Read Breizhcrops EOPatch files#
[1]:
import os
import numpy as np
from marshmallow import fields, Schema
from marshmallow.validate import OneOf
from eolearn.core import EOPatch, FeatureType
[2]:
eop = EOPatch.load("../../eopatches/eopatch_5453")
/home/elena/anaconda3/envs/aitlas-py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
and should_run_async(code)
[3]:
eop
[3]:
EOPatch(
data: {
FEATURES_S2: numpy.ndarray(shape=(28, 100, 100, 11), dtype=float32)
S1_COH_ASC_VH_VV_DES_VH_VV_mean_1month: numpy.ndarray(shape=(12, 100, 100, 4), dtype=float32)
S1_COH_ASC_VH_VV_DES_VH_VV_p10_1month: numpy.ndarray(shape=(12, 100, 100, 4), dtype=float32)
S1_COH_weekly_interp: numpy.ndarray(shape=(61, 100, 100, 4), dtype=float32)
S1_SIG_ASC_VH_VV_DES_VH_VV_cov_2months: numpy.ndarray(shape=(6, 100, 100, 4), dtype=float32)
S1_SIG_ASC_VH_VV_DES_VH_VV_mean_2months: numpy.ndarray(shape=(6, 100, 100, 4), dtype=float32)
S1_SIG_ASC_VVVH_DES_VVVH_ratio_weekly: numpy.ndarray(shape=(61, 100, 100, 2), dtype=float32)
S1_SIG_weekly_interp: numpy.ndarray(shape=(61, 100, 100, 4), dtype=float32)
}
mask: {}
scalar: {}
label: {}
vector: {}
data_timeless: {
S1_COH_ASC_VH_VV_DES_VH_VV_std_yearly: numpy.ndarray(shape=(100, 100, 4), dtype=float32)
}
mask_timeless: {
CROP_TYPE: numpy.ndarray(shape=(100, 100, 1), dtype=uint16)
CROP_TYPE_ERODED: numpy.ndarray(shape=(100, 100, 1), dtype=uint16)
CROP_TYPE_EU: numpy.ndarray(shape=(100, 100, 1), dtype=uint16)
CROP_TYPE_EU_ERODED: numpy.ndarray(shape=(100, 100, 1), dtype=uint16)
}
scalar_timeless: {}
label_timeless: {}
vector_timeless: {
CROP_TYPE_GDF: geopandas.GeoDataFrame(columns=['polygon_id', 'ct_code', 'ct_eu_code', 'geometry', 'ct_id', 'ct_eu_id'], length=95, crs=EPSG:32633)
}
meta_info: {
country: 'Slovenia'
ct_code_list: ct_code ct_name ct_id ct_eu_code \
0 0 ni v uporabi 1 NUAA+WA+FA9
1 1 pšenica (jara) 2 C1100
2 2 rž (jara) 3 C1200
3 3 pira (jara) 4 C1100
4 4 ajda 5 C1900
.. ... ... ... ...
184 825 trda pšenica (ozimna) 185 C1100
185 831 vrtni mak (ozimni) 186 I1190
186 833 krmni grah (ozimni) 187 P1100
187 835 pšenica horasan (ozimna) 188 C1900
188 900 hmelj 189 I4000
ct_eu_name ct_eu_id
0 Other farmland 105
1 Wheat and spelt 5
2 Rye and winter cereal mixtures (maslin) 10
3 Wheat and spelt 5
4 Other cereals n.e.c. (buckwheat, millet, canar... 22
.. ... ...
184 Wheat and spelt 5
185 Other oilseed crops n.e.c. 90
186 Field peas 110
187 Other cereals n.e.c. (buckwheat, millet, canar... 22
188 Hops 97
[189 rows x 6 columns]
ct_eu_hierarchy: ct_eu_code ct_eu_name level \
0 ARA Arable land level_2
1 ARA99 Other arable land crops n.e.c. level_3
2 C0000 Cereals for the production of grain (including... level_3
3 C1000 Cereals (excluding rice) for the production of... level_4
4 C1100 Wheat and spelt level_5
.. ... ... ...
194 W1120 Grapes for wines with protected geographical i... level_6
195 W1190 Grapes for other wines n.e.c. (without PDO/PGI) level_6
196 W1200 Grapes for table use level_5
197 W1300 Grapes for raisins level_5
198 W1900 Grapes for other purposes n.e.c. level_5
level_8 level_7 level_6 level_5 level_4 level_3 level_2 level_1
0 NaN NaN NaN NaN NaN NaN ARA UAA
1 NaN NaN NaN NaN NaN ARA99 ARA UAA
2 NaN NaN NaN NaN NaN C0000 ARA UAA
3 NaN NaN NaN NaN C1000 C0000 ARA UAA
4 NaN NaN NaN C1100 C1000 C0000 ARA UAA
.. ... ... ... ... ... ... ... ...
194 NaN NaN W1120 W1100 W1000 H0000 PECR UAA
195 NaN NaN W1190 W1100 W1000 H0000 PECR UAA
196 NaN NaN NaN W1200 W1000 H0000 PECR UAA
197 NaN NaN NaN W1300 W1000 H0000 PECR UAA
198 NaN NaN NaN W1900 W1000 H0000 PECR UAA
[199 rows x 11 columns]
maxcc: 0.8
service_type: 'processing'
size_x: 100
size_y: 100
time_difference: datetime.timedelta(seconds=7200)
time_interval: (datetime.datetime(2017, 1, 1, 0, 0), datetime.datetime(2017, 12, 31, 23, 59, 59))
year: 2017
}
bbox: BBox(((554000.0, 5091000.0), (555000.0, 5092000.0)), crs=CRS('32633'))
timestamp: [datetime.datetime(2017, 3, 1, 0, 0), ..., datetime.datetime(2017, 11, 26, 0, 0)], length=28
)
[4]:
eop.meta_info["ct_code_list"]
/home/elena/anaconda3/envs/aitlas-py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
and should_run_async(code)
[4]:
| ct_code | ct_name | ct_id | ct_eu_code | ct_eu_name | ct_eu_id | |
|---|---|---|---|---|---|---|
| 0 | 0 | ni v uporabi | 1 | NUAA+WA+FA9 | Other farmland | 105 |
| 1 | 1 | pšenica (jara) | 2 | C1100 | Wheat and spelt | 5 |
| 2 | 2 | rž (jara) | 3 | C1200 | Rye and winter cereal mixtures (maslin) | 10 |
| 3 | 3 | pira (jara) | 4 | C1100 | Wheat and spelt | 5 |
| 4 | 4 | ajda | 5 | C1900 | Other cereals n.e.c. (buckwheat, millet, canar... | 22 |
| ... | ... | ... | ... | ... | ... | ... |
| 184 | 825 | trda pšenica (ozimna) | 185 | C1100 | Wheat and spelt | 5 |
| 185 | 831 | vrtni mak (ozimni) | 186 | I1190 | Other oilseed crops n.e.c. | 90 |
| 186 | 833 | krmni grah (ozimni) | 187 | P1100 | Field peas | 110 |
| 187 | 835 | pšenica horasan (ozimna) | 188 | C1900 | Other cereals n.e.c. (buckwheat, millet, canar... | 22 |
| 188 | 900 | hmelj | 189 | I4000 | Hops | 97 |
189 rows × 6 columns
[5]:
eop.meta_info["ct_eu_hierarchy"]
/home/elena/anaconda3/envs/aitlas-py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
and should_run_async(code)
[5]:
| ct_eu_code | ct_eu_name | level | level_8 | level_7 | level_6 | level_5 | level_4 | level_3 | level_2 | level_1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ARA | Arable land | level_2 | NaN | NaN | NaN | NaN | NaN | NaN | ARA | UAA |
| 1 | ARA99 | Other arable land crops n.e.c. | level_3 | NaN | NaN | NaN | NaN | NaN | ARA99 | ARA | UAA |
| 2 | C0000 | Cereals for the production of grain (including... | level_3 | NaN | NaN | NaN | NaN | NaN | C0000 | ARA | UAA |
| 3 | C1000 | Cereals (excluding rice) for the production of... | level_4 | NaN | NaN | NaN | NaN | C1000 | C0000 | ARA | UAA |
| 4 | C1100 | Wheat and spelt | level_5 | NaN | NaN | NaN | C1100 | C1000 | C0000 | ARA | UAA |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 194 | W1120 | Grapes for wines with protected geographical i... | level_6 | NaN | NaN | W1120 | W1100 | W1000 | H0000 | PECR | UAA |
| 195 | W1190 | Grapes for other wines n.e.c. (without PDO/PGI) | level_6 | NaN | NaN | W1190 | W1100 | W1000 | H0000 | PECR | UAA |
| 196 | W1200 | Grapes for table use | level_5 | NaN | NaN | NaN | W1200 | W1000 | H0000 | PECR | UAA |
| 197 | W1300 | Grapes for raisins | level_5 | NaN | NaN | NaN | W1300 | W1000 | H0000 | PECR | UAA |
| 198 | W1900 | Grapes for other purposes n.e.c. | level_5 | NaN | NaN | NaN | W1900 | W1000 | H0000 | PECR | UAA |
199 rows × 11 columns
Each EOPatch is 100x100 pixels, containing timeseries for each pixel for multiple features
[6]:
import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
plt.imshow(eop.data['FEATURES_S2'][12][...,[0,1,2]] * 2.5)
/home/elena/anaconda3/envs/aitlas-py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
and should_run_async(code)
[6]:
<matplotlib.image.AxesImage at 0x7f0304317b00>
Each EOPatch contains information about the “crop fields” it contains?
[7]:
eop.vector_timeless["CROP_TYPE_GDF"]
/home/elena/anaconda3/envs/aitlas-py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
and should_run_async(code)
[7]:
| polygon_id | ct_code | ct_eu_code | geometry | ct_id | ct_eu_id | |
|---|---|---|---|---|---|---|
| 0 | 2207423.00000000000 | 204 | J0000 | MULTIPOLYGON (((554154.665 5091148.769, 554288... | 75 | 101 |
| 1 | 2964049.00000000000 | 801 | C1100 | MULTIPOLYGON (((554436.408 5091544.271, 554545... | 173 | 5 |
| 2 | 3077572.00000000000 | 801 | C1100 | MULTIPOLYGON (((554634.457 5091016.958, 554769... | 173 | 5 |
| 3 | 2964043.00000000000 | 801 | C1100 | MULTIPOLYGON (((554472.410 5091686.704, 554581... | 173 | 5 |
| 4 | 3051327.00000000000 | 5 | C1500 | MULTIPOLYGON (((554027.728 5091339.028, 554030... | 6 | 19 |
| ... | ... | ... | ... | ... | ... | ... |
| 90 | 2114371.00000000000 | 204 | J0000 | MULTIPOLYGON (((554156.819 5091203.299, 554155... | 75 | 101 |
| 91 | 2964307.00000000000 | 809 | C1300 | MULTIPOLYGON (((554376.712 5091902.957, 554379... | 179 | 13 |
| 92 | 2207417.00000000000 | 204 | J0000 | MULTIPOLYGON (((554335.377 5091010.159, 554491... | 75 | 101 |
| 93 | 2588116.00000000000 | 204 | J0000 | MULTIPOLYGON (((554233.185 5090966.385, 554125... | 75 | 101 |
| 94 | 3051299.00000000000 | 30 | I1130 | MULTIPOLYGON (((554619.762 5091061.089, 554624... | 27 | 87 |
95 rows × 6 columns
[ ]:
eop.vector_timeless["CROP_TYPE_GDF"].iloc[0].geometry
Crop types mask
[9]:
import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
plt.imshow(eop.mask_timeless['CROP_TYPE'])
[9]:
<matplotlib.image.AxesImage at 0x7f0304214d30>
