move to wsl
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.gitignore
vendored
46
.gitignore
vendored
@ -1,26 +1,20 @@
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# data file
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*.root
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*.log
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*.csv
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*.json
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*.png
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*.txt
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2016Q3D/
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result/
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!requirements.txt
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# env
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venv/
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# build cache
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__pycache__
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# config
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.vscode
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.vs
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# dev
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*.ipynb
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test*
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*.code-workspace
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# data file
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*.root
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*.log
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*.csv
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*.json
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*.png
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*.txt
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data/
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result/
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!requirements.txt
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# build cache
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__pycache__
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# config
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.vscode
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# dev
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*.ipynb
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test*
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@ -1,3 +1,3 @@
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from .Bind import Bind
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from .process import Process
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from .calibration import Calibration
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from .Bind import Bind
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from .process import Process
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from .calibration import Calibration
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@ -1,296 +1,296 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from .utils import get_hist, GMM_clip
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from .model import Linear1D, FixedSlopeLine, pXLine
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from .fit import fit_line, fit_hist_gaussian
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class Bind(object):
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"""Bind of a Block, using data under two energy conditions for fitting.
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The energy of an ADC device can be expressed as: $E = kx + b$.
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So the complete energy is represented by $E(x,y) = k_1x + b_1 + k_2y + b_2$.
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This formula can be converted to: $y = -\\frac{k_1}{k_2}x + \\frac{E-b_1-b_2}{k_2}$.
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Plotting the data in a 2D plane for linear regression, we can get $\\frac{k_1}{k_2}$ and $C_i$
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Using data of $E_1$ and $E_2$, now $k_2 = \\frac{E_1 - E_2}{C_1 - C_2}$.
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The incident position produces different responses in the left and right detectors, which can be expressed as:
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$Px = \\frac{k_2y - k_1x}{E}L+CL$.
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Attributes
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----------
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L/C : float
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parameters in $Px = \\frac{k_2y - k_1x}{E}L+CL$.
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k1, k2: float
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parameters in $Px = \\frac{k_2y - k_1x}{E}L+CL$.
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b : float
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$b = b_1 + b_2$
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K1/K2/C1/C2: float
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$K_i = -\\frac{k_1}{k_2}$
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$C_i = \\frac{E_i-b}{k_2}$
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"""
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def __init__(self, n, m):
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self.n, self.m = n, m
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self.px = [None, None]
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self.x, self.y = [None, None], [None, None]
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self.L, self.C = 40, 0
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self.k1, self.k2, self.b = 0, 0, 0
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self.K1, self.C1, self.K2, self.C2 = 0, 0, 0, 0
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def add_data(self, k, x, y, px):
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"""k-th energy data
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Parameters
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----------
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k : int
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k-th energy
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x : array
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left data
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y : array
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right data
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px : int
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px (set value)
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"""
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self.x[k] = np.concatenate((self.x[k], x)) if self.x[k] is not None else x
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self.y[k] = np.concatenate((self.y[k], y)) if self.y[k] is not None else y
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self.px[k] = (
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np.concatenate((self.px[k], np.full(len(x), px)))
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if self.px[k] is not None
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else np.full(len(x), px)
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)
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def clip(self):
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"""Using Gaussian Mixture Method (GMM) to decompose the data into noise and available data"""
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data = GMM_clip(
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np.array(list(zip(self.x[0], self.y[0], self.px[0])), dtype=object)
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)
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self.x[0] = data[:, 0]
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self.y[0] = data[:, 1]
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self.px[0] = data[:, 2]
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data = GMM_clip(
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np.array(list(zip(self.x[1], self.y[1], self.px[1])), dtype=object)
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)
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self.x[1] = data[:, 0]
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self.y[1] = data[:, 1]
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self.px[1] = data[:, 2]
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def get_line(self):
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"""Fit data with $y = -\\frac{k_1}{k_2}x + \\frac{E-b_1-b_2}{k_2}$."""
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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, step=0.01)
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self.E1 = fitted_model.mean.value
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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, step=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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peaks = np.unique(self.px[0])
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for px in peaks:
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idx = np.where(self.px[0] == px)[0]
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x, y = self.x[0][idx], self.y[0][idx]
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if len(idx) < 400:
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continue
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fitted_model = fit_hist_gaussian(x)
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self.fx.append(fitted_model.mean.value)
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fitted_model = fit_hist_gaussian(y)
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self.fy.append(fitted_model.mean.value)
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self.fz.append(px)
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self.fx = np.array(self.fx)
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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 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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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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data
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"""
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return self.k1 * x + self.k2 * y + self.b
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def predict_px(self, x, y):
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"""Use $Px = \\frac{k_2y - k_1x}{E}L+CL$ to calculate pX.
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Parameters
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----------
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x/y : array
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data
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"""
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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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ax = fig.add_subplot(1, 1, 1)
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ax.scatter(self.x[0], self.y[0], s=0.1, c="black", label=r"$E_1$")
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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.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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),
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)
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ax.plot(
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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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),
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)
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plt.title("Y - X Line")
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plt.xlabel("Output - Left (X) / (channel)")
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plt.ylabel("Output - Right (Y) / (channel)")
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ax.legend()
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fig.savefig(title, facecolor="w", transparent=False, bbox_inches='tight')
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plt.close()
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def draw_peak(self, title):
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fig = plt.figure(figsize=(8, 8))
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peaks = np.unique(self.px[0])
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n, k = len(peaks), 1
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for px in peaks:
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ax = fig.add_subplot(n, 1, k)
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ax.set_title("pX = {:.2f}".format(px))
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idx = np.where(self.px[0] == px)[0]
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x, y = self.x[0][idx], self.y[0][idx]
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count1, center1 = get_hist(x)
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count2, center2 = get_hist(y)
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ax.scatter(center1, count1, s=0.5)
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ax.scatter(center2, count2, s=0.5)
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ax.set_xlabel("Channel")
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ax.set_ylabel("Counts per Channel")
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k += 1
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fig.suptitle("Counts Curve")
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plt.tight_layout()
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fig.savefig(title, facecolor="w", transparent=False)
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plt.close()
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def draw_cluster(self, title):
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fig = plt.figure(figsize=(8, 8))
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ax = fig.add_subplot(1, 1, 1)
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peaks = np.unique(self.px[0])
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for px in peaks:
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idx = np.where(self.px[0] == px)[0]
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x, y = self.x[0][idx], self.y[0][idx]
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ax.scatter(x, y, s=0.1, label="pX={:.0f}".format(px))
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plt.title("Y - X Line")
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plt.xlabel("Output - Left (X) / (channel)")
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plt.ylabel("Output - Right (Y) / (channel)")
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plt.legend()
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plt.tight_layout()
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fig.savefig(title, facecolor="w", transparent=False)
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plt.close()
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@property
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def name(self):
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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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SST = np.sum((y - mean) ** 2)
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SSE = np.sum((y - yp) ** 2)
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return 1 - SSE / SST
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@property
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def RSquare1(self):
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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.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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import numpy as np
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import matplotlib.pyplot as plt
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from .utils import get_hist, GMM_clip
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from .model import Linear1D, FixedSlopeLine, pXLine
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from .fit import fit_line, fit_hist_gaussian
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class Bind(object):
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"""Bind of a Block, using data under two energy conditions for fitting.
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The energy of an ADC device can be expressed as: $E = kx + b$.
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So the complete energy is represented by $E(x,y) = k_1x + b_1 + k_2y + b_2$.
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This formula can be converted to: $y = -\\frac{k_1}{k_2}x + \\frac{E-b_1-b_2}{k_2}$.
|
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Plotting the data in a 2D plane for linear regression, we can get $\\frac{k_1}{k_2}$ and $C_i$
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|
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Using data of $E_1$ and $E_2$, now $k_2 = \\frac{E_1 - E_2}{C_1 - C_2}$.
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|
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The incident position produces different responses in the left and right detectors, which can be expressed as:
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$Px = \\frac{k_2y - k_1x}{E}L+CL$.
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Attributes
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----------
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L/C : float
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parameters in $Px = \\frac{k_2y - k_1x}{E}L+CL$.
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k1, k2: float
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parameters in $Px = \\frac{k_2y - k_1x}{E}L+CL$.
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b : float
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$b = b_1 + b_2$
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K1/K2/C1/C2: float
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$K_i = -\\frac{k_1}{k_2}$
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$C_i = \\frac{E_i-b}{k_2}$
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"""
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def __init__(self, n, m):
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self.n, self.m = n, m
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self.px = [None, None]
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self.x, self.y = [None, None], [None, None]
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self.L, self.C = 40, 0
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self.k1, self.k2, self.b = 0, 0, 0
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self.K1, self.C1, self.K2, self.C2 = 0, 0, 0, 0
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def add_data(self, k, x, y, px):
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"""k-th energy data
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Parameters
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----------
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k : int
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k-th energy
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x : array
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left data
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y : array
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right data
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px : int
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px (set value)
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"""
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self.x[k] = np.concatenate((self.x[k], x)) if self.x[k] is not None else x
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self.y[k] = np.concatenate((self.y[k], y)) if self.y[k] is not None else y
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self.px[k] = (
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np.concatenate((self.px[k], np.full(len(x), px)))
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if self.px[k] is not None
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else np.full(len(x), px)
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)
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def clip(self):
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"""Using Gaussian Mixture Method (GMM) to decompose the data into noise and available data"""
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data = GMM_clip(
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np.array(list(zip(self.x[0], self.y[0], self.px[0])), dtype=object)
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)
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self.x[0] = data[:, 0]
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self.y[0] = data[:, 1]
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self.px[0] = data[:, 2]
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data = GMM_clip(
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np.array(list(zip(self.x[1], self.y[1], self.px[1])), dtype=object)
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)
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self.x[1] = data[:, 0]
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self.y[1] = data[:, 1]
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self.px[1] = data[:, 2]
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def get_line(self):
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"""Fit data with $y = -\\frac{k_1}{k_2}x + \\frac{E-b_1-b_2}{k_2}$."""
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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
|
||||
----------
|
||||
bias : float
|
||||
bias $b = b_1 + b_2$
|
||||
deltaE : float, optional
|
||||
delta energy between two beams
|
||||
"""
|
||||
K = (self.K1 + self.K2) / 2
|
||||
self.K1 = self.K2 = K
|
||||
|
||||
model = FixedSlopeLine(slope=K, intercept=self.C1)
|
||||
fitted_model = fit_line(model, self.x[0], self.y[0])
|
||||
self.C1 = fitted_model.intercept.value
|
||||
|
||||
model = FixedSlopeLine(slope=K, intercept=self.C2)
|
||||
fitted_model = fit_line(model, self.x[1], self.y[1])
|
||||
self.C2 = fitted_model.intercept.value
|
||||
|
||||
self.k2 = deltaE / abs(self.C1 - self.C2)
|
||||
self.k1 = -self.k2 * K
|
||||
|
||||
eng = self.k1 * self.x[0] + self.k2 * self.y[0]
|
||||
fitted_model = fit_hist_gaussian(eng, step=0.01)
|
||||
self.E1 = fitted_model.mean.value
|
||||
|
||||
eng = self.k1 * self.x[1] + self.k2 * self.y[1]
|
||||
fitted_model = fit_hist_gaussian(eng, step=0.01)
|
||||
self.E2 = fitted_model.mean.value
|
||||
|
||||
self.b = bias - self.C1 * self.k2
|
||||
|
||||
self.E1 += self.b
|
||||
self.E2 += self.b
|
||||
|
||||
def get_peak_center(self):
|
||||
"""Get peak center (in channel) using Gaussian Model"""
|
||||
self.fx, self.fy, self.fz = [], [], []
|
||||
|
||||
peaks = np.unique(self.px[0])
|
||||
for px in peaks:
|
||||
idx = np.where(self.px[0] == px)[0]
|
||||
x, y = self.x[0][idx], self.y[0][idx]
|
||||
|
||||
if len(idx) < 400:
|
||||
continue
|
||||
|
||||
fitted_model = fit_hist_gaussian(x)
|
||||
self.fx.append(fitted_model.mean.value)
|
||||
|
||||
fitted_model = fit_hist_gaussian(y)
|
||||
self.fy.append(fitted_model.mean.value)
|
||||
|
||||
self.fz.append(px)
|
||||
|
||||
self.fx = np.array(self.fx)
|
||||
self.fy = np.array(self.fy)
|
||||
self.fz = np.array(self.fz)
|
||||
|
||||
def fit_px(self):
|
||||
"""Fit using $Px = \\frac{k_2y - k_1x}{E}L+CL$."""
|
||||
model = Linear1D()
|
||||
reg = fit_line(model, self.fx, self.fz)
|
||||
|
||||
E = np.mean(self.k1 * self.fx + self.k2 * self.fy + self.b)
|
||||
L = reg.slope.value * E / (self.k2 * self.K1 - self.k1)
|
||||
C = (reg.intercept.value - self.k2 * self.C1 * L / E) / L
|
||||
|
||||
model = pXLine(L=L, C=C, k1=self.k1, k2=self.k2, b=self.b)
|
||||
reg = fit_line(
|
||||
model, np.array(list(zip(self.fx, self.fy)), dtype=object), self.fz
|
||||
)
|
||||
self.L = reg.L.value
|
||||
self.C = reg.C.value
|
||||
|
||||
def predict_energy(self, x, y):
|
||||
"""Use $E(x,y) = k_1x + b_1 + k_2y + b_2$. to calculate energy.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x/y : array
|
||||
data
|
||||
"""
|
||||
return self.k1 * x + self.k2 * y + self.b
|
||||
|
||||
def predict_px(self, x, y):
|
||||
"""Use $Px = \\frac{k_2y - k_1x}{E}L+CL$ to calculate pX.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x/y : array
|
||||
data
|
||||
"""
|
||||
eng = self.predict_energy(x, y)
|
||||
return (self.k2 * y - self.k1 * x) / eng * self.L + self.C * self.L
|
||||
|
||||
def draw_fit_line(self, title):
|
||||
fig = plt.figure(figsize=(8, 8))
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
ax.scatter(self.x[0], self.y[0], s=0.1, c="black", label=r"$E_1$")
|
||||
ax.scatter(self.x[1], self.y[1], s=0.1, c="dimgray", label=r"$E_2$")
|
||||
ax.plot(
|
||||
self.x[0],
|
||||
self.K1 * self.x[0] + self.C1,
|
||||
c="red",
|
||||
label=r"$x_2={:.4f}x_1+{:.4f},\ R^2={:.5f}$".format(
|
||||
self.K1, self.C1, self.RSquare1
|
||||
),
|
||||
)
|
||||
ax.plot(
|
||||
self.x[1],
|
||||
self.K2 * self.x[1] + self.C2,
|
||||
c="orangered",
|
||||
label=r"$x_2={:.4f}x_1+{:.4f},\ R^2={:.5f}$".format(
|
||||
self.K2, self.C2, self.RSquare2
|
||||
),
|
||||
)
|
||||
|
||||
plt.title("Y - X Line")
|
||||
plt.xlabel("Output - Left (X) / (channel)")
|
||||
plt.ylabel("Output - Right (Y) / (channel)")
|
||||
|
||||
ax.legend()
|
||||
fig.savefig(title, facecolor="w", transparent=False, bbox_inches="tight")
|
||||
plt.close()
|
||||
|
||||
def draw_peak(self, title):
|
||||
fig = plt.figure(figsize=(8, 8))
|
||||
|
||||
peaks = np.unique(self.px[0])
|
||||
n, k = len(peaks), 1
|
||||
for px in peaks:
|
||||
ax = fig.add_subplot(n, 1, k)
|
||||
ax.set_title("pX = {:.2f}".format(px))
|
||||
|
||||
idx = np.where(self.px[0] == px)[0]
|
||||
x, y = self.x[0][idx], self.y[0][idx]
|
||||
|
||||
count1, center1 = get_hist(x)
|
||||
count2, center2 = get_hist(y)
|
||||
|
||||
ax.scatter(center1, count1, s=0.5)
|
||||
ax.scatter(center2, count2, s=0.5)
|
||||
|
||||
ax.set_xlabel("Channel")
|
||||
ax.set_ylabel("Counts per Channel")
|
||||
|
||||
k += 1
|
||||
|
||||
fig.suptitle("Counts Curve")
|
||||
plt.tight_layout()
|
||||
fig.savefig(title, facecolor="w", transparent=False)
|
||||
plt.close()
|
||||
|
||||
def draw_cluster(self, title):
|
||||
fig = plt.figure(figsize=(8, 8))
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
|
||||
peaks = np.unique(self.px[0])
|
||||
for px in peaks:
|
||||
idx = np.where(self.px[0] == px)[0]
|
||||
x, y = self.x[0][idx], self.y[0][idx]
|
||||
ax.scatter(x, y, s=0.1, label="pX={:.0f}".format(px))
|
||||
|
||||
plt.title("Y - X Line")
|
||||
plt.xlabel("Output - Left (X) / (channel)")
|
||||
plt.ylabel("Output - Right (Y) / (channel)")
|
||||
|
||||
plt.legend()
|
||||
plt.tight_layout()
|
||||
fig.savefig(title, facecolor="w", transparent=False)
|
||||
plt.close()
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return "{:d}-{:d}-{:.2f}-{:.2f}".format(self.n, self.m, self.E1, self.E2)
|
||||
|
||||
def _r_square(self, y, yp):
|
||||
mean = np.mean(y)
|
||||
SST = np.sum((y - mean) ** 2)
|
||||
SSE = np.sum((y - yp) ** 2)
|
||||
return 1 - SSE / SST
|
||||
|
||||
@property
|
||||
def RSquare1(self):
|
||||
return self._r_square(self.y[0], self.K1 * self.x[0] + self.C1)
|
||||
|
||||
@property
|
||||
def RSquare2(self):
|
||||
return self._r_square(self.y[1], self.K2 * self.x[1] + self.C2)
|
||||
|
||||
def __call__(self, data):
|
||||
"""Data is read to complete initialization"""
|
||||
self.k1 = data[0]
|
||||
self.k2 = data[1]
|
||||
self.b = data[2]
|
||||
self.L = data[3]
|
||||
self.C = data[4]
|
||||
@ -1,172 +1,172 @@
|
||||
import os
|
||||
import csv
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
from .Bind import Bind
|
||||
from .utils import readBlockData, get_hist
|
||||
|
||||
|
||||
class Calibration(object):
|
||||
"""Calibrate the detector according to the calibration data"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(self, file1, file2, bias_b, delta_e, n=6, m=8):
|
||||
"""Calibration
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file1/2 : str
|
||||
data file path of energy 1/2
|
||||
bias_b : float
|
||||
bias $b = b_1 + b_2$, in MeV
|
||||
delta_e : float
|
||||
delta energy between two beams, in MeV
|
||||
n : int, optional
|
||||
number of blocks, default 6
|
||||
m : int, optional
|
||||
number of binds, default 8
|
||||
"""
|
||||
# Initialization
|
||||
self.n, self.m = n, m
|
||||
self.binds = [[Bind(i, j) for j in range(m)] for i in range(n)]
|
||||
|
||||
# Read Data
|
||||
file_list = csv.reader(open(file1, "r"))
|
||||
pbar = tqdm(desc="Read Data E1", total=len(open(file1, "r").readlines()))
|
||||
for row in file_list:
|
||||
pn = int(row[1])
|
||||
ldata, rdata = readBlockData(row[0], int(row[5]), pn, m)
|
||||
for i in range(m):
|
||||
bind = self.binds[pn][i]
|
||||
bind.add_data(0, ldata[i], rdata[i], float(row[4]))
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
file_list = csv.reader(open(file2, "r"))
|
||||
pbar = tqdm(desc="Read Data E2", total=len(open(file2, "r").readlines()))
|
||||
for row in file_list:
|
||||
pn = int(row[1])
|
||||
ldata, rdata = readBlockData(row[0], int(row[5]), pn, m)
|
||||
for i in range(m):
|
||||
bind = self.binds[pn][i]
|
||||
bind.add_data(1, ldata[i], rdata[i], float(row[4]))
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
# Data preprocessing
|
||||
pbar = tqdm(desc="Bind Process", total=n * m)
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
bind: Bind = self.binds[i][j]
|
||||
bind.clip()
|
||||
bind.get_line()
|
||||
bind.get_kb(bias_b, delta_e)
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
# Fit
|
||||
pbar = tqdm(desc="Bind Fit", total=n * m)
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
bind: Bind = self.binds[i][j]
|
||||
bind.get_peak_center()
|
||||
bind.fit_px()
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
def draw_fit(self, path):
|
||||
"""Draw fit result
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str
|
||||
save folder, there must be `FIT-LINE`, `GMM`, and `PEAK` 3 subfolders.
|
||||
"""
|
||||
pbar = tqdm(desc="Draw Fit Figure", total=self.n * self.m)
|
||||
for i in range(self.n):
|
||||
for j in range(self.m):
|
||||
bind: Bind = self.binds[i][j]
|
||||
bind.draw_fit_line(os.path.join(path, "FIT-LINE", bind.name + ".png"))
|
||||
bind.draw_cluster(os.path.join(path, "GMM", bind.name + ".png"))
|
||||
bind.draw_peak(os.path.join(path, "PEAK", bind.name + ".png"))
|
||||
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
def draw_check(self, path="Check.png"):
|
||||
"""Draw check figure
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str, optional
|
||||
save path
|
||||
"""
|
||||
pbar = tqdm(desc="Draw Check Figure", total=self.n * self.m)
|
||||
fig = plt.figure(figsize=(24, 24), dpi=200)
|
||||
ax1 = fig.add_subplot(3, 1, 1)
|
||||
ax2 = fig.add_subplot(3, 1, 2)
|
||||
ax3 = fig.add_subplot(3, 1, 3)
|
||||
peaks = np.array([])
|
||||
|
||||
for i in range(self.n):
|
||||
engs = np.array([])
|
||||
for j in range(self.m):
|
||||
bind = self.binds[i][j]
|
||||
peaks = np.hstack((np.unique(bind.px[0]), peaks))
|
||||
|
||||
eng = bind.predict_energy(bind.x[0], bind.y[0])
|
||||
pX = bind.predict_px(bind.x[0], bind.y[0])
|
||||
count, center = get_hist(pX, step=0.5)
|
||||
engs = np.hstack((engs, eng))
|
||||
|
||||
ax2.scatter(pX, eng, s=0.1, color="k")
|
||||
ax3.scatter(center, count + 3000 * (7 - j), s=0.5, color="k")
|
||||
|
||||
pbar.update(1)
|
||||
count, center = get_hist(engs, step=0.01)
|
||||
ax1.plot(center, count)
|
||||
|
||||
peaks = np.unique(peaks)
|
||||
for x in peaks:
|
||||
ax3.vlines(x, 0, 3000 * self.m, color="gray", linestyles="dashed")
|
||||
for j in range(self.m):
|
||||
ax3.hlines(
|
||||
3000 * j,
|
||||
(np.min(peaks) // 50) * 50,
|
||||
(np.max(peaks) // 50 + 1) * 50,
|
||||
color="r",
|
||||
linestyles="dashdot",
|
||||
)
|
||||
|
||||
ax1.set_xlabel("Energy (MeV)")
|
||||
ax1.set_ylabel("Count per bin")
|
||||
ax2.set_xlabel("x (mm)")
|
||||
ax2.set_ylabel("Energy (MeV)")
|
||||
ax3.set_xlabel("x (mm)")
|
||||
ax3.set_ylabel("Count per bin")
|
||||
|
||||
fig.savefig(path, facecolor="w", transparent=False)
|
||||
plt.close()
|
||||
pbar.close()
|
||||
|
||||
def save(self, path="coef.csv"):
|
||||
"""Save coefficient to file
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str, optional
|
||||
save path
|
||||
"""
|
||||
f = open(path, "w")
|
||||
for i in range(self.n):
|
||||
for j in range(self.m):
|
||||
bind = self.binds[i][j]
|
||||
f.writelines(
|
||||
"{:d},{:d},{:.9f},{:.9f},{:.9f},{:.9f},{:.9f}\n".format(
|
||||
i, j, bind.k1, bind.k2, bind.b, bind.L, bind.C
|
||||
)
|
||||
)
|
||||
import os
|
||||
import csv
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
from .bind import Bind
|
||||
from .utils import readBlockData, get_hist
|
||||
|
||||
|
||||
class Calibration(object):
|
||||
"""Calibrate the detector according to the calibration data"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(self, file1, file2, bias_b, delta_e, n=6, m=8):
|
||||
"""Calibration
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file1/2 : str
|
||||
data file path of energy 1/2
|
||||
bias_b : float
|
||||
bias $b = b_1 + b_2$, in MeV
|
||||
delta_e : float
|
||||
delta energy between two beams, in MeV
|
||||
n : int, optional
|
||||
number of blocks, default 6
|
||||
m : int, optional
|
||||
number of binds, default 8
|
||||
"""
|
||||
# Initialization
|
||||
self.n, self.m = n, m
|
||||
self.binds = [[Bind(i, j) for j in range(m)] for i in range(n)]
|
||||
|
||||
# Read Data
|
||||
file_list = csv.reader(open(file1, "r"))
|
||||
pbar = tqdm(desc="Read Data E1", total=len(open(file1, "r").readlines()))
|
||||
for row in file_list:
|
||||
pn = int(row[1])
|
||||
ldata, rdata = readBlockData(row[0], int(row[3]), pn, m)
|
||||
for i in range(m):
|
||||
bind = self.binds[pn][i]
|
||||
bind.add_data(0, ldata[i], rdata[i], float(row[2]))
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
file_list = csv.reader(open(file2, "r"))
|
||||
pbar = tqdm(desc="Read Data E2", total=len(open(file2, "r").readlines()))
|
||||
for row in file_list:
|
||||
pn = int(row[1])
|
||||
ldata, rdata = readBlockData(row[0], int(row[3]), pn, m)
|
||||
for i in range(m):
|
||||
bind = self.binds[pn][i]
|
||||
bind.add_data(1, ldata[i], rdata[i], float(row[2]))
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
# Data preprocessing
|
||||
pbar = tqdm(desc="Bind Process", total=n * m)
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
bind: Bind = self.binds[i][j]
|
||||
bind.clip()
|
||||
bind.get_line()
|
||||
bind.get_kb(bias_b, delta_e)
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
# Fit
|
||||
pbar = tqdm(desc="Bind Fit", total=n * m)
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
bind: Bind = self.binds[i][j]
|
||||
bind.get_peak_center()
|
||||
bind.fit_px()
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
def draw_fit(self, path):
|
||||
"""Draw fit result
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str
|
||||
save folder, there must be `FIT-LINE`, `GMM`, and `PEAK` 3 subfolders.
|
||||
"""
|
||||
pbar = tqdm(desc="Draw Fit Figure", total=self.n * self.m)
|
||||
for i in range(self.n):
|
||||
for j in range(self.m):
|
||||
bind: Bind = self.binds[i][j]
|
||||
bind.draw_fit_line(os.path.join(path, "FIT-LINE", bind.name + ".png"))
|
||||
bind.draw_cluster(os.path.join(path, "GMM", bind.name + ".png"))
|
||||
bind.draw_peak(os.path.join(path, "PEAK", bind.name + ".png"))
|
||||
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
def draw_check(self, path="Check.png"):
|
||||
"""Draw check figure
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str, optional
|
||||
save path
|
||||
"""
|
||||
pbar = tqdm(desc="Draw Check Figure", total=self.n * self.m)
|
||||
fig = plt.figure(figsize=(24, 24), dpi=200)
|
||||
ax1 = fig.add_subplot(3, 1, 1)
|
||||
ax2 = fig.add_subplot(3, 1, 2)
|
||||
ax3 = fig.add_subplot(3, 1, 3)
|
||||
peaks = np.array([])
|
||||
|
||||
for i in range(self.n):
|
||||
engs = np.array([])
|
||||
for j in range(self.m):
|
||||
bind = self.binds[i][j]
|
||||
peaks = np.hstack((np.unique(bind.px[0]), peaks))
|
||||
|
||||
eng = bind.predict_energy(bind.x[0], bind.y[0])
|
||||
pX = bind.predict_px(bind.x[0], bind.y[0])
|
||||
count, center = get_hist(pX, step=0.5)
|
||||
engs = np.hstack((engs, eng))
|
||||
|
||||
ax2.scatter(pX, eng, s=0.1, color="k")
|
||||
ax3.scatter(center, count + 3000 * (7 - j), s=0.5, color="k")
|
||||
|
||||
pbar.update(1)
|
||||
count, center = get_hist(engs, step=0.01)
|
||||
ax1.plot(center, count)
|
||||
|
||||
peaks = np.unique(peaks)
|
||||
for x in peaks:
|
||||
ax3.vlines(x, 0, 3000 * self.m, color="gray", linestyles="dashed")
|
||||
for j in range(self.m):
|
||||
ax3.hlines(
|
||||
3000 * j,
|
||||
(np.min(peaks) // 50) * 50,
|
||||
(np.max(peaks) // 50 + 1) * 50,
|
||||
color="r",
|
||||
linestyles="dashdot",
|
||||
)
|
||||
|
||||
ax1.set_xlabel("Energy (MeV)")
|
||||
ax1.set_ylabel("Count per bin")
|
||||
ax2.set_xlabel("x (mm)")
|
||||
ax2.set_ylabel("Energy (MeV)")
|
||||
ax3.set_xlabel("x (mm)")
|
||||
ax3.set_ylabel("Count per bin")
|
||||
|
||||
fig.savefig(path, facecolor="w", transparent=False)
|
||||
plt.close()
|
||||
pbar.close()
|
||||
|
||||
def save(self, path="coef.csv"):
|
||||
"""Save coefficient to file
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str, optional
|
||||
save path
|
||||
"""
|
||||
f = open(path, "w")
|
||||
for i in range(self.n):
|
||||
for j in range(self.m):
|
||||
bind = self.binds[i][j]
|
||||
f.writelines(
|
||||
"{:d},{:d},{:.9f},{:.9f},{:.9f},{:.9f},{:.9f}\n".format(
|
||||
i, j, bind.k1, bind.k2, bind.b, bind.L, bind.C
|
||||
)
|
||||
)
|
||||
|
||||
112
qdx/fit.py
112
qdx/fit.py
@ -1,56 +1,56 @@
|
||||
import numpy as np
|
||||
from astropy.modeling import models, fitting
|
||||
|
||||
from .utils import get_hist
|
||||
|
||||
|
||||
def fit_line(model, x, y):
|
||||
"""
|
||||
Line fitting kx+b
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model : astropy.modeling.Model
|
||||
fit model
|
||||
x : array
|
||||
x data, can have two columns representing two independent variables
|
||||
y : array
|
||||
y data
|
||||
|
||||
Returns
|
||||
-------
|
||||
fitted_model : astropy fitting model
|
||||
fitting gaussian model
|
||||
"""
|
||||
fitter = fitting.LevMarLSQFitter()
|
||||
if np.ndim(x) == 1:
|
||||
fitted_model = fitter(model, x, y)
|
||||
else:
|
||||
fitted_model = fitter(model, x[:, 0], x[:, 1], y)
|
||||
|
||||
return fitted_model
|
||||
|
||||
|
||||
def fit_hist_gaussian(x, step=1):
|
||||
"""
|
||||
Gaussian fitting is performed on the histogram
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : array
|
||||
data point
|
||||
step : int, optional
|
||||
Minimum bin width. The bin width is an integer multiple of step.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fitted_model : astropy fitting model
|
||||
fitting gaussian model
|
||||
"""
|
||||
fitter = fitting.LMLSQFitter()
|
||||
|
||||
count, center = get_hist(x, step=step)
|
||||
model = models.Gaussian1D(amplitude=count.max(), mean=x.mean(), stddev=x.std())
|
||||
fitted_model = fitter(model, center, count)
|
||||
|
||||
return fitted_model
|
||||
import numpy as np
|
||||
from astropy.modeling import models, fitting
|
||||
|
||||
from .utils import get_hist
|
||||
|
||||
|
||||
def fit_line(model, x, y):
|
||||
"""
|
||||
Line fitting kx+b
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model : astropy.modeling.Model
|
||||
fit model
|
||||
x : array
|
||||
x data, can have two columns representing two independent variables
|
||||
y : array
|
||||
y data
|
||||
|
||||
Returns
|
||||
-------
|
||||
fitted_model : astropy fitting model
|
||||
fitting gaussian model
|
||||
"""
|
||||
fitter = fitting.LevMarLSQFitter()
|
||||
if np.ndim(x) == 1:
|
||||
fitted_model = fitter(model, x, y)
|
||||
else:
|
||||
fitted_model = fitter(model, x[:, 0], x[:, 1], y)
|
||||
|
||||
return fitted_model
|
||||
|
||||
|
||||
def fit_hist_gaussian(x, step=1):
|
||||
"""
|
||||
Gaussian fitting is performed on the histogram
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : array
|
||||
data point
|
||||
step : int, optional
|
||||
Minimum bin width. The bin width is an integer multiple of step.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fitted_model : astropy fitting model
|
||||
fitting gaussian model
|
||||
"""
|
||||
fitter = fitting.LMLSQFitter()
|
||||
|
||||
count, center = get_hist(x, step=step)
|
||||
model = models.Gaussian1D(amplitude=count.max(), mean=x.mean(), stddev=x.std())
|
||||
fitted_model = fitter(model, center, count)
|
||||
|
||||
return fitted_model
|
||||
|
||||
172
qdx/model.py
172
qdx/model.py
@ -1,86 +1,86 @@
|
||||
import numpy as np
|
||||
from astropy.modeling import Fittable1DModel, Fittable2DModel, Parameter
|
||||
|
||||
|
||||
class Linear1D(Fittable1DModel):
|
||||
"""
|
||||
One dimensional Line model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
slope : float
|
||||
Slope of the straight line
|
||||
intercept : float
|
||||
Intercept of the straight line
|
||||
"""
|
||||
|
||||
slope = Parameter(default=1, description="Slope of the straight line")
|
||||
intercept = Parameter(default=0, description="Intercept of the straight line")
|
||||
|
||||
@staticmethod
|
||||
def evaluate(x, slope, intercept):
|
||||
return slope * x + intercept
|
||||
|
||||
@staticmethod
|
||||
def fit_deriv(x, slope, intercept):
|
||||
d_slope = x
|
||||
d_intercept = np.ones_like(x)
|
||||
return [d_slope, d_intercept]
|
||||
|
||||
|
||||
class FixedSlopeLine(Fittable1DModel):
|
||||
"""
|
||||
Linear model, but fix the slope
|
||||
|
||||
Parameters
|
||||
----------
|
||||
slope : float
|
||||
Slope of the line
|
||||
intercept : float
|
||||
Intercept of the line
|
||||
"""
|
||||
|
||||
slope = Parameter()
|
||||
intercept = Parameter()
|
||||
|
||||
@staticmethod
|
||||
def evaluate(x, slope, intercept):
|
||||
return slope * x + intercept
|
||||
|
||||
@staticmethod
|
||||
def fit_deriv(x, slope, intercept):
|
||||
d_slope = np.zeros_like(x)
|
||||
d_intercept = np.ones_like(x)
|
||||
return [d_slope, d_intercept]
|
||||
|
||||
|
||||
class pXLine(Fittable2DModel):
|
||||
"""
|
||||
pX linear model.
|
||||
$Px = \\frac{k_2y - k_1x}{E}L+CL$.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
L : float
|
||||
Slope of the straight line
|
||||
C : float
|
||||
Intercept / Slope of the straight line
|
||||
"""
|
||||
|
||||
linear = True
|
||||
|
||||
L, C = Parameter(default=40), Parameter(default=0)
|
||||
k1, k2, b = Parameter(), Parameter(), Parameter()
|
||||
|
||||
@staticmethod
|
||||
def evaluate(x, y, L, C, k1, k2, b):
|
||||
E = k1 * x + k2 * y + b
|
||||
return (k2 * y - k1 * x) / E * L + C * L
|
||||
|
||||
@staticmethod
|
||||
def fit_deriv(x, y, L, C, k1, k2, b):
|
||||
E = k1 * x + k2 * y + b
|
||||
d_L = (k2 * y - k1 * x) / E + C
|
||||
d_C = np.full(x.shape, L)
|
||||
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]
|
||||
import numpy as np
|
||||
from astropy.modeling import Fittable1DModel, Fittable2DModel, Parameter
|
||||
|
||||
|
||||
class Linear1D(Fittable1DModel):
|
||||
"""
|
||||
One dimensional Line model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
slope : float
|
||||
Slope of the straight line
|
||||
intercept : float
|
||||
Intercept of the straight line
|
||||
"""
|
||||
|
||||
slope = Parameter(default=1, description="Slope of the straight line")
|
||||
intercept = Parameter(default=0, description="Intercept of the straight line")
|
||||
|
||||
@staticmethod
|
||||
def evaluate(x, slope, intercept):
|
||||
return slope * x + intercept
|
||||
|
||||
@staticmethod
|
||||
def fit_deriv(x, slope, intercept):
|
||||
d_slope = x
|
||||
d_intercept = np.ones_like(x)
|
||||
return [d_slope, d_intercept]
|
||||
|
||||
|
||||
class FixedSlopeLine(Fittable1DModel):
|
||||
"""
|
||||
Linear model, but fix the slope
|
||||
|
||||
Parameters
|
||||
----------
|
||||
slope : float
|
||||
Slope of the line
|
||||
intercept : float
|
||||
Intercept of the line
|
||||
"""
|
||||
|
||||
slope = Parameter()
|
||||
intercept = Parameter()
|
||||
|
||||
@staticmethod
|
||||
def evaluate(x, slope, intercept):
|
||||
return slope * x + intercept
|
||||
|
||||
@staticmethod
|
||||
def fit_deriv(x, slope, intercept):
|
||||
d_slope = np.zeros_like(x)
|
||||
d_intercept = np.ones_like(x)
|
||||
return [d_slope, d_intercept]
|
||||
|
||||
|
||||
class pXLine(Fittable2DModel):
|
||||
"""
|
||||
pX linear model.
|
||||
$Px = \\frac{k_2y - k_1x}{E}L+CL$.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
L : float
|
||||
Slope of the straight line
|
||||
C : float
|
||||
Intercept / Slope of the straight line
|
||||
"""
|
||||
|
||||
linear = True
|
||||
|
||||
L, C = Parameter(default=40), Parameter(default=0)
|
||||
k1, k2, b = Parameter(), Parameter(), Parameter()
|
||||
|
||||
@staticmethod
|
||||
def evaluate(x, y, L, C, k1, k2, b):
|
||||
E = k1 * x + k2 * y + b
|
||||
return (k2 * y - k1 * x) / E * L + C * L
|
||||
|
||||
@staticmethod
|
||||
def fit_deriv(x, y, L, C, k1, k2, b):
|
||||
E = k1 * x + k2 * y + b
|
||||
d_L = (k2 * y - k1 * x) / E + C
|
||||
d_C = np.full(x.shape, L)
|
||||
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]
|
||||
|
||||
262
qdx/process.py
262
qdx/process.py
@ -1,131 +1,131 @@
|
||||
import csv
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
from .Bind import Bind
|
||||
from .fit import fit_line
|
||||
from .model import Linear1D
|
||||
from .utils import readFileData, get_hist
|
||||
|
||||
|
||||
class Process(object):
|
||||
"""Process the experimental data according to the calibration results."""
|
||||
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def __call__(self, coef, task, n=6, m=8):
|
||||
"""Read Process Data
|
||||
coef : str
|
||||
coefficient file
|
||||
task : str
|
||||
task file
|
||||
n : int, optional
|
||||
number of blocks, default 6
|
||||
m : int, optional
|
||||
number of binds, default 8
|
||||
"""
|
||||
# Initialization
|
||||
self.n, self.m = n, m
|
||||
self.binds = [[Bind(i, j) for j in range(m)] for i in range(n)]
|
||||
|
||||
# Read Calibration Data
|
||||
pbar = tqdm(desc="Bind Initialization", total=n * m)
|
||||
data = list(csv.reader(open(coef, "r")))
|
||||
data = np.array(data, dtype=np.float64)
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
bind = self.binds[i][j]
|
||||
bind(data[j + i * m][2:])
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
# Read Data
|
||||
total = len(open(task, "r").readlines()) * n * m
|
||||
file_list = csv.reader(open(task, "r"))
|
||||
|
||||
self.pX = np.array([])
|
||||
self.eng = np.array([])
|
||||
|
||||
pbar = tqdm(desc="Read Data", total=total)
|
||||
for row in file_list:
|
||||
ldata, rdata = readFileData(row[0], n, m)
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
bind = self.binds[i][j]
|
||||
|
||||
x = bind.predict_px(ldata[j + i * m], rdata[j + i * m]) + float(
|
||||
row[1]
|
||||
)
|
||||
e = bind.predict_energy(ldata[j + i * m], rdata[j + i * m])
|
||||
edge_l = 5 + 130 * i + float(row[1]) - 35
|
||||
edge_r = edge_l + 65
|
||||
idx = np.where((x >= edge_l) & (x <= edge_r))[0]
|
||||
|
||||
self.pX = np.hstack((self.pX, x[idx]))
|
||||
self.eng = np.hstack((self.eng, e[idx]))
|
||||
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
def energy_filter(self, lower, upper, sigma=5.0, maxiters=5):
|
||||
"""Fit px - E line and do sigma clip iteratively.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
lower/upper : float
|
||||
Upper and lower bounds on the initial filter
|
||||
sigma: float, optional
|
||||
The number of standard deviations to use for both the lower and upper clipping limit.
|
||||
maxiters: int or None, optional
|
||||
The maximum number of sigma-clipping iterations to perform or None to clip until convergence is achieved.
|
||||
If convergence is achieved prior to maxiters iterations, the clipping iterations will stop.
|
||||
"""
|
||||
model = Linear1D()
|
||||
idx = np.where((self.eng >= lower) & (self.eng <= upper))[0]
|
||||
x, y = self.pX[idx], self.eng[idx]
|
||||
|
||||
for i in range(maxiters):
|
||||
reg = fit_line(model, x, y)
|
||||
err = np.abs(y - reg(x))
|
||||
idx = np.where(err <= sigma * np.std(err))[0]
|
||||
if len(idx) == len(x):
|
||||
break
|
||||
x, y = x[idx], y[idx]
|
||||
|
||||
self.pX_n = x
|
||||
self.eng_n = y
|
||||
self.reg = reg
|
||||
|
||||
def draw_result(self, path="result.png"):
|
||||
"""Draw the processing result
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str, optional
|
||||
save path
|
||||
"""
|
||||
fig = plt.figure(figsize=(24, 12), dpi=200)
|
||||
ax1 = fig.add_subplot(2, 1, 1)
|
||||
ax2 = fig.add_subplot(2, 1, 2)
|
||||
|
||||
count, center = get_hist(self.pX_n, step=0.1)
|
||||
ax1.scatter(self.pX, self.eng, s=0.01, color="black")
|
||||
ax1.scatter(self.pX_n, self.eng_n, s=0.01, color="orange")
|
||||
ax2.step(center, count, where="post", color="k")
|
||||
|
||||
px_min = (np.min(self.pX_n) // 50) * 50
|
||||
px_max = (np.max(self.pX_n) // 50 + 1) * 50
|
||||
px_x = np.linspace(px_min, px_max, int(px_max - px_min))
|
||||
ax1.plot(px_x, self.reg(px_x))
|
||||
|
||||
ax1.set_xticks(np.arange(px_min, px_max, 50))
|
||||
ax2.set_xticks(np.arange(px_min, px_max, 50))
|
||||
ax1.set_xlabel("x (mm)")
|
||||
ax1.set_ylabel("Energy (MeV)")
|
||||
ax2.set_xlabel("x (mm)")
|
||||
ax2.set_ylabel("Count per bin")
|
||||
|
||||
fig.savefig(path, facecolor="w", transparent=False)
|
||||
plt.close()
|
||||
import csv
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
from .bind import Bind
|
||||
from .fit import fit_line
|
||||
from .model import Linear1D
|
||||
from .utils import readFileData, get_hist
|
||||
|
||||
|
||||
class Process(object):
|
||||
"""Process the experimental data according to the calibration results."""
|
||||
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def __call__(self, coef, task, n=6, m=8):
|
||||
"""Read Process Data
|
||||
coef : str
|
||||
coefficient file
|
||||
task : str
|
||||
task file
|
||||
n : int, optional
|
||||
number of blocks, default 6
|
||||
m : int, optional
|
||||
number of binds, default 8
|
||||
"""
|
||||
# Initialization
|
||||
self.n, self.m = n, m
|
||||
self.binds = [[Bind(i, j) for j in range(m)] for i in range(n)]
|
||||
|
||||
# Read Calibration Data
|
||||
pbar = tqdm(desc="Bind Initialization", total=n * m)
|
||||
data = list(csv.reader(open(coef, "r")))
|
||||
data = np.array(data, dtype=np.float64)
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
bind = self.binds[i][j]
|
||||
bind(data[j + i * m][2:])
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
# Read Data
|
||||
total = len(open(task, "r").readlines()) * n * m
|
||||
file_list = csv.reader(open(task, "r"))
|
||||
|
||||
self.pX = np.array([])
|
||||
self.eng = np.array([])
|
||||
|
||||
pbar = tqdm(desc="Read Data", total=total)
|
||||
for row in file_list:
|
||||
ldata, rdata = readFileData(row[0], n, m)
|
||||
for i in range(n):
|
||||
for j in range(m):
|
||||
bind = self.binds[i][j]
|
||||
|
||||
x = bind.predict_px(ldata[j + i * m], rdata[j + i * m]) + float(
|
||||
row[1]
|
||||
)
|
||||
e = bind.predict_energy(ldata[j + i * m], rdata[j + i * m])
|
||||
edge_l = 5 + 130 * i + float(row[1]) - 35
|
||||
edge_r = edge_l + 65
|
||||
idx = np.where((x >= edge_l) & (x <= edge_r))[0]
|
||||
|
||||
self.pX = np.hstack((self.pX, x[idx]))
|
||||
self.eng = np.hstack((self.eng, e[idx]))
|
||||
|
||||
pbar.update(1)
|
||||
pbar.close()
|
||||
|
||||
def energy_filter(self, lower, upper, sigma=5.0, maxiters=5):
|
||||
"""Fit px - E line and do sigma clip iteratively.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
lower/upper : float
|
||||
Upper and lower bounds on the initial filter
|
||||
sigma: float, optional
|
||||
The number of standard deviations to use for both the lower and upper clipping limit.
|
||||
maxiters: int or None, optional
|
||||
The maximum number of sigma-clipping iterations to perform or None to clip until convergence is achieved.
|
||||
If convergence is achieved prior to maxiters iterations, the clipping iterations will stop.
|
||||
"""
|
||||
model = Linear1D()
|
||||
idx = np.where((self.eng >= lower) & (self.eng <= upper))[0]
|
||||
x, y = self.pX[idx], self.eng[idx]
|
||||
|
||||
for i in range(maxiters):
|
||||
reg = fit_line(model, x, y)
|
||||
err = np.abs(y - reg(x))
|
||||
idx = np.where(err <= sigma * np.std(err))[0]
|
||||
if len(idx) == len(x):
|
||||
break
|
||||
x, y = x[idx], y[idx]
|
||||
|
||||
self.pX_n = x
|
||||
self.eng_n = y
|
||||
self.reg = reg
|
||||
|
||||
def draw_result(self, path="result.png"):
|
||||
"""Draw the processing result
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str, optional
|
||||
save path
|
||||
"""
|
||||
fig = plt.figure(figsize=(24, 12), dpi=200)
|
||||
ax1 = fig.add_subplot(2, 1, 1)
|
||||
ax2 = fig.add_subplot(2, 1, 2)
|
||||
|
||||
count, center = get_hist(self.pX_n, step=0.1)
|
||||
ax1.scatter(self.pX, self.eng, s=0.01, color="black")
|
||||
ax1.scatter(self.pX_n, self.eng_n, s=0.01, color="orange")
|
||||
ax2.step(center, count, where="post", color="k")
|
||||
|
||||
px_min = (np.min(self.pX_n) // 50) * 50
|
||||
px_max = (np.max(self.pX_n) // 50 + 1) * 50
|
||||
px_x = np.linspace(px_min, px_max, int(px_max - px_min))
|
||||
ax1.plot(px_x, self.reg(px_x))
|
||||
|
||||
ax1.set_xticks(np.arange(px_min, px_max, 50))
|
||||
ax2.set_xticks(np.arange(px_min, px_max, 50))
|
||||
ax1.set_xlabel("x (mm)")
|
||||
ax1.set_ylabel("Energy (MeV)")
|
||||
ax2.set_xlabel("x (mm)")
|
||||
ax2.set_ylabel("Count per bin")
|
||||
|
||||
fig.savefig(path, facecolor="w", transparent=False)
|
||||
plt.close()
|
||||
|
||||
282
qdx/utils.py
282
qdx/utils.py
@ -1,141 +1,141 @@
|
||||
import uproot
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.mixture import GaussianMixture
|
||||
|
||||
|
||||
def readFileData(file, count, n=6, m=8, minT=800, maxT=4000):
|
||||
"""Read whole data from root file
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file : str
|
||||
root file path
|
||||
count : int
|
||||
count that normalized by counts of Faraday cylinder
|
||||
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")[:count]
|
||||
y = data["adc{:d}ch{:d}".format(na, nc + m)].array(library="np")[:count]
|
||||
idx = np.where((x + y >= minT) & (x + y <= maxT))[0]
|
||||
ldata.append(x[idx])
|
||||
rdata.append(y[idx])
|
||||
|
||||
return ldata, rdata
|
||||
|
||||
|
||||
def readBlockData(file, count, n, m=8, minT=800, maxT=4000):
|
||||
"""Read block data from root file
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file : str
|
||||
root file path
|
||||
count : int
|
||||
count that normalized by counts of Faraday cylinder
|
||||
n : int
|
||||
No.n block
|
||||
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 j in range(m):
|
||||
na = n // 2
|
||||
nc = j + 2 * m * (n % 2)
|
||||
x = data["adc{:d}ch{:d}".format(na, nc)].array(library="np")[:count]
|
||||
y = data["adc{:d}ch{:d}".format(na, nc + m)].array(library="np")[:count]
|
||||
idx = np.where((x + y >= minT) & (x + y <= maxT))[0]
|
||||
ldata.append(x[idx])
|
||||
rdata.append(y[idx])
|
||||
|
||||
return ldata, rdata
|
||||
|
||||
|
||||
def draw_scatter(data, title, s=0.1):
|
||||
"""Draw points using scatter
|
||||
|
||||
Parameters
|
||||
----------
|
||||
s : float, optional
|
||||
size of scatter point, default 0.1
|
||||
"""
|
||||
fig = plt.figure(figsize=(8, 8))
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
for cluster in data:
|
||||
ax.scatter(cluster[:, 0], cluster[:, 1], s=s)
|
||||
fig.savefig(title, facecolor="w", transparent=False)
|
||||
plt.close()
|
||||
|
||||
|
||||
def get_hist(data, step=1, maxN=50, return_edge=False):
|
||||
"""Gets the boundary of histogram that the maximum count is bigger than threshold
|
||||
|
||||
Parameters
|
||||
----------
|
||||
step : int, optional
|
||||
Minimum bin width. The bin width is an integer multiple of step.
|
||||
maxN : int, optional
|
||||
Maximum count threshold
|
||||
return_edge: bool, optional
|
||||
If True, then the bin edges are also returned.
|
||||
"""
|
||||
delta = step
|
||||
edge = np.arange(data.min(), data.max() + 1, delta)
|
||||
count, _ = np.histogram(data, bins=edge)
|
||||
try:
|
||||
while count.max() <= maxN:
|
||||
delta += step
|
||||
edge = np.arange(data.min(), data.max() + 1, delta)
|
||||
count, _ = np.histogram(data, bins=edge)
|
||||
except:
|
||||
edge = np.arange(data.min(), data.max() + 1, step)
|
||||
count, _ = np.histogram(data, bins=edge)
|
||||
|
||||
if return_edge:
|
||||
return count / delta, (edge[1:] + edge[:-1]) / 2, edge
|
||||
else:
|
||||
return count / delta, (edge[1:] + edge[:-1]) / 2
|
||||
|
||||
|
||||
def GMM_clip(data, return_all=False):
|
||||
"""Using Gaussian Mixture Method (GMM) to decompose the data into noise and available data
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : numpy.ndarray
|
||||
Data to be clipped
|
||||
return_all: bool, optional
|
||||
If True, then all data will be returned.
|
||||
"""
|
||||
fit_data = np.array([])
|
||||
|
||||
model = GaussianMixture(n_components=2)
|
||||
model.fit(data[:, :2])
|
||||
|
||||
ny = model.predict(data[:, :2])
|
||||
for i in np.unique(ny):
|
||||
idx = np.where(ny == i)[0]
|
||||
fit_data = idx if len(idx) > len(fit_data) else fit_data
|
||||
|
||||
if return_all:
|
||||
return ny
|
||||
else:
|
||||
return data[fit_data]
|
||||
import uproot
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.mixture import GaussianMixture
|
||||
|
||||
|
||||
def readFileData(file, count, n=6, m=8, minT=800, maxT=4000):
|
||||
"""Read whole data from root file
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file : str
|
||||
root file path
|
||||
count : int
|
||||
count that normalized by counts of Faraday cylinder
|
||||
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")[:count]
|
||||
y = data["adc{:d}ch{:d}".format(na, nc + m)].array(library="np")[:count]
|
||||
idx = np.where((x + y >= minT) & (x + y <= maxT))[0]
|
||||
ldata.append(x[idx])
|
||||
rdata.append(y[idx])
|
||||
|
||||
return ldata, rdata
|
||||
|
||||
|
||||
def readBlockData(file, count, n, m=8, minT=800, maxT=4000):
|
||||
"""Read block data from root file
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file : str
|
||||
root file path
|
||||
count : int
|
||||
count that normalized by counts of Faraday cylinder
|
||||
n : int
|
||||
No.n block
|
||||
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 j in range(m):
|
||||
na = n // 2
|
||||
nc = j + 2 * m * (n % 2)
|
||||
x = data["adc{:d}ch{:d}".format(na, nc)].array(library="np")[:count]
|
||||
y = data["adc{:d}ch{:d}".format(na, nc + m)].array(library="np")[:count]
|
||||
idx = np.where((x + y >= minT) & (x + y <= maxT))[0]
|
||||
ldata.append(x[idx])
|
||||
rdata.append(y[idx])
|
||||
|
||||
return ldata, rdata
|
||||
|
||||
|
||||
def draw_scatter(data, title, s=0.1):
|
||||
"""Draw points using scatter
|
||||
|
||||
Parameters
|
||||
----------
|
||||
s : float, optional
|
||||
size of scatter point, default 0.1
|
||||
"""
|
||||
fig = plt.figure(figsize=(8, 8))
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
for cluster in data:
|
||||
ax.scatter(cluster[:, 0], cluster[:, 1], s=s)
|
||||
fig.savefig(title, facecolor="w", transparent=False)
|
||||
plt.close()
|
||||
|
||||
|
||||
def get_hist(data, step=1, maxN=50, return_edge=False):
|
||||
"""Gets the boundary of histogram that the maximum count is bigger than threshold
|
||||
|
||||
Parameters
|
||||
----------
|
||||
step : int, optional
|
||||
Minimum bin width. The bin width is an integer multiple of step.
|
||||
maxN : int, optional
|
||||
Maximum count threshold
|
||||
return_edge: bool, optional
|
||||
If True, then the bin edges are also returned.
|
||||
"""
|
||||
delta = step
|
||||
edge = np.arange(data.min(), data.max() + 1, delta)
|
||||
count, _ = np.histogram(data, bins=edge)
|
||||
try:
|
||||
while count.max() <= maxN:
|
||||
delta += step
|
||||
edge = np.arange(data.min(), data.max() + 1, delta)
|
||||
count, _ = np.histogram(data, bins=edge)
|
||||
except:
|
||||
edge = np.arange(data.min(), data.max() + 1, step)
|
||||
count, _ = np.histogram(data, bins=edge)
|
||||
|
||||
if return_edge:
|
||||
return count / delta, (edge[1:] + edge[:-1]) / 2, edge
|
||||
else:
|
||||
return count / delta, (edge[1:] + edge[:-1]) / 2
|
||||
|
||||
|
||||
def GMM_clip(data, return_all=False):
|
||||
"""Using Gaussian Mixture Method (GMM) to decompose the data into noise and available data
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : numpy.ndarray
|
||||
Data to be clipped
|
||||
return_all: bool, optional
|
||||
If True, then all data will be returned.
|
||||
"""
|
||||
fit_data = np.array([])
|
||||
|
||||
model = GaussianMixture(n_components=2)
|
||||
model.fit(data[:, :2])
|
||||
|
||||
ny = model.predict(data[:, :2])
|
||||
for i in np.unique(ny):
|
||||
idx = np.where(ny == i)[0]
|
||||
fit_data = idx if len(idx) > len(fit_data) else fit_data
|
||||
|
||||
if return_all:
|
||||
return ny
|
||||
else:
|
||||
return data[fit_data]
|
||||
|
||||
BIN
requirements.txt
BIN
requirements.txt
Binary file not shown.
Loading…
Reference in New Issue
Block a user