Cleans up quantum/keymap situation, removes extra lufa folders (#416)

[qmk_firmware.git] / tmk_core / tool / mbed / mbed-sdk / workspace_tools / dev / dsp_fir.py

diff --git a/tmk_core/tool/mbed/mbed-sdk/workspace_tools/dev/dsp_fir.py b/tmk_core/tool/mbed/mbed-sdk/workspace_tools/dev/dsp_fir.py
deleted file mode 100644 (file)
index f62c2b4..0000000
--- a/tmk_core/tool/mbed/mbed-sdk/workspace_tools/dev/dsp_fir.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""
-mbed SDK
-Copyright (c) 2011-2013 ARM Limited
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-"""
-from numpy import sin, arange, pi
-from scipy.signal import lfilter, firwin
-from pylab import figure, plot, grid, show
-
-#------------------------------------------------
-# Create a signal for demonstration.
-#------------------------------------------------
-# 320 samples of (1000Hz + 15000 Hz) at 48 kHz
-sample_rate = 48000.
-nsamples = 320
-
-F_1KHz = 1000.
-A_1KHz = 1.0
-
-F_15KHz = 15000.
-A_15KHz = 0.5
-
-t = arange(nsamples) / sample_rate
-signal = A_1KHz * sin(2*pi*F_1KHz*t) + A_15KHz*sin(2*pi*F_15KHz*t)
-
-#------------------------------------------------
-# Create a FIR filter and apply it to signal.
-#------------------------------------------------
-# The Nyquist rate of the signal.
-nyq_rate = sample_rate / 2.
-
-# The cutoff frequency of the filter: 6KHz
-cutoff_hz = 6000.0
-
-# Length of the filter (number of coefficients, i.e. the filter order + 1)
-numtaps = 29
-
-# Use firwin to create a lowpass FIR filter
-fir_coeff = firwin(numtaps, cutoff_hz/nyq_rate)
-
-# Use lfilter to filter the signal with the FIR filter
-filtered_signal = lfilter(fir_coeff, 1.0, signal)
-
-#------------------------------------------------
-# Plot the original and filtered signals.
-#------------------------------------------------
-
-# The first N-1 samples are "corrupted" by the initial conditions
-warmup = numtaps - 1
-
-# The phase delay of the filtered signal
-delay = (warmup / 2) / sample_rate
-
-figure(1)
-# Plot the original signal
-plot(t, signal)
-
-# Plot the filtered signal, shifted to compensate for the phase delay
-plot(t-delay, filtered_signal, 'r-')
-
-# Plot just the "good" part of the filtered signal.  The first N-1
-# samples are "corrupted" by the initial conditions.
-plot(t[warmup:]-delay, filtered_signal[warmup:], 'g', linewidth=4)
-
-grid(True)
-
-show()
-
-#------------------------------------------------
-# Print values
-#------------------------------------------------
-def print_values(label, values):
-    var = "float32_t %s[%d]" % (label, len(values))
-    print "%-30s = {%s}" % (var, ', '.join(["%+.10f" % x for x in values]))
-
-print_values('signal', signal)
-print_values('fir_coeff', fir_coeff)
-print_values('filtered_signal', filtered_signal)