def plt_brom_ersem_wod(save=False):
dss = xr.open_dataset(brom_path)
#levels = sorted(dss.depth.values)
levels = sorted(dss.z.values)
fig = plt.figure(figsize=(7, 5.5), dpi=100)
'''
Data from World Ocean Database
https://www.nodc.noaa.gov/OC5/WOD/datageo.html
'''
ncfile = (wod_path)
vars = ['Oxygen', 'po4', 'si', 'no3']
titles = ['O$_2', 'PO$_4', 'Si $', 'NO$_3']
axes = []
cols = 2
gs = gridspec.GridSpec(len(vars) // cols, cols)
gs.update(hspace=0.3, wspace=0.3, top=0.92, bottom=0.02, right=0.97)
x_text, y_text = -0.05, 1.1
labels = ('A) ', 'B) ', 'C) ', 'D) ')
interps = [1, 3, 10, 3] #levels of interpolation
for n in range(0, 4):
row = (n // cols)
col = n % cols
axes.append(fig.add_subplot(gs[row, col]))
add_brom_plot(dss, axes[n], vars[n])
add_amk(axes[n], vars[n])
plot_data_wod(ncfile,
vars[n],
True,
True,
levels,
axes[n],
interps[n],
double_int=True,
only_clima_mean=True)
axes[n].set_ylim(90, 0)
axes[n].xaxis.set_ticks_position('top')
axes[n].set_ylabel('depth, m')
axes[n].set_title(r'{}\ \mu M$'.format(titles[n]), y=1.1)
axes[n].text(x_text,
y_text,
labels[n],
transform=axes[n].transAxes,
fontsize=14,
fontweight='bold',
va='top',
ha='right')
if save == True:
#plt.savefig('Fig/Figure6_WOD_vs_BROM.pdf',
# format = 'pdf',
# transparent = False)
plt.savefig('Fig/Figure6_WOD_vs_BROM.png',
format='png',
transparent=False)
else:
plt.show()
def plot_roms_average_year(var,title,cmap = 'gist',vmax = None,vmin = None) :
ds = xr.open_dataset('Data\ROMS_Laptev_Sea_NETCDF3_CLASSIC_east_var2.nc')
ds = ds.where((ds['time.year'] >= 1989), drop=True)
d = ds.depth.values
ds_1990 = ds.where((ds['time.year'] < 1992), drop=True)
ds_2002= ds.where((ds['time.year'] >= 2002), drop=True)
arr = ds[var].to_pandas().sort_index()
arr = arr.resample('D').ffill()
t = arr.index.dayofyear
arr['dayofyear'] = t
arr = arr.pivot_table(arr,index = 'dayofyear', aggfunc=np.median).T.sort_index() #columns = 'depth',
X,Y = np.meshgrid(arr.columns,arr.index)
fig = plt.figure(figsize=(8,9), dpi=100 )
fig.suptitle(title)
gs = gridspec.GridSpec(3,1)
gs.update(hspace=0.35,top = 0.95,bottom = 0.05, right = 1.05,left = 0.1)
ax = fig.add_subplot(gs[0])
ax1 = fig.add_subplot(gs[1])
ax2 = fig.add_subplot(gs[2])
ax2.set_title('Average year')
ds_1990[var].plot(ax=ax, x = 'time',y = 'depth',cmap=plt.get_cmap(cmap),vmax = vmax,vmin = vmin)
ds_2002[var].plot(ax=ax1, x = 'time',y = 'depth',cmap=plt.get_cmap(cmap),vmax = vmax,vmin = vmin)
cs = ax2.pcolormesh(X,Y,arr,cmap=plt.get_cmap(cmap),vmax = vmax,vmin = vmin)
plt.colorbar(cs, ax = ax2)
for axis in (ax,ax1,ax2):
axis.set_ylabel('depth, m')
axis.set_ylim(80,0)
start = datetime.datetime(1989, 1, 1, 23, 59)
end = datetime.datetime(2014, 1, 1, 23, 59)
rng = pd.date_range(start, end, freq='A-DEC')
ax.vlines(rng,0,80,linestyle = '--', lw = 0.5)
ax1.vlines(rng,0,80,linestyle = '--', lw = 0.5)
ax2.set_xlabel('number of day in a year')
#plt.savefig(r'C:\Users\elp\OneDrive\Python_workspace\arctic2030\Data\mean_roms_{}.png'.format(var))
plt.show()
plt.clf()
sub_second = int(round((start_time - int(start_time)) * 100))
timestamp = "%d-%02d-%02d %02d:%02d:%02d.%02d UT" % (
srt_time[0], srt_time[1], srt_time[2], srt_time[3], srt_time[4],
srt_time[5], sub_second)
# add and modify aspects of plot post-plotting
f.suptitle('%s %s %4.2f MHz' % (title, timestamp, cfreq / 1E6), fontsize=10)
ax.set_xlabel('time (UTC)', fontsize=8)
tl = ax.get_xticklabels()
for tk in tl:
tk.set_size(8)
del tl
tl = ax.get_yticklabels()
for tk in tl:
tk.set_size(8)
del tl
gridspec.update()
f.tight_layout()
f.subplots_adjust(top=0.95, right=0.88)
cax = f.add_axes([0.9, 0.12, 0.02, 0.80])
f.colorbar(im, cax=cax)
# save and show plot
matplotlib.pyplot.savefig(dir_plot)
#matplotlib.pyplot.show()
from make_movie_strip import process_fp
from make_movie_strip import process_phase
from make_movie_strip import add_no_over
import figure_util
import data.bio_film_data.strainmap as strainmaper
plt.style.use('../figstyle.mpl')
figall = plt.figure()
#gs = gs.GridSpec(2, 3, width_ratios=[0.4, 0.25, 0.25])
#plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1 )
gs = gs.GridSpec(2, 4, height_ratios=[0.7, 0.3])
#gs.update(left=0.05, right=0.95, wspace=0.15, hspace=0.00)
gs.update(left=0.06, right=0.98, wspace=0.15, hspace=0.05)
aximgr = plt.subplot(gs[0, 0:2])
aximgr.grid(False)
aximgr.axis('off')
aximgy = plt.subplot(gs[0, 2:4])
aximgy.grid(False)
aximgy.axis('off')
axall = [plt.subplot(gs[1, i]) for i in range(4)]
yticks = mticker.MaxNLocator(nbins=4)
## Images
"""
path = "movie_strip/sigB_biofilmpad6-O001_3-{0}-{1:03d}.tif"
image_num = 31
print("Loading data...")
x_test, _, _, _ = load_data(env['test_data_file'])
print("Initializing VIN...")
VIN = create_VIN(
env['input_image_shape'],
n_hidden_filters=150,
n_state_filters=10,
k=env['k'],
)
print("Loading pre-trained VIN parameterss...")
storage.load(VIN, env['pretrained_network_file'])
plt.figure(figsize=(8, 8))
gridspec = gridspec.GridSpec(5, 4, height_ratios=[0, 2, 2, 2, 2])
gridspec.update(wspace=0.1, hspace=0.1)
plt.suptitle('Trajectories between two points predicted by VIN ')
plt.subplot(gridspec[0, :])
plt.legend(
handles=[
mpatches.Patch(color='#A71C1B', label='Start'),
mpatches.Patch(color='#F35D47', label='Trajectory'),
mpatches.Patch(color='#007035', label='Goal'),
],
loc=3,
ncol=3,
mode="expand",
borderaxespad=0.,
bbox_to_anchor=(0., 1.02, 1., .102),
def for_2d_interpolation(ncfile, varname, file, max_depth, kxy=1):
fig = plt.figure(figsize=(6, 9), dpi=100)
gs = gridspec.GridSpec(3, 1)
gs.update(left=0.07,
right=0.99,
hspace=0.4,
wspace=0.05,
bottom=0.05,
top=0.95)
axis = fig.add_subplot(gs[0])
axis2 = fig.add_subplot(gs[1])
axis3 = fig.add_subplot(gs[2])
#axis4 = fig.add_subplot(gs[3])
ds = xr.open_dataset(ncfile)
df = ds.to_dataframe()
df = df[[
'date_time', 'var1', 'var2', 'var3', 'var4', 'var5', 'var6', 'var7',
'var9', 'var10', 'var12'
]]
df.columns = [
'date_time', 'var1', 'temp', 'sal', 'Oxygen', 'po4', 'si', 'no3', 'pH',
'chl', 'alk'
]
cmap = plt.get_cmap('rainbow') # get colormap
# Write name of variable to the text file with statistics
file.write('\n{}'.format(varname))
# Data cleaning! Different for each case
df['no3'] = df['no3'][df['no3'] < 17]
df['po4'] = df['po4'][df['po4'] < 2.5]
df['Oxygen'] = df['Oxygen'] * 44.6
df['alk'] = df['alk'] * 1000
df['Oxygen'] = df['Oxygen'][df['Oxygen'] > 200]
df = df[df.date_time.dt.year > 1950]
df['Date_of_year'] = df.date_time.dt.dayofyear.values
# crate the 1d array with numbers of days in a year
day_of_year = (np.array(df.date_time.dt.dayofyear.values)).flatten()
#day = (np.array(df.date_time.dt.date.values)).flatten()
depth2 = (np.array(df['var1'])).flatten()
var2 = (np.array(df[varname])).flatten()
# specify steps for interpolations for different vars
steps = {
'Oxygen': 1,
'si': 0.1,
'alk': 1,
'chl': 0.01,
'po4': 0.5,
'no3': 0.1,
'pH': 0.01
}
step = steps[varname]
#Remove nans
depth2_nan = depth2[~np.isnan(depth2)]
var2_nan = var2[~np.isnan(depth2)]
day_of_year_nan = day_of_year[~np.isnan(depth2)]
depth2_nan = depth2_nan[~np.isnan(var2_nan)]
var2_nan2 = var2_nan[~np.isnan(var2_nan)]
day_of_year_nan = day_of_year_nan[~np.isnan(var2_nan)]
vmax = np.nanmax(var2_nan2)
vmin = np.nanmin(var2_nan2)
file.write('\nMax over the whole period = {}'.format(vmax))
file.write('\nMin over the whole period = {}'.format(vmin))
# Create grid for linear 2d interpolation
gridsize = 1
xi = np.arange(0, 366, gridsize)
if varname == 'chl':
yi = np.arange(0, max_depth, gridsize)
else:
yi = np.arange(0, max_depth, gridsize)
z_griddata = griddata((day_of_year_nan, depth2_nan),
var2_nan2, (xi[None, :], yi[:, None]),
method='linear')
#Create 2d arrays with new x,y for plotting
X, Y = np.meshgrid(xi, yi)
# Interpolation 1 way
##f_int2d = interpolate.interp2d(day_of_year_nan,depth2_nan,var2_nan2)
###zi_int2d = np.transpose(f_int2d(xi,yi))
# Interpolate with bivariate spline. kxy = level of int. 1 linear, 3 cubic
f_biv_spline = interpolate.SmoothBivariateSpline(day_of_year_nan,
depth2_nan,
var2_nan2,
kx=kxy,
ky=kxy)
z_biv_spline = np.transpose(f_biv_spline(xi, yi))
levels = np.arange(vmin, vmax - 1, step)
axis.set_title('Long-term variability, raw data')
for tick in axis.get_xticklabels():
tick.set_rotation(45)
CS = axis.scatter(df.date_time.dt.date.values,
depth2,
c=var2,
alpha=1,
cmap=cmap,
s=5)
axis2.set_title('Seasonal variability, raw data')
CS2 = axis2.scatter(day_of_year_nan,
depth2_nan,
c=var2_nan2,
alpha=1,
cmap=cmap,
s=5,
vmin=vmin,
vmax=vmax)
axis3.set_title('Seasonal variability, griddata')
CS3 = axis3.scatter(X, Y, c=z_griddata, alpha=1, s=2,
cmap=cmap) #,vmin = vmin, vmax = vmax)
#CS3 = axis3.contourf(xi, yi,z_griddata,levels = levels ,cmap = cmap,vmin = vmin, vmax = vmax)
# Different options to plot interpolated data
#axis4.set_title('Seasonal variability, SmoothBivariateSpline')
#CS4 = axis4.contourf(xi,yi,z_biv_spline, cmap= cmap) #,vmin = vmin,vmax = vmax)
#CS4 = axis4.pcolormesh(xi,yi,z_griddata, cmap= cmap) #,vmin = vmin,vmax = vmax)
if varname == 'pH':
fig.suptitle(r'{}'.format(varname), y=0.995, size=14)
else:
fig.suptitle(r'{} $\mu M$'.format(varname), y=0.995, size=14)
plt.colorbar(CS2, ax=axis2)
plt.colorbar(CS, ax=axis)
plt.colorbar(CS3, ax=axis3)
#plt.colorbar(CS4, ax=axis4)
for axis in (axis, axis2, axis3): #,axis4):
axis.set_ylim(max_depth, 0)
for axis in (axis2, axis3): #,axis4):
axis.set_xlim(0, 365)
axis.set_xlabel('Day in a year')
fig.savefig('data/smeaheia_wod_2d_3{}.png'.format(varname))
#plt.show()
plt.clf()
print "Received ->", recv_msg
break
return recv_msg
if __name__ == "__main__":
UDPServer = UDPServer()
#recv_msg = UDPServer.tcpServer()
#print "main recv_msg", recv_msg
recv_msg = "2"
plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k')
gs = gs.GridSpec(1, 1)
gs.update(left=0.05, right=0.98, hspace=0.3)
leftArm = plt.subplot(gs[:-1, -1])
leftArm.axis('scaled')
plt.xticks(np.arange(0,80,20))
plt.xlim(0,80)
plt.ylim(-80,80)
leftArm.set_title('leftArm')
larm = arm.arm_class(leftArm)
plt.ion()
plt.show()
if recv_msg == "2":
print "2"
larm.arm_angle()