Adding support for streaming through the input arrays of a pipeline

README.rst

@@ -208,6 +208,104 @@ PDAL and Python:
     with tiledb.open("clamped") as a:
         print(a.schema)
 
+Reading using Numpy Arrays as buffers (advanced)
+................................................................................
+
+It's also possible to treat the Numpy arrays passed to PDAL as buffers that are iteratively populated through
+custom python functions during the execution of the pipeline.
+
+This may be useful in cases where you want the reading of the input data to be handled in a streamable fashion,
+like for example:
+
+* When the total Numpy array data wouldn't fit into memory.
+* To initiate execution of a streamable PDAL pipeline while the input data is still being read.
+
+To enable this mode, you just need to include the python populate function along with each corresponding Numpy array.
+
+.. code-block:: python
+
+    # Numpy array to be used as buffer
+    in_buffer = np.zeros(max_chunk_size, dtype=[("X", float), ("Y", float), ("Z", float)])
+
+    # The function to populate the buffer iteratively
+    def load_next_chunk() -> int:
+    """
+    Function called by PDAL before reading the data from the buffer.
+
+    IMPORTANT: must return the total number of items to be read from the buffer.
+    The Pipeline execution will keep calling this function in a loop until 0 is returned.
+    """
+        #
+        # Replace here with your code that populates the buffer and returns the number of elements to read
+        #
+        chunk_size = next_chunk.size
+        in_buffer[:chunk_size]["X"] = next_chunk[:]["X"]
+        in_buffer[:chunk_size]["Y"] = next_chunk[:]["Y"]
+        in_buffer[:chunk_size]["Z"] = next_chunk[:]["Z"]
+
+        return chunk_size
+
+    # Configure input array and handler during Pipeline initialization...
+    p = pdal.Pipeline(pipeline_json, arrays=[in_buffer], stream_handlers=[load_next_chunk])
+
+    # ...alternatively you can use the setter on an existing Pipeline
+    # p.inputs = [(in_buffer, load_next_chunk)]
+
+The following snippet provides a simple example of how to use a Numpy array as buffer to support writing through PDAL
+with total control over the maximum amount of memory to use.
+
+.. raw:: html
+
+   <details>
+   <summary>Example: Streaming the read and write of a very large LAZ file with low memory footprint</summary>
+
+.. code-block:: python
+
+    import numpy as np
+    import pdal
+
+    in_chunk_size = 10_000_000
+    in_pipeline = pdal.Reader.las(**{
+        "filename": "in_test.laz"
+    }).pipeline()
+
+    in_pipeline_it = in_pipeline.iterator(in_chunk_size).__iter__()
+
+    out_chunk_size = 50_000_000
+    out_file = "out_test.laz"
+    out_pipeline = pdal.Writer.las(
+        filename=out_file
+    ).pipeline()
+
+    out_buffer = np.zeros(in_chunk_size, dtype=[("X", float), ("Y", float), ("Z", float)])
+
+    def load_next_chunk():
+        try:
+            next_chunk = next(in_pipeline_it)
+        except StopIteration:
+            # Stops the streaming
+            return 0
+
+        chunk_size = next_chunk.size
+        out_buffer[:chunk_size]["X"] = next_chunk[:]["X"]
+        out_buffer[:chunk_size]["Y"] = next_chunk[:]["Y"]
+        out_buffer[:chunk_size]["Z"] = next_chunk[:]["Z"]
+
+        print(f"Loaded next chunk -> {chunk_size}")
+
+        return chunk_size
+
+    out_pipeline.inputs = [(out_buffer, load_next_chunk)]
+
+    out_pipeline.loglevel = 20 # INFO
+    count = out_pipeline.execute_streaming(out_chunk_size)
+
+    print(f"\nWROTE - {count}")
+
+.. raw:: html
+
+   </details>
+
 Executing Streamable Pipelines
 ................................................................................
 Streamable pipelines (pipelines that consist exclusively of streamable PDAL

src/pdal/PyArray.cpp

@@ -34,7 +34,6 @@
 
 #include "PyArray.hpp"
 #include <pdal/io/MemoryViewReader.hpp>
-#include <numpy/arrayobject.h>
 
 namespace pdal
 {
@@ -95,7 +94,8 @@ std::string pyObjectToString(PyObject *pname)
   #define PyDataType_NAMES(descr) ((descr)->names)
 #endif
 
-Array::Array(PyArrayObject* array) : m_array(array), m_rowMajor(true)
+Array::Array(PyArrayObject* array, std::shared_ptr<ArrayStreamHandler> stream_handler)
+    : m_array(array), m_rowMajor(true), m_stream_handler(std::move(stream_handler))
 {
     Py_XINCREF(array);
 
@@ -164,51 +164,93 @@ Array::~Array()
     Py_XDECREF(m_array);
 }
 
-
-ArrayIter& Array::iterator()
+std::shared_ptr<ArrayIter> Array::iterator()
 {
-    ArrayIter *it = new ArrayIter(m_array);
-    m_iterators.emplace_back((it));
-    return *it;
+    return std::make_shared<ArrayIter>(m_array, m_stream_handler);
 }
 
-
-ArrayIter::ArrayIter(PyArrayObject* np_array)
+ArrayIter::ArrayIter(PyArrayObject* np_array, std::shared_ptr<ArrayStreamHandler> stream_handler)
+    : m_stream_handler(std::move(stream_handler))
 {
+    // Create iterator
     m_iter = NpyIter_New(np_array,
-        NPY_ITER_EXTERNAL_LOOP | NPY_ITER_READONLY | NPY_ITER_REFS_OK,
-        NPY_KEEPORDER, NPY_NO_CASTING, NULL);
+                         NPY_ITER_EXTERNAL_LOOP | NPY_ITER_READONLY | NPY_ITER_REFS_OK,
+                         NPY_KEEPORDER, NPY_NO_CASTING, NULL);
     if (!m_iter)
         throw pdal_error("Unable to create numpy iterator.");
 
+    initIterator();
+}
+
+void ArrayIter::initIterator()
+{
+    // For a stream handler, first execute it to get the buffer populated and know the size of the data to iterate
+    int64_t stream_chunk_size = 0;
+    if (m_stream_handler) {
+        stream_chunk_size = (*m_stream_handler)();
+        if (!stream_chunk_size) {
+            m_done = true;
+            return;
+        }
+    }
+
+    // Initialize the iterator function
     char *itererr;
     m_iterNext = NpyIter_GetIterNext(m_iter, &itererr);
     if (!m_iterNext)
     {
         NpyIter_Deallocate(m_iter);
-        throw pdal_error(std::string("Unable to create numpy iterator: ") +
-            itererr);
+        m_iter = nullptr;
+        throw pdal_error(std::string("Unable to retrieve iteration function from numpy iterator: ") + itererr);
     }
     m_data = NpyIter_GetDataPtrArray(m_iter);
-    m_stride = NpyIter_GetInnerStrideArray(m_iter);
-    m_size = NpyIter_GetInnerLoopSizePtr(m_iter);
+    m_stride = *NpyIter_GetInnerStrideArray(m_iter);
+    m_size = *NpyIter_GetInnerLoopSizePtr(m_iter);
+    if (stream_chunk_size) {
+        // Ensure chunk size is valid and then limit iteration accordingly
+        if (0 < stream_chunk_size && stream_chunk_size <= m_size) {
+            m_size = stream_chunk_size;
+        } else {
+            throw pdal_error(std::string("Stream chunk size not in the range of array length: ") +
+                             std::to_string(stream_chunk_size));
+        }
+    }
     m_done = false;
 }
 
+void ArrayIter::resetIterator()
+{
+    // Reset the iterator to the initial state
+    if (NpyIter_Reset(m_iter, NULL) != NPY_SUCCEED) {
+        NpyIter_Deallocate(m_iter);
+        m_iter = nullptr;
+        throw pdal_error("Unable to reset numpy iterator.");
+    }
+
+    initIterator();
+}
+
 ArrayIter::~ArrayIter()
 {
-    NpyIter_Deallocate(m_iter);
+    if (m_iter != nullptr) {
+        NpyIter_Deallocate(m_iter);
+    }
 }
 
 ArrayIter& ArrayIter::operator++()
 {
     if (m_done)
         return *this;
 
-    if (--(*m_size))
-        *m_data += *m_stride;
-    else if (!m_iterNext(m_iter))
-        m_done = true;
+    if (--m_size) {
+        *m_data += m_stride;
+    } else if (!m_iterNext(m_iter)) {
+        if (m_stream_handler) {
+            resetIterator();
+        } else {
+            m_done = true;
+        }
+    }
     return *this;
 }
 

src/pdal/PyArray.hpp

@@ -58,14 +58,15 @@ namespace python
 
 class ArrayIter;
 
+using ArrayStreamHandler = std::function<int64_t()>;
 
 class PDAL_DLL Array
 {
 public:
     using Shape = std::array<size_t, 3>;
     using Fields = std::vector<MemoryViewReader::Field>;
 
-    Array(PyArrayObject* array);
+    Array(PyArrayObject* array, std::shared_ptr<ArrayStreamHandler> stream_handler = {});
     ~Array();
 
     Array(Array&& a) = default;
@@ -77,14 +78,14 @@ class PDAL_DLL Array
     bool rowMajor() const { return m_rowMajor; };
     Shape shape() const { return m_shape; }
     const Fields& fields() const { return m_fields; };
-    ArrayIter& iterator();
+    std::shared_ptr<ArrayIter> iterator();
 
 private:
     PyArrayObject* m_array;
     Fields m_fields;
     bool m_rowMajor;
     Shape m_shape {};
-    std::vector<std::unique_ptr<ArrayIter>> m_iterators;
+    std::shared_ptr<ArrayStreamHandler> m_stream_handler;
 };
 
 
@@ -94,20 +95,24 @@ class PDAL_DLL ArrayIter
     ArrayIter(const ArrayIter&) = delete;
     ArrayIter() = delete;
 
-    ArrayIter(PyArrayObject*);
+    ArrayIter(PyArrayObject*, std::shared_ptr<ArrayStreamHandler>);
     ~ArrayIter();
 
     ArrayIter& operator++();
     operator bool () const { return !m_done; }
     char* operator*() const { return *m_data; }
 
 private:
-    NpyIter *m_iter;
+    NpyIter *m_iter = nullptr;
     NpyIter_IterNextFunc *m_iterNext;
     char **m_data;
-    npy_intp *m_size;
-    npy_intp *m_stride;
+    npy_intp m_size;
+    npy_intp m_stride;
     bool m_done;
+
+    std::shared_ptr<ArrayStreamHandler> m_stream_handler;
+    void initIterator();
+    void resetIterator();
 };
 
 } // namespace python

src/pdal/PyPipeline.cpp

@@ -245,14 +245,20 @@ void PipelineExecutor::addArrayReaders(std::vector<std::shared_ptr<Array>> array
         for (auto f : array->fields())
             r.pushField(f);
 
-        ArrayIter& iter = array->iterator();
-        auto incrementer = [&iter](PointId id) -> char *
+        auto arrayIter = array->iterator();
+        auto incrementer = [arrayIter, firstPoint = true](PointId id) mutable -> char *
         {
-            if (! iter)
+            ArrayIter& iter = *arrayIter;
+            if (!firstPoint && iter) {
+                ++iter;
+            } else {
+                firstPoint = false;
+            }
+
+            if (!iter)
                 return nullptr;
 
             char *c = *iter;
-            ++iter;
             return c;
         };
 

src/pdal/libpdalpython.cpp

@@ -1,6 +1,7 @@
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
 #include <pybind11/numpy.h>
+#include <pybind11/functional.h>
 #include <pybind11/stl/filesystem.h>
 #include <iostream>
 
@@ -189,11 +190,22 @@ namespace pdal {
             ));
         }
 
-        void setInputs(std::vector<py::array> ndarrays) {
+        void setInputs(const std::vector<py::object>& inputs) {
             _inputs.clear();
-            for (const auto& ndarray: ndarrays) {
-                PyArrayObject* ndarray_ptr = (PyArrayObject*)ndarray.ptr();
-                _inputs.push_back(std::make_shared<pdal::python::Array>(ndarray_ptr));
+            for (const auto& input_obj: inputs) {
+                if (py::isinstance<py::array>(input_obj)) {
+                    // Backward compatibility for accepting list of numpy arrays
+                    auto ndarray = input_obj.cast<py::array>();
+                    _inputs.push_back(std::make_shared<pdal::python::Array>((PyArrayObject*)ndarray.ptr()));
+                } else {
+                    // Now expected to be a list of pairs: (numpy array, <optional> stream handler)
+                    auto input = input_obj.cast<std::pair<py::array, pdal::python::ArrayStreamHandler>>();
+                    _inputs.push_back(std::make_shared<pdal::python::Array>(
+                            (PyArrayObject*)input.first.ptr(),
+                            input.second ?
+                                std::make_shared<pdal::python::ArrayStreamHandler>(input.second)
+                                : nullptr));
+                }
             }
             delExecutor();
         }

src/pdal/pipeline.py

@@ -2,7 +2,7 @@
 
 import json
 import logging
-from typing import Any, Container, Dict, Iterator, List, Optional, Sequence, Union, cast
+from typing import Any, Container, Dict, Iterator, List, Optional, Sequence, Union, cast, Callable
 
 import numpy as np
 import pathlib
@@ -41,6 +41,7 @@ def __init__(
         loglevel: int = logging.ERROR,
         json: Optional[str] = None,
         dataframes: Sequence[DataFrame] = (),
+        stream_handlers: Sequence[Callable[[], int]] = (),
     ):
 
         if json:
@@ -58,7 +59,14 @@ def __init__(
             stages = _parse_stages(spec) if isinstance(spec, str) else spec
             for stage in stages:
                 self |= stage
-        self.inputs = arrays
+
+        if stream_handlers:
+            if len(stream_handlers) != len(arrays):
+                raise RuntimeError("stream_handlers must match the number of specified input arrays / dataframes")
+            self.inputs = [(a, h) for a, h in zip(arrays, stream_handlers)]
+        else:
+            self.inputs = [(a, None) for a in arrays]
+
         self.loglevel = loglevel
 
     def __getstate__(self):