def plot_data(lon,
lat,
data,
scalebar,
title,
lon_min=0,
lon_max=359,
lat_min=-90,
lat_max=90,
has_scale_bounds=False,
scale_min=0,
scale_max=0,
figsize_x=20,
figsize_y=15,
colormap="coolwarm",
has_rivers=True,
file_name=None):
data = np.asarray(data)
#make the figure
fig = plt.figure(figsize=(figsize_x, figsize_y))
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=0))
ax.coastlines()
ax.add_feature(cartopy.feature.LAKES, alpha=1)
if has_rivers: ax.add_feature(cartopy.feature.RIVERS, alpha=1)
#The latitudes break because of how they're set up in the NOAA renalysis data unless you do this
lat1 = em.find_closest_val(lat_min, lat)
lat2 = em.find_closest_val(lat_max, lat)
lon1 = em.find_closest_val(lon_min, lon)
lon2 = em.find_closest_val(lon_max, lon)
print(lat1)
print(lat2)
#label axes and the graph
ax.set_extent((lon_min, lon_max, lat_min, lat_max))
ax.set_xticks(lon[math.ceil(lon1 / 10) * 10:lon2][::10],
crs=ccrs.PlateCarree())
ax.set_yticks(lat[math.ceil(lat1 / 10) * 10:lat2][::10],
crs=ccrs.PlateCarree())
plt.xlabel('lon')
plt.ylabel('lat')
plt.title(title)
#
if not has_scale_bounds:
mesh = plt.pcolormesh(lon[lon1:lon2],
lat[lat1:lat2],
data[lat1:lat2, lon1:lon2],
cmap=colormap)
else:
mesh = plt.pcolormesh(lon[lon1:lon2],
lat[lat1:lat2],
data[lat1:lat2, lon1:lon2],
cmap=colormap,
vmin=scale_min,
vmax=scale_max)
cbar = plt.colorbar(mesh)
cbar.set_label(scalebar)
if file_name != None: plt.savefig(file_name)
def plot_vector_data(lon,
lat,
x_data,
y_data,
title,
scale_title,
lon_min=0,
lon_max=359,
lat_min=-90,
lat_max=90,
detail=3,
v_scale=400,
figsize_x=20,
figsize_y=15,
colormap="GnBu"):
#make the figure
fig = plt.figure(figsize=(figsize_x, figsize_y))
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=0))
ax.coastlines()
ax.add_feature(cartopy.feature.LAKES, alpha=1)
ax.add_feature(cartopy.feature.RIVERS, alpha=1)
lat1 = em.find_closest_val(lat_min, lat)
lat2 = em.find_closest_val(lat_max, lat)
#set labels and axes up
ax.set_extent((lon_min, lon_max, lat_min, lat_max))
ax.set_xticks(lon[math.ceil(lon_min / 10) * 10:lon_max][::10],
crs=ccrs.PlateCarree())
ax.set_yticks(lat[math.ceil(lat1 / 10) * 10:lat2][::10],
crs=ccrs.PlateCarree())
plt.xlabel('lon')
plt.ylabel('lat')
plt.title(title)
color = np.hypot(x_data[lat1:lat2, lon_min:lon_max][::detail, ::detail],
y_data[lat1:lat2, lon_min:lon_max][::detail, ::detail])
v_field = plt.quiver(lon[lon_min:lon_max][::detail],
lat[lat1:lat2][::detail],
x_data[lat1:lat2,
lon_min:lon_max][::detail, ::detail],
y_data[lat1:lat2,
lon_min:lon_max][::detail, ::detail],
color,
cmap=colormap,
scale=v_scale)
scalebar = plt.colorbar(v_field)
scalebar.set_label(scale_title)
def average_plot(lat_start, lat_end, lon_start, lon_end, lat, lon, pres_levels,
lev_x, data_package, title_str):
lat1 = em.find_closest_val(lat_start, lat)
lat2 = em.find_closest_val(lat_end, lat)
lon1 = em.find_closest_val(lon_start, lon)
lon2 = em.find_closest_val(lon_end, lon)
lev_x = em.find_closest_val(lev_x, pres_levels)
fig = plt.figure(figsize=(12, 4))
ax = plt.subplot(projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_xticks(np.asarray(lon[::5]) - 180)
ax.set_yticks(np.asarray(lat[::4]))
ax.set_xlabel('lon')
ax.set_ylabel('lat')
mesh = plt.pcolormesh(lon[lon1:lon2],
lat[lat1:lat2],
data_package['hgt'][lev_x, lat1:lat2, lon1:lon2],
cmap='coolwarm')
#plt.contour(lon[lon1:lon2], lat[lat1:lat2], data_package['hgt'][lev_x, lat1:lat2, lon1:lon2],
# cmap = 'coolwarm')
cb = plt.colorbar(mesh)
# ax.clabel(cont, inline=1, fontsize=5, colors = 'black')
cb.set_label('GPH at ' + str(pres_levels[lev_x]) + 'mb level [m]')
quiv = plt.quiver(lon[lon1:lon2], lat[lat1:lat2],
data_package['uwnd'][lev_x, lat1:lat2, lon1:lon2],
data_package['vwnd'][lev_x, lat1:lat2, lon1:lon2])
ax.quiverkey(quiv,
X=0.3,
Y=-0.1,
U=10,
label='Quiver key, length = 10 m/s',
labelpos='E')
ax.set_title(title_str)
ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax.add_feature(cartopy.feature.RIVERS)
return fig
def sst_anomaly_plot(lat_start, lat_end, lon_start, lon_end, lat, lon,
data_package, title_str, max_at_one):
lat1 = em.find_closest_val(lat_start, lat)
lat2 = em.find_closest_val(lat_end, lat)
lon1 = em.find_closest_val(lon_start, lon)
lon2 = em.find_closest_val(lon_end, lon)
fig = plt.figure(figsize=(12, 4))
ax = plt.subplot(projection=ccrs.PlateCarree())
ax.coastlines()
ax.add_feature(cartopy.feature.RIVERS)
ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
ax.set_xticks(np.asarray(lon[::5]) - 180)
ax.set_yticks(np.asarray(lat[::4]))
ax.set_xlabel('lon')
ax.set_ylabel('lat')
if max_at_one:
mesh = plt.pcolormesh(lon[lon1:lon2],
lat[lat1:lat2],
data_package['sst'][lat1:lat2, lon1:lon2],
cmap='coolwarm',
vmin=-1,
vmax=1)
else:
mesh = plt.pcolormesh(lon[lon1:lon2],
lat[lat1:lat2],
data_package['sst'][lat1:lat2, lon1:lon2],
cmap='coolwarm')
cb = plt.colorbar(mesh)
# ax.clabel(cont, inline=1, fontsize=5, colors = 'black')
cb.set_label('stdev from mean')
ax.set_title(title_str)
return fig
def height_anomaly_plot(lat_start, lat_end, lon_start, lon_end, lat, lon,
pres_levels, lev_x, data_package, title_str):
lat1 = em.find_closest_val(lat_start, lat)
lat2 = em.find_closest_val(lat_end, lat)
lon1 = em.find_closest_val(lon_start, lon)
lon2 = em.find_closest_val(lon_end, lon)
lev_x = em.find_closest_val(lev_x, pres_levels)
fig = plt.figure(figsize=(12, 4))
ax = plt.subplot(projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_xticks(np.asarray(lon[::4]) - 180)
ax.set_yticks(np.asarray(lat[::3]))
ax.set_xlabel('lon')
ax.set_ylabel('lat')
mesh = plt.pcolormesh(lon[lon1:lon2],
lat[lat1:lat2],
data_package['hgt'][lev_x, lat1:lat2, lon1:lon2],
cmap='coolwarm',
vmin=-1,
vmax=1)
#plt.contour(lon[lon1:lon2], lat[lat1:lat2], data_package['hgt'][lev_x, lat1:lat2, lon1:lon2],
# cmap = 'coolwarm')
cb = plt.colorbar(mesh)
# ax.clabel(cont, inline=1, fontsize=5, colors = 'black')
cb.set_label('stdev from mean')
ax.set_title(title_str)
ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax.add_feature(cartopy.feature.RIVERS)
return fig
def plot_pcasim_reg(tups, x_bin, y_bin, Y, lat, lon, path, title):
c = np.empty((len(lat), len(lon))) #lat/lon frame
tuple_list = em.generate_tuples(x_bin, y_bin) #list of binned tuples
lat = lat.tolist()
lon = lon.tolist()
for ind in range(
Y.shape[0]): #iterate through every lat/lon pair, in Y-order
x_val = x_bin[em.find_closest_val(
Y[ind, 0], x_bin)] #find the index of the closest val in x_bin
y_val = y_bin[em.find_closest_val(Y[ind, 1], y_bin)]
c_x = lat.index(tups[ind][0])
c_y = lon.index(tups[ind][1])
c[c_x, c_y] = tuple_list.index((x_val, y_val))
fig = plt.figure()
ax3 = plt.subplot(projection=ccrs.PlateCarree())
plt.title(title)
ax3.coastlines()
mesh = plt.pcolormesh(lon, lat, c)
plt.colorbar(mesh)
plt.savefig(path)
plt.close(fig)
frames1 = [] #for air
frames2 = [] #for pres
frames3 = [] #for prate
for d in dates: #iterate through top 10 dates
month = d.month
year = d.year
fname = str(i) + '_' + str(month) + '_' + str(
year) + '.png' #make file name: index_month_year.png
path = os.path.join(
folder, fname) #make file path: location folder + file_name
#find time index within NCEP-NCAR dataset - convert to days since 1/1/1800, subtract from time_greg
time_index = em.find_closest_val(
(d - dt.datetime(1800, 1, 1, 0, 0, 0)).days, time_greg)
#find the mean of the x months before the date
frame1 = np.mean(air_sub[time_index - num_months:time_index, :, :],
axis=0)
frame2 = np.mean(pres_sub[time_index -
num_months:time_index, :, :],
axis=0)
frame3 = np.mean(prate_sub[time_index -
num_months:time_index, :, :],
axis=0)
# if geospatial_subset == len(regions) - 1: #only generate these images for full-world
# vm.generate_3_var_img(frame1, frame2, frame3, time[time_index], path, lat_sub, lon_sub)
#append to means for that location
import numpy as np
import os
start_time = module_time.time()
directory = r'C:\Users\bydd1\Documents\Research\Data\gridded environmental'
file = r'sst.mon.mean.nc'
image_folder = r'C:\Users\bydd1\Documents\Research\SST Images'
time, lat, lon, var = em.get_sst_mon_mean(directory, file, 'sst')
lat_bounds = [29.6, 18]
lon_bounds = [360 -98, 360 - 75.8]
#lat_bounds = [max(lat), min(lat)]
#lon_bounds = [min(lon), max(lon)]
lat_ind = [em.find_closest_val(lat_bounds[0], lat), em.find_closest_val(lat_bounds[1], lat)]
lon_ind = [em.find_closest_val(lon_bounds[0], lon), em.find_closest_val(lon_bounds[1], lon)]
mois, _ = generate_doi.get() #retrieve mois from generate_doi
print("--- %s seconds ---" % (module_time.time() - start_time))
location_index = 2 #alphabetical, 0 for arkansas city, 1 for helena, 2 for vicksburg
moi = mois[location_index]
months_before = 6
time_ordinal = [dt.date.toordinal(x) for x in time]
for m in moi:
def plot_corr(dic, array, title, lat_start, lat_end, lon_start, lon_end, poi,
flipped_axis):
lat = dic['lat']
lon = dic['lon']
old_lon = lon
if flipped_axis:
lon = [180 - x for x in lon]
temp = lon_start
lon_start = lon_end
lon_end = temp
#print(lon)
print([lon_start, lon_end])
fig = plt.figure(figsize=(12, 5))
fig.patch.set_facecolor('white')
ax = plt.subplot(projection=ccrs.PlateCarree(central_longitude=0))
lat1 = em.find_closest_val(lat_start, lat)
lat2 = em.find_closest_val(lat_end, lat)
lon1 = em.find_closest_val(lon_start, lon)
lon2 = em.find_closest_val(lon_end, lon)
print([lon1, lon2])
ax.coastlines()
plt.title(title)
plt.xlabel('lon')
plt.ylabel('lat')
int_lon = int(len(old_lon[lon1:lon2]) / 10)
int_lat = int(len(lat[lat1:lat2]) / 10)
ax.set_xticks(np.round(np.asarray(old_lon[lon1:lon2][::int_lon])),
crs=ccrs.PlateCarree())
ax.set_yticks(np.round(np.asarray(lat[lat1:lat2][::int_lat])),
crs=ccrs.PlateCarree())
mesh = plt.pcolormesh(old_lon[lon1:lon2],
lat[lat1:lat2],
array[lat1:lat2, lon1:lon2],
cmap='coolwarm',
vmax=1,
vmin=-1)
cbar = plt.colorbar(mesh)
path1 = r'D:\Shapefiles\msrivs\msrivs.shp'
shp1 = geopandas.read_file(path1)
shp1.plot(ax=ax, edgecolor='gray', linewidth=1.3)
path2 = r'D:\Shapefiles\Miss_RiverBasin\Miss_RiverBasin.shp'
shp2 = geopandas.read_file(path2)
shp2 = shp2.to_crs("EPSG:4326")
shp2.plot(ax=ax, edgecolor='black', linewidth=1.3, facecolor='none')
ax.scatter(poi[1],
poi[0],
color='yellow',
s=30,
transform=ccrs.PlateCarree())
cbar.set_label('correlation coefficient to St. Louis')
ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
savepath = os.path.join(
r'C:\Users\bydd1\OneDrive\Documents\Research\miss-atmo correlation images',
title + '.png')
plt.savefig(savepath)
corr[i, j] = np.corrcoef(comp, comp0)[0, 1]
return corr
# #### run correlation on each dictionary
start_time = time.time()
dics = [pre_dic, air_dic, mois_dic]
name = ['precip', 'air', 'soilm']
c = []
for i in range(len(dics)):
d = dics[i]
poi = [
em.find_closest_val(st_lou[0], d['lat']),
em.find_closest_val(st_lou[1], d['lon'])
]
#conversion
if i > 0:
if st_lou[1] < 0:
poi = [
em.find_closest_val(st_lou[0], d['lat']),
em.find_closest_val(360 + st_lou[1], d['lon'])
]
if st_lou[1] > 0:
poi = [
em.find_closest_val(st_lou[0], d['lat']),
em.find_closest_val(st_lou[1], d['lon'])
]
location_index = 2
doi = dois[location_index]
lat_bounds = [48, 27]
lon_bounds = [360 - 100, 360 - 70]
for stop_date in doi: #now iterate over the dates, generate an image for each date with mean/stdev for temp + gph
if 1948 <= stop_date.year <= 2019:
start_date = stop_date - dt.timedelta(days=7)
air, gph, time, lat, lon = em.get_air_and_gph(start_date, stop_date)
lat_ind = [
em.find_closest_val(lat_bounds[0], lat),
em.find_closest_val(lat_bounds[1], lat)
]
lon_ind = [
em.find_closest_val(lon_bounds[0], lon),
em.find_closest_val(lon_bounds[1], lon)
]
lat = lat[lat_ind[0]:lat_ind[1]]
lon = lon[lon_ind[0]:lon_ind[1]]
air = air[:, lat_ind[0]:lat_ind[1], lon_ind[0]:lon_ind[1]]
gph = gph[:, lat_ind[0]:lat_ind[1], lon_ind[0]:lon_ind[1]]
air_mean = np.nanmean(air, axis=0)
air_stdev = np.std(air, axis=0)
gph_mean = np.nanmean(gph, axis=0)