diff --git a/utils/fft_analyser.py b/utils/fft_analyser.py
index 887d70a..a8359ca 100644
--- a/utils/fft_analyser.py
+++ b/utils/fft_analyser.py
@@ -74,15 +74,20 @@ class FFTAnalyser(QtCore.QThread):
         # np.argmax(fourier) = 2374
         # freq[2374] * .05 * self.song.frame_rate = 520 :O omg! thats the hz value
         # x values = freq * self.song.frame_rate * self.sampling_window_length
-        # print(freq * self.song.frame_rate * .05)
 
         point_range = 1 / self.resolution
 
         # Logarithmic frequency scaling
         min_freq = np.min(freq[freq > 0])  # minimum positive frequency
+        # print(
+        #     f"min freq: {min_freq * self.sampling_window_length * self.song.frame_rate}"
+        # )
         # 20hz
         max_freq = np.max(freq)  # maximum frequency
-        # 20khz
+        # print(
+        #     f"max freq: {max_freq * self.sampling_window_length * self.song.frame_rate}"
+        # )
+        # 23khz
         log_freqs = np.logspace(np.log10(min_freq), np.log10(max_freq), self.resolution)
 
         point_samples = []
diff --git a/utils/fft_analyser.py.dumb b/utils/fft_analyser.py.dumb
deleted file mode 100644
index 95ca2ce..0000000
--- a/utils/fft_analyser.py.dumb
+++ /dev/null
@@ -1,168 +0,0 @@
-# Credit
-# https://github.com/ravenkls/MilkPlayer/blob/master/audio/fft_analyser.py
-
-import time
-from PyQt5 import QtCore
-from pydub import AudioSegment
-import numpy as np
-from scipy.ndimage.filters import gaussian_filter1d
-from logging import debug, info
-
-
-class FFTAnalyser(QtCore.QThread):
-    """Analyses a song using FFTs."""
-
-    calculated_visual = QtCore.pyqtSignal(np.ndarray)
-
-    def __init__(self, player, x_resolution):  # noqa: F821
-        super().__init__()
-        self.player = player
-        self.reset_media()
-        self.player.currentMediaChanged.connect(self.reset_media)
-
-        self.resolution = x_resolution
-        # this length is a number, in seconds, of how much audio is sampled to determine the frequencies
-        # of the audio at a specific point in time
-        # in this case, it takes 5% of the samples at some point in time
-        self.sampling_window_length = 0.09
-        self.visual_delta_threshold = 1000
-        self.sensitivity = 10
-
-    def reset_media(self):
-        """Resets the media to the currently playing song."""
-        audio_file = self.player.currentMedia().canonicalUrl().path()
-        # if os.name == "nt" and audio_file.startswith("/"):
-        #     audio_file = audio_file[1:]
-        if audio_file:
-            try:
-                self.song = AudioSegment.from_file(audio_file).set_channels(1)
-            except PermissionError:
-                self.start_animate = False
-            else:
-                self.samples = np.array(self.song.get_array_of_samples())
-
-                self.max_sample = self.samples.max()
-                self.points = np.zeros(self.resolution)
-                self.start_animate = True
-        else:
-            self.start_animate = False
-
-    def calculate_amps(self):
-        """Calculates the amplitudes used for visualising the media."""
-
-        sample_count = int(self.song.frame_rate * self.sampling_window_length)
-        start_index = int((self.player.position() / 1000) * self.song.frame_rate)
-        v_sample = self.samples[
-            start_index : start_index + sample_count
-        ]  # samples to analyse
-
-
-        # use FFTs to analyse frequency and amplitudes
-        fourier = np.fft.fft(v_sample)
-        freq = np.fft.fftfreq(fourier.size, d=self.sampling_window_length)
-        amps = 2 / v_sample.size * np.abs(fourier)
-        data = np.array([freq, amps]).T
-        # print(freq * .05 * self.song.frame_rate)
-
-        # NOTE:
-        # given 520 hz sine wave
-        # np.argmax(fourier) = 2374
-        # freq[2374] * .05 * self.song.frame_rate = 520 :O omg! thats the hz value
-        # x values = freq * self.song.frame_rate * self.sampling_window_length
-        # print(freq * self.song.frame_rate * .05)
-
-        freq_log_intervals = [
-            (1,100),
-            (100,1000),
-            (1000, 10000),
-            (10000,22000)
-        ]
-
-        all_log_freqs = []
-
-        for low, high in freq_log_intervals:
-            # generate logarithmic frequencies for each range
-            log_freqs = np.logspace(np.log10(low + 1e-6), np.log10(high), self.resolution)
-            all_log_freqs.extend(log_freqs)
-
-        log_freqs = np.array(all_log_freqs)
-
-        # point_range = 1 / self.resolution
-
-        # Logarithmic frequency scaling
-        # min_freq = np.min(freq[freq > 0])  # minimum positive frequency
-        # 20hz
-        # print('min')
-        # print(min_freq * .05 * self.song.frame_rate)
-        # max_freq = np.max(freq)  # maximum frequency
-        # 20khz
-        # print('max')
-        # print(max_freq * .05 * self.song.frame_rate)
-        # log_freqs = np.logspace(np.log10(min_freq), np.log10(max_freq), self.resolution)
-
-        if len(self.points) != len(log_freqs):
-            self.points = np.zeros(len(log_freqs))
-
-        point_samples = []
-
-        if not data.size:
-            return
-
-        #for i, freq in enumerate(np.arange(0, 1, point_range), start=1):
-        for i, log_freq in enumerate(log_freqs):
-            # get the amps which are in between the frequency range
-            #amps = data[(freq - point_range < data[:, 0]) & (data[:, 0] < freq)]
-            freq_range = log_freq * 0.01
-            amps = data[(log_freq - freq_range < data[:, 0]) & (data[:, 0] < log_freq + freq_range)]
-            if not amps.size:
-                point_samples.append(0)
-            else:
-                point_samples.append(
-                    amps.max()
-                    * (
-                        (1 + self.sensitivity / 10 + (self.sensitivity - 1) / 10)
-                        ** (i / 50)
-                    )
-                )
-
-        # Add the point_samples to the self.points array, the reason we have a separate
-        # array (self.bars) is so that we can fade out the previous amplitudes from
-        # the past
-        for n, amp in enumerate(point_samples):
-            amp *= 2
-
-            if (
-                self.points[n] > 0
-                and amp < self.points[n]
-                or self.player.state()
-                in (self.player.PausedState, self.player.StoppedState)
-            ):
-                self.points[n] -= self.points[n] / 10  # fade out
-            elif abs(self.points[n] - amp) > self.visual_delta_threshold:
-                self.points[n] = amp
-            if self.points[n] < 1:
-                self.points[n] = 1e-5
-
-        # interpolate points
-        rs = gaussian_filter1d(self.points, sigma=2)
-
-        # Mirror the amplitudes, these are renamed to 'rs' because we are using them
-        # for polar plotting, which is plotted in terms of r and theta
-        # rs = np.concatenate((rs, np.flip(rs)))
-        # rs = np.concatenate((rs, np.flip(rs)))
-
-        # they are divided by the highest sample in the song to normalise the
-        # amps in terms of decimals from 0 -> 1
-        self.calculated_visual.emit(rs / self.max_sample)
-        # print(rs/self.max_sample)
-
-    def run(self):
-        """Runs the animate function depending on the song."""
-        while True:
-            if self.start_animate:
-                try:
-                    self.calculate_amps()
-                except ValueError:
-                    self.calculated_visual.emit(np.zeros(self.resolution))
-                    self.start_animate = False
-            time.sleep(0.025)
diff --git a/utils/fft_analyzer.py.ai b/utils/fft_analyzer.ai.py
similarity index 100%
rename from utils/fft_analyzer.py.ai
rename to utils/fft_analyzer.ai.py