# ======================================================== # == Boundary Conditions and resolutions == # Set the Target Boundaries (degrees) lon_min = 100 lon_max = 160 lat_min = -50 lat_max = 0 # Setting the approximate target resolution lonres = 10 # km latres = 10 # km # == Directories == # Main Directory basepath = ut.DefineAndCreateDirectory(r'C:\Users\jaspd\Documents\Python\00_bachelorthesis\bachelorthesis\THESIS_WORKINGDIR\\') # Directory where GFED files are stored GFED_path = os.path.join(basepath, '01_GFED_hdf5_files' + os.path.sep) # Path to gfed .hdf5 files EDGAR_path = os.path.join(basepath, '02_EDGAR_files' + os.path.sep) # Path to EDGAR .nc files CAMS_path = os.path.join(basepath, '02_Store_CAMS_data' + os.path.sep) # Path to CAMS .nc files # Set modelparams buffersizes = list(np.linspace(2, 8, 7).astype(int)) stdevs = list(np.round(np.linspace(1.4, 2.1, 8), 1)) # All loops that will have to be executed iterations = list(itertools.product(buffersizes, stdevs)) # Set variables estimator = np.zeros((len(buffersizes), len(stdevs))) # y-hat (total TROPOMI identifications)
def CreateWindVector(daily_data_dict, basepath, skip=30):
"""
Parameters
----------
daily_data_dict : dictionary
daily_data[<day>], contains data about TROPOMI measurement per day.
Contains at least: lat_min, lat_max, lon_min, lon_max, day, month, year,
and np.array with values to plot and count_t.
basepath : str
String to folder where output files will be stored.
skip : int, optional
Every 'skip' arrow is drawn in figure. The default is 30.
Returns
-------
Figure(s) stored as png in '...basepath...//04_output//plume_figures//'
"""
# Creating output directory
out_dir = ut.DefineAndCreateDirectory(
os.path.join(basepath + os.path.sep + r'04_output\plume_figures'))
# Creating output filename
curr_time = ut.GetCurrentTime()
tropomi_datestamp = f"{daily_data_dict['month']}_{daily_data_dict['day']}_{daily_data_dict['year']}"
current_timestamp = f"{curr_time['year']}{curr_time['month']}{curr_time['day']}{curr_time['hour']}{curr_time['minute']}{curr_time['second']}"
out_name = out_dir + rf"fig_subplots_{tropomi_datestamp}___{current_timestamp}.png"
# Define U and V wind
U = daily_data_dict['u_wind']
V = daily_data_dict['v_wind']
#windspeed = (U ** 2 + V ** 2) ** 0.5
#direction = 180 + (180/np.pi)*np.arctan2(V,U)
# Create a longitude and latitude np.meshgrid in target resolution
nlat_t, nlon_t = daily_data_dict['u_wind'].shape
# Creating lat/lon meshgrid, to correctly place grid cells
lon_t = np.linspace(daily_data_dict['lon_min'], daily_data_dict['lon_max'],
nlon_t)
lat_t = np.linspace(daily_data_dict['lat_min'], daily_data_dict['lat_max'],
nlat_t)
lon, lat = np.meshgrid(lon_t, lat_t)
# Prepare data for plotting
count_t = daily_data_dict['count_t']
mask = (count_t == 0) # Masking all zero values in count_t array
field_mt = ma.array(daily_data_dict['CO_ppb'],
mask=mask) # Apply this mask to figtype as well
# plot with cartopy
plt.ioff()
fig = plt.figure(figsize=(10, 6))
ax = plt.axes(projection=ccrs.PlateCarree())
gl = ax.gridlines(draw_labels=True)
gl.top_labels = False
gl.right_labels = False
# Add some cartopy features to the map
land_50m = cfeature.NaturalEarthFeature('physical', 'land', '50m')
ax.add_feature(land_50m,
edgecolor='k',
linewidth=0.5,
facecolor='None',
zorder=3)
# Draw vectors
cs = plt.quiver(lon[::skip, ::skip],
lat[::skip, ::skip],
U[::skip, ::skip],
V[::skip, ::skip],
transform=ccrs.PlateCarree(),
pivot='mid',
zorder=2)
# Use total column CO as background of wind vectors
cs = plt.pcolormesh(lon,
lat,
field_mt,
cmap='rainbow',
transform=ccrs.PlateCarree(),
zorder=1)
cbaxes = fig.add_axes([0.2, 0.03, 0.6, 0.03])
cb = plt.colorbar(cs, cax=cbaxes, orientation='horizontal')
cb.set_label('CO concentration (ppb)')
# Save the figure
plt.savefig(out_name, bbox_inches='tight') #, dpi=1200)
plt.close()
return
def NotePlumeCoordinates(daily_data_dict, basepath, lonres, latres, params,
compare_gfed_edgar):
"""
Parameters
----------
daily_data_dict : dictionary
daily_data[<day>], contains data about TROPOMI measurement per day.
TODO!!!!!!!!!!!!!!ct (txt file) of plume coordinates will be stored.
Returns
-------
Saves .txt file in coord_directory.
"""
# Output directories, for coordinates and figures
coord_directory = ut.DefineAndCreateDirectory(
os.path.join(basepath + r'\04_output\plume_coordinates'))
# Defining boundaries
lat_min = daily_data_dict['lat_min']
lat_max = daily_data_dict['lat_max']
lon_min = daily_data_dict['lon_min']
lon_max = daily_data_dict['lon_max']
# Deciding on the nlon_t and nlat_t
field_t = daily_data_dict['CO_ppb']
nlon_t = len(field_t[0])
nlat_t = len(field_t)
if compare_gfed_edgar:
# Define the plumes
plumes = daily_data_dict['plumes_explained']
# Label the plumes
labels, nlabels = ndimage.label(plumes)
# Get some statistics
daily_data_dict = GetStats(daily_data_dict, labels, nlabels)
else:
# Define the plumes
plumes = daily_data_dict['plume_mask']
# Label the plumes
labels, nlabels = ndimage.label(plumes)
# Get some label statistics, for file
max_xco = ndimage.measurements.maximum_position(field_t, labels,
np.arange(nlabels) + 1)
mean_xco_raw = ndimage.measurements.mean(field_t, labels,
np.arange(nlabels) + 1)
mean_xco = [round(num, 3) for num in mean_xco_raw]
plume_size = ndimage.labeled_comprehension(plumes, labels,
np.arange(nlabels) + 1, np.sum,
float, 0)
unexplained = round(
(100 - (daily_data_dict['explained plumes'] / nlabels) * 100), 2)
exp_by_gfed = round(
((daily_data_dict['explained by gfed'] / nlabels) * 100), 2)
exp_by_edgar = round(
((daily_data_dict['explained by edgar'] / nlabels) * 100), 2)
# Generate coordinate meshgrid
lon_t = np.linspace(lon_min, lon_max, nlon_t)
lat_t = np.linspace(lat_min, lat_max, nlat_t)
lon, lat = np.meshgrid(lon_t, lat_t)
# Write to txt file
day = daily_data_dict['day']
month = daily_data_dict['month']
year = daily_data_dict['year']
curr_time = ut.GetCurrentTime()
total = daily_data_dict['total_plumes']
bufferarea = round((np.pi * ((params[0] * ((lonres + latres) / 2))**2)), 1)
filename = os.path.join(coord_directory + \
'Plume_coordinates_{}_{}_{}.txt'.format(month, day, year))
headerstring = f"""#----------------------------------------
#----------------------------------------
This file was automatically generated at: {curr_time['year']}/{curr_time['month']}/{curr_time['day']} {curr_time['hour']}:{curr_time['minute']}
This file contains a list with information on Carbon Monoxide plumes at {month}/{day}/{year}, between:
longitudes: [{lon_min}, {lon_max}]
latitudes: [{lat_min}, {lat_max}]
column descriptions:
- type: Plume centre of mass origin: (Unknown, TROPOMI, TROPOMI+GFED, TROPOMI+EDGAR, TROPOMI+GFED+EDGAR)
- latitude: Latitude of northernmost edge of plume
- longitude: Longitude of westermost edge of plume
- grid_cells: Amount of grid cells (~{lonres}x{latres}km) in plume
- CO_max: Highest Carbon Monoxide concentration measured in plume (ppb)
- CO_average: Average Carbon Monoxide concentration measured in plume (ppb)
Total amount of plumes identified by TROPOMI: {total}
Percentage of TROPOMI plumes that can be explained by GFED: {exp_by_gfed}%
Percentage of TROPOMI plumes that can be explained by EDGAR: {exp_by_edgar}%
Percentage of TROPOMI plumes that cannot be explained by EDGAR or GFED:{unexplained}%
Note: a TROPOMI grid cell can be explained by EDGAR or GFED when an EDGAR or GFED enhancement was detected within
a circular buffer with radius {params[0]} (~{bufferarea} km^2) around a TROPOMI plume.
Other parameter settings:
Moving window size: {params[2]}x{params[2]} grid cells
Moving window step size: {params[3]} grid cells
Standard deviations treshold: {params[1]}
#----------------------------------------
#----------------------------------------
plume_origin; latitude; longitude; grid_cells; CO_max; CO_average;
"""
f = open(filename, 'w+')
f.write(headerstring)
for i in range(1, nlabels + 1):
plume = np.copy(labels)
plume[plume != i] = 0 # Keep only the labelled plume
# Retrieve plume value
y, x = np.where(
plume != 0) # Get the indices of the left top corner of plume
y = y[0] # Get only northernmost grid cell coordinate
x = x[0] # Get only westermost grid cell coordinate
x_co_max = int(max_xco[i - 1][1])
y_co_max = int(max_xco[i - 1][0])
plume_origin = 'Unknown' if (plumes[y,x] == 1) else 'Wildfire' \
if (plumes[y,x] == 11) else 'Anthropogenic' if (plumes[y,x] == 101) \
else 'Wildfire/anthropogenic' if (plumes[y,x] == 111) else 'Error'
f.write(
f"{plume_origin}; {lat[y, x]}; {lon[y, x]}; {plume_size[i-1]}; {round(field_t[y_co_max, x_co_max], 3)}; {mean_xco[i-1]}\n"
)
f.close()
# Notify a text file has been generated
#print(f'Generated: {filename}')
return
def PlotFigures(daily_data_dict,
basepath,
subplots=True,
mask_type='plume_mask'):
"""
Plot figures of:
- CO concentration as detected by TROPOMI
- Plumes detected in the TROPOMI data, compared to GFED and EDGAR databases
Parameters
----------
daily_data_dict : dictionary
daily_data[<day>], contains data about TROPOMI measurement per day.
Contains at least: lat_min, lat_max, lon_min, lon_max, day, month, year,
and np.array with values to plot and count_t.
basepath : str
String to folder where output files will be stored.
subplots : BOOL, optional
If True, both maps are plotted in one figure (subplots)
If False, two separate outputs are generated
The default is True.
Returns
-------
Figure(s) stored as png in '...'basepath'.../04_output/plume_figures'
"""
# Assert input is valid
assert mask_type in [
'plume_mask', 'plumes_explained', 'plumes_incl_gfed_edgar'
], 'Could not identify the plumes to plot'
# 1: DEFINING OUTPUT NAME AND STORAGE LOCATION
# Creating output directory
out_dir = ut.DefineAndCreateDirectory(
os.path.join(basepath + os.path.sep + r'04_output\plume_figures'))
# Creating output filename
curr_time = ut.GetCurrentTime()
tropomi_datestamp = f"{daily_data_dict['month']}_{daily_data_dict['day']}_{daily_data_dict['year']}"
current_timestamp = f"{curr_time['year']}{curr_time['month']}{curr_time['day']}{curr_time['hour']}{curr_time['minute']}{curr_time['second']}"
out_name_mask = out_dir + rf"fig_plumes_{tropomi_datestamp}___{current_timestamp}.png"
out_name_co = out_dir + rf"fig_CO_ppb_{tropomi_datestamp}___{current_timestamp}.png"
out_name = out_dir + rf"fig_subplots_{tropomi_datestamp}___{current_timestamp}.png"
# 2: CREATE LAT LON GRID TO GEOREFERENCE ALL GRID CELLS
# Setting target lon- and latitude
nlat_t, nlon_t = daily_data_dict['CO_ppb'].shape
# Creating lat/lon meshgrid, to correctly place grid cells
lon_t = np.linspace(daily_data_dict['lon_min'], daily_data_dict['lon_max'],
nlon_t)
lat_t = np.linspace(daily_data_dict['lat_min'], daily_data_dict['lat_max'],
nlat_t)
lon, lat = np.meshgrid(lon_t, lat_t)
# 3: PREPARE CO_DATA FOR PLOTTING BY MASKING IT
count_t = daily_data_dict['count_t']
mask = (count_t == 0) # Masking all zero values in count_t array
field_mt = ma.array(daily_data_dict['CO_ppb'],
mask=mask) # Apply this mask to figtype as well
# 4:
# # Correct data, for example if:
# TROPOMI (1) gfed_tropomi (1) & GFED (10) = 12 -> 11
plumes = daily_data_dict[mask_type]
plumes[plumes == 12] = 11
plumes[plumes == 102] = 101
plumes[plumes == 112] = 111
if subplots:
SubPlots(lon=lon,
lat=lat,
data1=field_mt,
data2=plumes,
out_name=out_name)
elif not subplots:
CreatePlumesMap(lon, lat, plumes, out_name=out_name_mask)
CreateCOMap(lon, lat, field_mt, out_name=out_name_co)
return
def PlotDatabase(dataset, in_path, out_path, bbox, xres, yres, day, month,
year):
"""
Function to plot data from the GFED, EDGAR and CAMS databases.
Parameters
----------
dataset : string
Database from which data will be plotted. Can be 'GFED', 'EDGAR' or 'CAMS'
in_path : string
Path to folder containing file with data to be plotted.
out_path : string
Path to folder where output will be saved
bbox : boundaries [lat_min, lat_max, lon_min, lon_max]
day : day of TROPOMI overpass
month : month of TROPOMI overpass
year : year of TROPOMI overpass
xres : resolution in lon direction
yres : resolution in lat direction
Returns
-------
Figure, stored in out_path
"""
# Assert inputs are valid
assert dataset in ['GFED', 'EDGAR', 'CAMS']
if not os.path.isdir(in_path):
print(f'Directory {in_path} was not found!')
print(f'Creating {in_path} ...')
in_path = ut.DefineAndCreateDirectory(in_path)
out_path = ut.DefineAndCreateDirectory(out_path)
# Check which database to read/open
if dataset == 'GFED':
data = gfed.OpenGFED(in_path, bbox, day, month, year, xres, yres)
label = 'g C m^2 month^-1'
if dataset == 'EDGAR':
in_path = os.path.join(in_path + r'v432_CO_2012_IPCC_1A2.0.1x0.1.nc')
data = edgar.OpenEDGAR(in_path, bbox, xres, yres)
label = 'kg m-2 s-1'
if dataset == 'CAMS':
data = cams.FetchCams(in_path,
dates=[day, month, year],
bbox=bbox,
xres=xres,
yres=yres)
label = 'ppb'
# Mask data values equal to zero
data = ma.array(data, mask=(data == 0))
# Latitude and Longitudinal ranges
latrange = np.linspace(bbox[0], bbox[1], data.shape[0])
lonrange = np.linspace(bbox[2], bbox[3], data.shape[1])
lon, lat = np.meshgrid(lonrange, latrange)
# plot with cartopy
fig = plt.figure(figsize=(10, 6))
ax = plt.axes(projection=ccrs.PlateCarree())
gl = ax.gridlines(draw_labels=True)
gl.top_labels = False
gl.right_labels = False
# Add some cartopy features to the map
land_50m = cfeature.NaturalEarthFeature('physical', 'land', '50m')
ax.add_feature(land_50m,
edgecolor='k',
linewidth=0.5,
facecolor='None',
zorder=3)
cs = plt.pcolormesh(lon,
lat,
data,
cmap='rainbow',
transform=ccrs.PlateCarree())
cbaxes = fig.add_axes([0.2, 0.03, 0.6, 0.03])
cb = plt.colorbar(cs, cax=cbaxes, orientation='horizontal')
cb.set_label(label)
# Save the figure
out_name = os.path.join(
out_path + rf'{dataset}_{bbox[0]}{bbox[1]}{bbox[2]}{bbox[3]}.png')
fig.savefig(out_name, bbox_inches='tight') #, dpi=1200)
# Close the figure and clear the axes
plt.cla()
plt.clf()
plt.close()
return