akey7

# This assumes frame numbering of 4 integer places like 0001, 0020, 0300, etc
ffmpeg -r 30 -s 1920x1080 -i heme_%04d.png -vcodec libx264 -crf 15 -pix_fmt yuv420p heme_movie.mp4

akey7

# Obviously, this will need to be modified for the particular situations at hand,
# but capturing every 50 timesteps at 2 fs/step is a good starting point for a
# 30 fps animation.

# Run with the following command line
# charmrun ++remote-shell ssh +p10 $(which namd2) 5eui_nvt.namd | tee 5eui_minimize_log.out

structure          5eui_nvt.psf
coordinates        5eui_nvt.pdb

akey7

# This is what executes NAMD on 5 cores of a 6 core i5 CPU and
# accelerates with the available GPU.
# Note that you should NOT tell it number of threads in the +p
# It will handle making multiple threads per core if it needs to.
# That is why I am NOT telling it +p10 to use 5 cores. Just tell
# it the number of cores!
#
# The isomalloc_sync has to do with some memory warning I was
# getting, and this fixed it.
charmrun +isomalloc_sync +p5 $(which namd2) 2K39_eq_10M.namd.conf | tee 2K39_eq_10M.namd.log

akey7

TF_FORCE_GPU_ALLOW_GROWTH=true jupyter notebook

akey7

This is the "Iris" dataset. Originally published at UCI Machine Learning Repository: Iris Data Set, this small dataset from 1936 is often used for testing out machine learning algorithms and visualizations (for example, Scatter Plot). Each row of the table represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters.

The HTML page provides the basic code required to load the data and display it on the page (as JSON) using D3.js.

Built with blockbuilder.org

akey7

# Credit where credit is due:
# https://stackoverflow.com/questions/14058453/making-python-loggers-output-all-messages-to-stdout-in-addition-to-log-file
# I am just keeping a copy of it here for easy reference in the future

import logging
import sys

self.log = logging.getLogger()
self.log.setLevel(logging.DEBUG)
handler = logging.StreamHandler(sys.stdout)

akey7

target_dir = os.environ['IMAGE_TARGET_DIR'] if 'IMAGE_TARGET_DIR' in os.environ else '/home/me'
source_dir = os.environ['IMAGE_SOURCE_DIR'] if 'IMAGE_SOURCE_DIR' in os.environ else '/home/me'

akey7

from datetime import datetime as dt
now = dt.now()
timestamp = f'{now.year}-{now.month}-{now.day}-{now.hour}-{now.minute}-{now.second}'

akey7

ZIP,LAT,LNG
00601,18.180555, -66.749961
00602,18.361945, -67.175597
00603,18.455183, -67.119887
00606,18.158345, -66.932911
00610,18.295366, -67.125135
00612,18.402253, -66.711397
00616,18.420412, -66.671979
00617,18.445147, -66.559696
00622,17.991245, -67.153993

akey7

class BSTNode:
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None


class BST:
    """
    Implements a binary search tree