Merge pull request #19 from Autostronomy/versionreqs

set req limits, fix logo missing, update trapz

.gitignore

@@ -2,4 +2,5 @@
 autoprofutils/oldcode.py
 docs/build_html.sh
 autoprof.egg-info
-dist
+dist
+build

autoprof/__init__.py

@@ -3,7 +3,7 @@
 
 from . import autoprofutils, Pipeline, pipeline_steps
 
-__version__ = "1.1.2"
+__version__ = "1.2.0"
 __author__ = "Connor Stone"
 __email__ = "connorstone628@gmail.com"
 

autoprof/autoprofutils/SharedFunctions.py

@@ -1,6 +1,6 @@
 import sys
 import os
-from scipy.integrate import trapz, quad
+from scipy.integrate import trapezoid, quad
 from scipy.stats import iqr, norm
 from scipy.interpolate import interp2d, SmoothBivariateSpline, Rbf, RectBivariateSpline
 from scipy.fftpack import fft, ifft
@@ -1111,7 +1111,9 @@ def fluxdens_to_fluxsum(R, I, axisratio):
     S = np.zeros(len(R))
     S[0] = I[0] * np.pi * axisratio[0] * (R[0] ** 2)
     for i in range(1, len(R)):
-        S[i] = trapz(2 * np.pi * I[: i + 1] * R[: i + 1] * axisratio[: i + 1], R[: i + 1]) + S[0]
+        S[i] = (
+            trapezoid(2 * np.pi * I[: i + 1] * R[: i + 1] * axisratio[: i + 1], R[: i + 1]) + S[0]
+        )
     return S
 
 
@@ -1223,7 +1225,7 @@ def Fmode_fluxdens_to_fluxsum(R, I, parameters, A=None):
     S[0] = I[0] * Aq[0]
     Adiff = np.array([Aq[0]] + list(Aq[1:] - Aq[:-1]))
     for i in range(1, len(R)):
-        S[i] = trapz(I[: i + 1] * Adiff[: i + 1], R[: i + 1]) + S[0]
+        S[i] = trapezoid(I[: i + 1] * Adiff[: i + 1], R[: i + 1]) + S[0]
     return S
 
 
@@ -1343,7 +1345,7 @@ def SBprof_to_COG(R, SB, parameters):
     #     R = arcsec_to_pc(np.array(R), D)
     #     # Integrate up to each radius in the profile
     #     for i in range(1,len(R)):
-    #         m[i] = abs_mag_to_app_mag(L_to_mag(trapz(2*np.pi*I[:i+1]*R[:i+1]*axisratio[:i+1],R[:i+1]) + \
+    #         m[i] = abs_mag_to_app_mag(L_to_mag(trapezoid(2*np.pi*I[:i+1]*R[:i+1]*axisratio[:i+1],R[:i+1]) + \
     #                                            mag_to_L(app_mag_to_abs_mag(m[0], D), band), band),D)
     # elif method == 1:
     #     # Compute the starting point assuming constant SB within first isophote

autoprof/pipeline_steps/Isophote_Fit.py

@@ -1,21 +1,16 @@
 import numpy as np
 from scipy.stats import iqr
-from scipy.fftpack import fft, ifft
+from scipy.fftpack import fft
 from scipy.optimize import minimize
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.pipeline import make_pipeline
-from sklearn.linear_model import RANSACRegressor, HuberRegressor
-from time import time
-from astropy.visualization import SqrtStretch, LogStretch
-from astropy.visualization.mpl_normalize import ImageNormalize
-from photutils.isophote import EllipseSample, EllipseGeometry, Isophote, IsophoteList
+from sklearn.linear_model import HuberRegressor
+from photutils.isophote import EllipseGeometry
 from photutils.isophote import Ellipse as Photutils_Ellipse
 import matplotlib.pyplot as plt
 from matplotlib.patches import Ellipse
 from copy import copy, deepcopy
 import logging
-import sys
-import os
 
 from ..autoprofutils.SharedFunctions import (
     _iso_extract,

requirements.txt

@@ -1,7 +1,7 @@
-astropy>=3.2.3
-astroquery>=0.3.10
+astropy>=3.2.3,<6.0
+astroquery>=0.4,<0.5
 matplotlib>=3.1.2
-numpy>=1.17.4
+numpy>=1.17.4,<2.0
 photutils>=0.7.2,<=1.5.0
 scikit-learn>=0.21.3
 scipy>=1.3.3

requirements_build.txt

@@ -1,7 +1,7 @@
-astropy>=3.2.3
-astroquery>=0.3.10
+astropy>=3.2.3,<6.0
+astroquery>=0.3.10,<0.4
 matplotlib>=3.1.2
-numpy>=1.17.4
+numpy>=1.17.4,<2.0
 photutils>=0.7.2,<=1.5.0
 scikit-learn>=0.21.3
 scipy>=1.3.3

setup.py

@@ -2,37 +2,34 @@
 import autoprof.__init__ as ap
 import os
 
+
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read()
 
+
 setup(
     name="autoprof",
-    version=ap.__version__,    
+    version=ap.__version__,
     description="Fast, robust, deep isophotal solutions for galaxy images",
     long_description=read("README.md"),
-    long_description_content_type='text/markdown',
+    long_description_content_type="text/markdown",
     url="https://github.com/Autostronomy/AutoProf",
     author=ap.__author__,
     author_email=ap.__email__,
     license="GPL-3.0 license",
     packages=find_packages(),
-    install_requires=["scipy",
-                      "numpy",
-                      "astropy",
-                      "matplotlib",
-                      "photutils",
-                      "requests",
-                      "scikit-learn",
-                      ],
-    entry_points = {
-        'console_scripts': [
-            'autoprof = autoprof:run_from_terminal',
+    package_data={"": ["*.png"]},
+    include_package_data=True,
+    install_requires=list(read("requirements.txt").split("\n")),
+    entry_points={
+        "console_scripts": [
+            "autoprof = autoprof:run_from_terminal",
         ],
     },
     classifiers=[
         "Development Status :: 5 - Production/Stable",
         "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",  
+        "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
         "Programming Language :: Python :: 3",
     ],
 )