Update initialize_params

pull/163/head
Shirley 2021-08-04 01:12:41 -05:00 committed by JulioV
parent 834bd3b93d
commit 4ddb2845a6
1 changed files with 108 additions and 102 deletions

View File

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