Merge pull request #141 from LSSTDESC/v3.0.2

update the timeouts and repair docs

config/auxtel.ini

@@ -16,9 +16,9 @@ SPECTRACTOR_DECONVOLUTION_FFM = True
 # value of sigma clip parameter for the spectractor deconvolution process PSF2D and FFM
 SPECTRACTOR_DECONVOLUTION_SIGMA_CLIP = 100
 # maximum time per gradient descent iteration before TimeoutError in seconds
-SPECTRACTOR_FIT_TIMEOUT_PER_ITER = 600
+SPECTRACTOR_FIT_TIMEOUT_PER_ITER = 1200
 # maximum time per gradient descent before TimeoutError in seconds
-SPECTRACTOR_FIT_TIMEOUT = 3600
+SPECTRACTOR_FIT_TIMEOUT = 7200
 
 [instrument]
 # instrument name

docs/requirements.txt

@@ -7,4 +7,5 @@ coloredlogs
 recommonmark
 mistune==2.0.3
 docutils<1.0,>=0.16
-sphinx-mdinclude
+sphinx-mdinclude
+sphinx-rtd-theme

setup.py

@@ -8,7 +8,7 @@
 if os.getenv('CONDA_PREFIX') is None:
     reqs = open('requirements.txt', 'r').read().strip().splitlines()
 
-if os.getenv('READTHEDOCS'):
+if os.getenv('READTHEDOCS') and 'mpi4py' in reqs:
     reqs.remove('mpi4py')
 
 with open('README.md') as file:

spectractor/extractor/chromaticpsf.py

@@ -2406,32 +2406,36 @@ def amplitude_derivatives(self):
 
     def jacobian(self, params, epsilon, model_input=None):
         r"""Generic function to compute the Jacobian matrix of a model, linear parameters being fixed (see Notes),
-        with analytical or numerical derivatives. Analytical derivatives are performed if self.analytical is True.
-        Let's write :math:`\theta`  the non-linear model parameters. If the model is written as:
+        with analytical or numerical derivatives. Analytical derivatives are performed if `self.analytical` is True.
+        Let's write :math:`\theta` the non-linear model parameters. If the model is written as:
+
         .. math::
 
             \mathbf{I} =  \mathbf{M}(\theta) \cdot \hat{\mathbf{A}}(\theta),
 
         this jacobian function returns:
+
         .. math::
 
             \frac{\partial \mathbf{I}}{\partial \theta} =   \frac{\partial \mathbf{M}}{\partial \theta} \cdot \hat{\mathbf{A}}.
 
         Notes
         -----
-            The gradient descent is performed on the non-linear parameters :math:`\theta` (PSF shape and position). Linear parameters :math:`\mathbf{A}`(amplitudes) are computed on the fly.
+            The gradient descent is performed on the non-linear parameters :math:`\theta` (PSF shape and position). Linear parameters :math:`\mathbf{A}` (amplitudes) are computed on the fly.
             Therefore, :math:`\chi^2` is a function of :math:`\theta` only
+
             .. math ::
 
                 \chi^2(\theta) = \chi'^2(\theta, \hat{\mathbf{A}}
 
-            whose partial derivatives on :math:`\theta` for gradient descent are
+            whose partial derivatives on :math:`\theta` for gradient descent are:
+
             .. math ::
 
-                \frac{\partial \chi^2}{\partial \theta} = \left.\left(\frac{\partial \chi'^2}{\partial \theta}  + \frac{\partial \chi'^2}{\partial \mathbf{A}} \frac{\partial \mathbf{A}}{\partial \theta}\right)\right\vert_{\mathbf{A} = \hat \mathbf{A}}
+                \frac{\partial \chi^2}{\partial \theta} = \left.\left(\frac{\partial \chi'^2}{\partial \theta}  + \frac{\partial \chi'^2}{\partial \mathbf{A}} \frac{\partial \mathbf{A}}{\partial \theta}\right)\right\vert_{\mathbf{A} = \hat{\mathbf{A}}}
 
-            By definition, :math:`\left.\partial \chi'^2/\partial \mathbf{A}\right\vert_{\mathbf{A} = \hat \mathbf{A}}=0` then :math:`\chi^2`  partial derivatives must be performed with fixed :math:`\mathbf{A} = \hat \mathbf{A}`
-            for gradient descent. `self.amplitude_priors_method` is temporarily switched to "keep" in `self.jacobian()` to use previously computed :math:`\hat \mathbf{A}` solution.
+            By definition, :math:`\left.\partial \chi'^2/\partial \mathbf{A}\right\vert_{\mathbf{A} = \hat{\mathbf{A}}}=0` then :math:`\chi^2`  partial derivatives must be performed with fixed :math:`\mathbf{A} = \hat{\mathbf{A}}`
+            for gradient descent. `self.amplitude_priors_method` is temporarily switched to "keep" in `self.jacobian()` to use previously computed :math:`\hat{\mathbf{A}}` solution.
 
 
         Parameters

spectractor/extractor/psf.py

@@ -18,10 +18,10 @@ def evaluate_moffat1d_normalisation(gamma, alpha):
 
     .. math ::
 
-        A = \frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}
+        A = \frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}
         \quad\text{with}, \alpha > 1/2
 
-    Note that this function is defined only for :math:`alpha > 1/2`.
+    Note that this function is defined only for :math:`\alpha > 1/2`.
 
     Parameters
     ----------
@@ -48,10 +48,10 @@ def evaluate_moffat1d_normalisation_dalpha(norm, alpha):
 
     .. math ::
 
-        A = \frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}
+        A = \frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}
         \quad\text{with}, \alpha > 1/2
 
-    Note that this function is defined only for :math:`alpha > 1/2`.
+    Note that this function is defined only for :math:`\alpha > 1/2`.
 
     Parameters
     ----------
@@ -83,8 +83,8 @@ def evaluate_moffat1d(y, amplitude, y_c, gamma, alpha, norm):  # pragma: no cove
         f(y) \propto \frac{A}{\left[ 1 +\left(\frac{y-y_c}{\gamma}\right)^2 \right]^\alpha}
         \quad\text{with}, \alpha > 1/2
 
-    Note that this function is defined only for :math:`alpha > 1/2`. The normalisation factor
-    :math:`\frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}` is not included as special functions
+    Note that this function is defined only for :math:`\alpha > 1/2`. The normalisation factor
+    :math:`\frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}` is not included as special functions
     are not supported by numba library.
 
     Parameters
@@ -100,7 +100,7 @@ def evaluate_moffat1d(y, amplitude, y_c, gamma, alpha, norm):  # pragma: no cove
     alpha: float
         Exponent :math:`\alpha` of the Moffat function.
     norm: float
-        Normalisation :math:`\frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}`.
+        Normalisation :math:`\frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}`.
 
     Returns
     -------
@@ -156,10 +156,10 @@ def evaluate_moffat1d_jacobian(y, amplitude, y_c, gamma, alpha, norm, dnormda, f
 
     .. math ::
 
-        f(y) \propto \frac{A}{\left[ 1 +\left(\frac{y-y_c}{\gamma}\right)^2 \right]^\alpha}\times \frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}
+        f(y) \propto \frac{A}{\left[ 1 +\left(\frac{y-y_c}{\gamma}\right)^2 \right]^\alpha}\times \frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}
         \quad\text{with}, \alpha > 1/2
 
-    Note that this function is defined only for :math:`alpha > 1/2`.
+    Note that this function is defined only for :math:`\alpha > 1/2`.
 
     Parameters
     ----------
@@ -174,7 +174,7 @@ def evaluate_moffat1d_jacobian(y, amplitude, y_c, gamma, alpha, norm, dnormda, f
     alpha: float
         Exponent :math:`\alpha` of the Moffat function.
     norm: float
-        Normalisation :math:`\frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}`.
+        Normalisation :math:`\frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}`.
     dnormda: float
         Derivatives of the normalisation with respect to alpha.
     fixed: array_like
@@ -241,8 +241,8 @@ def evaluate_moffatgauss1d(y, amplitude, y_c, gamma, alpha, eta_gauss, sigma, no
         \frac{1}{\left[ 1 +\left(\frac{y-y_c}{\gamma}\right)^2 \right]^\alpha}+ \eta e^{-(y-y_c)^2/(2\sigma^2)}\right\rbrace
         \quad\text{ and } \quad \eta < 0, \alpha > 1/2
 
-    Note that this function is defined only for :math:`alpha > 1/2`. The normalisation factor for the Moffat+Gauss
-    :math:`\frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)} + \eta \sqrt{2\pi} \sigma` is not included as special functions
+    Note that this function is defined only for :math:`\alpha > 1/2`. The normalisation factor for the Moffat+Gauss
+    :math:`\frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)} + \eta \sqrt{2\pi} \sigma` is not included as special functions
     are not supproted by the numba library.
 
     Parameters
@@ -262,7 +262,7 @@ def evaluate_moffatgauss1d(y, amplitude, y_c, gamma, alpha, eta_gauss, sigma, no
     sigma: float
         Width :math:`\sigma` of the Gaussian function.
     norm_moffat: float
-        Normalisation :math:`\frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}`.
+        Normalisation :math:`\frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}`.
 
     Returns
     -------
@@ -326,12 +326,12 @@ def evaluate_moffatgauss1d_jacobian(y, amplitude, y_c, gamma, alpha, eta_gauss,
     .. math ::
 
         f(y) \propto A \left\lbrace
-        \frac{1}{\left[ 1 +\left(\frac{y-y_c}{\gamma}\right)^2 \right]^\alpha} \times \frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}
+        \frac{1}{\left[ 1 +\left(\frac{y-y_c}{\gamma}\right)^2 \right]^\alpha} \times \frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}
          - \eta e^{-(y-y_c)^2/(2\sigma^2)}\right\rbrace
         \quad\text{ and } \quad \eta < 0, \alpha > 1/2
 
-    Note that this function is defined only for :math:`alpha > 1/2`. The normalisation factor for the Moffat
-    :math:`\frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)} + \eta \sqrt{2\pi} \sigma` is not included as special functions
+    Note that this function is defined only for :math:`\alpha > 1/2`. The normalisation factor for the Moffat
+    :math:`\frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)} + \eta \sqrt{2\pi} \sigma` is not included as special functions
     are not supproted by the numba library.
 
     Parameters
@@ -351,7 +351,7 @@ def evaluate_moffatgauss1d_jacobian(y, amplitude, y_c, gamma, alpha, eta_gauss,
     sigma: float
         Width :math:`\sigma` of the Gaussian function.
     norm_moffat: float
-        Normalisation :math:`\frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}`.
+        Normalisation :math:`\frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}`.
     dnormda: float
         Derivatives of the normalisation with respect to alpha.
     fixed: array_like
@@ -430,7 +430,7 @@ def evaluate_moffat2d(x, y, amplitude, x_c, y_c, gamma, alpha):  # pragma: no co
         \quad\text{with}\quad
         \int_{-\infty}^{\infty}\int_{-\infty}^{\infty}f(x, y) \mathrm{d}x \mathrm{d}y = A
 
-    Note that this function is defined only for :math:`alpha > 1`.
+    Note that this function is defined only for :math:`\alpha > 1`.
 
     Note that the normalisation of a 2D Moffat function is analytical so it is not expected that
     the sum of the output array is equal to :math:`A`, but lower.
@@ -589,7 +589,7 @@ def evaluate_moffatgauss2d(x, y, amplitude, x_c, y_c, gamma, alpha, eta_gauss, s
         \int_{-\infty}^{\infty}\int_{-\infty}^{\infty}f(x, y) \mathrm{d}x \mathrm{d}y = A
         \quad\text{and} \quad \eta < 0
 
-    Note that this function is defined only for :math:`alpha > 1`.
+    Note that this function is defined only for :math:`\alpha > 1`.
 
     Parameters
     ----------
@@ -672,8 +672,7 @@ def evaluate_gauss1d(y, amplitude, y_c, sigma):  # pragma: no cover
 
     .. math ::
 
-        f(x, y) = \frac{A}{\sigma \sqrt{2 \pi}\left\lbrace e^{-\left[ \left(x-x_c\right)^2\right]/(2 \sigma^2)}
-        \right\rbrace
+        f(y) = \frac{A}{\sigma \sqrt{2 \pi}\left\lbrace e^{-\left[ \left(y-y_c\right)^2\right]/(2 \sigma^2)}\right\rbrace
 
     .. math ::
         \quad\text{with}\quad
@@ -743,8 +742,7 @@ def evaluate_gauss1d_jacobian(y, amplitude, y_c, sigma, fixed):  # pragma: no co
 
     .. math ::
 
-        f(x, y) = \frac{A}{\sigma \sqrt{2 \pi}\left\lbrace e^{-\left[ \left(x-x_c\right)^2\right]/(2 \sigma^2)}
-        \right\rbrace
+        f(y) = \frac{A}{\sigma \sqrt{2 \pi}\left\lbrace e^{-\left[ \left(y-y_c\right)^2\right]/(2 \sigma^2)}\right\rbrace
 
     .. math ::
         \quad\text{with}\quad
@@ -1206,7 +1204,7 @@ def evaluate(self, pixels, values=None):
 
         .. math::
 
-            f(y) \propto \frac{A \Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)},
+            f(y) \propto \frac{A \Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)},
             \quad \int_{y_{\text{min}}}^{y_{\text{max}}} f(y) \mathrm{d}y = A
 
         Parameters
@@ -1576,7 +1574,7 @@ def evaluate(self, pixels, values=None):
 
         .. math::
 
-            f(y) \propto  \frac{A}{ \frac{\Gamma(alpha)}{\gamma \sqrt{\pi} \Gamma(alpha -1/2)}+\eta\sqrt{2\pi}\sigma},
+            f(y) \propto  \frac{A}{ \frac{\Gamma(\alpha)}{\gamma \sqrt{\pi} \Gamma(\alpha -1/2)}+\eta\sqrt{2\pi}\sigma},
             \quad \int_{y_{\text{min}}}^{y_{\text{max}}} f(y) \mathrm{d}y = A
 
         Parameters

spectractor/extractor/spectroscopy.py

@@ -430,6 +430,7 @@ def build_detected_line_table(self, amplitude_units="", calibration_only=False):
         --------
 
         Creation of a mock spectrum with emission and absorption lines
+
         >>> from spectractor.extractor.spectrum import Spectrum, detect_lines
         >>> lambdas = np.arange(300,1000,1)
         >>> spectrum = 1e4*np.exp(-((lambdas-600)/200)**2)
@@ -444,12 +445,14 @@ def build_detected_line_table(self, amplitude_units="", calibration_only=False):
         >>> fwhm_func = interp1d(lambdas, 0.01 * lambdas)
 
         Detect the lines
+
         >>> lines = Lines([HALPHA, HBETA, O2_1], hydrogen_only=True,
         ... atmospheric_lines=True, redshift=0, emission_spectrum=True)
         >>> global_chisq = detect_lines(lines, lambdas, spectrum, spectrum_err, fwhm_func=fwhm_func)
         >>> assert(global_chisq < 1)
 
         Print the result
+
         >>> spec.lines = lines
         >>> t = lines.build_detected_line_table()
         """

spectractor/extractor/spectrum.py

@@ -814,7 +814,8 @@ def load_spectrum(self, input_file_name, spectrogram_file_name_override=None,
         Examples
         --------
 
-        # Latest Spectractor output format: do not provide a config file (parameters are loaded from file header)
+        Latest Spectractor output format: do not provide a config file (parameters are loaded from file header)
+
         >>> from spectractor import parameters
         >>> s = Spectrum(config="")
         >>> s.load_spectrum('tests/data/reduc_20170530_134_spectrum.fits')
@@ -826,7 +827,8 @@ def load_spectrum(self, input_file_name, spectrogram_file_name_override=None,
             >>> assert parameters.CCD_REBIN == s.header["REBIN"]
             >>> assert s.parallactic_angle == s.header["PARANGLE"]
 
-        # Spectractor output format older than version <=2.3: must give the config file
+        Spectractor output format older than version <=2.3: must give the config file
+
         >>> parameters.VERBOSE = False
         >>> s = Spectrum(config="./config/ctio.ini")
         >>> s.load_spectrum('tests/data/reduc_20170605_028_spectrum.fits')