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plot_maps_old.py
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plot_maps_old.py
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import geopandas as gpd
from geopandas import GeoDataFrame
from shapely.geometry import Point
import numpy as np
import matplotlib
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as mpatches
from matplotlib.patches import PathPatch
from matplotlib.colors import LinearSegmentedColormap
import mapclassify as mc
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
def clip_area(sf, ax):
def coord_lister(geom):
coords = list(geom.exterior.coords)
return coords
vertices = sf.geometry.apply(coord_lister)[0]
codes = []
codes += [Path.MOVETO]
codes += [Path.LINETO] * (len(vertices) -2)
codes += [Path.CLOSEPOLY]
clip = Path(vertices, codes)
clip = PathPatch(clip, transform=ax.transData)
return clip
def mycmap(cmap, nbin):
return LinearSegmentedColormap.from_list('mycmap', cmap, N=nbin+2)
def set_limits(ax, shp):
env = shp.geometry.bounds
minx = env.minx.min()
maxx = env.maxx.max()
miny = env.miny.min()
maxy = env.maxy.max()
ax.set_xlim([minx-.1, maxx+.1])
ax.set_ylim([miny-.1, maxy+.1])
def __discret_legend(data, im, fontsize, ticks_label=0, values=0):
if not values:
values = np.sort(np.unique(data.ravel()))
colors = [ im.cmap(im.norm(value)) for value in values]
# create a patch (proxy artist) for every color
if ticks_label:
patches = [mpatches.Patch(color=colors[i], label=ticks_label[i]) for i in range(len(values)) ]
else:
patches = [ mpatches.Patch(color=colors[i], label="{l}".format(l=values[i]) ) for i in range(len(values)) ]
# put those patched as legend-handles into the legend
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,
fontsize=fontsize)
def __colorbar_legend(im, ticks, ticks_label, cbar_extend, vmin, vmax, nbin,
cbar_loc, cbar_label, cmap, norm, lbsize, lbpad,
fontsize, ax):
if not np.array(ticks).any():
ticks = np.linspace(vmin, vmax, nbin+1)
ticks = list(ticks)
if cbar_extend == 'both':
ticks.insert(0,ticks[0]-1)
ticks.append(ticks[-1]+1)
if not ticks_label:
ticks_label = ['{0:.2f}'.format(x) for x in ticks[1:-1]]
ticks2 = ticks[1:-1]
elif cbar_extend == 'max':
ticks.append(ticks[-1]+1)
if not ticks_label:
ticks_label = ['{0:.2f}'.format(x) for x in ticks[:-1]]
ticks2 = ticks[:-1]
elif cbar_extend == 'min':
ticks.insert(0,ticks[0]-1)
if not ticks_label:
ticks_label = ['{0:.2f}'.format(x) for x in ticks[1:]]
ticks2 = ticks[1:]
else:
if not ticks_label:
ticks_label = ['{0:.2f}'.format(x) for x in ticks]
ticks2 = ticks
axins1 = inset_axes(ax,
width="5%", # width = 5% of parent_bbox width
height="95%", # height : 95%
bbox_to_anchor=(.1, 0., 1, 1),
bbox_transform=ax.transAxes,
loc='center right')
cb = plt.colorbar(im, orientation=cbar_loc,
ticks=ticks2,
extendfrac = 'auto',
label=cbar_label, extend=cbar_extend,
boundaries=ticks, fraction=0.038,
cmap=cmap, norm=norm, cax=axins1
)
cb.ax.tick_params(labelsize=lbsize)
if cbar_loc == 'vertical':
cb.ax.set_yticklabels(ticks_label)
else:
cb.ax.set_xticklabels(ticks_label)
cb.set_label(cbar_label, labelpad=lbpad, size=fontsize)
def __legend(data, im, type_legend, ticks, ticks_label, cbar_extend, vmin,
vmax, nbin, cbar_loc, cbar_label, cmap, norm, lbsize, lbpad,
fontsize, ax):
if type_legend == 'colorbar':
__colorbar_legend(im, ticks, ticks_label, cbar_extend, vmin, vmax, nbin,
cbar_loc, cbar_label, cmap, norm, lbsize, lbpad, fontsize,
ax)
if type_legend == 'discret':
__discret_legend(data, im, fontsize, ticks_label, ticks, ax)
def imshow2(data, figsize=(10,10)):
plt.figure(figsize=figsize)
def imshow(data, lat, lon, shp, clip=None, vmin=0, vmax=100, cmap='seismic_r',
nbin=10, cbar_loc='vertical', cbar_label='My legend', grid=1,
cbar_extend='both', lbsize=10, lbpad=3, fontsize=11, aux_shapes=[],
title='', figname='', show=False, ticks=0, norm=None, figsize=(15,15),
ticks_label=[], pos_ticks=0, legend='colorbar', ax=False):
'''
Gera mapas a partir de matrix 2D.
Parameters
----------
data : array(2D)
Matriz com dados a serem usado para gerar o mapa.
lat : array
Latitudes correspondentes a cada ponto da matriz de dados.
lon : array
Longitudes correspondentes a cada ponto da matriz de dados.
shp : shapefile
Arquivo shapefile da região onde será plotado o mapa.
clip : bool, optional
Se True serão removidas partes da matriz de dados fora do shapefile.
The default is None.
vmin : float, optional
Mínimo valor a ser utilizado para legenda. The default is 0.
vmax : float, optional
Máximo valor a ser utilizado para legenda. The default is 100.
cmap : str or array, optional
Se str utiliza-se um dos mapas de cores padrões do matplotlib, se array
define mapa de cor personalizado. The default is 'seismic_r'.
nbin : int, optional
Número de divisões/cores para legenda/mapa. The default is 10.
cbar_loc : str, optional
Local para posicionar legenda ('vertical' ou 'horizontal'). The default is 'vertical'.
cbar_label : str, optional
Nome da legenda. The default is 'My legend'.
grid : float, optional
Tamanho da grade a ser utilizada no mapa. The default is 1.
cbar_extend : str, optional
Define se colorbar extender valores. The default is 'both'.
lbsize : int, optional
Tamanho das labels da lebenda. The default is 10.
lbpad : int, optional
Label pad. The default is 3.
fontsize : int, optional
Tamanho da fonte padrão do mapa. The default is 11.
aux_shapes : array(shp), optional
Lista de array auxiliares com complemento visual. The default is [].
title : str, optional
Título do mapa. The default is ''.
figname : str, optional
Se fornecido salva mapa no local e com nome especificado. The default is ''.
show : bool, optional
Se True mostra figura. The default is False.
ticks : array, optional
Ticks da barra de cores. The default is 0.
norm : bool, optional
If bool normalize colors. The default is None.
Returns
-------
None.
'''
gpd_shp = gpd.read_file(shp)
if ax == False:
fig, ax = plt.subplots(figsize=figsize)
xx,yy = np.meshgrid(lon, lat)
if clip:
clip = clip_area(gpd_shp, ax)
if not ticks:
ticks = np.linspace(vmin, vmax, nbin+1)
ticks = list(ticks)
if type(cmap) != str:
cmap = mycmap(cmap, nbin)
if legend == 'colorbar':
norm = mpl.colors.BoundaryNorm(ticks, cmap.N)
else:
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
im = plt.imshow(xx, yy, data, vmin=vmin, vmax=vmax, cmap=cmap,
clip_path=clip)
__legend(data, im, legend, ticks, ticks_label, cbar_extend, vmin, vmax,
nbin, cbar_loc, cbar_label, cmap, norm, lbsize, lbpad, fontsize, ax)
gpd_shp.plot(ax=ax, facecolor='none', edgecolor='black', lw=2)
if len(aux_shapes):
for aux_shape in aux_shapes:
aux_shape = gpd.read_file(aux_shape)
aux_shape.plot(ax=ax, facecolor='none', edgecolor='black', lw=2)
#draw parallels and meridians
parallels = np.arange(-90., 90., grid)
meridians = np.arange(-180., 180., grid)
ax.hlines(parallels, lon.min(), lon.max(), linewidth=.3)
ax.vlines(meridians, lat.min(), lat.max(), linewidth=.3)
xticks = parallels[(parallels>=lon.min()) & (parallels<=lon.max())]
ax.set_xticks(xticks)
xtickslabels = [f'{abs(x):0.1f}ºW' if x < 0 else f'{x:0.1f}º' if x == 0 else f'{x:0.1f}ºE' for x in xticks]
ax.set_xticklabels(xtickslabels, fontsize=fontsize)
yticks = meridians[(meridians>=lat.min()) & (meridians<=lat.max())]
ax.set_yticks(yticks)
yticklabels = [f'{abs(y):0.1f}ºS' if y < 0 else f'{y:0.1f}º' if y == 0 else f'{y:0.1f}ºN' for y in yticks]
ax.set_yticklabels(yticklabels, fontsize=fontsize)
set_limits(ax, gpd_shp)
if title:
ax.set_title(title, fontsize=fontsize+4)
if figname:
plt.savefig(figname, dpi=300)
plt.close()
if show:
plt.show()
def voronoi(lon, lat, data, shp, nbin=10, grid=.1, fontsize=11, title='', vmin=200, vmax=2000,
loc_legend='best', figname='', cmap='seismic', ticks=None,
legend_title='Legend', show=True):
'''
Gera mapa dos poligonos de Voronoi utilizados no método de Thiessen.
Parameters
----------
data : DataFrame
Array de dados da variável de interesse.
shp : shapefile
Arquivo .shp da área de interesse.
cmap : str ou array, optional
Se str utiliza-se um dos mapas de cores padrões do matplotlib, se array
define mapa de cor personalizado. The default is 'seismic'.
nbin : int, optional
Número de divisões/cores para legenda/mapa. The default is 10.
grid : float, optional
Tamanho da grade a ser utilizada no mapa. The default is .1.
fontsize : int, optional
Tamanho da fonte padrão do mapa. The default is 11.
title : str, optional
Título do mapa. The default is ''.
vmin : float, optional
Mínimo valor a ser utilizado para legenda. The default is 200.
vmax : float, optional
Máximo valor a ser utilizado para legenda. The default is 2000.
loc_legend : str, optional
Localização da legenda, valores padrões do matplolib. The default is 'best'.
figname : str, optional
Se fornecido salva figura no local e com nome informado. The default is ''.
cmap : str ou array, optional
Se str utiliza-se um dos mapas de cores padrões do matplotlib, se array
define mapa de cor personalizado. The default is 'seismic'.
ticks : array, optional
Ticks label. The default is None.
legend_title : str, optional
Nome da legenda. The default is 'Legend'.
show : bool, optional
Se True exibe figura. The default is True.
Returns
-------
None.
'''
gpd_shp = gpd.read_file(shp)
gpd_geom = gpd_shp['geometry']
env = gpd_geom.bounds
geometry = [Point(xy) for xy in zip(lon, lat)]
crs = 'epsg:4674'
#df = data.drop([0,1], axis=1)
df = data
df = df.rename(columns={1:'var'})
gdf = GeoDataFrame(df, crs=crs, geometry=geometry)
scheme = mc.Quantiles(df['var'], k=nbin)
quantiles = np.linspace(vmin, vmax, nbin)
scheme.bins = quantiles
if not ticks:
ticks = np.linspace(vmin, vmax, nbin+1)
ticks = list(ticks)
if type(cmap) != str:
cmap = mycmap(cmap, nbin)
fig, ax = plt.subplots(figsize=(15, 15))
gpd_shp.plot(ax=ax, facecolor='none', edgecolor='black', lw=2)
# geoplot.voronoi(gdf, clip=gpd_geom, hue='var', scheme=scheme,
# cmap=cmap, legend=True, ax=ax,
# legend_kwargs={'fontsize':fontsize+4, 'loc': loc_legend,
# 'title':legend_title, 'title_fontsize':fontsize+6})
gpd_shp.plot(ax=ax, facecolor='none', edgecolor='black', lw=2)
set_limits(ax, gpd_shp)
#draw parallels and meridians
parallels=np.arange(env.miny[0]+.1, env.maxy[0], grid)
# ax.hlines([env.miny[0], env.maxy[0]], env.minx[0], env.maxx[0], linewidth=1)
# ax.vlines([env.minx[0], env.maxx[0]], env.miny[0], env.maxy[0], linewidth=1)
meridians = np.arange(env.minx[0]+.5, env.maxx[0], grid)
ax.hlines(parallels, env.minx[0], env.maxx[0], linewidth=.3)
ax.vlines(meridians, env.miny[0], env.maxy[0], linewidth=.3)
xticks = meridians
yticks = parallels
yticklabels = [f'{y:0.1f}ºS' if y < 0 else f'{y:0.1}º' if y == 0 else 'f{y:0.1}ºN' for y in yticks]
xtickslabels = [f'{x:0.1f}ºW' if x < 0 else f'{x:0.1f}º' if x == 0 else f'{x:0.1f}ºE' for x in xticks]
for i in range(len(xticks)):
ax.text(xticks[i]-0.03, env.miny[0]-0.02, xtickslabels[i], fontsize=fontsize*1.5)
for i in range(len(yticks)):
ax.text(env.maxx[0], yticks[i], yticklabels[i], fontsize=fontsize*1.5)
plt.title(title, fontsize=fontsize*2)
if figname:
plt.savefig(figname, dpi=300)
if show:
plt.show()
def choropleth(data, shps, var='var', cmap='seismic', nbin=10, ticks=None,
vmin=200, vmax=2000, cbar_extend='both', cbar_label='Legend',
cbar_loc='vertical', lbsize=10, lbpad=3, fontsize=21, grid=0.5,
title='', figname='', show=True, ticks_label=[], figsize=(15,15),
pos_ticks=0, legend='colorbar', norm=0, fig=None, ax=None,
yticklabels=True, xtickslabels=True):
'''
Gera mapas coropléticos.
Parameters
----------
data : array
Dados da variável.
shps : list
Lista de shapefiles do mapa.
var : str, optional
Nome da variável de interesse. The default is 'var'.
cmap : str ou array, optional
Se str utiliza-se um dos mapas de cores padrões do matplotlib, se array
define mapa de cor personalizado. The default is 'seismic'.
nbin : int, optional
Número de divisões/cores para legenda/mapa. The default is 10.
ticks : array, optional
Ticks label. The default is None.
vmin : float, optional
Mínimo valor a ser utilizado para legenda. The default is 200.
vmax : float, optional
Máximo valor a ser utilizado para legenda. The default is 2000.
cbar_extend : str, optional
Define se colorbar extender valores. The default is 'both'.
cbar_label : str, optional
Nome da legenda. The default is 'Legend'.
cbar_loc : str, optional
Localização da legenda (vertical, horizontal). The default is 'vertical'.
lbsize : int, optional
Tamanho das labels da legenda. The default is 10.
lbpad : int, optional
Label pad. The default is 3.
fontsize : int, optional
Tamanho padrão da fonte do mapa. The default is 21.
grid : itn, optional
Tamanho da grade a ser utilizada no mapa.. The default is 0.5.
title : str, optional
Título do mapa. The default is ''.
figname : str, optional
Se fornecido salva figura no local e com nome informado. The default is ''.
show : boll, optional
Se True exibe figura. The default is True.
Returns
-------
None.
'''
if not fig and not ax:
fig, ax = plt.subplots(figsize=(15, 15))
#%%
gpd_shps = []
for i, shp in enumerate(shps):
gpd_shp = gpd.read_file(shp)
gpd_shp[var] = data[i]
if i == 0:
gpd_shps = gpd_shp
else:
gpd_shps = gpd_shps.append(gpd_shp)
#%%
if not np.array(ticks).any():
ticks = np.linspace(vmin, vmax, nbin+1)
ticks = list(ticks)
if type(cmap) != str:
cmap = mycmap(cmap, nbin)
#%%
gpd_shps.plot(column=var, cmap=cmap, vmin=vmin, vmax=vmax, ax=ax)
gpd_shps.plot(ax=ax, facecolor='none', edgecolor='black', lw=2)
im = plt.cm.ScalarMappable(cmap=cmap, norm=mpl.colors.BoundaryNorm(ticks, cmap.N))
if legend:
__legend(data, im, legend, ticks, ticks_label, cbar_extend, vmin, vmax,
nbin, cbar_loc, cbar_label, cmap, norm, lbsize, lbpad, fontsize, ax)
#%%
#draw parallels and meridians
env = gpd_shps.bounds
parallels=np.arange(env.miny.min()-grid, env.maxy.max()+grid, grid)
ax.hlines(parallels, env.minx.min()-grid, env.maxx.max()+grid, linewidth=.3)
yticks = parallels
if yticklabels:
ax.set_yticks(yticks)
yticklabels = [f'{y:0.1f}ºS' if y < 0 else f'{y:0.1}º' if y == 0 else 'f{y:0.1}ºN' for y in yticks]
ax.set_yticklabels(yticklabels, fontsize=fontsize)
else:
ax.set_yticklabels([])
ax.yaxis.set_ticks_position('none')
meridians = np.arange(env.minx.min()-grid, env.maxx.max()+grid, grid)
ax.vlines(meridians, env.miny.min()-grid, env.maxy.max()+grid, linewidth=.3)
xticks = meridians
if xtickslabels:
ax.set_xticks(xticks)
xtickslabels = [f'{x:0.1f}ºW' if x < 0 else f'{x:0.1f}º' if x == 0 else f'{x:0.1f}ºE' for x in xticks]
ax.set_xticklabels(xtickslabels, fontsize=fontsize)
else:
ax.set_xticklabels([])
ax.xaxis.set_ticks_position('none')
ax.set_xlim([env.minx.min(), env.maxx.max()])
ax.set_ylim([env.miny.min(), env.maxy.max()])
plt.title(title, fontsize=fontsize+6)
if figname:
plt.savefig(figname, dpi=300)
if show:
plt.show()
def quiver(u, v, lat, lon, m, shp, clip=None, vmin=0, vmax=100, cmap='seismic_r',
nbin=10, cbar_loc='vertical', cbar_label='My legend', grid=1,
cbar_extend='both', lbsize=10, lbpad=3, fontsize=11, aux_shapes=[],
title='', figname='', show=False, ticks=0, norm=None, figsize=(15,15),
ticks_label=[], pos_ticks=0, legend='colorbar'):
gpd_shp = gpd.read_file(shp)
fig, ax = plt.subplots(figsize=figsize)
xx,yy = np.meshgrid(lat, lon)
if clip:
clip = clip_area(gpd_shp, ax)
if not ticks:
ticks = np.linspace(vmin, vmax, nbin+1)
ticks = list(ticks)
if type(cmap) != str:
cmap = mycmap(cmap, nbin)
if legend == 'colorbar':
norm = mpl.colors.BoundaryNorm(ticks, cmap.N)
else:
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
# ax.set_title("pivot='tip'; scales with x view", fontsize=24)
im = plt.pcolormesh(yy, xx, m.T, cmap=cmap, clip_path=clip, norm=norm)
# im = plt.contourf(yy, xx, m.T, cmap=cmap, levels=10, vmin=0, vmax=vmax)
# plt.colorbar(orientation=cbar_loc,
# label=cbar_label, extend=cbar_extend,
# boundaries=[0, 1, 2], fraction=0.038,
# cmap=cmap)
q = plt.quiver(lon, lat, u, v,
scale = 1/0.03)
qk = ax.quiverkey(q, 0.7, 0.5, 1, r'$1 \frac{m}{s}$', labelpos='E',
coordinates='figure', fontproperties={'size':24})
__legend(m, im, legend, ticks, ticks_label, cbar_extend, vmin, vmax,
nbin, cbar_loc, cbar_label, cmap, norm, lbsize, lbpad, fontsize)
gpd_shp.plot(ax=ax, facecolor='none', edgecolor='black', lw=2)
if len(aux_shapes):
for aux_shape in aux_shapes:
aux_shape = gpd.read_file(aux_shape)
aux_shape.plot(ax=ax, facecolor='none', edgecolor='black', lw=2)
#draw parallels and meridians
parallels = np.arange(-90., 90., grid)
meridians = np.arange(-180., 180., grid)
ax.hlines(parallels, lon.min(), lon.max(), linewidth=.3)
ax.vlines(meridians, lat.min(), lat.max(), linewidth=.3)
xticks = parallels[(parallels>=lon.min()) & (parallels<=lon.max())]
ax.set_xticks(xticks)
xtickslabels = [f'{abs(x):0.1f}ºW' if x < 0 else f'{x:0.1f}º' if x == 0 else f'{x:0.1f}ºE' for x in xticks]
ax.set_xticklabels(xtickslabels, fontsize=fontsize)
yticks = meridians[(meridians>=lat.min()) & (meridians<=lat.max())]
ax.set_yticks(yticks)
yticklabels = [f'{abs(y):0.1f}ºS' if y < 0 else f'{y:0.1f}º' if y == 0 else f'{y:0.1f}ºN' for y in yticks]
ax.set_yticklabels(yticklabels, fontsize=fontsize)
set_limits(ax, gpd_shp)
if title:
plt.title(title, fontsize=fontsize+4)
if figname:
plt.savefig(figname, dpi=300)
plt.close()
if show:
plt.show()