f3/photometry.py
def do_photometry(self):
        """
        Does photometry and estimates uncertainties by calculating the scatter around a linear fit to the data
        in each orientation. This function is called by other functions and generally the user will not need
        to interact with it directly.
        """
        
        std_f = np.zeros(4)
        data_save = np.zeros_like(self.postcard)
        self.obs_flux = np.zeros_like(self.reference_flux)


        for i in range(4):
            g = np.where(self.qs == i)[0]
            wh = np.where(self.times[g] > 54947)

            data_save[g] = np.roll(self.postcard[g], int(self.roll_best[i,0]), axis=1)
            data_save[g] = np.roll(data_save[g], int(self.roll_best[i,1]), axis=2)

            self.target_flux_pixels = data_save[:,self.targets == 1]
            self.target_flux = np.sum(self.target_flux_pixels, axis=1)
            
            self.obs_flux[g] = self.target_flux[g] / self.reference_flux[g]
            self.obs_flux[g] /= np.median(self.obs_flux[g[wh]])
            
            fitline = np.polyfit(self.times[g][wh], self.obs_flux[g][wh], 1)
            std_f[i] = np.max([np.std(self.obs_flux[g][wh]/(fitline[0]*self.times[g][wh]+fitline[1])), 0.001])
        
        self.flux_uncert = std_f
def do_photometry(self):
        """
        Does photometry and estimates uncertainties by calculating the scatter around a linear fit to the data
        in each orientation. This function is called by other functions and generally the user will not need
        to interact with it directly.
        """
        
        std_f = np.zeros(4)
        data_save = np.zeros_like(self.postcard)
        self.obs_flux = np.zeros_like(self.reference_flux)


        for i in range(4):
            g = np.where(self.qs == i)[0]
            wh = np.where(self.times[g] > 54947)

            data_save[g] = np.roll(self.postcard[g], int(self.roll_best[i,0]), axis=1)
            data_save[g] = np.roll(data_save[g], int(self.roll_best[i,1]), axis=2)

            self.target_flux_pixels = data_save[:,self.targets == 1]
            self.target_flux = np.sum(self.target_flux_pixels, axis=1)
            
            self.obs_flux[g] = self.target_flux[g] / self.reference_flux[g]
            self.obs_flux[g] /= np.median(self.obs_flux[g[wh]])
            
            fitline = np.polyfit(self.times[g][wh], self.obs_flux[g][wh], 1)
            std_f[i] = np.max([np.std(self.obs_flux[g][wh]/(fitline[0]*self.times[g][wh]+fitline[1])), 0.001])
        
        self.flux_uncert = std_f
def do_photometry ( self ) : std_f = np . zeros ( 4 ) data_save = np . zeros_like ( self . postcard ) self . obs_flux = np . zeros_like ( self . reference_flux ) for i in range ( 4 ) : g = np . where ( self . qs == i ) [ 0 ] wh = np . where ( self . times [ g ] > 54947 ) data_save [ g ] = np . roll ( self . postcard [ g ] , int ( self . roll_best [ i , 0 ] ) , axis = 1 ) data_save [ g ] = np . roll ( data_save [ g ] , int ( self . roll_best [ i , 1 ] ) , axis = 2 ) self . target_flux_pixels = data_save [ : , self . targets == 1 ] self . target_flux = np . sum ( self . target_flux_pixels , axis = 1 ) self . obs_flux [ g ] = self . target_flux [ g ] / self . reference_flux [ g ] self . obs_flux [ g ] /= np . median ( self . obs_flux [ g [ wh ] ] ) fitline = np . polyfit ( self . times [ g ] [ wh ] , self . obs_flux [ g ] [ wh ] , 1 ) std_f [ i ] = np . max ( [ np . std ( self . obs_flux [ g ] [ wh ] / ( fitline [ 0 ] * self . times [ g ] [ wh ] + fitline [ 1 ] ) ) , 0.001 ] ) self . flux_uncert = std_f
Does photometry and estimates uncertainties by calculating the scatter around a linear fit to the data
        in each orientation. This function is called by other functions and generally the user will not need
        to interact with it directly.
Does photometry and estimates uncertainties by calculating the scatter around a linear fit to the data in each orientation . This function is called by other functions and generally the user will not need to interact with it directly .