diff --git a/_quarto.yml b/_quarto.yml index 9f7aead6..e630164e 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -71,9 +71,16 @@ website: href: https://github.com/saforem2/l2hmc-qcd aria-label: GitHub left: - - href: qmd/l2hmc-2DU1.qmd - text: "Example: 2D U(1)" + - href: qmd/examples.qmd + text: "[{{< fa solid book >}}]{.red-text} Examples" + - href: qmd/slides.qmd + text: "[{{< fa solid person-chalkboard >}}]{.blue-text} Slides" + # - href: https://saforem2.github.io/lattice23 + # text: "Slides" + # - href: qmd/l2hmc-2DU1.qmd + # text: "Example: 2D U(1)" sidebar: false +## [{{< fa solid hourglass-end >}}]{.blue-text} Appendix # sidebar: # style: "floating" # search: true diff --git a/docs/index.html b/docs/index.html index bf5c8d98..787d3f06 100644 --- a/docs/index.html +++ b/docs/index.html @@ -7,7 +7,7 @@ - + l2hmc-qcd + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
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+

Examples

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+ + + +
+ +
+
+
+

Sam Foreman

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+
+ +
+
+
+

September 6, 2023

+
+
+ + +
+ + +
+ + + + + + + + Back to top

Citation

BibTeX citation:
@misc{foreman2023,
+  author = {Foreman, Sam},
+  title = {Examples},
+  date = {2023-09-06},
+  url = {https://saforem2.github.io/l2hmc-qcd},
+  langid = {en}
+}
+
For attribution, please cite this work as:
+Foreman, Sam. 2023. “Examples.” L2HMC. https://saforem2.github.io/l2hmc-qcd. +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/qmd/l2hmc-2DU1.html b/docs/qmd/l2hmc-2DU1.html index 6a5103b5..fd7a3056 100644 --- a/docs/qmd/l2hmc-2DU1.html +++ b/docs/qmd/l2hmc-2DU1.html @@ -7,7 +7,7 @@ - + l2hmc-qcd – l2hmc-2du1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ +
+ + + + +
+ +
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+

4D SU(3) Model

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+ + + +
+ +
+
+
+

Sam Foreman

+
+
+ +
+
+
+

September 6, 2023

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+ + +
+ + +
+ + +
+
import os
+from pathlib import Path
+from typing import Optional
+
+import lovely_tensors as lt
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import yaml
+
+import l2hmc.group.su3.pytorch.group as g
+from l2hmc import get_logger
+from l2hmc.common import grab_tensor, print_dict
+from l2hmc.configs import dict_to_list_of_overrides, get_experiment
+from l2hmc.experiment.pytorch.experiment import Experiment, evaluate  # noqa  # noqa
+from l2hmc.utils.dist import setup_torch
+from l2hmc.utils.plot_helpers import set_plot_style
+
+lt.monkey_patch()
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '6'
+os.environ['COLORTERM'] = 'truecolor;'
+os.environ['MASTER_PORT'] = '5433'
+# os.environ['MPLBACKEND'] = 'module://matplotlib-backend-kitty'
+# plt.switch_backend('module://matplotlib-backend-kitty')
+from enrich.style import STYLES
+from rich.theme import Theme
+from enrich.console import Console
+
+log = get_logger(__name__)
+theme = Theme(STYLES)
+# log = get_logger('ClimRR')
+console = Console(theme=theme, log_path=False, markup=True)
+if console.is_jupyter:
+    console.is_jupyter = False
+
+_ = setup_torch(precision='float64', backend='DDP', seed=4351)
+
+set_plot_style()
+
+from l2hmc.utils.plot_helpers import (  # noqa
+    set_plot_style,
+    plot_scalar,
+    plot_chains,
+    plot_leapfrogs
+)
+
+
Using device: cpu
+
+
+
+
[2023-09-06 08:27:49,688] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to mps (auto detect)
+[09/06/23 08:27:50][INFO][dist.py:226] - Caught MASTER_PORT:5433 from environment!
+[09/06/23 08:27:50][WARNING][dist.py:332] - Setting default dtype: float64
+[09/06/23 08:27:50][INFO][dist.py:338] - Global Rank: 0 / 0
+
+
+
+
def savefig(fig: plt.Figure, fname: str, outdir: os.PathLike):
+    pngfile = Path(outdir).joinpath(f"pngs/{fname}.png")
+    svgfile = Path(outdir).joinpath(f"svgs/{fname}.svg")
+    pngfile.parent.mkdir(exist_ok=True, parents=True)
+    svgfile.parent.mkdir(exist_ok=True, parents=True)
+    fig.savefig(svgfile, transparent=True, bbox_inches='tight')
+    fig.savefig(pngfile, transparent=True, bbox_inches='tight', dpi=300)
+
+
+def plot_metrics(metrics: dict, title: Optional[str] = None, **kwargs):
+    from l2hmc.utils.rich import is_interactive
+    from l2hmc.configs import QUARTO_OUTPUTS_DIR
+    outdir = Path(f"{QUARTO_OUTPUTS_DIR}/plots-4dSU3/{title}")
+    outdir.mkdir(exist_ok=True, parents=True)
+    for key, val in metrics.items():
+        fig, ax = plot_metric(val, name=key, **kwargs)
+        if title is not None:
+            ax.set_title(title)
+        log.info(f"Saving {key} to {outdir}")
+        savefig(fig, f"{key}", outdir=outdir)
+        # fpath = outdir.joinpath(f"{key}")
+        # plt.savefig(f"{fpath}.svg", bbox_inches='tight')
+        # plt.savefig(f"{fpath}.png", bbox_inches='tight', dpi=300)
+        # log.info(f"Saving {title} {key} plot to {fpath}")
+        if not is_interactive():
+            plt.show()
+
+
+def plot_metric(
+        metric: torch.Tensor,
+        name: Optional[str] = None,
+        **kwargs,
+):
+    assert len(metric) > 0
+    if isinstance(metric[0], (int, float, bool, np.floating)):
+        y = np.stack(metric)
+        return plot_scalar(y, ylabel=name, **kwargs)
+    element_shape = metric[0].shape
+    if len(element_shape) == 2:
+        if isinstance(metric, torch.Tensor):
+            y = grab_tensor(torch.stack(metric))
+        else:
+            y = np.stack(metric)
+        return plot_leapfrogs(y, ylabel=name)
+    if len(element_shape) == 1:
+        if isinstance(metric, torch.Tensor):
+            y = grab_tensor(torch.stack(metric))
+        else:
+            y = np.stack(metric)
+        return plot_chains(y, ylabel=name, **kwargs)
+    if len(element_shape) == 0:
+        if isinstance(metric, torch.Tensor):
+            y = grab_tensor(torch.stack(metric))
+        else:
+            y = np.stack(metric)
+        return plot_scalar(y, ylabel=name, **kwargs)
+    raise ValueError
+
+
+
def main():
+    from l2hmc.experiment.pytorch.experiment import train_step
+    set_plot_style()
+
+    from l2hmc.configs import CONF_DIR
+    su3conf = Path(CONF_DIR).joinpath('su3-min-cpu.yaml')
+    assert su3conf.is_file()
+    # su3conf = Path('su3-min-cpu.yaml')
+    with su3conf.open('r') as stream:
+        conf = dict(yaml.safe_load(stream))
+
+    log.info(conf)
+    overrides = dict_to_list_of_overrides(conf)
+    ptExpSU3 = get_experiment(overrides=[*overrides], build_networks=True)
+    state = ptExpSU3.trainer.dynamics.random_state(6.0)
+    assert isinstance(state.x, torch.Tensor)
+    assert isinstance(state.beta, torch.Tensor)
+    assert isinstance(ptExpSU3, Experiment)
+    xhmc, history_hmc = evaluate(
+        nsteps=10,
+        exp=ptExpSU3,
+        beta=state.beta,
+        x=state.x,
+        eps=0.1,
+        nleapfrog=1,
+        job_type='hmc',
+        nlog=1,
+        nprint=2,
+        grab=True
+    )
+    xhmc = ptExpSU3.trainer.dynamics.unflatten(xhmc)
+    log.info(f"checkSU(x_hmc): {g.checkSU(xhmc)}")
+    plot_metrics(history_hmc.history, title='HMC', marker='.')
+    # ptExpSU3.trainer.dynamics.init_weights(
+    #     method='uniform',
+    #     min=-1e-16,
+    #     max=1e-16,
+    #     bias=True,
+    #     # xeps=0.001,
+    #     # veps=0.001,
+    # )
+    xeval, history_eval = evaluate(
+        nsteps=10,
+        exp=ptExpSU3,
+        beta=6.0,
+        x=state.x,
+        job_type='eval',
+        nlog=1,
+        nprint=2,
+        grab=True,
+    )
+    xeval = ptExpSU3.trainer.dynamics.unflatten(xeval)
+    log.info(f"checkSU(x_eval): {g.checkSU(xeval)}")
+    plot_metrics(history_eval.history, title='Evaluate', marker='.')
+
+    history = {}
+    x = state.x
+    for step in range(20):
+        log.info(f'TRAIN STEP: {step}')
+        x, metrics = ptExpSU3.trainer.train_step((x, state.beta))
+        if (step > 0 and step % 2 == 0):
+            print_dict(metrics, grab=True)
+        if (step > 0 and step % 1 == 0):
+            for key, val in metrics.items():
+                try:
+                    history[key].append(val)
+                except KeyError:
+                    history[key] = [val]
+
+    x = ptExpSU3.trainer.dynamics.unflatten(x)
+    log.info(f"checkSU(x_train): {g.checkSU(x)}")
+    plot_metrics(history, title='train', marker='.')
+    #
+    # for step in range(20):
+    #     log.info(f"train step: {step}")
+    #     x, metrics = ptExpSU3.trainer.train_step((x, state.beta))
+    #     if step % 5 == 0:
+    #         print_dict(metrics, grab=True)
+
+    return x, history
+
+
+
# main()
+from l2hmc.experiment.pytorch.experiment import train_step
+set_plot_style()
+
+from l2hmc.configs import CONF_DIR
+su3conf = Path(CONF_DIR).joinpath('su3-min-cpu.yaml')
+assert su3conf.is_file()
+# su3conf = Path('./conf/su3-min-cpu.yaml')
+with su3conf.open('r') as stream:
+    conf = dict(yaml.safe_load(stream))
+
+log.info(conf)
+overrides = dict_to_list_of_overrides(conf)
+ptExpSU3 = get_experiment(overrides=[*overrides], build_networks=True)
+
+
[09/06/23 08:27:50][INFO][2091541093.py:12] - {'annealing_schedule': {'beta_final': 6.0, 'beta_init': 6.0}, 'backend': 'DDP', 'conv': 'none', 'dynamics': {'eps': 0.1, 'eps_fixed': True, 'group': 'SU3', 'latvolume': [1, 1, 1, 1], 'nchains': 4, 'nleapfrog': 1, 'use_separate_networks': False, 'use_split_xnets': False, 'verbose': True}, 'framework': 'pytorch', 'init_aim': False, 'init_wandb': False, 'learning_rate': {'clip_norm': 1.0, 'lr_init': 1e-05}, 'loss': {'charge_weight': 0.0, 'plaq_weight': 0.0, 'rmse_weight': 1.0, 'use_mixed_loss': False}, 'net_weights': {'v': {'q': 1.0, 's': 1.0, 't': 1.0}, 'x': {'q': 0.0, 's': 0.0, 't': 0.0}}, 'network': {'activation_fn': 'tanh', 'dropout_prob': 0.0, 'units': [1], 'use_batch_norm': False}, 'restore': False, 'save': False, 'steps': {'log': 1, 'nepoch': 10, 'nera': 1, 'print': 1, 'test': 50}, 'use_tb': False, 'use_wandb': False}
+[09/06/23 08:27:50][INFO][dist.py:226] - Caught MASTER_PORT:5433 from environment!
+[09/06/23 08:27:50][INFO][dist.py:226] - Caught MASTER_PORT:5433 from environment!
+[09/06/23 08:27:50][WARNING][trainer.py:437] - Using `torch.optim.Adam` optimizer
+[09/06/23 08:27:50][INFO][trainer.py:285] - num_params in model: 788
+[09/06/23 08:27:50][WARNING][trainer.py:251] - logging with freq 1 for wandb.watch
+
+
+
+
state = ptExpSU3.trainer.dynamics.random_state(6.0)
+assert isinstance(state.x, torch.Tensor)
+assert isinstance(state.beta, torch.Tensor)
+assert isinstance(ptExpSU3, Experiment)
+
+
+
xhmc, history_hmc = evaluate(
+    nsteps=10,
+    exp=ptExpSU3,
+    beta=state.beta,
+    x=state.x,
+    eps=0.1,
+    nleapfrog=1,
+    job_type='hmc',
+    nlog=1,
+    nprint=2,
+    grab=True
+)
+xhmc = ptExpSU3.trainer.dynamics.unflatten(xhmc)
+log.info(f"checkSU(x_hmc): {g.checkSU(xhmc)}")
+plot_metrics(history_hmc.history, title='HMC', marker='.')
+
+
[09/06/23 08:27:50][INFO][experiment.py:117] - Running 10 steps of hmc at beta=6.0000
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 0
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 1
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 2
+[09/06/23 08:27:50][INFO][common.py:97] - energy: torch.Size([2, 4]) torch.float64 
+[[-10.75791732 -12.34937625  -0.41675933  -1.53564322]
+ [-10.75124452 -12.3522306   -0.41267615  -1.54108594]]
+logprob: torch.Size([2, 4]) torch.float64 
+[[-10.75791732 -12.34937625  -0.41675933  -1.53564322]
+ [-10.75124452 -12.3522306   -0.41267615  -1.54108594]]
+logdet: torch.Size([2, 4]) torch.float64 
+[[0. 0. 0. 0.]
+ [0. 0. 0. 0.]]
+acc: torch.Size([4]) torch.float64 
+[0.99334941 1.         0.99592515 1.        ]
+sumlogdet: torch.Size([4]) torch.float64 
+[0. 0. 0. 0.]
+acc_mask: torch.Size([4]) torch.float32 
+[1. 1. 1. 1.]
+plaqs: torch.Size([4]) torch.float64 
+[0.16450298 0.30335925 0.08750178 0.11000818]
+sinQ: torch.Size([4]) torch.float64 
+[0.06377054 0.02936343 0.0290893  0.0471427 ]
+intQ: torch.Size([4]) torch.float64 
+[0.00363448 0.00167351 0.00165789 0.00268681]
+dQint: torch.Size([4]) torch.float64 
+[0.00063734 0.00026722 0.00153373 0.00079181]
+dQsin: torch.Size([4]) torch.float64 
+[0.01118282 0.00468872 0.02691084 0.01389309]
+loss: None None 
+-0.008486187067784774
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 3
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 4
+[09/06/23 08:27:50][INFO][common.py:97] - energy: torch.Size([2, 4]) torch.float64 
+[[-11.11456032 -17.6499957   -3.95552998  -6.53463898]
+ [-11.13603858 -17.671668    -3.9389084   -6.51679905]]
+logprob: torch.Size([2, 4]) torch.float64 
+[[-11.11456032 -17.6499957   -3.95552998  -6.53463898]
+ [-11.13603858 -17.671668    -3.9389084   -6.51679905]]
+logdet: torch.Size([2, 4]) torch.float64 
+[[0. 0. 0. 0.]
+ [0. 0. 0. 0.]]
+acc: torch.Size([4]) torch.float64 
+[1.         1.         0.9835158  0.98231826]
+sumlogdet: torch.Size([4]) torch.float64 
+[0. 0. 0. 0.]
+acc_mask: torch.Size([4]) torch.float32 
+[1. 1. 1. 1.]
+plaqs: torch.Size([4]) torch.float64 
+[0.25275746 0.399866   0.08094436 0.14319661]
+sinQ: torch.Size([4]) torch.float64 
+[0.06062536 0.00580304 0.05690804 0.03510997]
+intQ: torch.Size([4]) torch.float64 
+[0.00345523 0.00033073 0.00324337 0.00200103]
+dQint: torch.Size([4]) torch.float64 
+[6.94769306e-04 1.54302575e-05 1.45796993e-03 7.05322708e-06]
+dQsin: torch.Size([4]) torch.float64 
+[0.0121904  0.00027074 0.02558149 0.00012376]
+loss: None None 
+-0.007962121892379256
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 5
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 6
+[09/06/23 08:27:50][INFO][common.py:97] - energy: torch.Size([2, 4]) torch.float64 
+[[ -7.92357836 -18.88468089  -3.00365154  -4.94505522]
+ [ -7.96783894 -18.86501731  -2.98482073  -4.97272838]]
+logprob: torch.Size([2, 4]) torch.float64 
+[[ -7.92357836 -18.88468089  -3.00365154  -4.94505522]
+ [ -7.96783894 -18.86501731  -2.98482073  -4.97272838]]
+logdet: torch.Size([2, 4]) torch.float64 
+[[0. 0. 0. 0.]
+ [0. 0. 0. 0.]]
+acc: torch.Size([4]) torch.float64 
+[1.         0.98052848 0.98134538 1.        ]
+sumlogdet: torch.Size([4]) torch.float64 
+[0. 0. 0. 0.]
+acc_mask: torch.Size([4]) torch.float32 
+[1. 1. 1. 1.]
+plaqs: torch.Size([4]) torch.float64 
+[0.40639896 0.38496604 0.09990116 0.24746026]
+sinQ: torch.Size([4]) torch.float64 
+[ 0.04504223  0.00155452 -0.00443203  0.02472714]
+intQ: torch.Size([4]) torch.float64 
+[ 2.56709954e-03  8.85969084e-05 -2.52595510e-04  1.40927794e-03]
+dQint: torch.Size([4]) torch.float64 
+[7.55078122e-04 4.03034083e-04 2.18911031e-03 8.63609620e-05]
+dQsin: torch.Size([4]) torch.float64 
+[0.01324857 0.00707162 0.03841005 0.00151529]
+loss: None None 
+-0.009571223668505026
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 7
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 8
+[09/06/23 08:27:50][INFO][common.py:97] - energy: torch.Size([2, 4]) torch.float64 
+[[-13.98883304  -7.11601894  -9.48035637  -3.92143691]
+ [-13.99184124  -7.1014651   -9.49438875  -4.11672224]]
+logprob: torch.Size([2, 4]) torch.float64 
+[[-13.98883304  -7.11601894  -9.48035637  -3.92143691]
+ [-13.99184124  -7.1014651   -9.49438875  -4.11672224]]
+logdet: torch.Size([2, 4]) torch.float64 
+[[0. 0. 0. 0.]
+ [0. 0. 0. 0.]]
+acc: torch.Size([4]) torch.float64 
+[1.         0.98555155 1.         1.        ]
+sumlogdet: torch.Size([4]) torch.float64 
+[0. 0. 0. 0.]
+acc_mask: torch.Size([4]) torch.float32 
+[1. 1. 1. 1.]
+plaqs: torch.Size([4]) torch.float64 
+[0.4385061  0.34539504 0.04537868 0.37245688]
+sinQ: torch.Size([4]) torch.float64 
+[0.0153556  0.00315862 0.11135176 0.00892928]
+intQ: torch.Size([4]) torch.float64 
+[0.00087516 0.00018002 0.00634629 0.00050891]
+dQint: torch.Size([4]) torch.float64 
+[0.00082473 0.00022505 0.00010956 0.00055608]
+dQsin: torch.Size([4]) torch.float64 
+[0.01447065 0.00394877 0.00192226 0.00975699]
+loss: None None 
+-0.010424735653495517
+[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 9
+[09/06/23 08:27:50][INFO][2419635544.py:14] - checkSU(x_hmc): (tensor[4] f64 x∈[2.562e-16, 4.133e-16] μ=3.159e-16 σ=7.356e-17 [2.562e-16, 2.620e-16, 3.323e-16, 4.133e-16], tensor[4] f64 x∈[3.601e-16, 7.415e-16] μ=5.139e-16 σ=1.696e-16 [3.601e-16, 4.134e-16, 5.404e-16, 7.415e-16])
+[09/06/23 08:27:50][INFO][2088423886.py:19] - Saving energy to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:50][INFO][2088423886.py:19] - Saving logprob to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving logdet to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving acc to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving sumlogdet to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving acc_mask to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving plaqs to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:52][INFO][2088423886.py:19] - Saving sinQ to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:52][INFO][2088423886.py:19] - Saving intQ to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:52][INFO][2088423886.py:19] - Saving dQint to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:52][INFO][2088423886.py:19] - Saving dQsin to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
+[09/06/23 08:27:53][INFO][2088423886.py:19] - Saving loss to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC
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# ptExpSU3.trainer.dynamics.init_weights(
+#     method='uniform',
+#     min=-1e-16,
+#     max=1e-16,
+#     bias=True,
+#     # xeps=0.001,
+#     # veps=0.001,
+# )
+xeval, history_eval = evaluate(
+    nsteps=10,
+    exp=ptExpSU3,
+    beta=6.0,
+    x=state.x,
+    job_type='eval',
+    nlog=1,
+    nprint=2,
+    grab=True,
+)
+xeval = ptExpSU3.trainer.dynamics.unflatten(xeval)
+log.info(f"checkSU(x_eval): {g.checkSU(xeval)}")
+plot_metrics(history_eval.history, title='Evaluate', marker='.')
+
+
[09/06/23 08:27:54][INFO][experiment.py:117] - Running 10 steps of eval at beta=6.0000
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 0
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 1
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 2
+[09/06/23 08:27:54][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 
+[[ -9.93909722 -11.71406924 -13.98390472  -6.91867768]
+ [ -9.64725014 -11.76741361 -13.84813463  -6.57255943]
+ [ -9.24332693 -11.64892261 -13.56752346  -6.14914933]]
+logprob: torch.Size([3, 4]) torch.float64 
+[[ -9.93909722 -11.71406924 -13.98390472  -6.91867768]
+ [ -9.57527769 -11.61717321 -13.6004816   -6.52241563]
+ [ -9.17429537 -11.68892811 -13.56843895  -6.15131339]]
+logdet: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [-0.07197245 -0.1502404  -0.24765303 -0.0501438 ]
+ [-0.06903157  0.0400055   0.00091548  0.00216406]]
+sldf: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [-0.07197245 -0.1502404  -0.24765303 -0.0501438 ]
+ [ 0.          0.          0.          0.        ]]
+sldb: torch.Size([3, 4]) torch.float64 
+[[0.         0.         0.         0.        ]
+ [0.         0.         0.         0.        ]
+ [0.00294088 0.1902459  0.24856851 0.05230786]]
+sld: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [-0.07197245 -0.1502404  -0.24765303 -0.0501438 ]
+ [-0.06903157  0.0400055   0.00091548  0.00216406]]
+xeps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+veps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+acc: torch.Size([4]) torch.float64 
+[0.46542615 0.97517227 0.66003278 0.46423505]
+sumlogdet: torch.Size([4]) torch.float64 
+[-0.06903157  0.0400055   0.00091548  0.00216406]
+beta: torch.Size([]) torch.float64 
+6.0
+acc_mask: torch.Size([4]) torch.float32 
+[1. 1. 1. 1.]
+plaqs: torch.Size([4]) torch.float64 
+[0.16767915 0.37381953 0.37383779 0.20119684]
+sinQ: torch.Size([4]) torch.float64 
+[ 0.04301257  0.01916423  0.01386744 -0.01006018]
+intQ: torch.Size([4]) torch.float64 
+[ 0.00245142  0.00109223  0.00079035 -0.00057336]
+dQint: torch.Size([4]) torch.float64 
+[0.00119233 0.00094291 0.00155372 0.00095932]
+dQsin: torch.Size([4]) torch.float64 
+[0.0209205  0.01654419 0.02726158 0.01683214]
+loss: None None 
+-0.020710585677662853
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 3
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 4
+[09/06/23 08:27:54][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 
+[[-12.85390515 -19.69124648 -23.34418961 -11.83338904]
+ [-12.61930827 -19.83006702 -23.39287265 -12.32972948]
+ [-12.22362162 -18.93555772 -22.24709415 -12.09553454]]
+logprob: torch.Size([3, 4]) torch.float64 
+[[-12.85390515 -19.69124648 -23.34418961 -11.83338904]
+ [-12.65387143 -19.71055885 -23.17380781 -12.2815458 ]
+ [-12.22896701 -18.91997701 -22.23396345 -12.07717473]]
+logdet: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.03456316 -0.11950817 -0.21906484 -0.04818368]
+ [ 0.00534539 -0.01558072 -0.01313071 -0.01835981]]
+sldf: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.03456316 -0.11950817 -0.21906484 -0.04818368]
+ [ 0.          0.          0.          0.        ]]
+sldb: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.          0.          0.          0.        ]
+ [-0.02921778  0.10392745  0.20593414  0.02982387]]
+sld: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.03456316 -0.11950817 -0.21906484 -0.04818368]
+ [ 0.00534539 -0.01558072 -0.01313071 -0.01835981]]
+xeps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+veps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+acc: torch.Size([4]) torch.float64 
+[0.53529454 0.46242566 0.32948444 1.        ]
+sumlogdet: torch.Size([4]) torch.float64 
+[ 0.00534539 -0.         -0.         -0.01835981]
+beta: torch.Size([]) torch.float64 
+6.0
+acc_mask: torch.Size([4]) torch.float32 
+[1. 0. 0. 1.]
+plaqs: torch.Size([4]) torch.float64 
+[0.30234966 0.55960731 0.69219294 0.31462515]
+sinQ: torch.Size([4]) torch.float64 
+[0.02246026 0.01034247 0.01407288 0.00475271]
+intQ: torch.Size([4]) torch.float64 
+[0.00128008 0.00058945 0.00080206 0.00027087]
+dQint: torch.Size([4]) torch.float64 
+[0.0017964  0.         0.         0.00236045]
+dQsin: torch.Size([4]) torch.float64 
+[0.03151961 0.         0.         0.04141642]
+loss: None None 
+-0.015773998192892276
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 5
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 6
+[09/06/23 08:27:54][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 
+[[-19.55645935 -20.86462233 -20.90289315 -11.80938497]
+ [-19.27554945 -20.60377855 -20.79816694 -11.77715066]
+ [-18.96590202 -20.34010565 -20.641842   -11.43213333]]
+logprob: torch.Size([3, 4]) torch.float64 
+[[-19.55645935 -20.86462233 -20.90289315 -11.80938497]
+ [-19.31960696 -20.45738215 -20.56264778 -11.72755736]
+ [-18.97671809 -20.35406438 -20.61748015 -11.4626642 ]]
+logdet: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.04405751 -0.14639641 -0.23551916 -0.0495933 ]
+ [ 0.01081607  0.01395873 -0.02436185  0.03053087]]
+sldf: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.04405751 -0.14639641 -0.23551916 -0.0495933 ]
+ [ 0.          0.          0.          0.        ]]
+sldb: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.          0.          0.          0.        ]
+ [-0.03324145  0.16035514  0.21115731  0.08012417]]
+sld: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.04405751 -0.14639641 -0.23551916 -0.0495933 ]
+ [ 0.01081607  0.01395873 -0.02436185  0.03053087]]
+xeps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+veps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+acc: torch.Size([4]) torch.float64 
+[0.56004325 0.60016062 0.75170374 0.70700272]
+sumlogdet: torch.Size([4]) torch.float64 
+[ 0.01081607  0.01395873 -0.02436185  0.03053087]
+beta: torch.Size([]) torch.float64 
+6.0
+acc_mask: torch.Size([4]) torch.float32 
+[1. 1. 1. 1.]
+plaqs: torch.Size([4]) torch.float64 
+[0.50284475 0.55960731 0.58704547 0.23919369]
+sinQ: torch.Size([4]) torch.float64 
+[-0.01053072  0.01034247  0.01918416 -0.03898123]
+intQ: torch.Size([4]) torch.float64 
+[-0.00060018  0.00058945  0.00109337 -0.00222166]
+dQint: torch.Size([4]) torch.float64 
+[0.00135638 0.00120177 0.00227558 0.00031006]
+dQsin: torch.Size([4]) torch.float64 
+[0.02379895 0.02108615 0.03992728 0.00544025]
+loss: None None 
+-0.016508422822688693
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 7
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 8
+[09/06/23 08:27:54][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 
+[[-18.07899101 -28.71248658 -13.48019371 -14.59335827]
+ [-17.76685303 -28.30437183 -12.76586787 -14.82013678]
+ [-17.05030815 -28.78619363 -11.86821313 -14.6282822 ]]
+logprob: torch.Size([3, 4]) torch.float64 
+[[-18.07899101 -28.71248658 -13.48019371 -14.59335827]
+ [-17.86336929 -28.37337191 -12.58082071 -14.75738913]
+ [-17.21060337 -28.90503573 -11.8680323  -14.61315147]]
+logdet: torch.Size([3, 4]) torch.float64 
+[[ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
+ [ 9.65162556e-02  6.90000737e-02 -1.85047158e-01 -6.27476486e-02]
+ [ 1.60295223e-01  1.18842098e-01 -1.80827316e-04 -1.51307243e-02]]
+sldf: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [ 0.09651626  0.06900007 -0.18504716 -0.06274765]
+ [ 0.          0.          0.          0.        ]]
+sldb: torch.Size([3, 4]) torch.float64 
+[[0.         0.         0.         0.        ]
+ [0.         0.         0.         0.        ]
+ [0.06377897 0.04984202 0.18486633 0.04761692]]
+sld: torch.Size([3, 4]) torch.float64 
+[[ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
+ [ 9.65162556e-02  6.90000737e-02 -1.85047158e-01 -6.27476486e-02]
+ [ 1.60295223e-01  1.18842098e-01 -1.80827316e-04 -1.51307243e-02]]
+xeps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+veps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+acc: torch.Size([4]) torch.float64 
+[0.41962759 1.         0.19945604 1.        ]
+sumlogdet: torch.Size([4]) torch.float64 
+[ 0.16029522  0.1188421  -0.         -0.01513072]
+beta: torch.Size([]) torch.float64 
+6.0
+acc_mask: torch.Size([4]) torch.float32 
+[1. 1. 0. 1.]
+plaqs: torch.Size([4]) torch.float64 
+[0.62270466 0.74549143 0.53797573 0.24602781]
+sinQ: torch.Size([4]) torch.float64 
+[ 0.01834668  0.00603678 -0.01653119 -0.05096747]
+intQ: torch.Size([4]) torch.float64 
+[ 0.00104564  0.00034406 -0.00094216 -0.0029048 ]
+dQint: torch.Size([4]) torch.float64 
+[0.00191687 0.00024381 0.         0.00046954]
+dQsin: torch.Size([4]) torch.float64 
+[0.03363338 0.0042779  0.         0.00823858]
+loss: None None 
+-0.01735800592796972
+[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 9
+[09/06/23 08:27:54][INFO][1629827420.py:20] - checkSU(x_eval): (tensor[4] f64 x∈[9.577e-14, 0.024] μ=0.009 σ=0.012 [0.024, 2.063e-13, 0.014, 9.577e-14], tensor[4] f64 x∈[1.790e-13, 0.034] μ=0.013 σ=0.016 [0.034, 3.935e-13, 0.016, 1.790e-13])
+[09/06/23 08:27:54][INFO][2088423886.py:19] - Saving energy to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:55][INFO][2088423886.py:19] - Saving logprob to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:55][INFO][2088423886.py:19] - Saving logdet to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:55][INFO][2088423886.py:19] - Saving sldf to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:55][INFO][2088423886.py:19] - Saving sldb to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:56][INFO][2088423886.py:19] - Saving sld to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:56][INFO][2088423886.py:19] - Saving xeps to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:56][INFO][2088423886.py:19] - Saving veps to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:56][INFO][2088423886.py:19] - Saving acc to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving sumlogdet to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving beta to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving acc_mask to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving plaqs to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving sinQ to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:58][INFO][2088423886.py:19] - Saving intQ to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:58][INFO][2088423886.py:19] - Saving dQint to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:58][INFO][2088423886.py:19] - Saving dQsin to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
+[09/06/23 08:27:58][INFO][2088423886.py:19] - Saving loss to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate
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history = {}
+x = state.x
+for step in range(20):
+    log.info(f'TRAIN STEP: {step}')
+    x, metrics = ptExpSU3.trainer.train_step((x, state.beta))
+    if (step > 0 and step % 2 == 0):
+        print_dict(metrics, grab=True)
+    if (step > 0 and step % 1 == 0):
+        for key, val in metrics.items():
+            try:
+                history[key].append(val)
+            except KeyError:
+                history[key] = [val]
+
+
[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 0
+[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 1
+[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 2
+[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 
+[[-20.11355156 -13.83759796  -4.51987377 -11.45297864]
+ [-19.63407248 -12.82094906  -3.89140218 -11.38944608]
+ [-18.91054695 -12.57901193  -3.60837652 -10.80563673]]
+logprob: torch.Size([3, 4]) torch.float64 
+[[-20.11355156 -13.83759796  -4.51987377 -11.45297864]
+ [-19.5349711  -12.66848527  -3.6937792  -11.31223158]
+ [-18.98136385 -12.5501776   -3.58081924 -10.80513544]]
+logdet: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [-0.09910138 -0.15246379 -0.19762298 -0.0772145 ]
+ [ 0.07081689 -0.02883433 -0.02755728 -0.00050128]]
+sldf: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [-0.09910138 -0.15246379 -0.19762298 -0.0772145 ]
+ [ 0.          0.          0.          0.        ]]
+sldb: torch.Size([3, 4]) torch.float64 
+[[0.         0.         0.         0.        ]
+ [0.         0.         0.         0.        ]
+ [0.16991827 0.12362946 0.17006571 0.07671321]]
+sld: torch.Size([3, 4]) torch.float64 
+[[ 0.          0.          0.          0.        ]
+ [-0.09910138 -0.15246379 -0.19762298 -0.0772145 ]
+ [ 0.07081689 -0.02883433 -0.02755728 -0.00050128]]
+xeps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+veps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+acc: torch.Size([4]) torch.float64 
+[0.32232732 0.2759818  0.39099734 0.52317294]
+sumlogdet: torch.Size([4]) torch.float64 
+[ 0.07081689 -0.         -0.02755728 -0.00050128]
+beta: torch.Size([]) torch.float64 
+6.0
+acc_mask: torch.Size([4]) torch.float32 
+[1. 0. 1. 1.]
+loss: None None 
+-0.009001684302756496
+plaqs: torch.Size([4]) torch.float64 
+[0.36183972 0.43746352 0.02919545 0.2244051 ]
+sinQ: torch.Size([4]) torch.float64 
+[ 0.01845919 -0.00937014  0.00653818  0.03579483]
+intQ: torch.Size([4]) torch.float64 
+[ 0.00105205 -0.00053403  0.00037263  0.00204006]
+dQint: torch.Size([4]) torch.float64 
+[0.00068181 0.         0.00033711 0.001986  ]
+dQsin: torch.Size([4]) torch.float64 
+[0.01196306 0.         0.00591496 0.0348463 ]
+[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 3
+[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 4
+[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 
+[[-16.99217625 -18.05099071 -10.04985909  -7.1340411 ]
+ [-16.61047626 -17.84035555  -9.68866118  -7.14912011]
+ [-16.28336399 -18.36191548  -9.14330041  -5.86613539]]
+logprob: torch.Size([3, 4]) torch.float64 
+[[-16.99217625 -18.05099071 -10.04985909  -7.1340411 ]
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+xeps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+veps: torch.Size([3]) torch.float64 
+[0.1 0.1 0.1]
+acc: torch.Size([4]) torch.float64 
+[0.21103823 0.38229083 0.30418792 0.43282093]
+sumlogdet: torch.Size([4]) torch.float64 
+[-0.         -0.08108449  0.          0.01506727]
+beta: torch.Size([]) torch.float64 
+6.0
+acc_mask: torch.Size([4]) torch.float32 
+[0. 1. 0. 1.]
+loss: None None 
+-0.00985098143962743
+plaqs: torch.Size([4]) torch.float64 
+[0.85381281 0.70579842 0.80681173 0.75974167]
+sinQ: torch.Size([4]) torch.float64 
+[-0.00332841 -0.00489329 -0.01271997  0.00506889]
+intQ: torch.Size([4]) torch.float64 
+[-0.0001897  -0.00027888 -0.00072495  0.00028889]
+dQint: torch.Size([4]) torch.float64 
+[0.         0.00045467 0.         0.00140838]
+dQsin: torch.Size([4]) torch.float64 
+[0.         0.00797764 0.         0.02471136]
+[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 19
+
+
+
+
x = ptExpSU3.trainer.dynamics.unflatten(x)
+log.info(f"checkSU(x_train): {g.checkSU(x)}")
+# plot_metrics(history, title='train', marker='.')
+
+
[09/06/23 08:28:01][INFO][1630590238.py:2] - checkSU(x_train): (tensor[4] f64 x∈[3.838e-16, 0.018] μ=0.008 σ=0.009 [0.018, 0.014, 3.838e-16, 9.135e-13], tensor[4] f64 x∈[6.307e-16, 0.026] μ=0.011 σ=0.013 [0.026, 0.017, 6.307e-16, 1.818e-12])
+
+
+
+
print(history.keys())
+
+
dict_keys(['energy', 'logprob', 'logdet', 'sldf', 'sldb', 'sld', 'xeps', 'veps', 'acc', 'sumlogdet', 'beta', 'acc_mask', 'loss', 'plaqs', 'sinQ', 'intQ', 'dQint', 'dQsin'])
+
+
+
+ + + + Back to top

Citation

BibTeX citation:
@misc{foreman2023,
+  author = {Foreman, Sam},
+  title = {4D {\$SU(3)\$} {Model}},
+  date = {2023-09-06},
+  url = {https://saforem2.github.io/l2hmc-qcd},
+  langid = {en}
+}
+
For attribution, please cite this work as:
+Foreman, Sam. 2023. “4D $SU(3)$ Model.” L2HMC. https://saforem2.github.io/l2hmc-qcd. +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-10.png b/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-10.png new file mode 100644 index 00000000..bdf51ca8 Binary files /dev/null and b/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-10.png differ diff --git a/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-11.png b/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-11.png new file mode 100644 index 00000000..8f1bf9ee Binary files /dev/null and b/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-11.png differ diff --git a/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-12.png b/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-12.png new file mode 100644 index 00000000..12c0b6e3 Binary files /dev/null and b/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-12.png differ diff --git a/docs/qmd/l2hmc-4DSU3_files/figure-html/cell-7-output-13.png 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+
+ +
+ +
+ + + + +
+ +
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+

Recent Talks

+
+ + + +
+ +
+
+
+

Sam Foreman

+
+
+ +
+
+
+

September 6, 2023

+
+
+ + +
+ + +
+ +
+ +
+
+ + +
+
+
+
+ +
+
+ + + +
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+
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+ +
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+ + +
+
+
+ + + + Back to top

Citation

BibTeX citation:
@misc{foreman2023,
+  author = {Foreman, Sam},
+  title = {Recent {Talks}},
+  date = {2023-09-06},
+  url = {https://saforem2.github.io/l2hmc-qcd},
+  langid = {en}
+}
+
For attribution, please cite this work as:
+Foreman, Sam. 2023. “Recent Talks.” L2HMC. https://saforem2.github.io/l2hmc-qcd. +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/search.json b/docs/search.json index 3b973e6b..a56bb77d 100644 --- a/docs/search.json +++ b/docs/search.json @@ -117,5 +117,26 @@ "title": "", "section": "", "text": "Documentation\n\n\n\n\n\n\nTODO\n\n\n\nComplete!\n\n\n\n\n\n\n Back to topCitationBibTeX citation:@misc{foreman2023,\n author = {Foreman, Sam},\n date = {2023-08-23},\n url = {https://saforem2.github.io/l2hmc-qcd},\n langid = {en}\n}\nFor attribution, please cite this work as:\nForeman, Sam. 2023. L2HMC. https://saforem2.github.io/l2hmc-qcd." + }, + { + "objectID": "qmd/l2hmc-4DSU3.html", + "href": "qmd/l2hmc-4DSU3.html", + "title": "4D SU(3) Model", + "section": "", + "text": "Slides\n\n\nimport os\nfrom pathlib import Path\nfrom typing import Optional\n\nimport lovely_tensors as lt\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport yaml\n\nimport l2hmc.group.su3.pytorch.group as g\nfrom l2hmc import get_logger\nfrom l2hmc.common import grab_tensor, print_dict\nfrom l2hmc.configs import dict_to_list_of_overrides, get_experiment\nfrom l2hmc.experiment.pytorch.experiment import Experiment, evaluate # noqa # noqa\nfrom l2hmc.utils.dist import setup_torch\nfrom l2hmc.utils.plot_helpers import set_plot_style\n\nlt.monkey_patch()\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '6'\nos.environ['COLORTERM'] = 'truecolor;'\nos.environ['MASTER_PORT'] = '5433'\n# os.environ['MPLBACKEND'] = 'module://matplotlib-backend-kitty'\n# plt.switch_backend('module://matplotlib-backend-kitty')\nfrom enrich.style import STYLES\nfrom rich.theme import Theme\nfrom enrich.console import Console\n\nlog = get_logger(__name__)\ntheme = Theme(STYLES)\n# log = get_logger('ClimRR')\nconsole = Console(theme=theme, log_path=False, markup=True)\nif console.is_jupyter:\n console.is_jupyter = False\n\n_ = setup_torch(precision='float64', backend='DDP', seed=4351)\n\nset_plot_style()\n\nfrom l2hmc.utils.plot_helpers import ( # noqa\n set_plot_style,\n plot_scalar,\n plot_chains,\n plot_leapfrogs\n)\n\nUsing device: cpu\n\n\n\n[2023-09-06 08:27:49,688] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to mps (auto detect)\n[09/06/23 08:27:50][INFO][dist.py:226] - Caught MASTER_PORT:5433 from environment!\n[09/06/23 08:27:50][WARNING][dist.py:332] - Setting default dtype: float64\n[09/06/23 08:27:50][INFO][dist.py:338] - Global Rank: 0 / 0\n\n\n\ndef savefig(fig: plt.Figure, fname: str, outdir: os.PathLike):\n pngfile = Path(outdir).joinpath(f\"pngs/{fname}.png\")\n svgfile = Path(outdir).joinpath(f\"svgs/{fname}.svg\")\n pngfile.parent.mkdir(exist_ok=True, parents=True)\n svgfile.parent.mkdir(exist_ok=True, parents=True)\n fig.savefig(svgfile, transparent=True, bbox_inches='tight')\n fig.savefig(pngfile, transparent=True, bbox_inches='tight', dpi=300)\n\n\ndef plot_metrics(metrics: dict, title: Optional[str] = None, **kwargs):\n from l2hmc.utils.rich import is_interactive\n from l2hmc.configs import QUARTO_OUTPUTS_DIR\n outdir = Path(f\"{QUARTO_OUTPUTS_DIR}/plots-4dSU3/{title}\")\n outdir.mkdir(exist_ok=True, parents=True)\n for key, val in metrics.items():\n fig, ax = plot_metric(val, name=key, **kwargs)\n if title is not None:\n ax.set_title(title)\n log.info(f\"Saving {key} to {outdir}\")\n savefig(fig, f\"{key}\", outdir=outdir)\n # fpath = outdir.joinpath(f\"{key}\")\n # plt.savefig(f\"{fpath}.svg\", bbox_inches='tight')\n # plt.savefig(f\"{fpath}.png\", bbox_inches='tight', dpi=300)\n # log.info(f\"Saving {title} {key} plot to {fpath}\")\n if not is_interactive():\n plt.show()\n\n\ndef plot_metric(\n metric: torch.Tensor,\n name: Optional[str] = None,\n **kwargs,\n):\n assert len(metric) > 0\n if isinstance(metric[0], (int, float, bool, np.floating)):\n y = np.stack(metric)\n return plot_scalar(y, ylabel=name, **kwargs)\n element_shape = metric[0].shape\n if len(element_shape) == 2:\n if isinstance(metric, torch.Tensor):\n y = grab_tensor(torch.stack(metric))\n else:\n y = np.stack(metric)\n return plot_leapfrogs(y, ylabel=name)\n if len(element_shape) == 1:\n if isinstance(metric, torch.Tensor):\n y = grab_tensor(torch.stack(metric))\n else:\n y = np.stack(metric)\n return plot_chains(y, ylabel=name, **kwargs)\n if len(element_shape) == 0:\n if isinstance(metric, torch.Tensor):\n y = grab_tensor(torch.stack(metric))\n else:\n y = np.stack(metric)\n return plot_scalar(y, ylabel=name, **kwargs)\n raise ValueError\n\n\ndef main():\n from l2hmc.experiment.pytorch.experiment import train_step\n set_plot_style()\n\n from l2hmc.configs import CONF_DIR\n su3conf = Path(CONF_DIR).joinpath('su3-min-cpu.yaml')\n assert su3conf.is_file()\n # su3conf = Path('su3-min-cpu.yaml')\n with su3conf.open('r') as stream:\n conf = dict(yaml.safe_load(stream))\n\n log.info(conf)\n overrides = dict_to_list_of_overrides(conf)\n ptExpSU3 = get_experiment(overrides=[*overrides], build_networks=True)\n state = ptExpSU3.trainer.dynamics.random_state(6.0)\n assert isinstance(state.x, torch.Tensor)\n assert isinstance(state.beta, torch.Tensor)\n assert isinstance(ptExpSU3, Experiment)\n xhmc, history_hmc = evaluate(\n nsteps=10,\n exp=ptExpSU3,\n beta=state.beta,\n x=state.x,\n eps=0.1,\n nleapfrog=1,\n job_type='hmc',\n nlog=1,\n nprint=2,\n grab=True\n )\n xhmc = ptExpSU3.trainer.dynamics.unflatten(xhmc)\n log.info(f\"checkSU(x_hmc): {g.checkSU(xhmc)}\")\n plot_metrics(history_hmc.history, title='HMC', marker='.')\n # ptExpSU3.trainer.dynamics.init_weights(\n # method='uniform',\n # min=-1e-16,\n # max=1e-16,\n # bias=True,\n # # xeps=0.001,\n # # veps=0.001,\n # )\n xeval, history_eval = evaluate(\n nsteps=10,\n exp=ptExpSU3,\n beta=6.0,\n x=state.x,\n job_type='eval',\n nlog=1,\n nprint=2,\n grab=True,\n )\n xeval = ptExpSU3.trainer.dynamics.unflatten(xeval)\n log.info(f\"checkSU(x_eval): {g.checkSU(xeval)}\")\n plot_metrics(history_eval.history, title='Evaluate', marker='.')\n\n history = {}\n x = state.x\n for step in range(20):\n log.info(f'TRAIN STEP: {step}')\n x, metrics = ptExpSU3.trainer.train_step((x, state.beta))\n if (step > 0 and step % 2 == 0):\n print_dict(metrics, grab=True)\n if (step > 0 and step % 1 == 0):\n for key, val in metrics.items():\n try:\n history[key].append(val)\n except KeyError:\n history[key] = [val]\n\n x = ptExpSU3.trainer.dynamics.unflatten(x)\n log.info(f\"checkSU(x_train): {g.checkSU(x)}\")\n plot_metrics(history, title='train', marker='.')\n #\n # for step in range(20):\n # log.info(f\"train step: {step}\")\n # x, metrics = ptExpSU3.trainer.train_step((x, state.beta))\n # if step % 5 == 0:\n # print_dict(metrics, grab=True)\n\n return x, history\n\n\n# main()\nfrom l2hmc.experiment.pytorch.experiment import train_step\nset_plot_style()\n\nfrom l2hmc.configs import CONF_DIR\nsu3conf = Path(CONF_DIR).joinpath('su3-min-cpu.yaml')\nassert su3conf.is_file()\n# su3conf = Path('./conf/su3-min-cpu.yaml')\nwith su3conf.open('r') as stream:\n conf = dict(yaml.safe_load(stream))\n\nlog.info(conf)\noverrides = dict_to_list_of_overrides(conf)\nptExpSU3 = get_experiment(overrides=[*overrides], build_networks=True)\n\n[09/06/23 08:27:50][INFO][2091541093.py:12] - {'annealing_schedule': {'beta_final': 6.0, 'beta_init': 6.0}, 'backend': 'DDP', 'conv': 'none', 'dynamics': {'eps': 0.1, 'eps_fixed': True, 'group': 'SU3', 'latvolume': [1, 1, 1, 1], 'nchains': 4, 'nleapfrog': 1, 'use_separate_networks': False, 'use_split_xnets': False, 'verbose': True}, 'framework': 'pytorch', 'init_aim': False, 'init_wandb': False, 'learning_rate': {'clip_norm': 1.0, 'lr_init': 1e-05}, 'loss': {'charge_weight': 0.0, 'plaq_weight': 0.0, 'rmse_weight': 1.0, 'use_mixed_loss': False}, 'net_weights': {'v': {'q': 1.0, 's': 1.0, 't': 1.0}, 'x': {'q': 0.0, 's': 0.0, 't': 0.0}}, 'network': {'activation_fn': 'tanh', 'dropout_prob': 0.0, 'units': [1], 'use_batch_norm': False}, 'restore': False, 'save': False, 'steps': {'log': 1, 'nepoch': 10, 'nera': 1, 'print': 1, 'test': 50}, 'use_tb': False, 'use_wandb': False}\n[09/06/23 08:27:50][INFO][dist.py:226] - Caught MASTER_PORT:5433 from environment!\n[09/06/23 08:27:50][INFO][dist.py:226] - Caught MASTER_PORT:5433 from environment!\n[09/06/23 08:27:50][WARNING][trainer.py:437] - Using `torch.optim.Adam` optimizer\n[09/06/23 08:27:50][INFO][trainer.py:285] - num_params in model: 788\n[09/06/23 08:27:50][WARNING][trainer.py:251] - logging with freq 1 for wandb.watch\n\n\n\nstate = ptExpSU3.trainer.dynamics.random_state(6.0)\nassert isinstance(state.x, torch.Tensor)\nassert isinstance(state.beta, torch.Tensor)\nassert isinstance(ptExpSU3, Experiment)\n\n\nxhmc, history_hmc = evaluate(\n nsteps=10,\n exp=ptExpSU3,\n beta=state.beta,\n x=state.x,\n eps=0.1,\n nleapfrog=1,\n job_type='hmc',\n nlog=1,\n nprint=2,\n grab=True\n)\nxhmc = ptExpSU3.trainer.dynamics.unflatten(xhmc)\nlog.info(f\"checkSU(x_hmc): {g.checkSU(xhmc)}\")\nplot_metrics(history_hmc.history, title='HMC', marker='.')\n\n[09/06/23 08:27:50][INFO][experiment.py:117] - Running 10 steps of hmc at beta=6.0000\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 0\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 1\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 2\n[09/06/23 08:27:50][INFO][common.py:97] - energy: torch.Size([2, 4]) torch.float64 \n[[-10.75791732 -12.34937625 -0.41675933 -1.53564322]\n [-10.75124452 -12.3522306 -0.41267615 -1.54108594]]\nlogprob: torch.Size([2, 4]) torch.float64 \n[[-10.75791732 -12.34937625 -0.41675933 -1.53564322]\n [-10.75124452 -12.3522306 -0.41267615 -1.54108594]]\nlogdet: torch.Size([2, 4]) torch.float64 \n[[0. 0. 0. 0.]\n [0. 0. 0. 0.]]\nacc: torch.Size([4]) torch.float64 \n[0.99334941 1. 0.99592515 1. ]\nsumlogdet: torch.Size([4]) torch.float64 \n[0. 0. 0. 0.]\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 1.]\nplaqs: torch.Size([4]) torch.float64 \n[0.16450298 0.30335925 0.08750178 0.11000818]\nsinQ: torch.Size([4]) torch.float64 \n[0.06377054 0.02936343 0.0290893 0.0471427 ]\nintQ: torch.Size([4]) torch.float64 \n[0.00363448 0.00167351 0.00165789 0.00268681]\ndQint: torch.Size([4]) torch.float64 \n[0.00063734 0.00026722 0.00153373 0.00079181]\ndQsin: torch.Size([4]) torch.float64 \n[0.01118282 0.00468872 0.02691084 0.01389309]\nloss: None None \n-0.008486187067784774\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 3\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 4\n[09/06/23 08:27:50][INFO][common.py:97] - energy: torch.Size([2, 4]) torch.float64 \n[[-11.11456032 -17.6499957 -3.95552998 -6.53463898]\n [-11.13603858 -17.671668 -3.9389084 -6.51679905]]\nlogprob: torch.Size([2, 4]) torch.float64 \n[[-11.11456032 -17.6499957 -3.95552998 -6.53463898]\n [-11.13603858 -17.671668 -3.9389084 -6.51679905]]\nlogdet: torch.Size([2, 4]) torch.float64 \n[[0. 0. 0. 0.]\n [0. 0. 0. 0.]]\nacc: torch.Size([4]) torch.float64 \n[1. 1. 0.9835158 0.98231826]\nsumlogdet: torch.Size([4]) torch.float64 \n[0. 0. 0. 0.]\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 1.]\nplaqs: torch.Size([4]) torch.float64 \n[0.25275746 0.399866 0.08094436 0.14319661]\nsinQ: torch.Size([4]) torch.float64 \n[0.06062536 0.00580304 0.05690804 0.03510997]\nintQ: torch.Size([4]) torch.float64 \n[0.00345523 0.00033073 0.00324337 0.00200103]\ndQint: torch.Size([4]) torch.float64 \n[6.94769306e-04 1.54302575e-05 1.45796993e-03 7.05322708e-06]\ndQsin: torch.Size([4]) torch.float64 \n[0.0121904 0.00027074 0.02558149 0.00012376]\nloss: None None \n-0.007962121892379256\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 5\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 6\n[09/06/23 08:27:50][INFO][common.py:97] - energy: torch.Size([2, 4]) torch.float64 \n[[ -7.92357836 -18.88468089 -3.00365154 -4.94505522]\n [ -7.96783894 -18.86501731 -2.98482073 -4.97272838]]\nlogprob: torch.Size([2, 4]) torch.float64 \n[[ -7.92357836 -18.88468089 -3.00365154 -4.94505522]\n [ -7.96783894 -18.86501731 -2.98482073 -4.97272838]]\nlogdet: torch.Size([2, 4]) torch.float64 \n[[0. 0. 0. 0.]\n [0. 0. 0. 0.]]\nacc: torch.Size([4]) torch.float64 \n[1. 0.98052848 0.98134538 1. ]\nsumlogdet: torch.Size([4]) torch.float64 \n[0. 0. 0. 0.]\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 1.]\nplaqs: torch.Size([4]) torch.float64 \n[0.40639896 0.38496604 0.09990116 0.24746026]\nsinQ: torch.Size([4]) torch.float64 \n[ 0.04504223 0.00155452 -0.00443203 0.02472714]\nintQ: torch.Size([4]) torch.float64 \n[ 2.56709954e-03 8.85969084e-05 -2.52595510e-04 1.40927794e-03]\ndQint: torch.Size([4]) torch.float64 \n[7.55078122e-04 4.03034083e-04 2.18911031e-03 8.63609620e-05]\ndQsin: torch.Size([4]) torch.float64 \n[0.01324857 0.00707162 0.03841005 0.00151529]\nloss: None None \n-0.009571223668505026\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 7\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 8\n[09/06/23 08:27:50][INFO][common.py:97] - energy: torch.Size([2, 4]) torch.float64 \n[[-13.98883304 -7.11601894 -9.48035637 -3.92143691]\n [-13.99184124 -7.1014651 -9.49438875 -4.11672224]]\nlogprob: torch.Size([2, 4]) torch.float64 \n[[-13.98883304 -7.11601894 -9.48035637 -3.92143691]\n [-13.99184124 -7.1014651 -9.49438875 -4.11672224]]\nlogdet: torch.Size([2, 4]) torch.float64 \n[[0. 0. 0. 0.]\n [0. 0. 0. 0.]]\nacc: torch.Size([4]) torch.float64 \n[1. 0.98555155 1. 1. ]\nsumlogdet: torch.Size([4]) torch.float64 \n[0. 0. 0. 0.]\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 1.]\nplaqs: torch.Size([4]) torch.float64 \n[0.4385061 0.34539504 0.04537868 0.37245688]\nsinQ: torch.Size([4]) torch.float64 \n[0.0153556 0.00315862 0.11135176 0.00892928]\nintQ: torch.Size([4]) torch.float64 \n[0.00087516 0.00018002 0.00634629 0.00050891]\ndQint: torch.Size([4]) torch.float64 \n[0.00082473 0.00022505 0.00010956 0.00055608]\ndQsin: torch.Size([4]) torch.float64 \n[0.01447065 0.00394877 0.00192226 0.00975699]\nloss: None None \n-0.010424735653495517\n[09/06/23 08:27:50][INFO][experiment.py:121] - STEP: 9\n[09/06/23 08:27:50][INFO][2419635544.py:14] - checkSU(x_hmc): (tensor[4] f64 x∈[2.562e-16, 4.133e-16] μ=3.159e-16 σ=7.356e-17 [2.562e-16, 2.620e-16, 3.323e-16, 4.133e-16], tensor[4] f64 x∈[3.601e-16, 7.415e-16] μ=5.139e-16 σ=1.696e-16 [3.601e-16, 4.134e-16, 5.404e-16, 7.415e-16])\n[09/06/23 08:27:50][INFO][2088423886.py:19] - Saving energy to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:50][INFO][2088423886.py:19] - Saving logprob to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving logdet to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving acc to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving sumlogdet to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving acc_mask to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:51][INFO][2088423886.py:19] - Saving plaqs to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:52][INFO][2088423886.py:19] - Saving sinQ to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:52][INFO][2088423886.py:19] - Saving intQ to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:52][INFO][2088423886.py:19] - Saving dQint to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:52][INFO][2088423886.py:19] - Saving dQsin to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n[09/06/23 08:27:53][INFO][2088423886.py:19] - Saving loss to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/HMC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# ptExpSU3.trainer.dynamics.init_weights(\n# method='uniform',\n# min=-1e-16,\n# max=1e-16,\n# bias=True,\n# # xeps=0.001,\n# # veps=0.001,\n# )\nxeval, history_eval = evaluate(\n nsteps=10,\n exp=ptExpSU3,\n beta=6.0,\n x=state.x,\n job_type='eval',\n nlog=1,\n nprint=2,\n grab=True,\n)\nxeval = ptExpSU3.trainer.dynamics.unflatten(xeval)\nlog.info(f\"checkSU(x_eval): {g.checkSU(xeval)}\")\nplot_metrics(history_eval.history, title='Evaluate', marker='.')\n\n[09/06/23 08:27:54][INFO][experiment.py:117] - Running 10 steps of eval at beta=6.0000\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 0\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 1\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 2\n[09/06/23 08:27:54][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[ -9.93909722 -11.71406924 -13.98390472 -6.91867768]\n [ -9.64725014 -11.76741361 -13.84813463 -6.57255943]\n [ -9.24332693 -11.64892261 -13.56752346 -6.14914933]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[ -9.93909722 -11.71406924 -13.98390472 -6.91867768]\n [ -9.57527769 -11.61717321 -13.6004816 -6.52241563]\n [ -9.17429537 -11.68892811 -13.56843895 -6.15131339]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.07197245 -0.1502404 -0.24765303 -0.0501438 ]\n [-0.06903157 0.0400055 0.00091548 0.00216406]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.07197245 -0.1502404 -0.24765303 -0.0501438 ]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[0. 0. 0. 0. ]\n [0. 0. 0. 0. ]\n [0.00294088 0.1902459 0.24856851 0.05230786]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.07197245 -0.1502404 -0.24765303 -0.0501438 ]\n [-0.06903157 0.0400055 0.00091548 0.00216406]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.46542615 0.97517227 0.66003278 0.46423505]\nsumlogdet: torch.Size([4]) torch.float64 \n[-0.06903157 0.0400055 0.00091548 0.00216406]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 1.]\nplaqs: torch.Size([4]) torch.float64 \n[0.16767915 0.37381953 0.37383779 0.20119684]\nsinQ: torch.Size([4]) torch.float64 \n[ 0.04301257 0.01916423 0.01386744 -0.01006018]\nintQ: torch.Size([4]) torch.float64 \n[ 0.00245142 0.00109223 0.00079035 -0.00057336]\ndQint: torch.Size([4]) torch.float64 \n[0.00119233 0.00094291 0.00155372 0.00095932]\ndQsin: torch.Size([4]) torch.float64 \n[0.0209205 0.01654419 0.02726158 0.01683214]\nloss: None None \n-0.020710585677662853\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 3\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 4\n[09/06/23 08:27:54][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-12.85390515 -19.69124648 -23.34418961 -11.83338904]\n [-12.61930827 -19.83006702 -23.39287265 -12.32972948]\n [-12.22362162 -18.93555772 -22.24709415 -12.09553454]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-12.85390515 -19.69124648 -23.34418961 -11.83338904]\n [-12.65387143 -19.71055885 -23.17380781 -12.2815458 ]\n [-12.22896701 -18.91997701 -22.23396345 -12.07717473]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.03456316 -0.11950817 -0.21906484 -0.04818368]\n [ 0.00534539 -0.01558072 -0.01313071 -0.01835981]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.03456316 -0.11950817 -0.21906484 -0.04818368]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [-0.02921778 0.10392745 0.20593414 0.02982387]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.03456316 -0.11950817 -0.21906484 -0.04818368]\n [ 0.00534539 -0.01558072 -0.01313071 -0.01835981]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.53529454 0.46242566 0.32948444 1. ]\nsumlogdet: torch.Size([4]) torch.float64 \n[ 0.00534539 -0. -0. -0.01835981]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 0. 0. 1.]\nplaqs: torch.Size([4]) torch.float64 \n[0.30234966 0.55960731 0.69219294 0.31462515]\nsinQ: torch.Size([4]) torch.float64 \n[0.02246026 0.01034247 0.01407288 0.00475271]\nintQ: torch.Size([4]) torch.float64 \n[0.00128008 0.00058945 0.00080206 0.00027087]\ndQint: torch.Size([4]) torch.float64 \n[0.0017964 0. 0. 0.00236045]\ndQsin: torch.Size([4]) torch.float64 \n[0.03151961 0. 0. 0.04141642]\nloss: None None \n-0.015773998192892276\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 5\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 6\n[09/06/23 08:27:54][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-19.55645935 -20.86462233 -20.90289315 -11.80938497]\n [-19.27554945 -20.60377855 -20.79816694 -11.77715066]\n [-18.96590202 -20.34010565 -20.641842 -11.43213333]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-19.55645935 -20.86462233 -20.90289315 -11.80938497]\n [-19.31960696 -20.45738215 -20.56264778 -11.72755736]\n [-18.97671809 -20.35406438 -20.61748015 -11.4626642 ]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.04405751 -0.14639641 -0.23551916 -0.0495933 ]\n [ 0.01081607 0.01395873 -0.02436185 0.03053087]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.04405751 -0.14639641 -0.23551916 -0.0495933 ]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [-0.03324145 0.16035514 0.21115731 0.08012417]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.04405751 -0.14639641 -0.23551916 -0.0495933 ]\n [ 0.01081607 0.01395873 -0.02436185 0.03053087]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.56004325 0.60016062 0.75170374 0.70700272]\nsumlogdet: torch.Size([4]) torch.float64 \n[ 0.01081607 0.01395873 -0.02436185 0.03053087]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 1.]\nplaqs: torch.Size([4]) torch.float64 \n[0.50284475 0.55960731 0.58704547 0.23919369]\nsinQ: torch.Size([4]) torch.float64 \n[-0.01053072 0.01034247 0.01918416 -0.03898123]\nintQ: torch.Size([4]) torch.float64 \n[-0.00060018 0.00058945 0.00109337 -0.00222166]\ndQint: torch.Size([4]) torch.float64 \n[0.00135638 0.00120177 0.00227558 0.00031006]\ndQsin: torch.Size([4]) torch.float64 \n[0.02379895 0.02108615 0.03992728 0.00544025]\nloss: None None \n-0.016508422822688693\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 7\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 8\n[09/06/23 08:27:54][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-18.07899101 -28.71248658 -13.48019371 -14.59335827]\n [-17.76685303 -28.30437183 -12.76586787 -14.82013678]\n [-17.05030815 -28.78619363 -11.86821313 -14.6282822 ]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-18.07899101 -28.71248658 -13.48019371 -14.59335827]\n [-17.86336929 -28.37337191 -12.58082071 -14.75738913]\n [-17.21060337 -28.90503573 -11.8680323 -14.61315147]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n [ 9.65162556e-02 6.90000737e-02 -1.85047158e-01 -6.27476486e-02]\n [ 1.60295223e-01 1.18842098e-01 -1.80827316e-04 -1.51307243e-02]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.09651626 0.06900007 -0.18504716 -0.06274765]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[0. 0. 0. 0. ]\n [0. 0. 0. 0. ]\n [0.06377897 0.04984202 0.18486633 0.04761692]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n [ 9.65162556e-02 6.90000737e-02 -1.85047158e-01 -6.27476486e-02]\n [ 1.60295223e-01 1.18842098e-01 -1.80827316e-04 -1.51307243e-02]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.41962759 1. 0.19945604 1. ]\nsumlogdet: torch.Size([4]) torch.float64 \n[ 0.16029522 0.1188421 -0. -0.01513072]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 0. 1.]\nplaqs: torch.Size([4]) torch.float64 \n[0.62270466 0.74549143 0.53797573 0.24602781]\nsinQ: torch.Size([4]) torch.float64 \n[ 0.01834668 0.00603678 -0.01653119 -0.05096747]\nintQ: torch.Size([4]) torch.float64 \n[ 0.00104564 0.00034406 -0.00094216 -0.0029048 ]\ndQint: torch.Size([4]) torch.float64 \n[0.00191687 0.00024381 0. 0.00046954]\ndQsin: torch.Size([4]) torch.float64 \n[0.03363338 0.0042779 0. 0.00823858]\nloss: None None \n-0.01735800592796972\n[09/06/23 08:27:54][INFO][experiment.py:121] - STEP: 9\n[09/06/23 08:27:54][INFO][1629827420.py:20] - checkSU(x_eval): (tensor[4] f64 x∈[9.577e-14, 0.024] μ=0.009 σ=0.012 [0.024, 2.063e-13, 0.014, 9.577e-14], tensor[4] f64 x∈[1.790e-13, 0.034] μ=0.013 σ=0.016 [0.034, 3.935e-13, 0.016, 1.790e-13])\n[09/06/23 08:27:54][INFO][2088423886.py:19] - Saving energy to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:55][INFO][2088423886.py:19] - Saving logprob to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:55][INFO][2088423886.py:19] - Saving logdet to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:55][INFO][2088423886.py:19] - Saving sldf to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:55][INFO][2088423886.py:19] - Saving sldb to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:56][INFO][2088423886.py:19] - Saving sld to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:56][INFO][2088423886.py:19] - Saving xeps to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:56][INFO][2088423886.py:19] - Saving veps to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:56][INFO][2088423886.py:19] - Saving acc to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving sumlogdet to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving beta to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving acc_mask to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving plaqs to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:57][INFO][2088423886.py:19] - Saving sinQ to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:58][INFO][2088423886.py:19] - Saving intQ to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:58][INFO][2088423886.py:19] - Saving dQint to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:58][INFO][2088423886.py:19] - Saving dQsin to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n[09/06/23 08:27:58][INFO][2088423886.py:19] - Saving loss to /Users/samforeman/projects/saforem2/l2hmc-qcd/qmd/outputs/plots-4dSU3/Evaluate\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nhistory = {}\nx = state.x\nfor step in range(20):\n log.info(f'TRAIN STEP: {step}')\n x, metrics = ptExpSU3.trainer.train_step((x, state.beta))\n if (step > 0 and step % 2 == 0):\n print_dict(metrics, grab=True)\n if (step > 0 and step % 1 == 0):\n for key, val in metrics.items():\n try:\n history[key].append(val)\n except KeyError:\n history[key] = [val]\n\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 0\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 1\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 2\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-20.11355156 -13.83759796 -4.51987377 -11.45297864]\n [-19.63407248 -12.82094906 -3.89140218 -11.38944608]\n [-18.91054695 -12.57901193 -3.60837652 -10.80563673]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-20.11355156 -13.83759796 -4.51987377 -11.45297864]\n [-19.5349711 -12.66848527 -3.6937792 -11.31223158]\n [-18.98136385 -12.5501776 -3.58081924 -10.80513544]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.09910138 -0.15246379 -0.19762298 -0.0772145 ]\n [ 0.07081689 -0.02883433 -0.02755728 -0.00050128]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.09910138 -0.15246379 -0.19762298 -0.0772145 ]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[0. 0. 0. 0. ]\n [0. 0. 0. 0. ]\n [0.16991827 0.12362946 0.17006571 0.07671321]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.09910138 -0.15246379 -0.19762298 -0.0772145 ]\n [ 0.07081689 -0.02883433 -0.02755728 -0.00050128]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.32232732 0.2759818 0.39099734 0.52317294]\nsumlogdet: torch.Size([4]) torch.float64 \n[ 0.07081689 -0. -0.02755728 -0.00050128]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 0. 1. 1.]\nloss: None None \n-0.009001684302756496\nplaqs: torch.Size([4]) torch.float64 \n[0.36183972 0.43746352 0.02919545 0.2244051 ]\nsinQ: torch.Size([4]) torch.float64 \n[ 0.01845919 -0.00937014 0.00653818 0.03579483]\nintQ: torch.Size([4]) torch.float64 \n[ 0.00105205 -0.00053403 0.00037263 0.00204006]\ndQint: torch.Size([4]) torch.float64 \n[0.00068181 0. 0.00033711 0.001986 ]\ndQsin: torch.Size([4]) torch.float64 \n[0.01196306 0. 0.00591496 0.0348463 ]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 3\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 4\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-16.99217625 -18.05099071 -10.04985909 -7.1340411 ]\n [-16.61047626 -17.84035555 -9.68866118 -7.14912011]\n [-16.28336399 -18.36191548 -9.14330041 -5.86613539]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-16.99217625 -18.05099071 -10.04985909 -7.1340411 ]\n [-16.4708106 -17.6654387 -9.64363079 -7.10061592]\n [-16.17915148 -18.35588077 -9.12314899 -5.81814653]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.13966566 -0.17491685 -0.04503039 -0.04850419]\n [-0.10421251 -0.0060347 -0.02015142 -0.04798886]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.13966566 -0.17491685 -0.04503039 -0.04850419]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[0. 0. 0. 0. ]\n [0. 0. 0. 0. ]\n [0.03545315 0.16888215 0.02487897 0.00051533]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.13966566 -0.17491685 -0.04503039 -0.04850419]\n [-0.10421251 -0.0060347 -0.02015142 -0.04798886]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.44351451 1. 0.39585389 0.26823426]\nsumlogdet: torch.Size([4]) torch.float64 \n[-0.10421251 -0.0060347 -0. -0. ]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 0. 0.]\nloss: None None \n-0.014134476436103167\nplaqs: torch.Size([4]) torch.float64 \n[0.46996628 0.45118214 0.18011337 0.30434348]\nsinQ: torch.Size([4]) torch.float64 \n[ 0.03170589 -0.01147666 0.05118318 -0.00268528]\nintQ: torch.Size([4]) torch.float64 \n[ 0.00180702 -0.00065409 0.00291709 -0.00015304]\ndQint: torch.Size([4]) torch.float64 \n[0.00065855 0.00207145 0. 0. ]\ndQsin: torch.Size([4]) torch.float64 \n[0.01155495 0.03634551 0. 0. ]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 5\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 6\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-22.17358114 -18.47427308 -2.95308721 -11.62559496]\n [-21.83570903 -18.22818944 -2.4429929 -11.20037746]\n [-21.81748065 -18.37604145 -2.70650275 -10.07465326]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-22.17358114 -18.47427308 -2.95308721 -11.62559496]\n [-21.84417608 -18.06424191 -2.60692839 -11.12021881]\n [-21.89172921 -18.39051785 -2.62673381 -10.08631078]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.00846706 -0.16394753 0.16393549 -0.08015865]\n [ 0.07424856 0.0144764 -0.07976894 0.01165751]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.00846706 -0.16394753 0.16393549 -0.08015865]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [ 0.0657815 0.17842393 -0.24370443 0.09181616]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.00846706 -0.16394753 0.16393549 -0.08015865]\n [ 0.07424856 0.0144764 -0.07976894 0.01165751]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.75438538 0.91965634 0.72155015 0.21453462]\nsumlogdet: torch.Size([4]) torch.float64 \n[ 0.07424856 0.0144764 -0.07976894 0.01165751]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 1.]\nloss: None None \n-0.019461443124629982\nplaqs: torch.Size([4]) torch.float64 \n[0.43455592 0.53410535 0.15486684 0.30434348]\nsinQ: torch.Size([4]) torch.float64 \n[-0.01746029 0.01216864 0.01077427 -0.00268528]\nintQ: torch.Size([4]) torch.float64 \n[-0.00099512 0.00069353 0.00061406 -0.00015304]\ndQint: torch.Size([4]) torch.float64 \n[0.0016496 0.00074394 0.00087095 0.0014133 ]\ndQsin: torch.Size([4]) torch.float64 \n[0.02894377 0.01305311 0.01528163 0.02479769]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 7\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 8\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-28.94977982 -23.93414904 -5.02251262 -20.14381224]\n [-28.26856486 -24.03288661 -4.19892196 -20.37397293]\n [-27.78459946 -23.56989642 -3.56347647 -20.06837893]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-28.94977982 -23.93414904 -5.02251262 -20.14381224]\n [-28.25995613 -23.85544268 -4.41858446 -20.21769226]\n [-27.77851779 -23.59675098 -3.59362754 -20.09340966]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.00860873 -0.17744392 0.2196625 -0.15628067]\n [-0.00608167 0.02685456 0.03015107 0.02503073]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.00860873 -0.17744392 0.2196625 -0.15628067]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [ 0.00252705 0.20429848 -0.18951143 0.1813114 ]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.00860873 -0.17744392 0.2196625 -0.15628067]\n [-0.00608167 0.02685456 0.03015107 0.02503073]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.3099755 0.71362471 0.23957588 0.95084655]\nsumlogdet: torch.Size([4]) torch.float64 \n[-0. 0. 0. 0.02503073]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[0. 0. 0. 1.]\nloss: None None \n-0.015584381253787876\nplaqs: torch.Size([4]) torch.float64 \n[0.7403035 0.65171197 0.31634181 0.47457693]\nsinQ: torch.Size([4]) torch.float64 \n[-0.00859781 -0.00165773 -0.00396142 0.00092218]\nintQ: torch.Size([4]) torch.float64 \n[-4.90016318e-04 -9.44793472e-05 -2.25773828e-04 5.25577319e-05]\ndQint: torch.Size([4]) torch.float64 \n[0. 0. 0. 0.00032025]\ndQsin: torch.Size([4]) torch.float64 \n[0. 0. 0. 0.00561914]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 9\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 10\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-31.39076793 -27.31985011 -19.54217484 -21.1104468 ]\n [-31.0614939 -27.06503105 -19.07066872 -21.3910664 ]\n [-30.84961158 -26.95602155 -18.69349894 -20.00516103]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-31.39076793 -27.31985011 -19.54217484 -21.1104468 ]\n [-31.06645234 -26.87717262 -19.24520362 -21.21648217]\n [-30.82929486 -26.99061806 -18.65210819 -20.00045772]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.00495844 -0.18785843 0.1745349 -0.17458422]\n [-0.02031672 0.03459652 -0.04139075 -0.00470331]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.00495844 -0.18785843 0.1745349 -0.17458422]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [-0.02527516 0.22245494 -0.21592565 0.16988091]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.00495844 -0.18785843 0.1745349 -0.17458422]\n [-0.02031672 0.03459652 -0.04139075 -0.00470331]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.57036826 0.71947605 0.41062839 0.32956256]\nsumlogdet: torch.Size([4]) torch.float64 \n[-0.02031672 0.03459652 -0.04139075 -0. ]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 0.]\nloss: None None \n-0.011982253415392767\nplaqs: torch.Size([4]) torch.float64 \n[0.7403035 0.65171197 0.45503328 0.62236101]\nsinQ: torch.Size([4]) torch.float64 \n[-0.00859781 -0.00165773 -0.00088107 0.01924983]\nintQ: torch.Size([4]) torch.float64 \n[-4.90016318e-04 -9.44793472e-05 -5.02151834e-05 1.09710862e-03]\ndQint: torch.Size([4]) torch.float64 \n[0.00043531 0.00061571 0.00043569 0. ]\ndQsin: torch.Size([4]) torch.float64 \n[0.00763793 0.01080326 0.00764459 0. ]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 11\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 12\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-31.42781825 -21.38032064 -19.16557544 -24.26085164]\n [-30.89428362 -21.22321069 -18.90945902 -23.55378881]\n [-30.46921912 -20.86892045 -18.93287679 -23.36450964]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-31.42781825 -21.38032064 -19.16557544 -24.26085164]\n [-30.88117485 -21.00988585 -19.11822465 -23.54366138]\n [-30.55010401 -20.84704698 -19.01058406 -23.31072478]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.01310877 -0.21332485 0.20876563 -0.01012743]\n [ 0.08088489 -0.02187348 0.07770727 -0.05378486]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.01310877 -0.21332485 0.20876563 -0.01012743]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [ 0.09399365 0.19145137 -0.13105835 -0.04365743]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.01310877 -0.21332485 0.20876563 -0.01012743]\n [ 0.08088489 -0.02187348 0.07770727 -0.05378486]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.41573209 0.58668123 0.85642256 0.38669196]\nsumlogdet: torch.Size([4]) torch.float64 \n[ 0.08088489 -0. 0.07770727 -0. ]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 0. 1. 0.]\nloss: None None \n-0.016004372395409854\nplaqs: torch.Size([4]) torch.float64 \n[0.84037172 0.65154716 0.42313337 0.73863172]\nsinQ: torch.Size([4]) torch.float64 \n[ 1.47285038e-03 -1.56397921e-03 -6.09823914e-03 -6.72724198e-05]\nintQ: torch.Size([4]) torch.float64 \n[ 8.39424059e-05 -8.91361261e-05 -3.47557954e-04 -3.83406818e-06]\ndQint: torch.Size([4]) torch.float64 \n[1.23274406e-05 0.00000000e+00 5.10271910e-04 0.00000000e+00]\ndQsin: torch.Size([4]) torch.float64 \n[0.0002163 0. 0.00895321 0. ]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 13\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 14\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-29.59240183 -21.94551736 -28.47707495 -20.59708329]\n [-29.48303373 -21.10556806 -28.05877271 -20.58985545]\n [-29.39854899 -20.73632654 -27.22089443 -19.77844503]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-29.59240183 -21.94551736 -28.47707495 -20.59708329]\n [-29.40940421 -20.98154179 -28.17860182 -20.541757 ]\n [-29.31460821 -20.72420952 -27.17276265 -19.77710774]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.07362952 -0.12402627 0.11982911 -0.04809845]\n [-0.08394078 -0.01211702 -0.04813178 -0.00133729]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.07362952 -0.12402627 0.11982911 -0.04809845]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [-0.01031126 0.11190925 -0.16796089 0.04676117]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.07362952 -0.12402627 0.11982911 -0.04809845]\n [-0.08394078 -0.01211702 -0.04813178 -0.00133729]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.75745313 0.2948443 0.27135909 0.44044242]\nsumlogdet: torch.Size([4]) torch.float64 \n[-0.08394078 -0.01211702 -0.04813178 -0.00133729]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 1. 1. 1.]\nloss: None None \n-0.012984717158635528\nplaqs: torch.Size([4]) torch.float64 \n[0.75491793 0.6547223 0.63304782 0.73863172]\nsinQ: torch.Size([4]) torch.float64 \n[ 3.85535809e-03 -1.41124889e-02 -1.46423245e-02 -6.72724198e-05]\nintQ: torch.Size([4]) torch.float64 \n[ 2.19729063e-04 -8.04315419e-04 -8.34512428e-04 -3.83406817e-06]\ndQint: torch.Size([4]) torch.float64 \n[0.00065824 0.00058615 0.00017334 0.00014198]\ndQsin: torch.Size([4]) torch.float64 \n[0.01154945 0.01028462 0.00304149 0.00249122]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 15\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 16\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-27.90336074 -24.58447835 -27.70507628 -25.1095963 ]\n [-27.4557147 -23.38939397 -26.86035304 -24.74188789]\n [-26.76730611 -23.12311187 -26.13507215 -24.81611966]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-27.90336074 -24.58447835 -27.70507628 -25.1095963 ]\n [-27.42721443 -23.25741435 -27.03208626 -24.69073936]\n [-26.83629231 -23.14150881 -26.14979085 -24.84522864]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.02850027 -0.13197962 0.17173322 -0.05114852]\n [ 0.0689862 0.01839694 0.0147187 0.02910898]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.02850027 -0.13197962 0.17173322 -0.05114852]\n [ 0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [ 0.09748647 0.15037655 -0.15701452 0.0802575 ]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [-0.02850027 -0.13197962 0.17173322 -0.05114852]\n [ 0.0689862 0.01839694 0.0147187 0.02910898]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.34401554 0.23622524 0.21112911 0.76769124]\nsumlogdet: torch.Size([4]) torch.float64 \n[0.0689862 0. 0.0147187 0.02910898]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[1. 0. 1. 1.]\nloss: None None \n-0.011869373548195221\nplaqs: torch.Size([4]) torch.float64 \n[0.82255532 0.72111137 0.69612456 0.72896623]\nsinQ: torch.Size([4]) torch.float64 \n[-0.00564947 -0.00444414 -0.01693866 0.00255894]\nintQ: torch.Size([4]) torch.float64 \n[-0.00032198 -0.00025329 -0.00096539 0.00014584]\ndQint: torch.Size([4]) torch.float64 \n[8.49293564e-05 0.00000000e+00 3.84992539e-04 3.15784764e-04]\ndQsin: torch.Size([4]) torch.float64 \n[0.00149017 0. 0.00675506 0.00554075]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 17\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 18\n[09/06/23 08:28:01][INFO][common.py:97] - energy: torch.Size([3, 4]) torch.float64 \n[[-28.74207541 -24.8965513 -32.81235279 -22.59228204]\n [-27.98828099 -23.91465789 -32.28912627 -22.61626646]\n [-27.23785728 -24.01606216 -31.51716178 -21.73978358]]\nlogprob: torch.Size([3, 4]) torch.float64 \n[[-28.74207541 -24.8965513 -32.81235279 -22.59228204]\n [-28.07501629 -23.97865766 -32.35996206 -22.61968433]\n [-27.18635944 -23.93497767 -31.62224319 -21.75485085]]\nlogdet: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.0867353 0.06399977 0.07083579 0.00341787]\n [-0.05149783 -0.08108449 0.10508142 0.01506727]]\nsldf: torch.Size([3, 4]) torch.float64 \n[[0. 0. 0. 0. ]\n [0.0867353 0.06399977 0.07083579 0.00341787]\n [0. 0. 0. 0. ]]\nsldb: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0. 0. 0. 0. ]\n [-0.13823313 -0.14508427 0.03424563 0.0116494 ]]\nsld: torch.Size([3, 4]) torch.float64 \n[[ 0. 0. 0. 0. ]\n [ 0.0867353 0.06399977 0.07083579 0.00341787]\n [-0.05149783 -0.08108449 0.10508142 0.01506727]]\nxeps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nveps: torch.Size([3]) torch.float64 \n[0.1 0.1 0.1]\nacc: torch.Size([4]) torch.float64 \n[0.21103823 0.38229083 0.30418792 0.43282093]\nsumlogdet: torch.Size([4]) torch.float64 \n[-0. -0.08108449 0. 0.01506727]\nbeta: torch.Size([]) torch.float64 \n6.0\nacc_mask: torch.Size([4]) torch.float32 \n[0. 1. 0. 1.]\nloss: None None \n-0.00985098143962743\nplaqs: torch.Size([4]) torch.float64 \n[0.85381281 0.70579842 0.80681173 0.75974167]\nsinQ: torch.Size([4]) torch.float64 \n[-0.00332841 -0.00489329 -0.01271997 0.00506889]\nintQ: torch.Size([4]) torch.float64 \n[-0.0001897 -0.00027888 -0.00072495 0.00028889]\ndQint: torch.Size([4]) torch.float64 \n[0. 0.00045467 0. 0.00140838]\ndQsin: torch.Size([4]) torch.float64 \n[0. 0.00797764 0. 0.02471136]\n[09/06/23 08:28:01][INFO][2642635469.py:4] - TRAIN STEP: 19\n\n\n\nx = ptExpSU3.trainer.dynamics.unflatten(x)\nlog.info(f\"checkSU(x_train): {g.checkSU(x)}\")\n# plot_metrics(history, title='train', marker='.')\n\n[09/06/23 08:28:01][INFO][1630590238.py:2] - checkSU(x_train): (tensor[4] f64 x∈[3.838e-16, 0.018] μ=0.008 σ=0.009 [0.018, 0.014, 3.838e-16, 9.135e-13], tensor[4] f64 x∈[6.307e-16, 0.026] μ=0.011 σ=0.013 [0.026, 0.017, 6.307e-16, 1.818e-12])\n\n\n\nprint(history.keys())\n\ndict_keys(['energy', 'logprob', 'logdet', 'sldf', 'sldb', 'sld', 'xeps', 'veps', 'acc', 'sumlogdet', 'beta', 'acc_mask', 'loss', 'plaqs', 'sinQ', 'intQ', 'dQint', 'dQsin'])\n\n\n\n\n\n\n Back to topCitationBibTeX citation:@misc{foreman2023,\n author = {Foreman, Sam},\n title = {4D {\\$SU(3)\\$} {Model}},\n date = {2023-09-06},\n url = {https://saforem2.github.io/l2hmc-qcd},\n langid = {en}\n}\nFor attribution, please cite this work as:\nForeman, Sam. 2023. “4D $SU(3)$ Model.” L2HMC. https://saforem2.github.io/l2hmc-qcd." + }, + { + "objectID": "qmd/slides.html", + "href": "qmd/slides.html", + "title": "Recent Talks", + "section": "", + "text": "\\hspace{2pt} MLMC: Machine Learning Monte Carlo @ Lattice 2023 (07/2023)\n\n\n\n\n\n\n\n<p>\nYour browser does not support iframes.\n</p>\n\n\n\n\n\n\n\n\n\n\n \\hspace{2pt} Generative Modeling and Efficient Sampling @ PASC23 (07/2023)\n\n\n\n\n\n\n\n\n<p>\nYour browser does not support iframes.\n</p>\n\n\n\n\n\n\n\n\n\n\n \\hspace{2pt} Efficient Sampling for Lattice Gauge Theory @ Deep Fridays @ U. Bologna (04/2023)\n\n\n\n\n\n\n\n<p>\nYour browser does not support iframes.\n</p>\n\n\n\n\n\n\n\n Back to topCitationBibTeX citation:@misc{foreman2023,\n author = {Foreman, Sam},\n title = {Recent {Talks}},\n date = {2023-09-06},\n url = {https://saforem2.github.io/l2hmc-qcd},\n langid = {en}\n}\nFor attribution, please cite this work as:\nForeman, Sam. 2023. “Recent Talks.” L2HMC. https://saforem2.github.io/l2hmc-qcd." + }, + { + "objectID": "qmd/examples.html", + "href": "qmd/examples.html", + "title": "Examples", + "section": "", + "text": "U(1) Model in 2D\nSU(3) Model in 4D\n\n\n\n\n Back to topCitationBibTeX citation:@misc{foreman2023,\n author = {Foreman, Sam},\n title = {Examples},\n date = {2023-09-06},\n url = {https://saforem2.github.io/l2hmc-qcd},\n langid = {en}\n}\nFor attribution, please cite this work as:\nForeman, Sam. 2023. “Examples.” L2HMC. https://saforem2.github.io/l2hmc-qcd." } ] \ No newline at end of file diff --git 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var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0); + animation-delay: var(--fa-animation-delay, 0); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + transition-delay: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, none)); + transform: rotate(var(--fa-rotate-angle, none)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-a::before { + content: "\41"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-address-card::before { + content: "\f2bb"; } + +.fa-contact-card::before { + content: "\f2bb"; } + +.fa-vcard::before { + content: "\f2bb"; } + +.fa-align-center::before { + content: "\f037"; } + +.fa-align-justify::before { + content: "\f039"; } + +.fa-align-left::before { + content: "\f036"; } + +.fa-align-right::before { + content: "\f038"; } + +.fa-anchor::before { + content: "\f13d"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-anchor-circle-exclamation::before { + content: "\e4ab"; } + +.fa-anchor-circle-xmark::before { + content: "\e4ac"; } + +.fa-anchor-lock::before { + content: "\e4ad"; } + +.fa-angle-down::before { + content: "\f107"; } + +.fa-angle-left::before { + content: "\f104"; } + +.fa-angle-right::before { + content: "\f105"; } + +.fa-angle-up::before { + content: "\f106"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-angles-left::before { + content: "\f100"; } + +.fa-angle-double-left::before { + content: "\f100"; } + +.fa-angles-right::before { + content: "\f101"; } + +.fa-angle-double-right::before { + content: "\f101"; } + +.fa-angles-up::before { + content: "\f102"; } + +.fa-angle-double-up::before { + content: "\f102"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-apple-whole::before { + content: "\f5d1"; } + +.fa-apple-alt::before { + content: "\f5d1"; } + +.fa-archway::before { + content: "\f557"; } + +.fa-arrow-down::before { + content: "\f063"; } + +.fa-arrow-down-1-9::before { + content: "\f162"; } + +.fa-sort-numeric-asc::before { + content: "\f162"; } + +.fa-sort-numeric-down::before { + content: "\f162"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-arrow-down-a-z::before { + content: "\f15d"; } + +.fa-sort-alpha-asc::before { + content: "\f15d"; } + +.fa-sort-alpha-down::before { + content: "\f15d"; } + +.fa-arrow-down-long::before { + content: "\f175"; } + +.fa-long-arrow-down::before { + content: "\f175"; } + +.fa-arrow-down-short-wide::before { + content: "\f884"; } + +.fa-sort-amount-desc::before { + content: "\f884"; } + +.fa-sort-amount-down-alt::before { + content: "\f884"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-arrow-down-up-lock::before { + content: "\e4b0"; } + +.fa-arrow-down-wide-short::before { + content: "\f160"; } + +.fa-sort-amount-asc::before { + content: "\f160"; } + +.fa-sort-amount-down::before { + content: "\f160"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-arrow-left::before { + content: "\f060"; } + +.fa-arrow-left-long::before { + content: "\f177"; } + +.fa-long-arrow-left::before { + content: "\f177"; } + +.fa-arrow-pointer::before { + content: "\f245"; } + +.fa-mouse-pointer::before { + content: "\f245"; } + +.fa-arrow-right::before { + content: "\f061"; } + +.fa-arrow-right-arrow-left::before { + content: "\f0ec"; } + +.fa-exchange::before { + content: "\f0ec"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-arrow-right-long::before { + content: "\f178"; } + +.fa-long-arrow-right::before { + content: "\f178"; } + +.fa-arrow-right-to-bracket::before { + content: "\f090"; } + +.fa-sign-in::before { + content: "\f090"; } + +.fa-arrow-right-to-city::before { + content: "\e4b3"; } + +.fa-arrow-rotate-left::before { + content: "\f0e2"; } + +.fa-arrow-left-rotate::before { + content: "\f0e2"; } + +.fa-arrow-rotate-back::before { + content: "\f0e2"; } + +.fa-arrow-rotate-backward::before { + content: "\f0e2"; } + +.fa-undo::before { + content: "\f0e2"; } + +.fa-arrow-rotate-right::before { + content: "\f01e"; } + +.fa-arrow-right-rotate::before { + content: "\f01e"; } + +.fa-arrow-rotate-forward::before { + content: "\f01e"; } + +.fa-redo::before { + content: "\f01e"; } + +.fa-arrow-trend-down::before { + content: "\e097"; } + +.fa-arrow-trend-up::before { + content: "\e098"; } + +.fa-arrow-turn-down::before { + content: "\f149"; } + +.fa-level-down::before { + content: "\f149"; } + +.fa-arrow-turn-up::before { + content: "\f148"; } + +.fa-level-up::before { + content: "\f148"; } + +.fa-arrow-up::before { + content: "\f062"; } + +.fa-arrow-up-1-9::before { + content: "\f163"; } + +.fa-sort-numeric-up::before { + content: "\f163"; } + +.fa-arrow-up-9-1::before { + content: "\f887"; } + +.fa-sort-numeric-up-alt::before { + content: "\f887"; } + +.fa-arrow-up-a-z::before { + content: "\f15e"; } + +.fa-sort-alpha-up::before { + content: "\f15e"; } + +.fa-arrow-up-from-bracket::before { + content: "\e09a"; } + +.fa-arrow-up-from-ground-water::before { + content: "\e4b5"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-arrow-up-right-dots::before { + content: "\e4b7"; } + +.fa-arrow-up-right-from-square::before { + content: "\f08e"; } + +.fa-external-link::before { + content: "\f08e"; } + +.fa-arrow-up-short-wide::before { + content: "\f885"; } + +.fa-sort-amount-up-alt::before { + content: "\f885"; } + +.fa-arrow-up-wide-short::before { + content: "\f161"; } + +.fa-sort-amount-up::before { + content: "\f161"; } + +.fa-arrow-up-z-a::before { + content: "\f882"; } + +.fa-sort-alpha-up-alt::before { + content: "\f882"; } + +.fa-arrows-down-to-line::before { + content: "\e4b8"; } + +.fa-arrows-down-to-people::before { + content: "\e4b9"; } + +.fa-arrows-left-right::before { + content: "\f07e"; } + +.fa-arrows-h::before { + content: "\f07e"; } + +.fa-arrows-left-right-to-line::before { + content: "\e4ba"; } + +.fa-arrows-rotate::before { + content: "\f021"; } + +.fa-refresh::before { + content: "\f021"; } + +.fa-sync::before { + content: "\f021"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-arrows-split-up-and-left::before { + content: "\e4bc"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-arrows-to-dot::before { + content: "\e4be"; } + +.fa-arrows-to-eye::before { + content: "\e4bf"; } + +.fa-arrows-turn-right::before { + content: "\e4c0"; } + +.fa-arrows-turn-to-dots::before { + content: "\e4c1"; } + +.fa-arrows-up-down::before { + content: "\f07d"; } + +.fa-arrows-v::before { + content: "\f07d"; } + +.fa-arrows-up-down-left-right::before { + content: "\f047"; } + +.fa-arrows::before { + content: "\f047"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-asterisk::before { + content: "\2a"; } + +.fa-at::before { + content: "\40"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-audio-description::before { + content: "\f29e"; } + +.fa-austral-sign::before { + content: "\e0a9"; } + +.fa-award::before { + content: "\f559"; } + +.fa-b::before { + content: "\42"; } + +.fa-baby::before { + content: "\f77c"; } + +.fa-baby-carriage::before { + content: "\f77d"; } + +.fa-carriage-baby::before { + content: "\f77d"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-backward-fast::before { + content: "\f049"; } + +.fa-fast-backward::before { + content: "\f049"; } + +.fa-backward-step::before { + content: "\f048"; } + +.fa-step-backward::before { + content: "\f048"; } + +.fa-bacon::before { + content: "\f7e5"; } + +.fa-bacteria::before { + content: "\e059"; } + 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content: "\f433"; } + +.fa-baseball-bat-ball::before { + content: "\f432"; } + +.fa-basket-shopping::before { + content: "\f291"; } + +.fa-shopping-basket::before { + content: "\f291"; } + +.fa-basketball::before { + content: "\f434"; } + +.fa-basketball-ball::before { + content: "\f434"; } + +.fa-bath::before { + content: "\f2cd"; } + +.fa-bathtub::before { + content: "\f2cd"; } + +.fa-battery-empty::before { + content: "\f244"; } + +.fa-battery-0::before { + content: "\f244"; } + +.fa-battery-full::before { + content: "\f240"; } + +.fa-battery::before { + content: "\f240"; } + +.fa-battery-5::before { + content: "\f240"; } + +.fa-battery-half::before { + content: "\f242"; } + +.fa-battery-3::before { + content: "\f242"; } + +.fa-battery-quarter::before { + content: "\f243"; } + +.fa-battery-2::before { + content: "\f243"; } + +.fa-battery-three-quarters::before { + content: "\f241"; } + +.fa-battery-4::before { + content: "\f241"; } + +.fa-bed::before { + content: "\f236"; } + 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content: "\f1e2"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-book::before { + content: "\f02d"; } + +.fa-book-atlas::before { + content: "\f558"; } + +.fa-atlas::before { + content: "\f558"; } + +.fa-book-bible::before { + content: "\f647"; } + +.fa-bible::before { + content: "\f647"; } + +.fa-book-bookmark::before { + content: "\e0bb"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-book-medical::before { + content: "\f7e6"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-open-reader::before { + content: "\f5da"; } + +.fa-book-reader::before { + content: "\f5da"; } + +.fa-book-quran::before { + content: "\f687"; } + +.fa-quran::before { + content: "\f687"; } + +.fa-book-skull::before { + content: "\f6b7"; } + +.fa-book-dead::before { + content: "\f6b7"; } + +.fa-bookmark::before { + content: "\f02e"; } + +.fa-border-all::before { + content: "\f84c"; } + +.fa-border-none::before { + content: "\f850"; } + +.fa-border-top-left::before { + content: "\f853"; } + +.fa-border-style::before { + content: "\f853"; } + +.fa-bore-hole::before { + content: "\e4c3"; } + +.fa-bottle-droplet::before { + content: "\e4c4"; } + +.fa-bottle-water::before { + content: "\e4c5"; } + +.fa-bowl-food::before { + content: "\e4c6"; } + +.fa-bowl-rice::before { + content: "\e2eb"; } + +.fa-bowling-ball::before { + content: "\f436"; } + +.fa-box::before { + content: "\f466"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-box-open::before { + content: "\f49e"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-boxes-packing::before { + content: "\e4c7"; } + +.fa-boxes-stacked::before { + content: "\f468"; } + +.fa-boxes::before { + content: "\f468"; } + +.fa-boxes-alt::before { + content: "\f468"; } + +.fa-braille::before { + content: "\f2a1"; } + +.fa-brain::before { + content: "\f5dc"; } + +.fa-brazilian-real-sign::before { + content: "\e46c"; } + +.fa-bread-slice::before { + content: "\f7ec"; } + +.fa-bridge::before { + content: "\e4c8"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-bridge-circle-exclamation::before { + content: "\e4ca"; } + +.fa-bridge-circle-xmark::before { + content: "\e4cb"; } + +.fa-bridge-lock::before { + content: "\e4cc"; } + +.fa-bridge-water::before { + content: "\e4ce"; } + +.fa-briefcase::before { + content: "\f0b1"; } + +.fa-briefcase-medical::before { + content: "\f469"; } + +.fa-broom::before { + content: "\f51a"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-bucket::before { + content: "\e4cf"; } + +.fa-bug::before { + content: "\f188"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-bugs::before { + content: "\e4d0"; } + 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content: "\f140"; } + +.fa-burger::before { + content: "\f805"; } + +.fa-hamburger::before { + content: "\f805"; } + +.fa-burst::before { + content: "\e4dc"; } + +.fa-bus::before { + content: "\f207"; } + +.fa-bus-simple::before { + content: "\f55e"; } + +.fa-bus-alt::before { + content: "\f55e"; } + +.fa-business-time::before { + content: "\f64a"; } + +.fa-briefcase-clock::before { + content: "\f64a"; } + +.fa-c::before { + content: "\43"; } + +.fa-cake-candles::before { + content: "\f1fd"; } + +.fa-birthday-cake::before { + content: "\f1fd"; } + +.fa-cake::before { + content: "\f1fd"; } + +.fa-calculator::before { + content: "\f1ec"; } + +.fa-calendar::before { + content: "\f133"; } + +.fa-calendar-check::before { + content: "\f274"; } + +.fa-calendar-day::before { + content: "\f783"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-calendar-minus::before { + content: "\f272"; } + +.fa-calendar-plus::before { + content: "\f271"; } + +.fa-calendar-week::before { + content: "\f784"; } + +.fa-calendar-xmark::before { + content: "\f273"; } + +.fa-calendar-times::before { + content: "\f273"; } + +.fa-camera::before { + content: "\f030"; } + +.fa-camera-alt::before { + content: "\f030"; } + +.fa-camera-retro::before { + content: "\f083"; } + +.fa-camera-rotate::before { + content: "\e0d8"; } + +.fa-campground::before { + content: "\f6bb"; } + +.fa-candy-cane::before { + content: "\f786"; } + +.fa-cannabis::before { + content: "\f55f"; } + +.fa-capsules::before { + content: "\f46b"; } + +.fa-car::before { + content: "\f1b9"; } + +.fa-automobile::before { + content: "\f1b9"; } + +.fa-car-battery::before { + content: "\f5df"; } + +.fa-battery-car::before { + content: "\f5df"; } + +.fa-car-burst::before { + content: "\f5e1"; } + +.fa-car-crash::before { + content: "\f5e1"; } + +.fa-car-on::before { + content: "\e4dd"; } + +.fa-car-rear::before { + content: "\f5de"; } + +.fa-car-alt::before { + content: "\f5de"; } + +.fa-car-side::before { + content: "\f5e4"; } + +.fa-car-tunnel::before { + content: "\e4de"; } + +.fa-caravan::before { + content: "\f8ff"; } + +.fa-caret-down::before { + content: "\f0d7"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-caret-up::before { + content: "\f0d8"; } + +.fa-carrot::before { + content: "\f787"; } + +.fa-cart-arrow-down::before { + content: "\f218"; } + +.fa-cart-flatbed::before { + content: "\f474"; } + +.fa-dolly-flatbed::before { + content: "\f474"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-cart-shopping::before { + content: "\f07a"; } + +.fa-shopping-cart::before { + content: "\f07a"; } + +.fa-cash-register::before { + content: "\f788"; } + +.fa-cat::before { + content: "\f6be"; } + +.fa-cedi-sign::before { + content: "\e0df"; } + +.fa-cent-sign::before { + content: "\e3f5"; } + +.fa-certificate::before { + content: "\f0a3"; } + +.fa-chair::before { + content: "\f6c0"; } + +.fa-chalkboard::before { + content: "\f51b"; } + +.fa-blackboard::before { + content: "\f51b"; } + +.fa-chalkboard-user::before { + content: "\f51c"; } + +.fa-chalkboard-teacher::before { + content: "\f51c"; } + +.fa-champagne-glasses::before { + content: "\f79f"; } + +.fa-glass-cheers::before { + content: "\f79f"; } + +.fa-charging-station::before { + content: "\f5e7"; } + +.fa-chart-area::before { + content: "\f1fe"; } + +.fa-area-chart::before { + content: "\f1fe"; } + +.fa-chart-bar::before { + content: "\f080"; } + +.fa-bar-chart::before { + content: "\f080"; } + +.fa-chart-column::before { + content: "\e0e3"; } + +.fa-chart-gantt::before { + content: "\e0e4"; } + +.fa-chart-line::before { + content: "\f201"; } + +.fa-line-chart::before { + content: "\f201"; } + +.fa-chart-pie::before { + content: "\f200"; } + +.fa-pie-chart::before { + content: "\f200"; } + 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"\f35a"; } + +.fa-circle-stop::before { + content: "\f28d"; } + +.fa-stop-circle::before { + content: "\f28d"; } + +.fa-circle-up::before { + content: "\f35b"; } + +.fa-arrow-alt-circle-up::before { + content: "\f35b"; } + +.fa-circle-user::before { + content: "\f2bd"; } + +.fa-user-circle::before { + content: "\f2bd"; } + +.fa-circle-xmark::before { + content: "\f057"; } + +.fa-times-circle::before { + content: "\f057"; } + +.fa-xmark-circle::before { + content: "\f057"; } + +.fa-city::before { + content: "\f64f"; } + +.fa-clapperboard::before { + content: "\e131"; } + +.fa-clipboard::before { + content: "\f328"; } + +.fa-clipboard-check::before { + content: "\f46c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-clipboard-question::before { + content: "\e4e3"; } + +.fa-clipboard-user::before { + content: "\f7f3"; } + +.fa-clock::before { + content: "\f017"; } + +.fa-clock-four::before { + content: "\f017"; } + +.fa-clock-rotate-left::before { + content: "\f1da"; } + 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} + +.fa-comments-dollar::before { + content: "\f653"; } + +.fa-compact-disc::before { + content: "\f51f"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-compass-drafting::before { + content: "\f568"; } + +.fa-drafting-compass::before { + content: "\f568"; } + +.fa-compress::before { + content: "\f066"; } + +.fa-computer::before { + content: "\e4e5"; } + +.fa-computer-mouse::before { + content: "\f8cc"; } + +.fa-mouse::before { + content: "\f8cc"; } + +.fa-cookie::before { + content: "\f563"; } + +.fa-cookie-bite::before { + content: "\f564"; } + +.fa-copy::before { + content: "\f0c5"; } + +.fa-copyright::before { + content: "\f1f9"; } + +.fa-couch::before { + content: "\f4b8"; } + +.fa-cow::before { + content: "\f6c8"; } + +.fa-credit-card::before { + content: "\f09d"; } + +.fa-credit-card-alt::before { + content: "\f09d"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-crop-simple::before { + content: "\f565"; } + +.fa-crop-alt::before { + content: "\f565"; } + 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content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-dolly::before { + content: "\f472"; } + +.fa-dolly-box::before { + content: "\f472"; } + +.fa-dong-sign::before { + content: "\e169"; } + +.fa-door-closed::before { + content: "\f52a"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-dove::before { + content: "\f4ba"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-down-long::before { + content: "\f309"; } + +.fa-long-arrow-alt-down::before { + content: "\f309"; } + +.fa-download::before { + content: "\f019"; } + +.fa-dragon::before { + content: "\f6d5"; } + +.fa-draw-polygon::before { + content: "\f5ee"; } + +.fa-droplet::before { + content: "\f043"; } + +.fa-tint::before { + content: "\f043"; } + +.fa-droplet-slash::before { + content: "\f5c7"; } + +.fa-tint-slash::before { + content: "\f5c7"; } + +.fa-drum::before { + content: "\f569"; } + +.fa-drum-steelpan::before { + content: "\f56a"; } + +.fa-drumstick-bite::before { + content: "\f6d7"; } + +.fa-dumbbell::before { + content: "\f44b"; } + +.fa-dumpster::before { + content: "\f793"; } + +.fa-dumpster-fire::before { + content: "\f794"; } + +.fa-dungeon::before { + content: "\f6d9"; } + +.fa-e::before { + content: "\45"; } + +.fa-ear-deaf::before { + content: "\f2a4"; } + +.fa-deaf::before { + content: "\f2a4"; } + +.fa-deafness::before { + content: "\f2a4"; } + +.fa-hard-of-hearing::before { + content: "\f2a4"; } + +.fa-ear-listen::before { + content: "\f2a2"; } + +.fa-assistive-listening-systems::before { + content: "\f2a2"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-earth-americas::before { + content: "\f57d"; } + +.fa-earth::before { + content: "\f57d"; } + +.fa-earth-america::before { + content: "\f57d"; } + +.fa-globe-americas::before { + content: "\f57d"; } + +.fa-earth-asia::before { + content: "\f57e"; } + 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content: "\f579"; } + +.fa-face-frown::before { + content: "\f119"; } + +.fa-frown::before { + content: "\f119"; } + +.fa-face-frown-open::before { + content: "\f57a"; } + +.fa-frown-open::before { + content: "\f57a"; } + +.fa-face-grimace::before { + content: "\f57f"; } + +.fa-grimace::before { + content: "\f57f"; } + +.fa-face-grin::before { + content: "\f580"; } + +.fa-grin::before { + content: "\f580"; } + +.fa-face-grin-beam::before { + content: "\f582"; } + +.fa-grin-beam::before { + content: "\f582"; } + +.fa-face-grin-beam-sweat::before { + content: "\f583"; } + +.fa-grin-beam-sweat::before { + content: "\f583"; } + +.fa-face-grin-hearts::before { + content: "\f584"; } + +.fa-grin-hearts::before { + content: "\f584"; } + +.fa-face-grin-squint::before { + content: "\f585"; } + +.fa-grin-squint::before { + content: "\f585"; } + +.fa-face-grin-squint-tears::before { + content: "\f586"; } + +.fa-grin-squint-tears::before { + content: "\f586"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-face-grin-tears::before { + content: "\f588"; } + +.fa-grin-tears::before { + content: "\f588"; } + +.fa-face-grin-tongue::before { + content: "\f589"; } + +.fa-grin-tongue::before { + content: "\f589"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-face-grin-tongue-wink::before { + content: "\f58b"; } + +.fa-grin-tongue-wink::before { + content: "\f58b"; } + +.fa-face-grin-wide::before { + content: "\f581"; } + +.fa-grin-alt::before { + content: "\f581"; } + +.fa-face-grin-wink::before { + content: "\f58c"; } + +.fa-grin-wink::before { + content: "\f58c"; } + +.fa-face-kiss::before { + content: "\f596"; } + +.fa-kiss::before { + content: "\f596"; } + +.fa-face-kiss-beam::before { + content: "\f597"; } + +.fa-kiss-beam::before { + content: "\f597"; } + +.fa-face-kiss-wink-heart::before { + content: "\f598"; } + 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{ + content: "\f118"; } + +.fa-smile::before { + content: "\f118"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-face-smile-wink::before { + content: "\f4da"; } + +.fa-smile-wink::before { + content: "\f4da"; } + +.fa-face-surprise::before { + content: "\f5c2"; } + +.fa-surprise::before { + content: "\f5c2"; } + +.fa-face-tired::before { + content: "\f5c8"; } + +.fa-tired::before { + content: "\f5c8"; } + +.fa-fan::before { + content: "\f863"; } + +.fa-faucet::before { + content: "\e005"; } + +.fa-faucet-drip::before { + content: "\e006"; } + +.fa-fax::before { + content: "\f1ac"; } + +.fa-feather::before { + content: "\f52d"; } + +.fa-feather-pointed::before { + content: "\f56b"; } + +.fa-feather-alt::before { + content: "\f56b"; } + +.fa-ferry::before { + content: "\e4ea"; } + +.fa-file::before { + content: "\f15b"; } + +.fa-file-arrow-down::before { + content: "\f56d"; } + +.fa-file-download::before { + content: "\f56d"; } + +.fa-file-arrow-up::before { + content: "\f574"; } + +.fa-file-upload::before { + content: "\f574"; } + +.fa-file-audio::before { + content: "\f1c7"; } + +.fa-file-circle-check::before { + content: "\e493"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-file-circle-minus::before { + content: "\e4ed"; } + +.fa-file-circle-plus::before { + content: "\e4ee"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-file-circle-xmark::before { + content: "\e494"; } + +.fa-file-code::before { + content: "\f1c9"; } + +.fa-file-contract::before { + content: "\f56c"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-file-excel::before { + content: "\f1c3"; } + +.fa-file-export::before { + content: "\f56e"; } + +.fa-arrow-right-from-file::before { + content: "\f56e"; } + +.fa-file-image::before { + content: "\f1c5"; } + +.fa-file-import::before { + content: "\f56f"; } + +.fa-arrow-right-to-file::before { + content: "\f56f"; } + 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{ + content: "\f575"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-film::before { + content: "\f008"; } + +.fa-filter::before { + content: "\f0b0"; } + +.fa-filter-circle-dollar::before { + content: "\f662"; } + +.fa-funnel-dollar::before { + content: "\f662"; } + +.fa-filter-circle-xmark::before { + content: "\e17b"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-fire::before { + content: "\f06d"; } + +.fa-fire-burner::before { + content: "\e4f1"; } + +.fa-fire-extinguisher::before { + content: "\f134"; } + +.fa-fire-flame-curved::before { + content: "\f7e4"; } + +.fa-fire-alt::before { + content: "\f7e4"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-fish::before { + content: "\f578"; } + +.fa-fish-fins::before { + content: "\e4f2"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-flag-usa::before { + content: "\f74d"; } + 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content: "\f62a"; } + +.fa-tachometer-fast::before { + content: "\f62a"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-gear::before { + content: "\f013"; } + +.fa-cog::before { + content: "\f013"; } + +.fa-gears::before { + content: "\f085"; } + +.fa-cogs::before { + content: "\f085"; } + +.fa-gem::before { + content: "\f3a5"; } + +.fa-genderless::before { + content: "\f22d"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-gift::before { + content: "\f06b"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-glass-water::before { + content: "\e4f4"; } + +.fa-glass-water-droplet::before { + content: "\e4f5"; } + +.fa-glasses::before { + content: "\f530"; } + +.fa-globe::before { + content: "\f0ac"; } + +.fa-golf-ball-tee::before { + content: "\f450"; } + +.fa-golf-ball::before { + content: "\f450"; } + +.fa-gopuram::before { + content: "\f664"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-greater-than::before { + content: "\3e"; } + +.fa-greater-than-equal::before { + content: "\f532"; } + +.fa-grip::before { + content: "\f58d"; } + +.fa-grip-horizontal::before { + content: "\f58d"; } + +.fa-grip-lines::before { + content: "\f7a4"; } + +.fa-grip-lines-vertical::before { + content: "\f7a5"; } + +.fa-grip-vertical::before { + content: "\f58e"; } + +.fa-group-arrows-rotate::before { + content: "\e4f6"; } + +.fa-guarani-sign::before { + content: "\e19a"; } + +.fa-guitar::before { + content: "\f7a6"; } + +.fa-gun::before { + content: "\e19b"; } + +.fa-h::before { + content: "\48"; } + +.fa-hammer::before { + content: "\f6e3"; } + +.fa-hamsa::before { + content: "\f665"; } + +.fa-hand::before { + content: "\f256"; } + +.fa-hand-paper::before { + content: "\f256"; } + +.fa-hand-back-fist::before { + content: "\f255"; } + +.fa-hand-rock::before { + content: "\f255"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-hand-fist::before { + content: "\f6de"; } + +.fa-fist-raised::before { + content: "\f6de"; } + +.fa-hand-holding::before { + content: "\f4bd"; } + +.fa-hand-holding-dollar::before { + content: "\f4c0"; } + +.fa-hand-holding-usd::before { + content: "\f4c0"; } + +.fa-hand-holding-droplet::before { + content: "\f4c1"; } + +.fa-hand-holding-water::before { + content: "\f4c1"; } + +.fa-hand-holding-hand::before { + content: "\e4f7"; } + +.fa-hand-holding-heart::before { + content: "\f4be"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-hand-lizard::before { + content: "\f258"; } + +.fa-hand-middle-finger::before { + content: "\f806"; } + +.fa-hand-peace::before { + content: "\f25b"; } + +.fa-hand-point-down::before { + content: "\f0a7"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-hand-point-up::before { + content: "\f0a6"; } + +.fa-hand-pointer::before { + content: "\f25a"; } + +.fa-hand-scissors::before { + content: "\f257"; } + +.fa-hand-sparkles::before { + content: "\e05d"; } + +.fa-hand-spock::before { + content: "\f259"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-hands::before { + content: "\f2a7"; } + +.fa-sign-language::before { + content: "\f2a7"; } + +.fa-signing::before { + content: "\f2a7"; } + +.fa-hands-asl-interpreting::before { + content: "\f2a3"; } + +.fa-american-sign-language-interpreting::before { + content: "\f2a3"; } + +.fa-asl-interpreting::before { + content: "\f2a3"; } + +.fa-hands-american-sign-language-interpreting::before { + content: "\f2a3"; } + +.fa-hands-bound::before { + content: "\e4f9"; } + +.fa-hands-bubbles::before { + content: "\e05e"; } + +.fa-hands-wash::before { + content: "\e05e"; } + +.fa-hands-clapping::before { + content: "\e1a8"; } + +.fa-hands-holding::before { + content: "\f4c2"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-hands-holding-circle::before { + content: "\e4fb"; } + +.fa-hands-praying::before { + content: "\f684"; } + +.fa-praying-hands::before { + content: "\f684"; } + +.fa-handshake::before { + content: "\f2b5"; } + +.fa-handshake-angle::before { + content: "\f4c4"; } + +.fa-hands-helping::before { + content: "\f4c4"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-handshake-simple-slash::before { + content: "\e05f"; } + +.fa-handshake-alt-slash::before { + content: "\e05f"; } + +.fa-handshake-slash::before { + content: "\e060"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-hard-drive::before { + content: "\f0a0"; } + +.fa-hdd::before { + content: "\f0a0"; } + +.fa-hashtag::before { + content: "\23"; } + +.fa-hat-cowboy::before { + content: "\f8c0"; } + +.fa-hat-cowboy-side::before { + content: "\f8c1"; } + +.fa-hat-wizard::before { + content: "\f6e8"; } + +.fa-head-side-cough::before { + content: "\e061"; } + +.fa-head-side-cough-slash::before { + content: "\e062"; } + +.fa-head-side-mask::before { + content: "\e063"; } + +.fa-head-side-virus::before { + content: "\e064"; } + +.fa-heading::before { + content: "\f1dc"; } + +.fa-header::before { + content: "\f1dc"; } + +.fa-headphones::before { + content: "\f025"; } + +.fa-headphones-simple::before { + content: "\f58f"; } + +.fa-headphones-alt::before { + content: "\f58f"; } + +.fa-headset::before { + content: "\f590"; } + +.fa-heart::before { + content: "\f004"; } + +.fa-heart-circle-bolt::before { + content: "\e4fc"; } + +.fa-heart-circle-check::before { + content: "\e4fd"; } + +.fa-heart-circle-exclamation::before { + content: "\e4fe"; } + +.fa-heart-circle-minus::before { + content: "\e4ff"; } + +.fa-heart-circle-plus::before { + content: "\e500"; } + +.fa-heart-circle-xmark::before { + content: "\e501"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-heart-pulse::before { + content: "\f21e"; } + 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content: "\f593"; } + +.fa-hot-tub::before { + content: "\f593"; } + +.fa-hotdog::before { + content: "\f80f"; } + +.fa-hotel::before { + content: "\f594"; } + +.fa-hourglass::before { + content: "\f254"; } + +.fa-hourglass-2::before { + content: "\f254"; } + +.fa-hourglass-half::before { + content: "\f254"; } + +.fa-hourglass-empty::before { + content: "\f252"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-hourglass-start::before { + content: "\f251"; } + +.fa-hourglass-1::before { + content: "\f251"; } + +.fa-house::before { + content: "\f015"; } + +.fa-home::before { + content: "\f015"; } + +.fa-home-alt::before { + content: "\f015"; } + +.fa-home-lg-alt::before { + content: "\f015"; } + +.fa-house-chimney::before { + content: "\e3af"; } + +.fa-home-lg::before { + content: "\e3af"; } + +.fa-house-chimney-crack::before { + content: "\f6f1"; } + +.fa-house-damage::before { + content: "\f6f1"; } + 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content: "\f1ab"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-laptop-file::before { + content: "\e51d"; } + +.fa-laptop-medical::before { + content: "\f812"; } + +.fa-lari-sign::before { + content: "\e1c8"; } + +.fa-layer-group::before { + content: "\f5fd"; } + +.fa-leaf::before { + content: "\f06c"; } + +.fa-left-long::before { + content: "\f30a"; } + +.fa-long-arrow-alt-left::before { + content: "\f30a"; } + +.fa-left-right::before { + content: "\f337"; } + +.fa-arrows-alt-h::before { + content: "\f337"; } + +.fa-lemon::before { + content: "\f094"; } + +.fa-less-than::before { + content: "\3c"; } + +.fa-less-than-equal::before { + content: "\f537"; } + +.fa-life-ring::before { + content: "\f1cd"; } + +.fa-lightbulb::before { + content: "\f0eb"; } + +.fa-lines-leaning::before { + content: "\e51e"; } + +.fa-link::before { + content: "\f0c1"; } + +.fa-chain::before { + content: "\f0c1"; } + +.fa-link-slash::before { + content: "\f127"; } + +.fa-chain-broken::before { + content: "\f127"; } + +.fa-chain-slash::before { + content: "\f127"; } + +.fa-unlink::before { + content: "\f127"; } + +.fa-lira-sign::before { + content: "\f195"; } + +.fa-list::before { + content: "\f03a"; } + +.fa-list-squares::before { + content: "\f03a"; } + +.fa-list-check::before { + content: "\f0ae"; } + +.fa-tasks::before { + content: "\f0ae"; } + +.fa-list-ol::before { + content: "\f0cb"; } + +.fa-list-1-2::before { + content: "\f0cb"; } + +.fa-list-numeric::before { + content: "\f0cb"; } + +.fa-list-ul::before { + content: "\f0ca"; } + +.fa-list-dots::before { + content: "\f0ca"; } + +.fa-litecoin-sign::before { + content: "\e1d3"; } + +.fa-location-arrow::before { + content: "\f124"; } + +.fa-location-crosshairs::before { + content: "\f601"; } + +.fa-location::before { + content: "\f601"; } + +.fa-location-dot::before { + content: "\f3c5"; } + +.fa-map-marker-alt::before { + content: "\f3c5"; } + +.fa-location-pin::before { + content: "\f041"; } + +.fa-map-marker::before { + content: "\f041"; } + +.fa-location-pin-lock::before { + content: "\e51f"; } + +.fa-lock::before { + content: "\f023"; } + +.fa-lock-open::before { + content: "\f3c1"; } + +.fa-locust::before { + content: "\e520"; } + +.fa-lungs::before { + content: "\f604"; } + +.fa-lungs-virus::before { + content: "\e067"; } + +.fa-m::before { + content: "\4d"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-magnifying-glass::before { + content: "\f002"; } + +.fa-search::before { + content: "\f002"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-magnifying-glass-chart::before { + content: "\e522"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + 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"\f044"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-carry-box::before { + content: "\f4ce"; } + +.fa-people-carry::before { + content: "\f4ce"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-people-pulling::before { + content: "\e535"; } + +.fa-people-robbery::before { + content: "\e536"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-pepper-hot::before { + content: "\f816"; } + +.fa-percent::before { + content: "\25"; } + +.fa-percentage::before { + content: "\25"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-person-arrow-down-to-line::before { + content: "\e538"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-person-biking::before { + content: "\f84a"; } + +.fa-biking::before { + content: "\f84a"; } + +.fa-person-booth::before { + content: "\f756"; } + +.fa-person-breastfeeding::before { + content: "\e53a"; } + +.fa-person-burst::before { + content: "\e53b"; } + +.fa-person-cane::before { + content: "\e53c"; } + +.fa-person-chalkboard::before { + content: "\e53d"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-person-circle-exclamation::before { + content: "\e53f"; } + +.fa-person-circle-minus::before { + content: "\e540"; } + +.fa-person-circle-plus::before { + content: "\e541"; } + +.fa-person-circle-question::before { + content: "\e542"; } + +.fa-person-circle-xmark::before { + content: "\e543"; } + +.fa-person-digging::before { + content: "\f85e"; } + +.fa-digging::before { + content: "\f85e"; } + +.fa-person-dots-from-line::before { + content: "\f470"; } + +.fa-diagnoses::before { + content: "\f470"; } + +.fa-person-dress::before { + content: "\f182"; } + +.fa-female::before { + content: "\f182"; } 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content: "\e554"; } + +.fa-person-walking-with-cane::before { + content: "\f29d"; } + +.fa-blind::before { + content: "\f29d"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-peso-sign::before { + content: "\e222"; } + +.fa-phone::before { + content: "\f095"; } + +.fa-phone-flip::before { + content: "\f879"; } + +.fa-phone-alt::before { + content: "\f879"; } + +.fa-phone-slash::before { + content: "\f3dd"; } + +.fa-phone-volume::before { + content: "\f2a0"; } + +.fa-volume-control-phone::before { + content: "\f2a0"; } + +.fa-photo-film::before { + content: "\f87c"; } + +.fa-photo-video::before { + content: "\f87c"; } + +.fa-piggy-bank::before { + content: "\f4d3"; } + +.fa-pills::before { + content: "\f484"; } + +.fa-pizza-slice::before { + content: "\f818"; } + +.fa-place-of-worship::before { + content: "\f67f"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-plane-arrival::before { + content: "\f5af"; } + +.fa-plane-circle-check::before { + content: "\e555"; } + 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} + +.fa-quote-right-alt::before { + content: "\f10e"; } + +.fa-r::before { + content: "\52"; } + +.fa-radiation::before { + content: "\f7b9"; } + +.fa-radio::before { + content: "\f8d7"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-ranking-star::before { + content: "\e561"; } + +.fa-receipt::before { + content: "\f543"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-rectangle-ad::before { + content: "\f641"; } + +.fa-ad::before { + content: "\f641"; } + +.fa-rectangle-list::before { + content: "\f022"; } + +.fa-list-alt::before { + content: "\f022"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-recycle::before { + content: "\f1b8"; } + +.fa-registered::before { + content: "\f25d"; } + +.fa-repeat::before { + content: "\f363"; } + +.fa-reply::before { + content: "\f3e5"; } + 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content: "\e564"; } + +.fa-road-circle-exclamation::before { + content: "\e565"; } + +.fa-road-circle-xmark::before { + content: "\e566"; } + +.fa-road-lock::before { + content: "\e567"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-robot::before { + content: "\f544"; } + +.fa-rocket::before { + content: "\f135"; } + +.fa-rotate::before { + content: "\f2f1"; } + +.fa-sync-alt::before { + content: "\f2f1"; } + +.fa-rotate-left::before { + content: "\f2ea"; } + +.fa-rotate-back::before { + content: "\f2ea"; } + +.fa-rotate-backward::before { + content: "\f2ea"; } + +.fa-undo-alt::before { + content: "\f2ea"; } + +.fa-rotate-right::before { + content: "\f2f9"; } + +.fa-redo-alt::before { + content: "\f2f9"; } + +.fa-rotate-forward::before { + content: "\f2f9"; } + +.fa-route::before { + content: "\f4d7"; } + +.fa-rss::before { + content: "\f09e"; } + +.fa-feed::before { + content: "\f09e"; } + +.fa-ruble-sign::before { + content: "\f158"; } + +.fa-rouble::before { + content: "\f158"; } + +.fa-rub::before { + content: "\f158"; } + +.fa-ruble::before { + content: "\f158"; } + +.fa-rug::before { + content: "\e569"; } + +.fa-ruler::before { + content: "\f545"; } + +.fa-ruler-combined::before { + content: "\f546"; } + +.fa-ruler-horizontal::before { + content: "\f547"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-rupee-sign::before { + content: "\f156"; } + +.fa-rupee::before { + content: "\f156"; } + +.fa-rupiah-sign::before { + content: "\e23d"; } + +.fa-s::before { + content: "\53"; } + +.fa-sack-dollar::before { + content: "\f81d"; } + +.fa-sack-xmark::before { + content: "\e56a"; } + +.fa-sailboat::before { + content: "\e445"; } + +.fa-satellite::before { + content: "\f7bf"; } + +.fa-satellite-dish::before { + content: "\f7c0"; } + +.fa-scale-balanced::before { + content: "\f24e"; } + +.fa-balance-scale::before { + content: "\f24e"; } + +.fa-scale-unbalanced::before { + content: "\f515"; } + +.fa-balance-scale-left::before { + content: "\f515"; } + +.fa-scale-unbalanced-flip::before { + content: "\f516"; } + +.fa-balance-scale-right::before { + content: "\f516"; } + +.fa-school::before { + content: "\f549"; } + +.fa-school-circle-check::before { + content: "\e56b"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-school-flag::before { + content: "\e56e"; } + +.fa-school-lock::before { + content: "\e56f"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-screwdriver::before { + content: "\f54a"; } + +.fa-screwdriver-wrench::before { + content: "\f7d9"; } + +.fa-tools::before { + content: "\f7d9"; } + +.fa-scroll::before { + content: "\f70e"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-sd-card::before { + content: "\f7c2"; } + +.fa-section::before { + content: "\e447"; } + +.fa-seedling::before { + content: "\f4d8"; } + 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content: "\e579"; } + +.fa-stairs::before { + content: "\e289"; } + +.fa-stamp::before { + content: "\f5bf"; } + +.fa-star::before { + content: "\f005"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-star-half::before { + content: "\f089"; } + +.fa-star-half-stroke::before { + content: "\f5c0"; } + +.fa-star-half-alt::before { + content: "\f5c0"; } + +.fa-star-of-david::before { + content: "\f69a"; } + +.fa-star-of-life::before { + content: "\f621"; } + +.fa-sterling-sign::before { + content: "\f154"; } + +.fa-gbp::before { + content: "\f154"; } + +.fa-pound-sign::before { + content: "\f154"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-stop::before { + content: "\f04d"; } + +.fa-stopwatch::before { + content: "\f2f2"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-store::before { + content: "\f54e"; } + +.fa-store-slash::before { + content: "\e071"; } + +.fa-street-view::before { + content: "\f21d"; } + +.fa-strikethrough::before { + 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} + +.fa-table-list::before { + content: "\f00b"; } + +.fa-th-list::before { + content: "\f00b"; } + +.fa-table-tennis-paddle-ball::before { + content: "\f45d"; } + +.fa-ping-pong-paddle-ball::before { + content: "\f45d"; } + +.fa-table-tennis::before { + content: "\f45d"; } + +.fa-tablet::before { + content: "\f3fb"; } + +.fa-tablet-android::before { + content: "\f3fb"; } + +.fa-tablet-button::before { + content: "\f10a"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-tablets::before { + content: "\f490"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-tag::before { + content: "\f02b"; } + +.fa-tags::before { + content: "\f02c"; } + +.fa-tape::before { + content: "\f4db"; } + +.fa-tarp::before { + content: "\e57b"; } + +.fa-tarp-droplet::before { + content: "\e57c"; } + +.fa-taxi::before { + content: "\f1ba"; } + +.fa-cab::before { + 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} + +.fa-temperature-high::before { + content: "\f769"; } + +.fa-temperature-low::before { + content: "\f76b"; } + +.fa-temperature-quarter::before { + content: "\f2ca"; } + +.fa-temperature-1::before { + content: "\f2ca"; } + +.fa-thermometer-1::before { + content: "\f2ca"; } + +.fa-thermometer-quarter::before { + content: "\f2ca"; } + +.fa-temperature-three-quarters::before { + content: "\f2c8"; } + +.fa-temperature-3::before { + content: "\f2c8"; } + +.fa-thermometer-3::before { + content: "\f2c8"; } + +.fa-thermometer-three-quarters::before { + content: "\f2c8"; } + +.fa-tenge-sign::before { + content: "\f7d7"; } + +.fa-tenge::before { + content: "\f7d7"; } + +.fa-tent::before { + content: "\e57d"; } + +.fa-tent-arrow-down-to-line::before { + content: "\e57e"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-tent-arrow-turn-left::before { + content: "\e580"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-tents::before { + content: "\e582"; } + 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content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.fa-tv::before { + content: "\f26c"; } + +.fa-television::before { + content: "\f26c"; } + +.fa-tv-alt::before { + content: "\f26c"; } + +.fa-u::before { + content: "\55"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-umbrella-beach::before { + content: "\f5ca"; } + +.fa-underline::before { + content: "\f0cd"; } + +.fa-universal-access::before { + content: "\f29a"; } + +.fa-unlock::before { + content: "\f09c"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-up-down::before { + content: "\f338"; } + +.fa-arrows-alt-v::before { + content: "\f338"; } + +.fa-up-down-left-right::before { + content: "\f0b2"; } + +.fa-arrows-alt::before { + content: "\f0b2"; } + +.fa-up-long::before { + content: "\f30c"; } + +.fa-long-arrow-alt-up::before { + content: "\f30c"; } + +.fa-up-right-and-down-left-from-center::before { + content: "\f424"; } + 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content: "\f4fa"; } + +.fa-user-lock::before { + content: "\f502"; } + +.fa-user-minus::before { + content: "\f503"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-user-pen::before { + content: "\f4ff"; } + +.fa-user-edit::before { + content: "\f4ff"; } + +.fa-user-plus::before { + content: "\f234"; } + +.fa-user-secret::before { + content: "\f21b"; } + +.fa-user-shield::before { + content: "\f505"; } + +.fa-user-slash::before { + content: "\f506"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-user-tie::before { + content: "\f508"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-users::before { + content: "\f0c0"; } + +.fa-users-between-lines::before { + content: "\e591"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-users-line::before { + content: "\e592"; } + +.fa-users-rays::before { + content: "\e593"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-users-viewfinder::before { + content: "\e595"; } + +.fa-utensils::before { + content: "\f2e7"; } + +.fa-cutlery::before { + content: "\f2e7"; } + +.fa-v::before { + content: "\56"; } + +.fa-van-shuttle::before { + content: "\f5b6"; } + +.fa-shuttle-van::before { + content: "\f5b6"; } + +.fa-vault::before { + content: "\e2c5"; } + +.fa-vector-square::before { + content: "\f5cb"; } + +.fa-venus::before { + content: "\f221"; } + +.fa-venus-double::before { + content: "\f226"; } + +.fa-venus-mars::before { + content: "\f228"; } + +.fa-vest::before { + content: "\e085"; } + +.fa-vest-patches::before { + content: "\e086"; } + +.fa-vial::before { + content: "\f492"; } + +.fa-vial-circle-check::before { + content: "\e596"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-vials::before { + content: "\f493"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-video-slash::before { + content: "\f4e2"; } + +.fa-vihara::before { + content: "\f6a7"; } + +.fa-virus::before { + content: "\e074"; } + +.fa-virus-covid::before { + content: "\e4a8"; } + +.fa-virus-covid-slash::before { + content: "\e4a9"; } + +.fa-virus-slash::before { + content: "\e075"; } + +.fa-viruses::before { + content: "\e076"; } + +.fa-voicemail::before { + content: "\f897"; } + +.fa-volcano::before { + content: "\f770"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-volume-high::before { + content: "\f028"; } + +.fa-volume-up::before { + content: "\f028"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-volume-off::before { + content: "\f026"; } + +.fa-volume-xmark::before { + content: "\f6a9"; } + +.fa-volume-mute::before { + content: "\f6a9"; } + +.fa-volume-times::before { + content: "\f6a9"; } + +.fa-vr-cardboard::before { + content: "\f729"; } + +.fa-w::before { + content: "\57"; } + +.fa-walkie-talkie::before { + content: "\f8ef"; } + +.fa-wallet::before { + content: "\f555"; } + +.fa-wand-magic::before { + content: "\f0d0"; } + +.fa-magic::before { + content: "\f0d0"; } + +.fa-wand-magic-sparkles::before { + content: "\e2ca"; } + +.fa-magic-wand-sparkles::before { + content: "\e2ca"; } + +.fa-wand-sparkles::before { + content: "\f72b"; } + +.fa-warehouse::before { + content: "\f494"; } + +.fa-water::before { + content: "\f773"; } + +.fa-water-ladder::before { + content: "\f5c5"; } + +.fa-ladder-water::before { + content: "\f5c5"; } + +.fa-swimming-pool::before { + content: "\f5c5"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-weight-hanging::before { + content: "\f5cd"; } + +.fa-weight-scale::before { + content: "\f496"; } + +.fa-weight::before { + content: "\f496"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-wheat-awn-circle-exclamation::before { + content: "\e598"; } + +.fa-wheelchair::before { + content: "\f193"; } + +.fa-wheelchair-move::before { + content: "\e2ce"; } + +.fa-wheelchair-alt::before { + content: "\e2ce"; } + +.fa-whiskey-glass::before { + content: "\f7a0"; } + +.fa-glass-whiskey::before { + content: "\f7a0"; } + +.fa-wifi::before { + content: "\f1eb"; } + +.fa-wifi-3::before { + content: "\f1eb"; } + +.fa-wifi-strong::before { + content: "\f1eb"; } + +.fa-wind::before { + content: "\f72e"; } + +.fa-window-maximize::before { + content: "\f2d0"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-window-restore::before { + content: "\f2d2"; } + +.fa-wine-bottle::before { + content: "\f72f"; } + +.fa-wine-glass::before { + content: "\f4e3"; } + +.fa-wine-glass-empty::before { + content: "\f5ce"; } + +.fa-wine-glass-alt::before { + content: "\f5ce"; } + +.fa-won-sign::before { + content: "\f159"; } + +.fa-krw::before { + content: "\f159"; } + +.fa-won::before { + content: "\f159"; } + +.fa-worm::before { + content: "\e599"; } + +.fa-wrench::before { + content: "\f0ad"; } + +.fa-x::before { + content: "\58"; } + +.fa-x-ray::before { + content: "\f497"; } + +.fa-xmark::before { + content: "\f00d"; } + +.fa-close::before { + content: "\f00d"; } + +.fa-multiply::before { + content: "\f00d"; } + +.fa-remove::before { + content: "\f00d"; } + +.fa-times::before { + content: "\f00d"; } + +.fa-xmarks-lines::before { + content: "\e59a"; } + +.fa-y::before { + content: "\59"; } + +.fa-yen-sign::before { + content: "\f157"; } + +.fa-cny::before { + content: "\f157"; } + +.fa-jpy::before { + content: "\f157"; } + +.fa-rmb::before { + content: "\f157"; } + +.fa-yen::before { + content: "\f157"; } + +.fa-yin-yang::before { + content: "\f6ad"; } + +.fa-z::before { + content: "\5a"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-font-brands: normal 400 1em/1 "Font Awesome 6 Brands"; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("1e21o67/fa-brands-400.woff2") format("woff2"), url("1e21o67/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa-42-group:before { + content: "\e080"; } + +.fa-innosoft:before { + content: "\e080"; } + +.fa-500px:before { + content: "\f26e"; } + +.fa-accessible-icon:before { + content: "\f368"; } + +.fa-accusoft:before { + content: "\f369"; } + +.fa-adn:before { + content: "\f170"; } + +.fa-adversal:before { + content: "\f36a"; } + +.fa-affiliatetheme:before { + content: "\f36b"; } + +.fa-airbnb:before { + content: "\f834"; } + +.fa-algolia:before { + content: "\f36c"; } + +.fa-alipay:before { + content: "\f642"; } + +.fa-amazon:before { + content: "\f270"; } + +.fa-amazon-pay:before { + content: "\f42c"; } + +.fa-amilia:before { + content: "\f36d"; } + +.fa-android:before { + content: "\f17b"; } + +.fa-angellist:before { + content: "\f209"; } + +.fa-angrycreative:before { + content: "\f36e"; } + +.fa-angular:before { + content: "\f420"; } + +.fa-app-store:before { + content: "\f36f"; } + +.fa-app-store-ios:before { + content: "\f370"; } + +.fa-apper:before { + content: "\f371"; } + +.fa-apple:before { + content: "\f179"; } + +.fa-apple-pay:before { + content: "\f415"; } + +.fa-artstation:before { + content: "\f77a"; } + +.fa-asymmetrik:before { + content: "\f372"; } + +.fa-atlassian:before { + content: "\f77b"; } + +.fa-audible:before { + content: "\f373"; } + +.fa-autoprefixer:before { + content: "\f41c"; } + +.fa-avianex:before { + content: "\f374"; } + +.fa-aviato:before { + content: "\f421"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-bandcamp:before { + content: "\f2d5"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-behance:before { + content: "\f1b4"; } + +.fa-behance-square:before { + content: "\f1b5"; } + +.fa-bilibili:before { + content: "\e3d9"; } + +.fa-bimobject:before { + content: "\f378"; } + +.fa-bitbucket:before { + content: "\f171"; } + +.fa-bitcoin:before { + content: "\f379"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-black-tie:before { + content: "\f27e"; } + +.fa-blackberry:before { + content: "\f37b"; } + +.fa-blogger:before { + content: "\f37c"; } + +.fa-blogger-b:before { + content: "\f37d"; } + +.fa-bluetooth:before { + content: "\f293"; } + +.fa-bluetooth-b:before { + content: "\f294"; } + +.fa-bootstrap:before { + content: "\f836"; } + +.fa-bots:before { + content: "\e340"; } + +.fa-btc:before { + content: "\f15a"; } + +.fa-buffer:before { + content: "\f837"; } + +.fa-buromobelexperte:before { + content: "\f37f"; } + +.fa-buy-n-large:before { + content: "\f8a6"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-canadian-maple-leaf:before { + content: "\f785"; } + +.fa-cc-amazon-pay:before { + content: "\f42d"; } + +.fa-cc-amex:before { + content: "\f1f3"; } + +.fa-cc-apple-pay:before { + content: "\f416"; } + +.fa-cc-diners-club:before { + content: "\f24c"; } + +.fa-cc-discover:before { + content: "\f1f2"; } + +.fa-cc-jcb:before { + content: "\f24b"; } + +.fa-cc-mastercard:before { + content: "\f1f1"; } + +.fa-cc-paypal:before { + content: "\f1f4"; } + +.fa-cc-stripe:before { + content: "\f1f5"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-centos:before { + content: "\f789"; } + +.fa-chrome:before { + content: "\f268"; } + +.fa-chromecast:before { + content: "\f838"; } + +.fa-cloudflare:before { + content: "\e07d"; } + 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+ +.fa-creative-commons-remix:before { + content: "\f4ee"; } + +.fa-creative-commons-sa:before { + content: "\f4ef"; } + +.fa-creative-commons-sampling:before { + content: "\f4f0"; } + +.fa-creative-commons-sampling-plus:before { + content: "\f4f1"; } + +.fa-creative-commons-share:before { + content: "\f4f2"; } + +.fa-creative-commons-zero:before { + content: "\f4f3"; } + +.fa-critical-role:before { + content: "\f6c9"; } + +.fa-css3:before { + content: "\f13c"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-cuttlefish:before { + content: "\f38c"; } + +.fa-d-and-d:before { + content: "\f38d"; } + +.fa-d-and-d-beyond:before { + content: "\f6ca"; } + +.fa-dailymotion:before { + content: "\e052"; } + +.fa-dashcube:before { + content: "\f210"; } + +.fa-deezer:before { + content: "\e077"; } + +.fa-delicious:before { + content: "\f1a5"; } + +.fa-deploydog:before { + content: "\f38e"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-dev:before { + content: "\f6cc"; } + 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{ + content: "\f423"; } + +.fa-empire:before { + content: "\f1d1"; } + +.fa-envira:before { + content: "\f299"; } + +.fa-erlang:before { + content: "\f39d"; } + +.fa-ethereum:before { + content: "\f42e"; } + +.fa-etsy:before { + content: "\f2d7"; } + +.fa-evernote:before { + content: "\f839"; } + +.fa-expeditedssl:before { + content: "\f23e"; } + +.fa-facebook:before { + content: "\f09a"; } + +.fa-facebook-f:before { + content: "\f39e"; } + +.fa-facebook-messenger:before { + content: "\f39f"; } + +.fa-facebook-square:before { + content: "\f082"; } + +.fa-fantasy-flight-games:before { + content: "\f6dc"; } + +.fa-fedex:before { + content: "\f797"; } + +.fa-fedora:before { + content: "\f798"; } + +.fa-figma:before { + content: "\f799"; } + +.fa-firefox:before { + content: "\f269"; } + +.fa-firefox-browser:before { + content: "\e007"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-first-order-alt:before { + content: "\f50a"; } + +.fa-firstdraft:before { + content: "\f3a1"; } + 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+ content: "\f5fa"; } + +.fa-keybase:before { + content: "\f4f5"; } + +.fa-keycdn:before { + content: "\f3ba"; } + +.fa-kickstarter:before { + content: "\f3bb"; } + +.fa-kickstarter-k:before { + content: "\f3bc"; } + +.fa-korvue:before { + content: "\f42f"; } + +.fa-laravel:before { + content: "\f3bd"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-lastfm-square:before { + content: "\f203"; } + +.fa-leanpub:before { + content: "\f212"; } + +.fa-less:before { + content: "\f41d"; } + +.fa-line:before { + content: "\f3c0"; } + +.fa-linkedin:before { + content: "\f08c"; } + +.fa-linkedin-in:before { + content: "\f0e1"; } + +.fa-linode:before { + content: "\f2b8"; } + +.fa-linux:before { + content: "\f17c"; } + +.fa-lyft:before { + content: "\f3c3"; } + +.fa-magento:before { + content: "\f3c4"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-mandalorian:before { + content: "\f50f"; } + +.fa-markdown:before { + content: "\f60f"; } + +.fa-mastodon:before { + content: "\f4f6"; } + +.fa-maxcdn:before { + content: "\f136"; } + +.fa-mdb:before { + content: "\f8ca"; } + +.fa-medapps:before { + content: "\f3c6"; } + +.fa-medium:before { + content: "\f23a"; } + +.fa-medium-m:before { + content: "\f23a"; } + +.fa-medrt:before { + content: "\f3c8"; } + +.fa-meetup:before { + content: "\f2e0"; } + +.fa-megaport:before { + content: "\f5a3"; } + +.fa-mendeley:before { + content: "\f7b3"; } + +.fa-microblog:before { + content: "\e01a"; } + +.fa-microsoft:before { + content: "\f3ca"; } + +.fa-mix:before { + content: "\f3cb"; } + +.fa-mixcloud:before { + content: "\f289"; } + +.fa-mixer:before { + content: "\e056"; } + +.fa-mizuni:before { + content: "\f3cc"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-napster:before { + content: "\f3d2"; } + +.fa-neos:before { + content: "\f612"; } + +.fa-nfc-directional:before { + content: "\e530"; } + +.fa-nfc-symbol:before { + content: "\e531"; } + +.fa-nimblr:before { + content: "\f5a8"; } + +.fa-node:before { + content: "\f419"; } + +.fa-node-js:before { + content: "\f3d3"; } + +.fa-npm:before { + content: "\f3d4"; } + +.fa-ns8:before { + content: "\f3d5"; } + +.fa-nutritionix:before { + content: "\f3d6"; } + +.fa-octopus-deploy:before { + content: "\e082"; } + +.fa-odnoklassniki:before { + content: "\f263"; } + +.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa-old-republic:before { + content: "\f510"; } + +.fa-opencart:before { + content: "\f23d"; } + +.fa-openid:before { + content: "\f19b"; } + +.fa-opera:before { + content: "\f26a"; } + +.fa-optin-monster:before { + content: "\f23c"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-osi:before { + content: "\f41a"; } + +.fa-padlet:before { + content: "\e4a0"; } + +.fa-page4:before { + content: "\f3d7"; } + +.fa-pagelines:before { + content: "\f18c"; } + +.fa-palfed:before { + content: "\f3d8"; } + +.fa-patreon:before { + content: "\f3d9"; } + +.fa-paypal:before { + content: "\f1ed"; } + +.fa-perbyte:before { + content: "\e083"; } + +.fa-periscope:before { + content: "\f3da"; } + +.fa-phabricator:before { + content: "\f3db"; } + +.fa-phoenix-framework:before { + content: "\f3dc"; } + +.fa-phoenix-squadron:before { + content: "\f511"; } + +.fa-php:before { + content: "\f457"; } + +.fa-pied-piper:before { + content: "\f2ae"; } + +.fa-pied-piper-alt:before { + content: "\f1a8"; } + +.fa-pied-piper-hat:before { + content: "\f4e5"; } + +.fa-pied-piper-pp:before { + content: "\f1a7"; } + +.fa-pied-piper-square:before { + content: "\e01e"; } + +.fa-pinterest:before { + content: "\f0d2"; } + +.fa-pinterest-p:before { + content: "\f231"; } + +.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa-pix:before { + content: "\e43a"; } + +.fa-playstation:before { + content: "\f3df"; } + +.fa-product-hunt:before { + content: "\f288"; } + +.fa-pushed:before { + content: "\f3e1"; } + +.fa-python:before { + content: "\f3e2"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-quinscape:before { + content: "\f459"; } + +.fa-quora:before { + content: "\f2c4"; } + +.fa-r-project:before { + content: "\f4f7"; } + +.fa-raspberry-pi:before { + content: "\f7bb"; } + +.fa-ravelry:before { + content: "\f2d9"; } + +.fa-react:before { + content: "\f41b"; } + +.fa-reacteurope:before { + content: "\f75d"; } + +.fa-readme:before { + content: "\f4d5"; } + +.fa-rebel:before { + content: "\f1d0"; } + +.fa-red-river:before { + content: "\f3e3"; } + +.fa-reddit:before { + content: "\f1a1"; } + +.fa-reddit-alien:before { + content: "\f281"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-renren:before { + content: "\f18b"; } + +.fa-replyd:before { + content: "\f3e6"; } + +.fa-researchgate:before { + content: "\f4f8"; } + +.fa-resolving:before { + content: "\f3e7"; } + +.fa-rev:before { + content: "\f5b2"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-rockrms:before { + content: "\f3e9"; } + +.fa-rust:before { + content: "\e07a"; } + +.fa-safari:before { + content: "\f267"; } + +.fa-salesforce:before { + content: "\f83b"; } + +.fa-sass:before { + content: "\f41e"; } + +.fa-schlix:before { + content: "\f3ea"; } + +.fa-screenpal:before { + content: "\e570"; } + +.fa-scribd:before { + content: "\f28a"; } + +.fa-searchengin:before { + content: "\f3eb"; } + +.fa-sellcast:before { + content: "\f2da"; } + +.fa-sellsy:before { + content: "\f213"; } + +.fa-servicestack:before { + content: "\f3ec"; } + +.fa-shirtsinbulk:before { + content: "\f214"; } + +.fa-shopify:before { + content: "\e057"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-simplybuilt:before { + content: "\f215"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-sith:before { + content: "\f512"; } + +.fa-sitrox:before { + content: "\e44a"; } + +.fa-sketch:before { + content: "\f7c6"; } + +.fa-skyatlas:before { + content: "\f216"; } + +.fa-skype:before { + content: "\f17e"; } + +.fa-slack:before { + content: "\f198"; } + +.fa-slack-hash:before { + content: "\f198"; } + +.fa-slideshare:before { + content: "\f1e7"; } + +.fa-snapchat:before { + content: "\f2ab"; } + +.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa-soundcloud:before { + content: "\f1be"; } + +.fa-sourcetree:before { + content: "\f7d3"; } + +.fa-speakap:before { + content: "\f3f3"; } + +.fa-speaker-deck:before { + content: "\f83c"; } + +.fa-spotify:before { + content: "\f1bc"; } + +.fa-square-font-awesome:before { + content: "\f425"; } + +.fa-square-font-awesome-stroke:before { + content: "\f35c"; } + +.fa-font-awesome-alt:before { + content: "\f35c"; } + +.fa-squarespace:before { + content: "\f5be"; } + +.fa-stack-exchange:before { + content: "\f18d"; } + +.fa-stack-overflow:before { + content: "\f16c"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-staylinked:before { + content: "\f3f5"; } + +.fa-steam:before { + content: "\f1b6"; } + +.fa-steam-square:before { + content: "\f1b7"; } + +.fa-steam-symbol:before { + content: "\f3f6"; } + +.fa-sticker-mule:before { + content: "\f3f7"; } + +.fa-strava:before { + content: "\f428"; } + +.fa-stripe:before { + content: "\f429"; } + +.fa-stripe-s:before { + content: "\f42a"; } + +.fa-studiovinari:before { + content: "\f3f8"; } + +.fa-stumbleupon:before { + content: "\f1a4"; } + +.fa-stumbleupon-circle:before { + content: "\f1a3"; } + +.fa-superpowers:before { + content: "\f2dd"; } + +.fa-supple:before { + content: "\f3f9"; } + +.fa-suse:before { + content: "\f7d6"; } + +.fa-swift:before { + content: "\f8e1"; } + +.fa-symfony:before { + content: "\f83d"; } + +.fa-teamspeak:before { + content: "\f4f9"; } + +.fa-telegram:before { + content: "\f2c6"; } + +.fa-telegram-plane:before { + content: "\f2c6"; } + +.fa-tencent-weibo:before { + content: "\f1d5"; } + +.fa-the-red-yeti:before { + content: "\f69d"; } + +.fa-themeco:before { + content: "\f5c6"; } + +.fa-themeisle:before { + content: "\f2b2"; } + +.fa-think-peaks:before { + content: "\f731"; } + +.fa-tiktok:before { + content: "\e07b"; } + +.fa-trade-federation:before { + content: "\f513"; } + +.fa-trello:before { + content: "\f181"; } + +.fa-tumblr:before { + content: "\f173"; } + +.fa-tumblr-square:before { + content: "\f174"; } + +.fa-twitch:before { + content: "\f1e8"; } + +.fa-twitter:before { + content: "\f099"; } + +.fa-twitter-square:before { + content: "\f081"; } + +.fa-typo3:before { + content: "\f42b"; } + +.fa-uber:before { + content: "\f402"; } + +.fa-ubuntu:before { + content: "\f7df"; } + +.fa-uikit:before { + content: "\f403"; } + +.fa-umbraco:before { + content: "\f8e8"; } + +.fa-uncharted:before { + content: "\e084"; } + +.fa-uniregistry:before { + content: "\f404"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-unsplash:before { + content: "\e07c"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-usb:before { + content: "\f287"; } + +.fa-usps:before { + content: "\f7e1"; } + +.fa-ussunnah:before { + content: "\f407"; } + +.fa-vaadin:before { + content: "\f408"; } + +.fa-viacoin:before { + content: "\f237"; } + +.fa-viadeo:before { + content: "\f2a9"; } + +.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa-viber:before { + content: "\f409"; } + +.fa-vimeo:before { + content: "\f40a"; } + +.fa-vimeo-square:before { + content: "\f194"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-vine:before { + content: "\f1ca"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-vuejs:before { + content: "\f41f"; } + +.fa-watchman-monitoring:before { + content: "\e087"; } + +.fa-waze:before { + content: "\f83f"; } + +.fa-weebly:before { + content: "\f5cc"; } + +.fa-weibo:before { + content: "\f18a"; } + +.fa-weixin:before { + content: "\f1d7"; } + +.fa-whatsapp:before { + content: "\f232"; } + +.fa-whatsapp-square:before { + content: "\f40c"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-wikipedia-w:before { + content: "\f266"; } + +.fa-windows:before { + content: "\f17a"; } + +.fa-wirsindhandwerk:before { + content: "\e2d0"; } + +.fa-wsh:before { + content: "\e2d0"; } + +.fa-wix:before { + content: "\f5cf"; } + +.fa-wizards-of-the-coast:before { + content: "\f730"; } + +.fa-wodu:before { + content: "\e088"; } + +.fa-wolf-pack-battalion:before { + content: "\f514"; } + +.fa-wordpress:before { + content: "\f19a"; } + +.fa-wordpress-simple:before { + content: "\f411"; } + +.fa-wpbeginner:before { + content: "\f297"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-wpforms:before { + content: "\f298"; } + +.fa-wpressr:before { + content: "\f3e4"; } + +.fa-xbox:before { + content: "\f412"; } + +.fa-xing:before { + content: "\f168"; } + +.fa-xing-square:before { + content: "\f169"; } + +.fa-y-combinator:before { + content: "\f23b"; } + +.fa-yahoo:before { + content: "\f19e"; } + +.fa-yammer:before { + content: "\f840"; } + +.fa-yandex:before { + content: "\f413"; } + +.fa-yandex-international:before { + content: "\f414"; } + +.fa-yarn:before { + content: "\f7e3"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-youtube:before { + content: "\f167"; } + +.fa-youtube-square:before { + content: "\f431"; } + +.fa-zhihu:before { + content: "\f63f"; } +:root, :host { + --fa-font-regular: normal 400 1em/1 "Font Awesome 6 Free"; } + +@font-face { + font-family: 'Font Awesome 6 Free'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("1e21o67/fa-regular-400.woff2") format("woff2"), url("1e21o67/fa-regular-400.ttf") format("truetype"); } + +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } +:root, :host { + --fa-font-solid: normal 900 1em/1 "Font Awesome 6 Free"; } + +@font-face { + font-family: 'Font Awesome 6 Free'; + font-style: normal; + font-weight: 900; + font-display: block; + src: url("1e21o67/fa-solid-900.woff2") format("woff2"), url("1e21o67/fa-solid-900.ttf") format("truetype"); } + +.fas, +.fa-solid { + font-family: 'Font Awesome 6 Free'; + font-weight: 900; } +@font-face { + font-family: "Font Awesome 5 Brands"; + font-display: block; + font-weight: 400; + src: url("1e21o67/fa-brands-400.woff2") format("woff2"), url("1e21o67/fa-brands-400.ttf") format("truetype"); } + +@font-face { + font-family: "Font Awesome 5 Free"; + font-display: block; + font-weight: 900; + src: url("1e21o67/fa-solid-900.woff2") format("woff2"), url("1e21o67/fa-solid-900.ttf") format("truetype"); } + +@font-face { + font-family: "Font Awesome 5 Free"; + font-display: block; + font-weight: 400; + src: url("1e21o67/fa-regular-400.woff2") format("woff2"), url("1e21o67/fa-regular-400.ttf") format("truetype"); } +@font-face { + font-family: "FontAwesome"; + font-display: block; + src: url("1e21o67/fa-solid-900.woff2") format("woff2"), url("1e21o67/fa-solid-900.ttf") format("truetype"); } + +@font-face { + font-family: "FontAwesome"; + font-display: block; + src: url("1e21o67/fa-brands-400.woff2") format("woff2"), url("1e21o67/fa-brands-400.ttf") format("truetype"); } + +@font-face { + font-family: "FontAwesome"; + font-display: block; + src: url("1e21o67/fa-regular-400.woff2") format("woff2"), url("1e21o67/fa-regular-400.ttf") format("truetype"); + unicode-range: U+F003,U+F006,U+F014,U+F016-F017,U+F01A-F01B,U+F01D,U+F022,U+F03E,U+F044,U+F046,U+F05C-F05D,U+F06E,U+F070,U+F087-F088,U+F08A,U+F094,U+F096-F097,U+F09D,U+F0A0,U+F0A2,U+F0A4-F0A7,U+F0C5,U+F0C7,U+F0E5-F0E6,U+F0EB,U+F0F6-F0F8,U+F10C,U+F114-F115,U+F118-F11A,U+F11C-F11D,U+F133,U+F147,U+F14E,U+F150-F152,U+F185-F186,U+F18E,U+F190-F192,U+F196,U+F1C1-F1C9,U+F1D9,U+F1DB,U+F1E3,U+F1EA,U+F1F7,U+F1F9,U+F20A,U+F247-F248,U+F24A,U+F24D,U+F255-F25B,U+F25D,U+F271-F274,U+F278,U+F27B,U+F28C,U+F28E,U+F29C,U+F2B5,U+F2B7,U+F2BA,U+F2BC,U+F2BE,U+F2C0-F2C1,U+F2C3,U+F2D0,U+F2D2,U+F2D4,U+F2DC; } + +@font-face { + font-family: "FontAwesome"; + font-display: block; + src: url("1e21o67/fa-v4compatibility.woff2") format("woff2"), url("1e21o67/fa-v4compatibility.ttf") format("truetype"); + unicode-range: U+F041,U+F047,U+F065-F066,U+F07D-F07E,U+F080,U+F08B,U+F08E,U+F090,U+F09A,U+F0AC,U+F0AE,U+F0B2,U+F0D0,U+F0D6,U+F0E4,U+F0EC,U+F10A-F10B,U+F123,U+F13E,U+F148-F149,U+F14C,U+F156,U+F15E,U+F160-F161,U+F163,U+F175-F178,U+F195,U+F1F8,U+F219,U+F250,U+F252,U+F27A; } diff --git a/docs/site_libs/quarto-contrib/fontawesome6-0.1.0/latex-fontsize.css b/docs/site_libs/quarto-contrib/fontawesome6-0.1.0/latex-fontsize.css new file mode 100644 index 00000000..45545ecf --- /dev/null +++ b/docs/site_libs/quarto-contrib/fontawesome6-0.1.0/latex-fontsize.css @@ -0,0 +1,30 @@ +.fa-tiny { + font-size: 0.5em; +} +.fa-scriptsize { + font-size: 0.7em; +} +.fa-footnotesize { + font-size: 0.8em; +} +.fa-small { + font-size: 0.9em; +} +.fa-normalsize { + font-size: 1em; +} +.fa-large { + font-size: 1.2em; +} +.fa-Large { + font-size: 1.5em; +} +.fa-LARGE { + font-size: 1.75em; +} +.fa-huge { + font-size: 2em; +} +.fa-Huge { + font-size: 2.5em; +} diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 68c3a665..8cf37a03 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -6,14 +6,26 @@ https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2DU1.html - 2023-08-23T19:24:28.224Z + 2023-09-06T13:53:05.758Z https://saforem2.github.io/l2hmc-qcd/index.html - 2023-08-23T19:22:13.517Z + 2023-09-06T13:16:43.611Z https://saforem2.github.io/l2hmc-qcd/qmd/docs.html 2023-08-23T19:18:41.228Z + + https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-4DSU3.html + 2023-09-06T13:28:03.638Z + + + https://saforem2.github.io/l2hmc-qcd/qmd/slides.html + 2023-09-06T13:17:15.533Z + + + https://saforem2.github.io/l2hmc-qcd/qmd/examples.html + 2023-09-06T13:23:16.811Z + diff --git a/docs/src/l2hmc/configs.py b/docs/src/l2hmc/configs.py index c60c1742..eb06030c 100644 --- a/docs/src/l2hmc/configs.py +++ b/docs/src/l2hmc/configs.py @@ -32,10 +32,12 @@ LOGS_DIR = PROJECT_DIR.joinpath('logs') AIM_DIR = HERE.joinpath('.aim') OUTPUTS_DIR = HERE.joinpath('outputs') +QUARTO_OUTPUTS_DIR = PROJECT_DIR.joinpath('qmd', 'outputs') CHECKPOINTS_DIR = HERE.joinpath('checkpoints') CONF_DIR.mkdir(exist_ok=True, parents=True) LOGS_DIR.mkdir(exist_ok=True, parents=True) +QUARTO_OUTPUTS_DIR.mkdir(exist_ok=True, parents=True) OUTPUTS_DIR.mkdir(exist_ok=True, parents=True) CHECKPOINTS_DIR.mkdir(exist_ok=True, parents=True) OUTDIRS_FILE = OUTPUTS_DIR.joinpath('outdirs.log') diff --git a/index.qmd b/index.qmd index c60f95c8..388eeb5e 100644 --- a/index.qmd +++ b/index.qmd @@ -13,7 +13,6 @@ execute: freeze: true --- - ::: {style="text-align:center;"} ![](https://raw.githubusercontent.com/saforem2/l2hmc-qcd/main/assets/logo-small.svg) diff --git a/src/l2hmc/configs.py b/src/l2hmc/configs.py index c60c1742..eb06030c 100644 --- a/src/l2hmc/configs.py +++ b/src/l2hmc/configs.py @@ -32,10 +32,12 @@ LOGS_DIR = PROJECT_DIR.joinpath('logs') AIM_DIR = HERE.joinpath('.aim') OUTPUTS_DIR = HERE.joinpath('outputs') +QUARTO_OUTPUTS_DIR = PROJECT_DIR.joinpath('qmd', 'outputs') CHECKPOINTS_DIR = HERE.joinpath('checkpoints') CONF_DIR.mkdir(exist_ok=True, parents=True) LOGS_DIR.mkdir(exist_ok=True, parents=True) +QUARTO_OUTPUTS_DIR.mkdir(exist_ok=True, parents=True) OUTPUTS_DIR.mkdir(exist_ok=True, parents=True) CHECKPOINTS_DIR.mkdir(exist_ok=True, parents=True) OUTDIRS_FILE = OUTPUTS_DIR.joinpath('outdirs.log')