57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
import tdm_ripper
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import numpy as np
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import matplotlib.pyplot as plt
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tdmpatha = b"samples/SineData.tdm"
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tdxpatha = b"samples/SineData.tdx"
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tdmpathb = b"/Users/mariofink/Downloads/CONTI_HBS_samples/config_id_1/001_Test_DIAdem/Messung.tdm"
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tdxpathb = b"/Users/mariofink/Downloads/CONTI_HBS_samples/config_id_1/001_Test_DIAdem/Messung.tdx"
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tdmpathchz = tdmpathb
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tdxpathchz = tdxpathb
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# create instance of ripper class
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# RP = tdm_ripper.pytdmripper(tdmpath)
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RP = tdm_ripper.pytdmripper(tdmpathchz,tdxpathchz)
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# provide overview of available channels
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RP.show_channels()
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print(RP.num_channels())
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print(RP.num_groups())
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for i in range(0,RP.num_groups()):
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print(str(i+1).rjust(10)+str(RP.no_channels(i)).rjust(10))
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# print particular channel to file
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RP.print_channel(1,b"SineData_extract.dat")
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# show some meta information
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print(RP.meta_info(b"SMP_Name").decode('utf-8'))
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print(RP.meta_info(b"Location").decode('utf-8'))
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print('\n')
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RP.print_meta(b"meta_information.dat")
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# extract channel and return it to numpy array
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# channels = RP.get_channel(1)
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# Nlen = len(channels)
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# channels = np.append(channels,RP.get_channel(2))
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# channels = np.append(channels,RP.get_channel(3))
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# channels = np.append(channels,RP.get_channel(4))
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# channels = np.append(channels,RP.get_channel(5))
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# channels = np.append(channels,RP.get_channel(6))
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# channels = np.append(channels,RP.get_channel(7))
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# channels = np.append(channels,RP.get_channel(8))
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# print(channels.shape)
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# print("\n\n")
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# print(channels[0:40])
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#
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# x = np.linspace(0,Nlen,Nlen)
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# plt.plot(x,channels[0:Nlen])
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# plt.plot(x,channels[Nlen:2*Nlen])
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# plt.plot(x,channels[2*Nlen:3*Nlen])
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#
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# plt.grid()
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# plt.show()
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