50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import os
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import nix_helpers as nh
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from IPython import embed
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res_df1 = np.load('res_df1.npy')
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res_df2 = np.load('res_df2.npy')
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res_df = np.load('res_df.npy')
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mres = []
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mdf = []
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currf = None
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idxlist = []
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for i, d in enumerate(res_df):
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if currf is None or currf == d[0]:
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currf = d[0]
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idxlist.append(i)
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else: # currf != f
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meanres = [] # lists to make mean of
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meandf = []
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for x in idxlist:
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meanres.append(res_df[x][1])
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meandf.append(res_df[x][0])
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meanedres = np.mean(meanres)
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meaneddf = np.mean(meandf)
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mres.append(meanedres)
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mdf.append(meaneddf)
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currf = d[0] # set back for next loop
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idxlist = [i]
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meanres = [] # lists to make mean of
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meandf = []
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for y in idxlist:
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meanres.append(res_df[y][1])
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meandf.append(res_df[y][0])
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meanedres = np.mean(meanres)
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meaneddf = np.mean(meandf)
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mres.append(meanedres)
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mdf.append(meaneddf)
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plt.plot(mdf, mres, 'o')
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plt.xlabel('deltaf [Hz]')
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plt.ylabel('JAR_respones [Hz]')
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plt.axhline(0, color='grey', lw =1)
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plt.axvline(0, color='grey', lw = 1)
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plt.title('JAR_response_to_deltaf_eigenmannia')
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plt.show() |