From: Pat Thoyts Date: Mon, 9 Jan 2023 21:41:23 +0000 (+0000) Subject: Lacquer tag raman plotting and Renishaw spectral data files X-Git-Url: https://privyetmir.co.uk/gitweb.cgi?a=commitdiff_plain;p=lacquertags%2Framanplot.git Lacquer tag raman plotting and Renishaw spectral data files --- 177cf8fffe7e311bcaa8c8cf5ab601490a271290 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f161a74 --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +.tox/ +.vscode/ +*.egg-info/ +__pycache__/ +build/ +dist/ \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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files +collected from the lacquer tags using a Renishaw RA802 and inVia Qontor spectrometer. + +Includes a prototype Python support package for Renishaw WDF data files and another +for Raman spectral data processing. diff --git a/data/Amber_Fingerprint.wdf b/data/Amber_Fingerprint.wdf new file mode 100755 index 0000000..9492c98 Binary files /dev/null and b/data/Amber_Fingerprint.wdf differ diff --git a/data/Benzoin_Fingerprint.wdf b/data/Benzoin_Fingerprint.wdf new file mode 100755 index 0000000..09820e1 Binary files /dev/null and b/data/Benzoin_Fingerprint.wdf differ diff --git a/data/Brass/Tile_10_AnilineYellow.wdf b/data/Brass/Tile_10_AnilineYellow.wdf new file mode 100755 index 0000000..f1ebc20 Binary files /dev/null and b/data/Brass/Tile_10_AnilineYellow.wdf differ diff --git a/data/Brass/Tile_11_MetalComplex.wdf b/data/Brass/Tile_11_MetalComplex.wdf new file mode 100755 index 0000000..abd9b97 Binary files /dev/null and b/data/Brass/Tile_11_MetalComplex.wdf differ diff --git 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0.01564 +3574.51061 0.01449 +3575.56724 0.01344 +3576.62344 0.01265 +3577.6792 0.0122 +3578.73454 0.012 +3579.78944 0.01183 +3580.8439 0.01146 +3581.89794 0.01074 +3582.95154 0.00968 +3584.00471 0.00844 +3585.05745 0.00726 +3586.10976 0.00626 +3587.16163 0.00552 +3588.21308 0.00498 +3589.26409 0.00457 +3590.31467 0.00419 +3591.36481 0.0038 +3592.41453 0.00332 +3593.46382 0.00269 +3594.51267 0.00184 +3595.5611 7.76039E-4 +3596.60909 -4.05061E-4 +3597.65665 -0.00155 +3598.70378 -0.00248 \ No newline at end of file diff --git a/demos/wdf_map_arith.py b/demos/wdf_map_arith.py new file mode 100644 index 0000000..04f0a2a --- /dev/null +++ b/demos/wdf_map_arith.py @@ -0,0 +1,190 @@ +#!python + +""" +Demonstration performing map image arithmetic and plotting the results using python, +numpy and matplotlib with the WiRE wdf access package. + +In this case, the matplotlib graphing widget has been embedded into a Tkinter GUI. +""" + +import sys +import argparse +import tkinter as tk +import tkinter.ttk as ttk +import tkinter.font +import tkinter.filedialog as tkfiledialog +import tkinter.messagebox as tkmessagebox +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.backends.backend_tkagg as tkagg +from wdf import Wdf, WdfDataType, WdfBlockId +from wdf.origin import WdfDataType + +if sys.platform == 'win32': + # Fix for tiny menus on high dpi display + from ctypes import windll + windll.shcore.SetProcessDpiAwareness(1) + +class Plot(ttk.Frame): + # ex: https://github.com/I2PC/scipion/blob/master/scipion-master/pyworkflow/matplotlib_image.py + def __init__(self, master, **kwargs): + if "figsize" in kwargs: + self.figsize = kwargs["figsize"] + del kwargs["figsize"] + super(Plot, self).__init__(master) + self.figure = matplotlib.figure.Figure(figsize=self.figsize, frameon=False, dpi=100) + self.canvas = tkagg.FigureCanvasTkAgg(self.figure, self) + self.toolbar = None + widget = self.canvas.get_tk_widget() + widget.grid(row=0, column=0, sticky=tk.NSEW) + self.grid_rowconfigure(0, weight=1) + self.grid_columnconfigure(0, weight=1) + self.configure(**kwargs) + + def add_toolbar(self): + self.toolbar = tkagg.NavigationToolbar2Tk(self.canvas, self, pack_toolbar=False) + self.toolbar.grid(row=1, column=0, sticky=tk.NSEW) + + def show(self): + self.canvas.show() + + @property + def widget(self): + return self.canvas.get_tk_widget() + + def configure(self, **kwargs): + return self.widget.configure(**kwargs) + + +class App(ttk.Frame): + """Application class.""" + def __init__(self, master, **kwargs): + super(App, self).__init__(master, **kwargs) + self.master.wm_minsize(800, 600) + self.create_ui() + self.create_menu() + self.grid(row=0, column=0, sticky=tk.NSEW) + self.master.grid_rowconfigure(0, weight=1) + self.master.grid_columnconfigure(0, weight=1) + + def create_ui(self): + self.plot = Plot(self, figsize=(11, 8), background="grey80") + self.plot.grid(row=0, column=0, sticky=tk.NSEW) + self.grid_rowconfigure(0, weight=1) + self.grid_columnconfigure(0, weight=1) + + def create_menu(self): + menu = tk.Menu(self.master) + fileMenu = tk.Menu(menu, tearoff=False) + optMenu = tk.Menu(menu, tearoff=False) + menu.add_cascade(label='File', menu=fileMenu) + menu.add_cascade(label='Options', menu=optMenu) + fileMenu.add_command(label='Open ...', command=self.on_file_open) + fileMenu.add_command(label='Exit', command=self.on_file_exit) + optMenu.add_command(label="Add toolbar", command=self.plot.add_toolbar) + self.master.configure(menu=menu) + + def on_file_open(self): + filename = tkfiledialog.askopenfilename(filetypes=(("WiRE Data Files", "*.wdf"), ("All files", "*.*"))) + if filename: + self.plot.figure.clear() + self.after_idle(self.run, filename) + + def on_file_exit(self): + self.master.destroy() + + def run(self, filename): + """Perform image arithmetic and plot inputs and result.""" + with Wdf(filename) as wdf: + shape = wdf.map_area.count.x, wdf.map_area.count.y + landscape_a4 = (11, 8) # inches (A4) + + lhs_props = wdf.get_section_properties(WdfBlockId.MAP, 1) + lhs_title = lhs_props['Label'].value + lhs = np.array(wdf.get_map_data(1), dtype=float) + rhs_props = wdf.get_section_properties(WdfBlockId.MAP, 2) + rhs_title = rhs_props['Label'].value + rhs = np.array(wdf.get_map_data(2), dtype=float) + + fig = self.plot.figure + fig.clear() + fig.set_tight_layout(True) + + ax = fig.add_subplot(2, 2, 1) + ax.set_title(lhs_title) + ax.imshow(lhs.reshape(shape)) + + ax = fig.add_subplot(2, 2, 2) + ax.set_title(rhs_title) + ax.imshow(rhs.reshape(shape)) + + ax = fig.add_subplot(2, 2, 3) + ax.set_title(f"{lhs_title} + {rhs_title}") + ax.imshow((lhs + rhs).reshape(shape)) + + try: + res_index = 4 + res_props = wdf.get_section_properties(WdfBlockId.MAP, res_index) + res_title = rhs_props['Label'].value + res = np.array(wdf.get_map_data(res_index), dtype=float) + + ax = fig.add_subplot(2, 2, 4) + ax.set_title(f"{res_title}") + ax.imshow(res.reshape((wdf.map_area.count.y, wdf.map_area.count.x))) + except Exception: + pass + self.plot.canvas.draw() + +def plot_contour(wdf, mapIndex): + """Plot the anadin data which is column-major with negative y step size""" + global app + maps = [section.uid for section in wdf.sections() if section.id == WdfBlockId.MAP] + if mapIndex not in maps: + raise Exception("invalid map index") + fig = app.plot.figure + fig.clear() + ax = fig.add_subplot(1, 1, 1) + x = np.array(wdf.origins[WdfDataType.Spatial_X], dtype=float) + y = np.array(wdf.origins[WdfDataType.Spatial_Y], dtype=float) + map = np.array(wdf.get_map_data(mapIndex), dtype=float) + props = wdf.get_section_properties(WdfBlockId.MAP, 2) + ax.set_title(props['Label'].value) + ax.contourf(y[0:wdf.map_area.count.y], x[0::wdf.map_area.count.y], + map.reshape(wdf.map_area.count.x, wdf.map_area.count.y), alpha=0.5) + ax.contour(y[0:wdf.map_area.count.y], x[0::wdf.map_area.count.y], + map.reshape(wdf.map_area.count.x, wdf.map_area.count.y)) + fig.canvas.draw() + + +def load_idle(root): + try: + import idlelib.pyshell as pyshell + sys.argv = [sys.argv[0], "-n"] + root.bind("", lambda ev: pyshell.main()) + root.bind("", lambda ev: pyshell.main()) + except ModuleNotFoundError: + pass + +def main(args=None): + """Main program entry.""" + global root, app, options # access to application for interactive idle mode + + parser = argparse.ArgumentParser(description="Demonstration of map image arithmetic using python") + parser.add_argument('-f', '--filename') + options = parser.parse_args(args) + + root = tk.Tk() + app = App(root) + if options.filename: + app.after(100, app.run, options.filename) + + root.after(200, load_idle, root) + try: + root.mainloop() + except Exception as e: + tkmessagebox.showerror('Error', e) + return 0 + +if __name__ == '__main__': + sys.exit(main(sys.argv[1:])) diff --git a/lacquertags_plot.md b/lacquertags_plot.md new file mode 100644 index 0000000..bf7027e --- /dev/null +++ b/lacquertags_plot.md @@ -0,0 +1,32 @@ +# Raman data processing + +Data was collected using a 785nm laser with the instrument initially set +to acquire over a 1 second interval at full power. The instrument was set to +continuously collect spectra with the sample exposed to laser the whole time. +This allows the user to observe the fluorcescence quenching over time. Once the +rate of signal drop slows down a final measurement was then saved. + +For some samples with excessive fluorcescence causing saturation of the detector either the +collection time or the laser power (or both) were decreased until a full spectrum could be +obtained without saturation using the quenching method described above. + +To plot the data, each spectrum is baseline subtracted using the Asymetric Least Squares [1] +This removes the baseline drift caused by fluorcescence which varies between +samples and permits comparison between the spectra. + +The spectal intensity values are then normalized to put all the intensity values between 0 and 1.0 so that +the different spectra can be compared. Utility functions from Numpy [3] and SciPy [4] were +helpful in processing the data. + +The spectra are then truncated to between 280 and 2000 wavenumbers to discard excessive +fluorescence noise at the ends of the spectrum. The sensitivity of +the CCD array is poor at near infra-red wavelengths. Given the use of a 785nm excitation +source this means the signal sensitivity becomes poor at high wavenumbers in this experiment. + +The data is the plotted using Matplotlib [2]. +The plots are then saved into a pdf file. + +[1] Eilers, P. and Boelens, H. "Asymetric Least Squares Smoothing" (2005). +[2] https://matplotlib.org/stable/users/project/citing.html +[3] https://numpy.org/citing-numpy/ +[4] https://scipy.org/citing-scipy/ \ No newline at end of file diff --git a/laquertags_plot.py b/laquertags_plot.py new file mode 100755 index 0000000..6053213 --- /dev/null +++ b/laquertags_plot.py @@ -0,0 +1,213 @@ +#!/usr/bin/python3 + +""" +Generate stacked plots of all the collected Lacquer project spectra. + +Each spectrum is baseline subtracted using the Assymetric LEast Squares method +included below in `baseline_als`. +They are then normalized to put all the intensity values between 0 and 1.0 so that +the different spectra can be compared. +The spectra are then truncated to between 280 and 2000 wavenumbers to discard excessive +flourescence noise at the ends of the spectrum. For the far end, as the sensitivity of +the CCD array at near infra-red wavelengths decreases then given a 785nm excitation +source we have poor signal over 2000 wavenumbers. + +The plots are then saved into a pdf file. +""" + + +import sys +import os +import argparse +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from matplotlib.backends.backend_pdf import PdfPages +from scipy import sparse +from scipy.sparse.linalg import spsolve +from scipy.signal import find_peaks, find_peaks_cwt +from wdf import Wdf, WdfBlockId + +#datadir = '/media/nas2/Software/LacquerTags/Raman' +datadir = 'data' + +def baseline_als(y, lam, p, niter=10): + """Assymetric Least Squares Smoothing + P. Eilers and H. Boelens (2005) + + p for asymmetry 0.001 ≤ p ≤ 0.1 is a good choice (for a signal with positive peaks) + λ for smoothness, 10^2 ≤ λ ≤ 10^9 + """ + L = len(y) + D = sparse.diags([1, -2, 1], [0, -1, -2], shape=(L, L-2)) + w = np.ones(L) + for i in range(niter): + W = sparse.spdiags(w, 0, L, L) + Z = W + lam * D.dot(D.transpose()) + z = spsolve(Z, w * y) + w = p * (y > z) + (1 - p) * (y < z) + return z + +def truncate(spectrum, begin:float, end:float): + a = np.where(spectrum[:, 0] > begin) + b = np.where(spectrum[:, 0] < end) + a_,b_ = sorted([a[0][-1], b[0][0]]) + i = range(a_, b_ + 1) + xlist = spectrum[:, 0][i] + ilist = spectrum[:, 1][i] + return np.array(list(zip(xlist, ilist))) + +def load_spectrum(filename:str, baseline_subtract=True, normalize=True, truncate=True): + with Wdf(filename) as data: + xlist = np.array(data.xlist(), dtype='float') + ilist = np.array(data[0], dtype='float') + if baseline_subtract: + baseline = baseline_als(data[0], 10**5, 0.001) + ilist = ilist - baseline # subtract baseline + if normalize: + ilist = np.divide(ilist, np.max(ilist)) # normalize [0..1] + if truncate: + a = np.where(xlist > 280.0) # truncate + b = np.where(xlist < 2000.0) + a_,b_ = sorted([a[0][-1], b[0][0]]) + xlist = xlist[a_:b_+1] + ilist = ilist[a_:b_+1] + return np.array(list(zip(xlist, ilist))) + +def plot_stack(data, title=None, offset=1.0, baseline_subtract=True, normalize=True, truncate=True): + count = 0 + fig, ax = plt.subplots(figsize=(11, 8)) + if title: + ax.set_title(title) + ax.set_xlabel('wavenumber (cm-1)') + for label, filename in data: + spectrum = load_spectrum(os.path.join(datadir, filename), baseline_subtract=baseline_subtract, normalize=normalize, truncate=truncate) + ax.plot(spectrum[:,0], spectrum[:,1] - (offset * count), label=label) + ax.set(yticklabels=[]) + ax.tick_params(left=False) + count += 1 + ax.legend(loc='upper right') + return fig, ax + +def plot_brass(data, title): + # Load a zincoxide spectrum from a csv file + df = pd.read_csv(os.path.join(datadir, 'ZincOxide.csv'), sep="\t", header=None, names=['xlist','ilist']) + zincoxide = np.array(list(zip(df['xlist'], df['ilist']))) + # Load oxidized brass and clean, scraped brass from wdf files + ox = load_spectrum(os.path.join(datadir, data[0][1])) + clean = load_spectrum(os.path.join(datadir, data[1][1])) + clean[:,1] /= 40 + fig, ax = plt.subplots(figsize=(11, 8)) + ax.set_title(title) + ax.plot(ox[:,0], ox[:,1], label='brass (oxidized)') + ax.plot(clean[:,0], clean[:,1], label='brass (oxidized)') + zno = truncate(zincoxide, 300, 1800) + ax.plot(zno[:,0], zno[:,1], label='zinc oxide') + ax.legend(loc='upper right') + return fig, ax + + +# ('orange shellac', 'Qontor/Tile_3_OrangeShellac.wdf'), +tile_files = ( + ('brass (oxidized)', 'Brass_Fingerprint.wdf'), + ('shellac', 'Brass/Tile_1_PlainLacquer.wdf'), + ('shellac+boric', 'Brass/Tile_2_PlainBoric.wdf'), + ('turmeric', 'Brass/Tile_3_Turmeric.wdf'), + ('turmeric+boric', 'Brass/Tile_4_TurmericBoric.wdf'), + ('turmeric faded', 'Brass/Tile_5_TurmericFaded.wdf'), + ('DGLB', 'Brass/Tile_8_DGLB.wdf'), + ('bismark brown', 'Brass/Tile_9_BismarkBrown.wdf'), + ('aniline yellow', 'Brass/Tile_10_AnilineYellow.wdf'), + ('metal complex', 'Brass/Tile_11_MetalComplex.wdf'), + ('cape aloes', 'Brass/Tile_12_CapeAloes.wdf'), + ('annatto', 'Brass/Tile_13_Annatto.wdf'), + ('madder', 'Brass/Tile_14_Madder.wdf'), + ('sandalwood', 'Brass/Tile_15_RedSandalwood.wdf') +) + +samples_files = ( + ('amber', 'Amber_Fingerprint.wdf'), + ('benzoin', 'Benzoin_Fingerprint.wdf'), + ('dragons blood', 'DragonsBlood_Fingerprint.wdf'), + ('gamboge', 'PipeGamboge_Fingerprint.wdf'), + ('sandarac', 'Sandarac_Fingerprint.wdf'), + ('colophony', 'Colophony_Fingerprint.wdf'), + ('copal', 'Copal_Fingerprint.wdf'), + ('dammar', 'Dammar_Fingerprint.wdf'), + ('elemi', 'Elemi_Fingerprint.wdf'), + ('mastic', 'Mastic_Fingerprint.wdf'), + ('shellac', 'Shellac_Fingerprint.wdf') +) + +qontortile_files = ( + ('blonde shellac', 'Qontor/Tile_1_BlondeShellac.wdf'), + ('orange shellac', 'Qontor/Tile_3_OrangeShellac.wdf'), + ('turmeric + boric', 'Qontor/Tile_6_TurmericBoric.wdf'), + ('turmeric (faded)', 'Qontor/Tile_7_TurmericFaded.wdf'), + ('DGLB', 'Qontor/Tile_8_DGLB.wdf'), + ('bismark brown', 'Qontor/Tile_9_BismarkBrown.wdf'), + ('aniline yellow', 'Qontor/Tile_10_AnilineYellow.wdf'), +) + +# ('Blonde Shellac', 'Qontor/BlondeShellac.wdf') +antique_files = ( + ('CarlZeissJena', 'Qontor/CarlZeissJena.wdf'), + ('Cary', 'Qontor/Cutts.wdf'), + ('Hartnac', 'Qontor/Hartnac.wdf'), + ('Lynch', 'Qontor/Mirror.wdf'), + ('Swift', 'Qontor/SwiftScrew.wdf'), + ('Tube', 'Qontor/BrassTube.wdf'), + ('Leitz', 'Qontor/Leitz.wdf'), + ('Smith & Beck', 'Qontor/SmithAndBeck.wdf'), + ('Verick', 'Qontor/Verick.wdf') +) + +brass_files = ( + ('brass (oxidized)', 'Brass_Fingerprint.wdf'), + ('brass (clean)', 'Qontor/BrassScraped.wdf'), +) + +steel_files = ( + ('yellow', 'Steel_YellowLacquer.wdf'), + ('brown', 'Steel_BrownLacquer.wdf'), + ('gamboge', 'PipeGamboge_Fingerprint.wdf'), + ('colophony', 'Colophony_Fingerprint.wdf'), +) + + +def pdfplot(path): + # Save to PDF + with PdfPages(path) as pdf: + fig, ax = plot_stack(tile_files, title='Raman spectra of lacquer on Brass at 785nm excitation') + pdf.savefig() + plt.close() + fig, ax = plot_stack(samples_files, title='Normalized Raman spectra of resins at 785nm excitation') + pdf.savefig() + plt.close() + fig, ax = plot_stack(antique_files, title='Antique lacquer samples on brass at 785 nm excitation') + pdf.savefig() + plt.close() + fig, ax = plot_stack(steel_files, title='Lacquers on steel slide at 785 nm excitation') + pdf.savefig() + plt.close() + fig, ax = plot_stack(qontortile_files, title='Confocal Raman spectra of lacquer on white tile (785 nm excitation)') + pdf.savefig() + plt.close() + fig, ax = plot_brass(brass_files, title='Brass and Oxidized Brass (785 nm excitation)') + pdf.savefig() + plt.close() + +def main(args=None): + parser = argparse.ArgumentParser(description="Plot lacquer tag spectra to PDF") + parser.add_argument('path', help='Path to PDF file to create') + parser.add_argument('--debug', action='store_true', help='Debug (show on-screen only)') + options = parser.parse_args(args) + if options.debug: + fig, ax = plot_stack(samples_files, title='Normalized Raman spectra of resins at 785nm excitation') + plt.show() + else: + pdfplot(options.path) + return 0 + +if __name__ == '__main__': + sys.exit(main(sys.argv[1:])) diff --git a/multiplot_demo.py b/multiplot_demo.py new file mode 100755 index 0000000..9c5ea3f --- /dev/null +++ b/multiplot_demo.py @@ -0,0 +1,52 @@ +#!/usr/bin/python3 + +""" +Plot all the spectral files in the specificed folder + +This plots each one in its own subplot. +Each is truncated to 250.0..1800.0 wavenumbers and +also has the default baseline subtracted to remove florescence. +""" + +import sys +import os +import argparse +import numpy as np +import matplotlib.pyplot as plt +from collections import OrderedDict +from wdf import Wdf +from rsl import Spectrum, TruncateStep, BaselineSubtractionStep, SmoothStep + + +def main(args=None): + parser = argparse.ArgumentParser(description='Plot multiple single spectra from a folder') + parser.add_argument('path', + help="Path to folder containing wdf files.") + parser.add_argument('--begin', type=float, default=250.0, + help='Truncation start xlist value') + parser.add_argument('--end', type=float, default=1800.0, + help='Truncation end value') + options = parser.parse_args(args) + + files = list(os.scandir(options.path)) + fig = plt.figure(figsize=(11, 8)) + index = 1 + count = len(files) + for filename in files: + with Wdf(filename.path) as data: + name = filename.name.split('_')[0] + + # Create a pipeline with a truncate and a baseline subtraction. + pipeline = OrderedDict() + pipeline['truncate'] = TruncateStep(options.begin, options.end) + pipeline['baseline'] = BaselineSubtractionStep(10**5, 0.001) + spectrum = Spectrum(data.xlist(), data[0], pipeline=pipeline) + + ax = fig.add_subplot(count, 1, index) + ax.plot(spectrum.xlist, spectrum.ilist, label=name) + ax.legend() + index += 1 + plt.show() + +if __name__ == '__main__': + sys.exit(main(sys.argv[1:])) diff --git a/pyre.py b/pyre.py new file mode 100755 index 0000000..edcc618 --- /dev/null +++ b/pyre.py @@ -0,0 +1,245 @@ +#!/usr/bin/python3 + +""" +Python Raman Environment +""" + +import sys +import os +import argparse +import tkinter as tk +import tkinter.ttk as ttk +import tkinter.filedialog as tkfiledialog +import tkinter.messagebox as tkmessagebox +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.backends.backend_tkagg as tkagg +from collections import OrderedDict +from typing import Tuple, Sequence +from wdf import Wdf, WdfDataType, WdfBlockId +from rsl import ( + Spectrum, TruncateStep, NormalizeStep, InterpolateStep, SmoothStep, + BaselineSubtractionStep, PeakPickStep) + +if sys.platform == 'win32': + # Fix for tiny menus on high dpi display + from ctypes import windll + windll.shcore.SetProcessDpiAwareness(1) + + +class Plot(ttk.Frame): + """Widget to hold the matplotlib plot.""" + # ex: https://github.com/I2PC/scipion/blob/master/scipion-master/pyworkflow/matplotlib_image.py + def __init__(self, master, **kwargs): + if "figsize" in kwargs: + self.figsize = kwargs["figsize"] + del kwargs["figsize"] + super(Plot, self).__init__(master) + self.figure = matplotlib.figure.Figure(figsize=self.figsize, frameon=False, dpi=100) + self.canvas = tkagg.FigureCanvasTkAgg(self.figure, self) + self.toolbar = None + widget = self.canvas.get_tk_widget() + widget.grid(row=0, column=0, sticky=tk.NSEW) + self.grid_rowconfigure(0, weight=1) + self.grid_columnconfigure(0, weight=1) + self.configure(**kwargs) + + def add_toolbar(self): + self.toolbar = tkagg.NavigationToolbar2Tk(self.canvas, self, pack_toolbar=False) + self.toolbar.grid(row=1, column=0, sticky=tk.NSEW) + + def show(self): + self.canvas.show() + + @property + def widget(self): + return self.canvas.get_tk_widget() + + def configure(self, **kwargs): + return self.widget.configure(**kwargs) + + +class PeaksView(ttk.Frame): + def __init__(self, master, **kwargs): + super(PeaksView, self).__init__(master) + self.header = ttk.Label(self, text="Peaks:", anchor=tk.NW) + self.text = tk.Text(self, background="white", width=16) + self.vs = ttk.Scrollbar(self, orient=tk.VERTICAL, command=self.text.yview) + self.text.configure(yscrollcommand=self.vs.set) + self.header.grid(row=0, column=0, sticky=tk.NSEW, columnspan=2) + self.text.grid(row=1, column=0, sticky=tk.NSEW) + self.vs.grid(row=1, column=1, sticky=tk.NSEW) + self.grid_rowconfigure(1, weight=1) + self.grid_columnconfigure(0, weight=1) + + def add_peak(self, peak: float): + self.text.insert(tk.END, f"{peak:.0f}", 'PEAK', "\n", '') + + def clear(self): + self.text.delete("0.0", tk.END) + + +class App(ttk.Frame): + """Application class.""" + def __init__(self, master, options, **kwargs): + super(App, self).__init__(master, **kwargs) + self.options = options + self.pipeline = [] + self.smoothVar = tk.BooleanVar() + self.smoothVar.set(self.options.smooth) + self.normalizeVar = tk.BooleanVar() + self.normalizeVar.set(self.options.normalize) + self.master.wm_title('PyRE') + self.master.wm_minsize(800, 600) + self.create_ui() + self.create_menu() + self.grid(row=0, column=0, sticky=tk.NSEW) + self.master.grid_rowconfigure(0, weight=1) + self.master.grid_columnconfigure(0, weight=1) + if options.filename: + self.after(100, self.run, options.filename) + + def create_ui(self): + self.plot = Plot(self, figsize=(11, 8), background="grey80") + self.plot.grid(row=0, column=0, sticky=tk.NSEW) + self.peakview = PeaksView(self) + self.peakview.grid(row=0, column=1, sticky=tk.NSEW) + self.grid_rowconfigure(0, weight=1) + self.grid_columnconfigure(0, weight=1) + + def create_menu(self): + menu = tk.Menu(self.master) + fileMenu = tk.Menu(menu, tearoff=False) + optMenu = tk.Menu(menu, tearoff=False) + menu.add_cascade(label='File', menu=fileMenu) + menu.add_cascade(label='Options', menu=optMenu) + fileMenu.add_command(label='Open ...', command=self.on_file_open) + fileMenu.add_command(label='Exit', command=self.on_file_exit) + optMenu.add_checkbutton(label='Smooth', variable=self.smoothVar, + command=self.on_click_smooth) + optMenu.add_checkbutton(label='Normalize', variable=self.normalizeVar, + command=self.on_click_normalize) + optMenu.add_command(label="Add toolbar", command=self.plot.add_toolbar) + self.master.configure(menu=menu) + + def on_click_smooth(self, ev): + self.options.smooth = self.smoothVar.get() + + def on_click_normalize(self, ev): + self.options.normalize = self.normalizeVar.get() + + def on_file_open(self): + filetypes = (("WiRE Data Files", "*.wdf"), ("All files", "*.*")) + filename = tkfiledialog.askopenfilename(filetypes=filetypes) + if filename: + self.plot.figure.clear() + # FIXME: set options.filename + self.options.filename = filename + self.after_idle(self.run, filename) + + def on_file_exit(self): + self.master.destroy() + + def run(self, filename): + """Perform image arithmetic and plot inputs and result.""" + name = os.path.basename(filename) + self.master.wm_title(f"{name} - PyRE") + with Wdf(filename) as data: + # shape = data.map_area.count.x, data.map_area.count.y + # landscape_a4 = (11, 8) # inches (A4) + + fig = self.plot.figure + # fig.clear() + fig.set_tight_layout(True) + + ax = fig.add_subplot(1, 1, 1) + ax.set_title(data.hdr.title) + + self.fig = fig + self.ax = ax + + self.pipeline = OrderedDict() + self.pipeline['baseline'] = BaselineSubtractionStep() + # self.pipeline['interpolate'] = InterpolateStep() + if options.normalize: + self.pipeline['normalize'] = NormalizeStep() + if options.smooth: + self.pipeline['smooth'] = SmoothStep(window=30, polynomial=2) + self.pipeline['peakpick'] = PeakPickStep(prominence=options.prominence, distance=10) + + spectrum_raw = Spectrum(data.xlist(), data[0]) + spectrum = Spectrum(data.xlist(), data[0], pipeline=self.pipeline) + spectrum.update() # apply the pipeline + + for key, step in self.pipeline.items(): + try: + print(f"{key} {dict(step)}") + except TypeError: + print(f"{key} {type(step)}") + + # Update the peak table with the xlist values for the peaks + peaks = self.pipeline['peakpick'].peaks + self.peakview.clear() + for peak in sorted(spectrum.xlist[peaks]): + self.peakview.add_peak(peak) + + if options.show_baseline: + ax.plot(spectrum_raw.xlist, spectrum_raw.ilist, alpha=0.2) + ax.plot(spectrum_raw.xlist, self.pipeline['baseline'].baseline, alpha=0.2) + if options.smooth: + ax.plot(step.original[0], self.pipeline['smooth'].original[1], alpha=0.2) + ax.plot(spectrum.xlist, spectrum.ilist) + ax.plot(spectrum.xlist[peaks], spectrum.ilist[peaks], 'o') + + if options.labels: + off = (max(spectrum.ilist) - min(spectrum.ilist)) / 30 + for peak in peaks: + x = spectrum.xlist[peak] - 10 + y = spectrum.ilist[peak] + off + text = f"{spectrum.xlist[peak]:.0f}" + ax.text(x, y, text, rotation=90 - 30, horizontalalignment='left') + self.plot.canvas.draw() + + +def load_idle(root): + """Load the IDLE shell""" + try: + import idlelib.pyshell as pyshell + sys.argv = [sys.argv[0], "-n"] + root.bind("", lambda ev: pyshell.main()) + root.bind("", lambda ev: pyshell.main()) + except ModuleNotFoundError: + pass + + +def main(args=None): + """Main program entry.""" + global root, app, options # access to application for interactive idle mode + + parser = argparse.ArgumentParser(description="Demonstration of map image arithmetic using python") + parser.add_argument('filename', help='Path to wdf file to open') + parser.add_argument('--show_baseline', action='store_true', + help='Show original and baseline plots.') + parser.add_argument('--no-labels', dest='labels', action='store_false', default=True, + help='Show peaks with xlist labels') + parser.add_argument('--no-smooth', dest='smooth', action='store_false', default=True, + help='Smooth spectral data') + parser.add_argument('--no-normalize', dest='normalize', action='store_false', default=True, + help='Normalize data') + parser.add_argument('--prominence', type=float, default=0.01, + help="Prominence value for peak pick") + options = parser.parse_args(args) + + root = tk.Tk() + app = App(root, options) + root.after(200, load_idle, root) + try: + root.mainloop() + except Exception as e: + tkmessagebox.showerror('Error', e) + return 0 + + +if __name__ == '__main__': + sys.exit(main(sys.argv[1:])) diff --git a/rsl/__init__.py b/rsl/__init__.py new file mode 100644 index 0000000..b80ee62 --- /dev/null +++ b/rsl/__init__.py @@ -0,0 +1,16 @@ +"""Spectrum processing classes.""" + + +__title__ = "renishaw_spectral_library" +__version__ = "1.0.0" +__author__ = "Renishaw plc" +__author_email__ = "spd_developers@renishaw.com" +__license__ = "Apache Software License" + + +from .spectrum import Spectrum, Region +from .steps import ( + TruncateStep, ZapStep, NormalizeStep, InterpolateStep, + AddStep, SubtractStep, DivideStep, MultiplyStep, PowerStep, + SmoothStep, PeakPickStep, BaselineSubtractionStep +) diff --git a/rsl/spectrum.py b/rsl/spectrum.py new file mode 100644 index 0000000..e08c114 --- /dev/null +++ b/rsl/spectrum.py @@ -0,0 +1,56 @@ +import numpy as np +from dataclasses import dataclass +from typing import Sequence + + +@dataclass +class Region: + begin: int + end: int + + def __init__(self, xlist, left: float, right: float): + inrange = np.where(np.logical_and(xlist >= left, xlist <= right))[0] + sorted(inrange) + self.begin, self.end = inrange[0], inrange[-1] + + def __iter__(self): + yield self.begin + yield self.end + + +class Spectrum(): + """Reference a single spectrum""" + def __init__(self, xlist: Sequence[float], ilist: Sequence[float], pipeline=None): + self.data = None # pipeline processed + self._data = np.array((xlist, ilist)) # original + self.pipeline = pipeline + + @property + def xlist(self) -> np.ndarray: + if self.data is None: + self.update() + return self.data[0] + + @property + def ilist(self) -> np.ndarray: + if self.data is None: + self.update() + return self.data[1] + + def update(self): + """Process the input using the defined pipeline + Caches the result so we only do this once. + The pipeline can be a list or ordered dict""" + data = self._data + if self.pipeline: + if isinstance(self.pipeline, dict): + for step in self.pipeline.values(): + data = step(data) + else: + for step in self.pipeline: + data = step(data) + self.data = data + + def __iter__(self): + for pair in zip(self.xlist, self.ilist): + yield pair diff --git a/rsl/steps.py b/rsl/steps.py new file mode 100644 index 0000000..8891350 --- /dev/null +++ b/rsl/steps.py @@ -0,0 +1,264 @@ +import numpy as np +from scipy import sparse +from scipy.sparse.linalg import spsolve +from scipy.interpolate import interp1d +from scipy.signal import savgol_filter, find_peaks +from . import Region + + +class DivideStep: + def __init__(self, operand: float): + self.operand = operand + + def __call__(self, *args, **kwargs): + spectrum, = args + spectrum = spectrum.copy() + spectrum[1] = np.divide(spectrum[1], self.operand) + return spectrum + + +class MultiplyStep: + def __init__(self, operand: float): + self.operand = operand + + def __call__(self, *args, **kwargs): + spectrum, = args + spectrum = spectrum.copy() + spectrum[1] = np.multiply(spectrum[1], self.operand) + return spectrum + + +class SubtractStep: + def __init__(self, operand: float): + self.operand = operand + + def __call__(self, *args, **kwargs): + spectrum, = args + spectrum = spectrum.copy() + spectrum[1] = np.subtract(spectrum[1], self.operand) + return spectrum + + +class AddStep: + def __init__(self, operand: float): + self.operand = operand + + def __call__(self, *args, **kwargs): + spectrum, = args + spectrum = spectrum.copy() + spectrum[1] = np.add(spectrum[1], self.operand) + return spectrum + + +class PowerStep: + def __init__(self, operand: float): + self.operand = operand + + def __call__(self, *args, **kwargs): + spectrum, = args + spectrum = spectrum.copy() + spectrum[1] = np.power(spectrum[1], self.operand) + return spectrum + + +class ZapStep: + def __init__(self, begin: float, end: float, kind='cubic'): + self.begin = begin + self.end = end + self.kind = kind + + def __call__(self, *args, **kwargs): + spectrum, = args + region = Region(spectrum[0], self.begin, self.end) + i = range(region.begin, region.end + 1) + regionx = [ + spectrum[0][region.begin - 1], spectrum[0][region.begin], + spectrum[0][region.end], spectrum[0][region.end + 1]] + regiony = [ + spectrum[1][region.begin - 1], spectrum[1][region.begin], + spectrum[1][region.end], spectrum[1][region.end + 1]] + interpolator = interp1d(regionx, regiony, kind=self.kind) + spectrum = spectrum.copy() + spectrum[1][i] = interpolator(spectrum[0][i]) + return spectrum + + +class TruncateStep: + """ + Truncate a spectrum to a range. + """ + def __init__(self, begin: float, end: float): + self.begin = begin + self.end = end + + def __call__(self, *args, **kwargs): + spectrum, = args + region = Region(spectrum[0], self.begin, self.end) + i = range(region.begin, region.end + 1) # inclusive of end point + return np.array([spectrum[0][i], spectrum[1][i]]) + + +class NormalizeStep: + """Normalize the spectrum intensity values between 0 and 1.0""" + def __init__(self): + self._original = None + + @property + def original(self): + return self._original + + def __repr__(self): + return f"{self.__class__.__name__}()" + + def __call__(self, *args, **kwargs): + self._original, = args + spectrum = self._original.copy() + spectrum[1] = np.divide(spectrum[1], np.max(spectrum[1])) + return spectrum + + +class InterpolateStep: + """Interpolate to even grid + + Same number of points and starting and ending at the same point + but evenly spaced.""" + def __init__(self): + self._original = None + + @property + def original(self): + return self._original + + def __repr__(self): + return f"{self.__class__.__name__}()" + + def __call__(self, *args, **kwargs): + self._original, = args + spectrum = self._original + num = len(spectrum[0]) + a, b = spectrum[0][0], spectrum[0][-1] + xlist = np.linspace(a, b, num=num, endpoint=True) + if a < b: + ilist = np.interp(xlist, spectrum[0], spectrum[1]) + else: + ilist = np.flip(np.interp(np.flip(xlist), np.flip(spectrum[0]), np.flip(spectrum[1]))) + return np.array([xlist, ilist], dtype='float') + + +class SmoothStep: + """Apply a Savitsky-Golay filter to smooth spectral data""" + def __init__(self, window: int = 30, polynomial: int = 2): + self.window = window + self.polynomial = polynomial + self._original = None + + @property + def original(self): + return self._original + + def __iter__(self): + yield "window", self.window + yield "poly", self.polynomial + + def __call__(self, *args, **kwargs): + self._original, = args + spectrum = self._original.copy() + spectrum[1] = savgol_filter(spectrum[1], self.window, self.polynomial) + return spectrum + + +class PeakPickStep: + def __init__(self, height=None, threshold=None, distance=None, prominence=None, width=None): + self.height = height + self.threshold = threshold + self.distance = distance + self.prominence = prominence + self.width = width + self._orignal = None + self._peaks = None + self._peak_data = None + + @property + def original(self): + return self._original + + @property + def peaks(self): + return self._peaks + + @property + def peak_data(self): + return self._peak_data + + def __iter__(self): + yield 'height', self.height + yield 'threshold', self.threshold + yield 'distance', self.distance + yield 'prominence', self.prominence + yield 'width', self.width + + def __call__(self, *args, **kwargs): + self._original, = args + self._peaks, self._peak_data = find_peaks( + self._original[1], height=self.height, threshold=None, + distance=self.distance, prominence=self.prominence, width=self.width) + return self._original + + +class BaselineSubtractionStep: + def __init__(self, l: float = 10**5, p: float = 0.001): + self.l = l + self.p = p + self._baseline = None + self._original = None + + @property + def baseline(self): + return self._baseline + + @property + def original(self): + return self._original + + def __iter__(self): + yield "l", self.l + yield "p", self.p + + def __call__(self, *args, **kwargs): + self._original, = args + spectrum = self._original.copy() + self._baseline = BaselineSubtractionStep.baseline_als(spectrum[1], self.l, self.p) + spectrum[1] -= self._baseline + return spectrum + + @staticmethod + def baseline_als(y, lam, p, niter=10): + """Assymetric Least Squares Smoothing + P. Eilers and H. Boelens (2005) + + p for asymmetry 0.001 ≤ p ≤ 0.1 is a good choice (for a signal with positive peaks) + λ for smoothness, 10^2 ≤ λ ≤ 10^9 + """ + L = len(y) + D = sparse.diags([1, -2, 1], [0, -1, -2], shape=(L, L - 2)) + w = np.ones(L) + for i in range(niter): + W = sparse.spdiags(w, 0, L, L) + Z = W + lam * D.dot(D.transpose()) + z = spsolve(Z, w * y) + w = p * (y > z) + (1 - p) * (y < z) + return z + +# def _1Voigt(x, ampG1, cenG1, sigmaG1, ampL1, cenL1, widL1): +# return (ampG1*(1/(sigmaG1*(np.sqrt(2*np.pi))))*(np.exp(-((x-cenG1)**2)/((2*sigmaG1)**2)))) +\ +# ((ampL1*widL1**2/((x-cenL1)**2+widL1**2)) ) + +# class CurveFitStep: +# def __init__(self): +# self._original = None +# @property +# def original(self): +# return self._original +# def __call__(self, *args, **kwargs): +# self._original, = args +# return self._original diff --git a/rsl/test/__init__.py b/rsl/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rsl/test/test_spectrum.py b/rsl/test/test_spectrum.py new file mode 100644 index 0000000..bbefc0e --- /dev/null +++ b/rsl/test/test_spectrum.py @@ -0,0 +1,46 @@ +import unittest +import numpy as np +from numpy.testing import assert_array_equal +from ..spectrum import Spectrum, Region +from ..steps import DivideStep, MultiplyStep, SubtractStep, AddStep, PowerStep + + +class TestRegion(unittest.TestCase): + def test_region(self): + xlist = np.linspace(1, 100, 10, endpoint=True) + region = Region(xlist, 25, 75) + self.assertEqual(region.begin, 3) + self.assertEqual(region.end, 6) + + def test_region_reverse(self): + xlist = np.array([100.0, 89.0, 78.0, 67.0, 56.0, 45.0, 34.0, 23.0, 12.0, 1.0], dtype='float') + region = Region(xlist, 25, 75) + self.assertEqual(region.begin, 3) + self.assertEqual(region.end, 6) + + +class TestSpectrum(unittest.TestCase): + xlist = [0, 1, 2, 3, 4, 5] + ilist = [2, 10, 12, 14, 12, 10] + aux = [2, 0, 2, 4, 2, 0] + + def test_spectrum(self): + spectrum = Spectrum(self.xlist, self.ilist) + assert_array_equal(self.xlist, spectrum.xlist) + assert_array_equal(self.ilist, spectrum.ilist) + + def test_spectrum_with_step(self): + pipeline = [SubtractStep(self.aux)] + spectrum = Spectrum(self.xlist, self.ilist, pipeline=pipeline) + assert_array_equal(self.xlist, spectrum.xlist) + assert_array_equal([0, 10, 10, 10, 10, 10], spectrum.ilist) + + def test_spectrum_with_two_steps(self): + pipeline = [SubtractStep(self.aux), AddStep(10.0)] + spectrum = Spectrum(self.xlist, self.ilist, pipeline=pipeline) + assert_array_equal(self.xlist, spectrum.xlist) + assert_array_equal([10, 20, 20, 20, 20, 20], spectrum.ilist) + + +if __name__ == '__main__': + unittest.main() diff --git a/rsl/test/test_steps.py b/rsl/test/test_steps.py new file mode 100644 index 0000000..44cdad2 --- /dev/null +++ b/rsl/test/test_steps.py @@ -0,0 +1,132 @@ +import unittest +import numpy as np +import matplotlib.pyplot as plt +import scipy.signal +from numpy.testing import assert_array_equal +from ..steps import ( + DivideStep, MultiplyStep, SubtractStep, AddStep, PowerStep, + ZapStep, TruncateStep, NormalizeStep, InterpolateStep, + SmoothStep, PeakPickStep) + + +class TestArithmeticSteps(unittest.TestCase): + xlist = [0, 1, 2, 3, 4, 5] + ilist = [2, 10, 12, 14, 12, 10] + + def test_multiply_3(self): + spectrum = np.array([self.xlist, self.ilist], dtype='float') + f = MultiplyStep(3.0) + result = f(spectrum) + assert_array_equal(spectrum[0], result[0]) + assert_array_equal(np.array([6, 30, 36, 42, 36, 30], dtype='float'), result[1]) + assert_array_equal(spectrum[1], self.ilist) # verify original is unmodified + + def test_divide_2(self): + spectrum = np.array([self.xlist, self.ilist], dtype='float') + f = DivideStep(2.0) + result = f(spectrum) + assert_array_equal(spectrum[0], result[0]) + assert_array_equal(np.array([1, 5, 6, 7, 6, 5], dtype='float'), result[1]) + assert_array_equal(spectrum[1], self.ilist) # verify original is unmodified + + def test_divide_2_minus_1(self): + spectrum = np.array([self.xlist, self.ilist], dtype='float') + f1 = DivideStep(2.0) + f2 = SubtractStep(1.0) + result = f2(f1(spectrum)) + assert_array_equal(spectrum[0], result[0]) + assert_array_equal(np.array([0, 4, 5, 6, 5, 4], dtype='float'), result[1]) + assert_array_equal(spectrum[1], self.ilist) # verify original is unmodified + + def test_divide_by_aux(self): + aux = np.array([self.xlist, [2, 5, 6, 7, 2, 2]], dtype='float') + spectrum = np.array([self.xlist, self.ilist], dtype='float') + f = DivideStep(aux[1]) + result = f(spectrum) + assert_array_equal(spectrum[0], result[0]) + assert_array_equal(np.array([1, 2, 2, 2, 6, 5], dtype='float'), result[1]) + assert_array_equal(spectrum[1], self.ilist) # verify original is unmodified + + def test_subtract_aux(self): + aux = np.array([self.xlist, [2, 5, 6, 7, 2, 2]], dtype='float') + spectrum = np.array([self.xlist, self.ilist], dtype='float') + f = SubtractStep(aux[1]) + result = f(spectrum) + assert_array_equal(spectrum[0], result[0]) + assert_array_equal(np.array([0, 5, 6, 7, 10, 8], dtype='float'), result[1]) + assert_array_equal(spectrum[1], self.ilist) # verify original is unmodified + + def test_add_2(self): + spectrum = np.array([self.xlist, self.ilist], dtype='float') + f = AddStep(2.0) + result = f(spectrum) + assert_array_equal(spectrum[0], result[0]) + assert_array_equal(np.array([4, 12, 14, 16, 14, 12], dtype='float'), result[1]) + assert_array_equal(spectrum[1], self.ilist) # verify original is unmodified + + def test_power_2(self): + spectrum = np.array([self.xlist, self.ilist], dtype='float') + f = PowerStep(2.0) + result = f(spectrum) + assert_array_equal(spectrum[0], result[0]) + assert_array_equal(np.array([4, 100, 144, 196, 144, 100], dtype='float'), result[1]) + assert_array_equal(spectrum[1], self.ilist) # verify original is unmodified + + +class TestProcessingSteps(unittest.TestCase): + def test_zap_linear(self): + xlist = [x for x in range(11)] + ilist = scipy.signal.gaussian(11, 2, True) + spectrum = np.array([xlist, ilist], dtype='float') + f = ZapStep(2, 8, kind='linear') + result = f(spectrum) + diff = [int(i * 100) for i in np.subtract(spectrum[1], result[1])] + assert_array_equal([0, 0, 0, 28, 55, 67, 55, 28, 0, 0, 0], diff) + self.assertEqual(result[1][3], result[1][7]) + + def test_zap_cubic(self): + xlist = [x for x in range(11)] + ilist = scipy.signal.gaussian(11, 2, True) + spectrum = np.array([xlist, ilist], dtype='float') + f = ZapStep(2, 8, kind='cubic') + result = f(spectrum) + diff = [int(i * 100) for i in np.subtract(spectrum[1], result[1])] + assert_array_equal([0, 0, 0, 14, 34, 43, 34, 14, 0, 0, 0], diff) + + def test_truncate(self): + xlist = [x for x in range(100)] + ilist = scipy.signal.gaussian(100, 10, True) + spectrum = np.array([xlist, ilist], dtype='float') + f = TruncateStep(20, 80) + result = f(spectrum) + self.assertEqual(20, result[0][0]) + self.assertEqual(80, result[0][-1]) + + def test_normalize(self): + xlist = [x for x in range(100)] + ilist = np.multiply(scipy.signal.gaussian(100, 10, True), 10.0) + spectrum = np.array([xlist, ilist], dtype='float') + f = NormalizeStep() + result = f(spectrum) + assert_array_equal(xlist, result[0]) + self.assertEqual(1.0, np.max(result[1])) + + def test_interpolate(self): + xlist = [0, .7, 1.5, 2.0, 3.0, 3.5, 4.0, 4.8, 5.5, 6.28] + ilist = np.sin(xlist) + spectrum = np.array([xlist, ilist], dtype='float') + f = InterpolateStep() + result = f(spectrum) + np.testing.assert_almost_equal(np.diff(result[0]), [0.69777778] * 9) + + def test_interpolate_reverse(self): + xlist = [6.28, 5.5, 4.8, 4.0, 3.5, 3.0, 2.0, 1.5, .7, 0] + ilist = np.sin(xlist) + spectrum = np.array([xlist, ilist], dtype='float') + f = InterpolateStep() + result = f(spectrum) + np.testing.assert_almost_equal(np.diff(result[0]), [-0.69777778] * 9) + + +if __name__ == '__main__': + unittest.main() diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..afdad55 --- /dev/null +++ b/setup.py @@ -0,0 +1,21 @@ +import setuptools +import wdf + +setuptools.setup( + name=wdf.__title__, + version=wdf.__version__, + description=wdf.__doc__, + author=wdf.__author__, + author_email=wdf.__author_email__, + license=wdf.__license__, + packages=['wdf'], + install_requires=[], + classifiers=[ + 'Development Status :: 4 - Beta', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: Apache Software License', + 'Operating System :: OS Independent', + 'Programming Language :: Python :: 3', + 'Topic :: Scientific/Engineering :: Physics', + ], +) diff --git a/tox.ini b/tox.ini new file mode 100644 index 0000000..10e0a48 --- /dev/null +++ b/tox.ini @@ -0,0 +1,23 @@ +[tox] +envlist = py310, py38, lint +skip_missing_interpreters = True + +[testenv] +setenv = + PYTHONPATH = {toxinidir};{toxinidir}/wdf +deps = + pytest +commands = + py.test + +[testenv:lint] +deps = + flake8 +commands = + flake8 wdf + +[flake8] +max-line-length = 110 +ignore = + # Constants imported into __init__ are not used there but are exposed to users here. + F401 diff --git a/uvdata/.gitignore b/uvdata/.gitignore new file mode 100644 index 0000000..8de5db4 --- /dev/null +++ b/uvdata/.gitignore @@ -0,0 +1,3 @@ +*.db +*~ + diff --git a/uvdata/README.md b/uvdata/README.md new file mode 100644 index 0000000..55bd9aa --- /dev/null +++ b/uvdata/README.md @@ -0,0 +1,17 @@ +# HASS databases + +The 20220319 database has data for 9 March 2022 to 19 March 2022. + +# Plotting + + import matplotlib.pyplot as plt + import matplotlib.dates as mdates + import pandas as pd + + df = pd.read_csv(filename, names=('Date', 'Value')) + # df['Date'] = df['Date'].map(lambda x: datetime.strptime(str(x), '%Y-%m-%d %H:%M:%S.%f')) + df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S.%f') + + plt.plot(df['Date'], df['Value']) + plt.gcf().autofmt_xdate() # prettify the x-labels + plt.show() diff --git a/uvdata/UVPlotting.ipynb b/uvdata/UVPlotting.ipynb new file mode 100644 index 0000000..0a6677f --- /dev/null +++ b/uvdata/UVPlotting.ipynb @@ -0,0 +1,185 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "id": "817bf74d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0516509433962264" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "from datetime import datetime, timezone\n", + "from scipy.integrate import trapz\n", + "from pandas.plotting import register_matplotlib_converters\n", + "register_matplotlib_converters()\n", + "\n", + "df = pd.read_csv('uvdata.csv', header=1, index_col=[],\n", + " names=('date','uvpower','temperature','light'),\n", + " dtype={'uvpower': float})\n", + "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%dT%H:%M:%S.%f')\n", + "\n", + "pdf = None\n", + "#pdf = PdfPages('uvplots.pdf')\n", + "\n", + "# Plot a single week of the raw uv data so we can see what we are dealing with\n", + "day = 1\n", + "start_date = datetime(2022, 5, day, tzinfo=timezone.utc)\n", + "end_date = datetime(2022, 5, day+8, tzinfo=timezone.utc)\n", + "inrange = np.where(np.logical_and(df['date'] >= start_date, df['date'] <= end_date))\n", + "xlist = df['date'][inrange[0]]\n", + "ylist = np.array(df['uvpower'][inrange[0]], dtype=float)\n", + "plt.figure(figsize=(20, 6))\n", + "plt.title('raw uv power over 1 week')\n", + "plt.plot(xlist, ylist)\n", + "if pdf:\n", + " pdf.savefig()\n", + " plt.close()\n", + "\n", + "# Calculate the area under the line for each week\n", + "plt.figure(figsize=(20, 6))\n", + "plt.title('Integrated uv power per week')\n", + "data = df.groupby([pd.Grouper(key='date', freq='W')])['uvpower'].agg(trapz)\n", + "_ = plt.plot(data, '-o')\n", + "if pdf:\n", + " pdf.savefig()\n", + " plt.close()\n", + "\n", + "# Plot the integrated area under the line for each day\n", + "plt.figure(figsize=(20, 6))\n", + "plt.title('Integrated uv power per day')\n", + "agg = df.groupby([pd.Grouper(key='date', freq='D')])['uvpower'].agg(trapz)\n", + "_ = plt.plot(agg, '-o')\n", + "if pdf:\n", + " pdf.savefig()\n", + " plt.close()\n", + "\n", + "# Get the average integrated value for each of the first 60 days (the duration of the experiment)\n", + "agg[0:60].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5912bb29", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from datetime import datetime, timezone\n", + "from scipy.integrate import trapz\n", + "from pandas.plotting import register_matplotlib_converters\n", + "register_matplotlib_converters()\n", + "\n", + "# Load the uvpower values extracted from the home assistant database\n", + "df = pd.read_csv('uvbox_uvpower.csv', header=0, index_col=[], dtype={'value': float})\n", + "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d %H:%M:%S.%f')\n", + "\n", + "day = 18\n", + "start_date = datetime(2022, 3, day)\n", + "end_date = datetime(2022, 3, day+1)\n", + "inrange = np.where(np.logical_and(df['date'] >= start_date, df['date'] <= end_date))\n", + "\n", + "xlist = df['date'][inrange[0]]\n", + "ylist = np.array(df['value'][inrange[0]], dtype=float)\n", + "ylist = np.multiply(ylist, 6.0)\n", + "total = trapz(ylist)\n", + "\n", + "fig = plt.figure(figsize=(20, 6))\n", + "plt.gcf().autofmt_xdate()\n", + "plt.plot(xlist, ylist)\n", + "_ = plt.annotate(f\"u.v. power {total:.3f}\", xy=(\"2022-03-18\", 0.0058))\n", + "if pdf:\n", + " pdf.savefig()\n", + " pdf.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6f633ffa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from pandas.plotting import register_matplotlib_converters\n", + "\n", + "register_matplotlib_converters()\n", + "\n", + "df = pd.read_csv('pressure.csv', header=0, names=('date', 'value'), dtype={'value': float})\n", + "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d %H:%M:%S.%f')\n", + "\n", + "plt.figure(figsize=(20, 4))\n", + "_ = plt.plot(df.date, df.value)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/uvdata/hass-extract.sh b/uvdata/hass-extract.sh new file mode 100755 index 0000000..b4b7cf3 --- /dev/null +++ b/uvdata/hass-extract.sh @@ -0,0 +1,19 @@ +#!/bin/bash +# +# Extract data for a given topic from the home assistant database file. +# Outputs CSV data to stdout. + +if [ $# -ne 2 ]; then + echo >&2 "usage: hass-extract sqlitedb topic" + echo >&2 " eg: hass/sensor/uvbox_veml6070_uvpower/state" + echo >&2 " hass/sensor/uvsensor_veml6070_uvpower/state" + echo >&2 " hass/sensor/uvbox_bme280_pressure/state" + echo >&2 " hass/sensor/uvbox_bh1750_illuminance/state" + exit 1 +fi + +sqlite3 -header -csv $1 << EOF +SELECT time_fired,json_extract(event_data,'\$.service_data.payload') AS value + FROM events WHERE json_extract(event_data, '\$.service_data.topic') = "$2"; +EOF + diff --git a/uvdata/hass-plot.py b/uvdata/hass-plot.py new file mode 100644 index 0000000..7b3e585 --- /dev/null +++ b/uvdata/hass-plot.py @@ -0,0 +1,49 @@ +#! /usr/bin/env python3 + +''' +Simple plot of 2 column (date, value) csv data as exported from SQLite3 +''' + +import sys +import argparse +import matplotlib.pyplot as plt +import matplotlib.dates as mdates +import pandas as pd +from scipy.integrate import trapz, simps + +from pandas.plotting import register_matplotlib_converters +register_matplotlib_converters() + +def main(args=None): + parser = argparse.ArgumentParser(description='plot date from two column csv files') + parser.add_argument('filename') + options = parser.parse_args(args) + + # Load the data assuming the first col is the date (needs fixing from sqlite) + df = pd.read_csv(options.filename, header=0, names=('Date', 'Value')) + df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S.%f') + + # we need to remove rows where the value is 'unavailable' or 'unknown' + # >>> df[df['Value'].isin(('unavailable', 'unknown'))] + # Date Value + # 334 2022-03-10 08:06:30.518897 unavailable + # 335 2022-03-10 08:06:40.223461 unavailable + # 1977 2022-03-15 23:35:24.848513 unknown + # ... + # Convert bad values to null - selects values in seq and negates. + df = df[~df['Value'].isin(('unavailable', 'unknown'))] + df.dropna() # drop the nulls + + xlist = df['Date'] + ylist = df['Value'] + + + #print(trapz(ylist, xlist)) + #print(simps(ylist, xlist)) + + plt.plot(xlist, ylist) + plt.gcf().autofmt_xdate() # prettify the x-labels + plt.show() + +if __name__ == '__main__': + sys.exit(main(sys.argv[1:])) diff --git a/uvdata/pressure.csv b/uvdata/pressure.csv new file mode 100644 index 0000000..14c2dcc --- /dev/null +++ b/uvdata/pressure.csv @@ -0,0 +1,2984 @@ +date,value +"2022-03-09 04:27:17.766140",1003.7 +"2022-03-09 04:32:17.760167",1003.7 +"2022-03-09 04:37:17.785287",1003.8 +"2022-03-09 04:42:17.772798",1003.9 +"2022-03-09 04:47:17.760189",1003.9 +"2022-03-09 04:52:17.788290",1003.8 +"2022-03-09 04:57:17.738026",1003.8 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Third-parties may +define their own block ids but should use lower-case letter values. +""" + + +FILE = 0x31464457 # 'W' 'D' 'F' '1' +DATA = 0x41544144 # 'D' 'A' 'T' 'A' +YLIST = 0x54534c59 # 'Y' 'L' 'S' 'T' +XLIST = 0x54534c58 # 'X' 'L' 'S' 'T' +ORIGIN = 0x4e47524f # 'O' 'R' 'G' 'N' +COMMENT = 0x54584554 # 'T' 'E' 'X' 'T' +WIREDATA = 0x41445857 # 'W' 'X' 'D' 'A' +DATASETDATA = 0x42445857 # 'W' 'X' 'D' 'B' +MEASUREMENT = 0x4d445857 # 'W' 'X' 'D' 'M' +CALIBRATION = 0x53435857 # 'W' 'X' 'C' 'S' +INSTRUMENT = 0x53495857 # 'W' 'X' 'I' 'S' +MAPAREA = 0x50414d57 # 'W' 'M' 'A' 'P' +WHITELIGHT = 0x4c544857 # 'W' 'H' 'T' 'L' +THUMBNAIL = 0x4c49414e # 'N' 'A' 'I' 'L' +MAP = 0x2050414d # 'M' 'A' 'P' ' ' +CURVEFIT = 0x52414643 # 'C' 'F' 'A' 'R' +COMPONENT = 0x534c4344 # 'D' 'C' 'L' 'S' +PCA = 0x52414350 # 'P' 'C' 'A' 'R' +EM = 0x4552434d # 'M' 'C' 'R' 'E' +ZELDAC = 0x43444c5a # 'Z' 'L' 'D' 'C' +RESPONSECAL = 0x4c414352 # 'R' 'C' 'A' 'L' +CAP = 0x20504143 # 'C' 'A' 'P' ' ' +PROCESSING = 0x50524157 # 'W' 'A' 'R' 'P' +ANALYSIS = 0x41524157 # 'W' 'A' 'R' 'A' +SPECTRUMLABELS = 0x4C424C57 # 'W' 'L' 'B' 'L' +CHECKSUM = 0x4b484357 # 'W' 'C' 'H' 'K' +RXCALDATA = 0x44435852 # 'R' 'X' 'C' 'D' +RXCALFIT = 0x46435852 # 'R' 'X' 'C' 'F' +XCAL = 0x4C414358 # 'X' 'C' 'A' 'L' +SPECSEARCH = 0x48435253 # 'S' 'R' 'C' 'H' +TEMPPROFILE = 0x504d4554 # 'T' 'E' 'M' 'P' +UNITCONVERT = 0x56434e55 # 'U' 'N' 'C' 'V' +ARPLATE = 0x52505241 # 'A' 'R' 'P' 'R' +ELECSIGN = 0x43454c45 # 'E' 'L' 'E' 'C' +BKXLIST = 0x4c584b42 # 'B' 'K' 'X' 'L' +AUXILARYDATA = 0x20585541 # 'A' 'U' 'X' ' ' +CHANGELOG = 0x474c4843 # 'C' 'H' 'L' 'G' +SURFACE = 0x46525553 # 'S' 'U' 'R' 'F' +ARCALPLATE = 0x50435241 # 'A' 'R' 'C' 'P' +PMC = 0x20434d50 # 'P' 'M' 'C' ' ' +CAMERAFIXEDFREQDATA = 0x44464643 # 'C' 'F' 'F' 'D' +CLUSTER = 0x53554c43 # 'C' 'L' 'U' 'S' +HIERARCHICALCLUSTER = 0x20414348 # 'H' 'C' 'A' ' ' +TEMPPTR = 0x52545054 # 'T' 'P' 'T' 'R' +UNKNOWN = 0x3f4b4e55 # 'U' 'N' 'K' '?' +WMSK = 0x4b534d57 # 'W' 'M' 'S' 'K' +STDV = 0x56445453 # 'S' 'T' 'D' 'V' +EDIT = 0x54494445 # 'E' 'D' 'I' 'T' +WSLS = 0x534c5357 # 'W' 'S' 'L' 'S' +WPAC = 0x43415057 # 'W' 'P' 'A' 'C' +ANY = 0xffffffff # reserved value for @ref Wdf_FindSection diff --git a/wdf/__init__.py b/wdf/__init__.py new file mode 100644 index 0000000..6c9e7a6 --- /dev/null +++ b/wdf/__init__.py @@ -0,0 +1,13 @@ +"""Provides python access to Renishaw Wdf spectral data files.""" + + +__title__ = "renishaw_wdf" +__version__ = "1.0.2" +__author__ = "Renishaw plc" +__author_email__ = "spd_developers@renishaw.com" +__license__ = "Apache Software License" + + +from .wdf import WdfBlock, WdfHeader, WdfIter, Wdf +from .pset import Pset +from .origin import WdfDataType, WdfDataUnit diff --git a/wdf/custom.py b/wdf/custom.py new file mode 100644 index 0000000..54ee7bf --- /dev/null +++ b/wdf/custom.py @@ -0,0 +1,45 @@ +from enum import Enum, _decompose +from types import MappingProxyType + + +_CUSTOM_FLAG = 0x40000000 + + +class CustomEnum(Enum): + """Enum class that allows for on-demand additional items that use the custom bit set""" + + def is_custom(self): + """Returns true if the enumeration value is a custom value.""" + return self.value & _CUSTOM_FLAG == _CUSTOM_FLAG + + @classmethod + def _missing_(cls, value): + """Returns member (possibly creating it) if one can be found for value""" + if not isinstance(value, int): + raise ValueError("%r is not a valid %s" % (value, cls.__qualname__)) + iscustom = value & _CUSTOM_FLAG == _CUSTOM_FLAG + if not iscustom: + raise ValueError("%r is not a valid custom %s" % (value, cls.__qualname__)) + new_member = cls._create_pseudo_member_(value) + return new_member + + @classmethod + def _create_pseudo_member_(cls, value): + """Create a custom member on demand""" + member = cls._value2member_map_.get(value, None) + if member is None: + # Ensure we have the custom flag + _, extra_flags = _decompose(cls, value) + if extra_flags & _CUSTOM_FLAG != _CUSTOM_FLAG: + raise ValueError("%r is not a valid %s" % (value, cls.__qualname__)) + # construct a new member + member = object.__new__(cls) + member._name_ = "Custom%d" % (value & ~_CUSTOM_FLAG) + member._value_ = value + # use setdefault in case another thread already created a composite + # with this value + member = cls._value2member_map_.setdefault(value, member) + # add the on-demand member into the enum type + cls._member_map_[member._name_] = member + cls._member_names_.append(member._name_) + return member diff --git a/wdf/origin.py b/wdf/origin.py new file mode 100644 index 0000000..d1abd68 --- /dev/null +++ b/wdf/origin.py @@ -0,0 +1,175 @@ +import sys +from dataclasses import dataclass +from enum import Enum +from os import SEEK_SET, SEEK_CUR +from io import BytesIO +from struct import unpack +from datetime import datetime, timezone, timedelta +from .custom import CustomEnum +from . import WdfBlockId + + +class WdfDataType(CustomEnum): + Arbitrary = 0 # < arbitrary type + Spectral = 1 # < DEPRECATED: Use Frequency instead (spectral data type) + Intensity = 2 # < intensity + Spatial_X = 3 # < X position + Spatial_Y = 4 # < Y axis position + Spatial_Z = 5 # < Z axis (vertical) position + Spatial_R = 6 # < rotary stage R axis position + Spatial_Theta = 7 # < rotary stage theta angle + Spatial_Phi = 8 # < rotary stage phi angle + Temperature = 9 # < temperature + Pressure = 10 # < pressure + Time = 11 # < time + Derived = 12 # < derivative type + Polarization = 13 # < polarization + FocusTrack = 14 # < focus track Z position + RampRate = 15 # < temperature ramp rate + Checksum = 16 # < spectrum data checksum + Flags = 17 # < bit flags + ElapsedTime = 18 # < elapsed time intervals + Frequency = 19 # < frequency + + # Microplate mapping origins + Mp_Well_Spatial_X = 20 + Mp_Well_Spatial_Y = 21 + Mp_LocationIndex = 22 + Mp_WellReference = 23 + + # PAF autofocus distance from focus + PAFZActual = 24 # < PAF distance from focus + PAFZError = 25 # < PAF distance between current and last requested positions + PAFSignalUsed = 26 # < PAF signal used (0 = None, 1 = Top, 2 = Bottom, 3 = Correlation) + + # Calculated exposure time used in exposure time normalisation + ExposureTime = 27 # < Measured exposure time in microseconds. + + ExternalSignal = 28 + + +class WdfDataUnit(Enum): + Arbitrary = 0 # < arbitrary units + RamanShift = 1 # < Raman shift (cm-1) + Wavenumber = 2 # < wavenumber (nm) + Nanometre = 3 # < 10-9 metres (nm) + ElectronVolt = 4 # < electron volts (eV) + Micron = 5 # < 10-6 metres (µm) + Counts = 6 # < counts + Electrons = 7 # < electrons + Millimetres = 8 # < 10-3 metres (mm) + Metres = 9 # < metres (m) + Kelvin = 10 # < degrees Kelvin (K) + Pascal = 11 # < Pascals (Pa) + Seconds = 12 # < seconds (s) + Milliseconds = 13 # < 10-3 seconds (ms) + Hours = 14 + Days = 15 + Pixels = 16 + Intensity = 17 + RelativeIntensity = 18 + Degrees = 19 + Radians = 20 + Celcius = 21 + Farenheit = 22 + KelvinPerMinute = 23 + FileTime = 24 # < date-time expressed as a Windows FILETIME + Microseconds = 25 # < 10-6 seconds (µs) + Volts = 26 + Amps = 27 + MilliAmps = 28 + Strain = 29 + Ohms = 30 + DegreesR = 31 + Coulombs = 32 + PicoCoulombs = 33 + + +class WdfOrigin: + """Present a Wdf file data origin as a list type object with additional properties. + + The datatype, units, label and a flag for alternate origins are provided as properties.""" + _EPOCH = datetime(year=1601, month=1, day=1, tzinfo=timezone.utc) + + def __init__(self, stream: BytesIO, count: int, position: int): + self._stream = stream # source of data + self._count = count # number of elements stored + self._position = position # start of the origin data header + stream.seek(position, SEEK_SET) + data = stream.read(24) + datatype, dataunits = unpack('> 31) & 1) + self.datatype = WdfDataType(datatype & 0x7fffffff) + self.dataunit = WdfDataUnit(dataunits) + # truncate the label at the first \0 or end and decode as utf-8 + label = data[8:] + b'\0' + ndx = label.index(b'\0') + label = label[:ndx] + self.label = label.decode('utf-8') + + def __str__(self): + return f"WdfOrigin(label='{self.label}' type='{self.datatype}', units='{self.dataunit}')" + + def __repr__(self): + return f"{self.__module__}.{self.__class__.__name__}({self._stream}, {self._count}, {self._position})" + + def __len__(self): + """Return the number of elements in the data origin.""" + return self._count + + def __getitem__(self, index: int): + """Return an element of the collection. Supports slices. + eg: origin[0:10] for the first 10 values, + origin[::2] for every other value, etc. + This allows the collection to be iterable as well.""" + + if isinstance(index, int): + start, stop, step = slice(index, index + 1, None).indices(self._count) + elif isinstance(index, slice): + start, stop, step = index.indices(self._count) + else: + raise TypeError("WdfOrigin indices must be integers or slices") + + self._stream.seek(self._position + 24 + (start * 8), SEEK_SET) + if step == 1: + result = self._readvals(stop - start) + else: + result = [] + for ndx in range(start, stop, step): + result.append(self._readvals(1)[0]) + self._stream.seek(step * 8, SEEK_CUR) + + return result if isinstance(index, slice) else result[0] + + def _readvals(self, len: int, format='d'): + """Read data origin values and convert as appropriate for the type and/or units. + + FileTime data is converted to python datetime objects. + Flags and Checksum types are read as 64 bit integers. All others are read as floating-point values. + The 'format' parameter can be used to change the default.""" + if self.dataunit == WdfDataUnit.FileTime \ + or self.datatype == WdfDataType.Flags \ + or self.datatype == WdfDataType.Checksum: + format = 'Q' + result = unpack(f"<{len}{format}", self._stream.read(len * 8)) + if self.dataunit == WdfDataUnit.FileTime: + result = [WdfOrigin._EPOCH + timedelta(microseconds=(t / 10)) for t in result] + return result + + +class WdfOriginSet(dict): + """Provides access to the data origin set from a WDF file. + + The origins are indexed by a DataType value. There can only be one origin for any + specific DataType. This class reads the origin data from the file on demand.""" + + def __init__(self, parent): + self.parent = parent + stream = parent.fd + _ = parent.find_section(WdfBlockId.ORIGIN) + origin_size = parent.hdr.nspectra * 8 + count = unpack(' bool: + """Test a stream for an embedded property set. + Reads 4 bytes from the stream. If the result is False it may be necessary + to reposition the stream seek position""" + magic = stream.read(4) + return magic == b'PSET' + + @staticmethod + def fromstream(stream: BinaryIO): + """Parse a stream and return the Pset decoded or None""" + result = None + if Pset.is_pset(stream): + size = unpack('> 15) & 1: + if item.opcode == PSET_TYPE_KEY: + customKeyNames[item.key] = item.value + else: + customItems[item.key] = item + else: + keyName = getKeyName(item.key) + result.items[keyName] = item + # Saved custom key names/items construct name:item pairs in the returned dictionary + for nameKey in customKeyNames: + for itemKey in customItems: + if nameKey == itemKey: + result.items[customKeyNames[nameKey]] = customItems[itemKey] + return result diff --git a/wdf/test/Si-map.orig.wdf b/wdf/test/Si-map.orig.wdf new file mode 100644 index 0000000..455162c Binary files /dev/null and b/wdf/test/Si-map.orig.wdf differ diff --git a/wdf/test/Si-map.wdf b/wdf/test/Si-map.wdf new file mode 100644 index 0000000..455162c Binary files /dev/null and b/wdf/test/Si-map.wdf differ diff --git a/wdf/test/__init__.py b/wdf/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/wdf/test/test_origin.py b/wdf/test/test_origin.py new file mode 100644 index 0000000..1c6790d --- /dev/null +++ b/wdf/test/test_origin.py @@ -0,0 +1,61 @@ +import unittest +import os +from datetime import datetime, timezone +from pytest import approx +from ..wdf import Wdf +from ..origin import WdfDataType, WdfDataUnit, WdfOrigin + + +class TestWdfOrigin(unittest.TestCase): + testfile = os.path.join(os.path.dirname(__file__), r'Si-map.wdf') + + def test_origin_repr(self): + with Wdf(self.testfile) as wdf: + origin = wdf.origins[WdfDataType.Spatial_X] + self.assertRegex(repr(origin), r"wdf.origin.WdfOrigin\(<[^>]+>, 88, 215231\)") + + def test_origin_str(self): + with Wdf(self.testfile) as wdf: + origin = wdf.origins[WdfDataType.Spatial_X] + expected = r"WdfOrigin(label='X' type='WdfDataType.Spatial_X', units='WdfDataUnit.Micron')" + self.assertEqual(str(origin), expected) + + def test_origin_instance(self): + with Wdf(self.testfile) as wdf: + xorigin = wdf.origins[WdfDataType.Spatial_X] + self.assertEqual(len(xorigin), wdf.hdr.nspectra) + self.assertEqual(xorigin.datatype, WdfDataType.Spatial_X) + self.assertEqual(xorigin.dataunit, WdfDataUnit.Micron) + self.assertEqual(xorigin.label, 'X') + self.assertTrue(xorigin.is_primary) + self.assertEqual(xorigin[0], approx(-7.090)) + + def test_origin_slice(self): + with Wdf(self.testfile) as wdf: + count = wdf.hdr.nspectra + origin = wdf.origins[WdfDataType.Flags] + self.assertEqual(len(origin), count) + self.assertEqual(len([v for v in origin]), count) + self.assertEqual(len(origin[0:4]), 4) + self.assertEqual(len(origin[0:9:2]), 5) + self.assertEqual(len(origin[0:]), count) + self.assertEqual(len(origin[0::2]), count // 2) + + def test_origin_type_conversion(self): + with Wdf(self.testfile) as wdf: + origin = wdf.origins[WdfDataType.Time] + self.assertIsInstance(origin[0], datetime) + self.assertEqual(origin[0], datetime(2005, 10, 27, 13, 54, 51, tzinfo=timezone.utc)) + + def test_origin_custom_type(self): + custom = WdfDataType(0x40000001) # Custom type with integer 1 + self.assertEqual(custom.name, "Custom1") + self.assertEqual(custom.value, 0x40000001) + self.assertIn(custom.name, WdfDataType.__members__, "New enum should be in collection") + + def test_origin_custom_reject_invalid(self): + self.assertRaises(ValueError, WdfDataType, 0x01000001) + + +if __name__ == '__main__': + unittest.main() diff --git a/wdf/test/test_pset.py b/wdf/test/test_pset.py new file mode 100644 index 0000000..b4c1a2b --- /dev/null +++ b/wdf/test/test_pset.py @@ -0,0 +1,118 @@ +import unittest +from io import BytesIO +from ..pset import ( + Pset, PsetItem, + PSET_TYPE_BOOL, PSET_TYPE_CHAR, PSET_TYPE_SHORT, PSET_TYPE_INT, + PSET_TYPE_WIDE, PSET_TYPE_FILETIME, PSET_TYPE_FLOAT, PSET_TYPE_DOUBLE, + PSET_TYPE_STRING, PSET_TYPE_BINARY, PSET_TYPE_KEY, PSET_TYPE_PSET) + + +class TestPsetItem(unittest.TestCase): + + def test_bool_zero_is_false(self): + data = BytesIO(b'?\x00\x01\x80\x00') + item = PsetItem.fromstream(data) + self.assertEqual(item.opcode, PSET_TYPE_BOOL) + self.assertEqual(item.key, 0x8001) + self.assertFalse(item.value) + + def test_bool_is_true(self): + data = BytesIO(b'?\x00\x01\x80\x01') + item = PsetItem.fromstream(data) + self.assertTrue(item.value) + + def test_bool_non_zero_is_true(self): + data = BytesIO(b'?\x00\x01\x80\xff') + item = PsetItem.fromstream(data) + self.assertTrue(item.value) + + def test_bool_array(self): + data = BytesIO(b'?\x80\x01\x80\x04\x00\x00\x00\xff\x00\x00\x01') + item = PsetItem.fromstream(data) + self.assertEqual(len(item.value), 4) + self.assertSequenceEqual(item.value, (True, False, False, True)) + + def test_short(self): + item = PsetItem.fromstream(BytesIO(b's\x00\x01\x80\x0d\xf0')) + self.assertEqual(item.opcode, PSET_TYPE_SHORT) + self.assertEqual(item.value, -4083) + + def test_int(self): + item = PsetItem.fromstream(BytesIO(b'i\x00\x01\x80\x04\x03\x02\x01')) + self.assertEqual(item.opcode, PSET_TYPE_INT) + self.assertEqual(item.value, 0x01020304) + + def test_float(self): + item = PsetItem.fromstream(BytesIO(b'r\x00\x01\x80\x00\x00\x80?')) + self.assertEqual(item.opcode, PSET_TYPE_FLOAT) + self.assertEqual(item.value, 1.0) + + def test_double(self): + item = PsetItem.fromstream(BytesIO(b'q\x00\x01\x80\0\0\0\0\0\0\xf0?')) + self.assertEqual(item.opcode, PSET_TYPE_DOUBLE) + self.assertEqual(item.value, 1.0) + + def test_string(self): + item = PsetItem.fromstream(BytesIO(b'u\x00\x01\x80\5\0\0\0hello')) + self.assertEqual(item.opcode, PSET_TYPE_STRING) + self.assertEqual(item.value, "hello") + + def test_string_unicode(self): + data = b'u\x00\x01\x80\x13\0\0\0\xd0\xbf\xd1\x80\xd0\xb8\xd0\xb2\xd0\xb5\xd1\x82' + data = data + b' \xd0\xbc\xd0\xb8\xd1\x80' + item = PsetItem.fromstream(BytesIO(data)) + self.assertEqual(item.opcode, PSET_TYPE_STRING) + self.assertEqual(item.value, "привет мир") + + def test_key(self): + item = PsetItem.fromstream(BytesIO(b'k\x00\x01\x80\6\0\0\0\xd0\xbc\xd0\xb8\xd1\x80')) + self.assertEqual(item.opcode, PSET_TYPE_KEY) + self.assertEqual(item.value, "мир") + + def test_pset(self): + item = PsetItem.fromstream(BytesIO(b'p\x00\x01\x80\x0d\x00\x00\x00?\0\2\x80\1i\0\3\x80\x0a\0\0\0')) + self.assertEqual(item.opcode, PSET_TYPE_PSET) + self.assertEqual(len(item.value), 13) + stream = BytesIO(item.value) + subitem1 = PsetItem.fromstream(stream) + subitem2 = PsetItem.fromstream(stream) + self.assertEqual(subitem1.opcode, PSET_TYPE_BOOL) + self.assertEqual(subitem2.opcode, PSET_TYPE_INT) + self.assertEqual(subitem2.key, 0x8003) + self.assertEqual(subitem2.value, 10) + + +class TestPset(unittest.TestCase): + def test_simple_pset(self): + """Check Pset.fromstream with a simple set that has predefined and custom defined items""" + stream = BytesIO(b'PSET\x1D\0\0\0u\0\x9B\1\4\0\0\0name?\0\1\x80\1k\0\1\x80\4\0\0\0test') + pset = Pset.fromstream(stream) + self.assertEqual(len(pset.items), 2) + self.assertEqual(pset.items['test'].key, 0x8001) + self.assertEqual(pset.items['test'].value, True) + self.assertEqual(pset.items['Label'].value, 'name') + + def test_simple_pset_key_before(self): + """Check a PSET where a custom key name is defined before the value that uses it.""" + stream = BytesIO(b'PSET\x1D\0\0\0u\0\x9B\1\4\0\0\0namek\0\1\x80\4\0\0\0test?\0\1\x80\xff') + pset = Pset.fromstream(stream) + self.assertEqual(len(pset.items), 2) + self.assertEqual(pset.items['test'].key, 0x8001) + self.assertEqual(pset.items['test'].value, True) + self.assertEqual(pset.items['Label'].value, 'name') + + def test_pset_get_item(self): + stream = BytesIO(b'PSET\x1D\0\0\0u\0\x9B\1\4\0\0\0namek\0\1\x80\4\0\0\0test?\0\1\x80\xff') + pset = Pset.fromstream(stream) + self.assertEqual(pset['test'].value, True) + self.assertEqual(pset['Label'].value, 'name') + + def test_pset_iter(self): + stream = BytesIO(b'PSET\x1D\0\0\0u\0\x9B\1\4\0\0\0namek\0\1\x80\4\0\0\0test?\0\1\x80\xff') + pset = Pset.fromstream(stream) + asdict = dict(pset) + self.assertIn('test', asdict) + + +if __name__ == '__main__': + unittest.main() diff --git a/wdf/test/test_wdf.py b/wdf/test/test_wdf.py new file mode 100644 index 0000000..2be7e2f --- /dev/null +++ b/wdf/test/test_wdf.py @@ -0,0 +1,147 @@ +import unittest +import os +from tempfile import NamedTemporaryFile +from ..wdf import Wdf, MapData, MapAreaFlags +from ..origin import WdfOrigin, WdfDataType, WdfDataUnit +from ..pset import Pset, PsetItem +from .. import WdfBlockId + + +class TestWdf(unittest.TestCase): + testfile = os.path.join(os.path.dirname(__file__), r'Si-map.wdf') + + def test_open(self): + wdf = Wdf() + wdf.open(self.testfile) + wdf.close() + + def test_create(self): + wdf = Wdf(self.testfile) + wdf.close() + + def test_with_support(self): + with Wdf(self.testfile): + pass + + def test_open_size(self): + with Wdf(self.testfile) as wdf: + self.assertEqual(wdf.hdr.signature, WdfBlockId.FILE) + self.assertEqual(wdf.hdr.nspectra, 88) + self.assertEqual(wdf.hdr.npoints, 575) + self.assertEqual(wdf.hdr.size, 512) + + def test_first_spectrum(self): + with Wdf(self.testfile) as wdf: + spectrum = wdf.spectrum(0) + self.assertEqual(wdf.hdr.npoints, len(spectrum)) + self.assertSequenceEqual([1595, 1565, 1590, 1580], [int(x) for x in spectrum[0:4]]) + + def test_find_block_hdr(self): + with Wdf(self.testfile) as wdf: + block, pos = wdf.find_section(WdfBlockId.FILE) + self.assertEqual(pos, 0) + self.assertEqual(block.id, WdfBlockId.FILE) + self.assertEqual(block.size, 512) + + def test_find_block_xlist(self): + with Wdf(self.testfile) as wdf: + block = wdf.find_section(WdfBlockId.XLIST)[0] + self.assertEqual(block.id, WdfBlockId.XLIST) + + def test_find_block_invalid(self): + with Wdf(self.testfile) as wdf: + self.assertRaises(EOFError, wdf.find_section, b'_z_z', 0) + + def test_xlist(self): + with Wdf(self.testfile) as wdf: + xlist = wdf.xlist() + self.assertEqual(wdf.hdr.xlistcount, len(xlist)) + + def test_comment(self): + check = 'This is a mapping measurement created by the map setup wizard' + with Wdf(self.testfile) as wdf: + comment = wdf.comment() + self.assertEqual(comment, check) + + def test_sections(self): + with Wdf(self.testfile) as wdf: + sections = [section.id for section in wdf.sections()] + self.assertIn(WdfBlockId.FILE, sections) + self.assertIn(WdfBlockId.MAPAREA, sections) + self.assertIn(WdfBlockId.ORIGIN, sections) + + def test_iterator(self): + with Wdf(self.testfile) as wdf: + values = [int(x[0]) for x in wdf] + self.assertEqual(len(values), wdf.hdr.nspectra) + + def test_indexing(self): + with Wdf(self.testfile) as wdf: + self.assertEqual(len(wdf[0]), wdf.hdr.npoints) + self.assertEqual(len(wdf[1]), wdf.hdr.npoints) + self.assertEqual(len(wdf[-1]), wdf.hdr.npoints) + self.assertEqual(len(wdf[-2]), wdf.hdr.npoints) + self.assertRaises(IndexError, wdf.__getitem__, wdf.hdr.nspectra) + + def test_len(self): + with Wdf(self.testfile) as wdf: + self.assertEqual(len(wdf), wdf.hdr.nspectra) + + def test_update_spectrum(self): + with NamedTemporaryFile(suffix=".wdf") as tmpfd: + with open(self.testfile, 'rb') as srcfd: + data = None + while not data or len(data) == 16384: + data = srcfd.read(16384) + tmpfd.write(data) + tmpfd.seek(0, 0) + with Wdf() as wdf: + wdf.open_fd(tmpfd) + expected = [100.0] * wdf.hdr.npoints + # orig = wdf.spectrum(10) + wdf.update_spectrum(10, expected) + new = wdf.spectrum(10) + self.assertSequenceEqual(new, expected) + + def test_get_map_data(self): + with Wdf(self.testfile) as wdf: + testMap = wdf.get_map_data() + self.assertIsInstance(testMap, MapData) + self.assertIsInstance(testMap.properties, Pset) + self.assertEqual(testMap.label, 'Intensity At Point 520') + self.assertEqual(len(testMap), wdf.hdr.nspectra) + self.assertIsInstance(testMap[0:-1], tuple) + # first and last, and 1 in from each end + self.assertAlmostEqual(testMap[0], 44131.98828125, 6) + self.assertAlmostEqual(testMap[-1], 44229.421875, 6) + self.assertAlmostEqual(testMap[1], 43564.88671875, 6) + self.assertAlmostEqual(testMap[-2], 44257.41796875, 6) + # check the slice parsing is ok + self.assertEqual(len(testMap[:]), len(testMap)) + self.assertEqual(len(testMap[0:]), len(testMap)) + self.assertEqual(len(testMap[0::2]), len(testMap) / 2) + self.assertEqual(len(testMap[-8::2]), 4) + + def test_origins_valid(self): + with Wdf(self.testfile) as wdf: + self.assertEqual(len(wdf.origins), 5) + self.assertIsInstance(wdf.origins[WdfDataType.Spatial_X], WdfOrigin) + + def test_origins_invalid(self): + with Wdf(self.testfile) as wdf: + self.assertRaises(KeyError, lambda: wdf.origins[WdfDataType.Pressure]) + + def test_map_area(self): + with Wdf(self.testfile) as wdf: + area = wdf.map_area + self.assertAlmostEqual(area.start.x, -7.090, 3) + self.assertAlmostEqual(area.step.x, 2.0, 3) + self.assertEqual(area.count.x, 8) + self.assertEqual(area.flags, MapAreaFlags.NoFlag) + + self.assertEqual(area.count.y, 11) + self.assertEqual(area.count.z, 0) + + +if __name__ == '__main__': + unittest.main() diff --git a/wdf/wdf.py b/wdf/wdf.py new file mode 100644 index 0000000..ddbe8c3 --- /dev/null +++ b/wdf/wdf.py @@ -0,0 +1,399 @@ +""" +Data accessor classes for Renishaw WDF spectral data files. +""" + + +import struct +from dataclasses import dataclass +from io import BufferedReader, BytesIO +from collections import namedtuple +from enum import IntFlag, Enum +from os import SEEK_SET, SEEK_CUR +from typing import List +from .pset import Pset +from .origin import WdfOriginSet +from . import WdfBlockId + + +# All of the fields found in the wdf file header +_FIELDS = [ + 'signature', 'version', 'size', 'flags', # 'uuid', 'unused0', 'unused1' + 'ntracks', 'status', 'npoints', 'nspectra', 'ncollected', 'naccum', + 'ylistcount', 'xlistcount', 'origincount', 'appname_', + 'appver_maj', 'appver_min', 'appver_patch', 'appver_build', + 'scantype', 'type', 'time_start', 'time_end', 'units', + 'laser_wavenumber', 'user_', 'title_', + # padding, free, reserved +] + + +def _tostr(data): + """Convert a binary buffer with null terminated utf-8 encoded text to a string.""" + try: + result = data.decode('utf-8').rstrip('\0') + except UnicodeDecodeError: + result = u'' + return result + + +class InvalidSectionError(Exception): + """Error raised when an attempt is made to open an invalid section.""" + def __init__(self, message): + super(InvalidSectionError, self).__init__(message) + self.message = message + + +@dataclass +class WdfBlock: + """Representation of the WDF section header structure.""" + id: int # block type id (see WdfBlockId) + uid: int # unique id for this specific block (withing the id type set) + size: int # total size of the block (including the header size) in bytes + position: int # offset of this block in the file (start of header) + + _PACKFMT = ' 'WdfBlock': + position = stream.tell() + data = stream.read(WdfBlock._SIZE) + if len(data) != WdfBlock._SIZE: + raise IOError() + id, uid, size = struct.unpack(WdfBlock._PACKFMT, data) + return WdfBlock(id, uid, size, position) + + +class WdfHeader(namedtuple('WdfHeader', _FIELDS)): + """Representation of the WDF file header structure.""" + _packstr = '= self.wdf.hdr.nspectra: + raise StopIteration + result = self.wdf[self.index] + self.index += 1 + return result + + +class MapData: + """Represent a map from the file.""" + + def __init__(self, fd: BufferedReader, section: WdfBlock): + self.fd = fd + self.section = section + self.fd.seek(section.position + WdfBlock._SIZE) + self.properties = Pset.fromstream(self.fd) + self.count = struct.unpack(' self.count: + raise IndexError("MapData index out of range") + + if step == 1: + # for step 1 use a single read. + length = stop - start + self.fd.seek(self.start + (start * 4), SEEK_SET) + values = struct.unpack(f"<{length}f", self.fd.read(length * 4)) + else: + values = [] + for index in range(start, stop, step): + self.fd.seek(self.start + (index * 4), SEEK_SET) + data = self.fd.read(4) + values.append(struct.unpack('= self.hdr.nspectra: + raise IndexError + return self.spectrum(index) + + def open(self, path, mode='rb'): + """open the specified path as a wdf file. + mode: provide the file access mode: 'rb' for read-only + or 'r+b' for read-write""" + self.open_fd(open(path, mode), owned=True) + + def open_fd(self, fd, owned=False): + """open the provided file-descriptor as a wdf file.""" + self.fd = fd + self.owned = owned + data = self.fd.read(WdfHeader._size) + self.hdr = WdfHeader._make(struct.unpack(WdfHeader._packstr, data)) + + def close(self): + """close the file descriptor (if owned by this object)""" + if self.owned: + self.fd.close() + self.fd = None + + def spectrum(self, index, count=1): + """Retrieve the unpacked spectrum i-list values. + If count is set then read count spectra in one go.""" + size = self.hdr.npoints * 4 + _ = self.find_section(WdfBlockId.DATA) + self.fd.seek(index * size, SEEK_CUR) + return struct.unpack(f'{self.hdr.npoints * count}f', self.fd.read(size * count)) + + def update_spectrum(self, index, data): + """Update the i-list for a spectrum. + data: a sequence of npoints values""" + if not self.fd.writable: + raise IOError('WDF object not writable') + self.find_section(WdfBlockId.DATA) + self.fd.seek(index * (self.hdr.npoints * 4), SEEK_CUR) + self.fd.write(struct.pack(f'{self.hdr.npoints}f', *data)) + + def xlist(self): + """Get the spectral x-list values.""" + block = self.find_section(WdfBlockId.XLIST)[0] + _, _ = struct.unpack(' List[WdfBlock]: + """Return a list of sections as (id,uid,size,pos)""" + pos = 0 + sections = [] + try: + while True: + self.fd.seek(pos, SEEK_SET) + block = WdfBlock.fromstream(self.fd) + sections.append(block) + pos += block.size + except IOError: + pass + return sections + + # Checkout numpy.fromfile for reading whole arrays. + # def read_all(self): + # block = self.find_section(b'DATA') # puts file pointer at start of data + # npoints = self.hdr.ncollected * self.hdr.npoints + # return np.fromfile(self.fd, dtype=float, count=npoints) + # + # eg: data = wdf.read_add() + # plt.plot(wdf.xlist(), data[wdf.hdr.npoints]) + + def get_map_data(self, uid=-1) -> MapData: + """Returns a MapData object holding information for a Wdf map. + The uid parameter selects a specific map. If uid is -1 then the first map is returned. + If no such map is present an InvalidSectionError exception is raised.""" + map_sections = [section for section in self.sections() if section.id == WdfBlockId.MAP] + valid = [section.uid for section in map_sections] + if valid and uid == -1: + uid = valid[0] + if uid not in valid: + raise InvalidSectionError(f"map {uid} not present") + section = [section for section in map_sections if section.uid == uid][0] + return MapData(self.fd, section) + + def get_section_properties(self, id, uid) -> Pset: + """If a section has a property collection, returns it else returns None""" + _ = self.find_section(id, uid) + return Pset.fromstream(self.fd) + + @property + def origins(self) -> WdfOriginSet: + return WdfOriginSet(self) + + @property + def map_area(self) -> MapArea: + """Get the map area definition for this file.""" + try: + _ = self.find_section(WdfBlockId.MAPAREA)[0] + flags, _ = struct.unpack('