getting lots of fixing done

DiamondLightSource · Aug 4, 2020 · 975f19e · 975f19e
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diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -23,10 +23,9 @@
 copyright = '2020, Andrew R. McCluskey'
 author = 'Andrew R. McCluskey'
 
-# The short X.Y version
-version = '0.1'
+version = '0.0.26'
 # The full version, including alpha/beta/rc tags
-release = '0.0.1'
+release = '0.0.26'
 
 
 # -- General configuration ---------------------------------------------------

diff --git a/docs/source/liquid_surface.ipynb b/docs/source/liquid_surface.ipynb
@@ -99,24 +99,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.zeros(10)\n",
+    "a[3] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a > 0).any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The file: refl_1.dat contains 61 images from q = 0.01 to q = 0.04.\n",
-      "The file: refl_2.dat contains 15 images from q = 0.03 to q = 0.1.\n",
-      "The file: refl_3.dat contains 14 images from q = 0.07 to q = 0.2.\n",
-      "The file: refl_4.dat contains 19 images from q = 0.16 to q = 0.34.\n",
-      "The file: refl_5.dat contains 16 images from q = 0.3 to q = 0.6.\n"
+      "[[0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " ...\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 0.]]\n",
+      "94965\n"
      ]
     }
    ],
    "source": [
     "for i in refl.scans:\n",
-    "    print(i)"
+    "    im = Image(i.data[\"file\"][0], i.data, i.metadata)\n",
+    "    plt.imshow(im.n)\n",
+    "    plt.show()\n",
+    "    print(im.n)\n",
+    "    print(im.n.size)\n",
+    "    break"
    ]
   },
   {
@@ -344,11 +394,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 61/61 [00:44<00:00,  1.36it/s]\n",
-      "100%|██████████| 15/15 [00:11<00:00,  1.36it/s]\n",
-      "100%|██████████| 14/14 [00:12<00:00,  1.11it/s]\n",
-      "100%|██████████| 19/19 [00:15<00:00,  1.21it/s]\n",
-      "100%|██████████| 16/16 [00:11<00:00,  1.45it/s]\n"
+      "100%|██████████| 61/61 [00:44<00:00,  1.39it/s]\n",
+      "100%|██████████| 15/15 [00:10<00:00,  1.49it/s]\n",
+      "100%|██████████| 14/14 [00:09<00:00,  1.51it/s]\n",
+      "100%|██████████| 19/19 [00:12<00:00,  1.54it/s]\n",
+      "100%|██████████| 16/16 [00:10<00:00,  1.51it/s]\n"
      ]
     }
    ],

diff --git a/islatu/background.py b/islatu/background.py
@@ -1,7 +1,8 @@
 """
-Background substraction is a necssary component of reflectometry reduction, where the background scattering is removed from the reflected intensity.
+Background substraction is a necessary component of reflectometry reduction,
+where the background scattering is removed from the reflected intensity.
 
-Herein are some function to enable that for a two-dimensional detector image.
+Herein are some functions to enable that for a two-dimensional detector image.
 """
 
 # Copyright (c) Andrew R. McCluskey
@@ -19,19 +20,23 @@ def bivariate_normal(data, mu_1, mu_2, sigma_1, sigma_2, offset, factor):
     """
     Produce a bivariate normal distribution.
 
-    *Note*: the covariance of the two dimensions is assumed to be zero to unsure greater stability.
+    *Note*: the covariance of the two dimensions is assumed to be zero to
+    unsure greater stability.
 
     Args:
         data (:py:attr:`array_like`): Two-dimensional abscissa data.
         mu_1 (:py:attr:`float`): Mean in dimension 0 (horizontal).
         mu_2 (:py:attr:`float`): Mean in dimension 1 (vertical).
         sigma_1 (:py:attr:`float`): Variance in dimension 0 (horizontal).
         sigma_2 (:py:attr:`float`): Variance in dimension 1 (vertical).
-        offset (:py:attr:`float`): Offset from the 0 for the ordinate, this is the background level.
-        factor (:py:attr:`float`): Multiplicative factor for area of normal distribution.
+        offset (:py:attr:`float`): Offset from the 0 for the ordinate, this is
+            the background level.
+        factor (:py:attr:`float`): Multiplicative factor for area of normal
+            distribution.
 
     Returns:
-        :py:attr:`array_like`: Flattened ordinate data for bivariate normal distribution.
+        :py:attr:`array_like`: Flattened ordinate data for bivariate normal
+            distribution.
     """
     # Setting the data up in the correct format
     pos = np.empty(data[0].shape + (2,))
@@ -51,42 +56,33 @@ def fit_gaussian_2d(image, image_e, p0=None, bounds=None):
     Args:
         image (:py:attr:`array_like`): The data to fit the Gaussian to.
         image_e (:py:attr:`array_like`): The data uncertainty.
-        p0 (:py:attr:`list`, optional): An initial guess at the parameters. Defaults to values based on the image.
-        bounds (:py:attr:`list` of :py:attr:`tuple`, optional): Bounds for the fitting. Defaults to values based on the image.
+        p0 (:py:attr:`list`, optional): An initial guess at the parameters.
+            Defaults to values based on the image.
+        bounds (:py:attr:`list` of :py:attr:`tuple`, optional): Bounds for the
+            fitting. Defaults to values based on the image.
 
     Returns:
         :py:attr:`tuple`: Containing:
-            - :py:attr:`array_like`: The results (with uncertainties) for each of the 6 parameters fit.
+            - :py:attr:`array_like`: The results (with uncertainties) for each
+                of the 6 parameters fit.
             - :py:attr:`int`: The index of the offset.
             - :py:attr:`int`: The index of the vertical distribution width.
     """
     # Setting default values
     if p0 is None:
-        p0 = [
-            image.shape[0] / 2,
-            image.shape[1] / 2,
-            1,
-            1,
-            image.min(),
-            image.max(),
-        ]
+        p0 = [image.shape[0] / 2, image.shape[1] / 2,
+              1, 1, image.min(), image.max()]
     if bounds is None:
         bounds = (
-            0,
-            [image.shape[0], image.shape[1], 100, 100, image.max(), image.max() * 10,],
-        )
-    abscissa = np.array(np.mgrid[0 : image.shape[0] : 1, 0 : image.shape[1] : 1])
+            0, [image.shape[0], image.shape[1], 100, 100,
+                image.max(), image.max() * 10])
+    abscissa = np.array(np.mgrid[0:image.shape[0]:1, 0:image.shape[1]:1])
     # Perform the fitting
     popt, pcov = curve_fit(
-        bivariate_normal,
-        abscissa,
-        image.flatten(),
-        bounds=bounds,
-        sigma=image_e.flatten(),
-        p0=p0,
-        maxfev=2000 * (len(p0) + 1),
-    )
-    # Determine uncertainty from covarience matrix
+        bivariate_normal, abscissa, image.flatten(),
+        bounds=bounds, sigma=image_e.flatten(), p0=p0,
+        maxfev=2000 * (len(p0) + 1))
+    # Determine uncertainty from covariance matrix
     p_sigma = np.sqrt(np.diag(pcov))
     return unp.uarray(popt, p_sigma), 4, 2
 
@@ -99,11 +95,14 @@ def univariate_normal(data, mu, sigma, offset, factor):
         data (:pyttr:`array_like`): Abscissa data.
         mu (:py:attr:`float`): Mean (horizontal).
         sigma (:py:attr:`float`): Variance (horizontal).
-        offset (:py:attr:`float`): Offset from the 0 for the ordinate, this is the background level.
-        factor (:py:attr:`float`): Multiplicative factor for area of normal distribution.
+        offset (:py:attr:`float`): Offset from the 0 for the ordinate, this is
+            the background level.
+        factor (:py:attr:`float`): Multiplicative factor for area of normal
+            distribution.
 
     Returns:
-        :py:attr:`array_like`: Ordinate data for uniivariate normal distribution.
+        :py:attr:`array_like`: Ordinate data for univariate normal
+            distribution.
     """
     # Creation of the bivariate normal distribution
     normal = norm(loc=mu, scale=sigma)
@@ -112,48 +111,39 @@ def univariate_normal(data, mu, sigma, offset, factor):
 
 def fit_gaussian_1d(image, image_e, p0=None, bounds=None, axis=0):
     """
-    Fit a one-dimensional Gaussian function with some ordinate offset to an image with uncertainty. 
-    This is achieved by averaging in a given ``axis`` before performing the fit. 
-    Return the results, and index of the offset.
+    Fit a one-dimensional Gaussian function with some ordinate offset to an
+    image with uncertainty. This is achieved by averaging in a given ``axis``
+    before performing the fit. Return the results, and index of the offset.
 
     Args:
         image (:py:attr:`array_like`): The data to fit the Gaussian to.
         image_e (:py:attr:`array_like`): The data uncertainty.
-        p0 (:py:attr:`list`, optional): An initial guess at the parameters. Defaults to values based on the image.
-        bounds (:py:attr:`list` of :py:attr:`tuple`, optional): Bounds for the fitting. Defaults to values based on the image.
+        p0 (:py:attr:`list`, optional): An initial guess at the parameters.
+            Defaults to values based on the image.
+        bounds (:py:attr:`list` of :py:attr:`tuple`, optional): Bounds for the
+            fitting. Defaults to values based on the image.
         axis (:py:attr:`int`): The dimension in which to perform the averaging.
 
     Returns:
         :py:attr:`tuple`: Containing:
-            - :py:attr:`array_like`: The results (with uncertainties) for each of the 6 parameters fit.
+            - :py:attr:`array_like`: The results (with uncertainties) for each
+                of the 6 parameters fit.
             - :py:attr:`int`: The index of the offset.
-            - :py:attr:`None`: As it is not possible to describe the reflected peak width.
+            - :py:attr:`None`: As it is not possible to describe the reflected
+                peak width.
     """
     ordinate = image.mean(axis=axis)
     ordinate_e = image_e.mean(axis=axis)
     # Setting default values
     if p0 is None:
-        p0 = [
-            ordinate.shape[0] / 2,
-            1,
-            image.min(),
-            image.max(),
-        ]
+        p0 = [ordinate.shape[0] / 2, 1, image.min(), image.max()]
     if bounds is None:
-        bounds = (
-            0,
-            [ordinate.shape[0], 100, image.max(), image.max() * 10,],
-        )
+        bounds = (0, [ordinate.shape[0], 100, image.max(), image.max() * 10])
     # Perform the fitting
     popt, pcov = curve_fit(
         univariate_normal,
-        np.arange(0, ordinate.shape[0], 1),
-        ordinate,
-        bounds=bounds,
-        sigma=ordinate_e.flatten(),
-        p0=p0,
-        maxfev=2000 * (len(p0) + 1),
-    )
+        np.arange(0, ordinate.shape[0], 1), ordinate, bounds=bounds,
+        sigma=ordinate_e.flatten(), p0=p0, maxfev=2000 * (len(p0) + 1))
     # Determine uncertainty from covarience matrix
     p_sigma = np.sqrt(np.diag(pcov))
-    return unp.uarray(popt, p_sigma), 2, None 
+    return unp.uarray(popt, p_sigma), 2, None
diff --git a/islatu/corrections.py b/islatu/corrections.py
@@ -1,6 +1,7 @@
 """
-Reflectometry data must be corrected as a part of reduction. 
-These functions facilitate this, including the footprint and DCD q-variance corrections.
+Reflectometry data must be corrected as a part of reduction.
+These functions facilitate this, including the footprint and
+DCD q-variance corrections.
 """
 
 # Copyright (c) Andrew R. McCluskey
@@ -9,9 +10,8 @@
 
 import numpy as np
 from scipy.stats import norm
-from uncertainties import unumpy as unp
-from islatu.io import i07_dat_parser
 from scipy.interpolate import splrep
+from uncertainties import unumpy as unp
 
 
 def footprint_correction(beam_width, sample_size, theta):
@@ -21,7 +21,8 @@ def footprint_correction(beam_width, sample_size, theta):
 
     Args:
         beam_width (:py:attr:`float`): Width of incident beam, in metres.
-        sample_size (:py:class:`uncertainties.core.Variable`): Width of sample in the dimension of the beam, in metres.
+        sample_size (:py:class:`uncertainties.core.Variable`): Width of sample
+            in the dimension of the beam, in metres.
         theta (:py:attr:`float`): Incident angle, in degrees.
 
     Returns:
@@ -33,22 +34,25 @@ def footprint_correction(beam_width, sample_size, theta):
     upper = (unp.nominal_values(length) + unp.std_devs(length)) / 2.0 / beam_sd
     lower = (unp.nominal_values(length) - unp.std_devs(length)) / 2.0 / beam_sd
     probability = 2.0 * (
-        unp.uarray(norm.cdf(mid), (norm.cdf(upper) - norm.cdf(lower)) / 2) - 0.5
-    )
+        unp.uarray(
+            norm.cdf(mid), (norm.cdf(upper) - norm.cdf(lower)) / 2) - 0.5)
     return probability
 
 
 def get_interpolator(
-    file_path, parser, q_axis_name="qdcd_", intensity_axis_name="adc2"
-):
+        file_path, parser, q_axis_name="qdcd_", intensity_axis_name="adc2"):
     """
-    Get an interpolator object from scipy, this is useful for the DCD q-normalisation step.
+    Get an interpolator object from scipy, this is useful for the DCD
+    q-normalisation step.
 
     Args:
         file_path (:py:attr:`str`): File path to the normalisation file.
-        parser (:py:attr:`callable`): Parser function for the normalisation file.
-        q_axis_name (:py:attr:`str`, optional): Label for the q-value in the normalisation file. Defaults to ``'qdcd_'``.
-        intensity_axis_name (:py:attr:`str`, optional): Label for the intensity in the normalisation file. Defaults to ``'adc2'``.
+        parser (:py:attr:`callable`): Parser function for the normalisation
+            file.
+        q_axis_name (:py:attr:`str`, optional): Label for the q-value in the
+            normalisation file. Defaults to ``'qdcd_'``.
+        intensity_axis_name (:py:attr:`str`, optional): Label for the
+            intensity in the normalisation file. Defaults to ``'adc2'``.
 
     Returns:
         :py:attr:`tuple`: Containing:
@@ -58,5 +62,5 @@ def get_interpolator(
     """
     normalisation_data = parser(file_path)[1]
     return splrep(
-        normalisation_data[q_axis_name], normalisation_data[intensity_axis_name]
-    )
+        normalisation_data[q_axis_name],
+        normalisation_data[intensity_axis_name])