{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f663000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "电离球半径约为 17.64 pc\n"
     ]
    }
   ],
   "source": [
    "#task_13假设一颗大质量的恒星为40,000 K的O型星，其热光度为太阳的10万倍，假设周围是中性星际气体（全部为氢原子，n~10cm-3)均匀分布，\n",
    "#其在10000年内能电离的球形空间的半径为多大？\n",
    "#请对银河系中的各种物质成分的质量比进行估算\n",
    "import numpy as np\n",
    "\n",
    "# 给定参数\n",
    "L_star = 1e5  # 恒星光度，单位为太阳光度\n",
    "n_H = 10  # 中性氢的数密度，单位为 cm^-3\n",
    "alpha_B = 2.6e-13  # 重组系数，单位为 cm^3/s\n",
    "\n",
    "# 估算 N_ion\n",
    "L_sun = 3.828e33  # 太阳光度，单位为 erg/s\n",
    "E_ion = 13.6 * 1.60218e-12  # 电离光子的能量，单位为 erg（13.6 eV 转换为 erg）\n",
    "L_star_erg = L_star * L_sun  # 将恒星光度转换为 erg/s\n",
    "N_ion = L_star_erg / E_ion  # 每秒释放的电离光子数\n",
    "\n",
    "# 计算 Stromgren 半径\n",
    "R_s = ((3 * N_ion) / (4 * np.pi * alpha_B * n_H**2))**(1/3)\n",
    "R_s_pc = R_s / 3.086e18  # 将单位从 cm 转换为 parsec\n",
    "\n",
    "print(f\"电离球半径约为 {R_s_pc:.2f} pc\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "399f11bb",
   "metadata": {},
   "source": [
    "### 2. 银河系中的物质成分质量比估算\n",
    "\n",
    "银河系中的物质成分可以分为以下几类：\n",
    "\n",
    "1. **恒星**：约占银河系总质量的 \\( \\sim 5\\% \\)。\n",
    "2. **气体**：主要由氢和氦组成，约占总质量的 \\( \\sim 10\\% \\)。\n",
    "3. **暗物质**：占总质量的 \\( \\sim 85\\% \\)。\n",
    "\n",
    "#### 质量比\n",
    "- **恒星与气体之比**：约为 1:2。\n",
    "- **恒星与暗物质之比**：约为 1:17。\n",
    "- **气体与暗物质之比**：约为 1:8.5。\n",
    "\n",
    "### 详细解释\n",
    "- 银河系的总质量约为 \\( 10^{12} M_\\odot \\)，其中暗物质是主导成分，恒星和气体只占少部分。\n",
    "- 气体主要分布在银盘和银晕区域，暗物质形成巨大的晕包围整个星系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "befecf79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIMPLE  =                    T                                                  BITPIX  =                   16                                                  NAXIS   =                    2                                                  NAXIS1  =                 2048                                                  NAXIS2  =                 1489                                                  BZERO   = 3.27680000000000E+04                                                  BSCALE  = 1.00000000000000E+00                                                  TAI     =        4513036852.23 / 1st row - Number of seconds since Nov 17 1858  RA      =            18.881586 / 1st row - Right ascension of telescope boresighDEC     =            0.000000  / 1st row - Declination of telescope boresight (dSPA     =              90.000  / 1st row - Camera col position angle wrt north (IPA     =              67.252  / 1st row - Instrument rotator position angle (deIPARATE =              0.0000  / 1st row - Instrument rotator angular velocity (AZ      =            332.50428 / 1st row - Azimuth  (encoder) of tele (0=N?) (deALT     =            54.152886 / 1st row - Altitude (encoder) of tele        (deFOCUS   =           -426.60000 / 1st row - Focus piston (microns?)              DATE-OBS= '2001-11-21'         / 1st row - TAI date                             TAIHMS  = '05:20:52.23'        / 1st row - TAI time (HH:MM:SS.SS) (TAI-UT = apprCOMMENT  TAI,RA,DEC,SPA,IPA,IPARATE,AZ,ALT,FOCUS at reading of col 0, row 0     BUNITS  = 'ADUs    '                                                            ORIGIN  = 'SDSS    '                                                            TELESCOP= '2.5m    '                                                            TIMESYS = 'TAI     '                                                            RUN     =                 2738 / Run number                                     FRAME   =                   48 / Frame sequence number within the run           CCDLOC  =                   31 / Survey location of CCD (e.g., rowCol)          STRIPE  =                   82 / Stripe index number (23 <--> eta=0)            STRIP   = 'N       '           / Strip in the stripe being tracked.             FLAVOR  = 'science '           / Flavor of this run                             SYS_SCN = 'mean    '           / System of the scan great circle (e.g., mean)   EQNX_SCN=           2000.00    / Equinox of the scan great circle. (years)      NODE    =           95.00000   / RA of the great circle's ascending node (deg)  INCL    =           0.00000    / Great circle's inclination wrt cel. eq. (deg)  XBORE   =           -22.74     / Boresight x offset from the array center (mm)  YBORE   =           0.00       / Boresight x offset from the array center (mm)  OBJECT  = '82 N    '           / e.g., 'stripe 50.6 degrees, north strip'       SEXPTIME= '53.907456'          / Exposure time (seconds)                        SYSTEM  = 'FK5     '           / System of the TCC coordinates (e.g., mean)     CCDMODE = 'DRIFT   '           / 'STARING' or 'DRIFT'                           C_OBS   =                26322 / CCD row clock rate (usec/row)                  COLBIN  =                    1 / Binning factor perpendicular to the columns    ROWBIN  =                    1 / Binning factor perpendicular to the rows       DAVERS  = 'v14_39  '           / Version of DA software                         COMMENT  CCD-specific parameters                                                CAMROW  =                    3 / Row in the imaging camera                      BADLINES=                    0 / Number of bad lines in frame                   EQUINOX = 2.00000000000000E+03                                                  SOFTBIAS=                 1000 / software \"bias\" added to all DN                BUNIT   = 'DNs     '                                                            FILTER  = 'u       '           / filter used                                    CAMCOL  =                    1 / column in the imaging camera                   VERSION = 'v5_4_28 '                                                            DERV_VER= 'v8_16   '                                                            ASTR_VER= 'v5_18   '                                                            ASTRO_ID= '2003-05-09T08:21:03 12437'                                           BIAS_ID = 'PS      '                                                            FRAME_ID= '2005-12-07T15:37:12 25022'                                           KO_VER  = 'devel   '                                                            PS_ID   = '2003-05-09T02:57:39 30178 camCol 1'                                  ATVSN   = 'v5_18   '           / ASTROTOOLS version tag                         RADECSYS= 'ICRS    '           / International Celestial Ref. System            CTYPE1  = 'RA---TAN'                                                            CTYPE2  = 'DEC--TAN'                                                            CUNIT1  = 'deg     '                                                            CUNIT2  = 'deg     '                                                            CRPIX1  = 1.02450000000000E+03 / Column Pixel Coordinate of Ref. Pixel          CRPIX2  = 7.44500000000000E+02 / Row Pixel Coordinate of Ref. Pixel             CRVAL1  = 1.88509276100000E+01 / RA at Reference Pixel                          CRVAL2  = -9.4509090000000E-01 / DEC at Reference Pixel                         CD1_1   = 4.67319230415372E-08 / RA  degrees per column pixel                   CD1_2   = 1.09982502499908E-04 / RA  degrees per row pixel                      CD2_1   = 1.09994165039063E-04 / DEC degrees per column pixel                   CD2_2   = 7.93682795695260E-09 / DEC degrees per row pixel                      FPC_END =                   0. / Header section found in fpC files ends here    FPC_NAME= 'fpC-002738-u1-0044.fit.gz' / Name of the original fpC file           FPC_MD5 = '21bdf2e2c0ddd91c7e878c58c0917dbb' / MD5 hash of the original fpC fileEXPTIME =            53.907456 / Exposure time (seconds)                        COMMENT Photometric transformation                                              COMMENT /datascope/sdss_das/das/imaging/2738/40/dr/1/drField-002738-1-40-0044.fiCOMMENT t                                                                       COMMENT See Lupton et al. (1999AJ....118.1406L)                                 COMMENT See Stoughton et al. (2002AJ....123..485S) section 4.4.5.1              COMMENT f/f0 = counts/exptime * 10^(0.4*(aa + kk * airmass))                    COMMENT Pogson mag = -2.5 * log10(f/f0)                                         COMMENT asinh mag = -(2.5/ln(10))*[asinh((f/f0)/2b)+ln(b)]                      PHT_AA  =    -23.9634990692139 / Photometric aa parameter                       PHTAAERR=  0.00264077004976571 / Photometric aa error parameter                 PHT_KK  =    0.465418010950089 / Photometric kk parameter                       PHTKKERR=   0.0237306002527475 / Photometric kk error parameter                 PHT_B   =              1.4E-10 / Softening parameter b                          AIRMASS =     1.24814112076017 / Airmass                                        FLUX0   =     121530566313.972 / Number of DN in 0th mag object                 FLUX20  =     1215.06746322974 / Number of DN in 20th mag object                ZP      =     27.7117138041356 / Phot. zero point m=-2.5log(DN)+ZP              COMMENT A maggie is a linear unit of flux                                       COMMENT counts = maggies*FLUX0                                                  SKY_FLUX= 1.78087000701765E-09 / average sky value (maggie/arcsec^2)            SKY     =     33.9361085119287 / sky level (DN/pixel)                           COMMENT astrometric transformation                                              COMMENT See Pier et al. (2003AJ....125.1559P)                                   COMMENT See Stoughton et al. (2002AJ....123..485S) section 3.2.2                COMMENT    r'-i' < riCut:                                                       COMMENT        rowm =row+dRow0+dRow1*col+dRow2*(col^2)+dRow3*(col^3)+csRow*colorCOMMENT        colm =col+dCol0+dCol1*col+dCol2*(col^2)+dCol3*(col^3)+csCol*colorCOMMENT      Which color to use depends on the filter as follows:               COMMENT        u: u-g    g: g-r    r,i,z: r-i                                   COMMENT    r'-i' >= riCut                                                       COMMENT        rowm = row+dRow0+dRow1*col+dRow2*(col^2)+dRow3*(col^3)+ccRow     COMMENT        colm = col+dCol0+dCol1*col+dCol2*(col^2)+dCol3*(col^3)+ccCol     COMMENT    mu = a + b * rowm + c * colm                                         COMMENT    nu = d + e * rowm + f * colm                                         COMMENT tan(ra-NODE)=[sin(mu-NODE)cos(nu)cos(INCL)-sin(nu)sin(INCL)]/           COMMENT              [cos(mu-NODE)cos(nu)]                                      COMMENT sin(dec)=sin(mu-NODE)cos(nu)sin(INCL)+sin(nu)cos(INCL)                  AST_A   =      18.769039709677 / Astrometric a parameter                        AST_B   = 0.000109998649086239 / Astrometric b parameter                        AST_C   = 4.74035619449056E-08 / Astrometric c parameter                        AST_D   =    -1.05773673841116 / Astrometric d parameter                        AST_E   = 8.17006880819384E-09 / Astrometric e parameter                        AST_F   = 0.000109994024385587 / Astrometric f parameter                        ASTDROW0=  0.00664257755850067 / Astrometric dRow0 parameter                    ASTDROW1= 1.40451896289158E-05 / Astrometric dRow1 parameter                    ASTDROW2= -5.39390956740478E-08 / Astrometric dRow2 parameter                   ASTDROW3= 2.54426524818614E-11 / Astrometric dRow3 parameter                    ASTDCOL0=   -0.102650100035123 / Astrometric dCol0 parameter                    ASTDCOL1= 0.000664758553338132 / Astrometric dCol1 parameter                    ASTDCOL2= -8.69974866066368E-07 / Astrometric dCol2 parameter                   ASTDCOL3= 2.97315954921411E-10 / Astrometric dCol3 parameter                    ASTCSCOL=   0.0399796232712038 / Astrometric csCol parameter                    ASTCSROW=   0.0163210622761398 / Astrometric csRow parameter                    ASTCCCOL=    0.119938869813611 / Astrometric ccCol parameter                    ASTCCROW=   0.0489631868284194 / Astrometric ccRow parameter                    ASTRICUT=                   3. / Astrometric riCut parameter                    ASTMUERR=   0.0326413586735725 / Astrometric muErr parameter (asec)             ASTNUERR=    0.037234116345644 / Astrometric nuErr parameter (asec)             MJD     =        52234.2228268 / MJD(TAI) when row 0 was read                   NUMSTARS=                    0 / Number of matching secondary stars in field    ROWOFSET=                   0. / Offset to add to transformed row coors (pix)   COLOFSET=                   0. / Offset to add to transformed col coords (pix)  SATLEVEL=                62168 / Saturation level                               NEFF_PSF=     37.7254524230957 / Effective area of the PSF (pixels)             SKY_PSP = 1.78087000701765E-09 / Sky from the point spread function             SKY_FRMS= 1.79307002579066E-09 / Global sky value in the corrected frame        SKYFRMSS= 1.79176995462882E-09 / Global sky value after object subtraction      SIGPIX  =    0.176158413290977 / Clipped sigma of pixel values in corrected framDEVAPCOR=     1.09011960029602 / De Vaucouleurs aperture correction             DEVAPCRE=                   0. / De Vaucouleurs aperture correction error       EXAPCOR =     1.09022641181946 / Exponential aperture correction                EXAPCORE=                   0. / Exponential aperture correction error          DEVMAP  =     1.08993971347809 / De Vaucouleurs model aperture correction       DEVMAPE = 5.41728004463948E-05 / De Vaucouleurs model aperture correction error EXMAPCOR=     1.08999741077423 / Exponential model aperture correction          EXMAPCRE= 5.98854530835524E-05 / Exponential model aperture correction error    NCR     =                  154 / Number of cosmic rays detected on the frame    NBTOBJ  =                   21 / Number of bright objects detected              NFTOBJ  =                  120 / Number of faint objects detected               MEDFIBCR=      1.4894050359726 / Median fiber colors of objects                 MEDPSFCR=     1.46341145038605 / Median PSF colors of objects                   Q       =   0.0635801628232002 / Mean Stokes Q parameter on the frame           U       =  0.00969945453107357 / Mean Stokes U parameter on the frame           PFITTYPE=                   66 / Type of PSF fit for each filter in the field   SKYSIG  =    0.177282676100731 / Sigma of distribution of sky values            SKYERR  = 6.49568983671078E-12 / The error of average sky value in the frame    SKYSLOPE= 6.03404989921164E-16 / The slope in the sky value along the columns   LBIAS   =                  22. / Left-hand bias level                           RBIAS   =                  58. / Right-hand bias level                          PSFNSTAR=                   10 / Number of stars used in psf measurement        PSFAPCRE=   0.0123021006584167 / Photometric error due to imperfect PSF model   PSF_S1  =     0.48703470826149 / Inner gaussian sigma for composite fit (asec)  PSF_S2  =     1.03454983234406 / Outer gaussian sigma for composite fit (asec)  PSF_B   =    0.203258231282234 / Ratio of inner to outer PSF at origin          PSF_P0  =  1.0000000116861E-07 / The value of the power law at the origin       PSF_BETA=                   3. / Slope of power law                             PSFSGMAP=                   5. / Width parameter for power law                  PSFWID  =      1.6288868188858 / Effective PSF width in 2-Gaussian fit (asec)   PSFCTS  =               -9999. / Flux via fit to the PSF (counts)               PSFS12G =     1.22992658615112 / PSF inner sigma in 2-Gaussian fit (pix)        PSFS22G =     2.61258673667908 / PSF outer sigma in 2-Gaussian fit (pix)        PSF_B_2G=    0.203258231282234 / Ratio of gaussian 2 to gaussian 1 at origin    PSFCTS  =     15.7225313186646 / PSF counts                                     GAIN    =     1.62000000476837 / Gain averaged over all amplifiers (e/DN)       DARK_VAR=     9.60999965667725 / combined variance from dark cur. and read noiseRDNOISE =     9.60999965667725 / combined variance from dark cur. and read noisePHOTO_ID= '2003-05-09T12:02:09 11473' / Photometric pipeline run ID             PHOT_VER= 'v5_4_19 '           / Version of frames pipeline used                TASTR_ID= '2008-03-07T09:56:25 17456' / Astrometric pipeline run ID for targe   TAST_VER= 'v3_8g   '           / Version of astrometric pipeline for t          TFCAL_ID= '2003-06-14T09:47:51 02165' / Photometric calibration run ID for ta   TFCA_VER= 'v2_1    '           / Version of fcalib for target                   EASTR_ID= '2008-03-07T09:56:25 17456' / Astrometric pipeline run ID for expor   EAST_VER= 'v3_8g   '           / Version of astrometric pipeline for e          EFCAL_ID= '2003-06-14T09:47:51 02165' / Photometric calibration run ID for ex   EFCA_VER= 'v2_1UFA '           / Version of fcalib for export                   CHECKSUM= 'S5VST4UPS4UPS4UP'   / HDU checksum updated 2024-11-17T08:55:33       DATASUM = '1251086843'         / data unit checksum updated 2024-11-17T08:55:33 END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 3000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#task_15. 下载一个LAMOST或者SDSS观测到的星系光谱文件，画出相应光\n",
    "#谱，测量至少一个Lick指数特征及其误差，并在图中标注\n",
    "#• SDSS的光谱分辨率是~2000，某星系其连续谱单位波长的典型信\n",
    "#噪比为5，该星系Ha线的等值宽度为50A，请问其Ha线流量的信噪\n",
    "#比是多少？\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "#from lick import Lick\n",
    "\n",
    "#specf = \"drC-002738-u1-0044.fits\"\n",
    "#spec = fits.open(specf)[0].data\n",
    "#wavelength = spec[\"WAVELENGTH\"][0]\n",
    "#flux = spec[\"FLUX\"][0]\n",
    "#\n",
    "#plt.figure(figsize=(10, 2), dpi=300)\n",
    "#plt.plot(wavelength, flux, linewidth=0.3)\n",
    "#plt.xlim(np.min(wavelength), np.max(wavelength))\n",
    "#plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#读取 FITS 文件\n",
    "filename = 'drC-002738-u1-0044.fits' \n",
    "hdul = fits.open(filename)\n",
    "data = hdul[0].data\n",
    "header = hdul[0].header\n",
    "print(header)\n",
    "plt.figure(figsize=(10, 2), dpi=300)\n",
    "plt.plot(data)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc0668b",
   "metadata": {},
   "source": [
    "SDSS的光谱分辨率是~2000，某星系其连续谱单位波长的典型信噪比为5，该星系Ha线的等值宽度为50A，请问其Ha线流量的信噪比是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d2f6fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number density of galaxies with r < 20.5 mag: 4.17e+00 per steradian\n",
      "Ratio of galaxy counts (M_r = -16 / M_r = -22): 18.65\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mz/pg3ztpgj1f10l242jx2qkjz80000gn/T/ipykernel_37509/2372526864.py:31: RuntimeWarning: divide by zero encountered in log10\n",
      "  return m - 5 * np.log10(d_l * 1e6) + 5\n",
      "/var/folders/mz/pg3ztpgj1f10l242jx2qkjz80000gn/T/ipykernel_37509/2372526864.py:38: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.\n",
      "  number_density = np.trapz(phi_values * dV_dz_values, z_values)\n",
      "/var/folders/mz/pg3ztpgj1f10l242jx2qkjz80000gn/T/ipykernel_37509/2372526864.py:54: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.\n",
      "  P_z /= np.trapz(P_z, z_values)  # Normalize to make it a probability distribution\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H-alpha line flux S/N: 19.52\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Schechter luminosity function parameters\n",
    "M_star = -20.83  # SDSS Mr*\n",
    "alpha = -1.0  # Faint-end slope\n",
    "phi_star = 0.01  # Normalization (Mpc^-3)\n",
    "\n",
    "# Constants\n",
    "H0 = 70  # Hubble constant (km/s/Mpc)\n",
    "c = 3e5  # Speed of light (km/s)\n",
    "\n",
    "# Function to calculate the Schechter luminosity function\n",
    "def schechter_function(M, M_star, alpha, phi_star):\n",
    "    term = 10**(0.4 * (M_star - M))\n",
    "    return 0.4 * np.log(10) * phi_star * term**(1 + alpha) * np.exp(-term)\n",
    "\n",
    "# Function to calculate comoving volume element (dV/dz in Mpc^3 per steradian)\n",
    "def comoving_volume_element(z, H0, Omega_m=0.3, Omega_L=0.7):\n",
    "    \"\"\"Comoving volume element per unit redshift per steradian.\"\"\"\n",
    "    Ez = np.sqrt(Omega_m * (1 + z)**3 + Omega_L)\n",
    "    dV_dz = c / H0 * (1 + z)**2 / Ez\n",
    "    return dV_dz\n",
    "\n",
    "# 1. Number of galaxies with r < 20.5 mag\n",
    "def number_density_r_limit(m_limit, M_star, alpha, phi_star, z_max):\n",
    "    \"\"\"Calculate the number density of galaxies with apparent magnitude limit.\"\"\"\n",
    "    def absolute_magnitude_limit(m, z):\n",
    "        \"Convert apparent magnitude to absolute magnitude at redshift z.\"\n",
    "        d_l = c * z / H0 * (1 + z / 2)  # Luminosity distance approximation (Mpc)\n",
    "        return m - 5 * np.log10(d_l * 1e6) + 5\n",
    "\n",
    "    z_values = np.linspace(0, z_max, 1000)\n",
    "    m_abs_limits = absolute_magnitude_limit(m_limit, z_values)\n",
    "    phi_values = schechter_function(m_abs_limits, M_star, alpha, phi_star)\n",
    "    dV_dz_values = comoving_volume_element(z_values, H0)\n",
    "    \n",
    "    number_density = np.trapz(phi_values * dV_dz_values, z_values)\n",
    "    return number_density\n",
    "\n",
    "# 2. Ratio of galaxy counts at M_r = -16 mag to M_r = -22 mag\n",
    "def galaxy_count_ratio(M1, M2, M_star, alpha):\n",
    "    phi1 = schechter_function(M1, M_star, alpha, phi_star)\n",
    "    phi2 = schechter_function(M2, M_star, alpha, phi_star)\n",
    "    return phi1 / phi2\n",
    "\n",
    "# 3. Redshift distribution for galaxies with M_r = -20 mag\n",
    "def redshift_distribution(M, M_star, alpha, phi_star, z_max):\n",
    "    z_values = np.linspace(0, z_max, 1000)\n",
    "    phi_values = schechter_function(M, M_star, alpha, phi_star)\n",
    "    dV_dz_values = comoving_volume_element(z_values, H0)\n",
    "    \n",
    "    P_z = phi_values * dV_dz_values\n",
    "    P_z /= np.trapz(P_z, z_values)  # Normalize to make it a probability distribution\n",
    "    return z_values, P_z\n",
    "\n",
    "# Inputs\n",
    "m_limit = 20.5\n",
    "z_max = 0.1  # Approximation for shallow surveys\n",
    "M1, M2 = -16, -22\n",
    "M_target = -20\n",
    "\n",
    "# 1. Number density of galaxies\n",
    "num_density = number_density_r_limit(m_limit, M_star, alpha, phi_star, z_max)\n",
    "print(f\"Number density of galaxies with r < {m_limit} mag: {num_density:.2e} per steradian\")\n",
    "\n",
    "# 2. Ratio of galaxy counts\n",
    "ratio = galaxy_count_ratio(M1, M2, M_star, alpha)\n",
    "print(f\"Ratio of galaxy counts (M_r = {M1} / M_r = {M2}): {ratio:.2f}\")\n",
    "\n",
    "# 3. Redshift distribution\n",
    "z_values, P_z = redshift_distribution(M_target, M_star, alpha, phi_star, z_max)\n",
    "\n",
    "# Plot redshift distribution\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(z_values, P_z, label=f\"M_r = {M_target}\")\n",
    "plt.xlabel(\"Redshift (z)\")\n",
    "plt.ylabel(\"Probability Density P(z)\")\n",
    "plt.title(\"Redshift Distribution for M_r = -20 mag Galaxies\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# 4. H-alpha line S/N calculation\n",
    "def ha_line_sn(SN_cont, W_lambda, R, lambda_ha=6563):\n",
    "    \"\"\"Calculate the S/N of H-alpha line flux.\"\"\"\n",
    "    delta_lambda = lambda_ha / R\n",
    "    return SN_cont * np.sqrt(W_lambda / delta_lambda)\n",
    "\n",
    "# Inputs for H-alpha calculation\n",
    "SN_cont = 5  # S/N of continuum\n",
    "W_lambda = 50  # Equivalent width in Angstroms\n",
    "R = 2000  # Spectral resolution\n",
    "\n",
    "SN_line = ha_line_sn(SN_cont, W_lambda, R)\n",
    "print(f\"H-alpha line flux S/N: {SN_line:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "006f8167",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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