x = np.random.standard_normal((NVALS, ))
y = np.random.standard_normal((NVALS, ))
y_r = y * (1 - x)
s = ""
s += """
#define NVALS %d
""" % (NVALS, )
s += """
static float y_result[] = {
"""
s = complete_array(y_r, s)
s += """
static float y[] = {
"""
s = complete_array(y, s)
s += """
static float x[] = {
"""
s = complete_array(x, s)
outfile = os.environ['OUTFILE']
with open(outfile, "w") as f:
# Generate what the one-pole filter should output
import numpy as np
import os
from gen_common import complete_array
a = 0.99
NVALS = 1007
y = np.power(a, np.arange(NVALS))
s = ""
s += """
#define LEN_YN_TRUE %d
""" % (NVALS, )
s += """
static float yn_true[] = {
"""
s = complete_array(y, s)
outfile = os.environ['OUTFILE']
with open(outfile, "w") as f:
f.write(s)
y1 = (x + 1) % M
y2 = (x + 2) % M
s = ""
s += """
#include <stdint.h>
#define NVALS %d
#define M %d
""" % (NVALS, M)
s += """
static uint32_t x[] = {
"""
s = complete_array(x, s, "%d,\n")
s += """
static uint32_t y0[] = {
"""
s = complete_array(y0, s, "%d,\n")
s += """
static uint32_t y1[] = {
"""
s = complete_array(y1, s, "%d,\n")
s += """
static uint32_t y2[] = {
"""
s = complete_array(y2, s, "%d,\n")
# Make some data to test the functions
import numpy as np
import os
from gen_common import complete_array
outfile = os.environ['OUTFILE']
N_VALS = 1000
x1 = np.random.standard_normal((N_VALS, ))
x2 = np.random.standard_normal((N_VALS, ))
y1 = x1 + x2
s = """static float x1_test_data[] = {
"""
s = complete_array(x1, s)
s += """static float x2_test_data[] = {
"""
s = complete_array(x2, s)
s += """static float y1_test_data[] = {
"""
s = complete_array(y1, s)
with open(outfile, "w") as f:
f.write(s)