diff --git a/PythonScripts/Calibration.py b/PythonScripts/Calibration.py
new file mode 100644
index 0000000000000000000000000000000000000000..1eb7c6b578da84ec8c18ce95d83865c99a379a1a
--- /dev/null
+++ b/PythonScripts/Calibration.py
@@ -0,0 +1,200 @@
+import numpy as np
+from scipy.optimize import curve_fit
+import matplotlib.pyplot as plt
+from matplotlib.offsetbox import AnchoredText
+import sys
+## Performs linear fit for calibration
+
+def FitAndPlot(xp, xm, z1p, z1m, z2p, z2m, z3p, z3m, zAllp, zAllm, Voltage, plots, raw):
+ #Linewidth
+ lw = 0.8
+
+ #
+ if(plots):
+ if(raw):
+ plt.title("Calibration Measurement " + str(Voltage))
+ plt.plot(xp, z1p, marker=".", color="tab:blue", label="z1-", linewidth=lw)
+ plt.plot(xp, z1m, marker=".", color="b", label="z1+", linewidth=lw)
+ plt.plot(xp, z2p, marker=".", color="tab:red", label="z2-", linewidth=lw)
+ plt.plot(xp, z2m, marker=".", color="chocolate", label="z2+", linewidth=lw)
+ plt.plot(xp, z3p, marker=".", color="tab:green", label="z3-", linewidth=lw)
+ plt.plot(xp, z3m, marker=".", color="darkolivegreen", label="z3+", linewidth=lw)
+ plt.plot(xp, zAllp, marker=".", color="tab:purple", label="zAll-", linewidth=lw)
+ plt.plot(xp, zAllm, marker=".", color="m", label="zAll+", linewidth=lw)
+ plt.ylabel(r"Measured Movement [$\mu m$]")
+ plt.xlabel("# of step")
+ plt.legend()
+ plt.grid()
+ plt.savefig("Plots\Calibration\\" + str(Voltage) + "_All_Data.pdf")
+ plt.show()
+
+ # Fit
+ # Errors are 1/sqrt(12) * one pixel length in mikrometers
+ error_z1 = 8.87 / np.sqrt(12)
+ error_z2 = 9.62 / np.sqrt(12)
+ error_z3 = 9.80 / np.sqrt(12)
+ error_zAll = 9.00 / np.sqrt(12)
+
+ popt_z1p, pcov_z1p = curve_fit(linear, xp, z1p, error_z1)
+ popt_z1m, pcov_z1m = curve_fit(linear, xp, z1m, error_z1)
+ popt_z2p, pcov_z2p = curve_fit(linear, xp, z2p, error_z2)
+ popt_z2m, pcov_z2m = curve_fit(linear, xp, z2m, error_z2)
+ popt_z3p, pcov_z3p = curve_fit(linear, xp, z3p, error_z3)
+ popt_z3m, pcov_z3m = curve_fit(linear, xp, z3m, error_z3)
+ popt_zAllp, pcov_zAllp = curve_fit(linear, xp, zAllp, error_zAll)
+ popt_zAllm, pcov_zAllm = curve_fit(linear, xp, zAllm, error_zAll)
+
+ # Chi squared Computation per degree of freedom
+ chi_z1p = np.sum(((z1p - linear(xp, popt_z1p[0]))**2 / error_z1**2)) / (len(z1p) - 1)
+ chi_z1m = np.sum(((z1m - linear(xp, popt_z1m[0]))**2 / error_z1**2)) / (len(z1m) - 1)
+ chi_z2p = np.sum(((z2p - linear(xp, popt_z2p[0]))**2 / error_z2**2)) / (len(z2p) - 1)
+ chi_z2m = np.sum(((z2m - linear(xp, popt_z2m[0]))**2 / error_z2**2)) / (len(z2m) - 1)
+ chi_z3p = np.sum(((z3p - linear(xp, popt_z3p[0]))**2 / error_z3**2)) / (len(z3p) - 1)
+ chi_z3m = np.sum(((z3m - linear(xp, popt_z3m[0]))**2 / error_z3**2)) / (len(z3m) - 1)
+ chi_zAllp = np.sum(((zAllp - linear(xp, popt_zAllp[0]))**2 / error_zAll**2)) / (len(zAllp) - 1)
+ chi_zAllm = np.sum(((zAllm - linear(xp, popt_zAllm[0]))**2 / error_zAll**2)) / (len(zAllm) - 1)
+
+ # Plots
+ fig, ax = plt.subplots(2, 2)
+ fig.set_figheight(9)
+ fig.set_figwidth(16)
+
+ # Z1
+ capsize = 2
+ if(plots):
+ ax[0,0].set_title("Z1 calibration fit " + str(Voltage))
+ ax[0,0].set_xlabel("# of steps")
+ ax[0,0].set_ylabel(r"Measured Movement [$\mu m$]")
+ ax[0,0].errorbar(xp, z1p, error_z1, linestyle="none", marker=".", linewidth=lw, color="tab:blue", label="z1- data", capsize=capsize)
+ ax[0,0].errorbar(xp, z1m, error_z1, linestyle="none", marker=".", linewidth=lw, color="b", label="z1+ data", capsize=capsize)
+ ax[0,0].plot(xp, linear(xp, popt_z1p[0]), linewidth=lw, color="tab:blue", label="z1- fit")
+ ax[0,0].plot(xp, linear(xp, popt_z1m[0]), linewidth=lw, color="b", label="z1+ fit")
+
+ #ax = plt.gca()
+ anchored_text = AnchoredText(r"$b_-$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z1p[0] * 1000, np.sqrt(pcov_z1p[0][0]) * 1000, chi_z1p) + "\n" + "$b_+$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z1m[0] * 1000, np.sqrt(pcov_z1m[0][0]) * 1000, chi_z1m), loc=4)
+ ax[0,0].add_artist(anchored_text)
+
+ ax[0,0].legend()
+ plt.grid()
+ #plt.savefig("Plots\Calibration\Z1_" + str(Voltage) + ".pdf")
+ #plt.show()
+
+ print("slope in nm/step z1-:", popt_z1p[0] * 1000, " +- ", np.sqrt(pcov_z1p[0][0]) * 1000, " chi^2 ", chi_z1p)
+ print("slope in nm/step z1+:", popt_z1m[0] * 1000, " +- ", np.sqrt(pcov_z1m[0][0]) * 1000, " chi^2 ", chi_z1m)
+
+ #Z2
+ if(plots):
+ ax[0,1].set_title("Z2 calibration fit " + str(Voltage))
+ ax[0,1].set_xlabel("# of steps")
+ ax[0,1].set_ylabel(r"Measured Movement [$\mu m$]")
+ ax[0,1].errorbar(xp, z2p, error_z2, linestyle="none", marker=".", linewidth=lw, color="tab:red", label="z2- data", capsize=capsize)
+ ax[0,1].errorbar(xp, z2m, error_z2, linestyle="none", marker=".", linewidth=lw, color="chocolate", label="z2+ data", capsize=capsize)
+ ax[0,1].plot(xp, linear(xp, popt_z2p[0]), linewidth=lw, color="tab:red", label="z2- fit")
+ ax[0,1].plot(xp, linear(xp, popt_z2m[0]), linewidth=lw, color="chocolate", label="z2+ fit")
+
+ #ax = plt.gca()
+ anchored_text = AnchoredText(r"$b_-$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z2p[0] * 1000, np.sqrt(pcov_z2p[0][0]) * 1000, chi_z2p) + "\n" + "$b_+$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z2m[0] * 1000, np.sqrt(pcov_z2m[0][0]) * 1000, chi_z2m), loc=4)
+ ax[0,1].add_artist(anchored_text)
+
+ ax[0,1].legend()
+ plt.grid()
+ #plt.savefig("Plots\Calibration\Z2_" + str(Voltage) + ".pdf")
+ #plt.show()
+
+ print("slope in nm/step z2-:", popt_z2p[0] * 1000, " +- ", np.sqrt(pcov_z2p[0][0]) * 1000, " chi^2 ", chi_z2p)
+ print("slope in nm/step z2+:", popt_z2m[0] * 1000, " +- ", np.sqrt(pcov_z2m[0][0]) * 1000, " chi^2 ", chi_z2m)
+
+ #Z3
+ if(plots):
+ ax[1,0].set_title("Z3 calibration fit " + str(Voltage))
+ ax[1,0].set_xlabel("# of steps")
+ ax[1,0].set_ylabel(r"Measured Movement [$\mu m$]")
+ ax[1,0].errorbar(xp, z3p, error_z3, linestyle="none", marker=".", linewidth=lw, color="tab:green", label="z3- data", capsize=capsize)
+ ax[1,0].errorbar(xp, z3m, error_z3, linestyle="none", marker=".", linewidth=lw, color="darkolivegreen", label="z3+ data", capsize=capsize)
+ ax[1,0].plot(xp, linear(xp, popt_z3p[0]), linewidth=lw, color="tab:green", label="z3- fit")
+ ax[1,0].plot(xp, linear(xp, popt_z3m[0]), linewidth=lw, color="darkolivegreen", label="z3+ fit")
+
+ #ax = plt.gca()
+ anchored_text = AnchoredText(r"$b_-$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z3p[0] * 1000, np.sqrt(pcov_z3p[0][0]) * 1000, chi_z3p) + "\n" + "$b_+$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z3m[0] * 1000, np.sqrt(pcov_z3m[0][0]) * 1000, chi_z3m), loc=4)
+ ax[1,0].add_artist(anchored_text)
+
+ ax[1,0].legend()
+ plt.grid()
+ #plt.savefig("Plots\Calibration\Z3_" + str(Voltage) + ".pdf")
+ #plt.show()
+
+ print("slope in nm/step z3-:", popt_z3p[0] * 1000, " +- ", np.sqrt(pcov_z3p[0][0]) * 1000, " chi^2 ", chi_z3p)
+ print("slope in nm/step z3+:", popt_z3m[0] * 1000, " +- ", np.sqrt(pcov_z3m[0][0]) * 1000, " chi^2 ", chi_z3p)
+
+ #ZALL
+ if(plots):
+ ax[1,1].set_title("ZAll calibration fit " + str(Voltage))
+ ax[1,1].set_xlabel("# of steps")
+ ax[1,1].set_ylabel(r"Measured Movement [$\mu m$]")
+ ax[1,1].errorbar(xp, zAllp, error_zAll, linestyle="none", marker=".", linewidth=lw, color="tab:purple", label="zAll- data", capsize=capsize)
+ ax[1,1].errorbar(xp, zAllm, error_zAll, linestyle="none", marker=".", linewidth=lw, color="m", label="zAll+ data", capsize=capsize)
+ ax[1,1].plot(xp, linear(xp, popt_zAllp[0]), linewidth=lw, color="tab:purple", label="zAll- fit")
+ ax[1,1].plot(xp, linear(xp, popt_zAllm[0]), linewidth=lw, color="m", label="zAll+ fit")
+
+ #ax = plt.gca()
+ anchored_text = AnchoredText(r"$b_-$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_zAllp[0] * 1000, np.sqrt(pcov_zAllp[0][0]) * 1000, chi_zAllp) + "\n" + "$b_+$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_zAllm[0] * 1000, np.sqrt(pcov_zAllm[0][0]) * 1000, chi_zAllm), loc=4)
+ ax[1,1].add_artist(anchored_text)
+
+ ax[1,1].legend()
+ plt.grid()
+ plt.savefig("Plots\Calibration\Z_" + str(Voltage) + ".pdf")
+ plt.savefig("Plots\Calibration\Z_" + str(Voltage) + ".png", dpi=300)
+ plt.show()
+
+ print("slope in nm/step zAll-:", popt_zAllp[0] * 1000, " +- ", np.sqrt(pcov_zAllp[0][0]) * 1000, " chi^2 ", chi_zAllp)
+ print("slope in nm/step zAll+:", popt_zAllm[0] * 1000, " +- ", np.sqrt(pcov_zAllm[0][0]) * 1000, " chi^2 ", chi_zAllp)
+
+def linear(x, b):
+ return b*x
+
+def dataHandling(data, plots, raw):
+ x = data[0]
+ z1p = data[1]
+ z1m = data[2]
+ z2p = data[3]
+ z2m = data[4]
+ z3p = data[5]
+ z3m = data[6]
+ # Check if calibration performed for all otherwise use dummy data.
+ if(len(data) > 7):
+ zAllp = data[7]
+ zAllm = data[8]
+ else:
+ zAllp = np.zeros(len(z1p))
+ zAllm = np.zeros(len(z1m))
+
+ FitAndPlot(x, x, z1p, z1m, z2p, z2m, z3p, z3m, zAllp, zAllm, 85, plots, raw)
+
+def main():
+ plots = True
+ raw = False
+
+ file = "C:\MaskAligner\Data\Calibration.csv"
+
+ # very Simple commandline parser
+ if("--no_plots" in sys.argv):
+ plots = False
+
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if("--file" in sys.argv[i] or "-f" == sys.argv[i]):
+ try:
+ file = sys.argv[i+1]
+ except:
+ print("No file or incorrect file specification.")
+
+ if("--raw_plot" in sys.argv):
+ raw = True
+
+ data = np.genfromtxt(file, skip_header=0, delimiter=",")
+ data = data.T
+
+ dataHandling(data, plots, raw)
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/CalibrationAfterRepair.py b/PythonScripts/CalibrationAfterRepair.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1ea162f3a988f03c8b80a0c3656c0e32f3f0625
--- /dev/null
+++ b/PythonScripts/CalibrationAfterRepair.py
@@ -0,0 +1,307 @@
+import numpy as np
+from scipy.optimize import curve_fit
+import matplotlib.pyplot as plt
+import matplotlib.font_manager as font_manager
+from matplotlib.offsetbox import AnchoredText
+import sys
+from sigfig import round
+
+#plt.rcParams['font.family'] = 'monospace'
+
+class var_with_err:
+ def __init__(self, var, err):
+ self.var = var
+ self.err = err
+
+ def __str__(self):
+ return round(self.var, uncertainty=self.err)
+
+def linear(x, b):
+ return b*x
+
+def FitAndPlot(x, z1p, z1m, z2p, z2m, z3p, z3m, z1p_err, z1m_err, z2p_err, z2m_err, z3p_err, z3m_err, voltage, sigma, plots = True, raw = False, residuals=True):
+
+ font = font_manager.FontProperties(family='DejaVu Sans Mono', style='normal')
+
+ fac = 1e6
+
+ popt_z1p, pcov_z1p = curve_fit(linear, x, z1p, sigma=z1p_err)
+ popt_z1m, pcov_z1m = curve_fit(linear, x, z1m, sigma=z1m_err)
+ popt_z2p, pcov_z2p = curve_fit(linear, x, z2p, sigma=z2p_err)
+ popt_z2m, pcov_z2m = curve_fit(linear, x, z2m, sigma=z2m_err)
+ popt_z3p, pcov_z3p = curve_fit(linear, x, z3p, sigma=z3p_err)
+ popt_z3m, pcov_z3m = curve_fit(linear, x, z3m, sigma=z3m_err)
+
+ step_z1p = var_with_err(popt_z1p[0] * fac, np.sqrt(pcov_z1p[0][0]) * fac)
+ step_z1m = var_with_err(popt_z1m[0] * fac, np.sqrt(pcov_z1m[0][0]) * fac)
+ step_z2p = var_with_err(popt_z2p[0] * fac, np.sqrt(pcov_z2p[0][0]) * fac)
+ step_z2m = var_with_err(popt_z2m[0] * fac, np.sqrt(pcov_z2m[0][0]) * fac)
+ step_z3p = var_with_err(popt_z3p[0] * fac, np.sqrt(pcov_z3p[0][0]) * fac)
+ step_z3m = var_with_err(popt_z3m[0] * fac, np.sqrt(pcov_z3m[0][0]) * fac)
+
+ chi_z1p = np.sum(((z1p - linear(x, popt_z1p[0]))/z1p_err)**2) / len(x - 2)
+ chi_z1m = np.sum(((z1m - linear(x, popt_z1m[0]))/z1m_err)**2) / len(x - 2)
+ chi_z2p = np.sum(((z2p - linear(x, popt_z2p[0]))/z2p_err)**2) / len(x - 2)
+ chi_z2m = np.sum(((z2m - linear(x, popt_z2m[0]))/z2m_err)**2) / len(x - 2)
+ chi_z3p = np.sum(((z3p - linear(x, popt_z3p[0]))/z3p_err)**2) / len(x - 2)
+ chi_z3m = np.sum(((z3m - linear(x, popt_z3m[0]))/z3m_err)**2) / len(x - 2)
+
+ err_z3z2p = popt_z3p[0] * fac / (popt_z2p[0] * fac)
+ err_z3z2m = popt_z3m[0] * fac / (popt_z2m[0] * fac)
+ err_z3z1p = popt_z3p[0] * fac / (popt_z1p[0] * fac)
+ err_z3z1m = popt_z3m[0] * fac / (popt_z1m[0] * fac)
+ err_z2z1p = popt_z2p[0] * fac / (popt_z1p[0] * fac)
+ err_z2z1m = popt_z2m[0] * fac / (popt_z1m[0] * fac)
+
+ print("### Voltage: ", voltage, " ###")
+
+ print("z1p: ", step_z1p, " nm/step ", "chi^2/ndof = ", chi_z1p)
+ print("z1m: ", step_z1m, " nm/step ", "chi^2/ndof = ", chi_z1m)
+ print("z2p: ", step_z2p, " nm/step ", "chi^2/ndof = ", chi_z2p)
+ print("z2m: ", step_z2m, " nm/step ", "chi^2/ndof = ", chi_z2m)
+ print("z3p: ", step_z3p, " nm/step ", "chi^2/ndof = ", chi_z3p)
+ print("z3m: ", step_z3m, " nm/step ", "chi^2/ndof = ", chi_z3m)
+
+ print("error (ratio) + z3/z2: ", popt_z3p[0] * fac / (popt_z2p[0] * fac))
+ print("error (ratio) - z3/z2: ", popt_z3m[0] * fac / (popt_z2m[0] * fac))
+ print("error (ratio) + z3/z1: ", popt_z3p[0] * fac / (popt_z1p[0] * fac))
+ print("error (ratio) - z3/z1: ", popt_z3m[0] * fac / (popt_z1m[0] * fac))
+ print("error (ratio) + z2/z1: ", popt_z2p[0] * fac / (popt_z1p[0] * fac))
+ print("error (ratio) - z2/z1: ", popt_z2m[0] * fac / (popt_z1m[0] * fac))
+
+
+ if plots:
+
+ x_inter = np.arange(min(x), max(x), 1)
+ l_jus = 5
+
+ if not residuals:
+ fig = plt.figure()
+ else:
+ fig, ax = plt.subplots(2,1, sharex=True, gridspec_kw={'height_ratios': [3,1]})
+
+ fig.set_figheight(4.5)
+ fig.set_figwidth(8)
+
+ if residuals:
+
+ ax[0].set_title("Mask Aligner Motors at " + str(voltage) + " V")
+ ax[0].errorbar(x + 80, z1p, z1p_err, marker=".", linestyle="none", capsize=3, label="z1+:".ljust(l_jus ) + str(step_z1p) + " nm/step ", color="tab:blue")
+ ax[0].errorbar(x + 40, z2p, z2p_err, marker=".", linestyle="none", capsize=3, label="z2+:".ljust(l_jus ) + str(step_z2p) + " nm/step ", color="tab:orange")
+ ax[0].errorbar(x - 40, z3p, z3p_err, marker=".", linestyle="none", capsize=3, label="z3+:".ljust(l_jus ) + str(step_z3p) + " nm/step ", color="tab:purple")
+
+ ax[0].plot(x_inter, linear(x_inter, popt_z1p[0]), color="tab:blue")
+ ax[0].plot(x_inter, linear(x_inter, popt_z1m[0]), color="tab:cyan")
+
+ ax[0].plot(x_inter, linear(x_inter, popt_z2p[0]), color="tab:orange")
+ ax[0].plot(x_inter, linear(x_inter, popt_z2m[0]), color="tab:red")
+
+ ax[0].errorbar(x - 80, z1m, z1m_err, marker=".", linestyle="none", capsize=3, label="z1-:".ljust(l_jus ) + str(step_z1m) + " nm/step ", color="tab:cyan")
+ ax[0].errorbar(x + 120, z2m, z2m_err, marker=".", linestyle="none", capsize=3, label="z2-:".ljust(l_jus ) + str(step_z2m) + " nm/step ", color="tab:red")
+ ax[0].errorbar(x, z3m, z3m_err, marker=".", linestyle="none", capsize=3, label="z3-:".ljust(l_jus ) + str(step_z3m) + " nm/step ", color="tab:pink")
+ ax[0].plot(x_inter, linear(x_inter, popt_z3p[0]), color="tab:purple")
+ ax[0].plot(x_inter, linear(x_inter, popt_z3m[0]), color="tab:pink")
+
+ ax[0].set_xlabel("Number of steps driven")
+ ax[0].set_ylabel("Distance travelled [mm]")
+ ax[0].legend(handletextpad=0.1, prop = font)
+ ax[0].grid()
+ plt.tight_layout()
+
+ #ax[1].set_title("residuals")
+ ax[1].errorbar(x + 80, z1p - linear(x, popt_z1p[0]), z1p_err, marker=".", linestyle="none", capsize=3, label="z1+:".ljust(l_jus ) + str(step_z1p) + " nm/step ", color="tab:blue")
+ ax[1].errorbar(x + 40, z2p - linear(x, popt_z2p[0]), z2p_err, marker=".", linestyle="none", capsize=3, label="z2+:".ljust(l_jus ) + str(step_z2p) + " nm/step ", color="tab:orange")
+ ax[1].errorbar(x- 40, z3p - linear(x, popt_z3p[0]), z3p_err, marker=".", linestyle="none", capsize=3, label="z3+:".ljust(l_jus ) + str(step_z3p) + " nm/step ", color="tab:purple")
+
+ ax[1].plot(x_inter, np.zeros(len(x_inter)), color="tab:blue")
+ ax[1].set_ylim(0 - max(np.absolute(z3m - linear(x, popt_z1m[0]))) - max(z3m_err) - 0.05, 0 + max(np.absolute(z3m - linear(x, popt_z1m[0]))) + max(z3m_err)+ 0.05)
+ #ax[1].plot(x_inter, linear(x_inter, popt_z1m[0]), color="tab:cyan")
+
+ #ax[1].plot(x_inter, linear(x_inter, popt_z2p[0]), color="tab:orange")
+ #ax[1].plot(x_inter, linear(x_inter, popt_z2m[0]), color="tab:red")
+
+ ax[1].errorbar(x - 80, z1m - linear(x, popt_z1m[0]), z1m_err, marker=".", linestyle="none", capsize=3, label="z1-:".ljust(l_jus ) + str(step_z1m) + " nm/step ", color="tab:cyan")
+ ax[1].errorbar(x + 120, z2m - linear(x, popt_z1m[0]), z2m_err, marker=".", linestyle="none", capsize=3, label="z2-:".ljust(l_jus ) + str(step_z2m) + " nm/step ", color="tab:red")
+ ax[1].errorbar(x, z3m - linear(x, popt_z1m[0]), z3m_err, marker=".", linestyle="none", capsize=3, label="z3-:".ljust(l_jus ) + str(step_z3m) + " nm/step ", color="tab:pink")
+ #ax[1].plot(x_inter, linear(x_inter, popt_z3p[0]), color="tab:purple")
+ #ax[1].plot(x_inter, linear(x_inter, popt_z3m[0]), color="tab:pink")
+
+ ax[1].set_xlabel("Number of steps driven")
+ ax[1].set_ylabel("Distance travelled [mm]")
+
+ ax[1].grid()
+
+ else:
+
+ plt.title("Mask Aligner Motors at " + str(voltage) + " V")
+ plt.errorbar(x + 80, z1p, z1p_err, marker=".", linestyle="none", capsize=3, label="z1+:".ljust(l_jus ) + str(step_z1p) + " nm/step ", color="tab:blue")
+ plt.errorbar(x + 40, z2p, z2p_err, marker=".", linestyle="none", capsize=3, label="z2+:".ljust(l_jus ) + str(step_z2p) + " nm/step ", color="tab:orange")
+ plt.errorbar(x - 40, z3p, z3p_err, marker=".", linestyle="none", capsize=3, label="z3+:".ljust(l_jus ) + str(step_z3p) + " nm/step ", color="tab:purple")
+
+ plt.plot(x_inter, linear(x_inter, popt_z1p[0]), color="tab:blue")
+ plt.plot(x_inter, linear(x_inter, popt_z1m[0]), color="tab:cyan")
+
+ plt.plot(x_inter, linear(x_inter, popt_z2p[0]), color="tab:orange")
+ plt.plot(x_inter, linear(x_inter, popt_z2m[0]), color="tab:red")
+
+ plt.errorbar(x - 80, z1m, z1m_err, marker=".", linestyle="none", capsize=3, label="z1-:".ljust(l_jus ) + str(step_z1m) + " nm/step ", color="tab:cyan")
+ plt.errorbar(x + 120, z2m, z2m_err, marker=".", linestyle="none", capsize=3, label="z2-:".ljust(l_jus ) + str(step_z2m) + " nm/step ", color="tab:red")
+ plt.errorbar(x, z3m, z3m_err, marker=".", linestyle="none", capsize=3, label="z3-:".ljust(l_jus ) + str(step_z3m) + " nm/step ", color="tab:pink")
+ plt.plot(x_inter, linear(x_inter, popt_z3p[0]), color="tab:purple")
+ plt.plot(x_inter, linear(x_inter, popt_z3m[0]), color="tab:pink")
+
+ plt.xlabel("Number of steps driven")
+ plt.ylabel("Distance travelled [mm]")
+ plt.legend(handletextpad=0.1, prop = font)
+ plt.grid()
+ plt.tight_layout()
+
+ plt.savefig("Plots/CalibrationAfterRepair/" + str(voltage) + "V.pdf")
+ plt.savefig("Plots/CalibrationAfterRepair/" + str(voltage) + "V.png", dpi=300)
+ plt.show()
+
+ return step_z1p, step_z1m, step_z2p, step_z2m, step_z3p, step_z3m, err_z3z2p, err_z3z2m, err_z3z1p, err_z3z1m, err_z2z1p, err_z2z1m
+
+def dataHandling(data, offset, voltage, plots, raw):
+ x = data[0 + offset]
+ z3m = data[1 + offset]
+ z3p = data[2 + offset]
+ z2m = data[3 + offset]
+ z2p = data[4 + offset]
+ z1m = data[5 + offset]
+ z1p = data[6 + offset]
+
+ z3m_err = data[7 + offset]
+ z3p_err = data[8 + offset]
+ z2m_err = data[9 + offset]
+ z2p_err = data[10 + offset]
+ z1m_err = data[11 + offset]
+ z1p_err = data[12 + offset]
+
+ sigma = np.ones(len(x)) * data[0][0]
+
+ #Check for empty data and fill with dummy
+ if np.isnan(z1p).any():
+ z1p = np.zeros(5)
+ if np.isnan(z1m).any():
+ z1m = np.zeros(5)
+ if np.isnan(z2p).any():
+ z2p = np.zeros(5)
+ if np.isnan(z2m).any():
+ z2m = np.zeros(5)
+ if np.isnan(z3p).any():
+ z3p = np.zeros(5)
+ if np.isnan(z3m).any():
+ z3m = np.zeros(5)
+
+ if np.isnan(z1p_err).any():
+ z1p_err = np.ones(5) * sigma
+ if np.isnan(z1m_err).any():
+ z1m_err = np.ones(5) * sigma
+ if np.isnan(z2p_err).any():
+ z2p_err = np.ones(5) * sigma
+ if np.isnan(z2m_err).any():
+ z2m_err = np.ones(5) * sigma
+ if np.isnan(z3p_err).any():
+ z3p_err = np.ones(5) * sigma
+ if np.isnan(z3m_err).any():
+ z3m_err = np.ones(5) * sigma
+
+ step_z1p, step_z1m, step_z2p, step_z2m, step_z3p, step_z3m, err_z3z2p, err_z3z2m, err_z3z1p, err_z3z1m, err_z2z1p, err_z2z1m = FitAndPlot(x, z1p, z1m, z2p, z2m, z3p, z3m, z1p_err, z1m_err, z2p_err, z2m_err, z3p_err, z3m_err, voltage, sigma, plots, raw)
+ return step_z1p, step_z1m, step_z2p, step_z2m, step_z3p, step_z3m, err_z3z2p, err_z3z2m, err_z3z1p, err_z3z1m, err_z2z1p, err_z2z1m
+
+def main():
+ plots = True
+ raw = False
+
+ file = "C:\MaskAligner\Data\Calibration\CalibrationAfterRepair_13_11_23.csv"
+
+ # very Simple commandline parser
+ if("--no_plots" in sys.argv):
+ plots = False
+
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if("--file" in sys.argv[i] or "-f" == sys.argv[i]):
+ try:
+ file = sys.argv[i+1]
+ except:
+ print("No file or incorrect file specification.")
+
+ if("--raw_plot" in sys.argv):
+ raw = True
+
+ data = np.genfromtxt(file, skip_header=1, delimiter=",")
+ data = data.T
+
+ arr = []
+
+ arr.append(dataHandling(data, 43, 70, plots, raw))
+ arr.append(dataHandling(data, 1, 80, plots, raw))
+ arr.append(dataHandling(data, 15, 100, plots, raw))
+ arr.append(dataHandling(data, 29, 120, plots, raw))
+
+ arr = np.array(arr).T
+
+ ## Plot motors errors against one another against voltage
+
+ fig = plt.figure()
+ fig.set_figheight(4.5)
+ fig.set_figwidth(8)
+
+ plt.plot([70, 80, 100, 120], arr[6], label="error z3/z2+", marker=".", linestyle="dashed")
+ plt.plot([70, 80, 100, 120], arr[8], label="error z3/z1+", marker=".", linestyle="dashed")
+ plt.plot([70, 80, 100, 120], arr[10], label="error z2/z1+", marker=".", linestyle="dashed")
+ plt.plot([70, 80, 100, 120], arr[7], label="error z3/z2-", marker=".", linestyle="dashed")
+ plt.plot([70, 80, 100, 120], arr[9], label="error z3/z1-", marker=".", linestyle="dashed")
+ plt.plot([70, 80, 100, 120], arr[11], label="error z2/z1-", marker=".", linestyle="dashed")
+ plt.xlabel("Voltage [V]")
+ plt.ylabel("ratio of motor's stepsizes")
+ plt.legend()
+ plt.tight_layout()
+ plt.savefig("Plots/CalibrationAfterRepair/Errors.pdf")
+ plt.savefig("Plots/CalibrationAfterRepair/Errors.png", dpi=300)
+ plt.show()
+
+ ## Plot stepsizes against voltage
+ fig = plt.figure()
+ fig.set_figheight(4.5)
+ fig.set_figwidth(8)
+
+ plt.errorbar([70, 80, 100, 120], [x.var for x in arr[0]], [x.err for x in arr[0]], label="z1+", marker=".", linestyle="dashed", capsize=3, color="tab:blue")
+ plt.errorbar([70, 80, 100, 120], [x.var for x in arr[1]], [x.err for x in arr[1]], label="z1-", marker=".", linestyle="dashed", capsize=3, color="tab:cyan")
+ plt.errorbar([70, 80, 100, 120], [x.var for x in arr[2]], [x.err for x in arr[2]], label="z2+", marker=".", linestyle="dashed", capsize=3, color="tab:orange")
+ plt.errorbar([70, 80, 100, 120], [x.var for x in arr[3]], [x.err for x in arr[3]], label="z2-", marker=".", linestyle="dashed", capsize=3, color="tab:red")
+ plt.errorbar([70, 80, 100, 120], [x.var for x in arr[4]], [x.err for x in arr[4]], label="z3+", marker=".", linestyle="dashed", capsize=3, color="tab:purple")
+ plt.errorbar([70, 80, 100, 120], [x.var for x in arr[5]], [x.err for x in arr[5]], label="z3-", marker=".", linestyle="dashed", capsize=3, color="tab:pink")
+ plt.xlabel("Voltage [V]")
+ plt.ylabel("stepsize [nm/step]")
+ plt.legend()
+ plt.tight_layout()
+ plt.savefig("Plots/CalibrationAfterRepair/Stepsizes.pdf")
+ plt.savefig("Plots/CalibrationAfterRepair/Stepsizes.png", dpi=300)
+ plt.show()
+
+
+ ## Plot up/down ratio against Voltage
+ z1_ud = var_with_err(np.array([x.var for x in arr[0]])/np.array([x.var for x in arr[1]]), np.sqrt((1/np.array([x.var for x in arr[1]]) * np.array([x.err for x in arr[0]]))**2 + (np.array([x.var for x in arr[0]])/np.array([x.var for x in arr[1]])**2 * np.array([x.err for x in arr[1]]))**2))
+ z2_ud = var_with_err(np.array([x.var for x in arr[2]])/np.array([x.var for x in arr[3]]), np.sqrt((1/np.array([x.var for x in arr[3]]) * np.array([x.err for x in arr[2]]))**2 + (np.array([x.var for x in arr[2]])/np.array([x.var for x in arr[3]])**2 * np.array([x.err for x in arr[3]]))**2))
+ z3_ud = var_with_err(np.array([x.var for x in arr[4]])/np.array([x.var for x in arr[5]]), np.sqrt((1/np.array([x.var for x in arr[5]]) * np.array([x.err for x in arr[4]]))**2 + (np.array([x.var for x in arr[4]])/np.array([x.var for x in arr[5]])**2 * np.array([x.err for x in arr[5]]))**2))
+
+ fig = plt.figure()
+ fig.set_figheight(4.5)
+ fig.set_figwidth(8)
+
+ plt.errorbar([70, 80, 100, 120], z1_ud.var, z1_ud.err, label="z1", marker=".", linestyle="dashed", capsize=3, color="tab:blue")
+ plt.errorbar([70, 80, 100, 120], z2_ud.var, z2_ud.err, label="z2", marker=".", linestyle="dashed", capsize=3, color="tab:cyan")
+ plt.errorbar([70, 80, 100, 120], z3_ud.var, z3_ud.err, label="z3", marker=".", linestyle="dashed", capsize=3, color="tab:orange")
+ plt.xlabel("Voltage [V]")
+ plt.ylabel("ratio approach/retract")
+ plt.legend()
+ plt.tight_layout()
+ plt.savefig("Plots/CalibrationAfterRepair/ratioUD.pdf")
+ plt.savefig("Plots/CalibrationAfterRepair/ratioUD.png", dpi=300)
+ plt.show()
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/CalibrationDragAndDrop.bat b/PythonScripts/CalibrationDragAndDrop.bat
new file mode 100644
index 0000000000000000000000000000000000000000..418a6bde516c75a95372e393ff8445c9fb074653
--- /dev/null
+++ b/PythonScripts/CalibrationDragAndDrop.bat
@@ -0,0 +1,3 @@
+@echo off
+cd /D "%~dp0"
+for %%a in (%*) do py .\Calibration.py --file %%a
diff --git a/PythonScripts/CalibrationTimeScrew.py b/PythonScripts/CalibrationTimeScrew.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3401892781628ec767b4325fe1d718ba69c18ec
--- /dev/null
+++ b/PythonScripts/CalibrationTimeScrew.py
@@ -0,0 +1,77 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+def plottingSingle(rot, zp, zm, err_t = 1, name="z1"):
+ plt.errorbar(rot, zp, err_t, label=name + " down")
+ plt.errorbar(rot, zm, err_t, label=name + " up")
+
+ plt.xlabel("screw rotations")
+ plt.ylabel("t [s]")
+ plt.legend()
+ plt.show()
+
+def plottingAll(rot, z1p, z1m, z2p, z2m, z3p, z3m, err_t = np.sqrt(2)):
+ plt.errorbar(rot, z1p, err_t, label="z1 down")
+ plt.errorbar(rot, z1m, err_t, label="z1 up")
+ plt.errorbar(rot, z2p, err_t, label="z2 down")
+ plt.errorbar(rot, z2m, err_t, label="z2 up")
+ plt.errorbar(rot, z3p, err_t, label="z3 down")
+ plt.errorbar(rot, z3m, err_t, label="z3 up")
+
+ plt.xlabel("screw rotations")
+ plt.ylabel("t [s]")
+ plt.legend()
+ plt.show()
+
+def plottingRatio(rot, z1, z2, z3, err_t = 1):
+ plt.errorbar(rot, z1, err_t, label="z1 up/down")
+ plt.errorbar(rot, z2, err_t, label="z2 up/down")
+ plt.errorbar(rot, z3, err_t, label="z3 up/down")
+
+ plt.xlabel("screw rotations")
+ plt.ylabel("t [s]")
+ plt.legend()
+ plt.show()
+
+def main():
+ if("--no_plots" in sys.argv):
+ plots = False
+
+ plt.style.use('tableau-colorblind10')
+
+ file = ""
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if("--file" in sys.argv[i] or "-f" == sys.argv[i]):
+ try:
+ file = sys.argv[i+1]
+ except:
+ print("No file or incorrect file specification.")
+
+ data = np.genfromtxt(file, delimiter=",", skip_header=1)
+ data = data.T
+ rot = data[0]
+ z1p = data[1]
+ z1m = data[2]
+ z2p = data[3]
+ z2m = data[4]
+ z3p = data[5]
+ z3m = data[6]
+
+ err_t = 1.5
+
+ plottingSingle(rot, z1p, z1m, err_t)
+ plottingSingle(rot, z2p, z2m, err_t, "z2")
+ plottingSingle(rot, z3p, z3m, err_t, "z3")
+
+ plottingAll(rot, z1p, z1m, z2p, z2m, z3p, z3m)
+
+ plottingRatio(rot, z1p/z1m, z2p/z2m, z3p/z3m)
+
+ print(z1p/z1m)
+ print(z2p/z2m)
+ print(z3p/z3m)
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/CalibrationVoltages.py b/PythonScripts/CalibrationVoltages.py
new file mode 100644
index 0000000000000000000000000000000000000000..dad569ab35c5360f7d1f7e879706cf6b58fdc44a
--- /dev/null
+++ b/PythonScripts/CalibrationVoltages.py
@@ -0,0 +1,244 @@
+import numpy as np
+from scipy.optimize import curve_fit
+import matplotlib.pyplot as plt
+from matplotlib.offsetbox import AnchoredText
+import sys
+## Performs linear fit for calibration
+
+def FitAndPlot(xp, xm, z1p, z1m, z2p, z2m, z3p, z3m, zAllp, zAllm, Voltage, plots):
+ #Linewidth
+ lw = 0.8
+
+ #
+ if(plots):
+ plt.title("Calibration Measurement " + str(Voltage) + " V")
+ plt.plot(xp, z1p, marker=".", color="tab:blue", label="z1-", linewidth=lw)
+ plt.plot(xp, z1m, marker=".", color="b", label="z1+", linewidth=lw)
+ plt.plot(xp, z2p, marker=".", color="tab:red", label="z2-", linewidth=lw)
+ plt.plot(xp, z2m, marker=".", color="chocolate", label="z2+", linewidth=lw)
+ plt.plot(xp, z3p, marker=".", color="tab:green", label="z3-", linewidth=lw)
+ plt.plot(xp, z3m, marker=".", color="darkolivegreen", label="z3+", linewidth=lw)
+ plt.plot(xp, zAllp, marker=".", color="tab:purple", label="zAll-", linewidth=lw)
+ plt.plot(xp, zAllm, marker=".", color="m", label="zAll+", linewidth=lw)
+ plt.ylabel(r"Measured Movement [$\mu m$]")
+ plt.xlabel("# of step")
+ plt.legend()
+ plt.grid()
+ plt.savefig("Plots\Calibration\\" + str(Voltage) + "_All_Data.pdf")
+ plt.show()
+
+ # Fit
+ # Errors are 1/sqrt(12) * one pixel length in mikrometers
+ error_z1 = 8.87 / np.sqrt(12)
+ error_z2 = 9.62 / np.sqrt(12)
+ error_z3 = 9.80 / np.sqrt(12)
+ error_zAll = 9.00 / np.sqrt(12)
+
+ popt_z1p, pcov_z1p = curve_fit(linear, xp, z1p, error_z1)
+ popt_z1m, pcov_z1m = curve_fit(linear, xp, z1m, error_z1)
+ popt_z2p, pcov_z2p = curve_fit(linear, xp, z2p, error_z2)
+ popt_z2m, pcov_z2m = curve_fit(linear, xp, z2m, error_z2)
+ popt_z3p, pcov_z3p = curve_fit(linear, xp, z3p, error_z3)
+ popt_z3m, pcov_z3m = curve_fit(linear, xp, z3m, error_z3)
+ popt_zAllp, pcov_zAllp = curve_fit(linear, xp, zAllp, error_zAll)
+ popt_zAllm, pcov_zAllm = curve_fit(linear, xp, zAllm, error_zAll)
+
+ # Chi squared Computation per degree of freedom
+ chi_z1p = np.sum(((z1p - linear(xp, popt_z1p[0]))**2 / error_z1**2)) / (len(z1p) - 1)
+ chi_z1m = np.sum(((z1m - linear(xp, popt_z1m[0]))**2 / error_z1**2)) / (len(z1m) - 1)
+ chi_z2p = np.sum(((z2p - linear(xp, popt_z2p[0]))**2 / error_z2**2)) / (len(z2p) - 1)
+ chi_z2m = np.sum(((z2m - linear(xp, popt_z2m[0]))**2 / error_z2**2)) / (len(z2m) - 1)
+ chi_z3p = np.sum(((z3p - linear(xp, popt_z3p[0]))**2 / error_z3**2)) / (len(z3p) - 1)
+ chi_z3m = np.sum(((z3m - linear(xp, popt_z3m[0]))**2 / error_z3**2)) / (len(z3m) - 1)
+ chi_zAllp = np.sum(((zAllp - linear(xp, popt_zAllp[0]))**2 / error_zAll**2)) / (len(zAllp) - 1)
+ chi_zAllm = np.sum(((zAllm - linear(xp, popt_zAllm[0]))**2 / error_zAll**2)) / (len(zAllm) - 1)
+
+ # Plots
+ fig, ax = plt.subplots(2, 2)
+ fig.set_figheight(9)
+ fig.set_figwidth(16)
+
+ # Z1
+ capsize = 2
+ if(plots):
+ ax[0,0].set_title("Z1 calibration fit " + str(Voltage) + " V")
+ ax[0,0].set_xlabel("# of steps")
+ ax[0,0].set_ylabel(r"Measured Movement [$\mu m$]")
+ ax[0,0].errorbar(xp, z1p, error_z1, linestyle="none", marker=".", linewidth=lw, color="tab:blue", label="z1- data", capsize=capsize)
+ ax[0,0].errorbar(xp, z1m, error_z1, linestyle="none", marker=".", linewidth=lw, color="b", label="z1+ data", capsize=capsize)
+ ax[0,0].plot(xp, linear(xp, popt_z1p[0]), linewidth=lw, color="tab:blue", label="z1- fit")
+ ax[0,0].plot(xp, linear(xp, popt_z1m[0]), linewidth=lw, color="b", label="z1+ fit")
+
+ #ax = plt.gca()
+ anchored_text = AnchoredText(r"$b_-$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z1p[0] * 1000, np.sqrt(pcov_z1p[0][0]) * 1000, chi_z1p) + "\n" + "$b_+$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z1m[0] * 1000, np.sqrt(pcov_z1m[0][0]) * 1000, chi_z1m), loc=4)
+ ax[0,0].add_artist(anchored_text)
+
+ ax[0,0].legend()
+ plt.grid()
+ #plt.savefig("Plots\Calibration\Z1_" + str(Voltage) + ".pdf")
+ #plt.show()
+
+ print("slope in nm/step z1-:", popt_z1p[0] * 1000, " +- ", np.sqrt(pcov_z1p[0][0]) * 1000, " chi^2 ", chi_z1p)
+ print("slope in nm/step z1+:", popt_z1m[0] * 1000, " +- ", np.sqrt(pcov_z1m[0][0]) * 1000, " chi^2 ", chi_z1m)
+
+ #Z2
+ if(plots):
+ ax[0,1].set_title("Z2 calibration fit " + str(Voltage) + " V")
+ ax[0,1].set_xlabel("# of steps")
+ ax[0,1].set_ylabel(r"Measured Movement [$\mu m$]")
+ ax[0,1].errorbar(xp, z2p, error_z2, linestyle="none", marker=".", linewidth=lw, color="tab:red", label="z2- data", capsize=capsize)
+ ax[0,1].errorbar(xp, z2m, error_z2, linestyle="none", marker=".", linewidth=lw, color="chocolate", label="z2+ data", capsize=capsize)
+ ax[0,1].plot(xp, linear(xp, popt_z2p[0]), linewidth=lw, color="tab:red", label="z2- fit")
+ ax[0,1].plot(xp, linear(xp, popt_z2m[0]), linewidth=lw, color="chocolate", label="z2+ fit")
+
+ #ax = plt.gca()
+ anchored_text = AnchoredText(r"$b_-$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z2p[0] * 1000, np.sqrt(pcov_z2p[0][0]) * 1000, chi_z2p) + "\n" + "$b_+$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z2m[0] * 1000, np.sqrt(pcov_z2m[0][0]) * 1000, chi_z2m), loc=4)
+ ax[0,1].add_artist(anchored_text)
+
+ ax[0,1].legend()
+ plt.grid()
+ #plt.savefig("Plots\Calibration\Z2_" + str(Voltage) + ".pdf")
+ #plt.show()
+
+ print("slope in nm/step z2-:", popt_z2p[0] * 1000, " +- ", np.sqrt(pcov_z2p[0][0]) * 1000, " chi^2 ", chi_z2p)
+ print("slope in nm/step z2+:", popt_z2m[0] * 1000, " +- ", np.sqrt(pcov_z2m[0][0]) * 1000, " chi^2 ", chi_z2m)
+
+ #Z3
+ if(plots):
+ ax[1,0].set_title("Z3 calibration fit " + str(Voltage) + " V")
+ ax[1,0].set_xlabel("# of steps")
+ ax[1,0].set_ylabel(r"Measured Movement [$\mu m$]")
+ ax[1,0].errorbar(xp, z3p, error_z3, linestyle="none", marker=".", linewidth=lw, color="tab:green", label="z3- data", capsize=capsize)
+ ax[1,0].errorbar(xp, z3m, error_z3, linestyle="none", marker=".", linewidth=lw, color="darkolivegreen", label="z3+ data", capsize=capsize)
+ ax[1,0].plot(xp, linear(xp, popt_z3p[0]), linewidth=lw, color="tab:green", label="z3- fit")
+ ax[1,0].plot(xp, linear(xp, popt_z3m[0]), linewidth=lw, color="darkolivegreen", label="z3+ fit")
+
+ #ax = plt.gca()
+ anchored_text = AnchoredText(r"$b_-$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z3p[0] * 1000, np.sqrt(pcov_z3p[0][0]) * 1000, chi_z3p) + "\n" + "$b_+$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_z3m[0] * 1000, np.sqrt(pcov_z3m[0][0]) * 1000, chi_z3m), loc=4)
+ ax[1,0].add_artist(anchored_text)
+
+ ax[1,0].legend()
+ plt.grid()
+ #plt.savefig("Plots\Calibration\Z3_" + str(Voltage) + ".pdf")
+ #plt.show()
+
+ print("slope in nm/step z3-:", popt_z3p[0] * 1000, " +- ", np.sqrt(pcov_z3p[0][0]) * 1000, " chi^2 ", chi_z3p)
+ print("slope in nm/step z3+:", popt_z3m[0] * 1000, " +- ", np.sqrt(pcov_z3m[0][0]) * 1000, " chi^2 ", chi_z3p)
+
+ #ZALL
+ if(plots):
+ ax[1,1].set_title("ZAll calibration fit " + str(Voltage) + " V")
+ ax[1,1].set_xlabel("# of steps")
+ ax[1,1].set_ylabel(r"Measured Movement [$\mu m$]")
+ ax[1,1].errorbar(xp, zAllp, error_zAll, linestyle="none", marker=".", linewidth=lw, color="tab:purple", label="zAll- data", capsize=capsize)
+ ax[1,1].errorbar(xp, zAllm, error_zAll, linestyle="none", marker=".", linewidth=lw, color="m", label="zAll+ data", capsize=capsize)
+ ax[1,1].plot(xp, linear(xp, popt_zAllp[0]), linewidth=lw, color="tab:purple", label="zAll- fit")
+ ax[1,1].plot(xp, linear(xp, popt_zAllm[0]), linewidth=lw, color="m", label="zAll+ fit")
+
+ #ax = plt.gca()
+ anchored_text = AnchoredText(r"$b_-$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_zAllp[0] * 1000, np.sqrt(pcov_zAllp[0][0]) * 1000, chi_zAllp) + "\n" + "$b_+$= {:.2f} $\pm$ {:.2f} $n m$/step, $\chi^2/ndof =${:.2f}".format(popt_zAllm[0] * 1000, np.sqrt(pcov_zAllm[0][0]) * 1000, chi_zAllm), loc=4)
+ ax[1,1].add_artist(anchored_text)
+
+ ax[1,1].legend()
+ plt.grid()
+ plt.savefig("Plots\Calibration\Z_" + str(Voltage) + ".pdf")
+ plt.savefig("Plots\Calibration\Z_" + str(Voltage) + ".png", dpi=300)
+ plt.show()
+
+ print("slope in nm/step zAll-:", popt_zAllp[0] * 1000, " +- ", np.sqrt(pcov_zAllp[0][0]) * 1000, " chi^2 ", chi_zAllp)
+ print("slope in nm/step zAll+:", popt_zAllm[0] * 1000, " +- ", np.sqrt(pcov_zAllm[0][0]) * 1000, " chi^2 ", chi_zAllp)
+
+def linear(x, b):
+ return b*x
+
+def Volt85(data, plots):
+ print("85 V")
+ x = data[0]
+ z1 = data[1]
+ z2 = data[2]
+ z3 = data[3]
+ zAll = data[4]
+
+ xp = x[:6]
+ xm = x[6:12]
+ z1p = z1[:6]
+ z1m = z1[6:12]
+ z2p = z2[:6]
+ z2m = z2[6:12]
+ z3p = z3[:6]
+ z3m = z3[6:12]
+ zAllp = zAll[:6]
+ zAllm = zAll[6:12]
+
+ FitAndPlot(xp, xm, z1p, z1m, z2p, z2m, z3p, z3m, zAllp, zAllm, 85, plots)
+
+def Volt100(data, plots):
+ print("100 V")
+ x = data[0]
+ z1 = data[1]
+ z2 = data[2]
+ z3 = data[3]
+ zAll = data[4]
+
+ xp = x[18:24]
+ xm = x[24:30]
+ z1p = z1[18:24]
+ z1m = z1[24:30]
+ z2p = z2[18:24]
+ z2m = z2[24:30]
+ z3p = z3[18:24]
+ z3m = z3[24:30]
+ zAllp = zAll[18:24]
+ zAllm = zAll[24:30]
+
+ FitAndPlot(xp, xm, z1p, z1m, z2p, z2m, z3p, z3m, zAllp, zAllm, 100, plots)
+
+def Volt125(data, plots):
+ print("125 V")
+ x = data[0]
+ z1 = data[1]
+ z2 = data[2]
+ z3 = data[3]
+ zAll = data[4]
+
+ xp = x[36:42]
+ xm = x[42:48]
+ z1p = z1[36:42]
+ z1m = z1[42:48]
+ z2p = z2[36:42]
+ z2m = z2[42:48]
+ z3p = z3[36:42]
+ z3m = z3[42:48]
+ zAllp = zAll[36:42]
+ zAllm = zAll[42:48]
+
+ FitAndPlot(xp, xm, z1p, z1m, z2p, z2m, z3p, z3m, zAllp, zAllm, 125, plots)
+
+
+def main():
+ plots = True
+
+ file = "C:\MaskAligner\Data\Calibration.csv"
+
+ # very Simple commandline parser
+ if("--no_plots" in sys.argv):
+ plots = False
+
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if("--file" in sys.argv[i] or "-f" == sys.argv[i]):
+ try:
+ file = sys.argv[i+1]
+ except:
+ print("No file or incorrect file specification.")
+
+
+ data = np.genfromtxt(file, skip_header=1, usecols=[1,2,3,4,5], delimiter=",")
+ data = data.T
+
+ Volt85(data, plots)
+ Volt100(data, plots)
+ Volt125(data, plots)
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/CheckScrewUpDownZ3Z2.py b/PythonScripts/CheckScrewUpDownZ3Z2.py
new file mode 100644
index 0000000000000000000000000000000000000000..df301ea1c34d0fb1bae66f67fe779f406d63ad31
--- /dev/null
+++ b/PythonScripts/CheckScrewUpDownZ3Z2.py
@@ -0,0 +1,195 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+def main():
+ if("--no_plots" in sys.argv):
+ plots = False
+
+ #plt.style.use('tableau-colorblind10')
+
+ file = "C:/MaskAligner/Data/CalibrationScrew/Calib_04_10_23.txt"
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if("--file" in sys.argv[i] or "-f" == sys.argv[i]):
+ try:
+ file = sys.argv[i+1]
+ except:
+ print("No file or incorrect file specification.")
+
+ factor = 9000
+ data = np.genfromtxt(file, delimiter="\t", skip_header=1)
+ data = data.T
+ rot = data[0]
+ z3m = data[1] / factor * 10**6
+ z3p = data[2] / factor * 10**6
+ z2m = data[3] / factor * 10**6
+ z2p = data[4] / factor * 10**6
+
+ z3mu = data[11] / factor * 10**6
+ z3md = data[12] / factor * 10**6
+ z3pu = data[13] / factor * 10**6
+ z3pd = data[14] / factor * 10**6
+
+ z3left01 = data[23]
+ z3left02 = data[24]
+ z3left03 = data[25]
+ z3mid = data[26]
+ z3right = data[27]
+ z3right02 = data[28]
+
+ z3left01_c = data[30]
+ z3left02_c = data[31]
+ z3mid_c = data[32]
+ z3right_c = data[33]
+ z3right02_c = data[34]
+
+ rot_d = data[37]
+ z3m_d = data[38] / factor * 10**6
+ z3p_d = data[40] / factor * 10**6
+ z2m_d = data[39] / factor * 10**6
+ z2p_d = data[41] / factor * 10**6
+
+ rot_d = rot_d[~np.isnan(rot_d)]
+ z3m_d = z3m_d[~np.isnan(z3m_d)]
+ z3p_d = z3p_d[~np.isnan(z3p_d)]
+ z2m_d = z2m_d[~np.isnan(z2m_d)]
+ z2p_d = z2p_d[~np.isnan(z2p_d)]
+
+ rot_dp = data[43]
+ z3_dp_m = data[44] / factor * 10**6
+ z3_dp_p = data[45] / factor * 10**6
+
+ z3AngleX = [z3left01[0], z3left02[0], z3left03[0], z3mid[0], z3right[0], z3right02[0]]
+ z3AngleX_copper = [z3left01_c[0], z3left02_c[0], z3mid_c[0], z3right_c[0], z3right02_c[0]]
+
+ z_err = 0.04 / factor * 10**6
+
+ z3p_vars = []
+ z3m_vars = []
+
+ rot_z3 = rot
+
+ for i in range(1):
+ z3p_vars.append(np.roll(z3p, i))
+ z3m_vars.append(np.roll(z3m, i))
+
+ for i in range(len(z3p_vars)):
+ plt.errorbar(rot_z3, z3p_vars[i], z_err, label="z3+ " + str(i), marker=".", capsize=3)
+ plt.errorbar(rot_z3, z3m_vars[i], z_err, label="z3- " + str(i), marker=".", capsize=3)
+ plt.errorbar(rot, z2p, z_err, label="z2+", marker=".", capsize=3)
+ plt.errorbar(rot, z2m, z_err, label="z2-", marker=".", capsize=3)
+ plt.xlabel("screw rotations from arbitrary 0. Larger means less firm screw.")
+ plt.ylabel("stepsize [nm]")
+
+ plt.legend()
+ plt.savefig("Plots/Z3Examine/ScrewRot.png", dpi=300)
+ plt.savefig("Plots/Z3Examine/ScrewRot.pdf", dpi=300)
+ plt.show()
+
+ plt.plot(rot, z2p/z2m, label="z2 ratio", marker=".")
+ plt.plot(rot, z3p/z3m, label="z3 ratio", marker=".")
+ plt.xlabel("screw rotations from arbitrary 0. Larger means less firm screw.")
+ plt.ylabel("ration up/down")
+ plt.legend()
+ plt.savefig("Plots/Z3Examine/UpDownRatio.png", dpi=300)
+ plt.savefig("Plots/Z3Examine/UpDownRatio.pdf", dpi=300)
+ plt.show()
+
+ plt.errorbar(1 + 0.1, np.nanmean(z3mu), np.nanstd(z3mu, ddof=1), label="wobble up", capsize=3, marker=".")
+ plt.errorbar(-1 - 0.1, np.nanmean(z3pu), np.nanstd(z3pu, ddof=1), label="wobble up", capsize=3, marker=".")
+ plt.errorbar(1 - 0.1, np.nanmean(z3md), np.nanstd(z3md, ddof=1), label="wobble down", capsize=3, marker=".")
+ plt.errorbar(-1 + 0.1, np.nanmean(z3pd), np.nanstd(z3pd, ddof=1), label="wobble down", capsize=3, marker=".")
+ plt.xlabel("movement direction -1 = approach / +1 = retract")
+ plt.ylabel("stepsize [nm]")
+ plt.legend()
+ plt.savefig("Plots/Z3Examine/ScrewWobble.png", dpi=300)
+ plt.savefig("Plots/Z3Examine/ScrewWobble.pdf", dpi=300)
+ plt.show()
+
+ z3AngleY = np.array([np.nanmean(z3left01[1:]), np.nanmean(z3left02[1:]), np.nanmean(z3left03[1:]), np.nanmean(z3mid[1:]), np.nanmean(z3right[1:]), np.nanmean(z3right02[1:])]) / factor * 10**6
+ z3AngleY_err = np.array([np.nanstd(z3left01[1:], ddof=1), np.nanstd(z3left02[1:], ddof=1), np.nanstd(z3left03[1:], ddof=1), np.nanstd(z3mid[1:], ddof=1), np.nanstd(z3right[1:], ddof=1), np.nanstd(z3right02[1:], ddof=1)]) / factor * 10**6
+
+ z3AngleY_copper = np.array([np.nanmean(z3left01_c[1:]), np.nanmean(z3left02_c[1:]), np.nanmean(z3mid_c[1:]), np.nanmean(z3right_c[1:]), np.nanmean(z3right02_c[1:])]) / factor * 10**6
+ z3AngleY_err_copper = np.array([np.nanstd(z3left01_c[1:], ddof=1), np.nanstd(z3left02_c[1:], ddof=1), np.nanstd(z3mid_c[1:], ddof=1), np.nanstd(z3right_c[1:], ddof=1), np.nanstd(z3right02_c[1:], ddof=1)]) / factor * 10**6
+
+ plt.errorbar(z3AngleX, z3AngleY, z3AngleY_err, capsize=3, marker=".", linestyle="none", label="w/o copper wire")
+ plt.errorbar(z3AngleX_copper, z3AngleY_copper, z3AngleY_err_copper, capsize=3, marker=".", linestyle="none", label="copper wire")
+ plt.xlabel("rotation of front piezo plate [°]")
+ plt.ylabel("stepsize [nm]")
+ plt.legend()
+ plt.savefig("Plots/Z3Examine/FrontPlateAngle.png", dpi=300)
+ plt.savefig("Plots/Z3Examine/FrontPlateAngle.pdf", dpi=300)
+ plt.show()
+
+ plt.errorbar(rot_d, z3p_d, z_err, label="z3+ ", marker=".", capsize=3, color="tab:blue")
+ plt.errorbar(rot_d, z3m_d, z_err, label="z3- ", marker=".", capsize=3, color="tab:orange")
+ plt.errorbar(rot_d, z2p_d, z_err, label="z2+", marker=".", capsize=3, color="tab:green")
+ plt.errorbar(rot_d, z2m_d, z_err, label="z2-", marker=".", capsize=3, color="tab:red")
+
+ #plt.errorbar(rot_z3[:3], z3p_vars[0][:3], z_err, label="z3+ " + str(i), marker=".", capsize=3, color="blue")
+ #plt.errorbar(rot_z3[:3], z3m_vars[0][:3], z_err, label="z3- " + str(i), marker=".", capsize=3, color="orange")
+ #plt.errorbar(rot[:3], z2p[:3], z_err, label="z2+", marker=".", capsize=3, color="green")
+ #plt.errorbar(rot[:3], z2m[:3], z_err, label="z2-", marker=".", capsize=3, color="red")
+
+ plt.xlabel("screw rotations from arbitrary 0. Larger means less firm screw.")
+ plt.ylabel("stepsize [nm]")
+ plt.legend()
+ plt.savefig("Plots/Z3Examine/ScrewRot_detail.png", dpi=300)
+ plt.savefig("Plots/Z3Examine/ScrewRot_detail.pdf", dpi=300)
+ plt.show()
+
+ plt.errorbar(rot_z3, z3p_vars[0], z_err, label="z3+ ", marker=".", capsize=3)
+ plt.errorbar(rot_z3, z3m_vars[0], z_err, label="z3- ", marker=".", capsize=3)
+ plt.errorbar(-rot_dp + 5, z3_dp_p, z_err, label="z3+ with z1 front plate", marker=".", capsize=3)
+ plt.errorbar(-rot_dp + 5, z3_dp_m, z_err, label="z3- with z1 front plate", marker=".", capsize=3)
+ plt.errorbar(rot, z2p, z_err, label="z2+", marker=".", capsize=3)
+ plt.errorbar(rot, z2m, z_err, label="z2-", marker=".", capsize=3)
+ plt.xlabel("screw rotations from arbitrary 0. Larger means less firm screw.")
+ plt.ylabel("stepsize [nm]")
+
+ data_2 = np.genfromtxt("C:/MaskAligner/Data/CalibrationScrew/31_10_23.csv", skip_header=1, delimiter=",")
+ data_2 = data_2.T
+ x = data_2[0]
+ y_up = data_2[1] / factor * 10**6
+ y_down = data_2[2] / factor * 10**6
+ y_err = 0.04 / factor * 10**6
+ y_err_2 = 0.1 / factor * 10**6
+
+ data_3 = np.genfromtxt("C:/MaskAligner/Data/CalibrationScrew/NewPlate_03_11_23.csv", skip_header=1, delimiter=",")
+ print(data_3)
+ data_3 = data_3.T
+ x_2 = data_3[0]
+ y_up_2 = data_3[1] / factor * 10**6
+ y_down_2 = data_3[2] / factor * 10**6
+ y_err_2 = data_3[3] / factor * 10**6
+ y_err_2_2 = data_3[4] / factor * 10**6
+
+ plt.errorbar(x, y_up, z_err, label="z3+ with new plate", marker=".", capsize=3)
+ plt.errorbar(x, y_down, z_err, label="z3- with new plate", marker=".", capsize=3)
+
+ plt.legend()
+ plt.savefig("Plots/Z3Examine/ScrewRot_CompareSwappedPlate.png", dpi=300)
+ plt.savefig("Plots/Z3Examine/ScrewRot_CompareSwappedPlate.pdf", dpi=300)
+ plt.show()
+
+ #plt.errorbar(-rot_dp + 5, z3_dp_p, z_err, label="z3+ with z1 front plate", marker=".", capsize=3)
+ #plt.errorbar(-rot_dp + 5, z3_dp_m, z_err, label="z3- with z1 front plate", marker=".", capsize=3)
+ plt.xlabel("screw rotations from arbitrary 0. Larger means less firm screw.")
+ plt.ylabel("stepsize [nm]")
+
+ plt.errorbar(x_2, y_up_2, y_err_2, label="z3+ with new plate 02_11", marker=".", capsize=3)
+ plt.errorbar(x_2, y_down_2, y_err_2_2, label="z3- with new plate 02_11", marker=".", capsize=3)
+ #plt.errorbar(x, y_up, y_err, label="z3+ with new plate", marker=".", capsize=3)
+ #plt.errorbar(x, y_down, y_err, label="z3- with new plate", marker=".", capsize=3)
+ plt.errorbar(rot, z2p, z_err, label="z2+", marker=".", capsize=3)
+ plt.errorbar(rot, z2m, z_err, label="z2-", marker=".", capsize=3)
+
+ plt.legend()
+ plt.savefig("Plots/Z3Examine/ScrewRot_SwappedPlate.png", dpi=300)
+ plt.savefig("Plots/Z3Examine/ScrewRot_SwappedPlate.pdf", dpi=300)
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/PlotApproachAndReversedretract.py b/PythonScripts/PlotApproachAndReversedretract.py
new file mode 100644
index 0000000000000000000000000000000000000000..22dd4fc7fa1f57eaec17cda4f9bba17ae948fb9e
--- /dev/null
+++ b/PythonScripts/PlotApproachAndReversedretract.py
@@ -0,0 +1,161 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def datahandling(data_app):
+ c1_app = data_app[0]
+ c2_app = data_app[1]
+ c3_app = data_app[2]
+
+ c1_app_err = data_app[3]
+ c2_app_err = data_app[4]
+ c3_app_err = data_app[5]
+
+ x = np.arange(0, len(c1_app), 1.0)
+
+ #Throw out mismeasurements, where error is very large and values where c is NaN
+ c1_app_err_mean = np.nanmean(c1_app_err)
+ mask_c1_app = np.where(c1_app_err > c1_app_err_mean * 4, False, True)
+ mask_c1_app_nan = np.where(np.isnan(c1_app), False, True)
+ c2_app_err_mean = np.nanmean(c2_app_err)
+ mask_c2_app = np.where(c2_app_err > c2_app_err_mean * 4, False, True)
+ mask_c2_app_nan = np.where(np.isnan(c2_app), False, True)
+ c3_app_err_mean = np.nanmean(c3_app_err)
+ mask_c3_app = np.where(c3_app_err > c3_app_err_mean * 4, False, True)
+ mask_c3_app_nan = np.where(np.isnan(c3_app), False, True)
+ full_mask = np.logical_and(np.logical_and(np.logical_and(np.logical_and(np.logical_and(mask_c1_app, mask_c2_app), mask_c3_app), mask_c1_app_nan), mask_c2_app_nan), mask_c3_app_nan)
+ c1_app = c1_app[full_mask]
+ c1_app_err = c1_app_err[full_mask]
+ c2_app = c2_app[full_mask]
+ c2_app_err = c2_app_err[full_mask]
+ c3_app = c3_app[full_mask]
+ c3_app_err = c3_app_err[full_mask]
+ x = x[full_mask]
+
+ #Ensure no 0 uncertainties for fitting purposes
+ c1_app_err = np.where(c1_app_err == 0, np.nanmax(c1_app_err), c1_app_err)
+ c2_app_err = np.where(c2_app_err == 0, np.nanmax(c2_app_err), c2_app_err)
+ c3_app_err = np.where(c3_app_err == 0, np.nanmax(c3_app_err), c3_app_err)
+
+ #Ensure no NaN values in errors and capacitances
+
+ c1_app_err = np.where(np.isnan(c1_app_err), np.nanmax(c1_app_err), c1_app_err)
+ c2_app_err = np.where(np.isnan(c2_app_err), np.nanmax(c2_app_err), c2_app_err)
+ c3_app_err = np.where(np.isnan(c3_app_err), np.nanmax(c3_app_err), c3_app_err)
+
+ try:
+ step_s = data_app[12][0]
+ step_s_err = data_app[12][1]
+ except:
+ step_s = 69
+ step_s_err = 1
+ print("No stepsizes specified! Assuming Hard Coded Default!")
+
+ c_diff_1 = np.ones(len(c1_app) - 1)
+ c_diff_2 = np.ones(len(c2_app) - 1)
+ c_diff_3 = np.ones(len(c3_app) - 1)
+
+ c_diff_1_err = np.ones(len(c1_app) - 1)
+ c_diff_2_err = np.ones(len(c2_app) - 1)
+ c_diff_3_err = np.ones(len(c3_app) - 1)
+
+ for i in range(len(c1_app)-1):
+ c_diff_1[i] = c1_app[i+1] - c1_app[i]
+ c_diff_2[i] = c2_app[i+1] - c2_app[i]
+ c_diff_3[i] = c3_app[i+1] - c3_app[i]
+
+ c_diff_1_err[i] = np.sqrt(c1_app_err[i+1]**2 + c1_app_err[i]**2)
+ c_diff_2_err[i] = np.sqrt(c2_app_err[i+1]**2 + c2_app_err[i]**2)
+ c_diff_3_err[i] = np.sqrt(c3_app_err[i+1]**2 + c3_app_err[i]**2)
+
+ return x, step_s, step_s_err, c1_app, c2_app, c3_app, c1_app_err, c2_app_err, c3_app_err, c_diff_1, c_diff_2, c_diff_3, c_diff_1_err, c_diff_2_err, c_diff_3_err
+
+def main():
+ no_err = False
+ no_diff_err = False
+
+ data_app = np.genfromtxt("C:\MaskAligner\Data\ApproachCurve21_11_23_ToFullContact.csv", delimiter=",").T
+ data_retr = np.genfromtxt("C:\MaskAligner\Data\RetractCurve_21_11_23_600.csv", delimiter=",").T
+
+ x, step_s, step_s_err, c1, c2, c3, c1_err, c2_err, c3_err, c_diff_1, c_diff_2, c_diff_3, c_diff_1_err, c_diff_2_err, c_diff_3_err = datahandling(data_app)
+ x_retr, _, _, c1_retr, c2_retr, c3_retr, c1_err_retr, c2_err_retr, c3_err_retr, c_diff_1_retr, c_diff_2_retr, c_diff_3_retr, c_diff_1_err_retr, c_diff_2_err_retr, c_diff_3_err_retr = datahandling(data_retr)
+
+ lw = 0.75
+ ms = 2
+
+ x_retr *= 1.18
+
+ fig, ax = plt.subplots(2, 3)
+ fig.set_figheight(9)
+ fig.set_figwidth(16)
+
+ if not no_err:
+ ax[0,0].errorbar(x, c1, c1_err, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="approach")
+ ax[0,0].errorbar(x_retr, c1_retr[::-1], c1_err_retr[::-1], color="tab:orange", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="retract reversed")
+ else:
+ ax[0,0].plot(x, c1, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,0].set_title("Capacitance 1")
+ ax[0,0].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,0].set_ylabel("Capacitance [pF]")
+
+ if not no_err:
+ ax[0,1].errorbar(x, c2, c2_err, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="approach")
+ ax[0,1].errorbar(x_retr, c2_retr[::-1], c2_err_retr[::-1], color="tab:olive", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="retract reversed")
+ else:
+ ax[0,1].plot(x, c2, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,1].set_title("Capacitance 2")
+ ax[0,1].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,1].set_ylabel("Capacitance [pF]")
+
+ if not no_err:
+ ax[0,2].errorbar(x, c3, c3_err, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="approach")
+ ax[0,2].errorbar(x_retr, c3_retr[::-1], c3_err_retr[::-1], color="tab:cyan", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="retract reversed")
+ else:
+ ax[0,2].plot(x, c3, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,2].set_title("Capacitance 3")
+ ax[0,2].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,2].set_ylabel("Capacitance [pF]")
+
+ if not no_diff_err and not no_err:
+ ax[1,0].errorbar(x[1:], c_diff_1, c_diff_1_err, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="approach")
+ ax[1,0].errorbar(x_retr[1:], c_diff_1_retr[::-1], c_diff_1_err_retr[::-1], color="tab:orange", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="retract reversed")
+ else:
+ ax[1,0].plot(x[1:], c_diff_1, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,0].set_title("Diff Capacitance 1")
+ ax[1,0].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,0].set_ylabel("Capacitance [pF]")
+ #ax[1,0].set_ylim(-0.0025, 0.02)
+
+ if not no_diff_err and not no_err:
+ ax[1,1].errorbar(x[1:], c_diff_2, c_diff_2_err, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="approach")
+ ax[1,1].errorbar(x_retr[1:], c_diff_2_retr[::-1], c_diff_2_err_retr[::-1], color="tab:olive", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="retract reversed")
+ else:
+ ax[1,1].plot(x[1:], c_diff_2, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,1].set_title("Diff Capacitance 2")
+ ax[1,1].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,1].set_ylabel("Capacitance [pF]")
+ #ax[1,1].set_ylim(-0.0015, 0.008)
+
+ if not no_diff_err and not no_err:
+ ax[1,2].errorbar(x[1:], c_diff_3, c_diff_3_err, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="approach")
+ ax[1,2].errorbar(x_retr[1:], c_diff_3_retr[::-1], c_diff_3_err_retr[::-1], color="tab:cyan", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3, label="retract reversed")
+ else:
+ ax[1,2].plot(x[1:], c_diff_3, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,2].set_title("Diff Capacitance 3")
+ ax[1,2].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,2].set_ylabel("Capacitance [pF]")
+ #ax[1,2].set_ylim(-0.002, 0.005)
+
+ ax[0,0].legend()
+ ax[0,1].legend()
+ ax[0,2].legend()
+ ax[1,0].legend()
+ ax[1,1].legend()
+ ax[1,2].legend()
+
+ fig.tight_layout()
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
+
\ No newline at end of file
diff --git a/PythonScripts/PlotApproachCurve.py b/PythonScripts/PlotApproachCurve.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2183a97f3996de5ab32d9567a14828e5fe467da
--- /dev/null
+++ b/PythonScripts/PlotApproachCurve.py
@@ -0,0 +1,302 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 9 14:45:10 2023
+
+@author: Luzifer
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+from matplotlib.offsetbox import AnchoredText
+from scipy.optimize import curve_fit
+
+def func(x, shift, stretch):
+ return shift + x * stretch
+
+def linear(x, a, b, c):
+ return a + x*b + x**2 * c
+
+def main():
+ path = "C:\MaskAligner\Data\ApproachCurve16_08_23.csv"
+ save = ""
+ no_err = False
+ no_diff_err = False
+ correlation = True
+ plot_correlation = True
+
+ #Simple command line parsing
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if(sys.argv[i] == "--file" or sys.argv[i] == "-f"):
+ try:
+ path = sys.argv[i+1]
+ except:
+ print("incorrect file path.")
+ return
+
+ if("--save" in sys.argv or "-s" in sys.argv):
+ for i in range(len(sys.argv)):
+ if(sys.argv[i] == "--save" or sys.argv[i] == "-s"):
+ try:
+ save = sys.argv[i+1]
+ except:
+ print("incorrect save file name.")
+ return
+
+ if("--no_diff_error" in sys.argv):
+ no_diff_err = True
+
+ if("--no_error" in sys.argv):
+ no_err = True
+
+ if("--no_correlation" in sys.argv):
+ correlation = False
+
+ if("--plot_correlation" in sys.argv):
+ plot_correlation = True
+
+ if("--help" in sys.argv or "-h" in sys.argv):
+ print("--file x or -f x: plots the approach curve of file at filepath x. File must be csv, txt and must be formatted: c1, c2, c3, c1_err, c2_err, c3_err, c1_raw, c2_raw, c3_raw, c1_raw_error, c2_raw_error, c3_raw_error, additional_info.")
+ print("--save x or -s x: saves the resulting plot with filename x. If x is left blank it will determine filename from input file. Should come as last command, since otherwise it will interpret following command as filename.")
+ print("--no_diff_err: Omits errorbars on the capacitance differences.")
+ print("--no_err: Omits errorbars on all plots.")
+ print("--no_correlation: skips c1/c2, c1/c3, c2/c3 correlation calculation.")
+ print("--plot_correlation: plots c1 and c2 with correlated other capacitances. Does nothing if --no_correlation is also given.")
+ return
+
+ data = np.genfromtxt(path, delimiter=",")
+ data = data.T
+
+ c1 = data[0]
+ c2 = data[1]
+ c3 = data[2]
+
+ c1_err = data[3]
+ c2_err = data[4]
+ c3_err = data[5]
+
+ x = np.arange(0,len(c1), 1)
+
+ #Throw out mismeasurements, where error is very large and values where c is NaN and where difference to neighbouring values is large
+ c1_err_mean = np.nanmean(c1_err)
+ mask_c1 = np.where(c1_err > c1_err_mean * 4, False, True)
+ mask_c1_nan = np.where(np.isnan(c1), False, True)
+ c2_err_mean = np.nanmean(c2_err)
+ mask_c2 = np.where(c2_err > c2_err_mean * 4, False, True)
+ mask_c2_nan = np.where(np.isnan(c2), False, True)
+ c3_err_mean = np.nanmean(c3_err)
+ mask_c3 = np.where(c3_err > c3_err_mean * 4, False, True)
+ mask_c3_nan = np.where(np.isnan(c3), False, True)
+ c1_dif = np.diff(c1)
+ c2_dif = np.diff(c2)
+ c3_dif = np.diff(c3)
+ full_mask = np.logical_and(np.logical_and(np.logical_and(np.logical_and(np.logical_and(mask_c1, mask_c2), mask_c3), mask_c1_nan), mask_c2_nan), mask_c3_nan)
+ c1 = c1[full_mask]
+ c1_err = c1_err[full_mask]
+ c2 = c2[full_mask]
+ c2_err = c2_err[full_mask]
+ c3 = c3[full_mask]
+ c3_err = c3_err[full_mask]
+ x = x[full_mask]
+
+ #Ensure no 0 uncertainties for fitting purposes
+ c1_err = np.where(c1_err == 0, np.nanmax(c1_err), c1_err)
+ c2_err = np.where(c2_err == 0, np.nanmax(c2_err), c2_err)
+ c3_err = np.where(c3_err == 0, np.nanmax(c3_err), c3_err)
+
+ #Ensure no NaN values in errors and capacitances
+
+ c1_err = np.where(np.isnan(c1_err), np.nanmax(c1_err), c1_err)
+ c2_err = np.where(np.isnan(c2_err), np.nanmax(c2_err), c2_err)
+ c3_err = np.where(np.isnan(c3_err), np.nanmax(c3_err), c3_err)
+
+ try:
+ step_s = data[12][0]
+ step_s_err = data[12][1]
+ except:
+ step_s = 69
+ step_s_err = 1
+ print("No stepsizes specified! Assuming Hard Coded Default!")
+
+ c_diff_1 = np.ones(len(c1) - 1)
+ c_diff_2 = np.ones(len(c2) - 1)
+ c_diff_3 = np.ones(len(c3) - 1)
+
+ c_diff_1_err = np.ones(len(c1) - 1)
+ c_diff_2_err = np.ones(len(c2) - 1)
+ c_diff_3_err = np.ones(len(c3) - 1)
+
+ for i in range(len(c1)-1):
+ c_diff_1[i] = c1[i+1] - c1[i]
+ c_diff_2[i] = c2[i+1] - c2[i]
+ c_diff_3[i] = c3[i+1] - c3[i]
+
+ c_diff_1_err[i] = np.sqrt(c1_err[i+1]**2 + c1_err[i]**2)
+ c_diff_2_err[i] = np.sqrt(c2_err[i+1]**2 + c2_err[i]**2)
+ c_diff_3_err[i] = np.sqrt(c3_err[i+1]**2 + c3_err[i]**2)
+
+ #for i in range(len(c1)-5):
+ # popt_c1, pcov_c1 = curve_fit(linear, x[i:i+5], c1[i:i+5], sigma=c1_err[i:i+5])
+ # popt_c2, pcov_c2 = curve_fit(linear, x[i:i+5], c2[i:i+5], sigma=c2_err[i:i+5])
+ # popt_c3, pcov_c3 = curve_fit(linear, x[i:i+5], c3[i:i+5], sigma=c3_err[i:i+5])
+
+ # c_diff_1[i] = popt_c1[1] + popt_c1[2] * c1[i]
+ # c_diff_2[i] = popt_c2[1] + popt_c2[2] * c2[i]
+ # c_diff_3[i] = popt_c3[1] + popt_c3[2] * c3[i]
+
+ # c_diff_1_err[i] = np.sqrt(pcov_c1[1][1])
+ # c_diff_2_err[i] = np.sqrt(pcov_c2[1][1])
+ # c_diff_3_err[i] = np.sqrt(pcov_c3[1][1])
+
+ del data
+
+ lw = 0.75
+ ms = 2
+
+ fig, ax = plt.subplots(2, 3)
+ fig.set_figheight(9)
+ fig.set_figwidth(16)
+
+ if not no_err:
+ ax[0,0].errorbar(x, c1, c1_err, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3)
+ else:
+ ax[0,0].plot(x, c1, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,0].set_title("Capacitance 1")
+ ax[0,0].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,0].set_ylabel("Capacitance [pF]")
+
+ if not no_err:
+ ax[0,1].errorbar(x, c2, c2_err, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3)
+ else:
+ ax[0,1].plot(x, c2, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,1].set_title("Capacitance 2")
+ ax[0,1].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,1].set_ylabel("Capacitance [pF]")
+
+ if not no_err:
+ ax[0,2].errorbar(x, c3, c3_err, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3)
+ else:
+ ax[0,2].plot(x, c3, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,2].set_title("Capacitance 3")
+ ax[0,2].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,2].set_ylabel("Capacitance [pF]")
+
+ if not no_diff_err and not no_err:
+ ax[1,0].errorbar(x[1:], c_diff_1, c_diff_1_err, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3)
+ else:
+ ax[1,0].plot(x[1:], c_diff_1, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,0].set_title("Diff Capacitance 1")
+ ax[1,0].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,0].set_ylabel("Capacitance [pF]")
+ #ax[1,0].set_ylim(-0.0025, 0.02)
+
+ if not no_diff_err and not no_err:
+ ax[1,1].errorbar(x[1:], c_diff_2, c_diff_2_err, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3)
+ else:
+ ax[1,1].plot(x[1:], c_diff_2, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,1].set_title("Diff Capacitance 2")
+ ax[1,1].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,1].set_ylabel("Capacitance [pF]")
+ #ax[1,1].set_ylim(-0.0015, 0.008)
+
+ if not no_diff_err and not no_err:
+ ax[1,2].errorbar(x[1:], c_diff_3, c_diff_3_err, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw, capsize=3)
+ else:
+ ax[1,2].plot(x[1:], c_diff_3, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,2].set_title("Diff Capacitance 3")
+ ax[1,2].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,2].set_ylabel("Capacitance [pF]")
+ #ax[1,2].set_ylim(-0.002, 0.005)
+
+ fig.tight_layout()
+ plt.show()
+
+ #Determine Savefile name if not already specified
+ if(save == ""):
+ path_parts = []
+ if("\\" in path):
+ path_parts = path.split("\\")
+ elif ("/" in path):
+ path_parts = path.split("/")
+
+ save = "Plots/ApproachCurves/" + path_parts[-1].replace(".csv", "")
+
+ fig.savefig(save + ".pdf")
+ fig.savefig(save + ".png", dpi=300)
+
+ if correlation:
+ #Throw out outlier points
+ c1_err_mean = np.mean(c1_err)
+ mask_c1 = np.where(c1_err > c1_err_mean * 4, False, True)
+ c2_err_mean = np.mean(c2_err)
+ mask_c2 = np.where(c2_err > c2_err_mean * 4, False, True)
+ c3_err_mean = np.mean(c3_err)
+ mask_c3 = np.where(c3_err > c3_err_mean * 4, False, True)
+ full_mask = np.logical_and(np.logical_and(mask_c1, mask_c2), mask_c3)
+ c1 = c1[full_mask]
+ c1_err = c1_err[full_mask]
+ c2 = c2[full_mask]
+ c2_err = c2_err[full_mask]
+ c3 = c3[full_mask]
+ c3_err = c3_err[full_mask]
+ x = x[full_mask]
+
+ #Determine correlation
+ popt_1_2, pcov_1_2 = curve_fit(func, c2, c1, sigma=c2_err)
+ popt_1_3, pcov_1_3 = curve_fit(func, c3, c1, sigma=c3_err)
+ popt_2_3, pcov_2_3 = curve_fit(func, c3, c2, sigma=c3_err)
+
+ chi_1_2 = np.sum(((c1 - func(c2, popt_1_2[0], popt_1_2[1]))**2/(c1_err**2 + (popt_1_2[1] * c2_err)**2))) / len(c1 - 2)
+ chi_1_3 = np.sum(((c1 - func(c3, popt_1_3[0], popt_1_3[1]))**2/(c1_err**2 + (popt_1_3[1] * c3_err)**2))) / len(c1 - 2)
+ chi_2_3 = np.sum(((c2 - func(c3, popt_2_3[0], popt_2_3[1]))**2/(c2_err**2 + (popt_2_3[1] * c3_err)**2))) / len(c2 - 2)
+
+ print("Small chi^2/ndof means high correlation of different capacitances. Careful though! Check plot! Since if data is sufficiently linear they can always be fitted to each other.")
+ print("chi^2/ndof similarity c1/c2: ", chi_1_2)
+ print("chi^2/ndof similarity c1/c3: ", chi_1_3)
+ print("chi^2/ndof similarity c2/c3: ", chi_2_3)
+
+ if(plot_correlation):
+ fig, ax = plt.subplots(2, 2)
+ fig.set_figheight(9)
+ fig.set_figwidth(16)
+
+ c1_max = max(np.absolute(c1))
+ c2_max = max(np.absolute(c2))
+
+ fig.suptitle('Correlation of different capacitances')
+
+ ax[0, 0].errorbar(x, func(c2, popt_1_2[0], popt_1_2[1]) / c1_max, func(c2_err, 0, np.absolute(popt_1_2[1])) / c1_max, label="C2 fit to C1", capsize=3, color="tab:green")
+ ax[0, 0].errorbar(x, func(c3, popt_1_3[0], popt_1_3[1]) / c1_max, func(c3_err, 0, np.absolute(popt_1_3[1])) / c1_max, label="C3 fit to C1", capsize=3, color="tab:blue")
+ ax[0, 0].errorbar(x, c1 / c1_max, c1_err/ c1_max, label="C1", capsize=3, color="tab:red",zorder=10)
+ ax[0, 0].set_xlabel(r"steps")
+ ax[0, 0].set_ylabel(r"$\frac{C}{C_{1,max}}$")
+ ax[0, 0].legend()
+
+ ax[0, 1].errorbar(x, func(c3, popt_2_3[0], popt_2_3[1]) / c2_max, func(c3_err, 0, np.absolute(popt_2_3[1])) / c2_max, label="C3 fit to C2", capsize=3, color="tab:blue")
+ ax[0, 1].errorbar(x, c2 / c2_max, c2_err / c2_max, label="C2", capsize=3, color="tab:green",zorder=10)
+ ax[0, 1].set_xlabel(r"steps")
+ ax[0, 1].set_ylabel(r"$\frac{C}{C_{2,max}}$")
+ ax[0, 1].legend()
+
+ y_lim = max(max(np.absolute(c1 - func(c2, popt_1_2[0], popt_1_2[1])) / c1_max + func(c2_err, 0, np.absolute(popt_1_2[1])) / c1_max) * 1.1, max(np.absolute(c1 - func(c3, popt_1_3[0], popt_1_3[1])) / c1_max + func(c3_err, 0, np.absolute(popt_1_3[1])) / c1_max) * 1.1)
+
+ ax[1, 0].errorbar(x, c1 / c1_max - func(c2, popt_1_2[0], popt_1_2[1]) / c1_max, func(c2_err, 0, np.absolute(popt_1_2[1])) / c1_max, label="C2 fit to C1", capsize=3, color="tab:green")
+ ax[1, 0].errorbar(x, c1 / c1_max - func(c3, popt_1_3[0], popt_1_3[1]) / c1_max, func(c3_err, 0, np.absolute(popt_1_3[1])) / c1_max, label="C3 fit to C1", capsize=3, color="tab:blue")
+ ax[1, 0].plot(x, np.ones(len(c1)) * 0, label="C1", color="tab:red", zorder=10)
+ ax[1, 0].set_ylim(-y_lim, y_lim)
+ ax[1, 0].set_ylabel(r"$\frac{C_1}{C_{1,max}}$ - fit")
+ ax[1, 0].set_xlabel(r"steps")
+ ax[1, 0].legend()
+
+ ax[1, 1].errorbar(x, c2 / c2_max - func(c3, popt_2_3[0], popt_2_3[1]) / c2_max, func(c3_err, 0, np.absolute(popt_2_3[1])) / c2_max, label="C3 fit to C2", capsize=3, color="tab:blue")
+ ax[1, 1].set_ylim(- max(np.absolute(c2 / c2_max - func(c3, popt_2_3[0], popt_2_3[1]) / c2_max + func(c3_err, 0, np.absolute(popt_2_3[1])) / c2_max)) * 1.1, max(np.absolute(c2 / c2_max - func(c3, popt_2_3[0], popt_2_3[1]) / c2_max + func(c3_err, 0, np.absolute(popt_2_3[1])) / c2_max)) * 1.1)
+ ax[1, 1].plot(x, np.ones(len(c2)) * 0, label="C2", color="tab:green",zorder=10)
+ ax[1, 1].set_ylabel(r"$\frac{C_2}{C_{2,max}}$ - fit")
+ ax[1, 1].set_xlabel(r"steps")
+ ax[1, 1].legend()
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/PlotApproachCurveDragAndDrop.bat b/PythonScripts/PlotApproachCurveDragAndDrop.bat
new file mode 100644
index 0000000000000000000000000000000000000000..bd112dd0a33cb025e322af4b2d2ade8ed280fd85
--- /dev/null
+++ b/PythonScripts/PlotApproachCurveDragAndDrop.bat
@@ -0,0 +1,4 @@
+@echo off
+cd /D "%~dp0"
+for %%a in (%*) do py .\PlotApproachCurve.py --file %%a
+pause
\ No newline at end of file
diff --git a/PythonScripts/PlotApproachCurve_old.py b/PythonScripts/PlotApproachCurve_old.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a9c596cef5e6481da91d8540d3e27de511c983f
--- /dev/null
+++ b/PythonScripts/PlotApproachCurve_old.py
@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 9 14:45:10 2023
+
+@author: Luzifer
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+from matplotlib.offsetbox import AnchoredText
+
+def main():
+ path = "C:\MaskAligner\Data\ApproachCurve16_08_23.csv"
+ save = ""
+
+ #Simple command line parsing
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if(sys.argv[i] == "--file" or sys.argv[i] == "--f"):
+ try:
+ path = sys.argv[i+1]
+ except:
+ print("incorrect file path.")
+ return
+
+ if("--save" in sys.argv or "-s" in sys.argv):
+ for i in range(len(sys.argv)):
+ if(sys.argv[i] == "--save" or sys.argv[i] == "--s"):
+ try:
+ save = sys.argv[i+1]
+ except:
+ print("incorrect save file name.")
+ return
+
+ data = np.genfromtxt(path, delimiter=",")
+
+ x = data.T[0]
+ c1 = data.T[1]
+ c2 = data.T[2]
+ c3 = data.T[3]
+
+ step_s = data.T[4][0]
+ step_s_err = data.T[5][0]
+
+
+ c_diff_1 = np.ones(len(c1) - 1)
+ c_diff_2 = np.ones(len(c2) - 1)
+ c_diff_3 = np.ones(len(c2) - 1)
+
+ for i in range(len(c1)-1):
+ c_diff_1[i] = c1[i+1] - c1[i]
+ c_diff_2[i] = c2[i+1] - c2[i]
+ c_diff_3[i] = c3[i+1] - c3[i]
+
+ del data
+
+ lw = 0.75
+ ms = 2
+
+ fig, ax = plt.subplots(2, 3)
+ fig.set_figheight(9)
+ fig.set_figwidth(16)
+
+ ax[0,0].plot(x, c1, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,0].set_title("Capacitance 1")
+ ax[0,0].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,0].set_ylabel("Capacitance [pF]")
+
+ ax[0,1].plot(x, c2, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,1].set_title("Capacitance 2")
+ ax[0,1].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,1].set_ylabel("Capacitance [pF]")
+
+ ax[0,2].plot(x, c3, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[0,2].set_title("Capacitance 3")
+ ax[0,2].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[0,2].set_ylabel("Capacitance [pF]")
+
+ ax[1,0].plot(x[1:], c_diff_1, color="tab:red", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,0].set_title("Diff Capacitance 1")
+ ax[1,0].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,0].set_ylabel("Capacitance [pF]")
+ #ax[1,0].set_ylim(-0.0025, 0.02)
+
+ ax[1,1].plot(x[1:], c_diff_2, color="tab:green", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,1].set_title("Diff Capacitance 2")
+ ax[1,1].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,1].set_ylabel("Capacitance [pF]")
+ #ax[1,1].set_ylim(-0.0015, 0.008)
+
+ ax[1,2].plot(x[1:], c_diff_3, color="tab:blue", marker="x", markersize=ms, linestyle="--", linewidth=lw)
+ ax[1,2].set_title("Diff Capacitance 3")
+ ax[1,2].set_xlabel("Step [" + str(step_s) + "±" + str(step_s_err) + " nm]")
+ ax[1,2].set_ylabel("Capacitance [pF]")
+ #ax[1,2].set_ylim(-0.002, 0.005)
+
+ fig.tight_layout()
+ plt.show()
+
+ #Determine Savefile name if not already specified
+ if(save == ""):
+ path_parts = []
+ if("\\" in path):
+ path_parts = path.split("\\")
+ elif ("/" in path):
+ path_parts = path.split("/")
+
+ save = "Plots/ApproachCurves/" + path_parts[-1].replace(".csv", "")
+
+ fig.savefig(save + ".pdf")
+ fig.savefig(save + ".png", dpi=300)
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/PlotEvaporationData.py b/PythonScripts/PlotEvaporationData.py
new file mode 100644
index 0000000000000000000000000000000000000000..666bcaeba26edcbbc576871611d1c88434743671
--- /dev/null
+++ b/PythonScripts/PlotEvaporationData.py
@@ -0,0 +1,94 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+def main():
+ path = "C:\MaskAligner\Data\ApproachCurve16_08_23.csv"
+ save = ""
+
+ plot_Fil = False
+ plot_Emis = False
+ plot_Flux = False
+ plot_Volt = False
+ plot_Pressure = False
+ plot_Temp = False
+
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if(sys.argv[i] == "--file" or sys.argv[i] == "--f"):
+ try:
+ path = sys.argv[i+1]
+ except:
+ print("incorrect file path.")
+ return
+
+ if("--FIL" in sys.argv):
+ plot_Fil = True
+ if("--EMIS" in sys.argv):
+ plot_Emis = True
+ if("--FLUX" in sys.argv):
+ plot_Flux = True
+ if("--Volt" in sys.argv):
+ plot_Volt = True
+ if("--Pressure" in sys.argv):
+ plot_Pressure = True
+ if("--Temp" in sys.argv):
+ plot_Temp = True
+ if("--All" in sys.argv):
+ plot_Temp = True
+ plot_Pressure = True
+ plot_Volt = True
+ plot_Flux = True
+ plot_Emis = True
+ plot_Fil = True
+
+ data = np.genfromtxt(path, delimiter=",", skip_header=1)
+ data = data.T
+ time = data[0]
+ FIL = data[1]
+ EMIS = data[2]
+ FLUX = data[3]
+ VOLT = data[4]
+ Pressure = data[5]
+ TEMP = data[6]
+
+ if(plot_Fil):
+ plt.plot(time, FIL, label="FIL")
+ #plt.legend()
+ plt.xlabel("time in min")
+ plt.ylabel("FIL in A")
+ plt.show()
+ if(plot_Emis):
+ plt.plot(time, EMIS, label="EMIS")
+ #plt.legend()
+ plt.xlabel("time in min")
+ plt.ylabel("EMIS in mA")
+ plt.show()
+ if(plot_Flux):
+ plt.plot(time, FLUX, label="Flux")
+ #plt.legend()
+ plt.xlabel("time in min")
+ plt.ylabel("Flux in nA")
+ plt.show()
+ if(plot_Volt):
+ plt.plot(time, VOLT, label="Volt")
+ #plt.legend()
+ plt.xlabel("time in min")
+ plt.ylabel("Volt in V")
+ plt.show()
+ if(plot_Pressure):
+ plt.plot(time, Pressure, label="Pressure")
+ #plt.legend()
+ plt.xlabel("time in min")
+ plt.ylabel("Pressure in mbar")
+ plt.show()
+ if(plot_Temp):
+ plt.plot(time, TEMP, label="Temp")
+ #plt.legend()
+ plt.xlabel("time in min")
+ plt.ylabel("Temp in °C")
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/PlotMultipleApproachCurves.py b/PythonScripts/PlotMultipleApproachCurves.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc786b2564027a824d8cee1bf0a81cba4eb01cd8
--- /dev/null
+++ b/PythonScripts/PlotMultipleApproachCurves.py
@@ -0,0 +1,157 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 9 14:45:10 2023
+
+@author: Luzifer
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+
+def main():
+ path = []
+ save = ""
+
+ #Simple command line parsing
+ if("--files" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if(sys.argv[i] == "--files" or sys.argv[i] == "--f"):
+ for j in range(i+1, len(sys.argv)):
+ if(sys.argv[j] == "--save" or sys.argv[j] == "-s"):
+ break
+ try:
+ path.append(sys.argv[j])
+ except:
+ print("incorrect file path.")
+ return
+
+ if("--save" in sys.argv or "-s" in sys.argv):
+ for i in range(len(sys.argv)):
+ if(sys.argv[i] == "--save" or sys.argv[i] == "-s"):
+ try:
+ save = sys.argv[i+1]
+ except:
+ print("incorrect save file name.")
+ return
+
+ if path == []:
+ print("No data to plot.")
+ return
+
+ xs = []
+
+ c1s = []
+ c2s = []
+ c3s = []
+
+ c_diff_1s = []
+ c_diff_2s = []
+ c_diff_3s = []
+
+ for i in range(len(path)):
+ data = np.genfromtxt(path[i], delimiter=",")
+
+ x = data.T[0]
+ c1 = data.T[1]
+ c2 = data.T[2]
+ c3 = data.T[3]
+
+ c_diff_1 = np.ones(len(c1) - 1)
+ c_diff_2 = np.ones(len(c2) - 1)
+ c_diff_3 = np.ones(len(c2) - 1)
+
+ xs.append(x)
+ c1s.append(c1)
+ c2s.append(c2)
+ c3s.append(c3)
+
+ c_diff_1s.append(c_diff_1)
+ c_diff_2s.append(c_diff_2)
+ c_diff_3s.append(c_diff_3)
+
+ for j in range(len(path)):
+ for i in range(len(c1s[j])-1):
+ c_diff_1s[j][i] = c1s[j][i+1] - c1s[j][i]
+ c_diff_2s[j][i] = c2s[j][i+1] - c2s[j][i]
+ c_diff_3s[j][i] = c3s[j][i+1] - c3s[j][i]
+
+ del data
+ del c1, c2, c3, c_diff_1, c_diff_2, c_diff_3
+
+ lw = 0.75
+ ms = 2
+
+ fig, ax = plt.subplots(2, 3)
+ fig.set_figheight(9)
+ fig.set_figwidth(16)
+
+ color_1 = ["tab:red", "#e24522", "#ec5f1a", "#f47711", "#fb8f05", "#ffa600"]
+ color_2 = ["tab:green", "#62a50d", "#8ca900", "#b3aa00", "#d9a900", "#ffa600"]
+ color_3 = ["tab:blue", "#7e75c8", "#cd6abc", "#ff6292", "#ff7957", "#ffa600"]
+
+ for i in range(len(path)):
+ x = np.arange(0, len(c1s[i]))
+
+ ax[0,0].plot(xs[i], c1s[i], color=color_1[i], marker="x", markersize=ms, linestyle="--", linewidth=lw, label=path[i].replace("\\", "/").split("/")[-1].replace(".csv", ""))
+ ax[0,0].set_title("Capacitance 1")
+ ax[0,0].set_xlabel("Step")
+ ax[0,0].set_ylabel("Capacitance [pF]")
+
+ ax[0,1].plot(xs[i], c2s[i], color=color_2[i], marker="x", markersize=ms, linestyle="--", linewidth=lw, label=path[i].replace("\\", "/").split("/")[-1].replace(".csv", ""))
+ ax[0,1].set_title("Capacitance 2")
+ ax[0,1].set_xlabel("Step")
+ ax[0,1].set_ylabel("Capacitance [pF]")
+
+ ax[0,2].plot(xs[i], c3s[i], color=color_3[i], marker="x", markersize=ms, linestyle="--", linewidth=lw, label=path[i].replace("\\", "/").split("/")[-1].replace(".csv", ""))
+ ax[0,2].set_title("Capacitance 3")
+ ax[0,2].set_xlabel("Step")
+ ax[0,2].set_ylabel("Capacitance [pF]")
+
+ ax[1,0].plot(xs[i][1:], c_diff_1s[i], color=color_1[i], marker="x", markersize=ms, linestyle="--", linewidth=lw, label=path[i].replace("\\", "/").split("/")[-1].replace(".csv", ""))
+ ax[1,0].set_title("Diff Capacitance 1")
+ ax[1,0].set_xlabel("Step")
+ ax[1,0].set_ylabel("Capacitance [pF]")
+ #ax[1,0].set_ylim(-0.0025, 0.02)
+
+ ax[1,1].plot(xs[i][1:], c_diff_2s[i], color=color_2[i], marker="x", markersize=ms, linestyle="--", linewidth=lw, label=path[i].replace("\\", "/").split("/")[-1].replace(".csv", ""))
+ ax[1,1].set_title("Diff Capacitance 2")
+ ax[1,1].set_xlabel("Step")
+ ax[1,1].set_ylabel("Capacitance [pF]")
+ #ax[1,1].set_ylim(-0.0015, 0.008)
+
+ ax[1,2].plot(xs[i][1:], c_diff_3s[i], color=color_3[i], marker="x", markersize=ms, linestyle="--", linewidth=lw, label=path[i].replace("\\", "/").split("/")[-1].replace(".csv", ""))
+ ax[1,2].set_title("Diff Capacitance 3")
+ ax[1,2].set_xlabel("Step")
+ ax[1,2].set_ylabel("Capacitance [pF]")
+ #ax[1,2].set_ylim(-0.002, 0.005)
+
+ ax[0,0].legend()
+ ax[0,1].legend()
+ ax[0,2].legend()
+ ax[1,0].legend()
+ ax[1,1].legend()
+ ax[1,2].legend()
+ ax[1,2].legend()
+ fig.tight_layout()
+ plt.show()
+
+ #Determine Savefile name if not already specified, by taking capital letters and number in string and concatenating
+ if(save == ""):
+ path_parts = []
+ for i in range(len(path)):
+ path_parts.append(path[i].replace("\\", "/").split("/")[-1].replace(".csv", ""))
+ path_parts[i] = [x for x in path_parts[i] if x.isupper() or x.isdigit()]
+ path_parts[i] = "".join(path_parts[i])
+
+ path_part = ""
+ for i in range(len(path)):
+ path_part += path_parts[i]
+
+ save = "Plots/ApproachCurves/" + path_part
+
+ fig.savefig(save + "comparison" + ".pdf")
+ fig.savefig(save + "comparison" + ".png", dpi=300)
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/PlotPressurePlort.py b/PythonScripts/PlotPressurePlort.py
new file mode 100644
index 0000000000000000000000000000000000000000..93ea96d33535b93eae794f2237e44a56e3f1134a
--- /dev/null
+++ b/PythonScripts/PlotPressurePlort.py
@@ -0,0 +1,37 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+from datetime import datetime
+
+def remove_day_of_week(string):
+ return string.replace("Mon", "").replace("Tue", "").replace("Wed", "").replace("Thu", "").replace("Fri", "").replace("Sat", "").replace("Son", "")
+
+def main():
+ file = "C:\MaskAligner\Data\Pressure\Pressure01.txt"
+ if("--file" in sys.argv or "-f" in sys.argv):
+ for i in range(len(sys.argv)):
+ if("--file" in sys.argv[i] or "-f" == sys.argv[i]):
+ try:
+ file = sys.argv[i+1]
+ except:
+ print("No file or incorrect file specification.")
+
+ times = []
+ pressures = []
+
+ with open(file, "r") as read_file:
+ lines = read_file.readlines()
+ pressures = np.zeros(len(lines) - 1)
+
+ for i in range(1, len(lines)):
+ temp = lines[i].split("\t")
+ t = datetime.strptime(remove_day_of_week(temp[0]).replace(" ", " "), "%H:%M:%S %d.%m.%y")
+ times.append(t)
+ pressures[i-1] = float(temp[1])
+
+ plt.plot(times, pressures)
+ plt.ylabel("pressure [mbar]")
+ plt.show()
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/PlotPressureRealtime.py b/PythonScripts/PlotPressureRealtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a73632d85fe19de0b36e5972ce29a61d17868e3
--- /dev/null
+++ b/PythonScripts/PlotPressureRealtime.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Nov 30 15:07:58 2023
+
+@author: admin
+"""
+
+import time
+import serial
+import numpy as np
+import matplotlib.pyplot as plt
+
+def reading():
+ ser = serial.Serial('COM7', 19200, timeout=0, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=8)
+ read_command = b"RFM%d\r" % 1
+ ser.write(read_command)
+ time.sleep(.03)
+ pressure = ''
+ while ser.inWaiting() > 0:
+ pressure += ser.read(1).decode('ascii')
+
+ print(pressure)
+
+ if pressure[0] == '0':
+ return float(pressure)
+ else:
+ return float('nan')
+
+ ser.close()
+
+def main():
+ for i in range(0, 100):
+ pressure = []
+ temp_press = reading()
+ pressure.append(temp_press)
+
+ plt.plot(pressure)
+ plt.pause(0.05)
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/PythonScripts/PlotRHKData.py b/PythonScripts/PlotRHKData.py
new file mode 100644
index 0000000000000000000000000000000000000000..d507f415e218808b04750b173a7e24bf1f10fa3a
--- /dev/null
+++ b/PythonScripts/PlotRHKData.py
@@ -0,0 +1,32 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def main():
+ path = "C:/MaskAligner/Data/RHKDAta.csv"
+ data = np.genfromtxt(path, skip_header=1, delimiter=",")
+ data = data.T
+ U_2ms = data[0]
+ U_1ms = data[3]
+ Down_2ms = data[1]
+ Down_1ms = data[4]
+ Up_2ms = data[2]
+ Up_1ms = data[5]
+
+ err = 1
+
+ plt.errorbar(U_2ms, Down_2ms, Down_2ms * 0.1, label="+ 2ms", capsize=3, color="tab:blue")
+ plt.errorbar(U_2ms, Up_2ms, Up_2ms * 0.1, label="- 2ms", capsize=3, color="tab:red")
+ plt.plot(U_2ms, Up_2ms /2, linestyle="dashed", color="tab:red")
+ plt.plot(U_2ms, Down_2ms /2, linestyle="dashed", color="tab:blue")
+ plt.errorbar(U_1ms, Down_1ms, Down_1ms * 0.1, label="+ 1ms", capsize=3, color="tab:cyan")
+ plt.errorbar(U_1ms, Up_1ms, Up_1ms * 0.1, label="- 1ms", capsize=3, color="tab:orange")
+
+ plt.xlabel("Voltage [V]")
+ plt.ylabel("1/speed [s/mm]")
+ plt.legend()
+ plt.savefig("Plots/RHKDatadep.pdf")
+ plt.savefig("Plots/RHKDatadep.png", dpi=300)
+ plt.show()
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
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