rahulbhadani

from flow.core.params import VehicleParams
from flow.controllers import IDMController, ContinuousRouter
from flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams
from flow.envs.ring.accel import AccelEnv, ADDITIONAL_ENV_PARAMS
from flow.core.experiment import Experiment
from flow.networks import Network
from numpy import pi, sin, cos, linspace


vehicles = VehicleParams()

rahulbhadani

# the TestEnv environment is used to simply simulate the network
from flow.envs import TestEnv

# the Experiment class is used for running simulations
from flow.core.experiment import Experiment

# all other imports are standard
from flow.core.params import VehicleParams
from flow.core.params import NetParams
from flow.core.params import InitialConfig

rahulbhadani

# the TestEnv environment is used to simply simulate the network
from flow.envs import TestEnv
import os

# the Experiment class is used for running simulations
from flow.core.experiment import Experiment

# all other imports are standard
from flow.core.params import VehicleParams
from flow.core.params import NetParams

rahulbhadani

Installation

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600

wget https://developer.download.nvidia.com/compute/cuda/11.3.0/local_installers/cuda-repo-ubuntu2004-11-3-local_11.3.0-465.19.01-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004-11-3-local_11.3.0-465.19.01-1_amd64.deb

sudo apt-key add /var/cuda-repo-ubuntu2004-11-3-local/7fa2af80.pub

rahulbhadani

diff --git a/LibCarla/source/test/common/test_streaming.cpp b/LibCarla/source/test/common/test_streaming.cpp
index ad4f35c3..e5183e43 100644
--- a/LibCarla/source/test/common/test_streaming.cpp
+++ b/LibCarla/source/test/common/test_streaming.cpp
@@ -55,12 +55,12 @@ TEST(streaming, low_level_sending_strings) {
 
   io_context_running io;
 
-  Server<tcp::Server> srv(io.service, TESTING_PORT);
+  carla::streaming::low_level::Server<tcp::Server> srv(io.service, TESTING_PORT);

rahulbhadani

#include <Eigen/Dense>
#include <iostream>
#include <cmath>
#include <vector>
#include <Eigen/QR>

void polyfit(	const std::vector<double> &t,
		const std::vector<double> &v,
		std::vector<double> &coeff,
		int order

rahulbhadani

import scipy as sp
import numpy as np
import matplotlib.pylab as pl
from mpl_toolkits.mplot3d import Axes3D  # noqa
import ot

n_samples = 50  # nb samples

mu_s = np.array([0, 0])

rahulbhadani

C1 = sp.spatial.distance.cdist(xs, xs)
C2 = sp.spatial.distance.cdist(xt, xt)

C1 /= C1.max()
C2 /= C2.max()

pl.figure()
pl.subplot(121)
pl.imshow(C1)
pl.subplot(122)

rahulbhadani

p = ot.unif(n_samples)
q = ot.unif(n_samples)

gw0, log0 = ot.gromov.gromov_wasserstein(
    C1, C2, p, q, 'square_loss', verbose=True, log=True)
print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))
pl.figure(1, (10, 5))
pl.imshow(gw0, cmap='jet')
pl.title('Gromov Wasserstein')
pl.colorbar()

rahulbhadani

import scanpy as sc
import pandas as pd


datafolder = "scRNAseqData/GSE146590"
adata = anndata.read_mtx(datafolder+'/GSM4396313_Ctrl_matrix.mtx.gz')
adata = adata1.T

adata.obs.index = pd.read_csv(datafolder+'/GSM4396313_Ctrl_barcodes.tsv.gz', header=None, index_col=0).index
genes = pd.read_csv(datafolder+'/GSM4396313_Ctrl_features.tsv.gz', header=None, index_col=0, sep='\t')[1].values