Merge 9bd04f0 into 44823cb

README.rst

@@ -62,5 +62,5 @@ That’s it! Now we can take a look at the extracted time-series spectra:
 
    obs.plot()
 
-.. figure:: specialsoss/figures/extracted_spectra.png
+.. figure:: specialsoss/files/extracted_spectra.png
    :alt: SOSS Extraction

setup.py

@@ -35,7 +35,7 @@
             'specialsoss=specialsoss.cli:main',
         ],
     },
-    install_requires=requirements,
+    install_requires=['numpy', 'astropy', 'bokeh'],
     license="MIT license",
     long_description=readme + '\n\n' + history,
     include_package_data=True,

specialsoss/__init__.py

@@ -5,3 +5,6 @@
 __author__ = """Joe Filippazzo"""
 __email__ = 'jfilippazzo@stsci.edu'
 __version__ = '0.1.0'
+
+from .crossdispersion import *
+from .specialsoss import *

specialsoss/crossdispersion.py

@@ -0,0 +1,178 @@
+# -*- coding: utf-8 -*-
+"""A module of functions that can be use to approximate the SOSS psf in the
+cross dispersion direction"""
+
+import os
+
+import numpy as np
+
+
+def batman(x, mu0, sigma0, A0, sigma1, A1, sep):
+    """
+    Generate a batman function of the given parameters
+
+    Parameters
+    ----------
+    x: array-like
+        The x-axis on which to generate the function
+    mu0: float
+        The x-position of the center peak
+    sigma0: float
+        The stanfard deviation of the center peak
+    A0: float
+        The amplitude of the center peak
+    sigma1: float
+        The stanfard deviation of the outer peaks
+    A1: float
+        The amplitude of the outer peaks
+    sep: float
+        The separation of the outer peaks from the center peak
+
+    Returns
+    -------
+    np.ndarray
+        The y-axis values of the mixed Gaussian
+    """
+    peak1 = gaussian(x, mu0-sep, sigma1, A1)
+    peak2 = gaussian(x, mu0, sigma0, A0)
+    peak3 = gaussian(x, mu0+sep, sigma1, A1)
+
+    return peak1 + peak2 + peak3
+
+
+def batmen(x, mu0_1, sigma0_1, A0_1, sigma1_1, A1_1, sep_1, mu0_2, sigma0_2, A0_2, sigma1_2, A1_2, sep_2):
+    """
+    Generate two batman functions of the given parameters
+
+    Parameters
+    ----------
+    x: array-like
+        The x-axis on which to generate the function
+    mu0_1: float
+        The x-position of the center peak, first psf
+    sigma0_1: float
+        The stanfard deviation of the center peak, first psf
+    A0_1: float
+        The amplitude of the center peak, first psf
+    sigma1_1: float
+        The stanfard deviation of the outer peaks, first psf
+    A1_1: float
+        The amplitude of the outer peaks, first psf
+    sep_1: float
+        The separation of the outer peaks from the center peak, first psf
+    mu0_2: float
+        The x-position of the center peak, second psf
+    sigma0_2: float
+        The stanfard deviation of the center peak, second psf
+    A0_2: float
+        The amplitude of the center peak, second psf
+    sigma1_2: float
+        The stanfard deviation of the outer peaks, second psf
+    A1_2: float
+        The amplitude of the outer peaks, second psf
+    sep_2: float
+        The separation of the outer peaks from the center peak, second psf
+
+    Returns
+    -------
+    np.ndarray
+        The y-axis values of the mixed Gaussian
+    """
+    batman1 = batman(x, mu0_1, sigma0_1, A0_1, sigma1_1, A1_1, sep_1)
+    batman2 = batman(x, mu0_2, sigma0_2, A0_2, sigma1_2, A1_2, sep_2)
+
+    return batman1 + batman2
+
+
+def bimodal(x, mu1, sigma1, A1, mu2, sigma2, A2):
+    """
+    Generate a bimodal function of two Gaussians of the given parameters
+
+    Parameters
+    ----------
+    x: array-like
+        The x-axis on which to generate the function
+    mu1: float
+        The x-position of the first peak center
+    sigma1: float
+        The stanfard deviation of the first distribution
+    A1: float
+        The amplitude of the first peak
+    mu2: float
+        The x-position of the second peak center
+    sigma2: float
+        The stanfard deviation of the second distribution
+    A2: float
+        The amplitude of the second peak
+
+    Returns
+    -------
+    np.ndarray
+        The y-axis values of the mixed Gaussian
+    """
+    g1 = gaussian(x, mu1, sigma1, A1)
+    g2 = gaussian(x, mu2, sigma2, A2)
+
+    return g1 + g2
+
+
+def gaussian(x, mu, sigma, A):
+    """
+    Generate a Gaussian function of the given parameters
+
+    Parameters
+    ----------
+    x: array-like
+        The x-axis on which to generate the Gaussian
+    mu: float
+        The x-position of the peak center
+    sigma: float
+        The stanfard deviation of the distribution
+    A: float
+        The amplitude of the peak
+
+    Returns
+    -------
+    np.ndarray
+        The y-axis values of the Gaussian
+    """
+    return A*np.exp(-(x-mu)**2/2/sigma**2)
+
+
+def trimodal(x, mu1, sigma1, A1, mu2, sigma2, A2, mu3, sigma3, A3):
+    """
+    Generate a trimodal function of three Gaussians of the given parameters
+
+    Parameters
+    ----------
+    x: array-like
+        The x-axis on which to generate the function
+    mu1: float
+        The x-position of the first peak center
+    sigma1: float
+        The stanfard deviation of the first distribution
+    A1: float
+        The amplitude of the first peak
+    mu2: float
+        The x-position of the second peak center
+    sigma2: float
+        The stanfard deviation of the second distribution
+    A2: float
+        The amplitude of the second peak
+    mu3: float
+        The x-position of the third peak center
+    sigma3: float
+        The stanfard deviation of the third distribution
+    A3: float
+        The amplitude of the third peak
+
+    Returns
+    -------
+    np.ndarray
+        The y-axis values of the mixed Gaussian
+    """
+    g1 = gaussian(x, mu1, sigma1, A1)
+    g2 = gaussian(x, mu2, sigma2, A2)
+    g3 = gaussian(x, mu3, sigma3, A3)
+
+    return g1 + g2 + g3