Aula 05 - Lendo dados oceanográficos em diversos formatos (netCDF, OPeNDAP, ERDDAP etc) e dimensões (AKA além das tabelas)]

Objetivos

Exibir dados em várias dimensões (Satélites, Modelos, etc)
Ler de dados diversas fontes binárias (NetCDF, HDF4/5, e Protocolos online)
Introduzir conceitos básicos de CDM (Common Data Models)

Relembrando Slices

Uma forma de lembrar como fazer slices em Python é pensar nos pontos entre os índices e não na célula do índice.

 +---+---+---+---+---+---+
 | P | y | t | h | o | n |
 +---+---+---+---+---+---+
 0   1   2   3   4   5   6
-6  -5  -4  -3  -2  -1



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t = 'Python'



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t[0:2]



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t[::2]



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t[::-1]



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import numpy as np

arr = np.array([[3, 6, 2, 1, 7],
                [4, 1, 3, 2, 8],
                [7, 9, 2, 1, 8],
                [8, 6, 9, 6, 7],
                [9, 1, 9, 2, 6],
                [9, 8, 1, 5, 6],
                [0, 4, 2, 0, 6],
                [0, 3, 1, 4, 7]])



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arr[:, 0]



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arr[2:6, 1:4]



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arr = np.array([[[3, 6, 2, 1, 7],
                 [4, 1, 3, 2, 8],
                 [7, 9, 2, 1, 8],
                 [8, 6, 9, 6, 7],
                 [9, 1, 9, 2, 6],
                 [9, 8, 1, 5, 6],
                 [0, 4, 2, 0, 6],
                 [0, 3, 1, 4, 7]],
                [[5, 5, 3, 9, 3],
                 [8, 3, 5, 1, 1],
                 [3, 4, 3, 0, 9],
                 [1, 4, 1, 0, 2],
                 [7, 1, 2, 0, 1],
                 [5, 1, 3, 7, 8],
                 [8, 0, 9, 6, 0],
                 [7, 7, 4, 4, 4]],
                [[1, 0, 8, 9, 1],
                 [7, 4, 8, 8, 2],
                 [9, 1, 8, 3, 6],
                 [5, 6, 2, 0, 1],
                 [7, 4, 2, 5, 7],
                 [9, 5, 6, 8, 6],
                 [7, 4, 4, 7, 1],
                 [8, 4, 4, 9, 1]]])



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arr.shape



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arr[0:2, 0:4, -1]



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arr[:, 0:2, 0:3]



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arr[:, 0, 2]

Mais que 3 dimensões

Exemplo real



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from netCDF4 import Dataset

nc = Dataset('./data/mdt_cnes_cls2009_global_v1.1.nc')

nc



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u = nc.variables['Grid_0002']
u



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v = nc.variables['Grid_0003']
v



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u, v = u[:], v[:]



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lon = nc.variables['NbLongitudes'][:]
lat = nc.variables['NbLatitudes'][:]



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import numpy as np

lon, lat = np.meshgrid(lon, lat)



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lon.shape, lat.shape, u.shape, v.shape



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%matplotlib inline
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
sub = 5
ax.quiver(lon[::sub, ::sub], lat[::sub, ::sub], u.T[::sub, ::sub], v.T[::sub, ::sub])



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from oceans import wrap_lon180
lon = wrap_lon180(lon)



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import matplotlib.pyplot as plt

import cartopy.crs as ccrs
from cartopy.io import shapereader
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER


def make_map(projection=ccrs.PlateCarree()):
    fig, ax = plt.subplots(figsize=(9, 13),
                           subplot_kw=dict(projection=projection))
    gl = ax.gridlines(draw_labels=True)
    gl.xlabels_top = gl.ylabels_right = False
    gl.xformatter = LONGITUDE_FORMATTER
    gl.yformatter = LATITUDE_FORMATTER
    return fig, ax



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mask_x = np.logical_and(lon > -40, lon < -36)
mask_y = np.logical_and(lat > -15, lat < -12)
mask = np.logical_and(mask_x, mask_y)



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import cartopy.feature as cfeature
land_10m = cfeature.NaturalEarthFeature('physical', 'land', '10m',
                                        edgecolor='face',
                                        facecolor=cfeature.COLORS['land'])

fig, ax = make_map()
ax.quiver(lon[mask], lat[mask], u.T[mask], v.T[mask])
ax.add_feature(land_10m)
ax.coastlines('10m')
ax.set_extent([-40, -36, -15, -12])