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Few corrections have been made on calibration, in particular, for the interpolation of the fine sediments profile. The user can invert several acoustic recording with a single calibration. There remains a prolem to display the plots, in particular, when the user switches from one to another acoustic data.
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@@ -207,70 +207,134 @@ class AcousticInversionMethodHighConcentration():
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# ------------- Computing interpolation of fine SSC data obtained from water sampling -------------
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# ------------- collected at various depth in the vertical sample -------------
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# def M_profile_SCC_fine_interpolated(self, sample_depth, M_profile, range_cells, r_bottom):
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# res = np.zeros((len(range_cells),)) * np.nan
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# for i in range(len(M_profile) - 1):
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# # print(f"i = {i}")
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# r_ini = sample_depth[i]
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# # print(f"r_ini = {r_ini}")
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# c_ini = M_profile[i]
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# # print(f"c_ini = {c_ini}")
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# r_end = sample_depth[i + 1]
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# # print(f"r_end = {r_end}")
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# c_end = M_profile[i + 1]
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# # print(f"c_end = {c_end}")
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#
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# # Computing the linear equation
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# a = (c_end - c_ini) / (r_end - r_ini)
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# # print(f"a = {a}")
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# b = c_ini - a * r_ini
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# # print(f"b = {b}")
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#
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# # Finding the indices of r_ini and r_end in the interpolated array
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# # print(f"range_cells = {range_cells}")
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# loc = (range_cells >= r_ini) * (range_cells < r_end)
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# # print(f"loc = {loc}")
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# # print(f"loc shape = {len(loc)}")
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#
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# # Filling the array with interpolation values
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# res[loc] = range_cells[loc] * a + b
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# # print(res.shape)
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# # print(f"res = {res}")
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# # print(f"1. res.shape = {res.shape}")
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#
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# # Filling first and last values
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# i = 0
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# while np.isnan(res[i]):
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# res[i] = M_profile[0]
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# i += 1
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#
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# # Filling the last values
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# i = -1
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# while np.isnan(res[i]):
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# res[i] = M_profile[-1]
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# i += -1
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# # print(f"res.shape = {res.shape}")
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# # print(f"res = {res}")
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# # print(f"r_bottom.shape = {r_bottom.shape}")
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# # print(f" = {res}")
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#
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# if r_bottom.shape != (0,):
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# res[np.where(range_cells > r_bottom)] = np.nan
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#
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# loc_point_lin_interp0 = range_cells[np.where((range_cells > sample_depth[0]) & (range_cells < sample_depth[-1]))]
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# # print(f"range_cells : {range_cells}")
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# # print(f"loc_point_lin_interp0 shape : {len(loc_point_lin_interp0)}")
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# # print(f"loc_point_lin_interp0 : {loc_point_lin_interp0}")
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# res0 = res[np.where((range_cells > sample_depth[0]) & (range_cells < sample_depth[-1]))]
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#
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# loc_point_lin_interp = loc_point_lin_interp0[np.where(loc_point_lin_interp0 > range_cells[0])]
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# # print(f"loc_point_lin_interp shape : {len(loc_point_lin_interp)}")
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# # print(f"loc_point_lin_interp : {loc_point_lin_interp}")
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# res = res0[np.where(loc_point_lin_interp0 > range_cells[0])]
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#
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# # fig, ax = plt.subplots(nrows=1, ncols=1)
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# # ax.plot(loc_point_lin_interp, res[:len(loc_point_lin_interp)], marker="*", mfc="blue")
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# # ax.plot(sample_depth, M_profile, marker="o", mfc="k", mec="k")
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# # plt.show()
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#
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# return (loc_point_lin_interp, res)
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def M_profile_SCC_fine_interpolated(self, sample_depth, M_profile, range_cells, r_bottom):
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res = np.zeros((len(range_cells),)) * np.nan
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for i in range(len(M_profile) - 1):
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# print(f"i = {i}")
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r_ini = sample_depth[i]
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# print(f"r_ini = {r_ini}")
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c_ini = M_profile[i]
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# print(f"c_ini = {c_ini}")
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r_end = sample_depth[i + 1]
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# print(f"r_end = {r_end}")
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c_end = M_profile[i + 1]
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# print(f"c_end = {c_end}")
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l0 = sample_depth
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print("l0 = ", l0)
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l1 = [l0.index(x) for x in sorted(l0)]
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print("l1 = ", l1)
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l2 = [l0[k] for k in l1]
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print("l2 = ", l2)
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c1 = [list(M_profile)[j] for j in l1]
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print("c1 = ", c1)
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for i in range(len(c1) - 1):
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# print("i = ", i)
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r_ini = l2[i]
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c_ini = c1[i]
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r_end = l2[i + 1]
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c_end = c1[i + 1]
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print("r_ini ", r_ini, "c_ini ", c_ini, "r_end ", r_end, "c_end ", c_end)
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# Computing the linear equation
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a = (c_end - c_ini) / (r_end - r_ini)
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# print(f"a = {a}")
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b = c_ini - a * r_ini
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# print(f"b = {b}")
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print("range_cells ", (range_cells))
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# Finding the indices of r_ini and r_end in the interpolated array
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# print(f"range_cells = {range_cells}")
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loc = (range_cells >= r_ini) * (range_cells < r_end)
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# print(f"loc = {loc}")
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# print(f"loc shape = {len(loc)}")
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print("range_cells >= r_ini ", range_cells >= r_ini)
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print("range_cells < r_end ", range_cells < r_end)
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print("loc ", loc)
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# Filling the array with interpolation values
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res[loc] = range_cells[loc] * a + b
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# print(res.shape)
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# print(f"res = {res}")
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# print(f"1. res.shape = {res.shape}")
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print("a = ", a, "b = ", b)
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print("res ", res)
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# Filling first and last values
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i = 0
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while np.isnan(res[i]):
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res[i] = M_profile[0]
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res[i] = c1[0]
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i += 1
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# Filling the last values
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i = -1
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while np.isnan(res[i]):
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res[i] = M_profile[-1]
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res[i] = c1[-1]
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i += -1
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# print(f"res.shape = {res.shape}")
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# print(f"res = {res}")
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# print(f"r_bottom.shape = {r_bottom.shape}")
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# print(f" = {res}")
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if r_bottom.shape != (0,):
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if r_bottom.size != 0:
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res[np.where(range_cells > r_bottom)] = np.nan
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loc_point_lin_interp0 = range_cells[np.where((range_cells > sample_depth[0]) & (range_cells < sample_depth[-1]))]
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# print(f"range_cells : {range_cells}")
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# print(f"loc_point_lin_interp0 shape : {len(loc_point_lin_interp0)}")
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# print(f"loc_point_lin_interp0 : {loc_point_lin_interp0}")
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res0 = res[np.where((range_cells > sample_depth[0]) & (range_cells < sample_depth[-1]))]
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loc_point_lin_interp0 = range_cells[np.where((range_cells > l2[0]) & (range_cells < l2[-1]))]
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res0 = res[np.where((range_cells > l2[0]) & (range_cells < l2[-1]))]
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loc_point_lin_interp = loc_point_lin_interp0[np.where(loc_point_lin_interp0 > range_cells[0])]
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# print(f"loc_point_lin_interp shape : {len(loc_point_lin_interp)}")
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# print(f"loc_point_lin_interp : {loc_point_lin_interp}")
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res = res0[np.where(loc_point_lin_interp0 > range_cells[0])]
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loc_point_lin_interp = loc_point_lin_interp0[np.where(loc_point_lin_interp0 > l2[0])]
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res = res0[np.where(loc_point_lin_interp0 > l2[0])]
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# fig, ax = plt.subplots(nrows=1, ncols=1)
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# ax.plot(loc_point_lin_interp, res[:len(loc_point_lin_interp)], marker="*", mfc="blue")
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# ax.plot(sample_depth, M_profile, marker="o", mfc="k", mec="k")
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# ax.plot(res[:len(loc_point_lin_interp)], -loc_point_lin_interp, marker="*", mfc="blue")
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# ax.plot(c1, [-x for x in l2], marker="o", mfc="k", mec="k", ls="None")
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# ax.set_xlabel("Concentration (g/L)")
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# ax.set_ylabel("Depth (m)")
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# plt.show()
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return (loc_point_lin_interp, res)
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