f_width = load_features.f_width
f_height = load_features.f_height
samples = load_features.samples
######################### Load Features #########################
features, eddy, polarity = get_feature_label(2) # X: feature matrix y1: eddy vector y2: polarity vertor
###################################################################
########################## Classifiers ##########################
# use the helper functions in the train_model.py file to train and validate and evaluate
### load trained classifieres
clf = pickle.load(open('clf_core.pck', 'rb'))
clf_polarity = pickle.load(open('clf_pol.pck', 'rb'))
###################### Load Vphase Snapshot ######################
for itnum in range(1):
tic = time.clock()
def get_mask():
# provide your own mask fuction for the region of your interest
# the function returns the location indecies and the correspondin lat/lon
# of the land data points
return cols, rows, mask_lon[cols], mask_lat[rows]
f_width = load_features.f_width
f_height = load_features.f_height
samples = load_features.samples
######################### Load Features #########################
# X: feature matrix y1: eddy vector y2: polarity vertor
features, eddy, polarity = get_feature_label(2)
###################################################################
########################## Classifiers ##########################
# use the helper functions in the train_model.py file to train and validate and evaluate
# load trained classifieres
clf = pickle.load(open('clf_core.pck', 'rb'))
clf_polarity = pickle.load(open('clf_pol.pck', 'rb'))
###################### Load Vphase Snapshot ######################
for itnum in range(1):
tic = time.clock()
vphase_path = 'provide file path'
dataset_2pi = np.load("{0}.npy".format(sys.argv[1]))