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