Source code for verde.base

"""
Base classes for all gridders.
"""
from warnings import warn

import xarray as xr
import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.metrics import r2_score
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression, Ridge

from .coordinates import grid_coordinates, profile_coordinates, scatter_points
from .utils import check_data

[docs]class BaseGridder(BaseEstimator): """ Base class for gridders. Most methods of this class requires the implementation of a :meth:~verde.base.BaseGridder.predict method. The data returned by it should be a 1d or 2d numpy array for scalar data or a tuple with 1d or 2d numpy arrays for each component of vector data. The :meth:~verde.base.BaseGridder.filter method requires the implementation of a :meth:~verde.base.BaseGridder.fit method to fit the gridder model to data. Doesn't define any new attributes. This is a subclass of :class:sklearn.base.BaseEstimator and must abide by the same rules of the scikit-learn classes. Mainly: * __init__ must **only** assign values to attributes based on the parameters it receives. All parameters must have default values. Parameter checking should be done in fit. * Estimated parameters should be stored as attributes with names ending in _. Examples -------- Let's create a class that interpolates by attributing the mean value of the data to every single point (it's not a very good interpolator). >>> import verde as vd >>> import numpy as np >>> from sklearn.utils.validation import check_is_fitted >>> class MeanGridder(vd.base.BaseGridder): ... "Gridder that always produces the mean of all data values" ... def __init__(self, multiplier=1): ... # Init should only assign the parameters to attributes ... self.multiplier = multiplier ... def fit(self, coordiantes, data): ... # Argument checking should be done in fit ... if self.multiplier <= 0: ... raise ValueError('Invalid multiplier {}' ... .format(self.multiplier)) ... self.mean_ = data.mean()*self.multiplier ... # fit should return self so that we can chain operations ... return self ... def predict(self, coordinates): ... # We know the gridder has been fitted if it has the mean ... check_is_fitted(self, ['mean_']) ... return np.ones_like(coordinates[0])*self.mean_ >>> # Try it on some synthetic data >>> synthetic = vd.datasets.CheckerBoard(region=(0, 5, -10, 8)) >>> data = synthetic.scatter() >>> print('{:.4f}'.format(data.scalars.mean())) -32.2182 >>> # Fit the gridder to our synthetic data >>> grd = MeanGridder().fit((data.easting, data.northing), data.scalars) >>> grd MeanGridder(multiplier=1) >>> # Interpolate on a regular grid >>> grid = grd.grid(region=(0, 5, -10, -8), shape=(30, 20)) >>> type(grid) <class 'xarray.core.dataset.Dataset'> >>> np.allclose(grid.scalars, -32.2182) True >>> # Interpolate along a profile >>> profile = grd.profile(point1=(0, -10), point2=(5, -8), size=10) >>> type(profile) <class 'pandas.core.frame.DataFrame'> >>> print(', '.join(['{:.2f}'.format(i) for i in profile.distance])) 0.00, 0.60, 1.20, 1.80, 2.39, 2.99, 3.59, 4.19, 4.79, 5.39 >>> print(', '.join(['{:.1f}'.format(i) for i in profile.scalars])) -32.2, -32.2, -32.2, -32.2, -32.2, -32.2, -32.2, -32.2, -32.2, -32.2 """
[docs] def predict(self, coordinates): """ Predict data on the given coordinate values. NOT IMPLEMENTED. This is a dummy placeholder for an actual method. Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). Returns ------- data : array The data predicted at the give coordinates. """ raise NotImplementedError()
[docs] def fit(self, coordinates, data, weights=None): """ Fit the gridder to observed data. NOT IMPLEMENTED. This is a dummy placeholder for an actual method. Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). data : array or tuple of arrays The data values of each data point. If the data has more than one component, *data* must be a tuple of arrays (one for each component). weights : None or array or tuple of arrays If not None, then the weights assigned to each data point. If more than one data component is provided, you must provide a weights array for each data component (if not None). Returns ------- self This instance of the gridder. Useful to chain operations. """ raise NotImplementedError()
[docs] def filter(self, coordinates, data, weights=None): """ Filter the data through the gridder and produce residuals. Calls fit on the data, evaluates the residuals (data - predicted data), and returns the coordinates, residuals, and weights. No very useful by itself but this interface makes gridders compatible with other processing operations and is used by :class:verde.Chain to join them together (for example, so you can fit a spline on the residuals of a trend). Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). data : array or tuple of arrays The data values of each data point. If the data has more than one component, *data* must be a tuple of arrays (one for each component). weights : None or array or tuple of arrays If not None, then the weights assigned to each data point. If more than one data component is provided, you must provide a weights array for each data component (if not None). Returns ------- coordinates, residuals, weights The coordinates and weights are same as the input. Residuals are the input data minus the predicted data. """ self.fit(coordinates, data, weights) data = check_data(data) pred = check_data(self.predict(coordinates)) residuals = tuple( datai - predi.reshape(datai.shape) for datai, predi in zip(data, pred) ) if len(residuals) == 1: residuals = residuals[0] return coordinates, residuals, weights
[docs] def score(self, coordinates, data, weights=None): """ Score the gridder predictions against the given data. Calculates the R^2 coefficient of determination of between the predicted values and the given data values. A maximum score of 1 means a perfect fit. The score can be negative. If the data has more than 1 component, the scores of each component will be averaged. Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). data : array or tuple of arrays The data values of each data point. If the data has more than one component, *data* must be a tuple of arrays (one for each component). weights : None or array or tuple of arrays If not None, then the weights assigned to each data point. If more than one data component is provided, you must provide a weights array for each data component (if not None). Returns ------- score : float The R^2 score """ coordinates, data, weights = check_fit_input( coordinates, data, weights, unpack=False ) pred = check_data(self.predict(coordinates)) result = np.mean( [ r2_score(datai.ravel(), predi.ravel(), sample_weight=weighti) for datai, predi, weighti in zip(data, pred, weights) ] ) return result
[docs] def grid( self, region=None, shape=None, spacing=None, dims=None, data_names=None, projection=None, **kwargs ): """ Interpolate the data onto a regular grid. The grid can be specified by either the number of points in each dimension (the *shape*) or by the grid node spacing. See :func:verde.grid_coordinates for details. Other arguments for :func:verde.grid_coordinates can be passed as extra keyword arguments (kwargs) to this method. If the interpolator collected the input data region, then it will be used if region=None. Otherwise, you must specify the grid region. Use the *dims* and *data_names* arguments to set custom names for the dimensions and the data field(s) in the output :class:xarray.Dataset. Default names will be provided if none are given. Parameters ---------- region : list = [W, E, S, N] The boundaries of a given region in Cartesian or geographic coordinates. shape : tuple = (n_north, n_east) or None The number of points in the South-North and West-East directions, respectively. spacing : tuple = (s_north, s_east) or None The grid spacing in the South-North and West-East directions, respectively. dims : list or None The names of the northing and easting data dimensions, respectively, in the output grid. Defaults to ['northing', 'easting']. **NOTE: This is an exception to the "easting" then "northing" pattern but is required for compatibility with xarray.** data_names : list of None The name(s) of the data variables in the output grid. Defaults to ['scalars'] for scalar data, ['east_component', 'north_component'] for 2D vector data, and ['east_component', 'north_component', 'vertical_component'] for 3D vector data. projection : callable or None If not None, then should be a callable object projection(easting, northing) -> (proj_easting, proj_northing) that takes in easting and northing coordinate arrays and returns projected northing and easting coordinate arrays. This function will be used to project the generated grid coordinates before passing them into predict. For example, you can use this to generate a geographic grid from a Cartesian gridder. Returns ------- grid : xarray.Dataset The interpolated grid. Metadata about the interpolator is written to the attrs attribute. See also -------- verde.grid_coordinates : Generate the coordinate values for the grid. """ dims = get_dims(dims) region = get_instance_region(self, region) coordinates = grid_coordinates(region, shape=shape, spacing=spacing, **kwargs) if projection is None: data = check_data(self.predict(coordinates)) else: data = check_data(self.predict(projection(*coordinates))) data_names = get_data_names(data, data_names) coords = {dims[1]: coordinates[0][0, :], dims[0]: coordinates[1][:, 0]} attrs = {"metadata": "Generated by {}".format(repr(self))} data_vars = { name: (dims, value, attrs) for name, value in zip(data_names, data) } return xr.Dataset(data_vars, coords=coords, attrs=attrs)
[docs] def scatter( self, region=None, size=300, random_state=0, dims=None, data_names=None, projection=None, **kwargs ): """ Interpolate values onto a random scatter of points. Point coordinates are generated by :func:verde.scatter_points. Other arguments for this function can be passed as extra keyword arguments (kwargs) to this method. If the interpolator collected the input data region, then it will be used if region=None. Otherwise, you must specify the grid region. Use the *dims* and *data_names* arguments to set custom names for the dimensions and the data field(s) in the output :class:pandas.DataFrame. Default names are provided. Parameters ---------- region : list = [W, E, S, N] The boundaries of a given region in Cartesian or geographic coordinates. size : int The number of points to generate. random_state : numpy.random.RandomState or an int seed A random number generator used to define the state of the random permutations. Use a fixed seed to make sure computations are reproducible. Use None to choose a seed automatically (resulting in different numbers with each run). dims : list or None The names of the northing and easting data dimensions, respectively, in the output dataframe. Defaults to ['northing', 'easting']. **NOTE: This is an exception to the "easting" then "northing" pattern but is required for compatibility with xarray.** data_names : list of None The name(s) of the data variables in the output dataframe. Defaults to ['scalars'] for scalar data, ['east_component', 'north_component'] for 2D vector data, and ['east_component', 'north_component', 'vertical_component'] for 3D vector data. projection : callable or None If not None, then should be a callable object projection(easting, northing) -> (proj_easting, proj_northing) that takes in easting and northing coordinate arrays and returns projected northing and easting coordinate arrays. This function will be used to project the generated scatter coordinates before passing them into predict. For example, you can use this to generate a geographic scatter from a Cartesian gridder. Returns ------- table : pandas.DataFrame The interpolated values on a random set of points. """ dims = get_dims(dims) region = get_instance_region(self, region) coordinates = scatter_points(region, size, random_state=random_state, **kwargs) if projection is None: data = check_data(self.predict(coordinates)) else: data = check_data(self.predict(projection(*coordinates))) data_names = get_data_names(data, data_names) columns = [(dims[0], coordinates[1]), (dims[1], coordinates[0])] columns.extend(zip(data_names, data)) return pd.DataFrame(dict(columns), columns=[c[0] for c in columns])
[docs] def profile( self, point1, point2, size, dims=None, data_names=None, projection=None, **kwargs ): """ Interpolate data along a profile between two points. Generates the profile along a straight line assuming Cartesian distances. Point coordinates are generated by :func:verde.profile_coordinates. Other arguments for this function can be passed as extra keyword arguments (kwargs) to this method. Use the *dims* and *data_names* arguments to set custom names for the dimensions and the data field(s) in the output :class:pandas.DataFrame. Default names are provided. Includes the calculated Cartesian distance from *point1* for each data point in the profile. Parameters ---------- point1 : tuple The easting and northing coordinates, respectively, of the first point. point2 : tuple The easting and northing coordinates, respectively, of the second point. size : int The number of points to generate. dims : list or None The names of the northing and easting data dimensions, respectively, in the output dataframe. Defaults to ['northing', 'easting']. **NOTE: This is an exception to the "easting" then "northing" pattern but is required for compatibility with xarray.** data_names : list of None The name(s) of the data variables in the output dataframe. Defaults to ['scalars'] for scalar data, ['east_component', 'north_component'] for 2D vector data, and ['east_component', 'north_component', 'vertical_component'] for 3D vector data. projection : callable or None If not None, then should be a callable object projection(easting, northing) -> (proj_easting, proj_northing) that takes in easting and northing coordinate arrays and returns projected northing and easting coordinate arrays. This function will be used to project the generated profile coordinates before passing them into predict. For example, you can use this to generate a geographic profile from a Cartesian gridder. Returns ------- table : pandas.DataFrame The interpolated values along the profile. """ dims = get_dims(dims) coordinates, distances = profile_coordinates(point1, point2, size, **kwargs) if projection is None: data = check_data(self.predict(coordinates)) else: data = check_data(self.predict(projection(*coordinates))) data_names = get_data_names(data, data_names) columns = [ (dims[0], coordinates[1]), (dims[1], coordinates[0]), ("distance", distances), ] columns.extend(zip(data_names, data)) return pd.DataFrame(dict(columns), columns=[c[0] for c in columns])
def get_dims(dims): """ Get default dimension names. Examples -------- >>> get_dims(dims=None) ('northing', 'easting') >>> get_dims(dims=('john', 'paul')) ('john', 'paul') """ if dims is not None: return dims return ("northing", "easting") def get_data_names(data, data_names): """ Get default names for data fields if none are given based on the data. Examples -------- >>> import numpy as np >>> east, north, up = [np.arange(10)]*3 >>> get_data_names((east,), data_names=None) ('scalars',) >>> get_data_names((east, north), data_names=None) ('east_component', 'north_component') >>> get_data_names((east, north, up), data_names=None) ('east_component', 'north_component', 'vertical_component') >>> get_data_names((up, north), data_names=('ringo', 'george')) ('ringo', 'george') """ if data_names is not None: if len(data) != len(data_names): raise ValueError( "Data has {} components but only {} names provided: {}".format( len(data), len(data_names), str(data_names) ) ) return data_names data_types = [ ("scalars",), ("east_component", "north_component"), ("east_component", "north_component", "vertical_component"), ] if len(data) > len(data_types): raise ValueError( " ".join( [ "Default data names only available for up to 3 components.", "Must provide custom names through the 'data_names' argument.", ] ) ) return data_types[len(data) - 1] def get_instance_region(instance, region): """ Get the region attribute stored in instance if one is not provided. """ if region is None: if not hasattr(instance, "region_"): raise ValueError("No default region found. Argument must be supplied.") region = getattr(instance, "region_") return region def check_fit_input(coordinates, data, weights, unpack=True): """ Validate the inputs to the fit method of gridders. Checks that the coordinates, data, and weights (if given) all have the same shape. Weights arrays are raveled. Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). data : array or tuple of arrays The data values of each data point. Data can have more than one component. In such cases, data should be a tuple of arrays. weights : None or array If not None, then the weights assigned to each data point. Typically, this should be 1 over the data uncertainty squared. If the data has multiple components, the weights have the same number of components. unoack : bool If False, data and weights will be tuples always. If they are single arrays, then they will be returned as a 1-element tuple. If True, will unpack the tuples if there is only 1 array in each. Returns ------- validated_inputs The validated inputs in the same order. If weights are given, will ravel the array before returning. """ data = check_data(data) weights = check_data(weights) if any(i.shape != j.shape for i in coordinates for j in data): raise ValueError("Coordinate and data arrays must have the same shape.") if any(w is not None for w in weights): if len(weights) != len(data): raise ValueError( "Number of data '{}' and weights '{}' must be equal.".format( len(data), len(weights) ) ) if any(i.size != j.size for i in weights for j in data): raise ValueError("Weights must have the same size as the data array.") weights = tuple(i.ravel() for i in weights) else: weights = tuple([None] * len(data)) if unpack: if len(weights) == 1: weights = weights[0] if len(data) == 1: data = data[0] return coordinates, data, weights def least_squares(jacobian, data, weights, damping=None): """ Estimate forces that fit the data using least-squares. Scales the Jacobian matrix to have unit standard deviation. This helps balance the regularization and the difference between forces. """ if jacobian.shape[0] < jacobian.shape[1]: warn( "Under-determined problem detected (ndata, nparams)={}.".format( jacobian.shape ) ) scaler = StandardScaler(copy=False, with_mean=False, with_std=True) jacobian = scaler.fit_transform(jacobian) if damping is None: regr = LinearRegression(fit_intercept=False, normalize=False) else: regr = Ridge(alpha=damping, fit_intercept=False, normalize=False) regr.fit(jacobian, data.ravel(), sample_weight=weights) params = regr.coef_ / scaler.scale_ return params