def start_controller(args): logger.info('Loading user interface') views.start_view() logger.info('User interface loaded') logger.info('Loading alarms') alarms = model.Alarms(args.alarmsFile) alarms.load_alarms() logger.info('Alarms loaded') if args.preload: logger.info('Loading physio data -- data will be preloaded') data = model.Data(args.rawJsonFile) data.load_physio_data() data.create_single_data_streams(load_then_display=True) logger.info('Physio data loaded') else: logger.info('Loading physio data -- data will be computed on the fly') data = model.Data(args.rawJsonFile) data.load_physio_data() data.create_single_data_streams(load_then_display=False) logger.info('Physio data loaded') return alarms, data
def save_steam_obj(steam_id, obj): data = model.Data.query(model.Data.steam_id == steam_id).get() if not data: data = model.Data() data.steam_id = obj["steam_id"] data.title = obj["title"] data.released = obj["released"] if len(obj["released"]) > 0: try: try: data.released_datetime = datetime.datetime.strptime( obj["released"], '%b %d, %Y') # print("invalid date:%s - %s" % ( obj["title"], obj["released"]) ) except: data.released_datetime = datetime.datetime.strptime( obj["released"], '%b %Y') # print("invalid date:%s - %s" % ( obj["title"], obj["released"]) ) except: print("invalid date:%s - %s" % (obj["steam_id"], obj["released"])) data.reviews = obj["reviews"] data.sentiment = obj["sentiment"] data.perc = obj["perc"] data.followers = obj["followers"] data.top_seller = obj["top_seller"] data.new_release = obj["new_release"] data.thumb_url = obj["thumb_url"] data.put() # create a time series followersOverTime = model.FollowersOverTime() followersOverTime.steam_id = obj["steam_id"] followersOverTime.followers = obj["followers"] followersOverTime.reviews = obj["reviews"] followersOverTime.sentiment = obj["sentiment"] followersOverTime.perc = obj["perc"] followersOverTime.put()
def prepare_data(is_char, seq_length): prepared_data = model.Data() if is_char: raw_text = get_seq_of_char() else: raw_text = get_seq_of_word() prepared_data.raw_text = raw_text chars = sorted(list(set(raw_text))) prepared_data.char_to_int = dict((c, i) for i, c in enumerate(chars)) prepared_data.int_to_char = dict((i, c) for i, c in enumerate(chars)) n_chars = len(raw_text) n_vocab = len(chars) prepared_data.n_vocab = n_vocab print("Total Thai Vocab: %s" % (n_chars)) print("Total Unique Vocab: %s" % (n_vocab)) # print(chars) # seq_length = 10 dataX = [] dataY = [] for i in range(0, n_chars - seq_length, 1): seq_in = raw_text[i:i + seq_length] seq_out = raw_text[i + seq_length] dataX.append([prepared_data.char_to_int[char] for char in seq_in]) dataY.append(prepared_data.char_to_int[seq_out]) n_patterns = len(dataX) prepared_data.dataX = dataX prepared_data.dataY = dataY print("Total Patterns: %s" % (n_patterns)) # print(dataX[0]) X = numpy.reshape(dataX, (n_patterns, seq_length, 1)) # print (X) prepared_data.X = X / float(n_vocab) prepared_data.Y = np_utils.to_categorical(dataY) # print (X) # print(Y) return prepared_data
def __init__(self): self.model = model.Data("todo_list.txt") self.view = view.Display() self.is_running = True
from sorting_algorithms import * import model import view from pygame.locals import * running = True data_model = model.Data() graphics = view.View(data_model) graphics.initialize() choice = '' while running: for event in pygame.event.get(): if event.type == QUIT: running = False choice = graphics.intro() graphics.screen.fill(graphics.WHITE) print(choice) graphics.show_array() pygame.time.wait(1000) if choice == "BUBBLE": bubble_sort(graphics, data_model) elif choice == "SELECTION": selection_sort(graphics, data_model) elif choice == "INSERTION": insertion_sort(graphics, data_model)
def main(): #################### BEGIN PARAMETER BLOCK ############################### #********** Manuscript parameter: N ************************** n_channels = ([16]) # Manuscript variable: N #n_channels = ([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20]) #********** Manuscript parameter: C ************************** n_cell_type = 4 # Manuscript variable: M #********** Manuscript parameter: M ************************** n_marker = 5 #********** Manuscript parameter: Delta m ************************** marker_mean_diff = ([50]) # Manuscript variable Delta M #marker_mean_diff = ([0, 1, 2, 3, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45, 50]) #********** Manuscript parameter: s ************************** marker_stdv = ([30]) # Manuscript variable s #marker_stdv = ([1, 2, 3, 4, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 150, 200, 300, 400, 500, 600, 700, 800, 900, 1000]) #********** Manuscript parameter: H ************************** cutoffs = [0] # Manuscript variable H #cutoffs = [0.9999, 0.9995, 0.999, 0.995, 0.99, 0.95, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.9, 0.89, 0.88, 0.87, 0.86, 0.85, 0.84, 0.83, 0.82, 0.81, 0.8] #################### END PARAMETER BLOCK ############################### n_cell = 22000 marker_base = 400 m_training_instance = 20000 m_test_instance = n_cell - m_training_instance #marker_mean_diff = np.random.uniform(1, 50, 1000) #marker_stdv = (np.random.uniform(0.1, 3,1000))**3 n_iter = 1 # Set this value to take the average accuracy calculations from multiple runs write_header = 1 file_accuracy = "accuracy.csv" for i in range(len(n_channels)): # loop over the channels n_ch = n_channels[i] for mm in range(len(marker_mean_diff)): # loop over the markers for k in range(len(marker_stdv)): marker_info = [] marker_info.append(marker_base) marker_info.append(marker_mean_diff[mm]) marker_info.append(marker_stdv[k]) marker_info.append(n_marker) data = model.Data(n_iter) for j in range( n_iter ): # iterations for the same set of channels and markers (however creates a new set of cells everytime) X, fw = model.get_cell_and_flowcytometer_data( n_ch, n_cell_type, n_cell, marker_info) n_cell, n_channel_used = X[0].shape # #model.plot_cell_marker_expression.plot_(fw.sample, fw.markers) # Uncomment if you would like to see he marker expression plot #model.plot_fingerprint.plot_(X) # Uncomment if you would like to see the fingerprints #model.plot_detector_signal_distributions.plot_(X) # Uncomment if you would like to see signal distribution in every detector X_train, y_train, X_test, y_test, train_sample, test_sample = \ model.get_training_and_test_sets(fw.sample, m_training_instance, m_test_instance) n_hidden_layer_node = 100 n_output_layer_node = n_cell_type input_shape_X = 1 # 1D image (only 1 pixel in the vertical direction) input_shape_Y = n_channel_used # Number of pixes in the horizontal direction dnn = model.create_dnn(input_shape_X, input_shape_Y, \ n_hidden_layer_node, n_output_layer_node) dnn = model.compile_dnn(dnn) epochs = 10 dnn = model.fit_dnn(dnn, X_train, y_train, epochs) type_of_interest = 0 #file_name = "roc_0_" + str(n_ch) + ".csv" #model.evaluate_roc(dnn, X_test, y_test, test_sample, type_of_interest, file_name) file_name = "purity_0_" + str(n_ch) + ".csv" #model.evaluate_purity(dnn, X_test, y_test, test_sample, file_name, cutoffs) data.accuracy[j] = model.evaluate_accuracy( dnn, X_test, y_test, test_sample) model.print_accuracy(data, n_ch, marker_mean_diff[mm], marker_stdv[k], file_accuracy, write_header) write_header = 0
def build_secondary_view(self, note, win): notaInicial = model.Data().notaIni distancia = model.Requests.interval_request(note) ascendente = _(str(model.lNotes[notaInicial])) + "-" + _( str(model.lNotes[(notaInicial + distancia) % 12])) descendente = _(str(model.lNotes[notaInicial])) + "-" + _( str(model.lNotes[(notaInicial - distancia) % 12])) win.set_default_size(300, 200) win.set_border_width(30) grid = Gtk.Grid() win.add(grid) label_Asc = Gtk.Label(label=_("Ascendente:"), xalign=0) label_Des = Gtk.Label(label=_("Descentente:"), xalign=0) notes_Asc = Gtk.Label(label=_("Exemplo:") + ascendente, xalign=0) notes_Des = Gtk.Label(label=_("Exemplo:") + descendente, xalign=0) lb_Asc = Gtk.ListBox() lb_Des = Gtk.ListBox() songsAsc = model.Requests.getSongs(note, "asc") for i in range(0, len(songsAsc)): if songsAsc[i][2] == "YES": text = "<a href=\"" + escape( songsAsc[i] [1]) + "\" > <b> " + songsAsc[i][0] + " </b></a>" else: text = "<a href=\"" + escape( songsAsc[i][1]) + "\" > " + songsAsc[i][0] + " </a>" escape(text, quote=True) song = Gtk.Label() song.set_markup(text) lb_Asc.add(song) songsDes = model.Requests.getSongs(note, "des") for i in range(0, len(songsDes)): if songsDes[i][2] == "YES": text = "<a href=\"" + escape( songsDes[i] [1]) + "\" > <b> " + songsDes[i][0] + " </b></a>" else: text = "<a href=\"" + escape( songsDes[i][1]) + "\" > " + songsDes[i][0] + " </a>" escape(text, quote=True) song = Gtk.Label() song.set_markup(text) lb_Des.add(song) #Grid properties grid.set_column_spacing(30) grid.set_row_spacing(10) #Grid elements label_Asc.set_hexpand(True) label_Des.set_hexpand(True) grid.add(label_Asc) grid.attach(label_Des, 1, 0, 2, 1) grid.attach_next_to(notes_Asc, label_Asc, Gtk.PositionType.BOTTOM, 1, 2) grid.attach_next_to(notes_Des, label_Des, Gtk.PositionType.BOTTOM, 1, 2) grid.attach_next_to(lb_Asc, notes_Asc, Gtk.PositionType.BOTTOM, 1, 1) grid.attach_next_to(lb_Des, lb_Asc, Gtk.PositionType.RIGHT, 1, 1) GLib.idle_add(self.show_secondary_all, win)