Merge branch 'develop' into sim_scheduler

install.sh

@@ -5,4 +5,5 @@ conda install numpy==1.26.4
 conda install cython==3.0.6
 conda install sep==1.2.1
 conda install mpi4py==3.1.6
-pip install -e .
+python3 setup.py install --user
\ No newline at end of file
+pip install -e .

requirements.txt

@@ -10,4 +10,5 @@ h5py==3.11.0
 Cython==3.0.6
 numba==0.59.1
 psutil==5.9.8
-toml==0.10.2
+toml==0.10.2
\ No newline at end of file
+lmfit==1.2.2
\ No newline at end of file

setup.py

@@ -98,7 +98,7 @@ setup(name='CSSTSim',
         'ObservationSim.Instrument.data.sls_conf': ['*.conf', '*.fits'],
         'ObservationSim.Instrument.data.flatCube': ['*.fits'],
         'Catalog.data': ['*.fits','*.so'],
-        'ObservationSim.Config.Header':['*.header','*.lst'],
+        'ObservationSim.Config.Header':['*.fits','*.lst'],
         'ObservationSim.Straylight.data': ['*.dat'],
         'ObservationSim.Straylight.data.sky': ['*.dat'],
         'ObservationSim.Straylight.lib': ['*'],

tests/det_effect_unit_test.py

@@ -138,15 +138,15 @@ class DetTest(unittest.TestCase):
         newimg.write(os.path.join(self.outDataFn,'test_badlines.fits'))
         del newimg,img
 
-    def test_cte(self):
-        img = galsim.Image(200,200,init_value=1000)
-        img.array[50,80] = 1e4
-        img.array[150,150] = 3e4
-        newimgcol = Effects.CTE_Effect(copy.deepcopy(img),direction='column')
-        newimgrow = Effects.CTE_Effect(copy.deepcopy(img),direction='row')
-        newimgcol.write(os.path.join(self.outDataFn,'test_ctecol.fits'))
-        newimgrow.write(os.path.join(self.outDataFn,'test_cterow.fits'))
-        del img,newimgcol,newimgrow
+    # def test_cte(self):
+    #     img = galsim.Image(200,200,init_value=1000)
+    #     img.array[50,80] = 1e4
+    #     img.array[150,150] = 3e4
+    #     newimgcol = Effects.CTE_Effect(copy.deepcopy(img),direction='column')
+    #     newimgrow = Effects.CTE_Effect(copy.deepcopy(img),direction='row')
+    #     newimgcol.write(os.path.join(self.outDataFn,'test_ctecol.fits'))
+    #     newimgrow.write(os.path.join(self.outDataFn,'test_cterow.fits'))
+    #     del img,newimgcol,newimgrow
 
     def test_readnoise(self):
         img = galsim.Image(200,200,init_value=1000)

tests/test_SpecDisperse.py

@@ -231,16 +231,16 @@ class TestSpecDisperse(unittest.TestCase):
         ids1 = wave_pix[ids] > 6500
         print('Spec disperse flux test')
         self.assertTrue(np.mean((wave_flux[ids][ids1] - sed_i(wave_pix[ids][ids1]))/sed_i(wave_pix[ids][ids1]))<0.004)
-        plt.figure()
-        plt.plot(wave_pix, wave_flux)
-        plt.plot(sed['WAVELENGTH'], sed['FLUX'])
-        plt.xlim(6200, 10000)
-        plt.ylim(1, 3)
-        plt.yscale('log')
-        plt.xlabel('$\lambda$')
-        plt.ylabel('$F\lambda$')
-        plt.legend(['extracted', 'input'])
-        plt.show()
+        # plt.figure()
+        # plt.plot(wave_pix, wave_flux)
+        # plt.plot(sed['WAVELENGTH'], sed['FLUX'])
+        # plt.xlim(6200, 10000)
+        # plt.ylim(1, 3)
+        # plt.yscale('log')
+        # plt.xlabel('$\lambda$')
+        # plt.ylabel('$F\lambda$')
+        # plt.legend(['extracted', 'input'])
+        # plt.show()
 
     def test_Specdistperse2(self):
 
@@ -303,16 +303,16 @@ class TestSpecDisperse(unittest.TestCase):
         self.assertTrue(fwhmx/deltLamda_pix*pix_scale - psf_fwhm  < np.abs(0.02))
         # print('error is ',np.mean((wave_flux[ids][ids1] - sed_i(wave_pix[ids][ids1]))/sed_i(wave_pix[ids][ids1])))
         # self.assertTrue(np.mean((wave_flux[ids][ids1] - sed_i(wave_pix[ids][ids1]))/sed_i(wave_pix[ids][ids1]))<0.004)
-        plt.figure()
-        plt.plot(wave_pix, wave_flux)
-        plt.plot(sed['WAVELENGTH'], sed['FLUX'])
-        plt.xlim(6200, 10000)
-        plt.ylim(1, 75)
-        plt.yscale('log')
-        plt.xlabel('$\lambda$')
-        plt.ylabel('$F\lambda$')
-        plt.legend(['extracted', 'input'])
-        plt.show()
+        # plt.figure()
+        # plt.plot(wave_pix, wave_flux)
+        # plt.plot(sed['WAVELENGTH'], sed['FLUX'])
+        # plt.xlim(6200, 10000)
+        # plt.ylim(1, 75)
+        # plt.yscale('log')
+        # plt.xlabel('$\lambda$')
+        # plt.ylabel('$F\lambda$')
+        # plt.legend(['extracted', 'input'])
+        # plt.show()
 
     def test_Specdistperse3(self):