Smith Chart Python

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# begin imports
import math
import os
import numpy as np
from smithplot import SmithAxes
from matplotlib import rcParams, pyplot as plt, cm
from matplotlib.colors import Normalize

# end imports

#####  DATA ACQUISITION FROM CSV FILE  #####

# open data file
if os.path.exists('load_pull.csv'):  # check for file
    file = np.genfromtxt("load_pull.csv", delimiter=',')  # open file of name load_pull.csv
else:
    print("error, file not found")

# populate data into arrays
z_mag = file[:, 2]
z_phase = file[:, 3] * (math.pi / 180)
gen_fwd = file[:, 7]
gen_rev = file[:, 8]
gen_load = gen_fwd - gen_rev
freq = file[:, 9]
v_rail = file[:, 10]
pai = file[:, 11] / 10
dc_pwr = np.array(pai * v_rail)
p_diss = file[:, 12]

# convert magnitude to real and imaginary parts
zr = z_mag * np.cos(z_phase)
zi = z_mag * np.sin(z_phase)

# combine real and imaginary parts
z = np.array(zr + (1j * zi))


#####  END DATA ACQUISITION FROM CSV FILE  #####

def z2gamma(input_array, load):
    """
    converts the impedance z to the reflection coefficient gamma using a reference impedance of z0 (load) ohms
    :param input_array: array of element style (real + imag)
    :param load: number in ohms
    :return: reflection coefficient
    """
    for i in np.nditer(input_array):
        reflection_coefficient = np.array(np.subtract(input_array, load) / np.add(input_array, load))
        return reflection_coefficient


gamma = z2gamma(z, 50)  # creates new array called gamma and calls the z2gamma function with 50 ohm
gr = np.real(gamma)  # the real part of each value from gamma
gi = np.imag(gamma)  # the imaginary part of each value from gamma

"""
Graphing
"""


def graph_PARail_vs_Load():
    min_vrail = min(v_rail)
    max_vrail = max(v_rail)

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(1, 1, 1, projection='smith', grid_major_xmaxn=5, grid_major_ymaxn=14, grid_minor_enable=False)

    norm_vrail = Normalize(vmin=int(float(max_vrail)), vmax=int(float(min_vrail)))  # normalize vrail for contour

    img = plt.imshow(np.array([[0, 1]]), cmap='YlOrRd_r', norm=norm_vrail)  # create countour chart
    img.set_visible(False)
    cmap = plt.cm.YlOrRd_r
    plt.colorbar()

    for g in gamma:
        plot = plt.plot(gamma, datatype=SmithAxes.S_PARAMETER, color=cmap(norm_vrail(float(v_rail))), marker='o',
                        markersize=12, alpha=0.3, markeredgewidth=0.5)

    plt.cla()
    plt.title("PA Rail V vs. Load")


graph_PARail_vs_Load()
plt.show()