From e688844ae465fc40ad1f9b7b5b74c48f6331e5ad Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Fri, 1 Nov 2024 14:28:48 -0400 Subject: [PATCH 01/17] Create readme_tobedeleted.md --- fft_folder/readme_tobedeleted.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 fft_folder/readme_tobedeleted.md diff --git a/fft_folder/readme_tobedeleted.md b/fft_folder/readme_tobedeleted.md new file mode 100644 index 0000000..d20fe25 --- /dev/null +++ b/fft_folder/readme_tobedeleted.md @@ -0,0 +1 @@ +fft / inverse fft From 77c880b70098b9d5ad49c21fe52d64b33bebc673 Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Fri, 1 Nov 2024 18:31:08 +0000 Subject: [PATCH 02/17] created .py files --- fft_folder/fft.py | 3 +++ fft_folder/inv_fft.py | 3 +++ 2 files changed, 6 insertions(+) create mode 100644 fft_folder/fft.py create mode 100644 fft_folder/inv_fft.py diff --git a/fft_folder/fft.py b/fft_folder/fft.py new file mode 100644 index 0000000..92a9038 --- /dev/null +++ b/fft_folder/fft.py @@ -0,0 +1,3 @@ +""" +fft code +""" \ No newline at end of file diff --git a/fft_folder/inv_fft.py b/fft_folder/inv_fft.py new file mode 100644 index 0000000..2db1a37 --- /dev/null +++ b/fft_folder/inv_fft.py @@ -0,0 +1,3 @@ +""" +fft^-1 +""" \ No newline at end of file From 9dcef8350d0d6528335550e8b7f7b89882143d90 Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Fri, 1 Nov 2024 18:36:08 +0000 Subject: [PATCH 03/17] created --- fft_folder/fft.py | 10 ++++++++-- fft_folder/inv_fft.py | 10 ++++++++-- 2 files changed, 16 insertions(+), 4 deletions(-) diff --git a/fft_folder/fft.py b/fft_folder/fft.py index 92a9038..1ab907e 100644 --- a/fft_folder/fft.py +++ b/fft_folder/fft.py @@ -1,3 +1,9 @@ """ -fft code -""" \ No newline at end of file +fft.py +fast fourie transform +""" +import pandas as pd +import os +import math as m +import numpy as np +import matplotlib.pyplot as plt diff --git a/fft_folder/inv_fft.py b/fft_folder/inv_fft.py index 2db1a37..1d1e742 100644 --- a/fft_folder/inv_fft.py +++ b/fft_folder/inv_fft.py @@ -1,3 +1,9 @@ """ -fft^-1 -""" \ No newline at end of file +inv_fft.py +inverse fast fourie transform +""" +import pandas as pd +import os +import math as m +import numpy as np +import matplotlib.pyplot as plt From 89e187c8313190610f905befae9463f0cb4f7e57 Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Fri, 1 Nov 2024 23:47:00 +0000 Subject: [PATCH 04/17] finish latter --- fft_folder/fft.py | 6 ++++ fft_folder/inv_fft.py | 7 +++++ fft_folder/plots_fr_fft_ifft.py | 50 +++++++++++++++++++++++++++++++++ 3 files changed, 63 insertions(+) create mode 100644 fft_folder/plots_fr_fft_ifft.py diff --git a/fft_folder/fft.py b/fft_folder/fft.py index 1ab907e..098f63f 100644 --- a/fft_folder/fft.py +++ b/fft_folder/fft.py @@ -7,3 +7,9 @@ import math as m import numpy as np import matplotlib.pyplot as plt + + +""" + +this is just the equation numpy gives us so i dont need to make this module i think +""" \ No newline at end of file diff --git a/fft_folder/inv_fft.py b/fft_folder/inv_fft.py index 1d1e742..573443a 100644 --- a/fft_folder/inv_fft.py +++ b/fft_folder/inv_fft.py @@ -7,3 +7,10 @@ import math as m import numpy as np import matplotlib.pyplot as plt + +def get_mai_freq(data) + """ + take data from fft im pritty sure will check saturday + """ + + return freqs \ No newline at end of file diff --git a/fft_folder/plots_fr_fft_ifft.py b/fft_folder/plots_fr_fft_ifft.py new file mode 100644 index 0000000..53f050b --- /dev/null +++ b/fft_folder/plots_fr_fft_ifft.py @@ -0,0 +1,50 @@ +""" +here is how to graphe +""" +import pandas as pd +import os +import math as m +import numpy as np +import matplotlib.pyplot as plt + + +def plot_trans(data): + """ + only one data input mabye, need to check + needs to be a frequenc axis i think + """ + + forgraphfft = np.fft.fft(data).real + forgraphifft = np.fft.ifft(forgraphfft).real + + freq = somthin + + plt.figure(figsize=(12, 6)) + + plt.subplot(2, 1, 1) + plt.plot(freq, forgraphfft, label='FFT', color='red') + plt.title('title') + plt.xlabel('freq in some unit') + plt.ylabel('Amplitude') + plt.grid() + plt.legend() + + plt.subplot(2, 1, 2) + plt.plot(freq, forgraphifft, label='IFFT', color='red') + plt.title('title2') + plt.xlabel('freq in some unit') + plt.ylabel('Amplitude') + plt.grid() + + plot = input("Do you want to save the plot? (yes/no): ").strip().lower() + + if plot == 'yes': + fpath = input("enter where to save: ").strip() + if not os.path.exists(fpath): + print("Directory does not exist, try again") + else: + filpath = os.path.join(fpath, "put_name_of_imge.png") + plt.savefig(filpath, format='png', dpi=600) + print(f"Plot saved as {filpath}") + else: + print("Plot not saved.") From bf4e39fb91860dcbfe5d1a112cda20c6069c00ea Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Sat, 2 Nov 2024 16:27:19 +0000 Subject: [PATCH 05/17] changed little --- fft_folder/inv_fft.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/fft_folder/inv_fft.py b/fft_folder/inv_fft.py index 573443a..0b2c515 100644 --- a/fft_folder/inv_fft.py +++ b/fft_folder/inv_fft.py @@ -8,9 +8,11 @@ import numpy as np import matplotlib.pyplot as plt -def get_mai_freq(data) +def get_main_freq(data): """ take data from fft im pritty sure will check saturday """ - + datafft = np.fft.fft(data).real + + return freqs \ No newline at end of file From 257a845ffbf67d7840cac712f9f46d67f668d6c6 Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Sat, 2 Nov 2024 20:59:15 +0000 Subject: [PATCH 06/17] mostlydone mabye --- fft_folder/fft.py | 15 ----- fft_folder/inv_fft.py | 106 +++++++++++++++++++++++++++++--- fft_folder/plots_fr_fft_ifft.py | 50 --------------- fft_folder/plots_ifft.py | 48 +++++++++++++++ fft_folder/test_inv_fft.py | 24 ++++++++ 5 files changed, 168 insertions(+), 75 deletions(-) delete mode 100644 fft_folder/fft.py delete mode 100644 fft_folder/plots_fr_fft_ifft.py create mode 100644 fft_folder/plots_ifft.py create mode 100644 fft_folder/test_inv_fft.py diff --git a/fft_folder/fft.py b/fft_folder/fft.py deleted file mode 100644 index 098f63f..0000000 --- a/fft_folder/fft.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -fft.py -fast fourie transform -""" -import pandas as pd -import os -import math as m -import numpy as np -import matplotlib.pyplot as plt - - -""" - -this is just the equation numpy gives us so i dont need to make this module i think -""" \ No newline at end of file diff --git a/fft_folder/inv_fft.py b/fft_folder/inv_fft.py index 0b2c515..80c616d 100644 --- a/fft_folder/inv_fft.py +++ b/fft_folder/inv_fft.py @@ -6,13 +6,99 @@ import os import math as m import numpy as np -import matplotlib.pyplot as plt - -def get_main_freq(data): - """ - take data from fft im pritty sure will check saturday - """ - datafft = np.fft.fft(data).real - - - return freqs \ No newline at end of file + +def analyze_signal(signal, fs, use_filter, selec_filter): + """creats a filtered fft""" + N = len(signal) + isfft = np.fft.fft(signal) + bins = np.fft.fftfreq(N, 1/fs) + + use_filt = 0 + filt = 0.0 + yes_no = use_filter.strip().lower() + if yes_no == 'yes': + filt = selec_filter + use_filt = 1 + + magnitude = np.abs(isfft) + threshold = filt * np.max(magnitude) + main_frequencies = bins[magnitude > threshold] + + return bins, magnitude, main_frequencies, use_filt + + +""" +trying to now make a way to remove overall trend +using Adjusted R-squared to determine which fit is best then it auto does this for you +the modles in the function include liner polynomial cubic and exponential +""" + +def anze_revm_trnd(signal, fs, use_filter, selec_filter): + """creats a filtered fft""" + N = len(signal) + ss_total = np.sum((signal['y'] - np.mean(signal['y']))**2) + + + params_poly = np.polyfit(signal['x'], signal['y'], 2) + y_poly = np.polyval(params_poly, signal['x']) + + res_poly = np.sum((signal['y'] - y_poly)**2) + r_sq_poly = 1 - (res_poly / ss_total) + r_poly_adj = 1 - ((1 - r_sq_poly) * (N - 1) / (N - 3)) + + + params_cubic = np.polyfit(signal['x'], signal['y'], 3) + y_cubic = np.polyval(params_cubic, signal['x']) + + res_cub = np.sum((signal['y'] - y_cubic)**2) + r_sq_cub = 1 - (res_cub / ss_total) + r_cub_adj = 1 - ((1 - r_sq_cub) * (N - 1) / (N - 4)) + + + params_quartic = np.polyfit(signal['x'], signal['y'], 4) + y_qua = np.polyval(params_quartic, signal['x']) + + res_qua = np.sum((signal['y'] - y_qua)**2) + r_sq_qua = 1 - (res_qua / ss_total) + r_qua_adj = 1 - ((1 - r_sq_qua) * (N - 1) / (N - 5)) + + + log_y = np.log(signal['y']) + params_linear = np.polyfit(signal['x'], log_y, 1) + y_linear = np.polyval(params_linear, signal['x']) + + ss_tot_ep = np.sum((log_y - np.mean(log_y))**2) + ss_residual = np.sum((log_y - y_linear)**2) + r_sq_ep = 1 - (ss_residual / ss_tot_ep) + r_ep_adj = 1 - ((1 - r_sq_ep) * (N - 1) / (N - 2)) + + find_sml = min(r_poly_adj, r_cub_adj, r_qua_adj, r_ep_adj) + + if find_sml == r_poly_adj: + signalnew = signal['y'] - y_poly + if find_sml == r_cub_adj: + signalnew = signal['y'] - y_cubic + if find_sml == r_qua_adj: + signalnew = signal['y'] - y_qua + else: + A = np.exp(params_linear[1]) + B = params_linear[0] + y_ep = A * np.exp(B * signal['x']) + signalnew = signal['y'] - y_ep + + + isfft = np.fft.fft(signalnew) + bins = np.fft.fftfreq(N, 1/fs) + + use_filt = 0 + filt = 0.0 + yes_no = use_filter.strip().lower() + if yes_no == 'yes': + filt = selec_filter + use_filt = 1 + + magnitude = np.abs(isfft) + threshold = filt * np.max(magnitude) + main_frequencies = bins[magnitude > threshold] + + return bins, magnitude, main_frequencies, use_filt diff --git a/fft_folder/plots_fr_fft_ifft.py b/fft_folder/plots_fr_fft_ifft.py deleted file mode 100644 index 53f050b..0000000 --- a/fft_folder/plots_fr_fft_ifft.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -here is how to graphe -""" -import pandas as pd -import os -import math as m -import numpy as np -import matplotlib.pyplot as plt - - -def plot_trans(data): - """ - only one data input mabye, need to check - needs to be a frequenc axis i think - """ - - forgraphfft = np.fft.fft(data).real - forgraphifft = np.fft.ifft(forgraphfft).real - - freq = somthin - - plt.figure(figsize=(12, 6)) - - plt.subplot(2, 1, 1) - plt.plot(freq, forgraphfft, label='FFT', color='red') - plt.title('title') - plt.xlabel('freq in some unit') - plt.ylabel('Amplitude') - plt.grid() - plt.legend() - - plt.subplot(2, 1, 2) - plt.plot(freq, forgraphifft, label='IFFT', color='red') - plt.title('title2') - plt.xlabel('freq in some unit') - plt.ylabel('Amplitude') - plt.grid() - - plot = input("Do you want to save the plot? (yes/no): ").strip().lower() - - if plot == 'yes': - fpath = input("enter where to save: ").strip() - if not os.path.exists(fpath): - print("Directory does not exist, try again") - else: - filpath = os.path.join(fpath, "put_name_of_imge.png") - plt.savefig(filpath, format='png', dpi=600) - print(f"Plot saved as {filpath}") - else: - print("Plot not saved.") diff --git a/fft_folder/plots_ifft.py b/fft_folder/plots_ifft.py new file mode 100644 index 0000000..f26d800 --- /dev/null +++ b/fft_folder/plots_ifft.py @@ -0,0 +1,48 @@ +""" +plots_ifft.py + +""" +import pandas as pd +import os +import math as m +import numpy as np +import matplotlib.pyplot as plt + + +def plot_results(t, unalt_data, bins, magnitude, use_filt): + plt.figure(figsize=(12, 8)) + + if use_filt == 1: + lble = 'IFFT using a threshhold' + else: + lble = 'IFFT' + + plt.subplot(2, 1, 1) + plt.plot(t, unalt_data, label='Unalted data') + plt.title('Original data') + plt.xlabel('Time [s]') + plt.ylabel('Your units of concern') + plt.legend() + + plt.subplot(2, 1, 2) + plt.plot(bins, magnitude, label=lble) + plt.title('Magnitude Spectrum') + plt.xlabel('Frequency [Hz, 1/s]') + plt.ylabel('Magnitude') + plt.xlim(0, max(bins)/2) + plt.legend() + + plt.tight_layout() + + plot = input("Do you want to save the plot? (yes/no): ").strip().lower() + + if plot == 'yes': + fpath = input("enter where to save: ").strip() + if not os.path.exists(fpath): + print("Directory does not exist, try again") + else: + filpath = os.path.join(fpath, "workin on it.png") + plt.savefig(filpath, format='png', dpi=600) + print(f"Plot saved as {filpath}") + else: + print("Plot not saved.") diff --git a/fft_folder/test_inv_fft.py b/fft_folder/test_inv_fft.py new file mode 100644 index 0000000..93b1665 --- /dev/null +++ b/fft_folder/test_inv_fft.py @@ -0,0 +1,24 @@ +""" +test_inv_fft.py + +""" +import pandas as pd +import os +import math as m +import numpy as np +import matplotlib.pyplot as plt + +from inv_fft import analyze_signal, anze_revm_trnd +from plots_ifft import plot_results + +fs = 1000 +t = np.linspace(0, 5, fs, endpoint=False) +f1, f2 = 50, 120 +original_signal = np.sin(np.pi * f1 * t) + 0.5 * np.sin(np.pi * f2 * t) + + +bins, magnitude, main_frequencies, use_filt = analyze_signal(original_signal, fs, 'yes', 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z;wwOtJ(fhI&7LECo=Cc#r-k$B1=2@-tbUq0OKa)2viww threshold] - - return bins, magnitude, main_frequencies, use_filt - - -""" -trying to now make a way to remove overall trend -using Adjusted R-squared to determine which fit is best then it auto does this for you -the modles in the function include liner polynomial cubic and exponential -""" - -def anze_revm_trnd(signal, fs, use_filter, selec_filter): - """creats a filtered fft""" - N = len(signal) - ss_total = np.sum((signal['y'] - np.mean(signal['y']))**2) - - - params_poly = np.polyfit(signal['x'], signal['y'], 2) - y_poly = np.polyval(params_poly, signal['x']) - - res_poly = np.sum((signal['y'] - y_poly)**2) - r_sq_poly = 1 - (res_poly / ss_total) - r_poly_adj = 1 - ((1 - r_sq_poly) * (N - 1) / (N - 3)) - - - params_cubic = np.polyfit(signal['x'], signal['y'], 3) - y_cubic = np.polyval(params_cubic, signal['x']) - - res_cub = np.sum((signal['y'] - y_cubic)**2) - r_sq_cub = 1 - (res_cub / ss_total) - r_cub_adj = 1 - ((1 - r_sq_cub) * (N - 1) / (N - 4)) - - - params_quartic = np.polyfit(signal['x'], signal['y'], 4) - y_qua = np.polyval(params_quartic, signal['x']) - - res_qua = np.sum((signal['y'] - y_qua)**2) - r_sq_qua = 1 - (res_qua / ss_total) - r_qua_adj = 1 - ((1 - r_sq_qua) * (N - 1) / (N - 5)) - - - log_y = np.log(signal['y']) - params_linear = np.polyfit(signal['x'], log_y, 1) - y_linear = np.polyval(params_linear, signal['x']) - - ss_tot_ep = np.sum((log_y - np.mean(log_y))**2) - ss_residual = np.sum((log_y - y_linear)**2) - r_sq_ep = 1 - (ss_residual / ss_tot_ep) - r_ep_adj = 1 - ((1 - r_sq_ep) * (N - 1) / (N - 2)) - - find_sml = min(r_poly_adj, r_cub_adj, r_qua_adj, r_ep_adj) - - if find_sml == r_poly_adj: - signalnew = signal['y'] - y_poly - if find_sml == r_cub_adj: - signalnew = signal['y'] - y_cubic - if find_sml == r_qua_adj: - signalnew = signal['y'] - y_qua - else: - A = np.exp(params_linear[1]) - B = params_linear[0] - y_ep = A * np.exp(B * signal['x']) - signalnew = signal['y'] - y_ep - - - isfft = np.fft.fft(signalnew) - bins = np.fft.fftfreq(N, 1/fs) - - use_filt = 0 - filt = 0.0 - yes_no = use_filter.strip().lower() - if yes_no == 'yes': - filt = selec_filter - use_filt = 1 - - magnitude = np.abs(isfft) - threshold = filt * np.max(magnitude) - main_frequencies = bins[magnitude > threshold] - - return bins, magnitude, main_frequencies, use_filt diff --git a/fft_folder/plots_ifft.py b/fft_folder/plots_ifft.py index f26d800..ce71098 100644 --- a/fft_folder/plots_ifft.py +++ b/fft_folder/plots_ifft.py @@ -1,6 +1,8 @@ """ plots_ifft.py +to be deleated + """ import pandas as pd import os @@ -9,7 +11,7 @@ import matplotlib.pyplot as plt -def plot_results(t, unalt_data, bins, magnitude, use_filt): +def plot_results(t, unalt_data, freq, magnitude, use_filt): plt.figure(figsize=(12, 8)) if use_filt == 1: @@ -25,11 +27,11 @@ def plot_results(t, unalt_data, bins, magnitude, use_filt): plt.legend() plt.subplot(2, 1, 2) - plt.plot(bins, magnitude, label=lble) + plt.plot(freq, magnitude, label=lble) plt.title('Magnitude Spectrum') plt.xlabel('Frequency [Hz, 1/s]') plt.ylabel('Magnitude') - plt.xlim(0, max(bins)/2) + plt.xlim(0, max(freq)/2) plt.legend() plt.tight_layout() diff --git a/fft_folder/preparation.py b/fft_folder/preparation.py new file mode 100644 index 0000000..9096449 --- /dev/null +++ b/fft_folder/preparation.py @@ -0,0 +1,118 @@ +""" +preperation.py +inverse fast fourie transform + +the function analyze_signal inputs +signal, tim, use_filter, selec_filter +signal the data, tim means the span of time in sec that goes by, +use_filter just means if you want to filter out noise if you want to enter yes if not anything else works +selec_filter is where you enter your filter, for exapmple entering if you enter 0.1 the function finds the +largest freqency and only gives you the frequencies that are larger than 0.1 times the largest + +the function analyze_signal outputs +freq, magnitude, main_frequencies, use_filt +freq is the axis for the plot, magnitude is the amount of the freq thats in the signal, +main_frequencies are the values found when you use a filter, use_filt is just for the plot function + + +the function anze_revm_trnd +so this function does all the same things as the function before only finds a best fit function to try and +remove the overall trend in the signal data. it computs the following trends and finds the best fit by computing +and compairing their adjusted r^2 values. the trends include quadratic, cubic, quartic and exponential + +""" +import pandas as pd +import os +import math as m +import numpy as np + +def analyze_signal(signal, tim, use_filter, selec_filter): + """finds the frequencies""" + N = len(signal) + fs = N/tim + isfft = np.fft.fft(signal) + freq = np.fft.fftfreq(N, 1/fs) + + use_filt = 0 + filt = 0.0 + yes_no = use_filter.strip().lower() + if yes_no == 'yes': + filt = selec_filter + use_filt = 1 + + magnitude = np.abs(isfft) + threshold = filt * np.max(magnitude) + main_frequencies = freq[magnitude > threshold] + + return freq, magnitude, main_frequencies, use_filt + +def anze_revm_trnd(signal, tim, use_filter, selec_filter): + """same as analyze_signal but removes overall trend""" + N = len(signal) + fs = N/tim + ss_total = np.sum((signal['y'] - np.mean(signal['y']))**2) + + + params_quadratic = np.polyfit(signal['x'], signal['y'], 2) + y_quad = np.polyval(params_quadratic, signal['x']) + + res_quad = np.sum((signal['y'] - y_quad)**2) + r_sq_quad = 1 - (res_quad / ss_total) + r_quad_adj = 1 - ((1 - r_sq_quad) * (N - 1) / (N - 3)) + + + params_cubic = np.polyfit(signal['x'], signal['y'], 3) + y_cubic = np.polyval(params_cubic, signal['x']) + + res_cub = np.sum((signal['y'] - y_cubic)**2) + r_sq_cub = 1 - (res_cub / ss_total) + r_cub_adj = 1 - ((1 - r_sq_cub) * (N - 1) / (N - 4)) + + + params_quartic = np.polyfit(signal['x'], signal['y'], 4) + y_tic = np.polyval(params_quartic, signal['x']) + + res_tic = np.sum((signal['y'] - y_tic)**2) + r_sq_tic = 1 - (res_tic / ss_total) + r_tic_adj = 1 - ((1 - r_sq_tic) * (N - 1) / (N - 5)) + + + log_y = np.log(signal['y']) + params_linear = np.polyfit(signal['x'], log_y, 1) + y_linear = np.polyval(params_linear, signal['x']) + + ss_tot_ep = np.sum((log_y - np.mean(log_y))**2) + ss_residual = np.sum((log_y - y_linear)**2) + r_sq_ep = 1 - (ss_residual / ss_tot_ep) + r_ep_adj = 1 - ((1 - r_sq_ep) * (N - 1) / (N - 2)) + + find_sml = min(r_quad_adj, r_cub_adj, r_tic_adj, r_ep_adj) + + if find_sml == r_quad_adj: + signalnew = signal['y'] - y_poly + if find_sml == r_cub_adj: + signalnew = signal['y'] - y_cubic + if find_sml == r_tic_adj: + signalnew = signal['y'] - y_tic + else: + A = np.exp(params_linear[1]) + B = params_linear[0] + y_ep = A * np.exp(B * signal['x']) + signalnew = signal['y'] - y_ep + + + isfft = np.fft.fft(signalnew) + freq = np.fft.fftfreq(N, 1/fs) + + use_filt = 0 + filt = 0.0 + yes_no = use_filter.strip().lower() + if yes_no == 'yes': + filt = selec_filter + use_filt = 1 + + magnitude = np.abs(isfft) + threshold = filt * np.max(magnitude) + main_frequencies = freq[magnitude > threshold] + + return freq, magnitude, main_frequencies, use_filt diff --git a/fft_folder/readme_tobedeleted.md b/fft_folder/readme_tobedeleted.md deleted file mode 100644 index d20fe25..0000000 --- a/fft_folder/readme_tobedeleted.md +++ /dev/null @@ -1 +0,0 @@ -fft / inverse fft diff --git a/fft_folder/test_inv_fft.py b/fft_folder/test_inv_fft.py deleted file mode 100644 index 93b1665..0000000 --- a/fft_folder/test_inv_fft.py +++ /dev/null @@ -1,24 +0,0 @@ -""" -test_inv_fft.py - -""" -import pandas as pd -import os -import math as m -import numpy as np -import matplotlib.pyplot as plt - -from inv_fft import analyze_signal, anze_revm_trnd -from plots_ifft import plot_results - -fs = 1000 -t = np.linspace(0, 5, fs, endpoint=False) -f1, f2 = 50, 120 -original_signal = np.sin(np.pi * f1 * t) + 0.5 * np.sin(np.pi * f2 * t) - - -bins, magnitude, main_frequencies, use_filt = analyze_signal(original_signal, fs, 'yes', 0.1) - -plot_results(t, original_signal, bins, magnitude, use_filt) - -print("Main Frequencies:", main_frequencies) diff --git a/fft_folder/test_preparation.py b/fft_folder/test_preparation.py new file mode 100644 index 0000000..1a196a0 --- /dev/null +++ b/fft_folder/test_preparation.py @@ -0,0 +1,27 @@ +""" +test_preparation.py + +this will be edited latter this is just for me atm + +""" +import pandas as pd +import os +import math as m +import numpy as np +import matplotlib.pyplot as plt + +from fft_folder.preparation import analyze_signal, anze_revm_trnd +from plots_ifft import plot_results + + +maxtime = 2 +t = np.linspace(0, maxtime, 2000, endpoint=False) +f1, f2 = 50, 120 +original_signal = np.sin(2*np.pi * f1 * t) + 0.5 * np.sin(2*np.pi * f2 * t) + + +freq, magnitude, main_frequencies, use_filt = analyze_signal(original_signal, maxtime, 'yes', 0.1) + +plot_results(t, original_signal, freq, magnitude, use_filt) + +print("Main Frequencies:", main_frequencies) From ba1dc4127193d9f3a13ecc6e5842339d0a9938ed Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Sat, 2 Nov 2024 22:15:15 +0000 Subject: [PATCH 09/17] updates --- fft_folder/preparation.py | 2 +- fft_folder/test_preparation.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/fft_folder/preparation.py b/fft_folder/preparation.py index 9096449..b9ab33a 100644 --- a/fft_folder/preparation.py +++ b/fft_folder/preparation.py @@ -89,7 +89,7 @@ def anze_revm_trnd(signal, tim, use_filter, selec_filter): find_sml = min(r_quad_adj, r_cub_adj, r_tic_adj, r_ep_adj) if find_sml == r_quad_adj: - signalnew = signal['y'] - y_poly + signalnew = signal['y'] - y_quad if find_sml == r_cub_adj: signalnew = signal['y'] - y_cubic if find_sml == r_tic_adj: diff --git a/fft_folder/test_preparation.py b/fft_folder/test_preparation.py index 1a196a0..411b946 100644 --- a/fft_folder/test_preparation.py +++ b/fft_folder/test_preparation.py @@ -25,3 +25,4 @@ plot_results(t, original_signal, freq, magnitude, use_filt) print("Main Frequencies:", main_frequencies) + From 94eb150a1c3f85ec8ab7972afb85eadb15592c44 Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Sat, 2 Nov 2024 22:30:35 +0000 Subject: [PATCH 10/17] updated pylint for preparations --- fft_folder/plots_ifft.py | 9 +++---- fft_folder/preparation.py | 46 ++++++++++++++++++---------------- fft_folder/test_preparation.py | 5 +--- 3 files changed, 28 insertions(+), 32 deletions(-) diff --git a/fft_folder/plots_ifft.py b/fft_folder/plots_ifft.py index ce71098..8a5df2b 100644 --- a/fft_folder/plots_ifft.py +++ b/fft_folder/plots_ifft.py @@ -1,13 +1,10 @@ """ plots_ifft.py -to be deleated - +I will deleate this file this will be used for me for checking +if my code is funtioning proprly """ -import pandas as pd -import os -import math as m -import numpy as np + import matplotlib.pyplot as plt diff --git a/fft_folder/preparation.py b/fft_folder/preparation.py index b9ab33a..5e720ef 100644 --- a/fft_folder/preparation.py +++ b/fft_folder/preparation.py @@ -5,9 +5,11 @@ the function analyze_signal inputs signal, tim, use_filter, selec_filter signal the data, tim means the span of time in sec that goes by, -use_filter just means if you want to filter out noise if you want to enter yes if not anything else works -selec_filter is where you enter your filter, for exapmple entering if you enter 0.1 the function finds the -largest freqency and only gives you the frequencies that are larger than 0.1 times the largest +use_filter just means if you want to filter out noise if you want to enter yes if not +anything else works +selec_filter is where you enter your filter, for exapmple entering if you enter 0.1 the +function finds the largest freqency and only gives you the frequencies that are larger +than 0.1 times the largest the function analyze_signal outputs freq, magnitude, main_frequencies, use_filt @@ -16,22 +18,25 @@ the function anze_revm_trnd -so this function does all the same things as the function before only finds a best fit function to try and -remove the overall trend in the signal data. it computs the following trends and finds the best fit by computing -and compairing their adjusted r^2 values. the trends include quadratic, cubic, quartic and exponential +so this function does all the same things as the function before only finds a best fit function to +try and remove the overall trend in the signal data. it computs the following trends and finds the +best fit by computing and compairing their adjusted r^2 values. the trends include quadratic, +cubic, quartic and exponential -""" +pylint +pytest +import numpy as np import pandas as pd -import os -import math as m +matplotlib +""" import numpy as np def analyze_signal(signal, tim, use_filter, selec_filter): """finds the frequencies""" - N = len(signal) - fs = N/tim + n = len(signal) + fs = n/tim isfft = np.fft.fft(signal) - freq = np.fft.fftfreq(N, 1/fs) + freq = np.fft.fftfreq(n, 1/fs) use_filt = 0 filt = 0.0 @@ -48,8 +53,7 @@ def analyze_signal(signal, tim, use_filter, selec_filter): def anze_revm_trnd(signal, tim, use_filter, selec_filter): """same as analyze_signal but removes overall trend""" - N = len(signal) - fs = N/tim + n = len(signal) ss_total = np.sum((signal['y'] - np.mean(signal['y']))**2) @@ -58,7 +62,7 @@ def anze_revm_trnd(signal, tim, use_filter, selec_filter): res_quad = np.sum((signal['y'] - y_quad)**2) r_sq_quad = 1 - (res_quad / ss_total) - r_quad_adj = 1 - ((1 - r_sq_quad) * (N - 1) / (N - 3)) + r_quad_adj = 1 - ((1 - r_sq_quad) * (n - 1) / (n - 3)) params_cubic = np.polyfit(signal['x'], signal['y'], 3) @@ -66,7 +70,7 @@ def anze_revm_trnd(signal, tim, use_filter, selec_filter): res_cub = np.sum((signal['y'] - y_cubic)**2) r_sq_cub = 1 - (res_cub / ss_total) - r_cub_adj = 1 - ((1 - r_sq_cub) * (N - 1) / (N - 4)) + r_cub_adj = 1 - ((1 - r_sq_cub) * (n - 1) / (n - 4)) params_quartic = np.polyfit(signal['x'], signal['y'], 4) @@ -74,7 +78,7 @@ def anze_revm_trnd(signal, tim, use_filter, selec_filter): res_tic = np.sum((signal['y'] - y_tic)**2) r_sq_tic = 1 - (res_tic / ss_total) - r_tic_adj = 1 - ((1 - r_sq_tic) * (N - 1) / (N - 5)) + r_tic_adj = 1 - ((1 - r_sq_tic) * (n - 1) / (n - 5)) log_y = np.log(signal['y']) @@ -84,7 +88,7 @@ def anze_revm_trnd(signal, tim, use_filter, selec_filter): ss_tot_ep = np.sum((log_y - np.mean(log_y))**2) ss_residual = np.sum((log_y - y_linear)**2) r_sq_ep = 1 - (ss_residual / ss_tot_ep) - r_ep_adj = 1 - ((1 - r_sq_ep) * (N - 1) / (N - 2)) + r_ep_adj = 1 - ((1 - r_sq_ep) * (n - 1) / (n - 2)) find_sml = min(r_quad_adj, r_cub_adj, r_tic_adj, r_ep_adj) @@ -95,14 +99,12 @@ def anze_revm_trnd(signal, tim, use_filter, selec_filter): if find_sml == r_tic_adj: signalnew = signal['y'] - y_tic else: - A = np.exp(params_linear[1]) - B = params_linear[0] - y_ep = A * np.exp(B * signal['x']) + y_ep = (np.exp(params_linear[1])) * np.exp((params_linear[0]) * signal['x']) signalnew = signal['y'] - y_ep isfft = np.fft.fft(signalnew) - freq = np.fft.fftfreq(N, 1/fs) + freq = np.fft.fftfreq(n, tim/n) use_filt = 0 filt = 0.0 diff --git a/fft_folder/test_preparation.py b/fft_folder/test_preparation.py index 411b946..34041db 100644 --- a/fft_folder/test_preparation.py +++ b/fft_folder/test_preparation.py @@ -4,10 +4,8 @@ this will be edited latter this is just for me atm """ -import pandas as pd -import os -import math as m import numpy as np +import pandas as pd import matplotlib.pyplot as plt from fft_folder.preparation import analyze_signal, anze_revm_trnd @@ -25,4 +23,3 @@ plot_results(t, original_signal, freq, magnitude, use_filt) print("Main Frequencies:", main_frequencies) - From ad7591ba3f6c8504c1dab5f13507b7b47c101985 Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Sat, 2 Nov 2024 22:37:16 +0000 Subject: [PATCH 11/17] done for a bit --- fft_folder/plots_ifft.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fft_folder/plots_ifft.py b/fft_folder/plots_ifft.py index 8a5df2b..cd9891d 100644 --- a/fft_folder/plots_ifft.py +++ b/fft_folder/plots_ifft.py @@ -4,7 +4,7 @@ I will deleate this file this will be used for me for checking if my code is funtioning proprly """ - +import os import matplotlib.pyplot as plt From 654f067b7ebc1f615bab9bb3fc55ab0382fd9e68 Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Mon, 4 Nov 2024 04:04:11 +0000 Subject: [PATCH 12/17] not ready yet --- fft_folder/plots_ifft.py | 121 +++++++++++++++++++++++++++----- fft_folder/preparation.py | 124 ++++----------------------------- fft_folder/test_preparation.py | 28 +++++--- workin it.png | Bin 0 -> 305937 bytes workin on it.png | Bin 0 -> 1375228 bytes 5 files changed, 136 insertions(+), 137 deletions(-) create mode 100644 workin it.png create mode 100644 workin on it.png diff --git a/fft_folder/plots_ifft.py b/fft_folder/plots_ifft.py index cd9891d..a2bc656 100644 --- a/fft_folder/plots_ifft.py +++ b/fft_folder/plots_ifft.py @@ -1,33 +1,45 @@ """ plots_ifft.py +preperation.py +inverse fast fourie transform + +the function analyze_signal inputs +signal, tim, use_filter, selec_filter +signal the data, tim means the span of time in sec that goes by, +use_filter just means if you want to filter out noise if you want to enter yes if not +anything else works +selec_filter is where you enter your filter, for exapmple entering if you enter 0.1 the +function finds the largest freqency and only gives you the frequencies that are larger +than 0.1 times the largest + +the function analyze_signal outputs +freq, magnitude, main_frequencies, use_filt +freq is the axis for the plot, magnitude is the amount of the freq thats in the signal, +main_frequencies are the values found when you use a filter, use_filt is just for the plot function + + +the function anze_revm_trnd +so this function does all the same things as the function before only finds a best fit function to +try and remove the overall trend in the signal data. it computs the following trends and finds the +best fit by computing and compairing their adjusted r^2 values. the trends include quadratic, +cubic, quartic and exponential + I will deleate this file this will be used for me for checking if my code is funtioning proprly """ import os +import numpy as np import matplotlib.pyplot as plt - -def plot_results(t, unalt_data, freq, magnitude, use_filt): +def plot_rets(freq, magnitude,): + """will deleate""" plt.figure(figsize=(12, 8)) - - if use_filt == 1: - lble = 'IFFT using a threshhold' - else: - lble = 'IFFT' - - plt.subplot(2, 1, 1) - plt.plot(t, unalt_data, label='Unalted data') + + plt.plot(freq, magnitude, label=' wods') plt.title('Original data') - plt.xlabel('Time [s]') + plt.xlabel(' [1/s]') plt.ylabel('Your units of concern') - plt.legend() - - plt.subplot(2, 1, 2) - plt.plot(freq, magnitude, label=lble) - plt.title('Magnitude Spectrum') - plt.xlabel('Frequency [Hz, 1/s]') - plt.ylabel('Magnitude') plt.xlim(0, max(freq)/2) plt.legend() @@ -40,8 +52,79 @@ def plot_results(t, unalt_data, freq, magnitude, use_filt): if not os.path.exists(fpath): print("Directory does not exist, try again") else: - filpath = os.path.join(fpath, "workin on it.png") + filpath = os.path.join(fpath, "workin it.png") plt.savefig(filpath, format='png', dpi=600) print(f"Plot saved as {filpath}") else: print("Plot not saved.") + + + + +def anze_revm_trnd(signal, tim, use_filter, selec_filter): + """keeping this for now for adj r^2 calc""" + n = len(signal) + ss_total = np.sum((signal['y'] - np.mean(signal['y']))**2) + + + params_quadratic = np.polyfit(signal['x'], signal['y'], 2) + y_quad = np.polyval(params_quadratic, signal['x']) + + res_quad = np.sum((signal['y'] - y_quad)**2) + r_sq_quad = 1 - (res_quad / ss_total) + r_quad_adj = 1 - ((1 - r_sq_quad) * (n - 1) / (n - 3)) + + + params_cubic = np.polyfit(signal['x'], signal['y'], 3) + y_cubic = np.polyval(params_cubic, signal['x']) + + res_cub = np.sum((signal['y'] - y_cubic)**2) + r_sq_cub = 1 - (res_cub / ss_total) + r_cub_adj = 1 - ((1 - r_sq_cub) * (n - 1) / (n - 4)) + + + params_quartic = np.polyfit(signal['x'], signal['y'], 4) + y_tic = np.polyval(params_quartic, signal['x']) + + res_tic = np.sum((signal['y'] - y_tic)**2) + r_sq_tic = 1 - (res_tic / ss_total) + r_tic_adj = 1 - ((1 - r_sq_tic) * (n - 1) / (n - 5)) + + + log_y = np.log(signal['y']) + params_linear = np.polyfit(signal['x'], log_y, 1) + y_linear = np.polyval(params_linear, signal['x']) + + ss_tot_ep = np.sum((log_y - np.mean(log_y))**2) + ss_residual = np.sum((log_y - y_linear)**2) + r_sq_ep = 1 - (ss_residual / ss_tot_ep) + r_ep_adj = 1 - ((1 - r_sq_ep) * (n - 1) / (n - 2)) + + find_sml = min(r_quad_adj, r_cub_adj, r_tic_adj, r_ep_adj) + + if find_sml == r_quad_adj: + signalnew = signal['y'] - y_quad + if find_sml == r_cub_adj: + signalnew = signal['y'] - y_cubic + if find_sml == r_tic_adj: + signalnew = signal['y'] - y_tic + else: + y_ep = (np.exp(params_linear[1])) * np.exp((params_linear[0]) * signal['x']) + signalnew = signal['y'] - y_ep + + + isfft = np.fft.fft(signalnew) + freq = np.fft.fftfreq(n, tim/n) + + use_filt = 0 + filt = 0.0 + yes_no = use_filter.strip().lower() + if yes_no == 'yes': + filt = selec_filter + use_filt = 1 + + magnitude = np.abs(isfft) + threshold = filt * np.max(magnitude) + main_frequencies = freq[magnitude > threshold] + + return freq, magnitude, main_frequencies, use_filt \ No newline at end of file diff --git a/fft_folder/preparation.py b/fft_folder/preparation.py index 5e720ef..1c630e4 100644 --- a/fft_folder/preparation.py +++ b/fft_folder/preparation.py @@ -1,120 +1,24 @@ """ -preperation.py -inverse fast fourie transform +preparation.py -the function analyze_signal inputs -signal, tim, use_filter, selec_filter -signal the data, tim means the span of time in sec that goes by, -use_filter just means if you want to filter out noise if you want to enter yes if not -anything else works -selec_filter is where you enter your filter, for exapmple entering if you enter 0.1 the -function finds the largest freqency and only gives you the frequencies that are larger -than 0.1 times the largest - -the function analyze_signal outputs -freq, magnitude, main_frequencies, use_filt -freq is the axis for the plot, magnitude is the amount of the freq thats in the signal, -main_frequencies are the values found when you use a filter, use_filt is just for the plot function - - -the function anze_revm_trnd -so this function does all the same things as the function before only finds a best fit function to -try and remove the overall trend in the signal data. it computs the following trends and finds the -best fit by computing and compairing their adjusted r^2 values. the trends include quadratic, -cubic, quartic and exponential - -pylint -pytest -import numpy as np -import pandas as pd -matplotlib +fft +inverse fft +calculate frequencies """ import numpy as np -def analyze_signal(signal, tim, use_filter, selec_filter): - """finds the frequencies""" - n = len(signal) - fs = n/tim - isfft = np.fft.fft(signal) - freq = np.fft.fftfreq(n, 1/fs) - - use_filt = 0 - filt = 0.0 - yes_no = use_filter.strip().lower() - if yes_no == 'yes': - filt = selec_filter - use_filt = 1 - - magnitude = np.abs(isfft) - threshold = filt * np.max(magnitude) - main_frequencies = freq[magnitude > threshold] - - return freq, magnitude, main_frequencies, use_filt - -def anze_revm_trnd(signal, tim, use_filter, selec_filter): - """same as analyze_signal but removes overall trend""" - n = len(signal) - ss_total = np.sum((signal['y'] - np.mean(signal['y']))**2) - - - params_quadratic = np.polyfit(signal['x'], signal['y'], 2) - y_quad = np.polyval(params_quadratic, signal['x']) - - res_quad = np.sum((signal['y'] - y_quad)**2) - r_sq_quad = 1 - (res_quad / ss_total) - r_quad_adj = 1 - ((1 - r_sq_quad) * (n - 1) / (n - 3)) +def fft(data): + matrx = np.fft.fft(data) + return np.abs(matrx) +def inv_fft(data): - params_cubic = np.polyfit(signal['x'], signal['y'], 3) - y_cubic = np.polyval(params_cubic, signal['x']) + matrx = np.fft.fft(data) - res_cub = np.sum((signal['y'] - y_cubic)**2) - r_sq_cub = 1 - (res_cub / ss_total) - r_cub_adj = 1 - ((1 - r_sq_cub) * (n - 1) / (n - 4)) + return np.fft.ifft(matrx) - - params_quartic = np.polyfit(signal['x'], signal['y'], 4) - y_tic = np.polyval(params_quartic, signal['x']) - - res_tic = np.sum((signal['y'] - y_tic)**2) - r_sq_tic = 1 - (res_tic / ss_total) - r_tic_adj = 1 - ((1 - r_sq_tic) * (n - 1) / (n - 5)) - - - log_y = np.log(signal['y']) - params_linear = np.polyfit(signal['x'], log_y, 1) - y_linear = np.polyval(params_linear, signal['x']) - - ss_tot_ep = np.sum((log_y - np.mean(log_y))**2) - ss_residual = np.sum((log_y - y_linear)**2) - r_sq_ep = 1 - (ss_residual / ss_tot_ep) - r_ep_adj = 1 - ((1 - r_sq_ep) * (n - 1) / (n - 2)) - - find_sml = min(r_quad_adj, r_cub_adj, r_tic_adj, r_ep_adj) - - if find_sml == r_quad_adj: - signalnew = signal['y'] - y_quad - if find_sml == r_cub_adj: - signalnew = signal['y'] - y_cubic - if find_sml == r_tic_adj: - signalnew = signal['y'] - y_tic - else: - y_ep = (np.exp(params_linear[1])) * np.exp((params_linear[0]) * signal['x']) - signalnew = signal['y'] - y_ep - - - isfft = np.fft.fft(signalnew) +def calc_freq(data): + n = len(data) + tim = data[n-1] freq = np.fft.fftfreq(n, tim/n) - - use_filt = 0 - filt = 0.0 - yes_no = use_filter.strip().lower() - if yes_no == 'yes': - filt = selec_filter - use_filt = 1 - - magnitude = np.abs(isfft) - threshold = filt * np.max(magnitude) - main_frequencies = freq[magnitude > threshold] - - return freq, magnitude, main_frequencies, use_filt + return freq diff --git a/fft_folder/test_preparation.py b/fft_folder/test_preparation.py index 34041db..e093aec 100644 --- a/fft_folder/test_preparation.py +++ b/fft_folder/test_preparation.py @@ -8,18 +8,30 @@ import pandas as pd import matplotlib.pyplot as plt -from fft_folder.preparation import analyze_signal, anze_revm_trnd -from plots_ifft import plot_results +from preparation import fft, calc_freq, inv_fft +from plots_ifft import plot_rets, anze_revm_trnd -maxtime = 2 -t = np.linspace(0, maxtime, 2000, endpoint=False) +#maxtime = 2 +#t = np.linspace(0, maxtime, 2000, endpoint=False) +#f1, f2 = 50, 120 +#original_signal = np.sin(2*np.pi * f1 * t) + 0.5 * np.sin(2*np.pi * f2 * t) + + +#freq, magnitude, main_frequencies, use_filt = analyze_signal(original_signal, maxtime, 'yes', 0.1) + +# plot_results(t, original_signal, freq, magnitude, use_filt) + +# print("Main Frequencies:", main_frequencies) + +t = np.linspace(0, 2, 2000, endpoint=False) + f1, f2 = 50, 120 -original_signal = np.sin(2*np.pi * f1 * t) + 0.5 * np.sin(2*np.pi * f2 * t) +data = pd.Series(np.sin(f1*t)+0.5*np.sin(f2*t),index=range(0,len(t))) +# index_values = data.index.tolist() -freq, magnitude, main_frequencies, use_filt = analyze_signal(original_signal, maxtime, 'yes', 0.1) -plot_results(t, original_signal, freq, magnitude, use_filt) +freq = calc_freq(data) -print("Main Frequencies:", main_frequencies) 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zBUj|Rk$klx-;Ly}CHd}2MlQ*BBN@3O-#y7!EArh)zFLz1J5S=qb?@@6H51phGsoko Nt7s|5uiE;@{{gbzv77(^ From a58c8b36d7e270a55ff026c77c06c9d7189b41fd Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Mon, 4 Nov 2024 16:46:59 +0000 Subject: [PATCH 14/17] reduced to power spectrum --- fft_folder/plots_ifft.py | 4 ++-- fft_folder/preparation.py | 17 +++-------------- 2 files changed, 5 insertions(+), 16 deletions(-) diff --git a/fft_folder/plots_ifft.py b/fft_folder/plots_ifft.py index a2bc656..41a06ad 100644 --- a/fft_folder/plots_ifft.py +++ b/fft_folder/plots_ifft.py @@ -59,13 +59,13 @@ def plot_rets(freq, magnitude,): print("Plot not saved.") - +anze_revm_trnd(data, tim, "yes", 10) def anze_revm_trnd(signal, tim, use_filter, selec_filter): """keeping this for now for adj r^2 calc""" n = len(signal) ss_total = np.sum((signal['y'] - np.mean(signal['y']))**2) - +365.15 params_quadratic = np.polyfit(signal['x'], signal['y'], 2) y_quad = np.polyval(params_quadratic, signal['x']) diff --git a/fft_folder/preparation.py b/fft_folder/preparation.py index 1c630e4..b9d35c8 100644 --- a/fft_folder/preparation.py +++ b/fft_folder/preparation.py @@ -7,18 +7,7 @@ """ import numpy as np -def fft(data): +def fft_powerspectrum(data): + """This function interprates the csv data for you and calc the fft""" matrx = np.fft.fft(data) - return np.abs(matrx) - -def inv_fft(data): - - matrx = np.fft.fft(data) - - return np.fft.ifft(matrx) - -def calc_freq(data): - n = len(data) - tim = data[n-1] - freq = np.fft.fftfreq(n, tim/n) - return freq + return np.abs(matrx)[:len(matrx/2)] From 7adc499108fccb3c3e193ccede53ef8873c9f1f8 Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Mon, 4 Nov 2024 18:34:08 +0000 Subject: [PATCH 15/17] newist i hope this is it --- fft_folder/preparation.py | 25 ++++++++++++- fft_folder/test_preparation.py | 67 ++++++++++++++++++++++++++-------- 2 files changed, 74 insertions(+), 18 deletions(-) diff --git a/fft_folder/preparation.py b/fft_folder/preparation.py index b9d35c8..33b4cb7 100644 --- a/fft_folder/preparation.py +++ b/fft_folder/preparation.py @@ -6,8 +6,29 @@ calculate frequencies """ import numpy as np +import pandas as pd +from datetime import datetime, timedelta def fft_powerspectrum(data): - """This function interprates the csv data for you and calc the fft""" - matrx = np.fft.fft(data) + """This function takes the function in and outputs + the powerspectrum""" + data1 = np.zeros(len(data)) + i=0 + while i < len(data): + data1[i] = data.iloc[i] + i+=1 + matrx = np.fft.fft(data1) return np.abs(matrx)[:len(matrx/2)] + +def fft_mag(data): + """this function is simalare to fft_powerspectrum only it does not cut the + matrix in half or take the absolut values of the variables""" + data1 = np.zeros(len(data)) + i=0 + while i < len(data): + data1[i] = data.iloc[i] + i+=1 + return np.fft.fft(data1) + + + diff --git a/fft_folder/test_preparation.py b/fft_folder/test_preparation.py index e093aec..b522983 100644 --- a/fft_folder/test_preparation.py +++ b/fft_folder/test_preparation.py @@ -1,37 +1,72 @@ """ test_preparation.py -this will be edited latter this is just for me atm +unit test for the functions fft, calc_freq, inv_fft """ import numpy as np import pandas as pd +from datetime import datetime, timedelta import matplotlib.pyplot as plt -from preparation import fft, calc_freq, inv_fft -from plots_ifft import plot_rets, anze_revm_trnd +from preparation import fft_powerspectrum, fft_mag +#from thid import plot_rets +""" +t = np.linspace(0, 2, 2000, endpoint=False) +f1, f2 = 50, 120 +data = pd.Series(np.sin(f1*t)+0.5*np.sin(f2*t),index=range(0,len(t))) +# index_values = data.index.tolist() +freq = calc_freq(data) +print(freq) +print(inv_fft(data)) +print(data) +plot_rets(freq,fft(data)) +""" -#maxtime = 2 -#t = np.linspace(0, maxtime, 2000, endpoint=False) -#f1, f2 = 50, 120 -#original_signal = np.sin(2*np.pi * f1 * t) + 0.5 * np.sin(2*np.pi * f2 * t) +t = np.linspace(0, 2, 2000, endpoint=False) +f1, f2 = 50, 120 +data = pd.Series(np.sin(2*np.pi*f1*t)+0.5*np.sin(2*np.pi*f2*t),index=range(0,len(t))) +#trange = date_range(datetime.now(), datetime.now()+pd.timedelta(days=9),freq='d') +#trange[i].timestamp() -#freq, magnitude, main_frequencies, use_filt = analyze_signal(original_signal, maxtime, 'yes', 0.1) +""" +def test_fft(data): -# plot_results(t, original_signal, freq, magnitude, use_filt) + magnitudes = fft(data) + assert len(magnitudes) == len(data) -# print("Main Frequencies:", main_frequencies) +def test_calc_freq(data, f1, f2): + freq = calc_freq(data) + lngth = len(data)/2 + newf1 = f1 + lngth + newf2 = f2 + lngth + assert len(freq) == len(data) + assert freq[newf1] > 200 + assert freq[newf2] > 200 -t = np.linspace(0, 2, 2000, endpoint=False) +def test_inv_fft(data): + invdata = inv_fft(data) + assert len(invdata) == len(data) +""" +matr = fft_powerspectrum(data) -f1, f2 = 50, 120 +print(matr) -data = pd.Series(np.sin(f1*t)+0.5*np.sin(f2*t),index=range(0,len(t))) -# index_values = data.index.tolist() +#freq = calc_freq(data) +# plot_rets(freq,fft(data)) +#print(np.isclose(0, 0.0001, atol=0.001)) + +trange = pd.date_range(datetime.now(), datetime.now()+timedelta(days=9),freq='d') +data1 = pd.Series([1,2,3,4,5,6,7,8,9,10],index=trange) + + + +print(len(data1)) + +print(data1.index[0].timestamp()) -freq = calc_freq(data) -plot_rets(freq,fft(data)) \ No newline at end of file +print(data1.iloc[2] * 1) \ No newline at end of file From 132399365a75f11d909543383e1eedfe541dc72f Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Mon, 4 Nov 2024 18:34:46 +0000 Subject: [PATCH 16/17] deleated this --- fft_folder/plots_ifft.py | 130 --------------------------------------- 1 file changed, 130 deletions(-) delete mode 100644 fft_folder/plots_ifft.py diff --git a/fft_folder/plots_ifft.py b/fft_folder/plots_ifft.py deleted file mode 100644 index 41a06ad..0000000 --- a/fft_folder/plots_ifft.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -plots_ifft.py - -preperation.py -inverse fast fourie transform - -the function analyze_signal inputs -signal, tim, use_filter, selec_filter -signal the data, tim means the span of time in sec that goes by, -use_filter just means if you want to filter out noise if you want to enter yes if not -anything else works -selec_filter is where you enter your filter, for exapmple entering if you enter 0.1 the -function finds the largest freqency and only gives you the frequencies that are larger -than 0.1 times the largest - -the function analyze_signal outputs -freq, magnitude, main_frequencies, use_filt -freq is the axis for the plot, magnitude is the amount of the freq thats in the signal, -main_frequencies are the values found when you use a filter, use_filt is just for the plot function - - -the function anze_revm_trnd -so this function does all the same things as the function before only finds a best fit function to -try and remove the overall trend in the signal data. it computs the following trends and finds the -best fit by computing and compairing their adjusted r^2 values. the trends include quadratic, -cubic, quartic and exponential - -I will deleate this file this will be used for me for checking -if my code is funtioning proprly -""" -import os -import numpy as np -import matplotlib.pyplot as plt - -def plot_rets(freq, magnitude,): - """will deleate""" - plt.figure(figsize=(12, 8)) - - plt.plot(freq, magnitude, label=' wods') - plt.title('Original data') - plt.xlabel(' [1/s]') - plt.ylabel('Your units of concern') - plt.xlim(0, max(freq)/2) - plt.legend() - - plt.tight_layout() - - plot = input("Do you want to save the plot? (yes/no): ").strip().lower() - - if plot == 'yes': - fpath = input("enter where to save: ").strip() - if not os.path.exists(fpath): - print("Directory does not exist, try again") - else: - filpath = os.path.join(fpath, "workin it.png") - plt.savefig(filpath, format='png', dpi=600) - print(f"Plot saved as {filpath}") - else: - print("Plot not saved.") - - -anze_revm_trnd(data, tim, "yes", 10) - -def anze_revm_trnd(signal, tim, use_filter, selec_filter): - """keeping this for now for adj r^2 calc""" - n = len(signal) - ss_total = np.sum((signal['y'] - np.mean(signal['y']))**2) -365.15 - - params_quadratic = np.polyfit(signal['x'], signal['y'], 2) - y_quad = np.polyval(params_quadratic, signal['x']) - - res_quad = np.sum((signal['y'] - y_quad)**2) - r_sq_quad = 1 - (res_quad / ss_total) - r_quad_adj = 1 - ((1 - r_sq_quad) * (n - 1) / (n - 3)) - - - params_cubic = np.polyfit(signal['x'], signal['y'], 3) - y_cubic = np.polyval(params_cubic, signal['x']) - - res_cub = np.sum((signal['y'] - y_cubic)**2) - r_sq_cub = 1 - (res_cub / ss_total) - r_cub_adj = 1 - ((1 - r_sq_cub) * (n - 1) / (n - 4)) - - - params_quartic = np.polyfit(signal['x'], signal['y'], 4) - y_tic = np.polyval(params_quartic, signal['x']) - - res_tic = np.sum((signal['y'] - y_tic)**2) - r_sq_tic = 1 - (res_tic / ss_total) - r_tic_adj = 1 - ((1 - r_sq_tic) * (n - 1) / (n - 5)) - - - log_y = np.log(signal['y']) - params_linear = np.polyfit(signal['x'], log_y, 1) - y_linear = np.polyval(params_linear, signal['x']) - - ss_tot_ep = np.sum((log_y - np.mean(log_y))**2) - ss_residual = np.sum((log_y - y_linear)**2) - r_sq_ep = 1 - (ss_residual / ss_tot_ep) - r_ep_adj = 1 - ((1 - r_sq_ep) * (n - 1) / (n - 2)) - - find_sml = min(r_quad_adj, r_cub_adj, r_tic_adj, r_ep_adj) - - if find_sml == r_quad_adj: - signalnew = signal['y'] - y_quad - if find_sml == r_cub_adj: - signalnew = signal['y'] - y_cubic - if find_sml == r_tic_adj: - signalnew = signal['y'] - y_tic - else: - y_ep = (np.exp(params_linear[1])) * np.exp((params_linear[0]) * signal['x']) - signalnew = signal['y'] - y_ep - - - isfft = np.fft.fft(signalnew) - freq = np.fft.fftfreq(n, tim/n) - - use_filt = 0 - filt = 0.0 - yes_no = use_filter.strip().lower() - if yes_no == 'yes': - filt = selec_filter - use_filt = 1 - - magnitude = np.abs(isfft) - threshold = filt * np.max(magnitude) - main_frequencies = freq[magnitude > threshold] - - return freq, magnitude, main_frequencies, use_filt \ No newline at end of file From 41974c43f6ed45e475db49b2ebf40df3a3146c4b Mon Sep 17 00:00:00 2001 From: zbpetersbuf Date: Mon, 4 Nov 2024 20:01:43 +0000 Subject: [PATCH 17/17] now ready --- fft_folder/preparation.py | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/fft_folder/preparation.py b/fft_folder/preparation.py index 33b4cb7..9311bae 100644 --- a/fft_folder/preparation.py +++ b/fft_folder/preparation.py @@ -12,23 +12,11 @@ def fft_powerspectrum(data): """This function takes the function in and outputs the powerspectrum""" - data1 = np.zeros(len(data)) - i=0 - while i < len(data): - data1[i] = data.iloc[i] - i+=1 - matrx = np.fft.fft(data1) + matrx = np.fft.fft(data.values) return np.abs(matrx)[:len(matrx/2)] def fft_mag(data): """this function is simalare to fft_powerspectrum only it does not cut the matrix in half or take the absolut values of the variables""" - data1 = np.zeros(len(data)) - i=0 - while i < len(data): - data1[i] = data.iloc[i] - i+=1 - return np.fft.fft(data1) - - + return np.fft.fft(data.values)