code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def Resolution(self, zoom):
'''Resolution (arc/pixel) for given zoom level (measured at Equator)'''
return self.resFact / 2 ** zoom
# return 180 / float( 1 << (8+zoom) ) | Resolution (arc/pixel) for given zoom level (measured at Equator) | Resolution | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def ZoomForPixelSize(self, pixelSize):
'''Maximal scaledown zoom of the pyramid closest to the pixelSize.'''
for i in range(MAXZOOMLEVEL):
if pixelSize > self.Resolution(i):
if i != 0:
return i - 1
else:
return 0 # We ... | Maximal scaledown zoom of the pyramid closest to the pixelSize. | ZoomForPixelSize | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def TileBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile'''
res = self.resFact / 2 ** zoom
return (tx * self.tileSize * res - 180, ty * self.tileSize
* res - 90, (tx + 1) * self.tileSize * res - 180, (ty
+... | Returns bounds of the given tile | TileBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def TileLatLonBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile in the SWNE form'''
b = self.TileBounds(tx, ty, zoom)
return (b[1], b[0], b[3], b[2]) | Returns bounds of the given tile in the SWNE form | TileLatLonBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def __init__(
self,
width,
height,
tilesize=256,
tileformat='jpg',
):
"""Initialization of the Zoomify tile tree"""
self.tilesize = tilesize
self.tileformat = tileformat
imagesize = (width, height)
tiles = (math.ceil(width / tilesi... | Initialization of the Zoomify tile tree | __init__ | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def tilefilename(
self,
x,
y,
z,
):
"""Returns filename for tile with given coordinates"""
tileIndex = x + y * self.tierSizeInTiles[z][0] \
+ self.tileCountUpToTier[z]
return os.path.join('TileGroup%.0f' % math.floor(tileIndex
... | Returns filename for tile with given coordinates | tilefilename | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def process(self):
"""The main processing function, runs all the main steps of processing"""
# Opening and preprocessing of the input file
self.open_input()
# Generation of main metadata files and HTML viewers
self.generate_metadata()
# Generation of the lowest tiles... | The main processing function, runs all the main steps of processing | process | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def error(self, msg, details=''):
"""Print an error message and stop the processing"""
if details:
self.parser.error(msg + '''
''' + details)
else:
self.parser.error(msg) | Print an error message and stop the processing | error | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def optparse_init(self):
"""Prepare the option parser for input (argv)"""
from optparse import OptionParser, OptionGroup
usage = 'Usage: %prog [options] input_file(s) [output]'
p = OptionParser(usage, version='%prog ' + __version__)
p.add_option(
'-p',
'-... | Prepare the option parser for input (argv) | optparse_init | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def open_input(self):
"""Initialization of the input raster, reprojection if necessary"""
gdal.UseExceptions()
gdal.AllRegister()
if not self.options.verbose:
gdal.PushErrorHandler('CPLQuietErrorHandler')
# Initialize necessary GDAL drivers
self.out_drv = g... | Initialization of the input raster, reprojection if necessary | open_input | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_metadata(self):
"""Generation of main metadata files and HTML viewers (metadata related to particular tiles are generated during the tile processing)."""
if not os.path.exists(self.output):
os.makedirs(self.output)
if self.options.profile == 'mercator':
(s... | Generation of main metadata files and HTML viewers (metadata related to particular tiles are generated during the tile processing). | generate_metadata | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_base_tiles(self, cpu):
"""Generation of the base tiles (the lowest in the pyramid) directly from the input raster"""
if self.options.verbose:
# mx, my = self.out_gt[0], self.out_gt[3] # OriginX, OriginY
# px, py = self.mercator.MetersToPixels( mx, my, self.tmaxz)
... | Generation of the base tiles (the lowest in the pyramid) directly from the input raster | generate_base_tiles | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_overview_tiles(self, cpu, tz):
"""Generation of the overview tiles (higher in the pyramid) based on existing tiles"""
tilebands = self.dataBandsCount + 1
# Usage of existing tiles: from 4 underlying tiles generate one as overview.
tcount = 0
for z in range(self.tm... | Generation of the overview tiles (higher in the pyramid) based on existing tiles | generate_overview_tiles | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def geo_query(
self,
ds,
ulx,
uly,
lrx,
lry,
querysize=0,
):
"""For given dataset and query in cartographic coordinates
returns parameters for ReadRaster() in raster coordinates and
x/y shifts (for border tiles). If the querysize is... | For given dataset and query in cartographic coordinates
returns parameters for ReadRaster() in raster coordinates and
x/y shifts (for border tiles). If the querysize is not given, the
extent is returned in the native resolution of dataset ds. | geo_query | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def scale_query_to_tile(
self,
dsquery,
dstile,
tilefilename='',
):
"""Scales down query dataset to the tile dataset"""
querysize = dsquery.RasterXSize
tilesize = dstile.RasterXSize
tilebands = dstile.RasterCount
if self.options.resamplin... | Scales down query dataset to the tile dataset | scale_query_to_tile | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_tilemapresource(self):
"""
Template for tilemapresource.xml. Returns filled string. Expected variables:
title, north, south, east, west, isepsg4326, projection, publishurl,
zoompixels, tilesize, tileformat, profile
"""
args = {}
args['title'] = s... |
Template for tilemapresource.xml. Returns filled string. Expected variables:
title, north, south, east, west, isepsg4326, projection, publishurl,
zoompixels, tilesize, tileformat, profile
| generate_tilemapresource | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_kml(
self,
tx,
ty,
tz,
children=[],
**args
):
"""
Template for the KML. Returns filled string.
"""
(args['tx'], args['ty'], args['tz']) = (tx, ty, tz)
args['tileformat'] = self.tileext
if 'tilesize' not... |
Template for the KML. Returns filled string.
| generate_kml | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_googlemaps(self):
"""
Template for googlemaps.html implementing Overlay of tiles for 'mercator' profile.
It returns filled string. Expected variables:
title, googlemapskey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
"""
args... |
Template for googlemaps.html implementing Overlay of tiles for 'mercator' profile.
It returns filled string. Expected variables:
title, googlemapskey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
| generate_googlemaps | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_openlayers(self):
"""
Template for openlayers.html implementing overlay of available Spherical Mercator layers.
It returns filled string. Expected variables:
title, bingkey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
"""
ar... |
Template for openlayers.html implementing overlay of available Spherical Mercator layers.
It returns filled string. Expected variables:
title, bingkey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
| generate_openlayers | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def __init__(self, tileSize=256):
'''Initialize the TMS Global Mercator pyramid'''
self.tileSize = tileSize
self.initialResolution = 2 * math.pi * 6378137 / self.tileSize
# 156543.03392804062 for tileSize 256 pixels
self.originShift = 2 * math.pi * 6378137 / 2.0
# 200... | Initialize the TMS Global Mercator pyramid | __init__ | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def LatLonToMeters(self, lat, lon):
'''Converts given lat/lon in WGS84 Datum to XY in Spherical Mercator EPSG:900913'''
mx = lon * self.originShift / 180.0
my = math.log(math.tan((90 + lat) * math.pi / 360.0)) \
/ (math.pi / 180.0)
my = my * self.originShift / 180.0
... | Converts given lat/lon in WGS84 Datum to XY in Spherical Mercator EPSG:900913 | LatLonToMeters | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def MetersToLatLon(self, mx, my):
'''Converts XY point from Spherical Mercator EPSG:900913 to lat/lon in WGS84 Datum'''
lon = mx / self.originShift * 180.0
lat = my / self.originShift * 180.0
lat = 180 / math.pi * (2 * math.atan(math.exp(lat * math.pi
/ 1... | Converts XY point from Spherical Mercator EPSG:900913 to lat/lon in WGS84 Datum | MetersToLatLon | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def PixelsToMeters(
self,
px,
py,
zoom,
):
'''Converts pixel coordinates in given zoom level of pyramid to EPSG:900913'''
res = self.Resolution(zoom)
mx = px * res - self.originShift
my = py * res - self.originShift
return (mx, my) | Converts pixel coordinates in given zoom level of pyramid to EPSG:900913 | PixelsToMeters | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def MetersToPixels(
self,
mx,
my,
zoom,
):
'''Converts EPSG:900913 to pyramid pixel coordinates in given zoom level'''
res = self.Resolution(zoom)
px = (mx + self.originShift) / res
py = (my + self.originShift) / res
return (px, py) | Converts EPSG:900913 to pyramid pixel coordinates in given zoom level | MetersToPixels | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def PixelsToTile(self, px, py):
'''Returns a tile covering region in given pixel coordinates'''
tx = int(math.ceil(px / float(self.tileSize)) - 1)
ty = int(math.ceil(py / float(self.tileSize)) - 1)
return (tx, ty) | Returns a tile covering region in given pixel coordinates | PixelsToTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def PixelsToRaster(
self,
px,
py,
zoom,
):
'''Move the origin of pixel coordinates to top-left corner'''
mapSize = self.tileSize << zoom
return (px, mapSize - py) | Move the origin of pixel coordinates to top-left corner | PixelsToRaster | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def MetersToTile(
self,
mx,
my,
zoom,
):
'''Returns tile for given mercator coordinates'''
(px, py) = self.MetersToPixels(mx, my, zoom)
return self.PixelsToTile(px, py) | Returns tile for given mercator coordinates | MetersToTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def TileBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile in EPSG:900913 coordinates'''
(minx, miny) = self.PixelsToMeters(tx * self.tileSize, ty
* self.tileSize, zoom)
(maxx, maxy) = self.PixelsToMeters((tx + 1) * self.ti... | Returns bounds of the given tile in EPSG:900913 coordinates | TileBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def TileLatLonBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile in latutude/longitude using WGS84 datum'''
bounds = self.TileBounds(tx, ty, zoom)
(minLat, minLon) = self.MetersToLatLon(bounds[0], bounds[1])
(maxLat, maxLon) = self... | Returns bounds of the given tile in latutude/longitude using WGS84 datum | TileLatLonBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def Resolution(self, zoom):
'''Resolution (meters/pixel) for given zoom level (measured at Equator)'''
# return (2 * math.pi * 6378137) / (self.tileSize * 2**zoom)
return self.initialResolution / 2 ** zoom | Resolution (meters/pixel) for given zoom level (measured at Equator) | Resolution | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def ZoomForPixelSize(self, pixelSize):
'''Maximal scaledown zoom of the pyramid closest to the pixelSize.'''
for i in range(MAXZOOMLEVEL):
if pixelSize > self.Resolution(i):
if i != 0:
return i - 1
else:
return 0 # We ... | Maximal scaledown zoom of the pyramid closest to the pixelSize. | ZoomForPixelSize | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def GoogleTile(
self,
tx,
ty,
zoom,
):
'''Converts TMS tile coordinates to Google Tile coordinates'''
# coordinate origin is moved from bottom-left to top-left corner of the extent
return (tx, 2 ** zoom - 1 - ty) | Converts TMS tile coordinates to Google Tile coordinates | GoogleTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def QuadTree(
self,
tx,
ty,
zoom,
):
'''Converts TMS tile coordinates to Microsoft QuadTree'''
quadKey = ''
ty = 2 ** zoom - 1 - ty
for i in range(zoom, 0, -1):
digit = 0
mask = 1 << i - 1
if tx & mask != 0:
... | Converts TMS tile coordinates to Microsoft QuadTree | QuadTree | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def LonLatToPixels(
self,
lon,
lat,
zoom,
):
'''Converts lon/lat to pixel coordinates in given zoom of the EPSG:4326 pyramid'''
res = self.resFact / 2 ** zoom
px = (180 + lon) / res
py = (90 + lat) / res
return (px, py) | Converts lon/lat to pixel coordinates in given zoom of the EPSG:4326 pyramid | LonLatToPixels | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def PixelsToTile(self, px, py):
'''Returns coordinates of the tile covering region in pixel coordinates'''
tx = int(math.ceil(px / float(self.tileSize)) - 1)
ty = int(math.ceil(py / float(self.tileSize)) - 1)
return (tx, ty) | Returns coordinates of the tile covering region in pixel coordinates | PixelsToTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def LonLatToTile(
self,
lon,
lat,
zoom,
):
'''Returns the tile for zoom which covers given lon/lat coordinates'''
(px, py) = self.LonLatToPixels(lon, lat, zoom)
return self.PixelsToTile(px, py) | Returns the tile for zoom which covers given lon/lat coordinates | LonLatToTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def Resolution(self, zoom):
'''Resolution (arc/pixel) for given zoom level (measured at Equator)'''
return self.resFact / 2 ** zoom
# return 180 / float( 1 << (8+zoom) ) | Resolution (arc/pixel) for given zoom level (measured at Equator) | Resolution | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def ZoomForPixelSize(self, pixelSize):
'''Maximal scaledown zoom of the pyramid closest to the pixelSize.'''
for i in range(MAXZOOMLEVEL):
if pixelSize > self.Resolution(i):
if i != 0:
return i - 1
else:
return 0 # We ... | Maximal scaledown zoom of the pyramid closest to the pixelSize. | ZoomForPixelSize | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def TileBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile'''
res = self.resFact / 2 ** zoom
return (tx * self.tileSize * res - 180, ty * self.tileSize
* res - 90, (tx + 1) * self.tileSize * res - 180, (ty
+... | Returns bounds of the given tile | TileBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def TileLatLonBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile in the SWNE form'''
b = self.TileBounds(tx, ty, zoom)
return (b[1], b[0], b[3], b[2]) | Returns bounds of the given tile in the SWNE form | TileLatLonBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def __init__(
self,
width,
height,
tilesize=256,
tileformat='jpg',
):
"""Initialization of the Zoomify tile tree"""
self.tilesize = tilesize
self.tileformat = tileformat
imagesize = (width, height)
tiles = (math.ceil(width / tilesi... | Initialization of the Zoomify tile tree | __init__ | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def tilefilename(
self,
x,
y,
z,
):
"""Returns filename for tile with given coordinates"""
tileIndex = x + y * self.tierSizeInTiles[z][0] \
+ self.tileCountUpToTier[z]
return os.path.join('TileGroup%.0f' % math.floor(tileIndex
... | Returns filename for tile with given coordinates | tilefilename | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def process(self):
"""The main processing function, runs all the main steps of processing"""
# Opening and preprocessing of the input file
self.open_input()
# Generation of main metadata files and HTML viewers
self.generate_metadata()
# Generation of the lowest tiles... | The main processing function, runs all the main steps of processing | process | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def error(self, msg, details=''):
"""Print an error message and stop the processing"""
if details:
self.parser.error(msg + '''
''' + details)
else:
self.parser.error(msg) | Print an error message and stop the processing | error | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def optparse_init(self):
"""Prepare the option parser for input (argv)"""
from optparse import OptionParser, OptionGroup
usage = 'Usage: %prog [options] input_file(s) [output]'
p = OptionParser(usage, version='%prog ' + __version__)
p.add_option(
'-p',
'-... | Prepare the option parser for input (argv) | optparse_init | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def open_input(self):
"""Initialization of the input raster, reprojection if necessary"""
gdal.AllRegister()
# Initialize necessary GDAL drivers
self.out_drv = gdal.GetDriverByName(self.tiledriver)
self.mem_drv = gdal.GetDriverByName('MEM')
if not self.out_drv:
... | Initialization of the input raster, reprojection if necessary | open_input | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def generate_metadata(self):
"""Generation of main metadata files and HTML viewers (metadata related to particular tiles are generated during the tile processing)."""
if not os.path.exists(self.output):
os.makedirs(self.output)
if self.options.profile == 'mercator':
(s... | Generation of main metadata files and HTML viewers (metadata related to particular tiles are generated during the tile processing). | generate_metadata | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def generate_base_tiles(self):
"""Generation of the base tiles (the lowest in the pyramid) directly from the input raster"""
print('Generating Base Tiles:')
if self.options.verbose:
# mx, my = self.out_gt[0], self.out_gt[3] # OriginX, OriginY
# px, py = self.mercator.M... | Generation of the base tiles (the lowest in the pyramid) directly from the input raster | generate_base_tiles | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def generate_overview_tiles(self):
"""Generation of the overview tiles (higher in the pyramid) based on existing tiles"""
print('Generating Overview Tiles:')
tilebands = self.dataBandsCount + 1
# Usage of existing tiles: from 4 underlying tiles generate one as overview.
tcoun... | Generation of the overview tiles (higher in the pyramid) based on existing tiles | generate_overview_tiles | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def geo_query(
self,
ds,
ulx,
uly,
lrx,
lry,
querysize=0,
):
"""For given dataset and query in cartographic coordinates
returns parameters for ReadRaster() in raster coordinates and
x/y shifts (for border tiles). If the querysize is... | For given dataset and query in cartographic coordinates
returns parameters for ReadRaster() in raster coordinates and
x/y shifts (for border tiles). If the querysize is not given, the
extent is returned in the native resolution of dataset ds. | geo_query | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def scale_query_to_tile(
self,
dsquery,
dstile,
tilefilename='',
):
"""Scales down query dataset to the tile dataset"""
querysize = dsquery.RasterXSize
tilesize = dstile.RasterXSize
tilebands = dstile.RasterCount
if self.options.resamplin... | Scales down query dataset to the tile dataset | scale_query_to_tile | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def generate_tilemapresource(self):
"""
Template for tilemapresource.xml. Returns filled string. Expected variables:
title, north, south, east, west, isepsg4326, projection, publishurl,
zoompixels, tilesize, tileformat, profile
"""
args = {}
args['title'] = s... |
Template for tilemapresource.xml. Returns filled string. Expected variables:
title, north, south, east, west, isepsg4326, projection, publishurl,
zoompixels, tilesize, tileformat, profile
| generate_tilemapresource | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def generate_kml(
self,
tx,
ty,
tz,
children=[],
**args
):
"""
Template for the KML. Returns filled string.
"""
(args['tx'], args['ty'], args['tz']) = (tx, ty, tz)
args['tileformat'] = self.tileext
if 'tilesize' not... |
Template for the KML. Returns filled string.
| generate_kml | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def generate_googlemaps(self):
"""
Template for googlemaps.html implementing Overlay of tiles for 'mercator' profile.
It returns filled string. Expected variables:
title, googlemapskey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
"""
args... |
Template for googlemaps.html implementing Overlay of tiles for 'mercator' profile.
It returns filled string. Expected variables:
title, googlemapskey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
| generate_googlemaps | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def generate_openlayers(self):
"""
Template for openlayers.html implementing overlay of available Spherical Mercator layers.
It returns filled string. Expected variables:
title, bingkey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
"""
ar... |
Template for openlayers.html implementing overlay of available Spherical Mercator layers.
It returns filled string. Expected variables:
title, bingkey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
| generate_openlayers | python | commenthol/gdal2tiles-leaflet | gdal2tiles.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles.py | MIT |
def build_dataset(tokenizer, config):
'''
We assume that we have preprocessed the dataset appropriately such that the sample is organized as follows:
{"positive": prompt + answer_positive, "negative": prompt + answer_negative}, where the positive response is preferred.
'''
def tokenize(sample):
... |
We assume that we have preprocessed the dataset appropriately such that the sample is organized as follows:
{"positive": prompt + answer_positive, "negative": prompt + answer_negative}, where the positive response is preferred.
| build_dataset | python | OptimalScale/LMFlow | contrib/rlhflow/reward_modeling.py | https://github.com/OptimalScale/LMFlow/blob/master/contrib/rlhflow/reward_modeling.py | Apache-2.0 |
def tokenize(
self,
dataset,
add_special_tokens=True,
*args,
**kwargs
) -> Dataset:
"""
Tokenize the full dataset.
Parameters
------------
dataset : lmflow.datasets.Dataset.
args : Optional.
Positional argu... |
Tokenize the full dataset.
Parameters
------------
dataset : lmflow.datasets.Dataset.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
------------
tokenized_d... | tokenize | python | OptimalScale/LMFlow | contrib/tool-finetune/function_call_finetune.py | https://github.com/OptimalScale/LMFlow/blob/master/contrib/tool-finetune/function_call_finetune.py | Apache-2.0 |
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA ra... |
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
| update_ema | python | OptimalScale/LMFlow | experimental/LISA-diffusion/diffusion_dpo/train_diffusion_dpo_lisa.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/diffusion_dpo/train_diffusion_dpo_lisa.py | Apache-2.0 |
def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
"""Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
lower case (Default va... | Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
lower case (Default value = True)
| group_by_keys_nothrow | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | Apache-2.0 |
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (... |
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (`int`, *optional*, defaults to 512):
dimension of the embeddings to ... | guidance_scale_embedding | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | Apache-2.0 |
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
return x[(...,) + (None,... | Appends dimensions to the end of a tensor until it has target_dims dimensions. | append_dims | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | Apache-2.0 |
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA ra... |
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
| update_ema | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | Apache-2.0 |
def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
"""Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
lower case (Default va... | Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
lower case (Default value = True)
| group_by_keys_nothrow | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py | Apache-2.0 |
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (... |
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (`int`, *optional*, defaults to 512):
dimension of the embeddings to ... | guidance_scale_embedding | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py | Apache-2.0 |
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
return x[(...,) + (None,... | Appends dimensions to the end of a tensor until it has target_dims dimensions. | append_dims | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py | Apache-2.0 |
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA ra... |
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
| update_ema | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/add_end_mark.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/add_end_mark.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/add_prompt.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/add_prompt.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/concat.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/concat.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/concat_shuffle_split.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/concat_shuffle_split.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/count.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/count.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/merge.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/merge.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/raw2textonly.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/raw2textonly.py | Apache-2.0 |
def raw2textonly(fin):
"""
Converts raw text to text-only format.
Args:
fin: the input file description of the raw text file.
Returns:
a dict with "text-only" format.
"""
data_dict = {
"type": "text_only",
"instances": [ { "text": line.strip() } for line in fin ]... |
Converts raw text to text-only format.
Args:
fin: the input file description of the raw text file.
Returns:
a dict with "text-only" format.
| raw2textonly | python | OptimalScale/LMFlow | scripts/data_preprocess/raw2textonly.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/raw2textonly.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/sample.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/sample.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/shuffle.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/shuffle.py | Apache-2.0 |
def _check_instance_format(self):
"""
Checks if data (instances) have required fields.
Raises messages with hints if not matched.
"""
fields = self.backend_dataset.features
correct_fields = INSTANCE_FIELDS_MAP[self.type]
if not set(correct_fields).issubset(set(fi... |
Checks if data (instances) have required fields.
Raises messages with hints if not matched.
| _check_instance_format | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def from_dict(self, dict_obj: dict, *args, **kwargs):
r"""
Create a Dataset object from a dictionary.
Return a Dataset given a dict with format:
{
"type": TYPE,
"instances": [
{
"key_1": VALUE_1.1,
... |
Create a Dataset object from a dictionary.
Return a Dataset given a dict with format:
{
"type": TYPE,
"instances": [
{
"key_1": VALUE_1.1,
"key_2": VALUE_1.2,
...
... | from_dict | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def create_from_dict(cls, dict_obj, *args, **kwargs):
r"""
Returns
--------
Returns a Dataset object given a dict.
"""
empty_data_args = DatasetArguments(dataset_path=None)
dataset = Dataset(empty_data_args)
return dataset.from_dict(dict_obj) |
Returns
--------
Returns a Dataset object given a dict.
| create_from_dict | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def to_dict(self):
r"""
Returns
---------
Return a dict represents the dataset:
{
"type": TYPE,
"instances": [
{
"key_1": VALUE_1.1,
"key_2": VALUE_1.2,
... |
Returns
---------
Return a dict represents the dataset:
{
"type": TYPE,
"instances": [
{
"key_1": VALUE_1.1,
"key_2": VALUE_1.2,
...
},
... | to_dict | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def map(self, *args, **kwargs):
r"""
Parameters
------------
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
---------
self : Dataset object.
"""
# If the dataset uses ... |
Parameters
------------
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
---------
self : Dataset object.
| map | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def save(
self,
file_path: str,
format: str="json"
):
r"""
Save the dataset to a json file.
Parameters
------------
file_path : str.
The path to the file where the dataset will be saved.
"""
if format == "json":
... |
Save the dataset to a json file.
Parameters
------------
file_path : str.
The path to the file where the dataset will be saved.
| save | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def sample(self, n: int, seed: int=42):
r"""
Sample n instances from the dataset.
Parameters
------------
n : int.
The number of instances to sample from the dataset.
Returns
---------
sample_dataset : Dataset object.
A new datas... |
Sample n instances from the dataset.
Parameters
------------
n : int.
The number of instances to sample from the dataset.
Returns
---------
sample_dataset : Dataset object.
A new dataset object containing the sampled instances.
| sample | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def train_test_split(self, test_size: float=0.2, shuffle: bool=True, seed: int=42):
r"""
Split the dataset into training and testing sets.
Parameters
------------
test_size : float, default=0.2.
The proportion of the dataset that will be used for testing.
Re... |
Split the dataset into training and testing sets.
Parameters
------------
test_size : float, default=0.2.
The proportion of the dataset that will be used for testing.
Returns
---------
train_dataset : Dataset object.
A new dataset objec... | train_test_split | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def drop_instances(self, indices: list):
r"""
Drop instances from the dataset.
Parameters
------------
indices : list.
A list of indices of the instances to drop from the dataset.
"""
if self.backend == "huggingface":
self.backend_dataset ... |
Drop instances from the dataset.
Parameters
------------
indices : list.
A list of indices of the instances to drop from the dataset.
| drop_instances | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def sanity_check(
self,
drop_invalid: bool=True,
):
r"""
Perform a sanity check on the dataset.
"""
if self.backend == "huggingface":
self.hf_dataset_sanity_check(drop_invalid)
else:
raise NotImplementedError(
f'Current... |
Perform a sanity check on the dataset.
| sanity_check | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def hf_dataset_sanity_check(
self,
drop_invalid: bool=True,
):
r"""
Perform a sanity check on the HuggingFace dataset.
"""
if self.backend_dataset is None or len(self.backend_dataset) == 0:
raise ValueError("Dataset is empty.")
if self.type == 'te... |
Perform a sanity check on the HuggingFace dataset.
| hf_dataset_sanity_check | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def preprocess_llama_from_llava_plain(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False):
"""
This function just add the image in the front of text.
And don't add any prompt.
Args:
sources: The input data with text and image.
tokenizer: The tokeni... |
This function just add the image in the front of text.
And don't add any prompt.
Args:
sources: The input data with text and image.
tokenizer: The tokenizer to process text.
has_image: Whether the input data has image.
Returns:
The input_ids and labels for the model.
... | preprocess_llama_from_llava_plain | python | OptimalScale/LMFlow | src/lmflow/datasets/multi_modal_dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/multi_modal_dataset.py | Apache-2.0 |
def preprocess_llama_from_llava_v1(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False):
"""
This function add the prompt and then put the image after the prompt.
So it needs additional code to generate the target label.
Args:
sources: The input data with t... |
This function add the prompt and then put the image after the prompt.
So it needs additional code to generate the target label.
Args:
sources: The input data with text and image.
tokenizer: The tokenizer to process text.
has_image: Whether the input data has image.
Returns:
... | preprocess_llama_from_llava_v1 | python | OptimalScale/LMFlow | src/lmflow/datasets/multi_modal_dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/multi_modal_dataset.py | Apache-2.0 |
def __init__(
self,
model_args,
tune_strategy='normal',
ds_config=None,
device="gpu",
use_accelerator=False,
*args,
**kwargs
):
"""
Initializes a HFDecoderModel instance.
:param model_args: dictionary with model arguments such a... |
Initializes a HFDecoderModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
:param tune_strategy: tuning strategy: normal, none, lora or adapter
:param ds_config: deepspeed configuration for distributed training
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def tokenize(
self,
dataset: Dataset,
add_special_tokens=True,
*args,
**kwargs
) -> Dataset:
"""
Tokenize the full dataset.
Parameters
------------
dataset : lmflow.datasets.Dataset.
args : Optional.
Positi... |
Tokenize the full dataset.
Parameters
------------
dataset : lmflow.datasets.Dataset.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
------------
tokenized_d... | tokenize | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def encode(self, input: Union[str, List[str]], *args, **kwargs ) -> Union[List[int], List[List[int]]]:
"""
Perform encoding process of the tokenizer.
Parameters
------------
inputs : str or list.
The text sequence.
args : Optional.
... |
Perform encoding process of the tokenizer.
Parameters
------------
inputs : str or list.
The text sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Re... | encode | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def decode(self, input, *args, **kwargs ) -> Union[str, List[str]]:
"""
Perform decoding process of the tokenizer.
Parameters
------------
inputs : list or tensor.
The token sequence.
args : Optional.
Positional arguments.
... |
Perform decoding process of the tokenizer.
Parameters
------------
inputs : list or tensor.
The token sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
... | decode | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def inference(
self,
inputs,
release_gpu: bool = False,
use_vllm: bool = False,
**kwargs
):
"""
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generatio... |
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generation or as model inputs to the model.
When using vllm inference, this should be a string or a list of strings.
When using normal... | inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def __inference(self, inputs, *args, **kwargs):
"""
Perform generation process of the model.
Parameters
------------
inputs :
The **tokenized** sequence used as a prompt for the generation or as model inputs to the model.
args : Optional.
... |
Perform generation process of the model.
Parameters
------------
inputs :
The **tokenized** sequence used as a prompt for the generation or as model inputs to the model.
args : Optional.
Positional arguments.
kwargs : Op... | __inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def __vllm_inference(
self,
inputs: Union[str, List[str]],
sampling_params: Optional['SamplingParams'] = None,
**kwargs,
) -> List[VLLMInferenceResultWithInput]:
"""Perform VLLM inference process of the model.
Parameters
----------
inputs : Union[str... | Perform VLLM inference process of the model.
Parameters
----------
inputs : Union[str, List[str]]
Prompt(s), string or a list of strings.
sampling_params : Optional[SamplingParams], optional
vllm SamplingParams object, by default None.
Returns
--... | __vllm_inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def prepare_inputs_for_inference(
self,
dataset: Dataset,
apply_chat_template: bool = True,
enable_distributed_inference: bool = False,
use_vllm: bool = False,
**kwargs,
) -> Union[List[str], "ray.data.Dataset", Dict[str, torch.Tensor]]:
"""
Prepare in... |
Prepare inputs for inference.
Parameters
------------
dataset : lmflow.datasets.Dataset.
The dataset used for inference.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
... | prepare_inputs_for_inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def save(self, dir, save_full_model=False, *args, **kwargs):
"""
Perform generation process of the model.
Parameters
------------
dir :
The directory to save model and tokenizer
save_full_model : Optional.
Whether to save full mod... |
Perform generation process of the model.
Parameters
------------
dir :
The directory to save model and tokenizer
save_full_model : Optional.
Whether to save full model.
kwargs : Optional.
Keyword arguments. ... | save | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def __init__(
self,
model_args,
tune_strategy='normal',
ds_config=None,
device="gpu",
use_accelerator=False,
custom_model=False,
with_deepspeed=True,
pipeline_args=None,
*args,
**kwargs
):
"""
Initializes a HFDec... |
Initializes a HFDecoderModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
:param tune_strategy: tuning strategy: normal, none, lora or adapter
:param ds_config: deepspeed configuration for distributed training
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def encode(self, input: Union[str, List[str]], *args, **kwargs ) -> Union[List[int], List[List[int]]]:
"""
Perform encoding process of the tokenizer.
Parameters
------------
inputs : str or list.
The text sequence.
args : Optional.
Positional arg... |
Perform encoding process of the tokenizer.
Parameters
------------
inputs : str or list.
The text sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
------------
o... | encode | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.