# In[12]:
import mplleaflet
#geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))
#fig, ax = make_map(projection=geodetic)
fig, ax = make_map()
kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray')
kw = dict(linestyle='-', color='red')
ax.triplot(triang, **kw) # or lon, lat, triangules;
ax.set_extent([-87.5, -82.5, 29.4, 31])
# In[ ]:
from cartopy.io.img_tiles import MapQuestOpenAerial, MapQuestOSM, OSM
geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))
fig = plt.figure(figsize=(12, 8))
tiler = MapQuestOpenAerial()
ax = plt.axes(projection=tiler.crs)
bbox = [-71, -69.3, 42, 42.8]
#ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(bbox)
ax.add_image(tiler, 8)
#ax.coastlines()
kw = dict(marker='.',
linestyle='-',
alpha=0.85,
color='darkgray',
ax2 = plt.axes(rect, projection=ccrs.PlateCarree(), ) ax2.set_extent((110, 160, -45, -10)) # ax2.set_global() will show the whole world as context ax2.coastlines(resolution='110m', zorder=2) ax2.add_feature(cfeature.LAND) ax2.add_feature(cfeature.OCEAN) ax2.gridlines() lon0, lon1, lat0, lat1 = ax.get_extent() box_x = [lon0, lon1, lon1, lon0, lon0] box_y = [lat0, lat0, lat1, lat1, lat0] plt.plot(box_x, box_y, color='red', transform=ccrs.Geodetic()) # -------------------------------- Title ----------------------------- # set up map title top 4% of figure, right 80% of figure left = 0.2 bottom = 0.95 width = 0.8 height = 0.04 rect = [left, bottom, width, height] ax6 = plt.axes(rect) ax6.text(0.5, 0.0, 'Multi-Axes Map Example', ha='center', fontsize=20) blank_axes(ax6) # ---------------------------------North Arrow ---------------------------- #
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import cartopy.crs as ccrs
import cartopy.io.img_tiles as cimgt
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from seisnn.io import read_hyp, read_event_list
from seisnn.utils import get_config
W, E, S, N = 119, 123, 21.5, 25.7
stamen_terrain = cimgt.Stamen('terrain-background')
fig = plt.figure(figsize=(8, 10))
ax = fig.add_subplot(1, 1, 1, projection=stamen_terrain.crs)
ax.set_extent([W, E, S, N], crs=ccrs.Geodetic())
ax.add_image(stamen_terrain, 11)
events = read_event_list('HL201718')
HL_eq = []
for event in events:
HL_eq.append([event.origins[0].longitude, event.origins[0].latitude])
HL_eq = np.array(HL_eq).T
ax.scatter(HL_eq[0],
HL_eq[1],
label='Earthquake',
transform=ccrs.Geodetic(),
color='#333333',
edgecolors='k',
linewidth=0.3,
marker='o',
#for tick in ax.xaxis.get_major_ticks():
# tick.label.set_fontsize(SMFONT)
# Label the end-points of the gridlines using the custom tick makers:
#ax.xaxis.set_major_formatter(LONGITUDE_FORMATTER)
#ax.yaxis.set_major_formatter(LATITUDE_FORMATTER)
#mct_xticks(ax, xticks)
#mct_yticks(ax, yticks)
# Set the map bounds
ax.set_xlim(cartopy_xlim(lsmask))
ax.set_ylim(cartopy_ylim(lsmask))
ax.plot(df_hko_obv['lon']/10., df_hko_obv['lat']/10.,
color='black', marker='o', linewidth=2, markersize=5, transform=ccrs.Geodetic(), label='HKO bestTrack')
ax.plot(df_cma_obv['lon']/10., df_cma_obv['lat']/10.,
color='dimgray', marker='*', linewidth=2, linestyle='dashed', markersize=8, transform=ccrs.Geodetic(), label='CMA bestTrack')
dateparse = lambda x: datetime.datetime.strptime(x, '%Y%m%d%H%M%S')
for (line_type,casename) in zip(line_libs,cases):
sen_path='/home/metctm1/array/data/1911-COAWST/'+casename+'/trck.'+casename+'.d02'
df_sen=pd.read_csv(sen_path,parse_dates=True,index_col='timestamp', sep='\s+', date_parser=dateparse)
df_sen_period=df_sen
df_sen_period.replace(0, np.nan, inplace=True) # replace 0 by nan
df_sen_period=df_sen_period.dropna()
ax.plot(df_sen_period['lon'], df_sen_period['lat'],
line_type, linewidth=1, markersize=3, transform=ccrs.Geodetic(), label=casename)
def plot_map(gf, snwe, vectorFile=None, zoom=8):
"""Plot dinosar inventory on a static map.
Parameters
----------
gf : GeoDataFrame
A geopandas GeoDataFrame
snwe : list
bounding coordinates [south, north, west, east].
vectorFile: str
path to region of interest polygon
zoom: int
zoom level for WMTS
"""
pad = 1
S, N, W, E = snwe
plot_CRS = ccrs.PlateCarree()
geodetic_CRS = ccrs.Geodetic()
x0, y0 = plot_CRS.transform_point(W - pad, S - pad, geodetic_CRS)
x1, y1 = plot_CRS.transform_point(E + pad, N + pad, geodetic_CRS)
fig, ax = plt.subplots(figsize=(8, 8),
dpi=100,
subplot_kw=dict(projection=plot_CRS))
ax.set_xlim((x0, x1))
ax.set_ylim((y0, y1))
url = 'http://tile.stamen.com/terrain/{z}/{x}/{y}.png'
tiler = GoogleTiles(url=url)
# NOTE: going higher than zoom=8 is slow...
ax.add_image(tiler, zoom)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='110m',
facecolor='none')
ax.add_feature(states_provinces, edgecolor='k', linestyle=':')
ax.coastlines(resolution='10m', color='black', linewidth=2)
ax.add_feature(cfeature.BORDERS)
# Add region of interest polygon in specified
if vectorFile:
tmp = gpd.read_file(vectorFile)
ax.add_geometries(tmp.geometry.values,
ccrs.PlateCarree(),
facecolor='none',
edgecolor='m',
lw=2,
linestyle='dashed')
orbits = gf.relativeOrbit.unique()
colors = plt.cm.jet(np.linspace(0, 1, orbits.size))
for orbit, color in zip(orbits, colors):
df = gf.query('relativeOrbit == @orbit')
poly = df.geometry.cascaded_union
if df.flightDirection.iloc[0] == 'ASCENDING':
linestyle = '--'
xpos, ypos = poly.centroid.x, poly.bounds[3]
else:
linestyle = '-'
xpos, ypos = poly.centroid.x, poly.bounds[1]
ax.add_geometries([poly],
ccrs.PlateCarree(),
facecolor='none',
edgecolor=color,
lw=2,
linestyle=linestyle)
ax.text(xpos,
ypos,
orbit,
color=color,
fontsize=16,
fontweight='bold',
transform=geodetic_CRS)
gl = ax.gridlines(plot_CRS,
draw_labels=True,
linewidth=0.5,
color='gray',
alpha=0.5,
linestyle='-')
gl.xlabels_top = False
gl.ylabels_left = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
plt.title('Orbital Footprints')
plt.savefig('map.pdf', bbox_inches='tight')
###############################################################################
# Create plot
# Create plot figure
fig = plt.figure(figsize=(8, 8))
# Add axes for lambert conformal map
# Set dimensions of axes with [X0, Y0, width, height] argument. Each value is a fraction of total figure size.
ax = plt.axes([.05, -.05, .9, 1],
projection=ccrs.LambertConformal(),
frameon=False)
# Set latitude and longitude extent of map
ax.set_extent([-119, -74, 18, 50], ccrs.Geodetic())
# Set shape name of map (which depicts the United States)
shapename = 'admin_1_states_provinces_lakes_shp'
states_shp = shpreader.natural_earth(resolution='110m',
category='cultural',
name=shapename)
# Set title and title fontsize of plot using gvutil function instead of matplotlib function call
gvutil.set_titles_and_labels(
ax,
maintitle=
"Average Annual Precipiation \n Computed for the period 1899-1999 \n NCDC climate division data \n",
maintitlefontsize=18)
# Add outlines of each state within the United States
def studyarea(path_plot):
'''Create a plot of the study area used in the Smartmove paper
Args
----
path_plot: str
Path and filename for plot to be saved. If 'None', no plot will be
saved (Defualt: None)
'''
def plot_coords(ax, proj, lon, lat, marker='o', color='black'):
'''Plot points at specified lon(s) and lat(s)'''
import cartopy.crs as ccrs
import numpy
lon = numpy.asarray(lon)
lat = numpy.asarray(lat)
points = proj.transform_points(ccrs.Geodetic(), lon, lat)
ax.scatter(points[:, 0], points[:, 1], marker='.', color=color)
return ax
import cartopy.crs as ccrs
from io import BytesIO
import matplotlib.pyplot as plt
from matplotlib import rcParams
import numpy
import os
from owslib.wms import WebMapService
import PIL
import seaborn
from . import maps
# Plotting configuration
rcParams.update({'figure.autolayout': True})
seaborn.set_context('paper')
sns_config = {
'axes.linewidth': 0.1,
'xtick.color': 'black',
'xtick.major.size': 4.0,
'xtick.direction': 'out',
'ytick.color': 'black',
'ytick.major.size': 4.0,
'ytick.direction': 'out'
}
seaborn.set_style('white', sns_config)
# Bounding box
lon0 = 18.565148 # BL
lat0 = 69.618893 # BL
lon1 = 19.071205 # TR
lat1 = 69.768302 # TR
layer = 'Vannflate'
srs = 'EPSG:32634'
# Create wms object for handling WMS calls
url = 'http://openwms.statkart.no/skwms1/wms.topo3?'
wms = WebMapService(url)
# Project bounding box
bbox = maps.project_bbox(srs, lon0, lat0, lon1, lat1)
owslib_img = maps.get_wms_png(wms,
bbox,
layer,
srs,
width=1600,
transparent=True)
# Convert binary png data to PIL Image data
color_land = (131, 196, 146, 255
) #(230, 230, 230, 255) # Hex color #f2f2f2
color_water = (237, 239, 242, 255) # Hex color #cccccc
# https://stackoverflow.com/a/43514640/943773
img = PIL.Image.open(BytesIO(owslib_img.read()))
land = PIL.Image.new('RGB', img.size, color_land)
water = PIL.Image.new('RGBA', img.size, color_water)
land.paste(water, mask=img.split()[3])
proj = ccrs.epsg(srs.split(':')[1])
# Create project axis and set extent to bounding box
ax = plt.axes(projection=proj)
extent = (bbox[0], bbox[2], bbox[1], bbox[3])
ax.set_extent(extent, proj)
# Plot projected image
ax.imshow(land, origin='upper', extent=extent, transform=proj)
# Project and plot Tromsø and study area positions
wgs84 = ccrs.Geodetic()
# Tromso
plot_coords(ax, proj, 18.955364, 69.649197, marker='o', color='#262626')
# Study area
plot_coords(ax, proj, 18.65125, 69.69942, marker='^', color='#a35d61')
# CTD
plot_coords(ax, proj, 18.65362, 69.70214, marker='o', color='#42a4f4')
# Create WGS84 axes labels with WGS84 axes positions
ticks_lon, xlabels = maps.map_ticks(lon0, lon1, 4)
ticks_lat, ylabels = maps.map_ticks(lat0, lat1, 4)
# Create list of lons/lats at axis edge for projecting ticks_lon/lats
lat_iter = numpy.array([lat0] * len(ticks_lon))
# Project ticks_lon/lats and set to axis labels
xpos = proj.transform_points(ccrs.Geodetic(), ticks_lon, lat_iter)[:, 0]
ypos = proj.transform_points(ccrs.Geodetic(), ticks_lon, ticks_lat)[:, 1]
ax.set_xticks(xpos)
ax.set_xticklabels(xlabels, ha='right', rotation=45)
ax.xaxis.tick_bottom()
ax.set_yticks(ypos)
ax.set_yticklabels(ylabels)
ax.yaxis.tick_right()
# Turn off grid for seaborn styling
ax.grid(False)
# Write image data if `path_plot` passed
if path_plot:
import pickle
# Save png of WMS data
filename_png = os.path.join(path_plot, '{}.png'.format(layer))
with open(filename_png, 'wb') as f:
f.write(owslib_img.read())
# Convert png to GEOTIFF data
maps.png2geotiff(filename_png, srs, bbox)
# Save pickle of `PIL` image data
file_pickle_img = os.path.join(path_plot, '{}_img.p'.format(layer))
pickle.dump(img, open(file_pickle_img, 'wb'))
# Save Cartopy plot
file_plot = os.path.join(path_plot, 'study_area.eps')
plt.savefig(file_plot, dpi=600)
plt.show()
return None
def add_inset(fig,extent,pos,bounds,label=None,polygon=None,anom=None,draw_cmap_y=None,hillshade=True,markup_sub=None,sub_pos=None,sub_adj=None):
sub_ax = fig.add_axes(pos,
projection=ccrs.Robinson(), label=label)
sub_ax.set_extent(extent, ccrs.Geodetic())
sub_ax.add_feature(cfeature.NaturalEarthFeature('physical', 'ocean', '50m', facecolor='gainsboro'))
sub_ax.add_feature(cfeature.NaturalEarthFeature('physical', 'land', '50m', facecolor='dimgrey'))
# if anom is not None:
# shape_feature = ShapelyFeature(Reader(shp_buff).geometries(), ccrs.PlateCarree(), edgecolor='black', alpha=0.5,
# facecolor='black', linewidth=1)
# sub_ax.add_feature(shape_feature)
if polygon is None and bounds is not None:
polygon = poly_from_extent(bounds)
if bounds is not None:
verts = mpath.Path(latlon_extent_to_robinson_axes_verts(polygon))
sub_ax.set_boundary(verts, transform=sub_ax.transAxes)
if hillshade:
def out_of_poly_mask(geoimg, poly_coords):
poly = ot.poly_from_coords(inter_poly_coords(poly_coords))
srs = osr.SpatialReference()
srs.ImportFromEPSG(54030)
# put in a memory vector
ds_shp = ot.create_mem_shp(poly, srs)
return ot.geoimg_mask_on_feat_shp_ds(ds_shp, geoimg)
def inter_poly_coords(polygon_coords):
list_lat_interp = []
list_lon_interp = []
for i in range(len(polygon_coords) - 1):
lon_interp = np.linspace(polygon_coords[i][0], polygon_coords[i + 1][0], 50)
lat_interp = np.linspace(polygon_coords[i][1], polygon_coords[i + 1][1], 50)
list_lon_interp.append(lon_interp)
list_lat_interp.append(lat_interp)
all_lon_interp = np.concatenate(list_lon_interp)
all_lat_interp = np.concatenate(list_lat_interp)
return np.array(list(zip(all_lon_interp, all_lat_interp)))
img = GeoImg(fn_hs)
hs_tmp = hs_land.copy()
hs_tmp_nl = hs_notland.copy()
mask = out_of_poly_mask(img, polygon)
hs_tmp[~mask] = 0
hs_tmp_nl[~mask] = 0
sub_ax.imshow(np.flip(hs_tmp[:, :], axis=0), extent=ext, transform=ccrs.Robinson(), cmap=cmap2, zorder=2)
sub_ax.imshow(np.flip(hs_tmp_nl[:, :], axis=0), extent=ext, transform=ccrs.Robinson(), cmap=cmap22, zorder=2)
sub_ax.outline_patch.set_edgecolor('white')
if anom is not None:
if anom == 'dh':
col_bounds = np.array([-0.8, -0.4, 0, 0.2, 0.4])
cb = []
cb_val = np.linspace(0, 1, len(col_bounds))
for j in range(len(cb_val)):
cb.append(mpl.cm.RdYlBu(cb_val[j]))
cmap_cus = mpl.colors.LinearSegmentedColormap.from_list('my_cb', list(
zip((col_bounds - min(col_bounds)) / (max(col_bounds - min(col_bounds))), cb)), N=1000)
vals = dhs
lab = 'Decadal difference in mean elevation change rate (m yr$^{-1}$)'
elif anom == 'dt':
col_bounds = np.array([-0.3, -0.15, 0, 0.3, 0.6])
cb = []
cb_val = np.linspace(0, 1, len(col_bounds))
for j in range(len(cb_val)):
cb.append(mpl.cm.RdBu_r(cb_val[j]))
cmap_cus = mpl.colors.LinearSegmentedColormap.from_list('my_cb', list(
zip((col_bounds - min(col_bounds)) / (max(col_bounds - min(col_bounds))), cb)), N=1000)
vals = dts
lab = 'Decadal difference in temperature (K)'
elif anom == 'dp':
col_bounds = np.array([-0.2, -0.1, 0, 0.1, 0.2])
cb = []
cb_val = np.linspace(0, 1, len(col_bounds))
for j in range(len(cb_val)):
cb.append(mpl.cm.BrBG(cb_val[j]))
cmap_cus = mpl.colors.LinearSegmentedColormap.from_list('my_cb', list(
zip((col_bounds - min(col_bounds)) / (max(col_bounds - min(col_bounds))), cb)), N=1000)
vals = dps
lab = 'Decadal difference in precipitation (m)'
# elif anom == 'du':
# col_bounds = np.array([-1, -0.5, 0, 0.5, 1])
# cb = []
# cb_val = np.linspace(0, 1, len(col_bounds))
# for j in range(len(cb_val)):
# cb.append(mpl.cm.RdBu_r(cb_val[j]))
# cmap_cus = mpl.colors.LinearSegmentedColormap.from_list('my_cb', list(
# zip((col_bounds - min(col_bounds)) / (max(col_bounds - min(col_bounds))), cb)), N=1000)
#
# vals = dus
# lab = 'Wind speed anomaly (m s$^{-1}$)'
#
# elif anom == 'dz':
#
# col_bounds = np.array([-100, -50, 0, 50, 100])
# cb = []
# cb_val = np.linspace(0, 1, len(col_bounds))
# for j in range(len(cb_val)):
# cb.append(mpl.cm.RdBu_r(cb_val[j]))
# cmap_cus = mpl.colors.LinearSegmentedColormap.from_list('my_cb', list(
# zip((col_bounds - min(col_bounds)) / (max(col_bounds - min(col_bounds))), cb)), N=1000)
#
# vals = dzs
# lab = 'Geopotential height anomaly at 500 hPa (m)'
#
# elif anom =='dk':
#
# col_bounds = np.array([-200000, -100000, 0, 100000, 200000])
# cb = []
# cb_val = np.linspace(0, 1, len(col_bounds))
# for j in range(len(cb_val)):
# cb.append(mpl.cm.RdBu_r(cb_val[j]))
# cmap_cus = mpl.colors.LinearSegmentedColormap.from_list('my_cb', list(
# zip((col_bounds - min(col_bounds)) / (max(col_bounds - min(col_bounds))), cb)), N=1000)
#
# vals = dks
# lab = 'Net clear-sky downwelling SW surface radiation anomaly (J m$^{-2}$)'
if draw_cmap_y is not None:
sub_ax_2 = fig.add_axes([0.2, draw_cmap_y, 0.6, 0.05])
sub_ax_2.set_xticks([])
sub_ax_2.set_yticks([])
sub_ax_2.spines['top'].set_visible(False)
sub_ax_2.spines['left'].set_visible(False)
sub_ax_2.spines['right'].set_visible(False)
sub_ax_2.spines['bottom'].set_visible(False)
cbaxes = sub_ax_2.inset_axes([0,0.85,1,0.2],label='legend_'+label)
norm = mpl.colors.Normalize(vmin=min(col_bounds), vmax=max(col_bounds))
sm = plt.cm.ScalarMappable(cmap=cmap_cus, norm=norm)
sm.set_array([])
cb = plt.colorbar(sm, cax=cbaxes, ticks=col_bounds, orientation='horizontal', extend='both', shrink=0.9)
# cb.ax.tick_params(labelsize=12)
cb.set_label(lab)
for i in range(len(tiles)):
lat, lon = SRTMGL1_naming_to_latlon(tiles[i])
if group_by_spec:
lat, lon, s = latlon_to_spec_center(lat, lon)
else:
lat = lat + 0.5
lon = lon + 0.5
s = (1,1)
# fac = 0.02
fac = 7000000.
rad = 10000. + np.nanmax((0.,0.2-errs[i]))**3*fac
div=(1+max(0,errs[i]-0.2)*10)**2
if ~np.isnan(vals[i]) and areas[i] > 0.2:
val = vals[i]
val_col = max(0.0001, min(0.9999, (val - min(col_bounds)) / (max(col_bounds) - min(col_bounds))))
col = cmap_cus(val_col)
elif areas[i] <= 5:
continue
else:
col = plt.cm.Greys(0.7)
# xy = [lon,lat]
xy = coordXform(ccrs.PlateCarree(), ccrs.Robinson(), np.array([lon]), np.array([lat]))[0][0:2]
rw = rad
rh = rad
x,y=xy
# sub_ax.add_patch(
# mpatches.Circle(xy=xy, radius=rad, color=col, alpha=1, transform=ccrs.Robinson(), zorder=30))
xl = 0.1*s[0] + 0.9*s[0]/div
yl =0.1*s[1] + 0.9*s[1]/div
sub_ax.add_patch(mpatches.Rectangle((lon-xl/2,lat-yl/2),xl,yl,facecolor=col,alpha=1,transform=ccrs.PlateCarree(),zorder=30))
if markup_sub is not None and anom == 'dh':
lon_min = np.min(list(zip(*polygon))[0])
lon_max = np.max(list(zip(*polygon))[0])
lon_mid = 0.5*(lon_min+lon_max)
lat_min = np.min(list(zip(*polygon))[1])
lat_max = np.max(list(zip(*polygon))[1])
lat_mid = 0.5*(lat_min+lat_max)
robin = np.array(list(zip([lon_min,lon_min,lon_min,lon_mid,lon_mid,lon_max,lon_max,lon_max],[lat_min,lat_mid,lat_max,lat_min,lat_max,lat_min,lat_mid,lat_max])))
if sub_pos=='lb':
rob_x = robin[0][0]
rob_y = robin[0][1]
ha='left'
va='bottom'
elif sub_pos=='lm':
rob_x = robin[1][0]
rob_y = robin[1][1]
ha='left'
va='center'
elif sub_pos=='lt':
rob_x = robin[2][0]
rob_y = robin[2][1]
ha='left'
va='top'
elif sub_pos=='mb':
rob_x = robin[3][0]
rob_y = robin[3][1]
ha='center'
va='bottom'
elif sub_pos=='mt':
rob_x = robin[4][0]
rob_y = robin[4][1]
ha='center'
va='top'
elif sub_pos=='rb':
rob_x = robin[5][0]
rob_y = robin[5][1]
ha='right'
va='bottom'
elif sub_pos=='rm':
rob_x = robin[6][0]
rob_y = robin[6][1]
ha='right'
va='center'
elif sub_pos=='rt':
rob_x = robin[7][0]
rob_y = robin[7][1]
ha='right'
va='top'
if sub_pos[0] == 'r':
rob_x = rob_x - 100000
elif sub_pos[0] == 'l':
rob_x = rob_x + 100000
if sub_pos[1] == 'b':
rob_y = rob_y + 100000
elif sub_pos[1] == 't':
rob_y = rob_y - 100000
if sub_adj is not None:
rob_x += sub_adj[0]
rob_y += sub_adj[1]
sub_ax.text(rob_x,rob_y,markup_sub,
horizontalalignment=ha, verticalalignment=va,
transform=ccrs.Robinson(), color='black',fontsize=10,bbox=dict(facecolor='white', alpha=1),fontweight='bold',zorder=25)
],
dtype=int),
coords=[
ds_force_yearly[start_year]['lat'],
ds_force_yearly[start_year]['lon']
],
dims=['lat', 'lon'])
# --- Plot VIC and GPM domain to check --- #
fig = plt.figure(figsize=(16, 8))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([
float(da_gpm_domain['lon'].min().values) - 0.5,
float(da_gpm_domain['lon'].max().values) + 0.5,
float(da_gpm_domain['lat'].min().values) - 0.5,
float(da_gpm_domain['lat'].max().values) + 0.5
], ccrs.Geodetic())
gl = add_gridlines(ax,
xlocs=np.arange(
float(da_gpm_domain['lon'].min().values) - 0.5,
float(da_gpm_domain['lon'].max().values) + 0.5, 1),
ylocs=np.arange(
float(da_gpm_domain['lat'].min().values) - 0.5,
float(da_gpm_domain['lat'].max().values) + 0.5, 1),
alpha=0)
# Plot GPM grids
lon_edges = edges_from_centers(da_gpm_domain['lon'].values)
lat_edges = edges_from_centers(da_gpm_domain['lat'].values)
lonlon, latlat = np.meshgrid(lon_edges, lat_edges)
cs = plt.pcolormesh(lonlon,
latlat,
da_gpm_domain.values,
inds.append(i)
else:
daily_mean_speed.append(np.mean(data.speed_2d[inds]))
next_day += 1
inds = []
plt.figure(figsize=(8, 6))
plt.plot(daily_mean_speed)
plt.xlabel("Day")
plt.ylabel("Mean Speed (m/s)")
import cartopy.crs as ccrs
import cartopy.feature as cfeature
proj = ccrs.Mercator()
plt.figure(figsize=(10, 10))
ax = plt.axes(projection=proj)
ax.set_extent((-25.0, 20.0, 52.0, 10.0))
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.BORDERS, linestyle=':')
for name in bird_names:
ix = birddata["bird_name"] == name
x, y = birddata.longitude[ix], birddata.latitude[ix]
ax.plot(x, y, ".", transform=ccrs.Geodetic(), label=name)
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.legend(loc="upper left")
plt.savefig("map.pdf")
norm=wv_norm)
# Add the text, complete with outline
text = ax.text(0.99,
0.01,
timestamp.strftime('%d %B %Y %H%MZ'),
horizontalalignment='right',
transform=ax.transAxes,
color='white',
fontsize='x-large',
weight='bold')
text.set_path_effects(
[patheffects.withStroke(linewidth=2, foreground='black')])
# PLOT 300-hPa Geopotential Heights and Wind Barbs
ax.set_extent([-132, -95, 25, 47], ccrs.Geodetic())
cs = ax.contour(lon, lat, Z_300, colors='black', transform=ccrs.PlateCarree())
ax.clabel(cs,
fontsize=12,
colors='k',
inline=1,
inline_spacing=8,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
ax.barbs(lon,
lat,
U_300.to('knots').m,
V_300.to('knots').m,
color='tab:red',
2 35946 98.4729 183.8200 0001770 165.0632 195.0663 14.37587360447041'''
s = '''ISS (ZARYA)
1 25544U 98067A 18107.44763100 .00001858 00000-0 35119-4 0 9997
2 25544 51.6430 321.5086 0002048 0.7561 69.2289 15.54271367109073'''
xtle = TwoLineElement(s)
msat = Satellite.Satellite(xtle)
#t = Tracker(msat)
#t.plot_position()
#t.plot_position(datetime.strptime('May 22 2018 12:20AM', '%b %d %Y %I:%M%p'))
#t.show_footprint()
plt.figure(figsize=(15, 25))
plt.axes(projection=ccrs.PlateCarree()).stock_img()
fig = plt.gcf()
fig.show()
fig.canvas.draw()
flag = True
color = ['r', 'g', 'b', 'y', 'm']
i = 0
t = datetime.utcnow()
while flag:
pos = msat.get_position(t)
plt.plot(pos[1], pos[0], 'ro', transform=ccrs.Geodetic())
t += timedelta(minutes=1)
plt.pause(1)
i = (i + 1) % len(color)
break
plt.show()
# fig = plt.figure(figsize = (20, 20))
# ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(ortho_trans[0], ortho_trans[1])) #(long, lat)
ax.add_feature(cfeature.OCEAN, zorder=0)
ax.add_feature(cfeature.LAND, zorder=0, edgecolor='grey')
ax.add_feature(cfeature.BORDERS, zorder=0, edgecolor='grey')
ax.add_feature(cfeature.LAKES, zorder=0)
ax.set_global()
ax.gridlines()
num_sta = len(list_of_stations)
longs = np.zeros(num_sta)
lats = np.zeros(num_sta)
for i in range(num_sta):
longs[i] = station_coords.longitude.loc[dict(station = list_of_stations[i])]
lats[i] = station_coords.latitude.loc[dict(station = list_of_stations[i])]
ax.scatter(longs, lats, transform = ccrs.Geodetic())
line, = ax.quiver(np.array([]), np.array([]), np.array([]), np.array([]), transform = ccrs.PlateCarree(), width = 0.002, color = "g")
def init():
# plswork = yz.plot_stations(list_of_stations, ortho_trans)
station_coords = csv_to_coords("First Pass/20190420-12-15-supermag-stations.csv")
# fig = plt.figure(figsize = (20, 20))
# ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(ortho_trans[0], ortho_trans[1])) #(long, lat)
ax.add_feature(cfeature.OCEAN, zorder=0)
ax.add_feature(cfeature.LAND, zorder=0, edgecolor='grey')
def main():
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal())
ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic())
shapename = 'admin_1_states_provinces_lakes_shp'
states_shp = shpreader.natural_earth(resolution='110m',
category='cultural',
name=shapename)
lons, lats = sample_data()
# to get the effect of having just the states without a map "background"
# turn off the outline and background patches
ax.background_patch.set_visible(False)
ax.outline_patch.set_visible(False)
ax.set_title('US States which intersect the track of '
'Hurricane Katrina (2005)')
# turn the lons and lats into a shapely LineString
track = sgeom.LineString(zip(lons, lats))
# buffer the linestring by two degrees (note: this is a non-physical
# distance)
track_buffer = track.buffer(2)
for state in shpreader.Reader(states_shp).geometries():
# pick a default color for the land with a black outline,
# this will change if the storm intersects with our track
facecolor = [0.9375, 0.9375, 0.859375]
edgecolor = 'black'
if state.intersects(track):
facecolor = 'red'
elif state.intersects(track_buffer):
facecolor = '#FF7E00'
ax.add_geometries([state],
ccrs.PlateCarree(),
facecolor=facecolor,
edgecolor=edgecolor)
ax.add_geometries([track_buffer],
ccrs.PlateCarree(),
facecolor='#C8A2C8',
alpha=0.5)
ax.add_geometries([track],
ccrs.PlateCarree(),
facecolor='none',
edgecolor='k')
# make two proxy artists to add to a legend
direct_hit = mpatches.Rectangle((0, 0), 1, 1, facecolor="red")
within_2_deg = mpatches.Rectangle((0, 0), 1, 1, facecolor="#FF7E00")
labels = [
'State directly intersects\nwith track',
'State is within \n2 degrees of track'
]
ax.legend([direct_hit, within_2_deg],
labels,
loc='lower left',
bbox_to_anchor=(0.025, -0.1),
fancybox=True)
plt.show()
ax.add_feature(feat.LAKES, zorder=-1)
ax.coastlines(resolution='110m', zorder=2, color='black')
ax.add_feature(state_boundaries, edgecolor='black')
ax.add_feature(feat.BORDERS, linewidth=2, edgecolor='black')
# Set plot bounds
ax.set_extent((-109.9, -101.8, 36.5, 41.3))
# Plot each station, labeling based on departure
for stn in range(len(pcpn)):
if pcpn_dep[stn] >= 0 and pcpn_dep[stn] < 2:
ax.plot(lon[stn],
lat[stn],
'g+',
markersize=7,
transform=ccrs.Geodetic())
ax.text(lon[stn], lat[stn], pcpn[stn], transform=ccrs.Geodetic())
elif pcpn_dep[stn] >= 2:
ax.plot(lon[stn],
lat[stn],
'm+',
markersize=7,
transform=ccrs.Geodetic())
ax.text(lon[stn], lat[stn], pcpn[stn], transform=ccrs.Geodetic())
elif pcpn_dep[stn] < 0:
ax.plot(lon[stn],
lat[stn],
'r+',
markersize=7,
transform=ccrs.Geodetic())
ax.text(lon[stn], lat[stn], pcpn[stn], transform=ccrs.Geodetic())
with gzip.open("srtm_1240-1300E_4740-4750N.asc.gz") as fp:
srtm = np.loadtxt(fp, skiprows=8)
# origin of data grid as stated in SRTM data file header
# create arrays with all lon/lat values from min to max and
lats = np.linspace(47.8333, 47.6666, srtm.shape[0])
lons = np.linspace(12.6666, 13.0000, srtm.shape[1])
# Prepare figure and Axis object with a proper projection and extent
projection = ccrs.PlateCarree()
fig = plt.figure(dpi=150)
ax = fig.add_subplot(111, projection=projection)
ax.set_extent((12.75, 12.95, 47.69, 47.81))
# create grids and compute map projection coordinates for lon/lat grid
grid = projection.transform_points(ccrs.Geodetic(),
*np.meshgrid(lons, lats), # unpacked x, y
srtm) # z from topo data
# Make contour plot
ax.contour(*grid.T, transform=projection, levels=30)
# Draw country borders with red
ax.add_feature(cfeature.BORDERS.with_scale('50m'), edgecolor='red')
# Draw a lon/lat grid and format the labels
gl = ax.gridlines(crs=projection, draw_labels=True)
gl.xlocator = mticker.FixedLocator([12.75, 12.8, 12.85, 12.9, 12.95])
gl.ylocator = mticker.FixedLocator([47.7, 47.75, 47.8])
gl.xformatter = LongitudeFormatter()
latitude.append(float(row1[6]))
IATA.append(row1[4])
#################
# map making part
#################
plt.figure(figsize=(15, 10))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_title("Flights From Tallinn")
# add coastline, land and borders to the map
ax.add_feature(cfeat.COASTLINE)
ax.add_feature(cfeat.OCEAN)
ax.add_feature(cfeat.LAND)
ax.add_feature(cfeat.BORDERS)
# add airports to map with IATA
for i in range(len(IATA)):
x = [src[1], longitude[i]]
y = [src[2], latitude[i]]
ax.plot(x, y, marker="o", transform=ccrs.Geodetic(), color="blue")
ax.text(longitude[i] + 0.2, latitude[i] + 0.2, IATA[i], weight="bold")
# add IATA to tallinn airport
ax.text(src[1], src[2], src[0], weight="bold")
# save figure
plt.savefig("FFT_map.png", dpi=300)
plt.show()
def _compute_extra_keys(self):
"""Compute our extra keys."""
global unknown_string
self.extra_keys = {}
# work out stuff based on these values from the message
edition = self.edition
# time-processed forcast time is from reference time to start of period
if edition == 2:
forecastTime = self.forecastTime
uft = np.uint32(forecastTime)
BILL = 2**30
# Workaround grib api's assumption that forecast time is positive.
# Handles correctly encoded -ve forecast times up to one -1 billion.
if hindcast_workaround:
if 2 * BILL < uft < 3 * BILL:
msg = "Re-interpreting negative forecastTime from " \
+ str(forecastTime)
forecastTime = -(uft - 2 * BILL)
msg += " to " + str(forecastTime)
warnings.warn(msg)
else:
forecastTime = self.startStep
#regular or rotated grid?
try:
longitudeOfSouthernPoleInDegrees = self.longitudeOfSouthernPoleInDegrees
latitudeOfSouthernPoleInDegrees = self.latitudeOfSouthernPoleInDegrees
except AttributeError:
longitudeOfSouthernPoleInDegrees = 0.0
latitudeOfSouthernPoleInDegrees = 90.0
centre = gribapi.grib_get_string(self.grib_message, "centre")
#default values
self.extra_keys = {
'_referenceDateTime': -1.0,
'_phenomenonDateTime': -1.0,
'_periodStartDateTime': -1.0,
'_periodEndDateTime': -1.0,
'_levelTypeName': unknown_string,
'_levelTypeUnits': unknown_string,
'_firstLevelTypeName': unknown_string,
'_firstLevelTypeUnits': unknown_string,
'_firstLevel': -1.0,
'_secondLevelTypeName': unknown_string,
'_secondLevel': -1.0,
'_originatingCentre': unknown_string,
'_forecastTime': None,
'_forecastTimeUnit': unknown_string,
'_coord_system': None,
'_x_circular': False,
'_x_coord_name': unknown_string,
'_y_coord_name': unknown_string,
# These are here to avoid repetition in the rules files,
# and reduce the very long line lengths.
'_x_points': None,
'_y_points': None,
'_cf_data': None
}
# cf phenomenon translation
if edition == 1:
# Get centre code (N.B. self.centre has default type = string)
centre_number = gribapi.grib_get_long(self.grib_message, "centre")
# Look for a known grib1-to-cf translation (or None).
cf_data = gptx.grib1_phenom_to_cf_info(
table2_version=self.table2Version,
centre_number=centre_number,
param_number=self.indicatorOfParameter)
self.extra_keys['_cf_data'] = cf_data
elif edition == 2:
# Don't attempt to interpret params if 'master tables version' is
# 255, as local params may then have same codes as standard ones.
if self.tablesVersion != 255:
# Look for a known grib2-to-cf translation (or None).
cf_data = gptx.grib2_phenom_to_cf_info(
param_discipline=self.discipline,
param_category=self.parameterCategory,
param_number=self.parameterNumber)
self.extra_keys['_cf_data'] = cf_data
#reference date
self.extra_keys['_referenceDateTime'] = \
datetime.datetime(int(self.year), int(self.month), int(self.day),
int(self.hour), int(self.minute))
# forecast time with workarounds
self.extra_keys['_forecastTime'] = forecastTime
#verification date
processingDone = self._get_processing_done()
#time processed?
if processingDone.startswith("time"):
if self.edition == 1:
validityDate = str(self.validityDate)
validityTime = "{:04}".format(int(self.validityTime))
endYear = int(validityDate[:4])
endMonth = int(validityDate[4:6])
endDay = int(validityDate[6:8])
endHour = int(validityTime[:2])
endMinute = int(validityTime[2:4])
elif self.edition == 2:
endYear = self.yearOfEndOfOverallTimeInterval
endMonth = self.monthOfEndOfOverallTimeInterval
endDay = self.dayOfEndOfOverallTimeInterval
endHour = self.hourOfEndOfOverallTimeInterval
endMinute = self.minuteOfEndOfOverallTimeInterval
# fixed forecastTime in hours
self.extra_keys['_periodStartDateTime'] = \
(self.extra_keys['_referenceDateTime'] +
datetime.timedelta(hours=int(forecastTime)))
self.extra_keys['_periodEndDateTime'] = \
datetime.datetime(endYear, endMonth, endDay, endHour, endMinute)
else:
self.extra_keys[
'_phenomenonDateTime'] = self._get_verification_date()
#originating centre
#TODO #574 Expand to include sub-centre
self.extra_keys['_originatingCentre'] = CENTRE_TITLES.get(
centre, "unknown centre %s" % centre)
#forecast time unit as a cm string
#TODO #575 Do we want PP or GRIB style forecast delta?
self.extra_keys['_forecastTimeUnit'] = self._timeunit_string()
#shape of the earth
#pre-defined sphere
if self.shapeOfTheEarth == 0:
geoid = coord_systems.GeogCS(semi_major_axis=6367470)
#custom sphere
elif self.shapeOfTheEarth == 1:
geoid = coord_systems.GeogCS(
self.scaledValueOfRadiusOfSphericalEarth *
10**-self.scaleFactorOfRadiusOfSphericalEarth)
#IAU65 oblate sphere
elif self.shapeOfTheEarth == 2:
geoid = coord_systems.GeogCS(6378160, inverse_flattening=297.0)
#custom oblate spheroid (km)
elif self.shapeOfTheEarth == 3:
geoid = coord_systems.GeogCS(
semi_major_axis=self.scaledValueOfEarthMajorAxis *
10**-self.scaleFactorOfEarthMajorAxis * 1000.,
semi_minor_axis=self.scaledValueOfEarthMinorAxis *
10**-self.scaleFactorOfEarthMinorAxis * 1000.)
#IAG-GRS80 oblate spheroid
elif self.shapeOfTheEarth == 4:
geoid = coord_systems.GeogCS(6378137, None, 298.257222101)
#WGS84
elif self.shapeOfTheEarth == 5:
geoid = \
coord_systems.GeogCS(6378137, inverse_flattening=298.257223563)
#pre-defined sphere
elif self.shapeOfTheEarth == 6:
geoid = coord_systems.GeogCS(6371229)
#custom oblate spheroid (m)
elif self.shapeOfTheEarth == 7:
geoid = coord_systems.GeogCS(
semi_major_axis=self.scaledValueOfEarthMajorAxis *
10**-self.scaleFactorOfEarthMajorAxis,
semi_minor_axis=self.scaledValueOfEarthMinorAxis *
10**-self.scaleFactorOfEarthMinorAxis)
elif self.shapeOfTheEarth == 8:
raise ValueError("unhandled shape of earth : grib earth shape = 8")
else:
raise ValueError("undefined shape of earth")
gridType = gribapi.grib_get_string(self.grib_message, "gridType")
if gridType in [
"regular_ll", "regular_gg", "reduced_ll", "reduced_gg"
]:
self.extra_keys['_x_coord_name'] = "longitude"
self.extra_keys['_y_coord_name'] = "latitude"
self.extra_keys['_coord_system'] = geoid
elif gridType == 'rotated_ll':
# TODO: Confirm the translation from angleOfRotation to
# north_pole_lon (usually 0 for both)
self.extra_keys['_x_coord_name'] = "grid_longitude"
self.extra_keys['_y_coord_name'] = "grid_latitude"
southPoleLon = longitudeOfSouthernPoleInDegrees
southPoleLat = latitudeOfSouthernPoleInDegrees
self.extra_keys['_coord_system'] = \
iris.coord_systems.RotatedGeogCS(
-southPoleLat,
math.fmod(southPoleLon + 180.0, 360.0),
self.angleOfRotation, geoid)
elif gridType == 'polar_stereographic':
self.extra_keys['_x_coord_name'] = "projection_x_coordinate"
self.extra_keys['_y_coord_name'] = "projection_y_coordinate"
if self.projectionCentreFlag == 0:
pole_lat = 90
elif self.projectionCentreFlag == 1:
pole_lat = -90
else:
raise TranslationError("Unhandled projectionCentreFlag")
# Note: I think the grib api defaults LaDInDegrees to 60 for grib1.
self.extra_keys['_coord_system'] = \
iris.coord_systems.Stereographic(
pole_lat, self.orientationOfTheGridInDegrees, 0, 0,
self.LaDInDegrees, ellipsoid=geoid)
elif gridType == 'lambert':
self.extra_keys['_x_coord_name'] = "projection_x_coordinate"
self.extra_keys['_y_coord_name'] = "projection_y_coordinate"
if self.edition == 1:
flag_name = "projectionCenterFlag"
else:
flag_name = "projectionCentreFlag"
if getattr(self, flag_name) == 0:
pole_lat = 90
elif getattr(self, flag_name) == 1:
pole_lat = -90
else:
raise TranslationError("Unhandled projectionCentreFlag")
LambertConformal = iris.coord_systems.LambertConformal
self.extra_keys['_coord_system'] = LambertConformal(
self.LaDInDegrees,
self.LoVInDegrees,
0,
0,
secant_latitudes=(self.Latin1InDegrees, self.Latin2InDegrees),
ellipsoid=geoid)
else:
raise TranslationError("unhandled grid type: {}".format(gridType))
if gridType in ["regular_ll", "rotated_ll"]:
self._regular_longitude_common()
j_step = self.jDirectionIncrementInDegrees
if not self.jScansPositively:
j_step = -j_step
self._y_points = (np.arange(self.Nj, dtype=np.float64) * j_step +
self.latitudeOfFirstGridPointInDegrees)
elif gridType in ['regular_gg']:
# longitude coordinate is straight-forward
self._regular_longitude_common()
# get the distinct latitudes, and make sure they are sorted
# (south-to-north) and then put them in the right direction
# depending on the scan direction
latitude_points = gribapi.grib_get_double_array(
self.grib_message, 'distinctLatitudes').astype(np.float64)
latitude_points.sort()
if not self.jScansPositively:
# we require latitudes north-to-south
self._y_points = latitude_points[::-1]
else:
self._y_points = latitude_points
elif gridType in ["polar_stereographic", "lambert"]:
# convert the starting latlon into meters
cartopy_crs = self.extra_keys['_coord_system'].as_cartopy_crs()
x1, y1 = cartopy_crs.transform_point(
self.longitudeOfFirstGridPointInDegrees,
self.latitudeOfFirstGridPointInDegrees, ccrs.Geodetic())
if not np.all(np.isfinite([x1, y1])):
raise TranslationError("Could not determine the first latitude"
" and/or longitude grid point.")
self._x_points = x1 + self.DxInMetres * np.arange(self.Nx,
dtype=np.float64)
self._y_points = y1 + self.DyInMetres * np.arange(self.Ny,
dtype=np.float64)
elif gridType in ["reduced_ll", "reduced_gg"]:
self._x_points = self.longitudes
self._y_points = self.latitudes
else:
raise TranslationError("unhandled grid type")
def PlateCarree_to_Orthographic(img,
llbd,
craters,
iglobe=None,
ctr_sub=False,
arad=1737.4,
origin="upper",
rgcoeff=1.2,
slivercut=0.):
"""Transform Plate Carree image and associated csv file into Orthographic.
Parameters
----------
img : PIL.Image.image or str
File or filename.
llbd : list-like
Long/lat limits (long_min, long_max, lat_min, lat_max) of image.
craters : pandas.DataFrame
Craters catalogue.
iglobe : cartopy.crs.Geodetic instance
Globe for images. If False, defaults to spherical Moon.
ctr_sub : bool, optional
If `True`, assumes craters dataframe includes only craters within
image. If `False` (default_, llbd used to cut craters from outside
image out of (copy of) dataframe.
arad : float
World radius in km. Default is Moon (1737.4 km).
origin : "lower" or "upper", optional
Based on imshow convention for displaying image y-axis. "upper"
(default) means that [0,0] is upper-left corner of image; "lower" means
it is bottom-left.
rgcoeff : float, optional
Fractional size increase of transformed image height. By default set
to 1.2 to prevent loss of fidelity during transform (though warping can
be so extreme that this might be meaningless).
slivercut : float from 0 to 1, optional
If transformed image aspect ratio is too narrow (and would lead to a
lot of padding, return null images).
Returns
-------
imgo : PIL.Image.image
Transformed, padded image in PIL.Image format.
ctr_xy : pandas.DataFrame
Craters with transformed x, y pixel positions and pixel radii.
distortion_coefficient : float
Ratio between the central heights of the transformed image and original
image.
centrallonglat_xy : pandas.DataFrame
xy position of the central longitude and latitude.
"""
# If user doesn't provide Moon globe properties.
#pdb.set_trace()
if not iglobe:
iglobe = ccrs.Globe(semimajor_axis=arad * 1000.,
semiminor_axis=arad * 1000.,
ellipse=None)
# Set up Geodetic (long/lat), Plate Carree (usually long/lat, but not when
# globe != WGS84) and Orthographic projections.
geoproj = ccrs.Geodetic(globe=iglobe)
iproj = ccrs.PlateCarree(globe=iglobe)
oproj = ccrs.Orthographic(central_longitude=np.mean(llbd[:2]),
central_latitude=np.mean(llbd[2:]),
globe=iglobe)
# Create and transform coordinates of image corners and edge midpoints.
# Due to Plate Carree and Orthographic's symmetries, max/min x/y values of
# these 9 points represent extrema of the transformed image.
xll = np.array([llbd[0], np.mean(llbd[:2]), llbd[1]])
yll = np.array([llbd[2], np.mean(llbd[2:]), llbd[3]])
xll, yll = np.meshgrid(xll, yll)
xll = xll.ravel()
yll = yll.ravel()
# [:,:2] because we don't need elevation data.
res = iproj.transform_points(x=xll, y=yll, src_crs=geoproj)[:, :2]
iextent = [min(res[:, 0]), max(res[:, 0]), min(res[:, 1]), max(res[:, 1])]
res = oproj.transform_points(x=xll, y=yll, src_crs=geoproj)[:, :2]
oextent = [min(res[:, 0]), max(res[:, 0]), min(res[:, 1]), max(res[:, 1])]
# Sanity check for narrow images; done before the most expensive part of
# the function.
oaspect = (oextent[1] - oextent[0]) / (oextent[3] - oextent[2])
# if oaspect < slivercut:
# return [None, None]
if type(img) != Image.Image:
Image.MAX_IMAGE_PIXELS = None
img = Image.open(img).convert("L")
# Warp image.
imgo, imgwshp, offset = WarpImagePad(img,
iproj,
iextent,
oproj,
oextent,
origin=origin,
rgcoeff=rgcoeff,
fillbg="black")
# Convert crater x, y position.
if ctr_sub:
llbd_in = None
else:
llbd_in = llbd
ctr_xy = WarpCraterLoc(craters,
geoproj,
oproj,
oextent,
imgwshp,
llbd=llbd_in,
origin=origin)
# Shift crater x, y positions by offset (origin doesn't matter for y-shift,
# since padding is symmetric).
ctr_xy.loc[:, "x"] += offset[0]
ctr_xy.loc[:, "y"] += offset[1]
# Pixel scale for orthographic determined (for images small enough that
# tan(x) approximately equals x + 1/3x^3 + ...) by l = R_moon*theta,
# where theta is the latitude extent of the centre of the image. Because
# projection transform doesn't guarantee central vertical axis will keep
# its pixel resolution, we need to calculate the conversion coefficient
# C = (res[7,1]- res[1,1])/(oextent[3] - oextent[2]).
# C0*pix height/C = theta
# Where theta is the latitude extent and C0 is the theta per pixel
# conversion for the Plate Carree image). Thus
# l_ctr = R_moon*C0*pix_ctr/C.
distortion_coefficient = ((res[7, 1] - res[1, 1]) /
(oextent[3] - oextent[2]))
if distortion_coefficient < 0.7:
raise ValueError("Distortion Coefficient cannot be"
" {0:.2f}!".format(distortion_coefficient))
pixperkm = trf.km2pix(imgo.size[1],
llbd[3] - llbd[2],
dc=distortion_coefficient,
a=arad)
ctr_xy["Diameter (pix)"] = ctr_xy["Diameter (km)"] * pixperkm
# Determine x, y position of central lat/long.
centrallonglat = pd.DataFrame({"Long": [xll[4]], "Lat": [yll[4]]})
centrallonglat_xy = WarpCraterLoc(centrallonglat,
geoproj,
oproj,
oextent,
imgwshp,
llbd=llbd_in,
origin=origin)
# Shift central long/lat
centrallonglat_xy.loc[:, "x"] += offset[0]
centrallonglat_xy.loc[:, "y"] += offset[1]
return [imgo, ctr_xy, distortion_coefficient, centrallonglat_xy]
def plot_cartopy(lons,
lats,
size,
color,
labels=None,
projection='global',
resolution='110m',
continent_fill_color='0.8',
water_fill_color='1.0',
colormap=None,
colorbar=None,
marker="o",
title=None,
colorbar_ticklabel_format=None,
show=True,
proj_kwargs=None,
**kwargs): # @UnusedVariable
"""
Creates a Cartopy plot with a data point scatter plot.
:type lons: list/tuple of floats
:param lons: Longitudes of the data points.
:type lats: list/tuple of floats
:param lats: Latitudes of the data points.
:type size: float or list/tuple of floats
:param size: Size of the individual points in the scatter plot.
:type color: list/tuple of floats (or objects that can be
converted to floats, like e.g.
:class:`~obspy.core.utcdatetime.UTCDateTime`)
:param color: Color information of the individual data points to be
used in the specified color map (e.g. origin depths,
origin times).
:type labels: list/tuple of str
:param labels: Annotations for the individual data points.
:type projection: str, optional
:param projection: The map projection.
Currently supported are:
* ``"global"`` (Will plot the whole world using
:class:`~cartopy.crs.Mollweide`.)
* ``"ortho"`` (Will center around the mean lat/long using
:class:`~cartopy.crs.Orthographic`.)
* ``"local"`` (Will plot around local events using
:class:`~cartopy.crs.AlbersEqualArea`.)
* Any other Cartopy :class:`~cartopy.crs.Projection`. An instance
of this class will be created using the supplied ``proj_kwargs``.
Defaults to "global"
:type resolution: str, optional
:param resolution: Resolution of the boundary database to use. Will be
passed directly to the Cartopy module. Possible values are:
* ``"110m"``
* ``"50m"``
* ``"10m"``
Defaults to ``"110m"``. For compatibility, you may also specify any of
the Basemap resolutions defined in :func:`plot_basemap`.
:type continent_fill_color: Valid matplotlib color, optional
:param continent_fill_color: Color of the continents. Defaults to
``"0.9"`` which is a light gray.
:type water_fill_color: Valid matplotlib color, optional
:param water_fill_color: Color of all water bodies.
Defaults to ``"white"``.
:type colormap: str, any matplotlib colormap, optional
:param colormap: The colormap for color-coding the events as provided
in `color` kwarg.
The event with the smallest `color` property will have the
color of one end of the colormap and the event with the highest
`color` property the color of the other end with all other events
in between.
Defaults to None which will use the default matplotlib colormap.
:type colorbar: bool, optional
:param colorbar: When left `None`, a colorbar is plotted if more than one
object is plotted. Using `True`/`False` the colorbar can be forced
on/off.
:type title: str
:param title: Title above plot.
:type colorbar_ticklabel_format: str or function or
subclass of :class:`matplotlib.ticker.Formatter`
:param colorbar_ticklabel_format: Format string or Formatter used to format
colorbar tick labels.
:type show: bool
:param show: Whether to show the figure after plotting or not. Can be used
to do further customization of the plot before showing it.
:type proj_kwargs: dict
:param proj_kwargs: Keyword arguments to pass to the Cartopy
:class:`~cartopy.ccrs.Projection`. In this dictionary, you may specify
``central_longitude='auto'`` or ``central_latitude='auto'`` to have
this function calculate the latitude or longitude as it would for other
projections. Some arguments may be ignored if you choose one of the
built-in ``projection`` choices.
"""
import matplotlib.pyplot as plt
min_color = min(color)
max_color = max(color)
if isinstance(color[0], (datetime.datetime, UTCDateTime)):
datetimeplot = True
color = [date2num(getattr(t, 'datetime', t)) for t in color]
else:
datetimeplot = False
scal_map = ScalarMappable(norm=Normalize(min_color, max_color),
cmap=colormap)
scal_map.set_array(np.linspace(0, 1, 1))
fig = plt.figure()
# The colorbar should only be plotted if more then one event is
# present.
if colorbar is not None:
show_colorbar = colorbar
else:
if len(lons) > 1 and hasattr(color, "__len__") and \
not isinstance(color, (str, native_str)):
show_colorbar = True
else:
show_colorbar = False
if projection == "local":
ax_x0, ax_width = 0.10, 0.80
elif projection == "global":
ax_x0, ax_width = 0.01, 0.98
else:
ax_x0, ax_width = 0.05, 0.90
proj_kwargs = proj_kwargs or {}
if projection == 'global':
proj_kwargs['central_longitude'] = np.mean(lons)
proj = ccrs.Mollweide(**proj_kwargs)
elif projection == 'ortho':
proj_kwargs['central_latitude'] = np.mean(lats)
proj_kwargs['central_longitude'] = mean_longitude(lons)
proj = ccrs.Orthographic(**proj_kwargs)
elif projection == 'local':
if min(lons) < -150 and max(lons) > 150:
max_lons = max(np.array(lons) % 360)
min_lons = min(np.array(lons) % 360)
else:
max_lons = max(lons)
min_lons = min(lons)
lat_0 = max(lats) / 2. + min(lats) / 2.
lon_0 = max_lons / 2. + min_lons / 2.
if lon_0 > 180:
lon_0 -= 360
deg2m_lat = 2 * np.pi * 6371 * 1000 / 360
deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi)
if len(lats) > 1:
height = (max(lats) - min(lats)) * deg2m_lat
width = (max_lons - min_lons) * deg2m_lon
margin = 0.2 * (width + height)
height += margin
width += margin
else:
height = 2.0 * deg2m_lat
width = 5.0 * deg2m_lon
# Do intelligent aspect calculation for local projection
# adjust to figure dimensions
w, h = fig.get_size_inches()
aspect = w / h
if show_colorbar:
aspect *= 1.2
if width / height < aspect:
width = height * aspect
else:
height = width / aspect
proj_kwargs['central_latitude'] = lat_0
proj_kwargs['central_longitude'] = lon_0
proj = ccrs.AlbersEqualArea(**proj_kwargs)
# User-supplied projection.
elif isinstance(projection, type):
if 'central_longitude' in proj_kwargs:
if proj_kwargs['central_longitude'] == 'auto':
proj_kwargs['central_longitude'] = mean_longitude(lons)
if 'central_latitude' in proj_kwargs:
if proj_kwargs['central_latitude'] == 'auto':
proj_kwargs['central_latitude'] = np.mean(lats)
if 'pole_longitude' in proj_kwargs:
if proj_kwargs['pole_longitude'] == 'auto':
proj_kwargs['pole_longitude'] = np.mean(lons)
if 'pole_latitude' in proj_kwargs:
if proj_kwargs['pole_latitude'] == 'auto':
proj_kwargs['pole_latitude'] = np.mean(lats)
proj = projection(**proj_kwargs)
else:
msg = "Projection '%s' not supported." % projection
raise ValueError(msg)
if show_colorbar:
map_ax = fig.add_axes([ax_x0, 0.13, ax_width, 0.77], projection=proj)
cm_ax = fig.add_axes([ax_x0, 0.05, ax_width, 0.05])
plt.sca(map_ax)
else:
ax_y0, ax_height = 0.05, 0.85
if projection == "local":
ax_y0 += 0.05
ax_height -= 0.05
map_ax = fig.add_axes([ax_x0, ax_y0, ax_width, ax_height],
projection=proj)
if projection == 'local':
x0, y0 = proj.transform_point(lon_0, lat_0, proj.as_geodetic())
map_ax.set_xlim(x0 - width / 2, x0 + width / 2)
map_ax.set_ylim(y0 - height / 2, y0 + height / 2)
else:
map_ax.set_global()
# Pick features at specified resolution.
resolution = _CARTOPY_RESOLUTIONS[resolution]
try:
borders, land, ocean = _CARTOPY_FEATURES[resolution]
except KeyError:
borders = cfeature.NaturalEarthFeature(cfeature.BORDERS.category,
cfeature.BORDERS.name,
resolution,
edgecolor='none',
facecolor='none')
land = cfeature.NaturalEarthFeature(cfeature.LAND.category,
cfeature.LAND.name,
resolution,
edgecolor='face',
facecolor='none')
ocean = cfeature.NaturalEarthFeature(cfeature.OCEAN.category,
cfeature.OCEAN.name,
resolution,
edgecolor='face',
facecolor='none')
_CARTOPY_FEATURES[resolution] = (borders, land, ocean)
# Draw coast lines, country boundaries, fill continents.
if MATPLOTLIB_VERSION >= [2, 0, 0]:
map_ax.set_facecolor(water_fill_color)
else:
map_ax.set_axis_bgcolor(water_fill_color)
map_ax.add_feature(ocean, facecolor=water_fill_color)
map_ax.add_feature(land, facecolor=continent_fill_color)
map_ax.add_feature(borders, edgecolor='0.75')
map_ax.coastlines(resolution=resolution, color='0.4')
# Draw grid lines - TODO: draw_labels=True doesn't work yet.
if projection == 'local':
map_ax.gridlines()
else:
# Draw lat/lon grid lines every 30 degrees.
map_ax.gridlines(xlocs=range(-180, 181, 30), ylocs=range(-90, 91, 30))
# Plot labels
if labels and len(lons) > 0:
with map_ax.hold_limits():
for name, xpt, ypt, _colorpt in zip(labels, lons, lats, color):
map_ax.text(xpt,
ypt,
name,
weight="heavy",
color="k",
zorder=100,
transform=ccrs.Geodetic(),
path_effects=[
PathEffects.withStroke(linewidth=3,
foreground="white")
])
scatter = map_ax.scatter(lons,
lats,
marker=marker,
s=size,
c=color,
zorder=10,
cmap=colormap,
transform=ccrs.Geodetic())
if title:
plt.suptitle(title)
# Only show the colorbar for more than one event.
if show_colorbar:
if colorbar_ticklabel_format is not None:
if isinstance(colorbar_ticklabel_format, (str, native_str)):
formatter = FormatStrFormatter(colorbar_ticklabel_format)
elif hasattr(colorbar_ticklabel_format, '__call__'):
formatter = FuncFormatter(colorbar_ticklabel_format)
elif isinstance(colorbar_ticklabel_format, Formatter):
formatter = colorbar_ticklabel_format
locator = MaxNLocator(5)
else:
if datetimeplot:
locator = AutoDateLocator()
formatter = AutoDateFormatter(locator)
# Compat with old matplotlib versions.
if hasattr(formatter, "scaled"):
formatter.scaled[1 / (24. * 60.)] = '%H:%M:%S'
else:
locator = None
formatter = None
cb = Colorbar(cm_ax,
scatter,
cmap=colormap,
orientation='horizontal',
ticks=locator,
format=formatter)
# Compat with old matplotlib versions.
if hasattr(cb, "update_ticks"):
cb.update_ticks()
if show:
plt.show()
return fig
def plot_trajectories_ens(traj, savepath, ls=None, config=None):
"""
plot multiple trajectories into one scene
Args:
traj (:class:`.trajectory`): trajectory to plot instance to plot
savepath (str): path to save
ls (:class:`trace_source.land_sfc.land_sfc`, optional): pre loaded land surface information
config (dict, optional): the toml derived config dict
Returns:
None
"""
import matplotlib
matplotlib.use('Agg')
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
if ls is None:
ls = trace_source.land_sfc.land_sfc()
colors = ['purple', 'darkorange', 'hotpink', 'dimgrey']
c = 'purple'
if not os.path.isdir(savepath):
os.makedirs(savepath)
###
# the map
###
fig = plt.figure(figsize=(8, 10))
if config is not None and "centerlon" in config['plotmap']:
ax = plt.axes(projection=ccrs.Miller(
central_longitude=config['plotmap']['centerlon']))
else:
ax = plt.axes(projection=ccrs.Miller(central_longitude=-170.))
ax = plt.axes(projection=ccrs.Miller())
# Projection for the north pole
# ax = plt.axes(projection=ccrs.NorthPolarStereo())
raise ValueError('provide plotmap.centerlon in the config file')
####
# make a color map of fixed colors
cmap = matplotlib.colors.ListedColormap([
'lightskyblue', 'darkgreen', 'khaki', 'palegreen', 'red', 'white',
'tan'
])
bounds = [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5]
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
####
pcm = ax.pcolormesh(ls.longs,
ls.lats,
ls.land_sfc,
cmap=cmap,
norm=norm,
transform=ccrs.PlateCarree())
ax.coastlines()
for k, v in traj.data.items():
ax.plot(v['longitude'],
v['latitude'],
linewidth=1.5,
color=c,
transform=ccrs.Geodetic())
ax.plot(v['longitude'][::24],
v['latitude'][::24],
'.',
color=c,
transform=ccrs.Geodetic())
ax.gridlines(linestyle=':')
if config is not None and "bounds" in config['plotmap']:
ax.set_extent(config['plotmap']['bounds'], crs=ccrs.PlateCarree())
else:
# bounds for punta arenas
ax.set_extent([-180, 180, -75, 0], crs=ccrs.PlateCarree())
# bounds for atlantic
#ax.set_extent([-180, 80, -70, 75], crs=ccrs.PlateCarree())
# bounds for the north pole
#ax.set_extent([-180, 180, 50, 90], crs=ccrs.PlateCarree())
raise ValueError('provide plotmap.bounds in the config file')
ax.set_title('Trajectory {}UTC\n{} {} '.format(
traj.info['date'].strftime('%Y-%m-%d %H'), traj.info['lat'],
traj.info['lon']),
fontweight='semibold',
fontsize=13)
savename = savepath + "/" + traj.info['date'].strftime("%Y%m%d_%H") \
+ '_' + '{:0>5.0f}'.format(traj.info['height']) + "_trajectories_map.png"
fig.savefig(savename, dpi=250)
plt.close()
###
# the profile
###
fig, ax = plt.subplots(4, 1, figsize=(6, 7), sharex=True)
ax[0].grid(True, axis='y')
for k, v in traj.data.items():
ax[0].plot(v['time'], v['height'])
ax[1].plot(v['time'], v['AIR_TEMP'][:] - 273.15)
ax[2].plot(v['time'], v['RELHUMID'][:])
ax[3].plot(v['time'], v['RAINFALL'][:])
ax[0].set_ylim(0, 10000)
ax[0].set_ylabel('Height [m]')
ax[0].xaxis.set_major_locator(matplotlib.dates.DayLocator(interval=2))
ax[0].xaxis.set_minor_locator(matplotlib.dates.HourLocator([0, 12]))
ax[0].xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%m-%d'))
# ax[1].set_ylim(0,10000)
ax[1].set_ylabel('Temperature [°C]')
ax[1].set_ylim(-40, 10)
ax[1].xaxis.set_major_locator(matplotlib.dates.DayLocator(interval=2))
ax[1].xaxis.set_minor_locator(matplotlib.dates.HourLocator([0, 12]))
ax[1].xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%m-%d'))
# ax[2].grid(True, axis='y')
ax[2].set_ylim(0, 100)
ax[2].set_ylabel('rel Humidity [%]')
ax[2].xaxis.set_major_locator(matplotlib.dates.DayLocator(interval=2))
ax[2].xaxis.set_minor_locator(matplotlib.dates.HourLocator([0, 12]))
ax[2].xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%m-%d'))
# ax[3].grid(True, axis='y')
ax[3].set_ylim(0, 20)
ax[3].set_xlabel('Day')
ax[3].set_ylabel('Precip [mm]')
ax[3].xaxis.set_major_locator(matplotlib.dates.DayLocator(interval=2))
ax[3].xaxis.set_minor_locator(matplotlib.dates.HourLocator([0, 12]))
ax[3].xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%m-%d'))
for axis in ax:
axis.tick_params(axis='both',
which='both',
right=True,
top=True,
direction='in')
ax[0].set_title('Trajectory {}UTC\n{} {} '.format(
traj.info['date'].strftime('%Y-%m-%d %H'), traj.info['lat'],
traj.info['lon']),
fontweight='semibold',
fontsize=13)
fig.tight_layout()
savename = savepath + "/" + traj.info['date'].strftime("%Y%m%d_%H") \
+ '_' + '{:0>5.0f}'.format(traj.info['height']) + "_trajectories_prof.png"
fig.savefig(savename, dpi=250)
plt.close()
def plot_all(self, out_path=None, Save = False, num_stations=1000):
"""
Plotting all stations on a map, divided in color of stations with
full observations in the reference period and not.
Arguments:
num_stations = 1000, selects the number of stations to construct the
dataset.
out_path, Path of the folder to save pdf of plots
Save = False, choosing to hide plots and not saving them.
"""
#plotting stations with full data in period 1960-1990:
fig = plt.figure(figsize=(12,6))
fig.suptitle("Stations with (red) and without (green) full interset period")
ax = fig.add_subplot(1,1,1,projection=ccrs.PlateCarree())
ax.set_global()
ax.coastlines()
ax.gridlines(draw_labels=True)
ax.add_feature(cfeature.BORDERS, edgecolor='gray')
ax.add_feature(cfeature.LAND)
w_interest_lon = []
w_interest_lat = []
wo_interest_lon =[]
wo_interest_lat =[]
for i in tqdm(range(num_stations)):
try:
Obs = Observation(self.file_list[i])
Obs.nan_interest_period()
if Obs.interest:
w_interest_lon.append([Obs.lon_line])
w_interest_lat.append([Obs.lat_line])
ax.plot([Obs.lon_line], [Obs.lat_line], color='red', linewidth=1, marker='o', markersize=2, transform=ccrs.Geodetic())
else:
wo_interest_lon.append([Obs.lon_line])
wo_interest_lat.append([Obs.lat_line])
ax.plot([Obs.lon_line], [Obs.lat_line], color='green', linewidth=1, marker='o', markersize=2, transform=ccrs.Geodetic())
except:
continue
#plotting separately stations with interest period and without interest period
fig1 = plt.figure(figsize=(12,6))
fig1.suptitle("Stations with full interset period")
ax1 = fig1.add_subplot(1,1,1,projection=ccrs.PlateCarree())
ax1.set_global()
ax1.coastlines()
ax1.gridlines(draw_labels=True)
ax1.add_feature(cfeature.BORDERS, edgecolor='gray')
ax1.add_feature(cfeature.LAND)
plt.scatter(w_interest_lon, w_interest_lat, color='red',s=3, linewidth=1, marker='o', transform=ccrs.Geodetic())
fig2 = plt.figure(figsize=(12,6))
fig2.suptitle("Stations without full interset period")
ax2 = fig2.add_subplot(1,1,1,projection=ccrs.PlateCarree())
ax2.set_global()
ax2.coastlines()
ax2.gridlines(draw_labels=True)
ax2.add_feature(cfeature.BORDERS, edgecolor='gray')
ax2.add_feature(cfeature.LAND)
plt.scatter(wo_interest_lon, wo_interest_lat, color='green',s = 3, linewidth=1, marker='o', transform=ccrs.Geodetic())
if Save:
#Saving plot
fig.savefig(out_path + '/All_Stations_Crutem.pdf')
fig1.savefig(out_path + '/Stations_full_interest.pdf')
fig2.savefig(out_path + '/Station_wo_full_interest.pdf')
plt.close()
nc_fid = Dataset(
'J:/REANALYSES/ERA5/SWE_Cumul_Nittasinan/climato/ERA5_Cumul_SWE_climatology_1990-2019.nc',
'r')
data = nc_fid.variables['sd'][:].squeeze()
lons = nc_fid.variables['longitude'][:].squeeze()
lats = nc_fid.variables['latitude'][:].squeeze()
#sr_clim_90_2019 = data.mean(axis=0)
#sr_clim_90_2019.data[sr_clim_90_2019 == 0] = np.nan
fig = plt.figure(figsize=(28, 16))
crs = ccrs.LambertConformal()
# crs=ccrs.PlateCarree()
ax = plt.axes(projection=crs)
plot_background(ax)
ax.plot(-69.72, 48.15, "bo", markersize=7, transform=ccrs.Geodetic())
ax.text(-69.5,
48.17,
"Tadoussac",
color='blue',
fontsize=20,
fontweight='bold',
transform=ccrs.Geodetic())
ax.plot(-66.4,
50.21,
"bo",
markersize=7,
color='blue',
transform=ccrs.Geodetic())
ax.text(-66.2,
def cleansing(self, out_path=None, Save = False, sliced = False):
"""
cleaning the dataframe of stations keeping only the stations with a
complete reference period 1960-1990. Subsequently it plots the result and
updated self.metadata and self.df
Arguments:
out_path, Path to save plots.
Save = False, Tell if the plots are going to be saved.
verbosity = False, Show or not show plots of the cleaning.
"""
taxis = pd.date_range('1850-01', '2019-01', freq='M')
dropped = pd.DataFrame({'time': taxis})
dropped = dropped.set_index(['time'])
try:
if sliced:
self.sliced
else:
self.df
except:
print('Full_dataset object needs to be converted into dataframe before cleansing. You can type data = Full_stations(file_list).to_dataset()')
return
if sliced:
for i in tqdm(self.meta_slice.index, desc = 'Cleaning df'):
if any(np.isnan(self.sliced[i]['1960-01-31':'1990-01-31'])):
dropped = pd.merge(dropped, self.sliced[i], on='time', how = 'left')
self.sliced = self.sliced.drop(i, 1)
self.meta_slice = self.meta_slice.drop(index = i, axis = 0)
self.df = self.df.drop(i, 1)
self.metadata = self.metadata.drop(index = i, axis = 0)
else:
for i in tqdm(self.df.columns, desc = 'Cleaning df'):
if any(np.isnan(self.df[i]['1960-01-31':'1990-01-31'])):
dropped = pd.merge(dropped, self.df[i], on='time', how = 'left')
self.df = self.df.drop(i, 1)
self.metadata = self.metadata.drop(index = i, axis = 0)
#Cleaning the gridded dataframe from dropped stations
try:
#if grid exist we go on
self.grid
#retrieve indices to apply loc.
df_indices = self.grid[self.grid['indices'] != 0].index
for i in tqdm(df_indices, desc = 'Grid cleaning'):
#redefine station_id indices of gridded dataset removing the ones dropped
self.grid.loc[i].indices = [x for x in self.grid.loc[i].indices if x not in dropped.columns]
self.grid.loc[i].number_station = len(self.grid.loc[i].indices)
#if the list is empty after the cleansing then we set the entry to zero
if len(self.grid.loc[i].indices) == 0:
self.grid.loc[i].indices = 0
self.grid.loc[i].number_station = 0
except:
pass
if Save:
fig = plt.figure(figsize=(12,6))
fig.suptitle("Visualizing cleansing")
ax = fig.add_subplot(1,1,1,projection=ccrs.PlateCarree())
ax.gridlines(draw_labels=True)
ax.coastlines()
ax.stock_img()
ax.add_feature(cfeature.LAND)
ax.set_global()
ax.add_feature(cfeature.BORDERS, edgecolor='gray')
if sliced:
ax.set_extent([self.ext_lon[0]-3, self.ext_lon[1]+3, self.ext_lat[0]-3, self.ext_lat[1]+3], ccrs.PlateCarree())
ax.plot(self.meta_slice['lon'].values, self.meta_slice['lat'].values, 'bo', markersize=2, transform=ccrs.Geodetic())
else:
count = 0
while count < len(self.metadata):
ax.plot(self.metadata.lon.values[count], self.metadata.lat.values[count], color='blue', marker='o', markersize=2, transform=ccrs.Geodetic())
count += 1
fig.savefig(out_path + "/Cleansing_slicing:{}.pdf".format(sliced))
def as_cartopy_crs(self):
return ccrs.Geodetic(self.as_cartopy_globe())
def grid_contribution(self, grid, out_path=None, Save = False, sliced = False, verbosity = True):
"""
Plotting which grids contribute with a station marked with an X.
Arguments:
grid, Takes the grid output of self.regridding to plot
out_path, Path to save plots.
Save = False, Tell if the plots are going to be saved.
verbosity = False, Show or not show plots of the cleaning.
"""
#plotting contribution cells along with point of the stations
#it is defined as a self method of Full_Stations to be able to
#record all the files simply. It still takes as input the grid which
#needs to be defined outside
#getting the steps of the grid
grid_lon_step = grid.loc[0, :]['max_long'] - grid.loc[0, :]['min_long']
grid_lat_step = grid.loc[0, :]['max_lat'] - grid.loc[0, :]['min_lat']
#plotting the grid based on the grid step
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(1,1,1,projection=ccrs.PlateCarree())
ax.set_global()
ax.add_feature(cfeature.LAND)
if sliced:
lat_ext = [int(grid_lat_step* mt.floor(self.ext_lat[0]/grid_lat_step)), int(grid_lat_step * mt.ceil(self.ext_lat[1]/grid_lat_step))]
lon_ext = [int(grid_lon_step * mt.floor(self.ext_lon[0]/grid_lon_step)), int(grid_lon_step * mt.ceil(self.ext_lon[1]/grid_lon_step))]
#ax.gridlines(xlocs=range(mt.floor(self.ext_lon[0]),mt.floor(self.ext_lon[1]),grid_lon_step), ylocs=range(mt.floor(self.ext_lat[0]),mt.floor(self.ext_lat[1]+1),grid_lat_step))
#ax.set_extent([mt.floor(self.ext_lon[0]),mt.ceil(self.ext_lon[1]), mt.floor(self.ext_lat[0]),mt.ceil(self.ext_lat[1])], ccrs.PlateCarree())
ax.gridlines(xlocs=range(lon_ext[0],lon_ext[1]+grid_lon_step,grid_lon_step), ylocs=range(lat_ext[0], lat_ext[1]+grid_lat_step,grid_lat_step))
ax.set_extent([lon_ext[0], lon_ext[1], lat_ext[0], lat_ext[1]], ccrs.PlateCarree())
else:
ax.gridlines(xlocs=range(-180,181,grid_lon_step), ylocs=range(-90,91,grid_lat_step))
ax.coastlines()
ax.add_feature(cfeature.BORDERS, edgecolor='gray')
#cycling on all grid entries and plotting
for i in tqdm(range(len(grid))):
#if the indices of the grid is not null (so the cell contains stations)
if grid.loc[i, :]['indices'] != 0:
#plots an x on the central latitude and longitude
central_lat = grid.loc[i, :]['max_lat'] - (grid.loc[i, :]['max_lat'] - grid.loc[i, :]['min_lat'])/2.
central_lon = grid.loc[i, :]['max_long'] - (grid.loc[i, :]['max_long'] - grid.loc[i, :]['min_long'])/2.
ax.plot([central_lon], [central_lat], color='red', marker='x', markersize=7, transform=ccrs.Geodetic())
for i in grid.loc[i, :]['indices']:
#for each station in the cell plots a point at its coordinate. Coordinates
#retrieved from the metadata dataset from self
if sliced:
ax.plot(self.meta_slice.loc[i]['lon'], self.meta_slice.loc[i]['lat'], color='blue', marker='o', markersize=1, transform=ccrs.Geodetic())
else:
ax.plot(self.metadata.loc[i]['lon'], self.metadata.loc[i]['lat'], color='blue', marker='o', markersize=1, transform=ccrs.Geodetic())
if not verbosity:
plt.close()
if Save:
fig.savefig(out_path + '/Contributing_Areas.pdf')
plt.close()
bird_names = bird_data['bird_name'].unique()
bird_data['date_time'] = pd.to_datetime(bird_data['date_time'])
bird_data['date'] = bird_data['date_time'].dt.date
grouped_bird_data = bird_data.groupby(['bird_name', 'date'])
plt.figure(figsize=(6, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_extent((-25, 20, 52, 10))
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.BORDERS, linestyle=':')
for name in bird_names:
ix = bird_data['bird_name'] == name
x, y = bird_data['longitude'][ix], bird_data['latitude'][ix]
plt.plot(x, y, '.', transform=ccrs.Geodetic(), label=name)
plt.title("Bird Routes")
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.legend(loc='upper left')
plt.savefig('plots/bird_migration_1')
plt.figure(figsize=(10, 4))
for name in bird_names:
ix = bird_data['bird_name'] == name
bird_data['speed_2d'][ix].plot(kind='hist',
histtype='step',
bins=np.linspace(0, 20, 20),
normed=True,
label=name)
plt.title("Speed Histogram")
def test_against_great_circle_and_gmt(self):
ll.info('testing depth against gmt extracted depths')
gmt = np.array([
-0.648317,
81.9962170001,
-2574.32325995,
-1.253283,
81.9983000001,
-2304.80896863,
-1.856417,
81.9994330001,
-1992.40861882,
-2.396967,
81.9947830001,
-2540.36409855,
-3.130133,
81.9987830001,
-3509.64727122,
-3.707333,
81.9998670001,
-4018.8108823,
-4.296033,
81.997083,
-4011.25898682,
-4.920717,
81.9994000001,
-3525.6963024,
-5.50345000001,
82.0028670001,
-3023.12097893,
-6.130917,
82.0015830001,
-3006.31591506,
-6.69468300003,
81.9966170001,
-3190.38107675,
-7.31206699999,
81.9941330001,
-3115.38103425,
-7.93959999997,
82.000267,
-2982.875422,
-8.19741700001,
81.9721330001,
-2911.32670751,
-0.62855,
81.9993830001,
-2589.30052845,
-0.051117,
81.999717,
-2822.43058789,
0.56,
82,
-3006.25619309,
1.705833,
81.9945,
-3435.51613899,
2.310417,
82.0015,
-2030.92420387,
2.89165,
81.9971670001,
-1309.44942805,
3.48035,
81.996817,
-1136.05331727,
4.093633,
81.99945,
-1098.94253697,
4.6825,
81.998917,
-1275.39851465,
5.290717,
81.998917,
-1345.6958582,
5.878783,
82.000133,
-989.180725733,
6.76653300003,
82.0143830001,
-796.276219227,
7.07896700002,
82.00315,
-777.077539801,
])
gmt = gmt.reshape(27, 3)
gmtz = gmt[:, 2]
lon = gmt[:, 0]
lat = gmt[:, 1]
xy = self.i.projection.transform_points(ccrs.Geodetic(), lon, lat)
dz = self.i.interp_depth(xy[:, 0], xy[:, 1])
mz = self.i.map_depth(xy[:, 0], xy[:, 1])
plt.figure()
plt.plot(lon, mz, label='map_depth')
plt.plot(lon, dz, label='interp_depth')
plt.plot(lon, gmtz, label='gmt')
plt.legend()
plt.title('depth')
plt.xlabel('Longitude')
plt.savefig(os.path.join(outdir, 'depth_vs_gmt.png'))
# high atol = 22 needed for python 3.4
np.testing.assert_allclose(gmtz, dz, atol=22)
np.testing.assert_allclose(gmtz, mz, atol=22)
flow_feature = ShapelyFeature(
Reader('../Structures_files/Shapefiles/UCRBstreams.shp').geometries(),
ccrs.PlateCarree(),
edgecolor='royalblue',
facecolor='None')
fig = plt.figure(figsize=(18, 9))
ax = plt.axes(projection=tiles.crs)
#ax.add_feature(rivers_10m, facecolor='None', edgecolor='royalblue', linewidth=2, zorder=4)
ax.add_image(tiles, 9, interpolation='none', alpha=0.8)
ax.set_extent(extent)
ax.add_feature(shape_feature, facecolor='#a1a384', alpha=0.6)
ax.add_feature(flow_feature, alpha=0.6, linewidth=1.5, zorder=4)
points = ax.scatter(structures['X'],
structures['Y'],
marker='.',
s=50,
c='black',
transform=ccrs.Geodetic(),
zorder=5)
#geom = geometry.box(extent[0],extent[2], extent[1], extent[3])
#fig = plt.figure(figsize=(18, 9))
#ax = plt.axes(projection=tiles.crs)
#ax.add_feature(rivers_10m, facecolor='None', edgecolor='royalblue', linewidth=2, zorder=4)
#ax.add_image(tiles, 7, interpolation='none',alpha = 0.8)
#ax.set_extent(extent_large)
#ax.add_feature(cpf.STATES)
#ax.add_geometries([geom], crs=ccrs.PlateCarree(), facecolor='None', edgecolor='black', linewidth=2,)
#ax.add_feature(shape_feature, facecolor='#a1a384',alpha = 0.6)
D = pc.data().from_h5('ramp_pass_ll.h5')
latR = np.array([np.nanmin(D.latitude), np.nanmax(D.latitude)])
lonR = np.array([np.nanmin(D.longitude), np.nanmax(D.longitude)])
if True:
# Create a Stamen terrain background instance.
bg_image = cimgt.GoogleTiles(style='satellite')
#Stamen('terrain-background')
fig = plt.figure(figsize=(10, 10))
# Create a GeoAxes in the tile's projection.
ax = fig.add_subplot(1, 1, 1, projection=bg_image.crs)
# Limit the extent of the map to a small longitude/latitude range.
ax.set_extent([lonR[0], lonR[1], latR[0], latR[1]], crs=ccrs.Geodetic())
# Add the Stamen data at zoom level 8.
ax.add_image(bg_image, 10)
#plt.plot(D.longitude.reshape([1, -1]), D.latitude.reshape([1, -1]), color='k', marker='.', transform=ccrs.Geodetic())
plt.scatter(D.longitude,
D.latitude,
2,
c=D.latitude,
marker='.',
transform=ccrs.Geodetic())
#for lati, loni in zip(D.latitude, D.longitude):
# plt.plot(loni, lati, 'k.', transform=ccrs.Geodetic())