thearn

from openmdao.core.explicitcomponent import ExplicitComponent
from openmdao.api import Problem, Group, ExecComp, IndepVarComp
from openmdao.devtools.generate_derivative import GenerateDerivative

class Paraboloid(ExplicitComponent):
    """
    Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3.
    """

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 numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import fmin, fminbound, fmin_bfgs
from scipy.misc import ascent
import pywt
from scipy.stats import kurtosis
true_image = ascent()

sigma = 25.0
test_image = true_image + sigma * np.random.randn(*true_image.shape)

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

from petsc4py import PETSc
rank = PETSc.COMM_WORLD.getRank()
num_ranks = PETSc.COMM_WORLD.getSize()

x = PETSc.Vec().createMPI(4) # VecCreateMPI: Creates a parallel vector.  size=4
x.setValues([0,1,2,3], [10,20,30,40]) # VecSetValues: Inserts or adds values into certain locations of a vector.  x[0]=10, x[1]=20, x[2]=30, x[3]=40

print ('Rank',rank,'has this portion of the MPI vector:', x.getArray() ) # VecGetArray: Returns a pointer to a contiguous array that contains this processor's portion of the vector data.

vec_sum = x.sum() # VecSum: Computes the sum of all the components of a vector. 10+20+30+40=100

thearn

import numpy as np

from openmdao.core.component import Component

from pycycle.constants import R_UNIVERSAL_ENG, MIN_VALID_CONCENTRATION
from openmdao.core.options import OptionsDictionary

from openmdao.components.indep_var_comp import IndepVarComp

from ad import adnumber

thearn

from openmdao.api import *
import numpy as np 

"""
Test model:
-------------
Find value of state variable "x" such that the line 2*x + 1 has a specific 
value of 'y1'
"""

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

import random
from sklearn import svm
from sklearn.grid_search import GridSearchCV
import pylab
import numpy as np


data = []
labels = []

thearn

Frontend Development

Attention: the list was moved to

https://github.com/dypsilon/frontend-dev-bookmarks

This page is not maintained anymore, please update your bookmarks.

---