for j in I:
print date_strs[j]
'''
'''
print 'num_items: ' + str(I.size)
print 'start: ' + date_strs[I[0]]
print 'end: ' + date_strs[I[-1]]
'''
times_series_vals[times_series_vals < 0] = np.nan
for state in unique_states:
#for state in unique_series_ids:
is_in_state = np.asarray([s.find(state) == 0 for s in unique_series_ids])
is_in_state = is_in_state.nonzero()[0]
if is_in_state.size > 8:
is_in_state = is_in_state[:8]
#y_val = times_series_vals[is_in_state, :800, 1].T
y_val = times_series_vals[:,is_in_state[:], 2]
x_val = range(y_val.shape[0])
#print unique_series_ids[to_use]
for i, s in enumerate(unique_series_ids[is_in_state]):
print str(i) + ': ' + s
array_functions.plot_2d_sub_multiple_y(np.asarray(x_val), y_val, title=None, sizes=10)
data = (times_series_vals,unique_series_ids)
helper_functions.save_object('processed_data.pkl', data)
pass
y_val.T,
alpha=1,
title=None,
sizes=None,
share_axis=True)
elif plot_multiple_stations:
for i in range(0, 400, 10):
is_in_state = np.arange(i, i + 10)
#y_val = times_series_vals[is_in_state, :800, 1].T
y_val = times_series_vals[:, is_in_state[:], y_to_plot]
x_val = range(y_val.shape[0])
#print unique_series_ids[to_use]
for i, s in enumerate(unique_series_ids[is_in_state]):
print str(i) + ': ' + s
array_functions.plot_2d_sub_multiple_y(np.asarray(x_val),
y_val,
title=None,
sizes=10)
else:
for i in range(times_series_vals.shape[1]):
y_val = times_series_vals[:, i, :]
x_val = np.arange(y_val.shape[0])
if not np.isfinite(y_val).sum(0).all():
print 'skipping - missing labels'
continue
print unique_series_ids[i]
array_functions.plot_2d_sub_multiple_y(np.asarray(x_val),
y_val,
title=unique_series_ids[i],
sizes=10)
data = (unique_locs, times_series_vals[:, :, y_to_use], unique_series_ids)
else:
y_val = times_series_vals[[i,60+i],:,y_to_plot]
y_val1 = times_series_vals[range(i,i+30),:,y_to_plot].mean(0)
y_val2 = times_series_vals[range(i+120, i + 150), :, y_to_plot].mean(0)
y_val = np.stack((y_val1, y_val2), 1).T
array_functions.plot_heatmap(unique_locs,y_val.T,alpha=1,title=None,sizes=None,share_axis=True)
elif plot_multiple_stations:
for i in range(0,400, 10):
is_in_state = np.arange(i,i+10)
#y_val = times_series_vals[is_in_state, :800, 1].T
y_val = times_series_vals[:,is_in_state[:], y_to_plot]
x_val = range(y_val.shape[0])
#print unique_series_ids[to_use]
for i, s in enumerate(unique_series_ids[is_in_state]):
print str(i) + ': ' + s
array_functions.plot_2d_sub_multiple_y(np.asarray(x_val), y_val, title=None, sizes=10)
else:
for i in range(times_series_vals.shape[1]):
y_val = times_series_vals[:, i, :]
x_val = np.arange(y_val.shape[0])
if not np.isfinite(y_val).sum(0).all():
print 'skipping - missing labels'
continue
print unique_series_ids[i]
array_functions.plot_2d_sub_multiple_y(np.asarray(x_val), y_val, title=unique_series_ids[i], sizes=10)
data = (unique_locs, times_series_vals[:,:,y_to_use],unique_series_ids)
suffix = y_names[y_to_use]
if use_monthly:
suffix += '-month'
'''
'''
print 'num_items: ' + str(I.size)
print 'start: ' + date_strs[I[0]]
print 'end: ' + date_strs[I[-1]]
'''
times_series_vals[times_series_vals < 0] = np.nan
for state in unique_states:
#for state in unique_series_ids:
is_in_state = np.asarray([s.find(state) == 0 for s in unique_series_ids])
is_in_state = is_in_state.nonzero()[0]
if is_in_state.size > 8:
is_in_state = is_in_state[:8]
#y_val = times_series_vals[is_in_state, :800, 1].T
y_val = times_series_vals[:, is_in_state[:], 2]
x_val = range(y_val.shape[0])
#print unique_series_ids[to_use]
for i, s in enumerate(unique_series_ids[is_in_state]):
print str(i) + ': ' + s
array_functions.plot_2d_sub_multiple_y(np.asarray(x_val),
y_val,
title=None,
sizes=10)
data = (times_series_vals, unique_series_ids)
helper_functions.save_object('processed_data.pkl', data)
pass
