def from_shapefile(cls, shapefile, *args, **kwargs):
"""
Loads a shapefile from disk and optionally merges
it with a dataset. See ``from_records`` for full
signature.
Parameters
----------
records: list of cartopy.io.shapereader.Record
Iterator containing Records.
dataset: holoviews.Dataset
Any HoloViews Dataset type.
on: str or list or dict
A mapping between the attribute names in the records and the
dimensions in the dataset.
value: str
The value dimension in the dataset the values will be drawn
from.
index: str or list
One or more dimensions in the dataset the Shapes will be
indexed by.
drop_missing: boolean
Whether to drop shapes which are missing from the provides
dataset.
Returns
-------
shapes: Polygons or Path object
A Polygons or Path object containing the geometries
"""
reader = Reader(shapefile)
return cls.from_records(reader.records(), *args, **kwargs)
def from_shapefile(cls, shapefile, *args, **kwargs):
"""
Loads a shapefile from disk and optionally merges
it with a dataset. See ``from_records`` for full
signature.
"""
reader = Reader(shapefile)
return cls.from_records(reader.records(), *args, **kwargs)
def distribution_geoaxes():
from cartopy.mpl.geoaxes import GeoAxes
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from mpl_toolkits.axes_grid1 import AxesGrid
projection = ccrs.PlateCarree()
axes_class = (GeoAxes,
dict(map_projection=projection))
fig = plt.figure(figsize=(16, 9))
axgr = AxesGrid(fig, 111, axes_class=axes_class,
nrows_ncols=(2, 2),
axes_pad=0.2,
cbar_location='right',
cbar_mode='single',
cbar_pad=0.2,
cbar_size='2%',
label_mode='') # note the empty label_mode
doc_fp = r'I:\Qinghai_Tibet_Plateau\GlcAnysis\SiteData\观测数据\GlcDOC\GlcDOC.shp'
doc_data = gpd.read_file(doc_fp)
sample_type_list = ['snow', 'snow_pit', 'ice', 'ice_core']
for i, ax in enumerate(axgr):
print(sample_type_list[i])
sample_type = sample_type_list[i]
data_type = doc_data[doc_data.Type==sample_type_list[i]]
lons, lats = data_type.CorrPt_x, data_type.CorrPt_y
# Tibetan plateau
# tp_fp = r'I:\Qinghai_Tibet_Plateau\GlcAnysis\Shapefile\TP_counties\TP_counties.shp'
tp_fp = r'I:\Qinghai_Tibet_Plateau\青藏高原范围与界线数据\DBATP\DBATP\DBATP_Polygon.shp'
tp_counties_fp = r'I:\Qinghai_Tibet_Plateau\GlcAnysis\Shapefile\TP_counties\TP_counties_wgs84.shp'
tp_shape_feature = ShapelyFeature(Reader(tp_fp).geometries(), ccrs.PlateCarree(), facecolor="#F6F7FC") # facecolor='#F6F7FC'
tp_counties_shape_feature = ShapelyFeature(Reader(tp_counties_fp).geometries(), ccrs.PlateCarree(), facecolor="#F6F7FC")
ax.add_feature(cfeature.LAND, facecolor='#E7E2DF',zorder=1)
ax.add_feature(cfeature.OCEAN, zorder=2)
ax.add_feature(cfeature.COASTLINE, zorder=3)
# ax.add_feature(cfeature.BORDERS, linestyle='-')
ax.add_feature(cfeature.LAKES, alpha=0.5, zorder=4)
# ax.add_feature(cfeature.RIVERS, zorder=5)
ax.set_extent([73, 105, 40, 25.5], crs=ccrs.PlateCarree())
# ax.set_xticks(np.linspace(-180, 180, 5), crs=projection)
# ax.set_yticks(np.linspace(-90, 90, 5), crs=projection)
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.add_feature(tp_shape_feature, edgecolor='gray', zorder=6)
ax.add_feature(tp_counties_shape_feature, edgecolor='gray', zorder=6.5)
cmap = mpl.cm.rainbow
s = ax.scatter(lons, lats, c=data_type.DOC, cmap=cmap, vmin=0.15, vmax=2.7, transform=ccrs.PlateCarree(), zorder=7)
ax.annotate(sample_type_dict[sample_type], xy=(10, 10), xycoords='axes points',
fontsize=14,
zorder=8)
axgr.cbar_axes[0].colorbar(s)
# fig.colorbar(s, shrink=0.6, location='right', extend='both')
plt.show()
def doc_distribution():
fig = plt.figure(figsize=(12.8, 5.4), constrained_layout=True)
# subfigs = fig.subfigures(1, 2, wspace=0.07)
# doc_fp = r'I:\Qinghai_Tibet_Plateau\GlcAnysis\SiteData\观测数据\GlcDOC\GlcDOC.shp'
doc_fp = r'I:\Data\DOC\Shapefile\GlcDOCMean\GlcDOCMean.shp'
doc_data = gpd.read_file(doc_fp)
sample_type_list = ['sonw', 'snow_pit', 'ice', 'ice_core']
sample_type_dict = {
'snow' : "Snow",
'snow_pit': 'Snow Pit',
'ice' : 'Ice',
'ice_core': 'Ice Core'
}
axes = []
for index, sample_type in enumerate(sample_type_dict.keys()):
ax = fig.add_subplot(2, 2, index+1, projection=ccrs.PlateCarree())
axes.append(ax)
ax.set_extent([73, 105, 40, 25.5], crs=ccrs.PlateCarree())
# Tibetan plateau
# tp_fp = r'I:\Qinghai_Tibet_Plateau\GlcAnysis\Shapefile\TP_counties\TP_counties.shp'
tp_fp = r'I:\Qinghai_Tibet_Plateau\青藏高原范围与界线数据\DBATP\DBATP\DBATP_Polygon.shp'
line_32 = r'I:\Qinghai_Tibet_Plateau\GlcAnysis\Shapefile\32N\32N.shp'
tp_counties_fp = r'I:\Qinghai_Tibet_Plateau\GlcAnysis\Shapefile\TP_counties\TP_counties_wgs84.shp'
tp_shape_feature = ShapelyFeature(Reader(tp_fp).geometries(), ccrs.PlateCarree(), facecolor="#F6F7FC") # facecolor='#F6F7FC'
tp_counties_shape_feature = ShapelyFeature(Reader(tp_counties_fp).geometries(), ccrs.PlateCarree(), facecolor="#F6F7FC")
line_32_feature = ShapelyFeature(Reader(line_32).geometries(), ccrs.PlateCarree(), facecolor="#F6F7FC")
ax.add_feature(cfeature.LAND, facecolor='#E7E2DF', zorder=1)
ax.add_feature(cfeature.OCEAN, zorder=2)
ax.add_feature(cfeature.COASTLINE, zorder=3)
# ax.add_feature(cfeature.BORDERS, linestyle='-')
ax.add_feature(cfeature.LAKES, alpha=0.5, zorder=4)
# ax.add_feature(cfeature.RIVERS, zorder=5)
# ax.add_feature(states_provinces, edgecolor='gray')
ax.add_feature(tp_shape_feature, edgecolor='gray', zorder=6)
# ax.add_feature(tp_counties_shape_feature, edgecolor='gray', zorder=6.5)
ax.add_feature(line_32_feature, edgecolor='black', linestyle='--', alpha=0.5, zorder=7)
data_type = doc_data[doc_data.Type==sample_type]
lons, lats = data_type.Lon, data_type.Lat
cmap = mpl.cm.rainbow
s = ax.scatter(lons, lats, c=data_type['DOC_mg_L_'], s=50, cmap=cmap, vmin=0.15, vmax=2.7, transform=ccrs.PlateCarree(), zorder=7)
# ax.annotate(sample_type_dict[sample_type], xy=(10, 10), xycoords='axes points',
# fontsize=14,
# zorder=8)
text = AnchoredText(sample_type_dict[sample_type], loc=3, prop={'size': 12}, frameon=True)
ax.add_artist(text)
ax.annotate("32°N", xy=(10, 88), xycoords='axes points',
fontsize=14,
zorder=8)
# fig.tight_layout(pad=1, w_pad=0.29, h_pad=0.64)
# plt.subplots_adjust(wspace=0.01, hspace=0.5) # 调整子图间距
# norm = mpl.colors.Normalize(vmin=0.15, vmax=2.7)
fig.colorbar(s, ax=axes, location='right', shrink=0.5, extend='both', pad=0.01)
# plt.colorbar(cax=data_type)
plt.show()
return
def shelf(self):
"""returns Mueller ice shelf as :class:`shapely.geometry.Polygon` based on :attr:`coastline_file`"""
R = Reader(os.path.join(self.path, self.coastline_file))
recs = [r for r in R.records() if r.attributes['FID_Coastl'] in self.shelf_features]
# order is important
geoms = [[r.geometry for r in recs if r.attributes['FID_Coastl']==f][0] for f in self.shelf_features]
pts = np.hstack([np.r_['1,2', p.xy] for g in geoms for p in g.geoms]).T.tolist()
# important! planar geometry ('False')
# https://developers.google.com/earth-engine/geometries_planar_geodesic
return ee.Geometry.Polygon(pts, 'EPSG:{}'.format(self.quanta_epsg), False)
def plot_basic_cappi(path, filename, level, shape_rivers, shape_states):
"""
"""
radar = read_sipam_cappi(filename)
date = pyart.util.datetime_from_grid(radar)
str_level = str(radar.z["data"][level] / 1000)
fig = plt.figure(figsize=(8, 7))
ax = fig.add_subplot(111, projection=ccrs.PlateCarree())
display = pyart.graph.GridMapDisplay(radar)
display.plot_grid("DBZc",
level=level,
vmin=0,
vmax=70,
ax=ax,
cmap="dbz",
embelish=False)
ax.add_geometries(
Reader(path + shape_rivers).geometries(),
ccrs.PlateCarree(),
linewidth=0.75,
facecolor="None",
edgecolor="darkblue",
alpha=0.3,
)
ax.add_geometries(
Reader(path + shape_states).geometries(),
ccrs.PlateCarree(),
linewidth=0.75,
facecolor="None",
edgecolor="darkgray",
alpha=0.8,
)
gl = ax.gridlines(
crs=ccrs.PlateCarree(),
draw_labels=True,
xlocs=np.arange(-70, -50, 1),
ylocs=np.arange(-10, 1, 1),
)
gl.top_labels = gl.right_labels = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
plt.savefig(
path + "radar_processing/figs/sipam_cappi/sipam_" + str_level +
"_dBZ_" + date.strftime("%Y%m%d%H%M%S") + ".png",
bbox_inches="tight",
dpi=300,
facecolor="None",
edgecolor="white",
)
plt.close()
print("Plotted! " + str(date) + " - " + str_level + " km")
def obtain_map_info(self, path):
""" Return the records (record = (attributes, geometry)) of the shapefile
Parameters :
- path = path of the shapefile to read
CountryMap(*args).obtain_map_info() ==> record :->generator"""
self.path_shp_commune = path
reader = Reader(path)
records = reader.records()
return records
def make_african_land():
from cartopy.io.shapereader import Reader, natural_earth
from shapely.ops import unary_union
shpfile = Reader(natural_earth(resolution='110m', category='cultural', name='admin_0_countries'))
filtered_records = shpfile.records()
filtered_records = filter(lambda x: x.attributes['continent'] == 'Africa', filtered_records)
region_poly = unary_union([r.geometry for r in filtered_records])
return region_poly
def plot(h):
msg_folder = cnst.GRIDSAT_PERU
htag1 = str(h).zfill(2) + 'UTC_2010-2017'
p1 = 'aggs/gridsat_WA_-40C_climatology_mean_mf_'+htag1+'.nc'
dat1 = xr.open_dataset(msg_folder + p1)
#ipdb.set_trace()
month1 = dat1['tir']
htag2 = str(h).zfill(2) + 'UTC_1985-1993'
p2 = 'aggs/gridsat_WA_-40C_climatology_mean_mf_'+htag2+'.nc'
dat2 = xr.open_dataset(msg_folder + p2)
month2 = dat2['tir']
for d, tag in [(month1, htag1), (month2,htag2)]:
d = d.sel(lon=slice(-79, -74), lat=slice(-12, -7))
simple = d.plot(x='lon', y='lat', col='month', col_wrap=4, cmap='jet', transform=ccrs.PlateCarree(),
subplot_kws={'projection': ccrs.PlateCarree()}, vmax=-40, vmin=-65, levels=10,
extend='both')
for ax in simple.axes.flat:
ax.coastlines()
ax.gridlines()
ax.plot(-77.55, -9.51, 'ko')
# ax.set_extent([-17.5, 30, -6, 20])
ax.set_aspect('equal', 'box-forced')
plt.savefig(cnst.network_data + '/figs/HUARAZ/mean_t_' + tag + '_small.png', dpi=300)
fname = '/home/ck/DIR/cornkle/data/HUARAZ/shapes/riosan_sel_one.shp'
shape_feature = ShapelyFeature(Reader(fname).geometries(),
ccrs.PlateCarree(), facecolor='none')
diff = month1-month2
diff = diff.sel(lon=slice(-79,-74), lat=slice(-12,-7))
simple = diff.plot(x='lon', y='lat', col='month', col_wrap=4, cmap='RdBu_r', transform=ccrs.PlateCarree(),
subplot_kws={'projection': ccrs.PlateCarree()}, vmax=5, vmin=-5, levels=10,
extend='both')
for ax in simple.axes.flat:
ax.coastlines()
ax.gridlines()
# ax.set_extent([-17.5, 30, -6, 20])
ax.set_aspect('equal', 'box-forced')
ax.add_feature(shape_feature)
ax.add_geometries(Reader(fname).geometries(),
ccrs.PlateCarree(),
facecolor='white', hatch='xxxx')
plt.savefig(cnst.network_data + '/figs/HUARAZ/mean_t_diff_fistlast8years_16UTC_small.png', dpi=300)
plt.close('all')
def maskcountries(self,
conf,
ax=None,
regions=["China"],
pathkw=dict(facecolor='none',
edgecolor='black',
linewidth=1.5)):
"""
在cartopy中, 对选定的国家进行白化.
refer to https://cloud.tencent.com/developer/article/1618341
Args:
conf (matplotlib object): matplotlib的contour或contourf绘图返回值.
ax (object, optional): Axes instance, if not None then overrides the default axes instance.
regions (list, optional): 指定无需白化的国家. Defaults to ["China"].
pathkw (dict, optional): PathPatch关键字参数. Defaults to dict(facecolor='none',edgecolor='black', linewidth=1.0).
Returns:
clipping path, matplotlib.patches.PathPatch
"""
#Get current axes if not specified
ax = ax or self._check_ax()
#Convert region name to upper
regions = list(regions)
regions = [region.upper() for region in regions]
# get shape file
shpfile = pkg_resources.resource_filename(
'nmc_met_graphics', "resources/maps/country1.shp")
# retrive region geometry
reader = Reader(shpfile)
countries = reader.records()
multipoly, = [
country.geometry for country in countries
if country.attributes['CNTRY_NAME'].upper() in regions
]
main_geom = sorted(multipoly.geoms, key=lambda geom: geom.area)[-1]
path, = geos_to_path(main_geom)
plate_carre_data_transform = ccrs.PlateCarree()._as_mpl_transform(ax)
for collection in conf.collections:
collection.set_clip_path(path, plate_carre_data_transform)
# Draw the path of interest.
path = PathPatch(path, transform=ccrs.PlateCarree(), **pathkw)
return path
def plot_base(self, bous = 1, bmap = None,
features = ['land','ocean','lake','river'],
gridline = True, lic = True):
"""
Plot map base
Parameters
----------
bous : TYPE, optional
DESCRIPTION. The default is 1.
bmap : TYPE, optional
DESCRIPTION. The default is None.
features : TYPE, optional
DESCRIPTION. The default is ['land','ocean','lake','river'].
gridline : TYPE, optional
DESCRIPTION. The default is True.
lic : TYPE, optional
DESCRIPTION. The default is True.
Returns
-------
None.
"""
if self.engine == 'gmt':
self.gmt_plot_base(self.region)
else:
self.fig.set_extent([72, 137, 10, 55])
self.fig.stock_img()
self.fig.coastlines()
self.fig.add_feature(cfeature.LAND)
self.fig.add_feature(cfeature.OCEAN)
self.fig.add_feature(cfeature.LAKES)
self.fig.add_feature(cfeature.RIVERS)
self.fig.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
datapath = Path(Path(catalog.__file__).parent,'data')
fname = Path(datapath, 'bou1_4l.shp')
f2name = Path(datapath, 'bou2_4l.shp')
faults = Path(datapath, 'gem_active_faults.shp')
self.fig.add_geometries(Reader(str(faults)).geometries(),
ccrs.PlateCarree(),facecolor = 'none',
edgecolor='red')
self.fig.add_geometries(Reader(str(f2name)).geometries(),
ccrs.PlateCarree(), facecolor = 'none',
edgecolor='gray', linestyle=':')
self.fig.add_geometries(Reader(str(fname)).geometries(),
ccrs.PlateCarree(), facecolor = 'none',
edgecolor='black')
def addFeatures(fname):
f = Reader(fname)
for item in f.records():
attributes = item.attributes
geometry = item.geometry
facecolor, edgecolor, alpha = categorizeBWS_s(attributes, column,
categories)
feature = ShapelyFeature(geometry,
ccrs.PlateCarree(),
facecolor=facecolor,
edgecolor=edgecolor,
alpha=alpha)
ax.add_feature(feature)
def make_np_mask(target_header):
"""Generate mask of cells that are in Redwood National Park"""
rdr = Reader(os.path.join("InputData", "nps_boundary", "nps_boundary.shp"))
redw_records = []
for x in rdr.records():
if 'Redwood' in x.attributes['UNIT_NAME']:
redw_records.append(x)
redw_shape_nps = redw_records[0].geometry[0]
NAD83_Cali_Albers = pyproj.Proj("+init=EPSG:3310")
nps_proj = pyproj.Proj("+init=EPSG:4269")
project = partial(pyproj.transform, nps_proj, NAD83_Cali_Albers)
redw_shape = transform(project, redw_shape_nps)
lower_x = np.array([
target_header['xllcorner'] + i * target_header['cellsize']
for i in range(target_header['ncols'])
])
upper_x = lower_x + target_header['cellsize']
lower_y = np.array([
target_header['yllcorner'] + i * target_header['cellsize']
for i in range(target_header['nrows'])
])[::-1]
upper_y = lower_y + target_header['cellsize']
np_array = np.zeros((target_header['nrows'], target_header['ncols']))
for i in range(target_header['nrows']):
for j in range(target_header['ncols']):
points = [[lower_x[j], lower_y[i]], [upper_x[j], lower_y[i]],
[upper_x[j], upper_y[i]], [lower_x[j], upper_y[i]]]
cell = geometry.Polygon(points)
intersection_area = redw_shape.intersection(cell).area / (
target_header['cellsize'] * target_header['cellsize'])
np_array[i, j] = intersection_area
np_raster = raster_tools.RasterData(
(target_header['nrows'], target_header['ncols']),
(target_header['xllcorner'], target_header['yllcorner']),
target_header['cellsize'],
array=np_array)
return np_raster
def shpplt(shp,
colors,
ax=None,
crs=ccrs.PlateCarree(),
field='type',
numbered=True,
**kwargs):
"""
Plot polygon .shp file from path using variable colors.
Requires that .shp file have field with numerical values that
will correspond to color.
Parameters:
shp: Path to .shp file
colors: List or dict of colors corresponding to field in .shp file
ax: Axes on which to plot polygon
crs: Cartopy projection for .shp file
field: Name of field with values used for color
Retruns:
df: Pandas dataframe of attribute table from .shp file
"""
reader = Reader(shp) # Creates reader object
units = reader.records() # Gets records from reader object
lst = [] # Set up empty list
for unit in units: # Iterate over each object in .shp file
# Get type number from attributes to assign color, requires 'type'
# field in imported .shp file
t = unit.attributes[field]
geo = unit.geometry # Get geometry to plot
# Plot geometry using field attribute to assign color
# If field sequential integers, adapt for Python counting
if numbered is True:
ax.add_geometries([geo], crs, facecolor=colors[t - 1], **kwargs)
# If field non-sequential, use color dictionary
elif numbered is False:
ax.add_geometries([geo], crs, facecolor=colors[t], **kwargs)
# Add attributes to list to create dataframe
lst.append(unit.attributes)
df = pd.DataFrame.from_records(lst) #create dataframe of attribute table
return (df)
def mapa_base(llat, llon):
"""
Mapa base para graficar las variables
"""
l_lat = llat
l_lon = np.array(llon) % 360 #Pasamos lon en [-180, 180] a [0, 360]
states_provinces = cpf.NaturalEarthFeature(category='cultural',
name='admin_1_states_provinces_lines',
scale='10m',
facecolor='none')
shp = Reader(natural_earth(resolution='10m', category='cultural',
name='admin_1_states_provinces_lines'))
countries = shp.records()
# Comenzamos la Figura
fig = plt.figure(figsize=(6, 8))
proj_lcc = ccrs.PlateCarree()
ax = plt.axes(projection=proj_lcc)
ax.coastlines(resolution='10m')
ax.add_feature(cpf.BORDERS, linestyle='-')
#ax.add_feature(states_provinces, edgecolor='gray')
for country in countries:
if country.attributes['adm0_name'] == 'Argentina':
ax.add_geometries( [country.geometry], ccrs.PlateCarree(),
edgecolor='black', facecolor='none',
linewidth=0.7 )
# ax.add_feature(shape_feature)
# Colocamos reticula personalizada
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=0.4, color='gray', alpha=0.7, linestyle=':')
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlocator = mticker.FixedLocator(np.linspace(llon[0], llon[1], 7))
gl.ylocator = mticker.FixedLocator(np.linspace(l_lat[0], l_lat[1], 9))
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
# Extension del mapa
ax.set_extent([l_lon[0], l_lon[1], l_lat[0], l_lat[1]], crs=proj_lcc)
# Posicion del eje (desplazamos un poco a la izquierda y más abajo)
pos1 = ax.get_position() # get the original position
pos2 = [pos1.x0 - 0.05, pos1.y0 - 0.06, pos1.width*1.16, pos1.height*1.22]
ax.set_position(pos2) # set a new position
ax.text(-79., -21., 'Fuente:\n NOAA - GFS',
horizontalalignment='left', verticalalignment='top',
fontweight='bold', fontsize=13,
transform=ccrs.Geodetic())
return fig, ax
def cartoplot_schools(mapsize, shapefile, data):
# Create a Stamen terrain background instance
stamen_terrain = cimgt.Stamen('terrain-background')
fig = plt.figure(figsize=(mapsize, mapsize))
ax = fig.add_subplot(1, 1, 1, projection=stamen_terrain.crs)
# Set range of map, stipulate zoom level
ax.set_extent([-122.7, -121.5, 37.15, 38.15], crs=ccrs.Geodetic())
ax.add_image(stamen_terrain, 12, zorder=0)
# set up colormap
from matplotlib import cm
import matplotlib.colors
cmap = cm.get_cmap('seismic_r', 100)
norm = matplotlib.colors.Normalize(vmin=min(data['School score']),
vmax=max(data['School score']))
color = cmap(norm(data['School score'].values))
# add colorbar
n_cmap = cm.ScalarMappable(norm=norm, cmap='seismic_r')
n_cmap.set_array([])
cax = fig.add_axes([0.185, 0.15, 0.02, 0.25])
cbar = ax.get_figure().colorbar(n_cmap, cax)
# set colorbar label, properties
cbar.set_label('School score\n(% proficient)',
rotation=0,
labelpad=15,
y=0.55,
ha='left')
cbar.ax.tick_params(labelsize=16)
cax.yaxis.set_ticks_position('left')
text = cax.yaxis.label
font = matplotlib.font_manager.FontProperties(family='Helvetica', size=20)
text.set_font_properties(font)
for tick in cbar.ax.yaxis.get_ticklabels():
tick.set_family('Helvetica')
# add shapefile features
shape_feature = ShapelyFeature(Reader(shapefile).geometries(),
ccrs.epsg(26910),
linewidth=2)
# Add commute data by zip code
for counter, geom in enumerate(shape_feature.geometries()):
if data['Area'][counter] < 50:
if data['Population'][counter] > 500:
ax.add_geometries([geom],
crs=shape_feature.crs,
facecolor=color[counter],
edgecolor='k',
alpha=0.8)
else:
continue
# save figure, show figure
fig = plt.gcf()
plt.savefig('schools_plot.jpg', bbox_inches='tight', dpi=600)
plt.show()
def add_river_shapes(ax, city, crs=None):
data_dir = Path(__file__).parent.parent / "canvec_250K"
city_to_province = {
"Toronto": "ON",
"Montreal": "QC",
"Ottawa-Gatineau": "ON",
"Vancouver": "BC",
"Calgary": "AB",
"Edmonton": "AB",
"Winnipeg": "MB",
"Halifax": "NS",
"Saskatoon": "SK",
"Moncton": "NB",
"Charlottetown": "PE",
"St Johns": "NL"
}
params = dict(facecolor=feature.COLORS["water"], linewidth=0)
for province_dir in data_dir.iterdir():
if province_dir.is_dir(
) and city_to_province[city] in province_dir.name:
for f in (province_dir / province_dir.name[:-4]).iterdir():
if f.name.startswith("waterbody_2") and f.name.endswith(
".shp"):
ax.add_geometries(
Reader(str(f)).geometries(), crs, **params)
def plot_basic_ppi(path, filename, level, shape_rivers, shape_states):
"""
"""
radar = pyart.aux_io.read_gamic(filename)
date = pyart.util.datetime_from_grid(radar)
str_level = str("%.1f" % radar.fixed_angle["data"][level])
fig = plt.figure(figsize=(8, 7))
ax = fig.add_subplot(111, projection=ccrs.PlateCarree())
display = pyart.graph.RadarMapDisplay(radar)
display.plot_ppi_map(
"corrected_reflectivity",
sweep=level,
vmin=0,
vmax=70,
ax=ax,
cmap="dbz",
shapefile=path + shape_rivers,
shapefile_kwargs={
"facecolor": "None",
"edgecolor": "darkblue",
"alpha": 0.3,
"linewidth": 0.75,
},
)
ax.add_geometries(
Reader(path + shape_states).geometries(),
ccrs.PlateCarree(),
linewidth=0.75,
facecolor="None",
edgecolor="gray",
alpha=0.8,
)
gl = ax.gridlines(
crs=ccrs.PlateCarree(),
draw_labels=True,
xlocs=np.arange(-70, -50, 1),
ylocs=np.arange(-10, 1, 1),
)
gl.top_labels = gl.right_labels = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
display.plot_range_rings([50, 100, 150, 200, 250],
ax=ax,
col="lightgray",
lw=1)
plt.savefig(
path + "radar_processing/figs/sipam_ppi/sipam_" + str_level + "_dBZ_" +
date.strftime("%Y%m%d%H%M%S") + ".png",
bbox_inches="tight",
dpi=300,
facecolor="None",
edgecolor="white",
)
plt.close()
print("Plotted! " + str(date) + " - " + str_level + "°")
def get_geometries_ocean(name0):
"""
Get an iterable of Shapely geometries corrresponding to given countries.
"""
# Using the Natural Earth feature interface provided by cartopy.
# You could use a different source, all you need is the geometries.
print(name0)
shape_records = Reader(
shpreader.natural_earth(resolution='110m',
category='Physical',
name='ocean')).records()
geoms = []
names = []
for country in shape_records:
try:
geoms += country.geometry
except TypeError:
geoms.append(country.geometry)
if name0 == 'MPac':
geoms = add_country(geoms, 'Taiwan')
if name0 == 'SCSPhi':
geoms = add_country(geoms, 'Philippines')
for i in range(len(geoms)):
names.append(name0)
return geoms, ccrs.PlateCarree()._as_mpl_transform, names
def add_china_map_2cartopy(ax, name='province', facecolor='none',
edgecolor='c', lw=2, **kwargs):
"""
Draw china boundary on cartopy map.
:param ax: matplotlib axes instance.
:param name: map name.
:param facecolor: fill color, default is none.
:param edgecolor: edge color.
:param lw: line width.
:return: None
"""
# map name
names = {'nation': "bou1_4p", 'province': "bou2_4p",
'county': "BOUNT_poly", 'river': "hyd1_4l",
'river_high': "hyd2_4l"}
# get shape filename
shpfile = pkg_resources.resource_filename(
'dk_met_graphics', "resources\\maps\\" + names[name] + ".shp")
# add map
ax.add_geometries(
Reader(shpfile).geometries(), ccrs.PlateCarree(),
facecolor=facecolor, edgecolor=edgecolor, lw=lw, **kwargs)
def plot_breakup_images(paths_2007, paths_2012, paths_2019, coastline_path, lon_min_MOD, lon_max_MOD, lat_min_MOD, lat_max_MOD, fontsz, figsz):
'''
This function plots n images from 2007, 2012, and 2019 Kotzebue Sound breakups in a 3xn grid
'''
gdal.UseExceptions()
cols = len(paths_2007)
coastline = ShapelyFeature(Reader(coastline_path).geometries(),ccrs.PlateCarree())
#extract coordinate system from first file to be used as projection of all subplots
fname = paths_2007[0]
ds = gdal.Open(fname)
proj = ds.GetProjection()
inproj = osr.SpatialReference()
inproj.ImportFromWkt(proj)
projcs = inproj.GetAuthorityCode('PROJCS')
projection = ccrs.epsg(projcs)
subplot_kw = dict(projection=projection)
#initialize figure
fig, axx = plt.subplots(nrows=3, ncols=cols, figsize=figsz, subplot_kw=subplot_kw, facecolor='w')
fig.suptitle('Satellite Imagery of the Sea Ice Breakup Process in Kotzebue Sound',y=0.95,fontsize=40)
for idx in np.arange(0,cols):
plot_MODIS_geotiff(axx[0,idx], paths_2007[idx], coastline, lon_min_MOD, lon_max_MOD, lat_min_MOD, lat_max_MOD )
plot_MODIS_geotiff(axx[1,idx], paths_2012[idx], coastline, lon_min_MOD, lon_max_MOD, lat_min_MOD, lat_max_MOD )
plot_MODIS_geotiff(axx[2,idx], paths_2019[idx], coastline, lon_min_MOD, lon_max_MOD, lat_min_MOD, lat_max_MOD )
axx[0,idx].add_patch(patches.Ellipse(xy=(-162.6139,66.8968), width=1.1, height=0.44, edgecolor='r', linewidth=4, transform=ccrs.PlateCarree(), facecolor='none',zorder=400))
axx[1,idx].add_patch(patches.Ellipse(xy=(-162.6139,66.8968), width=1.1, height=0.44, edgecolor='r', linewidth=4, transform=ccrs.PlateCarree(), facecolor='none',zorder=400))
axx[2,idx].add_patch(patches.Ellipse(xy=(-162.6139,66.8968), width=1.1, height=0.44, edgecolor='r', linewidth=4, transform=ccrs.PlateCarree(), facecolor='none',zorder=400))
for idx in np.arange(0,cols-1):
for row in np.arange(0,3):
con = ConnectionPatch(xyA=(1,0.5), coordsA=axx[row,idx].transAxes,
xyB=(0,0.5), coordsB=axx[row,idx+1].transAxes,
arrowstyle='->',linewidth=6,mutation_scale=50)
fig.add_artist(con)
axx[0,0].set_title('May 24',fontsize=fontsz)
axx[0,1].set_title('June 1',fontsize=fontsz)
axx[0,2].set_title('June 5',fontsize=fontsz)
axx[0,3].set_title('June 11',fontsize=fontsz)
axx[1,0].set_title('May 24',fontsize=fontsz)
axx[1,1].set_title('May 31',fontsize=fontsz)
axx[1,2].set_title('June 7',fontsize=fontsz)
axx[1,3].set_title('June 16',fontsize=fontsz)
axx[2,0].set_title('April 23',fontsize=fontsz)
axx[2,1].set_title('May 10',fontsize=fontsz)
axx[2,2].set_title('May 16',fontsize=fontsz)
axx[2,3].set_title('May 25',fontsize=fontsz)
plt.figtext(x=0.1,y=0.745,s='2007',rotation=90,fontsize=40)
plt.figtext(x=0.1,y=0.475,s='2012',rotation=90,fontsize=40)
plt.figtext(x=0.1,y=0.21,s='2019',rotation=90,fontsize=40)
def plot_polygons(regions):
"""
extract the polygon coordinate and plot it on a worldmap
:param regions: list of ISO abreviations for polygons
:return png: map_graphic.png
"""
from cartopy.io.shapereader import Reader
from cartopy.feature import ShapelyFeature
from os.path import curdir, abspath
from flyingpigeon import config
DIR_SHP = config.shapefiles_dir()
if type(regions) == str:
regions = list([regions])
fname = join(DIR_SHP, "countries.shp")
geos = Reader(fname).geometries()
records = Reader(fname).records()
LOGGER.debug('')
fig = plt.figure(figsize=(10, 10), facecolor='w',
edgecolor='k') # dpi=600,
projection = ccrs.Orthographic(central_longitude=0.0,
central_latitude=0.0,
globe=None) # Robinson()
ax = plt.axes(projection=projection)
for r in records:
geo = geos.next()
if r.attributes['ISO_A3'] in regions:
shape_feature = ShapelyFeature(geo,
ccrs.PlateCarree(),
edgecolor='black')
ax.add_feature(shape_feature)
ax.coastlines()
# ax.set_global()
o1, map_graphic = mkstemp(dir=abspath(curdir), suffix='.png')
fig.savefig(map_graphic)
plt.close()
return map_graphic
def plot_may2019_image(image_path, image_2path, coastline_path, river_path, figsz, lon_min_MOD, lon_max_MOD, lat_min_MOD, lat_max_MOD, sis_color, obt_color, fontsz):
'''
Function to plot simple map of the locations and deployment durations of the OBT and SIS
'''
gdal.UseExceptions()
coastline = ShapelyFeature(Reader(coastline_path).geometries(),ccrs.PlateCarree())
river = ShapelyFeature(Reader(river_path).geometries(),ccrs.PlateCarree())
#open tiff file and extract data, geotransform, and crs
ds = gdal.Open(image_path)
data = ds.ReadAsArray()
gt = ds.GetGeoTransform()
proj = ds.GetProjection()
inproj = osr.SpatialReference()
inproj.ImportFromWkt(proj)
projcs = inproj.GetAuthorityCode('PROJCS')
projection = ccrs.epsg(projcs)
subplot_kw = dict(projection=projection)
#initialize figure
fig, axx = plt.subplots(figsize=figsz, subplot_kw=subplot_kw)
extent = (gt[0], gt[0] + ds.RasterXSize * gt[1],
gt[3] + ds.RasterYSize * gt[5], gt[3])
img = axx.imshow(data[:3, :, :].transpose((1, 2, 0)), extent=extent,
origin='upper')
axx.set_extent([lon_min_MOD, lon_max_MOD, lat_min_MOD, lat_max_MOD])
plot_MODIS_geotiff(axx,image_2path, coastline, lon_min_MOD, lon_max_MOD, lat_min_MOD, lat_max_MOD)
#add coastlines and features
axx.add_feature(coastline,zorder=300, edgecolor='k', facecolor='#efebd3', alpha=1)
axx.add_feature(river,zorder=350, edgecolor='#46d3f6', facecolor='none', alpha=1,linewidth=4)
#add data source to map
axx.text(0.006, 0.01, ' Satellite Image From \n MODIS Visible, \n May 7 2019', fontsize=18, fontname='Calibri', horizontalalignment='left', verticalalignment='bottom', transform=axx.transAxes,
bbox=dict(boxstyle='square,pad=0.15', facecolor='w', alpha=0.8),zorder=400)
#label features on plot
axx.text(0.7, 0.475, 'Kobuk \n River', fontsize=18, transform=axx.transAxes, zorder=400)
axx.text(0.46, 0.75, 'Noatak \n River', fontsize=18, transform=axx.transAxes, zorder=400)
axx.text(0.538, 0.545, 'Kotzebue', fontsize=13, transform=axx.transAxes, zorder=400)
axx.add_patch(patches.Arrow(x=0.55, y=0.56, dx=-0.018, dy=0.03, width=0.01, facecolor='k',transform=axx.transAxes,zorder=400));
def plot_cmaq(monthly_tot,var_tot,title_2,cmap,vmaxs,vmins,crs_new,show,add_epa,version,div,shaped):
for i in range(0,len(monthly_tot)):
#for i in range(1):
# set var for plot
var= var_tot[i]
data= np.asarray(monthly_tot[i])
if var == 'RAINC': pass
else:
if i<len(cmaq_var): plon,plat = lon,lat
else: plon,plat = wlon,wlat
# make fig object
fig, axs = plt.subplots(subplot_kw={'projection': crs_new},figsize=(6, 6))
#set up data for plotting via levels
vmax,vmin=vmaxs[i],vmins[i]
#for i in range(1):
levels = np.arange(vmin, vmax, (vmax-vmin)/10)
# set boundary as outer extent by making a matplotlib path object and adding that geometry
if shaped: axs.set_boundary(mpath.Path(outsideofunion.T,closed=True), transform= crs_new, use_as_clip_path=True)
axs.add_geometries(Reader(path).geometries(), crs=crs_new,facecolor='None', edgecolor='black')
#plot the gridded data by using contourf
if div==False: cs=plt.pcolormesh(plon,plat,data,cmap= cmap , transform=crs_new, vmin=vmin,vmax=vmax)
if div == True:
divnorm = colors.DivergingNorm(vmin=vmin, vcenter=0, vmax= vmax)
cs=plt.pcolormesh(plon,plat,data,cmap= cmap , transform=crs_new, vmin=vmin,vmax=vmax, norm=divnorm)
#for i in range(1):
# add landmarks with scatterplot
midway= -87.7522,41.7868
ohare = -87.9073, 41.9842
loop = -87.6251,41.8786
axs.annotate(xy=midway,s="*",color='white')
axs.annotate(xy=ohare,s="*",color='white')
axs.annotate(xy=loop,s="*",color='white')
# set axes extents from shapefile
x=[chi_shapefile.bounds.minx.min(), chi_shapefile.bounds.maxx.max()]
y=[chi_shapefile.bounds.miny.min(), chi_shapefile.bounds.maxy.max()]
#
if shaped: axs.set_extent([x[0],x[1],y[0],y[1]],crs= crs_new)
else: axs.set_extent([x[0]-.3,x[1]+.3,y[0]-.3,y[1]+.3],crs= crs_new)
# title
axs.set_title(var+title_2)
#add colorbar and label
cbar=plt.colorbar(cs,boundaries=levels,shrink = 0.5)
cbar.set_ticks(levels)
# add state lines
states_provinces = cfeature.NaturalEarthFeature(category='cultural',name='admin_1_states_provinces_lines',scale='50m',facecolor='none')
#
land = cfeature.NaturalEarthFeature('physical', 'lakes', '10m',
edgecolor='black',
facecolor='none')
axs.add_feature(land, edgecolor='black')
axs.add_feature(states_provinces, edgecolor='black')
#add epa monitors if its CMAQ
if add_epa == True:
if i < len(cmaq_var):
try: axs.scatter(epa_avgs_latlon[i]['Longitude'],epa_avgs_latlon[i]['Latitude'],c=epa_avgs_latlon[i]['Arithmetic Mean'],vmin = vmin, vmax= vmax,s=75, cmap = cmap,edgecolors = 'black')
except: pass
#savefig
plt.savefig(var+version+'v7.png',transparent=True)
if show == True: plt.show()
def add_shapefile(ax,
shapefile,
projection=ccrs.PlateCarree(),
facecolor='none',
edgecolor='black'):
shape_feature = ShapelyFeature(Reader(shapefile).geometries(), projection)
ax.add_feature(shape_feature, facecolor=facecolor, edgecolor=edgecolor)
return ax
def plot_polygons(regions, file_extension='png'):
"""
extract the polygon coordinate and plot it on a worldmap
:param regions: list of ISO abreviations for polygons
:return png: map_graphic.png
"""
from cartopy.io.shapereader import Reader
from cartopy.feature import ShapelyFeature
from numpy import mean, append
from blackswan import config
DIR_SHP = config.shapefiles_path()
if type(regions) == str:
regions = list([regions])
fname = join(DIR_SHP, "countries.shp")
geos = Reader(fname).geometries()
records = Reader(fname).records()
central_latitude = []
central_longitude = []
for r in records:
geo = geos.next()
if r.attributes['ISO_A3'] in regions:
x, y = geo.centroid.coords.xy
central_longitude.append(x[0])
central_latitude.append(y[0])
fig = plt.figure(figsize=(20, 10))
projection = ccrs.Orthographic(central_longitude=mean(central_longitude),
central_latitude=mean(central_latitude),
globe=None) # Robinson()
ax = plt.axes(projection=projection)
geos = Reader(fname).geometries()
records = Reader(fname).records()
for r in records:
geo = geos.next()
if r.attributes['ISO_A3'] in regions:
shape_feature = ShapelyFeature(geo, ccrs.PlateCarree(), edgecolor='black', color='coral')
ax.add_feature(shape_feature)
ax.coastlines()
ax.gridlines()
ax.stock_img()
# ax.set_global()
map_graphic = fig2plot(fig=fig, file_extension=file_extension)
plt.close()
return map_graphic
def main():
# Iniciando a projeção e suas dimensões lat/lon
ax = plt.axes(projection=ccrs.PlateCarree())
# coords = (-54, -47, -25.8, -29.4)
coords = (-50, -48, -26, -29.4)
ax.set_extent(coords)
# ax.set_yticks((-25.8, -27.0, -28.2, -29.4))
ax.gridlines(draw_labels=True)
# Shapefiles que vão gerar os limites territoriais.
brasil = 'analysis/cartopy/shapefiles/Brasil-POLYGON.shp'
sc = 'analysis/cartopy/shapefiles/SC-POLYGON.shp'
laguna = 'analysis/cartopy/shapefiles/DestaqueLaguna-POLYGON.shp'
lagoas = 'analysis/cartopy/shapefiles/LagoasComplexoLagunar-POLYGON.shp'
# Vamos fazer uma lista para iterar um loop com a função add_geometries()
shapes = [brasil, sc, laguna, lagoas]
ax.stock_img()
# for loop para adicionar os shapes à figura
for item in shapes:
if item is lagoas:
ax.add_geometries(Reader(item).geometries(),
ccrs.PlateCarree(),
facecolor='skyblue',
edgecolor='black')
else:
ax.add_geometries(Reader(item).geometries(),
ccrs.PlateCarree(),
facecolor='beige',
edgecolor='black')
# p = patches.Rectangle(
# (-48.9, -28.55), 0.2, 0.15,
# fill = False, color = 'red',
# zorder = 6, lw = 3.0, ls = '-'
# )
# ax.add_patch(p)
plt.show()
def plot_continental_polygons(self,
ax,
facecolor=None,
edgecolor='none',
alpha=0.1):
print('Plotting continental polygons...')
shape_feature = ShapelyFeature(
Reader(continental_polygons_output_basename).geometries(),
ccrs.PlateCarree(),
edgecolor=edgecolor)
ax.add_feature(shape_feature, facecolor=facecolor, alpha=alpha)
def create_map(fig):
shp_path = r'/mnt/e/公众号/可视化/cartopy/China_shp_lqy/'
# --创建画图空间
proj = ccrs.LambertConformal(central_longitude=110,
central_latitude=35,
standard_parallels=(30, 60)) # 创建坐标系
ax = fig.subplots(1, 1, subplot_kw={'projection': proj})
# --设置shp
provinces = cfeat.ShapelyFeature(
Reader(shp_path + 'province.shp').geometries(),
ccrs.PlateCarree(),
edgecolor='k',
facecolor='none',
)
ax.add_feature(provinces, linewidth=0.6, zorder=2)
# --设置范围
ax.set_extent([80, 135, 15, 55], crs=ccrs.PlateCarree())
# --关闭边框
ax.spines['geo'].set_visible(False)
# --设置刻度
ax.tick_params(axis='both',
labelsize=5,
direction='out',
labelbottom=False,
labeltop=False,
labelleft=False,
labelright=False)
# --设置小地图
left, bottom, width, height = 0.675, 0.13, 0.23, 0.27
ax2 = fig.add_axes([left, bottom, width, height],
projection=ccrs.PlateCarree())
provinces = cfeat.ShapelyFeature(
Reader(shp_path + 'province.shp').geometries(),
ccrs.PlateCarree(),
edgecolor='k',
facecolor='gray',
)
ax2.add_feature(provinces, linewidth=0.6, zorder=2)
ax2.set_extent([105, 125, 0, 25], crs=ccrs.PlateCarree())
return ax
def add_country(geoms, name):
shape_records = Reader(
natural_earth(resolution='110m',
category='cultural',
name='admin_0_countries')).records()
for country in shape_records:
if country.attributes['NAME_LONG'] in name:
try:
geoms += country.geometry
except TypeError:
geoms.append(country.geometry)
return geoms
def get_country_geom(name_list):
country_shapes = Reader(
natural_earth(resolution='10m',
category='cultural',
name='admin_0_countries')).records()
geoms = []
for name in name_list:
for shape_temp in country_shapes:
if name == shape_temp.attributes['NAME_EN']:
print('Selecting: {}'.format(name))
geoms.append(shape_temp.geometry)
return geoms
new_d14c_cube.data = d14c_data_for_cube
new_dpco2_cube.data = dpco2_data_for_cube
new_d14c_cube.coord('time').points = (d14c_cube.coord('time').points-11)/100
new_dpco2_cube.coord('time').points = (d14c_cube.coord('time').points-11)/100
new_d14c_cube.long_name = d14c_cube.long_name
new_dpco2_cube.long_name = dpco2_cube.long_name
new_d14c_cube.standard_name = d14c_cube.standard_name
new_dpco2_cube.standard_name = dpco2_cube.standard_name
new_d14c_cube.units = d14c_cube.units
new_dpco2_cube.units = dpco2_cube.units
new_d14c_cube.var_name = d14c_cube.var_name
new_dpco2_cube.var_name = dpco2_cube.var_name
shpfilename = natural_earth(resolution='110m', category='physical', name='land')
reader = Reader(shpfilename)
continents = reader.records()
new_d14c_cube.coord('latitude').guess_bounds()
new_d14c_cube.coord('longitude').guess_bounds()
new_dpco2_cube.coord('latitude').guess_bounds()
new_dpco2_cube.coord('longitude').guess_bounds()
continent_geometries = reader.geometries() # NB. Switched from using records()
all_continents_geometry = cascaded_union(list(continent_geometries))
area_weights = geometry_area_weights(new_d14c_cube, all_continents_geometry)
land_mask = np.where(area_weights > 0, True, False)
new_d14c_cube.data = np.ma.array(new_d14c_cube.data, mask=land_mask)
area_weights = geometry_area_weights(new_dpco2_cube, all_continents_geometry)
land_mask = np.where(area_weights > 0, True, False)
new_dpco2_cube.data = np.ma.array(new_dpco2_cube.data, mask=land_mask)
from cartopy.feature import ShapelyFeature
import cartopy.feature as cfeature
import os
plt.clf()
#ax = geo_plots.add_map(bbox=(-89,-85,27.5,31))
ax = geo_plots.add_map(bbox=(-90, -84, 27.5, 31))
#land_10m = cfeature.NaturalEarthFeature('physical','land','50m',\
# edgecolor='face',facecolor='0.75')
##ax.add_feature(cfeature.LAND, facecolor='0.75')
#ax.add_feature(land_10m)
#if bbox not specified, this will use map bounds from bna
bathy_file = os.path.join('gom_bathy','Bathymetry.shp')
bathy = Reader(bathy_file)
for rec,geo in zip(bathy.records(),bathy.geometries()):
if rec.attributes['DEPTH_METR'] in ['100m','500m','1000m']:
shape_feature = ShapelyFeature(geo,ccrs.PlateCarree(), facecolor='none')
ax.add_feature(shape_feature)
#add particles at one time
t = datetime.datetime(2016, 9, 21, 1)
filename = 'gulf_tamoc.nc'
ax = geo_plots.contour_particles(ax,filename,t,levels=[0.1, 0.4, 0.6, 1])
#ax = geo_plots.plot_particles(ax,filename,t,depth=0,color='b')
#add initial location