'nearestIndex': modNearestIndex.values,
'depth': obsDepth,
'layer': modLayer
},
index=tf_index)
lonsize = [np.amin(lons), np.amax(lons)]
latsize = [np.amin(lats), np.amax(lats)]
errorNum = []
for i in range(
9): # just create a list with all zero to calculate error number
j = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
errorNum.append(j)
fig = plt.figure()
ax = fig.add_subplot(111)
draw_basemap(fig, ax, lonsize, latsize)
plt.plot([lonA, lonB, lonC, lonD, lonA], [latA, latB, latC, latD, latA], 'b-')
for i in range(
0, 75,
10): # Here use num smller than 81 because the last grid is too small
plt.plot([lons[i][0], lons[i][129]], [lats[i][0], lats[i][129]], 'b--')
for i in range(0, 129, 10):
plt.plot([lons[0][i], lons[81][i]], [lats[0][i], lats[81][i]],
'b--') # plot the box
r1 = range(0, 81, 10)
r2 = range(0, 129, 10)
nearestIndex = []
'''
for i in data.index:
diff = np.array(data['obstemp'][i]) - np.array(data['modtemp'][i])
indx = np.where(abs(diff)>criteria)[0]
161301,
161304,
172179,
172188,
175938,
175932,
175936,
175940
] #tu_102
lonsize = [-79, -69] #tu99
latsize = [35, 42] #tu199
obsdata = pd.read_csv(path1) # has both observed and modeled profiles
obstime = pd.Series(
(datetime.strptime(x, '%Y-%m-%d %H:%M:%S') for x in obsdata['datetime']))
obsLat = obsdata['lat']
obsLon = obsdata['lon']
obsturtle_id = obsdata['vessel_num']
ids = obsturtle_id.unique() # this is the interest turtle id
fig = plt.figure()
ax = fig.add_subplot(111)
plt.scatter(lon, lat, s=15, linewidths=None) #
draw_basemap(fig, ax, lonsize, latsize, interval_lon=2, interval_lat=2)
plt.title(
'ship_scatterplot') #('%s profiles(%s~%s)'% (e,obsTime[0],obsTime[-1]))
plt.legend(loc='lower right', ncol=2, fontsize='xx-small')
plt.savefig(path2 + 'ship_ScatterPlot.png', dpi=200)
if depthBottom[i]>200:
depthBottom[i]=200
'''
lonsize = [-79.5, -64]
latsize = [33, 46]
#lonsize = [np.amin(lonGoodCTD), np.amax(lonGoodCTD)]
#latsize = [np.amin(latGoodCTD), np.amax(latGoodCTD)]
fig = plt.figure()
ax = fig.add_subplot(111)
plt.scatter(lonGoodCTD,
latGoodCTD,
color='g',
s=1,
label='Turtle(good position)')
plt.scatter(lonship, latship, color='y', s=1, label='Ship')
draw_basemap(fig, ax, lonsize, latsize, interval_lon=4, interval_lat=4)
lon_is = np.linspace(lonsize[0], lonsize[1], 150)
lat_is = np.linspace(latsize[0], latsize[1], 150) #use for depth line
depth_i = griddata(np.array(contour_lon),
np.array(contour_lat),
np.array(contour_depth),
lon_is,
lat_is,
interp='linear')
cs = plt.contour(lon_is,
lat_is,
depth_i,
levels=[100],
colors='r',
linewidths=1.5,
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'))
p1=Rectangle((0, 0), 1, 1, fc="blue")
if 50<=number[q][i][j]<100:
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='yellow'))
p2=Rectangle((0, 0), 1, 1, fc="yellow")
if 100<=number[q][i][j]<150:
lat_10m=int(min(lat))+ten_minute*j
lon_10m=int(min(lon))+ten_minute*i
from mpl_toolkits.basemap import Basemap
from turtleModule import draw_basemap
from matplotlib.mlab import griddata
import glob
import time
import csv
lonsize = [-90, 0]
latsize = [25, 70]
color = ['g', 'darkviolet', 'orange', 'b', 'hotpink', 'r', 'peru', 'lime']
fig = plt.figure()
ax = fig.add_subplot(111)
csv_list = glob.glob('*_Summary.csv') #search csv files in current folder
print('%s csvfiles searched in total' % len(csv_list))
k = 0
for g in csv_list: #g is a filename which is processed
df = pd.read_csv(g)
latmin = df['lat_min'].min()
lonmin = df['lon_min'].min()
latmax = df['lat_max'].max()
lonmax = df['lon_max'].max()
plt.plot([lonmin, lonmax, lonmax, lonmin, lonmin],
[latmin, latmin, latmax, latmax, latmin],
color=color[k])
a = csv_list[k].find('_') #locate '_'
plt.text(lonmax, latmax, csv_list[k][0:a], size=13, color=color[k])
k += 1
draw_basemap(fig, ax, lonsize, latsize, interval_lon=20, interval_lat=10)
plt.title('database_boundary_comparison')
plt.savefig('visualize_db_boundary.png', dpi=200)
plt.show()
SEASON=[SUMMER,FALL,SPRING,WINTER]
lon_i = np.linspace(lonsize[0],lonsize[1],1000)
lat_i = np.linspace(latsize[0],latsize[1],1000) #use for mean error,absolute mean error and rms
lon_is = np.linspace(lonsize[0],lonsize[1],100)
lat_is = np.linspace(latsize[0],latsize[1],100) #use for depth line
depth_i = griddata(np.array(obsLons),np.array(obsLats),np.array(depthBottom),lon_is,lat_is,interp='linear') #use for plotting 100m depth line.
for i in [0,2]: #two seasons together plot.
modtemps=pd.Series(modtemp[SEASON[i]],index=SEASON[i])
obstemps=pd.Series(obstemp[SEASON[i]],index=SEASON[i])
print(str(len(modtemps)))
obsLon, obsLat = obsData['LON'][SEASON[i]], obsData['LAT'][SEASON[i]]
modtemp_i = griddata(np.array(obsLon),np.array(obsLat),np.array(modtemps),lon_i,lat_i,interp='linear')
obstemp_i = griddata(np.array(obsLon),np.array(obsLat),np.array(obstemps),lon_i,lat_i,interp='linear')
fig = plt.figure()
ax = fig.add_subplot(221) #plot model temperature
draw_basemap(fig, ax, lonsize, latsize)
CS = ax.contourf(lon_i, lat_i, modtemp_i, np.arange(modtemp_i.min(),modtemp_i.max(),0.1), cmap=plt.cm.rainbow,
vmax=modtemp_i.max(), vmin=modtemp_i.min())
CS1=ax.contour(lon_is, lat_is,depth_i,1,colors = 'r',linestyles=':',linewidths=2)
cbar=plt.colorbar(CS,ticks=np.arange(int(modtemp_i.min())-1,int(modtemp_i.max())+1,2))
cbar.ax.tick_params(labelsize=20)
cbar.ax.set_ylabel('Temperature($^\circ$C)', fontsize=20)
#plt.scatter(np.array(obsLon),np.array(obsLat), marker='o', c='b', s=5, zorder=10)
ax.set_title('Model_'+season[i])
ax.annotate('100m depth',xy=(-75.289,35.0395),xytext=(-75.0034,34.9842),arrowprops=dict(facecolor='black'))
ax1 = fig.add_subplot(222) #plot model temperature
draw_basemap(fig, ax1, lonsize, latsize)
CS = ax1.contourf(lon_i, lat_i, obstemp_i, np.arange(obstemp_i.min(),obstemp_i.max(),0.1), cmap=plt.cm.rainbow,
vmax=obstemp_i.max(), vmin=obstemp_i.min())
CS1=ax1.contour(lon_is, lat_is,depth_i,1,colors = 'r',linestyles=':',linewidths=2)
np.array(obsLat),
np.array(modtemps),
lon_i,
lat_i,
interp='linear')
obstemp_i = griddata(np.array(obsLon),
np.array(obsLat),
np.array(obstemps),
lon_i,
lat_i,
interp='linear')
m_mod = modtemp_i.max()
m_obs = obstemp_i.max()
fig = plt.figure()
ax = fig.add_subplot(221) #plot model temperature
draw_basemap(fig, ax, lonsize, latsize, fontsize=8)
CS = ax.contourf(lon_i,
lat_i,
modtemp_i,
np.arange(0, m_mod, 0.1),
cmap=plt.cm.rainbow,
vmax=m_mod,
vmin=0)
CS1 = ax.contour(lon_is,
lat_is,
depth_i,
1,
colors='r',
linestyles=':',
linewidths=2)
cbar = plt.colorbar(CS, ticks=np.arange(0, int(m_mod), 3))
gps = pd.read_csv('2014_04_16_rawgps.csv')
lonRawGPS, latRawGPS = gps['LON'], gps['LAT']
lonsize = [np.amin(lonRawGPS), np.amax(lonRawGPS)]
latsize = [np.amin(latRawGPS), np.amax(latRawGPS)]
plt.plot(lonRawGPS, latRawGPS, 'b.', label='RawGPS')
plt.title('RawGPS Positons', fontsize=FONTSIZE)
elif option == '5':
diag = pd.read_csv('2014_04_16_rawdiag.csv')
diagV = diag['V_MASK']
lonGoodDiag, latGoodDiag = diag['LON'][diagV != 20], diag['LAT'][
diagV != 20]
# lonsize = [-81, -62]
# latsize = [30, 42]
lonsize = [np.amin(lonGoodDiag), -25]
latsize = [np.amin(latGoodDiag), np.amax(latGoodDiag)]
plt.plot(lonGoodDiag, latGoodDiag, 'y.', label='GoodDiag')
plt.title('GoodDiag Positions Within the Mid-Atlantic Region',
fontsize=FONTSIZE)
elif option == '6':
diag = pd.read_csv('2014_04_16_rawdiag.csv')
lonRawDiag, latRawDiag = diag['LON'], diag['LAT']
lonsize = [np.amin(lonRawDiag), np.amax(lonRawDiag)]
latsize = [np.amin(latRawDiag), np.amax(latRawDiag)]
plt.plot(lonRawDiag, latRawDiag, 'ro', label='RawDiag')
plt.title('RawDiag Positions', fontsize=FONTSIZE)
elif option == '0':
sys.exit()
draw_basemap(fig, ax, lonsize, latsize, interval_lon=1, interval_lat=1)
plt.legend()
plt.show()
plt.title('GoodGPS Positions', fontsize=FONTSIZE)
elif option == '4':
gps = pd.read_csv('2014_04_16_rawgps.csv')
lonRawGPS, latRawGPS = gps['LON'], gps['LAT']
lonsize = [np.amin(lonRawGPS), np.amax(lonRawGPS)]
latsize = [np.amin(latRawGPS), np.amax(latRawGPS)]
plt.plot(lonRawGPS, latRawGPS, 'b.', label='RawGPS')
plt.title('RawGPS Positons', fontsize=FONTSIZE)
elif option == '5':
diag = pd.read_csv('2014_04_16_rawdiag.csv')
diagV = diag['V_MASK']
lonGoodDiag, latGoodDiag = diag['LON'][diagV!=20], diag['LAT'][diagV!=20]
# lonsize = [-81, -62]
# latsize = [30, 42]
lonsize = [np.amin(lonGoodDiag), -25]
latsize = [np.amin(latGoodDiag), np.amax(latGoodDiag)]
plt.plot(lonGoodDiag, latGoodDiag, 'y.', label='GoodDiag')
plt.title('GoodDiag Positions Within the Mid-Atlantic Region', fontsize=FONTSIZE)
elif option == '6':
diag = pd.read_csv('2014_04_16_rawdiag.csv')
lonRawDiag, latRawDiag = diag['LON'], diag['LAT']
lonsize = [np.amin(lonRawDiag), np.amax(lonRawDiag)]
latsize = [np.amin(latRawDiag), np.amax(latRawDiag)]
plt.plot(lonRawDiag, latRawDiag, 'ro', label='RawDiag')
plt.title('RawDiag Positions', fontsize=FONTSIZE)
elif option=='0':
sys.exit()
draw_basemap(fig, ax, lonsize, latsize, interval_lon=1, interval_lat=1)
plt.legend()
plt.show()
