Lacquer tag raman plotting and Renishaw spectral data files

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+.tox/
+.vscode/
+*.egg-info/
+__pycache__/
+build/
+dist/
\ No newline at end of file

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diff --git a/README.rst b/README.rst
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+# Laquer tags project
+
+Includes the data and scripts for plotting Raman spectra from 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.

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\ No newline at end of file

diff --git a/demos/wdf_map_arith.py b/demos/wdf_map_arith.py
new file mode 100644 (file)
index 0000000..04f0a2a
--- /dev/null
+++ b/demos/wdf_map_arith.py
@@ -0,0 +1,190 @@
+#!python\r
+ \r
+"""\r
+Demonstration performing map image arithmetic and plotting the results using python,\r
+numpy and matplotlib with the WiRE wdf access package.\r
+\r
+In this case, the matplotlib graphing widget has been embedded into a Tkinter GUI.\r
+"""\r
+\r
+import sys\r
+import argparse\r
+import tkinter as tk\r
+import tkinter.ttk as ttk\r
+import tkinter.font\r
+import tkinter.filedialog as tkfiledialog\r
+import tkinter.messagebox as tkmessagebox\r
+import numpy as np\r
+import matplotlib\r
+import matplotlib.pyplot as plt\r
+import matplotlib.backends.backend_tkagg as tkagg\r
+from wdf import Wdf, WdfDataType, WdfBlockId\r
+from wdf.origin import WdfDataType\r
+\r
+if sys.platform == 'win32':\r
+    # Fix for tiny menus on high dpi display\r
+    from ctypes import windll\r
+    windll.shcore.SetProcessDpiAwareness(1)\r
+\r
+class Plot(ttk.Frame):\r
+    # ex: https://github.com/I2PC/scipion/blob/master/scipion-master/pyworkflow/matplotlib_image.py\r
+    def __init__(self, master, **kwargs):\r
+        if "figsize" in kwargs:\r
+            self.figsize = kwargs["figsize"]\r
+            del kwargs["figsize"]\r
+        super(Plot, self).__init__(master)\r
+        self.figure = matplotlib.figure.Figure(figsize=self.figsize, frameon=False, dpi=100)\r
+        self.canvas = tkagg.FigureCanvasTkAgg(self.figure, self)\r
+        self.toolbar = None\r
+        widget = self.canvas.get_tk_widget()\r
+        widget.grid(row=0, column=0, sticky=tk.NSEW)\r
+        self.grid_rowconfigure(0, weight=1)\r
+        self.grid_columnconfigure(0, weight=1)\r
+        self.configure(**kwargs)\r
+\r
+    def add_toolbar(self):\r
+        self.toolbar = tkagg.NavigationToolbar2Tk(self.canvas, self, pack_toolbar=False)\r
+        self.toolbar.grid(row=1, column=0, sticky=tk.NSEW)\r
+\r
+    def show(self):\r
+        self.canvas.show()\r
+\r
+    @property\r
+    def widget(self):\r
+        return self.canvas.get_tk_widget()\r
+\r
+    def configure(self, **kwargs):\r
+        return self.widget.configure(**kwargs)\r
+\r
+\r
+class App(ttk.Frame):\r
+    """Application class."""\r
+    def __init__(self, master, **kwargs):\r
+        super(App, self).__init__(master, **kwargs)\r
+        self.master.wm_minsize(800, 600)\r
+        self.create_ui()\r
+        self.create_menu()\r
+        self.grid(row=0, column=0, sticky=tk.NSEW)\r
+        self.master.grid_rowconfigure(0, weight=1)\r
+        self.master.grid_columnconfigure(0, weight=1)\r
+\r
+    def create_ui(self):\r
+        self.plot = Plot(self, figsize=(11, 8), background="grey80")\r
+        self.plot.grid(row=0, column=0, sticky=tk.NSEW)\r
+        self.grid_rowconfigure(0, weight=1)\r
+        self.grid_columnconfigure(0, weight=1)\r
+\r
+    def create_menu(self):\r
+        menu = tk.Menu(self.master)\r
+        fileMenu = tk.Menu(menu, tearoff=False)\r
+        optMenu = tk.Menu(menu, tearoff=False)\r
+        menu.add_cascade(label='File', menu=fileMenu)\r
+        menu.add_cascade(label='Options', menu=optMenu)\r
+        fileMenu.add_command(label='Open ...', command=self.on_file_open)\r
+        fileMenu.add_command(label='Exit', command=self.on_file_exit)\r
+        optMenu.add_command(label="Add toolbar", command=self.plot.add_toolbar)\r
+        self.master.configure(menu=menu)\r
+\r
+    def on_file_open(self):\r
+        filename = tkfiledialog.askopenfilename(filetypes=(("WiRE Data Files", "*.wdf"), ("All files", "*.*")))\r
+        if filename:\r
+            self.plot.figure.clear()\r
+            self.after_idle(self.run, filename)\r
+\r
+    def on_file_exit(self):\r
+        self.master.destroy()\r
+\r
+    def run(self, filename):\r
+        """Perform image arithmetic and plot inputs and result."""\r
+        with Wdf(filename) as wdf:\r
+            shape = wdf.map_area.count.x, wdf.map_area.count.y\r
+            landscape_a4 = (11, 8) # inches (A4)\r
+\r
+            lhs_props = wdf.get_section_properties(WdfBlockId.MAP, 1)\r
+            lhs_title = lhs_props['Label'].value\r
+            lhs = np.array(wdf.get_map_data(1), dtype=float)\r
+            rhs_props = wdf.get_section_properties(WdfBlockId.MAP, 2)\r
+            rhs_title = rhs_props['Label'].value\r
+            rhs = np.array(wdf.get_map_data(2), dtype=float)\r
+            \r
+            fig = self.plot.figure\r
+            fig.clear()\r
+            fig.set_tight_layout(True)\r
+\r
+            ax = fig.add_subplot(2, 2, 1)\r
+            ax.set_title(lhs_title)\r
+            ax.imshow(lhs.reshape(shape))\r
+\r
+            ax = fig.add_subplot(2, 2, 2)\r
+            ax.set_title(rhs_title)\r
+            ax.imshow(rhs.reshape(shape))\r
+\r
+            ax = fig.add_subplot(2, 2, 3)\r
+            ax.set_title(f"{lhs_title} + {rhs_title}")\r
+            ax.imshow((lhs + rhs).reshape(shape))\r
+\r
+            try:\r
+                res_index = 4\r
+                res_props = wdf.get_section_properties(WdfBlockId.MAP, res_index)\r
+                res_title = rhs_props['Label'].value\r
+                res = np.array(wdf.get_map_data(res_index), dtype=float)\r
+\r
+                ax = fig.add_subplot(2, 2, 4)\r
+                ax.set_title(f"{res_title}")\r
+                ax.imshow(res.reshape((wdf.map_area.count.y, wdf.map_area.count.x)))\r
+            except Exception:\r
+                pass\r
+        self.plot.canvas.draw()\r
+\r
+def plot_contour(wdf, mapIndex):\r
+    """Plot the anadin data which is column-major with negative y step size"""\r
+    global app\r
+    maps = [section.uid for section in wdf.sections() if section.id == WdfBlockId.MAP]\r
+    if mapIndex not in maps:\r
+        raise Exception("invalid map index")\r
+    fig = app.plot.figure\r
+    fig.clear()\r
+    ax = fig.add_subplot(1, 1, 1)\r
+    x = np.array(wdf.origins[WdfDataType.Spatial_X], dtype=float)\r
+    y = np.array(wdf.origins[WdfDataType.Spatial_Y], dtype=float)\r
+    map = np.array(wdf.get_map_data(mapIndex), dtype=float)\r
+    props = wdf.get_section_properties(WdfBlockId.MAP, 2)\r
+    ax.set_title(props['Label'].value)\r
+    ax.contourf(y[0:wdf.map_area.count.y], x[0::wdf.map_area.count.y],\r
+        map.reshape(wdf.map_area.count.x, wdf.map_area.count.y), alpha=0.5)\r
+    ax.contour(y[0:wdf.map_area.count.y], x[0::wdf.map_area.count.y],\r
+        map.reshape(wdf.map_area.count.x, wdf.map_area.count.y))\r
+    fig.canvas.draw()\r
+\r
+\r
+def load_idle(root):\r
+    try:\r
+        import idlelib.pyshell as pyshell\r
+        sys.argv = [sys.argv[0], "-n"]\r
+        root.bind("<Control-F2>", lambda ev: pyshell.main())\r
+        root.bind("<Control-i>", lambda ev: pyshell.main())\r
+    except ModuleNotFoundError:\r
+        pass\r
+\r
+def main(args=None):\r
+    """Main program entry."""\r
+    global root, app, options # access to application for interactive idle mode\r
+\r
+    parser = argparse.ArgumentParser(description="Demonstration of map image arithmetic using python")\r
+    parser.add_argument('-f', '--filename')\r
+    options = parser.parse_args(args)\r
+\r
+    root = tk.Tk()\r
+    app = App(root)\r
+    if options.filename:\r
+        app.after(100, app.run, options.filename)\r
+\r
+    root.after(200, load_idle, root)\r
+    try:\r
+        root.mainloop()\r
+    except Exception as e:\r
+        tkmessagebox.showerror('Error', e)\r
+    return 0\r
+\r
+if __name__ == '__main__':\r
+    sys.exit(main(sys.argv[1:]))\r

diff --git a/lacquertags_plot.md b/lacquertags_plot.md
new file mode 100644 (file)
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 (executable)
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 (executable)
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 (executable)
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("<Control-F2>", lambda ev: pyshell.main())
+        root.bind("<Control-i>", 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 (file)
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 (file)
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 (file)
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 (file)
index 0000000..e69de29

diff --git a/rsl/test/test_spectrum.py b/rsl/test/test_spectrum.py
new file mode 100644 (file)
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 (file)
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 (file)
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 (file)
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 (file)
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 (file)
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 (file)
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": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "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": [
+       "<Figure size 1440x288 with 1 Axes>"
+      ]
+     },
+     "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 (executable)
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 (file)
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 (file)
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
+"2022-03-09 05:02:17.770314",1003.8
+"2022-03-09 05:07:17.779647",1003.9
+"2022-03-09 05:12:17.742826",1003.8
+"2022-03-09 05:17:17.769124",1003.9
+"2022-03-09 05:22:17.785306",1004.1
+"2022-03-09 05:27:17.783198",1004.0
+"2022-03-09 05:32:17.774294",1003.9
+"2022-03-09 05:37:17.805478",1004.0
+"2022-03-09 05:42:17.782318",1004.0
+"2022-03-09 05:47:17.833956",1004.0
+"2022-03-09 05:52:17.767860",1004.1
+"2022-03-09 05:57:17.803873",1004.1
+"2022-03-09 06:02:17.815715",1004.2
+"2022-03-09 06:07:17.828131",1004.2
+"2022-03-09 06:12:17.830036",1004.2
+"2022-03-09 06:17:17.808379",1004.2
+"2022-03-09 06:22:17.837630",1004.1
+"2022-03-09 06:27:17.802418",1004.3
+"2022-03-09 06:32:17.818307",1004.3
+"2022-03-09 06:37:17.855545",1004.3
+"2022-03-09 06:42:17.843955",1004.3
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diff --git a/uvdata/temperature.csv b/uvdata/temperature.csv
new file mode 100644 (file)
index 0000000..67a574a
--- /dev/null
+++ b/uvdata/temperature.csv
@@ -0,0 +1,2994 @@
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diff --git a/uvdata/uvbox-index-state.csv b/uvdata/uvbox-index-state.csv
new file mode 100644 (file)
index 0000000..2f49702
--- /dev/null
+++ b/uvdata/uvbox-index-state.csv
@@ -0,0 +1,2212 @@
+2022-03-09 04:28:01.557170|hass/sensor/uvbox_veml6070_uvlevel/state|65535
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diff --git a/uvdata/uvbox_uvpower.csv b/uvdata/uvbox_uvpower.csv
new file mode 100644 (file)
index 0000000..a06aac0
--- /dev/null
+++ b/uvdata/uvbox_uvpower.csv
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diff --git a/uvdata/uvdata.csv b/uvdata/uvdata.csv
new file mode 100644 (file)
index 0000000..242fd5c
--- /dev/null
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diff --git a/uvdata/uvplots.pdf b/uvdata/uvplots.pdf
new file mode 100644 (file)
index 0000000..c161a2a
Binary files /dev/null and b/uvdata/uvplots.pdf differ

diff --git a/uvdata/uvsensor_uvpower.csv b/uvdata/uvsensor_uvpower.csv
new file mode 100644 (file)
index 0000000..4580fd4
--- /dev/null
+++ b/uvdata/uvsensor_uvpower.csv
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diff --git a/wdf/WdfBlockId.py b/wdf/WdfBlockId.py
new file mode 100644 (file)
index 0000000..9f97686
--- /dev/null
+++ b/wdf/WdfBlockId.py
@@ -0,0 +1,59 @@
+"""
+Block identity values
+
+Renishaw will only use uppercase letter values. 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 (file)
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 (file)
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 (file)
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('<II', data[:8])
+        self.is_primary = bool((datatype >> 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('<I', stream.read(4))[0]
+        for _ in range(count):
+            origin = WdfOrigin(stream, parent.hdr.nspectra, stream.tell())
+            self[origin.datatype] = origin
+            stream.seek(origin_size, SEEK_CUR)

diff --git a/wdf/predefinedKeys.py b/wdf/predefinedKeys.py
new file mode 100644 (file)
index 0000000..dbeb9bc
--- /dev/null
+++ b/wdf/predefinedKeys.py
@@ -0,0 +1,82 @@
+def getKeyName(keyIn):
+    """Returns the predefined keyname for a given key number
+    in a property set present in a wdf file"""
+
+    keyNames = {
+        1: "WiRE2 Version",
+        2: "ErrorCode",
+        3: "CreationTime",
+        4: "StartTime",
+        5: "EndTime",
+        6: "ETA",
+        7: "wizardclsid",
+        8: "ExpectedDatasetCount",
+        9: "AcquisitionCount",
+        10: "NumberOfPoints",
+        11: "FileHandlerVersion",
+        12: "autoSaveInterval",
+        13: "MeasurementType",
+        14: "responseCalibration",
+        15: "restoreInstrumentState",
+        16: "closeLaserShutter",
+        17: "FocusTrackEnabled",
+        18: "FocusTrackInterval",
+        19: "LineFocusMode",
+        20: "yStepSize",
+        201: "DepthSeriesInterval",
+        202: "DepthSeriesStartPos",
+        203: "DepthSeriesFinalPos",
+
+        301: "ScanCompletionStatus",
+        302: "usingPixelIntensityVariationFunction",
+
+        # "Result":400, see 1010
+        401: "Results",
+        402: "Property",
+        403: "Properties",
+
+        410: "Data",  # Map items #
+        411: "Label",
+        412: "MapType",
+        420: "DataList0",
+        421: "DataList1",
+        422: "DataList2",
+        423: "DataList3",
+        424: "DataList4",
+
+        430: "Operator",
+        431: "Time",
+        432: "Version",
+
+        1001: "Name",
+        1002: "Description",
+        1003: "Language",
+        1004: "Code",
+        1005: "Status",
+        1006: "System",
+        1007: "DataFileFormat",
+        1008: "DataSaveMode",
+        1009: "DataSaveFile",
+        1010: "Result",
+        1011: "NamedItems",
+        1012: "Image",
+        1013: "AreaKey",
+        1014: "InstrumentState",
+        1015: "Laser Name",
+        1016: "BeamPath",
+        1017: "Focus Mode",
+        1018: "Grating Name",
+        1019: "Laser",
+        1020: "Camera",
+        1021: "Instrument",
+        1022: "Objective magnification",
+        1023: "Objective name",
+        # NOTE: these are actually UTF8 encoded strings (they just happen to also be ASCII).
+        #       For testing: here is a cyrillic string in utf-8 encoding (privyet).
+        1024: "\xd0\xbf\xd1\x80\xd0\xb8\xd0\xb2\xd0\xb5\xd1\x82"
+    }
+
+    for key in keyNames:
+        if key == keyIn:
+            return keyNames[key]
+    return ''

diff --git a/wdf/pset.py b/wdf/pset.py
new file mode 100644 (file)
index 0000000..cdb1ece
--- /dev/null
+++ b/wdf/pset.py
@@ -0,0 +1,137 @@
+from struct import unpack
+from os import SEEK_SET, SEEK_END, SEEK_CUR
+from typing import BinaryIO, Any
+from dataclasses import dataclass
+from .predefinedKeys import getKeyName
+from datetime import datetime, timezone, timedelta
+
+
+PSET_FLAG_NONE = 0,
+PSET_FLAG_COMPRESSED = 0x40,
+PSET_FLAG_ARRAY = 0x80
+
+PSET_TYPE_BOOL = ord(b'?')
+PSET_TYPE_CHAR = ord(b'c')
+PSET_TYPE_SHORT = ord('s')
+PSET_TYPE_INT = ord(b'i')
+PSET_TYPE_WIDE = ord(b'w')
+PSET_TYPE_FLOAT = ord(b'r')
+PSET_TYPE_DOUBLE = ord(b'q')
+PSET_TYPE_FILETIME = ord(b't')
+PSET_TYPE_PSET = ord(b'p')
+PSET_TYPE_KEY = ord(b'k')
+PSET_TYPE_BINARY = ord(b'b')
+PSET_TYPE_STRING = ord(b'u')
+
+
+class PsetParseError(Exception):
+    """Exception raise during pset parsing."""
+    def __init__(self, message):
+        super(PsetParseError, self).__init__(message)
+
+
+@dataclass
+class PsetItem:
+    """Representation of an item from a WDF property set.
+    Each item is a name:value pair from a limited set of types."""
+    opcode: int
+    flags: int
+    key: int
+    value: Any = None
+
+    @staticmethod
+    def fromstream(stream: BinaryIO):
+        """Returns an unpacked item from a wdf property set in
+        a form appropriate for python.
+        An array of data is returned in a tuple."""
+        data = stream.read(4)
+        item = PsetItem(*unpack('<BBH', data))
+        count = 1
+        if item.flags & PSET_FLAG_ARRAY:
+            count = unpack('<I', stream.read(4))[0]
+        if item.opcode == PSET_TYPE_BOOL:
+            item.value = unpack(f'<{count}?', stream.read(1 * count))
+        elif item.opcode == PSET_TYPE_CHAR:
+            item.value = unpack(f'<{count}c', stream.read(1 * count))
+        elif item.opcode == PSET_TYPE_SHORT:
+            item.value = unpack(f'<{count}h', stream.read(2 * count))
+        elif item.opcode == PSET_TYPE_INT:
+            item.value = unpack(f'<{count}i', stream.read(4 * count))
+        elif item.opcode == PSET_TYPE_WIDE:
+            item.value = unpack(f'<{count}q', stream.read(8 * count))
+        elif item.opcode == PSET_TYPE_FLOAT:
+            item.value = unpack(f'<{count}f', stream.read(4 * count))
+        elif item.opcode == PSET_TYPE_DOUBLE:
+            item.value = unpack(f'<{count}d', stream.read(8 * count))
+        elif item.opcode == PSET_TYPE_FILETIME:
+            fileTimes = unpack(f'<{count}Q', stream.read(8 * count))
+            item.value = ()
+            for fileTime in fileTimes:
+                # Convert from windows filetime to python datetime
+                epoch = datetime(year=1601, month=1, day=1, tzinfo=timezone.utc)
+                item.value = item.value + (epoch + timedelta(microseconds=fileTime / 10),)
+        elif item.opcode == PSET_TYPE_STRING or item.opcode == PSET_TYPE_KEY:
+            len = unpack('<I', stream.read(4))[0]
+            item.value = stream.read(len).decode('utf-8'),
+        elif item.opcode == PSET_TYPE_BINARY:
+            len = unpack('<I', stream.read(4))[0]
+            item.value = stream.read(len),
+        elif item.opcode == PSET_TYPE_PSET:
+            len = unpack('<I', stream.read(4))[0]
+            item.value = stream.read(len),
+        else:
+            raise PsetParseError(f"invalid pset item type '{item.opcode}'")
+        if count == 1:
+            item.value = item.value[0]
+        return item
+
+
+class Pset:
+    """Renishaw WDF file property collection parser."""
+    def __init__(self, parent=None):
+        self.parent = parent
+        self.items = dict()
+
+    def __getitem__(self, key):
+        return self.items[key]
+
+    def __iter__(self):
+        for key in self.items.keys():
+            yield key, self.items[key]
+
+    @staticmethod
+    def is_pset(stream: BinaryIO) -> 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('<I', stream.read(4))[0]
+            final = stream.tell() + size
+            result = Pset()
+            customKeyNames = {}
+            customItems = {}
+            while stream.tell() < final:
+                item = PsetItem.fromstream(stream)
+                # Check for custom keys or use predefined keys to name each PsetItem
+                # The first bit of 16 bit custom key value is 1 (hence predefined key values are < 2^15)
+                if (item.key >> 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 (file)
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 (file)
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 (file)
index 0000000..e69de29

diff --git a/wdf/test/test_origin.py b/wdf/test/test_origin.py
new file mode 100644 (file)
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 (file)
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 (file)
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 (file)
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 = '<IIQ'  # decoding format for a block header
+    _SIZE = 16  # size of the block header in bytes
+
+    @staticmethod
+    def fromstream(stream: BytesIO) -> '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 = '<IIQQ28x IIIQQI III24s4H IIQQI f48x32s160s 48x32x32x'
+    _size = 512
+
+    @property
+    def appversion(self):
+        return "%d.%d.%d.%d" % (self.appver_maj,
+                                self.appver_min,
+                                self.appver_patch,
+                                self.appver_build)
+
+    @property
+    def title(self):
+        return _tostr(self.title_)
+
+    @property
+    def user(self):
+        return _tostr(self.user_)
+
+    @property
+    def appname(self):
+        return _tostr(self.appname_)
+
+
+class WdfIter:
+    """Iterator for spectra in a WDF file."""
+    def __init__(self, parent, index=0):
+        self.wdf = parent
+        self.index = index
+
+    def next(self):
+        """Python2 iterator support."""
+        return self.__next__()
+
+    def __next__(self):
+        if self.index >= 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('<Q', self.fd.read(8))[0]
+        self.start = self.fd.tell()
+
+    def __len__(self):
+        return self.count
+
+    @property
+    def label(self):
+        return self.properties.items['Label'].value
+
+    @property
+    def values(self):
+        self.fd.seek(self.start, SEEK_SET)
+        return list(struct.unpack(f"<{self.count}f", self.fd.read(self.count * 4)))
+
+    def __getitem__(self, key):
+        if isinstance(key, int):
+            start, stop, step = slice(key, None, None).indices(self.count)
+            stop = start + 1
+        elif isinstance(key, slice):
+            start, stop, step = key.indices(self.count)
+        else:
+            raise TypeError("MapData indices must be integers or slices")
+
+        if stop > 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('<f', data)[0])
+        return values[0] if len(values) == 1 else tuple(values)
+
+
+class MapAreaFlags(IntFlag):
+    """Set of flags used to defined the layout of map points in a file."""
+
+    NoFlag = 0
+
+    RandomPoints = (1 << 0)
+    """File contains random points.
+    default (false) is rectangle area, otherwise is random points within a bound"""
+
+    ColumnMajor = (1 << 1)
+    """Data collection order. default (false) is X first then Y, otherwise is Y first then X"""
+
+    Alternating = (1 << 2)
+    """Data collection order of alternate major axis.
+    default (false) is raster, otherwise is snake (alternating)."""
+
+    LineFocusMapping = (1 << 3)
+    """Flag marks data collection order using line-focus mapping."""
+
+    # The following two values are deprecated; negative step-size is sufficient information.
+    # [Deprecated] InvertedRows = (1 << 4) # True if rows collected right to left
+    # [Deprecated] InvertedColumns = (1 << 5) # True if columns collected bottom to top
+
+    SurfaceProfile = (1 << 6)
+    """Flag to mark data with irregular Z positions (surface maps)."""
+
+    XYLine = (1 << 7)
+    """line or depth slice forming a single line along the XY plane
+    length.x contains number of points along line; length.y = 1 """
+
+
+@dataclass
+class FloatVector:
+    x: float
+    y: float
+    z: float
+
+    def __iter__(self):
+        yield "x", self.x
+        yield "y", self.y
+        yield "z", self.z
+
+
+@dataclass
+class IntVector:
+    x: int
+    y: int
+    z: int
+
+    def __iter__(self):
+        yield "x", self.x
+        yield "y", self.y
+        yield "z", self.z
+
+
+@dataclass
+class MapArea:
+    start: FloatVector
+    step: FloatVector
+    count: IntVector
+    flags: MapAreaFlags
+
+    def __iter__(self):
+        yield "start", dict(self.start)
+        yield "step", dict(self.step)
+        yield "count", dict(self.count)
+        yield "flags", self.flags
+
+
+class Wdf:
+    """Python accessor class for WDF file data."""
+    def __init__(self, path='', mode='rb'):
+        self.x = MapAreaFlags.RandomPoints
+        self.path = path
+        self.fd = None
+        self.hdr = None
+        self.owned = True
+        if path != "":
+            self.open(path, mode)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, vtype, value, traceback):
+        self.close()
+
+    def __iter__(self):
+        return WdfIter(self)
+
+    def __len__(self):
+        return self.hdr.nspectra
+
+    def __getitem__(self, index):
+        """return the spectrum at specified index.
+        if a negtive index is specifified return the spectrum
+        index spaces away from the end"""
+        if index < 0:
+            index = self.hdr.nspectra + index
+        if index < 0 or index >= 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('<II', self.fd.read(8))
+        return struct.unpack('<%df' % self.hdr.xlistcount, self.fd.read(block.size - 24))
+
+    def ylist(self):
+        """Get the spectral y-list values."""
+        block = self.find_section(WdfBlockId.YLIST)[0]
+        _, _ = struct.unpack('<II', self.fd.read(8))
+        return struct.unpack('<%df' % self.hdr.ylistcount, self.fd.read(block.size - 24))
+
+    def comment(self):
+        """Get the file comment block as text."""
+        try:
+            block = self.find_section(WdfBlockId.COMMENT)[0]
+        except EOFError:
+            return ""
+        size = block.size - WdfBlock._SIZE
+        data = self.fd.read(size)
+        try:
+            result = _tostr(data)
+        except UnicodeDecodeError:
+            pass
+        return result
+
+    def find_section(self, id: int, uid: int = -1, pos=0):
+        """Find a Wdf block using its id and optionally the uid.
+        On return, the starting file seek position can be specified
+        but is at the start of the block data by default.
+        The block structure and the position of the start
+        of the block is returned"""
+        try:
+            while True:
+                self.fd.seek(pos, SEEK_SET)
+                block = WdfBlock.fromstream(self.fd)
+                if block.id == id and (uid == -1 or uid == block.uid):
+                    return (block, pos)
+                pos += block.size
+        except IOError:
+            pass
+        raise EOFError()
+
+    def sections(self) -> 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('<II', self.fd.read(8))
+            startPos = struct.unpack('<fff', self.fd.read(12))
+            stepSize = struct.unpack('<fff', self.fd.read(12))
+            nSteps = struct.unpack('<LLL', self.fd.read(12))
+            return MapArea(FloatVector(*startPos),
+                           FloatVector(*stepSize),
+                           IntVector(*nSteps),
+                           MapAreaFlags(flags))
+        except EOFError:
+            raise EOFError('No map area data present')