Actual source code: getcolv.c

  1: #include <petsc/private/matimpl.h>

  3: /*@
  4:   MatGetColumnVector - Gets the values from a given column of a matrix.

  6:   Not Collective

  8:   Input Parameters:
  9: + A   - the matrix
 10: . yy  - the vector
 11: - col - the column requested (in global numbering)

 13:   Level: advanced

 15:   Notes:
 16:   If a `MatType` does not implement the operation, each processor for which this is called
 17:   gets the values for its rows using `MatGetRow()`.

 19:   The vector must have the same parallel row layout as the matrix.

 21:   Contributed by: Denis Vanderstraeten

 23: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatGetDiagonal()`, `MatMult()`
 24: @*/
 25: PetscErrorCode MatGetColumnVector(Mat A, Vec yy, PetscInt col)
 26: {
 27:   PetscScalar       *y;
 28:   const PetscScalar *v;
 29:   PetscInt           i, j, nz, N, Rs, Re, rs, re;
 30:   const PetscInt    *idx;

 32:   PetscFunctionBegin;
 36:   PetscCheck(col >= 0, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Requested negative column: %" PetscInt_FMT, col);
 37:   PetscCall(MatGetSize(A, NULL, &N));
 38:   PetscCheck(col < N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Requested column %" PetscInt_FMT " larger than number columns in matrix %" PetscInt_FMT, col, N);
 39:   PetscCall(MatGetOwnershipRange(A, &Rs, &Re));
 40:   PetscCall(VecGetOwnershipRange(yy, &rs, &re));
 41:   PetscCheck(Rs == rs && Re == re, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Matrix %" PetscInt_FMT " %" PetscInt_FMT " does not have same ownership range (size) as vector %" PetscInt_FMT " %" PetscInt_FMT, Rs, Re, rs, re);

 43:   if (A->ops->getcolumnvector) PetscUseTypeMethod(A, getcolumnvector, yy, col);
 44:   else {
 45:     PetscCall(VecSet(yy, 0.0));
 46:     PetscCall(VecGetArray(yy, &y));
 47:     /* TODO for general matrices */
 48:     for (i = Rs; i < Re; i++) {
 49:       PetscCall(MatGetRow(A, i, &nz, &idx, &v));
 50:       if (nz && idx[0] <= col) {
 51:         /*
 52:           Should use faster search here
 53:         */
 54:         for (j = 0; j < nz; j++) {
 55:           if (idx[j] >= col) {
 56:             if (idx[j] == col) y[i - rs] = v[j];
 57:             break;
 58:           }
 59:         }
 60:       }
 61:       PetscCall(MatRestoreRow(A, i, &nz, &idx, &v));
 62:     }
 63:     PetscCall(VecRestoreArray(yy, &y));
 64:   }
 65:   PetscFunctionReturn(PETSC_SUCCESS);
 66: }

 68: /*@
 69:   MatGetColumnNorms - Gets the norms of each column of a sparse or dense matrix.

 71:   Input Parameters:
 72: + A    - the matrix
 73: - type - `NORM_2`, `NORM_1` or `NORM_INFINITY`

 75:   Output Parameter:
 76: . norms - an array as large as the TOTAL number of columns in the matrix

 78:   Level: intermediate

 80:   Note:
 81:   Each process has ALL the column norms after the call. Because of the way this is computed each process gets all the values,
 82:   if each process wants only some of the values it should extract the ones it wants from the array.

 84: .seealso: [](ch_matrices), `Mat`, `NormType`, `MatNorm()`
 85: @*/
 86: PetscErrorCode MatGetColumnNorms(Mat A, NormType type, PetscReal norms[])
 87: {
 88:   /* NOTE: MatGetColumnNorms() could simply be a macro that calls MatGetColumnReductions().
 89:    * I've kept this as a function because it allows slightly more in the way of error checking,
 90:    * erroring out if MatGetColumnNorms() is not called with a valid NormType. */

 92:   PetscFunctionBegin;
 93:   PetscCheck(type == NORM_2 || type == NORM_1 || type == NORM_FROBENIUS || type == NORM_INFINITY || type == NORM_1_AND_2, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown NormType");
 94:   PetscCall(MatGetColumnReductions(A, type, norms));
 95:   PetscFunctionReturn(PETSC_SUCCESS);
 96: }

 98: /*@
 99:   MatGetColumnSumsRealPart - Gets the sums of the real part of each column of a sparse or dense matrix.

101:   Input Parameter:
102: . A - the matrix

104:   Output Parameter:
105: . sums - an array as large as the TOTAL number of columns in the matrix

107:   Level: intermediate

109:   Note:
110:   Each process has ALL the column sums after the call. Because of the way this is computed each process gets all the values,
111:   if each process wants only some of the values it should extract the ones it wants from the array.

113: .seealso: [](ch_matrices), `Mat`, `MatGetColumnSumsImaginaryPart()`, `VecSum()`, `MatGetColumnMeans()`, `MatGetColumnNorms()`, `MatGetColumnReductions()`
114: @*/
115: PetscErrorCode MatGetColumnSumsRealPart(Mat A, PetscReal sums[])
116: {
117:   PetscFunctionBegin;
118:   PetscCall(MatGetColumnReductions(A, REDUCTION_SUM_REALPART, sums));
119:   PetscFunctionReturn(PETSC_SUCCESS);
120: }

122: /*@
123:   MatGetColumnSumsImaginaryPart - Gets the sums of the imaginary part of each column of a sparse or dense matrix.

125:   Input Parameter:
126: . A - the matrix

128:   Output Parameter:
129: . sums - an array as large as the TOTAL number of columns in the matrix

131:   Level: intermediate

133:   Note:
134:   Each process has ALL the column sums after the call. Because of the way this is computed each process gets all the values,
135:   if each process wants only some of the values it should extract the ones it wants from the array.

137: .seealso: [](ch_matrices), `Mat`, `MatGetColumnSumsRealPart()`, `VecSum()`, `MatGetColumnMeans()`, `MatGetColumnNorms()`, `MatGetColumnReductions()`
138: @*/
139: PetscErrorCode MatGetColumnSumsImaginaryPart(Mat A, PetscReal sums[])
140: {
141:   PetscFunctionBegin;
142:   PetscCall(MatGetColumnReductions(A, REDUCTION_SUM_IMAGINARYPART, sums));
143:   PetscFunctionReturn(PETSC_SUCCESS);
144: }

146: /*@
147:   MatGetColumnSums - Gets the sums of each column of a sparse or dense matrix.

149:   Input Parameter:
150: . A - the matrix

152:   Output Parameter:
153: . sums - an array as large as the TOTAL number of columns in the matrix

155:   Level: intermediate

157:   Note:
158:   Each process has ALL the column sums after the call. Because of the way this is computed each process gets all the values,
159:   if each process wants only some of the values it should extract the ones it wants from the array.

161: .seealso: [](ch_matrices), `Mat`, `VecSum()`, `MatGetColumnMeans()`, `MatGetColumnNorms()`, `MatGetColumnReductions()`
162: @*/
163: PetscErrorCode MatGetColumnSums(Mat A, PetscScalar sums[])
164: {
165: #if defined(PETSC_USE_COMPLEX)
166:   PetscInt   i, n;
167:   PetscReal *work;
168: #endif

170:   PetscFunctionBegin;
171: #if !defined(PETSC_USE_COMPLEX)
172:   PetscCall(MatGetColumnSumsRealPart(A, sums));
173: #else
174:   PetscCall(MatGetSize(A, NULL, &n));
175:   PetscCall(PetscArrayzero(sums, n));
176:   PetscCall(PetscCalloc1(n, &work));
177:   PetscCall(MatGetColumnSumsRealPart(A, work));
178:   for (i = 0; i < n; i++) sums[i] = work[i];
179:   PetscCall(MatGetColumnSumsImaginaryPart(A, work));
180:   for (i = 0; i < n; i++) sums[i] += work[i] * PETSC_i;
181:   PetscCall(PetscFree(work));
182: #endif
183:   PetscFunctionReturn(PETSC_SUCCESS);
184: }

186: /*@
187:   MatGetColumnMeansRealPart - Gets the arithmetic means of the real part of each column of a sparse or dense matrix.

189:   Input Parameter:
190: . A - the matrix

192:   Output Parameter:
193: . means - an array as large as the TOTAL number of columns in the matrix

195:   Level: intermediate

197:   Note:
198:   Each process has ALL the column means after the call. Because of the way this is computed each process gets all the values,
199:   if each process wants only some of the values it should extract the ones it wants from the array.

201: .seealso: [](ch_matrices), `Mat`, `MatGetColumnMeansImaginaryPart()`, `VecSum()`, `MatGetColumnSums()`, `MatGetColumnNorms()`, `MatGetColumnReductions()`
202: @*/
203: PetscErrorCode MatGetColumnMeansRealPart(Mat A, PetscReal means[])
204: {
205:   PetscFunctionBegin;
206:   PetscCall(MatGetColumnReductions(A, REDUCTION_MEAN_REALPART, means));
207:   PetscFunctionReturn(PETSC_SUCCESS);
208: }

210: /*@
211:   MatGetColumnMeansImaginaryPart - Gets the arithmetic means of the imaginary part of each column of a sparse or dense matrix.

213:   Input Parameter:
214: . A - the matrix

216:   Output Parameter:
217: . means - an array as large as the TOTAL number of columns in the matrix

219:   Level: intermediate

221:   Note:
222:   Each process has ALL the column means after the call. Because of the way this is computed each process gets all the values,
223:   if each process wants only some of the values it should extract the ones it wants from the array.

225: .seealso: [](ch_matrices), `Mat`, `MatGetColumnMeansRealPart()`, `VecSum()`, `MatGetColumnSums()`, `MatGetColumnNorms()`, `MatGetColumnReductions()`
226: @*/
227: PetscErrorCode MatGetColumnMeansImaginaryPart(Mat A, PetscReal means[])
228: {
229:   PetscFunctionBegin;
230:   PetscCall(MatGetColumnReductions(A, REDUCTION_MEAN_IMAGINARYPART, means));
231:   PetscFunctionReturn(PETSC_SUCCESS);
232: }

234: /*@
235:   MatGetColumnMeans - Gets the arithmetic means of each column of a sparse or dense matrix.

237:   Input Parameter:
238: . A - the matrix

240:   Output Parameter:
241: . means - an array as large as the TOTAL number of columns in the matrix

243:   Level: intermediate

245:   Note:
246:   Each process has ALL the column means after the call. Because of the way this is computed each process gets all the values,
247:   if each process wants only some of the values it should extract the ones it wants from the array.

249: .seealso: [](ch_matrices), `Mat`, `VecSum()`, `MatGetColumnSums()`, `MatGetColumnNorms()`, `MatGetColumnReductions()`
250: @*/
251: PetscErrorCode MatGetColumnMeans(Mat A, PetscScalar means[])
252: {
253: #if defined(PETSC_USE_COMPLEX)
254:   PetscInt   i, n;
255:   PetscReal *work;
256: #endif

258:   PetscFunctionBegin;
259: #if !defined(PETSC_USE_COMPLEX)
260:   PetscCall(MatGetColumnMeansRealPart(A, means));
261: #else
262:   PetscCall(MatGetSize(A, NULL, &n));
263:   PetscCall(PetscArrayzero(means, n));
264:   PetscCall(PetscCalloc1(n, &work));
265:   PetscCall(MatGetColumnMeansRealPart(A, work));
266:   for (i = 0; i < n; i++) means[i] = work[i];
267:   PetscCall(MatGetColumnMeansImaginaryPart(A, work));
268:   for (i = 0; i < n; i++) means[i] += work[i] * PETSC_i;
269:   PetscCall(PetscFree(work));
270: #endif
271:   PetscFunctionReturn(PETSC_SUCCESS);
272: }

274: /*@
275:   MatGetColumnReductions - Gets the reductions of each column of a sparse or dense matrix.

277:   Input Parameters:
278: + A    - the matrix
279: - type - A constant defined in `NormType` or `ReductionType`: `NORM_2`, `NORM_1`, `NORM_INFINITY`, `REDUCTION_SUM_REALPART`,
280:           `REDUCTION_SUM_IMAGINARYPART`, `REDUCTION_MEAN_REALPART`, `REDUCTION_MEAN_IMAGINARYPART`

282:   Output Parameter:
283: . reductions - an array as large as the TOTAL number of columns in the matrix

285:   Level: developer

287:   Note:
288:   Each process has ALL the column reductions after the call. Because of the way this is computed each process gets all the values,
289:   if each process wants only some of the values it should extract the ones it wants from the array.

291:   Developer Notes:
292:   This routine is primarily intended as a back-end.
293:   `MatGetColumnNorms()`, `MatGetColumnSums()`, and `MatGetColumnMeans()` are implemented using this routine.

295: .seealso: [](ch_matrices), `Mat`, `ReductionType`, `NormType`, `MatGetColumnNorms()`, `MatGetColumnSums()`, `MatGetColumnMeans()`
296: @*/
297: PetscErrorCode MatGetColumnReductions(Mat A, PetscInt type, PetscReal reductions[])
298: {
299:   PetscFunctionBegin;
301:   PetscUseTypeMethod(A, getcolumnreductions, type, reductions);
302:   PetscFunctionReturn(PETSC_SUCCESS);
303: }