def truncate(
self, y
): # trunca el trapecio con la recta que se le pasa como parametro que es paralela a x
if y >= self.points[1].y:
# significa que la recta esta por encima del trapecio
return trapezoidal(self.points)
mr1 = slope(
self.points[0],
self.points[1]) # pendiente de los dos primero puntos del trapecio
mr2 = slope(
self.points[2],
self.points[3]) # pendiente de los dos ultimos puntos del trapecio
nr1 = trace(
mr1, self.points[0]
) # traza de la recta formada por los 2 primeros del trapecio
nr2 = trace(
mr2, self.points[2]
) # traza de la recta formada por los 2 ultimos del trapecio
x1 = (y -
nr1) / mr1 # x del punto de interseccion con la primera recta
x2 = (y -
nr2) / mr2 # x del punto de interseccion con la segunda recta
return trapezoidal(
[self.points[0],
point(x1, y),
point(x2, y), self.points[3]])
def truncate(
self, y
): # trunca el triangulo con una recta parametro que es paralela a x
if y >= self.points[
1].y: # significa que la recta esta por encima del triangulo y no lo trunca
return triangle(self.points)
mr1 = slope(self.points[0], self.points[1]
) # pendiente de los dos primero puntos del triangulo
mr2 = slope(self.points[1], self.points[2]
) # pendiente de los dos segundos puntos del triangulo
nr1 = trace(
mr1, self.points[0]
) # traza de la recta fromada por los 2 primeros puntos del triangulo
nr2 = trace(
mr2, self.points[1]
) # traza de la recta fromada por los 2 segundos puntos del triangulo
x1 = (y -
nr1) / mr1 # x del punto de interseccion con la primera recta
x2 = (y -
nr2) / mr1 # x del punto de interseccion con la segunda recta
return trapezoidal(
[self.points[0],
point(x1, y),
point(x2, y), self.points[2]])
def evaluate(self, x):
if x <= self.points[0].x or x >= self.points[2].x:
return 0
if x <= self.points[1].x:
m = slope(self.points[0], self.points[1])
n = trace(m, self.points[1])
else:
m = slope(self.points[1], self.points[2])
n = trace(m, self.points[1])
return m * x - n
def truncate(self, y): # si la recta esta por encima de la funcion
m = slope(self.points[0], self.points[1])
if (m > 0 and y >= self.points[1].y) or (m < 0
and y >= self.points[0].y):
return regular(self.points)
m = slope(self.points[0], self.points[1])
n = trace(m, self.points[0])
x = (y - n) / m
if m > 0:
return regular([self.points[0], point(x, y)])
return regular([point(x, y), self.points[1]])
def evaluate(self, x):
m = slope(self.points[0], self.points[1])
if x <= self.points[0].x:
if m > 0:
return 0
return 1
if x >= self.points[1].x:
if m > 0:
return 1
return 0
m = slope(self.points[0], self.points[1])
n = trace(m, self.points[0])
return m * x + n
def merge_df_files(df1, gj2, match):
df2 = geopandas.read_file(open(gj2))
if len(df1) == 0:
file_name2 = gj2.split('/')[-2]
for i in range(df2.shape[0]):
if df2.iloc[i]['id'] not in match.keys():
match[df2.iloc[i]['id']] = [(file_name2, df2.iloc[i]['id'])]
return df2, match
file_name2 = gj2.split('/')[-2]
repeat = list()
table2to1 = dict()
if not if_overlap(df1, df2):
for j in range(df2.shape[0]):
if df2.iloc[j]['id'] + df1.iloc[-1]['id'] + 1 not in match.keys():
match[df2.iloc[j]['id'] + df1.iloc[-1]['id'] + 1] = [
(file_name2, df2.iloc[j]['id'])
]
df2['id'] += df1.iloc[-1]['id'] + 1
df1 = df1.append(df2)
df1 = df1.reset_index(drop=True)
else:
matched_id = list()
base_id = df1.iloc[-1]['id'] + 1
for i in range(df1.shape[0]):
for j in range(df2.shape[0]):
row1 = df1.iloc[i]
line1 = np.array(row1['geometry']).reshape(-1, )
row2 = df2.iloc[j]
line2 = np.array(row2['geometry']).reshape(-1, )
#print("leftright:", leftright)
if abs(
utils.slope(np.array(line1[:2]), np.array(line1[2:])) -
utils.slope(np.array(line2[:2]), np.array(line2[2:]))
) > 0.2:
continue
if utils.l2_distance_2d(np.array(line1[:2]), np.array(
line2[:2])) > 40:
continue
d_line = utils.l2_distance_lines(np.array(line1[:2]),
np.array(line1[2:]),
np.array(line2[:2]),
np.array(line2[2:]))
if d_line > 2.5:
continue
ymin_coor,ymax_coor, cover_rate, baseline = \
utils.line2line_project(np.array(line1[:2]), np.array(line1[2:]),
np.array(line2[:2]), np.array(line2[2:]))
if utils.l2_distance_2d(ymin_coor, ymax_coor) > 30.0:
continue
#if (cover_rate < 1 and cover_rate > 0.7 and d_line < 0.5) or (cover_rate < 0.7 and d_line < 2.5):
if (cover_rate < config.cover_rate * 4
and d_line < 0.5) or (cover_rate < config.cover_rate
and d_line < 2.5):
if row2['id'] in matched_id:
pre_1_id = table2to1[row2['id']]
repeat.append(row1['id'])
index = df1[df1['id'] == pre_1_id].index[0]
row_pre = df1.loc[index]
line_pre = np.array(row_pre['geometry']).reshape(-1, )
ymin_coor,ymax_coor, cover_rate, baseline = \
utils.line2line_project(np.array(line_pre[:2]), np.array(line_pre[2:]),
np.array(line1[:2]), np.array(line1[2:]))
merge_line = LineString([(ymin_coor[0], ymin_coor[1]),
(ymax_coor[0], ymax_coor[1])])
df1.loc[index, 'min_h'] = min(row_pre['min_h'],
row1['min_h'])
df1.loc[index, 'max_h'] = min(row_pre['max_h'],
row1['max_h'])
df1.loc[index, 'ref_h'] = (row1['ref_h'] +
row_pre['ref_h']) / 2
df1.loc[index, 'depth'] = (row1['depth'] +
row_pre['depth']) / 2
df1.loc[index, 'dis'] = utils.l2_distance_2d(
ymin_coor[:2], ymax_coor[:2])
df1.loc[index, 'geometry'] = merge_line
filter_pair = list()
for pair in match[row1['id']]:
if pair not in match[row_pre['id']]:
filter_pair.append(pair)
if baseline == 1:
match[row_pre['id']] = match[
row_pre['id']] + filter_pair
else:
match[row_pre['id']] = filter_pair + match[
row_pre['id']]
else:
table2to1[row2['id']] = row1['id']
matched_id.append(row2['id'])
merge_line = LineString([(ymin_coor[0], ymin_coor[1]),
(ymax_coor[0], ymax_coor[1])])
index = df1[df1['id'] == row1['id']].index[0]
df1.loc[index, 'min_h'] = min(row1['min_h'],
row2['min_h'])
df1.loc[index, 'max_h'] = min(row1['max_h'],
row2['max_h'])
df1.loc[index,
'ref_h'] = (row1['ref_h'] + row2['ref_h']) / 2
df1.loc[index,
'depth'] = (row1['depth'] + row2['depth']) / 2
df1.loc[index, 'dis'] = utils.l2_distance_2d(
ymin_coor[:2], ymax_coor[:2])
df1.loc[index, 'geometry'] = merge_line
if baseline == 1:
match[row1['id']].append((file_name2, row2['id']))
else:
match[row1['id']].insert(0,
(file_name2, row2['id']))
for i in set(repeat):
df1.drop(index=df1[df1['id'] == i].index[0], inplace=True)
match.pop(i)
count = 1
base = df1.iloc[-1]['id']
for i in range(df2.shape[0]):
if i in matched_id:
continue
row2 = df2.iloc[i]
tmp_row2_id = row2['id']
row2['id'] = base + count
if row2['id'] not in match.keys():
match[row2['id']] = [(file_name2, tmp_row2_id)]
df1 = df1.append(row2)
count += 1
df1 = df1.reset_index(drop=True)
return df1, match
INPUT_FILE = '../output/02_19_16/UCBERKELEYAV45_02_19_16_regular_tp.csv'
df = pd.read_csv(INPUT_FILE)
df = df[['RID','EXAMDATE','WHOLECEREBELLUM','FRONTAL','CINGULATE','PARIETAL','TEMPORAL','COMPOSITE','SUMMARYSUVR_WHOLECEREBNORM','BRAIN_STEM']]
df.loc[:,'EXAMDATE'] = df.loc[:,'EXAMDATE'].apply(parseDate)
slopes = []
for rid, rows in df.groupby('RID'):
if len(rows.index) == 1:
continue
rows = rows.sort_values(by='EXAMDATE')
times = list(rows['EXAMDATE'])
years = [((t-times[0]).days)/365.0 for t in times]
brainstem = rows['BRAIN_STEM']
suvr_slope = slope(zip(years,list(rows['SUMMARYSUVR_WHOLECEREBNORM'])))
bl_suvr = rows.iloc[0]['SUMMARYSUVR_WHOLECEREBNORM']
slope_data = {'WHOLECEREBELLUM':slope(zip(years,list(rows['WHOLECEREBELLUM'].divide(brainstem)))),
'FRONTAL':slope(zip(years,list(rows['FRONTAL'].divide(brainstem)))),
'CINGULATE':slope(zip(years,list(rows['CINGULATE'].divide(brainstem)))),
'PARIETAL':slope(zip(years,list(rows['PARIETAL'].divide(brainstem)))),
'TEMPORAL':slope(zip(years,list(rows['TEMPORAL'].divide(brainstem)))),
'COMPOSITE':slope(zip(years,list(rows['COMPOSITE'].divide(brainstem)))),
'SUMMARYSUVR_WHOLECEREBNORM':slope(zip(years,list(rows['SUMMARYSUVR_WHOLECEREBNORM']))),
'INCREASER': 1 if suvr_slope > 0 else 0,
'BL_SUVR': bl_suvr,
'BL_POSITIVE': 1 if bl_suvr > 1.11 else 0,
'RID': rid}
slopes.append(slope_data)
def get_max_point(self):
m = slope(self.points[0], self.points[1])
if m > 0:
return self.points[1]
return self.points[0]