KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use prepare_network method in tempest

Best Python code snippet using tempest_python

gen_map.py

Source:gen_map.py

...57# case_path = '../../../examples/capabilities/ercot/case8/'58high_renew_wind_scaling = 2.0059# case_file = 'ercot_8'60# ========   END INPUT SETTINGS  ========================61def prepare_network(node, node_col, high_renewables_case, zero_pmin=False, zero_index=False, on_ehv=True, split_start_cost=False, high_ramp_rates=False, coal=True):62    if high_renewables_case:63        case_file = node + '_hi_system_case_config'64    else:65        case_file = node + '_system_case_config'66    book = xlrd.open_workbook(data_path + 'bus_generators.xlsx')67    if high_ramp_rates:68        sheet = book.sheet_by_name('Gen Info-high ramps')69    else:70        sheet = book.sheet_by_name('Gen Info')71    # os.rename(case_path + case_file + '.json', case_path + case_file + '_old.json')72    with open(case_path + case_file + '.json') as json_file:73        data = json.load(json_file)74        genCost = []75        genData = []76        genFuel = []77        if high_renewables_case:78            gentypes = ['solar', 'coal', 'gas', 'nuclear', 'wind', 'hydro']79        else:80            gentypes = ['coal', 'gas', 'nuclear', 'wind', 'hydro']81        if not coal:82            gentypes.remove('coal')83        for irow in range(1, sheet.nrows - 2):84            genidx = int(sheet.cell(irow, 0).value)85            if zero_index:86                busNo = int(sheet.cell(irow, node_col).value) - 187            else:88                busNo = int(sheet.cell(irow, node_col).value)89            mvabase = sheet.cell(irow, 3).value90            if zero_pmin:   # Mode to set pmin to zero as part of debugging91                pmin = 0.092            else:93                pmin = sheet.cell(irow, 4).value94            qmin = sheet.cell(irow, 5).value95            qmax = sheet.cell(irow, 6).value96            c2 = sheet.cell(irow, 7).value97            c1 = sheet.cell(irow, 8).value98            c0 = sheet.cell(irow, 9).value99            Gentype = sheet.cell(irow, 10).value100            RampRate = sheet.cell(irow, 11).value101            if split_start_cost:102                StartupCost = sheet.cell(irow, 12).value/2103                ShutdownCost = sheet.cell(irow, 12).value/2104            else:105                StartupCost = sheet.cell(irow, 12).value106                ShutdownCost = 0107            # For the 200 bus case determine if there is a high voltage bus that the generator should be connected to.108            # Solar and Wind are to remain on low-voltage buses:109            if node == '200' and on_ehv and "Wind" not in Gentype and "Solar" not in Gentype:  # 1 = 200 node case110                for branch in data['branch']:111                    if branch[0] == busNo and branch[1] > 200:112                        busNo = branch[1]113                        break114                    elif branch[1] == busNo and branch[0] > 200:115                        busNo = branch[1]116                        break117            # If high renewable case scale up the baseline wind capacities118            if high_renewables_case and "Wind" in Gentype:119                mvabase = high_renew_wind_scaling * mvabase120            if "Wind" in Gentype:121                fueltype = 'wind'122            elif "Nuclear" in Gentype:123                fueltype = 'nuclear'124            elif "Coal" in Gentype:125                fueltype = 'coal'126            elif "Gas" in Gentype:127                fueltype = 'gas'128            elif "Hydro" in Gentype:129                fueltype = 'hydro'130            elif "Solar" in Gentype:131                fueltype = 'solar'132            else:133                fueltype = 'other'134            if fueltype in gentypes:135                # gen_id[busNo] = gen_id[busNo] + 1136                genData.append([137                    busNo,138                    float(0),139                    float(0),140                    float(qmax),141                    float(qmin),142                    1.0,143                    float(mvabase),144                    1,145                    float(mvabase),146                    float(pmin),147                    0,148                    0,149                    0,150                    0,151                    0,152                    0,153                    float(RampRate),154                    0.0,155                    0.0,156                    0.0,157                    0.0158                ])159                genCost.append([160                    2,161                    float(StartupCost),162                    float(ShutdownCost),163                    3,164                    float(c2),165                    float(c1),166                    float(c0)167                ])168                genFuel.append([169                    fueltype,170                    Gentype,171                    genidx,172                    1173                ])174        # divide the generators into parts175        # testing on dividing first generator into 3176        '''177        oldQmax = genData[0][3]178        oldQmin = genData[0][4]179        oldPmax = genData[0][8]180        oldPmin = genData[0][9]181        newPmax = oldPmax/3.0182        newGenData = genData[0]183        newGenCost = genCost[0]184        newGenData[3] = oldQmax * newPmax/oldPmax185        newGenData[4] = oldQmin * newPmax/oldPmax186        newGenData[6] = newPmax187        newGenData[8] = newPmax188        newGenData[9] = oldPmin * newPmax/oldPmax189        data['gen'].pop(0)190        data['gencost'].pop(0)191        for j in range(3):192            data['gen'] = [newGenData] + data['gen']193            data['gencost'] = [newGenCost] + data['gencost']194        genData = data['gen']195        genCost = data['gencost']196        # assigning ramp rate based on fuel type197        for i in range(len(data['gen'])):198            # find the type of generation199            c2 = float(genCost[i][4])200            c1 = float(genCost[i][5])201            c0 = float(genCost[i][6])202            pmax = float(genData[i][8])203            # assign fuel types from the IA State default costs204            if c2 < 2e-5:  205                genfuel = 'wind'206            elif c2 < 0.0003:207                genfuel = 'nuclear'208            elif c1 < 25.0:209                genfuel = 'coal'210            else:211                genfuel = 'gas'212            # calculate ramprate based on fuel type213            ramprate = pctramprate[genfuel]*pmax214            minUpTime = math.ceil(minuptime[genfuel])215            minDownTime = math.ceil(mindowntime[genfuel])216            # update the original data variable217            data['gen'][i][16] = ramprate218            data['gen'][i].append(minUpTime)219            data['gen'][i].append(minDownTime)220        '''221        data['gen'] = genData222        data['gencost'] = genCost223        data['genfuel'] = genFuel224        print('Finished ' + case_file)225    json_file.close()226    # write it in the original data file227    with open(case_path + case_file + '.json', 'w') as outfile:228        json.dump(data, outfile, indent=2)229        outfile.close()230# ------- prepare_network(231#                 node,232#                 node_col,233#                 high_renewables_case,234#                 zero_pmin=False,235#                 zero_index=False,236#                 on_ehv=True,237#                 split_start_cost=False,238#                 high_ramp_rates=False,239#                 coal=True)240node = ['8', '200']241col = [2, 1]242for i in range(2):243    # ----- Use commands below to run with low ramp rates and defaults244    # prepare_network(node[i], col[i], True)245    # prepare_network(node[i], col[i], False)246    # ----- Use the commands below to turn off coal high renewables247    # prepare_network(node[i], col[i], True, False, False, True, False, False, False)248    # prepare_network(node[i], col[i], False, False, False, True, False, False, False)249    # ----- Use commands below to run with high ramp rates250    prepare_network(node[i], col[i], True, False, False, True, False, True)251    prepare_network(node[i], col[i], False, False, False, True, False, True)252    # ----- Use commands below to run with start up costs split 50/50 between start up and shutdown253    # prepare_network(node[i], col[i], True, False, False, True, True)...

exec_model.py

Source:exec_model.py

...22        parser.print_help()23        sys.exit(1)24    args = parser.parse_args()25    return args26def prepare_network(network):27    if network == 'LeNet':28        construct_func = construct_LeNet29        input_shape = (32, 32)30        custom_objects = {}31    elif network == 'VGG_F':32        construct_func = construct_VGG_F33        input_shape = (224, 224)34        custom_objects = {'LRN': LRN}35    else:36        raise ValueError('Unrecognized network "{}"'.format(network))37    return construct_func, input_shape, custom_objects38def make_onehots(labels, num_classes):39    onehot_labels = np.zeros((labels.shape[0], num_classes), dtype=np.float32)40    onehot_labels[range(labels.shape[0]), labels] = 1.041    return onehot_labels42def batch_resize(input_images, output_shape=(224, 224)):43    if input_images.shape[1:3] == output_shape:44        return input_images.astype(np.float32)45    def resize(image, output_shape):46        return scipy.misc.imresize(image, output_shape)47    output_images = map(lambda x:resize(x, output_shape), input_images)48    return np.array(output_images, dtype=np.float32)49def run_train(args):50    construct_func, input_shape, custom_objects = prepare_network(args.network)51    train_x = np.load('spectrogram_data/train_X.npy')52    train_x = batch_resize(train_x, input_shape)[..., np.newaxis]53    train_y = make_onehots(np.load('spectrogram_data/train_Y.npy').astype(np.int32), 20)54    val_x = np.load('spectrogram_data/val_X.npy')55    val_x = batch_resize(val_x, input_shape)[..., np.newaxis]56    val_y = make_onehots(np.load('spectrogram_data/val_Y.npy').astype(np.int32), 20)57    print('train_x: {}, train_y: {}'.format(train_x.shape, train_y.shape))58    print('val_x: {}, val_y: {}'.format(val_x.shape, val_y.shape))59    model = construct_func(input_shape=input_shape+(1,), num_classes=20)60    model.summary()61    optim = optimizers.Adam(lr=0.0001)62    model_checkpoint = callbacks.ModelCheckpoint(os.path.join('models/'+args.res_dir, 'best_model.h5'),63                                                  monitor='val_categorical_accuracy',64                                                  period=1, save_best_only=True)65    early_stopping = callbacks.EarlyStopping(monitor='val_categorical_accuracy',66                                             patience=10)67    csv_logger = callbacks.CSVLogger(os.path.join('models/'+args.res_dir, 'log.txt'))68    model.compile(loss='categorical_crossentropy', optimizer=optim,69                  metrics=['categorical_accuracy'])70    model.fit(x=train_x, y=train_y,71              validation_data=(val_x, val_y),72              callbacks=[model_checkpoint, csv_logger],73              batch_size=100, epochs=100, verbose=1, shuffle=True)74    return75def run_test(args):76    construct_func, input_shape, custom_objects = prepare_network(args.network)77    model = load_model(os.path.join('models/'+args.res_dir, 'best_model.h5'),78                       custom_objects=custom_objects)79    results = {}80    for split_name in ['train', 'val', 'test']:81        x = np.load('spectrogram_data/%s_X.npy' % split_name)82        x = batch_resize(x, input_shape)[..., np.newaxis]83        y = np.load('spectrogram_data/%s_Y.npy' % split_name).astype(np.int32)84        print('{}_x: {}, {}_y: {}'.format(split_name, x.shape,85                                          split_name, y.shape))86        pred = np.argmax(model.predict(x), axis=1)87        print('{}_pred: {}'.format(split_name, pred.shape))88        acc = float(np.sum(pred == y)) / float(y.shape[0])89        print('{} Accuracy: {:f}'.format(split_name.upper(), acc))90        results[split_name] = acc...

test_rootNode.py

Source:test_rootNode.py

...3__author__ = 'andyguo'4import pytest5from dayu_ffmpeg.network import *6class TestRootNode(object):7    def prepare_network(self):8        self.i1 = Input()9        self.root = RootNode()10        self.ih1 = self.root.create_node(InputHolder)11        self.root.set_input(self.i1)12        self.i2 = self.root.create_node(Input)13        self.cf = self.root.create_node(ComplexFilterGroup)14        self.ih2 = self.cf.create_node(InputHolder)15        self.ih3 = self.cf.create_node(InputHolder)16        self.cf.set_input(self.ih1, 0)17        self.cf.set_input(self.i2, 1)18        self.over = self.cf.create_node(Overlay)19        self.over.set_input(self.ih2, 0)20        self.over.set_input(self.ih3, 1)21        self.oh1 = self.cf.create_node(OutputHolder)22        self.oh1.set_input(self.over)23        self.o1 = self.root.create_node(Output)24        self.o1.set_input(self.cf)25    def test_cmd(self):26        from pprint import pprint27        self.prepare_network()28        # print self.root.cmd()29        pprint(self.root.to_script())30    def test__find_all_inputs(self):31        self.prepare_network()32        assert self.root._find_all_inputs(self.root._find_all_outputs()) == [self.i1, self.i2]33    def test__find_all_outputs(self):34        self.prepare_network()...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.