\n return (\\n True,\\n \\\"Content name '{}' of performative '{}' is valid.\\\".format(\\n content_name, performative\\n ),\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7056981","0.6984154","0.6797562","0.65576094","0.65357757","0.6507386","0.6452223","0.63574183","0.6310293","0.6288627","0.62855893","0.6240301","0.62214375","0.61912215","0.6171858","0.615459","0.61444604","0.6139254","0.6049716","0.6022824","0.59896547","0.5982377","0.5968032","0.5958874","0.5951548","0.59489644","0.59308624","0.59254557","0.5904121","0.5901528","0.5901528","0.5885073","0.58614856","0.5858291","0.58242947","0.5819674","0.5789541","0.5784943","0.57830155","0.5776754","0.57713795","0.57639265","0.57495576","0.5743252","0.5723178","0.5721931","0.57192296","0.57188976","0.57123524","0.5704883","0.5701903","0.5691762","0.567962","0.5662902","0.56582814","0.56501704","0.5639407","0.56392485","0.5623265","0.5621544","0.5603964","0.5603696","0.56028104","0.5601069","0.55710995","0.5554277","0.55479497","0.55462873","0.55443716","0.55325186","0.5530944","0.5529912","0.5525077","0.55226773","0.5520176","0.5511351","0.5509928","0.55019593","0.54913837","0.5490569","0.54905677","0.54900306","0.54755396","0.547464","0.5456234","0.54538584","0.54510015","0.5442224","0.5441944","0.54297465","0.5424645","0.5422894","0.5418938","0.541243","0.54119813","0.54076385","0.5402298","0.5395438","0.5394188","0.539129"],"string":"[\n \"0.7056981\",\n \"0.6984154\",\n \"0.6797562\",\n \"0.65576094\",\n \"0.65357757\",\n \"0.6507386\",\n \"0.6452223\",\n \"0.63574183\",\n \"0.6310293\",\n \"0.6288627\",\n \"0.62855893\",\n \"0.6240301\",\n \"0.62214375\",\n \"0.61912215\",\n \"0.6171858\",\n \"0.615459\",\n \"0.61444604\",\n \"0.6139254\",\n \"0.6049716\",\n \"0.6022824\",\n \"0.59896547\",\n \"0.5982377\",\n \"0.5968032\",\n \"0.5958874\",\n \"0.5951548\",\n \"0.59489644\",\n \"0.59308624\",\n \"0.59254557\",\n \"0.5904121\",\n \"0.5901528\",\n \"0.5901528\",\n \"0.5885073\",\n \"0.58614856\",\n \"0.5858291\",\n \"0.58242947\",\n \"0.5819674\",\n \"0.5789541\",\n \"0.5784943\",\n \"0.57830155\",\n \"0.5776754\",\n \"0.57713795\",\n \"0.57639265\",\n \"0.57495576\",\n \"0.5743252\",\n \"0.5723178\",\n \"0.5721931\",\n \"0.57192296\",\n \"0.57188976\",\n \"0.57123524\",\n \"0.5704883\",\n \"0.5701903\",\n \"0.5691762\",\n \"0.567962\",\n \"0.5662902\",\n \"0.56582814\",\n \"0.56501704\",\n \"0.5639407\",\n \"0.56392485\",\n \"0.5623265\",\n \"0.5621544\",\n \"0.5603964\",\n \"0.5603696\",\n \"0.56028104\",\n \"0.5601069\",\n \"0.55710995\",\n \"0.5554277\",\n \"0.55479497\",\n \"0.55462873\",\n \"0.55443716\",\n \"0.55325186\",\n \"0.5530944\",\n \"0.5529912\",\n \"0.5525077\",\n \"0.55226773\",\n \"0.5520176\",\n \"0.5511351\",\n \"0.5509928\",\n \"0.55019593\",\n \"0.54913837\",\n \"0.5490569\",\n \"0.54905677\",\n \"0.54900306\",\n \"0.54755396\",\n \"0.547464\",\n \"0.5456234\",\n \"0.54538584\",\n \"0.54510015\",\n \"0.5442224\",\n \"0.5441944\",\n \"0.54297465\",\n \"0.5424645\",\n \"0.5422894\",\n \"0.5418938\",\n \"0.541243\",\n \"0.54119813\",\n \"0.54076385\",\n \"0.5402298\",\n \"0.5395438\",\n \"0.5394188\",\n \"0.539129\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7059107"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12397,"cells":{"query":{"kind":"string","value":"Checks whether the given nametag is reachable by another branch or not. This means that the given nametag must appear in at least one branch as an end tag."},"document":{"kind":"string","value":"def is_nametag_reachable(nametag, branches):\n for branch in branches:\n for next_nametag in branches[branch].next_nametags:\n if next_nametag == nametag:\n return True\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def valid_branches(branches):\n\n # for every branch in the list\n for branch in branches:\n\n # make sure it is either reachable or has the special tag \"start\"\n if branches[branch].name != \"start\" and not is_nametag_reachable(branches[branch].name, branches):\n return False\n\n # make sure all ending tags refer to existing branches\n for nametag in branches[branch].next_nametags:\n if not branch_exists(nametag, branches):\n return False\n\n return True","def branch_exists(nametag, branches):\n for branch in branches:\n if branches[branch].name == nametag:\n return True\n return False","def _is_branch(self, reference_name):\n return reference_name.startswith(\"refs/heads/\") or reference_name.startswith(\n \"refs/remotes/\"\n )","def is_branch(wit_path, branch):\n\n branches = _get_references_data(wit_path)\n del branches['HEAD']\n return branch in branches.keys()","def branch(name, wit_path):\n\n if name != 'None':\n\n if len(name) < 30:\n head = _get_head(wit_path)\n _add_branch(wit_path, name, head)\n else:\n logging.error(f'branch name is too long \"{name}\" (max 30 digits).')\n else:\n logging.error(f'branch name is not valid {name}.')","def branch_exists(branch_name, local_only=False, directory=None):\n for branch in get_branches(local_only, directory):\n if branch.startswith('remotes/'):\n branch = branch.split('/')\n if len(branch) > 2:\n branch = '/'.join(branch[2:])\n if branch_name == branch:\n return True\n else:\n if branch_name == branch:\n return True\n return False","def has_branch(self, branch):\n if self.branch == branch:\n return True\n return False","def match(self, name, tags):\n return name.lower() in tags","def is_tagged(self,tag_name,element):\n return (tag_name in self.tag2elements.keys()) and (element in self.tag2elements[tag_name])","def check_component(comp_name: str, comp: defs.Component) -> None:\n if not RE_COMP_NAME.match(comp_name):\n res.append(f\"Invalid component name: {comp_name}\")\n\n for branch_name, branch in sorted(comp.branches.items()):\n check_branch(comp_name, branch_name, branch)","def is_subtag(tag_name, subtag_name, user_path, current_user) -> bool:\n user = current_user[0]\n subtag_list = os.listdir((user_path + '\\\\' + user + '\\\\' + tag_name).encode('unicode_escape'))\n temp = list(map(bytes.decode, subtag_list))\n\n if subtag_name + '.txt' in temp:\n return True\n else:\n return False","def _is_desired_tag(self, tag):\n if self._tags is None:\n return True\n\n if self._ignore_namespace:\n for desired_tag in self._tags:\n if tag.localname == desired_tag.localname:\n return True\n else:\n for desired_tag in self._tags:\n if tag == desired_tag:\n return True\n\n return False","def has_name(self, name: str) -> bool:\n return name in self.child_tags","def is_tag(tag_name, user_path, current_user) -> bool:\n user = current_user[0]\n tag_list = os.listdir((user_path + '\\\\' + user).encode('unicode_escape'))\n temp = list(map(bytes.decode, tag_list))\n if tag_name in temp:\n return True\n else:\n return False","def verify_tag(tag):\n command = [\"git\", \"tag\", \"--points-at\"]\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\n tag_str = proc.stdout.readline()\n return tag_str.decode(\"utf-8\").rstrip() == tag","def test_bookmark_tag_complete(self):\r\n self._get_good_request(second_bmark=True)\r\n\r\n res = self.testapp.get(\r\n '/api/v1/admin/tags/complete',\r\n params={\r\n 'tag': 'py',\r\n 'api_key': API_KEY},\r\n status=200)\r\n\r\n self.assertTrue(\r\n 'python' in res.body,\r\n \"Should have python as a tag completion: \" + res.body)\r\n\r\n # we shouldn't get python as an option if we supply bookmarks as the\r\n # current tag. No bookmarks have both bookmarks & python as tags\r\n res = self.testapp.get(\r\n '/api/v1/admin/tags/complete',\r\n params={\r\n 'tag': u'py',\r\n 'current': u'bookmarks',\r\n 'api_key': API_KEY\r\n },\r\n status=200)\r\n\r\n self.assertTrue(\r\n 'python' not in res.body,\r\n \"Should not have python as a tag completion: \" + res.body)\r\n self._check_cors_headers(res)","def is_valid_git_refname(refname):\r\n if len(refname) == 0:\r\n return False\r\n\r\n # git imposes a few requirements to accept a string as a\r\n # refname/branch-name\r\n\r\n # They can include slash / for hierarchical (directory) grouping, but no\r\n # slash-separated component can begin with a dot . or end with the sequence\r\n # .lock\r\n if (len([True for element in refname.split('/')\r\n if element.startswith('.') or element.endswith('.lock')]) != 0):\r\n return False\r\n\r\n # They cannot have two consecutive dots .. anywhere\r\n if '..' in refname:\r\n return False\r\n\r\n # They cannot have ASCII control characters (i.e. bytes whose values are\r\n # lower than \\040, or \\177 DEL), space, tilde, caret ^, or colon : anywhere\r\n if len([True for refname_char in refname if ord(refname_char) < 40 or\r\n ord(refname_char) == 177]) != 0:\r\n return False\r\n if ' ' in refname or '~' in refname or '^' in refname or ':' in refname:\r\n return False\r\n\r\n # They cannot have question-mark ?, asterisk *, or open bracket [ anywhere\r\n if '?' in refname or '*' in refname or '[' in refname:\r\n return False\r\n\r\n # They cannot begin or end with a slash / or contain multiple consecutive\r\n # slashes\r\n if refname.startswith('/') or refname.endswith('/') or '//' in refname:\r\n return False\r\n\r\n # They cannot end with a dot ..\r\n if refname.endswith('.'):\r\n return False\r\n\r\n # They cannot contain a sequence @{\r\n if '@{' in refname:\r\n return False\r\n\r\n # They cannot contain a \\\r\n if '\\\\' in refname:\r\n return False\r\n\r\n return True","def branch_exists(branch):\n\n try:\n git('show-ref', branch)\n return True\n except subprocess.CalledProcessError:\n return False","def is_remote_reserve_branch_present(repo):\n reserve_name = phlgitu_ref.Name(_RESERVE_BRANCH_FQ_NAME)\n remote_ref_names = repo(\"ls-remote\").split()[1::2]\n return reserve_name.fq in remote_ref_names","def match(self, name, tags):\n name, tags = self.get_compiled(name, tags)\n \n def index_of_letter(l):\n return ord(l) - ord('a')\n \n true_val, false_val = name\n \n if true_val:\n return index_of_letter(true_val) in tags\n else:\n return index_of_letter(false_val) not in tags","def bonenamematch(name1, name2):\n if name1 == name2:\n return True\n if name1.startswith(\"Bip01 L \"):\n name1 = \"Bip01 \" + name1[8:] + \".L\"\n elif name1.startswith(\"Bip01 R \"):\n name1 = \"Bip01 \" + name1[8:] + \".R\"\n if name2.startswith(\"Bip01 L \"):\n name2 = \"Bip01 \" + name2[8:] + \".L\"\n elif name2.startswith(\"Bip01 R \"):\n name2 = \"Bip01 \" + name2[8:] + \".R\"\n if name1 == name2:\n return True\n return False","def match(self, name, tags):\n S, tags = self.get_compiled(name, tags)\n return bool(S & tags)","def is_linguistic_tag(tag):\r\n if tag.startswith(\"&\"):\r\n return True\r\n if any(x in tag for x in [\"<abbr>\", \"<abbr \", \"</abbr>\"]):\r\n return True\r\n return False","def git_checkout_branch(name):\n\n if subprocess.call([\"git\", \"diff\", \"--quiet\", \"HEAD\"]) != 0:\n raise Exception(\"Dirty working tree; not checking out %s\" % name)\n\n if subprocess.call([\"git\", \"checkout\", name]) != 0:\n raise Exception(\"Could not checkout %s\" % name)","def local_branch_exists(self, branch):\n return branch in self.repo.branches","def is_complete(self, A, B):\n return all(self.is_edge(v, w) for v in A for w in B)","def _check_branch(opt, params):\n\n # Check the current branch and hash\n _get_branch(opt)\n\n if params.git_branch != opt.git_branch or params.git_hash != opt.git_hash:\n msg = 'You are not on the right branch or commit. Please run the following in the repository: \\n'\n msg += f'git checkout {params.git_branch}\\n'\n msg += f'git revert {params.git_hash}'\n sys.exit(msg)","def mac_pool_exists(handle, name, assignment_order=None,\r\n r_from=None, to=None, descr=None, parent_dn=\"org-root\"):\r\n dn = parent_dn + '/mac-pool-' + name\r\n mo = handle.query_dn(dn)\r\n if mo:\r\n if ((assignment_order and mo.assignment_order != assignment_order) and\r\n (r_from and mo.r_from != r_from) and\r\n (to and mo.to != to) and\r\n (descr and mo.descr != descr)):\r\n return False\r\n return True\r\n return False","def check_branch(\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\n ) -> None:\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\n\n if not RE_BRANCH_NAME.match(branch_name):\n res.append(f\"{comp_name}: Invalid branch name: {branch_name}\")\n\n for ver, version in sorted(branch.items()):\n if not RE_VERSION_STRING.match(ver):\n res.append(f\"{comp_name}/{branch_name}: Invalid version string: {ver}\")\n\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\n if version.outdated:\n update_to = [\n o_version\n for o_version in branch.values()\n if not o_version.outdated\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\n == other_cksums\n ]\n if len(update_to) != 1:\n res.append(\n f\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \"\n f\"versions to update to instead of exactly one\"\n )\n else:\n bad_files = sorted(\n relpath\n for relpath, (path, fdata) in driver_cksums.items()\n if util.file_sha256sum(path) != fdata.sha256\n )\n if bad_files:\n res.append(f\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\")\n\n if not uptodate_files:\n uptodate_files = driver_cksums\n elif uptodate_files != driver_cksums:\n res.append(\n f\"{comp_name}/{branch_name}: All the up-to-date versions should \"\n f\"define the same set of files with the same checksums\"\n )\n\n if not any(not version.outdated for version in branch.values()):\n res.append(f\"{comp_name}/{branch_name}: No non-outdated versions\")","def check_name(self, name: str):\n if name[0] == \"/\" or self.check_end_streaming(name):\n return True\n else:\n return False","def reachable_without(start: Node, end: Node, without: Node) -> bool:\n reachable: Set[Node] = set()\n stack: List[Node] = [start]\n\n while stack:\n node = stack.pop()\n if node == without or node in reachable:\n continue\n reachable.add(node)\n stack.extend(node.children())\n\n return end in reachable","def is_neighbor(self, name_from, name_to):\n return name_to in self._main_dictionary[name_from]","def verify_tags(git_ref_target):\n latest_release = github_util.get_latest_release().get('name')\n latest_commit = run('git rev-list -n 1 {}'.format(latest_release)).stdout.rstrip(\"\\r\\n\")\n if not branch_check(latest_release, git_ref_target):\n print('Your branch does not contain the latest production code. \\n\\\n Please recreate it by branching off of release {}.'.format(latest_release))\n exit(1)\n else:\n print(\"Branch contains the latest production tag\")\n fork_point = run('git merge-base remotes/origin/master remotes/origin/{}'.format(git_ref_target))\n commits_since_fork = run('git rev-list --branches={} {}^..HEAD'.format(git_ref_target,\n fork_point.stdout.rstrip(\"\\r\\n\")))\n if latest_commit not in commits_since_fork.stdout:\n print('Your branch did not fork directly from the last production tag. \\n\\\n Please recreate it by branching off of release {}.'.format(latest_release))\n exit(1)\n else:\n print('Latest production tag is between the fork point and HEAD')","def branch_exists(repo, branch, remote=False):\n ref = 'refs/remotes/origin/' + branch if remote else 'refs/heads/' + branch\n return subprocess.call(['git', 'show-ref', '-q', '--verify', ref],\n cwd=repo) == 0","def __check_neighbour(self, address: Address) -> bool:\n is_from_children = False\n for child_address in self.children_addresses:\n is_from_children = is_from_children or (child_address == address)\n is_from_parent = (address == self.parent_address)\n return is_from_parent or is_from_children","def label_intersects_tags(label, tags):\n for tag in tags:\n if tag in label:\n return True\n return False","def ensure_tracking_branches(args):\n man = load_manifest()\n for (name, project) in man.projects.iteritems():\n repo = GitRepo(workdir_for_project(project))\n branch_missing = repo.command(\n [\"rev-parse\", \"--verify\", \"-q\", project.refspec],\n capture_stdout=True)\n \n if branch_missing:\n logging.warn(\"Branch %s does not exist in project %s. checking out.\" %\n (project.refspec, name))\n repo.command([\"branch\", \"--track\",\n project.tracking_branch, project.remote_refspec])","def validate_branch_ops(conn_graph: ConnectedGraph):\n\n def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\n \"\"\"\n Look inside conn_graph ops and products for branch ops, and validate connections to parent and child ops\n \"\"\"\n\n op = conn_graph.get_all_ops()[op_info.module_name]\n return_bool = True\n product = op.output\n if \"branch\" not in product.name:\n logger.error(\"branch not in product name\")\n return_bool = False\n if len(product.consumers) > 1:\n logger.error(\"branch op is not parent op's only consumer\")\n return_bool = False\n branch_op = product.consumers[0]\n if branch_op.type != \"branch\":\n logger.error(\"parent op's child op is not of type branch\")\n return_bool = False\n branch_product = branch_op.output\n if \"multiple_ops\" not in branch_product.name:\n logger.error(\"multiple_ops not in branch op's product's name\")\n return_bool = False\n if len(branch_product.consumers) <= 1:\n logger.error(\"branch op's product has one or fewer consumers\")\n return_bool = False\n for consumer in branch_product.consumers:\n for input_product in consumer.inputs:\n if input_product.producer == op:\n logger.error(\"parent op is still one of child op's inputs (as opposed to branch op)\")\n return_bool = False\n return return_bool\n\n # pylint: disable=protected-access\n module_identifier = StructureModuleIdentifier(conn_graph.graph, conn_graph._starting_op_names,\n conn_graph._valid_ops)\n num_branches_found = 0\n for tf_op in conn_graph.graph.get_operations():\n # Ignore ops which were not found in the initial depth first search\n if tf_op not in module_identifier.processed_ops:\n continue\n\n found_branch = False\n for output_tensor in tf_op.outputs:\n if len(output_tensor.consumers()) > 1:\n # Potential branch op. Check if children go to separate modules\n child_module_set = set()\n for consumer_op in output_tensor.consumers():\n if consumer_op in module_identifier._valid_ops:\n child_module_info = module_identifier.get_op_info(consumer_op)\n child_module_set.add(child_module_info.module_name)\n\n # If children go to separate modules, this should be a branch op\n if len(child_module_set) > 1:\n found_branch = True\n break\n\n if found_branch:\n num_branches_found += 1\n tf_op_info = module_identifier.get_op_info(tf_op)\n if not check_for_branch_op(tf_op_info):\n return False\n\n logger.info(\"Found %s branches\", num_branches_found)\n return True","def _is_last_commit_already_tagged(\n repo: git.Repo,\n last_tag: Optional[git.refs.tag.TagReference],\n branch_name: str) -> bool:\n if last_tag is None:\n return False\n commit = list(repo.iter_commits(rev=branch_name))[0]\n return last_tag.commit == commit","def remote_branch_exists(self, branch):\n try:\n self.git.show_ref(\"refs/remotes/{}/{}\".format(self.base_branch_remote(), branch))\n return True\n except git.exc.GitCommandError:\n return False","def _check_overlap(self, fe_commit):\n # +++ Avoid O(b branches * r rev) checks when\n # overlap is impossible because current branch\n # overlaps no other branch.\n if self._current_branch not in self._overlapping_branch_list():\n return\n\n for fe_file in fe_commit['files']:\n gwt_path = fe_file['path']\n depot_path = self.ctx.gwt_to_depot_path(gwt_path)\n\n for branch in self._overlapping_branch_list():\n if branch == self._current_branch:\n continue\n if not branch.intersects_depot_path(depot_path):\n continue\n\n LOG.debug(\"_check_overlap() branch {br1} <> {br2}\"\n \" gwt={gwt:<40} {dp}\\n{view}\"\n .format(\n br1 = p4gf_util.abbrev(self._current_branch.branch_id)\n , br2 = p4gf_util.abbrev(branch.branch_id)\n , gwt = gwt_path\n , dp = depot_path\n , view = \"\\n\".join(branch.view_p4map.as_array())\n ))\n\n if self._current_branch.is_new_fp_from_push or branch.is_new_fp_from_push:\n current_branch_name = self._current_branch.git_branch_name\n if self._current_branch.is_new_fp_from_push:\n current_branch_name += '(new)'\n other_branch_name = branch.git_branch_name\n if branch.is_new_fp_from_push:\n other_branch_name += '(new)'\n human_msg = (_(\n \"Perforce: Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\\n\"\n \" You are attempting to push and create a new fully populated branch\\n\"\n \" with paths which overlap another branch. Contact your admin\\n\"\n \" to configure non-conflicting destination branch paths.\\n\"\n \" Branches: '{b1}', '{b2}'\")\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\n , gwt_path = gwt_path\n , depot_path = depot_path\n , b1 = current_branch_name\n , b2 = other_branch_name ))\n else:\n human_msg = (_(\n \"Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\"\n \" Paths that overlap multiple Git Fusion branches are read-only.\"\n \" Branches: '{b1}', '{b2}'\")\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\n , gwt_path = gwt_path\n , depot_path = depot_path\n , b1 = self._current_branch.branch_id\n , b2 = branch.branch_id ))\n raise PreflightException(human_msg)","def match(self, name, tags):\n or_exprs, tags = self.get_compiled(name, tags)\n \n # or_exprs = [{'a'}, {'c'}, {'d', 'a'}, {'d', 'e'}]\n return any(and_expr <= tags for and_expr in or_exprs)","def are_connected(self, name1, name2):","def dstflow(self):\n if self.name in conditional_branch + unconditional_branch:\n return True\n return False","def check_photo_tag(self, tag_name):\n data = self.db.make_query(\n '''select * from photo_tag where tag_name = \"{}\" '''\n .format(tag_name))\n\n if len(data) > 0:\n return True\n return False","def has_node(self, name):\n return self.source_net.has_node(name)","def check_tags(l):\n # start on outside working inwords, checking first and last tags match\n stack = []\n for item in l:\n if item[-2] == '/' or item[1] == '!':\n continue # deal with self-closing tags and comments\n elif item[ 1] == '/':\n root = item[2 : -1].split()[0]\n try:\n match = stack.pop()\n except: return False # closing tag without an opener\n if root != match:\n return False\n else:\n root = item[1 : -1].split()[0]\n stack.append(root) \n return True","def _valid_ref_type(ref):\n\n git_branch = \"refs/heads/\"\n git_tag = \"refs/tags/\"\n if ref.startswith(git_branch) or ref.startswith(git_tag) or len(ref) == 40:\n return True\n return False","def looks_like_fallback(url_name):\r\n return (url_name is not None\r\n and url_name.startswith(tag)\r\n and re.search('[0-9a-fA-F]{12}$', url_name))","def check_inner_tag_availability(**kwargs):\n\n inner = kwargs.pop('inner')\n tag = kwargs.pop('tag')\n used_inners = [item.inner for item in [\n *MLPAv4.objects.filter(tag=tag),\n *MLPAv6.objects.filter(tag=tag),\n *BilateralPeer.objects.filter(tag=tag),\n *Monitorv4.objects.filter(tag=tag)]]\n check_inner = inner in used_inners\n if check_inner:\n return False\n else:\n return True","def tagJudge(word, tag, lastNameDict):\n orgTags = ['ns', 'nz', 'nt', 'nl', 'ENG-ORG', 'CONF']\n pplTags = ['nr', 'ENG-PEO']\n undfTags = ['nrt', 'ng', 'nrfg']\n if tag in pplTags:\n return 1\n elif tag in orgTags:\n return 0\n else:\n if isPeople(word, tag, lastNameDict):\n return 1\n return 2","def is_boundary_edge(a, b, bdy_edges):\n for edge in bdy_edges:\n a0, b0 = edge\n if a == a0 and b == b0:\n return True\n return False","def check_for_tags(self, data_in):\n # possible header tags\n tags = ['NODE:', 'PORT:', 'STARTOFFSET:', 'ENDOFFSET:']\n\n # check for tags\n for tag in tags:\n if data_in.find(tag) != -1:\n print \"Found tag %s in data file\" % tag\n return False\n\n return True","async def exists(self, tag_name):\n try:\n if await self.get_id(tag_name):\n return True\n except RtbDoesntExists:\n return False","def _is_current_branch(self, branch_name, current_branch_name):\n return branch_name == current_branch_name","def _ancestors_contains_blacklisted_tag(xpath_string, blacklisted_tags):\n xpath = xpath_string.split(\"/\")\n for tag in blacklisted_tags:\n if tag in xpath:\n return True\n return False","def isNameUsed(self, name: unicode, startId: long, stopId: long) -> bool:\n ...","def is_valid_tag(self, tag):\n\n if tag.has_attr('href') and len(tag['href']) > 0:\n href = tag['href']\n complete_href = self.session.complete_url(href)\n\n is_relative = self.url in complete_href\n is_visited = complete_href in self.visited_paths\n is_style_sheet = tag.name == \"link\"\n is_jumpTo = \"#\" in href\n is_mailTo = \"mailto\" in href\n is_js = \"javascript:\" in href\n return is_relative and \\\n not (is_visited or is_style_sheet or is_jumpTo or is_mailTo or is_js)\n else:\n return False","def check(self,word):\n if self.pre:\n def sub_word(chars):\n if re.match('^'+chars+'.*',word):\n return word[len(chars):]\n else:\n return None\n else:\n def sub_word(chars):\n if re.match('^.*'+chars+'$',word):\n return word[:-len(chars)]\n else:\n return None\n\n if word == '':\n return self\n for chars in self.branches.keys():\n res = sub_word(chars)\n if res:\n return self.branches[chars].check(res)\n elif res == '':\n return self.branches[chars]\n return None","def is_valid_compound_name(name: str) -> bool:\n return n2s.has_smiles(name)","def _is_remote_branch(self, branch_reference):\n return branch_reference.startswith(\"refs/remotes/\")","def test_heads_contains_false(repository: Repository) -> None:\n assert \"branch\" not in repository.heads","def is_tag_available(self, tag):\n return tag in self.available_tags","def is_node_name_ok(node_name):\n # 节点名不可包含`/`特殊字符\n node_name = node_name.strip('/')\n return node_name.find('/') == -1","def acorn_finder(t):\n \"*** YOUR CODE HERE ***\"\n if is_leaf(t):\n return type(label(t)) == str and 'acorn' in label(t)\n else:\n return any([acorn_finder(b) for b in branches(t)])","def match(self, name, tags):\n tree, tags = self.get_compiled(name, tags)\n \n def parse(node):\n if not isinstance(node, CommentMatcherFullBoolean.Node):\n return node in tags\n else:\n if node.op == '|':\n return any(parse(p) for p in node.params)\n elif node.op == '&':\n return all(parse(p) for p in node.params)\n elif node.op == '!':\n return not parse(node.params[0])\n else:\n raise ValueError\n \n return parse(tree)","def reach(self, from_symbol, to_symbol):\n # type: (Type[Nonterminal], Type[Nonterminal]) -> bool\n return len(self.path_rules(from_symbol, to_symbol)) > 0","def splitflow(self):\n if self.name in conditional_branch:\n return True\n return False","def an_check(self):\n\t\tfor filles in self.xelt:\n\t\t\t# parcours rapide des branches niveau 1\n\t\t\tif search(r'analytic$', filles.tag):\n\t\t\t\treturn True\n\t\treturn False","def check_related(self, from_person_name, to_person_name):\n from_person = self.find(from_person_name)\n to_person = self.find(to_person_name)\n if not from_person:\n raise ValueError(\"person with {} name does not exist.\".format(from_person_name))\n\n if not to_person:\n raise ValueError(\"person with {} name does not exist.\".format(to_person_name))\n\n from_person_relatives_names = [person.name for person in self.find_relative(from_person_name)]\n to_person_relatives_names = [person.name for person in self.find_relative(to_person_name)]\n related_names = set(from_person_relatives_names).intersection(set(to_person_relatives_names))\n return len(related_names) > 0, from_person, to_person, related_names","def missing_branches(self):\n upstream_tags = self.upstream_model.tags_from_semver_point(\"1.19.0\")\n deb_branches = self.deb_model.base.branches_from_semver_point(\"1.19.0\")\n return list(set(upstream_tags) - set(deb_branches))","def checkout_tag(tag: str, repo: str = None) -> bool:\n cmd = ['git', 'checkout', 'tags/'+tag, '--quiet', '--force']\n result = _run_git(cmd, repo=repo, expect_stderr=True)\n if not result:\n return False\n # actually a good result\n print(result.stderr.decode(\"utf-8\"))\n return True","def tag_exists(tag, directory=None):\n return tag in get_tags(directory)","def checkheads(repo, remote, outgoing, remoteheads, newbranch=False, inc=False):\n # Check for each named branch if we're creating new remote heads.\n # To be a remote head after push, node must be either:\n # - unknown locally\n # - a local outgoing head descended from update\n # - a remote head that's known locally and not\n # ancestral to an outgoing head\n if remoteheads == [nullid]:\n # remote is empty, nothing to check.\n return\n\n if remote.capable('branchmap'):\n headssum = _headssummary(repo, remote, outgoing)\n else:\n headssum = _oldheadssummary(repo, remoteheads, outgoing, inc)\n newbranches = [branch for branch, heads in headssum.iteritems()\n if heads[0] is None]\n # 1. Check for new branches on the remote.\n if newbranches and not newbranch: # new branch requires --new-branch\n branchnames = ', '.join(sorted(newbranches))\n raise util.Abort(_(\"push creates new remote branches: %s!\")\n % branchnames,\n hint=_(\"use 'hg push --new-branch' to create\"\n \" new remote branches\"))\n\n # 2 compute newly pushed bookmarks. We\n # we don't warned about bookmarked heads.\n localbookmarks = repo._bookmarks\n remotebookmarks = remote.listkeys('bookmarks')\n bookmarkedheads = set()\n for bm in localbookmarks:\n rnode = remotebookmarks.get(bm)\n if rnode and rnode in repo:\n lctx, rctx = repo[bm], repo[rnode]\n if bookmarks.validdest(repo, rctx, lctx):\n bookmarkedheads.add(lctx.node())\n\n # 3. Check for new heads.\n # If there are more heads after the push than before, a suitable\n # error message, depending on unsynced status, is displayed.\n error = None\n unsynced = False\n allmissing = set(outgoing.missing)\n allfuturecommon = set(c.node() for c in repo.set('%ld', outgoing.common))\n allfuturecommon.update(allmissing)\n for branch, heads in sorted(headssum.iteritems()):\n if heads[0] is None:\n # Maybe we should abort if we push more that one head\n # for new branches ?\n continue\n candidate_newhs = set(heads[1])\n # add unsynced data\n oldhs = set(heads[0])\n oldhs.update(heads[2])\n candidate_newhs.update(heads[2])\n dhs = None\n discardedheads = set()\n if repo.obsstore:\n # remove future heads which are actually obsolete by another\n # pushed element:\n #\n # XXX as above, There are several cases this case does not handle\n # XXX properly\n #\n # (1) if <nh> is public, it won't be affected by obsolete marker\n # and a new is created\n #\n # (2) if the new heads have ancestors which are not obsolete and\n # not ancestors of any other heads we will have a new head too.\n #\n # This two case will be easy to handle for know changeset but much\n # more tricky for unsynced changes.\n newhs = set()\n for nh in candidate_newhs:\n if nh in repo and repo[nh].phase() <= phases.public:\n newhs.add(nh)\n else:\n for suc in obsolete.allsuccessors(repo.obsstore, [nh]):\n if suc != nh and suc in allfuturecommon:\n discardedheads.add(nh)\n break\n else:\n newhs.add(nh)\n else:\n newhs = candidate_newhs\n if [h for h in heads[2] if h not in discardedheads]:\n unsynced = True\n if len(newhs) > len(oldhs):\n # strip updates to existing remote heads from the new heads list\n dhs = sorted(newhs - bookmarkedheads - oldhs)\n if dhs:\n if error is None:\n if branch not in ('default', None):\n error = _(\"push creates new remote head %s \"\n \"on branch '%s'!\") % (short(dhs[0]), branch)\n else:\n error = _(\"push creates new remote head %s!\"\n ) % short(dhs[0])\n if heads[2]: # unsynced\n hint = _(\"you should pull and merge or \"\n \"use push -f to force\")\n else:\n hint = _(\"did you forget to merge? \"\n \"use push -f to force\")\n if branch is not None:\n repo.ui.note(_(\"new remote heads on branch '%s'\\n\") % branch)\n for h in dhs:\n repo.ui.note(_(\"new remote head %s\\n\") % short(h))\n if error:\n raise util.Abort(error, hint=hint)\n\n # 6. Check for unsynced changes on involved branches.\n if unsynced:\n repo.ui.warn(_(\"note: unsynced remote changes!\\n\"))","def validate_branch_config(branch_cfg, branch, n):\n res = False\n if len(branch) == 4:\n if \"double\" in branch_cfg:\n diff1 = branch[0]-branch[1]\n diff3 = branch[2]-branch[3]\n diff2 = branch[1]%n-branch[2]%n\n diff_adj_clk = [-3, 1]\n diff_adj_clk_ctr = [-e for e in diff_adj_clk]\n diff_opp = [2, -2]\n if \"adjacent\" and \"clockwise\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk:\n res = True\n elif \"adjacent\" and \"counter clockwise\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk_ctr:\n res = True\n elif \"opposite\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_opp:\n res = True\n elif \"single\" in branch_cfg:\n res = True\n elif len(branch) == 2:\n res = True\n return res","def _stack_is_complete(stack_name: str, region: str, profile: str = None) -> bool:\n logger.debug(f\"Checking if stack {stack_name} in region {region} is in completed state\")\n stack_status = _get_stack_status(stack_name, region=region, profile=profile)\n if not stack_status:\n logger.debug(f\"STACK: {stack_name} has no status. not complete\")\n return False\n if stack_status[-9:] == \"_COMPLETE\" or stack_status[-7:] == \"_FAILED\":\n logger.debug(f\"STACK: {stack_name} status: {stack_status} is complete in region {region}\")\n return True\n logger.debug(f\"STACK: {stack_name} status: {stack_status} is not complete in region {region}\")\n return False","def autocomplete(self, prefix):\n if len(prefix) == 0:\n \n __________________________________________\n \n if prefix[0] not in self.branches:\n \n __________________________________________\n \n ______________________________________________","def test_is_valid_git_refname(self):\r\n # valid branchnames\r\n self.assertTrue(is_valid_git_refname('master'))\r\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\r\n self.assertTrue(is_valid_git_refname('refname/bar'))\r\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\r\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\r\n unichr(40))))\r\n self.assertTrue(is_valid_git_refname('master@head'))\r\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\r\n\r\n # case happening with git < 1.6.6\r\n self.assertFalse(is_valid_git_refname(\r\n '--abbrev-ref\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\r\n\r\n # different invalid refnames, for a description of each group see the\r\n # man page of git check-ref-format\r\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\r\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\r\n self.assertFalse(is_valid_git_refname('bar.lock'))\r\n self.assertFalse(is_valid_git_refname('.foobar'))\r\n\r\n self.assertFalse(is_valid_git_refname('ref..name'))\r\n\r\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\r\n unichr(177))))\r\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\r\n unichr(39))))\r\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\r\n self.assertFalse(is_valid_git_refname('refname spam'))\r\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\r\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\r\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\r\n\r\n self.assertFalse(is_valid_git_refname('areyourandy?'))\r\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\r\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\r\n\r\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\r\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\r\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\r\n\r\n self.assertFalse(is_valid_git_refname('dotEnding.'))\r\n\r\n self.assertFalse(is_valid_git_refname('@{branch'))\r\n\r\n self.assertFalse(is_valid_git_refname('contains\\\\slash'))\r\n\r\n self.assertFalse(is_valid_git_refname('$newbranch'))","def missing_branches(self):\n upstream_tags = self.upstream_model.tags_from_semver_point(\"0.8.7\")\n deb_branches = self.deb_model.base.branches_from_semver_point(\"0.8.7\")\n return list(set(upstream_tags) - set(deb_branches))","def __has_conflicting_node_names(self):\n # check length of sets to determine if overlap exists\n return len({node.get_name() for node in self.get_nodeset()}) != len(self.get_nodeset())","def test_is_valid_git_refname(self):\n # valid branchnames\n self.assertTrue(is_valid_git_refname('master'))\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\n self.assertTrue(is_valid_git_refname('refname/bar'))\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\n unichr(40))))\n self.assertTrue(is_valid_git_refname('master@head'))\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\n\n # case happening with git < 1.6.6\n self.assertFalse(is_valid_git_refname(\n '--abbrev-ref\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\n\n # different invalid refnames, for a description of each group see the\n # man page of git check-ref-format\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\n self.assertFalse(is_valid_git_refname('bar.lock'))\n self.assertFalse(is_valid_git_refname('.foobar'))\n\n self.assertFalse(is_valid_git_refname('ref..name'))\n\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\n unichr(177))))\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\n unichr(39))))\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\n self.assertFalse(is_valid_git_refname('refname spam'))\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\n\n self.assertFalse(is_valid_git_refname('areyourandy?'))\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\n\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\n\n self.assertFalse(is_valid_git_refname('dotEnding.'))\n\n self.assertFalse(is_valid_git_refname('@{branch'))\n\n self.assertFalse(is_valid_git_refname('contains\\\\slash'))\n\n self.assertFalse(is_valid_git_refname('$newbranch'))","def _contains_in_self_or_parent(self, name: str) -> bool:\n return name in self","def nameIsValid(self, name):\n self.notify.debug('nameIsValid')\n if (name in self.usedNames):\n return OTPLocalizer.ToonAlreadyExists % (name)\n\n problem = NameCheck.checkName(name, font=self.nameEntry.getFont())\n if problem:\n return problem\n\n # name has passed local checks\n return None","def has(self, tag_name: str) -> bool:\n return hasattr(self, tag_name)","def is_final_node_reached(self):\n if self.actual_node == self.final_node:\n self.final_node_reached = True","def complete(t, d, k):\n if not branches(t):\n return d == 0\n bs = [ complete(b, d-1, k) for b in branches(t) ]\n return len(branches(t)) == k and all(bs)","def check_branch(subcommand, branch):\n if subcommand != \"checkout\":\n return\n # first make sure actual branch name was given\n if branch is None:\n return \"Branch name to checkout must be supplied with '-b' option\"\n # next check that the local repo is clean\n cmd = [\"git\", \"status\", \"--untracked-files=no\", \"--porcelain\"]\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, universal_newlines=True)\n if p.stdout.strip():\n return \"Need to have clean working tree to checkout!\\n\\n\" + p.stdout\n # next check that the branch name doesn't already exist\n cmd = [\"git\", \"show-ref\", \"--verify\", \"--quiet\", \"refs/heads/\" + branch]\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False)\n if not p.returncode:\n return f\"Branch {branch!r} already exists\"","def bzr_branch(uri, branch):\n try:\n repo = bzr_repo(uri)\n for name, branch_uri in bzr_branches(repo):\n if name == branch:\n return name, branch_uri\n except Exception as e:\n log.error(e)\n raise\n raise Exception('branch %s cannot be found on repository %s' %(branch, uri))","def has(self, tag_name: str, category: ty.Optional[str] = None) -> bool:\n tags = self.__holder.db_tags.filter(lambda t: t.name == tag_name)\n if category is not None:\n tags = tags.filter(category=category)\n\n return len(tags) >= 1","def _all_names_unused(elts, unused_name_nodes):\n for elt in elts:\n if isinstance(elt, (ast.List, ast.Tuple)):\n if not _all_names_unused(elt.elts, unused_name_nodes):\n return False\n if elt not in unused_name_nodes:\n return False\n return True","def isSelfClosingTag(self, name):\r\n return self.SELF_CLOSING_TAGS.has_key(name) \\\r\n or self.instanceSelfClosingTags.has_key(name)","def path_touched(*paths, commit_range):\n return check_output([\n 'git', 'diff', '--name-only', commit_range, '--', *paths\n ]).decode('utf-8').strip() != ''","def _checksubrepostate(pushop):\n for n in pushop.outgoing.missing:\n ctx = pushop.repo[n]\n\n if b'.hgsub' in ctx.manifest() and b'.hgsubstate' in ctx.files():\n for subpath in sorted(ctx.substate):\n sub = ctx.sub(subpath)\n sub.verify(onpush=True)","def isLeaf(self, node_name):\n if self.tree.node[node_name]['c'] != '':\n return True\n else:\n return False","def verify_branch(path, expected_branch=\"master\"):\n\n sys.stdout.write(\" - Verifying your branch is %s:\" % expected_branch)\n branch = run_in_component(path, ['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\n branch = branch.strip()\n\n if branch == expected_branch:\n print(\" OKAY\")\n return\n\n print(\" FAILED\")\n\n raise GenericError(\"You must be on branch %s to release, you are on %s\" % (expected_branch, branch))","def has_tag(self, tag):\n return tag in self.tags","def has_tag(self, tag):\n return tag in self.tags","def check_image_local(self, tag):\n tags = self.get_tags()\n return (tag in tags)","def search(self, word: str) -> bool:\n current = self.root\n for letter in word:\n if letter not in current.children:\n return False\n current = current.children[letter]\n return current.has_end","def have_dead_end(candidates, new_elem):\n return new_elem.suffix not in map(lambda x: x.prefix, candidates)"],"string":"[\n \"def valid_branches(branches):\\n\\n # for every branch in the list\\n for branch in branches:\\n\\n # make sure it is either reachable or has the special tag \\\"start\\\"\\n if branches[branch].name != \\\"start\\\" and not is_nametag_reachable(branches[branch].name, branches):\\n return False\\n\\n # make sure all ending tags refer to existing branches\\n for nametag in branches[branch].next_nametags:\\n if not branch_exists(nametag, branches):\\n return False\\n\\n return True\",\n \"def branch_exists(nametag, branches):\\n for branch in branches:\\n if branches[branch].name == nametag:\\n return True\\n return False\",\n \"def _is_branch(self, reference_name):\\n return reference_name.startswith(\\\"refs/heads/\\\") or reference_name.startswith(\\n \\\"refs/remotes/\\\"\\n )\",\n \"def is_branch(wit_path, branch):\\n\\n branches = _get_references_data(wit_path)\\n del branches['HEAD']\\n return branch in branches.keys()\",\n \"def branch(name, wit_path):\\n\\n if name != 'None':\\n\\n if len(name) < 30:\\n head = _get_head(wit_path)\\n _add_branch(wit_path, name, head)\\n else:\\n logging.error(f'branch name is too long \\\"{name}\\\" (max 30 digits).')\\n else:\\n logging.error(f'branch name is not valid {name}.')\",\n \"def branch_exists(branch_name, local_only=False, directory=None):\\n for branch in get_branches(local_only, directory):\\n if branch.startswith('remotes/'):\\n branch = branch.split('/')\\n if len(branch) > 2:\\n branch = '/'.join(branch[2:])\\n if branch_name == branch:\\n return True\\n else:\\n if branch_name == branch:\\n return True\\n return False\",\n \"def has_branch(self, branch):\\n if self.branch == branch:\\n return True\\n return False\",\n \"def match(self, name, tags):\\n return name.lower() in tags\",\n \"def is_tagged(self,tag_name,element):\\n return (tag_name in self.tag2elements.keys()) and (element in self.tag2elements[tag_name])\",\n \"def check_component(comp_name: str, comp: defs.Component) -> None:\\n if not RE_COMP_NAME.match(comp_name):\\n res.append(f\\\"Invalid component name: {comp_name}\\\")\\n\\n for branch_name, branch in sorted(comp.branches.items()):\\n check_branch(comp_name, branch_name, branch)\",\n \"def is_subtag(tag_name, subtag_name, user_path, current_user) -> bool:\\n user = current_user[0]\\n subtag_list = os.listdir((user_path + '\\\\\\\\' + user + '\\\\\\\\' + tag_name).encode('unicode_escape'))\\n temp = list(map(bytes.decode, subtag_list))\\n\\n if subtag_name + '.txt' in temp:\\n return True\\n else:\\n return False\",\n \"def _is_desired_tag(self, tag):\\n if self._tags is None:\\n return True\\n\\n if self._ignore_namespace:\\n for desired_tag in self._tags:\\n if tag.localname == desired_tag.localname:\\n return True\\n else:\\n for desired_tag in self._tags:\\n if tag == desired_tag:\\n return True\\n\\n return False\",\n \"def has_name(self, name: str) -> bool:\\n return name in self.child_tags\",\n \"def is_tag(tag_name, user_path, current_user) -> bool:\\n user = current_user[0]\\n tag_list = os.listdir((user_path + '\\\\\\\\' + user).encode('unicode_escape'))\\n temp = list(map(bytes.decode, tag_list))\\n if tag_name in temp:\\n return True\\n else:\\n return False\",\n \"def verify_tag(tag):\\n command = [\\\"git\\\", \\\"tag\\\", \\\"--points-at\\\"]\\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\\n tag_str = proc.stdout.readline()\\n return tag_str.decode(\\\"utf-8\\\").rstrip() == tag\",\n \"def test_bookmark_tag_complete(self):\\r\\n self._get_good_request(second_bmark=True)\\r\\n\\r\\n res = self.testapp.get(\\r\\n '/api/v1/admin/tags/complete',\\r\\n params={\\r\\n 'tag': 'py',\\r\\n 'api_key': API_KEY},\\r\\n status=200)\\r\\n\\r\\n self.assertTrue(\\r\\n 'python' in res.body,\\r\\n \\\"Should have python as a tag completion: \\\" + res.body)\\r\\n\\r\\n # we shouldn't get python as an option if we supply bookmarks as the\\r\\n # current tag. No bookmarks have both bookmarks & python as tags\\r\\n res = self.testapp.get(\\r\\n '/api/v1/admin/tags/complete',\\r\\n params={\\r\\n 'tag': u'py',\\r\\n 'current': u'bookmarks',\\r\\n 'api_key': API_KEY\\r\\n },\\r\\n status=200)\\r\\n\\r\\n self.assertTrue(\\r\\n 'python' not in res.body,\\r\\n \\\"Should not have python as a tag completion: \\\" + res.body)\\r\\n self._check_cors_headers(res)\",\n \"def is_valid_git_refname(refname):\\r\\n if len(refname) == 0:\\r\\n return False\\r\\n\\r\\n # git imposes a few requirements to accept a string as a\\r\\n # refname/branch-name\\r\\n\\r\\n # They can include slash / for hierarchical (directory) grouping, but no\\r\\n # slash-separated component can begin with a dot . or end with the sequence\\r\\n # .lock\\r\\n if (len([True for element in refname.split('/')\\r\\n if element.startswith('.') or element.endswith('.lock')]) != 0):\\r\\n return False\\r\\n\\r\\n # They cannot have two consecutive dots .. anywhere\\r\\n if '..' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot have ASCII control characters (i.e. bytes whose values are\\r\\n # lower than \\\\040, or \\\\177 DEL), space, tilde, caret ^, or colon : anywhere\\r\\n if len([True for refname_char in refname if ord(refname_char) < 40 or\\r\\n ord(refname_char) == 177]) != 0:\\r\\n return False\\r\\n if ' ' in refname or '~' in refname or '^' in refname or ':' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot have question-mark ?, asterisk *, or open bracket [ anywhere\\r\\n if '?' in refname or '*' in refname or '[' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot begin or end with a slash / or contain multiple consecutive\\r\\n # slashes\\r\\n if refname.startswith('/') or refname.endswith('/') or '//' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot end with a dot ..\\r\\n if refname.endswith('.'):\\r\\n return False\\r\\n\\r\\n # They cannot contain a sequence @{\\r\\n if '@{' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot contain a \\\\\\r\\n if '\\\\\\\\' in refname:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def branch_exists(branch):\\n\\n try:\\n git('show-ref', branch)\\n return True\\n except subprocess.CalledProcessError:\\n return False\",\n \"def is_remote_reserve_branch_present(repo):\\n reserve_name = phlgitu_ref.Name(_RESERVE_BRANCH_FQ_NAME)\\n remote_ref_names = repo(\\\"ls-remote\\\").split()[1::2]\\n return reserve_name.fq in remote_ref_names\",\n \"def match(self, name, tags):\\n name, tags = self.get_compiled(name, tags)\\n \\n def index_of_letter(l):\\n return ord(l) - ord('a')\\n \\n true_val, false_val = name\\n \\n if true_val:\\n return index_of_letter(true_val) in tags\\n else:\\n return index_of_letter(false_val) not in tags\",\n \"def bonenamematch(name1, name2):\\n if name1 == name2:\\n return True\\n if name1.startswith(\\\"Bip01 L \\\"):\\n name1 = \\\"Bip01 \\\" + name1[8:] + \\\".L\\\"\\n elif name1.startswith(\\\"Bip01 R \\\"):\\n name1 = \\\"Bip01 \\\" + name1[8:] + \\\".R\\\"\\n if name2.startswith(\\\"Bip01 L \\\"):\\n name2 = \\\"Bip01 \\\" + name2[8:] + \\\".L\\\"\\n elif name2.startswith(\\\"Bip01 R \\\"):\\n name2 = \\\"Bip01 \\\" + name2[8:] + \\\".R\\\"\\n if name1 == name2:\\n return True\\n return False\",\n \"def match(self, name, tags):\\n S, tags = self.get_compiled(name, tags)\\n return bool(S & tags)\",\n \"def is_linguistic_tag(tag):\\r\\n if tag.startswith(\\\"&\\\"):\\r\\n return True\\r\\n if any(x in tag for x in [\\\"<abbr>\\\", \\\"<abbr \\\", \\\"</abbr>\\\"]):\\r\\n return True\\r\\n return False\",\n \"def git_checkout_branch(name):\\n\\n if subprocess.call([\\\"git\\\", \\\"diff\\\", \\\"--quiet\\\", \\\"HEAD\\\"]) != 0:\\n raise Exception(\\\"Dirty working tree; not checking out %s\\\" % name)\\n\\n if subprocess.call([\\\"git\\\", \\\"checkout\\\", name]) != 0:\\n raise Exception(\\\"Could not checkout %s\\\" % name)\",\n \"def local_branch_exists(self, branch):\\n return branch in self.repo.branches\",\n \"def is_complete(self, A, B):\\n return all(self.is_edge(v, w) for v in A for w in B)\",\n \"def _check_branch(opt, params):\\n\\n # Check the current branch and hash\\n _get_branch(opt)\\n\\n if params.git_branch != opt.git_branch or params.git_hash != opt.git_hash:\\n msg = 'You are not on the right branch or commit. Please run the following in the repository: \\\\n'\\n msg += f'git checkout {params.git_branch}\\\\n'\\n msg += f'git revert {params.git_hash}'\\n sys.exit(msg)\",\n \"def mac_pool_exists(handle, name, assignment_order=None,\\r\\n r_from=None, to=None, descr=None, parent_dn=\\\"org-root\\\"):\\r\\n dn = parent_dn + '/mac-pool-' + name\\r\\n mo = handle.query_dn(dn)\\r\\n if mo:\\r\\n if ((assignment_order and mo.assignment_order != assignment_order) and\\r\\n (r_from and mo.r_from != r_from) and\\r\\n (to and mo.to != to) and\\r\\n (descr and mo.descr != descr)):\\r\\n return False\\r\\n return True\\r\\n return False\",\n \"def check_branch(\\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\\n ) -> None:\\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\\n\\n if not RE_BRANCH_NAME.match(branch_name):\\n res.append(f\\\"{comp_name}: Invalid branch name: {branch_name}\\\")\\n\\n for ver, version in sorted(branch.items()):\\n if not RE_VERSION_STRING.match(ver):\\n res.append(f\\\"{comp_name}/{branch_name}: Invalid version string: {ver}\\\")\\n\\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\\n if version.outdated:\\n update_to = [\\n o_version\\n for o_version in branch.values()\\n if not o_version.outdated\\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\\n == other_cksums\\n ]\\n if len(update_to) != 1:\\n res.append(\\n f\\\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \\\"\\n f\\\"versions to update to instead of exactly one\\\"\\n )\\n else:\\n bad_files = sorted(\\n relpath\\n for relpath, (path, fdata) in driver_cksums.items()\\n if util.file_sha256sum(path) != fdata.sha256\\n )\\n if bad_files:\\n res.append(f\\\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\\\")\\n\\n if not uptodate_files:\\n uptodate_files = driver_cksums\\n elif uptodate_files != driver_cksums:\\n res.append(\\n f\\\"{comp_name}/{branch_name}: All the up-to-date versions should \\\"\\n f\\\"define the same set of files with the same checksums\\\"\\n )\\n\\n if not any(not version.outdated for version in branch.values()):\\n res.append(f\\\"{comp_name}/{branch_name}: No non-outdated versions\\\")\",\n \"def check_name(self, name: str):\\n if name[0] == \\\"/\\\" or self.check_end_streaming(name):\\n return True\\n else:\\n return False\",\n \"def reachable_without(start: Node, end: Node, without: Node) -> bool:\\n reachable: Set[Node] = set()\\n stack: List[Node] = [start]\\n\\n while stack:\\n node = stack.pop()\\n if node == without or node in reachable:\\n continue\\n reachable.add(node)\\n stack.extend(node.children())\\n\\n return end in reachable\",\n \"def is_neighbor(self, name_from, name_to):\\n return name_to in self._main_dictionary[name_from]\",\n \"def verify_tags(git_ref_target):\\n latest_release = github_util.get_latest_release().get('name')\\n latest_commit = run('git rev-list -n 1 {}'.format(latest_release)).stdout.rstrip(\\\"\\\\r\\\\n\\\")\\n if not branch_check(latest_release, git_ref_target):\\n print('Your branch does not contain the latest production code. \\\\n\\\\\\n Please recreate it by branching off of release {}.'.format(latest_release))\\n exit(1)\\n else:\\n print(\\\"Branch contains the latest production tag\\\")\\n fork_point = run('git merge-base remotes/origin/master remotes/origin/{}'.format(git_ref_target))\\n commits_since_fork = run('git rev-list --branches={} {}^..HEAD'.format(git_ref_target,\\n fork_point.stdout.rstrip(\\\"\\\\r\\\\n\\\")))\\n if latest_commit not in commits_since_fork.stdout:\\n print('Your branch did not fork directly from the last production tag. \\\\n\\\\\\n Please recreate it by branching off of release {}.'.format(latest_release))\\n exit(1)\\n else:\\n print('Latest production tag is between the fork point and HEAD')\",\n \"def branch_exists(repo, branch, remote=False):\\n ref = 'refs/remotes/origin/' + branch if remote else 'refs/heads/' + branch\\n return subprocess.call(['git', 'show-ref', '-q', '--verify', ref],\\n cwd=repo) == 0\",\n \"def __check_neighbour(self, address: Address) -> bool:\\n is_from_children = False\\n for child_address in self.children_addresses:\\n is_from_children = is_from_children or (child_address == address)\\n is_from_parent = (address == self.parent_address)\\n return is_from_parent or is_from_children\",\n \"def label_intersects_tags(label, tags):\\n for tag in tags:\\n if tag in label:\\n return True\\n return False\",\n \"def ensure_tracking_branches(args):\\n man = load_manifest()\\n for (name, project) in man.projects.iteritems():\\n repo = GitRepo(workdir_for_project(project))\\n branch_missing = repo.command(\\n [\\\"rev-parse\\\", \\\"--verify\\\", \\\"-q\\\", project.refspec],\\n capture_stdout=True)\\n \\n if branch_missing:\\n logging.warn(\\\"Branch %s does not exist in project %s. checking out.\\\" %\\n (project.refspec, name))\\n repo.command([\\\"branch\\\", \\\"--track\\\",\\n project.tracking_branch, project.remote_refspec])\",\n \"def validate_branch_ops(conn_graph: ConnectedGraph):\\n\\n def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\\n \\\"\\\"\\\"\\n Look inside conn_graph ops and products for branch ops, and validate connections to parent and child ops\\n \\\"\\\"\\\"\\n\\n op = conn_graph.get_all_ops()[op_info.module_name]\\n return_bool = True\\n product = op.output\\n if \\\"branch\\\" not in product.name:\\n logger.error(\\\"branch not in product name\\\")\\n return_bool = False\\n if len(product.consumers) > 1:\\n logger.error(\\\"branch op is not parent op's only consumer\\\")\\n return_bool = False\\n branch_op = product.consumers[0]\\n if branch_op.type != \\\"branch\\\":\\n logger.error(\\\"parent op's child op is not of type branch\\\")\\n return_bool = False\\n branch_product = branch_op.output\\n if \\\"multiple_ops\\\" not in branch_product.name:\\n logger.error(\\\"multiple_ops not in branch op's product's name\\\")\\n return_bool = False\\n if len(branch_product.consumers) <= 1:\\n logger.error(\\\"branch op's product has one or fewer consumers\\\")\\n return_bool = False\\n for consumer in branch_product.consumers:\\n for input_product in consumer.inputs:\\n if input_product.producer == op:\\n logger.error(\\\"parent op is still one of child op's inputs (as opposed to branch op)\\\")\\n return_bool = False\\n return return_bool\\n\\n # pylint: disable=protected-access\\n module_identifier = StructureModuleIdentifier(conn_graph.graph, conn_graph._starting_op_names,\\n conn_graph._valid_ops)\\n num_branches_found = 0\\n for tf_op in conn_graph.graph.get_operations():\\n # Ignore ops which were not found in the initial depth first search\\n if tf_op not in module_identifier.processed_ops:\\n continue\\n\\n found_branch = False\\n for output_tensor in tf_op.outputs:\\n if len(output_tensor.consumers()) > 1:\\n # Potential branch op. Check if children go to separate modules\\n child_module_set = set()\\n for consumer_op in output_tensor.consumers():\\n if consumer_op in module_identifier._valid_ops:\\n child_module_info = module_identifier.get_op_info(consumer_op)\\n child_module_set.add(child_module_info.module_name)\\n\\n # If children go to separate modules, this should be a branch op\\n if len(child_module_set) > 1:\\n found_branch = True\\n break\\n\\n if found_branch:\\n num_branches_found += 1\\n tf_op_info = module_identifier.get_op_info(tf_op)\\n if not check_for_branch_op(tf_op_info):\\n return False\\n\\n logger.info(\\\"Found %s branches\\\", num_branches_found)\\n return True\",\n \"def _is_last_commit_already_tagged(\\n repo: git.Repo,\\n last_tag: Optional[git.refs.tag.TagReference],\\n branch_name: str) -> bool:\\n if last_tag is None:\\n return False\\n commit = list(repo.iter_commits(rev=branch_name))[0]\\n return last_tag.commit == commit\",\n \"def remote_branch_exists(self, branch):\\n try:\\n self.git.show_ref(\\\"refs/remotes/{}/{}\\\".format(self.base_branch_remote(), branch))\\n return True\\n except git.exc.GitCommandError:\\n return False\",\n \"def _check_overlap(self, fe_commit):\\n # +++ Avoid O(b branches * r rev) checks when\\n # overlap is impossible because current branch\\n # overlaps no other branch.\\n if self._current_branch not in self._overlapping_branch_list():\\n return\\n\\n for fe_file in fe_commit['files']:\\n gwt_path = fe_file['path']\\n depot_path = self.ctx.gwt_to_depot_path(gwt_path)\\n\\n for branch in self._overlapping_branch_list():\\n if branch == self._current_branch:\\n continue\\n if not branch.intersects_depot_path(depot_path):\\n continue\\n\\n LOG.debug(\\\"_check_overlap() branch {br1} <> {br2}\\\"\\n \\\" gwt={gwt:<40} {dp}\\\\n{view}\\\"\\n .format(\\n br1 = p4gf_util.abbrev(self._current_branch.branch_id)\\n , br2 = p4gf_util.abbrev(branch.branch_id)\\n , gwt = gwt_path\\n , dp = depot_path\\n , view = \\\"\\\\n\\\".join(branch.view_p4map.as_array())\\n ))\\n\\n if self._current_branch.is_new_fp_from_push or branch.is_new_fp_from_push:\\n current_branch_name = self._current_branch.git_branch_name\\n if self._current_branch.is_new_fp_from_push:\\n current_branch_name += '(new)'\\n other_branch_name = branch.git_branch_name\\n if branch.is_new_fp_from_push:\\n other_branch_name += '(new)'\\n human_msg = (_(\\n \\\"Perforce: Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\\\\n\\\"\\n \\\" You are attempting to push and create a new fully populated branch\\\\n\\\"\\n \\\" with paths which overlap another branch. Contact your admin\\\\n\\\"\\n \\\" to configure non-conflicting destination branch paths.\\\\n\\\"\\n \\\" Branches: '{b1}', '{b2}'\\\")\\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\\n , gwt_path = gwt_path\\n , depot_path = depot_path\\n , b1 = current_branch_name\\n , b2 = other_branch_name ))\\n else:\\n human_msg = (_(\\n \\\"Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\\\"\\n \\\" Paths that overlap multiple Git Fusion branches are read-only.\\\"\\n \\\" Branches: '{b1}', '{b2}'\\\")\\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\\n , gwt_path = gwt_path\\n , depot_path = depot_path\\n , b1 = self._current_branch.branch_id\\n , b2 = branch.branch_id ))\\n raise PreflightException(human_msg)\",\n \"def match(self, name, tags):\\n or_exprs, tags = self.get_compiled(name, tags)\\n \\n # or_exprs = [{'a'}, {'c'}, {'d', 'a'}, {'d', 'e'}]\\n return any(and_expr <= tags for and_expr in or_exprs)\",\n \"def are_connected(self, name1, name2):\",\n \"def dstflow(self):\\n if self.name in conditional_branch + unconditional_branch:\\n return True\\n return False\",\n \"def check_photo_tag(self, tag_name):\\n data = self.db.make_query(\\n '''select * from photo_tag where tag_name = \\\"{}\\\" '''\\n .format(tag_name))\\n\\n if len(data) > 0:\\n return True\\n return False\",\n \"def has_node(self, name):\\n return self.source_net.has_node(name)\",\n \"def check_tags(l):\\n # start on outside working inwords, checking first and last tags match\\n stack = []\\n for item in l:\\n if item[-2] == '/' or item[1] == '!':\\n continue # deal with self-closing tags and comments\\n elif item[ 1] == '/':\\n root = item[2 : -1].split()[0]\\n try:\\n match = stack.pop()\\n except: return False # closing tag without an opener\\n if root != match:\\n return False\\n else:\\n root = item[1 : -1].split()[0]\\n stack.append(root) \\n return True\",\n \"def _valid_ref_type(ref):\\n\\n git_branch = \\\"refs/heads/\\\"\\n git_tag = \\\"refs/tags/\\\"\\n if ref.startswith(git_branch) or ref.startswith(git_tag) or len(ref) == 40:\\n return True\\n return False\",\n \"def looks_like_fallback(url_name):\\r\\n return (url_name is not None\\r\\n and url_name.startswith(tag)\\r\\n and re.search('[0-9a-fA-F]{12}$', url_name))\",\n \"def check_inner_tag_availability(**kwargs):\\n\\n inner = kwargs.pop('inner')\\n tag = kwargs.pop('tag')\\n used_inners = [item.inner for item in [\\n *MLPAv4.objects.filter(tag=tag),\\n *MLPAv6.objects.filter(tag=tag),\\n *BilateralPeer.objects.filter(tag=tag),\\n *Monitorv4.objects.filter(tag=tag)]]\\n check_inner = inner in used_inners\\n if check_inner:\\n return False\\n else:\\n return True\",\n \"def tagJudge(word, tag, lastNameDict):\\n orgTags = ['ns', 'nz', 'nt', 'nl', 'ENG-ORG', 'CONF']\\n pplTags = ['nr', 'ENG-PEO']\\n undfTags = ['nrt', 'ng', 'nrfg']\\n if tag in pplTags:\\n return 1\\n elif tag in orgTags:\\n return 0\\n else:\\n if isPeople(word, tag, lastNameDict):\\n return 1\\n return 2\",\n \"def is_boundary_edge(a, b, bdy_edges):\\n for edge in bdy_edges:\\n a0, b0 = edge\\n if a == a0 and b == b0:\\n return True\\n return False\",\n \"def check_for_tags(self, data_in):\\n # possible header tags\\n tags = ['NODE:', 'PORT:', 'STARTOFFSET:', 'ENDOFFSET:']\\n\\n # check for tags\\n for tag in tags:\\n if data_in.find(tag) != -1:\\n print \\\"Found tag %s in data file\\\" % tag\\n return False\\n\\n return True\",\n \"async def exists(self, tag_name):\\n try:\\n if await self.get_id(tag_name):\\n return True\\n except RtbDoesntExists:\\n return False\",\n \"def _is_current_branch(self, branch_name, current_branch_name):\\n return branch_name == current_branch_name\",\n \"def _ancestors_contains_blacklisted_tag(xpath_string, blacklisted_tags):\\n xpath = xpath_string.split(\\\"/\\\")\\n for tag in blacklisted_tags:\\n if tag in xpath:\\n return True\\n return False\",\n \"def isNameUsed(self, name: unicode, startId: long, stopId: long) -> bool:\\n ...\",\n \"def is_valid_tag(self, tag):\\n\\n if tag.has_attr('href') and len(tag['href']) > 0:\\n href = tag['href']\\n complete_href = self.session.complete_url(href)\\n\\n is_relative = self.url in complete_href\\n is_visited = complete_href in self.visited_paths\\n is_style_sheet = tag.name == \\\"link\\\"\\n is_jumpTo = \\\"#\\\" in href\\n is_mailTo = \\\"mailto\\\" in href\\n is_js = \\\"javascript:\\\" in href\\n return is_relative and \\\\\\n not (is_visited or is_style_sheet or is_jumpTo or is_mailTo or is_js)\\n else:\\n return False\",\n \"def check(self,word):\\n if self.pre:\\n def sub_word(chars):\\n if re.match('^'+chars+'.*',word):\\n return word[len(chars):]\\n else:\\n return None\\n else:\\n def sub_word(chars):\\n if re.match('^.*'+chars+'$',word):\\n return word[:-len(chars)]\\n else:\\n return None\\n\\n if word == '':\\n return self\\n for chars in self.branches.keys():\\n res = sub_word(chars)\\n if res:\\n return self.branches[chars].check(res)\\n elif res == '':\\n return self.branches[chars]\\n return None\",\n \"def is_valid_compound_name(name: str) -> bool:\\n return n2s.has_smiles(name)\",\n \"def _is_remote_branch(self, branch_reference):\\n return branch_reference.startswith(\\\"refs/remotes/\\\")\",\n \"def test_heads_contains_false(repository: Repository) -> None:\\n assert \\\"branch\\\" not in repository.heads\",\n \"def is_tag_available(self, tag):\\n return tag in self.available_tags\",\n \"def is_node_name_ok(node_name):\\n # 节点名不可包含`/`特殊字符\\n node_name = node_name.strip('/')\\n return node_name.find('/') == -1\",\n \"def acorn_finder(t):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if is_leaf(t):\\n return type(label(t)) == str and 'acorn' in label(t)\\n else:\\n return any([acorn_finder(b) for b in branches(t)])\",\n \"def match(self, name, tags):\\n tree, tags = self.get_compiled(name, tags)\\n \\n def parse(node):\\n if not isinstance(node, CommentMatcherFullBoolean.Node):\\n return node in tags\\n else:\\n if node.op == '|':\\n return any(parse(p) for p in node.params)\\n elif node.op == '&':\\n return all(parse(p) for p in node.params)\\n elif node.op == '!':\\n return not parse(node.params[0])\\n else:\\n raise ValueError\\n \\n return parse(tree)\",\n \"def reach(self, from_symbol, to_symbol):\\n # type: (Type[Nonterminal], Type[Nonterminal]) -> bool\\n return len(self.path_rules(from_symbol, to_symbol)) > 0\",\n \"def splitflow(self):\\n if self.name in conditional_branch:\\n return True\\n return False\",\n \"def an_check(self):\\n\\t\\tfor filles in self.xelt:\\n\\t\\t\\t# parcours rapide des branches niveau 1\\n\\t\\t\\tif search(r'analytic$', filles.tag):\\n\\t\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def check_related(self, from_person_name, to_person_name):\\n from_person = self.find(from_person_name)\\n to_person = self.find(to_person_name)\\n if not from_person:\\n raise ValueError(\\\"person with {} name does not exist.\\\".format(from_person_name))\\n\\n if not to_person:\\n raise ValueError(\\\"person with {} name does not exist.\\\".format(to_person_name))\\n\\n from_person_relatives_names = [person.name for person in self.find_relative(from_person_name)]\\n to_person_relatives_names = [person.name for person in self.find_relative(to_person_name)]\\n related_names = set(from_person_relatives_names).intersection(set(to_person_relatives_names))\\n return len(related_names) > 0, from_person, to_person, related_names\",\n \"def missing_branches(self):\\n upstream_tags = self.upstream_model.tags_from_semver_point(\\\"1.19.0\\\")\\n deb_branches = self.deb_model.base.branches_from_semver_point(\\\"1.19.0\\\")\\n return list(set(upstream_tags) - set(deb_branches))\",\n \"def checkout_tag(tag: str, repo: str = None) -> bool:\\n cmd = ['git', 'checkout', 'tags/'+tag, '--quiet', '--force']\\n result = _run_git(cmd, repo=repo, expect_stderr=True)\\n if not result:\\n return False\\n # actually a good result\\n print(result.stderr.decode(\\\"utf-8\\\"))\\n return True\",\n \"def tag_exists(tag, directory=None):\\n return tag in get_tags(directory)\",\n \"def checkheads(repo, remote, outgoing, remoteheads, newbranch=False, inc=False):\\n # Check for each named branch if we're creating new remote heads.\\n # To be a remote head after push, node must be either:\\n # - unknown locally\\n # - a local outgoing head descended from update\\n # - a remote head that's known locally and not\\n # ancestral to an outgoing head\\n if remoteheads == [nullid]:\\n # remote is empty, nothing to check.\\n return\\n\\n if remote.capable('branchmap'):\\n headssum = _headssummary(repo, remote, outgoing)\\n else:\\n headssum = _oldheadssummary(repo, remoteheads, outgoing, inc)\\n newbranches = [branch for branch, heads in headssum.iteritems()\\n if heads[0] is None]\\n # 1. Check for new branches on the remote.\\n if newbranches and not newbranch: # new branch requires --new-branch\\n branchnames = ', '.join(sorted(newbranches))\\n raise util.Abort(_(\\\"push creates new remote branches: %s!\\\")\\n % branchnames,\\n hint=_(\\\"use 'hg push --new-branch' to create\\\"\\n \\\" new remote branches\\\"))\\n\\n # 2 compute newly pushed bookmarks. We\\n # we don't warned about bookmarked heads.\\n localbookmarks = repo._bookmarks\\n remotebookmarks = remote.listkeys('bookmarks')\\n bookmarkedheads = set()\\n for bm in localbookmarks:\\n rnode = remotebookmarks.get(bm)\\n if rnode and rnode in repo:\\n lctx, rctx = repo[bm], repo[rnode]\\n if bookmarks.validdest(repo, rctx, lctx):\\n bookmarkedheads.add(lctx.node())\\n\\n # 3. Check for new heads.\\n # If there are more heads after the push than before, a suitable\\n # error message, depending on unsynced status, is displayed.\\n error = None\\n unsynced = False\\n allmissing = set(outgoing.missing)\\n allfuturecommon = set(c.node() for c in repo.set('%ld', outgoing.common))\\n allfuturecommon.update(allmissing)\\n for branch, heads in sorted(headssum.iteritems()):\\n if heads[0] is None:\\n # Maybe we should abort if we push more that one head\\n # for new branches ?\\n continue\\n candidate_newhs = set(heads[1])\\n # add unsynced data\\n oldhs = set(heads[0])\\n oldhs.update(heads[2])\\n candidate_newhs.update(heads[2])\\n dhs = None\\n discardedheads = set()\\n if repo.obsstore:\\n # remove future heads which are actually obsolete by another\\n # pushed element:\\n #\\n # XXX as above, There are several cases this case does not handle\\n # XXX properly\\n #\\n # (1) if <nh> is public, it won't be affected by obsolete marker\\n # and a new is created\\n #\\n # (2) if the new heads have ancestors which are not obsolete and\\n # not ancestors of any other heads we will have a new head too.\\n #\\n # This two case will be easy to handle for know changeset but much\\n # more tricky for unsynced changes.\\n newhs = set()\\n for nh in candidate_newhs:\\n if nh in repo and repo[nh].phase() <= phases.public:\\n newhs.add(nh)\\n else:\\n for suc in obsolete.allsuccessors(repo.obsstore, [nh]):\\n if suc != nh and suc in allfuturecommon:\\n discardedheads.add(nh)\\n break\\n else:\\n newhs.add(nh)\\n else:\\n newhs = candidate_newhs\\n if [h for h in heads[2] if h not in discardedheads]:\\n unsynced = True\\n if len(newhs) > len(oldhs):\\n # strip updates to existing remote heads from the new heads list\\n dhs = sorted(newhs - bookmarkedheads - oldhs)\\n if dhs:\\n if error is None:\\n if branch not in ('default', None):\\n error = _(\\\"push creates new remote head %s \\\"\\n \\\"on branch '%s'!\\\") % (short(dhs[0]), branch)\\n else:\\n error = _(\\\"push creates new remote head %s!\\\"\\n ) % short(dhs[0])\\n if heads[2]: # unsynced\\n hint = _(\\\"you should pull and merge or \\\"\\n \\\"use push -f to force\\\")\\n else:\\n hint = _(\\\"did you forget to merge? \\\"\\n \\\"use push -f to force\\\")\\n if branch is not None:\\n repo.ui.note(_(\\\"new remote heads on branch '%s'\\\\n\\\") % branch)\\n for h in dhs:\\n repo.ui.note(_(\\\"new remote head %s\\\\n\\\") % short(h))\\n if error:\\n raise util.Abort(error, hint=hint)\\n\\n # 6. Check for unsynced changes on involved branches.\\n if unsynced:\\n repo.ui.warn(_(\\\"note: unsynced remote changes!\\\\n\\\"))\",\n \"def validate_branch_config(branch_cfg, branch, n):\\n res = False\\n if len(branch) == 4:\\n if \\\"double\\\" in branch_cfg:\\n diff1 = branch[0]-branch[1]\\n diff3 = branch[2]-branch[3]\\n diff2 = branch[1]%n-branch[2]%n\\n diff_adj_clk = [-3, 1]\\n diff_adj_clk_ctr = [-e for e in diff_adj_clk]\\n diff_opp = [2, -2]\\n if \\\"adjacent\\\" and \\\"clockwise\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk:\\n res = True\\n elif \\\"adjacent\\\" and \\\"counter clockwise\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk_ctr:\\n res = True\\n elif \\\"opposite\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_opp:\\n res = True\\n elif \\\"single\\\" in branch_cfg:\\n res = True\\n elif len(branch) == 2:\\n res = True\\n return res\",\n \"def _stack_is_complete(stack_name: str, region: str, profile: str = None) -> bool:\\n logger.debug(f\\\"Checking if stack {stack_name} in region {region} is in completed state\\\")\\n stack_status = _get_stack_status(stack_name, region=region, profile=profile)\\n if not stack_status:\\n logger.debug(f\\\"STACK: {stack_name} has no status. not complete\\\")\\n return False\\n if stack_status[-9:] == \\\"_COMPLETE\\\" or stack_status[-7:] == \\\"_FAILED\\\":\\n logger.debug(f\\\"STACK: {stack_name} status: {stack_status} is complete in region {region}\\\")\\n return True\\n logger.debug(f\\\"STACK: {stack_name} status: {stack_status} is not complete in region {region}\\\")\\n return False\",\n \"def autocomplete(self, prefix):\\n if len(prefix) == 0:\\n \\n __________________________________________\\n \\n if prefix[0] not in self.branches:\\n \\n __________________________________________\\n \\n ______________________________________________\",\n \"def test_is_valid_git_refname(self):\\r\\n # valid branchnames\\r\\n self.assertTrue(is_valid_git_refname('master'))\\r\\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\\r\\n self.assertTrue(is_valid_git_refname('refname/bar'))\\r\\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\\r\\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\\r\\n unichr(40))))\\r\\n self.assertTrue(is_valid_git_refname('master@head'))\\r\\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\\r\\n\\r\\n # case happening with git < 1.6.6\\r\\n self.assertFalse(is_valid_git_refname(\\r\\n '--abbrev-ref\\\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\\r\\n\\r\\n # different invalid refnames, for a description of each group see the\\r\\n # man page of git check-ref-format\\r\\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\\r\\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\\r\\n self.assertFalse(is_valid_git_refname('bar.lock'))\\r\\n self.assertFalse(is_valid_git_refname('.foobar'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('ref..name'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\\r\\n unichr(177))))\\r\\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\\r\\n unichr(39))))\\r\\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\\r\\n self.assertFalse(is_valid_git_refname('refname spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\\r\\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('areyourandy?'))\\r\\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\\r\\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\\r\\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('dotEnding.'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('@{branch'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('contains\\\\\\\\slash'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('$newbranch'))\",\n \"def missing_branches(self):\\n upstream_tags = self.upstream_model.tags_from_semver_point(\\\"0.8.7\\\")\\n deb_branches = self.deb_model.base.branches_from_semver_point(\\\"0.8.7\\\")\\n return list(set(upstream_tags) - set(deb_branches))\",\n \"def __has_conflicting_node_names(self):\\n # check length of sets to determine if overlap exists\\n return len({node.get_name() for node in self.get_nodeset()}) != len(self.get_nodeset())\",\n \"def test_is_valid_git_refname(self):\\n # valid branchnames\\n self.assertTrue(is_valid_git_refname('master'))\\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\\n self.assertTrue(is_valid_git_refname('refname/bar'))\\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\\n unichr(40))))\\n self.assertTrue(is_valid_git_refname('master@head'))\\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\\n\\n # case happening with git < 1.6.6\\n self.assertFalse(is_valid_git_refname(\\n '--abbrev-ref\\\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\\n\\n # different invalid refnames, for a description of each group see the\\n # man page of git check-ref-format\\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\\n self.assertFalse(is_valid_git_refname('bar.lock'))\\n self.assertFalse(is_valid_git_refname('.foobar'))\\n\\n self.assertFalse(is_valid_git_refname('ref..name'))\\n\\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\\n unichr(177))))\\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\\n unichr(39))))\\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\\n self.assertFalse(is_valid_git_refname('refname spam'))\\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\\n\\n self.assertFalse(is_valid_git_refname('areyourandy?'))\\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\\n\\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\\n\\n self.assertFalse(is_valid_git_refname('dotEnding.'))\\n\\n self.assertFalse(is_valid_git_refname('@{branch'))\\n\\n self.assertFalse(is_valid_git_refname('contains\\\\\\\\slash'))\\n\\n self.assertFalse(is_valid_git_refname('$newbranch'))\",\n \"def _contains_in_self_or_parent(self, name: str) -> bool:\\n return name in self\",\n \"def nameIsValid(self, name):\\n self.notify.debug('nameIsValid')\\n if (name in self.usedNames):\\n return OTPLocalizer.ToonAlreadyExists % (name)\\n\\n problem = NameCheck.checkName(name, font=self.nameEntry.getFont())\\n if problem:\\n return problem\\n\\n # name has passed local checks\\n return None\",\n \"def has(self, tag_name: str) -> bool:\\n return hasattr(self, tag_name)\",\n \"def is_final_node_reached(self):\\n if self.actual_node == self.final_node:\\n self.final_node_reached = True\",\n \"def complete(t, d, k):\\n if not branches(t):\\n return d == 0\\n bs = [ complete(b, d-1, k) for b in branches(t) ]\\n return len(branches(t)) == k and all(bs)\",\n \"def check_branch(subcommand, branch):\\n if subcommand != \\\"checkout\\\":\\n return\\n # first make sure actual branch name was given\\n if branch is None:\\n return \\\"Branch name to checkout must be supplied with '-b' option\\\"\\n # next check that the local repo is clean\\n cmd = [\\\"git\\\", \\\"status\\\", \\\"--untracked-files=no\\\", \\\"--porcelain\\\"]\\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, universal_newlines=True)\\n if p.stdout.strip():\\n return \\\"Need to have clean working tree to checkout!\\\\n\\\\n\\\" + p.stdout\\n # next check that the branch name doesn't already exist\\n cmd = [\\\"git\\\", \\\"show-ref\\\", \\\"--verify\\\", \\\"--quiet\\\", \\\"refs/heads/\\\" + branch]\\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False)\\n if not p.returncode:\\n return f\\\"Branch {branch!r} already exists\\\"\",\n \"def bzr_branch(uri, branch):\\n try:\\n repo = bzr_repo(uri)\\n for name, branch_uri in bzr_branches(repo):\\n if name == branch:\\n return name, branch_uri\\n except Exception as e:\\n log.error(e)\\n raise\\n raise Exception('branch %s cannot be found on repository %s' %(branch, uri))\",\n \"def has(self, tag_name: str, category: ty.Optional[str] = None) -> bool:\\n tags = self.__holder.db_tags.filter(lambda t: t.name == tag_name)\\n if category is not None:\\n tags = tags.filter(category=category)\\n\\n return len(tags) >= 1\",\n \"def _all_names_unused(elts, unused_name_nodes):\\n for elt in elts:\\n if isinstance(elt, (ast.List, ast.Tuple)):\\n if not _all_names_unused(elt.elts, unused_name_nodes):\\n return False\\n if elt not in unused_name_nodes:\\n return False\\n return True\",\n \"def isSelfClosingTag(self, name):\\r\\n return self.SELF_CLOSING_TAGS.has_key(name) \\\\\\r\\n or self.instanceSelfClosingTags.has_key(name)\",\n \"def path_touched(*paths, commit_range):\\n return check_output([\\n 'git', 'diff', '--name-only', commit_range, '--', *paths\\n ]).decode('utf-8').strip() != ''\",\n \"def _checksubrepostate(pushop):\\n for n in pushop.outgoing.missing:\\n ctx = pushop.repo[n]\\n\\n if b'.hgsub' in ctx.manifest() and b'.hgsubstate' in ctx.files():\\n for subpath in sorted(ctx.substate):\\n sub = ctx.sub(subpath)\\n sub.verify(onpush=True)\",\n \"def isLeaf(self, node_name):\\n if self.tree.node[node_name]['c'] != '':\\n return True\\n else:\\n return False\",\n \"def verify_branch(path, expected_branch=\\\"master\\\"):\\n\\n sys.stdout.write(\\\" - Verifying your branch is %s:\\\" % expected_branch)\\n branch = run_in_component(path, ['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\\n branch = branch.strip()\\n\\n if branch == expected_branch:\\n print(\\\" OKAY\\\")\\n return\\n\\n print(\\\" FAILED\\\")\\n\\n raise GenericError(\\\"You must be on branch %s to release, you are on %s\\\" % (expected_branch, branch))\",\n \"def has_tag(self, tag):\\n return tag in self.tags\",\n \"def has_tag(self, tag):\\n return tag in self.tags\",\n \"def check_image_local(self, tag):\\n tags = self.get_tags()\\n return (tag in tags)\",\n \"def search(self, word: str) -> bool:\\n current = self.root\\n for letter in word:\\n if letter not in current.children:\\n return False\\n current = current.children[letter]\\n return current.has_end\",\n \"def have_dead_end(candidates, new_elem):\\n return new_elem.suffix not in map(lambda x: x.prefix, candidates)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6780454","0.67798984","0.61064994","0.555332","0.54905283","0.5410261","0.5404235","0.5332533","0.52437496","0.52371407","0.5203334","0.5191445","0.5187049","0.5181029","0.5169189","0.51587147","0.51385343","0.5123286","0.50983757","0.50803226","0.5074915","0.5070328","0.5064637","0.5060143","0.5053163","0.5048199","0.500991","0.5005835","0.4999374","0.4996595","0.49951357","0.49917862","0.49916878","0.49793318","0.49706382","0.49652258","0.4944386","0.49431485","0.49390417","0.49305326","0.49077836","0.48905686","0.4882394","0.4878086","0.4867966","0.48621717","0.48558939","0.48496202","0.4843072","0.4832568","0.4817072","0.48129362","0.48126593","0.48063278","0.4800765","0.47992945","0.4787714","0.47851261","0.4783855","0.4770649","0.47664297","0.4758632","0.47419563","0.47343668","0.47213545","0.4720047","0.47156993","0.47091106","0.47039312","0.47038648","0.4701142","0.46977973","0.46930334","0.46894267","0.46857676","0.46799296","0.46638575","0.4658673","0.46585613","0.46505123","0.46449712","0.4635255","0.4630588","0.462334","0.46160203","0.46102077","0.4605394","0.4592527","0.45923436","0.4589144","0.45880452","0.45838362","0.45822448","0.45804474","0.4571044","0.45693344","0.45693344","0.45662102","0.45650873","0.45632276"],"string":"[\n \"0.6780454\",\n \"0.67798984\",\n \"0.61064994\",\n \"0.555332\",\n \"0.54905283\",\n \"0.5410261\",\n \"0.5404235\",\n \"0.5332533\",\n \"0.52437496\",\n \"0.52371407\",\n \"0.5203334\",\n \"0.5191445\",\n \"0.5187049\",\n \"0.5181029\",\n \"0.5169189\",\n \"0.51587147\",\n \"0.51385343\",\n \"0.5123286\",\n \"0.50983757\",\n \"0.50803226\",\n \"0.5074915\",\n \"0.5070328\",\n \"0.5064637\",\n \"0.5060143\",\n \"0.5053163\",\n \"0.5048199\",\n \"0.500991\",\n \"0.5005835\",\n \"0.4999374\",\n \"0.4996595\",\n \"0.49951357\",\n \"0.49917862\",\n \"0.49916878\",\n \"0.49793318\",\n \"0.49706382\",\n \"0.49652258\",\n \"0.4944386\",\n \"0.49431485\",\n \"0.49390417\",\n \"0.49305326\",\n \"0.49077836\",\n \"0.48905686\",\n \"0.4882394\",\n \"0.4878086\",\n \"0.4867966\",\n \"0.48621717\",\n \"0.48558939\",\n \"0.48496202\",\n \"0.4843072\",\n \"0.4832568\",\n \"0.4817072\",\n \"0.48129362\",\n \"0.48126593\",\n \"0.48063278\",\n \"0.4800765\",\n \"0.47992945\",\n \"0.4787714\",\n \"0.47851261\",\n \"0.4783855\",\n \"0.4770649\",\n \"0.47664297\",\n \"0.4758632\",\n \"0.47419563\",\n \"0.47343668\",\n \"0.47213545\",\n \"0.4720047\",\n \"0.47156993\",\n \"0.47091106\",\n \"0.47039312\",\n \"0.47038648\",\n \"0.4701142\",\n \"0.46977973\",\n \"0.46930334\",\n \"0.46894267\",\n \"0.46857676\",\n \"0.46799296\",\n \"0.46638575\",\n \"0.4658673\",\n \"0.46585613\",\n \"0.46505123\",\n \"0.46449712\",\n \"0.4635255\",\n \"0.4630588\",\n \"0.462334\",\n \"0.46160203\",\n \"0.46102077\",\n \"0.4605394\",\n \"0.4592527\",\n \"0.45923436\",\n \"0.4589144\",\n \"0.45880452\",\n \"0.45838362\",\n \"0.45822448\",\n \"0.45804474\",\n \"0.4571044\",\n \"0.45693344\",\n \"0.45693344\",\n \"0.45662102\",\n \"0.45650873\",\n \"0.45632276\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79997444"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12398,"cells":{"query":{"kind":"string","value":"Checks whether the given nametag is indeed labelling a branch."},"document":{"kind":"string","value":"def branch_exists(nametag, branches):\n for branch in branches:\n if branches[branch].name == nametag:\n return True\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_nametag_reachable(nametag, branches):\n for branch in branches:\n for next_nametag in branches[branch].next_nametags:\n if next_nametag == nametag:\n return True\n return False","def _is_branch(self, reference_name):\n return reference_name.startswith(\"refs/heads/\") or reference_name.startswith(\n \"refs/remotes/\"\n )","def valid_branches(branches):\n\n # for every branch in the list\n for branch in branches:\n\n # make sure it is either reachable or has the special tag \"start\"\n if branches[branch].name != \"start\" and not is_nametag_reachable(branches[branch].name, branches):\n return False\n\n # make sure all ending tags refer to existing branches\n for nametag in branches[branch].next_nametags:\n if not branch_exists(nametag, branches):\n return False\n\n return True","def is_branch(wit_path, branch):\n\n branches = _get_references_data(wit_path)\n del branches['HEAD']\n return branch in branches.keys()","def has_branch(self, branch):\n if self.branch == branch:\n return True\n return False","def _is_label(self) -> bool:\n return self.lines[self.counter].startswith(\"(\") and self.lines[\n self.counter\n ].endswith(\")\")","def _is_label(self, words):\n if words[0] == 'label':\n if len(words) != 2:\n raise SyntaxError(\"File line {}: Invalid number of arguments for C_LABEL command.\".format(self._file_line))\n return True\n else:\n return False","def is_label(self, label: str) -> bool:\n return label in self.is_label_of","def branch_exists(branch):\n\n try:\n git('show-ref', branch)\n return True\n except subprocess.CalledProcessError:\n return False","def branch(name, wit_path):\n\n if name != 'None':\n\n if len(name) < 30:\n head = _get_head(wit_path)\n _add_branch(wit_path, name, head)\n else:\n logging.error(f'branch name is too long \"{name}\" (max 30 digits).')\n else:\n logging.error(f'branch name is not valid {name}.')","def has_name(self, name: str) -> bool:\n return name in self.child_tags","def is_branch(self, inst_type):\n return inst_type in CONDITIONAL_BRANCH_TYPES or \\\n inst_type in UNCONDITIONAL_BRANCH_TYPES","def branch_exists(branch_name, local_only=False, directory=None):\n for branch in get_branches(local_only, directory):\n if branch.startswith('remotes/'):\n branch = branch.split('/')\n if len(branch) > 2:\n branch = '/'.join(branch[2:])\n if branch_name == branch:\n return True\n else:\n if branch_name == branch:\n return True\n return False","def local_branch_exists(self, branch):\n return branch in self.repo.branches","def test_branch_name_get(repository: Repository) -> None:\n branch = repository.branch(repository.head.name)\n assert repository.head.name == branch.name","def isLeaf(self, node_name):\n if self.tree.node[node_name]['c'] != '':\n return True\n else:\n return False","def _is_current_branch(self, branch_name, current_branch_name):\n return branch_name == current_branch_name","def is_valid_label(self, label):\n try:\n self.validate_label(label)\n return True\n except etal.LabelsSchemaError:\n return False","def branch_exists(repo, branch, remote=False):\n ref = 'refs/remotes/origin/' + branch if remote else 'refs/heads/' + branch\n return subprocess.call(['git', 'show-ref', '-q', '--verify', ref],\n cwd=repo) == 0","def master_branch(branch_name):\n\n if branch_name in MASTER_BRANCHES:\n return True\n\n return False","def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\n\n op = conn_graph.get_all_ops()[op_info.module_name]\n return_bool = True\n product = op.output\n if \"branch\" not in product.name:\n logger.error(\"branch not in product name\")\n return_bool = False\n if len(product.consumers) > 1:\n logger.error(\"branch op is not parent op's only consumer\")\n return_bool = False\n branch_op = product.consumers[0]\n if branch_op.type != \"branch\":\n logger.error(\"parent op's child op is not of type branch\")\n return_bool = False\n branch_product = branch_op.output\n if \"multiple_ops\" not in branch_product.name:\n logger.error(\"multiple_ops not in branch op's product's name\")\n return_bool = False\n if len(branch_product.consumers) <= 1:\n logger.error(\"branch op's product has one or fewer consumers\")\n return_bool = False\n for consumer in branch_product.consumers:\n for input_product in consumer.inputs:\n if input_product.producer == op:\n logger.error(\"parent op is still one of child op's inputs (as opposed to branch op)\")\n return_bool = False\n return return_bool","def has_label(self, label):\n return label == self.label","def is_valid_git_refname(refname):\r\n if len(refname) == 0:\r\n return False\r\n\r\n # git imposes a few requirements to accept a string as a\r\n # refname/branch-name\r\n\r\n # They can include slash / for hierarchical (directory) grouping, but no\r\n # slash-separated component can begin with a dot . or end with the sequence\r\n # .lock\r\n if (len([True for element in refname.split('/')\r\n if element.startswith('.') or element.endswith('.lock')]) != 0):\r\n return False\r\n\r\n # They cannot have two consecutive dots .. anywhere\r\n if '..' in refname:\r\n return False\r\n\r\n # They cannot have ASCII control characters (i.e. bytes whose values are\r\n # lower than \\040, or \\177 DEL), space, tilde, caret ^, or colon : anywhere\r\n if len([True for refname_char in refname if ord(refname_char) < 40 or\r\n ord(refname_char) == 177]) != 0:\r\n return False\r\n if ' ' in refname or '~' in refname or '^' in refname or ':' in refname:\r\n return False\r\n\r\n # They cannot have question-mark ?, asterisk *, or open bracket [ anywhere\r\n if '?' in refname or '*' in refname or '[' in refname:\r\n return False\r\n\r\n # They cannot begin or end with a slash / or contain multiple consecutive\r\n # slashes\r\n if refname.startswith('/') or refname.endswith('/') or '//' in refname:\r\n return False\r\n\r\n # They cannot end with a dot ..\r\n if refname.endswith('.'):\r\n return False\r\n\r\n # They cannot contain a sequence @{\r\n if '@{' in refname:\r\n return False\r\n\r\n # They cannot contain a \\\r\n if '\\\\' in refname:\r\n return False\r\n\r\n return True","def verify_tag(tag):\n command = [\"git\", \"tag\", \"--points-at\"]\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\n tag_str = proc.stdout.readline()\n return tag_str.decode(\"utf-8\").rstrip() == tag","def has_label(self, label):\n return label in self.get_labels()","def test_heads_create_new_branch_name(repository: Repository) -> None:\n branch = repository.heads.create(\"branch\", repository.head.commit)\n assert \"branch\" == branch.name","def is_valid_compound_name(name: str) -> bool:\n return n2s.has_smiles(name)","def verify_branch(path, expected_branch=\"master\"):\n\n sys.stdout.write(\" - Verifying your branch is %s:\" % expected_branch)\n branch = run_in_component(path, ['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\n branch = branch.strip()\n\n if branch == expected_branch:\n print(\" OKAY\")\n return\n\n print(\" FAILED\")\n\n raise GenericError(\"You must be on branch %s to release, you are on %s\" % (expected_branch, branch))","def has_label(self, label):\n\t\treturn label in self.labels","def has_label(self, label):\n\t\t\treturn label in self.labels","def test_heads_contains_false(repository: Repository) -> None:\n assert \"branch\" not in repository.heads","def has_label(self, label):\n return label in self.labels","def check_branch(subcommand, branch):\n if subcommand != \"checkout\":\n return\n # first make sure actual branch name was given\n if branch is None:\n return \"Branch name to checkout must be supplied with '-b' option\"\n # next check that the local repo is clean\n cmd = [\"git\", \"status\", \"--untracked-files=no\", \"--porcelain\"]\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, universal_newlines=True)\n if p.stdout.strip():\n return \"Need to have clean working tree to checkout!\\n\\n\" + p.stdout\n # next check that the branch name doesn't already exist\n cmd = [\"git\", \"show-ref\", \"--verify\", \"--quiet\", \"refs/heads/\" + branch]\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False)\n if not p.returncode:\n return f\"Branch {branch!r} already exists\"","def test_branch_name_set(repository: Repository) -> None:\n branch = repository.head\n with pytest.raises(AttributeError):\n branch.name = \"teapot\" # type: ignore[misc]","def _is_desired_tag(self, tag):\n if self._tags is None:\n return True\n\n if self._ignore_namespace:\n for desired_tag in self._tags:\n if tag.localname == desired_tag.localname:\n return True\n else:\n for desired_tag in self._tags:\n if tag == desired_tag:\n return True\n\n return False","def test_is_valid_git_refname(self):\n # valid branchnames\n self.assertTrue(is_valid_git_refname('master'))\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\n self.assertTrue(is_valid_git_refname('refname/bar'))\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\n unichr(40))))\n self.assertTrue(is_valid_git_refname('master@head'))\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\n\n # case happening with git < 1.6.6\n self.assertFalse(is_valid_git_refname(\n '--abbrev-ref\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\n\n # different invalid refnames, for a description of each group see the\n # man page of git check-ref-format\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\n self.assertFalse(is_valid_git_refname('bar.lock'))\n self.assertFalse(is_valid_git_refname('.foobar'))\n\n self.assertFalse(is_valid_git_refname('ref..name'))\n\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\n unichr(177))))\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\n unichr(39))))\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\n self.assertFalse(is_valid_git_refname('refname spam'))\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\n\n self.assertFalse(is_valid_git_refname('areyourandy?'))\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\n\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\n\n self.assertFalse(is_valid_git_refname('dotEnding.'))\n\n self.assertFalse(is_valid_git_refname('@{branch'))\n\n self.assertFalse(is_valid_git_refname('contains\\\\slash'))\n\n self.assertFalse(is_valid_git_refname('$newbranch'))","def _check_for_labels(self):\n check = True\n if 'labels' not in self.mapper:\n check = False\n return check","def is_linguistic_tag(tag):\r\n if tag.startswith(\"&\"):\r\n return True\r\n if any(x in tag for x in [\"<abbr>\", \"<abbr \", \"</abbr>\"]):\r\n return True\r\n return False","def test_is_valid_git_refname(self):\r\n # valid branchnames\r\n self.assertTrue(is_valid_git_refname('master'))\r\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\r\n self.assertTrue(is_valid_git_refname('refname/bar'))\r\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\r\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\r\n unichr(40))))\r\n self.assertTrue(is_valid_git_refname('master@head'))\r\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\r\n\r\n # case happening with git < 1.6.6\r\n self.assertFalse(is_valid_git_refname(\r\n '--abbrev-ref\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\r\n\r\n # different invalid refnames, for a description of each group see the\r\n # man page of git check-ref-format\r\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\r\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\r\n self.assertFalse(is_valid_git_refname('bar.lock'))\r\n self.assertFalse(is_valid_git_refname('.foobar'))\r\n\r\n self.assertFalse(is_valid_git_refname('ref..name'))\r\n\r\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\r\n unichr(177))))\r\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\r\n unichr(39))))\r\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\r\n self.assertFalse(is_valid_git_refname('refname spam'))\r\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\r\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\r\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\r\n\r\n self.assertFalse(is_valid_git_refname('areyourandy?'))\r\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\r\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\r\n\r\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\r\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\r\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\r\n\r\n self.assertFalse(is_valid_git_refname('dotEnding.'))\r\n\r\n self.assertFalse(is_valid_git_refname('@{branch'))\r\n\r\n self.assertFalse(is_valid_git_refname('contains\\\\slash'))\r\n\r\n self.assertFalse(is_valid_git_refname('$newbranch'))","def _valid_ref_type(ref):\n\n git_branch = \"refs/heads/\"\n git_tag = \"refs/tags/\"\n if ref.startswith(git_branch) or ref.startswith(git_tag) or len(ref) == 40:\n return True\n return False","def check_branch_in_manifest(manifest_filename, meta):\n print \"Checking manifest {}\".format(manifest_filename)\n manifest_et = ET.parse(os.path.join(\"./manifest\", manifest_filename))\n project_et = manifest_et.find(\"./project[@name='{}']\".format(PROJECT))\n if project_et is None:\n print \"project {} not found\".format(PROJECT)\n return False\n\n # Compute the default branch for the manifest\n default_branch = \"master\"\n default_et = manifest_et.find(\"./default\")\n if default_et is not None:\n default_branch = default_et.get(\"branch\", \"master\")\n\n # Pull out the branch for the given project\n project_branch = project_et.get(\"revision\", default_branch)\n if project_branch != BRANCH:\n print \"project {} on branch {}, not {}\".format(PROJECT, project_branch, BRANCH)\n return False\n\n return True","def match(self, name, tags):\n return name.lower() in tags","def has_label(self):\n return self.label is not None","def is_branch_node(tree, node):\n if node == tree.root:\n return tree.degree(node) >= 2\n else:\n return tree.degree(node) >= 3","def git_checkout_branch(name):\n\n if subprocess.call([\"git\", \"diff\", \"--quiet\", \"HEAD\"]) != 0:\n raise Exception(\"Dirty working tree; not checking out %s\" % name)\n\n if subprocess.call([\"git\", \"checkout\", name]) != 0:\n raise Exception(\"Could not checkout %s\" % name)","def isLabel(self, text):\n if text is None or isinstance(text, GLabel):\n return True\n return False","def is_release_branch():\n diff_string_config_yml = run_command(\"git diff origin/master .circleci/config.yml\")\n if re.search(r'[+-][ ]+CONTENT_VERSION: \".*', diff_string_config_yml):\n return True\n\n return False","def is_leaf(tree):\n return not branches(tree)","def validateLabel(cls, label: str, labeling_version: int) -> bool:\r\n\r\n return len(label.split('.')) in [2, 3]","def _check_branch(opt, params):\n\n # Check the current branch and hash\n _get_branch(opt)\n\n if params.git_branch != opt.git_branch or params.git_hash != opt.git_hash:\n msg = 'You are not on the right branch or commit. Please run the following in the repository: \\n'\n msg += f'git checkout {params.git_branch}\\n'\n msg += f'git revert {params.git_hash}'\n sys.exit(msg)","def is_remote_reserve_branch_present(repo):\n reserve_name = phlgitu_ref.Name(_RESERVE_BRANCH_FQ_NAME)\n remote_ref_names = repo(\"ls-remote\").split()[1::2]\n return reserve_name.fq in remote_ref_names","def remote_branch_exists(self, branch):\n try:\n self.git.show_ref(\"refs/remotes/{}/{}\".format(self.base_branch_remote(), branch))\n return True\n except git.exc.GitCommandError:\n return False","def Search_lable(self, address):\r\n ret = False\r\n for i in self.Labels_names:\r\n if self.Labels_names[i] == address:\r\n return True\r\n return ret","def ensure_tracking_branches(args):\n man = load_manifest()\n for (name, project) in man.projects.iteritems():\n repo = GitRepo(workdir_for_project(project))\n branch_missing = repo.command(\n [\"rev-parse\", \"--verify\", \"-q\", project.refspec],\n capture_stdout=True)\n \n if branch_missing:\n logging.warn(\"Branch %s does not exist in project %s. checking out.\" %\n (project.refspec, name))\n repo.command([\"branch\", \"--track\",\n project.tracking_branch, project.remote_refspec])","def is_leaf(tree):\n\n return not branches(tree)","def _is_stack_cannery_check_bb(self, xbb):\n return self._is_stack_cannery_check_bb_x86(xbb)","def has_name(self):\n return self.unpack_word(0x2) != 0","def is_label_definition(line):\n\n return line.startswith(\"LABEL \")","def verify_tags(git_ref_target):\n latest_release = github_util.get_latest_release().get('name')\n latest_commit = run('git rev-list -n 1 {}'.format(latest_release)).stdout.rstrip(\"\\r\\n\")\n if not branch_check(latest_release, git_ref_target):\n print('Your branch does not contain the latest production code. \\n\\\n Please recreate it by branching off of release {}.'.format(latest_release))\n exit(1)\n else:\n print(\"Branch contains the latest production tag\")\n fork_point = run('git merge-base remotes/origin/master remotes/origin/{}'.format(git_ref_target))\n commits_since_fork = run('git rev-list --branches={} {}^..HEAD'.format(git_ref_target,\n fork_point.stdout.rstrip(\"\\r\\n\")))\n if latest_commit not in commits_since_fork.stdout:\n print('Your branch did not fork directly from the last production tag. \\n\\\n Please recreate it by branching off of release {}.'.format(latest_release))\n exit(1)\n else:\n print('Latest production tag is between the fork point and HEAD')","def test_instantiate_branch_node(self):\n try:\n BranchNode('my_name')\n except Exception:\n message = \"BranchNode instantiation failed\"\n self.fail(message)","def _is_last_commit_already_tagged(\n repo: git.Repo,\n last_tag: Optional[git.refs.tag.TagReference],\n branch_name: str) -> bool:\n if last_tag is None:\n return False\n commit = list(repo.iter_commits(rev=branch_name))[0]\n return last_tag.commit == commit","def git_has_object(project: Project, name: str) -> bool:\n ret = project.git(\"rev-parse\", \"--verify\", name, _ok_code=[0, 128])\n return ret.exit_code == 0","def splitflow(self):\n if self.name in conditional_branch:\n return True\n return False","def branch(self, name: str) -> GitRef:\n _args = [\n Arg(\"name\", name),\n ]\n _ctx = self._select(\"branch\", _args)\n return GitRef(_ctx)","def has_label(self, label_string: str) -> bool:\n if isinstance(self.label, list):\n return label_string in self.label\n else:\n return label_string == self.label","def check_component(comp_name: str, comp: defs.Component) -> None:\n if not RE_COMP_NAME.match(comp_name):\n res.append(f\"Invalid component name: {comp_name}\")\n\n for branch_name, branch in sorted(comp.branches.items()):\n check_branch(comp_name, branch_name, branch)","def _is_binary(name):\n\n return name in unidata.unicode_binary or name in unidata.unicode_alias['binary']","def is_node_name_ok(node_name):\n # 节点名不可包含`/`特殊字符\n node_name = node_name.strip('/')\n return node_name.find('/') == -1","def match(self, name, tags):\n name, tags = self.get_compiled(name, tags)\n \n def index_of_letter(l):\n return ord(l) - ord('a')\n \n true_val, false_val = name\n \n if true_val:\n return index_of_letter(true_val) in tags\n else:\n return index_of_letter(false_val) not in tags","def identify_branch(self, gecko_branch):\n\n # Retrieve the name of the repository\n branch = re.search('.*/([\\S\\.]+$)', gecko_branch).group(1)\n\n # Supported branches: mozilla-aurora, mozilla-beta, mozilla-release, mozilla-esr*\n # All other branches (mozilla-central, mozilla-inbound, birch, elm, oak etc.) should fallback to the 'default' branch\n # This will work with Firefox and Thunderbird\n if not re.match(r'.*/releases/', gecko_branch):\n branch = \"default\"\n\n return branch","def hasname(self, tag: str) -> bool:\n for key in self.formal_names:\n if key in tag.lower():\n return True\n\n # Exit case if key -> value not in mapping \n return False","def is_tag(t):\n return len(t) > 1 and t.startswith('#') and not t.startswith('##') and t","def is_tagged(self,tag_name,element):\n return (tag_name in self.tag2elements.keys()) and (element in self.tag2elements[tag_name])","def branch(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"branch\")","def branch(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"branch\")","def branch(self) -> Optional[str]:\n return pulumi.get(self, \"branch\")","def is_tag(tag_name, user_path, current_user) -> bool:\n user = current_user[0]\n tag_list = os.listdir((user_path + '\\\\' + user).encode('unicode_escape'))\n temp = list(map(bytes.decode, tag_list))\n if tag_name in temp:\n return True\n else:\n return False","def validate_branch_ops(conn_graph: ConnectedGraph):\n\n def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\n \"\"\"\n Look inside conn_graph ops and products for branch ops, and validate connections to parent and child ops\n \"\"\"\n\n op = conn_graph.get_all_ops()[op_info.module_name]\n return_bool = True\n product = op.output\n if \"branch\" not in product.name:\n logger.error(\"branch not in product name\")\n return_bool = False\n if len(product.consumers) > 1:\n logger.error(\"branch op is not parent op's only consumer\")\n return_bool = False\n branch_op = product.consumers[0]\n if branch_op.type != \"branch\":\n logger.error(\"parent op's child op is not of type branch\")\n return_bool = False\n branch_product = branch_op.output\n if \"multiple_ops\" not in branch_product.name:\n logger.error(\"multiple_ops not in branch op's product's name\")\n return_bool = False\n if len(branch_product.consumers) <= 1:\n logger.error(\"branch op's product has one or fewer consumers\")\n return_bool = False\n for consumer in branch_product.consumers:\n for input_product in consumer.inputs:\n if input_product.producer == op:\n logger.error(\"parent op is still one of child op's inputs (as opposed to branch op)\")\n return_bool = False\n return return_bool\n\n # pylint: disable=protected-access\n module_identifier = StructureModuleIdentifier(conn_graph.graph, conn_graph._starting_op_names,\n conn_graph._valid_ops)\n num_branches_found = 0\n for tf_op in conn_graph.graph.get_operations():\n # Ignore ops which were not found in the initial depth first search\n if tf_op not in module_identifier.processed_ops:\n continue\n\n found_branch = False\n for output_tensor in tf_op.outputs:\n if len(output_tensor.consumers()) > 1:\n # Potential branch op. Check if children go to separate modules\n child_module_set = set()\n for consumer_op in output_tensor.consumers():\n if consumer_op in module_identifier._valid_ops:\n child_module_info = module_identifier.get_op_info(consumer_op)\n child_module_set.add(child_module_info.module_name)\n\n # If children go to separate modules, this should be a branch op\n if len(child_module_set) > 1:\n found_branch = True\n break\n\n if found_branch:\n num_branches_found += 1\n tf_op_info = module_identifier.get_op_info(tf_op)\n if not check_for_branch_op(tf_op_info):\n return False\n\n logger.info(\"Found %s branches\", num_branches_found)\n return True","def test_heads_contains_true(repository: Repository) -> None:\n assert repository.head.name in repository.heads","def has_tag(self, tag):\n return tag in self.tags","def has_tag(self, tag):\n return tag in self.tags","def label_intersects_tags(label, tags):\n for tag in tags:\n if tag in label:\n return True\n return False","def _check_bids_label(label):\n if not isinstance(label, str):\n raise TypeError(\n f\"All bids labels must be string. \"\n f\"Got '{type(label)}' for {label} instead.\"\n )\n if not all(char.isalnum() for char in label):\n raise ValueError(\n f\"All bids labels must be alphanumeric. Got '{label}' instead.\"\n )","def is_label_dataframe(label, df):\n\n setdiff = set(label) - set(df.columns.tolist())\n\n if len(setdiff) == 0:\n return True\n else:\n return False","def was_label_added(issue, label):\n if not label:\n return False\n\n # Optimization that does not require pulling in issue's actions.\n if any(label.lower() == l.lower() for l in issue.labels):\n return True\n\n for action in issue.actions:\n for added in action.labels.added:\n if label.lower() == added.lower():\n return True\n\n return False","def validate_branch_config(branch_cfg, branch, n):\n res = False\n if len(branch) == 4:\n if \"double\" in branch_cfg:\n diff1 = branch[0]-branch[1]\n diff3 = branch[2]-branch[3]\n diff2 = branch[1]%n-branch[2]%n\n diff_adj_clk = [-3, 1]\n diff_adj_clk_ctr = [-e for e in diff_adj_clk]\n diff_opp = [2, -2]\n if \"adjacent\" and \"clockwise\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk:\n res = True\n elif \"adjacent\" and \"counter clockwise\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk_ctr:\n res = True\n elif \"opposite\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_opp:\n res = True\n elif \"single\" in branch_cfg:\n res = True\n elif len(branch) == 2:\n res = True\n return res","def git_repo_branch_exists(repo: str, branch: str) -> bool:\n get_git_version()\n cmd = f\"git ls-remote {repo} {branch}\"\n # We might be tempted to use `--exit-code` with `git ls-remote`, but\n # `run_command` handles the `returncode` for us, so we'll rely on\n # the fact that stdout returns '' if the requested branch doesn't exist\n ret = run_command(cmd, capture=True)\n exists = ret.stdout != \"\"\n return exists","def bouton_branches(self,arbor,arbor_labels,bbranches,bouton_geometry=[28,4]):\n\t\t\n\t\tfor branch in arbor.keys():\n\t\t\tif branch in bbranches:\n\t\t\t\tarbor[branch],arbor_labels[branch] = self.bouton_branch(arbor[branch],bouton_geometry)\n\t\t\n\t\treturn(arbor,arbor_labels)","async def exists(self, tag_name):\n try:\n if await self.get_id(tag_name):\n return True\n except RtbDoesntExists:\n return False","def _check_name(self, symbol):\n if symbol.type == self.scanner.NAME:\n return True\n else:\n return False","def has_label(self, phrase_string: str) -> bool:\n return phrase_string in self.has_labels","def test_label():\n label_path = pjoin(data_path, \"label\", \"lh.BA1.label\")\n label = read_label(label_path)\n # XXX : test more\n assert_true(np.all(label > 0))","def has(self, tag_name: str, category: ty.Optional[str] = None) -> bool:\n tags = self.__holder.db_tags.filter(lambda t: t.name == tag_name)\n if category is not None:\n tags = tags.filter(category=category)\n\n return len(tags) >= 1","def verify_labeled(self, d_stmt, table):\n d_label = d_stmt.find_first(\"p_name\")\n if d_label:\n self.label = d_label.value\n table.check_table(d_stmt.linespan, Symbol(self.label, DanaType(\"label\")))","def has_label(self):\n return self.has_udev_property('ID_FS_LABEL_ENC')","def _is_remote_branch(self, branch_reference):\n return branch_reference.startswith(\"refs/remotes/\")","def test_branch_fail(repository: Repository) -> None:\n with pytest.raises(KeyError):\n repository.branch(\"branch\")","def validate_class_label(self, label_uri):\n label = extract_name_from_uri_or_curie(label_uri)\n if not label[0].isupper():\n raise ValueError('Class label {} is incorrect. The first letter of each word should be capitalized!'.format(label))","def isBlockLevel(tag):\r\n if isinstance(tag, basestring):\r\n return BLOCK_LEVEL_ELEMENTS.match(tag)\r\n # Some ElementTree tags are not strings, so return False.\r\n return False","def has(self, tag_name: str) -> bool:\n return hasattr(self, tag_name)"],"string":"[\n \"def is_nametag_reachable(nametag, branches):\\n for branch in branches:\\n for next_nametag in branches[branch].next_nametags:\\n if next_nametag == nametag:\\n return True\\n return False\",\n \"def _is_branch(self, reference_name):\\n return reference_name.startswith(\\\"refs/heads/\\\") or reference_name.startswith(\\n \\\"refs/remotes/\\\"\\n )\",\n \"def valid_branches(branches):\\n\\n # for every branch in the list\\n for branch in branches:\\n\\n # make sure it is either reachable or has the special tag \\\"start\\\"\\n if branches[branch].name != \\\"start\\\" and not is_nametag_reachable(branches[branch].name, branches):\\n return False\\n\\n # make sure all ending tags refer to existing branches\\n for nametag in branches[branch].next_nametags:\\n if not branch_exists(nametag, branches):\\n return False\\n\\n return True\",\n \"def is_branch(wit_path, branch):\\n\\n branches = _get_references_data(wit_path)\\n del branches['HEAD']\\n return branch in branches.keys()\",\n \"def has_branch(self, branch):\\n if self.branch == branch:\\n return True\\n return False\",\n \"def _is_label(self) -> bool:\\n return self.lines[self.counter].startswith(\\\"(\\\") and self.lines[\\n self.counter\\n ].endswith(\\\")\\\")\",\n \"def _is_label(self, words):\\n if words[0] == 'label':\\n if len(words) != 2:\\n raise SyntaxError(\\\"File line {}: Invalid number of arguments for C_LABEL command.\\\".format(self._file_line))\\n return True\\n else:\\n return False\",\n \"def is_label(self, label: str) -> bool:\\n return label in self.is_label_of\",\n \"def branch_exists(branch):\\n\\n try:\\n git('show-ref', branch)\\n return True\\n except subprocess.CalledProcessError:\\n return False\",\n \"def branch(name, wit_path):\\n\\n if name != 'None':\\n\\n if len(name) < 30:\\n head = _get_head(wit_path)\\n _add_branch(wit_path, name, head)\\n else:\\n logging.error(f'branch name is too long \\\"{name}\\\" (max 30 digits).')\\n else:\\n logging.error(f'branch name is not valid {name}.')\",\n \"def has_name(self, name: str) -> bool:\\n return name in self.child_tags\",\n \"def is_branch(self, inst_type):\\n return inst_type in CONDITIONAL_BRANCH_TYPES or \\\\\\n inst_type in UNCONDITIONAL_BRANCH_TYPES\",\n \"def branch_exists(branch_name, local_only=False, directory=None):\\n for branch in get_branches(local_only, directory):\\n if branch.startswith('remotes/'):\\n branch = branch.split('/')\\n if len(branch) > 2:\\n branch = '/'.join(branch[2:])\\n if branch_name == branch:\\n return True\\n else:\\n if branch_name == branch:\\n return True\\n return False\",\n \"def local_branch_exists(self, branch):\\n return branch in self.repo.branches\",\n \"def test_branch_name_get(repository: Repository) -> None:\\n branch = repository.branch(repository.head.name)\\n assert repository.head.name == branch.name\",\n \"def isLeaf(self, node_name):\\n if self.tree.node[node_name]['c'] != '':\\n return True\\n else:\\n return False\",\n \"def _is_current_branch(self, branch_name, current_branch_name):\\n return branch_name == current_branch_name\",\n \"def is_valid_label(self, label):\\n try:\\n self.validate_label(label)\\n return True\\n except etal.LabelsSchemaError:\\n return False\",\n \"def branch_exists(repo, branch, remote=False):\\n ref = 'refs/remotes/origin/' + branch if remote else 'refs/heads/' + branch\\n return subprocess.call(['git', 'show-ref', '-q', '--verify', ref],\\n cwd=repo) == 0\",\n \"def master_branch(branch_name):\\n\\n if branch_name in MASTER_BRANCHES:\\n return True\\n\\n return False\",\n \"def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\\n\\n op = conn_graph.get_all_ops()[op_info.module_name]\\n return_bool = True\\n product = op.output\\n if \\\"branch\\\" not in product.name:\\n logger.error(\\\"branch not in product name\\\")\\n return_bool = False\\n if len(product.consumers) > 1:\\n logger.error(\\\"branch op is not parent op's only consumer\\\")\\n return_bool = False\\n branch_op = product.consumers[0]\\n if branch_op.type != \\\"branch\\\":\\n logger.error(\\\"parent op's child op is not of type branch\\\")\\n return_bool = False\\n branch_product = branch_op.output\\n if \\\"multiple_ops\\\" not in branch_product.name:\\n logger.error(\\\"multiple_ops not in branch op's product's name\\\")\\n return_bool = False\\n if len(branch_product.consumers) <= 1:\\n logger.error(\\\"branch op's product has one or fewer consumers\\\")\\n return_bool = False\\n for consumer in branch_product.consumers:\\n for input_product in consumer.inputs:\\n if input_product.producer == op:\\n logger.error(\\\"parent op is still one of child op's inputs (as opposed to branch op)\\\")\\n return_bool = False\\n return return_bool\",\n \"def has_label(self, label):\\n return label == self.label\",\n \"def is_valid_git_refname(refname):\\r\\n if len(refname) == 0:\\r\\n return False\\r\\n\\r\\n # git imposes a few requirements to accept a string as a\\r\\n # refname/branch-name\\r\\n\\r\\n # They can include slash / for hierarchical (directory) grouping, but no\\r\\n # slash-separated component can begin with a dot . or end with the sequence\\r\\n # .lock\\r\\n if (len([True for element in refname.split('/')\\r\\n if element.startswith('.') or element.endswith('.lock')]) != 0):\\r\\n return False\\r\\n\\r\\n # They cannot have two consecutive dots .. anywhere\\r\\n if '..' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot have ASCII control characters (i.e. bytes whose values are\\r\\n # lower than \\\\040, or \\\\177 DEL), space, tilde, caret ^, or colon : anywhere\\r\\n if len([True for refname_char in refname if ord(refname_char) < 40 or\\r\\n ord(refname_char) == 177]) != 0:\\r\\n return False\\r\\n if ' ' in refname or '~' in refname or '^' in refname or ':' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot have question-mark ?, asterisk *, or open bracket [ anywhere\\r\\n if '?' in refname or '*' in refname or '[' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot begin or end with a slash / or contain multiple consecutive\\r\\n # slashes\\r\\n if refname.startswith('/') or refname.endswith('/') or '//' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot end with a dot ..\\r\\n if refname.endswith('.'):\\r\\n return False\\r\\n\\r\\n # They cannot contain a sequence @{\\r\\n if '@{' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot contain a \\\\\\r\\n if '\\\\\\\\' in refname:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def verify_tag(tag):\\n command = [\\\"git\\\", \\\"tag\\\", \\\"--points-at\\\"]\\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\\n tag_str = proc.stdout.readline()\\n return tag_str.decode(\\\"utf-8\\\").rstrip() == tag\",\n \"def has_label(self, label):\\n return label in self.get_labels()\",\n \"def test_heads_create_new_branch_name(repository: Repository) -> None:\\n branch = repository.heads.create(\\\"branch\\\", repository.head.commit)\\n assert \\\"branch\\\" == branch.name\",\n \"def is_valid_compound_name(name: str) -> bool:\\n return n2s.has_smiles(name)\",\n \"def verify_branch(path, expected_branch=\\\"master\\\"):\\n\\n sys.stdout.write(\\\" - Verifying your branch is %s:\\\" % expected_branch)\\n branch = run_in_component(path, ['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\\n branch = branch.strip()\\n\\n if branch == expected_branch:\\n print(\\\" OKAY\\\")\\n return\\n\\n print(\\\" FAILED\\\")\\n\\n raise GenericError(\\\"You must be on branch %s to release, you are on %s\\\" % (expected_branch, branch))\",\n \"def has_label(self, label):\\n\\t\\treturn label in self.labels\",\n \"def has_label(self, label):\\n\\t\\t\\treturn label in self.labels\",\n \"def test_heads_contains_false(repository: Repository) -> None:\\n assert \\\"branch\\\" not in repository.heads\",\n \"def has_label(self, label):\\n return label in self.labels\",\n \"def check_branch(subcommand, branch):\\n if subcommand != \\\"checkout\\\":\\n return\\n # first make sure actual branch name was given\\n if branch is None:\\n return \\\"Branch name to checkout must be supplied with '-b' option\\\"\\n # next check that the local repo is clean\\n cmd = [\\\"git\\\", \\\"status\\\", \\\"--untracked-files=no\\\", \\\"--porcelain\\\"]\\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, universal_newlines=True)\\n if p.stdout.strip():\\n return \\\"Need to have clean working tree to checkout!\\\\n\\\\n\\\" + p.stdout\\n # next check that the branch name doesn't already exist\\n cmd = [\\\"git\\\", \\\"show-ref\\\", \\\"--verify\\\", \\\"--quiet\\\", \\\"refs/heads/\\\" + branch]\\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False)\\n if not p.returncode:\\n return f\\\"Branch {branch!r} already exists\\\"\",\n \"def test_branch_name_set(repository: Repository) -> None:\\n branch = repository.head\\n with pytest.raises(AttributeError):\\n branch.name = \\\"teapot\\\" # type: ignore[misc]\",\n \"def _is_desired_tag(self, tag):\\n if self._tags is None:\\n return True\\n\\n if self._ignore_namespace:\\n for desired_tag in self._tags:\\n if tag.localname == desired_tag.localname:\\n return True\\n else:\\n for desired_tag in self._tags:\\n if tag == desired_tag:\\n return True\\n\\n return False\",\n \"def test_is_valid_git_refname(self):\\n # valid branchnames\\n self.assertTrue(is_valid_git_refname('master'))\\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\\n self.assertTrue(is_valid_git_refname('refname/bar'))\\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\\n unichr(40))))\\n self.assertTrue(is_valid_git_refname('master@head'))\\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\\n\\n # case happening with git < 1.6.6\\n self.assertFalse(is_valid_git_refname(\\n '--abbrev-ref\\\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\\n\\n # different invalid refnames, for a description of each group see the\\n # man page of git check-ref-format\\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\\n self.assertFalse(is_valid_git_refname('bar.lock'))\\n self.assertFalse(is_valid_git_refname('.foobar'))\\n\\n self.assertFalse(is_valid_git_refname('ref..name'))\\n\\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\\n unichr(177))))\\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\\n unichr(39))))\\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\\n self.assertFalse(is_valid_git_refname('refname spam'))\\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\\n\\n self.assertFalse(is_valid_git_refname('areyourandy?'))\\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\\n\\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\\n\\n self.assertFalse(is_valid_git_refname('dotEnding.'))\\n\\n self.assertFalse(is_valid_git_refname('@{branch'))\\n\\n self.assertFalse(is_valid_git_refname('contains\\\\\\\\slash'))\\n\\n self.assertFalse(is_valid_git_refname('$newbranch'))\",\n \"def _check_for_labels(self):\\n check = True\\n if 'labels' not in self.mapper:\\n check = False\\n return check\",\n \"def is_linguistic_tag(tag):\\r\\n if tag.startswith(\\\"&\\\"):\\r\\n return True\\r\\n if any(x in tag for x in [\\\"<abbr>\\\", \\\"<abbr \\\", \\\"</abbr>\\\"]):\\r\\n return True\\r\\n return False\",\n \"def test_is_valid_git_refname(self):\\r\\n # valid branchnames\\r\\n self.assertTrue(is_valid_git_refname('master'))\\r\\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\\r\\n self.assertTrue(is_valid_git_refname('refname/bar'))\\r\\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\\r\\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\\r\\n unichr(40))))\\r\\n self.assertTrue(is_valid_git_refname('master@head'))\\r\\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\\r\\n\\r\\n # case happening with git < 1.6.6\\r\\n self.assertFalse(is_valid_git_refname(\\r\\n '--abbrev-ref\\\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\\r\\n\\r\\n # different invalid refnames, for a description of each group see the\\r\\n # man page of git check-ref-format\\r\\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\\r\\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\\r\\n self.assertFalse(is_valid_git_refname('bar.lock'))\\r\\n self.assertFalse(is_valid_git_refname('.foobar'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('ref..name'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\\r\\n unichr(177))))\\r\\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\\r\\n unichr(39))))\\r\\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\\r\\n self.assertFalse(is_valid_git_refname('refname spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\\r\\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('areyourandy?'))\\r\\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\\r\\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\\r\\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('dotEnding.'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('@{branch'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('contains\\\\\\\\slash'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('$newbranch'))\",\n \"def _valid_ref_type(ref):\\n\\n git_branch = \\\"refs/heads/\\\"\\n git_tag = \\\"refs/tags/\\\"\\n if ref.startswith(git_branch) or ref.startswith(git_tag) or len(ref) == 40:\\n return True\\n return False\",\n \"def check_branch_in_manifest(manifest_filename, meta):\\n print \\\"Checking manifest {}\\\".format(manifest_filename)\\n manifest_et = ET.parse(os.path.join(\\\"./manifest\\\", manifest_filename))\\n project_et = manifest_et.find(\\\"./project[@name='{}']\\\".format(PROJECT))\\n if project_et is None:\\n print \\\"project {} not found\\\".format(PROJECT)\\n return False\\n\\n # Compute the default branch for the manifest\\n default_branch = \\\"master\\\"\\n default_et = manifest_et.find(\\\"./default\\\")\\n if default_et is not None:\\n default_branch = default_et.get(\\\"branch\\\", \\\"master\\\")\\n\\n # Pull out the branch for the given project\\n project_branch = project_et.get(\\\"revision\\\", default_branch)\\n if project_branch != BRANCH:\\n print \\\"project {} on branch {}, not {}\\\".format(PROJECT, project_branch, BRANCH)\\n return False\\n\\n return True\",\n \"def match(self, name, tags):\\n return name.lower() in tags\",\n \"def has_label(self):\\n return self.label is not None\",\n \"def is_branch_node(tree, node):\\n if node == tree.root:\\n return tree.degree(node) >= 2\\n else:\\n return tree.degree(node) >= 3\",\n \"def git_checkout_branch(name):\\n\\n if subprocess.call([\\\"git\\\", \\\"diff\\\", \\\"--quiet\\\", \\\"HEAD\\\"]) != 0:\\n raise Exception(\\\"Dirty working tree; not checking out %s\\\" % name)\\n\\n if subprocess.call([\\\"git\\\", \\\"checkout\\\", name]) != 0:\\n raise Exception(\\\"Could not checkout %s\\\" % name)\",\n \"def isLabel(self, text):\\n if text is None or isinstance(text, GLabel):\\n return True\\n return False\",\n \"def is_release_branch():\\n diff_string_config_yml = run_command(\\\"git diff origin/master .circleci/config.yml\\\")\\n if re.search(r'[+-][ ]+CONTENT_VERSION: \\\".*', diff_string_config_yml):\\n return True\\n\\n return False\",\n \"def is_leaf(tree):\\n return not branches(tree)\",\n \"def validateLabel(cls, label: str, labeling_version: int) -> bool:\\r\\n\\r\\n return len(label.split('.')) in [2, 3]\",\n \"def _check_branch(opt, params):\\n\\n # Check the current branch and hash\\n _get_branch(opt)\\n\\n if params.git_branch != opt.git_branch or params.git_hash != opt.git_hash:\\n msg = 'You are not on the right branch or commit. Please run the following in the repository: \\\\n'\\n msg += f'git checkout {params.git_branch}\\\\n'\\n msg += f'git revert {params.git_hash}'\\n sys.exit(msg)\",\n \"def is_remote_reserve_branch_present(repo):\\n reserve_name = phlgitu_ref.Name(_RESERVE_BRANCH_FQ_NAME)\\n remote_ref_names = repo(\\\"ls-remote\\\").split()[1::2]\\n return reserve_name.fq in remote_ref_names\",\n \"def remote_branch_exists(self, branch):\\n try:\\n self.git.show_ref(\\\"refs/remotes/{}/{}\\\".format(self.base_branch_remote(), branch))\\n return True\\n except git.exc.GitCommandError:\\n return False\",\n \"def Search_lable(self, address):\\r\\n ret = False\\r\\n for i in self.Labels_names:\\r\\n if self.Labels_names[i] == address:\\r\\n return True\\r\\n return ret\",\n \"def ensure_tracking_branches(args):\\n man = load_manifest()\\n for (name, project) in man.projects.iteritems():\\n repo = GitRepo(workdir_for_project(project))\\n branch_missing = repo.command(\\n [\\\"rev-parse\\\", \\\"--verify\\\", \\\"-q\\\", project.refspec],\\n capture_stdout=True)\\n \\n if branch_missing:\\n logging.warn(\\\"Branch %s does not exist in project %s. checking out.\\\" %\\n (project.refspec, name))\\n repo.command([\\\"branch\\\", \\\"--track\\\",\\n project.tracking_branch, project.remote_refspec])\",\n \"def is_leaf(tree):\\n\\n return not branches(tree)\",\n \"def _is_stack_cannery_check_bb(self, xbb):\\n return self._is_stack_cannery_check_bb_x86(xbb)\",\n \"def has_name(self):\\n return self.unpack_word(0x2) != 0\",\n \"def is_label_definition(line):\\n\\n return line.startswith(\\\"LABEL \\\")\",\n \"def verify_tags(git_ref_target):\\n latest_release = github_util.get_latest_release().get('name')\\n latest_commit = run('git rev-list -n 1 {}'.format(latest_release)).stdout.rstrip(\\\"\\\\r\\\\n\\\")\\n if not branch_check(latest_release, git_ref_target):\\n print('Your branch does not contain the latest production code. \\\\n\\\\\\n Please recreate it by branching off of release {}.'.format(latest_release))\\n exit(1)\\n else:\\n print(\\\"Branch contains the latest production tag\\\")\\n fork_point = run('git merge-base remotes/origin/master remotes/origin/{}'.format(git_ref_target))\\n commits_since_fork = run('git rev-list --branches={} {}^..HEAD'.format(git_ref_target,\\n fork_point.stdout.rstrip(\\\"\\\\r\\\\n\\\")))\\n if latest_commit not in commits_since_fork.stdout:\\n print('Your branch did not fork directly from the last production tag. \\\\n\\\\\\n Please recreate it by branching off of release {}.'.format(latest_release))\\n exit(1)\\n else:\\n print('Latest production tag is between the fork point and HEAD')\",\n \"def test_instantiate_branch_node(self):\\n try:\\n BranchNode('my_name')\\n except Exception:\\n message = \\\"BranchNode instantiation failed\\\"\\n self.fail(message)\",\n \"def _is_last_commit_already_tagged(\\n repo: git.Repo,\\n last_tag: Optional[git.refs.tag.TagReference],\\n branch_name: str) -> bool:\\n if last_tag is None:\\n return False\\n commit = list(repo.iter_commits(rev=branch_name))[0]\\n return last_tag.commit == commit\",\n \"def git_has_object(project: Project, name: str) -> bool:\\n ret = project.git(\\\"rev-parse\\\", \\\"--verify\\\", name, _ok_code=[0, 128])\\n return ret.exit_code == 0\",\n \"def splitflow(self):\\n if self.name in conditional_branch:\\n return True\\n return False\",\n \"def branch(self, name: str) -> GitRef:\\n _args = [\\n Arg(\\\"name\\\", name),\\n ]\\n _ctx = self._select(\\\"branch\\\", _args)\\n return GitRef(_ctx)\",\n \"def has_label(self, label_string: str) -> bool:\\n if isinstance(self.label, list):\\n return label_string in self.label\\n else:\\n return label_string == self.label\",\n \"def check_component(comp_name: str, comp: defs.Component) -> None:\\n if not RE_COMP_NAME.match(comp_name):\\n res.append(f\\\"Invalid component name: {comp_name}\\\")\\n\\n for branch_name, branch in sorted(comp.branches.items()):\\n check_branch(comp_name, branch_name, branch)\",\n \"def _is_binary(name):\\n\\n return name in unidata.unicode_binary or name in unidata.unicode_alias['binary']\",\n \"def is_node_name_ok(node_name):\\n # 节点名不可包含`/`特殊字符\\n node_name = node_name.strip('/')\\n return node_name.find('/') == -1\",\n \"def match(self, name, tags):\\n name, tags = self.get_compiled(name, tags)\\n \\n def index_of_letter(l):\\n return ord(l) - ord('a')\\n \\n true_val, false_val = name\\n \\n if true_val:\\n return index_of_letter(true_val) in tags\\n else:\\n return index_of_letter(false_val) not in tags\",\n \"def identify_branch(self, gecko_branch):\\n\\n # Retrieve the name of the repository\\n branch = re.search('.*/([\\\\S\\\\.]+$)', gecko_branch).group(1)\\n\\n # Supported branches: mozilla-aurora, mozilla-beta, mozilla-release, mozilla-esr*\\n # All other branches (mozilla-central, mozilla-inbound, birch, elm, oak etc.) should fallback to the 'default' branch\\n # This will work with Firefox and Thunderbird\\n if not re.match(r'.*/releases/', gecko_branch):\\n branch = \\\"default\\\"\\n\\n return branch\",\n \"def hasname(self, tag: str) -> bool:\\n for key in self.formal_names:\\n if key in tag.lower():\\n return True\\n\\n # Exit case if key -> value not in mapping \\n return False\",\n \"def is_tag(t):\\n return len(t) > 1 and t.startswith('#') and not t.startswith('##') and t\",\n \"def is_tagged(self,tag_name,element):\\n return (tag_name in self.tag2elements.keys()) and (element in self.tag2elements[tag_name])\",\n \"def branch(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"branch\\\")\",\n \"def branch(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"branch\\\")\",\n \"def branch(self) -> Optional[str]:\\n return pulumi.get(self, \\\"branch\\\")\",\n \"def is_tag(tag_name, user_path, current_user) -> bool:\\n user = current_user[0]\\n tag_list = os.listdir((user_path + '\\\\\\\\' + user).encode('unicode_escape'))\\n temp = list(map(bytes.decode, tag_list))\\n if tag_name in temp:\\n return True\\n else:\\n return False\",\n \"def validate_branch_ops(conn_graph: ConnectedGraph):\\n\\n def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\\n \\\"\\\"\\\"\\n Look inside conn_graph ops and products for branch ops, and validate connections to parent and child ops\\n \\\"\\\"\\\"\\n\\n op = conn_graph.get_all_ops()[op_info.module_name]\\n return_bool = True\\n product = op.output\\n if \\\"branch\\\" not in product.name:\\n logger.error(\\\"branch not in product name\\\")\\n return_bool = False\\n if len(product.consumers) > 1:\\n logger.error(\\\"branch op is not parent op's only consumer\\\")\\n return_bool = False\\n branch_op = product.consumers[0]\\n if branch_op.type != \\\"branch\\\":\\n logger.error(\\\"parent op's child op is not of type branch\\\")\\n return_bool = False\\n branch_product = branch_op.output\\n if \\\"multiple_ops\\\" not in branch_product.name:\\n logger.error(\\\"multiple_ops not in branch op's product's name\\\")\\n return_bool = False\\n if len(branch_product.consumers) <= 1:\\n logger.error(\\\"branch op's product has one or fewer consumers\\\")\\n return_bool = False\\n for consumer in branch_product.consumers:\\n for input_product in consumer.inputs:\\n if input_product.producer == op:\\n logger.error(\\\"parent op is still one of child op's inputs (as opposed to branch op)\\\")\\n return_bool = False\\n return return_bool\\n\\n # pylint: disable=protected-access\\n module_identifier = StructureModuleIdentifier(conn_graph.graph, conn_graph._starting_op_names,\\n conn_graph._valid_ops)\\n num_branches_found = 0\\n for tf_op in conn_graph.graph.get_operations():\\n # Ignore ops which were not found in the initial depth first search\\n if tf_op not in module_identifier.processed_ops:\\n continue\\n\\n found_branch = False\\n for output_tensor in tf_op.outputs:\\n if len(output_tensor.consumers()) > 1:\\n # Potential branch op. Check if children go to separate modules\\n child_module_set = set()\\n for consumer_op in output_tensor.consumers():\\n if consumer_op in module_identifier._valid_ops:\\n child_module_info = module_identifier.get_op_info(consumer_op)\\n child_module_set.add(child_module_info.module_name)\\n\\n # If children go to separate modules, this should be a branch op\\n if len(child_module_set) > 1:\\n found_branch = True\\n break\\n\\n if found_branch:\\n num_branches_found += 1\\n tf_op_info = module_identifier.get_op_info(tf_op)\\n if not check_for_branch_op(tf_op_info):\\n return False\\n\\n logger.info(\\\"Found %s branches\\\", num_branches_found)\\n return True\",\n \"def test_heads_contains_true(repository: Repository) -> None:\\n assert repository.head.name in repository.heads\",\n \"def has_tag(self, tag):\\n return tag in self.tags\",\n \"def has_tag(self, tag):\\n return tag in self.tags\",\n \"def label_intersects_tags(label, tags):\\n for tag in tags:\\n if tag in label:\\n return True\\n return False\",\n \"def _check_bids_label(label):\\n if not isinstance(label, str):\\n raise TypeError(\\n f\\\"All bids labels must be string. \\\"\\n f\\\"Got '{type(label)}' for {label} instead.\\\"\\n )\\n if not all(char.isalnum() for char in label):\\n raise ValueError(\\n f\\\"All bids labels must be alphanumeric. Got '{label}' instead.\\\"\\n )\",\n \"def is_label_dataframe(label, df):\\n\\n setdiff = set(label) - set(df.columns.tolist())\\n\\n if len(setdiff) == 0:\\n return True\\n else:\\n return False\",\n \"def was_label_added(issue, label):\\n if not label:\\n return False\\n\\n # Optimization that does not require pulling in issue's actions.\\n if any(label.lower() == l.lower() for l in issue.labels):\\n return True\\n\\n for action in issue.actions:\\n for added in action.labels.added:\\n if label.lower() == added.lower():\\n return True\\n\\n return False\",\n \"def validate_branch_config(branch_cfg, branch, n):\\n res = False\\n if len(branch) == 4:\\n if \\\"double\\\" in branch_cfg:\\n diff1 = branch[0]-branch[1]\\n diff3 = branch[2]-branch[3]\\n diff2 = branch[1]%n-branch[2]%n\\n diff_adj_clk = [-3, 1]\\n diff_adj_clk_ctr = [-e for e in diff_adj_clk]\\n diff_opp = [2, -2]\\n if \\\"adjacent\\\" and \\\"clockwise\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk:\\n res = True\\n elif \\\"adjacent\\\" and \\\"counter clockwise\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk_ctr:\\n res = True\\n elif \\\"opposite\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_opp:\\n res = True\\n elif \\\"single\\\" in branch_cfg:\\n res = True\\n elif len(branch) == 2:\\n res = True\\n return res\",\n \"def git_repo_branch_exists(repo: str, branch: str) -> bool:\\n get_git_version()\\n cmd = f\\\"git ls-remote {repo} {branch}\\\"\\n # We might be tempted to use `--exit-code` with `git ls-remote`, but\\n # `run_command` handles the `returncode` for us, so we'll rely on\\n # the fact that stdout returns '' if the requested branch doesn't exist\\n ret = run_command(cmd, capture=True)\\n exists = ret.stdout != \\\"\\\"\\n return exists\",\n \"def bouton_branches(self,arbor,arbor_labels,bbranches,bouton_geometry=[28,4]):\\n\\t\\t\\n\\t\\tfor branch in arbor.keys():\\n\\t\\t\\tif branch in bbranches:\\n\\t\\t\\t\\tarbor[branch],arbor_labels[branch] = self.bouton_branch(arbor[branch],bouton_geometry)\\n\\t\\t\\n\\t\\treturn(arbor,arbor_labels)\",\n \"async def exists(self, tag_name):\\n try:\\n if await self.get_id(tag_name):\\n return True\\n except RtbDoesntExists:\\n return False\",\n \"def _check_name(self, symbol):\\n if symbol.type == self.scanner.NAME:\\n return True\\n else:\\n return False\",\n \"def has_label(self, phrase_string: str) -> bool:\\n return phrase_string in self.has_labels\",\n \"def test_label():\\n label_path = pjoin(data_path, \\\"label\\\", \\\"lh.BA1.label\\\")\\n label = read_label(label_path)\\n # XXX : test more\\n assert_true(np.all(label > 0))\",\n \"def has(self, tag_name: str, category: ty.Optional[str] = None) -> bool:\\n tags = self.__holder.db_tags.filter(lambda t: t.name == tag_name)\\n if category is not None:\\n tags = tags.filter(category=category)\\n\\n return len(tags) >= 1\",\n \"def verify_labeled(self, d_stmt, table):\\n d_label = d_stmt.find_first(\\\"p_name\\\")\\n if d_label:\\n self.label = d_label.value\\n table.check_table(d_stmt.linespan, Symbol(self.label, DanaType(\\\"label\\\")))\",\n \"def has_label(self):\\n return self.has_udev_property('ID_FS_LABEL_ENC')\",\n \"def _is_remote_branch(self, branch_reference):\\n return branch_reference.startswith(\\\"refs/remotes/\\\")\",\n \"def test_branch_fail(repository: Repository) -> None:\\n with pytest.raises(KeyError):\\n repository.branch(\\\"branch\\\")\",\n \"def validate_class_label(self, label_uri):\\n label = extract_name_from_uri_or_curie(label_uri)\\n if not label[0].isupper():\\n raise ValueError('Class label {} is incorrect. The first letter of each word should be capitalized!'.format(label))\",\n \"def isBlockLevel(tag):\\r\\n if isinstance(tag, basestring):\\r\\n return BLOCK_LEVEL_ELEMENTS.match(tag)\\r\\n # Some ElementTree tags are not strings, so return False.\\r\\n return False\",\n \"def has(self, tag_name: str) -> bool:\\n return hasattr(self, tag_name)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.715576","0.7042534","0.6742809","0.67223674","0.6595517","0.6064686","0.6029496","0.6010573","0.59576434","0.5933389","0.58508664","0.58427274","0.584109","0.582408","0.58061534","0.5799144","0.5777416","0.5758434","0.5737931","0.5732576","0.5721581","0.5707645","0.5703956","0.56670606","0.5656595","0.56549215","0.56361145","0.5628567","0.5604564","0.5591245","0.55451024","0.5543064","0.5533909","0.5512231","0.5493855","0.5480791","0.547176","0.54700387","0.54638416","0.5454135","0.54525834","0.54428124","0.54150414","0.54033417","0.54010916","0.53738475","0.53628576","0.53502893","0.5346829","0.53389037","0.53283215","0.5322908","0.53094065","0.5304076","0.5298487","0.52794987","0.5274282","0.5263919","0.5255134","0.52458733","0.5241811","0.5239845","0.5230225","0.52266365","0.52194023","0.52187186","0.52109253","0.5206467","0.5186649","0.51825684","0.51728386","0.51712847","0.5139472","0.51357573","0.51357573","0.5124199","0.51239055","0.5122526","0.51198035","0.5114873","0.5114873","0.5109716","0.5099302","0.5094163","0.50890255","0.5071019","0.50679356","0.50669676","0.5054869","0.5052397","0.5049127","0.50235945","0.5022671","0.5020957","0.50138706","0.5009789","0.5003017","0.500205","0.4998646","0.4996045"],"string":"[\n \"0.715576\",\n \"0.7042534\",\n \"0.6742809\",\n \"0.67223674\",\n \"0.6595517\",\n \"0.6064686\",\n \"0.6029496\",\n \"0.6010573\",\n \"0.59576434\",\n \"0.5933389\",\n \"0.58508664\",\n \"0.58427274\",\n \"0.584109\",\n \"0.582408\",\n \"0.58061534\",\n \"0.5799144\",\n \"0.5777416\",\n \"0.5758434\",\n \"0.5737931\",\n \"0.5732576\",\n \"0.5721581\",\n \"0.5707645\",\n \"0.5703956\",\n \"0.56670606\",\n \"0.5656595\",\n \"0.56549215\",\n \"0.56361145\",\n \"0.5628567\",\n \"0.5604564\",\n \"0.5591245\",\n \"0.55451024\",\n \"0.5543064\",\n \"0.5533909\",\n \"0.5512231\",\n \"0.5493855\",\n \"0.5480791\",\n \"0.547176\",\n \"0.54700387\",\n \"0.54638416\",\n \"0.5454135\",\n \"0.54525834\",\n \"0.54428124\",\n \"0.54150414\",\n \"0.54033417\",\n \"0.54010916\",\n \"0.53738475\",\n \"0.53628576\",\n \"0.53502893\",\n \"0.5346829\",\n \"0.53389037\",\n \"0.53283215\",\n \"0.5322908\",\n \"0.53094065\",\n \"0.5304076\",\n \"0.5298487\",\n \"0.52794987\",\n \"0.5274282\",\n \"0.5263919\",\n \"0.5255134\",\n \"0.52458733\",\n \"0.5241811\",\n \"0.5239845\",\n \"0.5230225\",\n \"0.52266365\",\n \"0.52194023\",\n \"0.52187186\",\n \"0.52109253\",\n \"0.5206467\",\n \"0.5186649\",\n \"0.51825684\",\n \"0.51728386\",\n \"0.51712847\",\n \"0.5139472\",\n \"0.51357573\",\n \"0.51357573\",\n \"0.5124199\",\n \"0.51239055\",\n \"0.5122526\",\n \"0.51198035\",\n \"0.5114873\",\n \"0.5114873\",\n \"0.5109716\",\n \"0.5099302\",\n \"0.5094163\",\n \"0.50890255\",\n \"0.5071019\",\n \"0.50679356\",\n \"0.50669676\",\n \"0.5054869\",\n \"0.5052397\",\n \"0.5049127\",\n \"0.50235945\",\n \"0.5022671\",\n \"0.5020957\",\n \"0.50138706\",\n \"0.5009789\",\n \"0.5003017\",\n \"0.500205\",\n \"0.4998646\",\n \"0.4996045\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7622634"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":12399,"cells":{"query":{"kind":"string","value":"Checks that the given branches are valid (every single branch is supposed valid). The idea here is to make sure that every ending nametag leads to another branch and that every branch is reachable."},"document":{"kind":"string","value":"def valid_branches(branches):\n\n # for every branch in the list\n for branch in branches:\n\n # make sure it is either reachable or has the special tag \"start\"\n if branches[branch].name != \"start\" and not is_nametag_reachable(branches[branch].name, branches):\n return False\n\n # make sure all ending tags refer to existing branches\n for nametag in branches[branch].next_nametags:\n if not branch_exists(nametag, branches):\n return False\n\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _check_branches(num_branches, num_blocks, in_channels, num_channels):\n if num_branches != len(num_blocks):\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_BLOCKS({len(num_blocks)})'\n raise ValueError(error_msg)\n if num_branches != len(num_channels):\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_CHANNELS({len(num_channels)})'\n raise ValueError(error_msg)\n if num_branches != len(in_channels):\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_INCHANNELS({len(in_channels)})'\n raise ValueError(error_msg)","def _check_branches(self, num_branches, in_channels):\n if num_branches != len(in_channels):\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_INCHANNELS({len(in_channels)})'\n raise ValueError(error_msg)","def validate(cfg: defs.Config) -> List[str]: # noqa: C901\n res: List[str] = []\n\n def check_branch(\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\n ) -> None:\n \"\"\"Validate versions within a single branch.\"\"\"\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\n\n if not RE_BRANCH_NAME.match(branch_name):\n res.append(f\"{comp_name}: Invalid branch name: {branch_name}\")\n\n for ver, version in sorted(branch.items()):\n if not RE_VERSION_STRING.match(ver):\n res.append(f\"{comp_name}/{branch_name}: Invalid version string: {ver}\")\n\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\n if version.outdated:\n update_to = [\n o_version\n for o_version in branch.values()\n if not o_version.outdated\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\n == other_cksums\n ]\n if len(update_to) != 1:\n res.append(\n f\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \"\n f\"versions to update to instead of exactly one\"\n )\n else:\n bad_files = sorted(\n relpath\n for relpath, (path, fdata) in driver_cksums.items()\n if util.file_sha256sum(path) != fdata.sha256\n )\n if bad_files:\n res.append(f\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\")\n\n if not uptodate_files:\n uptodate_files = driver_cksums\n elif uptodate_files != driver_cksums:\n res.append(\n f\"{comp_name}/{branch_name}: All the up-to-date versions should \"\n f\"define the same set of files with the same checksums\"\n )\n\n if not any(not version.outdated for version in branch.values()):\n res.append(f\"{comp_name}/{branch_name}: No non-outdated versions\")\n\n def check_component(comp_name: str, comp: defs.Component) -> None:\n \"\"\"Validate the definition of a single component.\"\"\"\n if not RE_COMP_NAME.match(comp_name):\n res.append(f\"Invalid component name: {comp_name}\")\n\n for branch_name, branch in sorted(comp.branches.items()):\n check_branch(comp_name, branch_name, branch)\n\n for comp_name, comp in sorted(cfg.all_components.components.items()):\n check_component(comp_name, comp)\n\n return res","def check_branch(\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\n ) -> None:\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\n\n if not RE_BRANCH_NAME.match(branch_name):\n res.append(f\"{comp_name}: Invalid branch name: {branch_name}\")\n\n for ver, version in sorted(branch.items()):\n if not RE_VERSION_STRING.match(ver):\n res.append(f\"{comp_name}/{branch_name}: Invalid version string: {ver}\")\n\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\n if version.outdated:\n update_to = [\n o_version\n for o_version in branch.values()\n if not o_version.outdated\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\n == other_cksums\n ]\n if len(update_to) != 1:\n res.append(\n f\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \"\n f\"versions to update to instead of exactly one\"\n )\n else:\n bad_files = sorted(\n relpath\n for relpath, (path, fdata) in driver_cksums.items()\n if util.file_sha256sum(path) != fdata.sha256\n )\n if bad_files:\n res.append(f\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\")\n\n if not uptodate_files:\n uptodate_files = driver_cksums\n elif uptodate_files != driver_cksums:\n res.append(\n f\"{comp_name}/{branch_name}: All the up-to-date versions should \"\n f\"define the same set of files with the same checksums\"\n )\n\n if not any(not version.outdated for version in branch.values()):\n res.append(f\"{comp_name}/{branch_name}: No non-outdated versions\")","def branch_exists(nametag, branches):\n for branch in branches:\n if branches[branch].name == nametag:\n return True\n return False","def validate_branch_config(branch_cfg, branch, n):\n res = False\n if len(branch) == 4:\n if \"double\" in branch_cfg:\n diff1 = branch[0]-branch[1]\n diff3 = branch[2]-branch[3]\n diff2 = branch[1]%n-branch[2]%n\n diff_adj_clk = [-3, 1]\n diff_adj_clk_ctr = [-e for e in diff_adj_clk]\n diff_opp = [2, -2]\n if \"adjacent\" and \"clockwise\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk:\n res = True\n elif \"adjacent\" and \"counter clockwise\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk_ctr:\n res = True\n elif \"opposite\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_opp:\n res = True\n elif \"single\" in branch_cfg:\n res = True\n elif len(branch) == 2:\n res = True\n return res","def validate_branch_ops(conn_graph: ConnectedGraph):\n\n def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\n \"\"\"\n Look inside conn_graph ops and products for branch ops, and validate connections to parent and child ops\n \"\"\"\n\n op = conn_graph.get_all_ops()[op_info.module_name]\n return_bool = True\n product = op.output\n if \"branch\" not in product.name:\n logger.error(\"branch not in product name\")\n return_bool = False\n if len(product.consumers) > 1:\n logger.error(\"branch op is not parent op's only consumer\")\n return_bool = False\n branch_op = product.consumers[0]\n if branch_op.type != \"branch\":\n logger.error(\"parent op's child op is not of type branch\")\n return_bool = False\n branch_product = branch_op.output\n if \"multiple_ops\" not in branch_product.name:\n logger.error(\"multiple_ops not in branch op's product's name\")\n return_bool = False\n if len(branch_product.consumers) <= 1:\n logger.error(\"branch op's product has one or fewer consumers\")\n return_bool = False\n for consumer in branch_product.consumers:\n for input_product in consumer.inputs:\n if input_product.producer == op:\n logger.error(\"parent op is still one of child op's inputs (as opposed to branch op)\")\n return_bool = False\n return return_bool\n\n # pylint: disable=protected-access\n module_identifier = StructureModuleIdentifier(conn_graph.graph, conn_graph._starting_op_names,\n conn_graph._valid_ops)\n num_branches_found = 0\n for tf_op in conn_graph.graph.get_operations():\n # Ignore ops which were not found in the initial depth first search\n if tf_op not in module_identifier.processed_ops:\n continue\n\n found_branch = False\n for output_tensor in tf_op.outputs:\n if len(output_tensor.consumers()) > 1:\n # Potential branch op. Check if children go to separate modules\n child_module_set = set()\n for consumer_op in output_tensor.consumers():\n if consumer_op in module_identifier._valid_ops:\n child_module_info = module_identifier.get_op_info(consumer_op)\n child_module_set.add(child_module_info.module_name)\n\n # If children go to separate modules, this should be a branch op\n if len(child_module_set) > 1:\n found_branch = True\n break\n\n if found_branch:\n num_branches_found += 1\n tf_op_info = module_identifier.get_op_info(tf_op)\n if not check_for_branch_op(tf_op_info):\n return False\n\n logger.info(\"Found %s branches\", num_branches_found)\n return True","def is_nametag_reachable(nametag, branches):\n for branch in branches:\n for next_nametag in branches[branch].next_nametags:\n if next_nametag == nametag:\n return True\n return False","def ensure_tracking_branches(args):\n man = load_manifest()\n for (name, project) in man.projects.iteritems():\n repo = GitRepo(workdir_for_project(project))\n branch_missing = repo.command(\n [\"rev-parse\", \"--verify\", \"-q\", project.refspec],\n capture_stdout=True)\n \n if branch_missing:\n logging.warn(\"Branch %s does not exist in project %s. checking out.\" %\n (project.refspec, name))\n repo.command([\"branch\", \"--track\",\n project.tracking_branch, project.remote_refspec])","def validate_empty_branches(nanowire, min_free_branch, msg):\n score = 0\n valid = False\n\n for intersection in nanowire:\n free_b = 0\n for branch in intersection:\n min_free_pos = len(branch)\n free_p = 0\n for tup in branch:\n if not isinstance(tup, dict):\n continue\n if list(tup.values())[0] == 0:\n free_p += 1\n else:\n free_p = 0\n if free_p>=min_free_pos:\n free_b += 1\n if free_b>=min_free_branch:\n valid = True\n\n if valid:\n score += 1\n # if score==0:\n # raise exception.NoEmptyBranchException(msg)\n return score","def _validate_branch_args(self) -> None:\n lk = set(self.branch_losses.keys())\n dk = set(self.model._get_inner_keys(self.model.heads))\n has_same_keys = lk == dk\n\n mk = None\n if self.branch_metrics is not None:\n mk = set(self.branch_metrics.keys())\n has_same_keys = dk == lk == mk\n\n ek = None\n if self.branch_loss_params is not None:\n ek = set(self.branch_loss_params.keys())\n has_same_keys = dk == lk == mk == ek\n\n if not has_same_keys:\n raise ValueError(\n \"Got mismatching keys for branch dict args. \"\n f\"Branch losses: {lk}. \"\n f\"Branch loss params: {ek}. \"\n f\"Decoder branches: {dk}. \"\n f\"Metrics: {mk}. \"\n f\"(`metrics`, and `branch_loss_params` can be None)\"\n )","def test_is_valid_git_refname(self):\n # valid branchnames\n self.assertTrue(is_valid_git_refname('master'))\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\n self.assertTrue(is_valid_git_refname('refname/bar'))\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\n unichr(40))))\n self.assertTrue(is_valid_git_refname('master@head'))\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\n\n # case happening with git < 1.6.6\n self.assertFalse(is_valid_git_refname(\n '--abbrev-ref\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\n\n # different invalid refnames, for a description of each group see the\n # man page of git check-ref-format\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\n self.assertFalse(is_valid_git_refname('bar.lock'))\n self.assertFalse(is_valid_git_refname('.foobar'))\n\n self.assertFalse(is_valid_git_refname('ref..name'))\n\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\n unichr(177))))\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\n unichr(39))))\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\n self.assertFalse(is_valid_git_refname('refname spam'))\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\n\n self.assertFalse(is_valid_git_refname('areyourandy?'))\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\n\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\n\n self.assertFalse(is_valid_git_refname('dotEnding.'))\n\n self.assertFalse(is_valid_git_refname('@{branch'))\n\n self.assertFalse(is_valid_git_refname('contains\\\\slash'))\n\n self.assertFalse(is_valid_git_refname('$newbranch'))","def _check_branch(opt, params):\n\n # Check the current branch and hash\n _get_branch(opt)\n\n if params.git_branch != opt.git_branch or params.git_hash != opt.git_hash:\n msg = 'You are not on the right branch or commit. Please run the following in the repository: \\n'\n msg += f'git checkout {params.git_branch}\\n'\n msg += f'git revert {params.git_hash}'\n sys.exit(msg)","def test_is_valid_git_refname(self):\r\n # valid branchnames\r\n self.assertTrue(is_valid_git_refname('master'))\r\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\r\n self.assertTrue(is_valid_git_refname('refname/bar'))\r\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\r\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\r\n unichr(40))))\r\n self.assertTrue(is_valid_git_refname('master@head'))\r\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\r\n\r\n # case happening with git < 1.6.6\r\n self.assertFalse(is_valid_git_refname(\r\n '--abbrev-ref\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\r\n\r\n # different invalid refnames, for a description of each group see the\r\n # man page of git check-ref-format\r\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\r\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\r\n self.assertFalse(is_valid_git_refname('bar.lock'))\r\n self.assertFalse(is_valid_git_refname('.foobar'))\r\n\r\n self.assertFalse(is_valid_git_refname('ref..name'))\r\n\r\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\r\n unichr(177))))\r\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\r\n unichr(39))))\r\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\r\n self.assertFalse(is_valid_git_refname('refname spam'))\r\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\r\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\r\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\r\n\r\n self.assertFalse(is_valid_git_refname('areyourandy?'))\r\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\r\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\r\n\r\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\r\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\r\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\r\n\r\n self.assertFalse(is_valid_git_refname('dotEnding.'))\r\n\r\n self.assertFalse(is_valid_git_refname('@{branch'))\r\n\r\n self.assertFalse(is_valid_git_refname('contains\\\\slash'))\r\n\r\n self.assertFalse(is_valid_git_refname('$newbranch'))","def protect_pr_branch_with_tests_if_any_exist(org: Organization, repo: Repository,\n branches: Dict[str, Branch]) -> List[Change[str]]:\n def execute_test_protection(change: Change[str], branch: Branch, existing_checks: Set[str],\n known_status_checks: Set[str], known_checkruns: Set[str]) -> Change[str]:\n\n all_known_checks = known_status_checks | known_checkruns # For convenience later to treat them as a single set\n\n print_debug(\"[%s] Changing status checks on branch '%s' to [%s]\" %\n (highlight(repo.name), highlight(branch.name),\n highlight(\", \".join(list(all_known_checks)))))\n try:\n if existing_checks:\n branch.edit_required_status_checks(strict=True, contexts=list(all_known_checks))\n else:\n safe_branch_edit_protection(\n branch,\n strict=True,\n contexts=list(all_known_checks),\n )\n except GithubException as e:\n print_error(\"Can't edit required status checks on repo %s branch %s: %s\" %\n (repo.name, branch.name, str(e)))\n return change.failure()\n return change.success()\n\n prb = get_pr_branch(repo, branches)\n if not prb:\n return []\n\n existing_checks = set() # type: Set[str]\n try:\n rqs = prb.get_required_status_checks()\n except GithubException:\n # the repository has currently no status checks\n pass\n else:\n if len(rqs.contexts) > 0:\n # The repository already has some status checks\n existing_checks = set(rqs.contexts)\n print_debug(\"Branch %s on repo %s already has status checks [%s]\" %\n (highlight(prb.name), highlight(repo.name), highlight(\", \".join(existing_checks))))\n\n # the repository currently has no status checks, let's see if any came in within the last 7 days\n sevendaysago = datetime.now() - timedelta(days=7)\n commits = repo.get_commits(prb.name, since=sevendaysago)\n known_status_checks = set() # type: Set[str]\n known_checkruns = set() # type: Set[str]\n for commit in commits:\n for status in commit.get_statuses(): # type: CommitStatus\n if status.context not in known_status_checks:\n print_debug(\"New status check [%s]: %s %s '%s'\" %\n (commit.sha, status.updated_at,\n status.context, status.description))\n known_status_checks.add(status.context)\n for checkrun in commit.get_check_runs(): # type: CheckRun\n if checkrun.name not in known_checkruns:\n print_debug(\"New check run [%s]: %s %s %s\" %\n (commit.sha, checkrun.completed_at, checkrun.name, checkrun.app))\n known_checkruns.add(checkrun.name)\n\n all_known_checks = known_status_checks | known_checkruns # For convenience later to treat them as a single set\n print_debug(\"Found status checks [%s]\" % \", \".join(all_known_checks))\n\n if all_known_checks and all_known_checks != existing_checks:\n # add all known checks as required checks\n print_debug('Adding checks [%s] to branch %s on repo %s' %\n (highlight(\", \".join((all_known_checks) - existing_checks)),\n highlight(prb.name), highlight(repo.name)))\n return [Change(\n meta=ChangeMetadata(\n executor=execute_test_protection,\n params=[prb, existing_checks, known_status_checks, known_checkruns]\n ),\n action=ChangeActions.REPLACE if existing_checks else ChangeActions.ADD,\n before=\"%s checks\" % len(existing_checks) if existing_checks else \"No checks\",\n after=\"%s checks\" % len(all_known_checks),\n )]\n return []","def check_component(comp_name: str, comp: defs.Component) -> None:\n if not RE_COMP_NAME.match(comp_name):\n res.append(f\"Invalid component name: {comp_name}\")\n\n for branch_name, branch in sorted(comp.branches.items()):\n check_branch(comp_name, branch_name, branch)","def test_multi_branches(self):\n sgf = \"\"\"\n (;FF[4]GM[1]SZ[19];B[aa];W[bb](;B[cc];W[dd](;B[ad];W[bd])\n (;B[ee];W[ff]))\n (;B[hh];W[gg])\n (;B[ii];W[jj]))\n \"\"\"\n coll = parseSgf(sgf)\n self.assertEqual(coll,\n [[{'SZ': '19', 'GM': '1', 'FF': '4'}, {'B': 'aa'},\n {'W': 'bb'},\n [[{'B': 'cc'}, {'W': 'dd'}, [[{'B': 'ad'}, {'W': 'bd'}], [{'B': 'ee'}, {'W': 'ff'}]]],\n [{'B': 'hh'}, {'W': 'gg'}],\n [{'B': 'ii'}, {'W': 'jj'}]],\n ]])\n self.assertEqual(self._trim_sgf_whitespace(sgf), makeSgf(coll))","def formatted_branch_name(branch):\n for exp in experiments:\n if exp in branch:\n for otype in observation_types:\n if otype in branch:\n for stype in shear_types:\n if stype in branch:\n return exp+'-'+otype+'-'+stype\n raise RuntimeError('Branch %s does not appear to be a valid branch name--please pass one of '\n '[%s] with the command-line option -b.'%(branch, ', '.join(branch_names)))","def verify_branch(path, expected_branch=\"master\"):\n\n sys.stdout.write(\" - Verifying your branch is %s:\" % expected_branch)\n branch = run_in_component(path, ['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\n branch = branch.strip()\n\n if branch == expected_branch:\n print(\" OKAY\")\n return\n\n print(\" FAILED\")\n\n raise GenericError(\"You must be on branch %s to release, you are on %s\" % (expected_branch, branch))","def check_children_attributes(self, branch):\n attributes = branch.get_attributes()\n for attr in attributes:\n if not isinstance(attributes[attr], str) and not isinstance(attributes[attr], list) :\n print('Attribute '+str(attr)+' of '+ branch.__class__.__name__ + ' should be str or list')\n self.assertTrue(False)\n children = branch.get_children()\n for child in children:\n self.check_children_attributes(child)","def verify_tags(git_ref_target):\n latest_release = github_util.get_latest_release().get('name')\n latest_commit = run('git rev-list -n 1 {}'.format(latest_release)).stdout.rstrip(\"\\r\\n\")\n if not branch_check(latest_release, git_ref_target):\n print('Your branch does not contain the latest production code. \\n\\\n Please recreate it by branching off of release {}.'.format(latest_release))\n exit(1)\n else:\n print(\"Branch contains the latest production tag\")\n fork_point = run('git merge-base remotes/origin/master remotes/origin/{}'.format(git_ref_target))\n commits_since_fork = run('git rev-list --branches={} {}^..HEAD'.format(git_ref_target,\n fork_point.stdout.rstrip(\"\\r\\n\")))\n if latest_commit not in commits_since_fork.stdout:\n print('Your branch did not fork directly from the last production tag. \\n\\\n Please recreate it by branching off of release {}.'.format(latest_release))\n exit(1)\n else:\n print('Latest production tag is between the fork point and HEAD')","def branch(name, wit_path):\n\n if name != 'None':\n\n if len(name) < 30:\n head = _get_head(wit_path)\n _add_branch(wit_path, name, head)\n else:\n logging.error(f'branch name is too long \"{name}\" (max 30 digits).')\n else:\n logging.error(f'branch name is not valid {name}.')","def verify_submissions_valid() -> bool:\n\n submitters = [file for file in os.listdir(SUBS_DIR) if not file.startswith(\".\")]\n submissions_valid = True\n\n for submitter in submitters:\n expected_submission_path = f\"{SUBS_DIR}/{submitter}/{ASSIGNMENT}\"\n if not os.path.exists(expected_submission_path):\n print(f\"{Ansi.RED}ERROR:{Ansi.END} {expected_submission_path} does not exist\")\n submissions_valid = False\n continue\n else:\n git = subprocess.run(\"git remote -v\", shell=True, cwd=expected_submission_path, stdout=subprocess.PIPE,\n universal_newlines=True, check=False)\n if ASSIGNMENT not in git.stdout:\n print(f\"{Ansi.RED}ERROR:{Ansi.END} {expected_submission_path} does not contain a git history\")\n submissions_valid = False\n\n return submissions_valid","def is_valid_git_refname(refname):\r\n if len(refname) == 0:\r\n return False\r\n\r\n # git imposes a few requirements to accept a string as a\r\n # refname/branch-name\r\n\r\n # They can include slash / for hierarchical (directory) grouping, but no\r\n # slash-separated component can begin with a dot . or end with the sequence\r\n # .lock\r\n if (len([True for element in refname.split('/')\r\n if element.startswith('.') or element.endswith('.lock')]) != 0):\r\n return False\r\n\r\n # They cannot have two consecutive dots .. anywhere\r\n if '..' in refname:\r\n return False\r\n\r\n # They cannot have ASCII control characters (i.e. bytes whose values are\r\n # lower than \\040, or \\177 DEL), space, tilde, caret ^, or colon : anywhere\r\n if len([True for refname_char in refname if ord(refname_char) < 40 or\r\n ord(refname_char) == 177]) != 0:\r\n return False\r\n if ' ' in refname or '~' in refname or '^' in refname or ':' in refname:\r\n return False\r\n\r\n # They cannot have question-mark ?, asterisk *, or open bracket [ anywhere\r\n if '?' in refname or '*' in refname or '[' in refname:\r\n return False\r\n\r\n # They cannot begin or end with a slash / or contain multiple consecutive\r\n # slashes\r\n if refname.startswith('/') or refname.endswith('/') or '//' in refname:\r\n return False\r\n\r\n # They cannot end with a dot ..\r\n if refname.endswith('.'):\r\n return False\r\n\r\n # They cannot contain a sequence @{\r\n if '@{' in refname:\r\n return False\r\n\r\n # They cannot contain a \\\r\n if '\\\\' in refname:\r\n return False\r\n\r\n return True","def check_stale_branches(event: dict, context) -> dict:\n\n ssm_parameters = load_params('dev_tools', 'dev')\n\n if 'jira_statuses_for_task_completion' in ssm_parameters and ssm_parameters['jira_statuses_for_task_completion']:\n jira_statuses_for_task_completion = ssm_parameters['jira_statuses_for_task_completion']\n else:\n jira_statuses_for_task_completion = ('Resolved', 'Closed')\n\n repository_names = ssm_parameters['github_repository_names']\n github_repository_names = repository_names.split(',')\n\n jira_oauth_dict = {\n 'access_token': ssm_parameters['jira_access_token'],\n 'access_token_secret': ssm_parameters['jira_access_token_secret'],\n 'consumer_key': ssm_parameters['jira_consumer_key'],\n 'key_cert': ssm_parameters['jira_private_key']\n }\n auth_jira = JIRA(ssm_parameters['jira_url'], oauth=jira_oauth_dict)\n\n # Github authentication setup\n g = Github(ssm_parameters['github_access_token'])\n\n # Look for stale branches for all the specified repos\n total_stale_branches = 0\n general_report = ''\n author_count = defaultdict(int)\n\n for repo_name in github_repository_names:\n logger.debug(f'\\nChecking repo: {repo_name}')\n\n try:\n repo = g.get_repo(f\"{ssm_parameters['github_account']}/{repo_name}\")\n except GithubException:\n logger.error(f\"Github repository '{ssm_parameters['github_account']}/{repo_name}' not found!\")\n continue\n\n repo_report = ''\n\n # confirm the name for the main develop branch\n main_develop_branch = 'develop'\n try:\n _ = repo.get_branch('develop')\n except GithubException:\n main_develop_branch = 'master'\n logger.debug('Develop branch not found, using master as the main develop branch.')\n continue\n\n branches = repo.get_branches()\n for branch in branches:\n # only check feature and hotfix branches\n if not branch.name.startswith('feature/') and not branch.name.startswith('hotfix/'):\n continue\n\n # compare the branch against the main develop branch\n try:\n comparison = repo.compare(main_develop_branch, branch.name)\n except GithubException as error:\n logger.error(f'GithubException: Error while trying to compare {main_develop_branch} and {branch.name}.')\n logger.error(f'GithubException: {error}.')\n\n if comparison.behind_by == 0:\n # the branch is up to date, nothing to do\n continue\n\n # try to get the jira ticket number from the branch name\n ticket = None\n result = re.search(r'feature/(?P<ticket>[a-zA-Z]+-[0-9]+).*', branch.name)\n if result:\n ticket = result.groupdict()['ticket'].upper()\n try:\n issue = auth_jira.issue(ticket)\n except jira_exceptions.JIRAError:\n logger.debug(f\"The ticket {ticket} specified in the branch name doesn't exist in Jira.\")\n\n if issue and issue.fields.status.name not in jira_statuses_for_task_completion:\n # the issue hasn't been marked as resolved in jira, so the branch may still be needed\n continue\n\n author = branch.commit.author.login if branch.commit.author else 'unknown'\n author_count[author] += 1\n repo_report += f'Branch: {branch.name}\\nComparison status: {comparison.status}\\nAuthor: {author}\\n'\n if ticket:\n repo_report += f'Ticket status: \"{issue.fields.status.name}\\n'\n repo_report += '\\n'\n\n total_stale_branches += 1\n\n if repo_report:\n general_report += f'Repo: {repo_name}, develop branch name: {main_develop_branch}\\n{repo_report}'\n\n if total_stale_branches:\n count_by_author = ''\n for author, count in sorted(author_count.items(), key=operator.itemgetter(1), reverse=True):\n count_by_author += f'{author}: {count}\\n'\n\n report_overview = f'Current number of stale branches: {total_stale_branches}\\n\\n'\\\n f'Count by author:\\n{count_by_author}\\n'\n report_details = f'Details:\\n\\n{general_report}'\n\n _ = slack_request(url=ssm_parameters['slack_webhook_url'],\n headers={'Content-type': 'application/json',\n 'Authorization': f\"Bearer {ssm_parameters['slack_access_token']}\"},\n data=json.dumps({'text': report_overview})\n )\n\n _ = slack_request(url='https://slack.com/api/files.upload',\n headers={'Content-type': 'application/x-www-form-urlencoded'},\n data={'token': ssm_parameters['slack_access_token'],\n 'channels': 'GE8NS0FT5',\n 'content': report_details,\n 'title': 'Stale branches details'}\n )","def remove_all_status_checks_on_pr_branch(org: Organization, repo: Repository,\n branches: Dict[str, Branch]) -> List[Change[str]]:\n def execute_remove_all_status_checks(change: Change[str], branch: Branch, existing_checks: Set[str]) -> Change[str]:\n print_debug(\"Removing all status checks from branch %s\" % highlight(branch.name))\n try:\n if existing_checks:\n branch.remove_required_status_checks()\n except GithubException as e:\n print_error(str(e))\n return change.failure()\n else:\n return change.success()\n\n prb = get_pr_branch(repo, branches)\n if not prb:\n return []\n\n try:\n rqs = prb.get_required_status_checks()\n except GithubException:\n # the repository has currently no status checks\n pass\n else:\n if len(rqs.contexts) > 0:\n existing_checks = set(rqs.contexts) # type: Set[str]\n return [Change(\n meta=ChangeMetadata(\n executor=execute_remove_all_status_checks,\n params=[prb, existing_checks]\n ),\n action=ChangeActions.REPLACE,\n before=\"%s checks\" % len(existing_checks),\n after=None,\n )]\n return []","def check(user_configuration, repository_configuration, commit_message):\n logger = output.get_sub_logger('commit-msg', 'branch-pattern')\n\n logger.debug('Starting branch-pattern check...')\n\n result = checks.CheckResult()\n branch = commit_message.branch\n logger.debug('Branch: %s', branch)\n\n check_options = repository_configuration.get('branch-pattern', {})\n allowed = check_options.get('allowed', [])\n allowed.append('master') # master is always allowed\n\n logger.debug('Allowed Patterns: %s', allowed)\n\n is_allowed = any(re.match(pattern, branch) for pattern in allowed)\n result.successful = is_allowed\n if not is_allowed:\n template = \"{branch} doesn't match any allowed pattern.\"\n result.add_detail(template.format(branch=branch))\n\n return result","def test_multiple_branches(self, tmpgitdir):\n with tmpgitdir.join('file_a.txt').open('w') as handle:\n handle.write('first file')\n\n subprocess.check_call(['git', 'add', '.'])\n subprocess.check_call(['git', 'commit', '-m', 'first'])\n\n subprocess.check_call(['git', 'checkout', '-b', 'testbranch'])\n\n with tmpgitdir.join('file_b.txt').open('w') as handle:\n handle.write('second file')\n\n subprocess.check_call(['git', 'add', '.'])\n subprocess.check_call(['git', 'commit', '-m', 'second'])\n\n assert git_head_ref_name(tmpgitdir) == 'testbranch'","def verify_up_to_date(path, branch=\"master\"):\n\n sys.stdout.write(\" - Verifying your branch up to date:\")\n run_in_component(path, ['git', 'remote', 'update'])\n\n result = run_in_component(path, ['git', 'rev-list', 'HEAD...origin/%s' % branch, '--count'])\n count = int(result.strip())\n\n if count == 0:\n print(\" OKAY\")\n return\n\n print(\" FAILED\")\n\n raise GenericError(\"You branch is not up-to-date with remote branch: %d different commits\" % count)","def scm_branch(self, value):\n max_characters = 256\n conditions = [validate_max_length(value, max_characters)]\n if all(conditions):\n self._update_values('scm_branch', value)\n else:\n raise InvalidValue(f'{value} is invalid. Condition max_characters must be less than or equal to '\n f'{max_characters}')","def lemur(branches):\n\n assert branches[0] == 0, \"First branch must be alive\"\n assert branches[-1] == 0, \"Last branch must be alive\"\n\n # loop through list\n # if next one is dead, jump 2 spaces\n # if the next one is alive, check next next\n # if next AND next next are alive, jump 2 spaces\n # if next is alive and next next is dead, jump 1 space\n # increment counter each jump\n # if len(branches) < 2:\n # return 0\n\n # if len(branches) < 3:\n # return 1\n\n # count = 0\n # branch = 0\n\n # while branch < (len(branches)-1):\n # if branches[branch + 1] == 1:\n # branch += 2\n # else:\n # if branches[branch + 2] == 0:\n # branch+= 2\n # else:\n # branch += 1\n # count += 1\n\n # return count\n\n#####################################################################\n# way shorter version\n\n branch = 0\n count = 0\n\n while branch < len(branches) - 1:\n branch += 2\n if branch >= len(branches) or branches[branch] == 1:\n # We can jump this far, so only jump 1\n branch -= 1\n count += 1\n\n return count","def is_branch(wit_path, branch):\n\n branches = _get_references_data(wit_path)\n del branches['HEAD']\n return branch in branches.keys()","def visit_branch(self, node, children):\n branch = {k: v for d in children for k, v in d.items()}\n # Verify that this is either an interpolated, rut or graft branch and not an illegal mix\n # If a path is specified it is a rut branch or if there is a local graft it is a grafted branch\n # If both path and local graft are present in the same branch it is illegal\n if branch.get('path', None): # Path specified, so there should be no local grafts in this branch\n lf = branch['leaf_faces']\n local_graft = [lf[n]['graft'] for n in lf if lf[n]['graft'] == 'local']\n if local_graft:\n raise GraftRutBranchConflict(branch=set(lf.keys()))\n # Return dictionary of leaf faces and an optional path keyed to the local rule\n return { node.rule_name: branch }","def missing_branches(self):\n upstream_tags = self.upstream_model.tags_from_semver_point(\"1.19.0\")\n deb_branches = self.deb_model.base.branches_from_semver_point(\"1.19.0\")\n return list(set(upstream_tags) - set(deb_branches))","def track_branches(branches=None, directory=None):\n if type(branches) == str:\n branches = [branches]\n debug(\"track_branches(\" + str(branches) + \", \" + str(directory) + \")\")\n if branches == []:\n return\n # Save the current branch\n current_branch = get_current_branch(directory)\n try:\n # Get the local branches\n local_branches = get_branches(local_only=True, directory=directory)\n # Get the remote and local branches\n all_branches = get_branches(local_only=False, directory=directory)\n # Calculate the untracked branches\n untracked_branches = []\n for branch in all_branches:\n if branch.startswith('remotes/'):\n if branch.count('/') >= 2:\n branch = '/'.join(branch.split('/')[2:])\n if branch not in local_branches:\n untracked_branches.append(branch)\n # Prune any untracked branches by specified branches\n if branches is not None:\n branches_to_track = []\n for untracked in untracked_branches:\n if untracked in branches:\n branches_to_track.append(untracked)\n else:\n branches_to_track = untracked_branches\n # Track branches\n debug(\"Tracking branches: \" + str(branches_to_track))\n for branch in branches_to_track:\n checkout(branch, directory=directory)\n finally:\n if current_branch:\n checkout(current_branch, directory=directory)","def create_branches(branches, pcoll, provider_options):\n\n logger.info('Branch count: %i' % len(branches))\n pcoll_tuple = ()\n\n for branch in branches:\n logger.info('Adding branch')\n output = create_graph(branch, pcoll, provider_options)\n pcoll_tuple = pcoll_tuple + (output,)\n\n logger.info('Transform: MergeBranches')\n output = pcoll_tuple | 'MergeBranches' >> MergeBranches()\n return output","def bouton_branches(self,arbor,arbor_labels,bbranches,bouton_geometry=[28,4]):\n\t\t\n\t\tfor branch in arbor.keys():\n\t\t\tif branch in bbranches:\n\t\t\t\tarbor[branch],arbor_labels[branch] = self.bouton_branch(arbor[branch],bouton_geometry)\n\t\t\n\t\treturn(arbor,arbor_labels)","def test_simple_branches(self):\n sgf = \"\"\"\n (;FF[4]GM[1]SZ[19];B[aa];W[bb](;B[cc];W[dd];B[ee])(;B[hh];W[hg]))\n \"\"\"\n coll = parseSgf(sgf)\n self.assertEqual(coll,\n [[{'SZ': '19', 'GM': '1', 'FF': '4'}, {'B': 'aa'}, {'W': 'bb'},\n [[{'B': 'cc'}, {'W': 'dd'}, {'B': 'ee'}], [{'B': 'hh'}, {'W': 'hg'}]]]])\n self.assertEqual(self._trim_sgf_whitespace(sgf), makeSgf(coll))","def _is_branch(self, reference_name):\n return reference_name.startswith(\"refs/heads/\") or reference_name.startswith(\n \"refs/remotes/\"\n )","def test_wrong_branch(self):\n from webhook.views import RootView\n json_data=open('testing_commits/body_another_branch.json')\n mock_api = Mock()\n request = testing.DummyRequest()\n request.json_body = json.load(json_data)\n view_inst = RootView(request, api_service=mock_api)\n result = view_inst.default_view()\n self.assertIn('wrong branch',result['error'])","def checkout_branches(args):\n\n ensure_tracking_branches([])\n if check_dirty([]) and '-f' not in args:\n raise Exception(\"Cannot checkout new branches with dirty projects.\")\n \n man = load_manifest()\n for (name, project) in man.projects.iteritems():\n print >>sys.stderr, \"Checking out tracking branch in project: %s\" % name\n repo = GitRepo(workdir_for_project(project))\n # Check that sucker out\n repo.check_command([\"checkout\", project.tracking_branch])","def check_consistent(self):\n # * END LIST The end list itself must be consistent.\n # ** Each end must be of understood type\n # ** Each end must have a valid sequence or no sequence\n # ** There must be no more than one instance of each name\n # ** WARN if there are ends with no namecounts\n # * TILE LIST\n # ** each tile must be of understood type (must parse)\n # ** ends in the tile list must be consistent (must merge)\n # ** there must be no more than one tile with each name\n # self.tiles.check_consistent()\n endsfromtiles = self.tiles.glues_from_tiles()\n\n # ** WARN if any end that appears does not have a complement used or vice versa\n # ** WARN if there are tiles with no name\n # * TILE + END\n # ** The tile and end lists must merge validly\n # (checks sequences, adjacents, types, complements)\n self.glues | endsfromtiles\n\n # ** WARN if tilelist has end references not in ends\n # ** WARN if merge is not equal to the endlist\n # ** WARN if endlist has ends not used in tilelist\n # * ADAPTERS / SEEDS\n # SEED stuff was here","def test_with_known_mls_error():\n linestrings = samples.mls_from_these_linestrings_list\n target_area = [box(*MultiLineString(linestrings).bounds)]\n branches, nodes = branches_and_nodes.branches_and_nodes(\n gpd.GeoSeries(linestrings), gpd.GeoSeries(target_area), tests.snap_threshold\n )\n for branch in branches.geometry:\n assert EE_branch not in str(branches[CONNECTION_COLUMN])\n assert isinstance(branch, LineString)\n assert branch.is_simple\n assert not branch.is_empty\n for node in nodes.geometry:\n assert isinstance(node, Point)\n assert not node.is_empty","def missing_branches(self):\n upstream_tags = self.upstream_model.tags_from_semver_point(\"0.8.7\")\n deb_branches = self.deb_model.base.branches_from_semver_point(\"0.8.7\")\n return list(set(upstream_tags) - set(deb_branches))","def check_branch(subcommand, branch):\n if subcommand != \"checkout\":\n return\n # first make sure actual branch name was given\n if branch is None:\n return \"Branch name to checkout must be supplied with '-b' option\"\n # next check that the local repo is clean\n cmd = [\"git\", \"status\", \"--untracked-files=no\", \"--porcelain\"]\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, universal_newlines=True)\n if p.stdout.strip():\n return \"Need to have clean working tree to checkout!\\n\\n\" + p.stdout\n # next check that the branch name doesn't already exist\n cmd = [\"git\", \"show-ref\", \"--verify\", \"--quiet\", \"refs/heads/\" + branch]\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False)\n if not p.returncode:\n return f\"Branch {branch!r} already exists\"","def _check_brackets(tokens: List[str]) -> True:\r\n brackets = []\r\n for token in tokens:\r\n if token in \"([\":\r\n brackets.append(token)\r\n elif token in \")]\":\r\n if not brackets:\r\n raise ExtraBracket(token)\r\n closing_bracket = brackets.pop()\r\n if ((token == \"(\" and closing_bracket != \")\") or\r\n (token == \"[\" and closing_bracket != \"]\")):\r\n raise BracketsMismatch(token, closing_bracket)\r\n if brackets:\r\n raise NoClosingBracket(brackets[0])\r\n return True","def branches(self) -> list[str]:\n _args: list[Arg] = []\n _ctx = self._select(\"branches\", _args)\n return _ctx.execute_sync(list[str])","def bracket_validator(brackets):\n bracket_stack = []\n for b in brackets:\n if b == '(' or b == '{' or b == '[':\n bracket_stack.append(b)\n\n elif b == ')' or b == '}' or b == ']':\n top = bracket_stack.pop()\n\n if b != bracket_match(top):\n return False\n\n return True","def test_examples():\n assert nz_bank_validate(*'01-902-0068389-00'.split('-'))\n assert nz_bank_validate(*'08-6523-1954512-001'.split('-'))\n assert nz_bank_validate(*'26-2600-0320871-032'.split('-'))","def _check_sanity(self, tags: List[str], n_words: int):\n n_out = 0\n\n for tag in tags:\n if (\"<\" not in tag) and (\">\" not in tag):\n n_out += 1\n\n return n_out == n_words","def _check_sanity(self, tags: List[str], n_words: int):\n n_out = 0\n\n for tag in tags:\n if (\"<\" not in tag) and (\">\" not in tag):\n n_out += 1\n\n return n_out == n_words","def delete_branches(config, args):\n for ref_name in lib.input_json_lines():\n yield _delete_ref(config.repo, \"heads/\" + ref_name, args.force, args.dry_run)","def branches_full(config, args):\n for b in config.repo.branches():\n yield config.repo.branch(b.name)","def test_branching(self):\r\n repo_dir = self.GIT_REPO_DIR\r\n # Test successful import from command\r\n if not os.path.isdir(repo_dir):\r\n os.mkdir(repo_dir)\r\n self.addCleanup(shutil.rmtree, repo_dir)\r\n\r\n # Checkout non existent branch\r\n with self.assertRaisesRegexp(GitImportError, GitImportError.REMOTE_BRANCH_MISSING):\r\n git_import.add_repo(self.TEST_REPO, repo_dir / 'edx4edx_lite', 'asdfasdfasdf')\r\n\r\n # Checkout new branch\r\n git_import.add_repo(self.TEST_REPO,\r\n repo_dir / 'edx4edx_lite',\r\n self.TEST_BRANCH)\r\n def_ms = modulestore()\r\n # Validate that it is different than master\r\n self.assertIsNotNone(def_ms.get_course(self.TEST_BRANCH_COURSE))\r\n\r\n # Attempt to check out the same branch again to validate branch choosing\r\n # works\r\n git_import.add_repo(self.TEST_REPO,\r\n repo_dir / 'edx4edx_lite',\r\n self.TEST_BRANCH)\r\n\r\n # Delete to test branching back to master\r\n delete_course(def_ms, contentstore(),\r\n self.TEST_BRANCH_COURSE,\r\n True)\r\n self.assertIsNone(def_ms.get_course(self.TEST_BRANCH_COURSE))\r\n git_import.add_repo(self.TEST_REPO,\r\n repo_dir / 'edx4edx_lite',\r\n 'master')\r\n self.assertIsNone(def_ms.get_course(self.TEST_BRANCH_COURSE))\r\n self.assertIsNotNone(def_ms.get_course(SlashSeparatedCourseKey.from_deprecated_string(self.TEST_COURSE)))","def test_branch_fail(repository: Repository) -> None:\n with pytest.raises(KeyError):\n repository.branch(\"branch\")","def _check_completeness(\n tagmap,\n taglist,\n namelist,\n exact_match,\n exact_order,\n error_missing_message,\n error_order_message,\n error_excess_message,\n ):\n valids = []\n for itag, tagkey in enumerate(taglist):\n found_obj = None\n for obj, objtags in tagmap.items():\n if tagkey in objtags:\n found_obj = obj\n break\n if exact_order:\n # if we get here order is wrong\n err = self._format_message(\n error_order_message, missing=obj.get_display_name(looker=self.crafter)\n )\n self.msg(err)\n raise CraftingValidationError(err)\n\n # since we pop from the mapping, it gets ever shorter\n match = tagmap.pop(found_obj, None)\n if match:\n valids.append(found_obj)\n elif exact_match:\n err = self._format_message(\n error_missing_message,\n missing=namelist[itag] if namelist else tagkey.capitalize(),\n )\n self.msg(err)\n raise CraftingValidationError(err)\n\n if exact_match and tagmap:\n # something is left in tagmap, that means it was never popped and\n # thus this is not an exact match\n err = self._format_message(\n error_excess_message,\n excess=[obj.get_display_name(looker=self.crafter) for obj in tagmap],\n )\n self.msg(err)\n raise CraftingValidationError(err)\n\n return valids","def walkbranch(stem, ancestors, branches):\n for child in stem.children(0):\n if child not in ancestors:\n continue\n\n build, reason = needsbuild(child)\n\n if build:\n if reason in (PARENTNEWER, MISSING):\n yield child, reason\n else:\n raise RuntimeError(\"unexpected reason\")\n\n elif reason is PARENTMISSING:\n # A parent derived from another stem is missing, so delay\n # build until that branch is traversed\n pass\n\n elif not build:\n # Child is up to date, so append it as a new 'stem' to walk down\n if child not in branches:\n branches.append(child)","def verify_ballot_consistency(self) -> bool:\n sbb_contents = self._sbb.get_sbb_contents()\n \n # First, validate the commitment consistency with the initial vote lists and final vote lists.\n for list_idx, proof in sbb_contents.consistency_proof.items():\n for vote_idx in range(len(proof)):\n proved_sv = proof[vote_idx]\n tu_list = []\n tv_list = []\n for row_idx, sv in enumerate(proved_sv):\n # Ensure that we are consistent with the initial and the final commitments\n if sv.get('u', None) is not None:\n val_init = sv['u_init']\n val_fin = sv['u_fin']\n val_uv = sv['u']\n val_t = sbb_contents.t_values[list_idx][row_idx][vote_idx]['tu']\n original_commitment = sbb_contents.svr_commitments[row_idx][vote_idx]['com_u']\n final_commitment = sbb_contents.vote_lists[list_idx][vote_idx][row_idx].com_u\n else:\n val_init = sv['v_init']\n val_fin = sv['v_fin']\n val_uv = sv['v']\n val_t = sbb_contents.t_values[list_idx][row_idx][vote_idx]['tv']\n original_commitment = sbb_contents.svr_commitments[row_idx][vote_idx]['com_v']\n final_commitment = sbb_contents.vote_lists[list_idx][vote_idx][row_idx].com_v\n key_init = sv['k_init']\n key_fin = sv['k_fin']\n \n # Verify the input and output commitments\n com_init = util.get_COM(util.bigint_to_bytes(key_init), util.bigint_to_bytes(val_init))\n com_fin = util.get_COM(util.bigint_to_bytes(key_fin), util.bigint_to_bytes(val_fin))\n if com_init != original_commitment:\n raise Exception(\"Failed to open the initial vote commitment\")\n if com_fin != final_commitment:\n raise Exception(\"Failed to open the final vote commitment\")\n \n # Verify the t-values\n if util.t_val(util.bigint_to_bytes(val_init), util.bigint_to_bytes(val_uv), self._M) != val_t:\n raise Exception(\"Failed to verify t value\")\n \n # Add t-values to their respective lists for lagrange checks\n tu_list.append(sbb_contents.t_values[list_idx][row_idx][vote_idx]['tu'])\n tv_list.append(sbb_contents.t_values[list_idx][row_idx][vote_idx]['tv'])\n \n # Check that tu_list and tv_list lagrange to (t, -t)\n rows = len(proved_sv)\n tu0 = self._lagrange(tu_list, rows, rows-1, self._M)\n tv0 = self._lagrange(tv_list, rows, rows-1, self._M)\n if util.val(tu0, tv0, self._M) != 0:\n # TODO: This does not work\n #raise Exception(\"Failed lagrange verification of t values\")\n pass\n return True","def test_branch_exceptions(self):\r\n # create bare repo that we can mess with and attempt an import\r\n bare_repo = os.path.abspath('{0}/{1}'.format(settings.TEST_ROOT, 'bare.git'))\r\n os.mkdir(bare_repo)\r\n self.addCleanup(shutil.rmtree, bare_repo)\r\n subprocess.check_output(['git', '--bare', 'init', ], stderr=subprocess.STDOUT,\r\n cwd=bare_repo)\r\n\r\n # Build repo dir\r\n repo_dir = self.GIT_REPO_DIR\r\n if not os.path.isdir(repo_dir):\r\n os.mkdir(repo_dir)\r\n self.addCleanup(shutil.rmtree, repo_dir)\r\n\r\n rdir = '{0}/bare'.format(repo_dir)\r\n with self.assertRaisesRegexp(GitImportError, GitImportError.BAD_REPO):\r\n git_import.add_repo('file://{0}'.format(bare_repo), None, None)\r\n\r\n # Get logger for checking strings in logs\r\n output = StringIO.StringIO()\r\n test_log_handler = logging.StreamHandler(output)\r\n test_log_handler.setLevel(logging.DEBUG)\r\n glog = git_import.log\r\n glog.addHandler(test_log_handler)\r\n\r\n # Move remote so fetch fails\r\n shutil.move(bare_repo, '{0}/not_bare.git'.format(settings.TEST_ROOT))\r\n try:\r\n git_import.switch_branch('master', rdir)\r\n except GitImportError:\r\n self.assertIn('Unable to fetch remote', output.getvalue())\r\n shutil.move('{0}/not_bare.git'.format(settings.TEST_ROOT), bare_repo)\r\n output.truncate(0)\r\n\r\n # Replace origin with a different remote\r\n subprocess.check_output(\r\n ['git', 'remote', 'rename', 'origin', 'blah', ],\r\n stderr=subprocess.STDOUT, cwd=rdir\r\n )\r\n with self.assertRaises(GitImportError):\r\n git_import.switch_branch('master', rdir)\r\n self.assertIn('Getting a list of remote branches failed', output.getvalue())","def test_branch_length(self):\n exp = ['(', '(', 'h', ':', '4.0', ',', 'p', ':', '1.1461E-5', ')',\n 'hp', ':', '1351.146436', ',', 'g', ')', 'hpg', ';']\n content = '((h:4.0,p:1.1461E-5)hp:1351.146436,g)hpg;'\n self._do_test(content, exp)","def test_equality_function(self):\r\n self.assertFalse(directories_equal(self.version1_nodrafts, self.version0_nodrafts))\r\n self.assertFalse(directories_equal(self.version1_drafts_extra_branch, self.version1_drafts))","def check_commit_for_branch( self\n , commit\n , branch_id\n , any_locked_files\n , case_conflict_checker ):\n rev = commit['sha1']\n if LOG.isEnabledFor(logging.DEBUG):\n LOG.debug(\"check_commit_for_branch() \"\n \"Checking branch={} mark={} sha1={} file-ct={} -- {}\"\n .format( branch_id\n , commit['mark']\n , p4gf_util.abbrev(rev)\n , len(commit['files'])\n , repr(commit['data'])[:20].splitlines()[0]))\n\n if self._already_copied_commit(rev, branch_id):\n return\n\n # following checks assume client has been set for branch\n self.ensure_branch_preflight(commit, branch_id)\n with self.ctx.switched_to_branch(\n self._current_branch\n , set_client=self.set_client_on_branch_switch\n ):\n if case_conflict_checker:\n case_conflict_checker.read_fast_export_commit(\n commit, self._current_branch)\n\n # Empty commits require root-level .p4gf_placeholder to be mapped\n # in the current branch view.\n if not commit['files'] and not self._is_placeholder_mapped():\n raise PreflightException(\n _(\"Empty commit {sha1} not permitted. Git Fusion branch views\"\n \" must include root to permit empty commits.\")\n .format(sha1=p4gf_util.abbrev(rev)))\n\n with Timer(CHECK_PROTECTS):\n self._check_protects(commit['author_p4user'], commit['files'])\n\n with Timer(CHECK_OVERLAP):\n self._check_overlap(commit)\n\n # fetch the branch setting only, without cascading to repo/global config\n if self._current_branch.is_read_only:\n raise PreflightException(_(\"Push to branch {branch} prohibited.\")\n .format(branch=self._current_branch.git_branch_name))\n self._check_stream_writable(commit)\n self._check_stream_in_classic(commit)\n\n LOG.debug('checking locked files under //{}/...'.format(self.ctx.p4.client))\n if any_locked_files:\n # Convert the git commit paths to depotPaths\n files_in_commit = [self.ctx.gwt_path(f['path']).to_depot()\n for f in commit['files']]\n LOG.debug(\"files_in_commit {0}\".format(files_in_commit))\n for f in files_in_commit:\n if f in any_locked_files:\n # Collect the names (and clients) of users with locked files.\n # Report back to the pusher so they can take appropriate action.\n msg = _('{file} - locked by {user}').format(file=f,\n user=any_locked_files[f])\n LOG.info(msg)\n raise PreflightException(msg)\n\n # +++ Spend time extracting Jobs and P4Changelist owner\n # here if we actually do need to call\n # the preflight-commit hook.\n if self.ctx.preflight_hook.is_callable():\n jobs = G2PJob.extract_jobs(commit['data'])\n jobs2 = G2PJob.lookup_jobs(self.ctx, jobs)\n self.ctx.preflight_hook(\n ctx = self.ctx\n , fe_commit = commit\n , branch_id = branch_id\n , jobs = jobs2\n )","def _check_overlap(self, fe_commit):\n # +++ Avoid O(b branches * r rev) checks when\n # overlap is impossible because current branch\n # overlaps no other branch.\n if self._current_branch not in self._overlapping_branch_list():\n return\n\n for fe_file in fe_commit['files']:\n gwt_path = fe_file['path']\n depot_path = self.ctx.gwt_to_depot_path(gwt_path)\n\n for branch in self._overlapping_branch_list():\n if branch == self._current_branch:\n continue\n if not branch.intersects_depot_path(depot_path):\n continue\n\n LOG.debug(\"_check_overlap() branch {br1} <> {br2}\"\n \" gwt={gwt:<40} {dp}\\n{view}\"\n .format(\n br1 = p4gf_util.abbrev(self._current_branch.branch_id)\n , br2 = p4gf_util.abbrev(branch.branch_id)\n , gwt = gwt_path\n , dp = depot_path\n , view = \"\\n\".join(branch.view_p4map.as_array())\n ))\n\n if self._current_branch.is_new_fp_from_push or branch.is_new_fp_from_push:\n current_branch_name = self._current_branch.git_branch_name\n if self._current_branch.is_new_fp_from_push:\n current_branch_name += '(new)'\n other_branch_name = branch.git_branch_name\n if branch.is_new_fp_from_push:\n other_branch_name += '(new)'\n human_msg = (_(\n \"Perforce: Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\\n\"\n \" You are attempting to push and create a new fully populated branch\\n\"\n \" with paths which overlap another branch. Contact your admin\\n\"\n \" to configure non-conflicting destination branch paths.\\n\"\n \" Branches: '{b1}', '{b2}'\")\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\n , gwt_path = gwt_path\n , depot_path = depot_path\n , b1 = current_branch_name\n , b2 = other_branch_name ))\n else:\n human_msg = (_(\n \"Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\"\n \" Paths that overlap multiple Git Fusion branches are read-only.\"\n \" Branches: '{b1}', '{b2}'\")\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\n , gwt_path = gwt_path\n , depot_path = depot_path\n , b1 = self._current_branch.branch_id\n , b2 = branch.branch_id ))\n raise PreflightException(human_msg)","def hard_reset_branches(args):\n checkout_branches(args)\n man = load_manifest()\n for (name, project) in man.projects.iteritems():\n print >>sys.stderr, \"Hard resetting tracking branch in project: %s\" % name\n repo = GitRepo(workdir_for_project(project))\n repo.check_command([\"reset\", \"--hard\", project.remote_refspec])","def list_all_branches(self) -> dict:\n try:\n branches_response = self.repo.get_branches()\n branches_list = []\n for branch in branches_response:\n branches_list.append(branch.raw_data.get('name'))\n return make_success_response(200, branches_list)\n except GithubException as github_exc:\n return make_error_response(github_exc.status, github_exc.data)","def missing_branches(self):\n upstream_tags = self.upstream_model.tags_from_semver_point(\n enums.K8S_STARTING_SEMVER\n )\n deb_branches = self.deb_model.base.branches_from_semver_point(\n enums.K8S_STARTING_SEMVER\n )\n return list(set(upstream_tags) - set(deb_branches))","def _listBranches(self):\n assert self.wc.exists('branches')\n branches = self.wc.ls('branches')\n\n # Some early release branches used a different naming scheme\n # that doesn't sort properly with new-style release names. We\n # filter those out here, along with empty lines.\n branches = [b.strip('/') for b in branches\n if MELANGE_RELEASE_RE.match(b.strip('/'))]\n\n return sorted(branches)","def test_heads_contains_false(repository: Repository) -> None:\n assert \"branch\" not in repository.heads","def check_release_status(self, release_name, release_branch):\n logger.debug('GitHubAPI.check_release_status args: {}; {}'.format(\n release_name, release_branch)\n )\n release_version = extract_release_branch_version(release_name)\n release_branch_base = build_release_base_name(get_config())\n # Assume that this is a new release\n # Check if the release branch is merged into master\n try:\n merge_status = self.compare(\n 'master',\n release_branch\n ).get('status')\n except requests.exceptions.HTTPError as e:\n logger.debug('HTTPError: {}'.format(e.message))\n if not e.response.status_code == 404:\n raise e\n else:\n # can be one of diverged, ahead, behind, identical according to\n # http://stackoverflow.com/a/23969867\n if merge_status in ['diverged', 'ahead']:\n raise Exception(\n 'Release must be merged into master before release')\n return\n\n # if the release branch does not exist, then we end up here,\n # Assume that it is a hotfix\n raw_version = self.latest_release().get('name', '')\n if raw_version.startswith(release_branch_base):\n raw_version = raw_version[len(release_branch_base):]\n\n version = extract_year_week_version(raw_version)\n logger.debug(version)\n if extract_year_week_version(release_version) != version:\n raise Exception(\n 'New release version does not match the current release, '\n 'we expected a hotfix.'\n )\n\n return","def check_label_validity(labels):\n for label in labels:\n # If labels not in {0, 1} not valid\n if label not in [0, 1]:\n return False\n return True","def _valid_ref_type(ref):\n\n git_branch = \"refs/heads/\"\n git_tag = \"refs/tags/\"\n if ref.startswith(git_branch) or ref.startswith(git_tag) or len(ref) == 40:\n return True\n return False","def test_tracebranches_errors_on_invalid_model(mstr):\n with pytest.raises(CommandError):\n call_command('tracebranches', mstr)","def is_bank_name_valid(name_to_check: str):\n def is_name_short_enough():\n return True if len(name_to_check) <= 12 else False\n\n def is_name_only_letter():\n return True if name_to_check.isalpha() else False\n\n return True if is_name_short_enough() and is_name_only_letter() else False","def branch_exists(branch_name, local_only=False, directory=None):\n for branch in get_branches(local_only, directory):\n if branch.startswith('remotes/'):\n branch = branch.split('/')\n if len(branch) > 2:\n branch = '/'.join(branch[2:])\n if branch_name == branch:\n return True\n else:\n if branch_name == branch:\n return True\n return False","def branch_exists(branch):\n\n try:\n git('show-ref', branch)\n return True\n except subprocess.CalledProcessError:\n return False","def tree(label, branches=[]):\n for branch in branches:\n assert is_tree(branch), 'branches must be trees'\n return [label] + list(branches)","def check_valid(indexes):\n # Check if any substrings has any versions that are the opposite of it anywhere in that line.\n valid = False\n for k, v in indexes.items():\n swapped = k[::-1]\n other = indexes.get(swapped)\n # Check to see if the swapped version exists in the dictionary.\n if other:\n # 'aaa' case, these are invalid matches, don't bother checking further.\n if k == swapped:\n continue\n # single occurence case\n if len(v) == 1 and len(other) == 1:\n # Case where both occur inside or outsid brackets.\n if (int(v[0]) % 2) == (int(other[0]) % 2):\n continue\n else:\n valid = True\n else:\n # Use sets to eliminate duplicates in the same chunk.\n v_s = set(v)\n other_s = set(other)\n possible_combinations = [(x % 2, y % 2) for x in v_s for y in other_s]\n # For a pairing to be valid, one part needs to be in an even chunk and the other in an odd ([]) chunk.\n if (1, 0) in possible_combinations or (0, 1) in possible_combinations:\n valid = True\n return valid","def stale_pr_branches(config, args):\n repo = config.repo\n for pr in repo.pull_requests(state=\"closed\"):\n if pr.head.repo == pr.base.repo and repo.branch(pr.head.ref):\n yield {\n \"html_url\": pr.html_url,\n \"base_branch\": pr.base.ref,\n \"head_branch\": pr.head.ref,\n }","def has_branch(self, branch):\n if self.branch == branch:\n return True\n return False","def unique_branch_name(base_name):\n repo = git.repo()\n branches = repo.branches()\n collision = True\n count = 1\n while collision:\n new_branch = base_name + \"-bak-\" +str(count)\n collision = next((x for x in branches if x == new_branch), False)\n count += 1\n return new_branch","def branches(self):\r\n url = '{0}/branches/'.format(self.get_url())\r\n request = http.Request('GET', url)\r\n\r\n return request, parsers.parse_json","def delete_branch_on_merge(org: Organization, repo: Repository,\n branches: Dict[str, Branch]) -> List[Change[str]]:\n def _set_delete_branch_on_merge(change: Change[str]) -> Change[str]:\n print_debug(\"[%s] Enforcing branch deletion on PR merge\" % highlight(repo.name))\n try:\n repo.edit(delete_branch_on_merge=value)\n except GithubException:\n return change.failure()\n\n return change.success()\n\n if not repo.delete_branch_on_merge:\n change = Change(\n meta=ChangeMetadata(\n executor=_set_delete_branch_on_merge,\n ),\n action=ChangeActions.REPLACE,\n before=\"On PR merge: %s\" % \"Delete branch\" if repo.delete_branch_on_merge else \"Keep branch\",\n after=\"On PR merge: %s\" % \"Delete branch\" if value else \"Keep branch\"\n )\n return [change]\n return []","def test_commit_on_unborn_branch(tmp_path: Path) -> None:\n repository = Repository.init(tmp_path / \"repository\")\n repository.commit(message=\"initial\")\n\n assert not repository.head.commit.parents","def _validate_commit(\n commit_sha1, merge, author, committer, title, separator, body):\n errors = []\n\n # List of words a commit title can start with\n commit_title_start_words = filter(\n lambda x: x, COMMIT_TITLE_START_WORDS.splitlines())\n\n author_errors = _validate_email(author, 'Author')\n committer_errors = _validate_email(committer, 'Committer')\n\n if author_errors:\n errors.extend(author_errors)\n if committer_errors:\n errors.extend(committer_errors)\n\n title_words = title.split(' ', 1)\n\n # Check if in imperative tense\n if re.search(r'(ed|ing|s)$', title_words[0]):\n errors.append((\n 'title-imperative-tense-check',\n 'Commit title is not in imperative tense'))\n\n # Check if first word is capitalized\n if re.match(r'^[^A-Z]', title_words[0]):\n errors.append((\n 'title-capitalization-check',\n 'Commit title is not capitalized'))\n\n # Check if title begins with known start word\n if title_words[0] not in commit_title_start_words:\n errors.append((\n 'title-verb-check',\n 'Commit title does not begin with a verb'))\n\n # Check if this is a fixup! commit\n if re.match(r'^fixup!', title_words[0]):\n errors.append((\n 'title-fixup-check',\n 'Commit title starts with fixup! '))\n\n # Check if this is a squash! commit\n if re.match(r'^squash!', title_words[0]):\n errors.append((\n 'title-squash-check',\n 'Commit title starts with squash! '))\n\n # Check if the commit title ends in whitespace or punctuation\n if len(title_words) > 1 and re.search(r'[\\s\\W]$', title_words[1]):\n errors.append((\n 'title-whitespace-punctuation-check',\n 'Commit title ends in whitespace or punctuation'))\n\n # Check if the title is greater than 50 characters in length\n if len(title) > 50:\n errors.append((\n 'title-length-check',\n 'Commit title longer than 50 characters'))\n\n # Check if separator line (between title and body) is empty\n if separator is not None and separator != '':\n errors.append((\n 'message-separator-check',\n 'Missing blank line between title and body'))\n\n # Check if the commit message has a body\n if body == []:\n errors.append((\n 'body-check',\n 'Missing commit message body'))\n\n # Check if any line in the body is greater than 72 characters in legnth\n for body_line in body:\n if len(body_line) <= 72:\n continue\n errors.append((\n 'body-length-check',\n 'Commit message body line > 72 characters'))\n break\n\n # Check if commit is a merge commit\n if merge is not None:\n errors.append((\n 'commit-merge-check',\n 'Commit is a merge commit'))\n\n # Check commit diff for whitespace errors\n git_diff_cmd = shlex.split(\n 'git show --check {commit_sha1}'.format(\n commit_sha1=commit_sha1))\n\n has_whitespace_issue = None\n f, _ = tempfile.mkstemp()\n has_whitespace_issue = subprocess.call(git_diff_cmd,\n stdout=f, stderr=f, close_fds=True)\n os.close(f)\n\n if has_whitespace_issue:\n errors.append((\n 'diff-whitespace-check',\n 'Commit diff has whitespace issues'))\n\n return errors","def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\n\n op = conn_graph.get_all_ops()[op_info.module_name]\n return_bool = True\n product = op.output\n if \"branch\" not in product.name:\n logger.error(\"branch not in product name\")\n return_bool = False\n if len(product.consumers) > 1:\n logger.error(\"branch op is not parent op's only consumer\")\n return_bool = False\n branch_op = product.consumers[0]\n if branch_op.type != \"branch\":\n logger.error(\"parent op's child op is not of type branch\")\n return_bool = False\n branch_product = branch_op.output\n if \"multiple_ops\" not in branch_product.name:\n logger.error(\"multiple_ops not in branch op's product's name\")\n return_bool = False\n if len(branch_product.consumers) <= 1:\n logger.error(\"branch op's product has one or fewer consumers\")\n return_bool = False\n for consumer in branch_product.consumers:\n for input_product in consumer.inputs:\n if input_product.producer == op:\n logger.error(\"parent op is still one of child op's inputs (as opposed to branch op)\")\n return_bool = False\n return return_bool","def ticket_branches(self, msrp, cred_hash):\n branches = []\n repos = self.get_repos()\n if not repos['status']:\n return repos\n\n for repo in repos['data']:\n response = self.find_branch(repo_name=repo['name'], msrp=msrp, cred_hash=cred_hash)\n if response['status']:\n branches.append({'repo': repo['name'], 'branches': response['data'], 'all': response['all']})\n\n if len(branches) > 0:\n return {'status': True, 'data': branches}\n else:\n return {'status': False, 'data': f'No branches found with MSRP {msrp}'}","def _verify_all_states_reachable(states_list):\n\n # This queue stores state names.\n processed_queue = []\n curr_queue = [states_list[0]['name']]\n\n while curr_queue:\n curr_state = curr_queue[0]\n curr_queue = curr_queue[1:]\n\n if curr_state in processed_queue:\n continue\n\n processed_queue.append(curr_state)\n\n curr_state_ind = next(ind for ind, state in enumerate(states_list)\n if state['name'] == curr_state)\n\n for handler in states_list[curr_state_ind]['widget']['handlers']:\n for rule in handler['rule_specs']:\n dest_state = rule['dest']\n if (dest_state not in curr_queue and\n dest_state not in processed_queue and\n dest_state != feconf.END_DEST):\n curr_queue.append(dest_state)\n\n if len(states_list) != len(processed_queue):\n unseen_states = list(\n set([s['name'] for s in states_list]) - set(processed_queue))\n raise Exception('The following states are not reachable from the '\n 'initial state: %s' % ', '.join(unseen_states))","def multi_bracket_validation(input):\n brackets = Stack()\n for i in input:\n if i == '(' or i == '[' or i == '{':\n brackets.push(i)\n elif i == ')' or i == ']' or i == '}':\n if brackets._size == 0:\n return False\n elif i == ')' and brackets.top.val == '(':\n brackets.pop()\n elif i == ']' and brackets.top.val == '[':\n brackets.pop()\n elif i == '}' and brackets.top.val == '{':\n brackets.pop()\n else:\n return False\n\n if brackets._size != 0:\n return False\n return True","def is_valid_commits(args):\n if args.commits is not None:\n return True\n return False","def master_branch(branch_name):\n\n if branch_name in MASTER_BRANCHES:\n return True\n\n return False","def test_branch_name_set(repository: Repository) -> None:\n branch = repository.head\n with pytest.raises(AttributeError):\n branch.name = \"teapot\" # type: ignore[misc]","def _check_logic_defined(name_list, logic_list):\n _check_logic_defined.failed = False\n\n # We modify the behaviour of boolExp.parseString so that the parsing also\n # check if variables exist.\n def check_var(var):\n if var[0] not in name_list:\n print(\"Error: unkown variable %s\" % var[0], file=stderr)\n _check_logic_defined.failed = True\n return var\n\n varName.setParseAction(check_var)\n\n for string in logic_list:\n if not _check_logic_syntax(string):\n print(\"Error: syntax error %s\" % string, file=stderr)\n return False\n if _check_logic_defined.failed:\n varName.setParseAction(lambda x: x)\n return False\n\n varName.setParseAction(lambda x: x)\n return True","def test_str_not_equal_str(self):\n # compact version\n assert_that(Condition.is_valid(\n 'not \"{{ env.BRANCH_NAME }}\" == \"master\"'), equal_to(True))\n # more spaces around are allowed\n assert_that(Condition.is_valid(\n ' not \"{{ env.BRANCH_NAME }}\" == \"master\" '), equal_to(True))\n # compact version\n assert_that(Condition.is_valid(\n 'not \"{{ variables.BRANCH_NAME }}\" == \"master\"'), equal_to(True))\n # more spaces around are allowed\n assert_that(Condition.is_valid(\n ' not \"{{ variables.BRANCH_NAME }}\" == \"master\" '), equal_to(True))","def commit_check(ctx):\n result = ctx.run(f\"{VENV_PREFIX} cz check --rev-range master..\", warn=True)\n if result.exited == 3: # NO_COMMIT_FOUND\n exit(0)\n else:\n exit(result.exited)","def checkBC(bc):\n if isinstance(bc, string_types):\n bc = [bc, bc]\n assert isinstance(bc, list), 'bc must be a list'\n assert len(bc) == 2, 'bc must have two elements'\n\n for bc_i in bc:\n assert isinstance(bc_i, string_types), \"each bc must be a string\"\n assert bc_i in ['dirichlet', 'neumann'], (\"each bc must be either,\"\n \"'dirichlet' or 'neumann'\")\n return bc","def brepalgo_IsValid(*args):\n return _BRepAlgo.brepalgo_IsValid(*args)","def check(self):\n badNamedAssetNode = list()\n\n # prog = re.compile(\"^[A-Z]{4}[0-9]{2}_C_[0-9]{3}_GAST[0-9]{2}Shape$\")\n prog = re.compile(\"^[A-Z]{4}[0-9]{2}_C_[0-9]{3}_G[A-Z]{3}[0-9]{2}Shape$\")\n progFx = re.compile(\n \"^[A-Z]{4}[0-9]{2}_C_[0-9]{3}_G[A-Z]{2}[0-9]{2}Shape$\")\n\n for assetNode in pm.ls(type=\"gAsset\"):\n nodename = assetNode.nodeName(stripNamespace=True)\n if not prog.match(nodename):\n if not progFx.match(nodename):\n badNamedAssetNode.append(assetNode)\n\n if not badNamedAssetNode:\n self.status = \"OK\"\n else:\n self.status = self.errorMode\n self.errorNodes = badNamedAssetNode\n for mesh in badNamedAssetNode:\n self.addError(\"%s is not a legal asset node name\" % mesh)\n self.errorMessage = \"%s illegal asset node name(s)\" % (\n len(badNamedAssetNode))","def has_invalid_targets(self):\n return len(self._combined_invalid_versioned_targets.targets) > 0","def validate_change(ticket):\n # First ensure topic line mentions tickets, and pull them out.\n topic = COMMIT_MSG.split('\\n', 1)[0]\n fix_tickets = re.findall(\"[A-Z]{2,5}-[0-9]{1,6}\", topic)\n if len(fix_tickets) == 0:\n print \"\\n\\n\\n\\n\\n*********\\nERROR: commit message does not name a ticket!\"\n return False\n\n # Now get list of approved tickets from master ticket, and ensure\n # all \"fixed\" tickets are approved.\n approved_tickets = get_approved_tickets(ticket)\n for tick in fix_tickets:\n if not tick in approved_tickets:\n print \"\\n\\n\\n\\n\\n*********\\nERROR: ticket {} is not approved (see approval ticket {})\".format(\n tick, ticket)\n return False\n return True","def validateBed(df):\n try:\n msg = ''\n if df.isnull().values.any() == True: \n msg = 'Missing values' + '\\n' + str(df.isnull().sum())\n return [False, msg]\n if (all(x in ['+', '-'] for x in df['strand'].cat.categories.tolist())) != True:\n msg = 'Bad strand symbol(has to be + or -)'\n return [False, msg]\n return [True, msg]\n except (TypeError, AttributeError, KeyError):\n return [False, 'Not a valid dataframe']"],"string":"[\n \"def _check_branches(num_branches, num_blocks, in_channels, num_channels):\\n if num_branches != len(num_blocks):\\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_BLOCKS({len(num_blocks)})'\\n raise ValueError(error_msg)\\n if num_branches != len(num_channels):\\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_CHANNELS({len(num_channels)})'\\n raise ValueError(error_msg)\\n if num_branches != len(in_channels):\\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_INCHANNELS({len(in_channels)})'\\n raise ValueError(error_msg)\",\n \"def _check_branches(self, num_branches, in_channels):\\n if num_branches != len(in_channels):\\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_INCHANNELS({len(in_channels)})'\\n raise ValueError(error_msg)\",\n \"def validate(cfg: defs.Config) -> List[str]: # noqa: C901\\n res: List[str] = []\\n\\n def check_branch(\\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\\n ) -> None:\\n \\\"\\\"\\\"Validate versions within a single branch.\\\"\\\"\\\"\\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\\n\\n if not RE_BRANCH_NAME.match(branch_name):\\n res.append(f\\\"{comp_name}: Invalid branch name: {branch_name}\\\")\\n\\n for ver, version in sorted(branch.items()):\\n if not RE_VERSION_STRING.match(ver):\\n res.append(f\\\"{comp_name}/{branch_name}: Invalid version string: {ver}\\\")\\n\\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\\n if version.outdated:\\n update_to = [\\n o_version\\n for o_version in branch.values()\\n if not o_version.outdated\\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\\n == other_cksums\\n ]\\n if len(update_to) != 1:\\n res.append(\\n f\\\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \\\"\\n f\\\"versions to update to instead of exactly one\\\"\\n )\\n else:\\n bad_files = sorted(\\n relpath\\n for relpath, (path, fdata) in driver_cksums.items()\\n if util.file_sha256sum(path) != fdata.sha256\\n )\\n if bad_files:\\n res.append(f\\\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\\\")\\n\\n if not uptodate_files:\\n uptodate_files = driver_cksums\\n elif uptodate_files != driver_cksums:\\n res.append(\\n f\\\"{comp_name}/{branch_name}: All the up-to-date versions should \\\"\\n f\\\"define the same set of files with the same checksums\\\"\\n )\\n\\n if not any(not version.outdated for version in branch.values()):\\n res.append(f\\\"{comp_name}/{branch_name}: No non-outdated versions\\\")\\n\\n def check_component(comp_name: str, comp: defs.Component) -> None:\\n \\\"\\\"\\\"Validate the definition of a single component.\\\"\\\"\\\"\\n if not RE_COMP_NAME.match(comp_name):\\n res.append(f\\\"Invalid component name: {comp_name}\\\")\\n\\n for branch_name, branch in sorted(comp.branches.items()):\\n check_branch(comp_name, branch_name, branch)\\n\\n for comp_name, comp in sorted(cfg.all_components.components.items()):\\n check_component(comp_name, comp)\\n\\n return res\",\n \"def check_branch(\\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\\n ) -> None:\\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\\n\\n if not RE_BRANCH_NAME.match(branch_name):\\n res.append(f\\\"{comp_name}: Invalid branch name: {branch_name}\\\")\\n\\n for ver, version in sorted(branch.items()):\\n if not RE_VERSION_STRING.match(ver):\\n res.append(f\\\"{comp_name}/{branch_name}: Invalid version string: {ver}\\\")\\n\\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\\n if version.outdated:\\n update_to = [\\n o_version\\n for o_version in branch.values()\\n if not o_version.outdated\\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\\n == other_cksums\\n ]\\n if len(update_to) != 1:\\n res.append(\\n f\\\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \\\"\\n f\\\"versions to update to instead of exactly one\\\"\\n )\\n else:\\n bad_files = sorted(\\n relpath\\n for relpath, (path, fdata) in driver_cksums.items()\\n if util.file_sha256sum(path) != fdata.sha256\\n )\\n if bad_files:\\n res.append(f\\\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\\\")\\n\\n if not uptodate_files:\\n uptodate_files = driver_cksums\\n elif uptodate_files != driver_cksums:\\n res.append(\\n f\\\"{comp_name}/{branch_name}: All the up-to-date versions should \\\"\\n f\\\"define the same set of files with the same checksums\\\"\\n )\\n\\n if not any(not version.outdated for version in branch.values()):\\n res.append(f\\\"{comp_name}/{branch_name}: No non-outdated versions\\\")\",\n \"def branch_exists(nametag, branches):\\n for branch in branches:\\n if branches[branch].name == nametag:\\n return True\\n return False\",\n \"def validate_branch_config(branch_cfg, branch, n):\\n res = False\\n if len(branch) == 4:\\n if \\\"double\\\" in branch_cfg:\\n diff1 = branch[0]-branch[1]\\n diff3 = branch[2]-branch[3]\\n diff2 = branch[1]%n-branch[2]%n\\n diff_adj_clk = [-3, 1]\\n diff_adj_clk_ctr = [-e for e in diff_adj_clk]\\n diff_opp = [2, -2]\\n if \\\"adjacent\\\" and \\\"clockwise\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk:\\n res = True\\n elif \\\"adjacent\\\" and \\\"counter clockwise\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_adj_clk_ctr:\\n res = True\\n elif \\\"opposite\\\" in branch_cfg and diff1 == diff3 == 0 and diff2 in diff_opp:\\n res = True\\n elif \\\"single\\\" in branch_cfg:\\n res = True\\n elif len(branch) == 2:\\n res = True\\n return res\",\n \"def validate_branch_ops(conn_graph: ConnectedGraph):\\n\\n def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\\n \\\"\\\"\\\"\\n Look inside conn_graph ops and products for branch ops, and validate connections to parent and child ops\\n \\\"\\\"\\\"\\n\\n op = conn_graph.get_all_ops()[op_info.module_name]\\n return_bool = True\\n product = op.output\\n if \\\"branch\\\" not in product.name:\\n logger.error(\\\"branch not in product name\\\")\\n return_bool = False\\n if len(product.consumers) > 1:\\n logger.error(\\\"branch op is not parent op's only consumer\\\")\\n return_bool = False\\n branch_op = product.consumers[0]\\n if branch_op.type != \\\"branch\\\":\\n logger.error(\\\"parent op's child op is not of type branch\\\")\\n return_bool = False\\n branch_product = branch_op.output\\n if \\\"multiple_ops\\\" not in branch_product.name:\\n logger.error(\\\"multiple_ops not in branch op's product's name\\\")\\n return_bool = False\\n if len(branch_product.consumers) <= 1:\\n logger.error(\\\"branch op's product has one or fewer consumers\\\")\\n return_bool = False\\n for consumer in branch_product.consumers:\\n for input_product in consumer.inputs:\\n if input_product.producer == op:\\n logger.error(\\\"parent op is still one of child op's inputs (as opposed to branch op)\\\")\\n return_bool = False\\n return return_bool\\n\\n # pylint: disable=protected-access\\n module_identifier = StructureModuleIdentifier(conn_graph.graph, conn_graph._starting_op_names,\\n conn_graph._valid_ops)\\n num_branches_found = 0\\n for tf_op in conn_graph.graph.get_operations():\\n # Ignore ops which were not found in the initial depth first search\\n if tf_op not in module_identifier.processed_ops:\\n continue\\n\\n found_branch = False\\n for output_tensor in tf_op.outputs:\\n if len(output_tensor.consumers()) > 1:\\n # Potential branch op. Check if children go to separate modules\\n child_module_set = set()\\n for consumer_op in output_tensor.consumers():\\n if consumer_op in module_identifier._valid_ops:\\n child_module_info = module_identifier.get_op_info(consumer_op)\\n child_module_set.add(child_module_info.module_name)\\n\\n # If children go to separate modules, this should be a branch op\\n if len(child_module_set) > 1:\\n found_branch = True\\n break\\n\\n if found_branch:\\n num_branches_found += 1\\n tf_op_info = module_identifier.get_op_info(tf_op)\\n if not check_for_branch_op(tf_op_info):\\n return False\\n\\n logger.info(\\\"Found %s branches\\\", num_branches_found)\\n return True\",\n \"def is_nametag_reachable(nametag, branches):\\n for branch in branches:\\n for next_nametag in branches[branch].next_nametags:\\n if next_nametag == nametag:\\n return True\\n return False\",\n \"def ensure_tracking_branches(args):\\n man = load_manifest()\\n for (name, project) in man.projects.iteritems():\\n repo = GitRepo(workdir_for_project(project))\\n branch_missing = repo.command(\\n [\\\"rev-parse\\\", \\\"--verify\\\", \\\"-q\\\", project.refspec],\\n capture_stdout=True)\\n \\n if branch_missing:\\n logging.warn(\\\"Branch %s does not exist in project %s. checking out.\\\" %\\n (project.refspec, name))\\n repo.command([\\\"branch\\\", \\\"--track\\\",\\n project.tracking_branch, project.remote_refspec])\",\n \"def validate_empty_branches(nanowire, min_free_branch, msg):\\n score = 0\\n valid = False\\n\\n for intersection in nanowire:\\n free_b = 0\\n for branch in intersection:\\n min_free_pos = len(branch)\\n free_p = 0\\n for tup in branch:\\n if not isinstance(tup, dict):\\n continue\\n if list(tup.values())[0] == 0:\\n free_p += 1\\n else:\\n free_p = 0\\n if free_p>=min_free_pos:\\n free_b += 1\\n if free_b>=min_free_branch:\\n valid = True\\n\\n if valid:\\n score += 1\\n # if score==0:\\n # raise exception.NoEmptyBranchException(msg)\\n return score\",\n \"def _validate_branch_args(self) -> None:\\n lk = set(self.branch_losses.keys())\\n dk = set(self.model._get_inner_keys(self.model.heads))\\n has_same_keys = lk == dk\\n\\n mk = None\\n if self.branch_metrics is not None:\\n mk = set(self.branch_metrics.keys())\\n has_same_keys = dk == lk == mk\\n\\n ek = None\\n if self.branch_loss_params is not None:\\n ek = set(self.branch_loss_params.keys())\\n has_same_keys = dk == lk == mk == ek\\n\\n if not has_same_keys:\\n raise ValueError(\\n \\\"Got mismatching keys for branch dict args. \\\"\\n f\\\"Branch losses: {lk}. \\\"\\n f\\\"Branch loss params: {ek}. \\\"\\n f\\\"Decoder branches: {dk}. \\\"\\n f\\\"Metrics: {mk}. \\\"\\n f\\\"(`metrics`, and `branch_loss_params` can be None)\\\"\\n )\",\n \"def test_is_valid_git_refname(self):\\n # valid branchnames\\n self.assertTrue(is_valid_git_refname('master'))\\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\\n self.assertTrue(is_valid_git_refname('refname/bar'))\\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\\n unichr(40))))\\n self.assertTrue(is_valid_git_refname('master@head'))\\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\\n\\n # case happening with git < 1.6.6\\n self.assertFalse(is_valid_git_refname(\\n '--abbrev-ref\\\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\\n\\n # different invalid refnames, for a description of each group see the\\n # man page of git check-ref-format\\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\\n self.assertFalse(is_valid_git_refname('bar.lock'))\\n self.assertFalse(is_valid_git_refname('.foobar'))\\n\\n self.assertFalse(is_valid_git_refname('ref..name'))\\n\\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\\n unichr(177))))\\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\\n unichr(39))))\\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\\n self.assertFalse(is_valid_git_refname('refname spam'))\\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\\n\\n self.assertFalse(is_valid_git_refname('areyourandy?'))\\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\\n\\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\\n\\n self.assertFalse(is_valid_git_refname('dotEnding.'))\\n\\n self.assertFalse(is_valid_git_refname('@{branch'))\\n\\n self.assertFalse(is_valid_git_refname('contains\\\\\\\\slash'))\\n\\n self.assertFalse(is_valid_git_refname('$newbranch'))\",\n \"def _check_branch(opt, params):\\n\\n # Check the current branch and hash\\n _get_branch(opt)\\n\\n if params.git_branch != opt.git_branch or params.git_hash != opt.git_hash:\\n msg = 'You are not on the right branch or commit. Please run the following in the repository: \\\\n'\\n msg += f'git checkout {params.git_branch}\\\\n'\\n msg += f'git revert {params.git_hash}'\\n sys.exit(msg)\",\n \"def test_is_valid_git_refname(self):\\r\\n # valid branchnames\\r\\n self.assertTrue(is_valid_git_refname('master'))\\r\\n self.assertTrue(is_valid_git_refname('debuggatron_2000'))\\r\\n self.assertTrue(is_valid_git_refname('refname/bar'))\\r\\n self.assertTrue(is_valid_git_refname('ref.nameslu/_eggs_/spam'))\\r\\n self.assertTrue(is_valid_git_refname('valid{0}char'.format(\\r\\n unichr(40))))\\r\\n self.assertTrue(is_valid_git_refname('master@head'))\\r\\n self.assertTrue(is_valid_git_refname('bar{thing}foo'))\\r\\n\\r\\n # case happening with git < 1.6.6\\r\\n self.assertFalse(is_valid_git_refname(\\r\\n '--abbrev-ref\\\\nbaa350d7b7063d585ca293fc16ef15e0765dc9ee'))\\r\\n\\r\\n # different invalid refnames, for a description of each group see the\\r\\n # man page of git check-ref-format\\r\\n self.assertFalse(is_valid_git_refname('bar/.spam/eggs'))\\r\\n self.assertFalse(is_valid_git_refname('bar.lock/spam/eggs'))\\r\\n self.assertFalse(is_valid_git_refname('bar.lock'))\\r\\n self.assertFalse(is_valid_git_refname('.foobar'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('ref..name'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname(u'invalid{0}char'.format(\\r\\n unichr(177))))\\r\\n self.assertFalse(is_valid_git_refname('invalid{0}char'.format(\\r\\n unichr(39))))\\r\\n self.assertFalse(is_valid_git_refname('ref~name/bar'))\\r\\n self.assertFalse(is_valid_git_refname('refname spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar/foo/eggs~spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar:_spam_'))\\r\\n self.assertFalse(is_valid_git_refname('eggtastic^2'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('areyourandy?'))\\r\\n self.assertFalse(is_valid_git_refname('bar/*/spam'))\\r\\n self.assertFalse(is_valid_git_refname('bar[spam]/eggs'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('/barfooeggs'))\\r\\n self.assertFalse(is_valid_git_refname('barfooeggs/'))\\r\\n self.assertFalse(is_valid_git_refname('bar/foo//////eggs'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('dotEnding.'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('@{branch'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('contains\\\\\\\\slash'))\\r\\n\\r\\n self.assertFalse(is_valid_git_refname('$newbranch'))\",\n \"def protect_pr_branch_with_tests_if_any_exist(org: Organization, repo: Repository,\\n branches: Dict[str, Branch]) -> List[Change[str]]:\\n def execute_test_protection(change: Change[str], branch: Branch, existing_checks: Set[str],\\n known_status_checks: Set[str], known_checkruns: Set[str]) -> Change[str]:\\n\\n all_known_checks = known_status_checks | known_checkruns # For convenience later to treat them as a single set\\n\\n print_debug(\\\"[%s] Changing status checks on branch '%s' to [%s]\\\" %\\n (highlight(repo.name), highlight(branch.name),\\n highlight(\\\", \\\".join(list(all_known_checks)))))\\n try:\\n if existing_checks:\\n branch.edit_required_status_checks(strict=True, contexts=list(all_known_checks))\\n else:\\n safe_branch_edit_protection(\\n branch,\\n strict=True,\\n contexts=list(all_known_checks),\\n )\\n except GithubException as e:\\n print_error(\\\"Can't edit required status checks on repo %s branch %s: %s\\\" %\\n (repo.name, branch.name, str(e)))\\n return change.failure()\\n return change.success()\\n\\n prb = get_pr_branch(repo, branches)\\n if not prb:\\n return []\\n\\n existing_checks = set() # type: Set[str]\\n try:\\n rqs = prb.get_required_status_checks()\\n except GithubException:\\n # the repository has currently no status checks\\n pass\\n else:\\n if len(rqs.contexts) > 0:\\n # The repository already has some status checks\\n existing_checks = set(rqs.contexts)\\n print_debug(\\\"Branch %s on repo %s already has status checks [%s]\\\" %\\n (highlight(prb.name), highlight(repo.name), highlight(\\\", \\\".join(existing_checks))))\\n\\n # the repository currently has no status checks, let's see if any came in within the last 7 days\\n sevendaysago = datetime.now() - timedelta(days=7)\\n commits = repo.get_commits(prb.name, since=sevendaysago)\\n known_status_checks = set() # type: Set[str]\\n known_checkruns = set() # type: Set[str]\\n for commit in commits:\\n for status in commit.get_statuses(): # type: CommitStatus\\n if status.context not in known_status_checks:\\n print_debug(\\\"New status check [%s]: %s %s '%s'\\\" %\\n (commit.sha, status.updated_at,\\n status.context, status.description))\\n known_status_checks.add(status.context)\\n for checkrun in commit.get_check_runs(): # type: CheckRun\\n if checkrun.name not in known_checkruns:\\n print_debug(\\\"New check run [%s]: %s %s %s\\\" %\\n (commit.sha, checkrun.completed_at, checkrun.name, checkrun.app))\\n known_checkruns.add(checkrun.name)\\n\\n all_known_checks = known_status_checks | known_checkruns # For convenience later to treat them as a single set\\n print_debug(\\\"Found status checks [%s]\\\" % \\\", \\\".join(all_known_checks))\\n\\n if all_known_checks and all_known_checks != existing_checks:\\n # add all known checks as required checks\\n print_debug('Adding checks [%s] to branch %s on repo %s' %\\n (highlight(\\\", \\\".join((all_known_checks) - existing_checks)),\\n highlight(prb.name), highlight(repo.name)))\\n return [Change(\\n meta=ChangeMetadata(\\n executor=execute_test_protection,\\n params=[prb, existing_checks, known_status_checks, known_checkruns]\\n ),\\n action=ChangeActions.REPLACE if existing_checks else ChangeActions.ADD,\\n before=\\\"%s checks\\\" % len(existing_checks) if existing_checks else \\\"No checks\\\",\\n after=\\\"%s checks\\\" % len(all_known_checks),\\n )]\\n return []\",\n \"def check_component(comp_name: str, comp: defs.Component) -> None:\\n if not RE_COMP_NAME.match(comp_name):\\n res.append(f\\\"Invalid component name: {comp_name}\\\")\\n\\n for branch_name, branch in sorted(comp.branches.items()):\\n check_branch(comp_name, branch_name, branch)\",\n \"def test_multi_branches(self):\\n sgf = \\\"\\\"\\\"\\n (;FF[4]GM[1]SZ[19];B[aa];W[bb](;B[cc];W[dd](;B[ad];W[bd])\\n (;B[ee];W[ff]))\\n (;B[hh];W[gg])\\n (;B[ii];W[jj]))\\n \\\"\\\"\\\"\\n coll = parseSgf(sgf)\\n self.assertEqual(coll,\\n [[{'SZ': '19', 'GM': '1', 'FF': '4'}, {'B': 'aa'},\\n {'W': 'bb'},\\n [[{'B': 'cc'}, {'W': 'dd'}, [[{'B': 'ad'}, {'W': 'bd'}], [{'B': 'ee'}, {'W': 'ff'}]]],\\n [{'B': 'hh'}, {'W': 'gg'}],\\n [{'B': 'ii'}, {'W': 'jj'}]],\\n ]])\\n self.assertEqual(self._trim_sgf_whitespace(sgf), makeSgf(coll))\",\n \"def formatted_branch_name(branch):\\n for exp in experiments:\\n if exp in branch:\\n for otype in observation_types:\\n if otype in branch:\\n for stype in shear_types:\\n if stype in branch:\\n return exp+'-'+otype+'-'+stype\\n raise RuntimeError('Branch %s does not appear to be a valid branch name--please pass one of '\\n '[%s] with the command-line option -b.'%(branch, ', '.join(branch_names)))\",\n \"def verify_branch(path, expected_branch=\\\"master\\\"):\\n\\n sys.stdout.write(\\\" - Verifying your branch is %s:\\\" % expected_branch)\\n branch = run_in_component(path, ['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\\n branch = branch.strip()\\n\\n if branch == expected_branch:\\n print(\\\" OKAY\\\")\\n return\\n\\n print(\\\" FAILED\\\")\\n\\n raise GenericError(\\\"You must be on branch %s to release, you are on %s\\\" % (expected_branch, branch))\",\n \"def check_children_attributes(self, branch):\\n attributes = branch.get_attributes()\\n for attr in attributes:\\n if not isinstance(attributes[attr], str) and not isinstance(attributes[attr], list) :\\n print('Attribute '+str(attr)+' of '+ branch.__class__.__name__ + ' should be str or list')\\n self.assertTrue(False)\\n children = branch.get_children()\\n for child in children:\\n self.check_children_attributes(child)\",\n \"def verify_tags(git_ref_target):\\n latest_release = github_util.get_latest_release().get('name')\\n latest_commit = run('git rev-list -n 1 {}'.format(latest_release)).stdout.rstrip(\\\"\\\\r\\\\n\\\")\\n if not branch_check(latest_release, git_ref_target):\\n print('Your branch does not contain the latest production code. \\\\n\\\\\\n Please recreate it by branching off of release {}.'.format(latest_release))\\n exit(1)\\n else:\\n print(\\\"Branch contains the latest production tag\\\")\\n fork_point = run('git merge-base remotes/origin/master remotes/origin/{}'.format(git_ref_target))\\n commits_since_fork = run('git rev-list --branches={} {}^..HEAD'.format(git_ref_target,\\n fork_point.stdout.rstrip(\\\"\\\\r\\\\n\\\")))\\n if latest_commit not in commits_since_fork.stdout:\\n print('Your branch did not fork directly from the last production tag. \\\\n\\\\\\n Please recreate it by branching off of release {}.'.format(latest_release))\\n exit(1)\\n else:\\n print('Latest production tag is between the fork point and HEAD')\",\n \"def branch(name, wit_path):\\n\\n if name != 'None':\\n\\n if len(name) < 30:\\n head = _get_head(wit_path)\\n _add_branch(wit_path, name, head)\\n else:\\n logging.error(f'branch name is too long \\\"{name}\\\" (max 30 digits).')\\n else:\\n logging.error(f'branch name is not valid {name}.')\",\n \"def verify_submissions_valid() -> bool:\\n\\n submitters = [file for file in os.listdir(SUBS_DIR) if not file.startswith(\\\".\\\")]\\n submissions_valid = True\\n\\n for submitter in submitters:\\n expected_submission_path = f\\\"{SUBS_DIR}/{submitter}/{ASSIGNMENT}\\\"\\n if not os.path.exists(expected_submission_path):\\n print(f\\\"{Ansi.RED}ERROR:{Ansi.END} {expected_submission_path} does not exist\\\")\\n submissions_valid = False\\n continue\\n else:\\n git = subprocess.run(\\\"git remote -v\\\", shell=True, cwd=expected_submission_path, stdout=subprocess.PIPE,\\n universal_newlines=True, check=False)\\n if ASSIGNMENT not in git.stdout:\\n print(f\\\"{Ansi.RED}ERROR:{Ansi.END} {expected_submission_path} does not contain a git history\\\")\\n submissions_valid = False\\n\\n return submissions_valid\",\n \"def is_valid_git_refname(refname):\\r\\n if len(refname) == 0:\\r\\n return False\\r\\n\\r\\n # git imposes a few requirements to accept a string as a\\r\\n # refname/branch-name\\r\\n\\r\\n # They can include slash / for hierarchical (directory) grouping, but no\\r\\n # slash-separated component can begin with a dot . or end with the sequence\\r\\n # .lock\\r\\n if (len([True for element in refname.split('/')\\r\\n if element.startswith('.') or element.endswith('.lock')]) != 0):\\r\\n return False\\r\\n\\r\\n # They cannot have two consecutive dots .. anywhere\\r\\n if '..' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot have ASCII control characters (i.e. bytes whose values are\\r\\n # lower than \\\\040, or \\\\177 DEL), space, tilde, caret ^, or colon : anywhere\\r\\n if len([True for refname_char in refname if ord(refname_char) < 40 or\\r\\n ord(refname_char) == 177]) != 0:\\r\\n return False\\r\\n if ' ' in refname or '~' in refname or '^' in refname or ':' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot have question-mark ?, asterisk *, or open bracket [ anywhere\\r\\n if '?' in refname or '*' in refname or '[' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot begin or end with a slash / or contain multiple consecutive\\r\\n # slashes\\r\\n if refname.startswith('/') or refname.endswith('/') or '//' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot end with a dot ..\\r\\n if refname.endswith('.'):\\r\\n return False\\r\\n\\r\\n # They cannot contain a sequence @{\\r\\n if '@{' in refname:\\r\\n return False\\r\\n\\r\\n # They cannot contain a \\\\\\r\\n if '\\\\\\\\' in refname:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def check_stale_branches(event: dict, context) -> dict:\\n\\n ssm_parameters = load_params('dev_tools', 'dev')\\n\\n if 'jira_statuses_for_task_completion' in ssm_parameters and ssm_parameters['jira_statuses_for_task_completion']:\\n jira_statuses_for_task_completion = ssm_parameters['jira_statuses_for_task_completion']\\n else:\\n jira_statuses_for_task_completion = ('Resolved', 'Closed')\\n\\n repository_names = ssm_parameters['github_repository_names']\\n github_repository_names = repository_names.split(',')\\n\\n jira_oauth_dict = {\\n 'access_token': ssm_parameters['jira_access_token'],\\n 'access_token_secret': ssm_parameters['jira_access_token_secret'],\\n 'consumer_key': ssm_parameters['jira_consumer_key'],\\n 'key_cert': ssm_parameters['jira_private_key']\\n }\\n auth_jira = JIRA(ssm_parameters['jira_url'], oauth=jira_oauth_dict)\\n\\n # Github authentication setup\\n g = Github(ssm_parameters['github_access_token'])\\n\\n # Look for stale branches for all the specified repos\\n total_stale_branches = 0\\n general_report = ''\\n author_count = defaultdict(int)\\n\\n for repo_name in github_repository_names:\\n logger.debug(f'\\\\nChecking repo: {repo_name}')\\n\\n try:\\n repo = g.get_repo(f\\\"{ssm_parameters['github_account']}/{repo_name}\\\")\\n except GithubException:\\n logger.error(f\\\"Github repository '{ssm_parameters['github_account']}/{repo_name}' not found!\\\")\\n continue\\n\\n repo_report = ''\\n\\n # confirm the name for the main develop branch\\n main_develop_branch = 'develop'\\n try:\\n _ = repo.get_branch('develop')\\n except GithubException:\\n main_develop_branch = 'master'\\n logger.debug('Develop branch not found, using master as the main develop branch.')\\n continue\\n\\n branches = repo.get_branches()\\n for branch in branches:\\n # only check feature and hotfix branches\\n if not branch.name.startswith('feature/') and not branch.name.startswith('hotfix/'):\\n continue\\n\\n # compare the branch against the main develop branch\\n try:\\n comparison = repo.compare(main_develop_branch, branch.name)\\n except GithubException as error:\\n logger.error(f'GithubException: Error while trying to compare {main_develop_branch} and {branch.name}.')\\n logger.error(f'GithubException: {error}.')\\n\\n if comparison.behind_by == 0:\\n # the branch is up to date, nothing to do\\n continue\\n\\n # try to get the jira ticket number from the branch name\\n ticket = None\\n result = re.search(r'feature/(?P<ticket>[a-zA-Z]+-[0-9]+).*', branch.name)\\n if result:\\n ticket = result.groupdict()['ticket'].upper()\\n try:\\n issue = auth_jira.issue(ticket)\\n except jira_exceptions.JIRAError:\\n logger.debug(f\\\"The ticket {ticket} specified in the branch name doesn't exist in Jira.\\\")\\n\\n if issue and issue.fields.status.name not in jira_statuses_for_task_completion:\\n # the issue hasn't been marked as resolved in jira, so the branch may still be needed\\n continue\\n\\n author = branch.commit.author.login if branch.commit.author else 'unknown'\\n author_count[author] += 1\\n repo_report += f'Branch: {branch.name}\\\\nComparison status: {comparison.status}\\\\nAuthor: {author}\\\\n'\\n if ticket:\\n repo_report += f'Ticket status: \\\"{issue.fields.status.name}\\\\n'\\n repo_report += '\\\\n'\\n\\n total_stale_branches += 1\\n\\n if repo_report:\\n general_report += f'Repo: {repo_name}, develop branch name: {main_develop_branch}\\\\n{repo_report}'\\n\\n if total_stale_branches:\\n count_by_author = ''\\n for author, count in sorted(author_count.items(), key=operator.itemgetter(1), reverse=True):\\n count_by_author += f'{author}: {count}\\\\n'\\n\\n report_overview = f'Current number of stale branches: {total_stale_branches}\\\\n\\\\n'\\\\\\n f'Count by author:\\\\n{count_by_author}\\\\n'\\n report_details = f'Details:\\\\n\\\\n{general_report}'\\n\\n _ = slack_request(url=ssm_parameters['slack_webhook_url'],\\n headers={'Content-type': 'application/json',\\n 'Authorization': f\\\"Bearer {ssm_parameters['slack_access_token']}\\\"},\\n data=json.dumps({'text': report_overview})\\n )\\n\\n _ = slack_request(url='https://slack.com/api/files.upload',\\n headers={'Content-type': 'application/x-www-form-urlencoded'},\\n data={'token': ssm_parameters['slack_access_token'],\\n 'channels': 'GE8NS0FT5',\\n 'content': report_details,\\n 'title': 'Stale branches details'}\\n )\",\n \"def remove_all_status_checks_on_pr_branch(org: Organization, repo: Repository,\\n branches: Dict[str, Branch]) -> List[Change[str]]:\\n def execute_remove_all_status_checks(change: Change[str], branch: Branch, existing_checks: Set[str]) -> Change[str]:\\n print_debug(\\\"Removing all status checks from branch %s\\\" % highlight(branch.name))\\n try:\\n if existing_checks:\\n branch.remove_required_status_checks()\\n except GithubException as e:\\n print_error(str(e))\\n return change.failure()\\n else:\\n return change.success()\\n\\n prb = get_pr_branch(repo, branches)\\n if not prb:\\n return []\\n\\n try:\\n rqs = prb.get_required_status_checks()\\n except GithubException:\\n # the repository has currently no status checks\\n pass\\n else:\\n if len(rqs.contexts) > 0:\\n existing_checks = set(rqs.contexts) # type: Set[str]\\n return [Change(\\n meta=ChangeMetadata(\\n executor=execute_remove_all_status_checks,\\n params=[prb, existing_checks]\\n ),\\n action=ChangeActions.REPLACE,\\n before=\\\"%s checks\\\" % len(existing_checks),\\n after=None,\\n )]\\n return []\",\n \"def check(user_configuration, repository_configuration, commit_message):\\n logger = output.get_sub_logger('commit-msg', 'branch-pattern')\\n\\n logger.debug('Starting branch-pattern check...')\\n\\n result = checks.CheckResult()\\n branch = commit_message.branch\\n logger.debug('Branch: %s', branch)\\n\\n check_options = repository_configuration.get('branch-pattern', {})\\n allowed = check_options.get('allowed', [])\\n allowed.append('master') # master is always allowed\\n\\n logger.debug('Allowed Patterns: %s', allowed)\\n\\n is_allowed = any(re.match(pattern, branch) for pattern in allowed)\\n result.successful = is_allowed\\n if not is_allowed:\\n template = \\\"{branch} doesn't match any allowed pattern.\\\"\\n result.add_detail(template.format(branch=branch))\\n\\n return result\",\n \"def test_multiple_branches(self, tmpgitdir):\\n with tmpgitdir.join('file_a.txt').open('w') as handle:\\n handle.write('first file')\\n\\n subprocess.check_call(['git', 'add', '.'])\\n subprocess.check_call(['git', 'commit', '-m', 'first'])\\n\\n subprocess.check_call(['git', 'checkout', '-b', 'testbranch'])\\n\\n with tmpgitdir.join('file_b.txt').open('w') as handle:\\n handle.write('second file')\\n\\n subprocess.check_call(['git', 'add', '.'])\\n subprocess.check_call(['git', 'commit', '-m', 'second'])\\n\\n assert git_head_ref_name(tmpgitdir) == 'testbranch'\",\n \"def verify_up_to_date(path, branch=\\\"master\\\"):\\n\\n sys.stdout.write(\\\" - Verifying your branch up to date:\\\")\\n run_in_component(path, ['git', 'remote', 'update'])\\n\\n result = run_in_component(path, ['git', 'rev-list', 'HEAD...origin/%s' % branch, '--count'])\\n count = int(result.strip())\\n\\n if count == 0:\\n print(\\\" OKAY\\\")\\n return\\n\\n print(\\\" FAILED\\\")\\n\\n raise GenericError(\\\"You branch is not up-to-date with remote branch: %d different commits\\\" % count)\",\n \"def scm_branch(self, value):\\n max_characters = 256\\n conditions = [validate_max_length(value, max_characters)]\\n if all(conditions):\\n self._update_values('scm_branch', value)\\n else:\\n raise InvalidValue(f'{value} is invalid. Condition max_characters must be less than or equal to '\\n f'{max_characters}')\",\n \"def lemur(branches):\\n\\n assert branches[0] == 0, \\\"First branch must be alive\\\"\\n assert branches[-1] == 0, \\\"Last branch must be alive\\\"\\n\\n # loop through list\\n # if next one is dead, jump 2 spaces\\n # if the next one is alive, check next next\\n # if next AND next next are alive, jump 2 spaces\\n # if next is alive and next next is dead, jump 1 space\\n # increment counter each jump\\n # if len(branches) < 2:\\n # return 0\\n\\n # if len(branches) < 3:\\n # return 1\\n\\n # count = 0\\n # branch = 0\\n\\n # while branch < (len(branches)-1):\\n # if branches[branch + 1] == 1:\\n # branch += 2\\n # else:\\n # if branches[branch + 2] == 0:\\n # branch+= 2\\n # else:\\n # branch += 1\\n # count += 1\\n\\n # return count\\n\\n#####################################################################\\n# way shorter version\\n\\n branch = 0\\n count = 0\\n\\n while branch < len(branches) - 1:\\n branch += 2\\n if branch >= len(branches) or branches[branch] == 1:\\n # We can jump this far, so only jump 1\\n branch -= 1\\n count += 1\\n\\n return count\",\n \"def is_branch(wit_path, branch):\\n\\n branches = _get_references_data(wit_path)\\n del branches['HEAD']\\n return branch in branches.keys()\",\n \"def visit_branch(self, node, children):\\n branch = {k: v for d in children for k, v in d.items()}\\n # Verify that this is either an interpolated, rut or graft branch and not an illegal mix\\n # If a path is specified it is a rut branch or if there is a local graft it is a grafted branch\\n # If both path and local graft are present in the same branch it is illegal\\n if branch.get('path', None): # Path specified, so there should be no local grafts in this branch\\n lf = branch['leaf_faces']\\n local_graft = [lf[n]['graft'] for n in lf if lf[n]['graft'] == 'local']\\n if local_graft:\\n raise GraftRutBranchConflict(branch=set(lf.keys()))\\n # Return dictionary of leaf faces and an optional path keyed to the local rule\\n return { node.rule_name: branch }\",\n \"def missing_branches(self):\\n upstream_tags = self.upstream_model.tags_from_semver_point(\\\"1.19.0\\\")\\n deb_branches = self.deb_model.base.branches_from_semver_point(\\\"1.19.0\\\")\\n return list(set(upstream_tags) - set(deb_branches))\",\n \"def track_branches(branches=None, directory=None):\\n if type(branches) == str:\\n branches = [branches]\\n debug(\\\"track_branches(\\\" + str(branches) + \\\", \\\" + str(directory) + \\\")\\\")\\n if branches == []:\\n return\\n # Save the current branch\\n current_branch = get_current_branch(directory)\\n try:\\n # Get the local branches\\n local_branches = get_branches(local_only=True, directory=directory)\\n # Get the remote and local branches\\n all_branches = get_branches(local_only=False, directory=directory)\\n # Calculate the untracked branches\\n untracked_branches = []\\n for branch in all_branches:\\n if branch.startswith('remotes/'):\\n if branch.count('/') >= 2:\\n branch = '/'.join(branch.split('/')[2:])\\n if branch not in local_branches:\\n untracked_branches.append(branch)\\n # Prune any untracked branches by specified branches\\n if branches is not None:\\n branches_to_track = []\\n for untracked in untracked_branches:\\n if untracked in branches:\\n branches_to_track.append(untracked)\\n else:\\n branches_to_track = untracked_branches\\n # Track branches\\n debug(\\\"Tracking branches: \\\" + str(branches_to_track))\\n for branch in branches_to_track:\\n checkout(branch, directory=directory)\\n finally:\\n if current_branch:\\n checkout(current_branch, directory=directory)\",\n \"def create_branches(branches, pcoll, provider_options):\\n\\n logger.info('Branch count: %i' % len(branches))\\n pcoll_tuple = ()\\n\\n for branch in branches:\\n logger.info('Adding branch')\\n output = create_graph(branch, pcoll, provider_options)\\n pcoll_tuple = pcoll_tuple + (output,)\\n\\n logger.info('Transform: MergeBranches')\\n output = pcoll_tuple | 'MergeBranches' >> MergeBranches()\\n return output\",\n \"def bouton_branches(self,arbor,arbor_labels,bbranches,bouton_geometry=[28,4]):\\n\\t\\t\\n\\t\\tfor branch in arbor.keys():\\n\\t\\t\\tif branch in bbranches:\\n\\t\\t\\t\\tarbor[branch],arbor_labels[branch] = self.bouton_branch(arbor[branch],bouton_geometry)\\n\\t\\t\\n\\t\\treturn(arbor,arbor_labels)\",\n \"def test_simple_branches(self):\\n sgf = \\\"\\\"\\\"\\n (;FF[4]GM[1]SZ[19];B[aa];W[bb](;B[cc];W[dd];B[ee])(;B[hh];W[hg]))\\n \\\"\\\"\\\"\\n coll = parseSgf(sgf)\\n self.assertEqual(coll,\\n [[{'SZ': '19', 'GM': '1', 'FF': '4'}, {'B': 'aa'}, {'W': 'bb'},\\n [[{'B': 'cc'}, {'W': 'dd'}, {'B': 'ee'}], [{'B': 'hh'}, {'W': 'hg'}]]]])\\n self.assertEqual(self._trim_sgf_whitespace(sgf), makeSgf(coll))\",\n \"def _is_branch(self, reference_name):\\n return reference_name.startswith(\\\"refs/heads/\\\") or reference_name.startswith(\\n \\\"refs/remotes/\\\"\\n )\",\n \"def test_wrong_branch(self):\\n from webhook.views import RootView\\n json_data=open('testing_commits/body_another_branch.json')\\n mock_api = Mock()\\n request = testing.DummyRequest()\\n request.json_body = json.load(json_data)\\n view_inst = RootView(request, api_service=mock_api)\\n result = view_inst.default_view()\\n self.assertIn('wrong branch',result['error'])\",\n \"def checkout_branches(args):\\n\\n ensure_tracking_branches([])\\n if check_dirty([]) and '-f' not in args:\\n raise Exception(\\\"Cannot checkout new branches with dirty projects.\\\")\\n \\n man = load_manifest()\\n for (name, project) in man.projects.iteritems():\\n print >>sys.stderr, \\\"Checking out tracking branch in project: %s\\\" % name\\n repo = GitRepo(workdir_for_project(project))\\n # Check that sucker out\\n repo.check_command([\\\"checkout\\\", project.tracking_branch])\",\n \"def check_consistent(self):\\n # * END LIST The end list itself must be consistent.\\n # ** Each end must be of understood type\\n # ** Each end must have a valid sequence or no sequence\\n # ** There must be no more than one instance of each name\\n # ** WARN if there are ends with no namecounts\\n # * TILE LIST\\n # ** each tile must be of understood type (must parse)\\n # ** ends in the tile list must be consistent (must merge)\\n # ** there must be no more than one tile with each name\\n # self.tiles.check_consistent()\\n endsfromtiles = self.tiles.glues_from_tiles()\\n\\n # ** WARN if any end that appears does not have a complement used or vice versa\\n # ** WARN if there are tiles with no name\\n # * TILE + END\\n # ** The tile and end lists must merge validly\\n # (checks sequences, adjacents, types, complements)\\n self.glues | endsfromtiles\\n\\n # ** WARN if tilelist has end references not in ends\\n # ** WARN if merge is not equal to the endlist\\n # ** WARN if endlist has ends not used in tilelist\\n # * ADAPTERS / SEEDS\\n # SEED stuff was here\",\n \"def test_with_known_mls_error():\\n linestrings = samples.mls_from_these_linestrings_list\\n target_area = [box(*MultiLineString(linestrings).bounds)]\\n branches, nodes = branches_and_nodes.branches_and_nodes(\\n gpd.GeoSeries(linestrings), gpd.GeoSeries(target_area), tests.snap_threshold\\n )\\n for branch in branches.geometry:\\n assert EE_branch not in str(branches[CONNECTION_COLUMN])\\n assert isinstance(branch, LineString)\\n assert branch.is_simple\\n assert not branch.is_empty\\n for node in nodes.geometry:\\n assert isinstance(node, Point)\\n assert not node.is_empty\",\n \"def missing_branches(self):\\n upstream_tags = self.upstream_model.tags_from_semver_point(\\\"0.8.7\\\")\\n deb_branches = self.deb_model.base.branches_from_semver_point(\\\"0.8.7\\\")\\n return list(set(upstream_tags) - set(deb_branches))\",\n \"def check_branch(subcommand, branch):\\n if subcommand != \\\"checkout\\\":\\n return\\n # first make sure actual branch name was given\\n if branch is None:\\n return \\\"Branch name to checkout must be supplied with '-b' option\\\"\\n # next check that the local repo is clean\\n cmd = [\\\"git\\\", \\\"status\\\", \\\"--untracked-files=no\\\", \\\"--porcelain\\\"]\\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, universal_newlines=True)\\n if p.stdout.strip():\\n return \\\"Need to have clean working tree to checkout!\\\\n\\\\n\\\" + p.stdout\\n # next check that the branch name doesn't already exist\\n cmd = [\\\"git\\\", \\\"show-ref\\\", \\\"--verify\\\", \\\"--quiet\\\", \\\"refs/heads/\\\" + branch]\\n p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False)\\n if not p.returncode:\\n return f\\\"Branch {branch!r} already exists\\\"\",\n \"def _check_brackets(tokens: List[str]) -> True:\\r\\n brackets = []\\r\\n for token in tokens:\\r\\n if token in \\\"([\\\":\\r\\n brackets.append(token)\\r\\n elif token in \\\")]\\\":\\r\\n if not brackets:\\r\\n raise ExtraBracket(token)\\r\\n closing_bracket = brackets.pop()\\r\\n if ((token == \\\"(\\\" and closing_bracket != \\\")\\\") or\\r\\n (token == \\\"[\\\" and closing_bracket != \\\"]\\\")):\\r\\n raise BracketsMismatch(token, closing_bracket)\\r\\n if brackets:\\r\\n raise NoClosingBracket(brackets[0])\\r\\n return True\",\n \"def branches(self) -> list[str]:\\n _args: list[Arg] = []\\n _ctx = self._select(\\\"branches\\\", _args)\\n return _ctx.execute_sync(list[str])\",\n \"def bracket_validator(brackets):\\n bracket_stack = []\\n for b in brackets:\\n if b == '(' or b == '{' or b == '[':\\n bracket_stack.append(b)\\n\\n elif b == ')' or b == '}' or b == ']':\\n top = bracket_stack.pop()\\n\\n if b != bracket_match(top):\\n return False\\n\\n return True\",\n \"def test_examples():\\n assert nz_bank_validate(*'01-902-0068389-00'.split('-'))\\n assert nz_bank_validate(*'08-6523-1954512-001'.split('-'))\\n assert nz_bank_validate(*'26-2600-0320871-032'.split('-'))\",\n \"def _check_sanity(self, tags: List[str], n_words: int):\\n n_out = 0\\n\\n for tag in tags:\\n if (\\\"<\\\" not in tag) and (\\\">\\\" not in tag):\\n n_out += 1\\n\\n return n_out == n_words\",\n \"def _check_sanity(self, tags: List[str], n_words: int):\\n n_out = 0\\n\\n for tag in tags:\\n if (\\\"<\\\" not in tag) and (\\\">\\\" not in tag):\\n n_out += 1\\n\\n return n_out == n_words\",\n \"def delete_branches(config, args):\\n for ref_name in lib.input_json_lines():\\n yield _delete_ref(config.repo, \\\"heads/\\\" + ref_name, args.force, args.dry_run)\",\n \"def branches_full(config, args):\\n for b in config.repo.branches():\\n yield config.repo.branch(b.name)\",\n \"def test_branching(self):\\r\\n repo_dir = self.GIT_REPO_DIR\\r\\n # Test successful import from command\\r\\n if not os.path.isdir(repo_dir):\\r\\n os.mkdir(repo_dir)\\r\\n self.addCleanup(shutil.rmtree, repo_dir)\\r\\n\\r\\n # Checkout non existent branch\\r\\n with self.assertRaisesRegexp(GitImportError, GitImportError.REMOTE_BRANCH_MISSING):\\r\\n git_import.add_repo(self.TEST_REPO, repo_dir / 'edx4edx_lite', 'asdfasdfasdf')\\r\\n\\r\\n # Checkout new branch\\r\\n git_import.add_repo(self.TEST_REPO,\\r\\n repo_dir / 'edx4edx_lite',\\r\\n self.TEST_BRANCH)\\r\\n def_ms = modulestore()\\r\\n # Validate that it is different than master\\r\\n self.assertIsNotNone(def_ms.get_course(self.TEST_BRANCH_COURSE))\\r\\n\\r\\n # Attempt to check out the same branch again to validate branch choosing\\r\\n # works\\r\\n git_import.add_repo(self.TEST_REPO,\\r\\n repo_dir / 'edx4edx_lite',\\r\\n self.TEST_BRANCH)\\r\\n\\r\\n # Delete to test branching back to master\\r\\n delete_course(def_ms, contentstore(),\\r\\n self.TEST_BRANCH_COURSE,\\r\\n True)\\r\\n self.assertIsNone(def_ms.get_course(self.TEST_BRANCH_COURSE))\\r\\n git_import.add_repo(self.TEST_REPO,\\r\\n repo_dir / 'edx4edx_lite',\\r\\n 'master')\\r\\n self.assertIsNone(def_ms.get_course(self.TEST_BRANCH_COURSE))\\r\\n self.assertIsNotNone(def_ms.get_course(SlashSeparatedCourseKey.from_deprecated_string(self.TEST_COURSE)))\",\n \"def test_branch_fail(repository: Repository) -> None:\\n with pytest.raises(KeyError):\\n repository.branch(\\\"branch\\\")\",\n \"def _check_completeness(\\n tagmap,\\n taglist,\\n namelist,\\n exact_match,\\n exact_order,\\n error_missing_message,\\n error_order_message,\\n error_excess_message,\\n ):\\n valids = []\\n for itag, tagkey in enumerate(taglist):\\n found_obj = None\\n for obj, objtags in tagmap.items():\\n if tagkey in objtags:\\n found_obj = obj\\n break\\n if exact_order:\\n # if we get here order is wrong\\n err = self._format_message(\\n error_order_message, missing=obj.get_display_name(looker=self.crafter)\\n )\\n self.msg(err)\\n raise CraftingValidationError(err)\\n\\n # since we pop from the mapping, it gets ever shorter\\n match = tagmap.pop(found_obj, None)\\n if match:\\n valids.append(found_obj)\\n elif exact_match:\\n err = self._format_message(\\n error_missing_message,\\n missing=namelist[itag] if namelist else tagkey.capitalize(),\\n )\\n self.msg(err)\\n raise CraftingValidationError(err)\\n\\n if exact_match and tagmap:\\n # something is left in tagmap, that means it was never popped and\\n # thus this is not an exact match\\n err = self._format_message(\\n error_excess_message,\\n excess=[obj.get_display_name(looker=self.crafter) for obj in tagmap],\\n )\\n self.msg(err)\\n raise CraftingValidationError(err)\\n\\n return valids\",\n \"def walkbranch(stem, ancestors, branches):\\n for child in stem.children(0):\\n if child not in ancestors:\\n continue\\n\\n build, reason = needsbuild(child)\\n\\n if build:\\n if reason in (PARENTNEWER, MISSING):\\n yield child, reason\\n else:\\n raise RuntimeError(\\\"unexpected reason\\\")\\n\\n elif reason is PARENTMISSING:\\n # A parent derived from another stem is missing, so delay\\n # build until that branch is traversed\\n pass\\n\\n elif not build:\\n # Child is up to date, so append it as a new 'stem' to walk down\\n if child not in branches:\\n branches.append(child)\",\n \"def verify_ballot_consistency(self) -> bool:\\n sbb_contents = self._sbb.get_sbb_contents()\\n \\n # First, validate the commitment consistency with the initial vote lists and final vote lists.\\n for list_idx, proof in sbb_contents.consistency_proof.items():\\n for vote_idx in range(len(proof)):\\n proved_sv = proof[vote_idx]\\n tu_list = []\\n tv_list = []\\n for row_idx, sv in enumerate(proved_sv):\\n # Ensure that we are consistent with the initial and the final commitments\\n if sv.get('u', None) is not None:\\n val_init = sv['u_init']\\n val_fin = sv['u_fin']\\n val_uv = sv['u']\\n val_t = sbb_contents.t_values[list_idx][row_idx][vote_idx]['tu']\\n original_commitment = sbb_contents.svr_commitments[row_idx][vote_idx]['com_u']\\n final_commitment = sbb_contents.vote_lists[list_idx][vote_idx][row_idx].com_u\\n else:\\n val_init = sv['v_init']\\n val_fin = sv['v_fin']\\n val_uv = sv['v']\\n val_t = sbb_contents.t_values[list_idx][row_idx][vote_idx]['tv']\\n original_commitment = sbb_contents.svr_commitments[row_idx][vote_idx]['com_v']\\n final_commitment = sbb_contents.vote_lists[list_idx][vote_idx][row_idx].com_v\\n key_init = sv['k_init']\\n key_fin = sv['k_fin']\\n \\n # Verify the input and output commitments\\n com_init = util.get_COM(util.bigint_to_bytes(key_init), util.bigint_to_bytes(val_init))\\n com_fin = util.get_COM(util.bigint_to_bytes(key_fin), util.bigint_to_bytes(val_fin))\\n if com_init != original_commitment:\\n raise Exception(\\\"Failed to open the initial vote commitment\\\")\\n if com_fin != final_commitment:\\n raise Exception(\\\"Failed to open the final vote commitment\\\")\\n \\n # Verify the t-values\\n if util.t_val(util.bigint_to_bytes(val_init), util.bigint_to_bytes(val_uv), self._M) != val_t:\\n raise Exception(\\\"Failed to verify t value\\\")\\n \\n # Add t-values to their respective lists for lagrange checks\\n tu_list.append(sbb_contents.t_values[list_idx][row_idx][vote_idx]['tu'])\\n tv_list.append(sbb_contents.t_values[list_idx][row_idx][vote_idx]['tv'])\\n \\n # Check that tu_list and tv_list lagrange to (t, -t)\\n rows = len(proved_sv)\\n tu0 = self._lagrange(tu_list, rows, rows-1, self._M)\\n tv0 = self._lagrange(tv_list, rows, rows-1, self._M)\\n if util.val(tu0, tv0, self._M) != 0:\\n # TODO: This does not work\\n #raise Exception(\\\"Failed lagrange verification of t values\\\")\\n pass\\n return True\",\n \"def test_branch_exceptions(self):\\r\\n # create bare repo that we can mess with and attempt an import\\r\\n bare_repo = os.path.abspath('{0}/{1}'.format(settings.TEST_ROOT, 'bare.git'))\\r\\n os.mkdir(bare_repo)\\r\\n self.addCleanup(shutil.rmtree, bare_repo)\\r\\n subprocess.check_output(['git', '--bare', 'init', ], stderr=subprocess.STDOUT,\\r\\n cwd=bare_repo)\\r\\n\\r\\n # Build repo dir\\r\\n repo_dir = self.GIT_REPO_DIR\\r\\n if not os.path.isdir(repo_dir):\\r\\n os.mkdir(repo_dir)\\r\\n self.addCleanup(shutil.rmtree, repo_dir)\\r\\n\\r\\n rdir = '{0}/bare'.format(repo_dir)\\r\\n with self.assertRaisesRegexp(GitImportError, GitImportError.BAD_REPO):\\r\\n git_import.add_repo('file://{0}'.format(bare_repo), None, None)\\r\\n\\r\\n # Get logger for checking strings in logs\\r\\n output = StringIO.StringIO()\\r\\n test_log_handler = logging.StreamHandler(output)\\r\\n test_log_handler.setLevel(logging.DEBUG)\\r\\n glog = git_import.log\\r\\n glog.addHandler(test_log_handler)\\r\\n\\r\\n # Move remote so fetch fails\\r\\n shutil.move(bare_repo, '{0}/not_bare.git'.format(settings.TEST_ROOT))\\r\\n try:\\r\\n git_import.switch_branch('master', rdir)\\r\\n except GitImportError:\\r\\n self.assertIn('Unable to fetch remote', output.getvalue())\\r\\n shutil.move('{0}/not_bare.git'.format(settings.TEST_ROOT), bare_repo)\\r\\n output.truncate(0)\\r\\n\\r\\n # Replace origin with a different remote\\r\\n subprocess.check_output(\\r\\n ['git', 'remote', 'rename', 'origin', 'blah', ],\\r\\n stderr=subprocess.STDOUT, cwd=rdir\\r\\n )\\r\\n with self.assertRaises(GitImportError):\\r\\n git_import.switch_branch('master', rdir)\\r\\n self.assertIn('Getting a list of remote branches failed', output.getvalue())\",\n \"def test_branch_length(self):\\n exp = ['(', '(', 'h', ':', '4.0', ',', 'p', ':', '1.1461E-5', ')',\\n 'hp', ':', '1351.146436', ',', 'g', ')', 'hpg', ';']\\n content = '((h:4.0,p:1.1461E-5)hp:1351.146436,g)hpg;'\\n self._do_test(content, exp)\",\n \"def test_equality_function(self):\\r\\n self.assertFalse(directories_equal(self.version1_nodrafts, self.version0_nodrafts))\\r\\n self.assertFalse(directories_equal(self.version1_drafts_extra_branch, self.version1_drafts))\",\n \"def check_commit_for_branch( self\\n , commit\\n , branch_id\\n , any_locked_files\\n , case_conflict_checker ):\\n rev = commit['sha1']\\n if LOG.isEnabledFor(logging.DEBUG):\\n LOG.debug(\\\"check_commit_for_branch() \\\"\\n \\\"Checking branch={} mark={} sha1={} file-ct={} -- {}\\\"\\n .format( branch_id\\n , commit['mark']\\n , p4gf_util.abbrev(rev)\\n , len(commit['files'])\\n , repr(commit['data'])[:20].splitlines()[0]))\\n\\n if self._already_copied_commit(rev, branch_id):\\n return\\n\\n # following checks assume client has been set for branch\\n self.ensure_branch_preflight(commit, branch_id)\\n with self.ctx.switched_to_branch(\\n self._current_branch\\n , set_client=self.set_client_on_branch_switch\\n ):\\n if case_conflict_checker:\\n case_conflict_checker.read_fast_export_commit(\\n commit, self._current_branch)\\n\\n # Empty commits require root-level .p4gf_placeholder to be mapped\\n # in the current branch view.\\n if not commit['files'] and not self._is_placeholder_mapped():\\n raise PreflightException(\\n _(\\\"Empty commit {sha1} not permitted. Git Fusion branch views\\\"\\n \\\" must include root to permit empty commits.\\\")\\n .format(sha1=p4gf_util.abbrev(rev)))\\n\\n with Timer(CHECK_PROTECTS):\\n self._check_protects(commit['author_p4user'], commit['files'])\\n\\n with Timer(CHECK_OVERLAP):\\n self._check_overlap(commit)\\n\\n # fetch the branch setting only, without cascading to repo/global config\\n if self._current_branch.is_read_only:\\n raise PreflightException(_(\\\"Push to branch {branch} prohibited.\\\")\\n .format(branch=self._current_branch.git_branch_name))\\n self._check_stream_writable(commit)\\n self._check_stream_in_classic(commit)\\n\\n LOG.debug('checking locked files under //{}/...'.format(self.ctx.p4.client))\\n if any_locked_files:\\n # Convert the git commit paths to depotPaths\\n files_in_commit = [self.ctx.gwt_path(f['path']).to_depot()\\n for f in commit['files']]\\n LOG.debug(\\\"files_in_commit {0}\\\".format(files_in_commit))\\n for f in files_in_commit:\\n if f in any_locked_files:\\n # Collect the names (and clients) of users with locked files.\\n # Report back to the pusher so they can take appropriate action.\\n msg = _('{file} - locked by {user}').format(file=f,\\n user=any_locked_files[f])\\n LOG.info(msg)\\n raise PreflightException(msg)\\n\\n # +++ Spend time extracting Jobs and P4Changelist owner\\n # here if we actually do need to call\\n # the preflight-commit hook.\\n if self.ctx.preflight_hook.is_callable():\\n jobs = G2PJob.extract_jobs(commit['data'])\\n jobs2 = G2PJob.lookup_jobs(self.ctx, jobs)\\n self.ctx.preflight_hook(\\n ctx = self.ctx\\n , fe_commit = commit\\n , branch_id = branch_id\\n , jobs = jobs2\\n )\",\n \"def _check_overlap(self, fe_commit):\\n # +++ Avoid O(b branches * r rev) checks when\\n # overlap is impossible because current branch\\n # overlaps no other branch.\\n if self._current_branch not in self._overlapping_branch_list():\\n return\\n\\n for fe_file in fe_commit['files']:\\n gwt_path = fe_file['path']\\n depot_path = self.ctx.gwt_to_depot_path(gwt_path)\\n\\n for branch in self._overlapping_branch_list():\\n if branch == self._current_branch:\\n continue\\n if not branch.intersects_depot_path(depot_path):\\n continue\\n\\n LOG.debug(\\\"_check_overlap() branch {br1} <> {br2}\\\"\\n \\\" gwt={gwt:<40} {dp}\\\\n{view}\\\"\\n .format(\\n br1 = p4gf_util.abbrev(self._current_branch.branch_id)\\n , br2 = p4gf_util.abbrev(branch.branch_id)\\n , gwt = gwt_path\\n , dp = depot_path\\n , view = \\\"\\\\n\\\".join(branch.view_p4map.as_array())\\n ))\\n\\n if self._current_branch.is_new_fp_from_push or branch.is_new_fp_from_push:\\n current_branch_name = self._current_branch.git_branch_name\\n if self._current_branch.is_new_fp_from_push:\\n current_branch_name += '(new)'\\n other_branch_name = branch.git_branch_name\\n if branch.is_new_fp_from_push:\\n other_branch_name += '(new)'\\n human_msg = (_(\\n \\\"Perforce: Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\\\\n\\\"\\n \\\" You are attempting to push and create a new fully populated branch\\\\n\\\"\\n \\\" with paths which overlap another branch. Contact your admin\\\\n\\\"\\n \\\" to configure non-conflicting destination branch paths.\\\\n\\\"\\n \\\" Branches: '{b1}', '{b2}'\\\")\\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\\n , gwt_path = gwt_path\\n , depot_path = depot_path\\n , b1 = current_branch_name\\n , b2 = other_branch_name ))\\n else:\\n human_msg = (_(\\n \\\"Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\\\"\\n \\\" Paths that overlap multiple Git Fusion branches are read-only.\\\"\\n \\\" Branches: '{b1}', '{b2}'\\\")\\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\\n , gwt_path = gwt_path\\n , depot_path = depot_path\\n , b1 = self._current_branch.branch_id\\n , b2 = branch.branch_id ))\\n raise PreflightException(human_msg)\",\n \"def hard_reset_branches(args):\\n checkout_branches(args)\\n man = load_manifest()\\n for (name, project) in man.projects.iteritems():\\n print >>sys.stderr, \\\"Hard resetting tracking branch in project: %s\\\" % name\\n repo = GitRepo(workdir_for_project(project))\\n repo.check_command([\\\"reset\\\", \\\"--hard\\\", project.remote_refspec])\",\n \"def list_all_branches(self) -> dict:\\n try:\\n branches_response = self.repo.get_branches()\\n branches_list = []\\n for branch in branches_response:\\n branches_list.append(branch.raw_data.get('name'))\\n return make_success_response(200, branches_list)\\n except GithubException as github_exc:\\n return make_error_response(github_exc.status, github_exc.data)\",\n \"def missing_branches(self):\\n upstream_tags = self.upstream_model.tags_from_semver_point(\\n enums.K8S_STARTING_SEMVER\\n )\\n deb_branches = self.deb_model.base.branches_from_semver_point(\\n enums.K8S_STARTING_SEMVER\\n )\\n return list(set(upstream_tags) - set(deb_branches))\",\n \"def _listBranches(self):\\n assert self.wc.exists('branches')\\n branches = self.wc.ls('branches')\\n\\n # Some early release branches used a different naming scheme\\n # that doesn't sort properly with new-style release names. We\\n # filter those out here, along with empty lines.\\n branches = [b.strip('/') for b in branches\\n if MELANGE_RELEASE_RE.match(b.strip('/'))]\\n\\n return sorted(branches)\",\n \"def test_heads_contains_false(repository: Repository) -> None:\\n assert \\\"branch\\\" not in repository.heads\",\n \"def check_release_status(self, release_name, release_branch):\\n logger.debug('GitHubAPI.check_release_status args: {}; {}'.format(\\n release_name, release_branch)\\n )\\n release_version = extract_release_branch_version(release_name)\\n release_branch_base = build_release_base_name(get_config())\\n # Assume that this is a new release\\n # Check if the release branch is merged into master\\n try:\\n merge_status = self.compare(\\n 'master',\\n release_branch\\n ).get('status')\\n except requests.exceptions.HTTPError as e:\\n logger.debug('HTTPError: {}'.format(e.message))\\n if not e.response.status_code == 404:\\n raise e\\n else:\\n # can be one of diverged, ahead, behind, identical according to\\n # http://stackoverflow.com/a/23969867\\n if merge_status in ['diverged', 'ahead']:\\n raise Exception(\\n 'Release must be merged into master before release')\\n return\\n\\n # if the release branch does not exist, then we end up here,\\n # Assume that it is a hotfix\\n raw_version = self.latest_release().get('name', '')\\n if raw_version.startswith(release_branch_base):\\n raw_version = raw_version[len(release_branch_base):]\\n\\n version = extract_year_week_version(raw_version)\\n logger.debug(version)\\n if extract_year_week_version(release_version) != version:\\n raise Exception(\\n 'New release version does not match the current release, '\\n 'we expected a hotfix.'\\n )\\n\\n return\",\n \"def check_label_validity(labels):\\n for label in labels:\\n # If labels not in {0, 1} not valid\\n if label not in [0, 1]:\\n return False\\n return True\",\n \"def _valid_ref_type(ref):\\n\\n git_branch = \\\"refs/heads/\\\"\\n git_tag = \\\"refs/tags/\\\"\\n if ref.startswith(git_branch) or ref.startswith(git_tag) or len(ref) == 40:\\n return True\\n return False\",\n \"def test_tracebranches_errors_on_invalid_model(mstr):\\n with pytest.raises(CommandError):\\n call_command('tracebranches', mstr)\",\n \"def is_bank_name_valid(name_to_check: str):\\n def is_name_short_enough():\\n return True if len(name_to_check) <= 12 else False\\n\\n def is_name_only_letter():\\n return True if name_to_check.isalpha() else False\\n\\n return True if is_name_short_enough() and is_name_only_letter() else False\",\n \"def branch_exists(branch_name, local_only=False, directory=None):\\n for branch in get_branches(local_only, directory):\\n if branch.startswith('remotes/'):\\n branch = branch.split('/')\\n if len(branch) > 2:\\n branch = '/'.join(branch[2:])\\n if branch_name == branch:\\n return True\\n else:\\n if branch_name == branch:\\n return True\\n return False\",\n \"def branch_exists(branch):\\n\\n try:\\n git('show-ref', branch)\\n return True\\n except subprocess.CalledProcessError:\\n return False\",\n \"def tree(label, branches=[]):\\n for branch in branches:\\n assert is_tree(branch), 'branches must be trees'\\n return [label] + list(branches)\",\n \"def check_valid(indexes):\\n # Check if any substrings has any versions that are the opposite of it anywhere in that line.\\n valid = False\\n for k, v in indexes.items():\\n swapped = k[::-1]\\n other = indexes.get(swapped)\\n # Check to see if the swapped version exists in the dictionary.\\n if other:\\n # 'aaa' case, these are invalid matches, don't bother checking further.\\n if k == swapped:\\n continue\\n # single occurence case\\n if len(v) == 1 and len(other) == 1:\\n # Case where both occur inside or outsid brackets.\\n if (int(v[0]) % 2) == (int(other[0]) % 2):\\n continue\\n else:\\n valid = True\\n else:\\n # Use sets to eliminate duplicates in the same chunk.\\n v_s = set(v)\\n other_s = set(other)\\n possible_combinations = [(x % 2, y % 2) for x in v_s for y in other_s]\\n # For a pairing to be valid, one part needs to be in an even chunk and the other in an odd ([]) chunk.\\n if (1, 0) in possible_combinations or (0, 1) in possible_combinations:\\n valid = True\\n return valid\",\n \"def stale_pr_branches(config, args):\\n repo = config.repo\\n for pr in repo.pull_requests(state=\\\"closed\\\"):\\n if pr.head.repo == pr.base.repo and repo.branch(pr.head.ref):\\n yield {\\n \\\"html_url\\\": pr.html_url,\\n \\\"base_branch\\\": pr.base.ref,\\n \\\"head_branch\\\": pr.head.ref,\\n }\",\n \"def has_branch(self, branch):\\n if self.branch == branch:\\n return True\\n return False\",\n \"def unique_branch_name(base_name):\\n repo = git.repo()\\n branches = repo.branches()\\n collision = True\\n count = 1\\n while collision:\\n new_branch = base_name + \\\"-bak-\\\" +str(count)\\n collision = next((x for x in branches if x == new_branch), False)\\n count += 1\\n return new_branch\",\n \"def branches(self):\\r\\n url = '{0}/branches/'.format(self.get_url())\\r\\n request = http.Request('GET', url)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def delete_branch_on_merge(org: Organization, repo: Repository,\\n branches: Dict[str, Branch]) -> List[Change[str]]:\\n def _set_delete_branch_on_merge(change: Change[str]) -> Change[str]:\\n print_debug(\\\"[%s] Enforcing branch deletion on PR merge\\\" % highlight(repo.name))\\n try:\\n repo.edit(delete_branch_on_merge=value)\\n except GithubException:\\n return change.failure()\\n\\n return change.success()\\n\\n if not repo.delete_branch_on_merge:\\n change = Change(\\n meta=ChangeMetadata(\\n executor=_set_delete_branch_on_merge,\\n ),\\n action=ChangeActions.REPLACE,\\n before=\\\"On PR merge: %s\\\" % \\\"Delete branch\\\" if repo.delete_branch_on_merge else \\\"Keep branch\\\",\\n after=\\\"On PR merge: %s\\\" % \\\"Delete branch\\\" if value else \\\"Keep branch\\\"\\n )\\n return [change]\\n return []\",\n \"def test_commit_on_unborn_branch(tmp_path: Path) -> None:\\n repository = Repository.init(tmp_path / \\\"repository\\\")\\n repository.commit(message=\\\"initial\\\")\\n\\n assert not repository.head.commit.parents\",\n \"def _validate_commit(\\n commit_sha1, merge, author, committer, title, separator, body):\\n errors = []\\n\\n # List of words a commit title can start with\\n commit_title_start_words = filter(\\n lambda x: x, COMMIT_TITLE_START_WORDS.splitlines())\\n\\n author_errors = _validate_email(author, 'Author')\\n committer_errors = _validate_email(committer, 'Committer')\\n\\n if author_errors:\\n errors.extend(author_errors)\\n if committer_errors:\\n errors.extend(committer_errors)\\n\\n title_words = title.split(' ', 1)\\n\\n # Check if in imperative tense\\n if re.search(r'(ed|ing|s)$', title_words[0]):\\n errors.append((\\n 'title-imperative-tense-check',\\n 'Commit title is not in imperative tense'))\\n\\n # Check if first word is capitalized\\n if re.match(r'^[^A-Z]', title_words[0]):\\n errors.append((\\n 'title-capitalization-check',\\n 'Commit title is not capitalized'))\\n\\n # Check if title begins with known start word\\n if title_words[0] not in commit_title_start_words:\\n errors.append((\\n 'title-verb-check',\\n 'Commit title does not begin with a verb'))\\n\\n # Check if this is a fixup! commit\\n if re.match(r'^fixup!', title_words[0]):\\n errors.append((\\n 'title-fixup-check',\\n 'Commit title starts with fixup! '))\\n\\n # Check if this is a squash! commit\\n if re.match(r'^squash!', title_words[0]):\\n errors.append((\\n 'title-squash-check',\\n 'Commit title starts with squash! '))\\n\\n # Check if the commit title ends in whitespace or punctuation\\n if len(title_words) > 1 and re.search(r'[\\\\s\\\\W]$', title_words[1]):\\n errors.append((\\n 'title-whitespace-punctuation-check',\\n 'Commit title ends in whitespace or punctuation'))\\n\\n # Check if the title is greater than 50 characters in length\\n if len(title) > 50:\\n errors.append((\\n 'title-length-check',\\n 'Commit title longer than 50 characters'))\\n\\n # Check if separator line (between title and body) is empty\\n if separator is not None and separator != '':\\n errors.append((\\n 'message-separator-check',\\n 'Missing blank line between title and body'))\\n\\n # Check if the commit message has a body\\n if body == []:\\n errors.append((\\n 'body-check',\\n 'Missing commit message body'))\\n\\n # Check if any line in the body is greater than 72 characters in legnth\\n for body_line in body:\\n if len(body_line) <= 72:\\n continue\\n errors.append((\\n 'body-length-check',\\n 'Commit message body line > 72 characters'))\\n break\\n\\n # Check if commit is a merge commit\\n if merge is not None:\\n errors.append((\\n 'commit-merge-check',\\n 'Commit is a merge commit'))\\n\\n # Check commit diff for whitespace errors\\n git_diff_cmd = shlex.split(\\n 'git show --check {commit_sha1}'.format(\\n commit_sha1=commit_sha1))\\n\\n has_whitespace_issue = None\\n f, _ = tempfile.mkstemp()\\n has_whitespace_issue = subprocess.call(git_diff_cmd,\\n stdout=f, stderr=f, close_fds=True)\\n os.close(f)\\n\\n if has_whitespace_issue:\\n errors.append((\\n 'diff-whitespace-check',\\n 'Commit diff has whitespace issues'))\\n\\n return errors\",\n \"def check_for_branch_op(op_info: ModuleIdentifierOpInfo):\\n\\n op = conn_graph.get_all_ops()[op_info.module_name]\\n return_bool = True\\n product = op.output\\n if \\\"branch\\\" not in product.name:\\n logger.error(\\\"branch not in product name\\\")\\n return_bool = False\\n if len(product.consumers) > 1:\\n logger.error(\\\"branch op is not parent op's only consumer\\\")\\n return_bool = False\\n branch_op = product.consumers[0]\\n if branch_op.type != \\\"branch\\\":\\n logger.error(\\\"parent op's child op is not of type branch\\\")\\n return_bool = False\\n branch_product = branch_op.output\\n if \\\"multiple_ops\\\" not in branch_product.name:\\n logger.error(\\\"multiple_ops not in branch op's product's name\\\")\\n return_bool = False\\n if len(branch_product.consumers) <= 1:\\n logger.error(\\\"branch op's product has one or fewer consumers\\\")\\n return_bool = False\\n for consumer in branch_product.consumers:\\n for input_product in consumer.inputs:\\n if input_product.producer == op:\\n logger.error(\\\"parent op is still one of child op's inputs (as opposed to branch op)\\\")\\n return_bool = False\\n return return_bool\",\n \"def ticket_branches(self, msrp, cred_hash):\\n branches = []\\n repos = self.get_repos()\\n if not repos['status']:\\n return repos\\n\\n for repo in repos['data']:\\n response = self.find_branch(repo_name=repo['name'], msrp=msrp, cred_hash=cred_hash)\\n if response['status']:\\n branches.append({'repo': repo['name'], 'branches': response['data'], 'all': response['all']})\\n\\n if len(branches) > 0:\\n return {'status': True, 'data': branches}\\n else:\\n return {'status': False, 'data': f'No branches found with MSRP {msrp}'}\",\n \"def _verify_all_states_reachable(states_list):\\n\\n # This queue stores state names.\\n processed_queue = []\\n curr_queue = [states_list[0]['name']]\\n\\n while curr_queue:\\n curr_state = curr_queue[0]\\n curr_queue = curr_queue[1:]\\n\\n if curr_state in processed_queue:\\n continue\\n\\n processed_queue.append(curr_state)\\n\\n curr_state_ind = next(ind for ind, state in enumerate(states_list)\\n if state['name'] == curr_state)\\n\\n for handler in states_list[curr_state_ind]['widget']['handlers']:\\n for rule in handler['rule_specs']:\\n dest_state = rule['dest']\\n if (dest_state not in curr_queue and\\n dest_state not in processed_queue and\\n dest_state != feconf.END_DEST):\\n curr_queue.append(dest_state)\\n\\n if len(states_list) != len(processed_queue):\\n unseen_states = list(\\n set([s['name'] for s in states_list]) - set(processed_queue))\\n raise Exception('The following states are not reachable from the '\\n 'initial state: %s' % ', '.join(unseen_states))\",\n \"def multi_bracket_validation(input):\\n brackets = Stack()\\n for i in input:\\n if i == '(' or i == '[' or i == '{':\\n brackets.push(i)\\n elif i == ')' or i == ']' or i == '}':\\n if brackets._size == 0:\\n return False\\n elif i == ')' and brackets.top.val == '(':\\n brackets.pop()\\n elif i == ']' and brackets.top.val == '[':\\n brackets.pop()\\n elif i == '}' and brackets.top.val == '{':\\n brackets.pop()\\n else:\\n return False\\n\\n if brackets._size != 0:\\n return False\\n return True\",\n \"def is_valid_commits(args):\\n if args.commits is not None:\\n return True\\n return False\",\n \"def master_branch(branch_name):\\n\\n if branch_name in MASTER_BRANCHES:\\n return True\\n\\n return False\",\n \"def test_branch_name_set(repository: Repository) -> None:\\n branch = repository.head\\n with pytest.raises(AttributeError):\\n branch.name = \\\"teapot\\\" # type: ignore[misc]\",\n \"def _check_logic_defined(name_list, logic_list):\\n _check_logic_defined.failed = False\\n\\n # We modify the behaviour of boolExp.parseString so that the parsing also\\n # check if variables exist.\\n def check_var(var):\\n if var[0] not in name_list:\\n print(\\\"Error: unkown variable %s\\\" % var[0], file=stderr)\\n _check_logic_defined.failed = True\\n return var\\n\\n varName.setParseAction(check_var)\\n\\n for string in logic_list:\\n if not _check_logic_syntax(string):\\n print(\\\"Error: syntax error %s\\\" % string, file=stderr)\\n return False\\n if _check_logic_defined.failed:\\n varName.setParseAction(lambda x: x)\\n return False\\n\\n varName.setParseAction(lambda x: x)\\n return True\",\n \"def test_str_not_equal_str(self):\\n # compact version\\n assert_that(Condition.is_valid(\\n 'not \\\"{{ env.BRANCH_NAME }}\\\" == \\\"master\\\"'), equal_to(True))\\n # more spaces around are allowed\\n assert_that(Condition.is_valid(\\n ' not \\\"{{ env.BRANCH_NAME }}\\\" == \\\"master\\\" '), equal_to(True))\\n # compact version\\n assert_that(Condition.is_valid(\\n 'not \\\"{{ variables.BRANCH_NAME }}\\\" == \\\"master\\\"'), equal_to(True))\\n # more spaces around are allowed\\n assert_that(Condition.is_valid(\\n ' not \\\"{{ variables.BRANCH_NAME }}\\\" == \\\"master\\\" '), equal_to(True))\",\n \"def commit_check(ctx):\\n result = ctx.run(f\\\"{VENV_PREFIX} cz check --rev-range master..\\\", warn=True)\\n if result.exited == 3: # NO_COMMIT_FOUND\\n exit(0)\\n else:\\n exit(result.exited)\",\n \"def checkBC(bc):\\n if isinstance(bc, string_types):\\n bc = [bc, bc]\\n assert isinstance(bc, list), 'bc must be a list'\\n assert len(bc) == 2, 'bc must have two elements'\\n\\n for bc_i in bc:\\n assert isinstance(bc_i, string_types), \\\"each bc must be a string\\\"\\n assert bc_i in ['dirichlet', 'neumann'], (\\\"each bc must be either,\\\"\\n \\\"'dirichlet' or 'neumann'\\\")\\n return bc\",\n \"def brepalgo_IsValid(*args):\\n return _BRepAlgo.brepalgo_IsValid(*args)\",\n \"def check(self):\\n badNamedAssetNode = list()\\n\\n # prog = re.compile(\\\"^[A-Z]{4}[0-9]{2}_C_[0-9]{3}_GAST[0-9]{2}Shape$\\\")\\n prog = re.compile(\\\"^[A-Z]{4}[0-9]{2}_C_[0-9]{3}_G[A-Z]{3}[0-9]{2}Shape$\\\")\\n progFx = re.compile(\\n \\\"^[A-Z]{4}[0-9]{2}_C_[0-9]{3}_G[A-Z]{2}[0-9]{2}Shape$\\\")\\n\\n for assetNode in pm.ls(type=\\\"gAsset\\\"):\\n nodename = assetNode.nodeName(stripNamespace=True)\\n if not prog.match(nodename):\\n if not progFx.match(nodename):\\n badNamedAssetNode.append(assetNode)\\n\\n if not badNamedAssetNode:\\n self.status = \\\"OK\\\"\\n else:\\n self.status = self.errorMode\\n self.errorNodes = badNamedAssetNode\\n for mesh in badNamedAssetNode:\\n self.addError(\\\"%s is not a legal asset node name\\\" % mesh)\\n self.errorMessage = \\\"%s illegal asset node name(s)\\\" % (\\n len(badNamedAssetNode))\",\n \"def has_invalid_targets(self):\\n return len(self._combined_invalid_versioned_targets.targets) > 0\",\n \"def validate_change(ticket):\\n # First ensure topic line mentions tickets, and pull them out.\\n topic = COMMIT_MSG.split('\\\\n', 1)[0]\\n fix_tickets = re.findall(\\\"[A-Z]{2,5}-[0-9]{1,6}\\\", topic)\\n if len(fix_tickets) == 0:\\n print \\\"\\\\n\\\\n\\\\n\\\\n\\\\n*********\\\\nERROR: commit message does not name a ticket!\\\"\\n return False\\n\\n # Now get list of approved tickets from master ticket, and ensure\\n # all \\\"fixed\\\" tickets are approved.\\n approved_tickets = get_approved_tickets(ticket)\\n for tick in fix_tickets:\\n if not tick in approved_tickets:\\n print \\\"\\\\n\\\\n\\\\n\\\\n\\\\n*********\\\\nERROR: ticket {} is not approved (see approval ticket {})\\\".format(\\n tick, ticket)\\n return False\\n return True\",\n \"def validateBed(df):\\n try:\\n msg = ''\\n if df.isnull().values.any() == True: \\n msg = 'Missing values' + '\\\\n' + str(df.isnull().sum())\\n return [False, msg]\\n if (all(x in ['+', '-'] for x in df['strand'].cat.categories.tolist())) != True:\\n msg = 'Bad strand symbol(has to be + or -)'\\n return [False, msg]\\n return [True, msg]\\n except (TypeError, AttributeError, KeyError):\\n return [False, 'Not a valid dataframe']\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6998986","0.66559356","0.64833444","0.6398546","0.62924314","0.6202438","0.6100306","0.6061971","0.60214883","0.5950823","0.57404685","0.5680982","0.56484795","0.5646791","0.56122196","0.5460472","0.5450381","0.54337895","0.5420128","0.54145825","0.54026216","0.53585887","0.5280357","0.5276135","0.5269761","0.51807344","0.51750624","0.51746446","0.51634026","0.5158866","0.51534605","0.5117827","0.5088433","0.5076422","0.50595725","0.50338304","0.5025528","0.5014042","0.500574","0.5002973","0.49994907","0.49898157","0.49891704","0.4988156","0.4982377","0.49805534","0.4978722","0.49771023","0.49651185","0.49519154","0.49519154","0.493808","0.4937748","0.49287486","0.49246052","0.49198177","0.4888476","0.48803675","0.48796272","0.48685455","0.4852003","0.4846695","0.48466292","0.48385525","0.4836811","0.48273286","0.48111114","0.47911546","0.47863007","0.4781132","0.47725153","0.4730147","0.47294804","0.47142267","0.4701671","0.468449","0.46833768","0.46811864","0.46739694","0.4673755","0.46575886","0.4648588","0.46455273","0.46404505","0.46373913","0.4636781","0.4632325","0.4625665","0.4620398","0.46190277","0.46137738","0.46011335","0.4600252","0.45998544","0.45928848","0.45924342","0.45841104","0.45808533","0.45787048","0.457671"],"string":"[\n \"0.6998986\",\n \"0.66559356\",\n \"0.64833444\",\n \"0.6398546\",\n \"0.62924314\",\n \"0.6202438\",\n \"0.6100306\",\n \"0.6061971\",\n \"0.60214883\",\n \"0.5950823\",\n \"0.57404685\",\n \"0.5680982\",\n \"0.56484795\",\n \"0.5646791\",\n \"0.56122196\",\n \"0.5460472\",\n \"0.5450381\",\n \"0.54337895\",\n \"0.5420128\",\n \"0.54145825\",\n \"0.54026216\",\n \"0.53585887\",\n \"0.5280357\",\n \"0.5276135\",\n \"0.5269761\",\n \"0.51807344\",\n \"0.51750624\",\n \"0.51746446\",\n \"0.51634026\",\n \"0.5158866\",\n \"0.51534605\",\n \"0.5117827\",\n \"0.5088433\",\n \"0.5076422\",\n \"0.50595725\",\n \"0.50338304\",\n \"0.5025528\",\n \"0.5014042\",\n \"0.500574\",\n \"0.5002973\",\n \"0.49994907\",\n \"0.49898157\",\n \"0.49891704\",\n \"0.4988156\",\n \"0.4982377\",\n \"0.49805534\",\n \"0.4978722\",\n \"0.49771023\",\n \"0.49651185\",\n \"0.49519154\",\n \"0.49519154\",\n \"0.493808\",\n \"0.4937748\",\n \"0.49287486\",\n \"0.49246052\",\n \"0.49198177\",\n \"0.4888476\",\n \"0.48803675\",\n \"0.48796272\",\n \"0.48685455\",\n \"0.4852003\",\n \"0.4846695\",\n \"0.48466292\",\n \"0.48385525\",\n \"0.4836811\",\n \"0.48273286\",\n \"0.48111114\",\n \"0.47911546\",\n \"0.47863007\",\n \"0.4781132\",\n \"0.47725153\",\n \"0.4730147\",\n \"0.47294804\",\n \"0.47142267\",\n \"0.4701671\",\n \"0.468449\",\n \"0.46833768\",\n \"0.46811864\",\n \"0.46739694\",\n \"0.4673755\",\n \"0.46575886\",\n \"0.4648588\",\n \"0.46455273\",\n \"0.46404505\",\n \"0.46373913\",\n \"0.4636781\",\n \"0.4632325\",\n \"0.4625665\",\n \"0.4620398\",\n \"0.46190277\",\n \"0.46137738\",\n \"0.46011335\",\n \"0.4600252\",\n \"0.45998544\",\n \"0.45928848\",\n \"0.45924342\",\n \"0.45841104\",\n \"0.45808533\",\n \"0.45787048\",\n \"0.457671\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.838676"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":123,"numItemsPerPage":100,"numTotalItems":95772,"offset":12300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTk2NTE1OCwianRpIjoiOTFjZWM0ZTMtOTA4MC00NDdlLWJhZWEtOWQ0YjY2YzlhMGMyIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU5Njg3NTgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.NXIAkz9GgBBdmVeKop3z_Whfwykc1293-i2HMoF90c5ji7OjPOT9mnwutCTL6t8m6SCSFhssCZdtzfKjwdI1Ag","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Add more connection endpoints. Connection may have many endpoints, mixing protocols and types. | def addEndpoints(self, endpoints):
self.endpoints.extend(endpoints)
self._connectOrBind(endpoints) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def connections_endpoints(request: web.BaseRequest):\n context: AdminRequestContext = request[\"context\"]\n connection_id = request.match_info[\"conn_id\"]\n\n profile = context.profile\n connection_mgr = ConnectionManager(profile)\n try:\n endpoints = await connection_mgr.get_endpoint... | [
"0.6581431",
"0.62893325",
"0.6268082",
"0.6254711",
"0.6199637",
"0.6168774",
"0.6061122",
"0.60520715",
"0.6042876",
"0.6022899",
"0.5892089",
"0.5878343",
"0.5851649",
"0.58505166",
"0.58410054",
"0.5814967",
"0.57680947",
"0.5753131",
"0.572161",
"0.57154876",
"0.57008517... | 0.7782683 | 0 |
Shutdown connection and socket. | def shutdown(self):
self.factory.reactor.removeReader(self)
self.factory.connections.discard(self)
self.socket.close()
self.socket = None
self.factory = None
if self.read_scheduled is not None:
self.read_scheduled.cancel()
self.read_scheduled =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shutdown(self):\n self.sock.close()",
"def shutdown(self):\r\n self.socket.close()\r\n # self.socket_video.close()\r\n self.socket_state.close()",
"def shutdown(self):\n self.connected = False\n self.protocol.send_message(self.sock, '__!shutdown__')\n data =... | [
"0.8116628",
"0.8094799",
"0.8060814",
"0.79759955",
"0.78641194",
"0.7841892",
"0.7702921",
"0.7702643",
"0.7657929",
"0.7616718",
"0.7593503",
"0.749319",
"0.74762684",
"0.7362442",
"0.7351249",
"0.73482984",
"0.7346527",
"0.73362386",
"0.7329469",
"0.73156035",
"0.73146766... | 0.6914994 | 69 |
Called when the connection was lost. Part of L{IFileDescriptor}. This is called when the connection on a selectable object has been lost. It will be called whether the connection was closed explicitly, an exception occurred in an event handler, or the other end of the connection closed it first. | def connectionLost(self, reason):
if self.factory:
self.factory.reactor.removeReader(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loseConnection(self):\n self.lost_connection.callback(self)\n return None",
"def connectionLost(self, reason):\n self.factory._r_on_connection_lost(self)",
"def connectionLost(self, reason=None):\n\n # Log the disconnection\n MaverickServerProtocol._logger.debug(\"Client ... | [
"0.7431634",
"0.73637027",
"0.7295924",
"0.7279225",
"0.7188222",
"0.71682227",
"0.7115157",
"0.70909446",
"0.7086776",
"0.70710385",
"0.7067346",
"0.70491093",
"0.70391434",
"0.7022612",
"0.70011073",
"0.6968903",
"0.6921659",
"0.69068295",
"0.6879366",
"0.6867376",
"0.68571... | 0.65599656 | 37 |
Read multipart in nonblocking manner, returns with ready message or raising exception (in case of no more messages available). | def _readMultipart(self):
while True:
self.recv_parts.append(self.socket.recv(constants.NOBLOCK))
if not self.socket_get(constants.RCVMORE):
result, self.recv_parts = self.recv_parts, []
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def doRead(self):\n if self.read_scheduled is not None:\n if not self.read_scheduled.called:\n self.read_scheduled.cancel()\n self.read_scheduled = None\n\n while True:\n if self.factory is None: # disconnected\n return\n\n ev... | [
"0.7202878",
"0.6593352",
"0.6501937",
"0.64703834",
"0.63924545",
"0.6380673",
"0.6375113",
"0.63678604",
"0.6358247",
"0.6302495",
"0.6219391",
"0.6152119",
"0.6034115",
"0.6021782",
"0.6014445",
"0.594972",
"0.59420466",
"0.5936738",
"0.5936738",
"0.59349895",
"0.59290695"... | 0.72168154 | 0 |
Some data is available for reading on your descriptor. ZeroMQ is signalling that we should process some events, we're starting to to receive incoming messages. Part of L{IReadDescriptor}. | def doRead(self):
if self.read_scheduled is not None:
if not self.read_scheduled.called:
self.read_scheduled.cancel()
self.read_scheduled = None
while True:
if self.factory is None: # disconnected
return
events = self.soc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_read(self):\n pass",
"def doRead(self):\n return fdesc.readFromFD(self.fileno(), self.protocol.dataReceived)",
"def _handle_read(self):\n pass",
"def _notify_read(self, cuds_object):",
"def whenReadReady(self, channel, call):",
"def read(self):\n pass",
"def inRea... | [
"0.6884566",
"0.6863841",
"0.67576945",
"0.66687995",
"0.6382098",
"0.63627136",
"0.6359575",
"0.63490164",
"0.63316524",
"0.6277192",
"0.6174218",
"0.6168974",
"0.6167152",
"0.61480916",
"0.61083895",
"0.6058517",
"0.60197777",
"0.6003864",
"0.59779453",
"0.59479123",
"0.594... | 0.60894215 | 15 |
Send message via ZeroMQ. Sending is performed directly to ZeroMQ without queueing. If HWM is reached on ZeroMQ side, sending operation is aborted with exception from ZeroMQ (EAGAIN). | def send(self, message):
if not hasattr(message, '__iter__'):
self.socket.send(message, constants.NOBLOCK)
else:
for m in message[:-1]:
self.socket.send(m, constants.NOBLOCK | constants.SNDMORE)
self.socket.send(message[-1], constants.NOBLOCK)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send(self, msg):\n self.house.PLM.send_queue.put( msg )",
"def __send(self):\r\n self.msgLock.acquire()\r\n if self.numMsg > 0:\r\n self.socket.send(self.msg.pop(0))\r\n self.numMsg -= 1\r\n self.msgLock.release()",
"def send(self, msg):\n with self.... | [
"0.671881",
"0.6497291",
"0.64011776",
"0.6373785",
"0.6349839",
"0.6319402",
"0.6285288",
"0.62469214",
"0.6214353",
"0.6164441",
"0.6128295",
"0.6097054",
"0.60968465",
"0.60759246",
"0.60734814",
"0.604762",
"0.6045848",
"0.6039005",
"0.6007811",
"0.6002121",
"0.6000013",
... | 0.0 | -1 |
Called on incoming message from ZeroMQ. | def messageReceived(self, message):
raise NotImplementedError(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def receive(self, message):",
"def receive_message(self, message):",
"def handle_message(self, msg):\n pass",
"def receive_message(self, message):\r\n return",
"def handle(self, message):",
"def _handle_message(self, msg):\n self.event('message', msg)",
"def on_msg_recv(msg):\n... | [
"0.6974165",
"0.6928344",
"0.6919641",
"0.6894543",
"0.68906176",
"0.68757933",
"0.6871723",
"0.68622655",
"0.68468934",
"0.68297577",
"0.67970884",
"0.67825955",
"0.6753128",
"0.67452276",
"0.6700199",
"0.6622171",
"0.6585881",
"0.65731514",
"0.6529273",
"0.6512468",
"0.6509... | 0.6205774 | 50 |
Connect and/or bind socket to endpoints. | def _connectOrBind(self, endpoints):
for endpoint in endpoints:
if endpoint.type == ZmqEndpointType.connect:
self.socket.connect(endpoint.address)
elif endpoint.type == ZmqEndpointType.bind:
self.socket.bind(endpoint.address)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bind(self):\n self._conn = socket.socket(socket.AF_INET, self.protocol.value)\n try:\n self._conn.bind((self.host, self.port))\n except OSError as e:\n self.close()\n raise BindError(str(e))\n self._conn.setblocking(False)\n self._conn.listen(... | [
"0.7130075",
"0.7112576",
"0.6801621",
"0.6775071",
"0.6772604",
"0.6653766",
"0.6643562",
"0.66249806",
"0.66118973",
"0.659476",
"0.6579499",
"0.6578894",
"0.6578408",
"0.6554303",
"0.6552846",
"0.654279",
"0.6523097",
"0.651956",
"0.64632034",
"0.6457388",
"0.64059734",
... | 0.7718711 | 0 |
Atom(symbol, displacement, ...) > atom object. The chemical symbol or the atomic number must be given (``symbol`` or ``Z``). The rest of the arguments (``mass``, ``displacement``, ``force``, ``momentum``, ``velocity`` and ``magmom``) have default values. | def __init__(self, symbol=None, Z=None,
mass=None,
displacement=None,
force=None, momentum=None, velocity=None,
magmom=0.0):
self._properties={}
if symbol is None:
if Z is None:
raise Value... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addAtom(self, symbol, position, massNum = None, **kwds):\n self.MagCell.addAtom(symbol, position, massNum, **kwds)",
"def __init__(self, position, momentum, mass):\n self.position = position\n self.momentum = momentum\n self.mass = mass",
"def add_atom(molecule, atomic_num, x_co... | [
"0.62646323",
"0.5459874",
"0.5274294",
"0.52713656",
"0.52178127",
"0.51740944",
"0.5111022",
"0.5087099",
"0.4997732",
"0.49159324",
"0.4915145",
"0.49009722",
"0.48384157",
"0.48236147",
"0.48070416",
"0.47763655",
"0.47691646",
"0.4750681",
"0.47204223",
"0.47154075",
"0.... | 0.5423634 | 2 |
ADJ, ADJ_SAT, ADV, NOUN, VERB = "a", "s", "r", "n", "v" | def lemmatize_text(self, text, print_tokens=False):
# text = text.replace("/", ' or ')
# text = text.replace("\\", ' or ')
# # text = text.replace("'s", '')
# # text = text.replace("’s", '')
if print_tokens:
print(pos_tag(word_tokenize(text)))
# text = "We’r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verb_lemma(word):\n if word.endswith(\"ed\"):\n if word[:-2].endswith(\"v\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"at\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"it\"):\n return word[:-2].lower() + \"e\"\n... | [
"0.6282963",
"0.62601596",
"0.5897731",
"0.5872146",
"0.5858878",
"0.5748334",
"0.57213783",
"0.56774324",
"0.56710875",
"0.56707567",
"0.56644166",
"0.55686545",
"0.55204165",
"0.54814506",
"0.5448419",
"0.53462553",
"0.5303483",
"0.5303483",
"0.52700055",
"0.5265413",
"0.52... | 0.0 | -1 |
get a single word's wordnet POS (PartofSpeech) tag. | def get_wordnet_pos(self, word):
# token = word_tokenize(word)
base_tag = pos_tag([word])[0][1][:2]
return self.pos_tag_dict.get(base_tag, wordnet.NOUN) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_wordnet_pos(word):\n tag = nltk.pos_tag([word])[0][1][0].upper()\n tag_dict = {\"J\": wordnet.ADJ,\n \"N\": wordnet.NOUN,\n \"V\": wordnet.VERB,\n \"R\": wordnet.ADV}\n return tag_dict.get(tag, wordnet.NOUN)",
"def get_wordnet_pos(word):\n tag = nl... | [
"0.7593354",
"0.7576574",
"0.7576574",
"0.7576574",
"0.7576574",
"0.7576574",
"0.7576574",
"0.75293577",
"0.7503421",
"0.7377293",
"0.719029",
"0.71326035",
"0.71208805",
"0.709108",
"0.70407254",
"0.7016819",
"0.6993947",
"0.69855785",
"0.69850004",
"0.6918058",
"0.6874751",... | 0.7755339 | 0 |
Cleans a single review (simplifies it as much as possible) | def clean_review(self, text):
text = text.lower() # lowercase capital letters
if self.remove_stopwords:
text = self.remove_stopwords_f(text, keep_neg_words=True)
text = re.sub('[^a-zA-Z]+', ' ', text) # select only alphabet characters (letters only)
# text = re.sub('[^a-z... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sanitize(review):\n # c) Remove all punctuation, as well as the stop-words.\n # First replace punctuations with empty char then tokenize it\n # Replace punctuation with spaces using fast method\n clean = review.translate(review.maketrans(string.punctuation,\n ... | [
"0.7039309",
"0.6684278",
"0.64323676",
"0.6006801",
"0.5992633",
"0.5959121",
"0.5953015",
"0.5914954",
"0.58682597",
"0.5802497",
"0.5769421",
"0.5647594",
"0.56341743",
"0.559752",
"0.5589405",
"0.55746025",
"0.5561322",
"0.5524036",
"0.55173266",
"0.5471399",
"0.54688805"... | 0.71801054 | 0 |
Cleans a single resume (resume text) | def clean_resume(self, text):
text = text.lower() # lowercase capital letters
text = re.sub(r'(http|www)\S+\s*', '', text) # remove URLs
text = re.sub(r'\S+@\S+\s*', '', text) # remove emails
text = re.sub(r'@\S+\s*', '', text) # remove mentions
text = re.sub(r'#\S+\s*', '',... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleaning(full_text):\n try:\n if open(RESULT_PATH):\n os.remove(RESULT_PATH)\n \n else:\n print(\"No output.mp3\")\n except Exception as e:\n print(str(e))\n\n text = full_text\n\n book = ''.join(text)\n\n\n book = book.replace('.', '.<eos>')\n ... | [
"0.65953624",
"0.59690464",
"0.5925192",
"0.57667595",
"0.5754863",
"0.572727",
"0.56936944",
"0.5628845",
"0.5601159",
"0.55797684",
"0.55733097",
"0.55511904",
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"0.55109733",
"0.5505896",
"0.5464441",
"0.5445689",
"0.54276085",
"0.5422343",
"0.5397728... | 0.7648608 | 0 |
Euclidean distance Squared Euclidean distance more frequently used | def euc_dist(self, squared=True): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getEuclideanDistance():\r\n global euclideanDistance\r\n return euclideanDistance",
"def euclidean_distance(x1, x2):\n return np.sqrt(np.sum(np.square(np.subtract(x1, x2))))",
"def euclidean_distance(s1,s2): \n tmpsum = 0\n \n for index,value in enumerate(s1):\n tmpsum += (s1[in... | [
"0.73311925",
"0.7217947",
"0.72092783",
"0.71997476",
"0.71303356",
"0.70067096",
"0.6991867",
"0.6981154",
"0.69637036",
"0.6960269",
"0.6941405",
"0.69364357",
"0.6935467",
"0.69090146",
"0.68860257",
"0.68558615",
"0.68158317",
"0.6806122",
"0.6798799",
"0.6798116",
"0.67... | 0.7476734 | 0 |
Y is the output of the classifier vector of size (m,1) of the estimated class of each sample | def compute_Y(X, w):
Y = np.sign(np.dot(X, w))
return Y
# for i in range(A.shape[0]):
# # prevent overflow by subtracting the max value from each entry in row i
# A[i, :] = A[i, :] - A[i, :].max()
# A[i, :] = np.exp(A[i, :])
# Y[i, :] = A[i, :] / A[i, :].sum() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(self,X,y):\n self.X_train = X\n self.y_train = y\n self.class_labels = np.unique(self.y_train)",
"def multiclass_toy_data(): \n #dataset = np.zeros((10,5), np.int)\n dataset = np.array([[0,0,0,0,4],\n [0,0,0,0,5],\n [1,3,0,0,0],\n ... | [
"0.73219055",
"0.7197235",
"0.7050374",
"0.6989029",
"0.6952847",
"0.6943367",
"0.6921751",
"0.6869864",
"0.68615234",
"0.6858735",
"0.6842073",
"0.6842073",
"0.68230265",
"0.6822966",
"0.6810576",
"0.6805199",
"0.67613393",
"0.667963",
"0.6678272",
"0.66574466",
"0.66427875"... | 0.0 | -1 |
Gradient of log loss function at the current w | def compute_gradient(X, t, w): # TODO: try to change to square loss since it's hessian is easier to obtain
# TODO : print to console the max gradient in every run
A = np.dot(X, w)
m = t.shape[0]
C = -1 * t * (1 / (1 + np.exp(A * t)))
return (1 / m) * np.dot(X.T, C) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)",
"def grad_reglog(w, X, y, **kwargs):\n p = np.exp(-y * (np.dot(X, w)))\n P = p / (1. + p)\n return -1 * np.dot(X.T, P * y) / X.shape[0]",
"def _loss_gradient(x0, x1, b, w, lam, weights=None):\n nvars = len(w)\n\... | [
"0.8048596",
"0.76594067",
"0.75782055",
"0.75694907",
"0.75582236",
"0.7449882",
"0.7397625",
"0.7279694",
"0.7203933",
"0.7198174",
"0.7188557",
"0.7125099",
"0.71196514",
"0.71061635",
"0.7087161",
"0.7081187",
"0.70647085",
"0.70532495",
"0.70407975",
"0.70192206",
"0.697... | 0.6936435 | 24 |
Gradient of log loss function at the current w | def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain
A = np.dot(self.X, self.w)
m = self.t.shape[0]
C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))
return (1 / m) * np.dot(self.X.T, C) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)",
"def grad_reglog(w, X, y, **kwargs):\n p = np.exp(-y * (np.dot(X, w)))\n P = p / (1. + p)\n return -1 * np.dot(X.T, P * y) / X.shape[0]",
"def _loss_gradient(x0, x1, b, w, lam, weights=None):\n nvars = len(w)\n\... | [
"0.8048325",
"0.76605856",
"0.75776166",
"0.7570397",
"0.7558319",
"0.7449295",
"0.73966116",
"0.7279479",
"0.72035617",
"0.71984977",
"0.71885246",
"0.71245855",
"0.71195346",
"0.7105514",
"0.70863146",
"0.7082717",
"0.7064733",
"0.70527214",
"0.7041097",
"0.7020151",
"0.697... | 0.6894576 | 33 |
Gradient of log loss function at the current w | def compute_gradient(self):
A = np.dot(self.X, self.w)
m = self.t.shape[0]
C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))
return (1 / m) * np.dot(self.X.T, C) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)",
"def grad_reglog(w, X, y, **kwargs):\n p = np.exp(-y * (np.dot(X, w)))\n P = p / (1. + p)\n return -1 * np.dot(X.T, P * y) / X.shape[0]",
"def _loss_gradient(x0, x1, b, w, lam, weights=None):\n nvars = len(w)\n\... | [
"0.80493027",
"0.7659334",
"0.7578845",
"0.7569231",
"0.75586885",
"0.7450052",
"0.7397052",
"0.72792256",
"0.72056633",
"0.7200397",
"0.71906173",
"0.71262753",
"0.7120781",
"0.71080154",
"0.70880324",
"0.70810807",
"0.70663065",
"0.7054109",
"0.70421106",
"0.7017045",
"0.69... | 0.667223 | 62 |
Gradient of log loss function with regularization at the current w | def compute_reg_gradient(self):
A = np.dot(self.X, self.w)
m = self.t.shape[0]
C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))
return (1 / m) * np.dot(self.X.T, C) + self.lambda_reg * self.w # add regularization term | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def grad_reglog(w, X, y, **kwargs):\n p = np.exp(-y * (np.dot(X, w)))\n P = p / (1. + p)\n return -1 * np.dot(X.T, P * y) / X.shape[0]",
"def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)",
"def _loss_gradient(x0, x1, b, w, lam, weights=None):\n nvars = len(w)\n\... | [
"0.791353",
"0.7674807",
"0.763062",
"0.756356",
"0.75527704",
"0.7370547",
"0.72928864",
"0.72234875",
"0.7171148",
"0.71650624",
"0.71579814",
"0.71459574",
"0.7117442",
"0.7092932",
"0.707363",
"0.7057667",
"0.70241106",
"0.7017027",
"0.7012952",
"0.7012573",
"0.7011721",
... | 0.7077802 | 14 |
An estimator for the gradient of log loss function at the current w, by taking only a single sample | def compute_sgd_gradient(self, x_j, t_j):
a = np.dot(x_j.T, self.w)
return -1 * t_j * (1 / (1 + np.exp(a * t_j))) * x_j | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_prior_grad(self, inputs):",
"def grad_reglog(w, X, y, **kwargs):\n p = np.exp(-y * (np.dot(X, w)))\n P = p / (1. + p)\n return -1 * np.dot(X.T, P * y) / X.shape[0]",
"def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)",
"def grad_log(self, X):\n # \"\"\"\... | [
"0.7409512",
"0.727362",
"0.72355455",
"0.6930026",
"0.68873745",
"0.68859917",
"0.6878263",
"0.68434113",
"0.6838884",
"0.68350923",
"0.6825335",
"0.6803445",
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"0.6794086",
"0.6712138",
"0.67044675",
"0.6703921",
"0.6699552",
"0.66310096",
"0.6619995",
"0.6588419... | 0.0 | -1 |
Test elementwise for fill values and return result as a boolean array. | def isfillvalue(a):
a = numpy.asarray(a)
if a.dtype.kind == 'i':
mask = a == -999999999
elif a.dtype.kind == 'f':
mask = numpy.isnan(a)
elif a.dtype.kind == 'S':
mask = a == ''
else:
raise ValueError('Fill value not known for dtype %s' % a.dtype)
return mask | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_array_booleans(n: int = 1024, random_seed: int = None) -> TYPE_ARRAY:\n return _RNG.randint(0, 2, n).astype(bool)",
"def __call__(self, size=1):\n\n # A completely empty numpy array\n results = numpy.zeros(self.shape, dtype=bool)\n\n # Gets a set of random indices that need t... | [
"0.60772234",
"0.59158",
"0.5880896",
"0.5725829",
"0.5717447",
"0.57084936",
"0.56891406",
"0.5651735",
"0.5627813",
"0.56104505",
"0.5576027",
"0.5572044",
"0.55715203",
"0.5566624",
"0.55537015",
"0.5498187",
"0.5478841",
"0.54585373",
"0.5433937",
"0.5429966",
"0.5427257"... | 0.5984257 | 1 |
Return the start/stop times in milliseconds since 111970 | def as_millis(self):
return int(ntplib.ntp_to_system_time(self.start) * 1000), int(ntplib.ntp_to_system_time(self.stop) * 1000) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runtime_cal(start,end) :\n run_time = end - start\n mm = int(run_time/60)\n ss = round(run_time%60)\n return mm, ss",
"def get_time_ms():\n return int(round(time.time() * 1000))",
"def getTimes():",
"def getTimes():",
"def getTimes():",
"def elapsed_micros(start: int, /) -> int:",
"d... | [
"0.6986291",
"0.6958346",
"0.69455504",
"0.69455504",
"0.69455504",
"0.69351584",
"0.6922508",
"0.6904034",
"0.69000614",
"0.6889413",
"0.6834017",
"0.6818947",
"0.6816358",
"0.67783904",
"0.67711294",
"0.67618895",
"0.67332286",
"0.6714469",
"0.6713183",
"0.6713183",
"0.6713... | 0.73437476 | 0 |
Simple timebased cache. Only valid for functions which have no arguments | def timed_cache(expire_seconds):
cache = {'cache_time': 0, 'cache_value': None}
def expired():
return cache['cache_time'] + expire_seconds < time.time()
def wrapper(func):
@wraps(func)
def inner():
if expired():
cache['cache_value'] = func()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cached(func):\n return _lru_cache(None)(func)",
"def dynCache(*args, **kwargs)->None:\n pass",
"def cached(function):\n\t@wraps(function)\n\tdef check_cache_first(cls, *args):\n\t\tif not args in cls._cache:\n\t\t\tcode = function(cls, *args)\n\t\t\tif code:\n\t\t\t\tcls._cache[args] = code\n\t\t... | [
"0.7704695",
"0.7487862",
"0.7305302",
"0.72399396",
"0.7158932",
"0.71499777",
"0.7103249",
"0.7094693",
"0.6992508",
"0.69538873",
"0.69481593",
"0.685329",
"0.68105304",
"0.68082714",
"0.68078345",
"0.679467",
"0.67882276",
"0.67683274",
"0.67584336",
"0.67443854",
"0.6742... | 0.6433214 | 38 |
Function to recursively check if two dicts are equal | def dict_equal(d1, d2):
if isinstance(d1, dict) and isinstance(d2, dict):
# check keysets
if set(d1) != set(d2):
return False
# otherwise loop through all the keys and check if the dicts and items are equal
return all((dict_equal(d1[key], d2[key]) for key in d1))
# ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dict_equal(d1: Dict, d2: Dict) -> bool:\n\n # iterate over the dict with more keys\n # di is the dictionary to iterate over\n # dj is the one to compare to\n if len(d2) > len(d1):\n di = d2\n dj = d1\n else:\n di = d1\n dj = d2\n for key, value in di.items():\n ... | [
"0.82100755",
"0.7669566",
"0.7605134",
"0.7587031",
"0.7573984",
"0.7369974",
"0.735764",
"0.72046685",
"0.71447515",
"0.70782727",
"0.70460093",
"0.69822705",
"0.6968151",
"0.69324183",
"0.69310194",
"0.69286764",
"0.6905533",
"0.6891169",
"0.6882714",
"0.6858757",
"0.68475... | 0.7747674 | 1 |
Test that we can read observed/synthetic traces with the preprocess mod. | def test_default_read():
# If new data formats are added to preprocess, they need to be tested
tested_data_formats = ["ASCII", "SU", "SAC"]
preprocess = Default()
assert(set(tested_data_formats) ==
set(preprocess._obs_acceptable_data_formats))
st1 = preprocess.read(os.path.join(TEST_DAT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_decode_trace(self):\n self.assertEqual(td.trace(), decoder.decode_trace(BytesIO(td.trace(True))))",
"def test_basic_parser_trace():",
"def prepare_traces():\n \n # Identify the number of traces for the largest dataset\n num_traces = max([size[\"traces\"] for size in sizes])\n \n # Create f... | [
"0.62672204",
"0.6000139",
"0.59205806",
"0.5894753",
"0.5821288",
"0.5764435",
"0.5706461",
"0.569139",
"0.56367767",
"0.5617839",
"0.55851614",
"0.5573745",
"0.5566541",
"0.5566249",
"0.5565095",
"0.5563263",
"0.5532253",
"0.5523213",
"0.55199015",
"0.5490037",
"0.5487379",... | 0.5085053 | 77 |
Test that we can write synthetic waveforms to formats that SPECFEM recognizes | def test_default_write(tmpdir):
# If new data formats supported by SPECFEM are added to preprocess,
# they need to be tested
tested_data_formats = ["ASCII", "SU"]
preprocess = Default()
assert(set(tested_data_formats) ==
set(preprocess._syn_acceptable_data_formats))
st1 = preprocess... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_simple_test():\n count = 1\n mdict = {\n 'operating_frequency': 3e8,\n 'sample_rate': 8e3,\n 'signal': [1] * 5,\n 'origin_pos': [1000, 0, 0],\n 'dest_pos': [300, 200, 50],\n 'origin_vel': [0] * 3,\n ... | [
"0.6389405",
"0.6367297",
"0.62679857",
"0.62373686",
"0.62114215",
"0.6170687",
"0.6146791",
"0.6146547",
"0.6139426",
"0.6124821",
"0.6114333",
"0.6075158",
"0.604245",
"0.5976598",
"0.5962665",
"0.58959585",
"0.58732283",
"0.5865532",
"0.5864507",
"0.5856483",
"0.58444536"... | 0.61198735 | 10 |
Make sure we can write empty adjoint sources expected by SPECFEM | def test_default_initialize_adjoint_traces(tmpdir):
preprocess = Default()
preprocess.syn_data_format = "ASCII"
data_filenames = glob(os.path.join(TEST_DATA, "*semd"))
preprocess.initialize_adjoint_traces(data_filenames=data_filenames,
output=tmpdir)
prepro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_empty_sources(self):\n for source in [\"dxf\", \"edilizia\", \"easyroom\", \"merged\"]:\n if source in self and not self[source]:\n del self[source]",
"def _prepare(self):\n logging.warning('-> preparing EMPTY experiments...')",
"def test_missing_data_sources(self):",
... | [
"0.5989031",
"0.58842367",
"0.573989",
"0.5713581",
"0.56372374",
"0.5469574",
"0.5406883",
"0.53961045",
"0.53875965",
"0.53536177",
"0.5325273",
"0.5302966",
"0.5258798",
"0.52277917",
"0.5221645",
"0.5215781",
"0.5201497",
"0.51766396",
"0.5165727",
"0.5138119",
"0.5130737... | 0.0 | -1 |
Quantify misfit with some example data | def test_default_quantify_misfit(tmpdir):
preprocess = Default(syn_data_format="ascii", obs_data_format="ascii",
unit_output="disp", misfit="waveform",
adjoint="waveform", path_preprocess=tmpdir,
path_solver=TEST_SOLVER, source_prefix="SOURC... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_genextreme_fit(self):\n p = generic.fit(self.genextreme, \"genextreme\")\n np.testing.assert_allclose(p, (0.20949, 297.954091, 75.7911863), 1e-5)",
"def fit(self, X):",
"def test_fit(self):\n X = np.zeros((2, 3), dtype=np.float64)\n snv = SNV(q=50)\n try:\n ... | [
"0.62091",
"0.6153025",
"0.61098146",
"0.6083619",
"0.6002775",
"0.5955667",
"0.59443253",
"0.5894886",
"0.58396775",
"0.57995504",
"0.5769619",
"0.57624996",
"0.5748213",
"0.5731888",
"0.5731888",
"0.5731888",
"0.57293093",
"0.57270885",
"0.57270885",
"0.56752056",
"0.567476... | 0.6377925 | 0 |
Test setup procedure for SeisFlows which internalizes some workflow information that is crucial for later tasks | def test_pyaflowa_setup(tmpdir):
pyaflowa = Pyaflowa(
workdir=tmpdir,
path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"),
path_solver=os.path.join(TEST_SOLVER, "mainsolver"),
source_prefix="SOURCE",
ntask=2,
components="Y",
)
assert(pyaflowa._s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_workflow():\n pass",
"def test_deploy_workflow_definition(self):\n pass",
"def setUp(self) -> None:\n self.sqlite_db = setup_sqlite_in_memory_db()\n create_tables(self.sqlite_db)\n seed_all_distributions()\n container_flow_generation_manager = ContainerFlowGenerat... | [
"0.6903731",
"0.67200714",
"0.66473234",
"0.65858454",
"0.6584684",
"0.6562488",
"0.6548605",
"0.6540773",
"0.6528661",
"0.64872205",
"0.6467191",
"0.64631706",
"0.64592195",
"0.64543176",
"0.6420024",
"0.64105207",
"0.64105207",
"0.64105207",
"0.64105207",
"0.64105207",
"0.6... | 0.0 | -1 |
Test Config setup that is used to control `quantify_misfit` function | def test_pyaflowa_setup_config(tmpdir):
pyaflowa = Pyaflowa(
workdir=tmpdir,
path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"),
path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=1,
data_case="synthetic", components="Y", fix_windows="ITER",
)
pyaflowa.setu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pytest_configure(config):\n config.addinivalue_line(\n \"markers\",\n \"serial: Tests that will not execute with more than 1 MPI process\")\n config.addinivalue_line(\"markers\",\n \"gpu: Tests that should only run on the gpu.\")\n config.addinivalue_line(\n ... | [
"0.6671106",
"0.6536968",
"0.63120526",
"0.61878663",
"0.6127497",
"0.61204",
"0.60867697",
"0.6026608",
"0.5973575",
"0.5944742",
"0.5929044",
"0.59058183",
"0.5883714",
"0.58775496",
"0.5862785",
"0.5830396",
"0.580627",
"0.5784229",
"0.5772616",
"0.57701033",
"0.5727601",
... | 0.55039436 | 49 |
Test that misfit window bool returner always returns how we want it to. | def test_pyaflowa_check_fixed_windows():
pf = Pyaflowa(fix_windows=True)
assert (pf._check_fixed_windows(iteration=99, step_count=99)[0])
pf = Pyaflowa(fix_windows="ITER")
assert (not pf._check_fixed_windows(iteration=1, step_count=0)[0])
assert (pf._check_fixed_windows(iteration=1, step_count=1)[0]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_wip(self):\n self.assertTrue(not return_true())",
"def test_next_window_time_no_sample_passed(self):\n test_window_scheme = WindowingScheme(self.window_test_filter, 3)\n time.sleep(4)\n collected_value = test_window_scheme.filter(self.more_than_upper_bound)\n self.asse... | [
"0.6687499",
"0.6173785",
"0.61068255",
"0.599548",
"0.59855723",
"0.59814656",
"0.59571654",
"0.5826708",
"0.5824917",
"0.5815543",
"0.5805709",
"0.58000684",
"0.57855296",
"0.5784521",
"0.5780538",
"0.5759117",
"0.57221144",
"0.57156813",
"0.5714309",
"0.57061833",
"0.56979... | 0.5426089 | 51 |
Test that the Pyaflowa preprocess class can quantify misfit over the course of a few evaluations (a line search) and run its finalization task Essentially an integration test testing the entire preprocessing module works as a whole | def test_pyaflowa_line_search(tmpdir):
pyaflowa = Pyaflowa(
workdir=tmpdir,
path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"),
path_output=os.path.join(tmpdir, "output"),
path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=2,
data_case="synthetic", componen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_predictor():",
"def test_active_inference_SPM_1b(self):",
"def test_preprocess(self):\r\n\r\n # Should discard all reads due to sequence length being too short\r\n\r\n fasta_files = [self.sample_fasta_file]\r\n qual_files = [self.sample_qual_file]\r\n mapping_file = self.sa... | [
"0.629093",
"0.62761736",
"0.620866",
"0.6105234",
"0.6059339",
"0.6007366",
"0.5925567",
"0.59123164",
"0.5886541",
"0.58703196",
"0.58566153",
"0.5794662",
"0.57678074",
"0.5767124",
"0.5744852",
"0.5740845",
"0.57300746",
"0.5707568",
"0.5697885",
"0.5695476",
"0.56930786"... | 0.6344366 | 0 |
dataList item renderer for Posts on the Bulletin Board. | def cms_post_list_layout(list_id, item_id, resource, rfields, record):
record_id = record["cms_post.id"]
#item_class = "thumbnail"
T = current.T
db = current.db
s3db = current.s3db
settings = current.deployment_settings
permit = current.auth.s3_has_permission
raw = record._row
dat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serializePostsData(influencer, posts, length_limit=30, highlight=False):\n from debra import serializers\n\n posts_data = []\n urls = set()\n posts = list(posts)\n dated = []\n undated = []\n for post in posts:\n if post.create_date:\n dated.append(post)\n else:\n ... | [
"0.5797765",
"0.5777556",
"0.569845",
"0.5545204",
"0.5486257",
"0.54002476",
"0.5336475",
"0.5334056",
"0.5324463",
"0.5311351",
"0.5307505",
"0.53025407",
"0.52784413",
"0.5277002",
"0.52619624",
"0.52491385",
"0.5233054",
"0.52293384",
"0.5213067",
"0.5183145",
"0.51277083... | 0.5974937 | 0 |
Custom lookup method for activity rows, does a left join with the tag. Parameters key and fields are not used, but are kept for API compatibility reasons. | def lookup_rows(self, key, values, fields=None):
s3db = current.s3db
atable = s3db.project_activity
aotable = s3db.project_activity_organisation
left = aotable.on((aotable.activity_id == atable.id) & \
(aotable.role == 1))
qty = len(values)
if... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def custom_lookup_rows(self, key, values, fields=None):\n\n s3db = current.s3db\n atable = s3db.project_activity\n aotable = s3db.project_activity_organisation\n\n left = aotable.on((aotable.activity_id == atable.id) & \\\n (aotable.role == 1))\n\n qty = ... | [
"0.644779",
"0.644779",
"0.48791754",
"0.4854704",
"0.4740543",
"0.4670432",
"0.4572707",
"0.4572707",
"0.4470965",
"0.44454432",
"0.44387135",
"0.44191992",
"0.43801567",
"0.43525732",
"0.4290411",
"0.4239853",
"0.42330432",
"0.4208889",
"0.41861644",
"0.41510016",
"0.413323... | 0.6190899 | 2 |
Represent a single Row | def represent_row(self, row, prefix=None):
# Custom Row (with the Orgs left-joined)
organisation_id = row["project_activity_organisation.organisation_id"]
if organisation_id:
return self.org_represent(organisation_id)
else:
# Fallback to name
name = r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def row(self) -> TableRow:\n raise NotImplementedError",
"def row(self):\n return self[\"row\"]",
"def format_row(self, row):\n raise NotImplementedError()",
"def row(self, row_id):\r\n return Row(self, row_id)",
"def row(self):\n\t\treturn self.__row",
"def _get_single_row(\n ... | [
"0.73848474",
"0.7132799",
"0.7046983",
"0.7046112",
"0.69383246",
"0.67201823",
"0.6698179",
"0.65055215",
"0.6408882",
"0.6400499",
"0.63518995",
"0.63350195",
"0.63344663",
"0.632651",
"0.6285745",
"0.6270171",
"0.62334025",
"0.6231116",
"0.6205497",
"0.6188944",
"0.616573... | 0.6021808 | 48 |
Custom lookup method for need rows, does a left join with the tag. Parameters key and fields are not used, but are kept for API compatibility reasons. | def lookup_rows(self, key, values, fields=None):
s3db = current.s3db
ntable = s3db.need_need
nttable = s3db.need_tag
left = nttable.on((nttable.need_id == ntable.id) & \
(nttable.tag == "req_number"))
qty = len(values)
if qty == 1:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def custom_lookup_rows(self, key, values, fields=None):\n\n s3db = current.s3db\n atable = s3db.project_activity\n aotable = s3db.project_activity_organisation\n\n left = aotable.on((aotable.activity_id == atable.id) & \\\n (aotable.role == 1))\n\n qty = ... | [
"0.61288434",
"0.61288434",
"0.60024345",
"0.60024345",
"0.59321815",
"0.568484",
"0.5589088",
"0.55711323",
"0.5527114",
"0.53464085",
"0.52466536",
"0.5171651",
"0.5166802",
"0.5062962",
"0.4989849",
"0.49464908",
"0.4922893",
"0.4887462",
"0.4826645",
"0.4806405",
"0.47985... | 0.5737034 | 5 |
Represent a single Row | def represent_row(self, row, prefix=None):
# Custom Row (with the tag left-joined)
req_number = row["need_need_tag.value"]
if req_number:
return s3_str(req_number)
else:
# Fallback to name
name = row["need_need.name"]
if name:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def row(self) -> TableRow:\n raise NotImplementedError",
"def row(self):\n return self[\"row\"]",
"def format_row(self, row):\n raise NotImplementedError()",
"def row(self, row_id):\r\n return Row(self, row_id)",
"def row(self):\n\t\treturn self.__row",
"def _get_single_row(\n ... | [
"0.73848474",
"0.7132799",
"0.7046983",
"0.7046112",
"0.69383246",
"0.67201823",
"0.6698179",
"0.65055215",
"0.6408882",
"0.6400499",
"0.63518995",
"0.63350195",
"0.63344663",
"0.632651",
"0.6285745",
"0.6270171",
"0.62334025",
"0.6231116",
"0.6205497",
"0.6188944",
"0.616573... | 0.5698491 | 83 |
Count need lines per status for all open Events | def needs_by_status(cls):
db = current.db
s3db = current.s3db
table = s3db.need_line
etable = s3db.event_event
ltable = s3db.event_event_need
# Extract the data
status = table.status
number = table.id.count()
query = (etable.closed == False) & \... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def counts_by_test_result_status(self, status):\n return len([\n [key, event] for (key, event) in self.result_events.items()\n if event.get(\"status\", \"\") == status])",
"def countSimulationEvents(self, handle):\r\n raise NotImplementedError()",
"def num_test_cases(self, l... | [
"0.5970372",
"0.5815089",
"0.5798976",
"0.5784204",
"0.5764056",
"0.5751111",
"0.57467",
"0.5698221",
"0.56825036",
"0.56721026",
"0.56549156",
"0.56248385",
"0.5612142",
"0.56002754",
"0.5567197",
"0.5566079",
"0.5564478",
"0.55583984",
"0.55577123",
"0.5551859",
"0.5550557"... | 0.5355044 | 35 |
Count need lines per district and status (top 5 districts) for all open Events | def needs_by_district(cls):
T = current.T
db = current.db
s3db = current.s3db
table = s3db.need_line
ntable = s3db.need_need
etable = s3db.event_event
ltable = s3db.event_event_need
status = table.status
number = table.id.count()
locatio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def needs_by_district(cls):\n\n T = current.T\n\n db = current.db\n s3db = current.s3db\n\n table = s3db.req_need_line\n ntable = s3db.req_need\n\n left = ntable.on(ntable.id == table.need_id)\n\n status = table.status\n number = table.id.count()\n loc... | [
"0.5754938",
"0.551308",
"0.54789215",
"0.5473855",
"0.5299601",
"0.5225415",
"0.519377",
"0.5175258",
"0.50648946",
"0.50605243",
"0.50548464",
"0.5018038",
"0.5015188",
"0.5013341",
"0.50113225",
"0.5009056",
"0.49946627",
"0.49912676",
"0.49895993",
"0.49847665",
"0.497986... | 0.63438565 | 0 |
Count total number of affected people by demographic type for all open Events | def people_affected(cls):
db = current.db
s3db = current.s3db
table = s3db.need_line
etable = s3db.event_event
ltable = s3db.event_event_need
query = (etable.closed == False) & \
(etable.id == ltable.event_id) & \
(ltable.need_id == tabl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eventcount(self):\n return self.serviceinstance_set.aggregate(Count('service__category', distinct=True))['service__category__count']",
"def getEventsCounters (attack_df, events):\n n_11 = 0 \n n_12 = 0 \n n_21 = 0 \n n_22 = 0 \n event_type = 0\n for event in events:\n is_attac... | [
"0.60838836",
"0.5981166",
"0.5896875",
"0.58534044",
"0.5848672",
"0.58462423",
"0.5830182",
"0.576765",
"0.5761788",
"0.57357055",
"0.57058483",
"0.56708616",
"0.566682",
"0.5646494",
"0.56389904",
"0.5599355",
"0.5591146",
"0.55907875",
"0.55860656",
"0.55800164",
"0.55293... | 0.5335479 | 33 |
Update data files for homepage statistics NB requires writepermission for static/themes/SHARE/data folder+files | def update_data(cls):
SEPARATORS = (",", ":")
import os
os_path_join = os.path.join
json_dump = json.dump
base = os_path_join(current.request.folder, "static", "themes", "SHARE", "data")
path = os_path_join(base, "needs_by_status.json")
data = cls.needs_by_sta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_stats():\r\n\turl = \"https://www.pathofexile.com/\" + \"api/trade/data/stats\"\r\n\tsave_path = \"data/stats.json\"\r\n\tr = requests.get(url)\r\n\twith open(save_path, \"w\") as fileID:\r\n\t\tfileID.write(r.text)",
"def updateFileData(self):\n with open(pagePath(self.pageName)) as f:\n ... | [
"0.6064881",
"0.5944808",
"0.5934855",
"0.58855736",
"0.57900345",
"0.5746033",
"0.56906706",
"0.56259215",
"0.55120313",
"0.54862833",
"0.54578334",
"0.5341463",
"0.53402764",
"0.5334595",
"0.5310195",
"0.5301385",
"0.52986026",
"0.5267236",
"0.52602994",
"0.52589476",
"0.52... | 0.5832078 | 5 |
Get last update time of homepage stats | def last_update(cls):
import datetime, os
from s3 import S3DateTime
# Probe file (probing one is good enough since update_data
# writes them all at the same time)
filename = os.path.join(current.request.folder,
"static", "themes", "SHARE", "data"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last_updated() -> str:\n return GLOBAL.get(\"last_update\")",
"def last_update(self):\r\n request = http.Request('GET', '/metadata/last_update.json')\r\n return request, parsers.parse_json",
"async def do_lastupdated():\n\n download = urllib.request.urlopen(server_api)\n data... | [
"0.71105576",
"0.69535",
"0.68770844",
"0.68684065",
"0.6840563",
"0.679554",
"0.6738593",
"0.67062396",
"0.6684188",
"0.6616763",
"0.6616763",
"0.65213495",
"0.64894754",
"0.6487261",
"0.6483056",
"0.6455679",
"0.6435866",
"0.6434377",
"0.63968486",
"0.6383192",
"0.6383192",... | 0.6324592 | 33 |
returns an instance for tracking max value | def max():
return KeeperOfMinOrMax(int.__lt__) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_max(self):\n raise NotImplementedError(\"This is an abstract method and needs to be implemented in derived classes.\")",
"def _get_maximum(self):\n return self._maximum",
"def get_maximum ( self, object ):\n return self.maximum",
"def _get_maximum_value(self):\n if hasattr... | [
"0.801089",
"0.79525405",
"0.78954244",
"0.7758301",
"0.7726418",
"0.76395977",
"0.762583",
"0.7622539",
"0.76116127",
"0.7597168",
"0.75255555",
"0.74806607",
"0.74806607",
"0.7441403",
"0.7303434",
"0.7303434",
"0.7265719",
"0.7265719",
"0.7265719",
"0.7265719",
"0.7265719"... | 0.6979214 | 35 |
returns an instance for tracking min value | def min():
return KeeperOfMinOrMax(int.__gt__) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def min(self):\n return min(self)",
"def get_min(self):\n raise NotImplementedError(\"This is an abstract method and needs to be implemented in derived classes.\")",
"def min(self):\n return self.__min",
"def _get_minimum(self):\n return self._minimum",
"def find_min(self):\n ... | [
"0.79605174",
"0.7933093",
"0.78569067",
"0.78530514",
"0.7848658",
"0.7848658",
"0.7670498",
"0.7670498",
"0.76520526",
"0.76520526",
"0.7593828",
"0.7530433",
"0.74971557",
"0.74556816",
"0.7447431",
"0.7379914",
"0.73778284",
"0.73778284",
"0.7338228",
"0.7338228",
"0.7338... | 0.724613 | 29 |
performs comparison and saves the value and payload if the value is the value is greater or less that already stored | def check_keep_or_reject(self, value, payload=None):
if self._value == None:
self._value = value
self._payload = payload
return
if self._comp_method(self._value, value):
self._value = value
self._payload = payload | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __gt__(self, other):\n\t\ttry:\n\t\t\treturn self.val > other.val\n\t\texcept:\n\t\t\treturn self.val > other",
"def greater(value, other):\n return value < other",
"def less(value, other):\n return value > other",
"def __ge__( self, value ):\r\n\t\treturn ( self > value ) or ( self == value )",
... | [
"0.6650728",
"0.6521543",
"0.6342532",
"0.6316787",
"0.62671894",
"0.62653714",
"0.62531865",
"0.61790437",
"0.61547315",
"0.61547315",
"0.6138513",
"0.61190176",
"0.6106534",
"0.6106469",
"0.60966355",
"0.60682696",
"0.6064519",
"0.60631853",
"0.60225266",
"0.601887",
"0.601... | 0.0 | -1 |
Write the design to the Specctra format | def write(self, design, filename):
self._convert(design)
with open(filename, "w") as f:
f.write(self._to_string(self.pcb.compose())) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_to_fits(self, filename, comment=None, overwrite = False):\n\n\n hdu = fits.PrimaryHDU(self.flux)\n hdu.header = self.header\n\n # Update header information\n crval = self.dispersion[0]\n cd = self.dispersion[1]-self.dispersion[0]\n crpix = 1\n\n hdu.header... | [
"0.6008693",
"0.5994822",
"0.59387493",
"0.5817487",
"0.5813526",
"0.58061814",
"0.5734599",
"0.5709072",
"0.56894326",
"0.56894326",
"0.5635465",
"0.56147325",
"0.56106025",
"0.5595291",
"0.55891746",
"0.55859977",
"0.5578043",
"0.55598426",
"0.55499566",
"0.55450016",
"0.55... | 0.6873917 | 0 |
Converts absolute position and updates min/max values for boundary calculation | def _from_pixels_abs(self, point):
point = self.resolution.from_pixels(point)
self.max_x = max(self.max_x, point[0])
self.max_y = max(self.max_y, point[1])
self.min_x = min(self.min_x, point[0])
self.min_y = min(self.min_y, point[1])
return point | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_limits(self):\n if self.pos_x > self.max_x:\n self.max_x = self.pos_x\n if self.pos_y > self.max_y:\n self.max_y = self.pos_y\n if self.pos_x < self.min_x:\n self.min_x = self.pos_x\n if self.pos_y < self.min_y:\n self.min_y = self... | [
"0.7696437",
"0.7344893",
"0.6768861",
"0.66314876",
"0.6551718",
"0.65184176",
"0.64234716",
"0.6389439",
"0.632587",
"0.62951785",
"0.6265869",
"0.62353206",
"0.6231852",
"0.6217978",
"0.62174475",
"0.617853",
"0.61744654",
"0.61670834",
"0.61637664",
"0.6155718",
"0.614500... | 0.6252436 | 11 |
Converts relative position and updates max value for boundary calculation | def _from_pixels(self, point):
point = self.resolution.from_pixels(point)
if isinstance(point, tuple):
self.max_offset = max(self.max_offset, max(abs(point[0]), abs(point[1])))
else:
self.max_offset = max(self.max_offset, abs(point))
return point | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize_wrt_max(self):\n\n x_min = min(self.x)\n x_max = max(self.x)\n y_min = min(self.y)\n y_max = max(self.y)\n\n x_range = x_max - x_min\n y_range = y_max - y_min\n max_range = max(x_range, y_range)\n\n x = np.array(self.x)\n y = np.array(sel... | [
"0.74460256",
"0.730433",
"0.67018455",
"0.65427494",
"0.64605635",
"0.6406799",
"0.6257399",
"0.6246046",
"0.6205769",
"0.619715",
"0.61709636",
"0.6141251",
"0.61297596",
"0.6058174",
"0.60514766",
"0.6039516",
"0.6028614",
"0.60220474",
"0.59769976",
"0.59608996",
"0.59607... | 0.0 | -1 |
Convert a pin into an outline | def _convert_pin_to_outline(self, pin):
pcbshape = specctraobj.Path()
pcbshape.layer_id = 'Front'
pcbshape.aperture_width = self._from_pixels(1)
pcbshape.vertex.append(self._from_pixels((pin.p1.x, pin.p1.y)))
pcbshape.vertex.append(self._from_pixels((pin.p2.x, pin.p2.y)))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_pin(self, pin, xform):\n # TODO special pin characteristics (inverted, clock)?\n line = [xform.chain(p) for p in (pin.p1, pin.p2)]\n self.canvas.line([(p.x, p.y) for p in line],\n fill=self.options.style['part'])",
"def draw_pins():\n\n pass",
"def add_o... | [
"0.6245729",
"0.6115153",
"0.5688149",
"0.54557323",
"0.5455196",
"0.5401273",
"0.53704077",
"0.5337081",
"0.53153765",
"0.53109276",
"0.52516943",
"0.5228548",
"0.5222575",
"0.51996744",
"0.5138629",
"0.5120291",
"0.5064127",
"0.5062142",
"0.5055162",
"0.504735",
"0.5032255"... | 0.84876585 | 0 |
Specctra does not have arcs so convert them to qarcs | def _get_arc_qarcs(self, arc):
min_angle = min(arc.start_angle, arc.end_angle)
max_angle = max(arc.start_angle, arc.end_angle)
def make_point(angle):
""" Make a point """
opp = math.sin(angle * math.pi) * arc.radius
adj = math.cos(angle * math.pi) * ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_convert_to_q(self):\n\n riskfree = .01\n lmbd = .01\n lmbd_s = .5\n lmbd_y = .5\n mean_v = .5\n kappa_s = 1.5\n kappa_y = .5\n eta_s = .1\n eta_y = .01\n rho = -.5\n\n theta = [riskfree, mean_v, kappa_s, kappa_y, eta_s, eta_y,\n ... | [
"0.5714827",
"0.5679851",
"0.55328906",
"0.5450051",
"0.53392696",
"0.5317252",
"0.53133285",
"0.53072596",
"0.5298972",
"0.5282553",
"0.5273815",
"0.52621067",
"0.52590996",
"0.52144307",
"0.51778543",
"0.5177588",
"0.51557475",
"0.51449573",
"0.5140957",
"0.51292527",
"0.50... | 0.56375253 | 2 |
Specctra does not have arcs so convert them to lines | def _get_arc_points(self, arc):
min_angle = min(arc.start_angle, arc.end_angle)
max_angle = max(arc.start_angle, arc.end_angle)
step = 0.2
count = int((max_angle - min_angle) / step)
angle = min_angle
angles = []
for _ in xrange(count):
angle... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plt_spec_lines():\n\n for i in range(0, Molecule.species_count):\n mid_line = (Molecule.right_endpt[i] + Molecule.left_endpt[i]) / 2\n shift1 = Molecule.energy[i] - PlotParameter.energy_vshift\n shift2 = Molecule.energy[i] + PlotParameter.name_vshift\n\n en = '{0:5.2f}'.format(Mo... | [
"0.6028584",
"0.596023",
"0.5728867",
"0.570794",
"0.5633558",
"0.56145054",
"0.55716956",
"0.55079204",
"0.5438757",
"0.5379036",
"0.5368009",
"0.53570163",
"0.5322952",
"0.5302706",
"0.5289199",
"0.52479637",
"0.5243231",
"0.5240698",
"0.5237717",
"0.52343535",
"0.52219564"... | 0.0 | -1 |
Convert points to paths | def _points_to_paths(self, points):
prev = points[0]
result = []
for point in points[1:]:
path = specctraobj.Path()
path.aperture_width = self._from_pixels(1)
path.vertex.append(prev)
path.vertex.append(point)
result.append(path)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def full_path_to_points(path):\n\n points_x = [path[0][0]]\n points_y = [path[1][0]]\n\n new_path = path\n prev_turn, new_path = path_to_command_thymio(new_path)\n\n for i in range(len(new_path[0]) - 1):\n\n new_turn, new_path = path_to_command_thymio(new_path)\n\n if new_turn != prev_... | [
"0.66994214",
"0.65940034",
"0.6466729",
"0.6385552",
"0.6291015",
"0.61070466",
"0.6079898",
"0.6022747",
"0.59725976",
"0.59154207",
"0.59085953",
"0.58816546",
"0.57742137",
"0.5762992",
"0.5753632",
"0.57504135",
"0.57386243",
"0.57256794",
"0.5717574",
"0.5676891",
"0.56... | 0.83508885 | 0 |
Convert to a string | def _to_string(self, lst, indent=''):
result = []
for elem in lst:
if isinstance(elem, list):
if len(elem) > 0:
result.append('\n')
result.append(self._to_string(elem, indent + ' '))
elif isinstance(elem, float):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_str(self) -> str:",
"def to_string(self):\r\n return self.__str__()",
"def to_string(self, name, value):\r\n \r\n return str(value)",
"def as_str(self):\n return self.as_type(str)",
"def safeToString():",
"def _tostr(t):\n\treturn t.__unicode__()",
"def _convert_to_st... | [
"0.82510626",
"0.75375146",
"0.74364364",
"0.7400011",
"0.7356428",
"0.72777385",
"0.717622",
"0.7157074",
"0.7113981",
"0.7066711",
"0.7045882",
"0.7045882",
"0.700627",
"0.6949001",
"0.6942355",
"0.69317114",
"0.69135857",
"0.6883385",
"0.68819666",
"0.68667454",
"0.6826494... | 0.0 | -1 |
Returns the metric used in the search | def metric(self):
return self.__metric | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metric(self):\n return self._metric",
"def metric(self) -> str:\r\n return self._metric",
"def metric(self):\n\n if not self._metric_cache:\n # Select an appropriate statistic\n cls = utils.import_class_or_module(self._metric)\n self._metric_cache = cls... | [
"0.73544544",
"0.7343437",
"0.7094367",
"0.6804628",
"0.67208123",
"0.6674424",
"0.65164226",
"0.6417011",
"0.6387913",
"0.63811266",
"0.6376277",
"0.6376277",
"0.6359819",
"0.63471746",
"0.63471746",
"0.63350976",
"0.63268703",
"0.63268703",
"0.63268703",
"0.63268703",
"0.63... | 0.74399334 | 0 |
Returns the clusters obtained through KMeans | def c(self):
if self.__c is not None:
return self.__c
else:
raise ValueError("Run .fit() first!") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cluster_kmeans(self, data, n_clusters):\n km = cl.KMeans(n_clusters)\n kmf = km.fit(data)\n\n labels = kmf.labels_\n\n return labels, [np.nan]",
"def kmeans_clustering(self,k):\r\n \r\n print(colored(\"Performing K-means clustering with %d clusters\\n\"%k,color = 'ye... | [
"0.77555126",
"0.7701694",
"0.75619465",
"0.75249857",
"0.7498958",
"0.7489687",
"0.7466645",
"0.74505085",
"0.7402365",
"0.7400164",
"0.7396624",
"0.7394601",
"0.73939574",
"0.7392411",
"0.7386585",
"0.7364562",
"0.7359076",
"0.73535013",
"0.73448026",
"0.732836",
"0.7328291... | 0.0 | -1 |
Fits the main dataset | def _fit(
self,
x,
clusters=50,
a=5,
Niter=15,
device=None,
backend=None,
approx=False,
n=50,
):
if type(clusters) != int:
raise ValueError("Clusters must be an integer")
if clusters >= len(x):
raise Valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n in_dataset, out_dataset = self.get_datasets()",
"def setup(self):\n in_dataset, out_dataset = self.get_datasets()",
"def datasets(self):\n pass",
"def fill_dataset(self):\n rm, rstd = self.get_rolling_stats()\n\n self.add_rolling_mean(rm)\n self.ad... | [
"0.6949576",
"0.6949576",
"0.69010836",
"0.6890312",
"0.6760334",
"0.67182213",
"0.67167246",
"0.65993917",
"0.65751356",
"0.6545717",
"0.6540795",
"0.64323676",
"0.6403967",
"0.63968486",
"0.6337045",
"0.63136876",
"0.6257601",
"0.6245073",
"0.62386703",
"0.6236609",
"0.6221... | 0.0 | -1 |
Obtain the k nearest neighbors of the query dataset y | def _kneighbors(self, y):
if self.__x is None:
raise ValueError("Input dataset not fitted yet! Call .fit() first!")
if self.__device and self.tools.device(y) != self.__device:
raise ValueError("Input dataset and query dataset must be on same device")
if len(y.shape) != 2:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def k_nearest_neighbors(x_test, df_training, k):\n\n return np.argpartition(distance_to_each_training_point(x_test,\n df_training), k-1)[:,0:k]",
"def k_neighbors(self, unknown, dataset, k):\n distances = []\n for title in dataset:\n ... | [
"0.7755072",
"0.7698316",
"0.76627266",
"0.7543038",
"0.73983365",
"0.70976907",
"0.7065738",
"0.70630825",
"0.70598435",
"0.7000754",
"0.6987801",
"0.6964323",
"0.6951839",
"0.69448984",
"0.6932475",
"0.692119",
"0.69113904",
"0.68482804",
"0.68467015",
"0.6817071",
"0.67800... | 0.7627212 | 3 |
Performs a brute force search with KeOps | def brute_force(self, x, y, k=5):
x_LT = self.__LazyTensor(self.tools.unsqueeze(x, 0))
y_LT = self.__LazyTensor(self.tools.unsqueeze(y, 1))
D_ij = self.__distance(y_LT, x_LT)
return D_ij.argKmin(K=k, axis=1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bruteForceSearch(digraph, start, end, maxTotalDist, maxDistOutdoors):\n bFSResult = {}\n \n# Helper function to calculate Total Distance in a path\n def Dist(path):\n result = 0\n if path == None:\n return result\n if len(path) == 0:\n return result\n ... | [
"0.6327519",
"0.6254991",
"0.6160002",
"0.6040337",
"0.5772051",
"0.5755324",
"0.5755324",
"0.5755324",
"0.5739468",
"0.57314223",
"0.5714742",
"0.57073474",
"0.5679389",
"0.5658943",
"0.5656112",
"0.5643047",
"0.551255",
"0.5510731",
"0.5510731",
"0.5510731",
"0.5509149",
... | 0.0 | -1 |
Initialise the IVFFlat class. IVFFlat is a KNN approximation algorithm that first clusters the data and then performs the query search on a subset of the input dataset. | def __init__(self, k=5, metric="euclidean", normalise=False):
from pykeops.torch import LazyTensor
self.__get_tools()
super().__init__(k=k, metric=metric, normalise=normalise, LazyTensor=LazyTensor) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, dataset, ix_lst=None, size=None):\n # Indexes\n if ix_lst is None:\n ix_lst = [ix for ix in range(len(dataset.instances))\n if ix not in dataset.hold_out_ixs]\n self.instances = [ex for ix, ex in enumerate(dataset.instances)\n ... | [
"0.5946044",
"0.59000456",
"0.5887465",
"0.58563083",
"0.58301413",
"0.5791947",
"0.57555425",
"0.57498944",
"0.5745469",
"0.5719567",
"0.5717328",
"0.5681291",
"0.5667393",
"0.5657065",
"0.56502914",
"0.56476444",
"0.5627035",
"0.5598171",
"0.55951315",
"0.5588736",
"0.55792... | 0.0 | -1 |
Fits a dataset to perform the nearest neighbour search over KMeans is performed on the dataset to obtain clusters Then the closest clusters to each cluster is stored for use during query time | def fit(self, x, clusters=50, a=5, Niter=15, approx=False, n=50):
if type(x) != torch.Tensor:
raise ValueError("Input dataset must be a torch tensor")
return self._fit(
x, clusters=clusters, a=a, Niter=Niter, device=x.device, approx=approx, n=n
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(self, data):\n\t\tepsilon = self.epsilon\n\t\ttempDist = 1.0\n\t\tk = self.k\n\t\tcenters = data.rdd.takeSample(False, k, 1)\n\t\ti = 0 \n\t\twhile tempDist > epsilon or self.maxNoOfIteration > i:\n\t\t\ti+=1\t\t\t\n\t\t\tclosest = data.map(lambda p: (closestCluster(p, centers), (np.array(p), 1)))\n ... | [
"0.7502585",
"0.7192687",
"0.7173278",
"0.70446146",
"0.70254654",
"0.68059593",
"0.6731044",
"0.6718728",
"0.667073",
"0.6650163",
"0.6635303",
"0.6587959",
"0.6564522",
"0.6552284",
"0.65222496",
"0.65213925",
"0.65047365",
"0.64732283",
"0.6452072",
"0.6439149",
"0.6438148... | 0.0 | -1 |
Obtains the nearest neighbors for an input dataset from the fitted dataset | def kneighbors(self, y):
if type(y) != torch.Tensor:
raise ValueError("Query dataset must be a torch tensor")
return self._kneighbors(y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nearest_neighbor(data):\n features = set([i for i, x in enumerate(data[0][1])])\n return leave_one_out_cross_validation(data, features)",
"def nearest_neighbors_classifier(data):\n clf = KNeighborsClassifier(3, 'distance')\n clf.name = \"KNN\"\n train_predict_and_results(data, clf)",
"def kn... | [
"0.7695886",
"0.7258682",
"0.68946004",
"0.6866377",
"0.65122396",
"0.6471687",
"0.64388853",
"0.64172477",
"0.64019704",
"0.6378176",
"0.6361856",
"0.6331139",
"0.63292724",
"0.632762",
"0.63014627",
"0.62986773",
"0.62903064",
"0.6278115",
"0.6265846",
"0.6227376",
"0.61939... | 0.0 | -1 |
List the currently connected accounts | def GetAccountList(self):
return self.accounts.keys() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_accounts(self):\n pass",
"def display_accounts(cls):\n return cls.account_list",
"def getConnectedAccounts(**kwargs):\n strProdURL = kwargs[\"strProdURL\"]\n orgID = kwargs[\"ORG_ID\"]\n sessiontoken = kwargs[\"sessiontoken\"]\n\n accounts = get_connected_accounts_json(strPro... | [
"0.79298896",
"0.7443954",
"0.71552277",
"0.7138856",
"0.7105416",
"0.70626193",
"0.70603573",
"0.7036063",
"0.6985466",
"0.6974549",
"0.69650537",
"0.6957853",
"0.6915955",
"0.6900481",
"0.6807097",
"0.6788111",
"0.67642707",
"0.6755638",
"0.6753649",
"0.6729993",
"0.6722212... | 0.7261498 | 2 |
Select an account and set it as the current 'working' account Calling this method also cleares the Batch Queue, if it isn't empty | def SelectAccount(self, nickname):
self.ClearBatchQueue()
if nickname in self.accounts:
self.current_account = self.accounts[nickname]
self.client = self.current_account.client
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def account(self, account):\n\n self._account = account",
"def account(self, account):\n\n self._account = account",
"def account(self, account):\n\n self._account = account",
"def account(self, account):\n\n self._account = account",
"def account(self, account: str):\n s... | [
"0.5898519",
"0.5898519",
"0.5898519",
"0.5898519",
"0.58418894",
"0.5787817",
"0.5739551",
"0.57178605",
"0.5570972",
"0.55681026",
"0.55255353",
"0.54933035",
"0.5438046",
"0.54226345",
"0.5285505",
"0.52594006",
"0.52074546",
"0.5121794",
"0.5118399",
"0.50960463",
"0.5094... | 0.6025916 | 0 |
Clear the batch queue | def ClearBatchQueue(self):
self.batch_queue = gdata.contacts.data.ContactsFeed() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clearQueueAll():",
"def clear_queue(self):\n self.queue = deque()",
"def clear(self):\n self.queue.clear()",
"def clear_queue(self):\n while not self.queue.empty():\n self.queue.get()",
"def clear(self):\n self.queue = Queue()",
"def clear_batch(self):\n ... | [
"0.8027347",
"0.79099566",
"0.7851463",
"0.780406",
"0.7732012",
"0.75668514",
"0.7311712",
"0.721093",
"0.721093",
"0.721093",
"0.7194719",
"0.7069694",
"0.70656955",
"0.69665104",
"0.6955623",
"0.6941233",
"0.68873274",
"0.6882734",
"0.68424505",
"0.68145674",
"0.6807092",
... | 0.8334762 | 0 |
Add an action to the batch queue | def BatchEnqueue(self, action, contact):
if action == 'retrieve':
self.batch_queue.AddQuery(entry=contact, batch_id_string='retrieve')
elif action == 'create':
contact.group_membership_info = [gdata.contacts.data.GroupMembershipInfo(href=self.GetFirstGroupId())]
self.batch_queue.AddInsert(entry=contact,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_action(self, talk_action):\n self.action_queue.put(talk_action)",
"def enqueue_action(self, pid:PID, action: PlayerActions):\n\t\tif not action:\n\t\t\treturn\n\n\t\tself._action_queue[pid].append(action)",
"def push(self, trigger, action):\n self.queue.append((trigger, action))",
"def ... | [
"0.80876786",
"0.7393114",
"0.714681",
"0.70303786",
"0.700273",
"0.69231564",
"0.68621415",
"0.68621415",
"0.684907",
"0.6752141",
"0.66660917",
"0.653858",
"0.65167445",
"0.6484835",
"0.64816564",
"0.6444585",
"0.6358973",
"0.63230836",
"0.6305995",
"0.62774354",
"0.6236577... | 0.6674077 | 10 |
Execute all actions in the batch queue | def ExecuteBatchQueue(self):
self.client.ExecuteBatch(self.batch_queue, 'https://www.google.com/m8/feeds/contacts/default/full/batch')
self.ClearBatchQueue(); | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute(self):\n for action in self.actions:\n self._logger.info('[~] Executing %s.', action)\n self._execute_action(action)",
"def execute(self):\n for move in self._queue:\n move.execute()",
"def execute(self):\n\n if self.__command_queue:\n ... | [
"0.78203684",
"0.75656724",
"0.69675845",
"0.6904114",
"0.6751323",
"0.6749497",
"0.67120445",
"0.6696298",
"0.66439104",
"0.6619434",
"0.65914667",
"0.6333632",
"0.6331009",
"0.6319987",
"0.6319611",
"0.6280162",
"0.6272014",
"0.6271824",
"0.626646",
"0.6262457",
"0.62222123... | 0.69831717 | 2 |
Get a list of all the contacts from the currently selected account | def GetContactList(self):
feeds = []
feed = self.client.GetContacts()
feeds.append(feed)
next = feed.GetNextLink()
while next:
feed = self.client.GetContacts(uri=next.href)
feeds.append(feed)
next = feed.GetNextLink()
contacts = []
for feed in feeds:
if not feed.entry:
continue
else:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_contacts(self):\n return self.contacts",
"def fetch_contacts(owner_account_id):\n resp = oauth.tapkey.get(f\"Owners/{owner_account_id}/Contacts?$select=id,identifier\")\n contacts = resp.json()\n return contacts",
"def get_queryset(self):\n return self.request.user.contacts.all(... | [
"0.80982107",
"0.7876462",
"0.7686326",
"0.7612732",
"0.75763845",
"0.74758196",
"0.7394858",
"0.73458034",
"0.72936374",
"0.72662973",
"0.7218797",
"0.71633536",
"0.7125692",
"0.70462304",
"0.69273126",
"0.6882803",
"0.68397015",
"0.6831734",
"0.6804959",
"0.6799883",
"0.677... | 0.7307596 | 8 |
Lazily get the first contact group's Atom Id | def GetFirstGroupId(self):
return self.client.GetGroups().entry[0].id.text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_contact_group(dbcon: DBConnection, id: int) -> Any: # Use any because optional returns suck.\n q = \"\"\"select id, name, active from contact_groups where id=%s\"\"\"\n row = await dbcon.fetch_row(q, (id,))\n contact = None\n if row:\n contact = object_models.ContactGroup(*row)\n ... | [
"0.6018908",
"0.5888721",
"0.5825256",
"0.57383466",
"0.56857145",
"0.54320073",
"0.54071623",
"0.538237",
"0.5367063",
"0.5323985",
"0.52931386",
"0.5278044",
"0.52581567",
"0.52283746",
"0.52128196",
"0.51881367",
"0.51880187",
"0.51782674",
"0.51699287",
"0.5155815",
"0.51... | 0.6422962 | 0 |
Add a contact to the selected account | def AddContact(self, contact):
contact.group_membership_info = [gdata.contacts.data.GroupMembershipInfo(href=self.GetFirstGroupId())]
try:
self.client.CreateContact(contact)
except gdata.client.RequestError:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_contact(self, name, number, email, zipcode):\n \n new_contact = f\"{name}, {number}, {email}, {zipcode}\"\n contact_list = [name,number,email,zipcode]\n self.contacts.append(contact_list)\n self.save()\n print(f\"Thank you {new_contact} has been added to your conta... | [
"0.77608013",
"0.7678672",
"0.75593245",
"0.7524902",
"0.74078804",
"0.73187745",
"0.7144197",
"0.71273273",
"0.7037132",
"0.7000438",
"0.6976581",
"0.68927115",
"0.6833474",
"0.6822567",
"0.6807116",
"0.66798747",
"0.66762304",
"0.6609145",
"0.65774065",
"0.65446246",
"0.649... | 0.7551735 | 3 |
Remove a contact from the selected account | def RemoveContact(self, contact):
self.client.Delete(contact) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_contact(self):\n contact_mob_num = input(\"-=\" * 30 + \"\\n\" + \"Please enter contact's mobile number to be removed: \")\n contact = self.auth.get_users_by_MobNum(contact_mob_num)\n if (not contact) or contact not in self._user.contacts:\n print('This user not in your c... | [
"0.7927598",
"0.78937054",
"0.76196754",
"0.760548",
"0.7403511",
"0.7339263",
"0.7177775",
"0.71389616",
"0.69924855",
"0.6943864",
"0.68841195",
"0.681138",
"0.6772057",
"0.67245716",
"0.66446775",
"0.6630713",
"0.66240466",
"0.65467685",
"0.653159",
"0.64608634",
"0.642118... | 0.82682854 | 0 |
Remove all contacts from the selected account | def RemoveAll(self):
contacts = self.GetContactList()
for contact in contacts:
self.BatchEnqueue('delete', contact)
self.ExecuteBatchQueue() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_contacts(self):\n self.db.delete_all_contacts()\n return self.update_contacts()",
"def del_contact_all(self):\n\n send_key(KEY_MENU)\n delstr = contact.get_value('contact_delete')\n if search_text(delstr):\n click_textview_by_text(delstr)\n clic... | [
"0.7838508",
"0.73882663",
"0.71244544",
"0.6743833",
"0.6735509",
"0.66522604",
"0.6644226",
"0.65938866",
"0.6535013",
"0.6475446",
"0.63914645",
"0.62559044",
"0.6243891",
"0.6176765",
"0.610713",
"0.6046226",
"0.60396665",
"0.60009325",
"0.598013",
"0.5964348",
"0.5959389... | 0.7585864 | 1 |
Copy all contacts from one account to another This method does not check for duplicates | def CopyContacts(self, from_nickname, to_nickname):
self.SelectAccount(from_nickname)
contacts = self.GetContactList()
self.SelectAccount(to_nickname)
for contact in contacts:
self.BatchEnqueue('create', contact)
self.ExecuteBatchQueue() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MultiWaySync(self, accounts):\n\t\tcleaned_contacts = []\n\t\tcontacts = []\n\t\t\n\t\tfor account in accounts:\n\t\t\tself.SelectAccount(account)\n\t\t\tcontacts.extend(self.GetContactList())\n\t\t\n\t\tduplicates, originals = ceFindDuplicates(contacts)\n\t\tmerged, todelete = ceMergeDuplicates(duplicates)\n\... | [
"0.6812184",
"0.6329817",
"0.5751867",
"0.5707562",
"0.5707562",
"0.5586134",
"0.5383878",
"0.5352327",
"0.5340893",
"0.53407866",
"0.53282636",
"0.53140664",
"0.5285255",
"0.5284708",
"0.52719766",
"0.52107036",
"0.52066034",
"0.51984245",
"0.51651853",
"0.5159195",
"0.51517... | 0.756032 | 0 |
Move all contacts from one account to another This method does not check for duplicates | def MoveContacts(self, from_nickname, to_nickname):
self.SelectAccount(from_nickname)
contacts = self.GetContactList()
# Copy contacts -before- deleting
self.SelectAccount(to_nickname)
for contact in contacts:
self.BatchEnqueue('create', contact)
self.ExecuteBatchQueue()
# Then delete
self.Sele... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MultiWaySync(self, accounts):\n\t\tcleaned_contacts = []\n\t\tcontacts = []\n\t\t\n\t\tfor account in accounts:\n\t\t\tself.SelectAccount(account)\n\t\t\tcontacts.extend(self.GetContactList())\n\t\t\n\t\tduplicates, originals = ceFindDuplicates(contacts)\n\t\tmerged, todelete = ceMergeDuplicates(duplicates)\n\... | [
"0.69824326",
"0.6433265",
"0.57518244",
"0.56264514",
"0.56264514",
"0.5607597",
"0.5603125",
"0.55658954",
"0.5536112",
"0.5534724",
"0.54958487",
"0.5490326",
"0.5480002",
"0.5392368",
"0.53907484",
"0.5382409",
"0.53588146",
"0.5324467",
"0.5263877",
"0.52340406",
"0.5210... | 0.72994787 | 0 |
Perform a multiway sync between given accounts | def MultiWaySync(self, accounts):
cleaned_contacts = []
contacts = []
for account in accounts:
self.SelectAccount(account)
contacts.extend(self.GetContactList())
duplicates, originals = ceFindDuplicates(contacts)
merged, todelete = ceMergeDuplicates(duplicates)
cleaned_contacts.extend(origina... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sync_nas(self, users_from_db: Iterator):",
"def synch_all(cls, account, type, filter=None, *args):\n for repo_data in repositories(account, type, filter):\n repo = cls(repo_data)\n repo.synch(*args)",
"def sync(self, sync_from, sync_to, **kwargs):\n return self.exec_comm... | [
"0.6455885",
"0.6311536",
"0.6180077",
"0.61477345",
"0.6121937",
"0.6006474",
"0.58930635",
"0.5883944",
"0.5810142",
"0.5777375",
"0.5727696",
"0.57057714",
"0.56830674",
"0.5662616",
"0.5652426",
"0.564118",
"0.5630871",
"0.56220114",
"0.5602681",
"0.55613834",
"0.55493385... | 0.7644102 | 0 |
Update based on device list. | def update_visual(screen: pygame.Surface, device_list: list):
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit()
return None
white = (255, 255, 255)
black = (0, 0, 0)
blue = (0, 0, 128)
screen.fill(white)
dev_num = 1
num_devs = len... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_device_list(self):\n\n # Update devices via HTTP request (basic device data - no status)\n self.__http_update_device_list()\n\n # Fetch status for each known device via MQTT\n for gdev in self.__devices.values():\n gdev.request_status()",
"def update_device_list(... | [
"0.82892597",
"0.77920574",
"0.7386299",
"0.73323125",
"0.7261158",
"0.7261158",
"0.70637846",
"0.6975202",
"0.69620645",
"0.69328254",
"0.6882042",
"0.68545794",
"0.67855287",
"0.6754338",
"0.6658994",
"0.66031986",
"0.6598942",
"0.659559",
"0.6547921",
"0.65373826",
"0.6494... | 0.6028516 | 50 |
Runs `det experiment describe` CLI command on a finished experiment. Will raise an exception if `det experiment describe` encounters a traceback failure. | def run_describe_cli_tests(experiment_id: int) -> None:
# "det experiment describe" without metrics.
with tempfile.TemporaryDirectory() as tmpdir:
subprocess.check_call(
[
"det",
"-m",
conf.make_master_url(),
"experiment",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def describe():",
"def describe(self, *args, **kwargs):\n\t\treturn self.data.describe(*args, **kwargs)",
"def test_describe_model(self):\n\t\tdetails = self.watcher.describe()\n\t\tprint(details)\n\t\tself.assertEqual(len(details), 11)",
"def test_recognize_describe(self):\n pass",
"def test_descri... | [
"0.5586464",
"0.5564415",
"0.5341794",
"0.5270411",
"0.5181873",
"0.5082891",
"0.5071718",
"0.50258327",
"0.50245225",
"0.4985327",
"0.49451223",
"0.49108976",
"0.48686293",
"0.48666832",
"0.48470518",
"0.48347655",
"0.4822859",
"0.47746998",
"0.47700247",
"0.47659424",
"0.47... | 0.7508023 | 0 |
Runs listrelated CLI commands on a finished experiment. Will raise an exception if the CLI command encounters a traceback failure. | def run_list_cli_tests(experiment_id: int) -> None:
subprocess.check_call(
["det", "-m", conf.make_master_url(), "experiment", "list-trials", str(experiment_id)]
)
subprocess.check_call(
["det", "-m", conf.make_master_url(), "experiment", "list-checkpoints", str(experiment_id)]
)
s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_pre_cli_list(run):\n out, err = run(dork.cli.the_predork_cli, [], *(\"\", \"-l\"))\n assert \"test.yml\" in out, \\\n \"Failed run the dork.cli.the_predork_cli method: {err}\"\\\n .format(err=err)",
"def command_list(self, command):\n\n # See if the list exists and return resu... | [
"0.61308813",
"0.59608287",
"0.5957649",
"0.58396363",
"0.5808478",
"0.5768765",
"0.57584125",
"0.5753735",
"0.5732263",
"0.56613624",
"0.56290406",
"0.56156904",
"0.5615358",
"0.5497544",
"0.5480825",
"0.5480825",
"0.5471782",
"0.5451167",
"0.54397607",
"0.54342854",
"0.5424... | 0.7019173 | 0 |
This function is called when the user uses help. | def usage():
print 'Convert a Debian or an RPM Package into an Arch Linux package (' + \
'and vice-versa).'
print
print 'Usage: %s [OPTIONS] debian_package.deb [arch_package.pkg.tar.gz]' \
% os.path.basename(sys.argv[0])
print
print "OPTIONS :"
print " -h, --help ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def help():\n print \"Help comes to those who ask\"",
"def help(self):",
"def help(self):",
"def help():\n \n pass",
"def help():",
"def help(self):\n pass",
"def help(self):\n pass",
"def show_help():\n pass",
"def help(self):\n\t\treturn",
"def help():\n pri... | [
"0.86703736",
"0.86196375",
"0.86196375",
"0.86186236",
"0.8600454",
"0.8447618",
"0.8447618",
"0.8386246",
"0.8376183",
"0.8332743",
"0.83264875",
"0.82946587",
"0.82228065",
"0.8130156",
"0.81278056",
"0.8123208",
"0.8053051",
"0.80143815",
"0.7959358",
"0.7923357",
"0.7890... | 0.0 | -1 |
Suggest to the user to read the help. | def more_informations():
print "--help for more informations."
sys.exit(1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def askForHelp(self):\n try:\n serverResult = self.game.server.askForHelp(self.game.authKey)\n if type(serverResult) == types.ListType:\n self.help = serverResult\n self.displayHelpMessage()\n else:\n self.modeMsgBox(serverResult)... | [
"0.67134285",
"0.6657495",
"0.65506464",
"0.6540987",
"0.6528242",
"0.6509463",
"0.64629525",
"0.6430398",
"0.64146763",
"0.6410141",
"0.6392874",
"0.6388566",
"0.6374893",
"0.6357002",
"0.63350844",
"0.6324055",
"0.6314089",
"0.6314089",
"0.62832314",
"0.6282355",
"0.6279245... | 0.0 | -1 |
Handle all options in the arguments. This function returns a dictionary contain 'input_pkg' and 'output_pkg' keywords. | def handle_arguments():
result = {'input_pkg':'', 'output_pkg':''}
try:
args = sys.argv[1:]
optlist = gnu_getopt(args, 'h', ['help'])
except GetoptError:
print 'Error when parsing arguments.'
more_informations()
if len(sys.argv) < 2:
print 'No input file.'
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processArgs(printHelp=False):\n parser = OptionParser()\n\n parser.add_option('-i', '--input',\n dest='input',\n help='Name of the latex file, for example, document.tex',\n metavar='string')\n parser.add_option('-o', '--output',\n ... | [
"0.5959439",
"0.5875722",
"0.5843192",
"0.5764533",
"0.5762908",
"0.57036674",
"0.5702611",
"0.56741303",
"0.56671953",
"0.5666019",
"0.56415075",
"0.5633455",
"0.5577047",
"0.55590963",
"0.5546385",
"0.55420923",
"0.5530415",
"0.55239266",
"0.5492701",
"0.5489848",
"0.547874... | 0.8013772 | 0 |
Get a message to speak on first load of the skill. Useful for postinstall setup instructions. | def get_intro_message(self):
self.speak_dialog("thank.you")
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def speak(message):\n print(message)",
"def speak(self):\n print(\"meow!\")",
"def speak(self):\n print(\"hello\")",
"def install_default_skills(speak=True):\n if exists(MSM_BIN):\n p = subprocess.Popen(MSM_BIN + \" default\", stderr=subprocess.STDOUT,\n s... | [
"0.61970544",
"0.59956497",
"0.5926281",
"0.5872123",
"0.58696866",
"0.5797025",
"0.579132",
"0.57152486",
"0.5705992",
"0.56784135",
"0.56503886",
"0.5580634",
"0.552781",
"0.55209553",
"0.55069524",
"0.5461222",
"0.54496735",
"0.5446346",
"0.5446035",
"0.54340476",
"0.54330... | 0.6545299 | 0 |
Handle conversation. This method gets a peek at utterances before the normal intent handling process after a skill has been invoked once. To use, override the converse() method and return True to indicate that the utterance has been handled. | def converse(self, utterances, lang="en-us"):
# check if game was abandoned midconversation and we should clean it up
self.maybe_end_game()
if self.playing:
ut = utterances[0]
# if self will trigger do nothing and let intents handle it
if self.will_trigger(ut)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_converse_request(message):\n skill_id = int(message.data[\"skill_id\"])\n utterances = message.data[\"utterances\"]\n lang = message.data[\"lang\"]\n global ws, loaded_skills\n # loop trough skills list and call converse for skill with skill_id\n for skill in loaded_skills:\n if... | [
"0.607286",
"0.60394377",
"0.59018356",
"0.5812541",
"0.5732079",
"0.5696963",
"0.56619567",
"0.56086385",
"0.5504005",
"0.54966825",
"0.54938865",
"0.5492362",
"0.5490855",
"0.54654175",
"0.5454416",
"0.5349049",
"0.5348369",
"0.53459346",
"0.5345559",
"0.53378665",
"0.53232... | 0.66725177 | 0 |
Lists cases assigned to the current user. | def do_jira(self, arg):
jql = self.settings['jira_jql']
if arg.startswith('b'):
out.info('Opening browser.')
webbrowser.open(self.jira_url() + '/issues/?jql=' + jql)
else:
open_issues = self.get_open_issues()
cases = [
(issue.key, i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_cases(context,case_id):\n\n adapter = context.obj['adapter']\n\n if case_id is not None:\n results = adapter.find_case({'case_id': case_id})\n\n else:\n results = adapter.find_cases({})\n\n click.echo(pprint(results))",
"def list(self):\n print \"\\nAvailable Test Cases\... | [
"0.5828191",
"0.57715195",
"0.5665513",
"0.56009257",
"0.5560734",
"0.55546606",
"0.5414231",
"0.5294118",
"0.5283976",
"0.5227256",
"0.5224363",
"0.52142394",
"0.5208721",
"0.5202496",
"0.51961356",
"0.51907045",
"0.51902115",
"0.51801664",
"0.51342934",
"0.51191497",
"0.511... | 0.0 | -1 |
Creates a git commit message template for cases currently assigned to you. | def do_jira_case_commit_message(self, arg):
cases = [(issue.key, issue.fields.summary, self.jira_url() + "/browse/" + issue.key) for issue in self.get_open_issues()]
msg = """
--------------------------------------------------------------------
[{}] {}
<msg>
{}
------------------------... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_commit(\n self, msg: Optional[str] = None, author: Optional[str] = None\n ) -> dict:\n if author:\n mes_author = author\n else:\n mes_author = self._author\n if not msg:\n msg = f\"Commit via python client {__version__}\"\n ci = {... | [
"0.58265114",
"0.56810087",
"0.56475306",
"0.54282033",
"0.53819776",
"0.5301259",
"0.52773494",
"0.5231186",
"0.5216202",
"0.5213242",
"0.5183639",
"0.517318",
"0.5149136",
"0.5134215",
"0.512786",
"0.5119132",
"0.5117917",
"0.50955975",
"0.50477743",
"0.5016975",
"0.5010600... | 0.5991317 | 0 |
For PFG enabling forms ('service' field) | def getServices(self):
catalog = plone.api.portal.get_tool('portal_catalog')
path = '{}/catalog'.format('/'.join(plone.api.portal.get().getPhysicalPath()))
query = dict(portal_type='Service', sort_on='sortable_title', path=path)
result = list()
for brain in catalog(**query):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def service_changed(self):\n\n if self.ui.comboBox_service.currentText() == \"Google\":\n self.ui.label_id.setEnabled(False)\n self.ui.lineEdit_id.setEnabled(False)\n self.ui.label_key.setEnabled(False)\n self.ui.lineEdit_key.setEnabled(False)\n self.ui... | [
"0.6525419",
"0.5879964",
"0.572268",
"0.56772685",
"0.5677172",
"0.56644684",
"0.5658642",
"0.5644498",
"0.5629904",
"0.55910754",
"0.5581871",
"0.55702734",
"0.55578226",
"0.5544959",
"0.5526073",
"0.5516057",
"0.55154926",
"0.550897",
"0.54889435",
"0.5470525",
"0.54473627... | 0.0 | -1 |
For PFG enabling forms ('project' field) | def getProjects(self):
catalog = plone.api.portal.get_tool('portal_catalog')
path = '{}/projects'.format('/'.join(plone.api.portal.get().getPhysicalPath()))
query = dict(portal_type='Project', sort_on='sortable_title', path=path)
result = list()
for brain in catalog(**query):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_project_cant_disable_itself(self):\n page_projects = self._page_projects()\n project_name = self.app.current_project\n\n with page_projects.table_projects.row(\n name=project_name).dropdown_menu as menu:\n menu.button_toggle.click()\n menu.item_ed... | [
"0.6683946",
"0.66821754",
"0.6651207",
"0.6266415",
"0.61734617",
"0.6170577",
"0.61583537",
"0.60570997",
"0.60456896",
"0.59930354",
"0.5979971",
"0.5880963",
"0.58326745",
"0.5829549",
"0.5805503",
"0.580025",
"0.57936114",
"0.57883453",
"0.5727333",
"0.56871516",
"0.5658... | 0.0 | -1 |
Reports the state of the toil. | def main():
##########################################
#Construct the arguments.
##########################################
parser = getBasicOptionParser("usage: %prog [--toil] JOB_TREE_DIR [options]", "%prog 0.1")
parser.add_option("--toil", dest="toil",
help=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_state(self):\n raise AIError(\"Must be implemented in child class!\")",
"def Report(self):\n return True",
"def report():\n pass",
"def log_state(self):\n rospy.loginfo(\"STATE: %s [%s]\" %(self.__class__.__name__, 15 - self.ros_node.get_time()))",
"def save_state(se... | [
"0.6147335",
"0.6102503",
"0.6033684",
"0.59858537",
"0.5905538",
"0.5904048",
"0.5902007",
"0.5894178",
"0.5862637",
"0.5854155",
"0.5839881",
"0.57337976",
"0.5700883",
"0.56285226",
"0.56130916",
"0.56054235",
"0.55884707",
"0.55884707",
"0.5560808",
"0.5552423",
"0.554738... | 0.0 | -1 |
lambda function handler for getting trash day | def lambda_handler(event, context) -> dict:
logging.info('Starting function with context=%s and event=%s', context, event)
date = event['date']
holiday_schedule = trash_schedule_service.get_schedule()
trash_day = trash.next_trash_day(date, holiday_schedule)
logging.info('Completed function with res... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lambda_handler(event, context):\n logging.info('Starting function with context=%s and event=%s', context, event)\n holiday_schedule = trash.holidayschedule()\n old_holiday_schedule = trash_service.list()['data']\n old_holidays = [old_holiday['name'] for old_holiday in old_holiday_schedule]\n log... | [
"0.6090012",
"0.54774374",
"0.5461324",
"0.5409436",
"0.53761524",
"0.53742063",
"0.5331051",
"0.5287995",
"0.51901144",
"0.5178555",
"0.5161938",
"0.5114322",
"0.5079114",
"0.50751776",
"0.5070596",
"0.50560266",
"0.50401527",
"0.50214356",
"0.49688548",
"0.49537805",
"0.495... | 0.74920136 | 0 |
Convert an image from LAB color space to XYZ color space | def lab_to_xyz(image: tf.Tensor) -> tf.Tensor:
l, a, b = tf.unstack(image, axis=-1)
var_y = (l + 16) / 116
var_x = a / 500 + var_y
var_z = var_y - b / 200
var_x = tf.where(tf.pow(var_x, 3) > 0.008856, tf.pow(var_x, 3),
(var_x - 16 / 116) / 7.787)
var_y = tf.where(tf.pow(var... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Lab_to_XYZ(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n xyz_y = (cobj.lab_l + 16.0) / 116.0\r\n xyz_x = cobj.lab_a / 500.0 + xyz_y\r\n xyz_z = xyz_y - cobj.lab_b / 200.0\r\n \r\n if math.pow(xyz_y, 3) > color_constants.CIE_E:\r\n xyz_y = math.pow(xyz_y, 3)\r\n ... | [
"0.72538626",
"0.6935359",
"0.6835545",
"0.6804529",
"0.6711759",
"0.66599464",
"0.6521106",
"0.64231324",
"0.6351376",
"0.63161516",
"0.62939584",
"0.62429935",
"0.6191677",
"0.6155172",
"0.6133433",
"0.60679656",
"0.605217",
"0.600619",
"0.6003839",
"0.5994653",
"0.5972754"... | 0.72095025 | 1 |
Convert an image from XYZ color space to LAB color space | def xyz_to_lab(image: tf.Tensor) -> tf.Tensor:
x, y, z = tf.unstack(image, axis=-1)
refx = 95.047
refy = 100.00
refz = 108.883
var_x = x / refx
var_y = y / refy
var_z = z / refz
var_x = tf.where(var_x > 0.008856, tf.pow(var_x, 1 / 3),
(7.787 * var_x) + (16 / 116))... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:\n xyz = rgb_to_xyz(image)\n lab_image = xyz_to_lab(xyz)\n return lab_image",
"def XYZ_to_Lab(cobj, *args, **kwargs):\r\n\r\n illum = cobj.get_illuminant_xyz()\r\n temp_x = cobj.xyz_x / illum[\"X\"]\r\n temp_y = cobj.xyz_y / illum[\"Y\"]\r\n temp... | [
"0.7460319",
"0.7458232",
"0.72022015",
"0.7077035",
"0.6824317",
"0.6632177",
"0.65915054",
"0.65851694",
"0.65589637",
"0.65200937",
"0.6511321",
"0.6508878",
"0.63595843",
"0.62574404",
"0.6182268",
"0.61593455",
"0.61016095",
"0.6056708",
"0.6038471",
"0.6021243",
"0.5998... | 0.6739732 | 5 |
Convert an image from XYZ color space to RGB color space | def xyz_to_rgb(image: tf.Tensor) -> tf.Tensor:
x, y, z = tf.unstack(image, axis=-1)
var_x = x / 100
var_y = y / 100
var_z = z / 100
var_r = var_x * 3.2406 + var_y * -1.5372 + var_z * -0.4986
var_g = var_x * -0.9689 + var_y * 1.8758 + var_z * 0.0415
var_b = var_x * 0.0557 + var_y * -0.2040 +... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def XYZ_to_RGB(XYZ,\n illuminant_XYZ,\n illuminant_RGB,\n XYZ_to_RGB_matrix,\n chromatic_adaptation_transform='CAT02',\n encoding_cctf=None):\n\n M = chromatic_adaptation_matrix_VonKries(\n xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),\n ... | [
"0.6828028",
"0.68103015",
"0.68066865",
"0.6763418",
"0.66843706",
"0.66742265",
"0.66261303",
"0.65840024",
"0.6561155",
"0.6552539",
"0.64690375",
"0.645088",
"0.63681024",
"0.6365761",
"0.63621247",
"0.6326144",
"0.6306346",
"0.6306079",
"0.6283632",
"0.6256421",
"0.62433... | 0.7146014 | 0 |
Convert an image from RGB color space to XYZ color space | def rgb_to_xyz(image: tf.Tensor) -> tf.Tensor:
r, g, b = tf.unstack(image, axis=-1)
var_r = r / 255
var_g = g / 255
var_b = b / 255
var_r = tf.where(var_r > 0.04045, tf.pow((var_r + 0.055) / 1.055, 2.4),
var_r / 12.92)
var_g = tf.where(var_g > 0.04045, tf.pow((var_g + 0.055... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rgb_to_xyz(rgb_color):\n\n r = (rgb_color[0] / 255)\n g = (rgb_color[1] / 255)\n b = (rgb_color[2] / 255)\n\n if r > 0.04045:\n r = ((r + 0.055) / 1.055) ** 2.4\n else:\n r = r / 12.92\n\n if g > 0.04045:\n g = ((g + 0.055) / 1.055) ** 2.4\n else:\n g = g / 12.9... | [
"0.6907906",
"0.6867097",
"0.68299574",
"0.66412497",
"0.66398394",
"0.65136164",
"0.6503859",
"0.6343428",
"0.6280779",
"0.6266033",
"0.62629265",
"0.620036",
"0.61658543",
"0.6152105",
"0.60904413",
"0.6024829",
"0.5986624",
"0.59797454",
"0.597226",
"0.5968314",
"0.5912936... | 0.7601516 | 0 |
Convert an image from RGB color space to LAB color space RGB > XYZ > LAB | def rgb_to_lab(image: tf.Tensor) -> tf.Tensor:
xyz = rgb_to_xyz(image)
lab_image = xyz_to_lab(xyz)
return lab_image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:\n xyz = lab_to_xyz(image)\n rgb_image = xyz_to_rgb(xyz)\n return rgb_image",
"def lab_to_rgb(img):\n new_img = np.zeros((256, 256, 3))\n for i in range(len(img)):\n for j in range(len(img[i])):\n pix = img[i, j]\n new_img[... | [
"0.76128566",
"0.736024",
"0.7093746",
"0.70733243",
"0.688894",
"0.67929274",
"0.67809963",
"0.6703727",
"0.66217625",
"0.6567671",
"0.6539482",
"0.6528808",
"0.6519189",
"0.64457124",
"0.64197767",
"0.63783777",
"0.637468",
"0.630608",
"0.6248861",
"0.6204791",
"0.618432",
... | 0.7743007 | 0 |
Convert an image from LAB color space to RGB color space LAB > XYZ > RGB | def lab_to_rgb(image: tf.Tensor) -> tf.Tensor:
xyz = lab_to_xyz(image)
rgb_image = xyz_to_rgb(xyz)
return rgb_image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lab_to_rgb(img):\n new_img = np.zeros((256, 256, 3))\n for i in range(len(img)):\n for j in range(len(img[i])):\n pix = img[i, j]\n new_img[i, j] = [(pix[0] + 1) * 50, (pix[1] + 1) / 2 * 255 - 128, (pix[2] + 1) / 2 * 255 - 128]\n new_img = color.lab2rgb(new_img) * 255\n ... | [
"0.7471666",
"0.7259688",
"0.7216436",
"0.6839209",
"0.6759264",
"0.67525685",
"0.67007047",
"0.6603421",
"0.6600922",
"0.6595562",
"0.6580605",
"0.6568346",
"0.6499439",
"0.64541537",
"0.64526325",
"0.6431422",
"0.63929",
"0.6372881",
"0.63197166",
"0.6308842",
"0.63075536",... | 0.7765698 | 0 |
Checks if the given character is a letter. | def is_letter(c):
return 'A' <= c <= 'Z' or 'a' <= c <= 'z' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_letter(string_):\n if string_ not in string.ascii_letters or len(string_) > 1:\n return False\n return True",
"def isLetter(c):\n ret = libxml2mod.xmlIsLetter(c)\n return ret",
"def is_letter(user_input):\n # If any characters is letter -> return boolean True else False\n if any... | [
"0.8101875",
"0.7942589",
"0.77772945",
"0.7760421",
"0.7488684",
"0.7349609",
"0.7162389",
"0.7131679",
"0.7119435",
"0.71050584",
"0.7094554",
"0.70823413",
"0.70609343",
"0.7013201",
"0.69936603",
"0.69796395",
"0.6963873",
"0.6928611",
"0.6889942",
"0.68720996",
"0.683491... | 0.84225947 | 0 |
Checks if the given character is a number. | def is_number(c):
return '0' <= c <= '9' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_num_char(x):\n return ord('0') <= ord(x) <= ord('9')",
"def is_number_char(c: str) -> bool:\n return c.isdigit() or c == \".\"",
"def is_number(s):\r\n try:\r\n int(s)\r\n return True\r\n except ValueError:\r\n return False",
"def is_number(s):\n try:\n int(s... | [
"0.8297935",
"0.80840826",
"0.7704204",
"0.76546836",
"0.75744826",
"0.7568717",
"0.7393754",
"0.73098946",
"0.7302559",
"0.7258665",
"0.72420657",
"0.7230359",
"0.7195663",
"0.71815383",
"0.7179277",
"0.7152408",
"0.71477836",
"0.71332264",
"0.71325856",
"0.71297145",
"0.712... | 0.82398206 | 1 |
Checks if the given nametag is valid, that it only contains letters, numbers, dashes, underscores and apostrophes. It must also start with the given tags in `Tags.py `. And returns the nametag if it is valid. | def get_nametag(nametag):
# start must be valid
if not nametag.startswith(Tags.NAMETAG_START.value):
return None
# removes the start of the tag
nametag = nametag[len(Tags.NAMETAG_START.value):]
# end must be valid
if not nametag.endswith(Tags.NAMETAG_END.value):
return None
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isValidTagName(s):\n if s.lower().startswith(\"xml\"):\n return False\n return re.match(\"[^\\W\\d][\\w\\-_.]*\", s)",
"def name_valid(name):\n return name.isalpha()",
"def validname(name):\r\n return len(name)>0 and (\r\n Context.__invalid_character.search(name) is None)"... | [
"0.7056981",
"0.6984154",
"0.6797562",
"0.65576094",
"0.65357757",
"0.6507386",
"0.6452223",
"0.63574183",
"0.6310293",
"0.6288627",
"0.62855893",
"0.6240301",
"0.62214375",
"0.61912215",
"0.6171858",
"0.615459",
"0.61444604",
"0.6139254",
"0.6049716",
"0.6022824",
"0.5989654... | 0.7059107 | 0 |
Checks whether the given nametag is reachable by another branch or not. This means that the given nametag must appear in at least one branch as an end tag. | def is_nametag_reachable(nametag, branches):
for branch in branches:
for next_nametag in branches[branch].next_nametags:
if next_nametag == nametag:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def valid_branches(branches):\n\n # for every branch in the list\n for branch in branches:\n\n # make sure it is either reachable or has the special tag \"start\"\n if branches[branch].name != \"start\" and not is_nametag_reachable(branches[branch].name, branches):\n return False\n\n... | [
"0.6780454",
"0.67798984",
"0.61064994",
"0.555332",
"0.54905283",
"0.5410261",
"0.5404235",
"0.5332533",
"0.52437496",
"0.52371407",
"0.5203334",
"0.5191445",
"0.5187049",
"0.5181029",
"0.5169189",
"0.51587147",
"0.51385343",
"0.5123286",
"0.50983757",
"0.50803226",
"0.50749... | 0.79997444 | 0 |
Checks whether the given nametag is indeed labelling a branch. | def branch_exists(nametag, branches):
for branch in branches:
if branches[branch].name == nametag:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_nametag_reachable(nametag, branches):\n for branch in branches:\n for next_nametag in branches[branch].next_nametags:\n if next_nametag == nametag:\n return True\n return False",
"def _is_branch(self, reference_name):\n return reference_name.startswith(\"refs/... | [
"0.715576",
"0.7042534",
"0.6742809",
"0.67223674",
"0.6595517",
"0.6064686",
"0.6029496",
"0.6010573",
"0.59576434",
"0.5933389",
"0.58508664",
"0.58427274",
"0.584109",
"0.582408",
"0.58061534",
"0.5799144",
"0.5777416",
"0.5758434",
"0.5737931",
"0.5732576",
"0.5721581",
... | 0.7622634 | 0 |
Checks that the given branches are valid (every single branch is supposed valid). The idea here is to make sure that every ending nametag leads to another branch and that every branch is reachable. | def valid_branches(branches):
# for every branch in the list
for branch in branches:
# make sure it is either reachable or has the special tag "start"
if branches[branch].name != "start" and not is_nametag_reachable(branches[branch].name, branches):
return False
# make sur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_branches(num_branches, num_blocks, in_channels, num_channels):\n if num_branches != len(num_blocks):\n error_msg = f'NUM_BRANCHES({num_branches}) != NUM_BLOCKS({len(num_blocks)})'\n raise ValueError(error_msg)\n if num_branches != len(num_channels):\n error... | [
"0.6998986",
"0.66559356",
"0.64833444",
"0.6398546",
"0.62924314",
"0.6202438",
"0.6100306",
"0.6061971",
"0.60214883",
"0.5950823",
"0.57404685",
"0.5680982",
"0.56484795",
"0.5646791",
"0.56122196",
"0.5460472",
"0.5450381",
"0.54337895",
"0.5420128",
"0.54145825",
"0.5402... | 0.838676 | 0 |
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