plotly openmc.plot method #24
Comments
import openmc
import numpy as np
import warnings
import math
from tempfile import TemporaryDirectory
from pathlib import Path
def discrete_colorscale(bvals, colors):
"""
bvals - list of values bounding intervals/ranges of interest
colors - list of rgb or hex colorcodes for values in [bvals[k], bvals[k+1]],0<=k < len(bvals)-1
returns the plotly discrete colorscale
"""
if len(bvals) != len(colors)+1:
raise ValueError('len(boundary values) should be equal to len(colors)+1')
bvals = sorted(bvals)
nvals = [(v-bvals[0])/(bvals[-1]-bvals[0]) for v in bvals] #normalized values
dcolorscale = [] #discrete colorscale
for k in range(len(colors)):
dcolorscale.extend([[nvals[k], colors[k]], [nvals[k+1], colors[k]]])
return dcolorscale
def plot(
self,
origin=None,
width=None,
pixels=40000,
basis='xy',
color_by='cell',
colors=None,
seed=None,
openmc_exec='openmc',
axes=None,
legend=False,
axis_units='cm',
# legend_kwargs=_default_legend_kwargs,
outline=False,
**kwargs
):
"""Display a slice plot of the universe.
Parameters
----------
origin : iterable of float
Coordinates at the origin of the plot. If left as None,
universe.bounding_box.center will be used to attempt to ascertain
the origin with infinite values being replaced by 0.
width : iterable of float
Width of the plot in each basis direction. If left as none then the
universe.bounding_box.width() will be used to attempt to
ascertain the plot width. Defaults to (10, 10) if the bounding_box
contains inf values
pixels : Iterable of int or int
If iterable of ints provided then this directly sets the number of
pixels to use in each basis direction. If int provided then this
sets the total number of pixels in the plot and the number of
pixels in each basis direction is calculated from this total and
the image aspect ratio.
basis : {'xy', 'xz', 'yz'}
The basis directions for the plot
color_by : {'cell', 'material'}
Indicate whether the plot should be colored by cell or by material
colors : dict
Assigns colors to specific materials or cells. Keys are instances of
:class:`Cell` or :class:`Material` and values are RGB 3-tuples, RGBA
4-tuples, or strings indicating SVG color names. Red, green, blue,
and alpha should all be floats in the range [0.0, 1.0], for example:
.. code-block:: python
# Make water blue
water = openmc.Cell(fill=h2o)
universe.plot(..., colors={water: (0., 0., 1.))
seed : int
Seed for the random number generator
openmc_exec : str
Path to OpenMC executable.
axes : matplotlib.Axes
Axes to draw to
.. versionadded:: 0.13.1
legend : bool
Whether a legend showing material or cell names should be drawn
.. versionadded:: 0.14.0
legend_kwargs : dict
Keyword arguments passed to :func:`matplotlib.pyplot.legend`.
.. versionadded:: 0.14.0
outline : bool
Whether outlines between color boundaries should be drawn
.. versionadded:: 0.14.0
axis_units : {'km', 'm', 'cm', 'mm'}
Units used on the plot axis
.. versionadded:: 0.14.0
**kwargs
Keyword arguments passed to :func:`matplotlib.pyplot.imshow`
Returns
-------
matplotlib.axes.Axes
Axes containing resulting image
"""
import matplotlib.image as mpimg
# import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import plotly.graph_objects as go
# Determine extents of plot
if basis == 'xy':
x, y = 0, 1
xlabel, ylabel = f'x [{axis_units}]', f'y [{axis_units}]'
elif basis == 'yz':
x, y = 1, 2
xlabel, ylabel = f'y [{axis_units}]', f'z [{axis_units}]'
elif basis == 'xz':
x, y = 0, 2
xlabel, ylabel = f'x [{axis_units}]', f'z [{axis_units}]'
bb = self.bounding_box
# checks to see if bounding box contains -inf or inf values
if np.isinf(bb.extent[basis]).any():
if origin is None:
origin = (0, 0, 0)
if width is None:
width = (10, 10)
else:
if origin is None:
# if nan values in the bb.center they get replaced with 0.0
# this happens when the bounding_box contains inf values
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
origin = np.nan_to_num(bb.center)
if width is None:
bb_width = bb.width
x_width = bb_width['xyz'.index(basis[0])]
y_width = bb_width['xyz'.index(basis[1])]
width = (x_width, y_width)
if isinstance(pixels, int):
aspect_ratio = width[0] / width[1]
pixels_y = math.sqrt(pixels / aspect_ratio)
pixels = (int(pixels / pixels_y), int(pixels_y))
axis_scaling_factor = {'km': 0.00001, 'm': 0.01, 'cm': 1, 'mm': 10}
x_min = (origin[x] - 0.5*width[0]) * axis_scaling_factor[axis_units]
x_max = (origin[x] + 0.5*width[0]) * axis_scaling_factor[axis_units]
y_min = (origin[y] - 0.5*width[1]) * axis_scaling_factor[axis_units]
y_max = (origin[y] + 0.5*width[1]) * axis_scaling_factor[axis_units]
# with TemporaryDirectory() as tmpdir:
if 1==1:
model = openmc.Model()
model.geometry = openmc.Geometry(self)
if seed is not None:
model.settings.plot_seed = seed
# Determine whether any materials contains macroscopic data and if
# so, set energy mode accordingly
for mat in self.get_all_materials().values():
if mat._macroscopic is not None:
model.settings.energy_mode = 'multi-group'
break
# Create plot object matching passed arguments
plot = openmc.Plot()
plot.origin = origin
plot.width = width
plot.pixels = pixels
plot.basis = basis
plot.color_by = color_by
if colors is not None:
plot.colors = colors
model.plots.append(plot)
# Run OpenMC in geometry plotting mode
model.plot_geometry(False, openmc_exec=openmc_exec)
# model.plot_geometry(False, cwd=tmpdir, openmc_exec=openmc_exec)
# Read image from file
# img_path = Path(tmpdir) / f'plot_{plot.id}.png'
img_path = Path(f'plot_{plot.id}.png')
if not img_path.is_file():
img_path = img_path.with_suffix('.ppm')
img = mpimg.imread(str(img_path))
# Create a figure sized such that the size of the axes within
# exactly matches the number of pixels specified
# if axes is None:
# px = 1/plt.rcParams['figure.dpi']
# fig, axes = plt.subplots()
# axes.set_xlabel(xlabel)
# axes.set_ylabel(ylabel)
# params = fig.subplotpars
# width = pixels[0]*px/(params.right - params.left)
# height = pixels[1]*px/(params.top - params.bottom)
# fig.set_size_inches(width, height)
data = []
if outline:
# Combine R, G, B values into a single int
rgb = (img * 256).astype(int)
image_value = (rgb[..., 0] << 16) + \
(rgb[..., 1] << 8) + (rgb[..., 2])
# axes.contour(
# image_value,
# origin="upper",
# colors="k",
# linestyles="solid",
# linewidths=1,
# levels=np.unique(image_value),
# extent=(x_min, x_max, y_min, y_max),
# )
data.append(
go.Contour(
z=image_value,
contours_coloring='none',
# colorscale=dcolorsc,
showscale=False,
x0=x_min,
dx=abs(x_min - x_max) / (img.shape[0] - 1),
y0=y_min,
dy=abs(y_min - y_max) / (img.shape[1] - 1),
)
)
# add legend showing which colors represent which material
# or cell if that was requested
if legend:
if plot.colors == {}:
raise ValueError("Must pass 'colors' dictionary if you "
"are adding a legend via legend=True.")
if color_by == "cell":
expected_key_type = openmc.Cell
else:
expected_key_type = openmc.Material
# patches = []
for key, color in plot.colors.items():
if isinstance(key, int):
raise TypeError(
"Cannot use IDs in colors dict for auto legend.")
elif not isinstance(key, expected_key_type):
raise TypeError(
"Color dict key type does not match color_by")
# this works whether we're doing cells or materials
label = key.name if key.name != '' else key.id
# matplotlib takes RGB on 0-1 scale rather than 0-255. at
# this point PlotBase has already checked that 3-tuple
# based colors are already valid, so if the length is three
# then we know it just needs to be converted to the 0-1
# format.
if len(color) == 3 and not isinstance(color, str):
scaled_color = (
color[0]/255, color[1]/255, color[2]/255)
else:
scaled_color = color
# key_patch = mpatches.Patch(color=scaled_color, label=label)
# patches.append(key_patch)
# axes.legend(handles=patches, **legend_kwargs)
# Plot image and return the axes
# axes.imshow(img, extent=(x_min, x_max, y_min, y_max), **kwargs)
print(img)
print(image_value)
print(img.shape)
rgb = (img * 256).astype(int)
image_value = (rgb[..., 0] << 16) + \
(rgb[..., 1] << 8) + (rgb[..., 2])
list_of_unique_image_value= []
list_of_unique_colors = []
colors=[]
for i, x in enumerate(image_value):
for j, y in enumerate(x):
if y not in list_of_unique_image_value:
list_of_unique_image_value.append(y)
c= img[i][j]
colors.append(f'rgb({int(c[0]*255)}, {int(c[1]*255)}, {int(c[2]*255)})')
print('list_of_unique_image_value', list_of_unique_image_value)
print('list_of_unique_colors',list_of_unique_colors)
# for val, col in zip(list_of_unique_image_value, list_of_unique_colors):
# colors.append(f'rgb({int(col[0]*255)}, {int(col[1]*255)}, {int(col[2]*255)})')
# colorscale.append([val, f'rgb({int(col[0]*255)}, {int(col[1]*255)}, {int(col[2]*255)})'])
Z = [colors for _,colors in sorted(zip(list_of_unique_image_value,colors))]
print(Z)
dcolorsc=discrete_colorscale([1e12]+sorted(list_of_unique_image_value), Z)
# dcolorsc=discrete_colorscale(list_of_unique_image_value, colors)
# dcolorsc=discrete_colorscale(list_of_unique_image_value+[max(list_of_unique_image_value)+1], colors)
print('colors', colors)
print('dcolorsc', dcolorsc)
data.append(
go.Heatmap(
z=image_value,
showscale=True,
colorscale=dcolorsc,
x0=x_min,
dx=abs(x_min - x_max) / (img.shape[0] - 1),
y0=y_min,
dy=abs(y_min - y_max) / (img.shape[1] - 1),
)
)
plot = go.Figure(data=data)
plot.update_layout(
xaxis={"title": xlabel},
# reversed autorange is required to avoid image needing rotation/flipping in plotly
yaxis={"title": ylabel, "autorange": "reversed"},
# title=title,
autosize=False,
height=800,
)
plot.update_yaxes(
scaleanchor="x",
scaleratio=1,
)
return plot
openmc.Universe.plot = plot
surf = openmc.Sphere(r=10)
surf2 = openmc.ZCylinder(r=3)
cell = openmc.Cell(region=-surf & -surf2, cell_id=10)
cell2 = openmc.Cell(region=-surf & +surf2, cell_id=100)
geometry = openmc.Geometry([cell, cell2])
plot = geometry.plot(
outline=True,
basis='xz',
pixels=100000
)
plot.show() |
import openmc
import numpy as np
import warnings
import math
import typing
from tempfile import TemporaryDirectory
from pathlib import Path
from PIL import Image
def get_rgb_from_int(value: int) -> typing.Tuple[int, int, int]:
blue = value & 255
green = (value >> 8) & 255
red = (value >> 16) & 255
return red, green, blue
def get_int_from_rgb(rgb: typing.Tuple[int, int, int]) -> int:
red = rgb[0]
green = rgb[1]
blue = rgb[2]
return (red << 16) + (green << 8) + blue
def discrete_colorscale(bvals, colors):
"""
bvals - list of values bounding intervals/ranges of interest
colors - list of rgb or hex colorcodes for values in [bvals[k], bvals[k+1]],0<=k < len(bvals)-1
returns the plotly discrete colorscale
"""
print('bvals',bvals)
print('colors',colors)
if len(bvals) != len(colors)+1:
raise ValueError('len(boundary values) should be equal to len(colors)+1')
bvals = sorted(bvals)
nvals = [(v-bvals[0])/(bvals[-1]-bvals[0]) for v in bvals] #normalized values
dcolorscale = [] #discrete colorscale
for k in range(len(colors)):
dcolorscale.extend([[nvals[k], colors[k]], [nvals[k+1], colors[k]]])
return dcolorscale
def plot(
self,
origin=None,
width=None,
pixels=40000,
basis='xy',
color_by='cell',
colors=None,
seed=None,
openmc_exec='openmc',
axes=None,
legend=False,
axis_units='cm',
# legend_kwargs=_default_legend_kwargs,
outline=False,
**kwargs
):
"""Display a slice plot of the universe.
Parameters
----------
origin : iterable of float
Coordinates at the origin of the plot. If left as None,
universe.bounding_box.center will be used to attempt to ascertain
the origin with infinite values being replaced by 0.
width : iterable of float
Width of the plot in each basis direction. If left as none then the
universe.bounding_box.width() will be used to attempt to
ascertain the plot width. Defaults to (10, 10) if the bounding_box
contains inf values
pixels : Iterable of int or int
If iterable of ints provided then this directly sets the number of
pixels to use in each basis direction. If int provided then this
sets the total number of pixels in the plot and the number of
pixels in each basis direction is calculated from this total and
the image aspect ratio.
basis : {'xy', 'xz', 'yz'}
The basis directions for the plot
color_by : {'cell', 'material'}
Indicate whether the plot should be colored by cell or by material
colors : dict
Assigns colors to specific materials or cells. Keys are instances of
:class:`Cell` or :class:`Material` and values are RGB 3-tuples, RGBA
4-tuples, or strings indicating SVG color names. Red, green, blue,
and alpha should all be floats in the range [0.0, 1.0], for example:
.. code-block:: python
# Make water blue
water = openmc.Cell(fill=h2o)
universe.plot(..., colors={water: (0., 0., 1.))
seed : int
Seed for the random number generator
openmc_exec : str
Path to OpenMC executable.
axes : matplotlib.Axes
Axes to draw to
.. versionadded:: 0.13.1
legend : bool
Whether a legend showing material or cell names should be drawn
.. versionadded:: 0.14.0
legend_kwargs : dict
Keyword arguments passed to :func:`matplotlib.pyplot.legend`.
.. versionadded:: 0.14.0
outline : bool
Whether outlines between color boundaries should be drawn
.. versionadded:: 0.14.0
axis_units : {'km', 'm', 'cm', 'mm'}
Units used on the plot axis
.. versionadded:: 0.14.0
**kwargs
Keyword arguments passed to :func:`matplotlib.pyplot.imshow`
Returns
-------
matplotlib.axes.Axes
Axes containing resulting image
"""
import matplotlib.image as mpimg
# import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import plotly.graph_objects as go
# Determine extents of plot
if basis == 'xy':
x, y = 0, 1
xlabel, ylabel = f'x [{axis_units}]', f'y [{axis_units}]'
elif basis == 'yz':
x, y = 1, 2
xlabel, ylabel = f'y [{axis_units}]', f'z [{axis_units}]'
elif basis == 'xz':
x, y = 0, 2
xlabel, ylabel = f'x [{axis_units}]', f'z [{axis_units}]'
bb = self.bounding_box
# checks to see if bounding box contains -inf or inf values
if np.isinf(bb.extent[basis]).any():
if origin is None:
origin = (0, 0, 0)
if width is None:
width = (10, 10)
else:
if origin is None:
# if nan values in the bb.center they get replaced with 0.0
# this happens when the bounding_box contains inf values
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
origin = np.nan_to_num(bb.center)
if width is None:
bb_width = bb.width
x_width = bb_width['xyz'.index(basis[0])]
y_width = bb_width['xyz'.index(basis[1])]
width = (x_width, y_width)
if isinstance(pixels, int):
aspect_ratio = width[0] / width[1]
pixels_y = math.sqrt(pixels / aspect_ratio)
pixels = (int(pixels / pixels_y), int(pixels_y))
axis_scaling_factor = {'km': 0.00001, 'm': 0.01, 'cm': 1, 'mm': 10}
x_min = (origin[x] - 0.5*width[0]) * axis_scaling_factor[axis_units]
x_max = (origin[x] + 0.5*width[0]) * axis_scaling_factor[axis_units]
y_min = (origin[y] - 0.5*width[1]) * axis_scaling_factor[axis_units]
y_max = (origin[y] + 0.5*width[1]) * axis_scaling_factor[axis_units]
# with TemporaryDirectory() as tmpdir:
if 1==1:
model = openmc.Model()
model.geometry = openmc.Geometry(self)
if seed is not None:
model.settings.plot_seed = seed
# Determine whether any materials contains macroscopic data and if
# so, set energy mode accordingly
for mat in self.get_all_materials().values():
if mat._macroscopic is not None:
model.settings.energy_mode = 'multi-group'
break
# Create plot object matching passed arguments
plot = openmc.Plot()
plot.origin = origin
plot.width = width
plot.pixels = pixels
plot.basis = basis
plot.color_by = color_by
if colors is not None:
colors_based_on_ids = {}
for key, value in colors.items():
colors_based_on_ids[key] = get_rgb_from_int(key.id)
plot.colors = colors_based_on_ids
model.plots.append(plot)
# Run OpenMC in geometry plotting mode
model.plot_geometry(False, openmc_exec=openmc_exec)
# model.plot_geometry(False, cwd=tmpdir, openmc_exec=openmc_exec)
# Read image from file
# img_path = Path(tmpdir) / f'plot_{plot.id}.png'
img_path = Path(f'plot_{plot.id}.png')
if not img_path.is_file():
img_path = img_path.with_suffix('.ppm')
img = mpimg.imread(str(img_path))
image_values = Image.open(img_path)
print('img.shape,',img)
image_values = np.asarray(image_values)
image_values = [
[get_int_from_rgb(inner_entry) for inner_entry in outer_entry]
for outer_entry in image_values
]
image_values = np.array(image_values)
image_values[image_values == 16777215] = 0
# image_values[:] = [x if x != 16777215 else 0 for x in image_values]
# Create a figure sized such that the size of the axes within
# exactly matches the number of pixels specified
# if axes is None:
# px = 1/plt.rcParams['figure.dpi']
# fig, axes = plt.subplots()
# axes.set_xlabel(xlabel)
# axes.set_ylabel(ylabel)
# params = fig.subplotpars
# width = pixels[0]*px/(params.right - params.left)
# height = pixels[1]*px/(params.top - params.bottom)
# fig.set_size_inches(width, height)
data = []
# if outline:
# # Combine R, G, B values into a single int
# rgb = (img * 256).astype(int)
# image_value = (rgb[..., 0] << 16) + \
# (rgb[..., 1] << 8) + (rgb[..., 2])
# axes.contour(
# image_value,
# origin="upper",
# colors="k",
# linestyles="solid",
# linewidths=1,
# levels=np.unique(image_value),
# extent=(x_min, x_max, y_min, y_max),
# )
# data.append(
# go.Contour(
# z=image_value,
# contours_coloring='none',
# # colorscale=dcolorsc,
# showscale=False,
# x0=x_min,
# dx=abs(x_min - x_max) / (img.shape[0] - 1),
# y0=y_min,
# dy=abs(y_min - y_max) / (img.shape[1] - 1),
# )
# )
# add legend showing which colors represent which material
# or cell if that was requested
if legend:
if plot.colors == {}:
raise ValueError("Must pass 'colors' dictionary if you "
"are adding a legend via legend=True.")
if color_by == "cell":
expected_key_type = openmc.Cell
else:
expected_key_type = openmc.Material
# patches = []
for key, color in plot.colors.items():
if isinstance(key, int):
raise TypeError(
"Cannot use IDs in colors dict for auto legend.")
elif not isinstance(key, expected_key_type):
raise TypeError(
"Color dict key type does not match color_by")
# this works whether we're doing cells or materials
label = key.name if key.name != '' else key.id
# matplotlib takes RGB on 0-1 scale rather than 0-255. at
# this point PlotBase has already checked that 3-tuple
# based colors are already valid, so if the length is three
# then we know it just needs to be converted to the 0-1
# format.
if len(color) == 3 and not isinstance(color, str):
scaled_color = (
color[0]/255, color[1]/255, color[2]/255)
else:
scaled_color = color
# key_patch = mpatches.Patch(color=scaled_color, label=label)
# patches.append(key_patch)
# axes.legend(handles=patches, **legend_kwargs)
# Plot image and return the axes
# axes.imshow(img, extent=(x_min, x_max, y_min, y_max), **kwargs)
print(image_values)
# print(image_values.shape)
# rgb = (img * 256).astype(int)
# image_value = (rgb[..., 0] << 16) + \
# (rgb[..., 1] << 8) + (rgb[..., 2])
list_of_unique_image_value= []
list_of_unique_colors = []
# colors=[]
# for i, x in enumerate(image_value):
# for j, y in enumerate(x):
# if y not in list_of_unique_image_value:
# list_of_unique_image_value.append(y)
# c= img[i][j]
# colors.append(f'rgb({int(c[0]*255)}, {int(c[1]*255)}, {int(c[2]*255)})')
print('list_of_unique_image_value', list_of_unique_image_value)
print('list_of_unique_colors',list_of_unique_colors)
# for val, col in zip(list_of_unique_image_value, list_of_unique_colors):
# colors.append(f'rgb({int(col[0]*255)}, {int(col[1]*255)}, {int(col[2]*255)})')
# colorscale.append([val, f'rgb({int(col[0]*255)}, {int(col[1]*255)}, {int(col[2]*255)})'])
# Z = [colors for _,colors in sorted(zip(list_of_unique_image_value,colors))]
# print(Z)
# dcolorsc=discrete_colorscale([mat.id for mat in colors.keys()], [f'rgb({c[0]},{c[1]},{c[2]})' for c in list(colors.values())])
# dcolorsc.append
# dcolorsc=discrete_colorscale(list_of_unique_image_value, colors)
# dcolorsc=discrete_colorscale(list_of_unique_image_value+[max(list_of_unique_image_value)+1], colors)
dcolorsc=[
[0, 'green'],
# [(1/20)*2, 'red'],
# [(1/20)*20, 'blue'],
]
for rgb_col, mat_id in zip([f'rgb({c[0]},{c[1]},{c[2]})' for c in list(colors.values())], [mat.id for mat in colors.keys()]):
dcolorsc.append(((1/20)*mat_id,rgb_col))
print('dcolorsc', dcolorsc)
# [0.1, 'green'],
# [0.1, 'rgb(253, 237, 176)'],
# [0.2, 'rgb(249, 198, 139)'],
# [0.3, 'rgb(244, 159, 109)'],
# [0.4, 'rgb(234, 120, 88)'],
# [0.5, 'rgb(218, 83, 82)'],
# [0.6, 'rgb(191, 54, 91)'],
# [0.7, 'rgb(158, 35, 98)'],
# [0.8, 'rgb(120, 26, 97)'],
# [0.9, 'rgb(83, 22, 84)'],
# [1.0, 'rgb(47, 15, 61)']]
print('colors', colors)
print('dcolorsc', dcolorsc)
print(image_values)
data.append(
go.Heatmap(
z=image_values,
# showscale=True,
colorscale=dcolorsc,
x0=x_min,
dx=abs(x_min - x_max) / (img.shape[0] - 1),
y0=y_min,
dy=abs(y_min - y_max) / (img.shape[1] - 1),
)
)
plot = go.Figure(data=data)
plot.update_layout(
xaxis={"title": xlabel},
# reversed autorange is required to avoid image needing rotation/flipping in plotly
yaxis={"title": ylabel, "autorange": "reversed"},
# title=title,
autosize=False,
height=800,
)
plot.update_yaxes(
scaleanchor="x",
scaleratio=1,
)
return plot
openmc.Universe.plot = plot
surf = openmc.Sphere(r=10)
surf2 = openmc.ZCylinder(r=3)
mat1=openmc.Material(material_id=2)
mat1.add_nuclide('Li6',1)
mat1.set_density('g/cm3',1)
mat2=openmc.Material(material_id=20)
mat2.add_nuclide('Li6',1)
mat2.set_density('g/cm3',1)
cell = openmc.Cell(region=-surf & -surf2, cell_id=10, fill=mat1)
cell2 = openmc.Cell(region=-surf & +surf2, cell_id=100, fill=mat2)
geometry = openmc.Geometry([cell, cell2])
plot = geometry.plot(
outline=True,
basis='xz',
pixels=1000,
colors={mat1:[200,0,0], mat2:[0,0, 255]},
color_by='material'
)
plot.show() |
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adapting the openmc plot method to allow plotly
todo normalise the color scale so that it goes from 0 to 1 and the with spaces that match the id number gaps
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