Visualization with EOCube¶
[1]:
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
from pathlib import Path
import rasterio
import seaborn as sns
import matplotlib.pyplot as plt
# from eobox.raster import MultiRasterIO
from eobox import sampledata
from eobox.raster import cube
from eobox.raster import gdalutils
from eobox.raster.utils import cleanup_df_values_for_given_dtype
from eobox.raster.utils import dtype_checker_df
print(cube.__file__)
print(sampledata.__file__)
/home/ben/Devel/Packages/eo-box/raster/eobox/raster/cube.py
/home/ben/Devel/Packages/eo-box/sampledata/eobox/sampledata/__init__.py
Sample dataset¶
[2]:
def get_sampledata(year):
dataset = sampledata.get_dataset("lsts")
layers_paths = [Path(p) for p in dataset["raster_files"]]
layers_df = pd.Series([p.stem for p in layers_paths]).str.split("_", expand=True) \
.rename({0: "sceneid", 1:"band"}, axis=1)
layers_df["date"] = pd.to_datetime(layers_df.sceneid.str[9:16], format="%Y%j")
layers_df["uname"] = layers_df.sceneid.str[:3] + "_" + layers_df.date.dt.strftime("%Y-%m-%d") + "_" + layers_df.band.str[::]
layers_df["path"] = layers_paths
layers_df = layers_df.sort_values(["date", "band"])
layers_df = layers_df.reset_index(drop=True)
layers_df_year = layers_df[(layers_df.date >= str(year)) & (layers_df.date < str(year+1))]
layers_df_year = layers_df_year.reset_index(drop=True)
return layers_df_year
The sample data we are loading here contains 23 scenes each of which consists of three bands (b3, b4, b5) and a QA (quality assessment) band (here fmask).
fmask has the following categories:
0 - clear land
1 - clear water
2 - cloud
3 - snow
4 - shadow
255 - NoData
[3]:
df_layers = get_sampledata(2008)
display(df_layers.head(4))
print(df_layers.band.value_counts())
print(df_layers.date.dt.strftime("%Y-%m-%d").unique())
| sceneid | band | date | uname | path | |
|---|---|---|---|---|---|
| 0 | LT50350322008110PAC01 | b3 | 2008-04-19 | LT5_2008-04-19_b3 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 1 | LT50350322008110PAC01 | b4 | 2008-04-19 | LT5_2008-04-19_b4 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 2 | LT50350322008110PAC01 | b5 | 2008-04-19 | LT5_2008-04-19_b5 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 3 | LT50350322008110PAC01 | fmask | 2008-04-19 | LT5_2008-04-19_fmask | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
fmask 23
b4 23
b5 23
b3 23
Name: band, dtype: int64
['2008-04-19' '2008-04-27' '2008-05-05' '2008-05-21' '2008-05-29'
'2008-06-06' '2008-06-14' '2008-06-22' '2008-06-30' '2008-07-08'
'2008-07-16' '2008-07-24' '2008-08-01' '2008-08-09' '2008-08-17'
'2008-08-25' '2008-09-02' '2008-09-18' '2008-09-26' '2008-10-12'
'2008-10-28' '2008-11-21' '2008-12-07']
Initialize an ``EOCubeSceneCollection``
[4]:
df_layers=df_layers
chunksize=2**5
variables=["b3", "b4", "b5"]
qa="fmask"
qa_valid=[0, 1]
Get a chunk and read the data
[5]:
scoll = cube.EOCubeSceneCollection(df_layers=df_layers,
chunksize=chunksize,
variables=variables,
qa=qa,
qa_valid=qa_valid
)
display(scoll.df_layers.head())
scoll_chunk = scoll.get_chunk(1)
scoll_chunk.read_data()
scoll_chunk.data.shape
| sceneid | band | date | uname | path | |
|---|---|---|---|---|---|
| 0 | LT50350322008110PAC01 | b3 | 2008-04-19 | LT5_2008-04-19_b3 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 1 | LT50350322008110PAC01 | b4 | 2008-04-19 | LT5_2008-04-19_b4 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 2 | LT50350322008110PAC01 | b5 | 2008-04-19 | LT5_2008-04-19_b5 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 3 | LT50350322008110PAC01 | fmask | 2008-04-19 | LT5_2008-04-19_fmask | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 4 | LE70350322008118EDC00 | b3 | 2008-04-27 | LE7_2008-04-27_b3 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
[5]:
(32, 29, 92)
Plotting raster¶
Single layer¶
Continuous¶
[6]:
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 6))
aximg = scoll_chunk.plot_raster(0, spatial_bounds=False, ax=axes[0])
axes[0].set_title(scoll_chunk.df_layers.uname[0])
aximg = scoll_chunk.plot_raster(1, spatial_bounds=False, ax=axes[1])
axes[1].set_title(scoll_chunk.df_layers.uname[1])
aximg = scoll_chunk.plot_raster(2, spatial_bounds=False, ax=axes[2])
axes[2].set_title(scoll_chunk.df_layers.uname[2])
plt.tight_layout()
Categorical¶
ToDo
RGBs¶
There is a helper for getting the indices of commonly required for RGB plots:
[7]:
# three bands, one date
ilocs = scoll_chunk.get_df_ilocs(band=["b5", "b4", "b3"],
date="2008-06-30")
print(ilocs)
display(scoll_chunk.df_layers.iloc[ilocs])
# one band, three dates
ilocs = scoll_chunk.get_df_ilocs(band="b5",
date=['2008-04-19', '2008-07-08', '2008-09-26'])
print(ilocs)
display(scoll_chunk.df_layers.iloc[ilocs])
[34, 33, 32]
| sceneid | band | date | uname | path | |
|---|---|---|---|---|---|
| 34 | LE70350322008182EDC00 | b5 | 2008-06-30 | LE7_2008-06-30_b5 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 33 | LE70350322008182EDC00 | b4 | 2008-06-30 | LE7_2008-06-30_b4 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 32 | LE70350322008182EDC00 | b3 | 2008-06-30 | LE7_2008-06-30_b3 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
[2, 38, 74]
| sceneid | band | date | uname | path | |
|---|---|---|---|---|---|
| 2 | LT50350322008110PAC01 | b5 | 2008-04-19 | LT5_2008-04-19_b5 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 38 | LT50350322008190PAC01 | b5 | 2008-07-08 | LT5_2008-07-08_b5 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
| 74 | LT50350322008270PAC01 | b5 | 2008-09-26 | LT5_2008-09-26_b5 | /home/ben/Devel/Packages/eo-box/sampledata/eob... |
Plot false color RGB (SWIR-1, NIR, RED) of tree days. Each with its own robust color stretch.
[8]:
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 6))
ilocs = scoll_chunk.get_df_ilocs(band=["b5", "b4", "b3"],
date="2008-04-19")
print(ilocs)
scoll_chunk.plot_raster_rgb(ilocs,
spatial_bounds=False,
robust=True,
ax=axes[0])
axes[0].set_title("2008-04-19")
ilocs = scoll_chunk.get_df_ilocs(band=["b5", "b4", "b3"],
date="2008-07-08")
print(ilocs)
scoll_chunk.plot_raster_rgb(ilocs,
spatial_bounds=False,
robust=True,
ax=axes[1])
axes[1].set_title("2008-07-08")
ilocs = scoll_chunk.get_df_ilocs(band=["b5", "b4", "b3"],
date="2008-09-26")
print(ilocs)
scoll_chunk.plot_raster_rgb(ilocs,
spatial_bounds=False,
robust=True,
ax=axes[2])
axes[2].set_title("2008-09-26")
plt.tight_layout()
[2, 1, 0]
[38, 37, 36]
[74, 73, 72]
Plot false color RGB (SWIR-1, NIR, RED) of tree days. All with the same color stretch derived from the first image.
[9]:
master_ilocs = scoll_chunk.get_df_ilocs(band=["b5", "b4", "b3"],
date="2008-04-19")
master_array = scoll_chunk.data[:, :, master_ilocs]
vmin, vmax = scoll_chunk.robust_data_range(master_array, robust=True)
print(vmin)
print(vmax)
[339.0, 2016.0, 1092.0]
[734.0, 6972.180000000001, 16000.0]
[10]:
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 6))
ilocs = scoll_chunk.get_df_ilocs(band=["b5", "b4", "b3"],
date="2008-04-19")
scoll_chunk.plot_raster_rgb(ilocs,
spatial_bounds=False,
vmin=vmin, vmax=vmax,
ax=axes[0])
axes[0].set_title("2008-04-19")
ilocs = scoll_chunk.get_df_ilocs(band=["b5", "b4", "b3"],
date="2008-07-08")
scoll_chunk.plot_raster_rgb(ilocs,
spatial_bounds=False,
vmin=vmin, vmax=vmax,
ax=axes[1])
axes[1].set_title("2008-07-08")
ilocs = scoll_chunk.get_df_ilocs(band=["b5", "b4", "b3"],
date="2008-09-26")
scoll_chunk.plot_raster_rgb(ilocs,
spatial_bounds=False,
vmin=vmin, vmax=vmax,
ax=axes[2])
axes[2].set_title("2008-09-26")
plt.tight_layout()
IS HERE SOMETHING WRONG WITH THE CODE OR THE DATA?