int:\\n return self._icx_engine.get_total_supply(context)\",\n \"def _estimate_elasticsearch_requirement(\\n instance: Instance,\\n desires: CapacityDesires,\\n working_set: float,\\n reads_per_second: float,\\n max_rps_to_disk: int,\\n zones_per_region: int = 3,\\n copies_per_region: int = 3,\\n) -> CapacityRequirement:\\n # Keep half of the cores free for background work (merging mostly)\\n needed_cores = math.ceil(sqrt_staffed_cores(desires) * 1.5)\\n # Keep half of the bandwidth available for backup\\n needed_network_mbps = simple_network_mbps(desires) * 2\\n\\n needed_disk = math.ceil(\\n (1.0 / desires.data_shape.estimated_compression_ratio.mid)\\n * desires.data_shape.estimated_state_size_gib.mid\\n * copies_per_region,\\n )\\n\\n # Rough estimate of how many instances we would need just for the the CPU\\n # Note that this is a lower bound, we might end up with more.\\n needed_cores = math.ceil(\\n max(1, needed_cores // (instance.cpu_ghz / desires.core_reference_ghz))\\n )\\n rough_count = math.ceil(needed_cores / instance.cpu)\\n\\n # Generally speaking we want fewer than some number of reads per second\\n # hitting disk per instance. If we don't have many reads we don't need to\\n # hold much data in memory.\\n instance_rps = max(1, reads_per_second // rough_count)\\n disk_rps = instance_rps * _es_io_per_read(max(1, needed_disk // rough_count))\\n rps_working_set = min(1.0, disk_rps / max_rps_to_disk)\\n\\n # If disk RPS will be smaller than our target because there are no\\n # reads, we don't need to hold as much data in memory\\n needed_memory = min(working_set, rps_working_set) * needed_disk\\n\\n # Now convert to per zone\\n needed_cores = needed_cores // zones_per_region\\n needed_disk = needed_disk // zones_per_region\\n needed_memory = int(needed_memory // zones_per_region)\\n logger.debug(\\n \\\"Need (cpu, mem, disk, working) = (%s, %s, %s, %f)\\\",\\n needed_cores,\\n needed_memory,\\n needed_disk,\\n working_set,\\n )\\n\\n return CapacityRequirement(\\n requirement_type=\\\"elasticsearch-data-zonal\\\",\\n core_reference_ghz=desires.core_reference_ghz,\\n cpu_cores=certain_int(needed_cores),\\n mem_gib=certain_float(needed_memory),\\n disk_gib=certain_float(needed_disk),\\n network_mbps=certain_float(needed_network_mbps),\\n context={\\n \\\"working_set\\\": min(working_set, rps_working_set),\\n \\\"rps_working_set\\\": rps_working_set,\\n \\\"disk_slo_working_set\\\": working_set,\\n \\\"replication_factor\\\": copies_per_region,\\n \\\"compression_ratio\\\": round(\\n 1.0 / desires.data_shape.estimated_compression_ratio.mid, 2\\n ),\\n \\\"read_per_second\\\": reads_per_second,\\n },\\n )\",\n \"def new_capacity_rule(mod, g, p):\\n return 0\",\n \"def get_occupant_room_load_for_cooling_balanced(\\n l_cs: np.ndarray, l_cl: np.ndarray, q_d_trs_prt: np.ndarray) -> (np.ndarray, np.ndarray):\\n\\n l_d_cs = np.where(l_cs[0:5] > 0.0, l_cs[0:5] - q_d_trs_prt, 0.0)\\n l_d_cl = l_cl[0:5]\\n\\n return np.clip(l_d_cs, 0.0, None), np.clip(l_d_cl, 0.0, None)\",\n \"def calculate_lower_boundary(self, divisor):\\n\\n # see how low you can go\\n quotas = [0] * self.states\\n fair_shares = [0] * self.states\\n counter = 0\\n lowest_divisor = 0\\n prev_divisor = 0\\n estimator = 1000000000\\n while counter < 1000:\\n for i, population in enumerate(self.populations):\\n if divisor is None or population is None:\\n return None\\n quotas[i] = population / divisor\\n fair_shares[i] = math.floor(quotas[i])\\n if sum(fair_shares) != self.num_seats:\\n estimator = estimator / 10\\n prev_divisor = divisor\\n divisor = lowest_divisor - estimator\\n else:\\n lowest_divisor = divisor\\n divisor = prev_divisor - estimator\\n if lowest_divisor == divisor:\\n break\\n counter += 1\\n return math.ceil(lowest_divisor * 1000) / 1000\",\n \"def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t):\\n cap = self.get_capacity(meta, raven_vars, dispatch, t)[0][self._capacity_var]\\n try:\\n if abs(balance[self._capacity_var]) > abs(cap):\\n #ttttt\\n # do the inverse problem: how much can we make?\\n balance, meta = self.produce_max(meta, raven_vars, dispatch, t)\\n print('The full requested amount ({res}: {req}) was not possible, so accessing maximum available instead ({res}: {blc}).'.format(res=res, req=amt, blc=balance[res]))\\n except KeyError:\\n raise SyntaxError('Resource \\\"{}\\\" is listed as capacity limiter, but not an output of the component! Got: {}'.format(self._capacity_var, balance))\\n return balance, meta\",\n \"def calculateSaleReturnSolidity(S, R, F, T):\\n _supply = uint256(S)\\n _reserveBalance = uint256(R)\\n _reserveRatio = uint256(F)\\n _sellAmount = uint256(T)\\n \\n if ( _supply < _sellAmount):\\n raise Exception(\\\"Supply < Tokens\\\")\\n\\n _baseN = _supply - _sellAmount\\n\\n\\n if _reserveRatio == 100:\\n amount = uint256(_reserveBalance * _baseN ) / _supply\\n if _reserveBalance < amount:\\n raise Exception(\\\"_reservebalance < amount\\\")\\n\\n return _reserveBalance - amount\\n\\n resD = FIXED_ONE\\n #resN = power_sale(_supply, _baseN, 100, _reserveRatio)\\n resN = power(_supply, _baseN, 100, _reserveRatio)\\n resN = uint256(resN)\\n\\n reserveUpshifted = uint256(_reserveBalance * resN)\\n amount = uint256(_reserveBalance * resD) \\n\\n \\n result = (reserveUpshifted - amount) / resN\\n \\n if verbose:\\n print(\\\" rbal[%d] * resN[%d] / resD[%d] - rbal[%d] = %d \\\" %\\n (_reserveBalance, resN, resD, _reserveBalance, result))\\n\\n return uint256(result - minUnit(R))\",\n \"def load_factor(self) -> float:\\n return self.filled_count / self.table_size\",\n \"def _cost_caught_by_police(self):\\n if self.fine_frequency != 0:\\n if self.number_of_courses % self.fine_frequency == 0 and self.number_of_courses != 0:\\n if self.number_of_courses % self.fine_frequency_paid_by_driver == 0 and self.number_of_courses != 0:\\n self.fine_paid_number_of_courses += 1\\n fine_value = np.random.choice([100, 200, 500], p=[0.25, 0.4, 0.35])\\n self.total_penalty_points += self._add_penalty_points() # adding penalty points\\n return fine_value\\n else:\\n return 0\\n else:\\n return 0\\n else:\\n return 0\",\n \"def trucks_required(self) -> int:\\n daily_demand = (\\n self.total_demand() / self.operations.productivity.working_days_per_year\\n )\\n trucks_required = np.ceil(\\n daily_demand\\n / (self.operations.avg_vol_per_lift() * self.inputs.lifts_per_truck_day)\\n )\\n return trucks_required\",\n \"def get_utilization(self, node: int) -> float:\\n return self.busy[node].pmf(1)\",\n \"def calc(self, demands: Demand, routing: Routing) -> np.ndarray:\\n total_utilisation = np.zeros((self.num_nodes, self.num_nodes),\\n dtype=float)\\n\\n for commodity_idx in range(len(self.commodities)):\\n utilisation = self.calc_demand(routing,\\n demands[commodity_idx],\\n commodity_idx)\\n total_utilisation += utilisation\\n\\n return np.max(np.divide(total_utilisation, self.edge_capacities))\",\n \"def calc_pre_intertie_generation (self):\\n\\n self.pre_intertie_generation = \\\\\\n self.forecast.get_generation(self.start_year,self.end_year)\\n\\n gen_eff = self.cd[\\\"diesel generation efficiency\\\"]\\n self.pre_intertie_generation_fuel_used = \\\\\\n self.pre_intertie_generation / gen_eff\\n\\n #~ print 'self.baseline_generatio',self.baseline_generation\",\n \"def _cost(self, action):\\n return float(self.spacecraft.crashed)\",\n \"def calc_DC_supply(t_0, t_1):\\n if t_0 == 0:\\n t_0 = 1E6\\n if t_1 > 0:\\n tmin = min(t_0, t_1)\\n else:\\n tmin = t_0\\n return tmin\",\n \"def calc_electric_diesel_reduction (self):\\n gen_eff = self.cd[\\\"diesel generation efficiency\\\"]\\n\\n self.electric_diesel_reduction = self.net_generation_wind / gen_eff\\n\\n electric_diesel = self.generation/gen_eff\\n if self.electric_diesel_reduction > electric_diesel:\\n self.electric_diesel_reduction = electric_diesel\",\n \"def calc_annual_electric_savings (self):\\n costs = self.comp_specs['diesel generator o&m']\\n\\n for kW in costs.keys():\\n try:\\n if self.average_load < int(kW):\\n maintenance = self.comp_specs['diesel generator o&m'][kW]\\n break\\n except ValueError:\\n maintenance = self.comp_specs['diesel generator o&m'][kW]\\n\\n self.baseline_generation_cost = maintenance + \\\\\\n (self.pre_intertie_generation_fuel_used * self.diesel_prices)\\n\\n maintenance = self.capital_costs * \\\\\\n (self.comp_specs['percent o&m'] / 100.0)\\n self.proposed_generation_cost = maintenance + \\\\\\n self.intertie_offset_generation_fuel_used * \\\\\\n self.intertie_diesel_prices\\n self.annual_electric_savings = self.baseline_generation_cost -\\\\\\n self.proposed_generation_cost\\n #~ print len(self.annual_electric_savings)\\n #~ print 'self.annual_electric_savings',self.annual_electric_savings\",\n \"def get_load_factor(self):\\r\\n return self.num_items / self.table_size\",\n \"def inventoryCapacity(self):\\n # TODO: Worry about how +Strength and +Capacity gear could allow you to carry more than your capacity.\\n if self.totalStrength <= 15:\\n return int(6 * self.totalStrength + self._baseInventoryCapacity + self._equipmentCarryingCapacity)\\n else:\\n return int(90 + (self.totalStrength - 15) * 9 + self._baseInventoryCapacity + self._equipmentCarryingCapacity)\",\n \"def diff_l_sum_avail():\\n def subtract(raster1, raster2):\\n \\\"\\\"\\\"Subtract raster2 from raster1.\\\"\\\"\\\"\\n valid_mask = (\\n (raster1 != nodata) &\\n (raster2 != nodata))\\n result = numpy.empty(raster1.shape, dtype=numpy.float32)\\n result[:] = nodata\\n result[valid_mask] = raster1[valid_mask] - raster2[valid_mask]\\n return result\\n\\n outer_results_dir = r\\\"C:\\\\Users\\\\ginge\\\\Dropbox\\\\NatCap_backup\\\\USFS\\\\model_runs\\\\precip_scenarios\\\"\\n example_raster = r\\\"C:\\\\Users\\\\ginge\\\\Dropbox\\\\NatCap_backup\\\\USFS\\\\model_runs\\\\precip_scenarios\\\\0.5x\\\\post_decline\\\\L_sum_avail.tif\\\"\\n nodata = pygeoprocessing.get_raster_info(example_raster)['nodata'][0]\\n for multiply_factor in [0.5, 0.7, 0.9, 1.1, 1.3, 1.5]:\\n results_dir = os.path.join(\\n outer_results_dir, '{}x'.format(multiply_factor))\\n predecline_lavail_sum = os.path.join(\\n results_dir, 'pre_decline', 'L_sum_avail.tif')\\n postdecline_lavail_sum = os.path.join(\\n results_dir, 'post_decline', 'L_sum_avail.tif')\\n target_path = os.path.join(\\n results_dir, \\\"L_sum_avail_post_minus_pre.tif\\\")\\n pygeoprocessing.raster_calculator(\\n [(postdecline_lavail_sum, 1), (predecline_lavail_sum, 1)],\\n subtract, target_path, gdal.GDT_Float32, nodata)\",\n \"def get_total_supply() -> int:\\n return total_supply\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5882945","0.5823634","0.57726854","0.57467616","0.5741599","0.57414633","0.5710871","0.55506474","0.54651403","0.5430573","0.5428441","0.54128045","0.53944397","0.5361302","0.535642","0.53394634","0.5322019","0.53197694","0.5309506","0.5301232","0.530022","0.52987295","0.52906734","0.5287092","0.52850485","0.52696913","0.52629864","0.5246036","0.52322215","0.5228011","0.5210022","0.51905465","0.518204","0.5169943","0.51448846","0.51427823","0.5116554","0.511279","0.5110827","0.51092273","0.510617","0.50925726","0.5086132","0.508438","0.5083437","0.5081476","0.50776327","0.5075482","0.5049715","0.5046699","0.50456583","0.5042509","0.5033329","0.50328594","0.503198","0.5025129","0.50105274","0.5004199","0.4996772","0.49952048","0.49876794","0.49860978","0.49860513","0.49811885","0.49804294","0.497636","0.4974732","0.49664733","0.49632525","0.49581733","0.49543062","0.49542627","0.4953676","0.49495435","0.49447113","0.49438116","0.49423724","0.4937921","0.49325305","0.49250734","0.4923298","0.4921956","0.4918844","0.4917521","0.4917422","0.49152955","0.49150658","0.49057668","0.48993322","0.4898713","0.4898488","0.48982564","0.48913914","0.48863244","0.4885471","0.4884318","0.48762396","0.48742464","0.48735347","0.48719868"],"string":"[\n \"0.5882945\",\n \"0.5823634\",\n \"0.57726854\",\n \"0.57467616\",\n \"0.5741599\",\n \"0.57414633\",\n \"0.5710871\",\n \"0.55506474\",\n \"0.54651403\",\n \"0.5430573\",\n \"0.5428441\",\n \"0.54128045\",\n \"0.53944397\",\n \"0.5361302\",\n \"0.535642\",\n \"0.53394634\",\n \"0.5322019\",\n \"0.53197694\",\n \"0.5309506\",\n \"0.5301232\",\n \"0.530022\",\n \"0.52987295\",\n \"0.52906734\",\n \"0.5287092\",\n \"0.52850485\",\n \"0.52696913\",\n \"0.52629864\",\n \"0.5246036\",\n \"0.52322215\",\n \"0.5228011\",\n \"0.5210022\",\n \"0.51905465\",\n \"0.518204\",\n \"0.5169943\",\n \"0.51448846\",\n \"0.51427823\",\n \"0.5116554\",\n \"0.511279\",\n \"0.5110827\",\n \"0.51092273\",\n \"0.510617\",\n \"0.50925726\",\n \"0.5086132\",\n \"0.508438\",\n \"0.5083437\",\n \"0.5081476\",\n \"0.50776327\",\n \"0.5075482\",\n \"0.5049715\",\n \"0.5046699\",\n \"0.50456583\",\n \"0.5042509\",\n \"0.5033329\",\n \"0.50328594\",\n \"0.503198\",\n \"0.5025129\",\n \"0.50105274\",\n \"0.5004199\",\n \"0.4996772\",\n \"0.49952048\",\n \"0.49876794\",\n \"0.49860978\",\n \"0.49860513\",\n \"0.49811885\",\n \"0.49804294\",\n \"0.497636\",\n \"0.4974732\",\n \"0.49664733\",\n \"0.49632525\",\n \"0.49581733\",\n \"0.49543062\",\n \"0.49542627\",\n \"0.4953676\",\n \"0.49495435\",\n \"0.49447113\",\n \"0.49438116\",\n \"0.49423724\",\n \"0.4937921\",\n \"0.49325305\",\n \"0.49250734\",\n \"0.4923298\",\n \"0.4921956\",\n \"0.4918844\",\n \"0.4917521\",\n \"0.4917422\",\n \"0.49152955\",\n \"0.49150658\",\n \"0.49057668\",\n \"0.48993322\",\n \"0.4898713\",\n \"0.4898488\",\n \"0.48982564\",\n \"0.48913914\",\n \"0.48863244\",\n \"0.4885471\",\n \"0.4884318\",\n \"0.48762396\",\n \"0.48742464\",\n \"0.48735347\",\n \"0.48719868\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.517416"},"document_rank":{"kind":"string","value":"33"}}},{"rowIdx":6787,"cells":{"query":{"kind":"string","value":"Calculate the reserve factor for Euler Buckling Failure by calculating the critical load and comparing to the applied load. Return the reserve factor adjusted for inequalities based around =>0"},"document":{"kind":"string","value":"def eulerBuckle(dim):\n bst = dim[0]\n tst = dim[1]\n tsk = dim[2]\n\n ZEAZ = (Est*bst*tst*((tsk/2)+(bst/2)))\n ZEA = (Est*bst*tst)+(Esk*tsk*bsk)\n zbar = ZEAZ/ZEA # Neutral Axis\n\n EIbar = ((Esk*bsk*(tsk**3))/12)+(Esk*bsk*tsk*(zbar**2))+((Est*tst*bst**3)/12)+\\\n (Est*bst*tst*(((bst/2)+(tsk/2)-zbar)**2)) # Using Parallel Axis Theorm\n NxEuler = ((math.pi**2)*EIbar)/(ribSpace**2*bsk) # Critical Load\n rsf = NxEuler/Nx\n return rsf - 1.1 # Using a target Reserve Factor of >=1.1"},"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 get_load_factor(self):\n # Your code here\n return self.total_items / self.capacity","def internal_external_canopy_heat_capacity(lumped_cover_heat_capacity: float) -> float:\n return 0.1 * lumped_cover_heat_capacity","def calculate_lower_boundary(self, divisor):\n\n # see how low you can go\n quotas = [0] * self.states\n fair_shares = [0] * self.states\n counter = 0\n lowest_divisor = 0\n prev_divisor = 0\n estimator = 1000000000\n while counter < 1000:\n for i, population in enumerate(self.populations):\n if divisor is None or population is None:\n return None\n quotas[i] = population / divisor\n fair_shares[i] = math.floor(quotas[i])\n if sum(fair_shares) != self.num_seats:\n estimator = estimator / 10\n prev_divisor = divisor\n divisor = lowest_divisor - estimator\n else:\n lowest_divisor = divisor\n divisor = prev_divisor - estimator\n if lowest_divisor == divisor:\n break\n counter += 1\n return math.ceil(lowest_divisor * 1000) / 1000","def calculate_upper_boundary(self, divisor):\n\n # see how high you can go\n quotas = [0] * self.states\n fair_shares = [0] * self.states\n counter = 0\n highest_divisor = 0\n prev_divisor = 0\n estimator = 1000000000\n while counter < 1000:\n for i, population in enumerate(self.populations):\n if divisor is None:\n return None\n quotas[i] = population / divisor\n fair_shares[i] = math.floor(quotas[i])\n if sum(fair_shares) != self.num_seats:\n estimator = estimator / 10\n prev_divisor = divisor\n divisor = highest_divisor + estimator\n else:\n highest_divisor = divisor\n divisor = prev_divisor + estimator\n if highest_divisor == divisor:\n break\n counter += 1\n return math.floor(highest_divisor * 1000) / 1000","def get_amount_in(amount_out, reserve_in, reserve_out):\n assert amount_out > 0\n assert reserve_in > 0 and reserve_out > 0\n numerator = reserve_in*amount_out*1000\n denominator = (reserve_out - amount_out)*997\n return float(numerator/denominator + 1)","def penalty_calc(self):\n self.p_budget = (self.tx_oma_min - self.rx_unstressed_sensitivity - self.fiber_conn_loss)*self.l_1\n\n # fiber attenuation,\n self.p_atten = self.alpha*self.length # column B\n\n # calculate bandwidth for RIN test (exclude transmitter)\n rin_inverse_bw = np.sqrt(np.square(1.0/self.bw_cd) + np.square(1.0/self.bw_md) + (0.477/(self.rx_bw**2))*self.l_1)\n rin_bw = 1.0 / rin_inverse_bw\n\n # see FC-MSQS-2 equation B.47 in Annex B.4 for the following k_rin = math.sqrt(2.0/math.pi)*erfinv(0.8)\n k_rin = 0.7\n\n # v_rin,\n self.v_rin = (k_rin*1E6*(self.rin_test_isi**2)*rin_bw*\n math.pow(10.0,0.1*self.rin)) # column AK\n\n # Prin,\n print('v_rin: ', self.v_rin)\n print('Q: ',self.Q)\n print('isi_dj_refl_closed :', self.isi_dj_refl_closed)\n self.p_rin = -10.0*np.log10(np.sqrt(1.0-np.multiply(self.v_rin, np.square(self.Q/self.isi_dj_refl_closed)))) # column R\n print(\"P_rin : \", self.p_rin)\n self.beta = (3.14159E-6*self.speedup*self.br_nominal *self.delta_lambda*self.d1*self.length) # column O\n self.sigma_mpn = (self.k_mpn/math.sqrt(2.0)*(self.l_1 -np.exp(-np.square(self.beta)))) # column P\n self.p_mpn = (-10.0*np.log10(np.sqrt(self.l_1 - (self.Q**2)*np.square(self.sigma_mpn)))) # column Q\n self.p_blw = (-10.0*math.log10(math.sqrt(1.0- ((self.Q*self.sigma_blw)/ self.isi_tp4_rx)**2))*self.l_1) # cell T13\n self.p_reflection = -10.0*np.log10(self.isi_reflection) # column N\n self.v_mn = (((1.0-math.pow(10.0,-0.2*self.pmn))/ (self.Q)**2)*self.l_1) # cell AG7\n print(\"isi_center : \", self.isi_center)\n\n self.p_isi_center = -10.0*np.log10(self.isi_center) # column J\n\n self.p_isi_corners = (-10.0*np.log10(self.isi_corners) - self.p_isi_center) # column K\n self.p_isi_dj_center = (-10.0*np.log10(self.isi_dj_refl_closed) - self.p_isi_center) # column L\n self.p_isi_dj_corners = (-10.0*np.log10(self.isi_dj_corners) -self.p_isi_center -self.p_isi_corners) # column M\n\n\n # calculate the \"cross\" penalty contribution, column S\n arg1 = ((self.sigma_blw**2 + self.v_rin)/ np.square(self.isi_dj_refl_closed))\n arg2 = self.l_1 - (self.Q**2)*(arg1 + self.v_mn + np.square(self.sigma_mpn))\n arg3 = (-10.0*np.log10(np.multiply(self.isi_dj_refl_closed, np.sqrt(arg2))))\n self.p_cross_center = ( # column S\n arg3\n - self.p_blw # cell T13\n - self.p_isi_center # column J\n - self.p_isi_dj_center # column L\n - self.p_mpn # column Q\n - self.p_reflection # column N\n - self.p_rin # column R\n - self.pmn*self.l_1) # cell G13\n print('p_isi_center: ', self.p_isi_center)\n\n # calculate the total power budget evaluated at the center of the eye\n self.p_total_center = ( # column T\n self.p_isi_center # column J\n + self.p_isi_dj_center # column L\n + self.p_atten # column B\n + self.p_mpn # column Q\n + self.p_reflection # column N\n + self.p_rin # column R\n + self.p_cross_center # column S\n + self.pmn*self.l_1) # cell G13\n # calculate the total power budget evaluated at the corner of the eye\n self.p_total_corners = (\n self.p_isi_center # column J\n + self.p_isi_corners # column K\n + self.p_atten # column B\n + self.p_mpn # column Q\n + self.p_reflection # column N\n + self.p_rin # column R\n + self.p_cross_center # column S\n + self.pmn*self.l_1 # cell G13\n + self.p_isi_dj_corners)# column M\n\n # receiver stressed sensitivity\n self.margin = ( self.p_budget\n - self.p_total_center) # column W\n\n self.rx_stressed_sensitivity = (\n self.tx_oma_min*self.l_1\n - self.chil\n - self.p_mpn\n - self.p_reflection\n - self.p_rin\n - 0.5*self.p_cross_center\n - self.pmn*self.l_1\n - self.margin[self.lnum//2]*self.l_1)\n\n\n # end of GbE10.penalty_calc\n #======================================================================+","def ComputeRegenerativeBraking(self):\r\n pass","def _load_factor(self):\n return self.size / len(self.buckets)","def calculate_large_constant(self, bound, real_reduction_iterations):#factor):\n minimum_exponent = round(90/(real_reduction_iterations-1))#math.ceil(math.log(bound, 10) * factor)\n \n return ZZ(10 ** minimum_exponent)","def upper_bound(stock):\n counter=0\n for i in stock_price(stock):\n if i >= resistance(stock):\n counter+=1\n return counter","def get_additional_ball_capacity(self):\n return 999","def test_capacity_factor(pudl_out_eia):\n print(\"\\nCalculating generator capacity factors...\")\n cf = pudl_out_eia.capacity_factor()\n print(f\" capacity_factor: {len(cf)} records\")","def required_points(self):\n req_points = self.min_performance * self.initial_available_points()\n return np.maximum(0, np.int64(np.ceil(req_points)))","def calc_low_energy_bulb_ratio(lighting_outlets_total, lighting_outlets_low_energy):\n return int(100 * float(lighting_outlets_low_energy) / lighting_outlets_total + 0.5) / 100.0","def get_fuel_total_saved (self):\n #~ print self.lost_heat_recovery\n #~ print self.intertie_offset_generation_fuel_used\n #~ print self.pre_intertie_generation_fuel_used\n #~ gen_eff = self.cd[\"diesel generation efficiency\"]\n #~ fuel_used = self.intertie_offset_generation / gen_eff\n\n generation_diesel_reduction = \\\n np.array(self.pre_intertie_generation_fuel_used\\\n [:self.actual_project_life])\n return - np.array(self.lost_heat_recovery[:self.actual_project_life]) +\\\n generation_diesel_reduction","def get_additional_ball_capacity(cls):\n return 999","def load_factor_d_non_peak(self, data):\n lf_d = np.zeros((data['nr_of_fueltypes']))\n\n # Iterate fueltypes to calculate load factors for each fueltype\n for k, fueldata in enumerate(self.rs_fuels_tot_enduses_d):\n\n average_demand = sum(fueldata) / 365 # Averae_demand = yearly demand / nr of days\n max_demand_d = max(fueldata)\n\n if max_demand_d != 0:\n lf_d[k] = average_demand / max_demand_d # Calculate load factor\n\n lf_d = lf_d * 100 # Convert load factor to %\n\n return lf_d","def test_efficiency(self):\n effs = self.gr.calculate_global_efficiencies()\n ans = [a/6 for a in [1.5, 2., 1.5]]\n self.assertListEqual(effs.values(), ans)\n\n E = self.gr.global_efficiency()\n self.assertEqual(E, sum(ans))\n\n v_min = (E - (1/3)) / E\n mx, v = self.gr.vulnerability()\n # the middle node (2) is the most vulnerable\n self.assertEqual(mx, (2, v_min))","def calc_fuel_given_ore(ore, recipes):\n\n upper_bound = None\n lower_bound = 469536 # assume ore needed for 1 fuel is good lower bound\n\n\n while lower_bound + 1 != upper_bound:\n if upper_bound is None:\n guess = lower_bound * 2\n else:\n guess = (upper_bound + lower_bound) // 2\n \n ore_needed = calc_ore_needed(guess, recipes)\n if ore_needed > ore:\n upper_bound = guess\n else:\n lower_bound = guess\n \n return lower_bound","def _cost_refueling(self):\n if self.number_of_courses % self.refueling_frequency == 0 & self.number_of_courses != 0:\n lowest_amount = self.refueling_liter_range[0] # take a minimum value\n highest_amount = self.refueling_liter_range[1] # take a maximum value\n refueled_petrol = randint(lowest_amount, highest_amount)\n cost = refueled_petrol * self.petrol_cost\n return cost\n else:\n return 0","def E_BE(self,s,l):\n if s>l: return self.E_BE(l,s)\n if s == 0: return -100\n if l == 0: return -100\n delta = (((s+l)/l)-(l/s)) \n return delta-delta%self._tau","def load_factor(self) -> float:\n return self.filled_count / self.table_size","def getRepairCapacity(self):\n return int(self.myDesign.getSYCRequired() * (1-(self.strength/100.0)))","def get_allowable_fraction_without(mem_to_reserve, cuda_device_index):\n current_free = get_free_gpu_memory(cuda_device_index)\n allowable = current_free - mem_to_reserve # 1GB\n allowable_fraction = allowable / current_free\n if allowable_fraction <= 0.0:\n raise ValueError(f\"Can't leave 1GB over for the inference kernel, because\"\n f\" there is only {allowable} total free GPU memory.\")\n return allowable_fraction","def standard_init_price(self):\n # If a system can't use something, its price is zero.\n _good = self.tradeitem\n if self.planet.tech_level < _good.tu and _good.name not in 'fuel':\n base_price = 0\n else:\n base_price = _good.plt + (self.planet.tech_level * _good.pi)\n # if good is highly requested, increase the price\n if self.planet.status in [_good.dps]:\n base_price = base_price + (base_price * 0.5)\n # large system: high production decreases prices\n base_price = (base_price * (100 - self.planet.system_size)) / 100\n\n # price can't be negative\n if base_price < 0:\n base_price = 0\n\n return int(base_price)","def dilutionneeded(self) -> float:\n return self.stock*1.0/self.final","def MINIMUM_BET() -> int:\n return 10","def _cost_caught_by_police(self):\n if self.fine_frequency != 0:\n if self.number_of_courses % self.fine_frequency == 0 and self.number_of_courses != 0:\n if self.number_of_courses % self.fine_frequency_paid_by_driver == 0 and self.number_of_courses != 0:\n self.fine_paid_number_of_courses += 1\n fine_value = np.random.choice([100, 200, 500], p=[0.25, 0.4, 0.35])\n self.total_penalty_points += self._add_penalty_points() # adding penalty points\n return fine_value\n else:\n return 0\n else:\n return 0\n else:\n return 0","def fuel_required(mass):\n return max(math.floor(mass/3) - 2, 0)","def get_duct_linear_heat_loss_coefficient() -> float:\n return 0.49","def lower_bound(stock):\n counter=0\n for i in stock_price(stock):\n if i <= support(stock):\n counter+=1\n return counter","def get_load_factor(self):\r\n return self.num_items / self.table_size","def penalty(self):\n return 0","def revenue_landfill(self) -> float:\n return self.income_statement.revenue.operating_revenue * (\n 1 - self.inputs.allocation_to_collection_unit\n )","def compute_equilibrium(self): \r\n # First compute a valid lower bound for the total flow\r\n totalflow_lb = max(0, np.amax(1 - np.multiply(self.capacity, np.exp(self.beta * np.power(self.capacity, self.theta) + self.phi - self.b))))\r\n if totalflow_lb > 1:\r\n print(\"This network does not have equilibrium!\")\r\n else:\r\n # Compute the total flow at equilibrium z_star\r\n z_star = bisection_search(diff_totalflow_sumflow, totalflow_lb, 1, [1e-10, 1e-10], True, self) \r\n # Compute the flow over each route at equilibrium \r\n for i in range(self.num_routes):\r\n self.flow[i] = bisection_search(zeta, 0, self.capacity[i], [1e-10, 1e-10], True, self, z_star, i, 4)","def _compute_quantization_factor(self):\n self._quantization_factor = (float(self._full_value_range) /\n self._number_of_intervals)\n logging.debug(\n 'full value range: %r, number of intervals: %r',\n self._full_value_range, self._number_of_intervals)\n logging.debug('quantization factor: %r', self._quantization_factor)","def overall_reduction(self):\n return 84","def qty_available(quant) -> float:\n return quant.quantity - quant.reserved_quantity","def emission_factor(sheets, vehicleClass, speedRegime, pollutant):\r\n # get bc from excel. The year is hard coded to 2015\r\n f = sheets[\"Emissiefactoren CAR-VL3.0\"]\r\n idx = f[f.iloc[:,0] == vehicleClass + speedRegime + '2015'].index\r\n if len(idx) == 0:\r\n print(\"EFError: No ef corresponds to vehicle class {} and speed type {}.\".format(vehicleClass, speedRegime))\r\n return 0\r\n return float(f['EF_' + pollutant][idx])","def calculateSaleReturnSolidity(S, R, F, T):\n _supply = uint256(S)\n _reserveBalance = uint256(R)\n _reserveRatio = uint256(F)\n _sellAmount = uint256(T)\n \n if ( _supply < _sellAmount):\n raise Exception(\"Supply < Tokens\")\n\n _baseN = _supply - _sellAmount\n\n\n if _reserveRatio == 100:\n amount = uint256(_reserveBalance * _baseN ) / _supply\n if _reserveBalance < amount:\n raise Exception(\"_reservebalance < amount\")\n\n return _reserveBalance - amount\n\n resD = FIXED_ONE\n #resN = power_sale(_supply, _baseN, 100, _reserveRatio)\n resN = power(_supply, _baseN, 100, _reserveRatio)\n resN = uint256(resN)\n\n reserveUpshifted = uint256(_reserveBalance * resN)\n amount = uint256(_reserveBalance * resD) \n\n \n result = (reserveUpshifted - amount) / resN\n \n if verbose:\n print(\" rbal[%d] * resN[%d] / resD[%d] - rbal[%d] = %d \" %\n (_reserveBalance, resN, resD, _reserveBalance, result))\n\n return uint256(result - minUnit(R))","def calculate_effective_capacitance(self, load):\n c_load = load\n # In fF\n c_para = spice[\"min_tx_drain_c\"] * (self.nmos_size / parameter[\"min_tx_size\"])\n transition_prob = 0.1875\n return transition_prob * (c_load + c_para)","def calc_load_factor_h(data, fuels_tot_enduses_h, rs_fuels_peak_h):\n load_factor_h = np.zeros((data['nr_of_fueltypes']))\n\n # Iterate fueltypes to calculate load factors for each fueltype\n for fueltype, fuels in enumerate(fuels_tot_enduses_h):\n\n # Maximum fuel of an hour of the peak day\n maximum_h_of_day = rs_fuels_peak_h[fueltype]\n\n #Calculate average in full year\n average_demand_h = np.mean(fuels)\n\n # If there is a maximum day hour\n if maximum_h_of_day != 0:\n load_factor_h[fueltype] = average_demand_h / maximum_h_of_day # Calculate load factor\n\n # Convert load factor to %\n load_factor_h *= 100\n\n return load_factor_h","def fuel_required(mass):\n return int(floor(mass / 3) - 2)","def get_remaining_fuel(self):\n return min(self.liquid_fuel, self.oxidizer)","def _calc_worker_assign_limits(self, initial_count, occupied=None):\n occupied = occupied or dict()\n actual_count = initial_count - sum(occupied.values())\n\n endpoint_res = sorted(self._worker_slots.items(), key=operator.itemgetter(1),\n reverse=True)\n\n endpoints = [t[0] for t in endpoint_res]\n endpoint_cores = np.array([t[1] for t in endpoint_res]).astype(np.float32)\n\n # remove assigned nodes from limitations\n counts = initial_count * endpoint_cores / endpoint_cores.sum()\n for idx, ep in enumerate(endpoints):\n counts[idx] = max(0, counts[idx] - occupied.get(ep, 0))\n\n # all assigned, nothing to do\n if counts.sum() == 0:\n return dict((ep, 0) for ep in endpoints)\n\n counts = (actual_count * counts / counts.sum()).astype(np.int32)\n\n # assign remaining nodes\n pos = 0\n rest = actual_count - counts.sum()\n while rest > 0:\n counts[pos] += 1\n rest -= 1\n pos = (pos + 1) % len(counts)\n return dict(zip(endpoints, counts))","def checkFuel(self):\n return self.maze.checkFuelCost(self.checkpoint,currentLap = self.laps) - self.timeDriving","def compute_lb_totalflow(self):\r\n lb_totalflow = np.amax(1 - np.exp(self.beta - self.b + self.phi * np.minimum(1, 1/self.l))) \r\n return max(0, lb_totalflow)","def calculate_available_node_res (self, vnfs_to_be_left_in_place={},\n mode=MODE_ADD):\n # add available res attribute to all Infras and subtract the running\n # NFs` resources from the given max res\n for n in self.infras:\n setattr(self.network.node[n.id], 'availres',\n copy.deepcopy(self.network.node[n.id].resources))\n if mode == self.MODE_ADD:\n for vnf in self.running_nfs(n.id):\n # if a VNF needs to be left in place, then it is still mapped by the \n # mapping process, but with placement criteria, so its resource \n # requirements will be subtracted during the greedy process.\n if vnf.id not in vnfs_to_be_left_in_place:\n try:\n newres = self.network.node[n.id].availres.subtractNodeRes(\n self.network.node[vnf.id].resources,\n self.network.node[n.id].resources)\n except RuntimeError:\n raise RuntimeError(\n \"Infra node`s resources are expected to represent its maximal \"\n \"capabilities.\"\n \"The NodeNF(s) running on Infra node %s, use(s)more resource \"\n \"than the maximal.\" % n.id)\n else:\n try:\n newres = self.network.node[n.id].availres.subtractNodeRes(\n vnfs_to_be_left_in_place[vnf.id].resources,\n self.network.node[n.id].resources)\n except RuntimeError:\n raise RuntimeError(\"VNF %s cannot be kept on host %s with \"\n \"increased resource requirements due to not \"\n \"enough available resources!\" % (vnf.id, n.id))\n\n self.network.node[n.id].availres = newres","def workersNeeded(k, m):\n # formula: k/m\n from math import ceil\n return ceil(float(k)/float(m))","def calc_reduction_diesel_used (self):\n self.reduction_diesel_used = self.diesel_equiv_captured - \\\n self.loss_heat_recovery\n #~ print 'self.reduction_diesel_used',self.reduction_diesel_used","def _calc_freeze_probability(self, num_iterations, final_fraction):\n return 1.0 - (final_fraction ** (1.0 / num_iterations))","def get_risk_per_unit(price, sl_price):\n return abs(price - sl_price)","def get_amount_out(amount_in, reserve_in, reserve_out):\n assert amount_in > 0\n assert reserve_in > 0 and reserve_out > 0\n amount_in_with_fee = amount_in*997\n numerator = amount_in_with_fee*reserve_out\n denominator = reserve_in*1000 + amount_in_with_fee\n return float(numerator/denominator)","def branching_factor(data, loc):\n\n return 20","def base_reserve_0():\n print('Setting base reserve to 0')\n upgrade('basereserve', 'base_reserve_in_stroops', 0)","def overhead(readings):\n return 100.0 * (int(readings[0]) + int(readings[1])) / (int(readings[2]) + int(readings[3]))","def calculate_supply(self):\r\n \r\n for cell in self.cells:\r\n cell.supply = min(self.max_volume,\r\n self.wave_speed / self.free_speed *\r\n (self.cell_length * self.jam_density -\r\n cell.volume)) /self.interval\r\n self.supply = self.cells[0].supply","def calculate_br_up_metric(br_up):\n if br_up < 1:\n br_up = 1\n min_baud = 1200\n max_baud = 38400\n\n num = np.log(br_up) - np.log(min_baud)\n den = np.log(max_baud) - np.log(min_baud)\n\n return (num / den + 0.1).clip(min=0, max=1)","def bCheck(c, v, p, b):\n val = (v+1).floor()\n deg = c.degree()\n coeffs = c.coefficients(sparse=False)\n lcoeff = coeffs[deg]; coeffs.remove(lcoeff)\n check1 = [(coeffs[i].valuation(p) - lcoeff.valuation(p))/(deg - i) for i in range(0,len(coeffs)) if coeffs[i] != 0]\n check2 = (val - lcoeff.valuation(p))/deg\n check1.append(check2)\n bval = min(check1)\n return (bval).ceil()","def fibre_strain_energy(self, l_stretch):\n if l_stretch <= 1.0:\n # compressed region - no energy\n return 0.0\n\n # Note: this range should be '< lm' according to FEBio but we use '<=' to\n # make setting c6 easier -> there's no difference because it's cts.\n if l_stretch <= self.lm:\n # exponential energy\n return self.c3 * (exp(self.c4 * (l_stretch - 1.0)) - 1.0)\n\n # linear energy\n return self.c5 * l_stretch + self.c6","def calculate_demand(self):\r\n \r\n for cell in self.cells:\r\n cell.demand = min(cell.volume, self.max_volume) /self.interval\r\n self.demand = self.cells[-1].demand","def fuel_calc(mass):\n return max((mass / 3) - 2, 0)","def volneeded(self, totalvol: float) -> float:\n return totalvol*self.final*1.0/self.stock","def get_fuel_requirements(mass: int) -> int:\n return int(mass / 3) - 2","def verify_load_feasibility(self):\n max_load = 0\n for pp in self.powerplants:\n max_load += pp[\"pmax\"]\n\n min_load = max_load\n for pp in self.powerplants:\n min_load = min(pp[\"pmin\"], min_load)\n\n if self.load > max_load:\n self.feasible = False\n self.unfeasible_reason = f\"Requested load {self.load/10} too high for our powerstations \"\n return False\n\n if self.load < min_load:\n self.feasible = False\n self.unfeasible_reason = f\"Requested load {self.load/10} too low for our powerstations \"\n return False\n\n return True","def calc_pre_intertie_generation (self):\n\n self.pre_intertie_generation = \\\n self.forecast.get_generation(self.start_year,self.end_year)\n\n gen_eff = self.cd[\"diesel generation efficiency\"]\n self.pre_intertie_generation_fuel_used = \\\n self.pre_intertie_generation / gen_eff\n\n #~ print 'self.baseline_generatio',self.baseline_generation","def part3c_0():\n xs = exampleInput\n N = 10000\n\n difference = 0.0\n for ys, estimatedProb in submission.computeGibbsProbabilities( simpleCRF, \n submission.getCRFBlocks,\n submission.chooseGibbsCRF,\n xs, N ).iteritems():\n trueProb = nerUtils.computeProbability( simpleCRF, xs, ys )\n difference = abs( trueProb - estimatedProb )\n grader.requireIsLessThan( 5e-2, difference )","def calculate_217f_part_stress(**attributes): # pylint: disable=R0912, R0914\n _dic_ref_temp = {\n 1: 343.0,\n 2: {\n 1: 343.0,\n 2: 343.0,\n 3: 398.0,\n 4: 398.0\n },\n 3: 298.0,\n 5: 398.0,\n 6: 298.0,\n 7: 298.0,\n 9: 358.0,\n 10: 358.0,\n 11: 313.0,\n 12: 298.0,\n 13: 358.0,\n 14: 343.0,\n 15: 343.0\n }\n _dic_factors = {\n 1: [4.5E-9, 12.0, 1.0, 0.6, 1.0, 1.0],\n 2: {\n 1: [3.25E-4, 1.0, 3.0, 1.0, 1.0, 1.0],\n 2: [3.25E-4, 1.0, 3.0, 1.0, 1.0, 1.0],\n 3: [5.0E-5, 3.5, 1.0, 1.0, 1.0, 1.0],\n 4: [5.0E-5, 3.5, 1.0, 1.0, 1.0, 1.0]\n },\n 3: [7.33E-3, 0.202, 2.6, 1.45, 0.89, 1.3],\n 5: [0.0031, 1.0, 10.0, 1.0, 1.0, 1.5],\n 6: [0.00148, 1.0, 2.0, 0.5, 1.0, 1.0],\n 7: [0.00015, 2.64, 1.0, 0.466, 1.0, 1.0],\n 8: [0.021, 0.065, 0.105, 0.0, 0.0, 0.0],\n 9: [0.0062, 1.0, 5.0, 1.0, 1.0, 1.0],\n 10: [0.0735, 1.03, 4.45, 2.74, 3.51, 1.0],\n 11: [0.0398, 0.514, 5.28, 1.44, 4.46, 1.0],\n 12: [0.0481, 0.334, 4.66, 1.47, 2.83, 1.0],\n 13: [0.019, 0.445, 7.3, 2.69, 2.46, 1.0],\n 14: [0.0246, 0.459, 9.3, 2.32, 5.3, 1.0],\n 15: [0.018, 1.0, 7.4, 2.55, 3.6, 1.0]\n }\n _dic_piQ = {\n 1: [0.03, 0.1, 0.3, 1.0, 5.0, 15.0],\n 2: [0.03, 0.1, 0.3, 1.0, 5.0, 5.0, 15.0],\n 3: [1.0, 3.0],\n 4: [1.0, 3.0],\n 5: [0.03, 0.1, 0.3, 1.0, 5.0, 15.0],\n 6: [0.03, 0.1, 0.3, 1.0, 5.0, 15.0],\n 7: [0.03, 0.1, 0.3, 1.0, 5.0, 15.0],\n 8: [1.0, 15.0],\n 9: [0.02, 0.06, 0.2, 0.6, 3.0, 10.0],\n 10: [2.5, 5.0],\n 11: [2.0, 4.0],\n 12: [2.0, 4.0],\n 13: [0.02, 0.06, 0.2, 0.6, 3.0, 10.0],\n 14: [2.5, 5.0],\n 15: [2.0, 4.0]\n }\n _dic_piE = {\n 1: [\n 1.0, 3.0, 8.0, 5.0, 13.0, 4.0, 5.0, 7.0, 11.0, 19.0, 0.5, 11.0,\n 27.0, 490.0\n ],\n 2: [\n 1.0, 2.0, 8.0, 4.0, 14.0, 4.0, 8.0, 10.0, 18.0, 19.0, 0.2, 10.0,\n 28.0, 510.0\n ],\n 3: [\n 1.0, 2.0, 10.0, 5.0, 17.0, 6.0, 8.0, 14.0, 18.0, 25.0, 0.5, 14.0,\n 36.0, 660.0\n ],\n 4: [\n 1.0, 2.0, 10.0, 5.0, 17.0, 6.0, 8.0, 14.0, 18.0, 25.0, 0.5, 14.0,\n 36.0, 660.0\n ],\n 5: [\n 1.0, 2.0, 11.0, 5.0, 18.0, 15.0, 18.0, 28.0, 35.0, 27.0, 0.8, 14.0,\n 38.0, 610.0\n ],\n 6: [\n 1.0, 2.0, 10.0, 5.0, 16.0, 4.0, 8.0, 9.0, 18.0, 23.0, 0.3, 13.0,\n 34.0, 610.0\n ],\n 7: [\n 1.0, 2.0, 10.0, 5.0, 16.0, 4.0, 8.0, 9.0, 18.0, 23.0, 0.5, 13.0,\n 34.0, 610.0\n ],\n 8: [\n 1.0, 5.0, 21.0, 11.0, 24.0, 11.0, 30.0, 16.0, 42.0, 37.0, 0.5,\n 20.0, 53.0, 950.0\n ],\n 9: [\n 1.0, 2.0, 12.0, 6.0, 20.0, 5.0, 8.0, 9.0, 15.0, 33.0, 0.5, 18.0,\n 48.0, 870.0\n ],\n 10: [\n 1.0, 2.0, 18.0, 8.0, 30.0, 8.0, 12.0, 13.0, 18.0, 53.0, 0.5, 29.0,\n 76.0, 1400.0\n ],\n 11: [\n 1.0, 2.0, 16.0, 7.0, 28.0, 8.0, 12.0, 0.0, 0.0, 38.0, 0.5, 0.0,\n 0.0, 0.0\n ],\n 12: [\n 1.0, 3.0, 16.0, 7.0, 28.0, 8.0, 12.0, 0.0, 0.0, 38.0, 0.5, 0.0,\n 0.0, 0.0\n ],\n 13: [\n 1.0, 3.0, 14.0, 6.0, 24.0, 5.0, 7.0, 12.0, 18.0, 39.0, 0.5, 22.0,\n 57.0, 1000.0\n ],\n 14: [\n 1.0, 2.0, 19.0, 8.0, 29.0, 40.0, 65.0, 48.0, 78.0, 46.0, 0.5, 25.0,\n 66.0, 1200.0\n ],\n 15: [\n 1.0, 3.0, 14.0, 7.0, 24.0, 6.0, 12.0, 20.0, 30.0, 39.0, 0.5, 22.0,\n 57.0, 1000.0\n ]\n }\n # Resistance factor (piR) dictionary of values. The key is the\n # subcategory ID. The index in the returned list is the resistance range\n # breakpoint (breakpoint values are in _lst_breakpoints below). For\n # subcategory ID 6 and 7, the specification ID selects the correct set of\n # lists, then the style ID selects the proper list of piR values and then\n # the resistance range breakpoint is used to select\n _dic_piR = {\n 1: [1.0, 1.1, 1.6, 2.5],\n 2: [1.0, 1.1, 1.6, 2.5],\n 3: [1.0, 1.2, 1.3, 3.5],\n 5: [1.0, 1.7, 3.0, 5.0],\n 6: [[[1.0, 1.0, 1.2, 1.2, 1.6, 1.6, 1.6,\n 0.0], [1.0, 1.0, 1.0, 1.2, 1.6, 1.6, 0.0,\n 0.0], [1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.2,\n 1.6], [1.0, 1.2, 1.6, 1.6, 0.0, 0.0, 0.0, 0.0],\n [1.0, 1.6, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0], [1.0, 1.6, 1.6, 0.0, 0.0, 0.0, 0.0,\n 0.0], [1.0, 1.0, 1.1, 1.2, 1.2, 1.6, 0.0, 0.0],\n [1.0, 1.0, 1.4, 0.0, 0.0, 0.0, 0.0, 0.0]],\n [[1.0, 1.0, 1.0, 1.0, 1.2, 1.6], [1.0, 1.0, 1.0, 1.2, 1.6, 0.0],\n [1.0, 1.0, 1.2, 1.6, 0.0, 0.0], [1.0, 1.0, 1.0, 2.0, 0.0, 0.0], [\n 1.0, 1.0, 1.0, 2.0, 0.0, 0.0\n ], [1.0, 1.0, 1.2, 2.0, 0.0, 0.0], [1.0, 1.2, 1.4, 0.0, 0.0, 0.0],\n [1.0, 1.0, 1.6, 0.0, 0.0, 0.0], [1.0, 1.0, 1.2, 2.0, 0.0, 0.0], [\n 1.0, 1.0, 1.2, 1.6, 0.0, 0.0\n ], [1.0, 1.0, 1.0, 1.4, 0.0, 0.0], [1.0, 1.0, 1.0, 1.2, 0.0, 0.0],\n [1.0, 1.0, 1.4, 0.0, 0.0, 0.0], [1.0, 1.2, 1.6, 0.0, 0.0, 0.0], [\n 1.0, 1.0, 1.4, 0.0, 0.0, 0.0\n ], [1.0, 1.0, 1.2, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.4, 0.0, 0.0],\n [1.0, 1.0, 1.0, 1.4, 0.0, 0.0], [1.0, 1.0, 1.0, 1.4, 0.0, 0.0], [\n 1.0, 1.0, 1.2, 1.5, 0.0, 0.0\n ], [1.0, 1.0, 1.2, 1.6, 0.0, 0.0], [1.0, 1.0, 1.0, 1.4, 1.6, 0.0],\n [1.0, 1.0, 1.0, 1.4, 1.6, 2.0], [1.0, 1.0, 1.0, 1.4, 1.6, 2.0], [\n 1.0, 1.0, 1.4, 2.4, 0.0, 0.0\n ], [1.0, 1.0, 1.2, 2.6, 0.0,\n 0.0], [1.0, 1.0, 1.0, 0.0, 0.0,\n 0.0], [1.0, 1.0, 1.0, 0.0, 0.0,\n 0.0], [1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [\n 1.0, 1.2, 1.4, 0.0, 0.0, 0.0\n ], [1.0, 1.0, 1.2, 1.6, 0.0,\n 0.0], [1.0, 1.0, 1.0, 1.6, 0.0, 0.0], [\n 1.0, 1.0, 1.4, 0.0, 0.0, 0.0\n ], [1.0, 1.2, 1.5, 0.0, 0.0,\n 0.0], [1.0, 1.2, 0.0, 0.0, 0.0, 0.0]]],\n 7: [[[1.0, 1.2, 1.2, 1.6, 0.0, 0.0], [1.0, 1.0, 1.2, 1.6, 0.0, 0.0],\n [1.0, 1.0, 1.2, 1.2, 1.6, 0.0], [1.0, 1.0, 1.0, 1.1, 1.2, 1.6],\n [1.0, 1.0, 1.0, 1.0, 1.2, 1.6], [1.0, 1.0, 1.0, 1.0, 1.2, 1.6]],\n [[1.0, 1.2, 1.6, 0.0, 0.0, 0.0], [1.0, 1.2, 1.6, 0.0, 0.0, 0.0],\n [1.0, 1.0, 1.2, 1.6, 0.0, 0.0], [1.0, 1.0, 1.1, 1.2, 1.4, 0.0],\n [1.0, 1.0, 1.0, 1.2, 1.6, 0.0], [1.0, 1.0, 1.0, 1.1, 1.4, 0.0]]],\n 9: [1.0, 1.4, 2.0],\n 10: [1.0, 1.1, 1.4, 2.0, 2.5, 3.5],\n 11: [1.0, 1.4, 2.0],\n 12: [1.0, 1.4, 2.0],\n 13: [1.0, 1.1, 1.2, 1.4, 1.8],\n 14: [1.0, 1.1, 1.2, 1.4, 1.8],\n 15: [1.0, 1.1, 1.2, 1.4, 1.8]\n }\n # Dictionary containing the number of element breakpoints for determining\n # the resistance factor list to use.\n _dic_breakpoints = {\n 1: [1.0E5, 1.0E6, 1.0E7],\n 2: [1.0E5, 1.0E6, 1.0E7],\n 3: [100.0, 1.0E5, 1.0E6],\n 5: [1.0E4, 1.0E5, 1.0E6],\n 6: [[500.0, 1.0E3, 5.0E3, 7.5E3, 1.0E4, 1.5E4, 2.0E4],\n [100.0, 1.0E3, 1.0E4, 1.0E5, 1.5E5, 2.0E5]],\n 7: [500.0, 1.0E3, 5.0E3, 1.0E4, 2.0E4],\n 9: [2.0E3, 5.0E3],\n 10: [1.0E4, 2.0E4, 5.0E4, 1.0E5, 2.0E5],\n 11: [2.0E3, 5.0E3],\n 12: [2.0E3, 5.0E3],\n 13: [5.0E4, 1.0E5, 2.0E5, 5.0E5],\n 14: [5.0E4, 1.0E5, 2.0E5, 5.0E5],\n 15: [1.0E4, 5.0E4, 2.0E5, 1.0E6]\n }\n _dic_piV = {\n 9: [1.1, 1.05, 1.0, 1.1, 1.22, 1.4, 2.0],\n 10: [1.1, 1.05, 1.0, 1.1, 1.22, 1.4, 2.0],\n 11: [1.1, 1.05, 1.0, 1.1, 1.22, 1.4, 2.0],\n 12: [1.1, 1.05, 1.0, 1.1, 1.22, 1.4, 2.0],\n 13: [1.0, 1.05, 1.2],\n 14: [1.0, 1.05, 1.2],\n 15: [1.0, 1.05, 1.2]\n }\n _dic_piC = {10: [2.0, 1.0, 3.0, 1.5], 12: [2.0, 1.0]}\n _msg = ''\n\n # Calculate the base hazard rate.\n if attributes['subcategory_id'] == 2:\n _ref_temp = _dic_ref_temp[attributes['subcategory_id']][attributes[\n 'specification_id']]\n _f0 = _dic_factors[attributes['subcategory_id']][attributes[\n 'specification_id']][0]\n _f1 = _dic_factors[attributes['subcategory_id']][attributes[\n 'specification_id']][1]\n _f2 = _dic_factors[attributes['subcategory_id']][attributes[\n 'specification_id']][2]\n _f3 = _dic_factors[attributes['subcategory_id']][attributes[\n 'specification_id']][3]\n _f4 = _dic_factors[attributes['subcategory_id']][attributes[\n 'specification_id']][4]\n _f5 = _dic_factors[attributes['subcategory_id']][attributes[\n 'specification_id']][5]\n elif attributes['subcategory_id'] not in [4, 8]:\n _ref_temp = _dic_ref_temp[attributes['subcategory_id']]\n _f0 = _dic_factors[attributes['subcategory_id']][0]\n _f1 = _dic_factors[attributes['subcategory_id']][1]\n _f2 = _dic_factors[attributes['subcategory_id']][2]\n _f3 = _dic_factors[attributes['subcategory_id']][3]\n _f4 = _dic_factors[attributes['subcategory_id']][4]\n _f5 = _dic_factors[attributes['subcategory_id']][5]\n\n if attributes['subcategory_id'] == 4:\n attributes['lambda_b'] = 0.00006\n elif attributes['subcategory_id'] == 8:\n attributes['lambda_b'] = _dic_factors[attributes['subcategory_id']][\n attributes['type_id'] - 1]\n else:\n attributes['lambda_b'] = _f0 * exp(_f1 * (\n (attributes['temperature_active'] + 273.0) /\n _ref_temp))**_f2 * exp(((attributes['power_ratio'] / _f3) * (\n (attributes['temperature_active'] + 273.0) / 273.0)**_f4)**_f5)\n\n if attributes['lambda_b'] <= 0.0:\n _msg = _msg + 'RAMSTK WARNING: Base hazard rate is 0.0 when ' \\\n 'calculating resistor, hardware ID: ' \\\n '{0:d}'.format(attributes['hardware_id'])\n\n # Calculate the resistance factor (piR).\n if attributes['subcategory_id'] not in [4, 8]:\n _index = -1\n if attributes['subcategory_id'] == 6:\n _breaks = _dic_breakpoints[attributes['subcategory_id']][\n attributes['specification_id'] - 1]\n else:\n _breaks = _dic_breakpoints[attributes['subcategory_id']]\n\n for _index, _value in enumerate(_breaks):\n _diff = _value - attributes['n_elements']\n if len(_breaks) == 1 and _diff < 0:\n break\n elif _diff >= 0:\n break\n\n if attributes['subcategory_id'] in [6, 7]:\n attributes['piR'] = _dic_piR[attributes['subcategory_id']][\n attributes['specification_id'] - 1][attributes['family_id'] -\n 1][_index + 1]\n elif attributes['subcategory_id'] not in [4, 8]:\n attributes['piR'] = _dic_piR[attributes['subcategory_id']][_index +\n 1]\n\n # Determine the quality factor (piQ).\n attributes['piQ'] = _dic_piQ[attributes['subcategory_id']][\n attributes['quality_id'] - 1]\n\n if attributes['piQ'] <= 0.0:\n _msg = _msg + 'RAMSTK WARNING: piQ is 0.0 when calculating ' \\\n 'resistor, hardware ID: {0:d}'.format(attributes['hardware_id'])\n\n # Determine the environmental factor (piE).\n attributes['piE'] = _dic_piE[attributes['subcategory_id']][\n attributes['environment_active_id'] - 1]\n\n if attributes['piE'] <= 0.0:\n _msg = _msg + 'RAMSTK WARNING: piE is 0.0 when calculating ' \\\n 'resistor, hardware ID: {0:d}'.format(attributes['hardware_id'])\n\n # Calculate the temperature factor (piT).\n if attributes['subcategory_id'] == 4:\n attributes['temperature_case'] = (attributes['temperature_active'] +\n 55.0 * attributes['power_ratio'])\n attributes['piT'] = exp(-4056.0 * (\n (1.0 / (attributes['temperature_case'] + 273.0)) - 1.0 / 298.0))\n\n # Calculate the taps factor (piTAPS).\n if attributes['subcategory_id'] in [9, 10, 11, 12, 13, 14, 15]:\n attributes['piTAPS'] = (attributes['n_elements']**1.5 / 25.0) + 0.792\n\n # Calculate the voltage factor (piV).\n if attributes['subcategory_id'] > 8:\n _index = -1\n if attributes['subcategory_id'] in [9, 10, 11, 12]:\n _breaks = [0.1, 0.2, 0.6, 0.7, 0.8, 0.9]\n elif attributes['subcategory_id'] in [13, 14, 15]:\n _breaks = [0.8, 0.9]\n for _index, _value in enumerate(_breaks):\n _diff = _value - attributes['voltage_ratio']\n if len(_breaks) == 1 and _diff < 0.0:\n break\n elif _index == 0 and _diff >= 0.0:\n break\n elif _diff >= 0:\n break\n attributes['piV'] = _dic_piV[attributes['subcategory_id']][_index]\n\n # Determine the consruction class factor (piC).\n if attributes['subcategory_id'] in [10, 12]:\n attributes['piC'] = _dic_piC[attributes['subcategory_id']][\n attributes['construction_id'] - 1]\n\n # Calculate the active hazard rate.\n attributes['hazard_rate_active'] = (\n attributes['lambda_b'] * attributes['piQ'] * attributes['piE'])\n if attributes['subcategory_id'] == 4:\n attributes['hazard_rate_active'] = (\n attributes['hazard_rate_active'] * attributes['piT'] *\n attributes['n_elements'])\n elif attributes['subcategory_id'] in [9, 11, 13, 14, 15]:\n attributes['hazard_rate_active'] = (\n attributes['hazard_rate_active'] * attributes['piTAPS'] *\n attributes['piR'] * attributes['piV'])\n elif attributes['subcategory_id'] in [10, 12]:\n attributes['hazard_rate_active'] = (\n attributes['hazard_rate_active'] * attributes['piTAPS'] *\n attributes['piC'] * attributes['piR'] * attributes['piV'])\n elif attributes['subcategory_id'] != 8:\n attributes['hazard_rate_active'] = (\n attributes['hazard_rate_active'] * attributes['piR'])\n\n return attributes, _msg","def ComputeEAvailable(self):\r\n pass","def get_occupant_room_load_for_cooling_balanced(\n l_cs: np.ndarray, l_cl: np.ndarray, q_d_trs_prt: np.ndarray) -> (np.ndarray, np.ndarray):\n\n l_d_cs = np.where(l_cs[0:5] > 0.0, l_cs[0:5] - q_d_trs_prt, 0.0)\n l_d_cl = l_cl[0:5]\n\n return np.clip(l_d_cs, 0.0, None), np.clip(l_d_cl, 0.0, None)","def test_getThermalExpansionFactorConservedMassByLinearExpansionPercent(self):\n hotTemp = 700.0\n dLL = self.component.material.linearExpansionFactor(\n Tc=hotTemp, T0=self._coldTemp\n )\n ref = 1.0 + dLL\n cur = self.component.getThermalExpansionFactor(Tc=hotTemp)\n self.assertAlmostEqual(cur, ref)","def __call__(self, auctioneer):\n curr_bid = auctioneer.current_bid\n bid_price = curr_bid * self._bid_increase_perc\n if bid_price <= self._budget and self.get_bid_probability() > 0.3:\n self._highest_bid = bid_price\n return bid_price\n return 0","def convergence_check(self):\n air = self.air_alias.val\n flue_gas = self.fuel_alias.val + '_fg'\n fuel = self.fuel_alias.val\n\n for c in self.outl:\n if not c.fluid.val_set[air]:\n if c.fluid.val[air] > 0.95:\n c.fluid.val[air] = 0.95\n if c.fluid.val[air] < 0.5:\n c.fluid.val[air] = 0.5\n\n if not c.fluid.val_set[flue_gas]:\n if c.fluid.val[flue_gas] > 0.5:\n c.fluid.val[flue_gas] = 0.5\n if c.fluid.val[flue_gas] < 0.05:\n c.fluid.val[flue_gas] = 0.05\n\n if not c.fluid.val_set[fuel]:\n if c.fluid.val[fuel] > 0:\n c.fluid.val[fuel] = 0\n\n c.target.propagate_fluid_to_target(c, c.target)\n\n for i in self.inl:\n if i.m.val_SI < 0 and not i.m.val_set:\n i.m.val_SI = 0.01\n\n for c in self.outl:\n if c.m.val_SI < 0 and not c.m.val_set:\n c.m.val_SI = 10\n c.target.propagate_fluid_to_target(c, c.target)\n\n if self.lamb.val < 1 and not self.lamb.is_set:\n self.lamb.val = 2","def measure_allocation_diversity_bounds_errors(self, slots_assegnation_probabilities, LP_news_pool, iter=5000):\n for tech in [\"rand_1\", \"rand_2\", \"rand_3\"]:\n max_errors_per_iter = []\n for k in range(iter):\n tmp_slots_assegnation_probabilities = []\n for elem in slots_assegnation_probabilities:\n tmp_slots_assegnation_probabilities.append(elem.copy())\n constraints_error = [0] * len(self.categories)\n promenance_per_category = [0] * len(self.categories)\n result = self.__de_randomize_LP(LP_news_pool, tmp_slots_assegnation_probabilities, tech)\n for i in range(len(result)):\n category_index = self.categories.index(result[i].news_category)\n promenance_per_category[category_index] += self.real_slot_promenances[i]\n\n for i in range(len(promenance_per_category)):\n if promenance_per_category[i] < self.B[i] * -1:\n constraints_error[i] += (self.B[i] * -1 - promenance_per_category[i]) / (self.B[i] * -1)\n\n max_errors_per_iter.append(np.mean(constraints_error))\n if tech == \"rand_1\":\n self.rand_1_errors += max_errors_per_iter\n elif tech == \"rand_2\":\n self.rand_2_errors += max_errors_per_iter\n else:\n self.rand_3_errors += max_errors_per_iter","def cost(self):\n lg = len(self.guarantees.cnf)\n la = len(self.assumptions.cnf)\n\n \"\"\"heuristic\n Low: guarantees while assuming little (assumption set is bigger)\n High: guarantees while assuming a lot (assumption set is smaller)\"\"\"\n\n return la / lg","def sum_availability(val, quant) -> float:\n return val + qty_available(quant)","def _estimate_elasticsearch_requirement(\n instance: Instance,\n desires: CapacityDesires,\n working_set: float,\n reads_per_second: float,\n max_rps_to_disk: int,\n zones_per_region: int = 3,\n copies_per_region: int = 3,\n) -> CapacityRequirement:\n # Keep half of the cores free for background work (merging mostly)\n needed_cores = math.ceil(sqrt_staffed_cores(desires) * 1.5)\n # Keep half of the bandwidth available for backup\n needed_network_mbps = simple_network_mbps(desires) * 2\n\n needed_disk = math.ceil(\n (1.0 / desires.data_shape.estimated_compression_ratio.mid)\n * desires.data_shape.estimated_state_size_gib.mid\n * copies_per_region,\n )\n\n # Rough estimate of how many instances we would need just for the the CPU\n # Note that this is a lower bound, we might end up with more.\n needed_cores = math.ceil(\n max(1, needed_cores // (instance.cpu_ghz / desires.core_reference_ghz))\n )\n rough_count = math.ceil(needed_cores / instance.cpu)\n\n # Generally speaking we want fewer than some number of reads per second\n # hitting disk per instance. If we don't have many reads we don't need to\n # hold much data in memory.\n instance_rps = max(1, reads_per_second // rough_count)\n disk_rps = instance_rps * _es_io_per_read(max(1, needed_disk // rough_count))\n rps_working_set = min(1.0, disk_rps / max_rps_to_disk)\n\n # If disk RPS will be smaller than our target because there are no\n # reads, we don't need to hold as much data in memory\n needed_memory = min(working_set, rps_working_set) * needed_disk\n\n # Now convert to per zone\n needed_cores = needed_cores // zones_per_region\n needed_disk = needed_disk // zones_per_region\n needed_memory = int(needed_memory // zones_per_region)\n logger.debug(\n \"Need (cpu, mem, disk, working) = (%s, %s, %s, %f)\",\n needed_cores,\n needed_memory,\n needed_disk,\n working_set,\n )\n\n return CapacityRequirement(\n requirement_type=\"elasticsearch-data-zonal\",\n core_reference_ghz=desires.core_reference_ghz,\n cpu_cores=certain_int(needed_cores),\n mem_gib=certain_float(needed_memory),\n disk_gib=certain_float(needed_disk),\n network_mbps=certain_float(needed_network_mbps),\n context={\n \"working_set\": min(working_set, rps_working_set),\n \"rps_working_set\": rps_working_set,\n \"disk_slo_working_set\": working_set,\n \"replication_factor\": copies_per_region,\n \"compression_ratio\": round(\n 1.0 / desires.data_shape.estimated_compression_ratio.mid, 2\n ),\n \"read_per_second\": reads_per_second,\n },\n )","def erfc(x):\n return 0.0","def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t, level):\n # note \"amt\" has units of AMOUNT not RATE (resource, not resource per second)\n sign = np.sign(amt)\n # are we storing or providing?\n #print('DEBUGG supposed current level:', level)\n if sign < 0:\n # we are being asked to consume some\n cap, meta = self.get_capacity(meta, raven_vars, dispatch, t)\n available_amount = cap[res] - level\n #print('Supposed Capacity, Only calculated ins sign<0 (being asked to consumer)',cap)\n else:\n # we are being asked to produce some\n available_amount = level\n # the amount we can consume is the minimum of the requested or what's available\n delta = sign * min(available_amount, abs(amt))\n return {res: delta}, meta","def load_factor(self):\n return round(self._n / self._size, 2)","def recommend_contract(meter_load, percentile=100.0):\n\n load_value = np.nanpercentile(meter_load[meter_load != 0], percentile)\n\n for contract in contracts_available:\n if load_value < contract:\n return contract/kW\n\n return contracts_available[-1]/kW","def calc_annual_electric_savings (self):\n costs = self.comp_specs['diesel generator o&m']\n\n for kW in costs.keys():\n try:\n if self.average_load < int(kW):\n maintenance = self.comp_specs['diesel generator o&m'][kW]\n break\n except ValueError:\n maintenance = self.comp_specs['diesel generator o&m'][kW]\n\n self.baseline_generation_cost = maintenance + \\\n (self.pre_intertie_generation_fuel_used * self.diesel_prices)\n\n maintenance = self.capital_costs * \\\n (self.comp_specs['percent o&m'] / 100.0)\n self.proposed_generation_cost = maintenance + \\\n self.intertie_offset_generation_fuel_used * \\\n self.intertie_diesel_prices\n self.annual_electric_savings = self.baseline_generation_cost -\\\n self.proposed_generation_cost\n #~ print len(self.annual_electric_savings)\n #~ print 'self.annual_electric_savings',self.annual_electric_savings","def trucks_required(self) -> int:\n daily_demand = (\n self.total_demand() / self.operations.productivity.working_days_per_year\n )\n trucks_required = np.ceil(\n daily_demand\n / (self.operations.avg_vol_per_lift() * self.inputs.lifts_per_truck_day)\n )\n return trucks_required","def balanceFactor(self):\n leftHeight = self.left.height if self.left != None else 0\n rightHeight = self.right.height if self.right != None else 0\n return rightHeight - leftHeight","def initial_cash_balance(self) -> float:\n return self.buy_budget * len(self.stocks)","def bcRange(self):\n\t\treturn fabs(self.Upper - self.Lower)","def get_expected_cost(self):","def calculate_br_down_metric(br_down):\n if br_down < 1:\n br_down = 1\n min_baud = 1200\n max_baud = 38400\n\n num = np.log(br_down) - np.log(min_baud)\n den = np.log(max_baud) - np.log(min_baud)\n\n return (num / den + 0.1).clip(min=0, max=1)","def test_cpu_limitation(self):\n expected_dict = self.calculate_expected_values(\n load_dict=self.load_dict\n )\n assert sla_helpers.load_vm_and_check_the_load(\n load_dict=self.load_dict, expected_values=expected_dict\n )","def _get_new_capacity(self):\n for prime in primes:\n if prime > 2 * self.size:\n return prime\n raise ValueError(\"Error: Table size overflow!\")","def load_factor_d(self, data):\n lf_d = np.zeros((data['nr_of_fueltypes']))\n\n # Get day with maximum demand (in percentage of year)\n peak_d_demand = self.fuels_peak_d\n\n # Iterate fueltypes to calculate load factors for each fueltype\n for k, fueldata in enumerate(self.rs_fuels_tot_enduses_d):\n average_demand = np.sum(fueldata) / 365 # Averae_demand = yearly demand / nr of days\n\n if average_demand != 0:\n lf_d[k] = average_demand / peak_d_demand[k] # Calculate load factor\n\n lf_d = lf_d * 100 # Convert load factor to %\n\n return lf_d","def calc_electric_diesel_reduction (self):\n gen_eff = self.cd[\"diesel generation efficiency\"]\n\n self.electric_diesel_reduction = self.net_generation_wind / gen_eff\n\n electric_diesel = self.generation/gen_eff\n if self.electric_diesel_reduction > electric_diesel:\n self.electric_diesel_reduction = electric_diesel","def calc_GT_operation_partload(wdot_W, gt_size_W, eta0, m0_exhaust_from_GT_kgpers, fuel_type):\n assert wdot_W <= gt_size_W\n\n if fuel_type == 'NG':\n exitT = CC_EXIT_T_NG\n LHV = LHV_NG\n else:\n exitT = CC_EXIT_T_BG\n LHV = LHV_BG\n\n part_load_factor = (wdot_W + 1) / gt_size_W # avoid calculation errors # TODO: reference?\n if part_load_factor < GT_MIN_PART_LOAD:\n raise ValueError('The load (', wdot_W, ')is lower than minimum part load (', gt_size_W * GT_MIN_PART_LOAD, ').')\n\n eta = (0.4089 + 0.9624 * part_load_factor - 0.3726 * part_load_factor ** 2) * eta0 # (4.12) [C. Weber, 2008]_\n # mdot = (0.9934 + 0.0066 * part_load_factor) * mdot0\n T_exhaust_GT_K = (0.7379 + 0.2621 * part_load_factor) * exitT # (4.14) [C. Weber, 2008]_\n m_fuel_kgpers = wdot_W / (eta * LHV)\n\n if fuel_type == 'NG':\n m_exhaust_GT_kgpers = (103.7 * 44E-3 + 196.2 * 18E-3 + 761.4 * 28E-3 + 200.5 * 32E-3 * (CC_AIRRATIO - 1) +\n 200.5 * 3.773 * 28E-3 * (CC_AIRRATIO - 1)) * m_fuel_kgpers / 1.8156 # TODO: reference?\n\n else:\n m_exhaust_GT_kgpers = (98.5 * 44E-3 + 116 * 18E-3 + 436.8 * 28E-3 + 115.5 * 32E-3 * (CC_AIRRATIO - 1) + \\\n 115.5 * 3.773 * 28E-3 * (CC_AIRRATIO - 1)) * m_fuel_kgpers / 2.754 # TODO: reference?\n\n return eta, m_exhaust_GT_kgpers, T_exhaust_GT_K, m_fuel_kgpers","def calculate_risk_tol(*args):\n global total_score\n risk_tol_start = 0.0\n\n for risk_per_pg in risk_tol_per_qs.iterkeys():\n try:\n risk_tol_start = risk_tol_start + risk_tol_per_qs[risk_per_pg][-1] # this is the last item in the list of each information in the page\n except IndexError:\n pass\n total_score = risk_tol_start","def err_func(x,rv,valore,specn,lcrop,models='da2014'):\n tmp = tmp_func(x[0], x[1], rv, specn, lcrop, models)\n if tmp != 1: return abs(tmp[3]-(valore+1.)) #this is quantity that gets minimized \n else: return 1E30","def budget_used(self):\n return int(self.total_spent() / self.budget() * 100.0)","def real_reduce(self, bound, large_constant):\n approximation_matrix = self.generate_approximation_matrix(large_constant)\n LLL_matrix = self.generate_LLL_matrix(approximation_matrix)\n GS_matrix = self.generate_GS_matrix(LLL_matrix) \n minimal_vector_bound = self.calculate_minimal_vector_bound(large_constant, LLL_matrix, GS_matrix)\n S, T = self.calculate_S_and_T(self.coefficients[\"Z_bounds\"])\n print('ST',T**2 + S)\n new_bound = self.real_reduce_new_bound(large_constant, minimal_vector_bound, S, T)\n print('diff_bound',new_bound)\n return new_bound","def single_center_de_needs(servers: int, DE_capacity: int) -> int:\n\n return math.ceil(servers / DE_capacity)","def gss(self,LB,UB,tol,itr):\n \n GoldenRatio = (math.sqrt(5) + 1) / 2\n \n iterations=0\n gss=[]\n gss_x=[LB,UB]\n \n c = UB - (UB - LB) / GoldenRatio\n d = LB + (UB - LB) / GoldenRatio\n while abs(UB - LB) > tol and iterations < itr:\n if self.Func(c) < self.Func(d):\n UB = d\n gss_x.append(UB)\n iterations+=1\n else:\n LB = c\n \n gss_x.append(LB)\n iterations+=1\n c = UB - (UB - LB) / GoldenRatio\n d = LB + (UB - LB) / GoldenRatio\n \n \n #print(\" best at %.15f\"% ((UB + LB)/2) , \"itr = \",iterations)\n gss.append(gss_x)\n gss.append((LB+UB)/2)\n gss.append(iterations)\n \n return gss","def bottles_required(beer_recipe):\r\n bottles_demanded = upcoming_future_prediction(beer_recipe)\r\n bottles_ready = delivery_information[beer_recipe]\r\n if bottles_ready > bottles_demanded:\r\n bottles_needed = 0\r\n else:\r\n bottles_needed = bottles_demanded - bottles_ready\r\n return int(bottles_demanded), bottles_ready, int(bottles_needed)"],"string":"[\n \"def get_load_factor(self):\\n # Your code here\\n return self.total_items / self.capacity\",\n \"def internal_external_canopy_heat_capacity(lumped_cover_heat_capacity: float) -> float:\\n return 0.1 * lumped_cover_heat_capacity\",\n \"def calculate_lower_boundary(self, divisor):\\n\\n # see how low you can go\\n quotas = [0] * self.states\\n fair_shares = [0] * self.states\\n counter = 0\\n lowest_divisor = 0\\n prev_divisor = 0\\n estimator = 1000000000\\n while counter < 1000:\\n for i, population in enumerate(self.populations):\\n if divisor is None or population is None:\\n return None\\n quotas[i] = population / divisor\\n fair_shares[i] = math.floor(quotas[i])\\n if sum(fair_shares) != self.num_seats:\\n estimator = estimator / 10\\n prev_divisor = divisor\\n divisor = lowest_divisor - estimator\\n else:\\n lowest_divisor = divisor\\n divisor = prev_divisor - estimator\\n if lowest_divisor == divisor:\\n break\\n counter += 1\\n return math.ceil(lowest_divisor * 1000) / 1000\",\n \"def calculate_upper_boundary(self, divisor):\\n\\n # see how high you can go\\n quotas = [0] * self.states\\n fair_shares = [0] * self.states\\n counter = 0\\n highest_divisor = 0\\n prev_divisor = 0\\n estimator = 1000000000\\n while counter < 1000:\\n for i, population in enumerate(self.populations):\\n if divisor is None:\\n return None\\n quotas[i] = population / divisor\\n fair_shares[i] = math.floor(quotas[i])\\n if sum(fair_shares) != self.num_seats:\\n estimator = estimator / 10\\n prev_divisor = divisor\\n divisor = highest_divisor + estimator\\n else:\\n highest_divisor = divisor\\n divisor = prev_divisor + estimator\\n if highest_divisor == divisor:\\n break\\n counter += 1\\n return math.floor(highest_divisor * 1000) / 1000\",\n \"def get_amount_in(amount_out, reserve_in, reserve_out):\\n assert amount_out > 0\\n assert reserve_in > 0 and reserve_out > 0\\n numerator = reserve_in*amount_out*1000\\n denominator = (reserve_out - amount_out)*997\\n return float(numerator/denominator + 1)\",\n \"def penalty_calc(self):\\n self.p_budget = (self.tx_oma_min - self.rx_unstressed_sensitivity - self.fiber_conn_loss)*self.l_1\\n\\n # fiber attenuation,\\n self.p_atten = self.alpha*self.length # column B\\n\\n # calculate bandwidth for RIN test (exclude transmitter)\\n rin_inverse_bw = np.sqrt(np.square(1.0/self.bw_cd) + np.square(1.0/self.bw_md) + (0.477/(self.rx_bw**2))*self.l_1)\\n rin_bw = 1.0 / rin_inverse_bw\\n\\n # see FC-MSQS-2 equation B.47 in Annex B.4 for the following k_rin = math.sqrt(2.0/math.pi)*erfinv(0.8)\\n k_rin = 0.7\\n\\n # v_rin,\\n self.v_rin = (k_rin*1E6*(self.rin_test_isi**2)*rin_bw*\\n math.pow(10.0,0.1*self.rin)) # column AK\\n\\n # Prin,\\n print('v_rin: ', self.v_rin)\\n print('Q: ',self.Q)\\n print('isi_dj_refl_closed :', self.isi_dj_refl_closed)\\n self.p_rin = -10.0*np.log10(np.sqrt(1.0-np.multiply(self.v_rin, np.square(self.Q/self.isi_dj_refl_closed)))) # column R\\n print(\\\"P_rin : \\\", self.p_rin)\\n self.beta = (3.14159E-6*self.speedup*self.br_nominal *self.delta_lambda*self.d1*self.length) # column O\\n self.sigma_mpn = (self.k_mpn/math.sqrt(2.0)*(self.l_1 -np.exp(-np.square(self.beta)))) # column P\\n self.p_mpn = (-10.0*np.log10(np.sqrt(self.l_1 - (self.Q**2)*np.square(self.sigma_mpn)))) # column Q\\n self.p_blw = (-10.0*math.log10(math.sqrt(1.0- ((self.Q*self.sigma_blw)/ self.isi_tp4_rx)**2))*self.l_1) # cell T13\\n self.p_reflection = -10.0*np.log10(self.isi_reflection) # column N\\n self.v_mn = (((1.0-math.pow(10.0,-0.2*self.pmn))/ (self.Q)**2)*self.l_1) # cell AG7\\n print(\\\"isi_center : \\\", self.isi_center)\\n\\n self.p_isi_center = -10.0*np.log10(self.isi_center) # column J\\n\\n self.p_isi_corners = (-10.0*np.log10(self.isi_corners) - self.p_isi_center) # column K\\n self.p_isi_dj_center = (-10.0*np.log10(self.isi_dj_refl_closed) - self.p_isi_center) # column L\\n self.p_isi_dj_corners = (-10.0*np.log10(self.isi_dj_corners) -self.p_isi_center -self.p_isi_corners) # column M\\n\\n\\n # calculate the \\\"cross\\\" penalty contribution, column S\\n arg1 = ((self.sigma_blw**2 + self.v_rin)/ np.square(self.isi_dj_refl_closed))\\n arg2 = self.l_1 - (self.Q**2)*(arg1 + self.v_mn + np.square(self.sigma_mpn))\\n arg3 = (-10.0*np.log10(np.multiply(self.isi_dj_refl_closed, np.sqrt(arg2))))\\n self.p_cross_center = ( # column S\\n arg3\\n - self.p_blw # cell T13\\n - self.p_isi_center # column J\\n - self.p_isi_dj_center # column L\\n - self.p_mpn # column Q\\n - self.p_reflection # column N\\n - self.p_rin # column R\\n - self.pmn*self.l_1) # cell G13\\n print('p_isi_center: ', self.p_isi_center)\\n\\n # calculate the total power budget evaluated at the center of the eye\\n self.p_total_center = ( # column T\\n self.p_isi_center # column J\\n + self.p_isi_dj_center # column L\\n + self.p_atten # column B\\n + self.p_mpn # column Q\\n + self.p_reflection # column N\\n + self.p_rin # column R\\n + self.p_cross_center # column S\\n + self.pmn*self.l_1) # cell G13\\n # calculate the total power budget evaluated at the corner of the eye\\n self.p_total_corners = (\\n self.p_isi_center # column J\\n + self.p_isi_corners # column K\\n + self.p_atten # column B\\n + self.p_mpn # column Q\\n + self.p_reflection # column N\\n + self.p_rin # column R\\n + self.p_cross_center # column S\\n + self.pmn*self.l_1 # cell G13\\n + self.p_isi_dj_corners)# column M\\n\\n # receiver stressed sensitivity\\n self.margin = ( self.p_budget\\n - self.p_total_center) # column W\\n\\n self.rx_stressed_sensitivity = (\\n self.tx_oma_min*self.l_1\\n - self.chil\\n - self.p_mpn\\n - self.p_reflection\\n - self.p_rin\\n - 0.5*self.p_cross_center\\n - self.pmn*self.l_1\\n - self.margin[self.lnum//2]*self.l_1)\\n\\n\\n # end of GbE10.penalty_calc\\n #======================================================================+\",\n \"def ComputeRegenerativeBraking(self):\\r\\n pass\",\n \"def _load_factor(self):\\n return self.size / len(self.buckets)\",\n \"def calculate_large_constant(self, bound, real_reduction_iterations):#factor):\\n minimum_exponent = round(90/(real_reduction_iterations-1))#math.ceil(math.log(bound, 10) * factor)\\n \\n return ZZ(10 ** minimum_exponent)\",\n \"def upper_bound(stock):\\n counter=0\\n for i in stock_price(stock):\\n if i >= resistance(stock):\\n counter+=1\\n return counter\",\n \"def get_additional_ball_capacity(self):\\n return 999\",\n \"def test_capacity_factor(pudl_out_eia):\\n print(\\\"\\\\nCalculating generator capacity factors...\\\")\\n cf = pudl_out_eia.capacity_factor()\\n print(f\\\" capacity_factor: {len(cf)} records\\\")\",\n \"def required_points(self):\\n req_points = self.min_performance * self.initial_available_points()\\n return np.maximum(0, np.int64(np.ceil(req_points)))\",\n \"def calc_low_energy_bulb_ratio(lighting_outlets_total, lighting_outlets_low_energy):\\n return int(100 * float(lighting_outlets_low_energy) / lighting_outlets_total + 0.5) / 100.0\",\n \"def get_fuel_total_saved (self):\\n #~ print self.lost_heat_recovery\\n #~ print self.intertie_offset_generation_fuel_used\\n #~ print self.pre_intertie_generation_fuel_used\\n #~ gen_eff = self.cd[\\\"diesel generation efficiency\\\"]\\n #~ fuel_used = self.intertie_offset_generation / gen_eff\\n\\n generation_diesel_reduction = \\\\\\n np.array(self.pre_intertie_generation_fuel_used\\\\\\n [:self.actual_project_life])\\n return - np.array(self.lost_heat_recovery[:self.actual_project_life]) +\\\\\\n generation_diesel_reduction\",\n \"def get_additional_ball_capacity(cls):\\n return 999\",\n \"def load_factor_d_non_peak(self, data):\\n lf_d = np.zeros((data['nr_of_fueltypes']))\\n\\n # Iterate fueltypes to calculate load factors for each fueltype\\n for k, fueldata in enumerate(self.rs_fuels_tot_enduses_d):\\n\\n average_demand = sum(fueldata) / 365 # Averae_demand = yearly demand / nr of days\\n max_demand_d = max(fueldata)\\n\\n if max_demand_d != 0:\\n lf_d[k] = average_demand / max_demand_d # Calculate load factor\\n\\n lf_d = lf_d * 100 # Convert load factor to %\\n\\n return lf_d\",\n \"def test_efficiency(self):\\n effs = self.gr.calculate_global_efficiencies()\\n ans = [a/6 for a in [1.5, 2., 1.5]]\\n self.assertListEqual(effs.values(), ans)\\n\\n E = self.gr.global_efficiency()\\n self.assertEqual(E, sum(ans))\\n\\n v_min = (E - (1/3)) / E\\n mx, v = self.gr.vulnerability()\\n # the middle node (2) is the most vulnerable\\n self.assertEqual(mx, (2, v_min))\",\n \"def calc_fuel_given_ore(ore, recipes):\\n\\n upper_bound = None\\n lower_bound = 469536 # assume ore needed for 1 fuel is good lower bound\\n\\n\\n while lower_bound + 1 != upper_bound:\\n if upper_bound is None:\\n guess = lower_bound * 2\\n else:\\n guess = (upper_bound + lower_bound) // 2\\n \\n ore_needed = calc_ore_needed(guess, recipes)\\n if ore_needed > ore:\\n upper_bound = guess\\n else:\\n lower_bound = guess\\n \\n return lower_bound\",\n \"def _cost_refueling(self):\\n if self.number_of_courses % self.refueling_frequency == 0 & self.number_of_courses != 0:\\n lowest_amount = self.refueling_liter_range[0] # take a minimum value\\n highest_amount = self.refueling_liter_range[1] # take a maximum value\\n refueled_petrol = randint(lowest_amount, highest_amount)\\n cost = refueled_petrol * self.petrol_cost\\n return cost\\n else:\\n return 0\",\n \"def E_BE(self,s,l):\\n if s>l: return self.E_BE(l,s)\\n if s == 0: return -100\\n if l == 0: return -100\\n delta = (((s+l)/l)-(l/s)) \\n return delta-delta%self._tau\",\n \"def load_factor(self) -> float:\\n return self.filled_count / self.table_size\",\n \"def getRepairCapacity(self):\\n return int(self.myDesign.getSYCRequired() * (1-(self.strength/100.0)))\",\n \"def get_allowable_fraction_without(mem_to_reserve, cuda_device_index):\\n current_free = get_free_gpu_memory(cuda_device_index)\\n allowable = current_free - mem_to_reserve # 1GB\\n allowable_fraction = allowable / current_free\\n if allowable_fraction <= 0.0:\\n raise ValueError(f\\\"Can't leave 1GB over for the inference kernel, because\\\"\\n f\\\" there is only {allowable} total free GPU memory.\\\")\\n return allowable_fraction\",\n \"def standard_init_price(self):\\n # If a system can't use something, its price is zero.\\n _good = self.tradeitem\\n if self.planet.tech_level < _good.tu and _good.name not in 'fuel':\\n base_price = 0\\n else:\\n base_price = _good.plt + (self.planet.tech_level * _good.pi)\\n # if good is highly requested, increase the price\\n if self.planet.status in [_good.dps]:\\n base_price = base_price + (base_price * 0.5)\\n # large system: high production decreases prices\\n base_price = (base_price * (100 - self.planet.system_size)) / 100\\n\\n # price can't be negative\\n if base_price < 0:\\n base_price = 0\\n\\n return int(base_price)\",\n \"def dilutionneeded(self) -> float:\\n return self.stock*1.0/self.final\",\n \"def MINIMUM_BET() -> int:\\n return 10\",\n \"def _cost_caught_by_police(self):\\n if self.fine_frequency != 0:\\n if self.number_of_courses % self.fine_frequency == 0 and self.number_of_courses != 0:\\n if self.number_of_courses % self.fine_frequency_paid_by_driver == 0 and self.number_of_courses != 0:\\n self.fine_paid_number_of_courses += 1\\n fine_value = np.random.choice([100, 200, 500], p=[0.25, 0.4, 0.35])\\n self.total_penalty_points += self._add_penalty_points() # adding penalty points\\n return fine_value\\n else:\\n return 0\\n else:\\n return 0\\n else:\\n return 0\",\n \"def fuel_required(mass):\\n return max(math.floor(mass/3) - 2, 0)\",\n \"def get_duct_linear_heat_loss_coefficient() -> float:\\n return 0.49\",\n \"def lower_bound(stock):\\n counter=0\\n for i in stock_price(stock):\\n if i <= support(stock):\\n counter+=1\\n return counter\",\n \"def get_load_factor(self):\\r\\n return self.num_items / self.table_size\",\n \"def penalty(self):\\n return 0\",\n \"def revenue_landfill(self) -> float:\\n return self.income_statement.revenue.operating_revenue * (\\n 1 - self.inputs.allocation_to_collection_unit\\n )\",\n \"def compute_equilibrium(self): \\r\\n # First compute a valid lower bound for the total flow\\r\\n totalflow_lb = max(0, np.amax(1 - np.multiply(self.capacity, np.exp(self.beta * np.power(self.capacity, self.theta) + self.phi - self.b))))\\r\\n if totalflow_lb > 1:\\r\\n print(\\\"This network does not have equilibrium!\\\")\\r\\n else:\\r\\n # Compute the total flow at equilibrium z_star\\r\\n z_star = bisection_search(diff_totalflow_sumflow, totalflow_lb, 1, [1e-10, 1e-10], True, self) \\r\\n # Compute the flow over each route at equilibrium \\r\\n for i in range(self.num_routes):\\r\\n self.flow[i] = bisection_search(zeta, 0, self.capacity[i], [1e-10, 1e-10], True, self, z_star, i, 4)\",\n \"def _compute_quantization_factor(self):\\n self._quantization_factor = (float(self._full_value_range) /\\n self._number_of_intervals)\\n logging.debug(\\n 'full value range: %r, number of intervals: %r',\\n self._full_value_range, self._number_of_intervals)\\n logging.debug('quantization factor: %r', self._quantization_factor)\",\n \"def overall_reduction(self):\\n return 84\",\n \"def qty_available(quant) -> float:\\n return quant.quantity - quant.reserved_quantity\",\n \"def emission_factor(sheets, vehicleClass, speedRegime, pollutant):\\r\\n # get bc from excel. The year is hard coded to 2015\\r\\n f = sheets[\\\"Emissiefactoren CAR-VL3.0\\\"]\\r\\n idx = f[f.iloc[:,0] == vehicleClass + speedRegime + '2015'].index\\r\\n if len(idx) == 0:\\r\\n print(\\\"EFError: No ef corresponds to vehicle class {} and speed type {}.\\\".format(vehicleClass, speedRegime))\\r\\n return 0\\r\\n return float(f['EF_' + pollutant][idx])\",\n \"def calculateSaleReturnSolidity(S, R, F, T):\\n _supply = uint256(S)\\n _reserveBalance = uint256(R)\\n _reserveRatio = uint256(F)\\n _sellAmount = uint256(T)\\n \\n if ( _supply < _sellAmount):\\n raise Exception(\\\"Supply < Tokens\\\")\\n\\n _baseN = _supply - _sellAmount\\n\\n\\n if _reserveRatio == 100:\\n amount = uint256(_reserveBalance * _baseN ) / _supply\\n if _reserveBalance < amount:\\n raise Exception(\\\"_reservebalance < amount\\\")\\n\\n return _reserveBalance - amount\\n\\n resD = FIXED_ONE\\n #resN = power_sale(_supply, _baseN, 100, _reserveRatio)\\n resN = power(_supply, _baseN, 100, _reserveRatio)\\n resN = uint256(resN)\\n\\n reserveUpshifted = uint256(_reserveBalance * resN)\\n amount = uint256(_reserveBalance * resD) \\n\\n \\n result = (reserveUpshifted - amount) / resN\\n \\n if verbose:\\n print(\\\" rbal[%d] * resN[%d] / resD[%d] - rbal[%d] = %d \\\" %\\n (_reserveBalance, resN, resD, _reserveBalance, result))\\n\\n return uint256(result - minUnit(R))\",\n \"def calculate_effective_capacitance(self, load):\\n c_load = load\\n # In fF\\n c_para = spice[\\\"min_tx_drain_c\\\"] * (self.nmos_size / parameter[\\\"min_tx_size\\\"])\\n transition_prob = 0.1875\\n return transition_prob * (c_load + c_para)\",\n \"def calc_load_factor_h(data, fuels_tot_enduses_h, rs_fuels_peak_h):\\n load_factor_h = np.zeros((data['nr_of_fueltypes']))\\n\\n # Iterate fueltypes to calculate load factors for each fueltype\\n for fueltype, fuels in enumerate(fuels_tot_enduses_h):\\n\\n # Maximum fuel of an hour of the peak day\\n maximum_h_of_day = rs_fuels_peak_h[fueltype]\\n\\n #Calculate average in full year\\n average_demand_h = np.mean(fuels)\\n\\n # If there is a maximum day hour\\n if maximum_h_of_day != 0:\\n load_factor_h[fueltype] = average_demand_h / maximum_h_of_day # Calculate load factor\\n\\n # Convert load factor to %\\n load_factor_h *= 100\\n\\n return load_factor_h\",\n \"def fuel_required(mass):\\n return int(floor(mass / 3) - 2)\",\n \"def get_remaining_fuel(self):\\n return min(self.liquid_fuel, self.oxidizer)\",\n \"def _calc_worker_assign_limits(self, initial_count, occupied=None):\\n occupied = occupied or dict()\\n actual_count = initial_count - sum(occupied.values())\\n\\n endpoint_res = sorted(self._worker_slots.items(), key=operator.itemgetter(1),\\n reverse=True)\\n\\n endpoints = [t[0] for t in endpoint_res]\\n endpoint_cores = np.array([t[1] for t in endpoint_res]).astype(np.float32)\\n\\n # remove assigned nodes from limitations\\n counts = initial_count * endpoint_cores / endpoint_cores.sum()\\n for idx, ep in enumerate(endpoints):\\n counts[idx] = max(0, counts[idx] - occupied.get(ep, 0))\\n\\n # all assigned, nothing to do\\n if counts.sum() == 0:\\n return dict((ep, 0) for ep in endpoints)\\n\\n counts = (actual_count * counts / counts.sum()).astype(np.int32)\\n\\n # assign remaining nodes\\n pos = 0\\n rest = actual_count - counts.sum()\\n while rest > 0:\\n counts[pos] += 1\\n rest -= 1\\n pos = (pos + 1) % len(counts)\\n return dict(zip(endpoints, counts))\",\n \"def checkFuel(self):\\n return self.maze.checkFuelCost(self.checkpoint,currentLap = self.laps) - self.timeDriving\",\n \"def compute_lb_totalflow(self):\\r\\n lb_totalflow = np.amax(1 - np.exp(self.beta - self.b + self.phi * np.minimum(1, 1/self.l))) \\r\\n return max(0, lb_totalflow)\",\n \"def calculate_available_node_res (self, vnfs_to_be_left_in_place={},\\n mode=MODE_ADD):\\n # add available res attribute to all Infras and subtract the running\\n # NFs` resources from the given max res\\n for n in self.infras:\\n setattr(self.network.node[n.id], 'availres',\\n copy.deepcopy(self.network.node[n.id].resources))\\n if mode == self.MODE_ADD:\\n for vnf in self.running_nfs(n.id):\\n # if a VNF needs to be left in place, then it is still mapped by the \\n # mapping process, but with placement criteria, so its resource \\n # requirements will be subtracted during the greedy process.\\n if vnf.id not in vnfs_to_be_left_in_place:\\n try:\\n newres = self.network.node[n.id].availres.subtractNodeRes(\\n self.network.node[vnf.id].resources,\\n self.network.node[n.id].resources)\\n except RuntimeError:\\n raise RuntimeError(\\n \\\"Infra node`s resources are expected to represent its maximal \\\"\\n \\\"capabilities.\\\"\\n \\\"The NodeNF(s) running on Infra node %s, use(s)more resource \\\"\\n \\\"than the maximal.\\\" % n.id)\\n else:\\n try:\\n newres = self.network.node[n.id].availres.subtractNodeRes(\\n vnfs_to_be_left_in_place[vnf.id].resources,\\n self.network.node[n.id].resources)\\n except RuntimeError:\\n raise RuntimeError(\\\"VNF %s cannot be kept on host %s with \\\"\\n \\\"increased resource requirements due to not \\\"\\n \\\"enough available resources!\\\" % (vnf.id, n.id))\\n\\n self.network.node[n.id].availres = newres\",\n \"def workersNeeded(k, m):\\n # formula: k/m\\n from math import ceil\\n return ceil(float(k)/float(m))\",\n \"def calc_reduction_diesel_used (self):\\n self.reduction_diesel_used = self.diesel_equiv_captured - \\\\\\n self.loss_heat_recovery\\n #~ print 'self.reduction_diesel_used',self.reduction_diesel_used\",\n \"def _calc_freeze_probability(self, num_iterations, final_fraction):\\n return 1.0 - (final_fraction ** (1.0 / num_iterations))\",\n \"def get_risk_per_unit(price, sl_price):\\n return abs(price - sl_price)\",\n \"def get_amount_out(amount_in, reserve_in, reserve_out):\\n assert amount_in > 0\\n assert reserve_in > 0 and reserve_out > 0\\n amount_in_with_fee = amount_in*997\\n numerator = amount_in_with_fee*reserve_out\\n denominator = reserve_in*1000 + amount_in_with_fee\\n return float(numerator/denominator)\",\n \"def branching_factor(data, loc):\\n\\n return 20\",\n \"def base_reserve_0():\\n print('Setting base reserve to 0')\\n upgrade('basereserve', 'base_reserve_in_stroops', 0)\",\n \"def overhead(readings):\\n return 100.0 * (int(readings[0]) + int(readings[1])) / (int(readings[2]) + int(readings[3]))\",\n \"def calculate_supply(self):\\r\\n \\r\\n for cell in self.cells:\\r\\n cell.supply = min(self.max_volume,\\r\\n self.wave_speed / self.free_speed *\\r\\n (self.cell_length * self.jam_density -\\r\\n cell.volume)) /self.interval\\r\\n self.supply = self.cells[0].supply\",\n \"def calculate_br_up_metric(br_up):\\n if br_up < 1:\\n br_up = 1\\n min_baud = 1200\\n max_baud = 38400\\n\\n num = np.log(br_up) - np.log(min_baud)\\n den = np.log(max_baud) - np.log(min_baud)\\n\\n return (num / den + 0.1).clip(min=0, max=1)\",\n \"def bCheck(c, v, p, b):\\n val = (v+1).floor()\\n deg = c.degree()\\n coeffs = c.coefficients(sparse=False)\\n lcoeff = coeffs[deg]; coeffs.remove(lcoeff)\\n check1 = [(coeffs[i].valuation(p) - lcoeff.valuation(p))/(deg - i) for i in range(0,len(coeffs)) if coeffs[i] != 0]\\n check2 = (val - lcoeff.valuation(p))/deg\\n check1.append(check2)\\n bval = min(check1)\\n return (bval).ceil()\",\n \"def fibre_strain_energy(self, l_stretch):\\n if l_stretch <= 1.0:\\n # compressed region - no energy\\n return 0.0\\n\\n # Note: this range should be '< lm' according to FEBio but we use '<=' to\\n # make setting c6 easier -> there's no difference because it's cts.\\n if l_stretch <= self.lm:\\n # exponential energy\\n return self.c3 * (exp(self.c4 * (l_stretch - 1.0)) - 1.0)\\n\\n # linear energy\\n return self.c5 * l_stretch + self.c6\",\n \"def calculate_demand(self):\\r\\n \\r\\n for cell in self.cells:\\r\\n cell.demand = min(cell.volume, self.max_volume) /self.interval\\r\\n self.demand = self.cells[-1].demand\",\n \"def fuel_calc(mass):\\n return max((mass / 3) - 2, 0)\",\n \"def volneeded(self, totalvol: float) -> float:\\n return totalvol*self.final*1.0/self.stock\",\n \"def get_fuel_requirements(mass: int) -> int:\\n return int(mass / 3) - 2\",\n \"def verify_load_feasibility(self):\\n max_load = 0\\n for pp in self.powerplants:\\n max_load += pp[\\\"pmax\\\"]\\n\\n min_load = max_load\\n for pp in self.powerplants:\\n min_load = min(pp[\\\"pmin\\\"], min_load)\\n\\n if self.load > max_load:\\n self.feasible = False\\n self.unfeasible_reason = f\\\"Requested load {self.load/10} too high for our powerstations \\\"\\n return False\\n\\n if self.load < min_load:\\n self.feasible = False\\n self.unfeasible_reason = f\\\"Requested load {self.load/10} too low for our powerstations \\\"\\n return False\\n\\n return True\",\n \"def calc_pre_intertie_generation (self):\\n\\n self.pre_intertie_generation = \\\\\\n self.forecast.get_generation(self.start_year,self.end_year)\\n\\n gen_eff = self.cd[\\\"diesel generation efficiency\\\"]\\n self.pre_intertie_generation_fuel_used = \\\\\\n self.pre_intertie_generation / gen_eff\\n\\n #~ print 'self.baseline_generatio',self.baseline_generation\",\n \"def part3c_0():\\n xs = exampleInput\\n N = 10000\\n\\n difference = 0.0\\n for ys, estimatedProb in submission.computeGibbsProbabilities( simpleCRF, \\n submission.getCRFBlocks,\\n submission.chooseGibbsCRF,\\n xs, N ).iteritems():\\n trueProb = nerUtils.computeProbability( simpleCRF, xs, ys )\\n difference = abs( trueProb - estimatedProb )\\n grader.requireIsLessThan( 5e-2, difference )\",\n \"def calculate_217f_part_stress(**attributes): # pylint: disable=R0912, R0914\\n _dic_ref_temp = {\\n 1: 343.0,\\n 2: {\\n 1: 343.0,\\n 2: 343.0,\\n 3: 398.0,\\n 4: 398.0\\n },\\n 3: 298.0,\\n 5: 398.0,\\n 6: 298.0,\\n 7: 298.0,\\n 9: 358.0,\\n 10: 358.0,\\n 11: 313.0,\\n 12: 298.0,\\n 13: 358.0,\\n 14: 343.0,\\n 15: 343.0\\n }\\n _dic_factors = {\\n 1: [4.5E-9, 12.0, 1.0, 0.6, 1.0, 1.0],\\n 2: {\\n 1: [3.25E-4, 1.0, 3.0, 1.0, 1.0, 1.0],\\n 2: [3.25E-4, 1.0, 3.0, 1.0, 1.0, 1.0],\\n 3: [5.0E-5, 3.5, 1.0, 1.0, 1.0, 1.0],\\n 4: [5.0E-5, 3.5, 1.0, 1.0, 1.0, 1.0]\\n },\\n 3: [7.33E-3, 0.202, 2.6, 1.45, 0.89, 1.3],\\n 5: [0.0031, 1.0, 10.0, 1.0, 1.0, 1.5],\\n 6: [0.00148, 1.0, 2.0, 0.5, 1.0, 1.0],\\n 7: [0.00015, 2.64, 1.0, 0.466, 1.0, 1.0],\\n 8: [0.021, 0.065, 0.105, 0.0, 0.0, 0.0],\\n 9: [0.0062, 1.0, 5.0, 1.0, 1.0, 1.0],\\n 10: [0.0735, 1.03, 4.45, 2.74, 3.51, 1.0],\\n 11: [0.0398, 0.514, 5.28, 1.44, 4.46, 1.0],\\n 12: [0.0481, 0.334, 4.66, 1.47, 2.83, 1.0],\\n 13: [0.019, 0.445, 7.3, 2.69, 2.46, 1.0],\\n 14: [0.0246, 0.459, 9.3, 2.32, 5.3, 1.0],\\n 15: [0.018, 1.0, 7.4, 2.55, 3.6, 1.0]\\n }\\n _dic_piQ = {\\n 1: [0.03, 0.1, 0.3, 1.0, 5.0, 15.0],\\n 2: [0.03, 0.1, 0.3, 1.0, 5.0, 5.0, 15.0],\\n 3: [1.0, 3.0],\\n 4: [1.0, 3.0],\\n 5: [0.03, 0.1, 0.3, 1.0, 5.0, 15.0],\\n 6: [0.03, 0.1, 0.3, 1.0, 5.0, 15.0],\\n 7: [0.03, 0.1, 0.3, 1.0, 5.0, 15.0],\\n 8: [1.0, 15.0],\\n 9: [0.02, 0.06, 0.2, 0.6, 3.0, 10.0],\\n 10: [2.5, 5.0],\\n 11: [2.0, 4.0],\\n 12: [2.0, 4.0],\\n 13: [0.02, 0.06, 0.2, 0.6, 3.0, 10.0],\\n 14: [2.5, 5.0],\\n 15: [2.0, 4.0]\\n }\\n _dic_piE = {\\n 1: [\\n 1.0, 3.0, 8.0, 5.0, 13.0, 4.0, 5.0, 7.0, 11.0, 19.0, 0.5, 11.0,\\n 27.0, 490.0\\n ],\\n 2: [\\n 1.0, 2.0, 8.0, 4.0, 14.0, 4.0, 8.0, 10.0, 18.0, 19.0, 0.2, 10.0,\\n 28.0, 510.0\\n ],\\n 3: [\\n 1.0, 2.0, 10.0, 5.0, 17.0, 6.0, 8.0, 14.0, 18.0, 25.0, 0.5, 14.0,\\n 36.0, 660.0\\n ],\\n 4: [\\n 1.0, 2.0, 10.0, 5.0, 17.0, 6.0, 8.0, 14.0, 18.0, 25.0, 0.5, 14.0,\\n 36.0, 660.0\\n ],\\n 5: [\\n 1.0, 2.0, 11.0, 5.0, 18.0, 15.0, 18.0, 28.0, 35.0, 27.0, 0.8, 14.0,\\n 38.0, 610.0\\n ],\\n 6: [\\n 1.0, 2.0, 10.0, 5.0, 16.0, 4.0, 8.0, 9.0, 18.0, 23.0, 0.3, 13.0,\\n 34.0, 610.0\\n ],\\n 7: [\\n 1.0, 2.0, 10.0, 5.0, 16.0, 4.0, 8.0, 9.0, 18.0, 23.0, 0.5, 13.0,\\n 34.0, 610.0\\n ],\\n 8: [\\n 1.0, 5.0, 21.0, 11.0, 24.0, 11.0, 30.0, 16.0, 42.0, 37.0, 0.5,\\n 20.0, 53.0, 950.0\\n ],\\n 9: [\\n 1.0, 2.0, 12.0, 6.0, 20.0, 5.0, 8.0, 9.0, 15.0, 33.0, 0.5, 18.0,\\n 48.0, 870.0\\n ],\\n 10: [\\n 1.0, 2.0, 18.0, 8.0, 30.0, 8.0, 12.0, 13.0, 18.0, 53.0, 0.5, 29.0,\\n 76.0, 1400.0\\n ],\\n 11: [\\n 1.0, 2.0, 16.0, 7.0, 28.0, 8.0, 12.0, 0.0, 0.0, 38.0, 0.5, 0.0,\\n 0.0, 0.0\\n ],\\n 12: [\\n 1.0, 3.0, 16.0, 7.0, 28.0, 8.0, 12.0, 0.0, 0.0, 38.0, 0.5, 0.0,\\n 0.0, 0.0\\n ],\\n 13: [\\n 1.0, 3.0, 14.0, 6.0, 24.0, 5.0, 7.0, 12.0, 18.0, 39.0, 0.5, 22.0,\\n 57.0, 1000.0\\n ],\\n 14: [\\n 1.0, 2.0, 19.0, 8.0, 29.0, 40.0, 65.0, 48.0, 78.0, 46.0, 0.5, 25.0,\\n 66.0, 1200.0\\n ],\\n 15: [\\n 1.0, 3.0, 14.0, 7.0, 24.0, 6.0, 12.0, 20.0, 30.0, 39.0, 0.5, 22.0,\\n 57.0, 1000.0\\n ]\\n }\\n # Resistance factor (piR) dictionary of values. The key is the\\n # subcategory ID. The index in the returned list is the resistance range\\n # breakpoint (breakpoint values are in _lst_breakpoints below). For\\n # subcategory ID 6 and 7, the specification ID selects the correct set of\\n # lists, then the style ID selects the proper list of piR values and then\\n # the resistance range breakpoint is used to select\\n _dic_piR = {\\n 1: [1.0, 1.1, 1.6, 2.5],\\n 2: [1.0, 1.1, 1.6, 2.5],\\n 3: [1.0, 1.2, 1.3, 3.5],\\n 5: [1.0, 1.7, 3.0, 5.0],\\n 6: [[[1.0, 1.0, 1.2, 1.2, 1.6, 1.6, 1.6,\\n 0.0], [1.0, 1.0, 1.0, 1.2, 1.6, 1.6, 0.0,\\n 0.0], [1.0, 1.0, 1.0, 1.0, 1.2, 1.2, 1.2,\\n 1.6], [1.0, 1.2, 1.6, 1.6, 0.0, 0.0, 0.0, 0.0],\\n [1.0, 1.6, 0.0, 0.0, 0.0, 0.0, 0.0,\\n 0.0], [1.0, 1.6, 1.6, 0.0, 0.0, 0.0, 0.0,\\n 0.0], [1.0, 1.0, 1.1, 1.2, 1.2, 1.6, 0.0, 0.0],\\n [1.0, 1.0, 1.4, 0.0, 0.0, 0.0, 0.0, 0.0]],\\n [[1.0, 1.0, 1.0, 1.0, 1.2, 1.6], [1.0, 1.0, 1.0, 1.2, 1.6, 0.0],\\n [1.0, 1.0, 1.2, 1.6, 0.0, 0.0], [1.0, 1.0, 1.0, 2.0, 0.0, 0.0], [\\n 1.0, 1.0, 1.0, 2.0, 0.0, 0.0\\n ], [1.0, 1.0, 1.2, 2.0, 0.0, 0.0], [1.0, 1.2, 1.4, 0.0, 0.0, 0.0],\\n [1.0, 1.0, 1.6, 0.0, 0.0, 0.0], [1.0, 1.0, 1.2, 2.0, 0.0, 0.0], [\\n 1.0, 1.0, 1.2, 1.6, 0.0, 0.0\\n ], [1.0, 1.0, 1.0, 1.4, 0.0, 0.0], [1.0, 1.0, 1.0, 1.2, 0.0, 0.0],\\n [1.0, 1.0, 1.4, 0.0, 0.0, 0.0], [1.0, 1.2, 1.6, 0.0, 0.0, 0.0], [\\n 1.0, 1.0, 1.4, 0.0, 0.0, 0.0\\n ], [1.0, 1.0, 1.2, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.4, 0.0, 0.0],\\n [1.0, 1.0, 1.0, 1.4, 0.0, 0.0], [1.0, 1.0, 1.0, 1.4, 0.0, 0.0], [\\n 1.0, 1.0, 1.2, 1.5, 0.0, 0.0\\n ], [1.0, 1.0, 1.2, 1.6, 0.0, 0.0], [1.0, 1.0, 1.0, 1.4, 1.6, 0.0],\\n [1.0, 1.0, 1.0, 1.4, 1.6, 2.0], [1.0, 1.0, 1.0, 1.4, 1.6, 2.0], [\\n 1.0, 1.0, 1.4, 2.4, 0.0, 0.0\\n ], [1.0, 1.0, 1.2, 2.6, 0.0,\\n 0.0], [1.0, 1.0, 1.0, 0.0, 0.0,\\n 0.0], [1.0, 1.0, 1.0, 0.0, 0.0,\\n 0.0], [1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [\\n 1.0, 1.2, 1.4, 0.0, 0.0, 0.0\\n ], [1.0, 1.0, 1.2, 1.6, 0.0,\\n 0.0], [1.0, 1.0, 1.0, 1.6, 0.0, 0.0], [\\n 1.0, 1.0, 1.4, 0.0, 0.0, 0.0\\n ], [1.0, 1.2, 1.5, 0.0, 0.0,\\n 0.0], [1.0, 1.2, 0.0, 0.0, 0.0, 0.0]]],\\n 7: [[[1.0, 1.2, 1.2, 1.6, 0.0, 0.0], [1.0, 1.0, 1.2, 1.6, 0.0, 0.0],\\n [1.0, 1.0, 1.2, 1.2, 1.6, 0.0], [1.0, 1.0, 1.0, 1.1, 1.2, 1.6],\\n [1.0, 1.0, 1.0, 1.0, 1.2, 1.6], [1.0, 1.0, 1.0, 1.0, 1.2, 1.6]],\\n [[1.0, 1.2, 1.6, 0.0, 0.0, 0.0], [1.0, 1.2, 1.6, 0.0, 0.0, 0.0],\\n [1.0, 1.0, 1.2, 1.6, 0.0, 0.0], [1.0, 1.0, 1.1, 1.2, 1.4, 0.0],\\n [1.0, 1.0, 1.0, 1.2, 1.6, 0.0], [1.0, 1.0, 1.0, 1.1, 1.4, 0.0]]],\\n 9: [1.0, 1.4, 2.0],\\n 10: [1.0, 1.1, 1.4, 2.0, 2.5, 3.5],\\n 11: [1.0, 1.4, 2.0],\\n 12: [1.0, 1.4, 2.0],\\n 13: [1.0, 1.1, 1.2, 1.4, 1.8],\\n 14: [1.0, 1.1, 1.2, 1.4, 1.8],\\n 15: [1.0, 1.1, 1.2, 1.4, 1.8]\\n }\\n # Dictionary containing the number of element breakpoints for determining\\n # the resistance factor list to use.\\n _dic_breakpoints = {\\n 1: [1.0E5, 1.0E6, 1.0E7],\\n 2: [1.0E5, 1.0E6, 1.0E7],\\n 3: [100.0, 1.0E5, 1.0E6],\\n 5: [1.0E4, 1.0E5, 1.0E6],\\n 6: [[500.0, 1.0E3, 5.0E3, 7.5E3, 1.0E4, 1.5E4, 2.0E4],\\n [100.0, 1.0E3, 1.0E4, 1.0E5, 1.5E5, 2.0E5]],\\n 7: [500.0, 1.0E3, 5.0E3, 1.0E4, 2.0E4],\\n 9: [2.0E3, 5.0E3],\\n 10: [1.0E4, 2.0E4, 5.0E4, 1.0E5, 2.0E5],\\n 11: [2.0E3, 5.0E3],\\n 12: [2.0E3, 5.0E3],\\n 13: [5.0E4, 1.0E5, 2.0E5, 5.0E5],\\n 14: [5.0E4, 1.0E5, 2.0E5, 5.0E5],\\n 15: [1.0E4, 5.0E4, 2.0E5, 1.0E6]\\n }\\n _dic_piV = {\\n 9: [1.1, 1.05, 1.0, 1.1, 1.22, 1.4, 2.0],\\n 10: [1.1, 1.05, 1.0, 1.1, 1.22, 1.4, 2.0],\\n 11: [1.1, 1.05, 1.0, 1.1, 1.22, 1.4, 2.0],\\n 12: [1.1, 1.05, 1.0, 1.1, 1.22, 1.4, 2.0],\\n 13: [1.0, 1.05, 1.2],\\n 14: [1.0, 1.05, 1.2],\\n 15: [1.0, 1.05, 1.2]\\n }\\n _dic_piC = {10: [2.0, 1.0, 3.0, 1.5], 12: [2.0, 1.0]}\\n _msg = ''\\n\\n # Calculate the base hazard rate.\\n if attributes['subcategory_id'] == 2:\\n _ref_temp = _dic_ref_temp[attributes['subcategory_id']][attributes[\\n 'specification_id']]\\n _f0 = _dic_factors[attributes['subcategory_id']][attributes[\\n 'specification_id']][0]\\n _f1 = _dic_factors[attributes['subcategory_id']][attributes[\\n 'specification_id']][1]\\n _f2 = _dic_factors[attributes['subcategory_id']][attributes[\\n 'specification_id']][2]\\n _f3 = _dic_factors[attributes['subcategory_id']][attributes[\\n 'specification_id']][3]\\n _f4 = _dic_factors[attributes['subcategory_id']][attributes[\\n 'specification_id']][4]\\n _f5 = _dic_factors[attributes['subcategory_id']][attributes[\\n 'specification_id']][5]\\n elif attributes['subcategory_id'] not in [4, 8]:\\n _ref_temp = _dic_ref_temp[attributes['subcategory_id']]\\n _f0 = _dic_factors[attributes['subcategory_id']][0]\\n _f1 = _dic_factors[attributes['subcategory_id']][1]\\n _f2 = _dic_factors[attributes['subcategory_id']][2]\\n _f3 = _dic_factors[attributes['subcategory_id']][3]\\n _f4 = _dic_factors[attributes['subcategory_id']][4]\\n _f5 = _dic_factors[attributes['subcategory_id']][5]\\n\\n if attributes['subcategory_id'] == 4:\\n attributes['lambda_b'] = 0.00006\\n elif attributes['subcategory_id'] == 8:\\n attributes['lambda_b'] = _dic_factors[attributes['subcategory_id']][\\n attributes['type_id'] - 1]\\n else:\\n attributes['lambda_b'] = _f0 * exp(_f1 * (\\n (attributes['temperature_active'] + 273.0) /\\n _ref_temp))**_f2 * exp(((attributes['power_ratio'] / _f3) * (\\n (attributes['temperature_active'] + 273.0) / 273.0)**_f4)**_f5)\\n\\n if attributes['lambda_b'] <= 0.0:\\n _msg = _msg + 'RAMSTK WARNING: Base hazard rate is 0.0 when ' \\\\\\n 'calculating resistor, hardware ID: ' \\\\\\n '{0:d}'.format(attributes['hardware_id'])\\n\\n # Calculate the resistance factor (piR).\\n if attributes['subcategory_id'] not in [4, 8]:\\n _index = -1\\n if attributes['subcategory_id'] == 6:\\n _breaks = _dic_breakpoints[attributes['subcategory_id']][\\n attributes['specification_id'] - 1]\\n else:\\n _breaks = _dic_breakpoints[attributes['subcategory_id']]\\n\\n for _index, _value in enumerate(_breaks):\\n _diff = _value - attributes['n_elements']\\n if len(_breaks) == 1 and _diff < 0:\\n break\\n elif _diff >= 0:\\n break\\n\\n if attributes['subcategory_id'] in [6, 7]:\\n attributes['piR'] = _dic_piR[attributes['subcategory_id']][\\n attributes['specification_id'] - 1][attributes['family_id'] -\\n 1][_index + 1]\\n elif attributes['subcategory_id'] not in [4, 8]:\\n attributes['piR'] = _dic_piR[attributes['subcategory_id']][_index +\\n 1]\\n\\n # Determine the quality factor (piQ).\\n attributes['piQ'] = _dic_piQ[attributes['subcategory_id']][\\n attributes['quality_id'] - 1]\\n\\n if attributes['piQ'] <= 0.0:\\n _msg = _msg + 'RAMSTK WARNING: piQ is 0.0 when calculating ' \\\\\\n 'resistor, hardware ID: {0:d}'.format(attributes['hardware_id'])\\n\\n # Determine the environmental factor (piE).\\n attributes['piE'] = _dic_piE[attributes['subcategory_id']][\\n attributes['environment_active_id'] - 1]\\n\\n if attributes['piE'] <= 0.0:\\n _msg = _msg + 'RAMSTK WARNING: piE is 0.0 when calculating ' \\\\\\n 'resistor, hardware ID: {0:d}'.format(attributes['hardware_id'])\\n\\n # Calculate the temperature factor (piT).\\n if attributes['subcategory_id'] == 4:\\n attributes['temperature_case'] = (attributes['temperature_active'] +\\n 55.0 * attributes['power_ratio'])\\n attributes['piT'] = exp(-4056.0 * (\\n (1.0 / (attributes['temperature_case'] + 273.0)) - 1.0 / 298.0))\\n\\n # Calculate the taps factor (piTAPS).\\n if attributes['subcategory_id'] in [9, 10, 11, 12, 13, 14, 15]:\\n attributes['piTAPS'] = (attributes['n_elements']**1.5 / 25.0) + 0.792\\n\\n # Calculate the voltage factor (piV).\\n if attributes['subcategory_id'] > 8:\\n _index = -1\\n if attributes['subcategory_id'] in [9, 10, 11, 12]:\\n _breaks = [0.1, 0.2, 0.6, 0.7, 0.8, 0.9]\\n elif attributes['subcategory_id'] in [13, 14, 15]:\\n _breaks = [0.8, 0.9]\\n for _index, _value in enumerate(_breaks):\\n _diff = _value - attributes['voltage_ratio']\\n if len(_breaks) == 1 and _diff < 0.0:\\n break\\n elif _index == 0 and _diff >= 0.0:\\n break\\n elif _diff >= 0:\\n break\\n attributes['piV'] = _dic_piV[attributes['subcategory_id']][_index]\\n\\n # Determine the consruction class factor (piC).\\n if attributes['subcategory_id'] in [10, 12]:\\n attributes['piC'] = _dic_piC[attributes['subcategory_id']][\\n attributes['construction_id'] - 1]\\n\\n # Calculate the active hazard rate.\\n attributes['hazard_rate_active'] = (\\n attributes['lambda_b'] * attributes['piQ'] * attributes['piE'])\\n if attributes['subcategory_id'] == 4:\\n attributes['hazard_rate_active'] = (\\n attributes['hazard_rate_active'] * attributes['piT'] *\\n attributes['n_elements'])\\n elif attributes['subcategory_id'] in [9, 11, 13, 14, 15]:\\n attributes['hazard_rate_active'] = (\\n attributes['hazard_rate_active'] * attributes['piTAPS'] *\\n attributes['piR'] * attributes['piV'])\\n elif attributes['subcategory_id'] in [10, 12]:\\n attributes['hazard_rate_active'] = (\\n attributes['hazard_rate_active'] * attributes['piTAPS'] *\\n attributes['piC'] * attributes['piR'] * attributes['piV'])\\n elif attributes['subcategory_id'] != 8:\\n attributes['hazard_rate_active'] = (\\n attributes['hazard_rate_active'] * attributes['piR'])\\n\\n return attributes, _msg\",\n \"def ComputeEAvailable(self):\\r\\n pass\",\n \"def get_occupant_room_load_for_cooling_balanced(\\n l_cs: np.ndarray, l_cl: np.ndarray, q_d_trs_prt: np.ndarray) -> (np.ndarray, np.ndarray):\\n\\n l_d_cs = np.where(l_cs[0:5] > 0.0, l_cs[0:5] - q_d_trs_prt, 0.0)\\n l_d_cl = l_cl[0:5]\\n\\n return np.clip(l_d_cs, 0.0, None), np.clip(l_d_cl, 0.0, None)\",\n \"def test_getThermalExpansionFactorConservedMassByLinearExpansionPercent(self):\\n hotTemp = 700.0\\n dLL = self.component.material.linearExpansionFactor(\\n Tc=hotTemp, T0=self._coldTemp\\n )\\n ref = 1.0 + dLL\\n cur = self.component.getThermalExpansionFactor(Tc=hotTemp)\\n self.assertAlmostEqual(cur, ref)\",\n \"def __call__(self, auctioneer):\\n curr_bid = auctioneer.current_bid\\n bid_price = curr_bid * self._bid_increase_perc\\n if bid_price <= self._budget and self.get_bid_probability() > 0.3:\\n self._highest_bid = bid_price\\n return bid_price\\n return 0\",\n \"def convergence_check(self):\\n air = self.air_alias.val\\n flue_gas = self.fuel_alias.val + '_fg'\\n fuel = self.fuel_alias.val\\n\\n for c in self.outl:\\n if not c.fluid.val_set[air]:\\n if c.fluid.val[air] > 0.95:\\n c.fluid.val[air] = 0.95\\n if c.fluid.val[air] < 0.5:\\n c.fluid.val[air] = 0.5\\n\\n if not c.fluid.val_set[flue_gas]:\\n if c.fluid.val[flue_gas] > 0.5:\\n c.fluid.val[flue_gas] = 0.5\\n if c.fluid.val[flue_gas] < 0.05:\\n c.fluid.val[flue_gas] = 0.05\\n\\n if not c.fluid.val_set[fuel]:\\n if c.fluid.val[fuel] > 0:\\n c.fluid.val[fuel] = 0\\n\\n c.target.propagate_fluid_to_target(c, c.target)\\n\\n for i in self.inl:\\n if i.m.val_SI < 0 and not i.m.val_set:\\n i.m.val_SI = 0.01\\n\\n for c in self.outl:\\n if c.m.val_SI < 0 and not c.m.val_set:\\n c.m.val_SI = 10\\n c.target.propagate_fluid_to_target(c, c.target)\\n\\n if self.lamb.val < 1 and not self.lamb.is_set:\\n self.lamb.val = 2\",\n \"def measure_allocation_diversity_bounds_errors(self, slots_assegnation_probabilities, LP_news_pool, iter=5000):\\n for tech in [\\\"rand_1\\\", \\\"rand_2\\\", \\\"rand_3\\\"]:\\n max_errors_per_iter = []\\n for k in range(iter):\\n tmp_slots_assegnation_probabilities = []\\n for elem in slots_assegnation_probabilities:\\n tmp_slots_assegnation_probabilities.append(elem.copy())\\n constraints_error = [0] * len(self.categories)\\n promenance_per_category = [0] * len(self.categories)\\n result = self.__de_randomize_LP(LP_news_pool, tmp_slots_assegnation_probabilities, tech)\\n for i in range(len(result)):\\n category_index = self.categories.index(result[i].news_category)\\n promenance_per_category[category_index] += self.real_slot_promenances[i]\\n\\n for i in range(len(promenance_per_category)):\\n if promenance_per_category[i] < self.B[i] * -1:\\n constraints_error[i] += (self.B[i] * -1 - promenance_per_category[i]) / (self.B[i] * -1)\\n\\n max_errors_per_iter.append(np.mean(constraints_error))\\n if tech == \\\"rand_1\\\":\\n self.rand_1_errors += max_errors_per_iter\\n elif tech == \\\"rand_2\\\":\\n self.rand_2_errors += max_errors_per_iter\\n else:\\n self.rand_3_errors += max_errors_per_iter\",\n \"def cost(self):\\n lg = len(self.guarantees.cnf)\\n la = len(self.assumptions.cnf)\\n\\n \\\"\\\"\\\"heuristic\\n Low: guarantees while assuming little (assumption set is bigger)\\n High: guarantees while assuming a lot (assumption set is smaller)\\\"\\\"\\\"\\n\\n return la / lg\",\n \"def sum_availability(val, quant) -> float:\\n return val + qty_available(quant)\",\n \"def _estimate_elasticsearch_requirement(\\n instance: Instance,\\n desires: CapacityDesires,\\n working_set: float,\\n reads_per_second: float,\\n max_rps_to_disk: int,\\n zones_per_region: int = 3,\\n copies_per_region: int = 3,\\n) -> CapacityRequirement:\\n # Keep half of the cores free for background work (merging mostly)\\n needed_cores = math.ceil(sqrt_staffed_cores(desires) * 1.5)\\n # Keep half of the bandwidth available for backup\\n needed_network_mbps = simple_network_mbps(desires) * 2\\n\\n needed_disk = math.ceil(\\n (1.0 / desires.data_shape.estimated_compression_ratio.mid)\\n * desires.data_shape.estimated_state_size_gib.mid\\n * copies_per_region,\\n )\\n\\n # Rough estimate of how many instances we would need just for the the CPU\\n # Note that this is a lower bound, we might end up with more.\\n needed_cores = math.ceil(\\n max(1, needed_cores // (instance.cpu_ghz / desires.core_reference_ghz))\\n )\\n rough_count = math.ceil(needed_cores / instance.cpu)\\n\\n # Generally speaking we want fewer than some number of reads per second\\n # hitting disk per instance. If we don't have many reads we don't need to\\n # hold much data in memory.\\n instance_rps = max(1, reads_per_second // rough_count)\\n disk_rps = instance_rps * _es_io_per_read(max(1, needed_disk // rough_count))\\n rps_working_set = min(1.0, disk_rps / max_rps_to_disk)\\n\\n # If disk RPS will be smaller than our target because there are no\\n # reads, we don't need to hold as much data in memory\\n needed_memory = min(working_set, rps_working_set) * needed_disk\\n\\n # Now convert to per zone\\n needed_cores = needed_cores // zones_per_region\\n needed_disk = needed_disk // zones_per_region\\n needed_memory = int(needed_memory // zones_per_region)\\n logger.debug(\\n \\\"Need (cpu, mem, disk, working) = (%s, %s, %s, %f)\\\",\\n needed_cores,\\n needed_memory,\\n needed_disk,\\n working_set,\\n )\\n\\n return CapacityRequirement(\\n requirement_type=\\\"elasticsearch-data-zonal\\\",\\n core_reference_ghz=desires.core_reference_ghz,\\n cpu_cores=certain_int(needed_cores),\\n mem_gib=certain_float(needed_memory),\\n disk_gib=certain_float(needed_disk),\\n network_mbps=certain_float(needed_network_mbps),\\n context={\\n \\\"working_set\\\": min(working_set, rps_working_set),\\n \\\"rps_working_set\\\": rps_working_set,\\n \\\"disk_slo_working_set\\\": working_set,\\n \\\"replication_factor\\\": copies_per_region,\\n \\\"compression_ratio\\\": round(\\n 1.0 / desires.data_shape.estimated_compression_ratio.mid, 2\\n ),\\n \\\"read_per_second\\\": reads_per_second,\\n },\\n )\",\n \"def erfc(x):\\n return 0.0\",\n \"def _check_capacity_limit(self, res, amt, balance, meta, raven_vars, dispatch, t, level):\\n # note \\\"amt\\\" has units of AMOUNT not RATE (resource, not resource per second)\\n sign = np.sign(amt)\\n # are we storing or providing?\\n #print('DEBUGG supposed current level:', level)\\n if sign < 0:\\n # we are being asked to consume some\\n cap, meta = self.get_capacity(meta, raven_vars, dispatch, t)\\n available_amount = cap[res] - level\\n #print('Supposed Capacity, Only calculated ins sign<0 (being asked to consumer)',cap)\\n else:\\n # we are being asked to produce some\\n available_amount = level\\n # the amount we can consume is the minimum of the requested or what's available\\n delta = sign * min(available_amount, abs(amt))\\n return {res: delta}, meta\",\n \"def load_factor(self):\\n return round(self._n / self._size, 2)\",\n \"def recommend_contract(meter_load, percentile=100.0):\\n\\n load_value = np.nanpercentile(meter_load[meter_load != 0], percentile)\\n\\n for contract in contracts_available:\\n if load_value < contract:\\n return contract/kW\\n\\n return contracts_available[-1]/kW\",\n \"def calc_annual_electric_savings (self):\\n costs = self.comp_specs['diesel generator o&m']\\n\\n for kW in costs.keys():\\n try:\\n if self.average_load < int(kW):\\n maintenance = self.comp_specs['diesel generator o&m'][kW]\\n break\\n except ValueError:\\n maintenance = self.comp_specs['diesel generator o&m'][kW]\\n\\n self.baseline_generation_cost = maintenance + \\\\\\n (self.pre_intertie_generation_fuel_used * self.diesel_prices)\\n\\n maintenance = self.capital_costs * \\\\\\n (self.comp_specs['percent o&m'] / 100.0)\\n self.proposed_generation_cost = maintenance + \\\\\\n self.intertie_offset_generation_fuel_used * \\\\\\n self.intertie_diesel_prices\\n self.annual_electric_savings = self.baseline_generation_cost -\\\\\\n self.proposed_generation_cost\\n #~ print len(self.annual_electric_savings)\\n #~ print 'self.annual_electric_savings',self.annual_electric_savings\",\n \"def trucks_required(self) -> int:\\n daily_demand = (\\n self.total_demand() / self.operations.productivity.working_days_per_year\\n )\\n trucks_required = np.ceil(\\n daily_demand\\n / (self.operations.avg_vol_per_lift() * self.inputs.lifts_per_truck_day)\\n )\\n return trucks_required\",\n \"def balanceFactor(self):\\n leftHeight = self.left.height if self.left != None else 0\\n rightHeight = self.right.height if self.right != None else 0\\n return rightHeight - leftHeight\",\n \"def initial_cash_balance(self) -> float:\\n return self.buy_budget * len(self.stocks)\",\n \"def bcRange(self):\\n\\t\\treturn fabs(self.Upper - self.Lower)\",\n \"def get_expected_cost(self):\",\n \"def calculate_br_down_metric(br_down):\\n if br_down < 1:\\n br_down = 1\\n min_baud = 1200\\n max_baud = 38400\\n\\n num = np.log(br_down) - np.log(min_baud)\\n den = np.log(max_baud) - np.log(min_baud)\\n\\n return (num / den + 0.1).clip(min=0, max=1)\",\n \"def test_cpu_limitation(self):\\n expected_dict = self.calculate_expected_values(\\n load_dict=self.load_dict\\n )\\n assert sla_helpers.load_vm_and_check_the_load(\\n load_dict=self.load_dict, expected_values=expected_dict\\n )\",\n \"def _get_new_capacity(self):\\n for prime in primes:\\n if prime > 2 * self.size:\\n return prime\\n raise ValueError(\\\"Error: Table size overflow!\\\")\",\n \"def load_factor_d(self, data):\\n lf_d = np.zeros((data['nr_of_fueltypes']))\\n\\n # Get day with maximum demand (in percentage of year)\\n peak_d_demand = self.fuels_peak_d\\n\\n # Iterate fueltypes to calculate load factors for each fueltype\\n for k, fueldata in enumerate(self.rs_fuels_tot_enduses_d):\\n average_demand = np.sum(fueldata) / 365 # Averae_demand = yearly demand / nr of days\\n\\n if average_demand != 0:\\n lf_d[k] = average_demand / peak_d_demand[k] # Calculate load factor\\n\\n lf_d = lf_d * 100 # Convert load factor to %\\n\\n return lf_d\",\n \"def calc_electric_diesel_reduction (self):\\n gen_eff = self.cd[\\\"diesel generation efficiency\\\"]\\n\\n self.electric_diesel_reduction = self.net_generation_wind / gen_eff\\n\\n electric_diesel = self.generation/gen_eff\\n if self.electric_diesel_reduction > electric_diesel:\\n self.electric_diesel_reduction = electric_diesel\",\n \"def calc_GT_operation_partload(wdot_W, gt_size_W, eta0, m0_exhaust_from_GT_kgpers, fuel_type):\\n assert wdot_W <= gt_size_W\\n\\n if fuel_type == 'NG':\\n exitT = CC_EXIT_T_NG\\n LHV = LHV_NG\\n else:\\n exitT = CC_EXIT_T_BG\\n LHV = LHV_BG\\n\\n part_load_factor = (wdot_W + 1) / gt_size_W # avoid calculation errors # TODO: reference?\\n if part_load_factor < GT_MIN_PART_LOAD:\\n raise ValueError('The load (', wdot_W, ')is lower than minimum part load (', gt_size_W * GT_MIN_PART_LOAD, ').')\\n\\n eta = (0.4089 + 0.9624 * part_load_factor - 0.3726 * part_load_factor ** 2) * eta0 # (4.12) [C. Weber, 2008]_\\n # mdot = (0.9934 + 0.0066 * part_load_factor) * mdot0\\n T_exhaust_GT_K = (0.7379 + 0.2621 * part_load_factor) * exitT # (4.14) [C. Weber, 2008]_\\n m_fuel_kgpers = wdot_W / (eta * LHV)\\n\\n if fuel_type == 'NG':\\n m_exhaust_GT_kgpers = (103.7 * 44E-3 + 196.2 * 18E-3 + 761.4 * 28E-3 + 200.5 * 32E-3 * (CC_AIRRATIO - 1) +\\n 200.5 * 3.773 * 28E-3 * (CC_AIRRATIO - 1)) * m_fuel_kgpers / 1.8156 # TODO: reference?\\n\\n else:\\n m_exhaust_GT_kgpers = (98.5 * 44E-3 + 116 * 18E-3 + 436.8 * 28E-3 + 115.5 * 32E-3 * (CC_AIRRATIO - 1) + \\\\\\n 115.5 * 3.773 * 28E-3 * (CC_AIRRATIO - 1)) * m_fuel_kgpers / 2.754 # TODO: reference?\\n\\n return eta, m_exhaust_GT_kgpers, T_exhaust_GT_K, m_fuel_kgpers\",\n \"def calculate_risk_tol(*args):\\n global total_score\\n risk_tol_start = 0.0\\n\\n for risk_per_pg in risk_tol_per_qs.iterkeys():\\n try:\\n risk_tol_start = risk_tol_start + risk_tol_per_qs[risk_per_pg][-1] # this is the last item in the list of each information in the page\\n except IndexError:\\n pass\\n total_score = risk_tol_start\",\n \"def err_func(x,rv,valore,specn,lcrop,models='da2014'):\\n tmp = tmp_func(x[0], x[1], rv, specn, lcrop, models)\\n if tmp != 1: return abs(tmp[3]-(valore+1.)) #this is quantity that gets minimized \\n else: return 1E30\",\n \"def budget_used(self):\\n return int(self.total_spent() / self.budget() * 100.0)\",\n \"def real_reduce(self, bound, large_constant):\\n approximation_matrix = self.generate_approximation_matrix(large_constant)\\n LLL_matrix = self.generate_LLL_matrix(approximation_matrix)\\n GS_matrix = self.generate_GS_matrix(LLL_matrix) \\n minimal_vector_bound = self.calculate_minimal_vector_bound(large_constant, LLL_matrix, GS_matrix)\\n S, T = self.calculate_S_and_T(self.coefficients[\\\"Z_bounds\\\"])\\n print('ST',T**2 + S)\\n new_bound = self.real_reduce_new_bound(large_constant, minimal_vector_bound, S, T)\\n print('diff_bound',new_bound)\\n return new_bound\",\n \"def single_center_de_needs(servers: int, DE_capacity: int) -> int:\\n\\n return math.ceil(servers / DE_capacity)\",\n \"def gss(self,LB,UB,tol,itr):\\n \\n GoldenRatio = (math.sqrt(5) + 1) / 2\\n \\n iterations=0\\n gss=[]\\n gss_x=[LB,UB]\\n \\n c = UB - (UB - LB) / GoldenRatio\\n d = LB + (UB - LB) / GoldenRatio\\n while abs(UB - LB) > tol and iterations < itr:\\n if self.Func(c) < self.Func(d):\\n UB = d\\n gss_x.append(UB)\\n iterations+=1\\n else:\\n LB = c\\n \\n gss_x.append(LB)\\n iterations+=1\\n c = UB - (UB - LB) / GoldenRatio\\n d = LB + (UB - LB) / GoldenRatio\\n \\n \\n #print(\\\" best at %.15f\\\"% ((UB + LB)/2) , \\\"itr = \\\",iterations)\\n gss.append(gss_x)\\n gss.append((LB+UB)/2)\\n gss.append(iterations)\\n \\n return gss\",\n \"def bottles_required(beer_recipe):\\r\\n bottles_demanded = upcoming_future_prediction(beer_recipe)\\r\\n bottles_ready = delivery_information[beer_recipe]\\r\\n if bottles_ready > bottles_demanded:\\r\\n bottles_needed = 0\\r\\n else:\\r\\n bottles_needed = bottles_demanded - bottles_ready\\r\\n return int(bottles_demanded), bottles_ready, int(bottles_needed)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5978814","0.58166504","0.57432806","0.5645041","0.56362903","0.5612008","0.56099224","0.55491924","0.55470383","0.55240196","0.55133814","0.5455415","0.5440525","0.5410518","0.54084057","0.54057515","0.53936213","0.5387816","0.53826976","0.5381602","0.5380707","0.53788155","0.5361831","0.5352453","0.5351108","0.53442925","0.5342924","0.5335802","0.5322661","0.530402","0.5303664","0.530347","0.5297434","0.5293838","0.5282413","0.5255263","0.5252825","0.524803","0.52473193","0.52371544","0.5229624","0.5228288","0.522574","0.52226675","0.52146053","0.52080464","0.5207208","0.52047914","0.5200887","0.5191351","0.5188143","0.5186163","0.5175419","0.5172129","0.5171992","0.51683956","0.5164187","0.51630235","0.51609766","0.5148888","0.5144369","0.5138755","0.513478","0.512983","0.51272815","0.51267266","0.511937","0.51141244","0.5110717","0.50937605","0.5093549","0.5083582","0.5080394","0.50765574","0.507605","0.50646925","0.5060488","0.505711","0.5040375","0.5033611","0.50285846","0.5019012","0.50114226","0.5009145","0.50022846","0.49942455","0.49872074","0.49831644","0.49805102","0.49802208","0.49756026","0.49747667","0.49720865","0.49628016","0.4957792","0.4949948","0.49492633","0.4948299","0.49476707","0.49402002"],"string":"[\n \"0.5978814\",\n \"0.58166504\",\n \"0.57432806\",\n \"0.5645041\",\n \"0.56362903\",\n \"0.5612008\",\n \"0.56099224\",\n \"0.55491924\",\n \"0.55470383\",\n \"0.55240196\",\n \"0.55133814\",\n \"0.5455415\",\n \"0.5440525\",\n \"0.5410518\",\n \"0.54084057\",\n \"0.54057515\",\n \"0.53936213\",\n \"0.5387816\",\n \"0.53826976\",\n \"0.5381602\",\n \"0.5380707\",\n \"0.53788155\",\n \"0.5361831\",\n \"0.5352453\",\n \"0.5351108\",\n \"0.53442925\",\n \"0.5342924\",\n \"0.5335802\",\n \"0.5322661\",\n \"0.530402\",\n \"0.5303664\",\n \"0.530347\",\n \"0.5297434\",\n \"0.5293838\",\n \"0.5282413\",\n \"0.5255263\",\n \"0.5252825\",\n \"0.524803\",\n \"0.52473193\",\n \"0.52371544\",\n \"0.5229624\",\n \"0.5228288\",\n \"0.522574\",\n \"0.52226675\",\n \"0.52146053\",\n \"0.52080464\",\n \"0.5207208\",\n \"0.52047914\",\n \"0.5200887\",\n \"0.5191351\",\n \"0.5188143\",\n \"0.5186163\",\n \"0.5175419\",\n \"0.5172129\",\n \"0.5171992\",\n \"0.51683956\",\n \"0.5164187\",\n \"0.51630235\",\n \"0.51609766\",\n \"0.5148888\",\n \"0.5144369\",\n \"0.5138755\",\n \"0.513478\",\n \"0.512983\",\n \"0.51272815\",\n \"0.51267266\",\n \"0.511937\",\n \"0.51141244\",\n \"0.5110717\",\n \"0.50937605\",\n \"0.5093549\",\n \"0.5083582\",\n \"0.5080394\",\n \"0.50765574\",\n \"0.507605\",\n \"0.50646925\",\n \"0.5060488\",\n \"0.505711\",\n \"0.5040375\",\n \"0.5033611\",\n \"0.50285846\",\n \"0.5019012\",\n \"0.50114226\",\n \"0.5009145\",\n \"0.50022846\",\n \"0.49942455\",\n \"0.49872074\",\n \"0.49831644\",\n \"0.49805102\",\n \"0.49802208\",\n \"0.49756026\",\n \"0.49747667\",\n \"0.49720865\",\n \"0.49628016\",\n \"0.4957792\",\n \"0.4949948\",\n \"0.49492633\",\n \"0.4948299\",\n \"0.49476707\",\n \"0.49402002\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.555809"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":6788,"cells":{"query":{"kind":"string","value":"This function allows for testing of a set of dimensions, printing key info as a result."},"document":{"kind":"string","value":"def tester(dim):\n print(\"Unit Mass: \" + str(mass(dim)))\n print(\"Skin Buckle RSF: \" + str(skinBuckle(dim)+1))\n print(\"Stiffener Buckle RSF: \" + str(stiffenerBuckle(dim)+1))\n print(\"Mat Fail RSF: \" + str(matFail(dim)+1.1))\n print(\"Euler Fail RSF: \" + str(eulerBuckle(dim)+1.1))"},"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 print_dict_dimensions(entities_db, title='Title', message=''):\n total = 0\n m = f'{title}\\n'\n for k1, v1 in entities_db.items():\n m += f'\\n{message}{k1}:\\n'\n if isinstance(v1, dict):\n for k2, v2 in v1.items():\n if isinstance(v2, tuple):\n m += f'{k2} {v2[0]} mapped entities, {v2[1] * 100}% input {k2} coverage.\\n'\n else:\n print()\n m += f'{k2} {v2}\\n'\n else:\n if isinstance(v1, tuple):\n m += f'{v1[0]} mapped entities, {v1[1] * 100}% input coverage\\n'\n else:\n m += f'{v1}'\n\n print(f'{m}\\n')","def recursive_dict_key_print(dict_in, spacer=\"\"):\n if type(dict_in) is not dict:\n return\n next_spacer = spacer + \" \"\n for key, value in dict_in.items():\n try:\n print(spacer, f\"{key} : {value.shape}\")\n except(AttributeError):\n print(spacer, key)\n recursive_dict_key_print(value, next_spacer)","def test_dimensions(self):\n\t\t\n\t\t# default\t\n\t\tdetails = self.watcher.describe()\n\t\tprint(details)\n\t\t\n\t\t# default\t\n\t\tdetails = self.watcher.describe(layers=[self.first_layer])\n\t\tprint(details)\n\t\tN = details.N.to_numpy()[0]\n\t\tM = details.M.to_numpy()[0]\n\t\trf = details.rf.to_numpy()[0]\n\t\tnum_evals = details.num_evals.to_numpy()[0]\n\t\tprint(N,M,rf,num_evals)\n\t\t\n\t\tself.assertEqual(N,64)\n\t\tself.assertEqual(M,3)\n\t\tself.assertEqual(rf,9)\n\t\tself.assertEqual(num_evals,M*rf)","def run_print_dict_examples():\n print()\n print_dict_keys(NAME_DICT)\n print()\n print_dict_items(NAME_DICT)","def run_print_dict_examples():\n print()\n print_dict_keys(NAME_DICT)\n print()\n print_dict_items(NAME_DICT)","def printt(dictionnary):\n for key, value in dictionnary.iteritems():\n print('{key}, size: {size}, {values}'.format(key=key, \n size=len(value), values=value[0:4]))","def report_keyset(self):\n for i, matchset in enumerate(self.matches):\n if len(matchset) == 1:\n print \"[%02d]\" % i, fmt(sorted([k for k, data in matchset.items()]), BLUE)\n elif len(matchset) != 0:\n print \"[%02d]\" % i, fmt(sorted([k for k, data in matchset.items()]), WHITE)\n else:\n print \"[%02d]\" % i, fmt(\"[X]\", RED)","def printDict(myDict):\n for key in myDict:\n print(f\"Version: --> {myDict[key]['version']} \")\n print(f\"Accuracy: --> {myDict[key]['accuracy']}\")\n print(f\"Time --> {myDict[key]['time_per_target']}\")\n print(f\"Penalty --> {myDict[key]['target_w_penalty']}\")\n print(f\"ID --> {myDict[key]['assessed_by']}\")\n print(f\"# --> {myDict[key]['attempt']}\")\n\n print()","def metadata_print(metadata):\n\n print('{0:<10} {1}'.format('parameter', 'value'))\n for key in metadata:\n print('{0:<10} {1}'.format(key, metadata[key]))","def printMap(values, klab, vlab, precision, offset=16):\n\tprint(klab.ljust(offset, \" \") + vlab)\n\tfor k in values.keys():\n\t\tv = values[k]\n\t\tks = toStr(k, precision).ljust(offset, \" \")\n\t\tvs = toStr(v, precision)\n\t\tprint(ks + vs)","def printValues(self):\n\n for layer in self.LayerNames:\n print \"-------- {0} --------\".format(layer)\n print \"nWorkingModules: {0}\".format(self.nWorkingModules[layer])\n print \"Pixels per Layer\"\n print \" Pixels hit: {0}\".format(self.hitPix[layer])\n print \" Occupancy: {0}\".format(self.occupancies[layer])\n print \" Pixels hit per Module: {0}\".format(self.hitPixPerModule[layer])\n print \" Pixels hit per Area: {0}\".format(self.hitPixPerArea[layer])\n print \" Pixels hit per Area per sec: {0}\".format(self.hitPixPerAreaSec[layer])\n print \"Pixels per Det\"\n print \" Occupancy (Det): {0}\".format(self.Detoccupancies[layer])\n print \" Pixels hit per Det: {0}\".format(self.hitPixPerDet[layer])\n print \" Pixels hit per DetArea: {0}\".format(self.hitPixPerDetArea[layer])\n print \" Pixels hit per DetArea per sec: {0}\".format(self.hitPixPerDetAreaSec[layer])\n print \"Cluster per Layer\"\n print \" Clusters hit: {0}\".format(self.hitClusters[layer])\n print \" Clusters hit per module: {0}\".format(self.hitClustersPerModule[layer])\n print \" Clusters hit per Area: {0}\".format(self.hitClustersPerArea[layer])\n print \" Clusters hit per Area per sec: {0}\".format(self.hitClustersPerAreaSec[layer])\n print \"Clusters per Det\"\n print \" Clusters hit per Det: {0}\".format(self.hitClustersPerDet[layer])\n print \" Clusters hit per DetArea: {0}\".format(self.hitClustersPerDetArea[layer])\n print \" Clusters hit per DetArea per sec: {0}\".format(self.hitClustersPerDetAreaSec[layer])","def test_get_dimension(self):\n\n v = Vector({ 'x': 1 })\n self.assertEqual(1, v.dimensions['x'])","def printInfo(matrix):\n\n print(\"Groups:\")\n for group in matrix.matrix.group_labels:\n print(\"\\t{0}\".format(group))\n\n print(\"Samples:\")\n for sample in matrix.matrix.sample_labels:\n print(\"\\t{0}\".format(sample))","def main():\n dims = params['dims']\n\n for d in dims:\n print('**** Running test for d={0:d} ****'.format(d))\n run_test(d)","def visualize_debug(example_dict, prefix):\n region_string = example_dict[\"example_metadata\"][0].strip(\"\\x00\").split(\";\")[0].split(\"=\")[1].replace(\":\", \"-\")\n region_prefix = \"{}.{}\".format(prefix, region_string)\n \n for key in example_dict.keys():\n\n # visualize importances\n if \"importances\" in key:\n plot_name = \"{}.{}.pdf\".format(region_prefix, key)\n print plot_name\n plot_weights(np.squeeze(example_dict[key]), plot_name)\n\n # visualize pwm scores\n if \"pwm-scores\" in key:\n pass\n \n\n return None","def test_has_correct_number_of_keys_and_values(self):\n self.has_correct_number_of_keys_and_values(2, 1)","def test_has_correct_number_of_keys_and_values(self):\n self.has_correct_number_of_keys_and_values(2, 1)","def print_image(img):\n for k in img:\n print(\"\".join([str(x) for x in k] ))","def display_dict() -> None:\n for key in ascii_dict:\n print(key, ': ')\n for line in ascii_dict[key]:\n print(line)","def show_info(self, group):\n print(group)\n for child in self.dict[group]:\n if child=='Version':\n print(child, ':', self(group, child))\n elif not self(group, child).shape:\n print(child, ':',self(group, child))\n else:\n print(child,': size', self(group, child).shape)","def _format_dimensions(dimensions):\n if not dimensions:\n return \"\"\n\n dim_pairs = [\"%s=%s\" % (k, v) for k, v in dimensions.items()]\n return \"[%s]\" % (\",\".join(dim_pairs))","def print_table():\n for key in _op_table.keys():\n print(key)\n for sub_key in _op_table[key]:\n print('\\t--' + sub_key)","def debug(self):\n \n #path\n print('Path information:')\n for k, v in self.__path.items():\n print(k, v)\n \n #sample count\n print('Sample statistic of each phase')\n for k, v in self.__phase_sample_count.items():\n print(k, v)\n \n print('Sample statistic of each class')\n for k, v in self.__area_sample_count.items():\n print(k, v)\n \n print('Sample statistic of each train')\n for k, v in self.__train_sample_count.items():\n print(k, v)","def recursively_print_structure(item, leading = ''):\n for key in item:\n if isinstance(item[key], h5py.Dataset):\n print(leading + key + ': ' + str(item[key].shape))\n else:\n print(leading + key)\n recursively_print_structure(item[key], leading + ' ')","def printResults(self):\n for key in self.mDict.keys():\n print ('for {:d}, entries = {:d} and exits = {:d}'.format (key, self.mDict.get(key).get ('entries'), self.mDict.get(key).get ('exits')))","def print_configuration_info():\n print(\"Selected dataset:\", DATASET) \n print(\"Dataset base directory:\", BASE_INPUT_DIR) \n print(\"Daytime option:\", DAYTIME) \n print(\"Nones option:\", NONES) \n print(\"Selected action/activity representation:\", OP)\n print(\"Number of epochs: \", EPOCHS)\n print(\"Number of folds for cross-validation: \", FOLDS)\n print(\"Input directory for data files:\", INPUT_DIR) \n print(\"Embedding matrix file:\", EMBEDDING_WEIGHTS)\n print(\"Action sequences (X) file:\", X_FILE) \n print(\"Word embedding file for activities:\", ACTIVITY_EMBEDDINGS) \n print(\"Activity to int mappings:\", ACTIVITY_TO_INT)\n print(\"Int to activity mappings:\", INT_TO_ACTIVITY) \n print(\"Experiment ID:\", EXPERIMENT_ID)\n print(\"Treat imbalance data:\", TREAT_IMBALANCE)\n print(\"Save intermediate plots:\", SAVE)\n print(\"Batch size:\", BATCH_SIZE)\n print(\"Dropout:\", DROPOUT)\n print(\"Loss:\", LOSS)","def show(matrix):\n print(\"\",end=\" \")\n for k in sorted(matrix.keys()):\n print(k,end=\" \")\n \n for i,row in sorted(matrix.items()):\n print(\"\\n\" + str(i),end=\" \")\n for j in row:\n print(matrix[i][j],end=\" \")\n print()","def print_anagram_sets(d):\r\n for v in d.values():\r\n if len(v) > 1:\r\n print(len(v), v)","def print_info(self, i):\n\n im_size = self.image_size(i)\n print 'The path of the image is: {}'.format(self.image_path_at(i))\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\n \n attr_i = self.gtdb['attr'][i, :]\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))","def printsection(section):\n print('===')\n for key in section.keys():\n print(\"Key: %s\" % key)\n for item in section[key]:\n print(' %s' % item)","def view_shapes(xm_train, xm_dev, xm_test, ym_train, ym_dev, ym_test):\n\n print('X_train shape: ' + str(xm_train.shape))\n print('Y_train shape: ' + str(ym_train.shape))\n print('X_dev shape: ' + str(xm_dev.shape))\n print('Y_dev shape: ' + str(ym_dev.shape))\n print('X_test shape: ' + str(xm_test.shape))\n print('Y_test shape: ' + str(ym_test.shape))\n print('\\n')","def dimensions():","def print_model_definitions(self):\n sys.stdout.write(\"Model Dimensions\\n\")\n sys.stdout.write(\"----------------\\n\")\n for key, val in self.dimensions.iteritems():\n sys.stdout.write(\"{key}: {val}\\n\".format(key=key, val=val))","def print_kwargs(**kwargs):\n for key in kwargs:\n print('%s %s' %(key, kwargs[key]))","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_categories)","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_categories)","def show_shape_metrics(shape):","def test_basic_property_of_random_matrix():\n for name, random_matrix in all_random_matrix.items():\n print(name)\n\n check_input_size_random_matrix(random_matrix)\n check_size_generated(random_matrix)\n if name != \"random_subsample_normalized\":\n check_zero_mean_and_unit_norm(random_matrix)\n check_approximate_isometry(random_matrix)","def test_RegionDimensionsBox_extractFromLinesWithKey(self):\r\n\r\n regionDimensions = RegionDimensions.RegionDimensionsBox()\r\n\r\n numberParameters = 6\r\n self.assertEquals(numberParameters, len(regionDimensions._keys))\r\n\r\n line = \"RegionParameters=0.000000 10000000000.000000 -10000000000.000000 10000000000.000000 0.000000 20000000000.000000\"\r\n regionDimensions.extractFromLinesWithKey(line)\r\n\r\n self.assertEquals(0.0, regionDimensions.minimumX)\r\n self.assertEquals(10000000000.0, regionDimensions.maximumX)\r\n self.assertEquals(-10000000000.0, regionDimensions.minimumY)\r\n self.assertEquals(10000000000.0, regionDimensions.maximumY)\r\n self.assertEquals(0.0, regionDimensions.minimumZ)\r\n self.assertEquals(20000000000.0, regionDimensions.maximumZ)\r\n\r\n #self.fail(\"Test if the testcase is working.\")\r","def print_config(config_dic, logger):\n for k, v in config_dic.items():\n logger.info(\"{}:\\t{}\".format(k.ljust(15), v))","def print_q(q):\n for key in sorted(q.keys()):\n print(key, end=\" \")\n value = q[key]\n for i in range(len(value)):\n print(value[i], end=\" \")\n print()","def dimension_check():\n print(\"### DIMENSION CHECK ###\")\n print(X.shape,\n y.shape,\n X_train.shape,\n y_train.shape,\n X_test.shape,\n y_test.shape,\n weights.shape)\n print(\"### END ###\")","def print_metric_dict(self, metric_dict):\n print(\"\".join([\" {}: {:4f},\".format(k, v) for k, v in metric_dict.items()]))","def printdict(input_dict):\n for key in input_dict:\n print key, \":\", input_dict[key]","def print_device_dict(device_dict):\n for device_id in device_dict:\n print(str(device_id) + ':', device_dict[device_id]['name'])","def info(d, return_dict=False, print_=True):\n info_ = {}\n for k,v in d.items():\n if isinstance(v, dict):\n info_.update(info(v))\n else:\n info_[k] = {\n 'size': np.asarray(v).ravel().shape,\n 'shape' :np.asarray(v).shape,\n 'dtype': np.asarray(v).dtype.name\n }\n if print_:\n _v = np.asarray(v)\n print('key -', k)\n print('dtype -', _v.dtype.name)\n print('size -', np.asarray(v).ravel().shape)\n print('shape -', _v.shape)\n print()\n if return_dict:\n return info_","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_sample_ids)","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_sample_ids)","def print_output():\n print(\"count: [primary: \"+str(primary_shards)+\", replica: \"+str(secondary_shards)+\"]\")\n print(\"size: [primary: \"+pretty_print_storage(total_size_primary)+\", replica: \"+pretty_print_storage(total_size_secondary)+\"]\")\n print(\"disk-max-node: \"+max_size_node_name)\n print(\"watermark-breached: \"+str(watermark_breached))","def info(self, n=80):\n # concatenate list of index dimensions and levels\n info = f\"{type(self)}\\nIndex dimensions and data coordinates:\\n\"\n c1 = max([len(i) for i in self.dimensions]) + 1\n c2 = n - c1 - 5\n info += \"\\n\".join(\n [\n f\" {i:{c1}}: {print_list(getattr(self, i), c2)}\"\n for i in self.dimensions\n ]\n )\n\n return info","def show_map(map_):\n for r in map_.matrix:\n print(''.join(r))\n print()","def display(self):\n # type: ()->None\n print('============')\n for key, value in self._ifAttributes.items():\n if isinstance(value, list):\n print(key + ': ')\n for item in value:\n print('\\t' + item)\n elif isinstance(value, dict):\n print(key + ': ')\n for item in value.keys():\n print('\\t' + item + ': ' + value[item])\n else:\n print(key + ': ' + str(value))\n print('============')","def dimension_testing(self, dimension):\n if dimension in self.allowed_dimensions:\n return ''\n else:\n error_msg = 'Dimension %s is not supported. Choose from %s' % (dimension, self.allowed_dimensions)\n return error_msg","def print_map(self):\n y_max,x_max = map(max, zip(*self.mp.keys()))\n for row in range(0,y_max+1):\n msg = []\n for k in range(0,x_max+1):\n msg.append(chr(self.mp[row,k]))\n print(\"\".join(msg))","def display_layers(layers, wide, tall):\n\n colours = {\n \"0\": \" \",\n \"1\": \" # \",\n }\n\n for row in range(tall):\n for col in range(wide):\n pixels = [layer[row][col] for layer in layers]\n line = next(colours[p] for p in pixels if p in colours)\n print(line, end=\"\")\n print()","def print_infrastructure(aws_key, aws_secret):\n _, _, _, redshift_client = create_clients(aws_key, aws_secret)\n for k, v in get_cluster_properties(redshift_client):\n print(k, v)","def valid_config_keys():\n click.echo(', '.join(get_class_properties(GenConfig)))","def printPicnic(itemsDict: dict, leftWidth: int, rightWidth: int) -> None:\n print('PICNIC ITEMS'.center(leftWidth + rightWidth, '-'))\n for k, v in itemsDict.items():\n print(k.ljust(leftWidth, '.') + str(v).rjust(rightWidth))","def show_values():\n dic_drg = {}\n dic_age = {}\n dic_sex = {}\n dic_sline = {}\n for tup in all_data:\n drg = tup[7]\n age = tup[9]\n sex = tup[10]\n sline = tup[14]\n\n dic_drg[drg] = 1\n dic_age[age] = 1\n dic_sex[sex] = 1\n dic_sline[sline] = 1\n\n print \"Age values\"\n for key in sorted(dic_age.keys()):\n print key\n\n print \"Sex values\"\n for key in sorted(dic_sex.keys()):\n print key\n\n print \"Service line values\"\n for key in sorted(dic_sline.keys()):\n if key is None or len(key) == 0:\n continue\n print \"'\" + key + \"',\",\n print\n\n print \"Drg values\"\n for key in sorted(dic_drg.keys()):\n if key is None or len(key) == 0:\n continue\n print\"'\" + key + \"',\",\n print","def test_section_keys(self):\n ars = self.ar[2009][11]['general']\n self.assertEqual(ars.keys(), self.__class__.wanted_lines)","def test_getitem(self):\n space = Space()\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", 2, 3, 4)\n dim = Categorical(\"yolo\", OrderedDict(zip(categories, probs)), shape=2)\n space.register(dim)\n dim = Integer(\"yolo2\", \"uniform\", -3, 6)\n space.register(dim)\n dim = Real(\"yolo3\", \"norm\", 0.9)\n space.register(dim)\n\n assert space[\"yolo\"].type == \"categorical\"\n assert space[0].type == \"categorical\"\n\n with pytest.raises(KeyError):\n space[\"asdf\"]\n\n with pytest.raises(IndexError):\n space[3]","def test_main_multiple_keys(self):\n args = [\n \"--layout\",\n self.layout_double_signed_path,\n \"--layout-keys\",\n self.alice_path,\n self.bob_path,\n ]\n self.assert_cli_sys_exit(args, 0)","def test_main_multiple_keys(self):\n args = [\n \"--layout\",\n self.layout_double_signed_path,\n \"--layout-keys\",\n self.alice_path,\n self.bob_path,\n ]\n self.assert_cli_sys_exit(args, 0)","def test_partially_one(self):\n setups = self.get_setup().decompress([\"dimensions.species_id\"])\n assert isinstance(setups, PlotSetupGroup)\n assert all(isinstance(setup, PlotSetup) for setup in setups)\n assert len(setups) == 2\n res = {\n (\n s.panels.collect_equal(\"dimensions\").variable,\n s.panels.collect_equal(\"dimensions\").species_id,\n s.panels.collect_equal(\"dimensions\").time,\n )\n for s in setups\n }\n sol = {\n ((\"dry_deposition\", \"wet_deposition\"), 1, (1, 2, 3)),\n ((\"dry_deposition\", \"wet_deposition\"), 2, (1, 2, 3)),\n }\n assert res == sol","def test_all_keys(self):\n\t\tdocumentation: dict = self.handlerObject.build_swagger_documentation()\n\t\t# print(\"Test documentation was generated. The corresponding object is:\\n{0}\".format(documentation))\n\t\tself.assertTrue(compare_keys(documentation, TEST_ALL_KEYS_EXPECTED_OUTPUT))","def cli(dimensions):\r\n click.echo(\"Iris Flower classifier\\n\")\r\n \r\n\r\n click.echo(\"Calculating result...\")\r\n time.sleep(1)\r\n results = zip(dimm_names, dimensions)\r\n click.echo(\"Input data:\")\r\n for i, j in results:\r\n click.echo(\"{:12} -> {}\".format(i, j))\r\n\r\n click.echo()\r\n click.echo(\"Your flower seems to be fine example of:\")\r\n click.secho(\"{}\".format(\"species\"), fg='green', bold=True)","def check_and_print_if_error(self): # pragma: no cover\n dupes, empties, not_detected = self._get_aberrations()\n if dupes:\n print 'duplicate entries for:'\n for dup, matches in dupes:\n print ' %s: %s' % (dup, [f.func_name for f in matches])\n if empties:\n print 'empty entries for:'\n for empty in empties:\n print ' ' + str(empty)\n if not_detected:\n print 'dimensions not detected:'\n for n_d in not_detected:\n print ' ' + str(n_d)\n return self.is_correct","def test_dict_keywords(self):\n output, _err = self.executor.docker.run('lego:1', env=dict(SPECIAL='emett',\n SONG='awesome')).batch()\n self.assertEqual(output, 'everything')","def printDicts():\n for k in key:\n print k, key[k]\n \n for f in freq:\n print f, freq[f]\n \n for e in english:\n print e, english[e]","def print_data():\r\n\r\n d = data()\r\n for i in d:\r\n for key, value in i.items():\r\n print(key, \" : \", value)\r\n print()","def test_get_dimensions_by_label(self):\n\n dimensions, dim_names = pyjstat.get_dimensions(\n self.oecd_datasets['oecd'], 'label')\n self.assertTrue(dim_names[2] == '2003-2014')\n self.assertTrue(dimensions[0].iloc[0]['label'] == 'Unemployment rate')","def print_possibility_space():\n\n print(\"Possibility space:\")\n print(\" {} unique sword images\".format(calculate_image_possibilities()))","def test_product_initialization(product_design_space):\n assert product_design_space.name == 'my design space'\n assert product_design_space.description == 'does some things'\n assert len(product_design_space.dimensions) == 3\n assert product_design_space.dimensions[0].descriptor.key == 'alpha'\n assert product_design_space.dimensions[1].descriptor.key == 'beta'\n assert product_design_space.dimensions[2].descriptor.key == 'gamma'","def print_img_data(img,title='Image'):\n print(f'________ {title} _______')\n if len(image.shape) == 2:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\n')\n elif len(image.shape) == 3:\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\n\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\n')","def test17():\n\n\twardrobe = {\"shirt\":[\"red\",\"blue\",\"white\"], \"jeans\":[\"blue\",\"black\"]}\n\tfor cloth in wardrobe.keys():\n\t\tfor color in wardrobe[cloth]:\n\t\t\tprint(\"{} {}\".format(color,cloth))","def print_info(self):\n print(\"Experiment key: \" + self.key)\n print(\"Experiment name: \" + self.name)\n print(\"Experiment path: \" + self.output_path)\n print(\"Auto-sync activated: \" + str(self.auto_sync))\n print(\"\")\n print(\"Experiment metadata: \")\n print(self.exp_metadata.to_str())","def print_stats(ds):\n print(\"Dataset Name: \" + ds.name)\n print(\"Dataset Mode: \" + ds.mode)\n print(\"Band Count: \" + str(ds.count))\n print(\"Dataset Width: \" + str(ds.width))\n print(\"Dataset Height: \" + str(ds.height))\n print(\"Dataset Bounds: \", ds.bounds)\n print(\"Dataset Transform: \", ds.transform)\n ul = ds.transform * (0, 0)\n print(\"Upper Left Corner: \", ul)\n lr = ds.transform * (ds.width, ds.height)\n print(\"Lower Right Corner: \", lr)\n {i: dtype for i, dtype in zip(ds.indexes, ds.dtypes)}","def print_args(args):\n _args = vars(args)\n max_length = max([len(k) for k, _ in _args.items()])\n for k, v in _args.items():\n print(' ' * (max_length - len(k)) + k + ': ' + str(v))","def print_eval(eval_dict):\n\n status = ''.join(\n ['{}: {:.6f}, '.format(key, eval_dict[key]) for key in sorted(eval_dict)])\n print(status[:-2])","def view_keys(dict):\n claves=list(dict.keys())\n claves.sort()\n for line in claves:\n print(line.upper(),' = ',dict[line])","def test_antialiasing():\n images = generate_test_images()\n loaded = load_test_images(images)\n print(list(loaded.keys()))\n for description in images.keys():\n assert (images[description] == loaded[description]).all()","def test_map_basics(self):\n self.assertDigitizerMapBasics(self.map, self.dgroup)","def print_seeds(self):\n for key in self.CONFIG.keys():\n if \"EMI\" in key:\n print key","def display_map():\r\n for row in range(0, len(map1)): #for all rows\r\n for column in range(0, len(map1[0])): #for all columns\r\n print(map1[row][column], end=' ')\r\n print()","def var_sizes_check(global_dic):\n import types\n def _print(a, b):\n print(\"|{:>15}|{:>13}|\".format(a, b))\n\n _print(\"Variable\", \"Size\")\n print(\"-\"*31)\n for k, v in global_dic.items():\n if not k.startswith('_') and not isinstance(v, types.ModuleType):\n # print size of variable\n if hasattr(v, 'size'):\n try:\n _print(k, v.size)\n except:\n continue\n # print length of variable\n elif hasattr(v, '__len__'):\n try:\n _print(k, len(v))\n except:\n continue","def helper():\n for mode in PackingMode:\n for bin_algo in PackingBin:\n for size, w, h in ('big_enough', 50, 50), ('too_small', 5, 5):\n name = '_'.join(('test', mode, bin_algo, size))\n print(\"\"\"\\\n def %s(self):\n # create bins that are %s to hold the rectangles\n p = self._common(PackingMode.%s, PackingBin.%s, %s, %s)\"\"\" %\n (name, size.replace('_', ' '), mode, bin_algo, w, h))\n if size == 'big_enough':\n print(\"\"\"\\\n # check that bins were created\n self.assertGreater(len(p.bin_list()), 0)\n # check that all of the rectangles made it in\n self.assertEqual(len(p.rect_list()), len(self.rectangles))\n\"\"\")\n else:\n print(\"\"\"\\\n # check that none of the rectangles made it in\n self.assertEqual(len(p.rect_list()), 0)\n\"\"\")","def print_character(character):\n for k, v in character.items():\n print(str(k) + ': ' + str(v)) # Prints every key:value pair in character dictionary\n if len(character) == 8:\n print(\"Inventory:\" + str(character[7])) # If there is an inventory, then print it as well","def pretty_keys(dictionary):\n if not dictionary:\n return []\n # - number of keys printed per line\n num = 5\n # - turn into sorted list\n keys = list(dictionary.keys())\n keys.sort()\n # - fill with blank elements to width num\n missing = (len(keys) % num)\n if missing != 0:\n to_add = num - missing\n keys.extend([''] * to_add)\n # - turn into 2D matrix\n matrix = [[keys[i+j] for i in range(0, num)]\n for j in range(0, len(keys), num)]\n # - calculate max width for each column\n len_matrix = [[len(col) for col in row] for row in matrix]\n max_len_col = [max([row[j] for row in len_matrix])\n for j in range(0, num)]\n # - pad with spaces\n matrix = [[row[j].ljust(max_len_col[j]) for j in range(0, num)]\n for row in matrix]\n # - return list of lines to print\n matrix = [' '.join(row) for row in matrix]\n return matrix","def testFourDimensions(self):\n false1 = (True, True, True, False)\n false2 = (True, False, True, False)\n true1 = (False, True, False, True)\n true2 = (True, True, False, False)\n tt = cros_test_lib.TruthTable(inputs=(false1, false2), input_result=False)\n self.assertEquals(len(tt), pow(2, 4))\n\n # Check truth table output.\n self.assertFalse(tt.GetOutput(false1))\n self.assertFalse(tt.GetOutput(false2))\n self.assertTrue(tt.GetOutput(true1))\n self.assertTrue(tt.GetOutput(true2))\n\n # Check assertions on bad input to GetOutput.\n self.assertRaises(TypeError, tt.GetOutput, True)\n self.assertRaises(ValueError, tt.GetOutput, (True, True, True))\n\n # Check iteration over input lines.\n lines = list(tt)\n self.assertEquals((False, False, False, False), lines[0])\n self.assertEquals((False, False, False, True), lines[1])\n self.assertEquals((False, True, True, True), lines[7])\n self.assertEquals((True, True, True, True), lines[15])\n\n self._TestTableSanity(tt, lines)","def test_overall_report_keys():\n keys = overall_data.keys()\n assert('banner_report' in keys)\n assert('rewarded_report' in keys)\n assert('interstitial_report' in keys)\n assert('overall_report' in keys)","def printMetadata(metadata):\n\tif metadata:\n\t\tfor x in metadata:\n\t\t print(x+': {}'.format(metadata[x]) )\n\t\treturn True\n\telse:\n\t\tprint('metadata empty')\n\t\tprint('unable to find metadata at this location')\n\t\tprint('')\n\t\treturn False","def print_dict(dictionary):\n for x,y in dictionary.items():\n print(x, y)","def print_args():\n for key, value in vars(ARGS).items():\n print(key + ' : ' + str(value))","def _debug_print(self, cfg, list_iterstore):\n def __print_params(iterstore, edataset):\n if (edataset not in iterstore): return\n gen = iterstore[edataset]\n if (gen is not None):\n if (gen._params is not None):\n print('{} : {}'.format(edataset, gen))\n print(\"\\tIterator Parameters: (iter_param, iter_pp_param)\")\n print(\"\\t-------------------------------------------------\")\n for key, val in gen._params.items():\n if (key.startswith('_')): continue\n print('\\t{} : {}'.format(key, val))\n \n if (gen._pp_params is not None):\n print(\"\\t-------------------------------------------------\")\n for key, val in gen._pp_params.items():\n if (key.startswith('_')): continue\n print('\\t{} : {}'.format(key, val))\n print(\"\")\n\n if (list_iterstore is None):\n return\n\n print(\"\\nNNCfg\")\n print(\"=====\")\n for key, val in cfg.__dict__.items():\n if (key.startswith('_')): continue\n print('\\t{} : {}'.format(key, val))\n\n for i, iterstore in enumerate(list_iterstore):\n print(\"\\nIterator Store:{}\".format(i))\n print(\"=================\")\n __print_params(iterstore, Dataset.TR)\n __print_params(iterstore, Dataset.VAL)\n __print_params(iterstore, Dataset.TE)\n __print_params(iterstore, Dataset.TR_OUT)\n __print_params(iterstore, Dataset.VAL_OUT)\n __print_params(iterstore, Dataset.TE_OUT)","def printCounterByKey(counter):\n for k in counter.keys():\n print(k, counter[k])","def print(self):\n tiles = list(map(list, zip(*self.tiles))) # transposed\n print('tiles = [')\n for row in tiles:\n print('\\t' + repr(row))\n print(']')\n print('props = [')\n for prop in self.props:\n print('\\t' + repr(prop))\n print(']')","def _print_issue(issue, ntabs):\n for key, value in issue.items():\n if isinstance(value, dict):\n tabs = TAB*ntabs\n print('%s%s (section):' % (tabs, key))\n ntabs += 1\n print_issue(value, ntabs=ntabs)\n elif isinstance(value, bool):\n if value == False:\n tabs = TAB*ntabs\n print('%s%s parameter is missing.' % (tabs, key))\n continue\n else:\n tabs = TAB*ntabs\n print('%s%s (parameter):' % (tabs, key))\n tabs = TAB*(ntabs+1)\n print('%s%s' % (tabs, value))","def test_print_args_dict(self):\n log_file = os.path.join(DATA_DIR, 'sample_log.txt')\n if os.path.exists(log_file):\n os.remove(log_file)\n self.assertEqual(os.path.exists(log_file), False)\n manager = execution.LogManager('MainThread', log_file)\n\n args = {}\n manager.print_args(args)\n self.assertEqual(count_lines(log_file), 4)\n os.remove(log_file)","def show(self, keys=None, sort_keys_function=None):\n output_keys = keys or self.keys\n if not self.items:\n print(\"No items to show\")\n else:\n for item in self.__get_items(sort_keys_function):\n for output_key in output_keys:\n print(\"{0:25}: {1!s}\".format(output_key, getattr(item, self.mapping[output_key])))\n print(\"-\" * 25)","def _validate_dimensions(config):\n logging.info(\"Checking provided dimensions are valid\")\n for feature in config.get(\"test-suites\").values():\n for test_name, test in feature.items():\n for dimensions_config in test[\"dimensions\"]:\n _validate_schedulers(config, dimensions_config.get(\"schedulers\", []))\n if [] in dimensions_config.values():\n logging.error(\"Values assigned to dimensions in test %s cannot be empty\", test_name)\n raise AssertionError"],"string":"[\n \"def print_dict_dimensions(entities_db, title='Title', message=''):\\n total = 0\\n m = f'{title}\\\\n'\\n for k1, v1 in entities_db.items():\\n m += f'\\\\n{message}{k1}:\\\\n'\\n if isinstance(v1, dict):\\n for k2, v2 in v1.items():\\n if isinstance(v2, tuple):\\n m += f'{k2} {v2[0]} mapped entities, {v2[1] * 100}% input {k2} coverage.\\\\n'\\n else:\\n print()\\n m += f'{k2} {v2}\\\\n'\\n else:\\n if isinstance(v1, tuple):\\n m += f'{v1[0]} mapped entities, {v1[1] * 100}% input coverage\\\\n'\\n else:\\n m += f'{v1}'\\n\\n print(f'{m}\\\\n')\",\n \"def recursive_dict_key_print(dict_in, spacer=\\\"\\\"):\\n if type(dict_in) is not dict:\\n return\\n next_spacer = spacer + \\\" \\\"\\n for key, value in dict_in.items():\\n try:\\n print(spacer, f\\\"{key} : {value.shape}\\\")\\n except(AttributeError):\\n print(spacer, key)\\n recursive_dict_key_print(value, next_spacer)\",\n \"def test_dimensions(self):\\n\\t\\t\\n\\t\\t# default\\t\\n\\t\\tdetails = self.watcher.describe()\\n\\t\\tprint(details)\\n\\t\\t\\n\\t\\t# default\\t\\n\\t\\tdetails = self.watcher.describe(layers=[self.first_layer])\\n\\t\\tprint(details)\\n\\t\\tN = details.N.to_numpy()[0]\\n\\t\\tM = details.M.to_numpy()[0]\\n\\t\\trf = details.rf.to_numpy()[0]\\n\\t\\tnum_evals = details.num_evals.to_numpy()[0]\\n\\t\\tprint(N,M,rf,num_evals)\\n\\t\\t\\n\\t\\tself.assertEqual(N,64)\\n\\t\\tself.assertEqual(M,3)\\n\\t\\tself.assertEqual(rf,9)\\n\\t\\tself.assertEqual(num_evals,M*rf)\",\n \"def run_print_dict_examples():\\n print()\\n print_dict_keys(NAME_DICT)\\n print()\\n print_dict_items(NAME_DICT)\",\n \"def run_print_dict_examples():\\n print()\\n print_dict_keys(NAME_DICT)\\n print()\\n print_dict_items(NAME_DICT)\",\n \"def printt(dictionnary):\\n for key, value in dictionnary.iteritems():\\n print('{key}, size: {size}, {values}'.format(key=key, \\n size=len(value), values=value[0:4]))\",\n \"def report_keyset(self):\\n for i, matchset in enumerate(self.matches):\\n if len(matchset) == 1:\\n print \\\"[%02d]\\\" % i, fmt(sorted([k for k, data in matchset.items()]), BLUE)\\n elif len(matchset) != 0:\\n print \\\"[%02d]\\\" % i, fmt(sorted([k for k, data in matchset.items()]), WHITE)\\n else:\\n print \\\"[%02d]\\\" % i, fmt(\\\"[X]\\\", RED)\",\n \"def printDict(myDict):\\n for key in myDict:\\n print(f\\\"Version: --> {myDict[key]['version']} \\\")\\n print(f\\\"Accuracy: --> {myDict[key]['accuracy']}\\\")\\n print(f\\\"Time --> {myDict[key]['time_per_target']}\\\")\\n print(f\\\"Penalty --> {myDict[key]['target_w_penalty']}\\\")\\n print(f\\\"ID --> {myDict[key]['assessed_by']}\\\")\\n print(f\\\"# --> {myDict[key]['attempt']}\\\")\\n\\n print()\",\n \"def metadata_print(metadata):\\n\\n print('{0:<10} {1}'.format('parameter', 'value'))\\n for key in metadata:\\n print('{0:<10} {1}'.format(key, metadata[key]))\",\n \"def printMap(values, klab, vlab, precision, offset=16):\\n\\tprint(klab.ljust(offset, \\\" \\\") + vlab)\\n\\tfor k in values.keys():\\n\\t\\tv = values[k]\\n\\t\\tks = toStr(k, precision).ljust(offset, \\\" \\\")\\n\\t\\tvs = toStr(v, precision)\\n\\t\\tprint(ks + vs)\",\n \"def printValues(self):\\n\\n for layer in self.LayerNames:\\n print \\\"-------- {0} --------\\\".format(layer)\\n print \\\"nWorkingModules: {0}\\\".format(self.nWorkingModules[layer])\\n print \\\"Pixels per Layer\\\"\\n print \\\" Pixels hit: {0}\\\".format(self.hitPix[layer])\\n print \\\" Occupancy: {0}\\\".format(self.occupancies[layer])\\n print \\\" Pixels hit per Module: {0}\\\".format(self.hitPixPerModule[layer])\\n print \\\" Pixels hit per Area: {0}\\\".format(self.hitPixPerArea[layer])\\n print \\\" Pixels hit per Area per sec: {0}\\\".format(self.hitPixPerAreaSec[layer])\\n print \\\"Pixels per Det\\\"\\n print \\\" Occupancy (Det): {0}\\\".format(self.Detoccupancies[layer])\\n print \\\" Pixels hit per Det: {0}\\\".format(self.hitPixPerDet[layer])\\n print \\\" Pixels hit per DetArea: {0}\\\".format(self.hitPixPerDetArea[layer])\\n print \\\" Pixels hit per DetArea per sec: {0}\\\".format(self.hitPixPerDetAreaSec[layer])\\n print \\\"Cluster per Layer\\\"\\n print \\\" Clusters hit: {0}\\\".format(self.hitClusters[layer])\\n print \\\" Clusters hit per module: {0}\\\".format(self.hitClustersPerModule[layer])\\n print \\\" Clusters hit per Area: {0}\\\".format(self.hitClustersPerArea[layer])\\n print \\\" Clusters hit per Area per sec: {0}\\\".format(self.hitClustersPerAreaSec[layer])\\n print \\\"Clusters per Det\\\"\\n print \\\" Clusters hit per Det: {0}\\\".format(self.hitClustersPerDet[layer])\\n print \\\" Clusters hit per DetArea: {0}\\\".format(self.hitClustersPerDetArea[layer])\\n print \\\" Clusters hit per DetArea per sec: {0}\\\".format(self.hitClustersPerDetAreaSec[layer])\",\n \"def test_get_dimension(self):\\n\\n v = Vector({ 'x': 1 })\\n self.assertEqual(1, v.dimensions['x'])\",\n \"def printInfo(matrix):\\n\\n print(\\\"Groups:\\\")\\n for group in matrix.matrix.group_labels:\\n print(\\\"\\\\t{0}\\\".format(group))\\n\\n print(\\\"Samples:\\\")\\n for sample in matrix.matrix.sample_labels:\\n print(\\\"\\\\t{0}\\\".format(sample))\",\n \"def main():\\n dims = params['dims']\\n\\n for d in dims:\\n print('**** Running test for d={0:d} ****'.format(d))\\n run_test(d)\",\n \"def visualize_debug(example_dict, prefix):\\n region_string = example_dict[\\\"example_metadata\\\"][0].strip(\\\"\\\\x00\\\").split(\\\";\\\")[0].split(\\\"=\\\")[1].replace(\\\":\\\", \\\"-\\\")\\n region_prefix = \\\"{}.{}\\\".format(prefix, region_string)\\n \\n for key in example_dict.keys():\\n\\n # visualize importances\\n if \\\"importances\\\" in key:\\n plot_name = \\\"{}.{}.pdf\\\".format(region_prefix, key)\\n print plot_name\\n plot_weights(np.squeeze(example_dict[key]), plot_name)\\n\\n # visualize pwm scores\\n if \\\"pwm-scores\\\" in key:\\n pass\\n \\n\\n return None\",\n \"def test_has_correct_number_of_keys_and_values(self):\\n self.has_correct_number_of_keys_and_values(2, 1)\",\n \"def test_has_correct_number_of_keys_and_values(self):\\n self.has_correct_number_of_keys_and_values(2, 1)\",\n \"def print_image(img):\\n for k in img:\\n print(\\\"\\\".join([str(x) for x in k] ))\",\n \"def display_dict() -> None:\\n for key in ascii_dict:\\n print(key, ': ')\\n for line in ascii_dict[key]:\\n print(line)\",\n \"def show_info(self, group):\\n print(group)\\n for child in self.dict[group]:\\n if child=='Version':\\n print(child, ':', self(group, child))\\n elif not self(group, child).shape:\\n print(child, ':',self(group, child))\\n else:\\n print(child,': size', self(group, child).shape)\",\n \"def _format_dimensions(dimensions):\\n if not dimensions:\\n return \\\"\\\"\\n\\n dim_pairs = [\\\"%s=%s\\\" % (k, v) for k, v in dimensions.items()]\\n return \\\"[%s]\\\" % (\\\",\\\".join(dim_pairs))\",\n \"def print_table():\\n for key in _op_table.keys():\\n print(key)\\n for sub_key in _op_table[key]:\\n print('\\\\t--' + sub_key)\",\n \"def debug(self):\\n \\n #path\\n print('Path information:')\\n for k, v in self.__path.items():\\n print(k, v)\\n \\n #sample count\\n print('Sample statistic of each phase')\\n for k, v in self.__phase_sample_count.items():\\n print(k, v)\\n \\n print('Sample statistic of each class')\\n for k, v in self.__area_sample_count.items():\\n print(k, v)\\n \\n print('Sample statistic of each train')\\n for k, v in self.__train_sample_count.items():\\n print(k, v)\",\n \"def recursively_print_structure(item, leading = ''):\\n for key in item:\\n if isinstance(item[key], h5py.Dataset):\\n print(leading + key + ': ' + str(item[key].shape))\\n else:\\n print(leading + key)\\n recursively_print_structure(item[key], leading + ' ')\",\n \"def printResults(self):\\n for key in self.mDict.keys():\\n print ('for {:d}, entries = {:d} and exits = {:d}'.format (key, self.mDict.get(key).get ('entries'), self.mDict.get(key).get ('exits')))\",\n \"def print_configuration_info():\\n print(\\\"Selected dataset:\\\", DATASET) \\n print(\\\"Dataset base directory:\\\", BASE_INPUT_DIR) \\n print(\\\"Daytime option:\\\", DAYTIME) \\n print(\\\"Nones option:\\\", NONES) \\n print(\\\"Selected action/activity representation:\\\", OP)\\n print(\\\"Number of epochs: \\\", EPOCHS)\\n print(\\\"Number of folds for cross-validation: \\\", FOLDS)\\n print(\\\"Input directory for data files:\\\", INPUT_DIR) \\n print(\\\"Embedding matrix file:\\\", EMBEDDING_WEIGHTS)\\n print(\\\"Action sequences (X) file:\\\", X_FILE) \\n print(\\\"Word embedding file for activities:\\\", ACTIVITY_EMBEDDINGS) \\n print(\\\"Activity to int mappings:\\\", ACTIVITY_TO_INT)\\n print(\\\"Int to activity mappings:\\\", INT_TO_ACTIVITY) \\n print(\\\"Experiment ID:\\\", EXPERIMENT_ID)\\n print(\\\"Treat imbalance data:\\\", TREAT_IMBALANCE)\\n print(\\\"Save intermediate plots:\\\", SAVE)\\n print(\\\"Batch size:\\\", BATCH_SIZE)\\n print(\\\"Dropout:\\\", DROPOUT)\\n print(\\\"Loss:\\\", LOSS)\",\n \"def show(matrix):\\n print(\\\"\\\",end=\\\" \\\")\\n for k in sorted(matrix.keys()):\\n print(k,end=\\\" \\\")\\n \\n for i,row in sorted(matrix.items()):\\n print(\\\"\\\\n\\\" + str(i),end=\\\" \\\")\\n for j in row:\\n print(matrix[i][j],end=\\\" \\\")\\n print()\",\n \"def print_anagram_sets(d):\\r\\n for v in d.values():\\r\\n if len(v) > 1:\\r\\n print(len(v), v)\",\n \"def print_info(self, i):\\n\\n im_size = self.image_size(i)\\n print 'The path of the image is: {}'.format(self.image_path_at(i))\\n print 'width: {}, height: {}'.format(im_size[0], im_size[1])\\n \\n attr_i = self.gtdb['attr'][i, :]\\n print 'The attributes are: {}'.format(','.join([self._classes[i] for i in np.where(attr_i==1)[0]]))\",\n \"def printsection(section):\\n print('===')\\n for key in section.keys():\\n print(\\\"Key: %s\\\" % key)\\n for item in section[key]:\\n print(' %s' % item)\",\n \"def view_shapes(xm_train, xm_dev, xm_test, ym_train, ym_dev, ym_test):\\n\\n print('X_train shape: ' + str(xm_train.shape))\\n print('Y_train shape: ' + str(ym_train.shape))\\n print('X_dev shape: ' + str(xm_dev.shape))\\n print('Y_dev shape: ' + str(ym_dev.shape))\\n print('X_test shape: ' + str(xm_test.shape))\\n print('Y_test shape: ' + str(ym_test.shape))\\n print('\\\\n')\",\n \"def dimensions():\",\n \"def print_model_definitions(self):\\n sys.stdout.write(\\\"Model Dimensions\\\\n\\\")\\n sys.stdout.write(\\\"----------------\\\\n\\\")\\n for key, val in self.dimensions.iteritems():\\n sys.stdout.write(\\\"{key}: {val}\\\\n\\\".format(key=key, val=val))\",\n \"def print_kwargs(**kwargs):\\n for key in kwargs:\\n print('%s %s' %(key, kwargs[key]))\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_categories)\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_categories)\",\n \"def show_shape_metrics(shape):\",\n \"def test_basic_property_of_random_matrix():\\n for name, random_matrix in all_random_matrix.items():\\n print(name)\\n\\n check_input_size_random_matrix(random_matrix)\\n check_size_generated(random_matrix)\\n if name != \\\"random_subsample_normalized\\\":\\n check_zero_mean_and_unit_norm(random_matrix)\\n check_approximate_isometry(random_matrix)\",\n \"def test_RegionDimensionsBox_extractFromLinesWithKey(self):\\r\\n\\r\\n regionDimensions = RegionDimensions.RegionDimensionsBox()\\r\\n\\r\\n numberParameters = 6\\r\\n self.assertEquals(numberParameters, len(regionDimensions._keys))\\r\\n\\r\\n line = \\\"RegionParameters=0.000000 10000000000.000000 -10000000000.000000 10000000000.000000 0.000000 20000000000.000000\\\"\\r\\n regionDimensions.extractFromLinesWithKey(line)\\r\\n\\r\\n self.assertEquals(0.0, regionDimensions.minimumX)\\r\\n self.assertEquals(10000000000.0, regionDimensions.maximumX)\\r\\n self.assertEquals(-10000000000.0, regionDimensions.minimumY)\\r\\n self.assertEquals(10000000000.0, regionDimensions.maximumY)\\r\\n self.assertEquals(0.0, regionDimensions.minimumZ)\\r\\n self.assertEquals(20000000000.0, regionDimensions.maximumZ)\\r\\n\\r\\n #self.fail(\\\"Test if the testcase is working.\\\")\\r\",\n \"def print_config(config_dic, logger):\\n for k, v in config_dic.items():\\n logger.info(\\\"{}:\\\\t{}\\\".format(k.ljust(15), v))\",\n \"def print_q(q):\\n for key in sorted(q.keys()):\\n print(key, end=\\\" \\\")\\n value = q[key]\\n for i in range(len(value)):\\n print(value[i], end=\\\" \\\")\\n print()\",\n \"def dimension_check():\\n print(\\\"### DIMENSION CHECK ###\\\")\\n print(X.shape,\\n y.shape,\\n X_train.shape,\\n y_train.shape,\\n X_test.shape,\\n y_test.shape,\\n weights.shape)\\n print(\\\"### END ###\\\")\",\n \"def print_metric_dict(self, metric_dict):\\n print(\\\"\\\".join([\\\" {}: {:4f},\\\".format(k, v) for k, v in metric_dict.items()]))\",\n \"def printdict(input_dict):\\n for key in input_dict:\\n print key, \\\":\\\", input_dict[key]\",\n \"def print_device_dict(device_dict):\\n for device_id in device_dict:\\n print(str(device_id) + ':', device_dict[device_id]['name'])\",\n \"def info(d, return_dict=False, print_=True):\\n info_ = {}\\n for k,v in d.items():\\n if isinstance(v, dict):\\n info_.update(info(v))\\n else:\\n info_[k] = {\\n 'size': np.asarray(v).ravel().shape,\\n 'shape' :np.asarray(v).shape,\\n 'dtype': np.asarray(v).dtype.name\\n }\\n if print_:\\n _v = np.asarray(v)\\n print('key -', k)\\n print('dtype -', _v.dtype.name)\\n print('size -', np.asarray(v).ravel().shape)\\n print('shape -', _v.shape)\\n print()\\n if return_dict:\\n return info_\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_sample_ids)\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_sample_ids)\",\n \"def print_output():\\n print(\\\"count: [primary: \\\"+str(primary_shards)+\\\", replica: \\\"+str(secondary_shards)+\\\"]\\\")\\n print(\\\"size: [primary: \\\"+pretty_print_storage(total_size_primary)+\\\", replica: \\\"+pretty_print_storage(total_size_secondary)+\\\"]\\\")\\n print(\\\"disk-max-node: \\\"+max_size_node_name)\\n print(\\\"watermark-breached: \\\"+str(watermark_breached))\",\n \"def info(self, n=80):\\n # concatenate list of index dimensions and levels\\n info = f\\\"{type(self)}\\\\nIndex dimensions and data coordinates:\\\\n\\\"\\n c1 = max([len(i) for i in self.dimensions]) + 1\\n c2 = n - c1 - 5\\n info += \\\"\\\\n\\\".join(\\n [\\n f\\\" {i:{c1}}: {print_list(getattr(self, i), c2)}\\\"\\n for i in self.dimensions\\n ]\\n )\\n\\n return info\",\n \"def show_map(map_):\\n for r in map_.matrix:\\n print(''.join(r))\\n print()\",\n \"def display(self):\\n # type: ()->None\\n print('============')\\n for key, value in self._ifAttributes.items():\\n if isinstance(value, list):\\n print(key + ': ')\\n for item in value:\\n print('\\\\t' + item)\\n elif isinstance(value, dict):\\n print(key + ': ')\\n for item in value.keys():\\n print('\\\\t' + item + ': ' + value[item])\\n else:\\n print(key + ': ' + str(value))\\n print('============')\",\n \"def dimension_testing(self, dimension):\\n if dimension in self.allowed_dimensions:\\n return ''\\n else:\\n error_msg = 'Dimension %s is not supported. Choose from %s' % (dimension, self.allowed_dimensions)\\n return error_msg\",\n \"def print_map(self):\\n y_max,x_max = map(max, zip(*self.mp.keys()))\\n for row in range(0,y_max+1):\\n msg = []\\n for k in range(0,x_max+1):\\n msg.append(chr(self.mp[row,k]))\\n print(\\\"\\\".join(msg))\",\n \"def display_layers(layers, wide, tall):\\n\\n colours = {\\n \\\"0\\\": \\\" \\\",\\n \\\"1\\\": \\\" # \\\",\\n }\\n\\n for row in range(tall):\\n for col in range(wide):\\n pixels = [layer[row][col] for layer in layers]\\n line = next(colours[p] for p in pixels if p in colours)\\n print(line, end=\\\"\\\")\\n print()\",\n \"def print_infrastructure(aws_key, aws_secret):\\n _, _, _, redshift_client = create_clients(aws_key, aws_secret)\\n for k, v in get_cluster_properties(redshift_client):\\n print(k, v)\",\n \"def valid_config_keys():\\n click.echo(', '.join(get_class_properties(GenConfig)))\",\n \"def printPicnic(itemsDict: dict, leftWidth: int, rightWidth: int) -> None:\\n print('PICNIC ITEMS'.center(leftWidth + rightWidth, '-'))\\n for k, v in itemsDict.items():\\n print(k.ljust(leftWidth, '.') + str(v).rjust(rightWidth))\",\n \"def show_values():\\n dic_drg = {}\\n dic_age = {}\\n dic_sex = {}\\n dic_sline = {}\\n for tup in all_data:\\n drg = tup[7]\\n age = tup[9]\\n sex = tup[10]\\n sline = tup[14]\\n\\n dic_drg[drg] = 1\\n dic_age[age] = 1\\n dic_sex[sex] = 1\\n dic_sline[sline] = 1\\n\\n print \\\"Age values\\\"\\n for key in sorted(dic_age.keys()):\\n print key\\n\\n print \\\"Sex values\\\"\\n for key in sorted(dic_sex.keys()):\\n print key\\n\\n print \\\"Service line values\\\"\\n for key in sorted(dic_sline.keys()):\\n if key is None or len(key) == 0:\\n continue\\n print \\\"'\\\" + key + \\\"',\\\",\\n print\\n\\n print \\\"Drg values\\\"\\n for key in sorted(dic_drg.keys()):\\n if key is None or len(key) == 0:\\n continue\\n print\\\"'\\\" + key + \\\"',\\\",\\n print\",\n \"def test_section_keys(self):\\n ars = self.ar[2009][11]['general']\\n self.assertEqual(ars.keys(), self.__class__.wanted_lines)\",\n \"def test_getitem(self):\\n space = Space()\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", 2, 3, 4)\\n dim = Categorical(\\\"yolo\\\", OrderedDict(zip(categories, probs)), shape=2)\\n space.register(dim)\\n dim = Integer(\\\"yolo2\\\", \\\"uniform\\\", -3, 6)\\n space.register(dim)\\n dim = Real(\\\"yolo3\\\", \\\"norm\\\", 0.9)\\n space.register(dim)\\n\\n assert space[\\\"yolo\\\"].type == \\\"categorical\\\"\\n assert space[0].type == \\\"categorical\\\"\\n\\n with pytest.raises(KeyError):\\n space[\\\"asdf\\\"]\\n\\n with pytest.raises(IndexError):\\n space[3]\",\n \"def test_main_multiple_keys(self):\\n args = [\\n \\\"--layout\\\",\\n self.layout_double_signed_path,\\n \\\"--layout-keys\\\",\\n self.alice_path,\\n self.bob_path,\\n ]\\n self.assert_cli_sys_exit(args, 0)\",\n \"def test_main_multiple_keys(self):\\n args = [\\n \\\"--layout\\\",\\n self.layout_double_signed_path,\\n \\\"--layout-keys\\\",\\n self.alice_path,\\n self.bob_path,\\n ]\\n self.assert_cli_sys_exit(args, 0)\",\n \"def test_partially_one(self):\\n setups = self.get_setup().decompress([\\\"dimensions.species_id\\\"])\\n assert isinstance(setups, PlotSetupGroup)\\n assert all(isinstance(setup, PlotSetup) for setup in setups)\\n assert len(setups) == 2\\n res = {\\n (\\n s.panels.collect_equal(\\\"dimensions\\\").variable,\\n s.panels.collect_equal(\\\"dimensions\\\").species_id,\\n s.panels.collect_equal(\\\"dimensions\\\").time,\\n )\\n for s in setups\\n }\\n sol = {\\n ((\\\"dry_deposition\\\", \\\"wet_deposition\\\"), 1, (1, 2, 3)),\\n ((\\\"dry_deposition\\\", \\\"wet_deposition\\\"), 2, (1, 2, 3)),\\n }\\n assert res == sol\",\n \"def test_all_keys(self):\\n\\t\\tdocumentation: dict = self.handlerObject.build_swagger_documentation()\\n\\t\\t# print(\\\"Test documentation was generated. The corresponding object is:\\\\n{0}\\\".format(documentation))\\n\\t\\tself.assertTrue(compare_keys(documentation, TEST_ALL_KEYS_EXPECTED_OUTPUT))\",\n \"def cli(dimensions):\\r\\n click.echo(\\\"Iris Flower classifier\\\\n\\\")\\r\\n \\r\\n\\r\\n click.echo(\\\"Calculating result...\\\")\\r\\n time.sleep(1)\\r\\n results = zip(dimm_names, dimensions)\\r\\n click.echo(\\\"Input data:\\\")\\r\\n for i, j in results:\\r\\n click.echo(\\\"{:12} -> {}\\\".format(i, j))\\r\\n\\r\\n click.echo()\\r\\n click.echo(\\\"Your flower seems to be fine example of:\\\")\\r\\n click.secho(\\\"{}\\\".format(\\\"species\\\"), fg='green', bold=True)\",\n \"def check_and_print_if_error(self): # pragma: no cover\\n dupes, empties, not_detected = self._get_aberrations()\\n if dupes:\\n print 'duplicate entries for:'\\n for dup, matches in dupes:\\n print ' %s: %s' % (dup, [f.func_name for f in matches])\\n if empties:\\n print 'empty entries for:'\\n for empty in empties:\\n print ' ' + str(empty)\\n if not_detected:\\n print 'dimensions not detected:'\\n for n_d in not_detected:\\n print ' ' + str(n_d)\\n return self.is_correct\",\n \"def test_dict_keywords(self):\\n output, _err = self.executor.docker.run('lego:1', env=dict(SPECIAL='emett',\\n SONG='awesome')).batch()\\n self.assertEqual(output, 'everything')\",\n \"def printDicts():\\n for k in key:\\n print k, key[k]\\n \\n for f in freq:\\n print f, freq[f]\\n \\n for e in english:\\n print e, english[e]\",\n \"def print_data():\\r\\n\\r\\n d = data()\\r\\n for i in d:\\r\\n for key, value in i.items():\\r\\n print(key, \\\" : \\\", value)\\r\\n print()\",\n \"def test_get_dimensions_by_label(self):\\n\\n dimensions, dim_names = pyjstat.get_dimensions(\\n self.oecd_datasets['oecd'], 'label')\\n self.assertTrue(dim_names[2] == '2003-2014')\\n self.assertTrue(dimensions[0].iloc[0]['label'] == 'Unemployment rate')\",\n \"def print_possibility_space():\\n\\n print(\\\"Possibility space:\\\")\\n print(\\\" {} unique sword images\\\".format(calculate_image_possibilities()))\",\n \"def test_product_initialization(product_design_space):\\n assert product_design_space.name == 'my design space'\\n assert product_design_space.description == 'does some things'\\n assert len(product_design_space.dimensions) == 3\\n assert product_design_space.dimensions[0].descriptor.key == 'alpha'\\n assert product_design_space.dimensions[1].descriptor.key == 'beta'\\n assert product_design_space.dimensions[2].descriptor.key == 'gamma'\",\n \"def print_img_data(img,title='Image'):\\n print(f'________ {title} _______')\\n if len(image.shape) == 2:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0])}\\\\n')\\n elif len(image.shape) == 3:\\n print(f'min:{img.min()} max:{img.max()} shape:{img.shape}\\\\\\n\\\\nmean:{img.mean()} std:{img.std()} type:{type(img[0,0,0])}\\\\n')\",\n \"def test17():\\n\\n\\twardrobe = {\\\"shirt\\\":[\\\"red\\\",\\\"blue\\\",\\\"white\\\"], \\\"jeans\\\":[\\\"blue\\\",\\\"black\\\"]}\\n\\tfor cloth in wardrobe.keys():\\n\\t\\tfor color in wardrobe[cloth]:\\n\\t\\t\\tprint(\\\"{} {}\\\".format(color,cloth))\",\n \"def print_info(self):\\n print(\\\"Experiment key: \\\" + self.key)\\n print(\\\"Experiment name: \\\" + self.name)\\n print(\\\"Experiment path: \\\" + self.output_path)\\n print(\\\"Auto-sync activated: \\\" + str(self.auto_sync))\\n print(\\\"\\\")\\n print(\\\"Experiment metadata: \\\")\\n print(self.exp_metadata.to_str())\",\n \"def print_stats(ds):\\n print(\\\"Dataset Name: \\\" + ds.name)\\n print(\\\"Dataset Mode: \\\" + ds.mode)\\n print(\\\"Band Count: \\\" + str(ds.count))\\n print(\\\"Dataset Width: \\\" + str(ds.width))\\n print(\\\"Dataset Height: \\\" + str(ds.height))\\n print(\\\"Dataset Bounds: \\\", ds.bounds)\\n print(\\\"Dataset Transform: \\\", ds.transform)\\n ul = ds.transform * (0, 0)\\n print(\\\"Upper Left Corner: \\\", ul)\\n lr = ds.transform * (ds.width, ds.height)\\n print(\\\"Lower Right Corner: \\\", lr)\\n {i: dtype for i, dtype in zip(ds.indexes, ds.dtypes)}\",\n \"def print_args(args):\\n _args = vars(args)\\n max_length = max([len(k) for k, _ in _args.items()])\\n for k, v in _args.items():\\n print(' ' * (max_length - len(k)) + k + ': ' + str(v))\",\n \"def print_eval(eval_dict):\\n\\n status = ''.join(\\n ['{}: {:.6f}, '.format(key, eval_dict[key]) for key in sorted(eval_dict)])\\n print(status[:-2])\",\n \"def view_keys(dict):\\n claves=list(dict.keys())\\n claves.sort()\\n for line in claves:\\n print(line.upper(),' = ',dict[line])\",\n \"def test_antialiasing():\\n images = generate_test_images()\\n loaded = load_test_images(images)\\n print(list(loaded.keys()))\\n for description in images.keys():\\n assert (images[description] == loaded[description]).all()\",\n \"def test_map_basics(self):\\n self.assertDigitizerMapBasics(self.map, self.dgroup)\",\n \"def print_seeds(self):\\n for key in self.CONFIG.keys():\\n if \\\"EMI\\\" in key:\\n print key\",\n \"def display_map():\\r\\n for row in range(0, len(map1)): #for all rows\\r\\n for column in range(0, len(map1[0])): #for all columns\\r\\n print(map1[row][column], end=' ')\\r\\n print()\",\n \"def var_sizes_check(global_dic):\\n import types\\n def _print(a, b):\\n print(\\\"|{:>15}|{:>13}|\\\".format(a, b))\\n\\n _print(\\\"Variable\\\", \\\"Size\\\")\\n print(\\\"-\\\"*31)\\n for k, v in global_dic.items():\\n if not k.startswith('_') and not isinstance(v, types.ModuleType):\\n # print size of variable\\n if hasattr(v, 'size'):\\n try:\\n _print(k, v.size)\\n except:\\n continue\\n # print length of variable\\n elif hasattr(v, '__len__'):\\n try:\\n _print(k, len(v))\\n except:\\n continue\",\n \"def helper():\\n for mode in PackingMode:\\n for bin_algo in PackingBin:\\n for size, w, h in ('big_enough', 50, 50), ('too_small', 5, 5):\\n name = '_'.join(('test', mode, bin_algo, size))\\n print(\\\"\\\"\\\"\\\\\\n def %s(self):\\n # create bins that are %s to hold the rectangles\\n p = self._common(PackingMode.%s, PackingBin.%s, %s, %s)\\\"\\\"\\\" %\\n (name, size.replace('_', ' '), mode, bin_algo, w, h))\\n if size == 'big_enough':\\n print(\\\"\\\"\\\"\\\\\\n # check that bins were created\\n self.assertGreater(len(p.bin_list()), 0)\\n # check that all of the rectangles made it in\\n self.assertEqual(len(p.rect_list()), len(self.rectangles))\\n\\\"\\\"\\\")\\n else:\\n print(\\\"\\\"\\\"\\\\\\n # check that none of the rectangles made it in\\n self.assertEqual(len(p.rect_list()), 0)\\n\\\"\\\"\\\")\",\n \"def print_character(character):\\n for k, v in character.items():\\n print(str(k) + ': ' + str(v)) # Prints every key:value pair in character dictionary\\n if len(character) == 8:\\n print(\\\"Inventory:\\\" + str(character[7])) # If there is an inventory, then print it as well\",\n \"def pretty_keys(dictionary):\\n if not dictionary:\\n return []\\n # - number of keys printed per line\\n num = 5\\n # - turn into sorted list\\n keys = list(dictionary.keys())\\n keys.sort()\\n # - fill with blank elements to width num\\n missing = (len(keys) % num)\\n if missing != 0:\\n to_add = num - missing\\n keys.extend([''] * to_add)\\n # - turn into 2D matrix\\n matrix = [[keys[i+j] for i in range(0, num)]\\n for j in range(0, len(keys), num)]\\n # - calculate max width for each column\\n len_matrix = [[len(col) for col in row] for row in matrix]\\n max_len_col = [max([row[j] for row in len_matrix])\\n for j in range(0, num)]\\n # - pad with spaces\\n matrix = [[row[j].ljust(max_len_col[j]) for j in range(0, num)]\\n for row in matrix]\\n # - return list of lines to print\\n matrix = [' '.join(row) for row in matrix]\\n return matrix\",\n \"def testFourDimensions(self):\\n false1 = (True, True, True, False)\\n false2 = (True, False, True, False)\\n true1 = (False, True, False, True)\\n true2 = (True, True, False, False)\\n tt = cros_test_lib.TruthTable(inputs=(false1, false2), input_result=False)\\n self.assertEquals(len(tt), pow(2, 4))\\n\\n # Check truth table output.\\n self.assertFalse(tt.GetOutput(false1))\\n self.assertFalse(tt.GetOutput(false2))\\n self.assertTrue(tt.GetOutput(true1))\\n self.assertTrue(tt.GetOutput(true2))\\n\\n # Check assertions on bad input to GetOutput.\\n self.assertRaises(TypeError, tt.GetOutput, True)\\n self.assertRaises(ValueError, tt.GetOutput, (True, True, True))\\n\\n # Check iteration over input lines.\\n lines = list(tt)\\n self.assertEquals((False, False, False, False), lines[0])\\n self.assertEquals((False, False, False, True), lines[1])\\n self.assertEquals((False, True, True, True), lines[7])\\n self.assertEquals((True, True, True, True), lines[15])\\n\\n self._TestTableSanity(tt, lines)\",\n \"def test_overall_report_keys():\\n keys = overall_data.keys()\\n assert('banner_report' in keys)\\n assert('rewarded_report' in keys)\\n assert('interstitial_report' in keys)\\n assert('overall_report' in keys)\",\n \"def printMetadata(metadata):\\n\\tif metadata:\\n\\t\\tfor x in metadata:\\n\\t\\t print(x+': {}'.format(metadata[x]) )\\n\\t\\treturn True\\n\\telse:\\n\\t\\tprint('metadata empty')\\n\\t\\tprint('unable to find metadata at this location')\\n\\t\\tprint('')\\n\\t\\treturn False\",\n \"def print_dict(dictionary):\\n for x,y in dictionary.items():\\n print(x, y)\",\n \"def print_args():\\n for key, value in vars(ARGS).items():\\n print(key + ' : ' + str(value))\",\n \"def _debug_print(self, cfg, list_iterstore):\\n def __print_params(iterstore, edataset):\\n if (edataset not in iterstore): return\\n gen = iterstore[edataset]\\n if (gen is not None):\\n if (gen._params is not None):\\n print('{} : {}'.format(edataset, gen))\\n print(\\\"\\\\tIterator Parameters: (iter_param, iter_pp_param)\\\")\\n print(\\\"\\\\t-------------------------------------------------\\\")\\n for key, val in gen._params.items():\\n if (key.startswith('_')): continue\\n print('\\\\t{} : {}'.format(key, val))\\n \\n if (gen._pp_params is not None):\\n print(\\\"\\\\t-------------------------------------------------\\\")\\n for key, val in gen._pp_params.items():\\n if (key.startswith('_')): continue\\n print('\\\\t{} : {}'.format(key, val))\\n print(\\\"\\\")\\n\\n if (list_iterstore is None):\\n return\\n\\n print(\\\"\\\\nNNCfg\\\")\\n print(\\\"=====\\\")\\n for key, val in cfg.__dict__.items():\\n if (key.startswith('_')): continue\\n print('\\\\t{} : {}'.format(key, val))\\n\\n for i, iterstore in enumerate(list_iterstore):\\n print(\\\"\\\\nIterator Store:{}\\\".format(i))\\n print(\\\"=================\\\")\\n __print_params(iterstore, Dataset.TR)\\n __print_params(iterstore, Dataset.VAL)\\n __print_params(iterstore, Dataset.TE)\\n __print_params(iterstore, Dataset.TR_OUT)\\n __print_params(iterstore, Dataset.VAL_OUT)\\n __print_params(iterstore, Dataset.TE_OUT)\",\n \"def printCounterByKey(counter):\\n for k in counter.keys():\\n print(k, counter[k])\",\n \"def print(self):\\n tiles = list(map(list, zip(*self.tiles))) # transposed\\n print('tiles = [')\\n for row in tiles:\\n print('\\\\t' + repr(row))\\n print(']')\\n print('props = [')\\n for prop in self.props:\\n print('\\\\t' + repr(prop))\\n print(']')\",\n \"def _print_issue(issue, ntabs):\\n for key, value in issue.items():\\n if isinstance(value, dict):\\n tabs = TAB*ntabs\\n print('%s%s (section):' % (tabs, key))\\n ntabs += 1\\n print_issue(value, ntabs=ntabs)\\n elif isinstance(value, bool):\\n if value == False:\\n tabs = TAB*ntabs\\n print('%s%s parameter is missing.' % (tabs, key))\\n continue\\n else:\\n tabs = TAB*ntabs\\n print('%s%s (parameter):' % (tabs, key))\\n tabs = TAB*(ntabs+1)\\n print('%s%s' % (tabs, value))\",\n \"def test_print_args_dict(self):\\n log_file = os.path.join(DATA_DIR, 'sample_log.txt')\\n if os.path.exists(log_file):\\n os.remove(log_file)\\n self.assertEqual(os.path.exists(log_file), False)\\n manager = execution.LogManager('MainThread', log_file)\\n\\n args = {}\\n manager.print_args(args)\\n self.assertEqual(count_lines(log_file), 4)\\n os.remove(log_file)\",\n \"def show(self, keys=None, sort_keys_function=None):\\n output_keys = keys or self.keys\\n if not self.items:\\n print(\\\"No items to show\\\")\\n else:\\n for item in self.__get_items(sort_keys_function):\\n for output_key in output_keys:\\n print(\\\"{0:25}: {1!s}\\\".format(output_key, getattr(item, self.mapping[output_key])))\\n print(\\\"-\\\" * 25)\",\n \"def _validate_dimensions(config):\\n logging.info(\\\"Checking provided dimensions are valid\\\")\\n for feature in config.get(\\\"test-suites\\\").values():\\n for test_name, test in feature.items():\\n for dimensions_config in test[\\\"dimensions\\\"]:\\n _validate_schedulers(config, dimensions_config.get(\\\"schedulers\\\", []))\\n if [] in dimensions_config.values():\\n logging.error(\\\"Values assigned to dimensions in test %s cannot be empty\\\", test_name)\\n raise AssertionError\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6196142","0.6052784","0.60204434","0.60203207","0.60203207","0.5951763","0.5832673","0.5800634","0.57597065","0.57452214","0.5724362","0.57028264","0.5698949","0.5673566","0.55920947","0.55707216","0.55707216","0.55645055","0.55363876","0.55324614","0.5518316","0.55090666","0.5499341","0.548178","0.5478478","0.54699993","0.5468296","0.54496485","0.5448419","0.5438902","0.543411","0.54339284","0.54306924","0.54299736","0.542923","0.542923","0.54172844","0.5412934","0.5412774","0.5400309","0.5394097","0.5367837","0.53635","0.5345432","0.53444993","0.533711","0.5334534","0.5334534","0.5322644","0.5293944","0.5291729","0.52869475","0.52826405","0.5278914","0.5275319","0.5264936","0.52647614","0.52609456","0.52555794","0.5238312","0.5233251","0.5221002","0.5221002","0.52190477","0.52118474","0.5211352","0.5200684","0.5199169","0.51973724","0.51934826","0.5193201","0.5190238","0.518372","0.5182519","0.51745087","0.51735187","0.517007","0.51644474","0.51624066","0.5154629","0.514665","0.5140157","0.51400524","0.5138696","0.51369697","0.51302654","0.51292753","0.51231873","0.5122311","0.5121215","0.5119518","0.5116301","0.5110447","0.51083237","0.510648","0.5098904","0.5092542","0.50807106","0.5080643","0.5078626"],"string":"[\n \"0.6196142\",\n \"0.6052784\",\n \"0.60204434\",\n \"0.60203207\",\n \"0.60203207\",\n \"0.5951763\",\n \"0.5832673\",\n \"0.5800634\",\n \"0.57597065\",\n \"0.57452214\",\n \"0.5724362\",\n \"0.57028264\",\n \"0.5698949\",\n \"0.5673566\",\n \"0.55920947\",\n \"0.55707216\",\n \"0.55707216\",\n \"0.55645055\",\n \"0.55363876\",\n \"0.55324614\",\n \"0.5518316\",\n \"0.55090666\",\n \"0.5499341\",\n \"0.548178\",\n \"0.5478478\",\n \"0.54699993\",\n \"0.5468296\",\n \"0.54496485\",\n \"0.5448419\",\n \"0.5438902\",\n \"0.543411\",\n \"0.54339284\",\n \"0.54306924\",\n \"0.54299736\",\n \"0.542923\",\n \"0.542923\",\n \"0.54172844\",\n \"0.5412934\",\n \"0.5412774\",\n \"0.5400309\",\n \"0.5394097\",\n \"0.5367837\",\n \"0.53635\",\n \"0.5345432\",\n \"0.53444993\",\n \"0.533711\",\n \"0.5334534\",\n \"0.5334534\",\n \"0.5322644\",\n \"0.5293944\",\n \"0.5291729\",\n \"0.52869475\",\n \"0.52826405\",\n \"0.5278914\",\n \"0.5275319\",\n \"0.5264936\",\n \"0.52647614\",\n \"0.52609456\",\n \"0.52555794\",\n \"0.5238312\",\n \"0.5233251\",\n \"0.5221002\",\n \"0.5221002\",\n \"0.52190477\",\n \"0.52118474\",\n \"0.5211352\",\n \"0.5200684\",\n \"0.5199169\",\n \"0.51973724\",\n \"0.51934826\",\n \"0.5193201\",\n \"0.5190238\",\n \"0.518372\",\n \"0.5182519\",\n \"0.51745087\",\n \"0.51735187\",\n \"0.517007\",\n \"0.51644474\",\n \"0.51624066\",\n \"0.5154629\",\n \"0.514665\",\n \"0.5140157\",\n \"0.51400524\",\n \"0.5138696\",\n \"0.51369697\",\n \"0.51302654\",\n \"0.51292753\",\n \"0.51231873\",\n \"0.5122311\",\n \"0.5121215\",\n \"0.5119518\",\n \"0.5116301\",\n \"0.5110447\",\n \"0.51083237\",\n \"0.510648\",\n \"0.5098904\",\n \"0.5092542\",\n \"0.50807106\",\n \"0.5080643\",\n \"0.5078626\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5100301"},"document_rank":{"kind":"string","value":"95"}}},{"rowIdx":6789,"cells":{"query":{"kind":"string","value":"The callback function for the optimize process. Manages recording of iteration values and monitoring current optimiser solutions."},"document":{"kind":"string","value":"def callbackMonitor(dim):\n bstRecord.append(dim[0])\n tstRecord.append(dim[1])\n tskRecord.append(dim[2])\n massRecord.append(mass(dim))\n rsfSkinRecord.append(skinBuckle(dim)+1)\n rsfStiffRecord.append(stiffenerBuckle(dim)+1)\n rsfMatRecord.append(matFail(dim)+1.1)\n rsfEulerRecord.append(eulerBuckle(dim)+1.1)\n print(dim, mass(dim))"},"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 Optimize(self):\n self._OpenOutputFiles()\n while self.n_iter < self.n_maxiter and not self.is_converged:\n self.n_iter += 1\n self._ChooseStepDirection(self.opt_type)\n self._LineSearch(-1.0 * self.step_dir)\n self._UpdateEnergy()\n self._UpdateGradient()\n self.traj.AppendStep(self.mol)\n self._UpdateCriteria()\n self._CheckConvergence()\n self._PrintStatus()\n self._CloseOutputFiles()","def optimize(self):\n \n if self.verbose:\n print('Starting grid search with bounds: [' + \\\n ';'.join(['%5g to %5g']*len(self.steps))%tuple([(self.steps[i][0], self.steps[i][-1]) for i in range(len(self.steps))]) +']')\n\n for params in self._get_next_point():\n self.transform.set_params(params)\n\n v, _ = self.measure.value_and_derivatives(self.transform)\n\n if v < self.best_value:\n self.best_value = v\n self.best_params = params\n# print('New best value %2.4f at ('%v, ', '.join(['%8.3f']*len(params))%tuple(params), ')')\n\n self.value_history.append(v)\n self.last_value = v\n self.iteration += 1\n\n if self.report_freq > 0 and (self.iteration % self.report_freq == 0) and self.report_func is not None:\n self.report_func(self)\n\n # Set the best transform\n self.transform.set_params(self.best_params)\n self.last_value = self.best_value\n return self.best_value","def _optimization_loop(self, iteration=0):\n self.logger.print_optimization_header()\n\n while iteration < self.iterations:\n try:\n self._execute_experiment()\n except RepeatedExperimentError:\n # G.debug_(F'Skipping repeated Experiment: {_ex!s}\\n')\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\n G.log_(f\"Hyperparameter search space has been exhausted\")\n break\n self.skipped_iterations += 1\n continue\n except StopIteration:\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\n G.log_(f\"Hyperparameter search space has been exhausted\")\n break\n # G.debug_(f'Re-initializing hyperparameter grid after testing {len(self.tested_keys)} keys')\n self._set_hyperparameter_space()\n continue\n\n self.logger.print_result(\n self.current_hyperparameters_list,\n self.current_score,\n experiment_id=self.current_experiment.experiment_id,\n )\n\n if (\n (self.best_experiment is None) # First evaluation\n or (self.do_maximize and (self.best_score < self.current_score)) # New best max\n or (not self.do_maximize and (self.best_score > self.current_score)) # New best min\n ):\n self.best_experiment = self.current_experiment.experiment_id\n self.best_score = self.current_score\n\n iteration += 1","def go(self):\n if self.model_initializer is None:\n raise ValueError(\"Experiment guidelines must be set before starting optimization\")\n\n _reporter_params = dict(dict(do_maximize=self.do_maximize), **self.reporter_parameters)\n self.logger = OptimizationReporter([_.name for _ in self.dimensions], **_reporter_params)\n\n self.tested_keys = []\n self._set_hyperparameter_space()\n self._find_similar_experiments()\n\n loop_start_time = datetime.now()\n self._optimization_loop()\n loop_end_time = datetime.now()\n G.log_(f\"Optimization loop completed in {loop_end_time - loop_start_time}\")\n G.log_(f'Best score was {self.best_score} from Experiment \"{self.best_experiment}\"')\n self._clean_up_optimization()","def iterations(self):\n i = 0\n stateVectorConv = self.stateVectorConvThreshold * 1.0e6\n n = len(self.model.stateVector)\n self.answer = None\n \n while ((i < self.maxiter) \n and (stateVectorConv > self.stateVectorConvThreshold)\n ):\n \n F, K = self.model()\n \n if np.any(np.isnan(F)) or np.any(np.isnan(K)):\n m = \"Iteration {0} failure of model.\"\n raise OptimalEstimationException(m.format(i))\n \n if self.model.verbose > 0:\n self.model.plot(i+1, stateVectorConv)\n \n try:\n self.DecomposeJacobian(K)\n except np.linalg.LinAlgError:\n m = \"Iteration {0} failure in decomposition.\"\n raise OptimalEstimationException(m.format(i))\n \n statevectorOffset = (self.V.T * self.priorSinvh * \n np.matrix(np.array(self.model.stateVector) - np.array(self.model.prior) ).T)\n measurementOffset = (self.U.T * self.errSinvh * \n np.matrix(self.model.observation - F).T)\n \n newState = np.matrix((self.w * \n (measurementOffset.A1 + \n self.w * statevectorOffset.A1))/(self.w**2+1.0)).T\n newState = self.priorSh * self.V * newState\n newState = newState.A1 + self.model.prior\n \n stateVectorConv = ((np.matrix(newState - self.model.stateVector) * \n self.Sinv * np.matrix(newState - self.model.stateVector).T)/n)[0,0]\n self.model.stateVector = newState\n\n if i == 0:\n \n stateVectorConv = self.stateVectorConvThreshold * 1.0e6\n \n print('cost Function for iteration {}:'.format(i), self.costFunction)\n\n i += 1\n \n F, K = self.model()\n if self.model.verbose > 0:\n self.model.plot(i+1, stateVectorConv)\n \n try:\n self.DecomposeJacobian(K)\n except np.linalg.LinAlgError:\n raise OptimalEstimationException(\"Failure in decomposition.\")\n \n Wplus2 = np.matrix(np.diag(1.0/(self.w**2+1.0)))\n self.model.covariance = (self.priorSh * self.V * Wplus2 * \n self.V.T * self.priorSh)\n \n\n \n return i, stateVectorConv","def optimize(self):\n self.vbe_step()\n self.compute_responsibilities()\n self.compute_sufficient_stats()\n self.vbmstep()","def solve(self):\n\n # Open status display\n fmtstr, nsep = self.display_start()\n\n # Start solve timer\n self.timer.start(['solve', 'solve_wo_func', 'solve_wo_rsdl',\n 'solve_wo_btrack'])\n\n # Main optimisation iterations\n for self.k in range(self.k, self.k + self.opt['MaxMainIter']):\n\n # Update record of X and Y from previous iteration\n self.on_iteration_start()\n\n # Compute backtracking\n if self.opt['Backtrack'] is not None and self.k >= 0:\n self.timer.stop('solve_wo_btrack')\n # Compute backtracking\n self.backtrack.update(self)\n self.timer.start('solve_wo_btrack')\n else:\n # Compute just proximal step\n self.xstep()\n # Update by combining previous iterates\n self.ystep()\n\n # Compute residuals and stopping thresholds\n self.timer.stop(['solve_wo_rsdl', 'solve_wo_btrack'])\n if not self.opt['FastSolve']:\n frcxd, adapt_tol = self.compute_residuals()\n self.timer.start('solve_wo_rsdl')\n\n # Compute and record other iteration statistics and\n # display iteration stats if Verbose option enabled\n self.timer.stop(['solve_wo_func', 'solve_wo_rsdl',\n 'solve_wo_btrack'])\n if not self.opt['FastSolve']:\n itst = self.iteration_stats(self.k, frcxd)\n self.itstat.append(itst)\n self.display_status(fmtstr, itst)\n self.timer.start(['solve_wo_func', 'solve_wo_rsdl',\n 'solve_wo_btrack'])\n\n # Call callback function if defined\n if self.opt['Callback'] is not None:\n if self.opt['Callback'](self):\n break\n\n # Stop if residual-based stopping tolerances reached\n if not self.opt['FastSolve']:\n if frcxd < adapt_tol:\n break\n\n # Increment iteration count\n self.k += 1\n\n # Record solve time\n self.timer.stop(['solve', 'solve_wo_func', 'solve_wo_rsdl',\n 'solve_wo_btrack'])\n\n # Print final separator string if Verbose option enabled\n self.display_end(nsep)\n\n return self.getmin()","def _optimise(self):\n pass","def optimizer_callback(current_theta):\n\n global optimizer_data\n global optimizer_len\n\n optimizer_data.append(current_theta)\n optimizer_len += 1\n return False","def optimize(self, iterations=1000):\r\n prev = None\r\n finalx = None\r\n finaly = None\r\n while iterations:\r\n maxei, eis = self.acquisition()\r\n new_y = self.f(maxei)\r\n if maxei == prev:\r\n break\r\n self.gp.update(maxei, new_y)\r\n pycodehack = finaly is None or self.minimize and finaly > new_y\r\n if ((pycodehack or not self.minimize and finaly < new_y)):\r\n finaly = new_y\r\n finalx = maxei\r\n prev = maxei\r\n iterations -= 1\r\n return finalx, finaly","def accept_optimize():\n pass","def run_optimization(self):\n # Get batch\n (obs, action, old_logp, old_value, return_, advantage) = self.buffer.eject()\n\n # Train pi\n print(\"-\" * 20 + \"\\nPi Update\" + \"\\n\" + \"-\" * 20)\n (policy_loss, entropy,\n kl_divergence, clipping_fraction, steps) = self.update_actor(obs, action, old_logp, advantage)\n\n # Train value function\n print(\"-\" * 20 + \"\\nValue Function Update\" + \"\\n\" + \"-\" * 20)\n (value_loss,\n explained_variance) = self.update_critic(obs, old_value, return_)\n\n # Logging\n self.update_counter += 1\n self.log_update(policy_loss, entropy, kl_divergence, clipping_fraction,\n value_loss, explained_variance, steps)\n\n # Update learning rate\n self.decay_lr()\n\n # Save current weights (overwrites previous weights)\n self.save_weights()\n\n # Empty scenario counter\n self.scenario_counter = dict.fromkeys(self.scenario_counter, 0)","def _optimization_closure(self, iteration, step):\n aug = self._get_augmentation(iteration)\n if iteration == self.num_iter_per_step - 1:\n reg_noise_std = 0\n aug = 0\n else:\n reg_noise_std = (1 / 1000.) * (iteration // 400)\n # creates left_net_inputs and right_net_inputs by adding small noise\n clean_nets_inputs = [clean_net_input[aug] + (clean_net_input[aug].clone().normal_() * reg_noise_std)\n for clean_net_input in self.clean_nets_inputs]\n watermark_net_input = self.watermark_net_input[aug] # + (self.watermark_net_input[aug].clone().normal_() * reg_noise_std)\n mask_net_input = self.mask_net_input[aug]\n # applies the nets\n self.clean_nets_outputs = [clean_net(clean_net_input) for clean_net, clean_net_input\n in zip(self.clean_nets, clean_nets_inputs)]\n self.watermark_net_output = self.watermark_net(watermark_net_input)\n self.mask_net_output = self.mask_net(mask_net_input)\n self.total_loss = 0\n self.blur = 0\n\n self.total_loss += sum(self.l1_loss(self.watermark_net_output * self.mask_net_output +\n clean_net_output * (1 - self.mask_net_output), image_torch[aug])\n for clean_net_output, image_torch in zip(self.clean_nets_outputs, self.images_torch))\n self.total_loss.backward(retain_graph=True)","def Optimize(self):\n return _gmat_py.ExternalOptimizer_Optimize(self)","def fit(self):\n if self.minimizer == \"differential_evolution\":\n kwargs = {\"maxiter\": self._maxiter}\n elif self.minimizer == \"shgo\":\n kwargs = {\"options\": {\"maxiter\": self._maxiter,\n \"jac\": self.cost_func.jac_cost}}\n elif self.minimizer == \"dual_annealing\":\n kwargs = {\"maxiter\": self._maxiter, \"local_search_options\": {\n \"jac\": self.cost_func.jac_cost}}\n fun = self.cost_func.eval_cost\n bounds = self.value_ranges\n algorithm = getattr(optimize, self.minimizer)\n result = algorithm(fun, bounds, **kwargs)\n self._popt = result.x\n if result.success:\n self._status = 0\n elif \"Maximum number of iteration\" in result.message:\n self._status = 1\n else:\n self._status = 2","def run_and_store(self):\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient,2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Stored updates\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\n stored_predictive_likelihood = np.zeros(self.iterations)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n gradient = self.cv_gradient(self.draw_normal())\n gradient[np.isnan(gradient)] = 0\n new_parameters = self.optim.update(gradient)\n self.change_parameters(new_parameters)\n\n stored_means[i] = self.optim.parameters[::2]\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\n\n if self.printer is True:\n self.print_progress(i,self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records","def run_and_store(self):\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient,2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Stored updates\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\n stored_predictive_likelihood = np.zeros(self.iterations)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n gradient = self.cv_gradient(self.draw_normal())\n gradient[np.isnan(gradient)] = 0\n new_parameters = self.optim.update(gradient)\n self.change_parameters(new_parameters)\n\n stored_means[i] = self.optim.parameters[::2]\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\n\n if self.printer is True:\n self.print_progress(i,self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records","def run_qae_optimization(training_states, n_repetitions, exact=no_noise, noisy=gate_error):\n result_list = []\n def proxy(params, training_states, n_repetitions, exact=no_noise, noisy=gate_error):\n \"\"\"Embedded function version\n \"\"\"\n input_list = fix_list(params, all_param_array=all_param, var_param_array=var_param, fixed_vals_array=fixed_vals)\n fidelities = []\n for training_state in training_states:\n fid = cusp_stage2.compute_stage2_cost_function(*input_list, alpha=training_state, n_repetitions=n_repetitions,\n exact=exact, noisy=noisy)\n fidelities.append(fid)\n avg_fid = np.mean(fidelities)\n result_list.append(1-avg_fid)\n print(1-avg_fid)\n return 1. - avg_fid\n\n \n # Initialize parameters\n half_turn_min = 0\n half_turn_max = 2\n init_params = np.random.uniform(low=half_turn_min, high=half_turn_max,\n size=num_param)\n\n # Optimization using Nelder-Mead.\n h2_qae_wrap = lambda params: proxy(params, training_states=training_states,\n n_repetitions=n_repetitions, exact=exact, noisy=noisy)\n \n if noisy:\n maxiter = 60\n else:\n maxiter = None\n \n res = minimize(h2_qae_wrap, init_params, args=(),\n method='Nelder-Mead', tol=None, \n options={'disp': False, 'maxiter': maxiter, 'xatol': 0.001,\n 'return_all': False, 'fatol': 0.001})\n np.savetxt('stage2_data.csv',result_list, delimiter=',')\n return res.x","def example():\n Optimizer = BFGS(f, g)\n startPoint = 100 * numpy.ones(2);\n res = Optimizer.optimize(startPoint,\n epsilon=1e-5,\n maxIterations=10)\n print res\n pass","def optimize(self, optimizer=None, start=None, messages=False, max_iters=1000, ipython_notebook=True, clear_after_finish=False, **kwargs):\n self.inference_method.on_optimization_start()\n try:\n super(GPMSGP, self).optimize(optimizer, start, messages, max_iters, ipython_notebook, clear_after_finish, **kwargs)\n except KeyboardInterrupt:\n print(\"KeyboardInterrupt caught, calling on_optimization_end() to round things up\")\n self.inference_method.on_optimization_end()\n raise\n \n self.posterior_prediction = self.inference_method.update_prediction_vectors(self.kern,self.posterior,self.grad_dict,self.likelihood)","def _on_step(self) -> None:\n self._n_calls += 1\n # Account for multiple environments\n # each call to step() corresponds to n_envs transitions\n if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:\n polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\n # Copy running stats, see GH issue #996\n polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)\n\n self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\n self.logger.record(\"rollout/exploration_rate\", self.exploration_rate)","def optimization_step(self):\n \n if \"CSS\" in self.algorithm:\n \n input_dict = {self.x: self.train_inputs[self.minibatch_set,:]}\n \n var_list = [self.x_tilda, self.minibatch_set]\n \n if (self.num_samples > 0) and (not self.mixture):\n \n if ((self.mf_steps > 0) and self.alpha >0) or\\\n self.gibbs_steps > 0: \n \n var_list.append(self.sampler_theta)\n \n elif \"CD\" in self.algorithm:\n \n input_dict = {self.x : self.train_inputs[self.minibatch_set,:]} \n \n var_list = [self.minibatch_set]\n \n var_list.append(self.learning_rate)\n \n if self.use_momentum:\n \n var_list.append(self.momentum)\n \n output_vars = [self.pseudo_cost]\n \n if self.report_p_tilda:\n \n output_vars.append(self.p_tilda)\n \n else:\n \n output_vars.append(theano.shared(0))\n \n opt_step = theano.function(inputs = var_list,\n outputs = output_vars,\n updates = self.updates,\n givens = input_dict,\n on_unused_input='warn')\n \n return opt_step","def opt_wrapper(m, **kwargs):\r\n m.optimize(**kwargs)\r\n return m.optimization_runs[-1]","def algorithm_loop(self):","def propose_optimize():\n pass","def run(self, C, p0 = None):\n global algorithm \n algorithm = AdaptiveMM(self.g, C, p0 = p0, lambda0 = 2000)\n solve()","def Solve(self, cost, termination=None, ExtraArgs=(), **kwds):\n # process and activate input settings\n sigint_callback = kwds.pop('sigint_callback', None)\n settings = self._process_inputs(kwds)\n disp = settings['disp'] if 'disp' in settings else False\n echo = settings['callback'] if 'callback' in settings else None\n# for key in settings:\n# exec \"%s = settings['%s']\" % (key,key)\n if disp in ['verbose', 'all']: verbose = True\n else: verbose = False\n #-------------------------------------------------------------\n\n from python_map import python_map\n if self._map != python_map:\n #FIXME: EvaluationMonitor fails for MPI, throws error for 'pp'\n from mystic.monitors import Null\n evalmon = Null()\n else: evalmon = self._evalmon\n fcalls, cost = wrap_function(cost, ExtraArgs, evalmon)\n\n # set up signal handler\n #self._EARLYEXIT = False\n self._generateHandler(sigint_callback) \n\n # activate signal_handler\n #import threading as thread\n #mainthread = isinstance(thread.current_thread(), thread._MainThread)\n #if mainthread: #XXX: if not mainthread, signal will raise ValueError\n import signal\n if self._handle_sigint:\n signal.signal(signal.SIGINT,self.signal_handler)\n\n # register termination function\n if termination is not None: self.SetTermination(termination)\n\n # get the nested solver instance\n solver = self._AbstractEnsembleSolver__get_solver_instance()\n #-------------------------------------------------------------\n\n # generate starting points\n initial_values = self._InitialPoints()\n\n # run optimizer for each grid point\n from copy import deepcopy as _copy\n op = [_copy(solver) for i in range(len(initial_values))]\n #cf = [cost for i in range(len(initial_values))]\n vb = [verbose for i in range(len(initial_values))]\n cb = [echo for i in range(len(initial_values))] #XXX: remove?\n at = self.id if self.id else 0 # start at self.id\n id = range(at,at+len(initial_values))\n\n # generate the local_optimize function\n def local_optimize(solver, x0, rank=None, disp=False, callback=None):\n from copy import deepcopy as _copy\n from mystic.tools import isNull\n solver.id = rank\n solver.SetInitialPoints(x0)\n if solver._useStrictRange: #XXX: always, settable, or sync'd ?\n solver.SetStrictRanges(min=solver._strictMin, \\\n max=solver._strictMax) # or lower,upper ?\n solver.Solve(cost, disp=disp, callback=callback)\n sm = solver._stepmon\n em = solver._evalmon\n if isNull(sm): sm = ([],[],[],[])\n else: sm = (_copy(sm._x),_copy(sm._y),_copy(sm._id),_copy(sm._info))\n if isNull(em): em = ([],[],[],[])\n else: em = (_copy(em._x),_copy(em._y),_copy(em._id),_copy(em._info))\n return solver, sm, em\n\n # map:: solver = local_optimize(solver, x0, id, verbose)\n results = self._map(local_optimize, op, initial_values, id, \\\n vb, cb, **self._mapconfig)\n\n # save initial state\n self._AbstractSolver__save_state()\n #XXX: HACK TO GET CONTENT OF ALL MONITORS\n # reconnect monitors; save all solvers\n from mystic.monitors import Monitor\n while results: #XXX: option to not save allSolvers? skip this and _copy\n _solver, _stepmon, _evalmon = results.pop()\n sm = Monitor()\n sm._x,sm._y,sm._id,sm._info = _stepmon\n _solver._stepmon.extend(sm)\n del sm\n em = Monitor()\n em._x,em._y,em._id,em._info = _evalmon\n _solver._evalmon.extend(em)\n del em\n self._allSolvers[len(results)] = _solver\n del results, _solver, _stepmon, _evalmon\n #XXX: END HACK\n\n # get the results with the lowest energy\n self._bestSolver = self._allSolvers[0]\n bestpath = self._bestSolver._stepmon\n besteval = self._bestSolver._evalmon\n self._total_evals = self._bestSolver.evaluations\n for solver in self._allSolvers[1:]:\n self._total_evals += solver.evaluations # add func evals\n if solver.bestEnergy < self._bestSolver.bestEnergy:\n self._bestSolver = solver\n bestpath = solver._stepmon\n besteval = solver._evalmon\n\n # return results to internals\n self.population = self._bestSolver.population #XXX: pointer? copy?\n self.popEnergy = self._bestSolver.popEnergy #XXX: pointer? copy?\n self.bestSolution = self._bestSolver.bestSolution #XXX: pointer? copy?\n self.bestEnergy = self._bestSolver.bestEnergy\n self.trialSolution = self._bestSolver.trialSolution #XXX: pointer? copy?\n self._fcalls = self._bestSolver._fcalls #XXX: pointer? copy?\n self._maxiter = self._bestSolver._maxiter\n self._maxfun = self._bestSolver._maxfun\n\n # write 'bests' to monitors #XXX: non-best monitors may be useful too\n self._stepmon = bestpath #XXX: pointer? copy?\n self._evalmon = besteval #XXX: pointer? copy?\n self.energy_history = None\n self.solution_history = None\n #from mystic.tools import isNull\n #if isNull(bestpath):\n # self._stepmon = bestpath\n #else:\n # for i in range(len(bestpath.y)):\n # self._stepmon(bestpath.x[i], bestpath.y[i], self.id)\n # #XXX: could apply callback here, or in exec'd code\n #if isNull(besteval):\n # self._evalmon = besteval\n #else:\n # for i in range(len(besteval.y)):\n # self._evalmon(besteval.x[i], besteval.y[i])\n #-------------------------------------------------------------\n\n # restore default handler for signal interrupts\n if self._handle_sigint:\n signal.signal(signal.SIGINT,signal.default_int_handler)\n\n # log any termination messages\n msg = self.Terminated(disp=disp, info=True)\n if msg: self._stepmon.info('STOP(\"%s\")' % msg)\n # save final state\n self._AbstractSolver__save_state(force=True)\n return","def run(self):\n # print(\"111\"+\"--- %s seconds ---\" % (time.time() ))\n err = self.params.tolerance + 1\n for iter_num in range(self.params.max_iter):\n if err <= self.params.tolerance:\n break\n # print(\"11\"+str(iter_num)+\"--- %s seconds ---\" % (time.time() - start_time))\n qprev = self.sigma2\n\n self._expectation_iter(iter_num)\n self._maximization_iter(iter_num)\n\n if self.sigma2 <= 0:\n self.sigma2 = self.params.tolerance / 10\n err = np.abs(self.sigma2 - qprev)\n\n if callable(self.callback):\n kwargs = {\n 'iteration': iter_num,\n 'error': err,\n 'X': self.X,\n 'Y': self.TY,\n 'W': self.W,\n 'P': self.P\n }\n self.callback(**kwargs)\n return self.TY","def opt_wrapper(m, **kwargs):\n m.optimize(**kwargs)\n return m.optimization_runs[-1]","def Optimize(self):\n return _gmat_py.Optimizer_Optimize(self)","def optimize(cls, trials, score, evals_rounds, mon_cons, categorical):\n raise NotImplementedError","def optimize(self, objectivefct, logger=None, verb_disp=20, iterations=None):\r\n if logger is None:\r\n if hasattr(self, 'logger'):\r\n logger = self.logger\r\n\r\n citer = 0\r\n while not self.stop():\r\n if iterations is not None and citer >= iterations:\r\n return self.result()\r\n citer += 1\r\n\r\n X = self.ask() # deliver candidate solutions\r\n fitvals = [objectivefct(x) for x in X]\r\n self.tell(X, fitvals) # all the work is done here\r\n\r\n self.disp(verb_disp)\r\n logger.add(self) if logger else None\r\n\r\n logger.add(self, modulo=bool(logger.modulo)) if logger else None\r\n if verb_disp:\r\n self.disp(1)\r\n if verb_disp in (1, True):\r\n print('termination by', self.stop())\r\n print('best f-value =', self.result()[1])\r\n print('solution =', self.result()[0])\r\n\r\n return self.result() + (self.stop(), self, logger)","def run_optimization(config,\n blackbox_optimizer,\n init_current_input,\n init_best_input,\n init_best_core_hyperparameters,\n init_best_value,\n init_iteration,\n workers,\n log_bool=False):\n current_input = init_current_input\n best_input = init_best_input\n best_core_hyperparameters = init_best_core_hyperparameters\n best_value = [init_best_value]\n iteration = init_iteration\n\n while True:\n print(iteration)\n sys.stdout.flush()\n success, current_input = run_step_rpc_blackbox_optimizer(\n config, current_input, blackbox_optimizer, workers, iteration,\n best_input, best_core_hyperparameters, best_value, log_bool)\n if success:\n iteration += 1\n if iteration == config.nb_iterations:\n break","def iterate(self, update_func):\n if self.disc_param_save_on_error:\n update_func = param_file.wrap_with_save_on_error(\n self.gan.discriminator,\n self.datastore.path('disc_param', 'pre_error.npz'),\n self.datastore.path('disc_param', 'post_error.npz'),\n )(update_func)\n\n self.pre_loop()\n logger.info('%s: start iterations', self.__class__.__name__)\n with recording_exit_reason(self.datastore):\n for gen_step in range(self.iterations):\n self.post_update(gen_step, update_func(gen_step))\n logger.info('%s: maximum iterations reached', self.__class__.__name__)","def __call__(self, fgraph):\r\n return self.optimize(fgraph)","def step(self, closure=None):\n orig_loss,err,pred = closure()\n loss = orig_loss\n\n group = self.param_groups[0]\n lr = group['lr']\n decay_lr = group['decay_lr']\n max_iter = group['max_iter']\n reg = group['reg']\n backtrack = group['backtrack']\n bt_alpha = group['bt_alpha']\n bt_beta = group['bt_beta']\n sketch_size = group['sketch_size']\n tolerance = group['tolerance']\n\n #import pdb; pdb.set_trace()\n n = err.shape[0] #batch size\n #If sketching the jacobian, randomly select [sketch_size] samples\n \n if sketch_size is not None:\n idx = torch.randperm(n)[:sketch_size]\n else:\n idx = torch.arange(n) #Don't sketch, use all samples\n \n w0 = nn.utils.parameters_to_vector(self._params) #weight parameters in vector form\n \n #Compute Gauss-Newton vector product \n grad, ggnvp = _make_ggnvp(err,self._params,w0,n,reg,idx) #return gradient in vector form + ggnvp function\n #Solve for the Conjugate Gradient Direction\n dw, cost_log = _conjugate_gradient(ggnvp, grad, max_iter, tolerance)\n\n #Perform backtracking line search\n val = loss + 0.5 * reg * torch.norm(w0)**2\n fprime = -1*dw @ grad\n \n self.grad_update += 1\n if backtrack > 0:\n t = lr\n\n #TODO: If using backtracking, get new loss with (w0 - t*dw) as network parameters\n bts = 0\n alpha = bt_alpha\n beta = bt_beta \n while (loss + 0.5 * reg * torch.norm(w0 - t*dw)**2 > val + alpha * t * fprime):\n t = beta * t\n bts += 1\n if bts > backtrack:\n print('Maximum backtracking reached, accuracy not guaranteed')\n break\n elif decay_lr: #decay lr\n t = lr/np.maximum(1, self.grad_update-10)\n else: #use lr step-size\n t = lr\n\n print('step size: {}'.format(t))\n\n #Update the model parameters\n self._add_grad(-t, dw)\n \n return val, pred","def callback(self, parameters):\n print(\"\\nFinsished Iteration: {}\".format(self.current_iteration), flush=True)\n print(\"Current Parameters: {}\".format(parameters), flush=True)\n\n # If getting the value history, perform an evaluation with current parameters\n\n print(\"Starting Next Iteration...\", flush=True)\n\n # Update currrent_iteration index and add new blank history\n self.current_iteration += 1","def do_optimisation(self):\n\n print('--> Parameters for optimisation:')\n print('--> Using measurements : {}'.format(self.stoma_cfg.comparison_helper.optimisation_keys))\n print('')\n\n x0 = self.initial_guess()\n\n tol, eps = 1e-4, 0.001\n\n print('--> Using SLSQP with tol={} and eps={}'.format(tol, eps))\n\n soln = opt.minimize(fun=self.optimise_fn,\n x0=x0,\n method='SLSQP',\n tol=tol,\n options={'eps': eps})\n\n print('*' * 120)\n print('--> Optimisation procedure has finished...')\n print(soln)\n print('*' * 120)\n\n if soln.success:\n print('--> Optimisation succeeded. Result is...')\n self._set_material_parameters(soln.x)\n print('--> {}'.format(self.material_model))\n else:\n print('--> The optimisation failed!')\n\n print('*' * 120)\n\n return soln","def VFI(method) :\n iteration=0 # Iteration Counter\n converged = 0 # Convergence Flag|\n \n#----- Initial Settings \n v_update = zeros(n_grid)\n v_func = empty(n_grid)\n k_next_vec = empty(n_grid)\n run_time = empty(2)\n \n def obj(k_next) :\n \"\"\"\n This function is used in value function iteration.\n It represents the objective function to be maximized for one node (state) of current capitals.\n Resulting value is maximized one corresponding to next period's capital as a maximizer. \n Next period's value is computed by interpolation.\n \n Input : k_next (next period's capital)\n \n Output : value_vec (maximized value resulting from choosing optimal capital in the next period)\n \"\"\" \n \n if method==1 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*linear_interp(k_grid,v_update,k_next))\n elif method==2 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*quad_interp(k_grid,v_update,k_next))\n elif method==3 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*cubic_interp(k_grid,v_update,k_next))\n \n return value_vec\n\n#----- Value function iteration\n start = time.time() # start time\n while converged==0 :\n index = 0\n for k_current in k_grid :\n k_next = fminbound(obj,k_grid[0],k_grid[-1])\n v_func[index] = (-1) * obj(k_next)\n k_next_vec[index] = k_next\n index = index + 1\n dist = abs(max(v_func - v_update))\n if dist<tol :\n converged = 1\n v_k, g_k = v_func, k_next_vec\n v_update = v_func\n print \"Iteration : \",iteration,\"\",\"Distance : \",dist # convergence process\n iteration = iteration + 1\n v_func = empty(n_grid) \n k_next_vec = empty(n_grid)\n \n end = time.time() # end time\n run_time[0],run_time[1] = runtime_cal(start,end) # total running time\n \n return v_k, g_k, run_time, iteration","def run(self):\n\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient, 2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n x = self.draw_normal()\n gradient = self.cv_gradient(x)\n gradient[np.isnan(gradient)] = 0\n self.change_parameters(self.optim.update(gradient))\n\n if self.printer is True:\n self.print_progress(i, self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, elbo_records","def run(self):\n\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient, 2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n x = self.draw_normal()\n gradient = self.cv_gradient(x)\n gradient[np.isnan(gradient)] = 0\n self.change_parameters(self.optim.update(gradient))\n\n if self.printer is True:\n self.print_progress(i, self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, elbo_records","def optimize_parameters(self):\n pass","def optimize_parameters(self):\n pass","def optimize_parameters(self):\n pass","def intermediate(self, alg_mod, iter_count, obj_value,\n inf_pr, inf_du, mu, d_norm, regularization_size,\n alpha_du, alpha_pr, ls_trials):\n # TODO: Document the arguments\n pass","def _function(self):\n\n\n\n def calculate_weights():\n \"\"\"\n calculate a weight inversely proportional to the expected to duration of the two steps in the\n script\n\n Returns: weights as a dictionary for the two steps\n\n \"\"\"\n weights = {}\n\n\n # estimate run time of step 1 (fast sweep)\n f_range = sweeper_script.settings['stop'] - sweeper_script.settings['start']\n N_samples = sweeper_script.settings['samplecount']\n df = f_range / N_samples\n\n t = N_samples / df\n\n weights['quick scan'] = t\n\n # estimate run time of step 2 (high res sweep)\n df = self.settings['high_res_df']\n N_samples = self.settings['high_res_N']\n\n t = N_samples / df\n\n weights['high res scan'] = t\n\n\n total_time = sum([v for k, v in weights.iteritems()])\n\n weights = {k: v/total_time for k, v in weights.iteritems()}\n\n print('weights',weights)\n\n return weights\n\n def run_scan(name):\n self.current_subscript = name\n sweeper_script.start()\n while self.current_subscript is name:\n time.sleep(0.1)\n\n def calc_new_range():\n\n\n df = self.settings['high_res_df']\n N = self.settings['high_res_N']\n\n r = sweeper_script.data[-1]['r']\n freq = sweeper_script.data[-1]['frequency']\n freq = freq[np.isfinite(r)]\n r = r[np.isfinite(r)]\n\n fo = freq[np.argmax(r)]\n\n f_start, f_end = fo - N/2 *df, fo + N/2 *df\n\n\n # make sure that we convert back to native python types (numpy file types don't pass the Parameter validation)\n return float(f_start), float(f_end), int(N)\n\n\n sweeper_script = self.scripts['zi sweep']\n #save initial settings, so that we can rest at the end of the script\n initial_settings = deepcopy(sweeper_script.settings)\n self.weights = calculate_weights()\n\n # take the signal from the subscript and route it to a function that takes care of it\n sweeper_script.updateProgress.connect(self._receive_signal)\n\n print('====== start quick scan ============')\n\n run_scan('quick scan')\n\n print('====== calculate new scan range ====')\n f_start, f_stop, N = calc_new_range()\n\n print('f_start, f_stop, N', f_start, f_stop, N)\n\n print('====== update sweeper ==============')\n sweeper_script.update({\n 'start' : f_start,\n 'stop' : f_stop,\n 'samplecount' : N\n })\n\n print('====== start high res scan =========')\n # print(sweeper_script.sweeper.finished())\n # print(sweeper_script.sweeper.progress())\n\n run_scan('high res scan')\n\n sweeper_script.updateProgress.disconnect()\n self.data = sweeper_script.data[-1]\n\n self._recording = False\n\n if self.settings['save']:\n self.save()\n\n # set the sweeper script back to initial settings\n sweeper_script.update(initial_settings)\n # make sure that progess is set 1o 100 because we check that in the old_gui\n self.updateProgress.emit(100)","def run(self):\n if not self._no_progress and self._verbose:\n from progressbar import ProgressBar\n progress = ProgressBar()\n iter_range = progress(range(self._iters))\n else:\n iter_range = range(self._iters)\n\n if self._no_progress and self._time_iters:\n from time import time\n\n i = 0\n try:\n for i in iter_range:\n if self._verbose and self._no_progress:\n print(\"Iteration \" + repr(i))\n\n if self._no_progress and self._time_iters:\n start = time()\n\n self.iteration += 1\n\n self._forward(self._p_k, self._v_k)\n sigma_k = measure(self._p_k, self._v_k)\n alpha_k = self._rho_k / sigma_k\n if self._double:\n update_m_double(self._m, alpha_k, self._p_k)\n sub_scaled_vector_double(self._residual_k,\n self._residual_k,\n alpha_k, self._v_k)\n else:\n update_m(self._m, alpha_k, self._p_k)\n sub_scaled_vector(self._residual_k, self._residual_k,\n alpha_k, self._v_k)\n self._v_k = gpuarray_copy(self._residual_k)\n rho_k_plus_1 = measure(self._v_k, self._residual_k)\n rho_k_t = np.abs(rho_k_plus_1)\n\n if (rho_k_t / self._rho_0 <= self._relative_tolerance) \\\n or (rho_k_t <= self._absolute_tolerance):\n print(\"Converged.\")\n self.converged = True\n break\n\n if self._double:\n add_scaled_vector_double(self._p_k, self._v_k,\n rho_k_plus_1/self._rho_k,\n self._p_k)\n else:\n add_scaled_vector(self._p_k, self._v_k,\n rho_k_plus_1/self._rho_k, self._p_k)\n\n self._rho_k = rho_k_plus_1\n\n if self._noisy:\n print(\" Residual=\" + str(rho_k_t))\n\n if self._no_progress and self._time_iters:\n print(\"Elapsed time for iteration \" + str(i) + \": \" +\n str(time() - start) + \" seconds\")\n\n if self._save_images:\n save_image(np.abs(self._m.get().reshape(self._data.nX1,\n self._data.nX2)),\n self._out_dir, i, self._image_format)\n if self._save_matlab:\n save_matlab(self._m.get().reshape(self._data.nX1,\n self._data.nX2),\n self._out_dir, i)\n except KeyboardInterrupt:\n print(\"Reconstruction aborted (CTRL-C) at iteration \" + str(i))\n finally:\n if self._save_images:\n save_image(np.abs(self._m.get().reshape(self._data.nX1,\n self._data.nX2)),\n self._out_dir, \"result\", self._image_format)\n if self._save_matlab:\n save_matlab(self._m.get().reshape(self._data.nX1,\n self._data.nX2),\n self._out_dir, \"result\")\n self.iteration = i+1\n return (self._m.get().reshape(self._data.nX1, self._data.nX2),\n self.iteration)","def update():\n global iteration, result\n iteration += 1\n # Stop iterating after max_iterations\n if iteration >= max_iterations:\n timer.stop()\n print \"Output is\", result\n else:\n result = get_next(result)","def objective(hyperparams): \n global iteration #necessary with a global variable because of implementation from hyperopt. \n iteration += 1\n\n result = run_model(hyperparams, iteration)\n loss = -result #transform to loss in order to minimize\n\n return {'loss': loss, 'hyperparams': hyperparams, 'iteration': iteration, 'status': STATUS_OK}","def on_iteration_start(self):\n\n self.Xprv = self.X.copy()\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\n BacktrackRobust)):\n self.Yprv = self.Y.copy()\n\n if self.opt['Monotone']:\n if self.k == 0:\n self.objfn = self.eval_objfn()\n self.objfn_prev = self.objfn","def fit(self):\n self._minuit_problem.migrad() # run optimizer\n self._status = 0 if self._minuit_problem.migrad_ok() else 1","def optimize(self,max_iter=100):\n\n\n for itr in range(max_iter):\n opt_logs = self.opt.minimize(self.tot_neg_elbo,sum([expert.trainable_variables for expert in self.experts],())) \n print(self.neg_elbo)","def optimize(self):\n prm = (self.b,self.c)\n d = self.d\n no = int(d*d)\n bounds = [(-1,1)]*no\n resG = differential_evolution(inpSc.entBias, bounds, args = prm, popsize = 40, disp = False)\n\n xOpt = resG.x\n xOpt = xOpt/(np.linalg.norm(xOpt))\n\n #Refine the global optimization by performing a second local optimizaiton\n x0 = xOpt\n\n res = minimize(inpSc.entBias, x0, args = prm, method='BFGS', options={'disp': False})\n xOpt = res.x\n xOpt = xOpt/(np.linalg.norm(xOpt))\n self.rhoOp = inpSc.getMat(xOpt, d)\n self.Q1 = -res.fun","def search(self):\n self.iteration = 0\n while self.iteration < self.maxIterations:\n self.GAStep()\n self.iteration += 1\n\n print (\"Total iterations: \",self.iteration)\n print (\"Best Solution: \", self.best.getFitness())","def run_step_blackbox_optimizer(config,\n current_input,\n blackbox_optimizer,\n proposed_perturbations,\n finished_dnas,\n results,\n logging_data=None):\n core_hyperparameters = blackbox_optimizer.get_hyperparameters()\n function_values = [0.0] * len(proposed_perturbations)\n rewards_for_controller = []\n perturbations = proposed_perturbations\n evaluation_stats = []\n current_value_exact = 0.0\n current_value_exact_counter = 0\n\n for i in range(len(results)):\n rewards_for_controller.append(results[i]['function_value'])\n tag = results[i]['tag']\n index = 0\n if tag > 0:\n if config.est_type == 'antithetic':\n index = (tag - 1) * 2\n function_values[index] += results[i]['function_value']\n else:\n index = tag - 1\n function_values[index] += results[i]['function_value']\n if tag < 0:\n index = (-tag - 1) * 2 + 1\n function_values[index] += results[i]['function_value']\n if tag == 0:\n current_value_exact += results[i]['function_value']\n current_value_exact_counter += 1\n current_value_exact /= float(current_value_exact_counter)\n\n for result in results:\n evaluation_stat = list(result['evaluation_stat'])\n evaluation_stats.append(evaluation_stat)\n\n function_values_reshaped = np.array(function_values)\n perturbations_reshaped = np.array(perturbations)\n\n logging.info('LIST OF FUNCTION VALUES')\n logging.info(function_values_reshaped)\n\n logging.info('MAX VALUE SEEN CURRENTLY')\n logging.info(np.max(function_values_reshaped))\n\n logging.info('MEAN OF VALUES')\n logging.info(np.mean(function_values_reshaped))\n\n if logging_data is not None:\n iteration = logging_data['iteration']\n best_value = logging_data['best_value']\n iteration = logging_data['iteration']\n best_input = logging_data['best_input']\n best_core_hyperparameters = logging_data['best_core_hyperparameters']\n optimizer_state = blackbox_optimizer.get_state()\n\n if current_value_exact > best_value[0]:\n best_value[0] = current_value_exact\n best_input = current_input\n best_core_hyperparameters = core_hyperparameters\n\n # Writing logs.\n if iteration % config.log_frequency == 0:\n util.log_row(config.params_file, current_input)\n util.log_row(config.best_params_file, best_input)\n util.log_row(config.best_core_hyperparameters_file,\n best_core_hyperparameters)\n util.log_row(config.best_value_file, best_value)\n util.log_row(config.optimizer_internal_state_file, optimizer_state)\n util.log_row(config.current_values_list_file, [current_value_exact])\n util.log_row(config.best_values_list_file, [best_value[0]])\n util.log_row(config.fvalues_file, function_values_reshaped)\n util.log_row(config.iteration_file, [iteration])\n\n print('Current exact value estimate:')\n print(current_value_exact)\n sys.stdout.flush()\n\n new_current_input = blackbox_optimizer.run_step(perturbations_reshaped,\n function_values_reshaped,\n current_input,\n current_value_exact)\n config.controller.collect_rewards_and_train(rewards_for_controller,\n finished_dnas)\n\n evaluation_stats_reduced = [sum(x) for x in zip(*evaluation_stats)]\n blackbox_optimizer.update_state(evaluation_stats_reduced)\n\n return [True, new_current_input]","def optimize(self): \n if self.model == 'ARD':\n estimate = minimize(\n fun=optim_func,\n x0=np.array([self.alpha, self.beta]),\n args=(self,),\n method='L-BFGS-B',\n bounds=((0, 50), (0, 50)),\n )\n # logger.info(estimate)\n\n # organize into a dict\n result = {\n \"alpha\": estimate.x[0],\n \"beta\": estimate.x[1],\n \"Lik\": estimate.fun,\n \"negLogLik\": -np.log(-estimate.fun),\n \"convergence\": estimate.success,\n }\n logger.debug(result)\n\n elif self.model == 'ER':\n estimate = minimize(\n fun=optim_func,\n x0=np.array([self.alpha]),\n args=(self,),\n method='L-BFGS-B',\n bounds=[(0, 50)],\n )\n\n result = {\n \"alpha\": estimate.x[0],\n \"Lik\": estimate.fun, \n \"negLogLik\": -np.log(-estimate.fun),\n \"convergence\": estimate.success,\n }\n logger.debug(result)\n\n else:\n raise Exception('model must be specified as either ARD or ER')\n\n # get scaled likelihood values\n self.log_lik = result[\"negLogLik\"]\n self.tree = self.tree.set_node_values(\n 'likelihood',\n values={\n node.idx: np.array(node.likelihood) / sum(node.likelihood)\n for node in self.tree.idx_dict.values()\n }\n )","def iterate_value(self):\n self.V = np.zeros(self.stateCount)\n for i in range(self.maxIter):\n last_V = np.copy(self.V)\n for state_index in range(self.stateCount):\n current_state = self.env.statespace[state_index]\n for action in self.env.actionspace:\n next_state = self.env.next_state(current_state,action)\n reward = self.env.compute_reward(next_state)\n next_state_index = self.env.stateDict[next_state]\n self.Q[state_index][action] = reward + self.gamma*last_V[next_state_index]\n if self.mode == 'debug':\n print(\"Q(s={}):{}\".format(current_state,self.Q[state_index]))\n self.V[state_index] = max(self.Q[state_index])\n if np.sum(np.fabs(last_V - self.V)) <= self.th:\n print (\"Convergene Achieved in {}th iteration. \"\n \"Breaking V_Iteration loop!\".format(i))\n break","def optimize(self, ngen):\n res = 0\n for res in self(ngen):\n pass\n return res","def on_iteration_start(self):\n\n self.Xfprv = self.Xf.copy()\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\n BacktrackRobust)):\n self.Yfprv = self.Yf.copy()\n\n if self.opt['Monotone']:\n if self.k == 0:\n self.objfn = self.eval_objfn()\n self.objfn_prev = self.objfn","def run(self):\n\n # init\n base_value = self._problem.evaluate()\n self._problem.set_as_best(base_value)\n\n # init iteration (used to count the amount of iterations)\n iteration = 0\n\n # add to data\n self._data_append(self.data, iteration, base_value, base_value)\n\n # init termination criterion\n self._termination_criterion.check_first_value(base_value)\n self._termination_criterion.start_timing()\n\n # main loop\n while self._termination_criterion.keep_running():\n\n # search the neighbourhood for the best move\n best_found_delta = self._best_found_delta_base_value\n best_found_move = None\n\n for move in self._problem.get_moves():\n\n # check quality move\n delta = self._problem.evaluate_move(move)\n\n # checks how the move alters the current state\n diff = self._diff(move)\n\n # if not in tabu list --> not similar to earlier performed\n # moves --> if delta better than old best move\n # --> becomes the best move\n\n if not self._tabu_list.contains(diff) and \\\n self._is_better(best_found_delta, delta):\n best_found_delta = delta\n best_found_move = move\n best_found_diff = diff\n\n # the best found move will be used as the next move\n # alter state problem\n base_value = base_value + best_found_delta\n\n # check if a move was found\n if best_found_move is not None:\n\n self._problem.move(best_found_move)\n\n # if better than best found --> new best_found\n if self._is_better(self._problem.best_order_value,\n base_value):\n self._problem.set_as_best(base_value)\n # log the better solution\n self._log_improvement(base_value)\n\n # add diff to tabu list\n self._tabu_list.add(best_found_diff)\n\n # add to data\n self._data_append(self.data, iteration,\n base_value, self._problem.best_order_value)\n\n self._termination_criterion.check_new_value(base_value)\n\n # functions _termination_criterion called\n self._termination_criterion.check_new_value(base_value)\n\n else:\n # no move found --> we're stuck --> break loop\n break\n\n iteration += 1\n self._termination_criterion.iteration_done()\n\n # last data point\n self._data_append(self.data, iteration, base_value,\n self._problem.best_order_value)\n\n # if we have data:\n # convert data to something easier to plot\n if self.data is not None:\n\n # convert to tuple of list\n data = convert_data(self.data)\n\n # make namedtuple\n DataAsLists = namedtuple(\n 'Data', ['time', 'iteration', 'value', 'best_value'])\n\n data = DataAsLists(data[0], data[1], data[2], data[3])\n\n else:\n data = None\n\n # return results\n\n Results = namedtuple('Results', ['best_order', 'best_value', 'data'])\n\n return Results(self._problem.best_order,\n self._problem.best_order_value,\n data)","def compute_gradient(self, verbose):\n from inversionson.helpers.autoinverter_helpers import IterationListener\n\n # Attempt to dispatch model smoothing right at the beginning.\n # So there is no smoothing bottleneck when updates are not smoothed.\n it_listen = IterationListener(\n self.comm,\n events=self.comm.project.events_in_iteration)\n it_listen.listen()\n\n self.task_dict[\"forward_submitted\"] = True\n self.task_dict[\"misfit_completed\"] = True\n self.task_dict[\"gradient_completed\"] = True\n self._update_task_file()\n self.finish_task()","def solve(self, regparam):\n self.regparam = regparam\n \n #Some counters for bookkeeping\n self.stepcounter = 0\n self.flipcounter = 0\n self.nochangecounter = 0\n \n #Cached results\n self.evals = np.multiply(self.svals, self.svals)\n self.newevals = 1. / (self.evals + self.regparam)\n newevalslamtilde = np.multiply(self.evals, self.newevals)\n self.D = np.sqrt(newevalslamtilde)\n #self.D = -newevalslamtilde\n \n self.VTY = self.svecs.T * self.Y\n DVTY = np.multiply(self.D.T, self.svecs.T * self.Y)\n \n #Using lists in order to avoid unnecessary matrix slicings\n self.DVTY_list = []\n self.YTVDDVTY_list = []\n self.classFitnessList = []\n for i in range(self.labelcount):\n DVTY_i = DVTY[:,i]\n self.DVTY_list.append(DVTY_i)\n YTVDDVTY_i = DVTY_i.T * DVTY_i\n self.YTVDDVTY_list.append(YTVDDVTY_i)\n fitness_i = self.size - DVTY_i.T * DVTY_i\n self.classFitnessList.append(fitness_i)\n \n self.Dsvecs_list = []\n self.svecsDDsvecs_list = []\n for i in range(self.size):\n Dsvec = np.multiply(self.D.T, self.svecs[i].T)\n self.Dsvecs_list.append(Dsvec)\n self.svecsDDsvecs_list.append(Dsvec.T*Dsvec)\n \n self.updateA()\n \n \n converged = False\n print(self.classcounts.T)\n if self.callbackfun is not None:\n self.callbackfun.callback(self)\n while True:\n \n converged = self.roundRobin()\n print(self.classcounts.T)\n if self.callbackfun is not None:\n self.callbackfun.callback(self)\n if converged: break\n \n if self.oneclass:\n self.Y = self.Y[:, 0]\n self.A = self.A[:, 0]\n self.results['predicted_clusters_for_training_data'] = self.Y\n self.predictor = self.svdad.createModel(self)","def step(self, closure: Callable = None):\n loss = self.optimizer.step(closure)\n self._k_counter += 1\n\n if self._k_counter >= self.k:\n self._k_counter = 0\n # Lookahead and cache the current optimizer parameters\n for group in self.optimizer.param_groups:\n for p in group[\"params\"]:\n param_state = self.state[p]\n p.data.mul_(self.alpha).add_(\n param_state[\"slow_params\"], alpha=1.0 - self.alpha\n )\n param_state[\"slow_params\"].copy_(p.data)\n return loss","def callback(_locals, _globals):\n global n_steps, best_mean_reward\n # Print stats every 1000 calls\n if (n_steps + 1) % 1000 == 0:\n # Evaluate policy training performance\n x, y = ts2xy(load_results(log_dir), 'timesteps')\n if len(x) > 0:\n mean_reward = np.mean(y[-100:])\n print(x[-1], 'timesteps')\n print(\"Best mean reward: {:.2f} - Last mean reward per episode: {:.2f}\".format(best_mean_reward, mean_reward))\n\n # New best model, you could save the agent here\n if mean_reward > best_mean_reward:\n best_mean_reward = mean_reward\n # Example for saving best model\n print(\"Saving new best model\")\n _locals['self'].save(log_dir + str(x[-1])+'best_model.pkl')\n n_steps += 1\n return True","def optimize(self, num_restarts=1, max_iters=100, max_f_eval=300.0, method='Anneal'):\n dic = DictVectorizer()\n # flatten the parameters\n init_params,bounds=dic.fit_transform(self.params)\n #we minimise minus the marginal likelihood\n def objective(params_flatten):\n self.params=dic.inverse_transform(params_flatten,bounds)\n val = -self.log_marginal_likelihood()\n return val# we want to maximize it\n \n \n #run ptimisation with multiple restarts\n optml=np.inf\n for i in range(num_restarts):\n #minimise function\n if method=='Anneal':\n res=dual_annealing(objective,bounds, maxiter=max_iters, maxfun=max_f_eval, x0=init_params)\n else:\n \n res = minimize(objective, init_params, \n bounds=bounds, method=method,options={'maxiter': max_iters, 'disp': False})\n #print(\"Iteration \"+str(i)+\" \",-res.fun)\n if res.fun<optml:\n params_best=res.x #init_params \n optml=res.fun\n init_params=bounds[:,0]+(bounds[:,1]-bounds[:,0])*np.random.rand(len(bounds[:,0]))\n print(\"Iteration \"+str(i)+\" \",-res.fun)\n #params_best=res.x\n #optml=res.fun\n self.params=dic.inverse_transform(params_best,bounds)\n return -optml","def minimize(self):\n pass","def optimize(self, return_teacher_params_bool = False):\n\n gen_batches = self.DATASET.data_stream(self.BATCH_SIZE)\n \n num_complete_batches, leftover = divmod(self.DATASET.num_example['train'], self.BATCH_SIZE)\n\n # number of minibatches per epoch\n num_mini_batches_per_epochs = num_complete_batches + bool(leftover)\n\n # number of total iterations\n num_total_iters = self.NUM_EPOCHS * num_mini_batches_per_epochs\n\n # number of time that the sparisty levels get updated\n num_sparsity_updates = num_total_iters // self.MASK_UPDATE_FREQ \n \n mask_update_limit = num_total_iters - self.MASK_UPDATE_FREQ\n \n if self.SAVE_BOOL == True:\n # save the transferred results in the desinated directory.\n\n trans_model_dir = self.unique_model_dir\n\n# while os.path.exists(trans_model_dir):\n# trans_model_dir = trans_model_dir + '_0'\n \n if not os.path.exists(trans_model_dir):\n os.makedirs(trans_model_dir)\n\n np.save(trans_model_dir + '/param_dict.npy', self.param_dict) \n \n \n\n nt_trans_params_all_sparsities_all_runs = []\n nt_trans_masks_all_sparsities_all_runs = []\n nt_trans_vali_all_sparsities_all_runs = []\n teacher_params_all_sparsities_all_runs = []\n \n \n num_sparisty_levels = len(self.NN_DENSITY_LEVEL_LIST) \n num_runs = len(range(self.INIT_RUN_INDEX, self.INIT_RUN_INDEX + self.NUM_RUNS ))\n all_density_all_run_num_total_iters = num_sparisty_levels * num_runs * num_total_iters\n \n \n for nn_density_level in self.NN_DENSITY_LEVEL_LIST: \n \n \n nt_trans_params_all_runs = []\n nt_trans_masks_all_runs = []\n nt_trans_vali_all_runs = []\n teacher_params_all_runs = []\n\n\n for run_index in range(self.INIT_RUN_INDEX, self.INIT_RUN_INDEX + self.NUM_RUNS ):\n\n # do logging\n for handler in logging.root.handlers[:]:\n logging.root.removeHandler(handler)\n\n # a string that summarizes the current ntt experiment\n model_summary_str = self.model_str + '_density_' + str(round(nn_density_level, 2) ) + '_run_' + str(run_index)\n\n if self.SAVE_BOOL == True:\n model_dir_density_run = trans_model_dir + '/' + 'density_' + str(round(nn_density_level, 2) ) + '/' + 'run_' + str(run_index) + '/'\n\n os.makedirs(model_dir_density_run)\n \n logging.basicConfig(filename = model_dir_density_run + \"/\" + model_summary_str + \"_log.log\", format='%(asctime)s %(message)s', filemode='w', level=logging.DEBUG)\n\n else: \n logging.basicConfig(filename = model_summary_str + \"_log.log\" , format='%(asctime)s %(message)s', filemode='w', level=logging.DEBUG)\n \n \n # for different run indices, randomly draw teacher net's parameters\n _, teacher_net_params = self.init_fun(random.PRNGKey(run_index), tuple(self.batch_input_shape))\n \n # the prediction of the teacher net evaluated on validation samples\n vali_teacher_prediction = self.apply_fn(teacher_net_params, self.vali_samples)\n\n vali_teacher_ntk_mat = self.emp_ntk_fn(self.vali_inputs_1, self.vali_inputs_2, teacher_net_params) \n\n # the initial binary mask\n \n if self.PRUNE_METHOD == 'magnitude': \n masks = get_masks_from_jax_params(teacher_net_params, nn_density_level, global_bool = self.GLOBAL_PRUNE_BOOL)\n elif self.PRUNE_METHOD == 'logit_snip':\n logger.info(\"Use logit snip method to get the initial mask\")\n num_examples_snip = 128\n\n# gen_batches_logit_snip = self.DATASET.data_stream(num_examples_snip)\n \n snip_input = self.DATASET.dataset['train']['input'][:num_examples_snip, :]\n \n if self.GLOBAL_PRUNE_BOOL == False:\n logger.warning(\"layerwise sparse net initialized with logit_snip\") \n masks = get_logit_snip_masks(teacher_net_params, nn_density_level, self.apply_fn, snip_input, self.batch_input_shape, GlOBAL_PRUNE_BOOL = self.GLOBAL_PRUNE_BOOL) \n else:\n raise NotImplementedError(\"not implemented\")\n \n\n # the initial student parameters\n masked_student_net_params = get_sparse_params_filtered_by_masks(teacher_net_params, masks)\n\n # instantiate the optimizer triple \n opt_init, opt_update, get_params = self.OPTIMIZER_WITH_PARAMS\n\n opt_state = opt_init(teacher_net_params) \n\n # one step of NTK transfer\n @jit\n def nt_transfer_step(i, opt_state, x, masks):\n\n # parameters in the current optimizer state\n student_net_params = get_params(opt_state)\n\n # gradients that flow through the binary masks\n masked_g = grad(self.nt_transfer_loss)(student_net_params, masks, teacher_net_params, x, nn_density_level)\n\n return opt_update(i, masked_g, opt_state)\n\n # a list of validation loss\n vali_loss_list = []\n\n # calculate the initial validation loss. \n vali_loss = self.eval_nt_transfer_loss_on_vali_data(masked_student_net_params, vali_teacher_prediction, vali_teacher_ntk_mat, nn_density_level)\n\n vali_loss_list.append(vali_loss)\n\n logger.info(\"Before transfer: trans dist %.3f | ntk dist %.3f | targ dist %.3f | l2 pentalty %.3f | nn density %.2f\", vali_loss[0], vali_loss[1], vali_loss[2], vali_loss[3], nn_density_level)\n itercount = itertools.count()\n\n t = time.time()\n\n # loop through iterations\n for num_iter in range(1, num_total_iters + 1): \n \n # a batch of input data\n batch_xs, _ = next(gen_batches) \n\n # reshape the input to a proper format (2d array for MLP and 3d for CNN)\n batch_xs = batch_xs.reshape(self.batch_input_shape) \n\n # update the optimizer state\n opt_state = nt_transfer_step(next(itercount), opt_state, batch_xs, masks )\n\n\n if num_iter % 100 == 0:\n elapsed_time = time.time() - t\n \n if (num_iter <= 500) and (run_index == self.INIT_RUN_INDEX) and (nn_density_level == self.NN_DENSITY_LEVEL_LIST[0]): \n # estimate the program end time.\n remaining_iter_num = all_density_all_run_num_total_iters - num_iter\n remaining_seconds = elapsed_time * ( remaining_iter_num / 100 )\n expected_end_time = str(datetime.now() + timedelta(seconds = remaining_seconds))\n\n # get parameters from the current optimizer state\n student_net_params = get_params(opt_state) \n\n # filter the paramters by masks\n masked_student_net_params = get_sparse_params_filtered_by_masks(student_net_params , masks)\n \n # validation loss\n vali_loss = self.eval_nt_transfer_loss_on_vali_data(masked_student_net_params, vali_teacher_prediction, vali_teacher_ntk_mat, nn_density_level) \n\n vali_loss_list.append(vali_loss)\n\n logger.info('run: %02d/%02d | iter %04d/%04d | trans. dist %.3f | ntk dist %.3f | targ. dist %.3f | l2 %.3f | nn density %.2f | time %.2f [s] | expected finish time %s', run_index, self.NUM_RUNS + self.INIT_RUN_INDEX - 1, num_iter, num_total_iters, vali_loss[0], vali_loss[1], vali_loss[2], vali_loss[3], nn_density_level, elapsed_time, expected_end_time)\n t = time.time()\n\n\n if (num_iter % self.MASK_UPDATE_FREQ == 0) and (num_iter < mask_update_limit):\n # get parameters from the current optimizer state\n student_net_params = get_params(opt_state) \n \n # update masks\n masks = get_masks_from_jax_params(student_net_params, nn_density_level, global_bool = self.GLOBAL_PRUNE_BOOL)\n \n# if self.PRUNE_METHOD == 'logit_snip':\n# logit_snip_batch_xs, _ = next(gen_batches_logit_snip)\n# masks = get_logit_snip_masks(student_net_params, nn_density_level, self.apply_fn, snip_input, self.batch_input_shape, GlOBAL_PRUNE_BOOL = self.GLOBAL_PRUNE_BOOL) \n# else:\n# masks = get_masks_from_jax_params(student_net_params, nn_density_level, global_bool = self.GLOBAL_PRUNE_BOOL)\n\n\n \n elapsed_time = time.time() - t\n \n student_net_params = get_params(opt_state) \n \n # filter the paramters by masks\n masked_student_net_params = get_sparse_params_filtered_by_masks(student_net_params , masks)\n \n vali_loss = self.eval_nt_transfer_loss_on_vali_data(masked_student_net_params, vali_teacher_prediction, vali_teacher_ntk_mat, nn_density_level) \n\n vali_loss_list.append(vali_loss)\n \n logger.info('run: %02d/%02d | iter %04d/%04d | trans. dist %.3f | ntk dist %.3f | targ. dist %.3f | l2 %.3f | nn density %.2f | time %.2f [s]', run_index, self.NUM_RUNS + self.INIT_RUN_INDEX - 1, num_iter, num_total_iters, vali_loss[0], vali_loss[1], vali_loss[2], vali_loss[3], nn_density_level, elapsed_time )\n \n vali_loss_array = np.array(vali_loss_list)\n\n nt_trans_params_all_runs.append(masked_student_net_params)\n nt_trans_masks_all_runs.append(masks)\n nt_trans_vali_all_runs.append(vali_loss_array)\n teacher_params_all_runs.append(teacher_net_params )\n\n if self.SAVE_BOOL == True:\n\n model_summary_str = self.model_str + '_density_' + str(round(nn_density_level, 2) ) + '_run_' + str(run_index)\n\n teacher_param_fileName = model_dir_density_run + 'teacher_params_' + model_summary_str\n np.save(teacher_param_fileName, teacher_net_params)\n\n student_param_fileName = model_dir_density_run + 'transferred_params_' + model_summary_str\n np.save(student_param_fileName, masked_student_net_params)\n\n mask_fileName = model_dir_density_run + 'transferred_masks_' + model_summary_str\n np.save(mask_fileName, masks)\n\n loss_array_fileName = model_dir_density_run + 'loss_array_' + model_summary_str\n np.save(loss_array_fileName, vali_loss_array)\n \n\n nt_trans_params_all_sparsities_all_runs.append( nt_trans_params_all_runs )\n nt_trans_masks_all_sparsities_all_runs.append( nt_trans_masks_all_runs )\n nt_trans_vali_all_sparsities_all_runs.append( nt_trans_vali_all_runs )\n teacher_params_all_sparsities_all_runs.append( teacher_params_all_runs )\n \n if return_teacher_params_bool:\n return nt_trans_params_all_sparsities_all_runs, nt_trans_masks_all_sparsities_all_runs, nt_trans_vali_all_sparsities_all_runs, teacher_params_all_sparsities_all_runs\n\n else:\n return nt_trans_params_all_sparsities_all_runs, nt_trans_masks_all_sparsities_all_runs, nt_trans_vali_all_sparsities_all_runs","def step(self, closure=None):\n loss = None\n if closure is not None:\n loss = closure()\n for m in self.optimizers:\n m.step()\n return loss","def ExecuteInstanceStochasticAdaptiveRefinementAux_Functionality(current_global_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_index,current_analysis_stage,previous_computational_time,open_mp_threads,mapping_flag,pickled_mapping_reference_model,print_to_file,filename):\n\n start_time = time.time()\n # unpickle model and build Kratos Model object\n serialized_model = pickle.loads(pickled_coarse_model)\n current_model = KratosMultiphysics.Model()\n serialized_model.Load(\"ModelSerialization\",current_model)\n del(serialized_model)\n # unpickle parameters and build Kratos Parameters object\n serialized_project_parameters = pickle.loads(pickled_coarse_project_parameters)\n current_project_parameters = KratosMultiphysics.Parameters()\n serialized_project_parameters.Load(\"ParametersSerialization\",current_project_parameters)\n del(serialized_project_parameters)\n # refine if current current_global_index > 0, adaptive refinement based on the solution of previous index\n if (current_index > 0):\n # unpickle metric and remesh refinement parameters and build Kratos Parameters objects\n serialized_custom_metric_refinement_parameters = pickle.loads(pickled_custom_metric_refinement_parameters)\n serialized_custom_remesh_refinement_parameters = pickle.loads(pickled_custom_remesh_refinement_parameters)\n current_custom_metric_refinement_parameters = KratosMultiphysics.Parameters()\n current_custom_remesh_refinement_parameters = KratosMultiphysics.Parameters()\n serialized_custom_metric_refinement_parameters.Load(\"MetricRefinementParametersSerialization\",current_custom_metric_refinement_parameters)\n serialized_custom_remesh_refinement_parameters.Load(\"RemeshRefinementParametersSerialization\",current_custom_remesh_refinement_parameters)\n del(serialized_custom_metric_refinement_parameters,serialized_custom_remesh_refinement_parameters)\n # refine the model Kratos object\n adaptive_refinement_manager = AdaptiveRefinement(current_index,current_model,current_project_parameters,current_custom_metric_refinement_parameters,current_custom_remesh_refinement_parameters)\n refined_model,refined_project_parameters = adaptive_refinement_manager.ComputeAdaptiveRefinement()\n current_model = refined_model\n del(refined_model,refined_project_parameters)\n # constructor analysis stage\n simulation = current_analysis_stage(current_model,current_project_parameters,random_variable)\n # add filename flag print_to_file is true\n if (print_to_file):\n simulation.filename = filename\n # add flag if current index is maximum index\n if (current_index == current_global_index):\n simulation.is_current_index_maximum_index = True\n else:\n simulation.is_current_index_maximum_index = False\n # mapping if in current finest level and mapping flag is true\n # otherwise standard behavior\n if (mapping_flag is True and current_index == current_global_index):\n # unpickle mapping reference model and build Kratos Model object\n serialized_mapping_reference_model = pickle.loads(pickled_mapping_reference_model)\n mapping_reference_model = KratosMultiphysics.Model()\n serialized_mapping_reference_model.Load(\"ModelSerialization\",mapping_reference_model)\n del(serialized_mapping_reference_model)\n # send reference model to analysis stage for mapping and set mapping flag to true\n simulation.mapping_reference_model = mapping_reference_model\n simulation.mapping = True\n simulation.Run()\n # mapping if in current finest level and mapping flag is true\n # otherwise standard qoi evaluation\n if (mapping_flag is True and current_index == current_global_index):\n qoi = simulation.MappingAndEvaluateQuantityOfInterest()\n else:\n qoi = simulation.EvaluateQuantityOfInterest()\n # save model and parameters as MpiSerializer Kratos objects\n serialized_finer_model = KratosMultiphysics.MpiSerializer()\n serialized_finer_model.Save(\"ModelSerialization\",simulation.model)\n # pickle model and parameters\n pickled_finer_model = pickle.dumps(serialized_finer_model, 2) # second argument is the protocol and is NECESSARY (according to pybind11 docs)\n del(simulation)\n end_time = time.time()\n computational_time = previous_computational_time + open_mp_threads*(end_time-start_time) # multiply by open mp threads to consider real machine cost\n return qoi,pickled_finer_model,computational_time","def step(self, closure):\r\n self.state['step'] += 1\r\n for y_ind in range(self.dim_out):\r\n err = closure(y_ind)\r\n for group_ind in range(len(self.param_groups)):\r\n group = self.param_groups[group_ind]\r\n iekf_mat = self.state['iekf_groups'][group_ind]\r\n for ii, w in enumerate(group['params']):\r\n if w.grad is None:\r\n continue\r\n H_n = iekf_mat[ii]['H']\r\n grad = w.grad.data.detach()\r\n if len(w.size()) > 1:\r\n grad = grad.transpose(1, 0)\r\n grad = grad.contiguous().view((1, -1))\r\n if y_ind == 0:\r\n H_n = grad\r\n else:\r\n H_n = torch.cat([H_n, grad], dim=0)\r\n self.state['iekf_groups'][group_ind][ii]['H'] = H_n\r\n\r\n err_T = err.transpose(0, 1)\r\n\r\n for group_ind in range(len(self.param_groups)):\r\n group = self.param_groups[group_ind]\r\n iekf_mat = self.state['iekf_groups'][group_ind]\r\n for ii, w in enumerate(group['params']):\r\n if w.grad is None:\r\n continue\r\n\r\n lbd_n = iekf_mat[ii]['lbd']\r\n P_n = iekf_mat[ii]['P']\r\n EPS = iekf_mat[ii]['EPS']\r\n H_n = iekf_mat[ii]['H']\r\n H_n_T = H_n.transpose(0, 1)\r\n if group['lbd_decay']:\r\n miu = 1.0 / min(self.state['step'], group['lbd_max_step'])\r\n lbd_n = lbd_n + miu * (err_T.mm(err).flatten()[0] / self.dim_out - lbd_n)\r\n self.state['iekf_groups'][group_ind][ii]['lbd'] = lbd_n\r\n R_n = lbd_n * torch.eye(self.dim_out, dtype=torch.float, device=iekf_mat[ii]['device'],\r\n requires_grad=False)\r\n\r\n g_n = R_n + H_n.mm(P_n).mm(H_n_T)\r\n g_n = g_n.inverse()\r\n\r\n K_n = P_n.mm(H_n_T).mm(g_n)\r\n delta_w = K_n.mm(err)\r\n if len(w.size()) > 1:\r\n delta_w = delta_w.view((w.size(1), w.size(0))).transpose(1, 0)\r\n else:\r\n delta_w = delta_w.view(w.size())\r\n\r\n new_P = (group['alpha'] + 1) * (P_n - K_n.mm(H_n).mm(P_n) + EPS)\r\n self.state['iekf_groups'][group_ind][ii]['P'] = new_P\r\n\r\n w.data.add_(delta_w)\r\n\r\n return err","def optimize_library_descent(self, target, direction='max', steps=100,\n multiprocessing=False, ret_info=False,\n args=None):\n # get the target function to call\n target_function = getattr(self, target)\n if args is not None:\n target_function = functools.partial(target_function, **args)\n\n # initialize the optimizer\n value = target_function()\n value_best, state_best = value, self.sens_mat.copy()\n \n if ret_info:\n # store extra information\n start_time = time.time()\n info = {'values': {}}\n values_count = self.parameters['optimizer_values_count']\n values_step = max(1, steps // values_count)\n \n if multiprocessing:\n # run the calculations in multiple processes\n pool_size = self.get_number_of_cores()\n pool = mp.Pool(processes=pool_size)\n if ret_info:\n values_step = max(1, values_step // pool_size)\n \n # iterate for given number of steps\n for step in range(int(steps) // pool_size):\n joblist = []\n init_arguments = self.init_arguments\n for _ in range(pool_size):\n # modify the current state and add it to the job list\n i = random.randrange(self.sens_mat.size)\n self.sens_mat.flat[i] = 1 - self.sens_mat.flat[i]\n params = init_arguments['parameters'] \n params['sensitivity_matrix'] = self.sens_mat\n params['initialize_state']['sensitivity'] = 'exact'\n \n joblist.append((copy.deepcopy(init_arguments), target))\n self.sens_mat.flat[i] = 1 - self.sens_mat.flat[i]\n \n # run all the jobs\n results = pool.map(_run_job, joblist)\n \n # find the best result \n if direction == 'max':\n res_best = np.argmax(results)\n if results[res_best] > value_best:\n value_best = results[res_best]\n state_best = joblist[res_best][0]['parameters']['sensitivity_matrix']\n # use the best state as a basis for the next iteration\n self.sens_mat = state_best\n \n elif direction == 'min':\n res_best = np.argmin(results)\n if results[res_best] < value_best:\n value_best = results[res_best]\n state_best = joblist[res_best][0]['parameters']['sensitivity_matrix']\n # use the best state as a basis for the next iteration\n self.sens_mat = state_best\n \n else:\n raise ValueError('Unsupported direction `%s`' % direction)\n \n if ret_info and step % values_step == 0:\n info['values'][step * pool_size] = results[res_best]\n \n else:\n # run the calculations in this process\n for step in range(int(steps)):\n # modify the current state\n i = random.randrange(self.sens_mat.size)\n self.sens_mat.flat[i] = 1 - self.sens_mat.flat[i]\n \n # get the value of the new state\n value = target_function()\n \n improved = ((direction == 'max' and value > value_best) or\n (direction == 'min' and value < value_best))\n if improved:\n # save the state as the new best value\n value_best, state_best = value, self.sens_mat.copy()\n else:\n # undo last change\n self.sens_mat.flat[i] = 1 - self.sens_mat.flat[i]\n \n if ret_info and step % values_step == 0:\n info['values'][step] = value_best\n\n # sort the best state and store it in the current object\n state_best = self.sort_sensitivity_matrix(state_best)\n self.sens_mat = state_best.copy()\n\n if ret_info:\n info['total_time'] = time.time() - start_time \n info['states_considered'] = steps\n info['performance'] = steps / info['total_time']\n return value_best, state_best, info\n else:\n return value_best, state_best","def post_apply(self): #pragma no cover\n for e in self.obs_queue:\n\n # translate operation wire labels to the device's wire labels\n device_wires = self.map_wires(e.wires)\n\n self.measure += \"set resultArray w/= {wires[0]} <- \".format(wires=device_wires.tolist())\n self.measure += self._observable_map[e.name].format(wires=device_wires.tolist())\n self.measure += \" \"\n\n self._source_code = PROGRAM.format(wires=self.num_wires, operations=self.prog, measurements=self.measure)\n self.qs = qsharp.compile(self._source_code)","def optimize(self, input_val_dict):\n logger.log(\"Start CG optimization\")\n\n logger.log(\"computing loss before\")\n loss_before = self.loss(input_val_dict)\n\n logger.log(\"performing update\")\n\n logger.log(\"computing gradient\")\n gradient = self.gradient(input_val_dict)\n logger.log(\"gradient computed\")\n\n logger.log(\"computing descent direction\")\n Hx = self._hvp_approach.build_eval(input_val_dict)\n descent_direction = conjugate_gradients(Hx, gradient, cg_iters=self._cg_iters)\n\n initial_step_size = np.sqrt(2.0 * self._max_constraint_val *\n (1. / (descent_direction.dot(Hx(descent_direction)) + 1e-8)))\n if np.isnan(initial_step_size):\n logger.log(\"Initial step size is NaN! Rejecting the step!\")\n return\n\n initial_descent_step = initial_step_size * descent_direction\n logger.log(\"descent direction computed\")\n\n prev_params = self._target.get_param_values()\n prev_params_values = _flatten_params(prev_params)\n\n loss, constraint_val, n_iter, violated = 0, 0, 0, False\n for n_iter, ratio in enumerate(self._backtrack_ratio ** np.arange(self._max_backtracks)):\n cur_step = ratio * initial_descent_step\n cur_params_values = prev_params_values - cur_step\n cur_params = _unflatten_params(cur_params_values, params_example=prev_params)\n self._target.set_params(cur_params)\n\n loss, constraint_val = self.loss(input_val_dict), self.constraint_val(input_val_dict)\n if loss < loss_before and constraint_val <= self._max_constraint_val:\n break\n\n \"\"\" ------------------- Logging Stuff -------------------------- \"\"\"\n if np.isnan(loss):\n violated = True\n logger.log(\"Line search violated because loss is NaN\")\n if np.isnan(constraint_val):\n violated = True\n logger.log(\"Line search violated because constraint %s is NaN\" % self._constraint_name)\n if loss >= loss_before:\n violated = True\n logger.log(\"Line search violated because loss not improving\")\n if constraint_val >= self._max_constraint_val:\n violated = True\n logger.log(\"Line search violated because constraint %s is violated\" % self._constraint_name)\n\n if violated and not self._accept_violation:\n logger.log(\"Line search condition violated. Rejecting the step!\")\n self._target.set_params(prev_params)\n\n logger.log(\"backtrack iters: %d\" % n_iter)\n logger.log(\"computing loss after\")\n logger.log(\"optimization finished\")","def optimize5():\n xl = xl_app()\n qt_app = get_qt_app() # pragma noqc\n # Get the initial values of the input cells\n msgBox = OpDialog()\n result = msgBox.exec_()\n if not result: # user cancelled\n return\n\n in_range = get_range(msgBox.in_range.text())\n out_cell = get_range(msgBox.out_cell.text())\n in_values = list(in_range.Value)\n X = np.array([x[0] for x in in_values])\n\n orig_calc_mode = xl.Calculation\n try:\n # switch Excel to manual calculation\n # and disable screen updating\n xl.Calculation = constants.xlManual\n xl.ScreenUpdating = False\n\n # run the minimization routine\n xl_obj_func = partial(obj_func, xl, in_range, out_cell)\n print(f\"X = {X}\")\n result = minimize(xl_obj_func, X, method=\"nelder-mead\")\n in_range.Value = [(float(x),) for x in result.x]\n xl.ScreenUpdating = True\n mbox = QMessageBox()\n mbox.setIcon(QMessageBox.Information)\n mbox.setText(\"Optimization results shown below.\" \"\\nMake changes permanent?\")\n mbox.setWindowTitle(\"Optimization Complete\")\n mbox.setInformativeText(\n \"\\n\".join(\n [\n \"Successful: %s\" % result.success,\n result.message,\n \"After %d iterations\" % result.nit,\n ]\n )\n )\n mbox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n yes_no = mbox.exec_()\n if yes_no != QMessageBox.Ok:\n in_range.Value = in_values\n else:\n in_range.Value = [(float(x),) for x in result.x]\n\n finally:\n # restore the original calculation\n # and screen updating mode\n xl.ScreenUpdating = True\n xl.Calculation = orig_calc_mode","def finalize(self):\n self.report('Finalizing optimization procedure.')\n with self.optimizer() as opt:\n optimal_process_output = opt.result_value\n optimal_process_output.store()\n self.out('optimal_process_output', optimal_process_output)\n result_index = opt.result_index\n optimal_process = self.ctx[self.eval_key(result_index)]\n self.out('optimal_process_uuid', Str(optimal_process.uuid).store())","def _optimize(self) -> None:\n\n for i, agent in enumerate(self.agents):\n states, actions, rewards, next_states, dones = self.memory.sample()\n\n actor_next_state = self._agent_states(i, next_states)\n next_actions = torch.cat(\n [a.actor_target(actor_next_state) for a in self.agents], 1\n )\n next_q = agent.critic_target(next_states, next_actions).detach()\n target_q = rewards[:, i].view(-1, 1) + self.gamma * next_q * (\n 1 - dones[:, i].view(-1, 1)\n )\n local_q = agent.critic_local(states, actions)\n\n value_loss = agent.loss_fn(local_q, target_q)\n agent.value_optimizer.zero_grad()\n value_loss.backward()\n agent.value_optimizer.step()\n\n local_actions = []\n for i, a in enumerate(self.agents):\n local_states = self._agent_states(i, states)\n local_actions.append(\n a.actor_local(local_states)\n if a == agent\n else a.actor_local(local_states).detach()\n )\n local_actions = torch.cat(local_actions, 1)\n policy_loss = -agent.critic_local(states, local_actions).mean()\n\n agent.policy_optimizer.zero_grad()\n policy_loss.backward()\n agent.policy_optimizer.step()\n\n self._update_target_model(agent.critic_local, agent.critic_target)\n self._update_target_model(agent.actor_local, agent.actor_target)","def main_function(self):\n self.ana_cont_probl = cont.AnalyticContinuationProblem(im_axis=self.input_data.mats,\n im_data=self.input_data.value.real,\n re_axis=self.realgrid.grid,\n kernel_mode='freq_bosonic')\n model = np.ones_like(self.realgrid.grid)\n model /= np.trapz(model, self.realgrid.grid)\n\n preblur, bw = self.get_preblur()\n\n sol = self.ana_cont_probl.solve(method='maxent_svd',\n optimizer='newton',\n alpha_determination='chi2kink',\n model=model,\n stdev=self.input_data.error,\n interactive=False, alpha_start=1e10, alpha_end=1e-3,\n preblur=preblur, blur_width=bw)\n\n inp_str = 'atom {}, orb {}, spin {}, blur {}: '.format(self.input_data.atom,\n self.input_data.orbital,\n self.input_data.spin,\n bw)\n all_chis = np.isfinite(np.array([s.chi2 for s in sol[1]]))\n res_str = 'alpha_opt={:3.2f}, chi2(alpha_opt)={:3.2f}, min(chi2)={:3.2f}'.format(\n sol[0].alpha, sol[0].chi2, np.amin(all_chis)\n )\n self.text_output.append(inp_str + res_str)\n alphas = [s.alpha for s in sol[1]]\n chis = [s.chi2 for s in sol[1]]\n\n self.output_data.update(self.realgrid.grid, sol[0].A_opt, self.input_data)\n\n fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(11.75, 8.25)) # A4 paper size\n ax[0, 0].loglog(alphas, chis, marker='s', color='black')\n ax[0, 0].loglog(sol[0].alpha, sol[0].chi2, marker='*', color='red', markersize=15)\n ax[0, 0].set_xlabel(r'$\\alpha$')\n ax[0, 0].set_ylabel(r'$\\chi^2(\\alpha)$')\n\n ax[1, 0].plot(self.realgrid.grid, sol[0].A_opt)\n ax[1, 0].set_xlabel(r'$\\omega$')\n ax[1, 0].set_ylabel('spectrum')\n\n ax[0, 1].plot(self.input_data.mats, self.input_data.value.real,\n color='blue', ls=':', marker='x', markersize=5,\n label='Re[data]')\n ax[0, 1].plot(self.input_data.mats, self.input_data.value.imag,\n color='green', ls=':', marker='+', markersize=5,\n label='Im[data]')\n ax[0, 1].plot(self.input_data.mats, sol[0].backtransform.real,\n ls='--', color='gray', label='Re[fit]')\n ax[0, 1].plot(self.input_data.mats, sol[0].backtransform.imag,\n color='gray', label='Im[fit]')\n ax[0, 1].set_xlabel(r'$\\nu_n$')\n ax[0, 1].set_ylabel(self.input_data.data_type)\n ax[0, 1].legend()\n\n ax[1, 1].plot(self.input_data.mats, (self.input_data.value - sol[0].backtransform).real,\n ls='--', label='real part')\n ax[1, 1].plot(self.input_data.mats, (self.input_data.value - sol[0].backtransform).imag,\n label='imaginary part')\n ax[1, 1].set_xlabel(r'$\\nu_n$')\n ax[1, 1].set_ylabel('data $-$ fit')\n ax[1, 1].legend()\n plt.tight_layout()\n plt.show()","def callback(_locals, _globals):\n global n_steps\n # Print stats every 20 calls\n if (n_steps + 1) % 1 == 0:\n # Evaluate policy training performance\n episode_rewards, episode_lengths = evaluate_policy(_locals['self'], eval_real_env,\n n_eval_episodes=n_eval_episodes,\n render=False,\n deterministic=False,\n return_episode_rewards=False)\n print(\"Last mean reward per episode at target: {:.2f}\".format(episode_rewards))\n\n episode_rewards_grnd, episode_lengths_grnd = evaluate_policy(_locals['self'], eval_grnd_env,\n n_eval_episodes=n_eval_episodes,\n render=False,\n deterministic=False,\n return_episode_rewards=False)\n print(\"Last mean reward per episode at grounded environment: {:.2f}\".format(episode_rewards_grnd))\n\n with open(os.path.join(log_dir, 'eval_at_target.txt'), 'a') as f:\n f.write(\"{}, {}, {}\\n\".format(n_steps, episode_rewards, episode_lengths/n_eval_episodes))\n f.close()\n with open(os.path.join(log_dir, 'eval_at_grnd.txt'), 'a') as f:\n f.write(\"{}, {}, {}\\n\".format(n_steps, episode_rewards_grnd, episode_lengths_grnd/n_eval_episodes))\n f.close()\n n_steps += 1\n return True","def run(self):\n i = 0\n try:\n for i in range(0, self._iters):\n if self._verbose:\n print(\" Inner CG Iteration \" + repr(i))\n\n self._forward(self._p_k, self._v_k)\n sigma_k = measure(self._p_k, self._v_k)\n alpha_k = self._rho_k / sigma_k\n update_m(self._m, alpha_k, self._p_k)\n sub_scaled_vector(self._residual_k, self._residual_k, alpha_k,\n self._v_k)\n self._v_k = gpuarray_copy(self._residual_k)\n rho_k_plus_1 = measure(self._v_k, self._residual_k)\n rho_k_t = np.abs(rho_k_plus_1)\n\n if (rho_k_t / self._rho_0 <= self._relative_tolerance) \\\n or (rho_k_t <= self._absolute_tolerance):\n if self._verbose:\n print(\"Converged at Iteration \" + str(i) + \".\")\n self.converged = True\n self.iteration = i+1\n return\n\n add_scaled_vector(self._p_k, self._v_k,\n rho_k_plus_1/self._rho_k,\n self._p_k)\n self._rho_k = rho_k_plus_1\n\n if self._verbose >= 3:\n print(\" Residual=\" + repr(rho_k_t))\n except KeyboardInterrupt:\n raise\n finally:\n self.iteration = i+1","def save_cma_optimization_results(self, es):\n # code extra verbose to understand what is going on\n generation = es.result.iterations\n evals = es.result.evaluations # number of evals at start of each gen\n xfavorite = es.result.xfavorite # center of distribution, best est\n stds = es.result.stds # stds of distribution, stds of xfavorite\n fbest = es.result.fbest # best ever measured\n xbest = es.result.xbest # coordinates of best ever measured\n evals_best = es.result.evals_best # index of best measurement\n\n if not self.minimize_optimization:\n fbest = -fbest\n\n results_array = np.concatenate([[generation, evals],\n xfavorite, stds,\n [fbest], xbest, [evals_best]])\n if (not 'optimization_result'\n in self.data_object[EXPERIMENTAL_DATA_GROUP_NAME].keys()):\n opt_res_grp = self.data_object[EXPERIMENTAL_DATA_GROUP_NAME]\n self.opt_res_dset = opt_res_grp.create_dataset(\n 'optimization_result', (0, len(results_array)),\n maxshape=(None, len(results_array)),\n dtype='float64')\n\n # FIXME: Jan 2018, add the names of the parameters to column names\n self.opt_res_dset.attrs['column_names'] = h5d.encode_to_utf8(\n 'generation, ' + 'evaluations, ' +\n 'xfavorite, ' * len(xfavorite) +\n 'stds, '*len(stds) +\n 'fbest, ' + 'xbest, '*len(xbest) +\n 'best evaluation,')\n\n old_shape = self.opt_res_dset.shape\n new_shape = (old_shape[0]+1, old_shape[1])\n self.opt_res_dset.resize(new_shape)\n self.opt_res_dset[-1, :] = results_array","def run(self):\n self._fitness = self._fitness_func(self._bitstring, *self._args, **self._kwargs)","def Optimize(fun, \n dimensions = 10,\n dataType = float64,\n lowerDomain = -5.0,\n upperDomain = 5.0,\n constrainToLower = False,\n constrainToUpper = False,\n maxMutations = 3, \n maxIndexes = 3, \n gamma = 0.99, \n minImprovements = 3,\n scale = 1.0,\n popSize = 10, \n maxIterations = 1000000,\n targetLoss = 1.0e-8,\n minScale = 1.0e-10):\n pop = Population( memberLength = dimensions,\n memberDataType = dataType,\n lowerDomain = lowerDomain, \n upperDomain = upperDomain,\n maxMutations = maxMutations, \n maxIndexes = maxIndexes, \n gamma = gamma, \n minImprovements = minImprovements,\n scale = scale)\n pop.prepare(popSize, fun)\n loss = pop.elite.loss\n startTime = time.time()\n print(f\"[{0:7d}] Loss: {loss:<13.10g} S: {pop.scale:<12.7g} I:{PI(pop.improvements)} elapsed: {0.0:>9.6f} hours\")\n try:\n #-----------------------------------------------------------------\n for trial in range(1, maxIterations):\n pop.minimize(fun, constrainToLower=constrainToLower, constrainToUpper=constrainToUpper)\n if loss > pop.elite.loss:\n loss = pop.elite.loss\n elapsedTime = (time.time() - startTime)/(60*60)\n print(f\"[{trial:7d}] Loss: {loss:<13.10g} S: {pop.scale:<12.7g} I:{PI(pop.improvements)} elapsed: {elapsedTime:>9.6f} hours\")\n if (loss < targetLoss) or (pop.scale < minScale):\n break\n #-----------------------------------------------------------------\n except KeyboardInterrupt:\n pass\n finally:\n print(f\"\\n[{trial:7d}] Loss: {pop.elite.loss:<13.10g} S: {pop.scale:<12.7g} I:{PI(pop.improvements)} elapsed: {elapsedTime:>9.6f} hours\")\n return pop.elite","def runIteration(self, task, Sol, Fitness, xb, fxb, A, S, Q, v, **dparams):\n\t\tfor i in range(self.NP):\n\t\t\tQ[i] = self.Qmin + (self.Qmax - self.Qmin) * self.uniform(0, 1)\n\t\t\tv[i] += (Sol[i] - xb) * Q[i]\n\t\t\tif self.rand() > self.r: S[i] = self.localSearch(best=xb, A=A[i], task=task, i=i, Sol=Sol)\n\t\t\telse: S[i] = task.repair(Sol[i] + v[i], rnd=self.Rand)\n\t\t\tFnew = task.eval(S[i])\n\t\t\tif (Fnew <= Fitness[i]) and (self.rand() < A[i]): Sol[i], Fitness[i] = S[i], Fnew\n\t\t\tif Fnew <= fxb: xb, fxb, A[i] = S[i].copy(), Fnew, self.updateLoudness(A[i])\n\t\treturn Sol, Fitness, xb, fxb, {'A': A, 'S': S, 'Q': Q, 'v': v}","def iterate(self, update_func):\n self.pre_loop()\n logger.info('%s: start iterations', self.__class__.__name__)\n with recording_exit_reason(self.datastore):\n for gen_step in range(self.iterations):\n self.post_update(gen_step, update_func(gen_step))\n logger.info('%s: maximum iterations reached', self.__class__.__name__)","def eval_energy(Optimizer, individ):\n #logger = initialize_logger(Optimizer.loggername)\n logger = logging.getLogger(Optimizer.loggername)\n if 'MAST' in Optimizer.calc_method:\n energy = individ.energy\n bul = individ.bulki\n signal = 'Received MAST structure\\n'\n logger.info('Received individual index = {0} from MAST with energy {1}. Returning with no evaluation'.format(\n individ.index, individ.energy))\n else:\n if Optimizer.parallel: \n rank = MPI.COMM_WORLD.Get_rank()\n logger.info('Received individual HI = {0} with energy {1} for energy evaluation'.format(\n individ.history_index, individ.energy))\n STR='----Individual ' + str(individ.history_index)+ ' Optimization----\\n'\n indiv=individ[0]\n if 'EE' in Optimizer.debug:\n debug = True\n else:\n debug = False\n if debug: \n write_xyz(Optimizer.debugfile,indiv,'Received by eval_energy')\n Optimizer.debugfile.flush()\n logger.debug('Writing recieved individual to debug file')\n # Establish individual structure for evaluation. Piece together regions when necessary.\n if Optimizer.structure=='Defect':\n indi=indiv.copy()\n bulk=individ.bulki\n nat=indi.get_number_of_atoms()\n if debug: \n logger.info('Extending defect structure to include bulk len(r1+r2)={0} len(bulk)={1}'.format(nat,len(bulk)))\n csize=bulk.get_cell() \n totalsol=Atoms(cell=csize, pbc=True)\n totalsol.extend(indi)\n totalsol.extend(bulk)\n for sym,c,m,u in Optimizer.atomlist:\n nc=len([atm for atm in totalsol if atm.symbol==sym])\n STR+='Defect configuration contains '+repr(nc)+' '+repr(sym)+' atoms\\n'\n elif Optimizer.structure=='Surface':\n totalsol=Atoms()\n totalsol.extend(indiv)\n nat=indiv.get_number_of_atoms()\n totalsol.extend(individ.bulki)\n if debug:\n logger.info('Extending surface structure to include bulk len(r1+r2)={0} len(bulk)={1}'.format(nat,len(individ.bulki)))\n for sym,c,m,u in Optimizer.atomlist:\n nc=len([atm for atm in totalsol if atm.symbol==sym])\n STR+='Surface-Bulk configuration contains '+repr(nc)+' '+repr(sym)+' atoms\\n'\n cell=numpy.maximum.reduce(indiv.get_cell())\n totalsol.set_cell([cell[0],cell[1],500])\n totalsol.set_pbc([True,True,False])\n elif Optimizer.structure=='Cluster':\n totalsol = indiv.copy()\n nat = len(totalsol)\n if debug:\n logger.info('Extending cluster with {0} atoms to center of evaluation box of size {1}'.format(nat,Optimizer.large_box_size))\n origcell = indiv.get_cell()\n totalsol.set_cell([Optimizer.large_box_size,Optimizer.large_box_size,Optimizer.large_box_size])\n totalsol.translate([Optimizer.large_box_size/2.0,Optimizer.large_box_size/2.0,Optimizer.large_box_size/2.0])\n elif Optimizer.structure=='Crystal':\n totalsol = indiv.copy()\n nat = len(totalsol)\n else:\n print 'WARNING: In EvalEnergy. Optimizer.structure not recognized'\n logger.warning('Optimizer.structure not recognized')\n \n # Check for atoms that are too close or out of constrained location\n if Optimizer.constrain_position:\n if Optimizer.structure=='Defect':\n if debug:\n logger.info('Constraining positions of defect')\n totalsol, stro = constrain_positions(totalsol, Optimizer.solidbulk, Optimizer.sf)\n if debug:\n logger.info(stro)\n STR+=str0\n min_len=0.7\n if not Optimizer.fixed_region:\n if debug:\n logger.info('Running check minimum distance')\n totalsol, STR = check_min_dist(totalsol, Optimizer.structure, nat, min_len, STR)\n if debug:\n write_xyz(Optimizer.debugfile,totalsol,'After minlength check')\n Optimizer.debugfile.flush()\n logger.debug('Writing individual after checking minimum length')\n \n # Set calculator to use to get forces/energies\n if Optimizer.parallel:\n calc = setup_calculator(Optimizer)\n if Optimizer.fixed_region:\n if debug:\n logger.info('Setting up fixed region calculator')\n pms=copy.deepcopy(calc.parameters)\n try:\n pms['mass'][len(pms['mass'])-1] += '\\ngroup RO id >= {0}\\nfix freeze RO setforce 0.0 0.0 0.0\\n'.format(nat)\n except KeyError:\n pms['pair_coeff'][0] += '\\ngroup RO id >= {0}\\nfix freeze RO setforce 0.0 0.0 0.0\\n'.format(nat)\n calc = LAMMPS(parameters=pms, files=calc.files, keep_tmp_files=calc.keep_tmp_files, tmp_dir=calc.tmp_dir)\n lmin = copy.copy(Optimizer.lammps_min)\n if debug:\n logger.info('Setting up no local minimization calculator')\n Optimizer.lammps_min = None\n Optimizer.static_calc = setup_calculator(Optimizer)\n Optimizer.lammps_min = lmin\n else:\n calc=Optimizer.calc\n totalsol.set_calculator(calc)\n totalsol.set_pbc(True)\n \n # Perform Energy Minimization\n if not Optimizer.parallel:\n if debug: \n write_xyz(Optimizer.debugfile,totalsol,'Individual sent to Energy Minimizer')\n logger.debug('Writing structure sent to energy minimizer')\n try:\n cwd = os.getcwd()\n if Optimizer.ase_min == True:\n if debug:\n logger.info('Running ASE minimizer')\n if Optimizer.calc_method=='LennardJones':\n logger.warn('Must run ase LJ calculator with pbc=False')\n totalsol.set_pbc(False)\n totalsol, energy, pressure, volume, STR = run_ase_min(totalsol, Optimizer.ase_min_fmax, Optimizer.ase_min_maxsteps, Optimizer.fitness_scheme, STR)\n else:\n if debug:\n logger.info('Running local energy calculator')\n if Optimizer.fixed_region:\n totalsol, energy, pressure, volume, STR = run_energy_eval(totalsol, Optimizer.calc_method, Optimizer.fixed_region, Optimizer.fitness_scheme, STR, Optimizer.static_calc)\n else:\n totalsol, energy, pressure, volume, STR = run_energy_eval(totalsol, Optimizer.calc_method, False, Optimizer.fitness_scheme, STR)\n except Exception, e:\n logger.critical('Error in energy evaluation: {0}'.format(e), exc_info=True)\n path = os.path.join(cwd,'TroubledLammps')\n if not os.path.exists(path):\n os.mkdir(path)\n #Copy files over\n shutil.copyfile(calc.trajfile,os.path.join(path,os.path.basename(calc.trajfile)))\n shutil.copyfile(calc.infile,os.path.join(path,os.path.basename(calc.infile)))\n shutil.copyfile(calc.logfile,os.path.join(path,os.path.basename(calc.logfile)))\n shutil.copyfile(calc.datafile,os.path.join(path,os.path.basename(calc.datafile)))\n raise RuntimeError('{0}:{1}'.format(Exception,e))\n if not Optimizer.parallel:\n if debug:\n write_xyz(Optimizer.debugfile,totalsol,'Individual after Energy Minimization')\n Optimizer.debugfile.flush()\n logger.debug('Writing structure recieved from energy minimizer')\n \n # Separate structures into distinct pieces\n if Optimizer.structure=='Defect':\n if Optimizer.fixed_region==True or Optimizer.finddefects==False:\n if debug:\n logger.info('Identifying atoms in defect structure based on ID')\n individ[0]=totalsol[0:nat]\n bul=totalsol[(nat):len(totalsol)]\n individ[0].set_cell(csize)\n else:\n if debug:\n logger.info('Applying find defects scheme to identify R1 and R2 for Defect')\n if 'FD' in Optimizer.debug:\n outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=Optimizer.debugfile)\n else:\n outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=False)\n individ[0]=outt[0]\n bul=outt[1]\n individ.vacancies = outt[2]\n individ.swaps = outt[3]\n STR += outt[4]\n indiv=individ[0]\n elif Optimizer.structure=='Surface':\n if debug:\n logger.info('Finding surface top layer')\n top,bul=find_top_layer(totalsol,Optimizer.surftopthick)\n indiv=top.copy()\n individ[0]=top.copy()\n bul = Atoms()\n elif Optimizer.structure=='Crystal':\n if debug:\n logger.info('Checking crystal cell type')\n celltype = check_cell_type(totalsol)\n STR+='Cell structure = {0}\\n'.format(celltype)\n bul = Atoms()\n individ[0] = totalsol.copy()\n elif Optimizer.structure=='Cluster':\n volume = get_cluster_volume(totalsol)\n bul = Atoms()\n if debug:\n logger.info('Translating cluster back to smaller box size location')\n totalsol.translate([-Optimizer.large_box_size/2.0,-Optimizer.large_box_size/2.0,-Optimizer.large_box_size/2.0])\n totalsol.set_cell(origcell)\n individ[0] = totalsol.copy()\n \n # Add concentration energy dependence\n if Optimizer.forcing=='energy_bias':\n if debug:\n logger.info('Applying energy bias for atoms with different number of atoms of type than in atomlist')\n n=[0]*len(Optimizer.atomlist)\n for i in range(len(Optimizer.atomlist)):\n n[i]=len([inds for inds in totalsol if inds.symbol==Optimizer.atomlist[i][0]])\n n[i]=abs(n[i]-Optimizer.atomlist[i][1])\n factor=sum(n)**3\n energy=(energy+factor)/totalsol.get_number_of_atoms()\n STR+='Energy with Bias = {0}\\n'.format(energy)\n elif Optimizer.forcing=='chem_pot':\n if debug:\n logger.info('Applying chemical potential bias for atoms with different number of atoms of type than in atomlist')\n n=[0]*len(Optimizer.atomlist)\n for i in range(len(Optimizer.atomlist)):\n n[i]=len([inds for inds in totalsol if inds.symbol==Optimizer.atomlist[i][0]])\n n[i]=n[i]*Optimizer.atomlist[i][3]\n factor=sum(n)\n energy=(energy+factor)/totalsol.get_number_of_atoms()\n STR+='Energy with Chemical Potential = {0}\\n'.format(energy)\n\n individ.energy=energy\n individ.buli=bul\n individ.pressure=pressure\n individ.volume=volume\n \n if Optimizer.fingerprinting:\n if debug:\n logger.info('Identifying fingerprint of new structure')\n individ.fingerprint=get_fingerprint(Optimizer,individ,Optimizer.fpbin,Optimizer.fpcutoff)\n if Optimizer.parallel:\n calc.clean()\n signal = 'Evaluated individual {0} on {1}\\n'.format(individ.index,rank)\n signal +=STR\n else:\n signal=STR\n\n return energy, bul, individ, signal","def search_method(self): \n self.setup_problem()\n self.problem_results = minimize(self._problem, \n self.algorithm, \n self.termination,\n seed=1,\n verbose=False)\n \n self.log_debug('Core design variables determined: {}'.format(self.current_design_variables))","def __call__(self):\n self.start()\n numberOfIterations, stateVectorConv = self.iterations()\n if numberOfIterations <= 1:\n self.answer = None\n raise DidNotConvergeWarning(\"Number of iterations <= 1.\")\n result = self.diagnostic(numberOfIterations, stateVectorConv)\n return result","def _step1_optimization_closure(self, iteration, step):\n if iteration == self.num_iter_first_step - 1:\n reg_noise_std = 0\n else:\n reg_noise_std = (1 / 1000.) * (iteration // 300) # TODO: make it dependant in the max number of iterations\n aug = self._get_augmentation(iteration)\n if iteration == self.num_iter_first_step - 1:\n aug = 0\n # creates left_net_inputs and right_net_inputs by adding small noise\n clean_net_input = self.clean_net_inputs[aug] + (self.clean_net_inputs[aug].clone().normal_() * reg_noise_std)\n # watermark_net_input = self.watermark_net_inputs[aug] # + (self.watermark_net_input.clone().normal_())\n # mask_net_input = self.mask_net_inputs[aug]\n # applies the nets\n self.clean_net_output = self.clean_net(clean_net_input)\n self.total_loss = 0\n self.blur = 0\n self.total_loss += self.extended_l1_loss(self.clean_net_output,\n self.image_torchs[aug],\n (1 - self.watermark_hint_torchs[aug]))\n self.total_loss.backward(retain_graph=True)","def step(self) -> ResultDict:\n # Do we have to run `self.evaluate()` this iteration?\n # `self.iteration` gets incremented after this function returns,\n # meaning that e. g. the first time this function is called,\n # self.iteration will be 0.\n evaluate_this_iter = (\n self.config.evaluation_interval is not None\n and (self.iteration + 1) % self.config.evaluation_interval == 0\n )\n\n # Results dict for training (and if appolicable: evaluation).\n results: ResultDict = {}\n\n # Parallel eval + training: Kick off evaluation-loop and parallel train() call.\n if evaluate_this_iter and self.config.evaluation_parallel_to_training:\n (\n results,\n train_iter_ctx,\n ) = self._run_one_training_iteration_and_evaluation_in_parallel()\n # - No evaluation necessary, just run the next training iteration.\n # - We have to evaluate in this training iteration, but no parallelism ->\n # evaluate after the training iteration is entirely done.\n else:\n results, train_iter_ctx = self._run_one_training_iteration()\n\n # Sequential: Train (already done above), then evaluate.\n if evaluate_this_iter and not self.config.evaluation_parallel_to_training:\n results.update(self._run_one_evaluation(train_future=None))\n\n # Attach latest available evaluation results to train results,\n # if necessary.\n if not evaluate_this_iter and self.config.always_attach_evaluation_results:\n assert isinstance(\n self.evaluation_metrics, dict\n ), \"Algorithm.evaluate() needs to return a dict.\"\n results.update(self.evaluation_metrics)\n\n if hasattr(self, \"workers\") and isinstance(self.workers, WorkerSet):\n # Sync filters on workers.\n self._sync_filters_if_needed(\n central_worker=self.workers.local_worker(),\n workers=self.workers,\n config=self.config,\n )\n # TODO (avnishn): Remove the execution plan API by q1 2023\n # Collect worker metrics and add combine them with `results`.\n if self.config._disable_execution_plan_api:\n episodes_this_iter = collect_episodes(\n self.workers,\n self._remote_worker_ids_for_metrics(),\n timeout_seconds=self.config.metrics_episode_collection_timeout_s,\n )\n results = self._compile_iteration_results(\n episodes_this_iter=episodes_this_iter,\n step_ctx=train_iter_ctx,\n iteration_results=results,\n )\n\n # Check `env_task_fn` for possible update of the env's task.\n if self.config.env_task_fn is not None:\n if not callable(self.config.env_task_fn):\n raise ValueError(\n \"`env_task_fn` must be None or a callable taking \"\n \"[train_results, env, env_ctx] as args!\"\n )\n\n def fn(env, env_context, task_fn):\n new_task = task_fn(results, env, env_context)\n cur_task = env.get_task()\n if cur_task != new_task:\n env.set_task(new_task)\n\n fn = functools.partial(fn, task_fn=self.config.env_task_fn)\n self.workers.foreach_env_with_context(fn)\n\n return results","def runIteration(self, task, Sol, Fitness, xb, fxb, A, r, S, Q, v, **dparams):\n\t\tfor i in range(self.NP):\n\t\t\tA[i], r[i] = self.selfAdaptation(A[i], r[i])\n\t\t\tQ[i] = self.Qmin + (self.Qmax - self.Qmin) * self.uniform(0, 1)\n\t\t\tv[i] += (Sol[i] - xb) * Q[i]\n\t\t\tif self.rand() > r[i]: S[i] = self.localSearch(best=xb, A=A[i], task=task, i=i, Sol=Sol)\n\t\t\telse: S[i] = task.repair(Sol[i] + v[i], rnd=self.Rand)\n\t\t\tFnew = task.eval(S[i])\n\t\t\tif (Fnew <= Fitness[i]) and (self.rand() < (self.A_l - A[i]) / self.A): Sol[i], Fitness[i] = S[i], Fnew\n\t\t\tif Fnew <= fxb: xb, fxb = S[i].copy(), Fnew\n\t\treturn Sol, Fitness, xb, fxb, {'A': A, 'r': r, 'S': S, 'Q': Q, 'v': v}","def run(self, function, beta):\n if self.info_requested(Info.ok):\n self.info_set(Info.ok, False)\n\n# step = function.step(beta)\n\n z = betanew = betaold = beta\n\n if self.info_requested(Info.time):\n t = []\n if self.info_requested(Info.fvalue):\n f = []\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, False)\n\n for i in xrange(1, max(self.min_iter, self.max_iter) + 1):\n\n if self.info_requested(Info.time):\n tm = utils.time_cpu()\n\n z = betanew + ((i - 2.0) / (i + 1.0)) * (betanew - betaold)\n\n step = function.step(z)\n\n betaold = betanew\n betanew = function.prox(z - step * function.grad(z),\n step)\n\n if self.info_requested(Info.time):\n t.append(utils.time_cpu() - tm)\n if self.info_requested(Info.fvalue):\n f.append(function.f(betanew))\n\n if self.conesta_stop is not None:\n mu_min = self.conesta_stop[0]\n# print \"mu_min:\", mu_min\n mu_old = function.set_mu(mu_min)\n# print \"mu_old:\", mu_old\n stop_step = function.step(betanew)\n# print \"step :\", step\n # Take one ISTA step for use in the stopping criterion.\n stop_z = function.prox(betanew - stop_step \\\n * function.grad(betanew),\n stop_step)\n function.set_mu(mu_old)\n# print \"err :\", maths.norm(betanew - z)\n# print \"sc err:\", (1.0 / step) * maths.norm(betanew - z)\n# print \"eps :\", self.eps\n\n if (1. / stop_step) * maths.norm(betanew - stop_z) < self.eps \\\n and i >= self.min_iter:\n\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, True)\n\n break\n\n else:\n if step > 0.0:\n if (1.0 / step) * maths.norm(betanew - z) < self.eps \\\n and i >= self.min_iter:\n\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, True)\n\n break\n\n else: # TODO: Fix this!\n if maths.norm(betanew - z) < self.eps \\\n and i >= self.min_iter:\n\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, True)\n\n break\n\n self.num_iter = i\n\n if self.info_requested(Info.num_iter):\n self.info_set(Info.num_iter, i)\n if self.info_requested(Info.time):\n self.info_set(Info.time, t)\n if self.info_requested(Info.fvalue):\n self.info_set(Info.fvalue, f)\n if self.info_requested(Info.ok):\n self.info_set(Info.ok, True)\n\n return betanew","def run(self):\n results = self._optimization.run()\n self._optimization.sim.fdtd.close()\n \n # plot optimization recap figure\n plt.show()\n \n return [results[0], np.array(results[1])]","def optimize(w, b, X, Y, num_iterations,learning_rate,print_cost = False):\n costs = []\n for i in range(num_iterations):\n\n # Cost and gradient calculation (≈ 1-4 lines of code)\n ### START CODE HERE ###\n grads,cost = propagate(w,b,X,Y)\n ### END CODE HERE ###\n\n # Retrieve derivatives from grads\n dw = grads[\"dw\"]\n db = grads[\"db\"]\n\n # update rule (≈ 2 lines of code)\n ### START CODE HERE ###\n w = w - learning_rate*dw\n b = b - learning_rate*db\n ### END CODE HERE ###\n\n # Record the costs\n if i % 100 == 0:\n costs.append(cost)\n\n # Print the cost every 100 training examples\n if print_cost and i%100==0:\n print(\"Cost after iteration %i: %f\"%(i,cost))\n\n params = {\n \"w\":w,\n \"b\":b\n }\n grads = {\n \"dw\":dw,\n \"db\":db\n }\n return params,grads,costs","def solve(self, **kwargs) -> OptimizationResult:\n raise NotImplementedError","def optimize(self, maxiter):\n for iteration in range(maxiter):\n self.sortParticles()\n self.phi = int(phiMin + iteration *((phiMax - phiMin) / float(maxiter)))\n self.cluster()\n #self.ConnectClusters()\n for i in range(self.n_particles):\n x = self.particles_pos[i]\n v = self.velocities[i]\n p_best = self.p_best[i]\n self.velocities[i] = self.update_velocity(x, v, p_best , self.g_best , self.getLbestOfCluster(self.getClusterOfParticle(i)) , i)\n self.particles_pos[i] = self.update_position(x, v)\n # Update the best position for particle i\n if self.func(self.particles_pos[i]) < self.func(p_best):\n self.p_best[i] = self.particles_pos[i]\n # Update the best position overall\n if self.func(self.particles_pos[i]) < self.func(self.g_best):\n \n self.g_best = self.particles_pos[i]\n return self.g_best, self.func(self.g_best)","def run():\n return estimate(0,1,0)","def optimize(self, startPoint=0, epsilon=1e-5, maxIterations=100):\n n = len(startPoint)\n alpha = 1\n Hk = numpy.eye(n)\n I = numpy.eye(n)\n k = 0\n xk = startPoint\n gk = self.g(xk)\n \n while 1:\n # Compute the norm of the gradient.\n gradNorm = numpy.sqrt(numpy.dot(gk, gk))\n\n # Display the function value for the current iteration.\n fk = f(xk)\n print \"%d: fval = %d, norm = %f\" % (k, fk, gradNorm) \n \n # Termination based on tolerenace criterion.\n if (gradNorm <= epsilon):\n print \"Terminating: Tolerence %f (fval = %f, norm = %f)\"\\\n % (epsilon, fk, gradNorm)\n return {'optimalPoint':xk, 'functVal':fk}\n\n # Termination due to maximum iterations.\n if (k > maxIterations):\n print \"Terminating: Max iterations %d (fval = %f, norm = %f)\" \\\n % (i, fk, gradNorm) \n return {'optimalPoint':xk, 'functVal':fk}\n\n # Computing the search direction.\n pk = -numpy.dot(Hk, gk)\n sk = alpha * pk\n xk1 = xk + sk\n gk1 = self.g(xk1)\n yk = gk1 - gk\n\n # Computing Hk1.\n rhok = 1.0 / numpy.dot(yk, sk)\n A = I - (rhok * numpy.outer(sk, yk))\n B = rhok * numpy.outer(sk, sk)\n Hk = numpy.dot(numpy.dot(A, Hk), A) + B\n\n # Update the variables for the next iteration.\n xk = xk1\n gk = gk1\n k += 1\n pass \n pass","def value_iteration(self):\n #Create a utility function of the environment shape\n gamma = 0.9\n epsilon = 0.01\n iteration = 0\n\n #create a utility function that matches the size of the number of states\n u = np.zeros(self.env.observation_space.n, dtype=float)\n\n u_copy = u.copy()\n\n #Create the reward grid\n reward = np.array([state_map.get(sublist) for state in frozen_lake.MAPS[self.env.spec._kwargs.get('map_name')] for sublist in state])\n\n T = self.frozen_transition()\n\n graph_list = list()\n\n #keep track of the convergence\n policy_convergence = list()\n\n while True:\n delta = 0\n iteration += 1\n u = u_copy.copy()\n graph_list.append(u)\n start_time = time()\n for s in range(self.env.observation_space.n):\n r = reward[s]\n v = np.zeros((1, self.env.observation_space.n), dtype=float)\n v[0, s] = 1.0\n u_copy[s] = self.return_state_utility(v, T, u, r, gamma)\n delta = max(delta, np.abs(u_copy[s] - u[s]))\n policy_convergence.append({'iter': iteration, 'delta': delta})\n if delta < epsilon * (1 - gamma) / gamma:\n print(\"Total Iterations: {}\".format(iteration))\n print(\"=================== VALUE ITERATION RESULT ==================\")\n print(\"Iterations: \" + str(iteration))\n print(\"Delta: \" + str(delta))\n print(\"Gamma: \" + str(gamma))\n print(\"Epsilon: \" + str(epsilon))\n print(\"Time to converge: {} seconds\".format(time() - start_time))\n print(\"===================================================\")\n utility_reshape = np.reshape(u, (int(np.sqrt(self.env.observation_space.n)), int(np.sqrt(self.env.observation_space.n))))\n print (np.array(utility_reshape, dtype=float))\n print(\"===================================================\")\n break\n\n return u","def algorithm(self):\n convergence_threshold = 50\n reward_num_threshold = 300\n alpha = 1\n gamma = 0.5\n while (self.reward_num < reward_num_threshold) and (self.count<convergence_threshold):\n print('------')\n print('Iteration', self.reward_num, '/', reward_num_threshold)\n print('Iterations w/out Q-update:', self.count, '/', convergence_threshold)\n # select a possible action (any of them; all are valid)\n s = self.get_state_num()\n print(\"Initial state:\", s)\n a = random.choice(np.arange(3))\n self.apply_action(a)\n while self.reward == None:\n #print(\"Sleeping to wait for reward\")\n rospy.sleep(0.5)\n reward = self.reward\n print(\"REWARD =\", reward)\n self.reward = None\n if reward == 0:\n next_state = self.get_state_num()\n mx = np.amax(self.Q[next_state])\n else:\n ## There is no next state if nonzero reward seen\n mx = 0\n update = self.Q[s][a] + alpha*(reward+gamma*mx-self.Q[s][a])\n if self.Q[s][a] != update:\n print(\"Update Q matrix\")\n self.Q[s][a] = update\n self.count = 0\n else:\n self.count += 1\n\n print(\"Finished calculating Q-Matrix\\n\\n\\n\\n\\n\\n\\n\")","def experiment_callback(self, args):\n # If args is None, that means that an exception was raised during the\n # execution of the experiment. In such case, ignore it\n if not args:\n self.n_fail += 1\n return\n # Extract parameters\n params, results, duration = args\n self.n_success += 1\n # Store results\n self.results.add(params, results)\n self.exp_durations.append(duration)\n if self.n_success % self.summary_freq == 0:\n # Number of experiments scheduled to be executed\n n_scheduled = self.n_exp - (self.n_fail + self.n_success)\n # Compute ETA\n n_cores = min(mp.cpu_count(), self.n_proc)\n mean_duration = sum(self.exp_durations) / len(self.exp_durations)\n eta = timestr(n_scheduled * mean_duration / n_cores, False)\n # Print summary\n logger.info('SUMMARY | Completed: %d, Failed: %d, Scheduled: %d, ETA: %s',\n self.n_success, self.n_fail, n_scheduled, eta)","def optimize_self(self):\n self.compute_predicate_values();\n \"\"\" Firstly,adjust the f(x) into > alpha_0; \"\"\"\n for i in range(len(self.RING_PARA_PAIR_CD)):\n self.adjust_to_excepted_value(self.RING_PARA_PAIR_CD[i]);\n self.RING_PARA_PAIR_CD[i][0].VECTOR = RUNNING_DATA[self.RING_PARA_PAIR_CD[i][0].WORD_ID]; # 更新内部VECTOR;\n self.RING_PARA_PAIR_CD[i][2] = self.RING_PARA_PAIR_CD[i][0].polynomial_func(self.RING_PARA_PAIR_CD[i][1]); # 更新内部ALPHA;\n for i in range(len(self.RING_PARA_PAIR_CC)):\n # ************** BUG 0627 **********************\n # if self.RING_PARA_PAIR_CC[i][2]<41 and self.RING_PARA_PAIR_CC[i][2]>40: # ---------------------------------------------------------------->>>>>\n # print( RUNNING_DATA[self.RING_PARA_PAIR_CC[i][0].WORD_ID] ); # ---------------------------------------------------------------->>>>>\n self.adjust_to_excepted_value(self.RING_PARA_PAIR_CC[i]); \n # ************** BUG 0627 **********************\n # if self.RING_PARA_PAIR_CC[i][2]<41 and self.RING_PARA_PAIR_CC[i][2]>40: # ---------------------------------------------------------------->>>>> \n # print(self.RING_PARA_PAIR_CC[i][0].VECTOR); # ---------------------------------------------------------------->>>>>\n # print(RUNNING_DATA[self.RING_PARA_PAIR_CC[i][0].WORD_ID]); # ---------------------------------------------------------------->>>>>\n # print(self.RING_PARA_PAIR_CC[i][0].polynomial_func(self.RING_PARA_PAIR_CC[i][1])); # ---------------------------------------------------------------->>>>>\n # print( np.cumprod( self.RING_PARA_PAIR_CC[i][1] - self.RING_PARA_PAIR_CC[i][0].VECTOR )[-1] ); # ---------------------------------------------------------------->>>>>\n self.RING_PARA_PAIR_CC[i][0].VECTOR = RUNNING_DATA[self.RING_PARA_PAIR_CC[i][0].WORD_ID]; # 更新内部VECTOR;\n self.RING_PARA_PAIR_CC[i][2] = self.RING_PARA_PAIR_CC[i][0].polynomial_func(self.RING_PARA_PAIR_CC[i][1]); # 更新内部ALPHA;\n \"\"\" Secondly,adjust the alpha_0 < p_min(y_n) < q_min(x_n)\"\"\" \n P_MIN = 99999999.0; Q_MIN = 99999999.0;P_MAX = 0.0;Q_MAX = 0.0;\n CD_ID = 0;CD_MIN=0;\n # 找到结论中谓词的最小多项式值\n for RING_PARA_PAIR in self.RING_PARA_PAIR_CC:\n if Q_MIN>RING_PARA_PAIR[2]: Q_MIN=RING_PARA_PAIR[2];\n # if Q_MAX<RING_PARA_PAIR[2]: Q_MAX=RING_PARA_PAIR[2];\n # 不满足小于结论最小值的那些环的TARGET_ALPHA更新; \n for RING_PARA_PAIR in self.RING_PARA_PAIR_CD:\n if P_MIN>RING_PARA_PAIR[2]: P_MIN=RING_PARA_PAIR[2];CD_MIN=CD_ID;# self.RING_PARA_PAIR_CD[CD_ID][3]=-Q_MIN;\n # if P_MAX<RING_PARA_PAIR[2]: P_MAX=RING_PARA_PAIR[2];self.RING_PARA_PAIR_CD[CD_ID][3]=-Q_MAX;\n CD_ID+=1;\n self.RING_PARA_PAIR_CD[CD_MIN][3]=-Q_MIN; \n # 优化不满足小于结论最小值的那些环 \n for i in range(len(self.RING_PARA_PAIR_CD)): \n if self.RING_PARA_PAIR_CD[i][3]!=ALPHA:\n if P_MIN>Q_MIN:\n self.adjust_to_excepted_value(self.RING_PARA_PAIR_CD[i]); \n self.RING_PARA_PAIR_CD[i][0].VECTOR = RUNNING_DATA[self.RING_PARA_PAIR_CD[i][0].WORD_ID]; # 更新内部VECTOR;\n self.RING_PARA_PAIR_CD[i][2] = self.RING_PARA_PAIR_CD[i][0].polynomial_func(self.RING_PARA_PAIR_CD[i][1]); # 更新内部ALPHA;"],"string":"[\n \"def Optimize(self):\\n self._OpenOutputFiles()\\n while self.n_iter < self.n_maxiter and not self.is_converged:\\n self.n_iter += 1\\n self._ChooseStepDirection(self.opt_type)\\n self._LineSearch(-1.0 * self.step_dir)\\n self._UpdateEnergy()\\n self._UpdateGradient()\\n self.traj.AppendStep(self.mol)\\n self._UpdateCriteria()\\n self._CheckConvergence()\\n self._PrintStatus()\\n self._CloseOutputFiles()\",\n \"def optimize(self):\\n \\n if self.verbose:\\n print('Starting grid search with bounds: [' + \\\\\\n ';'.join(['%5g to %5g']*len(self.steps))%tuple([(self.steps[i][0], self.steps[i][-1]) for i in range(len(self.steps))]) +']')\\n\\n for params in self._get_next_point():\\n self.transform.set_params(params)\\n\\n v, _ = self.measure.value_and_derivatives(self.transform)\\n\\n if v < self.best_value:\\n self.best_value = v\\n self.best_params = params\\n# print('New best value %2.4f at ('%v, ', '.join(['%8.3f']*len(params))%tuple(params), ')')\\n\\n self.value_history.append(v)\\n self.last_value = v\\n self.iteration += 1\\n\\n if self.report_freq > 0 and (self.iteration % self.report_freq == 0) and self.report_func is not None:\\n self.report_func(self)\\n\\n # Set the best transform\\n self.transform.set_params(self.best_params)\\n self.last_value = self.best_value\\n return self.best_value\",\n \"def _optimization_loop(self, iteration=0):\\n self.logger.print_optimization_header()\\n\\n while iteration < self.iterations:\\n try:\\n self._execute_experiment()\\n except RepeatedExperimentError:\\n # G.debug_(F'Skipping repeated Experiment: {_ex!s}\\\\n')\\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\\n G.log_(f\\\"Hyperparameter search space has been exhausted\\\")\\n break\\n self.skipped_iterations += 1\\n continue\\n except StopIteration:\\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\\n G.log_(f\\\"Hyperparameter search space has been exhausted\\\")\\n break\\n # G.debug_(f'Re-initializing hyperparameter grid after testing {len(self.tested_keys)} keys')\\n self._set_hyperparameter_space()\\n continue\\n\\n self.logger.print_result(\\n self.current_hyperparameters_list,\\n self.current_score,\\n experiment_id=self.current_experiment.experiment_id,\\n )\\n\\n if (\\n (self.best_experiment is None) # First evaluation\\n or (self.do_maximize and (self.best_score < self.current_score)) # New best max\\n or (not self.do_maximize and (self.best_score > self.current_score)) # New best min\\n ):\\n self.best_experiment = self.current_experiment.experiment_id\\n self.best_score = self.current_score\\n\\n iteration += 1\",\n \"def go(self):\\n if self.model_initializer is None:\\n raise ValueError(\\\"Experiment guidelines must be set before starting optimization\\\")\\n\\n _reporter_params = dict(dict(do_maximize=self.do_maximize), **self.reporter_parameters)\\n self.logger = OptimizationReporter([_.name for _ in self.dimensions], **_reporter_params)\\n\\n self.tested_keys = []\\n self._set_hyperparameter_space()\\n self._find_similar_experiments()\\n\\n loop_start_time = datetime.now()\\n self._optimization_loop()\\n loop_end_time = datetime.now()\\n G.log_(f\\\"Optimization loop completed in {loop_end_time - loop_start_time}\\\")\\n G.log_(f'Best score was {self.best_score} from Experiment \\\"{self.best_experiment}\\\"')\\n self._clean_up_optimization()\",\n \"def iterations(self):\\n i = 0\\n stateVectorConv = self.stateVectorConvThreshold * 1.0e6\\n n = len(self.model.stateVector)\\n self.answer = None\\n \\n while ((i < self.maxiter) \\n and (stateVectorConv > self.stateVectorConvThreshold)\\n ):\\n \\n F, K = self.model()\\n \\n if np.any(np.isnan(F)) or np.any(np.isnan(K)):\\n m = \\\"Iteration {0} failure of model.\\\"\\n raise OptimalEstimationException(m.format(i))\\n \\n if self.model.verbose > 0:\\n self.model.plot(i+1, stateVectorConv)\\n \\n try:\\n self.DecomposeJacobian(K)\\n except np.linalg.LinAlgError:\\n m = \\\"Iteration {0} failure in decomposition.\\\"\\n raise OptimalEstimationException(m.format(i))\\n \\n statevectorOffset = (self.V.T * self.priorSinvh * \\n np.matrix(np.array(self.model.stateVector) - np.array(self.model.prior) ).T)\\n measurementOffset = (self.U.T * self.errSinvh * \\n np.matrix(self.model.observation - F).T)\\n \\n newState = np.matrix((self.w * \\n (measurementOffset.A1 + \\n self.w * statevectorOffset.A1))/(self.w**2+1.0)).T\\n newState = self.priorSh * self.V * newState\\n newState = newState.A1 + self.model.prior\\n \\n stateVectorConv = ((np.matrix(newState - self.model.stateVector) * \\n self.Sinv * np.matrix(newState - self.model.stateVector).T)/n)[0,0]\\n self.model.stateVector = newState\\n\\n if i == 0:\\n \\n stateVectorConv = self.stateVectorConvThreshold * 1.0e6\\n \\n print('cost Function for iteration {}:'.format(i), self.costFunction)\\n\\n i += 1\\n \\n F, K = self.model()\\n if self.model.verbose > 0:\\n self.model.plot(i+1, stateVectorConv)\\n \\n try:\\n self.DecomposeJacobian(K)\\n except np.linalg.LinAlgError:\\n raise OptimalEstimationException(\\\"Failure in decomposition.\\\")\\n \\n Wplus2 = np.matrix(np.diag(1.0/(self.w**2+1.0)))\\n self.model.covariance = (self.priorSh * self.V * Wplus2 * \\n self.V.T * self.priorSh)\\n \\n\\n \\n return i, stateVectorConv\",\n \"def optimize(self):\\n self.vbe_step()\\n self.compute_responsibilities()\\n self.compute_sufficient_stats()\\n self.vbmstep()\",\n \"def solve(self):\\n\\n # Open status display\\n fmtstr, nsep = self.display_start()\\n\\n # Start solve timer\\n self.timer.start(['solve', 'solve_wo_func', 'solve_wo_rsdl',\\n 'solve_wo_btrack'])\\n\\n # Main optimisation iterations\\n for self.k in range(self.k, self.k + self.opt['MaxMainIter']):\\n\\n # Update record of X and Y from previous iteration\\n self.on_iteration_start()\\n\\n # Compute backtracking\\n if self.opt['Backtrack'] is not None and self.k >= 0:\\n self.timer.stop('solve_wo_btrack')\\n # Compute backtracking\\n self.backtrack.update(self)\\n self.timer.start('solve_wo_btrack')\\n else:\\n # Compute just proximal step\\n self.xstep()\\n # Update by combining previous iterates\\n self.ystep()\\n\\n # Compute residuals and stopping thresholds\\n self.timer.stop(['solve_wo_rsdl', 'solve_wo_btrack'])\\n if not self.opt['FastSolve']:\\n frcxd, adapt_tol = self.compute_residuals()\\n self.timer.start('solve_wo_rsdl')\\n\\n # Compute and record other iteration statistics and\\n # display iteration stats if Verbose option enabled\\n self.timer.stop(['solve_wo_func', 'solve_wo_rsdl',\\n 'solve_wo_btrack'])\\n if not self.opt['FastSolve']:\\n itst = self.iteration_stats(self.k, frcxd)\\n self.itstat.append(itst)\\n self.display_status(fmtstr, itst)\\n self.timer.start(['solve_wo_func', 'solve_wo_rsdl',\\n 'solve_wo_btrack'])\\n\\n # Call callback function if defined\\n if self.opt['Callback'] is not None:\\n if self.opt['Callback'](self):\\n break\\n\\n # Stop if residual-based stopping tolerances reached\\n if not self.opt['FastSolve']:\\n if frcxd < adapt_tol:\\n break\\n\\n # Increment iteration count\\n self.k += 1\\n\\n # Record solve time\\n self.timer.stop(['solve', 'solve_wo_func', 'solve_wo_rsdl',\\n 'solve_wo_btrack'])\\n\\n # Print final separator string if Verbose option enabled\\n self.display_end(nsep)\\n\\n return self.getmin()\",\n \"def _optimise(self):\\n pass\",\n \"def optimizer_callback(current_theta):\\n\\n global optimizer_data\\n global optimizer_len\\n\\n optimizer_data.append(current_theta)\\n optimizer_len += 1\\n return False\",\n \"def optimize(self, iterations=1000):\\r\\n prev = None\\r\\n finalx = None\\r\\n finaly = None\\r\\n while iterations:\\r\\n maxei, eis = self.acquisition()\\r\\n new_y = self.f(maxei)\\r\\n if maxei == prev:\\r\\n break\\r\\n self.gp.update(maxei, new_y)\\r\\n pycodehack = finaly is None or self.minimize and finaly > new_y\\r\\n if ((pycodehack or not self.minimize and finaly < new_y)):\\r\\n finaly = new_y\\r\\n finalx = maxei\\r\\n prev = maxei\\r\\n iterations -= 1\\r\\n return finalx, finaly\",\n \"def accept_optimize():\\n pass\",\n \"def run_optimization(self):\\n # Get batch\\n (obs, action, old_logp, old_value, return_, advantage) = self.buffer.eject()\\n\\n # Train pi\\n print(\\\"-\\\" * 20 + \\\"\\\\nPi Update\\\" + \\\"\\\\n\\\" + \\\"-\\\" * 20)\\n (policy_loss, entropy,\\n kl_divergence, clipping_fraction, steps) = self.update_actor(obs, action, old_logp, advantage)\\n\\n # Train value function\\n print(\\\"-\\\" * 20 + \\\"\\\\nValue Function Update\\\" + \\\"\\\\n\\\" + \\\"-\\\" * 20)\\n (value_loss,\\n explained_variance) = self.update_critic(obs, old_value, return_)\\n\\n # Logging\\n self.update_counter += 1\\n self.log_update(policy_loss, entropy, kl_divergence, clipping_fraction,\\n value_loss, explained_variance, steps)\\n\\n # Update learning rate\\n self.decay_lr()\\n\\n # Save current weights (overwrites previous weights)\\n self.save_weights()\\n\\n # Empty scenario counter\\n self.scenario_counter = dict.fromkeys(self.scenario_counter, 0)\",\n \"def _optimization_closure(self, iteration, step):\\n aug = self._get_augmentation(iteration)\\n if iteration == self.num_iter_per_step - 1:\\n reg_noise_std = 0\\n aug = 0\\n else:\\n reg_noise_std = (1 / 1000.) * (iteration // 400)\\n # creates left_net_inputs and right_net_inputs by adding small noise\\n clean_nets_inputs = [clean_net_input[aug] + (clean_net_input[aug].clone().normal_() * reg_noise_std)\\n for clean_net_input in self.clean_nets_inputs]\\n watermark_net_input = self.watermark_net_input[aug] # + (self.watermark_net_input[aug].clone().normal_() * reg_noise_std)\\n mask_net_input = self.mask_net_input[aug]\\n # applies the nets\\n self.clean_nets_outputs = [clean_net(clean_net_input) for clean_net, clean_net_input\\n in zip(self.clean_nets, clean_nets_inputs)]\\n self.watermark_net_output = self.watermark_net(watermark_net_input)\\n self.mask_net_output = self.mask_net(mask_net_input)\\n self.total_loss = 0\\n self.blur = 0\\n\\n self.total_loss += sum(self.l1_loss(self.watermark_net_output * self.mask_net_output +\\n clean_net_output * (1 - self.mask_net_output), image_torch[aug])\\n for clean_net_output, image_torch in zip(self.clean_nets_outputs, self.images_torch))\\n self.total_loss.backward(retain_graph=True)\",\n \"def Optimize(self):\\n return _gmat_py.ExternalOptimizer_Optimize(self)\",\n \"def fit(self):\\n if self.minimizer == \\\"differential_evolution\\\":\\n kwargs = {\\\"maxiter\\\": self._maxiter}\\n elif self.minimizer == \\\"shgo\\\":\\n kwargs = {\\\"options\\\": {\\\"maxiter\\\": self._maxiter,\\n \\\"jac\\\": self.cost_func.jac_cost}}\\n elif self.minimizer == \\\"dual_annealing\\\":\\n kwargs = {\\\"maxiter\\\": self._maxiter, \\\"local_search_options\\\": {\\n \\\"jac\\\": self.cost_func.jac_cost}}\\n fun = self.cost_func.eval_cost\\n bounds = self.value_ranges\\n algorithm = getattr(optimize, self.minimizer)\\n result = algorithm(fun, bounds, **kwargs)\\n self._popt = result.x\\n if result.success:\\n self._status = 0\\n elif \\\"Maximum number of iteration\\\" in result.message:\\n self._status = 1\\n else:\\n self._status = 2\",\n \"def run_and_store(self):\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient,2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Stored updates\\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\\n stored_predictive_likelihood = np.zeros(self.iterations)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n gradient = self.cv_gradient(self.draw_normal())\\n gradient[np.isnan(gradient)] = 0\\n new_parameters = self.optim.update(gradient)\\n self.change_parameters(new_parameters)\\n\\n stored_means[i] = self.optim.parameters[::2]\\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\\n\\n if self.printer is True:\\n self.print_progress(i,self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records\",\n \"def run_and_store(self):\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient,2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Stored updates\\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\\n stored_predictive_likelihood = np.zeros(self.iterations)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n gradient = self.cv_gradient(self.draw_normal())\\n gradient[np.isnan(gradient)] = 0\\n new_parameters = self.optim.update(gradient)\\n self.change_parameters(new_parameters)\\n\\n stored_means[i] = self.optim.parameters[::2]\\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\\n\\n if self.printer is True:\\n self.print_progress(i,self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records\",\n \"def run_qae_optimization(training_states, n_repetitions, exact=no_noise, noisy=gate_error):\\n result_list = []\\n def proxy(params, training_states, n_repetitions, exact=no_noise, noisy=gate_error):\\n \\\"\\\"\\\"Embedded function version\\n \\\"\\\"\\\"\\n input_list = fix_list(params, all_param_array=all_param, var_param_array=var_param, fixed_vals_array=fixed_vals)\\n fidelities = []\\n for training_state in training_states:\\n fid = cusp_stage2.compute_stage2_cost_function(*input_list, alpha=training_state, n_repetitions=n_repetitions,\\n exact=exact, noisy=noisy)\\n fidelities.append(fid)\\n avg_fid = np.mean(fidelities)\\n result_list.append(1-avg_fid)\\n print(1-avg_fid)\\n return 1. - avg_fid\\n\\n \\n # Initialize parameters\\n half_turn_min = 0\\n half_turn_max = 2\\n init_params = np.random.uniform(low=half_turn_min, high=half_turn_max,\\n size=num_param)\\n\\n # Optimization using Nelder-Mead.\\n h2_qae_wrap = lambda params: proxy(params, training_states=training_states,\\n n_repetitions=n_repetitions, exact=exact, noisy=noisy)\\n \\n if noisy:\\n maxiter = 60\\n else:\\n maxiter = None\\n \\n res = minimize(h2_qae_wrap, init_params, args=(),\\n method='Nelder-Mead', tol=None, \\n options={'disp': False, 'maxiter': maxiter, 'xatol': 0.001,\\n 'return_all': False, 'fatol': 0.001})\\n np.savetxt('stage2_data.csv',result_list, delimiter=',')\\n return res.x\",\n \"def example():\\n Optimizer = BFGS(f, g)\\n startPoint = 100 * numpy.ones(2);\\n res = Optimizer.optimize(startPoint,\\n epsilon=1e-5,\\n maxIterations=10)\\n print res\\n pass\",\n \"def optimize(self, optimizer=None, start=None, messages=False, max_iters=1000, ipython_notebook=True, clear_after_finish=False, **kwargs):\\n self.inference_method.on_optimization_start()\\n try:\\n super(GPMSGP, self).optimize(optimizer, start, messages, max_iters, ipython_notebook, clear_after_finish, **kwargs)\\n except KeyboardInterrupt:\\n print(\\\"KeyboardInterrupt caught, calling on_optimization_end() to round things up\\\")\\n self.inference_method.on_optimization_end()\\n raise\\n \\n self.posterior_prediction = self.inference_method.update_prediction_vectors(self.kern,self.posterior,self.grad_dict,self.likelihood)\",\n \"def _on_step(self) -> None:\\n self._n_calls += 1\\n # Account for multiple environments\\n # each call to step() corresponds to n_envs transitions\\n if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:\\n polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\\n # Copy running stats, see GH issue #996\\n polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)\\n\\n self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\\n self.logger.record(\\\"rollout/exploration_rate\\\", self.exploration_rate)\",\n \"def optimization_step(self):\\n \\n if \\\"CSS\\\" in self.algorithm:\\n \\n input_dict = {self.x: self.train_inputs[self.minibatch_set,:]}\\n \\n var_list = [self.x_tilda, self.minibatch_set]\\n \\n if (self.num_samples > 0) and (not self.mixture):\\n \\n if ((self.mf_steps > 0) and self.alpha >0) or\\\\\\n self.gibbs_steps > 0: \\n \\n var_list.append(self.sampler_theta)\\n \\n elif \\\"CD\\\" in self.algorithm:\\n \\n input_dict = {self.x : self.train_inputs[self.minibatch_set,:]} \\n \\n var_list = [self.minibatch_set]\\n \\n var_list.append(self.learning_rate)\\n \\n if self.use_momentum:\\n \\n var_list.append(self.momentum)\\n \\n output_vars = [self.pseudo_cost]\\n \\n if self.report_p_tilda:\\n \\n output_vars.append(self.p_tilda)\\n \\n else:\\n \\n output_vars.append(theano.shared(0))\\n \\n opt_step = theano.function(inputs = var_list,\\n outputs = output_vars,\\n updates = self.updates,\\n givens = input_dict,\\n on_unused_input='warn')\\n \\n return opt_step\",\n \"def opt_wrapper(m, **kwargs):\\r\\n m.optimize(**kwargs)\\r\\n return m.optimization_runs[-1]\",\n \"def algorithm_loop(self):\",\n \"def propose_optimize():\\n pass\",\n \"def run(self, C, p0 = None):\\n global algorithm \\n algorithm = AdaptiveMM(self.g, C, p0 = p0, lambda0 = 2000)\\n solve()\",\n \"def Solve(self, cost, termination=None, ExtraArgs=(), **kwds):\\n # process and activate input settings\\n sigint_callback = kwds.pop('sigint_callback', None)\\n settings = self._process_inputs(kwds)\\n disp = settings['disp'] if 'disp' in settings else False\\n echo = settings['callback'] if 'callback' in settings else None\\n# for key in settings:\\n# exec \\\"%s = settings['%s']\\\" % (key,key)\\n if disp in ['verbose', 'all']: verbose = True\\n else: verbose = False\\n #-------------------------------------------------------------\\n\\n from python_map import python_map\\n if self._map != python_map:\\n #FIXME: EvaluationMonitor fails for MPI, throws error for 'pp'\\n from mystic.monitors import Null\\n evalmon = Null()\\n else: evalmon = self._evalmon\\n fcalls, cost = wrap_function(cost, ExtraArgs, evalmon)\\n\\n # set up signal handler\\n #self._EARLYEXIT = False\\n self._generateHandler(sigint_callback) \\n\\n # activate signal_handler\\n #import threading as thread\\n #mainthread = isinstance(thread.current_thread(), thread._MainThread)\\n #if mainthread: #XXX: if not mainthread, signal will raise ValueError\\n import signal\\n if self._handle_sigint:\\n signal.signal(signal.SIGINT,self.signal_handler)\\n\\n # register termination function\\n if termination is not None: self.SetTermination(termination)\\n\\n # get the nested solver instance\\n solver = self._AbstractEnsembleSolver__get_solver_instance()\\n #-------------------------------------------------------------\\n\\n # generate starting points\\n initial_values = self._InitialPoints()\\n\\n # run optimizer for each grid point\\n from copy import deepcopy as _copy\\n op = [_copy(solver) for i in range(len(initial_values))]\\n #cf = [cost for i in range(len(initial_values))]\\n vb = [verbose for i in range(len(initial_values))]\\n cb = [echo for i in range(len(initial_values))] #XXX: remove?\\n at = self.id if self.id else 0 # start at self.id\\n id = range(at,at+len(initial_values))\\n\\n # generate the local_optimize function\\n def local_optimize(solver, x0, rank=None, disp=False, callback=None):\\n from copy import deepcopy as _copy\\n from mystic.tools import isNull\\n solver.id = rank\\n solver.SetInitialPoints(x0)\\n if solver._useStrictRange: #XXX: always, settable, or sync'd ?\\n solver.SetStrictRanges(min=solver._strictMin, \\\\\\n max=solver._strictMax) # or lower,upper ?\\n solver.Solve(cost, disp=disp, callback=callback)\\n sm = solver._stepmon\\n em = solver._evalmon\\n if isNull(sm): sm = ([],[],[],[])\\n else: sm = (_copy(sm._x),_copy(sm._y),_copy(sm._id),_copy(sm._info))\\n if isNull(em): em = ([],[],[],[])\\n else: em = (_copy(em._x),_copy(em._y),_copy(em._id),_copy(em._info))\\n return solver, sm, em\\n\\n # map:: solver = local_optimize(solver, x0, id, verbose)\\n results = self._map(local_optimize, op, initial_values, id, \\\\\\n vb, cb, **self._mapconfig)\\n\\n # save initial state\\n self._AbstractSolver__save_state()\\n #XXX: HACK TO GET CONTENT OF ALL MONITORS\\n # reconnect monitors; save all solvers\\n from mystic.monitors import Monitor\\n while results: #XXX: option to not save allSolvers? skip this and _copy\\n _solver, _stepmon, _evalmon = results.pop()\\n sm = Monitor()\\n sm._x,sm._y,sm._id,sm._info = _stepmon\\n _solver._stepmon.extend(sm)\\n del sm\\n em = Monitor()\\n em._x,em._y,em._id,em._info = _evalmon\\n _solver._evalmon.extend(em)\\n del em\\n self._allSolvers[len(results)] = _solver\\n del results, _solver, _stepmon, _evalmon\\n #XXX: END HACK\\n\\n # get the results with the lowest energy\\n self._bestSolver = self._allSolvers[0]\\n bestpath = self._bestSolver._stepmon\\n besteval = self._bestSolver._evalmon\\n self._total_evals = self._bestSolver.evaluations\\n for solver in self._allSolvers[1:]:\\n self._total_evals += solver.evaluations # add func evals\\n if solver.bestEnergy < self._bestSolver.bestEnergy:\\n self._bestSolver = solver\\n bestpath = solver._stepmon\\n besteval = solver._evalmon\\n\\n # return results to internals\\n self.population = self._bestSolver.population #XXX: pointer? copy?\\n self.popEnergy = self._bestSolver.popEnergy #XXX: pointer? copy?\\n self.bestSolution = self._bestSolver.bestSolution #XXX: pointer? copy?\\n self.bestEnergy = self._bestSolver.bestEnergy\\n self.trialSolution = self._bestSolver.trialSolution #XXX: pointer? copy?\\n self._fcalls = self._bestSolver._fcalls #XXX: pointer? copy?\\n self._maxiter = self._bestSolver._maxiter\\n self._maxfun = self._bestSolver._maxfun\\n\\n # write 'bests' to monitors #XXX: non-best monitors may be useful too\\n self._stepmon = bestpath #XXX: pointer? copy?\\n self._evalmon = besteval #XXX: pointer? copy?\\n self.energy_history = None\\n self.solution_history = None\\n #from mystic.tools import isNull\\n #if isNull(bestpath):\\n # self._stepmon = bestpath\\n #else:\\n # for i in range(len(bestpath.y)):\\n # self._stepmon(bestpath.x[i], bestpath.y[i], self.id)\\n # #XXX: could apply callback here, or in exec'd code\\n #if isNull(besteval):\\n # self._evalmon = besteval\\n #else:\\n # for i in range(len(besteval.y)):\\n # self._evalmon(besteval.x[i], besteval.y[i])\\n #-------------------------------------------------------------\\n\\n # restore default handler for signal interrupts\\n if self._handle_sigint:\\n signal.signal(signal.SIGINT,signal.default_int_handler)\\n\\n # log any termination messages\\n msg = self.Terminated(disp=disp, info=True)\\n if msg: self._stepmon.info('STOP(\\\"%s\\\")' % msg)\\n # save final state\\n self._AbstractSolver__save_state(force=True)\\n return\",\n \"def run(self):\\n # print(\\\"111\\\"+\\\"--- %s seconds ---\\\" % (time.time() ))\\n err = self.params.tolerance + 1\\n for iter_num in range(self.params.max_iter):\\n if err <= self.params.tolerance:\\n break\\n # print(\\\"11\\\"+str(iter_num)+\\\"--- %s seconds ---\\\" % (time.time() - start_time))\\n qprev = self.sigma2\\n\\n self._expectation_iter(iter_num)\\n self._maximization_iter(iter_num)\\n\\n if self.sigma2 <= 0:\\n self.sigma2 = self.params.tolerance / 10\\n err = np.abs(self.sigma2 - qprev)\\n\\n if callable(self.callback):\\n kwargs = {\\n 'iteration': iter_num,\\n 'error': err,\\n 'X': self.X,\\n 'Y': self.TY,\\n 'W': self.W,\\n 'P': self.P\\n }\\n self.callback(**kwargs)\\n return self.TY\",\n \"def opt_wrapper(m, **kwargs):\\n m.optimize(**kwargs)\\n return m.optimization_runs[-1]\",\n \"def Optimize(self):\\n return _gmat_py.Optimizer_Optimize(self)\",\n \"def optimize(cls, trials, score, evals_rounds, mon_cons, categorical):\\n raise NotImplementedError\",\n \"def optimize(self, objectivefct, logger=None, verb_disp=20, iterations=None):\\r\\n if logger is None:\\r\\n if hasattr(self, 'logger'):\\r\\n logger = self.logger\\r\\n\\r\\n citer = 0\\r\\n while not self.stop():\\r\\n if iterations is not None and citer >= iterations:\\r\\n return self.result()\\r\\n citer += 1\\r\\n\\r\\n X = self.ask() # deliver candidate solutions\\r\\n fitvals = [objectivefct(x) for x in X]\\r\\n self.tell(X, fitvals) # all the work is done here\\r\\n\\r\\n self.disp(verb_disp)\\r\\n logger.add(self) if logger else None\\r\\n\\r\\n logger.add(self, modulo=bool(logger.modulo)) if logger else None\\r\\n if verb_disp:\\r\\n self.disp(1)\\r\\n if verb_disp in (1, True):\\r\\n print('termination by', self.stop())\\r\\n print('best f-value =', self.result()[1])\\r\\n print('solution =', self.result()[0])\\r\\n\\r\\n return self.result() + (self.stop(), self, logger)\",\n \"def run_optimization(config,\\n blackbox_optimizer,\\n init_current_input,\\n init_best_input,\\n init_best_core_hyperparameters,\\n init_best_value,\\n init_iteration,\\n workers,\\n log_bool=False):\\n current_input = init_current_input\\n best_input = init_best_input\\n best_core_hyperparameters = init_best_core_hyperparameters\\n best_value = [init_best_value]\\n iteration = init_iteration\\n\\n while True:\\n print(iteration)\\n sys.stdout.flush()\\n success, current_input = run_step_rpc_blackbox_optimizer(\\n config, current_input, blackbox_optimizer, workers, iteration,\\n best_input, best_core_hyperparameters, best_value, log_bool)\\n if success:\\n iteration += 1\\n if iteration == config.nb_iterations:\\n break\",\n \"def iterate(self, update_func):\\n if self.disc_param_save_on_error:\\n update_func = param_file.wrap_with_save_on_error(\\n self.gan.discriminator,\\n self.datastore.path('disc_param', 'pre_error.npz'),\\n self.datastore.path('disc_param', 'post_error.npz'),\\n )(update_func)\\n\\n self.pre_loop()\\n logger.info('%s: start iterations', self.__class__.__name__)\\n with recording_exit_reason(self.datastore):\\n for gen_step in range(self.iterations):\\n self.post_update(gen_step, update_func(gen_step))\\n logger.info('%s: maximum iterations reached', self.__class__.__name__)\",\n \"def __call__(self, fgraph):\\r\\n return self.optimize(fgraph)\",\n \"def step(self, closure=None):\\n orig_loss,err,pred = closure()\\n loss = orig_loss\\n\\n group = self.param_groups[0]\\n lr = group['lr']\\n decay_lr = group['decay_lr']\\n max_iter = group['max_iter']\\n reg = group['reg']\\n backtrack = group['backtrack']\\n bt_alpha = group['bt_alpha']\\n bt_beta = group['bt_beta']\\n sketch_size = group['sketch_size']\\n tolerance = group['tolerance']\\n\\n #import pdb; pdb.set_trace()\\n n = err.shape[0] #batch size\\n #If sketching the jacobian, randomly select [sketch_size] samples\\n \\n if sketch_size is not None:\\n idx = torch.randperm(n)[:sketch_size]\\n else:\\n idx = torch.arange(n) #Don't sketch, use all samples\\n \\n w0 = nn.utils.parameters_to_vector(self._params) #weight parameters in vector form\\n \\n #Compute Gauss-Newton vector product \\n grad, ggnvp = _make_ggnvp(err,self._params,w0,n,reg,idx) #return gradient in vector form + ggnvp function\\n #Solve for the Conjugate Gradient Direction\\n dw, cost_log = _conjugate_gradient(ggnvp, grad, max_iter, tolerance)\\n\\n #Perform backtracking line search\\n val = loss + 0.5 * reg * torch.norm(w0)**2\\n fprime = -1*dw @ grad\\n \\n self.grad_update += 1\\n if backtrack > 0:\\n t = lr\\n\\n #TODO: If using backtracking, get new loss with (w0 - t*dw) as network parameters\\n bts = 0\\n alpha = bt_alpha\\n beta = bt_beta \\n while (loss + 0.5 * reg * torch.norm(w0 - t*dw)**2 > val + alpha * t * fprime):\\n t = beta * t\\n bts += 1\\n if bts > backtrack:\\n print('Maximum backtracking reached, accuracy not guaranteed')\\n break\\n elif decay_lr: #decay lr\\n t = lr/np.maximum(1, self.grad_update-10)\\n else: #use lr step-size\\n t = lr\\n\\n print('step size: {}'.format(t))\\n\\n #Update the model parameters\\n self._add_grad(-t, dw)\\n \\n return val, pred\",\n \"def callback(self, parameters):\\n print(\\\"\\\\nFinsished Iteration: {}\\\".format(self.current_iteration), flush=True)\\n print(\\\"Current Parameters: {}\\\".format(parameters), flush=True)\\n\\n # If getting the value history, perform an evaluation with current parameters\\n\\n print(\\\"Starting Next Iteration...\\\", flush=True)\\n\\n # Update currrent_iteration index and add new blank history\\n self.current_iteration += 1\",\n \"def do_optimisation(self):\\n\\n print('--> Parameters for optimisation:')\\n print('--> Using measurements : {}'.format(self.stoma_cfg.comparison_helper.optimisation_keys))\\n print('')\\n\\n x0 = self.initial_guess()\\n\\n tol, eps = 1e-4, 0.001\\n\\n print('--> Using SLSQP with tol={} and eps={}'.format(tol, eps))\\n\\n soln = opt.minimize(fun=self.optimise_fn,\\n x0=x0,\\n method='SLSQP',\\n tol=tol,\\n options={'eps': eps})\\n\\n print('*' * 120)\\n print('--> Optimisation procedure has finished...')\\n print(soln)\\n print('*' * 120)\\n\\n if soln.success:\\n print('--> Optimisation succeeded. Result is...')\\n self._set_material_parameters(soln.x)\\n print('--> {}'.format(self.material_model))\\n else:\\n print('--> The optimisation failed!')\\n\\n print('*' * 120)\\n\\n return soln\",\n \"def VFI(method) :\\n iteration=0 # Iteration Counter\\n converged = 0 # Convergence Flag|\\n \\n#----- Initial Settings \\n v_update = zeros(n_grid)\\n v_func = empty(n_grid)\\n k_next_vec = empty(n_grid)\\n run_time = empty(2)\\n \\n def obj(k_next) :\\n \\\"\\\"\\\"\\n This function is used in value function iteration.\\n It represents the objective function to be maximized for one node (state) of current capitals.\\n Resulting value is maximized one corresponding to next period's capital as a maximizer. \\n Next period's value is computed by interpolation.\\n \\n Input : k_next (next period's capital)\\n \\n Output : value_vec (maximized value resulting from choosing optimal capital in the next period)\\n \\\"\\\"\\\" \\n \\n if method==1 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*linear_interp(k_grid,v_update,k_next))\\n elif method==2 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*quad_interp(k_grid,v_update,k_next))\\n elif method==3 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*cubic_interp(k_grid,v_update,k_next))\\n \\n return value_vec\\n\\n#----- Value function iteration\\n start = time.time() # start time\\n while converged==0 :\\n index = 0\\n for k_current in k_grid :\\n k_next = fminbound(obj,k_grid[0],k_grid[-1])\\n v_func[index] = (-1) * obj(k_next)\\n k_next_vec[index] = k_next\\n index = index + 1\\n dist = abs(max(v_func - v_update))\\n if dist<tol :\\n converged = 1\\n v_k, g_k = v_func, k_next_vec\\n v_update = v_func\\n print \\\"Iteration : \\\",iteration,\\\"\\\",\\\"Distance : \\\",dist # convergence process\\n iteration = iteration + 1\\n v_func = empty(n_grid) \\n k_next_vec = empty(n_grid)\\n \\n end = time.time() # end time\\n run_time[0],run_time[1] = runtime_cal(start,end) # total running time\\n \\n return v_k, g_k, run_time, iteration\",\n \"def run(self):\\n\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient, 2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n x = self.draw_normal()\\n gradient = self.cv_gradient(x)\\n gradient[np.isnan(gradient)] = 0\\n self.change_parameters(self.optim.update(gradient))\\n\\n if self.printer is True:\\n self.print_progress(i, self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, elbo_records\",\n \"def run(self):\\n\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient, 2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n x = self.draw_normal()\\n gradient = self.cv_gradient(x)\\n gradient[np.isnan(gradient)] = 0\\n self.change_parameters(self.optim.update(gradient))\\n\\n if self.printer is True:\\n self.print_progress(i, self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, elbo_records\",\n \"def optimize_parameters(self):\\n pass\",\n \"def optimize_parameters(self):\\n pass\",\n \"def optimize_parameters(self):\\n pass\",\n \"def intermediate(self, alg_mod, iter_count, obj_value,\\n inf_pr, inf_du, mu, d_norm, regularization_size,\\n alpha_du, alpha_pr, ls_trials):\\n # TODO: Document the arguments\\n pass\",\n \"def _function(self):\\n\\n\\n\\n def calculate_weights():\\n \\\"\\\"\\\"\\n calculate a weight inversely proportional to the expected to duration of the two steps in the\\n script\\n\\n Returns: weights as a dictionary for the two steps\\n\\n \\\"\\\"\\\"\\n weights = {}\\n\\n\\n # estimate run time of step 1 (fast sweep)\\n f_range = sweeper_script.settings['stop'] - sweeper_script.settings['start']\\n N_samples = sweeper_script.settings['samplecount']\\n df = f_range / N_samples\\n\\n t = N_samples / df\\n\\n weights['quick scan'] = t\\n\\n # estimate run time of step 2 (high res sweep)\\n df = self.settings['high_res_df']\\n N_samples = self.settings['high_res_N']\\n\\n t = N_samples / df\\n\\n weights['high res scan'] = t\\n\\n\\n total_time = sum([v for k, v in weights.iteritems()])\\n\\n weights = {k: v/total_time for k, v in weights.iteritems()}\\n\\n print('weights',weights)\\n\\n return weights\\n\\n def run_scan(name):\\n self.current_subscript = name\\n sweeper_script.start()\\n while self.current_subscript is name:\\n time.sleep(0.1)\\n\\n def calc_new_range():\\n\\n\\n df = self.settings['high_res_df']\\n N = self.settings['high_res_N']\\n\\n r = sweeper_script.data[-1]['r']\\n freq = sweeper_script.data[-1]['frequency']\\n freq = freq[np.isfinite(r)]\\n r = r[np.isfinite(r)]\\n\\n fo = freq[np.argmax(r)]\\n\\n f_start, f_end = fo - N/2 *df, fo + N/2 *df\\n\\n\\n # make sure that we convert back to native python types (numpy file types don't pass the Parameter validation)\\n return float(f_start), float(f_end), int(N)\\n\\n\\n sweeper_script = self.scripts['zi sweep']\\n #save initial settings, so that we can rest at the end of the script\\n initial_settings = deepcopy(sweeper_script.settings)\\n self.weights = calculate_weights()\\n\\n # take the signal from the subscript and route it to a function that takes care of it\\n sweeper_script.updateProgress.connect(self._receive_signal)\\n\\n print('====== start quick scan ============')\\n\\n run_scan('quick scan')\\n\\n print('====== calculate new scan range ====')\\n f_start, f_stop, N = calc_new_range()\\n\\n print('f_start, f_stop, N', f_start, f_stop, N)\\n\\n print('====== update sweeper ==============')\\n sweeper_script.update({\\n 'start' : f_start,\\n 'stop' : f_stop,\\n 'samplecount' : N\\n })\\n\\n print('====== start high res scan =========')\\n # print(sweeper_script.sweeper.finished())\\n # print(sweeper_script.sweeper.progress())\\n\\n run_scan('high res scan')\\n\\n sweeper_script.updateProgress.disconnect()\\n self.data = sweeper_script.data[-1]\\n\\n self._recording = False\\n\\n if self.settings['save']:\\n self.save()\\n\\n # set the sweeper script back to initial settings\\n sweeper_script.update(initial_settings)\\n # make sure that progess is set 1o 100 because we check that in the old_gui\\n self.updateProgress.emit(100)\",\n \"def run(self):\\n if not self._no_progress and self._verbose:\\n from progressbar import ProgressBar\\n progress = ProgressBar()\\n iter_range = progress(range(self._iters))\\n else:\\n iter_range = range(self._iters)\\n\\n if self._no_progress and self._time_iters:\\n from time import time\\n\\n i = 0\\n try:\\n for i in iter_range:\\n if self._verbose and self._no_progress:\\n print(\\\"Iteration \\\" + repr(i))\\n\\n if self._no_progress and self._time_iters:\\n start = time()\\n\\n self.iteration += 1\\n\\n self._forward(self._p_k, self._v_k)\\n sigma_k = measure(self._p_k, self._v_k)\\n alpha_k = self._rho_k / sigma_k\\n if self._double:\\n update_m_double(self._m, alpha_k, self._p_k)\\n sub_scaled_vector_double(self._residual_k,\\n self._residual_k,\\n alpha_k, self._v_k)\\n else:\\n update_m(self._m, alpha_k, self._p_k)\\n sub_scaled_vector(self._residual_k, self._residual_k,\\n alpha_k, self._v_k)\\n self._v_k = gpuarray_copy(self._residual_k)\\n rho_k_plus_1 = measure(self._v_k, self._residual_k)\\n rho_k_t = np.abs(rho_k_plus_1)\\n\\n if (rho_k_t / self._rho_0 <= self._relative_tolerance) \\\\\\n or (rho_k_t <= self._absolute_tolerance):\\n print(\\\"Converged.\\\")\\n self.converged = True\\n break\\n\\n if self._double:\\n add_scaled_vector_double(self._p_k, self._v_k,\\n rho_k_plus_1/self._rho_k,\\n self._p_k)\\n else:\\n add_scaled_vector(self._p_k, self._v_k,\\n rho_k_plus_1/self._rho_k, self._p_k)\\n\\n self._rho_k = rho_k_plus_1\\n\\n if self._noisy:\\n print(\\\" Residual=\\\" + str(rho_k_t))\\n\\n if self._no_progress and self._time_iters:\\n print(\\\"Elapsed time for iteration \\\" + str(i) + \\\": \\\" +\\n str(time() - start) + \\\" seconds\\\")\\n\\n if self._save_images:\\n save_image(np.abs(self._m.get().reshape(self._data.nX1,\\n self._data.nX2)),\\n self._out_dir, i, self._image_format)\\n if self._save_matlab:\\n save_matlab(self._m.get().reshape(self._data.nX1,\\n self._data.nX2),\\n self._out_dir, i)\\n except KeyboardInterrupt:\\n print(\\\"Reconstruction aborted (CTRL-C) at iteration \\\" + str(i))\\n finally:\\n if self._save_images:\\n save_image(np.abs(self._m.get().reshape(self._data.nX1,\\n self._data.nX2)),\\n self._out_dir, \\\"result\\\", self._image_format)\\n if self._save_matlab:\\n save_matlab(self._m.get().reshape(self._data.nX1,\\n self._data.nX2),\\n self._out_dir, \\\"result\\\")\\n self.iteration = i+1\\n return (self._m.get().reshape(self._data.nX1, self._data.nX2),\\n self.iteration)\",\n \"def update():\\n global iteration, result\\n iteration += 1\\n # Stop iterating after max_iterations\\n if iteration >= max_iterations:\\n timer.stop()\\n print \\\"Output is\\\", result\\n else:\\n result = get_next(result)\",\n \"def objective(hyperparams): \\n global iteration #necessary with a global variable because of implementation from hyperopt. \\n iteration += 1\\n\\n result = run_model(hyperparams, iteration)\\n loss = -result #transform to loss in order to minimize\\n\\n return {'loss': loss, 'hyperparams': hyperparams, 'iteration': iteration, 'status': STATUS_OK}\",\n \"def on_iteration_start(self):\\n\\n self.Xprv = self.X.copy()\\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\\n BacktrackRobust)):\\n self.Yprv = self.Y.copy()\\n\\n if self.opt['Monotone']:\\n if self.k == 0:\\n self.objfn = self.eval_objfn()\\n self.objfn_prev = self.objfn\",\n \"def fit(self):\\n self._minuit_problem.migrad() # run optimizer\\n self._status = 0 if self._minuit_problem.migrad_ok() else 1\",\n \"def optimize(self,max_iter=100):\\n\\n\\n for itr in range(max_iter):\\n opt_logs = self.opt.minimize(self.tot_neg_elbo,sum([expert.trainable_variables for expert in self.experts],())) \\n print(self.neg_elbo)\",\n \"def optimize(self):\\n prm = (self.b,self.c)\\n d = self.d\\n no = int(d*d)\\n bounds = [(-1,1)]*no\\n resG = differential_evolution(inpSc.entBias, bounds, args = prm, popsize = 40, disp = False)\\n\\n xOpt = resG.x\\n xOpt = xOpt/(np.linalg.norm(xOpt))\\n\\n #Refine the global optimization by performing a second local optimizaiton\\n x0 = xOpt\\n\\n res = minimize(inpSc.entBias, x0, args = prm, method='BFGS', options={'disp': False})\\n xOpt = res.x\\n xOpt = xOpt/(np.linalg.norm(xOpt))\\n self.rhoOp = inpSc.getMat(xOpt, d)\\n self.Q1 = -res.fun\",\n \"def search(self):\\n self.iteration = 0\\n while self.iteration < self.maxIterations:\\n self.GAStep()\\n self.iteration += 1\\n\\n print (\\\"Total iterations: \\\",self.iteration)\\n print (\\\"Best Solution: \\\", self.best.getFitness())\",\n \"def run_step_blackbox_optimizer(config,\\n current_input,\\n blackbox_optimizer,\\n proposed_perturbations,\\n finished_dnas,\\n results,\\n logging_data=None):\\n core_hyperparameters = blackbox_optimizer.get_hyperparameters()\\n function_values = [0.0] * len(proposed_perturbations)\\n rewards_for_controller = []\\n perturbations = proposed_perturbations\\n evaluation_stats = []\\n current_value_exact = 0.0\\n current_value_exact_counter = 0\\n\\n for i in range(len(results)):\\n rewards_for_controller.append(results[i]['function_value'])\\n tag = results[i]['tag']\\n index = 0\\n if tag > 0:\\n if config.est_type == 'antithetic':\\n index = (tag - 1) * 2\\n function_values[index] += results[i]['function_value']\\n else:\\n index = tag - 1\\n function_values[index] += results[i]['function_value']\\n if tag < 0:\\n index = (-tag - 1) * 2 + 1\\n function_values[index] += results[i]['function_value']\\n if tag == 0:\\n current_value_exact += results[i]['function_value']\\n current_value_exact_counter += 1\\n current_value_exact /= float(current_value_exact_counter)\\n\\n for result in results:\\n evaluation_stat = list(result['evaluation_stat'])\\n evaluation_stats.append(evaluation_stat)\\n\\n function_values_reshaped = np.array(function_values)\\n perturbations_reshaped = np.array(perturbations)\\n\\n logging.info('LIST OF FUNCTION VALUES')\\n logging.info(function_values_reshaped)\\n\\n logging.info('MAX VALUE SEEN CURRENTLY')\\n logging.info(np.max(function_values_reshaped))\\n\\n logging.info('MEAN OF VALUES')\\n logging.info(np.mean(function_values_reshaped))\\n\\n if logging_data is not None:\\n iteration = logging_data['iteration']\\n best_value = logging_data['best_value']\\n iteration = logging_data['iteration']\\n best_input = logging_data['best_input']\\n best_core_hyperparameters = logging_data['best_core_hyperparameters']\\n optimizer_state = blackbox_optimizer.get_state()\\n\\n if current_value_exact > best_value[0]:\\n best_value[0] = current_value_exact\\n best_input = current_input\\n best_core_hyperparameters = core_hyperparameters\\n\\n # Writing logs.\\n if iteration % config.log_frequency == 0:\\n util.log_row(config.params_file, current_input)\\n util.log_row(config.best_params_file, best_input)\\n util.log_row(config.best_core_hyperparameters_file,\\n best_core_hyperparameters)\\n util.log_row(config.best_value_file, best_value)\\n util.log_row(config.optimizer_internal_state_file, optimizer_state)\\n util.log_row(config.current_values_list_file, [current_value_exact])\\n util.log_row(config.best_values_list_file, [best_value[0]])\\n util.log_row(config.fvalues_file, function_values_reshaped)\\n util.log_row(config.iteration_file, [iteration])\\n\\n print('Current exact value estimate:')\\n print(current_value_exact)\\n sys.stdout.flush()\\n\\n new_current_input = blackbox_optimizer.run_step(perturbations_reshaped,\\n function_values_reshaped,\\n current_input,\\n current_value_exact)\\n config.controller.collect_rewards_and_train(rewards_for_controller,\\n finished_dnas)\\n\\n evaluation_stats_reduced = [sum(x) for x in zip(*evaluation_stats)]\\n blackbox_optimizer.update_state(evaluation_stats_reduced)\\n\\n return [True, new_current_input]\",\n \"def optimize(self): \\n if self.model == 'ARD':\\n estimate = minimize(\\n fun=optim_func,\\n x0=np.array([self.alpha, self.beta]),\\n args=(self,),\\n method='L-BFGS-B',\\n bounds=((0, 50), (0, 50)),\\n )\\n # logger.info(estimate)\\n\\n # organize into a dict\\n result = {\\n \\\"alpha\\\": estimate.x[0],\\n \\\"beta\\\": estimate.x[1],\\n \\\"Lik\\\": estimate.fun,\\n \\\"negLogLik\\\": -np.log(-estimate.fun),\\n \\\"convergence\\\": estimate.success,\\n }\\n logger.debug(result)\\n\\n elif self.model == 'ER':\\n estimate = minimize(\\n fun=optim_func,\\n x0=np.array([self.alpha]),\\n args=(self,),\\n method='L-BFGS-B',\\n bounds=[(0, 50)],\\n )\\n\\n result = {\\n \\\"alpha\\\": estimate.x[0],\\n \\\"Lik\\\": estimate.fun, \\n \\\"negLogLik\\\": -np.log(-estimate.fun),\\n \\\"convergence\\\": estimate.success,\\n }\\n logger.debug(result)\\n\\n else:\\n raise Exception('model must be specified as either ARD or ER')\\n\\n # get scaled likelihood values\\n self.log_lik = result[\\\"negLogLik\\\"]\\n self.tree = self.tree.set_node_values(\\n 'likelihood',\\n values={\\n node.idx: np.array(node.likelihood) / sum(node.likelihood)\\n for node in self.tree.idx_dict.values()\\n }\\n )\",\n \"def iterate_value(self):\\n self.V = np.zeros(self.stateCount)\\n for i in range(self.maxIter):\\n last_V = np.copy(self.V)\\n for state_index in range(self.stateCount):\\n current_state = self.env.statespace[state_index]\\n for action in self.env.actionspace:\\n next_state = self.env.next_state(current_state,action)\\n reward = self.env.compute_reward(next_state)\\n next_state_index = self.env.stateDict[next_state]\\n self.Q[state_index][action] = reward + self.gamma*last_V[next_state_index]\\n if self.mode == 'debug':\\n print(\\\"Q(s={}):{}\\\".format(current_state,self.Q[state_index]))\\n self.V[state_index] = max(self.Q[state_index])\\n if np.sum(np.fabs(last_V - self.V)) <= self.th:\\n print (\\\"Convergene Achieved in {}th iteration. \\\"\\n \\\"Breaking V_Iteration loop!\\\".format(i))\\n break\",\n \"def optimize(self, ngen):\\n res = 0\\n for res in self(ngen):\\n pass\\n return res\",\n \"def on_iteration_start(self):\\n\\n self.Xfprv = self.Xf.copy()\\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\\n BacktrackRobust)):\\n self.Yfprv = self.Yf.copy()\\n\\n if self.opt['Monotone']:\\n if self.k == 0:\\n self.objfn = self.eval_objfn()\\n self.objfn_prev = self.objfn\",\n \"def run(self):\\n\\n # init\\n base_value = self._problem.evaluate()\\n self._problem.set_as_best(base_value)\\n\\n # init iteration (used to count the amount of iterations)\\n iteration = 0\\n\\n # add to data\\n self._data_append(self.data, iteration, base_value, base_value)\\n\\n # init termination criterion\\n self._termination_criterion.check_first_value(base_value)\\n self._termination_criterion.start_timing()\\n\\n # main loop\\n while self._termination_criterion.keep_running():\\n\\n # search the neighbourhood for the best move\\n best_found_delta = self._best_found_delta_base_value\\n best_found_move = None\\n\\n for move in self._problem.get_moves():\\n\\n # check quality move\\n delta = self._problem.evaluate_move(move)\\n\\n # checks how the move alters the current state\\n diff = self._diff(move)\\n\\n # if not in tabu list --> not similar to earlier performed\\n # moves --> if delta better than old best move\\n # --> becomes the best move\\n\\n if not self._tabu_list.contains(diff) and \\\\\\n self._is_better(best_found_delta, delta):\\n best_found_delta = delta\\n best_found_move = move\\n best_found_diff = diff\\n\\n # the best found move will be used as the next move\\n # alter state problem\\n base_value = base_value + best_found_delta\\n\\n # check if a move was found\\n if best_found_move is not None:\\n\\n self._problem.move(best_found_move)\\n\\n # if better than best found --> new best_found\\n if self._is_better(self._problem.best_order_value,\\n base_value):\\n self._problem.set_as_best(base_value)\\n # log the better solution\\n self._log_improvement(base_value)\\n\\n # add diff to tabu list\\n self._tabu_list.add(best_found_diff)\\n\\n # add to data\\n self._data_append(self.data, iteration,\\n base_value, self._problem.best_order_value)\\n\\n self._termination_criterion.check_new_value(base_value)\\n\\n # functions _termination_criterion called\\n self._termination_criterion.check_new_value(base_value)\\n\\n else:\\n # no move found --> we're stuck --> break loop\\n break\\n\\n iteration += 1\\n self._termination_criterion.iteration_done()\\n\\n # last data point\\n self._data_append(self.data, iteration, base_value,\\n self._problem.best_order_value)\\n\\n # if we have data:\\n # convert data to something easier to plot\\n if self.data is not None:\\n\\n # convert to tuple of list\\n data = convert_data(self.data)\\n\\n # make namedtuple\\n DataAsLists = namedtuple(\\n 'Data', ['time', 'iteration', 'value', 'best_value'])\\n\\n data = DataAsLists(data[0], data[1], data[2], data[3])\\n\\n else:\\n data = None\\n\\n # return results\\n\\n Results = namedtuple('Results', ['best_order', 'best_value', 'data'])\\n\\n return Results(self._problem.best_order,\\n self._problem.best_order_value,\\n data)\",\n \"def compute_gradient(self, verbose):\\n from inversionson.helpers.autoinverter_helpers import IterationListener\\n\\n # Attempt to dispatch model smoothing right at the beginning.\\n # So there is no smoothing bottleneck when updates are not smoothed.\\n it_listen = IterationListener(\\n self.comm,\\n events=self.comm.project.events_in_iteration)\\n it_listen.listen()\\n\\n self.task_dict[\\\"forward_submitted\\\"] = True\\n self.task_dict[\\\"misfit_completed\\\"] = True\\n self.task_dict[\\\"gradient_completed\\\"] = True\\n self._update_task_file()\\n self.finish_task()\",\n \"def solve(self, regparam):\\n self.regparam = regparam\\n \\n #Some counters for bookkeeping\\n self.stepcounter = 0\\n self.flipcounter = 0\\n self.nochangecounter = 0\\n \\n #Cached results\\n self.evals = np.multiply(self.svals, self.svals)\\n self.newevals = 1. / (self.evals + self.regparam)\\n newevalslamtilde = np.multiply(self.evals, self.newevals)\\n self.D = np.sqrt(newevalslamtilde)\\n #self.D = -newevalslamtilde\\n \\n self.VTY = self.svecs.T * self.Y\\n DVTY = np.multiply(self.D.T, self.svecs.T * self.Y)\\n \\n #Using lists in order to avoid unnecessary matrix slicings\\n self.DVTY_list = []\\n self.YTVDDVTY_list = []\\n self.classFitnessList = []\\n for i in range(self.labelcount):\\n DVTY_i = DVTY[:,i]\\n self.DVTY_list.append(DVTY_i)\\n YTVDDVTY_i = DVTY_i.T * DVTY_i\\n self.YTVDDVTY_list.append(YTVDDVTY_i)\\n fitness_i = self.size - DVTY_i.T * DVTY_i\\n self.classFitnessList.append(fitness_i)\\n \\n self.Dsvecs_list = []\\n self.svecsDDsvecs_list = []\\n for i in range(self.size):\\n Dsvec = np.multiply(self.D.T, self.svecs[i].T)\\n self.Dsvecs_list.append(Dsvec)\\n self.svecsDDsvecs_list.append(Dsvec.T*Dsvec)\\n \\n self.updateA()\\n \\n \\n converged = False\\n print(self.classcounts.T)\\n if self.callbackfun is not None:\\n self.callbackfun.callback(self)\\n while True:\\n \\n converged = self.roundRobin()\\n print(self.classcounts.T)\\n if self.callbackfun is not None:\\n self.callbackfun.callback(self)\\n if converged: break\\n \\n if self.oneclass:\\n self.Y = self.Y[:, 0]\\n self.A = self.A[:, 0]\\n self.results['predicted_clusters_for_training_data'] = self.Y\\n self.predictor = self.svdad.createModel(self)\",\n \"def step(self, closure: Callable = None):\\n loss = self.optimizer.step(closure)\\n self._k_counter += 1\\n\\n if self._k_counter >= self.k:\\n self._k_counter = 0\\n # Lookahead and cache the current optimizer parameters\\n for group in self.optimizer.param_groups:\\n for p in group[\\\"params\\\"]:\\n param_state = self.state[p]\\n p.data.mul_(self.alpha).add_(\\n param_state[\\\"slow_params\\\"], alpha=1.0 - self.alpha\\n )\\n param_state[\\\"slow_params\\\"].copy_(p.data)\\n return loss\",\n \"def callback(_locals, _globals):\\n global n_steps, best_mean_reward\\n # Print stats every 1000 calls\\n if (n_steps + 1) % 1000 == 0:\\n # Evaluate policy training performance\\n x, y = ts2xy(load_results(log_dir), 'timesteps')\\n if len(x) > 0:\\n mean_reward = np.mean(y[-100:])\\n print(x[-1], 'timesteps')\\n print(\\\"Best mean reward: {:.2f} - Last mean reward per episode: {:.2f}\\\".format(best_mean_reward, mean_reward))\\n\\n # New best model, you could save the agent here\\n if mean_reward > best_mean_reward:\\n best_mean_reward = mean_reward\\n # Example for saving best model\\n print(\\\"Saving new best model\\\")\\n _locals['self'].save(log_dir + str(x[-1])+'best_model.pkl')\\n n_steps += 1\\n return True\",\n \"def optimize(self, num_restarts=1, max_iters=100, max_f_eval=300.0, method='Anneal'):\\n dic = DictVectorizer()\\n # flatten the parameters\\n init_params,bounds=dic.fit_transform(self.params)\\n #we minimise minus the marginal likelihood\\n def objective(params_flatten):\\n self.params=dic.inverse_transform(params_flatten,bounds)\\n val = -self.log_marginal_likelihood()\\n return val# we want to maximize it\\n \\n \\n #run ptimisation with multiple restarts\\n optml=np.inf\\n for i in range(num_restarts):\\n #minimise function\\n if method=='Anneal':\\n res=dual_annealing(objective,bounds, maxiter=max_iters, maxfun=max_f_eval, x0=init_params)\\n else:\\n \\n res = minimize(objective, init_params, \\n bounds=bounds, method=method,options={'maxiter': max_iters, 'disp': False})\\n #print(\\\"Iteration \\\"+str(i)+\\\" \\\",-res.fun)\\n if res.fun<optml:\\n params_best=res.x #init_params \\n optml=res.fun\\n init_params=bounds[:,0]+(bounds[:,1]-bounds[:,0])*np.random.rand(len(bounds[:,0]))\\n print(\\\"Iteration \\\"+str(i)+\\\" \\\",-res.fun)\\n #params_best=res.x\\n #optml=res.fun\\n self.params=dic.inverse_transform(params_best,bounds)\\n return -optml\",\n \"def minimize(self):\\n pass\",\n \"def optimize(self, return_teacher_params_bool = False):\\n\\n gen_batches = self.DATASET.data_stream(self.BATCH_SIZE)\\n \\n num_complete_batches, leftover = divmod(self.DATASET.num_example['train'], self.BATCH_SIZE)\\n\\n # number of minibatches per epoch\\n num_mini_batches_per_epochs = num_complete_batches + bool(leftover)\\n\\n # number of total iterations\\n num_total_iters = self.NUM_EPOCHS * num_mini_batches_per_epochs\\n\\n # number of time that the sparisty levels get updated\\n num_sparsity_updates = num_total_iters // self.MASK_UPDATE_FREQ \\n \\n mask_update_limit = num_total_iters - self.MASK_UPDATE_FREQ\\n \\n if self.SAVE_BOOL == True:\\n # save the transferred results in the desinated directory.\\n\\n trans_model_dir = self.unique_model_dir\\n\\n# while os.path.exists(trans_model_dir):\\n# trans_model_dir = trans_model_dir + '_0'\\n \\n if not os.path.exists(trans_model_dir):\\n os.makedirs(trans_model_dir)\\n\\n np.save(trans_model_dir + '/param_dict.npy', self.param_dict) \\n \\n \\n\\n nt_trans_params_all_sparsities_all_runs = []\\n nt_trans_masks_all_sparsities_all_runs = []\\n nt_trans_vali_all_sparsities_all_runs = []\\n teacher_params_all_sparsities_all_runs = []\\n \\n \\n num_sparisty_levels = len(self.NN_DENSITY_LEVEL_LIST) \\n num_runs = len(range(self.INIT_RUN_INDEX, self.INIT_RUN_INDEX + self.NUM_RUNS ))\\n all_density_all_run_num_total_iters = num_sparisty_levels * num_runs * num_total_iters\\n \\n \\n for nn_density_level in self.NN_DENSITY_LEVEL_LIST: \\n \\n \\n nt_trans_params_all_runs = []\\n nt_trans_masks_all_runs = []\\n nt_trans_vali_all_runs = []\\n teacher_params_all_runs = []\\n\\n\\n for run_index in range(self.INIT_RUN_INDEX, self.INIT_RUN_INDEX + self.NUM_RUNS ):\\n\\n # do logging\\n for handler in logging.root.handlers[:]:\\n logging.root.removeHandler(handler)\\n\\n # a string that summarizes the current ntt experiment\\n model_summary_str = self.model_str + '_density_' + str(round(nn_density_level, 2) ) + '_run_' + str(run_index)\\n\\n if self.SAVE_BOOL == True:\\n model_dir_density_run = trans_model_dir + '/' + 'density_' + str(round(nn_density_level, 2) ) + '/' + 'run_' + str(run_index) + '/'\\n\\n os.makedirs(model_dir_density_run)\\n \\n logging.basicConfig(filename = model_dir_density_run + \\\"/\\\" + model_summary_str + \\\"_log.log\\\", format='%(asctime)s %(message)s', filemode='w', level=logging.DEBUG)\\n\\n else: \\n logging.basicConfig(filename = model_summary_str + \\\"_log.log\\\" , format='%(asctime)s %(message)s', filemode='w', level=logging.DEBUG)\\n \\n \\n # for different run indices, randomly draw teacher net's parameters\\n _, teacher_net_params = self.init_fun(random.PRNGKey(run_index), tuple(self.batch_input_shape))\\n \\n # the prediction of the teacher net evaluated on validation samples\\n vali_teacher_prediction = self.apply_fn(teacher_net_params, self.vali_samples)\\n\\n vali_teacher_ntk_mat = self.emp_ntk_fn(self.vali_inputs_1, self.vali_inputs_2, teacher_net_params) \\n\\n # the initial binary mask\\n \\n if self.PRUNE_METHOD == 'magnitude': \\n masks = get_masks_from_jax_params(teacher_net_params, nn_density_level, global_bool = self.GLOBAL_PRUNE_BOOL)\\n elif self.PRUNE_METHOD == 'logit_snip':\\n logger.info(\\\"Use logit snip method to get the initial mask\\\")\\n num_examples_snip = 128\\n\\n# gen_batches_logit_snip = self.DATASET.data_stream(num_examples_snip)\\n \\n snip_input = self.DATASET.dataset['train']['input'][:num_examples_snip, :]\\n \\n if self.GLOBAL_PRUNE_BOOL == False:\\n logger.warning(\\\"layerwise sparse net initialized with logit_snip\\\") \\n masks = get_logit_snip_masks(teacher_net_params, nn_density_level, self.apply_fn, snip_input, self.batch_input_shape, GlOBAL_PRUNE_BOOL = self.GLOBAL_PRUNE_BOOL) \\n else:\\n raise NotImplementedError(\\\"not implemented\\\")\\n \\n\\n # the initial student parameters\\n masked_student_net_params = get_sparse_params_filtered_by_masks(teacher_net_params, masks)\\n\\n # instantiate the optimizer triple \\n opt_init, opt_update, get_params = self.OPTIMIZER_WITH_PARAMS\\n\\n opt_state = opt_init(teacher_net_params) \\n\\n # one step of NTK transfer\\n @jit\\n def nt_transfer_step(i, opt_state, x, masks):\\n\\n # parameters in the current optimizer state\\n student_net_params = get_params(opt_state)\\n\\n # gradients that flow through the binary masks\\n masked_g = grad(self.nt_transfer_loss)(student_net_params, masks, teacher_net_params, x, nn_density_level)\\n\\n return opt_update(i, masked_g, opt_state)\\n\\n # a list of validation loss\\n vali_loss_list = []\\n\\n # calculate the initial validation loss. \\n vali_loss = self.eval_nt_transfer_loss_on_vali_data(masked_student_net_params, vali_teacher_prediction, vali_teacher_ntk_mat, nn_density_level)\\n\\n vali_loss_list.append(vali_loss)\\n\\n logger.info(\\\"Before transfer: trans dist %.3f | ntk dist %.3f | targ dist %.3f | l2 pentalty %.3f | nn density %.2f\\\", vali_loss[0], vali_loss[1], vali_loss[2], vali_loss[3], nn_density_level)\\n itercount = itertools.count()\\n\\n t = time.time()\\n\\n # loop through iterations\\n for num_iter in range(1, num_total_iters + 1): \\n \\n # a batch of input data\\n batch_xs, _ = next(gen_batches) \\n\\n # reshape the input to a proper format (2d array for MLP and 3d for CNN)\\n batch_xs = batch_xs.reshape(self.batch_input_shape) \\n\\n # update the optimizer state\\n opt_state = nt_transfer_step(next(itercount), opt_state, batch_xs, masks )\\n\\n\\n if num_iter % 100 == 0:\\n elapsed_time = time.time() - t\\n \\n if (num_iter <= 500) and (run_index == self.INIT_RUN_INDEX) and (nn_density_level == self.NN_DENSITY_LEVEL_LIST[0]): \\n # estimate the program end time.\\n remaining_iter_num = all_density_all_run_num_total_iters - num_iter\\n remaining_seconds = elapsed_time * ( remaining_iter_num / 100 )\\n expected_end_time = str(datetime.now() + timedelta(seconds = remaining_seconds))\\n\\n # get parameters from the current optimizer state\\n student_net_params = get_params(opt_state) \\n\\n # filter the paramters by masks\\n masked_student_net_params = get_sparse_params_filtered_by_masks(student_net_params , masks)\\n \\n # validation loss\\n vali_loss = self.eval_nt_transfer_loss_on_vali_data(masked_student_net_params, vali_teacher_prediction, vali_teacher_ntk_mat, nn_density_level) \\n\\n vali_loss_list.append(vali_loss)\\n\\n logger.info('run: %02d/%02d | iter %04d/%04d | trans. dist %.3f | ntk dist %.3f | targ. dist %.3f | l2 %.3f | nn density %.2f | time %.2f [s] | expected finish time %s', run_index, self.NUM_RUNS + self.INIT_RUN_INDEX - 1, num_iter, num_total_iters, vali_loss[0], vali_loss[1], vali_loss[2], vali_loss[3], nn_density_level, elapsed_time, expected_end_time)\\n t = time.time()\\n\\n\\n if (num_iter % self.MASK_UPDATE_FREQ == 0) and (num_iter < mask_update_limit):\\n # get parameters from the current optimizer state\\n student_net_params = get_params(opt_state) \\n \\n # update masks\\n masks = get_masks_from_jax_params(student_net_params, nn_density_level, global_bool = self.GLOBAL_PRUNE_BOOL)\\n \\n# if self.PRUNE_METHOD == 'logit_snip':\\n# logit_snip_batch_xs, _ = next(gen_batches_logit_snip)\\n# masks = get_logit_snip_masks(student_net_params, nn_density_level, self.apply_fn, snip_input, self.batch_input_shape, GlOBAL_PRUNE_BOOL = self.GLOBAL_PRUNE_BOOL) \\n# else:\\n# masks = get_masks_from_jax_params(student_net_params, nn_density_level, global_bool = self.GLOBAL_PRUNE_BOOL)\\n\\n\\n \\n elapsed_time = time.time() - t\\n \\n student_net_params = get_params(opt_state) \\n \\n # filter the paramters by masks\\n masked_student_net_params = get_sparse_params_filtered_by_masks(student_net_params , masks)\\n \\n vali_loss = self.eval_nt_transfer_loss_on_vali_data(masked_student_net_params, vali_teacher_prediction, vali_teacher_ntk_mat, nn_density_level) \\n\\n vali_loss_list.append(vali_loss)\\n \\n logger.info('run: %02d/%02d | iter %04d/%04d | trans. dist %.3f | ntk dist %.3f | targ. dist %.3f | l2 %.3f | nn density %.2f | time %.2f [s]', run_index, self.NUM_RUNS + self.INIT_RUN_INDEX - 1, num_iter, num_total_iters, vali_loss[0], vali_loss[1], vali_loss[2], vali_loss[3], nn_density_level, elapsed_time )\\n \\n vali_loss_array = np.array(vali_loss_list)\\n\\n nt_trans_params_all_runs.append(masked_student_net_params)\\n nt_trans_masks_all_runs.append(masks)\\n nt_trans_vali_all_runs.append(vali_loss_array)\\n teacher_params_all_runs.append(teacher_net_params )\\n\\n if self.SAVE_BOOL == True:\\n\\n model_summary_str = self.model_str + '_density_' + str(round(nn_density_level, 2) ) + '_run_' + str(run_index)\\n\\n teacher_param_fileName = model_dir_density_run + 'teacher_params_' + model_summary_str\\n np.save(teacher_param_fileName, teacher_net_params)\\n\\n student_param_fileName = model_dir_density_run + 'transferred_params_' + model_summary_str\\n np.save(student_param_fileName, masked_student_net_params)\\n\\n mask_fileName = model_dir_density_run + 'transferred_masks_' + model_summary_str\\n np.save(mask_fileName, masks)\\n\\n loss_array_fileName = model_dir_density_run + 'loss_array_' + model_summary_str\\n np.save(loss_array_fileName, vali_loss_array)\\n \\n\\n nt_trans_params_all_sparsities_all_runs.append( nt_trans_params_all_runs )\\n nt_trans_masks_all_sparsities_all_runs.append( nt_trans_masks_all_runs )\\n nt_trans_vali_all_sparsities_all_runs.append( nt_trans_vali_all_runs )\\n teacher_params_all_sparsities_all_runs.append( teacher_params_all_runs )\\n \\n if return_teacher_params_bool:\\n return nt_trans_params_all_sparsities_all_runs, nt_trans_masks_all_sparsities_all_runs, nt_trans_vali_all_sparsities_all_runs, teacher_params_all_sparsities_all_runs\\n\\n else:\\n return nt_trans_params_all_sparsities_all_runs, nt_trans_masks_all_sparsities_all_runs, nt_trans_vali_all_sparsities_all_runs\",\n \"def step(self, closure=None):\\n loss = None\\n if closure is not None:\\n loss = closure()\\n for m in self.optimizers:\\n m.step()\\n return loss\",\n \"def ExecuteInstanceStochasticAdaptiveRefinementAux_Functionality(current_global_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_index,current_analysis_stage,previous_computational_time,open_mp_threads,mapping_flag,pickled_mapping_reference_model,print_to_file,filename):\\n\\n start_time = time.time()\\n # unpickle model and build Kratos Model object\\n serialized_model = pickle.loads(pickled_coarse_model)\\n current_model = KratosMultiphysics.Model()\\n serialized_model.Load(\\\"ModelSerialization\\\",current_model)\\n del(serialized_model)\\n # unpickle parameters and build Kratos Parameters object\\n serialized_project_parameters = pickle.loads(pickled_coarse_project_parameters)\\n current_project_parameters = KratosMultiphysics.Parameters()\\n serialized_project_parameters.Load(\\\"ParametersSerialization\\\",current_project_parameters)\\n del(serialized_project_parameters)\\n # refine if current current_global_index > 0, adaptive refinement based on the solution of previous index\\n if (current_index > 0):\\n # unpickle metric and remesh refinement parameters and build Kratos Parameters objects\\n serialized_custom_metric_refinement_parameters = pickle.loads(pickled_custom_metric_refinement_parameters)\\n serialized_custom_remesh_refinement_parameters = pickle.loads(pickled_custom_remesh_refinement_parameters)\\n current_custom_metric_refinement_parameters = KratosMultiphysics.Parameters()\\n current_custom_remesh_refinement_parameters = KratosMultiphysics.Parameters()\\n serialized_custom_metric_refinement_parameters.Load(\\\"MetricRefinementParametersSerialization\\\",current_custom_metric_refinement_parameters)\\n serialized_custom_remesh_refinement_parameters.Load(\\\"RemeshRefinementParametersSerialization\\\",current_custom_remesh_refinement_parameters)\\n del(serialized_custom_metric_refinement_parameters,serialized_custom_remesh_refinement_parameters)\\n # refine the model Kratos object\\n adaptive_refinement_manager = AdaptiveRefinement(current_index,current_model,current_project_parameters,current_custom_metric_refinement_parameters,current_custom_remesh_refinement_parameters)\\n refined_model,refined_project_parameters = adaptive_refinement_manager.ComputeAdaptiveRefinement()\\n current_model = refined_model\\n del(refined_model,refined_project_parameters)\\n # constructor analysis stage\\n simulation = current_analysis_stage(current_model,current_project_parameters,random_variable)\\n # add filename flag print_to_file is true\\n if (print_to_file):\\n simulation.filename = filename\\n # add flag if current index is maximum index\\n if (current_index == current_global_index):\\n simulation.is_current_index_maximum_index = True\\n else:\\n simulation.is_current_index_maximum_index = False\\n # mapping if in current finest level and mapping flag is true\\n # otherwise standard behavior\\n if (mapping_flag is True and current_index == current_global_index):\\n # unpickle mapping reference model and build Kratos Model object\\n serialized_mapping_reference_model = pickle.loads(pickled_mapping_reference_model)\\n mapping_reference_model = KratosMultiphysics.Model()\\n serialized_mapping_reference_model.Load(\\\"ModelSerialization\\\",mapping_reference_model)\\n del(serialized_mapping_reference_model)\\n # send reference model to analysis stage for mapping and set mapping flag to true\\n simulation.mapping_reference_model = mapping_reference_model\\n simulation.mapping = True\\n simulation.Run()\\n # mapping if in current finest level and mapping flag is true\\n # otherwise standard qoi evaluation\\n if (mapping_flag is True and current_index == current_global_index):\\n qoi = simulation.MappingAndEvaluateQuantityOfInterest()\\n else:\\n qoi = simulation.EvaluateQuantityOfInterest()\\n # save model and parameters as MpiSerializer Kratos objects\\n serialized_finer_model = KratosMultiphysics.MpiSerializer()\\n serialized_finer_model.Save(\\\"ModelSerialization\\\",simulation.model)\\n # pickle model and parameters\\n pickled_finer_model = pickle.dumps(serialized_finer_model, 2) # second argument is the protocol and is NECESSARY (according to pybind11 docs)\\n del(simulation)\\n end_time = time.time()\\n computational_time = previous_computational_time + open_mp_threads*(end_time-start_time) # multiply by open mp threads to consider real machine cost\\n return qoi,pickled_finer_model,computational_time\",\n \"def step(self, closure):\\r\\n self.state['step'] += 1\\r\\n for y_ind in range(self.dim_out):\\r\\n err = closure(y_ind)\\r\\n for group_ind in range(len(self.param_groups)):\\r\\n group = self.param_groups[group_ind]\\r\\n iekf_mat = self.state['iekf_groups'][group_ind]\\r\\n for ii, w in enumerate(group['params']):\\r\\n if w.grad is None:\\r\\n continue\\r\\n H_n = iekf_mat[ii]['H']\\r\\n grad = w.grad.data.detach()\\r\\n if len(w.size()) > 1:\\r\\n grad = grad.transpose(1, 0)\\r\\n grad = grad.contiguous().view((1, -1))\\r\\n if y_ind == 0:\\r\\n H_n = grad\\r\\n else:\\r\\n H_n = torch.cat([H_n, grad], dim=0)\\r\\n self.state['iekf_groups'][group_ind][ii]['H'] = H_n\\r\\n\\r\\n err_T = err.transpose(0, 1)\\r\\n\\r\\n for group_ind in range(len(self.param_groups)):\\r\\n group = self.param_groups[group_ind]\\r\\n iekf_mat = self.state['iekf_groups'][group_ind]\\r\\n for ii, w in enumerate(group['params']):\\r\\n if w.grad is None:\\r\\n continue\\r\\n\\r\\n lbd_n = iekf_mat[ii]['lbd']\\r\\n P_n = iekf_mat[ii]['P']\\r\\n EPS = iekf_mat[ii]['EPS']\\r\\n H_n = iekf_mat[ii]['H']\\r\\n H_n_T = H_n.transpose(0, 1)\\r\\n if group['lbd_decay']:\\r\\n miu = 1.0 / min(self.state['step'], group['lbd_max_step'])\\r\\n lbd_n = lbd_n + miu * (err_T.mm(err).flatten()[0] / self.dim_out - lbd_n)\\r\\n self.state['iekf_groups'][group_ind][ii]['lbd'] = lbd_n\\r\\n R_n = lbd_n * torch.eye(self.dim_out, dtype=torch.float, device=iekf_mat[ii]['device'],\\r\\n requires_grad=False)\\r\\n\\r\\n g_n = R_n + H_n.mm(P_n).mm(H_n_T)\\r\\n g_n = g_n.inverse()\\r\\n\\r\\n K_n = P_n.mm(H_n_T).mm(g_n)\\r\\n delta_w = K_n.mm(err)\\r\\n if len(w.size()) > 1:\\r\\n delta_w = delta_w.view((w.size(1), w.size(0))).transpose(1, 0)\\r\\n else:\\r\\n delta_w = delta_w.view(w.size())\\r\\n\\r\\n new_P = (group['alpha'] + 1) * (P_n - K_n.mm(H_n).mm(P_n) + EPS)\\r\\n self.state['iekf_groups'][group_ind][ii]['P'] = new_P\\r\\n\\r\\n w.data.add_(delta_w)\\r\\n\\r\\n return err\",\n \"def optimize_library_descent(self, target, direction='max', steps=100,\\n multiprocessing=False, ret_info=False,\\n args=None):\\n # get the target function to call\\n target_function = getattr(self, target)\\n if args is not None:\\n target_function = functools.partial(target_function, **args)\\n\\n # initialize the optimizer\\n value = target_function()\\n value_best, state_best = value, self.sens_mat.copy()\\n \\n if ret_info:\\n # store extra information\\n start_time = time.time()\\n info = {'values': {}}\\n values_count = self.parameters['optimizer_values_count']\\n values_step = max(1, steps // values_count)\\n \\n if multiprocessing:\\n # run the calculations in multiple processes\\n pool_size = self.get_number_of_cores()\\n pool = mp.Pool(processes=pool_size)\\n if ret_info:\\n values_step = max(1, values_step // pool_size)\\n \\n # iterate for given number of steps\\n for step in range(int(steps) // pool_size):\\n joblist = []\\n init_arguments = self.init_arguments\\n for _ in range(pool_size):\\n # modify the current state and add it to the job list\\n i = random.randrange(self.sens_mat.size)\\n self.sens_mat.flat[i] = 1 - self.sens_mat.flat[i]\\n params = init_arguments['parameters'] \\n params['sensitivity_matrix'] = self.sens_mat\\n params['initialize_state']['sensitivity'] = 'exact'\\n \\n joblist.append((copy.deepcopy(init_arguments), target))\\n self.sens_mat.flat[i] = 1 - self.sens_mat.flat[i]\\n \\n # run all the jobs\\n results = pool.map(_run_job, joblist)\\n \\n # find the best result \\n if direction == 'max':\\n res_best = np.argmax(results)\\n if results[res_best] > value_best:\\n value_best = results[res_best]\\n state_best = joblist[res_best][0]['parameters']['sensitivity_matrix']\\n # use the best state as a basis for the next iteration\\n self.sens_mat = state_best\\n \\n elif direction == 'min':\\n res_best = np.argmin(results)\\n if results[res_best] < value_best:\\n value_best = results[res_best]\\n state_best = joblist[res_best][0]['parameters']['sensitivity_matrix']\\n # use the best state as a basis for the next iteration\\n self.sens_mat = state_best\\n \\n else:\\n raise ValueError('Unsupported direction `%s`' % direction)\\n \\n if ret_info and step % values_step == 0:\\n info['values'][step * pool_size] = results[res_best]\\n \\n else:\\n # run the calculations in this process\\n for step in range(int(steps)):\\n # modify the current state\\n i = random.randrange(self.sens_mat.size)\\n self.sens_mat.flat[i] = 1 - self.sens_mat.flat[i]\\n \\n # get the value of the new state\\n value = target_function()\\n \\n improved = ((direction == 'max' and value > value_best) or\\n (direction == 'min' and value < value_best))\\n if improved:\\n # save the state as the new best value\\n value_best, state_best = value, self.sens_mat.copy()\\n else:\\n # undo last change\\n self.sens_mat.flat[i] = 1 - self.sens_mat.flat[i]\\n \\n if ret_info and step % values_step == 0:\\n info['values'][step] = value_best\\n\\n # sort the best state and store it in the current object\\n state_best = self.sort_sensitivity_matrix(state_best)\\n self.sens_mat = state_best.copy()\\n\\n if ret_info:\\n info['total_time'] = time.time() - start_time \\n info['states_considered'] = steps\\n info['performance'] = steps / info['total_time']\\n return value_best, state_best, info\\n else:\\n return value_best, state_best\",\n \"def post_apply(self): #pragma no cover\\n for e in self.obs_queue:\\n\\n # translate operation wire labels to the device's wire labels\\n device_wires = self.map_wires(e.wires)\\n\\n self.measure += \\\"set resultArray w/= {wires[0]} <- \\\".format(wires=device_wires.tolist())\\n self.measure += self._observable_map[e.name].format(wires=device_wires.tolist())\\n self.measure += \\\" \\\"\\n\\n self._source_code = PROGRAM.format(wires=self.num_wires, operations=self.prog, measurements=self.measure)\\n self.qs = qsharp.compile(self._source_code)\",\n \"def optimize(self, input_val_dict):\\n logger.log(\\\"Start CG optimization\\\")\\n\\n logger.log(\\\"computing loss before\\\")\\n loss_before = self.loss(input_val_dict)\\n\\n logger.log(\\\"performing update\\\")\\n\\n logger.log(\\\"computing gradient\\\")\\n gradient = self.gradient(input_val_dict)\\n logger.log(\\\"gradient computed\\\")\\n\\n logger.log(\\\"computing descent direction\\\")\\n Hx = self._hvp_approach.build_eval(input_val_dict)\\n descent_direction = conjugate_gradients(Hx, gradient, cg_iters=self._cg_iters)\\n\\n initial_step_size = np.sqrt(2.0 * self._max_constraint_val *\\n (1. / (descent_direction.dot(Hx(descent_direction)) + 1e-8)))\\n if np.isnan(initial_step_size):\\n logger.log(\\\"Initial step size is NaN! Rejecting the step!\\\")\\n return\\n\\n initial_descent_step = initial_step_size * descent_direction\\n logger.log(\\\"descent direction computed\\\")\\n\\n prev_params = self._target.get_param_values()\\n prev_params_values = _flatten_params(prev_params)\\n\\n loss, constraint_val, n_iter, violated = 0, 0, 0, False\\n for n_iter, ratio in enumerate(self._backtrack_ratio ** np.arange(self._max_backtracks)):\\n cur_step = ratio * initial_descent_step\\n cur_params_values = prev_params_values - cur_step\\n cur_params = _unflatten_params(cur_params_values, params_example=prev_params)\\n self._target.set_params(cur_params)\\n\\n loss, constraint_val = self.loss(input_val_dict), self.constraint_val(input_val_dict)\\n if loss < loss_before and constraint_val <= self._max_constraint_val:\\n break\\n\\n \\\"\\\"\\\" ------------------- Logging Stuff -------------------------- \\\"\\\"\\\"\\n if np.isnan(loss):\\n violated = True\\n logger.log(\\\"Line search violated because loss is NaN\\\")\\n if np.isnan(constraint_val):\\n violated = True\\n logger.log(\\\"Line search violated because constraint %s is NaN\\\" % self._constraint_name)\\n if loss >= loss_before:\\n violated = True\\n logger.log(\\\"Line search violated because loss not improving\\\")\\n if constraint_val >= self._max_constraint_val:\\n violated = True\\n logger.log(\\\"Line search violated because constraint %s is violated\\\" % self._constraint_name)\\n\\n if violated and not self._accept_violation:\\n logger.log(\\\"Line search condition violated. Rejecting the step!\\\")\\n self._target.set_params(prev_params)\\n\\n logger.log(\\\"backtrack iters: %d\\\" % n_iter)\\n logger.log(\\\"computing loss after\\\")\\n logger.log(\\\"optimization finished\\\")\",\n \"def optimize5():\\n xl = xl_app()\\n qt_app = get_qt_app() # pragma noqc\\n # Get the initial values of the input cells\\n msgBox = OpDialog()\\n result = msgBox.exec_()\\n if not result: # user cancelled\\n return\\n\\n in_range = get_range(msgBox.in_range.text())\\n out_cell = get_range(msgBox.out_cell.text())\\n in_values = list(in_range.Value)\\n X = np.array([x[0] for x in in_values])\\n\\n orig_calc_mode = xl.Calculation\\n try:\\n # switch Excel to manual calculation\\n # and disable screen updating\\n xl.Calculation = constants.xlManual\\n xl.ScreenUpdating = False\\n\\n # run the minimization routine\\n xl_obj_func = partial(obj_func, xl, in_range, out_cell)\\n print(f\\\"X = {X}\\\")\\n result = minimize(xl_obj_func, X, method=\\\"nelder-mead\\\")\\n in_range.Value = [(float(x),) for x in result.x]\\n xl.ScreenUpdating = True\\n mbox = QMessageBox()\\n mbox.setIcon(QMessageBox.Information)\\n mbox.setText(\\\"Optimization results shown below.\\\" \\\"\\\\nMake changes permanent?\\\")\\n mbox.setWindowTitle(\\\"Optimization Complete\\\")\\n mbox.setInformativeText(\\n \\\"\\\\n\\\".join(\\n [\\n \\\"Successful: %s\\\" % result.success,\\n result.message,\\n \\\"After %d iterations\\\" % result.nit,\\n ]\\n )\\n )\\n mbox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\\n yes_no = mbox.exec_()\\n if yes_no != QMessageBox.Ok:\\n in_range.Value = in_values\\n else:\\n in_range.Value = [(float(x),) for x in result.x]\\n\\n finally:\\n # restore the original calculation\\n # and screen updating mode\\n xl.ScreenUpdating = True\\n xl.Calculation = orig_calc_mode\",\n \"def finalize(self):\\n self.report('Finalizing optimization procedure.')\\n with self.optimizer() as opt:\\n optimal_process_output = opt.result_value\\n optimal_process_output.store()\\n self.out('optimal_process_output', optimal_process_output)\\n result_index = opt.result_index\\n optimal_process = self.ctx[self.eval_key(result_index)]\\n self.out('optimal_process_uuid', Str(optimal_process.uuid).store())\",\n \"def _optimize(self) -> None:\\n\\n for i, agent in enumerate(self.agents):\\n states, actions, rewards, next_states, dones = self.memory.sample()\\n\\n actor_next_state = self._agent_states(i, next_states)\\n next_actions = torch.cat(\\n [a.actor_target(actor_next_state) for a in self.agents], 1\\n )\\n next_q = agent.critic_target(next_states, next_actions).detach()\\n target_q = rewards[:, i].view(-1, 1) + self.gamma * next_q * (\\n 1 - dones[:, i].view(-1, 1)\\n )\\n local_q = agent.critic_local(states, actions)\\n\\n value_loss = agent.loss_fn(local_q, target_q)\\n agent.value_optimizer.zero_grad()\\n value_loss.backward()\\n agent.value_optimizer.step()\\n\\n local_actions = []\\n for i, a in enumerate(self.agents):\\n local_states = self._agent_states(i, states)\\n local_actions.append(\\n a.actor_local(local_states)\\n if a == agent\\n else a.actor_local(local_states).detach()\\n )\\n local_actions = torch.cat(local_actions, 1)\\n policy_loss = -agent.critic_local(states, local_actions).mean()\\n\\n agent.policy_optimizer.zero_grad()\\n policy_loss.backward()\\n agent.policy_optimizer.step()\\n\\n self._update_target_model(agent.critic_local, agent.critic_target)\\n self._update_target_model(agent.actor_local, agent.actor_target)\",\n \"def main_function(self):\\n self.ana_cont_probl = cont.AnalyticContinuationProblem(im_axis=self.input_data.mats,\\n im_data=self.input_data.value.real,\\n re_axis=self.realgrid.grid,\\n kernel_mode='freq_bosonic')\\n model = np.ones_like(self.realgrid.grid)\\n model /= np.trapz(model, self.realgrid.grid)\\n\\n preblur, bw = self.get_preblur()\\n\\n sol = self.ana_cont_probl.solve(method='maxent_svd',\\n optimizer='newton',\\n alpha_determination='chi2kink',\\n model=model,\\n stdev=self.input_data.error,\\n interactive=False, alpha_start=1e10, alpha_end=1e-3,\\n preblur=preblur, blur_width=bw)\\n\\n inp_str = 'atom {}, orb {}, spin {}, blur {}: '.format(self.input_data.atom,\\n self.input_data.orbital,\\n self.input_data.spin,\\n bw)\\n all_chis = np.isfinite(np.array([s.chi2 for s in sol[1]]))\\n res_str = 'alpha_opt={:3.2f}, chi2(alpha_opt)={:3.2f}, min(chi2)={:3.2f}'.format(\\n sol[0].alpha, sol[0].chi2, np.amin(all_chis)\\n )\\n self.text_output.append(inp_str + res_str)\\n alphas = [s.alpha for s in sol[1]]\\n chis = [s.chi2 for s in sol[1]]\\n\\n self.output_data.update(self.realgrid.grid, sol[0].A_opt, self.input_data)\\n\\n fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(11.75, 8.25)) # A4 paper size\\n ax[0, 0].loglog(alphas, chis, marker='s', color='black')\\n ax[0, 0].loglog(sol[0].alpha, sol[0].chi2, marker='*', color='red', markersize=15)\\n ax[0, 0].set_xlabel(r'$\\\\alpha$')\\n ax[0, 0].set_ylabel(r'$\\\\chi^2(\\\\alpha)$')\\n\\n ax[1, 0].plot(self.realgrid.grid, sol[0].A_opt)\\n ax[1, 0].set_xlabel(r'$\\\\omega$')\\n ax[1, 0].set_ylabel('spectrum')\\n\\n ax[0, 1].plot(self.input_data.mats, self.input_data.value.real,\\n color='blue', ls=':', marker='x', markersize=5,\\n label='Re[data]')\\n ax[0, 1].plot(self.input_data.mats, self.input_data.value.imag,\\n color='green', ls=':', marker='+', markersize=5,\\n label='Im[data]')\\n ax[0, 1].plot(self.input_data.mats, sol[0].backtransform.real,\\n ls='--', color='gray', label='Re[fit]')\\n ax[0, 1].plot(self.input_data.mats, sol[0].backtransform.imag,\\n color='gray', label='Im[fit]')\\n ax[0, 1].set_xlabel(r'$\\\\nu_n$')\\n ax[0, 1].set_ylabel(self.input_data.data_type)\\n ax[0, 1].legend()\\n\\n ax[1, 1].plot(self.input_data.mats, (self.input_data.value - sol[0].backtransform).real,\\n ls='--', label='real part')\\n ax[1, 1].plot(self.input_data.mats, (self.input_data.value - sol[0].backtransform).imag,\\n label='imaginary part')\\n ax[1, 1].set_xlabel(r'$\\\\nu_n$')\\n ax[1, 1].set_ylabel('data $-$ fit')\\n ax[1, 1].legend()\\n plt.tight_layout()\\n plt.show()\",\n \"def callback(_locals, _globals):\\n global n_steps\\n # Print stats every 20 calls\\n if (n_steps + 1) % 1 == 0:\\n # Evaluate policy training performance\\n episode_rewards, episode_lengths = evaluate_policy(_locals['self'], eval_real_env,\\n n_eval_episodes=n_eval_episodes,\\n render=False,\\n deterministic=False,\\n return_episode_rewards=False)\\n print(\\\"Last mean reward per episode at target: {:.2f}\\\".format(episode_rewards))\\n\\n episode_rewards_grnd, episode_lengths_grnd = evaluate_policy(_locals['self'], eval_grnd_env,\\n n_eval_episodes=n_eval_episodes,\\n render=False,\\n deterministic=False,\\n return_episode_rewards=False)\\n print(\\\"Last mean reward per episode at grounded environment: {:.2f}\\\".format(episode_rewards_grnd))\\n\\n with open(os.path.join(log_dir, 'eval_at_target.txt'), 'a') as f:\\n f.write(\\\"{}, {}, {}\\\\n\\\".format(n_steps, episode_rewards, episode_lengths/n_eval_episodes))\\n f.close()\\n with open(os.path.join(log_dir, 'eval_at_grnd.txt'), 'a') as f:\\n f.write(\\\"{}, {}, {}\\\\n\\\".format(n_steps, episode_rewards_grnd, episode_lengths_grnd/n_eval_episodes))\\n f.close()\\n n_steps += 1\\n return True\",\n \"def run(self):\\n i = 0\\n try:\\n for i in range(0, self._iters):\\n if self._verbose:\\n print(\\\" Inner CG Iteration \\\" + repr(i))\\n\\n self._forward(self._p_k, self._v_k)\\n sigma_k = measure(self._p_k, self._v_k)\\n alpha_k = self._rho_k / sigma_k\\n update_m(self._m, alpha_k, self._p_k)\\n sub_scaled_vector(self._residual_k, self._residual_k, alpha_k,\\n self._v_k)\\n self._v_k = gpuarray_copy(self._residual_k)\\n rho_k_plus_1 = measure(self._v_k, self._residual_k)\\n rho_k_t = np.abs(rho_k_plus_1)\\n\\n if (rho_k_t / self._rho_0 <= self._relative_tolerance) \\\\\\n or (rho_k_t <= self._absolute_tolerance):\\n if self._verbose:\\n print(\\\"Converged at Iteration \\\" + str(i) + \\\".\\\")\\n self.converged = True\\n self.iteration = i+1\\n return\\n\\n add_scaled_vector(self._p_k, self._v_k,\\n rho_k_plus_1/self._rho_k,\\n self._p_k)\\n self._rho_k = rho_k_plus_1\\n\\n if self._verbose >= 3:\\n print(\\\" Residual=\\\" + repr(rho_k_t))\\n except KeyboardInterrupt:\\n raise\\n finally:\\n self.iteration = i+1\",\n \"def save_cma_optimization_results(self, es):\\n # code extra verbose to understand what is going on\\n generation = es.result.iterations\\n evals = es.result.evaluations # number of evals at start of each gen\\n xfavorite = es.result.xfavorite # center of distribution, best est\\n stds = es.result.stds # stds of distribution, stds of xfavorite\\n fbest = es.result.fbest # best ever measured\\n xbest = es.result.xbest # coordinates of best ever measured\\n evals_best = es.result.evals_best # index of best measurement\\n\\n if not self.minimize_optimization:\\n fbest = -fbest\\n\\n results_array = np.concatenate([[generation, evals],\\n xfavorite, stds,\\n [fbest], xbest, [evals_best]])\\n if (not 'optimization_result'\\n in self.data_object[EXPERIMENTAL_DATA_GROUP_NAME].keys()):\\n opt_res_grp = self.data_object[EXPERIMENTAL_DATA_GROUP_NAME]\\n self.opt_res_dset = opt_res_grp.create_dataset(\\n 'optimization_result', (0, len(results_array)),\\n maxshape=(None, len(results_array)),\\n dtype='float64')\\n\\n # FIXME: Jan 2018, add the names of the parameters to column names\\n self.opt_res_dset.attrs['column_names'] = h5d.encode_to_utf8(\\n 'generation, ' + 'evaluations, ' +\\n 'xfavorite, ' * len(xfavorite) +\\n 'stds, '*len(stds) +\\n 'fbest, ' + 'xbest, '*len(xbest) +\\n 'best evaluation,')\\n\\n old_shape = self.opt_res_dset.shape\\n new_shape = (old_shape[0]+1, old_shape[1])\\n self.opt_res_dset.resize(new_shape)\\n self.opt_res_dset[-1, :] = results_array\",\n \"def run(self):\\n self._fitness = self._fitness_func(self._bitstring, *self._args, **self._kwargs)\",\n \"def Optimize(fun, \\n dimensions = 10,\\n dataType = float64,\\n lowerDomain = -5.0,\\n upperDomain = 5.0,\\n constrainToLower = False,\\n constrainToUpper = False,\\n maxMutations = 3, \\n maxIndexes = 3, \\n gamma = 0.99, \\n minImprovements = 3,\\n scale = 1.0,\\n popSize = 10, \\n maxIterations = 1000000,\\n targetLoss = 1.0e-8,\\n minScale = 1.0e-10):\\n pop = Population( memberLength = dimensions,\\n memberDataType = dataType,\\n lowerDomain = lowerDomain, \\n upperDomain = upperDomain,\\n maxMutations = maxMutations, \\n maxIndexes = maxIndexes, \\n gamma = gamma, \\n minImprovements = minImprovements,\\n scale = scale)\\n pop.prepare(popSize, fun)\\n loss = pop.elite.loss\\n startTime = time.time()\\n print(f\\\"[{0:7d}] Loss: {loss:<13.10g} S: {pop.scale:<12.7g} I:{PI(pop.improvements)} elapsed: {0.0:>9.6f} hours\\\")\\n try:\\n #-----------------------------------------------------------------\\n for trial in range(1, maxIterations):\\n pop.minimize(fun, constrainToLower=constrainToLower, constrainToUpper=constrainToUpper)\\n if loss > pop.elite.loss:\\n loss = pop.elite.loss\\n elapsedTime = (time.time() - startTime)/(60*60)\\n print(f\\\"[{trial:7d}] Loss: {loss:<13.10g} S: {pop.scale:<12.7g} I:{PI(pop.improvements)} elapsed: {elapsedTime:>9.6f} hours\\\")\\n if (loss < targetLoss) or (pop.scale < minScale):\\n break\\n #-----------------------------------------------------------------\\n except KeyboardInterrupt:\\n pass\\n finally:\\n print(f\\\"\\\\n[{trial:7d}] Loss: {pop.elite.loss:<13.10g} S: {pop.scale:<12.7g} I:{PI(pop.improvements)} elapsed: {elapsedTime:>9.6f} hours\\\")\\n return pop.elite\",\n \"def runIteration(self, task, Sol, Fitness, xb, fxb, A, S, Q, v, **dparams):\\n\\t\\tfor i in range(self.NP):\\n\\t\\t\\tQ[i] = self.Qmin + (self.Qmax - self.Qmin) * self.uniform(0, 1)\\n\\t\\t\\tv[i] += (Sol[i] - xb) * Q[i]\\n\\t\\t\\tif self.rand() > self.r: S[i] = self.localSearch(best=xb, A=A[i], task=task, i=i, Sol=Sol)\\n\\t\\t\\telse: S[i] = task.repair(Sol[i] + v[i], rnd=self.Rand)\\n\\t\\t\\tFnew = task.eval(S[i])\\n\\t\\t\\tif (Fnew <= Fitness[i]) and (self.rand() < A[i]): Sol[i], Fitness[i] = S[i], Fnew\\n\\t\\t\\tif Fnew <= fxb: xb, fxb, A[i] = S[i].copy(), Fnew, self.updateLoudness(A[i])\\n\\t\\treturn Sol, Fitness, xb, fxb, {'A': A, 'S': S, 'Q': Q, 'v': v}\",\n \"def iterate(self, update_func):\\n self.pre_loop()\\n logger.info('%s: start iterations', self.__class__.__name__)\\n with recording_exit_reason(self.datastore):\\n for gen_step in range(self.iterations):\\n self.post_update(gen_step, update_func(gen_step))\\n logger.info('%s: maximum iterations reached', self.__class__.__name__)\",\n \"def eval_energy(Optimizer, individ):\\n #logger = initialize_logger(Optimizer.loggername)\\n logger = logging.getLogger(Optimizer.loggername)\\n if 'MAST' in Optimizer.calc_method:\\n energy = individ.energy\\n bul = individ.bulki\\n signal = 'Received MAST structure\\\\n'\\n logger.info('Received individual index = {0} from MAST with energy {1}. Returning with no evaluation'.format(\\n individ.index, individ.energy))\\n else:\\n if Optimizer.parallel: \\n rank = MPI.COMM_WORLD.Get_rank()\\n logger.info('Received individual HI = {0} with energy {1} for energy evaluation'.format(\\n individ.history_index, individ.energy))\\n STR='----Individual ' + str(individ.history_index)+ ' Optimization----\\\\n'\\n indiv=individ[0]\\n if 'EE' in Optimizer.debug:\\n debug = True\\n else:\\n debug = False\\n if debug: \\n write_xyz(Optimizer.debugfile,indiv,'Received by eval_energy')\\n Optimizer.debugfile.flush()\\n logger.debug('Writing recieved individual to debug file')\\n # Establish individual structure for evaluation. Piece together regions when necessary.\\n if Optimizer.structure=='Defect':\\n indi=indiv.copy()\\n bulk=individ.bulki\\n nat=indi.get_number_of_atoms()\\n if debug: \\n logger.info('Extending defect structure to include bulk len(r1+r2)={0} len(bulk)={1}'.format(nat,len(bulk)))\\n csize=bulk.get_cell() \\n totalsol=Atoms(cell=csize, pbc=True)\\n totalsol.extend(indi)\\n totalsol.extend(bulk)\\n for sym,c,m,u in Optimizer.atomlist:\\n nc=len([atm for atm in totalsol if atm.symbol==sym])\\n STR+='Defect configuration contains '+repr(nc)+' '+repr(sym)+' atoms\\\\n'\\n elif Optimizer.structure=='Surface':\\n totalsol=Atoms()\\n totalsol.extend(indiv)\\n nat=indiv.get_number_of_atoms()\\n totalsol.extend(individ.bulki)\\n if debug:\\n logger.info('Extending surface structure to include bulk len(r1+r2)={0} len(bulk)={1}'.format(nat,len(individ.bulki)))\\n for sym,c,m,u in Optimizer.atomlist:\\n nc=len([atm for atm in totalsol if atm.symbol==sym])\\n STR+='Surface-Bulk configuration contains '+repr(nc)+' '+repr(sym)+' atoms\\\\n'\\n cell=numpy.maximum.reduce(indiv.get_cell())\\n totalsol.set_cell([cell[0],cell[1],500])\\n totalsol.set_pbc([True,True,False])\\n elif Optimizer.structure=='Cluster':\\n totalsol = indiv.copy()\\n nat = len(totalsol)\\n if debug:\\n logger.info('Extending cluster with {0} atoms to center of evaluation box of size {1}'.format(nat,Optimizer.large_box_size))\\n origcell = indiv.get_cell()\\n totalsol.set_cell([Optimizer.large_box_size,Optimizer.large_box_size,Optimizer.large_box_size])\\n totalsol.translate([Optimizer.large_box_size/2.0,Optimizer.large_box_size/2.0,Optimizer.large_box_size/2.0])\\n elif Optimizer.structure=='Crystal':\\n totalsol = indiv.copy()\\n nat = len(totalsol)\\n else:\\n print 'WARNING: In EvalEnergy. Optimizer.structure not recognized'\\n logger.warning('Optimizer.structure not recognized')\\n \\n # Check for atoms that are too close or out of constrained location\\n if Optimizer.constrain_position:\\n if Optimizer.structure=='Defect':\\n if debug:\\n logger.info('Constraining positions of defect')\\n totalsol, stro = constrain_positions(totalsol, Optimizer.solidbulk, Optimizer.sf)\\n if debug:\\n logger.info(stro)\\n STR+=str0\\n min_len=0.7\\n if not Optimizer.fixed_region:\\n if debug:\\n logger.info('Running check minimum distance')\\n totalsol, STR = check_min_dist(totalsol, Optimizer.structure, nat, min_len, STR)\\n if debug:\\n write_xyz(Optimizer.debugfile,totalsol,'After minlength check')\\n Optimizer.debugfile.flush()\\n logger.debug('Writing individual after checking minimum length')\\n \\n # Set calculator to use to get forces/energies\\n if Optimizer.parallel:\\n calc = setup_calculator(Optimizer)\\n if Optimizer.fixed_region:\\n if debug:\\n logger.info('Setting up fixed region calculator')\\n pms=copy.deepcopy(calc.parameters)\\n try:\\n pms['mass'][len(pms['mass'])-1] += '\\\\ngroup RO id >= {0}\\\\nfix freeze RO setforce 0.0 0.0 0.0\\\\n'.format(nat)\\n except KeyError:\\n pms['pair_coeff'][0] += '\\\\ngroup RO id >= {0}\\\\nfix freeze RO setforce 0.0 0.0 0.0\\\\n'.format(nat)\\n calc = LAMMPS(parameters=pms, files=calc.files, keep_tmp_files=calc.keep_tmp_files, tmp_dir=calc.tmp_dir)\\n lmin = copy.copy(Optimizer.lammps_min)\\n if debug:\\n logger.info('Setting up no local minimization calculator')\\n Optimizer.lammps_min = None\\n Optimizer.static_calc = setup_calculator(Optimizer)\\n Optimizer.lammps_min = lmin\\n else:\\n calc=Optimizer.calc\\n totalsol.set_calculator(calc)\\n totalsol.set_pbc(True)\\n \\n # Perform Energy Minimization\\n if not Optimizer.parallel:\\n if debug: \\n write_xyz(Optimizer.debugfile,totalsol,'Individual sent to Energy Minimizer')\\n logger.debug('Writing structure sent to energy minimizer')\\n try:\\n cwd = os.getcwd()\\n if Optimizer.ase_min == True:\\n if debug:\\n logger.info('Running ASE minimizer')\\n if Optimizer.calc_method=='LennardJones':\\n logger.warn('Must run ase LJ calculator with pbc=False')\\n totalsol.set_pbc(False)\\n totalsol, energy, pressure, volume, STR = run_ase_min(totalsol, Optimizer.ase_min_fmax, Optimizer.ase_min_maxsteps, Optimizer.fitness_scheme, STR)\\n else:\\n if debug:\\n logger.info('Running local energy calculator')\\n if Optimizer.fixed_region:\\n totalsol, energy, pressure, volume, STR = run_energy_eval(totalsol, Optimizer.calc_method, Optimizer.fixed_region, Optimizer.fitness_scheme, STR, Optimizer.static_calc)\\n else:\\n totalsol, energy, pressure, volume, STR = run_energy_eval(totalsol, Optimizer.calc_method, False, Optimizer.fitness_scheme, STR)\\n except Exception, e:\\n logger.critical('Error in energy evaluation: {0}'.format(e), exc_info=True)\\n path = os.path.join(cwd,'TroubledLammps')\\n if not os.path.exists(path):\\n os.mkdir(path)\\n #Copy files over\\n shutil.copyfile(calc.trajfile,os.path.join(path,os.path.basename(calc.trajfile)))\\n shutil.copyfile(calc.infile,os.path.join(path,os.path.basename(calc.infile)))\\n shutil.copyfile(calc.logfile,os.path.join(path,os.path.basename(calc.logfile)))\\n shutil.copyfile(calc.datafile,os.path.join(path,os.path.basename(calc.datafile)))\\n raise RuntimeError('{0}:{1}'.format(Exception,e))\\n if not Optimizer.parallel:\\n if debug:\\n write_xyz(Optimizer.debugfile,totalsol,'Individual after Energy Minimization')\\n Optimizer.debugfile.flush()\\n logger.debug('Writing structure recieved from energy minimizer')\\n \\n # Separate structures into distinct pieces\\n if Optimizer.structure=='Defect':\\n if Optimizer.fixed_region==True or Optimizer.finddefects==False:\\n if debug:\\n logger.info('Identifying atoms in defect structure based on ID')\\n individ[0]=totalsol[0:nat]\\n bul=totalsol[(nat):len(totalsol)]\\n individ[0].set_cell(csize)\\n else:\\n if debug:\\n logger.info('Applying find defects scheme to identify R1 and R2 for Defect')\\n if 'FD' in Optimizer.debug:\\n outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=Optimizer.debugfile)\\n else:\\n outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=False)\\n individ[0]=outt[0]\\n bul=outt[1]\\n individ.vacancies = outt[2]\\n individ.swaps = outt[3]\\n STR += outt[4]\\n indiv=individ[0]\\n elif Optimizer.structure=='Surface':\\n if debug:\\n logger.info('Finding surface top layer')\\n top,bul=find_top_layer(totalsol,Optimizer.surftopthick)\\n indiv=top.copy()\\n individ[0]=top.copy()\\n bul = Atoms()\\n elif Optimizer.structure=='Crystal':\\n if debug:\\n logger.info('Checking crystal cell type')\\n celltype = check_cell_type(totalsol)\\n STR+='Cell structure = {0}\\\\n'.format(celltype)\\n bul = Atoms()\\n individ[0] = totalsol.copy()\\n elif Optimizer.structure=='Cluster':\\n volume = get_cluster_volume(totalsol)\\n bul = Atoms()\\n if debug:\\n logger.info('Translating cluster back to smaller box size location')\\n totalsol.translate([-Optimizer.large_box_size/2.0,-Optimizer.large_box_size/2.0,-Optimizer.large_box_size/2.0])\\n totalsol.set_cell(origcell)\\n individ[0] = totalsol.copy()\\n \\n # Add concentration energy dependence\\n if Optimizer.forcing=='energy_bias':\\n if debug:\\n logger.info('Applying energy bias for atoms with different number of atoms of type than in atomlist')\\n n=[0]*len(Optimizer.atomlist)\\n for i in range(len(Optimizer.atomlist)):\\n n[i]=len([inds for inds in totalsol if inds.symbol==Optimizer.atomlist[i][0]])\\n n[i]=abs(n[i]-Optimizer.atomlist[i][1])\\n factor=sum(n)**3\\n energy=(energy+factor)/totalsol.get_number_of_atoms()\\n STR+='Energy with Bias = {0}\\\\n'.format(energy)\\n elif Optimizer.forcing=='chem_pot':\\n if debug:\\n logger.info('Applying chemical potential bias for atoms with different number of atoms of type than in atomlist')\\n n=[0]*len(Optimizer.atomlist)\\n for i in range(len(Optimizer.atomlist)):\\n n[i]=len([inds for inds in totalsol if inds.symbol==Optimizer.atomlist[i][0]])\\n n[i]=n[i]*Optimizer.atomlist[i][3]\\n factor=sum(n)\\n energy=(energy+factor)/totalsol.get_number_of_atoms()\\n STR+='Energy with Chemical Potential = {0}\\\\n'.format(energy)\\n\\n individ.energy=energy\\n individ.buli=bul\\n individ.pressure=pressure\\n individ.volume=volume\\n \\n if Optimizer.fingerprinting:\\n if debug:\\n logger.info('Identifying fingerprint of new structure')\\n individ.fingerprint=get_fingerprint(Optimizer,individ,Optimizer.fpbin,Optimizer.fpcutoff)\\n if Optimizer.parallel:\\n calc.clean()\\n signal = 'Evaluated individual {0} on {1}\\\\n'.format(individ.index,rank)\\n signal +=STR\\n else:\\n signal=STR\\n\\n return energy, bul, individ, signal\",\n \"def search_method(self): \\n self.setup_problem()\\n self.problem_results = minimize(self._problem, \\n self.algorithm, \\n self.termination,\\n seed=1,\\n verbose=False)\\n \\n self.log_debug('Core design variables determined: {}'.format(self.current_design_variables))\",\n \"def __call__(self):\\n self.start()\\n numberOfIterations, stateVectorConv = self.iterations()\\n if numberOfIterations <= 1:\\n self.answer = None\\n raise DidNotConvergeWarning(\\\"Number of iterations <= 1.\\\")\\n result = self.diagnostic(numberOfIterations, stateVectorConv)\\n return result\",\n \"def _step1_optimization_closure(self, iteration, step):\\n if iteration == self.num_iter_first_step - 1:\\n reg_noise_std = 0\\n else:\\n reg_noise_std = (1 / 1000.) * (iteration // 300) # TODO: make it dependant in the max number of iterations\\n aug = self._get_augmentation(iteration)\\n if iteration == self.num_iter_first_step - 1:\\n aug = 0\\n # creates left_net_inputs and right_net_inputs by adding small noise\\n clean_net_input = self.clean_net_inputs[aug] + (self.clean_net_inputs[aug].clone().normal_() * reg_noise_std)\\n # watermark_net_input = self.watermark_net_inputs[aug] # + (self.watermark_net_input.clone().normal_())\\n # mask_net_input = self.mask_net_inputs[aug]\\n # applies the nets\\n self.clean_net_output = self.clean_net(clean_net_input)\\n self.total_loss = 0\\n self.blur = 0\\n self.total_loss += self.extended_l1_loss(self.clean_net_output,\\n self.image_torchs[aug],\\n (1 - self.watermark_hint_torchs[aug]))\\n self.total_loss.backward(retain_graph=True)\",\n \"def step(self) -> ResultDict:\\n # Do we have to run `self.evaluate()` this iteration?\\n # `self.iteration` gets incremented after this function returns,\\n # meaning that e. g. the first time this function is called,\\n # self.iteration will be 0.\\n evaluate_this_iter = (\\n self.config.evaluation_interval is not None\\n and (self.iteration + 1) % self.config.evaluation_interval == 0\\n )\\n\\n # Results dict for training (and if appolicable: evaluation).\\n results: ResultDict = {}\\n\\n # Parallel eval + training: Kick off evaluation-loop and parallel train() call.\\n if evaluate_this_iter and self.config.evaluation_parallel_to_training:\\n (\\n results,\\n train_iter_ctx,\\n ) = self._run_one_training_iteration_and_evaluation_in_parallel()\\n # - No evaluation necessary, just run the next training iteration.\\n # - We have to evaluate in this training iteration, but no parallelism ->\\n # evaluate after the training iteration is entirely done.\\n else:\\n results, train_iter_ctx = self._run_one_training_iteration()\\n\\n # Sequential: Train (already done above), then evaluate.\\n if evaluate_this_iter and not self.config.evaluation_parallel_to_training:\\n results.update(self._run_one_evaluation(train_future=None))\\n\\n # Attach latest available evaluation results to train results,\\n # if necessary.\\n if not evaluate_this_iter and self.config.always_attach_evaluation_results:\\n assert isinstance(\\n self.evaluation_metrics, dict\\n ), \\\"Algorithm.evaluate() needs to return a dict.\\\"\\n results.update(self.evaluation_metrics)\\n\\n if hasattr(self, \\\"workers\\\") and isinstance(self.workers, WorkerSet):\\n # Sync filters on workers.\\n self._sync_filters_if_needed(\\n central_worker=self.workers.local_worker(),\\n workers=self.workers,\\n config=self.config,\\n )\\n # TODO (avnishn): Remove the execution plan API by q1 2023\\n # Collect worker metrics and add combine them with `results`.\\n if self.config._disable_execution_plan_api:\\n episodes_this_iter = collect_episodes(\\n self.workers,\\n self._remote_worker_ids_for_metrics(),\\n timeout_seconds=self.config.metrics_episode_collection_timeout_s,\\n )\\n results = self._compile_iteration_results(\\n episodes_this_iter=episodes_this_iter,\\n step_ctx=train_iter_ctx,\\n iteration_results=results,\\n )\\n\\n # Check `env_task_fn` for possible update of the env's task.\\n if self.config.env_task_fn is not None:\\n if not callable(self.config.env_task_fn):\\n raise ValueError(\\n \\\"`env_task_fn` must be None or a callable taking \\\"\\n \\\"[train_results, env, env_ctx] as args!\\\"\\n )\\n\\n def fn(env, env_context, task_fn):\\n new_task = task_fn(results, env, env_context)\\n cur_task = env.get_task()\\n if cur_task != new_task:\\n env.set_task(new_task)\\n\\n fn = functools.partial(fn, task_fn=self.config.env_task_fn)\\n self.workers.foreach_env_with_context(fn)\\n\\n return results\",\n \"def runIteration(self, task, Sol, Fitness, xb, fxb, A, r, S, Q, v, **dparams):\\n\\t\\tfor i in range(self.NP):\\n\\t\\t\\tA[i], r[i] = self.selfAdaptation(A[i], r[i])\\n\\t\\t\\tQ[i] = self.Qmin + (self.Qmax - self.Qmin) * self.uniform(0, 1)\\n\\t\\t\\tv[i] += (Sol[i] - xb) * Q[i]\\n\\t\\t\\tif self.rand() > r[i]: S[i] = self.localSearch(best=xb, A=A[i], task=task, i=i, Sol=Sol)\\n\\t\\t\\telse: S[i] = task.repair(Sol[i] + v[i], rnd=self.Rand)\\n\\t\\t\\tFnew = task.eval(S[i])\\n\\t\\t\\tif (Fnew <= Fitness[i]) and (self.rand() < (self.A_l - A[i]) / self.A): Sol[i], Fitness[i] = S[i], Fnew\\n\\t\\t\\tif Fnew <= fxb: xb, fxb = S[i].copy(), Fnew\\n\\t\\treturn Sol, Fitness, xb, fxb, {'A': A, 'r': r, 'S': S, 'Q': Q, 'v': v}\",\n \"def run(self, function, beta):\\n if self.info_requested(Info.ok):\\n self.info_set(Info.ok, False)\\n\\n# step = function.step(beta)\\n\\n z = betanew = betaold = beta\\n\\n if self.info_requested(Info.time):\\n t = []\\n if self.info_requested(Info.fvalue):\\n f = []\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, False)\\n\\n for i in xrange(1, max(self.min_iter, self.max_iter) + 1):\\n\\n if self.info_requested(Info.time):\\n tm = utils.time_cpu()\\n\\n z = betanew + ((i - 2.0) / (i + 1.0)) * (betanew - betaold)\\n\\n step = function.step(z)\\n\\n betaold = betanew\\n betanew = function.prox(z - step * function.grad(z),\\n step)\\n\\n if self.info_requested(Info.time):\\n t.append(utils.time_cpu() - tm)\\n if self.info_requested(Info.fvalue):\\n f.append(function.f(betanew))\\n\\n if self.conesta_stop is not None:\\n mu_min = self.conesta_stop[0]\\n# print \\\"mu_min:\\\", mu_min\\n mu_old = function.set_mu(mu_min)\\n# print \\\"mu_old:\\\", mu_old\\n stop_step = function.step(betanew)\\n# print \\\"step :\\\", step\\n # Take one ISTA step for use in the stopping criterion.\\n stop_z = function.prox(betanew - stop_step \\\\\\n * function.grad(betanew),\\n stop_step)\\n function.set_mu(mu_old)\\n# print \\\"err :\\\", maths.norm(betanew - z)\\n# print \\\"sc err:\\\", (1.0 / step) * maths.norm(betanew - z)\\n# print \\\"eps :\\\", self.eps\\n\\n if (1. / stop_step) * maths.norm(betanew - stop_z) < self.eps \\\\\\n and i >= self.min_iter:\\n\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, True)\\n\\n break\\n\\n else:\\n if step > 0.0:\\n if (1.0 / step) * maths.norm(betanew - z) < self.eps \\\\\\n and i >= self.min_iter:\\n\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, True)\\n\\n break\\n\\n else: # TODO: Fix this!\\n if maths.norm(betanew - z) < self.eps \\\\\\n and i >= self.min_iter:\\n\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, True)\\n\\n break\\n\\n self.num_iter = i\\n\\n if self.info_requested(Info.num_iter):\\n self.info_set(Info.num_iter, i)\\n if self.info_requested(Info.time):\\n self.info_set(Info.time, t)\\n if self.info_requested(Info.fvalue):\\n self.info_set(Info.fvalue, f)\\n if self.info_requested(Info.ok):\\n self.info_set(Info.ok, True)\\n\\n return betanew\",\n \"def run(self):\\n results = self._optimization.run()\\n self._optimization.sim.fdtd.close()\\n \\n # plot optimization recap figure\\n plt.show()\\n \\n return [results[0], np.array(results[1])]\",\n \"def optimize(w, b, X, Y, num_iterations,learning_rate,print_cost = False):\\n costs = []\\n for i in range(num_iterations):\\n\\n # Cost and gradient calculation (≈ 1-4 lines of code)\\n ### START CODE HERE ###\\n grads,cost = propagate(w,b,X,Y)\\n ### END CODE HERE ###\\n\\n # Retrieve derivatives from grads\\n dw = grads[\\\"dw\\\"]\\n db = grads[\\\"db\\\"]\\n\\n # update rule (≈ 2 lines of code)\\n ### START CODE HERE ###\\n w = w - learning_rate*dw\\n b = b - learning_rate*db\\n ### END CODE HERE ###\\n\\n # Record the costs\\n if i % 100 == 0:\\n costs.append(cost)\\n\\n # Print the cost every 100 training examples\\n if print_cost and i%100==0:\\n print(\\\"Cost after iteration %i: %f\\\"%(i,cost))\\n\\n params = {\\n \\\"w\\\":w,\\n \\\"b\\\":b\\n }\\n grads = {\\n \\\"dw\\\":dw,\\n \\\"db\\\":db\\n }\\n return params,grads,costs\",\n \"def solve(self, **kwargs) -> OptimizationResult:\\n raise NotImplementedError\",\n \"def optimize(self, maxiter):\\n for iteration in range(maxiter):\\n self.sortParticles()\\n self.phi = int(phiMin + iteration *((phiMax - phiMin) / float(maxiter)))\\n self.cluster()\\n #self.ConnectClusters()\\n for i in range(self.n_particles):\\n x = self.particles_pos[i]\\n v = self.velocities[i]\\n p_best = self.p_best[i]\\n self.velocities[i] = self.update_velocity(x, v, p_best , self.g_best , self.getLbestOfCluster(self.getClusterOfParticle(i)) , i)\\n self.particles_pos[i] = self.update_position(x, v)\\n # Update the best position for particle i\\n if self.func(self.particles_pos[i]) < self.func(p_best):\\n self.p_best[i] = self.particles_pos[i]\\n # Update the best position overall\\n if self.func(self.particles_pos[i]) < self.func(self.g_best):\\n \\n self.g_best = self.particles_pos[i]\\n return self.g_best, self.func(self.g_best)\",\n \"def run():\\n return estimate(0,1,0)\",\n \"def optimize(self, startPoint=0, epsilon=1e-5, maxIterations=100):\\n n = len(startPoint)\\n alpha = 1\\n Hk = numpy.eye(n)\\n I = numpy.eye(n)\\n k = 0\\n xk = startPoint\\n gk = self.g(xk)\\n \\n while 1:\\n # Compute the norm of the gradient.\\n gradNorm = numpy.sqrt(numpy.dot(gk, gk))\\n\\n # Display the function value for the current iteration.\\n fk = f(xk)\\n print \\\"%d: fval = %d, norm = %f\\\" % (k, fk, gradNorm) \\n \\n # Termination based on tolerenace criterion.\\n if (gradNorm <= epsilon):\\n print \\\"Terminating: Tolerence %f (fval = %f, norm = %f)\\\"\\\\\\n % (epsilon, fk, gradNorm)\\n return {'optimalPoint':xk, 'functVal':fk}\\n\\n # Termination due to maximum iterations.\\n if (k > maxIterations):\\n print \\\"Terminating: Max iterations %d (fval = %f, norm = %f)\\\" \\\\\\n % (i, fk, gradNorm) \\n return {'optimalPoint':xk, 'functVal':fk}\\n\\n # Computing the search direction.\\n pk = -numpy.dot(Hk, gk)\\n sk = alpha * pk\\n xk1 = xk + sk\\n gk1 = self.g(xk1)\\n yk = gk1 - gk\\n\\n # Computing Hk1.\\n rhok = 1.0 / numpy.dot(yk, sk)\\n A = I - (rhok * numpy.outer(sk, yk))\\n B = rhok * numpy.outer(sk, sk)\\n Hk = numpy.dot(numpy.dot(A, Hk), A) + B\\n\\n # Update the variables for the next iteration.\\n xk = xk1\\n gk = gk1\\n k += 1\\n pass \\n pass\",\n \"def value_iteration(self):\\n #Create a utility function of the environment shape\\n gamma = 0.9\\n epsilon = 0.01\\n iteration = 0\\n\\n #create a utility function that matches the size of the number of states\\n u = np.zeros(self.env.observation_space.n, dtype=float)\\n\\n u_copy = u.copy()\\n\\n #Create the reward grid\\n reward = np.array([state_map.get(sublist) for state in frozen_lake.MAPS[self.env.spec._kwargs.get('map_name')] for sublist in state])\\n\\n T = self.frozen_transition()\\n\\n graph_list = list()\\n\\n #keep track of the convergence\\n policy_convergence = list()\\n\\n while True:\\n delta = 0\\n iteration += 1\\n u = u_copy.copy()\\n graph_list.append(u)\\n start_time = time()\\n for s in range(self.env.observation_space.n):\\n r = reward[s]\\n v = np.zeros((1, self.env.observation_space.n), dtype=float)\\n v[0, s] = 1.0\\n u_copy[s] = self.return_state_utility(v, T, u, r, gamma)\\n delta = max(delta, np.abs(u_copy[s] - u[s]))\\n policy_convergence.append({'iter': iteration, 'delta': delta})\\n if delta < epsilon * (1 - gamma) / gamma:\\n print(\\\"Total Iterations: {}\\\".format(iteration))\\n print(\\\"=================== VALUE ITERATION RESULT ==================\\\")\\n print(\\\"Iterations: \\\" + str(iteration))\\n print(\\\"Delta: \\\" + str(delta))\\n print(\\\"Gamma: \\\" + str(gamma))\\n print(\\\"Epsilon: \\\" + str(epsilon))\\n print(\\\"Time to converge: {} seconds\\\".format(time() - start_time))\\n print(\\\"===================================================\\\")\\n utility_reshape = np.reshape(u, (int(np.sqrt(self.env.observation_space.n)), int(np.sqrt(self.env.observation_space.n))))\\n print (np.array(utility_reshape, dtype=float))\\n print(\\\"===================================================\\\")\\n break\\n\\n return u\",\n \"def algorithm(self):\\n convergence_threshold = 50\\n reward_num_threshold = 300\\n alpha = 1\\n gamma = 0.5\\n while (self.reward_num < reward_num_threshold) and (self.count<convergence_threshold):\\n print('------')\\n print('Iteration', self.reward_num, '/', reward_num_threshold)\\n print('Iterations w/out Q-update:', self.count, '/', convergence_threshold)\\n # select a possible action (any of them; all are valid)\\n s = self.get_state_num()\\n print(\\\"Initial state:\\\", s)\\n a = random.choice(np.arange(3))\\n self.apply_action(a)\\n while self.reward == None:\\n #print(\\\"Sleeping to wait for reward\\\")\\n rospy.sleep(0.5)\\n reward = self.reward\\n print(\\\"REWARD =\\\", reward)\\n self.reward = None\\n if reward == 0:\\n next_state = self.get_state_num()\\n mx = np.amax(self.Q[next_state])\\n else:\\n ## There is no next state if nonzero reward seen\\n mx = 0\\n update = self.Q[s][a] + alpha*(reward+gamma*mx-self.Q[s][a])\\n if self.Q[s][a] != update:\\n print(\\\"Update Q matrix\\\")\\n self.Q[s][a] = update\\n self.count = 0\\n else:\\n self.count += 1\\n\\n print(\\\"Finished calculating Q-Matrix\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\")\",\n \"def experiment_callback(self, args):\\n # If args is None, that means that an exception was raised during the\\n # execution of the experiment. In such case, ignore it\\n if not args:\\n self.n_fail += 1\\n return\\n # Extract parameters\\n params, results, duration = args\\n self.n_success += 1\\n # Store results\\n self.results.add(params, results)\\n self.exp_durations.append(duration)\\n if self.n_success % self.summary_freq == 0:\\n # Number of experiments scheduled to be executed\\n n_scheduled = self.n_exp - (self.n_fail + self.n_success)\\n # Compute ETA\\n n_cores = min(mp.cpu_count(), self.n_proc)\\n mean_duration = sum(self.exp_durations) / len(self.exp_durations)\\n eta = timestr(n_scheduled * mean_duration / n_cores, False)\\n # Print summary\\n logger.info('SUMMARY | Completed: %d, Failed: %d, Scheduled: %d, ETA: %s',\\n self.n_success, self.n_fail, n_scheduled, eta)\",\n \"def optimize_self(self):\\n self.compute_predicate_values();\\n \\\"\\\"\\\" Firstly,adjust the f(x) into > alpha_0; \\\"\\\"\\\"\\n for i in range(len(self.RING_PARA_PAIR_CD)):\\n self.adjust_to_excepted_value(self.RING_PARA_PAIR_CD[i]);\\n self.RING_PARA_PAIR_CD[i][0].VECTOR = RUNNING_DATA[self.RING_PARA_PAIR_CD[i][0].WORD_ID]; # 更新内部VECTOR;\\n self.RING_PARA_PAIR_CD[i][2] = self.RING_PARA_PAIR_CD[i][0].polynomial_func(self.RING_PARA_PAIR_CD[i][1]); # 更新内部ALPHA;\\n for i in range(len(self.RING_PARA_PAIR_CC)):\\n # ************** BUG 0627 **********************\\n # if self.RING_PARA_PAIR_CC[i][2]<41 and self.RING_PARA_PAIR_CC[i][2]>40: # ---------------------------------------------------------------->>>>>\\n # print( RUNNING_DATA[self.RING_PARA_PAIR_CC[i][0].WORD_ID] ); # ---------------------------------------------------------------->>>>>\\n self.adjust_to_excepted_value(self.RING_PARA_PAIR_CC[i]); \\n # ************** BUG 0627 **********************\\n # if self.RING_PARA_PAIR_CC[i][2]<41 and self.RING_PARA_PAIR_CC[i][2]>40: # ---------------------------------------------------------------->>>>> \\n # print(self.RING_PARA_PAIR_CC[i][0].VECTOR); # ---------------------------------------------------------------->>>>>\\n # print(RUNNING_DATA[self.RING_PARA_PAIR_CC[i][0].WORD_ID]); # ---------------------------------------------------------------->>>>>\\n # print(self.RING_PARA_PAIR_CC[i][0].polynomial_func(self.RING_PARA_PAIR_CC[i][1])); # ---------------------------------------------------------------->>>>>\\n # print( np.cumprod( self.RING_PARA_PAIR_CC[i][1] - self.RING_PARA_PAIR_CC[i][0].VECTOR )[-1] ); # ---------------------------------------------------------------->>>>>\\n self.RING_PARA_PAIR_CC[i][0].VECTOR = RUNNING_DATA[self.RING_PARA_PAIR_CC[i][0].WORD_ID]; # 更新内部VECTOR;\\n self.RING_PARA_PAIR_CC[i][2] = self.RING_PARA_PAIR_CC[i][0].polynomial_func(self.RING_PARA_PAIR_CC[i][1]); # 更新内部ALPHA;\\n \\\"\\\"\\\" Secondly,adjust the alpha_0 < p_min(y_n) < q_min(x_n)\\\"\\\"\\\" \\n P_MIN = 99999999.0; Q_MIN = 99999999.0;P_MAX = 0.0;Q_MAX = 0.0;\\n CD_ID = 0;CD_MIN=0;\\n # 找到结论中谓词的最小多项式值\\n for RING_PARA_PAIR in self.RING_PARA_PAIR_CC:\\n if Q_MIN>RING_PARA_PAIR[2]: Q_MIN=RING_PARA_PAIR[2];\\n # if Q_MAX<RING_PARA_PAIR[2]: Q_MAX=RING_PARA_PAIR[2];\\n # 不满足小于结论最小值的那些环的TARGET_ALPHA更新; \\n for RING_PARA_PAIR in self.RING_PARA_PAIR_CD:\\n if P_MIN>RING_PARA_PAIR[2]: P_MIN=RING_PARA_PAIR[2];CD_MIN=CD_ID;# self.RING_PARA_PAIR_CD[CD_ID][3]=-Q_MIN;\\n # if P_MAX<RING_PARA_PAIR[2]: P_MAX=RING_PARA_PAIR[2];self.RING_PARA_PAIR_CD[CD_ID][3]=-Q_MAX;\\n CD_ID+=1;\\n self.RING_PARA_PAIR_CD[CD_MIN][3]=-Q_MIN; \\n # 优化不满足小于结论最小值的那些环 \\n for i in range(len(self.RING_PARA_PAIR_CD)): \\n if self.RING_PARA_PAIR_CD[i][3]!=ALPHA:\\n if P_MIN>Q_MIN:\\n self.adjust_to_excepted_value(self.RING_PARA_PAIR_CD[i]); \\n self.RING_PARA_PAIR_CD[i][0].VECTOR = RUNNING_DATA[self.RING_PARA_PAIR_CD[i][0].WORD_ID]; # 更新内部VECTOR;\\n self.RING_PARA_PAIR_CD[i][2] = self.RING_PARA_PAIR_CD[i][0].polynomial_func(self.RING_PARA_PAIR_CD[i][1]); # 更新内部ALPHA;\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65901256","0.6476958","0.64748776","0.639814","0.63832265","0.6313094","0.6264372","0.6213871","0.6205301","0.61710924","0.6140338","0.6129752","0.60806435","0.6051773","0.6009665","0.60076827","0.60076827","0.5998235","0.59467274","0.5933865","0.59092945","0.5902032","0.5897183","0.58852327","0.58791524","0.5870821","0.587045","0.58695805","0.5826524","0.58217716","0.5811564","0.58035845","0.57533544","0.57514393","0.57400596","0.5728194","0.5716536","0.5714253","0.5691625","0.56797856","0.5678571","0.5673598","0.5673598","0.5673598","0.56608415","0.565868","0.5620482","0.5611053","0.5607042","0.56070036","0.56066334","0.56029123","0.5579653","0.557771","0.5566541","0.5564588","0.55512685","0.5538573","0.5522365","0.5516557","0.5514994","0.551131","0.5507184","0.54994106","0.5497955","0.54953885","0.5484313","0.54802305","0.54674804","0.546602","0.545959","0.5452608","0.5442806","0.5436651","0.5433038","0.5431733","0.54188627","0.5415806","0.5414562","0.5409718","0.54096365","0.5408973","0.54072285","0.54042345","0.54035723","0.54016334","0.54009604","0.5389468","0.5389379","0.53853405","0.5384501","0.53743196","0.5370644","0.5367415","0.5362922","0.5360071","0.5356477","0.5356105","0.5351363","0.534829","0.53475076"],"string":"[\n \"0.65901256\",\n \"0.6476958\",\n \"0.64748776\",\n \"0.639814\",\n \"0.63832265\",\n \"0.6313094\",\n \"0.6264372\",\n \"0.6213871\",\n \"0.6205301\",\n \"0.61710924\",\n \"0.6140338\",\n \"0.6129752\",\n \"0.60806435\",\n \"0.6051773\",\n \"0.6009665\",\n \"0.60076827\",\n \"0.60076827\",\n \"0.5998235\",\n \"0.59467274\",\n \"0.5933865\",\n \"0.59092945\",\n \"0.5902032\",\n \"0.5897183\",\n \"0.58852327\",\n \"0.58791524\",\n \"0.5870821\",\n \"0.587045\",\n \"0.58695805\",\n \"0.5826524\",\n \"0.58217716\",\n \"0.5811564\",\n \"0.58035845\",\n \"0.57533544\",\n \"0.57514393\",\n \"0.57400596\",\n \"0.5728194\",\n \"0.5716536\",\n \"0.5714253\",\n \"0.5691625\",\n \"0.56797856\",\n \"0.5678571\",\n \"0.5673598\",\n \"0.5673598\",\n \"0.5673598\",\n \"0.56608415\",\n \"0.565868\",\n \"0.5620482\",\n \"0.5611053\",\n \"0.5607042\",\n \"0.56070036\",\n \"0.56066334\",\n \"0.56029123\",\n \"0.5579653\",\n \"0.557771\",\n \"0.5566541\",\n \"0.5564588\",\n \"0.55512685\",\n \"0.5538573\",\n \"0.5522365\",\n \"0.5516557\",\n \"0.5514994\",\n \"0.551131\",\n \"0.5507184\",\n \"0.54994106\",\n \"0.5497955\",\n \"0.54953885\",\n \"0.5484313\",\n \"0.54802305\",\n \"0.54674804\",\n \"0.546602\",\n \"0.545959\",\n \"0.5452608\",\n \"0.5442806\",\n \"0.5436651\",\n \"0.5433038\",\n \"0.5431733\",\n \"0.54188627\",\n \"0.5415806\",\n \"0.5414562\",\n \"0.5409718\",\n \"0.54096365\",\n \"0.5408973\",\n \"0.54072285\",\n \"0.54042345\",\n \"0.54035723\",\n \"0.54016334\",\n \"0.54009604\",\n \"0.5389468\",\n \"0.5389379\",\n \"0.53853405\",\n \"0.5384501\",\n \"0.53743196\",\n \"0.5370644\",\n \"0.5367415\",\n \"0.5362922\",\n \"0.5360071\",\n \"0.5356477\",\n \"0.5356105\",\n \"0.5351363\",\n \"0.534829\",\n \"0.53475076\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":6790,"cells":{"query":{"kind":"string","value":"About Nurevam or person by mention info"},"document":{"kind":"string","value":"async def info(self,ctx,*,person:discord.Member = None):\n\n if not person:\n guild = len(self.bot.guilds)\n member = len(set(self.bot.get_all_members()))\n app = await self.bot.application_info()\n msg = \"Name:{}\".format(self.bot.user)\n if ctx.message.guild.me.nick:\n msg += \"\\nNickname:{}\".format(ctx.message.guild.me.nick)\n msg += \"\\nCreator: {}\".format(app.owner)\n msg += \"\\nServer:{}\\nMembers:{}\".format(guild,member)\n link = \"If you want to invite this bot to your server, you can check it out here <http://nurevam.site>!\"\n return await self.bot.say(ctx,content = \"```xl\\n{}\\n```\\n{}\\n\".format(msg,link))\n else:\n e = discord.Embed()\n e.title = \"{} - {}\".format(person,person.id)\n e.set_thumbnail(url = person.avatar_url)\n e.add_field(name = \"Created at\", value=\"{} - ({})\".format(person.created_at,self.get_time_delta(person.created_at)),inline=False)\n e.add_field(name = \"Joined at\", value=\"{} - ({})\".format(person.joined_at,self.get_time_delta(person.joined_at)),inline=False)\n e.add_field(name = \"Total Roles\", value=str(len(person.roles)),inline=False)\n\n if person.colour.value:\n e.colour = person.color\n await self.bot.say(ctx,embed = e)"},"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 mention(self) -> str:","def mention(self) -> str:","def display_person(person):\n name = person['name']\n followers = person['follower_count']\n description = person['description']\n country = person['country']\n print(f'{name}, a(n) {description}, from {country}.')\n return followers","async def user(self, ctx, member: discord.Member = None):\r\n rank = 1\r\n if member is None:\r\n member = ctx.author\r\n x = member.name\r\n for (k, v) in sorted(player.items(), key=lambda x: expose(x[1]), reverse=True):\r\n if k == x:\r\n start = '```md' + u\"\\u000A\" + '-'*68 + u\"\\u000A\" + u\"\\u000A\" + 'User Info: '\r\n middle = u\"\\u000A\" + u\"\\u000A\" + '[rank][#' + str(rank) + '] [rating]['\r\n end = str(round(expose(v), 2)) + '] [skill][' + str(round(v.mu, 2)) + '] [uncertainty]['\r\n await ctx.send(start + k + middle + end + str(round(v.sigma, 2)) + ']' + u\"\\u000A\" + u\"\\u000A\" + '-'*68 + u\"\\u000A\" + '```')\r\n if rank == 1:\r\n await ctx.send(k + ' is the G O A T 🐐')\r\n break\r\n rank += 1","def get_person_text(self, uid):\n words = \"\"\n\n query = \"\"\"\nSELECT ?overview ?researchO ?label\nWHERE\n{\n <%s> <http://vivoweb.org/ontology/core#overview> ?overview .\n <%s> <http://vivoweb.org/ontology/core#researchOverview> ?researchO .\n <%s> <http://www.w3.org/2000/01/rdf-schema#label> ?label .\n}\n \"\"\" % (uid, uid, uid)\n self.setQuery(query)\n try:\n rval = self.query()\n try:\n g = rval.convert()\n except:\n pass\n words = \"%s %s %s\" % (g['results']['bindings'][0]['overview']['value'], g['results']['bindings'][0]['researchO']['value'], g['results']['bindings'][0]['label']['value'])\n except:\n print \"Select failed: %s\" % query\n\n self.setQuery(\"\"\"\nPREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\nPREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\nPREFIX vivo: <http://vivoweb.org/ontology/core#>\nPREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\nSELECT ?name\nWHERE\n{\n ?auth vivo:relates <%s> .\n ?auth rdf:type vivo:Authorship .\n ?auth vivo:relates ?art .\n filter (?art!=<%s>) .\n ?art <http://vivoweb.org/ontology/core#dateTimeValue> ?date .\n ?date <http://vivoweb.org/ontology/core#dateTime> ?year .\n filter (?year>\"2009-01-01T00:00:00Z\"^^xsd:dateTime) .\n ?art rdfs:label ?name .\n}\nLIMIT 20\n\"\"\" % (uid, uid))\n try:\n rval = self.query()\n try:\n g = rval.convert()\n except:\n pass\n for t in g['results']['bindings']:\n words = words + \" \" + t['name']['value']\n\n except:\n print \"Select failed\"\n traceback.print_exc(file=sys.stdout)\n\n self.setQuery(\"\"\"\nPREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\nPREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\nPREFIX vivo: <http://vivoweb.org/ontology/core#>\nPREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\n\nSELECT ?name\nWHERE\n{\n ?grant vivo:relates <%s> .\n ?grant rdf:type vivo:Grant .\n ?grant <http://vivoweb.org/ontology/core#dateTimeInterval> ?date .\n ?date <http://vivoweb.org/ontology/core#end> ?end .\n ?end <http://vivoweb.org/ontology/core#dateTime> ?year .\n filter (?year>\"2009-01-01T00:00:00Z\"^^xsd:dateTime) .\n ?grant rdfs:label ?name .\n}\n\n \"\"\" % (uid))\n try:\n rval = self.query()\n try:\n g = rval.convert()\n except:\n pass\n\n for t in g['results']['bindings']:\n words = words + \" \" + t['name']['value']\n\n except:\n print \"Select failed\"\n traceback.print_exc(file=sys.stdout)\n\n\n\n\n return words","def author_following(self):\n\t\tpass","async def ign_whoami(self, ctx):\n user = ctx.message.author\n igns = self.names.get(user.mention)\n if not igns:\n await self.bot.say(\"You have not yet entered any IGN info. :cry:\".format(user.mention))\n else:\n await self.bot.say(self.format_igns(user, igns))","def mention(result):\n return result.text.find('@') != -1","def mention(self) -> str:\n raise NotImplementedError","async def ign_whois(self, user: discord.Member):\n igns = self.names.get(user.mention)\n if not igns:\n await self.bot.say(\"{0} has not yet entered any IGN info. :cry:\".format(user.mention))\n else:\n await self.bot.say(self.format_igns(user, igns))","def mention(cls, user, message, mentioned):\r\n pass","def detect_author(user_to_tweets: Dict[str, List[tuple]], tweet_text: str) -> \\\n str:\n acc = []\n \n for keys in user_to_tweets:\n author_hashes = hashtag_seperator(user_to_tweets[keys])\n text_hashes = extract_hashtags(tweet_text)\n if set(text_hashes).issubset(author_hashes):\n acc.append(keys)\n if len(acc) == 1:\n return acc[0]\n return 'unknown'","def get_info(hit):\n mention = Mention(hit)\n return dict(\n url = mention.info[\"url\"],\n title = mention.info[\"title\"],\n date = mention.info[\"datetime_date\"] or datetime.date(1970, 1, 1),\n type = 'news' if mention.in_the_news else 'print',\n author = '(need author)',\n media = mention.info[\"media\"],\n )","def _get_user_mention(self, name: str) -> str:\n try:\n return self.known_user_ids[name]\n except KeyError:\n pass\n\n mention = name\n users = get_value_from_redis('slack-members') or []\n user = self._get_user(name, users)\n if user:\n mention = f'<@{user[\"id\"]}>'\n\n self.known_user_ids[name] = mention\n\n return mention","def get_author_info(self, author: str):\n for writer_word in self._writer_words:\n data = json.loads(requests.get(WIKIDATA_SEARCH + \"&srsearch=\" + author + \" \" + writer_word).text)\n pages = data.get(\"query\").get(\"search\")\n if len(pages) >= 1:\n pageid = pages[0].get(\"title\")\n author_details = self._reference.author_map.get(author)\n if author_details:\n return author_details\n if pageid == -1:\n continue\n\n else:\n response = requests.get(WIKIDATA_PARSE + pageid + \".json\")\n data = json.loads(response.text)\n if author.lower() not in data.get(\"entities\").get(pageid).get(\"labels\").get(\"en\").get(\"value\").lower():\n continue\n else:\n try:\n id = data.get(\"entities\").get(pageid).get(\"claims\").get(\"P31\")[0].get(\"mainsnak\").get(\"datavalue\").get(\"value\").get(\"id\")\n if str(id) != \"Q5\": # the id for human\n continue\n except IndexError:\n continue\n properties = data.get(\"entities\").get(pageid).get(\"claims\")\n author_details = {\"id\": pageid, \"gender\": self.get_gender(properties)}\n country_details = self.get_country(properties)\n author_details[\"country\"] = country_details\n self._reference.author_map[author] = author_details\n return author_details\n return {\"id\": \"Unknown\", \"gender\": \"Unknown\", \"country\": [{\"name\": \"Unknown\", \"region\": \"Unknown\"}]}","async def love(ctx, user: discord.Member):\r\n author = ctx.message.author\r\n if user.id == ctx.bot.user.id:\r\n await ctx.send(\"I am not capable of loving like you can. I'm sorry.\" )\r\n else:\r\n await ctx.send(author.mention + \" is capable of loving \" + user.mention + \" a whopping \" +\r\n str(randint(0, 100)) + \"%!\")\r\n ctx.counter(n)","def replace_user_mentions(text):\n text = re.sub('\\B@\\w*[a-zA-Z]+\\w*', 'USERMENTION', text)\n return text","def author(self):\n\t\tauthor = re.search(r\"([Ff]rom\\s)(.+) ([tT]\\s*o)([^,]+),\",self.raw_text()[:150])\n\t\treport = re.search(r\".*[Rr]eport of ([^,]+),\",self.raw_text()[:150])\n\t\torder = re.search(r\".*[Oo]rder of ([^,]+)[,.]* to\",self.raw_text()[:250])\n\t\torder2 = re.search(r\".*[Oo]rder of ([^,]+),\",self.raw_text()[:250])\n\t\tlogbook = re.search(r\".*[lL]og of ([^,]+)[,.]\",self.raw_text()[:250])\n\t\tif order:\n\t\t\torder = order.group(1)\n\t\t\treturn order\n\t\tif order2:\n\t\t\torder2 = order2.group(1)\n\t\t\treturn order2\n\t\tif logbook:\n\t\t\tlogbook = logbook.group(1)\n\t\t\treturn logbook\n\t\tif report:\n\t\t\treport = report.group(1)\n\t\t\treturn report\n\t\tif author: \t\n\t\t\tauthor = author.group(2) \n\t\t\tauthor = re.sub(r\"([^,]*),.*\",r\"\\1\",author)\t \n\t\t\treturn author\n\t\t\n\t\treturn \"Unknown\"","def covid_mention(text, synonyms=covid_list):\n for term in synonyms:\n if term in text:\n return 'Yes'\n continue\n return 'No'","def mention(bot, msg):\n\n if msg.command != \"PRIVMSG\":\n return\n\n message = msg.args[1]\n\n if bot.nickname.lower() in message.lower():\n bot.privmsg(msg.sendername, \"You called?\")","def followed_by_hillary_and_donald(users, twitter):\n ###TODO-- Completed\n for user in users:\n if user['screen_name'] == 'HillaryClinton':\n friends_Hillary = user['friends']\n #print(len(friends_Hillary))\n elif user['screen_name'] == 'realDonaldTrump':\n friends_donald = user['friends']\n #print(len(friends_donald))\n\n common_followed_id = list(set(friends_Hillary) & set(friends_donald))\n\n commn_followed_user = robust_request(twitter,'users/lookup',{'user_id':common_followed_id}).json()\n #print(commn_followed_user[0]['screen_name'])#['screen_name'])\n return commn_followed_user[0]['screen_name']\n #pass","def parse_direct_mention(message_text):\n # print(message_text)\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def _parse_text_as_direct_mention(\n message_text: str,\n ) -> tuple[Optional[str], Optional[str]]:\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n if matches:\n return matches.group(1), matches.group(2).strip()\n return None, None","def mention_user(self, username: str) -> str:\n member = self.get_user(username)\n if member:\n return member.mention\n return username","def crawl_person_infos(uid):\n if not uid:\n return\n\n user = user_get.get_profile(uid)\n # If it's enterprise user, just skip it\n if user.verify_type == 2:\n set_seed_other_crawled(uid)\n return\n\n # Crawl fans and followers\n celery.send_task('celery_tasks.weibo.user.crawl_follower_fans', args=(uid,), queue='fans_followers',\n routing_key='for_fans_followers')","def displayFolowers(database):\n firstname=str(input(\"who do you want to display followers :\"))\n usr,find=getByName(database,firstname)\n if find:\n print(f\"{usr.firstname} {usr.lastname} is folowed by:\")\n for folower in usr.folowed:\n print(folower)","def mention(cls, user, message, mentioned):\n pass","def test_refersto_author_multi_name(self):\n inv_search = 'author:ellis refersto:author:\"parke, s. j.\"'\n spi_search = 'find a ellis and refersto author \"parke, s. j.\"'\n self._compare_searches(inv_search, spi_search)","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n print (matches)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def userFollowers(nick):\n if (len(nick) != 1):\n print \"Has d'introduir només un nick\"\n return\n i.userFollow(nick[0])","def parse_direct_mention(message_text):\n\tmatches = re.search(MENTION_REGEX, message_text)\n\t# the first group contains the username, the second group contains the remaining message\n\treturn (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # 1st gourp has username, 2nd group called remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def detect_person(snap):\n pass","def parse_direct_mention(self,message_text):\n matches = re.search(self.MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def parse_direct_mention(message_text):\r\n matches = re.search(MENTION_REGEX, message_text)\r\n # the first group contains the username, the second group contains the remaining message\r\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def mentor_list_view(request):\n # TODO: this view\n pass","def user(inp):\n user = inp.text.lower().replace(' ', '-')\n return 'http://www.wikidot.com/user:info/' + user","def test_author_many_lastnames(self):\n inv_search = 'author:\"alvarez gaume, j* r* r*\"'\n spi_search = 'find a alvarez gaume, j r r'\n self._compare_searches(inv_search, spi_search)","def followed_by_hillary_and_donald(users, twitter):\n\n str = ''\n set1 = set()\n set2 = set()\n for u_dict in users:\n \tif u_dict['screen_name'] == 'HillaryClinton':\n \t\tset1 = set(u_dict['friends'])\n \telif u_dict['screen_name'] == 'realDonaldTrump':\n \t\tset2 = set(u_dict['friends'])\n \t\t\n common = set.intersection(set1, set2)\n request = robust_request(twitter, 'users/lookup', {'user_id': common}, max_tries=5)\n for user in request:\n \tstr = user['screen_name']\t\n return str","async def info(self, context):\n await context.send('creador: debellisnahuel@gmail.com\\ncolabs:\\n emi: https://twitter.com/emilianosce/ o https://www.instagram.com/emilianosce/ \\n garza: https://twitter.com/Matias_Garcia00 o https://www.twitch.tv/garzangb')","def compute_user_mentions_name(row):\n entity_series = pd.read_json(json.dumps(row['entities']), typ='series')\n user_mentions_name = list(map(lambda entry: entry['name'], entity_series['user_mentions']))\n return ','.join(user_mentions_name)","def mentee_list_view(request):\n # TODO: this view\n pass","def is_explicit_mention(self, bot):\n for um in self.original.entities['user_mentions']:\n if um['screen_name'] == bot.screen_name:\n this_is_an_xm = um['indices'][0] >= self.original.display_text_range[0]\n this_is_an_xm &= um['indices'][1] <= self.original.display_text_range[1]\n if this_is_an_xm:\n return True\n return False","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains\n # the remaining message\n return (matches.group(1),\n matches.group(2).strip()) if matches else (None, None)","def like_to_party(msg):\n if message.rate_limit(msg.settings, 'like_to_party'):\n return\n return \"%s, I know for a fact you don't party. You do *not* party.\" \\\n % msg.name","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def who_am_i():\n return {'name': 'Jonathan Martinez', 'id': '201095569', 'email': 'martijon@post.bgu.ac.il'}","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group\n # contains the remaining message\n return (matches.group(1),\n matches.group(2).strip()) if matches else (None, None)","def get_mentions(texts, word):\r\n mentions = {}\r\n for text, label, text_id in texts:\r\n if word in text.lower():\r\n if label not in mentions:\r\n mentions[label] = [text_id]\r\n else:\r\n if text_id not in mentions[label]:\r\n mentions[label].append(text_id)\r\n return mentions","def _talk_about(self, about, data):\n if not data:\n return\n\n if about == 'faces':\n for face in data:\n self._seen_faces.append(face['identity'])\n\n all_people = list(set(self._seen_faces))\n all_known_people = [name for name in all_people if name is not None]\n\n if len(all_known_people) == 0:\n text = \"Hello. I haven't seen you before. Welcome!\"\n elif len(all_known_people) == 1:\n text = \"Hello {}\".format(all_known_people[0])\n elif len(all_people) <= 3:\n all_but_last = ', '.join([name or 'stranger' for name in all_people[:-1]])\n last = all_people[-1] or 'stranger'\n names = ' and '.join([all_but_last, last])\n text = 'Hello {}'.format(names)\n elif len(all_known_people) > 3:\n text = 'Hi everybody. What a crowd! Why don\\'t you get back to work?'\n self._say_prudent(text)","def parse_direct_mention(message_text):\n matches = re.search(MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, message_text.strip())","def person_f(x: Text) -> Tuple[Text, Text]:\n return \"person\", x.lower()","def get_bookshare_user_info(patron):\n pass","def find_new_people(self):\n #greets people, only greets once while they're in the camera's view and are center of attention\n\n\n if (self.person is not None) and (self.person.acknowledged == False):\n self.person.acknowledged = True\n print \"I see you!\"\n self.idle_pub.publish(\"idle:stop\")\n time.sleep(2)\n\n greeting = [\"R_nudge\",\"R_look\"]\n for msg in greeting:\n self.behavior_pub.publish(msg)\n self.check_completion()\n\n\n self.detection_pub.publish('found')\n\n elif self.person is None:\n print \"I don't see you\"\n self.detection_pub.publish('nothing')","async def info(self, ctx, *, member: disnake.Member = None):\n\n member = member or ctx.author\n\n e = disnake.Embed(description=\"\")\n\n if member.bot:\n e.description = \"This account is a bot.\\n\\n\"\n\n e.description += member.mention\n\n e.add_field(name=\"Status\", value=member.status)\n\n if member.activity:\n e.add_field(name=\"Activity\", value=member.activity.name)\n\n e.set_author(name=str(member), icon_url=member.display_avatar.url)\n\n now = datetime.now(timezone.utc)\n created = member.created_at\n joined = member.joined_at\n\n e.add_field(\n name=\"Account age\",\n value=\"{0} • Created <t:{1}:F>\".format(\n pretty_timedelta(now - created), round(created.timestamp())\n ),\n inline=False,\n )\n\n e.add_field(\n name=\"Member for\",\n value=\"{0} • Joined <t:{1}:F>\".format(\n pretty_timedelta(now - joined), round(joined.timestamp())\n ),\n )\n\n if len(member.roles) > 1:\n e.add_field(\n name=\"Roles\",\n value=\" \".join(role.mention for role in reversed(member.roles[1:])),\n inline=False,\n )\n\n e.set_footer(text=\"ID: \" + str(member.id))\n\n await ctx.send(embed=e)","def test_citedby_author(self):\n inv_search = 'citedby:author:doggy'\n spi_search = 'find citedby author doggy'\n self._compare_searches(inv_search, spi_search)","def mention(self) -> str:\n return f\"<@{self.id}>\"","def process_person(self,msg):\n self.personx = msg.x\n self.persony = msg.y","def somebody(g, name, homepage, who=None):\n FOAF = Namespace(\"http://xmlns.com/foaf/0.1/\")\n if not who:\n who = BNode()\n g.add((who, FOAF.name, Literal(name)))\n g.add((who, FOAF.homepage, URIRef(homepage)))\n #if email:\n # g.add((who, FOAF.homepage, URIRef(\"mailto:%s\" % email)))\n return who","def test_refersto_author(self):\n inv_search = 'refersto:author:kitty'\n spi_search = 'find refersto author kitty'\n self._compare_searches(inv_search, spi_search)","def personUp(person):\n\timport os\n\timport wikipedia\n\timport speak\n\ttry:\n\t\tsay(wikipedia.summary(person,sentences=2))\n\t\t# os.system('clear')\n\texcept wikipedia.exceptions.DisambiguationError:\n\t\tresults = wikipedia.search(person)\n\t\tos.system('clear')\n\t\tspeak.speak('The closest I could find was : ' + results[1])\n\t\tsay(wikipedia.summary(results[1],sentences=2))\n\texcept Exception:\n\t\tsay(\"Couldn't find anything...\")","def test_refersto_author_multi_name_no_quotes(self):\n inv_search = 'author:ellis refersto:(author:\"parke, sj*\" or exactauthor:\"parke, s *\" or exactauthor:\"parke, s\" or author:\"parke, sj, *\")'\n spi_search = \"find a ellis and refersto author parke, sj\"\n self._compare_searches(inv_search, spi_search)","async def info_user(self, ctx, member: Optional[discord.Member]):\n member1 = member or ctx.author\n embed = discord.Embed(title=\"Member Information\",\n color=discord.Color.blurple(),\n timestamp=datetime.utcnow())\n\n embed.add_field(name=\"ID\", value=f\"{member1.id}\", inline=False)\n embed.add_field(\n name=\"Name\", value=f\"{member1.name}#{member1.discriminator}\")\n embed.add_field(name=\"Top role\", value=f\"{member1.top_role.mention}\")\n embed.add_field(name=\"status\",\n value=f\"{str(member1.activity.type).split('.') if member1.activity else 'N/A'} {member1.activity.name if member1.activity else ''}\")\n embed.add_field(\n name=\"created at\", value=f\"{member1.created_at.strftime('%d/%m/%y %H:%M:%S')}\")\n embed.add_field(\n name=\"Joined at\", value=f\"{member1.joined_at.strftime('%d/%m/%y %H:%M:%S')}\")\n embed.add_field(name=\"Boosted?\", value=f\"{member1.premium_since}\")\n\n await ctx.reply(embed=embed)","def test_citedby_refersto_author(self):\n inv_search = 'refersto:citedby:author:penguin'\n spi_search = 'find refersto citedby author penguin'\n self._compare_searches(inv_search, spi_search)","def get_mentions(fname):\n capture = re.compile('NPRI in ?.* ?(news|print)', re.I)\n with open(fname) as fp:\n for line in fp:\n line = line.strip()\n if capture.search(line):\n yield get_info(line)","async def confused(self, ctx, member: discord.Member = None):\n member = member if member else ctx.author\n members = [m for m in ctx.guild.members if not m == member]\n async with ctx.typing():\n image = await self.request(endpoint=\"generators/confused\", params=f\"?avatar={member.avatar_url_as(format='png')}&photo={random.choice(members).avatar_url_as(format='png')}\")\n await ctx.send(f\"**{member.name}** is confusion.\", file=discord.File(image, filename=\"file.png\"))","def who(id, client, name):\r\n exists = person.locate(id)\r\n\r\n if not exists:\r\n person.create(id, name)\r\n client.send_message(id, 'Hello ' + name + '''!\r\nI am your Journal Buddy :) My purpose is to help you manage your productivity!\r\n\r\nYou can:\r\n - Set up habits\r\n - Schedule events\r\n - Create a task pool\r\n - Set reminders\r\n - Add notes - tell me things you don’t want to forget! May it be a quote, or a movie you liked\r\n\r\nEvery month we can choose which tasks you would like to complete in the month ahead!\r\nEvery week I will help you set goals in terms of tasks and habits for the week ahead!\r\n\r\nThe more we get to know each other the more I will be able to help! Providing weekly analytics and suggestions for future performance. Tell me how you feel throughout your tasks, habits and events and I will share with you what I think will work best.\r\n\r\nType /setup to begin!''')\r\n\r\n users[id] = {\"last\": \"new user\"}\r\n elif not (users.keys().__contains__(id)):\r\n users[id] = {\"last\": \"welcome back\"}","def parse_direct_mention(self, message_text):\n matches = re.search(self.MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def parse_direct_mention(self, message_text):\n matches = re.search(self.MENTION_REGEX, message_text)\n # the first group contains the username, the second group contains the remaining message\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)","def taco_mention_callback(bot, message):\n\n cid = get_cid(message)\n store_name(message.from_user)\n\n chat = Chats.get(Chats.cid == message.chat.id)\n clean_chat(chat.mids, chat.cid, bot, None)\n\n ok_button = InlineKeyboardButton('OK', callback_data='delete:{}'.format(message.from_user.id))\n ok_keyboard = InlineKeyboardMarkup([[ok_button]])\n\n mentioned_users = list()\n for entity in message.entities:\n if entity.type == 'mention':\n user = message.text[entity.offset: entity.offset + entity.length].lower()\n mentioned_users.append(user)\n mentioned_users = list(set(mentioned_users))\n\n if len(mentioned_users) > 1:\n if chat.less is True:\n text = only_one_receiver_phrase.split('\\n')[0]\n else:\n text = only_one_receiver_phrase\n mid = bot.send_message(chat_id=cid,\n text=text,\n reply_to_message_id=get_mid(message),\n reply_markup=ok_keyboard,\n parse_mode='html').message_id\n\n chat.mids = [mid]\n chat.save()\n return\n\n sender = message.from_user\n receiver_username: str = ensure_no_at_sign(mentioned_users[0])\n\n try:\n receiver = bot.get_chat_member(chat_id=cid,\n user_id=receiver_username).user\n\n except Exception:\n \"\"\" here should be except UserNotParticipant, but it still raises this exception \"\"\"\n if chat.less is True:\n text = user_not_present_phrase.split('\\n')[0]\n else:\n text = user_not_present_phrase\n mid = bot.send_message(chat_id=cid,\n text=text.format(ensure_username(receiver_username)),\n reply_to_message_id=get_mid(message),\n reply_markup=ok_keyboard,\n parse_mode='html').message_id\n\n chat.mids = [mid]\n chat.save()\n\n return\n\n give_tacos(bot, message, sender, receiver)","async def profilepicture(self, ctx):\n if not ctx.message.mentions:\n await ctx.send(embed=discord.Embed().set_image(\n url=ctx.author.avatar_url))\n else:\n for member in ctx.message.mentions:\n await ctx.send(embed=discord.Embed().set_image(\n url=member.avatar_url))","def user_suggested(username):\n raise NotImplementedError()","def follow_reciprocated(self, target):\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\n self.tweet_user(target)\n else:\n try:\n self.dm_user(target)\n except:\n pass","def whois( self, mess, args):\n user = self.get_sender_username(mess)\n args = args.strip().replace(' ', '_')\n if user in self.users:\n self.log.info('%s queried whois %s.' % (user, args))\n if args in self.users.values():\n return filter(lambda u: self.users[u] == args, self.users)[0]\n else:\n return 'Nobody!'","def subject_info(intent, extra_info=[]):\n\n text = intent['inputTranscript'].lower()\n utterances = AS.load_file('sample_utterances.txt')\n\n # add \"book\" and \"books\" to every utterance\n for line in list(utterances):\n utterances.insert(0, line + \" book\")\n utterances.insert(0, line + \" books\")\n\n # tells how many characters needs to be dropped before the subject starts\n to_drop = 0\n\n for line in utterances:\n if text.startswith(line):\n to_drop = len(line)\n break\n\n # drops the characters and makes a list from the strings that are left\n text = text[to_drop:].strip()\n text_list = text.split(' ', len(text))\n\n subject_list = []\n keywords = [\"books\", \"book\", \"by\", \"published\", \"written\"]\n keyword = \"\"\n\n # Find out when the book name ends\n for word in text_list:\n if word not in keywords:\n subject_list.append(word)\n else:\n break\n\n subject = \" \".join(subject_list)\n\n # Get all the keywords in the middle, so they can be\n # all be dropped at once, eg written by, books by\n text_list = text_list[len(subject_list):]\n if text_list:\n word = text_list[0]\n while word in keywords:\n keyword += word + \" \"\n text_list = text_list[1:]\n if text_list:\n word = text_list[0]\n else:\n break\n\n # search for an author from the rest of the characters\n author_text = text[len(keyword):].strip()\n author = AS.search(author_text, False)\n if author is \"\":\n author = None\n\n # There might be old info in the extra_info (author), so \n # we need to clear it\n extra_info.clear()\n\n # add the author to extra info so it can be used in the Finna API call\n if author:\n extra_info += [\"author:\\\"\" + author + \"\\\"\"]\n elif intent['sessionAttributes'].get('author'):\n extra_info += [\n \"author:\\\"\" + intent['sessionAttributes']['author'] + \"\\\"\"\n ]\n\n # The Finna API call\n request = lookfor(term=subject, filter=extra_info)['json']\n\n return parse_subject(request, subject, {'author': author})","def substantiate():","def _guess_author(talk_url, document):\r\n elements = _AUTHOR_BIO_XPATH(document)\r\n if elements:\r\n author_bio_url = urljoin(SITE_URL, elements[0].get('href'))\r\n author_bio_document = html.parse(author_bio_url)\r\n return _clean_up_file_name(\r\n author_bio_document.find('/head/title').text.split('|')[0].strip()\r\n )\r\n \r\n logging.warning(\"Failed to guess the author of '%s'\", talk_url)\r\n return 'Unknown'","async def imitate(self, ctx):\n message = ctx.message.content.strip(' ').split(' ')\n user_id = ''\n if len(message) == 1:\n response = self.message.returnMarkovMsgError()\n await self.bot.say(response)\n elif message[1].casefold() == 'me'.casefold():\n user_id = ctx.message.author.id\n print(user_id)\n else:\n user_id = message[1][2:-1]\n print(user_id)\n\n # if(does_markov_exist(user_id)):\n # markov_chain = get_markov_chain(user_id)\n # # Do something with chain\n # else:\n # # Make chain","async def whoowns(ctx, bot: typing.Union[discord.Member, discord.User]):\n if not bot.bot:\n # pyright: reportUndefinedVariable=false\n return await r(ctx, \"Not a bot.\")\n\n data = await make_request(\"https://www.motiondevelopment.top/api/v1.2/bots/\", bot.id)\n\n e = discord.Embed(color=0xfecdea)\n e.description = f'**{data[\"owner_name\"]}** owns **{bot}**'\n\n await em(ctx, embed=e)","async def info(\n self, ctx, *, user: Union[discord.Member, FetchUserConverter] = None\n ):\n\n user = user or ctx.author\n if ctx.guild is not None and isinstance(user, discord.User):\n user = ctx.guild.get_member(user.id) or user\n\n embed = discord.Embed()\n embed.set_author(name=str(user))\n\n embed.add_field(name=\"ID\", value=user.id, inline=False)\n embed.add_field(\n name=\"Joined\",\n value=format_date(getattr(user, \"joined_at\", None)),\n inline=False,\n )\n embed.add_field(\n name=\"Created\",\n value=format_date(user.created_at),\n inline=False,\n )\n\n if isinstance(user, discord.Member):\n roles = [role.name.replace(\"@\", \"@\\u200b\") for role in user.roles]\n if len(roles) > 10:\n roles = [*roles[:9], f\"and {len(roles) - 9} more\"]\n embed.add_field(name=\"Roles\", value=\", \".join(roles), inline=False)\n else:\n embed.set_footer(text=\"This user is not in this server.\")\n\n async def reputation(user_id):\n vouches = await self.bot.mongo.db.vouch.count_documents({str(user_id): True})\n reports = await self.bot.mongo.db.vouch.count_documents({str(user_id): False})\n return (vouches, reports)\n \n rep = await reputation(user.id)\n vouch_content = []\n if rep[0]:\n vouch_content.append(f\"{rep[0]} vouches\")\n if rep[1]:\n vouch_content.append(f\"{rep[1]} reports\")\n if len(vouch_content) == 0:\n vouch_content.append(\"User is not vouched or reported by anyone.\")\n\n embed.add_field(\n name = \"Vouches and Reports\",\n value = \", \".join(vouch_content)\n )\n\n embed.color = user.color\n embed.set_thumbnail(url=user.avatar_url)\n\n await ctx.send(embed=embed)","def test_author_simple(self):\n invenio_search = 'author:\"ellis, j*\"'\n spires_search = 'find a ellis, j'\n self._compare_searches(invenio_search, spires_search)","def get_article_author(self, article_webpage):\n pass","def author_name(text):\n tag = text.split()\n\n \"\"\"\n We take the beginning of the text since the\n author name will likely be there\n \"\"\"\n\n tag = tag[:100]\n author = []\n\n current_tag = 0\n \"\"\"\n We go through each word until we find the first instance\n of the word 'by' or 'author', which should mean the author\n will be written right after that.\n We save the first word after 'by' or 'author' since it should\n be the authors first name\n \"\"\"\n\n for word in tag:\n if (word.lower() == ('by') or\n word.lower() == ('author') or\n word.lower() == ('author:')):\n\n author.append(tag[current_tag+1].decode(encoding='UTF8',\n errors='ignore'))\n current_tag += 1\n tag = tag[current_tag+1:]\n break\n current_tag += 1\n\n \"\"\"\n We go through each word after the first name of the author\n until we find a word that is not capitalized. We assume that\n it marks the end of the author name.\n We then return a list of the author's name split up.\n \"\"\"\n current_tag = 0\n for word in tag:\n if tag[current_tag].lower() == 'this':\n break\n if tag[current_tag].istitle():\n author.append(tag[current_tag].decode(encoding='UTF8',\n errors='ignore'))\n current_tag += 1\n\n return author","async def devox(self, ctx):\n member = discord.utils.find(lambda m: m.id == 250865328194715658, ctx.channel.guild.members)\n await ctx.send(\"{} The great man who created this bot some people say he has too much power, but the truth is he doesnt have enough\".format(member.mention))","async def follow(follow):\n await follow.edit(\n f\"`FOLLOW {DEFAULTUSER} ON` \\n\\n\"\n f\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\n\\n\"\n f\"[FaceBook](https://www.facebook.com/mkaraniya) \\n\\n\"\n f\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \"\n )","def __add_author(self, key_name, others_names, personal_information):\n for name in others_names:\n self.author_to_authorID[name] = (key_name, personal_information)","def is_judges_or_author(line_text):\n # line can't be too long\n if len(line_text) > 80:\n return False\n\n line_lower = line_text.lower()\n\n # 'Before DYKMAN, VERGERONT and LUNDSTEN, JJ.'\n # 'Present: All the Justices.'\n if any(line_lower.startswith(s) for s in (\"before\", \"present\", \"considered and decided by\")) or 'en banc' in line_lower:\n return \"judges\"\n\n # 'PER CURIAM.'\n if any(s in line_lower for s in ('per curiam', 'opinion by', 'by the court')):\n return \"author\"\n\n if any(s in line_lower for s in judge_titles):\n if \" and \" in line_text or \" & \" in line_text:\n return \"judges\"\n else:\n return \"author\"\n\n return None","def extra_bibparse(db):\n for key,entry in db.entries.items():\n for auth in entry.persons[\"author\"]:\n if (\"Harrison\" not in auth.first_names or\n \"Chapman\" not in auth.last_names):\n entry.add_person(auth, \"otherauthor\")","def getName(sentence): #Jasper, Suraj\n userWords = sentence.lower()\n userWords = userWords.split()\n \n # ways of introduction:\n # \"Hello, my name is ___\"\n # \"Hi, I'm ____\"\n # \"Howdy, I'm called ____\"\n # Order: Greeting -> pronoun -> Name -> question (optional)\n # eg. \"Hello, I'm Jasper. How are you?\"\n\n if (userWords[0] in greetings): #the added code that stops iam from being added into the name if 2 greeting are added\n userWords.pop(0) #pop and not .remove because\n \n \n if (userWords[0] == \"i\" and len(userWords) > 1):\n if (userWords[1] in [\"m\",\"am\"]):\n userWords.insert(0, \" \".join(userWords[0:2]))\n userWords.pop(2)\n userWords.pop(1)\n \n userName = \"\"\n for userWord in userWords: #iterate throught the user's words\n foundWord = False #sets True when there's a similar word in the other list\n for word in greetings: #iterates and compares the chosen word from the user's list of words to the words list\n if userWord == word and foundWord == False:\n foundWord = True\n if foundWord == False:\n userName = userName + userWord + \" \"\n return userName #this is the found name","def is_mention(self, bot):\n for um in self.original.entities['user_mentions']:\n if um['screen_name'] == bot.screen_name:\n return True\n return False","def on_whoisuser(self, raw_msg, nick, user, host, **kwargs):","def test_super_short_author_name(self):\n spi_search = \"fin a er and cn cms\"\n inv_search = \"author:er collaboration:cms\"\n self._compare_searches(inv_search, spi_search)","def entity_mentions(adm):\n for entity in adm['attributes']['entities']['items']:\n for mention in entity['mentions']:\n # Augment mentions with the entity type of the entity they refer to\n mention['type'] = entity.get('type')\n yield mention","def show_user(title, fname, lname):\n person = \"{} {} {}\".format(title, fname, lname)\n logger.info(\"{}\".format(person))\n return person","async def treatme(self, ctx):\n await ctx.send(await self.cure_user(ctx, ctx.author))","def test_author_full_initial(self):\n inv_search = 'author:\"klebanov, ig* r*\" or exactauthor:\"klebanov, i r\"'\n spi_search = \"find a klebanov, ig.r.\"\n self._compare_searches(inv_search, spi_search)"],"string":"[\n \"def mention(self) -> str:\",\n \"def mention(self) -> str:\",\n \"def display_person(person):\\n name = person['name']\\n followers = person['follower_count']\\n description = person['description']\\n country = person['country']\\n print(f'{name}, a(n) {description}, from {country}.')\\n return followers\",\n \"async def user(self, ctx, member: discord.Member = None):\\r\\n rank = 1\\r\\n if member is None:\\r\\n member = ctx.author\\r\\n x = member.name\\r\\n for (k, v) in sorted(player.items(), key=lambda x: expose(x[1]), reverse=True):\\r\\n if k == x:\\r\\n start = '```md' + u\\\"\\\\u000A\\\" + '-'*68 + u\\\"\\\\u000A\\\" + u\\\"\\\\u000A\\\" + 'User Info: '\\r\\n middle = u\\\"\\\\u000A\\\" + u\\\"\\\\u000A\\\" + '[rank][#' + str(rank) + '] [rating]['\\r\\n end = str(round(expose(v), 2)) + '] [skill][' + str(round(v.mu, 2)) + '] [uncertainty]['\\r\\n await ctx.send(start + k + middle + end + str(round(v.sigma, 2)) + ']' + u\\\"\\\\u000A\\\" + u\\\"\\\\u000A\\\" + '-'*68 + u\\\"\\\\u000A\\\" + '```')\\r\\n if rank == 1:\\r\\n await ctx.send(k + ' is the G O A T 🐐')\\r\\n break\\r\\n rank += 1\",\n \"def get_person_text(self, uid):\\n words = \\\"\\\"\\n\\n query = \\\"\\\"\\\"\\nSELECT ?overview ?researchO ?label\\nWHERE\\n{\\n <%s> <http://vivoweb.org/ontology/core#overview> ?overview .\\n <%s> <http://vivoweb.org/ontology/core#researchOverview> ?researchO .\\n <%s> <http://www.w3.org/2000/01/rdf-schema#label> ?label .\\n}\\n \\\"\\\"\\\" % (uid, uid, uid)\\n self.setQuery(query)\\n try:\\n rval = self.query()\\n try:\\n g = rval.convert()\\n except:\\n pass\\n words = \\\"%s %s %s\\\" % (g['results']['bindings'][0]['overview']['value'], g['results']['bindings'][0]['researchO']['value'], g['results']['bindings'][0]['label']['value'])\\n except:\\n print \\\"Select failed: %s\\\" % query\\n\\n self.setQuery(\\\"\\\"\\\"\\nPREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\\nPREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\\nPREFIX vivo: <http://vivoweb.org/ontology/core#>\\nPREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\\nSELECT ?name\\nWHERE\\n{\\n ?auth vivo:relates <%s> .\\n ?auth rdf:type vivo:Authorship .\\n ?auth vivo:relates ?art .\\n filter (?art!=<%s>) .\\n ?art <http://vivoweb.org/ontology/core#dateTimeValue> ?date .\\n ?date <http://vivoweb.org/ontology/core#dateTime> ?year .\\n filter (?year>\\\"2009-01-01T00:00:00Z\\\"^^xsd:dateTime) .\\n ?art rdfs:label ?name .\\n}\\nLIMIT 20\\n\\\"\\\"\\\" % (uid, uid))\\n try:\\n rval = self.query()\\n try:\\n g = rval.convert()\\n except:\\n pass\\n for t in g['results']['bindings']:\\n words = words + \\\" \\\" + t['name']['value']\\n\\n except:\\n print \\\"Select failed\\\"\\n traceback.print_exc(file=sys.stdout)\\n\\n self.setQuery(\\\"\\\"\\\"\\nPREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\\nPREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\\nPREFIX vivo: <http://vivoweb.org/ontology/core#>\\nPREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\\n\\nSELECT ?name\\nWHERE\\n{\\n ?grant vivo:relates <%s> .\\n ?grant rdf:type vivo:Grant .\\n ?grant <http://vivoweb.org/ontology/core#dateTimeInterval> ?date .\\n ?date <http://vivoweb.org/ontology/core#end> ?end .\\n ?end <http://vivoweb.org/ontology/core#dateTime> ?year .\\n filter (?year>\\\"2009-01-01T00:00:00Z\\\"^^xsd:dateTime) .\\n ?grant rdfs:label ?name .\\n}\\n\\n \\\"\\\"\\\" % (uid))\\n try:\\n rval = self.query()\\n try:\\n g = rval.convert()\\n except:\\n pass\\n\\n for t in g['results']['bindings']:\\n words = words + \\\" \\\" + t['name']['value']\\n\\n except:\\n print \\\"Select failed\\\"\\n traceback.print_exc(file=sys.stdout)\\n\\n\\n\\n\\n return words\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"async def ign_whoami(self, ctx):\\n user = ctx.message.author\\n igns = self.names.get(user.mention)\\n if not igns:\\n await self.bot.say(\\\"You have not yet entered any IGN info. :cry:\\\".format(user.mention))\\n else:\\n await self.bot.say(self.format_igns(user, igns))\",\n \"def mention(result):\\n return result.text.find('@') != -1\",\n \"def mention(self) -> str:\\n raise NotImplementedError\",\n \"async def ign_whois(self, user: discord.Member):\\n igns = self.names.get(user.mention)\\n if not igns:\\n await self.bot.say(\\\"{0} has not yet entered any IGN info. :cry:\\\".format(user.mention))\\n else:\\n await self.bot.say(self.format_igns(user, igns))\",\n \"def mention(cls, user, message, mentioned):\\r\\n pass\",\n \"def detect_author(user_to_tweets: Dict[str, List[tuple]], tweet_text: str) -> \\\\\\n str:\\n acc = []\\n \\n for keys in user_to_tweets:\\n author_hashes = hashtag_seperator(user_to_tweets[keys])\\n text_hashes = extract_hashtags(tweet_text)\\n if set(text_hashes).issubset(author_hashes):\\n acc.append(keys)\\n if len(acc) == 1:\\n return acc[0]\\n return 'unknown'\",\n \"def get_info(hit):\\n mention = Mention(hit)\\n return dict(\\n url = mention.info[\\\"url\\\"],\\n title = mention.info[\\\"title\\\"],\\n date = mention.info[\\\"datetime_date\\\"] or datetime.date(1970, 1, 1),\\n type = 'news' if mention.in_the_news else 'print',\\n author = '(need author)',\\n media = mention.info[\\\"media\\\"],\\n )\",\n \"def _get_user_mention(self, name: str) -> str:\\n try:\\n return self.known_user_ids[name]\\n except KeyError:\\n pass\\n\\n mention = name\\n users = get_value_from_redis('slack-members') or []\\n user = self._get_user(name, users)\\n if user:\\n mention = f'<@{user[\\\"id\\\"]}>'\\n\\n self.known_user_ids[name] = mention\\n\\n return mention\",\n \"def get_author_info(self, author: str):\\n for writer_word in self._writer_words:\\n data = json.loads(requests.get(WIKIDATA_SEARCH + \\\"&srsearch=\\\" + author + \\\" \\\" + writer_word).text)\\n pages = data.get(\\\"query\\\").get(\\\"search\\\")\\n if len(pages) >= 1:\\n pageid = pages[0].get(\\\"title\\\")\\n author_details = self._reference.author_map.get(author)\\n if author_details:\\n return author_details\\n if pageid == -1:\\n continue\\n\\n else:\\n response = requests.get(WIKIDATA_PARSE + pageid + \\\".json\\\")\\n data = json.loads(response.text)\\n if author.lower() not in data.get(\\\"entities\\\").get(pageid).get(\\\"labels\\\").get(\\\"en\\\").get(\\\"value\\\").lower():\\n continue\\n else:\\n try:\\n id = data.get(\\\"entities\\\").get(pageid).get(\\\"claims\\\").get(\\\"P31\\\")[0].get(\\\"mainsnak\\\").get(\\\"datavalue\\\").get(\\\"value\\\").get(\\\"id\\\")\\n if str(id) != \\\"Q5\\\": # the id for human\\n continue\\n except IndexError:\\n continue\\n properties = data.get(\\\"entities\\\").get(pageid).get(\\\"claims\\\")\\n author_details = {\\\"id\\\": pageid, \\\"gender\\\": self.get_gender(properties)}\\n country_details = self.get_country(properties)\\n author_details[\\\"country\\\"] = country_details\\n self._reference.author_map[author] = author_details\\n return author_details\\n return {\\\"id\\\": \\\"Unknown\\\", \\\"gender\\\": \\\"Unknown\\\", \\\"country\\\": [{\\\"name\\\": \\\"Unknown\\\", \\\"region\\\": \\\"Unknown\\\"}]}\",\n \"async def love(ctx, user: discord.Member):\\r\\n author = ctx.message.author\\r\\n if user.id == ctx.bot.user.id:\\r\\n await ctx.send(\\\"I am not capable of loving like you can. I'm sorry.\\\" )\\r\\n else:\\r\\n await ctx.send(author.mention + \\\" is capable of loving \\\" + user.mention + \\\" a whopping \\\" +\\r\\n str(randint(0, 100)) + \\\"%!\\\")\\r\\n ctx.counter(n)\",\n \"def replace_user_mentions(text):\\n text = re.sub('\\\\B@\\\\w*[a-zA-Z]+\\\\w*', 'USERMENTION', text)\\n return text\",\n \"def author(self):\\n\\t\\tauthor = re.search(r\\\"([Ff]rom\\\\s)(.+) ([tT]\\\\s*o)([^,]+),\\\",self.raw_text()[:150])\\n\\t\\treport = re.search(r\\\".*[Rr]eport of ([^,]+),\\\",self.raw_text()[:150])\\n\\t\\torder = re.search(r\\\".*[Oo]rder of ([^,]+)[,.]* to\\\",self.raw_text()[:250])\\n\\t\\torder2 = re.search(r\\\".*[Oo]rder of ([^,]+),\\\",self.raw_text()[:250])\\n\\t\\tlogbook = re.search(r\\\".*[lL]og of ([^,]+)[,.]\\\",self.raw_text()[:250])\\n\\t\\tif order:\\n\\t\\t\\torder = order.group(1)\\n\\t\\t\\treturn order\\n\\t\\tif order2:\\n\\t\\t\\torder2 = order2.group(1)\\n\\t\\t\\treturn order2\\n\\t\\tif logbook:\\n\\t\\t\\tlogbook = logbook.group(1)\\n\\t\\t\\treturn logbook\\n\\t\\tif report:\\n\\t\\t\\treport = report.group(1)\\n\\t\\t\\treturn report\\n\\t\\tif author: \\t\\n\\t\\t\\tauthor = author.group(2) \\n\\t\\t\\tauthor = re.sub(r\\\"([^,]*),.*\\\",r\\\"\\\\1\\\",author)\\t \\n\\t\\t\\treturn author\\n\\t\\t\\n\\t\\treturn \\\"Unknown\\\"\",\n \"def covid_mention(text, synonyms=covid_list):\\n for term in synonyms:\\n if term in text:\\n return 'Yes'\\n continue\\n return 'No'\",\n \"def mention(bot, msg):\\n\\n if msg.command != \\\"PRIVMSG\\\":\\n return\\n\\n message = msg.args[1]\\n\\n if bot.nickname.lower() in message.lower():\\n bot.privmsg(msg.sendername, \\\"You called?\\\")\",\n \"def followed_by_hillary_and_donald(users, twitter):\\n ###TODO-- Completed\\n for user in users:\\n if user['screen_name'] == 'HillaryClinton':\\n friends_Hillary = user['friends']\\n #print(len(friends_Hillary))\\n elif user['screen_name'] == 'realDonaldTrump':\\n friends_donald = user['friends']\\n #print(len(friends_donald))\\n\\n common_followed_id = list(set(friends_Hillary) & set(friends_donald))\\n\\n commn_followed_user = robust_request(twitter,'users/lookup',{'user_id':common_followed_id}).json()\\n #print(commn_followed_user[0]['screen_name'])#['screen_name'])\\n return commn_followed_user[0]['screen_name']\\n #pass\",\n \"def parse_direct_mention(message_text):\\n # print(message_text)\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def _parse_text_as_direct_mention(\\n message_text: str,\\n ) -> tuple[Optional[str], Optional[str]]:\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n if matches:\\n return matches.group(1), matches.group(2).strip()\\n return None, None\",\n \"def mention_user(self, username: str) -> str:\\n member = self.get_user(username)\\n if member:\\n return member.mention\\n return username\",\n \"def crawl_person_infos(uid):\\n if not uid:\\n return\\n\\n user = user_get.get_profile(uid)\\n # If it's enterprise user, just skip it\\n if user.verify_type == 2:\\n set_seed_other_crawled(uid)\\n return\\n\\n # Crawl fans and followers\\n celery.send_task('celery_tasks.weibo.user.crawl_follower_fans', args=(uid,), queue='fans_followers',\\n routing_key='for_fans_followers')\",\n \"def displayFolowers(database):\\n firstname=str(input(\\\"who do you want to display followers :\\\"))\\n usr,find=getByName(database,firstname)\\n if find:\\n print(f\\\"{usr.firstname} {usr.lastname} is folowed by:\\\")\\n for folower in usr.folowed:\\n print(folower)\",\n \"def mention(cls, user, message, mentioned):\\n pass\",\n \"def test_refersto_author_multi_name(self):\\n inv_search = 'author:ellis refersto:author:\\\"parke, s. j.\\\"'\\n spi_search = 'find a ellis and refersto author \\\"parke, s. j.\\\"'\\n self._compare_searches(inv_search, spi_search)\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n print (matches)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def userFollowers(nick):\\n if (len(nick) != 1):\\n print \\\"Has d'introduir només un nick\\\"\\n return\\n i.userFollow(nick[0])\",\n \"def parse_direct_mention(message_text):\\n\\tmatches = re.search(MENTION_REGEX, message_text)\\n\\t# the first group contains the username, the second group contains the remaining message\\n\\treturn (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # 1st gourp has username, 2nd group called remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def detect_person(snap):\\n pass\",\n \"def parse_direct_mention(self,message_text):\\n matches = re.search(self.MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def parse_direct_mention(message_text):\\r\\n matches = re.search(MENTION_REGEX, message_text)\\r\\n # the first group contains the username, the second group contains the remaining message\\r\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def mentor_list_view(request):\\n # TODO: this view\\n pass\",\n \"def user(inp):\\n user = inp.text.lower().replace(' ', '-')\\n return 'http://www.wikidot.com/user:info/' + user\",\n \"def test_author_many_lastnames(self):\\n inv_search = 'author:\\\"alvarez gaume, j* r* r*\\\"'\\n spi_search = 'find a alvarez gaume, j r r'\\n self._compare_searches(inv_search, spi_search)\",\n \"def followed_by_hillary_and_donald(users, twitter):\\n\\n str = ''\\n set1 = set()\\n set2 = set()\\n for u_dict in users:\\n \\tif u_dict['screen_name'] == 'HillaryClinton':\\n \\t\\tset1 = set(u_dict['friends'])\\n \\telif u_dict['screen_name'] == 'realDonaldTrump':\\n \\t\\tset2 = set(u_dict['friends'])\\n \\t\\t\\n common = set.intersection(set1, set2)\\n request = robust_request(twitter, 'users/lookup', {'user_id': common}, max_tries=5)\\n for user in request:\\n \\tstr = user['screen_name']\\t\\n return str\",\n \"async def info(self, context):\\n await context.send('creador: debellisnahuel@gmail.com\\\\ncolabs:\\\\n emi: https://twitter.com/emilianosce/ o https://www.instagram.com/emilianosce/ \\\\n garza: https://twitter.com/Matias_Garcia00 o https://www.twitch.tv/garzangb')\",\n \"def compute_user_mentions_name(row):\\n entity_series = pd.read_json(json.dumps(row['entities']), typ='series')\\n user_mentions_name = list(map(lambda entry: entry['name'], entity_series['user_mentions']))\\n return ','.join(user_mentions_name)\",\n \"def mentee_list_view(request):\\n # TODO: this view\\n pass\",\n \"def is_explicit_mention(self, bot):\\n for um in self.original.entities['user_mentions']:\\n if um['screen_name'] == bot.screen_name:\\n this_is_an_xm = um['indices'][0] >= self.original.display_text_range[0]\\n this_is_an_xm &= um['indices'][1] <= self.original.display_text_range[1]\\n if this_is_an_xm:\\n return True\\n return False\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains\\n # the remaining message\\n return (matches.group(1),\\n matches.group(2).strip()) if matches else (None, None)\",\n \"def like_to_party(msg):\\n if message.rate_limit(msg.settings, 'like_to_party'):\\n return\\n return \\\"%s, I know for a fact you don't party. You do *not* party.\\\" \\\\\\n % msg.name\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def who_am_i():\\n return {'name': 'Jonathan Martinez', 'id': '201095569', 'email': 'martijon@post.bgu.ac.il'}\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group\\n # contains the remaining message\\n return (matches.group(1),\\n matches.group(2).strip()) if matches else (None, None)\",\n \"def get_mentions(texts, word):\\r\\n mentions = {}\\r\\n for text, label, text_id in texts:\\r\\n if word in text.lower():\\r\\n if label not in mentions:\\r\\n mentions[label] = [text_id]\\r\\n else:\\r\\n if text_id not in mentions[label]:\\r\\n mentions[label].append(text_id)\\r\\n return mentions\",\n \"def _talk_about(self, about, data):\\n if not data:\\n return\\n\\n if about == 'faces':\\n for face in data:\\n self._seen_faces.append(face['identity'])\\n\\n all_people = list(set(self._seen_faces))\\n all_known_people = [name for name in all_people if name is not None]\\n\\n if len(all_known_people) == 0:\\n text = \\\"Hello. I haven't seen you before. Welcome!\\\"\\n elif len(all_known_people) == 1:\\n text = \\\"Hello {}\\\".format(all_known_people[0])\\n elif len(all_people) <= 3:\\n all_but_last = ', '.join([name or 'stranger' for name in all_people[:-1]])\\n last = all_people[-1] or 'stranger'\\n names = ' and '.join([all_but_last, last])\\n text = 'Hello {}'.format(names)\\n elif len(all_known_people) > 3:\\n text = 'Hi everybody. What a crowd! Why don\\\\'t you get back to work?'\\n self._say_prudent(text)\",\n \"def parse_direct_mention(message_text):\\n matches = re.search(MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, message_text.strip())\",\n \"def person_f(x: Text) -> Tuple[Text, Text]:\\n return \\\"person\\\", x.lower()\",\n \"def get_bookshare_user_info(patron):\\n pass\",\n \"def find_new_people(self):\\n #greets people, only greets once while they're in the camera's view and are center of attention\\n\\n\\n if (self.person is not None) and (self.person.acknowledged == False):\\n self.person.acknowledged = True\\n print \\\"I see you!\\\"\\n self.idle_pub.publish(\\\"idle:stop\\\")\\n time.sleep(2)\\n\\n greeting = [\\\"R_nudge\\\",\\\"R_look\\\"]\\n for msg in greeting:\\n self.behavior_pub.publish(msg)\\n self.check_completion()\\n\\n\\n self.detection_pub.publish('found')\\n\\n elif self.person is None:\\n print \\\"I don't see you\\\"\\n self.detection_pub.publish('nothing')\",\n \"async def info(self, ctx, *, member: disnake.Member = None):\\n\\n member = member or ctx.author\\n\\n e = disnake.Embed(description=\\\"\\\")\\n\\n if member.bot:\\n e.description = \\\"This account is a bot.\\\\n\\\\n\\\"\\n\\n e.description += member.mention\\n\\n e.add_field(name=\\\"Status\\\", value=member.status)\\n\\n if member.activity:\\n e.add_field(name=\\\"Activity\\\", value=member.activity.name)\\n\\n e.set_author(name=str(member), icon_url=member.display_avatar.url)\\n\\n now = datetime.now(timezone.utc)\\n created = member.created_at\\n joined = member.joined_at\\n\\n e.add_field(\\n name=\\\"Account age\\\",\\n value=\\\"{0} • Created <t:{1}:F>\\\".format(\\n pretty_timedelta(now - created), round(created.timestamp())\\n ),\\n inline=False,\\n )\\n\\n e.add_field(\\n name=\\\"Member for\\\",\\n value=\\\"{0} • Joined <t:{1}:F>\\\".format(\\n pretty_timedelta(now - joined), round(joined.timestamp())\\n ),\\n )\\n\\n if len(member.roles) > 1:\\n e.add_field(\\n name=\\\"Roles\\\",\\n value=\\\" \\\".join(role.mention for role in reversed(member.roles[1:])),\\n inline=False,\\n )\\n\\n e.set_footer(text=\\\"ID: \\\" + str(member.id))\\n\\n await ctx.send(embed=e)\",\n \"def test_citedby_author(self):\\n inv_search = 'citedby:author:doggy'\\n spi_search = 'find citedby author doggy'\\n self._compare_searches(inv_search, spi_search)\",\n \"def mention(self) -> str:\\n return f\\\"<@{self.id}>\\\"\",\n \"def process_person(self,msg):\\n self.personx = msg.x\\n self.persony = msg.y\",\n \"def somebody(g, name, homepage, who=None):\\n FOAF = Namespace(\\\"http://xmlns.com/foaf/0.1/\\\")\\n if not who:\\n who = BNode()\\n g.add((who, FOAF.name, Literal(name)))\\n g.add((who, FOAF.homepage, URIRef(homepage)))\\n #if email:\\n # g.add((who, FOAF.homepage, URIRef(\\\"mailto:%s\\\" % email)))\\n return who\",\n \"def test_refersto_author(self):\\n inv_search = 'refersto:author:kitty'\\n spi_search = 'find refersto author kitty'\\n self._compare_searches(inv_search, spi_search)\",\n \"def personUp(person):\\n\\timport os\\n\\timport wikipedia\\n\\timport speak\\n\\ttry:\\n\\t\\tsay(wikipedia.summary(person,sentences=2))\\n\\t\\t# os.system('clear')\\n\\texcept wikipedia.exceptions.DisambiguationError:\\n\\t\\tresults = wikipedia.search(person)\\n\\t\\tos.system('clear')\\n\\t\\tspeak.speak('The closest I could find was : ' + results[1])\\n\\t\\tsay(wikipedia.summary(results[1],sentences=2))\\n\\texcept Exception:\\n\\t\\tsay(\\\"Couldn't find anything...\\\")\",\n \"def test_refersto_author_multi_name_no_quotes(self):\\n inv_search = 'author:ellis refersto:(author:\\\"parke, sj*\\\" or exactauthor:\\\"parke, s *\\\" or exactauthor:\\\"parke, s\\\" or author:\\\"parke, sj, *\\\")'\\n spi_search = \\\"find a ellis and refersto author parke, sj\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"async def info_user(self, ctx, member: Optional[discord.Member]):\\n member1 = member or ctx.author\\n embed = discord.Embed(title=\\\"Member Information\\\",\\n color=discord.Color.blurple(),\\n timestamp=datetime.utcnow())\\n\\n embed.add_field(name=\\\"ID\\\", value=f\\\"{member1.id}\\\", inline=False)\\n embed.add_field(\\n name=\\\"Name\\\", value=f\\\"{member1.name}#{member1.discriminator}\\\")\\n embed.add_field(name=\\\"Top role\\\", value=f\\\"{member1.top_role.mention}\\\")\\n embed.add_field(name=\\\"status\\\",\\n value=f\\\"{str(member1.activity.type).split('.') if member1.activity else 'N/A'} {member1.activity.name if member1.activity else ''}\\\")\\n embed.add_field(\\n name=\\\"created at\\\", value=f\\\"{member1.created_at.strftime('%d/%m/%y %H:%M:%S')}\\\")\\n embed.add_field(\\n name=\\\"Joined at\\\", value=f\\\"{member1.joined_at.strftime('%d/%m/%y %H:%M:%S')}\\\")\\n embed.add_field(name=\\\"Boosted?\\\", value=f\\\"{member1.premium_since}\\\")\\n\\n await ctx.reply(embed=embed)\",\n \"def test_citedby_refersto_author(self):\\n inv_search = 'refersto:citedby:author:penguin'\\n spi_search = 'find refersto citedby author penguin'\\n self._compare_searches(inv_search, spi_search)\",\n \"def get_mentions(fname):\\n capture = re.compile('NPRI in ?.* ?(news|print)', re.I)\\n with open(fname) as fp:\\n for line in fp:\\n line = line.strip()\\n if capture.search(line):\\n yield get_info(line)\",\n \"async def confused(self, ctx, member: discord.Member = None):\\n member = member if member else ctx.author\\n members = [m for m in ctx.guild.members if not m == member]\\n async with ctx.typing():\\n image = await self.request(endpoint=\\\"generators/confused\\\", params=f\\\"?avatar={member.avatar_url_as(format='png')}&photo={random.choice(members).avatar_url_as(format='png')}\\\")\\n await ctx.send(f\\\"**{member.name}** is confusion.\\\", file=discord.File(image, filename=\\\"file.png\\\"))\",\n \"def who(id, client, name):\\r\\n exists = person.locate(id)\\r\\n\\r\\n if not exists:\\r\\n person.create(id, name)\\r\\n client.send_message(id, 'Hello ' + name + '''!\\r\\nI am your Journal Buddy :) My purpose is to help you manage your productivity!\\r\\n\\r\\nYou can:\\r\\n - Set up habits\\r\\n - Schedule events\\r\\n - Create a task pool\\r\\n - Set reminders\\r\\n - Add notes - tell me things you don’t want to forget! May it be a quote, or a movie you liked\\r\\n\\r\\nEvery month we can choose which tasks you would like to complete in the month ahead!\\r\\nEvery week I will help you set goals in terms of tasks and habits for the week ahead!\\r\\n\\r\\nThe more we get to know each other the more I will be able to help! Providing weekly analytics and suggestions for future performance. Tell me how you feel throughout your tasks, habits and events and I will share with you what I think will work best.\\r\\n\\r\\nType /setup to begin!''')\\r\\n\\r\\n users[id] = {\\\"last\\\": \\\"new user\\\"}\\r\\n elif not (users.keys().__contains__(id)):\\r\\n users[id] = {\\\"last\\\": \\\"welcome back\\\"}\",\n \"def parse_direct_mention(self, message_text):\\n matches = re.search(self.MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def parse_direct_mention(self, message_text):\\n matches = re.search(self.MENTION_REGEX, message_text)\\n # the first group contains the username, the second group contains the remaining message\\n return (matches.group(1), matches.group(2).strip()) if matches else (None, None)\",\n \"def taco_mention_callback(bot, message):\\n\\n cid = get_cid(message)\\n store_name(message.from_user)\\n\\n chat = Chats.get(Chats.cid == message.chat.id)\\n clean_chat(chat.mids, chat.cid, bot, None)\\n\\n ok_button = InlineKeyboardButton('OK', callback_data='delete:{}'.format(message.from_user.id))\\n ok_keyboard = InlineKeyboardMarkup([[ok_button]])\\n\\n mentioned_users = list()\\n for entity in message.entities:\\n if entity.type == 'mention':\\n user = message.text[entity.offset: entity.offset + entity.length].lower()\\n mentioned_users.append(user)\\n mentioned_users = list(set(mentioned_users))\\n\\n if len(mentioned_users) > 1:\\n if chat.less is True:\\n text = only_one_receiver_phrase.split('\\\\n')[0]\\n else:\\n text = only_one_receiver_phrase\\n mid = bot.send_message(chat_id=cid,\\n text=text,\\n reply_to_message_id=get_mid(message),\\n reply_markup=ok_keyboard,\\n parse_mode='html').message_id\\n\\n chat.mids = [mid]\\n chat.save()\\n return\\n\\n sender = message.from_user\\n receiver_username: str = ensure_no_at_sign(mentioned_users[0])\\n\\n try:\\n receiver = bot.get_chat_member(chat_id=cid,\\n user_id=receiver_username).user\\n\\n except Exception:\\n \\\"\\\"\\\" here should be except UserNotParticipant, but it still raises this exception \\\"\\\"\\\"\\n if chat.less is True:\\n text = user_not_present_phrase.split('\\\\n')[0]\\n else:\\n text = user_not_present_phrase\\n mid = bot.send_message(chat_id=cid,\\n text=text.format(ensure_username(receiver_username)),\\n reply_to_message_id=get_mid(message),\\n reply_markup=ok_keyboard,\\n parse_mode='html').message_id\\n\\n chat.mids = [mid]\\n chat.save()\\n\\n return\\n\\n give_tacos(bot, message, sender, receiver)\",\n \"async def profilepicture(self, ctx):\\n if not ctx.message.mentions:\\n await ctx.send(embed=discord.Embed().set_image(\\n url=ctx.author.avatar_url))\\n else:\\n for member in ctx.message.mentions:\\n await ctx.send(embed=discord.Embed().set_image(\\n url=member.avatar_url))\",\n \"def user_suggested(username):\\n raise NotImplementedError()\",\n \"def follow_reciprocated(self, target):\\n if random.randint(1, 1000) == 1: # 1 in 20 are public @replies\\n self.tweet_user(target)\\n else:\\n try:\\n self.dm_user(target)\\n except:\\n pass\",\n \"def whois( self, mess, args):\\n user = self.get_sender_username(mess)\\n args = args.strip().replace(' ', '_')\\n if user in self.users:\\n self.log.info('%s queried whois %s.' % (user, args))\\n if args in self.users.values():\\n return filter(lambda u: self.users[u] == args, self.users)[0]\\n else:\\n return 'Nobody!'\",\n \"def subject_info(intent, extra_info=[]):\\n\\n text = intent['inputTranscript'].lower()\\n utterances = AS.load_file('sample_utterances.txt')\\n\\n # add \\\"book\\\" and \\\"books\\\" to every utterance\\n for line in list(utterances):\\n utterances.insert(0, line + \\\" book\\\")\\n utterances.insert(0, line + \\\" books\\\")\\n\\n # tells how many characters needs to be dropped before the subject starts\\n to_drop = 0\\n\\n for line in utterances:\\n if text.startswith(line):\\n to_drop = len(line)\\n break\\n\\n # drops the characters and makes a list from the strings that are left\\n text = text[to_drop:].strip()\\n text_list = text.split(' ', len(text))\\n\\n subject_list = []\\n keywords = [\\\"books\\\", \\\"book\\\", \\\"by\\\", \\\"published\\\", \\\"written\\\"]\\n keyword = \\\"\\\"\\n\\n # Find out when the book name ends\\n for word in text_list:\\n if word not in keywords:\\n subject_list.append(word)\\n else:\\n break\\n\\n subject = \\\" \\\".join(subject_list)\\n\\n # Get all the keywords in the middle, so they can be\\n # all be dropped at once, eg written by, books by\\n text_list = text_list[len(subject_list):]\\n if text_list:\\n word = text_list[0]\\n while word in keywords:\\n keyword += word + \\\" \\\"\\n text_list = text_list[1:]\\n if text_list:\\n word = text_list[0]\\n else:\\n break\\n\\n # search for an author from the rest of the characters\\n author_text = text[len(keyword):].strip()\\n author = AS.search(author_text, False)\\n if author is \\\"\\\":\\n author = None\\n\\n # There might be old info in the extra_info (author), so \\n # we need to clear it\\n extra_info.clear()\\n\\n # add the author to extra info so it can be used in the Finna API call\\n if author:\\n extra_info += [\\\"author:\\\\\\\"\\\" + author + \\\"\\\\\\\"\\\"]\\n elif intent['sessionAttributes'].get('author'):\\n extra_info += [\\n \\\"author:\\\\\\\"\\\" + intent['sessionAttributes']['author'] + \\\"\\\\\\\"\\\"\\n ]\\n\\n # The Finna API call\\n request = lookfor(term=subject, filter=extra_info)['json']\\n\\n return parse_subject(request, subject, {'author': author})\",\n \"def substantiate():\",\n \"def _guess_author(talk_url, document):\\r\\n elements = _AUTHOR_BIO_XPATH(document)\\r\\n if elements:\\r\\n author_bio_url = urljoin(SITE_URL, elements[0].get('href'))\\r\\n author_bio_document = html.parse(author_bio_url)\\r\\n return _clean_up_file_name(\\r\\n author_bio_document.find('/head/title').text.split('|')[0].strip()\\r\\n )\\r\\n \\r\\n logging.warning(\\\"Failed to guess the author of '%s'\\\", talk_url)\\r\\n return 'Unknown'\",\n \"async def imitate(self, ctx):\\n message = ctx.message.content.strip(' ').split(' ')\\n user_id = ''\\n if len(message) == 1:\\n response = self.message.returnMarkovMsgError()\\n await self.bot.say(response)\\n elif message[1].casefold() == 'me'.casefold():\\n user_id = ctx.message.author.id\\n print(user_id)\\n else:\\n user_id = message[1][2:-1]\\n print(user_id)\\n\\n # if(does_markov_exist(user_id)):\\n # markov_chain = get_markov_chain(user_id)\\n # # Do something with chain\\n # else:\\n # # Make chain\",\n \"async def whoowns(ctx, bot: typing.Union[discord.Member, discord.User]):\\n if not bot.bot:\\n # pyright: reportUndefinedVariable=false\\n return await r(ctx, \\\"Not a bot.\\\")\\n\\n data = await make_request(\\\"https://www.motiondevelopment.top/api/v1.2/bots/\\\", bot.id)\\n\\n e = discord.Embed(color=0xfecdea)\\n e.description = f'**{data[\\\"owner_name\\\"]}** owns **{bot}**'\\n\\n await em(ctx, embed=e)\",\n \"async def info(\\n self, ctx, *, user: Union[discord.Member, FetchUserConverter] = None\\n ):\\n\\n user = user or ctx.author\\n if ctx.guild is not None and isinstance(user, discord.User):\\n user = ctx.guild.get_member(user.id) or user\\n\\n embed = discord.Embed()\\n embed.set_author(name=str(user))\\n\\n embed.add_field(name=\\\"ID\\\", value=user.id, inline=False)\\n embed.add_field(\\n name=\\\"Joined\\\",\\n value=format_date(getattr(user, \\\"joined_at\\\", None)),\\n inline=False,\\n )\\n embed.add_field(\\n name=\\\"Created\\\",\\n value=format_date(user.created_at),\\n inline=False,\\n )\\n\\n if isinstance(user, discord.Member):\\n roles = [role.name.replace(\\\"@\\\", \\\"@\\\\u200b\\\") for role in user.roles]\\n if len(roles) > 10:\\n roles = [*roles[:9], f\\\"and {len(roles) - 9} more\\\"]\\n embed.add_field(name=\\\"Roles\\\", value=\\\", \\\".join(roles), inline=False)\\n else:\\n embed.set_footer(text=\\\"This user is not in this server.\\\")\\n\\n async def reputation(user_id):\\n vouches = await self.bot.mongo.db.vouch.count_documents({str(user_id): True})\\n reports = await self.bot.mongo.db.vouch.count_documents({str(user_id): False})\\n return (vouches, reports)\\n \\n rep = await reputation(user.id)\\n vouch_content = []\\n if rep[0]:\\n vouch_content.append(f\\\"{rep[0]} vouches\\\")\\n if rep[1]:\\n vouch_content.append(f\\\"{rep[1]} reports\\\")\\n if len(vouch_content) == 0:\\n vouch_content.append(\\\"User is not vouched or reported by anyone.\\\")\\n\\n embed.add_field(\\n name = \\\"Vouches and Reports\\\",\\n value = \\\", \\\".join(vouch_content)\\n )\\n\\n embed.color = user.color\\n embed.set_thumbnail(url=user.avatar_url)\\n\\n await ctx.send(embed=embed)\",\n \"def test_author_simple(self):\\n invenio_search = 'author:\\\"ellis, j*\\\"'\\n spires_search = 'find a ellis, j'\\n self._compare_searches(invenio_search, spires_search)\",\n \"def get_article_author(self, article_webpage):\\n pass\",\n \"def author_name(text):\\n tag = text.split()\\n\\n \\\"\\\"\\\"\\n We take the beginning of the text since the\\n author name will likely be there\\n \\\"\\\"\\\"\\n\\n tag = tag[:100]\\n author = []\\n\\n current_tag = 0\\n \\\"\\\"\\\"\\n We go through each word until we find the first instance\\n of the word 'by' or 'author', which should mean the author\\n will be written right after that.\\n We save the first word after 'by' or 'author' since it should\\n be the authors first name\\n \\\"\\\"\\\"\\n\\n for word in tag:\\n if (word.lower() == ('by') or\\n word.lower() == ('author') or\\n word.lower() == ('author:')):\\n\\n author.append(tag[current_tag+1].decode(encoding='UTF8',\\n errors='ignore'))\\n current_tag += 1\\n tag = tag[current_tag+1:]\\n break\\n current_tag += 1\\n\\n \\\"\\\"\\\"\\n We go through each word after the first name of the author\\n until we find a word that is not capitalized. We assume that\\n it marks the end of the author name.\\n We then return a list of the author's name split up.\\n \\\"\\\"\\\"\\n current_tag = 0\\n for word in tag:\\n if tag[current_tag].lower() == 'this':\\n break\\n if tag[current_tag].istitle():\\n author.append(tag[current_tag].decode(encoding='UTF8',\\n errors='ignore'))\\n current_tag += 1\\n\\n return author\",\n \"async def devox(self, ctx):\\n member = discord.utils.find(lambda m: m.id == 250865328194715658, ctx.channel.guild.members)\\n await ctx.send(\\\"{} The great man who created this bot some people say he has too much power, but the truth is he doesnt have enough\\\".format(member.mention))\",\n \"async def follow(follow):\\n await follow.edit(\\n f\\\"`FOLLOW {DEFAULTUSER} ON` \\\\n\\\\n\\\"\\n f\\\"[InstaGram](https://www.instagram.com/mayur_karaniya) \\\\n\\\\n\\\"\\n f\\\"[FaceBook](https://www.facebook.com/mkaraniya) \\\\n\\\\n\\\"\\n f\\\"[YouTube](https://www.youtube.com/channel/UCeKQxQK7XZ3jGi3541uWATg?sub_confirmation=1) \\\"\\n )\",\n \"def __add_author(self, key_name, others_names, personal_information):\\n for name in others_names:\\n self.author_to_authorID[name] = (key_name, personal_information)\",\n \"def is_judges_or_author(line_text):\\n # line can't be too long\\n if len(line_text) > 80:\\n return False\\n\\n line_lower = line_text.lower()\\n\\n # 'Before DYKMAN, VERGERONT and LUNDSTEN, JJ.'\\n # 'Present: All the Justices.'\\n if any(line_lower.startswith(s) for s in (\\\"before\\\", \\\"present\\\", \\\"considered and decided by\\\")) or 'en banc' in line_lower:\\n return \\\"judges\\\"\\n\\n # 'PER CURIAM.'\\n if any(s in line_lower for s in ('per curiam', 'opinion by', 'by the court')):\\n return \\\"author\\\"\\n\\n if any(s in line_lower for s in judge_titles):\\n if \\\" and \\\" in line_text or \\\" & \\\" in line_text:\\n return \\\"judges\\\"\\n else:\\n return \\\"author\\\"\\n\\n return None\",\n \"def extra_bibparse(db):\\n for key,entry in db.entries.items():\\n for auth in entry.persons[\\\"author\\\"]:\\n if (\\\"Harrison\\\" not in auth.first_names or\\n \\\"Chapman\\\" not in auth.last_names):\\n entry.add_person(auth, \\\"otherauthor\\\")\",\n \"def getName(sentence): #Jasper, Suraj\\n userWords = sentence.lower()\\n userWords = userWords.split()\\n \\n # ways of introduction:\\n # \\\"Hello, my name is ___\\\"\\n # \\\"Hi, I'm ____\\\"\\n # \\\"Howdy, I'm called ____\\\"\\n # Order: Greeting -> pronoun -> Name -> question (optional)\\n # eg. \\\"Hello, I'm Jasper. How are you?\\\"\\n\\n if (userWords[0] in greetings): #the added code that stops iam from being added into the name if 2 greeting are added\\n userWords.pop(0) #pop and not .remove because\\n \\n \\n if (userWords[0] == \\\"i\\\" and len(userWords) > 1):\\n if (userWords[1] in [\\\"m\\\",\\\"am\\\"]):\\n userWords.insert(0, \\\" \\\".join(userWords[0:2]))\\n userWords.pop(2)\\n userWords.pop(1)\\n \\n userName = \\\"\\\"\\n for userWord in userWords: #iterate throught the user's words\\n foundWord = False #sets True when there's a similar word in the other list\\n for word in greetings: #iterates and compares the chosen word from the user's list of words to the words list\\n if userWord == word and foundWord == False:\\n foundWord = True\\n if foundWord == False:\\n userName = userName + userWord + \\\" \\\"\\n return userName #this is the found name\",\n \"def is_mention(self, bot):\\n for um in self.original.entities['user_mentions']:\\n if um['screen_name'] == bot.screen_name:\\n return True\\n return False\",\n \"def on_whoisuser(self, raw_msg, nick, user, host, **kwargs):\",\n \"def test_super_short_author_name(self):\\n spi_search = \\\"fin a er and cn cms\\\"\\n inv_search = \\\"author:er collaboration:cms\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def entity_mentions(adm):\\n for entity in adm['attributes']['entities']['items']:\\n for mention in entity['mentions']:\\n # Augment mentions with the entity type of the entity they refer to\\n mention['type'] = entity.get('type')\\n yield mention\",\n \"def show_user(title, fname, lname):\\n person = \\\"{} {} {}\\\".format(title, fname, lname)\\n logger.info(\\\"{}\\\".format(person))\\n return person\",\n \"async def treatme(self, ctx):\\n await ctx.send(await self.cure_user(ctx, ctx.author))\",\n \"def test_author_full_initial(self):\\n inv_search = 'author:\\\"klebanov, ig* r*\\\" or exactauthor:\\\"klebanov, i r\\\"'\\n spi_search = \\\"find a klebanov, ig.r.\\\"\\n self._compare_searches(inv_search, spi_search)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6522878","0.6522878","0.5954495","0.5830652","0.58269155","0.5811767","0.5793768","0.5775662","0.5751016","0.5696697","0.5679133","0.55970377","0.55823743","0.556282","0.5555041","0.55549055","0.5526657","0.5525049","0.55118614","0.5511843","0.5495225","0.54822","0.54801834","0.54798365","0.5477462","0.5470072","0.54672986","0.5444114","0.54390556","0.54197454","0.5412081","0.5409958","0.54058385","0.53903574","0.53784794","0.53738165","0.5357082","0.53565294","0.5342198","0.53417104","0.5338186","0.5332554","0.53312904","0.5323259","0.5323022","0.5316893","0.5316893","0.5316893","0.5316893","0.5316893","0.5309676","0.530525","0.5283775","0.52744484","0.5273108","0.52709967","0.52532136","0.5252192","0.5251863","0.52419126","0.52391523","0.5238473","0.523477","0.5230162","0.5225416","0.5219187","0.52038586","0.51963174","0.5190695","0.5190605","0.51889414","0.5179796","0.5179796","0.5174712","0.5170297","0.5169988","0.5156333","0.5153297","0.51532114","0.51485777","0.51446694","0.5139777","0.5128867","0.512589","0.5122284","0.51210326","0.51205784","0.51199114","0.51136684","0.51109666","0.5108836","0.5104004","0.5098577","0.50908923","0.5090302","0.50813615","0.5070207","0.5067306","0.5060124","0.50574946"],"string":"[\n \"0.6522878\",\n \"0.6522878\",\n \"0.5954495\",\n \"0.5830652\",\n \"0.58269155\",\n \"0.5811767\",\n \"0.5793768\",\n \"0.5775662\",\n \"0.5751016\",\n \"0.5696697\",\n \"0.5679133\",\n \"0.55970377\",\n \"0.55823743\",\n \"0.556282\",\n \"0.5555041\",\n \"0.55549055\",\n \"0.5526657\",\n \"0.5525049\",\n \"0.55118614\",\n \"0.5511843\",\n \"0.5495225\",\n \"0.54822\",\n \"0.54801834\",\n \"0.54798365\",\n \"0.5477462\",\n \"0.5470072\",\n \"0.54672986\",\n \"0.5444114\",\n \"0.54390556\",\n \"0.54197454\",\n \"0.5412081\",\n \"0.5409958\",\n \"0.54058385\",\n \"0.53903574\",\n \"0.53784794\",\n \"0.53738165\",\n \"0.5357082\",\n \"0.53565294\",\n \"0.5342198\",\n \"0.53417104\",\n \"0.5338186\",\n \"0.5332554\",\n \"0.53312904\",\n \"0.5323259\",\n \"0.5323022\",\n \"0.5316893\",\n \"0.5316893\",\n \"0.5316893\",\n \"0.5316893\",\n \"0.5316893\",\n \"0.5309676\",\n \"0.530525\",\n \"0.5283775\",\n \"0.52744484\",\n \"0.5273108\",\n \"0.52709967\",\n \"0.52532136\",\n \"0.5252192\",\n \"0.5251863\",\n \"0.52419126\",\n \"0.52391523\",\n \"0.5238473\",\n \"0.523477\",\n \"0.5230162\",\n \"0.5225416\",\n \"0.5219187\",\n \"0.52038586\",\n \"0.51963174\",\n \"0.5190695\",\n \"0.5190605\",\n \"0.51889414\",\n \"0.5179796\",\n \"0.5179796\",\n \"0.5174712\",\n \"0.5170297\",\n \"0.5169988\",\n \"0.5156333\",\n \"0.5153297\",\n \"0.51532114\",\n \"0.51485777\",\n \"0.51446694\",\n \"0.5139777\",\n \"0.5128867\",\n \"0.512589\",\n \"0.5122284\",\n \"0.51210326\",\n \"0.51205784\",\n \"0.51199114\",\n \"0.51136684\",\n \"0.51109666\",\n \"0.5108836\",\n \"0.5104004\",\n \"0.5098577\",\n \"0.50908923\",\n \"0.5090302\",\n \"0.50813615\",\n \"0.5070207\",\n \"0.5067306\",\n \"0.5060124\",\n \"0.50574946\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5873194"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":6791,"cells":{"query":{"kind":"string","value":"Give info about this server"},"document":{"kind":"string","value":"async def serverinfo(self,ctx):\n g = ctx.guild\n embed = discord.Embed()\n embed.set_thumbnail(url = g.icon_url)\n embed.title = \"{} - {}\".format(g.name,g.id)\n embed.add_field(name = \"Owner\",value=\"{} - {}\".format(g.owner,g.owner.id),inline=False)\n embed.add_field(name = \"Created at\", value = str(g.created_at), inline=False)\n embed.add_field(name = \"Total Roles\", value= str(len(g.roles)), inline=False)\n embed.add_field(name = \"Total Members\", value= str(g.member_count), inline=False)\n embed.add_field(name = \"Premium Member\", value= str(g.premium_subscription_count), inline=False)\n embed.add_field(name = \"Premium Tier\", value= str(g.premium_tier), inline=False)\n await self.bot.say(ctx,embed = embed)"},"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 do_serverinfo(self, server):\n print('QManager server:', self._server)\n server_info = self._qm.get_server_info()\n for k, v in server_info.items():\n print(' %s: %s' % (k, v))","def server_info(ctx):\n data = ctx.obj.get_server_info()\n output_json_data(data)","def serverInfo(self):\n return self._server.getServerInfo(self._token)","async def serverInfo(self, ctx):\n\t\ts = ctx.message.server\n\t\te = discord.Embed()\n\t\t\n\t\te.title = \"Server Statistics\"\n\t\te.description = \"\"\"\nName: {n}\nNumber of Roles: {r}\nServer Region: {sr}\nNumber of Emojis: {em}\nNumber of Members: {m}\nID: {i}\nOwner: {o}\nCreated at: {c}\n\t\t\t\t\t\t\"\"\".format(n = s.name, r = len(s.roles), sr = s.region, em = len(s.emojis), \n\t\t\t\t\t\t\t\t\tm = s.member_count, i = s.id, o = s.owner.name, c = s.created_at)\n\t\te.set_thumbnail(url = s.icon_url)\n\t\t\n\t\tawait self.bot.say(embed = e)","async def serverinfo_command(self, ctx):\n owner = str(ctx.guild.owner.mention)\n id = str(ctx.guild.id)\n region = str(ctx.guild.region)\n memberCount = str(ctx.guild.member_count)\n textChannels = len(ctx.guild.text_channels)\n voiceChannels = len(ctx.guild.voice_channels)\n roles = len(ctx.guild.roles)\n guildCreatedate = ctx.guild.created_at.strftime(\"%a, %#d %B %Y, %I:%M %p\")\n\n embed = Embed(\n title=f\"Info of {ctx.guild.name} Server\",\n color=Color.blurple(),\n timestamp=datetime.utcnow(),\n )\n embed.set_footer(text=f\"Requested by {ctx.author.name}\")\n embed.set_thumbnail(url=ctx.guild.icon_url)\n fields = [\n (\"Server ID\", id, True),\n (\"Server Region\", region.capitalize(), True),\n (\"Owner\", owner, True),\n (\"Member Count\", memberCount, True),\n (\"Text Channels\", textChannels, True),\n (\"Voice Channels\", voiceChannels, True),\n (\"Role Count\", roles, True),\n (\"Created on\", guildCreatedate, True),\n ]\n for name, value, inline in fields:\n embed.add_field(name=name, value=value, inline=inline)\n await ctx.send(embed=embed)","async def server_info(self, ctx):\n guild = ctx.guild\n id = guild.id\n boost_count = guild.premium_subscription_count\n region = str(guild.region)\n channels = len(guild.channels)\n vc = len(guild.voice_channels)\n text_channels = len(guild.text_channels)\n emoji_limit = guild.emoji_limit\n bitrate = guild.bitrate_limit\n filesize = guild.filesize_limit\n members = str(len(guild.members))\n owner = guild.owner.name\n icon = guild.icon_url\n roles = len(guild.roles)\n banned = len(await guild.bans())\n invites = len(await guild.invites())\n created = str(guild.created_at)\n embed = discord.Embed(\n title=guild.name,\n description=\"Server Info:\",\n color=discord.Colour.purple()\n )\n embed.set_thumbnail(url=icon)\n embed.add_field(name=\"ID:\", value=str(id))\n embed.add_field(name=\"Owner: \", value=owner)\n embed.add_field(name=\"Region: \", value=region)\n embed.add_field(name=\"created at: \", value=created)\n embed.add_field(name=\"Boost count: \", value=boost_count)\n embed.add_field(name=\"Members: \", value=members)\n embed.add_field(name=\"Roles:\", value=str(roles))\n embed.add_field(name=\"Channels:\", value=str(channels))\n embed.add_field(name=\"Text Channels:\", value=str(text_channels))\n embed.add_field(name=\"Voice Channels:\", value=str(vc))\n embed.add_field(name=\"Emoji Limit:\", value=str(emoji_limit))\n embed.add_field(name=\"Max Bitrate:\", value=bitrate)\n embed.add_field(name=\"Max Filesize:\", value=filesize)\n embed.add_field(name=\"Banned Members:\", value=str(banned))\n embed.add_field(name=\"Active Invites:\", value=str(invites))\n await ctx.send(\"\", embed=embed)","def get_servers_info(self):\n return self.mrr_obj.get('/info/servers')","async def serverinfo(self, context: Context) -> None:\n roles = [role.name for role in context.guild.roles]\n if len(roles) > 50:\n roles = roles[:50]\n roles.append(f\">>>> Displaying[50/{len(roles)}] Roles\")\n roles = \", \".join(roles)\n\n embed = discord.Embed(\n title=\"**Server Name:**\", description=f\"{context.guild}\", color=0x9C84EF\n )\n if context.guild.icon is not None:\n embed.set_thumbnail(url=context.guild.icon.url)\n embed.add_field(name=\"Server ID\", value=context.guild.id)\n embed.add_field(name=\"Member Count\", value=context.guild.member_count)\n embed.add_field(\n name=\"Text/Voice Channels\", value=f\"{len(context.guild.channels)}\"\n )\n embed.add_field(name=f\"Roles ({len(context.guild.roles)})\", value=roles)\n embed.set_footer(text=f\"Created at: {context.guild.created_at}\")\n await context.send(embed=embed)","def get_server_info(self):\n\t\treturn handle_to_object(call_sdk_function('PrlSrv_GetServerInfo', self.handle))","def serverinfo( self, mess, args):\n version = open('/proc/version').read().strip()\n loadavg = open('/proc/loadavg').read().strip()\n\n return '%snn%s' % ( version, loadavg, )","def serverinfo( self, mess, args):\n version = open('/proc/version').read().strip()\n loadavg = open('/proc/loadavg').read().strip()\n\n return '%s\\n\\n%s' % ( version, loadavg, )","def get_server_info_list(self):\n # TODO: 不要では？特に理由がなければ削除する\n result = []\n if self._server_sock is not None:\n result.append(\"Sever address: %s\" %\n str(self._server_sock.getsockname()))\n else:\n result.append(\"Sever address: Not initialized yet.\")\n result.append(\"Handler: %s\" %\n str(self._data_handler.__class__))\n result.append(\"Sessions: %d\" % len(self._sessions))\n for idx, session_thread in enumerate(self._sessions):\n result.append(\"Session[%d]: %s\" % (\n idx, str(session_thread.client_address)))\n return result","def redis_info(self):\n def func(server):\n return server.info()\n self.__run_redis_cmd(func)","def info(self):\n self._info()","def get_server(self):\n\n pass","def info(self):\n return requests.get(self.info_url + self.pid).json()","def test_server_info(self):\n pass","def info(self):\n return self.client.call('GET', self.name + 'info')","def get_db_info(self):\n db_info = {}\n db_info[\"Mongo Server Info\"] = self.db_client.server_info()\n return db_info","def get_server_info(self):\n raise NotImplementedError('Database.get_version()')","def get_info(self):\n pass","def get_info(self):\n pass","def info(self) -> dict:","def info() -> None:","def info(self):\r\n return self._get('info', {})","def details(self):\n return self.sock.getsockname()","def getInfo():","async def server_info(self, ctx):\n if ctx.author.bot == False:\n embed = discord.Embed(title= ctx.guild.name,\n colour= ctx.guild.owner.colour\n )\n\n embed.set_thumbnail(url=ctx.guild.icon_url)\n\n statuses = [len(list(filter(lambda m: str(m.status) == \"online\", ctx.guild.members))),\n len(list(filter(lambda m: str(m.status) == \"idle\", ctx.guild.members))),\n len(list(filter(lambda m: str(m.status) == \"dnd\", ctx.guild.members))),\n len(list(filter(lambda m: str(m.status) == \"offline\", ctx.guild.members)))]\n\n fields =[\n (\"Owner\", f'{ctx.guild.owner.mention}', True),\n (\"Region\", f'{str(ctx.guild.region).title()}', True),\n (\"Created on\", ctx.guild.created_at.strftime(\"%b %d, %Y \"), True),\n \n (\"Members\", f\"{len(list(filter(lambda m: not m.bot, ctx.guild.members)))} | {len(list(filter(lambda m: m.bot, ctx.guild.members)))}\", True),\n (\"Roles\", len(ctx.guild.roles), True),\n (\"Banned\", len(await ctx.guild.bans()), True),\n\n (\"Emojis\", len(ctx.guild.emojis), True),\n (\"Text channels\", len(ctx.guild.text_channels), True),\n (\"Voice channels\", len(ctx.guild.voice_channels), True),\n \n\n (\"\\u200b\",f\"**🟢 {statuses[0]}｜🟠 {statuses[1]}｜🔴 {statuses[2]}｜⚪ {statuses[3]}**\", False)\n ]\n for name, value, inline in fields:\n embed.add_field(name=name, value=value, inline=inline)\n await ctx.send(embed=embed)","def getHostInfo():","def rpc_info():","def info(self):","def info(self):","def info(client):\n\n return client.get_info()","def __repr__(self):\n return \"API Server Running at: \" + str(self.host) + \" on Port:\" + str(self.port) \\\n + \"/records: Return records\\n\" + \"/response_time: Return All response_times\\n\" \\\n + \"/response_time/max: Return max response_times\\n\" \\\n + \"/response_time/min: Return min response_times\\n\" \\\n + \"/response_time/average: Return average response_times\\n\" \\\n + \"/response_time/all_stats: Return tuple of(min, max, average) response_times\\n\"","def info(self):\n resp = requests.get(\"%s/api/info\"%self.urlbase, verify=False)\n return resp.json","def info(self):\n return {}","def server(self):\n return self.the_server","def info(self, *path):\n self._download_server_info()\n if self._info:\n return self._info.get(path, {})\n path = list(path)\n path[-1] += \".info\"\n t = self._open(*path)\n if t.status_code == 200:\n return json.loads(t.text)\n else:\n return {}","def info(self):\n pp = pprint.PrettyPrinter(indent=4)\n print_text_box('Info')\n pp.pprint(self.manager.data[\"info\"])\n print('')","def info(self) -> str:\n return pulumi.get(self, \"info\")","async def info(self, ctx):\n self.logger.info(misolog.format_log(ctx, f\"\"))\n appinfo = await self.client.application_info()\n membercount = sum(1 for x in self.client.get_all_members())\n info_embed = discord.Embed(title=f\"Miso Bot | version {main.version}\",\n description=f\"Created by {appinfo.owner.mention}\\n\\n\"\n f\"Use `{self.client.command_prefix}help` to get the list of commands, \"\n f\"or visit the documention website for more help.\"\n f\"\\n\\nCurrently active in **{len(self.client.guilds)}** \"\n f\"servers totaling **{membercount}** unique users\",\n colour=discord.Colour.red())\n\n # info_embed.set_footer(text=f'version 2.0')\n info_embed.set_thumbnail(url=self.client.user.avatar_url)\n info_embed.add_field(name='Github', value='https://github.com/joinemm/miso-bot', inline=False)\n info_embed.add_field(name='Documentation', value=\"http://joinemm.me/misobot\", inline=False)\n info_embed.add_field(name='Patreon', value=\"https://www.patreon.com/joinemm\", inline=False)\n await ctx.send(embed=info_embed)","def get_info(self) -> str:\n return self.info","def get(self):\n try:\n server_response = self.get_unauthenticated_request(self.baseurl)\n except ServerResponseError as e:\n if e.code == \"404003\":\n raise ServerInfoEndpointNotFoundError(e)\n if e.code == \"404001\":\n raise EndpointUnavailableError(e)\n raise e\n\n self._info = ServerInfoItem.from_response(server_response.content, self.parent_srv.namespace)\n return self._info","async def serverinfo(self, ctx):\n guild = ctx.guild\n\n embed = discord.Embed()\n embed.title = str(guild)\n if guild.icon_url is not None:\n embed.description = '**ID**: {0.id}\\n[Icon URL]({0.icon_url})'.format(guild)\n embed.set_thumbnail(url=guild.icon_url)\n else:\n embed.description = '**ID**: {0.id}'.format(guild)\n\n if guild.me.permissions_in(ctx.channel).kick_members and ctx.author.permissions_in(ctx.channel).kick_members:\n dead_members = await ctx.guild.estimate_pruned_members(days=7)\n members = '{} members, {} of which were active in the past 7 days'.format(guild.member_count,\n guild.member_count - dead_members)\n else:\n members = guild.member_count\n\n embed.add_field(name='Members', value=members)\n\n roles = [x.mention for x in guild.role_hierarchy if not x.is_default()]\n if roles: # only show roles if the server has any\n roles = ', '.join(roles)\n if len(roles) <= 1024: # deal with limits\n embed.add_field(name='Roles', value=roles)\n\n channels = [x[1] for x in sorted([(x.position, x.mention) for x in guild.channels if\n isinstance(x, discord.TextChannel)])]\n channels = ', '.join(channels)\n if len(channels) <= 1024:\n embed.add_field(name='Text channels', value=channels)\n\n if guild.verification_level == discord.VerificationLevel.none:\n verification_level = 'None'\n elif guild.verification_level == discord.VerificationLevel.low:\n verification_level = 'Low'\n elif guild.verification_level == discord.VerificationLevel.medium:\n verification_level = 'Medium'\n elif guild.verification_level == discord.VerificationLevel.high:\n verification_level = '(╯°□°）╯︵ ┻━┻'\n else:\n verification_level = '┻━┻ ﾐヽ(ಠ益ಠ)ノ彡┻━┻'\n\n if guild.explicit_content_filter == discord.ContentFilter.disabled:\n explicit_level = 'Don\\'t scan any messages'\n elif guild.explicit_content_filter == discord.ContentFilter.no_role:\n explicit_level = 'Scan messages from members without a role'\n else:\n explicit_level = 'Scan messages sent by all members'\n\n info = '**AFK channel**: {0.afk_channel}\\n**AFK timeout**: {0.afk_timeout} seconds\\n' \\\n '**Owner**: {0.owner.mention}\\n**Region**: `{0.region.value}`\\n' \\\n '**Verification level**: {1}\\n**Explicit content filter**: {2}'.format(guild, verification_level,\n explicit_level)\n\n embed.add_field(name='Other miscellaneous info', value=info)\n\n embed.timestamp = guild.created_at\n embed.set_footer(text='Created on')\n\n if ctx.channel.permissions_for(ctx.guild.me).embed_links:\n if ctx.author.id == self.liara.user.id:\n await ctx.message.edit(embed=embed)\n else:\n await ctx.send(embed=embed)\n else:\n await ctx.send('Unable to post serverinfo, please allow the Embed Links permission.')","def info(self):\n return self.__dict__[self.sid]","def info(self):\n return self._fetch_json('/api/info')","def info(self):\n path = self._get_path('info')\n \n response = self._GET(path)\n self._set_attrs_to_values(response)\n return response","def get_node_details(self, node):\n node_details = self.parser.find_server_by_ip(node.get('ip')) or \\\n self.parser.find_server_by_hostname(node.get('host'))\n\n return node_details","def SetServerInformation(self, server, port):\n self.hostname = server\n self.port = port","def default_server_info():\n # If not set or purposely set = None, then set default\n if MDC.get('server') is None:\n try:\n server = socket.getfqdn()\n except Exception:\n try:\n server = socket.gethostname()\n except Exception:\n server = ''\n MDC.put('server', server)\n if MDC.get('serverIPAddress') is None:\n try:\n server_ip_address = socket.gethostbyname(MDC.get('server'))\n except Exception:\n server_ip_address = \"\"\n MDC.put('serverIPAddress', server_ip_address)","def fp_meta(self):\n for server in self.machines:\n s = self.machines[server]\n print \"%s: %s (%s)\" % (s.id, s.adminPass, s)","def getInfo(self):\n return self.info","def sipserver_status(self) -> str:","def get_info(self):\n return \"TODO !\"","def info(self):\n _, data = yield from self.transport.perform_request('GET', '/')\n return data","def _request_bootstrap_server_info() -> str:\n if __debug__:\n logger.info(\"Requesting bootstrap server...\")\n req = BootstrapServerRequest()\n DistroStreamClientHandler.request(req)\n\n # Retrieve answer\n req.wait_processed()\n error = req.get_error_code()\n if error != 0:\n raise BackendException(error, req.get_error_msg())\n\n # Parse answer\n answer = req.get_response_msg()\n if __debug__:\n logger.debug(\"Retrieved bootstrap server information: %s\", answer)\n\n return answer","def __repr__(self):\n return 'Server %s at Node %s' % (self.id_number,\n self.node.id_number)","def info(self) -> str:\n return self._info","def info(self) -> str:\n return self._info","def info(self):\n return self._info","def get_info(self) -> str:\n raise NotImplementedError()","def get_info(self):\n return {}","def server_info(self, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.server_info_with_http_info(**kwargs)\n else:\n (data) = self.server_info_with_http_info(**kwargs)\n return data","def show_info(self):\n print 'Querying the station for the configuration...'\n config = self.station.getConfig()\n for key in sorted(config):\n print '%s: %s' % (key, config[key])","async def info(self):\n # [p]info\n\n await self.bot.say(strings.info.format(\n CacheAPI.get(key='dwarf_repository'),\n CacheAPI.get(key='dwarf_invite_link')))","def info(self):\n return self._info","def get_server():\n pass","def save(self):\n return {\n \"type\": \"Server\",\n \"fd\": self.socket.fileno()\n }","def get_server(self):\n return self.__server","def info(self):\n\n if self.running:\n return INFO_RUNNING_FORMAT.format(**self.__dict__)\n else:\n return INFO_ENDED_FORMAT.format(**self.__dict__)","def info(self) -> Info:\n raw = self._call('GET', 'info')\n return Info.parse_raw(raw)","def get_info(self):\n return None","def getInfo(self):\n return self._info","def info(self) -> Optional[Dict[str, Any]]:\n return self._state.get(\"info\", None)","def server_info(subresource, server, proxyfilename, baseurl):\n server = HTTPRequests(url=server, localcert=proxyfilename, localkey=proxyfilename, version='HC')\n\n dictresult, status, reason = server.get(baseurl, {'subresource' : subresource})\n\n return dictresult['result'][0]","def server(self):\n return self._server","def server(self):\n return self._server","def get_info():\n message = \"GET information about glancesync server\"\n\n logger_api.info(message)\n\n message = '''\n {\n \"id\": \"%s\",\n \"owner\": \"%s\",\n \"status\": \"%s\",\n \"version\": \"%s\",\n \"updated\": \"%s\",\n \"runningfrom\": \"%s\",\n \"href\": \"%s\"\n }\n ''' % (ID, OWNER, STATUS, VERSION, UPDATED, RUNNINGFROM, API_INFO_URL)\n\n resp = make_response(message, httplib.OK)\n resp.headers[SERVER_HEADER] = SERVER\n resp.headers[CONTENT_TYPE] = JSON_TYPE\n\n logger_api.info('Return result: %s', message)\n\n return resp","async def serverinfo(ctx):\n server = ctx.message.server\n online = len([m.status for m in server.members\n if m.status == discord.Status.online or\n m.status == discord.Status.idle])\n total_users = len(server.members)\n text_channels = len([x for x in server.channels\n if x.type == discord.ChannelType.text])\n voice_channels = len(server.channels) - text_channels\n passed = (ctx.message.timestamp - server.created_at).days\n created_at = (\"Since {}. That's over {} days ago!\"\n \"\".format(server.created_at.strftime(\"%d %b %Y %H:%M\"), passed))\n\n colour = ''.join([random.choice('0123456789ABCDEF') for x in range(6)])\n colour = int(colour, 16)\n embed = discord.Embed(description = created_at, colour = discord.Colour(value = colour), timestamp = datetime.datetime.utcnow())\n embed.add_field(name = \"Region\", value = str(server.region))\n embed.add_field(name = \"Users Online\", value = \"{}/{}\".format(online, total_users))\n embed.add_field(name = \"Text Channels\", value = text_channels)\n embed.add_field(name = \"Voice Channels\", value = voice_channels)\n embed.add_field(name = \"Roles\", value = len(server.roles))\n embed.add_field(name = \"Owner\", value = str(server.owner))\n embed.set_footer(text = \"Server ID: \" + server.id)\n embed.add_field(name = \"AFK Timeout\", value = \"{} minutes\".format(server.afk_timeout/60).replace(\".0\", \"\"))\n embed.add_field(name = \"AFK Channel\", value = str(server.afk_channel))\n embed.add_field(name = \"Verification Level\", value = str(server.verification_level))\n embed.set_footer(text= \"{} | Requested by: {}\".format(version, ctx.message.author))\n\n if server.icon_url:\n embed.set_author(name = server.name, url = server.icon_url)\n embed.set_thumbnail(url = server.icon_url)\n else:\n embed.set_author(name=server.name)\n\n await bot.say(embed = embed)","def get_server_metadata(self, name):\n raise NotImplementedError","def device_info(self):\n info = {\n \"connections\": {(CONNECTION_NETWORK_MAC, self._data[\"port-mac-address\"])},\n \"manufacturer\": self._ctrl.data[\"resource\"][\"platform\"],\n \"model\": self._ctrl.data[\"resource\"][\"board-name\"],\n \"name\": f\"{self._inst} {self._data['default-name']}\",\n }\n return info","def device_info(self) -> dict:\n return {\n \"connections\": {(DOMAIN, self._unique_id)},\n \"name\": self._host,\n \"manufacturer\": \"IMAP E-Mail\",\n \"sw_version\": VERSION,\n }","def get_description(self):\n return self['host_name']","def server_type(self):\n ...","async def info(self, ctx):\n\t\tembed = discord.Embed(\n\t\t\tdescription=\"Created By Seperoph#1399 and AkaBaka#4654\",\n\t\t\tcolor=config[\"success\"]\n\t\t)\n\t\tembed.set_author(\n\t\t\tname=\"Bot Information\"\n\t\t)\n\t\tembed.add_field(\n\t\t\tname=\"Head Programmers:\",\n\t\t\tvalue=\"Seperoph#1399 and AkaBaka#4654\",\n\t\t\tinline=True\n\t\t)\n\t\tembed.add_field(\n\t\t\tname=\"Python Version:\",\n\t\t\tvalue=f\"{platform.python_version()}\",\n\t\t\tinline=True\n\t\t)\n\t\tawait ctx.respond(embed=embed)","def info(self):\n import string\n results = self.info_list()\n labels = \"%-8s %-9s %-4s %-8s %-8s %-4s\" % \\\n ('MACHINE','CPU','GHZ','MB TOTAL',\n 'MB FREE','LOAD')\n print labels\n for i in range(len(self.workers)):\n name = string.split(self.workers[i].host,'.')[0]\n res = results[i]\n s = \"%-8s %2dx%-6s %4.1f %8.1f %8.1f %4.2f\" % \\\n (name[-8:], res['cpu_count'],res['cpu_type'][-6:], \\\n res['cpu_speed'],res['mem_total'],res['mem_free'],\\\n res['load_1'])\n print s","def info(self):\n self.update_info()\n print('Number of electrodes: ' + str(self.n_elecs))\n print('Recording time in seconds: ' + str(self.dur))\n print('Sample Rate in Hz: '+ str(self.sample_rate))\n print('Number of sessions: ' + str(self.n_sessions))\n print('Date created: ' + str(self.date_created))\n print('Meta data: ' + str(self.meta))","def info() -> Dict[str, Any]:","def describe(self) -> None:\n return {\n 'cluster_metadata': self.cluster_metadata,\n 'master_url': self.master_url\n }","async def _info(self, ctx: Context):\n\n embed = discord.Embed(colour=await ctx.embed_colour())\n\n perm_int = discord.Permissions(268494928)\n\n data = await self.bot.application_info()\n invite_url = discord.utils.oauth_url(data.id, permissions=perm_int)\n\n embed.description = (\n \"TvM Assistant is a Discord bot with utility commands to make hosting TvMs easier.\"\n \"\\n\\nSome of the bot features include:\"\n \"\\n\\n- Setup roles and channel creation\"\n \"\\n- Management of sign-ups, sign-outs, spectators and replacements\"\n \"\\n- In-built logging to detect and ignore private channels\"\n \"\\n- Quick creation of player, mafia and spectator chats\"\n \"\\n- Vote counts and time since day/night started\"\n )\n\n links = (\n f\"\\n- [Invite to your server]({invite_url})\"\n f\"\\n- [Quickstart]({QUICKSTART})\"\n f\"\\n- [Commands Reference]({COMMANDS_REFERENCE})\"\n f\"\\n- [Source Code]({SOURCE_CODE})\"\n )\n\n embed.add_field(name=\"\\u200b\\nQuick Links\", value=links)\n embed.set_author(name=f\"About {ctx.me.name}\", icon_url=ctx.me.avatar_url)\n\n await ctx.send(embed=embed)","def show_hostname(self):\n if self.hostname is None:\n self.get_version()\n print self.hostname","def server_name(self) -> str:\n return pulumi.get(self, \"server_name\")","def manage_info():","def info(self):\n return self.info_text","def get_host_stats(self):\n status, data, errors, messages = self._make_get_request(CraftyAPIRoutes.SERVER_STATS)\n \n if status == 200:\n return data\n elif status == 500:\n self._check_errors(errors, messages)","def server():","def server():","async def _serverinfo(self, ctx, details: bool = True):\n # if not id:\n # guild = ctx.guild\n # else:\n # try:\n # guild = ctx.bot.get_guild(int(id))\n # if not guild:\n # guild = await ctx.bot.fetch_guild(int(id))\n # except Exception as e:\n # await ctx.send(f\"Error: {e}\")\n\n guild = ctx.guild\n online = len([m.status for m in guild.members if m.status != discord.Status.offline])\n if not details:\n desc = \"Created at {date}\".format(date=guild.created_at.strftime(\"%d %b %Y %H:%M\"))\n data = discord.Embed(color=ctx.guild.me.top_role.color, description=desc)\n data.set_author(name=guild.name)\n data.set_thumbnail(url=guild.icon_url)\n data.add_field(name=\"Region\", value=bold(guild.region))\n data.add_field(name=\"Users Online\", value=bold(f\"{online}/{guild.member_count}\"))\n data.add_field(name=\"Roles\", value=bold(len(guild.roles)))\n data.add_field(name=\"Owner\", value=bold(str(guild.owner)))\n data.set_footer(text=f\"ID: {guild.id}\")\n\n else:\n passed = (ctx.message.created_at - guild.created_at).days\n created_at = (\"Created on {date}. That's **{num}** days ago!\").format(\n date=guild.created_at.strftime(\"%d %b %Y %H:%M\"), num=passed,\n )\n total_users = guild.member_count\n text_channels = len(guild.text_channels)\n voice_channels = len(guild.voice_channels)\n\n def _size(num: int):\n for unit in [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\"]:\n if abs(num) < 1024.0:\n return \"{0:.1f}{1}\".format(num, unit)\n num /= 1024.0\n return \"{0:.1f}{1}\".format(num, \"YB\")\n\n def _bitsize(num: int):\n for unit in [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\"]:\n if abs(num) < 1000.0:\n return \"{0:.1f}{1}\".format(num, unit)\n num /= 1000.0\n return \"{0:.1f}{1}\".format(num, \"YB\")\n\n shard_info = (\n \"\\nShard ID: **{shard_id}/{shard_count}**\".format(\n shard_id=guild.shard_id + 1,\n shard_count=ctx.bot.shard_count,\n )\n if ctx.bot.shard_count > 1\n else \"\"\n )\n online_stats = {\n \"Humans: \": lambda x: not x.bot,\n \" • Bots: \": lambda x: x.bot,\n \"\\N{LARGE GREEN CIRCLE}\": lambda x: x.status is discord.Status.online,\n \"\\N{LARGE ORANGE CIRCLE}\": lambda x: x.status is discord.Status.idle,\n \"\\N{LARGE RED CIRCLE}\": lambda x: x.status is discord.Status.do_not_disturb,\n \"\\N{MEDIUM WHITE CIRCLE}\": lambda x: x.status is discord.Status.offline,\n \"\\N{LARGE PURPLE CIRCLE}\": lambda x: any(\n a.type is discord.ActivityType.streaming for a in x.activities\n ),\n \"\\N{MOBILE PHONE}\": lambda x: x.is_on_mobile(),\n }\n member_msg = (\"Users online: **{onlineusr}/{total_users}**\\n\").format(\n onlineusr=online, total_users=total_users\n )\n count = 1\n for emoji, value in online_stats.items():\n try:\n num = len([m for m in guild.members if value(m)])\n except Exception as error:\n print(error)\n continue\n else:\n member_msg += f\"{emoji} {bold(num)} \" + (\n \"\\n\" if count % 2 == 0 else \"\"\n )\n count += 1\n\n vc_regions = {\n \"vip-us-east\": \"__VIP__ US East \" + \"\\U0001F1FA\\U0001F1F8\",\n \"vip-us-west\": \"__VIP__ US West \" + \"\\U0001F1FA\\U0001F1F8\",\n \"vip-amsterdam\": \"__VIP__ Amsterdam \" + \"\\U0001F1F3\\U0001F1F1\",\n \"eu-west\": \"EU West \" + \"\\U0001F1EA\\U0001F1FA\",\n \"eu-central\": \"EU Central \" + \"\\U0001F1EA\\U0001F1FA\",\n \"europe\": \"Europe \" + \"\\U0001F1EA\\U0001F1FA\",\n \"london\": \"London \" + \"\\U0001F1EC\\U0001F1E7\",\n \"frankfurt\": \"Frankfurt \" + \"\\U0001F1E9\\U0001F1EA\",\n \"amsterdam\": \"Amsterdam \" + \"\\U0001F1F3\\U0001F1F1\",\n \"us-west\": \"US West \" + \"\\U0001F1FA\\U0001F1F8\",\n \"us-east\": \"US East \" + \"\\U0001F1FA\\U0001F1F8\",\n \"us-south\": \"US South \" + \"\\U0001F1FA\\U0001F1F8\",\n \"us-central\": \"US Central \" + \"\\U0001F1FA\\U0001F1F8\",\n \"singapore\": \"Singapore \" + \"\\U0001F1F8\\U0001F1EC\",\n \"sydney\": \"Sydney \" + \"\\U0001F1E6\\U0001F1FA\",\n \"brazil\": \"Brazil \" + \"\\U0001F1E7\\U0001F1F7\",\n \"hongkong\": \"Hong Kong \" + \"\\U0001F1ED\\U0001F1F0\",\n \"russia\": \"Russia \" + \"\\U0001F1F7\\U0001F1FA\",\n \"japan\": \"Japan \" + \"\\U0001F1EF\\U0001F1F5\",\n \"southafrica\": \"South Africa \" + \"\\U0001F1FF\\U0001F1E6\",\n \"india\": \"India \" + \"\\U0001F1EE\\U0001F1F3\",\n \"dubai\": \"Dubai \" + \"\\U0001F1E6\\U0001F1EA\",\n \"south-korea\": \"South Korea \" + \"\\U0001f1f0\\U0001f1f7\",\n }\n verif = {\n \"none\": \"0 - None\",\n \"low\": \"1 - Low\",\n \"medium\": \"2 - Medium\",\n \"high\": \"3 - High\",\n \"extreme\": \"4 - Extreme\",\n }\n features = {\n \"PARTNERED\": \"Partnered\",\n \"VERIFIED\": \"Verified\",\n \"DISCOVERABLE\": \"Server Discovery\",\n \"FEATURABLE\": \"Featurable\",\n \"PUBLIC\": \"Public\",\n \"PUBLIC_DISABLED\": \"Public disabled\",\n \"INVITE_SPLASH\": \"Splash Invite\",\n \"VIP_REGIONS\": \"VIP Voice Servers\",\n \"VANITY_URL\": \"Vanity URL\",\n \"MORE_EMOJI\": \"More Emojis\",\n \"COMMERCE\": \"Commerce\",\n \"NEWS\": \"News Channels\",\n \"ANIMATED_ICON\": \"Animated Icon\",\n \"BANNER\": \"Banner Image\",\n \"MEMBER_LIST_DISABLED\": \"Member list disabled\",\n }\n guild_features_list = [\n f\"✅ {name}\" for feature, name in features.items() if feature in guild.features\n ]\n joined_on = (\n \"{bot_name} joined this server on {bot_join}. That's {since_join} days ago!\"\n ).format(\n bot_name=ctx.bot.user.name,\n bot_join=guild.me.joined_at.strftime(\"%d %b %Y %H:%M:%S\"),\n since_join=(ctx.message.created_at - guild.me.joined_at).days,\n )\n data = discord.Embed(\n description=(f\"{guild.description}\\n\\n\" if guild.description else \"\") + created_at,\n colour=ctx.guild.me.top_role.color,\n )\n data.set_author(\n name=guild.name,\n icon_url=\"https://cdn.discordapp.com/emojis/457879292152381443.png\"\n if \"VERIFIED\" in guild.features\n else \"https://cdn.discordapp.com/emojis/508929941610430464.png\"\n if \"PARTNERED\" in guild.features\n else discord.Embed.Empty,\n )\n if guild.icon_url:\n data.set_thumbnail(url=guild.icon_url)\n data.add_field(name=\"Members:\", value=member_msg)\n data.add_field(\n name= \"Channels:\",\n value= (\n \"\\N{SPEECH BALLOON} Text: {text}\\n\"\n \"\\N{SPEAKER WITH THREE SOUND WAVES} Voice: {voice}\"\n ).format(text=bold(text_channels), voice=bold(voice_channels)),\n )\n data.add_field(\n name=\"Utility:\",\n value=(\n \"Owner: {owner}\\nVoice region: {region}\\nVerif. level: {verif}\\nServer ID: {id}{shard_info}\"\n ).format(\n owner=bold(str(guild.owner)),\n region=bold(f\"{vc_regions.get(str(guild.region)) or str(guild.region)}\"),\n verif=bold(verif[str(guild.verification_level)]),\n id=bold(str(guild.id)),\n shard_info=shard_info,\n ),\n inline=False,\n )\n data.add_field(\n name=\"Misc:\",\n value=(\n \"AFK channel: {afk_chan}\\nAFK timeout: {afk_timeout}\\nCustom emojis: {emoji_count}\\nRoles: {role_count}\"\n ).format(\n afk_chan=bold(str(guild.afk_channel))\n if guild.afk_channel\n else bold(\"Not Set\"),\n afk_timeout=bold(guild.afk_timeout),\n emoji_count=bold(len(guild.emojis)),\n role_count=bold(len(guild.roles)),\n ),\n inline=False,\n )\n if guild_features_list:\n data.add_field(name=\"Server features:\", value=\"\\n\".join(guild_features_list))\n if guild.premium_tier != 0:\n nitro_boost = (\n \"Tier {boostlevel} with {nitroboosters} boosters\\n\"\n \"File size limit: {filelimit}\\n\"\n \"Emoji limit: {emojis_limit}\\n\"\n \"VCs max bitrate: {bitrate}\"\n ).format(\n boostlevel=bold(str(guild.premium_tier)),\n nitroboosters=bold(guild.premium_subscription_count),\n filelimit=bold(_size(guild.filesize_limit)),\n emojis_limit=bold(str(guild.emoji_limit)),\n bitrate=bold(_bitsize(guild.bitrate_limit)),\n )\n data.add_field(name=\"Nitro Boost:\", value=nitro_boost)\n if guild.splash:\n data.set_image(url=guild.splash_url_as(format=\"png\"))\n data.set_footer(text=joined_on)\n\n await ctx.send(embed=data)","def get_name(self):\n \n return 'TCP/IP Server'","def get_ded_info(server, show=False):\n # start Requests session\n sc = requests.Session()\n\n # import cookies from Firefox\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\n\n # send request\n vpx = sc.get('https://imhsc.imhadmin.net/index.php',\n params={'v': \"Dedicated\", 'selectServer': server})\n\n # check if login failed\n check_sc_login(vpx.text)\n\n # parse with BS4\n bs = BeautifulSoup(vpx.text, \"xml\")\n\n # server=0 ip=4 net=5 psc=6 user=11 type=14\n trr = bs.tbody.find_all('tr')\n if len(trr) > 0:\n tsrv = {\n 'hostname': trr[0].find_all('td')[0].string,\n 'ip': trr[0].find_all('td')[2].string,\n 'net': trr[0].find_all('td')[3].string,\n 'psc': trr[0].find_all('td')[4].a.string,\n 'user': trr[0].find_all('td')[9].string,\n 'type': trr[0].find_all('td')[12].string,\n 'status': trr[0].find_all('td')[13].string.strip()\n }\n else:\n tsrv = None\n\n if show:\n if tsrv:\n print(\"[%(hostname)s] IP: %(ip)s (%(net)s) / PSC: %(psc)s / User: %(user)s / Type: %(type)s / Status: %(status)s\" % tsrv)\n else:\n print(\"!! Server '%s' not found\" % (server))\n\n return tsrv"],"string":"[\n \"def do_serverinfo(self, server):\\n print('QManager server:', self._server)\\n server_info = self._qm.get_server_info()\\n for k, v in server_info.items():\\n print(' %s: %s' % (k, v))\",\n \"def server_info(ctx):\\n data = ctx.obj.get_server_info()\\n output_json_data(data)\",\n \"def serverInfo(self):\\n return self._server.getServerInfo(self._token)\",\n \"async def serverInfo(self, ctx):\\n\\t\\ts = ctx.message.server\\n\\t\\te = discord.Embed()\\n\\t\\t\\n\\t\\te.title = \\\"Server Statistics\\\"\\n\\t\\te.description = \\\"\\\"\\\"\\nName: {n}\\nNumber of Roles: {r}\\nServer Region: {sr}\\nNumber of Emojis: {em}\\nNumber of Members: {m}\\nID: {i}\\nOwner: {o}\\nCreated at: {c}\\n\\t\\t\\t\\t\\t\\t\\\"\\\"\\\".format(n = s.name, r = len(s.roles), sr = s.region, em = len(s.emojis), \\n\\t\\t\\t\\t\\t\\t\\t\\t\\tm = s.member_count, i = s.id, o = s.owner.name, c = s.created_at)\\n\\t\\te.set_thumbnail(url = s.icon_url)\\n\\t\\t\\n\\t\\tawait self.bot.say(embed = e)\",\n \"async def serverinfo_command(self, ctx):\\n owner = str(ctx.guild.owner.mention)\\n id = str(ctx.guild.id)\\n region = str(ctx.guild.region)\\n memberCount = str(ctx.guild.member_count)\\n textChannels = len(ctx.guild.text_channels)\\n voiceChannels = len(ctx.guild.voice_channels)\\n roles = len(ctx.guild.roles)\\n guildCreatedate = ctx.guild.created_at.strftime(\\\"%a, %#d %B %Y, %I:%M %p\\\")\\n\\n embed = Embed(\\n title=f\\\"Info of {ctx.guild.name} Server\\\",\\n color=Color.blurple(),\\n timestamp=datetime.utcnow(),\\n )\\n embed.set_footer(text=f\\\"Requested by {ctx.author.name}\\\")\\n embed.set_thumbnail(url=ctx.guild.icon_url)\\n fields = [\\n (\\\"Server ID\\\", id, True),\\n (\\\"Server Region\\\", region.capitalize(), True),\\n (\\\"Owner\\\", owner, True),\\n (\\\"Member Count\\\", memberCount, True),\\n (\\\"Text Channels\\\", textChannels, True),\\n (\\\"Voice Channels\\\", voiceChannels, True),\\n (\\\"Role Count\\\", roles, True),\\n (\\\"Created on\\\", guildCreatedate, True),\\n ]\\n for name, value, inline in fields:\\n embed.add_field(name=name, value=value, inline=inline)\\n await ctx.send(embed=embed)\",\n \"async def server_info(self, ctx):\\n guild = ctx.guild\\n id = guild.id\\n boost_count = guild.premium_subscription_count\\n region = str(guild.region)\\n channels = len(guild.channels)\\n vc = len(guild.voice_channels)\\n text_channels = len(guild.text_channels)\\n emoji_limit = guild.emoji_limit\\n bitrate = guild.bitrate_limit\\n filesize = guild.filesize_limit\\n members = str(len(guild.members))\\n owner = guild.owner.name\\n icon = guild.icon_url\\n roles = len(guild.roles)\\n banned = len(await guild.bans())\\n invites = len(await guild.invites())\\n created = str(guild.created_at)\\n embed = discord.Embed(\\n title=guild.name,\\n description=\\\"Server Info:\\\",\\n color=discord.Colour.purple()\\n )\\n embed.set_thumbnail(url=icon)\\n embed.add_field(name=\\\"ID:\\\", value=str(id))\\n embed.add_field(name=\\\"Owner: \\\", value=owner)\\n embed.add_field(name=\\\"Region: \\\", value=region)\\n embed.add_field(name=\\\"created at: \\\", value=created)\\n embed.add_field(name=\\\"Boost count: \\\", value=boost_count)\\n embed.add_field(name=\\\"Members: \\\", value=members)\\n embed.add_field(name=\\\"Roles:\\\", value=str(roles))\\n embed.add_field(name=\\\"Channels:\\\", value=str(channels))\\n embed.add_field(name=\\\"Text Channels:\\\", value=str(text_channels))\\n embed.add_field(name=\\\"Voice Channels:\\\", value=str(vc))\\n embed.add_field(name=\\\"Emoji Limit:\\\", value=str(emoji_limit))\\n embed.add_field(name=\\\"Max Bitrate:\\\", value=bitrate)\\n embed.add_field(name=\\\"Max Filesize:\\\", value=filesize)\\n embed.add_field(name=\\\"Banned Members:\\\", value=str(banned))\\n embed.add_field(name=\\\"Active Invites:\\\", value=str(invites))\\n await ctx.send(\\\"\\\", embed=embed)\",\n \"def get_servers_info(self):\\n return self.mrr_obj.get('/info/servers')\",\n \"async def serverinfo(self, context: Context) -> None:\\n roles = [role.name for role in context.guild.roles]\\n if len(roles) > 50:\\n roles = roles[:50]\\n roles.append(f\\\">>>> Displaying[50/{len(roles)}] Roles\\\")\\n roles = \\\", \\\".join(roles)\\n\\n embed = discord.Embed(\\n title=\\\"**Server Name:**\\\", description=f\\\"{context.guild}\\\", color=0x9C84EF\\n )\\n if context.guild.icon is not None:\\n embed.set_thumbnail(url=context.guild.icon.url)\\n embed.add_field(name=\\\"Server ID\\\", value=context.guild.id)\\n embed.add_field(name=\\\"Member Count\\\", value=context.guild.member_count)\\n embed.add_field(\\n name=\\\"Text/Voice Channels\\\", value=f\\\"{len(context.guild.channels)}\\\"\\n )\\n embed.add_field(name=f\\\"Roles ({len(context.guild.roles)})\\\", value=roles)\\n embed.set_footer(text=f\\\"Created at: {context.guild.created_at}\\\")\\n await context.send(embed=embed)\",\n \"def get_server_info(self):\\n\\t\\treturn handle_to_object(call_sdk_function('PrlSrv_GetServerInfo', self.handle))\",\n \"def serverinfo( self, mess, args):\\n version = open('/proc/version').read().strip()\\n loadavg = open('/proc/loadavg').read().strip()\\n\\n return '%snn%s' % ( version, loadavg, )\",\n \"def serverinfo( self, mess, args):\\n version = open('/proc/version').read().strip()\\n loadavg = open('/proc/loadavg').read().strip()\\n\\n return '%s\\\\n\\\\n%s' % ( version, loadavg, )\",\n \"def get_server_info_list(self):\\n # TODO: 不要では？特に理由がなければ削除する\\n result = []\\n if self._server_sock is not None:\\n result.append(\\\"Sever address: %s\\\" %\\n str(self._server_sock.getsockname()))\\n else:\\n result.append(\\\"Sever address: Not initialized yet.\\\")\\n result.append(\\\"Handler: %s\\\" %\\n str(self._data_handler.__class__))\\n result.append(\\\"Sessions: %d\\\" % len(self._sessions))\\n for idx, session_thread in enumerate(self._sessions):\\n result.append(\\\"Session[%d]: %s\\\" % (\\n idx, str(session_thread.client_address)))\\n return result\",\n \"def redis_info(self):\\n def func(server):\\n return server.info()\\n self.__run_redis_cmd(func)\",\n \"def info(self):\\n self._info()\",\n \"def get_server(self):\\n\\n pass\",\n \"def info(self):\\n return requests.get(self.info_url + self.pid).json()\",\n \"def test_server_info(self):\\n pass\",\n \"def info(self):\\n return self.client.call('GET', self.name + 'info')\",\n \"def get_db_info(self):\\n db_info = {}\\n db_info[\\\"Mongo Server Info\\\"] = self.db_client.server_info()\\n return db_info\",\n \"def get_server_info(self):\\n raise NotImplementedError('Database.get_version()')\",\n \"def get_info(self):\\n pass\",\n \"def get_info(self):\\n pass\",\n \"def info(self) -> dict:\",\n \"def info() -> None:\",\n \"def info(self):\\r\\n return self._get('info', {})\",\n \"def details(self):\\n return self.sock.getsockname()\",\n \"def getInfo():\",\n \"async def server_info(self, ctx):\\n if ctx.author.bot == False:\\n embed = discord.Embed(title= ctx.guild.name,\\n colour= ctx.guild.owner.colour\\n )\\n\\n embed.set_thumbnail(url=ctx.guild.icon_url)\\n\\n statuses = [len(list(filter(lambda m: str(m.status) == \\\"online\\\", ctx.guild.members))),\\n len(list(filter(lambda m: str(m.status) == \\\"idle\\\", ctx.guild.members))),\\n len(list(filter(lambda m: str(m.status) == \\\"dnd\\\", ctx.guild.members))),\\n len(list(filter(lambda m: str(m.status) == \\\"offline\\\", ctx.guild.members)))]\\n\\n fields =[\\n (\\\"Owner\\\", f'{ctx.guild.owner.mention}', True),\\n (\\\"Region\\\", f'{str(ctx.guild.region).title()}', True),\\n (\\\"Created on\\\", ctx.guild.created_at.strftime(\\\"%b %d, %Y \\\"), True),\\n \\n (\\\"Members\\\", f\\\"{len(list(filter(lambda m: not m.bot, ctx.guild.members)))} | {len(list(filter(lambda m: m.bot, ctx.guild.members)))}\\\", True),\\n (\\\"Roles\\\", len(ctx.guild.roles), True),\\n (\\\"Banned\\\", len(await ctx.guild.bans()), True),\\n\\n (\\\"Emojis\\\", len(ctx.guild.emojis), True),\\n (\\\"Text channels\\\", len(ctx.guild.text_channels), True),\\n (\\\"Voice channels\\\", len(ctx.guild.voice_channels), True),\\n \\n\\n (\\\"\\\\u200b\\\",f\\\"**🟢 {statuses[0]}｜🟠 {statuses[1]}｜🔴 {statuses[2]}｜⚪ {statuses[3]}**\\\", False)\\n ]\\n for name, value, inline in fields:\\n embed.add_field(name=name, value=value, inline=inline)\\n await ctx.send(embed=embed)\",\n \"def getHostInfo():\",\n \"def rpc_info():\",\n \"def info(self):\",\n \"def info(self):\",\n \"def info(client):\\n\\n return client.get_info()\",\n \"def __repr__(self):\\n return \\\"API Server Running at: \\\" + str(self.host) + \\\" on Port:\\\" + str(self.port) \\\\\\n + \\\"/records: Return records\\\\n\\\" + \\\"/response_time: Return All response_times\\\\n\\\" \\\\\\n + \\\"/response_time/max: Return max response_times\\\\n\\\" \\\\\\n + \\\"/response_time/min: Return min response_times\\\\n\\\" \\\\\\n + \\\"/response_time/average: Return average response_times\\\\n\\\" \\\\\\n + \\\"/response_time/all_stats: Return tuple of(min, max, average) response_times\\\\n\\\"\",\n \"def info(self):\\n resp = requests.get(\\\"%s/api/info\\\"%self.urlbase, verify=False)\\n return resp.json\",\n \"def info(self):\\n return {}\",\n \"def server(self):\\n return self.the_server\",\n \"def info(self, *path):\\n self._download_server_info()\\n if self._info:\\n return self._info.get(path, {})\\n path = list(path)\\n path[-1] += \\\".info\\\"\\n t = self._open(*path)\\n if t.status_code == 200:\\n return json.loads(t.text)\\n else:\\n return {}\",\n \"def info(self):\\n pp = pprint.PrettyPrinter(indent=4)\\n print_text_box('Info')\\n pp.pprint(self.manager.data[\\\"info\\\"])\\n print('')\",\n \"def info(self) -> str:\\n return pulumi.get(self, \\\"info\\\")\",\n \"async def info(self, ctx):\\n self.logger.info(misolog.format_log(ctx, f\\\"\\\"))\\n appinfo = await self.client.application_info()\\n membercount = sum(1 for x in self.client.get_all_members())\\n info_embed = discord.Embed(title=f\\\"Miso Bot | version {main.version}\\\",\\n description=f\\\"Created by {appinfo.owner.mention}\\\\n\\\\n\\\"\\n f\\\"Use `{self.client.command_prefix}help` to get the list of commands, \\\"\\n f\\\"or visit the documention website for more help.\\\"\\n f\\\"\\\\n\\\\nCurrently active in **{len(self.client.guilds)}** \\\"\\n f\\\"servers totaling **{membercount}** unique users\\\",\\n colour=discord.Colour.red())\\n\\n # info_embed.set_footer(text=f'version 2.0')\\n info_embed.set_thumbnail(url=self.client.user.avatar_url)\\n info_embed.add_field(name='Github', value='https://github.com/joinemm/miso-bot', inline=False)\\n info_embed.add_field(name='Documentation', value=\\\"http://joinemm.me/misobot\\\", inline=False)\\n info_embed.add_field(name='Patreon', value=\\\"https://www.patreon.com/joinemm\\\", inline=False)\\n await ctx.send(embed=info_embed)\",\n \"def get_info(self) -> str:\\n return self.info\",\n \"def get(self):\\n try:\\n server_response = self.get_unauthenticated_request(self.baseurl)\\n except ServerResponseError as e:\\n if e.code == \\\"404003\\\":\\n raise ServerInfoEndpointNotFoundError(e)\\n if e.code == \\\"404001\\\":\\n raise EndpointUnavailableError(e)\\n raise e\\n\\n self._info = ServerInfoItem.from_response(server_response.content, self.parent_srv.namespace)\\n return self._info\",\n \"async def serverinfo(self, ctx):\\n guild = ctx.guild\\n\\n embed = discord.Embed()\\n embed.title = str(guild)\\n if guild.icon_url is not None:\\n embed.description = '**ID**: {0.id}\\\\n[Icon URL]({0.icon_url})'.format(guild)\\n embed.set_thumbnail(url=guild.icon_url)\\n else:\\n embed.description = '**ID**: {0.id}'.format(guild)\\n\\n if guild.me.permissions_in(ctx.channel).kick_members and ctx.author.permissions_in(ctx.channel).kick_members:\\n dead_members = await ctx.guild.estimate_pruned_members(days=7)\\n members = '{} members, {} of which were active in the past 7 days'.format(guild.member_count,\\n guild.member_count - dead_members)\\n else:\\n members = guild.member_count\\n\\n embed.add_field(name='Members', value=members)\\n\\n roles = [x.mention for x in guild.role_hierarchy if not x.is_default()]\\n if roles: # only show roles if the server has any\\n roles = ', '.join(roles)\\n if len(roles) <= 1024: # deal with limits\\n embed.add_field(name='Roles', value=roles)\\n\\n channels = [x[1] for x in sorted([(x.position, x.mention) for x in guild.channels if\\n isinstance(x, discord.TextChannel)])]\\n channels = ', '.join(channels)\\n if len(channels) <= 1024:\\n embed.add_field(name='Text channels', value=channels)\\n\\n if guild.verification_level == discord.VerificationLevel.none:\\n verification_level = 'None'\\n elif guild.verification_level == discord.VerificationLevel.low:\\n verification_level = 'Low'\\n elif guild.verification_level == discord.VerificationLevel.medium:\\n verification_level = 'Medium'\\n elif guild.verification_level == discord.VerificationLevel.high:\\n verification_level = '(╯°□°）╯︵ ┻━┻'\\n else:\\n verification_level = '┻━┻ ﾐヽ(ಠ益ಠ)ノ彡┻━┻'\\n\\n if guild.explicit_content_filter == discord.ContentFilter.disabled:\\n explicit_level = 'Don\\\\'t scan any messages'\\n elif guild.explicit_content_filter == discord.ContentFilter.no_role:\\n explicit_level = 'Scan messages from members without a role'\\n else:\\n explicit_level = 'Scan messages sent by all members'\\n\\n info = '**AFK channel**: {0.afk_channel}\\\\n**AFK timeout**: {0.afk_timeout} seconds\\\\n' \\\\\\n '**Owner**: {0.owner.mention}\\\\n**Region**: `{0.region.value}`\\\\n' \\\\\\n '**Verification level**: {1}\\\\n**Explicit content filter**: {2}'.format(guild, verification_level,\\n explicit_level)\\n\\n embed.add_field(name='Other miscellaneous info', value=info)\\n\\n embed.timestamp = guild.created_at\\n embed.set_footer(text='Created on')\\n\\n if ctx.channel.permissions_for(ctx.guild.me).embed_links:\\n if ctx.author.id == self.liara.user.id:\\n await ctx.message.edit(embed=embed)\\n else:\\n await ctx.send(embed=embed)\\n else:\\n await ctx.send('Unable to post serverinfo, please allow the Embed Links permission.')\",\n \"def info(self):\\n return self.__dict__[self.sid]\",\n \"def info(self):\\n return self._fetch_json('/api/info')\",\n \"def info(self):\\n path = self._get_path('info')\\n \\n response = self._GET(path)\\n self._set_attrs_to_values(response)\\n return response\",\n \"def get_node_details(self, node):\\n node_details = self.parser.find_server_by_ip(node.get('ip')) or \\\\\\n self.parser.find_server_by_hostname(node.get('host'))\\n\\n return node_details\",\n \"def SetServerInformation(self, server, port):\\n self.hostname = server\\n self.port = port\",\n \"def default_server_info():\\n # If not set or purposely set = None, then set default\\n if MDC.get('server') is None:\\n try:\\n server = socket.getfqdn()\\n except Exception:\\n try:\\n server = socket.gethostname()\\n except Exception:\\n server = ''\\n MDC.put('server', server)\\n if MDC.get('serverIPAddress') is None:\\n try:\\n server_ip_address = socket.gethostbyname(MDC.get('server'))\\n except Exception:\\n server_ip_address = \\\"\\\"\\n MDC.put('serverIPAddress', server_ip_address)\",\n \"def fp_meta(self):\\n for server in self.machines:\\n s = self.machines[server]\\n print \\\"%s: %s (%s)\\\" % (s.id, s.adminPass, s)\",\n \"def getInfo(self):\\n return self.info\",\n \"def sipserver_status(self) -> str:\",\n \"def get_info(self):\\n return \\\"TODO !\\\"\",\n \"def info(self):\\n _, data = yield from self.transport.perform_request('GET', '/')\\n return data\",\n \"def _request_bootstrap_server_info() -> str:\\n if __debug__:\\n logger.info(\\\"Requesting bootstrap server...\\\")\\n req = BootstrapServerRequest()\\n DistroStreamClientHandler.request(req)\\n\\n # Retrieve answer\\n req.wait_processed()\\n error = req.get_error_code()\\n if error != 0:\\n raise BackendException(error, req.get_error_msg())\\n\\n # Parse answer\\n answer = req.get_response_msg()\\n if __debug__:\\n logger.debug(\\\"Retrieved bootstrap server information: %s\\\", answer)\\n\\n return answer\",\n \"def __repr__(self):\\n return 'Server %s at Node %s' % (self.id_number,\\n self.node.id_number)\",\n \"def info(self) -> str:\\n return self._info\",\n \"def info(self) -> str:\\n return self._info\",\n \"def info(self):\\n return self._info\",\n \"def get_info(self) -> str:\\n raise NotImplementedError()\",\n \"def get_info(self):\\n return {}\",\n \"def server_info(self, **kwargs):\\n kwargs['_return_http_data_only'] = True\\n if kwargs.get('callback'):\\n return self.server_info_with_http_info(**kwargs)\\n else:\\n (data) = self.server_info_with_http_info(**kwargs)\\n return data\",\n \"def show_info(self):\\n print 'Querying the station for the configuration...'\\n config = self.station.getConfig()\\n for key in sorted(config):\\n print '%s: %s' % (key, config[key])\",\n \"async def info(self):\\n # [p]info\\n\\n await self.bot.say(strings.info.format(\\n CacheAPI.get(key='dwarf_repository'),\\n CacheAPI.get(key='dwarf_invite_link')))\",\n \"def info(self):\\n return self._info\",\n \"def get_server():\\n pass\",\n \"def save(self):\\n return {\\n \\\"type\\\": \\\"Server\\\",\\n \\\"fd\\\": self.socket.fileno()\\n }\",\n \"def get_server(self):\\n return self.__server\",\n \"def info(self):\\n\\n if self.running:\\n return INFO_RUNNING_FORMAT.format(**self.__dict__)\\n else:\\n return INFO_ENDED_FORMAT.format(**self.__dict__)\",\n \"def info(self) -> Info:\\n raw = self._call('GET', 'info')\\n return Info.parse_raw(raw)\",\n \"def get_info(self):\\n return None\",\n \"def getInfo(self):\\n return self._info\",\n \"def info(self) -> Optional[Dict[str, Any]]:\\n return self._state.get(\\\"info\\\", None)\",\n \"def server_info(subresource, server, proxyfilename, baseurl):\\n server = HTTPRequests(url=server, localcert=proxyfilename, localkey=proxyfilename, version='HC')\\n\\n dictresult, status, reason = server.get(baseurl, {'subresource' : subresource})\\n\\n return dictresult['result'][0]\",\n \"def server(self):\\n return self._server\",\n \"def server(self):\\n return self._server\",\n \"def get_info():\\n message = \\\"GET information about glancesync server\\\"\\n\\n logger_api.info(message)\\n\\n message = '''\\n {\\n \\\"id\\\": \\\"%s\\\",\\n \\\"owner\\\": \\\"%s\\\",\\n \\\"status\\\": \\\"%s\\\",\\n \\\"version\\\": \\\"%s\\\",\\n \\\"updated\\\": \\\"%s\\\",\\n \\\"runningfrom\\\": \\\"%s\\\",\\n \\\"href\\\": \\\"%s\\\"\\n }\\n ''' % (ID, OWNER, STATUS, VERSION, UPDATED, RUNNINGFROM, API_INFO_URL)\\n\\n resp = make_response(message, httplib.OK)\\n resp.headers[SERVER_HEADER] = SERVER\\n resp.headers[CONTENT_TYPE] = JSON_TYPE\\n\\n logger_api.info('Return result: %s', message)\\n\\n return resp\",\n \"async def serverinfo(ctx):\\n server = ctx.message.server\\n online = len([m.status for m in server.members\\n if m.status == discord.Status.online or\\n m.status == discord.Status.idle])\\n total_users = len(server.members)\\n text_channels = len([x for x in server.channels\\n if x.type == discord.ChannelType.text])\\n voice_channels = len(server.channels) - text_channels\\n passed = (ctx.message.timestamp - server.created_at).days\\n created_at = (\\\"Since {}. That's over {} days ago!\\\"\\n \\\"\\\".format(server.created_at.strftime(\\\"%d %b %Y %H:%M\\\"), passed))\\n\\n colour = ''.join([random.choice('0123456789ABCDEF') for x in range(6)])\\n colour = int(colour, 16)\\n embed = discord.Embed(description = created_at, colour = discord.Colour(value = colour), timestamp = datetime.datetime.utcnow())\\n embed.add_field(name = \\\"Region\\\", value = str(server.region))\\n embed.add_field(name = \\\"Users Online\\\", value = \\\"{}/{}\\\".format(online, total_users))\\n embed.add_field(name = \\\"Text Channels\\\", value = text_channels)\\n embed.add_field(name = \\\"Voice Channels\\\", value = voice_channels)\\n embed.add_field(name = \\\"Roles\\\", value = len(server.roles))\\n embed.add_field(name = \\\"Owner\\\", value = str(server.owner))\\n embed.set_footer(text = \\\"Server ID: \\\" + server.id)\\n embed.add_field(name = \\\"AFK Timeout\\\", value = \\\"{} minutes\\\".format(server.afk_timeout/60).replace(\\\".0\\\", \\\"\\\"))\\n embed.add_field(name = \\\"AFK Channel\\\", value = str(server.afk_channel))\\n embed.add_field(name = \\\"Verification Level\\\", value = str(server.verification_level))\\n embed.set_footer(text= \\\"{} | Requested by: {}\\\".format(version, ctx.message.author))\\n\\n if server.icon_url:\\n embed.set_author(name = server.name, url = server.icon_url)\\n embed.set_thumbnail(url = server.icon_url)\\n else:\\n embed.set_author(name=server.name)\\n\\n await bot.say(embed = embed)\",\n \"def get_server_metadata(self, name):\\n raise NotImplementedError\",\n \"def device_info(self):\\n info = {\\n \\\"connections\\\": {(CONNECTION_NETWORK_MAC, self._data[\\\"port-mac-address\\\"])},\\n \\\"manufacturer\\\": self._ctrl.data[\\\"resource\\\"][\\\"platform\\\"],\\n \\\"model\\\": self._ctrl.data[\\\"resource\\\"][\\\"board-name\\\"],\\n \\\"name\\\": f\\\"{self._inst} {self._data['default-name']}\\\",\\n }\\n return info\",\n \"def device_info(self) -> dict:\\n return {\\n \\\"connections\\\": {(DOMAIN, self._unique_id)},\\n \\\"name\\\": self._host,\\n \\\"manufacturer\\\": \\\"IMAP E-Mail\\\",\\n \\\"sw_version\\\": VERSION,\\n }\",\n \"def get_description(self):\\n return self['host_name']\",\n \"def server_type(self):\\n ...\",\n \"async def info(self, ctx):\\n\\t\\tembed = discord.Embed(\\n\\t\\t\\tdescription=\\\"Created By Seperoph#1399 and AkaBaka#4654\\\",\\n\\t\\t\\tcolor=config[\\\"success\\\"]\\n\\t\\t)\\n\\t\\tembed.set_author(\\n\\t\\t\\tname=\\\"Bot Information\\\"\\n\\t\\t)\\n\\t\\tembed.add_field(\\n\\t\\t\\tname=\\\"Head Programmers:\\\",\\n\\t\\t\\tvalue=\\\"Seperoph#1399 and AkaBaka#4654\\\",\\n\\t\\t\\tinline=True\\n\\t\\t)\\n\\t\\tembed.add_field(\\n\\t\\t\\tname=\\\"Python Version:\\\",\\n\\t\\t\\tvalue=f\\\"{platform.python_version()}\\\",\\n\\t\\t\\tinline=True\\n\\t\\t)\\n\\t\\tawait ctx.respond(embed=embed)\",\n \"def info(self):\\n import string\\n results = self.info_list()\\n labels = \\\"%-8s %-9s %-4s %-8s %-8s %-4s\\\" % \\\\\\n ('MACHINE','CPU','GHZ','MB TOTAL',\\n 'MB FREE','LOAD')\\n print labels\\n for i in range(len(self.workers)):\\n name = string.split(self.workers[i].host,'.')[0]\\n res = results[i]\\n s = \\\"%-8s %2dx%-6s %4.1f %8.1f %8.1f %4.2f\\\" % \\\\\\n (name[-8:], res['cpu_count'],res['cpu_type'][-6:], \\\\\\n res['cpu_speed'],res['mem_total'],res['mem_free'],\\\\\\n res['load_1'])\\n print s\",\n \"def info(self):\\n self.update_info()\\n print('Number of electrodes: ' + str(self.n_elecs))\\n print('Recording time in seconds: ' + str(self.dur))\\n print('Sample Rate in Hz: '+ str(self.sample_rate))\\n print('Number of sessions: ' + str(self.n_sessions))\\n print('Date created: ' + str(self.date_created))\\n print('Meta data: ' + str(self.meta))\",\n \"def info() -> Dict[str, Any]:\",\n \"def describe(self) -> None:\\n return {\\n 'cluster_metadata': self.cluster_metadata,\\n 'master_url': self.master_url\\n }\",\n \"async def _info(self, ctx: Context):\\n\\n embed = discord.Embed(colour=await ctx.embed_colour())\\n\\n perm_int = discord.Permissions(268494928)\\n\\n data = await self.bot.application_info()\\n invite_url = discord.utils.oauth_url(data.id, permissions=perm_int)\\n\\n embed.description = (\\n \\\"TvM Assistant is a Discord bot with utility commands to make hosting TvMs easier.\\\"\\n \\\"\\\\n\\\\nSome of the bot features include:\\\"\\n \\\"\\\\n\\\\n- Setup roles and channel creation\\\"\\n \\\"\\\\n- Management of sign-ups, sign-outs, spectators and replacements\\\"\\n \\\"\\\\n- In-built logging to detect and ignore private channels\\\"\\n \\\"\\\\n- Quick creation of player, mafia and spectator chats\\\"\\n \\\"\\\\n- Vote counts and time since day/night started\\\"\\n )\\n\\n links = (\\n f\\\"\\\\n- [Invite to your server]({invite_url})\\\"\\n f\\\"\\\\n- [Quickstart]({QUICKSTART})\\\"\\n f\\\"\\\\n- [Commands Reference]({COMMANDS_REFERENCE})\\\"\\n f\\\"\\\\n- [Source Code]({SOURCE_CODE})\\\"\\n )\\n\\n embed.add_field(name=\\\"\\\\u200b\\\\nQuick Links\\\", value=links)\\n embed.set_author(name=f\\\"About {ctx.me.name}\\\", icon_url=ctx.me.avatar_url)\\n\\n await ctx.send(embed=embed)\",\n \"def show_hostname(self):\\n if self.hostname is None:\\n self.get_version()\\n print self.hostname\",\n \"def server_name(self) -> str:\\n return pulumi.get(self, \\\"server_name\\\")\",\n \"def manage_info():\",\n \"def info(self):\\n return self.info_text\",\n \"def get_host_stats(self):\\n status, data, errors, messages = self._make_get_request(CraftyAPIRoutes.SERVER_STATS)\\n \\n if status == 200:\\n return data\\n elif status == 500:\\n self._check_errors(errors, messages)\",\n \"def server():\",\n \"def server():\",\n \"async def _serverinfo(self, ctx, details: bool = True):\\n # if not id:\\n # guild = ctx.guild\\n # else:\\n # try:\\n # guild = ctx.bot.get_guild(int(id))\\n # if not guild:\\n # guild = await ctx.bot.fetch_guild(int(id))\\n # except Exception as e:\\n # await ctx.send(f\\\"Error: {e}\\\")\\n\\n guild = ctx.guild\\n online = len([m.status for m in guild.members if m.status != discord.Status.offline])\\n if not details:\\n desc = \\\"Created at {date}\\\".format(date=guild.created_at.strftime(\\\"%d %b %Y %H:%M\\\"))\\n data = discord.Embed(color=ctx.guild.me.top_role.color, description=desc)\\n data.set_author(name=guild.name)\\n data.set_thumbnail(url=guild.icon_url)\\n data.add_field(name=\\\"Region\\\", value=bold(guild.region))\\n data.add_field(name=\\\"Users Online\\\", value=bold(f\\\"{online}/{guild.member_count}\\\"))\\n data.add_field(name=\\\"Roles\\\", value=bold(len(guild.roles)))\\n data.add_field(name=\\\"Owner\\\", value=bold(str(guild.owner)))\\n data.set_footer(text=f\\\"ID: {guild.id}\\\")\\n\\n else:\\n passed = (ctx.message.created_at - guild.created_at).days\\n created_at = (\\\"Created on {date}. That's **{num}** days ago!\\\").format(\\n date=guild.created_at.strftime(\\\"%d %b %Y %H:%M\\\"), num=passed,\\n )\\n total_users = guild.member_count\\n text_channels = len(guild.text_channels)\\n voice_channels = len(guild.voice_channels)\\n\\n def _size(num: int):\\n for unit in [\\\"B\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\", \\\"ZB\\\"]:\\n if abs(num) < 1024.0:\\n return \\\"{0:.1f}{1}\\\".format(num, unit)\\n num /= 1024.0\\n return \\\"{0:.1f}{1}\\\".format(num, \\\"YB\\\")\\n\\n def _bitsize(num: int):\\n for unit in [\\\"B\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\", \\\"ZB\\\"]:\\n if abs(num) < 1000.0:\\n return \\\"{0:.1f}{1}\\\".format(num, unit)\\n num /= 1000.0\\n return \\\"{0:.1f}{1}\\\".format(num, \\\"YB\\\")\\n\\n shard_info = (\\n \\\"\\\\nShard ID: **{shard_id}/{shard_count}**\\\".format(\\n shard_id=guild.shard_id + 1,\\n shard_count=ctx.bot.shard_count,\\n )\\n if ctx.bot.shard_count > 1\\n else \\\"\\\"\\n )\\n online_stats = {\\n \\\"Humans: \\\": lambda x: not x.bot,\\n \\\" • Bots: \\\": lambda x: x.bot,\\n \\\"\\\\N{LARGE GREEN CIRCLE}\\\": lambda x: x.status is discord.Status.online,\\n \\\"\\\\N{LARGE ORANGE CIRCLE}\\\": lambda x: x.status is discord.Status.idle,\\n \\\"\\\\N{LARGE RED CIRCLE}\\\": lambda x: x.status is discord.Status.do_not_disturb,\\n \\\"\\\\N{MEDIUM WHITE CIRCLE}\\\": lambda x: x.status is discord.Status.offline,\\n \\\"\\\\N{LARGE PURPLE CIRCLE}\\\": lambda x: any(\\n a.type is discord.ActivityType.streaming for a in x.activities\\n ),\\n \\\"\\\\N{MOBILE PHONE}\\\": lambda x: x.is_on_mobile(),\\n }\\n member_msg = (\\\"Users online: **{onlineusr}/{total_users}**\\\\n\\\").format(\\n onlineusr=online, total_users=total_users\\n )\\n count = 1\\n for emoji, value in online_stats.items():\\n try:\\n num = len([m for m in guild.members if value(m)])\\n except Exception as error:\\n print(error)\\n continue\\n else:\\n member_msg += f\\\"{emoji} {bold(num)} \\\" + (\\n \\\"\\\\n\\\" if count % 2 == 0 else \\\"\\\"\\n )\\n count += 1\\n\\n vc_regions = {\\n \\\"vip-us-east\\\": \\\"__VIP__ US East \\\" + \\\"\\\\U0001F1FA\\\\U0001F1F8\\\",\\n \\\"vip-us-west\\\": \\\"__VIP__ US West \\\" + \\\"\\\\U0001F1FA\\\\U0001F1F8\\\",\\n \\\"vip-amsterdam\\\": \\\"__VIP__ Amsterdam \\\" + \\\"\\\\U0001F1F3\\\\U0001F1F1\\\",\\n \\\"eu-west\\\": \\\"EU West \\\" + \\\"\\\\U0001F1EA\\\\U0001F1FA\\\",\\n \\\"eu-central\\\": \\\"EU Central \\\" + \\\"\\\\U0001F1EA\\\\U0001F1FA\\\",\\n \\\"europe\\\": \\\"Europe \\\" + \\\"\\\\U0001F1EA\\\\U0001F1FA\\\",\\n \\\"london\\\": \\\"London \\\" + \\\"\\\\U0001F1EC\\\\U0001F1E7\\\",\\n \\\"frankfurt\\\": \\\"Frankfurt \\\" + \\\"\\\\U0001F1E9\\\\U0001F1EA\\\",\\n \\\"amsterdam\\\": \\\"Amsterdam \\\" + \\\"\\\\U0001F1F3\\\\U0001F1F1\\\",\\n \\\"us-west\\\": \\\"US West \\\" + \\\"\\\\U0001F1FA\\\\U0001F1F8\\\",\\n \\\"us-east\\\": \\\"US East \\\" + \\\"\\\\U0001F1FA\\\\U0001F1F8\\\",\\n \\\"us-south\\\": \\\"US South \\\" + \\\"\\\\U0001F1FA\\\\U0001F1F8\\\",\\n \\\"us-central\\\": \\\"US Central \\\" + \\\"\\\\U0001F1FA\\\\U0001F1F8\\\",\\n \\\"singapore\\\": \\\"Singapore \\\" + \\\"\\\\U0001F1F8\\\\U0001F1EC\\\",\\n \\\"sydney\\\": \\\"Sydney \\\" + \\\"\\\\U0001F1E6\\\\U0001F1FA\\\",\\n \\\"brazil\\\": \\\"Brazil \\\" + \\\"\\\\U0001F1E7\\\\U0001F1F7\\\",\\n \\\"hongkong\\\": \\\"Hong Kong \\\" + \\\"\\\\U0001F1ED\\\\U0001F1F0\\\",\\n \\\"russia\\\": \\\"Russia \\\" + \\\"\\\\U0001F1F7\\\\U0001F1FA\\\",\\n \\\"japan\\\": \\\"Japan \\\" + \\\"\\\\U0001F1EF\\\\U0001F1F5\\\",\\n \\\"southafrica\\\": \\\"South Africa \\\" + \\\"\\\\U0001F1FF\\\\U0001F1E6\\\",\\n \\\"india\\\": \\\"India \\\" + \\\"\\\\U0001F1EE\\\\U0001F1F3\\\",\\n \\\"dubai\\\": \\\"Dubai \\\" + \\\"\\\\U0001F1E6\\\\U0001F1EA\\\",\\n \\\"south-korea\\\": \\\"South Korea \\\" + \\\"\\\\U0001f1f0\\\\U0001f1f7\\\",\\n }\\n verif = {\\n \\\"none\\\": \\\"0 - None\\\",\\n \\\"low\\\": \\\"1 - Low\\\",\\n \\\"medium\\\": \\\"2 - Medium\\\",\\n \\\"high\\\": \\\"3 - High\\\",\\n \\\"extreme\\\": \\\"4 - Extreme\\\",\\n }\\n features = {\\n \\\"PARTNERED\\\": \\\"Partnered\\\",\\n \\\"VERIFIED\\\": \\\"Verified\\\",\\n \\\"DISCOVERABLE\\\": \\\"Server Discovery\\\",\\n \\\"FEATURABLE\\\": \\\"Featurable\\\",\\n \\\"PUBLIC\\\": \\\"Public\\\",\\n \\\"PUBLIC_DISABLED\\\": \\\"Public disabled\\\",\\n \\\"INVITE_SPLASH\\\": \\\"Splash Invite\\\",\\n \\\"VIP_REGIONS\\\": \\\"VIP Voice Servers\\\",\\n \\\"VANITY_URL\\\": \\\"Vanity URL\\\",\\n \\\"MORE_EMOJI\\\": \\\"More Emojis\\\",\\n \\\"COMMERCE\\\": \\\"Commerce\\\",\\n \\\"NEWS\\\": \\\"News Channels\\\",\\n \\\"ANIMATED_ICON\\\": \\\"Animated Icon\\\",\\n \\\"BANNER\\\": \\\"Banner Image\\\",\\n \\\"MEMBER_LIST_DISABLED\\\": \\\"Member list disabled\\\",\\n }\\n guild_features_list = [\\n f\\\"✅ {name}\\\" for feature, name in features.items() if feature in guild.features\\n ]\\n joined_on = (\\n \\\"{bot_name} joined this server on {bot_join}. That's {since_join} days ago!\\\"\\n ).format(\\n bot_name=ctx.bot.user.name,\\n bot_join=guild.me.joined_at.strftime(\\\"%d %b %Y %H:%M:%S\\\"),\\n since_join=(ctx.message.created_at - guild.me.joined_at).days,\\n )\\n data = discord.Embed(\\n description=(f\\\"{guild.description}\\\\n\\\\n\\\" if guild.description else \\\"\\\") + created_at,\\n colour=ctx.guild.me.top_role.color,\\n )\\n data.set_author(\\n name=guild.name,\\n icon_url=\\\"https://cdn.discordapp.com/emojis/457879292152381443.png\\\"\\n if \\\"VERIFIED\\\" in guild.features\\n else \\\"https://cdn.discordapp.com/emojis/508929941610430464.png\\\"\\n if \\\"PARTNERED\\\" in guild.features\\n else discord.Embed.Empty,\\n )\\n if guild.icon_url:\\n data.set_thumbnail(url=guild.icon_url)\\n data.add_field(name=\\\"Members:\\\", value=member_msg)\\n data.add_field(\\n name= \\\"Channels:\\\",\\n value= (\\n \\\"\\\\N{SPEECH BALLOON} Text: {text}\\\\n\\\"\\n \\\"\\\\N{SPEAKER WITH THREE SOUND WAVES} Voice: {voice}\\\"\\n ).format(text=bold(text_channels), voice=bold(voice_channels)),\\n )\\n data.add_field(\\n name=\\\"Utility:\\\",\\n value=(\\n \\\"Owner: {owner}\\\\nVoice region: {region}\\\\nVerif. level: {verif}\\\\nServer ID: {id}{shard_info}\\\"\\n ).format(\\n owner=bold(str(guild.owner)),\\n region=bold(f\\\"{vc_regions.get(str(guild.region)) or str(guild.region)}\\\"),\\n verif=bold(verif[str(guild.verification_level)]),\\n id=bold(str(guild.id)),\\n shard_info=shard_info,\\n ),\\n inline=False,\\n )\\n data.add_field(\\n name=\\\"Misc:\\\",\\n value=(\\n \\\"AFK channel: {afk_chan}\\\\nAFK timeout: {afk_timeout}\\\\nCustom emojis: {emoji_count}\\\\nRoles: {role_count}\\\"\\n ).format(\\n afk_chan=bold(str(guild.afk_channel))\\n if guild.afk_channel\\n else bold(\\\"Not Set\\\"),\\n afk_timeout=bold(guild.afk_timeout),\\n emoji_count=bold(len(guild.emojis)),\\n role_count=bold(len(guild.roles)),\\n ),\\n inline=False,\\n )\\n if guild_features_list:\\n data.add_field(name=\\\"Server features:\\\", value=\\\"\\\\n\\\".join(guild_features_list))\\n if guild.premium_tier != 0:\\n nitro_boost = (\\n \\\"Tier {boostlevel} with {nitroboosters} boosters\\\\n\\\"\\n \\\"File size limit: {filelimit}\\\\n\\\"\\n \\\"Emoji limit: {emojis_limit}\\\\n\\\"\\n \\\"VCs max bitrate: {bitrate}\\\"\\n ).format(\\n boostlevel=bold(str(guild.premium_tier)),\\n nitroboosters=bold(guild.premium_subscription_count),\\n filelimit=bold(_size(guild.filesize_limit)),\\n emojis_limit=bold(str(guild.emoji_limit)),\\n bitrate=bold(_bitsize(guild.bitrate_limit)),\\n )\\n data.add_field(name=\\\"Nitro Boost:\\\", value=nitro_boost)\\n if guild.splash:\\n data.set_image(url=guild.splash_url_as(format=\\\"png\\\"))\\n data.set_footer(text=joined_on)\\n\\n await ctx.send(embed=data)\",\n \"def get_name(self):\\n \\n return 'TCP/IP Server'\",\n \"def get_ded_info(server, show=False):\\n # start Requests session\\n sc = requests.Session()\\n\\n # import cookies from Firefox\\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\\n\\n # send request\\n vpx = sc.get('https://imhsc.imhadmin.net/index.php',\\n params={'v': \\\"Dedicated\\\", 'selectServer': server})\\n\\n # check if login failed\\n check_sc_login(vpx.text)\\n\\n # parse with BS4\\n bs = BeautifulSoup(vpx.text, \\\"xml\\\")\\n\\n # server=0 ip=4 net=5 psc=6 user=11 type=14\\n trr = bs.tbody.find_all('tr')\\n if len(trr) > 0:\\n tsrv = {\\n 'hostname': trr[0].find_all('td')[0].string,\\n 'ip': trr[0].find_all('td')[2].string,\\n 'net': trr[0].find_all('td')[3].string,\\n 'psc': trr[0].find_all('td')[4].a.string,\\n 'user': trr[0].find_all('td')[9].string,\\n 'type': trr[0].find_all('td')[12].string,\\n 'status': trr[0].find_all('td')[13].string.strip()\\n }\\n else:\\n tsrv = None\\n\\n if show:\\n if tsrv:\\n print(\\\"[%(hostname)s] IP: %(ip)s (%(net)s) / PSC: %(psc)s / User: %(user)s / Type: %(type)s / Status: %(status)s\\\" % tsrv)\\n else:\\n print(\\\"!! Server '%s' not found\\\" % (server))\\n\\n return tsrv\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8096221","0.80244446","0.7995032","0.7823503","0.7384904","0.7376968","0.72559196","0.7244653","0.7027462","0.70059526","0.6965059","0.69347584","0.6865889","0.68443227","0.6838706","0.6834391","0.68235415","0.68076944","0.67965454","0.67949927","0.673637","0.673637","0.67306","0.6726609","0.66893345","0.66709757","0.66625744","0.66491467","0.66244173","0.6607118","0.6567703","0.6567703","0.65262425","0.65192264","0.65134716","0.64855","0.647899","0.64526916","0.6449917","0.6441995","0.64403266","0.6437109","0.64324033","0.6389654","0.637861","0.6365638","0.6355285","0.6334399","0.63249046","0.6312996","0.6306648","0.6299358","0.6296493","0.62924063","0.6290568","0.6288605","0.62871","0.6286396","0.6286396","0.62767327","0.6270174","0.6269","0.6265381","0.6238064","0.62374544","0.623738","0.6225643","0.6223159","0.6219124","0.6208915","0.6206577","0.618729","0.617808","0.6175303","0.61747926","0.6173345","0.6173345","0.61700964","0.6160715","0.6151498","0.6145647","0.61405915","0.6139291","0.6138384","0.61099184","0.6103785","0.60952926","0.60909075","0.6087721","0.60845184","0.60832405","0.60619926","0.606013","0.6054223","0.60485566","0.60416615","0.60416615","0.6038813","0.6022923","0.60111845"],"string":"[\n \"0.8096221\",\n \"0.80244446\",\n \"0.7995032\",\n \"0.7823503\",\n \"0.7384904\",\n \"0.7376968\",\n \"0.72559196\",\n \"0.7244653\",\n \"0.7027462\",\n \"0.70059526\",\n \"0.6965059\",\n \"0.69347584\",\n \"0.6865889\",\n \"0.68443227\",\n \"0.6838706\",\n \"0.6834391\",\n \"0.68235415\",\n \"0.68076944\",\n \"0.67965454\",\n \"0.67949927\",\n \"0.673637\",\n \"0.673637\",\n \"0.67306\",\n \"0.6726609\",\n \"0.66893345\",\n \"0.66709757\",\n \"0.66625744\",\n \"0.66491467\",\n \"0.66244173\",\n \"0.6607118\",\n \"0.6567703\",\n \"0.6567703\",\n \"0.65262425\",\n \"0.65192264\",\n \"0.65134716\",\n \"0.64855\",\n \"0.647899\",\n \"0.64526916\",\n \"0.6449917\",\n \"0.6441995\",\n \"0.64403266\",\n \"0.6437109\",\n \"0.64324033\",\n \"0.6389654\",\n \"0.637861\",\n \"0.6365638\",\n \"0.6355285\",\n \"0.6334399\",\n \"0.63249046\",\n \"0.6312996\",\n \"0.6306648\",\n \"0.6299358\",\n \"0.6296493\",\n \"0.62924063\",\n \"0.6290568\",\n \"0.6288605\",\n \"0.62871\",\n \"0.6286396\",\n \"0.6286396\",\n \"0.62767327\",\n \"0.6270174\",\n \"0.6269\",\n \"0.6265381\",\n \"0.6238064\",\n \"0.62374544\",\n \"0.623738\",\n \"0.6225643\",\n \"0.6223159\",\n \"0.6219124\",\n \"0.6208915\",\n \"0.6206577\",\n \"0.618729\",\n \"0.617808\",\n \"0.6175303\",\n \"0.61747926\",\n \"0.6173345\",\n \"0.6173345\",\n \"0.61700964\",\n \"0.6160715\",\n \"0.6151498\",\n \"0.6145647\",\n \"0.61405915\",\n \"0.6139291\",\n \"0.6138384\",\n \"0.61099184\",\n \"0.6103785\",\n \"0.60952926\",\n \"0.60909075\",\n \"0.6087721\",\n \"0.60845184\",\n \"0.60832405\",\n \"0.60619926\",\n \"0.606013\",\n \"0.6054223\",\n \"0.60485566\",\n \"0.60416615\",\n \"0.60416615\",\n \"0.6038813\",\n \"0.6022923\",\n \"0.60111845\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71475893"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":6792,"cells":{"query":{"kind":"string","value":"Type !help {command} for more info on a command. You can also type !help {category} for more info on a category. For example, !help level (If you have level plugin enable!)"},"document":{"kind":"string","value":"async def command(self,ctx):\n await ctx.send(\"Yes this is a command.\")"},"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 help_help(self):\n print(\"List commands or print details about a command\")","def command_help(self, command):\n self.commands[command].command_help()","def command_help(self, *args, **kwargs):\n print(\"Commands available:\\n\")\n for name in dir(self):\n if not name.startswith(\"command_\"):\n continue\n name_clean = name[len(\"command_\"):]\n print(\"%s:\\n - %s\\n\" % (name_clean, getattr(self, name).__doc__.strip()))","def do_command_help(self, command):\n summary = self.base.commands[command].get_summary()\n usage = self.get_command_usage(command)\n description = self.base.commands[command].get_description()\n sys.stdout.write('%s\\n%s' % (summary, usage))\n if description != None:\n sys.stdout.write('Arguments Description:\\n%s\\n' %\n (description, ))","def help(command=None):\n if command is None: \n # print first line of docstring\n for cmd in commands:\n ds = commands[cmd].__doc__.split('\\n')[0]\n print \"%-16s %s\" % (cmd,ds)\n else:\n print commands[command].__doc__","def _help(self):\n self.onecmd('help')","def cmd_help(args):","def help():","def help():\n \n pass","async def _help(ctx, *, command_name: str=None):\n if command_name:\n command = bot.get_command(command_name)\n if not command:\n return await ctx.send(\"No such command!\")\n return await ctx.send(f\"```\\n{ctx.prefix}{command.name} {command.signature}\\n\\n{command.help or 'Missing description'}```\")\n description = []\n for name, cog in bot.cogs.items():\n entries = [\" - \".join([cmd.name, cmd.short_doc or \"Missing description\"]) for cmd in cog.get_commands() if await _can_run(cmd, ctx) and not cmd.hidden]\n if entries:\n description.append(f\"**{name}**:\")\n description.append(\"• \" + \"\\n• \".join(entries))\n await ctx.send(embed=discord.Embed(description=\"\\n\".join(description), color=ctx.me.color))","def help_command(server, output, conf):\n server.tell(output.name, 'Available commands:')\n for key in COMMANDS.keys():\n cmd_func = COMMANDS[key]\n if cmd_func.__doc__:\n server.tell(output.name, '%s: %s' % (key[1:], cmd_func.__doc__))\n else:\n server.tell(output.name, key[1:])\n return","def help():\n print(UI.HELP)","def do_help(self, command_name):\n if not command_name:\n print _PVBlotInterp.__doc__\n print \"The following commands are supported:\"\n print \" \",\n blotish_commands = self._blotish_commands.keys()\n blotish_commands.sort()\n for c in blotish_commands:\n print c,\n print\n print\n print \"For more information on any command, try help <command>.\"\n return\n try:\n command = self.get_unique_command(command_name)\n print command.__doc__\n except blotish.BlotishError, err:\n blot_common.print_blot_error(err)","def command_help(args):\n\tprint_usage()\n\treturn 0","async def do_help(self, arg):\n if arg:\n if arg in self._commands:\n _, doc = self._commands[arg]\n print(doc)\n else:\n self.error('No command {}'.format(arg))\n else:\n print('Valid commands:')\n for c in self._commands:\n print(' ' + c)","def show_help():\n pass","def command_help(self):\n print(\"Command \", self)\n print(\"\\t\\thelp (Get help for command)\")\n\n params = self.params.copy()\n del params[\"help\"]\n\n if len(params) == 0:\n print(\"This command has no parameters\")\n return\n\n print(\"Parameters:\")\n for info in params.values():\n print(\" %s\" % info.get_basic_info())\n description = info.get_desc()\n if description != \"\":\n print(textwrap.fill(description,\n initial_indent=\" \",\n subsequent_indent=\" \",\n width=70))","def help(self):\r\n self._short_help(None, None, None, None)","def get_command_help(self, module_name, command_name):\r\n command = self.env.get_command(module_name, command_name)\r\n\r\n default_format = 'raw'\r\n if sys.stdout.isatty():\r\n default_format = 'table'\r\n\r\n arg_doc = command.__doc__\r\n\r\n if 'confirm' in command.options:\r\n arg_doc += \"\"\"\r\nPrompt Options:\r\n -y, --really Confirm all prompt actions\r\n\"\"\"\r\n\r\n if '[options]' in arg_doc:\r\n arg_doc += \"\"\"\r\nStandard Options:\r\n --format=ARG Output format. [Options: table, raw] [Default: %s]\r\n -C FILE --config=FILE Config file location. [Default: ~/.softlayer]\r\n --debug=LEVEL Specifies the debug noise level\r\n 1=warn, 2=info, 3=debug\r\n --timings Time each API call and display after results\r\n --proxy=PROTO:PROXY_URL HTTP[s] proxy to be use to make API calls\r\n -h --help Show this screen\r\n\"\"\" % default_format\r\n return arg_doc.strip()","def help(self):","def help(self):","def help(self):\n pass","def help(self):\n pass","def help(self, args):\n print('No commands available for this consumer')","def help():\n print \"Help comes to those who ask\"","def cmd_help(ctx):\n echo(ctx.parent.get_help())","async def help(self, ctx, *, command_name: str=None):\n bot_prefix = '@Randy '\n # Shortcut to command search\n if command_name is not None:\n return await ctx.invoke(self.cmd('help command'), cmd_name=command_name)\n\n em = discord.Embed(title='Help',\n description='**Permissions:** The permissions required to function :-\\n'\n '`Send Messages`, `Manage Messages`, `Embed Links`\\n'\n '--\\nTo get help or more information on a specific command, use:\\n'\n '`{bot_prefix}help <command name>`\\n'\n '--\\nRead my messy code [here](http://github.com/xKynn/RandomRumble)'\n '--\\nIf you like my work and would like to help me, '\n 'Ko-Fi/Paypal: [Link](https://ko-fi.com/D1D6EXXV)\\n',\n color=self.color)\n\n em.set_footer(text=\"Contact me at Demo#7645\")\n\n # This can't go in the init because help isn't loaded last & thus misses some commands\n em.add_field(name=\"Commands\", value=' • '+'\\n • '.join(f\"***{c.name}*** - {c.short_doc}\" for c in self.bot.commands if\n c.name not in ['pob', 'link', 'convert']))\n try:\n await ctx.send(embed=em)\n except:\n await ctx.send(\"`Embed Links` permission is required to see the help!\")","def command_short():\n pass","def help_util():\r\n for cmd, f in COMMANDS.items():\r\n print(\"POM \" + cmd + \":\")\r\n print(f.__doc__.lstrip(\"\\n\"))","def test_cli_help(self):\n output = self.update_command('-h')","async def send_command_help(self, command):\n ctx = self.context\n embed = discord.Embed(title=command.name.upper(), description=command.description, color=discord.Color.green())\n alias = command.aliases\n if alias:\n embed.add_field(name=\"Aliases\", value=\", \".join(alias), inline=False)\n if command.usage != None:\n embed.add_field(name=\"How to use:\", value=f'`{command.usage}`')\n\n await ctx.reply(embed=embed)","def command_help(self, bot, update):\n\n messages = [\n 'Available commands:',\n '/who - Who is Myles?',\n '/where - Where is Myles?',\n '/tweet - What was the last tweet Myles sent?',\n '/photo - What was the last Instagram photo Myles took?',\n '/web - Where can I find Myles on the interwebs?',\n ]\n\n self.send_messages(bot, update, messages)","def printhelp():","def print_help(self):\n\n print((\"Help is not defined for command \" + self.command))","async def help(self, *args, **kwargs):\n if len(args) == 0:\n return self.help.__doc__\n docs = []\n for command in args:\n if command in self.user_special_bang_set:\n docs.append(self.user_special_bang_set[command].bang_skill.__doc__)\n elif command in self.control_bang_set:\n docs.append(self.control_bang_set[command].bang_skill.__doc__)\n\n return \"| \".join(docs)","def CommandHelp(paser):\n\n\tprint \"\\n===============Commands List===============\\n\"\n\t\t\n\tprint \"NewProject - {}\".format(NewProject.__doc__)\n\tprint \"DelProject - {}\".format(DelProject.__doc__)\n\tprint \"ShareProject - {}\".format(ShareProject.__doc__)\n\tprint \"StopProject - {}\".format(StopProject.__doc__)\n\tprint \"Help - {}\".format(CommandHelp.__doc__)\n\tprint \"Exit - Finaliza la sesion en la terminal.\"","def _help():\n text = \"\"\"\n```\nUsage: @bot [command] (message)\n\n*Commands*:\n \n add Adds specified users to notify list | To add yourself use key 'myself'\n list Lists users on notify list\n remove Removes specified users from notify list | To remove yourself use key 'myself'\n help This help\n\n > Obs: All commands are optional\n\n*Examples*:\n\n @bot add myself @Fulano <= Will add yourself and @Fulano in list.\n @bot this a messge test <= Send 'this a messge test' to all list.\n @bot remove myself @Fulano <= Remove yourself and @fulano from list.\n\n```\n\"\"\"\n return text","def help(cmd, cmdArgs):\n global commandDict\n retInfo = []\n if len(cmdArgs) > 0:\n #return help on a single function\n if cmdArgs[0] in commandDict.keys():\n return commandDict[cmdArgs[0]].__doc__\n\n #else, return general info\n retInfo = ['pypeople: A command line tool for vCard management',\n 'Version:' + __version__,\n 'Available Commands:']\n #fill in more info here\n for cmdName in commandDict.keys():\n cmdFunc = commandDict[cmdName]\n cmdDoc = str(cmdName) + ': ' + str(cmdFunc.__doc__) if cmdFunc.__doc__ is not None else 'Undocumented Function'\n retInfo.append('\\t' + cmdDoc)\n\n return '\\n'.join(retInfo)","def print_generic_help():\r\n print ART_NAME\r\n print 'Version {1}\\nby {2}'.format(NAME, VERSION, AUTHOR)\r\n print DESCRIPTION\r\n tools = sorted(AvailableCommands.commands.keys(), key=lambda v: v.upper())\r\n # Do not show CUSTOM command in the help\r\n tools.remove('CUSTOM')\r\n tools.remove('CUSTOM_NO_OUTPUT')\r\n print '\\n\\nSupported tools are:\\n{0}'.format('\\n'.join(tools))\r\n print '\\nHint: Check tool specific help with --help <tool_name>\\n'","async def help(ctx, command:str=None):\n if command == None:\n embed = assemble_embed(\n title=\"Looking for help?\",\n desc=(\"Hey there, I'm a resident bot of Scioly.org!\\n\\n\" +\n \"On Discord, you can send me commands using `!` before the command name, and I will process it to help you! \" +\n \"For example, `!states`, `!events`, and `!fish` are all valid commands that can be used!\\n\\n\" +\n \"If you want to see some commands that you can use on me, just type `!list`! \" +\n \"If you need more help, please feel free to reach out to a staff member!\")\n )\n return await ctx.send(embed=embed)\n hlp = await get_help(ctx, command)\n await ctx.send(embed=hlp)","def help(self, msg=None):\n\n # Print the message if given.\n if not msg == None:\n print str(msg) + \"\\n\"\n\n # Display the list of commands, in the alphabetical order.\n print \"Use one of the following commands:\"\n for action in sorted(self.actions.keys()):\n info = self.actions[action]\n joined_oblig = ' '.join(info['required'])\n if len(info['additional']) > 0:\n add = [\"<%s>\" % x for x in info['additional']]\n joined_add = '[' + ' '.join(add) + ']'\n else:\n joined_add = ''\n print \"\\t* %s %s %s\" % (action, joined_oblig, joined_add)","def help_command(update: Update, _: CallbackContext) -> None:\n update.message.reply_text(\"These are the list of commands supported. \\n\\n /deadlines. \\n\\n \"\n \"Hey! I am still being enhanced, more features to come...!\")","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text(\"Laughybot\\n\\n VERSION\\n 0.1 \\n\\nUSAGE\\n /{command} : Entrer une commande parmi celles disponibles\\n\\nCOMMADES\\n/joke => recherche une blague sur la toile\\n/start => Affiche le message d'accueil\\n/help => Affiche l'aide\")","def get_help(self,command):\n if \"help\" in self.commands[command]:\n return self.commands[command][\"help\"]\n else:\n return \"No help defined for this command.\"","def do_help(self, args): \n if args.command:\n if len(args.command) > 1:\n command = args.command[0] +\"-\" + args.command[1]\n else:\n command = args.command[0]\n if command in self.subcommands:\n self.subcommands[command].print_help()\n else:\n print((\"'%s' is not a valid subcommand\") %\n args.command)\n else:\n self.parser.print_help()","def help_command(update, context):\n update.message.reply_text('Help!')","def get_help(self) -> None: \n print(messages.get_help())","async def help(self, ctx):\n self.log_command_call(\"help\", ctx.message)\n await ctx.send(HELP_TEXT)\n embed_output = create_embed(description=MORE_INFO_TEXT)\n await ctx.send(embed=embed_output)","def dispatch_help(args):\n command = args.subcommand\n if command is None:\n command = 'help'\n args.parsers[command].print_help()\n\n cmd_func = getattr(EtcMaint, 'cmd_%s' % command, None)\n if cmd_func:\n lines = cmd_func.__doc__.splitlines()\n print('\\n%s\\n' % lines[0])\n paragraph = []\n for l in dedent('\\n'.join(lines[2:])).splitlines():\n if l == '':\n if paragraph:\n print('\\n'.join(wrap(' '.join(paragraph), width=78)))\n print()\n paragraph = []\n continue\n paragraph.append(l)\n if paragraph:\n print('\\n'.join(wrap(' '.join(paragraph), width=78)))","def print_specific_help(tool_name):\r\n if tool_name not in AvailableCommands.commands:\r\n print 'Command is not supported: {0}'.format(tool_name)\r\n return\r\n cmd = AvailableCommands.commands[tool_name]\r\n\r\n print 'Usage of {0}:'.format(cmd.name)\r\n print '\\nAccepted input types:\\n{0}'.format(str(list(cmd.input_types)))\r\n print '\\nOutput types:\\n{0}'.format(str(cmd.output_types))\r\n print '\\nMandatory arguments:\\n{0}'.format(str(cmd.user_mandatory_args))\r\n print '\\nOptional arguments:\\n{0}'.format(str(cmd.user_optional_args))\r\n print '\\nParallelizable:\\n{0}'.format(str(cmd.parallelizable))\r\n print '\\nAdditional description:\\n{0}'.format(str(cmd.help_description))\r\n print ''","def display_help(self):\n pass","async def help_command(self, ctx, *, cmd_name: str=None):\n bot_prefix = '@Randy '\n # Get command object\n cmd_obj = self.cmd(cmd_name)\n\n # Handle no command found\n if cmd_obj is None:\n return await ctx.error(f'Command {cmd_name} not found')\n em = discord.Embed(title=cmd_obj.name, description=cmd_obj.help, color=self.color)\n\n # Input aliases and parameters to embed\n if cmd_obj.aliases:\n em.add_field(name='Aliases', value='\\n'.join([f'\\u2022 {x}' for x in cmd_obj.aliases]))\n if cmd_obj.clean_params:\n em.add_field(name='Parameters', value='\\n'.join([f'\\u2022 {x}' for x in cmd_obj.clean_params]))\n\n # Handle group commands\n if isinstance(cmd_obj, commands.core.Group):\n em.add_field(name='Group commands',\n value='\\n'.join([f'\\u2022 {x}' for x in cmd_obj.commands]),\n inline=False)\n\n # Add usage last\n em.add_field(name='Usage',\n value=f'```{bot_prefix}\\u200b{cmd_name} '\n f'{\" \".join([f\"<{x}>\" for x in cmd_obj.clean_params])}```',\n inline=False)\n\n await ctx.send(embed=em)","def showHelp(self):\n\t\tfor i in range(0,20):\n\t\t\tprint \"\"\n\t\tprint \" _ _ \"\n\t\tprint \"| | | | \"\n\t\tprint \"| |__ _ __ ___ | |__ \"\n\t\tprint \"| '_ \\ | '_ \\ / __|| '_ \\ \"\n\t\tprint \"| | | || | | |\\__ \\| | | |\"\n\t\tprint \"|_| |_||_| |_||___/|_| |_|\"\n\t\tprint \"A program by Scott Jackson\"\n\t\tprint \"\"\n\t\tprint \"To enter a command, type the key and press Return.\"\n\t\tprint \"NB: parentheses indicate which of two options is the default.\"\n\t\tprint \"\"\n\t\tprint \"Basic Commands:\"\n\t\tprint \"j / k -- show lower-ranked / higher-ranked stories.\"\n\t\tprint \"r -- get the latest stories from Hacker News.\"\n\t\tprint \"q -- quit.\"\n\t\tprint \"# -- open story number # in your web browser.\"\n\t\tprint \"c# -- open comments for story number # in your web browser.\"\n\t\tprint \"#+ -- open up story number # AND its comments in your web browser.\"\n\t\tprint \"top / new -- switch between showing the top and newest stories on HN. (top)\"\n\t\tprint \"c / e -- collapse stories you've already read / don't collapse them. (e)\"\n\t\tprint \"u -- update hnsh to the latest version.\"\n\t\tprint \"==========================\"\n\t\tprint \"For more commands, see the man.txt file.\"\n\t\tinput = raw_input(\"Press Return to go back to the Hacker News stories.\")","def help(self, irc, msg, args, command):\n command = map(callbacks.canonicalName, command)\n (maxL, cbs) = irc.findCallbacksForArgs(command)\n if maxL == command:\n if len(cbs) > 1:\n names = sorted([cb.name() for cb in cbs])\n irc.error(format('That command exists in the %L plugins. '\n 'Please specify exactly which plugin command '\n 'you want help with.', names))\n else:\n assert cbs, 'Odd, maxL == command, but no cbs.'\n irc.reply(cbs[0].getCommandHelp(command, False))\n else:\n irc.error(format('There is no command %q.',\n callbacks.formatCommand(command)))","def help(self):\n\t\treturn","def help(ctx):\n click.echo(ctx.parent.get_help())","def help_command(update: Update, _: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, _: CallbackContext) -> None:\n update.message.reply_text('Help!')","async def do_help():\n\n if len(message.content.split()) > 1:\n for i in cmd_dict:\n if message.content.split()[1] == i:\n await bot.send_message(c, f'Help for {i}: {cmd_dict[i].__doc__}')\n return\n\n cmdstr = 'Commands: '\n for i in cmd_dict:\n cmdstr += '{}, '.format(i)\n await bot.send_message(c, cmdstr)","def help(self, cmd=\"\", *, fail=\"\"):\n class_dict = dict(type(self).__dict__)\n # Add this function to class, so that when subclassing,\n # help for help is found\n class_dict.update({\"help\": self.help})\n if cmd.startswith(self.predicate):\n # Strip predicate\n cmd = cmd[len(self.predicate) :]\n # Check that command exists and is not\n # private, protected or special method\n if (not cmd.startswith(\"_\")) and cmd in class_dict.keys():\n item = class_dict[cmd]\n if callable(item):\n if item.__doc__:\n return \"Help on command '{}':\\n. {}\".format(\n cmd, \"\\n. \".join(cleandoc(item.__doc__).split(\"\\n\"))\n )\n return \"No help on command '{}'\".format(cmd)\n # If no cmd given or wrong cmd given, return commands\n commands = []\n for key, value in class_dict.items():\n if not key.startswith(\"_\"):\n if callable(value):\n commands.append(key)\n msg = (\n \"Commands:\\n {}\".format(\", \".join(commands))\n + \"\\n for more help on command, use \"\n + \"{}help command\".format(self.predicate)\n )\n if fail:\n msg = fail + \"\\n\" + msg\n return msg","def help():\n version()\n print(\"USAGE: python3 notes.py [COMMAND]\")\n print(\"COMMAND:\")\n help_dict = {\n \"help\": \"Prints out the help text.\",\n \"version\": \"Prints out the version.\",\n \"init\": \"Initializes a new set of notes.\",\n \"modify\": \"Modify the current set of notes.\",\n }\n help_dict = OrderedDict(sorted(help_dict.items()))\n for entry in help_dict:\n print((\" \" + entry).ljust(10), help_dict[entry])","def do_help(self, message):\r\n\t\tcommands = [command[3:] for command in dir(self) if command.startswith('do_')]\r\n\t\t# self.trace(f'Help: {\", \".join(commands)}')\r\n\r\n\t\tfor command in commands:\r\n\t\t\tmethod = getattr(self, f'do_{command}')\r\n\t\t\tcommand_help = getattr(method, '__doc__')\r\n\t\t\tif command_help:\r\n\t\t\t\tself.trace(f'{command}: {command_help}')","def help_command(update: Update, context: CallbackContext) -> None:\n commands = [\"/login <pwd>\\n\",\n \"/status\\n\",\n \"/heat\\n\",\n \"/temp\\n\",\n \"/off\\n\",\n \"/help\\n\",\n \"/set\\n\",\n \"/unset\\n\",\n \"/heatmore\\n\",\n \"/lighton\\n\",\n \"/lightoff\\n\"]\n\n cmd: str = \" \".join(commands)\n update.message.reply_text('commands are:\\n' + cmd)","def Help(topic=None):\n\n if not topic:\n print \"Avaliable commands are: \"\n print \"\\n\".join(map(lambda x:\" \"*3+x, filter(lambda c:c!=\"help\", commands)))\n print \"Type help command to get help with a specific command.\"\n\n elif topic in commands:\n print \"Help for %s:\"%topic\n print commands[topic].__doc__","def do_help(self, arg):\n if arg:\n # Getting help for a specific command\n funcname = self._func_named(arg)\n if funcname:\n # No special behavior needed, delegate to cmd base class do_help()\n cmd.Cmd.do_help(self, funcname[3:])\n else:\n # Show a menu of what commands help can be gotten for\n self._help_menu()","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Help!')","def show_help(message, commands):\n commands_help = [\n {\n 'name': 'help',\n 'text': 'show this help message.'\n },\n ]\n\n commands_help.extend((c.help for key, c in commands.items()))\n command_text = '\\n'.join([' {0[name]:<12}{0[text]}'.format(cmd)\n for cmd in commands_help])\n help_message = '**redditfy-bot** available commands:\\n{}'.format(command_text)\n print(help_message)\n return help_message","def help_command(update: Update) -> None:\n #update.message.reply_text('Help!')","def help_command(update: Update, context: CallbackContext) -> None:\r\n update.message.reply_text('Help!')","async def help(self, args):\n if not args:\n maxw = max([len(x) for x in self.commands]) + 1\n commands = list(self.commands)\n commands.sort()\n message = '\\n'.join(['`{name:{width}}|` {desc}'.format(\n name=command, width=maxw,\n desc=(self.commands[command].__doc__ or 'No description.').splitlines()[0]\n ) for command in commands])\n await self.send(\"Unlisted commands are forwarded to the Minecraft server.\\n\" + message)\n elif args.lower() not in self.commands:\n await self.send_error(\"Unknown command: {command}. This might be a Minecraft command.\".format(command=args))\n else:\n args = args.lower()\n await self.send(\"**`{name}`** - {doc}\".format(name=args, doc=self.commands[args].__doc__ or 'No description.'))","def help(self):\n help = ''\n cmds = [(x, y) for x, y in Commands.__dict__.iteritems()]\n cmds.sort(key=lambda x: x[0])\n for name, member in cmds:\n if name.startswith('cmd_') and callable(member):\n help += ' %s\\n' % ' '.join([name[4:]] +\n ['<%s>' % x for x in\n inspect.getargspec(member).args[1:]])\n if member.__doc__:\n help += ' %s\\n' % member.__doc__.splitlines()[0]\n return 'Available commands:\\n%s' % help","def help_command(update, context):\n update.message.reply_text('Let me help you. \\r\\n /help print this help \\r\\n /safety prints safety instructions \\r\\n /play start the game\\r\\n /joingroup Join CTF tg group')","def help(ctx, topic, **kw):\n # The help command implementation is taken from\n # https://www.burgundywall.com/post/having-click-help-subcommand\n if topic is None:\n click.echo(ctx.parent.get_help())\n else:\n click.echo(main.commands[topic].get_help(ctx))","async def getHelp(ctx, cmd):\n wikiMods = discord.utils.get(ctx.message.author.guild.roles, name=\"Wiki Moderator\")\n cmdInfo = next((c for c in COMMAND_INFO if c[\"name\"] == cmd or cmd in c[\"aliases\"]), None)\n if cmdInfo == None:\n return assembleEmbed(\n title=f\"`{cmd}`\",\n desc=\"Cannot find command with this name. Try again buddy.\",\n webcolor=\"red\"\n )\n else:\n roles = [(discord.utils.get(ctx.message.author.guild.roles, name=r)) for r in cmdInfo['access']]\n commandFields = [\n {\n \"name\": \"Parameters\",\n \"value\": \"\\n\".join([f\"`{p['name']}` - {p['description']}\" for p in cmdInfo['parameters']]) if len(cmdInfo['parameters']) > 0 else \"`none`\",\n \"inline\": False\n }\n ]\n # If command has flags show those, if not do nothing\n if 'flags' in cmdInfo:\n commandFields.append({\n \"name\": \"Flags\",\n \"value\": \"\\n\".join([f\"`-{u['name']}` - {u['description']}\" for u in cmdInfo['flags']]),\n \"inline\": False\n })\n # Add available roles\n commandFields.extend([\n {\n \"name\": \"Usage\",\n \"value\": \"\\n\".join([f\"`{u['cmd']}` - {u['result']}\" for u in cmdInfo['usage']]),\n \"inline\": False\n },\n {\n \"name\": \"Available To\",\n \"value\": \"\\n\".join([f\"{r.mention}\" for r in roles]),\n \"inline\": False\n }\n ]\n )\n return assembleEmbed(\n title=f\"`!{cmdInfo['name']}`\",\n desc=f\"{cmdInfo['description']}\",\n fields=commandFields,\n webcolor=\"gold\"\n )","def HelpCommand(self, unused_args, unused_sub_opts=None, unused_headers=None,\n unused_debug=None):\n self.OutputUsageAndExit()","def print_help():\n\tprint(\"Help text\")","async def help(self, ctx, *, command: str = None):\n try:\n if command is None:\n p = await HelpPaginator.from_bot(ctx)\n else:\n new_names = {\"Infamous RPG v2\": \"Rpg2\", \"Image Manipulation\": \"Imagem\"}\n if command in new_names.keys():\n command = new_names[command]\n entity = self.bot.get_cog(command) or self.bot.get_command(command)\n\n if entity is None:\n clean = command.replace('@', '@\\u200b')\n return await ctx.send(f'Looks like \"{clean}\" is not a command or category.')\n elif isinstance(entity, commands.Command):\n p = await HelpPaginator.from_command(ctx, entity)\n else:\n p = await HelpPaginator.from_cog(ctx, entity)\n\n await p.paginate()\n except Exception as e:\n await ctx.send(e)","def do_help(self, arg):\n\t\tif arg:\n\t\t\tfuncarg = arg.replace(\" \", \"_\")\n\t\t\ttry:\n\t\t\t\tfunc = getattr(self, 'help_' + funcarg)\n\t\t\texcept AttributeError:\n\t\t\t\ttry:\n\t\t\t\t\tdoc=getattr(self, 'do_' + funcarg).__doc__\n\t\t\t\t\tif doc:\n\t\t\t\t\t\t\tself.stdout.write(\"%s\\n\"%str(doc))\n\t\t\t\t\t\t\treturn\n\t\t\t\texcept AttributeError:\n\t\t\t\t\tpass\n\t\t\t\tself.stdout.write(\"%s\\n\"%str(self.nohelp % (arg,)))\n\t\t\t\treturn\n\t\t\tfunc()\n\t\telse:\n\t\t\tprint \"\\nAvailable commands (type help <topic> for help on topic):\\n\"\n\t\t\tnames = self.get_names()\n\t\t\tfor name in sorted(names):\n\t\t\t\tif name[:3] == \"do_\":\n\t\t\t\t\tname = name[3:]\n\t\t\t\t\tif name == \"EOF\":\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tself.parent.printHelpLine(name, \"\", getattr(self, \"do_\" + name).__doc__ or \"\")\n\t\t\tprint \"\"","async def get_help(ctx, commandename=\"\"):\n to_send = \"\"\"\\n> \\n> **Commands List** : \\n\"\"\"\n to_send += \"> \\n\"\n for commande in client.commands:\n to_send += \"> \\n\"\n to_send += f\"\"\"> `!{commande.name}` : {commande.description}\\n\"\"\"\n\n to_send += \"> \\n\"\n to_send += f\"\"\"> _Bot created for the **Ursina** discord server_ \\n\"\"\"\n await ctx.send(content=to_send)","def do_help(self, args):\n ## The only reason to define this method is for the help text in the doc string\n cmd.Cmd.do_help(self, args)","def do_help(self, args):\n ## The only reason to define this method is for the help text in the doc string\n cmd.Cmd.do_help(self, args)","def help(self) -> str:\n\t\treturn None","def DoHelp(options, args):\n __pychecker__ = 'unusednames=options'\n if len(args) == 1 and args[0] in COMMAND_USAGE_TEXT:\n print(COMMAND_USAGE_TEXT[args[0]])\n else:\n raise gclient_utils.Error(\"unknown subcommand '%s'; see 'gclient help'\" %\n args[0])","def help(command = None):\n if command is None:\n option = \" {:12} {}\"\n help_text = [\n \"Usage: tracker <command> [<args>]\",\n \"\",\n \"Available commands:\",\n option.format(\"help\", \"display this dialog\"),\n option.format(\"update\", \"save data to tracker\"),\n option.format(\"list\", \"list available trackers\"),\n option.format(\"show\", \"display raw tracker data\"),\n option.format(\"rename\", \"rename tracker\"),\n option.format(\"delete\", \"remove tracker\"),\n option.format(\"stats\", \"show statistics\"),\n option.format(\"plot\", \"show graph\"),\n \"\",\n \"Use 'tracker help <command>' for a command's detailed usage.\"\n ]\n print(\"\\n\".join(help_text))\n else:\n # commands = [\"update\", \"list\", \"show\", \"rename\", \"delete\", \"stats\", \"plot\"]\n usage = \" {}\"\n desc = \" {}\"\n if command == \"update\":\n help_text = [\n \"Update: command which adds (numerical) data to a tracker.\",\n \"\",\n \"Usage:\",\n usage.format(\"tracker update <tracker> <data>\"),\n usage.format(\"tracker update <tracker>\"),\n \"\",\n \"Description:\",\n usage.format(\"tracker update <tracker> <data>\"),\n desc.format(\"This form is shorthand for saving <data> to \" +\n \"<tracker> for today's date.\"),\n \"\",\n usage.format(\"tracker update <tracker>\"),\n desc.format(\"This form is used to set the value for an \" +\n \"arbitrary date for <tracker>.\"),\n desc.format(\"The date must be in the format YYYY-MM-DD.\"),\n \"\",\n \"Options:\",\n usage.format(\"<tracker>\"),\n desc.format(\"The name of the tracker to update, converted to lowercase.\"),\n desc.format(\"If <tracker> does not exist, you will be prompted to create it.\"),\n \"\",\n usage.format(\"<data>\"),\n desc.format(\"The value to save to the tracker to update, must be numerical.\")\n ]\n elif command == \"list\":\n help_text = [\n \"List: displays a list of trackers which have been created\",\n \"\",\n \"Usage:\",\n usage.format(\"tracker list\")\n ]\n elif command == \"show\":\n help_text = [\n \"Show: displays raw dates and values for a tracker\",\n \"\",\n \"Usage:\",\n usage.format(\"tracker show <tracker>\"),\n \"\",\n \"Description:\",\n usage.format(\"tracker show <tracker>\"),\n desc.format(\"Displays all data for <tracker> in the form '<date> | <value>'.\"),\n desc.format(\"Note: <date> is formatted as 'YYYY-MM-DD'.\"),\n \"\",\n \"Options:\",\n usage.format(\"<tracker>\"),\n desc.format(\"The name of the tracker to show, converted to lowercase.\"),\n ]\n elif command == \"rename\":\n help_text = [\n \"Rename: change name of a tracker\",\n \"\",\n \"Usage:\",\n usage.format(\"tracker rename <tracker> <new_tracker>\"),\n \"\",\n \"Description:\",\n usage.format(\"tracker rename <tracker> <new_tracker>\"),\n desc.format(\"All <tracker> entries will not be <new_tracker> entries.\"),\n \"\",\n \"Options:\",\n usage.format(\"<tracker>\"),\n desc.format(\"The name of the existing tracker to change, converted to lowercase.\"),\n \"\",\n usage.format(\"<new_tracker>\"),\n desc.format(\"The name of the new tracker (must not already exist), converted to lowercase.\")\n ]\n elif command == \"delete\":\n help_text = [\n \"Delete: permanently removes all data entries for a given tracker\",\n \"\",\n \"Usage:\",\n usage.format(\"tracker delete <tracker>\"),\n \"\",\n \"Description:\",\n usage.format(\"tracker delete <tracker>\"),\n desc.format(\"All sqlite entries associated with <tracker> are deleted.\"),\n \"\",\n \"Options:\",\n usage.format(\"<tracker>\"),\n desc.format(\"The name of the tracker to delete, converted to lowercase.\")\n ]\n elif command == \"stats\":\n help_text = [\n \"Stats: show statistics for tracker(s)\",\n \"\",\n \"Usage:\",\n usage.format(\"tracker stats <tracker> <tracker>\"),\n usage.format(\"tracker stats <tracker>\"),\n usage.format(\"tracker stats\"),\n \"\",\n \"Description:\",\n usage.format(\"tracker stats <tracker> <tracker>\"),\n desc.format(\"Show correlation coefficient between two trackers.\"),\n \"\",\n usage.format(\"tracker stats <tracker>\"),\n desc.format(\"Display information for each weekday and entire time period.\"),\n desc.format(\"Stats included: total, mean, min, max.\"),\n \"\",\n usage.format(\"tracker stats\"),\n desc.format(\"Displays information about all trackers.\"),\n desc.format(\"Stats included: total entries, entries per tracker.\"),\n \"\",\n \"Options:\",\n usage.format(\"<tracker>\"),\n desc.format(\"The name of the tracker to show stats for, converted to lowercase.\")\n ]\n elif command == \"plot\":\n help_text = [\n \"Plot: show graph for tracker\",\n \"\",\n \"Usage:\",\n usage.format(\"tracker plot <tracker>\"),\n \"\",\n \"Description:\",\n usage.format(\"tracker stats <tracker>\"),\n desc.format(\"Displays graph for <tracker> from first entry to last entry.\"),\n \"\",\n \"Options:\",\n usage.format(\"<tracker>\"),\n desc.format(\"The name of the tracker to graph, converted to lowercase.\")\n ]\n else:\n error(\"Invalid command: '{}'\".format(command))\n print(\"\\n\".join(help_text))\n sys.exit(1)","def cmd_help(self, commands=None, usage=False):\n if commands:\n usage = True\n commands = {self.approx.decmd(c.lower()) for c in commands}\n rejects = commands - self.approx.keys()\n for reject in rejects:\n self.put_pretty(\"No command named %r\" % reject)\n continue\n commands -= rejects\n if self.debug:\n assert not any(self.approx.encmd(r) in self.mod_commands for\n r in rejects)\n assert all(self.approx.encmd(c) in self.mod_commands for\n c in commands)\n if not commands:\n return\n requested = zip(commands, (self.approx[c] for c in commands))\n else:\n requested = self.approx.items()\n help = znc.CTable()\n help.AddColumn(\"Command\")\n help.AddColumn(\"Usage\" if usage else \"Description\")\n from itertools import zip_longest\n #\n for command, parser in requested:\n if usage:\n upre = \"usage: %s\" % command\n rest = (parser.format_usage()\n .replace(upre, \"\", 1)\n .replace(\"[-h] \", \"\", 1))\n desc = [l.strip() for l in rest.split(\"\\n\") if l.strip()]\n else:\n desc = [parser.description]\n for line, comm in zip_longest(desc, (command,), fillvalue=\"\"):\n help.AddRow()\n help.SetCell(\"Command\", comm)\n help.SetCell(\"Usage\" if usage else \"Description\", line)\n #\n s_line = znc.String()\n strung = []\n while help.GetLine(len(strung), s_line):\n strung.append(s_line.s)\n also = \" (<command> [-h] for details)\"\n strung[1] = strung[1].replace(len(also) * \" \", also, 1)\n self.put_pretty(\"\\n\".join(strung))","async def help(message, *args, **kwargs):\r\n\r\n if not args:\r\n docs = {name: f.__doc__ for name, f in usercommands.items()}\r\n docs = {k:v.split(\"\\n\",1)[0] for k,v in docs.items() if v and k.startswith(prefix)}\r\n output = dictstr(docs) + f\"\\nType \\\"{prefix}help func\\\" for detailed information about func\"\r\n output += \"\\n\\nFunction arguments are words separated by spaces\\n ex. $sort enemydata HP\"\r\n output += \"\\nKeyword arguments have the format: key=value\\n no spaces, unless value is in quotes\"\r\n output += \"\\n\\nDataTables:\"\r\n for name in DataTables:\r\n output += \"\\n \" + name\r\n if name == \"enemydata-h\": output += \" (hard-mode stats)\"\r\n await reply(message, f\"```{output}```\")\r\n return\r\n await reply(message, f\"```{inspect.getdoc(usercommands['$'+args[0]])}```\")","def quick_test():\n do_command('Help: Command=Help')\n do_command('Help: Command=\"GetInfo\"')\n #do_command('SetPreference: Name=GUI/Theme Value=classic Reload=1')","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text(messages.HELP_TEXT)","async def help(ctx, *, command: str = None):\n try:\n if command is None:\n p = await HelpPaginator.from_bot(ctx)\n else:\n entity = bot.get_cog(command)\n if entity is None:\n clean = command.replace('@', '@\\u200b')\n return await ctx.send(f'Command or category \"{clean}\" not found.')\n elif isinstance(entity, commands.Command):\n p = await HelpPaginator.from_command(ctx, entity)\n else:\n p = await HelpPaginator.from_cog(ctx, entity)\n\n await p.paginate()\n except Exception as e:\n await ctx.send(e)","async def help(self, msg, *args, intro=None):\n sudo = Guard.allow_sudo(msg)\n if intro is not None:\n intro = f'{intro.strip()} '\n else:\n intro = ''\n if msg.channel.type == discord.ChannelType.private:\n nick = '@DayRInfo'\n else:\n nick = f'@{msg.channel.guild.me.nick}'\n content = f'{intro}I understand the following commands (tag me at the start of the message):\\n'\n for command, (args, desc, enabled, delay) in Controller.commands.items():\n if not sudo and not enabled:\n continue\n if args:\n args = f' {args.strip()}'\n if desc:\n desc = f'\\n\\t{desc}'\n content = f'{content}`{Controller.HELP_KEY}{command}{args}`{desc}\\n'\n content = f'{content}----------\\n'\n content = f'{content}• Also, if you tag this bot ({nick}) on a message containing a link to the interactive Day R map 🗺️ with a location URL, I will send you a snapshot of the location.\\n'\n content = f'{content}• React with ❌ to any of my messages to delete it (if I still remember that it was my message). You can only delete my messages that are directed to you.'\n await msg.author.send(**{\n 'content': content,\n })\n await msg.channel.send(**{\n 'content': 'Command list sent via DM!',\n 'reference': msg.to_reference(),\n 'mention_author': True,\n 'delete_after': 3,\n })","def do_help():\n return True, \"\"\"I can understand these commands:\nOFF - Shut down robot\nHELP - provide information about commands\nFORWARD - move forward by specified number of steps, e.g. 'FORWARD 10'\nBACK - move backward by specified number of steps, e.g. 'BACK 10'\nRIGHT - turn right by 90 degrees\nLEFT - turn left by 90 degrees\nSPRINT - sprint forward according to a formula\n\"\"\"","def help(update, context):\n update.message.reply_text(\"\"\"THESE ARE THE COMMANDS\n /hi\n /gokul\n /bala\n /competition\n /form\n \"\"\")","async def help(self, ctx, *, command: str = None):\n try:\n if command is None:\n p = await HelpPaginator.from_bot(ctx)\n else:\n entity = self.bot.get_cog(command) or self.bot.get_command(command)\n\n if entity is None:\n clean = command.replace('@', '@\\u200b')\n return await ctx.send(f'Command or category \"{clean}\" not found.')\n elif isinstance(entity, commands.Command):\n p = await HelpPaginator.from_command(ctx, entity)\n else:\n p = await HelpPaginator.from_cog(ctx, entity)\n\n await p.paginate()\n except Exception as e:\n await ctx.send(e)","def print_help(self):\n print self.get_help()","def helpme(self):\n\n print(\"{}{}{}\".format(' ', 'Commands', ' '))\n print(\"{}{}{}\".format(' ', '--------', ' '))\n print(\"{} {} {}\".format('help ', '|', 'Display all usable commands'))\n print(\"{} {} {}\".format('look ', '|', 'Explore the room to find current location, exits and potential items.'))\n print(\"{} {} {}\".format('go ', '|', 'The prefix required to navigate your player.'))\n print(\"{} {} {}\".format('get ', '|', 'The prefix for picking up items.'))\n print(\"{} {} {}\".format('drop ', '|', 'The prefix for dropping items.'))\n print(\"{} {} {}\".format('inv ', '|', 'Displays the player inventory'))\n print(\"{} {} {}\".format('health ', '|', 'Displays player health'))\n print(\"{} {} {}\".format('eat ', '|', 'Allows the player to use consumables to gain health'))\n print(\"{} {} {}\".format('equip ', '|', 'Equip a weapon in your inventory'))\n print(\"{} {} {}\".format('unequip', '|', 'Unequip a current weapon'))\n print(\"{} {} {}\".format('attack ', '|', 'Allows the player to attack a non-player'))\n print(\"{} {} {}\".format('push ', '|', 'Returns NPC to spawn'))\n print(\"{} {} {}\".format('save ', '|', 'Save current player progress'))\n print(\"{} {} {}\".format('load ', '|', 'Load a previous character'))","def initialize_help_commands(self) -> None:\n\n @self.command(name=\"help\")\n @logger(\"all\")\n async def help_command(ctx, *args):\n if len(args) == 0:\n await ctx.message.channel.send(indie_help.summary())\n else:\n await ctx.message.channel.send(indie_help.specific(args))","def do_help(self, line):\n Cmd.do_help(self, line)","def channel_help(message):\n message.reply(Strings['HELP'].format(config.HELP_URL))"],"string":"[\n \"def help_help(self):\\n print(\\\"List commands or print details about a command\\\")\",\n \"def command_help(self, command):\\n self.commands[command].command_help()\",\n \"def command_help(self, *args, **kwargs):\\n print(\\\"Commands available:\\\\n\\\")\\n for name in dir(self):\\n if not name.startswith(\\\"command_\\\"):\\n continue\\n name_clean = name[len(\\\"command_\\\"):]\\n print(\\\"%s:\\\\n - %s\\\\n\\\" % (name_clean, getattr(self, name).__doc__.strip()))\",\n \"def do_command_help(self, command):\\n summary = self.base.commands[command].get_summary()\\n usage = self.get_command_usage(command)\\n description = self.base.commands[command].get_description()\\n sys.stdout.write('%s\\\\n%s' % (summary, usage))\\n if description != None:\\n sys.stdout.write('Arguments Description:\\\\n%s\\\\n' %\\n (description, ))\",\n \"def help(command=None):\\n if command is None: \\n # print first line of docstring\\n for cmd in commands:\\n ds = commands[cmd].__doc__.split('\\\\n')[0]\\n print \\\"%-16s %s\\\" % (cmd,ds)\\n else:\\n print commands[command].__doc__\",\n \"def _help(self):\\n self.onecmd('help')\",\n \"def cmd_help(args):\",\n \"def help():\",\n \"def help():\\n \\n pass\",\n \"async def _help(ctx, *, command_name: str=None):\\n if command_name:\\n command = bot.get_command(command_name)\\n if not command:\\n return await ctx.send(\\\"No such command!\\\")\\n return await ctx.send(f\\\"```\\\\n{ctx.prefix}{command.name} {command.signature}\\\\n\\\\n{command.help or 'Missing description'}```\\\")\\n description = []\\n for name, cog in bot.cogs.items():\\n entries = [\\\" - \\\".join([cmd.name, cmd.short_doc or \\\"Missing description\\\"]) for cmd in cog.get_commands() if await _can_run(cmd, ctx) and not cmd.hidden]\\n if entries:\\n description.append(f\\\"**{name}**:\\\")\\n description.append(\\\"• \\\" + \\\"\\\\n• \\\".join(entries))\\n await ctx.send(embed=discord.Embed(description=\\\"\\\\n\\\".join(description), color=ctx.me.color))\",\n \"def help_command(server, output, conf):\\n server.tell(output.name, 'Available commands:')\\n for key in COMMANDS.keys():\\n cmd_func = COMMANDS[key]\\n if cmd_func.__doc__:\\n server.tell(output.name, '%s: %s' % (key[1:], cmd_func.__doc__))\\n else:\\n server.tell(output.name, key[1:])\\n return\",\n \"def help():\\n print(UI.HELP)\",\n \"def do_help(self, command_name):\\n if not command_name:\\n print _PVBlotInterp.__doc__\\n print \\\"The following commands are supported:\\\"\\n print \\\" \\\",\\n blotish_commands = self._blotish_commands.keys()\\n blotish_commands.sort()\\n for c in blotish_commands:\\n print c,\\n print\\n print\\n print \\\"For more information on any command, try help <command>.\\\"\\n return\\n try:\\n command = self.get_unique_command(command_name)\\n print command.__doc__\\n except blotish.BlotishError, err:\\n blot_common.print_blot_error(err)\",\n \"def command_help(args):\\n\\tprint_usage()\\n\\treturn 0\",\n \"async def do_help(self, arg):\\n if arg:\\n if arg in self._commands:\\n _, doc = self._commands[arg]\\n print(doc)\\n else:\\n self.error('No command {}'.format(arg))\\n else:\\n print('Valid commands:')\\n for c in self._commands:\\n print(' ' + c)\",\n \"def show_help():\\n pass\",\n \"def command_help(self):\\n print(\\\"Command \\\", self)\\n print(\\\"\\\\t\\\\thelp (Get help for command)\\\")\\n\\n params = self.params.copy()\\n del params[\\\"help\\\"]\\n\\n if len(params) == 0:\\n print(\\\"This command has no parameters\\\")\\n return\\n\\n print(\\\"Parameters:\\\")\\n for info in params.values():\\n print(\\\" %s\\\" % info.get_basic_info())\\n description = info.get_desc()\\n if description != \\\"\\\":\\n print(textwrap.fill(description,\\n initial_indent=\\\" \\\",\\n subsequent_indent=\\\" \\\",\\n width=70))\",\n \"def help(self):\\r\\n self._short_help(None, None, None, None)\",\n \"def get_command_help(self, module_name, command_name):\\r\\n command = self.env.get_command(module_name, command_name)\\r\\n\\r\\n default_format = 'raw'\\r\\n if sys.stdout.isatty():\\r\\n default_format = 'table'\\r\\n\\r\\n arg_doc = command.__doc__\\r\\n\\r\\n if 'confirm' in command.options:\\r\\n arg_doc += \\\"\\\"\\\"\\r\\nPrompt Options:\\r\\n -y, --really Confirm all prompt actions\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n if '[options]' in arg_doc:\\r\\n arg_doc += \\\"\\\"\\\"\\r\\nStandard Options:\\r\\n --format=ARG Output format. [Options: table, raw] [Default: %s]\\r\\n -C FILE --config=FILE Config file location. [Default: ~/.softlayer]\\r\\n --debug=LEVEL Specifies the debug noise level\\r\\n 1=warn, 2=info, 3=debug\\r\\n --timings Time each API call and display after results\\r\\n --proxy=PROTO:PROXY_URL HTTP[s] proxy to be use to make API calls\\r\\n -h --help Show this screen\\r\\n\\\"\\\"\\\" % default_format\\r\\n return arg_doc.strip()\",\n \"def help(self):\",\n \"def help(self):\",\n \"def help(self):\\n pass\",\n \"def help(self):\\n pass\",\n \"def help(self, args):\\n print('No commands available for this consumer')\",\n \"def help():\\n print \\\"Help comes to those who ask\\\"\",\n \"def cmd_help(ctx):\\n echo(ctx.parent.get_help())\",\n \"async def help(self, ctx, *, command_name: str=None):\\n bot_prefix = '@Randy '\\n # Shortcut to command search\\n if command_name is not None:\\n return await ctx.invoke(self.cmd('help command'), cmd_name=command_name)\\n\\n em = discord.Embed(title='Help',\\n description='**Permissions:** The permissions required to function :-\\\\n'\\n '`Send Messages`, `Manage Messages`, `Embed Links`\\\\n'\\n '--\\\\nTo get help or more information on a specific command, use:\\\\n'\\n '`{bot_prefix}help <command name>`\\\\n'\\n '--\\\\nRead my messy code [here](http://github.com/xKynn/RandomRumble)'\\n '--\\\\nIf you like my work and would like to help me, '\\n 'Ko-Fi/Paypal: [Link](https://ko-fi.com/D1D6EXXV)\\\\n',\\n color=self.color)\\n\\n em.set_footer(text=\\\"Contact me at Demo#7645\\\")\\n\\n # This can't go in the init because help isn't loaded last & thus misses some commands\\n em.add_field(name=\\\"Commands\\\", value=' • '+'\\\\n • '.join(f\\\"***{c.name}*** - {c.short_doc}\\\" for c in self.bot.commands if\\n c.name not in ['pob', 'link', 'convert']))\\n try:\\n await ctx.send(embed=em)\\n except:\\n await ctx.send(\\\"`Embed Links` permission is required to see the help!\\\")\",\n \"def command_short():\\n pass\",\n \"def help_util():\\r\\n for cmd, f in COMMANDS.items():\\r\\n print(\\\"POM \\\" + cmd + \\\":\\\")\\r\\n print(f.__doc__.lstrip(\\\"\\\\n\\\"))\",\n \"def test_cli_help(self):\\n output = self.update_command('-h')\",\n \"async def send_command_help(self, command):\\n ctx = self.context\\n embed = discord.Embed(title=command.name.upper(), description=command.description, color=discord.Color.green())\\n alias = command.aliases\\n if alias:\\n embed.add_field(name=\\\"Aliases\\\", value=\\\", \\\".join(alias), inline=False)\\n if command.usage != None:\\n embed.add_field(name=\\\"How to use:\\\", value=f'`{command.usage}`')\\n\\n await ctx.reply(embed=embed)\",\n \"def command_help(self, bot, update):\\n\\n messages = [\\n 'Available commands:',\\n '/who - Who is Myles?',\\n '/where - Where is Myles?',\\n '/tweet - What was the last tweet Myles sent?',\\n '/photo - What was the last Instagram photo Myles took?',\\n '/web - Where can I find Myles on the interwebs?',\\n ]\\n\\n self.send_messages(bot, update, messages)\",\n \"def printhelp():\",\n \"def print_help(self):\\n\\n print((\\\"Help is not defined for command \\\" + self.command))\",\n \"async def help(self, *args, **kwargs):\\n if len(args) == 0:\\n return self.help.__doc__\\n docs = []\\n for command in args:\\n if command in self.user_special_bang_set:\\n docs.append(self.user_special_bang_set[command].bang_skill.__doc__)\\n elif command in self.control_bang_set:\\n docs.append(self.control_bang_set[command].bang_skill.__doc__)\\n\\n return \\\"| \\\".join(docs)\",\n \"def CommandHelp(paser):\\n\\n\\tprint \\\"\\\\n===============Commands List===============\\\\n\\\"\\n\\t\\t\\n\\tprint \\\"NewProject - {}\\\".format(NewProject.__doc__)\\n\\tprint \\\"DelProject - {}\\\".format(DelProject.__doc__)\\n\\tprint \\\"ShareProject - {}\\\".format(ShareProject.__doc__)\\n\\tprint \\\"StopProject - {}\\\".format(StopProject.__doc__)\\n\\tprint \\\"Help - {}\\\".format(CommandHelp.__doc__)\\n\\tprint \\\"Exit - Finaliza la sesion en la terminal.\\\"\",\n \"def _help():\\n text = \\\"\\\"\\\"\\n```\\nUsage: @bot [command] (message)\\n\\n*Commands*:\\n \\n add Adds specified users to notify list | To add yourself use key 'myself'\\n list Lists users on notify list\\n remove Removes specified users from notify list | To remove yourself use key 'myself'\\n help This help\\n\\n > Obs: All commands are optional\\n\\n*Examples*:\\n\\n @bot add myself @Fulano <= Will add yourself and @Fulano in list.\\n @bot this a messge test <= Send 'this a messge test' to all list.\\n @bot remove myself @Fulano <= Remove yourself and @fulano from list.\\n\\n```\\n\\\"\\\"\\\"\\n return text\",\n \"def help(cmd, cmdArgs):\\n global commandDict\\n retInfo = []\\n if len(cmdArgs) > 0:\\n #return help on a single function\\n if cmdArgs[0] in commandDict.keys():\\n return commandDict[cmdArgs[0]].__doc__\\n\\n #else, return general info\\n retInfo = ['pypeople: A command line tool for vCard management',\\n 'Version:' + __version__,\\n 'Available Commands:']\\n #fill in more info here\\n for cmdName in commandDict.keys():\\n cmdFunc = commandDict[cmdName]\\n cmdDoc = str(cmdName) + ': ' + str(cmdFunc.__doc__) if cmdFunc.__doc__ is not None else 'Undocumented Function'\\n retInfo.append('\\\\t' + cmdDoc)\\n\\n return '\\\\n'.join(retInfo)\",\n \"def print_generic_help():\\r\\n print ART_NAME\\r\\n print 'Version {1}\\\\nby {2}'.format(NAME, VERSION, AUTHOR)\\r\\n print DESCRIPTION\\r\\n tools = sorted(AvailableCommands.commands.keys(), key=lambda v: v.upper())\\r\\n # Do not show CUSTOM command in the help\\r\\n tools.remove('CUSTOM')\\r\\n tools.remove('CUSTOM_NO_OUTPUT')\\r\\n print '\\\\n\\\\nSupported tools are:\\\\n{0}'.format('\\\\n'.join(tools))\\r\\n print '\\\\nHint: Check tool specific help with --help <tool_name>\\\\n'\",\n \"async def help(ctx, command:str=None):\\n if command == None:\\n embed = assemble_embed(\\n title=\\\"Looking for help?\\\",\\n desc=(\\\"Hey there, I'm a resident bot of Scioly.org!\\\\n\\\\n\\\" +\\n \\\"On Discord, you can send me commands using `!` before the command name, and I will process it to help you! \\\" +\\n \\\"For example, `!states`, `!events`, and `!fish` are all valid commands that can be used!\\\\n\\\\n\\\" +\\n \\\"If you want to see some commands that you can use on me, just type `!list`! \\\" +\\n \\\"If you need more help, please feel free to reach out to a staff member!\\\")\\n )\\n return await ctx.send(embed=embed)\\n hlp = await get_help(ctx, command)\\n await ctx.send(embed=hlp)\",\n \"def help(self, msg=None):\\n\\n # Print the message if given.\\n if not msg == None:\\n print str(msg) + \\\"\\\\n\\\"\\n\\n # Display the list of commands, in the alphabetical order.\\n print \\\"Use one of the following commands:\\\"\\n for action in sorted(self.actions.keys()):\\n info = self.actions[action]\\n joined_oblig = ' '.join(info['required'])\\n if len(info['additional']) > 0:\\n add = [\\\"<%s>\\\" % x for x in info['additional']]\\n joined_add = '[' + ' '.join(add) + ']'\\n else:\\n joined_add = ''\\n print \\\"\\\\t* %s %s %s\\\" % (action, joined_oblig, joined_add)\",\n \"def help_command(update: Update, _: CallbackContext) -> None:\\n update.message.reply_text(\\\"These are the list of commands supported. \\\\n\\\\n /deadlines. \\\\n\\\\n \\\"\\n \\\"Hey! I am still being enhanced, more features to come...!\\\")\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text(\\\"Laughybot\\\\n\\\\n VERSION\\\\n 0.1 \\\\n\\\\nUSAGE\\\\n /{command} : Entrer une commande parmi celles disponibles\\\\n\\\\nCOMMADES\\\\n/joke => recherche une blague sur la toile\\\\n/start => Affiche le message d'accueil\\\\n/help => Affiche l'aide\\\")\",\n \"def get_help(self,command):\\n if \\\"help\\\" in self.commands[command]:\\n return self.commands[command][\\\"help\\\"]\\n else:\\n return \\\"No help defined for this command.\\\"\",\n \"def do_help(self, args): \\n if args.command:\\n if len(args.command) > 1:\\n command = args.command[0] +\\\"-\\\" + args.command[1]\\n else:\\n command = args.command[0]\\n if command in self.subcommands:\\n self.subcommands[command].print_help()\\n else:\\n print((\\\"'%s' is not a valid subcommand\\\") %\\n args.command)\\n else:\\n self.parser.print_help()\",\n \"def help_command(update, context):\\n update.message.reply_text('Help!')\",\n \"def get_help(self) -> None: \\n print(messages.get_help())\",\n \"async def help(self, ctx):\\n self.log_command_call(\\\"help\\\", ctx.message)\\n await ctx.send(HELP_TEXT)\\n embed_output = create_embed(description=MORE_INFO_TEXT)\\n await ctx.send(embed=embed_output)\",\n \"def dispatch_help(args):\\n command = args.subcommand\\n if command is None:\\n command = 'help'\\n args.parsers[command].print_help()\\n\\n cmd_func = getattr(EtcMaint, 'cmd_%s' % command, None)\\n if cmd_func:\\n lines = cmd_func.__doc__.splitlines()\\n print('\\\\n%s\\\\n' % lines[0])\\n paragraph = []\\n for l in dedent('\\\\n'.join(lines[2:])).splitlines():\\n if l == '':\\n if paragraph:\\n print('\\\\n'.join(wrap(' '.join(paragraph), width=78)))\\n print()\\n paragraph = []\\n continue\\n paragraph.append(l)\\n if paragraph:\\n print('\\\\n'.join(wrap(' '.join(paragraph), width=78)))\",\n \"def print_specific_help(tool_name):\\r\\n if tool_name not in AvailableCommands.commands:\\r\\n print 'Command is not supported: {0}'.format(tool_name)\\r\\n return\\r\\n cmd = AvailableCommands.commands[tool_name]\\r\\n\\r\\n print 'Usage of {0}:'.format(cmd.name)\\r\\n print '\\\\nAccepted input types:\\\\n{0}'.format(str(list(cmd.input_types)))\\r\\n print '\\\\nOutput types:\\\\n{0}'.format(str(cmd.output_types))\\r\\n print '\\\\nMandatory arguments:\\\\n{0}'.format(str(cmd.user_mandatory_args))\\r\\n print '\\\\nOptional arguments:\\\\n{0}'.format(str(cmd.user_optional_args))\\r\\n print '\\\\nParallelizable:\\\\n{0}'.format(str(cmd.parallelizable))\\r\\n print '\\\\nAdditional description:\\\\n{0}'.format(str(cmd.help_description))\\r\\n print ''\",\n \"def display_help(self):\\n pass\",\n \"async def help_command(self, ctx, *, cmd_name: str=None):\\n bot_prefix = '@Randy '\\n # Get command object\\n cmd_obj = self.cmd(cmd_name)\\n\\n # Handle no command found\\n if cmd_obj is None:\\n return await ctx.error(f'Command {cmd_name} not found')\\n em = discord.Embed(title=cmd_obj.name, description=cmd_obj.help, color=self.color)\\n\\n # Input aliases and parameters to embed\\n if cmd_obj.aliases:\\n em.add_field(name='Aliases', value='\\\\n'.join([f'\\\\u2022 {x}' for x in cmd_obj.aliases]))\\n if cmd_obj.clean_params:\\n em.add_field(name='Parameters', value='\\\\n'.join([f'\\\\u2022 {x}' for x in cmd_obj.clean_params]))\\n\\n # Handle group commands\\n if isinstance(cmd_obj, commands.core.Group):\\n em.add_field(name='Group commands',\\n value='\\\\n'.join([f'\\\\u2022 {x}' for x in cmd_obj.commands]),\\n inline=False)\\n\\n # Add usage last\\n em.add_field(name='Usage',\\n value=f'```{bot_prefix}\\\\u200b{cmd_name} '\\n f'{\\\" \\\".join([f\\\"<{x}>\\\" for x in cmd_obj.clean_params])}```',\\n inline=False)\\n\\n await ctx.send(embed=em)\",\n \"def showHelp(self):\\n\\t\\tfor i in range(0,20):\\n\\t\\t\\tprint \\\"\\\"\\n\\t\\tprint \\\" _ _ \\\"\\n\\t\\tprint \\\"| | | | \\\"\\n\\t\\tprint \\\"| |__ _ __ ___ | |__ \\\"\\n\\t\\tprint \\\"| '_ \\\\ | '_ \\\\ / __|| '_ \\\\ \\\"\\n\\t\\tprint \\\"| | | || | | |\\\\__ \\\\| | | |\\\"\\n\\t\\tprint \\\"|_| |_||_| |_||___/|_| |_|\\\"\\n\\t\\tprint \\\"A program by Scott Jackson\\\"\\n\\t\\tprint \\\"\\\"\\n\\t\\tprint \\\"To enter a command, type the key and press Return.\\\"\\n\\t\\tprint \\\"NB: parentheses indicate which of two options is the default.\\\"\\n\\t\\tprint \\\"\\\"\\n\\t\\tprint \\\"Basic Commands:\\\"\\n\\t\\tprint \\\"j / k -- show lower-ranked / higher-ranked stories.\\\"\\n\\t\\tprint \\\"r -- get the latest stories from Hacker News.\\\"\\n\\t\\tprint \\\"q -- quit.\\\"\\n\\t\\tprint \\\"# -- open story number # in your web browser.\\\"\\n\\t\\tprint \\\"c# -- open comments for story number # in your web browser.\\\"\\n\\t\\tprint \\\"#+ -- open up story number # AND its comments in your web browser.\\\"\\n\\t\\tprint \\\"top / new -- switch between showing the top and newest stories on HN. (top)\\\"\\n\\t\\tprint \\\"c / e -- collapse stories you've already read / don't collapse them. (e)\\\"\\n\\t\\tprint \\\"u -- update hnsh to the latest version.\\\"\\n\\t\\tprint \\\"==========================\\\"\\n\\t\\tprint \\\"For more commands, see the man.txt file.\\\"\\n\\t\\tinput = raw_input(\\\"Press Return to go back to the Hacker News stories.\\\")\",\n \"def help(self, irc, msg, args, command):\\n command = map(callbacks.canonicalName, command)\\n (maxL, cbs) = irc.findCallbacksForArgs(command)\\n if maxL == command:\\n if len(cbs) > 1:\\n names = sorted([cb.name() for cb in cbs])\\n irc.error(format('That command exists in the %L plugins. '\\n 'Please specify exactly which plugin command '\\n 'you want help with.', names))\\n else:\\n assert cbs, 'Odd, maxL == command, but no cbs.'\\n irc.reply(cbs[0].getCommandHelp(command, False))\\n else:\\n irc.error(format('There is no command %q.',\\n callbacks.formatCommand(command)))\",\n \"def help(self):\\n\\t\\treturn\",\n \"def help(ctx):\\n click.echo(ctx.parent.get_help())\",\n \"def help_command(update: Update, _: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, _: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"async def do_help():\\n\\n if len(message.content.split()) > 1:\\n for i in cmd_dict:\\n if message.content.split()[1] == i:\\n await bot.send_message(c, f'Help for {i}: {cmd_dict[i].__doc__}')\\n return\\n\\n cmdstr = 'Commands: '\\n for i in cmd_dict:\\n cmdstr += '{}, '.format(i)\\n await bot.send_message(c, cmdstr)\",\n \"def help(self, cmd=\\\"\\\", *, fail=\\\"\\\"):\\n class_dict = dict(type(self).__dict__)\\n # Add this function to class, so that when subclassing,\\n # help for help is found\\n class_dict.update({\\\"help\\\": self.help})\\n if cmd.startswith(self.predicate):\\n # Strip predicate\\n cmd = cmd[len(self.predicate) :]\\n # Check that command exists and is not\\n # private, protected or special method\\n if (not cmd.startswith(\\\"_\\\")) and cmd in class_dict.keys():\\n item = class_dict[cmd]\\n if callable(item):\\n if item.__doc__:\\n return \\\"Help on command '{}':\\\\n. {}\\\".format(\\n cmd, \\\"\\\\n. \\\".join(cleandoc(item.__doc__).split(\\\"\\\\n\\\"))\\n )\\n return \\\"No help on command '{}'\\\".format(cmd)\\n # If no cmd given or wrong cmd given, return commands\\n commands = []\\n for key, value in class_dict.items():\\n if not key.startswith(\\\"_\\\"):\\n if callable(value):\\n commands.append(key)\\n msg = (\\n \\\"Commands:\\\\n {}\\\".format(\\\", \\\".join(commands))\\n + \\\"\\\\n for more help on command, use \\\"\\n + \\\"{}help command\\\".format(self.predicate)\\n )\\n if fail:\\n msg = fail + \\\"\\\\n\\\" + msg\\n return msg\",\n \"def help():\\n version()\\n print(\\\"USAGE: python3 notes.py [COMMAND]\\\")\\n print(\\\"COMMAND:\\\")\\n help_dict = {\\n \\\"help\\\": \\\"Prints out the help text.\\\",\\n \\\"version\\\": \\\"Prints out the version.\\\",\\n \\\"init\\\": \\\"Initializes a new set of notes.\\\",\\n \\\"modify\\\": \\\"Modify the current set of notes.\\\",\\n }\\n help_dict = OrderedDict(sorted(help_dict.items()))\\n for entry in help_dict:\\n print((\\\" \\\" + entry).ljust(10), help_dict[entry])\",\n \"def do_help(self, message):\\r\\n\\t\\tcommands = [command[3:] for command in dir(self) if command.startswith('do_')]\\r\\n\\t\\t# self.trace(f'Help: {\\\", \\\".join(commands)}')\\r\\n\\r\\n\\t\\tfor command in commands:\\r\\n\\t\\t\\tmethod = getattr(self, f'do_{command}')\\r\\n\\t\\t\\tcommand_help = getattr(method, '__doc__')\\r\\n\\t\\t\\tif command_help:\\r\\n\\t\\t\\t\\tself.trace(f'{command}: {command_help}')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n commands = [\\\"/login <pwd>\\\\n\\\",\\n \\\"/status\\\\n\\\",\\n \\\"/heat\\\\n\\\",\\n \\\"/temp\\\\n\\\",\\n \\\"/off\\\\n\\\",\\n \\\"/help\\\\n\\\",\\n \\\"/set\\\\n\\\",\\n \\\"/unset\\\\n\\\",\\n \\\"/heatmore\\\\n\\\",\\n \\\"/lighton\\\\n\\\",\\n \\\"/lightoff\\\\n\\\"]\\n\\n cmd: str = \\\" \\\".join(commands)\\n update.message.reply_text('commands are:\\\\n' + cmd)\",\n \"def Help(topic=None):\\n\\n if not topic:\\n print \\\"Avaliable commands are: \\\"\\n print \\\"\\\\n\\\".join(map(lambda x:\\\" \\\"*3+x, filter(lambda c:c!=\\\"help\\\", commands)))\\n print \\\"Type help command to get help with a specific command.\\\"\\n\\n elif topic in commands:\\n print \\\"Help for %s:\\\"%topic\\n print commands[topic].__doc__\",\n \"def do_help(self, arg):\\n if arg:\\n # Getting help for a specific command\\n funcname = self._func_named(arg)\\n if funcname:\\n # No special behavior needed, delegate to cmd base class do_help()\\n cmd.Cmd.do_help(self, funcname[3:])\\n else:\\n # Show a menu of what commands help can be gotten for\\n self._help_menu()\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def show_help(message, commands):\\n commands_help = [\\n {\\n 'name': 'help',\\n 'text': 'show this help message.'\\n },\\n ]\\n\\n commands_help.extend((c.help for key, c in commands.items()))\\n command_text = '\\\\n'.join([' {0[name]:<12}{0[text]}'.format(cmd)\\n for cmd in commands_help])\\n help_message = '**redditfy-bot** available commands:\\\\n{}'.format(command_text)\\n print(help_message)\\n return help_message\",\n \"def help_command(update: Update) -> None:\\n #update.message.reply_text('Help!')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\r\\n update.message.reply_text('Help!')\",\n \"async def help(self, args):\\n if not args:\\n maxw = max([len(x) for x in self.commands]) + 1\\n commands = list(self.commands)\\n commands.sort()\\n message = '\\\\n'.join(['`{name:{width}}|` {desc}'.format(\\n name=command, width=maxw,\\n desc=(self.commands[command].__doc__ or 'No description.').splitlines()[0]\\n ) for command in commands])\\n await self.send(\\\"Unlisted commands are forwarded to the Minecraft server.\\\\n\\\" + message)\\n elif args.lower() not in self.commands:\\n await self.send_error(\\\"Unknown command: {command}. This might be a Minecraft command.\\\".format(command=args))\\n else:\\n args = args.lower()\\n await self.send(\\\"**`{name}`** - {doc}\\\".format(name=args, doc=self.commands[args].__doc__ or 'No description.'))\",\n \"def help(self):\\n help = ''\\n cmds = [(x, y) for x, y in Commands.__dict__.iteritems()]\\n cmds.sort(key=lambda x: x[0])\\n for name, member in cmds:\\n if name.startswith('cmd_') and callable(member):\\n help += ' %s\\\\n' % ' '.join([name[4:]] +\\n ['<%s>' % x for x in\\n inspect.getargspec(member).args[1:]])\\n if member.__doc__:\\n help += ' %s\\\\n' % member.__doc__.splitlines()[0]\\n return 'Available commands:\\\\n%s' % help\",\n \"def help_command(update, context):\\n update.message.reply_text('Let me help you. \\\\r\\\\n /help print this help \\\\r\\\\n /safety prints safety instructions \\\\r\\\\n /play start the game\\\\r\\\\n /joingroup Join CTF tg group')\",\n \"def help(ctx, topic, **kw):\\n # The help command implementation is taken from\\n # https://www.burgundywall.com/post/having-click-help-subcommand\\n if topic is None:\\n click.echo(ctx.parent.get_help())\\n else:\\n click.echo(main.commands[topic].get_help(ctx))\",\n \"async def getHelp(ctx, cmd):\\n wikiMods = discord.utils.get(ctx.message.author.guild.roles, name=\\\"Wiki Moderator\\\")\\n cmdInfo = next((c for c in COMMAND_INFO if c[\\\"name\\\"] == cmd or cmd in c[\\\"aliases\\\"]), None)\\n if cmdInfo == None:\\n return assembleEmbed(\\n title=f\\\"`{cmd}`\\\",\\n desc=\\\"Cannot find command with this name. Try again buddy.\\\",\\n webcolor=\\\"red\\\"\\n )\\n else:\\n roles = [(discord.utils.get(ctx.message.author.guild.roles, name=r)) for r in cmdInfo['access']]\\n commandFields = [\\n {\\n \\\"name\\\": \\\"Parameters\\\",\\n \\\"value\\\": \\\"\\\\n\\\".join([f\\\"`{p['name']}` - {p['description']}\\\" for p in cmdInfo['parameters']]) if len(cmdInfo['parameters']) > 0 else \\\"`none`\\\",\\n \\\"inline\\\": False\\n }\\n ]\\n # If command has flags show those, if not do nothing\\n if 'flags' in cmdInfo:\\n commandFields.append({\\n \\\"name\\\": \\\"Flags\\\",\\n \\\"value\\\": \\\"\\\\n\\\".join([f\\\"`-{u['name']}` - {u['description']}\\\" for u in cmdInfo['flags']]),\\n \\\"inline\\\": False\\n })\\n # Add available roles\\n commandFields.extend([\\n {\\n \\\"name\\\": \\\"Usage\\\",\\n \\\"value\\\": \\\"\\\\n\\\".join([f\\\"`{u['cmd']}` - {u['result']}\\\" for u in cmdInfo['usage']]),\\n \\\"inline\\\": False\\n },\\n {\\n \\\"name\\\": \\\"Available To\\\",\\n \\\"value\\\": \\\"\\\\n\\\".join([f\\\"{r.mention}\\\" for r in roles]),\\n \\\"inline\\\": False\\n }\\n ]\\n )\\n return assembleEmbed(\\n title=f\\\"`!{cmdInfo['name']}`\\\",\\n desc=f\\\"{cmdInfo['description']}\\\",\\n fields=commandFields,\\n webcolor=\\\"gold\\\"\\n )\",\n \"def HelpCommand(self, unused_args, unused_sub_opts=None, unused_headers=None,\\n unused_debug=None):\\n self.OutputUsageAndExit()\",\n \"def print_help():\\n\\tprint(\\\"Help text\\\")\",\n \"async def help(self, ctx, *, command: str = None):\\n try:\\n if command is None:\\n p = await HelpPaginator.from_bot(ctx)\\n else:\\n new_names = {\\\"Infamous RPG v2\\\": \\\"Rpg2\\\", \\\"Image Manipulation\\\": \\\"Imagem\\\"}\\n if command in new_names.keys():\\n command = new_names[command]\\n entity = self.bot.get_cog(command) or self.bot.get_command(command)\\n\\n if entity is None:\\n clean = command.replace('@', '@\\\\u200b')\\n return await ctx.send(f'Looks like \\\"{clean}\\\" is not a command or category.')\\n elif isinstance(entity, commands.Command):\\n p = await HelpPaginator.from_command(ctx, entity)\\n else:\\n p = await HelpPaginator.from_cog(ctx, entity)\\n\\n await p.paginate()\\n except Exception as e:\\n await ctx.send(e)\",\n \"def do_help(self, arg):\\n\\t\\tif arg:\\n\\t\\t\\tfuncarg = arg.replace(\\\" \\\", \\\"_\\\")\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tfunc = getattr(self, 'help_' + funcarg)\\n\\t\\t\\texcept AttributeError:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tdoc=getattr(self, 'do_' + funcarg).__doc__\\n\\t\\t\\t\\t\\tif doc:\\n\\t\\t\\t\\t\\t\\t\\tself.stdout.write(\\\"%s\\\\n\\\"%str(doc))\\n\\t\\t\\t\\t\\t\\t\\treturn\\n\\t\\t\\t\\texcept AttributeError:\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\tself.stdout.write(\\\"%s\\\\n\\\"%str(self.nohelp % (arg,)))\\n\\t\\t\\t\\treturn\\n\\t\\t\\tfunc()\\n\\t\\telse:\\n\\t\\t\\tprint \\\"\\\\nAvailable commands (type help <topic> for help on topic):\\\\n\\\"\\n\\t\\t\\tnames = self.get_names()\\n\\t\\t\\tfor name in sorted(names):\\n\\t\\t\\t\\tif name[:3] == \\\"do_\\\":\\n\\t\\t\\t\\t\\tname = name[3:]\\n\\t\\t\\t\\t\\tif name == \\\"EOF\\\":\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tself.parent.printHelpLine(name, \\\"\\\", getattr(self, \\\"do_\\\" + name).__doc__ or \\\"\\\")\\n\\t\\t\\tprint \\\"\\\"\",\n \"async def get_help(ctx, commandename=\\\"\\\"):\\n to_send = \\\"\\\"\\\"\\\\n> \\\\n> **Commands List** : \\\\n\\\"\\\"\\\"\\n to_send += \\\"> \\\\n\\\"\\n for commande in client.commands:\\n to_send += \\\"> \\\\n\\\"\\n to_send += f\\\"\\\"\\\"> `!{commande.name}` : {commande.description}\\\\n\\\"\\\"\\\"\\n\\n to_send += \\\"> \\\\n\\\"\\n to_send += f\\\"\\\"\\\"> _Bot created for the **Ursina** discord server_ \\\\n\\\"\\\"\\\"\\n await ctx.send(content=to_send)\",\n \"def do_help(self, args):\\n ## The only reason to define this method is for the help text in the doc string\\n cmd.Cmd.do_help(self, args)\",\n \"def do_help(self, args):\\n ## The only reason to define this method is for the help text in the doc string\\n cmd.Cmd.do_help(self, args)\",\n \"def help(self) -> str:\\n\\t\\treturn None\",\n \"def DoHelp(options, args):\\n __pychecker__ = 'unusednames=options'\\n if len(args) == 1 and args[0] in COMMAND_USAGE_TEXT:\\n print(COMMAND_USAGE_TEXT[args[0]])\\n else:\\n raise gclient_utils.Error(\\\"unknown subcommand '%s'; see 'gclient help'\\\" %\\n args[0])\",\n \"def help(command = None):\\n if command is None:\\n option = \\\" {:12} {}\\\"\\n help_text = [\\n \\\"Usage: tracker <command> [<args>]\\\",\\n \\\"\\\",\\n \\\"Available commands:\\\",\\n option.format(\\\"help\\\", \\\"display this dialog\\\"),\\n option.format(\\\"update\\\", \\\"save data to tracker\\\"),\\n option.format(\\\"list\\\", \\\"list available trackers\\\"),\\n option.format(\\\"show\\\", \\\"display raw tracker data\\\"),\\n option.format(\\\"rename\\\", \\\"rename tracker\\\"),\\n option.format(\\\"delete\\\", \\\"remove tracker\\\"),\\n option.format(\\\"stats\\\", \\\"show statistics\\\"),\\n option.format(\\\"plot\\\", \\\"show graph\\\"),\\n \\\"\\\",\\n \\\"Use 'tracker help <command>' for a command's detailed usage.\\\"\\n ]\\n print(\\\"\\\\n\\\".join(help_text))\\n else:\\n # commands = [\\\"update\\\", \\\"list\\\", \\\"show\\\", \\\"rename\\\", \\\"delete\\\", \\\"stats\\\", \\\"plot\\\"]\\n usage = \\\" {}\\\"\\n desc = \\\" {}\\\"\\n if command == \\\"update\\\":\\n help_text = [\\n \\\"Update: command which adds (numerical) data to a tracker.\\\",\\n \\\"\\\",\\n \\\"Usage:\\\",\\n usage.format(\\\"tracker update <tracker> <data>\\\"),\\n usage.format(\\\"tracker update <tracker>\\\"),\\n \\\"\\\",\\n \\\"Description:\\\",\\n usage.format(\\\"tracker update <tracker> <data>\\\"),\\n desc.format(\\\"This form is shorthand for saving <data> to \\\" +\\n \\\"<tracker> for today's date.\\\"),\\n \\\"\\\",\\n usage.format(\\\"tracker update <tracker>\\\"),\\n desc.format(\\\"This form is used to set the value for an \\\" +\\n \\\"arbitrary date for <tracker>.\\\"),\\n desc.format(\\\"The date must be in the format YYYY-MM-DD.\\\"),\\n \\\"\\\",\\n \\\"Options:\\\",\\n usage.format(\\\"<tracker>\\\"),\\n desc.format(\\\"The name of the tracker to update, converted to lowercase.\\\"),\\n desc.format(\\\"If <tracker> does not exist, you will be prompted to create it.\\\"),\\n \\\"\\\",\\n usage.format(\\\"<data>\\\"),\\n desc.format(\\\"The value to save to the tracker to update, must be numerical.\\\")\\n ]\\n elif command == \\\"list\\\":\\n help_text = [\\n \\\"List: displays a list of trackers which have been created\\\",\\n \\\"\\\",\\n \\\"Usage:\\\",\\n usage.format(\\\"tracker list\\\")\\n ]\\n elif command == \\\"show\\\":\\n help_text = [\\n \\\"Show: displays raw dates and values for a tracker\\\",\\n \\\"\\\",\\n \\\"Usage:\\\",\\n usage.format(\\\"tracker show <tracker>\\\"),\\n \\\"\\\",\\n \\\"Description:\\\",\\n usage.format(\\\"tracker show <tracker>\\\"),\\n desc.format(\\\"Displays all data for <tracker> in the form '<date> | <value>'.\\\"),\\n desc.format(\\\"Note: <date> is formatted as 'YYYY-MM-DD'.\\\"),\\n \\\"\\\",\\n \\\"Options:\\\",\\n usage.format(\\\"<tracker>\\\"),\\n desc.format(\\\"The name of the tracker to show, converted to lowercase.\\\"),\\n ]\\n elif command == \\\"rename\\\":\\n help_text = [\\n \\\"Rename: change name of a tracker\\\",\\n \\\"\\\",\\n \\\"Usage:\\\",\\n usage.format(\\\"tracker rename <tracker> <new_tracker>\\\"),\\n \\\"\\\",\\n \\\"Description:\\\",\\n usage.format(\\\"tracker rename <tracker> <new_tracker>\\\"),\\n desc.format(\\\"All <tracker> entries will not be <new_tracker> entries.\\\"),\\n \\\"\\\",\\n \\\"Options:\\\",\\n usage.format(\\\"<tracker>\\\"),\\n desc.format(\\\"The name of the existing tracker to change, converted to lowercase.\\\"),\\n \\\"\\\",\\n usage.format(\\\"<new_tracker>\\\"),\\n desc.format(\\\"The name of the new tracker (must not already exist), converted to lowercase.\\\")\\n ]\\n elif command == \\\"delete\\\":\\n help_text = [\\n \\\"Delete: permanently removes all data entries for a given tracker\\\",\\n \\\"\\\",\\n \\\"Usage:\\\",\\n usage.format(\\\"tracker delete <tracker>\\\"),\\n \\\"\\\",\\n \\\"Description:\\\",\\n usage.format(\\\"tracker delete <tracker>\\\"),\\n desc.format(\\\"All sqlite entries associated with <tracker> are deleted.\\\"),\\n \\\"\\\",\\n \\\"Options:\\\",\\n usage.format(\\\"<tracker>\\\"),\\n desc.format(\\\"The name of the tracker to delete, converted to lowercase.\\\")\\n ]\\n elif command == \\\"stats\\\":\\n help_text = [\\n \\\"Stats: show statistics for tracker(s)\\\",\\n \\\"\\\",\\n \\\"Usage:\\\",\\n usage.format(\\\"tracker stats <tracker> <tracker>\\\"),\\n usage.format(\\\"tracker stats <tracker>\\\"),\\n usage.format(\\\"tracker stats\\\"),\\n \\\"\\\",\\n \\\"Description:\\\",\\n usage.format(\\\"tracker stats <tracker> <tracker>\\\"),\\n desc.format(\\\"Show correlation coefficient between two trackers.\\\"),\\n \\\"\\\",\\n usage.format(\\\"tracker stats <tracker>\\\"),\\n desc.format(\\\"Display information for each weekday and entire time period.\\\"),\\n desc.format(\\\"Stats included: total, mean, min, max.\\\"),\\n \\\"\\\",\\n usage.format(\\\"tracker stats\\\"),\\n desc.format(\\\"Displays information about all trackers.\\\"),\\n desc.format(\\\"Stats included: total entries, entries per tracker.\\\"),\\n \\\"\\\",\\n \\\"Options:\\\",\\n usage.format(\\\"<tracker>\\\"),\\n desc.format(\\\"The name of the tracker to show stats for, converted to lowercase.\\\")\\n ]\\n elif command == \\\"plot\\\":\\n help_text = [\\n \\\"Plot: show graph for tracker\\\",\\n \\\"\\\",\\n \\\"Usage:\\\",\\n usage.format(\\\"tracker plot <tracker>\\\"),\\n \\\"\\\",\\n \\\"Description:\\\",\\n usage.format(\\\"tracker stats <tracker>\\\"),\\n desc.format(\\\"Displays graph for <tracker> from first entry to last entry.\\\"),\\n \\\"\\\",\\n \\\"Options:\\\",\\n usage.format(\\\"<tracker>\\\"),\\n desc.format(\\\"The name of the tracker to graph, converted to lowercase.\\\")\\n ]\\n else:\\n error(\\\"Invalid command: '{}'\\\".format(command))\\n print(\\\"\\\\n\\\".join(help_text))\\n sys.exit(1)\",\n \"def cmd_help(self, commands=None, usage=False):\\n if commands:\\n usage = True\\n commands = {self.approx.decmd(c.lower()) for c in commands}\\n rejects = commands - self.approx.keys()\\n for reject in rejects:\\n self.put_pretty(\\\"No command named %r\\\" % reject)\\n continue\\n commands -= rejects\\n if self.debug:\\n assert not any(self.approx.encmd(r) in self.mod_commands for\\n r in rejects)\\n assert all(self.approx.encmd(c) in self.mod_commands for\\n c in commands)\\n if not commands:\\n return\\n requested = zip(commands, (self.approx[c] for c in commands))\\n else:\\n requested = self.approx.items()\\n help = znc.CTable()\\n help.AddColumn(\\\"Command\\\")\\n help.AddColumn(\\\"Usage\\\" if usage else \\\"Description\\\")\\n from itertools import zip_longest\\n #\\n for command, parser in requested:\\n if usage:\\n upre = \\\"usage: %s\\\" % command\\n rest = (parser.format_usage()\\n .replace(upre, \\\"\\\", 1)\\n .replace(\\\"[-h] \\\", \\\"\\\", 1))\\n desc = [l.strip() for l in rest.split(\\\"\\\\n\\\") if l.strip()]\\n else:\\n desc = [parser.description]\\n for line, comm in zip_longest(desc, (command,), fillvalue=\\\"\\\"):\\n help.AddRow()\\n help.SetCell(\\\"Command\\\", comm)\\n help.SetCell(\\\"Usage\\\" if usage else \\\"Description\\\", line)\\n #\\n s_line = znc.String()\\n strung = []\\n while help.GetLine(len(strung), s_line):\\n strung.append(s_line.s)\\n also = \\\" (<command> [-h] for details)\\\"\\n strung[1] = strung[1].replace(len(also) * \\\" \\\", also, 1)\\n self.put_pretty(\\\"\\\\n\\\".join(strung))\",\n \"async def help(message, *args, **kwargs):\\r\\n\\r\\n if not args:\\r\\n docs = {name: f.__doc__ for name, f in usercommands.items()}\\r\\n docs = {k:v.split(\\\"\\\\n\\\",1)[0] for k,v in docs.items() if v and k.startswith(prefix)}\\r\\n output = dictstr(docs) + f\\\"\\\\nType \\\\\\\"{prefix}help func\\\\\\\" for detailed information about func\\\"\\r\\n output += \\\"\\\\n\\\\nFunction arguments are words separated by spaces\\\\n ex. $sort enemydata HP\\\"\\r\\n output += \\\"\\\\nKeyword arguments have the format: key=value\\\\n no spaces, unless value is in quotes\\\"\\r\\n output += \\\"\\\\n\\\\nDataTables:\\\"\\r\\n for name in DataTables:\\r\\n output += \\\"\\\\n \\\" + name\\r\\n if name == \\\"enemydata-h\\\": output += \\\" (hard-mode stats)\\\"\\r\\n await reply(message, f\\\"```{output}```\\\")\\r\\n return\\r\\n await reply(message, f\\\"```{inspect.getdoc(usercommands['$'+args[0]])}```\\\")\",\n \"def quick_test():\\n do_command('Help: Command=Help')\\n do_command('Help: Command=\\\"GetInfo\\\"')\\n #do_command('SetPreference: Name=GUI/Theme Value=classic Reload=1')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text(messages.HELP_TEXT)\",\n \"async def help(ctx, *, command: str = None):\\n try:\\n if command is None:\\n p = await HelpPaginator.from_bot(ctx)\\n else:\\n entity = bot.get_cog(command)\\n if entity is None:\\n clean = command.replace('@', '@\\\\u200b')\\n return await ctx.send(f'Command or category \\\"{clean}\\\" not found.')\\n elif isinstance(entity, commands.Command):\\n p = await HelpPaginator.from_command(ctx, entity)\\n else:\\n p = await HelpPaginator.from_cog(ctx, entity)\\n\\n await p.paginate()\\n except Exception as e:\\n await ctx.send(e)\",\n \"async def help(self, msg, *args, intro=None):\\n sudo = Guard.allow_sudo(msg)\\n if intro is not None:\\n intro = f'{intro.strip()} '\\n else:\\n intro = ''\\n if msg.channel.type == discord.ChannelType.private:\\n nick = '@DayRInfo'\\n else:\\n nick = f'@{msg.channel.guild.me.nick}'\\n content = f'{intro}I understand the following commands (tag me at the start of the message):\\\\n'\\n for command, (args, desc, enabled, delay) in Controller.commands.items():\\n if not sudo and not enabled:\\n continue\\n if args:\\n args = f' {args.strip()}'\\n if desc:\\n desc = f'\\\\n\\\\t{desc}'\\n content = f'{content}`{Controller.HELP_KEY}{command}{args}`{desc}\\\\n'\\n content = f'{content}----------\\\\n'\\n content = f'{content}• Also, if you tag this bot ({nick}) on a message containing a link to the interactive Day R map 🗺️ with a location URL, I will send you a snapshot of the location.\\\\n'\\n content = f'{content}• React with ❌ to any of my messages to delete it (if I still remember that it was my message). You can only delete my messages that are directed to you.'\\n await msg.author.send(**{\\n 'content': content,\\n })\\n await msg.channel.send(**{\\n 'content': 'Command list sent via DM!',\\n 'reference': msg.to_reference(),\\n 'mention_author': True,\\n 'delete_after': 3,\\n })\",\n \"def do_help():\\n return True, \\\"\\\"\\\"I can understand these commands:\\nOFF - Shut down robot\\nHELP - provide information about commands\\nFORWARD - move forward by specified number of steps, e.g. 'FORWARD 10'\\nBACK - move backward by specified number of steps, e.g. 'BACK 10'\\nRIGHT - turn right by 90 degrees\\nLEFT - turn left by 90 degrees\\nSPRINT - sprint forward according to a formula\\n\\\"\\\"\\\"\",\n \"def help(update, context):\\n update.message.reply_text(\\\"\\\"\\\"THESE ARE THE COMMANDS\\n /hi\\n /gokul\\n /bala\\n /competition\\n /form\\n \\\"\\\"\\\")\",\n \"async def help(self, ctx, *, command: str = None):\\n try:\\n if command is None:\\n p = await HelpPaginator.from_bot(ctx)\\n else:\\n entity = self.bot.get_cog(command) or self.bot.get_command(command)\\n\\n if entity is None:\\n clean = command.replace('@', '@\\\\u200b')\\n return await ctx.send(f'Command or category \\\"{clean}\\\" not found.')\\n elif isinstance(entity, commands.Command):\\n p = await HelpPaginator.from_command(ctx, entity)\\n else:\\n p = await HelpPaginator.from_cog(ctx, entity)\\n\\n await p.paginate()\\n except Exception as e:\\n await ctx.send(e)\",\n \"def print_help(self):\\n print self.get_help()\",\n \"def helpme(self):\\n\\n print(\\\"{}{}{}\\\".format(' ', 'Commands', ' '))\\n print(\\\"{}{}{}\\\".format(' ', '--------', ' '))\\n print(\\\"{} {} {}\\\".format('help ', '|', 'Display all usable commands'))\\n print(\\\"{} {} {}\\\".format('look ', '|', 'Explore the room to find current location, exits and potential items.'))\\n print(\\\"{} {} {}\\\".format('go ', '|', 'The prefix required to navigate your player.'))\\n print(\\\"{} {} {}\\\".format('get ', '|', 'The prefix for picking up items.'))\\n print(\\\"{} {} {}\\\".format('drop ', '|', 'The prefix for dropping items.'))\\n print(\\\"{} {} {}\\\".format('inv ', '|', 'Displays the player inventory'))\\n print(\\\"{} {} {}\\\".format('health ', '|', 'Displays player health'))\\n print(\\\"{} {} {}\\\".format('eat ', '|', 'Allows the player to use consumables to gain health'))\\n print(\\\"{} {} {}\\\".format('equip ', '|', 'Equip a weapon in your inventory'))\\n print(\\\"{} {} {}\\\".format('unequip', '|', 'Unequip a current weapon'))\\n print(\\\"{} {} {}\\\".format('attack ', '|', 'Allows the player to attack a non-player'))\\n print(\\\"{} {} {}\\\".format('push ', '|', 'Returns NPC to spawn'))\\n print(\\\"{} {} {}\\\".format('save ', '|', 'Save current player progress'))\\n print(\\\"{} {} {}\\\".format('load ', '|', 'Load a previous character'))\",\n \"def initialize_help_commands(self) -> None:\\n\\n @self.command(name=\\\"help\\\")\\n @logger(\\\"all\\\")\\n async def help_command(ctx, *args):\\n if len(args) == 0:\\n await ctx.message.channel.send(indie_help.summary())\\n else:\\n await ctx.message.channel.send(indie_help.specific(args))\",\n \"def do_help(self, line):\\n Cmd.do_help(self, line)\",\n \"def channel_help(message):\\n message.reply(Strings['HELP'].format(config.HELP_URL))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8511808","0.8454938","0.83023393","0.82100123","0.8172162","0.8119534","0.8045609","0.7997151","0.79934657","0.79888207","0.7869247","0.7839074","0.7831564","0.7807893","0.7801458","0.77975965","0.77936643","0.7725134","0.76936215","0.7667932","0.7667932","0.76497036","0.76497036","0.7628276","0.7610771","0.75922734","0.75838995","0.758352","0.7563916","0.7556451","0.755237","0.75399286","0.7539473","0.7525681","0.75214773","0.7514472","0.75070816","0.7498093","0.748434","0.7472967","0.745708","0.7456372","0.7448633","0.7441096","0.74235225","0.742008","0.741299","0.74123985","0.74046856","0.7401341","0.73991734","0.73990095","0.73945934","0.73850703","0.7384522","0.73833996","0.7372972","0.7372972","0.73705924","0.7369859","0.73683614","0.73671097","0.73603016","0.73593056","0.7333431","0.733177","0.733177","0.733177","0.733177","0.73265743","0.7319092","0.73185515","0.7303384","0.72921866","0.7282114","0.7278147","0.72738814","0.72723234","0.7265674","0.726526","0.72568154","0.7256574","0.72528225","0.72528225","0.7251022","0.72443396","0.72423077","0.72289395","0.7228725","0.7226511","0.7218349","0.7213249","0.7212109","0.72093403","0.72060156","0.71885115","0.71863073","0.7174839","0.7161957","0.71515167","0.7147234"],"string":"[\n \"0.8511808\",\n \"0.8454938\",\n \"0.83023393\",\n \"0.82100123\",\n \"0.8172162\",\n \"0.8119534\",\n \"0.8045609\",\n \"0.7997151\",\n \"0.79934657\",\n \"0.79888207\",\n \"0.7869247\",\n \"0.7839074\",\n \"0.7831564\",\n \"0.7807893\",\n \"0.7801458\",\n \"0.77975965\",\n \"0.77936643\",\n \"0.7725134\",\n \"0.76936215\",\n \"0.7667932\",\n \"0.7667932\",\n \"0.76497036\",\n \"0.76497036\",\n \"0.7628276\",\n \"0.7610771\",\n \"0.75922734\",\n \"0.75838995\",\n \"0.758352\",\n \"0.7563916\",\n \"0.7556451\",\n \"0.755237\",\n \"0.75399286\",\n \"0.7539473\",\n \"0.7525681\",\n \"0.75214773\",\n \"0.7514472\",\n \"0.75070816\",\n \"0.7498093\",\n \"0.748434\",\n \"0.7472967\",\n \"0.745708\",\n \"0.7456372\",\n \"0.7448633\",\n \"0.7441096\",\n \"0.74235225\",\n \"0.742008\",\n \"0.741299\",\n \"0.74123985\",\n \"0.74046856\",\n \"0.7401341\",\n \"0.73991734\",\n \"0.73990095\",\n \"0.73945934\",\n \"0.73850703\",\n \"0.7384522\",\n \"0.73833996\",\n \"0.7372972\",\n \"0.7372972\",\n \"0.73705924\",\n \"0.7369859\",\n \"0.73683614\",\n \"0.73671097\",\n \"0.73603016\",\n \"0.73593056\",\n \"0.7333431\",\n \"0.733177\",\n \"0.733177\",\n \"0.733177\",\n \"0.733177\",\n \"0.73265743\",\n \"0.7319092\",\n \"0.73185515\",\n \"0.7303384\",\n \"0.72921866\",\n \"0.7282114\",\n \"0.7278147\",\n \"0.72738814\",\n \"0.72723234\",\n \"0.7265674\",\n \"0.726526\",\n \"0.72568154\",\n \"0.7256574\",\n \"0.72528225\",\n \"0.72528225\",\n \"0.7251022\",\n \"0.72443396\",\n \"0.72423077\",\n \"0.72289395\",\n \"0.7228725\",\n \"0.7226511\",\n \"0.7218349\",\n \"0.7213249\",\n \"0.7212109\",\n \"0.72093403\",\n \"0.72060156\",\n \"0.71885115\",\n \"0.71863073\",\n \"0.7174839\",\n \"0.7161957\",\n \"0.71515167\",\n \"0.7147234\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":6793,"cells":{"query":{"kind":"string","value":"Type !help command for additional info on a command. You can also type !help category for additional info on a category. For example, type !help Level (If you have the level plugin enable!)"},"document":{"kind":"string","value":"async def category(self,ctx):\n await ctx.send(\"Yes this is a category.\")"},"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 help_help(self):\n print(\"List commands or print details about a command\")","def command_help(self, command):\n self.commands[command].command_help()","def _help(self):\n self.onecmd('help')","def cmd_help(args):","def help():\n \n pass","def help():","def command_help(self, *args, **kwargs):\n print(\"Commands available:\\n\")\n for name in dir(self):\n if not name.startswith(\"command_\"):\n continue\n name_clean = name[len(\"command_\"):]\n print(\"%s:\\n - %s\\n\" % (name_clean, getattr(self, name).__doc__.strip()))","def do_command_help(self, command):\n summary = self.base.commands[command].get_summary()\n usage = self.get_command_usage(command)\n description = self.base.commands[command].get_description()\n sys.stdout.write('%s\\n%s' % (summary, usage))\n if description != None:\n sys.stdout.write('Arguments Description:\\n%s\\n' %\n (description, ))","def help():\n print(UI.HELP)","def help(self):\r\n self._short_help(None, None, None, None)","def cmd_help(ctx):\n echo(ctx.parent.get_help())","def show_help():\n pass","async def help(self, ctx):\n self.log_command_call(\"help\", ctx.message)\n await ctx.send(HELP_TEXT)\n embed_output = create_embed(description=MORE_INFO_TEXT)\n await ctx.send(embed=embed_output)","async def _help(ctx, *, command_name: str=None):\n if command_name:\n command = bot.get_command(command_name)\n if not command:\n return await ctx.send(\"No such command!\")\n return await ctx.send(f\"```\\n{ctx.prefix}{command.name} {command.signature}\\n\\n{command.help or 'Missing description'}```\")\n description = []\n for name, cog in bot.cogs.items():\n entries = [\" - \".join([cmd.name, cmd.short_doc or \"Missing description\"]) for cmd in cog.get_commands() if await _can_run(cmd, ctx) and not cmd.hidden]\n if entries:\n description.append(f\"**{name}**:\")\n description.append(\"• \" + \"\\n• \".join(entries))\n await ctx.send(embed=discord.Embed(description=\"\\n\".join(description), color=ctx.me.color))","def help(command=None):\n if command is None: \n # print first line of docstring\n for cmd in commands:\n ds = commands[cmd].__doc__.split('\\n')[0]\n print \"%-16s %s\" % (cmd,ds)\n else:\n print commands[command].__doc__","def help(self):","def help(self):","def help(self):\n pass","def help(self):\n pass","def command_help(self):\n print(\"Command \", self)\n print(\"\\t\\thelp (Get help for command)\")\n\n params = self.params.copy()\n del params[\"help\"]\n\n if len(params) == 0:\n print(\"This command has no parameters\")\n return\n\n print(\"Parameters:\")\n for info in params.values():\n print(\" %s\" % info.get_basic_info())\n description = info.get_desc()\n if description != \"\":\n print(textwrap.fill(description,\n initial_indent=\" \",\n subsequent_indent=\" \",\n width=70))","def help():\n print \"Help comes to those who ask\"","def help_command(server, output, conf):\n server.tell(output.name, 'Available commands:')\n for key in COMMANDS.keys():\n cmd_func = COMMANDS[key]\n if cmd_func.__doc__:\n server.tell(output.name, '%s: %s' % (key[1:], cmd_func.__doc__))\n else:\n server.tell(output.name, key[1:])\n return","def help(ctx):\n click.echo(ctx.parent.get_help())","def help(ctx, topic, **kw):\n # The help command implementation is taken from\n # https://www.burgundywall.com/post/having-click-help-subcommand\n if topic is None:\n click.echo(ctx.parent.get_help())\n else:\n click.echo(main.commands[topic].get_help(ctx))","def print_generic_help():\r\n print ART_NAME\r\n print 'Version {1}\\nby {2}'.format(NAME, VERSION, AUTHOR)\r\n print DESCRIPTION\r\n tools = sorted(AvailableCommands.commands.keys(), key=lambda v: v.upper())\r\n # Do not show CUSTOM command in the help\r\n tools.remove('CUSTOM')\r\n tools.remove('CUSTOM_NO_OUTPUT')\r\n print '\\n\\nSupported tools are:\\n{0}'.format('\\n'.join(tools))\r\n print '\\nHint: Check tool specific help with --help <tool_name>\\n'","def help_command(update, context):\n update.message.reply_text('Help!')","async def help(self, *args, **kwargs):\n if len(args) == 0:\n return self.help.__doc__\n docs = []\n for command in args:\n if command in self.user_special_bang_set:\n docs.append(self.user_special_bang_set[command].bang_skill.__doc__)\n elif command in self.control_bang_set:\n docs.append(self.control_bang_set[command].bang_skill.__doc__)\n\n return \"| \".join(docs)","def help_command(update, context):\n update.message.reply_text('Let me help you. \\r\\n /help print this help \\r\\n /safety prints safety instructions \\r\\n /play start the game\\r\\n /joingroup Join CTF tg group')","def command_help(self, bot, update):\n\n messages = [\n 'Available commands:',\n '/who - Who is Myles?',\n '/where - Where is Myles?',\n '/tweet - What was the last tweet Myles sent?',\n '/photo - What was the last Instagram photo Myles took?',\n '/web - Where can I find Myles on the interwebs?',\n ]\n\n self.send_messages(bot, update, messages)","def do_help(self, command_name):\n if not command_name:\n print _PVBlotInterp.__doc__\n print \"The following commands are supported:\"\n print \" \",\n blotish_commands = self._blotish_commands.keys()\n blotish_commands.sort()\n for c in blotish_commands:\n print c,\n print\n print\n print \"For more information on any command, try help <command>.\"\n return\n try:\n command = self.get_unique_command(command_name)\n print command.__doc__\n except blotish.BlotishError, err:\n blot_common.print_blot_error(err)","def printhelp():","def display_help(self):\n pass","def help_command(update: Update, _: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, _: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help(self, msg=None):\n\n # Print the message if given.\n if not msg == None:\n print str(msg) + \"\\n\"\n\n # Display the list of commands, in the alphabetical order.\n print \"Use one of the following commands:\"\n for action in sorted(self.actions.keys()):\n info = self.actions[action]\n joined_oblig = ' '.join(info['required'])\n if len(info['additional']) > 0:\n add = [\"<%s>\" % x for x in info['additional']]\n joined_add = '[' + ' '.join(add) + ']'\n else:\n joined_add = ''\n print \"\\t* %s %s %s\" % (action, joined_oblig, joined_add)","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Help!')","def help_command(update: Update, context: CallbackContext) -> None:\r\n update.message.reply_text('Help!')","def help(self):\n\t\treturn","async def do_help(self, arg):\n if arg:\n if arg in self._commands:\n _, doc = self._commands[arg]\n print(doc)\n else:\n self.error('No command {}'.format(arg))\n else:\n print('Valid commands:')\n for c in self._commands:\n print(' ' + c)","def print_specific_help(tool_name):\r\n if tool_name not in AvailableCommands.commands:\r\n print 'Command is not supported: {0}'.format(tool_name)\r\n return\r\n cmd = AvailableCommands.commands[tool_name]\r\n\r\n print 'Usage of {0}:'.format(cmd.name)\r\n print '\\nAccepted input types:\\n{0}'.format(str(list(cmd.input_types)))\r\n print '\\nOutput types:\\n{0}'.format(str(cmd.output_types))\r\n print '\\nMandatory arguments:\\n{0}'.format(str(cmd.user_mandatory_args))\r\n print '\\nOptional arguments:\\n{0}'.format(str(cmd.user_optional_args))\r\n print '\\nParallelizable:\\n{0}'.format(str(cmd.parallelizable))\r\n print '\\nAdditional description:\\n{0}'.format(str(cmd.help_description))\r\n print ''","def help(cmd, cmdArgs):\n global commandDict\n retInfo = []\n if len(cmdArgs) > 0:\n #return help on a single function\n if cmdArgs[0] in commandDict.keys():\n return commandDict[cmdArgs[0]].__doc__\n\n #else, return general info\n retInfo = ['pypeople: A command line tool for vCard management',\n 'Version:' + __version__,\n 'Available Commands:']\n #fill in more info here\n for cmdName in commandDict.keys():\n cmdFunc = commandDict[cmdName]\n cmdDoc = str(cmdName) + ': ' + str(cmdFunc.__doc__) if cmdFunc.__doc__ is not None else 'Undocumented Function'\n retInfo.append('\\t' + cmdDoc)\n\n return '\\n'.join(retInfo)","async def help(self, ctx, *, command_name: str=None):\n bot_prefix = '@Randy '\n # Shortcut to command search\n if command_name is not None:\n return await ctx.invoke(self.cmd('help command'), cmd_name=command_name)\n\n em = discord.Embed(title='Help',\n description='**Permissions:** The permissions required to function :-\\n'\n '`Send Messages`, `Manage Messages`, `Embed Links`\\n'\n '--\\nTo get help or more information on a specific command, use:\\n'\n '`{bot_prefix}help <command name>`\\n'\n '--\\nRead my messy code [here](http://github.com/xKynn/RandomRumble)'\n '--\\nIf you like my work and would like to help me, '\n 'Ko-Fi/Paypal: [Link](https://ko-fi.com/D1D6EXXV)\\n',\n color=self.color)\n\n em.set_footer(text=\"Contact me at Demo#7645\")\n\n # This can't go in the init because help isn't loaded last & thus misses some commands\n em.add_field(name=\"Commands\", value=' • '+'\\n • '.join(f\"***{c.name}*** - {c.short_doc}\" for c in self.bot.commands if\n c.name not in ['pob', 'link', 'convert']))\n try:\n await ctx.send(embed=em)\n except:\n await ctx.send(\"`Embed Links` permission is required to see the help!\")","def test_cli_help(self):\n output = self.update_command('-h')","def help_command(update: Update, _: CallbackContext) -> None:\n update.message.reply_text(\"These are the list of commands supported. \\n\\n /deadlines. \\n\\n \"\n \"Hey! I am still being enhanced, more features to come...!\")","def command_help(args):\n\tprint_usage()\n\treturn 0","def help_command(update: Update) -> None:\n #update.message.reply_text('Help!')","async def do_help():\n\n if len(message.content.split()) > 1:\n for i in cmd_dict:\n if message.content.split()[1] == i:\n await bot.send_message(c, f'Help for {i}: {cmd_dict[i].__doc__}')\n return\n\n cmdstr = 'Commands: '\n for i in cmd_dict:\n cmdstr += '{}, '.format(i)\n await bot.send_message(c, cmdstr)","def get_command_help(self, module_name, command_name):\r\n command = self.env.get_command(module_name, command_name)\r\n\r\n default_format = 'raw'\r\n if sys.stdout.isatty():\r\n default_format = 'table'\r\n\r\n arg_doc = command.__doc__\r\n\r\n if 'confirm' in command.options:\r\n arg_doc += \"\"\"\r\nPrompt Options:\r\n -y, --really Confirm all prompt actions\r\n\"\"\"\r\n\r\n if '[options]' in arg_doc:\r\n arg_doc += \"\"\"\r\nStandard Options:\r\n --format=ARG Output format. [Options: table, raw] [Default: %s]\r\n -C FILE --config=FILE Config file location. [Default: ~/.softlayer]\r\n --debug=LEVEL Specifies the debug noise level\r\n 1=warn, 2=info, 3=debug\r\n --timings Time each API call and display after results\r\n --proxy=PROTO:PROXY_URL HTTP[s] proxy to be use to make API calls\r\n -h --help Show this screen\r\n\"\"\" % default_format\r\n return arg_doc.strip()","def print_help(self):\n\n print((\"Help is not defined for command \" + self.command))","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text(\"Laughybot\\n\\n VERSION\\n 0.1 \\n\\nUSAGE\\n /{command} : Entrer une commande parmi celles disponibles\\n\\nCOMMADES\\n/joke => recherche une blague sur la toile\\n/start => Affiche le message d'accueil\\n/help => Affiche l'aide\")","def help_command(update: Update, context: CallbackContext) -> None:\n update.message.reply_text(messages.HELP_TEXT)","def get_help(self) -> None: \n print(messages.get_help())","def Help(topic=None):\n\n if not topic:\n print \"Avaliable commands are: \"\n print \"\\n\".join(map(lambda x:\" \"*3+x, filter(lambda c:c!=\"help\", commands)))\n print \"Type help command to get help with a specific command.\"\n\n elif topic in commands:\n print \"Help for %s:\"%topic\n print commands[topic].__doc__","def dispatch_help(args):\n command = args.subcommand\n if command is None:\n command = 'help'\n args.parsers[command].print_help()\n\n cmd_func = getattr(EtcMaint, 'cmd_%s' % command, None)\n if cmd_func:\n lines = cmd_func.__doc__.splitlines()\n print('\\n%s\\n' % lines[0])\n paragraph = []\n for l in dedent('\\n'.join(lines[2:])).splitlines():\n if l == '':\n if paragraph:\n print('\\n'.join(wrap(' '.join(paragraph), width=78)))\n print()\n paragraph = []\n continue\n paragraph.append(l)\n if paragraph:\n print('\\n'.join(wrap(' '.join(paragraph), width=78)))","def help(self) -> str:\n\t\treturn None","async def send_command_help(self, command):\n ctx = self.context\n embed = discord.Embed(title=command.name.upper(), description=command.description, color=discord.Color.green())\n alias = command.aliases\n if alias:\n embed.add_field(name=\"Aliases\", value=\", \".join(alias), inline=False)\n if command.usage != None:\n embed.add_field(name=\"How to use:\", value=f'`{command.usage}`')\n\n await ctx.reply(embed=embed)","def _help():\n text = \"\"\"\n```\nUsage: @bot [command] (message)\n\n*Commands*:\n \n add Adds specified users to notify list | To add yourself use key 'myself'\n list Lists users on notify list\n remove Removes specified users from notify list | To remove yourself use key 'myself'\n help This help\n\n > Obs: All commands are optional\n\n*Examples*:\n\n @bot add myself @Fulano <= Will add yourself and @Fulano in list.\n @bot this a messge test <= Send 'this a messge test' to all list.\n @bot remove myself @Fulano <= Remove yourself and @fulano from list.\n\n```\n\"\"\"\n return text","def do_help(self, args): \n if args.command:\n if len(args.command) > 1:\n command = args.command[0] +\"-\" + args.command[1]\n else:\n command = args.command[0]\n if command in self.subcommands:\n self.subcommands[command].print_help()\n else:\n print((\"'%s' is not a valid subcommand\") %\n args.command)\n else:\n self.parser.print_help()","def print_help():\n\tprint(\"Help text\")","def help(update, context):\n update.message.reply_text(\"\"\"usage \n /bus <bus name> or /bus <bus name> <stop name>\n /addstop <stop name> <stop code>\n /delstop <stop name>\n /showstops\n /help\n \"\"\")\n\n # log info\n logger.info(\"help used username:{0}\".format(update.message.from_user.username))","def do_help(self, args):\n ## The only reason to define this method is for the help text in the doc string\n cmd.Cmd.do_help(self, args)","def do_help(self, args):\n ## The only reason to define this method is for the help text in the doc string\n cmd.Cmd.do_help(self, args)","def print_help(self):\n print self.get_help()","def do_help(self, line):\n Cmd.do_help(self, line)","def show_help(self):\n self.slack.reply('\\n\\n'.join(self.help_lines))","def help(self, args):\n print('No commands available for this consumer')","def help_util():\r\n for cmd, f in COMMANDS.items():\r\n print(\"POM \" + cmd + \":\")\r\n print(f.__doc__.lstrip(\"\\n\"))","def help(self) -> str:\n raise NotImplementedError","def do_help(self, arg):\n if arg:\n # Getting help for a specific command\n funcname = self._func_named(arg)\n if funcname:\n # No special behavior needed, delegate to cmd base class do_help()\n cmd.Cmd.do_help(self, funcname[3:])\n else:\n # Show a menu of what commands help can be gotten for\n self._help_menu()","async def help_command(self, ctx, *, cmd_name: str=None):\n bot_prefix = '@Randy '\n # Get command object\n cmd_obj = self.cmd(cmd_name)\n\n # Handle no command found\n if cmd_obj is None:\n return await ctx.error(f'Command {cmd_name} not found')\n em = discord.Embed(title=cmd_obj.name, description=cmd_obj.help, color=self.color)\n\n # Input aliases and parameters to embed\n if cmd_obj.aliases:\n em.add_field(name='Aliases', value='\\n'.join([f'\\u2022 {x}' for x in cmd_obj.aliases]))\n if cmd_obj.clean_params:\n em.add_field(name='Parameters', value='\\n'.join([f'\\u2022 {x}' for x in cmd_obj.clean_params]))\n\n # Handle group commands\n if isinstance(cmd_obj, commands.core.Group):\n em.add_field(name='Group commands',\n value='\\n'.join([f'\\u2022 {x}' for x in cmd_obj.commands]),\n inline=False)\n\n # Add usage last\n em.add_field(name='Usage',\n value=f'```{bot_prefix}\\u200b{cmd_name} '\n f'{\" \".join([f\"<{x}>\" for x in cmd_obj.clean_params])}```',\n inline=False)\n\n await ctx.send(embed=em)","def show_help():\n print('\\n' + Colors.BLACK_ON_WHITE +\n 'track: Input info for daily tracking' + Colors.NORMAL + '\\n'\n ' * Mood\\n' +\n ' * Short Daily Summary\\n' +\n ' * Accomplishments\\n' +\n ' * Long Journal Entry\\n' +\n \" * Tomorrow's Most Important Task\\n\" +\n '\\n' +\n 'Usage: track.py [options]\\n'\n '\\n' +\n 'Options:\\n' +\n ' [none] Input and record daily tracking\\n' +\n ' y Input and record tracking for previous day\\n' +\n ' ! Print random daily entry\\n' +\n ' accs Print all recent accomplishments\\n' +\n ' help Print this help menu\\n' +\n ' mood Print average mood using past entries\\n' +\n ' overviews Print headers of all recent entries.\\n')","def do_help(self, message):\r\n\t\tcommands = [command[3:] for command in dir(self) if command.startswith('do_')]\r\n\t\t# self.trace(f'Help: {\", \".join(commands)}')\r\n\r\n\t\tfor command in commands:\r\n\t\t\tmethod = getattr(self, f'do_{command}')\r\n\t\t\tcommand_help = getattr(method, '__doc__')\r\n\t\t\tif command_help:\r\n\t\t\t\tself.trace(f'{command}: {command_help}')","def get_help(self,command):\n if \"help\" in self.commands[command]:\n return self.commands[command][\"help\"]\n else:\n return \"No help defined for this command.\"","def HelpCommand(self, unused_args, unused_sub_opts=None, unused_headers=None,\n unused_debug=None):\n self.OutputUsageAndExit()","def help(update, context):\n update.message.reply_text('Help!')","def help(update, context):\n update.message.reply_text('Help!')","def help(update, context):\n update.message.reply_text('Help!')","def help(update, context):\n update.message.reply_text('Help!')","def printHelp(self,):\n print man\n return 0","def help_description():\n pass","def help(self, cmd=\"\", *, fail=\"\"):\n class_dict = dict(type(self).__dict__)\n # Add this function to class, so that when subclassing,\n # help for help is found\n class_dict.update({\"help\": self.help})\n if cmd.startswith(self.predicate):\n # Strip predicate\n cmd = cmd[len(self.predicate) :]\n # Check that command exists and is not\n # private, protected or special method\n if (not cmd.startswith(\"_\")) and cmd in class_dict.keys():\n item = class_dict[cmd]\n if callable(item):\n if item.__doc__:\n return \"Help on command '{}':\\n. {}\".format(\n cmd, \"\\n. \".join(cleandoc(item.__doc__).split(\"\\n\"))\n )\n return \"No help on command '{}'\".format(cmd)\n # If no cmd given or wrong cmd given, return commands\n commands = []\n for key, value in class_dict.items():\n if not key.startswith(\"_\"):\n if callable(value):\n commands.append(key)\n msg = (\n \"Commands:\\n {}\".format(\", \".join(commands))\n + \"\\n for more help on command, use \"\n + \"{}help command\".format(self.predicate)\n )\n if fail:\n msg = fail + \"\\n\" + msg\n return msg","def help(self):\n return None","def help():\n version()\n print(\"USAGE: python3 notes.py [COMMAND]\")\n print(\"COMMAND:\")\n help_dict = {\n \"help\": \"Prints out the help text.\",\n \"version\": \"Prints out the version.\",\n \"init\": \"Initializes a new set of notes.\",\n \"modify\": \"Modify the current set of notes.\",\n }\n help_dict = OrderedDict(sorted(help_dict.items()))\n for entry in help_dict:\n print((\" \" + entry).ljust(10), help_dict[entry])","async def help(self, msg, *args, intro=None):\n sudo = Guard.allow_sudo(msg)\n if intro is not None:\n intro = f'{intro.strip()} '\n else:\n intro = ''\n if msg.channel.type == discord.ChannelType.private:\n nick = '@DayRInfo'\n else:\n nick = f'@{msg.channel.guild.me.nick}'\n content = f'{intro}I understand the following commands (tag me at the start of the message):\\n'\n for command, (args, desc, enabled, delay) in Controller.commands.items():\n if not sudo and not enabled:\n continue\n if args:\n args = f' {args.strip()}'\n if desc:\n desc = f'\\n\\t{desc}'\n content = f'{content}`{Controller.HELP_KEY}{command}{args}`{desc}\\n'\n content = f'{content}----------\\n'\n content = f'{content}• Also, if you tag this bot ({nick}) on a message containing a link to the interactive Day R map 🗺️ with a location URL, I will send you a snapshot of the location.\\n'\n content = f'{content}• React with ❌ to any of my messages to delete it (if I still remember that it was my message). You can only delete my messages that are directed to you.'\n await msg.author.send(**{\n 'content': content,\n })\n await msg.channel.send(**{\n 'content': 'Command list sent via DM!',\n 'reference': msg.to_reference(),\n 'mention_author': True,\n 'delete_after': 3,\n })","def print_help_info(self, global_options):\r\n usage = ['',\"Type '%s help <subcommand>' for help on a specific subcommand.\" % self.prog_name,'']\r\n usage.append('Available subcommands:')\r\n commands = self.get_commands(global_options).keys()\r\n commands.sort()\r\n for cmd in commands:\r\n usage.append(' %s' % cmd)\r\n return '\\n'.join(usage)","def help_command(update: Update, _: CallbackContext) -> None:\n update.message.reply_text('Usage: Basic example to make face recognition' +\n 'Just upload foto, pls.')","def show_help():\n messagebox.showinfo(title='How to Use', message=\"It's really easy.\")","def helpme(self):\n\n print(\"{}{}{}\".format(' ', 'Commands', ' '))\n print(\"{}{}{}\".format(' ', '--------', ' '))\n print(\"{} {} {}\".format('help ', '|', 'Display all usable commands'))\n print(\"{} {} {}\".format('look ', '|', 'Explore the room to find current location, exits and potential items.'))\n print(\"{} {} {}\".format('go ', '|', 'The prefix required to navigate your player.'))\n print(\"{} {} {}\".format('get ', '|', 'The prefix for picking up items.'))\n print(\"{} {} {}\".format('drop ', '|', 'The prefix for dropping items.'))\n print(\"{} {} {}\".format('inv ', '|', 'Displays the player inventory'))\n print(\"{} {} {}\".format('health ', '|', 'Displays player health'))\n print(\"{} {} {}\".format('eat ', '|', 'Allows the player to use consumables to gain health'))\n print(\"{} {} {}\".format('equip ', '|', 'Equip a weapon in your inventory'))\n print(\"{} {} {}\".format('unequip', '|', 'Unequip a current weapon'))\n print(\"{} {} {}\".format('attack ', '|', 'Allows the player to attack a non-player'))\n print(\"{} {} {}\".format('push ', '|', 'Returns NPC to spawn'))\n print(\"{} {} {}\".format('save ', '|', 'Save current player progress'))\n print(\"{} {} {}\".format('load ', '|', 'Load a previous character'))","def DoHelp(options, args):\n __pychecker__ = 'unusednames=options'\n if len(args) == 1 and args[0] in COMMAND_USAGE_TEXT:\n print(COMMAND_USAGE_TEXT[args[0]])\n else:\n raise gclient_utils.Error(\"unknown subcommand '%s'; see 'gclient help'\" %\n args[0])","def help(update, context):\n update.message.reply_text(\"\"\"THESE ARE THE COMMANDS\n /hi\n /gokul\n /bala\n /competition\n /form\n \"\"\")","async def help(ctx, command:str=None):\n if command == None:\n embed = assemble_embed(\n title=\"Looking for help?\",\n desc=(\"Hey there, I'm a resident bot of Scioly.org!\\n\\n\" +\n \"On Discord, you can send me commands using `!` before the command name, and I will process it to help you! \" +\n \"For example, `!states`, `!events`, and `!fish` are all valid commands that can be used!\\n\\n\" +\n \"If you want to see some commands that you can use on me, just type `!list`! \" +\n \"If you need more help, please feel free to reach out to a staff member!\")\n )\n return await ctx.send(embed=embed)\n hlp = await get_help(ctx, command)\n await ctx.send(embed=hlp)","def channel_help(message):\n message.reply(Strings['HELP'].format(config.HELP_URL))","def do_help(self, arg):\n\t\tif arg:\n\t\t\tfuncarg = arg.replace(\" \", \"_\")\n\t\t\ttry:\n\t\t\t\tfunc = getattr(self, 'help_' + funcarg)\n\t\t\texcept AttributeError:\n\t\t\t\ttry:\n\t\t\t\t\tdoc=getattr(self, 'do_' + funcarg).__doc__\n\t\t\t\t\tif doc:\n\t\t\t\t\t\t\tself.stdout.write(\"%s\\n\"%str(doc))\n\t\t\t\t\t\t\treturn\n\t\t\t\texcept AttributeError:\n\t\t\t\t\tpass\n\t\t\t\tself.stdout.write(\"%s\\n\"%str(self.nohelp % (arg,)))\n\t\t\t\treturn\n\t\t\tfunc()\n\t\telse:\n\t\t\tprint \"\\nAvailable commands (type help <topic> for help on topic):\\n\"\n\t\t\tnames = self.get_names()\n\t\t\tfor name in sorted(names):\n\t\t\t\tif name[:3] == \"do_\":\n\t\t\t\t\tname = name[3:]\n\t\t\t\t\tif name == \"EOF\":\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tself.parent.printHelpLine(name, \"\", getattr(self, \"do_\" + name).__doc__ or \"\")\n\t\t\tprint \"\"","def showHelp(self):\n\t\tfor i in range(0,20):\n\t\t\tprint \"\"\n\t\tprint \" _ _ \"\n\t\tprint \"| | | | \"\n\t\tprint \"| |__ _ __ ___ | |__ \"\n\t\tprint \"| '_ \\ | '_ \\ / __|| '_ \\ \"\n\t\tprint \"| | | || | | |\\__ \\| | | |\"\n\t\tprint \"|_| |_||_| |_||___/|_| |_|\"\n\t\tprint \"A program by Scott Jackson\"\n\t\tprint \"\"\n\t\tprint \"To enter a command, type the key and press Return.\"\n\t\tprint \"NB: parentheses indicate which of two options is the default.\"\n\t\tprint \"\"\n\t\tprint \"Basic Commands:\"\n\t\tprint \"j / k -- show lower-ranked / higher-ranked stories.\"\n\t\tprint \"r -- get the latest stories from Hacker News.\"\n\t\tprint \"q -- quit.\"\n\t\tprint \"# -- open story number # in your web browser.\"\n\t\tprint \"c# -- open comments for story number # in your web browser.\"\n\t\tprint \"#+ -- open up story number # AND its comments in your web browser.\"\n\t\tprint \"top / new -- switch between showing the top and newest stories on HN. (top)\"\n\t\tprint \"c / e -- collapse stories you've already read / don't collapse them. (e)\"\n\t\tprint \"u -- update hnsh to the latest version.\"\n\t\tprint \"==========================\"\n\t\tprint \"For more commands, see the man.txt file.\"\n\t\tinput = raw_input(\"Press Return to go back to the Hacker News stories.\")","def more_informations():\n print \"--help for more informations.\"\n sys.exit(1)","def CommandHelp(paser):\n\n\tprint \"\\n===============Commands List===============\\n\"\n\t\t\n\tprint \"NewProject - {}\".format(NewProject.__doc__)\n\tprint \"DelProject - {}\".format(DelProject.__doc__)\n\tprint \"ShareProject - {}\".format(ShareProject.__doc__)\n\tprint \"StopProject - {}\".format(StopProject.__doc__)\n\tprint \"Help - {}\".format(CommandHelp.__doc__)\n\tprint \"Exit - Finaliza la sesion en la terminal.\"","def help_command(update: Update, context: CallbackContext) -> None:\n commands = [\"/login <pwd>\\n\",\n \"/status\\n\",\n \"/heat\\n\",\n \"/temp\\n\",\n \"/off\\n\",\n \"/help\\n\",\n \"/set\\n\",\n \"/unset\\n\",\n \"/heatmore\\n\",\n \"/lighton\\n\",\n \"/lightoff\\n\"]\n\n cmd: str = \" \".join(commands)\n update.message.reply_text('commands are:\\n' + cmd)","def help():\n return statement(help_text)"],"string":"[\n \"def help_help(self):\\n print(\\\"List commands or print details about a command\\\")\",\n \"def command_help(self, command):\\n self.commands[command].command_help()\",\n \"def _help(self):\\n self.onecmd('help')\",\n \"def cmd_help(args):\",\n \"def help():\\n \\n pass\",\n \"def help():\",\n \"def command_help(self, *args, **kwargs):\\n print(\\\"Commands available:\\\\n\\\")\\n for name in dir(self):\\n if not name.startswith(\\\"command_\\\"):\\n continue\\n name_clean = name[len(\\\"command_\\\"):]\\n print(\\\"%s:\\\\n - %s\\\\n\\\" % (name_clean, getattr(self, name).__doc__.strip()))\",\n \"def do_command_help(self, command):\\n summary = self.base.commands[command].get_summary()\\n usage = self.get_command_usage(command)\\n description = self.base.commands[command].get_description()\\n sys.stdout.write('%s\\\\n%s' % (summary, usage))\\n if description != None:\\n sys.stdout.write('Arguments Description:\\\\n%s\\\\n' %\\n (description, ))\",\n \"def help():\\n print(UI.HELP)\",\n \"def help(self):\\r\\n self._short_help(None, None, None, None)\",\n \"def cmd_help(ctx):\\n echo(ctx.parent.get_help())\",\n \"def show_help():\\n pass\",\n \"async def help(self, ctx):\\n self.log_command_call(\\\"help\\\", ctx.message)\\n await ctx.send(HELP_TEXT)\\n embed_output = create_embed(description=MORE_INFO_TEXT)\\n await ctx.send(embed=embed_output)\",\n \"async def _help(ctx, *, command_name: str=None):\\n if command_name:\\n command = bot.get_command(command_name)\\n if not command:\\n return await ctx.send(\\\"No such command!\\\")\\n return await ctx.send(f\\\"```\\\\n{ctx.prefix}{command.name} {command.signature}\\\\n\\\\n{command.help or 'Missing description'}```\\\")\\n description = []\\n for name, cog in bot.cogs.items():\\n entries = [\\\" - \\\".join([cmd.name, cmd.short_doc or \\\"Missing description\\\"]) for cmd in cog.get_commands() if await _can_run(cmd, ctx) and not cmd.hidden]\\n if entries:\\n description.append(f\\\"**{name}**:\\\")\\n description.append(\\\"• \\\" + \\\"\\\\n• \\\".join(entries))\\n await ctx.send(embed=discord.Embed(description=\\\"\\\\n\\\".join(description), color=ctx.me.color))\",\n \"def help(command=None):\\n if command is None: \\n # print first line of docstring\\n for cmd in commands:\\n ds = commands[cmd].__doc__.split('\\\\n')[0]\\n print \\\"%-16s %s\\\" % (cmd,ds)\\n else:\\n print commands[command].__doc__\",\n \"def help(self):\",\n \"def help(self):\",\n \"def help(self):\\n pass\",\n \"def help(self):\\n pass\",\n \"def command_help(self):\\n print(\\\"Command \\\", self)\\n print(\\\"\\\\t\\\\thelp (Get help for command)\\\")\\n\\n params = self.params.copy()\\n del params[\\\"help\\\"]\\n\\n if len(params) == 0:\\n print(\\\"This command has no parameters\\\")\\n return\\n\\n print(\\\"Parameters:\\\")\\n for info in params.values():\\n print(\\\" %s\\\" % info.get_basic_info())\\n description = info.get_desc()\\n if description != \\\"\\\":\\n print(textwrap.fill(description,\\n initial_indent=\\\" \\\",\\n subsequent_indent=\\\" \\\",\\n width=70))\",\n \"def help():\\n print \\\"Help comes to those who ask\\\"\",\n \"def help_command(server, output, conf):\\n server.tell(output.name, 'Available commands:')\\n for key in COMMANDS.keys():\\n cmd_func = COMMANDS[key]\\n if cmd_func.__doc__:\\n server.tell(output.name, '%s: %s' % (key[1:], cmd_func.__doc__))\\n else:\\n server.tell(output.name, key[1:])\\n return\",\n \"def help(ctx):\\n click.echo(ctx.parent.get_help())\",\n \"def help(ctx, topic, **kw):\\n # The help command implementation is taken from\\n # https://www.burgundywall.com/post/having-click-help-subcommand\\n if topic is None:\\n click.echo(ctx.parent.get_help())\\n else:\\n click.echo(main.commands[topic].get_help(ctx))\",\n \"def print_generic_help():\\r\\n print ART_NAME\\r\\n print 'Version {1}\\\\nby {2}'.format(NAME, VERSION, AUTHOR)\\r\\n print DESCRIPTION\\r\\n tools = sorted(AvailableCommands.commands.keys(), key=lambda v: v.upper())\\r\\n # Do not show CUSTOM command in the help\\r\\n tools.remove('CUSTOM')\\r\\n tools.remove('CUSTOM_NO_OUTPUT')\\r\\n print '\\\\n\\\\nSupported tools are:\\\\n{0}'.format('\\\\n'.join(tools))\\r\\n print '\\\\nHint: Check tool specific help with --help <tool_name>\\\\n'\",\n \"def help_command(update, context):\\n update.message.reply_text('Help!')\",\n \"async def help(self, *args, **kwargs):\\n if len(args) == 0:\\n return self.help.__doc__\\n docs = []\\n for command in args:\\n if command in self.user_special_bang_set:\\n docs.append(self.user_special_bang_set[command].bang_skill.__doc__)\\n elif command in self.control_bang_set:\\n docs.append(self.control_bang_set[command].bang_skill.__doc__)\\n\\n return \\\"| \\\".join(docs)\",\n \"def help_command(update, context):\\n update.message.reply_text('Let me help you. \\\\r\\\\n /help print this help \\\\r\\\\n /safety prints safety instructions \\\\r\\\\n /play start the game\\\\r\\\\n /joingroup Join CTF tg group')\",\n \"def command_help(self, bot, update):\\n\\n messages = [\\n 'Available commands:',\\n '/who - Who is Myles?',\\n '/where - Where is Myles?',\\n '/tweet - What was the last tweet Myles sent?',\\n '/photo - What was the last Instagram photo Myles took?',\\n '/web - Where can I find Myles on the interwebs?',\\n ]\\n\\n self.send_messages(bot, update, messages)\",\n \"def do_help(self, command_name):\\n if not command_name:\\n print _PVBlotInterp.__doc__\\n print \\\"The following commands are supported:\\\"\\n print \\\" \\\",\\n blotish_commands = self._blotish_commands.keys()\\n blotish_commands.sort()\\n for c in blotish_commands:\\n print c,\\n print\\n print\\n print \\\"For more information on any command, try help <command>.\\\"\\n return\\n try:\\n command = self.get_unique_command(command_name)\\n print command.__doc__\\n except blotish.BlotishError, err:\\n blot_common.print_blot_error(err)\",\n \"def printhelp():\",\n \"def display_help(self):\\n pass\",\n \"def help_command(update: Update, _: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, _: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help(self, msg=None):\\n\\n # Print the message if given.\\n if not msg == None:\\n print str(msg) + \\\"\\\\n\\\"\\n\\n # Display the list of commands, in the alphabetical order.\\n print \\\"Use one of the following commands:\\\"\\n for action in sorted(self.actions.keys()):\\n info = self.actions[action]\\n joined_oblig = ' '.join(info['required'])\\n if len(info['additional']) > 0:\\n add = [\\\"<%s>\\\" % x for x in info['additional']]\\n joined_add = '[' + ' '.join(add) + ']'\\n else:\\n joined_add = ''\\n print \\\"\\\\t* %s %s %s\\\" % (action, joined_oblig, joined_add)\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Help!')\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\r\\n update.message.reply_text('Help!')\",\n \"def help(self):\\n\\t\\treturn\",\n \"async def do_help(self, arg):\\n if arg:\\n if arg in self._commands:\\n _, doc = self._commands[arg]\\n print(doc)\\n else:\\n self.error('No command {}'.format(arg))\\n else:\\n print('Valid commands:')\\n for c in self._commands:\\n print(' ' + c)\",\n \"def print_specific_help(tool_name):\\r\\n if tool_name not in AvailableCommands.commands:\\r\\n print 'Command is not supported: {0}'.format(tool_name)\\r\\n return\\r\\n cmd = AvailableCommands.commands[tool_name]\\r\\n\\r\\n print 'Usage of {0}:'.format(cmd.name)\\r\\n print '\\\\nAccepted input types:\\\\n{0}'.format(str(list(cmd.input_types)))\\r\\n print '\\\\nOutput types:\\\\n{0}'.format(str(cmd.output_types))\\r\\n print '\\\\nMandatory arguments:\\\\n{0}'.format(str(cmd.user_mandatory_args))\\r\\n print '\\\\nOptional arguments:\\\\n{0}'.format(str(cmd.user_optional_args))\\r\\n print '\\\\nParallelizable:\\\\n{0}'.format(str(cmd.parallelizable))\\r\\n print '\\\\nAdditional description:\\\\n{0}'.format(str(cmd.help_description))\\r\\n print ''\",\n \"def help(cmd, cmdArgs):\\n global commandDict\\n retInfo = []\\n if len(cmdArgs) > 0:\\n #return help on a single function\\n if cmdArgs[0] in commandDict.keys():\\n return commandDict[cmdArgs[0]].__doc__\\n\\n #else, return general info\\n retInfo = ['pypeople: A command line tool for vCard management',\\n 'Version:' + __version__,\\n 'Available Commands:']\\n #fill in more info here\\n for cmdName in commandDict.keys():\\n cmdFunc = commandDict[cmdName]\\n cmdDoc = str(cmdName) + ': ' + str(cmdFunc.__doc__) if cmdFunc.__doc__ is not None else 'Undocumented Function'\\n retInfo.append('\\\\t' + cmdDoc)\\n\\n return '\\\\n'.join(retInfo)\",\n \"async def help(self, ctx, *, command_name: str=None):\\n bot_prefix = '@Randy '\\n # Shortcut to command search\\n if command_name is not None:\\n return await ctx.invoke(self.cmd('help command'), cmd_name=command_name)\\n\\n em = discord.Embed(title='Help',\\n description='**Permissions:** The permissions required to function :-\\\\n'\\n '`Send Messages`, `Manage Messages`, `Embed Links`\\\\n'\\n '--\\\\nTo get help or more information on a specific command, use:\\\\n'\\n '`{bot_prefix}help <command name>`\\\\n'\\n '--\\\\nRead my messy code [here](http://github.com/xKynn/RandomRumble)'\\n '--\\\\nIf you like my work and would like to help me, '\\n 'Ko-Fi/Paypal: [Link](https://ko-fi.com/D1D6EXXV)\\\\n',\\n color=self.color)\\n\\n em.set_footer(text=\\\"Contact me at Demo#7645\\\")\\n\\n # This can't go in the init because help isn't loaded last & thus misses some commands\\n em.add_field(name=\\\"Commands\\\", value=' • '+'\\\\n • '.join(f\\\"***{c.name}*** - {c.short_doc}\\\" for c in self.bot.commands if\\n c.name not in ['pob', 'link', 'convert']))\\n try:\\n await ctx.send(embed=em)\\n except:\\n await ctx.send(\\\"`Embed Links` permission is required to see the help!\\\")\",\n \"def test_cli_help(self):\\n output = self.update_command('-h')\",\n \"def help_command(update: Update, _: CallbackContext) -> None:\\n update.message.reply_text(\\\"These are the list of commands supported. \\\\n\\\\n /deadlines. \\\\n\\\\n \\\"\\n \\\"Hey! I am still being enhanced, more features to come...!\\\")\",\n \"def command_help(args):\\n\\tprint_usage()\\n\\treturn 0\",\n \"def help_command(update: Update) -> None:\\n #update.message.reply_text('Help!')\",\n \"async def do_help():\\n\\n if len(message.content.split()) > 1:\\n for i in cmd_dict:\\n if message.content.split()[1] == i:\\n await bot.send_message(c, f'Help for {i}: {cmd_dict[i].__doc__}')\\n return\\n\\n cmdstr = 'Commands: '\\n for i in cmd_dict:\\n cmdstr += '{}, '.format(i)\\n await bot.send_message(c, cmdstr)\",\n \"def get_command_help(self, module_name, command_name):\\r\\n command = self.env.get_command(module_name, command_name)\\r\\n\\r\\n default_format = 'raw'\\r\\n if sys.stdout.isatty():\\r\\n default_format = 'table'\\r\\n\\r\\n arg_doc = command.__doc__\\r\\n\\r\\n if 'confirm' in command.options:\\r\\n arg_doc += \\\"\\\"\\\"\\r\\nPrompt Options:\\r\\n -y, --really Confirm all prompt actions\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n if '[options]' in arg_doc:\\r\\n arg_doc += \\\"\\\"\\\"\\r\\nStandard Options:\\r\\n --format=ARG Output format. [Options: table, raw] [Default: %s]\\r\\n -C FILE --config=FILE Config file location. [Default: ~/.softlayer]\\r\\n --debug=LEVEL Specifies the debug noise level\\r\\n 1=warn, 2=info, 3=debug\\r\\n --timings Time each API call and display after results\\r\\n --proxy=PROTO:PROXY_URL HTTP[s] proxy to be use to make API calls\\r\\n -h --help Show this screen\\r\\n\\\"\\\"\\\" % default_format\\r\\n return arg_doc.strip()\",\n \"def print_help(self):\\n\\n print((\\\"Help is not defined for command \\\" + self.command))\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text(\\\"Laughybot\\\\n\\\\n VERSION\\\\n 0.1 \\\\n\\\\nUSAGE\\\\n /{command} : Entrer une commande parmi celles disponibles\\\\n\\\\nCOMMADES\\\\n/joke => recherche une blague sur la toile\\\\n/start => Affiche le message d'accueil\\\\n/help => Affiche l'aide\\\")\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text(messages.HELP_TEXT)\",\n \"def get_help(self) -> None: \\n print(messages.get_help())\",\n \"def Help(topic=None):\\n\\n if not topic:\\n print \\\"Avaliable commands are: \\\"\\n print \\\"\\\\n\\\".join(map(lambda x:\\\" \\\"*3+x, filter(lambda c:c!=\\\"help\\\", commands)))\\n print \\\"Type help command to get help with a specific command.\\\"\\n\\n elif topic in commands:\\n print \\\"Help for %s:\\\"%topic\\n print commands[topic].__doc__\",\n \"def dispatch_help(args):\\n command = args.subcommand\\n if command is None:\\n command = 'help'\\n args.parsers[command].print_help()\\n\\n cmd_func = getattr(EtcMaint, 'cmd_%s' % command, None)\\n if cmd_func:\\n lines = cmd_func.__doc__.splitlines()\\n print('\\\\n%s\\\\n' % lines[0])\\n paragraph = []\\n for l in dedent('\\\\n'.join(lines[2:])).splitlines():\\n if l == '':\\n if paragraph:\\n print('\\\\n'.join(wrap(' '.join(paragraph), width=78)))\\n print()\\n paragraph = []\\n continue\\n paragraph.append(l)\\n if paragraph:\\n print('\\\\n'.join(wrap(' '.join(paragraph), width=78)))\",\n \"def help(self) -> str:\\n\\t\\treturn None\",\n \"async def send_command_help(self, command):\\n ctx = self.context\\n embed = discord.Embed(title=command.name.upper(), description=command.description, color=discord.Color.green())\\n alias = command.aliases\\n if alias:\\n embed.add_field(name=\\\"Aliases\\\", value=\\\", \\\".join(alias), inline=False)\\n if command.usage != None:\\n embed.add_field(name=\\\"How to use:\\\", value=f'`{command.usage}`')\\n\\n await ctx.reply(embed=embed)\",\n \"def _help():\\n text = \\\"\\\"\\\"\\n```\\nUsage: @bot [command] (message)\\n\\n*Commands*:\\n \\n add Adds specified users to notify list | To add yourself use key 'myself'\\n list Lists users on notify list\\n remove Removes specified users from notify list | To remove yourself use key 'myself'\\n help This help\\n\\n > Obs: All commands are optional\\n\\n*Examples*:\\n\\n @bot add myself @Fulano <= Will add yourself and @Fulano in list.\\n @bot this a messge test <= Send 'this a messge test' to all list.\\n @bot remove myself @Fulano <= Remove yourself and @fulano from list.\\n\\n```\\n\\\"\\\"\\\"\\n return text\",\n \"def do_help(self, args): \\n if args.command:\\n if len(args.command) > 1:\\n command = args.command[0] +\\\"-\\\" + args.command[1]\\n else:\\n command = args.command[0]\\n if command in self.subcommands:\\n self.subcommands[command].print_help()\\n else:\\n print((\\\"'%s' is not a valid subcommand\\\") %\\n args.command)\\n else:\\n self.parser.print_help()\",\n \"def print_help():\\n\\tprint(\\\"Help text\\\")\",\n \"def help(update, context):\\n update.message.reply_text(\\\"\\\"\\\"usage \\n /bus <bus name> or /bus <bus name> <stop name>\\n /addstop <stop name> <stop code>\\n /delstop <stop name>\\n /showstops\\n /help\\n \\\"\\\"\\\")\\n\\n # log info\\n logger.info(\\\"help used username:{0}\\\".format(update.message.from_user.username))\",\n \"def do_help(self, args):\\n ## The only reason to define this method is for the help text in the doc string\\n cmd.Cmd.do_help(self, args)\",\n \"def do_help(self, args):\\n ## The only reason to define this method is for the help text in the doc string\\n cmd.Cmd.do_help(self, args)\",\n \"def print_help(self):\\n print self.get_help()\",\n \"def do_help(self, line):\\n Cmd.do_help(self, line)\",\n \"def show_help(self):\\n self.slack.reply('\\\\n\\\\n'.join(self.help_lines))\",\n \"def help(self, args):\\n print('No commands available for this consumer')\",\n \"def help_util():\\r\\n for cmd, f in COMMANDS.items():\\r\\n print(\\\"POM \\\" + cmd + \\\":\\\")\\r\\n print(f.__doc__.lstrip(\\\"\\\\n\\\"))\",\n \"def help(self) -> str:\\n raise NotImplementedError\",\n \"def do_help(self, arg):\\n if arg:\\n # Getting help for a specific command\\n funcname = self._func_named(arg)\\n if funcname:\\n # No special behavior needed, delegate to cmd base class do_help()\\n cmd.Cmd.do_help(self, funcname[3:])\\n else:\\n # Show a menu of what commands help can be gotten for\\n self._help_menu()\",\n \"async def help_command(self, ctx, *, cmd_name: str=None):\\n bot_prefix = '@Randy '\\n # Get command object\\n cmd_obj = self.cmd(cmd_name)\\n\\n # Handle no command found\\n if cmd_obj is None:\\n return await ctx.error(f'Command {cmd_name} not found')\\n em = discord.Embed(title=cmd_obj.name, description=cmd_obj.help, color=self.color)\\n\\n # Input aliases and parameters to embed\\n if cmd_obj.aliases:\\n em.add_field(name='Aliases', value='\\\\n'.join([f'\\\\u2022 {x}' for x in cmd_obj.aliases]))\\n if cmd_obj.clean_params:\\n em.add_field(name='Parameters', value='\\\\n'.join([f'\\\\u2022 {x}' for x in cmd_obj.clean_params]))\\n\\n # Handle group commands\\n if isinstance(cmd_obj, commands.core.Group):\\n em.add_field(name='Group commands',\\n value='\\\\n'.join([f'\\\\u2022 {x}' for x in cmd_obj.commands]),\\n inline=False)\\n\\n # Add usage last\\n em.add_field(name='Usage',\\n value=f'```{bot_prefix}\\\\u200b{cmd_name} '\\n f'{\\\" \\\".join([f\\\"<{x}>\\\" for x in cmd_obj.clean_params])}```',\\n inline=False)\\n\\n await ctx.send(embed=em)\",\n \"def show_help():\\n print('\\\\n' + Colors.BLACK_ON_WHITE +\\n 'track: Input info for daily tracking' + Colors.NORMAL + '\\\\n'\\n ' * Mood\\\\n' +\\n ' * Short Daily Summary\\\\n' +\\n ' * Accomplishments\\\\n' +\\n ' * Long Journal Entry\\\\n' +\\n \\\" * Tomorrow's Most Important Task\\\\n\\\" +\\n '\\\\n' +\\n 'Usage: track.py [options]\\\\n'\\n '\\\\n' +\\n 'Options:\\\\n' +\\n ' [none] Input and record daily tracking\\\\n' +\\n ' y Input and record tracking for previous day\\\\n' +\\n ' ! Print random daily entry\\\\n' +\\n ' accs Print all recent accomplishments\\\\n' +\\n ' help Print this help menu\\\\n' +\\n ' mood Print average mood using past entries\\\\n' +\\n ' overviews Print headers of all recent entries.\\\\n')\",\n \"def do_help(self, message):\\r\\n\\t\\tcommands = [command[3:] for command in dir(self) if command.startswith('do_')]\\r\\n\\t\\t# self.trace(f'Help: {\\\", \\\".join(commands)}')\\r\\n\\r\\n\\t\\tfor command in commands:\\r\\n\\t\\t\\tmethod = getattr(self, f'do_{command}')\\r\\n\\t\\t\\tcommand_help = getattr(method, '__doc__')\\r\\n\\t\\t\\tif command_help:\\r\\n\\t\\t\\t\\tself.trace(f'{command}: {command_help}')\",\n \"def get_help(self,command):\\n if \\\"help\\\" in self.commands[command]:\\n return self.commands[command][\\\"help\\\"]\\n else:\\n return \\\"No help defined for this command.\\\"\",\n \"def HelpCommand(self, unused_args, unused_sub_opts=None, unused_headers=None,\\n unused_debug=None):\\n self.OutputUsageAndExit()\",\n \"def help(update, context):\\n update.message.reply_text('Help!')\",\n \"def help(update, context):\\n update.message.reply_text('Help!')\",\n \"def help(update, context):\\n update.message.reply_text('Help!')\",\n \"def help(update, context):\\n update.message.reply_text('Help!')\",\n \"def printHelp(self,):\\n print man\\n return 0\",\n \"def help_description():\\n pass\",\n \"def help(self, cmd=\\\"\\\", *, fail=\\\"\\\"):\\n class_dict = dict(type(self).__dict__)\\n # Add this function to class, so that when subclassing,\\n # help for help is found\\n class_dict.update({\\\"help\\\": self.help})\\n if cmd.startswith(self.predicate):\\n # Strip predicate\\n cmd = cmd[len(self.predicate) :]\\n # Check that command exists and is not\\n # private, protected or special method\\n if (not cmd.startswith(\\\"_\\\")) and cmd in class_dict.keys():\\n item = class_dict[cmd]\\n if callable(item):\\n if item.__doc__:\\n return \\\"Help on command '{}':\\\\n. {}\\\".format(\\n cmd, \\\"\\\\n. \\\".join(cleandoc(item.__doc__).split(\\\"\\\\n\\\"))\\n )\\n return \\\"No help on command '{}'\\\".format(cmd)\\n # If no cmd given or wrong cmd given, return commands\\n commands = []\\n for key, value in class_dict.items():\\n if not key.startswith(\\\"_\\\"):\\n if callable(value):\\n commands.append(key)\\n msg = (\\n \\\"Commands:\\\\n {}\\\".format(\\\", \\\".join(commands))\\n + \\\"\\\\n for more help on command, use \\\"\\n + \\\"{}help command\\\".format(self.predicate)\\n )\\n if fail:\\n msg = fail + \\\"\\\\n\\\" + msg\\n return msg\",\n \"def help(self):\\n return None\",\n \"def help():\\n version()\\n print(\\\"USAGE: python3 notes.py [COMMAND]\\\")\\n print(\\\"COMMAND:\\\")\\n help_dict = {\\n \\\"help\\\": \\\"Prints out the help text.\\\",\\n \\\"version\\\": \\\"Prints out the version.\\\",\\n \\\"init\\\": \\\"Initializes a new set of notes.\\\",\\n \\\"modify\\\": \\\"Modify the current set of notes.\\\",\\n }\\n help_dict = OrderedDict(sorted(help_dict.items()))\\n for entry in help_dict:\\n print((\\\" \\\" + entry).ljust(10), help_dict[entry])\",\n \"async def help(self, msg, *args, intro=None):\\n sudo = Guard.allow_sudo(msg)\\n if intro is not None:\\n intro = f'{intro.strip()} '\\n else:\\n intro = ''\\n if msg.channel.type == discord.ChannelType.private:\\n nick = '@DayRInfo'\\n else:\\n nick = f'@{msg.channel.guild.me.nick}'\\n content = f'{intro}I understand the following commands (tag me at the start of the message):\\\\n'\\n for command, (args, desc, enabled, delay) in Controller.commands.items():\\n if not sudo and not enabled:\\n continue\\n if args:\\n args = f' {args.strip()}'\\n if desc:\\n desc = f'\\\\n\\\\t{desc}'\\n content = f'{content}`{Controller.HELP_KEY}{command}{args}`{desc}\\\\n'\\n content = f'{content}----------\\\\n'\\n content = f'{content}• Also, if you tag this bot ({nick}) on a message containing a link to the interactive Day R map 🗺️ with a location URL, I will send you a snapshot of the location.\\\\n'\\n content = f'{content}• React with ❌ to any of my messages to delete it (if I still remember that it was my message). You can only delete my messages that are directed to you.'\\n await msg.author.send(**{\\n 'content': content,\\n })\\n await msg.channel.send(**{\\n 'content': 'Command list sent via DM!',\\n 'reference': msg.to_reference(),\\n 'mention_author': True,\\n 'delete_after': 3,\\n })\",\n \"def print_help_info(self, global_options):\\r\\n usage = ['',\\\"Type '%s help <subcommand>' for help on a specific subcommand.\\\" % self.prog_name,'']\\r\\n usage.append('Available subcommands:')\\r\\n commands = self.get_commands(global_options).keys()\\r\\n commands.sort()\\r\\n for cmd in commands:\\r\\n usage.append(' %s' % cmd)\\r\\n return '\\\\n'.join(usage)\",\n \"def help_command(update: Update, _: CallbackContext) -> None:\\n update.message.reply_text('Usage: Basic example to make face recognition' +\\n 'Just upload foto, pls.')\",\n \"def show_help():\\n messagebox.showinfo(title='How to Use', message=\\\"It's really easy.\\\")\",\n \"def helpme(self):\\n\\n print(\\\"{}{}{}\\\".format(' ', 'Commands', ' '))\\n print(\\\"{}{}{}\\\".format(' ', '--------', ' '))\\n print(\\\"{} {} {}\\\".format('help ', '|', 'Display all usable commands'))\\n print(\\\"{} {} {}\\\".format('look ', '|', 'Explore the room to find current location, exits and potential items.'))\\n print(\\\"{} {} {}\\\".format('go ', '|', 'The prefix required to navigate your player.'))\\n print(\\\"{} {} {}\\\".format('get ', '|', 'The prefix for picking up items.'))\\n print(\\\"{} {} {}\\\".format('drop ', '|', 'The prefix for dropping items.'))\\n print(\\\"{} {} {}\\\".format('inv ', '|', 'Displays the player inventory'))\\n print(\\\"{} {} {}\\\".format('health ', '|', 'Displays player health'))\\n print(\\\"{} {} {}\\\".format('eat ', '|', 'Allows the player to use consumables to gain health'))\\n print(\\\"{} {} {}\\\".format('equip ', '|', 'Equip a weapon in your inventory'))\\n print(\\\"{} {} {}\\\".format('unequip', '|', 'Unequip a current weapon'))\\n print(\\\"{} {} {}\\\".format('attack ', '|', 'Allows the player to attack a non-player'))\\n print(\\\"{} {} {}\\\".format('push ', '|', 'Returns NPC to spawn'))\\n print(\\\"{} {} {}\\\".format('save ', '|', 'Save current player progress'))\\n print(\\\"{} {} {}\\\".format('load ', '|', 'Load a previous character'))\",\n \"def DoHelp(options, args):\\n __pychecker__ = 'unusednames=options'\\n if len(args) == 1 and args[0] in COMMAND_USAGE_TEXT:\\n print(COMMAND_USAGE_TEXT[args[0]])\\n else:\\n raise gclient_utils.Error(\\\"unknown subcommand '%s'; see 'gclient help'\\\" %\\n args[0])\",\n \"def help(update, context):\\n update.message.reply_text(\\\"\\\"\\\"THESE ARE THE COMMANDS\\n /hi\\n /gokul\\n /bala\\n /competition\\n /form\\n \\\"\\\"\\\")\",\n \"async def help(ctx, command:str=None):\\n if command == None:\\n embed = assemble_embed(\\n title=\\\"Looking for help?\\\",\\n desc=(\\\"Hey there, I'm a resident bot of Scioly.org!\\\\n\\\\n\\\" +\\n \\\"On Discord, you can send me commands using `!` before the command name, and I will process it to help you! \\\" +\\n \\\"For example, `!states`, `!events`, and `!fish` are all valid commands that can be used!\\\\n\\\\n\\\" +\\n \\\"If you want to see some commands that you can use on me, just type `!list`! \\\" +\\n \\\"If you need more help, please feel free to reach out to a staff member!\\\")\\n )\\n return await ctx.send(embed=embed)\\n hlp = await get_help(ctx, command)\\n await ctx.send(embed=hlp)\",\n \"def channel_help(message):\\n message.reply(Strings['HELP'].format(config.HELP_URL))\",\n \"def do_help(self, arg):\\n\\t\\tif arg:\\n\\t\\t\\tfuncarg = arg.replace(\\\" \\\", \\\"_\\\")\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tfunc = getattr(self, 'help_' + funcarg)\\n\\t\\t\\texcept AttributeError:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tdoc=getattr(self, 'do_' + funcarg).__doc__\\n\\t\\t\\t\\t\\tif doc:\\n\\t\\t\\t\\t\\t\\t\\tself.stdout.write(\\\"%s\\\\n\\\"%str(doc))\\n\\t\\t\\t\\t\\t\\t\\treturn\\n\\t\\t\\t\\texcept AttributeError:\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\tself.stdout.write(\\\"%s\\\\n\\\"%str(self.nohelp % (arg,)))\\n\\t\\t\\t\\treturn\\n\\t\\t\\tfunc()\\n\\t\\telse:\\n\\t\\t\\tprint \\\"\\\\nAvailable commands (type help <topic> for help on topic):\\\\n\\\"\\n\\t\\t\\tnames = self.get_names()\\n\\t\\t\\tfor name in sorted(names):\\n\\t\\t\\t\\tif name[:3] == \\\"do_\\\":\\n\\t\\t\\t\\t\\tname = name[3:]\\n\\t\\t\\t\\t\\tif name == \\\"EOF\\\":\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tself.parent.printHelpLine(name, \\\"\\\", getattr(self, \\\"do_\\\" + name).__doc__ or \\\"\\\")\\n\\t\\t\\tprint \\\"\\\"\",\n \"def showHelp(self):\\n\\t\\tfor i in range(0,20):\\n\\t\\t\\tprint \\\"\\\"\\n\\t\\tprint \\\" _ _ \\\"\\n\\t\\tprint \\\"| | | | \\\"\\n\\t\\tprint \\\"| |__ _ __ ___ | |__ \\\"\\n\\t\\tprint \\\"| '_ \\\\ | '_ \\\\ / __|| '_ \\\\ \\\"\\n\\t\\tprint \\\"| | | || | | |\\\\__ \\\\| | | |\\\"\\n\\t\\tprint \\\"|_| |_||_| |_||___/|_| |_|\\\"\\n\\t\\tprint \\\"A program by Scott Jackson\\\"\\n\\t\\tprint \\\"\\\"\\n\\t\\tprint \\\"To enter a command, type the key and press Return.\\\"\\n\\t\\tprint \\\"NB: parentheses indicate which of two options is the default.\\\"\\n\\t\\tprint \\\"\\\"\\n\\t\\tprint \\\"Basic Commands:\\\"\\n\\t\\tprint \\\"j / k -- show lower-ranked / higher-ranked stories.\\\"\\n\\t\\tprint \\\"r -- get the latest stories from Hacker News.\\\"\\n\\t\\tprint \\\"q -- quit.\\\"\\n\\t\\tprint \\\"# -- open story number # in your web browser.\\\"\\n\\t\\tprint \\\"c# -- open comments for story number # in your web browser.\\\"\\n\\t\\tprint \\\"#+ -- open up story number # AND its comments in your web browser.\\\"\\n\\t\\tprint \\\"top / new -- switch between showing the top and newest stories on HN. (top)\\\"\\n\\t\\tprint \\\"c / e -- collapse stories you've already read / don't collapse them. (e)\\\"\\n\\t\\tprint \\\"u -- update hnsh to the latest version.\\\"\\n\\t\\tprint \\\"==========================\\\"\\n\\t\\tprint \\\"For more commands, see the man.txt file.\\\"\\n\\t\\tinput = raw_input(\\\"Press Return to go back to the Hacker News stories.\\\")\",\n \"def more_informations():\\n print \\\"--help for more informations.\\\"\\n sys.exit(1)\",\n \"def CommandHelp(paser):\\n\\n\\tprint \\\"\\\\n===============Commands List===============\\\\n\\\"\\n\\t\\t\\n\\tprint \\\"NewProject - {}\\\".format(NewProject.__doc__)\\n\\tprint \\\"DelProject - {}\\\".format(DelProject.__doc__)\\n\\tprint \\\"ShareProject - {}\\\".format(ShareProject.__doc__)\\n\\tprint \\\"StopProject - {}\\\".format(StopProject.__doc__)\\n\\tprint \\\"Help - {}\\\".format(CommandHelp.__doc__)\\n\\tprint \\\"Exit - Finaliza la sesion en la terminal.\\\"\",\n \"def help_command(update: Update, context: CallbackContext) -> None:\\n commands = [\\\"/login <pwd>\\\\n\\\",\\n \\\"/status\\\\n\\\",\\n \\\"/heat\\\\n\\\",\\n \\\"/temp\\\\n\\\",\\n \\\"/off\\\\n\\\",\\n \\\"/help\\\\n\\\",\\n \\\"/set\\\\n\\\",\\n \\\"/unset\\\\n\\\",\\n \\\"/heatmore\\\\n\\\",\\n \\\"/lighton\\\\n\\\",\\n \\\"/lightoff\\\\n\\\"]\\n\\n cmd: str = \\\" \\\".join(commands)\\n update.message.reply_text('commands are:\\\\n' + cmd)\",\n \"def help():\\n return statement(help_text)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.84459895","0.81877965","0.81058455","0.8060183","0.80382925","0.803564","0.7979417","0.79499114","0.79247713","0.79205954","0.7903753","0.7890884","0.7862196","0.78568035","0.7844785","0.78190106","0.78190106","0.77970946","0.77970946","0.7784749","0.7760896","0.77318525","0.77285266","0.7727433","0.77265805","0.7683609","0.7667311","0.7646955","0.7614347","0.7612728","0.75966346","0.7581593","0.7577692","0.7577692","0.7572426","0.75653255","0.75653255","0.75653255","0.75653255","0.756396","0.75449127","0.75311625","0.7525225","0.7519658","0.7516538","0.75124586","0.7504183","0.74994963","0.74739295","0.7470314","0.74689704","0.7466509","0.7462467","0.74598455","0.74544287","0.7453879","0.74479777","0.7443314","0.7441935","0.7438914","0.7434473","0.74256635","0.7398089","0.739702","0.739702","0.7369509","0.7364543","0.7363899","0.73567545","0.7345411","0.7341273","0.7316813","0.7313173","0.73117816","0.73106086","0.7300159","0.7294036","0.72884285","0.72884285","0.72884285","0.72884285","0.72780025","0.7276247","0.7273482","0.72729075","0.7272734","0.7265219","0.7261052","0.72573024","0.7256481","0.72552156","0.72530943","0.7251562","0.7244723","0.72393715","0.72393614","0.7233769","0.7228423","0.72272223","0.7217316","0.7217257"],"string":"[\n \"0.84459895\",\n \"0.81877965\",\n \"0.81058455\",\n \"0.8060183\",\n \"0.80382925\",\n \"0.803564\",\n \"0.7979417\",\n \"0.79499114\",\n \"0.79247713\",\n \"0.79205954\",\n \"0.7903753\",\n \"0.7890884\",\n \"0.7862196\",\n \"0.78568035\",\n \"0.7844785\",\n \"0.78190106\",\n \"0.78190106\",\n \"0.77970946\",\n \"0.77970946\",\n \"0.7784749\",\n \"0.7760896\",\n \"0.77318525\",\n \"0.77285266\",\n \"0.7727433\",\n \"0.77265805\",\n \"0.7683609\",\n \"0.7667311\",\n \"0.7646955\",\n \"0.7614347\",\n \"0.7612728\",\n \"0.75966346\",\n \"0.7581593\",\n \"0.7577692\",\n \"0.7577692\",\n \"0.7572426\",\n \"0.75653255\",\n \"0.75653255\",\n \"0.75653255\",\n \"0.75653255\",\n \"0.756396\",\n \"0.75449127\",\n \"0.75311625\",\n \"0.7525225\",\n \"0.7519658\",\n \"0.7516538\",\n \"0.75124586\",\n \"0.7504183\",\n \"0.74994963\",\n \"0.74739295\",\n \"0.7470314\",\n \"0.74689704\",\n \"0.7466509\",\n \"0.7462467\",\n \"0.74598455\",\n \"0.74544287\",\n \"0.7453879\",\n \"0.74479777\",\n \"0.7443314\",\n \"0.7441935\",\n \"0.7438914\",\n \"0.7434473\",\n \"0.74256635\",\n \"0.7398089\",\n \"0.739702\",\n \"0.739702\",\n \"0.7369509\",\n \"0.7364543\",\n \"0.7363899\",\n \"0.73567545\",\n \"0.7345411\",\n \"0.7341273\",\n \"0.7316813\",\n \"0.7313173\",\n \"0.73117816\",\n \"0.73106086\",\n \"0.7300159\",\n \"0.7294036\",\n \"0.72884285\",\n \"0.72884285\",\n \"0.72884285\",\n \"0.72884285\",\n \"0.72780025\",\n \"0.7276247\",\n \"0.7273482\",\n \"0.72729075\",\n \"0.7272734\",\n \"0.7265219\",\n \"0.7261052\",\n \"0.72573024\",\n \"0.7256481\",\n \"0.72552156\",\n \"0.72530943\",\n \"0.7251562\",\n \"0.7244723\",\n \"0.72393715\",\n \"0.72393614\",\n \"0.7233769\",\n \"0.7228423\",\n \"0.72272223\",\n \"0.7217316\",\n \"0.7217257\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":6794,"cells":{"query":{"kind":"string","value":"Red = Disable Blue = Enable Any problem such as plugins on dashboard is enable but show disable here, info Owner"},"document":{"kind":"string","value":"async def plugin(self,ctx):\n special_case = {\"Anime\":\"myanimelist\",\"Anti Raid\":\"antiraid\"}\n plugin_setting = await self.redis.hgetall(\"{}:Config:Cogs\".format(ctx.message.guild.id))\n embed = discord.Embed()\n cogs = self.bot.cogs.keys()\n for x in cogs:\n setting = \tu\"\\U0001F534\" #red\n if x in (\"Core\", \"Remindme\", \"Tools\", \"REPL\",\"Events\"): # A Owner's thing only.\n if ctx.message.author.id != self.bot.owner.id:\n continue\n setting = u\"\\U0001F535\" #blue\n if x.lower() in plugin_setting or special_case.get(x) in plugin_setting:\n setting = \tu\"\\U0001F535\" #blue\n embed.add_field(name = x,value = setting)\n if ctx.message.guild.me.colour.value:\n embed.colour = ctx.message.guild.me.colour\n\n embed.set_footer(text = \"{} = Disable | {} = Enable\".format(u\"\\U0001F534\",u\"\\U0001F535\"))\n await ctx.send(embed=embed)"},"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_enabled(self):","def check_disabled(self):\n return None","def get_status(self):\n return super(Cabling, self).get_status()","def enable(self):","def Enabled(self) -> bool:","def disable(self):","def isEnabled(self):","def disable_feature(self,reason,source=\"gff3_maniger\"):\r\n date = datetime.datetime.now().strftime(\"%Y-%m-%d\")\r\n self.add_history(date,source,reason)\r\n self.active = False\r\n if self._owner_line.type == 'SNP':\r\n self._owner_line._owner_set.all_snp_disabled()","async def cog_check(self, ctx):\n guild_doc = await db.PLUGINS.find_one({\"_id\": ctx.guild.id})\n\n if guild_doc.get(\"Verification\"):\n return True\n\n else:\n await ctx.send(\n embed=discord.Embed(\n description=(\n f\"{var.E_DISABLE} The Verification plugin\"\n \" is disabled in this server\"\n ),\n color=var.C_ORANGE\n )\n )","def enable(self) -> None:","def disable(ctx):\n config_db = ConfigDBConnector()\n config_db.connect()\n config_db.mod_entry(\"NAT_GLOBAL\", \"Values\", {\"admin_mode\": \"disabled\"})","def disability_specify(self, instance):\r\n return instance.user.profile.disability_specify","def disable():\n boutonPierre[\"state\"] = \"disabled\"\n boutonFeuille[\"state\"] = \"disabled\"\n boutonCiseaux[\"state\"] = \"disabled\"","def check_enable_mode(self, *args, **kwargs):\n pass","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def setEnabled(*args):","def get_everyone_denied(self):","def enable():\n boutonPierre[\"state\"] = \"normal\"\n boutonFeuille[\"state\"] = \"normal\"\n boutonCiseaux[\"state\"] = \"normal\"","def disable(self) -> None:","def disable():\n configdb = ConfigDBConnector()\n configdb.connect()\n rif_info = {}\n rif_info['FLEX_COUNTER_STATUS'] = 'disable'\n configdb.mod_entry(\"FLEX_COUNTER_TABLE\", \"RIF\", rif_info)","def disabled(name):\n return not enabled(name)","async def toggle(self, ctx: BBContext):\n\n self.code_enabled = not self.code_enabled\n e = 'enabled.' if self.code_enabled else 'disabled.'\n await ctx.send(f\"Bunker code auto reaction has been : **{e}**\")\n self.bot.logger.info('Bunker code listener %s by %s', e, str(ctx.author))","def enable(self):\n pass","def isEnabled(self) -> bool:\n ...","def _isdisable(self):\n return self.dp.state()==PyTango.DevState.DISABLE","def can_be_disabled(self) -> bool:\n return True","async def _antiadv(self, ctx):\r\n serverid = ctx.message.server.id\r\n if ctx.invoked_subcommand is None:\r\n await send_cmd_help(ctx)\r\n if serverid not in self.adkillr:\r\n self.adkillr[serverid] = {'toggle': False, 'message': '{0.mention} don\\'t send links!', 'filters': []}\r\n dataIO.save_json(\"data/adkillr/adkillr.json\", self.adkillr)","def enableVocolaTakesUnimacroActions(self):\n key = \"VocolaTakesUnimacroActions\"\n self.userregnl.set(key, 1)","async def admin_disable(self, ctx: commands.Context):\n if ctx.guild.id not in self.guilds:\n await ctx.send('Team management is already disabled in this guild.')\n return\n await self._disable_guild(guild=ctx.guild)\n await ctx.send('Team management disabled.')","def disable(self):\n pass","def disable(ctx):\n\n fc_group_cfg = {}\n fc_group_cfg['FLEX_COUNTER_STATUS'] = DISABLE\n ctx.obj.mod_entry(\"FLEX_COUNTER_TABLE\", ACL, fc_group_cfg)","def disable():\n configdb = ConfigDBConnector()\n configdb.connect()\n tunnel_info = {}\n tunnel_info['FLEX_COUNTER_STATUS'] = DISABLE\n configdb.mod_entry(\"FLEX_COUNTER_TABLE\", \"TUNNEL\", tunnel_info)","def status():\n with spinner():\n is_enabled = is_witness_enabled()\n signing_key = current_signing_key()\n misses = total_missed()\n\n t = PrettyTable([\"Enabled\", \"Misses\", \"Key\"])\n t.align = \"l\"\n t.add_row([is_enabled, misses, signing_key])\n\n output(t, 'Status')\n output(get_config(), 'Configuration')","def check_enable_mode(self, check_string='#'):\n return True","def check_if_enabled(self, user):\n\t\tfrom webnotes.utils import cint\n\t\tif user=='Administrator': return\n\t\tif not cint(webnotes.conn.get_value('Profile', user, 'enabled')):\n\t\t\tself.fail('User disabled or missing')","def affection_status_switch_on(self):\n self._affection_status_switch = False","def disable():\n configdb = ConfigDBConnector()\n configdb.connect()\n fc_info = {}\n fc_info['FLEX_COUNTER_STATUS'] = 'disable'\n configdb.mod_entry(\"FLEX_COUNTER_TABLE\", \"QUEUE_WATERMARK\", fc_info)\n configdb.mod_entry(\"FLEX_COUNTER_TABLE\", \"PG_WATERMARK\", fc_info)\n configdb.mod_entry(\"FLEX_COUNTER_TABLE\", BUFFER_POOL_WATERMARK, fc_info)","def disableVocolaTakesUnimacroActions(self):\n key = \"VocolaTakesUnimacroActions\"\n self.userregnl.set(key, 0)","def enable(ctx):\n\n config_db = ConfigDBConnector()\n config_db.connect()\n config_db.mod_entry(\"NAT_GLOBAL\", \"Values\", {\"admin_mode\": \"enabled\"})","def organization_enable_status(self) -> str:\n return pulumi.get(self, \"organization_enable_status\")","def ha_status_icon_should_appear_and_it_should_reflect_that_ha_is_enabled_when_clicked(driver):\n wait_on_element(driver, 0.5, 5, '//button[@ix-auto=\"button__haStatus\"]')\n driver.find_element_by_xpath('//button[@ix-auto=\"button__haStatus\"]').click()\n wait_on_element(driver, 0.5, 5, '//h1[contains(.,\"HA Enabled)]')\n driver.find_element_by_xpath('//span[contains(.,\"HA is enabled\")]')\n driver.find_element_by_xpath('//button[@ix-auto=\"button__CLOSE\"]').click()","def check_disabled(self):\n if self._treat_counter % 6 != 0:\n return\n if not self.site.logged_in():\n self.site.login()\n page = pywikibot.Page(\n self.site,\n 'User:{username}/shutoff/{class_name}'.format(\n username=self.site.user(), class_name=self.__class__.__name__\n ),\n )\n if page.exists():\n content = page.get(force=True).strip()\n if content:\n e = '{} disabled:\\n{}'.format(self.__class__.__name__, content)\n pywikibot.error(e)\n self.quit()","def test_verification_status_invisible(self):\r\n self.client.login(username=\"jack\", password=\"test\")\r\n self.check_verification_status_off('verified', 'You\\'re enrolled as a verified student')\r\n self.check_verification_status_off('honor', 'You\\'re enrolled as an honor code student')\r\n self.check_verification_status_off('audit', 'You\\'re auditing this course')","def setDisabledColor(*args):","def setDisabledColor(*args):","def test_get_current_component_status_DISABLED(self):\n self._ucr({\n 'repository/online/component/a': 'no',\n })\n ORIG = UU.FN_UPDATER_APTSOURCES_COMPONENT\n try:\n tmp = NamedTemporaryFile()\n UU.FN_UPDATER_APTSOURCES_COMPONENT = tmp.name\n self.assertEqual(UU.COMPONENT_DISABLED, self.u.get_current_component_status('a'))\n finally:\n UU.FN_UPDATER_APTSOURCES_COMPONENT = ORIG\n tmp.close()","def get_status():\n return ('off', 'off')","def toggled_comunication(self):\n if self.actionPC_Monitor.isChecked() and self.actionPC_Monitor.isEnabled():\n self.actionPC_Monitor.setEnabled(0)\n self.actionPC_Sensor_Actuador.setChecked(0)\n self.actionPC_Sensor_Actuador.setEnabled(1)\n self.monitor_environment()\n \n elif self.actionPC_Sensor_Actuador.isChecked() and self.actionPC_Sensor_Actuador.isEnabled():\n self.actionPC_Sensor_Actuador.setEnabled(0)\n self.actionPC_Monitor.setChecked(0)\n self.actionPC_Monitor.setEnabled(1)\n self.actuator_environment()","def test_listWithDisabled(self):\n self.userbase('create', 'alice', 'localhost', SECRET)\n self.userbase('create', 'bob', 'localhost', SECRET)\n\n def cb(xxx_todo_changeme2):\n (interface, avatar, logout) = xxx_todo_changeme2\n avatar.disabled = 1\n output = self.userbase('list')\n self.assertEqual(output,\n ['alice@localhost', 'bob@localhost [DISABLED]'])\n\n return self._login('bob@localhost', SECRET).addCallback(cb)","def disableButtons(self):\n self.ui.b_run.setEnabled(False)\n self.ui.b_colour.setEnabled(False)\n self.ui.b_ground_truth.setEnabled(False)\n self.ui.b_vid.setEnabled(False)\n self.ui.b_save.setEnabled(False)\n self.ui.t_low.setEnabled(False)\n self.ui.t_high.setEnabled(False)\n self.ui.t_fps.setEnabled(False)\n self.ui.combo_superpixel.setEnabled(False)\n self.ui.c_super_pixel_video.setEnabled(False)\n self.ui.c_csv.setEnabled(False)\n self.ui.c_draw.setEnabled(False)\n self.ui.c_velocity.setEnabled(False)\n self.ui.c_of.setEnabled(False)\n self.ui.c_back_of.setEnabled(False)\n self.ui.c_depth.setEnabled(False)\n self.ui.c_speed_plot.setEnabled(False)\n self.ui.c_error_plot.setEnabled(False)\n self.ui.c_crash_plot.setEnabled(False)\n self.ui.c_error_plot_video.setEnabled(False)\n self.ui.c_speed_plot_video.setEnabled(False)\n self.ui.c_crash_plot_video.setEnabled(False)\n self.ui.c_optimize.setEnabled(False)\n self.ui.c_object_detection.setEnabled(False)","async def enable(self, ctx):\n self.bot.db.execute(\"UPDATE starboards SET enabled = 1 WHERE channel_id = ?\", (ctx.channel.id,))\n await ctx.say(\"star.enabled\")","def _get_enable(self):\n return self.__enable","def _get_enable(self):\n return self.__enable","def custom_assess_status_check(self):\n options = self.options\n # can check options.thing to ensure that it makes sense\n # if wrong return 'blocked', \"The driver is badly configured ...\"\n return None, None","def admin_non_activated(request):\r\n ret = []\r\n res = UserMgr.non_activated_account()\r\n if res:\r\n ret = [u.username for u in res]\r\n\r\n return _api_response(request, {\r\n 'count': len(ret),\r\n 'status': True,\r\n 'data': ret,\r\n })","def _iotool_enable_yellow_command(self):\n return self._iotool.commands.set_low(self._spconfig.IOTOOL_GREEN_YELLOW_SWITCH_PIN)","def test_enabled(self):\n # OSA script should have been installed in setUp function, which sets\n # enabled to True by default.\n self.assertTrue(self.run_function(\"assistive.enabled\", [OSA_SCRIPT]))\n # Disable OSA Script\n self.run_function(\"assistive.enable\", [OSA_SCRIPT, False])\n # Assert against new disabled status\n self.assertFalse(self.run_function(\"assistive.enabled\", [OSA_SCRIPT]))","def styled_status(enabled, bold=True):\n return click.style('Enabled' if enabled else 'Disabled', 'green' if enabled else 'red', bold=bold)","def enabled(self):\n raise NotImplementedError","def _disable(self):\n self.enabled = False","def disable():\n request = dict(id='gbn')\n _gbn_disable(request)","async def goodbye_toggle(self, ctx, value: bool):\n await queries.update_setting(ctx, \"goodbye_settings\", \"is_enabled\", value)\n if value:\n await util.send_success(ctx, \"Goodbye messages are now **enabled**\")\n else:\n await util.send_success(ctx, \"Goodbye messages are now **disabled**\")","async def toggle(self, ctx):\r\n serverid = ctx.message.server.id\r\n if self.adkillr[serverid]['toggle'] is True:\r\n self.adkillr[serverid]['toggle'] = False\r\n e = discord.Embed(description='**AntiAdv is now disabled.**')\r\n await self.bot.say(embed=e)\r\n elif self.adkillr[serverid]['toggle'] is False:\r\n self.adkillr[serverid]['toggle'] = True\r\n e = discord.Embed(description='**AntiAdv is now enabled.**')\r\n await self.bot.say(embed=e)\r\n dataIO.save_json(\"data/adkillr/adkillr.json\", self.adkillr)","def disable(self) -> Awaitable[Dict]:\n return self.client.send(\"Security.disable\", {})","def __disable__(self) -> None:\n pass","def disabled_by_microsoft(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"disabled_by_microsoft\")","def get_id(self):\n return \"always_false_plugin\"","def disable_dialog_boxes(self):\n self.exec_command('SilentUpdateFW')\n self.exec_command('SuppressInfoUpdateFW')\n self.exec_command('SetBatchMode = 1')\n\n # SuppressControlPanel\n self.exec_command(\"HideDeviceSelection = 1\")\n self.exec_command(\"SuppressControlPanel\")\n # Hide Flash Windows\n self.exec_command(\"DisableInfoWinFlashDL\")\n self.exec_command(\"DisableInfoWinFlashBPs\")","def enable_btns(self):\n self.saveBtn.setEnabled(True)\n self.openVideoBtn.setEnabled(True)\n self.openAnnotationBtn.setEnabled(True)\n self.resetBtn.setEnabled(True)\n self.speedCombo.setEnabled(True)\n self.newFileBtn.setEnabled(True)\n self.HelpBtn.setEnabled(True)","def enable(self):\n if not self.labExperiment:\n super().enable()\n else:\n self.connection.command('open_dm', self.DMserial)\n status = self.connection.query('get_status')\n assert status == 0, 'Error connecting to DM. Error: ' + str(status)\n numActProfile = self.connection.query('num_actuators')\n assert numActProfile == self.numActProfile, 'Wrong number of profile actuators entered'\n print(\"'BM1k' is now enabled\")","def disable(self):\n\t\tresponse = self.client.post(self._endpoint + \"/disable\")\n\t\treturn bool(response.json[\"success\"])","def test_disable(self):\n self.assertTrue(self.user1.active)\n self.assertFalse(self.user1.ad_deleted)\n url = '/api/users/{}/'.format(self.user1.ad_guid)\n data = {\n 'Enabled': False,\n }\n response = self.client.put(url, json.dumps(data), content_type='application/json')\n self.assertEqual(response.status_code, 202)\n user = DepartmentUser.objects.get(pk=self.user1.pk) # Refresh from db\n self.assertFalse(user.ad_deleted)\n self.assertFalse(user.active)\n self.assertTrue(user.in_sync)","def change_log_success_status(self, value):\n if value:\n self.embed.colour = 3066993 # Discord color format\n elif value is False:\n self.embed.colour = 15158332\n elif not value:\n self.embed.colour = 15105570","def add_option_enable(self):\n logger.debug(\"Adding enable option\")\n chkenable = ttk.Checkbutton(self.optsframe,\n variable=self.vars[\"enabled\"],\n text=\"Enable {}\".format(self.tabname),\n command=self.on_chkenable_change)\n chkenable.pack(side=tk.RIGHT, padx=5, anchor=tk.W)\n Tooltip(chkenable,\n text=\"Enable or disable {} display\".format(self.tabname),\n wraplength=200)","def disable():\n configdb = ConfigDBConnector()\n configdb.connect()\n port_info = {}\n port_info['FLEX_COUNTER_STATUS'] = 'disable'\n configdb.mod_entry(\"FLEX_COUNTER_TABLE\", \"PORT\", port_info)","def test_disabled_feature(self):\r\n user = UserFactory()\r\n staff = AdminFactory()\r\n\r\n # if feature is disabled user can keep reusing same password\r\n self.assertTrue(PasswordHistory.is_allowable_password_reuse(user, \"test\"))\r\n self.assertTrue(PasswordHistory.is_allowable_password_reuse(staff, \"test\"))\r\n\r\n self.assertFalse(PasswordHistory.should_user_reset_password_now(user))\r\n self.assertFalse(PasswordHistory.should_user_reset_password_now(staff))","def disable_setup(self):\n self.high_ver_entry.config(state=\"disabled\")\n self.low_ver_entry.config(state=\"disabled\")\n self.left_hor_entry.config(state=\"disabled\")\n self.right_hor_entry.config(state=\"disabled\")","def _iotool_enable_green_command(self):\n return self._iotool.commands.set_high(self._spconfig.IOTOOL_GREEN_YELLOW_SWITCH_PIN)","def is_enabled(self):\n enabled = self.settings.get('show_status_bar_text', False)\n if self.template and not enabled:\n self.template = None\n self.vars['repo'] = None\n self.erase()\n return enabled","async def disable(self, ctx):\n\n server = ctx.message.server\n\n settings = self.bot.dota_ticker_settings.get(server.id)\n\n if settings is not None:\n settings['enabled'] = False\n await self.bot.dota_ticker_settings.put(server.id, settings)\n\n await self.bot.say('The match ticker has been disabled on {0.name}.'.format(server))","def disabled(username):\r\n msg = u\"{0} is a disabled user account\".format(username)\r\n\r\n data = {\r\n 'user': username,\r\n 'component': AuthLog.component\r\n }\r\n\r\n AuthLog.store(Log.INFO, msg, **data)","def enabled(self):\n return True","def enabled(self):\n return True","async def active(self, ctx: commands.Context, toggle: bool = None):\n guild = ctx.guild\n tog = self.config.guild(guild)\n role_config = [\n await tog.temprole(),\n await tog.autoroles(),\n ]\n if not role_config[1]:\n role_config[1] = None\n if toggle is None:\n message = \"The Verification settings is set to {}.\".format(await tog.toggle())\n if role_config.count(None) == 2 and await tog.toggle():\n await tog.toggle.set(False)\n message = (\n \"I have disabled verification since roles instructions has been \"\n \"removed. Check settings for more informations\"\n )\n return await ctx.send(message)\n\n if role_config.count(None) == 2:\n return await ctx.send(\n \"I am missing informations; I don't know if I should either give a temprorary \"\n \"role while verifying or give a role after the verification.\"\n )\n await tog.toggle.set(toggle)\n await ctx.send(\"Verification settings is now set to {choice}.\".format(choice=toggle))","async def admin_enable(self, ctx: commands.Context):\n if ctx.guild.id in self.guilds:\n await ctx.send('Team management is already enabled in this guild.')\n return\n await self._enable_guild(guild=ctx.guild)\n await ctx.send('Team management enabled.')","def fact():\n status = \"disabled\"\n pref = CFPreferencesCopyAppValue(\n \"AutomaticCheckEnabled\", \"/Library/Preferences/com.apple.SoftwareUpdate\"\n )\n if pref:\n status = \"enabled\"\n\n return {factoid: status}","def disable(self): \n self.feed_button.config(state=\"disabled\")\n self.eat_button.config(state=\"disabled\") \n for t in range(self.player.game.trait_limit): \n self.add_trait_buttons[t].config(state=\"disabled\") \n self.add_population_button.config(state=\"disabled\")\n self.add_body_size_button.config(state=\"disabled\")","def toggle_failed_banner_off(request):\r\n user_id = request.user.id\r\n SoftwareSecurePhotoVerification.display_off(user_id)\r\n return HttpResponse('Success')","async def toggle(self, ctx):\n guild = ctx.message.guild\n\n enabled = await self.config.guild(guild).enabled()\n\n enabled = not enabled\n await self.config.guild(guild).enabled.set(enabled)\n\n if enabled is True:\n await ctx.send(\"AntiSpam has been enabled\")\n else:\n await ctx.send(\"AntiSpam has been disabled\")","def _get_admin_status(self):\n return self.__admin_status","def disable_color(self):\n self.disable_color = True"],"string":"[\n \"def is_enabled(self):\",\n \"def check_disabled(self):\\n return None\",\n \"def get_status(self):\\n return super(Cabling, self).get_status()\",\n \"def enable(self):\",\n \"def Enabled(self) -> bool:\",\n \"def disable(self):\",\n \"def isEnabled(self):\",\n \"def disable_feature(self,reason,source=\\\"gff3_maniger\\\"):\\r\\n date = datetime.datetime.now().strftime(\\\"%Y-%m-%d\\\")\\r\\n self.add_history(date,source,reason)\\r\\n self.active = False\\r\\n if self._owner_line.type == 'SNP':\\r\\n self._owner_line._owner_set.all_snp_disabled()\",\n \"async def cog_check(self, ctx):\\n guild_doc = await db.PLUGINS.find_one({\\\"_id\\\": ctx.guild.id})\\n\\n if guild_doc.get(\\\"Verification\\\"):\\n return True\\n\\n else:\\n await ctx.send(\\n embed=discord.Embed(\\n description=(\\n f\\\"{var.E_DISABLE} The Verification plugin\\\"\\n \\\" is disabled in this server\\\"\\n ),\\n color=var.C_ORANGE\\n )\\n )\",\n \"def enable(self) -> None:\",\n \"def disable(ctx):\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n config_db.mod_entry(\\\"NAT_GLOBAL\\\", \\\"Values\\\", {\\\"admin_mode\\\": \\\"disabled\\\"})\",\n \"def disability_specify(self, instance):\\r\\n return instance.user.profile.disability_specify\",\n \"def disable():\\n boutonPierre[\\\"state\\\"] = \\\"disabled\\\"\\n boutonFeuille[\\\"state\\\"] = \\\"disabled\\\"\\n boutonCiseaux[\\\"state\\\"] = \\\"disabled\\\"\",\n \"def check_enable_mode(self, *args, **kwargs):\\n pass\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def setEnabled(*args):\",\n \"def get_everyone_denied(self):\",\n \"def enable():\\n boutonPierre[\\\"state\\\"] = \\\"normal\\\"\\n boutonFeuille[\\\"state\\\"] = \\\"normal\\\"\\n boutonCiseaux[\\\"state\\\"] = \\\"normal\\\"\",\n \"def disable(self) -> None:\",\n \"def disable():\\n configdb = ConfigDBConnector()\\n configdb.connect()\\n rif_info = {}\\n rif_info['FLEX_COUNTER_STATUS'] = 'disable'\\n configdb.mod_entry(\\\"FLEX_COUNTER_TABLE\\\", \\\"RIF\\\", rif_info)\",\n \"def disabled(name):\\n return not enabled(name)\",\n \"async def toggle(self, ctx: BBContext):\\n\\n self.code_enabled = not self.code_enabled\\n e = 'enabled.' if self.code_enabled else 'disabled.'\\n await ctx.send(f\\\"Bunker code auto reaction has been : **{e}**\\\")\\n self.bot.logger.info('Bunker code listener %s by %s', e, str(ctx.author))\",\n \"def enable(self):\\n pass\",\n \"def isEnabled(self) -> bool:\\n ...\",\n \"def _isdisable(self):\\n return self.dp.state()==PyTango.DevState.DISABLE\",\n \"def can_be_disabled(self) -> bool:\\n return True\",\n \"async def _antiadv(self, ctx):\\r\\n serverid = ctx.message.server.id\\r\\n if ctx.invoked_subcommand is None:\\r\\n await send_cmd_help(ctx)\\r\\n if serverid not in self.adkillr:\\r\\n self.adkillr[serverid] = {'toggle': False, 'message': '{0.mention} don\\\\'t send links!', 'filters': []}\\r\\n dataIO.save_json(\\\"data/adkillr/adkillr.json\\\", self.adkillr)\",\n \"def enableVocolaTakesUnimacroActions(self):\\n key = \\\"VocolaTakesUnimacroActions\\\"\\n self.userregnl.set(key, 1)\",\n \"async def admin_disable(self, ctx: commands.Context):\\n if ctx.guild.id not in self.guilds:\\n await ctx.send('Team management is already disabled in this guild.')\\n return\\n await self._disable_guild(guild=ctx.guild)\\n await ctx.send('Team management disabled.')\",\n \"def disable(self):\\n pass\",\n \"def disable(ctx):\\n\\n fc_group_cfg = {}\\n fc_group_cfg['FLEX_COUNTER_STATUS'] = DISABLE\\n ctx.obj.mod_entry(\\\"FLEX_COUNTER_TABLE\\\", ACL, fc_group_cfg)\",\n \"def disable():\\n configdb = ConfigDBConnector()\\n configdb.connect()\\n tunnel_info = {}\\n tunnel_info['FLEX_COUNTER_STATUS'] = DISABLE\\n configdb.mod_entry(\\\"FLEX_COUNTER_TABLE\\\", \\\"TUNNEL\\\", tunnel_info)\",\n \"def status():\\n with spinner():\\n is_enabled = is_witness_enabled()\\n signing_key = current_signing_key()\\n misses = total_missed()\\n\\n t = PrettyTable([\\\"Enabled\\\", \\\"Misses\\\", \\\"Key\\\"])\\n t.align = \\\"l\\\"\\n t.add_row([is_enabled, misses, signing_key])\\n\\n output(t, 'Status')\\n output(get_config(), 'Configuration')\",\n \"def check_enable_mode(self, check_string='#'):\\n return True\",\n \"def check_if_enabled(self, user):\\n\\t\\tfrom webnotes.utils import cint\\n\\t\\tif user=='Administrator': return\\n\\t\\tif not cint(webnotes.conn.get_value('Profile', user, 'enabled')):\\n\\t\\t\\tself.fail('User disabled or missing')\",\n \"def affection_status_switch_on(self):\\n self._affection_status_switch = False\",\n \"def disable():\\n configdb = ConfigDBConnector()\\n configdb.connect()\\n fc_info = {}\\n fc_info['FLEX_COUNTER_STATUS'] = 'disable'\\n configdb.mod_entry(\\\"FLEX_COUNTER_TABLE\\\", \\\"QUEUE_WATERMARK\\\", fc_info)\\n configdb.mod_entry(\\\"FLEX_COUNTER_TABLE\\\", \\\"PG_WATERMARK\\\", fc_info)\\n configdb.mod_entry(\\\"FLEX_COUNTER_TABLE\\\", BUFFER_POOL_WATERMARK, fc_info)\",\n \"def disableVocolaTakesUnimacroActions(self):\\n key = \\\"VocolaTakesUnimacroActions\\\"\\n self.userregnl.set(key, 0)\",\n \"def enable(ctx):\\n\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n config_db.mod_entry(\\\"NAT_GLOBAL\\\", \\\"Values\\\", {\\\"admin_mode\\\": \\\"enabled\\\"})\",\n \"def organization_enable_status(self) -> str:\\n return pulumi.get(self, \\\"organization_enable_status\\\")\",\n \"def ha_status_icon_should_appear_and_it_should_reflect_that_ha_is_enabled_when_clicked(driver):\\n wait_on_element(driver, 0.5, 5, '//button[@ix-auto=\\\"button__haStatus\\\"]')\\n driver.find_element_by_xpath('//button[@ix-auto=\\\"button__haStatus\\\"]').click()\\n wait_on_element(driver, 0.5, 5, '//h1[contains(.,\\\"HA Enabled)]')\\n driver.find_element_by_xpath('//span[contains(.,\\\"HA is enabled\\\")]')\\n driver.find_element_by_xpath('//button[@ix-auto=\\\"button__CLOSE\\\"]').click()\",\n \"def check_disabled(self):\\n if self._treat_counter % 6 != 0:\\n return\\n if not self.site.logged_in():\\n self.site.login()\\n page = pywikibot.Page(\\n self.site,\\n 'User:{username}/shutoff/{class_name}'.format(\\n username=self.site.user(), class_name=self.__class__.__name__\\n ),\\n )\\n if page.exists():\\n content = page.get(force=True).strip()\\n if content:\\n e = '{} disabled:\\\\n{}'.format(self.__class__.__name__, content)\\n pywikibot.error(e)\\n self.quit()\",\n \"def test_verification_status_invisible(self):\\r\\n self.client.login(username=\\\"jack\\\", password=\\\"test\\\")\\r\\n self.check_verification_status_off('verified', 'You\\\\'re enrolled as a verified student')\\r\\n self.check_verification_status_off('honor', 'You\\\\'re enrolled as an honor code student')\\r\\n self.check_verification_status_off('audit', 'You\\\\'re auditing this course')\",\n \"def setDisabledColor(*args):\",\n \"def setDisabledColor(*args):\",\n \"def test_get_current_component_status_DISABLED(self):\\n self._ucr({\\n 'repository/online/component/a': 'no',\\n })\\n ORIG = UU.FN_UPDATER_APTSOURCES_COMPONENT\\n try:\\n tmp = NamedTemporaryFile()\\n UU.FN_UPDATER_APTSOURCES_COMPONENT = tmp.name\\n self.assertEqual(UU.COMPONENT_DISABLED, self.u.get_current_component_status('a'))\\n finally:\\n UU.FN_UPDATER_APTSOURCES_COMPONENT = ORIG\\n tmp.close()\",\n \"def get_status():\\n return ('off', 'off')\",\n \"def toggled_comunication(self):\\n if self.actionPC_Monitor.isChecked() and self.actionPC_Monitor.isEnabled():\\n self.actionPC_Monitor.setEnabled(0)\\n self.actionPC_Sensor_Actuador.setChecked(0)\\n self.actionPC_Sensor_Actuador.setEnabled(1)\\n self.monitor_environment()\\n \\n elif self.actionPC_Sensor_Actuador.isChecked() and self.actionPC_Sensor_Actuador.isEnabled():\\n self.actionPC_Sensor_Actuador.setEnabled(0)\\n self.actionPC_Monitor.setChecked(0)\\n self.actionPC_Monitor.setEnabled(1)\\n self.actuator_environment()\",\n \"def test_listWithDisabled(self):\\n self.userbase('create', 'alice', 'localhost', SECRET)\\n self.userbase('create', 'bob', 'localhost', SECRET)\\n\\n def cb(xxx_todo_changeme2):\\n (interface, avatar, logout) = xxx_todo_changeme2\\n avatar.disabled = 1\\n output = self.userbase('list')\\n self.assertEqual(output,\\n ['alice@localhost', 'bob@localhost [DISABLED]'])\\n\\n return self._login('bob@localhost', SECRET).addCallback(cb)\",\n \"def disableButtons(self):\\n self.ui.b_run.setEnabled(False)\\n self.ui.b_colour.setEnabled(False)\\n self.ui.b_ground_truth.setEnabled(False)\\n self.ui.b_vid.setEnabled(False)\\n self.ui.b_save.setEnabled(False)\\n self.ui.t_low.setEnabled(False)\\n self.ui.t_high.setEnabled(False)\\n self.ui.t_fps.setEnabled(False)\\n self.ui.combo_superpixel.setEnabled(False)\\n self.ui.c_super_pixel_video.setEnabled(False)\\n self.ui.c_csv.setEnabled(False)\\n self.ui.c_draw.setEnabled(False)\\n self.ui.c_velocity.setEnabled(False)\\n self.ui.c_of.setEnabled(False)\\n self.ui.c_back_of.setEnabled(False)\\n self.ui.c_depth.setEnabled(False)\\n self.ui.c_speed_plot.setEnabled(False)\\n self.ui.c_error_plot.setEnabled(False)\\n self.ui.c_crash_plot.setEnabled(False)\\n self.ui.c_error_plot_video.setEnabled(False)\\n self.ui.c_speed_plot_video.setEnabled(False)\\n self.ui.c_crash_plot_video.setEnabled(False)\\n self.ui.c_optimize.setEnabled(False)\\n self.ui.c_object_detection.setEnabled(False)\",\n \"async def enable(self, ctx):\\n self.bot.db.execute(\\\"UPDATE starboards SET enabled = 1 WHERE channel_id = ?\\\", (ctx.channel.id,))\\n await ctx.say(\\\"star.enabled\\\")\",\n \"def _get_enable(self):\\n return self.__enable\",\n \"def _get_enable(self):\\n return self.__enable\",\n \"def custom_assess_status_check(self):\\n options = self.options\\n # can check options.thing to ensure that it makes sense\\n # if wrong return 'blocked', \\\"The driver is badly configured ...\\\"\\n return None, None\",\n \"def admin_non_activated(request):\\r\\n ret = []\\r\\n res = UserMgr.non_activated_account()\\r\\n if res:\\r\\n ret = [u.username for u in res]\\r\\n\\r\\n return _api_response(request, {\\r\\n 'count': len(ret),\\r\\n 'status': True,\\r\\n 'data': ret,\\r\\n })\",\n \"def _iotool_enable_yellow_command(self):\\n return self._iotool.commands.set_low(self._spconfig.IOTOOL_GREEN_YELLOW_SWITCH_PIN)\",\n \"def test_enabled(self):\\n # OSA script should have been installed in setUp function, which sets\\n # enabled to True by default.\\n self.assertTrue(self.run_function(\\\"assistive.enabled\\\", [OSA_SCRIPT]))\\n # Disable OSA Script\\n self.run_function(\\\"assistive.enable\\\", [OSA_SCRIPT, False])\\n # Assert against new disabled status\\n self.assertFalse(self.run_function(\\\"assistive.enabled\\\", [OSA_SCRIPT]))\",\n \"def styled_status(enabled, bold=True):\\n return click.style('Enabled' if enabled else 'Disabled', 'green' if enabled else 'red', bold=bold)\",\n \"def enabled(self):\\n raise NotImplementedError\",\n \"def _disable(self):\\n self.enabled = False\",\n \"def disable():\\n request = dict(id='gbn')\\n _gbn_disable(request)\",\n \"async def goodbye_toggle(self, ctx, value: bool):\\n await queries.update_setting(ctx, \\\"goodbye_settings\\\", \\\"is_enabled\\\", value)\\n if value:\\n await util.send_success(ctx, \\\"Goodbye messages are now **enabled**\\\")\\n else:\\n await util.send_success(ctx, \\\"Goodbye messages are now **disabled**\\\")\",\n \"async def toggle(self, ctx):\\r\\n serverid = ctx.message.server.id\\r\\n if self.adkillr[serverid]['toggle'] is True:\\r\\n self.adkillr[serverid]['toggle'] = False\\r\\n e = discord.Embed(description='**AntiAdv is now disabled.**')\\r\\n await self.bot.say(embed=e)\\r\\n elif self.adkillr[serverid]['toggle'] is False:\\r\\n self.adkillr[serverid]['toggle'] = True\\r\\n e = discord.Embed(description='**AntiAdv is now enabled.**')\\r\\n await self.bot.say(embed=e)\\r\\n dataIO.save_json(\\\"data/adkillr/adkillr.json\\\", self.adkillr)\",\n \"def disable(self) -> Awaitable[Dict]:\\n return self.client.send(\\\"Security.disable\\\", {})\",\n \"def __disable__(self) -> None:\\n pass\",\n \"def disabled_by_microsoft(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"disabled_by_microsoft\\\")\",\n \"def get_id(self):\\n return \\\"always_false_plugin\\\"\",\n \"def disable_dialog_boxes(self):\\n self.exec_command('SilentUpdateFW')\\n self.exec_command('SuppressInfoUpdateFW')\\n self.exec_command('SetBatchMode = 1')\\n\\n # SuppressControlPanel\\n self.exec_command(\\\"HideDeviceSelection = 1\\\")\\n self.exec_command(\\\"SuppressControlPanel\\\")\\n # Hide Flash Windows\\n self.exec_command(\\\"DisableInfoWinFlashDL\\\")\\n self.exec_command(\\\"DisableInfoWinFlashBPs\\\")\",\n \"def enable_btns(self):\\n self.saveBtn.setEnabled(True)\\n self.openVideoBtn.setEnabled(True)\\n self.openAnnotationBtn.setEnabled(True)\\n self.resetBtn.setEnabled(True)\\n self.speedCombo.setEnabled(True)\\n self.newFileBtn.setEnabled(True)\\n self.HelpBtn.setEnabled(True)\",\n \"def enable(self):\\n if not self.labExperiment:\\n super().enable()\\n else:\\n self.connection.command('open_dm', self.DMserial)\\n status = self.connection.query('get_status')\\n assert status == 0, 'Error connecting to DM. Error: ' + str(status)\\n numActProfile = self.connection.query('num_actuators')\\n assert numActProfile == self.numActProfile, 'Wrong number of profile actuators entered'\\n print(\\\"'BM1k' is now enabled\\\")\",\n \"def disable(self):\\n\\t\\tresponse = self.client.post(self._endpoint + \\\"/disable\\\")\\n\\t\\treturn bool(response.json[\\\"success\\\"])\",\n \"def test_disable(self):\\n self.assertTrue(self.user1.active)\\n self.assertFalse(self.user1.ad_deleted)\\n url = '/api/users/{}/'.format(self.user1.ad_guid)\\n data = {\\n 'Enabled': False,\\n }\\n response = self.client.put(url, json.dumps(data), content_type='application/json')\\n self.assertEqual(response.status_code, 202)\\n user = DepartmentUser.objects.get(pk=self.user1.pk) # Refresh from db\\n self.assertFalse(user.ad_deleted)\\n self.assertFalse(user.active)\\n self.assertTrue(user.in_sync)\",\n \"def change_log_success_status(self, value):\\n if value:\\n self.embed.colour = 3066993 # Discord color format\\n elif value is False:\\n self.embed.colour = 15158332\\n elif not value:\\n self.embed.colour = 15105570\",\n \"def add_option_enable(self):\\n logger.debug(\\\"Adding enable option\\\")\\n chkenable = ttk.Checkbutton(self.optsframe,\\n variable=self.vars[\\\"enabled\\\"],\\n text=\\\"Enable {}\\\".format(self.tabname),\\n command=self.on_chkenable_change)\\n chkenable.pack(side=tk.RIGHT, padx=5, anchor=tk.W)\\n Tooltip(chkenable,\\n text=\\\"Enable or disable {} display\\\".format(self.tabname),\\n wraplength=200)\",\n \"def disable():\\n configdb = ConfigDBConnector()\\n configdb.connect()\\n port_info = {}\\n port_info['FLEX_COUNTER_STATUS'] = 'disable'\\n configdb.mod_entry(\\\"FLEX_COUNTER_TABLE\\\", \\\"PORT\\\", port_info)\",\n \"def test_disabled_feature(self):\\r\\n user = UserFactory()\\r\\n staff = AdminFactory()\\r\\n\\r\\n # if feature is disabled user can keep reusing same password\\r\\n self.assertTrue(PasswordHistory.is_allowable_password_reuse(user, \\\"test\\\"))\\r\\n self.assertTrue(PasswordHistory.is_allowable_password_reuse(staff, \\\"test\\\"))\\r\\n\\r\\n self.assertFalse(PasswordHistory.should_user_reset_password_now(user))\\r\\n self.assertFalse(PasswordHistory.should_user_reset_password_now(staff))\",\n \"def disable_setup(self):\\n self.high_ver_entry.config(state=\\\"disabled\\\")\\n self.low_ver_entry.config(state=\\\"disabled\\\")\\n self.left_hor_entry.config(state=\\\"disabled\\\")\\n self.right_hor_entry.config(state=\\\"disabled\\\")\",\n \"def _iotool_enable_green_command(self):\\n return self._iotool.commands.set_high(self._spconfig.IOTOOL_GREEN_YELLOW_SWITCH_PIN)\",\n \"def is_enabled(self):\\n enabled = self.settings.get('show_status_bar_text', False)\\n if self.template and not enabled:\\n self.template = None\\n self.vars['repo'] = None\\n self.erase()\\n return enabled\",\n \"async def disable(self, ctx):\\n\\n server = ctx.message.server\\n\\n settings = self.bot.dota_ticker_settings.get(server.id)\\n\\n if settings is not None:\\n settings['enabled'] = False\\n await self.bot.dota_ticker_settings.put(server.id, settings)\\n\\n await self.bot.say('The match ticker has been disabled on {0.name}.'.format(server))\",\n \"def disabled(username):\\r\\n msg = u\\\"{0} is a disabled user account\\\".format(username)\\r\\n\\r\\n data = {\\r\\n 'user': username,\\r\\n 'component': AuthLog.component\\r\\n }\\r\\n\\r\\n AuthLog.store(Log.INFO, msg, **data)\",\n \"def enabled(self):\\n return True\",\n \"def enabled(self):\\n return True\",\n \"async def active(self, ctx: commands.Context, toggle: bool = None):\\n guild = ctx.guild\\n tog = self.config.guild(guild)\\n role_config = [\\n await tog.temprole(),\\n await tog.autoroles(),\\n ]\\n if not role_config[1]:\\n role_config[1] = None\\n if toggle is None:\\n message = \\\"The Verification settings is set to {}.\\\".format(await tog.toggle())\\n if role_config.count(None) == 2 and await tog.toggle():\\n await tog.toggle.set(False)\\n message = (\\n \\\"I have disabled verification since roles instructions has been \\\"\\n \\\"removed. Check settings for more informations\\\"\\n )\\n return await ctx.send(message)\\n\\n if role_config.count(None) == 2:\\n return await ctx.send(\\n \\\"I am missing informations; I don't know if I should either give a temprorary \\\"\\n \\\"role while verifying or give a role after the verification.\\\"\\n )\\n await tog.toggle.set(toggle)\\n await ctx.send(\\\"Verification settings is now set to {choice}.\\\".format(choice=toggle))\",\n \"async def admin_enable(self, ctx: commands.Context):\\n if ctx.guild.id in self.guilds:\\n await ctx.send('Team management is already enabled in this guild.')\\n return\\n await self._enable_guild(guild=ctx.guild)\\n await ctx.send('Team management enabled.')\",\n \"def fact():\\n status = \\\"disabled\\\"\\n pref = CFPreferencesCopyAppValue(\\n \\\"AutomaticCheckEnabled\\\", \\\"/Library/Preferences/com.apple.SoftwareUpdate\\\"\\n )\\n if pref:\\n status = \\\"enabled\\\"\\n\\n return {factoid: status}\",\n \"def disable(self): \\n self.feed_button.config(state=\\\"disabled\\\")\\n self.eat_button.config(state=\\\"disabled\\\") \\n for t in range(self.player.game.trait_limit): \\n self.add_trait_buttons[t].config(state=\\\"disabled\\\") \\n self.add_population_button.config(state=\\\"disabled\\\")\\n self.add_body_size_button.config(state=\\\"disabled\\\")\",\n \"def toggle_failed_banner_off(request):\\r\\n user_id = request.user.id\\r\\n SoftwareSecurePhotoVerification.display_off(user_id)\\r\\n return HttpResponse('Success')\",\n \"async def toggle(self, ctx):\\n guild = ctx.message.guild\\n\\n enabled = await self.config.guild(guild).enabled()\\n\\n enabled = not enabled\\n await self.config.guild(guild).enabled.set(enabled)\\n\\n if enabled is True:\\n await ctx.send(\\\"AntiSpam has been enabled\\\")\\n else:\\n await ctx.send(\\\"AntiSpam has been disabled\\\")\",\n \"def _get_admin_status(self):\\n return self.__admin_status\",\n \"def disable_color(self):\\n self.disable_color = True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64461994","0.61870736","0.6155342","0.606294","0.60158396","0.59407747","0.5909308","0.5869749","0.5851598","0.58354104","0.5772434","0.5748269","0.574007","0.5675615","0.5668143","0.5668143","0.5668143","0.5668143","0.5668143","0.5668143","0.5668143","0.5668143","0.5668143","0.5668143","0.5668143","0.56668574","0.56544757","0.5642185","0.56349945","0.55991596","0.55842316","0.55806315","0.55688715","0.5526911","0.5517144","0.5492399","0.54842997","0.546942","0.5462072","0.5458034","0.5420423","0.54133695","0.5409734","0.54094636","0.5401592","0.5399459","0.53950405","0.5389266","0.5385976","0.5376283","0.53623945","0.5362315","0.5360602","0.5360602","0.53574705","0.53508556","0.53494215","0.5349183","0.5340616","0.5337646","0.5336186","0.5336186","0.53294474","0.5324071","0.5316508","0.53143376","0.5308128","0.53059644","0.5304992","0.52990156","0.52943707","0.5290482","0.52880156","0.5286479","0.52804244","0.5278929","0.52674896","0.5266074","0.5262645","0.52546716","0.5253695","0.5252237","0.52518606","0.5242113","0.5238136","0.5237907","0.52341616","0.523326","0.52307504","0.5229827","0.52198815","0.52198815","0.52158785","0.5211892","0.52094036","0.520762","0.5205642","0.52001923","0.5198957","0.5194029"],"string":"[\n \"0.64461994\",\n \"0.61870736\",\n \"0.6155342\",\n \"0.606294\",\n \"0.60158396\",\n \"0.59407747\",\n \"0.5909308\",\n \"0.5869749\",\n \"0.5851598\",\n \"0.58354104\",\n \"0.5772434\",\n \"0.5748269\",\n \"0.574007\",\n \"0.5675615\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.5668143\",\n \"0.56668574\",\n \"0.56544757\",\n \"0.5642185\",\n \"0.56349945\",\n \"0.55991596\",\n \"0.55842316\",\n \"0.55806315\",\n \"0.55688715\",\n \"0.5526911\",\n \"0.5517144\",\n \"0.5492399\",\n \"0.54842997\",\n \"0.546942\",\n \"0.5462072\",\n \"0.5458034\",\n \"0.5420423\",\n \"0.54133695\",\n \"0.5409734\",\n \"0.54094636\",\n \"0.5401592\",\n \"0.5399459\",\n \"0.53950405\",\n \"0.5389266\",\n \"0.5385976\",\n \"0.5376283\",\n \"0.53623945\",\n \"0.5362315\",\n \"0.5360602\",\n \"0.5360602\",\n \"0.53574705\",\n \"0.53508556\",\n \"0.53494215\",\n \"0.5349183\",\n \"0.5340616\",\n \"0.5337646\",\n \"0.5336186\",\n \"0.5336186\",\n \"0.53294474\",\n \"0.5324071\",\n \"0.5316508\",\n \"0.53143376\",\n \"0.5308128\",\n \"0.53059644\",\n \"0.5304992\",\n \"0.52990156\",\n \"0.52943707\",\n \"0.5290482\",\n \"0.52880156\",\n \"0.5286479\",\n \"0.52804244\",\n \"0.5278929\",\n \"0.52674896\",\n \"0.5266074\",\n \"0.5262645\",\n \"0.52546716\",\n \"0.5253695\",\n \"0.5252237\",\n \"0.52518606\",\n \"0.5242113\",\n \"0.5238136\",\n \"0.5237907\",\n \"0.52341616\",\n \"0.523326\",\n \"0.52307504\",\n \"0.5229827\",\n \"0.52198815\",\n \"0.52198815\",\n \"0.52158785\",\n \"0.5211892\",\n \"0.52094036\",\n \"0.520762\",\n \"0.5205642\",\n \"0.52001923\",\n \"0.5198957\",\n \"0.5194029\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65774465"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6795,"cells":{"query":{"kind":"string","value":"store input into filename used pickle.dump"},"document":{"kind":"string","value":"def store (input, filename) :\n cout = open (filename, 'w')\n pickle.dump (input, cout)\n cout.close ()"},"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 store(self, filename):","def pickle(self,data,filename):\n pickle.dump(data, open(filename, 'wb'))","def save_file_data(name, obj, input_path='/inputs'):\n filename = '{}/{}.pkl'.format(input_path, name)\n if not os.path.exists(os.path.dirname(filename)):\n try:\n os.makedirs(os.path.dirname(filename))\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n raise\n with open(filename.format(name), 'wb+') as output:\n pickle.dump(obj, output)","def save(self, output, data):","def save(data, filename):\r\n with open(filename, 'wb') as fp:\r\n pickle.dump(data, fp)","def save(self,filename): \n with open(filename, 'wb') as f:\n pickle.dump(self,f)","def save(self,filename):\n with open(filename,'wb') as f:\n pickle.dump(self,f)","def save(self, filename):\n pickle.dump(self, open(filename + '.p', 'wb'), 2)","def store_pickle(fname, info, mode='w'):\n assert fname[-2:] == '.p'\n f = open(fname, mode)\n pickle.dump(info, f)\n f.close()","def psave(var, filename):\n pickle.dump(var, open(filename, 'wb'))","def save_object(self, filename, data):\n with open(filename, 'wb') as outp: # Overwrites any existing file.\n pickle.dump(data, outp, pickle.HIGHEST_PROTOCOL)","def save_as(self, filename):\n assert type(filename) == str, 'ERROR: filename should be type str'\n if '.pkl' in filename:\n with open(filename, 'wb') as f:\n dill.dump(self, f)\n else:\n with open(filename + '.pkl', 'wb') as f:\n dill.dump(self, f)","def save(fname, data):\r\n with open(fname, 'wb') as f:\r\n pickle.dump(data, f)","def save(self, filename):\n if '.pkl' not in filename:\n filename = filename + '.pkl'\n with open(filename, 'wb') as f:\n pickle.dump(self, f)","def save_input(self):\n if not os.path.exists(self.wdir):\n os.makedirs(self.wdir)\n\n with open(self.filepath, \"w\") as f:\n f.write(self.input_string)\n print(f\"-- Input file [{self.filename}] written successfully.\")","def save_var(filename, data, protocol = -1, allow_dill=False):\n if filename.endswith('.gz') :\n open_method = gzip.open\n else:\n open_method = open\n\n output = open_method(filename, 'wb')\n try:\n # Pickle dictionary using given protocol\n std_pickle.dump(data, output, protocol)\n finally:\n output.close()\n\n return","def save(self, filename):\n with open(filename, \"wb\") as f:\n pkl.dump(self, f)","def pickle_data(file_name, data):\n outfile = open(file_name, \"wb\")\n pickle.dump(data, outfile)\n outfile.close()","def save(self, filename, **kwargs):\n with open(filename, 'wb') as fin:\n pickle.dump(self, fin, **kwargs)","def save(self,filename):\n f = open(filename, 'wb')\n pickle.dump(self,f)\n f.close()","def save_pickle(file, path):\n with open(path, 'wb') as f:\n pickle.dump(file, f)\n file_name = re.findall(r\"/?[^/]+\", path)[-1].strip(\"/\")\n print(f\"Stored {file_name}.\")","def write_pickle_object_to_file(self, inpobj):\n with gzip.open('%s.tmp' % self.pickle_file, 'wb') as pkl_file:\n pickle.dump(inpobj, pkl_file, pickle.HIGHEST_PROTOCOL)\n run_command('mv %s.tmp %s' % (self.pickle_file, self.pickle_file))\n return True","def save_pickle(filename, data, override=True):\n filename = \"{}.pkl\".format(filename)\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n\n if override == False:\n filename = add_unique_postfix(filename)\n\n with open(filename, \"wb\") as f:\n pickle.dump(data, f)\n\n return filename","def save(self,fn):\n fn = fn if fn[-4:] == \".pkl\" else fn+\".pkl\"\n with open(fn,\"wb+\") as f:\n pickle.dump(self,f)\n log(\"Saved reader to {}\".format(fn))","def save(object, filename, protocol = 0):\n file = gzip.GzipFile(filename, 'wb')\n file.write(pickle.dumps(object, protocol))\n file.close()","def pickleSave(object, filename):\n #Todo: Handle exceptions from pickle\n filehandler = open(\"obj/\" + filename + \".obj\", 'wb')\n pickle.dump(object, filehandler)","def save(self, filename):\n cPickle.dump(self, open(filename, \"wb\"))","def pickle_data(filename, data):\n f = open(filename, \"wb\")\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n f.close()","def save(self, file_name):\n saved_data = { \"start_config\" : self.start_config, \"action_storage\" : self.action_storage } \n with open(file_name, 'wb') as fh:\n pickle.dump(saved_data, fh)","def saveVar(var,name):\n with open(name+'.pickle','wb') as fl:\n pickle.dump(var,fl)","def saveStuff(stuff, path=None):\n if path == None:\n # TODO take name from something\n output = open('results/i-will-be-overwritten.pkl', 'wb')\n else:\n output = open(path, 'wb')\n\n # Pickle the list using the highest protocol available.\n cPickle.dump(stuff, output, -1)\n output.close()","def saveStuff(stuff, path=None):\n if path == None:\n # TODO take name from something\n output = open('results/i-will-be-overwritten.pkl', 'wb')\n else:\n output = open(path, 'wb')\n\n # Pickle the list using the highest protocol available.\n cPickle.dump(stuff, output, -1)\n output.close()","def save(self, filename='test'):\n file = open(filename+'.txt','w')\n pickle.dump(self, file)\n file.close()","def save_pickle(obj, filename):\n with open(filename, 'wb') as file:\n pickle.dump(obj, file)","def save(self, output, data):\n pass","def save_data(data: Any, file_name: str) -> None:\n with open(file_name, \"wb\") as output:\n pickle.dump(data, output)","def do_pickle(self, arg):\n try:\n from pickling import Pickling\n Pickling(arg, input(\"Please enter the name of file: \")).pickle_it()\n except TypeError as e:\n print(e)\n except():\n print(\"Error!!\")","def save(self, filename):\n with open(filename, 'wb') as f:\n pickle.dump({'wi': self.W_input_to_hidden, 'wo': self.W_hidden_to_output}, f)","def save_data_pickle(PATH, data, dataset, filename):\n with open(PATH + '/' + dataset + \"_\" + filename + \".pkl\",\"wb\") as f:\n pickle.dump(data,f)\n print(filename, \"created\")","def storePickle(filename, fit_diff_matrix):\n store_filename = filename + \".pickle\"\n with open(store_filename, \"wb\") as f:\n pickle.dump(fit_diff_matrix, f)","def write_to_file(name, obj):\n\n print 'writing structures to pickle'\n print '----------------------------'\n\n path = os.getcwd() + '/pickles/' + name + '.pkl'\n file = open(path, 'wb')\n pickle.dump(obj, file)\n file.close()","def save(self):\n pickle.dump(self, open(self.path, \"wb\"))","def save(self, filename):\n with gzip.open(filename, \"w\") as f:\n f.write(pickle.dumps(self))","def save(self, filename):\n Util.savePickle(self.V, filename + self.ext, overwrite=True)\n return filename + self.ext","def save(self, filename):\n pass","def save_to_disk(name, object):\n shortname = _dumpify(_compress_name(name) + '.pkl')\n print 'save_to_disk(%s)' % shortname\n pkl_file = open(shortname , 'wb')\n pickle.dump(object, pkl_file, -1) # Pickle the list using the highest protocol available.\n pkl_file.close()","def save_pickle(file, variable):\n if file.split('.')[-1] != \"pkl\":\n file += \".pkl\"\n\n with open(file, 'wb') as f:\n pickle.dump(variable, f)\n print(\"Variable successfully saved in \" + file)","def to_pickle(self, filename, **kwargs):\n with open(filename, 'wb') as f:\n pickle.dump(self, f)","def pickle_dump(file_name: str, data: Any):\n with open(file_name, 'wb') as file:\n pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL)","def save(self, fname):\n pass","def save_as(self, filename: str) -> None:\n save_data = lzma.compress(pickle.dumps(self))\n with open(filename, \"wb\") as f:\n f.write(save_data)","def _save_obj(obj, name):\n with open('/bigdisk/pickles/' + name, 'w') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)","def save(data, phonebook):\n\n with open(phonebook, \"w\") as outfile:\n cPickle.dump(data, outfile)","def savePickle(object, name):\n epoch = time.time()\n filename = name + str(epoch) + \".pkl\" # Save name\n fullPath = path.join(PICKLE_DIR, filename) # Save path\n\n # Get permissions and save the file\n with open(fullPath, \"w\") as outfile:\n pickle.dump(object, outfile)","def save(object, filename, protocol = 0):\n file = gzip.GzipFile(filename, 'wb')\n file.write(pickle.dumps(object, protocol))\n file.close()","def save(self, fname):\n with open(fname, \"wb\") as f:\n cloudpickle.dump(self, f)\n # pickle.dump(self, open(fname, 'wb'))","def _save(self):\n # TODO: Use local.punny dump (when written)\n with open(filename, 'w') as f:\n pickle = Pickler(f)\n pickle.dump(self.pungen.puns)","def save(self, filename = 'array_zest', path = '/home/eric/dev/insitu/data/zs_recovery/'):\n filename = filename# + '_Lx_' + str(self.Lx) + 'm_Ly_' + str(self.Ly) + 'm'\n self.path_filename = path + filename + '.pkl'\n f = open(self.path_filename, 'wb')\n pickle.dump(self.__dict__, f, 2)\n f.close()","def save(self, target):\n from six.moves.cPickle import dump\n data = self.serialize()\n with open(target, 'wb') as f:\n dump(data, f)","def save(self, target):\n from six.moves.cPickle import dump\n data = self.serialize()\n with open(target, 'wb') as f:\n dump(data, f)","def dump_pickle_data(obj, filename):\n path = \"../tmp/{}.pckl\".format(filename)\n f = open(path, 'wb')\n pickle.dump(obj, f)\n f.close()","def save(self, p):\n pickle.dump(p, open('save.dat', 'wb'))\n print(\"Game Saved!\")","def save_model(self, step):\n\n # file_name = params['name']\n # pickle.dump(self, gzip.open(file_name, 'wb'))","def writePickle(self, filename):\n \n assert filename.endswith('.pkl')\n file = open(filename, 'wb')\n cPickle.dump(self, file, cPickle.HIGHEST_PROTOCOL)","def save_pkl(dictionnary,directory,file_name):\n with open(f'saved_exp_info/{directory}/{file_name}.pkl', 'wb') as output:\n pickle.dump(dictionnary, output)","def save_file(self, file_name: str):\n pickle.dump(self, open(file_name, \"wb\"))","def save_dict_pickle(PATH, data, filename):\n with open(PATH + '/' + filename + \".pkl\",\"wb\") as f:\n pickle.dump(data,f)\n print(filename, \"created\")","def save(self, file_name: str) -> None:\n with open(file_name, \"wb\") as f:\n pickle.dump(self, f)","def save(self, filename):\n pass","def save(self):\n if self.hasChanged:\n filePath = self.path\n tempPath = filePath+'.tmp'\n fileDir = os.path.split(filePath)[0]\n if not os.path.exists(fileDir): os.makedirs(fileDir)\n cPickle.dump(self.data,open(tempPath,'w'))\n renameFile(tempPath,filePath,True)\n self.hasChanged = False","def save(self, filename):\n with open(filename, 'wb') as f:\n pickle.dump({'wi': self.W_input_to_hidden, 'wh':\n self.W_hidden_to_hidden, 'wo': self.W_hidden_to_output}, f)","def pickleToDisk(population, fName):\n pickle.dump(population, open(fName + \".pkl\", 'wb'), protocol=2)","def write_pickle(obj, file_name):\n with open(file_name, 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)","def pickle_files(filename, stuff):\n save_stuff = open(filename, \"wb\")\n pickle.dump(stuff, save_stuff)\n save_stuff.close()","def save_reaction(self, filename):\n filename = filename.replace('.pkl', '.gpkl')\n filename = filename.replace('.bpkl', '.gpkl')\n # Overwrites any existing file.\n with gzip.GzipFile(filename, 'wb') as output:\n pickle.dump(self, output, pickle.HIGHEST_PROTOCOL)","def store_outcome(model_name, dataset, strict, forgiving):\n model_name = model_name.replace('/', '-')\n\n with open(f'outcomes-{model_name}-{dataset}.pkl', 'wb') as file:\n\n pickle.dump((strict, forgiving), file)","def save(self, target, withdP=False):\n from six.moves.cPickle import dump\n data = self.serialize(withdP=withdP)\n with open(target, 'wb') as f:\n dump(data, f)","def save(self, target, withdP=False):\n from six.moves.cPickle import dump\n data = self.serialize(withdP=withdP)\n with open(target, 'wb') as f:\n dump(data, f)","def pickle_dump(data, file):\n with open(file, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)","def serialize(self): \n with open(self.path+self.name, \"wb\") as pfile:\n pickle.dump(self.pyObj, pfile)","def save_object(obj, filename):\r\n with open(filename, 'wb') as output:\r\n pickle.dump(obj, output)","def save(self, filename=\"fitter.pickle\"):\n\n with open(filename, \"wb\") as outfile:\n pickle.dump(self, outfile)","def save_obj(obj, name):\n \n with open(name + '.pkl', 'wb') as objec:\n pickle.dump(obj, objec)","def storeData(data, filename='laue.dat'):\r\n import cPickle\r\n with open(filename, 'wb') as fp:\r\n cPickle.dump(data, fp)","def save_object(obj, filename):\n with open(filename, 'wb') as output_file: # Overwrites any existing file.\n pickle.dump(obj, output_file, pickle.HIGHEST_PROTOCOL)","def save(self, filename):\n import pickle\n if path.dirname(filename) and not path.exists(path.dirname(filename)):\n makedirs(path.dirname(filename))\n pickle.dump(self, open(filename, 'wb'), protocol=-1)","def save_obj(obj, name):\r\n with open('../pickle/' + name + '.pkl', 'wb') as fout:\r\n pickle.dump(obj, fout, pickle.HIGHEST_PROTOCOL)\r\n # end with\r","def save(self, filename):\n raise NotImplementedError","def save_object(obj, filename):\n with open(filename, 'wb') as output:\n pickle.dump(obj, output, protocol=2)","def _save(self):\n\t\t\n\t\tdirectory = self.Output_path\n\n\t\t# replace with \n\t\t# file_name = hermes.mk_themis_file_name(themis_obj = self)\n\t\tfile_name = f'Themis_{self.CELL_ID[\"experiment\"]}_u{self.CELL_ID[\"unit\"]}_c{self.CELL_ID[\"cell\"]}_r{self.CELL_ID[\"run\"]}.pkl'\n\n\t\tsave_path = directory / file_name\n\n\t\t# Atomic saving (helpful?)\n\t\ttemp_path = save_path.with_suffix(save_path.suffix + '.tmp')\n\t\t\n\t\tself.SavePath = save_path\n\n\t\t\n\t\twith open(temp_path, 'wb') as f:\n\t\t\tpickle.dump(self, f)\n\n\t\ttemp_path.rename(save_path)\n\n\t\tprint(f'Saved {self.RUN_KEY} as {save_path}')","def save_obj(obj, saved_name ):\n with open( saved_name + '.pkl', 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)","def dump(self, filename):\n\n pickle.dump(self, open(filename, \"w\"))","def dump(filename, data):\n _savez(filename, [], data, True, allow_pickle=False)","def save_obj(obj, name):\n with open('../../data/' + name + '.pkl', 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)","def save_obj(obj, name):\n with open('../../data/' + name + '.pkl', 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)","def save(obj, filename):\n import pickle\n with open(filename, 'w') as f:\n pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)","def _persist_output(self, output, dir):\r\n try:\r\n mkdirp(dir)\r\n filename = os.path.join(dir, 'output.pkl')\r\n numpy_pickle.dump(output, filename, compress=self.compress)\r\n if self._verbose > 10:\r\n print('Persisting in %s' % dir)\r\n except OSError:\r\n \" Race condition in the creation of the directory \"","def save(self, output, data):\n return","def dump_pickle(path, data):\n with open(path, 'wb') as f:\n pickle.dump(data, f)","def dumpme(self) :\n fileName = \"./data/oP4_ModelBuilder.dump\"\n with open(fileName,\"wb\") as dumpedFile:\n oPickler = pickle.Pickler(dumpedFile)\n oPickler.dump(self)"],"string":"[\n \"def store(self, filename):\",\n \"def pickle(self,data,filename):\\n pickle.dump(data, open(filename, 'wb'))\",\n \"def save_file_data(name, obj, input_path='/inputs'):\\n filename = '{}/{}.pkl'.format(input_path, name)\\n if not os.path.exists(os.path.dirname(filename)):\\n try:\\n os.makedirs(os.path.dirname(filename))\\n except OSError as exc: # Guard against race condition\\n if exc.errno != errno.EEXIST:\\n raise\\n with open(filename.format(name), 'wb+') as output:\\n pickle.dump(obj, output)\",\n \"def save(self, output, data):\",\n \"def save(data, filename):\\r\\n with open(filename, 'wb') as fp:\\r\\n pickle.dump(data, fp)\",\n \"def save(self,filename): \\n with open(filename, 'wb') as f:\\n pickle.dump(self,f)\",\n \"def save(self,filename):\\n with open(filename,'wb') as f:\\n pickle.dump(self,f)\",\n \"def save(self, filename):\\n pickle.dump(self, open(filename + '.p', 'wb'), 2)\",\n \"def store_pickle(fname, info, mode='w'):\\n assert fname[-2:] == '.p'\\n f = open(fname, mode)\\n pickle.dump(info, f)\\n f.close()\",\n \"def psave(var, filename):\\n pickle.dump(var, open(filename, 'wb'))\",\n \"def save_object(self, filename, data):\\n with open(filename, 'wb') as outp: # Overwrites any existing file.\\n pickle.dump(data, outp, pickle.HIGHEST_PROTOCOL)\",\n \"def save_as(self, filename):\\n assert type(filename) == str, 'ERROR: filename should be type str'\\n if '.pkl' in filename:\\n with open(filename, 'wb') as f:\\n dill.dump(self, f)\\n else:\\n with open(filename + '.pkl', 'wb') as f:\\n dill.dump(self, f)\",\n \"def save(fname, data):\\r\\n with open(fname, 'wb') as f:\\r\\n pickle.dump(data, f)\",\n \"def save(self, filename):\\n if '.pkl' not in filename:\\n filename = filename + '.pkl'\\n with open(filename, 'wb') as f:\\n pickle.dump(self, f)\",\n \"def save_input(self):\\n if not os.path.exists(self.wdir):\\n os.makedirs(self.wdir)\\n\\n with open(self.filepath, \\\"w\\\") as f:\\n f.write(self.input_string)\\n print(f\\\"-- Input file [{self.filename}] written successfully.\\\")\",\n \"def save_var(filename, data, protocol = -1, allow_dill=False):\\n if filename.endswith('.gz') :\\n open_method = gzip.open\\n else:\\n open_method = open\\n\\n output = open_method(filename, 'wb')\\n try:\\n # Pickle dictionary using given protocol\\n std_pickle.dump(data, output, protocol)\\n finally:\\n output.close()\\n\\n return\",\n \"def save(self, filename):\\n with open(filename, \\\"wb\\\") as f:\\n pkl.dump(self, f)\",\n \"def pickle_data(file_name, data):\\n outfile = open(file_name, \\\"wb\\\")\\n pickle.dump(data, outfile)\\n outfile.close()\",\n \"def save(self, filename, **kwargs):\\n with open(filename, 'wb') as fin:\\n pickle.dump(self, fin, **kwargs)\",\n \"def save(self,filename):\\n f = open(filename, 'wb')\\n pickle.dump(self,f)\\n f.close()\",\n \"def save_pickle(file, path):\\n with open(path, 'wb') as f:\\n pickle.dump(file, f)\\n file_name = re.findall(r\\\"/?[^/]+\\\", path)[-1].strip(\\\"/\\\")\\n print(f\\\"Stored {file_name}.\\\")\",\n \"def write_pickle_object_to_file(self, inpobj):\\n with gzip.open('%s.tmp' % self.pickle_file, 'wb') as pkl_file:\\n pickle.dump(inpobj, pkl_file, pickle.HIGHEST_PROTOCOL)\\n run_command('mv %s.tmp %s' % (self.pickle_file, self.pickle_file))\\n return True\",\n \"def save_pickle(filename, data, override=True):\\n filename = \\\"{}.pkl\\\".format(filename)\\n os.makedirs(os.path.dirname(filename), exist_ok=True)\\n\\n if override == False:\\n filename = add_unique_postfix(filename)\\n\\n with open(filename, \\\"wb\\\") as f:\\n pickle.dump(data, f)\\n\\n return filename\",\n \"def save(self,fn):\\n fn = fn if fn[-4:] == \\\".pkl\\\" else fn+\\\".pkl\\\"\\n with open(fn,\\\"wb+\\\") as f:\\n pickle.dump(self,f)\\n log(\\\"Saved reader to {}\\\".format(fn))\",\n \"def save(object, filename, protocol = 0):\\n file = gzip.GzipFile(filename, 'wb')\\n file.write(pickle.dumps(object, protocol))\\n file.close()\",\n \"def pickleSave(object, filename):\\n #Todo: Handle exceptions from pickle\\n filehandler = open(\\\"obj/\\\" + filename + \\\".obj\\\", 'wb')\\n pickle.dump(object, filehandler)\",\n \"def save(self, filename):\\n cPickle.dump(self, open(filename, \\\"wb\\\"))\",\n \"def pickle_data(filename, data):\\n f = open(filename, \\\"wb\\\")\\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\\n f.close()\",\n \"def save(self, file_name):\\n saved_data = { \\\"start_config\\\" : self.start_config, \\\"action_storage\\\" : self.action_storage } \\n with open(file_name, 'wb') as fh:\\n pickle.dump(saved_data, fh)\",\n \"def saveVar(var,name):\\n with open(name+'.pickle','wb') as fl:\\n pickle.dump(var,fl)\",\n \"def saveStuff(stuff, path=None):\\n if path == None:\\n # TODO take name from something\\n output = open('results/i-will-be-overwritten.pkl', 'wb')\\n else:\\n output = open(path, 'wb')\\n\\n # Pickle the list using the highest protocol available.\\n cPickle.dump(stuff, output, -1)\\n output.close()\",\n \"def saveStuff(stuff, path=None):\\n if path == None:\\n # TODO take name from something\\n output = open('results/i-will-be-overwritten.pkl', 'wb')\\n else:\\n output = open(path, 'wb')\\n\\n # Pickle the list using the highest protocol available.\\n cPickle.dump(stuff, output, -1)\\n output.close()\",\n \"def save(self, filename='test'):\\n file = open(filename+'.txt','w')\\n pickle.dump(self, file)\\n file.close()\",\n \"def save_pickle(obj, filename):\\n with open(filename, 'wb') as file:\\n pickle.dump(obj, file)\",\n \"def save(self, output, data):\\n pass\",\n \"def save_data(data: Any, file_name: str) -> None:\\n with open(file_name, \\\"wb\\\") as output:\\n pickle.dump(data, output)\",\n \"def do_pickle(self, arg):\\n try:\\n from pickling import Pickling\\n Pickling(arg, input(\\\"Please enter the name of file: \\\")).pickle_it()\\n except TypeError as e:\\n print(e)\\n except():\\n print(\\\"Error!!\\\")\",\n \"def save(self, filename):\\n with open(filename, 'wb') as f:\\n pickle.dump({'wi': self.W_input_to_hidden, 'wo': self.W_hidden_to_output}, f)\",\n \"def save_data_pickle(PATH, data, dataset, filename):\\n with open(PATH + '/' + dataset + \\\"_\\\" + filename + \\\".pkl\\\",\\\"wb\\\") as f:\\n pickle.dump(data,f)\\n print(filename, \\\"created\\\")\",\n \"def storePickle(filename, fit_diff_matrix):\\n store_filename = filename + \\\".pickle\\\"\\n with open(store_filename, \\\"wb\\\") as f:\\n pickle.dump(fit_diff_matrix, f)\",\n \"def write_to_file(name, obj):\\n\\n print 'writing structures to pickle'\\n print '----------------------------'\\n\\n path = os.getcwd() + '/pickles/' + name + '.pkl'\\n file = open(path, 'wb')\\n pickle.dump(obj, file)\\n file.close()\",\n \"def save(self):\\n pickle.dump(self, open(self.path, \\\"wb\\\"))\",\n \"def save(self, filename):\\n with gzip.open(filename, \\\"w\\\") as f:\\n f.write(pickle.dumps(self))\",\n \"def save(self, filename):\\n Util.savePickle(self.V, filename + self.ext, overwrite=True)\\n return filename + self.ext\",\n \"def save(self, filename):\\n pass\",\n \"def save_to_disk(name, object):\\n shortname = _dumpify(_compress_name(name) + '.pkl')\\n print 'save_to_disk(%s)' % shortname\\n pkl_file = open(shortname , 'wb')\\n pickle.dump(object, pkl_file, -1) # Pickle the list using the highest protocol available.\\n pkl_file.close()\",\n \"def save_pickle(file, variable):\\n if file.split('.')[-1] != \\\"pkl\\\":\\n file += \\\".pkl\\\"\\n\\n with open(file, 'wb') as f:\\n pickle.dump(variable, f)\\n print(\\\"Variable successfully saved in \\\" + file)\",\n \"def to_pickle(self, filename, **kwargs):\\n with open(filename, 'wb') as f:\\n pickle.dump(self, f)\",\n \"def pickle_dump(file_name: str, data: Any):\\n with open(file_name, 'wb') as file:\\n pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL)\",\n \"def save(self, fname):\\n pass\",\n \"def save_as(self, filename: str) -> None:\\n save_data = lzma.compress(pickle.dumps(self))\\n with open(filename, \\\"wb\\\") as f:\\n f.write(save_data)\",\n \"def _save_obj(obj, name):\\n with open('/bigdisk/pickles/' + name, 'w') as f:\\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\",\n \"def save(data, phonebook):\\n\\n with open(phonebook, \\\"w\\\") as outfile:\\n cPickle.dump(data, outfile)\",\n \"def savePickle(object, name):\\n epoch = time.time()\\n filename = name + str(epoch) + \\\".pkl\\\" # Save name\\n fullPath = path.join(PICKLE_DIR, filename) # Save path\\n\\n # Get permissions and save the file\\n with open(fullPath, \\\"w\\\") as outfile:\\n pickle.dump(object, outfile)\",\n \"def save(object, filename, protocol = 0):\\n file = gzip.GzipFile(filename, 'wb')\\n file.write(pickle.dumps(object, protocol))\\n file.close()\",\n \"def save(self, fname):\\n with open(fname, \\\"wb\\\") as f:\\n cloudpickle.dump(self, f)\\n # pickle.dump(self, open(fname, 'wb'))\",\n \"def _save(self):\\n # TODO: Use local.punny dump (when written)\\n with open(filename, 'w') as f:\\n pickle = Pickler(f)\\n pickle.dump(self.pungen.puns)\",\n \"def save(self, filename = 'array_zest', path = '/home/eric/dev/insitu/data/zs_recovery/'):\\n filename = filename# + '_Lx_' + str(self.Lx) + 'm_Ly_' + str(self.Ly) + 'm'\\n self.path_filename = path + filename + '.pkl'\\n f = open(self.path_filename, 'wb')\\n pickle.dump(self.__dict__, f, 2)\\n f.close()\",\n \"def save(self, target):\\n from six.moves.cPickle import dump\\n data = self.serialize()\\n with open(target, 'wb') as f:\\n dump(data, f)\",\n \"def save(self, target):\\n from six.moves.cPickle import dump\\n data = self.serialize()\\n with open(target, 'wb') as f:\\n dump(data, f)\",\n \"def dump_pickle_data(obj, filename):\\n path = \\\"../tmp/{}.pckl\\\".format(filename)\\n f = open(path, 'wb')\\n pickle.dump(obj, f)\\n f.close()\",\n \"def save(self, p):\\n pickle.dump(p, open('save.dat', 'wb'))\\n print(\\\"Game Saved!\\\")\",\n \"def save_model(self, step):\\n\\n # file_name = params['name']\\n # pickle.dump(self, gzip.open(file_name, 'wb'))\",\n \"def writePickle(self, filename):\\n \\n assert filename.endswith('.pkl')\\n file = open(filename, 'wb')\\n cPickle.dump(self, file, cPickle.HIGHEST_PROTOCOL)\",\n \"def save_pkl(dictionnary,directory,file_name):\\n with open(f'saved_exp_info/{directory}/{file_name}.pkl', 'wb') as output:\\n pickle.dump(dictionnary, output)\",\n \"def save_file(self, file_name: str):\\n pickle.dump(self, open(file_name, \\\"wb\\\"))\",\n \"def save_dict_pickle(PATH, data, filename):\\n with open(PATH + '/' + filename + \\\".pkl\\\",\\\"wb\\\") as f:\\n pickle.dump(data,f)\\n print(filename, \\\"created\\\")\",\n \"def save(self, file_name: str) -> None:\\n with open(file_name, \\\"wb\\\") as f:\\n pickle.dump(self, f)\",\n \"def save(self, filename):\\n pass\",\n \"def save(self):\\n if self.hasChanged:\\n filePath = self.path\\n tempPath = filePath+'.tmp'\\n fileDir = os.path.split(filePath)[0]\\n if not os.path.exists(fileDir): os.makedirs(fileDir)\\n cPickle.dump(self.data,open(tempPath,'w'))\\n renameFile(tempPath,filePath,True)\\n self.hasChanged = False\",\n \"def save(self, filename):\\n with open(filename, 'wb') as f:\\n pickle.dump({'wi': self.W_input_to_hidden, 'wh':\\n self.W_hidden_to_hidden, 'wo': self.W_hidden_to_output}, f)\",\n \"def pickleToDisk(population, fName):\\n pickle.dump(population, open(fName + \\\".pkl\\\", 'wb'), protocol=2)\",\n \"def write_pickle(obj, file_name):\\n with open(file_name, 'wb') as f:\\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\",\n \"def pickle_files(filename, stuff):\\n save_stuff = open(filename, \\\"wb\\\")\\n pickle.dump(stuff, save_stuff)\\n save_stuff.close()\",\n \"def save_reaction(self, filename):\\n filename = filename.replace('.pkl', '.gpkl')\\n filename = filename.replace('.bpkl', '.gpkl')\\n # Overwrites any existing file.\\n with gzip.GzipFile(filename, 'wb') as output:\\n pickle.dump(self, output, pickle.HIGHEST_PROTOCOL)\",\n \"def store_outcome(model_name, dataset, strict, forgiving):\\n model_name = model_name.replace('/', '-')\\n\\n with open(f'outcomes-{model_name}-{dataset}.pkl', 'wb') as file:\\n\\n pickle.dump((strict, forgiving), file)\",\n \"def save(self, target, withdP=False):\\n from six.moves.cPickle import dump\\n data = self.serialize(withdP=withdP)\\n with open(target, 'wb') as f:\\n dump(data, f)\",\n \"def save(self, target, withdP=False):\\n from six.moves.cPickle import dump\\n data = self.serialize(withdP=withdP)\\n with open(target, 'wb') as f:\\n dump(data, f)\",\n \"def pickle_dump(data, file):\\n with open(file, 'wb') as f:\\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\",\n \"def serialize(self): \\n with open(self.path+self.name, \\\"wb\\\") as pfile:\\n pickle.dump(self.pyObj, pfile)\",\n \"def save_object(obj, filename):\\r\\n with open(filename, 'wb') as output:\\r\\n pickle.dump(obj, output)\",\n \"def save(self, filename=\\\"fitter.pickle\\\"):\\n\\n with open(filename, \\\"wb\\\") as outfile:\\n pickle.dump(self, outfile)\",\n \"def save_obj(obj, name):\\n \\n with open(name + '.pkl', 'wb') as objec:\\n pickle.dump(obj, objec)\",\n \"def storeData(data, filename='laue.dat'):\\r\\n import cPickle\\r\\n with open(filename, 'wb') as fp:\\r\\n cPickle.dump(data, fp)\",\n \"def save_object(obj, filename):\\n with open(filename, 'wb') as output_file: # Overwrites any existing file.\\n pickle.dump(obj, output_file, pickle.HIGHEST_PROTOCOL)\",\n \"def save(self, filename):\\n import pickle\\n if path.dirname(filename) and not path.exists(path.dirname(filename)):\\n makedirs(path.dirname(filename))\\n pickle.dump(self, open(filename, 'wb'), protocol=-1)\",\n \"def save_obj(obj, name):\\r\\n with open('../pickle/' + name + '.pkl', 'wb') as fout:\\r\\n pickle.dump(obj, fout, pickle.HIGHEST_PROTOCOL)\\r\\n # end with\\r\",\n \"def save(self, filename):\\n raise NotImplementedError\",\n \"def save_object(obj, filename):\\n with open(filename, 'wb') as output:\\n pickle.dump(obj, output, protocol=2)\",\n \"def _save(self):\\n\\t\\t\\n\\t\\tdirectory = self.Output_path\\n\\n\\t\\t# replace with \\n\\t\\t# file_name = hermes.mk_themis_file_name(themis_obj = self)\\n\\t\\tfile_name = f'Themis_{self.CELL_ID[\\\"experiment\\\"]}_u{self.CELL_ID[\\\"unit\\\"]}_c{self.CELL_ID[\\\"cell\\\"]}_r{self.CELL_ID[\\\"run\\\"]}.pkl'\\n\\n\\t\\tsave_path = directory / file_name\\n\\n\\t\\t# Atomic saving (helpful?)\\n\\t\\ttemp_path = save_path.with_suffix(save_path.suffix + '.tmp')\\n\\t\\t\\n\\t\\tself.SavePath = save_path\\n\\n\\t\\t\\n\\t\\twith open(temp_path, 'wb') as f:\\n\\t\\t\\tpickle.dump(self, f)\\n\\n\\t\\ttemp_path.rename(save_path)\\n\\n\\t\\tprint(f'Saved {self.RUN_KEY} as {save_path}')\",\n \"def save_obj(obj, saved_name ):\\n with open( saved_name + '.pkl', 'wb') as f:\\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\",\n \"def dump(self, filename):\\n\\n pickle.dump(self, open(filename, \\\"w\\\"))\",\n \"def dump(filename, data):\\n _savez(filename, [], data, True, allow_pickle=False)\",\n \"def save_obj(obj, name):\\n with open('../../data/' + name + '.pkl', 'wb') as f:\\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\",\n \"def save_obj(obj, name):\\n with open('../../data/' + name + '.pkl', 'wb') as f:\\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\",\n \"def save(obj, filename):\\n import pickle\\n with open(filename, 'w') as f:\\n pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)\",\n \"def _persist_output(self, output, dir):\\r\\n try:\\r\\n mkdirp(dir)\\r\\n filename = os.path.join(dir, 'output.pkl')\\r\\n numpy_pickle.dump(output, filename, compress=self.compress)\\r\\n if self._verbose > 10:\\r\\n print('Persisting in %s' % dir)\\r\\n except OSError:\\r\\n \\\" Race condition in the creation of the directory \\\"\",\n \"def save(self, output, data):\\n return\",\n \"def dump_pickle(path, data):\\n with open(path, 'wb') as f:\\n pickle.dump(data, f)\",\n \"def dumpme(self) :\\n fileName = \\\"./data/oP4_ModelBuilder.dump\\\"\\n with open(fileName,\\\"wb\\\") as dumpedFile:\\n oPickler = pickle.Pickler(dumpedFile)\\n oPickler.dump(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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pickle.load"},"document":{"kind":"string","value":"def grab (filename) :\n cin = open (filename, 'r')\n return pickle.load (cin)"},"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 load(filename):\n with open(filename,'rb') as f:\n return pickle.load(self,f)","def load_data(file_name):\n with open(file_name, 'rb') as f:\n data = pickle.load(f)\n return data","def load_pickle_data(filename):\n path = \"../tmp/{}.pckl\".format(filename)\n if os.path.exists(path):\n print(\"LOADING PCKL FILE FROM {}\".format(path))\n f = open(path, 'rb')\n obj = pickle.load(f)\n f.close()\n return obj","def load(filename):\n with open(filename, 'rb') as f:\n return pickle.load(f)","def load(filename):\n import pickle\n return pickle.load(open(filename, 'r'))","def load(fname):\r\n with open(fname, 'rb') as f:\r\n data = pickle.load(f)\r\n return data","def load_file(self, filename):\n with open(filename, \"rb\") as pickle_handle:\n return pickle.load(pickle_handle)","def load_data_loader_from_file(cls, filename):\n print(\"Loading data loader from file: {}\".format(filename))\n\n with open(filename, \"rb\") as file:\n return pickle.load(file)","def load_object(self, filename):\n with open(filename, 'rb') as inp: # Overwrites any existing file.\n data = pickle.load(inp)\n return data","def _unpickle(filename):\n\n # Create full path for the file.\n file_path = _get_file_path(filename)\n\n print(\"Loading data: \" + file_path)\n\n with open(file_path, mode='rb') as file:\n # In Python 3.X it is important to set the encoding,\n # otherwise an exception is raised here.\n data = pickle.load(file, encoding='bytes')\n\n return data","def pload(filename):\n return pickle.load(open(filename, 'rb'))","def _load_file(name):\n filename = 'ind.{}.{}'.format(dataset_name, name)\n filename = os.path.join(path, filename)\n with open(filename, 'rb') as f:\n if sys.version_info > (3, 0):\n return pickle.load(f, encoding='latin1') # pylint: disable=unexpected-keyword-arg\n else:\n return pickle.load(f)","def load_data():\n with open('data.pickle', 'rb') as f:\n data = pickle.load(f)\n return data","def load(filename):\n try:\n with open(filename, 'rb') as f:\n data = pickle.load(f)\n return data\n except FileNotFoundError:\n return None","def load(self, filename):\n import pickle\n return pickle.load(open(filename, 'rb'))","def __init__(self, name, loadfile=None, loadpath=''):\n \n self.name = name\n \n if loadfile==None:\n self.data = []\n else:\n with open(loadpath+loadfile) as currentfile:\n self.data = pickle.load(currentfile)","def load_data_pickle(PATH, dataset, filename):\n with open(PATH + '/' + dataset + \"_\" + filename + \".pkl\",\"rb\") as f:\n new_data = pickle.load(f)\n\n # print(filename, \"opened\")\n return new_data","def load_data(file_name: str) -> Optional[Any]:\n with open(file_name, \"rb\") as input_data:\n data = pickle.load(input_data)\n return data","def load_data(filename) :\r\n data = Data()\r\n data.load(filename)\r\n return data","def load(self, filename):\n raise NotImplementedError","def load(cls, filename):\n return cPickle.load(open(filename, \"rb\"))","def load(self, filename):\n pass","def Load_Data(savedfilename):\n import pickle\n\n try:\n\n with open(savedfilename, 'rb') as handle:\n loaded_data = pickle.load(handle)\n print\n 'loaded successfully, fileloaded as as:\\nloaded_data'\n return loaded_data\n except:\n import numpy as np\n loaded_data = np.load(savedfilename)\n return loaded_data","def pickle_load(file_name: str) -> Any:\n with open(file_name, 'rb') as file:\n return pickle.load(file)","def load(self):\n logger.debug('Loading state from file %s', self.file_path)\n\n with open(self.file_path, 'rb') as f:\n self.data = pickle.load(f)","def load (self, filename) :\n\t\tserialFile = open (filename, \"rb\")\n\t\tself.production_rules = pickle.load (serialFile)\n\t\tself.unitrelation = pickle.load (serialFile)\n\t\tself.labels = pickle.load (serialFile)\n\t\tself.keeper = pickle.load (serialFile)\n\t\tself.strnodes = pickle.load(serialFile)\n\t\tself.tokens = pickle.load (serialFile)\n\t\tserialFile.close()","def pickle_from_file(fname):\n\ttry:\n\t\tfh = open(fname, 'r')\n\t\tdata = cPickle.load(fh)\n\t\tfh.close()\n\texcept:\n\t\t#raise\n\t\tprint \"Loading pickled data failed!\", sys.exc_info()[0]\n\t\tdata = None\n \n\treturn data","def pickleLoad(filename):\n #Todo: Handle exceptions from pickle\n filehandler = open(\"obj/\" + filename + \".obj\", 'rb')\n object = pickle.load(filehandler)\n return object","def load_pkl_data(path):\n with open(path, 'rb') as fi:\n data = pickle.load(fi)\n return data","def load(self, filename):\n with open(filename, 'r') as f:\n self.components, self.mean = pickle.load(f)","def load_pickle(path):\n with open(path, 'rb') as f:\n data = pickle.load(f)\n return data","def load_obj(name):\n with open('../../data/' + name + '.pkl', 'rb') as f:\n return pickle.load(f)","def load_obj(name):\n with open('../../data/' + name + '.pkl', 'rb') as f:\n return pickle.load(f)","def load_pickle(filename):\n with open(filename, 'rb') as file:\n obj = pickle.load(file)\n return obj","def load(cls,filename):\n obj = None\n f = open(filename,'r')\n try:\n obj = pickle.load(f)\n obj.filename = filename\n finally:\n f.close()\n return obj","def load_pickle(filename):\n\n with open(filename, 'rb') as file:\n if filename.split('.')[-1] == 'dill':\n obj = dill.load(file)\n else:\n obj = pickle.load(file)\n return obj","def load_pickle(filename):\n with open(filename, \"rb\") as f:\n obj = pickle.load(f)\n\n return obj","def load_files(filename):\n saved_stuff = open(filename, \"rb\")\n stuff = pickle.load(saved_stuff)\n saved_stuff.close()\n return stuff","def load(self, filename = 'array_zest', path = '/home/eric/dev/insitu/data/zs_recovery/'):\n lpath_filename = path + filename + '.pkl'\n f = open(lpath_filename, 'rb')\n tmp_dict = pickle.load(f)\n f.close()\n self.__dict__.update(tmp_dict)","def load_from_file(self, name):\n if os.path.isdir(\"saved_data\"):\n with open(f'saved_data/{name}.txt', 'rb') as file:\n data = pickle.load(file)\n print(\"Successfully load from file\")\n return data\n else:\n os.mkdir(\"saved_data\")\n self.load_from_file(name)","def load_pkl(file_name):\n with open(file_name) as fp:\n data = pkl.load(fp)\n return data","def load(self, filename=None, data=None):\n if filename is not None and data is None:\n with open(filename, 'rb') as fd:\n data = pickle.load(fd)\n elif filename is None and data is not None:\n pass\n else:\n raise ValueError(\"Exactly one of filename and data must be specified\")\n\n return self._load(data)","def loadVar(name):\n with open(name+'.pickle','rb') as fl:\n return pickle.load(fl)","def loadObj(name):\n\n with open(name + '.pkl', 'rb') as f:\n return pickle.load(f)","def loadPickle(pickle_file):\n print(\"Loading pickle data from file: \"+pickle_file)\n\n data = None\n try:\n with open(pickle_file, \"rb\") as fd:\n data = pickle.load(fd)\n except EOFError:\n pass\n except pickle.UnpicklingError as upe:\n print(\"Failed: Loading Pickle Data\")\n except IOError:\n data = {}\n\n return data","def inputfile(fileName):\n with open(fileName, \"rb\") as file:\n data = pickle.load(file)\n return data","def load_pickle(file_name):\n with open(file_name, \"rb\") as handle:\n pickle_file = pickle.load(handle)\n\n return pickle_file","def restoreData(filename='laue.dat'):\r\n import cPickle\r\n with open(filename, 'rb') as fp:\r\n return cPickle.load(fp)","def pickle_load(path):\n data = pickle.load(open(os.path.join(os.getcwd(), path), 'rb'))\n return data","def load(self,filename=None): # return True\r\n pass","def load_data(self) -> None:","def load(file_path):\n\t# assert type(file_path) == str, 'File_path must be a string'\n\t\n\twith open(file_path, 'rb') as f:\n\t\treturn pickle.load(f)","def load(self):\r\n self.read(self.filename)","def load_pickle(file):\n with open(file, 'rb') as fh:\n datum = pickle.load(fh)\n\n return datum","def read_data(self, workfile='workfile_tmp.p'):\n self.data = pickle.load(open(workfile, 'rb'))","def read_data(self, workfile='workfile_tmp.p'):\n self.data = pickle.load(open(workfile, 'rb'))","def parse_pickle(filename):\n \n with open(filename) as file:\n return pickle.load(file)","def load_obj(name):\r\n with open('../pickle/' + name + '.pkl', 'rb') as fout:\r\n return pickle.load(fout)\r\n # end with\r","def read_from_file(name):\n print 'reading structures from pickle'\n print '------------------------------'\n\n path = os.getcwd() + '/pickles/' + name + '.pkl'\n file = open(path, 'rb')\n new_obj = pickle.load(file)\n file.close()\n\n return new_obj","def unpickle(filename: str) -> dict:\n with open(os.path.join(f\"{ROOT_DIR}/dataset/\", filename), \"rb\") as file:\n dict = pickle.load(file, encoding=\"bytes\")\n return dict","def load(cls, filename):\n \n raise NotImplementedError(\"not implemented!\")","def _load_data(self, filename):\n if not os.path.isfile(filename):\n return False\n\n with open(filename) as f:\n data = pickle.load(f)\n if data:\n self.profiles = data['profiles']\n self.user_data = data['user_data']\n self.api_data = data['api_data']\n else:\n return False","def pickle_loader(fileobj):\n if isinstance(fileobj, bytes):\n data = pickle.loads(fileobj, encoding=\"latin1\")\n elif isinstance(fileobj, six.string_types):\n with open(fileobj, 'rb') as f:\n data = pickle.load(f, encoding=\"latin1\")\n elif hasattr(fileobj, 'read'):\n data = pickle.load(fileobj, encoding=\"latin1\")\n else:\n raise ValueError('fileobj is not a filename or a file object')\n return data","def load_synthetic_data():\n\n pickle_object = FM().data_file \n\n with pickle_object.open('rb') as data_file: \n return pickle.load(data_file)","def _load_obj(name):\n with open('/bigdisk/pickles/' + name, 'r') as f:\n return pickle.load(f)","def load(self, which):\n\t\tpath = os.path.join(self.storagedir, which)\n\t\tprint(\"Loading from\", path)\n\t\twith open(path, \"rb\") as handle:\n\t\t\tsetattr(self, which, _pickle.load(handle))","def safe_pickle_load(file_name):\n try:\n f = open(file_name, \"r\")\n try:\n data = pickle.load(f)\n except EOFError:\n data = None\n finally:\n f.close()\n except IOError:\n data = None\n\n return data","def load(cls, filename, **kwargs):\n with open(filename, 'rb') as fin:\n self = pickle.load(fin, **kwargs)\n self._check_types()\n return self","def load_data(filename):\r\n with open(filename,'rb') as f:\r\n data = pk.load(f,encoding='bytes')\r\n return data[b'data'],data[b'labels']","def load_from_file(self, filename):\n # clear datastore mape\n self._datastoreMap = {}\n # citanje filea\n with open(filename, 'rb') as f:\n binstr = f.read()\n inMap = pickle.loads(binstr)\n # za svaki kanal moramo dodati element u _datastoreMap\n for kanal in inMap:\n # stvaramo instancu Datastore\n self._datastoreMap[kanal] = DataStore()\n # instanca Datastore zna se otpakirati iz mape (dictionary)\n self._datastoreMap[kanal].dict2store(inMap[kanal])","def _load_binary(file_name):\n try:\n with open(file_name, 'rb') as f:\n return cp.load(f)\n except UnicodeDecodeError: # When loading Python 2 pickle from Python 3\n with open(file_name, 'rb') as f:\n return cp.load(f, encoding=\"latin1\")","def load(self, filename):\n f = open(filename, 'rb')\n try:\n data = pickle.load(f)\n activities, schedules, resources, resourceAsignaments = data\n except (pickle.UnpicklingError, AttributeError, EOFError, ImportError, IndexError, ValueError, KeyError):\n raise InvalidFileFormatException('Unpickle failed')\n\n # Check activities, schedules, resources, resourceAsignaments have the right data structure\n for row in activities:\n if len(row) != 9:\n raise InvalidFileFormatException('Incorrect data on file')\n \n f.close()\n return data","def load(self, filename):\n with open(filename, 'rb') as f:\n data = pickle.load(f)\n # Set biases and weights\n self.W_input_to_hidden = data['wi']\n self.W_hidden_to_output = data['wo']","def load_pickle(path):\n with open(path, 'rb') as handle:\n return pickle.load(handle)","def read_pickle(file_name):\n with open(file_name, 'rb') as f:\n obj = pickle.load(f)\n return obj","def loads(data):\n return cPickle.loads(data)","def load(cls, file_id):\n if not isinstance(file_id, file):\n handle = open(\n \"{:s}{:s}-{:d}.pckl\".format(\n DUMP_PATH,\n cls.__name__,\n file_id\n ),\n \"rb\")\n else:\n handle = file_id\n return pickle.load(handle)","def load_model(self, file_name):\n with open(file_name, 'rb') as file:\n self.lin_reg = pickle.load(file)","def loadData(dataPathFile):\r\n if dataPathFile[-3:] == 'pkl':\r\n dataBaseDict = pickle.load(open(dataPathFile, 'rb'))\r\n return dataBaseDict\r\n else:\r\n raise Exception('File that is trying to be loaded is not a pickle file\\n')","def load_data():\r\n print ('Loadng all the file one time......')\r\n if not os.path.exists('cifar.pkl'):\r\n set_data()\r\n with open('cifar.pkl', 'rb') as cifar_pickle:\r\n data = six.moves.cPickle.load(cifar_pickle)\r\n return data","def readDataFromPickle(config, filename):\n path = config.picklePath + filename\n\n # if stopid in pickle variable and it is a dataframe-> sort columns\n return pd.read_pickle(path)","def pickle_load(path):\n try:\n data = pickle.load(open(path, \"rb\"))\n return data\n except UnpicklingError:\n unix_path = path.replace(\".pkl\", \"_unix.pkl\")\n try:\n data = pickle.load(open(unix_path, \"rb\"))\n return data\n except FileNotFoundError:\n path = _to_unix(path)\n data = pickle.load(open(path, \"rb\"))\n return data","def load_data(self):","def unpickle_data(file_name):\n infile = open(file_name, \"rb\")\n try:\n data = pickle.load(infile)\n except:\n data = {}\n infile.close()\n\n return data","def loadPickle(filepath):\n\tf = open(filepath, 'rb')\n\tobj = pickle.load(f)\n\tf.close()\n\treturn obj","def load_groups(filename):\r\n with open(filename, 'rb') as f:\r\n saved_data = pickle.load(f)\r\n return saved_data","def load_object(filename):\r\n with open(filename, 'rb') as input:\r\n obj = pickle.load(input)\r\n return obj","def load_mapping(filename):\n with open(filename + '.pkl', 'rb') as handle:\n return pickle.load(handle)","def pickle(self,data,filename):\n pickle.dump(data, open(filename, 'wb'))","def load(self, filename):\n with open(filename, 'rb') as f:\n data = pickle.load(f)\n # Set biases and weights\n self.W_input_to_hidden = data['wi']\n self.W_hidden_to_hidden = data['wh']\n self.W_hidden_to_output = data['wo']","def load_pickle(filepath):\n logging.info('Loading object from pickle: {}'.format(filepath))\n with open(filepath, 'rb') as infile:\n return pickle.load(infile)","def read_pickle(path):\n with open(path, \"rb\") as f:\n data = pickle.load(f)\n\n return data","def load_pickle(path):\n with open(path, 'rb') as f:\n pickle_file = pickle.load(f)\n file_name = re.findall(r\"/?[^/]+\", path)[-1].strip(\"/\")\n print(f\"Loaded {file_name}.\")\n return pickle_file","def pickle_load(file_path):\n if not os.path.isfile(file_path):\n return None\n\n with open(file_path, 'rb') as f:\n return pickle.load(f)","def load_data_set_from_pickle(file_name=None):\n if not file_name:\n try:\n file_name = max(glob.glob(os.path.join(__pickled_data_directory__, '*.chars74k-lite.gz')), key=os.path.getctime)\n except ValueError as e:\n log.error('Unable to load data set from file since no pickled files could be found, ')\n return None\n\n log.debug('Loading data set from file: %s' % file_name)\n return unpickle_data(file_name)","def load_data(\n self, file_path: str = os.path.join(os.getcwd(), \"data_breast_cancer.p\")\n ) -> None:\n with open(file_path, \"rb\") as file:\n data = pickle.load(file)\n self.x_train, self.y_train = data[\"x_train\"], data[\"y_train\"]\n self.x_test, self.y_test = data[\"x_test\"], data[\"y_test\"]","def open_pickle_file(file_name):\n print(\"Unpickling file \" + file_name)\n full_file_name = full_path(file_name)\n with open(full_file_name, mode='rb') as f:\n return pickle.load(f)","def load_data_pickle(self, load_full=False):\n self.train = pd.read_pickle('../input/train_mod.pkl')\n self.test = pd.read_pickle('../input/test_mod.pkl')\n if load_full:\n self.train_full = pd.read_pickle('../input/train_full_mod.pkl')","def load_dictionary(filename):\n filename = os.path.join(FILE_DIR, 'assets/obj/' + filename)\n try:\n with open(filename, 'rb') as input:\n return pickle.load(input)\n except Exception as e:\n print(\"exception\", e)","def pickleload(path):\n with open(path, 'rb') as file:\n loaded = pickle.load(file)\n return loaded"],"string":"[\n \"def load(filename):\\n with open(filename,'rb') as f:\\n return pickle.load(self,f)\",\n \"def load_data(file_name):\\n with open(file_name, 'rb') as f:\\n data = pickle.load(f)\\n return data\",\n \"def load_pickle_data(filename):\\n path = \\\"../tmp/{}.pckl\\\".format(filename)\\n if os.path.exists(path):\\n print(\\\"LOADING PCKL FILE FROM {}\\\".format(path))\\n f = open(path, 'rb')\\n obj = pickle.load(f)\\n f.close()\\n return obj\",\n \"def load(filename):\\n with open(filename, 'rb') as f:\\n return pickle.load(f)\",\n \"def load(filename):\\n import pickle\\n return pickle.load(open(filename, 'r'))\",\n \"def load(fname):\\r\\n with open(fname, 'rb') as f:\\r\\n data = pickle.load(f)\\r\\n return data\",\n \"def load_file(self, filename):\\n with open(filename, \\\"rb\\\") as pickle_handle:\\n return pickle.load(pickle_handle)\",\n \"def load_data_loader_from_file(cls, filename):\\n print(\\\"Loading data loader from file: {}\\\".format(filename))\\n\\n with open(filename, \\\"rb\\\") as file:\\n return pickle.load(file)\",\n \"def load_object(self, filename):\\n with open(filename, 'rb') as inp: # Overwrites any existing file.\\n data = pickle.load(inp)\\n return data\",\n \"def _unpickle(filename):\\n\\n # Create full path for the file.\\n file_path = _get_file_path(filename)\\n\\n print(\\\"Loading data: \\\" + file_path)\\n\\n with open(file_path, mode='rb') as file:\\n # In Python 3.X it is important to set the encoding,\\n # otherwise an exception is raised here.\\n data = pickle.load(file, encoding='bytes')\\n\\n return data\",\n \"def pload(filename):\\n return pickle.load(open(filename, 'rb'))\",\n \"def _load_file(name):\\n filename = 'ind.{}.{}'.format(dataset_name, name)\\n filename = os.path.join(path, filename)\\n with open(filename, 'rb') as f:\\n if sys.version_info > (3, 0):\\n return pickle.load(f, encoding='latin1') # pylint: disable=unexpected-keyword-arg\\n else:\\n return pickle.load(f)\",\n \"def load_data():\\n with open('data.pickle', 'rb') as f:\\n data = pickle.load(f)\\n return data\",\n \"def load(filename):\\n try:\\n with open(filename, 'rb') as f:\\n data = pickle.load(f)\\n return data\\n except FileNotFoundError:\\n return None\",\n \"def load(self, filename):\\n import pickle\\n return pickle.load(open(filename, 'rb'))\",\n \"def __init__(self, name, loadfile=None, loadpath=''):\\n \\n self.name = name\\n \\n if loadfile==None:\\n self.data = []\\n else:\\n with open(loadpath+loadfile) as currentfile:\\n self.data = pickle.load(currentfile)\",\n \"def load_data_pickle(PATH, dataset, filename):\\n with open(PATH + '/' + dataset + \\\"_\\\" + filename + \\\".pkl\\\",\\\"rb\\\") as f:\\n new_data = pickle.load(f)\\n\\n # print(filename, \\\"opened\\\")\\n return new_data\",\n \"def load_data(file_name: str) -> Optional[Any]:\\n with open(file_name, \\\"rb\\\") as input_data:\\n data = pickle.load(input_data)\\n return data\",\n \"def load_data(filename) :\\r\\n data = Data()\\r\\n data.load(filename)\\r\\n return data\",\n \"def load(self, filename):\\n raise NotImplementedError\",\n \"def load(cls, filename):\\n return cPickle.load(open(filename, \\\"rb\\\"))\",\n \"def load(self, filename):\\n pass\",\n \"def Load_Data(savedfilename):\\n import pickle\\n\\n try:\\n\\n with open(savedfilename, 'rb') as handle:\\n loaded_data = pickle.load(handle)\\n print\\n 'loaded successfully, fileloaded as as:\\\\nloaded_data'\\n return loaded_data\\n except:\\n import numpy as np\\n loaded_data = np.load(savedfilename)\\n return loaded_data\",\n \"def pickle_load(file_name: str) -> Any:\\n with open(file_name, 'rb') as file:\\n return pickle.load(file)\",\n \"def load(self):\\n logger.debug('Loading state from file %s', self.file_path)\\n\\n with open(self.file_path, 'rb') as f:\\n self.data = pickle.load(f)\",\n \"def load (self, filename) :\\n\\t\\tserialFile = open (filename, \\\"rb\\\")\\n\\t\\tself.production_rules = pickle.load (serialFile)\\n\\t\\tself.unitrelation = pickle.load (serialFile)\\n\\t\\tself.labels = pickle.load (serialFile)\\n\\t\\tself.keeper = pickle.load (serialFile)\\n\\t\\tself.strnodes = pickle.load(serialFile)\\n\\t\\tself.tokens = pickle.load (serialFile)\\n\\t\\tserialFile.close()\",\n \"def pickle_from_file(fname):\\n\\ttry:\\n\\t\\tfh = open(fname, 'r')\\n\\t\\tdata = cPickle.load(fh)\\n\\t\\tfh.close()\\n\\texcept:\\n\\t\\t#raise\\n\\t\\tprint \\\"Loading pickled data failed!\\\", sys.exc_info()[0]\\n\\t\\tdata = None\\n \\n\\treturn data\",\n \"def pickleLoad(filename):\\n #Todo: Handle exceptions from pickle\\n filehandler = open(\\\"obj/\\\" + filename + \\\".obj\\\", 'rb')\\n object = pickle.load(filehandler)\\n return object\",\n \"def load_pkl_data(path):\\n with open(path, 'rb') as fi:\\n data = pickle.load(fi)\\n return data\",\n \"def load(self, filename):\\n with open(filename, 'r') as f:\\n self.components, self.mean = pickle.load(f)\",\n \"def load_pickle(path):\\n with open(path, 'rb') as f:\\n data = pickle.load(f)\\n return data\",\n \"def load_obj(name):\\n with open('../../data/' + name + '.pkl', 'rb') as f:\\n return pickle.load(f)\",\n \"def load_obj(name):\\n with open('../../data/' + name + '.pkl', 'rb') as f:\\n return pickle.load(f)\",\n \"def load_pickle(filename):\\n with open(filename, 'rb') as file:\\n obj = pickle.load(file)\\n return obj\",\n \"def load(cls,filename):\\n obj = None\\n f = open(filename,'r')\\n try:\\n obj = pickle.load(f)\\n obj.filename = filename\\n finally:\\n f.close()\\n return obj\",\n \"def load_pickle(filename):\\n\\n with open(filename, 'rb') as file:\\n if filename.split('.')[-1] == 'dill':\\n obj = dill.load(file)\\n else:\\n obj = pickle.load(file)\\n return obj\",\n \"def load_pickle(filename):\\n with open(filename, \\\"rb\\\") as f:\\n obj = pickle.load(f)\\n\\n return obj\",\n \"def load_files(filename):\\n saved_stuff = open(filename, \\\"rb\\\")\\n stuff = pickle.load(saved_stuff)\\n saved_stuff.close()\\n return stuff\",\n \"def load(self, filename = 'array_zest', path = '/home/eric/dev/insitu/data/zs_recovery/'):\\n lpath_filename = path + filename + '.pkl'\\n f = open(lpath_filename, 'rb')\\n tmp_dict = pickle.load(f)\\n f.close()\\n self.__dict__.update(tmp_dict)\",\n \"def load_from_file(self, name):\\n if os.path.isdir(\\\"saved_data\\\"):\\n with open(f'saved_data/{name}.txt', 'rb') as file:\\n data = pickle.load(file)\\n print(\\\"Successfully load from file\\\")\\n return data\\n else:\\n os.mkdir(\\\"saved_data\\\")\\n self.load_from_file(name)\",\n \"def load_pkl(file_name):\\n with open(file_name) as fp:\\n data = pkl.load(fp)\\n return data\",\n \"def load(self, filename=None, data=None):\\n if filename is not None and data is None:\\n with open(filename, 'rb') as fd:\\n data = pickle.load(fd)\\n elif filename is None and data is not None:\\n pass\\n else:\\n raise ValueError(\\\"Exactly one of filename and data must be specified\\\")\\n\\n return self._load(data)\",\n \"def loadVar(name):\\n with open(name+'.pickle','rb') as fl:\\n return pickle.load(fl)\",\n \"def loadObj(name):\\n\\n with open(name + '.pkl', 'rb') as f:\\n return pickle.load(f)\",\n \"def loadPickle(pickle_file):\\n print(\\\"Loading pickle data from file: \\\"+pickle_file)\\n\\n data = None\\n try:\\n with open(pickle_file, \\\"rb\\\") as fd:\\n data = pickle.load(fd)\\n except EOFError:\\n pass\\n except pickle.UnpicklingError as upe:\\n print(\\\"Failed: Loading Pickle Data\\\")\\n except IOError:\\n data = {}\\n\\n return data\",\n \"def inputfile(fileName):\\n with open(fileName, \\\"rb\\\") as file:\\n data = pickle.load(file)\\n return data\",\n \"def load_pickle(file_name):\\n with open(file_name, \\\"rb\\\") as handle:\\n pickle_file = pickle.load(handle)\\n\\n return pickle_file\",\n \"def restoreData(filename='laue.dat'):\\r\\n import cPickle\\r\\n with open(filename, 'rb') as fp:\\r\\n return cPickle.load(fp)\",\n \"def pickle_load(path):\\n data = pickle.load(open(os.path.join(os.getcwd(), path), 'rb'))\\n return data\",\n \"def load(self,filename=None): # return True\\r\\n pass\",\n \"def load_data(self) -> None:\",\n \"def load(file_path):\\n\\t# assert type(file_path) == str, 'File_path must be a string'\\n\\t\\n\\twith open(file_path, 'rb') as f:\\n\\t\\treturn pickle.load(f)\",\n \"def load(self):\\r\\n self.read(self.filename)\",\n \"def load_pickle(file):\\n with open(file, 'rb') as fh:\\n datum = pickle.load(fh)\\n\\n return datum\",\n \"def read_data(self, workfile='workfile_tmp.p'):\\n self.data = pickle.load(open(workfile, 'rb'))\",\n \"def read_data(self, workfile='workfile_tmp.p'):\\n self.data = pickle.load(open(workfile, 'rb'))\",\n \"def parse_pickle(filename):\\n \\n with open(filename) as file:\\n return pickle.load(file)\",\n \"def load_obj(name):\\r\\n with open('../pickle/' + name + '.pkl', 'rb') as fout:\\r\\n return pickle.load(fout)\\r\\n # end with\\r\",\n \"def read_from_file(name):\\n print 'reading structures from pickle'\\n print '------------------------------'\\n\\n path = os.getcwd() + '/pickles/' + name + '.pkl'\\n file = open(path, 'rb')\\n new_obj = pickle.load(file)\\n file.close()\\n\\n return new_obj\",\n \"def unpickle(filename: str) -> dict:\\n with open(os.path.join(f\\\"{ROOT_DIR}/dataset/\\\", filename), \\\"rb\\\") as file:\\n dict = pickle.load(file, encoding=\\\"bytes\\\")\\n return dict\",\n \"def load(cls, filename):\\n \\n raise NotImplementedError(\\\"not implemented!\\\")\",\n \"def _load_data(self, filename):\\n if not os.path.isfile(filename):\\n return False\\n\\n with open(filename) as f:\\n data = pickle.load(f)\\n if data:\\n self.profiles = data['profiles']\\n self.user_data = data['user_data']\\n self.api_data = data['api_data']\\n else:\\n return False\",\n \"def pickle_loader(fileobj):\\n if isinstance(fileobj, bytes):\\n data = pickle.loads(fileobj, encoding=\\\"latin1\\\")\\n elif isinstance(fileobj, six.string_types):\\n with open(fileobj, 'rb') as f:\\n data = pickle.load(f, encoding=\\\"latin1\\\")\\n elif hasattr(fileobj, 'read'):\\n data = pickle.load(fileobj, encoding=\\\"latin1\\\")\\n else:\\n raise ValueError('fileobj is not a filename or a file object')\\n return data\",\n \"def load_synthetic_data():\\n\\n pickle_object = FM().data_file \\n\\n with pickle_object.open('rb') as data_file: \\n return pickle.load(data_file)\",\n \"def _load_obj(name):\\n with open('/bigdisk/pickles/' + name, 'r') as f:\\n return pickle.load(f)\",\n \"def load(self, which):\\n\\t\\tpath = os.path.join(self.storagedir, which)\\n\\t\\tprint(\\\"Loading from\\\", path)\\n\\t\\twith open(path, \\\"rb\\\") as handle:\\n\\t\\t\\tsetattr(self, which, _pickle.load(handle))\",\n \"def safe_pickle_load(file_name):\\n try:\\n f = open(file_name, \\\"r\\\")\\n try:\\n data = pickle.load(f)\\n except EOFError:\\n data = None\\n finally:\\n f.close()\\n except IOError:\\n data = None\\n\\n return data\",\n \"def load(cls, filename, **kwargs):\\n with open(filename, 'rb') as fin:\\n self = pickle.load(fin, **kwargs)\\n self._check_types()\\n return self\",\n \"def load_data(filename):\\r\\n with open(filename,'rb') as f:\\r\\n data = pk.load(f,encoding='bytes')\\r\\n return data[b'data'],data[b'labels']\",\n \"def load_from_file(self, filename):\\n # clear datastore mape\\n self._datastoreMap = {}\\n # citanje filea\\n with open(filename, 'rb') as f:\\n binstr = f.read()\\n inMap = pickle.loads(binstr)\\n # za svaki kanal moramo dodati element u _datastoreMap\\n for kanal in inMap:\\n # stvaramo instancu Datastore\\n self._datastoreMap[kanal] = DataStore()\\n # instanca Datastore zna se otpakirati iz mape (dictionary)\\n self._datastoreMap[kanal].dict2store(inMap[kanal])\",\n \"def _load_binary(file_name):\\n try:\\n with open(file_name, 'rb') as f:\\n return cp.load(f)\\n except UnicodeDecodeError: # When loading Python 2 pickle from Python 3\\n with open(file_name, 'rb') as f:\\n return cp.load(f, encoding=\\\"latin1\\\")\",\n \"def load(self, filename):\\n f = open(filename, 'rb')\\n try:\\n data = pickle.load(f)\\n activities, schedules, resources, resourceAsignaments = data\\n except (pickle.UnpicklingError, AttributeError, EOFError, ImportError, IndexError, ValueError, KeyError):\\n raise InvalidFileFormatException('Unpickle failed')\\n\\n # Check activities, schedules, resources, resourceAsignaments have the right data structure\\n for row in activities:\\n if len(row) != 9:\\n raise InvalidFileFormatException('Incorrect data on file')\\n \\n f.close()\\n return data\",\n \"def load(self, filename):\\n with open(filename, 'rb') as f:\\n data = pickle.load(f)\\n # Set biases and weights\\n self.W_input_to_hidden = data['wi']\\n self.W_hidden_to_output = data['wo']\",\n \"def load_pickle(path):\\n with open(path, 'rb') as handle:\\n return pickle.load(handle)\",\n \"def read_pickle(file_name):\\n with open(file_name, 'rb') as f:\\n obj = pickle.load(f)\\n return obj\",\n \"def loads(data):\\n return cPickle.loads(data)\",\n \"def load(cls, file_id):\\n if not isinstance(file_id, file):\\n handle = open(\\n \\\"{:s}{:s}-{:d}.pckl\\\".format(\\n DUMP_PATH,\\n cls.__name__,\\n file_id\\n ),\\n \\\"rb\\\")\\n else:\\n handle = file_id\\n return pickle.load(handle)\",\n \"def load_model(self, file_name):\\n with open(file_name, 'rb') as file:\\n self.lin_reg = pickle.load(file)\",\n \"def loadData(dataPathFile):\\r\\n if dataPathFile[-3:] == 'pkl':\\r\\n dataBaseDict = pickle.load(open(dataPathFile, 'rb'))\\r\\n return dataBaseDict\\r\\n else:\\r\\n raise Exception('File that is trying to be loaded is not a pickle file\\\\n')\",\n \"def load_data():\\r\\n print ('Loadng all the file one time......')\\r\\n if not os.path.exists('cifar.pkl'):\\r\\n set_data()\\r\\n with open('cifar.pkl', 'rb') as cifar_pickle:\\r\\n data = six.moves.cPickle.load(cifar_pickle)\\r\\n return data\",\n \"def readDataFromPickle(config, filename):\\n path = config.picklePath + filename\\n\\n # if stopid in pickle variable and it is a dataframe-> sort columns\\n return pd.read_pickle(path)\",\n \"def pickle_load(path):\\n try:\\n data = pickle.load(open(path, \\\"rb\\\"))\\n return data\\n except UnpicklingError:\\n unix_path = path.replace(\\\".pkl\\\", \\\"_unix.pkl\\\")\\n try:\\n data = pickle.load(open(unix_path, \\\"rb\\\"))\\n return data\\n except FileNotFoundError:\\n path = _to_unix(path)\\n data = pickle.load(open(path, \\\"rb\\\"))\\n return data\",\n \"def load_data(self):\",\n \"def unpickle_data(file_name):\\n infile = open(file_name, \\\"rb\\\")\\n try:\\n data = pickle.load(infile)\\n except:\\n data = {}\\n infile.close()\\n\\n return data\",\n \"def loadPickle(filepath):\\n\\tf = open(filepath, 'rb')\\n\\tobj = pickle.load(f)\\n\\tf.close()\\n\\treturn obj\",\n \"def load_groups(filename):\\r\\n with open(filename, 'rb') as f:\\r\\n saved_data = pickle.load(f)\\r\\n return saved_data\",\n \"def load_object(filename):\\r\\n with open(filename, 'rb') as input:\\r\\n obj = pickle.load(input)\\r\\n return obj\",\n \"def load_mapping(filename):\\n with open(filename + '.pkl', 'rb') as handle:\\n return pickle.load(handle)\",\n \"def pickle(self,data,filename):\\n pickle.dump(data, open(filename, 'wb'))\",\n \"def load(self, filename):\\n with open(filename, 'rb') as f:\\n data = pickle.load(f)\\n # Set biases and weights\\n self.W_input_to_hidden = data['wi']\\n self.W_hidden_to_hidden = data['wh']\\n self.W_hidden_to_output = data['wo']\",\n \"def load_pickle(filepath):\\n logging.info('Loading object from pickle: {}'.format(filepath))\\n with open(filepath, 'rb') as infile:\\n return pickle.load(infile)\",\n \"def read_pickle(path):\\n with open(path, \\\"rb\\\") as f:\\n data = pickle.load(f)\\n\\n return data\",\n \"def load_pickle(path):\\n with open(path, 'rb') as f:\\n pickle_file = pickle.load(f)\\n file_name = re.findall(r\\\"/?[^/]+\\\", path)[-1].strip(\\\"/\\\")\\n print(f\\\"Loaded {file_name}.\\\")\\n return pickle_file\",\n \"def pickle_load(file_path):\\n if not os.path.isfile(file_path):\\n return None\\n\\n with open(file_path, 'rb') as f:\\n return pickle.load(f)\",\n \"def load_data_set_from_pickle(file_name=None):\\n if not file_name:\\n try:\\n file_name = max(glob.glob(os.path.join(__pickled_data_directory__, '*.chars74k-lite.gz')), key=os.path.getctime)\\n except ValueError as e:\\n log.error('Unable to load data set from file since no pickled files could be found, ')\\n return None\\n\\n log.debug('Loading data set from file: %s' % file_name)\\n return unpickle_data(file_name)\",\n \"def load_data(\\n self, file_path: str = os.path.join(os.getcwd(), \\\"data_breast_cancer.p\\\")\\n ) -> None:\\n with open(file_path, \\\"rb\\\") as file:\\n data = pickle.load(file)\\n self.x_train, self.y_train = data[\\\"x_train\\\"], data[\\\"y_train\\\"]\\n self.x_test, self.y_test = data[\\\"x_test\\\"], data[\\\"y_test\\\"]\",\n \"def open_pickle_file(file_name):\\n print(\\\"Unpickling file \\\" + file_name)\\n full_file_name = full_path(file_name)\\n with open(full_file_name, mode='rb') as f:\\n return pickle.load(f)\",\n \"def load_data_pickle(self, load_full=False):\\n self.train = pd.read_pickle('../input/train_mod.pkl')\\n self.test = pd.read_pickle('../input/test_mod.pkl')\\n if load_full:\\n self.train_full = pd.read_pickle('../input/train_full_mod.pkl')\",\n \"def load_dictionary(filename):\\n filename = os.path.join(FILE_DIR, 'assets/obj/' + filename)\\n try:\\n with open(filename, 'rb') as input:\\n return pickle.load(input)\\n except Exception as e:\\n print(\\\"exception\\\", e)\",\n \"def pickleload(path):\\n with open(path, 'rb') as file:\\n loaded = pickle.load(file)\\n return loaded\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7869546","0.7829951","0.7812556","0.77086544","0.76826143","0.7677001","0.76607376","0.7654821","0.7640084","0.7521244","0.75156707","0.7463171","0.7457279","0.743854","0.7430627","0.7389005","0.7344408","0.73272413","0.7275342","0.72668594","0.7232986","0.7232883","0.7228265","0.72269726","0.72066486","0.71936893","0.71740854","0.71422744","0.7137935","0.71364707","0.7132823","0.7108738","0.7108738","0.7079098","0.7074106","0.7035405","0.70230824","0.7009045","0.6985634","0.69816726","0.6972461","0.69690603","0.69661814","0.6965648","0.6958563","0.69576585","0.6945246","0.69389296","0.69364303","0.6932124","0.6924645","0.6923586","0.69224226","0.69188523","0.6916983","0.6916983","0.6909045","0.6907214","0.68619585","0.6843989","0.6836591","0.68264335","0.681761","0.68156236","0.6798232","0.67977726","0.6795571","0.6795194","0.6789722","0.67873406","0.6780652","0.67763317","0.67752737","0.6755705","0.67475593","0.67463636","0.6740027","0.67399514","0.6734372","0.6731503","0.6727815","0.6709846","0.6706473","0.67027295","0.66999316","0.66965574","0.6692341","0.66900307","0.66849905","0.6684788","0.6682241","0.66821694","0.66701895","0.6665038","0.66571206","0.66533077","0.6647079","0.6640107","0.6636412","0.6636155"],"string":"[\n \"0.7869546\",\n \"0.7829951\",\n \"0.7812556\",\n \"0.77086544\",\n \"0.76826143\",\n \"0.7677001\",\n \"0.76607376\",\n \"0.7654821\",\n \"0.7640084\",\n \"0.7521244\",\n \"0.75156707\",\n \"0.7463171\",\n \"0.7457279\",\n \"0.743854\",\n \"0.7430627\",\n \"0.7389005\",\n \"0.7344408\",\n \"0.73272413\",\n \"0.7275342\",\n \"0.72668594\",\n \"0.7232986\",\n \"0.7232883\",\n \"0.7228265\",\n \"0.72269726\",\n \"0.72066486\",\n \"0.71936893\",\n \"0.71740854\",\n \"0.71422744\",\n \"0.7137935\",\n \"0.71364707\",\n \"0.7132823\",\n \"0.7108738\",\n \"0.7108738\",\n \"0.7079098\",\n \"0.7074106\",\n \"0.7035405\",\n \"0.70230824\",\n \"0.7009045\",\n \"0.6985634\",\n \"0.69816726\",\n \"0.6972461\",\n \"0.69690603\",\n \"0.69661814\",\n \"0.6965648\",\n \"0.6958563\",\n \"0.69576585\",\n \"0.6945246\",\n \"0.69389296\",\n \"0.69364303\",\n \"0.6932124\",\n \"0.6924645\",\n \"0.6923586\",\n \"0.69224226\",\n \"0.69188523\",\n \"0.6916983\",\n \"0.6916983\",\n \"0.6909045\",\n \"0.6907214\",\n \"0.68619585\",\n \"0.6843989\",\n \"0.6836591\",\n \"0.68264335\",\n \"0.681761\",\n \"0.68156236\",\n \"0.6798232\",\n \"0.67977726\",\n \"0.6795571\",\n \"0.6795194\",\n \"0.6789722\",\n \"0.67873406\",\n \"0.6780652\",\n \"0.67763317\",\n \"0.67752737\",\n \"0.6755705\",\n \"0.67475593\",\n \"0.67463636\",\n \"0.6740027\",\n \"0.67399514\",\n \"0.6734372\",\n \"0.6731503\",\n \"0.6727815\",\n \"0.6709846\",\n \"0.6706473\",\n \"0.67027295\",\n \"0.66999316\",\n \"0.66965574\",\n \"0.6692341\",\n \"0.66900307\",\n \"0.66849905\",\n \"0.6684788\",\n \"0.6682241\",\n \"0.66821694\",\n \"0.66701895\",\n \"0.6665038\",\n \"0.66571206\",\n \"0.66533077\",\n \"0.6647079\",\n \"0.6640107\",\n \"0.6636412\",\n \"0.6636155\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68825763"},"document_rank":{"kind":"string","value":"58"}}},{"rowIdx":6797,"cells":{"query":{"kind":"string","value":"Sends args and kwargs to any configured callbacks. This handles the cases where the 'callbacks' variable is ``None``, a single function, or a list."},"document":{"kind":"string","value":"def _multiple_callbacks(callbacks, *args, **kwargs):\n if isinstance(callbacks, list):\n for cb in callbacks:\n cb(*args, **kwargs)\n return\n if callbacks:\n callbacks(*args, **kwargs)"},"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 run_callbacks(self, **kwargs):\n for callback in self.CALLBACKS:\n getattr(self, callback)(**kwargs)","def callbacks(*args, addCallback: Script=None, clearAllCallbacks: bool=True, clearCallbacks:\n bool=True, describeHooks: bool=True, dumpCallbacks: bool=True, executeCallbacks:\n bool=True, hook: AnyStr=\"\", listCallbacks: bool=True, owner: AnyStr=\"\",\n removeCallback: Script=None, **kwargs)->List[AnyStr]:\n pass","def __call__(self, args, kwargs):\n callback = self._callback_ref()\n if callback is not None:\n callback(*args, **kwargs)","def subscribe_callbacks(self, *args):\n for arg in args:\n self.callbacks.append(arg)","def trigger(self, callback_type, *args):\n if self.callbacks.has_key(callback_type):\n for cb in self.callbacks[callback_type]:\n cb(*args)","def callback(self, *args, **kwargs):\n\n for key, (methodwrapper, onetime) in list(self.callbacks.items()):\n try:\n methodwrapper(*args, **kwargs)\n except:\n log.err()\n\n if onetime:\n del self.callbacks[key]","def callback(self, fun: Callable[[], None] | None) -> None:","def _forward_cb(self, *args, **kwargs):\n for callback_function in self.changeCallbacks:\n callback_function(*args, **kwargs)","def _run_callbacks(cls, cb_method, *args):\n global CALLBACKS\n for c in CALLBACKS:\n attr = getattr(c, cb_method)\n attr(*args)","def execute_callbacks(self, name, *args, **kwargs):\n callbacks = self.callbacks.get(name, {}).items()\n for order, func in callbacks:\n func(self, *args, **kwargs)\n\n return len(callbacks)","def handle_admincallbacks(bot, ievent):\n cbs = getcallbacktable()\n if not ievent.rest: ievent.reply(\"callbacks: \", cbs)\n else:\n try: ievent.reply(\"%s callbacks: \" % ievent.rest, cbs[ievent.rest])\n except KeyError: ievent.reply(\"no such callbacks available\")","def get_callbacks(callbacks: List[str]) -> List:\n callback_functions = []\n for callback in callbacks:\n try:\n callback_functions.append(eval(callback))\n except (NameError, AttributeError) as err:\n sys.exit(f'{err}. Accepted callbacks from {tf}, {sm}, {tfa}')\n return callback_functions","def set_callbacks(self, **dic_functions):\n for action in self.interface.CALLBACKS:\n try:\n f = dic_functions[action]\n except KeyError:\n pass\n else:\n setattr(self.interface.callbacks, action, f)\n manquantes = [\n a for a in self.interface.CALLBACKS if not a in dic_functions]\n if not manquantes:\n logging.debug(\n f\"{self.__class__.__name__} : Tous les callbacks demandés sont fournis.\")\n else:\n logging.warning(\n f\"{self.__class__.__name__} didn't set asked callbacks {manquantes}\")","def dispatch_callback(self, callback):\n self.callback_queue.put(lambda: callback.func(*callback.args))","def _trigger_callback(self, *args, **kwargs):\n for callback_function in self.changeCallbacks:\n callback_function(self, *args, **kwargs)","def execute(self):\n args = self.args\n \n for callNumber in xrange(len(self.callback)):\n args = (self.callback.pop(0))(*args)\n \n if not (isinstance(args,tuple) or isinstance(args,list)):\n args = [args]\n \n return args","def initialise_callbacks():\n adapter = mice.ice.createObjectAdapterWithEndpoints(\"Callback.Client\", \"tcp -h 127.0.0.1\")\n adapter.activate()\n cb=mice.Murmur.ServerCallbackPrx.uncheckedCast(adapter.addWithUUID(ServerCallbackI(s, adapter)))\n s.addCallback(cb)","def handle_cb(self, obj, cb, kwargs):\n callbacks = kwargs.get(\"callbacks\", {})\n callback_exception_failure = kwargs.get(\"callback_exception_failure\", True)\n\n callback = callbacks.get(cb, None)\n if callback:\n m = \"Running callback function {f} for {n}\".format\n m = m(f=callback, n=cb)\n self.mylog.debug(m)\n try:\n obj = callback(handler=self, obj=obj, kwargs=kwargs)\n except Exception as e:\n if callback_exception_failure:\n raise\n else:\n m = \"Exception occurred in callback function {f} for {n}: {e}\".format\n m = m(f=callback, n=cb, e=e)\n self.mylog.exception(m)\n else:\n m = \"No callback function specified for {n}\".format\n m = m(n=cb)\n self.mylog.debug(m)\n return obj","def perform_callback(self, *args, **kwargs):\n pass","def execute(self):\n\t\tfor callback in self:\n\t\t\tcallback()","def setEventCallbacks(self, callbacks):\n self.ws.setEventCallbacks(callbacks)","def fit_callbacks():\n # pylint: disable=no-value-for-parameter\n return model_callbacks() + logging_callbacks()\n # pylint: enable=no-value-for-parameter","def __init__(self, callback, *args, **kwargs):\n self.callback = lambda: callback(*args, **kwargs)","def runCallback(self, callback=\"help\"):\n self.initialize()\n\n # run the start callback\n tools.run_callback(\"start\", {'request': self._request})\n\n config = self._request.getConfig()\n data = self._request.getData()\n\n # invoke all callbacks for the 'callback'\n handled = tools.run_callback(callback,\n {'request': self._request},\n mappingfunc=lambda x,y:x,\n donefunc=lambda x:x)\n\n # do end callback\n tools.run_callback(\"end\", {'request': request})","def execute(self):\n results = []\n \n for callback in self.callback:\n results.append(callback(*self.args))\n \n return results","def do_callback(self, sensor):\n if sensor in self._callbacks:\n for callback in self._callbacks[sensor]:\n try:\n callback(None)\n except Exception as e:\n self.stick.logger.error(\n \"Error while executing all callback : %s\",\n e,\n )","def collecting_callback():\n calls = []\n\n def cb(**kwargs):\n calls.append(kwargs)\n\n return cb, calls","def set_callbacks(self):\n\t\tself.set_floor_callbacks()\n\t\tself.set_button_callbacks()\n\t\tself.set_stop_callback()","def on_post_execution(**kwargs):\n logging.debug(\"Calling callbacks: %s\", __post_exec_callbacks)\n for callback in __post_exec_callbacks:\n try:\n callback(**kwargs)\n except Exception:\n logging.exception(\"Failed on post-execution callback using %s\", callback)","def on_pre_execution(**kwargs):\n logging.debug(\"Calling callbacks: %s\", __pre_exec_callbacks)\n for callback in __pre_exec_callbacks:\n try:\n callback(**kwargs)\n except Exception:\n logging.exception(\"Failed on pre-execution callback using %s\", callback)","def setObjectCallbacks(self, callbacks):\n self.ws.setObjectCallbacks(callbacks)","def fire_event(self, callback: Callable[..., bool], **kwargs):\n if not callable(callback):\n raise RuntimeError('Callback method (callback) is not a callable.')\n\n # get developer passed trigger_ids\n trigger_ids: list | None = kwargs.pop('trigger_ids', None)\n\n for trigger_id, config in list(self.configs.items()):\n if trigger_ids is not None and trigger_id not in trigger_ids:\n # skip config that don't match developer provided trigger ids\n continue\n\n try:\n # get a session_id specifically for this thread\n session_id: str = self.create_session_id()\n\n # only required for testing in tcex framework\n self._tcex_testing(session_id, trigger_id)\n\n # get an instance of PB module with current\n # session_id and outputs to pass to callback\n outputs: list | str = config.tc_playbook_out_variables or []\n if isinstance(outputs, str):\n outputs = outputs.split(',')\n playbook = self.get_playbook(context=session_id, output_variables=outputs)\n\n self.log.info(f'feature=trigger-service, event=fire-event, trigger-id={session_id}')\n\n # current thread has session_id as name\n self.service_thread(\n name=session_id,\n target=self.fire_event_trigger,\n args=(\n callback,\n playbook,\n session_id,\n trigger_id,\n config,\n ),\n kwargs=kwargs,\n session_id=session_id,\n trigger_id=trigger_id,\n )\n except Exception:\n self.log.trace(traceback.format_exc())","def execute():\n command_line_args = argv[1:]\n args = cli(command_line_args)\n\n callback = args.callback\n kwargs = {\n k: v\n for k, v in args.__dict__.items()\n if k != \"callback\"\n }\n\n main(callback, **kwargs)","def set_callback(self,callback = None):\n self.callback = callback","def set_callback(self, callback_frames=None, callback_audio=None):\n if callback_frames is not None:\n if not callable(callback_frames):\n raise ValueError(\"`callback_frames` must be callable.\")\n self._callback_frames = callback_frames\n if callback_audio is not None:\n if not callable(callback_audio):\n raise ValueError(\"`callback_audio` must be callable.\")\n self._audio_player.set_callback(callback_audio)","def call_on(self, target):\n for cb in self._callbacks:\n if hasattr(target, cb):\n c = getattr(target, cb)\n if callable(c):\n c()","def _callback(self, *args):\n\n # Check to see if the first argument is a 'help' call\n if((args) and (args[0] in [\"help\",\"?\"])):\n self.console.writeln(self.help)\n else:\n self.callback(*args)","def callback(self, callback, *args, **kwds):\n\n def _exit_wrapper(exc_type, exc, tb):\n callback(*args, **kwds)\n\n # We changed the signature, so using @wraps is not appropriate, but\n # setting __wrapped__ may still help with introspection\n _exit_wrapper.__wrapped__ = callback\n self.push(_exit_wrapper)\n return callback # Allow use as a decorator","def register_callback(self, callback: Callable[[], None]) -> None:\r\n print(\"register callback called\")\r\n self._callbacks.add(callback)","def dispatch_post_action_callbacks(atexit, s_db, s_cur, d_db, d_cur):\n if not atexit:\n pa = nori.core.cfg['post_action_callbacks']\n else:\n pa = post_action_callbacks\n num_cbs = len(pa)\n for i, cb_t in enumerate(pa):\n cb, args, kwargs = cb_t[0:3] # there might be a 4th\n nori.core.status_logger.info(\n 'Calling post-action callback {0} of {1}...' .\n format((i + 1), num_cbs)\n )\n ret = cb(*args, s_db=s_db, s_cur=s_cur, d_db=d_db, d_cur=d_cur,\n **kwargs)\n nori.core.status_logger.info(\n 'Callback complete.' if ret else 'Callback failed.'\n )\n if (not atexit) and ((cb, args, kwargs) in post_action_callbacks):\n post_action_callbacks.remove((cb, args, kwargs))","def create_callback(self, chain):","def add_callbacks(self, defaults):\n defaults.update(self.mouse_callbacks)\n self.mouse_callbacks = defaults","def _set_callback(self):\n self.callback_f = None\n if self.callback:\n self.callback_f = inspector_mtw(**self.callback_kwargs)","def get_callback_list(hyperparams: Dict[str, Any]) -> List[BaseCallback]:\n\n def get_module_name(callback_name):\n return \".\".join(callback_name.split(\".\")[:-1])\n\n def get_class_name(callback_name):\n return callback_name.split(\".\")[-1]\n\n callbacks = []\n\n if \"callback\" in hyperparams.keys():\n callback_name = hyperparams.get(\"callback\")\n\n if callback_name is None:\n return callbacks\n\n if not isinstance(callback_name, list):\n callback_names = [callback_name]\n else:\n callback_names = callback_name\n\n # Handle multiple wrappers\n for callback_name in callback_names:\n # Handle keyword arguments\n if isinstance(callback_name, dict):\n assert len(callback_name) == 1, (\n \"You have an error in the formatting \"\n f\"of your YAML file near {callback_name}. \"\n \"You should check the indentation.\"\n )\n callback_dict = callback_name\n callback_name = list(callback_dict.keys())[0]\n kwargs = callback_dict[callback_name]\n else:\n kwargs = {}\n callback_module = importlib.import_module(get_module_name(callback_name))\n callback_class = getattr(callback_module, get_class_name(callback_name))\n callbacks.append(callback_class(**kwargs))\n\n return callbacks","def execute_callbacks(query, result):\n for func in self.callbacks:\n func(query, result)","def Invoke (self, owner: typing.Any, eventArguments: typing.Optional[EventArguments]) -> None:\n\n\t\tif not isinstance(eventArguments, EventArguments) and eventArguments is not None:\n\t\t\traise Exceptions.IncorrectTypeException(eventArguments, \"eventArguments\", (EventArguments, None))\n\n\t\tfor callback in self: # type: typing.Callable\n\t\t\tcallback(owner, eventArguments)","def callback(self, function: Optional[Callable[[int], None]]) -> None:","def bind(self, event_type, callback):\n\n # Each event type has a list of callback functions to be called.\n # If we have not seen the event type yet, then create an empty list,\n # otherwise append to the existing list:\n self.callbacks.setdefault(event_type, []).append(callback)","def forward_callbacks(self, obj):\n obj.add_change_callback(self._forward_cb)","def execute(self):\n results = []\n\n for callNumber in xrange(len(self.callback)):\n results.append( (self.callback.pop(0))(*self.args.pop(0)) )\n\n return results","def set_asyncgen_hooks(*args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__\n pass","def callback(cb): \n def cb_func(*args):\n self = args[0]\n (value, is_last) = cb(*args)\n if (value is not None):\n self._cb_return[cb.__name__] = self._cb_return.get(cb.__name__, []) + \\\n [value]\n if (is_last):\n self._cb_event[cb.__name__] = True\n return cb_func","def addCallback(self,newCallback,*newArgs):\n self.callback.append(newCallback)\n self.args.append(newArgs)","def apply_callback(self, all_values: Namespace) -> Any:\n if not callable(self.callback):\n return self.value\n\n try:\n val = self.callback(self.value, all_values)\n except TypeError as terr: # pragma: no cover\n # len() takes exactly one argument (2 given)\n # <lambda>() takes 1 positional argument but 2 were given\n if not re.search(r\"takes .+ argument .+ given\", str(terr)):\n raise\n val = self.callback(self.value)\n\n if isinstance(val, Exception):\n raise PyParamTypeError(str(val))\n return val","def _notify_callback_event(self, event, data):\n # Notify callbacks\n for cback in self.callbacks:\n cback.invoke(self, event, data)","def connect(\n cls,\n on_start=None,\n on_resume=None,\n on_pause=None,\n on_stop=None,\n on_save=None,\n on_reset=None,\n ):\n if on_start and on_start not in cls.start_callback:\n cls.start_callback.append(on_start)\n\n if on_resume and on_resume not in cls.resume_callback:\n cls.resume_callback.append(on_resume)\n\n if on_pause and on_pause not in cls.pause_callback:\n cls.pause_callback.append(on_pause)\n\n if on_stop and on_stop not in cls.stop_callback:\n cls.stop_callback.append(on_stop)\n\n if on_save and on_save not in cls.save_callback:\n cls.save_callback.append(on_save)\n\n if on_reset and on_reset not in cls.reset_callback:\n cls.reset_callback.append(on_reset)","def callback_method(value):\n assert value == 'With callback'","def callback(self, *args):\n raise NotImplementedError()","def dummy_callback(obj):\n pass","def set_stream_callbacks(self):\n from telnetlib3.telopt import TTYPE, TSPEED, XDISPLOC, NEW_ENVIRON\n from telnetlib3.telopt import CHARSET, NAWS\n\n # wire extended rfc callbacks for terminal atributes, etc.\n for (opt, func) in (\n (TTYPE, self.send_ttype),\n (TSPEED, self.send_tspeed),\n (XDISPLOC, self.send_xdisploc),\n (NEW_ENVIRON, self.send_env),\n (NAWS, self.send_naws),\n (CHARSET, self.send_charset),\n ):\n self.stream.set_ext_send_callback(opt, func)","def restipy(callback,\n pre_call=lambda env,sr,args,kwargs: None,\n post_call=lambda env,sr,call_ret: None):\n _func_table[callback.__name__] = (callback, pre_call, post_call)","def register(self, callback, filters = []):\n\t\tself.callbacks.append((callback, filters))\n\t\tself.events[str(callback)] = []","def set_callback(self, callback):\n if not callable(callback):\n raise TypeError(\"'callback' must be callable\")\n\n self._callback = callback","def call_start_cbs(self):\n try:\n for (cb,args) in self._start_cbs:\n cb(self.userdata, self.get_initial_states(), *args)\n except:\n smach.logerr(\"Could not execute start callback: \"+traceback.format_exc())","def __trigger(self, toBeExecuted, args = []):\n\n self.__connect()\n [ f(args) for f in toBeExecuted ]\n self.__disconnect()","def subscribe(self, callback: Callable) -> None:\n self.callbacks.add(callback)","def add_callback(self, done_cb: Callable[[], None] = None) -> None:\n\n if done_cb is not None:\n self.callbacks.append(done_cb)","def callbacks(self, val_generator: DataGenerator) -> List[Callback]:\n if self.train_mode in [\"classifier\", \"both\"]:\n model = self.classifier\n model_dir = \"class_model\"\n elif self.train_mode == \"combined\":\n model = self.combined\n model_dir = \"com_model\"\n\n # Callback for evaluating the validation dataset\n eval_callback = EvalCallback(\n model=model, val_generator=val_generator, layers=self.n_blocks\n )\n\n # callback for saving the best model\n checkpoint_callback = ModelCheckpoint(\n f\"{model_dir}/{model_dir}\" + \"_{epoch:04d}_{val_acc:.4f}.h5\",\n monitor=\"val_acc\",\n verbose=0,\n save_best_only=True,\n save_weights_only=False,\n mode=\"max\",\n )\n\n \"Make sure checkpoint callback is after the eval_callback, dependency\"\n return [eval_callback, checkpoint_callback]","def add_callback(self, callback):\n if callback is not None:\n self.callbacks.append(callback)","def subscribe(callback, keys):\n assert type(keys) in (set, list, tuple)\n _subscribers[callback] = keys","def append(self, callback):\n\t\tif callback is None:\n\t\t\tpass\n\t\telif callable(callback):\n\t\t\tlist.append(self, WeakMethod(callback))\n\t\telif isinstance(callback, list, tuple):\n\t\t\tfor i in callback:\n\t\t\t\tself.append(i)\n\t\telse:\n\t\t\tassert False","def add(self, callback):\n self._callbacks += as_cb_list(callback)","async def send(self, *args, **kwargs) -> None:\n for callback in self:\n res = callback(*args, **kwargs)\n if asyncio.iscoroutine(res) or isinstance(res, asyncio.Future):\n await res","def run_callback(self, name, handle_exceptions=True, show_traceback=None,\n **kwargs):\n for fcn in self._cb_dict[name]:\n try:\n fcn(**kwargs)\n except Exception as ex:\n if handle_exceptions:\n self.logger.debug('Callback %s failed' % (name, ),\n exc_info=True)\n if show_traceback is not None:\n print('Callback %s failed' % (name, ))\n util.print_traceback(ex, f=show_traceback)\n\n else:\n raise","def set_update_callbacks(self, update_tr_pos_callback, update_view_model_callback) -> None:\n\n self.update_tr_position = update_tr_pos_callback\n self.update_view_model = update_view_model_callback","def _dummy_callback():\n assert False, \"TRESPASS - set_callback() must be called\"","def set_request_handler(self, callback: Callable[[ACLMessage], Any]):\n self.callback = callback","def add(self, callback, *args, **kwargs):\n\n self.list.append((callback, args, kwargs))","def add_callbacks(am, ifttkey, ifttevent):\n if _debug:\n print(\"add_callbacks\", am)\n\n xurl = IFTT_URL.format(ifttevent, ifttkey)\n for c in am.mac_targets.values():\n call_val = {\n 'url': xurl,\n 'id': c.name,\n 'cur_val': -1\n }\n c.add_callback(iftt_callback, call_val)","def on_events(self, insert_callback=None, set_callback=None,\n del_callback=None, reverse_callback=None, sort_callback=None):\n self.on_insert(insert_callback)\n self.on_set(set_callback)\n self.on_del(del_callback)\n self.on_reverse(reverse_callback)\n self.on_sort(sort_callback)","def add_asyn_callback(self, fcn, **kwargs):\n assert(hasattr(fcn, '__call__'))\n self._asyn_callbacks[fcn] = kwargs","def Callbacks (self) -> typing.List[typing.Callable]:\n\n\t\tcallbacks = list()\n\n\t\tcallbackReferenceIndex = 0\n\t\twhile callbackReferenceIndex < len(self._callbackReferences):\n\t\t\tcallback = self._callbackReferences[callbackReferenceIndex]()\n\n\t\t\tif callback is None:\n\t\t\t\tself._callbackReferences.pop(callbackReferenceIndex)\n\t\t\t\tcontinue\n\n\t\t\tcallbacks.append(callback)\n\t\t\tcallbackReferenceIndex += 1\n\n\t\treturn callbacks","def register_callbacks(self, cbs):\n assert isinstance(cbs, dict) # cbs must be a dictionary\n cbs_key_list = list(cbs.keys())\n enum_key_list = [enum.value for enum in self._action_type_enum]\n \n # TODO replace by check for compulsory keys only, all action types do not apply to all actions\n #assert(len(set(enum_key_list) - set(cbs_key_list)) == 0)\n\n self._cbs = {}\n for key in cbs_key_list: # check syntax\n print \"Registered callback for action %s\" % key\n self._cbs[key] = cbs[key]\n rospy.logdebug(\"Callbacks registered.\")","def prepare_callbacks(output_dir: str) -> Tuple[Callable]:\n time = datetime.now().strftime(\"%y%m%d-%H%M\")\n log_dir = os.path.join(output_dir, \"tensorboard\" + time)\n logging.info(\"Tensorboard directory: %s\" % log_dir)\n tensorboard = TensorBoard(log_dir=log_dir, batch_size=1000, write_images=True,\n write_graph=True)\n csv_path = os.path.join(output_dir, \"csv_logger_\" + time + \".txt\")\n logging.info(\"CSV logs: %s\" % csv_path)\n csv_logger = CSVLogger(csv_path)\n\n filepath = os.path.join(output_dir, \"best_\" + time + \".model\")\n model_saver = ModelCheckpoint(filepath, monitor=\"val_recall\", verbose=1, save_best_only=True,\n mode=\"max\")\n return tensorboard, csv_logger, model_saver","def register_callbacks(self):\n self.either_edge_cb = self.pi.callback(\n self.gpio,\n pigpio.EITHER_EDGE,\n self.either_edge_callback\n )","def runEventCallbacks(self, event, *args):\n\n if not event in self.EVENT_TYPES:\n raise Exception(\"XnatIo (onEvent): invalid event type '%s'\"%(\\\n event))\n if not hasattr(self, 'eventCallbacks__'):\n print('self has no attribute eventCallbacks__')\n return\n\n for callback in self.eventCallbacks__[event]:\n #print(f\"EVENT CALLBACK {event}\")\n callback(*args)","def _setup_callbacks(lst, log_models, metrics_logger):\n # pylint: disable=no-name-in-module\n from mlflow.tensorflow._autolog import _TensorBoard, __MLflowTfKeras2Callback\n\n tb = _get_tensorboard_callback(lst)\n if tb is None:\n log_dir = _TensorBoardLogDir(location=tempfile.mkdtemp(), is_temp=True)\n\n out_list = lst + [_TensorBoard(log_dir.location)]\n else:\n log_dir = _TensorBoardLogDir(location=tb.log_dir, is_temp=False)\n out_list = lst\n out_list += [__MLflowTfKeras2Callback(log_models, metrics_logger, _LOG_EVERY_N_STEPS)]\n return out_list, log_dir","def fire_event(self, event = None):\n for e in self.events:\n if e[\"event\"] == event:\n if type(e[\"args\"]) == type([]):\n e[\"callback\"](*e[\"args\"])\n elif type(e[\"args\"]) == type({}):\n e[\"callback\"](**e[\"args\"])\n elif e[\"args\"] == None:\n e[\"callback\"]()\n else:\n e[\"callback\"](e[\"args\"])\n return True","def Handler(self, *events: str, colon: bool = False,\n ircv3: bool = False) -> Callable:\n ...","def cbhandler(cls, session, cb_input):\n commandhandler = dict()\n commandhandler.update(dict({'sleep': cls.sleep}))\n commandhandler.update(dict({'dupsession': cls.reinitsession}))\n for i in range(len(cb_input)):\n command = cb_input[i]['cmd']\n arg = cb_input[i]['arg']\n if command in commandhandler.keys():\n cb_input[i]['ret'] = commandhandler[command](arg, session)\n else:\n print(\"Unknown command passed\", command)\n cls.log(3, \"Unknown command passed\", command)","def on_recv(self, callback):\n if callback is None:\n self._on_recv = callback\n else:\n\n def wrap_recv(header, body):\n callback(body)\n\n self._on_recv = wrap_recv","def async_callback(self, callback, *args, **kwargs):\n if callback is None:\n return None\n\n if args or kwargs:\n callback = functools.partial(callback, *args, **kwargs)\n\n def wrapper(*args, **kwargs):\n try:\n return callback(*args, **kwargs)\n except Exception, e:\n logging.error('Exception during callback', exc_info=True)\n\n return wrapper","def call(manager, *args):\n for instance in manager:\n instance.callback(*args)","def call_subscribers(self, *args, **kwargs) -> None:\n for subscriber in self.get_subscribers():\n subscriber(*args, **kwargs)","def on_set(self, callback):\n self._set_callback = callback if callable(callback) else _void","def on_set(self, callback):\n self._set_callback = callback if callable(callback) else _void","def __call__(self, *args, **kwargs):\n value = None\n for callback in self.callbacks:\n try:\n local_value = callback(*args, **kwargs)\n except Exception as e:\n ip = get_ipython()\n if ip is None:\n self.log.warning(\"Exception in callback %s: %s\", callback, e, exc_info=True)\n else:\n ip.showtraceback()\n else:\n value = local_value if local_value is not None else value\n return value","def process_callbacks(host, typ):\n \n ip = host.replace(\"-\", \".\")\n src = typ\n call_time = strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())\n\n if os.path.isfile(\"/tmp/cc/calls.log\"): # write callback to calls log\n with open(\"/tmp/cc/calls.log\", 'a') as f:\n s = \"{0:<25} {1:<16} {2:<10}\\n\".format(call_time, ip, src)\n f.write(s)\n else:\n with open(\"/tmp/cc/calls.log\", 'w') as f:\n s = \"{0:<25} {1:<16} {2:<10}\\n\".format(call_time, ip, src)\n f.write(s)\n\n com_file = \"/tmp/cc/hosts/\" + ip # read commands from the appropriate file\n if os.path.isfile(com_file):\n with open(com_file, 'r') as f:\n c = f.read()\n os.remove(com_file)\n return c + \"\\n\"\n else:\n return \"#lmao\\n\"","def __call__(self, event_type, details):\n cb = self.callback\n if cb is None:\n return\n if self._details_filter is not None:\n if not self._details_filter(details):\n return\n kwargs = dict(self._kwargs)\n kwargs['details'] = details\n cb(event_type, *self._args, **kwargs)","def setup_callbacks(self):\n\n super(ArtellaMayaPlugin, self).setup_callbacks()\n\n callbacks.register(callback.Callbacks().AfterOpenCallback, self._after_open)\n callbacks.register(callback.Callbacks().SceneBeforeSaveCallback, self._before_save)\n callbacks.register(callback.Callbacks().BeforeOpenCheckCallback, self._before_open_check)\n callbacks.register(callback.Callbacks().AfterLoadReferenceCallback, self._after_load_reference)\n callbacks.register(callback.Callbacks().BeforeCreateReferenceCheckCallback, self._before_reference_check)"],"string":"[\n \"def run_callbacks(self, **kwargs):\\n for callback in self.CALLBACKS:\\n getattr(self, callback)(**kwargs)\",\n \"def callbacks(*args, addCallback: Script=None, clearAllCallbacks: bool=True, clearCallbacks:\\n bool=True, describeHooks: bool=True, dumpCallbacks: bool=True, executeCallbacks:\\n bool=True, hook: AnyStr=\\\"\\\", listCallbacks: bool=True, owner: AnyStr=\\\"\\\",\\n removeCallback: Script=None, **kwargs)->List[AnyStr]:\\n pass\",\n \"def __call__(self, args, kwargs):\\n callback = self._callback_ref()\\n if callback is not None:\\n callback(*args, **kwargs)\",\n \"def subscribe_callbacks(self, *args):\\n for arg in args:\\n self.callbacks.append(arg)\",\n \"def trigger(self, callback_type, *args):\\n if self.callbacks.has_key(callback_type):\\n for cb in self.callbacks[callback_type]:\\n cb(*args)\",\n \"def callback(self, *args, **kwargs):\\n\\n for key, (methodwrapper, onetime) in list(self.callbacks.items()):\\n try:\\n methodwrapper(*args, **kwargs)\\n except:\\n log.err()\\n\\n if onetime:\\n del self.callbacks[key]\",\n \"def callback(self, fun: Callable[[], None] | None) -> None:\",\n \"def _forward_cb(self, *args, **kwargs):\\n for callback_function in self.changeCallbacks:\\n callback_function(*args, **kwargs)\",\n \"def _run_callbacks(cls, cb_method, *args):\\n global CALLBACKS\\n for c in CALLBACKS:\\n attr = getattr(c, cb_method)\\n attr(*args)\",\n \"def execute_callbacks(self, name, *args, **kwargs):\\n callbacks = self.callbacks.get(name, {}).items()\\n for order, func in callbacks:\\n func(self, *args, **kwargs)\\n\\n return len(callbacks)\",\n \"def handle_admincallbacks(bot, ievent):\\n cbs = getcallbacktable()\\n if not ievent.rest: ievent.reply(\\\"callbacks: \\\", cbs)\\n else:\\n try: ievent.reply(\\\"%s callbacks: \\\" % ievent.rest, cbs[ievent.rest])\\n except KeyError: ievent.reply(\\\"no such callbacks available\\\")\",\n \"def get_callbacks(callbacks: List[str]) -> List:\\n callback_functions = []\\n for callback in callbacks:\\n try:\\n callback_functions.append(eval(callback))\\n except (NameError, AttributeError) as err:\\n sys.exit(f'{err}. Accepted callbacks from {tf}, {sm}, {tfa}')\\n return callback_functions\",\n \"def set_callbacks(self, **dic_functions):\\n for action in self.interface.CALLBACKS:\\n try:\\n f = dic_functions[action]\\n except KeyError:\\n pass\\n else:\\n setattr(self.interface.callbacks, action, f)\\n manquantes = [\\n a for a in self.interface.CALLBACKS if not a in dic_functions]\\n if not manquantes:\\n logging.debug(\\n f\\\"{self.__class__.__name__} : Tous les callbacks demandés sont fournis.\\\")\\n else:\\n logging.warning(\\n f\\\"{self.__class__.__name__} didn't set asked callbacks {manquantes}\\\")\",\n \"def dispatch_callback(self, callback):\\n self.callback_queue.put(lambda: callback.func(*callback.args))\",\n \"def _trigger_callback(self, *args, **kwargs):\\n for callback_function in self.changeCallbacks:\\n callback_function(self, *args, **kwargs)\",\n \"def execute(self):\\n args = self.args\\n \\n for callNumber in xrange(len(self.callback)):\\n args = (self.callback.pop(0))(*args)\\n \\n if not (isinstance(args,tuple) or isinstance(args,list)):\\n args = [args]\\n \\n return args\",\n \"def initialise_callbacks():\\n adapter = mice.ice.createObjectAdapterWithEndpoints(\\\"Callback.Client\\\", \\\"tcp -h 127.0.0.1\\\")\\n adapter.activate()\\n cb=mice.Murmur.ServerCallbackPrx.uncheckedCast(adapter.addWithUUID(ServerCallbackI(s, adapter)))\\n s.addCallback(cb)\",\n \"def handle_cb(self, obj, cb, kwargs):\\n callbacks = kwargs.get(\\\"callbacks\\\", {})\\n callback_exception_failure = kwargs.get(\\\"callback_exception_failure\\\", True)\\n\\n callback = callbacks.get(cb, None)\\n if callback:\\n m = \\\"Running callback function {f} for {n}\\\".format\\n m = m(f=callback, n=cb)\\n self.mylog.debug(m)\\n try:\\n obj = callback(handler=self, obj=obj, kwargs=kwargs)\\n except Exception as e:\\n if callback_exception_failure:\\n raise\\n else:\\n m = \\\"Exception occurred in callback function {f} for {n}: {e}\\\".format\\n m = m(f=callback, n=cb, e=e)\\n self.mylog.exception(m)\\n else:\\n m = \\\"No callback function specified for {n}\\\".format\\n m = m(n=cb)\\n self.mylog.debug(m)\\n return obj\",\n \"def perform_callback(self, *args, **kwargs):\\n pass\",\n \"def execute(self):\\n\\t\\tfor callback in self:\\n\\t\\t\\tcallback()\",\n \"def setEventCallbacks(self, callbacks):\\n self.ws.setEventCallbacks(callbacks)\",\n \"def fit_callbacks():\\n # pylint: disable=no-value-for-parameter\\n return model_callbacks() + logging_callbacks()\\n # pylint: enable=no-value-for-parameter\",\n \"def __init__(self, callback, *args, **kwargs):\\n self.callback = lambda: callback(*args, **kwargs)\",\n \"def runCallback(self, callback=\\\"help\\\"):\\n self.initialize()\\n\\n # run the start callback\\n tools.run_callback(\\\"start\\\", {'request': self._request})\\n\\n config = self._request.getConfig()\\n data = self._request.getData()\\n\\n # invoke all callbacks for the 'callback'\\n handled = tools.run_callback(callback,\\n {'request': self._request},\\n mappingfunc=lambda x,y:x,\\n donefunc=lambda x:x)\\n\\n # do end callback\\n tools.run_callback(\\\"end\\\", {'request': request})\",\n \"def execute(self):\\n results = []\\n \\n for callback in self.callback:\\n results.append(callback(*self.args))\\n \\n return results\",\n \"def do_callback(self, sensor):\\n if sensor in self._callbacks:\\n for callback in self._callbacks[sensor]:\\n try:\\n callback(None)\\n except Exception as e:\\n self.stick.logger.error(\\n \\\"Error while executing all callback : %s\\\",\\n e,\\n )\",\n \"def collecting_callback():\\n calls = []\\n\\n def cb(**kwargs):\\n calls.append(kwargs)\\n\\n return cb, calls\",\n \"def set_callbacks(self):\\n\\t\\tself.set_floor_callbacks()\\n\\t\\tself.set_button_callbacks()\\n\\t\\tself.set_stop_callback()\",\n \"def on_post_execution(**kwargs):\\n logging.debug(\\\"Calling callbacks: %s\\\", __post_exec_callbacks)\\n for callback in __post_exec_callbacks:\\n try:\\n callback(**kwargs)\\n except Exception:\\n logging.exception(\\\"Failed on post-execution callback using %s\\\", callback)\",\n \"def on_pre_execution(**kwargs):\\n logging.debug(\\\"Calling callbacks: %s\\\", __pre_exec_callbacks)\\n for callback in __pre_exec_callbacks:\\n try:\\n callback(**kwargs)\\n except Exception:\\n logging.exception(\\\"Failed on pre-execution callback using %s\\\", callback)\",\n \"def setObjectCallbacks(self, callbacks):\\n self.ws.setObjectCallbacks(callbacks)\",\n \"def fire_event(self, callback: Callable[..., bool], **kwargs):\\n if not callable(callback):\\n raise RuntimeError('Callback method (callback) is not a callable.')\\n\\n # get developer passed trigger_ids\\n trigger_ids: list | None = kwargs.pop('trigger_ids', None)\\n\\n for trigger_id, config in list(self.configs.items()):\\n if trigger_ids is not None and trigger_id not in trigger_ids:\\n # skip config that don't match developer provided trigger ids\\n continue\\n\\n try:\\n # get a session_id specifically for this thread\\n session_id: str = self.create_session_id()\\n\\n # only required for testing in tcex framework\\n self._tcex_testing(session_id, trigger_id)\\n\\n # get an instance of PB module with current\\n # session_id and outputs to pass to callback\\n outputs: list | str = config.tc_playbook_out_variables or []\\n if isinstance(outputs, str):\\n outputs = outputs.split(',')\\n playbook = self.get_playbook(context=session_id, output_variables=outputs)\\n\\n self.log.info(f'feature=trigger-service, event=fire-event, trigger-id={session_id}')\\n\\n # current thread has session_id as name\\n self.service_thread(\\n name=session_id,\\n target=self.fire_event_trigger,\\n args=(\\n callback,\\n playbook,\\n session_id,\\n trigger_id,\\n config,\\n ),\\n kwargs=kwargs,\\n session_id=session_id,\\n trigger_id=trigger_id,\\n )\\n except Exception:\\n self.log.trace(traceback.format_exc())\",\n \"def execute():\\n command_line_args = argv[1:]\\n args = cli(command_line_args)\\n\\n callback = args.callback\\n kwargs = {\\n k: v\\n for k, v in args.__dict__.items()\\n if k != \\\"callback\\\"\\n }\\n\\n main(callback, **kwargs)\",\n \"def set_callback(self,callback = None):\\n self.callback = callback\",\n \"def set_callback(self, callback_frames=None, callback_audio=None):\\n if callback_frames is not None:\\n if not callable(callback_frames):\\n raise ValueError(\\\"`callback_frames` must be callable.\\\")\\n self._callback_frames = callback_frames\\n if callback_audio is not None:\\n if not callable(callback_audio):\\n raise ValueError(\\\"`callback_audio` must be callable.\\\")\\n self._audio_player.set_callback(callback_audio)\",\n \"def call_on(self, target):\\n for cb in self._callbacks:\\n if hasattr(target, cb):\\n c = getattr(target, cb)\\n if callable(c):\\n c()\",\n \"def _callback(self, *args):\\n\\n # Check to see if the first argument is a 'help' call\\n if((args) and (args[0] in [\\\"help\\\",\\\"?\\\"])):\\n self.console.writeln(self.help)\\n else:\\n self.callback(*args)\",\n \"def callback(self, callback, *args, **kwds):\\n\\n def _exit_wrapper(exc_type, exc, tb):\\n callback(*args, **kwds)\\n\\n # We changed the signature, so using @wraps is not appropriate, but\\n # setting __wrapped__ may still help with introspection\\n _exit_wrapper.__wrapped__ = callback\\n self.push(_exit_wrapper)\\n return callback # Allow use as a decorator\",\n \"def register_callback(self, callback: Callable[[], None]) -> None:\\r\\n print(\\\"register callback called\\\")\\r\\n self._callbacks.add(callback)\",\n \"def dispatch_post_action_callbacks(atexit, s_db, s_cur, d_db, d_cur):\\n if not atexit:\\n pa = nori.core.cfg['post_action_callbacks']\\n else:\\n pa = post_action_callbacks\\n num_cbs = len(pa)\\n for i, cb_t in enumerate(pa):\\n cb, args, kwargs = cb_t[0:3] # there might be a 4th\\n nori.core.status_logger.info(\\n 'Calling post-action callback {0} of {1}...' .\\n format((i + 1), num_cbs)\\n )\\n ret = cb(*args, s_db=s_db, s_cur=s_cur, d_db=d_db, d_cur=d_cur,\\n **kwargs)\\n nori.core.status_logger.info(\\n 'Callback complete.' if ret else 'Callback failed.'\\n )\\n if (not atexit) and ((cb, args, kwargs) in post_action_callbacks):\\n post_action_callbacks.remove((cb, args, kwargs))\",\n \"def create_callback(self, chain):\",\n \"def add_callbacks(self, defaults):\\n defaults.update(self.mouse_callbacks)\\n self.mouse_callbacks = defaults\",\n \"def _set_callback(self):\\n self.callback_f = None\\n if self.callback:\\n self.callback_f = inspector_mtw(**self.callback_kwargs)\",\n \"def get_callback_list(hyperparams: Dict[str, Any]) -> List[BaseCallback]:\\n\\n def get_module_name(callback_name):\\n return \\\".\\\".join(callback_name.split(\\\".\\\")[:-1])\\n\\n def get_class_name(callback_name):\\n return callback_name.split(\\\".\\\")[-1]\\n\\n callbacks = []\\n\\n if \\\"callback\\\" in hyperparams.keys():\\n callback_name = hyperparams.get(\\\"callback\\\")\\n\\n if callback_name is None:\\n return callbacks\\n\\n if not isinstance(callback_name, list):\\n callback_names = [callback_name]\\n else:\\n callback_names = callback_name\\n\\n # Handle multiple wrappers\\n for callback_name in callback_names:\\n # Handle keyword arguments\\n if isinstance(callback_name, dict):\\n assert len(callback_name) == 1, (\\n \\\"You have an error in the formatting \\\"\\n f\\\"of your YAML file near {callback_name}. \\\"\\n \\\"You should check the indentation.\\\"\\n )\\n callback_dict = callback_name\\n callback_name = list(callback_dict.keys())[0]\\n kwargs = callback_dict[callback_name]\\n else:\\n kwargs = {}\\n callback_module = importlib.import_module(get_module_name(callback_name))\\n callback_class = getattr(callback_module, get_class_name(callback_name))\\n callbacks.append(callback_class(**kwargs))\\n\\n return callbacks\",\n \"def execute_callbacks(query, result):\\n for func in self.callbacks:\\n func(query, result)\",\n \"def Invoke (self, owner: typing.Any, eventArguments: typing.Optional[EventArguments]) -> None:\\n\\n\\t\\tif not isinstance(eventArguments, EventArguments) and eventArguments is not None:\\n\\t\\t\\traise Exceptions.IncorrectTypeException(eventArguments, \\\"eventArguments\\\", (EventArguments, None))\\n\\n\\t\\tfor callback in self: # type: typing.Callable\\n\\t\\t\\tcallback(owner, eventArguments)\",\n \"def callback(self, function: Optional[Callable[[int], None]]) -> None:\",\n \"def bind(self, event_type, callback):\\n\\n # Each event type has a list of callback functions to be called.\\n # If we have not seen the event type yet, then create an empty list,\\n # otherwise append to the existing list:\\n self.callbacks.setdefault(event_type, []).append(callback)\",\n \"def forward_callbacks(self, obj):\\n obj.add_change_callback(self._forward_cb)\",\n \"def execute(self):\\n results = []\\n\\n for callNumber in xrange(len(self.callback)):\\n results.append( (self.callback.pop(0))(*self.args.pop(0)) )\\n\\n return results\",\n \"def set_asyncgen_hooks(*args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__\\n pass\",\n \"def callback(cb): \\n def cb_func(*args):\\n self = args[0]\\n (value, is_last) = cb(*args)\\n if (value is not None):\\n self._cb_return[cb.__name__] = self._cb_return.get(cb.__name__, []) + \\\\\\n [value]\\n if (is_last):\\n self._cb_event[cb.__name__] = True\\n return cb_func\",\n \"def addCallback(self,newCallback,*newArgs):\\n self.callback.append(newCallback)\\n self.args.append(newArgs)\",\n \"def apply_callback(self, all_values: Namespace) -> Any:\\n if not callable(self.callback):\\n return self.value\\n\\n try:\\n val = self.callback(self.value, all_values)\\n except TypeError as terr: # pragma: no cover\\n # len() takes exactly one argument (2 given)\\n # <lambda>() takes 1 positional argument but 2 were given\\n if not re.search(r\\\"takes .+ argument .+ given\\\", str(terr)):\\n raise\\n val = self.callback(self.value)\\n\\n if isinstance(val, Exception):\\n raise PyParamTypeError(str(val))\\n return val\",\n \"def _notify_callback_event(self, event, data):\\n # Notify callbacks\\n for cback in self.callbacks:\\n cback.invoke(self, event, data)\",\n \"def connect(\\n cls,\\n on_start=None,\\n on_resume=None,\\n on_pause=None,\\n on_stop=None,\\n on_save=None,\\n on_reset=None,\\n ):\\n if on_start and on_start not in cls.start_callback:\\n cls.start_callback.append(on_start)\\n\\n if on_resume and on_resume not in cls.resume_callback:\\n cls.resume_callback.append(on_resume)\\n\\n if on_pause and on_pause not in cls.pause_callback:\\n cls.pause_callback.append(on_pause)\\n\\n if on_stop and on_stop not in cls.stop_callback:\\n cls.stop_callback.append(on_stop)\\n\\n if on_save and on_save not in cls.save_callback:\\n cls.save_callback.append(on_save)\\n\\n if on_reset and on_reset not in cls.reset_callback:\\n cls.reset_callback.append(on_reset)\",\n \"def callback_method(value):\\n assert value == 'With callback'\",\n \"def callback(self, *args):\\n raise NotImplementedError()\",\n \"def dummy_callback(obj):\\n pass\",\n \"def set_stream_callbacks(self):\\n from telnetlib3.telopt import TTYPE, TSPEED, XDISPLOC, NEW_ENVIRON\\n from telnetlib3.telopt import CHARSET, NAWS\\n\\n # wire extended rfc callbacks for terminal atributes, etc.\\n for (opt, func) in (\\n (TTYPE, self.send_ttype),\\n (TSPEED, self.send_tspeed),\\n (XDISPLOC, self.send_xdisploc),\\n (NEW_ENVIRON, self.send_env),\\n (NAWS, self.send_naws),\\n (CHARSET, self.send_charset),\\n ):\\n self.stream.set_ext_send_callback(opt, func)\",\n \"def restipy(callback,\\n pre_call=lambda env,sr,args,kwargs: None,\\n post_call=lambda env,sr,call_ret: None):\\n _func_table[callback.__name__] = (callback, pre_call, post_call)\",\n \"def register(self, callback, filters = []):\\n\\t\\tself.callbacks.append((callback, filters))\\n\\t\\tself.events[str(callback)] = []\",\n \"def set_callback(self, callback):\\n if not callable(callback):\\n raise TypeError(\\\"'callback' must be callable\\\")\\n\\n self._callback = callback\",\n \"def call_start_cbs(self):\\n try:\\n for (cb,args) in self._start_cbs:\\n cb(self.userdata, self.get_initial_states(), *args)\\n except:\\n smach.logerr(\\\"Could not execute start callback: \\\"+traceback.format_exc())\",\n \"def __trigger(self, toBeExecuted, args = []):\\n\\n self.__connect()\\n [ f(args) for f in toBeExecuted ]\\n self.__disconnect()\",\n \"def subscribe(self, callback: Callable) -> None:\\n self.callbacks.add(callback)\",\n \"def add_callback(self, done_cb: Callable[[], None] = None) -> None:\\n\\n if done_cb is not None:\\n self.callbacks.append(done_cb)\",\n \"def callbacks(self, val_generator: DataGenerator) -> List[Callback]:\\n if self.train_mode in [\\\"classifier\\\", \\\"both\\\"]:\\n model = self.classifier\\n model_dir = \\\"class_model\\\"\\n elif self.train_mode == \\\"combined\\\":\\n model = self.combined\\n model_dir = \\\"com_model\\\"\\n\\n # Callback for evaluating the validation dataset\\n eval_callback = EvalCallback(\\n model=model, val_generator=val_generator, layers=self.n_blocks\\n )\\n\\n # callback for saving the best model\\n checkpoint_callback = ModelCheckpoint(\\n f\\\"{model_dir}/{model_dir}\\\" + \\\"_{epoch:04d}_{val_acc:.4f}.h5\\\",\\n monitor=\\\"val_acc\\\",\\n verbose=0,\\n save_best_only=True,\\n save_weights_only=False,\\n mode=\\\"max\\\",\\n )\\n\\n \\\"Make sure checkpoint callback is after the eval_callback, dependency\\\"\\n return [eval_callback, checkpoint_callback]\",\n \"def add_callback(self, callback):\\n if callback is not None:\\n self.callbacks.append(callback)\",\n \"def subscribe(callback, keys):\\n assert type(keys) in (set, list, tuple)\\n _subscribers[callback] = keys\",\n \"def append(self, callback):\\n\\t\\tif callback is None:\\n\\t\\t\\tpass\\n\\t\\telif callable(callback):\\n\\t\\t\\tlist.append(self, WeakMethod(callback))\\n\\t\\telif isinstance(callback, list, tuple):\\n\\t\\t\\tfor i in callback:\\n\\t\\t\\t\\tself.append(i)\\n\\t\\telse:\\n\\t\\t\\tassert False\",\n \"def add(self, callback):\\n self._callbacks += as_cb_list(callback)\",\n \"async def send(self, *args, **kwargs) -> None:\\n for callback in self:\\n res = callback(*args, **kwargs)\\n if asyncio.iscoroutine(res) or isinstance(res, asyncio.Future):\\n await res\",\n \"def run_callback(self, name, handle_exceptions=True, show_traceback=None,\\n **kwargs):\\n for fcn in self._cb_dict[name]:\\n try:\\n fcn(**kwargs)\\n except Exception as ex:\\n if handle_exceptions:\\n self.logger.debug('Callback %s failed' % (name, ),\\n exc_info=True)\\n if show_traceback is not None:\\n print('Callback %s failed' % (name, ))\\n util.print_traceback(ex, f=show_traceback)\\n\\n else:\\n raise\",\n \"def set_update_callbacks(self, update_tr_pos_callback, update_view_model_callback) -> None:\\n\\n self.update_tr_position = update_tr_pos_callback\\n self.update_view_model = update_view_model_callback\",\n \"def _dummy_callback():\\n assert False, \\\"TRESPASS - set_callback() must be called\\\"\",\n \"def set_request_handler(self, callback: Callable[[ACLMessage], Any]):\\n self.callback = callback\",\n \"def add(self, callback, *args, **kwargs):\\n\\n self.list.append((callback, args, kwargs))\",\n \"def add_callbacks(am, ifttkey, ifttevent):\\n if _debug:\\n print(\\\"add_callbacks\\\", am)\\n\\n xurl = IFTT_URL.format(ifttevent, ifttkey)\\n for c in am.mac_targets.values():\\n call_val = {\\n 'url': xurl,\\n 'id': c.name,\\n 'cur_val': -1\\n }\\n c.add_callback(iftt_callback, call_val)\",\n \"def on_events(self, insert_callback=None, set_callback=None,\\n del_callback=None, reverse_callback=None, sort_callback=None):\\n self.on_insert(insert_callback)\\n self.on_set(set_callback)\\n self.on_del(del_callback)\\n self.on_reverse(reverse_callback)\\n self.on_sort(sort_callback)\",\n \"def add_asyn_callback(self, fcn, **kwargs):\\n assert(hasattr(fcn, '__call__'))\\n self._asyn_callbacks[fcn] = kwargs\",\n \"def Callbacks (self) -> typing.List[typing.Callable]:\\n\\n\\t\\tcallbacks = list()\\n\\n\\t\\tcallbackReferenceIndex = 0\\n\\t\\twhile callbackReferenceIndex < len(self._callbackReferences):\\n\\t\\t\\tcallback = self._callbackReferences[callbackReferenceIndex]()\\n\\n\\t\\t\\tif callback is None:\\n\\t\\t\\t\\tself._callbackReferences.pop(callbackReferenceIndex)\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tcallbacks.append(callback)\\n\\t\\t\\tcallbackReferenceIndex += 1\\n\\n\\t\\treturn callbacks\",\n \"def register_callbacks(self, cbs):\\n assert isinstance(cbs, dict) # cbs must be a dictionary\\n cbs_key_list = list(cbs.keys())\\n enum_key_list = [enum.value for enum in self._action_type_enum]\\n \\n # TODO replace by check for compulsory keys only, all action types do not apply to all actions\\n #assert(len(set(enum_key_list) - set(cbs_key_list)) == 0)\\n\\n self._cbs = {}\\n for key in cbs_key_list: # check syntax\\n print \\\"Registered callback for action %s\\\" % key\\n self._cbs[key] = cbs[key]\\n rospy.logdebug(\\\"Callbacks registered.\\\")\",\n \"def prepare_callbacks(output_dir: str) -> Tuple[Callable]:\\n time = datetime.now().strftime(\\\"%y%m%d-%H%M\\\")\\n log_dir = os.path.join(output_dir, \\\"tensorboard\\\" + time)\\n logging.info(\\\"Tensorboard directory: %s\\\" % log_dir)\\n tensorboard = TensorBoard(log_dir=log_dir, batch_size=1000, write_images=True,\\n write_graph=True)\\n csv_path = os.path.join(output_dir, \\\"csv_logger_\\\" + time + \\\".txt\\\")\\n logging.info(\\\"CSV logs: %s\\\" % csv_path)\\n csv_logger = CSVLogger(csv_path)\\n\\n filepath = os.path.join(output_dir, \\\"best_\\\" + time + \\\".model\\\")\\n model_saver = ModelCheckpoint(filepath, monitor=\\\"val_recall\\\", verbose=1, save_best_only=True,\\n mode=\\\"max\\\")\\n return tensorboard, csv_logger, model_saver\",\n \"def register_callbacks(self):\\n self.either_edge_cb = self.pi.callback(\\n self.gpio,\\n pigpio.EITHER_EDGE,\\n self.either_edge_callback\\n )\",\n \"def runEventCallbacks(self, event, *args):\\n\\n if not event in self.EVENT_TYPES:\\n raise Exception(\\\"XnatIo (onEvent): invalid event type '%s'\\\"%(\\\\\\n event))\\n if not hasattr(self, 'eventCallbacks__'):\\n print('self has no attribute eventCallbacks__')\\n return\\n\\n for callback in self.eventCallbacks__[event]:\\n #print(f\\\"EVENT CALLBACK {event}\\\")\\n callback(*args)\",\n \"def _setup_callbacks(lst, log_models, metrics_logger):\\n # pylint: disable=no-name-in-module\\n from mlflow.tensorflow._autolog import _TensorBoard, __MLflowTfKeras2Callback\\n\\n tb = _get_tensorboard_callback(lst)\\n if tb is None:\\n log_dir = _TensorBoardLogDir(location=tempfile.mkdtemp(), is_temp=True)\\n\\n out_list = lst + [_TensorBoard(log_dir.location)]\\n else:\\n log_dir = _TensorBoardLogDir(location=tb.log_dir, is_temp=False)\\n out_list = lst\\n out_list += [__MLflowTfKeras2Callback(log_models, metrics_logger, _LOG_EVERY_N_STEPS)]\\n return out_list, log_dir\",\n \"def fire_event(self, event = None):\\n for e in self.events:\\n if e[\\\"event\\\"] == event:\\n if type(e[\\\"args\\\"]) == type([]):\\n e[\\\"callback\\\"](*e[\\\"args\\\"])\\n elif type(e[\\\"args\\\"]) == type({}):\\n e[\\\"callback\\\"](**e[\\\"args\\\"])\\n elif e[\\\"args\\\"] == None:\\n e[\\\"callback\\\"]()\\n else:\\n e[\\\"callback\\\"](e[\\\"args\\\"])\\n return True\",\n \"def Handler(self, *events: str, colon: bool = False,\\n ircv3: bool = False) -> Callable:\\n ...\",\n \"def cbhandler(cls, session, cb_input):\\n commandhandler = dict()\\n commandhandler.update(dict({'sleep': cls.sleep}))\\n commandhandler.update(dict({'dupsession': cls.reinitsession}))\\n for i in range(len(cb_input)):\\n command = cb_input[i]['cmd']\\n arg = cb_input[i]['arg']\\n if command in commandhandler.keys():\\n cb_input[i]['ret'] = commandhandler[command](arg, session)\\n else:\\n print(\\\"Unknown command passed\\\", command)\\n cls.log(3, \\\"Unknown command passed\\\", command)\",\n \"def on_recv(self, callback):\\n if callback is None:\\n self._on_recv = callback\\n else:\\n\\n def wrap_recv(header, body):\\n callback(body)\\n\\n self._on_recv = wrap_recv\",\n \"def async_callback(self, callback, *args, **kwargs):\\n if callback is None:\\n return None\\n\\n if args or kwargs:\\n callback = functools.partial(callback, *args, **kwargs)\\n\\n def wrapper(*args, **kwargs):\\n try:\\n return callback(*args, **kwargs)\\n except Exception, e:\\n logging.error('Exception during callback', exc_info=True)\\n\\n return wrapper\",\n \"def call(manager, *args):\\n for instance in manager:\\n instance.callback(*args)\",\n \"def call_subscribers(self, *args, **kwargs) -> None:\\n for subscriber in self.get_subscribers():\\n subscriber(*args, **kwargs)\",\n \"def on_set(self, callback):\\n self._set_callback = callback if callable(callback) else _void\",\n \"def on_set(self, callback):\\n self._set_callback = callback if callable(callback) else _void\",\n \"def __call__(self, *args, **kwargs):\\n value = None\\n for callback in self.callbacks:\\n try:\\n local_value = callback(*args, **kwargs)\\n except Exception as e:\\n ip = get_ipython()\\n if ip is None:\\n self.log.warning(\\\"Exception in callback %s: %s\\\", callback, e, exc_info=True)\\n else:\\n ip.showtraceback()\\n else:\\n value = local_value if local_value is not None else value\\n return value\",\n \"def process_callbacks(host, typ):\\n \\n ip = host.replace(\\\"-\\\", \\\".\\\")\\n src = typ\\n call_time = strftime(\\\"%Y-%m-%d %H:%M:%S\\\", gmtime())\\n\\n if os.path.isfile(\\\"/tmp/cc/calls.log\\\"): # write callback to calls log\\n with open(\\\"/tmp/cc/calls.log\\\", 'a') as f:\\n s = \\\"{0:<25} {1:<16} {2:<10}\\\\n\\\".format(call_time, ip, src)\\n f.write(s)\\n else:\\n with open(\\\"/tmp/cc/calls.log\\\", 'w') as f:\\n s = \\\"{0:<25} {1:<16} {2:<10}\\\\n\\\".format(call_time, ip, src)\\n f.write(s)\\n\\n com_file = \\\"/tmp/cc/hosts/\\\" + ip # read commands from the appropriate file\\n if os.path.isfile(com_file):\\n with open(com_file, 'r') as f:\\n c = f.read()\\n os.remove(com_file)\\n return c + \\\"\\\\n\\\"\\n else:\\n return \\\"#lmao\\\\n\\\"\",\n \"def __call__(self, event_type, details):\\n cb = self.callback\\n if cb is None:\\n return\\n if self._details_filter is not None:\\n if not self._details_filter(details):\\n return\\n kwargs = dict(self._kwargs)\\n kwargs['details'] = details\\n cb(event_type, *self._args, **kwargs)\",\n \"def setup_callbacks(self):\\n\\n super(ArtellaMayaPlugin, self).setup_callbacks()\\n\\n callbacks.register(callback.Callbacks().AfterOpenCallback, self._after_open)\\n callbacks.register(callback.Callbacks().SceneBeforeSaveCallback, self._before_save)\\n callbacks.register(callback.Callbacks().BeforeOpenCheckCallback, self._before_open_check)\\n callbacks.register(callback.Callbacks().AfterLoadReferenceCallback, self._after_load_reference)\\n callbacks.register(callback.Callbacks().BeforeCreateReferenceCheckCallback, self._before_reference_check)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.702277","0.66514504","0.65428776","0.651266","0.6477689","0.61787194","0.6089447","0.60846204","0.60464215","0.60348064","0.59095436","0.590122","0.5900241","0.5882658","0.5858614","0.5747529","0.57051486","0.5680165","0.56781036","0.5653725","0.5636546","0.5616152","0.55880874","0.55756146","0.55564874","0.5518041","0.5517807","0.5497091","0.54964554","0.5467474","0.54554087","0.54337764","0.53963876","0.5376686","0.53637636","0.53586507","0.53429586","0.5341724","0.5330287","0.53171694","0.53044516","0.5258516","0.52428","0.52409726","0.52407306","0.5232459","0.5208991","0.51972187","0.5185989","0.51786935","0.5166441","0.5166003","0.51637214","0.51533335","0.51472944","0.51440096","0.5140474","0.5125386","0.5092434","0.50855476","0.50774086","0.5075293","0.506917","0.5054927","0.5029005","0.5028113","0.5025376","0.5016435","0.5013147","0.5012792","0.5011033","0.5005025","0.5000638","0.49992755","0.49989694","0.49805203","0.49717963","0.49652502","0.4951834","0.49488762","0.4946779","0.4941704","0.4939725","0.49365917","0.49338582","0.49238867","0.49184102","0.49156764","0.49146008","0.49060258","0.49052462","0.4904084","0.49033925","0.4894868","0.48883152","0.48883152","0.48833558","0.48815137","0.48797992","0.48743755"],"string":"[\n \"0.702277\",\n \"0.66514504\",\n \"0.65428776\",\n \"0.651266\",\n \"0.6477689\",\n \"0.61787194\",\n \"0.6089447\",\n \"0.60846204\",\n \"0.60464215\",\n \"0.60348064\",\n \"0.59095436\",\n \"0.590122\",\n \"0.5900241\",\n \"0.5882658\",\n \"0.5858614\",\n \"0.5747529\",\n \"0.57051486\",\n \"0.5680165\",\n \"0.56781036\",\n \"0.5653725\",\n \"0.5636546\",\n \"0.5616152\",\n \"0.55880874\",\n \"0.55756146\",\n \"0.55564874\",\n \"0.5518041\",\n \"0.5517807\",\n \"0.5497091\",\n \"0.54964554\",\n \"0.5467474\",\n \"0.54554087\",\n \"0.54337764\",\n \"0.53963876\",\n \"0.5376686\",\n \"0.53637636\",\n \"0.53586507\",\n \"0.53429586\",\n \"0.5341724\",\n \"0.5330287\",\n \"0.53171694\",\n \"0.53044516\",\n \"0.5258516\",\n \"0.52428\",\n \"0.52409726\",\n \"0.52407306\",\n \"0.5232459\",\n \"0.5208991\",\n \"0.51972187\",\n \"0.5185989\",\n \"0.51786935\",\n \"0.5166441\",\n \"0.5166003\",\n \"0.51637214\",\n \"0.51533335\",\n \"0.51472944\",\n \"0.51440096\",\n \"0.5140474\",\n \"0.5125386\",\n \"0.5092434\",\n \"0.50855476\",\n \"0.50774086\",\n \"0.5075293\",\n \"0.506917\",\n \"0.5054927\",\n \"0.5029005\",\n \"0.5028113\",\n \"0.5025376\",\n \"0.5016435\",\n \"0.5013147\",\n \"0.5012792\",\n \"0.5011033\",\n \"0.5005025\",\n \"0.5000638\",\n \"0.49992755\",\n \"0.49989694\",\n \"0.49805203\",\n \"0.49717963\",\n \"0.49652502\",\n \"0.4951834\",\n \"0.49488762\",\n \"0.4946779\",\n \"0.4941704\",\n \"0.4939725\",\n \"0.49365917\",\n \"0.49338582\",\n \"0.49238867\",\n \"0.49184102\",\n \"0.49156764\",\n \"0.49146008\",\n \"0.49060258\",\n \"0.49052462\",\n \"0.4904084\",\n \"0.49033925\",\n \"0.4894868\",\n \"0.48883152\",\n \"0.48883152\",\n \"0.48833558\",\n \"0.48815137\",\n \"0.48797992\",\n \"0.48743755\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7574884"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6798,"cells":{"query":{"kind":"string","value":"Adds and connects attributes from default encore FKIK switch anim setup to rig nodes in scene Imports default control setup from file or you may specify source_ctrl in args to override"},"document":{"kind":"string","value":"def make_fkikSwitch_connection_attrs(partpre=None, side='Lt', source_ctrl=None, tag_name='switch', snapTo=None,\n add_attrs=None):\n\n switch_anim = ''\n if source_ctrl is not None:\n switch_anim = source_ctrl\n\n partpre = partpre\n if partpre == '':\n partpre = 'mypart_'\n\n if source_ctrl is None:\n # filepath = r'C:/Users/Nicob/Documents/maya/scripts/rigBot/rigBot/config/switcher_anim.mb'\n system_base_path = os.path.dirname(utils.__file__)\n base_path = os.path.join(system_base_path, 'config')\n file_path = os.path.join(base_path, 'switcher_anim.mb')\n newnodes = mc.file(filepath, i=1, ignoreVersion=1, rnn=1, mergeNamespacesOnClash=0, rpr=partpre, ra=1,\n options=\"v=0;\", pr=1)\n\n switch_anim = partpre + '_CTL'\n\n # pos switcher grpOffset node if snapTo\n\n if snapTo is not None:\n utils.snap_to_transform(snapTo, switch_anim.replace('CTL', 'grpOffset'))\n mc.setAttr(switch_anim.replace('CTL', 'grpOffset') + '.r', 0, 0, 0)\n\n # get value of tags and sort into ik and fk vis groups\n\n iks = []\n fks = []\n nodes = mc.ls('*.' + tag_name)\n\n for node in nodes:\n if partpre in node and side in node:\n mode = mc.getAttr(node)\n if mode:\n mode = mode.lower()\n if 'ik' in mode:\n iks.append(node.split('.')[0])\n if 'fk' in mode:\n fks.append(node.split('.')[0])\n for ik in iks:\n # ikparpar=utils.get_parent(ik)\n ikpar = utils.get_parent(ik)\n if ikpar is None:\n mc.connectAttr(switch_anim + '.FK_IK', ik + '.visiblity', f=1)\n else:\n mc.connectAttr(switch_anim + '.FK_IK', ikpar + '.visibility', f=1)\n rvn = mc.createNode('reverse', name=switch_anim + '_fkik_vis_rv')\n mc.connectAttr(switch_anim + '.FK_IK', rvn + '.inputX')\n for fk in fks:\n fkpar = utils.get_parent(fk)\n if fkpar:\n mc.connectAttr(rvn + '.outputX', fkpar + '.visibility', f=1)\n if add_attrs is not None:\n for att in add_attrs:\n mc.addAttr(switch_anim, ln=att, min=0, max=1, dv=0, k=1)\n\n nns = []\n\n for nn in reversed(newnodes):\n nnn = ''\n sn = nn.split(\"|\")\n nnn = mc.rename(nn, sn[-1])\n nns.append(nnn)\n\n anim = mc.ls(partpre + '_CTL')\n\n # if mc.objExists (partpre+'_skeleton_grp'):\n # mc.parent (anim, partpre+'_skeleton_grp' )\n return anim"},"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 switch_setup(params, rig, ik_joints):\n\n # Duplicate for bind skeleton\n skeleton = [x.name() for x in params['ikSkeleton']]\n bind_skeleton = cmds.duplicate(skeleton, n=skeleton[0] + '_bnd_0')\n #bind_skeleton\n\n # Hide all attribute on Controller\n fkikcontrol = params['fkIkSwitch'].name()\n attrs = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\n for i in attrs:\n cmds.setAttr('{node}.{attr}'.format(node=fkikcontrol, attr=i), k=False, cb=False)\n\n # Create FK/IK Switch attributes\n cmds.addAttr(fkikcontrol, sn='FKIKBlend', at='float', min=0, max=1, dv=0, k=True)\n cmds.addAttr(fkikcontrol, sn='AutoVis', at='bool', dv=1, k=True)\n cmds.addAttr(fkikcontrol, ln='FKVis', at='bool', dv=1, k=True)\n cmds.addAttr(fkikcontrol, ln='IKVis', at='bool', dv=1, k=True)\n\n # create control offset transforms\n # par = cmds.listRelatives(fkikcontrol, parent=True)\n # buf = create_offset_transform(fkikcontrol, BUF)\n # cmds.parent(fkikcontrol, buf)\n # if par: cmds.parent(buf, par[0])\n\n # Parent Skeleton to rig group\n ik_skeleton = [x.name() for x in params['ikSkeleton']]\n fk_skeleton = [x.name() for x in params['fkSkeleton']]\n cmds.parent(ik_skeleton[0], rig['rigGroup'])\n cmds.parent(fk_skeleton[0], rig['rigGroup'])\n\n # Constraint Bind Skeleton\n fk_ik_finish(ik_joints, bind_skeleton, params)","def _set_attr(self):\n self.as_skeletal = self._import_as_skeleton()\n self.materials = self._import_materials()\n self.textures = self._import_textures()","def init_from_file(self):\n self.src.load('start.00') \n self.oe1.load('start.01')\n #self.det.load('start.02')\n print('NOTE: variables loaded from start.00/start.01 files')","def setup(args):\n cfg = get_cfg()\n add_imaterialist_config(cfg)\n cfg.merge_from_file(model_zoo.get_config_file(\"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\"))\n cfg.merge_from_file(args.config_file)\n \n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n # Setup logger for \"imaterialist\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"imaterialist\")\n return cfg","def setup_threeCtrl(lf_lidrails, rt_lidrails):\n # Declare control variables\n lf_up = ['lf_lid01_tp01_ccc', 'lf_lid01_tp02_ccc', 'lf_lid01_tp03_ccc']\n lf_dn = ['lf_lid01_dn01_ccc', 'lf_lid01_dn02_ccc', 'lf_lid01_dn03_ccc']\n rt_up = ['rt_lid01_tp01_ccc', 'rt_lid01_tp02_ccc', 'rt_lid01_tp03_ccc']\n rt_dn = ['rt_lid01_dn01_ccc', 'rt_lid01_dn02_ccc', 'rt_lid01_dn03_ccc']\n\n # Connect lidRails ramps to lid profile controls\n\n # ========\n # lf_up\n\n # inner\n cmds.connectAttr(lf_up[0] + '.tx', lf_lidrails + '.offsettop[0].offsettop_Position', f=True)\n cmds.connectAttr(lf_up[0] + '.ty', lf_lidrails + '.offsettop[0].offsettop_FloatValue', f=True)\n # mid\n lf_lid01_um01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_um01_addDoubleLinear')\n cmds.connectAttr(lf_up[1] + '.tx', lf_lid01_um01_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_um01_adn + '.input2', 0.5)\n cmds.connectAttr(lf_lid01_um01_adn + '.output', lf_lidrails + '.offsettop[1].offsettop_Position', f=True)\n cmds.connectAttr(lf_up[1] + '.ty', lf_lidrails + '.offsettop[1].offsettop_FloatValue', f=True)\n # outer\n lf_lid01_uo01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_uo01_addDoubleLinear')\n cmds.connectAttr(lf_up[2] + '.tx', lf_lid01_uo01_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_uo01_adn + '.input2', 1.0)\n cmds.connectAttr(lf_lid01_uo01_adn + '.output', lf_lidrails + '.offsettop[2].offsettop_Position', f=True)\n cmds.connectAttr(lf_up[2] + '.ty', lf_lidrails + '.offsettop[2].offsettop_FloatValue', f=True)\n\n # ========\n # lf_dn\n\n # Reverse node\n lf_dn_rvn = cmds.createNode('reverse', n='lf_lid01_dn01_reverse')\n # inner\n cmds.connectAttr(lf_dn[0] + '.tx', lf_lidrails + '.offsetbottom[0].offsetbottom_Position', f=True)\n cmds.connectAttr(lf_dn[0] + '.ty', lf_dn_rvn + '.inputX', f=True)\n cmds.connectAttr(lf_dn_rvn + '.outputX', lf_lidrails + '.offsetbottom[0].offsetbottom_FloatValue', f=True)\n # mid\n lf_lid01_dm01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_dm01_addDoubleLinear')\n cmds.connectAttr(lf_dn[1] + '.tx', lf_lid01_dm01_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_dm01_adn + '.input2', 0.5)\n cmds.connectAttr(lf_lid01_dm01_adn + '.output', lf_lidrails + '.offsetbottom[1].offsetbottom_Position', f=True)\n cmds.connectAttr(lf_dn[1] + '.ty', lf_dn_rvn + '.inputY', f=True)\n cmds.connectAttr(lf_dn_rvn + '.outputY', lf_lidrails + '.offsetbottom[1].offsetbottom_FloatValue', f=True)\n # outer\n lf_lid01_do01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_do01_addDoubleLinear')\n cmds.connectAttr(lf_dn[2] + '.tx', lf_lid01_do01_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_do01_adn + '.input2', 1.0)\n cmds.connectAttr(lf_lid01_do01_adn + '.output', lf_lidrails + '.offsetbottom[2].offsetbottom_Position', f=True)\n cmds.connectAttr(lf_dn[2] + '.ty', lf_dn_rvn + '.inputZ', f=True)\n cmds.connectAttr(lf_dn_rvn + '.outputZ', lf_lidrails + '.offsetbottom[2].offsetbottom_FloatValue', f=True)\n\n # ========\n # rt_up\n\n # inner\n rt_lid01_ui01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_ui01_plusMinusAverage')\n cmds.setAttr(rt_lid01_ui01_asn + '.operation', 2)\n cmds.setAttr(rt_lid01_ui01_asn + '.input1D[0]', 1.0)\n cmds.connectAttr(rt_up[0] + '.tx', rt_lid01_ui01_asn + '.input1D[1]', f=True)\n cmds.connectAttr(rt_lid01_ui01_asn + '.output1D', rt_lidrails + '.offsettop[2].offsettop_Position', f=True)\n cmds.connectAttr(rt_up[0] + '.ty', rt_lidrails + '.offsettop[2].offsettop_FloatValue', f=True)\n # mid\n rt_lid01_um01_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_um01_multDoubleLinear')\n rt_lid01_um01_adn = cmds.createNode('addDoubleLinear', n='rt_lid01_um01_addDoubleLinear')\n cmds.connectAttr(rt_up[1] + '.tx', rt_lid01_um01_mdn + '.input1', f=True)\n cmds.setAttr(rt_lid01_um01_mdn + '.input2', -1.0)\n cmds.connectAttr(rt_lid01_um01_mdn + '.output', rt_lid01_um01_adn + '.input1', f=True)\n cmds.setAttr(rt_lid01_um01_adn + '.input2', 0.5)\n cmds.connectAttr(rt_lid01_um01_adn + '.output', rt_lidrails + '.offsettop[1].offsettop_Position', f=True)\n cmds.connectAttr(rt_up[1] + '.ty', rt_lidrails + '.offsettop[1].offsettop_FloatValue', f=True)\n # outer\n rt_lid01_uo_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_uo_multDoubleLinear')\n cmds.connectAttr(rt_up[2] + '.tx', rt_lid01_uo_mdn + '.input1', f=True)\n cmds.setAttr(rt_lid01_uo_mdn + '.input2', -1.0)\n cmds.connectAttr(rt_lid01_uo_mdn + '.output', rt_lidrails + '.offsettop[0].offsettop_Position', f=True)\n cmds.connectAttr(rt_up[2] + '.ty', rt_lidrails + '.offsettop[0].offsettop_FloatValue', f=True)\n\n # ========\n # rt_dn\n\n # Reverse node\n rt_dn_rvn = cmds.createNode('reverse', n='rt_lid01_dn01_reverse')\n # inner\n rt_lid01_di01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_di01_plusMinusAverage')\n cmds.setAttr(rt_lid01_di01_asn + '.operation', 2)\n cmds.setAttr(rt_lid01_di01_asn + '.input1D[0]', 1.0)\n cmds.connectAttr(rt_dn[0] + '.tx', rt_lid01_di01_asn + '.input1D[1]', f=True)\n cmds.connectAttr(rt_lid01_di01_asn + '.output1D', rt_lidrails + '.offsetbottom[0].offsetbottom_Position', f=True)\n cmds.connectAttr(rt_dn[0] + '.ty', rt_dn_rvn + '.inputX', f=True)\n cmds.connectAttr(rt_dn_rvn + '.outputX', rt_lidrails + '.offsetbottom[0].offsetbottom_FloatValue', f=True)\n # mid\n rt_lid01_dm01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_dm01_plusMinusAverage')\n cmds.setAttr(rt_lid01_dm01_asn + '.operation', 2)\n cmds.setAttr(rt_lid01_dm01_asn + '.input1D[0]', 0.5)\n cmds.connectAttr(rt_dn[1] + '.tx', rt_lid01_dm01_asn + '.input1D[1]', f=True)\n cmds.connectAttr(rt_lid01_dm01_asn + '.output1D', rt_lidrails + '.offsetbottom[1].offsetbottom_Position', f=True)\n cmds.connectAttr(rt_dn[1] + '.ty', rt_dn_rvn + '.inputY', f=True)\n cmds.connectAttr(rt_dn_rvn + '.outputY', rt_lidrails + '.offsetbottom[1].offsetbottom_FloatValue', f=True)\n # outer\n rt_lid01_do01_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_do01_multDoubleLinear')\n cmds.connectAttr(rt_dn[2] + '.tx', rt_lid01_do01_mdn + '.input1', f=True)\n cmds.setAttr(rt_lid01_do01_mdn + '.input2', -1.0)\n cmds.connectAttr(rt_lid01_do01_mdn + '.output', rt_lidrails + '.offsetbottom[2].offsetbottom_Position', f=True)\n cmds.connectAttr(rt_dn[2] + '.ty', rt_dn_rvn + '.inputZ', f=True)\n cmds.connectAttr(rt_dn_rvn + '.outputZ', rt_lidrails + '.offsetbottom[2].offsetbottom_FloatValue', f=True)","def __init__(self, yaml_file = 'options_modeling.yaml'):\n\n self.reproj_th = 2.5\n self.min_matched_views = 3\n self.descriptors = {'SIFT': 'sift'} # Descriptor name and module name\n self.mask_suffix = '*_mask.png'\n \n # If there is an options file, it will overwrite the defaults \n if yaml_file is not None:\n self.load(yaml_file)","def connectControl(*args, fileName: bool=True, index: int=0, preventContextualMenu: bool=True,\n preventOverride: bool=True, **kwargs)->None:\n pass","def setup_fourCtrl(lf_lidrails, rt_lidrails):\n # Declare control variables\n lf_up = ['L_upperLid1_ctrl', 'L_upperLid2_ctrl', 'L_upperLid3_ctrl', 'L_upperLid4_ctrl']\n lf_dn = ['L_lowerLid1_ctrl', 'L_lowerLid2_ctrl', 'L_lowerLid3_ctrl', 'L_lowerLid4_ctrl']\n rt_up = ['R_upperLid1_ctrl', 'R_upperLid2_ctrl', 'R_upperLid3_ctrl', 'R_upperLid4_ctrl']\n rt_dn = ['R_lowerLid1_ctrl', 'R_lowerLid2_ctrl', 'R_lowerLid3_ctrl', 'R_lowerLid4_ctrl']\n\n # Connect lidRails ramps to lid profile controls\n\n # lf_up =========\n\n # inner\n cmds.connectAttr(lf_up[0] + '.tx', lf_lidrails + '.offsettop[0].offsettop_Position', f=True)\n cmds.connectAttr(lf_up[0] + '.ty', lf_lidrails + '.offsettop[0].offsettop_FloatValue', f=True)\n # mid - inner\n lf_lid01_um01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_um01_addDoubleLinear')\n cmds.connectAttr(lf_up[1] + '.tx', lf_lid01_um01_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_um01_adn + '.input2', 0.333)\n cmds.connectAttr(lf_lid01_um01_adn + '.output', lf_lidrails + '.offsettop[1].offsettop_Position', f=True)\n cmds.connectAttr(lf_up[1] + '.ty', lf_lidrails + '.offsettop[1].offsettop_FloatValue', f=True)\n # mid - outer\n lf_lid01_um02_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_um02_addDoubleLinear')\n cmds.connectAttr(lf_up[2] + '.tx', lf_lid01_um02_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_um02_adn + '.input2', 0.666)\n cmds.connectAttr(lf_lid01_um02_adn + '.output', lf_lidrails + '.offsettop[2].offsettop_Position', f=True)\n cmds.connectAttr(lf_up[2] + '.ty', lf_lidrails + '.offsettop[2].offsettop_FloatValue', f=True)\n # outer\n lf_lid01_uo01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_uo01_addDoubleLinear')\n cmds.connectAttr(lf_up[3] + '.tx', lf_lid01_uo01_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_uo01_adn + '.input2', 1.0)\n cmds.connectAttr(lf_lid01_uo01_adn + '.output', lf_lidrails + '.offsettop[3].offsettop_Position', f=True)\n cmds.connectAttr(lf_up[3] + '.ty', lf_lidrails + '.offsettop[3].offsettop_FloatValue', f=True)\n\n # lf_dn =========\n\n lf_dn_rvn = cmds.createNode('reverse', n='lf_lid01_dn01_reverse')\n lf_dn02_rvn = cmds.createNode('reverse', n='lf_lid01_dn02_reverse')\n # inner\n cmds.connectAttr(lf_dn[0] + '.tx', lf_lidrails + '.offsetbottom[0].offsetbottom_Position', f=True)\n cmds.connectAttr(lf_dn[0] + '.ty', lf_dn_rvn + '.inputX', f=True)\n cmds.connectAttr(lf_dn_rvn + '.outputX', lf_lidrails + '.offsetbottom[0].offsetbottom_FloatValue', f=True)\n # mid - inner\n lf_lid01_dm01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_dm01_addDoubleLinear')\n cmds.connectAttr(lf_dn[1] + '.tx', lf_lid01_dm01_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_dm01_adn + '.input2', 0.333)\n cmds.connectAttr(lf_lid01_dm01_adn + '.output', lf_lidrails + '.offsetbottom[1].offsetbottom_Position', f=True)\n cmds.connectAttr(lf_dn[1] + '.ty', lf_dn_rvn + '.inputY', f=True)\n cmds.connectAttr(lf_dn_rvn + '.outputY', lf_lidrails + '.offsetbottom[1].offsetbottom_FloatValue', f=True)\n # mid - outer\n lf_lid01_dm02_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_dm02_addDoubleLinear')\n cmds.connectAttr(lf_dn[2] + '.tx', lf_lid01_dm02_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_dm02_adn + '.input2', 0.666)\n cmds.connectAttr(lf_lid01_dm02_adn + '.output', lf_lidrails + '.offsetbottom[2].offsetbottom_Position', f=True)\n cmds.connectAttr(lf_dn[2] + '.ty', lf_dn02_rvn + '.inputX', f=True)\n cmds.connectAttr(lf_dn02_rvn + '.outputX', lf_lidrails + '.offsetbottom[2].offsetbottom_FloatValue', f=True)\n # outer\n lf_lid01_do01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_do01_addDoubleLinear')\n cmds.connectAttr(lf_dn[3] + '.tx', lf_lid01_do01_adn + '.input1', f=True)\n cmds.setAttr(lf_lid01_do01_adn + '.input2', 1.0)\n cmds.connectAttr(lf_lid01_do01_adn + '.output', lf_lidrails + '.offsetbottom[3].offsetbottom_Position', f=True)\n cmds.connectAttr(lf_dn[3] + '.ty', lf_dn02_rvn + '.inputY')\n cmds.connectAttr(lf_dn02_rvn + '.outputY', lf_lidrails + '.offsetbottom[3].offsetbottom_FloatValue', f=True)\n\n # rt_up =========\n\n # inner\n rt_lid01_ui01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_ui01_plusMinusAverage')\n cmds.setAttr(rt_lid01_ui01_asn + '.input1D[0]', 1.0)\n cmds.connectAttr(rt_up[0] + '.tx', rt_lid01_ui01_asn + '.input1D[1]', f=True)\n cmds.connectAttr(rt_lid01_ui01_asn + '.output1D', rt_lidrails + '.offsettop[3].offsettop_Position', f=True)\n cmds.connectAttr(rt_up[0] + '.ty', rt_lidrails + '.offsettop[3].offsettop_FloatValue', f=True)\n # mid -inner\n rt_lid01_um01_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_um01_multDoubleLinear')\n rt_lid01_um01_adn = cmds.createNode('addDoubleLinear', n='rt_lid01_um01_addDoubleLinear')\n cmds.connectAttr(rt_up[2] + '.tx', rt_lid01_um01_mdn + '.input1', f=True)\n cmds.setAttr(rt_lid01_um01_mdn + '.input2', -1.0)\n cmds.connectAttr(rt_lid01_um01_mdn + '.output', rt_lid01_um01_adn + '.input1', f=True)\n cmds.setAttr(rt_lid01_um01_adn + '.input2', 0.333)\n cmds.connectAttr(rt_lid01_um01_adn + '.output', rt_lidrails + '.offsettop[2].offsettop_Position', f=True)\n cmds.connectAttr(rt_up[2] + '.ty', rt_lidrails + '.offsettop[2].offsettop_FloatValue', f=True)\n\n # mid - outer\n rt_lid01_um02_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_um02_multDoubleLinear')\n rt_lid01_um02_adn = cmds.createNode('addDoubleLinear', n='rt_lid01_um02_addDoubleLinear')\n cmds.connectAttr(rt_up[1] + '.tx', rt_lid01_um02_mdn + '.input1', f=True)\n cmds.setAttr(rt_lid01_um02_mdn + '.input2', -1.0)\n cmds.connectAttr(rt_lid01_um02_mdn + '.output', rt_lid01_um02_adn + '.input1', f=True)\n cmds.setAttr(rt_lid01_um02_adn + '.input2', 0.666)\n cmds.connectAttr(rt_lid01_um02_adn + '.output', rt_lidrails + '.offsettop[1].offsettop_Position', f=True)\n cmds.connectAttr(rt_up[1] + '.ty', rt_lidrails + '.offsettop[1].offsettop_FloatValue', f=True)\n\n # outer\n rt_lid01_uo_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_uo_multDoubleLinear')\n cmds.connectAttr(rt_up[3] + '.tx', rt_lid01_uo_mdn + '.input1', f=True)\n cmds.setAttr(rt_lid01_uo_mdn + '.input2', -1.0)\n cmds.connectAttr(rt_lid01_uo_mdn + '.output', rt_lidrails + '.offsettop[0].offsettop_Position', f=True)\n cmds.connectAttr(rt_up[3] + '.ty', rt_lidrails + '.offsettop[0].offsettop_FloatValue', f=True)\n\n # rt_dn =========\n\n rt_dn_rvn = cmds.createNode('reverse', n='rt_lid01_dn01_reverse')\n rt_dn02_rvn = cmds.createNode('reverse', n='rt_lid01_dn02_reverse')\n # inner\n rt_lid01_di01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_di01_plusMinusAverage')\n cmds.setAttr(rt_lid01_di01_asn + '.operation', 2)\n cmds.setAttr(rt_lid01_di01_asn + '.input1D[0]', 1.0)\n cmds.connectAttr(rt_dn[0] + '.tx', rt_lid01_di01_asn + '.input1D[1]', f=True)\n cmds.connectAttr(rt_lid01_di01_asn + '.output1D', rt_lidrails + '.offsetbottom[0].offsetbottom_Position', f=True)\n cmds.connectAttr(rt_dn[0] + '.ty', rt_dn_rvn + '.inputX', f=True)\n cmds.connectAttr(rt_dn_rvn + '.outputX', rt_lidrails + '.offsetbottom[0].offsetbottom_FloatValue', f=True)\n # mid - inner\n rt_lid01_dm01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_dm01_plusMinusAverage')\n cmds.setAttr(rt_lid01_dm01_asn + '.operation', 2)\n cmds.setAttr(rt_lid01_dm01_asn + '.input1D[0]', 0.333)\n cmds.connectAttr(rt_dn[2] + '.tx', rt_lid01_dm01_asn + '.input1D[1]', f=True)\n cmds.connectAttr(rt_lid01_dm01_asn + '.output1D', rt_lidrails + '.offsetbottom[1].offsetbottom_Position', f=True)\n cmds.connectAttr(rt_dn[2] + '.ty', rt_dn_rvn + '.inputY', f=True)\n cmds.connectAttr(rt_dn_rvn + '.outputY', rt_lidrails + '.offsetbottom[1].offsetbottom_FloatValue', f=True)\n # mid - outer\n rt_lid01_dm02_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_dm02_plusMinusAverage')\n cmds.setAttr(rt_lid01_dm02_asn + '.operation', 2)\n cmds.setAttr(rt_lid01_dm02_asn + '.input1D[0]', 0.666)\n cmds.connectAttr(rt_dn[1] + '.tx', rt_lid01_dm02_asn + '.input1D[1]', f=True)\n cmds.connectAttr(rt_lid01_dm02_asn + '.output1D', rt_lidrails + '.offsetbottom[2].offsetbottom_Position', f=True)\n cmds.connectAttr(rt_dn[1] + '.ty', rt_dn02_rvn + '.inputX', f=True)\n cmds.connectAttr(rt_dn02_rvn + '.outputX', rt_lidrails + '.offsetbottom[2].offsetbottom_FloatValue', f=True)\n # outer\n rt_lid01_do01_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_do01_multDoubleLinear')\n cmds.connectAttr(rt_dn[3] + '.tx', rt_lid01_do01_mdn + '.input1', f=True)\n cmds.setAttr(rt_lid01_do01_mdn + '.input2', -1.0)\n cmds.connectAttr(rt_lid01_do01_mdn + '.output', rt_lidrails + '.offsetbottom[3].offsetbottom_Position', f=True)\n cmds.connectAttr(rt_dn[3] + '.ty', rt_dn02_rvn + '.inputY', f=True)\n cmds.connectAttr(rt_dn02_rvn + '.outputY', rt_lidrails + '.offsetbottom[3].offsetbottom_FloatValue')","def init():\n \n # General parameters\n vect_path = '/home/laura/Documents/STAGE3/outputVectorisation_1705_new_invert/output_60_70/' # graphs directory\n csv_path = '/home/laura/Documents/STAGE3/outputVectorisation_1705_new_invert/output_60_70/tracking3/nodes_for_tracking.csv' # csv file \n dest_path = '/home/laura/Documents/STAGE3/outputVectorisation_1705_new_invert/output_60_70/tracking3/' # output directory\n verbose = True\n main_params = [vect_path, csv_path, dest_path, verbose]\n \n # Linking parameters\n createCSV = True \n forced_matching = True\n search_range = 10\n memory = 3\n adaptive_stop = 5 \n link_params = [createCSV, forced_matching, search_range, memory, \n adaptive_stop]\n \n # Tracking check parameters\n check = True # True to create a check image\n img_path = '/home/laura/Documents/STAGE3/outputVectorisation_1705_new_invert/output_60_70/tracking2/MosaicTest_t070.jpg' # image file on which to draw\n size = 1 # size of the nodes drawing\n check_params = [check, img_path, size]\n \n return main_params, link_params, check_params","def create_ik_setup(controls, joints):\n\n # Create control offset transforms\n exp_tf_ms = []\n for ctl in controls:\n par = cmds.listRelatives(ctl, parent=True)\n buf = create_offset_transform(ctl, BUF)\n exp = create_offset_transform(ctl, EXP)\n off = create_offset_transform(ctl, OFF)\n cmds.parent(ctl, off)\n cmds.parent(off, exp)\n cmds.parent(exp, buf)\n if par:\n cmds.parent(buf, par[0])\n exp_tf_ms.append(buf)\n\n root_control, pole_control, goal_control = controls\n handle, effector = cmds.ikHandle(sj=joints[0], ee=joints[-1], sol='ikRPsolver')\n cmds.setAttr('{}.hiddenInOutliner'.format(handle), True)\n cmds.orientConstraint(goal_control, joints[-1], mo=True)\n cmds.parent(handle, goal_control)\n cmds.hide(handle)\n\n # Connect root control to ik joint offset group\n ik_joints_offset = cmds.listRelatives(joints[0], p=True)[0]\n cmds.parentConstraint(root_control, ik_joints_offset, mo=True)\n cmds.scaleConstraint(root_control, ik_joints_offset, mo=True)\n\n # Connect twisting and pole vector control\n cmds.addAttr(goal_control, ln='twist', at='float', k=True)\n cmds.connectAttr('{}.twist'.format(goal_control), '{}.twist'.format(handle))\n cmds.poleVectorConstraint(pole_control, handle)\n\n # Add PV visibility attribute\n cmds.addAttr(goal_control, shortName='pv', longName='poleVector', at='bool', k=True)\n cmds.connectAttr('{}.pv'.format(goal_control), '{}.v'.format(pole_control))\n cmds.setAttr('{}.pv'.format(goal_control),1)\n\n # Add curve that points elbow to pole control\n crv = cmds.curve(p=[[0, 0, 0], [0, 1, 0]], d=1)\n cmds.connectAttr('{}.visibility'.format(pole_control), '{}.visibility'.format(crv))\n lock_hide_attrs(crv, attrs=['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz'])\n cmds.setAttr('{}.overrideEnabled'.format(crv), True)\n cmds.setAttr('{}.overrideDisplayType'.format(crv), 2)\n decomp_joint = cmds.createNode('decomposeMatrix')\n decomp_control = cmds.createNode('decomposeMatrix')\n cmds.connectAttr('{}.worldMatrix'.format(joints[1]), '{}.inputMatrix'.format(decomp_joint))\n cmds.connectAttr('{}.worldMatrix'.format(pole_control), '{}.inputMatrix'.format(decomp_control))\n cmds.connectAttr('{}.outputTranslate'.format(decomp_joint), '{}.controlPoints[0]'.format(crv))\n cmds.connectAttr('{}.outputTranslate'.format(decomp_control), '{}.controlPoints[1]'.format(crv))\n\n return handle, crv, exp_tf_ms","def setup(instname):\n global reducer, inst_name,van_mass,bleed_switch,rate,pixels\n # debugging (allows to reload changed DirectEnergyConversion package from Mantid)\n\n if instname=='MAR' or instname=='mar':\n print 'setup mari'\n inst_name='MAR'\n reducer = DRC.setup_reducer('MARI')\n bleed_switch=False\n rate=0.0\n pixels=0\n elif instname=='MER' or instname=='mer':\n print 'setup merlin'\n inst_name='MER'\n reducer = DRC.setup_reducer('MERLIN')\n bleed_switch=True\n rate=0.01\n pixels=80\n elif instname=='MAP' or instname=='map':\n print 'setup maps'\n inst_name='MAP'\n reducer = DRC.setup_reducer('MAPS')\n bleed_switch=False\n rate=0.0\n pixels=0.0\n elif instname=='LET' or instname=='let':\n print 'setup let'\n inst_name='LET'\n reducer = DRC.setup_reducer('LET')\n bleed_switch=True\n rate=0.01\n pixels=80\n elif instname=='ARCS' or instname=='arcs':\n print 'setup Arcs'\n inst_name='ARC'\n reducer = DRC.setup_reducer('ARCS')\n bleed_switch=False\n rate=0.01\n pixels=80\n elif instname=='SEQ' or instname=='seq':\n print 'setup Sequoia'\n inst_name='SEQ'\n reducer = DRC.setup_reducer('SEQUOIA')\n bleed_switch=False\n rate=0.01\n pixels=80\n elif instname=='CNCS' or instname=='cncs':\n print 'setup cncs'\n inst_name='SEQ'\n reducer = DRC.setup_reducer('CNCS')\n bleed_switch=False\n rate=0.01\n pixels=80\n elif instname=='HYSPEC' or instname=='hyspec':\n print 'setup hyspec'\n inst_name='SEQ'\n reducer = DRC.setup_reducer('HYSPEC')\n bleed_switch=False\n rate=0.01\n pixels=80\n else:\n print 'Instrument name not defined'\n return \n van_mass=reducer.get_default_parameter('vanadium-mass')","def __init__(self, parent):\n super(Demo4, self).__init__(parent)\n self.scenes = []\n self.draw_axes = True\n self.lighting = True\n self.current_scene = 0\n self.objects = []\n self.diffuse_light = [0.8, 0.8, 0.8, 1]","def __init__(self, cat_path, img_path, orig_json_path, rst_json_path,\n layers, **kwargs):\n\n # Initialize class attributes\n self.name = \"AI2D-RST\" # Dataset name\n self.cat_path = Path(cat_path) # Path to categories JSON\n self.img_path = Path(img_path) # Path to AI2D images\n self.orig_json_path = Path(orig_json_path) # Path to AI2D JSON\n self.rst_json_path = Path(rst_json_path) # Path to AI2D-RST JSON\n\n # Check input types\n assert self.cat_path.is_file()\n assert self.img_path.is_dir()\n assert self.orig_json_path.is_dir()\n assert self.rst_json_path.is_dir()\n assert layers in ['grouping', 'grouping+connectivity', 'connectivity',\n 'discourse', 'discourse+connectivity']\n\n # Load node and edge dictionaries\n self.node_dict = node_dicts\n self.edge_dict = edge_dicts\n\n # Load diagram labels from the labels JSON file\n categories = self._load_annotation(cat_path)\n\n # Initialize label encoder and encode integer labels\n le = LabelEncoder().fit(list(categories.values()))\n\n # Create a dictionary mapping encoded class integers to their names\n self.class_names = {k: v for k, v in zip(le.transform(le.classes_),\n le.classes_)}\n\n # Create a dictionary mapping filenames to labels\n label_dict = {k: le.transform([v]) for k, v in categories.items()}\n\n # Convert labels into a numpy array for calculating class weights\n label_arr = np.concatenate(list(label_dict.values()))\n\n # Calculate class weights\n class_weights = compute_class_weight(class_weight='balanced',\n classes=np.unique(label_arr),\n y=label_arr)\n\n # Wrap class weights into a torch Tensor and make available through\n # attribute\n self.class_weights = torch.FloatTensor(class_weights)\n\n # Get diagram identifiers and labels\n self.file_ids = list(label_dict.keys())\n self.labels = list(label_dict.values())\n\n # Return DGL graph objects by default\n self._return_nx = False\n\n # Check if NetworkX graphs have been requested\n if kwargs and 'nx' in kwargs:\n\n # Set the flag for returning NetworkX graphs to True\n if kwargs['nx']:\n \n self._return_nx = True\n\n # Check if node type information should be added to node features\n if kwargs and kwargs['node_types']:\n\n # Set add node types flag to True\n self._add_node_types = True\n\n # Check which node label dictionary to use: this depends on the kind\n # of annotation layers requested\n if 'discourse' in layers:\n\n # Get the node labels from the node dictionary & cast to array\n node_labels = list(self.node_dict['discourse'].values())\n node_labels = np.asarray(node_labels)\n\n else:\n\n # Get the node labels from the node dictionary & cast to array\n node_labels = list(self.node_dict['grouping'].values())\n node_labels = np.asarray(node_labels)\n\n # Initialize label binarizer and fit to node labels\n self._node_binarizer = LabelBinarizer().fit(node_labels)\n\n else:\n\n self._add_node_types = False\n\n # Check if smoothed labels have been requested\n if 'smooth' in kwargs and kwargs['smooth']:\n\n # Set the flag for smoothed labels to True\n self._smooth_labels = True\n\n else:\n\n self._smooth_labels = False\n\n # Initialize label binarizer for RST relations if needed\n if 'discourse' in layers:\n\n # Get the RST relations from the node dictionary\n rst_relations = np.asarray(list(self.node_dict['relations'].values()))\n\n # Initialize label binarizer and fit to node labels\n self._rst_binarizer = LabelBinarizer().fit(rst_relations)\n\n # Load the requested annotation and create the graphs accordingly\n self._load(layers)\n\n # Get number of unique diagram classes in the dataset\n self.n_classes = len(np.unique(self.labels))\n\n # Get the number of node and edge classes for DGL graphs (grouping +\n # connectivity)\n if 'discourse' not in layers and not self._return_nx:\n\n # Get unique node and edge types for graphs that don't use typed\n # nodes or edges\n node_list = [x.ndata['kind'].flatten() for x in self.diagrams]\n self.n_node_classes = len(np.unique(torch.cat(node_list).numpy()))\n\n edge_list = [x.edata['kind'].flatten() for x in self.diagrams]\n self.n_edge_classes = len(np.unique(torch.cat(edge_list).numpy()))\n\n # Do the same for DGLHeteroGraphs (discourse)\n if 'discourse' in layers and not self._return_nx:\n\n node_list = np.concatenate(np.asarray([x.ntypes for x in\n self.diagrams]))\n self.n_node_classes = len(np.unique(node_list))\n\n edge_list = np.concatenate(np.asarray([x.etypes for x in\n self.diagrams]))\n self.n_edge_classes = len(np.unique(edge_list))","def setup(args):\n # chaparral,denseForest,lake,canyon,burning,burnt = neighbours\n config_path = args[0]\n config = utils.load(config_path)\n # -- THE CA MUST BE RELOADED IN THE GUI IF ANY OF THE BELOW ARE CHANGED --\n config.title = \"Forest Fire\"\n config.dimensions = 2\n config.states = \\\n (\n CHAPARRAL,\n DENSE_FORREST,\n LAKE,\n CANYON,\n BURNING,\n BURNT,\n START_BURN,\n END_BURN\n )\n\n # ------------ -------------------------------------------------------------\n\n config.state_colors = \\\n [\n (0.6,0.6,0), #chaparral\n (0,0.4,0), #dense forrest\n (0,0.5,1), #lake\n (0.5,0.5,0.5), #canyon\n (1,0,0), #burning\n (0.25,0.25,0.25), #burnt\n (1,0.7,0), #starting to burn\n (0.8,0,0.2) #ending burn\n ]\n\n config.grid_dims = (grid_size, grid_size)\n config.num_generations = 1000\n config.set_initial_grid(initial_grid)\n config.wrap = False\n\n # --------------------------------------------------------------------\n\n # the GUI calls this to pass the user defined config\n # into the main system with an extra argument\n # do not change\n if len(args) == 2:\n config.save()\n sys.exit()\n return config","def setup(args):\n cfg = get_cfg()\n\n cfg.merge_from_file(model_zoo.get_config_file(\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\"))\n cfg.merge_from_list(args.opts)\n\n # configs for training\n if args.small_vidor: # cfg.DATASETS.VIDOR.SIZE == 'small':\n cfg.DATASETS.TRAIN = (\"vidor_small_train\",)\n elif args.small_vidor_10imgs: # cfg.DATASETS.VIDOR.SIZE == 'small-10imgs':\n cfg.DATASETS.TRAIN = (\"vidor_small_10imgs_train\",)\n else:\n cfg.DATASETS.TRAIN = (\"vidor_large_train\",)\n # cfg.DATALOADER.NUM_WORKERS = 2\n if not args.eval_only:\n cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\") # Let training initialize from model zoo\n factor = 4\n cfg.SOLVER.IMS_PER_BATCH = 16 * factor\n cfg.SOLVER.BASE_LR = 0.0001 * factor # finetune using 10x smaller base_lr\n cfg.SOLVER.MAX_ITER = 270000 // factor \n cfg.SOLVER.STEPS = [210000 // factor, 250000 // factor]\n # cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # default: 512\n cfg.MODEL.ROI_HEADS.NUM_CLASSES = 78\n\n # configs for testing\n # cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \"model_final.pth\")\n if args.small_vidor: # cfg.DATASETS.VIDOR.SIZE == 'small':\n cfg.DATASETS.TEST = (\"vidor_small_val\",)\n elif args.small_vidor_10imgs: # cfg.DATASETS.VIDOR.SIZE == 'small-10imgs':\n cfg.DATASETS.TEST = (\"vidor_small_10imgs_val\",)\n else:\n cfg.DATASETS.TEST = (\"vidor_large_val\",)\n # cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\n\n # cfg.OUTPUT_DIR = './output/train_vidor_with_pseudo_labels'\n \n \n if not args.eval_only:\n os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\n cfg.freeze()\n default_setup(cfg, args)\n return cfg","def __init__(self,config = None):\n \n self.join_path = join_path\n self.label_path = cfg['labels_path']\n self.pick_path = (cfg['result_path'] + cfg['pickle_path'])\n self.label_dir = os.path.join(CWD_PATH,self.join_path, self.label_path)\n\n #Variables inherent to the Fluent data: \n self.num_ins = 4\n\n self.scale_var = cfg['scale_var']\n # User set values are below. These can be adjusted in config.yml \n self.MSE_thresh1 = (cfg['thresh1']*self.scale_var)**2\n self.MSE_thresh2 = (cfg['thresh2']*self.scale_var)**2\n self.MSE_thresh3 = (cfg['thresh3']*self.scale_var)**2\n \n self.rew_goal = cfg['reward'] * self.scale_var\n\n self.noise = cfg['noise']\n self.minmaxbuffer = cfg['minmaxbuffer']\n\n # Get the function of input-output mapping, and max & min:\n [self.O_CH4_flow_uniformity, mins,maxes] = self.get_funcs('O_CH4_flow_uniformity')\n [self.O_CH4_mol_frac, mins,maxes] = self.get_funcs('O_CH4_mol_frac')\n [self.O_t, mins, maxes] = self.get_funcs('O_t')\n \n self.mins = mins# * self.scale_var\n self.maxes = maxes#* self.scale_var\n #Action range is a percentage of the total range\n self.action_range = cfg['action_range']*self.scale_var\n\n #Action space is the up & down range for the 4 actions \n self.action_space = Box(-self.action_range, self.action_range, shape=(self.num_ins,), dtype=np.float32)\n\n # For ref, this is a 10d state space:\n #in: 1 ch4 flow, 2 ch4 t, 3 o2 flow, 4 o2 t,\n #out: 5 flow unif, 6 mol frac, 7 temp\n #out - target: 8 flow unif, 9 mol frac, 10 temp\n \n self.observation_space = Tuple((Box(self.mins.values[0],self.maxes.values[0],shape=(1,), dtype=np.float32),\n Box(self.mins.values[1],self.maxes.values[1],shape=(1,), dtype=np.float32),\n Box(self.mins.values[2],self.maxes.values[2],shape=(1,), dtype=np.float32),\n Box(self.mins.values[3],self.maxes.values[3],shape=(1,), dtype=np.float32),\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32),\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32)))\n \n # TODO this isn't really a proper gym spec\n self._spec = lambda: None\n self._spec.id = \"AllVar-v0\"\n \n # For rendering:\n self.viewer = None\n self.labels = cfg['labels']\n \n #initialize variables for tracking:\n self.episode = 0\n self.reward = 0\n self.reset()","def __init__(self, cfg_index, conditions, pars_dir, step_title, use_defaults, input_cfg_json_data):\n super().__init__(cfg_index, conditions, pars_dir, step_title, use_defaults, input_cfg_json_data)\n self.set_name = \"alignment\"\n if input_cfg_json_data:\n self._read_custom_pars()\n else:\n self._combine_conditions()","def init_vars():\n\tda_vinci.base.usepackage(\"pgfkeys\")\n\tda_vinci.base.add_preamble(setup_script)","def setup_flags(self):\n self.io_args.color = self.io_args.color_full\n self.io_args.rig_in = self.io_args.rig\n self.io_args.matches = os.path.join(self.io_args.output_root, \"matches.json\")\n self.io_args.rig_out = os.path.join(self.io_args.output_root, \"rig.json\")","def init_trainers(self, args):\n self.actors_cur = [None for _ in range(self.num_agents)]\n self.critics_cur = [None for _ in range(self.num_agents)]\n self.actors_tar = [None for _ in range(self.num_agents)]\n self.critics_tar = [None for _ in range(self.num_agents)]\n self.optimizers_c = [None for _ in range(self.num_agents)]\n self.optimizers_a = [None for _ in range(self.num_agents)]\n input_size_global = sum(self.obs_shape_n) + sum(self.action_shape_n)\n\n if args.restore == True: # restore the model\n game_step = int(args.old_model_name.split('_')[-1][:-1])\n for idx in range(self.num_agents):\n self.actors_cur[idx] = torch.load(args.old_model_name+'a_c_{}.pt'.format(idx))\n self.actors_tar[idx] = torch.load(args.old_model_name+'a_t_{}.pt'.format(idx))\n self.critics_cur[idx] = torch.load(args.old_model_name+'c_c_{}.pt'.format(idx))\n self.critics_tar[idx] = torch.load(args.old_model_name+'c_t_{}.pt'.format(idx))\n self.optimizers_a[idx] = optim.Adam(self.actors_cur[idx].parameters(), args.lr_a)\n self.optimizers_c[idx] = optim.Adam(self.critics_cur[idx].parameters(), args.lr_c)\n self.var = self.var - (game_step-args.learning_start_episode*args.per_episode_max_len)*args.var_discount\n self.var = self.min_var if self.var < self.min_var else self.var\n old_data = {'game_step':game_step, 'episode_gone_old':int(game_step/args.per_episode_max_len)}\n\n # Note: if you need load old model, there should be a procedure for juding if the trainers[idx] is None\n for i in range(self.num_agents):\n self.actors_cur[i] = actor_agent(self.obs_shape_n[i], self.action_shape_n[i], \\\n args).to(args.device)\n self.critics_cur[i] = critic_agent(sum(self.obs_shape_n), sum(self.action_shape_n), \\\n args).to(args.device)\n self.actors_tar[i] = actor_agent(self.obs_shape_n[i], self.action_shape_n[i], \\\n args).to(args.device)\n self.critics_tar[i] = critic_agent(sum(self.obs_shape_n), sum(self.action_shape_n), \\\n args).to(args.device)\n self.optimizers_a[i] = optim.Adam(self.actors_cur[i].parameters(), args.lr_a)\n self.optimizers_c[i] = optim.Adam(self.critics_cur[i].parameters(), args.lr_c)\n\n # return the old data, no need to update the trainers\n if args.restore == True: return old_data\n\n self.actors_tar = self.update_trainers(self.actors_cur, self.actors_tar, 1.0) # update the target par using the cur\n self.critics_tar = self.update_trainers(self.critics_cur, self.critics_tar, 1.0) # update the target par using the cur","def __init__(self, modifications = {}):\n CoveredState.__init__(self, BASE_CONFIG, coverLayers = TOTAL_LAYERS)\n for name in modifications:\n self.Edit(name, modifications[name])\n if unix:\n self.Cover(\"BuilderDir\", VELAUNCHER_DIR, layer = UNAVAILABLE_LAYER)","def _augment_pipeline_cfg(self):","def set_defaults(args):\n # Check critical files exist\n args.train_file = os.path.join(args.data_dir, args.train_file)\n if not os.path.isfile(args.train_file):\n raise IOError('No such file: %s' % args.train_file)\n args.dev_file = os.path.join(args.data_dir, args.dev_file)\n if not os.path.isfile(args.dev_file):\n raise IOError('No such file: %s' % args.dev_file)\n if args.embedding_file:\n args.embedding_file = os.path.join(args.embed_dir, args.embedding_file)\n if not os.path.isfile(args.embedding_file):\n raise IOError('No such file: %s' % args.embedding_file)\n\n # Set model directory\n subprocess.call(['mkdir', '-p', args.model_dir])\n\n # Set model name\n if not args.model_name:\n import uuid\n import time\n args.model_name = time.strftime(\"%Y%m%d-\") + str(uuid.uuid4())[:8]\n\n # Set log + model file names\n args.log_file = os.path.join(args.model_dir, args.model_name + '.txt')\n args.model_file = os.path.join(args.model_dir, args.model_name + '.pt')\n\n # Embeddings options\n if args.embedding_file:\n with open(args.embedding_file) as f:\n dim = len(f.readline().strip().split(' ')) - 1\n args.embedding_dim = dim\n elif not args.embedding_dim:\n raise RuntimeError('Either embedding_file or embedding_dim '\n 'needs to be specified.')\n\n # Make sure fix_embeddings and embedding_file are consistent\n if args.fix_embeddings:\n if not (args.embedding_file or args.pretrained):\n logger.warning('WARN: fix_embeddings set to False '\n 'as embeddings are random.')\n args.fix_embeddings = False\n return args","def fill_args(args):\n args.agent_module = 'dstar_sgolam_walker'\n args.checkpoint_path = None\n args.exp_config = 'configs/baselines/dstar_proto_sgolam.yaml'\n args.num_episodes = 25\n \n return args","def addControl(*args):","def addControl(*args):","def addControl(*args):","def addControl(*args):","def add_source_achors():\n pass","def __init__(self, parent=None):\n ScriptedLoadableModuleWidget.__init__(self, parent)\n VTKObservationMixin.__init__(self) # needed for parameter node observation\n self.logic = None","def __init__(self, parent=None):\n ScriptedLoadableModuleWidget.__init__(self, parent)\n VTKObservationMixin.__init__(self) # needed for parameter node observation\n self.logic = None\n self._parameterNode = None\n self.T1_LLN_Node = None\n self.T1_LLE_Node = None\n self.ECVMapNode = None\n self.LLE_Node = None\n self.LLN_Node = None\n self.ArefNode = None\n self.T1_LLE_Name = 'T1 Enhanced'\n self.T1_LLN_Name = 'T1 Native'\n self.ResetSliceViews()\n self.LinkSlices()\n self.ColorBarEnabled()\n self.setupVolumeNodeViewLayout()\n self.Warning = True","def controls_setup(self):\n pass","def load_defaults(self):\n self.set_motor_limits(self.MOTOR_LEFT, self.LEFT_DEFAULT)\n self.set_motor_limits(self.MOTOR_RIGHT, self.RIGHT_DEFAULT)\n self.set_servo(self.SERVO_1, self.MIDPOINT)","def load_defaults(self):\n self.set_motor_limits(self.MOTOR_LEFT, self.LEFT_DEFAULT)\n self.set_motor_limits(self.MOTOR_RIGHT, self.RIGHT_DEFAULT)\n self.set_servo(self.SERVO_1, self.MIDPOINT)","def load_defaults(self):\n self.set_motor_limits(self.MOTOR_LEFT, self.LEFT_DEFAULT)\n self.set_motor_limits(self.MOTOR_RIGHT, self.RIGHT_DEFAULT)\n self.set_servo(self.SERVO_1, self.MIDPOINT)","def __init__(self, *args, **kwargs):\n super(MayaScene, self).__init__(*args, **kwargs)","def onReset(self):\n #productive\n profprint()\n fileName = pathToScene = slicer.modules.needlefinder.path.replace(\"NeedleFinder.py\",\"Config/default.cfg\")\n self.logic.loadParameters(fileName)","def setup(self) -> None:\n mlflow.set_tracking_uri('file://' + hutils.get_original_cwd() + '/mlruns')\n if self.log_mlflow:\n mlflow.set_experiment(self.config.runner.exp_name)\n \n if self.log_mlflow:\n self.log_parameters(self.config)\n mlflow.log_param('node', os.uname()[1])","def setUp(params, spec):\n\n if not params['fkSkeleton']:\n joints = []\n\n for x in range(params['numberOfSegments']):\n jnt = cmds.createNode('joint')\n jnt = cmds.rename(jnt, '{baseName}{index}_{JNT}'.format(baseName=spec.name, index=x, JNT=JNT)).split('|')[-1]\n if x > 0:\n cmds.parent(jnt, joints[x - 1])\n cmds.setAttr('{node}.translateY'.format(node=jnt), 1.0)\n joints.append(jnt)\n\n spec.params()['fkSkeleton'] = dragonfly.node.dfNode.fromList(joints)\n\n if not params['fkControls']:\n controls = []\n\n for x in range(params['fkNumberOfSegments']):\n ctl = cmds.curve(**CONTROL_SHAPE_DATA)\n cmds.controller(ctl)\n ctl = cmds.rename(ctl, '{baseName}{index}_{CTL}'.format(baseName=spec.name, index=x, CTL=CTL))\n match_nodes(params['fkSkeleton'][x].name(), ctl)\n if x > 0:\n cmds.parent(ctl, controls[x-1])\n controls.append(ctl)\n\n spec.params()['fkControls'] = dragonfly.node.dfNode.fromList(controls)","def setupOptionsFrame(self):\n\n # CPU / CUDA options\n self.device.addItems([\"cuda\", \"cpu\"])\n self.scriptedEffect.addLabeledOptionsWidget(\"Device:\", self.device)\n\n self.modality.addItems([\"CT\", \"MRI\"])\n self.scriptedEffect.addLabeledOptionsWidget(\"Modality:\", self.modality)\n\n # Add ROI options\n self.roiSelector.nodeTypes = ['vtkMRMLMarkupsROINode']\n self.roiSelector.noneEnabled = True\n self.roiSelector.setMRMLScene(slicer.mrmlScene)\n self.scriptedEffect.addLabeledOptionsWidget(\"ROI: \", self.roiSelector)\n\n # Toggle ROI visibility button\n toggleROIVisibilityButton = qt.QPushButton(\"Toggle ROI Visibility\")\n toggleROIVisibilityButton.objectName = self.__class__.__name__ + 'ToggleROIVisibility'\n toggleROIVisibilityButton.setToolTip(\"Toggle selected ROI visibility\")\n toggleROIVisibilityButton.connect('clicked()', self.toggleROIVisibility)\n self.scriptedEffect.addOptionsWidget(toggleROIVisibilityButton)\n\n # Apply button\n applyButton = qt.QPushButton(\"Apply\")\n applyButton.objectName = self.__class__.__name__ + 'Apply'\n applyButton.setToolTip(\"Extract liver from input volume\")\n applyButton.connect('clicked()', self.onApply)\n self.scriptedEffect.addOptionsWidget(applyButton)","def setup(self, path_to_conf_file):\n\n self.track = Track.SENSORS\n self.num_frames = 0\n\n with open(path_to_conf_file, 'r') as f:\n config = yaml.safe_load(f)\n\n for key, value in config.items():\n setattr(self, key, value)\n\n self.device = torch.device('cuda')\n\n self.image_model = CameraModel(config).to(self.device)\n self.image_model.load_state_dict(torch.load(self.main_model_dir))\n self.image_model.eval()\n\n self.vizs = []\n\n self.waypointer = None\n\n if self.log_wandb:\n wandb.init(project='carla_evaluate')\n \n self.steers = torch.tensor(np.linspace(-self.max_steers,self.max_steers,self.num_steers)).float().to(self.device)\n self.throts = torch.tensor(np.linspace(0,self.max_throts,self.num_throts)).float().to(self.device)\n\n self.prev_steer = 0\n self.lane_change_counter = 0\n self.stop_counter = 0","def setup(self):\n ScriptedLoadableModuleWidget.setup(self)\n\n globalPath, _ = os.path.split( os.path.splitext( slicer.modules.regularizedfastmarching.path )[0] )\n self.globalPath = globalPath + \"/Resources/SegmentationFastMarching/\"\n self.volumesPath = self.globalPath + \"Volumes/\"\n self.seedsCsvPath = self.globalPath + \"SeedsLabels/\"\n self.seedsPath = self.globalPath + \"Seeds/\"\n self.regularizationsPath = self.globalPath + \"Regularizations/\"\n self.segmentationsPath = self.globalPath + \"Segmentations/\"\n\n self.logic = RegularizedFastMarchingLogic()\n # self.logic.previousVolumeName = None\n # self.logic.imgLabel = np.array([])\n # self.logic.previousImgIds = np.array([])\n # self.logic.imgDist = np.array([])\n\n # Hide the Slicer Logo to increase space\n slicer.util.findChild(slicer.util.mainWindow(), 'LogoLabel').visible = False\n \n #\n #region Restart Slicer Area\n #\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\n parametersCollapsibleButton.text = \"Reload\"\n self.layout.addWidget(parametersCollapsibleButton)\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\n self.restartSlicerButton = qt.QPushButton(\"Restart Slicer\")\n self.restartSlicerButton.connect('clicked(bool)', slicer.util.restart)\n parametersFormLayout.addRow(self.restartSlicerButton)\n #endregion \n\n #\n #region Load Data Area\n #\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\n parametersCollapsibleButton.text = \"Seeds\"\n self.layout.addWidget(parametersCollapsibleButton)\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\n \n #\n # File name volume .nii\n # \n # self.volueNameComboBox = qt.QComboBox() \n # self.volueNameComboBox = self.fillComboBox(self.volueNameComboBox, self.volumesPath, \".nii\")\n # parametersFormLayout.addRow(\"Volumes .nii: \", self.volueNameComboBox)\n \n #\n # Load volume buttons\n #\n # horizontalLayout = qt.QHBoxLayout()\n # self.loadBrainVolumeButton = qt.QPushButton(\"Load Example Volume\")\n # self.loadBrainVolumeButton.enabled = True\n # horizontalLayout.addWidget(self.loadBrainVolumeButton)\n \n #\n # Load custom volumes buttons\n #\n # self.loadCustomVolumeButton = qt.QPushButton(\"Load Selected Volume\")\n # self.loadCustomVolumeButton.enabled = True\n # horizontalLayout.addWidget(self.loadCustomVolumeButton) \n # parametersFormLayout.addRow(horizontalLayout)\n \n \n #\n # Add vertical spacing\n # \n # verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n # parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Seeds files and a text line to load / save seeds from the scene\n #\n horizontalLayout = qt.QHBoxLayout()\n fileNameSeedsLabel = qt.QLabel(\"Seeds: \")\n self.fileNameSeedsComboBox = qt.QComboBox() \n self.fileNameSeedsComboBox = self.fillComboBox(self.fileNameSeedsComboBox, self.seedsPath, \".seed\") \n self.fileNameSeedsLineEdit = qt.QLineEdit()\n horizontalLayout.addWidget(fileNameSeedsLabel)\n horizontalLayout.addWidget(self.fileNameSeedsComboBox)\n horizontalLayout.addWidget(self.fileNameSeedsLineEdit)\n self.fileNameSeedsComboBox.currentTextChanged.connect(self.setSelectedSeedsFile)\n self.setSelectedSeedsFile(self.fileNameSeedsComboBox.currentText)\n parametersFormLayout.addRow(horizontalLayout)\n \n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Save markers Button\n #\n horizontalLayout = qt.QHBoxLayout()\n self.saveMarkersButton = qt.QPushButton(\"Save markers\")\n self.saveMarkersButton.toolTip = \"Save the markers in a fcsv file\"\n self.saveMarkersButton.enabled = True\n horizontalLayout.addWidget(self.saveMarkersButton)\n \n #\n # Load markers Button\n #\n self.loadMarkersButton = qt.QPushButton(\"Load markers\")\n self.loadMarkersButton.toolTip = \"Load the markers from fcsv file.\"\n self.loadMarkersButton.enabled = True\n horizontalLayout.addWidget(self.loadMarkersButton) \n \n #\n # Clear markups Button\n #\n self.clearButton = qt.QPushButton(\"Clear all markups\")\n self.clearButton.toolTip = \"Remove all fiducial markers.\"\n self.clearButton.enabled = True\n horizontalLayout.addWidget(self.clearButton)\n parametersFormLayout.addRow(horizontalLayout)\n \n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 25, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Fiducial placement button\n #\n \"\"\"\n Add this markup in the markupsList and rename it depending on seeds combobox's value\n \"\"\"\n @vtk.calldata_type(vtk.VTK_INT)\n def onMarkupAdded(caller, event, index): \n markupsNode = caller\n # Label, Name\n seedTokens = self.currentSeedNameComboBox.currentText.replace(\" \", \"\").split(\":\")\n markupsNode.SetNthFiducialLabel(index, seedTokens[1])\n markupsNode.SetNthControlPointDescription(index, seedTokens[0])\n \n self.w=slicer.qSlicerMarkupsPlaceWidget()\n self.w.setMRMLScene(slicer.mrmlScene)\n \n markupsNode = slicer.mrmlScene.GetFirstNodeByName(\"MarkupsFiducial\")\n if markupsNode == None: \n markupsNode = slicer.mrmlScene.AddNewNodeByClass(\"vtkMRMLMarkupsFiducialNode\")\n markupsNode.CreateDefaultDisplayNodes()\n \n # On markup added event: format the added markup\n markupsNode.AddObserver(slicer.vtkMRMLMarkupsNode.PointPositionDefinedEvent, onMarkupAdded)\n self.w.setCurrentNode(markupsNode)\n # Hide all buttons and only show place button\n self.w.buttonsVisible=True\n self.w.placeButton().show()\n self.w.show()\n parametersFormLayout.addRow(self.w)\n \n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # File name data's seeds\n # \n self.fileNameDataComboBox = qt.QComboBox() \n self.fileNameDataComboBox = self.fillComboBox(self.fileNameDataComboBox, self.seedsCsvPath, \".csv\") \n self.fileNameDataComboBox.currentTextChanged.connect(self.setSeedsLabelFromFile)\n parametersFormLayout.addRow(\"Seeds labels csv: \", self.fileNameDataComboBox)\n\n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n horizontalLayout = qt.QHBoxLayout()\n # Seeds label \n self.seedsLabelText = qt.QLabel()\n self.seedsLabelText.text = \"Labels:\"\n horizontalLayout.addWidget(self.seedsLabelText)\n \n # ComboxBox containing names and labels\n self.currentSeedNameComboBox= qt.QComboBox()\n \n # Add labels depending on first csv file found\n self.setSeedsLabelFromFile(self.fileNameDataComboBox.currentText)\n horizontalLayout.addWidget(self.currentSeedNameComboBox)\n \n self.clearOrganButton = qt.QPushButton(\"Clear this organ\")\n self.clearOrganButton.toolTip = \"Remove all fiducial markers for this organ.\"\n self.clearOrganButton.enabled = True\n horizontalLayout.addWidget(self.clearOrganButton)\n parametersFormLayout.addRow(horizontalLayout)\n \n #endregion\n\n\n #\n #region Segmentation Parameters Area\n #\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\n parametersCollapsibleButton.text = \"Parameters\"\n self.layout.addWidget(parametersCollapsibleButton)\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\n\n #\n # input volume selector\n #\n self.inputSelector = slicer.qMRMLNodeComboBox()\n self.inputSelector.nodeTypes = [\"vtkMRMLScalarVolumeNode\"]\n self.inputSelector.selectNodeUponCreation = True\n self.inputSelector.addEnabled = False\n self.inputSelector.removeEnabled = False\n self.inputSelector.noneEnabled = False\n self.inputSelector.showHidden = False\n self.inputSelector.showChildNodeTypes = False\n self.inputSelector.setMRMLScene( slicer.mrmlScene )\n self.inputSelector.setToolTip( \"Pick the input to the algorithm.\" )\n parametersFormLayout.addRow(\"Input Volume: \", self.inputSelector)\n\n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Segmentation distance to stop the algo\n #\n self.distance = ctk.ctkSliderWidget()\n self.distance.singleStep = 10\n self.distance.minimum = 0\n self.distance.maximum = 1000\n self.distance.value = 100\n self.distance.setToolTip(\"Set the maximum geodesic distance allowed from a seed\")\n parametersFormLayout.addRow(\"Distance\", self.distance)\n \n #\n # Margin to build masks\n #\n self.marginMask = ctk.ctkSliderWidget()\n self.marginMask.singleStep = 1\n self.marginMask.minimum = 0\n self.marginMask.maximum = 50\n self.marginMask.value = 15\n self.marginMask.setToolTip(\"Margin used to build masks for each seed\")\n parametersFormLayout.addRow(\"Mask Margin (voxel)\", self.marginMask)\n\n #\n # Gamma to prevent the seeds to cross bridges between organs\n #\n self.gammaSlider = qt.QSlider(qt.Qt.Horizontal)\n self.gammaSlider.setRange(0, 1000)\n self.gammaSlider.setSingleStep(1)\n self.gammaSlider.setPageStep(10)\n self.gammaSlider.setValue(25)\n\n self.gammaSpinBox = qt.QDoubleSpinBox()\n self.gammaSpinBox.setDecimals(3)\n self.gammaSpinBox.setRange(0, 1)\n self.gammaSpinBox.setSingleStep(0.001)\n self.gammaSpinBox.setValue(0.025)\n\n self.gammaSlider.valueChanged.connect(self.setGammaValueFromSlider)\n self.gammaSpinBox.valueChanged.connect(self.setGammaValueFromSpinBox)\n \n horizontalLayout = qt.QHBoxLayout()\n horizontalLayout.addWidget(self.gammaSlider)\n horizontalLayout.addWidget(self.gammaSpinBox)\n\n parametersFormLayout.addRow(\"Regularization weight\", horizontalLayout)\n \n #\n # Regularization radius\n #\n self.regularizationDiameter = ctk.ctkSliderWidget()\n self.regularizationDiameter.singleStep = 1\n self.regularizationDiameter.minimum = 1\n self.regularizationDiameter.maximum = 20\n self.regularizationDiameter.value = 4\n self.regularizationDiameter.setToolTip(\"Margin used to build masks for each seed\")\n parametersFormLayout.addRow(\"Regularization Diameter (voxels)\", self.regularizationDiameter)\n \n #\n # Threshold to prevent the seeds to spread over a range intensities\n #\n self.minThresholdSlider = ctk.ctkSliderWidget()\n self.minThresholdSlider.singleStep = 1\n self.minThresholdSlider.minimum = 0\n self.minThresholdSlider.maximum = 255\n self.minThresholdSlider.value = 40\n self.minThresholdSlider.setToolTip(\"Seeds cannot spread below this value\")\n self.minThresholdSlider.valueChanged.connect(self.setMinThresholdValue)\n parametersFormLayout.addRow(\"Min Threshold (digital level)\", self.minThresholdSlider)\n\n self.maxThresholdSlider = ctk.ctkSliderWidget()\n self.maxThresholdSlider.singleStep = 1\n self.maxThresholdSlider.minimum = 0\n self.maxThresholdSlider.maximum = 255\n self.maxThresholdSlider.value = 255\n self.maxThresholdSlider.setToolTip(\"Seeds cannot spread over this value\")\n self.maxThresholdSlider.valueChanged.connect(self.setMaxThresholdValue)\n parametersFormLayout.addRow(\"Max Threshold (digital level)\", self.maxThresholdSlider)\n\n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Check box to keep or not last segmentation\n #\n self.removeLastSegmentationCheckBox = qt.QCheckBox(\"\")\n self.removeLastSegmentationCheckBox.setChecked(True)\n parametersFormLayout.addRow(\"Remove last segmentation\", self.removeLastSegmentationCheckBox)\n \n #\n # Show the background seed of the next segmentation\n #\n self.showBackGroundCheckBox = qt.QCheckBox(\"\")\n self.showBackGroundCheckBox.setChecked(False)\n parametersFormLayout.addRow(\"Show background\", self.showBackGroundCheckBox)\n \n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Segment Button\n #\n self.segmentButton = qt.QPushButton(\"Segment\")\n self.segmentButton.toolTip = \"Run the algorithm.\"\n self.segmentButton.enabled = False\n parametersFormLayout.addRow(self.segmentButton)\n #endregion \n\n #\n #region Load Save segmentations\n #\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\n parametersCollapsibleButton.text = \"Segmentations\"\n self.layout.addWidget(parametersCollapsibleButton)\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\n\n #\n # Checkbox to save the segmentation using the labels \n #\n self.saveByLabelsCheckBox = qt.QCheckBox(\"\")\n self.saveByLabelsCheckBox.setChecked(True)\n parametersFormLayout.addRow(\"Save by labels\", self.saveByLabelsCheckBox)\n\n #\n # Checkbox to save the segmentation using the voxels intensities \n #\n self.saveByIntensitiesCheckBox = qt.QCheckBox(\"\")\n self.saveByIntensitiesCheckBox.setChecked(False)\n parametersFormLayout.addRow(\"Save by intensities\", self.saveByIntensitiesCheckBox)\n\n #\n # Checkbox to generate segments data \n #\n self.generateDataCsvCheckBox = qt.QCheckBox(\"\")\n self.generateDataCsvCheckBox.setChecked(False)\n parametersFormLayout.addRow(\"Generate segments data CSV\", self.generateDataCsvCheckBox)\n\n\n #\n # Name of the segmentation to save\n #\n self.saveSegmentationName = qt.QLineEdit()\n self.saveSegmentationName.textChanged.connect(self.onSaveSegmentationFileChange)\n parametersFormLayout.addRow(\"Segmentation name:\", self.saveSegmentationName) \n\n #\n # Save segmentationButton Button\n #\n self.saveSegmentationButton = qt.QPushButton(\"Save segmentation\")\n self.saveSegmentationButton.toolTip = \"Save this segmentation with used parameters.\"\n parametersFormLayout.addRow(self.saveSegmentationButton) \n\n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n\n horizontalLayout = qt.QHBoxLayout()\n self.segmentationLoadLabelText = qt.QLabel()\n self.segmentationLoadLabelText.text = \"Segmentation files:\"\n horizontalLayout.addWidget(self.segmentationLoadLabelText)\n \n #\n # Segmentation files \n # \n self.segmentationFilesComboBox = qt.QComboBox() \n self.segmentationFilesComboBox = self.fillComboBox(self.segmentationFilesComboBox, self.segmentationsPath, \".seg.nrrd\")\n horizontalLayout.addWidget(self.segmentationFilesComboBox)\n parametersFormLayout.addRow(horizontalLayout)\n \n #\n # Load segmentationButton Button\n #\n self.loadSegmentationButton = qt.QPushButton(\"Load segmentation\")\n self.loadSegmentationButton.toolTip = \"Load the segmenation from this file.\"\n parametersFormLayout.addRow(self.loadSegmentationButton) \n #end region\n \n\n #\n #region Connections & Shortcuts\n #\n # self.loadBrainVolumeButton.connect('clicked(bool)', self.onLoadBrainVolumeButton)\n # self.loadCustomVolumeButton.connect('clicked(bool)', self.onLoadCustomVolumeButton)\n \n self.clearButton.connect('clicked(bool)', self.onClearButton)\n self.clearOrganButton.connect('clicked(bool)', self.onClearOrganButton)\n self.segmentButton.connect('clicked(bool)', self.onSegmentButton)\n self.saveSegmentationButton.connect('clicked(bool)', self.onSaveSegmentationButton)\n self.loadSegmentationButton.connect('clicked(bool)', self.onLoadSegmentationButton)\n self.saveMarkersButton.connect('clicked(bool)', self.onSaveMarkersButton)\n self.loadMarkersButton.connect('clicked(bool)', self.onLoadMarkersButton)\n self.inputSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelect)\n \n # Keyboard shortcut bind: press ctrl button to add markup \n markupShortcut = qt.QShortcut(slicer.util.mainWindow())\n markupShortcut.setKey( qt.QKeySequence(\"w\") )\n markupShortcut.connect('activated()', self.addMarkupcallback)\n \n markupShortcut = qt.QShortcut(slicer.util.mainWindow())\n markupShortcut.setKey( qt.QKeySequence(\"x\") )\n markupShortcut.connect('activated()', self.addBgMarkupcallback)\n #endregion \n\n # Refresh Apply button state\n self.onSelect()\n \n # Refresh segmentation save button state\n self.onSaveSegmentationFileChange()","def init_turn(self):\n sender = self.sender()\n cur_settings = self.setting_dropdown.currentText()\n\n if sender:\n cur_disp = self.rad_grp.checkedButton()\n self.start_frm = self.start_frm_le.text()\n self.end_frm = self.end_frm_le.text()\n file_path = self.save_loc.text()\n if self.arg_check():\n\n wireframe = self.wireframe\n # Instantiate the tool logic with the selected values.\n start_turn = tl.Turntable(cur_disp.objectName(),\n self.start_frm,\n self.end_frm,\n file_path,\n wireframe)\n\n # If discipline is surface, set render settings.\n if cur_disp.objectName() == 'surface':\n set_turn = tl.RenderTurntable()\n set_turn.set_render_settings(cur_settings,self.start_frm,self.end_frm)\n\n start_turn.launch_tool()","def load_params(self, event):\n \n self.robot_type = rospy.get_param(\"robot_type\" , 'pendulum' )\n self.robot_config = rospy.get_param(\"robot_config\", 'wrist-only' )\n self.robot_ctl = rospy.get_param(\"controller\", 'RfixCTC' )\n self.fixed_mode = rospy.get_param(\"fixed_mode\", 1 )\n \n \n ###############################################\n # Load robot model for the right configuration\n if self.robot_config == 'wrist-only':\n self.R = Proto.SingleRevoluteDSDM()\n \n elif self.robot_config == 'dual-plane' :\n self.R = Proto.TwoPlanarSerialDSDM()\n \n else:\n self.R = None\n \n ###############################################\n # Load controller\n if self.robot_ctl == 'RfixCTC' :\n self.Ctl = RminCTC.RfixComputedTorqueController( self.R , self.fixed_mode )\n \n elif self.robot_ctl == 'RminCTC' :\n self.Ctl = RminCTC.RminComputedTorqueController( self.R )\n \n elif self.robot_ctl == 'RfixSLD' :\n self.Ctl = RminCTC.RfixSlidingModeController( self.R , self.fixed_mode )\n \n elif self.robot_ctl == 'RminSLD' :\n self.Ctl = RminCTC.RminSlidingModeController( self.R )\n \n elif self.robot_ctl == 'RollCTC' :\n self.Ctl = RollCTC.RolloutComputedTorqueController( self.R )\n \n elif self.robot_ctl == 'RollSLD' :\n self.Ctl = RollCTC.RolloutSlidingModeController( self.R )\n \n else:\n self.Ctl = None\n \n \n if self.robot_config == 'wrist-only':\n self.Ctl.n_gears = rospy.get_param(\"n_gears\", 2 )\n self.x_d = np.array( rospy.get_param(\"goal\", [0,0] ) )\n \n elif self.robot_config == 'dual-plane' :\n self.Ctl.n_gears = rospy.get_param(\"n_gears\", 4 )\n self.x_d = np.array( rospy.get_param(\"goal\", [0.0,0.0,0.0,0.0] ) )\n #self.x_d = np.array( [-3.14 , 0 , 0 , 0] )\n \n # Gen ctl params\n self.Ctl.hysteresis = rospy.get_param(\"hysteresis\", True )\n self.Ctl.min_delay = rospy.get_param(\"min_delay\", 0.5 )\n \n self.Ctl.w0 = rospy.get_param(\"w0\", 1 )\n self.Ctl.zeta = rospy.get_param(\"zeta\", 0.7 )\n \n self.Ctl.lam = rospy.get_param(\"lam\", 1 )\n self.Ctl.nab = rospy.get_param(\"nab\", 1 )\n self.Ctl.D = rospy.get_param(\"D\", 0 )\n \n self.Ctl.horizon = rospy.get_param(\"horizon\", 0.5 )\n self.Ctl.sim_dt = rospy.get_param(\"sim_dt\", 0.1 )\n \n self.Ctl.domain_check = rospy.get_param(\"domain_check\", False )\n \n # Base policy param for roll \n if self.robot_ctl == 'RollCTC' :\n self.Ctl.FixCtl.lam = self.Ctl.lam\n \n elif self.robot_ctl == 'RollSLD' :\n self.Ctl.FixCtl.lam = self.Ctl.lam \n self.Ctl.FixCtl.nab = self.Ctl.nab \n self.Ctl.FixCtl.D = self.Ctl.D","def __init__(self):\n ros_ws_abspath = rospy.get_param(\"/drone/ros_ws_abspath\", None)\n assert ros_ws_abspath is not None, \"You forgot to set ros_ws_abspath in your yaml file of your main RL script. Set ros_ws_abspath: \\'YOUR/SIM_WS/PATH\\'\"\n assert os.path.exists(ros_ws_abspath), \"The Simulation ROS Workspace path \" + ros_ws_abspath + \\\n \" DOESNT exist, execute: mkdir -p \" + ros_ws_abspath + \\\n \"/src;cd \" + ros_ws_abspath + \";catkin_make\"\n\n ROSLauncher(rospackage_name=\"drone_construct\",\n launch_file_name=\"start_world.launch\",\n ros_ws_abspath=ros_ws_abspath)\n\n # Load Params from the desired Yaml file\n LoadYamlFileParamsTest(rospackage_name=\"openai_ros\",\n rel_path_from_package_to_file=\"src/openai_ros/task_envs/parrotdrone/config\",\n yaml_file_name=\"parrotdrone_goto.yaml\")\n\n # Only variable needed to be set here\n number_actions = rospy.get_param('/drone/n_actions')\n self.action_space = spaces.Discrete(number_actions)\n\n # We set the reward range, which is not compulsory but here we do it.\n self.reward_range = (-numpy.inf, numpy.inf)\n\n # Actions and Observations\n self.linear_forward_speed = rospy.get_param(\n '/drone/linear_forward_speed')\n self.angular_turn_speed = rospy.get_param('/drone/angular_turn_speed')\n self.angular_speed = rospy.get_param('/drone/angular_speed')\n\n self.init_linear_speed_vector = Vector3()\n self.init_linear_speed_vector.x = rospy.get_param(\n '/drone/init_linear_speed_vector/x')\n self.init_linear_speed_vector.y = rospy.get_param(\n '/drone/init_linear_speed_vector/y')\n self.init_linear_speed_vector.z = rospy.get_param(\n '/drone/init_linear_speed_vector/z')\n\n self.init_angular_turn_speed = rospy.get_param(\n '/drone/init_angular_turn_speed')\n\n self.min_sonar_value = rospy.get_param('/drone/min_sonar_value')\n self.max_sonar_value = rospy.get_param('/drone/max_sonar_value')\n\n # Get WorkSpace Cube Dimensions\n self.work_space_x_max = rospy.get_param(\"/drone/work_space/x_max\")\n self.work_space_x_min = rospy.get_param(\"/drone/work_space/x_min\")\n self.work_space_y_max = rospy.get_param(\"/drone/work_space/y_max\")\n self.work_space_y_min = rospy.get_param(\"/drone/work_space/y_min\")\n self.work_space_z_max = rospy.get_param(\"/drone/work_space/z_max\")\n self.work_space_z_min = rospy.get_param(\"/drone/work_space/z_min\")\n\n # Maximum RPY values\n self.max_roll = rospy.get_param(\"/drone/max_roll\")\n self.max_pitch = rospy.get_param(\"/drone/max_pitch\")\n self.max_yaw = rospy.get_param(\"/drone/max_yaw\")\n\n # Get Desired Point to Get\n self.desired_point = Point()\n self.desired_point.x = rospy.get_param(\"/drone/desired_pose/x\")\n self.desired_point.y = rospy.get_param(\"/drone/desired_pose/y\")\n self.desired_point.z = rospy.get_param(\"/drone/desired_pose/z\")\n\n self.desired_point_epsilon = rospy.get_param(\n \"/drone/desired_point_epsilon\")\n\n # We place the Maximum and minimum values of the X,Y,Z,R,P,Yof the pose\n\n high = numpy.array([self.work_space_x_max,\n self.work_space_y_max,\n self.work_space_z_max,\n self.max_roll,\n self.max_pitch,\n self.max_yaw,\n self.max_sonar_value])\n\n low = numpy.array([self.work_space_x_min,\n self.work_space_y_min,\n self.work_space_z_min,\n -1*self.max_roll,\n -1*self.max_pitch,\n -numpy.inf,\n self.min_sonar_value])\n\n self.observation_space = spaces.Box(low, high)\n\n rospy.logdebug(\"ACTION SPACES TYPE===>\"+str(self.action_space))\n rospy.logdebug(\"OBSERVATION SPACES TYPE===>\" +\n str(self.observation_space))\n\n # Rewards\n self.closer_to_point_reward = rospy.get_param(\n \"/drone/closer_to_point_reward\")\n self.not_ending_point_reward = rospy.get_param(\n \"/drone/not_ending_point_reward\")\n self.end_episode_points = rospy.get_param(\"/drone/end_episode_points\")\n\n self.cumulated_steps = 0.0\n\n # Here we will add any init functions prior to starting the MyRobotEnv\n super(ParrotDroneGotoEnv, self).__init__(ros_ws_abspath)","def __init__(self, env, action_repeat=1):\n super().__init__(env)\n if self.env.mujoco_robot.name == \"sawyer\":\n from robosuite.controllers import SawyerIKController\n\n self.controller = SawyerIKController(\n bullet_data_path=os.path.join(robosuite.models.assets_root, \"bullet_data\"),\n robot_jpos_getter=self._robot_jpos_getter,\n )\n elif self.env.mujoco_robot.name == \"baxter\":\n from robosuite.controllers import BaxterIKController\n\n self.controller = BaxterIKController(\n bullet_data_path=os.path.join(robosuite.models.assets_root, \"bullet_data\"),\n robot_jpos_getter=self._robot_jpos_getter,\n )\n else:\n raise Exception(\n \"Only Sawyer and Baxter robot environments are supported for IK \"\n \"control currently.\"\n )\n\n self.action_repeat = action_repeat","def __init__(self, source, label_config_center, name=None, header=None, \n view_min=None, view_max=None, alpha=255, colormap='gray',\n cross_pos=None):\n if isinstance(source, np.ndarray):\n self._data = np.rot90(source)\n if name == None:\n self._name = 'new_image'\n else:\n self._name = str(name)\n if not isinstance(header, nib.nifti1.Nifti1Header):\n raise ValueError(\"Parameter header must be specified!\")\n elif header.get_data_shape() == source.shape:\n self._header = header\n self._img = None\n else:\n raise ValueError(\"Data dimension does not match.\")\n else:\n self._img = nib.load(source)\n self._header = self._img.get_header()\n basename = os.path.basename(source.strip('/'))\n self._name = re.sub(r'(.*)\\.nii(\\.gz)?', r'\\1', basename)\n self.save_mem_load()\n\n # For convenience, define a shift variable\n self._y_shift = self.get_data_shape()[1] - 1\n\n if view_min == None:\n self._view_min = self._data.min()\n else:\n self._view_min = view_min\n\n if view_max == None:\n self._view_max = self._data.max()\n else:\n self._view_max = view_max\n\n self._alpha = alpha\n self._colormap = colormap\n self._rgba_list = range(self.get_data_shape()[2])\n \n # bool status for the item\n self._visible = True\n if len(self.get_data_shape()) == 3:\n self._4d = False\n else:\n self._4d = True\n self._time_point = 0\n\n # temporal variant for OrthView\n self._cross_pos = cross_pos\n\n # define a dictionary \n self.label_config_center = label_config_center\n self.label_config_center.single_roi_view_update.connect(self.update_single_roi)\n \n # undo redo stacks\n self.undo_stack = DoStack()\n self.redo_stack = DoStack()\n\n self.update_rgba()\n if self._cross_pos:\n self.update_orth_rgba()","def __init__(self):\n self.brainstate = {}\n\n self.export = ['run_ai']","def setup(self):\r\n ScriptedLoadableModuleWidget.setup(self)\r\n\r\n # Load widget from .ui file (created by Qt Designer).\r\n # Additional widgets can be instantiated manually and added to self.layout.\r\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/RecordHerniaData.ui'))\r\n self.layout.addWidget(uiWidget)\r\n self.ui = slicer.util.childWidgetVariables(uiWidget)\r\n\r\n # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\r\n # \"mrmlSceneChanged(vtkMRMLScene*)\" signal in is connected to each MRML widget's.\r\n # \"setMRMLScene(vtkMRMLScene*)\" slot.\r\n uiWidget.setMRMLScene(slicer.mrmlScene)\r\n\r\n # Create logic class. Logic implements all computations that should be possible to run\r\n # in batch mode, without a graphical user interface.\r\n self.logic = TMSRecordDataModuleLogic()\r\n self.recordingStarted = False\r\n self.camerasStarted = False\r\n self.moduleDir = os.path.dirname(slicer.modules.tmsrecorddatamodule.path)\r\n self.logic.setupScene()\r\n\r\n # Buttons\r\n self.ui.StartStopRecordingButton.connect('clicked(bool)', self.onStartStopRecordingClicked)\r\n self.ui.startCamerasButton.connect('clicked(bool)',self.onStartStopCamerasClicked)","def setup(args):\n cfg = get_cfg()\n\n cfg.merge_from_file(model_zoo.get_config_file(args.model_zoo))\n cfg.DATASETS.TRAIN = (args.train_dataset, )\n cfg.DATASETS.TEST = (args.test_dataset, )\n cfg.DATALOADER.NUM_WORKERS = args.num_workers\n cfg.OUTPUT_DIR = args.output_dir\n os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\n\n cfg.image_w = args.size[0]\n cfg.image_h = args.size[1]\n\n cfg.MODEL.WEIGHTS = args.model_zoo_weights\n cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.roi_thresh # set a custom testing threshold\n\n default_setup(cfg, args)\n return cfg","def loadData(self, data=None):\n\n super(SimpleControlComponentRig, self).loadData( data )\n\n ctrlSize = data.get('ctrlSize', 1.0)\n ctrlXfo = data.get('ctrlXfo', Xfo())\n\n # ================\n # Resize Controls\n # ================\n self.mainCtrl.setShape('square')\n self.mainCtrl.rotatePoints(90, 0, 0)\n self.mainCtrl.scalePoints(Vec3(ctrlSize, ctrlSize, ctrlSize))\n\n # =======================\n # Set Control Transforms\n # =======================\n self.mainCtrlSpace.xfo = ctrlXfo\n self.mainCtrl.xfo = ctrlXfo\n\n # ============\n # Set IO Xfos\n # ============\n self.mainInputTgt.xfo = ctrlXfo\n self.mainDef.xfo = ctrlXfo\n self.outputTgt.xfo = ctrlXfo\n\n # ====================\n # Evaluate Constraints\n # ====================\n self.mainInputConstraint.evaluate()\n self.mainOutputConstraint.evaluate()\n self.mainDefConstraint.evaluate()\n\n # ====================\n # Evaluate Canvas Ops\n # ====================","def __init__(self, env):\n super(PlayerOneNetworkControllerWrapper, self).__init__(env)\n buttons = [\"B\", \"A\", \"MODE\", \"START\", \"UP\", \"DOWN\", \"LEFT\", \"RIGHT\", \"C\", \"Y\", \"X\", \"Z\"]\n actions = [['LEFT'], ['RIGHT'], ['LEFT', 'DOWN'], ['RIGHT', 'DOWN'],['LEFT', 'UP'],['RIGHT', 'UP'],\n ['DOWN', 'B'],['LEFT', 'UP'],['RIGHT', 'DOWN','B'],['RIGHT', 'DOWN','A'],\n ['RIGHT', 'UP','B'],['RIGHT', 'UP','A'],['RIGHT', 'UP','C'],\n ['LEFT', 'UP','B'],['LEFT', 'UP','A'],['LEFT', 'UP','C'],\n ['C'],['START'], ['B'],['Y'],['X'],['Z'],['A'],['UP'],['MODE']]\n self._actions = []\n for action in actions:\n arr = np.array([False] * 12)\n for button in action:\n arr[buttons.index(button)] = True\n self._actions.append(arr)\n self.action_space = gym.spaces.Discrete(len(self._actions))","def handle(self, args, unknown):\n\n settings = Dict2Obj(**runpy.run_path(\"%s/%s\" % (os.getcwd(), 'settings.py')))\n project_name = os.getcwd().split('/')[-1]\n extra_args = self.parse_unknown(unknown)\n Train(project_name=project_name, settings=settings, args=args, **extra_args).begin()","def build_guide(self, **kwargs):\n\n # This builds your guide master and updates your options\n self.create_guide_master(**kwargs)\n\n prefix = self.prefix # Naming prefix. Use this for every new node you create and there should be no name clashes.\n options = self.options # Build options\n mirror_value = self.mirror_value # 1.0 for left and center sided parts and -1.0 for right sided part.\n\n mc.setAttr(self.guide_master+'.offsetTranslateY', -0.2)\n\n l_prefix = prefix.replace('C','L', 1)\n r_prefix = prefix.replace('C','R', 1)\n mirror_values = [1, -1]\n enable_steering = options.get('enableSteering')\n\n colors = ['green', 'red']\n\n for mi, prefix in enumerate([l_prefix, r_prefix]):\n\n mirror_value = mirror_values[mi]\n color = colors[mi]\n\n l_main_zero, l_main_plc = self.guide_joint('main', alt_prefix=prefix, placer_only=1)\n\n # create hub\n hub_zero, hub_plc, hub_jnt = self.guide_joint('wheelhub', alt_prefix=prefix, constraint_type='point')\n hub_end_zero, hub_end_plc, hub_end_jnt = self.guide_joint('wheelhub_end', alt_prefix=prefix, constraint_type='point')\n\n mc.xform(hub_end_zero, r=1, t=[1,0,0])\n mc.parent(hub_end_jnt, hub_jnt)\n mc.aimConstraint(hub_end_plc, hub_jnt, aim=[mirror_value,0,0], u=[0,1,0], wu=[0,1,0], wut='vector')\n mc.parentConstraint(hub_plc, hub_end_zero , mo=1)\n\n # Create steering arm\n steer_zero, steer_plc, steer_jnt = self.guide_joint('steeringArm', alt_prefix=prefix, constraint_type='parent')\n mc.xform(steer_zero, r=1, t=[-1,0,0])\n mc.parent(hub_jnt, steer_jnt)\n\n # Create shocks\n shock_a_zero, shock_a_plc, shock_a_jnt = self.guide_joint('shock_A', alt_prefix=prefix, constraint_type='point')\n shock_b_zero, shock_b_plc, shock_b_jnt = self.guide_joint('shock_B', alt_prefix=prefix, constraint_type='point')\n\n mc.xform(shock_a_zero, ws=1, t=[-2,2,0])\n mc.xform(shock_b_zero, ws=1, t=[-0.5,0.25,0])\n\n mc.parent(shock_b_jnt, shock_a_jnt)\n\n mc.aimConstraint(shock_b_plc, shock_a_jnt, aim=[mirror_value,0,0], u=[0,0,1], wu=[0,0,1], wut='vector')\n mc.aimConstraint(shock_a_plc, shock_b_jnt, aim=[-mirror_value,0,0], u=[0,0,1], wu=[0,0,1], wut='vector')\n\n # upper arm\n up_arm_zero, up_arm_plc, up_arm_jnt = self.guide_joint('upperArm', alt_prefix=prefix, constraint_type='point')\n up_arm_end_zero, up_arm_end_plc, up_arm_end_jnt = self.guide_joint('upperArm_end', alt_prefix=prefix, constraint_type='point')\n\n mc.xform(up_arm_end_zero, r=1, t=[-3.5,1,0])\n mc.xform(up_arm_zero, r=1, t=[-1,0.5,0])\n mc.parent(up_arm_end_jnt, up_arm_jnt)\n mc.aimConstraint(up_arm_end_plc, up_arm_jnt, aim=[mirror_value,0,0], u=[0,0,1], wu=[0,0,mirror_value], wut='objectRotation', wuo=up_arm_plc)\n\n # lower arm\n lo_arm_zero, lo_arm_plc, lo_arm_jnt = self.guide_joint('lowerArm', alt_prefix=prefix, constraint_type='point')\n lo_arm_end_zero, lo_arm_end_plc, lo_arm_end_jnt = self.guide_joint('lowerArm_end', alt_prefix=prefix, constraint_type='point')\n\n mc.xform(lo_arm_end_zero, r=1, t=[-4,-0.5,0])\n mc.xform(lo_arm_zero, r=1, t=[-1,-0.5,0])\n mc.parent(lo_arm_end_jnt, lo_arm_jnt)\n mc.aimConstraint(lo_arm_end_plc, lo_arm_jnt, aim=[mirror_value,0,0], u=[0,0,1], wu=[0,0,mirror_value], wut='objectRotation', wuo=lo_arm_plc)\n\n # steeringArm\n if enable_steering:\n steeringArm_a_zero, steeringArm_a_plc, steeringArm_a_jnt = self.guide_joint('steeringArm_A', alt_prefix=prefix, constraint_type='point')\n steeringArm_b_zero, steeringArm_b_plc, steeringArm_b_jnt = self.guide_joint('steeringArm_B', alt_prefix=prefix, constraint_type='point')\n\n mc.xform(steeringArm_b_zero, r=1, t=[-1.5,0,1])\n mc.xform(steeringArm_a_zero, r=1, t=[-4,0,1])\n\n mc.parent(steeringArm_b_jnt, steeringArm_a_jnt)\n mc.aimConstraint(steeringArm_b_plc, steeringArm_a_jnt, aim=[mirror_value,0,0], u=[0,0,1], wu=[0,0,1], wut='vector')\n\n # Create control\n zero, ctrl = self.guide_ctrl('wheel', alt_prefix=prefix, driver=hub_end_jnt, color=color, shape='circle', axis='X', scale=[3]*3, create_pivot=0)\n mc.setAttr(ctrl+'.numOffsetCtrls', 1)\n mc.addAttr(ctrl+'.numOffsetCtrls', e=1, min=1)\n mc.xform(ctrl.replace('_CTL','_A_OFF_CTL.cv[*]'), r=1, s=[0.8]*3)\n\n control.create_shape('wheel', ctrl, axis='X', scale=[3]*3)\n\n #suspension_zero, suspension_ctrl = self.guide_ctrl('suspension', create_pivot=0, driver=shock_a_jnt, axis='X', shape='pyramid', color=color, scale=[1.5,1,1], alt_prefix=prefix)\n ground_zero, ground_ctrl = self.guide_ctrl('ground', create_pivot=0, shape='square', color='grass', alt_prefix=prefix)\n mc.delete(mc.pointConstraint(hub_jnt, ground_zero))\n\n # constraint to placer\n childs = [prefix+'_wheelhub_JNT_PLC_ZERO',\n prefix+'_steeringArm_JNT_PLC_ZERO',\n prefix+'_shock_A_JNT_PLC_ZERO',\n prefix+'_shock_B_JNT_PLC_ZERO',\n prefix+'_upperArm_JNT_PLC_ZERO',\n prefix+'_upperArm_end_JNT_PLC_ZERO',\n prefix+'_lowerArm_JNT_PLC_ZERO',\n prefix+'_lowerArm_end_JNT_PLC_ZERO']\n\n for c in childs:\n mc.parentConstraint(l_main_plc, c, mo=1)\n\n mc.setAttr(l_main_plc+'.offsetTranslateY', mirror_value*0.5)\n\n # ################3\n # Place it all\n hub_pos = mc.ls(options.get('hubCenter') or '')\n if hub_pos:\n loc = utils.snap_locator(hub_pos)\n mc.delete(mc.pointConstraint(loc, self.guide_master))\n mc.setAttr(self.guide_master+'.tx', 0)\n mc.delete(mc.pointConstraint(loc, l_main_plc), loc)\n\n hub_end_pos = mc.ls(options.get('hubEndCenter') or '')\n if hub_end_pos:\n loc = utils.snap_locator(hub_end_pos)\n mc.delete(mc.pointConstraint(loc, hub_end_plc), loc)\n\n else:\n mc.xform(self.guide_master, ws=1, t=[0,2,10])\n mc.xform(l_main_plc, r=1, t=[mirror_value*6,0,0])\n\n mc.setAttr(self.guide_master+'.jointAxisVis', 1)\n\n l = utils.snap_locator(hub_jnt)\n mc.setAttr(l+'.ty', 0)\n mc.delete(mc.pointConstraint(l, ground_zero), l)\n\n chassis_plc_zero, chassis_plc = self.guide_joint('chassis_driver', placer_only=1)\n mc.setAttr(chassis_plc+'.radius', 1)\n mc.setAttr(chassis_plc+'.color', 0.96, 0.71, .01)\n mc.setAttr(chassis_plc+'.otherType', 'Leg IK Driver', type='string');\n mc.setAttr(chassis_plc+'.type', 18)\n\n mc.pointConstraint(l_prefix+'_lowerArm_end_JNT_PLC', r_prefix+'_lowerArm_end_JNT_PLC', chassis_plc_zero)\n utils.set_attrs(chassis_plc, l=1, k=0)\n\n # This finalizes your guide.\n self.finalize_guide()\n self.mirror_guide()","def loadDefault(self):\n self.addStart()","def importMasterSettings(self):\n\t\tpickleData = pickle.load( open( self.masterPath.path, \"rb\" ) )\n\t\tmaster = rlayer.RenderLayer( 'defaultRenderLayer' )\n\t\tmaster.makeCurrent()\n\t\tfor a in pickleData.keys():\n\t\t\ttry:\n\t\t\t\ta.v = pickleData[a]\n\t\t\texcept:\n\t\t\t\tcontinue","def set_default_params(self):\n print('------------------')\n print('Setting default parameters with file ', self.input_file_name)\n if 'ssephem' not in self.__dict__:\n self.__dict__['ssephem'] = 'DE436'\n print('Setting default Solar System Ephemeris: DE436')\n if 'clock' not in self.__dict__:\n self.__dict__['clock'] = None\n print('Setting a default Enterprise clock convention (check the code)')\n if 'setupsamp' not in self.__dict__:\n self.__dict__['setupsamp'] = False\n if 'psrlist' in self.__dict__:\n self.psrlist = np.loadtxt(self.psrlist, dtype=np.unicode_)\n print('Only using pulsars from psrlist')\n else:\n self.__dict__['psrlist'] = []\n print('Using all available pulsars from .par/.tim directory')\n if 'psrcachefile' not in self.__dict__:\n self.psrcachefile = None\n if 'tm' not in self.__dict__:\n self.tm = 'default'\n print('Setting a default linear timing model')\n if 'inc_events' not in self.__dict__:\n self.inc_events = True\n print('Including transient events to specific pulsar models')\n if 'fref' not in self.__dict__:\n self.fref = 1400 # MHz\n print('Setting reference radio frequency to 1400 MHz')\n if 'mcmc_covm_csv' in self.__dict__ and os.path.isfile(self.mcmc_covm_csv):\n print('MCMC jump covariance matrix is available')\n self.__dict__['mcmc_covm'] = pd.read_csv(self.mcmc_covm_csv, index_col=0)\n else:\n self.__dict__['mcmc_covm'] = None\n # Copying default priors from StandardModels/CustomModels object\n # Priors are chosen not to be model-specific because HyperModel\n # (which is the only reason to have multiple models) does not support\n # different priors for different models\n for prior_key, prior_default in self.noise_model_obj().priors.items():\n if prior_key not in self.__dict__.keys():\n self.__dict__[prior_key] = prior_default\n\n # Model-dependent parameters\n for mkey in self.models:\n\n self.models[mkey].modeldict = dict()\n\n print('------------------')","def set_up_scenes():\n cmd.zoom('Cathepsin', 10) # Zoom out to get a view on the whole complex\n cmd.scene('001', 'store', message='This is the first scene with a view on the complex!')\n cmd.set_view(closeup) # Get a close-up of the ligand by using the manually chosen viewpoint\n cmd.scene('002', 'store', message='This is the second scene with a close-up on the ligand!')","def setup_populated_instr_McXtrace():\n instr = setup_instr_root_path_McXtrace()\n\n instr.add_parameter(\"double\", \"theta\")\n instr.add_parameter(\"double\", \"has_default\", value=37)\n instr.add_declare_var(\"double\", \"two_theta\")\n instr.append_initialize(\"two_theta = 2.0*theta;\")\n\n instr.add_component(\"first_component\", \"test_for_reading\")\n instr.add_component(\"second_component\", \"test_for_reading\")\n instr.add_component(\"third_component\", \"test_for_reading\")\n\n return instr","def set_parameters(self, args):\n self.args = args\n\n if args.testing:\n self.delay_close()\n\n if args.source == \"simulation\":\n log.info(\"Create simulated spectra device\")\n self.dev = simulation.SimulatedSpectraDevice()\n\n elif args.source == \"sled\":\n log.info(\"Create single sled cobra\")\n self.dev = simulation.SimulatedCobraSLED()\n\n elif args.source == \"cobra\":\n log.info(\"Create DALSA cobra device\")\n #self.dev = devices.DalsaCobraDevice()\n self.dev = DALSA.Cobra()\n\n elif args.source == \"opto\":\n log.info(\"Create OPTO sensor cobra device\")\n self.dev = DALSA.OPTOCobra()\n\n elif args.source == \"basler\":\n log.info(\"Create DALSA basler device\")\n #self.dev = devices.DalsaBaslerDevice()\n self.dev = DALSA.BaslerSprint4K()\n\n self.dev.setup_pipe()\n self.setup_pipe_timer()","def __init__(self):\r\n ScriptedLoadableModuleLogic.__init__(self)\r\n self.rgbport = 18944\r\n self.depthPort = 18945","def __init__(self, io, default_camera='right'):\n parts.part.ReachyPart.__init__(self, name='head', io=io)\n\n #self.neck = self.create_orbita_actuator('neck', Head.orbita_config)\n self.attach_dxl_motors(parts.Head.dxl_motors)\n #self.camera = self.io.find_dual_camera(default_camera)","def __set_control_elements(*args):\n args[0].Controls.valve_number = args[1]\n args[0].Controls.set_valve_number()\n args[0].Controls.go_elements = args[2]\n args[0].Controls.set_go_elements()","def import_control_section(self, filename_suffix='run'):\n pass","def __setup_ui_controls(self, list_of_names):\n\n ##################################################\n self.scene.append_to_caption('<h3>Scene Settings</h3>\\n')\n # Button to reset camera\n reset_button = button(\n bind=self.__reset_camera, text=\"Reset Camera\")\n self.__ui_controls.btn_reset = reset_button\n self.scene.append_to_caption('\\t')\n\n screenshot_button = button(bind=self.__screenshot, text=\"Take Screenshot\")\n self.__ui_controls.btn_ss = screenshot_button\n self.scene.append_to_caption('\\n')\n\n camera_lock_checkbox = checkbox(bind=self.__camera_lock_checkbox, text=\"Camera Lock\",\n checked=self.__camera_lock)\n self.__ui_controls.chkbox_cam = camera_lock_checkbox\n self.scene.append_to_caption('\\t')\n\n grid_relative_checkbox = checkbox(\n bind=self.__grid_relative_checkbox,\n text=\"Grid Relative\", checked=self.__grid_relative)\n self.__ui_controls.chkbox_rel = grid_relative_checkbox\n\n self.scene.append_to_caption('\\t')\n # Checkbox for grid visibility\n checkbox_grid_visibility = checkbox(\n bind=self.__grid_visibility_checkbox, text=\"Grid Visibility\",\n checked=self.__grid_visibility)\n self.__ui_controls.chkbox_grid = checkbox_grid_visibility\n self.scene.append_to_caption('\\n')\n\n ##################################################\n self.scene.append_to_caption('<h3>Robot</h3>\\n')\n # Drop down for robots / joints in frame\n menu_robots_list = menu(bind=self.__menu_item_chosen, choices=list_of_names)\n if not len(list_of_names) == 0:\n menu_robots_list.index = self.__selected_robot\n self.__ui_controls.menu_robots = menu_robots_list\n self.scene.append_to_caption('\\t')\n\n # Button to delete the selected robot\n delete_button = button(bind=self.__del_robot, text=\"Delete Robot\")\n self.__ui_controls.btn_del = delete_button\n self.scene.append_to_caption('\\t')\n\n # Button to clear the robots in screen\n clear_button = button(bind=self.clear_scene, text=\"Clear Scene\")\n self.__ui_controls.btn_clr = clear_button\n self.scene.append_to_caption('\\n')\n\n ##################################################\n self.scene.append_to_caption('<h3>Characteristics</h3>\\n')\n # Checkbox for reference frame visibilities\n if len(self.__robots) == 0:\n reference_checkbox = checkbox(\n bind=self.__reference_frame_checkbox,\n text=\"Show Reference Frames\", checked=True)\n else:\n chk = self.__robots[self.__selected_robot].ref_shown\n reference_checkbox = checkbox(\n bind=self.__reference_frame_checkbox,\n text=\"Show Reference Frames\", checked=chk)\n self.__ui_controls.chkbox_ref = reference_checkbox\n self.scene.append_to_caption('\\t')\n\n # Checkbox for robot visibility\n if len(self.__robots) == 0:\n robot_vis_checkbox = checkbox(\n bind=self.__robot_visibility_checkbox,\n text=\"Show Robot\", checked=True)\n else:\n chk = self.__robots[self.__selected_robot].rob_shown\n robot_vis_checkbox = checkbox(\n bind=self.__robot_visibility_checkbox,\n text=\"Show Robot\", checked=chk)\n self.__ui_controls.chkbox_rob = robot_vis_checkbox\n self.scene.append_to_caption('\\n')\n\n # Slider for robot opacity\n self.scene.append_to_caption('Robot Opacity:')\n if len(self.__robots) == 0:\n opacity_slider = slider(bind=self.__opacity_slider, value=1)\n else:\n opc = self.__robots[self.__selected_robot].opacity\n opacity_slider = slider(bind=self.__opacity_slider, value=opc)\n self.__ui_controls.sld_opc = opacity_slider\n self.scene.append_to_caption('\\n')\n\n ##################################################\n # Control manual\n controls_str = '<h3>Controls</h3><br>' \\\n '<b>PAN</b><br>' \\\n 'W , S | <i>forward / backward</i><br>' \\\n 'A , D | <i>left / right</i><br>' \\\n 'SPACE , SHIFT | <i>up / down</i><br>' \\\n '<b>ROTATE</b><br>' \\\n 'CTRL + LMB | <i>free spin</i><br>' \\\n 'ARROWS KEYS | <i>rotate direction</i><br>' \\\n 'Q , E | <i>roll left / right</i><br>' \\\n '<b>ZOOM</b></br>' \\\n 'MOUSEWHEEL | <i>zoom in / out</i>'\n\n self.scene.append_to_caption(controls_str)","def import_forward(self):\n self.import_property('OG')\n self.import_property('IBU')\n self.import_property('ABV')\n self.import_property('SRM')","def __init__(self, settings, name, linear_scaling_factor=0.1, angular_scaling_factor=0.1):\n super(ArmControlInterpreter, self).__init__(rate=0.033)\n\n self.settings = settings\n\n self.linear_scaling_factor = linear_scaling_factor\n self.angular_scaling_factor = angular_scaling_factor\n\n self.arm_velocity_client = actionlib.SimpleActionClient('/arm_controller/velocity', rose_arm_controller_msgs.msg.set_velocityAction)\n self.arm_gripper_client = actionlib.SimpleActionClient('/arm_controller/gripper_width', rose_arm_controller_msgs.msg.set_gripper_widthAction)\n\n self.arm_name = name\n\n self.velocity_goal = set_velocityGoal()\n self.gripper_goal = set_gripper_widthGoal()\n\n self.gripper_width = ArmControlInterpreter.gripper_open\n self.open_close_toggle = self.settings[\"open_close\"]","def __init__(self):\n self.label = \"RAPID Tools\"\n self.alias = \"RAPIDTools\"\n\n # List of tool classes associated with this toolbox\n self.tools = [AddSPTFields,\n AutomaticRAPIDfileGenerator, \n CopyDataToServer,\n CreateNetworkConnectivityFile,\n CreateNetworkConnectivityFileNHDPlus,\n CreateMuskingumParameterFiles,\n CreateMuskingumKFile,\n CreateMuskingumKfacFile,\n CreateMuskingumXField, \n CreateMuskingumXFile, \n CreateRivIDGageFile, \n CreateSubsetFile,\n CreateWeightTableFromWRFGeogrid,\n CreateInflowFileFromWRFHydroRunoff,\n CreateWeightTableFromECMWFRunoff,\n CreateInflowFileFromECMWFRunoff,\n CreateWeightTableFromLDASRunoff,\n CreateWeightTableFrom2DLatLonRunoff,\n CreateDischargeTable,\n CreateDischargeMap,\n FlowlineToPoint,\n DEMtoStreamNetwork,\n PublishDischargeMap,\n StreamNetworktoRAPID,\n StreamNetworktoSPT,\n UpdateWeightTable,\n UpdateDischargeMap,\n ]","def __init__(self, root, annFile, feature_file, caption_idx):\n\t\timport torch\n\n\t\tsuper().__init__(root, annFile)\n\t\tself.features = torch.load(feature_file, map_location='cpu')\n\t\tself.caption_idx = caption_idx","def zero_target(*args):\n robots = get_robot_roots()\n if not robots:\n pm.warning('Nothing Selected; Select a valid robot')\n return\n\n try:\n for robot in robots:\n target_ctrl_path = get_target_ctrl_path(robot)\n tool_ctrl_path = get_tool_ctrl_path(robot)\n\n ik_mode = pm.getAttr(target_ctrl_path + '.ik')\n\n if ik_mode:\n if pm.objExists(tool_ctrl_path):\n pm.setAttr(tool_ctrl_path + '.translate', 0, 0, 0)\n pm.setAttr(tool_ctrl_path + '.rotate', 0, 0, 0)\n else:\n pm.setAttr(target_ctrl_path + '.translate', 0, 0, 0)\n pm.setAttr(target_ctrl_path + '.rotate', 0, 0, 0)\n else:\n a1_fk_ctrl_path = format_path(__A1_FK_CTRL_PATH, robot)\n a2_fk_ctrl_path = format_path(__A2_FK_CTRL_PATH, robot)\n a3_fk_ctrl_path = format_path(__A3_FK_CTRL_PATH, robot)\n a4_fk_ctrl_path = format_path(__A4_FK_CTRL_PATH, robot)\n a5_fk_ctrl_path = format_path(__A5_FK_CTRL_PATH, robot)\n a6_fk_ctrl_path = format_path(__A6_FK_CTRL_PATH, robot)\n\n pm.setAttr(a1_fk_ctrl_path + '.rotateY', 0)\n pm.setAttr(a2_fk_ctrl_path + '.rotateX', 0)\n pm.setAttr(a3_fk_ctrl_path + '.rotateX', 0)\n pm.setAttr(a4_fk_ctrl_path + '.rotateZ', 0)\n pm.setAttr(a5_fk_ctrl_path + '.rotateX', 0)\n pm.setAttr(a6_fk_ctrl_path + '.rotateZ', 0)\n except:\n pm.warning('Cannot zero target')","def __init__(self, meta: SceneDescription):\n super().__init__(meta)\n self.scenes = []\n self.nodes = []\n self.meshes = []\n self.materials = []\n self.images = []\n self.samplers = []\n self.textures = []\n\n self.path = None\n self.scene = None\n self.gltf = None","def __init__(self, qasmsourcelines,\n name='main',\n filepath=None,\n no_unknown=False,\n save_pgm_source=False, save_element_source=False,\n save_gate_source=False,\n show_gate_decls=False,\n include_path='.'):\n\n # Control factors\n self.no_unknown = no_unknown\n self.save_pgm_source = save_pgm_source\n self.save_element_source = save_element_source\n self.save_gate_source = save_gate_source\n self.show_gate_decls = show_gate_decls\n self.include_path = include_path\n\n # Init sections\n self.t_sect = T_Sect(name)\n self.c_sect = C_Sect()\n self.g_sect = G_Sect()\n\n if save_pgm_source is None:\n self.s_sect = None\n else:\n self.s_sect = S_Sect()\n\n self.translation = {\n 't_sect': self.t_sect.t_sect,\n 'c_sect': self.c_sect.c_sect,\n 'g_sect': self.g_sect.g_sect,\n 's_sect': self.s_sect.s_sect\n }\n\n # Prepare to process initial source\n self.source_frame_stack = Source_Frame_Stack()\n self.push_source(filepath, qasmsourcelines)","def __init__(self):\n self.label = \"Data Assistant\"\n self.alias = \"dla\"\n\n # List of tool classes associated with this toolbox\n self.tools = [Append, Stage, NewFile, Preview, Replace]","def _setup_pipeline_cfg(self):","def setupConnections(self):\n self.T1Button.connect('clicked(bool)', self.onApplyButton)\n self.RViewButton.connect('clicked(bool)', self.onApplyRViewButton)\n self.CheckButton.connect('stateChanged(int)', self.onCheckbuttonChecked)\n self.LLE_Selector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelectLLENode)\n self.LLN_Selector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelectLLNNode)\n self.Aref_Selector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelectArefNode)\n\n self.ThSlider_LLE.Slider.connect(\"positionsChanged(double,double)\",self.ThSlider_LLE.onSliderChanged)\n self.ThSlider_LLE.SpinBoxL.connect(\"valueChanged(int)\", self.ThSlider_LLE.onSpinBoxLChanged)\n self.ThSlider_LLE.SpinBoxR.connect(\"valueChanged(int)\", self.ThSlider_LLE.onSpinBoxRChanged)\n \n self.ThSlider_LLN.Slider.connect(\"positionsChanged(double,double)\",self.ThSlider_LLN.onSliderChanged)\n self.ThSlider_LLN.SpinBoxL.connect(\"valueChanged(int)\", self.ThSlider_LLN.onSpinBoxLChanged)\n self.ThSlider_LLN.SpinBoxR.connect(\"valueChanged(int)\", self.ThSlider_LLN.onSpinBoxRChanged) \n\n self.ThSlider_ECV.Slider.connect(\"positionsChanged(double,double)\",self.ThSlider_ECV.onSliderChanged)\n self.ThSlider_ECV.SpinBoxL.connect(\"valueChanged(int)\", self.ThSlider_ECV.onSpinBoxLChanged)\n self.ThSlider_ECV.SpinBoxR.connect(\"valueChanged(int)\", self.ThSlider_ECV.onSpinBoxRChanged)\n \n self.Stats.segmentationSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.Stats.onScalarSelectorChanged)\n self.Stats.scalarSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.Stats.onScalarSelectorChanged)\n self.Stats.scalarSelector2.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.Stats.onScalarSelector2Changed)\n self.Stats.SButton.connect('clicked(bool)', self.onApplyGetStatistics)\n\n self.NativeT1_Selector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelectNT1Node)\n self.EnhancedT1_Selector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelectET1Node)\n self.SB_NBlodd.connect(\"valueChanged(Double)\", self.onSpinBoxNBChanged)\n self.SB_EBlodd.connect(\"valueChanged(Double)\", self.onSpinBoxEBChanged)\n self.SB_Haematocrit.connect(\"valueChanged(Double)\", self.onSpinBoxHChanged)\n self.ECVButton.connect('clicked(bool)',self.onApplyECVButton)","def config_extra_settings(self, data_dir):\n # load data directory configuration\n self.label_path = data_dir\n self.label_config_dir = os.path.join(self.label_path, 'labelconfig')\n self.label_config_suffix = 'lbl'\n\n # set icon configuration\n self._icon_dir=get_icon_dir()\n\n # set window title\n self.setWindowTitle('FreeROI')\n #self.resize(1280, 1000)\n self.center()\n # set window icon\n self.setWindowIcon(QIcon(os.path.join(self._icon_dir,'icon.png')))\n\n self._init_configuration()\n self._init_label_config_center()\n\n # create actions\n self._create_actions()\n\n # create menus\n self._create_menus()","def load(settings):\n\n aircraft_file = settings.paths('f_aircraft')\n logger.info(f\"Reading aircraft model from file '{truncate_filepath(aircraft_file)}'...\")\n\n if not os.path.exists(aircraft_file):\n raise IOError(f\"file '{aircraft_file}' not found\")\n\n with open(aircraft_file, 'r') as fp:\n aircraft_dict = json.load(fp)\n\n # ====== Aircraft top level =====\n aircraft = Aircraft()\n aircraft.uid = aircraft_dict['uid']\n\n for key, value in aircraft_dict['refs'].items():\n aircraft.refs[key] = value\n\n # ====== Wings =====\n for wing_entry in aircraft_dict['wings']:\n wing = aircraft.add_wing(wing_entry['uid'])\n wing.symmetry = wing_entry['symmetry']\n\n # ====== Segments =====\n for segment_entry in wing_entry['segments']:\n segment = wing.add_segment(segment_entry['uid'])\n\n for key, value in segment_entry['vertices'].items():\n segment.vertices[key] = value\n\n if segment_entry.get('geometry', None):\n for key, value in segment_entry['geometry'].items():\n segment.geometry[key] = value\n\n for key, value in segment_entry['airfoils'].items():\n # From now on use the absolute file path\n if value.startswith(PATHS.DIR.AIRFOILS):\n value = os.path.join(settings.paths('root'), value)\n segment.airfoils[key] = value\n\n if segment_entry.get('panels', None):\n for key, value in segment_entry['panels'].items():\n segment.panels[key] = value\n\n # ====== Controls =====\n for control_entry in wing_entry.get('controls', []):\n control = wing.add_control(control_entry['uid'])\n control.device_type = control_entry['device_type']\n control.deflection = control_entry['deflection']\n control.deflection_mirror = control_entry.get('deflection_mirror', None)\n\n for key, value in control_entry['segment_uid'].items():\n control.segment_uid[key] = value\n\n for key, value in control_entry['rel_vertices'].items():\n control.rel_vertices[key] = value\n\n for key, value in control_entry['rel_hinge_vertices'].items():\n control.rel_hinge_vertices[key] = value\n\n control.panels['num_c'] = control_entry['panels']['num_c']\n control.check()\n\n aircraft.generate()\n return aircraft","def __init__(self):\n self.label = \"Neural network input files\"\n self.description = \"Use this tool to create the input ASCII files for the GeoXplore neural network. Before using this tool, the evidence must be combined into a unique conditions raster with the Combine tool and the band statistics must be obtained for all the evidence using the Band Collection Statistics tool. If desired fuzzy membership attribute can be added to each of the training sites. See the ArcMap Tools Options discussion in Usage Tips in the Help about adjusting default setting for this tool.\"\n self.canRunInBackground = False\n self.category = \"Neural network\"","def setup(self, initdir, settings):\n\n print(\"Moving files into initial run directory:\", initdir)\n initdir = os.path.abspath(initdir)\n for p in os.listdir(self.relaxdir):\n if (p in (io.VASP_INPUT_FILE_LIST + self.settings[\"extra_input_files\"])) and (os.path.join(self.relaxdir, p) != initdir):\n os.rename(os.path.join(self.relaxdir,p), os.path.join(initdir,p))\n print(\"\")\n sys.stdout.flush()\n\n # Keep a backup copy of the base INCAR\n shutil.copyfile(os.path.join(initdir,\"INCAR\"),os.path.join(self.relaxdir,\"INCAR.base\"))\n\n # If an initial incar is called for, copy it in and set the appropriate flag\n if (self.settings[\"initial\"] != None) and (os.path.isfile(os.path.join(self.relaxdir,self.settings[\"initial\"]))):\n new_values = io.Incar(os.path.join(self.relaxdir,self.settings[\"initial\"])).tags\n io.set_incar_tag(new_values, initdir)\n print(\" Set INCAR tags:\", new_values, \"\\n\")\n sys.stdout.flush()","def settings(args):\n data = {}\n data['train_x'] = load_pkl(os.path.join(args.data_dir, 'train_images.pkl'))\n data['train_y'] = load_pkl(os.path.join(args.data_dir, 'train_labels.pkl'))\n data['valid_x'] = load_pkl(os.path.join(args.data_dir, 'valid_images.pkl'))\n data['valid_y'] = load_pkl(os.path.join(args.data_dir, 'valid_labels.pkl'))\n if args.combine_train_val:\n data['train_x'].update(data['valid_x'])\n data['train_y'].update(data['valid_y'])\n data['valid_x'] = load_pkl(os.path.join(args.data_dir, 'test_images.pkl'))\n data['valid_y'] = load_pkl(os.path.join(args.data_dir, './data/bsd_pkl_float/test_labels.pkl'))\n args.display_step = len(data['train_x']) / 46\n # Default configuration\n if args.default_settings:\n args.n_epochs = 250\n args.batch_size = 10\n args.learning_rate = 3e-2\n args.std_mult = 0.8\n args.delay = 8\n args.filter_gain = 2\n args.filter_size = 5\n args.n_rings = 4\n args.n_filters = 7\n args.save_step = 5\n args.height = 321\n args.width = 481\n\n args.n_channels = 3\n args.lr_div = 10.\n args.augment = True\n args.sparsity = True\n\n args.test_path = args.save_name\n args.log_path = './logs'\n args.checkpoint_path = './checkpoints'\n\n make_dirs(args, args.test_path)\n make_dirs(args, args.log_path)\n make_dirs(args, args.checkpoint_path)\n\n return args, data","def create_net(args):\n\n # Load config file for this experiment\n xinfo = yaml.load(open(args.exp)) # experiment info\n\n # copy config to run directory\n assert osp.isdir(args.cache_dir), 'Working directory not found: ' + args.cache_dir\n # output config file\n yaml.dump(xinfo, open(args.exp_config_path, 'w'),\n default_flow_style=False)\n\n # Load dataset config file\n dcfg_path = osp.join(args.data_config_path, xinfo['INPUT']['DATASET'])\n dinfo = yaml.load(open(dcfg_path)) # dataset info\n data_dir = dinfo['ROOT']\n\n layout = xinfo['INPUT']['LAYOUT']\n inps = [s.strip() for l in layout for s in l.split(',')]\n outs = [s.strip() for s in xinfo['REFINE']['TARGETS'].split(',')]\n\n supports = ['seg', 'flow', 'norm', 'rgb', 'depth']\n\n nets = {}\n for split in ['train', 'test']:\n net_inps = []\n net_outs = []\n for inp in inps:\n match = re.search('^(gt|pr)({})'.format('|'.join(supports)), inp)\n assert match is not None, 'Error in config INPUT-LAYOUT: ' + inp\n\n modality = match.group(2)\n nchannels = dinfo[modality]['n']\n path = osp.join(data_dir, dinfo[modality][match.group(1) + '-' + split])\n\n assert osp.exists(path), 'File not found: ' + path\n net_inps.append((inp, path, nchannels))\n\n for out in outs:\n # TODO: read target type: zero couplings, tight, loose couplings\n match = re.search('({})'.format('|'.join(supports)), out)\n assert match is not None, 'Error in config REFINE-TARGET: '+ out\n\n modality = match.group(1)\n nchannels = dinfo[modality]['n']\n path = osp.join(data_dir, dinfo[modality]['gt-' + split])\n\n assert osp.exists(path), 'File not found: ' + path\n net_outs.append((out, path, nchannels))\n\n loss_params = dict()\n mapping = None\n if 'mapping' in dinfo['seg']:\n idx = dinfo['seg']['mapping']\n mapping = dict(zip(idx, xrange(len(idx))))\n\n if split == 'train':\n\n # if the class weights is not in the dataset config file\n if 'gt-train-weights' not in dinfo['seg']:\n print 'Generating median frequency balancing weights.'\n (weights, mapping) = gcw.get_mfb(osp.join(dinfo['ROOT'], dinfo['seg']['gt-train']),\n dinfo['seg']['ignore_label'],\n mapping)\n # save back to dataset config\n dinfo['seg']['gt-train-weights'] = weights\n yaml.dump(dinfo, open(dcfg_path, 'w'), default_flow_style=False)\n else:\n weights = dinfo['seg']['gt-train-weights']\n # update data\n # update loss parameter\n ignore_label = dinfo['seg']['ignore_label']\n ignore_label = mapping[ignore_label] if mapping is not None else ignore_label\n loss_params['loss_param'] = {\n 'ignore_label': ignore_label,\n 'class_weighting': weights\n }\n\n # generate net prototxt\n loader = dinfo['NAME'] + '_loader'\n net_proto = arch.create_net(net_inps, net_outs, split, loader, layout, mapping, **loss_params)\n\n # output to file\n path = osp.join(args.cache_dir, getattr(args, 'exp_{}_path'.format(split)))\n open(path, 'w').write(str(net_proto))\n nets[split] = net_proto\n\n return nets","def prepare_node(self, node):\n self.interface = IpmiInterface(\n node.get('fencer-ip'),\n node.get('fencer-user'),\n node.get('fencer-password'),\n verbose=CONF.debug)","def setup(args):\n cfg = get_cfg()\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n # customize reszied parameters\n # cfg['INPUT']['MIN_SIZE_TRAIN'] = (20,)\n # cfg['INPUT']['MAX_SIZE_TRAIN'] = 50\n cfg.freeze()\n default_setup(\n cfg, args\n ) # if you don't like any of the default setup, write your own setup code\n return cfg","def _setup(self, orig=False):\n log_method_call(self, self.name, orig=orig, status=self.status,\n controllable=self.controllable)\n disks = []\n for member in self.devices:\n member.setup(orig=orig)\n disks.append(member.path)\n\n mdraid.mdactivate(self.path,\n members=disks,\n array_uuid=self.mdadmFormatUUID)","def node_setup(self):\n self.nav_tasks = {} # id -> nav_task\n self.missions = {} # id -> mission\n self.mission_state = {} # mission_id -> current task index.\n self.trigger_nav_task_active = False\n self.trigger_local_path_published = False","def __init__(self, parent=None):\n\t\tScriptedLoadableModuleWidget.__init__(self, parent)\n\t\tVTKObservationMixin.__init__(self) # needed for parameter node observation\n\t\tself.logic = None\n\t\tself._parameterNode = None\n\t\tself._updatingGUIFromParameterNode = False\n\t\t\n\t\tself.elecModel = None\n\t\tself.elecModelLastButton = None\n\t\tself.elecModelButton = 0\n\t\tself.elecChanLastButton = None\n\t\tself.elecChanButton = 0\n\t\tself.lastPolButton=0\n\t\tself.active = False","def init_extra_module(self, component_instance, function, mw_data):\n init_extra_actuator(self, component_instance, function, mw_data, ors_genpos_poster)","def setup(self):\n super().setup()\n self.ctx.restart_calc = None\n self.ctx.inputs = AttributeDict(self.exposed_inputs(XspectraCalculation, 'xspectra'))\n\n self.ctx.inputs.parameters = self.ctx.inputs.parameters.get_dict()","def __init__(self, animPickerUI, modulesToAdd, parent=None):\n super(ART_AddModuleToCanvas, self).__init__()\n # get the directory path of the tools\n settings = QtCore.QSettings(\"Epic Games\", \"ARTv2\")\n self.toolsPath = settings.value(\"toolsPath\")\n self.iconsPath = settings.value(\"iconPath\")\n self.scriptPath = settings.value(\"scriptPath\")\n self.projectPath = settings.value(\"projectPath\")\n\n self.pickerUI = animPickerUI\n self.modules = []\n self.modulesToAdd = modulesToAdd\n\n # assign close event\n self.closeEvent = self.closeWin\n\n # build the UI\n self.buildUI()","def changeNodeLib(ned, createNodeWin):\n pass","def get_init(self, storyboard):\n init = etree.SubElement(storyboard, \"Init\")\n init_act = etree.SubElement(init, \"Actions\")\n self.get_environment_actions(init_act)\n\n # Ego Vehicle\n if QgsProject.instance().mapLayersByName(\"Vehicles - Ego\"):\n vehicle_ego_layer = QgsProject.instance().mapLayersByName(\"Vehicles - Ego\")[0]\n for feature in vehicle_ego_layer.getFeatures():\n veh_id = \"Ego_\" + str(feature[\"id\"])\n orientation = feature[\"Orientation\"]\n pos_x = feature[\"Pos X\"]\n pos_y = feature[\"Pos Y\"]\n pos_z = feature[\"Pos Z\"]\n init_speed = feature[\"Init Speed\"]\n agent = feature[\"Agent\"]\n agent_camera = str(feature[\"Agent Camera\"]).lower()\n\n if agent == \"User Defined\":\n agent = feature[\"Agent User Defined\"]\n\n entity = etree.SubElement(init_act, \"Private\")\n entity.set(\"entityRef\", str(veh_id))\n self.entity_teleport_action(entity, orientation, pos_x, pos_y, pos_z)\n self.vehicle_controller(entity, str(feature[\"id\"]), agent, agent_camera, is_ego=True)\n if init_speed != 0:\n self.set_init_speed(entity, init_speed)\n\n # Vehicle\n if QgsProject.instance().mapLayersByName(\"Vehicles\"):\n vehicle_layer = QgsProject.instance().mapLayersByName(\"Vehicles\")[0]\n for feature in vehicle_layer.getFeatures():\n veh_id = \"Vehicle_\" + str(feature[\"id\"])\n orientation = feature[\"Orientation\"]\n pos_x = feature[\"Pos X\"]\n pos_y = feature[\"Pos Y\"]\n pos_z = feature[\"Pos Z\"]\n init_speed = feature[\"Init Speed\"]\n agent = feature[\"Agent\"]\n agent_camera = str(feature[\"Agent Camera\"]).lower()\n\n if agent == \"User Defined\":\n agent = feature[\"Agent User Defined\"]\n\n entity = etree.SubElement(init_act, \"Private\")\n entity.set(\"entityRef\", str(veh_id))\n self.entity_teleport_action(entity, orientation, pos_x, pos_y, pos_z)\n self.vehicle_controller(entity, str(feature[\"id\"]), agent, agent_camera, is_ego=False)\n if init_speed != 0:\n self.set_init_speed(entity, init_speed)\n\n # Pedestrian\n if QgsProject.instance().mapLayersByName(\"Pedestrians\"):\n walker_layer = QgsProject.instance().mapLayersByName(\"Pedestrians\")[0]\n for feature in walker_layer.getFeatures():\n ped_id = \"Pedestrian_\" + str(feature[\"id\"])\n orientation = feature[\"Orientation\"]\n pos_x = feature[\"Pos X\"]\n pos_y = feature[\"Pos Y\"]\n pos_z = feature[\"Pos Z\"]\n init_speed = feature[\"Init Speed\"]\n\n entity = etree.SubElement(init_act, \"Private\")\n entity.set(\"entityRef\", ped_id)\n self.entity_teleport_action(entity, orientation, pos_x, pos_y, pos_z)\n if init_speed != 0:\n self.set_init_speed(entity, init_speed)\n\n # Static Objects\n if QgsProject.instance().mapLayersByName(\"Static Objects\"):\n props_layer = QgsProject.instance().mapLayersByName(\"Static Objects\")[0]\n for feature in props_layer.getFeatures():\n prop_id = \"Prop_\" + str(feature[\"id\"])\n orientation = feature[\"Orientation\"]\n pos_x = feature[\"Pos X\"]\n pos_y = feature[\"Pos Y\"]\n pos_z = feature[\"Pos Z\"]\n\n entity = etree.SubElement(init_act, \"Private\")\n entity.set(\"entityRef\", prop_id)\n self.entity_teleport_action(entity, orientation, pos_x, pos_y, pos_z)","def test_sceneImport24281(self):\r\n\r\n self.delayDisplay(\"Starting the test\")\r\n #\r\n # first, get some data\r\n #\r\n self.delayDisplay(\"Getting Data\")\r\n import SampleData\r\n head = SampleData.downloadSample(\"MRHead\")\r\n\r\n #\r\n # create a label map and set it for editing\r\n #\r\n self.delayDisplay(\"Setting up LabelMap\")\r\n volumesLogic = slicer.modules.volumes.logic()\r\n headLabel = volumesLogic.CreateAndAddLabelVolume( slicer.mrmlScene, head, head.GetName() + '-label' )\r\n selectionNode = slicer.app.applicationLogic().GetSelectionNode()\r\n selectionNode.SetActiveVolumeID( head.GetID() )\r\n selectionNode.SetActiveLabelVolumeID( headLabel.GetID() )\r\n slicer.app.applicationLogic().PropagateVolumeSelection(0)\r\n\r\n #\r\n # got to the editor and do some drawing\r\n #\r\n self.delayDisplay(\"Setting up Editor and drawing\")\r\n parameterNode = EditUtil.getParameterNode()\r\n lm = slicer.app.layoutManager()\r\n paintEffectOptions = EditorLib.PaintEffectOptions()\r\n paintEffectOptions.setMRMLDefaults()\r\n paintEffectOptions.__del__()\r\n\r\n self.delayDisplay('Paint radius is %s' % parameterNode.GetParameter('PaintEffect,radius'))\r\n sliceWidget = lm.sliceWidget('Red')\r\n size = min(sliceWidget.width,sliceWidget.height)\r\n step = int(size / 12)\r\n center = int(size / 2)\r\n parameterNode.SetParameter('PaintEffect,radius', '20')\r\n paintTool = EditorLib.PaintEffectTool(sliceWidget)\r\n self.delayDisplay('Paint radius is %s, tool radius is %d' % (parameterNode.GetParameter('PaintEffect,radius'),paintTool.radius))\r\n for label in range(1,5):\r\n EditUtil.setLabel(label)\r\n pos = center - 2*step + (step * label)\r\n self.delayDisplay('Painting %d, at (%d,%d)' % (label,pos,pos),200)\r\n paintTool.paintAddPoint(pos,pos)\r\n paintTool.paintApply()\r\n paintTool.cleanup()\r\n paintTool = None\r\n\r\n #\r\n # now build:\r\n # create a model using the command line module\r\n # based on the current editor parameters\r\n # - make a new hierarchy node\r\n #\r\n\r\n self.delayDisplay( \"Building...\" )\r\n\r\n parameters = {}\r\n parameters[\"InputVolume\"] = headLabel.GetID()\r\n # create models for all labels\r\n parameters[\"JointSmoothing\"] = True\r\n parameters[\"StartLabel\"] = -1\r\n parameters[\"EndLabel\"] = -1\r\n outHierarchy = slicer.vtkMRMLModelHierarchyNode()\r\n outHierarchy.SetScene( slicer.mrmlScene )\r\n outHierarchy.SetName( \"sceneImport2428Hierachy\" )\r\n slicer.mrmlScene.AddNode( outHierarchy )\r\n parameters[\"ModelSceneFile\"] = outHierarchy\r\n\r\n modelMaker = slicer.modules.modelmaker\r\n self.CLINode = None\r\n self.CLINode = slicer.cli.runSync(modelMaker, self.CLINode, parameters, delete_temporary_files=False)\r\n\r\n self.delayDisplay(\"Models built\")\r\n\r\n success = self.verifyModels()\r\n\r\n success = success and (slicer.mrmlScene.GetNumberOfNodesByClass( \"vtkMRMLModelNode\" ) > 3)\r\n\r\n self.delayDisplay(\"Test finished\")\r\n\r\n if success:\r\n self.delayDisplay(\"Ahh... test passed.\")\r\n else:\r\n self.delayDisplay(\"!$!$!#!@#!@!@$%! Test Failed!!\")\r\n\r\n self.assertTrue(success)","def onResetParameters(self):\r\n # productive #button\r\n profprint()\r\n fileName = pathToScene = slicer.modules.needlefinder.path.replace(\"NeedleFinder.py\", \"Config/default.cfg\")\r\n self.logic.loadParameters(fileName)","def config_and_train(self, sys_args):\n \n self.run_config_function(sys_args)\n self.set_model_name('vgg_16')\n self.set_trainable_and_exclude_scopes(constants.checkpoint_exclude_scopes,\n constants.trainable_scopes)\n self.set_optimizer('sgd')\n self.set_max_number_of_steps(6000)\n self.train_or_eval_net(sys_args)","def configure_basics_in_routers(self):\n \n basics_script = Basics.get_script_file()\n \n # Alteracao do nome e senha do router no script\n basics_script = Basics.replace_vars(itter_list=basics_script, pattern='{%hostname%}', replace=self.router.nome)\n basics_script = Basics.replace_vars(itter_list=basics_script, pattern='{%password%}', replace=Basics.get_default_password())\n\n _path = f'router-config/cisco/{self.router.nome}.ios'\n\n router_configfile = open(_path, 'w')\n\n # Gravacao dos dados nos arquivos de cada router em cisco/\n try:\n for line in basics_script:\n router_configfile.write(line)\n \n return f'BASICS: Arquivo de configuracao de {self.router.nome} criado com sucesso em {_path}.'\n \n except: f'BASICS: Erro na configuracao do arquivo de {self.router.nome} em {_path}!'\n\n router_configfile.close()","def setup(self, trainer: \"pl.Trainer\") -> None:\n trainer_fn = trainer.state.fn\n if trainer_fn == TrainerFn.FITTING:\n super().setup(trainer)\n else:\n assert self.accelerator is not None\n self.accelerator.setup(trainer)\n\n # move the model to the correct device\n self.model_to_device()\n self.setup_precision_plugin()\n assert self.model is not None","def __init__(self, experiment, label=None):\n\n super(DrawTransitions, self).__init__(experiment, name=\"DrawTransitions\", label=label)\n\n self.epoch_start = self.experiment.config.getint(self.config_section, 'epoch_start', 0)\n self.epoch_end = self.experiment.config.getint(self.config_section, 'epoch_end',\n default=self.experiment.config.getint('Experiment', 'epochs', default=-1))\n self.frequency = self.experiment.config.getint(self.config_section, 'frequency', 1)\n self.priority = self.experiment.config.getint(self.config_section, 'priority', 0)\n self.filename = self.experiment.config.get(self.config_section, 'filename', default='transitions')\n self.format = self.experiment.config.get(self.config_section, 'format', default='png')\n self.transparent = self.experiment.config.getboolean(self.config_section, 'transparent', default=False)\n self.display_epoch = self.experiment.config.getboolean(self.config_section, 'display_epoch', default=False)\n\n self.colors = self.experiment.population._cell_class.type_colors\n self.types = self.experiment.population._cell_class.types\n self.max_types = self.experiment.population._cell_class.max_types\n\n self.node_labels = {}","def _create_default_setting(path):\n try:\n from configparser import ConfigParser\n except ImportError:\n from ConfigParser import ConfigParser # ver. < 3.0\n\n # instantiate\n config = ConfigParser()\n\n # update existing value\n config['Assets Paths'] = {\n 'background': 'assets\\\\images\\\\background.png',\n 'bullet': 'assets\\\\images\\\\bullet.png',\n 'bullet_red': 'assets\\\\images\\\\bullet_red.png',\n 'icon' : 'assets\\\\images\\\\RedInvader.png',\n\n 'ship': 'assets\\\\images\\\\Ship.png',\n 'ship_cr': 'assets\\\\images\\\\ShipCrushedRight.png',\n 'ship_cl': 'assets\\\\images\\\\ShipCrushedLeft.png',\n 'ship_cc': 'assets\\\\images\\\\ShipWhite.png',\n\n 'invadera1': 'assets\\\\images\\\\InvaderA1.png',\n 'invadera2': 'assets\\\\images\\\\InvaderA2.png',\n 'invaderb1': 'assets\\\\images\\\\InvaderB1.png',\n 'invaderb2': 'assets\\\\images\\\\InvaderB2.png',\n 'invaderc1': 'assets\\\\images\\\\InvaderC1.png',\n 'invaderc2': 'assets\\\\images\\\\InvaderC2.png',\n\n }\n config['castle'] = {\n 'castle_location': [\n [1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1],\n [1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1],\n [1, 0, 0, 0, 1],\n ],\n 'start_x': 50,\n 'start_y': 500,\n 'column': 5,\n 'row': 5,\n 'block_l3': (9, 255, 14),\n 'block_l2': (27, 255, 30),\n 'block_l1': (114, 255, 133),\n }\n config['alien'] = {\n 'margin_width': 200,\n 'margin_height': 20,\n 'column': 'd',\n 'Row': 5,\n 'width_x': 10,\n 'width_y': 10,\n 'movement': 10,\n 'alien_column_config': r'{\"0\":{\"path1\":\"InvaderA1\",\"path2\":\"InvaderA2\"},\"1\":{\"path1\":\"InvaderB1\",\"path2\":\"InvaderB2\"},\"2\":{\"path1\":\"InvaderB1\",\"path2\":\"InvaderB2\"},\"3\":{\"path1\":\"InvaderC1\",\"path2\":\"InvaderC2\"},\"4\":{\"path1\":\"InvaderC1\",\"path2\":\"InvaderC2\"}}'\n }\n config['player 1'] = {\n 'margin': 20,\n 'speed': 3\n }\n\n with open(path, 'w') as configfile:\n config.write(configfile)","def enter(self):\r\n self.turnOffLightboxes()\r\n self.installShortcutKeys()\r\n\r\n # Set parameter set node if absent\r\n self.selectParameterNode()\r\n self.editor.updateWidgetFromMRML()\r\n \r\n # If no segmentation node exists then create one so that the user does not have to create one manually\r\n if not self.editor.segmentationNodeID():\r\n newSegmentationNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLSegmentationNode')\r\n slicer.mrmlScene.AddNode(newSegmentationNode)\r\n self.editor.setSegmentationNode(newSegmentationNode)\r\n masterVolumeNodeID = self.getDefaultMasterVolumeNodeID()\r\n self.editor.setMasterVolumeNodeID(masterVolumeNodeID)","def __init__(self, pklfile=None, preffile=_preffile):\n\n super(config_controller, self).__init__()\n self.pklfile = pklfile\n self.preffile = preffile"],"string":"[\n \"def switch_setup(params, rig, ik_joints):\\n\\n # Duplicate for bind skeleton\\n skeleton = [x.name() for x in params['ikSkeleton']]\\n bind_skeleton = cmds.duplicate(skeleton, n=skeleton[0] + '_bnd_0')\\n #bind_skeleton\\n\\n # Hide all attribute on Controller\\n fkikcontrol = params['fkIkSwitch'].name()\\n attrs = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\\n for i in attrs:\\n cmds.setAttr('{node}.{attr}'.format(node=fkikcontrol, attr=i), k=False, cb=False)\\n\\n # Create FK/IK Switch attributes\\n cmds.addAttr(fkikcontrol, sn='FKIKBlend', at='float', min=0, max=1, dv=0, k=True)\\n cmds.addAttr(fkikcontrol, sn='AutoVis', at='bool', dv=1, k=True)\\n cmds.addAttr(fkikcontrol, ln='FKVis', at='bool', dv=1, k=True)\\n cmds.addAttr(fkikcontrol, ln='IKVis', at='bool', dv=1, k=True)\\n\\n # create control offset transforms\\n # par = cmds.listRelatives(fkikcontrol, parent=True)\\n # buf = create_offset_transform(fkikcontrol, BUF)\\n # cmds.parent(fkikcontrol, buf)\\n # if par: cmds.parent(buf, par[0])\\n\\n # Parent Skeleton to rig group\\n ik_skeleton = [x.name() for x in params['ikSkeleton']]\\n fk_skeleton = [x.name() for x in params['fkSkeleton']]\\n cmds.parent(ik_skeleton[0], rig['rigGroup'])\\n cmds.parent(fk_skeleton[0], rig['rigGroup'])\\n\\n # Constraint Bind Skeleton\\n fk_ik_finish(ik_joints, bind_skeleton, params)\",\n \"def _set_attr(self):\\n self.as_skeletal = self._import_as_skeleton()\\n self.materials = self._import_materials()\\n self.textures = self._import_textures()\",\n \"def init_from_file(self):\\n self.src.load('start.00') \\n self.oe1.load('start.01')\\n #self.det.load('start.02')\\n print('NOTE: variables loaded from start.00/start.01 files')\",\n \"def setup(args):\\n cfg = get_cfg()\\n add_imaterialist_config(cfg)\\n cfg.merge_from_file(model_zoo.get_config_file(\\\"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\\\"))\\n cfg.merge_from_file(args.config_file)\\n \\n cfg.merge_from_list(args.opts)\\n cfg.freeze()\\n default_setup(cfg, args)\\n # Setup logger for \\\"imaterialist\\\" module\\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\\\"imaterialist\\\")\\n return cfg\",\n \"def setup_threeCtrl(lf_lidrails, rt_lidrails):\\n # Declare control variables\\n lf_up = ['lf_lid01_tp01_ccc', 'lf_lid01_tp02_ccc', 'lf_lid01_tp03_ccc']\\n lf_dn = ['lf_lid01_dn01_ccc', 'lf_lid01_dn02_ccc', 'lf_lid01_dn03_ccc']\\n rt_up = ['rt_lid01_tp01_ccc', 'rt_lid01_tp02_ccc', 'rt_lid01_tp03_ccc']\\n rt_dn = ['rt_lid01_dn01_ccc', 'rt_lid01_dn02_ccc', 'rt_lid01_dn03_ccc']\\n\\n # Connect lidRails ramps to lid profile controls\\n\\n # ========\\n # lf_up\\n\\n # inner\\n cmds.connectAttr(lf_up[0] + '.tx', lf_lidrails + '.offsettop[0].offsettop_Position', f=True)\\n cmds.connectAttr(lf_up[0] + '.ty', lf_lidrails + '.offsettop[0].offsettop_FloatValue', f=True)\\n # mid\\n lf_lid01_um01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_um01_addDoubleLinear')\\n cmds.connectAttr(lf_up[1] + '.tx', lf_lid01_um01_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_um01_adn + '.input2', 0.5)\\n cmds.connectAttr(lf_lid01_um01_adn + '.output', lf_lidrails + '.offsettop[1].offsettop_Position', f=True)\\n cmds.connectAttr(lf_up[1] + '.ty', lf_lidrails + '.offsettop[1].offsettop_FloatValue', f=True)\\n # outer\\n lf_lid01_uo01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_uo01_addDoubleLinear')\\n cmds.connectAttr(lf_up[2] + '.tx', lf_lid01_uo01_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_uo01_adn + '.input2', 1.0)\\n cmds.connectAttr(lf_lid01_uo01_adn + '.output', lf_lidrails + '.offsettop[2].offsettop_Position', f=True)\\n cmds.connectAttr(lf_up[2] + '.ty', lf_lidrails + '.offsettop[2].offsettop_FloatValue', f=True)\\n\\n # ========\\n # lf_dn\\n\\n # Reverse node\\n lf_dn_rvn = cmds.createNode('reverse', n='lf_lid01_dn01_reverse')\\n # inner\\n cmds.connectAttr(lf_dn[0] + '.tx', lf_lidrails + '.offsetbottom[0].offsetbottom_Position', f=True)\\n cmds.connectAttr(lf_dn[0] + '.ty', lf_dn_rvn + '.inputX', f=True)\\n cmds.connectAttr(lf_dn_rvn + '.outputX', lf_lidrails + '.offsetbottom[0].offsetbottom_FloatValue', f=True)\\n # mid\\n lf_lid01_dm01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_dm01_addDoubleLinear')\\n cmds.connectAttr(lf_dn[1] + '.tx', lf_lid01_dm01_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_dm01_adn + '.input2', 0.5)\\n cmds.connectAttr(lf_lid01_dm01_adn + '.output', lf_lidrails + '.offsetbottom[1].offsetbottom_Position', f=True)\\n cmds.connectAttr(lf_dn[1] + '.ty', lf_dn_rvn + '.inputY', f=True)\\n cmds.connectAttr(lf_dn_rvn + '.outputY', lf_lidrails + '.offsetbottom[1].offsetbottom_FloatValue', f=True)\\n # outer\\n lf_lid01_do01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_do01_addDoubleLinear')\\n cmds.connectAttr(lf_dn[2] + '.tx', lf_lid01_do01_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_do01_adn + '.input2', 1.0)\\n cmds.connectAttr(lf_lid01_do01_adn + '.output', lf_lidrails + '.offsetbottom[2].offsetbottom_Position', f=True)\\n cmds.connectAttr(lf_dn[2] + '.ty', lf_dn_rvn + '.inputZ', f=True)\\n cmds.connectAttr(lf_dn_rvn + '.outputZ', lf_lidrails + '.offsetbottom[2].offsetbottom_FloatValue', f=True)\\n\\n # ========\\n # rt_up\\n\\n # inner\\n rt_lid01_ui01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_ui01_plusMinusAverage')\\n cmds.setAttr(rt_lid01_ui01_asn + '.operation', 2)\\n cmds.setAttr(rt_lid01_ui01_asn + '.input1D[0]', 1.0)\\n cmds.connectAttr(rt_up[0] + '.tx', rt_lid01_ui01_asn + '.input1D[1]', f=True)\\n cmds.connectAttr(rt_lid01_ui01_asn + '.output1D', rt_lidrails + '.offsettop[2].offsettop_Position', f=True)\\n cmds.connectAttr(rt_up[0] + '.ty', rt_lidrails + '.offsettop[2].offsettop_FloatValue', f=True)\\n # mid\\n rt_lid01_um01_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_um01_multDoubleLinear')\\n rt_lid01_um01_adn = cmds.createNode('addDoubleLinear', n='rt_lid01_um01_addDoubleLinear')\\n cmds.connectAttr(rt_up[1] + '.tx', rt_lid01_um01_mdn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_um01_mdn + '.input2', -1.0)\\n cmds.connectAttr(rt_lid01_um01_mdn + '.output', rt_lid01_um01_adn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_um01_adn + '.input2', 0.5)\\n cmds.connectAttr(rt_lid01_um01_adn + '.output', rt_lidrails + '.offsettop[1].offsettop_Position', f=True)\\n cmds.connectAttr(rt_up[1] + '.ty', rt_lidrails + '.offsettop[1].offsettop_FloatValue', f=True)\\n # outer\\n rt_lid01_uo_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_uo_multDoubleLinear')\\n cmds.connectAttr(rt_up[2] + '.tx', rt_lid01_uo_mdn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_uo_mdn + '.input2', -1.0)\\n cmds.connectAttr(rt_lid01_uo_mdn + '.output', rt_lidrails + '.offsettop[0].offsettop_Position', f=True)\\n cmds.connectAttr(rt_up[2] + '.ty', rt_lidrails + '.offsettop[0].offsettop_FloatValue', f=True)\\n\\n # ========\\n # rt_dn\\n\\n # Reverse node\\n rt_dn_rvn = cmds.createNode('reverse', n='rt_lid01_dn01_reverse')\\n # inner\\n rt_lid01_di01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_di01_plusMinusAverage')\\n cmds.setAttr(rt_lid01_di01_asn + '.operation', 2)\\n cmds.setAttr(rt_lid01_di01_asn + '.input1D[0]', 1.0)\\n cmds.connectAttr(rt_dn[0] + '.tx', rt_lid01_di01_asn + '.input1D[1]', f=True)\\n cmds.connectAttr(rt_lid01_di01_asn + '.output1D', rt_lidrails + '.offsetbottom[0].offsetbottom_Position', f=True)\\n cmds.connectAttr(rt_dn[0] + '.ty', rt_dn_rvn + '.inputX', f=True)\\n cmds.connectAttr(rt_dn_rvn + '.outputX', rt_lidrails + '.offsetbottom[0].offsetbottom_FloatValue', f=True)\\n # mid\\n rt_lid01_dm01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_dm01_plusMinusAverage')\\n cmds.setAttr(rt_lid01_dm01_asn + '.operation', 2)\\n cmds.setAttr(rt_lid01_dm01_asn + '.input1D[0]', 0.5)\\n cmds.connectAttr(rt_dn[1] + '.tx', rt_lid01_dm01_asn + '.input1D[1]', f=True)\\n cmds.connectAttr(rt_lid01_dm01_asn + '.output1D', rt_lidrails + '.offsetbottom[1].offsetbottom_Position', f=True)\\n cmds.connectAttr(rt_dn[1] + '.ty', rt_dn_rvn + '.inputY', f=True)\\n cmds.connectAttr(rt_dn_rvn + '.outputY', rt_lidrails + '.offsetbottom[1].offsetbottom_FloatValue', f=True)\\n # outer\\n rt_lid01_do01_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_do01_multDoubleLinear')\\n cmds.connectAttr(rt_dn[2] + '.tx', rt_lid01_do01_mdn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_do01_mdn + '.input2', -1.0)\\n cmds.connectAttr(rt_lid01_do01_mdn + '.output', rt_lidrails + '.offsetbottom[2].offsetbottom_Position', f=True)\\n cmds.connectAttr(rt_dn[2] + '.ty', rt_dn_rvn + '.inputZ', f=True)\\n cmds.connectAttr(rt_dn_rvn + '.outputZ', rt_lidrails + '.offsetbottom[2].offsetbottom_FloatValue', f=True)\",\n \"def __init__(self, yaml_file = 'options_modeling.yaml'):\\n\\n self.reproj_th = 2.5\\n self.min_matched_views = 3\\n self.descriptors = {'SIFT': 'sift'} # Descriptor name and module name\\n self.mask_suffix = '*_mask.png'\\n \\n # If there is an options file, it will overwrite the defaults \\n if yaml_file is not None:\\n self.load(yaml_file)\",\n \"def connectControl(*args, fileName: bool=True, index: int=0, preventContextualMenu: bool=True,\\n preventOverride: bool=True, **kwargs)->None:\\n pass\",\n \"def setup_fourCtrl(lf_lidrails, rt_lidrails):\\n # Declare control variables\\n lf_up = ['L_upperLid1_ctrl', 'L_upperLid2_ctrl', 'L_upperLid3_ctrl', 'L_upperLid4_ctrl']\\n lf_dn = ['L_lowerLid1_ctrl', 'L_lowerLid2_ctrl', 'L_lowerLid3_ctrl', 'L_lowerLid4_ctrl']\\n rt_up = ['R_upperLid1_ctrl', 'R_upperLid2_ctrl', 'R_upperLid3_ctrl', 'R_upperLid4_ctrl']\\n rt_dn = ['R_lowerLid1_ctrl', 'R_lowerLid2_ctrl', 'R_lowerLid3_ctrl', 'R_lowerLid4_ctrl']\\n\\n # Connect lidRails ramps to lid profile controls\\n\\n # lf_up =========\\n\\n # inner\\n cmds.connectAttr(lf_up[0] + '.tx', lf_lidrails + '.offsettop[0].offsettop_Position', f=True)\\n cmds.connectAttr(lf_up[0] + '.ty', lf_lidrails + '.offsettop[0].offsettop_FloatValue', f=True)\\n # mid - inner\\n lf_lid01_um01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_um01_addDoubleLinear')\\n cmds.connectAttr(lf_up[1] + '.tx', lf_lid01_um01_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_um01_adn + '.input2', 0.333)\\n cmds.connectAttr(lf_lid01_um01_adn + '.output', lf_lidrails + '.offsettop[1].offsettop_Position', f=True)\\n cmds.connectAttr(lf_up[1] + '.ty', lf_lidrails + '.offsettop[1].offsettop_FloatValue', f=True)\\n # mid - outer\\n lf_lid01_um02_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_um02_addDoubleLinear')\\n cmds.connectAttr(lf_up[2] + '.tx', lf_lid01_um02_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_um02_adn + '.input2', 0.666)\\n cmds.connectAttr(lf_lid01_um02_adn + '.output', lf_lidrails + '.offsettop[2].offsettop_Position', f=True)\\n cmds.connectAttr(lf_up[2] + '.ty', lf_lidrails + '.offsettop[2].offsettop_FloatValue', f=True)\\n # outer\\n lf_lid01_uo01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_uo01_addDoubleLinear')\\n cmds.connectAttr(lf_up[3] + '.tx', lf_lid01_uo01_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_uo01_adn + '.input2', 1.0)\\n cmds.connectAttr(lf_lid01_uo01_adn + '.output', lf_lidrails + '.offsettop[3].offsettop_Position', f=True)\\n cmds.connectAttr(lf_up[3] + '.ty', lf_lidrails + '.offsettop[3].offsettop_FloatValue', f=True)\\n\\n # lf_dn =========\\n\\n lf_dn_rvn = cmds.createNode('reverse', n='lf_lid01_dn01_reverse')\\n lf_dn02_rvn = cmds.createNode('reverse', n='lf_lid01_dn02_reverse')\\n # inner\\n cmds.connectAttr(lf_dn[0] + '.tx', lf_lidrails + '.offsetbottom[0].offsetbottom_Position', f=True)\\n cmds.connectAttr(lf_dn[0] + '.ty', lf_dn_rvn + '.inputX', f=True)\\n cmds.connectAttr(lf_dn_rvn + '.outputX', lf_lidrails + '.offsetbottom[0].offsetbottom_FloatValue', f=True)\\n # mid - inner\\n lf_lid01_dm01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_dm01_addDoubleLinear')\\n cmds.connectAttr(lf_dn[1] + '.tx', lf_lid01_dm01_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_dm01_adn + '.input2', 0.333)\\n cmds.connectAttr(lf_lid01_dm01_adn + '.output', lf_lidrails + '.offsetbottom[1].offsetbottom_Position', f=True)\\n cmds.connectAttr(lf_dn[1] + '.ty', lf_dn_rvn + '.inputY', f=True)\\n cmds.connectAttr(lf_dn_rvn + '.outputY', lf_lidrails + '.offsetbottom[1].offsetbottom_FloatValue', f=True)\\n # mid - outer\\n lf_lid01_dm02_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_dm02_addDoubleLinear')\\n cmds.connectAttr(lf_dn[2] + '.tx', lf_lid01_dm02_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_dm02_adn + '.input2', 0.666)\\n cmds.connectAttr(lf_lid01_dm02_adn + '.output', lf_lidrails + '.offsetbottom[2].offsetbottom_Position', f=True)\\n cmds.connectAttr(lf_dn[2] + '.ty', lf_dn02_rvn + '.inputX', f=True)\\n cmds.connectAttr(lf_dn02_rvn + '.outputX', lf_lidrails + '.offsetbottom[2].offsetbottom_FloatValue', f=True)\\n # outer\\n lf_lid01_do01_adn = cmds.createNode('addDoubleLinear', n='lf_lid01_do01_addDoubleLinear')\\n cmds.connectAttr(lf_dn[3] + '.tx', lf_lid01_do01_adn + '.input1', f=True)\\n cmds.setAttr(lf_lid01_do01_adn + '.input2', 1.0)\\n cmds.connectAttr(lf_lid01_do01_adn + '.output', lf_lidrails + '.offsetbottom[3].offsetbottom_Position', f=True)\\n cmds.connectAttr(lf_dn[3] + '.ty', lf_dn02_rvn + '.inputY')\\n cmds.connectAttr(lf_dn02_rvn + '.outputY', lf_lidrails + '.offsetbottom[3].offsetbottom_FloatValue', f=True)\\n\\n # rt_up =========\\n\\n # inner\\n rt_lid01_ui01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_ui01_plusMinusAverage')\\n cmds.setAttr(rt_lid01_ui01_asn + '.input1D[0]', 1.0)\\n cmds.connectAttr(rt_up[0] + '.tx', rt_lid01_ui01_asn + '.input1D[1]', f=True)\\n cmds.connectAttr(rt_lid01_ui01_asn + '.output1D', rt_lidrails + '.offsettop[3].offsettop_Position', f=True)\\n cmds.connectAttr(rt_up[0] + '.ty', rt_lidrails + '.offsettop[3].offsettop_FloatValue', f=True)\\n # mid -inner\\n rt_lid01_um01_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_um01_multDoubleLinear')\\n rt_lid01_um01_adn = cmds.createNode('addDoubleLinear', n='rt_lid01_um01_addDoubleLinear')\\n cmds.connectAttr(rt_up[2] + '.tx', rt_lid01_um01_mdn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_um01_mdn + '.input2', -1.0)\\n cmds.connectAttr(rt_lid01_um01_mdn + '.output', rt_lid01_um01_adn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_um01_adn + '.input2', 0.333)\\n cmds.connectAttr(rt_lid01_um01_adn + '.output', rt_lidrails + '.offsettop[2].offsettop_Position', f=True)\\n cmds.connectAttr(rt_up[2] + '.ty', rt_lidrails + '.offsettop[2].offsettop_FloatValue', f=True)\\n\\n # mid - outer\\n rt_lid01_um02_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_um02_multDoubleLinear')\\n rt_lid01_um02_adn = cmds.createNode('addDoubleLinear', n='rt_lid01_um02_addDoubleLinear')\\n cmds.connectAttr(rt_up[1] + '.tx', rt_lid01_um02_mdn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_um02_mdn + '.input2', -1.0)\\n cmds.connectAttr(rt_lid01_um02_mdn + '.output', rt_lid01_um02_adn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_um02_adn + '.input2', 0.666)\\n cmds.connectAttr(rt_lid01_um02_adn + '.output', rt_lidrails + '.offsettop[1].offsettop_Position', f=True)\\n cmds.connectAttr(rt_up[1] + '.ty', rt_lidrails + '.offsettop[1].offsettop_FloatValue', f=True)\\n\\n # outer\\n rt_lid01_uo_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_uo_multDoubleLinear')\\n cmds.connectAttr(rt_up[3] + '.tx', rt_lid01_uo_mdn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_uo_mdn + '.input2', -1.0)\\n cmds.connectAttr(rt_lid01_uo_mdn + '.output', rt_lidrails + '.offsettop[0].offsettop_Position', f=True)\\n cmds.connectAttr(rt_up[3] + '.ty', rt_lidrails + '.offsettop[0].offsettop_FloatValue', f=True)\\n\\n # rt_dn =========\\n\\n rt_dn_rvn = cmds.createNode('reverse', n='rt_lid01_dn01_reverse')\\n rt_dn02_rvn = cmds.createNode('reverse', n='rt_lid01_dn02_reverse')\\n # inner\\n rt_lid01_di01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_di01_plusMinusAverage')\\n cmds.setAttr(rt_lid01_di01_asn + '.operation', 2)\\n cmds.setAttr(rt_lid01_di01_asn + '.input1D[0]', 1.0)\\n cmds.connectAttr(rt_dn[0] + '.tx', rt_lid01_di01_asn + '.input1D[1]', f=True)\\n cmds.connectAttr(rt_lid01_di01_asn + '.output1D', rt_lidrails + '.offsetbottom[0].offsetbottom_Position', f=True)\\n cmds.connectAttr(rt_dn[0] + '.ty', rt_dn_rvn + '.inputX', f=True)\\n cmds.connectAttr(rt_dn_rvn + '.outputX', rt_lidrails + '.offsetbottom[0].offsetbottom_FloatValue', f=True)\\n # mid - inner\\n rt_lid01_dm01_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_dm01_plusMinusAverage')\\n cmds.setAttr(rt_lid01_dm01_asn + '.operation', 2)\\n cmds.setAttr(rt_lid01_dm01_asn + '.input1D[0]', 0.333)\\n cmds.connectAttr(rt_dn[2] + '.tx', rt_lid01_dm01_asn + '.input1D[1]', f=True)\\n cmds.connectAttr(rt_lid01_dm01_asn + '.output1D', rt_lidrails + '.offsetbottom[1].offsetbottom_Position', f=True)\\n cmds.connectAttr(rt_dn[2] + '.ty', rt_dn_rvn + '.inputY', f=True)\\n cmds.connectAttr(rt_dn_rvn + '.outputY', rt_lidrails + '.offsetbottom[1].offsetbottom_FloatValue', f=True)\\n # mid - outer\\n rt_lid01_dm02_asn = cmds.createNode('plusMinusAverage', n='rt_lid01_dm02_plusMinusAverage')\\n cmds.setAttr(rt_lid01_dm02_asn + '.operation', 2)\\n cmds.setAttr(rt_lid01_dm02_asn + '.input1D[0]', 0.666)\\n cmds.connectAttr(rt_dn[1] + '.tx', rt_lid01_dm02_asn + '.input1D[1]', f=True)\\n cmds.connectAttr(rt_lid01_dm02_asn + '.output1D', rt_lidrails + '.offsetbottom[2].offsetbottom_Position', f=True)\\n cmds.connectAttr(rt_dn[1] + '.ty', rt_dn02_rvn + '.inputX', f=True)\\n cmds.connectAttr(rt_dn02_rvn + '.outputX', rt_lidrails + '.offsetbottom[2].offsetbottom_FloatValue', f=True)\\n # outer\\n rt_lid01_do01_mdn = cmds.createNode('multDoubleLinear', n='rt_lid01_do01_multDoubleLinear')\\n cmds.connectAttr(rt_dn[3] + '.tx', rt_lid01_do01_mdn + '.input1', f=True)\\n cmds.setAttr(rt_lid01_do01_mdn + '.input2', -1.0)\\n cmds.connectAttr(rt_lid01_do01_mdn + '.output', rt_lidrails + '.offsetbottom[3].offsetbottom_Position', f=True)\\n cmds.connectAttr(rt_dn[3] + '.ty', rt_dn02_rvn + '.inputY', f=True)\\n cmds.connectAttr(rt_dn02_rvn + '.outputY', rt_lidrails + '.offsetbottom[3].offsetbottom_FloatValue')\",\n \"def init():\\n \\n # General parameters\\n vect_path = '/home/laura/Documents/STAGE3/outputVectorisation_1705_new_invert/output_60_70/' # graphs directory\\n csv_path = '/home/laura/Documents/STAGE3/outputVectorisation_1705_new_invert/output_60_70/tracking3/nodes_for_tracking.csv' # csv file \\n dest_path = '/home/laura/Documents/STAGE3/outputVectorisation_1705_new_invert/output_60_70/tracking3/' # output directory\\n verbose = True\\n main_params = [vect_path, csv_path, dest_path, verbose]\\n \\n # Linking parameters\\n createCSV = True \\n forced_matching = True\\n search_range = 10\\n memory = 3\\n adaptive_stop = 5 \\n link_params = [createCSV, forced_matching, search_range, memory, \\n adaptive_stop]\\n \\n # Tracking check parameters\\n check = True # True to create a check image\\n img_path = '/home/laura/Documents/STAGE3/outputVectorisation_1705_new_invert/output_60_70/tracking2/MosaicTest_t070.jpg' # image file on which to draw\\n size = 1 # size of the nodes drawing\\n check_params = [check, img_path, size]\\n \\n return main_params, link_params, check_params\",\n \"def create_ik_setup(controls, joints):\\n\\n # Create control offset transforms\\n exp_tf_ms = []\\n for ctl in controls:\\n par = cmds.listRelatives(ctl, parent=True)\\n buf = create_offset_transform(ctl, BUF)\\n exp = create_offset_transform(ctl, EXP)\\n off = create_offset_transform(ctl, OFF)\\n cmds.parent(ctl, off)\\n cmds.parent(off, exp)\\n cmds.parent(exp, buf)\\n if par:\\n cmds.parent(buf, par[0])\\n exp_tf_ms.append(buf)\\n\\n root_control, pole_control, goal_control = controls\\n handle, effector = cmds.ikHandle(sj=joints[0], ee=joints[-1], sol='ikRPsolver')\\n cmds.setAttr('{}.hiddenInOutliner'.format(handle), True)\\n cmds.orientConstraint(goal_control, joints[-1], mo=True)\\n cmds.parent(handle, goal_control)\\n cmds.hide(handle)\\n\\n # Connect root control to ik joint offset group\\n ik_joints_offset = cmds.listRelatives(joints[0], p=True)[0]\\n cmds.parentConstraint(root_control, ik_joints_offset, mo=True)\\n cmds.scaleConstraint(root_control, ik_joints_offset, mo=True)\\n\\n # Connect twisting and pole vector control\\n cmds.addAttr(goal_control, ln='twist', at='float', k=True)\\n cmds.connectAttr('{}.twist'.format(goal_control), '{}.twist'.format(handle))\\n cmds.poleVectorConstraint(pole_control, handle)\\n\\n # Add PV visibility attribute\\n cmds.addAttr(goal_control, shortName='pv', longName='poleVector', at='bool', k=True)\\n cmds.connectAttr('{}.pv'.format(goal_control), '{}.v'.format(pole_control))\\n cmds.setAttr('{}.pv'.format(goal_control),1)\\n\\n # Add curve that points elbow to pole control\\n crv = cmds.curve(p=[[0, 0, 0], [0, 1, 0]], d=1)\\n cmds.connectAttr('{}.visibility'.format(pole_control), '{}.visibility'.format(crv))\\n lock_hide_attrs(crv, attrs=['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz'])\\n cmds.setAttr('{}.overrideEnabled'.format(crv), True)\\n cmds.setAttr('{}.overrideDisplayType'.format(crv), 2)\\n decomp_joint = cmds.createNode('decomposeMatrix')\\n decomp_control = cmds.createNode('decomposeMatrix')\\n cmds.connectAttr('{}.worldMatrix'.format(joints[1]), '{}.inputMatrix'.format(decomp_joint))\\n cmds.connectAttr('{}.worldMatrix'.format(pole_control), '{}.inputMatrix'.format(decomp_control))\\n cmds.connectAttr('{}.outputTranslate'.format(decomp_joint), '{}.controlPoints[0]'.format(crv))\\n cmds.connectAttr('{}.outputTranslate'.format(decomp_control), '{}.controlPoints[1]'.format(crv))\\n\\n return handle, crv, exp_tf_ms\",\n \"def setup(instname):\\n global reducer, inst_name,van_mass,bleed_switch,rate,pixels\\n # debugging (allows to reload changed DirectEnergyConversion package from Mantid)\\n\\n if instname=='MAR' or instname=='mar':\\n print 'setup mari'\\n inst_name='MAR'\\n reducer = DRC.setup_reducer('MARI')\\n bleed_switch=False\\n rate=0.0\\n pixels=0\\n elif instname=='MER' or instname=='mer':\\n print 'setup merlin'\\n inst_name='MER'\\n reducer = DRC.setup_reducer('MERLIN')\\n bleed_switch=True\\n rate=0.01\\n pixels=80\\n elif instname=='MAP' or instname=='map':\\n print 'setup maps'\\n inst_name='MAP'\\n reducer = DRC.setup_reducer('MAPS')\\n bleed_switch=False\\n rate=0.0\\n pixels=0.0\\n elif instname=='LET' or instname=='let':\\n print 'setup let'\\n inst_name='LET'\\n reducer = DRC.setup_reducer('LET')\\n bleed_switch=True\\n rate=0.01\\n pixels=80\\n elif instname=='ARCS' or instname=='arcs':\\n print 'setup Arcs'\\n inst_name='ARC'\\n reducer = DRC.setup_reducer('ARCS')\\n bleed_switch=False\\n rate=0.01\\n pixels=80\\n elif instname=='SEQ' or instname=='seq':\\n print 'setup Sequoia'\\n inst_name='SEQ'\\n reducer = DRC.setup_reducer('SEQUOIA')\\n bleed_switch=False\\n rate=0.01\\n pixels=80\\n elif instname=='CNCS' or instname=='cncs':\\n print 'setup cncs'\\n inst_name='SEQ'\\n reducer = DRC.setup_reducer('CNCS')\\n bleed_switch=False\\n rate=0.01\\n pixels=80\\n elif instname=='HYSPEC' or instname=='hyspec':\\n print 'setup hyspec'\\n inst_name='SEQ'\\n reducer = DRC.setup_reducer('HYSPEC')\\n bleed_switch=False\\n rate=0.01\\n pixels=80\\n else:\\n print 'Instrument name not defined'\\n return \\n van_mass=reducer.get_default_parameter('vanadium-mass')\",\n \"def __init__(self, parent):\\n super(Demo4, self).__init__(parent)\\n self.scenes = []\\n self.draw_axes = True\\n self.lighting = True\\n self.current_scene = 0\\n self.objects = []\\n self.diffuse_light = [0.8, 0.8, 0.8, 1]\",\n \"def __init__(self, cat_path, img_path, orig_json_path, rst_json_path,\\n layers, **kwargs):\\n\\n # Initialize class attributes\\n self.name = \\\"AI2D-RST\\\" # Dataset name\\n self.cat_path = Path(cat_path) # Path to categories JSON\\n self.img_path = Path(img_path) # Path to AI2D images\\n self.orig_json_path = Path(orig_json_path) # Path to AI2D JSON\\n self.rst_json_path = Path(rst_json_path) # Path to AI2D-RST JSON\\n\\n # Check input types\\n assert self.cat_path.is_file()\\n assert self.img_path.is_dir()\\n assert self.orig_json_path.is_dir()\\n assert self.rst_json_path.is_dir()\\n assert layers in ['grouping', 'grouping+connectivity', 'connectivity',\\n 'discourse', 'discourse+connectivity']\\n\\n # Load node and edge dictionaries\\n self.node_dict = node_dicts\\n self.edge_dict = edge_dicts\\n\\n # Load diagram labels from the labels JSON file\\n categories = self._load_annotation(cat_path)\\n\\n # Initialize label encoder and encode integer labels\\n le = LabelEncoder().fit(list(categories.values()))\\n\\n # Create a dictionary mapping encoded class integers to their names\\n self.class_names = {k: v for k, v in zip(le.transform(le.classes_),\\n le.classes_)}\\n\\n # Create a dictionary mapping filenames to labels\\n label_dict = {k: le.transform([v]) for k, v in categories.items()}\\n\\n # Convert labels into a numpy array for calculating class weights\\n label_arr = np.concatenate(list(label_dict.values()))\\n\\n # Calculate class weights\\n class_weights = compute_class_weight(class_weight='balanced',\\n classes=np.unique(label_arr),\\n y=label_arr)\\n\\n # Wrap class weights into a torch Tensor and make available through\\n # attribute\\n self.class_weights = torch.FloatTensor(class_weights)\\n\\n # Get diagram identifiers and labels\\n self.file_ids = list(label_dict.keys())\\n self.labels = list(label_dict.values())\\n\\n # Return DGL graph objects by default\\n self._return_nx = False\\n\\n # Check if NetworkX graphs have been requested\\n if kwargs and 'nx' in kwargs:\\n\\n # Set the flag for returning NetworkX graphs to True\\n if kwargs['nx']:\\n \\n self._return_nx = True\\n\\n # Check if node type information should be added to node features\\n if kwargs and kwargs['node_types']:\\n\\n # Set add node types flag to True\\n self._add_node_types = True\\n\\n # Check which node label dictionary to use: this depends on the kind\\n # of annotation layers requested\\n if 'discourse' in layers:\\n\\n # Get the node labels from the node dictionary & cast to array\\n node_labels = list(self.node_dict['discourse'].values())\\n node_labels = np.asarray(node_labels)\\n\\n else:\\n\\n # Get the node labels from the node dictionary & cast to array\\n node_labels = list(self.node_dict['grouping'].values())\\n node_labels = np.asarray(node_labels)\\n\\n # Initialize label binarizer and fit to node labels\\n self._node_binarizer = LabelBinarizer().fit(node_labels)\\n\\n else:\\n\\n self._add_node_types = False\\n\\n # Check if smoothed labels have been requested\\n if 'smooth' in kwargs and kwargs['smooth']:\\n\\n # Set the flag for smoothed labels to True\\n self._smooth_labels = True\\n\\n else:\\n\\n self._smooth_labels = False\\n\\n # Initialize label binarizer for RST relations if needed\\n if 'discourse' in layers:\\n\\n # Get the RST relations from the node dictionary\\n rst_relations = np.asarray(list(self.node_dict['relations'].values()))\\n\\n # Initialize label binarizer and fit to node labels\\n self._rst_binarizer = LabelBinarizer().fit(rst_relations)\\n\\n # Load the requested annotation and create the graphs accordingly\\n self._load(layers)\\n\\n # Get number of unique diagram classes in the dataset\\n self.n_classes = len(np.unique(self.labels))\\n\\n # Get the number of node and edge classes for DGL graphs (grouping +\\n # connectivity)\\n if 'discourse' not in layers and not self._return_nx:\\n\\n # Get unique node and edge types for graphs that don't use typed\\n # nodes or edges\\n node_list = [x.ndata['kind'].flatten() for x in self.diagrams]\\n self.n_node_classes = len(np.unique(torch.cat(node_list).numpy()))\\n\\n edge_list = [x.edata['kind'].flatten() for x in self.diagrams]\\n self.n_edge_classes = len(np.unique(torch.cat(edge_list).numpy()))\\n\\n # Do the same for DGLHeteroGraphs (discourse)\\n if 'discourse' in layers and not self._return_nx:\\n\\n node_list = np.concatenate(np.asarray([x.ntypes for x in\\n self.diagrams]))\\n self.n_node_classes = len(np.unique(node_list))\\n\\n edge_list = np.concatenate(np.asarray([x.etypes for x in\\n self.diagrams]))\\n self.n_edge_classes = len(np.unique(edge_list))\",\n \"def setup(args):\\n # chaparral,denseForest,lake,canyon,burning,burnt = neighbours\\n config_path = args[0]\\n config = utils.load(config_path)\\n # -- THE CA MUST BE RELOADED IN THE GUI IF ANY OF THE BELOW ARE CHANGED --\\n config.title = \\\"Forest Fire\\\"\\n config.dimensions = 2\\n config.states = \\\\\\n (\\n CHAPARRAL,\\n DENSE_FORREST,\\n LAKE,\\n CANYON,\\n BURNING,\\n BURNT,\\n START_BURN,\\n END_BURN\\n )\\n\\n # ------------ -------------------------------------------------------------\\n\\n config.state_colors = \\\\\\n [\\n (0.6,0.6,0), #chaparral\\n (0,0.4,0), #dense forrest\\n (0,0.5,1), #lake\\n (0.5,0.5,0.5), #canyon\\n (1,0,0), #burning\\n (0.25,0.25,0.25), #burnt\\n (1,0.7,0), #starting to burn\\n (0.8,0,0.2) #ending burn\\n ]\\n\\n config.grid_dims = (grid_size, grid_size)\\n config.num_generations = 1000\\n config.set_initial_grid(initial_grid)\\n config.wrap = False\\n\\n # --------------------------------------------------------------------\\n\\n # the GUI calls this to pass the user defined config\\n # into the main system with an extra argument\\n # do not change\\n if len(args) == 2:\\n config.save()\\n sys.exit()\\n return config\",\n \"def setup(args):\\n cfg = get_cfg()\\n\\n cfg.merge_from_file(model_zoo.get_config_file(\\\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\\\"))\\n cfg.merge_from_list(args.opts)\\n\\n # configs for training\\n if args.small_vidor: # cfg.DATASETS.VIDOR.SIZE == 'small':\\n cfg.DATASETS.TRAIN = (\\\"vidor_small_train\\\",)\\n elif args.small_vidor_10imgs: # cfg.DATASETS.VIDOR.SIZE == 'small-10imgs':\\n cfg.DATASETS.TRAIN = (\\\"vidor_small_10imgs_train\\\",)\\n else:\\n cfg.DATASETS.TRAIN = (\\\"vidor_large_train\\\",)\\n # cfg.DATALOADER.NUM_WORKERS = 2\\n if not args.eval_only:\\n cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\\\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\\\") # Let training initialize from model zoo\\n factor = 4\\n cfg.SOLVER.IMS_PER_BATCH = 16 * factor\\n cfg.SOLVER.BASE_LR = 0.0001 * factor # finetune using 10x smaller base_lr\\n cfg.SOLVER.MAX_ITER = 270000 // factor \\n cfg.SOLVER.STEPS = [210000 // factor, 250000 // factor]\\n # cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # default: 512\\n cfg.MODEL.ROI_HEADS.NUM_CLASSES = 78\\n\\n # configs for testing\\n # cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \\\"model_final.pth\\\")\\n if args.small_vidor: # cfg.DATASETS.VIDOR.SIZE == 'small':\\n cfg.DATASETS.TEST = (\\\"vidor_small_val\\\",)\\n elif args.small_vidor_10imgs: # cfg.DATASETS.VIDOR.SIZE == 'small-10imgs':\\n cfg.DATASETS.TEST = (\\\"vidor_small_10imgs_val\\\",)\\n else:\\n cfg.DATASETS.TEST = (\\\"vidor_large_val\\\",)\\n # cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\\n\\n # cfg.OUTPUT_DIR = './output/train_vidor_with_pseudo_labels'\\n \\n \\n if not args.eval_only:\\n os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\\n cfg.freeze()\\n default_setup(cfg, args)\\n return cfg\",\n \"def __init__(self,config = None):\\n \\n self.join_path = join_path\\n self.label_path = cfg['labels_path']\\n self.pick_path = (cfg['result_path'] + cfg['pickle_path'])\\n self.label_dir = os.path.join(CWD_PATH,self.join_path, self.label_path)\\n\\n #Variables inherent to the Fluent data: \\n self.num_ins = 4\\n\\n self.scale_var = cfg['scale_var']\\n # User set values are below. These can be adjusted in config.yml \\n self.MSE_thresh1 = (cfg['thresh1']*self.scale_var)**2\\n self.MSE_thresh2 = (cfg['thresh2']*self.scale_var)**2\\n self.MSE_thresh3 = (cfg['thresh3']*self.scale_var)**2\\n \\n self.rew_goal = cfg['reward'] * self.scale_var\\n\\n self.noise = cfg['noise']\\n self.minmaxbuffer = cfg['minmaxbuffer']\\n\\n # Get the function of input-output mapping, and max & min:\\n [self.O_CH4_flow_uniformity, mins,maxes] = self.get_funcs('O_CH4_flow_uniformity')\\n [self.O_CH4_mol_frac, mins,maxes] = self.get_funcs('O_CH4_mol_frac')\\n [self.O_t, mins, maxes] = self.get_funcs('O_t')\\n \\n self.mins = mins# * self.scale_var\\n self.maxes = maxes#* self.scale_var\\n #Action range is a percentage of the total range\\n self.action_range = cfg['action_range']*self.scale_var\\n\\n #Action space is the up & down range for the 4 actions \\n self.action_space = Box(-self.action_range, self.action_range, shape=(self.num_ins,), dtype=np.float32)\\n\\n # For ref, this is a 10d state space:\\n #in: 1 ch4 flow, 2 ch4 t, 3 o2 flow, 4 o2 t,\\n #out: 5 flow unif, 6 mol frac, 7 temp\\n #out - target: 8 flow unif, 9 mol frac, 10 temp\\n \\n self.observation_space = Tuple((Box(self.mins.values[0],self.maxes.values[0],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[1],self.maxes.values[1],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[2],self.maxes.values[2],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[3],self.maxes.values[3],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32)))\\n \\n # TODO this isn't really a proper gym spec\\n self._spec = lambda: None\\n self._spec.id = \\\"AllVar-v0\\\"\\n \\n # For rendering:\\n self.viewer = None\\n self.labels = cfg['labels']\\n \\n #initialize variables for tracking:\\n self.episode = 0\\n self.reward = 0\\n self.reset()\",\n \"def __init__(self, cfg_index, conditions, pars_dir, step_title, use_defaults, input_cfg_json_data):\\n super().__init__(cfg_index, conditions, pars_dir, step_title, use_defaults, input_cfg_json_data)\\n self.set_name = \\\"alignment\\\"\\n if input_cfg_json_data:\\n self._read_custom_pars()\\n else:\\n self._combine_conditions()\",\n \"def init_vars():\\n\\tda_vinci.base.usepackage(\\\"pgfkeys\\\")\\n\\tda_vinci.base.add_preamble(setup_script)\",\n \"def setup_flags(self):\\n self.io_args.color = self.io_args.color_full\\n self.io_args.rig_in = self.io_args.rig\\n self.io_args.matches = os.path.join(self.io_args.output_root, \\\"matches.json\\\")\\n self.io_args.rig_out = os.path.join(self.io_args.output_root, \\\"rig.json\\\")\",\n \"def init_trainers(self, args):\\n self.actors_cur = [None for _ in range(self.num_agents)]\\n self.critics_cur = [None for _ in range(self.num_agents)]\\n self.actors_tar = [None for _ in range(self.num_agents)]\\n self.critics_tar = [None for _ in range(self.num_agents)]\\n self.optimizers_c = [None for _ in range(self.num_agents)]\\n self.optimizers_a = [None for _ in range(self.num_agents)]\\n input_size_global = sum(self.obs_shape_n) + sum(self.action_shape_n)\\n\\n if args.restore == True: # restore the model\\n game_step = int(args.old_model_name.split('_')[-1][:-1])\\n for idx in range(self.num_agents):\\n self.actors_cur[idx] = torch.load(args.old_model_name+'a_c_{}.pt'.format(idx))\\n self.actors_tar[idx] = torch.load(args.old_model_name+'a_t_{}.pt'.format(idx))\\n self.critics_cur[idx] = torch.load(args.old_model_name+'c_c_{}.pt'.format(idx))\\n self.critics_tar[idx] = torch.load(args.old_model_name+'c_t_{}.pt'.format(idx))\\n self.optimizers_a[idx] = optim.Adam(self.actors_cur[idx].parameters(), args.lr_a)\\n self.optimizers_c[idx] = optim.Adam(self.critics_cur[idx].parameters(), args.lr_c)\\n self.var = self.var - (game_step-args.learning_start_episode*args.per_episode_max_len)*args.var_discount\\n self.var = self.min_var if self.var < self.min_var else self.var\\n old_data = {'game_step':game_step, 'episode_gone_old':int(game_step/args.per_episode_max_len)}\\n\\n # Note: if you need load old model, there should be a procedure for juding if the trainers[idx] is None\\n for i in range(self.num_agents):\\n self.actors_cur[i] = actor_agent(self.obs_shape_n[i], self.action_shape_n[i], \\\\\\n args).to(args.device)\\n self.critics_cur[i] = critic_agent(sum(self.obs_shape_n), sum(self.action_shape_n), \\\\\\n args).to(args.device)\\n self.actors_tar[i] = actor_agent(self.obs_shape_n[i], self.action_shape_n[i], \\\\\\n args).to(args.device)\\n self.critics_tar[i] = critic_agent(sum(self.obs_shape_n), sum(self.action_shape_n), \\\\\\n args).to(args.device)\\n self.optimizers_a[i] = optim.Adam(self.actors_cur[i].parameters(), args.lr_a)\\n self.optimizers_c[i] = optim.Adam(self.critics_cur[i].parameters(), args.lr_c)\\n\\n # return the old data, no need to update the trainers\\n if args.restore == True: return old_data\\n\\n self.actors_tar = self.update_trainers(self.actors_cur, self.actors_tar, 1.0) # update the target par using the cur\\n self.critics_tar = self.update_trainers(self.critics_cur, self.critics_tar, 1.0) # update the target par using the cur\",\n \"def __init__(self, modifications = {}):\\n CoveredState.__init__(self, BASE_CONFIG, coverLayers = TOTAL_LAYERS)\\n for name in modifications:\\n self.Edit(name, modifications[name])\\n if unix:\\n self.Cover(\\\"BuilderDir\\\", VELAUNCHER_DIR, layer = UNAVAILABLE_LAYER)\",\n \"def _augment_pipeline_cfg(self):\",\n \"def set_defaults(args):\\n # Check critical files exist\\n args.train_file = os.path.join(args.data_dir, args.train_file)\\n if not os.path.isfile(args.train_file):\\n raise IOError('No such file: %s' % args.train_file)\\n args.dev_file = os.path.join(args.data_dir, args.dev_file)\\n if not os.path.isfile(args.dev_file):\\n raise IOError('No such file: %s' % args.dev_file)\\n if args.embedding_file:\\n args.embedding_file = os.path.join(args.embed_dir, args.embedding_file)\\n if not os.path.isfile(args.embedding_file):\\n raise IOError('No such file: %s' % args.embedding_file)\\n\\n # Set model directory\\n subprocess.call(['mkdir', '-p', args.model_dir])\\n\\n # Set model name\\n if not args.model_name:\\n import uuid\\n import time\\n args.model_name = time.strftime(\\\"%Y%m%d-\\\") + str(uuid.uuid4())[:8]\\n\\n # Set log + model file names\\n args.log_file = os.path.join(args.model_dir, args.model_name + '.txt')\\n args.model_file = os.path.join(args.model_dir, args.model_name + '.pt')\\n\\n # Embeddings options\\n if args.embedding_file:\\n with open(args.embedding_file) as f:\\n dim = len(f.readline().strip().split(' ')) - 1\\n args.embedding_dim = dim\\n elif not args.embedding_dim:\\n raise RuntimeError('Either embedding_file or embedding_dim '\\n 'needs to be specified.')\\n\\n # Make sure fix_embeddings and embedding_file are consistent\\n if args.fix_embeddings:\\n if not (args.embedding_file or args.pretrained):\\n logger.warning('WARN: fix_embeddings set to False '\\n 'as embeddings are random.')\\n args.fix_embeddings = False\\n return args\",\n \"def fill_args(args):\\n args.agent_module = 'dstar_sgolam_walker'\\n args.checkpoint_path = None\\n args.exp_config = 'configs/baselines/dstar_proto_sgolam.yaml'\\n args.num_episodes = 25\\n \\n return args\",\n \"def addControl(*args):\",\n \"def addControl(*args):\",\n \"def addControl(*args):\",\n \"def addControl(*args):\",\n \"def add_source_achors():\\n pass\",\n \"def __init__(self, parent=None):\\n ScriptedLoadableModuleWidget.__init__(self, parent)\\n VTKObservationMixin.__init__(self) # needed for parameter node observation\\n self.logic = None\",\n \"def __init__(self, parent=None):\\n ScriptedLoadableModuleWidget.__init__(self, parent)\\n VTKObservationMixin.__init__(self) # needed for parameter node observation\\n self.logic = None\\n self._parameterNode = None\\n self.T1_LLN_Node = None\\n self.T1_LLE_Node = None\\n self.ECVMapNode = None\\n self.LLE_Node = None\\n self.LLN_Node = None\\n self.ArefNode = None\\n self.T1_LLE_Name = 'T1 Enhanced'\\n self.T1_LLN_Name = 'T1 Native'\\n self.ResetSliceViews()\\n self.LinkSlices()\\n self.ColorBarEnabled()\\n self.setupVolumeNodeViewLayout()\\n self.Warning = True\",\n \"def controls_setup(self):\\n pass\",\n \"def load_defaults(self):\\n self.set_motor_limits(self.MOTOR_LEFT, self.LEFT_DEFAULT)\\n self.set_motor_limits(self.MOTOR_RIGHT, self.RIGHT_DEFAULT)\\n self.set_servo(self.SERVO_1, self.MIDPOINT)\",\n \"def load_defaults(self):\\n self.set_motor_limits(self.MOTOR_LEFT, self.LEFT_DEFAULT)\\n self.set_motor_limits(self.MOTOR_RIGHT, self.RIGHT_DEFAULT)\\n self.set_servo(self.SERVO_1, self.MIDPOINT)\",\n \"def load_defaults(self):\\n self.set_motor_limits(self.MOTOR_LEFT, self.LEFT_DEFAULT)\\n self.set_motor_limits(self.MOTOR_RIGHT, self.RIGHT_DEFAULT)\\n self.set_servo(self.SERVO_1, self.MIDPOINT)\",\n \"def __init__(self, *args, **kwargs):\\n super(MayaScene, self).__init__(*args, **kwargs)\",\n \"def onReset(self):\\n #productive\\n profprint()\\n fileName = pathToScene = slicer.modules.needlefinder.path.replace(\\\"NeedleFinder.py\\\",\\\"Config/default.cfg\\\")\\n self.logic.loadParameters(fileName)\",\n \"def setup(self) -> None:\\n mlflow.set_tracking_uri('file://' + hutils.get_original_cwd() + '/mlruns')\\n if self.log_mlflow:\\n mlflow.set_experiment(self.config.runner.exp_name)\\n \\n if self.log_mlflow:\\n self.log_parameters(self.config)\\n mlflow.log_param('node', os.uname()[1])\",\n \"def setUp(params, spec):\\n\\n if not params['fkSkeleton']:\\n joints = []\\n\\n for x in range(params['numberOfSegments']):\\n jnt = cmds.createNode('joint')\\n jnt = cmds.rename(jnt, '{baseName}{index}_{JNT}'.format(baseName=spec.name, index=x, JNT=JNT)).split('|')[-1]\\n if x > 0:\\n cmds.parent(jnt, joints[x - 1])\\n cmds.setAttr('{node}.translateY'.format(node=jnt), 1.0)\\n joints.append(jnt)\\n\\n spec.params()['fkSkeleton'] = dragonfly.node.dfNode.fromList(joints)\\n\\n if not params['fkControls']:\\n controls = []\\n\\n for x in range(params['fkNumberOfSegments']):\\n ctl = cmds.curve(**CONTROL_SHAPE_DATA)\\n cmds.controller(ctl)\\n ctl = cmds.rename(ctl, '{baseName}{index}_{CTL}'.format(baseName=spec.name, index=x, CTL=CTL))\\n match_nodes(params['fkSkeleton'][x].name(), ctl)\\n if x > 0:\\n cmds.parent(ctl, controls[x-1])\\n controls.append(ctl)\\n\\n spec.params()['fkControls'] = dragonfly.node.dfNode.fromList(controls)\",\n \"def setupOptionsFrame(self):\\n\\n # CPU / CUDA options\\n self.device.addItems([\\\"cuda\\\", \\\"cpu\\\"])\\n self.scriptedEffect.addLabeledOptionsWidget(\\\"Device:\\\", self.device)\\n\\n self.modality.addItems([\\\"CT\\\", \\\"MRI\\\"])\\n self.scriptedEffect.addLabeledOptionsWidget(\\\"Modality:\\\", self.modality)\\n\\n # Add ROI options\\n self.roiSelector.nodeTypes = ['vtkMRMLMarkupsROINode']\\n self.roiSelector.noneEnabled = True\\n self.roiSelector.setMRMLScene(slicer.mrmlScene)\\n self.scriptedEffect.addLabeledOptionsWidget(\\\"ROI: \\\", self.roiSelector)\\n\\n # Toggle ROI visibility button\\n toggleROIVisibilityButton = qt.QPushButton(\\\"Toggle ROI Visibility\\\")\\n toggleROIVisibilityButton.objectName = self.__class__.__name__ + 'ToggleROIVisibility'\\n toggleROIVisibilityButton.setToolTip(\\\"Toggle selected ROI visibility\\\")\\n toggleROIVisibilityButton.connect('clicked()', self.toggleROIVisibility)\\n self.scriptedEffect.addOptionsWidget(toggleROIVisibilityButton)\\n\\n # Apply button\\n applyButton = qt.QPushButton(\\\"Apply\\\")\\n applyButton.objectName = self.__class__.__name__ + 'Apply'\\n applyButton.setToolTip(\\\"Extract liver from input volume\\\")\\n applyButton.connect('clicked()', self.onApply)\\n self.scriptedEffect.addOptionsWidget(applyButton)\",\n \"def setup(self, path_to_conf_file):\\n\\n self.track = Track.SENSORS\\n self.num_frames = 0\\n\\n with open(path_to_conf_file, 'r') as f:\\n config = yaml.safe_load(f)\\n\\n for key, value in config.items():\\n setattr(self, key, value)\\n\\n self.device = torch.device('cuda')\\n\\n self.image_model = CameraModel(config).to(self.device)\\n self.image_model.load_state_dict(torch.load(self.main_model_dir))\\n self.image_model.eval()\\n\\n self.vizs = []\\n\\n self.waypointer = None\\n\\n if self.log_wandb:\\n wandb.init(project='carla_evaluate')\\n \\n self.steers = torch.tensor(np.linspace(-self.max_steers,self.max_steers,self.num_steers)).float().to(self.device)\\n self.throts = torch.tensor(np.linspace(0,self.max_throts,self.num_throts)).float().to(self.device)\\n\\n self.prev_steer = 0\\n self.lane_change_counter = 0\\n self.stop_counter = 0\",\n \"def setup(self):\\n ScriptedLoadableModuleWidget.setup(self)\\n\\n globalPath, _ = os.path.split( os.path.splitext( slicer.modules.regularizedfastmarching.path )[0] )\\n self.globalPath = globalPath + \\\"/Resources/SegmentationFastMarching/\\\"\\n self.volumesPath = self.globalPath + \\\"Volumes/\\\"\\n self.seedsCsvPath = self.globalPath + \\\"SeedsLabels/\\\"\\n self.seedsPath = self.globalPath + \\\"Seeds/\\\"\\n self.regularizationsPath = self.globalPath + \\\"Regularizations/\\\"\\n self.segmentationsPath = self.globalPath + \\\"Segmentations/\\\"\\n\\n self.logic = RegularizedFastMarchingLogic()\\n # self.logic.previousVolumeName = None\\n # self.logic.imgLabel = np.array([])\\n # self.logic.previousImgIds = np.array([])\\n # self.logic.imgDist = np.array([])\\n\\n # Hide the Slicer Logo to increase space\\n slicer.util.findChild(slicer.util.mainWindow(), 'LogoLabel').visible = False\\n \\n #\\n #region Restart Slicer Area\\n #\\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\\n parametersCollapsibleButton.text = \\\"Reload\\\"\\n self.layout.addWidget(parametersCollapsibleButton)\\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\\n self.restartSlicerButton = qt.QPushButton(\\\"Restart Slicer\\\")\\n self.restartSlicerButton.connect('clicked(bool)', slicer.util.restart)\\n parametersFormLayout.addRow(self.restartSlicerButton)\\n #endregion \\n\\n #\\n #region Load Data Area\\n #\\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\\n parametersCollapsibleButton.text = \\\"Seeds\\\"\\n self.layout.addWidget(parametersCollapsibleButton)\\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\\n \\n #\\n # File name volume .nii\\n # \\n # self.volueNameComboBox = qt.QComboBox() \\n # self.volueNameComboBox = self.fillComboBox(self.volueNameComboBox, self.volumesPath, \\\".nii\\\")\\n # parametersFormLayout.addRow(\\\"Volumes .nii: \\\", self.volueNameComboBox)\\n \\n #\\n # Load volume buttons\\n #\\n # horizontalLayout = qt.QHBoxLayout()\\n # self.loadBrainVolumeButton = qt.QPushButton(\\\"Load Example Volume\\\")\\n # self.loadBrainVolumeButton.enabled = True\\n # horizontalLayout.addWidget(self.loadBrainVolumeButton)\\n \\n #\\n # Load custom volumes buttons\\n #\\n # self.loadCustomVolumeButton = qt.QPushButton(\\\"Load Selected Volume\\\")\\n # self.loadCustomVolumeButton.enabled = True\\n # horizontalLayout.addWidget(self.loadCustomVolumeButton) \\n # parametersFormLayout.addRow(horizontalLayout)\\n \\n \\n #\\n # Add vertical spacing\\n # \\n # verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n # parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Seeds files and a text line to load / save seeds from the scene\\n #\\n horizontalLayout = qt.QHBoxLayout()\\n fileNameSeedsLabel = qt.QLabel(\\\"Seeds: \\\")\\n self.fileNameSeedsComboBox = qt.QComboBox() \\n self.fileNameSeedsComboBox = self.fillComboBox(self.fileNameSeedsComboBox, self.seedsPath, \\\".seed\\\") \\n self.fileNameSeedsLineEdit = qt.QLineEdit()\\n horizontalLayout.addWidget(fileNameSeedsLabel)\\n horizontalLayout.addWidget(self.fileNameSeedsComboBox)\\n horizontalLayout.addWidget(self.fileNameSeedsLineEdit)\\n self.fileNameSeedsComboBox.currentTextChanged.connect(self.setSelectedSeedsFile)\\n self.setSelectedSeedsFile(self.fileNameSeedsComboBox.currentText)\\n parametersFormLayout.addRow(horizontalLayout)\\n \\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Save markers Button\\n #\\n horizontalLayout = qt.QHBoxLayout()\\n self.saveMarkersButton = qt.QPushButton(\\\"Save markers\\\")\\n self.saveMarkersButton.toolTip = \\\"Save the markers in a fcsv file\\\"\\n self.saveMarkersButton.enabled = True\\n horizontalLayout.addWidget(self.saveMarkersButton)\\n \\n #\\n # Load markers Button\\n #\\n self.loadMarkersButton = qt.QPushButton(\\\"Load markers\\\")\\n self.loadMarkersButton.toolTip = \\\"Load the markers from fcsv file.\\\"\\n self.loadMarkersButton.enabled = True\\n horizontalLayout.addWidget(self.loadMarkersButton) \\n \\n #\\n # Clear markups Button\\n #\\n self.clearButton = qt.QPushButton(\\\"Clear all markups\\\")\\n self.clearButton.toolTip = \\\"Remove all fiducial markers.\\\"\\n self.clearButton.enabled = True\\n horizontalLayout.addWidget(self.clearButton)\\n parametersFormLayout.addRow(horizontalLayout)\\n \\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 25, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Fiducial placement button\\n #\\n \\\"\\\"\\\"\\n Add this markup in the markupsList and rename it depending on seeds combobox's value\\n \\\"\\\"\\\"\\n @vtk.calldata_type(vtk.VTK_INT)\\n def onMarkupAdded(caller, event, index): \\n markupsNode = caller\\n # Label, Name\\n seedTokens = self.currentSeedNameComboBox.currentText.replace(\\\" \\\", \\\"\\\").split(\\\":\\\")\\n markupsNode.SetNthFiducialLabel(index, seedTokens[1])\\n markupsNode.SetNthControlPointDescription(index, seedTokens[0])\\n \\n self.w=slicer.qSlicerMarkupsPlaceWidget()\\n self.w.setMRMLScene(slicer.mrmlScene)\\n \\n markupsNode = slicer.mrmlScene.GetFirstNodeByName(\\\"MarkupsFiducial\\\")\\n if markupsNode == None: \\n markupsNode = slicer.mrmlScene.AddNewNodeByClass(\\\"vtkMRMLMarkupsFiducialNode\\\")\\n markupsNode.CreateDefaultDisplayNodes()\\n \\n # On markup added event: format the added markup\\n markupsNode.AddObserver(slicer.vtkMRMLMarkupsNode.PointPositionDefinedEvent, onMarkupAdded)\\n self.w.setCurrentNode(markupsNode)\\n # Hide all buttons and only show place button\\n self.w.buttonsVisible=True\\n self.w.placeButton().show()\\n self.w.show()\\n parametersFormLayout.addRow(self.w)\\n \\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # File name data's seeds\\n # \\n self.fileNameDataComboBox = qt.QComboBox() \\n self.fileNameDataComboBox = self.fillComboBox(self.fileNameDataComboBox, self.seedsCsvPath, \\\".csv\\\") \\n self.fileNameDataComboBox.currentTextChanged.connect(self.setSeedsLabelFromFile)\\n parametersFormLayout.addRow(\\\"Seeds labels csv: \\\", self.fileNameDataComboBox)\\n\\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n horizontalLayout = qt.QHBoxLayout()\\n # Seeds label \\n self.seedsLabelText = qt.QLabel()\\n self.seedsLabelText.text = \\\"Labels:\\\"\\n horizontalLayout.addWidget(self.seedsLabelText)\\n \\n # ComboxBox containing names and labels\\n self.currentSeedNameComboBox= qt.QComboBox()\\n \\n # Add labels depending on first csv file found\\n self.setSeedsLabelFromFile(self.fileNameDataComboBox.currentText)\\n horizontalLayout.addWidget(self.currentSeedNameComboBox)\\n \\n self.clearOrganButton = qt.QPushButton(\\\"Clear this organ\\\")\\n self.clearOrganButton.toolTip = \\\"Remove all fiducial markers for this organ.\\\"\\n self.clearOrganButton.enabled = True\\n horizontalLayout.addWidget(self.clearOrganButton)\\n parametersFormLayout.addRow(horizontalLayout)\\n \\n #endregion\\n\\n\\n #\\n #region Segmentation Parameters Area\\n #\\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\\n parametersCollapsibleButton.text = \\\"Parameters\\\"\\n self.layout.addWidget(parametersCollapsibleButton)\\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\\n\\n #\\n # input volume selector\\n #\\n self.inputSelector = slicer.qMRMLNodeComboBox()\\n self.inputSelector.nodeTypes = [\\\"vtkMRMLScalarVolumeNode\\\"]\\n self.inputSelector.selectNodeUponCreation = True\\n self.inputSelector.addEnabled = False\\n self.inputSelector.removeEnabled = False\\n self.inputSelector.noneEnabled = False\\n self.inputSelector.showHidden = False\\n self.inputSelector.showChildNodeTypes = False\\n self.inputSelector.setMRMLScene( slicer.mrmlScene )\\n self.inputSelector.setToolTip( \\\"Pick the input to the algorithm.\\\" )\\n parametersFormLayout.addRow(\\\"Input Volume: \\\", self.inputSelector)\\n\\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Segmentation distance to stop the algo\\n #\\n self.distance = ctk.ctkSliderWidget()\\n self.distance.singleStep = 10\\n self.distance.minimum = 0\\n self.distance.maximum = 1000\\n self.distance.value = 100\\n self.distance.setToolTip(\\\"Set the maximum geodesic distance allowed from a seed\\\")\\n parametersFormLayout.addRow(\\\"Distance\\\", self.distance)\\n \\n #\\n # Margin to build masks\\n #\\n self.marginMask = ctk.ctkSliderWidget()\\n self.marginMask.singleStep = 1\\n self.marginMask.minimum = 0\\n self.marginMask.maximum = 50\\n self.marginMask.value = 15\\n self.marginMask.setToolTip(\\\"Margin used to build masks for each seed\\\")\\n parametersFormLayout.addRow(\\\"Mask Margin (voxel)\\\", self.marginMask)\\n\\n #\\n # Gamma to prevent the seeds to cross bridges between organs\\n #\\n self.gammaSlider = qt.QSlider(qt.Qt.Horizontal)\\n self.gammaSlider.setRange(0, 1000)\\n self.gammaSlider.setSingleStep(1)\\n self.gammaSlider.setPageStep(10)\\n self.gammaSlider.setValue(25)\\n\\n self.gammaSpinBox = qt.QDoubleSpinBox()\\n self.gammaSpinBox.setDecimals(3)\\n self.gammaSpinBox.setRange(0, 1)\\n self.gammaSpinBox.setSingleStep(0.001)\\n self.gammaSpinBox.setValue(0.025)\\n\\n self.gammaSlider.valueChanged.connect(self.setGammaValueFromSlider)\\n self.gammaSpinBox.valueChanged.connect(self.setGammaValueFromSpinBox)\\n \\n horizontalLayout = qt.QHBoxLayout()\\n horizontalLayout.addWidget(self.gammaSlider)\\n horizontalLayout.addWidget(self.gammaSpinBox)\\n\\n parametersFormLayout.addRow(\\\"Regularization weight\\\", horizontalLayout)\\n \\n #\\n # Regularization radius\\n #\\n self.regularizationDiameter = ctk.ctkSliderWidget()\\n self.regularizationDiameter.singleStep = 1\\n self.regularizationDiameter.minimum = 1\\n self.regularizationDiameter.maximum = 20\\n self.regularizationDiameter.value = 4\\n self.regularizationDiameter.setToolTip(\\\"Margin used to build masks for each seed\\\")\\n parametersFormLayout.addRow(\\\"Regularization Diameter (voxels)\\\", self.regularizationDiameter)\\n \\n #\\n # Threshold to prevent the seeds to spread over a range intensities\\n #\\n self.minThresholdSlider = ctk.ctkSliderWidget()\\n self.minThresholdSlider.singleStep = 1\\n self.minThresholdSlider.minimum = 0\\n self.minThresholdSlider.maximum = 255\\n self.minThresholdSlider.value = 40\\n self.minThresholdSlider.setToolTip(\\\"Seeds cannot spread below this value\\\")\\n self.minThresholdSlider.valueChanged.connect(self.setMinThresholdValue)\\n parametersFormLayout.addRow(\\\"Min Threshold (digital level)\\\", self.minThresholdSlider)\\n\\n self.maxThresholdSlider = ctk.ctkSliderWidget()\\n self.maxThresholdSlider.singleStep = 1\\n self.maxThresholdSlider.minimum = 0\\n self.maxThresholdSlider.maximum = 255\\n self.maxThresholdSlider.value = 255\\n self.maxThresholdSlider.setToolTip(\\\"Seeds cannot spread over this value\\\")\\n self.maxThresholdSlider.valueChanged.connect(self.setMaxThresholdValue)\\n parametersFormLayout.addRow(\\\"Max Threshold (digital level)\\\", self.maxThresholdSlider)\\n\\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Check box to keep or not last segmentation\\n #\\n self.removeLastSegmentationCheckBox = qt.QCheckBox(\\\"\\\")\\n self.removeLastSegmentationCheckBox.setChecked(True)\\n parametersFormLayout.addRow(\\\"Remove last segmentation\\\", self.removeLastSegmentationCheckBox)\\n \\n #\\n # Show the background seed of the next segmentation\\n #\\n self.showBackGroundCheckBox = qt.QCheckBox(\\\"\\\")\\n self.showBackGroundCheckBox.setChecked(False)\\n parametersFormLayout.addRow(\\\"Show background\\\", self.showBackGroundCheckBox)\\n \\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Segment Button\\n #\\n self.segmentButton = qt.QPushButton(\\\"Segment\\\")\\n self.segmentButton.toolTip = \\\"Run the algorithm.\\\"\\n self.segmentButton.enabled = False\\n parametersFormLayout.addRow(self.segmentButton)\\n #endregion \\n\\n #\\n #region Load Save segmentations\\n #\\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\\n parametersCollapsibleButton.text = \\\"Segmentations\\\"\\n self.layout.addWidget(parametersCollapsibleButton)\\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\\n\\n #\\n # Checkbox to save the segmentation using the labels \\n #\\n self.saveByLabelsCheckBox = qt.QCheckBox(\\\"\\\")\\n self.saveByLabelsCheckBox.setChecked(True)\\n parametersFormLayout.addRow(\\\"Save by labels\\\", self.saveByLabelsCheckBox)\\n\\n #\\n # Checkbox to save the segmentation using the voxels intensities \\n #\\n self.saveByIntensitiesCheckBox = qt.QCheckBox(\\\"\\\")\\n self.saveByIntensitiesCheckBox.setChecked(False)\\n parametersFormLayout.addRow(\\\"Save by intensities\\\", self.saveByIntensitiesCheckBox)\\n\\n #\\n # Checkbox to generate segments data \\n #\\n self.generateDataCsvCheckBox = qt.QCheckBox(\\\"\\\")\\n self.generateDataCsvCheckBox.setChecked(False)\\n parametersFormLayout.addRow(\\\"Generate segments data CSV\\\", self.generateDataCsvCheckBox)\\n\\n\\n #\\n # Name of the segmentation to save\\n #\\n self.saveSegmentationName = qt.QLineEdit()\\n self.saveSegmentationName.textChanged.connect(self.onSaveSegmentationFileChange)\\n parametersFormLayout.addRow(\\\"Segmentation name:\\\", self.saveSegmentationName) \\n\\n #\\n # Save segmentationButton Button\\n #\\n self.saveSegmentationButton = qt.QPushButton(\\\"Save segmentation\\\")\\n self.saveSegmentationButton.toolTip = \\\"Save this segmentation with used parameters.\\\"\\n parametersFormLayout.addRow(self.saveSegmentationButton) \\n\\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n\\n horizontalLayout = qt.QHBoxLayout()\\n self.segmentationLoadLabelText = qt.QLabel()\\n self.segmentationLoadLabelText.text = \\\"Segmentation files:\\\"\\n horizontalLayout.addWidget(self.segmentationLoadLabelText)\\n \\n #\\n # Segmentation files \\n # \\n self.segmentationFilesComboBox = qt.QComboBox() \\n self.segmentationFilesComboBox = self.fillComboBox(self.segmentationFilesComboBox, self.segmentationsPath, \\\".seg.nrrd\\\")\\n horizontalLayout.addWidget(self.segmentationFilesComboBox)\\n parametersFormLayout.addRow(horizontalLayout)\\n \\n #\\n # Load segmentationButton Button\\n #\\n self.loadSegmentationButton = qt.QPushButton(\\\"Load segmentation\\\")\\n self.loadSegmentationButton.toolTip = \\\"Load the segmenation from this file.\\\"\\n parametersFormLayout.addRow(self.loadSegmentationButton) \\n #end region\\n \\n\\n #\\n #region Connections & Shortcuts\\n #\\n # self.loadBrainVolumeButton.connect('clicked(bool)', self.onLoadBrainVolumeButton)\\n # self.loadCustomVolumeButton.connect('clicked(bool)', self.onLoadCustomVolumeButton)\\n \\n self.clearButton.connect('clicked(bool)', self.onClearButton)\\n self.clearOrganButton.connect('clicked(bool)', self.onClearOrganButton)\\n self.segmentButton.connect('clicked(bool)', self.onSegmentButton)\\n self.saveSegmentationButton.connect('clicked(bool)', self.onSaveSegmentationButton)\\n self.loadSegmentationButton.connect('clicked(bool)', self.onLoadSegmentationButton)\\n self.saveMarkersButton.connect('clicked(bool)', self.onSaveMarkersButton)\\n self.loadMarkersButton.connect('clicked(bool)', self.onLoadMarkersButton)\\n self.inputSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.onSelect)\\n \\n # Keyboard shortcut bind: press ctrl button to add markup \\n markupShortcut = qt.QShortcut(slicer.util.mainWindow())\\n markupShortcut.setKey( qt.QKeySequence(\\\"w\\\") )\\n markupShortcut.connect('activated()', self.addMarkupcallback)\\n \\n markupShortcut = qt.QShortcut(slicer.util.mainWindow())\\n markupShortcut.setKey( qt.QKeySequence(\\\"x\\\") )\\n markupShortcut.connect('activated()', self.addBgMarkupcallback)\\n #endregion \\n\\n # Refresh Apply button state\\n self.onSelect()\\n \\n # Refresh segmentation save button state\\n self.onSaveSegmentationFileChange()\",\n \"def init_turn(self):\\n sender = self.sender()\\n cur_settings = self.setting_dropdown.currentText()\\n\\n if sender:\\n cur_disp = self.rad_grp.checkedButton()\\n self.start_frm = self.start_frm_le.text()\\n self.end_frm = self.end_frm_le.text()\\n file_path = self.save_loc.text()\\n if self.arg_check():\\n\\n wireframe = self.wireframe\\n # Instantiate the tool logic with the selected values.\\n start_turn = tl.Turntable(cur_disp.objectName(),\\n self.start_frm,\\n self.end_frm,\\n file_path,\\n wireframe)\\n\\n # If discipline is surface, set render settings.\\n if cur_disp.objectName() == 'surface':\\n set_turn = tl.RenderTurntable()\\n set_turn.set_render_settings(cur_settings,self.start_frm,self.end_frm)\\n\\n start_turn.launch_tool()\",\n \"def load_params(self, event):\\n \\n self.robot_type = rospy.get_param(\\\"robot_type\\\" , 'pendulum' )\\n self.robot_config = rospy.get_param(\\\"robot_config\\\", 'wrist-only' )\\n self.robot_ctl = rospy.get_param(\\\"controller\\\", 'RfixCTC' )\\n self.fixed_mode = rospy.get_param(\\\"fixed_mode\\\", 1 )\\n \\n \\n ###############################################\\n # Load robot model for the right configuration\\n if self.robot_config == 'wrist-only':\\n self.R = Proto.SingleRevoluteDSDM()\\n \\n elif self.robot_config == 'dual-plane' :\\n self.R = Proto.TwoPlanarSerialDSDM()\\n \\n else:\\n self.R = None\\n \\n ###############################################\\n # Load controller\\n if self.robot_ctl == 'RfixCTC' :\\n self.Ctl = RminCTC.RfixComputedTorqueController( self.R , self.fixed_mode )\\n \\n elif self.robot_ctl == 'RminCTC' :\\n self.Ctl = RminCTC.RminComputedTorqueController( self.R )\\n \\n elif self.robot_ctl == 'RfixSLD' :\\n self.Ctl = RminCTC.RfixSlidingModeController( self.R , self.fixed_mode )\\n \\n elif self.robot_ctl == 'RminSLD' :\\n self.Ctl = RminCTC.RminSlidingModeController( self.R )\\n \\n elif self.robot_ctl == 'RollCTC' :\\n self.Ctl = RollCTC.RolloutComputedTorqueController( self.R )\\n \\n elif self.robot_ctl == 'RollSLD' :\\n self.Ctl = RollCTC.RolloutSlidingModeController( self.R )\\n \\n else:\\n self.Ctl = None\\n \\n \\n if self.robot_config == 'wrist-only':\\n self.Ctl.n_gears = rospy.get_param(\\\"n_gears\\\", 2 )\\n self.x_d = np.array( rospy.get_param(\\\"goal\\\", [0,0] ) )\\n \\n elif self.robot_config == 'dual-plane' :\\n self.Ctl.n_gears = rospy.get_param(\\\"n_gears\\\", 4 )\\n self.x_d = np.array( rospy.get_param(\\\"goal\\\", [0.0,0.0,0.0,0.0] ) )\\n #self.x_d = np.array( [-3.14 , 0 , 0 , 0] )\\n \\n # Gen ctl params\\n self.Ctl.hysteresis = rospy.get_param(\\\"hysteresis\\\", True )\\n self.Ctl.min_delay = rospy.get_param(\\\"min_delay\\\", 0.5 )\\n \\n self.Ctl.w0 = rospy.get_param(\\\"w0\\\", 1 )\\n self.Ctl.zeta = rospy.get_param(\\\"zeta\\\", 0.7 )\\n \\n self.Ctl.lam = rospy.get_param(\\\"lam\\\", 1 )\\n self.Ctl.nab = rospy.get_param(\\\"nab\\\", 1 )\\n self.Ctl.D = rospy.get_param(\\\"D\\\", 0 )\\n \\n self.Ctl.horizon = rospy.get_param(\\\"horizon\\\", 0.5 )\\n self.Ctl.sim_dt = rospy.get_param(\\\"sim_dt\\\", 0.1 )\\n \\n self.Ctl.domain_check = rospy.get_param(\\\"domain_check\\\", False )\\n \\n # Base policy param for roll \\n if self.robot_ctl == 'RollCTC' :\\n self.Ctl.FixCtl.lam = self.Ctl.lam\\n \\n elif self.robot_ctl == 'RollSLD' :\\n self.Ctl.FixCtl.lam = self.Ctl.lam \\n self.Ctl.FixCtl.nab = self.Ctl.nab \\n self.Ctl.FixCtl.D = self.Ctl.D\",\n \"def __init__(self):\\n ros_ws_abspath = rospy.get_param(\\\"/drone/ros_ws_abspath\\\", None)\\n assert ros_ws_abspath is not None, \\\"You forgot to set ros_ws_abspath in your yaml file of your main RL script. Set ros_ws_abspath: \\\\'YOUR/SIM_WS/PATH\\\\'\\\"\\n assert os.path.exists(ros_ws_abspath), \\\"The Simulation ROS Workspace path \\\" + ros_ws_abspath + \\\\\\n \\\" DOESNT exist, execute: mkdir -p \\\" + ros_ws_abspath + \\\\\\n \\\"/src;cd \\\" + ros_ws_abspath + \\\";catkin_make\\\"\\n\\n ROSLauncher(rospackage_name=\\\"drone_construct\\\",\\n launch_file_name=\\\"start_world.launch\\\",\\n ros_ws_abspath=ros_ws_abspath)\\n\\n # Load Params from the desired Yaml file\\n LoadYamlFileParamsTest(rospackage_name=\\\"openai_ros\\\",\\n rel_path_from_package_to_file=\\\"src/openai_ros/task_envs/parrotdrone/config\\\",\\n yaml_file_name=\\\"parrotdrone_goto.yaml\\\")\\n\\n # Only variable needed to be set here\\n number_actions = rospy.get_param('/drone/n_actions')\\n self.action_space = spaces.Discrete(number_actions)\\n\\n # We set the reward range, which is not compulsory but here we do it.\\n self.reward_range = (-numpy.inf, numpy.inf)\\n\\n # Actions and Observations\\n self.linear_forward_speed = rospy.get_param(\\n '/drone/linear_forward_speed')\\n self.angular_turn_speed = rospy.get_param('/drone/angular_turn_speed')\\n self.angular_speed = rospy.get_param('/drone/angular_speed')\\n\\n self.init_linear_speed_vector = Vector3()\\n self.init_linear_speed_vector.x = rospy.get_param(\\n '/drone/init_linear_speed_vector/x')\\n self.init_linear_speed_vector.y = rospy.get_param(\\n '/drone/init_linear_speed_vector/y')\\n self.init_linear_speed_vector.z = rospy.get_param(\\n '/drone/init_linear_speed_vector/z')\\n\\n self.init_angular_turn_speed = rospy.get_param(\\n '/drone/init_angular_turn_speed')\\n\\n self.min_sonar_value = rospy.get_param('/drone/min_sonar_value')\\n self.max_sonar_value = rospy.get_param('/drone/max_sonar_value')\\n\\n # Get WorkSpace Cube Dimensions\\n self.work_space_x_max = rospy.get_param(\\\"/drone/work_space/x_max\\\")\\n self.work_space_x_min = rospy.get_param(\\\"/drone/work_space/x_min\\\")\\n self.work_space_y_max = rospy.get_param(\\\"/drone/work_space/y_max\\\")\\n self.work_space_y_min = rospy.get_param(\\\"/drone/work_space/y_min\\\")\\n self.work_space_z_max = rospy.get_param(\\\"/drone/work_space/z_max\\\")\\n self.work_space_z_min = rospy.get_param(\\\"/drone/work_space/z_min\\\")\\n\\n # Maximum RPY values\\n self.max_roll = rospy.get_param(\\\"/drone/max_roll\\\")\\n self.max_pitch = rospy.get_param(\\\"/drone/max_pitch\\\")\\n self.max_yaw = rospy.get_param(\\\"/drone/max_yaw\\\")\\n\\n # Get Desired Point to Get\\n self.desired_point = Point()\\n self.desired_point.x = rospy.get_param(\\\"/drone/desired_pose/x\\\")\\n self.desired_point.y = rospy.get_param(\\\"/drone/desired_pose/y\\\")\\n self.desired_point.z = rospy.get_param(\\\"/drone/desired_pose/z\\\")\\n\\n self.desired_point_epsilon = rospy.get_param(\\n \\\"/drone/desired_point_epsilon\\\")\\n\\n # We place the Maximum and minimum values of the X,Y,Z,R,P,Yof the pose\\n\\n high = numpy.array([self.work_space_x_max,\\n self.work_space_y_max,\\n self.work_space_z_max,\\n self.max_roll,\\n self.max_pitch,\\n self.max_yaw,\\n self.max_sonar_value])\\n\\n low = numpy.array([self.work_space_x_min,\\n self.work_space_y_min,\\n self.work_space_z_min,\\n -1*self.max_roll,\\n -1*self.max_pitch,\\n -numpy.inf,\\n self.min_sonar_value])\\n\\n self.observation_space = spaces.Box(low, high)\\n\\n rospy.logdebug(\\\"ACTION SPACES TYPE===>\\\"+str(self.action_space))\\n rospy.logdebug(\\\"OBSERVATION SPACES TYPE===>\\\" +\\n str(self.observation_space))\\n\\n # Rewards\\n self.closer_to_point_reward = rospy.get_param(\\n \\\"/drone/closer_to_point_reward\\\")\\n self.not_ending_point_reward = rospy.get_param(\\n \\\"/drone/not_ending_point_reward\\\")\\n self.end_episode_points = rospy.get_param(\\\"/drone/end_episode_points\\\")\\n\\n self.cumulated_steps = 0.0\\n\\n # Here we will add any init functions prior to starting the MyRobotEnv\\n super(ParrotDroneGotoEnv, self).__init__(ros_ws_abspath)\",\n \"def __init__(self, env, action_repeat=1):\\n super().__init__(env)\\n if self.env.mujoco_robot.name == \\\"sawyer\\\":\\n from robosuite.controllers import SawyerIKController\\n\\n self.controller = SawyerIKController(\\n bullet_data_path=os.path.join(robosuite.models.assets_root, \\\"bullet_data\\\"),\\n robot_jpos_getter=self._robot_jpos_getter,\\n )\\n elif self.env.mujoco_robot.name == \\\"baxter\\\":\\n from robosuite.controllers import BaxterIKController\\n\\n self.controller = BaxterIKController(\\n bullet_data_path=os.path.join(robosuite.models.assets_root, \\\"bullet_data\\\"),\\n robot_jpos_getter=self._robot_jpos_getter,\\n )\\n else:\\n raise Exception(\\n \\\"Only Sawyer and Baxter robot environments are supported for IK \\\"\\n \\\"control currently.\\\"\\n )\\n\\n self.action_repeat = action_repeat\",\n \"def __init__(self, source, label_config_center, name=None, header=None, \\n view_min=None, view_max=None, alpha=255, colormap='gray',\\n cross_pos=None):\\n if isinstance(source, np.ndarray):\\n self._data = np.rot90(source)\\n if name == None:\\n self._name = 'new_image'\\n else:\\n self._name = str(name)\\n if not isinstance(header, nib.nifti1.Nifti1Header):\\n raise ValueError(\\\"Parameter header must be specified!\\\")\\n elif header.get_data_shape() == source.shape:\\n self._header = header\\n self._img = None\\n else:\\n raise ValueError(\\\"Data dimension does not match.\\\")\\n else:\\n self._img = nib.load(source)\\n self._header = self._img.get_header()\\n basename = os.path.basename(source.strip('/'))\\n self._name = re.sub(r'(.*)\\\\.nii(\\\\.gz)?', r'\\\\1', basename)\\n self.save_mem_load()\\n\\n # For convenience, define a shift variable\\n self._y_shift = self.get_data_shape()[1] - 1\\n\\n if view_min == None:\\n self._view_min = self._data.min()\\n else:\\n self._view_min = view_min\\n\\n if view_max == None:\\n self._view_max = self._data.max()\\n else:\\n self._view_max = view_max\\n\\n self._alpha = alpha\\n self._colormap = colormap\\n self._rgba_list = range(self.get_data_shape()[2])\\n \\n # bool status for the item\\n self._visible = True\\n if len(self.get_data_shape()) == 3:\\n self._4d = False\\n else:\\n self._4d = True\\n self._time_point = 0\\n\\n # temporal variant for OrthView\\n self._cross_pos = cross_pos\\n\\n # define a dictionary \\n self.label_config_center = label_config_center\\n self.label_config_center.single_roi_view_update.connect(self.update_single_roi)\\n \\n # undo redo stacks\\n self.undo_stack = DoStack()\\n self.redo_stack = DoStack()\\n\\n self.update_rgba()\\n if self._cross_pos:\\n self.update_orth_rgba()\",\n \"def __init__(self):\\n self.brainstate = {}\\n\\n self.export = ['run_ai']\",\n \"def setup(self):\\r\\n ScriptedLoadableModuleWidget.setup(self)\\r\\n\\r\\n # Load widget from .ui file (created by Qt Designer).\\r\\n # Additional widgets can be instantiated manually and added to self.layout.\\r\\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/RecordHerniaData.ui'))\\r\\n self.layout.addWidget(uiWidget)\\r\\n self.ui = slicer.util.childWidgetVariables(uiWidget)\\r\\n\\r\\n # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\\r\\n # \\\"mrmlSceneChanged(vtkMRMLScene*)\\\" signal in is connected to each MRML widget's.\\r\\n # \\\"setMRMLScene(vtkMRMLScene*)\\\" slot.\\r\\n uiWidget.setMRMLScene(slicer.mrmlScene)\\r\\n\\r\\n # Create logic class. Logic implements all computations that should be possible to run\\r\\n # in batch mode, without a graphical user interface.\\r\\n self.logic = TMSRecordDataModuleLogic()\\r\\n self.recordingStarted = False\\r\\n self.camerasStarted = False\\r\\n self.moduleDir = os.path.dirname(slicer.modules.tmsrecorddatamodule.path)\\r\\n self.logic.setupScene()\\r\\n\\r\\n # Buttons\\r\\n self.ui.StartStopRecordingButton.connect('clicked(bool)', self.onStartStopRecordingClicked)\\r\\n self.ui.startCamerasButton.connect('clicked(bool)',self.onStartStopCamerasClicked)\",\n \"def setup(args):\\n cfg = get_cfg()\\n\\n cfg.merge_from_file(model_zoo.get_config_file(args.model_zoo))\\n cfg.DATASETS.TRAIN = (args.train_dataset, )\\n cfg.DATASETS.TEST = (args.test_dataset, )\\n cfg.DATALOADER.NUM_WORKERS = args.num_workers\\n cfg.OUTPUT_DIR = args.output_dir\\n os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\\n\\n cfg.image_w = args.size[0]\\n cfg.image_h = args.size[1]\\n\\n cfg.MODEL.WEIGHTS = args.model_zoo_weights\\n cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.roi_thresh # set a custom testing threshold\\n\\n default_setup(cfg, args)\\n return cfg\",\n \"def loadData(self, data=None):\\n\\n super(SimpleControlComponentRig, self).loadData( data )\\n\\n ctrlSize = data.get('ctrlSize', 1.0)\\n ctrlXfo = data.get('ctrlXfo', Xfo())\\n\\n # ================\\n # Resize Controls\\n # ================\\n self.mainCtrl.setShape('square')\\n self.mainCtrl.rotatePoints(90, 0, 0)\\n self.mainCtrl.scalePoints(Vec3(ctrlSize, ctrlSize, ctrlSize))\\n\\n # =======================\\n # Set Control Transforms\\n # =======================\\n self.mainCtrlSpace.xfo = ctrlXfo\\n self.mainCtrl.xfo = ctrlXfo\\n\\n # ============\\n # Set IO Xfos\\n # ============\\n self.mainInputTgt.xfo = ctrlXfo\\n self.mainDef.xfo = ctrlXfo\\n self.outputTgt.xfo = ctrlXfo\\n\\n # ====================\\n # Evaluate Constraints\\n # ====================\\n self.mainInputConstraint.evaluate()\\n self.mainOutputConstraint.evaluate()\\n self.mainDefConstraint.evaluate()\\n\\n # ====================\\n # Evaluate Canvas Ops\\n # ====================\",\n \"def __init__(self, env):\\n super(PlayerOneNetworkControllerWrapper, self).__init__(env)\\n buttons = [\\\"B\\\", \\\"A\\\", \\\"MODE\\\", \\\"START\\\", \\\"UP\\\", \\\"DOWN\\\", \\\"LEFT\\\", \\\"RIGHT\\\", \\\"C\\\", \\\"Y\\\", \\\"X\\\", \\\"Z\\\"]\\n actions = [['LEFT'], ['RIGHT'], ['LEFT', 'DOWN'], ['RIGHT', 'DOWN'],['LEFT', 'UP'],['RIGHT', 'UP'],\\n ['DOWN', 'B'],['LEFT', 'UP'],['RIGHT', 'DOWN','B'],['RIGHT', 'DOWN','A'],\\n ['RIGHT', 'UP','B'],['RIGHT', 'UP','A'],['RIGHT', 'UP','C'],\\n ['LEFT', 'UP','B'],['LEFT', 'UP','A'],['LEFT', 'UP','C'],\\n ['C'],['START'], ['B'],['Y'],['X'],['Z'],['A'],['UP'],['MODE']]\\n self._actions = []\\n for action in actions:\\n arr = np.array([False] * 12)\\n for button in action:\\n arr[buttons.index(button)] = True\\n self._actions.append(arr)\\n self.action_space = gym.spaces.Discrete(len(self._actions))\",\n \"def handle(self, args, unknown):\\n\\n settings = Dict2Obj(**runpy.run_path(\\\"%s/%s\\\" % (os.getcwd(), 'settings.py')))\\n project_name = os.getcwd().split('/')[-1]\\n extra_args = self.parse_unknown(unknown)\\n Train(project_name=project_name, settings=settings, args=args, **extra_args).begin()\",\n \"def build_guide(self, **kwargs):\\n\\n # This builds your guide master and updates your options\\n self.create_guide_master(**kwargs)\\n\\n prefix = self.prefix # Naming prefix. Use this for every new node you create and there should be no name clashes.\\n options = self.options # Build options\\n mirror_value = self.mirror_value # 1.0 for left and center sided parts and -1.0 for right sided part.\\n\\n mc.setAttr(self.guide_master+'.offsetTranslateY', -0.2)\\n\\n l_prefix = prefix.replace('C','L', 1)\\n r_prefix = prefix.replace('C','R', 1)\\n mirror_values = [1, -1]\\n enable_steering = options.get('enableSteering')\\n\\n colors = ['green', 'red']\\n\\n for mi, prefix in enumerate([l_prefix, r_prefix]):\\n\\n mirror_value = mirror_values[mi]\\n color = colors[mi]\\n\\n l_main_zero, l_main_plc = self.guide_joint('main', alt_prefix=prefix, placer_only=1)\\n\\n # create hub\\n hub_zero, hub_plc, hub_jnt = self.guide_joint('wheelhub', alt_prefix=prefix, constraint_type='point')\\n hub_end_zero, hub_end_plc, hub_end_jnt = self.guide_joint('wheelhub_end', alt_prefix=prefix, constraint_type='point')\\n\\n mc.xform(hub_end_zero, r=1, t=[1,0,0])\\n mc.parent(hub_end_jnt, hub_jnt)\\n mc.aimConstraint(hub_end_plc, hub_jnt, aim=[mirror_value,0,0], u=[0,1,0], wu=[0,1,0], wut='vector')\\n mc.parentConstraint(hub_plc, hub_end_zero , mo=1)\\n\\n # Create steering arm\\n steer_zero, steer_plc, steer_jnt = self.guide_joint('steeringArm', alt_prefix=prefix, constraint_type='parent')\\n mc.xform(steer_zero, r=1, t=[-1,0,0])\\n mc.parent(hub_jnt, steer_jnt)\\n\\n # Create shocks\\n shock_a_zero, shock_a_plc, shock_a_jnt = self.guide_joint('shock_A', alt_prefix=prefix, constraint_type='point')\\n shock_b_zero, shock_b_plc, shock_b_jnt = self.guide_joint('shock_B', alt_prefix=prefix, constraint_type='point')\\n\\n mc.xform(shock_a_zero, ws=1, t=[-2,2,0])\\n mc.xform(shock_b_zero, ws=1, t=[-0.5,0.25,0])\\n\\n mc.parent(shock_b_jnt, shock_a_jnt)\\n\\n mc.aimConstraint(shock_b_plc, shock_a_jnt, aim=[mirror_value,0,0], u=[0,0,1], wu=[0,0,1], wut='vector')\\n mc.aimConstraint(shock_a_plc, shock_b_jnt, aim=[-mirror_value,0,0], u=[0,0,1], wu=[0,0,1], wut='vector')\\n\\n # upper arm\\n up_arm_zero, up_arm_plc, up_arm_jnt = self.guide_joint('upperArm', alt_prefix=prefix, constraint_type='point')\\n up_arm_end_zero, up_arm_end_plc, up_arm_end_jnt = self.guide_joint('upperArm_end', alt_prefix=prefix, constraint_type='point')\\n\\n mc.xform(up_arm_end_zero, r=1, t=[-3.5,1,0])\\n mc.xform(up_arm_zero, r=1, t=[-1,0.5,0])\\n mc.parent(up_arm_end_jnt, up_arm_jnt)\\n mc.aimConstraint(up_arm_end_plc, up_arm_jnt, aim=[mirror_value,0,0], u=[0,0,1], wu=[0,0,mirror_value], wut='objectRotation', wuo=up_arm_plc)\\n\\n # lower arm\\n lo_arm_zero, lo_arm_plc, lo_arm_jnt = self.guide_joint('lowerArm', alt_prefix=prefix, constraint_type='point')\\n lo_arm_end_zero, lo_arm_end_plc, lo_arm_end_jnt = self.guide_joint('lowerArm_end', alt_prefix=prefix, constraint_type='point')\\n\\n mc.xform(lo_arm_end_zero, r=1, t=[-4,-0.5,0])\\n mc.xform(lo_arm_zero, r=1, t=[-1,-0.5,0])\\n mc.parent(lo_arm_end_jnt, lo_arm_jnt)\\n mc.aimConstraint(lo_arm_end_plc, lo_arm_jnt, aim=[mirror_value,0,0], u=[0,0,1], wu=[0,0,mirror_value], wut='objectRotation', wuo=lo_arm_plc)\\n\\n # steeringArm\\n if enable_steering:\\n steeringArm_a_zero, steeringArm_a_plc, steeringArm_a_jnt = self.guide_joint('steeringArm_A', alt_prefix=prefix, constraint_type='point')\\n steeringArm_b_zero, steeringArm_b_plc, steeringArm_b_jnt = self.guide_joint('steeringArm_B', alt_prefix=prefix, constraint_type='point')\\n\\n mc.xform(steeringArm_b_zero, r=1, t=[-1.5,0,1])\\n mc.xform(steeringArm_a_zero, r=1, t=[-4,0,1])\\n\\n mc.parent(steeringArm_b_jnt, steeringArm_a_jnt)\\n mc.aimConstraint(steeringArm_b_plc, steeringArm_a_jnt, aim=[mirror_value,0,0], u=[0,0,1], wu=[0,0,1], wut='vector')\\n\\n # Create control\\n zero, ctrl = self.guide_ctrl('wheel', alt_prefix=prefix, driver=hub_end_jnt, color=color, shape='circle', axis='X', scale=[3]*3, create_pivot=0)\\n mc.setAttr(ctrl+'.numOffsetCtrls', 1)\\n mc.addAttr(ctrl+'.numOffsetCtrls', e=1, min=1)\\n mc.xform(ctrl.replace('_CTL','_A_OFF_CTL.cv[*]'), r=1, s=[0.8]*3)\\n\\n control.create_shape('wheel', ctrl, axis='X', scale=[3]*3)\\n\\n #suspension_zero, suspension_ctrl = self.guide_ctrl('suspension', create_pivot=0, driver=shock_a_jnt, axis='X', shape='pyramid', color=color, scale=[1.5,1,1], alt_prefix=prefix)\\n ground_zero, ground_ctrl = self.guide_ctrl('ground', create_pivot=0, shape='square', color='grass', alt_prefix=prefix)\\n mc.delete(mc.pointConstraint(hub_jnt, ground_zero))\\n\\n # constraint to placer\\n childs = [prefix+'_wheelhub_JNT_PLC_ZERO',\\n prefix+'_steeringArm_JNT_PLC_ZERO',\\n prefix+'_shock_A_JNT_PLC_ZERO',\\n prefix+'_shock_B_JNT_PLC_ZERO',\\n prefix+'_upperArm_JNT_PLC_ZERO',\\n prefix+'_upperArm_end_JNT_PLC_ZERO',\\n prefix+'_lowerArm_JNT_PLC_ZERO',\\n prefix+'_lowerArm_end_JNT_PLC_ZERO']\\n\\n for c in childs:\\n mc.parentConstraint(l_main_plc, c, mo=1)\\n\\n mc.setAttr(l_main_plc+'.offsetTranslateY', mirror_value*0.5)\\n\\n # ################3\\n # Place it all\\n hub_pos = mc.ls(options.get('hubCenter') or '')\\n if hub_pos:\\n loc = utils.snap_locator(hub_pos)\\n mc.delete(mc.pointConstraint(loc, self.guide_master))\\n mc.setAttr(self.guide_master+'.tx', 0)\\n mc.delete(mc.pointConstraint(loc, l_main_plc), loc)\\n\\n hub_end_pos = mc.ls(options.get('hubEndCenter') or '')\\n if hub_end_pos:\\n loc = utils.snap_locator(hub_end_pos)\\n mc.delete(mc.pointConstraint(loc, hub_end_plc), loc)\\n\\n else:\\n mc.xform(self.guide_master, ws=1, t=[0,2,10])\\n mc.xform(l_main_plc, r=1, t=[mirror_value*6,0,0])\\n\\n mc.setAttr(self.guide_master+'.jointAxisVis', 1)\\n\\n l = utils.snap_locator(hub_jnt)\\n mc.setAttr(l+'.ty', 0)\\n mc.delete(mc.pointConstraint(l, ground_zero), l)\\n\\n chassis_plc_zero, chassis_plc = self.guide_joint('chassis_driver', placer_only=1)\\n mc.setAttr(chassis_plc+'.radius', 1)\\n mc.setAttr(chassis_plc+'.color', 0.96, 0.71, .01)\\n mc.setAttr(chassis_plc+'.otherType', 'Leg IK Driver', type='string');\\n mc.setAttr(chassis_plc+'.type', 18)\\n\\n mc.pointConstraint(l_prefix+'_lowerArm_end_JNT_PLC', r_prefix+'_lowerArm_end_JNT_PLC', chassis_plc_zero)\\n utils.set_attrs(chassis_plc, l=1, k=0)\\n\\n # This finalizes your guide.\\n self.finalize_guide()\\n self.mirror_guide()\",\n \"def loadDefault(self):\\n self.addStart()\",\n \"def importMasterSettings(self):\\n\\t\\tpickleData = pickle.load( open( self.masterPath.path, \\\"rb\\\" ) )\\n\\t\\tmaster = rlayer.RenderLayer( 'defaultRenderLayer' )\\n\\t\\tmaster.makeCurrent()\\n\\t\\tfor a in pickleData.keys():\\n\\t\\t\\ttry:\\n\\t\\t\\t\\ta.v = pickleData[a]\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tcontinue\",\n \"def set_default_params(self):\\n print('------------------')\\n print('Setting default parameters with file ', self.input_file_name)\\n if 'ssephem' not in self.__dict__:\\n self.__dict__['ssephem'] = 'DE436'\\n print('Setting default Solar System Ephemeris: DE436')\\n if 'clock' not in self.__dict__:\\n self.__dict__['clock'] = None\\n print('Setting a default Enterprise clock convention (check the code)')\\n if 'setupsamp' not in self.__dict__:\\n self.__dict__['setupsamp'] = False\\n if 'psrlist' in self.__dict__:\\n self.psrlist = np.loadtxt(self.psrlist, dtype=np.unicode_)\\n print('Only using pulsars from psrlist')\\n else:\\n self.__dict__['psrlist'] = []\\n print('Using all available pulsars from .par/.tim directory')\\n if 'psrcachefile' not in self.__dict__:\\n self.psrcachefile = None\\n if 'tm' not in self.__dict__:\\n self.tm = 'default'\\n print('Setting a default linear timing model')\\n if 'inc_events' not in self.__dict__:\\n self.inc_events = True\\n print('Including transient events to specific pulsar models')\\n if 'fref' not in self.__dict__:\\n self.fref = 1400 # MHz\\n print('Setting reference radio frequency to 1400 MHz')\\n if 'mcmc_covm_csv' in self.__dict__ and os.path.isfile(self.mcmc_covm_csv):\\n print('MCMC jump covariance matrix is available')\\n self.__dict__['mcmc_covm'] = pd.read_csv(self.mcmc_covm_csv, index_col=0)\\n else:\\n self.__dict__['mcmc_covm'] = None\\n # Copying default priors from StandardModels/CustomModels object\\n # Priors are chosen not to be model-specific because HyperModel\\n # (which is the only reason to have multiple models) does not support\\n # different priors for different models\\n for prior_key, prior_default in self.noise_model_obj().priors.items():\\n if prior_key not in self.__dict__.keys():\\n self.__dict__[prior_key] = prior_default\\n\\n # Model-dependent parameters\\n for mkey in self.models:\\n\\n self.models[mkey].modeldict = dict()\\n\\n print('------------------')\",\n \"def set_up_scenes():\\n cmd.zoom('Cathepsin', 10) # Zoom out to get a view on the whole complex\\n cmd.scene('001', 'store', message='This is the first scene with a view on the complex!')\\n cmd.set_view(closeup) # Get a close-up of the ligand by using the manually chosen viewpoint\\n cmd.scene('002', 'store', message='This is the second scene with a close-up on the ligand!')\",\n \"def setup_populated_instr_McXtrace():\\n instr = setup_instr_root_path_McXtrace()\\n\\n instr.add_parameter(\\\"double\\\", \\\"theta\\\")\\n instr.add_parameter(\\\"double\\\", \\\"has_default\\\", value=37)\\n instr.add_declare_var(\\\"double\\\", \\\"two_theta\\\")\\n instr.append_initialize(\\\"two_theta = 2.0*theta;\\\")\\n\\n instr.add_component(\\\"first_component\\\", \\\"test_for_reading\\\")\\n instr.add_component(\\\"second_component\\\", \\\"test_for_reading\\\")\\n instr.add_component(\\\"third_component\\\", \\\"test_for_reading\\\")\\n\\n return instr\",\n \"def set_parameters(self, args):\\n self.args = args\\n\\n if args.testing:\\n self.delay_close()\\n\\n if args.source == \\\"simulation\\\":\\n log.info(\\\"Create simulated spectra device\\\")\\n self.dev = simulation.SimulatedSpectraDevice()\\n\\n elif args.source == \\\"sled\\\":\\n log.info(\\\"Create single sled cobra\\\")\\n self.dev = simulation.SimulatedCobraSLED()\\n\\n elif args.source == \\\"cobra\\\":\\n log.info(\\\"Create DALSA cobra device\\\")\\n #self.dev = devices.DalsaCobraDevice()\\n self.dev = DALSA.Cobra()\\n\\n elif args.source == \\\"opto\\\":\\n log.info(\\\"Create OPTO sensor cobra device\\\")\\n self.dev = DALSA.OPTOCobra()\\n\\n elif args.source == \\\"basler\\\":\\n log.info(\\\"Create DALSA basler device\\\")\\n #self.dev = devices.DalsaBaslerDevice()\\n self.dev = DALSA.BaslerSprint4K()\\n\\n self.dev.setup_pipe()\\n self.setup_pipe_timer()\",\n \"def __init__(self):\\r\\n ScriptedLoadableModuleLogic.__init__(self)\\r\\n self.rgbport = 18944\\r\\n self.depthPort = 18945\",\n \"def __init__(self, io, default_camera='right'):\\n parts.part.ReachyPart.__init__(self, name='head', io=io)\\n\\n #self.neck = self.create_orbita_actuator('neck', Head.orbita_config)\\n self.attach_dxl_motors(parts.Head.dxl_motors)\\n #self.camera = self.io.find_dual_camera(default_camera)\",\n \"def __set_control_elements(*args):\\n args[0].Controls.valve_number = args[1]\\n args[0].Controls.set_valve_number()\\n args[0].Controls.go_elements = args[2]\\n args[0].Controls.set_go_elements()\",\n \"def import_control_section(self, filename_suffix='run'):\\n pass\",\n \"def __setup_ui_controls(self, list_of_names):\\n\\n ##################################################\\n self.scene.append_to_caption('<h3>Scene Settings</h3>\\\\n')\\n # Button to reset camera\\n reset_button = button(\\n bind=self.__reset_camera, text=\\\"Reset Camera\\\")\\n self.__ui_controls.btn_reset = reset_button\\n self.scene.append_to_caption('\\\\t')\\n\\n screenshot_button = button(bind=self.__screenshot, text=\\\"Take Screenshot\\\")\\n self.__ui_controls.btn_ss = screenshot_button\\n self.scene.append_to_caption('\\\\n')\\n\\n camera_lock_checkbox = checkbox(bind=self.__camera_lock_checkbox, text=\\\"Camera Lock\\\",\\n checked=self.__camera_lock)\\n self.__ui_controls.chkbox_cam = camera_lock_checkbox\\n self.scene.append_to_caption('\\\\t')\\n\\n grid_relative_checkbox = checkbox(\\n bind=self.__grid_relative_checkbox,\\n text=\\\"Grid Relative\\\", checked=self.__grid_relative)\\n self.__ui_controls.chkbox_rel = grid_relative_checkbox\\n\\n self.scene.append_to_caption('\\\\t')\\n # Checkbox for grid visibility\\n checkbox_grid_visibility = checkbox(\\n bind=self.__grid_visibility_checkbox, text=\\\"Grid Visibility\\\",\\n checked=self.__grid_visibility)\\n self.__ui_controls.chkbox_grid = checkbox_grid_visibility\\n self.scene.append_to_caption('\\\\n')\\n\\n ##################################################\\n self.scene.append_to_caption('<h3>Robot</h3>\\\\n')\\n # Drop down for robots / joints in frame\\n menu_robots_list = menu(bind=self.__menu_item_chosen, choices=list_of_names)\\n if not len(list_of_names) == 0:\\n menu_robots_list.index = self.__selected_robot\\n self.__ui_controls.menu_robots = menu_robots_list\\n self.scene.append_to_caption('\\\\t')\\n\\n # Button to delete the selected robot\\n delete_button = button(bind=self.__del_robot, text=\\\"Delete Robot\\\")\\n self.__ui_controls.btn_del = delete_button\\n self.scene.append_to_caption('\\\\t')\\n\\n # Button to clear the robots in screen\\n clear_button = button(bind=self.clear_scene, text=\\\"Clear Scene\\\")\\n self.__ui_controls.btn_clr = clear_button\\n self.scene.append_to_caption('\\\\n')\\n\\n ##################################################\\n self.scene.append_to_caption('<h3>Characteristics</h3>\\\\n')\\n # Checkbox for reference frame visibilities\\n if len(self.__robots) == 0:\\n reference_checkbox = checkbox(\\n bind=self.__reference_frame_checkbox,\\n text=\\\"Show Reference Frames\\\", checked=True)\\n else:\\n chk = self.__robots[self.__selected_robot].ref_shown\\n reference_checkbox = checkbox(\\n bind=self.__reference_frame_checkbox,\\n text=\\\"Show Reference Frames\\\", checked=chk)\\n self.__ui_controls.chkbox_ref = reference_checkbox\\n self.scene.append_to_caption('\\\\t')\\n\\n # Checkbox for robot visibility\\n if len(self.__robots) == 0:\\n robot_vis_checkbox = checkbox(\\n bind=self.__robot_visibility_checkbox,\\n text=\\\"Show Robot\\\", checked=True)\\n else:\\n chk = self.__robots[self.__selected_robot].rob_shown\\n robot_vis_checkbox = checkbox(\\n bind=self.__robot_visibility_checkbox,\\n text=\\\"Show Robot\\\", checked=chk)\\n self.__ui_controls.chkbox_rob = robot_vis_checkbox\\n self.scene.append_to_caption('\\\\n')\\n\\n # Slider for robot opacity\\n self.scene.append_to_caption('Robot Opacity:')\\n if len(self.__robots) == 0:\\n opacity_slider = slider(bind=self.__opacity_slider, value=1)\\n else:\\n opc = self.__robots[self.__selected_robot].opacity\\n opacity_slider = slider(bind=self.__opacity_slider, value=opc)\\n self.__ui_controls.sld_opc = opacity_slider\\n self.scene.append_to_caption('\\\\n')\\n\\n ##################################################\\n # Control manual\\n controls_str = '<h3>Controls</h3><br>' \\\\\\n '<b>PAN</b><br>' \\\\\\n 'W , S | <i>forward / backward</i><br>' \\\\\\n 'A , D | <i>left / right</i><br>' \\\\\\n 'SPACE , SHIFT | <i>up / down</i><br>' \\\\\\n '<b>ROTATE</b><br>' \\\\\\n 'CTRL + LMB | <i>free spin</i><br>' \\\\\\n 'ARROWS KEYS | <i>rotate direction</i><br>' \\\\\\n 'Q , E | <i>roll left / right</i><br>' \\\\\\n '<b>ZOOM</b></br>' \\\\\\n 'MOUSEWHEEL | <i>zoom in / out</i>'\\n\\n self.scene.append_to_caption(controls_str)\",\n \"def import_forward(self):\\n self.import_property('OG')\\n self.import_property('IBU')\\n self.import_property('ABV')\\n self.import_property('SRM')\",\n \"def __init__(self, settings, name, linear_scaling_factor=0.1, angular_scaling_factor=0.1):\\n super(ArmControlInterpreter, self).__init__(rate=0.033)\\n\\n self.settings = settings\\n\\n self.linear_scaling_factor = linear_scaling_factor\\n self.angular_scaling_factor = angular_scaling_factor\\n\\n self.arm_velocity_client = actionlib.SimpleActionClient('/arm_controller/velocity', rose_arm_controller_msgs.msg.set_velocityAction)\\n self.arm_gripper_client = actionlib.SimpleActionClient('/arm_controller/gripper_width', rose_arm_controller_msgs.msg.set_gripper_widthAction)\\n\\n self.arm_name = name\\n\\n self.velocity_goal = set_velocityGoal()\\n self.gripper_goal = set_gripper_widthGoal()\\n\\n self.gripper_width = ArmControlInterpreter.gripper_open\\n self.open_close_toggle = self.settings[\\\"open_close\\\"]\",\n \"def __init__(self):\\n self.label = \\\"RAPID Tools\\\"\\n self.alias = \\\"RAPIDTools\\\"\\n\\n # List of tool classes associated with this toolbox\\n self.tools = [AddSPTFields,\\n AutomaticRAPIDfileGenerator, \\n CopyDataToServer,\\n CreateNetworkConnectivityFile,\\n CreateNetworkConnectivityFileNHDPlus,\\n CreateMuskingumParameterFiles,\\n CreateMuskingumKFile,\\n CreateMuskingumKfacFile,\\n CreateMuskingumXField, \\n CreateMuskingumXFile, \\n CreateRivIDGageFile, \\n CreateSubsetFile,\\n CreateWeightTableFromWRFGeogrid,\\n CreateInflowFileFromWRFHydroRunoff,\\n CreateWeightTableFromECMWFRunoff,\\n CreateInflowFileFromECMWFRunoff,\\n CreateWeightTableFromLDASRunoff,\\n CreateWeightTableFrom2DLatLonRunoff,\\n CreateDischargeTable,\\n CreateDischargeMap,\\n FlowlineToPoint,\\n DEMtoStreamNetwork,\\n PublishDischargeMap,\\n StreamNetworktoRAPID,\\n StreamNetworktoSPT,\\n UpdateWeightTable,\\n UpdateDischargeMap,\\n ]\",\n \"def __init__(self, root, annFile, feature_file, caption_idx):\\n\\t\\timport torch\\n\\n\\t\\tsuper().__init__(root, annFile)\\n\\t\\tself.features = torch.load(feature_file, map_location='cpu')\\n\\t\\tself.caption_idx = caption_idx\",\n \"def zero_target(*args):\\n robots = get_robot_roots()\\n if not robots:\\n pm.warning('Nothing Selected; Select a valid robot')\\n return\\n\\n try:\\n for robot in robots:\\n target_ctrl_path = get_target_ctrl_path(robot)\\n tool_ctrl_path = get_tool_ctrl_path(robot)\\n\\n ik_mode = pm.getAttr(target_ctrl_path + '.ik')\\n\\n if ik_mode:\\n if pm.objExists(tool_ctrl_path):\\n pm.setAttr(tool_ctrl_path + '.translate', 0, 0, 0)\\n pm.setAttr(tool_ctrl_path + '.rotate', 0, 0, 0)\\n else:\\n pm.setAttr(target_ctrl_path + '.translate', 0, 0, 0)\\n pm.setAttr(target_ctrl_path + '.rotate', 0, 0, 0)\\n else:\\n a1_fk_ctrl_path = format_path(__A1_FK_CTRL_PATH, robot)\\n a2_fk_ctrl_path = format_path(__A2_FK_CTRL_PATH, robot)\\n a3_fk_ctrl_path = format_path(__A3_FK_CTRL_PATH, robot)\\n a4_fk_ctrl_path = format_path(__A4_FK_CTRL_PATH, robot)\\n a5_fk_ctrl_path = format_path(__A5_FK_CTRL_PATH, robot)\\n a6_fk_ctrl_path = format_path(__A6_FK_CTRL_PATH, robot)\\n\\n pm.setAttr(a1_fk_ctrl_path + '.rotateY', 0)\\n pm.setAttr(a2_fk_ctrl_path + '.rotateX', 0)\\n pm.setAttr(a3_fk_ctrl_path + '.rotateX', 0)\\n pm.setAttr(a4_fk_ctrl_path + '.rotateZ', 0)\\n pm.setAttr(a5_fk_ctrl_path + '.rotateX', 0)\\n pm.setAttr(a6_fk_ctrl_path + '.rotateZ', 0)\\n except:\\n pm.warning('Cannot zero target')\",\n \"def __init__(self, meta: SceneDescription):\\n super().__init__(meta)\\n self.scenes = []\\n self.nodes = []\\n self.meshes = []\\n self.materials = []\\n self.images = []\\n self.samplers = []\\n self.textures = []\\n\\n self.path = None\\n self.scene = None\\n self.gltf = None\",\n \"def __init__(self, qasmsourcelines,\\n name='main',\\n filepath=None,\\n no_unknown=False,\\n save_pgm_source=False, save_element_source=False,\\n save_gate_source=False,\\n show_gate_decls=False,\\n include_path='.'):\\n\\n # Control factors\\n self.no_unknown = no_unknown\\n self.save_pgm_source = save_pgm_source\\n self.save_element_source = save_element_source\\n self.save_gate_source = save_gate_source\\n self.show_gate_decls = show_gate_decls\\n self.include_path = include_path\\n\\n # Init sections\\n self.t_sect = T_Sect(name)\\n self.c_sect = C_Sect()\\n self.g_sect = G_Sect()\\n\\n if save_pgm_source is None:\\n self.s_sect = None\\n else:\\n self.s_sect = S_Sect()\\n\\n self.translation = {\\n 't_sect': self.t_sect.t_sect,\\n 'c_sect': self.c_sect.c_sect,\\n 'g_sect': self.g_sect.g_sect,\\n 's_sect': self.s_sect.s_sect\\n }\\n\\n # Prepare to process initial source\\n self.source_frame_stack = Source_Frame_Stack()\\n self.push_source(filepath, qasmsourcelines)\",\n \"def __init__(self):\\n self.label = \\\"Data Assistant\\\"\\n self.alias = \\\"dla\\\"\\n\\n # List of tool classes associated with this toolbox\\n self.tools = [Append, Stage, NewFile, Preview, Replace]\",\n \"def _setup_pipeline_cfg(self):\",\n \"def setupConnections(self):\\n self.T1Button.connect('clicked(bool)', self.onApplyButton)\\n self.RViewButton.connect('clicked(bool)', self.onApplyRViewButton)\\n self.CheckButton.connect('stateChanged(int)', self.onCheckbuttonChecked)\\n self.LLE_Selector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.onSelectLLENode)\\n self.LLN_Selector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.onSelectLLNNode)\\n self.Aref_Selector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.onSelectArefNode)\\n\\n self.ThSlider_LLE.Slider.connect(\\\"positionsChanged(double,double)\\\",self.ThSlider_LLE.onSliderChanged)\\n self.ThSlider_LLE.SpinBoxL.connect(\\\"valueChanged(int)\\\", self.ThSlider_LLE.onSpinBoxLChanged)\\n self.ThSlider_LLE.SpinBoxR.connect(\\\"valueChanged(int)\\\", self.ThSlider_LLE.onSpinBoxRChanged)\\n \\n self.ThSlider_LLN.Slider.connect(\\\"positionsChanged(double,double)\\\",self.ThSlider_LLN.onSliderChanged)\\n self.ThSlider_LLN.SpinBoxL.connect(\\\"valueChanged(int)\\\", self.ThSlider_LLN.onSpinBoxLChanged)\\n self.ThSlider_LLN.SpinBoxR.connect(\\\"valueChanged(int)\\\", self.ThSlider_LLN.onSpinBoxRChanged) \\n\\n self.ThSlider_ECV.Slider.connect(\\\"positionsChanged(double,double)\\\",self.ThSlider_ECV.onSliderChanged)\\n self.ThSlider_ECV.SpinBoxL.connect(\\\"valueChanged(int)\\\", self.ThSlider_ECV.onSpinBoxLChanged)\\n self.ThSlider_ECV.SpinBoxR.connect(\\\"valueChanged(int)\\\", self.ThSlider_ECV.onSpinBoxRChanged)\\n \\n self.Stats.segmentationSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.Stats.onScalarSelectorChanged)\\n self.Stats.scalarSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.Stats.onScalarSelectorChanged)\\n self.Stats.scalarSelector2.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.Stats.onScalarSelector2Changed)\\n self.Stats.SButton.connect('clicked(bool)', self.onApplyGetStatistics)\\n\\n self.NativeT1_Selector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.onSelectNT1Node)\\n self.EnhancedT1_Selector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.onSelectET1Node)\\n self.SB_NBlodd.connect(\\\"valueChanged(Double)\\\", self.onSpinBoxNBChanged)\\n self.SB_EBlodd.connect(\\\"valueChanged(Double)\\\", self.onSpinBoxEBChanged)\\n self.SB_Haematocrit.connect(\\\"valueChanged(Double)\\\", self.onSpinBoxHChanged)\\n self.ECVButton.connect('clicked(bool)',self.onApplyECVButton)\",\n \"def config_extra_settings(self, data_dir):\\n # load data directory configuration\\n self.label_path = data_dir\\n self.label_config_dir = os.path.join(self.label_path, 'labelconfig')\\n self.label_config_suffix = 'lbl'\\n\\n # set icon configuration\\n self._icon_dir=get_icon_dir()\\n\\n # set window title\\n self.setWindowTitle('FreeROI')\\n #self.resize(1280, 1000)\\n self.center()\\n # set window icon\\n self.setWindowIcon(QIcon(os.path.join(self._icon_dir,'icon.png')))\\n\\n self._init_configuration()\\n self._init_label_config_center()\\n\\n # create actions\\n self._create_actions()\\n\\n # create menus\\n self._create_menus()\",\n \"def load(settings):\\n\\n aircraft_file = settings.paths('f_aircraft')\\n logger.info(f\\\"Reading aircraft model from file '{truncate_filepath(aircraft_file)}'...\\\")\\n\\n if not os.path.exists(aircraft_file):\\n raise IOError(f\\\"file '{aircraft_file}' not found\\\")\\n\\n with open(aircraft_file, 'r') as fp:\\n aircraft_dict = json.load(fp)\\n\\n # ====== Aircraft top level =====\\n aircraft = Aircraft()\\n aircraft.uid = aircraft_dict['uid']\\n\\n for key, value in aircraft_dict['refs'].items():\\n aircraft.refs[key] = value\\n\\n # ====== Wings =====\\n for wing_entry in aircraft_dict['wings']:\\n wing = aircraft.add_wing(wing_entry['uid'])\\n wing.symmetry = wing_entry['symmetry']\\n\\n # ====== Segments =====\\n for segment_entry in wing_entry['segments']:\\n segment = wing.add_segment(segment_entry['uid'])\\n\\n for key, value in segment_entry['vertices'].items():\\n segment.vertices[key] = value\\n\\n if segment_entry.get('geometry', None):\\n for key, value in segment_entry['geometry'].items():\\n segment.geometry[key] = value\\n\\n for key, value in segment_entry['airfoils'].items():\\n # From now on use the absolute file path\\n if value.startswith(PATHS.DIR.AIRFOILS):\\n value = os.path.join(settings.paths('root'), value)\\n segment.airfoils[key] = value\\n\\n if segment_entry.get('panels', None):\\n for key, value in segment_entry['panels'].items():\\n segment.panels[key] = value\\n\\n # ====== Controls =====\\n for control_entry in wing_entry.get('controls', []):\\n control = wing.add_control(control_entry['uid'])\\n control.device_type = control_entry['device_type']\\n control.deflection = control_entry['deflection']\\n control.deflection_mirror = control_entry.get('deflection_mirror', None)\\n\\n for key, value in control_entry['segment_uid'].items():\\n control.segment_uid[key] = value\\n\\n for key, value in control_entry['rel_vertices'].items():\\n control.rel_vertices[key] = value\\n\\n for key, value in control_entry['rel_hinge_vertices'].items():\\n control.rel_hinge_vertices[key] = value\\n\\n control.panels['num_c'] = control_entry['panels']['num_c']\\n control.check()\\n\\n aircraft.generate()\\n return aircraft\",\n \"def __init__(self):\\n self.label = \\\"Neural network input files\\\"\\n self.description = \\\"Use this tool to create the input ASCII files for the GeoXplore neural network. Before using this tool, the evidence must be combined into a unique conditions raster with the Combine tool and the band statistics must be obtained for all the evidence using the Band Collection Statistics tool. If desired fuzzy membership attribute can be added to each of the training sites. See the ArcMap Tools Options discussion in Usage Tips in the Help about adjusting default setting for this tool.\\\"\\n self.canRunInBackground = False\\n self.category = \\\"Neural network\\\"\",\n \"def setup(self, initdir, settings):\\n\\n print(\\\"Moving files into initial run directory:\\\", initdir)\\n initdir = os.path.abspath(initdir)\\n for p in os.listdir(self.relaxdir):\\n if (p in (io.VASP_INPUT_FILE_LIST + self.settings[\\\"extra_input_files\\\"])) and (os.path.join(self.relaxdir, p) != initdir):\\n os.rename(os.path.join(self.relaxdir,p), os.path.join(initdir,p))\\n print(\\\"\\\")\\n sys.stdout.flush()\\n\\n # Keep a backup copy of the base INCAR\\n shutil.copyfile(os.path.join(initdir,\\\"INCAR\\\"),os.path.join(self.relaxdir,\\\"INCAR.base\\\"))\\n\\n # If an initial incar is called for, copy it in and set the appropriate flag\\n if (self.settings[\\\"initial\\\"] != None) and (os.path.isfile(os.path.join(self.relaxdir,self.settings[\\\"initial\\\"]))):\\n new_values = io.Incar(os.path.join(self.relaxdir,self.settings[\\\"initial\\\"])).tags\\n io.set_incar_tag(new_values, initdir)\\n print(\\\" Set INCAR tags:\\\", new_values, \\\"\\\\n\\\")\\n sys.stdout.flush()\",\n \"def settings(args):\\n data = {}\\n data['train_x'] = load_pkl(os.path.join(args.data_dir, 'train_images.pkl'))\\n data['train_y'] = load_pkl(os.path.join(args.data_dir, 'train_labels.pkl'))\\n data['valid_x'] = load_pkl(os.path.join(args.data_dir, 'valid_images.pkl'))\\n data['valid_y'] = load_pkl(os.path.join(args.data_dir, 'valid_labels.pkl'))\\n if args.combine_train_val:\\n data['train_x'].update(data['valid_x'])\\n data['train_y'].update(data['valid_y'])\\n data['valid_x'] = load_pkl(os.path.join(args.data_dir, 'test_images.pkl'))\\n data['valid_y'] = load_pkl(os.path.join(args.data_dir, './data/bsd_pkl_float/test_labels.pkl'))\\n args.display_step = len(data['train_x']) / 46\\n # Default configuration\\n if args.default_settings:\\n args.n_epochs = 250\\n args.batch_size = 10\\n args.learning_rate = 3e-2\\n args.std_mult = 0.8\\n args.delay = 8\\n args.filter_gain = 2\\n args.filter_size = 5\\n args.n_rings = 4\\n args.n_filters = 7\\n args.save_step = 5\\n args.height = 321\\n args.width = 481\\n\\n args.n_channels = 3\\n args.lr_div = 10.\\n args.augment = True\\n args.sparsity = True\\n\\n args.test_path = args.save_name\\n args.log_path = './logs'\\n args.checkpoint_path = './checkpoints'\\n\\n make_dirs(args, args.test_path)\\n make_dirs(args, args.log_path)\\n make_dirs(args, args.checkpoint_path)\\n\\n return args, data\",\n \"def create_net(args):\\n\\n # Load config file for this experiment\\n xinfo = yaml.load(open(args.exp)) # experiment info\\n\\n # copy config to run directory\\n assert osp.isdir(args.cache_dir), 'Working directory not found: ' + args.cache_dir\\n # output config file\\n yaml.dump(xinfo, open(args.exp_config_path, 'w'),\\n default_flow_style=False)\\n\\n # Load dataset config file\\n dcfg_path = osp.join(args.data_config_path, xinfo['INPUT']['DATASET'])\\n dinfo = yaml.load(open(dcfg_path)) # dataset info\\n data_dir = dinfo['ROOT']\\n\\n layout = xinfo['INPUT']['LAYOUT']\\n inps = [s.strip() for l in layout for s in l.split(',')]\\n outs = [s.strip() for s in xinfo['REFINE']['TARGETS'].split(',')]\\n\\n supports = ['seg', 'flow', 'norm', 'rgb', 'depth']\\n\\n nets = {}\\n for split in ['train', 'test']:\\n net_inps = []\\n net_outs = []\\n for inp in inps:\\n match = re.search('^(gt|pr)({})'.format('|'.join(supports)), inp)\\n assert match is not None, 'Error in config INPUT-LAYOUT: ' + inp\\n\\n modality = match.group(2)\\n nchannels = dinfo[modality]['n']\\n path = osp.join(data_dir, dinfo[modality][match.group(1) + '-' + split])\\n\\n assert osp.exists(path), 'File not found: ' + path\\n net_inps.append((inp, path, nchannels))\\n\\n for out in outs:\\n # TODO: read target type: zero couplings, tight, loose couplings\\n match = re.search('({})'.format('|'.join(supports)), out)\\n assert match is not None, 'Error in config REFINE-TARGET: '+ out\\n\\n modality = match.group(1)\\n nchannels = dinfo[modality]['n']\\n path = osp.join(data_dir, dinfo[modality]['gt-' + split])\\n\\n assert osp.exists(path), 'File not found: ' + path\\n net_outs.append((out, path, nchannels))\\n\\n loss_params = dict()\\n mapping = None\\n if 'mapping' in dinfo['seg']:\\n idx = dinfo['seg']['mapping']\\n mapping = dict(zip(idx, xrange(len(idx))))\\n\\n if split == 'train':\\n\\n # if the class weights is not in the dataset config file\\n if 'gt-train-weights' not in dinfo['seg']:\\n print 'Generating median frequency balancing weights.'\\n (weights, mapping) = gcw.get_mfb(osp.join(dinfo['ROOT'], dinfo['seg']['gt-train']),\\n dinfo['seg']['ignore_label'],\\n mapping)\\n # save back to dataset config\\n dinfo['seg']['gt-train-weights'] = weights\\n yaml.dump(dinfo, open(dcfg_path, 'w'), default_flow_style=False)\\n else:\\n weights = dinfo['seg']['gt-train-weights']\\n # update data\\n # update loss parameter\\n ignore_label = dinfo['seg']['ignore_label']\\n ignore_label = mapping[ignore_label] if mapping is not None else ignore_label\\n loss_params['loss_param'] = {\\n 'ignore_label': ignore_label,\\n 'class_weighting': weights\\n }\\n\\n # generate net prototxt\\n loader = dinfo['NAME'] + '_loader'\\n net_proto = arch.create_net(net_inps, net_outs, split, loader, layout, mapping, **loss_params)\\n\\n # output to file\\n path = osp.join(args.cache_dir, getattr(args, 'exp_{}_path'.format(split)))\\n open(path, 'w').write(str(net_proto))\\n nets[split] = net_proto\\n\\n return nets\",\n \"def prepare_node(self, node):\\n self.interface = IpmiInterface(\\n node.get('fencer-ip'),\\n node.get('fencer-user'),\\n node.get('fencer-password'),\\n verbose=CONF.debug)\",\n \"def setup(args):\\n cfg = get_cfg()\\n cfg.merge_from_file(args.config_file)\\n cfg.merge_from_list(args.opts)\\n # customize reszied parameters\\n # cfg['INPUT']['MIN_SIZE_TRAIN'] = (20,)\\n # cfg['INPUT']['MAX_SIZE_TRAIN'] = 50\\n cfg.freeze()\\n default_setup(\\n cfg, args\\n ) # if you don't like any of the default setup, write your own setup code\\n return cfg\",\n \"def _setup(self, orig=False):\\n log_method_call(self, self.name, orig=orig, status=self.status,\\n controllable=self.controllable)\\n disks = []\\n for member in self.devices:\\n member.setup(orig=orig)\\n disks.append(member.path)\\n\\n mdraid.mdactivate(self.path,\\n members=disks,\\n array_uuid=self.mdadmFormatUUID)\",\n \"def node_setup(self):\\n self.nav_tasks = {} # id -> nav_task\\n self.missions = {} # id -> mission\\n self.mission_state = {} # mission_id -> current task index.\\n self.trigger_nav_task_active = False\\n self.trigger_local_path_published = False\",\n \"def __init__(self, parent=None):\\n\\t\\tScriptedLoadableModuleWidget.__init__(self, parent)\\n\\t\\tVTKObservationMixin.__init__(self) # needed for parameter node observation\\n\\t\\tself.logic = None\\n\\t\\tself._parameterNode = None\\n\\t\\tself._updatingGUIFromParameterNode = False\\n\\t\\t\\n\\t\\tself.elecModel = None\\n\\t\\tself.elecModelLastButton = None\\n\\t\\tself.elecModelButton = 0\\n\\t\\tself.elecChanLastButton = None\\n\\t\\tself.elecChanButton = 0\\n\\t\\tself.lastPolButton=0\\n\\t\\tself.active = False\",\n \"def init_extra_module(self, component_instance, function, mw_data):\\n init_extra_actuator(self, component_instance, function, mw_data, ors_genpos_poster)\",\n \"def setup(self):\\n super().setup()\\n self.ctx.restart_calc = None\\n self.ctx.inputs = AttributeDict(self.exposed_inputs(XspectraCalculation, 'xspectra'))\\n\\n self.ctx.inputs.parameters = self.ctx.inputs.parameters.get_dict()\",\n \"def __init__(self, animPickerUI, modulesToAdd, parent=None):\\n super(ART_AddModuleToCanvas, self).__init__()\\n # get the directory path of the tools\\n settings = QtCore.QSettings(\\\"Epic Games\\\", \\\"ARTv2\\\")\\n self.toolsPath = settings.value(\\\"toolsPath\\\")\\n self.iconsPath = settings.value(\\\"iconPath\\\")\\n self.scriptPath = settings.value(\\\"scriptPath\\\")\\n self.projectPath = settings.value(\\\"projectPath\\\")\\n\\n self.pickerUI = animPickerUI\\n self.modules = []\\n self.modulesToAdd = modulesToAdd\\n\\n # assign close event\\n self.closeEvent = self.closeWin\\n\\n # build the UI\\n self.buildUI()\",\n \"def changeNodeLib(ned, createNodeWin):\\n pass\",\n \"def get_init(self, storyboard):\\n init = etree.SubElement(storyboard, \\\"Init\\\")\\n init_act = etree.SubElement(init, \\\"Actions\\\")\\n self.get_environment_actions(init_act)\\n\\n # Ego Vehicle\\n if QgsProject.instance().mapLayersByName(\\\"Vehicles - Ego\\\"):\\n vehicle_ego_layer = QgsProject.instance().mapLayersByName(\\\"Vehicles - Ego\\\")[0]\\n for feature in vehicle_ego_layer.getFeatures():\\n veh_id = \\\"Ego_\\\" + str(feature[\\\"id\\\"])\\n orientation = feature[\\\"Orientation\\\"]\\n pos_x = feature[\\\"Pos X\\\"]\\n pos_y = feature[\\\"Pos Y\\\"]\\n pos_z = feature[\\\"Pos Z\\\"]\\n init_speed = feature[\\\"Init Speed\\\"]\\n agent = feature[\\\"Agent\\\"]\\n agent_camera = str(feature[\\\"Agent Camera\\\"]).lower()\\n\\n if agent == \\\"User Defined\\\":\\n agent = feature[\\\"Agent User Defined\\\"]\\n\\n entity = etree.SubElement(init_act, \\\"Private\\\")\\n entity.set(\\\"entityRef\\\", str(veh_id))\\n self.entity_teleport_action(entity, orientation, pos_x, pos_y, pos_z)\\n self.vehicle_controller(entity, str(feature[\\\"id\\\"]), agent, agent_camera, is_ego=True)\\n if init_speed != 0:\\n self.set_init_speed(entity, init_speed)\\n\\n # Vehicle\\n if QgsProject.instance().mapLayersByName(\\\"Vehicles\\\"):\\n vehicle_layer = QgsProject.instance().mapLayersByName(\\\"Vehicles\\\")[0]\\n for feature in vehicle_layer.getFeatures():\\n veh_id = \\\"Vehicle_\\\" + str(feature[\\\"id\\\"])\\n orientation = feature[\\\"Orientation\\\"]\\n pos_x = feature[\\\"Pos X\\\"]\\n pos_y = feature[\\\"Pos Y\\\"]\\n pos_z = feature[\\\"Pos Z\\\"]\\n init_speed = feature[\\\"Init Speed\\\"]\\n agent = feature[\\\"Agent\\\"]\\n agent_camera = str(feature[\\\"Agent Camera\\\"]).lower()\\n\\n if agent == \\\"User Defined\\\":\\n agent = feature[\\\"Agent User Defined\\\"]\\n\\n entity = etree.SubElement(init_act, \\\"Private\\\")\\n entity.set(\\\"entityRef\\\", str(veh_id))\\n self.entity_teleport_action(entity, orientation, pos_x, pos_y, pos_z)\\n self.vehicle_controller(entity, str(feature[\\\"id\\\"]), agent, agent_camera, is_ego=False)\\n if init_speed != 0:\\n self.set_init_speed(entity, init_speed)\\n\\n # Pedestrian\\n if QgsProject.instance().mapLayersByName(\\\"Pedestrians\\\"):\\n walker_layer = QgsProject.instance().mapLayersByName(\\\"Pedestrians\\\")[0]\\n for feature in walker_layer.getFeatures():\\n ped_id = \\\"Pedestrian_\\\" + str(feature[\\\"id\\\"])\\n orientation = feature[\\\"Orientation\\\"]\\n pos_x = feature[\\\"Pos X\\\"]\\n pos_y = feature[\\\"Pos Y\\\"]\\n pos_z = feature[\\\"Pos Z\\\"]\\n init_speed = feature[\\\"Init Speed\\\"]\\n\\n entity = etree.SubElement(init_act, \\\"Private\\\")\\n entity.set(\\\"entityRef\\\", ped_id)\\n self.entity_teleport_action(entity, orientation, pos_x, pos_y, pos_z)\\n if init_speed != 0:\\n self.set_init_speed(entity, init_speed)\\n\\n # Static Objects\\n if QgsProject.instance().mapLayersByName(\\\"Static Objects\\\"):\\n props_layer = QgsProject.instance().mapLayersByName(\\\"Static Objects\\\")[0]\\n for feature in props_layer.getFeatures():\\n prop_id = \\\"Prop_\\\" + str(feature[\\\"id\\\"])\\n orientation = feature[\\\"Orientation\\\"]\\n pos_x = feature[\\\"Pos X\\\"]\\n pos_y = feature[\\\"Pos Y\\\"]\\n pos_z = feature[\\\"Pos Z\\\"]\\n\\n entity = etree.SubElement(init_act, \\\"Private\\\")\\n entity.set(\\\"entityRef\\\", prop_id)\\n self.entity_teleport_action(entity, orientation, pos_x, pos_y, pos_z)\",\n \"def test_sceneImport24281(self):\\r\\n\\r\\n self.delayDisplay(\\\"Starting the test\\\")\\r\\n #\\r\\n # first, get some data\\r\\n #\\r\\n self.delayDisplay(\\\"Getting Data\\\")\\r\\n import SampleData\\r\\n head = SampleData.downloadSample(\\\"MRHead\\\")\\r\\n\\r\\n #\\r\\n # create a label map and set it for editing\\r\\n #\\r\\n self.delayDisplay(\\\"Setting up LabelMap\\\")\\r\\n volumesLogic = slicer.modules.volumes.logic()\\r\\n headLabel = volumesLogic.CreateAndAddLabelVolume( slicer.mrmlScene, head, head.GetName() + '-label' )\\r\\n selectionNode = slicer.app.applicationLogic().GetSelectionNode()\\r\\n selectionNode.SetActiveVolumeID( head.GetID() )\\r\\n selectionNode.SetActiveLabelVolumeID( headLabel.GetID() )\\r\\n slicer.app.applicationLogic().PropagateVolumeSelection(0)\\r\\n\\r\\n #\\r\\n # got to the editor and do some drawing\\r\\n #\\r\\n self.delayDisplay(\\\"Setting up Editor and drawing\\\")\\r\\n parameterNode = EditUtil.getParameterNode()\\r\\n lm = slicer.app.layoutManager()\\r\\n paintEffectOptions = EditorLib.PaintEffectOptions()\\r\\n paintEffectOptions.setMRMLDefaults()\\r\\n paintEffectOptions.__del__()\\r\\n\\r\\n self.delayDisplay('Paint radius is %s' % parameterNode.GetParameter('PaintEffect,radius'))\\r\\n sliceWidget = lm.sliceWidget('Red')\\r\\n size = min(sliceWidget.width,sliceWidget.height)\\r\\n step = int(size / 12)\\r\\n center = int(size / 2)\\r\\n parameterNode.SetParameter('PaintEffect,radius', '20')\\r\\n paintTool = EditorLib.PaintEffectTool(sliceWidget)\\r\\n self.delayDisplay('Paint radius is %s, tool radius is %d' % (parameterNode.GetParameter('PaintEffect,radius'),paintTool.radius))\\r\\n for label in range(1,5):\\r\\n EditUtil.setLabel(label)\\r\\n pos = center - 2*step + (step * label)\\r\\n self.delayDisplay('Painting %d, at (%d,%d)' % (label,pos,pos),200)\\r\\n paintTool.paintAddPoint(pos,pos)\\r\\n paintTool.paintApply()\\r\\n paintTool.cleanup()\\r\\n paintTool = None\\r\\n\\r\\n #\\r\\n # now build:\\r\\n # create a model using the command line module\\r\\n # based on the current editor parameters\\r\\n # - make a new hierarchy node\\r\\n #\\r\\n\\r\\n self.delayDisplay( \\\"Building...\\\" )\\r\\n\\r\\n parameters = {}\\r\\n parameters[\\\"InputVolume\\\"] = headLabel.GetID()\\r\\n # create models for all labels\\r\\n parameters[\\\"JointSmoothing\\\"] = True\\r\\n parameters[\\\"StartLabel\\\"] = -1\\r\\n parameters[\\\"EndLabel\\\"] = -1\\r\\n outHierarchy = slicer.vtkMRMLModelHierarchyNode()\\r\\n outHierarchy.SetScene( slicer.mrmlScene )\\r\\n outHierarchy.SetName( \\\"sceneImport2428Hierachy\\\" )\\r\\n slicer.mrmlScene.AddNode( outHierarchy )\\r\\n parameters[\\\"ModelSceneFile\\\"] = outHierarchy\\r\\n\\r\\n modelMaker = slicer.modules.modelmaker\\r\\n self.CLINode = None\\r\\n self.CLINode = slicer.cli.runSync(modelMaker, self.CLINode, parameters, delete_temporary_files=False)\\r\\n\\r\\n self.delayDisplay(\\\"Models built\\\")\\r\\n\\r\\n success = self.verifyModels()\\r\\n\\r\\n success = success and (slicer.mrmlScene.GetNumberOfNodesByClass( \\\"vtkMRMLModelNode\\\" ) > 3)\\r\\n\\r\\n self.delayDisplay(\\\"Test finished\\\")\\r\\n\\r\\n if success:\\r\\n self.delayDisplay(\\\"Ahh... test passed.\\\")\\r\\n else:\\r\\n self.delayDisplay(\\\"!$!$!#!@#!@!@$%! Test Failed!!\\\")\\r\\n\\r\\n self.assertTrue(success)\",\n \"def onResetParameters(self):\\r\\n # productive #button\\r\\n profprint()\\r\\n fileName = pathToScene = slicer.modules.needlefinder.path.replace(\\\"NeedleFinder.py\\\", \\\"Config/default.cfg\\\")\\r\\n self.logic.loadParameters(fileName)\",\n \"def config_and_train(self, sys_args):\\n \\n self.run_config_function(sys_args)\\n self.set_model_name('vgg_16')\\n self.set_trainable_and_exclude_scopes(constants.checkpoint_exclude_scopes,\\n constants.trainable_scopes)\\n self.set_optimizer('sgd')\\n self.set_max_number_of_steps(6000)\\n self.train_or_eval_net(sys_args)\",\n \"def configure_basics_in_routers(self):\\n \\n basics_script = Basics.get_script_file()\\n \\n # Alteracao do nome e senha do router no script\\n basics_script = Basics.replace_vars(itter_list=basics_script, pattern='{%hostname%}', replace=self.router.nome)\\n basics_script = Basics.replace_vars(itter_list=basics_script, pattern='{%password%}', replace=Basics.get_default_password())\\n\\n _path = f'router-config/cisco/{self.router.nome}.ios'\\n\\n router_configfile = open(_path, 'w')\\n\\n # Gravacao dos dados nos arquivos de cada router em cisco/\\n try:\\n for line in basics_script:\\n router_configfile.write(line)\\n \\n return f'BASICS: Arquivo de configuracao de {self.router.nome} criado com sucesso em {_path}.'\\n \\n except: f'BASICS: Erro na configuracao do arquivo de {self.router.nome} em {_path}!'\\n\\n router_configfile.close()\",\n \"def setup(self, trainer: \\\"pl.Trainer\\\") -> None:\\n trainer_fn = trainer.state.fn\\n if trainer_fn == TrainerFn.FITTING:\\n super().setup(trainer)\\n else:\\n assert self.accelerator is not None\\n self.accelerator.setup(trainer)\\n\\n # move the model to the correct device\\n self.model_to_device()\\n self.setup_precision_plugin()\\n assert self.model is not None\",\n \"def __init__(self, experiment, label=None):\\n\\n super(DrawTransitions, self).__init__(experiment, name=\\\"DrawTransitions\\\", label=label)\\n\\n self.epoch_start = self.experiment.config.getint(self.config_section, 'epoch_start', 0)\\n self.epoch_end = self.experiment.config.getint(self.config_section, 'epoch_end',\\n default=self.experiment.config.getint('Experiment', 'epochs', default=-1))\\n self.frequency = self.experiment.config.getint(self.config_section, 'frequency', 1)\\n self.priority = self.experiment.config.getint(self.config_section, 'priority', 0)\\n self.filename = self.experiment.config.get(self.config_section, 'filename', default='transitions')\\n self.format = self.experiment.config.get(self.config_section, 'format', default='png')\\n self.transparent = self.experiment.config.getboolean(self.config_section, 'transparent', default=False)\\n self.display_epoch = self.experiment.config.getboolean(self.config_section, 'display_epoch', default=False)\\n\\n self.colors = self.experiment.population._cell_class.type_colors\\n self.types = self.experiment.population._cell_class.types\\n self.max_types = self.experiment.population._cell_class.max_types\\n\\n self.node_labels = {}\",\n \"def _create_default_setting(path):\\n try:\\n from configparser import ConfigParser\\n except ImportError:\\n from ConfigParser import ConfigParser # ver. < 3.0\\n\\n # instantiate\\n config = ConfigParser()\\n\\n # update existing value\\n config['Assets Paths'] = {\\n 'background': 'assets\\\\\\\\images\\\\\\\\background.png',\\n 'bullet': 'assets\\\\\\\\images\\\\\\\\bullet.png',\\n 'bullet_red': 'assets\\\\\\\\images\\\\\\\\bullet_red.png',\\n 'icon' : 'assets\\\\\\\\images\\\\\\\\RedInvader.png',\\n\\n 'ship': 'assets\\\\\\\\images\\\\\\\\Ship.png',\\n 'ship_cr': 'assets\\\\\\\\images\\\\\\\\ShipCrushedRight.png',\\n 'ship_cl': 'assets\\\\\\\\images\\\\\\\\ShipCrushedLeft.png',\\n 'ship_cc': 'assets\\\\\\\\images\\\\\\\\ShipWhite.png',\\n\\n 'invadera1': 'assets\\\\\\\\images\\\\\\\\InvaderA1.png',\\n 'invadera2': 'assets\\\\\\\\images\\\\\\\\InvaderA2.png',\\n 'invaderb1': 'assets\\\\\\\\images\\\\\\\\InvaderB1.png',\\n 'invaderb2': 'assets\\\\\\\\images\\\\\\\\InvaderB2.png',\\n 'invaderc1': 'assets\\\\\\\\images\\\\\\\\InvaderC1.png',\\n 'invaderc2': 'assets\\\\\\\\images\\\\\\\\InvaderC2.png',\\n\\n }\\n config['castle'] = {\\n 'castle_location': [\\n [1, 1, 1, 1, 1],\\n [1, 1, 1, 1, 1],\\n [1, 1, 0, 1, 1],\\n [1, 1, 0, 1, 1],\\n [1, 0, 0, 0, 1],\\n ],\\n 'start_x': 50,\\n 'start_y': 500,\\n 'column': 5,\\n 'row': 5,\\n 'block_l3': (9, 255, 14),\\n 'block_l2': (27, 255, 30),\\n 'block_l1': (114, 255, 133),\\n }\\n config['alien'] = {\\n 'margin_width': 200,\\n 'margin_height': 20,\\n 'column': 'd',\\n 'Row': 5,\\n 'width_x': 10,\\n 'width_y': 10,\\n 'movement': 10,\\n 'alien_column_config': r'{\\\"0\\\":{\\\"path1\\\":\\\"InvaderA1\\\",\\\"path2\\\":\\\"InvaderA2\\\"},\\\"1\\\":{\\\"path1\\\":\\\"InvaderB1\\\",\\\"path2\\\":\\\"InvaderB2\\\"},\\\"2\\\":{\\\"path1\\\":\\\"InvaderB1\\\",\\\"path2\\\":\\\"InvaderB2\\\"},\\\"3\\\":{\\\"path1\\\":\\\"InvaderC1\\\",\\\"path2\\\":\\\"InvaderC2\\\"},\\\"4\\\":{\\\"path1\\\":\\\"InvaderC1\\\",\\\"path2\\\":\\\"InvaderC2\\\"}}'\\n }\\n config['player 1'] = {\\n 'margin': 20,\\n 'speed': 3\\n }\\n\\n with open(path, 'w') as configfile:\\n config.write(configfile)\",\n \"def enter(self):\\r\\n self.turnOffLightboxes()\\r\\n self.installShortcutKeys()\\r\\n\\r\\n # Set parameter set node if absent\\r\\n self.selectParameterNode()\\r\\n self.editor.updateWidgetFromMRML()\\r\\n \\r\\n # If no segmentation node exists then create one so that the user does not have to create one manually\\r\\n if not self.editor.segmentationNodeID():\\r\\n newSegmentationNode = slicer.mrmlScene.CreateNodeByClass('vtkMRMLSegmentationNode')\\r\\n slicer.mrmlScene.AddNode(newSegmentationNode)\\r\\n self.editor.setSegmentationNode(newSegmentationNode)\\r\\n masterVolumeNodeID = self.getDefaultMasterVolumeNodeID()\\r\\n self.editor.setMasterVolumeNodeID(masterVolumeNodeID)\",\n \"def __init__(self, pklfile=None, preffile=_preffile):\\n\\n super(config_controller, self).__init__()\\n self.pklfile = pklfile\\n self.preffile = preffile\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5719306","0.5415949","0.5388942","0.5385029","0.5336632","0.5299819","0.5200528","0.5145229","0.5140653","0.51194525","0.5079638","0.50723904","0.50585204","0.50376135","0.5010488","0.49763772","0.49707508","0.49594924","0.4954051","0.4949411","0.4947352","0.4943901","0.4934593","0.49284354","0.4923892","0.4923892","0.4923892","0.4923892","0.49217173","0.4919505","0.49172956","0.49127904","0.48924997","0.48924997","0.48924997","0.48894072","0.488293","0.48692843","0.4862807","0.48608032","0.48587546","0.48572928","0.4835457","0.48325357","0.48309034","0.4825999","0.4813325","0.4805297","0.4794852","0.47910696","0.47814056","0.47706217","0.47663957","0.4762909","0.4758006","0.4757204","0.47485957","0.47461104","0.47403783","0.47369528","0.47366032","0.47349852","0.47309327","0.47273844","0.47201413","0.47201335","0.4719329","0.47168612","0.47165364","0.4716268","0.4712119","0.47087467","0.47043702","0.4704265","0.46983424","0.4697081","0.46935275","0.4692958","0.46838683","0.4681121","0.4679565","0.4674944","0.4671631","0.4668328","0.46670285","0.4664907","0.46619886","0.46619788","0.4661319","0.46597382","0.46553832","0.4652706","0.46501863","0.46484464","0.46462658","0.4645533","0.46400067","0.4628762","0.46283883","0.46265304"],"string":"[\n \"0.5719306\",\n \"0.5415949\",\n \"0.5388942\",\n \"0.5385029\",\n \"0.5336632\",\n \"0.5299819\",\n \"0.5200528\",\n \"0.5145229\",\n \"0.5140653\",\n \"0.51194525\",\n \"0.5079638\",\n \"0.50723904\",\n \"0.50585204\",\n \"0.50376135\",\n \"0.5010488\",\n \"0.49763772\",\n \"0.49707508\",\n \"0.49594924\",\n \"0.4954051\",\n \"0.4949411\",\n \"0.4947352\",\n \"0.4943901\",\n \"0.4934593\",\n \"0.49284354\",\n \"0.4923892\",\n \"0.4923892\",\n \"0.4923892\",\n \"0.4923892\",\n \"0.49217173\",\n \"0.4919505\",\n \"0.49172956\",\n \"0.49127904\",\n \"0.48924997\",\n \"0.48924997\",\n \"0.48924997\",\n \"0.48894072\",\n \"0.488293\",\n \"0.48692843\",\n \"0.4862807\",\n \"0.48608032\",\n \"0.48587546\",\n \"0.48572928\",\n \"0.4835457\",\n \"0.48325357\",\n \"0.48309034\",\n \"0.4825999\",\n \"0.4813325\",\n \"0.4805297\",\n \"0.4794852\",\n \"0.47910696\",\n \"0.47814056\",\n \"0.47706217\",\n \"0.47663957\",\n \"0.4762909\",\n \"0.4758006\",\n \"0.4757204\",\n \"0.47485957\",\n \"0.47461104\",\n \"0.47403783\",\n \"0.47369528\",\n \"0.47366032\",\n \"0.47349852\",\n \"0.47309327\",\n \"0.47273844\",\n \"0.47201413\",\n \"0.47201335\",\n \"0.4719329\",\n \"0.47168612\",\n \"0.47165364\",\n \"0.4716268\",\n \"0.4712119\",\n \"0.47087467\",\n \"0.47043702\",\n \"0.4704265\",\n \"0.46983424\",\n \"0.4697081\",\n \"0.46935275\",\n \"0.4692958\",\n \"0.46838683\",\n \"0.4681121\",\n \"0.4679565\",\n \"0.4674944\",\n \"0.4671631\",\n \"0.4668328\",\n \"0.46670285\",\n \"0.4664907\",\n \"0.46619886\",\n \"0.46619788\",\n \"0.4661319\",\n \"0.46597382\",\n \"0.46553832\",\n \"0.4652706\",\n \"0.46501863\",\n \"0.46484464\",\n \"0.46462658\",\n \"0.4645533\",\n \"0.46400067\",\n \"0.4628762\",\n \"0.46283883\",\n \"0.46265304\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5789509"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6799,"cells":{"query":{"kind":"string","value":"This will be used for FK IK matching"},"document":{"kind":"string","value":"def create_snapto_node(ctrl, jnt):\n\n snapto = ctrl+'_SNAPTO'\n if mc.objExists(snapto):\n jnt = utils.get_parent(snapto)\n mc.delete(snapto)\n\n snapto_grp = mc.duplicate(ctrl, po=1, n=ctrl+'_SNAPTO_GRP')[0]\n snapto = mc.duplicate(ctrl, po=1, n=ctrl+'_SNAPTO')[0]\n utils.set_attrs([snapto_grp, snapto], k=1, l=0)\n mc.parent(snapto_grp, jnt)\n mc.parent(snapto, snapto_grp)\n\n ua = mc.listAttr(snapto, ud=1)\n\n for a in ua:\n try:\n mc.setAttr(snapto+'.'+a, l=0)\n mc.deleteAttr(snapto+'.'+a)\n\n mc.setAttr(snapto_grp+'.'+a, l=0)\n mc.deleteAttr(snapto_grp+'.'+a)\n\n except:\n pass\n\n return snapto"},"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 ikFkMatch(\n namespace,\n ikfk_attr,\n ui_host,\n fks,\n ik,\n upv,\n ik_rot=None,\n key=None):\n\n # returns a pymel node on the given name\n def _get_node(name):\n # type: (Text) -> pm.nodetypes.Transform\n name = anim_utils.stripNamespace(name)\n if namespace:\n node = anim_utils.getNode(\":\".join([namespace, name]))\n else:\n node = anim_utils.getNode(name)\n\n if not node:\n mgear.log(\"Can't find object : {0}\".format(name), mgear.sev_error)\n\n return node\n\n # returns matching node\n def _get_mth(name):\n # type: (str) -> pm.nodetypes.Transform\n tmp = name.split(\"_\")\n tmp[-1] = \"mth\"\n query = \"_\".join(tmp)\n n = _get_node(query)\n\n if not n:\n mgear.log(\"Can't find mth object : {0} for {1}\".format(query, name), mgear.sev_comment)\n return _get_node(name)\n else:\n return n\n\n # get things ready\n fk_ctrls = [_get_node(x) for x in fks]\n fk_goals = [_get_mth(x) for x in fks]\n ik_ctrl = _get_node(ik)\n ik_goal = _get_mth(ik)\n upv_ctrl = _get_node(upv)\n\n if ik_rot:\n ik_rot_node = _get_node(ik_rot)\n ik_rot_goal = _get_mth(ik_rot)\n\n ui_node = _get_node(ui_host)\n o_attr = ui_node.attr(ikfk_attr)\n\n switch_to_fk = (o_attr.get() == 1.0)\n switch_to_ik = (not switch_to_fk)\n\n # sets keyframes before snapping\n if key:\n _all_controls = []\n _all_controls.extend(fk_ctrls)\n _all_controls.extend([ik_ctrl, upv_ctrl, ui_node])\n if ik_rot:\n _all_controls.extend([ik_rot_node])\n [cmds.setKeyframe(\"{}\".format(elem),\n time=(cmds.currentTime(query=True) - 1.0))\n for elem in _all_controls]\n\n # if is IKw then snap FK\n if switch_to_fk:\n\n world_matrices = []\n for src, _ in zip(fk_goals, fk_ctrls):\n world_matrices.append(getMatrix(src))\n\n o_attr.set(0.0)\n\n for mat, dst in zip(world_matrices, fk_ctrls):\n setMatrix(dst, mat)\n\n for mat, dst in zip(world_matrices, fk_ctrls):\n setMatrix(dst, mat)\n\n # if is FKw then sanp IK\n elif switch_to_ik:\n\n shoulder_mat = getMatrix(fk_goals[0])\n ik_mat = getMatrix(ik_goal)\n\n # transform.matchWorldTransform(ik_goal, ik_ctrl)\n if ik_rot:\n rot_mat = getMatrix(ik_rot_goal)\n # transform.matchWorldTransform(ik_rot_goal, ik_rot_node)\n\n upv_mat = getMatrix(fk_goals[2])\n\n o_attr.set(1.0)\n\n setMatrix(ik_ctrl, ik_mat)\n setMatrix(upv_ctrl, upv_mat)\n # for _ in range(10):\n # fk_ctrls[0].setMatrix(shoulder_mat, worldSpace=True)\n\n for _ in range(20):\n cmds.xform(fk_ctrls[0].name(), ws=True, matrix=shoulder_mat)\n if ik_rot:\n setMatrix(ik_rot_node, rot_mat)\n\n # transform.matchWorldTransform(fk_goals[1], upv_ctrl)\n # calculates new pole vector position\n start_end = (fk_goals[-1].getTranslation(space=\"world\") - fk_goals[1].getTranslation(space=\"world\"))\n start_mid = (fk_goals[2].getTranslation(space=\"world\") - fk_goals[1].getTranslation(space=\"world\"))\n\n dot_p = start_mid * start_end\n proj = float(dot_p) / float(start_end.length())\n proj_vector = start_end.normal() * proj\n arrow_vector = (start_mid - proj_vector) * 1.5\n arrow_vector *= start_end.normal().length()\n final_vector = (arrow_vector + fk_goals[2].getTranslation(space=\"world\"))\n upv_ctrl.setTranslation(final_vector, space=\"world\")\n\n # sets blend attribute new value\n # o_attr.set(1.0)\n roll_att = ui_node.attr(ikfk_attr.replace(\"blend\", \"roll\"))\n roll_att.set(0.0)\n\n setMatrix(ik_ctrl, ik_mat)\n if ik_rot:\n setMatrix(ik_rot_node, rot_mat)\n # upv_ctrl.setMatrix(upv_mat, worldSpace=True)\n for _ in range(20):\n cmds.xform(fk_ctrls[0].name(), ws=True, matrix=shoulder_mat)\n\n # sets keyframes\n if key:\n [cmds.setKeyframe(\"{}\".format(elem),\n time=(cmds.currentTime(query=True)))\n for elem in _all_controls]","def test_fk():\n\n joints = [0.0, 2.9, 1.3, 4.2, 1.4, 0.0]\n\n path_planner = PathPlanner(\"manipulator\")\n\n pose = path_planner.get_fk(joints)\n\n print pose","def matching(self, Y, K):\n\n ### sort data Y\n sidx = np.argsort(Y.SID)\n Y.SID = Y.SID[sidx]\n Y.Y = Y.Y[sidx,:]\n\n ### sort data K\n for kitem in K:\n sidx = np.argsort(kitem.SID)\n kitem.SID = kitem.SID[sidx]\n kitem.K = kitem.K[sidx,:][:,sidx]\n\n ### find reference set samples\n ### to boring to finish this crap....","def ik_to_fk(node):\n ik_main_off = get_parent(node.ik_main_conn)\n fk_01_off = get_parent(node.fk_01_conn)\n fk_02_off = get_parent(node.fk_02_conn)\n fk_03_off = get_parent(node.fk_03_conn)\n\n ik_main_world_trans = get_world_trans(node.ik_main_conn)\n fk_01_world_trans = get_world_trans(node.fk_01_conn)\n ik_main_off_world_trans = get_world_trans(ik_main_off)\n fk_01_off_world_trans = get_world_trans(fk_01_off)\n fk_02_off_world_trans = get_world_trans(fk_02_off)\n fk_03_off_world_trans = get_world_trans(fk_03_off)\n\n # calculate base information\n def_len = (ik_main_off_world_trans - fk_01_off_world_trans).length()\n\n # Calculate ik direction\n ik_dir_01 = ik_main_off_world_trans - fk_01_off_world_trans\n ik_dir_02 = ik_main_world_trans - fk_01_world_trans\n\n ik_dir_rot = ik_dir_01.rotateTo(ik_dir_02).asEulerRotation()\n\n # Apply ik direction -> important to calculate correct pole rotations\n fk_01_rot_plugs = get_rot_plugs(node.fk_01_conn)\n for i, plug in enumerate(fk_01_rot_plugs):\n plug.setMAngle(oMa.MAngle(ik_dir_rot[i], oMa.MAngle.kRadians))\n\n # Calculate ik pole rotations\n ik_pole_world_mat = get_world_matrix(node.ik_pole_conn, 0)\n fk_03_world_inv_mat = get_world_inv_matrix(node.fk_01_conn, 0)\n\n ik_pole_rot_mat = ik_pole_world_mat * fk_03_world_inv_mat\n\n ik_pole_vec = oMa.MTransformationMatrix(ik_pole_rot_mat).translation(oMa.MSpace.kWorld)\n ik_pole_vec.y = 0\n\n ik_pole_rot = oMa.MVector.kZaxisVector.rotateTo(ik_pole_vec).asEulerRotation()\n\n # Calculate ik rotations\n tri_a_len = (fk_02_off_world_trans - fk_01_off_world_trans).length()\n tri_b_len = (fk_03_off_world_trans - fk_02_off_world_trans).length()\n tri_c_len = (ik_main_world_trans - fk_01_world_trans).length()\n\n if tri_c_len >= def_len:\n fk_02_angle = 0\n fk_01_angle = 0\n else:\n fk_02_angle = math.pi - solve_triangle(tri_a_len, tri_b_len, tri_c_len, \"C\")\n fk_01_angle = -solve_triangle(tri_a_len, tri_b_len, tri_c_len, \"B\")\n\n # Add rotations together\n fk_01_temp = oMa.MEulerRotation(fk_01_angle, ik_pole_rot.y, 0)\n\n ik_dir_mat = compose_mat(ik_dir_rot)\n fk_01_mat = compose_mat(fk_01_temp)\n rot_mat = fk_01_mat * ik_dir_mat\n\n # Apply everything\n fk_01_rot = get_rot_from_mat(rot_mat)\n fk_02_rot = (fk_02_angle, 0, 0)\n\n fk_01_rot_plugs = get_rot_plugs(node.fk_01_conn)\n for i, plug in enumerate(fk_01_rot_plugs):\n plug.setMAngle(oMa.MAngle(fk_01_rot[i], oMa.MAngle.kRadians))\n\n fk_02_rot_plugs = get_rot_plugs(node.fk_02_conn)\n for i, plug in enumerate(fk_02_rot_plugs):\n if not plug.isLocked:\n plug.setMAngle(oMa.MAngle(fk_02_rot[i], oMa.MAngle.kRadians))\n\n # Calculate ankle rotation\n fk_03_rot = rot_world_space_to_local_space(node.ik_main_conn, get_parent(node.fk_03_conn))\n\n fk_03_rot_plugs = get_rot_plugs(node.fk_03_conn)\n for i, plug in enumerate(fk_03_rot_plugs):\n plug.setMAngle(oMa.MAngle(fk_03_rot[i], oMa.MAngle.kRadians))","def test_match_table_post(self):\n pass","def key_fk(*args):\n\n robots = get_robot_roots()\n if not robots:\n pm.warning('No robots selected')\n return\n\n for robot in robots:\n # If the robot's IK attribute is on, switch the robot to\n # FK mode before proceeding\n target_ctrl_path = get_target_ctrl_path(robot)\n tool_ctrl_path = get_tool_ctrl_path(robot)\n\n if pm.getAttr(target_ctrl_path + '.ik'):\n switch_to_fk(robot)\n\n # We first check if the target/tool controller transformation and\n # orientation is already aligned with the FK chain. If so, it\n # indicates that we're performing an IK to FK switch, and we\n # keyframe its position and orientation directly, without\n # snapping the IK control to the FK hierarchy. This is to avoid\n # unneccessarily changing the controllers Euler Angle rotation\n # representation that can cause unpredictable behavior between frames\n\n if pm.objExists(tool_ctrl_path):\n ctrl_ik = tool_ctrl_path\n ctrl_fk = format_path(__TOOL_CTRL_FK_PATH, robot)\n\n # If robot doesn't have a tool controller, use target_CTRL.\n else:\n ctrl_ik = target_ctrl_path\n ctrl_fk = format_path(__TCP_HDL_PATH, robot)\n\n if not _ik_and_fk_aligned(ctrl_ik, ctrl_fk):\n _snap_ik_target_to_fk(robot)\n\n # Key all FK elements\n try:\n pm.setKeyframe(format_path(__A1_FK_CTRL_PATH, robot),\n attribute='rotateY')\n pm.setKeyframe(format_path(__A2_FK_CTRL_PATH, robot),\n attribute='rotateX')\n pm.setKeyframe(format_path(__A3_FK_CTRL_PATH, robot),\n attribute='rotateX')\n pm.setKeyframe(format_path(__A4_FK_CTRL_PATH, robot),\n attribute='rotateZ')\n pm.setKeyframe(format_path(__A5_FK_CTRL_PATH, robot),\n attribute='rotateX')\n pm.setKeyframe(format_path(__A6_FK_CTRL_PATH, robot),\n attribute='rotateZ')\n\n # Key visibility of FK controllers\n for i in range(6):\n pm.setKeyframe(format_path(__FK_CTRLS_PATH, robot),\n attribute='visibility')\n except:\n pm.warning('Error setting FK keys in FK mode')\n\n # Key all IK elements\n try:\n pm.setKeyframe(target_ctrl_path, attribute='ik')\n pm.setKeyframe(target_ctrl_path, attribute='v', value=0)\n\n if pm.objExists(tool_ctrl_path):\n pm.setKeyframe(tool_ctrl_path, attribute='v')\n\n # Key tool controllers\n if pm.checkBox('cb_keyToolCtrl', query=True, value=True):\n if pm.objExists(tool_ctrl_path):\n pm.setKeyframe(tool_ctrl_path, attribute='translate')\n pm.setKeyframe(tool_ctrl_path, attribute='rotate')\n else:\n pm.setKeyframe(target_ctrl_path, attribute='translate')\n pm.setKeyframe(target_ctrl_path, attribute='rotate')\n\n except:\n pm.warning('Error setting IK keys in FK mode')","def match_keypoints(desc1, desc2, k=2, thresh=.9, matchertype=None):\n if not matchertype:\n # default is brute forcce\n matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n matches = matcher.knnMatch(desc1, desc2, k=3)\n print(matches)\n\n elif matchertype == \"FlannORB\":\n #\n FLANN_INDEX_LSH = 6\n search_params = dict(checks=50)\n index_params = dict(algorithm=FLANN_INDEX_LSH,\n table_number=6, # 12\n key_size=12, # 20\n multi_probe_level=1) # 2\n matcher = cv2.FlannBasedMatcher(index_params, search_params)\n matches = matcher.knnMatch(desc1, desc2, k=2)\n\n elif matchertype == \"FlannSURF\":\n FLANN_INDEX_KDTREE = 2\n index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=500)\n search_params = dict(checks=50)\n matcher = cv2.FlannBasedMatcher(index_params, search_params)\n matches = matcher.knnMatch(desc1, desc2, k=2)\n\n goodmatches = []\n for m, n in matches:\n if m.distance < thresh * n.distance:\n goodmatches.append(m)\n return goodmatches","def match(self, dc):\n raise NotImplemented","def secondary_keys(self):","def match(self, other):","def correspondence_points(img1, img2, tag='c'):\n if len(img1.shape) == 3:\n img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n if len(img2.shape) == 3:\n img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n\n detector = cv2.SURF(800)\n norm = cv2.NORM_L2\n flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)\n kp1, desc1 = detector.detectAndCompute(img1, None)\n kp2, desc2 = detector.detectAndCompute(img2, None)\n raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2\n p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)\n\n if len(p1) >= 4:\n H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)\n print '%d / %d inliers/matched' % (np.sum(status), len(status))\n status = status.reshape(-1) # flatten\n p1 = p1[status == 1]\n p2 = p2[status == 1]\n kp_pairs = [kp_pairs[i] for i in range(len(kp_pairs)) if status[i] == 1]\n else:\n # Just depend on the thresholding for filtering matches\n p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches, ratio=0.3)\n\n draw_correspondence_points(img1, img2, kp_pairs, tag=tag)\n return p1, p2, kp_pairs","def newmatch(self):\n\n objectpnts = self.kif.getObjectPcd()\n # normals\n objectnormals = tools.estimatenormals(objectpnts)","def match_source_key(self, match):\n raise NotImplementedError","def needleMatching(self):\r\n # productive\r\n profprint()\r\n modelNodes = slicer.mrmlScene.GetNodesByClass('vtkMRMLModelNode')\r\n nbNode = modelNodes.GetNumberOfItems()\r\n result = []\r\n found = []\r\n # print nbNode\r\n for nthNode in range(nbNode):\r\n node = slicer.mrmlScene.GetNthNodeByClass(nthNode, 'vtkMRMLModelNode')\r\n if node.GetID() not in found and node.GetAttribute('type') != 'Validation':\r\n dist = []\r\n polydata = node.GetPolyData()\r\n if polydata != None:\r\n bounds = polydata.GetBounds()\r\n for nthNode2 in range(nbNode):\r\n node2 = slicer.mrmlScene.GetNthNodeByClass(nthNode2, 'vtkMRMLModelNode')\r\n if node2.GetID() not in found and node2.GetAttribute('type') == 'Validation':\r\n polydata2 = node2.GetPolyData()\r\n if polydata2 != None and polydata2.GetNumberOfPoints() > 100 and polydata.GetNumberOfPoints() > 100:\r\n tipDistance = self.distTip(int(node.GetID().strip('vtkMRMLModelNode')) , int(node2.GetID().strip('vtkMRMLModelNode')))\r\n baseDistance = self.distBase(int(node.GetID().strip('vtkMRMLModelNode')) , int(node2.GetID().strip('vtkMRMLModelNode')))\r\n name = node.GetName()\r\n manualName = name.lstrip('auto-seg_').lstrip('manual-seg_').lstrip('obturator-seg_').lstrip('0123456789').lstrip('-ID-vtkMRMLModelNode').lstrip('0123456789-')\r\n if manualName==node2.GetName(): dist.append([tipDistance, node2.GetID(), node2.GetName()])\r\n # print tipDistance\r\n if dist != []:\r\n match = [min(dist)[0], min(dist)[1], node.GetID(), min(dist)[2]]\r\n result.append(match)\r\n found.append(min(dist)[1])\r\n found.append(node.GetID())\r\n node.GetDisplayNode().SetSliceIntersectionVisibility(1)\r\n # print result\r\n return result","def findMatchesBetweenImages(image_1, image_2):\n # matches - type: list of cv2.DMath\n matches = None\n # image_1_kp - type: list of cv2.KeyPoint items.\n image_1_kp = None\n # image_1_desc - type: numpy.ndarray of numpy.uint8 values.\n image_1_desc = None\n # image_2_kp - type: list of cv2.KeyPoint items.\n image_2_kp = None\n # image_2_desc - type: numpy.ndarray of numpy.uint8 values.\n image_2_desc = None\n # WRITE YOUR CODE HERE.\n\n sift = cv2.ORB_create()\n image_1_kp, image_1_desc = sift.detectAndCompute(image_1, None)\n image_2_kp, image_2_desc = sift.detectAndCompute(image_2, None)\n\n # bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n # matches = bf.match(image_1_desc, image_2_desc)\n # matches = sorted(matches, key = lambda x:x.distance)\n # matches = matches[:10]\n\n bf = cv2.BFMatcher()\n matches = bf.knnMatch(image_1_desc, image_2_desc, k=2)\n\n # Apply ratio test\n good = []\n for m, n in matches:\n print(m.distance, n.distance, m.distance < .75*n.distance)\n if m.distance < (0.75 * n.distance):\n good.append([m])\n\n # We coded the return statement for you. You are free to modify it -- just\n # make sure the tests pass.\n print(len(good), good)\n return image_1_kp, image_2_kp, matches\n # END OF FUNCTION.","def needleMatching(self):\n #productive\n profprint()\n modelNodes=slicer.mrmlScene.GetNodesByClass('vtkMRMLModelNode')\n nbNode=modelNodes.GetNumberOfItems()\n result=[]\n found=[]\n #print nbNode\n for nthNode in range(nbNode):\n node=slicer.mrmlScene.GetNthNodeByClass(nthNode,'vtkMRMLModelNode')\n if node.GetID() not in found and node.GetAttribute('type')!='Validation':\n dist=[]\n polydata = node.GetPolyData()\n if polydata!=None:\n bounds = polydata.GetBounds()\n for nthNode2 in range(nbNode):\n node2=slicer.mrmlScene.GetNthNodeByClass(nthNode2,'vtkMRMLModelNode')\n if node2.GetID() not in found and node2.GetAttribute('type')=='Validation':\n polydata2 = node2.GetPolyData()\n if polydata2!=None and polydata2.GetNumberOfPoints()>100 and polydata.GetNumberOfPoints()>100:\n\n axialDistance=self.distTip( int(node.GetID().strip('vtkMRMLModelNode')) , int(node2.GetID().strip('vtkMRMLModelNode')))\n \n dist.append([axialDistance,node2.GetID()])\n # print axialDistance\n \n if dist!=[]:\n match=[min(dist)[0],min(dist)[1],node.GetID()]\n result.append(match)\n found.append(min(dist)[1])\n found.append(node.GetID()) \n node.GetDisplayNode().SetSliceIntersectionVisibility(1)\n # print result\n return result","def find(self, image, k=None, ratio=None):\n if not self._targets:\n return []\n k = 2 if k is None else k\n ratio = 0.75 if ratio is None else ratio\n keypoints, descriptors = self._detector.detectAndCompute(image, None)\n if len(keypoints) < self.min_match_count:\n return []\n matches = self._matcher.knnMatch(descriptors, k=int(k))\n matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * ratio]\n if len(matches) < self.min_match_count:\n return []\n matches_by_id = [[] for _ in xrange(len(self._targets))]\n for m in matches:\n matches_by_id[m.imgIdx].append(m)\n tracked = []\n for imgIdx, matches in enumerate(matches_by_id):\n if len(matches) < self.min_match_count:\n continue\n target = self._targets[imgIdx]\n p0 = [target.keypoints[m.trainIdx].pt for m in matches]\n p1 = [keypoints[m.queryIdx].pt for m in matches]\n p0, p1 = np.float32((p0, p1))\n H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)\n status = status.ravel() != 0\n if status.sum() < self.min_match_count:\n continue\n p0, p1 = np.int32((p0, p1))\n inliers = [((x0, y0), (x1, y1)) for (x0, y0), (x1, y1), s in zip(p0, p1, status) if s]\n outliers = [((x0, y0), (x1, y1)) for (x0, y0), (x1, y1), s in zip(p0, p1, status) if not s]\n quad = cv2.perspectiveTransform(target.quad.reshape(1, -1, 2), H).reshape(-1, 2)\n track = TrackedTarget(target=target, image=image, inliers=inliers, outliers=outliers, H=H, quad=quad)\n tracked.append(track)\n tracked.sort(key = lambda t: len(t.inliers), reverse=True)\n return tracked","def match_features(kp1, kp2, des1, des2):\n FLANN_INDEX_LSH = 6\n index_params= dict(algorithm = FLANN_INDEX_LSH,\n table_number = 6, # 12\n key_size = 12, # 20\n multi_probe_level = 2) #2\n\n search_params = dict(checks=50) # or pass empty dictionary\n flann = cv2.FlannBasedMatcher(index_params,search_params)\n matches = flann.knnMatch(des1,des2,k=2)\n good = []\n for (m,n) in matches:\n if m.distance < 0.8*n.distance: ## Lowe's ratio imp for tuning\n good.append(m)\n\n if len(good) < 20:\n return []\n return good","def secondary_keys_dicts(self):","def key_ik_fk(*args):\n if not pm.window(\"mimic_win\", exists=True):\n return\n\n current_tab = pm.tabLayout('switcher_tab_layout',\n query=True,\n selectTabIndex=True)\n\n try:\n if current_tab == 1:\n key_ik()\n elif current_tab == 2:\n key_fk()\n except:\n pm.warning('Error keying IK/FK')","def delete_ik_fk_keys(*args):\n if not check_robot_selection():\n pm.warning('No robots selected; ' \\\n 'Select at least one robot.')\n\n keyed_attrs = {__TARGET_CTRL_PATH: ['ik',\n 'visibility',\n 'ikSolution1',\n 'ikSolution2',\n 'ikSolution3'],\n __FK_CTRLS_PATH: ['visibility'],\n __A1_FK_CTRL_PATH: ['rotateY'],\n __A2_FK_CTRL_PATH: ['rotateX'],\n __A3_FK_CTRL_PATH: ['rotateX'],\n __A4_FK_CTRL_PATH: ['rotateZ'],\n __A5_FK_CTRL_PATH: ['rotateX'],\n __A6_FK_CTRL_PATH: ['rotateZ']}\n\n robots = get_robot_roots()\n\n current_frame = pm.currentTime()\n for robot in robots:\n target_ctrl_path = get_target_ctrl_path(robot)\n tool_ctrl_path = get_tool_ctrl_path(robot)\n # Check if there's a keyframe set on the target_CTRL.ik attribute\n key = pm.keyframe(target_ctrl_path,\n attribute='ik',\n query=True,\n time=current_frame)\n\n # If there is no keyframe set on the IK attribute, continue to the\n # next robot\n if not key:\n pm.warning('{} has no IK|FK keyframe at frame {}' \\\n .format(robot, current_frame))\n continue\n\n # If there is a keyframe on the IK attribute, we also check if there's\n # a keyframe on an FK controller as well, as we only consider there to\n # be a proper IK or FK keyframe if both are true\n # Note, we only need to check a single FK controller as they should all\n # be keyframed (or not) together\n fk_test_handle_path = format_path(__A1_FK_CTRL_PATH + '.rotateY', robot)\n fk_key = pm.keyframe(fk_test_handle_path,\n query=True,\n time=current_frame)\n # If there is no keyframe set on the FK controller attribute,\n # continue to the next robot\n if not fk_key:\n pm.warning('{} has no IK|FK keyframe at frame {}' \\\n .format(robot, current_frame))\n continue \n\n for obj in keyed_attrs:\n for attr in keyed_attrs[obj]:\n pm.cutKey(format_path(obj, robot),\n time=current_frame,\n attribute=attr,\n option=\"keys\")\n\n if pm.objExists(tool_ctrl_path):\n pm.cutKey(tool_ctrl_path,\n time=current_frame,\n attribute='visibility',\n option=\"keys\")\n\n if pm.checkBox('cb_keyToolCtrl', query=True, value=True):\n if pm.objExists(tool_ctrl_path):\n pm.cutKey(tool_ctrl_path,\n time=current_frame,\n attribute='translate',\n option=\"keys\")\n pm.cutKey(tool_ctrl_path,\n time=current_frame,\n attribute='rotate',\n option=\"keys\")\n else:\n pm.cutKey(target_ctrl_path,\n time=current_frame,\n attribute='translate',\n option=\"keys\")\n pm.cutKey(target_ctrl_path,\n time=current_frame,\n attribute='rotate',\n option=\"keys\")","def altloc_match(self, other: AtomKey) -> bool:\n ...","def visulize_matches(matches, k2, k1, img2, img1):\n\n import scipy as sp\n img2 = cv.cvtColor(img2, cv.COLOR_GRAY2BGR)\n h1, w1 = img1.shape[:2]\n h2, w2 = img2.shape[:2]\n view = sp.zeros((max(h1, h2), w1 + w2, 3), sp.uint8)\n view[:h1, :w1, :] = img1 \n view[:h2, w1:, :] = img2\n view[:, :, 1] = view[:, :, 0] \n view[:, :, 2] = view[:, :, 0]\n\n for m in matches:\n m = m[0]\n # draw the keypoints\n # print m.queryIdx, m.trainIdx, m.distance\n color = tuple([sp.random.randint(0, 255) for _ in xrange(3)])\n pt1 = (int(k1[m.queryIdx].pt[0]), int(k1[m.queryIdx].pt[1]))\n pt2 = (int(k2[m.trainIdx].pt[0] + w1), int(k2[m.trainIdx].pt[1]))\n\n cv.line(view, pt1, pt2, color)\n return view","def test_getCpfRelations(self):\n pass","def lookup(self, key):","def relationships(self):","def keys(targets):","def matches(self):\n pass","def theidfobjectmentioningobjs(idfindex, keyindex, objindex):\n idf, edges = eppystuff.an_idfedges(idfindex)\n objnames = idf_helpers.idfobjectkeys(idf)\n objname = objnames[keyindex]\n idfobjects = idf.idfobjects[objname]\n idfobject = idfobjects[objindex]\n from eppy import walk_hvac\n try:\n idfobjectname = idfobject.Name\n except BadEPFieldError as e:\n idfobjectname = idfobject.Zone_Name\n \n nextnodes = walk_hvac.nextnode(edges, idfobjectname)\n nextobjs = [eppystuff.hvacname2idfobj(idf, nnode) for nnode in nextnodes]\n keyobjids = [eppystuff.idfobjectindices(idf, nobj) for nobj in nextobjs]\n nurls = [\"../../%s/%s\" % (key_id, obj_id) for key_id, obj_id in keyobjids]\n nextlinks = ['<a href=%s>%s</a>' % (url, nnode)\n for nnode, url in zip(nextnodes, nurls)]\n firstlines = [\n \"HVAC connections from %s named '%s'\" % (objname, idfobjectname),\n \"\",\n \"Next Objects\",\n ]\n betweenlines = [\"\", \"Previous Objects\"]\n prevnodes = walk_hvac.prevnode(edges, idfobjectname)\n prevobjs = [eppystuff.hvacname2idfobj(idf, pnode) for pnode in prevnodes]\n keyobjids = [eppystuff.idfobjectindices(idf, nobj) for nobj in prevobjs]\n try:\n purls = [\"../../%s/%s\" % (key_id, obj_id) \n for key_id, obj_id in keyobjids]\n except TypeError as e:\n purls = []\n prevlinks = ['<a href=%s>%s</a>' % (url, pnode)\n for pnode, url in zip(prevnodes, purls)]\n # image snippet\n onlythis = [idfobjectname, ] + nextnodes + prevnodes\n trimmed = eppystuff.trimedges(edges, onlythis)\n from eppy.useful_scripts import loopdiagram\n imgname = '%s_%s_%s' % (idfindex, keyindex, objindex)\n eppystuff.save_imagesnippets(IMGFOLDER,imgname, trimmed)\n imgline = ['<img src=\"../../../../../static/%s.png\" alt=\"snippet\">' % (imgname, )]\n html = '<br>'.join(firstlines + nextlinks + betweenlines + prevlinks + imgline)\n return html","def keyEquivalent( self ):\n\t\treturn None","def fk_to_ik(node):\n # Get relevant data\n ik_pole_off = get_parent(node.ik_pole_conn)\n\n world_trans_ik_pole_off = get_world_trans(ik_pole_off)\n world_trans_fk_01 = get_world_trans(node.fk_01_conn)\n world_trans_fk_02 = get_world_trans(node.fk_02_conn)\n world_trans_fk_03 = get_world_trans(node.fk_03_conn)\n world_trans_ik_pole = get_world_trans(node.ik_pole_conn)\n\n world_rot_fk_03 = get_world_rot(node.fk_03_conn)\n\n # calculate ik pole position\n ik_pole_mid_point = (world_trans_fk_01 + world_trans_fk_03) / 2\n ik_pole_base = world_trans_fk_02 - ik_pole_mid_point\n\n # Handle the case when the leg is fully stretched\n if ik_pole_base.length() <= 0.0001:\n rot_fk_01 = get_rot_as_quat(node.fk_01_conn)\n rot_fk_02 = get_rot_as_quat(node.fk_02_conn)\n\n rot = rot_fk_01 * rot_fk_02\n\n ik_pole_base = oMa.MVector(2 * (rot.x * rot.z + rot.w * rot.y),\n 2 * (rot.y * rot.z - rot.w * rot.x),\n 1 - 2 * (rot.x * rot.x + rot.y * rot.y))\n\n ik_pole_len = (world_trans_ik_pole - world_trans_fk_02).length()\n\n pos_ik_pole = world_trans_fk_02 + ik_pole_base.normalize() * ik_pole_len - world_trans_ik_pole_off\n\n # Get the destination MPlugs\n ik_main_trans_plugs = get_trans_plugs(node.ik_main_conn)\n ik_main_rot_plugs = get_rot_plugs(node.ik_main_conn)\n ik_pole_trans_plugs = get_trans_plugs(node.ik_pole_conn)\n\n # Set the new values\n for i, plug in enumerate(ik_main_trans_plugs):\n plug.setFloat(world_trans_fk_03[i])\n\n for i, plug in enumerate(ik_main_rot_plugs):\n plug.setMAngle(oMa.MAngle(world_rot_fk_03[i], oMa.MAngle.kRadians))\n\n for i, plug in enumerate(ik_pole_trans_plugs):\n plug.setFloat(pos_ik_pole[i])","def MatchRelationshipConfrontation(C):\n for c in C:\n if c.relationships is None:\n continue\n for i, longname in enumerate(c.relationships):\n found = False\n for cor in C:\n if longname.lower() == cor.longname.lower():\n c.relationships[i] = cor\n found = True\n return C","def find_matching_points(img1, img2, max_pix_movement=50, normalize=True, show=False):\n\n # Initiate ORB detector\n orb = cv2.ORB_create()\n\n # find the keypoints and descriptors with ORB\n kp1, des1 = orb.detectAndCompute(img1, None)\n kp2, des2 = orb.detectAndCompute(img2, None)\n\n # create BFMatcher object\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n # Match descriptors.\n matches = bf.match(des1,des2)\n # Sort them in the order of their distance.\n matches = sorted(matches, key = lambda x:x.distance)\n # Draw first 10 matches.\n if show:\n img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:500], None,flags=2)\n plt.imshow(img3),plt.show()\n # Get the matching keypoints for each of the images\n\n list_kp1 = []\n list_kp2 = []\n for mat in matches:\n img1_idx = mat.queryIdx\n img2_idx = mat.trainIdx\n\n # x - columns\n # y - rows\n list_kp1.append(kp1[img1_idx].pt)\n list_kp2.append(kp2[img2_idx].pt)\n\n n_kp1, n_kp2 = np.float32(list_kp1), np.float32(list_kp2)\n n_kp1 /= np.asarray([img1.shape[1], img1.shape[0]], np.float32)\n n_kp2 /= np.asarray([img2.shape[1], img2.shape[0]], np.float32)\n n_kp1 = n_kp1 * 2. - 1.\n n_kp2 = n_kp2 * 2. - 1.\n\n return np.int32(list_kp1), np.int32(list_kp2), n_kp1, n_kp2","def nn_match(descs1, descs2):\n # diff = descs1[:, None, :] - descs2[None, :, :]\n # diff = np.linalg.norm(diff, ord=2, axis=2)\n # indices = np.argmin(diff, axis=1)\n\n flann = cv2.FlannBasedMatcher_create()\n matches = flann.match(descs1.astype(np.float32), descs2.astype(np.float32))\n indices = [x.trainIdx for x in matches]\n\n return indices","def check_keys(self):","def match(image1,image2,threshold,useRansac=False,t_orientation=30,t_scale=0.5):\r\n im1, keypoints1, descriptors1 = ReadKeys(image1)\r\n im2, keypoints2, descriptors2 = ReadKeys(image2)\r\n #\r\n # REPLACE THIS CODE WITH YOUR SOLUTION (ASSIGNMENT 5, QUESTION 3)\r\n #\r\n # Generate five random matches (for testing purposes)\r\n # matched_pairs = []\r\n # num = 5\r\n # for i in range(num):\r\n # matched_pairs.append([keypoints1[i],keypoints2[i]])\r\n # return DisplayMatches(im1, im2, matched_pairs)\r\n\r\n # END OF SECTION OF CODE TO REPLACE\r\n #\r\n\r\n #q3\r\n matched_pairs = []\r\n between_angles = np.arccos(np.dot(descriptors1, np.transpose(descriptors2)))\r\n for i, row in enumerate(between_angles):\r\n \tratio = sorted(row)[0] / sorted(row)[1]\r\n \tif ratio <= threshold:\r\n\t \tmatched_pairs.append([keypoints1[i], keypoints2[np.where(row == sorted(row)[0])[0][0]]])\r\n # print(matched_pairs)\r\n if useRansac is False:\r\n return DisplayMatches(im1, im2, matched_pairs)\r\n\t# \r\n\r\n #q4\r\n repetition = 10\r\n subsets = [[]] * repetition\r\n for i in range(repetition):\r\n r = random.randint(0, len(matched_pairs))\r\n for match in matched_pairs:\r\n ds1, ds2 = matched_pairs[r][1][2]/matched_pairs[r][0][2], match[1][2]/match[0][2]\r\n do1, do2 = (matched_pairs[r][1][3]-matched_pairs[r][0][3]), (match[1][3]-match[0][3])\r\n if abs(ds2 - ds1) <= t_scale * ds1 and abs(do2 - do1) % (2 * math.pi) <= t_orientation:\r\n subsets[i].append(match)\r\n\r\n max_i, max_len = 0, subsets[0]\r\n for i in range(10):\r\n l = len(subsets[i])\r\n if l > max_len:\r\n max_len = l\r\n max_i = i\r\n\r\n im3 = DisplayMatches(im1, im2, subsets[max_i])\r\n return im3","def match(self, other):\n matches = match_descriptors(self.base_view.descriptors, other.descriptors,\n cross_check=True)\n matches = pd.Series({m[0]: m[1] for m in matches}).reindex(\n self._match_table.index)\n self._match_table[other.position.id] = matches","def compute_matches(self):\n\t\tself.local_database[\"figure_number\"] = [0] * len(self.local_database[\"feature_vectors\"])\n\t\tForensics = wbForensicsHOG(Database=self.local_database)\n\t\tForensics.KDTree_pairs(leaf_size = len(self.local_database)+1)\n\t\tForensics.d_rank(pairs=Forensics.pairs, distances=Forensics.dists, ratios=Forensics.ratios)\n\n\t\tself.local_matches = Forensics.Dist_Rank","def IK_geometric(dh_params, pose):\n pass","def _digest_fkeys(self):\n for schema in self.schemas.values():\n for fkey in schema._fkeys.values():\n ref_col = fkey._fkey_doc.get('referenced_columns', [{}])[0]\n pk_sname, pk_tname = ref_col.get('schema_name'), ref_col.get('table_name')\n if pk_sname in self.schemas and pk_tname in self.schemas[pk_sname].tables:\n fkey.pk_table = self.schemas[pk_sname].tables[pk_tname]\n else:\n fkey.pk_table = self._unknown_table","def test4(self):\n tgts = [ ('CCC(O)C(=O)O',\n ('CCC','OCC','OCC=O','OCCO','CCCC','OC=O','CC(O)C')),\n ]\n for smi,matches in tgts:\n m = Chem.MolFromSmiles(smi)\n fp1 = Chem.RDKFingerprint(m,2,7,9192,4,0)\n obs = fp1.GetOnBits()\n for match in matches:\n m2 = Chem.MolFromSmiles(match)\n fp2 = Chem.RDKFingerprint(m2,2,7,9192,4,0)\n v1,v2 = DataStructs.OnBitProjSimilarity(fp2,fp1)\n assert feq(v1,1.0000),'substruct %s not properly contained in %s'%(match,smi)","def associated_coroot(self):","def matchById(firstcat, othercat, otherid='SeqNr', selfid='SeqNr'):\n order = {}\n for i, x in enumerate(firstcat[selfid]):\n order[x] = i\n\n keeporder = []\n for x in othercat[otherid]:\n if x in order:\n keeporder.append(order[x])\n\n keep = np.array(keeporder)\n matched = firstcat[keep]\n print \"INFO: %i matched galaxies kept\" % len(matched)\n return matched","def match(self, item):","def _ik_and_fk_aligned(ik_ctrl, tcp_handle):\n\n # Define some small number to threshold our output\n delta = .0001\n\n # Initialize variables\n # translation_is_aligned = False\n # rotation_is_aligned = False\n ik_fk_are_aligned = False\n\n # Find the translation of each object and compare them\n ik_trans = pm.xform(ik_ctrl, q=True, rp=True, ws=True)\n tcp_trans = pm.xform(tcp_handle, q=True, rp=True, ws=True)\n\n # Find the distance between the ik controller and the tcp handle\n trans_diff = math.sqrt((ik_trans[0] - tcp_trans[0]) ** 2\n + (ik_trans[1] - tcp_trans[1]) ** 2\n + (ik_trans[2] - tcp_trans[2]) ** 2)\n\n if round(trans_diff, 6) < delta:\n ik_fk_are_aligned = True\n\n return ik_fk_are_aligned","def matchModelPose(self):\n\n pass","def _add_matches(self):\r\n for record in self.records:\r\n match_dict={key_to_track: record.get(key_to_track)\r\n for key_to_track in self.key_matcher.keys()}\r\n self.key_matcher.add(obj=record,\r\n match_dict=match_dict)","def matches(self, target):\n raise NotImplementedError()","def MatchMatrixs (self,Node1,Node2):\n\n T1Native_Node = Node1\n T1Native_Matrix = slicer.util.arrayFromVolume(T1Native_Node)\n DimN = T1Native_Matrix.shape\n T1Enhanced_Node = Node2\n T1Enhanced_Matrix = slicer.util.arrayFromVolume(T1Enhanced_Node)\n DimE = T1Enhanced_Matrix.shape\n\n NMatrix = self.GetIJKToRASnpArray(T1Native_Node)\n NVector = NMatrix[:-1,-1]\n EMatrix = self.GetIJKToRASnpArray(T1Enhanced_Node)\n EVector = EMatrix[:-1,-1]\n NPixelSize = [np.linalg.norm(NMatrix[:-1,0]), np.linalg.norm(NMatrix[:-1,1])]\n EPixelSize = [np.linalg.norm(EMatrix[:-1,0]), np.linalg.norm(EMatrix[:-1,1])]\n\n Niversor = NMatrix[:-1,0]/NPixelSize[0]\n Njversor = NMatrix[:-1,1]/NPixelSize[1]\n Nkversor = np.round(np.cross(Niversor,Njversor),3)\n Nkstep = round(np.linalg.norm(NMatrix[:-1,2]),3)\n\n Eiversor = EMatrix[:-1,0]/EPixelSize[0]\n Ejversor = EMatrix[:-1,1]/EPixelSize[1]\n Ekversor = np.round(np.cross(Eiversor,Ejversor),3)\n Ekstep = round(np.linalg.norm(EMatrix[:-1,2]),3)\n print(Nkversor,Ekversor,Nkstep,Ekstep,NVector,EVector,(NVector-EVector).dot(Ekversor))\n if not ( np.sum(Nkversor==Ekversor) == 3 and Nkstep==Ekstep and ((NVector-EVector).dot(Ekversor)) == 0 ): # it verifies if the slices are oriented in the same direction, with the same step between slices and if the first images are complanar.\n slicer.util.warningDisplay('The geometry of the LL Native and LL Enhanced volume doesn\\'t match. It could deteriorate the ECV map', windowTitle= 'Warning')\n\n if (DimE == DimN):\n T1_ECVMappingLogic.setupNodeFromNode(self,self.ECVMapNode , self.NativeT1_Selector.currentNode()) \n return [T1Native_Matrix,T1Enhanced_Matrix]\n if (DimE[1:3] == DimN[1:3]):\n k = min([DimE[1],DimN[1]])\n T1_ECVMappingLogic.setupNodeFromNode(self,self.ECVMapNode , self.NativeT1_Selector.currentNode())\n return [T1Native_Matrix[:k,:,:],T1Enhanced_Matrix[:k,:,:]]\n\n jN = np.arange(0,DimN[2]*NPixelSize[1],NPixelSize[1])+NPixelSize[1]/2+(NVector-EVector).dot(Njversor)\n iN = np.arange(0,DimN[1]*NPixelSize[0],NPixelSize[0])+NPixelSize[0]/2+(NVector-EVector).dot(Niversor)\n iE = np.arange(0,DimE[1]*EPixelSize[0],EPixelSize[0])+EPixelSize[0]/2\n jE = np.arange(0,DimE[2]*EPixelSize[1],EPixelSize[1])+EPixelSize[1]/2 \n if DimE[1] > DimN[1]: ## I concidered a square image\n T1Nreshaped = np.zeros(DimE)\n for k in range(DimN[0]):\n f = interpolate.interp2d(iN, jN, np.nan_to_num(T1Native_Matrix[k,:,:]), fill_value = 0)\n T1Nreshaped[k,:,:] = f(iE, jE)\n T1Ereshaped = T1Enhanced_Matrix[:k+1,:,:]\n T1_ECVMappingLogic.setupNodeFromNode(self,self.ECVMapNode , self.EnhancedT1_Selector.currentNode())\n return [T1Nreshaped,T1Ereshaped]\n else:\n T1Ereshaped = np.zeros(DimN)\n for k in range(DimE[0]):\n f = interpolate.interp2d(iE, jE, np.nan_to_num(T1Enhanced_Matrix[k,:,:]), fill_value = 0)\n T1Ereshaped[k,:,:] = f(iN, jN) \n T1Nreshaped = T1Native_Matrix[:k+1,:,:]\n T1_ECVMappingLogic.setupNodeFromNode(self,self.ECVMapNode , self.NativeT1_Selector.currentNode()) \n return [T1Nreshaped,T1Ereshaped]","def matches(self, accession):\n pass","def vsone_feature_matching(kpts1, vecs1, kpts2, vecs2, dlen_sqrd2, cfgdict={},\n flann1=None, flann2=None, verbose=None):\n import vtool as vt\n import pyflann\n from vtool import spatial_verification as sver\n #import vtool as vt\n sver_xy_thresh = cfgdict.get('sver_xy_thresh', .01)\n ratio_thresh = cfgdict.get('ratio_thresh', .625)\n refine_method = cfgdict.get('refine_method', 'homog')\n symmetric = cfgdict.get('symmetric', False)\n K = cfgdict.get('K', 1)\n Knorm = cfgdict.get('Knorm', 1)\n #ratio_thresh = .99\n # GET NEAREST NEIGHBORS\n checks = 800\n #pseudo_max_dist_sqrd = (np.sqrt(2) * 512) ** 2\n #pseudo_max_dist_sqrd = 2 * (512 ** 2)\n if verbose is None:\n verbose = True\n\n flann_params = {'algorithm': 'kdtree', 'trees': 8}\n if flann1 is None:\n flann1 = vt.flann_cache(vecs1, flann_params=flann_params, verbose=verbose)\n\n #print('symmetric = %r' % (symmetric,))\n if symmetric:\n if flann2 is None:\n flann2 = vt.flann_cache(vecs2, flann_params=flann_params, verbose=verbose)\n\n try:\n try:\n num_neighbors = K + Knorm\n fx2_to_fx1, fx2_to_dist = normalized_nearest_neighbors(flann1, vecs2, num_neighbors, checks)\n #fx2_to_fx1, _fx2_to_dist = flann1.nn_index(vecs2, num_neighbors=K, checks=checks)\n if symmetric:\n fx1_to_fx2, fx1_to_dist = normalized_nearest_neighbors(flann2, vecs1, K, checks)\n\n except pyflann.FLANNException:\n print('vecs1.shape = %r' % (vecs1.shape,))\n print('vecs2.shape = %r' % (vecs2.shape,))\n print('vecs1.dtype = %r' % (vecs1.dtype,))\n print('vecs2.dtype = %r' % (vecs2.dtype,))\n raise\n if symmetric:\n is_symmetric = flag_symmetric_matches(fx2_to_fx1, fx1_to_fx2)\n fx2_to_fx1 = fx2_to_fx1.compress(is_symmetric, axis=0)\n fx2_to_dist = fx2_to_dist.compress(is_symmetric, axis=0)\n\n assigntup = assign_unconstrained_matches(fx2_to_fx1, fx2_to_dist)\n\n fx2_match, fx1_match, fx1_norm, match_dist, norm_dist = assigntup\n fm_ORIG = np.vstack((fx1_match, fx2_match)).T\n fs_ORIG = 1 - np.divide(match_dist, norm_dist)\n # APPLY RATIO TEST\n fm_RAT, fs_RAT, fm_norm_RAT = ratio_test(fx2_match, fx1_match, fx1_norm,\n match_dist, norm_dist,\n ratio_thresh)\n\n # SPATIAL VERIFICATION FILTER\n #with ut.EmbedOnException():\n match_weights = np.ones(len(fm_RAT))\n svtup = sver.spatially_verify_kpts(kpts1, kpts2, fm_RAT, sver_xy_thresh,\n dlen_sqrd2, match_weights=match_weights,\n refine_method=refine_method)\n if svtup is not None:\n (homog_inliers, homog_errors, H_RAT) = svtup[0:3]\n else:\n H_RAT = np.eye(3)\n homog_inliers = []\n fm_RAT_SV = fm_RAT.take(homog_inliers, axis=0)\n fs_RAT_SV = fs_RAT.take(homog_inliers, axis=0)\n fm_norm_RAT_SV = fm_norm_RAT[homog_inliers]\n\n top_percent = .5\n top_idx = ut.take_percentile(fx2_to_dist.T[0].argsort(), top_percent)\n fm_TOP = fm_ORIG.take(top_idx, axis=0)\n fs_TOP = fx2_to_dist.T[0].take(top_idx)\n #match_weights = np.ones(len(fm_TOP))\n #match_weights = (np.exp(fs_TOP) / np.sqrt(np.pi * 2))\n match_weights = 1 - fs_TOP\n #match_weights = np.ones(len(fm_TOP))\n svtup = sver.spatially_verify_kpts(kpts1, kpts2, fm_TOP, sver_xy_thresh,\n dlen_sqrd2, match_weights=match_weights,\n refine_method=refine_method)\n if svtup is not None:\n (homog_inliers, homog_errors, H_TOP) = svtup[0:3]\n np.sqrt(homog_errors[0] / dlen_sqrd2)\n else:\n H_TOP = np.eye(3)\n homog_inliers = []\n fm_TOP_SV = fm_TOP.take(homog_inliers, axis=0)\n fs_TOP_SV = fs_TOP.take(homog_inliers, axis=0)\n\n matches = {\n 'ORIG' : MatchTup2(fm_ORIG, fs_ORIG),\n 'RAT' : MatchTup3(fm_RAT, fs_RAT, fm_norm_RAT),\n 'RAT+SV' : MatchTup3(fm_RAT_SV, fs_RAT_SV, fm_norm_RAT_SV),\n 'TOP' : MatchTup2(fm_TOP, fs_TOP),\n 'TOP+SV' : MatchTup2(fm_TOP_SV, fs_TOP_SV),\n }\n output_metdata = {\n 'H_RAT': H_RAT,\n 'H_TOP': H_TOP,\n }\n\n except MatchingError:\n fm_ERR = np.empty((0, 2), dtype=np.int32)\n fs_ERR = np.empty((0, 1), dtype=np.float32)\n H_ERR = np.eye(3)\n matches = {\n 'ORIG' : MatchTup2(fm_ERR, fs_ERR),\n 'RAT' : MatchTup3(fm_ERR, fs_ERR, fm_ERR),\n 'RAT+SV' : MatchTup3(fm_ERR, fs_ERR, fm_ERR),\n 'TOP' : MatchTup2(fm_ERR, fs_ERR),\n 'TOP+SV' : MatchTup2(fm_ERR, fs_ERR),\n }\n output_metdata = {\n 'H_RAT': H_ERR,\n 'H_TOP': H_ERR,\n }\n\n return matches, output_metdata","def try1():\n path = '/Users/mayankkejriwal/datasets/eswc2017/disasters/'\n model = Word2Vec.load_word2vec_format(path+'GoogleNews-vectors-negative300.bin', binary=True)\n model.init_sims(replace=True)\n keys = ['charlotte', 'Charlotte', 'yorktown', 'LA']\n for key in keys:\n try:\n # print model.most_similar(positive=['woman', 'king'], negative=['man'])\n j = model[key]\n print 'found...',\n print key\n except KeyError:\n print 'not found...',\n print key\n continue\n print model.similarity('charlotte', 'carolina')\n print model.similarity('LA', 'California')","def hit(self):","def test_two_referrers(dumper, db):\n dumper.reader.load_db(\n db.create_sample(\n 5,\n fkeys=[\n (\"table1\", \"t2id\", \"table2\", \"id\"),\n (\"table1\", \"t3id\", \"table3\", \"id\"),\n (\"table2\", \"t24id\", \"table4\", \"id\"),\n (\"table3\", \"t34id\", \"table4\", \"id\"),\n ],\n )\n )\n dumper.add_config({\"db_objects\": [{\"name\": \"table1\"}]})\n dumper.perform_dump()\n objs = [obj for obj, match in dumper.writer.dumped if isinstance(obj, Table)]\n assert len(objs) == 4\n\n (match,) = [match for obj, match in dumper.writer.dumped if obj.name == \"table4\"]\n assert len(match.referenced_by) == 2\n assert sorted(fkey.name for fkey in match.referenced_by) == [\n \"t24id_table4_id_fkey\",\n \"t34id_table4_id_fkey\",\n ]","def test_fkey_nav_rec(dumper, db):\n dumper.reader.load_db(\n db.create_sample(\n 4,\n fkeys=[\n (\"table1\", \"t2id\", \"table2\", \"id\"),\n (\"table2\", \"t3id\", \"table3\", \"id\"),\n ],\n )\n )\n dumper.add_config({\"db_objects\": [{\"name\": \"table1\"}]})\n dumper.perform_dump()\n objs = [obj for obj, match in dumper.writer.dumped if isinstance(obj, Table)]\n assert len(objs) == 3","def fit(self):\n\n self.bow_archives_by_paperid = {userid: [self.dictionary.doc2bow(doc) for doc in archive] \\\n for userid, archive in self.kp_archives_by_paperid.items()}\n\n self.bow_archives_by_userid = {userid: [self.dictionary.doc2bow(doc) for doc in archive] \\\n for userid, archive in self.kp_archives_by_userid.items()}\n\n flattened_archives = [\n bow for archive in self.bow_archives_by_paperid.values() for bow in archive]\n\n self.index = SparseMatrixSimilarity(\n [self.tfidf[bow] for bow in flattened_archives],\n num_features=len(self.dictionary)\n )","def test_desy_keyword_translation(self):\n spi_search = \"find dk \\\"B --> pi pi\\\"\"\n inv_search = \"695__a:\\\"B --> pi pi\\\"\"\n self._compare_searches(inv_search, spi_search)","def match(desc1,desc2):\n desc1 = array([d/linalg.norm(d) for d in desc1])\n desc2 = array([d/linalg.norm(d) for d in desc2])\n dist_ratio = 0.6\n desc1_size = desc1.shape\n matchscores = zeros((desc1_size[0],1),'int')\n desc2t = desc2.T # precompute matrix transpose\n for i in range(desc1_size[0]):\n dotprods = dot(desc1[i, :], desc2t) # vector of dot products\n dotprods *= 0.9999\n # inverse cosine and sort, return index for features in second image\n indx = argsort(arccos(dotprods))\n # check if nearest neighbor has angle less than dist_ratio times 2nd\n if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\n matchscores[i] = int(indx[0])\n return matchscores","def beam_search(X, u, w, b, relLabels):\n\n candidate_paths = [[] for _ in range(10)] # contains the candidate label sets\n candidate_vals =[[] for _ in range(10)] # contains the label values (-1/1) for each candidate set\n candidate_scores = [0. for _ in range(10)]\n min_score = -1000\n\n iter = 0\n start = 0\n while True:\n # print(\"Iter: \", iter)\n intermediate_paths = {}\n # intermediate_paths_val = []\n interim_scores = []\n hash_table = {}\n\n cnt_paths = 0\n for cp in range(5):\n labels_curr = candidate_paths[cp]\n labels_val_curr = candidate_vals[cp]\n scores_curr = candidate_scores[cp]\n Y = -np.ones((10, 1))\n for lv in range(len(labels_val_curr)):\n Y[labels_curr[lv]] = labels_val_curr[lv]\n\n for l in range(10):\n candidate_interim = labels_curr[:]\n candidate_vals_interim = labels_val_curr[:]\n # if l in labels_curr:\n # continue\n\n temp_relLabels = []\n for lc in range(len(labels_curr)):\n temp_relLabels.extend(relLabels[labels_curr[lc]])\n\n # temp_relLabels = np.array(list(set(temp_relLabels)))\n temp_relLabels = np.array(list(set(relLabels[l]).intersection(set(labels_curr))))\n model_pos = returnModelVal(X, Y, 1.0, u[l], u[l], b[l][0], np.array(temp_relLabels))\n candidate_interim.append(l)\n\n if model_pos < 0:\n # print('hello')\n candidate_vals_interim.append(-1)\n interim_scores.append(-model_pos)\n else:\n candidate_vals_interim.append(1)\n interim_scores.append(model_pos)\n\n hash_table[cnt_paths] = candidate_interim\n intermediate_paths[cnt_paths] = candidate_vals_interim\n cnt_paths += 1\n # For the first iteration, just iterate once - all labels in one iteration\n if start == 0:\n start = 1\n break\n\n temp_paths = intermediate_paths\n interim_zip = zip(intermediate_paths, interim_scores)\n sorted_scores = sorted(interim_zip, key=lambda x: x[1], reverse=True)[:5]\n intermediate_paths, scores = zip(*sorted_scores)\n\n temp_cand = []\n temp_val = []\n for i in range(len(intermediate_paths)):\n temp_cand.append(hash_table[intermediate_paths[i]])\n temp_val.append(temp_paths[intermediate_paths[i]])\n # candidate_scores[i] += scores[i]\n\n candidate_paths = temp_cand\n candidate_vals = temp_val\n print(candidate_paths)\n print(candidate_vals)\n # print(scores)\n # candidate_scores = scores\n\n # Exit condition from loop\n # if max(interim_scores) < min_score:\n # break\n #\n # min_score = min(interim_scores)\n\n iter += 1\n if iter > 5:\n break\n\n candidate_dict = {}\n for i in range(5):\n for c in range(len(candidate_paths[i])):\n if candidate_paths[i][c] not in candidate_dict:\n candidate_dict[candidate_paths[i][c]] = candidate_vals[i][c]\n elif candidate_dict[candidate_paths[i][c]] != 2:\n if candidate_dict[candidate_paths[i][c]] != candidate_vals[i][c]:\n candidate_dict[candidate_paths[i][c]] = 2.\n\n print(candidate_dict)\n exit()\n return candidate_dict","def match(desc1,desc2):\n\t\n\tdesc1 = array([d/linalg.norm(d) for d in desc1])\n\tdesc2 = array([d/linalg.norm(d) for d in desc2])\n\t\n\tdist_ratio = 0.6\n\tdesc1_size = desc1.shape\n\t\n\tmatchscores = zeros((desc1_size[0],1))\n\tdesc2t = desc2.T #precompute matrix transpose\n\tfor i in range(desc1_size[0]):\n\t\tdotprods = dot(desc1[i,:],desc2t) #vector of dot products\n\t\tdotprods = 0.9999*dotprods\n\t\t#inverse cosine and sort, return index for features in second image\n\t\tindx = argsort(arccos(dotprods))\n\t\t\n\t\t#check if nearest neighbor has angle less than dist_ratio times 2nd\n#\t\tif arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\n\t\tmatchscores[i] = int(indx[0])\n\t\n\treturn matchscores","def pg_secondary_keys(self):","def key_ik(*args):\n\n robots = get_robot_roots()\n if not robots:\n pm.warning('No robots selected')\n return\n\n for robot in robots:\n target_ctrl_path = get_target_ctrl_path(robot)\n tool_ctrl_path = get_tool_ctrl_path(robot)\n fk_ctrls_path = format_path(__FK_CTRLS_PATH, robot)\n\n if not pm.getAttr(target_ctrl_path + '.ik'):\n switch_to_ik(robot)\n\n ik_attributes = ['ik',\n 'v',\n 'ikSolution1',\n 'ikSolution2',\n 'ikSolution3']\n\n # Key all IK elements\n for attr in ik_attributes:\n pm.setKeyframe(target_ctrl_path, attribute=attr)\n\n if pm.objExists(tool_ctrl_path):\n pm.setKeyframe(tool_ctrl_path, attribute='v')\n\n fk_pose = find_fk_config(robot)\n\n # Key all FK elements\n pm.setKeyframe(format_path(__A1_FK_CTRL_PATH, robot),\n attribute='rotateY',\n value=fk_pose[0])\n pm.setKeyframe(format_path(__A2_FK_CTRL_PATH, robot),\n attribute='rotateX',\n value=fk_pose[1])\n pm.setKeyframe(format_path(__A3_FK_CTRL_PATH, robot),\n attribute='rotateX',\n value=fk_pose[2])\n pm.setKeyframe(format_path(__A4_FK_CTRL_PATH, robot),\n attribute='rotateZ',\n value=fk_pose[3])\n pm.setKeyframe(format_path(__A5_FK_CTRL_PATH, robot),\n attribute='rotateX',\n value=fk_pose[4])\n pm.setKeyframe(format_path(__A6_FK_CTRL_PATH, robot),\n attribute='rotateZ',\n value=fk_pose[5])\n\n # Key visibility of FK controllers\n pm.setKeyframe(fk_ctrls_path, attribute='visibility')\n\n # Key tool controllers\n if pm.checkBox('cb_keyToolCtrl', query=True, value=True):\n if pm.objExists(tool_ctrl_path):\n pm.setKeyframe(tool_ctrl_path, attribute='translate')\n pm.setKeyframe(tool_ctrl_path, attribute='rotate')\n else:\n pm.setKeyframe(target_ctrl_path, attribute='translate')\n pm.setKeyframe(target_ctrl_path, attribute='rotate')","def refseq_based_clustering(self):\n self.refseq_based = Usefuls.NonRedSet.NonRedSetDict()\n for prey in self.ivv_info.Prey_info().preys():\n refseqid = self.get_refseq(prey)\n if refseqid:\n self.refseq_based.append_Dict(refseqid, prey)","def edge_mapping(self):\n ...","def testFindCorrespondence(self):\n # Create some dummy keypoints and descriptors to match. Make the descriptors really far apart to be sure.\n keypoints1 = []\n descriptors1 = numpy.zeros(shape=(3, 1))\n keypoint = cv2.KeyPoint()\n for i in range(3):\n keypoint.pt = (float(i), 0.0)\n keypoints1.append(keypoint)\n descriptors1[i] = i*100.0\n keypoints2 = []\n descriptors2 = numpy.zeros(shape=(5, 1))\n for i in range(5):\n keypoint.pt = (0.0, float(i))\n keypoints2.append(keypoint)\n descriptors2[i] = i*105.0\n (first_points, second_points) = self.evaluator._findCorrespondence(\n keypoints1, descriptors1, keypoints2, descriptors2)\n expected_first = numpy.array([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])\n expected_second = numpy.array([[0.0, 0.0], [0.0, 1.0], [0.0, 2.0]])\n self.assertTrue(numpy.array_equal(first_points, expected_first))\n self.assertTrue(numpy.array_equal(second_points, expected_second))","def illustrate_matching(self):\n work_dir = self.work_dir\n draw_keys_oriented(work_dir+'matching_keys_im0.txt',\n work_dir+'input_0.orig.png',\n work_dir+'matching_keys_im0.png')\n draw_keys_oriented(work_dir+'matching_keys_im1.txt',\n work_dir+'input_1.orig.png',\n work_dir+'matching_keys_im1.png')\n draw_matches(work_dir+'matches.txt',\n work_dir+'input_0.orig.png',\n work_dir+'input_1.orig.png',\n work_dir+'OUTmatches.png')\n return 1","def look_for_reference_image(image):\n match_list = []\n thresh = 8\n final_value = -1\n references = import_reference_images()\n # Initialize the ORB detector algorithm\n orb = cv2.ORB_create()\n\n # Now detect the keypoints and compute\n # the descriptors for the query image\n imgKeypoints, imgDescriptors = orb.detectAndCompute(image, None)\n try:\n for ref in references:\n # Now detect the keypoints and compute\n # the descriptors for the train image\n ref.refKeypoints, ref.refDescriptors = orb.detectAndCompute(ref.img, None)\n\n # Initialize the Matcher for matching\n # the keypoints and then match the\n # keypoints\n matcher = cv2.BFMatcher()\n matches = matcher.knnMatch(imgDescriptors, ref.refDescriptors, k=2)\n\n for m, n in matches:\n if m.distance < 0.75 * n.distance:\n ref.refMatches.append([m])\n\n match_list.append(len(ref.refMatches))\n except:\n pass\n if len(match_list) != 0:\n if max(match_list) > thresh:\n final_value = match_list.index(max(match_list))\n\n return references[final_value].name","def test5(self):\n tgts = [ ('CCC(O)C(=O)O',\n ('O[CH-][CH2-]','O[CH-][C-]=O')),\n ]\n for smi,matches in tgts:\n m = Chem.MolFromSmiles(smi)\n fp1 = Chem.RDKFingerprint(m,2,7,9192,4,1)\n obs = fp1.GetOnBits()\n for match in matches:\n m2 = Chem.MolFromSmiles(match)\n fp2 = Chem.RDKFingerprint(m2,2,7,9192,4,1)\n v1,v2 = DataStructs.OnBitProjSimilarity(fp2,fp1)\n assert feq(v1,1.0000),'substruct %s not properly contained in %s'%(match,smi)","def test_post_chain_search(self):\n pass","def object_detection(self):\r\n pass","def test_normal_fields_can_be_defined_between_primary_keys(self):","def similarity(self, e1, e2):\n\t\tpass","def find(image):\n keypoint, description = describe(image)\n # load keypoints, descriptions from mongodb\n\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n\n best_match_size = float(\"inf\")\n best_match_index = -1\n match_index = 0\n best_matches = 0\n\n for desc in descriptions:\n matches = bf.match(desc,description)\n matches = sorted(matches, key = lambda x:x.distance)\n if len(matches) > 0:\n match_size = sum(x.distance for x in matches[:10])\n\n print \"match size is \", match_size\n if match_size < best_match_size:\n best_match_size = match_size\n best_match_index = match_index\n best_matches = matches\n\n match_index += 1\n\n needle_color = cv2.imread('needle-stripped.png')[:,:,::-1] # needle\n best_match_image = cv2.imread(\"haystack/\"+files[best_match_index])\n print \"best match is \", files[best_match_index]\n\n # Draw first 10 matches.\n outImg = cv2.imread(\"output/outImg.png\")\n match = cv2.drawMatches(needle_color,keypoint,best_match_image[:,:,::-1],keypoints[best_match_index],best_matches[-20:],outImg, flags=6)\n\n plt.imshow(match),plt.show()\n return","def _fit_exact_match(self):\n entity_map = self._resource_loader.get_entity_map(self.type)\n self._exact_match_mapping = self._process_entity_map(\n self.type, entity_map, self._normalizer\n )","def matches(self, feature):\n pass","def fix_annotmatch_pzmaster1():\n import wbia\n\n ibs = wbia.opendb('PZ_Master1')\n infr = wbia.AnnotInference(ibs=ibs, aids=ibs.get_valid_aids(), verbose=5)\n infr.initialize_graph()\n annots = ibs.annots()\n aid_to_nid = ut.dzip(annots.aids, annots.nids)\n\n if False:\n infr.reset_feedback()\n infr.ensure_mst()\n infr.apply_feedback_edges()\n infr.relabel_using_reviews()\n infr.start_qt_interface()\n\n # Get annotmatch rowids that agree with current labeling\n if False:\n annotmatch = ibs.db.get_table_as_pandas('annotmatch')\n import pandas as pd\n\n flags1 = pd.isnull(annotmatch['annotmatch_evidence_decision'])\n flags2 = annotmatch['annotmatch_tag_text'] == ''\n bad_part = annotmatch[flags1 & flags2]\n rowids = bad_part.index.tolist()\n ibs.delete_annotmatch(rowids)\n\n if False:\n # Delete bidirectional annotmatches\n annotmatch = ibs.db.get_table_as_pandas('annotmatch')\n df = annotmatch.set_index(['annot_rowid1', 'annot_rowid2'])\n\n # Find entires that have both directions\n pairs1 = annotmatch[['annot_rowid1', 'annot_rowid2']].values\n f_edges = {tuple(p) for p in pairs1}\n b_edges = {tuple(p[::-1]) for p in pairs1}\n isect_edges = {tuple(sorted(p)) for p in b_edges.intersection(f_edges)}\n isect_edges1 = list(isect_edges)\n isect_edges2 = [p[::-1] for p in isect_edges]\n\n # cols = ['annotmatch_evidence_decision', 'annotmatch_tag_text']\n import pandas as pd\n\n custom_ = {\n (559, 4909): (False, ['photobomb']),\n (7918, 8041): (False, ['photobomb']),\n (6634, 6754): (False, ['photobomb']),\n (3707, 3727): (False, ['photobomb']),\n (86, 103): (False, ['photobomb']),\n }\n extra_ = {}\n\n fixme_edges = []\n\n d1 = df.loc[isect_edges1].reset_index(drop=False)\n d2 = df.loc[isect_edges2].reset_index(drop=False)\n flags = d1['annotmatch_evidence_decision'] != d2['annotmatch_evidence_decision']\n from wbia.tag_funcs import _parse_tags\n\n for f, r1, r2 in zip(flags, d1.iterrows(), d2.iterrows()):\n v1, v2 = r1[1], r2[1]\n aid1 = v1['annot_rowid1']\n aid2 = v1['annot_rowid2']\n truth_real = (\n ibs.const.EVIDENCE_DECISION.POSITIVE\n if aid_to_nid[aid1] == aid_to_nid[aid2]\n else ibs.const.EVIDENCE_DECISION.NEGATIVE\n )\n truth1 = v1['annotmatch_evidence_decision']\n truth2 = v2['annotmatch_evidence_decision']\n t1 = _parse_tags(v1['annotmatch_tag_text'])\n t2 = _parse_tags(v2['annotmatch_tag_text'])\n newtag = ut.union_ordered(t1, t2)\n if (aid1, aid2) in custom_:\n continue\n fixme_flag = False\n if not pd.isnull(truth1):\n if truth_real != truth1:\n fixme_flag = True\n if not pd.isnull(truth2):\n if truth_real != truth2:\n fixme_flag = True\n if fixme_flag:\n logger.info('newtag = {!r}'.format(newtag))\n logger.info('truth_real = {!r}'.format(truth_real))\n logger.info('truth1 = {!r}'.format(truth1))\n logger.info('truth2 = {!r}'.format(truth2))\n logger.info('aid1 = {!r}'.format(aid1))\n logger.info('aid2 = {!r}'.format(aid2))\n fixme_edges.append((aid1, aid2))\n else:\n extra_[(aid1, aid2)] = (truth_real, newtag)\n\n extra_.update(custom_)\n new_pairs = extra_.keys()\n new_truths = ut.take_column(ut.dict_take(extra_, new_pairs), 0)\n new_tags = ut.take_column(ut.dict_take(extra_, new_pairs), 1)\n new_tag_texts = [';'.join(t) for t in new_tags]\n aids1, aids2 = ut.listT(new_pairs)\n\n # Delete the old\n ibs.delete_annotmatch(\n d1['annotmatch_rowid'].values.tolist()\n + d2['annotmatch_rowid'].values.tolist()\n )\n\n # Add the new\n ams = ibs.add_annotmatch_undirected(aids1, aids2)\n ibs.set_annotmatch_evidence_decision(ams, new_truths)\n ibs.set_annotmatch_tag_text(ams, new_tag_texts)\n\n if False:\n import wbia.guitool as gt\n\n gt.ensure_qapp()\n ut.qtensure()\n from wbia.gui import inspect_gui\n\n inspect_gui.show_vsone_tuner(ibs, aid1, aid2)\n\n # pairs2 = pairs1.T[::-1].T\n # idx1, idx2 = ut.isect_indices(list(map(tuple, pairs1)),\n # list(map(tuple, pairs2)))\n # r_edges = list(set(map(tuple, map(sorted, pairs1[idx1]))))\n # unique_pairs = list(set(map(tuple, map(sorted, pairs1[idx1]))))\n # df = annotmatch.set_index(['annot_rowid1', 'annot_rowid2'])\n\n x = ut.ddict(list)\n annotmatch = ibs.db.get_table_as_pandas('annotmatch')\n import ubelt as ub\n\n _iter = annotmatch.iterrows()\n prog = ub.ProgIter(_iter, length=len(annotmatch))\n for k, m in prog:\n aid1 = m['annot_rowid1']\n aid2 = m['annot_rowid2']\n if m['annotmatch_evidence_decision'] == ibs.const.EVIDENCE_DECISION.POSITIVE:\n if aid_to_nid[aid1] == aid_to_nid[aid2]:\n x['agree1'].append(k)\n else:\n x['disagree1'].append(k)\n elif m['annotmatch_evidence_decision'] == ibs.const.EVIDENCE_DECISION.NEGATIVE:\n if aid_to_nid[aid1] == aid_to_nid[aid2]:\n x['disagree2'].append(k)\n else:\n x['agree2'].append(k)\n\n ub.map_vals(len, x)\n ut.dict_hist(annotmatch.loc[x['disagree1']]['annotmatch_tag_text'])\n\n disagree1 = annotmatch.loc[x['disagree1']]\n pb_disagree1 = disagree1[disagree1['annotmatch_tag_text'] == 'photobomb']\n aids1 = pb_disagree1['annot_rowid1'].values.tolist()\n aids2 = pb_disagree1['annot_rowid2'].values.tolist()\n aid_pairs = list(zip(aids1, aids2))\n infr = wbia.AnnotInference.from_pairs(aid_pairs, ibs=ibs, verbose=5)\n if False:\n feedback = infr.read_wbia_annotmatch_feedback(edges=infr.edges())\n infr.external_feedback = feedback\n infr.apply_feedback_edges()\n infr.start_qt_interface(loop=False)\n\n # Delete these values\n if False:\n nonpb_disagree1 = disagree1[disagree1['annotmatch_tag_text'] != 'photobomb']\n disagree2 = annotmatch.loc[x['disagree2']]\n ibs.delete_annotmatch(nonpb_disagree1['annotmatch_rowid'])\n ibs.delete_annotmatch(disagree2['annotmatch_rowid'])\n\n # ut.dict_hist(disagree1['annotmatch_tag_text'])\n import networkx as nx\n\n graph = nx.Graph()\n graph.add_edges_from(zip(pb_disagree1['annot_rowid1'], pb_disagree1['annot_rowid2']))\n list(nx.connected_components(graph))\n\n set(annotmatch.loc[x['disagree2']]['annotmatch_tag_text'])\n\n # aid1, aid2 = 2585, 1875\n # # pd.unique(annotmatch['annotmatch_evidence_decision'])\n # from wbia.gui import inspect_gui\n # inspect_gui.show_vsone_tuner(ibs, aid1, aid2)\n # from vtool import inspect_matches\n\n # aid1, aid2 = 2108, 2040\n\n # pd.unique(annotmatch['annotmatch_tag_text'])\n\n # infr.reset_feedback()\n # infr.relabel_using_reviews()","def test_labels_match_geographies(self):\n geo = self.geographies.find_one({ 'geoid': '15' })\n labels = self.labels.find_one({ 'dataset': 'SF1' })\n\n geo_tables = geo['data']['2010']\n labels_tables = labels['tables']\n\n self.assertEqual(sorted(geo_tables.keys()), sorted(labels_tables.keys()))\n\n # Test table has labels\n for table_name, geo_keys in geo_tables.items():\n label_keys = labels_tables[table_name]['labels']\n\n self.assertEqual(sorted(geo_keys.keys()), sorted(label_keys.keys()))\n\n for table_name, label_data in labels_tables.items():\n label_keys = label_data['labels']\n geo_keys = geo_tables[table_name]\n\n self.assertEqual(sorted(geo_keys.keys()), sorted(label_keys.keys()))","def refseq_based_clustering(self):\n self.refseq_based = NonRedSetDict()\n for prey in self.ivv_info.Prey_info().preys():\n refseqid = self.get_refseq(prey)\n if refseqid:\n self.refseq_based.append_Dict(refseqid, prey)","def _attentive_matching(self, h1, h2, cosine_matrix, w):\n # h1 * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)\n h1 = self._time_distributed_multiply(h1, w)\n # attentive vector (batch_size, h1_timesteps, embedding_szie)\n attentive_vec = self._mean_attentive_vectors(h2, cosine_matrix)\n # attentive_vec * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)\n attentive_vec = self._time_distributed_multiply(attentive_vec, w)\n # matching vector, (batch_size, h1_timesteps, mp_dim)\n matching = self._cosine_similarity(h1, attentive_vec)\n return matching","def match_against_database(list_of_face_vectors):\n\n threshold_of_similarity = 0.4\n\n if len(list_of_face_vectors) != 0:\n #Load the database, if it exists.\n if (file_path/\"names_and_faces.pkl\").exists():\n with open(file_path/\"names_and_faces.pkl\", mode = \"rb\") as opened_file:\n names_and_faces = pickle.load(opened_file)\n\n #Calculate the mean for each key.\n for key in names_and_faces:\n names_and_faces[key] = np.array(names_and_faces[key]).mean(axis = 0)\n\n\n #Here comes the fun part! Iterate thru our list of face vectors to find the best candidate name for each face vector.\n face_vectors = np.array(list_of_face_vectors)\n #print(face_vectors.shape)\n names = np.array(list(names_and_faces.keys()))\n faces = np.array(list(names_and_faces.values()))\n candidates = L2_dists_vectorized(face_vectors, faces)\n\n minimum_indices = np.argmin(candidates, axis = 1)\n #print(\"candidates are\", candidates)\n minimum_args = np.min(candidates, axis = 1)\n names_to_return = names[minimum_indices]\n names_to_return[minimum_args > threshold_of_similarity] = \"unknown\"\n\n\n return names_to_return\n\n\n else:\n return 0\n else:\n return 0","def icontains(self, other):","def keyDependsOnKey(self, k1Name, k2Name):\n if k1Name == k2Name: return 0\n k1 = self.infoKinds[k1Name]\n k2 = self.infoKinds[k2Name]\n if k1.superNames != k2.superNames:\n allSuperK1 = set()\n toDoK1 = list(k1.superNames)\n allSuperK2 = set()\n toDoK2 = list(k2.superNames)\n while (len(toDoK1) > 0 or len(toDoK2) > 0):\n if len(toDoK1) > 0:\n el1Name = toDoK1.pop()\n if k2Name == el1Name:\n return 1\n el1 = self.infoKinds[el1Name]\n if el1.kindStr in self and not el1.kindStr in allSuperK1:\n toDoK1.append(el1.kindStr)\n for subEl in el1.superNames:\n if not subEl in allSuperK1:\n toDoK1.append(subEl)\n allSuperK1.update(el1.superNames)\n if len(toDoK2) > 0:\n el2Name = toDoK2.pop()\n if k1Name == el2Name:\n return -1\n el2 = self.infoKinds[el2Name]\n if el2.kindStr in self and not el2.kindStr in allSuperK2:\n toDoK2.append(el2.kindStr)\n for subEl in el2.superNames:\n if not subEl in allSuperK2:\n toDoK2.append(subEl)\n allSuperK2.update(el2.superNames)\n return None","def GetSubkeys(self):","def test_fkey_nav(dumper, db):\n dumper.reader.load_db(\n db.create_sample(3, fkeys=[(\"table1\", \"t2id\", \"table2\", \"id\")])\n )\n dumper.add_config({\"db_objects\": [{\"name\": \"table1\"}]})\n dumper.perform_dump()\n objs = [obj for obj, match in dumper.writer.dumped if isinstance(obj, Table)]\n assert len(objs) == 2","def lookup():","def testBeliefs2sk(self):","def test_associations(self):\n\n oj = {}\n\n if self.DEBUG:\n print \"Create A\"\n\n # create a\n a = self.save_base_metadata()\n\n oj[a.uuid] = 'A'\n\n if self.DEBUG:\n print \"Create B, point B to A\"\n\n # create b, point b to a\n b = self.save_base_metadata()\n\n oj[b.uuid] = 'B'\n\n b.add_association_to(a)\n\n # save a, b\n b.save()\n a.save()\n\n if self.DEBUG:\n self.printy(oj, a, b, None, None, None)\n\n # test b pointing to a\n self.assertIn(a, b.my_associations)\n # test to see if a is aware of association from b\n # self.assertIn(b, a.associations_to_me)\n\n if self.DEBUG:\n print \"Create C, point C to B\"\n\n # create c, point c to b\n c = self.save_base_metadata()\n\n oj[c.uuid] = 'C'\n\n c.add_association_to(b)\n\n # a.save()\n\n # save b, c\n c.save()\n b.save()\n\n if self.DEBUG:\n self.printy(oj, a, b, c, None, None)\n\n # test c pointing to a\n self.assertIn(a, c.my_associations)\n # test c aware of association from a\n self.assertIn(c, a.associations_to_me)\n\n # test c pointing to b\n self.assertIn(b, c.my_associations)\n # test b aware of association from c\n self.assertIn(c, b.associations_to_me)\n\n if self.DEBUG:\n print \"create D, point to B\"\n\n # create d, point to b\n d = self.save_base_metadata()\n\n oj[d.uuid] = 'D'\n\n d.add_association_to(b)\n\n # a.save()\n\n # save d, b\n d.save()\n b.save()\n\n if self.DEBUG:\n self.printy(oj, a, b, c, d, None)\n\n # test d pointing to a\n self.assertIn(a, d.my_associations)\n # test a aware of assoication from d\n self.assertIn(d, a.associations_to_me)\n\n # test d pointing to b\n self.assertIn(b, d.my_associations)\n # test b aware of association from d\n self.assertIn(d, b.associations_to_me)\n\n if self.DEBUG:\n print \"Create E, point to A\"\n\n # create e, point to a\n e = self.save_base_metadata()\n\n oj[e.uuid] = 'E'\n\n e.add_association_to(a)\n\n # save e, a\n e.save()\n a.save()\n\n if self.DEBUG:\n self.printy(oj, a, b, c, d, e)\n\n # test e pointing to a\n self.assertIn(a, e.my_associations)\n # test a aware of association from e\n self.assertIn(b, a.associations_to_me)","def detail_matching(self):\n paradic = self.cfg['param']['paradic']\n work_dir = self.work_dir\n \n x = float(self.cfg['param']['x']) # selected pixel in the first image\n y = float(self.cfg['param']['y'])\n \n # sift parameters\n # number of bins in the orientation histogram\n n_bins = int(paradic['n_bins']) \n n_hist = int(paradic['n_hist']) \n # descriptor of n_hist X n_hist weighted histograms with n_ori\n n_ori = int(paradic['n_ori']) \n delta_min = float(paradic['delta_min'])\n sigma_min = float(paradic['sigma_min'])\n sigma_in = float(paradic['sigma_in'])\n lambda_ori = float(paradic['lambda_ori'])\n lambda_descr = float(paradic['lambda_descr'])\n #threshold defining reference orientations\n n_spo = int(paradic['n_spo'])\n \n # Read feature vectors from output files\n if (os.path.getsize(work_dir+'OUTmatches.txt') > 0 ):\n pairdata = find_nearest_keypoint(work_dir+'OUTmatches.txt', y, x)\n \n illustrate_pair(pairdata, n_bins, n_hist, n_ori, work_dir)\n\n \n # Read keys coordinates.\n d = 6+n_bins+n_hist*n_hist*n_ori # size of keydata inside pairdata\n v = n_hist*n_hist*n_ori\n [x1, y1, sigma1, theta1] = [float(x) for x in pairdata[0:4]]\n [o1, s1] = [float(x) for x in pairdata[4+v:4+v+2]]\n [x2a, y2a, sigma2a, theta2a] = [float(x) for x in pairdata[d:d+4]]\n [o2a, s2a] = [float(x) for x in pairdata[d+4+v:d+4+v+2]]\n [x2b, y2b, sigma2b, theta2b] = \\\n [float(x) for x in pairdata[2*d:2*d+4]]\n [o2b, s2b] = [float(x) for x in pairdata[2*d+4+v:2*d+4+v+2]]\n \n draw_one_match(pairdata,\n work_dir+'input_0.png',\n work_dir+'input_1.png',\n d,\n lambda_ori,\n lambda_descr,\n n_hist,\n work_dir+'OUTonepair.png')\n \n \n # Extract thumbnails.\n # keypoint 1 (image 1)\n print ' '.join(['demo_extract_patch', work_dir+'input_0.png',\n str(x1), str(y1), str(sigma1), str(theta1), str(o1), str(s1),\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\n str(lambda_ori), str(lambda_descr), str(n_hist),\n work_dir+\"detail_im1\"])\n proc = self.run_proc(['demo_extract_patch', work_dir+'input_0.png',\n str(x1), str(y1), str(sigma1), str(theta1), str(o1), str(s1),\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\n str(lambda_ori), str(lambda_descr), str(n_hist),\n work_dir+\"detail_im1\"])\n self.wait_proc(proc, timeout=self.timeout)\n \n # keypoint 2a (nearest neighbor in image 2)\n print ' '.join(['demo_extract_patch', work_dir+'input_1.png',\n str(x2a), str(y2a), str(sigma2a), str(theta2a), str(o2a), str(s2a),\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\n str(lambda_ori), str(lambda_descr), str(n_hist),\n work_dir+\"detail_im2a\"])\n proc = self.run_proc(['demo_extract_patch', work_dir+'input_1.png',\n str(x2a), str(y2a), str(sigma2a), str(theta2a), str(o2a), str(s2a),\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\n str(lambda_ori), str(lambda_descr), str(n_hist),\n work_dir+\"detail_im2a\"])\n self.wait_proc(proc, timeout=self.timeout) \n \n # keypoint 2b (second nearest neighbor in image 2)\n proc = self.run_proc(['demo_extract_patch', work_dir+'input_1.png',\n str(x2b), str(y2b), str(sigma2b), str(theta2b), str(o2b), str(s2b),\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\n str(lambda_ori), str(lambda_descr), str(n_hist),\n work_dir+\"detail_im2b\"])\n self.wait_proc(proc, timeout=self.timeout) \n \n \n return 1","def _backreference_keys(self, ref, key_in_ref):\n return (self.__class__.REFERENCE_FIELDS +\n self.__class__.DEPENDENT_LINES +\n self.__class__.OTHER_REFERENCES)","def additionalMatch(handIn, indx):","def _candidate_generation(self):\n doc = self.nlp(self.text)\n named_entity_dict = {}\n named_entity_key_list = []\n named_entity_value_list = []\n entity_from_text_list = []\n offline_dic_list = []\n matched_element_list = []\n\n for ent in doc.ents:\n named_entity = (str(ent.text) + ':' + str(ent.label_))\n named_entity = (named_entity.split(':'))\n # named_entity_key = named_entity[0].replace('\\n', '')\n # named_entity_key_list.append(named_entity_key)\n # named_entity_value = named_entity[1].replace('\\n', '')\n # named_entity_value_list.append(named_entity_value)\n named_entity_value = named_entity[1].replace('\\n', '')\n named_entity_value_list.append(named_entity_value)\n filtered_words = (str(ent.text).split())\n filtered_words = [w for w in filtered_words if w.lower() not in self.english_stopwords]\n named_entity_key = [' '.join(filtered_words)]\n for i in named_entity_key:\n named_entity_key_list.append(i)\n for key in named_entity_key_list:\n named_entity_dict[key] = []\n i = 0\n for key in named_entity_key_list:\n named_entity_dict[key].append(named_entity_value_list[i])\n i = i + 1\n\n entities = \"ORG PERSON LOC GPE\".split()\n for entity in entities:\n entity_from_text = [k for k, v in named_entity_dict.items() if entity in v]\n for item in entity_from_text:\n entity_from_text_list.append(item)\n\n if not entity_from_text_list:\n self.logger.info('No named entity found in the input text')\n else:\n self.logger.info('Entities which are identified from the input sentence')\n self.logger.info(entity_from_text_list)\n\n for key, value in self.offline_dic.items():\n offline_dic_list.append(key)\n\n for item in entity_from_text_list:\n for item1 in offline_dic_list:\n if item == item1:\n matched_element_list.append(item)\n\n big_final_dict = []\n for i in matched_element_list:\n candidate_list = [v for k, v in self.offline_dic.items() if str(k) == str(i)]\n final_dict = dict(zip(i.split('\\n'), candidate_list))\n big_final_dict.append(final_dict)\n\n if not big_final_dict:\n self.logger.warning(\"No Match found in the KB\")\n return matched_element_list, None\n else:\n self.logger.info('found entities')\n return matched_element_list, big_final_dict","def match(desc1, desc2):\n desc1 = array([d/linalg.norm(d) for d in desc1])\n desc2 = array([d/linalg.norm(d) for d in desc2])\n\n dist_ratio = 0.6\n disc1_size = desc1.shape\n\n matchscores = zeros((desc1_size[0]), \"int\")\n desc2t = desc2.T\n for i in range(desc1_size[0]):\n dotprods = dot(desc1[i, :], desc2t)\n dotprods = 0.9999 * dotprods\n\n indx = argsort(arccos(dotprods))\n\n if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\n matchscores[i] = int(indx[0])\n\n return matchscores","def allow_relation(self, obj1, obj2, **hints):\n if (\n obj1._meta.label_lower in self.route_encuestas or\n obj2._meta.label_lower in self.route_encuestas\n ):\n return True\n return None","def _create_auto_key(self,identifier2organism={}):\n # when this is a locus in a dbwarehouse, abstract the genomedirname\n realdirname = os.path.realpath(self.dirname)\n if realdirname.find(\"/loci/\") > 0:\n key = os.path.basename(realdirname[0:realdirname.find(\"/loci/\")])\n if key: return key\n # if this point is reached, NOT a locus in dbwarehouse\n # check if we can map the gene's id to an organism ID\n if identifier2organism:\n for identifierpart,organism in identifier2organism.iteritems():\n if self.fref.find(identifierpart) == 0:\n # succesfull mapping\n return organism\n else:\n # mapping was not succesfull\n return self.fref\n else:\n return self.fref","def __contains__(self, y): # real signature unknown; restored from __doc__\r\n pass","def targetids(obj, reftype):","def bipartite_matching(data=None, is_ascend=_Null, threshold=_Null, topk=_Null, out=None, name=None, **kwargs):\n return (0,)","def match(self, product):\n\n raise NotImplementedError, 'need impletent match method'","def toggle_ik_fk(*args):\n\n current_tab = pm.tabLayout('switcher_tab_layout',\n query=True,\n selectTab=True)\n\n if current_tab == 'ikTab':\n ik_tab = 1\n else:\n ik_tab = 0\n\n robots = get_robot_roots(1)\n if not robots:\n return\n\n for robot in robots:\n target_ctrl_path = get_target_ctrl_path(robot)\n \n if ik_tab:\n if pm.getAttr(target_ctrl_path + '.ik'):\n continue\n\n switch_to_ik(robot)\n\n else:\n if not pm.getAttr(target_ctrl_path + '.ik'):\n continue\n\n switch_to_fk(robot)\n \n # Maintain appropriate selections on each robot\n try:\n selection = []\n active_robots = get_robot_roots()\n if active_robots:\n if ik_tab:\n for robot in active_robots:\n target_ctrl_path = get_target_ctrl_path(robot)\n tool_ctrl_path = get_tool_ctrl_path(robot)\n\n if pm.objExists(tool_ctrl_path):\n selection.append(tool_ctrl_path)\n else:\n selection.append(target_ctrl_path)\n else:\n for robot in active_robots:\n selection.append(format_path(__A6_FK_CTRL_PATH, robot))\n \n pm.select(selection)\n else:\n pass\n\n except:\n pm.warning('Error selecting after IK/FK switch')","def expected_relations():\n reldict = {('project', 'userspec'): ('specs', False),\n ('project', 'userdoc'): ('docs', False),\n ('project', 'userwijz'): ('rfcs', False),\n ('project', 'userprob'): ('probs', False),\n ('project', 'funcdoc'): ('fdocs', False),\n ('project', 'gebrtaak'): ('gtaken', False),\n ('project', 'funcproc'): ('fprocs', False),\n ('project', 'entiteit'): ('fdata', False),\n ('project', 'attribuut'): (None, None),\n ('project', 'techtaak'): ('ttask', False),\n ('project', 'techproc'): ('tproc', False),\n ('project', 'dataitem'): ('tdata', False),\n ('project', 'element'): (None, None),\n ('project', 'layout'): ('layout', False),\n ('project', 'programma'): ('pproc', False),\n ('project', 'testplan'): ('tplan', False),\n ('project', 'testcase'): ('tcase', False),\n ('project', 'bevinding'): ('tbev', False),\n ('userspec', 'project'): ('project', False),\n ('userspec', 'gebrtaak'): ('gtaken', False),\n ('userspec', 'funcproc'): ('fprocs', False),\n ('userdoc', 'project'): ('project', False),\n ('userwijz', 'project'): ('project', False),\n ('userwijz', 'gebrtaak'): ('gtaken', True),\n ('userwijz', 'funcproc'): ('fprocs', True),\n ('userwijz', 'entiteit'): ('fdata', True),\n ('userprob', 'project'): ('project', False),\n ('funcdoc', 'project'): ('project', False),\n ('gebrtaak', 'project'): ('project', False),\n ('gebrtaak', 'userspec'): ('spec', False),\n ('gebrtaak', 'userwijz'): ('rfc', True),\n ('gebrtaak', 'funcproc'): ('fprocs', True),\n ('gebrtaak', 'techtaak'): ('ttask', False),\n ('gebrtaak', 'layout'): ('layout', True),\n ('gebrtaak', 'testplan'): ('tplan', True),\n ('funcproc', 'project'): ('project', False),\n ('funcproc', 'userspec'): ('spec', False),\n ('funcproc', 'userwijz'): ('rfc', True),\n ('funcproc', 'gebrtaak'): ('gt', True),\n ('funcproc', 'funcproc'): ('used_by', True),\n ('funcproc', 'entiteit'): ('fdata', True),\n ('funcproc', 'techproc'): ('tproc', True),\n ('funcproc', 'testplan'): ('tplan', True),\n ('entiteit', 'project'): ('project', False),\n ('entiteit', 'userwijz'): ('rfc', True),\n ('entiteit', 'funcproc'): ('fp', True),\n ('entiteit', 'attribuut'): ('attrs', False),\n ('entiteit', 'dataitem'): ('tdata', True),\n ('entiteit', 'testplan'): ('tplan', True),\n ('attribuut', 'entiteit'): ('hoort_bij', False),\n ('techtaak', 'project'): ('project', False),\n ('techtaak', 'gebrtaak'): ('gt', False),\n ('techtaak', 'techproc'): ('tproc', True),\n ('techproc', 'project'): ('project', False),\n ('techproc', 'funcproc'): ('fp', True),\n ('techproc', 'techtaak'): ('tt', True),\n ('techproc', 'techproc'): ('used_by', True),\n ('techproc', 'dataitem'): ('tdata', True),\n ('techproc', 'layout'): ('layout', True),\n ('techproc', 'programma'): ('pproc', True),\n ('dataitem', 'project'): ('project', False),\n ('dataitem', 'entiteit'): ('ent', True),\n ('dataitem', 'techproc'): ('tp', True),\n ('element', 'dataitem'): ('hoort_bij', False),\n ('layout', 'project'): ('project', False),\n ('layout', 'gebrtaak'): ('gt', True),\n ('layout', 'techproc'): ('tp', True),\n ('programma', 'project'): ('project', False),\n ('programma', 'techproc'): ('tp', True),\n ('testplan', 'project'): ('project', False),\n ('testplan', 'gebrtaak'): ('gt', True),\n ('testplan', 'funcproc'): ('fp', True),\n ('testplan', 'entiteit'): ('ent', True),\n ('testplan', 'testcase'): ('tcase', True),\n ('testplan', 'bevinding'): ('tbev', True),\n ('testcase', 'project'): ('project', False),\n ('testcase', 'testplan'): ('tplan', True),\n ('bevinding', 'project'): ('project', False),\n ('bevinding', 'testplan'): ('tplan', True)}\n return reldict","def foreign_key_check(self):\n # MyRocks doesn't support foreign key\n if self.is_myrocks_table:\n log.info(\n \"SKip foreign key check because MyRocks doesn't support \" \"this yet\"\n )\n return True\n foreign_keys = self.query(\n sql.foreign_key_cnt,\n (\n self.table_name,\n self._current_db,\n self.table_name,\n self._current_db,\n ),\n )\n if foreign_keys:\n fk = \"CONSTRAINT `{}` FOREIGN KEY (`{}`) REFERENCES `{}` (`{}`)\".format(\n foreign_keys[0][\"constraint_name\"],\n foreign_keys[0][\"col_name\"],\n foreign_keys[0][\"ref_tab\"],\n foreign_keys[0][\"ref_col_name\"],\n )\n raise OSCError(\n \"FOREIGN_KEY_FOUND\",\n {\"db\": self._current_db, \"table\": self.table_name, \"fk\": fk},\n )"],"string":"[\n \"def ikFkMatch(\\n namespace,\\n ikfk_attr,\\n ui_host,\\n fks,\\n ik,\\n upv,\\n ik_rot=None,\\n key=None):\\n\\n # returns a pymel node on the given name\\n def _get_node(name):\\n # type: (Text) -> pm.nodetypes.Transform\\n name = anim_utils.stripNamespace(name)\\n if namespace:\\n node = anim_utils.getNode(\\\":\\\".join([namespace, name]))\\n else:\\n node = anim_utils.getNode(name)\\n\\n if not node:\\n mgear.log(\\\"Can't find object : {0}\\\".format(name), mgear.sev_error)\\n\\n return node\\n\\n # returns matching node\\n def _get_mth(name):\\n # type: (str) -> pm.nodetypes.Transform\\n tmp = name.split(\\\"_\\\")\\n tmp[-1] = \\\"mth\\\"\\n query = \\\"_\\\".join(tmp)\\n n = _get_node(query)\\n\\n if not n:\\n mgear.log(\\\"Can't find mth object : {0} for {1}\\\".format(query, name), mgear.sev_comment)\\n return _get_node(name)\\n else:\\n return n\\n\\n # get things ready\\n fk_ctrls = [_get_node(x) for x in fks]\\n fk_goals = [_get_mth(x) for x in fks]\\n ik_ctrl = _get_node(ik)\\n ik_goal = _get_mth(ik)\\n upv_ctrl = _get_node(upv)\\n\\n if ik_rot:\\n ik_rot_node = _get_node(ik_rot)\\n ik_rot_goal = _get_mth(ik_rot)\\n\\n ui_node = _get_node(ui_host)\\n o_attr = ui_node.attr(ikfk_attr)\\n\\n switch_to_fk = (o_attr.get() == 1.0)\\n switch_to_ik = (not switch_to_fk)\\n\\n # sets keyframes before snapping\\n if key:\\n _all_controls = []\\n _all_controls.extend(fk_ctrls)\\n _all_controls.extend([ik_ctrl, upv_ctrl, ui_node])\\n if ik_rot:\\n _all_controls.extend([ik_rot_node])\\n [cmds.setKeyframe(\\\"{}\\\".format(elem),\\n time=(cmds.currentTime(query=True) - 1.0))\\n for elem in _all_controls]\\n\\n # if is IKw then snap FK\\n if switch_to_fk:\\n\\n world_matrices = []\\n for src, _ in zip(fk_goals, fk_ctrls):\\n world_matrices.append(getMatrix(src))\\n\\n o_attr.set(0.0)\\n\\n for mat, dst in zip(world_matrices, fk_ctrls):\\n setMatrix(dst, mat)\\n\\n for mat, dst in zip(world_matrices, fk_ctrls):\\n setMatrix(dst, mat)\\n\\n # if is FKw then sanp IK\\n elif switch_to_ik:\\n\\n shoulder_mat = getMatrix(fk_goals[0])\\n ik_mat = getMatrix(ik_goal)\\n\\n # transform.matchWorldTransform(ik_goal, ik_ctrl)\\n if ik_rot:\\n rot_mat = getMatrix(ik_rot_goal)\\n # transform.matchWorldTransform(ik_rot_goal, ik_rot_node)\\n\\n upv_mat = getMatrix(fk_goals[2])\\n\\n o_attr.set(1.0)\\n\\n setMatrix(ik_ctrl, ik_mat)\\n setMatrix(upv_ctrl, upv_mat)\\n # for _ in range(10):\\n # fk_ctrls[0].setMatrix(shoulder_mat, worldSpace=True)\\n\\n for _ in range(20):\\n cmds.xform(fk_ctrls[0].name(), ws=True, matrix=shoulder_mat)\\n if ik_rot:\\n setMatrix(ik_rot_node, rot_mat)\\n\\n # transform.matchWorldTransform(fk_goals[1], upv_ctrl)\\n # calculates new pole vector position\\n start_end = (fk_goals[-1].getTranslation(space=\\\"world\\\") - fk_goals[1].getTranslation(space=\\\"world\\\"))\\n start_mid = (fk_goals[2].getTranslation(space=\\\"world\\\") - fk_goals[1].getTranslation(space=\\\"world\\\"))\\n\\n dot_p = start_mid * start_end\\n proj = float(dot_p) / float(start_end.length())\\n proj_vector = start_end.normal() * proj\\n arrow_vector = (start_mid - proj_vector) * 1.5\\n arrow_vector *= start_end.normal().length()\\n final_vector = (arrow_vector + fk_goals[2].getTranslation(space=\\\"world\\\"))\\n upv_ctrl.setTranslation(final_vector, space=\\\"world\\\")\\n\\n # sets blend attribute new value\\n # o_attr.set(1.0)\\n roll_att = ui_node.attr(ikfk_attr.replace(\\\"blend\\\", \\\"roll\\\"))\\n roll_att.set(0.0)\\n\\n setMatrix(ik_ctrl, ik_mat)\\n if ik_rot:\\n setMatrix(ik_rot_node, rot_mat)\\n # upv_ctrl.setMatrix(upv_mat, worldSpace=True)\\n for _ in range(20):\\n cmds.xform(fk_ctrls[0].name(), ws=True, matrix=shoulder_mat)\\n\\n # sets keyframes\\n if key:\\n [cmds.setKeyframe(\\\"{}\\\".format(elem),\\n time=(cmds.currentTime(query=True)))\\n for elem in _all_controls]\",\n \"def test_fk():\\n\\n joints = [0.0, 2.9, 1.3, 4.2, 1.4, 0.0]\\n\\n path_planner = PathPlanner(\\\"manipulator\\\")\\n\\n pose = path_planner.get_fk(joints)\\n\\n print pose\",\n \"def matching(self, Y, K):\\n\\n ### sort data Y\\n sidx = np.argsort(Y.SID)\\n Y.SID = Y.SID[sidx]\\n Y.Y = Y.Y[sidx,:]\\n\\n ### sort data K\\n for kitem in K:\\n sidx = np.argsort(kitem.SID)\\n kitem.SID = kitem.SID[sidx]\\n kitem.K = kitem.K[sidx,:][:,sidx]\\n\\n ### find reference set samples\\n ### to boring to finish this crap....\",\n \"def ik_to_fk(node):\\n ik_main_off = get_parent(node.ik_main_conn)\\n fk_01_off = get_parent(node.fk_01_conn)\\n fk_02_off = get_parent(node.fk_02_conn)\\n fk_03_off = get_parent(node.fk_03_conn)\\n\\n ik_main_world_trans = get_world_trans(node.ik_main_conn)\\n fk_01_world_trans = get_world_trans(node.fk_01_conn)\\n ik_main_off_world_trans = get_world_trans(ik_main_off)\\n fk_01_off_world_trans = get_world_trans(fk_01_off)\\n fk_02_off_world_trans = get_world_trans(fk_02_off)\\n fk_03_off_world_trans = get_world_trans(fk_03_off)\\n\\n # calculate base information\\n def_len = (ik_main_off_world_trans - fk_01_off_world_trans).length()\\n\\n # Calculate ik direction\\n ik_dir_01 = ik_main_off_world_trans - fk_01_off_world_trans\\n ik_dir_02 = ik_main_world_trans - fk_01_world_trans\\n\\n ik_dir_rot = ik_dir_01.rotateTo(ik_dir_02).asEulerRotation()\\n\\n # Apply ik direction -> important to calculate correct pole rotations\\n fk_01_rot_plugs = get_rot_plugs(node.fk_01_conn)\\n for i, plug in enumerate(fk_01_rot_plugs):\\n plug.setMAngle(oMa.MAngle(ik_dir_rot[i], oMa.MAngle.kRadians))\\n\\n # Calculate ik pole rotations\\n ik_pole_world_mat = get_world_matrix(node.ik_pole_conn, 0)\\n fk_03_world_inv_mat = get_world_inv_matrix(node.fk_01_conn, 0)\\n\\n ik_pole_rot_mat = ik_pole_world_mat * fk_03_world_inv_mat\\n\\n ik_pole_vec = oMa.MTransformationMatrix(ik_pole_rot_mat).translation(oMa.MSpace.kWorld)\\n ik_pole_vec.y = 0\\n\\n ik_pole_rot = oMa.MVector.kZaxisVector.rotateTo(ik_pole_vec).asEulerRotation()\\n\\n # Calculate ik rotations\\n tri_a_len = (fk_02_off_world_trans - fk_01_off_world_trans).length()\\n tri_b_len = (fk_03_off_world_trans - fk_02_off_world_trans).length()\\n tri_c_len = (ik_main_world_trans - fk_01_world_trans).length()\\n\\n if tri_c_len >= def_len:\\n fk_02_angle = 0\\n fk_01_angle = 0\\n else:\\n fk_02_angle = math.pi - solve_triangle(tri_a_len, tri_b_len, tri_c_len, \\\"C\\\")\\n fk_01_angle = -solve_triangle(tri_a_len, tri_b_len, tri_c_len, \\\"B\\\")\\n\\n # Add rotations together\\n fk_01_temp = oMa.MEulerRotation(fk_01_angle, ik_pole_rot.y, 0)\\n\\n ik_dir_mat = compose_mat(ik_dir_rot)\\n fk_01_mat = compose_mat(fk_01_temp)\\n rot_mat = fk_01_mat * ik_dir_mat\\n\\n # Apply everything\\n fk_01_rot = get_rot_from_mat(rot_mat)\\n fk_02_rot = (fk_02_angle, 0, 0)\\n\\n fk_01_rot_plugs = get_rot_plugs(node.fk_01_conn)\\n for i, plug in enumerate(fk_01_rot_plugs):\\n plug.setMAngle(oMa.MAngle(fk_01_rot[i], oMa.MAngle.kRadians))\\n\\n fk_02_rot_plugs = get_rot_plugs(node.fk_02_conn)\\n for i, plug in enumerate(fk_02_rot_plugs):\\n if not plug.isLocked:\\n plug.setMAngle(oMa.MAngle(fk_02_rot[i], oMa.MAngle.kRadians))\\n\\n # Calculate ankle rotation\\n fk_03_rot = rot_world_space_to_local_space(node.ik_main_conn, get_parent(node.fk_03_conn))\\n\\n fk_03_rot_plugs = get_rot_plugs(node.fk_03_conn)\\n for i, plug in enumerate(fk_03_rot_plugs):\\n plug.setMAngle(oMa.MAngle(fk_03_rot[i], oMa.MAngle.kRadians))\",\n \"def test_match_table_post(self):\\n pass\",\n \"def key_fk(*args):\\n\\n robots = get_robot_roots()\\n if not robots:\\n pm.warning('No robots selected')\\n return\\n\\n for robot in robots:\\n # If the robot's IK attribute is on, switch the robot to\\n # FK mode before proceeding\\n target_ctrl_path = get_target_ctrl_path(robot)\\n tool_ctrl_path = get_tool_ctrl_path(robot)\\n\\n if pm.getAttr(target_ctrl_path + '.ik'):\\n switch_to_fk(robot)\\n\\n # We first check if the target/tool controller transformation and\\n # orientation is already aligned with the FK chain. If so, it\\n # indicates that we're performing an IK to FK switch, and we\\n # keyframe its position and orientation directly, without\\n # snapping the IK control to the FK hierarchy. This is to avoid\\n # unneccessarily changing the controllers Euler Angle rotation\\n # representation that can cause unpredictable behavior between frames\\n\\n if pm.objExists(tool_ctrl_path):\\n ctrl_ik = tool_ctrl_path\\n ctrl_fk = format_path(__TOOL_CTRL_FK_PATH, robot)\\n\\n # If robot doesn't have a tool controller, use target_CTRL.\\n else:\\n ctrl_ik = target_ctrl_path\\n ctrl_fk = format_path(__TCP_HDL_PATH, robot)\\n\\n if not _ik_and_fk_aligned(ctrl_ik, ctrl_fk):\\n _snap_ik_target_to_fk(robot)\\n\\n # Key all FK elements\\n try:\\n pm.setKeyframe(format_path(__A1_FK_CTRL_PATH, robot),\\n attribute='rotateY')\\n pm.setKeyframe(format_path(__A2_FK_CTRL_PATH, robot),\\n attribute='rotateX')\\n pm.setKeyframe(format_path(__A3_FK_CTRL_PATH, robot),\\n attribute='rotateX')\\n pm.setKeyframe(format_path(__A4_FK_CTRL_PATH, robot),\\n attribute='rotateZ')\\n pm.setKeyframe(format_path(__A5_FK_CTRL_PATH, robot),\\n attribute='rotateX')\\n pm.setKeyframe(format_path(__A6_FK_CTRL_PATH, robot),\\n attribute='rotateZ')\\n\\n # Key visibility of FK controllers\\n for i in range(6):\\n pm.setKeyframe(format_path(__FK_CTRLS_PATH, robot),\\n attribute='visibility')\\n except:\\n pm.warning('Error setting FK keys in FK mode')\\n\\n # Key all IK elements\\n try:\\n pm.setKeyframe(target_ctrl_path, attribute='ik')\\n pm.setKeyframe(target_ctrl_path, attribute='v', value=0)\\n\\n if pm.objExists(tool_ctrl_path):\\n pm.setKeyframe(tool_ctrl_path, attribute='v')\\n\\n # Key tool controllers\\n if pm.checkBox('cb_keyToolCtrl', query=True, value=True):\\n if pm.objExists(tool_ctrl_path):\\n pm.setKeyframe(tool_ctrl_path, attribute='translate')\\n pm.setKeyframe(tool_ctrl_path, attribute='rotate')\\n else:\\n pm.setKeyframe(target_ctrl_path, attribute='translate')\\n pm.setKeyframe(target_ctrl_path, attribute='rotate')\\n\\n except:\\n pm.warning('Error setting IK keys in FK mode')\",\n \"def match_keypoints(desc1, desc2, k=2, thresh=.9, matchertype=None):\\n if not matchertype:\\n # default is brute forcce\\n matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\\n matches = matcher.knnMatch(desc1, desc2, k=3)\\n print(matches)\\n\\n elif matchertype == \\\"FlannORB\\\":\\n #\\n FLANN_INDEX_LSH = 6\\n search_params = dict(checks=50)\\n index_params = dict(algorithm=FLANN_INDEX_LSH,\\n table_number=6, # 12\\n key_size=12, # 20\\n multi_probe_level=1) # 2\\n matcher = cv2.FlannBasedMatcher(index_params, search_params)\\n matches = matcher.knnMatch(desc1, desc2, k=2)\\n\\n elif matchertype == \\\"FlannSURF\\\":\\n FLANN_INDEX_KDTREE = 2\\n index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=500)\\n search_params = dict(checks=50)\\n matcher = cv2.FlannBasedMatcher(index_params, search_params)\\n matches = matcher.knnMatch(desc1, desc2, k=2)\\n\\n goodmatches = []\\n for m, n in matches:\\n if m.distance < thresh * n.distance:\\n goodmatches.append(m)\\n return goodmatches\",\n \"def match(self, dc):\\n raise NotImplemented\",\n \"def secondary_keys(self):\",\n \"def match(self, other):\",\n \"def correspondence_points(img1, img2, tag='c'):\\n if len(img1.shape) == 3:\\n img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\\n if len(img2.shape) == 3:\\n img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\\n\\n detector = cv2.SURF(800)\\n norm = cv2.NORM_L2\\n flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\\n matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)\\n kp1, desc1 = detector.detectAndCompute(img1, None)\\n kp2, desc2 = detector.detectAndCompute(img2, None)\\n raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2\\n p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)\\n\\n if len(p1) >= 4:\\n H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)\\n print '%d / %d inliers/matched' % (np.sum(status), len(status))\\n status = status.reshape(-1) # flatten\\n p1 = p1[status == 1]\\n p2 = p2[status == 1]\\n kp_pairs = [kp_pairs[i] for i in range(len(kp_pairs)) if status[i] == 1]\\n else:\\n # Just depend on the thresholding for filtering matches\\n p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches, ratio=0.3)\\n\\n draw_correspondence_points(img1, img2, kp_pairs, tag=tag)\\n return p1, p2, kp_pairs\",\n \"def newmatch(self):\\n\\n objectpnts = self.kif.getObjectPcd()\\n # normals\\n objectnormals = tools.estimatenormals(objectpnts)\",\n \"def match_source_key(self, match):\\n raise NotImplementedError\",\n \"def needleMatching(self):\\r\\n # productive\\r\\n profprint()\\r\\n modelNodes = slicer.mrmlScene.GetNodesByClass('vtkMRMLModelNode')\\r\\n nbNode = modelNodes.GetNumberOfItems()\\r\\n result = []\\r\\n found = []\\r\\n # print nbNode\\r\\n for nthNode in range(nbNode):\\r\\n node = slicer.mrmlScene.GetNthNodeByClass(nthNode, 'vtkMRMLModelNode')\\r\\n if node.GetID() not in found and node.GetAttribute('type') != 'Validation':\\r\\n dist = []\\r\\n polydata = node.GetPolyData()\\r\\n if polydata != None:\\r\\n bounds = polydata.GetBounds()\\r\\n for nthNode2 in range(nbNode):\\r\\n node2 = slicer.mrmlScene.GetNthNodeByClass(nthNode2, 'vtkMRMLModelNode')\\r\\n if node2.GetID() not in found and node2.GetAttribute('type') == 'Validation':\\r\\n polydata2 = node2.GetPolyData()\\r\\n if polydata2 != None and polydata2.GetNumberOfPoints() > 100 and polydata.GetNumberOfPoints() > 100:\\r\\n tipDistance = self.distTip(int(node.GetID().strip('vtkMRMLModelNode')) , int(node2.GetID().strip('vtkMRMLModelNode')))\\r\\n baseDistance = self.distBase(int(node.GetID().strip('vtkMRMLModelNode')) , int(node2.GetID().strip('vtkMRMLModelNode')))\\r\\n name = node.GetName()\\r\\n manualName = name.lstrip('auto-seg_').lstrip('manual-seg_').lstrip('obturator-seg_').lstrip('0123456789').lstrip('-ID-vtkMRMLModelNode').lstrip('0123456789-')\\r\\n if manualName==node2.GetName(): dist.append([tipDistance, node2.GetID(), node2.GetName()])\\r\\n # print tipDistance\\r\\n if dist != []:\\r\\n match = [min(dist)[0], min(dist)[1], node.GetID(), min(dist)[2]]\\r\\n result.append(match)\\r\\n found.append(min(dist)[1])\\r\\n found.append(node.GetID())\\r\\n node.GetDisplayNode().SetSliceIntersectionVisibility(1)\\r\\n # print result\\r\\n return result\",\n \"def findMatchesBetweenImages(image_1, image_2):\\n # matches - type: list of cv2.DMath\\n matches = None\\n # image_1_kp - type: list of cv2.KeyPoint items.\\n image_1_kp = None\\n # image_1_desc - type: numpy.ndarray of numpy.uint8 values.\\n image_1_desc = None\\n # image_2_kp - type: list of cv2.KeyPoint items.\\n image_2_kp = None\\n # image_2_desc - type: numpy.ndarray of numpy.uint8 values.\\n image_2_desc = None\\n # WRITE YOUR CODE HERE.\\n\\n sift = cv2.ORB_create()\\n image_1_kp, image_1_desc = sift.detectAndCompute(image_1, None)\\n image_2_kp, image_2_desc = sift.detectAndCompute(image_2, None)\\n\\n # bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\\n # matches = bf.match(image_1_desc, image_2_desc)\\n # matches = sorted(matches, key = lambda x:x.distance)\\n # matches = matches[:10]\\n\\n bf = cv2.BFMatcher()\\n matches = bf.knnMatch(image_1_desc, image_2_desc, k=2)\\n\\n # Apply ratio test\\n good = []\\n for m, n in matches:\\n print(m.distance, n.distance, m.distance < .75*n.distance)\\n if m.distance < (0.75 * n.distance):\\n good.append([m])\\n\\n # We coded the return statement for you. You are free to modify it -- just\\n # make sure the tests pass.\\n print(len(good), good)\\n return image_1_kp, image_2_kp, matches\\n # END OF FUNCTION.\",\n \"def needleMatching(self):\\n #productive\\n profprint()\\n modelNodes=slicer.mrmlScene.GetNodesByClass('vtkMRMLModelNode')\\n nbNode=modelNodes.GetNumberOfItems()\\n result=[]\\n found=[]\\n #print nbNode\\n for nthNode in range(nbNode):\\n node=slicer.mrmlScene.GetNthNodeByClass(nthNode,'vtkMRMLModelNode')\\n if node.GetID() not in found and node.GetAttribute('type')!='Validation':\\n dist=[]\\n polydata = node.GetPolyData()\\n if polydata!=None:\\n bounds = polydata.GetBounds()\\n for nthNode2 in range(nbNode):\\n node2=slicer.mrmlScene.GetNthNodeByClass(nthNode2,'vtkMRMLModelNode')\\n if node2.GetID() not in found and node2.GetAttribute('type')=='Validation':\\n polydata2 = node2.GetPolyData()\\n if polydata2!=None and polydata2.GetNumberOfPoints()>100 and polydata.GetNumberOfPoints()>100:\\n\\n axialDistance=self.distTip( int(node.GetID().strip('vtkMRMLModelNode')) , int(node2.GetID().strip('vtkMRMLModelNode')))\\n \\n dist.append([axialDistance,node2.GetID()])\\n # print axialDistance\\n \\n if dist!=[]:\\n match=[min(dist)[0],min(dist)[1],node.GetID()]\\n result.append(match)\\n found.append(min(dist)[1])\\n found.append(node.GetID()) \\n node.GetDisplayNode().SetSliceIntersectionVisibility(1)\\n # print result\\n return result\",\n \"def find(self, image, k=None, ratio=None):\\n if not self._targets:\\n return []\\n k = 2 if k is None else k\\n ratio = 0.75 if ratio is None else ratio\\n keypoints, descriptors = self._detector.detectAndCompute(image, None)\\n if len(keypoints) < self.min_match_count:\\n return []\\n matches = self._matcher.knnMatch(descriptors, k=int(k))\\n matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * ratio]\\n if len(matches) < self.min_match_count:\\n return []\\n matches_by_id = [[] for _ in xrange(len(self._targets))]\\n for m in matches:\\n matches_by_id[m.imgIdx].append(m)\\n tracked = []\\n for imgIdx, matches in enumerate(matches_by_id):\\n if len(matches) < self.min_match_count:\\n continue\\n target = self._targets[imgIdx]\\n p0 = [target.keypoints[m.trainIdx].pt for m in matches]\\n p1 = [keypoints[m.queryIdx].pt for m in matches]\\n p0, p1 = np.float32((p0, p1))\\n H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)\\n status = status.ravel() != 0\\n if status.sum() < self.min_match_count:\\n continue\\n p0, p1 = np.int32((p0, p1))\\n inliers = [((x0, y0), (x1, y1)) for (x0, y0), (x1, y1), s in zip(p0, p1, status) if s]\\n outliers = [((x0, y0), (x1, y1)) for (x0, y0), (x1, y1), s in zip(p0, p1, status) if not s]\\n quad = cv2.perspectiveTransform(target.quad.reshape(1, -1, 2), H).reshape(-1, 2)\\n track = TrackedTarget(target=target, image=image, inliers=inliers, outliers=outliers, H=H, quad=quad)\\n tracked.append(track)\\n tracked.sort(key = lambda t: len(t.inliers), reverse=True)\\n return tracked\",\n \"def match_features(kp1, kp2, des1, des2):\\n FLANN_INDEX_LSH = 6\\n index_params= dict(algorithm = FLANN_INDEX_LSH,\\n table_number = 6, # 12\\n key_size = 12, # 20\\n multi_probe_level = 2) #2\\n\\n search_params = dict(checks=50) # or pass empty dictionary\\n flann = cv2.FlannBasedMatcher(index_params,search_params)\\n matches = flann.knnMatch(des1,des2,k=2)\\n good = []\\n for (m,n) in matches:\\n if m.distance < 0.8*n.distance: ## Lowe's ratio imp for tuning\\n good.append(m)\\n\\n if len(good) < 20:\\n return []\\n return good\",\n \"def secondary_keys_dicts(self):\",\n \"def key_ik_fk(*args):\\n if not pm.window(\\\"mimic_win\\\", exists=True):\\n return\\n\\n current_tab = pm.tabLayout('switcher_tab_layout',\\n query=True,\\n selectTabIndex=True)\\n\\n try:\\n if current_tab == 1:\\n key_ik()\\n elif current_tab == 2:\\n key_fk()\\n except:\\n pm.warning('Error keying IK/FK')\",\n \"def delete_ik_fk_keys(*args):\\n if not check_robot_selection():\\n pm.warning('No robots selected; ' \\\\\\n 'Select at least one robot.')\\n\\n keyed_attrs = {__TARGET_CTRL_PATH: ['ik',\\n 'visibility',\\n 'ikSolution1',\\n 'ikSolution2',\\n 'ikSolution3'],\\n __FK_CTRLS_PATH: ['visibility'],\\n __A1_FK_CTRL_PATH: ['rotateY'],\\n __A2_FK_CTRL_PATH: ['rotateX'],\\n __A3_FK_CTRL_PATH: ['rotateX'],\\n __A4_FK_CTRL_PATH: ['rotateZ'],\\n __A5_FK_CTRL_PATH: ['rotateX'],\\n __A6_FK_CTRL_PATH: ['rotateZ']}\\n\\n robots = get_robot_roots()\\n\\n current_frame = pm.currentTime()\\n for robot in robots:\\n target_ctrl_path = get_target_ctrl_path(robot)\\n tool_ctrl_path = get_tool_ctrl_path(robot)\\n # Check if there's a keyframe set on the target_CTRL.ik attribute\\n key = pm.keyframe(target_ctrl_path,\\n attribute='ik',\\n query=True,\\n time=current_frame)\\n\\n # If there is no keyframe set on the IK attribute, continue to the\\n # next robot\\n if not key:\\n pm.warning('{} has no IK|FK keyframe at frame {}' \\\\\\n .format(robot, current_frame))\\n continue\\n\\n # If there is a keyframe on the IK attribute, we also check if there's\\n # a keyframe on an FK controller as well, as we only consider there to\\n # be a proper IK or FK keyframe if both are true\\n # Note, we only need to check a single FK controller as they should all\\n # be keyframed (or not) together\\n fk_test_handle_path = format_path(__A1_FK_CTRL_PATH + '.rotateY', robot)\\n fk_key = pm.keyframe(fk_test_handle_path,\\n query=True,\\n time=current_frame)\\n # If there is no keyframe set on the FK controller attribute,\\n # continue to the next robot\\n if not fk_key:\\n pm.warning('{} has no IK|FK keyframe at frame {}' \\\\\\n .format(robot, current_frame))\\n continue \\n\\n for obj in keyed_attrs:\\n for attr in keyed_attrs[obj]:\\n pm.cutKey(format_path(obj, robot),\\n time=current_frame,\\n attribute=attr,\\n option=\\\"keys\\\")\\n\\n if pm.objExists(tool_ctrl_path):\\n pm.cutKey(tool_ctrl_path,\\n time=current_frame,\\n attribute='visibility',\\n option=\\\"keys\\\")\\n\\n if pm.checkBox('cb_keyToolCtrl', query=True, value=True):\\n if pm.objExists(tool_ctrl_path):\\n pm.cutKey(tool_ctrl_path,\\n time=current_frame,\\n attribute='translate',\\n option=\\\"keys\\\")\\n pm.cutKey(tool_ctrl_path,\\n time=current_frame,\\n attribute='rotate',\\n option=\\\"keys\\\")\\n else:\\n pm.cutKey(target_ctrl_path,\\n time=current_frame,\\n attribute='translate',\\n option=\\\"keys\\\")\\n pm.cutKey(target_ctrl_path,\\n time=current_frame,\\n attribute='rotate',\\n option=\\\"keys\\\")\",\n \"def altloc_match(self, other: AtomKey) -> bool:\\n ...\",\n \"def visulize_matches(matches, k2, k1, img2, img1):\\n\\n import scipy as sp\\n img2 = cv.cvtColor(img2, cv.COLOR_GRAY2BGR)\\n h1, w1 = img1.shape[:2]\\n h2, w2 = img2.shape[:2]\\n view = sp.zeros((max(h1, h2), w1 + w2, 3), sp.uint8)\\n view[:h1, :w1, :] = img1 \\n view[:h2, w1:, :] = img2\\n view[:, :, 1] = view[:, :, 0] \\n view[:, :, 2] = view[:, :, 0]\\n\\n for m in matches:\\n m = m[0]\\n # draw the keypoints\\n # print m.queryIdx, m.trainIdx, m.distance\\n color = tuple([sp.random.randint(0, 255) for _ in xrange(3)])\\n pt1 = (int(k1[m.queryIdx].pt[0]), int(k1[m.queryIdx].pt[1]))\\n pt2 = (int(k2[m.trainIdx].pt[0] + w1), int(k2[m.trainIdx].pt[1]))\\n\\n cv.line(view, pt1, pt2, color)\\n return view\",\n \"def test_getCpfRelations(self):\\n pass\",\n \"def lookup(self, key):\",\n \"def relationships(self):\",\n \"def keys(targets):\",\n \"def matches(self):\\n pass\",\n \"def theidfobjectmentioningobjs(idfindex, keyindex, objindex):\\n idf, edges = eppystuff.an_idfedges(idfindex)\\n objnames = idf_helpers.idfobjectkeys(idf)\\n objname = objnames[keyindex]\\n idfobjects = idf.idfobjects[objname]\\n idfobject = idfobjects[objindex]\\n from eppy import walk_hvac\\n try:\\n idfobjectname = idfobject.Name\\n except BadEPFieldError as e:\\n idfobjectname = idfobject.Zone_Name\\n \\n nextnodes = walk_hvac.nextnode(edges, idfobjectname)\\n nextobjs = [eppystuff.hvacname2idfobj(idf, nnode) for nnode in nextnodes]\\n keyobjids = [eppystuff.idfobjectindices(idf, nobj) for nobj in nextobjs]\\n nurls = [\\\"../../%s/%s\\\" % (key_id, obj_id) for key_id, obj_id in keyobjids]\\n nextlinks = ['<a href=%s>%s</a>' % (url, nnode)\\n for nnode, url in zip(nextnodes, nurls)]\\n firstlines = [\\n \\\"HVAC connections from %s named '%s'\\\" % (objname, idfobjectname),\\n \\\"\\\",\\n \\\"Next Objects\\\",\\n ]\\n betweenlines = [\\\"\\\", \\\"Previous Objects\\\"]\\n prevnodes = walk_hvac.prevnode(edges, idfobjectname)\\n prevobjs = [eppystuff.hvacname2idfobj(idf, pnode) for pnode in prevnodes]\\n keyobjids = [eppystuff.idfobjectindices(idf, nobj) for nobj in prevobjs]\\n try:\\n purls = [\\\"../../%s/%s\\\" % (key_id, obj_id) \\n for key_id, obj_id in keyobjids]\\n except TypeError as e:\\n purls = []\\n prevlinks = ['<a href=%s>%s</a>' % (url, pnode)\\n for pnode, url in zip(prevnodes, purls)]\\n # image snippet\\n onlythis = [idfobjectname, ] + nextnodes + prevnodes\\n trimmed = eppystuff.trimedges(edges, onlythis)\\n from eppy.useful_scripts import loopdiagram\\n imgname = '%s_%s_%s' % (idfindex, keyindex, objindex)\\n eppystuff.save_imagesnippets(IMGFOLDER,imgname, trimmed)\\n imgline = ['<img src=\\\"../../../../../static/%s.png\\\" alt=\\\"snippet\\\">' % (imgname, )]\\n html = '<br>'.join(firstlines + nextlinks + betweenlines + prevlinks + imgline)\\n return html\",\n \"def keyEquivalent( self ):\\n\\t\\treturn None\",\n \"def fk_to_ik(node):\\n # Get relevant data\\n ik_pole_off = get_parent(node.ik_pole_conn)\\n\\n world_trans_ik_pole_off = get_world_trans(ik_pole_off)\\n world_trans_fk_01 = get_world_trans(node.fk_01_conn)\\n world_trans_fk_02 = get_world_trans(node.fk_02_conn)\\n world_trans_fk_03 = get_world_trans(node.fk_03_conn)\\n world_trans_ik_pole = get_world_trans(node.ik_pole_conn)\\n\\n world_rot_fk_03 = get_world_rot(node.fk_03_conn)\\n\\n # calculate ik pole position\\n ik_pole_mid_point = (world_trans_fk_01 + world_trans_fk_03) / 2\\n ik_pole_base = world_trans_fk_02 - ik_pole_mid_point\\n\\n # Handle the case when the leg is fully stretched\\n if ik_pole_base.length() <= 0.0001:\\n rot_fk_01 = get_rot_as_quat(node.fk_01_conn)\\n rot_fk_02 = get_rot_as_quat(node.fk_02_conn)\\n\\n rot = rot_fk_01 * rot_fk_02\\n\\n ik_pole_base = oMa.MVector(2 * (rot.x * rot.z + rot.w * rot.y),\\n 2 * (rot.y * rot.z - rot.w * rot.x),\\n 1 - 2 * (rot.x * rot.x + rot.y * rot.y))\\n\\n ik_pole_len = (world_trans_ik_pole - world_trans_fk_02).length()\\n\\n pos_ik_pole = world_trans_fk_02 + ik_pole_base.normalize() * ik_pole_len - world_trans_ik_pole_off\\n\\n # Get the destination MPlugs\\n ik_main_trans_plugs = get_trans_plugs(node.ik_main_conn)\\n ik_main_rot_plugs = get_rot_plugs(node.ik_main_conn)\\n ik_pole_trans_plugs = get_trans_plugs(node.ik_pole_conn)\\n\\n # Set the new values\\n for i, plug in enumerate(ik_main_trans_plugs):\\n plug.setFloat(world_trans_fk_03[i])\\n\\n for i, plug in enumerate(ik_main_rot_plugs):\\n plug.setMAngle(oMa.MAngle(world_rot_fk_03[i], oMa.MAngle.kRadians))\\n\\n for i, plug in enumerate(ik_pole_trans_plugs):\\n plug.setFloat(pos_ik_pole[i])\",\n \"def MatchRelationshipConfrontation(C):\\n for c in C:\\n if c.relationships is None:\\n continue\\n for i, longname in enumerate(c.relationships):\\n found = False\\n for cor in C:\\n if longname.lower() == cor.longname.lower():\\n c.relationships[i] = cor\\n found = True\\n return C\",\n \"def find_matching_points(img1, img2, max_pix_movement=50, normalize=True, show=False):\\n\\n # Initiate ORB detector\\n orb = cv2.ORB_create()\\n\\n # find the keypoints and descriptors with ORB\\n kp1, des1 = orb.detectAndCompute(img1, None)\\n kp2, des2 = orb.detectAndCompute(img2, None)\\n\\n # create BFMatcher object\\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\\n # Match descriptors.\\n matches = bf.match(des1,des2)\\n # Sort them in the order of their distance.\\n matches = sorted(matches, key = lambda x:x.distance)\\n # Draw first 10 matches.\\n if show:\\n img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:500], None,flags=2)\\n plt.imshow(img3),plt.show()\\n # Get the matching keypoints for each of the images\\n\\n list_kp1 = []\\n list_kp2 = []\\n for mat in matches:\\n img1_idx = mat.queryIdx\\n img2_idx = mat.trainIdx\\n\\n # x - columns\\n # y - rows\\n list_kp1.append(kp1[img1_idx].pt)\\n list_kp2.append(kp2[img2_idx].pt)\\n\\n n_kp1, n_kp2 = np.float32(list_kp1), np.float32(list_kp2)\\n n_kp1 /= np.asarray([img1.shape[1], img1.shape[0]], np.float32)\\n n_kp2 /= np.asarray([img2.shape[1], img2.shape[0]], np.float32)\\n n_kp1 = n_kp1 * 2. - 1.\\n n_kp2 = n_kp2 * 2. - 1.\\n\\n return np.int32(list_kp1), np.int32(list_kp2), n_kp1, n_kp2\",\n \"def nn_match(descs1, descs2):\\n # diff = descs1[:, None, :] - descs2[None, :, :]\\n # diff = np.linalg.norm(diff, ord=2, axis=2)\\n # indices = np.argmin(diff, axis=1)\\n\\n flann = cv2.FlannBasedMatcher_create()\\n matches = flann.match(descs1.astype(np.float32), descs2.astype(np.float32))\\n indices = [x.trainIdx for x in matches]\\n\\n return indices\",\n \"def check_keys(self):\",\n \"def match(image1,image2,threshold,useRansac=False,t_orientation=30,t_scale=0.5):\\r\\n im1, keypoints1, descriptors1 = ReadKeys(image1)\\r\\n im2, keypoints2, descriptors2 = ReadKeys(image2)\\r\\n #\\r\\n # REPLACE THIS CODE WITH YOUR SOLUTION (ASSIGNMENT 5, QUESTION 3)\\r\\n #\\r\\n # Generate five random matches (for testing purposes)\\r\\n # matched_pairs = []\\r\\n # num = 5\\r\\n # for i in range(num):\\r\\n # matched_pairs.append([keypoints1[i],keypoints2[i]])\\r\\n # return DisplayMatches(im1, im2, matched_pairs)\\r\\n\\r\\n # END OF SECTION OF CODE TO REPLACE\\r\\n #\\r\\n\\r\\n #q3\\r\\n matched_pairs = []\\r\\n between_angles = np.arccos(np.dot(descriptors1, np.transpose(descriptors2)))\\r\\n for i, row in enumerate(between_angles):\\r\\n \\tratio = sorted(row)[0] / sorted(row)[1]\\r\\n \\tif ratio <= threshold:\\r\\n\\t \\tmatched_pairs.append([keypoints1[i], keypoints2[np.where(row == sorted(row)[0])[0][0]]])\\r\\n # print(matched_pairs)\\r\\n if useRansac is False:\\r\\n return DisplayMatches(im1, im2, matched_pairs)\\r\\n\\t# \\r\\n\\r\\n #q4\\r\\n repetition = 10\\r\\n subsets = [[]] * repetition\\r\\n for i in range(repetition):\\r\\n r = random.randint(0, len(matched_pairs))\\r\\n for match in matched_pairs:\\r\\n ds1, ds2 = matched_pairs[r][1][2]/matched_pairs[r][0][2], match[1][2]/match[0][2]\\r\\n do1, do2 = (matched_pairs[r][1][3]-matched_pairs[r][0][3]), (match[1][3]-match[0][3])\\r\\n if abs(ds2 - ds1) <= t_scale * ds1 and abs(do2 - do1) % (2 * math.pi) <= t_orientation:\\r\\n subsets[i].append(match)\\r\\n\\r\\n max_i, max_len = 0, subsets[0]\\r\\n for i in range(10):\\r\\n l = len(subsets[i])\\r\\n if l > max_len:\\r\\n max_len = l\\r\\n max_i = i\\r\\n\\r\\n im3 = DisplayMatches(im1, im2, subsets[max_i])\\r\\n return im3\",\n \"def match(self, other):\\n matches = match_descriptors(self.base_view.descriptors, other.descriptors,\\n cross_check=True)\\n matches = pd.Series({m[0]: m[1] for m in matches}).reindex(\\n self._match_table.index)\\n self._match_table[other.position.id] = matches\",\n \"def compute_matches(self):\\n\\t\\tself.local_database[\\\"figure_number\\\"] = [0] * len(self.local_database[\\\"feature_vectors\\\"])\\n\\t\\tForensics = wbForensicsHOG(Database=self.local_database)\\n\\t\\tForensics.KDTree_pairs(leaf_size = len(self.local_database)+1)\\n\\t\\tForensics.d_rank(pairs=Forensics.pairs, distances=Forensics.dists, ratios=Forensics.ratios)\\n\\n\\t\\tself.local_matches = Forensics.Dist_Rank\",\n \"def IK_geometric(dh_params, pose):\\n pass\",\n \"def _digest_fkeys(self):\\n for schema in self.schemas.values():\\n for fkey in schema._fkeys.values():\\n ref_col = fkey._fkey_doc.get('referenced_columns', [{}])[0]\\n pk_sname, pk_tname = ref_col.get('schema_name'), ref_col.get('table_name')\\n if pk_sname in self.schemas and pk_tname in self.schemas[pk_sname].tables:\\n fkey.pk_table = self.schemas[pk_sname].tables[pk_tname]\\n else:\\n fkey.pk_table = self._unknown_table\",\n \"def test4(self):\\n tgts = [ ('CCC(O)C(=O)O',\\n ('CCC','OCC','OCC=O','OCCO','CCCC','OC=O','CC(O)C')),\\n ]\\n for smi,matches in tgts:\\n m = Chem.MolFromSmiles(smi)\\n fp1 = Chem.RDKFingerprint(m,2,7,9192,4,0)\\n obs = fp1.GetOnBits()\\n for match in matches:\\n m2 = Chem.MolFromSmiles(match)\\n fp2 = Chem.RDKFingerprint(m2,2,7,9192,4,0)\\n v1,v2 = DataStructs.OnBitProjSimilarity(fp2,fp1)\\n assert feq(v1,1.0000),'substruct %s not properly contained in %s'%(match,smi)\",\n \"def associated_coroot(self):\",\n \"def matchById(firstcat, othercat, otherid='SeqNr', selfid='SeqNr'):\\n order = {}\\n for i, x in enumerate(firstcat[selfid]):\\n order[x] = i\\n\\n keeporder = []\\n for x in othercat[otherid]:\\n if x in order:\\n keeporder.append(order[x])\\n\\n keep = np.array(keeporder)\\n matched = firstcat[keep]\\n print \\\"INFO: %i matched galaxies kept\\\" % len(matched)\\n return matched\",\n \"def match(self, item):\",\n \"def _ik_and_fk_aligned(ik_ctrl, tcp_handle):\\n\\n # Define some small number to threshold our output\\n delta = .0001\\n\\n # Initialize variables\\n # translation_is_aligned = False\\n # rotation_is_aligned = False\\n ik_fk_are_aligned = False\\n\\n # Find the translation of each object and compare them\\n ik_trans = pm.xform(ik_ctrl, q=True, rp=True, ws=True)\\n tcp_trans = pm.xform(tcp_handle, q=True, rp=True, ws=True)\\n\\n # Find the distance between the ik controller and the tcp handle\\n trans_diff = math.sqrt((ik_trans[0] - tcp_trans[0]) ** 2\\n + (ik_trans[1] - tcp_trans[1]) ** 2\\n + (ik_trans[2] - tcp_trans[2]) ** 2)\\n\\n if round(trans_diff, 6) < delta:\\n ik_fk_are_aligned = True\\n\\n return ik_fk_are_aligned\",\n \"def matchModelPose(self):\\n\\n pass\",\n \"def _add_matches(self):\\r\\n for record in self.records:\\r\\n match_dict={key_to_track: record.get(key_to_track)\\r\\n for key_to_track in self.key_matcher.keys()}\\r\\n self.key_matcher.add(obj=record,\\r\\n match_dict=match_dict)\",\n \"def matches(self, target):\\n raise NotImplementedError()\",\n \"def MatchMatrixs (self,Node1,Node2):\\n\\n T1Native_Node = Node1\\n T1Native_Matrix = slicer.util.arrayFromVolume(T1Native_Node)\\n DimN = T1Native_Matrix.shape\\n T1Enhanced_Node = Node2\\n T1Enhanced_Matrix = slicer.util.arrayFromVolume(T1Enhanced_Node)\\n DimE = T1Enhanced_Matrix.shape\\n\\n NMatrix = self.GetIJKToRASnpArray(T1Native_Node)\\n NVector = NMatrix[:-1,-1]\\n EMatrix = self.GetIJKToRASnpArray(T1Enhanced_Node)\\n EVector = EMatrix[:-1,-1]\\n NPixelSize = [np.linalg.norm(NMatrix[:-1,0]), np.linalg.norm(NMatrix[:-1,1])]\\n EPixelSize = [np.linalg.norm(EMatrix[:-1,0]), np.linalg.norm(EMatrix[:-1,1])]\\n\\n Niversor = NMatrix[:-1,0]/NPixelSize[0]\\n Njversor = NMatrix[:-1,1]/NPixelSize[1]\\n Nkversor = np.round(np.cross(Niversor,Njversor),3)\\n Nkstep = round(np.linalg.norm(NMatrix[:-1,2]),3)\\n\\n Eiversor = EMatrix[:-1,0]/EPixelSize[0]\\n Ejversor = EMatrix[:-1,1]/EPixelSize[1]\\n Ekversor = np.round(np.cross(Eiversor,Ejversor),3)\\n Ekstep = round(np.linalg.norm(EMatrix[:-1,2]),3)\\n print(Nkversor,Ekversor,Nkstep,Ekstep,NVector,EVector,(NVector-EVector).dot(Ekversor))\\n if not ( np.sum(Nkversor==Ekversor) == 3 and Nkstep==Ekstep and ((NVector-EVector).dot(Ekversor)) == 0 ): # it verifies if the slices are oriented in the same direction, with the same step between slices and if the first images are complanar.\\n slicer.util.warningDisplay('The geometry of the LL Native and LL Enhanced volume doesn\\\\'t match. It could deteriorate the ECV map', windowTitle= 'Warning')\\n\\n if (DimE == DimN):\\n T1_ECVMappingLogic.setupNodeFromNode(self,self.ECVMapNode , self.NativeT1_Selector.currentNode()) \\n return [T1Native_Matrix,T1Enhanced_Matrix]\\n if (DimE[1:3] == DimN[1:3]):\\n k = min([DimE[1],DimN[1]])\\n T1_ECVMappingLogic.setupNodeFromNode(self,self.ECVMapNode , self.NativeT1_Selector.currentNode())\\n return [T1Native_Matrix[:k,:,:],T1Enhanced_Matrix[:k,:,:]]\\n\\n jN = np.arange(0,DimN[2]*NPixelSize[1],NPixelSize[1])+NPixelSize[1]/2+(NVector-EVector).dot(Njversor)\\n iN = np.arange(0,DimN[1]*NPixelSize[0],NPixelSize[0])+NPixelSize[0]/2+(NVector-EVector).dot(Niversor)\\n iE = np.arange(0,DimE[1]*EPixelSize[0],EPixelSize[0])+EPixelSize[0]/2\\n jE = np.arange(0,DimE[2]*EPixelSize[1],EPixelSize[1])+EPixelSize[1]/2 \\n if DimE[1] > DimN[1]: ## I concidered a square image\\n T1Nreshaped = np.zeros(DimE)\\n for k in range(DimN[0]):\\n f = interpolate.interp2d(iN, jN, np.nan_to_num(T1Native_Matrix[k,:,:]), fill_value = 0)\\n T1Nreshaped[k,:,:] = f(iE, jE)\\n T1Ereshaped = T1Enhanced_Matrix[:k+1,:,:]\\n T1_ECVMappingLogic.setupNodeFromNode(self,self.ECVMapNode , self.EnhancedT1_Selector.currentNode())\\n return [T1Nreshaped,T1Ereshaped]\\n else:\\n T1Ereshaped = np.zeros(DimN)\\n for k in range(DimE[0]):\\n f = interpolate.interp2d(iE, jE, np.nan_to_num(T1Enhanced_Matrix[k,:,:]), fill_value = 0)\\n T1Ereshaped[k,:,:] = f(iN, jN) \\n T1Nreshaped = T1Native_Matrix[:k+1,:,:]\\n T1_ECVMappingLogic.setupNodeFromNode(self,self.ECVMapNode , self.NativeT1_Selector.currentNode()) \\n return [T1Nreshaped,T1Ereshaped]\",\n \"def matches(self, accession):\\n pass\",\n \"def vsone_feature_matching(kpts1, vecs1, kpts2, vecs2, dlen_sqrd2, cfgdict={},\\n flann1=None, flann2=None, verbose=None):\\n import vtool as vt\\n import pyflann\\n from vtool import spatial_verification as sver\\n #import vtool as vt\\n sver_xy_thresh = cfgdict.get('sver_xy_thresh', .01)\\n ratio_thresh = cfgdict.get('ratio_thresh', .625)\\n refine_method = cfgdict.get('refine_method', 'homog')\\n symmetric = cfgdict.get('symmetric', False)\\n K = cfgdict.get('K', 1)\\n Knorm = cfgdict.get('Knorm', 1)\\n #ratio_thresh = .99\\n # GET NEAREST NEIGHBORS\\n checks = 800\\n #pseudo_max_dist_sqrd = (np.sqrt(2) * 512) ** 2\\n #pseudo_max_dist_sqrd = 2 * (512 ** 2)\\n if verbose is None:\\n verbose = True\\n\\n flann_params = {'algorithm': 'kdtree', 'trees': 8}\\n if flann1 is None:\\n flann1 = vt.flann_cache(vecs1, flann_params=flann_params, verbose=verbose)\\n\\n #print('symmetric = %r' % (symmetric,))\\n if symmetric:\\n if flann2 is None:\\n flann2 = vt.flann_cache(vecs2, flann_params=flann_params, verbose=verbose)\\n\\n try:\\n try:\\n num_neighbors = K + Knorm\\n fx2_to_fx1, fx2_to_dist = normalized_nearest_neighbors(flann1, vecs2, num_neighbors, checks)\\n #fx2_to_fx1, _fx2_to_dist = flann1.nn_index(vecs2, num_neighbors=K, checks=checks)\\n if symmetric:\\n fx1_to_fx2, fx1_to_dist = normalized_nearest_neighbors(flann2, vecs1, K, checks)\\n\\n except pyflann.FLANNException:\\n print('vecs1.shape = %r' % (vecs1.shape,))\\n print('vecs2.shape = %r' % (vecs2.shape,))\\n print('vecs1.dtype = %r' % (vecs1.dtype,))\\n print('vecs2.dtype = %r' % (vecs2.dtype,))\\n raise\\n if symmetric:\\n is_symmetric = flag_symmetric_matches(fx2_to_fx1, fx1_to_fx2)\\n fx2_to_fx1 = fx2_to_fx1.compress(is_symmetric, axis=0)\\n fx2_to_dist = fx2_to_dist.compress(is_symmetric, axis=0)\\n\\n assigntup = assign_unconstrained_matches(fx2_to_fx1, fx2_to_dist)\\n\\n fx2_match, fx1_match, fx1_norm, match_dist, norm_dist = assigntup\\n fm_ORIG = np.vstack((fx1_match, fx2_match)).T\\n fs_ORIG = 1 - np.divide(match_dist, norm_dist)\\n # APPLY RATIO TEST\\n fm_RAT, fs_RAT, fm_norm_RAT = ratio_test(fx2_match, fx1_match, fx1_norm,\\n match_dist, norm_dist,\\n ratio_thresh)\\n\\n # SPATIAL VERIFICATION FILTER\\n #with ut.EmbedOnException():\\n match_weights = np.ones(len(fm_RAT))\\n svtup = sver.spatially_verify_kpts(kpts1, kpts2, fm_RAT, sver_xy_thresh,\\n dlen_sqrd2, match_weights=match_weights,\\n refine_method=refine_method)\\n if svtup is not None:\\n (homog_inliers, homog_errors, H_RAT) = svtup[0:3]\\n else:\\n H_RAT = np.eye(3)\\n homog_inliers = []\\n fm_RAT_SV = fm_RAT.take(homog_inliers, axis=0)\\n fs_RAT_SV = fs_RAT.take(homog_inliers, axis=0)\\n fm_norm_RAT_SV = fm_norm_RAT[homog_inliers]\\n\\n top_percent = .5\\n top_idx = ut.take_percentile(fx2_to_dist.T[0].argsort(), top_percent)\\n fm_TOP = fm_ORIG.take(top_idx, axis=0)\\n fs_TOP = fx2_to_dist.T[0].take(top_idx)\\n #match_weights = np.ones(len(fm_TOP))\\n #match_weights = (np.exp(fs_TOP) / np.sqrt(np.pi * 2))\\n match_weights = 1 - fs_TOP\\n #match_weights = np.ones(len(fm_TOP))\\n svtup = sver.spatially_verify_kpts(kpts1, kpts2, fm_TOP, sver_xy_thresh,\\n dlen_sqrd2, match_weights=match_weights,\\n refine_method=refine_method)\\n if svtup is not None:\\n (homog_inliers, homog_errors, H_TOP) = svtup[0:3]\\n np.sqrt(homog_errors[0] / dlen_sqrd2)\\n else:\\n H_TOP = np.eye(3)\\n homog_inliers = []\\n fm_TOP_SV = fm_TOP.take(homog_inliers, axis=0)\\n fs_TOP_SV = fs_TOP.take(homog_inliers, axis=0)\\n\\n matches = {\\n 'ORIG' : MatchTup2(fm_ORIG, fs_ORIG),\\n 'RAT' : MatchTup3(fm_RAT, fs_RAT, fm_norm_RAT),\\n 'RAT+SV' : MatchTup3(fm_RAT_SV, fs_RAT_SV, fm_norm_RAT_SV),\\n 'TOP' : MatchTup2(fm_TOP, fs_TOP),\\n 'TOP+SV' : MatchTup2(fm_TOP_SV, fs_TOP_SV),\\n }\\n output_metdata = {\\n 'H_RAT': H_RAT,\\n 'H_TOP': H_TOP,\\n }\\n\\n except MatchingError:\\n fm_ERR = np.empty((0, 2), dtype=np.int32)\\n fs_ERR = np.empty((0, 1), dtype=np.float32)\\n H_ERR = np.eye(3)\\n matches = {\\n 'ORIG' : MatchTup2(fm_ERR, fs_ERR),\\n 'RAT' : MatchTup3(fm_ERR, fs_ERR, fm_ERR),\\n 'RAT+SV' : MatchTup3(fm_ERR, fs_ERR, fm_ERR),\\n 'TOP' : MatchTup2(fm_ERR, fs_ERR),\\n 'TOP+SV' : MatchTup2(fm_ERR, fs_ERR),\\n }\\n output_metdata = {\\n 'H_RAT': H_ERR,\\n 'H_TOP': H_ERR,\\n }\\n\\n return matches, output_metdata\",\n \"def try1():\\n path = '/Users/mayankkejriwal/datasets/eswc2017/disasters/'\\n model = Word2Vec.load_word2vec_format(path+'GoogleNews-vectors-negative300.bin', binary=True)\\n model.init_sims(replace=True)\\n keys = ['charlotte', 'Charlotte', 'yorktown', 'LA']\\n for key in keys:\\n try:\\n # print model.most_similar(positive=['woman', 'king'], negative=['man'])\\n j = model[key]\\n print 'found...',\\n print key\\n except KeyError:\\n print 'not found...',\\n print key\\n continue\\n print model.similarity('charlotte', 'carolina')\\n print model.similarity('LA', 'California')\",\n \"def hit(self):\",\n \"def test_two_referrers(dumper, db):\\n dumper.reader.load_db(\\n db.create_sample(\\n 5,\\n fkeys=[\\n (\\\"table1\\\", \\\"t2id\\\", \\\"table2\\\", \\\"id\\\"),\\n (\\\"table1\\\", \\\"t3id\\\", \\\"table3\\\", \\\"id\\\"),\\n (\\\"table2\\\", \\\"t24id\\\", \\\"table4\\\", \\\"id\\\"),\\n (\\\"table3\\\", \\\"t34id\\\", \\\"table4\\\", \\\"id\\\"),\\n ],\\n )\\n )\\n dumper.add_config({\\\"db_objects\\\": [{\\\"name\\\": \\\"table1\\\"}]})\\n dumper.perform_dump()\\n objs = [obj for obj, match in dumper.writer.dumped if isinstance(obj, Table)]\\n assert len(objs) == 4\\n\\n (match,) = [match for obj, match in dumper.writer.dumped if obj.name == \\\"table4\\\"]\\n assert len(match.referenced_by) == 2\\n assert sorted(fkey.name for fkey in match.referenced_by) == [\\n \\\"t24id_table4_id_fkey\\\",\\n \\\"t34id_table4_id_fkey\\\",\\n ]\",\n \"def test_fkey_nav_rec(dumper, db):\\n dumper.reader.load_db(\\n db.create_sample(\\n 4,\\n fkeys=[\\n (\\\"table1\\\", \\\"t2id\\\", \\\"table2\\\", \\\"id\\\"),\\n (\\\"table2\\\", \\\"t3id\\\", \\\"table3\\\", \\\"id\\\"),\\n ],\\n )\\n )\\n dumper.add_config({\\\"db_objects\\\": [{\\\"name\\\": \\\"table1\\\"}]})\\n dumper.perform_dump()\\n objs = [obj for obj, match in dumper.writer.dumped if isinstance(obj, Table)]\\n assert len(objs) == 3\",\n \"def fit(self):\\n\\n self.bow_archives_by_paperid = {userid: [self.dictionary.doc2bow(doc) for doc in archive] \\\\\\n for userid, archive in self.kp_archives_by_paperid.items()}\\n\\n self.bow_archives_by_userid = {userid: [self.dictionary.doc2bow(doc) for doc in archive] \\\\\\n for userid, archive in self.kp_archives_by_userid.items()}\\n\\n flattened_archives = [\\n bow for archive in self.bow_archives_by_paperid.values() for bow in archive]\\n\\n self.index = SparseMatrixSimilarity(\\n [self.tfidf[bow] for bow in flattened_archives],\\n num_features=len(self.dictionary)\\n )\",\n \"def test_desy_keyword_translation(self):\\n spi_search = \\\"find dk \\\\\\\"B --> pi pi\\\\\\\"\\\"\\n inv_search = \\\"695__a:\\\\\\\"B --> pi pi\\\\\\\"\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def match(desc1,desc2):\\n desc1 = array([d/linalg.norm(d) for d in desc1])\\n desc2 = array([d/linalg.norm(d) for d in desc2])\\n dist_ratio = 0.6\\n desc1_size = desc1.shape\\n matchscores = zeros((desc1_size[0],1),'int')\\n desc2t = desc2.T # precompute matrix transpose\\n for i in range(desc1_size[0]):\\n dotprods = dot(desc1[i, :], desc2t) # vector of dot products\\n dotprods *= 0.9999\\n # inverse cosine and sort, return index for features in second image\\n indx = argsort(arccos(dotprods))\\n # check if nearest neighbor has angle less than dist_ratio times 2nd\\n if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\\n matchscores[i] = int(indx[0])\\n return matchscores\",\n \"def beam_search(X, u, w, b, relLabels):\\n\\n candidate_paths = [[] for _ in range(10)] # contains the candidate label sets\\n candidate_vals =[[] for _ in range(10)] # contains the label values (-1/1) for each candidate set\\n candidate_scores = [0. for _ in range(10)]\\n min_score = -1000\\n\\n iter = 0\\n start = 0\\n while True:\\n # print(\\\"Iter: \\\", iter)\\n intermediate_paths = {}\\n # intermediate_paths_val = []\\n interim_scores = []\\n hash_table = {}\\n\\n cnt_paths = 0\\n for cp in range(5):\\n labels_curr = candidate_paths[cp]\\n labels_val_curr = candidate_vals[cp]\\n scores_curr = candidate_scores[cp]\\n Y = -np.ones((10, 1))\\n for lv in range(len(labels_val_curr)):\\n Y[labels_curr[lv]] = labels_val_curr[lv]\\n\\n for l in range(10):\\n candidate_interim = labels_curr[:]\\n candidate_vals_interim = labels_val_curr[:]\\n # if l in labels_curr:\\n # continue\\n\\n temp_relLabels = []\\n for lc in range(len(labels_curr)):\\n temp_relLabels.extend(relLabels[labels_curr[lc]])\\n\\n # temp_relLabels = np.array(list(set(temp_relLabels)))\\n temp_relLabels = np.array(list(set(relLabels[l]).intersection(set(labels_curr))))\\n model_pos = returnModelVal(X, Y, 1.0, u[l], u[l], b[l][0], np.array(temp_relLabels))\\n candidate_interim.append(l)\\n\\n if model_pos < 0:\\n # print('hello')\\n candidate_vals_interim.append(-1)\\n interim_scores.append(-model_pos)\\n else:\\n candidate_vals_interim.append(1)\\n interim_scores.append(model_pos)\\n\\n hash_table[cnt_paths] = candidate_interim\\n intermediate_paths[cnt_paths] = candidate_vals_interim\\n cnt_paths += 1\\n # For the first iteration, just iterate once - all labels in one iteration\\n if start == 0:\\n start = 1\\n break\\n\\n temp_paths = intermediate_paths\\n interim_zip = zip(intermediate_paths, interim_scores)\\n sorted_scores = sorted(interim_zip, key=lambda x: x[1], reverse=True)[:5]\\n intermediate_paths, scores = zip(*sorted_scores)\\n\\n temp_cand = []\\n temp_val = []\\n for i in range(len(intermediate_paths)):\\n temp_cand.append(hash_table[intermediate_paths[i]])\\n temp_val.append(temp_paths[intermediate_paths[i]])\\n # candidate_scores[i] += scores[i]\\n\\n candidate_paths = temp_cand\\n candidate_vals = temp_val\\n print(candidate_paths)\\n print(candidate_vals)\\n # print(scores)\\n # candidate_scores = scores\\n\\n # Exit condition from loop\\n # if max(interim_scores) < min_score:\\n # break\\n #\\n # min_score = min(interim_scores)\\n\\n iter += 1\\n if iter > 5:\\n break\\n\\n candidate_dict = {}\\n for i in range(5):\\n for c in range(len(candidate_paths[i])):\\n if candidate_paths[i][c] not in candidate_dict:\\n candidate_dict[candidate_paths[i][c]] = candidate_vals[i][c]\\n elif candidate_dict[candidate_paths[i][c]] != 2:\\n if candidate_dict[candidate_paths[i][c]] != candidate_vals[i][c]:\\n candidate_dict[candidate_paths[i][c]] = 2.\\n\\n print(candidate_dict)\\n exit()\\n return candidate_dict\",\n \"def match(desc1,desc2):\\n\\t\\n\\tdesc1 = array([d/linalg.norm(d) for d in desc1])\\n\\tdesc2 = array([d/linalg.norm(d) for d in desc2])\\n\\t\\n\\tdist_ratio = 0.6\\n\\tdesc1_size = desc1.shape\\n\\t\\n\\tmatchscores = zeros((desc1_size[0],1))\\n\\tdesc2t = desc2.T #precompute matrix transpose\\n\\tfor i in range(desc1_size[0]):\\n\\t\\tdotprods = dot(desc1[i,:],desc2t) #vector of dot products\\n\\t\\tdotprods = 0.9999*dotprods\\n\\t\\t#inverse cosine and sort, return index for features in second image\\n\\t\\tindx = argsort(arccos(dotprods))\\n\\t\\t\\n\\t\\t#check if nearest neighbor has angle less than dist_ratio times 2nd\\n#\\t\\tif arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\\n\\t\\tmatchscores[i] = int(indx[0])\\n\\t\\n\\treturn matchscores\",\n \"def pg_secondary_keys(self):\",\n \"def key_ik(*args):\\n\\n robots = get_robot_roots()\\n if not robots:\\n pm.warning('No robots selected')\\n return\\n\\n for robot in robots:\\n target_ctrl_path = get_target_ctrl_path(robot)\\n tool_ctrl_path = get_tool_ctrl_path(robot)\\n fk_ctrls_path = format_path(__FK_CTRLS_PATH, robot)\\n\\n if not pm.getAttr(target_ctrl_path + '.ik'):\\n switch_to_ik(robot)\\n\\n ik_attributes = ['ik',\\n 'v',\\n 'ikSolution1',\\n 'ikSolution2',\\n 'ikSolution3']\\n\\n # Key all IK elements\\n for attr in ik_attributes:\\n pm.setKeyframe(target_ctrl_path, attribute=attr)\\n\\n if pm.objExists(tool_ctrl_path):\\n pm.setKeyframe(tool_ctrl_path, attribute='v')\\n\\n fk_pose = find_fk_config(robot)\\n\\n # Key all FK elements\\n pm.setKeyframe(format_path(__A1_FK_CTRL_PATH, robot),\\n attribute='rotateY',\\n value=fk_pose[0])\\n pm.setKeyframe(format_path(__A2_FK_CTRL_PATH, robot),\\n attribute='rotateX',\\n value=fk_pose[1])\\n pm.setKeyframe(format_path(__A3_FK_CTRL_PATH, robot),\\n attribute='rotateX',\\n value=fk_pose[2])\\n pm.setKeyframe(format_path(__A4_FK_CTRL_PATH, robot),\\n attribute='rotateZ',\\n value=fk_pose[3])\\n pm.setKeyframe(format_path(__A5_FK_CTRL_PATH, robot),\\n attribute='rotateX',\\n value=fk_pose[4])\\n pm.setKeyframe(format_path(__A6_FK_CTRL_PATH, robot),\\n attribute='rotateZ',\\n value=fk_pose[5])\\n\\n # Key visibility of FK controllers\\n pm.setKeyframe(fk_ctrls_path, attribute='visibility')\\n\\n # Key tool controllers\\n if pm.checkBox('cb_keyToolCtrl', query=True, value=True):\\n if pm.objExists(tool_ctrl_path):\\n pm.setKeyframe(tool_ctrl_path, attribute='translate')\\n pm.setKeyframe(tool_ctrl_path, attribute='rotate')\\n else:\\n pm.setKeyframe(target_ctrl_path, attribute='translate')\\n pm.setKeyframe(target_ctrl_path, attribute='rotate')\",\n \"def refseq_based_clustering(self):\\n self.refseq_based = Usefuls.NonRedSet.NonRedSetDict()\\n for prey in self.ivv_info.Prey_info().preys():\\n refseqid = self.get_refseq(prey)\\n if refseqid:\\n self.refseq_based.append_Dict(refseqid, prey)\",\n \"def edge_mapping(self):\\n ...\",\n \"def testFindCorrespondence(self):\\n # Create some dummy keypoints and descriptors to match. Make the descriptors really far apart to be sure.\\n keypoints1 = []\\n descriptors1 = numpy.zeros(shape=(3, 1))\\n keypoint = cv2.KeyPoint()\\n for i in range(3):\\n keypoint.pt = (float(i), 0.0)\\n keypoints1.append(keypoint)\\n descriptors1[i] = i*100.0\\n keypoints2 = []\\n descriptors2 = numpy.zeros(shape=(5, 1))\\n for i in range(5):\\n keypoint.pt = (0.0, float(i))\\n keypoints2.append(keypoint)\\n descriptors2[i] = i*105.0\\n (first_points, second_points) = self.evaluator._findCorrespondence(\\n keypoints1, descriptors1, keypoints2, descriptors2)\\n expected_first = numpy.array([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])\\n expected_second = numpy.array([[0.0, 0.0], [0.0, 1.0], [0.0, 2.0]])\\n self.assertTrue(numpy.array_equal(first_points, expected_first))\\n self.assertTrue(numpy.array_equal(second_points, expected_second))\",\n \"def illustrate_matching(self):\\n work_dir = self.work_dir\\n draw_keys_oriented(work_dir+'matching_keys_im0.txt',\\n work_dir+'input_0.orig.png',\\n work_dir+'matching_keys_im0.png')\\n draw_keys_oriented(work_dir+'matching_keys_im1.txt',\\n work_dir+'input_1.orig.png',\\n work_dir+'matching_keys_im1.png')\\n draw_matches(work_dir+'matches.txt',\\n work_dir+'input_0.orig.png',\\n work_dir+'input_1.orig.png',\\n work_dir+'OUTmatches.png')\\n return 1\",\n \"def look_for_reference_image(image):\\n match_list = []\\n thresh = 8\\n final_value = -1\\n references = import_reference_images()\\n # Initialize the ORB detector algorithm\\n orb = cv2.ORB_create()\\n\\n # Now detect the keypoints and compute\\n # the descriptors for the query image\\n imgKeypoints, imgDescriptors = orb.detectAndCompute(image, None)\\n try:\\n for ref in references:\\n # Now detect the keypoints and compute\\n # the descriptors for the train image\\n ref.refKeypoints, ref.refDescriptors = orb.detectAndCompute(ref.img, None)\\n\\n # Initialize the Matcher for matching\\n # the keypoints and then match the\\n # keypoints\\n matcher = cv2.BFMatcher()\\n matches = matcher.knnMatch(imgDescriptors, ref.refDescriptors, k=2)\\n\\n for m, n in matches:\\n if m.distance < 0.75 * n.distance:\\n ref.refMatches.append([m])\\n\\n match_list.append(len(ref.refMatches))\\n except:\\n pass\\n if len(match_list) != 0:\\n if max(match_list) > thresh:\\n final_value = match_list.index(max(match_list))\\n\\n return references[final_value].name\",\n \"def test5(self):\\n tgts = [ ('CCC(O)C(=O)O',\\n ('O[CH-][CH2-]','O[CH-][C-]=O')),\\n ]\\n for smi,matches in tgts:\\n m = Chem.MolFromSmiles(smi)\\n fp1 = Chem.RDKFingerprint(m,2,7,9192,4,1)\\n obs = fp1.GetOnBits()\\n for match in matches:\\n m2 = Chem.MolFromSmiles(match)\\n fp2 = Chem.RDKFingerprint(m2,2,7,9192,4,1)\\n v1,v2 = DataStructs.OnBitProjSimilarity(fp2,fp1)\\n assert feq(v1,1.0000),'substruct %s not properly contained in %s'%(match,smi)\",\n \"def test_post_chain_search(self):\\n pass\",\n \"def object_detection(self):\\r\\n pass\",\n \"def test_normal_fields_can_be_defined_between_primary_keys(self):\",\n \"def similarity(self, e1, e2):\\n\\t\\tpass\",\n \"def find(image):\\n keypoint, description = describe(image)\\n # load keypoints, descriptions from mongodb\\n\\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\\n\\n best_match_size = float(\\\"inf\\\")\\n best_match_index = -1\\n match_index = 0\\n best_matches = 0\\n\\n for desc in descriptions:\\n matches = bf.match(desc,description)\\n matches = sorted(matches, key = lambda x:x.distance)\\n if len(matches) > 0:\\n match_size = sum(x.distance for x in matches[:10])\\n\\n print \\\"match size is \\\", match_size\\n if match_size < best_match_size:\\n best_match_size = match_size\\n best_match_index = match_index\\n best_matches = matches\\n\\n match_index += 1\\n\\n needle_color = cv2.imread('needle-stripped.png')[:,:,::-1] # needle\\n best_match_image = cv2.imread(\\\"haystack/\\\"+files[best_match_index])\\n print \\\"best match is \\\", files[best_match_index]\\n\\n # Draw first 10 matches.\\n outImg = cv2.imread(\\\"output/outImg.png\\\")\\n match = cv2.drawMatches(needle_color,keypoint,best_match_image[:,:,::-1],keypoints[best_match_index],best_matches[-20:],outImg, flags=6)\\n\\n plt.imshow(match),plt.show()\\n return\",\n \"def _fit_exact_match(self):\\n entity_map = self._resource_loader.get_entity_map(self.type)\\n self._exact_match_mapping = self._process_entity_map(\\n self.type, entity_map, self._normalizer\\n )\",\n \"def matches(self, feature):\\n pass\",\n \"def fix_annotmatch_pzmaster1():\\n import wbia\\n\\n ibs = wbia.opendb('PZ_Master1')\\n infr = wbia.AnnotInference(ibs=ibs, aids=ibs.get_valid_aids(), verbose=5)\\n infr.initialize_graph()\\n annots = ibs.annots()\\n aid_to_nid = ut.dzip(annots.aids, annots.nids)\\n\\n if False:\\n infr.reset_feedback()\\n infr.ensure_mst()\\n infr.apply_feedback_edges()\\n infr.relabel_using_reviews()\\n infr.start_qt_interface()\\n\\n # Get annotmatch rowids that agree with current labeling\\n if False:\\n annotmatch = ibs.db.get_table_as_pandas('annotmatch')\\n import pandas as pd\\n\\n flags1 = pd.isnull(annotmatch['annotmatch_evidence_decision'])\\n flags2 = annotmatch['annotmatch_tag_text'] == ''\\n bad_part = annotmatch[flags1 & flags2]\\n rowids = bad_part.index.tolist()\\n ibs.delete_annotmatch(rowids)\\n\\n if False:\\n # Delete bidirectional annotmatches\\n annotmatch = ibs.db.get_table_as_pandas('annotmatch')\\n df = annotmatch.set_index(['annot_rowid1', 'annot_rowid2'])\\n\\n # Find entires that have both directions\\n pairs1 = annotmatch[['annot_rowid1', 'annot_rowid2']].values\\n f_edges = {tuple(p) for p in pairs1}\\n b_edges = {tuple(p[::-1]) for p in pairs1}\\n isect_edges = {tuple(sorted(p)) for p in b_edges.intersection(f_edges)}\\n isect_edges1 = list(isect_edges)\\n isect_edges2 = [p[::-1] for p in isect_edges]\\n\\n # cols = ['annotmatch_evidence_decision', 'annotmatch_tag_text']\\n import pandas as pd\\n\\n custom_ = {\\n (559, 4909): (False, ['photobomb']),\\n (7918, 8041): (False, ['photobomb']),\\n (6634, 6754): (False, ['photobomb']),\\n (3707, 3727): (False, ['photobomb']),\\n (86, 103): (False, ['photobomb']),\\n }\\n extra_ = {}\\n\\n fixme_edges = []\\n\\n d1 = df.loc[isect_edges1].reset_index(drop=False)\\n d2 = df.loc[isect_edges2].reset_index(drop=False)\\n flags = d1['annotmatch_evidence_decision'] != d2['annotmatch_evidence_decision']\\n from wbia.tag_funcs import _parse_tags\\n\\n for f, r1, r2 in zip(flags, d1.iterrows(), d2.iterrows()):\\n v1, v2 = r1[1], r2[1]\\n aid1 = v1['annot_rowid1']\\n aid2 = v1['annot_rowid2']\\n truth_real = (\\n ibs.const.EVIDENCE_DECISION.POSITIVE\\n if aid_to_nid[aid1] == aid_to_nid[aid2]\\n else ibs.const.EVIDENCE_DECISION.NEGATIVE\\n )\\n truth1 = v1['annotmatch_evidence_decision']\\n truth2 = v2['annotmatch_evidence_decision']\\n t1 = _parse_tags(v1['annotmatch_tag_text'])\\n t2 = _parse_tags(v2['annotmatch_tag_text'])\\n newtag = ut.union_ordered(t1, t2)\\n if (aid1, aid2) in custom_:\\n continue\\n fixme_flag = False\\n if not pd.isnull(truth1):\\n if truth_real != truth1:\\n fixme_flag = True\\n if not pd.isnull(truth2):\\n if truth_real != truth2:\\n fixme_flag = True\\n if fixme_flag:\\n logger.info('newtag = {!r}'.format(newtag))\\n logger.info('truth_real = {!r}'.format(truth_real))\\n logger.info('truth1 = {!r}'.format(truth1))\\n logger.info('truth2 = {!r}'.format(truth2))\\n logger.info('aid1 = {!r}'.format(aid1))\\n logger.info('aid2 = {!r}'.format(aid2))\\n fixme_edges.append((aid1, aid2))\\n else:\\n extra_[(aid1, aid2)] = (truth_real, newtag)\\n\\n extra_.update(custom_)\\n new_pairs = extra_.keys()\\n new_truths = ut.take_column(ut.dict_take(extra_, new_pairs), 0)\\n new_tags = ut.take_column(ut.dict_take(extra_, new_pairs), 1)\\n new_tag_texts = [';'.join(t) for t in new_tags]\\n aids1, aids2 = ut.listT(new_pairs)\\n\\n # Delete the old\\n ibs.delete_annotmatch(\\n d1['annotmatch_rowid'].values.tolist()\\n + d2['annotmatch_rowid'].values.tolist()\\n )\\n\\n # Add the new\\n ams = ibs.add_annotmatch_undirected(aids1, aids2)\\n ibs.set_annotmatch_evidence_decision(ams, new_truths)\\n ibs.set_annotmatch_tag_text(ams, new_tag_texts)\\n\\n if False:\\n import wbia.guitool as gt\\n\\n gt.ensure_qapp()\\n ut.qtensure()\\n from wbia.gui import inspect_gui\\n\\n inspect_gui.show_vsone_tuner(ibs, aid1, aid2)\\n\\n # pairs2 = pairs1.T[::-1].T\\n # idx1, idx2 = ut.isect_indices(list(map(tuple, pairs1)),\\n # list(map(tuple, pairs2)))\\n # r_edges = list(set(map(tuple, map(sorted, pairs1[idx1]))))\\n # unique_pairs = list(set(map(tuple, map(sorted, pairs1[idx1]))))\\n # df = annotmatch.set_index(['annot_rowid1', 'annot_rowid2'])\\n\\n x = ut.ddict(list)\\n annotmatch = ibs.db.get_table_as_pandas('annotmatch')\\n import ubelt as ub\\n\\n _iter = annotmatch.iterrows()\\n prog = ub.ProgIter(_iter, length=len(annotmatch))\\n for k, m in prog:\\n aid1 = m['annot_rowid1']\\n aid2 = m['annot_rowid2']\\n if m['annotmatch_evidence_decision'] == ibs.const.EVIDENCE_DECISION.POSITIVE:\\n if aid_to_nid[aid1] == aid_to_nid[aid2]:\\n x['agree1'].append(k)\\n else:\\n x['disagree1'].append(k)\\n elif m['annotmatch_evidence_decision'] == ibs.const.EVIDENCE_DECISION.NEGATIVE:\\n if aid_to_nid[aid1] == aid_to_nid[aid2]:\\n x['disagree2'].append(k)\\n else:\\n x['agree2'].append(k)\\n\\n ub.map_vals(len, x)\\n ut.dict_hist(annotmatch.loc[x['disagree1']]['annotmatch_tag_text'])\\n\\n disagree1 = annotmatch.loc[x['disagree1']]\\n pb_disagree1 = disagree1[disagree1['annotmatch_tag_text'] == 'photobomb']\\n aids1 = pb_disagree1['annot_rowid1'].values.tolist()\\n aids2 = pb_disagree1['annot_rowid2'].values.tolist()\\n aid_pairs = list(zip(aids1, aids2))\\n infr = wbia.AnnotInference.from_pairs(aid_pairs, ibs=ibs, verbose=5)\\n if False:\\n feedback = infr.read_wbia_annotmatch_feedback(edges=infr.edges())\\n infr.external_feedback = feedback\\n infr.apply_feedback_edges()\\n infr.start_qt_interface(loop=False)\\n\\n # Delete these values\\n if False:\\n nonpb_disagree1 = disagree1[disagree1['annotmatch_tag_text'] != 'photobomb']\\n disagree2 = annotmatch.loc[x['disagree2']]\\n ibs.delete_annotmatch(nonpb_disagree1['annotmatch_rowid'])\\n ibs.delete_annotmatch(disagree2['annotmatch_rowid'])\\n\\n # ut.dict_hist(disagree1['annotmatch_tag_text'])\\n import networkx as nx\\n\\n graph = nx.Graph()\\n graph.add_edges_from(zip(pb_disagree1['annot_rowid1'], pb_disagree1['annot_rowid2']))\\n list(nx.connected_components(graph))\\n\\n set(annotmatch.loc[x['disagree2']]['annotmatch_tag_text'])\\n\\n # aid1, aid2 = 2585, 1875\\n # # pd.unique(annotmatch['annotmatch_evidence_decision'])\\n # from wbia.gui import inspect_gui\\n # inspect_gui.show_vsone_tuner(ibs, aid1, aid2)\\n # from vtool import inspect_matches\\n\\n # aid1, aid2 = 2108, 2040\\n\\n # pd.unique(annotmatch['annotmatch_tag_text'])\\n\\n # infr.reset_feedback()\\n # infr.relabel_using_reviews()\",\n \"def test_labels_match_geographies(self):\\n geo = self.geographies.find_one({ 'geoid': '15' })\\n labels = self.labels.find_one({ 'dataset': 'SF1' })\\n\\n geo_tables = geo['data']['2010']\\n labels_tables = labels['tables']\\n\\n self.assertEqual(sorted(geo_tables.keys()), sorted(labels_tables.keys()))\\n\\n # Test table has labels\\n for table_name, geo_keys in geo_tables.items():\\n label_keys = labels_tables[table_name]['labels']\\n\\n self.assertEqual(sorted(geo_keys.keys()), sorted(label_keys.keys()))\\n\\n for table_name, label_data in labels_tables.items():\\n label_keys = label_data['labels']\\n geo_keys = geo_tables[table_name]\\n\\n self.assertEqual(sorted(geo_keys.keys()), sorted(label_keys.keys()))\",\n \"def refseq_based_clustering(self):\\n self.refseq_based = NonRedSetDict()\\n for prey in self.ivv_info.Prey_info().preys():\\n refseqid = self.get_refseq(prey)\\n if refseqid:\\n self.refseq_based.append_Dict(refseqid, prey)\",\n \"def _attentive_matching(self, h1, h2, cosine_matrix, w):\\n # h1 * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)\\n h1 = self._time_distributed_multiply(h1, w)\\n # attentive vector (batch_size, h1_timesteps, embedding_szie)\\n attentive_vec = self._mean_attentive_vectors(h2, cosine_matrix)\\n # attentive_vec * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)\\n attentive_vec = self._time_distributed_multiply(attentive_vec, w)\\n # matching vector, (batch_size, h1_timesteps, mp_dim)\\n matching = self._cosine_similarity(h1, attentive_vec)\\n return matching\",\n \"def match_against_database(list_of_face_vectors):\\n\\n threshold_of_similarity = 0.4\\n\\n if len(list_of_face_vectors) != 0:\\n #Load the database, if it exists.\\n if (file_path/\\\"names_and_faces.pkl\\\").exists():\\n with open(file_path/\\\"names_and_faces.pkl\\\", mode = \\\"rb\\\") as opened_file:\\n names_and_faces = pickle.load(opened_file)\\n\\n #Calculate the mean for each key.\\n for key in names_and_faces:\\n names_and_faces[key] = np.array(names_and_faces[key]).mean(axis = 0)\\n\\n\\n #Here comes the fun part! Iterate thru our list of face vectors to find the best candidate name for each face vector.\\n face_vectors = np.array(list_of_face_vectors)\\n #print(face_vectors.shape)\\n names = np.array(list(names_and_faces.keys()))\\n faces = np.array(list(names_and_faces.values()))\\n candidates = L2_dists_vectorized(face_vectors, faces)\\n\\n minimum_indices = np.argmin(candidates, axis = 1)\\n #print(\\\"candidates are\\\", candidates)\\n minimum_args = np.min(candidates, axis = 1)\\n names_to_return = names[minimum_indices]\\n names_to_return[minimum_args > threshold_of_similarity] = \\\"unknown\\\"\\n\\n\\n return names_to_return\\n\\n\\n else:\\n return 0\\n else:\\n return 0\",\n \"def icontains(self, other):\",\n \"def keyDependsOnKey(self, k1Name, k2Name):\\n if k1Name == k2Name: return 0\\n k1 = self.infoKinds[k1Name]\\n k2 = self.infoKinds[k2Name]\\n if k1.superNames != k2.superNames:\\n allSuperK1 = set()\\n toDoK1 = list(k1.superNames)\\n allSuperK2 = set()\\n toDoK2 = list(k2.superNames)\\n while (len(toDoK1) > 0 or len(toDoK2) > 0):\\n if len(toDoK1) > 0:\\n el1Name = toDoK1.pop()\\n if k2Name == el1Name:\\n return 1\\n el1 = self.infoKinds[el1Name]\\n if el1.kindStr in self and not el1.kindStr in allSuperK1:\\n toDoK1.append(el1.kindStr)\\n for subEl in el1.superNames:\\n if not subEl in allSuperK1:\\n toDoK1.append(subEl)\\n allSuperK1.update(el1.superNames)\\n if len(toDoK2) > 0:\\n el2Name = toDoK2.pop()\\n if k1Name == el2Name:\\n return -1\\n el2 = self.infoKinds[el2Name]\\n if el2.kindStr in self and not el2.kindStr in allSuperK2:\\n toDoK2.append(el2.kindStr)\\n for subEl in el2.superNames:\\n if not subEl in allSuperK2:\\n toDoK2.append(subEl)\\n allSuperK2.update(el2.superNames)\\n return None\",\n \"def GetSubkeys(self):\",\n \"def test_fkey_nav(dumper, db):\\n dumper.reader.load_db(\\n db.create_sample(3, fkeys=[(\\\"table1\\\", \\\"t2id\\\", \\\"table2\\\", \\\"id\\\")])\\n )\\n dumper.add_config({\\\"db_objects\\\": [{\\\"name\\\": \\\"table1\\\"}]})\\n dumper.perform_dump()\\n objs = [obj for obj, match in dumper.writer.dumped if isinstance(obj, Table)]\\n assert len(objs) == 2\",\n \"def lookup():\",\n \"def testBeliefs2sk(self):\",\n \"def test_associations(self):\\n\\n oj = {}\\n\\n if self.DEBUG:\\n print \\\"Create A\\\"\\n\\n # create a\\n a = self.save_base_metadata()\\n\\n oj[a.uuid] = 'A'\\n\\n if self.DEBUG:\\n print \\\"Create B, point B to A\\\"\\n\\n # create b, point b to a\\n b = self.save_base_metadata()\\n\\n oj[b.uuid] = 'B'\\n\\n b.add_association_to(a)\\n\\n # save a, b\\n b.save()\\n a.save()\\n\\n if self.DEBUG:\\n self.printy(oj, a, b, None, None, None)\\n\\n # test b pointing to a\\n self.assertIn(a, b.my_associations)\\n # test to see if a is aware of association from b\\n # self.assertIn(b, a.associations_to_me)\\n\\n if self.DEBUG:\\n print \\\"Create C, point C to B\\\"\\n\\n # create c, point c to b\\n c = self.save_base_metadata()\\n\\n oj[c.uuid] = 'C'\\n\\n c.add_association_to(b)\\n\\n # a.save()\\n\\n # save b, c\\n c.save()\\n b.save()\\n\\n if self.DEBUG:\\n self.printy(oj, a, b, c, None, None)\\n\\n # test c pointing to a\\n self.assertIn(a, c.my_associations)\\n # test c aware of association from a\\n self.assertIn(c, a.associations_to_me)\\n\\n # test c pointing to b\\n self.assertIn(b, c.my_associations)\\n # test b aware of association from c\\n self.assertIn(c, b.associations_to_me)\\n\\n if self.DEBUG:\\n print \\\"create D, point to B\\\"\\n\\n # create d, point to b\\n d = self.save_base_metadata()\\n\\n oj[d.uuid] = 'D'\\n\\n d.add_association_to(b)\\n\\n # a.save()\\n\\n # save d, b\\n d.save()\\n b.save()\\n\\n if self.DEBUG:\\n self.printy(oj, a, b, c, d, None)\\n\\n # test d pointing to a\\n self.assertIn(a, d.my_associations)\\n # test a aware of assoication from d\\n self.assertIn(d, a.associations_to_me)\\n\\n # test d pointing to b\\n self.assertIn(b, d.my_associations)\\n # test b aware of association from d\\n self.assertIn(d, b.associations_to_me)\\n\\n if self.DEBUG:\\n print \\\"Create E, point to A\\\"\\n\\n # create e, point to a\\n e = self.save_base_metadata()\\n\\n oj[e.uuid] = 'E'\\n\\n e.add_association_to(a)\\n\\n # save e, a\\n e.save()\\n a.save()\\n\\n if self.DEBUG:\\n self.printy(oj, a, b, c, d, e)\\n\\n # test e pointing to a\\n self.assertIn(a, e.my_associations)\\n # test a aware of association from e\\n self.assertIn(b, a.associations_to_me)\",\n \"def detail_matching(self):\\n paradic = self.cfg['param']['paradic']\\n work_dir = self.work_dir\\n \\n x = float(self.cfg['param']['x']) # selected pixel in the first image\\n y = float(self.cfg['param']['y'])\\n \\n # sift parameters\\n # number of bins in the orientation histogram\\n n_bins = int(paradic['n_bins']) \\n n_hist = int(paradic['n_hist']) \\n # descriptor of n_hist X n_hist weighted histograms with n_ori\\n n_ori = int(paradic['n_ori']) \\n delta_min = float(paradic['delta_min'])\\n sigma_min = float(paradic['sigma_min'])\\n sigma_in = float(paradic['sigma_in'])\\n lambda_ori = float(paradic['lambda_ori'])\\n lambda_descr = float(paradic['lambda_descr'])\\n #threshold defining reference orientations\\n n_spo = int(paradic['n_spo'])\\n \\n # Read feature vectors from output files\\n if (os.path.getsize(work_dir+'OUTmatches.txt') > 0 ):\\n pairdata = find_nearest_keypoint(work_dir+'OUTmatches.txt', y, x)\\n \\n illustrate_pair(pairdata, n_bins, n_hist, n_ori, work_dir)\\n\\n \\n # Read keys coordinates.\\n d = 6+n_bins+n_hist*n_hist*n_ori # size of keydata inside pairdata\\n v = n_hist*n_hist*n_ori\\n [x1, y1, sigma1, theta1] = [float(x) for x in pairdata[0:4]]\\n [o1, s1] = [float(x) for x in pairdata[4+v:4+v+2]]\\n [x2a, y2a, sigma2a, theta2a] = [float(x) for x in pairdata[d:d+4]]\\n [o2a, s2a] = [float(x) for x in pairdata[d+4+v:d+4+v+2]]\\n [x2b, y2b, sigma2b, theta2b] = \\\\\\n [float(x) for x in pairdata[2*d:2*d+4]]\\n [o2b, s2b] = [float(x) for x in pairdata[2*d+4+v:2*d+4+v+2]]\\n \\n draw_one_match(pairdata,\\n work_dir+'input_0.png',\\n work_dir+'input_1.png',\\n d,\\n lambda_ori,\\n lambda_descr,\\n n_hist,\\n work_dir+'OUTonepair.png')\\n \\n \\n # Extract thumbnails.\\n # keypoint 1 (image 1)\\n print ' '.join(['demo_extract_patch', work_dir+'input_0.png',\\n str(x1), str(y1), str(sigma1), str(theta1), str(o1), str(s1),\\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\\n str(lambda_ori), str(lambda_descr), str(n_hist),\\n work_dir+\\\"detail_im1\\\"])\\n proc = self.run_proc(['demo_extract_patch', work_dir+'input_0.png',\\n str(x1), str(y1), str(sigma1), str(theta1), str(o1), str(s1),\\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\\n str(lambda_ori), str(lambda_descr), str(n_hist),\\n work_dir+\\\"detail_im1\\\"])\\n self.wait_proc(proc, timeout=self.timeout)\\n \\n # keypoint 2a (nearest neighbor in image 2)\\n print ' '.join(['demo_extract_patch', work_dir+'input_1.png',\\n str(x2a), str(y2a), str(sigma2a), str(theta2a), str(o2a), str(s2a),\\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\\n str(lambda_ori), str(lambda_descr), str(n_hist),\\n work_dir+\\\"detail_im2a\\\"])\\n proc = self.run_proc(['demo_extract_patch', work_dir+'input_1.png',\\n str(x2a), str(y2a), str(sigma2a), str(theta2a), str(o2a), str(s2a),\\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\\n str(lambda_ori), str(lambda_descr), str(n_hist),\\n work_dir+\\\"detail_im2a\\\"])\\n self.wait_proc(proc, timeout=self.timeout) \\n \\n # keypoint 2b (second nearest neighbor in image 2)\\n proc = self.run_proc(['demo_extract_patch', work_dir+'input_1.png',\\n str(x2b), str(y2b), str(sigma2b), str(theta2b), str(o2b), str(s2b),\\n str(delta_min), str(sigma_min), str(sigma_in), str(n_spo),\\n str(lambda_ori), str(lambda_descr), str(n_hist),\\n work_dir+\\\"detail_im2b\\\"])\\n self.wait_proc(proc, timeout=self.timeout) \\n \\n \\n return 1\",\n \"def _backreference_keys(self, ref, key_in_ref):\\n return (self.__class__.REFERENCE_FIELDS +\\n self.__class__.DEPENDENT_LINES +\\n self.__class__.OTHER_REFERENCES)\",\n \"def additionalMatch(handIn, indx):\",\n \"def _candidate_generation(self):\\n doc = self.nlp(self.text)\\n named_entity_dict = {}\\n named_entity_key_list = []\\n named_entity_value_list = []\\n entity_from_text_list = []\\n offline_dic_list = []\\n matched_element_list = []\\n\\n for ent in doc.ents:\\n named_entity = (str(ent.text) + ':' + str(ent.label_))\\n named_entity = (named_entity.split(':'))\\n # named_entity_key = named_entity[0].replace('\\\\n', '')\\n # named_entity_key_list.append(named_entity_key)\\n # named_entity_value = named_entity[1].replace('\\\\n', '')\\n # named_entity_value_list.append(named_entity_value)\\n named_entity_value = named_entity[1].replace('\\\\n', '')\\n named_entity_value_list.append(named_entity_value)\\n filtered_words = (str(ent.text).split())\\n filtered_words = [w for w in filtered_words if w.lower() not in self.english_stopwords]\\n named_entity_key = [' '.join(filtered_words)]\\n for i in named_entity_key:\\n named_entity_key_list.append(i)\\n for key in named_entity_key_list:\\n named_entity_dict[key] = []\\n i = 0\\n for key in named_entity_key_list:\\n named_entity_dict[key].append(named_entity_value_list[i])\\n i = i + 1\\n\\n entities = \\\"ORG PERSON LOC GPE\\\".split()\\n for entity in entities:\\n entity_from_text = [k for k, v in named_entity_dict.items() if entity in v]\\n for item in entity_from_text:\\n entity_from_text_list.append(item)\\n\\n if not entity_from_text_list:\\n self.logger.info('No named entity found in the input text')\\n else:\\n self.logger.info('Entities which are identified from the input sentence')\\n self.logger.info(entity_from_text_list)\\n\\n for key, value in self.offline_dic.items():\\n offline_dic_list.append(key)\\n\\n for item in entity_from_text_list:\\n for item1 in offline_dic_list:\\n if item == item1:\\n matched_element_list.append(item)\\n\\n big_final_dict = []\\n for i in matched_element_list:\\n candidate_list = [v for k, v in self.offline_dic.items() if str(k) == str(i)]\\n final_dict = dict(zip(i.split('\\\\n'), candidate_list))\\n big_final_dict.append(final_dict)\\n\\n if not big_final_dict:\\n self.logger.warning(\\\"No Match found in the KB\\\")\\n return matched_element_list, None\\n else:\\n self.logger.info('found entities')\\n return matched_element_list, big_final_dict\",\n \"def match(desc1, desc2):\\n desc1 = array([d/linalg.norm(d) for d in desc1])\\n desc2 = array([d/linalg.norm(d) for d in desc2])\\n\\n dist_ratio = 0.6\\n disc1_size = desc1.shape\\n\\n matchscores = zeros((desc1_size[0]), \\\"int\\\")\\n desc2t = desc2.T\\n for i in range(desc1_size[0]):\\n dotprods = dot(desc1[i, :], desc2t)\\n dotprods = 0.9999 * dotprods\\n\\n indx = argsort(arccos(dotprods))\\n\\n if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\\n matchscores[i] = int(indx[0])\\n\\n return matchscores\",\n \"def allow_relation(self, obj1, obj2, **hints):\\n if (\\n obj1._meta.label_lower in self.route_encuestas or\\n obj2._meta.label_lower in self.route_encuestas\\n ):\\n return True\\n return None\",\n \"def _create_auto_key(self,identifier2organism={}):\\n # when this is a locus in a dbwarehouse, abstract the genomedirname\\n realdirname = os.path.realpath(self.dirname)\\n if realdirname.find(\\\"/loci/\\\") > 0:\\n key = os.path.basename(realdirname[0:realdirname.find(\\\"/loci/\\\")])\\n if key: return key\\n # if this point is reached, NOT a locus in dbwarehouse\\n # check if we can map the gene's id to an organism ID\\n if identifier2organism:\\n for identifierpart,organism in identifier2organism.iteritems():\\n if self.fref.find(identifierpart) == 0:\\n # succesfull mapping\\n return organism\\n else:\\n # mapping was not succesfull\\n return self.fref\\n else:\\n return self.fref\",\n \"def __contains__(self, y): # real signature unknown; restored from __doc__\\r\\n pass\",\n \"def targetids(obj, reftype):\",\n \"def bipartite_matching(data=None, is_ascend=_Null, threshold=_Null, topk=_Null, out=None, name=None, **kwargs):\\n return (0,)\",\n \"def match(self, product):\\n\\n raise NotImplementedError, 'need impletent match method'\",\n \"def toggle_ik_fk(*args):\\n\\n current_tab = pm.tabLayout('switcher_tab_layout',\\n query=True,\\n selectTab=True)\\n\\n if current_tab == 'ikTab':\\n ik_tab = 1\\n else:\\n ik_tab = 0\\n\\n robots = get_robot_roots(1)\\n if not robots:\\n return\\n\\n for robot in robots:\\n target_ctrl_path = get_target_ctrl_path(robot)\\n \\n if ik_tab:\\n if pm.getAttr(target_ctrl_path + '.ik'):\\n continue\\n\\n switch_to_ik(robot)\\n\\n else:\\n if not pm.getAttr(target_ctrl_path + '.ik'):\\n continue\\n\\n switch_to_fk(robot)\\n \\n # Maintain appropriate selections on each robot\\n try:\\n selection = []\\n active_robots = get_robot_roots()\\n if active_robots:\\n if ik_tab:\\n for robot in active_robots:\\n target_ctrl_path = get_target_ctrl_path(robot)\\n tool_ctrl_path = get_tool_ctrl_path(robot)\\n\\n if pm.objExists(tool_ctrl_path):\\n selection.append(tool_ctrl_path)\\n else:\\n selection.append(target_ctrl_path)\\n else:\\n for robot in active_robots:\\n selection.append(format_path(__A6_FK_CTRL_PATH, robot))\\n \\n pm.select(selection)\\n else:\\n pass\\n\\n except:\\n pm.warning('Error selecting after IK/FK switch')\",\n \"def expected_relations():\\n reldict = {('project', 'userspec'): ('specs', False),\\n ('project', 'userdoc'): ('docs', False),\\n ('project', 'userwijz'): ('rfcs', False),\\n ('project', 'userprob'): ('probs', False),\\n ('project', 'funcdoc'): ('fdocs', False),\\n ('project', 'gebrtaak'): ('gtaken', False),\\n ('project', 'funcproc'): ('fprocs', False),\\n ('project', 'entiteit'): ('fdata', False),\\n ('project', 'attribuut'): (None, None),\\n ('project', 'techtaak'): ('ttask', False),\\n ('project', 'techproc'): ('tproc', False),\\n ('project', 'dataitem'): ('tdata', False),\\n ('project', 'element'): (None, None),\\n ('project', 'layout'): ('layout', False),\\n ('project', 'programma'): ('pproc', False),\\n ('project', 'testplan'): ('tplan', False),\\n ('project', 'testcase'): ('tcase', False),\\n ('project', 'bevinding'): ('tbev', False),\\n ('userspec', 'project'): ('project', False),\\n ('userspec', 'gebrtaak'): ('gtaken', False),\\n ('userspec', 'funcproc'): ('fprocs', False),\\n ('userdoc', 'project'): ('project', False),\\n ('userwijz', 'project'): ('project', False),\\n ('userwijz', 'gebrtaak'): ('gtaken', True),\\n ('userwijz', 'funcproc'): ('fprocs', True),\\n ('userwijz', 'entiteit'): ('fdata', True),\\n ('userprob', 'project'): ('project', False),\\n ('funcdoc', 'project'): ('project', False),\\n ('gebrtaak', 'project'): ('project', False),\\n ('gebrtaak', 'userspec'): ('spec', False),\\n ('gebrtaak', 'userwijz'): ('rfc', True),\\n ('gebrtaak', 'funcproc'): ('fprocs', True),\\n ('gebrtaak', 'techtaak'): ('ttask', False),\\n ('gebrtaak', 'layout'): ('layout', True),\\n ('gebrtaak', 'testplan'): ('tplan', True),\\n ('funcproc', 'project'): ('project', False),\\n ('funcproc', 'userspec'): ('spec', False),\\n ('funcproc', 'userwijz'): ('rfc', True),\\n ('funcproc', 'gebrtaak'): ('gt', True),\\n ('funcproc', 'funcproc'): ('used_by', True),\\n ('funcproc', 'entiteit'): ('fdata', True),\\n ('funcproc', 'techproc'): ('tproc', True),\\n ('funcproc', 'testplan'): ('tplan', True),\\n ('entiteit', 'project'): ('project', False),\\n ('entiteit', 'userwijz'): ('rfc', True),\\n ('entiteit', 'funcproc'): ('fp', True),\\n ('entiteit', 'attribuut'): ('attrs', False),\\n ('entiteit', 'dataitem'): ('tdata', True),\\n ('entiteit', 'testplan'): ('tplan', True),\\n ('attribuut', 'entiteit'): ('hoort_bij', False),\\n ('techtaak', 'project'): ('project', False),\\n ('techtaak', 'gebrtaak'): ('gt', False),\\n ('techtaak', 'techproc'): ('tproc', True),\\n ('techproc', 'project'): ('project', False),\\n ('techproc', 'funcproc'): ('fp', True),\\n ('techproc', 'techtaak'): ('tt', True),\\n ('techproc', 'techproc'): ('used_by', True),\\n ('techproc', 'dataitem'): ('tdata', True),\\n ('techproc', 'layout'): ('layout', True),\\n ('techproc', 'programma'): ('pproc', True),\\n ('dataitem', 'project'): ('project', False),\\n ('dataitem', 'entiteit'): ('ent', True),\\n ('dataitem', 'techproc'): ('tp', True),\\n ('element', 'dataitem'): ('hoort_bij', False),\\n ('layout', 'project'): ('project', False),\\n ('layout', 'gebrtaak'): ('gt', True),\\n ('layout', 'techproc'): ('tp', True),\\n ('programma', 'project'): ('project', False),\\n ('programma', 'techproc'): ('tp', True),\\n ('testplan', 'project'): ('project', False),\\n ('testplan', 'gebrtaak'): ('gt', True),\\n ('testplan', 'funcproc'): ('fp', True),\\n ('testplan', 'entiteit'): ('ent', True),\\n ('testplan', 'testcase'): ('tcase', True),\\n ('testplan', 'bevinding'): ('tbev', True),\\n ('testcase', 'project'): ('project', False),\\n ('testcase', 'testplan'): ('tplan', True),\\n ('bevinding', 'project'): ('project', False),\\n ('bevinding', 'testplan'): ('tplan', True)}\\n return reldict\",\n \"def foreign_key_check(self):\\n # MyRocks doesn't support foreign key\\n if self.is_myrocks_table:\\n log.info(\\n \\\"SKip foreign key check because MyRocks doesn't support \\\" \\\"this yet\\\"\\n )\\n return True\\n foreign_keys = self.query(\\n sql.foreign_key_cnt,\\n (\\n self.table_name,\\n self._current_db,\\n self.table_name,\\n self._current_db,\\n ),\\n )\\n if foreign_keys:\\n fk = \\\"CONSTRAINT `{}` FOREIGN KEY (`{}`) REFERENCES `{}` (`{}`)\\\".format(\\n foreign_keys[0][\\\"constraint_name\\\"],\\n foreign_keys[0][\\\"col_name\\\"],\\n foreign_keys[0][\\\"ref_tab\\\"],\\n foreign_keys[0][\\\"ref_col_name\\\"],\\n )\\n raise OSCError(\\n \\\"FOREIGN_KEY_FOUND\\\",\\n {\\\"db\\\": self._current_db, \\\"table\\\": self.table_name, \\\"fk\\\": fk},\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64621747","0.6233758","0.6001383","0.5866673","0.5858059","0.57838565","0.5618625","0.5599867","0.55876","0.55551654","0.54261523","0.54143274","0.54062307","0.5399775","0.5396218","0.5388583","0.53567606","0.52986205","0.52962285","0.52947336","0.5270456","0.52457756","0.52436656","0.5234153","0.5215751","0.5209741","0.52039987","0.51976883","0.51751417","0.513496","0.5124755","0.51244444","0.5111415","0.51066446","0.5094734","0.5087002","0.50820637","0.50728524","0.5069215","0.5069034","0.5059516","0.5058986","0.505482","0.50440556","0.50200105","0.50195944","0.5015092","0.49933347","0.49906576","0.49774295","0.49707735","0.4969237","0.49641386","0.49542177","0.49400368","0.49381348","0.49343276","0.49323833","0.49271497","0.49226353","0.49123183","0.49109057","0.49082664","0.4907925","0.49060917","0.49055988","0.4892699","0.4889825","0.48897797","0.48859495","0.4878314","0.48775724","0.48763362","0.48691982","0.4868415","0.48675773","0.4866646","0.48627332","0.4855944","0.48543733","0.48530525","0.48523977","0.48483887","0.48477188","0.48449484","0.4842367","0.4834079","0.48284882","0.48250896","0.48239818","0.48229423","0.48184416","0.48154104","0.48120958","0.48113972","0.48113298","0.48109913","0.48106575","0.48094448","0.4789479","0.47865412"],"string":"[\n \"0.64621747\",\n \"0.6233758\",\n \"0.6001383\",\n \"0.5866673\",\n \"0.5858059\",\n \"0.57838565\",\n \"0.5618625\",\n \"0.5599867\",\n \"0.55876\",\n \"0.55551654\",\n \"0.54261523\",\n \"0.54143274\",\n \"0.54062307\",\n \"0.5399775\",\n \"0.5396218\",\n \"0.5388583\",\n \"0.53567606\",\n \"0.52986205\",\n \"0.52962285\",\n \"0.52947336\",\n \"0.5270456\",\n \"0.52457756\",\n \"0.52436656\",\n \"0.5234153\",\n \"0.5215751\",\n \"0.5209741\",\n \"0.52039987\",\n \"0.51976883\",\n \"0.51751417\",\n \"0.513496\",\n \"0.5124755\",\n \"0.51244444\",\n \"0.5111415\",\n \"0.51066446\",\n \"0.5094734\",\n \"0.5087002\",\n \"0.50820637\",\n \"0.50728524\",\n \"0.5069215\",\n \"0.5069034\",\n \"0.5059516\",\n \"0.5058986\",\n \"0.505482\",\n \"0.50440556\",\n \"0.50200105\",\n \"0.50195944\",\n \"0.5015092\",\n \"0.49933347\",\n \"0.49906576\",\n \"0.49774295\",\n \"0.49707735\",\n \"0.4969237\",\n \"0.49641386\",\n \"0.49542177\",\n \"0.49400368\",\n \"0.49381348\",\n \"0.49343276\",\n \"0.49323833\",\n \"0.49271497\",\n \"0.49226353\",\n \"0.49123183\",\n \"0.49109057\",\n \"0.49082664\",\n \"0.4907925\",\n \"0.49060917\",\n \"0.49055988\",\n \"0.4892699\",\n \"0.4889825\",\n \"0.48897797\",\n \"0.48859495\",\n \"0.4878314\",\n \"0.48775724\",\n \"0.48763362\",\n \"0.48691982\",\n \"0.4868415\",\n \"0.48675773\",\n \"0.4866646\",\n \"0.48627332\",\n \"0.4855944\",\n \"0.48543733\",\n \"0.48530525\",\n \"0.48523977\",\n \"0.48483887\",\n \"0.48477188\",\n \"0.48449484\",\n \"0.4842367\",\n \"0.4834079\",\n \"0.48284882\",\n \"0.48250896\",\n \"0.48239818\",\n \"0.48229423\",\n \"0.48184416\",\n \"0.48154104\",\n \"0.48120958\",\n \"0.48113972\",\n \"0.48113298\",\n \"0.48109913\",\n \"0.48106575\",\n \"0.48094448\",\n \"0.4789479\",\n \"0.47865412\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":67,"numItemsPerPage":100,"numTotalItems":95772,"offset":6700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3ODcwMDk1Nywic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3Nzg3MDQ1NTcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.unJY_-LHRQBmRVZrj0Yl8TYAlD1wsM_c9tjaBGpY-V8Pg8SjNQvcYPg0LbHChdw_j7NRrPbSNP0-B_XymFCmDw","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 |
|---|---|---|---|---|---|---|
Run the task compose full series + add to our results | def run(self):
while self.i < len(self.series):
# Grab line + RSS
s = self.series[self.i]
rss = self.request_rss(s.feedUrl)
# Compose Episodes
ep_dicts = []
for entry in rss['entries']:
ep_dicts.append(Episode(s, entry).__dict__)
# Build result dict
result_d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute_series(self):\n for n in xrange(self.conf[\"n_runs\"]):\n self.runs[n].execute()",
"def run(self):\n if self.task == 'all':\n self.produce_all_term_data()\n else:\n self.produce_next_term_data()",
"def run(self):\n import sacc\n im... | [
"0.63525146",
"0.5947928",
"0.5696838",
"0.5634496",
"0.5608928",
"0.56013626",
"0.5599923",
"0.5587855",
"0.55873567",
"0.55794024",
"0.5530784",
"0.551449",
"0.55136603",
"0.54978615",
"0.5480734",
"0.5459616",
"0.5439924",
"0.5439924",
"0.54238594",
"0.54176867",
"0.540211... | 0.54119116 | 20 |
Test the configuration is not overrided but extend for `suppress` | def test_rich_traceback_configuration_extend_suppress(mocker, default_logging_config):
import click
rich_traceback_install = mocker.patch("rich.traceback.install")
rich_pretty_install = mocker.patch("rich.pretty.install")
sys_executable_path = str(Path(sys.executable).parent)
traceback_install_def... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_suppress_import(self):\n logging.info(\"testing suppress import\")\n\n generated_file = os.path.join(self._test_workspace,\n \"generated.suppress\")\n\n extract_cmd = ['CodeChecker', 'parse',\n os.path.join(self._test_workspac... | [
"0.6450392",
"0.6399377",
"0.63585526",
"0.62900907",
"0.6012756",
"0.59775937",
"0.5947511",
"0.59247243",
"0.5893758",
"0.5858456",
"0.5848734",
"0.5842272",
"0.58085376",
"0.5727631",
"0.56761247",
"0.56608504",
"0.56291807",
"0.5626758",
"0.56165975",
"0.56113476",
"0.560... | 0.6306293 | 3 |
Prints solution on console. | def print_solution(data, manager, routing, solution):
time_dimension = routing.GetDimensionOrDie('Time')
total_time = 0
for vehicle_id in range(data['num_vehicles']):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
while not routing.IsEnd(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_solution():\n pass",
"def solve(self):\n print(\"Problem %s Answer: %s\" % (self.number, self.solution()))",
"def printSolution(self):\n print \"----- Solution -----\"\n for feature in self.features:\n print \"Name = \" + feature.name + \" Value = \" + str(feature.v... | [
"0.87028384",
"0.74389744",
"0.7377259",
"0.708527",
"0.7000077",
"0.69975126",
"0.69795585",
"0.6975123",
"0.6821493",
"0.6821493",
"0.6821493",
"0.6735901",
"0.6602481",
"0.65324736",
"0.65144485",
"0.64514583",
"0.64233005",
"0.64180934",
"0.63984954",
"0.6382045",
"0.6381... | 0.5708058 | 89 |
Returns the travel time between the two nodes. | def time_callback(from_index, to_index):
# Convert from routing variable Index to time matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return self.data['time_matrix'][from_node][to_node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nodes_time(self):\n return self._nodes_time",
"def _node_distance(self, first, second):\r\n\r\n name_1 = first.name.split(' ')[0]\r\n name_2 = second.name.split(' ')[0]\r\n\r\n seq1 = self.msa_by_name[name_1]\r\n seq2 = self.msa_by_name[name_2]\r\n\r\n distance = sel... | [
"0.69388837",
"0.6491376",
"0.64647245",
"0.644064",
"0.64244837",
"0.64106727",
"0.64093053",
"0.6311803",
"0.6295542",
"0.61754763",
"0.6144564",
"0.613612",
"0.61312324",
"0.5971906",
"0.59415174",
"0.5910606",
"0.5904154",
"0.5904154",
"0.59017485",
"0.58911026",
"0.58676... | 0.0 | -1 |
Variable assignment can include assigning array elements. | def assign_variable(executor, variable, value):
variable = variable.replace(" ", "")
# TODO Should move parsing of this to ParsedStatementLet.
# TODO Need to handle N-dimensional array element assignment.
i = variable.find("(")
if i != -1:
# Array reference
j = variable.find(")", i+1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_50_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 2]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texp... | [
"0.6699032",
"0.6667143",
"0.66010433",
"0.64327574",
"0.6394654",
"0.632295",
"0.62098926",
"0.61762494",
"0.6125874",
"0.6119721",
"0.60467184",
"0.6016822",
"0.5979976",
"0.5958759",
"0.58768624",
"0.57980937",
"0.5793912",
"0.5786944",
"0.57581115",
"0.5734274",
"0.573222... | 0.7048861 | 0 |
Declares an array. Initializes it to zeros. TODO Handle more than two dimensions. | def stmt_dim(executor, stmt:ParsedStatementDim):
for name, value in stmt._dimensions:
initializer = init_array(value)
executor.put_symbol(name, initializer, SymbolType.ARRAY, arg=None) # Not right, but for now. | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zeros_numpy_array(self,\n type_name,\n as_matrix = True):\n if as_matrix:\n return np.zeros((1, self.get_max_id(type_name)), dtype='float32')\n else:\n return np.zeros((self.get_max_id(type_name),), dtype='float32')",
"def zero_init(self, shape):\... | [
"0.66444355",
"0.66228235",
"0.65934485",
"0.65253294",
"0.63993794",
"0.63414425",
"0.6330485",
"0.63030803",
"0.6281445",
"0.62749404",
"0.6273253",
"0.62706274",
"0.62627155",
"0.62333333",
"0.6128069",
"0.6122897",
"0.60999733",
"0.60854125",
"0.6048962",
"0.6042979",
"0.... | 0.0 | -1 |
An if statement works by skipping to the next line, if the THEN clause is false, otherwise it continues to execute the clauses after the THEN. | def stmt_if(executor, stmt):
e = Expression()
result = e.eval(stmt._tokens, symbols=executor._symbols)
if not result:
executor.goto_next_line() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def end_ifeq(self):\n self.indent_left()\n self.write_line(\"endif\")",
"def test_28_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a>0 then return; else return 0; end\n\t\tend\... | [
"0.6381344",
"0.6276441",
"0.6248517",
"0.6244961",
"0.62422997",
"0.61599344",
"0.6136429",
"0.6136429",
"0.5794875",
"0.5792966",
"0.575992",
"0.57597315",
"0.5758323",
"0.5756217",
"0.573461",
"0.56686",
"0.5654149",
"0.5635589",
"0.5634545",
"0.563404",
"0.5616345",
"0.... | 0.741904 | 0 |
Define a userdefined function. 470 DEF FND(D)=SQR((K(I,1)S1)^2+(K(I,2)S2)^2) | def stmt_def(executor, stmt:ParsedStatementDef):
executor.put_symbol(stmt._variable, stmt._tokens, SymbolType.FUNCTION, stmt._function_arg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dmy_fn(x):\n return 0.4*(2.0*(np.exp(x*4) + np.exp(-x*4)) - 8 + 0.6*x - 6*x**2)",
"def calculate_fn_value(self) :\r\n\r\n self.fn_value = self.gn_value + self.hn_value #f(n) = g(n) + h(n)\r",
"def d2f2dx1x5_func(self,X):\n return(\n -(self.rj**2)*self.rm*self.k_spr*(self.b_spr**2)... | [
"0.6282961",
"0.6064702",
"0.59194523",
"0.5878748",
"0.58671814",
"0.5861088",
"0.5792502",
"0.57803655",
"0.5707842",
"0.5690402",
"0.56867594",
"0.5677876",
"0.566242",
"0.5646247",
"0.5644403",
"0.56438243",
"0.56058556",
"0.5597457",
"0.5594543",
"0.5573553",
"0.5563041"... | 0.0 | -1 |
The WIDTH statement is only for compatibility with some versions of BASIC. It set the width of the screen. Ignored. | def stmt_width(executor, stmt):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def width(self):\n return(self.SCREEN_W)",
"def width(self):\n\t\tpass",
"def setWidth(*args):",
"def setWidth(*args):",
"def setWidth(*args):",
"def setWidth(*args):",
"def setWidth(*args):",
"def setWidth(*args):",
"def setWidth(*args):",
"def setWidth(*args):",
"def setWidth(*args):",... | [
"0.7544066",
"0.7302459",
"0.72391605",
"0.72391605",
"0.72391605",
"0.72391605",
"0.72391605",
"0.72391605",
"0.72391605",
"0.72391605",
"0.72391605",
"0.72391605",
"0.72391605",
"0.69712067",
"0.69448876",
"0.6928398",
"0.6928398",
"0.6913508",
"0.6904323",
"0.68787444",
"0... | 0.0 | -1 |
mode must be "claims" or "channels" | def make_graph(mode, show=True):
if mode != "claims" and mode != "channels":
return
plt.close("all")
# Open the DB
db_file = "/home/brewer/local/lbry-sdk/lbry/lbryum-data/claims.db"
conn = sqlite3.connect(db_file)
c = conn.cursor()
# List for results
times = []
# Query
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mode (self, mode) :\r\n self.mode_ = mode",
"def set_mode(self, mode):\n\t\tif mode not in (self.MODE_PREVIEW, self.MODE_ANALYZE, self.MODE_SEND):\n\t\t\traise ValueError('mode must be one of the MODE_* constants')\n\t\tself._mode = mode\n\t\tif mode == self.MODE_ANALYZE:\n\t\t\tself.attachment_images =... | [
"0.60435784",
"0.57577735",
"0.55922663",
"0.558323",
"0.5573023",
"0.554792",
"0.55342835",
"0.55269134",
"0.55122584",
"0.5491209",
"0.5439935",
"0.54331106",
"0.5416338",
"0.54091513",
"0.5399344",
"0.5399344",
"0.5399344",
"0.5391328",
"0.53808737",
"0.5337275",
"0.533549... | 0.0 | -1 |
Calculate tips over past X amount of time and write JSON output | def aggregate_tips():
# The SQL query to perform
now = time.time()
print("Computing tip stats...", end="", flush=True)
labels = ["30_days", "7_days", "24_hours", "1_hour"]
windows = [30*86400.0, 7*86400.0, 1*86400.0, 3600.0]
result = {}
result["unix_time"] = now
result["human_time_utc"]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_tip(meal_base, tip_rate):",
"def time_taken(json_cutlist, laser):\r\n\tcutlist = json.loads(json_cutlist)\r\n\ttime = 0\r\n\tcoordinate_array = [0, 0]\r\n\tfor a in cutlist:\r\n\t\tif a[0] == \"jump\" or a[0] == \"mark\":\r\n\t\t\tcoordinate_array = [float(a[1]) - coordinate_array[0], float(a[2]) -... | [
"0.5688149",
"0.55947673",
"0.5528684",
"0.55030835",
"0.5432563",
"0.53793204",
"0.5325283",
"0.53094494",
"0.52589875",
"0.51901346",
"0.51684177",
"0.5168089",
"0.5163765",
"0.5146831",
"0.511869",
"0.5105191",
"0.50967735",
"0.5090349",
"0.5079822",
"0.5079822",
"0.507982... | 0.6560097 | 0 |
Publish files to somewhere on the internet. | def publish_files():
print("Publishing files to the internet...", end="", flush=True)
import subprocess
try:
subprocess.run("./upload.sh", timeout=120.0)
print("done.\n")
except:
print("failed.\n") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def publish(self, filename):\n # 1) Encrypt file\n # 2) Publish to remote cloud server\n # 3) Wait for the result\n # 4) Store results in files located inside RAM folder",
"def publish():\n if sys.argv[-1] == 'publish':\n os.system('python setup.py sdist')\n os.system... | [
"0.74543023",
"0.6922994",
"0.6866991",
"0.676951",
"0.642343",
"0.6293285",
"0.6157238",
"0.6132221",
"0.60680485",
"0.59941494",
"0.5980355",
"0.5970285",
"0.59354484",
"0.5896326",
"0.58699507",
"0.5835118",
"0.5805512",
"0.58000696",
"0.57821625",
"0.5779796",
"0.57461995... | 0.77630585 | 0 |
create a database connection to a SQLite database | def create_connection(db_file):
conn = None
try:
conn = sqlite3.connect(db_file)
return conn
except Error as e:
print(e)
return conn | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect_db(self) -> sqlite3.Connection:",
"def create_connection():\n dir_path = os.path.dirname(os.path.abspath(__file__))\n db_file = dir_path + '/py_sqlite.db'\n try:\n DbUtil.connection = sqlite3.connect(db_file)\n except Error as e:\n print(e)\n f... | [
"0.8269675",
"0.8154287",
"0.8153167",
"0.80855244",
"0.8006094",
"0.79054296",
"0.7893724",
"0.78821623",
"0.7875023",
"0.7869494",
"0.7866777",
"0.7866777",
"0.7866777",
"0.7866019",
"0.78544617",
"0.7850683",
"0.78433925",
"0.7838268",
"0.78342485",
"0.78307635",
"0.782310... | 0.77235234 | 53 |
Start a thread and consume messages there. | def async_consume(self, callback, auto_ack=False):
logging.info("Async consume")
if self.thread is not None:
return
self.thread_stop = False
def wrapped_callback(ch, method, properties, body):
#logging.info("Wrapped callback'd")
callback(ch, method, p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_thread(self):\n self.thread = Thread(target=self.put_lines_into_queue)\n self.thread.daemon = True\n self.thread.start()",
"def start(self):\n\n def pubsub_thread():\n \"\"\" Call get_message in loop to fire _handler. \"\"\"\n\n while not self._stop.is_... | [
"0.71411765",
"0.6965287",
"0.68307364",
"0.6813745",
"0.6759223",
"0.66143614",
"0.65682524",
"0.6550619",
"0.65052104",
"0.64535457",
"0.6452136",
"0.6439701",
"0.6390061",
"0.63585454",
"0.6282679",
"0.6282162",
"0.62618804",
"0.6250061",
"0.6245712",
"0.6242766",
"0.62239... | 0.0 | -1 |
Build the command word. Note | def get_cmd_word(cmd, d_width, d_length):
word = 0x1 # cmd valid
word = word | (d_width - 1) << 1 # cmd dataWidth (3->4B, 1->2B, 0->1B)
word = word | cmd << 3 # cmd type (1->RD, 0->WR)
word = word | d_length << 8 # cmd burst length (1->1 word)
word... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_command(self, cmd, unit):\n return '#' + unit + cmd + NEWLINE",
"def _build_simple_command(self, cmd):\n return cmd+SBE37_NEWLINE",
"def _build_direct_command(self, cmd, arg):\n return \"%s%s\" % (arg, self._newline)",
"def _buildCmd(self, cmd, cmdArg=0x00):\n res = [cm... | [
"0.79212874",
"0.744362",
"0.7122005",
"0.707842",
"0.70561314",
"0.69483036",
"0.6882322",
"0.6856377",
"0.6743266",
"0.67065525",
"0.66842824",
"0.6522843",
"0.6477476",
"0.64324766",
"0.6427607",
"0.6427607",
"0.6427607",
"0.64067274",
"0.6403515",
"0.63853884",
"0.6316158... | 0.0 | -1 |
Return a new instance of a DevMode object. | def __init__(self, mb_info, switch_config):
self.microblaze = Arduino(mb_info, ARDUINO_MAILBOX_PROGRAM)
self.iop_switch_config = switch_config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _new_instance(self):\n return self.__class__(self._vmodule)",
"def _new_instance(self):\n return self.__class__(self._fmodule)",
"def _new_instance(self):\n return self.__class__(self._fmodule)",
"def debug(self):\n return Debug(self)",
"def dev_mode(self):\r\n return... | [
"0.5734324",
"0.55724305",
"0.55724305",
"0.5561178",
"0.55437505",
"0.55043155",
"0.5407992",
"0.53603274",
"0.5352191",
"0.53068537",
"0.5200111",
"0.5169847",
"0.51558495",
"0.51315564",
"0.51260436",
"0.5092145",
"0.5049877",
"0.5045362",
"0.500854",
"0.49994266",
"0.4944... | 0.0 | -1 |
Start the Microblaze Processor. The processor instance will start automatically after instantiation. | def start(self):
self.microblaze.run()
self.microblaze.write(MAILBOX_OFFSET + MAILBOX_PY2IOP_CMD_OFFSET, 0)
self.load_switch_config(self.iop_switch_config) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def startup(self):\n if self.initialize_mp:\n self.initialize_multiprocessing()\n self.startup_run()\n self.startup_finish()",
"def start(params) -> None:\n check_root()\n start_microservice(params)\n load_kernel_module(params)\n start_streamer(params)",
"def platfor... | [
"0.6218762",
"0.60426116",
"0.5927747",
"0.5888535",
"0.5852811",
"0.58245885",
"0.5803731",
"0.57714486",
"0.57650065",
"0.5744394",
"0.5729824",
"0.5724727",
"0.5718365",
"0.5661167",
"0.5647819",
"0.5586553",
"0.5540475",
"0.553082",
"0.551599",
"0.5512633",
"0.5512412",
... | 0.6880082 | 0 |
Put the Microblaze processor into reset. This method will set processor status as "STOPPED". | def stop(self):
self.microblaze.reset() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self):\n # The camera will give no response to this command\n self._serial_io('\\x55\\x99\\x66\\x11', None)\n while True:\n try:\n self.system_state = 0x11\n if self.system_state == 0x11:\n break\n except CygnetEx... | [
"0.62783825",
"0.6214647",
"0.62101734",
"0.62012935",
"0.6188777",
"0.6144242",
"0.6094757",
"0.60869604",
"0.6043643",
"0.6032759",
"0.6026657",
"0.6011027",
"0.600109",
"0.59982294",
"0.5969956",
"0.59564924",
"0.5952704",
"0.59244686",
"0.5921813",
"0.5914156",
"0.5907323... | 0.70397276 | 0 |
Load the Microblaze processor's switch configuration. This method will update switch config. Each pin requires 8 bits for configuration. | def load_switch_config(self, config=None):
if config is None:
config = ARDUINO_SWCFG_DIOALL
elif not len(config) == 4*ARDUINO_SWITCHCONFIG_NUMREGS:
raise TypeError('Invalid switch config {}.'.format(config))
# Build switch config word
self.iop_switch_config = con... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure_switch(self, number: str, config: SwitchConfig, platform_config: dict) -> \"SwitchPlatformInterface\":\n raise NotImplementedError",
"def configure_switch(self, config):\n raise NotImplementedError",
"def __init__(self, mb_info, switch_config):\n self.microblaze = Arduino(mb_... | [
"0.62282544",
"0.5851503",
"0.5757967",
"0.5553404",
"0.5420665",
"0.5405235",
"0.5385367",
"0.53103375",
"0.5305637",
"0.52973616",
"0.5270049",
"0.52667636",
"0.5264424",
"0.52271485",
"0.5182084",
"0.51805335",
"0.51612717",
"0.515244",
"0.5107933",
"0.50897145",
"0.507896... | 0.67974967 | 0 |
Returns the status of the Microblaze processor. Returns str The processor status ("IDLE", "RUNNING", or "STOPPED"). | def status(self):
return self.microblaze.state | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_status(self):\n return self.read_register(259, 0, 3)",
"async def get_status(self) -> str:\n return await self.hw_device.status()",
"def get_status(self):\n if self.vm.get_cloud_status() != \"ACTIVE\":\n return \"stopped\"\n #wait for the vm to be ready and SSH-ab... | [
"0.66442674",
"0.65349734",
"0.6357901",
"0.61985266",
"0.61985266",
"0.6195143",
"0.61590946",
"0.6113441",
"0.6109538",
"0.6081476",
"0.6031187",
"0.6031187",
"0.6031187",
"0.6031187",
"0.6031187",
"0.6031187",
"0.6031187",
"0.6031187",
"0.6031187",
"0.6031187",
"0.6031187"... | 0.692374 | 0 |
Send a write command to the mailbox. | def write_cmd(self, address, data, d_width=4, d_length=1, timeout=10):
# Write the address and data
self.microblaze.write(MAILBOX_OFFSET + MAILBOX_PY2IOP_ADDR_OFFSET,
address)
self.microblaze.write(MAILBOX_OFFSET + MAILBOX_PY2IOP_DATA_OFFSET,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(self, cmd):\n self._chan.send(\"{}\\n\".format(cmd))\n logger.debug(\"sent '{}'\".format(cmd))",
"def write(self, cmd):\n if self.tn:\n try:\n self.wlock.acquire()\n if not isinstance(cmd, bytes): cmd = cmd.encode('ascii')\n s... | [
"0.6980138",
"0.68226683",
"0.6813809",
"0.6756463",
"0.66383994",
"0.6596419",
"0.6563577",
"0.6360743",
"0.6337061",
"0.63311714",
"0.6312822",
"0.6311199",
"0.62932336",
"0.6277489",
"0.6255314",
"0.6235374",
"0.62292373",
"0.6213919",
"0.62082815",
"0.6193863",
"0.6186107... | 0.646782 | 7 |
Send a read command to the mailbox. | def read_cmd(self, address, d_width=4, d_length=1, timeout=10):
# Write the address
self.microblaze.write(MAILBOX_OFFSET + MAILBOX_PY2IOP_ADDR_OFFSET,
address)
# Build the read command
cmd_word = get_cmd_word(READ_CMD, d_width, d_length)
self.microb... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(self):\n if self.status == 'read':\n return\n self.status = 'read'\n self.emit('read')\n self.emit('modified')",
"def read(self, read):\n\n self._read = read",
"def mifare_read(self,address):\n return self.in_data_exchange(bytearray([MIFARE_COMMAND_... | [
"0.6737285",
"0.65308905",
"0.6498505",
"0.64222383",
"0.63628715",
"0.6303914",
"0.62050015",
"0.6119331",
"0.6107073",
"0.60777915",
"0.6036015",
"0.6027135",
"0.6019354",
"0.5988685",
"0.5962931",
"0.594067",
"0.59229904",
"0.5910504",
"0.5862011",
"0.58523464",
"0.5848433... | 0.6479234 | 3 |
Check whether the command mailbox is idle. Returns bool True if the command in the mailbox is idle. | def is_cmd_mailbox_idle(self):
mb_cmd_word = self.microblaze.read(MAILBOX_OFFSET +
MAILBOX_PY2IOP_CMD_OFFSET)
return (mb_cmd_word & 0x1) == 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def is_idle(self) -> bool:\n return (\n await self.send_command_and_read_reply(Protocol1Command(command=\"F\")) == \"Y\"\n )",
"def is_idle(self) -> bool:\n\n return self.get_runningstate == self.cmd.C815_IDLE_STATE",
"def is_idle(self) -> bool:",
"def is_idle(self) -> b... | [
"0.7600817",
"0.7516991",
"0.7223411",
"0.7089875",
"0.6917131",
"0.66209525",
"0.6557727",
"0.6422757",
"0.6312891",
"0.6253275",
"0.62224126",
"0.6158467",
"0.6102417",
"0.60107124",
"0.5923812",
"0.58288354",
"0.58169097",
"0.5816199",
"0.5791001",
"0.57627046",
"0.5757289... | 0.8497326 | 0 |
Encode truncated classical image into quantum datapoint. | def convert_to_circuit(x):
y = np.arcsin(x)
z = np.arccos(x**2)
qubits = cirq.GridQubit.rect(5, 1)
circuit = cirq.Circuit()
for i in range(5):
circuit.append(cirq.ry(y).on(qubits[i]))
circuit.append(cirq.rz(z).on(qubits[i]))
return circuit | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encode(self, encode_data, image):\r\n raise NotImplementedError(\"Not Implemented\")",
"def encode(self, signal: np.ndarray) -> np.ndarray:\n pass",
"def encode_image(self, image):\n image = self.clip_preprocess(image).unsqueeze(0).to(self.device)\n image_features = self.clip_mo... | [
"0.5878991",
"0.58486336",
"0.5828887",
"0.57244307",
"0.56981575",
"0.54445195",
"0.5440825",
"0.5357206",
"0.53013325",
"0.5262915",
"0.5184929",
"0.5019855",
"0.5011705",
"0.49735066",
"0.49495837",
"0.49373123",
"0.49359372",
"0.4922893",
"0.49128926",
"0.49051914",
"0.48... | 0.0 | -1 |
Detects labels given a GCS path. | def main(path):
video_client = (video_intelligence_service_client.
VideoIntelligenceServiceClient())
features = [enums.Feature.LABEL_DETECTION]
video_context = video_intelligence_pb2.VideoContext()
video_context.stationary_camera = True
video_context.label_detection_mode = video_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def detect_labels(path):\n client = vision.ImageAnnotatorClient()\n with io.open(path, 'rb') as image_file:\n content = image_file.read()\n image = vision.types.Image(content=content)\n response = client.label_detection(image=image)\n labels = response.label_annotations\n print('Labels:')\... | [
"0.75087816",
"0.7491403",
"0.7457117",
"0.61335844",
"0.6064078",
"0.5983722",
"0.58968204",
"0.588334",
"0.5836181",
"0.58187306",
"0.57689315",
"0.57617164",
"0.5745488",
"0.5734802",
"0.57158095",
"0.5700895",
"0.5700895",
"0.5694585",
"0.5681159",
"0.5674216",
"0.5672181... | 0.0 | -1 |
Downloads a blob from the bucket. | def download_blob(bucket_name, source_blob_name, destination_file_name):
storage_client = storage.Client()
try:
bucket = storage_client.get_bucket(bucket_name)
blob = bucket.blob(source_blob_name)
blob.download_to_filename(destination_file_name)
print('Blob {} downloade... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_blob(bucket_name, source_blob_name):\n\n storage_client = storage.Client()\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n\n return blob",
"def blob_download(blob_url):\n blob = storage.Object.from_url(blob_url)\n blobc = blob.download()\n ... | [
"0.8135342",
"0.8125911",
"0.78897315",
"0.7834108",
"0.77213085",
"0.77190185",
"0.7650273",
"0.7502587",
"0.7473543",
"0.7457528",
"0.73454875",
"0.7343923",
"0.7278197",
"0.72137046",
"0.71286523",
"0.708186",
"0.70657223",
"0.6937438",
"0.6928341",
"0.6895048",
"0.6828916... | 0.7573154 | 7 |
Computes the hamming distance for sequences in seqs_mat indicated by pairs of indices. | def nb_vector_hamming_distance(indices, seqs_mat, seqs_L, check_lengths=True):
return _nb_vector_hamming_distance(indices, seqs_mat, seqs_L, check_lengths) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hamming_distance(s1, s2):\n assert(len(s1) == len(s2))\n return np.sum([1 if c1 != c2 else 0 for c1, c2 in zip(s1, s2)])",
"def hamming_distance(s1, s2):\n if len(s1) != len(s2):\n raise ValueError(\"Undefined for sequences of unequal lenght.\")\n return sum(ch1 != ch2 for ch1, ch2 in zip(... | [
"0.63828456",
"0.6093228",
"0.6056598",
"0.59507614",
"0.5795671",
"0.578761",
"0.57800555",
"0.574706",
"0.5681204",
"0.56678975",
"0.56452966",
"0.56424135",
"0.563103",
"0.558626",
"0.55659384",
"0.5539205",
"0.5504324",
"0.5493676",
"0.5407647",
"0.5401868",
"0.53662705",... | 0.6753979 | 0 |
Computes the Levenshtein edit distance between two sequences, with the AA substitution distances provided in distance_matrix. The default distance matrix has a 1 for mismatches and 0 for matches. | def nb_editdistance(seq_vec1, seq_vec2, distance_matrix=identity_nb_distance_matrix, gap_penalty=1):
q_L = seq_vec1.shape[0]
s_L = seq_vec2.shape[0]
if q_L == s_L:
"""No gaps: substitution distance
This will make it differ from a strict edit-distance since
the optimal edit-dista... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def word_embedding_levenshtein(seq1, seq2, embeddings, average_distance, r=0.9, normalise=False):\n\tx1 = 1 + len(seq1)\n\tx2 = 1 + len(seq2)\n\n\talpha = r / ((1 - r) * average_distance)\n\n\t# Initialisation of the matrix\n\td = [] # Using Numpy structures for this is probably not more efficient\n\td.append(list... | [
"0.6711283",
"0.6683752",
"0.6674949",
"0.6592512",
"0.6581954",
"0.64532",
"0.64532",
"0.6445244",
"0.6432772",
"0.64265794",
"0.63696915",
"0.6358784",
"0.62909234",
"0.62251955",
"0.6190301",
"0.61244994",
"0.6103599",
"0.6082008",
"0.60701114",
"0.60523444",
"0.60067487",... | 0.6989441 | 0 |
Compute "tcrdist" distance between two TCR CDR3 sequences. Using default weight, gap penalty, ntrim and ctrim is equivalent to the original distance published in Dash et al, (2017). By setting ntrim and ctrim to 0 and adjusting the dist_weight, it is also possible to compute the CDR1/2 loop distances which can be combi... | def nb_tcrdist(seq_vec1, seq_vec2, distance_matrix=tcr_nb_distance_matrix, dist_weight=3, gap_penalty=4, ntrim=3, ctrim=2, fixed_gappos=True):
q_L = seq_vec1.shape[0]
s_L = seq_vec2.shape[0]
if q_L == s_L:
"""No gaps: substitution distance"""
tmp_dist = 0
for i in range(ntrim, q_L - ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _nb_vector_tcrdist(indices, seqs_mat, seqs_L, distance_matrix=tcr_nb_distance_matrix, dist_weight=3, gap_penalty=4, ntrim=3, ctrim=2, fixed_gappos=True):\n assert seqs_mat.shape[0] == seqs_L.shape[0]\n\n dist = np.zeros(indices.shape[0], dtype=np.int16)\n for ind_i in nb.prange(indices.shape[0]):\n ... | [
"0.53767455",
"0.4826406",
"0.47805288",
"0.4753317",
"0.47484082",
"0.46879807",
"0.46756828",
"0.46334925",
"0.45374662",
"0.4479596",
"0.44660226",
"0.44558287",
"0.44372138",
"0.44101122",
"0.43605304",
"0.4340066",
"0.43351212",
"0.4289836",
"0.4284168",
"0.42829153",
"0... | 0.6212199 | 0 |
Computes the tcrdist distance for sequences in seqs_mat indicated by pairs of indices. | def nb_vector_tcrdist(indices, seqs_mat, seqs_L, distance_matrix=tcr_nb_distance_matrix, dist_weight=3, gap_penalty=4, ntrim=3, ctrim=2, fixed_gappos=True):
return _nb_vector_tcrdist(indices, seqs_mat, seqs_L, distance_matrix, dist_weight, gap_penalty, ntrim, ctrim, fixed_gappos) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _nb_vector_tcrdist(indices, seqs_mat, seqs_L, distance_matrix=tcr_nb_distance_matrix, dist_weight=3, gap_penalty=4, ntrim=3, ctrim=2, fixed_gappos=True):\n assert seqs_mat.shape[0] == seqs_L.shape[0]\n\n dist = np.zeros(indices.shape[0], dtype=np.int16)\n for ind_i in nb.prange(indices.shape[0]):\n ... | [
"0.6867072",
"0.6173414",
"0.61204684",
"0.59654254",
"0.5629023",
"0.5607086",
"0.55902237",
"0.55837727",
"0.55610365",
"0.5435831",
"0.54259413",
"0.5407338",
"0.5377603",
"0.5373048",
"0.5354197",
"0.5333928",
"0.5265103",
"0.52603585",
"0.5210351",
"0.5201209",
"0.518305... | 0.6814871 | 1 |
This function works OK on its own. Wrapping it with the above python function was a workaround because joblib and multiprocessing seem to have an issue retaining default arguments with numba functions. | def _nb_vector_tcrdist(indices, seqs_mat, seqs_L, distance_matrix=tcr_nb_distance_matrix, dist_weight=3, gap_penalty=4, ntrim=3, ctrim=2, fixed_gappos=True):
assert seqs_mat.shape[0] == seqs_L.shape[0]
dist = np.zeros(indices.shape[0], dtype=np.int16)
for ind_i in nb.prange(indices.shape[0]):
query... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_default_args(self):\n cfunc = njit(g)\n self.assertEqual(cfunc(1, 2, 3), g(1, 2, 3))\n self.assertEqual(cfunc(1, y=2, z=3), g(1, 2, 3))",
"def __call__(self, *args, **kwargs):\n if Numba.numba_flag:\n return self.numba_fn(*args, **kwargs)\n else:\n ... | [
"0.65683377",
"0.61231744",
"0.61210066",
"0.58272725",
"0.58008033",
"0.57025033",
"0.56774867",
"0.565421",
"0.5610815",
"0.5579183",
"0.5561648",
"0.5541063",
"0.552428",
"0.54533285",
"0.5434301",
"0.5418918",
"0.5369601",
"0.5330608",
"0.52911884",
"0.5281962",
"0.519551... | 0.0 | -1 |
Computes the Levenshtein edit distance for sequences in seqs_mat indicated by pairs of indices. | def nb_vector_editdistance(indices, seqs_mat, seqs_L, distance_matrix=identity_nb_distance_matrix, gap_penalty=1):
#print(indices.shape)
#print(seqs_mat.shape)
#print(seqs_L.shape)
return _nb_vector_editdistance(indices, seqs_mat, seqs_L, distance_matrix, gap_penalty) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _nb_vector_editdistance(indices, seqs_mat, seqs_L, distance_matrix=identity_nb_distance_matrix, gap_penalty=1):\n assert seqs_mat.shape[0] == seqs_L.shape[0]\n mx_L = nb.int_(np.max(seqs_L))\n\n dist = np.zeros(indices.shape[0], dtype=np.int16)\n \n \"\"\"As long as ldmat is big enough to accomo... | [
"0.7585912",
"0.65193325",
"0.6018155",
"0.5973261",
"0.5960617",
"0.5927266",
"0.5902716",
"0.5879615",
"0.5879615",
"0.5809641",
"0.57759446",
"0.5760104",
"0.57229745",
"0.5714925",
"0.5702276",
"0.5691489",
"0.5681923",
"0.5659949",
"0.5631824",
"0.56016165",
"0.55863863"... | 0.7149528 | 1 |
This function works OK on its own. Wrapping it with the above python function was a workaround because joblib and multiprocessing seem to have an issue retaining default arguments with numba functions. | def _nb_vector_editdistance(indices, seqs_mat, seqs_L, distance_matrix=identity_nb_distance_matrix, gap_penalty=1):
assert seqs_mat.shape[0] == seqs_L.shape[0]
mx_L = nb.int_(np.max(seqs_L))
dist = np.zeros(indices.shape[0], dtype=np.int16)
"""As long as ldmat is big enough to accomodate the large... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_default_args(self):\n cfunc = njit(g)\n self.assertEqual(cfunc(1, 2, 3), g(1, 2, 3))\n self.assertEqual(cfunc(1, y=2, z=3), g(1, 2, 3))",
"def __call__(self, *args, **kwargs):\n if Numba.numba_flag:\n return self.numba_fn(*args, **kwargs)\n else:\n ... | [
"0.65683377",
"0.61231744",
"0.61210066",
"0.58272725",
"0.58008033",
"0.57025033",
"0.56774867",
"0.565421",
"0.5610815",
"0.5579183",
"0.5561648",
"0.5541063",
"0.552428",
"0.54533285",
"0.5434301",
"0.5418918",
"0.5369601",
"0.5330608",
"0.52911884",
"0.5281962",
"0.519551... | 0.0 | -1 |
Computes sequence similarity based on the substitution matrix. Requires that sequences are prealigned and equal length. Operates on strings and a dict substitution matrix | def _nb_subst_metric(seq1, seq2, subst_dict, as_similarity=False):
assert len(seq1) == len(seq2)
def _sim_func(s1, s2, subst):
sim12 = 0.
for i in range(len(s1)):
k1 = s1[i] + '|' + s2[i]
k2 = s2[i] + '|' + s1[i]
sim12 += subst.get(k1, subst.get(k2, subst['n|... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ComputeDistMatrix(dict_alignedSequences):\r\n \r\n # check if dictionary with keys as tuples containing integers and values as tuples containing strings\r\n check = True \r\n #1 Check Input is dict\r\n if isinstance(dict_alignedSequences, dict) == False:\r\n check = False\r\n \r\n ... | [
"0.69739157",
"0.647544",
"0.6389687",
"0.635343",
"0.63365984",
"0.6257071",
"0.62165403",
"0.6069753",
"0.59501123",
"0.5907441",
"0.5907441",
"0.589913",
"0.5876702",
"0.5853206",
"0.5807996",
"0.5804335",
"0.5802121",
"0.58003086",
"0.5793083",
"0.5780481",
"0.57682025",
... | 0.6120387 | 7 |
Store the names and grades of school students. | def __init__(self):
self.students = {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_student(self, name: str, grade: int) -> None:\n school_grade = self.students.setdefault(grade, [])\n school_grade.append(name)\n school_grade.sort()",
"def __init__(self):\n self.students = []\n self.grades = {}\n self.isSorted = True",
"def add_student():\n\n\... | [
"0.70357305",
"0.61918634",
"0.6180321",
"0.61655444",
"0.61187875",
"0.6078942",
"0.6064046",
"0.60455054",
"0.60356414",
"0.60095865",
"0.59675676",
"0.5928267",
"0.5887183",
"0.5879371",
"0.5865206",
"0.58481586",
"0.58402777",
"0.5822257",
"0.5813019",
"0.5812019",
"0.580... | 0.6250706 | 1 |
Add a student to a grade in the roster. | def add_student(self, name: str, grade: int) -> None:
school_grade = self.students.setdefault(grade, [])
school_grade.append(name)
school_grade.sort() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_grade(self, student, grade):\n try:\n self.grades[student.id].append(grade)\n except KeyError:\n raise ValueError('Student not in Grade Book.')",
"def addGrade(self, student, grade):\n try:\n self.grades[student.getIDNumber()].append(grade)\n e... | [
"0.812951",
"0.7889351",
"0.7747675",
"0.75414544",
"0.7533531",
"0.74755746",
"0.74255633",
"0.7281956",
"0.7157385",
"0.70832974",
"0.70647204",
"0.7050774",
"0.7042668",
"0.70276797",
"0.68648064",
"0.67618066",
"0.6695062",
"0.661738",
"0.6594988",
"0.65615714",
"0.653173... | 0.7927675 | 1 |
Find all students in the school regardless of grade. | def roster(self) -> list:
return [student
for grade in sorted(self.students)
for student in self.students[grade]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def students(self):\n\t\treturn self.grade_set.all().distinct()",
"def get_all_failing_students(self):\n students = MyCollection()\n for student in self.__student_repository.get_all():\n failing = False\n for grade in self.__grade_repository.get_all():\n if \".\... | [
"0.76901233",
"0.66902846",
"0.65749806",
"0.6472811",
"0.64696026",
"0.64033043",
"0.63738966",
"0.6277681",
"0.62386876",
"0.6223855",
"0.6206561",
"0.6142386",
"0.61358625",
"0.6032616",
"0.6021176",
"0.6009096",
"0.5954611",
"0.59262055",
"0.59205323",
"0.58794457",
"0.58... | 0.5828101 | 22 |
Find all students in a particular grade. | def grade(self, grade_number: int):
return self.students.setdefault(grade_number, []) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def students(self):\n\t\treturn self.grade_set.all().distinct()",
"def find_students(self):\n from quizzer.models.attendance import Attendance\n from quizzer.models.semester import Semester\n\n semester = Semester.get_current()\n\n for attendance in Attendance.objects: # TODO: Use in... | [
"0.772303",
"0.6642984",
"0.6595034",
"0.6356183",
"0.6333465",
"0.62915266",
"0.6277043",
"0.62721986",
"0.6257745",
"0.6209728",
"0.62009317",
"0.61742765",
"0.6173197",
"0.61293",
"0.6112251",
"0.6028895",
"0.60229445",
"0.5965616",
"0.5958776",
"0.5954945",
"0.58713627",
... | 0.64473695 | 3 |
If username and password are not provided, https request will not send authentication headers | def __init__(self, server, ssl=True, username=None, password=None):
self.server = server
authstring = 'Basic ' + string.strip(base64.encodestring(username + ':' + password))
self.auth = authstring if username and password else None
self.connection_method = httplib.HTTPSConnection if... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def basic_authentication(self, username: str, password: str) -> None:\n self.api_session.auth = (username, password)",
"def authenticate():\n return Response(\n 'Could not verify your credentials for that url', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})",
"def authenticate()... | [
"0.68551093",
"0.6853429",
"0.6646717",
"0.66217005",
"0.6620933",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
"0.66114116",
... | 0.5987323 | 83 |
Ensures that the path starts with a '/' | def ensure_path(self, page):
return page if page.startswith('/') else "/{0}".format(page) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_short_path(short_path):",
"def test_normalize_path(self):\n self.assertEqual(normalize_path(\"//////\"), \"/\")\n self.assertEqual(normalize_path(\"//\"), \"/\")\n self.assertEqual(normalize_path(\"//foo/bar//baz\"), \"/foo/bar/baz\")\n self.assertEqual(normalize_path(\"/... | [
"0.7186039",
"0.71806985",
"0.7127312",
"0.71078247",
"0.7051519",
"0.69835657",
"0.68522096",
"0.6767365",
"0.66817963",
"0.6630477",
"0.6626429",
"0.6597407",
"0.65462995",
"0.64854527",
"0.64659435",
"0.64395905",
"0.6419547",
"0.63963103",
"0.63918716",
"0.63726133",
"0.6... | 0.7040683 | 5 |
serializes the python object into json string | def to_json(self, json_file):
try:
json.dump(self.container, open(json_file, 'w'), indent=4)
except (FileNotFoundError, IOError) as err:
print(err) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serialize(self, obj):\n return json.dumps(obj)",
"def toJSON(cls, obj):\n return json.dumps(obj)",
"def toJSON(object):\n\treturn json.dumps(object, ensure_ascii=False)",
"def serialize(cls, obj):\n return json.dumps(obj, cls=CustomTypeEncoder)",
"def encode_json(obj):\n\treturn js... | [
"0.84162724",
"0.82060945",
"0.7896136",
"0.7808678",
"0.7746026",
"0.7719003",
"0.769862",
"0.7666531",
"0.7664871",
"0.76284343",
"0.76284343",
"0.76284343",
"0.76284343",
"0.76254743",
"0.7620307",
"0.7614118",
"0.7614118",
"0.76006883",
"0.7582615",
"0.754739",
"0.7535313... | 0.0 | -1 |
This function will watch the given file for any updates. | def watch(log_file):
log_file.seek(0, os.SEEK_END)
while True:
line = LogParser.read_line(log_file)
if not line:
time.sleep(1)
continue
yield line | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def statusupdate(filepath):\n pass",
"def watch(self):",
"def watch_changes(directories):\n default_watcher.watch(directories)\n return default_watcher.get_notification_thread()",
"def _file_watcher(self, filename, interval):\n fp = open(filename)\n\n line = \"\"\n while self._w... | [
"0.7057741",
"0.68224883",
"0.6613068",
"0.6612686",
"0.65972984",
"0.6454981",
"0.6450486",
"0.6387383",
"0.6343412",
"0.62934804",
"0.6246352",
"0.6237782",
"0.6216912",
"0.6172237",
"0.6156002",
"0.615293",
"0.615024",
"0.61491203",
"0.61296344",
"0.60973674",
"0.6014658",... | 0.5519304 | 41 |
Get CPF data for a given date The ephemeris data is stored in a dictionary with tables of times and position under the "positions" key. This table is interpolated in the calculate_initial_values method. | def get_ephemeris(rundate, sat_name):
file_key = "slr_ephemeris"
ephemeris_data = get_satellite_vars(sat_name)
provider_list = config.tech.prediction_providers.list
# Find the latest version of the observation file
versions = config.files.glob_variable(file_key, "version", r"\d+", file_vars=ephemeri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_initial_values(eph, rundate):\n data = sorted(eph[\"positions\"].items())\n pos_itrs = np.zeros((len(data), 3))\n mjd1, mjd2 = zip(*[t for t, d in data])\n rotation_mat = rotation.trs2gcrs(time.Time(val=mjd1, val2=mjd2, fmt=\"mjd\", scale=\"utc\"))\n tbl = time.Time(val=mjd1, val2=mjd2... | [
"0.6184481",
"0.5654636",
"0.55653894",
"0.55446994",
"0.5504313",
"0.5501529",
"0.5354049",
"0.5346375",
"0.5329468",
"0.5328793",
"0.52968925",
"0.52822536",
"0.5271903",
"0.5270245",
"0.5265283",
"0.526407",
"0.52583337",
"0.5231366",
"0.5223594",
"0.5191087",
"0.5191087",... | 0.5595784 | 2 |
Computing initial values for position and velocity in GCRS system This is for later use in orbit integration, from tables in the prediction files. Use a lagrange polynomial in order to interpolate in the tables. | def calculate_initial_values(eph, rundate):
data = sorted(eph["positions"].items())
pos_itrs = np.zeros((len(data), 3))
mjd1, mjd2 = zip(*[t for t, d in data])
rotation_mat = rotation.trs2gcrs(time.Time(val=mjd1, val2=mjd2, fmt="mjd", scale="utc"))
tbl = time.Time(val=mjd1, val2=mjd2, fmt="mjd", sca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize(self):\n self.positions = self._generate_initial_positions()\n self.scores = np.array(self.compute_scores(self.positions))\n\n self._pso_data.best_positions = self.positions\n self._pso_data.best_scores = self.scores\n\n magic_constant = 2 # feel free to change FI... | [
"0.6120319",
"0.6116722",
"0.5932199",
"0.5788035",
"0.57826406",
"0.57638705",
"0.5757198",
"0.5753652",
"0.57429856",
"0.57152313",
"0.57009196",
"0.5668376",
"0.5657558",
"0.5629421",
"0.5624367",
"0.5592595",
"0.5591798",
"0.5582761",
"0.5570598",
"0.55646664",
"0.5564146... | 0.6997615 | 0 |
Do the initialization and setup for building a postage stamp. In the base class, we check for and parse the appropriate size and position values in config (aka base['stamp'] or base['image']. Values given in base['stamp'] take precedence if these are given in both places (which would be confusing, so probably shouldn't... | def setup(self, config, base, xsize, ysize, ignore, logger):
# .. Do any custom setup you need to do.
# Probably want to call the base class setup function to do the normal determination
# of the size and position values.
# Extra processing of 'bandpass' argument
# Most needed t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self, config, base, file_num, logger):\n # This is a copy of the base class code\n seed = galsim.config.SetupConfigRNG(base, logger=logger)\n logger.debug('file %d: seed = %d',file_num,seed)\n\n if 'det_num' not in config:\n config['det_num'] = { 'type': 'Sequence',... | [
"0.5815659",
"0.5406954",
"0.53990644",
"0.53855544",
"0.5383662",
"0.5338992",
"0.5320965",
"0.5303831",
"0.5302118",
"0.5297782",
"0.52322584",
"0.51910156",
"0.51902056",
"0.5185881",
"0.5184995",
"0.5149911",
"0.5142809",
"0.51350987",
"0.51125443",
"0.5085775",
"0.508431... | 0.64838976 | 0 |
Before drawing the profile, see whether this object can be trivially skipped. The base method checks if the object is completely off the main image, so the intersection bounds will be undefined. In this case, don't bother drawing the postage stamp for this object. prof The profile to draw. image The image onto which to... | def updateSkip(self, prof, image, method, offset, config, base, logger):
# NOTE: There are currently unresolved issues with the image size checking of chromatic
# objects. For now, we ignore any possible speed increases and skip the check.
# if isinstance(prof, galsim.ChromaticObject):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw(self, prof, image, method, offset, config, base, logger, **kwargs):\n # ... draw prof onto the given image (making a new Image if necessary)\n if prof is None:\n return image\n else:\n logger = galsim.config.LoggerWrapper(logger)\n # Setup the kwargs t... | [
"0.6533734",
"0.51599497",
"0.47828445",
"0.47232333",
"0.45972005",
"0.45731962",
"0.45694324",
"0.45572576",
"0.45465982",
"0.45340365",
"0.44940332",
"0.44605252",
"0.44413173",
"0.44082165",
"0.4281743",
"0.4263862",
"0.42636248",
"0.42611814",
"0.42450166",
"0.42342076",
... | 0.67846763 | 0 |
Draw the profile on the postage stamp image. This is a slightly modified version of `stamp.DrawBasic()` which allows drawing of chromatic objects. prof The profile to draw. image The image onto which to draw the profile (which may be None). method The method to use in drawImage. offset The offset to apply when drawing.... | def draw(self, prof, image, method, offset, config, base, logger, **kwargs):
# ... draw prof onto the given image (making a new Image if necessary)
if prof is None:
return image
else:
logger = galsim.config.LoggerWrapper(logger)
# Setup the kwargs to pass to d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateSkip(self, prof, image, method, offset, config, base, logger):\n\n # NOTE: There are currently unresolved issues with the image size checking of chromatic\n # objects. For now, we ignore any possible speed increases and skip the check.\n # if isinstance(prof, galsim.ChromaticObject):... | [
"0.56249416",
"0.49046072",
"0.4773243",
"0.4728293",
"0.47255272",
"0.46967715",
"0.460233",
"0.45648476",
"0.45566934",
"0.45022318",
"0.44824857",
"0.44786713",
"0.44598132",
"0.4454975",
"0.44341892",
"0.4383001",
"0.43778774",
"0.43481213",
"0.43244997",
"0.43213403",
"0... | 0.70576626 | 0 |
Take a draft_dict that was already validated by draft_dict_validator then further sanitize, validate, and transform it. Ultimately return this "further validated" draft dict. It will have a slightly different set of keys the values for which can be used to directly create a Draft object. | def further_validated_draft_dict(
draft_dict: Dict[str, Any], user_profile: UserProfile
) -> Dict[str, Any]:
content = normalize_body(draft_dict["content"])
timestamp = draft_dict.get("timestamp", time.time())
timestamp = round(timestamp, 6)
if timestamp < 0:
# While it's not exactly an in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _draft_from_response(data):\n return Draft(\n uuid=UUID(data['uuid']),\n bundle_uuid=UUID(data['bundle_uuid']),\n name=data['name'],\n updated_at=dateutil.parser.parse(data['staged_draft']['updated_at']),\n files={\n path: DraftFile(path=path, **file)\n ... | [
"0.56550765",
"0.56206477",
"0.5450413",
"0.52529806",
"0.52475804",
"0.5194902",
"0.51102144",
"0.51091665",
"0.50821674",
"0.5033902",
"0.5002",
"0.49568045",
"0.4842201",
"0.48251075",
"0.48187992",
"0.48105076",
"0.48069793",
"0.47712836",
"0.47657195",
"0.47256124",
"0.4... | 0.7376656 | 0 |
Create drafts in bulk for a given user based on the draft dicts. Since currently, the only place this method is being used (apart from tests) is from the create_draft view, we assume that the drafts_dicts are syntactically valid (i.e. they satisfy the draft_dict_validator). | def do_create_drafts(draft_dicts: List[Dict[str, Any]], user_profile: UserProfile) -> List[Draft]:
draft_objects = []
for draft_dict in draft_dicts:
valid_draft_dict = further_validated_draft_dict(draft_dict, user_profile)
draft_objects.append(
Draft(
user_profile=use... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_draft(auth, subject, body, addresses, user_id, cc_addresses=[], attachments_list=None):\r\n data = {}\r\n data['Subject'] = subject\r\n data['Body'] = {}\r\n data['Body']['ContentType'] = 'HTML'\r\n data['Body']['Content'] = body\r\n data['ToRecipients'] = [{'EmailAddress': {'Address':... | [
"0.56473714",
"0.55276287",
"0.5469983",
"0.5152256",
"0.5134393",
"0.5032918",
"0.4999463",
"0.49490035",
"0.49176887",
"0.49063164",
"0.48992178",
"0.48911917",
"0.48814285",
"0.48810473",
"0.48643064",
"0.48627475",
"0.48573655",
"0.48487023",
"0.48398086",
"0.48395732",
"... | 0.7956114 | 0 |
Edit/update a single draft for a given user. Since the only place this method is being used from (apart from tests) is the edit_draft view, we assume that the drafts_dict is syntactically valid (i.e. it satisfies the draft_dict_validator). | def do_edit_draft(draft_id: int, draft_dict: Dict[str, Any], user_profile: UserProfile) -> None:
try:
draft_object = Draft.objects.get(id=draft_id, user_profile=user_profile)
except Draft.DoesNotExist:
raise ResourceNotFoundError(_("Draft does not exist"))
valid_draft_dict = further_validate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def edit_draft(self):\r\n EmptyPromise(\r\n lambda: self.q(css='.create-draft').present,\r\n 'Wait for edit draft link to be present'\r\n ).fulfill()\r\n\r\n self.q(css='.create-draft').first.click()\r\n\r\n EmptyPromise(\r\n lambda: self.q(css='.editing... | [
"0.63595945",
"0.6280873",
"0.623346",
"0.62005955",
"0.618698",
"0.61712956",
"0.616757",
"0.6136594",
"0.6136562",
"0.61018544",
"0.60939956",
"0.60843796",
"0.5883826",
"0.58486557",
"0.5807267",
"0.57433337",
"0.5733624",
"0.5710894",
"0.56908655",
"0.56681085",
"0.563653... | 0.7761259 | 0 |
Delete a draft belonging to a particular user. | def do_delete_draft(draft_id: int, user_profile: UserProfile) -> None:
try:
draft_object = Draft.objects.get(id=draft_id, user_profile=user_profile)
except Draft.DoesNotExist:
raise ResourceNotFoundError(_("Draft does not exist"))
draft_id = draft_object.id
draft_object.delete()
ev... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_draft(draft_uuid):\n api_request('delete', api_url('drafts', str(draft_uuid)))",
"def delete(self,\n draft_id,\n ):\n return self._invoke('delete',\n {\n 'draft_id': draft_id,\n })",
... | [
"0.7564674",
"0.7383004",
"0.6840054",
"0.6774973",
"0.67743796",
"0.66971105",
"0.6603117",
"0.65684706",
"0.654473",
"0.6492794",
"0.6456428",
"0.63981634",
"0.6362671",
"0.63221097",
"0.62822104",
"0.62783",
"0.62724733",
"0.62385744",
"0.61420053",
"0.61398315",
"0.612225... | 0.77435535 | 0 |
Get zero version `0.0.0` | def zero(cls: Type[_R]) -> _R:
return cls("0.0.0") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_version():\n return \"0.0.1 (prerelease prototype)\"",
"def version_number() -> int:\n return 0",
"def get_version():\n return '%d.%d.%d' % version_info",
"def current_version(self):\n try:\n return self.release_set.order_by('-created')[0].version\n except IndexError... | [
"0.7245187",
"0.668971",
"0.66882396",
"0.6665648",
"0.6570469",
"0.64901686",
"0.64345765",
"0.64268774",
"0.63672644",
"0.6360634",
"0.63367814",
"0.6328667",
"0.6325577",
"0.63126534",
"0.6253386",
"0.6247568",
"0.62471354",
"0.6241517",
"0.6227065",
"0.6226674",
"0.621898... | 0.68623227 | 1 |
Create a copy of a current version instance. | def copy(self: _R) -> _R:
return self.__class__(self.dumps()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def version_clone(self, version_id):\n raise Exception(\"TODO\")",
"def __copy__(self):\n return self.__class__(self.baseurl, self.template, self.selection[:],\n self.slice[:], self.application)",
"def make_reversion_to_copy(self, copy):\n if copy.copy_of != se... | [
"0.69766486",
"0.68571734",
"0.6853616",
"0.6696837",
"0.66747206",
"0.6633099",
"0.6621376",
"0.6601393",
"0.65898925",
"0.65894204",
"0.65894204",
"0.6584453",
"0.65257055",
"0.6520473",
"0.6510755",
"0.6490696",
"0.6472705",
"0.6456239",
"0.6437196",
"0.6430004",
"0.641598... | 0.63095486 | 32 |
Get next release version. | def bump_release(
self: _R,
release_type: Literal["major", "minor", "micro"] = VersionParts.MICRO,
inc: int = 1,
) -> _R:
if release_type == VersionParts.MAJOR:
return self.bump_major(inc)
if release_type == VersionParts.MINOR:
return self.bump_minor(i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_version(major=False, minor=False, patch=True):\n try:\n r = Release.objects.latest()\n except Release.DoesNotExist:\n return Version('0.0.0')\n\n v = r.version\n if major:\n v = v.next_major()\n elif minor:\n v = v.next_minor()\n else:\n v = v.next_patc... | [
"0.8136026",
"0.8127919",
"0.7517721",
"0.71033996",
"0.6974199",
"0.6932763",
"0.6864649",
"0.6853223",
"0.6828289",
"0.6774772",
"0.67444694",
"0.6615308",
"0.6605916",
"0.6588041",
"0.65674776",
"0.65634",
"0.65570605",
"0.6548462",
"0.6492869",
"0.64794105",
"0.6404592",
... | 0.0 | -1 |
Get next major version. | def bump_major(self: _R, inc: int = 1) -> _R:
if not self.is_stable and self.minor == 0 and self.micro == 0:
return self.get_stable().bump_major(inc - 1)
return self._replace(
BaseVersion(
epoch=0,
release=(self.major + inc, 0, 0),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_version(major=False, minor=False, patch=True):\n try:\n r = Release.objects.latest()\n except Release.DoesNotExist:\n return Version('0.0.0')\n\n v = r.version\n if major:\n v = v.next_major()\n elif minor:\n v = v.next_minor()\n else:\n v = v.next_patc... | [
"0.82029897",
"0.7808802",
"0.72779334",
"0.71113545",
"0.7102261",
"0.702806",
"0.69866174",
"0.69342357",
"0.6925223",
"0.69188356",
"0.6805731",
"0.67799294",
"0.6777864",
"0.66487956",
"0.6535175",
"0.6533176",
"0.6531343",
"0.65151787",
"0.6488693",
"0.6478734",
"0.64504... | 0.63819283 | 24 |
Get next minor version. | def bump_minor(self: _R, inc: int = 1) -> _R:
if not self.is_stable and self.micro == 0:
return self.get_stable().bump_minor(inc - 1)
return self._replace(
BaseVersion(
epoch=0,
release=(self.major, self.minor + inc, 0),
pre=None,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_version(major=False, minor=False, patch=True):\n try:\n r = Release.objects.latest()\n except Release.DoesNotExist:\n return Version('0.0.0')\n\n v = r.version\n if major:\n v = v.next_major()\n elif minor:\n v = v.next_minor()\n else:\n v = v.next_patc... | [
"0.8125392",
"0.763029",
"0.7602886",
"0.7530592",
"0.7165768",
"0.6980305",
"0.69532543",
"0.69383895",
"0.6928541",
"0.6895772",
"0.687624",
"0.6815259",
"0.67866236",
"0.67418855",
"0.67331123",
"0.66247994",
"0.65771586",
"0.6505136",
"0.64826417",
"0.64826417",
"0.647756... | 0.6520823 | 17 |
Get next micro version. | def bump_micro(self: _R, inc: int = 1) -> _R:
if not self.is_stable:
return self.get_stable().bump_micro(inc - 1)
return self._replace(
BaseVersion(
epoch=0,
release=(self.major, self.minor, self.micro + inc),
pre=None,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_version(major=False, minor=False, patch=True):\n try:\n r = Release.objects.latest()\n except Release.DoesNotExist:\n return Version('0.0.0')\n\n v = r.version\n if major:\n v = v.next_major()\n elif minor:\n v = v.next_minor()\n else:\n v = v.next_patc... | [
"0.6825203",
"0.6565859",
"0.6408749",
"0.6236738",
"0.6216092",
"0.60519856",
"0.60511297",
"0.6045051",
"0.5854707",
"0.5835003",
"0.58286273",
"0.577659",
"0.57726276",
"0.5756602",
"0.5756459",
"0.5703252",
"0.56749344",
"0.5653698",
"0.5628476",
"0.5624355",
"0.5614559",... | 0.6671826 | 1 |
Get next prerelease version. If version is stable bump `micro` for a proper versioning as well. Defaults to `rc` prereleases. | def bump_prerelease(
self: _R,
inc: int = 1,
release_type: Literal["rc", "alpha", "beta", "a", "b"] = None,
bump_release: Literal["major", "minor", "micro"] = VersionParts.MICRO,
) -> _R:
prerelease_type = release_type or self.prerelease_type or VersionParts.RC
increm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_version(major=False, minor=False, patch=True):\n try:\n r = Release.objects.latest()\n except Release.DoesNotExist:\n return Version('0.0.0')\n\n v = r.version\n if major:\n v = v.next_major()\n elif minor:\n v = v.next_minor()\n else:\n v = v.next_patc... | [
"0.74618196",
"0.73501813",
"0.72785693",
"0.6688966",
"0.6573726",
"0.6516371",
"0.63295984",
"0.632609",
"0.61778533",
"0.60363823",
"0.60113215",
"0.60052055",
"0.59608686",
"0.59551674",
"0.594888",
"0.5904299",
"0.5897116",
"0.5894186",
"0.5864545",
"0.58246434",
"0.5817... | 0.6240163 | 8 |
Get next postrelease version. | def bump_postrelease(self: _R, inc: int = 1) -> _R:
post = (VersionParts.POST, max(inc, 1))
base_post: Optional[Tuple[str, int]] = self._version.post
if base_post:
post = (VersionParts.POST, max(base_post[1], 1) + inc)
base = BaseVersion(
epoch=0,
rele... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_version(major=False, minor=False, patch=True):\n try:\n r = Release.objects.latest()\n except Release.DoesNotExist:\n return Version('0.0.0')\n\n v = r.version\n if major:\n v = v.next_major()\n elif minor:\n v = v.next_minor()\n else:\n v = v.next_patc... | [
"0.74370474",
"0.7424086",
"0.7209765",
"0.7072295",
"0.68549937",
"0.68467635",
"0.6822434",
"0.6782135",
"0.675786",
"0.6439284",
"0.64025617",
"0.6362353",
"0.6344141",
"0.63141763",
"0.6309239",
"0.6289336",
"0.6222575",
"0.610926",
"0.6093002",
"0.60867167",
"0.6048308",... | 0.68445814 | 6 |
Whether version is not prerelease or devrelease. | def is_stable(self) -> bool:
return not self.is_prerelease | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_release():\n return VERSION[-1]",
"def is_release(self):\n # version string: N.N.N.N is for release.\n return bool(re.match(r'^[\\d.]+$', self.version))",
"def is_production(version=None):\n return is_host_google() and is_default_version(version)",
"def is_0_release(release: str) -> ... | [
"0.7988306",
"0.7390222",
"0.72329897",
"0.71225476",
"0.70509773",
"0.674931",
"0.67251575",
"0.66882837",
"0.6623015",
"0.6601849",
"0.65394694",
"0.64932364",
"0.64372605",
"0.639243",
"0.63218457",
"0.6310093",
"0.62938845",
"0.6283808",
"0.6193949",
"0.6171282",
"0.60062... | 0.7183544 | 3 |
Get stable version from pre or post release. | def get_stable(self: _R) -> _R:
return self._replace(
BaseVersion(
epoch=0,
release=(self.major, self.minor, self.micro),
pre=None,
post=None,
dev=None,
local=None,
)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_version(major=False, minor=False, patch=True):\n try:\n r = Release.objects.latest()\n except Release.DoesNotExist:\n return Version('0.0.0')\n\n v = r.version\n if major:\n v = v.next_major()\n elif minor:\n v = v.next_minor()\n else:\n v = v.next_patc... | [
"0.65761906",
"0.64690495",
"0.64539593",
"0.6419955",
"0.6369132",
"0.6336212",
"0.62359613",
"0.62115157",
"0.61312795",
"0.612242",
"0.6118793",
"0.61041874",
"0.6091029",
"0.6033105",
"0.59679747",
"0.59453046",
"0.5935324",
"0.5899036",
"0.58957833",
"0.58908105",
"0.588... | 0.6867934 | 0 |
v_1 w_1 + ... + v_n w_n | def dot(v, w):
return sum(v_i * w_i for v_i, w_i in zip(v, w)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vector_add(v, w):\n return [v_i + w_i for v_i, w_i in zip(v,w)]",
"def vector_add(v, w):\n return [v_i + w_i for v_i, w_i in zip(v, w)]",
"def vector_add(v, w):\n\treturn [v_i + w_i for v_i, w_i in zip(v, w)]",
"def add(v: Vector, w: Vector) -> Vector:\n assert len(v) == len(w), 'both vectors mu... | [
"0.70978653",
"0.70766723",
"0.70762354",
"0.65042007",
"0.6414303",
"0.63884014",
"0.63861686",
"0.63567185",
"0.63381374",
"0.63309324",
"0.6324586",
"0.62767076",
"0.6206558",
"0.617885",
"0.608573",
"0.6083657",
"0.6048553",
"0.6001456",
"0.5981003",
"0.59767973",
"0.5976... | 0.6353202 | 12 |
makes variables for subsets of the unicode char sets | def assignCharacters(otherChars, where):
if(isinstance(otherChars, list)):
where.append(otherChars)
if(isinstance(otherChars, str)):
tmp = " ".join(otherChars).split()
where.append(tmp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _characters(self):\n self.characters = list(\n set([item for sublist in self.grid for item in sublist])\n )\n return self.characters",
"def unique_characters(self):\n charset = set()\n for doc in self.docs.values():\n charset |= set(doc)\n\n ret... | [
"0.58729345",
"0.5847239",
"0.58128977",
"0.56627953",
"0.5647866",
"0.56441444",
"0.56391495",
"0.5624327",
"0.55717635",
"0.55333704",
"0.5514946",
"0.5514946",
"0.5495993",
"0.5446798",
"0.544425",
"0.5440407",
"0.54331005",
"0.54176193",
"0.5314018",
"0.5313979",
"0.53131... | 0.53112906 | 21 |
checks for common characters in the password and returns the characters contained somewhere in the password string | def makeList(username, url, caseSensitive = False, wildCards = True):
charList = []
for ch in lower:
# check for ch in
if(checkPasswordCharacter(str(ch), username, url)):
charList.append(str(ch))
print(ch)
for ch in numbers:
if(checkPasswordCharacter(str(ch), username, url)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def analyze_password(password):\n vowels = number_of_vowels(password)\n if valid_password(password) is True:\n result = password + \" is a valid password and contains \" + str(vowels) + \" vowels.\"\n else:\n result = password + \" is not a valid password and contains \" + str(vowels) + \" v... | [
"0.66974944",
"0.64420664",
"0.6421295",
"0.642044",
"0.63667226",
"0.6365107",
"0.6325546",
"0.63089836",
"0.6299588",
"0.62728053",
"0.62592286",
"0.62546295",
"0.6249797",
"0.62487674",
"0.6225312",
"0.6205716",
"0.62037766",
"0.62024164",
"0.6148876",
"0.61458707",
"0.614... | 0.0 | -1 |
checks for a character in position i, and records it when the character at postion i was found. | def checkPass(username, url, charList, n):
# dikt = {}
password = ""
for i in range(0, n):
if(testPassword(password, username, url)):
return password #password is found!
# https://stackoverflow.com/questions/189645/how-to-break-out-of-multiple-loops-in-python
ch = findChar(us... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inside_char(self, char, marker, tracker, i):\n if char == marker[0]:\n tracker.append(i)\n elif char == marker[1]:\n try:\n tracker.pop()\n except IndexError:\n pass\n return tracker",
"def _charToIndex(self,ch): \n ... | [
"0.7345728",
"0.63285416",
"0.6295855",
"0.6295855",
"0.6295855",
"0.6295855",
"0.6248521",
"0.6134753",
"0.6081834",
"0.6037675",
"0.60087425",
"0.60087425",
"0.60087425",
"0.5981202",
"0.58713824",
"0.5847541",
"0.5844085",
"0.5784419",
"0.5778379",
"0.57567716",
"0.5748923... | 0.0 | -1 |
helper function for checkPass returns the first element of charList found that works for the password at index i if it fails to find a character at i, prints i and returns an empty string instead of returning i. | def findChar(username, url, charList, i):
for ch in charList:
if(checkPasswordCharacter(ch, username, url, index = i)):
return ch
#only runs if no ch in charList match:
# return i #oof, there's no match if i is out of bounds, e.g. len(password) < i
print("Missing: " + i) #so I know when... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkPass(username, url, charList, n):\n # dikt = {}\n password = \"\"\n for i in range(0, n):\n if(testPassword(password, username, url)):\n return password #password is found! \n # https://stackoverflow.com/questions/189645/how-to-break-out-of-multiple-loops-in-python\n ch... | [
"0.71826273",
"0.6203832",
"0.6179515",
"0.6156986",
"0.61567867",
"0.60574234",
"0.6030019",
"0.59959686",
"0.59700435",
"0.5946914",
"0.5921345",
"0.5909927",
"0.5908256",
"0.58809274",
"0.5878235",
"0.5816463",
"0.5804979",
"0.5787845",
"0.5780203",
"0.57457215",
"0.572765... | 0.77611613 | 0 |
List of characters in table names | def makeTableList(url, caseSensitive = False, wildCards = True):
charList = []
for ch in lower:
# ch = str(ch)
if(characterInTableName(ch, url)):
charList.append(ch)
for ch in numbers:
ch = str(ch)
if(characterInTableName(ch, url)):
charList.append(ch)
for ch in spe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def table_names(self, cursor=None):\r\n return [kind.key().name() for kind in Query(kind='__kind__').Run()]",
"def makeTableNamesList(n, ):",
"def get_table_column_name(self, table):\n c = self.conn.cursor()\n c.execute(\"SELECT * FROM %s\" % table)\n names = list(map(lambda x: x[0]... | [
"0.6977511",
"0.68698466",
"0.66727006",
"0.6658325",
"0.6603546",
"0.65911764",
"0.6573043",
"0.6499986",
"0.6483981",
"0.6470874",
"0.6378552",
"0.6191215",
"0.61647415",
"0.614972",
"0.61342114",
"0.610629",
"0.6102741",
"0.6078974",
"0.60369515",
"0.5990207",
"0.5947591",... | 0.57981765 | 28 |
List of characters in database names | def makeDatabaseList():
charList = []
for ch in lower:
# ch = str(ch)
if(characterInDatabaseName(ch, url)):
charList.append(ch)
for ch in numbers:
ch = str(ch)
if(characterInDatabaseName(ch, url)):
charList.append(ch)
for ch in special:
ch = str(ch)
if(c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeDatabaseNamesList(n, ):",
"def get_database_names(self) -> Iterable[str]:\n custom_database_name = self.service_connection.__dict__.get(\"databaseName\")\n\n database_name = self.service_connection.__dict__.get(\n \"database\", custom_database_name or \"default\"\n )\n ... | [
"0.6628727",
"0.5976508",
"0.5936507",
"0.5901102",
"0.58635503",
"0.5853042",
"0.5845213",
"0.58389676",
"0.58144695",
"0.58012706",
"0.5732453",
"0.5721888",
"0.5696421",
"0.5669424",
"0.56576335",
"0.5632975",
"0.56264454",
"0.56206435",
"0.5605173",
"0.5598442",
"0.557013... | 0.7128734 | 0 |
List of table names | def makeTableNamesList(n, ): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getTableNames(self):\n\n # The specific command depends on whether we are using mysql or sqlite\n if self.connector == 'mysql':\n sqlcmd = (\"SELECT table_name FROM INFORMATION_SCHEMA.TABLES \" +\n \"WHERE table_schema='\" + self.dbname + \"'\")\n else:\n ... | [
"0.8072151",
"0.8006719",
"0.8002535",
"0.7987537",
"0.78607845",
"0.78607196",
"0.78587925",
"0.78372866",
"0.7792003",
"0.7758121",
"0.7746107",
"0.7651905",
"0.76492196",
"0.76076037",
"0.7603518",
"0.75557685",
"0.7516995",
"0.7435231",
"0.74144286",
"0.7379448",
"0.73682... | 0.698688 | 32 |
List of database names | def makeDatabaseNamesList(n, ): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_databases():\n config = load_config()\n\n databases = [x for x in config.keys() if \"schemas\" in config[x]]\n return databases",
"def get_database_names(self) -> Iterable[str]:\n custom_database_name = self.service_connection.__dict__.get(\"databaseName\")\n\n database_name = sel... | [
"0.80087274",
"0.78206784",
"0.77191776",
"0.7642941",
"0.7639768",
"0.7624062",
"0.7575778",
"0.75723255",
"0.7553429",
"0.75302035",
"0.752453",
"0.73743457",
"0.7372475",
"0.72860897",
"0.7283433",
"0.7283433",
"0.72620213",
"0.72427213",
"0.72408473",
"0.7234212",
"0.7225... | 0.6749918 | 29 |
makeList generalized to use the boolean function f. | def makeListF(f, url, *argsf, caseSensitive = False, wildCards = True): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_bool(bool_list):\n new_list = []\n for lst in bool_list:\n for item in lst:\n new_list.append(item)\n if True in new_list:\n return True\n else:\n return False",
"def get_list_of_bool2(self):\n pass",
"def build():\n return [5,2,1,3,6] # true\n ... | [
"0.63551515",
"0.63446385",
"0.6126535",
"0.5955366",
"0.5880952",
"0.5871879",
"0.58662516",
"0.5822809",
"0.57801163",
"0.5777717",
"0.5704812",
"0.56913203",
"0.55946165",
"0.55707604",
"0.55597055",
"0.5552575",
"0.55456346",
"0.55446935",
"0.5511639",
"0.54347694",
"0.54... | 0.56456155 | 12 |
returns list of characters that appear in any username | def userNameCharacters(url, tableName, caseSensitive = False, wildCards = True):
"""
sqlzoo characters
['a', 'c', 'd', 'e', 'h', 'i', 'j', 'k', 'n', 'o', 'p', 'r', 't', 'w', '_', '%']
"""
lst = []
for ch in special:
if(checkUsernameCharacter(ch, url, tableName, notLike = False, notLikeName = "", index ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def all_users():\n\treturn [unicode(name[:-4]).lower() for name in os.listdir(os.path.join(WORLD_DIR, 'players'))]",
"def check_user_name(self, username):\n usernames = []\n for user in self.__users:\n if user['username'] == username:\n usernames.append(user)\n retu... | [
"0.65302527",
"0.6494948",
"0.63683933",
"0.6244168",
"0.6195335",
"0.61639106",
"0.61376554",
"0.6136397",
"0.6090331",
"0.6048526",
"0.6043042",
"0.6035452",
"0.60292345",
"0.6010415",
"0.59815353",
"0.5924206",
"0.5911422",
"0.58786315",
"0.5807217",
"0.57925546",
"0.57895... | 0.68477184 | 0 |
construct sequences and use those to inform the choice of strings. So if a,b,c,d matches, check aa, ab, ac, ad, ba, bb, bc, bd, ca, cb, cc, cd, da, db, dc, dd. | def checkUsernameSequences(n, ch, url, tableName, minLen = 1, maxLen = 2):
if(minLen == 1):
strLst = ch
# assumes all of ch is a match
else:
strLst = []
for k in range(minLen, maxLen + 1):
lst = generateSubSequences(k, ch)
sublst = [x for x in lst if userNameLike(x, url, tableName)]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coding_strand_to_AA(dna):\n Seq = ''\n for i in range(0,len(dna),3): \n triple = dna[i:i+3]\n print triple\n for k in range(len(codons)):\n if triple in codons[k]: \n print \"Casey Rocks\"\n print codons[k]\n amino = aa[k]\n ... | [
"0.58296806",
"0.57344776",
"0.57000065",
"0.569295",
"0.5626267",
"0.5622596",
"0.56082696",
"0.56058365",
"0.5604716",
"0.55872947",
"0.5555464",
"0.5515502",
"0.55089283",
"0.54992986",
"0.5488709",
"0.5476587",
"0.54706156",
"0.54512507",
"0.5450281",
"0.5441451",
"0.5432... | 0.5192423 | 60 |
generates all subsequences of ch with length k | def generateSubSequences(k, ch):
seq = ["".join(c) for c in itertools.product(ch, repeat = k)]
# discussion about the best way to do this:
# https://stackoverflow.com/questions/7074051/what-is-the-best-way-to-generate-all-possible-three-letter-strings
return seq | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_kmers(seq, k):\n\n return [seq[i:i+k] for i in range(len(seq)-k+1)]",
"def cut_kmer(sequence, k_mer):\n for i in range(0, len(sequence)-k_mer + 1):\n yield sequence[i:i+k_mer]",
"def kmer_list(s, k):\n kmer = []\n n = len(s)\n # n-k+1 is the available range of values or probabliti... | [
"0.6393046",
"0.63518596",
"0.6346528",
"0.63429147",
"0.62951434",
"0.62502956",
"0.62439233",
"0.61887056",
"0.6169127",
"0.6153225",
"0.61380696",
"0.6062542",
"0.6060054",
"0.6024283",
"0.6011528",
"0.6003349",
"0.59933025",
"0.5982021",
"0.5982021",
"0.59802747",
"0.5954... | 0.85543895 | 0 |
returns a list of usernames | def userNames(lst, url, tableName):
n = len(lst)
# https://docs.python.org/3/library/itertools.html#itertools.product
# https://stackoverflow.com/questions/3034014/how-to-apply-itertools-product-to-elements-of-a-list-of-lists
lst2 = list(itertools.product(*lst))
lst3 = list(map("".join, lst2))
#
#... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_usernames(self) -> list:\n db_list = list(self.cursor.execute('SELECT * FROM sqlite_master'))\n users = [db_list[i][1] for i in range(0, len(db_list), 2)]\n return users",
"def user_list(self):\n self.cur.execute(\"SELECT username FROM users\")\n users = []\n for... | [
"0.8277952",
"0.8237998",
"0.821286",
"0.8186258",
"0.80364215",
"0.79730445",
"0.78307825",
"0.7717235",
"0.7714539",
"0.7674189",
"0.7632501",
"0.7603505",
"0.75877964",
"0.75004154",
"0.7474869",
"0.74285185",
"0.7409626",
"0.7409626",
"0.73354083",
"0.7305028",
"0.7255649... | 0.0 | -1 |
filters lst. returns sublist | def filt(seq, lst):
regex = "(" + ")|(".join(seq) + ")"
regex = re.compile(regex)
slst = list(filter(regex.search, lst))
return slst
# still need a checkUsername function | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sublist(self, lst, exclude, length):\n for sublist in itertools.combinations([e for e in lst if e not in exclude], length):\n yield list(sublist)",
"def my_filter(function,lst):\n return list(x for x in lst if function(x))",
"def filter(lst):\n res = []\n for name in lst:\n ... | [
"0.674777",
"0.67473686",
"0.6627806",
"0.63558894",
"0.63215184",
"0.6267986",
"0.6242654",
"0.6209429",
"0.6174839",
"0.60629857",
"0.6052656",
"0.5999534",
"0.596492",
"0.59513366",
"0.59276193",
"0.59161556",
"0.58984244",
"0.58968115",
"0.5890496",
"0.5857357",
"0.581732... | 0.54409194 | 42 |
Readd a user's postgresql database. | def _add_postgresql(user, options, dump = None):
if dump is not None:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def postgres_rebuild(name=None, user=None):\n name = check(name, 'name: The dabatase name to create.')\n user = check(user, 'user: the user to grant privileges.')\n\n drop_postgres_db(name=name, user=user)\n create_postgres_db(name=name, user=user, password=password)",
"def recreate_db():\n drop_d... | [
"0.7393419",
"0.6982639",
"0.6812037",
"0.642954",
"0.6232582",
"0.62139505",
"0.6179955",
"0.5981221",
"0.5960473",
"0.5953316",
"0.5928391",
"0.59170234",
"0.58603674",
"0.58522147",
"0.5849216",
"0.5843206",
"0.5840966",
"0.5813732",
"0.5810215",
"0.58094305",
"0.580628",
... | 0.60755813 | 7 |
Adds a user's mysql tables back into the OCF database. | def _add_mysql(user, options, dump = None):
# Access the new username with user["username"]
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_db():\n populate_tables()",
"def create_tables_and_apply_patches(self):\n\n if self.authorized and not self.db_tables_initiated:\n with self.connection.cursor() as cursor:\n for statement in self.parse_mysql_sql_file():\n cursor.execute(statement)... | [
"0.6519952",
"0.62828916",
"0.61585516",
"0.6156607",
"0.61473525",
"0.6075949",
"0.5991981",
"0.5991254",
"0.5913686",
"0.5912704",
"0.5908939",
"0.5908939",
"0.5899569",
"0.5880359",
"0.5877914",
"0.5871357",
"0.5836821",
"0.58103013",
"0.57808405",
"0.5778543",
"0.57273936... | 0.6568307 | 0 |
Class for handling all minidump symbolizing code on Android. | def __init__(self, dump_finder, build_dir, symbols_dir=None):
# Map from minidump path (string) to minidump_dump output (string).
self._minidump_dump_output = {}
# Map from minidump path (string) to the directory that should be used when
# looking for symbol binaries (string).
self._minidump_symbol_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def obfuscate():\r\n smali_file_list = u.load_smali_file() # Load smali files\r\n set()\r\n change_all_direct_method(\r\n set(\r\n find_all_direct_method(\r\n list(u.get_android_method_names()) + list(set(find_all_native_method(smali_file_list))),\r\n smali... | [
"0.5797563",
"0.53851175",
"0.5382028",
"0.5153331",
"0.4875175",
"0.48596218",
"0.47921395",
"0.4589166",
"0.45467687",
"0.45267266",
"0.45106924",
"0.44272697",
"0.441203",
"0.43683136",
"0.43274632",
"0.43264234",
"0.43178105",
"0.43031862",
"0.43031862",
"0.42946658",
"0.... | 0.6551352 | 0 |
Returns a list of paths to binaries where symbols may be located. | def GetSymbolBinaries(self, minidump):
libraries = self._ExtractLibraryNamesFromDump(minidump)
symbol_binary_dir = self._GetSymbolBinaryDirectory(minidump, libraries)
if not symbol_binary_dir:
return []
return [os.path.join(symbol_binary_dir, lib) for lib in libraries] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_binaries():\n\n builddir = Path(__file__).parent.parent / \"builddir\"\n\n bins = []\n\n for folder in [\"examples\", \"tests\", \"tools\"]:\n for path in sorted((builddir / folder).rglob(\"*\")):\n if path.stem.startswith(\"xnvme_single\"):\n continue\n ... | [
"0.7554683",
"0.65706384",
"0.6247244",
"0.6195482",
"0.61567324",
"0.61457145",
"0.6059669",
"0.60321474",
"0.60022146",
"0.60022146",
"0.59460664",
"0.5926765",
"0.5923688",
"0.5919705",
"0.58894503",
"0.5882842",
"0.5870094",
"0.5855827",
"0.58501244",
"0.5832909",
"0.5810... | 0.7863217 | 0 |
Extracts library names that may contain symbols from the minidump. This is a duplicate of the logic in Chromium's //build/android/stacktrace/crashpad_stackwalker.py. | def _ExtractLibraryNamesFromDump(self, minidump):
default_library_name = 'libmonochrome.so'
minidump_dump_output = self._GetMinidumpDumpOutput(minidump)
if not minidump_dump_output:
logging.warning(
'Could not get minidump_dump output, defaulting to library %s',
default_library_na... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetSymbolBinaries(self, minidump):\n libraries = self._ExtractLibraryNamesFromDump(minidump)\n symbol_binary_dir = self._GetSymbolBinaryDirectory(minidump, libraries)\n if not symbol_binary_dir:\n return []\n\n return [os.path.join(symbol_binary_dir, lib) for lib in libraries]",
"def get_pla... | [
"0.606551",
"0.5674643",
"0.56701034",
"0.5526038",
"0.5405917",
"0.5370439",
"0.5335965",
"0.52976125",
"0.52932",
"0.52613264",
"0.523408",
"0.52253866",
"0.5208155",
"0.51849467",
"0.51835585",
"0.5180916",
"0.51393825",
"0.51383364",
"0.513191",
"0.5112789",
"0.5104473",
... | 0.74637324 | 0 |
Gets the directory that should contain symbol binaries for |minidump|. | def _GetSymbolBinaryDirectory(self, minidump, libraries):
if minidump in self._minidump_symbol_binaries_directories:
return self._minidump_symbol_binaries_directories[minidump]
# Get the processor architecture reported by the minidump.
arch = None
matcher = re.compile(_PROCESSOR_ARCH_REGEX)
f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bin_dir():\n return os.path.abspath(os.path.join(get_root_dir(), 'bin/'))",
"def GetSymbolBinaries(self, minidump):\n libraries = self._ExtractLibraryNamesFromDump(minidump)\n symbol_binary_dir = self._GetSymbolBinaryDirectory(minidump, libraries)\n if not symbol_binary_dir:\n return []\... | [
"0.6687989",
"0.668011",
"0.6666962",
"0.6321019",
"0.63035196",
"0.6107489",
"0.6062449",
"0.6049191",
"0.6031628",
"0.6012374",
"0.5988652",
"0.5959031",
"0.5932072",
"0.5915141",
"0.59118515",
"0.59096825",
"0.5886469",
"0.5836009",
"0.58122456",
"0.58107346",
"0.58003944"... | 0.7879615 | 0 |
Runs minidump_dump on the given minidump. Caches the result for reuse. | def _GetMinidumpDumpOutput(self, minidump):
if minidump in self._minidump_dump_output:
logging.debug('Returning cached minidump_dump output for %s', minidump)
return self._minidump_dump_output[minidump]
dumper_path = local_first_binary_manager.GetInstance().FetchPath(
'minidump_dump')
i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testPullMinidumps(self):\n def GetDumpLocation(_=None):\n return '/sdcard/dumps/'\n\n platform_backend = self._browser_backend.platform_backend\n time_offset = platform_backend.GetDeviceHostClockOffset()\n platform_backend.GetDumpLocation = GetDumpLocation\n remote_path = posixpath.join(Get... | [
"0.57792044",
"0.53705996",
"0.52849925",
"0.52743363",
"0.5234966",
"0.518306",
"0.51442665",
"0.5070806",
"0.5030404",
"0.49894577",
"0.49146363",
"0.49089596",
"0.48556525",
"0.4828357",
"0.48120502",
"0.47813582",
"0.47782636",
"0.4737786",
"0.46923456",
"0.46380943",
"0.... | 0.6442122 | 0 |
Make sure message is formatted correctly. | def test_setting_failure(self):
with mock.patch.object(ip_lib, 'set_ip_nonlocal_bind', return_value=1):
ip_lib.set_ip_nonlocal_bind_for_namespace('foo', value=1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format(self, message):",
"def _formatMessage(self, msg, standardMsg):\r\n if not self.longMessage:\r\n return msg or standardMsg\r\n if msg is None:\r\n return standardMsg\r\n try:\r\n return '%s : %s' % (standardMsg, msg)\r\n except UnicodeDecodeE... | [
"0.7444605",
"0.69293326",
"0.690031",
"0.67062855",
"0.66425115",
"0.64710456",
"0.64351165",
"0.62836003",
"0.62724143",
"0.61536384",
"0.6117061",
"0.6076582",
"0.6061003",
"0.6058752",
"0.6057646",
"0.60355955",
"0.6029556",
"0.60024786",
"0.599572",
"0.59727937",
"0.5924... | 0.0 | -1 |
Calculate the reserve factor for Skin Buckling by calculating the critical load and comparing to the applied load. Return the reserve factor adjusted for inequalities based around ==0 | def skinBuckle(dim):
bst = dim[0]
tst = dim[1]
tsk = dim[2]
epsilonk = kk*((tsk/bsk))**2
Et = (Esk*tsk)+(Est*((bst*tst)/bsk))
Nsk = Et*epsilonk # Critical Load
rsf = Nsk/Nx
return rsf - 1 # Using a target Reserve Factor of 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_load_factor(self):\n # Your code here\n return self.total_items / self.capacity",
"def internal_external_canopy_heat_capacity(lumped_cover_heat_capacity: float) -> float:\n return 0.1 * lumped_cover_heat_capacity",
"def calculate_effective_capacitance(self, load):\n c_load = loa... | [
"0.6202992",
"0.59245586",
"0.57835984",
"0.57430667",
"0.56664133",
"0.56359935",
"0.55884063",
"0.55883443",
"0.5531915",
"0.54831076",
"0.5470007",
"0.54357696",
"0.5415667",
"0.5415524",
"0.5412604",
"0.5383835",
"0.53750026",
"0.5356168",
"0.53061926",
"0.52848005",
"0.5... | 0.5172448 | 27 |
Calculate the reserve factor for Stiffener Buckling by calculating the critical load and comparing to the applied load. Return the reserve factor adjusted for inequalities based around ==0 | def stiffenerBuckle(dim):
bst = dim[0]
tst = dim[1]
tsk = dim[2]
epsilont = kt * ((tst / bst)) ** 2
Et = (Esk * tsk) + (Est * ((bst * tst) / bsk))
Nst = Et*epsilont # Critical Load
rsf = Nst/Nx
return rsf - 1 # Using a target Reserve Factor of 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_lower_boundary(self, divisor):\n\n # see how low you can go\n quotas = [0] * self.states\n fair_shares = [0] * self.states\n counter = 0\n lowest_divisor = 0\n prev_divisor = 0\n estimator = 1000000000\n while counter < 1000:\n for i,... | [
"0.6212213",
"0.5960155",
"0.59591246",
"0.58988106",
"0.57863295",
"0.5742012",
"0.56890213",
"0.5643248",
"0.5618332",
"0.56007034",
"0.55502635",
"0.55463886",
"0.5537825",
"0.551547",
"0.5508426",
"0.54879606",
"0.5471865",
"0.54627365",
"0.5456973",
"0.545685",
"0.544993... | 0.49897957 | 86 |
Calculate the reserve factor for Material Failure by calculating the critical load and comparing to the applied load. Return the reserve factor adjusted for inequalities based around =>0 | def matFail(dim):
bst = dim[0]
tst = dim[1]
tsk = dim[2]
Et = (Esk * tsk) + (Est * ((bst * tst) / bsk))
Nmat = Et*maxstrain # Critical Load
rsf = Nmat/Nx
return rsf - 1.1 # Using a target Reserve Factor of >=1.1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_load_factor(self):\n # Your code here\n return self.total_items / self.capacity",
"def fuel_required(mass):\n return max(math.floor(mass/3) - 2, 0)",
"def getRepairCapacity(self):\n return int(self.myDesign.getSYCRequired() * (1-(self.strength/100.0)))",
"def internal_external... | [
"0.5882945",
"0.5823634",
"0.57726854",
"0.57467616",
"0.5741599",
"0.57414633",
"0.5710871",
"0.55506474",
"0.54651403",
"0.5430573",
"0.5428441",
"0.54128045",
"0.53944397",
"0.5361302",
"0.535642",
"0.53394634",
"0.5322019",
"0.53197694",
"0.5309506",
"0.5301232",
"0.53002... | 0.517416 | 33 |
Calculate the reserve factor for Euler Buckling Failure by calculating the critical load and comparing to the applied load. Return the reserve factor adjusted for inequalities based around =>0 | def eulerBuckle(dim):
bst = dim[0]
tst = dim[1]
tsk = dim[2]
ZEAZ = (Est*bst*tst*((tsk/2)+(bst/2)))
ZEA = (Est*bst*tst)+(Esk*tsk*bsk)
zbar = ZEAZ/ZEA # Neutral Axis
EIbar = ((Esk*bsk*(tsk**3))/12)+(Esk*bsk*tsk*(zbar**2))+((Est*tst*bst**3)/12)+\
(Est*bst*tst*(((bst/2)+(tsk/2)-z... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_load_factor(self):\n # Your code here\n return self.total_items / self.capacity",
"def internal_external_canopy_heat_capacity(lumped_cover_heat_capacity: float) -> float:\n return 0.1 * lumped_cover_heat_capacity",
"def calculate_lower_boundary(self, divisor):\n\n # see how low ... | [
"0.5978814",
"0.58166504",
"0.57432806",
"0.5645041",
"0.56362903",
"0.5612008",
"0.56099224",
"0.55491924",
"0.55470383",
"0.55240196",
"0.55133814",
"0.5455415",
"0.5440525",
"0.5410518",
"0.54084057",
"0.54057515",
"0.53936213",
"0.5387816",
"0.53826976",
"0.5381602",
"0.5... | 0.555809 | 7 |
This function allows for testing of a set of dimensions, printing key info as a result. | def tester(dim):
print("Unit Mass: " + str(mass(dim)))
print("Skin Buckle RSF: " + str(skinBuckle(dim)+1))
print("Stiffener Buckle RSF: " + str(stiffenerBuckle(dim)+1))
print("Mat Fail RSF: " + str(matFail(dim)+1.1))
print("Euler Fail RSF: " + str(eulerBuckle(dim)+1.1)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_dict_dimensions(entities_db, title='Title', message=''):\n total = 0\n m = f'{title}\\n'\n for k1, v1 in entities_db.items():\n m += f'\\n{message}{k1}:\\n'\n if isinstance(v1, dict):\n for k2, v2 in v1.items():\n if isinstance(v2, tuple):\n ... | [
"0.6196142",
"0.6052784",
"0.60204434",
"0.60203207",
"0.60203207",
"0.5951763",
"0.5832673",
"0.5800634",
"0.57597065",
"0.57452214",
"0.5724362",
"0.57028264",
"0.5698949",
"0.5673566",
"0.55920947",
"0.55707216",
"0.55707216",
"0.55645055",
"0.55363876",
"0.55324614",
"0.5... | 0.5100301 | 95 |
The callback function for the optimize process. Manages recording of iteration values and monitoring current optimiser solutions. | def callbackMonitor(dim):
bstRecord.append(dim[0])
tstRecord.append(dim[1])
tskRecord.append(dim[2])
massRecord.append(mass(dim))
rsfSkinRecord.append(skinBuckle(dim)+1)
rsfStiffRecord.append(stiffenerBuckle(dim)+1)
rsfMatRecord.append(matFail(dim)+1.1)
rsfEulerRecord.append(eulerBuckle(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Optimize(self):\n self._OpenOutputFiles()\n while self.n_iter < self.n_maxiter and not self.is_converged:\n self.n_iter += 1\n self._ChooseStepDirection(self.opt_type)\n self._LineSearch(-1.0 * self.step_dir)\n self._UpdateEnergy()\n self._UpdateGradient()\n self.traj.Append... | [
"0.65901256",
"0.6476958",
"0.64748776",
"0.639814",
"0.63832265",
"0.6313094",
"0.6264372",
"0.6213871",
"0.6205301",
"0.61710924",
"0.6140338",
"0.6129752",
"0.60806435",
"0.6051773",
"0.6009665",
"0.60076827",
"0.60076827",
"0.5998235",
"0.59467274",
"0.5933865",
"0.590929... | 0.0 | -1 |
About Nurevam or person by mention info | async def info(self,ctx,*,person:discord.Member = None):
if not person:
guild = len(self.bot.guilds)
member = len(set(self.bot.get_all_members()))
app = await self.bot.application_info()
msg = "Name:{}".format(self.bot.user)
if ctx.message.guild.me.ni... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mention(self) -> str:",
"def mention(self) -> str:",
"def display_person(person):\n name = person['name']\n followers = person['follower_count']\n description = person['description']\n country = person['country']\n print(f'{name}, a(n) {description}, from {country}.')\n return followers",... | [
"0.6522878",
"0.6522878",
"0.5954495",
"0.5830652",
"0.58269155",
"0.5811767",
"0.5793768",
"0.5775662",
"0.5751016",
"0.5696697",
"0.5679133",
"0.55970377",
"0.55823743",
"0.556282",
"0.5555041",
"0.55549055",
"0.5526657",
"0.5525049",
"0.55118614",
"0.5511843",
"0.5495225",... | 0.5873194 | 3 |
Give info about this server | async def serverinfo(self,ctx):
g = ctx.guild
embed = discord.Embed()
embed.set_thumbnail(url = g.icon_url)
embed.title = "{} - {}".format(g.name,g.id)
embed.add_field(name = "Owner",value="{} - {}".format(g.owner,g.owner.id),inline=False)
embed.add_field(name = "Created ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_serverinfo(self, server):\n print('QManager server:', self._server)\n server_info = self._qm.get_server_info()\n for k, v in server_info.items():\n print(' %s: %s' % (k, v))",
"def server_info(ctx):\n data = ctx.obj.get_server_info()\n output_json_data(data)",
"def... | [
"0.8096221",
"0.80244446",
"0.7995032",
"0.7823503",
"0.7384904",
"0.7376968",
"0.72559196",
"0.7244653",
"0.7027462",
"0.70059526",
"0.6965059",
"0.69347584",
"0.6865889",
"0.68443227",
"0.6838706",
"0.6834391",
"0.68235415",
"0.68076944",
"0.67965454",
"0.67949927",
"0.6736... | 0.71475893 | 8 |
Type !help {command} for more info on a command. You can also type !help {category} for more info on a category. For example, !help level (If you have level plugin enable!) | async def command(self,ctx):
await ctx.send("Yes this is a command.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def help_help(self):\n print(\"List commands or print details about a command\")",
"def command_help(self, command):\n self.commands[command].command_help()",
"def command_help(self, *args, **kwargs):\n print(\"Commands available:\\n\")\n for name in dir(self):\n if not n... | [
"0.8511808",
"0.8454938",
"0.83023393",
"0.82100123",
"0.8172162",
"0.8119534",
"0.8045609",
"0.7997151",
"0.79934657",
"0.79888207",
"0.7869247",
"0.7839074",
"0.7831564",
"0.7807893",
"0.7801458",
"0.77975965",
"0.77936643",
"0.7725134",
"0.76936215",
"0.7667932",
"0.766793... | 0.0 | -1 |
Type !help command for additional info on a command. You can also type !help category for additional info on a category. For example, type !help Level (If you have the level plugin enable!) | async def category(self,ctx):
await ctx.send("Yes this is a category.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def help_help(self):\n print(\"List commands or print details about a command\")",
"def command_help(self, command):\n self.commands[command].command_help()",
"def _help(self):\n self.onecmd('help')",
"def cmd_help(args):",
"def help():\n \n pass",
"def help():",
"def com... | [
"0.84459895",
"0.81877965",
"0.81058455",
"0.8060183",
"0.80382925",
"0.803564",
"0.7979417",
"0.79499114",
"0.79247713",
"0.79205954",
"0.7903753",
"0.7890884",
"0.7862196",
"0.78568035",
"0.7844785",
"0.78190106",
"0.78190106",
"0.77970946",
"0.77970946",
"0.7784749",
"0.77... | 0.0 | -1 |
Red = Disable Blue = Enable Any problem such as plugins on dashboard is enable but show disable here, info Owner | async def plugin(self,ctx):
special_case = {"Anime":"myanimelist","Anti Raid":"antiraid"}
plugin_setting = await self.redis.hgetall("{}:Config:Cogs".format(ctx.message.guild.id))
embed = discord.Embed()
cogs = self.bot.cogs.keys()
for x in cogs:
setting = u"\U0001F5... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_enabled(self):",
"def check_disabled(self):\n return None",
"def get_status(self):\n return super(Cabling, self).get_status()",
"def enable(self):",
"def Enabled(self) -> bool:",
"def disable(self):",
"def isEnabled(self):",
"def disable_feature(self,reason,source=\"gff3_maniger\... | [
"0.64461994",
"0.61870736",
"0.6155342",
"0.606294",
"0.60158396",
"0.59407747",
"0.5909308",
"0.5869749",
"0.5851598",
"0.58354104",
"0.5772434",
"0.5748269",
"0.574007",
"0.5675615",
"0.5668143",
"0.5668143",
"0.5668143",
"0.5668143",
"0.5668143",
"0.5668143",
"0.5668143",
... | 0.65774465 | 0 |
store input into filename used pickle.dump | def store (input, filename) :
cout = open (filename, 'w')
pickle.dump (input, cout)
cout.close () | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def store(self, filename):",
"def pickle(self,data,filename):\n pickle.dump(data, open(filename, 'wb'))",
"def save_file_data(name, obj, input_path='/inputs'):\n filename = '{}/{}.pkl'.format(input_path, name)\n if not os.path.exists(os.path.dirname(filename)):\n try:\n os.makedi... | [
"0.7272676",
"0.7110179",
"0.69229895",
"0.67838925",
"0.6766273",
"0.67407393",
"0.67352134",
"0.6724542",
"0.6718559",
"0.6689364",
"0.6684626",
"0.6666956",
"0.6617081",
"0.6609867",
"0.65701944",
"0.65609634",
"0.6513856",
"0.6507621",
"0.6492011",
"0.64900124",
"0.648344... | 0.89019084 | 0 |
load data from filename used pickle.load | def grab (filename) :
cin = open (filename, 'r')
return pickle.load (cin) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(filename):\n with open(filename,'rb') as f:\n return pickle.load(self,f)",
"def load_data(file_name):\n with open(file_name, 'rb') as f:\n data = pickle.load(f)\n return data",
"def load_pickle_data(filename):\n path = \"../tmp/{}.pckl\".format(filename)\n if os.pa... | [
"0.7869546",
"0.7829951",
"0.7812556",
"0.77086544",
"0.76826143",
"0.7677001",
"0.76607376",
"0.7654821",
"0.7640084",
"0.7521244",
"0.75156707",
"0.7463171",
"0.7457279",
"0.743854",
"0.7430627",
"0.7389005",
"0.7344408",
"0.73272413",
"0.7275342",
"0.72668594",
"0.7232986"... | 0.68825763 | 58 |
Sends args and kwargs to any configured callbacks. This handles the cases where the 'callbacks' variable is ``None``, a single function, or a list. | def _multiple_callbacks(callbacks, *args, **kwargs):
if isinstance(callbacks, list):
for cb in callbacks:
cb(*args, **kwargs)
return
if callbacks:
callbacks(*args, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_callbacks(self, **kwargs):\n for callback in self.CALLBACKS:\n getattr(self, callback)(**kwargs)",
"def callbacks(*args, addCallback: Script=None, clearAllCallbacks: bool=True, clearCallbacks:\n bool=True, describeHooks: bool=True, dumpCallbacks: bool=True, executeCallbacks... | [
"0.702277",
"0.66514504",
"0.65428776",
"0.651266",
"0.6477689",
"0.61787194",
"0.6089447",
"0.60846204",
"0.60464215",
"0.60348064",
"0.59095436",
"0.590122",
"0.5900241",
"0.5882658",
"0.5858614",
"0.5747529",
"0.57051486",
"0.5680165",
"0.56781036",
"0.5653725",
"0.5636546... | 0.7574884 | 0 |
Adds and connects attributes from default encore FKIK switch anim setup to rig nodes in scene Imports default control setup from file or you may specify source_ctrl in args to override | def make_fkikSwitch_connection_attrs(partpre=None, side='Lt', source_ctrl=None, tag_name='switch', snapTo=None,
add_attrs=None):
switch_anim = ''
if source_ctrl is not None:
switch_anim = source_ctrl
partpre = partpre
if partpre == '':
partpre = 'my... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def switch_setup(params, rig, ik_joints):\n\n # Duplicate for bind skeleton\n skeleton = [x.name() for x in params['ikSkeleton']]\n bind_skeleton = cmds.duplicate(skeleton, n=skeleton[0] + '_bnd_0')\n #bind_skeleton\n\n # Hide all attribute on Controller\n fkikcontrol = params['fkIkSwitch'].name(... | [
"0.5719306",
"0.5415949",
"0.5388942",
"0.5385029",
"0.5336632",
"0.5299819",
"0.5200528",
"0.5145229",
"0.5140653",
"0.51194525",
"0.5079638",
"0.50723904",
"0.50585204",
"0.50376135",
"0.5010488",
"0.49763772",
"0.49707508",
"0.49594924",
"0.4954051",
"0.4949411",
"0.494735... | 0.5789509 | 0 |
This will be used for FK IK matching | def create_snapto_node(ctrl, jnt):
snapto = ctrl+'_SNAPTO'
if mc.objExists(snapto):
jnt = utils.get_parent(snapto)
mc.delete(snapto)
snapto_grp = mc.duplicate(ctrl, po=1, n=ctrl+'_SNAPTO_GRP')[0]
snapto = mc.duplicate(ctrl, po=1, n=ctrl+'_SNAPTO')[0]
utils.set_attrs([snapto_grp, sn... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ikFkMatch(\n namespace,\n ikfk_attr,\n ui_host,\n fks,\n ik,\n upv,\n ik_rot=None,\n key=None):\n\n # returns a pymel node on the given name\n def _get_node(name):\n # type: (Text) -> pm.nodetypes.Transform\n name = anim_utils.stripNam... | [
"0.64621747",
"0.6233758",
"0.6001383",
"0.5866673",
"0.5858059",
"0.57838565",
"0.5618625",
"0.5599867",
"0.55876",
"0.55551654",
"0.54261523",
"0.54143274",
"0.54062307",
"0.5399775",
"0.5396218",
"0.5388583",
"0.53567606",
"0.52986205",
"0.52962285",
"0.52947336",
"0.52704... | 0.0 | -1 |
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