thearn

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import random
import pickle
import matplotlib.dates as mdates
from matplotlib.ticker import LinearLocator

thearn

import numpy as np
import openmdao.api as om

class SIRvec(om.ExplicitComponent):
    """Basic epidemiological infection model
       S (suceptible), I (infected), R (recovered).
    """

    def initialize(self):
        self.options.declare('num_nodes', types=int)

thearn

import numpy as np
import openmdao.api as om

class SIRvec(om.ExplicitComponent):
    """Basic epidemiological infection model
       S (suceptible), I (infected), R (recovered).
    """

    def initialize(self):
        self.options.declare('num_nodes', types=int)

thearn

    4   21  894
  419  794  987
  424  797  125
  651  305  558
  655  631  963
    2  628  436
  736   50  363
  657  707  408
  252  705   98
  532  173  878

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 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

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'
"""