Elektrologi

from __future__ import print_function
from __future__ import division

import numpy as np

# Example code, computes the coefficients of a low-pass windowed-sinc filter.

# Configuration.
fS = 20000 # Sampling rate.
fL = 800 # Cutoff frequency.
N = 53 # Filter length, must be odd.

# Compute sinc filter.
h = np.sinc(2 * fL / fS * (np.arange(N) - (N - 1) / 2))

# Apply window.
h *= np.blackman(N)

# Normalize to get unity gain.
h /= np.sum(h)

print(h)

# Applying the filter to a signal s can be as simple as writing
# s = np.convolve(s, h)

[-4.29317419e-20 -4.18549678e-21 -1.79819385e-05 -8.38178513e-05
 -2.28616892e-04 -4.79528638e-04 -8.51847897e-04 -1.33864303e-03
 -1.89910369e-03 -2.44776137e-03 -2.84740649e-03 -2.90866942e-03
 -2.39870072e-03 -1.06016948e-03  1.35995551e-03  5.07437980e-03
  1.02166209e-02  1.68050560e-02  2.47160945e-02  3.36720917e-02
  4.32479500e-02  5.28978134e-02  6.20002342e-02  6.99171799e-02
  7.60597872e-02  7.99523091e-02  8.12855501e-02  7.99523091e-02
  7.60597872e-02  6.99171799e-02  6.20002342e-02  5.28978134e-02
  4.32479500e-02  3.36720917e-02  2.47160945e-02  1.68050560e-02
  1.02166209e-02  5.07437980e-03  1.35995551e-03 -1.06016948e-03
 -2.39870072e-03 -2.90866942e-03 -2.84740649e-03 -2.44776137e-03
 -1.89910369e-03 -1.33864303e-03 -8.51847897e-04 -4.79528638e-04
 -2.28616892e-04 -8.38178513e-05 -1.79819385e-05 -4.18549678e-21
 -4.29317419e-20]

from __future__ import print_function
from __future__ import division

import numpy as np
import math
import matplotlib.pyplot as plt

# Example code, computes the coefficients of a low-pass windowed-sinc filter.

# Configuration.
fS = 20000 # Sampling rate.
fL = 800 # Cutoff frequency.
N = 53 # Filter length, must be odd.

# Compute sinc filter.
h = np.sinc(2 * fL / fS * (np.arange(N) - (N - 1) / 2))

# Apply window.
h *= np.blackman(N)

# Normalize to get unity gain.
h /= np.sum(h)

print(h)

# Applying the filter to a signal s can be as simple as writing
# s = np.convolve(s, h)

x = np.linspace(0,1, 20001)

# filter digital
signal_frequency=8000 # diubah-ubah sesuai keperluan
y=np.sin(x*2*math.pi * signal_frequency)
# Applying the filter to a signal s can be as simple as writing
y_out = np.convolve(y, h)
plt.figure(figsize=(16,6))
plt.plot(x, y_out[0:20001],label="output")
plt.plot(x, y[0:20001],label="input")
plt.title("signal:{} Hz".format(signal_frequency))
plt.legend()
plt.axis('tight')
plt.xlim([0, 0.01])
plt.savefig("simulasi-signal-{}.jpg".format(signal_frequency))

#include <stdio.h>
#include <stdlib.h>
#include "math.h"

int main()
{

#define ORDE_FILTER 53
int i,j,k;
FILE * fp;
char filename[100];
printf("Hello world!\n");
// simulasi 1 detik saja

float signal_frequency=8; // frekuensi input signal
float filter_coefficients[]= {-4.29317419e-20, -4.18549678e-21, -1.79819385e-05, -8.38178513e-05,
-2.28616892e-04, -4.79528638e-04, -8.51847897e-04, -1.33864303e-03,
-1.89910369e-03, -2.44776137e-03, -2.84740649e-03, -2.90866942e-03,
-2.39870072e-03, -1.06016948e-03, 1.35995551e-03, 5.07437980e-03,
1.02166209e-02, 1.68050560e-02, 2.47160945e-02, 3.36720917e-02,
4.32479500e-02, 5.28978134e-02, 6.20002342e-02, 6.99171799e-02,
7.60597872e-02, 7.99523091e-02, 8.12855501e-02, 7.99523091e-02,
7.60597872e-02, 6.99171799e-02, 6.20002342e-02, 5.28978134e-02,
4.32479500e-02, 3.36720917e-02, 2.47160945e-02, 1.68050560e-02,
1.02166209e-02, 5.07437980e-03, 1.35995551e-03, -1.06016948e-03,
-2.39870072e-03, -2.90866942e-03, -2.84740649e-03, -2.44776137e-03,
-1.89910369e-03, -1.33864303e-03, -8.51847897e-04, -4.79528638e-04,
-2.28616892e-04, -8.38178513e-05, -1.79819385e-05, -4.18549678e-21,
-4.29317419e-20
};
int orde_filter=ORDE_FILTER;
float y_history[ORDE_FILTER];
signal_frequency=8000;
sprintf(filename,"simulasi-%d.csv",(int)signal_frequency);
fp = fopen (filename, "w");
fprintf(fp,"time,y,y_out\n");

// zeros y_history
for (i=0; i<orde_filter; i++)
{
y_history[i]=0;
}
for (i=0; i<20000; i++)
{
printf(".");
float t,y;
t=(float)i/(float)20000; // time axis
y=sin(2 * M_PI *signal_frequency* t);
// fprintf(fp,"%f,%f\n",t,y);

// hitung history signal
for (j=orde_filter-1; j>=1; j--)
{
int src=j-1;
int dst=j;
y_history[dst]=y_history[src];
// printf("%d %d\n",src,dst);
}
y_history[0]=y;

// hitung konvolusi
float y_out=0;
for (k=0; k<orde_filter; k++)
{
// fprintf(fp,"%5.3f ",y_history[k]);
y_out=y_out+y_history[k]*filter_coefficients[k];
}
// fprintf(fp,"\n");
fprintf(fp,"%f,%f,%f\n",t,y,y_out);
}
fclose(fp);
return 0;
}



// https://www.tutorialspoint.com/c_standard_library/c_function_fprintf.htm

import pandas as pd
import math
import matplotlib.pyplot as plt

name="simulasi-8"
name="simulasi-80"
name="simulasi-800"
name="simulasi-8000"
df = pd.read_csv('../filter-simulasi-c/{}.csv'.format(name))

plt.figure(figsize=(16,6))
plt.plot(df["time"], df["y"],marker='o',label="input")
plt.plot(df["time"], df["y_out"],marker='o',label="output")
plt.legend()
plt.axis('tight')
plt.xlim([0, 0.001])
plt.savefig("visualisasi-{}.jpg".format(name))

# graph simulasi C filter
import pandas as pd
import math
import matplotlib.pyplot as plt

name="esp32-8.csv"
name="esp32-80.csv"
#name="esp32-800.csv"
#name="esp32-8000.csv"
df = pd.read_csv('{}'.format(name))

plt.figure(figsize=(16,6))
plt.plot(df["time"], df["y"],label="input")
plt.plot(df["time"], df["y_out"],label="output")
plt.legend()
plt.axis('tight')
plt.xlim([0, 0.1])
plt.title(name)
plt.savefig("visualisasi-{}.jpg".format(name))