42 lines
1.8 KiB
Python
42 lines
1.8 KiB
Python
run("Diagnostics/sig_process_wrapper")
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root = "2017/05/04/20170504_182411_WireScan.h5|/"
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wire = "x"
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blms = ["",""]
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cycles = 3
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calc_values = []
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for i in range(len(blms)):
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path_back = root + "background/blm" + str(i+1)
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bg = get_attributes(path_back)["Mean"]
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samples = [[], [], [], []]
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for cycle in range (cycles):
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pos = load_data(root + wire+"_" + ("%04d" % (cycle+1)) + "/w_pos")
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path = root + wire+"_" + ("%04d" % (cycle+1)) + "/blm" + str(i+1)
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data = load_data(path)
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sp = blm_remove_spikes(data)
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sig = sp if bg is None else [v-bg for v in sp]
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rms_com, rms = profile_rms_stats(pos, sig,noise_std=0, n_sigma=3.5)
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#set_attribute(path, "RMS", float("nan") if (rms is None) else rms)
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#set_attribute(path, "RMS COM", float("nan") if (rms_com is None) else rms_com)
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print [rms_com, rms]
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off, amp, com, sigma = profile_gauss_stats(pos, sig, off=None, amp=None, com=None, sigma=None)
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#set_attribute(path, "Gauss COM", float("nan") if (com is None) else com)
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#set_attribute(path, "Gauss Sigma", float("nan") if (sigma is None) else sigma)
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print [off, amp, com, sigma]
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samples[0].append(rms_com);samples[1].append(rms);samples[2].append(com);samples[3].append(sigma)
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from mathutils import Gaussian
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g = Gaussian(amp, com, sigma)
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gauss = [g.value(v)+off for v in pos]
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plot([data, sp, sig, gauss], ["data", "sp", "signal", "gauss", ], xdata = pos, title="Fit blm" + str(i+1) + " - " + str(cycle+1))
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stats = []
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for sample in samples:
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stats.apppend((mean(sample), stdev(sample)))
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print "-------------"
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print i, stats
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