Chris fix error handle
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@ -126,8 +126,8 @@ def canny_shock_finder(time_list, pressure_list, sigma = 4, derivative_threshold
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import numpy as np
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import copy
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print ('-'*60)
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print ("Running Canny shock finder version {0}".format(VERSION_STRING))
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print('-'*60)
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print("Running Canny shock finder version {0}".format(VERSION_STRING))
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# do some basic input checking
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if not isinstance(sigma, (float, int)):
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@ -148,7 +148,7 @@ def canny_shock_finder(time_list, pressure_list, sigma = 4, derivative_threshold
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start_time = time_list[0]
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if post_shock_pressure:
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print ("Using post-shock pressure scaling so the post_suppression_threshold will be calculated using a post-shock pressure estimate.")
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print("Using post-shock pressure scaling so the post_suppression_threshold will be calculated using a post-shock pressure estimate.")
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# we need to calculate our post_suppression_threshold here based on the expected post-shock pressure and the
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# scaling caused by the first order gaussian data based on the maximum of the Gaussian
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@ -178,10 +178,10 @@ def canny_shock_finder(time_list, pressure_list, sigma = 4, derivative_threshold
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post_suppression_threshold = post_shock_pressure * gaussian_first_derivative_max
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#post_suppression_threshold = 0.5 * post_shock_pressure * gaussian_max/2.0
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print ("Calculated post_suppression_threshold is {0}".format(post_suppression_threshold))
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print("Calculated post_suppression_threshold is {0}".format(post_suppression_threshold))
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if calculate_automatic_derivative_threshold:
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print ("Calculating automatic derivative threshold as the user has asked for this.")
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print("Calculating automatic derivative threshold as the user has asked for this.")
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# this commented out code here was my original model, based on the actual second derivative of the Gaussian,
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# but it didn't seem to work too well (it got too small at very high sigma values, i.e. above 6 or so)
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@ -199,7 +199,7 @@ def canny_shock_finder(time_list, pressure_list, sigma = 4, derivative_threshold
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else:
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derivative_threshold = post_shock_pressure / 2.5 * np.exp(-6) / 10.0
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print ("Calculated derivative_threshold is {0}.".format(derivative_threshold))
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print("Calculated derivative_threshold is {0}.".format(derivative_threshold))
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# make the input data arrays incase they didn't come in that way...
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time_list = np.array(time_list)
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@ -224,14 +224,14 @@ def canny_shock_finder(time_list, pressure_list, sigma = 4, derivative_threshold
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first_value_uncertainty = None
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if auto_derivative:
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print ("Doing auto-derivative!")
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print("Doing auto-derivative!")
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# remove points which have the same gradient on either side
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for i in range(0,len(first_order_data)-1):
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if np.sign(second_order_data[i-1]) == np.sign(second_order_data[i+1]):
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suppressed_data[i] = 0
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else:
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print ("Not doing auto-derivative!")
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print("Not doing auto-derivative!")
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for i in range(0,len(first_order_data)-1):
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# check the gradients on either side using the second order data
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@ -345,7 +345,7 @@ def canny_shock_finder(time_list, pressure_list, sigma = 4, derivative_threshold
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try: # this is mainly so the code doesn't bail out if one closes a window before it has loaded properly
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import matplotlib.pyplot as mplt
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figure, (data_ax, convolution_ax) = mplt.subplots(2,1, sharex=True, figsize = (14,8))
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data_ax.plot(time_list*plot_time_scale, pressure_list, '-o', label = 'original data')
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@ -476,7 +476,7 @@ def canny_shock_finder(time_list, pressure_list, sigma = 4, derivative_threshold
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mplt.show()
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except Exception as e:
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print ("{0}: {1}".format(type(e).__name__, e.message))
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print (e)
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print ("There was an issue plotting the result.")
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mplt.close('all')
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