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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()...

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