From 8ba4258ba6632cb9bcdd52713b4caecba1353226 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Dutrieux?= Date: Wed, 28 Aug 2024 18:02:29 +0200 Subject: [PATCH] Removed nrt.data subpackage --- MANIFEST.in | 1 - nrt/__init__.py | 2 +- nrt/data/__init__.py | 363 ------------------ nrt/data/mreCritValTable.json | 1 - nrt/data/sentinel2_cube_subset_romania_10m.nc | 3 - nrt/data/sentinel2_cube_subset_romania_20m.nc | 3 - nrt/data/sentinel2_subset.nc | 3 - nrt/data/tree_cover_density_2018_romania.tif | 3 - nrt/monitor/__init__.py | 2 + pyproject.toml | 52 +++ setup.py | 59 --- 11 files changed, 55 insertions(+), 437 deletions(-) delete mode 100644 nrt/data/__init__.py delete mode 100644 nrt/data/mreCritValTable.json delete mode 100644 nrt/data/sentinel2_cube_subset_romania_10m.nc delete mode 100644 nrt/data/sentinel2_cube_subset_romania_20m.nc delete mode 100644 nrt/data/sentinel2_subset.nc delete mode 100644 nrt/data/tree_cover_density_2018_romania.tif create mode 100644 pyproject.toml delete mode 100644 setup.py diff --git a/MANIFEST.in b/MANIFEST.in index 836df78..aaa0265 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,6 +1,5 @@ include LICENSE README.rst setup.py include tests*/*.py -include tests/data/*.csv include integration_test/*.py recursive-include docs *.rst recursive-include docs *.png diff --git a/nrt/__init__.py b/nrt/__init__.py index 3ced358..69e3be5 100644 --- a/nrt/__init__.py +++ b/nrt/__init__.py @@ -1 +1 @@ -__version__ = "0.2.1" +__path__ = __import__('pkgutil').extend_path(__path__, __name__) diff --git a/nrt/data/__init__.py b/nrt/data/__init__.py deleted file mode 100644 index 7015d1f..0000000 --- a/nrt/data/__init__.py +++ /dev/null @@ -1,363 +0,0 @@ -# Copyright (C) 2022 European Union (Joint Research Centre) -# -# Licensed under the EUPL, Version 1.2 or - as soon they will be approved by -# the European Commission - subsequent versions of the EUPL (the "Licence"); -# You may not use this work except in compliance with the Licence. -# You may obtain a copy of the Licence at: -# -# https://joinup.ec.europa.eu/software/page/eupl -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the Licence is distributed on an "AS IS" basis, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the Licence for the specific language governing permissions and -# limitations under the Licence. - -import os -import json - -import xarray as xr -import rasterio -import numpy as np - - -data_dir = os.path.abspath(os.path.dirname(__file__)) - - -def _load(f, **kwargs): - """Load a ncdf file located in the data directory as a xarray Dataset - - Args: - f (str): File basename - **kwargs: Keyword arguments passed to ``xarray.open_dataset`` - - Return: - xarray.Dataset: The Dataset - """ - xr_dataset = xr.open_dataset(os.path.join(data_dir, f), - **kwargs) - return xr_dataset - - -def romania_10m(**kwargs): - """Sentinel 2 datacube of a small forested area in Romania at 10 m resolution - - Examples: - >>> from nrt import data - - >>> s2_cube = data.romania_10m() - >>> # Compute NDVI - >>> s2_cube['ndvi'] = (s2_cube.B8 - s2_cube.B4) / (s2_cube.B8 + s2_cube.B4) - >>> # Filter clouds - >>> s2_cube = s2_cube.where(s2_cube.SCL.isin([4,5,7])) - """ - return _load(f='sentinel2_cube_subset_romania_10m.nc', **kwargs) - - -def romania_20m(**kwargs): - """Sentinel 2 datacube of a small forested area in Romania at 20 m resolution - - Examples: - >>> from nrt import data - - >>> s2_cube = data.romania_20m() - >>> # Compute NDVI - >>> s2_cube['ndvi'] = (s2_cube.B8A - s2_cube.B4) / (s2_cube.B8A + s2_cube.B4) - >>> # Filter clouds - >>> s2_cube = s2_cube.where(s2_cube.SCL.isin([4,5,7])) - """ - return _load(f='sentinel2_cube_subset_romania_20m.nc', **kwargs) - - -def romania_forest_cover_percentage(): - """Subset of Copernicus HR layer tree cover percentage - 20 m - Romania - """ - file_basename = 'tree_cover_density_2018_romania.tif' - filename = os.path.join(data_dir, file_basename) - with rasterio.open(filename) as src: - arr = src.read(1) - return arr - - -def mre_crit_table(): - """Contains a dictionary equivalent to strucchange's ``mreCritValTable`` - The key 'sig_level' is a list of the available pre-computed significance - (1-alpha) values. - - The other keys contain nested dictionaries, where the keys are the - available relative window sizes (0.25, 0.5, 1), the second keys are the - available periods (2, 4, 6, 8, 10) and the third keys are the functional - types ("max", "range"). - - Example: - >>> from nrt import data - >>> crit_table = data.mre_crit_table() - >>> win_size = 0.5 - >>> period = 10 - >>> functional = "max" - >>> alpha=0.025 - >>> crit_values = crit_table.get(str(win_size))\ - .get(str(period))\ - .get(functional) - >>> sig_level = crit_table.get('sig_levels') - >>> crit_level = np.interp(1-alpha, sig_level, crit_values) - """ - with open(os.path.join(data_dir, "mreCritValTable.json")) as crit: - crit_table = json.load(crit) - return crit_table - - -def make_ts(dates, break_idx=-1, intercept=0.7, amplitude=0.15, magnitude=0.25, - recovery_time=1095, sigma_noise=0.02, n_outlier=3, - outlier_value=-0.1, n_nan=3): - """Simulate a harmonic time-series with optional breakpoint, noise and outliers - - The time-series is generated by adding; - - an intercept/trend component which varies depending on the phase of the time-series - (stable, recovery) - - An annual seasonal component - - Random noise drawn from a normal distribution (white noise) - Optional outliers are then added to randomly chosen observation as well as ``np.Nan`` values. - Note that the seasonal cycles simulation approach used here is rather simplistic, - using a sinusoidal model and therefore assuming symetrical and regular behaviour - around the peak of the simulated variable. Actual vegetation signal is often more - asymetrical and irregular. - - Args: - dates (array-like): List or array of dates (numpy.datetime64) - break_idx (int): Breakpoint index in the date array provided. Defaults to - ``-1``, corresponding to a stable time-series - intercept (float): Intercept of the time-series - amplitude (float): Amplitude of the harmonic model (note that at every point - of the time-series, the actual model amplitude is multiplied by the intercept - magnitude (float): Break magnitude (always a drop in y value) - recovery_time (int): Time (in days) to recover the initial intersect value - following a break - sigma_noise (float): Sigma value of the normal distribution (mean = 0) from which - noise values are drawn - n_outlier (int): Number of outliers randomly assigned to observations of the - time-series - outlier_value (float): Value to assign to outliers - n_nan (int): Number of ``np.nan`` (no data) assigned to observations of the - time-series - - Example: - >>> from nrt import data - >>> import numpy as np - >>> import matplotlib.pyplot as plt - - >>> dates = np.arange('2018-01-01', '2022-06-15', dtype='datetime64[W]') - >>> ts = data.make_ts(dates=dates, break_idx=30) - - >>> plt.plot(dates, ts) - >>> plt.show() - - Returns: - np.ndarray: Array of simulated values of same size as ``dates`` - """ - dates = dates.astype('datetime64[D]') - timestamps = dates.astype(int) - ydays = (dates - dates.astype('datetime64[Y]')).astype(int) + 1 - y = np.empty_like(dates, dtype=np.float64) - # Intercept array - y[:] = intercept - # Build trend segment if break - if break_idx != -1: - # Segment bounds - segment_start_y = intercept - magnitude - segment_start_timestamp = timestamps[break_idx] - segment_end_timestamp = segment_start_timestamp + recovery_time - segment_end_idx = np.abs(segment_end_timestamp - timestamps).argmin() - # Compute y values - recovery_rate = magnitude / recovery_time - days_since_break = timestamps - segment_start_timestamp - trend_segment = (recovery_rate * days_since_break + segment_start_y)[break_idx + 1:segment_end_idx + 1] - # include into y - y[break_idx + 1:segment_end_idx + 1] = trend_segment - # Seasonality - amplitude_values = amplitude * y - season = amplitude * np.sin(2 * np.pi * timestamps / 365.25 - 2) - # noise and outliers - noise = np.random.normal(0, sigma_noise, dates.size) - # Combine the 3 (trend, season, noise) components - ts = y + season + noise - # Add optional outliers and Nans - outliers_idx = np.random.choice(np.arange(0, dates.size), size=n_outlier, replace=False) - nan_idx = np.random.choice(np.arange(0, dates.size), size=n_nan) - ts[outliers_idx] = outlier_value - ts[nan_idx] = np.nan - return ts - - -def make_cube_parameters(shape=(100,100), - break_idx_interval=(0,100), - intercept_interval=(0.6, 0.8), - amplitude_interval=(0.12, 0.2), - magnitude_interval=(0.2, 0.3), - recovery_time_interval=(800,1400), - sigma_noise_interval=(0.02, 0.04), - n_outliers_interval=(0,5), - n_nan_interval=(0,5), - unstable_proportion=0.5): - """Create ``xarray.Dataset`` of paramters for generation of synthetic data cube - - Prepares the main input required by the the ``make_cube`` function. This - intermediary step eases the creation of multiple synthetic DataArrays sharing - similar characteristics (e.g. to simulate multispectral data) - - Args: - shape (tuple): A size two integer tuple giving the x,y size of the Dataset to be - generated - break_idx_interval (tuple): A tuple of two integers indicating the interval - from which the breakpoint position in the time-series is drawn. Generate - array of random values passed to the ``break_idx` argument of ``make_ts``. - Similarly to python ranges, upper bound value is excluded from the resulting - array. To produce a zero filled array ``(0,1)`` can therefore be used - TODO: add a default to allow breakpoint at any location (conflict with Nan that indicate no break) - intercept_interval (tuple): A tuple of two floats providing the interval - from which intercept is drawn. Generate array of random values passed - to the ``intercept`` argument of ``make_ts`` - amplitude_interval (tuple): A tuple of two floats indicating the interval - from which the seasonal amplitude parameter is drawn. Generate array - of random values passed to the ``amplitude`` argument of ``make_ts`` - magnitude_interval (tuple): A tuple of two floats indicating the interval - from which the breakpoint magnitude parameter is drawn. Generate array - of random values passed to the ``magnitude`` argument of ``make_ts`` - recovery_time_interval (tuple): A tuple of two integers indicating the interval - from which the recovery time parameter (in days) is drawn. Generate array - of random values passed to the ``recovery_time` argument of ``make_ts`` - sigma_noise_interval (tuple): A tuple of two floats indicating the interval - from which the white noise level is drawn. Generate array of random - values passed to the ``sigma_noise` argument of ``make_ts`` - n_outliers_interval (tuple): A tuple of two integers indicating the interval - from which the number of outliers is drawn. Generate array - of random values passed to the ``n_outliers` argument of ``make_ts`` - n_nan_interval (tuple): A tuple of two integers indicating the interval - from which the number of no-data observations is drawn. Generate array - of random values passed to the ``n_nan` argument of ``make_ts`` - unstable_proportion (float): Proportion of time-series containing a breakpoint. - The other time-series are stable. - - Returns: - xarray.Dataset: Dataset with arrays of parameters required for the generation - of synthetic time-series using the spatialized version of ``make_ts`` - (see ``make_cube``) - - Examples: - >>> import time - >>> import numpy as np - >>> import xarray as xr - >>> from nrt import data - >>> import matplotlib.pyplot as plt - >>> params_nir = data.make_cube_parameters(shape=(20,20), - ... n_outliers_interval=(0,1), - ... n_nan_interval=(0,1), - ... break_idx_interval=(50,100)) - >>> params_red = params_nir.copy(deep=True) - >>> # create parameters for red, green, blue cubes by slightly adjusting intercept, - >>> # magnitude and amplitude parameters - >>> params_red['intercept'].data = np.random.uniform(0.09, 0.12, size=(20,20)) - >>> params_red['magnitude'].data = np.random.uniform(-0.1, -0.03, size=(20,20)) - >>> params_red['amplitude'].data = np.random.uniform(0.03, 0.07, size=(20,20)) - >>> params_green = params_nir.copy(deep=True) - >>> params_green['intercept'].data = np.random.uniform(0.12, 0.20, size=(20,20)) - >>> params_green['magnitude'].data = np.random.uniform(0.05, 0.1, size=(20,20)) - >>> params_green['amplitude'].data = np.random.uniform(0.05, 0.08, size=(20,20)) - >>> params_blue = params_nir.copy(deep=True) - >>> params_blue['intercept'].data = np.random.uniform(0.08, 0.13, size=(20,20)) - >>> params_blue['magnitude'].data = np.random.uniform(-0.01, 0.01, size=(20,20)) - >>> params_blue['amplitude'].data = np.random.uniform(0.02, 0.04, size=(20,20)) - >>> dates = np.arange('2018-01-01', '2022-06-15', dtype='datetime64[W]') - >>> # Create cubes (DataArrays) and merge them into a sligle Dataset - >>> nir = data.make_cube(dates, name='nir', params_ds=params_nir) - >>> red = data.make_cube(dates, name='red', params_ds=params_red) - >>> green = data.make_cube(dates, name='green', params_ds=params_green) - >>> blue = data.make_cube(dates, name='blue', params_ds=params_blue) - >>> cube = xr.merge([blue, green, red, nir]).to_array() - >>> # PLot one ts - >>> cube.isel(x=5, y=5).plot(row='variable') - >>> plt.show() - """ - intercept = np.random.uniform(*intercept_interval, size=shape) - amplitude = np.random.uniform(*amplitude_interval, size=shape) - magnitude = np.random.uniform(*magnitude_interval, size=shape) - recovery_time = np.random.randint(*recovery_time_interval, size=shape) - sigma_noise = np.random.uniform(*sigma_noise_interval, size=shape) - n_outlier = np.random.randint(*n_outliers_interval, size=shape) - n_nan = np.random.randint(*n_nan_interval, size=shape) - break_idx = np.random.randint(*break_idx_interval, size=shape) - # Make a proportion of these cells stable - size = np.multiply(*shape) - stable_size = size - round(unstable_proportion * size) - break_idx.ravel()[np.random.choice(size, stable_size, replace=False)] = -1 - # Build Dataset of parameters - params = xr.Dataset(data_vars={'intercept': (['y', 'x'], intercept), - 'amplitude': (['y', 'x'], amplitude), - 'magnitude': (['y', 'x'], magnitude), - 'recovery_time': (['y', 'x'], recovery_time), - 'sigma_noise': (['y', 'x'], sigma_noise), - 'n_outlier': (['y', 'x'], n_outlier), - 'n_nan': (['y', 'x'], n_nan), - 'break_idx': (['y', 'x'], break_idx)}, - coords={'y': np.arange(shape[0]), - 'x': np.arange(shape[1])}) - return params - - -def make_cube(dates, params_ds, outlier_value=0.1, name='ndvi'): - """Generate a cube of synthetic time-series - - See ``make_ts`` for more details on how every single time-series is generated - - Args: - dates (array-like): List or array of dates (numpy.datetime64) - params_ds (xarray.Dataset): Dataset containing arrays of time-series generation - parameters. See ``make_cube_parameters`` for a helper to generate such Dataset. - Spatial dimensions of the params_ds Dataset are used for the generated cube - outlier_value (float): Value to assign to outliers - name (str): Name of the generated variable in the DataArray - - Return: - xarray.DataArray: Cube of synthetic time-series generated using the paramters - provided via ``param_ds`` Dataset. - - Example: - >>> import time - >>> import numpy as np - >>> from nrt import data - >>> import matplotlib.pyplot as plt - >>> dates = np.arange('2018-01-01', '2022-06-15', dtype='datetime64[W]') - >>> params_ds = data.make_cube_parameters(shape=(100,100), - ... n_outliers_interval=(0,5), - ... n_nan_interval=(0,7), - ... break_idx_interval=(100,dates.size - 20)) - >>> cube = data.make_cube(dates=dates, params_ds=params_ds) - >>> # PLot one ts - >>> cube.isel(x=5, y=5).plot() - >>> plt.show() - """ - nrows, ncols = params_ds.intercept.data.shape - # Vectorize function - make_ts_v = np.vectorize(make_ts, signature='(n),(),(),(),(),(),(),(),(),()->(n)') - # Create output array - out = make_ts_v(dates=dates, - break_idx=params_ds.break_idx.data, - intercept=params_ds.intercept.data, - amplitude=params_ds.amplitude.data, - magnitude=params_ds.magnitude.data, - recovery_time=params_ds.recovery_time.data, - sigma_noise=params_ds.sigma_noise.data, - n_outlier=params_ds.n_outlier.data, - outlier_value=outlier_value, - n_nan=params_ds.n_nan.data) - # Build xarray dataset - xr_cube = xr.DataArray(data=np.moveaxis(out, -1, 0), - coords={'time': dates, - 'y': np.arange(nrows), - 'x': np.arange(ncols)}, - name=name) - return xr_cube - -if __name__ == "__main__": - import doctest - doctest.testmod() diff --git a/nrt/data/mreCritValTable.json b/nrt/data/mreCritValTable.json deleted file mode 100644 index 9bf7935..0000000 --- a/nrt/data/mreCritValTable.json +++ /dev/null @@ -1 +0,0 @@ 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diff --git a/nrt/data/sentinel2_cube_subset_romania_10m.nc b/nrt/data/sentinel2_cube_subset_romania_10m.nc deleted file mode 100644 index 01bc460..0000000 --- a/nrt/data/sentinel2_cube_subset_romania_10m.nc +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:10165376979ed20f988bef09bf79135ea415e741256ba50fd4fef679b1c1fa9d -size 14017443 diff --git a/nrt/data/sentinel2_cube_subset_romania_20m.nc b/nrt/data/sentinel2_cube_subset_romania_20m.nc deleted file mode 100644 index c65920d..0000000 --- a/nrt/data/sentinel2_cube_subset_romania_20m.nc +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e22c3545dea25c93b31dea017fb70ba18578c1a6c954bf1040e58218b9fa28ca -size 3515395 diff --git a/nrt/data/sentinel2_subset.nc b/nrt/data/sentinel2_subset.nc deleted file mode 100644 index 7276bb5..0000000 --- a/nrt/data/sentinel2_subset.nc +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:71e976b88e6029a897b0bf300e1d203af2a4b4412550081fdc6bff77551592a6 -size 161395 diff --git a/nrt/data/tree_cover_density_2018_romania.tif b/nrt/data/tree_cover_density_2018_romania.tif deleted file mode 100644 index 9f709d3..0000000 --- a/nrt/data/tree_cover_density_2018_romania.tif +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9e3798baaa7f52e6b9b27bf7f0a81b321a45afa3c83f04dded8a608199f86201 -size 3276 diff --git a/nrt/monitor/__init__.py b/nrt/monitor/__init__.py index a48db82..c906558 100644 --- a/nrt/monitor/__init__.py +++ b/nrt/monitor/__init__.py @@ -13,6 +13,8 @@ # See the Licence for the specific language governing permissions and # limitations under the Licence. +__path__ = __import__('pkgutil').extend_path(__path__, __name__) + import abc import warnings import datetime diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..93e57e2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,52 @@ +[build-system] +requires = ["setuptools>=40.8.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "nrt" +version = "0.3.0" +description = "Online monitoring with xarray" +readme = "README.rst" +keywords = ["sentinel2", "xarray", "datacube", "monitoring", "change"] +authors = [ + { name = "Loic Dutrieux", email = "loic.dutrieux@ec.europa.eu" }, + { name = "Jonas Viehweger" }, + { name = "Chris Holden" } +] +license = {file = "LICENSE"} +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12" +] +requires-python = ">=3.9" +dependencies = [ + "numpy", + "scipy", + "xarray", + "rasterio", + "netCDF4", + "numba!=0.59.*", + "pandas", + "affine", + "nrt-data" +] + +[project.urls] +"Homepage" = "https://github.com/ec-jrc/nrt.git" + +[project.optional-dependencies] +tests = ["pytest"] +docs = [ + "sphinx==7.4.7", + "dask", + "sphinx_rtd_theme==2.0.0", + "matplotlib==3.9.1", + "sphinx-gallery==0.17.0" +] + +[tool.setuptools.packages.find] +where = ["."] + diff --git a/setup.py b/setup.py deleted file mode 100644 index 65f8d94..0000000 --- a/setup.py +++ /dev/null @@ -1,59 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import codecs -from setuptools import setup, find_packages -import os - -# Parse the version from the main __init__.py -with open('nrt/__init__.py') as f: - for line in f: - if line.find("__version__") >= 0: - version = line.split("=")[1].strip() - version = version.strip('"') - version = version.strip("'") - continue - - -with codecs.open('README.rst', encoding='utf-8') as f: - readme = f.read() - -extra_reqs = {'tests': ['pytest'], - 'docs': ['sphinx==7.4.7', - 'dask', - 'sphinx_rtd_theme==2.0.0', - 'matplotlib==3.9.1', - 'sphinx-gallery==0.17.0']} - -setup(name='nrt', - version=version, - description=u"Online monitoring with xarray", - long_description_content_type="text/x-rst", - long_description=readme, - keywords='sentinel2, xarray, datacube, monitoring, change', - author=u"Loic Dutrieux, Jonas Viehweger, Chris Holden", - author_email='loic.dutrieux@ec.europa.eu', - url='https://github.com/ec-jrc/nrt.git', - license='EUPL-v1.2', - classifiers=[ - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.9', - 'Programming Language :: Python :: 3.10', - 'Programming Language :: Python :: 3.11', - 'Programming Language :: Python :: 3.12', - ], - packages=find_packages(), - package_data={'nrt': ['data/*.nc', 'data/*.tif', 'data/*.json']}, - install_requires=[ - 'numpy', - 'scipy', - 'xarray', - 'rasterio', - 'netCDF4', - 'numba!=0.59.*', - 'pandas', - 'affine' - ], - python_requires=">=3.9", - extras_require=extra_reqs) -