def __init__(self): initialise.init() super(mind_id, self).__init__() self.dirname = os.path.dirname(__file__) filename = os.path.join(self.dirname, 'login_window.ui') # print (dirname) loadUi(filename, self) filename = os.path.join(self.dirname, 'background.png') self.label_2.setPixmap(QtGui.QPixmap(filename)) ####################### #### MY CODE STARTS HERE self.reset() self.login_b.clicked.connect(self.login_clicked) self.signup_b.clicked.connect(self.signup_clicked) self.verify.clicked.connect(self.verify_clicked) self.submit.clicked.connect(self.submit_clicked) self.browse.clicked.connect(self.browse_clicked) self.home_button.clicked.connect(self.home_clicked) self.verify_email.clicked.connect(self.verify_email_clicked) self.regex = '^[a-zA-Z0-9_+&*-]+(?:\\.[a-zA-Z0-9_+&*-]+)*@(?:[a-zA-Z0-9-]+\\.)+[a-zA-Z]{2,7}$' self.home_button_2.clicked.connect(self.home_clicked) self.signup_sig = Communicate() # self.signup_sig = pyqtSignal() self.signup_sig.sig.connect(self.signup_in_progress) self.login_sig = Communicate() # self.signup_sig = pyqtSignal() self.login_sig.sig.connect(self.login_in_progress)
def main(): initialise.init() # colorcube_analysis() # histogram_analysis() # entropy_cc_analysis() # colorfulness_analysis() # r_on_fair_distribution() # data_analysis() # confusion() # select_ft_data('densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22.hdf5', [], 0, do_plot_boxes=True) # bdd100k_analysis('densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22.hdf5', do_plot_boxes=True) # bdd100k_cc_analysis() # main()
def __init__(self, ndim=10, nwalkers=200, nsteps=100, run_id="1808/test1", obsname='1808_obs.txt', burstname='1808_bursts.txt', gtiname='1808_gti.txt', theta=(0.44, 0.01, 0.18, 2.1, 3.5, 0.108, 0.90, 0.5, 1.4, 11.2), numburstssim=3, numburstsobs=4, bc=2.21, ref_ind=1, gti_checking=0, threads=4, restart=False, train=None): from initialise import init from run_model import runmodel # Set up initial conditions: self.ndim = ndim self.nwalkers = nwalkers # nwalkers and nsteps are the number of walkers and number of steps for emcee to do self.nsteps = nsteps self.run_id = run_id # Where you want output to be saved and under what name self.theta = theta # Set starting value for each theta parameter, Recall odering, theta: X, Z, Q_b, f_a, f_E, r1, r2, r3 self.threads = threads # Number of threads for emcee to use (e.g. number of cores your computer has) self.numburstssim = numburstssim # this needs to be an integer value of half the number of bursts you want to simulate. I.e. simulate this many from the reference burst in either direction. Don't forget to account for missed bursts! self.numburstsobs = numburstsobs # number of observed bursts in your dataset self.ref_ind = ref_ind # Define index of reference burst (should be middle of predicted burst train). This burst will not be simulated but will be used as a reference to predict the other bursts. self.gti_checking = gti_checking #Option to turn on gti time checking (1 for on, 0 for off): self.obsname = obsname #set location of your observation data files self.burstname = burstname #set location of your burst data files self.gtiname = gtiname #set location of your gti data files self.bc = bc #bolometric correction to apply to your persistent flux (1.0 if they are already bolometric fluxes): self.restart = restart #if your run crashed and you would like to restart from a previous run, with run_id above, set this to True if train is None: train = 1 self.train = train # 1 For whether you want to generate a burst train or 0 for work on non contigius bursts else: train = 0 self.train = train self.x, self.y, self.yerr, self.tref, self.bstart, self.pflux, self.pfluxe, self.tobs, self.fluen, self.st, self.et = init( ndim, nwalkers, theta, run_id, threads, numburstssim, numburstsobs, ref_ind, gti_checking, obsname, burstname, gtiname, bc, restart, train) print(self.st, self.et) # # -------------------------------------------------------------------------# # # TEST THE MODEL WORKS # # -------------------------------------------------------------------------# print( "# -------------------------------------------------------------------------#" ) print("Doing Initialisation..") print("Testing the model works..") test, valid = runmodel(self.theta, self.y, self.tref, self.bstart, self.pflux, self.pfluxe, self.tobs, self.numburstssim, self.numburstsobs, self.ref_ind, self.gti_checking, self.train, self.st, self.et, debug=False) # set debug to True for testing print("result: ", test, valid) self.plot_model(test)
# Options UPLOAD_FOLDER = paths.upload_folder ALLOWED_EXTENSIONS = {'mp3', 'wav'} previous_upload_dir = None previous_filenames = None genre_data_stats = True app = Flask(__name__, static_url_path='') CORS(app) app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER app.config['MAX_CONTENT_LENGTH'] = 128 * 1024 * 1024 app.secret_key = 'key' # Load all data from disk data = initialise.init() # Calculate genre stats on load if option is enabled if genre_data_stats: from collections import Counter gsl = multi_logging.setup_logger('genre_stats', 'logs/genre_stats.log') genre_stats = Counter([song.listed_genre for song in data['song_data']]) for key, value in genre_stats.most_common(): gsl.info(str(key) + " " + str(value)) # Utility function for formatting predicted genres def make_string(list): return ", ".join(list)
def main(): initialise.init() # Tests # show_distribution() # retinanet_train() # retinanet_tiny_train() # retinanet_test() # analyse.entropy_cc_analysis() # analyse.histogram_analysis() # analyse.colorcube_analysis() # imagenet_test() # cifar_color_domains_test() # show_ids() # box_size() # test_do_boxes_cross() # check_obj_annotations() # test_extract_non_superposing_boxes() # classification_dataset() # cc_grant() # return history_files = [ 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_ref_hist.pkl', 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_city_street_ft_hist.pkl', 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_daytime_ft_hist.pkl', 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_highway_ft_hist.pkl', 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_night_ft_hist.pkl', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_ft_hist.pkl', ] model_files = [ # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_refep30_vl0.23.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_daytime_ftep20_vl0.27.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_night_ftep01_vl0.16.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_highway_ftep02_vl0.22.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_city_street_ftep30_vl0.26.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_ref2_ft_ep05_vl0.25.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_daytime2_ftep04_vl0.24.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_night2_ftep03_vl0.15.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_highway2_ftep02_vl0.21.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_city_street2_ftep04_vl0.21.hdf5', 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_ref3_ft_ep11_vl0.23.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_daytime3_ftep06_vl0.24.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_night3_ftep01_vl0.15.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_highway3_ftep02_vl0.21.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_city_street3_ftep14_vl0.25.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_ref_ftep02_vl0.23.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_daytime_ftep03_vl0.25.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_night_ftep01_vl0.16.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_highway_ftep01_vl0.21.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_city_street_ftep04_vl0.23.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_ref2_ft_ep02_vl0.25.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_daytime2_ft_ep02_vl0.23.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_night2_ft_ep03_vl0.16.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_highway2_ft_ep01_vl0.20.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_city_street2_ft_ep01_vl0.21.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_ref3_ft_ep01_vl0.26.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_daytime3_ft_ep02_vl0.23.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_night3_ft_ep01_vl0.15.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_highway3_ft_ep01_vl0.20.hdf5', # 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24_city_street3_ft_ep02_vl0.21.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_ref_ftep10_vl0.26.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_daytime_ftep07_vl0.25.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_night_ftep04_vl0.16.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_highway_ftep07_vl0.24.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_city_street_ftep05_vl0.23.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_ref2_ft_ep01_vl0.21.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_daytime2_ft_ep08_vl0.25.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_night2_ft_ep03_vl0.15.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_highway2_ft_ep04_vl0.21.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_city_street2_ft_ep01_vl0.21.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_city_street2_ft_ep02_vl0.22.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_ref3_ft_ep10_vl0.23.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_daytime3_ft_ep02_vl0.23.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_night3_ft_ep03_vl0.15.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_highway3_ft_ep02_vl0.24.hdf5', # 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22_city_street3_ft_ep02_vl0.22.hdf5', # 'nasnet_bdd100k_cl0-500k_20ep_woda_ep17_vl0.24.hdf5', # 'resnet50_bdd100k_cl0-500k_20ep_woda_ep13_vl0.27.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_ft_day_ep06_vl0.24.hdf5', # 'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22_refep30_vl0.23.hdf5', ] # plotting.imshow(cv2.imread('../../bdd100k/classification/images/val/bbadf190-864c9a43-9.jpg')) # return # for mf in model_files: # analyse.bdd100k_analysis(mf, do_plot_boxes=True) # analyse.analyse_attributes(model_files) # analyse.bdd100k_compare(model_files[1], model_files[0], 'scene', 'score') # load_model_test(model_files) # bdd100k_sel_partition_test() # bdd100k_global_finetune_test() # bdd100k_local_finetune_test(model_files) # show_history_test(history_files, log_path) # show_history_test(tb_path + ) # subset_selection_test() # adjectives_finding_test() # train_bdd100k_cl() plotting.color_3channels_hist('/Users/user/Desktop/IEEEAITEST/grant.jpg')