many updates

development/update_tess_sectors.ipynb

@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f699122a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "from astropy.time import Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "07bebdbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/db/hdghk6ts5g11hr10jq0ss625nf1ny2/T/ipykernel_10556/1019568273.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n",
+      "  data = pd.read_csv('https://tess.mit.edu/public/files/TESS_orbit_times.csv',skipfooter=1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('https://tess.mit.edu/public/files/TESS_orbit_times.csv',skipfooter=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3ebad035",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sectors = data['Sector'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "6d49e9f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame()\n",
+    "for sector in sectors:\n",
+    "    s = data.loc[data['Sector'] == sector]\n",
+    "    start = Time(s['Start of Orbit'].values[0])\n",
+    "    end = Time(s['End of Orbit'].values[1])\n",
+    "    entry = pd.DataFrame()\n",
+    "    entry['Sector'] = [sector]\n",
+    "    entry['mjd_start'] = [start.mjd]\n",
+    "    entry['mjd_end'] = [end.mjd]\n",
+    "    df = pd.concat([df,entry])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "dfc55f06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sector</th>\n",
+       "      <th>mjd_start</th>\n",
+       "      <th>mjd_end</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "    <tr>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>97 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Sector     mjd_start       mjd_end\n",
+       "0        1  58324.815972  58352.684028\n",
+       "0        2  58353.607639  58381.020833\n",
+       "0        3  58382.225694  58408.875000\n",
+       "0        4  58410.406250  58436.357639\n",
+       "0        5  58437.482639  58463.795139\n",
+       "..     ...           ...           ...\n",
+       "0       93  60829.357639  60842.638889\n",
+       "0       94  60855.763889  60868.236111\n",
+       "0       95  60881.833333  60894.173611\n",
+       "0       96  60907.274306  60919.989583\n",
+       "0       97  60933.159722  60946.371528\n",
+       "\n",
+       "[97 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8efcc1f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_sector_times():\n",
+    "    import pandas as pd \n",
+    "    from astropy.time import Time\n",
+    "    data = pd.read_csv('https://tess.mit.edu/public/files/TESS_orbit_times.csv',skipfooter=1)\n",
+    "    sectors = data['Sector'].unique()\n",
+    "    df = pd.DataFrame()\n",
+    "    for sector in sectors:\n",
+    "        s = data.loc[data['Sector'] == sector]\n",
+    "        start = Time(s['Start of Orbit'].values[0])\n",
+    "        end = Time(s['End of Orbit'].values[1])\n",
+    "        entry = pd.DataFrame()\n",
+    "        entry['Sector'] = [sector]\n",
+    "        entry['mjd_start'] = [start.mjd]\n",
+    "        entry['mjd_end'] = [end.mjd]\n",
+    "        df = pd.concat([df,entry])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

setup.py

@@ -24,7 +24,7 @@
 
 # What packages are required for this module to be executed?
 REQUIRED = ['lightkurve>=2.0.0',
-            'numpy',           
+            'numpy<2.0.0',           
             'photutils>=1.4',
             'pandas',
             'scipy!=1.4.0,!=1.4.1,>=0.19.0',

tessreduce/calibration_tools.py

@@ -192,8 +192,8 @@ def Tonry_reduce(Data,plot=False,savename=None,system='ps1'):
         colours = Make_colours(dat.iloc[ind],tonry,compare,Extinction = res.x, Tonry = True,system=system)
         clip = Tonry_clip(colours,tonry)
         #clips += [clip]
-        dat['locus'].iloc[ind] = clip 
-    dat2 = dat.iloc[dat['locus'].values > 0]
+        dat.loc[ind,'locus'] = clip * 1
+    dat2 = dat.loc[dat['locus'] > 0]
     #print('Pass ' + str(i+1) + ': '  + str(res.x[0]))
     #clips[0][clips[0]] = clips[1]
     if plot:

tessreduce/cat_mask.py

@@ -25,7 +25,7 @@ def circle_app(rad):
     """
     Makes a kinda circular aperture, probably not worth using.
     """
-    mask = np.zeros((int(rad*2+.5)+1,int(rad*2+.5)+1))
+    mask = np.zeros((int(np.round(rad*2,0))+1,int(np.round(rad*2))+1))
     c = rad
     x,y =np.where(mask==0)
     dist = np.sqrt((x-c)**2 + (y-c)**2)
@@ -42,8 +42,8 @@ def ps1_auto_mask(table,Image,scale=1):
     image = np.zeros_like(Image)
     x = table.x.values
     y = table.y.values
-    x = (x+.5).astype(int)
-    y = (y+.5).astype(int)
+    x = (np.round(x,0)).astype(int)
+    y = (np.round(y,0)).astype(int)
     m = table.mag.values
     ind = size_limit(x,y,image)
     x = x[ind]; y = y[ind]; m = m[ind]
@@ -91,8 +91,8 @@ def gaia_auto_mask(table,Image,scale=1):
     image = np.zeros_like(Image)
     x = table.x.values
     y = table.y.values
-    x = (x+.5).astype(int)
-    y = (y+.5).astype(int)
+    x = (np.round(x,0)).astype(int)
+    y = (np.round(y,0)).astype(int)
     m = table.mag.values
     ind = size_limit(x,y,image)
     x = x[ind]; y = y[ind]; m = m[ind]
@@ -105,10 +105,15 @@ def gaia_auto_mask(table,Image,scale=1):
     mags = [[18,17],[17,16],[16,15],[15,14],[14,13.5],[13.5,12],[12,10],[10,9],[9,8],[8,7]]
     size = (np.array([3,4,5,6,7,8,10,14,16,18])*scale).astype(int)
     for i, mag in enumerate(mags):
-        m = ((magim > mag[1]) & (magim <= mag[0])) * 1.
-        k = np.ones((size[i],size[i]))
-        conv = fftconvolve(m, k,mode='same')#.astype(int)
-        masks[str(mag[0])] = (conv >.1) * 1.
+        ind = (m > mag[1]) & (m <= mag[0])
+        magim = np.zeros_like(image)
+        magim[y[ind],x[ind]] = 1.
+        if size[i] >0:
+            k = np.ones((size[i],size[i]))
+            conv = fftconvolve(magim, k,mode='same')#.astype(int)
+            masks[str(mag[0])] = (conv >.1) * 1.
+        else:
+            conv = magim
     masks['all'] = np.zeros_like(image,dtype=float)
     for key in masks:
         masks['all'] += masks[key]
@@ -143,8 +148,8 @@ def Big_sat(table,Image,scale=1):
     sat = table.iloc[i]
     x = sat.x.values
     y = sat.y.values
-    x = (x+.5).astype(int)
-    y = (y+.5).astype(int)
+    x = (np.round(x,0)).astype(int)
+    y = (np.round(y,0)).astype(int)
     m = sat.mag.values
     ind = size_limit(x,y,image)
 
@@ -217,7 +222,7 @@ def Strap_mask(Image,col,size=4):
     big_strap = fftconvolve(strap_mask,np.ones((size,size)),mode='same') > .5
     return big_strap
 
-def Cat_mask(tpf,catalogue_path=None,maglim=19,scale=1,strapsize=3,ref=None,sigma=3):
+def Cat_mask(tpf,catalogue_path=None,maglim=19,scale=1,strapsize=3,ref=None,sigma=3,col_offset=0):
 
 	"""
 	Make a source mask from the PS1 and Gaia catalogs.
@@ -277,7 +282,7 @@ def Cat_mask(tpf,catalogue_path=None,maglim=19,scale=1,strapsize=3,ref=None,sigm
 	sat = (np.nansum(sat,axis=0) > 0).astype(int) * 2 # assign 2 bit 
 
 	if strapsize > 0: 
-		strap = Strap_mask(image,tpf.column,strapsize).astype(int) * 4 # assign 4 bit 
+		strap = Strap_mask(image,tpf.column+col_offset,strapsize).astype(int) * 4 # assign 4 bit 
 	else:
 		strap = np.zeros_like(image,dtype=int)
 

tessreduce/delta_function_fitting.py

@@ -5,8 +5,8 @@
 
 from scipy.optimize import minimize
 
-def Delta_basis(Size = 11):
-    kernel = np.zeros((Size,Size))
+def Delta_basis(size = 11):
+    kernel = np.zeros((size,size))
     x,y = np.where(kernel==0)
     middle = int(len(x)/2)
     basis = []
@@ -22,17 +22,17 @@ def Delta_basis(Size = 11):
     coeff = np.ones(len(basis))
     return basis, coeff
 
-def Delta_kernel(reference,image,Size=11,mask=None):
+def Delta_kernel(reference,image,size=11,mask=None):
     if mask is None:
         mask = np.ones_like(image)
     mask[mask == 0] = np.nan
-    Basis, coeff_0 = Delta_basis(Size)
+    Basis, coeff_0 = Delta_basis(size)
     bds = []
     for i in range(len(coeff_0)):
         bds += [(0,1)]
     coeff_0 *= 0.01
-    coeff_0[Size//2+1] = 0.95
-    res = minimize(optimize_delta, coeff_0, args=(Basis,reference,image,Size,mask),
+    coeff_0[size//2+1] = 0.95
+    res = minimize(optimize_delta, coeff_0, args=(Basis,reference,image,size,mask),
                    bounds=bds,method='Powell')
     k = np.nansum(res.x[:,np.newaxis,np.newaxis]*Basis,axis=0)
     return k
@@ -50,7 +50,7 @@ def optimize_delta(Coeff, Basis, reference, image,size,mask):
 
 
 def parallel_delta_diff(image,reference,mask=None,size=11):
-    kernel = Delta_kernel(reference,image,Size=11,mask=mask)
+    kernel = Delta_kernel(reference,image,size=size,mask=mask)
     template = signal.fftconvolve(reference, kernel, mode='same')
     diff = image - template
     return diff, kernel