nd::array nd::view(const nd::array &arr, const ndt::type &tp)
{
if (arr.get_type() == tp) {
// If the types match exactly, simply return 'arr'
return arr;
} else if (tp.get_type_id() == bytes_type_id) {
// If it's a request to view the data as raw bytes
nd::array result = view_as_bytes(arr, tp);
if (!result.is_null()) {
return result;
}
} else if (arr.get_type().get_type_id() == bytes_type_id) {
// If it's a request to view raw bytes as something else
nd::array result = view_from_bytes(arr, tp);
if (!result.is_null()) {
return result;
}
} else if (arr.get_ndim() == tp.get_ndim()) {
// If the type is symbolic, e.g. has a "Fixed" symbolic dimension,
// first substitute in the shape from the array
if (tp.is_symbolic()) {
dimvector shape(arr.get_ndim());
arr.get_shape(shape.get());
return view_concrete(arr, substitute_shape(tp, arr.get_ndim(), shape.get()));
} else {
return view_concrete(arr, tp);
}
}
stringstream ss;
ss << "Unable to view nd::array of type " << arr.get_type();
ss << " as type " << tp;
throw type_error(ss.str());
}
nd::array dynd::format_json(const nd::array &n, bool struct_as_list)
{
// Create a UTF-8 string
nd::array result = nd::empty(ndt::string_type::make());
// Initialize the output with some memory
output_data out;
out.out_string.resize(1024);
out.out_begin = out.out_string.begin();
out.out_capacity_end = out.out_string.end();
out.out_end = out.out_begin;
out.struct_as_list = struct_as_list;
if (!n.get_type().is_expression()) {
::format_json(out, n.get_type(), n.get_arrmeta(), n.get_readonly_originptr());
} else {
nd::array tmp = n.eval();
::format_json(out, tmp.get_type(), tmp.get_arrmeta(), tmp.get_readonly_originptr());
}
// Shrink the memory to fit, and set the pointers in the output
string *d = reinterpret_cast<string *>(result.get_readwrite_originptr());
d->assign(out.out_string.data(), out.out_end - out.out_begin);
// Finalize processing and mark the result as immutable
result.get_type().extended()->arrmeta_finalize_buffers(result.get_arrmeta());
result.flag_as_immutable();
return result;
}
nd::array nd::view(const nd::array& arr, const ndt::type& tp)
{
// If the types match exactly, simply return 'arr'
if (arr.get_type() == tp) {
return arr;
} else if (arr.get_ndim() == tp.get_ndim()) {
// Allocate a result array to attempt the view in it
array result(make_array_memory_block(tp.get_metadata_size()));
// Copy the fields
result.get_ndo()->m_data_pointer = arr.get_ndo()->m_data_pointer;
if (arr.get_ndo()->m_data_reference == NULL) {
// Embedded data, need reference to the array
result.get_ndo()->m_data_reference = arr.get_memblock().release();
} else {
// Use the same data reference, avoid producing a chain
result.get_ndo()->m_data_reference = arr.get_data_memblock().release();
}
result.get_ndo()->m_type = ndt::type(tp).release();
result.get_ndo()->m_flags = arr.get_ndo()->m_flags;
// Now try to copy the metadata as a view
if (try_view(arr.get_type(), arr.get_ndo_meta(), tp,
result.get_ndo_meta(), arr.get_memblock().get())) {
// If it succeeded, return it
return result;
}
// Otherwise fall through, let it get destructed, and raise an error
}
stringstream ss;
ss << "Unable to view nd::array of type " << arr.get_type();
ss << "as type " << tp;
throw type_error(ss.str());
}
nd::array dynd::format_json(const nd::array& n)
{
// Create a UTF-8 string
nd::array result = nd::empty(ndt::make_string());
// Initialize the output with some memory
output_data out;
out.blockref = reinterpret_cast<const string_type_metadata *>(result.get_ndo_meta())->blockref;
out.api = get_memory_block_pod_allocator_api(out.blockref);
out.api->allocate(out.blockref, 1024, 1, &out.out_begin, &out.out_capacity_end);
out.out_end = out.out_begin;
if (!n.get_type().is_expression()) {
::format_json(out, n.get_type(), n.get_ndo_meta(), n.get_readonly_originptr());
} else {
nd::array tmp = n.eval();
::format_json(out, tmp.get_type(), tmp.get_ndo_meta(), tmp.get_readonly_originptr());
}
// Shrink the memory to fit, and set the pointers in the output
string_type_data *d = reinterpret_cast<string_type_data *>(result.get_readwrite_originptr());
d->begin = out.out_begin;
d->end = out.out_capacity_end;
out.api->resize(out.blockref, out.out_end - out.out_begin, &d->begin, &d->end);
// Finalize processing and mark the result as immutable
result.get_type().extended()->metadata_finalize_buffers(result.get_ndo_meta());
result.flag_as_immutable();
return result;
}
inline string broadcast_error_message(const nd::array& dst, const nd::array& src)
{
vector<intptr_t> dst_shape = dst.get_shape(), src_shape = src.get_shape();
stringstream ss;
ss << "cannot broadcast dynd array with type ";
ss << src.get_type() << " and shape ";
print_shape(ss, src_shape);
ss << " to type " << dst.get_type() << " and shape ";
print_shape(ss, dst_shape);
return ss.str();
}
// Constructor which creates the output based on the input's broadcast shape
array_iter(const ndt::type& op0_dtype, nd::array& out_op0, const nd::array& op1, const nd::array& op2, const nd::array& op3) {
create_broadcast_result(op0_dtype, op1, op2, op3, out_op0, m_iter_ndim[0], m_itershape);
nd::array ops[4] = {out_op0, op1, op2, op3};
m_array_tp[0] = out_op0.get_type();
m_array_tp[1] = op1.get_type();
m_array_tp[2] = op2.get_type();
m_array_tp[3] = op3.get_type();
m_itersize = 1;
m_iter_ndim[1] = m_array_tp[1].get_ndim();
m_iter_ndim[2] = m_array_tp[2].get_ndim();
m_iter_ndim[3] = m_array_tp[3].get_ndim();
// Allocate and initialize the iterdata
if (m_iter_ndim[0] != 0) {
m_iterindex.init(m_iter_ndim[0]);
memset(m_iterindex.get(), 0, sizeof(intptr_t) * m_iter_ndim[0]);
// The destination iterdata
size_t iterdata_size = m_array_tp[0].get_iterdata_size(m_iter_ndim[0]);
m_iterdata[0] = reinterpret_cast<iterdata_common *>(malloc(iterdata_size));
if (!m_iterdata[0]) {
throw std::bad_alloc();
}
m_metadata[0] = out_op0.get_ndo_meta();
m_array_tp[0].iterdata_construct(m_iterdata[0],
&m_metadata[0], m_iter_ndim[0], m_itershape.get(), m_uniform_tp[0]);
m_data[0] = m_iterdata[0]->reset(m_iterdata[0], out_op0.get_readwrite_originptr(), m_iter_ndim[0]);
// The op iterdata
for (int i = 1; i < 4; ++i) {
iterdata_size = m_array_tp[i].get_broadcasted_iterdata_size(m_iter_ndim[i]);
m_iterdata[i] = reinterpret_cast<iterdata_common *>(malloc(iterdata_size));
if (!m_iterdata[i]) {
throw std::bad_alloc();
}
m_metadata[i] = ops[i].get_ndo_meta();
m_array_tp[i].broadcasted_iterdata_construct(m_iterdata[i],
&m_metadata[i], m_iter_ndim[i],
m_itershape.get() + (m_iter_ndim[0] - m_iter_ndim[i]), m_uniform_tp[i]);
m_data[i] = m_iterdata[i]->reset(m_iterdata[i], ops[i].get_ndo()->m_data_pointer, m_iter_ndim[0]);
}
for (size_t i = 0, i_end = m_iter_ndim[0]; i != i_end; ++i) {
m_itersize *= m_itershape[i];
}
} else {
for (size_t i = 0; i < 4; ++i) {
m_iterdata[i] = NULL;
m_uniform_tp[i] = m_array_tp[i];
m_data[i] = ops[i].get_ndo()->m_data_pointer;
m_metadata[i] = ops[i].get_ndo_meta();
}
}
}
static nd::array view_concrete(const nd::array &arr, const ndt::type &tp)
{
// Allocate a result array to attempt the view in it
nd::array result(make_array_memory_block(tp.get_arrmeta_size()));
// Copy the fields
result.get_ndo()->data.ptr = arr.get_ndo()->data.ptr;
if (arr.get_ndo()->data.ref == NULL) {
// Embedded data, need reference to the array
result.get_ndo()->data.ref = arr.get_memblock().release();
} else {
// Use the same data reference, avoid producing a chain
result.get_ndo()->data.ref = arr.get_data_memblock().release();
}
result.get_ndo()->m_type = ndt::type(tp).release();
result.get_ndo()->m_flags = arr.get_ndo()->m_flags;
// First handle a special case of viewing outermost "var" as "fixed[#]"
if (arr.get_type().get_type_id() == var_dim_type_id && tp.get_type_id() == fixed_dim_type_id) {
const var_dim_type_arrmeta *in_am = reinterpret_cast<const var_dim_type_arrmeta *>(arr.get_arrmeta());
const var_dim_type_data *in_dat = reinterpret_cast<const var_dim_type_data *>(arr.get_readonly_originptr());
fixed_dim_type_arrmeta *out_am = reinterpret_cast<fixed_dim_type_arrmeta *>(result.get_arrmeta());
out_am->dim_size = tp.extended<ndt::fixed_dim_type>()->get_fixed_dim_size();
out_am->stride = in_am->stride;
if ((intptr_t)in_dat->size == out_am->dim_size) {
// Use the more specific data reference from the var arrmeta if possible
if (in_am->blockref != NULL) {
memory_block_decref(result.get_ndo()->data.ref);
memory_block_incref(in_am->blockref);
result.get_ndo()->data.ref = in_am->blockref;
}
result.get_ndo()->data.ptr = in_dat->begin + in_am->offset;
// Try to copy the rest of the arrmeta as a view
if (try_view(arr.get_type().extended<ndt::base_dim_type>()->get_element_type(),
arr.get_arrmeta() + sizeof(var_dim_type_arrmeta),
tp.extended<ndt::base_dim_type>()->get_element_type(),
result.get_arrmeta() + sizeof(fixed_dim_type_arrmeta), arr.get_memblock().get())) {
return result;
}
}
}
// Otherwise try to copy the arrmeta as a view
else if (try_view(arr.get_type(), arr.get_arrmeta(), tp, result.get_arrmeta(), arr.get_memblock().get())) {
// If it succeeded, return it
return result;
}
stringstream ss;
ss << "Unable to view nd::array of type " << arr.get_type();
ss << " as type " << tp;
throw type_error(ss.str());
}
const arrfunc_type *get_is_avail_arrfunc_type() const
{
return m_nafunc.get_type()
.extended<base_tuple_type>()
->get_field_type(0)
.extended<arrfunc_type>();
}
void dynd::parse_json(nd::array &out, const char *json_begin,
const char *json_end, const eval::eval_context *ectx)
{
try {
const char *begin = json_begin, *end = json_end;
ndt::type tp = out.get_type();
::parse_json(tp, out.get_ndo_meta(), out.get_readwrite_originptr(), begin, end, ectx);
begin = skip_whitespace(begin, end);
if (begin != end) {
throw json_parse_error(begin, "unexpected trailing JSON text", tp);
}
} catch (const json_parse_error& e) {
stringstream ss;
string line_prev, line_cur;
int line, column;
get_error_line_column(json_begin, json_end, e.get_position(),
line_prev, line_cur, line, column);
ss << "Error parsing JSON at line " << line << ", column " << column << "\n";
if (e.get_type().get_type_id() != uninitialized_type_id) {
ss << "DType: " << e.get_type() << "\n";
}
ss << "Message: " << e.get_message() << "\n";
print_json_parse_error_marker(ss, line_prev, line_cur, line, column);
throw runtime_error(ss.str());
}
}
inline static bool run(nd::array &a)
{
const ndt::type &tp = a.get_type();
if (a.is_immutable() && tp.get_type_id() == fixed_dim_type_id) {
// It's immutable and "N * <something>"
const ndt::type &et = tp.extended<fixed_dim_type>()->get_element_type();
const fixed_dim_type_arrmeta *md =
reinterpret_cast<const fixed_dim_type_arrmeta *>(a.get_arrmeta());
if (et.get_type_id() == type_type_id &&
md->stride == sizeof(ndt::type)) {
// It also has the right type and is contiguous,
// so no modification necessary.
return true;
}
}
// We have to make a copy, check that it's a 1D array, and that
// it has the same array kind as the requested type.
if (tp.get_ndim() == 1) {
// It's a 1D array
const ndt::type &et = tp.get_type_at_dimension(NULL, 1).value_type();
if (et.get_type_id() == type_type_id) {
// It also has the same array type as requested
nd::array tmp = nd::empty(a.get_dim_size(), ndt::make_type());
tmp.vals() = a;
tmp.flag_as_immutable();
a.swap(tmp);
return true;
}
}
// It's not compatible, so return false
return false;
}
uint32_t ndt::categorical_type::get_value_from_category(const nd::array &category) const
{
nd::array c;
if (category.get_type() == m_category_tp) {
// If the type is right, get the category value directly
c = category;
}
else {
// Otherwise convert to the correct type, then get the category value
c = nd::empty(m_category_tp);
c.assign(category);
}
intptr_t i = nd::binary_search(m_categories, c).as<intptr_t>();
if (i < 0) {
stringstream ss;
ss << "Unrecognized category value ";
m_category_tp.print_data(ss, c.get()->metadata(), c.data());
ss << " assigning to dynd type " << type(this, true);
throw std::runtime_error(ss.str());
}
else {
return (uint32_t)unchecked_fixed_dim_get<intptr_t>(m_category_index_to_value, i);
}
}
static nd::array array_function_dereference(const nd::array &self)
{
// Follow the pointers to eliminate them
ndt::type dt = self.get_type();
const char *arrmeta = self.get_arrmeta();
char *data = self.get_ndo()->m_data_pointer;
memory_block_data *dataref = self.get_ndo()->m_data_reference;
if (dataref == NULL) {
dataref = self.get_memblock().get();
}
uint64_t flags = self.get_ndo()->m_flags;
while (dt.get_type_id() == pointer_type_id) {
const pointer_type_arrmeta *md =
reinterpret_cast<const pointer_type_arrmeta *>(arrmeta);
dt = dt.extended<ndt::pointer_type>()->get_target_type();
arrmeta += sizeof(pointer_type_arrmeta);
data = *reinterpret_cast<char **>(data) + md->offset;
dataref = md->blockref;
}
// Create an array without the pointers
nd::array result(make_array_memory_block(dt.get_arrmeta_size()));
if (!dt.is_builtin()) {
dt.extended()->arrmeta_copy_construct(result.get_arrmeta(), arrmeta,
&self.get_ndo()->m_memblockdata);
}
result.get_ndo()->m_type = dt.release();
result.get_ndo()->m_data_pointer = data;
result.get_ndo()->m_data_reference = dataref;
memory_block_incref(result.get_ndo()->m_data_reference);
result.get_ndo()->m_flags = flags;
return result;
}
const arrfunc_type *get_assign_na_arrfunc_type() const
{
return m_nafunc.get_type()
.extended<base_tuple_type>()
->get_field_type(1)
.extended<arrfunc_type>();
}
void dynd::typed_data_assign(const ndt::type &dst_tp, const char *dst_arrmeta,
char *dst_data, const nd::array &src_arr,
const eval::eval_context *ectx)
{
typed_data_assign(dst_tp, dst_arrmeta, dst_data, src_arr.get_type(),
src_arr.get_arrmeta(), src_arr.get_readonly_originptr(),
ectx);
}
static nd::array property_ndo_get_groups(const nd::array& n) {
ndt::type d = n.get_type();
while (d.get_type_id() != groupby_type_id) {
d = d.at_single(0);
}
const groupby_type *gd = d.extended<groupby_type>();
return gd->get_groups_type().p("categories");
}
static void set(const ndt::type& paramtype, char *metadata, char *data, const nd::array& value) {
if (paramtype.get_type_id() == void_pointer_type_id) {
// TODO: switch to a better mechanism for passing nd::array references
*reinterpret_cast<const array_preamble **>(data) = value.get_ndo();
} else {
typed_data_assign(paramtype, metadata, data, value.get_type(), value.get_ndo_meta(), value.get_ndo()->m_data_pointer);
}
}
static nd::array view_as_bytes(const nd::array &arr, const ndt::type &tp)
{
if (arr.get_type().get_flags() & type_flag_destructor) {
// Can't view arrays of object type
return nd::array();
}
// Get the essential components of the array to analyze
memory_block_ptr data_ref = arr.get_data_memblock();
char *data_ptr = arr.get_ndo()->data.ptr;
ndt::type data_tp = arr.get_type();
const char *data_meta = arr.get_arrmeta();
intptr_t data_dim_size = -1, data_stride = 0;
// Repeatedly refine the data
while (data_tp.get_type_id() != uninitialized_type_id) {
refine_bytes_view(data_ref, data_ptr, data_tp, data_meta, data_dim_size, data_stride);
}
// Check that it worked, and that the resulting data pointer is aligned
if (data_dim_size < 0 ||
!offset_is_aligned(reinterpret_cast<size_t>(data_ptr), tp.extended<ndt::bytes_type>()->get_target_alignment())) {
// This signals we could not view the data as a
// contiguous chunk of bytes
return nd::array();
}
char *result_data_ptr = NULL;
nd::array result(make_array_memory_block(tp.extended()->get_arrmeta_size(), tp.get_data_size(),
tp.get_data_alignment(), &result_data_ptr));
// Set the bytes extents
((char **)result_data_ptr)[0] = data_ptr;
((char **)result_data_ptr)[1] = data_ptr + data_dim_size;
// Set the array arrmeta
array_preamble *ndo = result.get_ndo();
ndo->m_type = ndt::type(tp).release();
ndo->data.ptr = result_data_ptr;
ndo->data.ref = NULL;
ndo->m_flags = arr.get_flags();
// Set the bytes arrmeta
bytes_type_arrmeta *ndo_meta = reinterpret_cast<bytes_type_arrmeta *>(result.get_arrmeta());
ndo_meta->blockref = data_ref.release();
return result;
}
uint32_t categorical_type::get_value_from_category(const nd::array& category) const
{
if (category.get_type() == m_category_tp) {
// If the type is right, get the category value directly
return get_value_from_category(category.get_arrmeta(), category.get_readonly_originptr());
} else {
// Otherwise convert to the correct type, then get the category value
nd::array c = nd::empty(m_category_tp);
c.val_assign(category);
return get_value_from_category(c.get_arrmeta(), c.get_readonly_originptr());
}
}
array_iter(const nd::array& op0, const nd::array& op1) {
nd::array ops[2] = {op0, op1};
m_array_tp[0] = op0.get_type();
m_array_tp[1] = op1.get_type();
m_itersize = 1;
shortvector<int> axis_perm; // TODO: Use this to affect the iteration order
broadcast_input_shapes(2, ops, m_iter_ndim, m_itershape, axis_perm);
// Allocate and initialize the iterdata
if (m_iter_ndim != 0) {
m_iterindex.init(m_iter_ndim);
memset(m_iterindex.get(), 0, sizeof(intptr_t) * m_iter_ndim);
// The op iterdata
for (int i = 0; i < 2; ++i) {
size_t iter_ndim_i = m_array_tp[i].get_ndim();
size_t iterdata_size = m_array_tp[i].get_broadcasted_iterdata_size(iter_ndim_i);
m_iterdata[i] = reinterpret_cast<iterdata_common *>(malloc(iterdata_size));
if (!m_iterdata[i]) {
throw std::bad_alloc();
}
m_metadata[i] = ops[i].get_ndo_meta();
m_array_tp[i].broadcasted_iterdata_construct(m_iterdata[i],
&m_metadata[i], iter_ndim_i,
m_itershape.get() + (m_iter_ndim - iter_ndim_i), m_uniform_tp[i]);
m_data[i] = m_iterdata[i]->reset(m_iterdata[i], ops[i].get_ndo()->m_data_pointer, m_iter_ndim);
}
for (size_t i = 0, i_end = m_iter_ndim; i != i_end; ++i) {
m_itersize *= m_itershape[i];
}
} else {
for (size_t i = 0; i < 2; ++i) {
m_iterdata[i] = NULL;
m_uniform_tp[i] = m_array_tp[i];
m_data[i] = ops[i].get_ndo()->m_data_pointer;
m_metadata[i] = ops[i].get_ndo_meta();
}
}
}
nd::callable::callable(const nd::array &rhs)
{
if (!rhs.is_null()) {
if (rhs.get_type().get_type_id() == callable_type_id) {
const callable_type_data *af =
reinterpret_cast<const callable_type_data *>(
rhs.cdata());
if (af->instantiate != NULL) {
// It's valid: callable type, contains instantiate function.
m_value = rhs;
} else {
throw invalid_argument("Require a non-empty callable, "
"provided callable has NULL "
"instantiate function");
}
} else {
stringstream ss;
ss << "Cannot implicitly convert nd::array of type "
<< rhs.get_type().value_type() << " to callable";
throw type_error(ss.str());
}
}
}
static void json_as_buffer(const nd::array &json, nd::array &out_tmp_ref,
const char *&begin, const char *&end)
{
// Check the type of 'json', and get pointers to the begin/end of a UTF-8
// buffer
ndt::type json_type = json.get_type().value_type();
switch (json_type.get_kind()) {
case string_kind: {
const ndt::base_string_type *sdt =
json_type.extended<ndt::base_string_type>();
switch (sdt->get_encoding()) {
case string_encoding_ascii:
case string_encoding_utf_8:
out_tmp_ref = json.eval();
// The data is already UTF-8, so use the buffer directly
sdt->get_string_range(&begin, &end, out_tmp_ref.get_arrmeta(),
out_tmp_ref.get_readonly_originptr());
break;
default: {
// The data needs to be converted to UTF-8 before parsing
ndt::type utf8_tp = ndt::string_type::make(string_encoding_utf_8);
out_tmp_ref = json.ucast(utf8_tp).eval();
sdt = static_cast<const ndt::base_string_type *>(utf8_tp.extended());
sdt->get_string_range(&begin, &end, out_tmp_ref.get_arrmeta(),
out_tmp_ref.get_readonly_originptr());
break;
}
}
break;
}
case bytes_kind: {
out_tmp_ref = json.eval();
const ndt::base_bytes_type *bdt =
json_type.extended<ndt::base_bytes_type>();
bdt->get_bytes_range(&begin, &end, out_tmp_ref.get_arrmeta(),
out_tmp_ref.get_readonly_originptr());
break;
}
default: {
stringstream ss;
ss << "Input for JSON parsing must be either bytes (interpreted as UTF-8) "
"or a string, not \"" << json_type << "\"";
throw runtime_error(ss.str());
break;
}
}
}
/**
* Given a buffer array of type "strided * T" which was
* created by nd::empty, resets it so it can be used
* as a buffer again.
*
* NOTE: If the array is not of type "strided * T" and default
* initialized by nd::empty, undefined behavior will result.
*
*/
inline void reset_strided_buffer_array(const nd::array& buf)
{
const ndt::type &buf_tp = buf.get_type();
base_type_members::flags_type flags = buf_tp.extended()->get_flags();
if (flags &
(type_flag_blockref | type_flag_zeroinit | type_flag_destructor)) {
char *buf_arrmeta = buf.get_ndo()->get_arrmeta();
char *buf_data = buf.get_readwrite_originptr();
buf_tp.extended()->arrmeta_reset_buffers(buf.get_ndo()->get_arrmeta());
strided_dim_type_arrmeta *am =
reinterpret_cast<strided_dim_type_arrmeta *>(buf_arrmeta);
if (flags & type_flag_destructor) {
buf_tp.extended()->data_destruct(buf_arrmeta, buf_data);
}
memset(buf_data, 0, am->dim_size * am->stride);
}
}
void dynd::parse_json(nd::array &out, const char *json_begin, const char *json_end, const eval::eval_context *ectx)
{
try
{
const char *begin = json_begin, *end = json_end;
ndt::type tp = out.get_type();
::parse_json(tp, out.get()->metadata(), out.data(), begin, end, ectx);
begin = skip_whitespace(begin, end);
if (begin != end) {
throw json_parse_error(begin, "unexpected trailing JSON text", tp);
}
}
catch (const json_parse_error &e)
{
stringstream ss;
std::string line_prev, line_cur;
int line, column;
get_error_line_column(json_begin, json_end, e.get_position(), line_prev, line_cur, line, column);
ss << "Error parsing JSON at line " << line << ", column " << column << "\n";
ss << "DyND Type: " << e.get_type() << "\n";
ss << "Message: " << e.what() << "\n";
print_json_parse_error_marker(ss, line_prev, line_cur, line, column);
throw invalid_argument(ss.str());
}
catch (const parse::parse_error &e)
{
stringstream ss;
std::string line_prev, line_cur;
int line, column;
get_error_line_column(json_begin, json_end, e.get_position(), line_prev, line_cur, line, column);
ss << "Error parsing JSON at line " << line << ", column " << column << "\n";
ss << "Message: " << e.what() << "\n";
print_json_parse_error_marker(ss, line_prev, line_cur, line, column);
throw invalid_argument(ss.str());
}
}
ndt::categorical_type::categorical_type(const nd::array &categories, bool presorted)
: base_type(categorical_id, 4, 4, type_flag_none, 0, 0, 0)
{
intptr_t category_count;
if (presorted) {
// This is construction shortcut, for the case when the categories are
// already
// sorted. No validation of this is done, the caller should have ensured it
// was correct already, typically by construction.
m_categories = categories.eval_immutable();
m_category_tp = m_categories.get_type().at(0);
category_count = categories.get_dim_size();
m_value_to_category_index = nd::range(category_count);
m_value_to_category_index.flag_as_immutable();
m_category_index_to_value = m_value_to_category_index;
}
else {
// Process the categories array to make sure it's valid
const type &cdt = categories.get_type();
if (cdt.get_id() != fixed_dim_id) {
throw dynd::type_error("categorical_type only supports construction from "
"a fixed-dim array of categories");
}
m_category_tp = categories.get_type().at(0);
if (!m_category_tp.is_scalar()) {
throw dynd::type_error("categorical_type only supports construction from "
"a 1-dimensional strided array of categories");
}
category_count = categories.get_dim_size();
intptr_t categories_stride = reinterpret_cast<const fixed_dim_type_arrmeta *>(categories.get()->metadata())->stride;
const char *categories_element_arrmeta = categories.get()->metadata() + sizeof(fixed_dim_type_arrmeta);
nd::kernel_builder k;
kernel_single_t fn = k.get()->get_function<kernel_single_t>();
cmp less(fn, k.get());
set<const char *, cmp> uniques(less);
m_value_to_category_index = nd::empty(category_count, make_type<intptr_t>());
m_category_index_to_value = nd::empty(category_count, make_type<intptr_t>());
// create the mapping from indices of (to be lexicographically sorted)
// categories to values
for (size_t i = 0; i != (size_t)category_count; ++i) {
unchecked_fixed_dim_get_rw<intptr_t>(m_category_index_to_value, i) = i;
const char *category_value = categories.cdata() + i * categories_stride;
if (uniques.find(category_value) == uniques.end()) {
uniques.insert(category_value);
}
else {
stringstream ss;
ss << "categories must be unique: category value ";
m_category_tp.print_data(ss, categories_element_arrmeta, category_value);
ss << " appears more than once";
throw std::runtime_error(ss.str());
}
}
// TODO: Putting everything in a set already caused a sort operation to
// occur,
// there's no reason we should need a second sort.
std::sort(&unchecked_fixed_dim_get_rw<intptr_t>(m_category_index_to_value, 0),
&unchecked_fixed_dim_get_rw<intptr_t>(m_category_index_to_value, category_count),
sorter(categories.cdata(), categories_stride, fn, k.get()));
// invert the m_category_index_to_value permutation
for (intptr_t i = 0; i < category_count; ++i) {
unchecked_fixed_dim_get_rw<intptr_t>(m_value_to_category_index,
unchecked_fixed_dim_get<intptr_t>(m_category_index_to_value, i)) = i;
}
m_categories = make_sorted_categories(uniques, m_category_tp, categories_element_arrmeta);
}
// Use the number of categories to set which underlying integer storage to use
if (category_count <= 256) {
m_storage_type = make_type<uint8_t>();
}
else if (category_count <= 65536) {
m_storage_type = make_type<uint16_t>();
}
else {
m_storage_type = make_type<uint32_t>();
}
this->data_size = m_storage_type.get_data_size();
this->data_alignment = (uint8_t)m_storage_type.get_data_alignment();
}
/**
* Adds a ckernel layer for processing one dimension of the reduction.
* This is for a strided dimension which is being broadcast, and is
* the final dimension before the accumulation operation.
*/
static size_t make_strided_inner_broadcast_dimension_kernel(
const callable_type_data *elwise_reduction_const,
const ndt::callable_type *elwise_reduction_tp,
const callable_type_data *dst_initialization_const,
const ndt::callable_type *dst_initialization_tp, void *ckb,
intptr_t ckb_offset, intptr_t dst_stride, intptr_t src_stride,
intptr_t src_size, const ndt::type &dst_tp, const char *dst_arrmeta,
const ndt::type &src_tp, const char *src_arrmeta, bool right_associative,
const nd::array &reduction_identity, kernel_request_t kernreq,
const eval::eval_context *ectx)
{
callable_type_data *elwise_reduction =
const_cast<callable_type_data *>(elwise_reduction_const);
callable_type_data *dst_initialization =
const_cast<callable_type_data *>(dst_initialization_const);
intptr_t root_ckb_offset = ckb_offset;
strided_inner_broadcast_kernel_extra *e =
reinterpret_cast<ckernel_builder<kernel_request_host> *>(ckb)
->alloc_ck<strided_inner_broadcast_kernel_extra>(ckb_offset);
e->destructor = &strided_inner_broadcast_kernel_extra::destruct;
// Cannot have both a dst_initialization kernel and a reduction identity
if (dst_initialization != NULL && !reduction_identity.is_null()) {
throw invalid_argument(
"make_lifted_reduction_ckernel: cannot specify"
" both a dst_initialization kernel and a reduction_identity");
}
if (reduction_identity.is_null()) {
// Get the function pointer for the first_call, for the case with
// no reduction identity
if (kernreq == kernel_request_single) {
e->set_first_call_function(
&strided_inner_broadcast_kernel_extra::single_first);
} else if (kernreq == kernel_request_strided) {
e->set_first_call_function(
&strided_inner_broadcast_kernel_extra::strided_first);
} else {
stringstream ss;
ss << "make_lifted_reduction_ckernel: unrecognized request "
<< (int)kernreq;
throw runtime_error(ss.str());
}
} else {
// Get the function pointer for the first_call, for the case with
// a reduction identity
if (kernreq == kernel_request_single) {
e->set_first_call_function(
&strided_inner_broadcast_kernel_extra::single_first_with_ident);
} else if (kernreq == kernel_request_strided) {
e->set_first_call_function(
&strided_inner_broadcast_kernel_extra::strided_first_with_ident);
} else {
stringstream ss;
ss << "make_lifted_reduction_ckernel: unrecognized request "
<< (int)kernreq;
throw runtime_error(ss.str());
}
if (reduction_identity.get_type() != dst_tp) {
stringstream ss;
ss << "make_lifted_reduction_ckernel: reduction identity type ";
ss << reduction_identity.get_type() << " does not match dst type ";
ss << dst_tp;
throw runtime_error(ss.str());
}
e->ident_data = reduction_identity.get_readonly_originptr();
e->ident_ref = reduction_identity.get_memblock().release();
}
// The function pointer for followup accumulation calls
e->set_followup_call_function(
&strided_inner_broadcast_kernel_extra::strided_followup);
// The striding parameters
e->dst_stride = dst_stride;
e->src_stride = src_stride;
e->size = src_size;
// Validate that the provided callables are unary operations,
// and have the correct types
if (elwise_reduction_tp->get_npos() != 1 &&
elwise_reduction_tp->get_npos() != 2) {
stringstream ss;
ss << "make_lifted_reduction_ckernel: elwise reduction ckernel ";
ss << "funcproto must be unary or a binary expr with all equal types";
throw runtime_error(ss.str());
}
if (elwise_reduction_tp->get_return_type() != dst_tp) {
stringstream ss;
ss << "make_lifted_reduction_ckernel: elwise reduction ckernel ";
ss << "dst type is " << elwise_reduction_tp->get_return_type();
ss << ", expected " << dst_tp;
throw type_error(ss.str());
}
if (elwise_reduction_tp->get_pos_type(0) != src_tp) {
stringstream ss;
ss << "make_lifted_reduction_ckernel: elwise reduction ckernel ";
ss << "src type is " << elwise_reduction_tp->get_return_type();
ss << ", expected " << src_tp;
throw type_error(ss.str());
}
if (dst_initialization != NULL) {
check_dst_initialization(dst_initialization_tp, dst_tp, src_tp);
}
if (elwise_reduction_tp->get_npos() == 2) {
ckb_offset = kernels::wrap_binary_as_unary_reduction_ckernel(
ckb, ckb_offset, right_associative, kernel_request_strided);
ndt::type src_tp_doubled[2] = {src_tp, src_tp};
const char *src_arrmeta_doubled[2] = {src_arrmeta, src_arrmeta};
ckb_offset = elwise_reduction->instantiate(
elwise_reduction->static_data, 0, NULL, ckb, ckb_offset, dst_tp,
dst_arrmeta, elwise_reduction_tp->get_npos(), src_tp_doubled,
src_arrmeta_doubled, kernel_request_strided, ectx, nd::array(),
std::map<nd::string, ndt::type>());
} else {
ckb_offset = elwise_reduction->instantiate(
elwise_reduction->static_data, 0, NULL, ckb, ckb_offset, dst_tp,
dst_arrmeta, elwise_reduction_tp->get_npos(), &src_tp, &src_arrmeta,
kernel_request_strided, ectx, nd::array(),
std::map<nd::string, ndt::type>());
}
// Make sure there's capacity for the next ckernel
reinterpret_cast<ckernel_builder<kernel_request_host> *>(ckb)
->reserve(ckb_offset + sizeof(ckernel_prefix));
// Need to retrieve 'e' again because it may have moved
e = reinterpret_cast<ckernel_builder<kernel_request_host> *>(ckb)
->get_at<strided_inner_broadcast_kernel_extra>(root_ckb_offset);
e->dst_init_kernel_offset = ckb_offset - root_ckb_offset;
if (dst_initialization != NULL) {
ckb_offset = dst_initialization->instantiate(
dst_initialization->static_data, 0, NULL, ckb, ckb_offset, dst_tp,
dst_arrmeta, elwise_reduction_tp->get_npos(), &src_tp, &src_arrmeta,
kernel_request_strided, ectx, nd::array(),
std::map<nd::string, ndt::type>());
} else if (reduction_identity.is_null()) {
ckb_offset =
make_assignment_kernel(ckb, ckb_offset, dst_tp, dst_arrmeta, src_tp,
src_arrmeta, kernel_request_strided, ectx);
} else {
ckb_offset = make_assignment_kernel(
ckb, ckb_offset, dst_tp, dst_arrmeta, reduction_identity.get_type(),
reduction_identity.get_arrmeta(), kernel_request_strided, ectx);
}
return ckb_offset;
}
categorical_type::categorical_type(const nd::array& categories, bool presorted)
: base_type(categorical_type_id, custom_kind, 4, 4, type_flag_scalar, 0, 0, 0)
{
intptr_t category_count;
if (presorted) {
// This is construction shortcut, for the case when the categories are already
// sorted. No validation of this is done, the caller should have ensured it
// was correct already, typically by construction.
m_categories = categories.eval_immutable();
m_category_tp = m_categories.get_type().at(0);
category_count = categories.get_dim_size();
m_value_to_category_index.resize(category_count);
m_category_index_to_value.resize(category_count);
for (size_t i = 0; i != (size_t)category_count; ++i) {
m_value_to_category_index[i] = i;
m_category_index_to_value[i] = i;
}
} else {
// Process the categories array to make sure it's valid
const ndt::type& cdt = categories.get_type();
if (cdt.get_type_id() != strided_dim_type_id) {
throw dynd::type_error("categorical_type only supports construction from a strided array of categories");
}
m_category_tp = categories.get_type().at(0);
if (!m_category_tp.is_scalar()) {
throw dynd::type_error("categorical_type only supports construction from a 1-dimensional strided array of categories");
}
category_count = categories.get_dim_size();
intptr_t categories_stride = reinterpret_cast<const strided_dim_type_arrmeta *>(categories.get_arrmeta())->stride;
const char *categories_element_arrmeta = categories.get_arrmeta() + sizeof(strided_dim_type_arrmeta);
comparison_ckernel_builder k;
::make_comparison_kernel(&k, 0,
m_category_tp, categories_element_arrmeta,
m_category_tp, categories_element_arrmeta,
comparison_type_sorting_less, &eval::default_eval_context);
cmp less(k.get_function(), k.get());
set<const char *, cmp> uniques(less);
m_value_to_category_index.resize(category_count);
m_category_index_to_value.resize(category_count);
// create the mapping from indices of (to be lexicographically sorted) categories to values
for (size_t i = 0; i != (size_t)category_count; ++i) {
m_category_index_to_value[i] = i;
const char *category_value = categories.get_readonly_originptr() +
i * categories_stride;
if (uniques.find(category_value) == uniques.end()) {
uniques.insert(category_value);
} else {
stringstream ss;
ss << "categories must be unique: category value ";
m_category_tp.print_data(ss, categories_element_arrmeta, category_value);
ss << " appears more than once";
throw std::runtime_error(ss.str());
}
}
// TODO: Putting everything in a set already caused a sort operation to occur,
// there's no reason we should need a second sort.
std::sort(m_category_index_to_value.begin(), m_category_index_to_value.end(),
sorter(categories.get_readonly_originptr(), categories_stride,
k.get_function(), k.get()));
// invert the m_category_index_to_value permutation
for (uint32_t i = 0; i < m_category_index_to_value.size(); ++i) {
m_value_to_category_index[m_category_index_to_value[i]] = i;
}
m_categories = make_sorted_categories(uniques, m_category_tp,
categories_element_arrmeta);
}
// Use the number of categories to set which underlying integer storage to use
if (category_count <= 256) {
m_storage_type = ndt::make_type<uint8_t>();
} else if (category_count <= 65536) {
m_storage_type = ndt::make_type<uint16_t>();
} else {
m_storage_type = ndt::make_type<uint32_t>();
}
m_members.data_size = m_storage_type.get_data_size();
m_members.data_alignment = (uint8_t)m_storage_type.get_data_alignment();
}
dynd::nd::array pydynd::nd_fields(const nd::array &n, PyObject *field_list)
{
vector<std::string> selected_fields;
pyobject_as_vector_string(field_list, selected_fields);
// TODO: Move this implementation into dynd
ndt::type fdt = n.get_dtype();
if (fdt.get_kind() != struct_kind) {
stringstream ss;
ss << "nd.fields must be given a dynd array of 'struct' kind, not ";
ss << fdt;
throw runtime_error(ss.str());
}
const ndt::struct_type *bsd = fdt.extended<ndt::struct_type>();
if (selected_fields.empty()) {
throw runtime_error(
"nd.fields requires at least one field name to be specified");
}
// Construct the field mapping and output field types
vector<intptr_t> selected_index(selected_fields.size());
vector<ndt::type> selected__types(selected_fields.size());
for (size_t i = 0; i != selected_fields.size(); ++i) {
selected_index[i] = bsd->get_field_index(selected_fields[i]);
if (selected_index[i] < 0) {
stringstream ss;
ss << "field name ";
print_escaped_utf8_string(ss, selected_fields[i]);
ss << " does not exist in dynd type " << fdt;
throw runtime_error(ss.str());
}
selected__types[i] = bsd->get_field_type(selected_index[i]);
}
// Create the result udt
ndt::type rudt = ndt::struct_type::make(selected_fields, selected__types);
ndt::type result_tp = n.get_type().with_replaced_dtype(rudt);
const ndt::struct_type *rudt_bsd = rudt.extended<ndt::struct_type>();
// Allocate the new memory block.
size_t arrmeta_size = result_tp.get_arrmeta_size();
nd::array result(reinterpret_cast<array_preamble *>(
make_array_memory_block(arrmeta_size).get()),
true);
// Clone the data pointer
result.get()->data = n.get()->data;
result.get()->owner = n.get()->owner;
if (!result.get()->owner) {
result.get()->owner = n.get();
}
// Copy the flags
result.get()->flags = n.get()->flags;
// Set the type and transform the arrmeta
result.get()->tp = result_tp;
// First copy all the array data type arrmeta
ndt::type tmp_dt = result_tp;
char *dst_arrmeta = result.get()->metadata();
const char *src_arrmeta = n.get()->metadata();
while (tmp_dt.get_ndim() > 0) {
if (tmp_dt.get_kind() != dim_kind) {
throw runtime_error(
"nd.fields doesn't support dimensions with pointers yet");
}
const ndt::base_dim_type *budd = tmp_dt.extended<ndt::base_dim_type>();
size_t offset = budd->arrmeta_copy_construct_onedim(
dst_arrmeta, src_arrmeta,
intrusive_ptr<memory_block_data>(n.get(), true));
dst_arrmeta += offset;
src_arrmeta += offset;
tmp_dt = budd->get_element_type();
}
// Then create the arrmeta for the new struct
const size_t *arrmeta_offsets = bsd->get_arrmeta_offsets_raw();
const size_t *result_arrmeta_offsets = rudt_bsd->get_arrmeta_offsets_raw();
const size_t *data_offsets = bsd->get_data_offsets(src_arrmeta);
size_t *result_data_offsets = reinterpret_cast<size_t *>(dst_arrmeta);
for (size_t i = 0; i != selected_fields.size(); ++i) {
const ndt::type &dt = selected__types[i];
// Copy the data offset
result_data_offsets[i] = data_offsets[selected_index[i]];
// Copy the arrmeta for this field
if (dt.get_arrmeta_size() > 0) {
dt.extended()->arrmeta_copy_construct(
dst_arrmeta + result_arrmeta_offsets[i],
src_arrmeta + arrmeta_offsets[selected_index[i]],
intrusive_ptr<memory_block_data>(n.get(), true));
}
}
return result;
}
void dynd::lift_reduction_arrfunc(arrfunc_type_data *out_ar,
const nd::arrfunc& elwise_reduction_arr,
const ndt::type& lifted_arr_type,
const nd::arrfunc& dst_initialization_arr,
bool keepdims,
intptr_t reduction_ndim,
const bool *reduction_dimflags,
bool associative,
bool commutative,
bool right_associative,
const nd::array& reduction_identity)
{
// Validate the input elwise_reduction arrfunc
if (elwise_reduction_arr.is_null()) {
throw runtime_error("lift_reduction_arrfunc: 'elwise_reduction' may not be empty");
}
const arrfunc_type_data *elwise_reduction = elwise_reduction_arr.get();
if (elwise_reduction->get_param_count() != 1 &&
!(elwise_reduction->get_param_count() == 2 &&
elwise_reduction->get_param_type(0) ==
elwise_reduction->get_param_type(1) &&
elwise_reduction->get_param_type(0) ==
elwise_reduction->get_return_type())) {
stringstream ss;
ss << "lift_reduction_arrfunc: 'elwise_reduction' must contain a"
" unary operation ckernel or a binary expr ckernel with all "
"equal types, its prototype is " << elwise_reduction->func_proto;
throw invalid_argument(ss.str());
}
lifted_reduction_arrfunc_data *self = new lifted_reduction_arrfunc_data;
*out_ar->get_data_as<lifted_reduction_arrfunc_data *>() = self;
out_ar->free_func = &delete_lifted_reduction_arrfunc_data;
self->child_elwise_reduction = elwise_reduction_arr;
self->child_dst_initialization = dst_initialization_arr;
if (!reduction_identity.is_null()) {
if (reduction_identity.is_immutable() &&
reduction_identity.get_type() == elwise_reduction->get_return_type()) {
self->reduction_identity = reduction_identity;
} else {
self->reduction_identity = nd::empty(elwise_reduction->get_return_type());
self->reduction_identity.vals() = reduction_identity;
self->reduction_identity.flag_as_immutable();
}
}
// Figure out the result type
ndt::type lifted_dst_type = elwise_reduction->get_return_type();
for (intptr_t i = reduction_ndim - 1; i >= 0; --i) {
if (reduction_dimflags[i]) {
if (keepdims) {
lifted_dst_type = ndt::make_strided_dim(lifted_dst_type);
}
} else {
ndt::type subtype = lifted_arr_type.get_type_at_dimension(NULL, i);
switch (subtype.get_type_id()) {
case strided_dim_type_id:
case cfixed_dim_type_id:
lifted_dst_type = ndt::make_strided_dim(lifted_dst_type);
break;
case var_dim_type_id:
lifted_dst_type = ndt::make_var_dim(lifted_dst_type);
break;
default: {
stringstream ss;
ss << "lift_reduction_arrfunc: don't know how to process ";
ss << "dimension of type " << subtype;
throw type_error(ss.str());
}
}
}
}
self->data_types[0] = lifted_dst_type;
self->data_types[1] = lifted_arr_type;
self->reduction_ndim = reduction_ndim;
self->associative = associative;
self->commutative = commutative;
self->right_associative = right_associative;
self->reduction_dimflags.init(reduction_ndim);
memcpy(self->reduction_dimflags.get(), reduction_dimflags, sizeof(bool) * reduction_ndim);
out_ar->instantiate = &instantiate_lifted_reduction_arrfunc_data;
out_ar->func_proto = ndt::make_funcproto(lifted_arr_type, lifted_dst_type);
}
static size_t array_param(const nd::array& n) {
return n.get_type().get_ndim();
}
array_iter(const nd::array& op0) {
init(op0.get_type(), op0.get_ndo_meta(), op0.get_readwrite_originptr());
}