if not indx.size: continue
nearestIndex.extend([modNearestIndex[i]] * indx.size) # calculate all error data
'''
for i in data.index:
if data['layer'][i][0] - data['layer'][i][-1] != 0:
d_each = (data['depth'][i][-1] - data['depth'][i][0]) * 1.0 / (
data['layer'][i][0] - data['layer'][i][-1])
if 36 * d_each - data['depth'][i][-1] < 10:
diff = np.array(data['obstemp'][i][-1]) - np.array(
data['modtemp'][i][-1])
indx = np.where(abs(diff) > criteria)[0]
if not indx.size: continue
nearestIndex.extend([modNearestIndex[i]] *
indx.size) #calculate error data in bottom
for i in nearestIndex:
m = whichArea(i[0], r1)
n = whichArea(i[1], r2)
errorNum[m][n] += 1
m1, m2 = 34.05, 39.84 # m1, m2 are the location to put Text.
n1, n2 = -75.83, -67.72 # n1, n2 are the location to put Text.
for s in range(8):
# a = np.arange(-75.83, -67.72, 0.631)
# b = np.arange(34.05, 39.84, 0.47)
a = np.arange(n1, n2, 0.631)
b = np.arange(m1, m2, 0.47)
for i, j, k in zip(a, b, errorNum[s]):
if k > 0:
print(i, j, k)
plt.text(i,
j,
str(round(k, 1)),
for i in range(len(data)):
data['datet'][i]=datetime.strptime(data['datet'][i],'%Y-%m-%d %H:%M:%S')
lat_i=np.arange(int(min(lat)),int(max(lat)),ten_minute)
lon_i=np.arange(int(min(lon)),int(max(lon)),ten_minute)
number=[]
for q in range(10):
number.append([])
for i in range(len(lon_i)):
j=[0]*len(lat_i)
number[q].append(j)
lat_n=[]
lon_m=[]
for i in range(len(lat)):
for j in range(10): #10 is 10 years,from 2006 to 2015
if data['datet'][i].year==2006+j:
n=whichArea(lat[i],lat_i)
m=whichArea(lon[i],lon_i)
number[j][m][n]+=1 # calculate number in 10 minute square
lonsize=[min(lon),max(lon)]
latsize=[min(lat),max(lat)]
for q in range(len(number)):
fig=plt.figure(figsize=(12,10))
ax=fig.add_subplot(111)
draw_basemap(fig,ax,lonsize,latsize)
for i in range(len(number[q])):
for j in range(len(number[q][i])):
if 0<number[q][i][j]<50:
lat_10m=int(min(lat))+ten_minute*j
lon_10m=int(min(lon))+ten_minute*i
ax.add_patch(patches.Rectangle((lon_10m, lat_10m),ten_minute,ten_minute,color='blue'))
from turtleModule import draw_basemap, whichArea
########################################################################
one_minute = 1.0 / 60 #1.0/60 is 1 minute
data = pd.read_csv('binned_td_hoey.csv')
lat, lon = dm2dd(data['LATITUDE'],
data['LONGITUDE']) #convert ddmm.m to dd.ddd
lat_i = np.arange(min(lat), max(lat), one_minute)
lon_i = np.arange(min(lon), max(lon), one_minute)
number = []
for i in range(len(lon_i)):
j = [0] * len(lat_i)
number.append(j)
lat_n = []
lon_m = []
for i in range(len(lat)):
n = whichArea(lat[i], lat_i)
m = whichArea(lon[i], lon_i)
number[m][n] += 1 # calculate number in 1 minute square
lonsize = [min(lon), max(lon)]
latsize = [min(lat), max(lat)]
lon_is = np.linspace(lonsize[0], lonsize[1], 1000)
lat_is = np.linspace(latsize[0], latsize[1], 1000)
LON, LAT, NUMBER = [], [], []
for j in range(len(lon_i)):
for i in range(len(lat_i)):
LON.append(lon_i[j])
LAT.append(lat_i[i])
NUMBER.append(number[j][i]) #use for griddata
number_i = griddata(np.array(LON),
np.array(LAT),
np.array(NUMBER),
'''
for a in data.index:
diff = np.array(data['obstemp'][a]) - np.array(data['modtemp'][a])
indx = np.where(abs(diff)>criteria)[0]
if not indx.size: continue
nearestIndex.extend([modNearestIndex[a]] * indx.size) # calculate all error data
'''
for i in data.index:
if abs(obsDepth[i][-1]-depthBottom[i])<10: #diff<10m,wo can use the data
diff = np.array(data['obstemp'][i][-1]) - np.array(data['modtemp'][i][-1])
indx = np.where(abs(diff)>criteria)[0]
if not indx.size: continue
nearestIndex.extend([modNearestIndex[i]] * indx.size) #calculate error data in bottom
for b in nearestIndex:
m = whichArea(b[0], r1)
n = whichArea(b[1], r2)
errorNum[m][n] += 1 #calculate error data in bottom
m1, m2 = 34.05, 39.84 # m1, m2 are the location to put Text.
n1, n2 = -75.83, -67.72 # n1, n2 are the location to put Text.
for s in range(8):
a = np.arange(n1, n2, 0.631)
b = np.arange(m1, m2, 0.47)
for i, j, q in zip(a, b, errorNum[s]):
plt.text(i, j, str(q), color='r',multialignment='center')
m1 = m1 + 0.408
m2 = m2 + 0.408
n1 = n1 - 0.45
n2 = n2 - 0.45
CS1=plt.contour(lon_i, lat_i,depth_i,1,colors = 'r')
ax.annotate('100m depth',xy=(-72.0963,38.9144),xytext=(-71.5662,38.8756),arrowprops=dict(facecolor='black'))