thearn

Connections for inlet.flow_in:
------------------------------
.foo                                                                        -> {unconnected}
fc.subgroup.fs.exit_static.flow_static.Fl_O:stat:V (fc.Fl_O:stat:V) [ft/s]  -> .Fl_I:stat:V (inlet.Fl_I:stat:V) [ft/s]
fc.subgroup.fs.exit_static.flow_static.Fl_O:stat:W (fc.Fl_O:stat:W) [lbm/s] -> .Fl_I:stat:W (inlet.Fl_I:stat:W) [lbm/s]
fc.subgroup.fs.totals.flow.Fl_O:tot:Cp (fc.Fl_O:tot:Cp) [Btu/(lbm*degR)]    -> .Fl_I:tot:Cp (inlet.Fl_I:tot:Cp) [Btu/(lbm*degR)]
fc.subgroup.fs.totals.flow.Fl_O:tot:Cv (fc.Fl_O:tot:Cv) [Btu/(lbm*degR)]    -> .Fl_I:tot:Cv (inlet.Fl_I:tot:Cv) [Btu/(lbm*degR)]
fc.subgroup.fs.totals.flow.Fl_O:tot:P (fc.Fl_O:tot:P) [lbf/inch**2]         -> .Fl_I:tot:P (inlet.Fl_I:tot:P) [lbf/inch**2]
fc.subgroup.fs.totals.flow.Fl_O:tot:S (fc.Fl_O:tot:S) [Btu/(lbm*degR)]      -> .Fl_I:tot:S (inlet.Fl_I:tot:S) [Btu/(lbm*degR)]
fc.subgroup.fs.totals.flow.Fl_O:tot:T (fc.Fl_O:tot:T) [degR]                -> .Fl_I:tot:T (inlet.Fl_I:tot:T) [degR]

thearn

Abraham Gotthelf Kästner \nUniversität Leipzig (1739) -> Bernhard Friedrich Thibaut \nGeorg-August-Universität Göttingen (1796)
Abraham Gotthelf Kästner \nUniversität Leipzig (1739) -> Georg Christoph Lichtenberg \nGeorg-August-Universität Göttingen (1765)
Abraham Gotthelf Kästner \nUniversität Leipzig (1739) -> Heinrich Wilhelm Brandes \nGeorg-August-Universität Göttingen (1800)
Abraham Gotthelf Kästner \nUniversität Leipzig (1739) -> Johann Friedrich Pfaff \nGeorg-August-Universität Göttingen (1786)
Abraham Klein (Calovius) \nUniversität Rostock (1632) -> Aegidius Strauch \nMartin-Luther-Universität Halle-Wittenberg (1657)
Adolph Vorstius \nUniversità di Padova (1622) -> Franciscus de le Boë Sylvius \nUniversiteit Leiden and Universität Basel (1634)
Adriaan van den Spieghel \nUniversità di Padova and Université Catholique de Louvain (1603) -> Adolph Vorstius \nUniversità di Padova (1622)
Adriaan van den Spieghel \nUniversità di Padova and Université Catholique de Louvain (1603) -> Werner Rolfinck \nMartin-L

thearn

import requests
import json
import sys
import time
timer = {}

hub_url = "192.168.1.2"
hours_to_refresh_token = 8

class ZWaveController(object):

thearn

import autograd.numpy as np
from autograd import grad

def f(A):

    B = np.zeros((4,4))

    B[:2, :2] = A

thearn

os:
  - linux
  - osx

sudo: false

language: generic

env:
- PY=2.7

thearn

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

thearn

int flexSensorPin_0 = A0; //analog pin for flex sensor

int flexSensorPin_1 = A4; //analog pin for flex sensor

int hand = 0;

int x_pin = A2; 
int y_pin = A3; 
int z_pin = A6; 
int accelSrc = 3;

thearn

from openmdao.main.api import Component, Assembly
from openmdao.main.datatypes.api import Float
from pyoptsparse_driver.pyoptsparse_driver import pyOptSparseDriver
from pyopt_driver import pyopt_driver
from simple import SimpleComp, SimpleCompNoDeriv
import time

class parallel(Assembly):

    def configure(self):

thearn

    def doctor(self):
        print "-- OpenMDAO Doctor --"
        print 30*"-"
        uncon_inputs = self.get_unconnected_inputs()
        n_uncon = len(uncon_inputs)
        if n_uncon > 0:
            print "- Assembly contains %i unconnected inputs:" % n_uncon
            for name in uncon_inputs:
                print "   " + name

thearn

import numpy as np

from openmdao.main.api import Component, Assembly, set_as_top
from openmdao.lib.datatypes.api import Float, Array


class KSComponent(Component): 
    """
    Aggregates a number of functions to a single value via the 
    Kreisselmeier-Steinhauser Function. Often used in the aggregation of