fix: line fit formula, b1+b2 calculation;add: data process
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parent
927120b632
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543d5e106b
@ -1,12 +1,14 @@
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import csv
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import numpy as np
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from qdx import Bind
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from qdx.utils import readData, get_hist
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from qdx.utils import readBlockData, get_hist
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from tqdm import tqdm
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from matplotlib import pyplot as plt
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# Initialization
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n, m = 5, 8
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bias = 12.97
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deltaE = 4
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binds = [[Bind(i, j) for j in range(m)] for i in range(n)]
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# Read Data
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@ -14,7 +16,7 @@ file_list = csv.reader(open("./config1.csv", "r"))
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pbar = tqdm(desc="Read Data E1", total=len(open("./config1.csv", "r").readlines()))
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for row in file_list:
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pn = int(row[1])
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ldata, rdata = readData("2016Q3D/root/raw/201609Q3D" + row[0] + ".root", pn, m)
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ldata, rdata = readBlockData(row[0], pn, m)
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for i in range(m):
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bind = binds[pn][i]
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bind.add_data(0, ldata[i], rdata[i], 585 - float(row[2]))
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@ -25,7 +27,7 @@ file_list = csv.reader(open("./config2.csv", "r"))
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pbar = tqdm(desc="Read Data E2", total=len(open("./config2.csv", "r").readlines()))
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for row in file_list:
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pn = int(row[1])
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ldata, rdata = readData("2016Q3D/root/raw/201609Q3D" + row[0] + ".root", pn, m)
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ldata, rdata = readBlockData(row[0], pn, m)
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for i in range(m):
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bind = binds[pn][i]
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bind.add_data(1, ldata[i], rdata[i], 585 - float(row[2]))
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@ -38,12 +40,11 @@ for i in range(n):
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for j in range(m):
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bind: Bind = binds[i][j]
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bind.slash()
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bind.get_energy()
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bind.get_line()
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bind.get_kb()
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# bind.draw_fit_line("result/FIT-LINE/" + bind.name + ".png")
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# bind.draw_cluster("result/GMM/" + bind.name + ".png")
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# bind.draw_peak("result/PEAK/" + bind.name + ".png")
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bind.get_kb(bias, deltaE)
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bind.draw_fit_line("result/FIT-LINE/" + bind.name + ".png")
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bind.draw_cluster("result/GMM/" + bind.name + ".png")
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bind.draw_peak("result/PEAK/" + bind.name + ".png")
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pbar.update(1)
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pbar.close()
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@ -59,7 +60,7 @@ pbar.close()
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# Draw check figure
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pbar = tqdm(desc="Figure Check", total=n * m)
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fig = plt.figure(figsize=(16, 16), dpi=200)
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fig = plt.figure(figsize=(16, 10), dpi=200)
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ax1 = fig.add_subplot(2, 1, 1)
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ax2 = fig.add_subplot(2, 1, 2)
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peaks = np.array([])
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@ -72,7 +73,6 @@ for i in range(n):
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eng = bind.predict_energy(bind.x[0], bind.y[0])
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pX = bind.predict_px(bind.x[0], bind.y[0])
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count, center = get_hist(pX, delta=0.5)
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ax1.scatter(pX, eng, s=0.1, color="k")
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ax2.scatter(center, count + 2500 * (7 - j), s=0.5, color="k")
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@ -82,19 +82,19 @@ peaks = np.unique(peaks)
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for x in peaks:
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ax2.vlines(x, 0, 20000, color="gray", linestyles="dashed")
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for j in range(m):
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ax2.hlines(2500 * j, 0, 650, color="r", linestyles="dashdot")
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ax2.hlines(2500 * j, -50, 600, color="r", linestyles="dashdot")
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fig.savefig("./result/Check.png", facecolor="w", transparent=False)
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plt.close()
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pbar.close()
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# Save coefficient to file
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f = open("./coef.txt", "w")
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f = open("./coef.csv", "w")
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for i in range(n):
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for j in range(m):
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bind = binds[i][j]
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f.writelines(
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"{:d} {:d} {:.9f} {:.9f} {:.9f} {:.9f} {:.9f}\n".format(
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"{:d},{:d},{:.9f},{:.9f},{:.9f},{:.9f},{:.9f}\n".format(
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i, j, bind.k1, bind.k2, bind.b, bind.L, bind.C
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)
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)
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69
process.py
69
process.py
@ -0,0 +1,69 @@
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import csv
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import numpy as np
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from qdx import Bind
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from qdx.utils import readFileData, get_hist
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from tqdm import tqdm
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from matplotlib import pyplot as plt
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# Initialization
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n, m = 5, 8
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binds = [[Bind(i, j) for j in range(m)] for i in range(n)]
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# Read Calibration Data
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pbar = tqdm(desc="Bind Initialization", total=n * m)
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data = list(csv.reader(open("coef1.csv", "r")))
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data = np.array(data, dtype=np.float64)
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for i in range(n):
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for j in range(m):
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bind = binds[i][j]
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bind(data[j + i * m][2:])
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pbar.update(1)
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pbar.close()
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# Read Data
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total = len(open("task3.csv", "r").readlines()) * n * m
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file_list = csv.reader(open("task3.csv", "r"))
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pX = np.array([])
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eng = np.array([])
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pX_full = np.array([])
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eng_full = np.array([])
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pbar = tqdm(desc="Task - Mg25(d,p)Mg26*", total=total)
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for row in file_list:
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ldata, rdata = readFileData(row[0], n, m)
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for i in range(n):
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for j in range(m):
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bind = binds[i][j]
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x = bind.predict_px(ldata[j + i * m], rdata[j + i * m]) + float(row[1])
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e = bind.predict_energy(ldata[j + i * m], rdata[j + i * m])
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edge_l = 5 + 130 * i + float(row[1]) - 35
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edge_r = edge_l + 65
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idx = np.where((x >= edge_l) & (x <= edge_r))[0]
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pX = np.hstack((pX, x[idx]))
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eng = np.hstack((eng, e[idx]))
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pX_full = np.hstack((pX_full, x))
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eng_full = np.hstack((eng_full, e))
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pbar.update(1)
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pbar.close()
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# Draw
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fig = plt.figure(figsize=(20, 8), dpi=200)
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ax1 = fig.add_subplot(2, 1, 1)
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ax2 = fig.add_subplot(2, 1, 2)
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count, center = get_hist(pX, delta=0.1)
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ax1.scatter(pX, eng, s=0.05, color="k")
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py, = ax2.step(center, count, where="post", color="k")
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ax1.set_xticks(np.arange((np.min(pX) // 50) * 50, (np.max(pX) // 50 + 1) * 50, 50))
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ax2.set_xticks(np.arange((np.min(pX) // 50) * 50, (np.max(pX) // 50 + 1) * 50, 50))
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fig.savefig("Task3.png")
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plt.close()
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118
qdx/Bind.py
118
qdx/Bind.py
@ -81,16 +81,56 @@ class Bind(object):
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self.y[1] = data[:, 1]
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self.px[1] = data[:, 2]
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def get_energy(self):
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"""Get energy (in channel) by fit Gaussian Model with ldata + rdata"""
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eng = self.x[0] + self.y[0]
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fitted_model = fit_hist_gaussian(eng)
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def get_line(self):
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"""Fit data with $x = -\\frac{k_2}{k_1}y + \\frac{E-b_1-b_2}{k_1}$."""
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model = Linear1D()
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reg = fit_line(model, self.x[0], self.y[0])
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self.K1 = reg.slope.value
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self.C1 = reg.intercept.value
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reg = fit_line(model, self.x[1], self.y[1])
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self.K2 = reg.slope.value
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self.C2 = reg.intercept.value
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def get_kb(self, bias, deltaE=4):
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"""Get $k_2$, $k_2$, $b$ from $K_i$, $C_i$
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Set slope equal average of $K_i$.
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Parameters
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----------
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bias : float
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bias $b = b_1 + b_2$
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deltaE : float, optional
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delta energy between two beams
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"""
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K = (self.K1 + self.K2) / 2
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self.K1 = self.K2 = K
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model = FixedSlopeLine(slope=K, intercept=self.C1)
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fitted_model = fit_line(model, self.x[0], self.y[0])
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self.C1 = fitted_model.intercept.value
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model = FixedSlopeLine(slope=K, intercept=self.C2)
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fitted_model = fit_line(model, self.x[1], self.y[1])
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self.C2 = fitted_model.intercept.value
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self.k2 = deltaE / abs(self.C1 - self.C2)
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self.k1 = -self.k2 * K
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eng = self.k1 * self.x[0] + self.k2 * self.y[0]
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fitted_model = fit_hist_gaussian(eng, delta=0.01)
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self.E1 = fitted_model.mean.value
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eng = self.x[1] + self.y[1]
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fitted_model = fit_hist_gaussian(eng)
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eng = self.k1 * self.x[1] + self.k2 * self.y[1]
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fitted_model = fit_hist_gaussian(eng, delta=0.01)
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self.E2 = fitted_model.mean.value
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self.b = bias - self.C1 * self.k2
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self.E1 += self.b
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self.E2 += self.b
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def get_peak_center(self):
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"""Get peak center (in channel) using Gaussian Model"""
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self.fx, self.fy, self.fz = [], [], []
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@ -115,52 +155,25 @@ class Bind(object):
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self.fy = np.array(self.fy)
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self.fz = np.array(self.fz)
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def get_line(self):
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"""Fit data with $x = -\\frac{k_2}{k_1}y + \\frac{E-b_1-b_2}{k_1}$."""
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model = Linear1D()
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self.reg1 = fit_line(model, self.x[0], self.y[0])
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self.K1 = self.reg1.slope.value
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self.C1 = self.reg1.intercept.value
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self.reg2 = fit_line(model, self.x[1], self.y[1])
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self.K2 = self.reg2.slope.value
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self.C2 = self.reg2.intercept.value
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def get_kb(self):
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"""Get $k_2$, $k_2$, $b$ from $K_i$, $C_i$
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Set slope equal average of $K_i$.
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"""
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K = (self.K1 + self.K2) / 2
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self.K1 = self.K2 = K
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model = FixedSlopeLine(slope=K, intercept=self.C1)
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fitted_model = fit_line(model, self.x[0], self.y[0])
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self.C1 = fitted_model.intercept.value
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model = FixedSlopeLine(slope=K, intercept=self.C2)
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fitted_model = fit_line(model, self.x[1], self.y[1])
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self.C2 = fitted_model.intercept.value
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self.k1 = (self.E1 - self.E2) / (self.C1 - self.C2)
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self.k2 = -(self.K1 + self.K2) * self.k1 / 2
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self.b = (self.E1 - self.C1 * self.k1 + self.E2 - self.C2 * self.k1) / 2
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def fit_px(self):
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"""Fit using $Px = \\frac{k_2y - k_1x}{E}L+CL$."""
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model = Linear1D()
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reg = fit_line(model, self.fx, self.fz)
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L = reg.slope.value * self.E1 / (self.k2 * self.K1 - self.k1)
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C = (reg.intercept.value - self.k2 * self.C1 * L / self.E1) / L
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model = pXLine(L=L, C=C, k1=self.k1, k2=self.k2, E=self.E1)
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reg = fit_line(model, np.array(list(zip(self.fx, self.fy)), dtype=object), self.fz)
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E = np.mean(self.k1 * self.fx + self.k2 * self.fy + self.b)
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L = reg.slope.value * E / (self.k2 * self.K1 - self.k1)
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C = (reg.intercept.value - self.k2 * self.C1 * L / E) / L
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model = pXLine(L=L, C=C, k1=self.k1, k2=self.k2, b=self.b)
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reg = fit_line(
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model, np.array(list(zip(self.fx, self.fy)), dtype=object), self.fz
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)
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self.L = reg.L.value
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self.C = reg.C.value
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def predict_energy(self, x, y):
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"""Use $E(x,y) = k_1x + b_1 + k_2y + b_2$. to calculate energy.
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Parameters
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----------
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x/y : array
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@ -176,7 +189,8 @@ class Bind(object):
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x/y : array
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data
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"""
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return (self.k2 * y - self.k1 * x) / self.E1 * self.L + self.C * self.L
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eng = self.predict_energy(x, y)
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return (self.k2 * y - self.k1 * x) / eng * self.L + self.C * self.L
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def draw_fit_line(self, title):
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fig = plt.figure(figsize=(8, 8))
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@ -185,7 +199,7 @@ class Bind(object):
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ax.scatter(self.x[1], self.y[1], s=0.1, c="dimgray", label=r"$E_2$")
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ax.plot(
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self.x[0],
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self.reg1(self.x[0]),
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self.K1 * self.x[0] + self.C1,
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c="red",
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label=r"$x_2={:.4f}x_1+{:.4f},\ R^2={:.5f}$".format(
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self.K1, self.C1, self.RSquare1
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@ -193,7 +207,7 @@ class Bind(object):
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)
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ax.plot(
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self.x[1],
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self.reg2(self.x[1]),
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self.K2 * self.x[1] + self.C2,
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c="orangered",
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label=r"$x_2={:.4f}x_1+{:.4f},\ R^2={:.5f}$".format(
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self.K2, self.C2, self.RSquare2
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@ -244,7 +258,7 @@ class Bind(object):
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@property
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def name(self):
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return "{:d}-{:d}-{:.1f}-{:.1f}".format(self.n, self.m, self.E1, self.E2)
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return "{:d}-{:d}-{:.2f}-{:.2f}".format(self.n, self.m, self.E1, self.E2)
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def _r_square(self, y, yp):
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mean = np.mean(y)
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@ -254,8 +268,16 @@ class Bind(object):
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@property
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def RSquare1(self):
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return self._r_square(self.y[0], self.reg1(self.x[0]))
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return self._r_square(self.y[0], self.K1 * self.x[0] + self.C1)
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@property
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def RSquare2(self):
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return self._r_square(self.y[1], self.reg2(self.x[1]))
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return self._r_square(self.y[1], self.K2 * self.x[1] + self.C2)
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def __call__(self, data):
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"""Data is read to complete initialization"""
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self.k1 = data[0]
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self.k2 = data[1]
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self.b = data[2]
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self.L = data[3]
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self.C = data[4]
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@ -31,7 +31,7 @@ def fit_line(model, x, y):
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return fitted_model
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def fit_hist_gaussian(x):
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def fit_hist_gaussian(x, delta=1):
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"""
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Gaussian fitting is performed on the histogram
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@ -39,6 +39,8 @@ def fit_hist_gaussian(x):
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----------
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x : array
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data point
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delta : int, optional
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Minimum bin width. The bin width is an integer multiple of delta.
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Returns
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-------
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@ -47,7 +49,7 @@ def fit_hist_gaussian(x):
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"""
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fitter = fitting.LMLSQFitter()
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count, center = get_hist(x)
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count, center = get_hist(x, delta=delta)
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model = models.Gaussian1D(amplitude=count.max(), mean=x.mean(), stddev=x.std())
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fitted_model = fitter(model, center, count)
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12
qdx/model.py
12
qdx/model.py
@ -70,15 +70,17 @@ class pXLine(Fittable2DModel):
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linear = True
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L, C = Parameter(default=40), Parameter(default=0)
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k1, k2, E = Parameter(), Parameter(), Parameter()
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k1, k2, b = Parameter(), Parameter(), Parameter()
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@staticmethod
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def evaluate(x, y, L, C, k1, k2, E):
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def evaluate(x, y, L, C, k1, k2, b):
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E = k1 * x + k2 * y + b
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return (k2 * y - k1 * x) / E * L + C * L
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@staticmethod
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def fit_deriv(x, y, L, C, k1, k2, E):
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def fit_deriv(x, y, L, C, k1, k2, b):
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E = k1 * x + k2 * y + b
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d_L = (k2 * y - k1 * x) / E + C
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d_C = np.full(x.shape, L)
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d_k1, d_k2, d_E = np.zeros_like(x), np.zeros_like(x), np.zeros_like(x)
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return [d_L, d_C, d_k1, d_k2, d_E]
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||||
d_k1, d_k2, d_b = np.zeros_like(x), np.zeros_like(x), np.zeros_like(x)
|
||||
return [d_L, d_C, d_k1, d_k2, d_b]
|
||||
|
||||
39
qdx/utils.py
39
qdx/utils.py
@ -4,8 +4,39 @@ import matplotlib.pyplot as plt
|
||||
from sklearn.mixture import GaussianMixture
|
||||
|
||||
|
||||
def readData(file, n, m=8, minT=800, maxT=4000):
|
||||
"""Read data from root file
|
||||
def readFileData(file, n=6, m=8, minT=800, maxT=4000):
|
||||
"""Read whole data from root file
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file : str
|
||||
root file path
|
||||
n : int, optional
|
||||
number of blocks, default 6
|
||||
m : int, optional
|
||||
number of binds, default 8
|
||||
minT/maxT : int, optional
|
||||
Filtering data, the sum of the left and right sides needs to be in the interval [minT, maxT]
|
||||
min / max threshold
|
||||
"""
|
||||
data = uproot.open(file)["Tree1"]
|
||||
|
||||
ldata, rdata = [], []
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
na = i // 2
|
||||
nc = j + 2 * m * (i % 2)
|
||||
x = data["adc{:d}ch{:d}".format(na, nc)].array(library="np")
|
||||
y = data["adc{:d}ch{:d}".format(na, nc + m)].array(library="np")
|
||||
idx = np.where((x + y >= minT) & (x + y <= maxT))[0]
|
||||
ldata.append(x[idx])
|
||||
rdata.append(y[idx])
|
||||
|
||||
return ldata, rdata
|
||||
|
||||
|
||||
def readBlockData(file, n, m=8, minT=800, maxT=4000):
|
||||
"""Read block data from root file
|
||||
|
||||
Parameters
|
||||
----------
|
||||
@ -25,8 +56,8 @@ def readData(file, n, m=8, minT=800, maxT=4000):
|
||||
for j in range(m):
|
||||
na = n // 2
|
||||
nc = j + 2 * m * (n % 2)
|
||||
x = data["adc{:d}ch{:d}".format(na, nc)].array()
|
||||
y = data["adc{:d}ch{:d}".format(na, nc + m)].array()
|
||||
x = data["adc{:d}ch{:d}".format(na, nc)].array(library="np")
|
||||
y = data["adc{:d}ch{:d}".format(na, nc + m)].array(library="np")
|
||||
idx = np.where((x + y >= minT) & (x + y <= maxT))[0]
|
||||
ldata.append(x[idx])
|
||||
rdata.append(y[idx])
|
||||
|
||||
Loading…
Reference in New Issue
Block a user