Update initialize_params
Shirley
JulioV
parent
834bd3b93d
commit
4ddb2845a6
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@ -22,40 +22,42 @@ def run_barnett_features_for_rapids(input_dataframe, accuracy_limit=51.0, timezo
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lonlat, r, w = preprocessing(data_frame, interval=interval, acc_threshold=accuracy_limit, r=r, w=w, tint_m=tint_m, tint_k=tint_k)
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lonlat, r, w = preprocessing(data_frame, interval=interval, acc_threshold=accuracy_limit, r=r, w=w, tint_m=tint_m, tint_k=tint_k)
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mobmatmiss = convert_to_flights_pauses(lonlat, r, w)
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mobmatmiss = convert_to_flights_pauses(lonlat, r, w)
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mobmat = guess_pause(mobmatmiss, min_pause_dur, min_pause_dist)
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mobmat = guess_pause(mobmatmiss, min_pause_dur, min_pause_dist)
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obj = initialize_params(mobmat)
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obj = initialize_params(mobmat)
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lsmf = []
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lssigloc = []
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for i in range(n_reps):
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print("Sim #: ", i+1)
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out3 = simulate_mobility_gaps(mobmat, obj, wtype, spread_pars)
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out3 = np.array(out3)
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IDundef=np.where(out3[:,0]==3)[0]
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if len(IDundef) > 0:
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out3 = np.delete(out3, IDundef,axis=0)
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obj3 = initialize_params(out3)
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output_features, slout, row_names = get_mobility_features(out3, obj3, mobmatmiss, timezone, center_rad, interval)
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lsmf.append(output_features)
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lssigloc.append(slout)
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#get average
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np_lsmf = np.array((lsmf))
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avg_output_features = [] #convert to pandas dataframe and then return this
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for j in range(len(row_names)):
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temp_avg = []
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for i in range(n_reps):
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temp_avg.append(np_lsmf[i][j])
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avg_output_features.append(np.mean(temp_avg, axis=0))
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data_for_pandas = []
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data_for_pandas = []
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for idx, row in enumerate(avg_output_features):
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if obj:
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date = row_names[idx]
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lsmf = []
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date_str = str(date[0]) + "-" + str(date[1]) + "-" + str(date[2])
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lssigloc = []
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features = [date_str]
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for i in range(n_reps):
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for feature in row:
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print("Sim #: ", i+1)
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features.append(feature)
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out3 = simulate_mobility_gaps(mobmat, obj, wtype, spread_pars)
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data_for_pandas.append(features)
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out3 = np.array(out3)
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IDundef=np.where(out3[:,0]==3)[0]
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if len(IDundef) > 0:
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out3 = np.delete(out3, IDundef,axis=0)
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obj3 = initialize_params(out3)
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output_features, slout, row_names = get_mobility_features(out3, obj3, mobmatmiss, timezone, center_rad, interval)
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lsmf.append(output_features)
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lssigloc.append(slout)
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#get average
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np_lsmf = np.array((lsmf))
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avg_output_features = [] #convert to pandas dataframe and then return this
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for j in range(len(row_names)):
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temp_avg = []
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for i in range(n_reps):
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temp_avg.append(np_lsmf[i][j])
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avg_output_features.append(np.mean(temp_avg, axis=0))
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for idx, row in enumerate(avg_output_features):
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date = row_names[idx]
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date_str = str(date[0]) + "-" + str(date[1]) + "-" + str(date[2])
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features = [date_str]
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for feature in row:
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features.append(feature)
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data_for_pandas.append(features)
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column_names = ["local_date", "hometime", "disttravelled", "rog", "maxdiam", "maxhomedist", "siglocsvisited", "avgflightlen", "stdflightlen", "avgflightdur", "stdflightdur", "probpause", "siglocentropy", "minsmissing", "circdnrtn", "wkenddayrtn"]
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column_names = ["local_date", "hometime", "disttravelled", "rog", "maxdiam", "maxhomedist", "siglocsvisited", "avgflightlen", "stdflightlen", "avgflightdur", "stdflightdur", "probpause", "siglocentropy", "minsmissing", "circdnrtn", "wkenddayrtn"]
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df = pd.DataFrame(data_for_pandas, columns = column_names)
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df = pd.DataFrame(data_for_pandas, columns = column_names)
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@ -533,7 +535,7 @@ def collapse_pause_in_matrix(matrix, flatmat):
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else:
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else:
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output_mat = []
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output_mat = []
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if flatmat[0][0] > 1:
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if flatmat[0][0] > 1:
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output_mat = matrix[0:flatmat[0][0]-1,]
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output_mat = list(matrix[0:flatmat[0][0]-1,])
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for i in range(len(flatmat)):
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for i in range(len(flatmat)):
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start_idx = flatmat[i][0]
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start_idx = flatmat[i][0]
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@ -733,82 +735,86 @@ def initialize_params(matrix):
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two = np.where(np_matrix[:,0] == 2)
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two = np.where(np_matrix[:,0] == 2)
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three = np.where(np_matrix[:,0] == 3)
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three = np.where(np_matrix[:,0] == 3)
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four = np.where(np_matrix[:,0] == 4)
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four = np.where(np_matrix[:,0] == 4)
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ID1p1 = one[0]+1
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condition1 = one[0][len(one[0])-1]
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if len(one[0]) > 0 and condition1 == len(np_matrix)-1:
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ID1p1 = ID1p1[:len(ID1p1)-1]
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all_timestamp = np.apply_along_axis(np.mean, 1, np_matrix[:,[3,6]])
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all_x = np_matrix[:, 1]
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all_y = np_matrix[:, 2]
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#which code 1 is followed by 1
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ind11 = ID1p1[np.where(np_matrix[ID1p1,0] == 1)]
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#which flight(1) followed by pause (2)
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ind12 = ID1p1[np.where(np_matrix[ID1p1,0] == 2)]
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l1 = len(ind11)
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l2 = len(ind12)
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if (l1 + l2) > 0:
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phatall = l2/(l1+l2)
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if (l1+l2) == 0:
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if len(one) > 1:
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phatall = len(two)/(len(one) + len(two))
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ID1p1 = one[0]+1
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condition1 = one[0][len(one[0])-1]
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if len(one[0]) > 0 and condition1 == len(np_matrix)-1:
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ID1p1 = ID1p1[:len(ID1p1)-1]
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all_timestamp = np.apply_along_axis(np.mean, 1, np_matrix[:,[3,6]])
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all_x = np_matrix[:, 1]
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all_y = np_matrix[:, 2]
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#which code 1 is followed by 1
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ind11 = ID1p1[np.where(np_matrix[ID1p1,0] == 1)]
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#which flight(1) followed by pause (2)
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ind12 = ID1p1[np.where(np_matrix[ID1p1,0] == 2)]
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l1 = len(ind11)
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l2 = len(ind12)
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if (l1 + l2) > 0:
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phatall = l2/(l1+l2)
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if (l1+l2) == 0:
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phatall = len(two)/(len(one) + len(two))
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# ------ flight distances & times & pauses -------
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# ------ flight distances & times & pauses -------
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flight_distances = []
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flight_distances = []
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flight_times = []
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flight_times = []
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pause_times = []
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pause_times = []
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for row in np_matrix:
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if row[0] == 1:
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distance = np.sqrt((row[1]-row[4])**2 + (row[2] - row[5])**2)
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flight_distances.append(distance)
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times = row[6] - row[3]
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flight_times.append(times)
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if row[0] == 2:
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pause = row[6] - row[3]
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pause_times.append(pause)
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fxs = np_matrix[one][:,1]
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fys = np_matrix[one][:,2]
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fa = np.zeros(len(one[0]))
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for row in np_matrix:
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if row[0] == 1:
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yvals = np_matrix[one][:,5] - np_matrix[one][:,2]
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distance = np.sqrt((row[1]-row[4])**2 + (row[2] - row[5])**2)
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xvals = np_matrix[one][:,4] - np_matrix[one][:,1]
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flight_distances.append(distance)
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times = row[6] - row[3]
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flight_times.append(times)
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if row[0] == 2:
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pause = row[6] - row[3]
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pause_times.append(pause)
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IDyg0 = np.where(yvals[:] >=0)[0]
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fxs = np_matrix[one][:,1]
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IDxg0 = np.where(xvals[:] >=0)[0]
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fys = np_matrix[one][:,2]
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IDyl0 = np.where(yvals[:] < 0)[0]
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IDxl0 = np.where(xvals[:] < 0)[0]
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IDgg = list(set(IDyg0) & set(IDxg0))
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fa = np.zeros(len(one[0]))
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IDlg = list(set(IDyg0) & set(IDxl0))
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IDgl = list(set(IDyl0) & set(IDxg0))
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IDll = list(set(IDyl0) & set(IDxl0))
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IDgg.sort()
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IDlg.sort()
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IDgl.sort()
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IDll.sort()
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fa[IDgg] = np.arctan(list(yvals[IDgg]/xvals[IDgg]))
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fa[IDgl] = np.arctan(list(yvals[IDgl]/xvals[IDgl]))+2*math.pi
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fa[IDlg] = np.arctan(list(yvals[IDlg]/xvals[IDlg])) +math.pi
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fa[IDll] = np.arctan(list(yvals[IDll]/xvals[IDll])) +math.pi
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#flight timestamps
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yvals = np_matrix[one][:,5] - np_matrix[one][:,2]
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flight_timestamps = np_matrix[one][:,3]
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xvals = np_matrix[one][:,4] - np_matrix[one][:,1]
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pxs = np_matrix[two][:,1] #x
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IDyg0 = np.where(yvals[:] >=0)[0]
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pys = np_matrix[two][:,2] #y
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IDxg0 = np.where(xvals[:] >=0)[0]
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pts = np_matrix[two][:,3] #pause timestamps
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IDyl0 = np.where(yvals[:] < 0)[0]
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IDxl0 = np.where(xvals[:] < 0)[0]
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the_dict = {'ID1': one, "ID2": two, "ID3": three, "ID4":four, "ID1p1": ID1p1, "allts": all_timestamp, "ind11": ind11, "ind12": ind12, "phatall": phatall, "fd": flight_distances, "ft": flight_times, "fa": fa, "fts": flight_timestamps, "pt": pause_times, "pts": pts, "fxs": fxs, "fys": fys, "pxs": pxs, "pys": pys, "allxs": all_x, "allys": all_y}
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IDgg = list(set(IDyg0) & set(IDxg0))
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return the_dict
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IDlg = list(set(IDyg0) & set(IDxl0))
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IDgl = list(set(IDyl0) & set(IDxg0))
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IDll = list(set(IDyl0) & set(IDxl0))
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IDgg.sort()
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IDlg.sort()
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IDgl.sort()
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IDll.sort()
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fa[IDgg] = np.arctan(list(yvals[IDgg]/xvals[IDgg]))
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fa[IDgl] = np.arctan(list(yvals[IDgl]/xvals[IDgl]))+2*math.pi
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fa[IDlg] = np.arctan(list(yvals[IDlg]/xvals[IDlg])) +math.pi
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fa[IDll] = np.arctan(list(yvals[IDll]/xvals[IDll])) +math.pi
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#flight timestamps
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flight_timestamps = np_matrix[one][:,3]
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pxs = np_matrix[two][:,1] #x
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pys = np_matrix[two][:,2] #y
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pts = np_matrix[two][:,3] #pause timestamps
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the_dict = {'ID1': one, "ID2": two, "ID3": three, "ID4":four, "ID1p1": ID1p1, "allts": all_timestamp, "ind11": ind11, "ind12": ind12, "phatall": phatall, "fd": flight_distances, "ft": flight_times, "fa": fa, "fts": flight_timestamps, "pt": pause_times, "pts": pts, "fxs": fxs, "fys": fys, "pxs": pxs, "pys": pys, "allxs": all_x, "allys": all_y}
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return the_dict
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else:
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print("No flight")
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return None
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#simulate_mobility_gaps
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#simulate_mobility_gaps
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#impute the missing gaps hot-tech computation
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#impute the missing gaps hot-tech computation
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@ -1524,4 +1530,4 @@ def get_mobility_features(mobmat, obj, mobmatmiss, timezone, center_rad, interva
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outmat[i][14] = dri_output[1] #separately
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outmat[i][14] = dri_output[1] #separately
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return outmat, slout, daystr_v #daystr_v = row names
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return outmat, slout, daystr_v #daystr_v = row names
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