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Pipelines are the operative construct in PDAL, and it is how data are modeled from reading, processing, and writing. PDAL internally constructs a pipeline to perform data translation operations using Lancer Patentleather Pointtoe Flats White Jimmy Choo London GCKOHUDM
, for example. While specific Adidas Originals Tubular Doom Sock Primeknit sneakers Black adidas sfybeQf
are useful in many contexts, a pipeline provides useful advantages for more complex things:

Note

pipeline is used to invoke pipeline operations via the command line.

Warning

As of PDAL 1.2, is the preferred specification language for PDAL pipelines. XML was dropped at the 1.6 release.

## Introduction note print sneakers Green Maison Martin Margiela 4m0Uu5d3k4

A JSON object represents a PDAL processing pipeline. The structure is always a JSON object, with the primary object called  pipeline  being an array of inferred or explicit PDAL Stage Objects representations.

A simple PDAL pipeline, inferring the appropriate drivers for the reader and writer from filenames, and able to be specified as a set of sequential steps:

A simple pipeline to convert LAS to BPF while only keeping points inside the box [ 0 x 100 , 0 y 100 ] .

A more complex PDAL pipeline reprojects the stage tagged  A1  , merges the result with  B  , and writes the merged output to a GeoTIFF file with the White HighTop Sneakers Maison Kitsun�� WxuZXPQ
writer:

A more complex pipeline that merges two inputs together but uses Air Footscape Magista Flyknit sneakers Black Nike PCs0CDPg
to transform the coordinate system of file from to X Officine Creative Freki gladiator sandals Black Damir Doma jbYZKqAF
.

## Processing Modes ¶

PDAL process data in one of two ways: standard mode or stream mode. With standard mode, all input is read into memory before it is processed. Many algorithms require standard mode processing because they need access to all points. Operations that do sorting or require neighbors of points, for example, require access to all points.

For operations that don’t require access to all points, PDAL provides stream mode. Stream mode processes points through a pipeline in chunks, which reduces memory requirements.

When using pdal translate or pdal pipeline PDAL uses stream mode if possible. If stream mode can’t use used the applications fall back to standard mode processing. Users can explicitly choose to use standard mode by using the option.

Users of the PDAL API can explicitly control the selection of the PDAL processing mode.

PDAL JSON pipelines always consist of a single object. This object (referred to as the PDAL JSON object below) represents a processing pipeline.

Besides, Poisson approximation is also considered for a wide class of discrete random variables via operator method and method of probability distance [see Hung and Thao ( ) and Hung and Giang ( ), for more details].

The main purpose of this paper is to use the Stein–Chen method for providing the bounds of Le Cam-type inequality ( ) and ( ) in Poisson approximation for row-wise arrays of independent negative-binomial distributed random variables. The results obtained in this paper are extensions and generalizations of some results in Teerapabolarn and Wongkasem ( ), Teerapabolarn ( , ).

During the last several decades the Stein–Chen method has risen to become one of the most important tools available for studying in Poisson approximation problems. The Stein–Chen method has been dealt with in detail in many articles [the reader is referred to Stein ( ), Chen ( ), Chen and Röllin ( ), Barbour etal. ( ) and Barbour and Chen ( ) for fuller development]. The Stein–Chen method can be summarized as follows:

\begin{aligned} \Delta = \mathop {\sup }\limits _x \left| {F_{X}\left( A \right) - P_{\alpha _n} \left( A \right) } \right| \end{aligned}
\begin{aligned} {\alpha _n} f(x+1)-xf(x)=h(x)-P_{\alpha _n} h. \end{aligned}
\begin{aligned} h(x)=h_{A}(x)= {\left\{ \begin{array}{ll} 1, \quad \text {if}\; x\in A,\\ 0, \quad \text {if}\; x\notin A. \end{array}\right. } \end{aligned}
\begin{aligned} F_{X}\left( A \right) - P_{\alpha _n} \left( A \right) = E\left[ {{\alpha _n} f\left( X+1 \right) - X f\left( X \right) } \right] . \end{aligned}
\begin{aligned} \left| {E{\alpha _n} f\left( X+1 \right) - E X f\left( X \right) } \right| . \end{aligned}

Before starting the main results in the next section we first recall the following remarkable lemmas:

Lemma 1
\begin{aligned} \mathop {sup}\limits _{w \ge k} \left| {V{f_A}\left( w \right) } \right| \le \min \left\{ {{{\alpha _n} ^{ - 1}}\left( {1 - {e^{ - {\alpha _n} }}} \right) ,\frac{1}{k}} \right\} . \end{aligned}
Lemma 2
\begin{aligned} \mathop {\sup }\limits _{w \ge k} \left| {V{f_{{C_{{w_0}}}}}\left( w \right) } \right| \le {{\gamma _n} ^{ - 1}}\left( {{e^{\gamma _n} } - 1} \right) \min \left\{ {\frac{1}{{{w_0} + 1}},\frac{1}{k}} \right\} . \end{aligned}
Lemma 3
\begin{aligned} 0 < \mathop {\sup }\limits _{w \ge k} f\left( w \right) \le {{\bar{{\gamma _n}}} ^{ - 1}}\left( {{e^{\bar{{\gamma _n}}} } - 1} \right) \min \left\{ {\frac{1}{k},\frac{1}{{{w_0} + 1}}} \right\} . \end{aligned}
Lemma 4

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