Commit 115b0b08 authored by Frederik Hennig's avatar Frederik Hennig
Browse files

Updated boundaries_in_kernel

parent fe36b5a3
Pipeline #27519 canceled with stage
in 3 minutes and 2 seconds
......@@ -5,6 +5,7 @@ import pystencils as ps
from pystencils.data_types import TypedSymbol, create_type
from pystencils.backends.cbackend import CustomCodeNode
from lbmpy.stencils import get_stencil
from lbmpy.advanced_streaming.utility import get_accessor, inverse_dir_index, is_inplace, Timestep
from itertools import product
......@@ -30,6 +31,8 @@ class BetweenTimestepsIndexing:
@property
def inverse_dir_symbol(self):
"""Symbol denoting the inversion of a PDF field index.
Use only at top-level of index to f_out or f_in, otherwise it can't be correctly replaced."""
return sp.IndexedBase('invdir')
# =============================
......@@ -42,6 +45,9 @@ class BetweenTimestepsIndexing:
raise ValueError('Cannot create index arrays for both kinds of timesteps for inplace streaming pattern '
+ streaming_pattern)
if isinstance(stencil, str):
stencil = get_stencil(stencil)
prev_accessor = get_accessor(streaming_pattern, prev_timestep)
next_accessor = get_accessor(streaming_pattern, prev_timestep.next())
......@@ -193,9 +199,12 @@ class BetweenTimestepsIndexing:
class NeighbourOffsetArrays(CustomCodeNode):
@staticmethod
def symbolic_neighbour_offset(dir_idx, dim):
return tuple([sp.IndexedBase(symbol, shape=(1,))[dir_idx]
for symbol in NeighbourOffsetArrays._offset_symbols(dim)])
def neighbour_offset(dir_idx, stencil):
if isinstance(sp.sympify(dir_idx), sp.Integer):
return stencil[dir_idx]
else:
return tuple([sp.IndexedBase(symbol, shape=(1,))[dir_idx]
for symbol in NeighbourOffsetArrays._offset_symbols(len(stencil[0]))])
@staticmethod
def _offset_symbols(dim):
......
import sympy as sp
from lbmpy.boundaries.boundaryhandling import LbmWeightInfo
from lbmpy.advanced_streaming.indexing import BetweenTimestepsIndexing
from lbmpy.advanced_streaming.utility import Timestep, get_accessor
from pystencils.boundaries.boundaryhandling import BoundaryOffsetInfo
from pystencils.assignment import Assignment
from pystencils.astnodes import Block, Conditional, LoopOverCoordinate, SympyAssignment
......@@ -56,6 +58,8 @@ def border_conditions(direction, field, ghost_layers=1):
result = sp.And(border_condition, *other_min, *other_max)
return type_all_numbers(result, loop_ctr.dtype)
# TODO: Function is called nowhere... remove it?
def transformed_boundary_rule(boundary, accessor_func, field, direction_symbol, lb_method, **kwargs):
tmp_field = field.new_field_with_different_name("t")
......@@ -82,27 +86,24 @@ def transformed_boundary_rule(boundary, accessor_func, field, direction_symbol,
return ac.main_assignments[0].rhs
def boundary_conditional(boundary, direction, read_of_next_accessor, lb_method, output_field, cse=False):
def boundary_conditional(boundary, direction, streaming_pattern, prev_timestep, lb_method, output_field, cse=False):
stencil = lb_method.stencil
tmp_field = output_field.new_field_with_different_name("t")
dir_indices = direction_indices_in_direction(direction, stencil)
indexing = BetweenTimestepsIndexing(output_field, lb_method.stencil, prev_timestep, streaming_pattern)
f_out, f_in = indexing.proxy_fields
inv_dir = indexing.inverse_dir_symbol
assignments = []
for direction_idx in dir_indices:
rule = boundary(tmp_field, direction_idx, lb_method, index_field=None)
boundary_subs = boundary_substitutions(lb_method)
rule = [a.subs(boundary_subs) for a in rule]
rhs_substitutions = {tmp_field(i): sym for i, sym in enumerate(lb_method.post_collision_pdf_symbols)}
offset = stencil[direction_idx]
inv_offset = inverse_direction(offset)
inv_idx = stencil.index(inv_offset)
lhs_substitutions = {
tmp_field[offset](inv_idx): read_of_next_accessor(output_field, stencil)[inv_idx]}
rule = [Assignment(a.lhs.subs(lhs_substitutions), a.rhs.subs(rhs_substitutions)) for a in rule]
ac = AssignmentCollection([rule[-1]], rule[:-1]).new_without_subexpressions()
rule = boundary(f_out, f_in, direction_idx, inv_dir, lb_method, index_field=None)
# rhs: replace f_out by post collision symbols.
rhs_substitutions = {f_out(i): sym for i, sym in enumerate(lb_method.post_collision_pdf_symbols)}
rule = AssignmentCollection([rule]).new_with_substitutions(rhs_substitutions)
rule = indexing.substitute_proxies(rule)
ac = rule.new_without_subexpressions()
assignments += ac.main_assignments
border_cond = border_conditions(direction, output_field, ghost_layers=1)
......@@ -112,8 +113,10 @@ def boundary_conditional(boundary, direction, read_of_next_accessor, lb_method,
return Conditional(border_cond, Block(assignments))
def update_rule_with_push_boundaries(collision_rule, field, boundary_spec, accessor, read_of_next_accessor):
def update_rule_with_push_boundaries(collision_rule, field, boundary_spec,
streaming_pattern='pull', timestep=Timestep.BOTH):
method = collision_rule.method
accessor = get_accessor(streaming_pattern, timestep)
loads = [Assignment(a, b) for a, b in zip(method.pre_collision_pdf_symbols, accessor.read(field, method.stencil))]
stores = [Assignment(a, b) for a, b in
zip(accessor.write(field, method.stencil), method.post_collision_pdf_symbols)]
......@@ -122,7 +125,7 @@ def update_rule_with_push_boundaries(collision_rule, field, boundary_spec, acces
result.subexpressions = loads + result.subexpressions
result.main_assignments += stores
for direction, boundary in boundary_spec.items():
cond = boundary_conditional(boundary, direction, read_of_next_accessor, method, field)
cond = boundary_conditional(boundary, direction, streaming_pattern, timestep, method, field)
result.main_assignments.append(cond)
if 'split_groups' in result.simplification_hints:
......
......@@ -137,7 +137,7 @@ class UBB(Boundary):
assert self.dim == lb_method.dim, \
f"Dimension of UBB ({self.dim}) does not match dimension of method ({lb_method.dim})"
neighbor_offset = NeighbourOffsetArrays.symbolic_neighbour_offset(direction, lb_method.dim)
neighbor_offset = NeighbourOffsetArrays.neighbour_offset(direction, lb_method.stencil)
velocity = tuple(v_i.get_shifted(*neighbor_offset)
if isinstance(v_i, Field.Access) and not vel_from_idx_field
......@@ -153,7 +153,7 @@ class UBB(Boundary):
weight_of_direction = LbmWeightInfo.weight_of_direction
vel_term = 2 / c_s_sq \
* sum([d_i * v_i for d_i, v_i in zip(neighbor_offset, velocity)]) \
* weight_of_direction(direction)
* weight_of_direction(direction, lb_method)
# Better alternative: in conserved value computation
# rename what is currently called density to "virtual_density"
......@@ -227,7 +227,7 @@ class NeumannByCopy(Boundary):
return [NeighbourOffsetArrays(lb_method.stencil)]
def __call__(self, f_out, f_in, dir_symbol, inv_dir, lb_method, index_field):
neighbour_offset = NeighbourOffsetArrays.symbolic_neighbour_offset(dir_symbol, lb_method.dim)
neighbour_offset = NeighbourOffsetArrays.neighbour_offset(dir_symbol, lb_method.stencil)
return [Assignment(f_in(inv_dir[dir_symbol]), f_out(inv_dir[dir_symbol])),
Assignment(f_out[neighbour_offset](dir_symbol), f_out(dir_symbol))]
......@@ -249,7 +249,7 @@ class StreamInConstant(Boundary):
return [NeighbourOffsetArrays(lb_method.stencil)]
def __call__(self, f_out, f_in, dir_symbol, inv_dir, lb_method, index_field):
neighbour_offset = NeighbourOffsetArrays.symbolic_neighbour_offset(dir_symbol, lb_method.dim)
neighbour_offset = NeighbourOffsetArrays.neighbour_offset(dir_symbol, lb_method.stencil)
return [Assignment(f_in(inv_dir[dir_symbol]), self._constant),
Assignment(f_out[neighbour_offset](dir_symbol), self._constant)]
......
......@@ -155,8 +155,11 @@ class LbmWeightInfo(CustomCodeNode):
# --------------------------- Functions to be used by boundaries --------------------------
@staticmethod
def weight_of_direction(dir_idx):
return sp.IndexedBase(LbmWeightInfo.WEIGHTS_SYMBOL, shape=(1,))[dir_idx]
def weight_of_direction(dir_idx, lb_method=None):
if isinstance(sp.sympify(dir_idx), sp.Integer):
return lb_method.weights[dir_idx].evalf()
else:
return sp.IndexedBase(LbmWeightInfo.WEIGHTS_SYMBOL, shape=(1,))[dir_idx]
# ---------------------------------- Internal ---------------------------------------------
......
......@@ -3,6 +3,7 @@ import sympy as sp
import lbmpy.plot as plt
import pystencils as ps
from lbmpy.advanced_streaming import *
from lbmpy.boundaries import *
from lbmpy.creationfunctions import *
from lbmpy.geometry import *
......
%% Cell type:code id: tags:
``` python
from lbmpy.session import *
from lbmpy.boundaries.boundaries_in_kernel import update_rule_with_push_boundaries
from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter
from collections import OrderedDict
from time import perf_counter
```
%% Cell type:markdown id: tags:
# Version 1: compile-in boundaries
%% Cell type:code id: tags:
``` python
domain_size = (32, 32, 32)
relaxation_rate = 1.8
time_steps = 100
lid_velocity = 0.05
```
%% Cell type:code id: tags:
``` python
dh = create_data_handling(domain_size, default_target='cpu')
pdfs = dh.add_array('pdfs', values_per_cell=19)
u = dh.add_array('u', values_per_cell=len(domain_size))
streaming_pattern = 'aa'
```
%% Cell type:code id: tags:
``` python
boundaries = OrderedDict((
((0, 1, 0), UBB([lid_velocity, 0, 0])),
((1, 0, 0), NoSlip()),
((-1, 0, 0), NoSlip()),
((0, -1, 0), NoSlip()),
((0, 0, 1), NoSlip()),
((0, 0, -1), NoSlip()),
((0, 0, -1), NeumannByCopy()),
))
opt = {'symbolic_field': pdfs, 'cse_global': False, 'cse_pdfs': True}
cr_even = create_lb_collision_rule(stencil="D3Q19", relaxation_rate=relaxation_rate, compressible=False, optimization=opt)
cr_odd = create_lb_collision_rule(stencil="D3Q19", relaxation_rate=relaxation_rate, compressible=False, optimization=opt)
update_rule_aa_even = update_rule_with_push_boundaries(cr_even, pdfs, boundaries, AAEvenTimeStepAccessor, AAOddTimeStepAccessor.read)
update_rule_aa_odd = update_rule_with_push_boundaries(cr_odd, pdfs, boundaries, AAOddTimeStepAccessor, AAEvenTimeStepAccessor.read)
update_rule_aa_even = update_rule_with_push_boundaries(cr_even, pdfs, boundaries, streaming_pattern, Timestep.EVEN)
update_rule_aa_odd = update_rule_with_push_boundaries(cr_odd, pdfs, boundaries, streaming_pattern, Timestep.ODD)
getter_assignments = macroscopic_values_getter(update_rule_aa_even.method, velocity=u.center_vector,
pdfs=pdfs.center_vector, density=None)
pdfs=pdfs, density=None,
streaming_pattern=streaming_pattern,
previous_timestep=Timestep.EVEN)
getter_kernel = ps.create_kernel(getter_assignments, target=dh.default_target).compile()
even_kernel = ps.create_kernel(update_rule_aa_even, target=dh.default_target, ghost_layers=1).compile()
odd_kernel = ps.create_kernel(update_rule_aa_odd, target=dh.default_target, ghost_layers=1).compile()
```
%% Cell type:code id: tags:
``` python
def init():
dh.fill(pdfs.name, 0, ghost_layers=True)
def aa_time_loop(steps=100):
assert steps % 2 == 0, "Works only for an even number of time steps"
dh.all_to_gpu()
for i in range(steps // 2):
dh.run_kernel(odd_kernel)
dh.run_kernel(even_kernel)
dh.run_kernel(getter_kernel)
dh.all_to_cpu()
```
%% Cell type:code id: tags:
``` python
init()
aa_time_loop(time_steps)
vel_version1 = dh.gather_array(u.name, ghost_layers=False).copy()
plt.vector_field_magnitude(vel_version1[:, :, domain_size[2]//2, :])
plt.colorbar();
plt.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7fa8b1205490>
%%%% Output: display_data
![](data:image/png;base64,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)
![](data:image/png;base64,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)
![](data:image/svg+xml;utf8,<?xml version="1.0" encoding="utf-8" standalone="no"?> <!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"> <!-- Created with matplotlib (https://matplotlib.org/) --> <svg height="357.238125pt" version="1.1" viewBox="0 0 844.941125 357.238125" width="844.941125pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <metadata> <rdf:RDF xmlns:cc="http://creativecommons.org/ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"> <cc:Work> <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/> <dc:date>2020-10-27T11:08:18.449885</dc:date> <dc:format>image/svg+xml</dc:format> <dc:creator> <cc:Agent> <dc:title>Matplotlib v3.3.1, https://matplotlib.org/</dc:title> </cc:Agent> </dc:creator> </cc:Work> </rdf:RDF> </metadata> <defs> <style type="text/css">*{stroke-linecap:butt;stroke-linejoin:round;}</style> </defs> <g id="figure_1"> <g id="patch_1"> <path d="M 0 357.238125 L 844.941125 357.238125 L 844.941125 0 L 0 0 z " style="fill:none;"/> </g> <g id="axes_1"> <g id="patch_2"> <path d="M 26.925 333.36 L 741.165 333.36 L 741.165 7.2 L 26.925 7.2 z " style="fill:#ffffff;"/> </g> <g clip-path="url(#pb06035e813)"> <image height="327" id="imagea717631e5d" transform="scale(1 -1)translate(0 -327)" width="327" x="220.965" xlink:href="data:image/png;base64, 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" y="-6.36"/> </g> <g id="matplotlib.axis_1"> <g id="xtick_1"> <g id="line2d_1"> <defs> <path d="M 0 0 L 0 3.5 " id="m225dc936d9" style="stroke:#000000;stroke-width:0.8;"/> </defs> <g> <use style="stroke:#000000;stroke-width:0.8;" x="124.13625" xlink:href="#m225dc936d9" y="333.36"/> </g> </g> <g id="text_1"> <!-- −10 --> <g transform="translate(113.583906 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 10.59375 35.5 L 73.1875 35.5 L 73.1875 27.203125 L 10.59375 27.203125 z " id="DejaVuSans-8722"/> <path d="M 12.40625 8.296875 L 28.515625 8.296875 L 28.515625 63.921875 L 10.984375 60.40625 L 10.984375 69.390625 L 28.421875 72.90625 L 38.28125 72.90625 L 38.28125 8.296875 L 54.390625 8.296875 L 54.390625 0 L 12.40625 0 z " id="DejaVuSans-49"/> <path d="M 31.78125 66.40625 Q 24.171875 66.40625 20.328125 58.90625 Q 16.5 51.421875 16.5 36.375 Q 16.5 21.390625 20.328125 13.890625 Q 24.171875 6.390625 31.78125 6.390625 Q 39.453125 6.390625 43.28125 13.890625 Q 47.125 21.390625 47.125 36.375 Q 47.125 51.421875 43.28125 58.90625 Q 39.453125 66.40625 31.78125 66.40625 z M 31.78125 74.21875 Q 44.046875 74.21875 50.515625 64.515625 Q 56.984375 54.828125 56.984375 36.375 Q 56.984375 17.96875 50.515625 8.265625 Q 44.046875 -1.421875 31.78125 -1.421875 Q 19.53125 -1.421875 13.0625 8.265625 Q 6.59375 17.96875 6.59375 36.375 Q 6.59375 54.828125 13.0625 64.515625 Q 19.53125 74.21875 31.78125 74.21875 z " id="DejaVuSans-48"/> </defs> <use xlink:href="#DejaVuSans-8722"/> <use x="83.789062" xlink:href="#DejaVuSans-49"/> <use x="147.412109" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_2"> <g id="line2d_2"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="226.06125" xlink:href="#m225dc936d9" y="333.36"/> </g> </g> <g id="text_2"> <!-- 0 --> <g transform="translate(222.88 347.958438)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_3"> <g id="line2d_3"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="327.98625" xlink:href="#m225dc936d9" y="333.36"/> </g> </g> <g id="text_3"> <!-- 10 --> <g transform="translate(321.62375 347.958438)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-49"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_4"> <g id="line2d_4"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="429.91125" xlink:href="#m225dc936d9" y="333.36"/> </g> </g> <g id="text_4"> <!-- 20 --> <g transform="translate(423.54875 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 19.1875 8.296875 L 53.609375 8.296875 L 53.609375 0 L 7.328125 0 L 7.328125 8.296875 Q 12.9375 14.109375 22.625 23.890625 Q 32.328125 33.6875 34.8125 36.53125 Q 39.546875 41.84375 41.421875 45.53125 Q 43.3125 49.21875 43.3125 52.78125 Q 43.3125 58.59375 39.234375 62.25 Q 35.15625 65.921875 28.609375 65.921875 Q 23.96875 65.921875 18.8125 64.3125 Q 13.671875 62.703125 7.8125 59.421875 L 7.8125 69.390625 Q 13.765625 71.78125 18.9375 73 Q 24.125 74.21875 28.421875 74.21875 Q 39.75 74.21875 46.484375 68.546875 Q 53.21875 62.890625 53.21875 53.421875 Q 53.21875 48.921875 51.53125 44.890625 Q 49.859375 40.875 45.40625 35.40625 Q 44.1875 33.984375 37.640625 27.21875 Q 31.109375 20.453125 19.1875 8.296875 z " id="DejaVuSans-50"/> </defs> <use xlink:href="#DejaVuSans-50"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_5"> <g id="line2d_5"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="531.83625" xlink:href="#m225dc936d9" y="333.36"/> </g> </g> <g id="text_5"> <!-- 30 --> <g transform="translate(525.47375 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 40.578125 39.3125 Q 47.65625 37.796875 51.625 33 Q 55.609375 28.21875 55.609375 21.1875 Q 55.609375 10.40625 48.1875 4.484375 Q 40.765625 -1.421875 27.09375 -1.421875 Q 22.515625 -1.421875 17.65625 -0.515625 Q 12.796875 0.390625 7.625 2.203125 L 7.625 11.71875 Q 11.71875 9.328125 16.59375 8.109375 Q 21.484375 6.890625 26.8125 6.890625 Q 36.078125 6.890625 40.9375 10.546875 Q 45.796875 14.203125 45.796875 21.1875 Q 45.796875 27.640625 41.28125 31.265625 Q 36.765625 34.90625 28.71875 34.90625 L 20.21875 34.90625 L 20.21875 43.015625 L 29.109375 43.015625 Q 36.375 43.015625 40.234375 45.921875 Q 44.09375 48.828125 44.09375 54.296875 Q 44.09375 59.90625 40.109375 62.90625 Q 36.140625 65.921875 28.71875 65.921875 Q 24.65625 65.921875 20.015625 65.03125 Q 15.375 64.15625 9.8125 62.3125 L 9.8125 71.09375 Q 15.4375 72.65625 20.34375 73.4375 Q 25.25 74.21875 29.59375 74.21875 Q 40.828125 74.21875 47.359375 69.109375 Q 53.90625 64.015625 53.90625 55.328125 Q 53.90625 49.265625 50.4375 45.09375 Q 46.96875 40.921875 40.578125 39.3125 z " id="DejaVuSans-51"/> </defs> <use xlink:href="#DejaVuSans-51"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_6"> <g id="line2d_6"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="633.76125" xlink:href="#m225dc936d9" y="333.36"/> </g> </g> <g id="text_6"> <!-- 40 --> <g transform="translate(627.39875 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 37.796875 64.3125 L 12.890625 25.390625 L 37.796875 25.390625 z M 35.203125 72.90625 L 47.609375 72.90625 L 47.609375 25.390625 L 58.015625 25.390625 L 58.015625 17.1875 L 47.609375 17.1875 L 47.609375 0 L 37.796875 0 L 37.796875 17.1875 L 4.890625 17.1875 L 4.890625 26.703125 z " id="DejaVuSans-52"/> </defs> <use xlink:href="#DejaVuSans-52"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_7"> <g id="line2d_7"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="735.68625" xlink:href="#m225dc936d9" y="333.36"/> </g> </g> <g id="text_7"> <!-- 50 --> <g transform="translate(729.32375 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 10.796875 72.90625 L 49.515625 72.90625 L 49.515625 64.59375 L 19.828125 64.59375 L 19.828125 46.734375 Q 21.96875 47.46875 24.109375 47.828125 Q 26.265625 48.1875 28.421875 48.1875 Q 40.625 48.1875 47.75 41.5 Q 54.890625 34.8125 54.890625 23.390625 Q 54.890625 11.625 47.5625 5.09375 Q 40.234375 -1.421875 26.90625 -1.421875 Q 22.3125 -1.421875 17.546875 -0.640625 Q 12.796875 0.140625 7.71875 1.703125 L 7.71875 11.625 Q 12.109375 9.234375 16.796875 8.0625 Q 21.484375 6.890625 26.703125 6.890625 Q 35.15625 6.890625 40.078125 11.328125 Q 45.015625 15.765625 45.015625 23.390625 Q 45.015625 31 40.078125 35.4375 Q 35.15625 39.890625 26.703125 39.890625 Q 22.75 39.890625 18.8125 39.015625 Q 14.890625 38.140625 10.796875 36.28125 z " id="DejaVuSans-53"/> </defs> <use xlink:href="#DejaVuSans-53"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> </g> <g id="matplotlib.axis_2"> <g id="ytick_1"> <g id="line2d_8"> <defs> <path d="M 0 0 L -3.5 0 " id="m019c7e846d" style="stroke:#000000;stroke-width:0.8;"/> </defs> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#m019c7e846d" y="328.26375"/> </g> </g> <g id="text_8"> <!-- 0 --> <g transform="translate(13.5625 332.062969)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_2"> <g id="line2d_9"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#m019c7e846d" y="277.30125"/> </g> </g> <g id="text_9"> <!-- 5 --> <g transform="translate(13.5625 281.100469)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_3"> <g id="line2d_10"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#m019c7e846d" y="226.33875"/> </g> </g> <g id="text_10"> <!-- 10 --> <g transform="translate(7.2 230.137969)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-49"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_4"> <g id="line2d_11"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#m019c7e846d" y="175.37625"/> </g> </g> <g id="text_11"> <!-- 15 --> <g transform="translate(7.2 179.175469)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-49"/> <use x="63.623047" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_5"> <g id="line2d_12"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#m019c7e846d" y="124.41375"/> </g> </g> <g id="text_12"> <!-- 20 --> <g transform="translate(7.2 128.212969)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-50"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_6"> <g id="line2d_13"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#m019c7e846d" y="73.45125"/> </g> </g> <g id="text_13"> <!-- 25 --> <g transform="translate(7.2 77.250469)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-50"/> <use x="63.623047" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_7"> <g id="line2d_14"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#m019c7e846d" y="22.48875"/> </g> </g> <g id="text_14"> <!-- 30 --> <g transform="translate(7.2 26.287969)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-51"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> </g> <g id="patch_3"> <path d="M 26.925 333.36 L 26.925 7.2 " style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> </g> <g id="patch_4"> <path d="M 741.165 333.36 L 741.165 7.2 " style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> </g> <g id="patch_5"> <path d="M 26.925 333.36 L 741.165 333.36 " style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> </g> <g id="patch_6"> <path d="M 26.925 7.2 L 741.165 7.2 " style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> </g> </g> <g id="axes_2"> <g id="patch_7"> <path clip-path="url(#p4da09dcd2a)" d="M 785.805 333.36 L 785.805 332.085938 L 785.805 8.474063 L 785.805 7.2 L 802.113 7.2 L 802.113 8.474063 L 802.113 332.085938 L 802.113 333.36 z " style="fill:#ffffff;stroke:#ffffff;stroke-linejoin:miter;stroke-width:0.01;"/> </g> <image height="326" id="image1be7469932" transform="scale(1 -1)translate(0 -326)" width="16" x="786" xlink:href="data:image/png;base64, iVBORw0KGgoAAAANSUhEUgAAABAAAAFGCAYAAABjUx8/AAABrklEQVR4nO2cwW3EMAwEKdmlpYT0X0qcIobArATe37r1aJYWDvCtn/X7Ffi8tTa5vt61F1tAT8C+vjpuoThEm8HalAG8hbfsBA0iUQ/WmDgitTDgEC8Qaco0IjVA/DgDmEDfxgAT9V0IYDAQIxiw6xPa+OkT6YMQA0TCHlzQRr9MVKSENvJboAvoP8L4DEakgVgZEPWxfr4HEQcMuECAiQMRJ5hDVgTE8xlMG0ekpgTs+gSI+i4EMPBF8tt4PoMxMUKkYTBtrGljS4IOBuyM4jMIgHg+A+yBD9GfBz4DXmfdRH8bAyAmdMFmAH+Lu4IBX6Dssd7ggb0LNEEERJzAZoA9wAx0ExMg2gkSGFCRNk8wEC+AuOHT2YcYIJLuQQDEBpHsXQiAaN8C3sYrGNgLBLSxgcGfm2AgdjB47IFyg0jP8QywBx1t1BnABAHPxgeLRFWmCTogQgZ+nWmCCIgXjHXKYNrIxzo+oTSYaENsMBEf83QG/kQ6v4380WbPxAQG+kRKOB9gBvYC8D3ZCBNxggAG8IWoBgY4gf2eawNEuEADRIah46SKGcB/qdIZ/AMyVI2OEN2MIAAAAABJRU5ErkJggg==" y="-7"/> <g id="matplotlib.axis_3"/> <g id="matplotlib.axis_4"> <g id="ytick_8"> <g id="line2d_15"> <defs> <path d="M 0 0 L 3.5 0 " id="m8a31f06eee" style="stroke:#000000;stroke-width:0.8;"/> </defs> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#m8a31f06eee" y="291.918742"/> </g> </g> <g id="text_15"> <!-- 0.005 --> <g transform="translate(809.113 295.71796)scale(0.1 -0.1)"> <defs> <path d="M 10.6875 12.40625 L 21 12.40625 L 21 0 L 10.6875 0 z " id="DejaVuSans-46"/> </defs> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-48"/> <use x="222.65625" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_9"> <g id="line2d_16"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#m8a31f06eee" y="250.466707"/> </g> </g> <g id="text_16"> <!-- 0.010 --> <g transform="translate(809.113 254.265926)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-49"/> <use x="222.65625" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_10"> <g id="line2d_17"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#m8a31f06eee" y="209.014673"/> </g> </g> <g id="text_17"> <!-- 0.015 --> <g transform="translate(809.113 212.813892)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-49"/> <use x="222.65625" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_11"> <g id="line2d_18"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#m8a31f06eee" y="167.562639"/> </g> </g> <g id="text_18"> <!-- 0.020 --> <g transform="translate(809.113 171.361858)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-50"/> <use x="222.65625" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_12"> <g id="line2d_19"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#m8a31f06eee" y="126.110605"/> </g> </g> <g id="text_19"> <!-- 0.025 --> <g transform="translate(809.113 129.909824)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-50"/> <use x="222.65625" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_13"> <g id="line2d_20"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#m8a31f06eee" y="84.658571"/> </g> </g> <g id="text_20"> <!-- 0.030 --> <g transform="translate(809.113 88.457789)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-51"/> <use x="222.65625" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_14"> <g id="line2d_21"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#m8a31f06eee" y="43.206536"/> </g> </g> <g id="text_21"> <!-- 0.035 --> <g transform="translate(809.113 47.005755)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-51"/> <use x="222.65625" xlink:href="#DejaVuSans-53"/> </g> </g> </g> </g> <g id="patch_8"> <path d="M 785.805 333.36 L 785.805 332.085938 L 785.805 8.474063 L 785.805 7.2 L 802.113 7.2 L 802.113 8.474063 L 802.113 332.085938 L 802.113 333.36 z " style="fill:none;stroke:#000000;stroke-linejoin:miter;stroke-width:0.8;"/> </g> </g> </g> <defs> <clipPath id="pb06035e813"> <rect height="326.16" width="714.24" x="26.925" y="7.2"/> </clipPath> <clipPath id="p4da09dcd2a"> <rect height="326.16" width="16.308" x="785.805" y="7.2"/> </clipPath> </defs> </svg>)
%% Cell type:markdown id: tags:
# Version 2: Normal boundary handling
%% Cell type:code id: tags:
``` python
ldc = create_lid_driven_cavity(domain_size, relaxation_rate=relaxation_rate, lid_velocity=lid_velocity)
ldc.run(time_steps)
vel_version2 = ldc.velocity[:, :, :, :]
plt.vector_field_magnitude(vel_version2[:, :, domain_size[2]//2, :])
plt.colorbar();
plt.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7fa8b15ff7f0>
%%%% Output: display_data
![](data:image/png;base64,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)
![](data:image/png;base64,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)
![](data:image/svg+xml;utf8,<?xml version="1.0" encoding="utf-8" standalone="no"?> <!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"> <!-- Created with matplotlib (https://matplotlib.org/) --> <svg height="357.238125pt" version="1.1" viewBox="0 0 844.941125 357.238125" width="844.941125pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <metadata> <rdf:RDF xmlns:cc="http://creativecommons.org/ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"> <cc:Work> <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/> <dc:date>2020-10-27T11:08:27.352601</dc:date> <dc:format>image/svg+xml</dc:format> <dc:creator> <cc:Agent> <dc:title>Matplotlib v3.3.1, https://matplotlib.org/</dc:title> </cc:Agent> </dc:creator> </cc:Work> </rdf:RDF> </metadata> <defs> <style type="text/css">*{stroke-linecap:butt;stroke-linejoin:round;}</style> </defs> <g id="figure_1"> <g id="patch_1"> <path d="M 0 357.238125 L 844.941125 357.238125 L 844.941125 0 L 0 0 z " style="fill:none;"/> </g> <g id="axes_1"> <g id="patch_2"> <path d="M 26.925 333.36 L 741.165 333.36 L 741.165 7.2 L 26.925 7.2 z " style="fill:#ffffff;"/> </g> <g clip-path="url(#p75585fa4a0)"> <image height="327" id="image9296eb896c" transform="scale(1 -1)translate(0 -327)" width="327" x="220.965" xlink:href="data:image/png;base64, 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" y="-6.36"/> </g> <g id="matplotlib.axis_1"> <g id="xtick_1"> <g id="line2d_1"> <defs> <path d="M 0 0 L 0 3.5 " id="m35d5e751ae" style="stroke:#000000;stroke-width:0.8;"/> </defs> <g> <use style="stroke:#000000;stroke-width:0.8;" x="124.13625" xlink:href="#m35d5e751ae" y="333.36"/> </g> </g> <g id="text_1"> <!-- −10 --> <g transform="translate(113.583906 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 10.59375 35.5 L 73.1875 35.5 L 73.1875 27.203125 L 10.59375 27.203125 z " id="DejaVuSans-8722"/> <path d="M 12.40625 8.296875 L 28.515625 8.296875 L 28.515625 63.921875 L 10.984375 60.40625 L 10.984375 69.390625 L 28.421875 72.90625 L 38.28125 72.90625 L 38.28125 8.296875 L 54.390625 8.296875 L 54.390625 0 L 12.40625 0 z " id="DejaVuSans-49"/> <path d="M 31.78125 66.40625 Q 24.171875 66.40625 20.328125 58.90625 Q 16.5 51.421875 16.5 36.375 Q 16.5 21.390625 20.328125 13.890625 Q 24.171875 6.390625 31.78125 6.390625 Q 39.453125 6.390625 43.28125 13.890625 Q 47.125 21.390625 47.125 36.375 Q 47.125 51.421875 43.28125 58.90625 Q 39.453125 66.40625 31.78125 66.40625 z M 31.78125 74.21875 Q 44.046875 74.21875 50.515625 64.515625 Q 56.984375 54.828125 56.984375 36.375 Q 56.984375 17.96875 50.515625 8.265625 Q 44.046875 -1.421875 31.78125 -1.421875 Q 19.53125 -1.421875 13.0625 8.265625 Q 6.59375 17.96875 6.59375 36.375 Q 6.59375 54.828125 13.0625 64.515625 Q 19.53125 74.21875 31.78125 74.21875 z " id="DejaVuSans-48"/> </defs> <use xlink:href="#DejaVuSans-8722"/> <use x="83.789062" xlink:href="#DejaVuSans-49"/> <use x="147.412109" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_2"> <g id="line2d_2"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="226.06125" xlink:href="#m35d5e751ae" y="333.36"/> </g> </g> <g id="text_2"> <!-- 0 --> <g transform="translate(222.88 347.958438)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_3"> <g id="line2d_3"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="327.98625" xlink:href="#m35d5e751ae" y="333.36"/> </g> </g> <g id="text_3"> <!-- 10 --> <g transform="translate(321.62375 347.958438)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-49"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_4"> <g id="line2d_4"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="429.91125" xlink:href="#m35d5e751ae" y="333.36"/> </g> </g> <g id="text_4"> <!-- 20 --> <g transform="translate(423.54875 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 19.1875 8.296875 L 53.609375 8.296875 L 53.609375 0 L 7.328125 0 L 7.328125 8.296875 Q 12.9375 14.109375 22.625 23.890625 Q 32.328125 33.6875 34.8125 36.53125 Q 39.546875 41.84375 41.421875 45.53125 Q 43.3125 49.21875 43.3125 52.78125 Q 43.3125 58.59375 39.234375 62.25 Q 35.15625 65.921875 28.609375 65.921875 Q 23.96875 65.921875 18.8125 64.3125 Q 13.671875 62.703125 7.8125 59.421875 L 7.8125 69.390625 Q 13.765625 71.78125 18.9375 73 Q 24.125 74.21875 28.421875 74.21875 Q 39.75 74.21875 46.484375 68.546875 Q 53.21875 62.890625 53.21875 53.421875 Q 53.21875 48.921875 51.53125 44.890625 Q 49.859375 40.875 45.40625 35.40625 Q 44.1875 33.984375 37.640625 27.21875 Q 31.109375 20.453125 19.1875 8.296875 z " id="DejaVuSans-50"/> </defs> <use xlink:href="#DejaVuSans-50"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_5"> <g id="line2d_5"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="531.83625" xlink:href="#m35d5e751ae" y="333.36"/> </g> </g> <g id="text_5"> <!-- 30 --> <g transform="translate(525.47375 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 40.578125 39.3125 Q 47.65625 37.796875 51.625 33 Q 55.609375 28.21875 55.609375 21.1875 Q 55.609375 10.40625 48.1875 4.484375 Q 40.765625 -1.421875 27.09375 -1.421875 Q 22.515625 -1.421875 17.65625 -0.515625 Q 12.796875 0.390625 7.625 2.203125 L 7.625 11.71875 Q 11.71875 9.328125 16.59375 8.109375 Q 21.484375 6.890625 26.8125 6.890625 Q 36.078125 6.890625 40.9375 10.546875 Q 45.796875 14.203125 45.796875 21.1875 Q 45.796875 27.640625 41.28125 31.265625 Q 36.765625 34.90625 28.71875 34.90625 L 20.21875 34.90625 L 20.21875 43.015625 L 29.109375 43.015625 Q 36.375 43.015625 40.234375 45.921875 Q 44.09375 48.828125 44.09375 54.296875 Q 44.09375 59.90625 40.109375 62.90625 Q 36.140625 65.921875 28.71875 65.921875 Q 24.65625 65.921875 20.015625 65.03125 Q 15.375 64.15625 9.8125 62.3125 L 9.8125 71.09375 Q 15.4375 72.65625 20.34375 73.4375 Q 25.25 74.21875 29.59375 74.21875 Q 40.828125 74.21875 47.359375 69.109375 Q 53.90625 64.015625 53.90625 55.328125 Q 53.90625 49.265625 50.4375 45.09375 Q 46.96875 40.921875 40.578125 39.3125 z " id="DejaVuSans-51"/> </defs> <use xlink:href="#DejaVuSans-51"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_6"> <g id="line2d_6"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="633.76125" xlink:href="#m35d5e751ae" y="333.36"/> </g> </g> <g id="text_6"> <!-- 40 --> <g transform="translate(627.39875 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 37.796875 64.3125 L 12.890625 25.390625 L 37.796875 25.390625 z M 35.203125 72.90625 L 47.609375 72.90625 L 47.609375 25.390625 L 58.015625 25.390625 L 58.015625 17.1875 L 47.609375 17.1875 L 47.609375 0 L 37.796875 0 L 37.796875 17.1875 L 4.890625 17.1875 L 4.890625 26.703125 z " id="DejaVuSans-52"/> </defs> <use xlink:href="#DejaVuSans-52"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="xtick_7"> <g id="line2d_7"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="735.68625" xlink:href="#m35d5e751ae" y="333.36"/> </g> </g> <g id="text_7"> <!-- 50 --> <g transform="translate(729.32375 347.958438)scale(0.1 -0.1)"> <defs> <path d="M 10.796875 72.90625 L 49.515625 72.90625 L 49.515625 64.59375 L 19.828125 64.59375 L 19.828125 46.734375 Q 21.96875 47.46875 24.109375 47.828125 Q 26.265625 48.1875 28.421875 48.1875 Q 40.625 48.1875 47.75 41.5 Q 54.890625 34.8125 54.890625 23.390625 Q 54.890625 11.625 47.5625 5.09375 Q 40.234375 -1.421875 26.90625 -1.421875 Q 22.3125 -1.421875 17.546875 -0.640625 Q 12.796875 0.140625 7.71875 1.703125 L 7.71875 11.625 Q 12.109375 9.234375 16.796875 8.0625 Q 21.484375 6.890625 26.703125 6.890625 Q 35.15625 6.890625 40.078125 11.328125 Q 45.015625 15.765625 45.015625 23.390625 Q 45.015625 31 40.078125 35.4375 Q 35.15625 39.890625 26.703125 39.890625 Q 22.75 39.890625 18.8125 39.015625 Q 14.890625 38.140625 10.796875 36.28125 z " id="DejaVuSans-53"/> </defs> <use xlink:href="#DejaVuSans-53"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> </g> <g id="matplotlib.axis_2"> <g id="ytick_1"> <g id="line2d_8"> <defs> <path d="M 0 0 L -3.5 0 " id="mbef11f7315" style="stroke:#000000;stroke-width:0.8;"/> </defs> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#mbef11f7315" y="328.26375"/> </g> </g> <g id="text_8"> <!-- 0 --> <g transform="translate(13.5625 332.062969)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_2"> <g id="line2d_9"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#mbef11f7315" y="277.30125"/> </g> </g> <g id="text_9"> <!-- 5 --> <g transform="translate(13.5625 281.100469)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_3"> <g id="line2d_10"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#mbef11f7315" y="226.33875"/> </g> </g> <g id="text_10"> <!-- 10 --> <g transform="translate(7.2 230.137969)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-49"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_4"> <g id="line2d_11"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#mbef11f7315" y="175.37625"/> </g> </g> <g id="text_11"> <!-- 15 --> <g transform="translate(7.2 179.175469)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-49"/> <use x="63.623047" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_5"> <g id="line2d_12"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#mbef11f7315" y="124.41375"/> </g> </g> <g id="text_12"> <!-- 20 --> <g transform="translate(7.2 128.212969)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-50"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_6"> <g id="line2d_13"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#mbef11f7315" y="73.45125"/> </g> </g> <g id="text_13"> <!-- 25 --> <g transform="translate(7.2 77.250469)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-50"/> <use x="63.623047" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_7"> <g id="line2d_14"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="26.925" xlink:href="#mbef11f7315" y="22.48875"/> </g> </g> <g id="text_14"> <!-- 30 --> <g transform="translate(7.2 26.287969)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-51"/> <use x="63.623047" xlink:href="#DejaVuSans-48"/> </g> </g> </g> </g> <g id="patch_3"> <path d="M 26.925 333.36 L 26.925 7.2 " style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> </g> <g id="patch_4"> <path d="M 741.165 333.36 L 741.165 7.2 " style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> </g> <g id="patch_5"> <path d="M 26.925 333.36 L 741.165 333.36 " style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> </g> <g id="patch_6"> <path d="M 26.925 7.2 L 741.165 7.2 " style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> </g> </g> <g id="axes_2"> <g id="patch_7"> <path clip-path="url(#p3d8b2788c0)" d="M 785.805 333.36 L 785.805 332.085938 L 785.805 8.474063 L 785.805 7.2 L 802.113 7.2 L 802.113 8.474063 L 802.113 332.085938 L 802.113 333.36 z " style="fill:#ffffff;stroke:#ffffff;stroke-linejoin:miter;stroke-width:0.01;"/> </g> <image height="326" id="imageca91ea68cd" transform="scale(1 -1)translate(0 -326)" width="16" x="786" xlink:href="data:image/png;base64, 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" y="-7"/> <g id="matplotlib.axis_3"/> <g id="matplotlib.axis_4"> <g id="ytick_8"> <g id="line2d_15"> <defs> <path d="M 0 0 L 3.5 0 " id="me59db57928" style="stroke:#000000;stroke-width:0.8;"/> </defs> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#me59db57928" y="291.992282"/> </g> </g> <g id="text_15"> <!-- 0.005 --> <g transform="translate(809.113 295.791501)scale(0.1 -0.1)"> <defs> <path d="M 10.6875 12.40625 L 21 12.40625 L 21 0 L 10.6875 0 z " id="DejaVuSans-46"/> </defs> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-48"/> <use x="222.65625" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_9"> <g id="line2d_16"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#me59db57928" y="250.574889"/> </g> </g> <g id="text_16"> <!-- 0.010 --> <g transform="translate(809.113 254.374108)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-49"/> <use x="222.65625" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_10"> <g id="line2d_17"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#me59db57928" y="209.157495"/> </g> </g> <g id="text_17"> <!-- 0.015 --> <g transform="translate(809.113 212.956714)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-49"/> <use x="222.65625" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_11"> <g id="line2d_18"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#me59db57928" y="167.740101"/> </g> </g> <g id="text_18"> <!-- 0.020 --> <g transform="translate(809.113 171.53932)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-50"/> <use x="222.65625" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_12"> <g id="line2d_19"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#me59db57928" y="126.322708"/> </g> </g> <g id="text_19"> <!-- 0.025 --> <g transform="translate(809.113 130.121927)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-50"/> <use x="222.65625" xlink:href="#DejaVuSans-53"/> </g> </g> </g> <g id="ytick_13"> <g id="line2d_20"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#me59db57928" y="84.905314"/> </g> </g> <g id="text_20"> <!-- 0.030 --> <g transform="translate(809.113 88.704533)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-51"/> <use x="222.65625" xlink:href="#DejaVuSans-48"/> </g> </g> </g> <g id="ytick_14"> <g id="line2d_21"> <g> <use style="stroke:#000000;stroke-width:0.8;" x="802.113" xlink:href="#me59db57928" y="43.487921"/> </g> </g> <g id="text_21"> <!-- 0.035 --> <g transform="translate(809.113 47.287139)scale(0.1 -0.1)"> <use xlink:href="#DejaVuSans-48"/> <use x="63.623047" xlink:href="#DejaVuSans-46"/> <use x="95.410156" xlink:href="#DejaVuSans-48"/> <use x="159.033203" xlink:href="#DejaVuSans-51"/> <use x="222.65625" xlink:href="#DejaVuSans-53"/> </g> </g> </g> </g> <g id="patch_8"> <path d="M 785.805 333.36 L 785.805 332.085938 L 785.805 8.474063 L 785.805 7.2 L 802.113 7.2 L 802.113 8.474063 L 802.113 332.085938 L 802.113 333.36 z " style="fill:none;stroke:#000000;stroke-linejoin:miter;stroke-width:0.8;"/> </g> </g> </g> <defs> <clipPath id="p75585fa4a0"> <rect height="326.16" width="714.24" x="26.925" y="7.2"/> </clipPath> <clipPath id="p3d8b2788c0"> <rect height="326.16" width="16.308" x="785.805" y="7.2"/> </clipPath> </defs> </svg>)
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment