id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
12,438 | import os
import warnings
from typing import Optional, Any, Dict
import ee
import ipyleaflet
import ipywidgets as widgets
from box import Box
from bqplot import pyplot as plt
from IPython.display import display
from .basemaps import get_xyz_dict, xyz_to_leaflet
from .common import *
from .conversion import *
from .ee_t... | Create linked maps of Earth Engine data layers. Args: rows (int, optional): The number of rows of maps to create. Defaults to 2. cols (int, optional): The number of columns of maps to create. Defaults to 2. height (str, optional): The height of each map in pixels. Defaults to "400px". ee_objects (list, optional): The l... |
12,439 | import os
import warnings
from typing import Optional, Any, Dict
import ee
import ipyleaflet
import ipywidgets as widgets
from box import Box
from bqplot import pyplot as plt
from IPython.display import display
from .basemaps import get_xyz_dict, xyz_to_leaflet
from .common import *
from .conversion import *
from .ee_t... | Creates a time series inspector. Args: layers_dict (dict, optional): A dictionary of layers to be shown on the map. Defaults to None. left_name (str, optional): A name for the left layer. Defaults to None. right_name (str, optional): A name for the right layer. Defaults to None. width (str, optional): Width of the drop... |
12,440 | import os
import warnings
from typing import Optional, Any, Dict
import ee
import ipyleaflet
import ipywidgets as widgets
from box import Box
from bqplot import pyplot as plt
from IPython.display import display
from .basemaps import get_xyz_dict, xyz_to_leaflet
from .common import *
from .conversion import *
from .ee_t... | Gets a basemap tile layer by name. Args: name (str): The name of the basemap. Returns: ipylealfet.TileLayer | ipyleaflet.WMSLayer: The basemap layer. |
12,441 | import pandas as pd
import plotly.express as px
from .common import *
def github_raw_url(url):
"""Get the raw URL for a GitHub file.
Args:
url (str): The GitHub URL.
Returns:
str: The raw URL.
"""
if isinstance(url, str) and url.startswith("https://github.com/") and "blob" in url:
... | Create a bar chart with plotly.express, Args: data: DataFrame | array-like | dict | str (local file path or HTTP URL) This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constru... |
12,442 | import pandas as pd
import plotly.express as px
from .common import *
def github_raw_url(url):
"""Get the raw URL for a GitHub file.
Args:
url (str): The GitHub URL.
Returns:
str: The raw URL.
"""
if isinstance(url, str) and url.startswith("https://github.com/") and "blob" in url:
... | Create a line chart with plotly.express, Args: data: DataFrame | array-like | dict | str (local file path or HTTP URL) This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constr... |
12,443 | import pandas as pd
import plotly.express as px
from .common import *
def github_raw_url(url):
"""Get the raw URL for a GitHub file.
Args:
url (str): The GitHub URL.
Returns:
str: The raw URL.
"""
if isinstance(url, str) and url.startswith("https://github.com/") and "blob" in url:
... | Create a line chart with plotly.express, Args: data: DataFrame | array-like | dict | str (local file path or HTTP URL) This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constr... |
12,444 | import pandas as pd
import plotly.express as px
from .common import *
def github_raw_url(url):
"""Get the raw URL for a GitHub file.
Args:
url (str): The GitHub URL.
Returns:
str: The raw URL.
"""
if isinstance(url, str) and url.startswith("https://github.com/") and "blob" in url:
... | Create a plotly pie chart. Args: data: DataFrame or array-like or dict This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments. ... |
12,445 | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from box import Box
The provided code snippet includes necessary dependencies for implementing the `get_colorbar` function. Write a Python function `def get_colorbar( colors, vmin=0, vmax=1, width=6.0, height=0.4, orien... | Creates a colorbar based on custom colors. Args: colors (list): A list of hex colors. vmin (float, optional): The minimum value range. Defaults to 0. vmax (float, optional): The maximum value range. Defaults to 1.0. width (float, optional): The width of the colormap. Defaults to 6.0. height (float, optional): The heigh... |
12,446 | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from box import Box
The provided code snippet includes necessary dependencies for implementing the `plot_colormap` function. Write a Python function `def plot_colormap( cmap, width=8.0, height=0.4, orientation="horizontal", ... | Plot a matplotlib colormap. Args: cmap (str): The name of the colormap. width (float, optional): The width of the colormap. Defaults to 8.0. height (float, optional): The height of the colormap. Defaults to 0.4. orientation (str, optional): The orientation of the colormap. Defaults to "horizontal". vmin (float, optiona... |
12,447 | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from box import Box
def list_colormaps(add_extra=False, lowercase=False):
"""List all available colormaps. See a complete lost of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.
Returns:
list: The lis... | Plot all available colormaps. Args: width (float, optional): Width of the colormap. Defaults to 8.0. height (float, optional): Height of the colormap. Defaults to 0.4. |
12,448 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
class Feature_ByFeature(BarChart):
"""A object to define variables and get_data method."""
def __init__(
self, features, xProper... | Generates a Chart from a set of features. Plots the value of one or more properties for each feature. Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturebyfeature Args: features (ee.FeatureCollection): The feature collection to generate a chart from. xProperty (str): Features labe... |
12,449 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
class Feature_ByProperty(BarChart):
"""A object to define variables and get_data method."""
def __init__(
self, features, xPrope... | Generates a Chart from a set of features. Plots property values of one or more features. Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturebyproperty Args: features (ee.FeatureCollection): The features to include in the chart. xProperties (list | dict): One of (1) a list of prope... |
12,450 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
class Feature_Groups(BarChart):
"""A object to define variables and get_data method."""
def __init__(
self,
features,
... | Generates a Chart from a set of features. Plots the value of one property for each feature. Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturegroups Args: features (ee.FeatureCollection): The feature collection to make a chart from. xProperty (str): Features labeled by xProperty.... |
12,451 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
The provided code snippet includes necessary dependencies for implementing the `feature_histogram` function. Write a Python function `def featu... | Generates a Chart from a set of features. Computes and plots a histogram of the given property. - X-axis = Histogram buckets (of property value). - Y-axis = Frequency Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturehistogram Args: features (ee.FeatureCollection): The features t... |
12,452 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_byClass(
image, classBand, region, reducer, scale, classLabels, xLabels, **kwargs
):
# TODO
pass | null |
12,453 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_byRegion(image, regions, reducer, scale, xProperty, **kwargs):
# TODO
pass | null |
12,454 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_doySeries(
imageCollection,
region,
regionReducer,
scale,
yearReducer,
startDay,
endDay,
**kwargs,
):... | null |
12,455 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_doySeriesByRegion(
imageCollection,
bandName,
regions,
regionReducer,
scale,
yearReducer,
seriesProperty,... | null |
12,456 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_doySeriesByYear(
imageCollection,
bandName,
region,
regionReducer,
scale,
sameDayReducer,
startDay,
e... | null |
12,457 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_histogram(
image, region, scale, maxBuckets, minBucketWidth, maxRaw, maxPixels, **kwargs
):
# TODO
pass | null |
12,458 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_regions(image, regions, reducer, scale, seriesProperty, xLabels, **kwargs):
# TODO
pass | null |
12,459 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_series(imageCollection, region, reducer, scale, xProperty, **kwargs):
# TODO
pass | null |
12,460 | import ee
import pandas as pd
import numpy as np
from bqplot import Tooltip
from bqplot import pyplot as plt
from .common import ee_to_df, zonal_stats
from typing import Union
def image_seriesByRegion(
imageCollection, regions, reducer, band, scale, xProperty, seriesProperty, **kwargs
):
# TODO
pass | null |
12,461 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
The p... | Create a toolbar widget. Args: m (geemap.Map, optional): The geemap.Map instance. Defaults to None. opened (bool, optional): Whether to open the toolbar. Defaults to True. |
12,462 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
The p... | Create a toolbar widget. Args: m (geemap.Map, optional): The geemap.Map instance. Defaults to None. opened (bool, optional): Whether to open the toolbar. Defaults to True. show_close_button (bool, optional): Whether to show the close button. Defaults to True. |
12,463 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def sp... | null |
12,464 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def _... | null |
12,465 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
The p... | Wraps a toolbar item callback to clean up the widget when unselected. |
12,466 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def _... | null |
12,467 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def ee... | null |
12,468 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def ti... | null |
12,469 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def co... | null |
12,470 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def _... | null |
12,471 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def op... | null |
12,472 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def bu... | null |
12,473 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def ge... | null |
12,474 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def ti... | null |
12,475 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def co... | null |
12,476 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def pl... | null |
12,477 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def sa... | null |
12,478 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def in... | null |
12,479 | import os
from dataclasses import dataclass
import ee
import ipyevents
import ipyleaflet
import ipywidgets as widgets
from ipyfilechooser import FileChooser
from IPython.core.display import display
from typing import Any, Callable, Optional
from .common import *
from .timelapse import *
from . import map_widgets
def pl... | Creates the main toolbar and adds it to the map. Args: m (plotlymap.Map): The plotly Map object. |
12,480 | from playwright.sync_api import sync_playwright
import time
from sys import argv, exit, platform
import openai
import os
def print_help():
print(
"(g) to visit url\n(u) scroll up\n(d) scroll down\n(c) to click\n(t) to type\n" +
"(h) to view commands again\n(r/enter) to run suggested command\n(o) change objecti... | null |
12,481 | from playwright.sync_api import sync_playwright
import time
from sys import argv, exit, platform
import openai
import os
prompt_template = """
You are an agent controlling a browser. You are given:
(1) an objective that you are trying to achieve
(2) the URL of your current web page
(3) a simplified text description ... | null |
12,482 | from playwright.sync_api import sync_playwright
import time
from sys import argv, exit, platform
import openai
import os
def run_cmd(cmd):
cmd = cmd.split("\n")[0]
if cmd.startswith("SCROLL UP"):
_crawler.scroll("up")
elif cmd.startswith("SCROLL DOWN"):
_crawler.scroll("down")
elif cmd.startswith("CLICK... | null |
12,483 | import os
import time
import h5py
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `write_hdf5` function. Write a Python function `def write_hdf5(file, tensor, key = 'tensor')` to solve the following problem:
Write a simple tensor, i.e. numpy array ,to HDF5.... | Write a simple tensor, i.e. numpy array ,to HDF5. :param file: path to file to write :type file: str :param tensor: tensor to write :type tensor: numpy.ndarray :param key: key to use for tensor :type key: str |
12,484 | import os
import time
import h5py
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `read_hdf5` function. Write a Python function `def read_hdf5(file, key = 'tensor')` to solve the following problem:
Read a tensor, i.e. numpy array, from HDF5. :param file: pa... | Read a tensor, i.e. numpy array, from HDF5. :param file: path to file to read :type file: str :param key: key to read :type key: str :return: tensor :rtype: numpy.ndarray |
12,485 | import os
import time
import h5py
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `write_off` function. Write a Python function `def write_off(file, vertices, faces)` to solve the following problem:
Writes the given vertices and faces to OFF. :param vertice... | Writes the given vertices and faces to OFF. :param vertices: vertices as tuples of (x, y, z) coordinates :type vertices: [(float)] :param faces: faces as tuples of (num_vertices, vertex_id_1, vertex_id_2, ...) :type faces: [(int)] |
12,486 | import os
import time
import h5py
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `read_off` function. Write a Python function `def read_off(file)` to solve the following problem:
Reads vertices and faces from an off file. :param file: path to file to read ... | Reads vertices and faces from an off file. :param file: path to file to read :type file: str :return: vertices and faces as lists of tuples :rtype: [(float)], [(int)] |
12,487 | import os
import time
import h5py
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `write_obj` function. Write a Python function `def write_obj(file, vertices, faces)` to solve the following problem:
Writes the given vertices and faces to OBJ. :param vertice... | Writes the given vertices and faces to OBJ. :param vertices: vertices as tuples of (x, y, z) coordinates :type vertices: [(float)] :param faces: faces as tuples of (num_vertices, vertex_id_1, vertex_id_2, ...) :type faces: [(int)] |
12,488 | import os
import time
import h5py
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `read_obj` function. Write a Python function `def read_obj(file)` to solve the following problem:
Reads vertices and faces from an obj file. :param file: path to file to read ... | Reads vertices and faces from an obj file. :param file: path to file to read :type file: str :return: vertices and faces as lists of tuples :rtype: [(float)], [(int)] |
12,489 | import os
import time
import h5py
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `makedir` function. Write a Python function `def makedir(dir)` to solve the following problem:
Creates directory if it does not exist. :param dir: directory path :type dir: st... | Creates directory if it does not exist. :param dir: directory path :type dir: str |
12,490 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `export_obj` function. Write a Python function `def export_obj(vertices, triangles, filename)` to solve the following problem:
Exports a mesh in the (.obj) format.
Here is the function:
def export_obj(vertices, triangle... | Exports a mesh in the (.obj) format. |
12,491 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `export_off` function. Write a Python function `def export_off(vertices, triangles, filename)` to solve the following problem:
Exports a mesh in the (.off) format.
Here is the function:
def export_off(vertices, triangle... | Exports a mesh in the (.off) format. |
12,492 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `export_mesh` function. Write a Python function `def export_mesh(vertices, triangles, filename, mesh_name="mcubes_mesh")` to solve the following problem:
Exports a mesh in the COLLADA (.dae) format. Needs PyCollada (https... | Exports a mesh in the COLLADA (.dae) format. Needs PyCollada (https://github.com/pycollada/pycollada). |
12,493 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.r2n2 import models, training, generation
from im2mesh import data
encoder_dict = {
'simple_conv': conv.ConvEncoder,
'resnet18': conv.Resnet18,
'resnet34': conv.Resnet34,
'resnet50': conv.Resnet50,
'resnet101': conv.Resnet101,
'r2n2... | Return the model. Args: cfg (dict): loaded yaml config device (device): pytorch device |
12,494 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.r2n2 import models, training, generation
from im2mesh import data
The provided code snippet includes necessary dependencies for implementing the `get_trainer` function. Write a Python function `def get_trainer(model, optimizer, cfg, device, **kwargs)` to ... | Returns the trainer object. Args: model (nn.Module): R2N2 model optimizer (optimizer): pytorch optimizer cfg (dict): loaded yaml config device (device): pytorch device |
12,495 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.r2n2 import models, training, generation
from im2mesh import data
The provided code snippet includes necessary dependencies for implementing the `get_generator` function. Write a Python function `def get_generator(model, cfg, device, **kwargs)` to solve t... | Returns the generator object. Args: model (nn.Module): R2N2 model cfg (dict): loaded yaml config device (device): pytorch device |
12,496 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.r2n2 import models, training, generation
from im2mesh import data
The provided code snippet includes necessary dependencies for implementing the `get_data_fields` function. Write a Python function `def get_data_fields(split, cfg, **kwargs)` to solve the f... | Returns the data fields. Args: split (str): the split which should be used cfg (dict): loaded yaml config |
12,497 | import os
import logging
from torch.utils import data
import numpy as np
import yaml
The provided code snippet includes necessary dependencies for implementing the `collate_remove_none` function. Write a Python function `def collate_remove_none(batch)` to solve the following problem:
Collater that puts each data field... | Collater that puts each data field into a tensor with outer dimension batch size. Args: batch: batch |
12,498 | import os
import logging
from torch.utils import data
import numpy as np
import yaml
The provided code snippet includes necessary dependencies for implementing the `worker_init_fn` function. Write a Python function `def worker_init_fn(worker_id)` to solve the following problem:
Worker init function to ensure true rand... | Worker init function to ensure true randomness. |
12,499 | import os
import urllib
import torch
from torch.utils import model_zoo
def is_url(url):
scheme = urllib.parse.urlparse(url).scheme
return scheme in ('http', 'https') | null |
12,500 | import yaml
from torchvision import transforms
from im2mesh import data
from im2mesh import onet, r2n2, psgn, pix2mesh, dmc
from im2mesh import preprocess
def update_recursive(dict1, dict2):
''' Update two config dictionaries recursively.
Args:
dict1 (dict): first dictionary to be updated
dict2 ... | Loads config file. Args: path (str): path to config file default_path (bool): whether to use default path |
12,501 | import yaml
from torchvision import transforms
from im2mesh import data
from im2mesh import onet, r2n2, psgn, pix2mesh, dmc
from im2mesh import preprocess
method_dict = {
'onet': onet,
'r2n2': r2n2,
'psgn': psgn,
'pix2mesh': pix2mesh,
'dmc': dmc,
}
The provided code snippet includes necessary depen... | Returns the model instance. Args: cfg (dict): config dictionary device (device): pytorch device dataset (dataset): dataset |
12,502 | import yaml
from torchvision import transforms
from im2mesh import data
from im2mesh import onet, r2n2, psgn, pix2mesh, dmc
from im2mesh import preprocess
method_dict = {
'onet': onet,
'r2n2': r2n2,
'psgn': psgn,
'pix2mesh': pix2mesh,
'dmc': dmc,
}
The provided code snippet includes necessary depen... | Returns a trainer instance. Args: model (nn.Module): the model which is used optimizer (optimizer): pytorch optimizer cfg (dict): config dictionary device (device): pytorch device |
12,503 | import yaml
from torchvision import transforms
from im2mesh import data
from im2mesh import onet, r2n2, psgn, pix2mesh, dmc
from im2mesh import preprocess
method_dict = {
'onet': onet,
'r2n2': r2n2,
'psgn': psgn,
'pix2mesh': pix2mesh,
'dmc': dmc,
}
The provided code snippet includes necessary depen... | Returns a generator instance. Args: model (nn.Module): the model which is used cfg (dict): config dictionary device (device): pytorch device |
12,504 | import yaml
from torchvision import transforms
from im2mesh import data
from im2mesh import onet, r2n2, psgn, pix2mesh, dmc
from im2mesh import preprocess
method_dict = {
'onet': onet,
'r2n2': r2n2,
'psgn': psgn,
'pix2mesh': pix2mesh,
'dmc': dmc,
}
def get_inputs_field(mode, cfg):
''' Returns th... | Returns the dataset. Args: model (nn.Module): the model which is used cfg (dict): config dictionary return_idx (bool): whether to include an ID field |
12,505 | import os
from im2mesh.dmc import models, training, generation
from im2mesh import data
def get_model(cfg, device=None, **kwargs):
encoder = cfg['model']['encoder']
decoder = cfg['model']['decoder']
c_dim = cfg['model']['c_dim']
encoder_kwargs = cfg['model']['encoder_kwargs']
decoder_kwargs = cfg['... | null |
12,506 | import os
from im2mesh.dmc import models, training, generation
from im2mesh import data
def get_trainer(model, optimizer, cfg, device, **kwargs):
input_type = cfg['data']['input_type']
out_dir = cfg['training']['out_dir']
vis_dir = os.path.join(out_dir, 'vis')
num_voxels = cfg['model']['num_voxels']
... | null |
12,507 | import os
from im2mesh.dmc import models, training, generation
from im2mesh import data
def get_generator(model, cfg, device, **kwargs):
num_voxels = cfg['model']['num_voxels']
generator = generation.Generator3D(
model, device=device, num_voxels=num_voxels
)
return generator | null |
12,508 | import os
from im2mesh.dmc import models, training, generation
from im2mesh import data
def get_data_fields(split, cfg, **kwargs):
with_transforms = cfg['data']['with_transforms']
# TODO: put this into config
pointcloud_n = 3000
pointcloud_transform = data.SubsamplePointcloud(pointcloud_n)
fields ... | null |
12,509 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_accept_topology` function. Write a Python function `def get_accept_topology(num_tri=3)` to solve the following problem:
Get the list of the singly connected topologies up to a specified number of triangles
Here is t... | Get the list of the singly connected topologies up to a specified number of triangles |
12,510 | import numpy as np
def check_connected(triangles, v1, v2):
# number of all triangles
T = len(triangles)
compatible_mat = np.zeros((T, T))
match = {}
for ind,x in enumerate(v1):
match[x] = v2[ind]
for i in range(T):
for j in range(T):
t1 = triangles[i]
t2... | return connected pairs in x, y, z directions |
12,511 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_occupancy_table` function. Write a Python function `def get_occupancy_table()` to solve the following problem:
Return binary occupancy status of 8 vertices for all 256 topology types
Here is the function:
def get_o... | Return binary occupancy status of 8 vertices for all 256 topology types |
12,512 | import numpy as np
import torch
from torch.autograd import Variable
import time
one = Variable(torch.ones(1).type(torch.FloatTensor), requires_grad=True)
eps = 1e-8
def pointTriangleDistance(TRI, P):
# function [dist,PP0] = pointTriangleDistance(TRI,P)
# calculate distance between a point and a triangle in 3D
... | null |
12,513 | import numpy as np
import torch
from torch.autograd import Variable
from im2mesh.dmc.utils.pointTriangleDistance import pointTriangleDistance, pointTriangleDistanceFast
from im2mesh.dmc.ops.table import get_triangle_table, get_unique_triangles, vertices_on_location
topologys = get_triangle_table()
def dis_to_mesh(pts, ... | Return the distances from a point set to all acceptable topology types in a single cell Input: pts, (Nx3) a set of points pts_index, (Nx1) indicating if a point is in the cell or not vertices, (3x12) the 12 vertices on each edge of the cell x_, the offset of the cell in x direction y_, the offset of the cell in y direc... |
12,514 | import numpy as np
import torch
from torch.autograd import Variable
from im2mesh.dmc.utils.pointTriangleDistance import pointTriangleDistance, pointTriangleDistanceFast
from im2mesh.dmc.ops.table import get_triangle_table, get_unique_triangles, vertices_on_location
The provided code snippet includes necessary dependen... | get the point indices incide of a given cell (pyTorch) Input: pts, a set of points in pytorch format cell, a list of 6 numbers {x1, y1, z1, x2, y2, z2} Output: inds, a list of indices for points inside the cell |
12,515 | import numpy as np
import torch
from torch.autograd import Variable
from im2mesh.dmc.utils.pointTriangleDistance import pointTriangleDistance, pointTriangleDistanceFast
from im2mesh.dmc.ops.table import get_triangle_table, get_unique_triangles, vertices_on_location
The provided code snippet includes necessary dependen... | get the point indices incide of a given cell (numpy) Input: pts, a set of points in numpy format cell, a list of 6 numbers {x1, y1, z1, x2, y2, z2} Output: inds, a list of indices for points inside the cell |
12,516 | import numpy as np
import torch
from torch.autograd import Variable
from im2mesh.dmc.utils.pointTriangleDistance import pointTriangleDistance, pointTriangleDistanceFast
from im2mesh.dmc.ops.table import get_triangle_table, get_unique_triangles, vertices_on_location
def offset_to_vertices(offset, x, y, z):
""" get 1... | get normal vector of all triangles |
12,517 | import numpy as np
import torch
from torch.autograd import Variable
from im2mesh.dmc.utils.pointTriangleDistance import pointTriangleDistance, pointTriangleDistanceFast
from im2mesh.dmc.ops.table import get_triangle_table, get_unique_triangles, vertices_on_location
The provided code snippet includes necessary dependen... | get the gaussian kernel https://stackoverflow.com/questions/29731726/how-to-calculate-a-gaussian-kernel-matrix-efficiently-in-numpy |
12,518 | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import torch
import os
import numpy as np
from utils.util import write_to_off, unique_rows
from _ext import eval_util
def write_to_off(vertices, faces, filename):
"""write the given vertices and faces to off"""
f = open(filename, 'w')
... | save the estimated mesh with maximum likelihood as image |
12,519 | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import torch
import os
import numpy as np
from utils.util import write_to_off, unique_rows
from _ext import eval_util
The provided code snippet includes necessary dependencies for implementing the `save_occupancy_fig` function. Write a Python func... | save the estimated occupancy as image |
12,520 | import argparse
import os
from ConfigParser import SafeConfigParser
from im2mesh.dmc.ops.table import get_triangle_table
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.Ar... | null |
12,521 | import torch
import numpy as np
from im2mesh.dmc.ops.cpp_modules import pred2mesh
The provided code snippet includes necessary dependencies for implementing the `unique_rows` function. Write a Python function `def unique_rows(a)` to solve the following problem:
Return the matrix with unique rows
Here is the function:... | Return the matrix with unique rows |
12,522 | import torch
import numpy as np
from im2mesh.dmc.ops.cpp_modules import pred2mesh
The provided code snippet includes necessary dependencies for implementing the `pred_to_mesh_max` function. Write a Python function `def pred_to_mesh_max(offset, topology)` to solve the following problem:
Converts the predicted offset va... | Converts the predicted offset variable and topology to a mesh by choosing the most likely topology Input ---------- arg1 : tensor offset variables [3 x W+1 x H+1 x D+1] arg2 : tensor topology probabilities [W*H*D x T] Returns ------- trimesh format mesh |
12,523 | import torch
import torch.distributions as dist
from torch import nn
import os
from im2mesh.encoder import encoder_dict
from im2mesh.onet import models, training, generation
from im2mesh import data
from im2mesh import config
def get_prior_z(cfg, device, **kwargs):
''' Returns prior distribution for latent code z.
... | Return the Occupancy Network model. Args: cfg (dict): imported yaml config device (device): pytorch device dataset (dataset): dataset |
12,524 | import torch
import torch.distributions as dist
from torch import nn
import os
from im2mesh.encoder import encoder_dict
from im2mesh.onet import models, training, generation
from im2mesh import data
from im2mesh import config
The provided code snippet includes necessary dependencies for implementing the `get_trainer` ... | Returns the trainer object. Args: model (nn.Module): the Occupancy Network model optimizer (optimizer): pytorch optimizer object cfg (dict): imported yaml config device (device): pytorch device |
12,525 | import torch
import torch.distributions as dist
from torch import nn
import os
from im2mesh.encoder import encoder_dict
from im2mesh.onet import models, training, generation
from im2mesh import data
from im2mesh import config
The provided code snippet includes necessary dependencies for implementing the `get_generator... | Returns the generator object. Args: model (nn.Module): Occupancy Network model cfg (dict): imported yaml config device (device): pytorch device |
12,526 | import torch
import torch.distributions as dist
from torch import nn
import os
from im2mesh.encoder import encoder_dict
from im2mesh.onet import models, training, generation
from im2mesh import data
from im2mesh import config
The provided code snippet includes necessary dependencies for implementing the `get_data_fiel... | Returns the data fields. Args: mode (str): the mode which is used cfg (dict): imported yaml config |
12,527 | import torch
import torch.nn as nn
import torch.nn.functional as F
def maxpool(x, dim=-1, keepdim=False):
out, _ = x.max(dim=dim, keepdim=keepdim)
return out | null |
12,528 | import torch
import torch.nn as nn
from im2mesh.layers import ResnetBlockFC
def maxpool(x, dim=-1, keepdim=False):
out, _ = x.max(dim=dim, keepdim=keepdim)
return out | null |
12,529 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.pix2mesh import models, training, generation
from im2mesh import data
import pickle
import numpy as np
encoder_dict = {
'simple_conv': conv.ConvEncoder,
'resnet18': conv.Resnet18,
'resnet34': conv.Resnet34,
'resnet50': conv.Resnet50,
'... | Returns the Pixel2Mesh model. Args: cfg (yaml file): config file device (PyTorch device): PyTorch device |
12,530 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.pix2mesh import models, training, generation
from im2mesh import data
import pickle
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_trainer` function. Write a Python function `def get_trainer(model, o... | Return the trainer object for the Pixel2Mesh model. Args: model (PyTorch model): Pixel2Mesh model optimizer( PyTorch optimizer): The optimizer that should be used cfg (yaml file): config file device (PyTorch device): The PyTorch device that should be used |
12,531 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.pix2mesh import models, training, generation
from im2mesh import data
import pickle
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_generator` function. Write a Python function `def get_generator(mode... | Returns a generator object for the Pixel2Mesh model. Args: model (PyTorch model): Pixel2Mesh model cfg (yaml file): config file device (PyTorch device): The PyTorch device that should be used |
12,532 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.pix2mesh import models, training, generation
from im2mesh import data
import pickle
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_data_fields` function. Write a Python function `def get_data_fields(... | Returns the respective data fields. Args: mode (string): which split should be performed (train/test) cfg (yaml file): config file |
12,533 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.psgn import models, training, generation
from im2mesh import data
encoder_dict = {
'simple_conv': conv.ConvEncoder,
'resnet18': conv.Resnet18,
'resnet34': conv.Resnet34,
'resnet50': conv.Resnet50,
'resnet101': conv.Resnet101,
'r2n2... | r''' Returns the model instance. Args: cfg (yaml object): the config file device (PyTorch device): the PyTorch device |
12,534 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.psgn import models, training, generation
from im2mesh import data
The provided code snippet includes necessary dependencies for implementing the `get_trainer` function. Write a Python function `def get_trainer(model, optimizer, cfg, device, **kwargs)` to ... | r''' Returns the trainer instance. Args: model (nn.Module): PSGN model optimizer (PyTorch optimizer): The optimizer that should be used cfg (yaml object): the config file device (PyTorch device): the PyTorch device |
12,535 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.psgn import models, training, generation
from im2mesh import data
The provided code snippet includes necessary dependencies for implementing the `get_generator` function. Write a Python function `def get_generator(model, cfg, device, **kwargs)` to solve t... | r''' Returns the generator instance. Args: cfg (yaml object): the config file device (PyTorch device): the PyTorch device |
12,536 | import os
from im2mesh.encoder import encoder_dict
from im2mesh.psgn import models, training, generation
from im2mesh import data
The provided code snippet includes necessary dependencies for implementing the `get_data_fields` function. Write a Python function `def get_data_fields(mode, cfg, **kwargs)` to solve the fo... | r''' Returns the data fields. Args: mode (string): The split that is used (train/val/test) cfg (yaml object): the config file |
12,537 | import torch
from im2mesh.utils.io import export_pointcloud
import tempfile
import subprocess
import os
import trimesh
FILTER_SCRIPT_RECONSTRUCTION = '''
<!DOCTYPE FilterScript>
<FilterScript>
<filter name="Surface Reconstruction: Ball Pivoting">
<Param value="0" type="RichAbsPerc" max="1.4129" name="BallRadius" des... | r''' Runs the meshlab ball pivoting algorithm. Args: pointcloud (numpy tensor): input point cloud |
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