-1 * topoin,
levels=batym_levs,
cmap=cmapdef,
vmin=baty_min,
vmax=baty_max,
extend='max')
bmap.drawcountries()
cb = plt.colorbar(fraction=0.027, pad=0.04)
# cb.set_ticks(range(0,200,20))
# plt.clim(0,200)
cb.ax.invert_yaxis()
#ACTUAL DATA
start = mp.dates.datetime.datetime(1000, 5, 5)
end = mp.dates.datetime.datetime(3030, 5, 5)
for f, col, lab in zip(filest_to_plot, colors, labels):
a = qa.PointData(f, 1, start, end, "argonc")
x, y = bmap(a.obs['ape']['lon'][:], a.obs['ape']['lat'][:])
# bmap.plot(x,y,color=col,linewidth=2,alpha=0.5)
if (hasattr(x, 'mask')):
x = x[~x.mask]
y = y[~y.mask]
bmap.plot(x, y, color=col, linewidth=2,
alpha=0.8) #x,y masks are there to crop non values.
bmap.plot(x[0],
y[0],
'*',
color=col,
markersize=12,
alpha=1.0,
zorder=10)
bmap.plot(x[-1],
bmap = Basemap(llcrnrlon=lon_min,llcrnrlat=lat_min,urcrnrlon=lon_max,urcrnrlat=lat_max, \
resolution = 'i',fix_aspect=False)
bmap.drawcoastlines()
bmap.fillcontinents()
bmap.drawparallels(np.arange(50., 69, 2.),
labels=[1, 0, 0, 0],
linewidth=0,
dashes=[5, 10])
bmap.drawmeridians(np.arange(12., 30, 2.),
labels=[0, 0, 0, 1],
linewidth=0,
dashes=[5, 10])
data_path = "C:\\Data\\Pape1\\"
a = qa.PointData(data_path + data_file_names[0], 1, start, end, "argonc")
lon_dat = a.obs['ape']['lon'][~a.obs['ape']['lon'].mask]
lat_dat = a.obs['ape']['lat'][~a.obs['ape']['lat'].mask]
date_axis = a.obs['ape']['date']
col = 'r'
x, y = bmap(np.array(lon_dat), np.array(lat_dat))
bmap.plot(x, y, '-', color=col, linewidth=2, alpha=0.4)
bmap.plot(x, y, '.', color=col, linewidth=2, alpha=0.8)
bmap.plot(x[-1], y[-1], 'x', color='k', markersize=8, alpha=1.0)
bmap.plot(x[0], y[0], 'o', color='k', markersize=8, alpha=1.0)
plt.savefig(save_path + '{}_route_new.png'.format(alue))
print("some statistics")
ah.file_names_converted = [data_path + data_file_names[0]]
ah.give_statistics()
@author: siirias
"""
import sys
sys.path.insert(0, 'D:\\svnfmi_merimallit\\qa\\nemo')
import matplotlib as mp
import matplotlib.pyplot as plt
import numpy as np
import ModelQATools as qa
import ModelPltTools
from scipy.io import netcdf
from mpl_toolkits.basemap import Basemap
data = qa.GriddedData('fmi_hirlam_forecastv4_sd_20151216_12_D4.nc',
'hbm',
varlist=['temp', 'salt'])
salt = data.get_var('salt')[0, 0, :, :].copy().T
"""
#Oma yritys lataamiseen
ncf=netcdf.netcdf_file('fmi_hirlam_forecastv4_sd_20151216_12_D4.nc','r')
salt=ncf.variables['salt'][0][0][:][:].copy()
salt=salt
mask=salt<-9000
salt_m=salt[:].copy()
salt_m[mask]=np.nan
lat=ncf.variables['lat'][:].copy()
lat=np.tile(lat,(salt_m.shape[0],1))
lon=ncf.variables['lon'][:].copy()
import sys
sys.path.insert(0,'D:\\svnfmi_merimallit\\qa\\nemo')
import matplotlib as mp
import matplotlib.pyplot as plt
import numpy as np
import ModelQATools as qa
import ModelPltTools
from scipy.io import netcdf
from mpl_toolkits.basemap import Basemap
runfile('plot_full_data.py')
start=mp.dates.datetime.datetime(1000,5,5)
end=mp.dates.datetime.datetime(3030,5,5)
a1=qa.PointData("6902014_prof.nc",1,start,end,"argonc");
a2=qa.PointData("6902019_prof.nc",1,start,end,"argonc");
a3=qa.PointData("6902020_prof.nc",1,start,end,"argonc");
#a1=qa.PointData("IM_6902014_20130814_20140821.nc",1,start,end,"argonc");
#a2=qa.PointData("IM_6902019_20140821_20150805.nc",1,start,end,"argonc");
#a3=qa.PointData("IM_6902020_20150805_20160331_active.nc",1,start,end,"argonc");
for dataset in range(3):
if(dataset==0):
a=a1;offset=0
if(dataset==1):
a=a2;offset=a1.obs['ape']['sal'].shape[0]
if(dataset==2):
a=a3;offset=a1.obs['ape']['sal'].shape[0]+a2.obs['ape']['sal'].shape[0]