Merge pull request #26 from rahil-makadia/dev

GRSS v1 is ready!

CMakeLists.txt

@@ -7,20 +7,14 @@ message(STATUS "GRSS version: ${ver}")
 project(grss VERSION ${ver})
 
 # specify the C++ standard and compiler flags
-# TODO: add gprof profiling, iee standards for floats
 set(CMAKE_CXX_STANDARD 11)
 set(CMAKE_CXX_STANDARD_REQUIRED True)
 add_compile_options(-std=c++11 -O3 -fPIC) # operational flags
-# add_compile_options(-std=c++11 -DLONGDOUBLE -O2 -fPIC -Werror -Wall -Wextra -pedantic -Wno-unused-but-set-variable -Wno-unused-result)
-# if not clang, add -Wno-maybe-uninitialized
-if(NOT CMAKE_CXX_COMPILER_ID MATCHES "Clang")
-    add_compile_options(-Wno-maybe-uninitialized)
-endif()
+# add_compile_options(-std=c++11 -DLONGDOUBLE -g3 -fPIC -Werror -Wall -Wextra -pedantic -Wno-unused-but-set-variable -Wno-unused-result) # debugging flags
 
 # Set header file directories
 include_directories(${CMAKE_SOURCE_DIR}/include)
 include_directories(${CMAKE_SOURCE_DIR}/extern/cspice/include)
-# include_directories(${CMAKE_SOURCE_DIR}/extern/pybind11/include)
 
 # Set source code directories
 add_subdirectory(extern/pybind11)

README.md

@@ -1,6 +1,6 @@
 # grss
 
-[![PyPi Version](https://img.shields.io/pypi/v/grss.svg)](https://pypi.python.org/pypi/grss/)
+[![PyPi Version](https://img.shields.io/pypi/v/grss?color=green)](https://pypi.python.org/pypi/grss/)
 [![Build Sphinx docs)](https://github.com/rahil-makadia/grss/actions/workflows/docs.yml/badge.svg)](https://github.com/rahil-makadia/grss/actions/workflows/docs.yml)
 [![Python tests)](https://github.com/rahil-makadia/grss/actions/workflows/python_tests.yml/badge.svg)](https://github.com/rahil-makadia/grss/actions/workflows/python_tests.yml)
 [![C++ tests)](https://github.com/rahil-makadia/grss/actions/workflows/cpp_tests.yml/badge.svg)](https://github.com/rahil-makadia/grss/actions/workflows/cpp_tests.yml)
@@ -49,7 +49,7 @@ Once the GRSS library has been installed, it can be imported into a Python scrip
    import grss
 ```
 
-The first time the library is imported, it will download some data files such as NAIF SPICE kernels and the data needed to debias optical astrometry. This should not take more than a couple minutes, and if the download was completed, the following message will be printed:
+The first time the library is imported, it will download some data files such as NAIF SPICE kernels and the data needed to debias optical astrometry. This should should take a few minutes, and if the download was completed, the following message will be printed for each file:
 
 ```console
    YYYY-MM-DD HH:MM:SS URL:url-of-downloaded-file [filesize] -> path/to/downloaded/file [1]

docs/source/start.rst

@@ -42,7 +42,7 @@ Once the GRSS library has been installed, it can be imported into a Python scrip
 
    import grss
 
-The first time the library is imported, it will download some data files such as NAIF SPICE kernels and the data needed to debias optical astrometry. This should not take more than a couple minutes, and if the download was completed, the following message will be printed:
+The first time the library is imported, it will download some data files such as NAIF SPICE kernels and the data needed to debias optical astrometry. This should should take a few minutes, and if the download was completed, the following message will be printed for each file:
 
 .. code-block:: console
 

grss/fit/fit_simulation.py

@@ -430,7 +430,7 @@ def plot_chi(self, t_arr, ra_chi, dec_chi, delay_chi, doppler_chi,
         plt.axhline(sigma_limit, c='red', linestyle='--', alpha=0.5)
         plt.legend(ncol=2)
         plt.xlabel('Time [UTC]')
-        plt.ylabel(r'Weighted Residuals, (O-C)/$\sigma$ $[\cdot]$')
+        plt.ylabel(r'Weighted Residuals, (O-C) $[\sigma]$')
         plt.grid(True, which='both', axis='both', alpha=0.2)
         if show_logarithmic:
             plt.yscale('log')
@@ -451,7 +451,7 @@ def plot_chi(self, t_arr, ra_chi, dec_chi, delay_chi, doppler_chi,
                         markersize=radar_scale*markersize, label='Doppler')
             plt.legend(ncol=2)
             plt.xlabel('Time [UTC]')
-            plt.ylabel(r'$\chi^2$, (O-C)$^2/\sigma^2$ $[\cdot]$')
+            plt.ylabel(r'$\chi^2$')
             plt.grid(True, which='both', axis='both', alpha=0.2)
             # if show_logarithmic: plt.yscale('log')
             plt.yscale('log')

grss/prop/__init__.py

@@ -1,3 +1,4 @@
 """GRSS orbit propagation subpackage"""
 from .prop_simulation import *
+from .prop_unscented import *
 from .prop_utils import *

grss/prop/prop_unscented.py

@@ -0,0 +1,36 @@
+"""Unscented transformations for the GRSS orbit propagation code"""
+import numpy as np
+
+def generate_sigma_points(sol, cov):
+    """
+    Generate sigma points for propagating uncertainties using the unscented
+    transformation.
+
+    Parameters
+    ----------
+    sol : dict
+        solution dictionary from the SBDB API
+    cov : numpy.ndarray
+        covariance matrix of the solution
+
+    Returns
+    -------
+    sigma_points : list
+        list of dictionaries containing each of the sigma points
+    """
+    assert len(sol)-1 == cov.shape[0]
+    assert cov.shape[0] == cov.shape[1]
+    dim = cov.shape[0]
+    sqrt_cov = np.linalg.cholesky(cov)
+    sol_array = np.array([sol[key] for key in sol if key != 't'])
+    sigma_points = [list(sol_array)]
+    fac = dim**0.5
+    for i in range(dim):
+        plus = sol_array + fac*sqrt_cov.T[i]
+        minus = sol_array - fac*sqrt_cov.T[i]
+        sigma_points.append(list(plus))
+        sigma_points.append(list(minus))
+    # convert sigma points to dict
+    for i in range(2*dim+1):
+        sigma_points[i] = {key: sigma_points[i][j-1] for j, key in enumerate(sol) if key != 't'}
+    return sigma_points