jordelver

Find the SD card device

In this case, the SD card is /dev/disk4. DO NOT get this wrong or you may destroy all the data on the wrong disk/card/drive.

diskutil list

/dev/disk0
   #:                       TYPE NAME                    SIZE       IDENTIFIER
   0:      GUID_partition_scheme                        *160.0 GB   disk0

1: EFI 209.7 MB disk0s1

schwehr

iamtekeste

Download Google Drive files with WGET

Example Google Drive download link:

https://docs.google.com/open?id=[ID]
To download the file with WGET you need to use this link:

https://googledrive.com/host/[ID]

Example WGET command:

kylemcdonald

import PIL.Image
from cStringIO import StringIO
import IPython.display
import numpy as np
def showarray(a, fmt='png'):
    a = np.uint8(a)
    f = StringIO()
    PIL.Image.fromarray(a).save(f, fmt)
    IPython.display.display(IPython.display.Image(data=f.getvalue()))

awesomebytes

#/usr/bin/env python

# Based on:
# http://mathworld.wolfram.com/MomentofInertia.html


def get_cube_inertia_matrix(mass, x, y, z):
    """Given mass and dimensions of a cube return intertia matrix.
    :return: ixx, ixy, ixz, ixy, iyy, iyz, ixz, iyz, izz
    From https://www.wolframalpha.com/input/?i=moment+of+inertia+cube"""

stonehippo

Getting a USB device to show up in a Docker container on OS X

Some background

I was trying to get the Arduino IDE to work inside a Docker container on OS X. I was able to get the IDE working (see Getting X11 GUI applications to work on OS X with Docker), but I could not figure out how to make the USB port for the Arduino visible.

The solution

I first tried to directly map hardware serial port into the Docker container, doing something like this:

hagerty

with tf.device(gpu):
# Generator
    x8 = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 8])
    x3 = tf.placeholder(tf.float32, shape=[None, scale * FLAGS.ws, scale * FLAGS.ws, 3])
    label_distance = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 1])
    for i in range(layers):
        alpha[i] = tf.Variable(0.9, name='alpha_' + str(i))
        beta[i] = tf.maximum( 0.0 , tf.minimum ( 1.0 , alpha[i] ), name='beta_'+str(i))
        bi[i] = tf.Variable(tf.constant(0.0,shape=[FLAGS.filters]), name='bi_'+str(i))
        bo[i] = tf.Variable(tf.constant(0.0,shape=[FLAGS.filters]), name='bo_'+str(i))

kolergy

#!/usr/freeware/bin/python
## #!/home/QtPalmtop/bin/python

#-------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------
# Ustructured NN
# Kolergy
# Written in Python.  See http://www.python.org/
#-------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------

ndronen

#!/usr/bin/env python
"""
A quick, partial implementation of ENet (https://arxiv.org/abs/1606.02147) using PyTorch.
The original Torch ENet implementation can process a 480x360 image in ~12 ms (on a P2 AWS
instance).  TensorFlow takes ~35 ms.  The PyTorch implementation takes ~25 ms, an improvement
over TensorFlow, but worse than the original Torch.
"""

from __future__ import absolute_import

lirnli