LSST Applications  21.0.0+04719a4bac,21.0.0-1-ga51b5d4+f5e6047307,21.0.0-11-g2b59f77+a9c1acf22d,21.0.0-11-ga42c5b2+86977b0b17,21.0.0-12-gf4ce030+76814010d2,21.0.0-13-g1721dae+760e7a6536,21.0.0-13-g3a573fe+768d78a30a,21.0.0-15-g5a7caf0+f21cbc5713,21.0.0-16-g0fb55c1+b60e2d390c,21.0.0-19-g4cded4ca+71a93a33c0,21.0.0-2-g103fe59+bb20972958,21.0.0-2-g45278ab+04719a4bac,21.0.0-2-g5242d73+3ad5d60fb1,21.0.0-2-g7f82c8f+8babb168e8,21.0.0-2-g8f08a60+06509c8b61,21.0.0-2-g8faa9b5+616205b9df,21.0.0-2-ga326454+8babb168e8,21.0.0-2-gde069b7+5e4aea9c2f,21.0.0-2-gecfae73+1d3a86e577,21.0.0-2-gfc62afb+3ad5d60fb1,21.0.0-25-g1d57be3cd+e73869a214,21.0.0-3-g357aad2+ed88757d29,21.0.0-3-g4a4ce7f+3ad5d60fb1,21.0.0-3-g4be5c26+3ad5d60fb1,21.0.0-3-g65f322c+e0b24896a3,21.0.0-3-g7d9da8d+616205b9df,21.0.0-3-ge02ed75+a9c1acf22d,21.0.0-4-g591bb35+a9c1acf22d,21.0.0-4-g65b4814+b60e2d390c,21.0.0-4-gccdca77+0de219a2bc,21.0.0-4-ge8a399c+6c55c39e83,21.0.0-5-gd00fb1e+05fce91b99,21.0.0-6-gc675373+3ad5d60fb1,21.0.0-64-g1122c245+4fb2b8f86e,21.0.0-7-g04766d7+cd19d05db2,21.0.0-7-gdf92d54+04719a4bac,21.0.0-8-g5674e7b+d1bd76f71f,master-gac4afde19b+a9c1acf22d,w.2021.13
LSST Data Management Base Package
ChebyshevBoundedField.cc
Go to the documentation of this file.
1 // -*- LSST-C++ -*-
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23 
24 #include <memory>
25 
26 #include "ndarray/eigen.h"
35 
36 namespace lsst {
37 namespace afw {
38 
39 template std::shared_ptr<math::ChebyshevBoundedField> table::io::PersistableFacade<
40  math::ChebyshevBoundedField>::dynamicCast(std::shared_ptr<table::io::Persistable> const&);
41 
42 namespace math {
43 
45 
46 // ------------------ Constructors and helpers ---------------------------------------------------------------
47 
48 namespace {
49 
50 // Compute an affine transform that maps an arbitrary box to [-1,1]x[-1,1]
51 lsst::geom::AffineTransform makeChebyshevRangeTransform(lsst::geom::Box2D const bbox) {
53  lsst::geom::LinearTransform::makeScaling(2.0 / bbox.getWidth(), 2.0 / bbox.getHeight()),
54  lsst::geom::Extent2D(-(2.0 * bbox.getCenterX()) / bbox.getWidth(),
55  -(2.0 * bbox.getCenterY()) / bbox.getHeight()));
56 }
57 
58 } // namespace
59 
61  ndarray::Array<double const, 2, 2> const& coefficients)
62  : BoundedField(bbox),
63  _toChebyshevRange(makeChebyshevRangeTransform(lsst::geom::Box2D(bbox))),
64  _coefficients(coefficients) {}
65 
67  : BoundedField(bbox), _toChebyshevRange(makeChebyshevRangeTransform(lsst::geom::Box2D(bbox))) {}
68 
69 ChebyshevBoundedField::ChebyshevBoundedField(ChebyshevBoundedField const&) = default;
70 ChebyshevBoundedField::ChebyshevBoundedField(ChebyshevBoundedField&&) = default;
72 
73 // ------------------ fit() and helpers ---------------------------------------------------------------------
74 
75 namespace {
76 
78 typedef detail::TrapezoidalPacker Packer;
79 
80 // fill an array with 1-d Chebyshev functions of the 1st kind T(x), evaluated at the given point x
81 void evaluateBasis1d(ndarray::Array<double, 1, 1> const& t, double x) {
82  int const n = t.getSize<0>();
83  if (n > 0) {
84  t[0] = 1.0;
85  }
86  if (n > 1) {
87  t[1] = x;
88  }
89  for (int i = 2; i < n; ++i) {
90  t[i] = 2.0 * x * t[i - 1] - t[i - 2];
91  }
92 }
93 
94 // Create a matrix of 2-d Chebyshev functions evaluated at a set of positions, with
95 // Chebyshev order along columns and evaluation positions along rows. We pack the
96 // 2-d functions using the TrapezoidalPacker class, because we don't want any columns
97 // that correspond to coefficients that should be set to zero.
98 ndarray::Array<double, 2, 2> makeMatrix(ndarray::Array<double const, 1> const& x,
99  ndarray::Array<double const, 1> const& y,
100  lsst::geom::AffineTransform const& toChebyshevRange,
101  Packer const& packer, Control const& ctrl) {
102  int const nPoints = x.getSize<0>();
103  ndarray::Array<double, 1, 1> tx = ndarray::allocate(packer.nx);
104  ndarray::Array<double, 1, 1> ty = ndarray::allocate(packer.ny);
105  ndarray::Array<double, 2, 2> out = ndarray::allocate(nPoints, packer.size);
106  // Loop over x and y together, computing T_i(x) and T_j(y) arrays for each point,
107  // then packing them together.
108  for (int p = 0; p < nPoints; ++p) {
109  lsst::geom::Point2D sxy = toChebyshevRange(lsst::geom::Point2D(x[p], y[p]));
110  evaluateBasis1d(tx, sxy.getX());
111  evaluateBasis1d(ty, sxy.getY());
112  packer.pack(out[p], tx, ty); // this sets a row of out to the packed outer product of tx and ty
113  }
114  return out;
115 }
116 
117 // Create a matrix of 2-d Chebyshev functions evaluated on a grid of positions, with
118 // Chebyshev order along columns and evaluation positions along rows. We pack the
119 // 2-d functions using the TrapezoidalPacker class, because we don't want any columns
120 // that correspond to coefficients that should be set to zero.
121 ndarray::Array<double, 2, 2> makeMatrix(lsst::geom::Box2I const& bbox,
122  lsst::geom::AffineTransform const& toChebyshevRange,
123  Packer const& packer, Control const& ctrl) {
124  // Create a 2-d array that contains T_j(x) for each x value, with x values in rows and j in columns
125  ndarray::Array<double, 2, 2> tx = ndarray::allocate(bbox.getWidth(), packer.nx);
126  for (int x = bbox.getBeginX(), p = 0; p < bbox.getWidth(); ++p, ++x) {
127  evaluateBasis1d(tx[p], toChebyshevRange[lsst::geom::AffineTransform::XX] * x +
128  toChebyshevRange[lsst::geom::AffineTransform::X]);
129  }
130 
131  // Loop over y values, and at each point, compute T_i(y), then loop over x and multiply by the T_j(x)
132  // we already computed and stored above.
133  ndarray::Array<double, 2, 2> out = ndarray::allocate(bbox.getArea(), packer.size);
134  ndarray::Array<double, 2, 2>::Iterator outIter = out.begin();
135  ndarray::Array<double, 1, 1> ty = ndarray::allocate(ctrl.orderY + 1);
136  for (int y = bbox.getBeginY(), i = 0; i < bbox.getHeight(); ++i, ++y) {
137  evaluateBasis1d(ty, toChebyshevRange[lsst::geom::AffineTransform::YY] * y +
138  toChebyshevRange[lsst::geom::AffineTransform::Y]);
139  for (int j = 0; j < bbox.getWidth(); ++j, ++outIter) {
140  // this sets a row of out to the packed outer product of tx and ty
141  packer.pack(*outIter, tx[j], ty);
142  }
143  }
144  return out;
145 }
146 
147 } // namespace
148 
150  ndarray::Array<double const, 1> const& x,
151  ndarray::Array<double const, 1> const& y,
152  ndarray::Array<double const, 1> const& z,
153  Control const& ctrl) {
154  // Initialize the result object, so we can make use of the AffineTransform it builds
156  // This packer object knows how to map the 2-d Chebyshev functions onto a 1-d array,
157  // using only those that the control says should have nonzero coefficients.
158  Packer const packer(ctrl);
159  // Create a "design matrix" for the linear least squares problem (A in min||Ax-b||)
160  ndarray::Array<double, 2, 2> matrix = makeMatrix(x, y, result->_toChebyshevRange, packer, ctrl);
161  // Solve the linear least squares problem.
163  // Unpack the solution into a 2-d matrix, with zeros for values we didn't fit.
164  result->_coefficients = packer.unpack(lstsq.getSolution());
165  return result;
166 }
167 
169  ndarray::Array<double const, 1> const& x,
170  ndarray::Array<double const, 1> const& y,
171  ndarray::Array<double const, 1> const& z,
172  ndarray::Array<double const, 1> const& w,
173  Control const& ctrl) {
174  // Initialize the result object, so we can make use of the AffineTransform it builds
176  // This packer object knows how to map the 2-d Chebyshev functions onto a 1-d array,
177  // using only those that the control says should have nonzero coefficients.
178  Packer const packer(ctrl);
179  // Create a "design matrix" for the linear least squares problem ('A' in min||Ax-b||)
180  ndarray::Array<double, 2, 2> matrix = makeMatrix(x, y, result->_toChebyshevRange, packer, ctrl);
181  // We want to do weighted least squares, so we multiply both the data vector 'b' and the
182  // matrix 'A' by the weights.
183  ndarray::asEigenArray(matrix).colwise() *= ndarray::asEigenArray(w);
184  ndarray::Array<double, 1, 1> wz = ndarray::copy(z);
185  ndarray::asEigenArray(wz) *= ndarray::asEigenArray(w);
186  // Solve the linear least squares problem.
188  // Unpack the solution into a 2-d matrix, with zeros for values we didn't fit.
189  result->_coefficients = packer.unpack(lstsq.getSolution());
190  return result;
191 }
192 
193 template <typename T>
195  Control const& ctrl) {
196  // Initialize the result object, so we can make use of the AffineTransform it builds
199  // This packer object knows how to map the 2-d Chebyshev functions onto a 1-d array,
200  // using only those that the control says should have nonzero coefficients.
201  Packer const packer(ctrl);
202  ndarray::Array<double, 2, 2> matrix = makeMatrix(bbox, result->_toChebyshevRange, packer, ctrl);
203  // Flatten the data image into a 1-d vector.
204  ndarray::Array<double, 2, 2> imgCopy = ndarray::allocate(img.getArray().getShape());
205  imgCopy.deep() = img.getArray();
206  ndarray::Array<double const, 1, 1> z = ndarray::flatten<1>(imgCopy);
207  // Solve the linear least squares problem.
209  // Unpack the solution into a 2-d matrix, with zeros for values we didn't fit.
210  result->_coefficients = packer.unpack(lstsq.getSolution());
211  return result;
212 }
213 
214 // ------------------ modifier factories ---------------------------------------------------------------
215 
217  if (static_cast<std::size_t>(ctrl.orderX) >= _coefficients.getSize<1>()) {
219  (boost::format("New x order (%d) exceeds old x order (%d)") % ctrl.orderX %
220  (_coefficients.getSize<1>() - 1))
221  .str());
222  }
223  if (static_cast<std::size_t>(ctrl.orderY) >= _coefficients.getSize<0>()) {
225  (boost::format("New y order (%d) exceeds old y order (%d)") % ctrl.orderY %
226  (_coefficients.getSize<0>() - 1))
227  .str());
228  }
229  ndarray::Array<double, 2, 2> coefficients = ndarray::allocate(ctrl.orderY + 1, ctrl.orderX + 1);
230  coefficients.deep() = _coefficients[ndarray::view(0, ctrl.orderY + 1)(0, ctrl.orderX + 1)];
231  if (ctrl.triangular) {
232  Packer packer(ctrl);
233  ndarray::Array<double, 1, 1> packed = ndarray::allocate(packer.size);
234  packer.pack(packed, coefficients);
235  packer.unpack(coefficients, packed);
236  }
237  return std::make_shared<ChebyshevBoundedField>(getBBox(), coefficients);
238 }
239 
241  return std::make_shared<ChebyshevBoundedField>(bbox, _coefficients);
242 }
243 
244 // ------------------ evaluate() and helpers ---------------------------------------------------------------
245 
246 namespace {
247 
248 // To evaluate a 1-d Chebyshev function without needing to have workspace, we use the
249 // Clenshaw algorith, which is like going through the recurrence relation in reverse.
250 // The CoeffGetter argument g is something that behaves like an array, providing access
251 // to the coefficients.
252 template <typename CoeffGetter>
253 double evaluateFunction1d(CoeffGetter g, double x, int size) {
254  double b_kp2 = 0.0, b_kp1 = 0.0;
255  for (int k = (size - 1); k > 0; --k) {
256  double b_k = g[k] + 2 * x * b_kp1 - b_kp2;
257  b_kp2 = b_kp1;
258  b_kp1 = b_k;
259  }
260  return g[0] + x * b_kp1 - b_kp2;
261 }
262 
263 // This class imitates a 1-d array, by running evaluateFunction1d on a nested dimension;
264 // this lets us reuse the logic in evaluateFunction1d for both dimensions. Essentially,
265 // we run evaluateFunction1d on a column of coefficients to evaluate T_i(x), then pass
266 // the result of that to evaluateFunction1d with the results as the "coefficients" associated
267 // with the T_j(y) functions.
268 struct RecursionArrayImitator {
269  double operator[](int i) const {
270  return evaluateFunction1d(coefficients[i], x, coefficients.getSize<1>());
271  }
272 
273  RecursionArrayImitator(ndarray::Array<double const, 2, 2> const& coefficients_, double x_)
274  : coefficients(coefficients_), x(x_) {}
275 
276  ndarray::Array<double const, 2, 2> coefficients;
277  double x;
278 };
279 
280 } // namespace
281 
283  lsst::geom::Point2D p = _toChebyshevRange(position);
284  return evaluateFunction1d(RecursionArrayImitator(_coefficients, p.getX()), p.getY(),
285  _coefficients.getSize<0>());
286 }
287 
288 // The integral of T_n(x) over [-1,1]:
289 // https://en.wikipedia.org/wiki/Chebyshev_polynomials#Differentiation_and_integration
290 double integrateTn(int n) {
291  if (n % 2 == 1)
292  return 0;
293  else
294  return 2.0 / (1.0 - double(n * n));
295 }
296 
298  double result = 0;
299  double determinant = getBBox().getArea() / 4.0;
300  for (ndarray::Size j = 0; j < _coefficients.getSize<0>(); j++) {
301  for (ndarray::Size i = 0; i < _coefficients.getSize<1>(); i++) {
302  result += _coefficients[j][i] * integrateTn(i) * integrateTn(j);
303  }
304  }
305  return result * determinant;
306 }
307 
308 double ChebyshevBoundedField::mean() const { return integrate() / getBBox().getArea(); }
309 
310 // ------------------ persistence ---------------------------------------------------------------------------
311 
312 namespace {
313 
314 struct PersistenceHelper {
315  table::Schema schema;
316  table::Key<int> orderX;
318  table::Key<table::Array<double> > coefficients;
319 
320  PersistenceHelper(int nx, int ny)
321  : schema(),
322  orderX(schema.addField<int>("order_x", "maximum Chebyshev function order in x")),
323  bbox(table::Box2IKey::addFields(schema, "bbox", "bounding box", "pixel")),
324  coefficients(schema.addField<table::Array<double> >(
325  "coefficients", "Chebyshev function coefficients, ordered by y then x", nx * ny)) {}
326 
327  PersistenceHelper(table::Schema const& s)
328  : schema(s), orderX(s["order_x"]), bbox(s["bbox"]), coefficients(s["coefficients"]) {}
329 };
330 
331 class ChebyshevBoundedFieldFactory : public table::io::PersistableFactory {
332 public:
333  explicit ChebyshevBoundedFieldFactory(std::string const& name)
334  : afw::table::io::PersistableFactory(name) {}
335 
336  std::shared_ptr<table::io::Persistable> read(InputArchive const& archive,
337  CatalogVector const& catalogs) const override {
338  LSST_ARCHIVE_ASSERT(catalogs.size() == 1u);
339  LSST_ARCHIVE_ASSERT(catalogs.front().size() == 1u);
340  table::BaseRecord const& record = catalogs.front().front();
341  PersistenceHelper const keys(record.getSchema());
342  lsst::geom::Box2I bbox(record.get(keys.bbox));
343  int nx = record.get(keys.orderX) + 1;
344  int ny = keys.coefficients.getSize() / nx;
345  LSST_ARCHIVE_ASSERT(nx * ny == keys.coefficients.getSize());
346  ndarray::Array<double, 2, 2> coefficients = ndarray::allocate(ny, nx);
347  ndarray::flatten<1>(coefficients) = record.get(keys.coefficients);
348  return std::make_shared<ChebyshevBoundedField>(bbox, coefficients);
349  }
350 };
351 
352 std::string getChebyshevBoundedFieldPersistenceName() { return "ChebyshevBoundedField"; }
353 
354 ChebyshevBoundedFieldFactory registration(getChebyshevBoundedFieldPersistenceName());
355 
356 } // namespace
357 
359  return getChebyshevBoundedFieldPersistenceName();
360 }
361 
362 std::string ChebyshevBoundedField::getPythonModule() const { return "lsst.afw.math"; }
363 
365  PersistenceHelper const keys(_coefficients.getSize<1>(), _coefficients.getSize<0>());
366  table::BaseCatalog catalog = handle.makeCatalog(keys.schema);
367  std::shared_ptr<table::BaseRecord> record = catalog.addNew();
368  record->set(keys.orderX, _coefficients.getSize<1>() - 1);
369  record->set(keys.bbox, getBBox());
370  (*record)[keys.coefficients].deep() = ndarray::flatten<1>(_coefficients);
371  handle.saveCatalog(catalog);
372 }
373 
374 // ------------------ operators -----------------------------------------------------------------------------
375 
377  return std::make_shared<ChebyshevBoundedField>(getBBox(), ndarray::copy(getCoefficients() * scale));
378 }
379 
381  auto rhsCasted = dynamic_cast<ChebyshevBoundedField const*>(&rhs);
382  if (!rhsCasted) return false;
383 
384  return (getBBox() == rhsCasted->getBBox()) &&
385  (_coefficients.getShape() == rhsCasted->_coefficients.getShape()) &&
386  all(equal(_coefficients, rhsCasted->_coefficients));
387 }
388 
389 std::string ChebyshevBoundedField::toString() const {
391  os << "ChebyshevBoundedField (" << _coefficients.getShape() << " coefficients in y,x)";
392  return os.str();
393 }
394 
395 // ------------------ explicit instantiation ----------------------------------------------------------------
396 
397 #ifndef DOXYGEN
398 
399 #define INSTANTIATE(T) \
400  template std::shared_ptr<ChebyshevBoundedField> ChebyshevBoundedField::fit(image::Image<T> const& image, \
401  Control const& ctrl)
402 
403 INSTANTIATE(float);
404 INSTANTIATE(double);
405 
406 #endif
407 } // namespace math
408 } // namespace afw
409 } // namespace lsst
py::object result
Definition: _schema.cc:430
table::Key< std::string > name
Definition: Amplifier.cc:116
table::Key< int > orderX
table::Box2IKey bbox
table::Schema schema
ndarray::Array< double const, 2, 2 > coefficients
double x
#define INSTANTIATE(FROMSYS, TOSYS)
Definition: Detector.cc:484
#define LSST_EXCEPT(type,...)
Create an exception with a given type.
Definition: Exception.h:48
double z
Definition: Match.cc:44
std::ostream * os
Definition: Schema.cc:746
int y
Definition: SpanSet.cc:49
#define LSST_ARCHIVE_ASSERT(EXPR)
An assertion macro used to validate the structure of an InputArchive.
Definition: Persistable.h:48
lsst::geom::Box2I getBBox(ImageOrigin origin=PARENT) const
Definition: ImageBase.h:445
A class to represent a 2-dimensional array of pixels.
Definition: Image.h:58
An abstract base class for 2-d functions defined on an integer bounding boxes.
Definition: BoundedField.h:55
lsst::geom::Box2I getBBox() const
Return the bounding box that defines the region where the field is valid.
Definition: BoundedField.h:112
A control object used when fitting ChebyshevBoundedField to data (see ChebyshevBoundedField::fit)
int computeSize() const
Return the number of nonzero coefficients in the Chebyshev function defined by this object.
bool triangular
"if true, only include terms where the sum of the x and y order " "is less than or equal to max(order...
int orderY
"maximum Chebyshev function order in y" ;
int orderX
"maximum Chebyshev function order in x" ;
A BoundedField based on 2-d Chebyshev polynomials of the first kind.
static std::shared_ptr< ChebyshevBoundedField > fit(lsst::geom::Box2I const &bbox, ndarray::Array< double const, 1 > const &x, ndarray::Array< double const, 1 > const &y, ndarray::Array< double const, 1 > const &z, Control const &ctrl)
Fit a Chebyshev approximation to non-gridded data with equal weights.
std::shared_ptr< ChebyshevBoundedField > relocate(lsst::geom::Box2I const &bbox) const
Return a new ChebyshevBoundedField with domain set to the given bounding box.
bool operator==(BoundedField const &rhs) const override
BoundedFields (of the same sublcass) are equal if their bounding boxes and parameters are equal.
std::shared_ptr< BoundedField > operator*(double const scale) const override
Return a scaled BoundedField.
void write(OutputArchiveHandle &handle) const override
Write the object to one or more catalogs.
std::string getPythonModule() const override
Return the fully-qualified Python module that should be imported to guarantee that its factory is reg...
double mean() const override
Compute the mean of this function over its bounding-box.
ndarray::Array< double const, 2, 2 > getCoefficients() const
Return the coefficient matrix.
std::shared_ptr< ChebyshevBoundedField > truncate(Control const &ctrl) const
Return a new ChebyshevBoudedField with maximum orders set by the given control object.
std::string getPersistenceName() const override
Return the unique name used to persist this object and look up its factory.
ChebyshevBoundedField(lsst::geom::Box2I const &bbox, ndarray::Array< double const, 2, 2 > const &coefficients)
Initialize the field from its bounding box an coefficients.
double integrate() const override
Compute the integral of this function over its bounding-box.
virtual double evaluate(lsst::geom::Point2D const &position) const=0
Evaluate the field at the given point.
Solver for linear least-squares problems.
Definition: LeastSquares.h:67
ndarray::Array< double const, 1, 1 > getSolution()
Return the vector solution to the least squares problem.
static LeastSquares fromDesignMatrix(ndarray::Array< T1, 2, C1 > const &design, ndarray::Array< T2, 1, C2 > const &data, Factorization factorization=NORMAL_EIGENSYSTEM)
Initialize from the design matrix and data vector given as ndarrays.
Definition: LeastSquares.h:100
@ NORMAL_EIGENSYSTEM
Use the normal equations with a symmetric Eigensystem decomposition.
Definition: LeastSquares.h:72
std::shared_ptr< RecordT > addNew()
Create a new record, add it to the end of the catalog, and return a pointer to it.
Definition: Catalog.h:485
An object passed to Persistable::write to allow it to persist itself.
void saveCatalog(BaseCatalog const &catalog)
Save a catalog in the archive.
BaseCatalog makeCatalog(Schema const &schema)
Return a new, empty catalog with the given schema.
An affine coordinate transformation consisting of a linear transformation and an offset.
A floating-point coordinate rectangle geometry.
Definition: Box.h:413
An integer coordinate rectangle.
Definition: Box.h:55
int getArea() const
Definition: Box.h:189
static LinearTransform makeScaling(double s) noexcept
Reports attempts to exceed implementation-defined length limits for some classes.
Definition: Runtime.h:76
def scale(algorithm, min, max=None, frame=None)
Definition: ds9.py:108
BoxKey< lsst::geom::Box2I > Box2IKey
Definition: aggregates.h:201
bool all(CoordinateExpr< N > const &expr) noexcept
Return true if all elements are true.
FastFinder::Iterator Iterator
Definition: FastFinder.cc:178
def format(config, name=None, writeSourceLine=True, prefix="", verbose=False)
Definition: history.py:174
A base class for image defects.
double w
Definition: CoaddPsf.cc:69
A helper class ChebyshevBoundedField, for mapping trapezoidal matrices to 1-d arrays.