'Events':
'-',
'n_avg':
'-',
'Time': [event.time for event in events],
'Distance': ['-'] * len(events),
'x': [event.x for event in events],
'y': [event.y for event in events],
'Generation': [0] * len(events),
'Distance_from_origin': [event.distance_from_origin for event in events]
})
cols = [
'n_avg', 'Events', 'Magnitude', 'Generation', 'x', 'y', 'Distance', 'Time',
'Distance_from_origin'
]
catalog = catalog.reindex(columns=cols)
r0 = np.array([
np.mean([event.x for event in events]),
np.mean([event.y for event in events])
]) #np.array([3557.418383, -324.384367])
catalog['x'] = r0[0] - catalog.x # shift so that main shock position is (0,0)
catalog['y'] = r0[1] - 50 - catalog.y
catalog['Distance_from_origin'] = (catalog.x**2 + catalog.y**2)**0.5
#for i in range(1000,3886):
eq.plot_catalog(
catalog, 1, np.array([0, 0]), color='Density'
) #, savepath = 'fentonprog_frames/{}.png'.format(i), saveplot = True)
print((datetime.now() - start).total_seconds())
catalog = catalog.reindex(columns=cols)
r0 = np.array([
np.mean([event.x for event in events]),
np.mean([event.y for event in events])
]) #np.array([3557.418383, -324.384367])
catalog['x'] = r0[0] - catalog.x # shift so that main shock position is (0,0)
catalog['y'] = r0[1] - catalog.y
catalog['Distance_from_origin'] = (catalog.x**2 + catalog.y**2)**0.5
catalog = catalog.dropna()
# reduce events to a random sample of k elements
#catalog = catalog.sample(frac=0.45, replace=False)
#==============================================================================
eq.plot_catalog(catalog, 1, np.array([0, 0]), color='Generation')
N = len(catalog)
k = 20 # int(N**0.5)
r, densities = eq.plot_ED(catalog, k=k, plot=False) # get distance, density
df_dens = pd.DataFrame({'distance': r, 'density': densities})
#df_dens = df_dens[(df_dens.distance > 10**1.2) & (df_dens.distance < 10**2.6)]
r = df_dens.distance
densities = df_dens.density #* np.exp()
#rho0 = np.mean(densities[0:100])
#rmax = (r.max())
#rmin = (r.min())
#n_edges = 30
##bin_edges = np.linspace(np.log10(rmin), np.log10(rmax), n_edges) #np.array([r[i] for i in range(0, len(r), q)])
data_raw = pd.read_csv('habanero_data.csv')
data = data_raw.drop(['z','elapsed time (s)'], axis = 1)
data = data.rename(index = str, columns = {'elapsed time (s) corrected': 'Time'})
nrows = data.shape[0]
data['n_avg'] = pd.Series(['-'] * nrows, index = data.index)
data['Events'] = pd.Series(['-'] * nrows, index = data.index)
data['Magnitude'] = pd.Series([2] * nrows, index = data.index)
data['Generation'] = pd.Series([0] * nrows, index = data.index)
data['Distance'] = pd.Series(['-'] * nrows, index = data.index)
cols = ['n_avg','Events','Magnitude','Generation','x','y','Distance','Time','Distance_from_origin']
data = data.reindex(columns = cols)
start = datetime.now()
# plot data
#x0 = 475513.1
#y0 = 6922782.2
x0 = np.mean(data.x)
y0 = np.mean(data.y)
data['x'] = x0 - data.x # shift so that main shock position is (0,0)
data['y'] = y0 - data.y
data['Distance_from_origin'] = (data.x**2 + data.y**2)**0.5
data = data[data.Distance_from_origin < 10**3.2]
for i in range(322,4001):
eq.plot_catalog(data[:i], 1, np.array([0,0]), color = 'Generation', savepath = 'habanero_frames/{}.png'.format(i), saveplot = True)
print(datetime.now() - start)
# format events in the pd dataframe format defined by generate_catalog etc.
catalog0 = pd.DataFrame({'Magnitude': [event.magnitude for event in events_all],
'Time':[event.time for event in events_all],
'x':[event.x for event in events_all],
'y':[event.y for event in events_all],
'Generation':[0] * len(events_all),
'Distance_from_origin': [event.distance_from_origin for event in events_all],
'Date':[datetime(int(event.split()[0]),int(event.split()[2]),int(event.split()[3]),hour=int(event.split()[4]),minute=int(event.split()[5]),second=int(float(event.split()[6]))) for event in flines]})
cols = ['Magnitude','Generation','x','y','Time','Distance_from_origin','Date']
catalog0 = catalog0.reindex(columns = cols)
catalog0 = catalog0[metrics.loc[fname].range] # get events from time period of interest
#catalog0 = catalog0[0:2000]
catalog0.Time = catalog0.Time - catalog0.Time.min() + metrics.loc[fname].T_adjust # shift time so that first event occurs however long after injection began
N = len(catalog0)
#k = 6
eq.plot_catalog(catalog0, 1, np.array([0,0]), color = 'Generation', saveplot = False, savepath = fname.split(sep='.')[0]+'_positions.png')
nsplit = 3
amount = np.ceil(np.linspace(N/nsplit, N, nsplit))
r_all = []
dens_all = []
t = []
T = metrics.loc[fname].T_inj
for i, n in enumerate(amount):
catalog = catalog0.copy()
catalog = catalog[:int(n)] # only get first injection round
k = int(np.ceil(len(catalog)**0.5))
# print(len(catalog))
assert len(catalog) > k, "Number of nearest neighbours exceed catalog size"
r, densities = eq.plot_ED(catalog, k = k, plot = False) # get distance, density