def test_calc_area(self):
self.assertEqual(3, calc_area(1))
self.assertEqual(196350, calc_area(250))
self.assertEqual(0, calc_area(0))
self.assertEqual(31416, calc_area(100))
self.assertEqual(7, calc_area(1.5))
self.assertEqual(314159265, calc_area(10000))
self.assertTrue(isinstance(calc_area(1.5), int))
def budget_anc(netCDF, word):
var=word
ncbase=netCDF
data = ncbase.variables[var] # kg.m-2.-1
lat = np.array(ncbase.variables['latitude'],dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'],dtype=np.float64)[:]
area=calc_area.calc_area(lon,lat) # m2.gridcell-1
budget=int(round(np.sum(np.mean(data*area, axis=0))*3600*24*360*1E-9)) # Tg.yr-1
unit = r'$\mathbf{\ Tg\ yr^{-1}}$'
return budget, unit
def budget_anc(netCDF, word):
var = word
ncbase = netCDF
data = ncbase.variables[var] # kg.m-2.-1
lat = np.array(ncbase.variables['latitude'], dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'], dtype=np.float64)[:]
area = calc_area.calc_area(lon, lat) # m2.gridcell-1
budget = int(
round(np.sum(np.mean(data * area, axis=0)) * 3600 * 24 * 360 *
1E-9)) # Tg.yr-1
unit = r'$\mathbf{\ Tg\ yr^{-1}}$'
return budget, unit
def budget(netCDF, word):
var=word
ncbase=netCDF
data = ncbase.variables[var]
if var=='ch4_oh_rxn_flux':
budget = int(round(np.sum(np.mean(data, axis=0)*16)*3600*24*360*1E-12))
elif var=='ch4_ems' :
lat = np.array(ncbase.variables['latitude'],dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'],dtype=np.float64)[:]
area=calc_area.calc_area(lon,lat) # m2.gridcell-1
budget=int(round(np.sum(np.mean(data*area, axis=0))*3600*24*360*1E-9)) # Tg.yr-1
unit = r'$\mathrm{\ Tg(CH_{4})\ yr^{-1}}$'
return budget, unit
def yearly_budget(netCDF, word):
var=word
ncbase=netCDF
data = ncbase.variables[var]
budget=[]
for y in range(0,np.shape(data)[0]/12):
if var=='ch4_oh_rxn_flux':
budget.append(int(round(np.sum(np.mean(data[y*12:(y+1)*12], axis=0)*16)*3600*24*360*1E-12)))
elif var=='ch4_ems':
lat = np.array(ncbase.variables['latitude'],dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'],dtype=np.float64)[:]
area=calc_area.calc_area(lon,lat) # m2.gridcell-1
budget=int(round(np.sum(np.mean(data*area[y*12:(y+1)*12], axis=0))*3600*24*360*1E-9)) # Tg.yr-1
unit = r'$\mathrm{\ Tg(CH_{4})\ yr^{-1}}$'
return budget, unit
def seasonal_budget(netCDF, word):
var=word
ncbase=netCDF
season=seasonal_data.season(ncbase, var)
budget=[]
for i in range(0,4):
if var=='ch4_oh_rxn_flux':
budget.append(int(round(np.sum(season[i]*16)*3600*24*360*1E-12)))
elif var=='ch4_ems':
lat = np.array(ncbase.variables['latitude'],dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'],dtype=np.float64)[:]
area=calc_area.calc_area(lon,lat) # m2.gridcell-1
budget.append(int(round(np.sum(season[i]*area)*3600*24*360*1E-9))) # Tg.yr-1
unit = r'$\mathrm{\ Tg(CH_{4})\ yr^{-1}}$'
return budget, unit
def seasonal_budget(netCDF, word):
var = word
ncbase = netCDF
season = seasonal_data.season(ncbase, var)
budget = []
for i in range(0, 4):
if var == 'ch4_oh_rxn_flux':
budget.append(
int(round(np.sum(season[i] * 16) * 3600 * 24 * 360 * 1E-12)))
elif var == 'ch4_ems':
lat = np.array(ncbase.variables['latitude'], dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'], dtype=np.float64)[:]
area = calc_area.calc_area(lon, lat) # m2.gridcell-1
budget.append(
int(round(np.sum(season[i] * area) * 3600 * 24 * 360 *
1E-9))) # Tg.yr-1
unit = r'$\mathrm{\ Tg(CH_{4})\ yr^{-1}}$'
return budget, unit
def budget(netCDF, word):
var = word
ncbase = netCDF
data = ncbase.variables[var]
if var == 'ch4_oh_rxn_flux':
budget = int(
round(
np.sum(np.mean(data, axis=0) * 16) * 3600 * 24 * 360 * 1E-12))
elif var == 'ch4_ems':
lat = np.array(ncbase.variables['latitude'], dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'], dtype=np.float64)[:]
area = calc_area.calc_area(lon, lat) # m2.gridcell-1
budget = int(
round(
np.sum(np.mean(data * area, axis=0)) * 3600 * 24 * 360 *
1E-9)) # Tg.yr-1
unit = r'$\mathrm{\ Tg(CH_{4})\ yr^{-1}}$'
return budget, unit
def yearly_budget(netCDF, word):
var = word
ncbase = netCDF
data = ncbase.variables[var]
budget = []
for y in range(0, np.shape(data)[0] / 12):
if var == 'ch4_oh_rxn_flux':
budget.append(
int(
round(
np.sum(
np.mean(data[y * 12:(y + 1) * 12], axis=0) * 16) *
3600 * 24 * 360 * 1E-12)))
elif var == 'ch4_ems':
lat = np.array(ncbase.variables['latitude'], dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'], dtype=np.float64)[:]
area = calc_area.calc_area(lon, lat) # m2.gridcell-1
budget = int(
round(
np.sum(np.mean(data * area[y * 12:(y + 1) * 12], axis=0)) *
3600 * 24 * 360 * 1E-9)) # Tg.yr-1
unit = r'$\mathrm{\ Tg(CH_{4})\ yr^{-1}}$'
return budget, unit
writer = csv.writer(f, lineterminator='\n')
writer.writerow(title)
for i, file in enumerate(files, 721):
if int(i) in delete:
pass
else:
img = cv2.imread(file)
# 画像のトリミング
trimmed_img = img[600:1600, 1000:2000]
cv2.imwrite(path + '/trimmed/' + str(i) + '_trimmed.png',
trimmed_img)
# 明度の調整
reg_img = reg_brightness(trimmed_img)
cv2.imwrite(path + '/reg/' + str(i) + '_reg.png', reg_img)
# 緑地面積の計算
area = calc_area(reg_img)
green_area = area[0]
# pillowで緑地部分のみの切り抜き画像を作成
cv2.imwrite(path + '/bw/' + str(i) + '_bw.png', area[1])
src = Image.open(path + '/reg/' + str(i) + '_reg.png')
mask = Image.open(path + '/bw/' + str(i) + '_bw.png')
masked_src = src.copy()
masked_src.putalpha(mask)
masked_src.save(path + '/masked/' + str(i) + '_masked.png')
# データ水増し
masked_src.save(path + '/dataset/' + str(i) + '_dataset_0.png')
masked_src.transpose(
Image.ROTATE_180).save(path + '/dataset/' + str(i) +
'_dataset_1.png')