Merge pull request #23 from lsst/tickets/DM-34226

DM-34226: Update Spectractor fork with PR #91 from upstream

.github/workflows/build.yaml

@@ -38,7 +38,12 @@ jobs:
         run: |
           pip install -v -e .
 
-      - name: Run tests
+      - name: Run nosetests
         shell: bash -l {0}
         run: |
           python setup.py nosetests
+
+      - name: Run full chain
+        shell: bash -l {0}
+        run: |
+          nosetests tests/run_full_chain.py --all --debug --detailed-errors --verbose --process-restartworker --with-coverage --cover-package=spectractor

spectractor/extractor/dispersers.py

@@ -361,35 +361,36 @@ def load_files(self, verbose=False):
         0
 
         """
-        filename = self.data_dir + self.label + "/N.txt"
+        filename = os.path.join(self.data_dir, self.label, "N.txt")
         if os.path.isfile(filename):
             a = np.loadtxt(filename)
             self.N_input = a[0]
             self.N_err = a[1]
         else:
             raise FileNotFoundError(f"Failed to load {filename} for {self.label}")
 
-        filename = self.data_dir + self.label + "/full_name.txt"
+        filename = os.path.join(self.data_dir, self.label, "full_name.txt")
         if os.path.isfile(filename):
             with open(filename, 'r') as f:
                 for line in f:  # MFL: you really just want the last line of the file?
                     self.full_name = line.rstrip('\n')
         else:
             raise FileNotFoundError(f"Failed to load {filename} for {self.label}")
 
-        filename = self.data_dir + self.label + "/transmission.txt"
+        filename = os.path.join(self.data_dir, self.label, "transmission.txt")
         if os.path.isfile(filename):
             a = np.loadtxt(filename)
             l, t, e = a.T
             self.transmission = interpolate.interp1d(l, t, bounds_error=False, fill_value=0.)
             self.transmission_err = interpolate.interp1d(l, e, bounds_error=False, fill_value=0.)
         else:
-            self.transmission = lambda x: np.ones_like(x).astype(float)
-            self.transmission_err = lambda x: np.zeros_like(x).astype(float)
+            ones = np.ones_like(parameters.LAMBDAS).astype(float)
+            self.transmission = interpolate.interp1d(parameters.LAMBDAS, ones, bounds_error=False, fill_value=0.)
+            self.transmission_err = interpolate.interp1d(parameters.LAMBDAS, 0*ones, bounds_error=False, fill_value=0.)
             msg = f"Failed to load {filename} for {self.label}, using default (perfect) transmission"
             self.my_logger.info(msg)
 
-        filename = self.data_dir + self.label + "/ratio_order_2over1.txt"
+        filename = os.path.join(self.data_dir, self.label, "ratio_order_2over1.txt")
         if os.path.isfile(filename):
             a = np.loadtxt(filename)
             if a.T.shape[0] == 2:
@@ -400,9 +401,11 @@ def load_files(self, verbose=False):
                                                            fill_value="extrapolate")  # "(0, t[-1]))
             self.flat_ratio_order_2over1 = False
         else:
-            self.ratio_order_2over1 = lambda x: parameters.GRATING_ORDER_2OVER1 * np.ones_like(x).astype(float)
+            ratio = parameters.GRATING_ORDER_2OVER1 * np.ones_like(parameters.LAMBDAS).astype(float)
+            self.ratio_order_2over1 = interpolate.interp1d(parameters.LAMBDAS, ratio, bounds_error=False, kind="linear",
+                                                           fill_value="extrapolate")  # "(0, t[-1]))
             self.flat_ratio_order_2over1 = True
-        filename = self.data_dir + self.label + "/hologram_center.txt"
+        filename = os.path.join(self.data_dir, self.label, "hologram_center.txt")
         if os.path.isfile(filename):
             lines = [ll.rstrip('\n') for ll in open(filename)]
             self.theta_tilt = float(lines[1].split(' ')[2])
@@ -625,7 +628,7 @@ def __init__(self, label, D=parameters.DISTANCE2CCD, data_dir=parameters.DISPERS
         """
         Grating.__init__(self, 350, D=D, label=label, data_dir=data_dir, verbose=False)
         self.holo_center = None  # center of symmetry of the hologram interferences in pixels
-        self.theta = None  # interpolated rotation angle map of the hologram from data in degrees
+        self.theta_interp = None  # interpolated rotation angle map of the hologram from data in degrees
         self.theta_data = None  # rotation angle map data of the hologram from data in degrees
         self.theta_x = None  # x coordinates for the interpolated rotation angle map
         self.theta_y = None  # y coordinates for the interpolated rotation angle map
@@ -670,11 +673,34 @@ def N(self, x):
 
         if x[0] < np.min(self.N_x) or x[0] > np.max(self.N_x) \
                 or x[1] < np.min(self.N_y) or x[1] > np.max(self.N_y):
-            N = self.N_fit(x[0], x[1])
+            N = float(self.N_fit(*x))
         else:
-            N = int(self.N_interp(x))
+            N = int(self.N_interp(*x))
         return N
 
+    def theta(self, x):
+        """Return the mean dispersion angle of the grating at position x.
+
+        Parameters
+        ----------
+        x: float, array
+            The [x,y] pixel position on the CCD.
+
+        Returns
+        -------
+        theta: float
+            The mean dispersion angle at position x in degrees.
+
+        Examples
+        --------
+        >>> h = Hologram('HoloPhP')
+        >>> h.theta((500,500))
+        -1.3393287109201792
+        >>> h.theta((0,0))
+        -2.0936702173289983
+        """
+        return float(self.theta_interp(*x))
+
     def load_specs(self, verbose=True):
         """Load the files in data_dir/label/ to set the main
         characteristics of the hologram. If they do not exist, default values are used.
@@ -699,57 +725,57 @@ def load_specs(self, verbose=True):
 
         The files do not exist:
 
-        >>> h = Hologram(label='XXX')
-        >>> h.N((500,500))
-        350
-        >>> h.theta((500,500))
-        0
-        >>> h.holo_center
-        [1024.0, 1024.0]
+        >>> h = Hologram(label='XXX')  # doctest: +ELLIPSIS
+        Traceback (most recent call last):
+        ...
+        FileNotFoundError:...
 
         """
         if verbose:
             self.my_logger.info(f'\n\tLoad disperser {self.label}:\n\tfrom {os.path.join(self.data_dir, self.label)}')
-        filename = self.data_dir + self.label + "/hologram_grooves_per_mm.txt"
+        filename = os.path.join(self.data_dir, self.label, "hologram_grooves_per_mm.txt")
         if os.path.isfile(filename):
             a = np.loadtxt(filename)
             self.N_x, self.N_y, self.N_data = a.T
             if parameters.CCD_REBIN > 1:
                 self.N_x /= parameters.CCD_REBIN
                 self.N_y /= parameters.CCD_REBIN
-            N_interp = interpolate.interp2d(self.N_x, self.N_y, self.N_data, kind='cubic')
+            self.N_interp = interpolate.interp2d(self.N_x, self.N_y, self.N_data, kind='cubic')
             self.N_fit = fit_poly2d(self.N_x, self.N_y, self.N_data, order=2)
-            self.N_interp = lambda x: float(N_interp(x[0], x[1]))
         else:
             self.is_hologram = False
             self.N_x = np.arange(0, parameters.CCD_IMSIZE)
             self.N_y = np.arange(0, parameters.CCD_IMSIZE)
-            filename = self.data_dir + self.label + "/N.txt"
+            filename = os.path.join(self.data_dir, self.label, "N.txt")
             if os.path.isfile(filename):
                 a = np.loadtxt(filename)
-                self.N_interp = lambda x: a[0]
-                self.N_fit = lambda x, y: a[0]
+
+                def N_func(x, y):
+                    return a[0]
+                self.N_interp = N_func
+                self.N_fit = N_func
             else:
                 raise ValueError("To define an hologram, you must provide hologram_grooves_per_mm.txt or N.txt files.")
-        filename = self.data_dir + self.label + "/hologram_center.txt"
+        filename = os.path.join(self.data_dir, self.label, "hologram_center.txt")
         if os.path.isfile(filename):
             lines = [ll.rstrip('\n') for ll in open(filename)]
             self.holo_center = list(map(float, lines[1].split(' ')[:2]))
             self.theta_tilt = float(lines[1].split(' ')[2])
         else:
             self.holo_center = [0.5 * parameters.CCD_IMSIZE, 0.5 * parameters.CCD_IMSIZE]
             self.theta_tilt = 0
-        filename = self.data_dir + self.label + "/hologram_rotation_angles.txt"
+        filename = os.path.join(self.data_dir, self.label, "hologram_rotation_angles.txt")
         if os.path.isfile(filename):
             a = np.loadtxt(filename)
             self.theta_x, self.theta_y, self.theta_data = a.T
             if parameters.CCD_REBIN > 1:
                 self.theta_x /= parameters.CCD_REBIN
                 self.theta_y /= parameters.CCD_REBIN
-            theta_interp = interpolate.interp2d(self.theta_x, self.theta_y, self.theta_data, kind='cubic')
-            self.theta = lambda x: float(theta_interp(x[0], x[1]))
+            self.theta_interp = interpolate.interp2d(self.theta_x, self.theta_y, self.theta_data, kind='cubic')
         else:
-            self.theta = lambda x: self.theta_tilt
+            def theta_func(x, y):
+                return self.theta_tilt
+            self.theta_interp = theta_func
         self.x_lines, self.line1, self.line2 = neutral_lines(self.holo_center[0], self.holo_center[1], self.theta_tilt)
         if verbose:
             if self.is_hologram:

spectractor/extractor/extractor.py

@@ -18,11 +18,13 @@
 from spectractor.simulation.adr import adr_calib, flip_and_rotate_adr_to_image_xy_coordinates
 from spectractor.fit.fitter import run_minimisation, run_minimisation_sigma_clipping, RegFitWorkspace, FitWorkspace
 
+
 def dumpParameters():
     for item in dir(parameters):
         if not item.startswith("__"):
             print(item, getattr(parameters, item))
 
+
 class FullForwardModelFitWorkspace(FitWorkspace):
 
     def __init__(self, spectrum, amplitude_priors_method="noprior", nwalkers=18, nsteps=1000, burnin=100, nbins=10,
@@ -789,6 +791,7 @@ def run_ffm_minimisation(w, method="newton", niter=2):
 
     return w.spectrum
 
+
 def Spectractor(file_name, output_directory, target_label, guess=None, disperser_label="", config='./config/ctio.ini',
                 atmospheric_lines=True, line_detection=True):
     """ Spectractor

spectractor/extractor/images.py

@@ -1190,7 +1190,7 @@ def compute_rotation_angle_hessian(image, angle_range=(-10, 10), width_cut=param
         theta_mask = np.copy(theta)
         theta_mask[mask] = np.nan
         # print len(theta_mask[~np.isnan(theta_mask)]), lambda_threshold
-    theta_guess = image.disperser.theta(image.target_pixcoords)
+    theta_guess = float(image.disperser.theta(image.target_pixcoords))
     mask2 = np.logical_or(angle_range[0] > theta - theta_guess, theta - theta_guess > angle_range[1])
     theta_mask[mask2] = np.nan
     theta_mask = theta_mask[2:-2, 2:-2]

spectractor/extractor/targets.py

@@ -366,7 +366,8 @@ def build_sed(self, index=0):
         array(1.67605113e-11)
         """
         if len(self.spectra) == 0:
-            self.sed = lambda x: np.zeros_like(x)
+            self.sed = interp1d(parameters.LAMBDAS, np.zeros_like(parameters.LAMBDAS), kind='linear', bounds_error=False,
+                                fill_value=0.)
         else:
             self.sed = interp1d(self.wavelengths[index], self.spectra[index], kind='linear', bounds_error=False,
                                 fill_value=0.)

spectractor/parameters.py

@@ -103,7 +103,7 @@ def __getattr__(name):
 ROT_PREFILTER = True  # must be set to true, otherwise create residuals and correlated noise
 ROT_ORDER = 5  # must be above 3
 ROT_ANGLE_MIN = -10
-ROT_ANGL_MAX = 10  # in the Hessian analysis to compute rotation angle, cut all angles outside this range [degrees]
+ROT_ANGLE_MAX = 10  # in the Hessian analysis to compute rotation angle, cut all angles outside this range [degrees]
 
 # Range for spectrum
 LAMBDA_MIN = 300  # minimum wavelength for spectrum extraction (in nm)

spectractor/simulation/image_simulation.py

@@ -165,7 +165,7 @@ def set_star_list(self):
             mask = np.zeros(data.shape, dtype=bool)
             for y in range(int(y0) - 100, int(y0) + 100):
                 for x in range(parameters.CCD_IMSIZE):
-                    u, v = pixel_rotation(x, y, self.image.disperser.theta([x0, y0]) * np.pi / 180., x0, y0)
+                    u, v = pixel_rotation(x, y, self.image.disperser.theta(x0, y0) * np.pi / 180., x0, y0)
                     if margin > v > -margin:
                         mask[y, x] = True
             # remove background and detect sources