wolfhece.lazviewer.laz_viewer

Module Contents

class wolfhece.lazviewer.laz_viewer.Colors_Lazviewer[source]

Bases: enum.Enum

Inheritance diagram of wolfhece.lazviewer.laz_viewer.Colors_Lazviewer

Generic enumeration.

Derive from this class to define new enumerations.

ORTHO_2012_2013 = 2013[source]
ORTHO_2015 = 2015[source]
ORTHO_2021 = 2021[source]
ORTHO_2023 = 2023[source]
ORTHO_2006_2007 = 2006[source]
CODE_2013 = 0[source]
CODE_2023 = 2[source]
FROM_FILE = 1[source]
class wolfhece.lazviewer.laz_viewer.Classification_LAZ[source]
test_wx()[source]
init_2013()[source]
init_2023()[source]
callback_colors()[source]

Update from wx GUI

callback_destroy()[source]
interactive_update_colors()[source]

set GUI

wolfhece.lazviewer.laz_viewer.choices_laz_colormap() → list[str][source]
class wolfhece.lazviewer.laz_viewer.xyz_laz(fn: str = '', format: Literal[las, numpy] = 'las', to_read: bool = True)[source]

Classe de gestion des fichiers XYZ+Class issus d’un gros fichier laz

property size[source]
split(dir_out: str, nbparts: int)[source]

Split file into ‘nb’ parts along X and Y

get_bounds()[source]
test_bounds(bounds: list[list[float, float], list[float, float]])[source]
read_bin_xyz()[source]

Lecture d’un fichier binaire de points XYZ+classification généré par la fonction sort_grid_np Le format est une succession de trame binaire de la forme :

nbpoints (np.int32) X[nbpoints] (np.float32) ou (np.float64) Y[nbpoints] (np.float32) ou (np.float64) Z[nbpoints] (np.float32) Classif[nbpoints] (np.int8)

Il est possible de récupérer une matrice numpy shape(nbtot,4) ou un objet laspy via l’argument ‘out’ (par défaut à ‘las’)

to_las()[source]
class wolfhece.lazviewer.laz_viewer.xyz_laz_grid(mydir: str)[source]

Gestion d’un grid de données LAZ

_read_gridinfo(gridinfo: str) → list[source]
scan(bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])[source]

Find all points in bounds

_split_xyz(dirout: str, nbparts: int = 10)[source]

Split XYZ file into ‘nb’ parts along X and Y

_sort_grid_np(fn_in: str, fn_out: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]], gridsize: list[int], chunksize: int = 5000000, force_format=np.float64)[source]

Create .bin files from .laz

class wolfhece.lazviewer.laz_viewer.xyz_laz_grids(dir_grids: str, create: bool = False)[source]

Ensemble de grids

scan(bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]]) → numpy.ndarray[source]

Scan all LAZ to find used data

Parameters:

bounds (Union[tuple[tuple[float,float],tuple[float,float]], list[list[float, float],list[float, float]]]) – [[xmin,xmax], [ymin,ymax]]

Returns:

np.ndarray

read_dir(dir_grids)[source]
scan_around(xy: shapely.geometry.LineString | list[list[float], list[float]], length_buffer=5.0)[source]

Récupération de points LAZ autour de la section

plot_laz(xy: shapely.geometry.LineString | list[list[float], list[float]], length_buffer=5.0, figax: tuple[matplotlib.figure.Figure, matplotlib.axes.Axes] = None, show=False)[source]

Dessin des points LAZ sur un graphique Matplotlib

create_from_laz(dir_laz: str, shape: str, ds: float = 50, force_format=np.float64)[source]
wolfhece.lazviewer.laz_viewer.find_pointsXYZ(xyz: numpy.ndarray, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]]) → numpy.ndarray[source]
wolfhece.lazviewer.laz_viewer.find_points(las: laspy.LasData, xb: list[float, float], yb: list[float, float]) → laspy.LasData[source]

Get arrays which indicate invalid X, Y, or Z values

wolfhece.lazviewer.laz_viewer.read_laz(fn: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]] = None) → numpy.ndarray | laspy.LasData[source]

Lecture d’un fichier LAZ, LAS ou NPZ

wolfhece.lazviewer.laz_viewer.xyzlaz_scandir(mydir: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])[source]

Scan for XYZ files

wolfhece.lazviewer.laz_viewer.laz_scandir(mydir: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]]) → list[laspy.LasData][source]

Scan directory and treat .laz files

wolfhece.lazviewer.laz_viewer.clip_data_xyz(dir_in: str, fn_out: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]])[source]

Get data and write zip numpy file

wolfhece.lazviewer.laz_viewer.clip_data_laz(fn_in: str, fn_out: str, bounds: tuple[tuple[float, float], tuple[float, float]] | list[list[float, float], list[float, float]], chunksize: int = 5000000)[source]
wolfhece.lazviewer.laz_viewer.get_concat_h(im1: PIL.Image.Image, im2: PIL.Image.Image)[source]

Concatenate 2 images horizontally

wolfhece.lazviewer.laz_viewer.get_concat_v(im1: PIL.Image.Image, im2: PIL.Image.Image)[source]

Concatenate 2 images vertically

wolfhece.lazviewer.laz_viewer.get_Orthos_Walonmap(bounds, fn, cat='IMAGERIE/ORTHO_2012_2013', size=3000)[source]

Récupération des orthos depuis Walonmap fn = filename sans extension –> .png sera ajouté automatiquement

catégories possibles :

  • ‘IMAGERIE/ORTHO_2012_2013’

  • ‘IMAGERIE/ORTHO_2015’

  • ‘IMAGERIE/ORTHO_2021’

  • ‘IMAGERIE/ORTHO_2006_2007’

wolfhece.lazviewer.laz_viewer.get_colors(las: laspy.LasData, which_colors: Colors_Lazviewer, imsize=2000, fname='', palette_classif: Classification_LAZ = None)[source]
wolfhece.lazviewer.laz_viewer.myviewer(las: numpy.ndarray | list[laspy.LasData] | laspy.LasData, which_colors: Colors_Lazviewer, fname='', palette_classif: Classification_LAZ = None)[source]

Get viewer for las data

wolfhece.lazviewer.laz_viewer.grids[source]