{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 6: Solutions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this lab, we'll work through some of the basics of using Pandas, using a few different tabular data sets. Ultimately, one need not do anything particularly fancy with DataFrames for them to be useful as data containers. But we would like to highlight a few extra abilities these objects have, that illustrate situations where we may actually have a strong reason to use pandas over another library. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 1: HII regions + Planetary Nebulae measurements in M81\n", "\n", "For our first data set, we're going to look at a file (`table2.dat`), which contains measurements of the flux and intensity of various ions' line emission from a set of known emitting objects (PNs and HII regions) in the M81 galaxy. \n", "\n", "The columns of this file are `name`, `ion`, `wl`, `flux`, and `I` (intensity). Two of the columns are string-valued (name and ion), three are numerical-values (wl, flux, I). This mix of strings and floats tells us before we even decide how to read in this file that `numpy` data structures won't be usable, as they demand all values in an array to have the same `dtype`. \n", "\n", "### Problem 1.1 \n", "\n", "Using the `pd.read_csv()` function shown in the lecture, read this data file into a dataframe called `df`, and print it. \n", "```{hint}\n", "You can get a \"pretty\" visualization of a dataframe by simply typing its name into a jupyter cell -- as long as it's the last line of the cell, the dataframe will print more nicely than typing `print(df)`. This does not work outside of notebooks.\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "%matplotlib inline \n", "df = pd.read_csv('table2.dat')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " name ion wl flux rms I\n", "0 PN3m [OII] 3727 373.9 58.6 517.3\n", "1 PN3m HeI 3805 0.0 0.0 0.0\n", "2 PN3m HI 3835 0.0 0.0 0.0\n", "3 PN3m [NeIII] 3869 0.0 0.0 0.0\n", "4 PN3m HeI 3889 0.0 0.0 0.0\n", "... ... ... ... ... ... ...\n", "1629 HII403 [ArIII] 7135 15.5 3.1 8.2\n", "1630 HII403 [OII] 7320 0.0 0.0 0.0\n", "1631 HII403 [OII] 7330 0.0 0.0 0.0\n", "1632 HII403 [ArIII] 7751 0.0 0.0 0.0\n", "1633 HII403 [SIII] 9069 51.1 3.7 17.3\n", "\n", "[1634 rows x 6 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 1.2 \n", "\n", "Though it doesn't show up in the clean representation above, the strings associated with the name and ion columns above have trailing and leading spaces that we don't want. \n", "\n", "Use a *list comprehension* to modify the data frame such that each value in the name and ion columns are replaced with a `.strip()` version of themselves." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df['name'] = [i.strip() for i in df.name]\n", "df['ion'] = [i.strip() for i in df.ion]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 1.3 \n", "\n", "Write a function `select_object` which takes in as an argument the name of an HII region or planetrary nebula, and filters the dataframe for only the entries for that object using `df.loc[]`. Consider having the dataframe be an optional argument you set to `df`, the dataframe we are \n", "working with.\n", "\n", "Have your function take in an optional argument `drop_empty=True` which additionally selects only those rows where the flux/intensity is **not** zero." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def select_object(name,df=df,drop_empty=True):\n", " if drop_empty:\n", " out = df.loc[(df.name==name)&(df.flux!=0)&(df.I!=0)]\n", " else:\n", " out = df.loc[(df.name==name)]\n", " return out" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " name ion wl flux rms I\n", "0 PN3m [OII] 3727 373.9 58.6 517.3\n", "8 PN3m HI 4340 50.0 3.5 58.0\n", "15 PN3m HI 4861 100.0 4.5 100.0\n", "16 PN3m [OIII] 4959 35.4 3.8 34.4\n", "17 PN3m [OIII] 5007 104.2 5.2 99.9\n", "19 PN3m [NII] 5755 1.3 0.3 1.1\n", "20 PN3m HeI 5876 9.1 0.3 7.2\n", "24 PN3m [NII] 6548 59.8 3.5 42.2\n", "25 PN3m HI 6563 412.0 6.9 290.1\n", "26 PN3m [NII] 6584 142.5 4.5 100.0\n", "28 PN3m [SII] 6717 60.3 3.8 41.4\n", "29 PN3m [SII] 6731 44.3 3.4 30.4" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "select_object('PN3m')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 1.4 \n", "\n", "Write a function `select_ion_by_wavelength()` which takes in the name of an ion and its wavelength (and a dataframe), and returns the filtered data frame for all objects, but only ions for the selected wavelengths. \n", "\n", "As before, have a `drop_empty` optional argument to not include entries where the flux and intensity are zero.\n", "\n", "Additionally, as the index is now uniquely identified by the name of the PN/HII region, set the index to be the name column." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "def select_ion_by_wavelength(ion,wavelength,df=df,drop_empty=True): \n", " if drop_empty:\n", " out = df.loc[(df.ion==ion)&(df.wl==wavelength)&(df.flux!=0)&(df.I!=0)]\n", " else:\n", " out = df.loc[(df.ion==ion)&(df.wl==wavelength)]\n", " out = out.set_index('name')\n", " return out" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " ion wl flux rms I\n", "name \n", "PN3m [OII] 3727 373.9 58.6 517.3\n", "PN5 [OII] 3727 195.5 0.6 374.9\n", "PN9m [OII] 3727 260.4 0.7 415.4\n", "PN17m [OII] 3727 225.5 0.7 333.5\n", "PN29m [OII] 3727 305.2 2.4 507.4" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "select_ion_by_wavelength('[OII]',3727).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 1.5\n", "It will be helpful to know for a given ion which wavelengths are avalable and have data in the dataframe. Write a function `get_wavelenghs_by_ion()` that determines for a given input ion, which wavelengths are available. \n", "\n", "**Bonus + 0.5: Some ions of forbidden transitions like `[OII]` have brackets in the name. Add a bit to your get_wavelengths_by_ion code that allows the user to enter either `\"[OII]\"` or `\"OII\"` and get the same answer.**\n", "\n", "Additionally, make a convenience function `get_ions()` that just returns the full list of ions represented in the dataframe.\n", "\n", "```{hint}\n", "The `.unique()` method in pandas will be useful here.\n", "```" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def get_ions(df=df):\n", " return df.ion.unique()\n", "\n", "def get_wavelengths_by_ion(ion,df=df,drop_empty=True):\n", " return df.loc[df.ion==ion,'wl'].unique()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['[OII]', 'HeI', 'HI', '[NeIII]', '[NeIII]/HI', '[SII]', '[OIII]',\n", " 'HeII', 'HeI/[ArIV]', '[ArIV]', '[NII]', '[OI]', '[SIII]', '[ArV]',\n", " '[ArIII]'], dtype=object)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_ions()\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5755, 6548, 6584])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_wavelengths_by_ion('[NII]')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3727, 7320, 7330])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_wavelengths_by_ion('[OII]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 1.6 \n", "\n", "Rather than having all these convenience functions littered around our code, let's go ahead and make a class, `FluxTable`, which initializes with our dataframe, and then has all of the functions created above as methods. The input DataFrame, `df`, should be accessible as an attribute as well. \n", "\n", "When you're done, you should be able to do something like the following \n", "```\n", "ions = FluxTable(df)\n", "print(ions.df)\n", "ions.get_ions()\n", "ions.get_wavelengths_by_ions('[OII]')\n", "PN3m = ions.select_object('PN3m')\n", "```" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "class FluxTable():\n", " def __init__(self,df):\n", " self.df=df\n", " def select_object(self,name,drop_empty=True):\n", " df=self.df\n", " if drop_empty:\n", " out = df.loc[(df.name==name)&(df.flux!=0)&(df.I!=0)]\n", " else:\n", " out = df.loc[(df.name==name)]\n", " return out\n", " def select_ion_by_wavelength(self,ion,wavelength,drop_empty=True): \n", " df=self.df\n", " if drop_empty:\n", " out = df.loc[(df.ion==ion)&(df.wl==wavelength)&(df.flux!=0)&(df.I!=0)]\n", " else:\n", " out = df.loc[(df.ion==ion)&(df.wl==wavelength)]\n", " out = out.set_index('name')\n", " return out\n", " def get_ions(self):\n", " df=self.df\n", " return df.ion.unique()\n", "\n", " def get_wavelengths_by_ion(self,ion,drop_empty=True):\n", " df=self.df\n", " return df.loc[df.ion==ion,'wl'].unique()\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "ft = FluxTable(df)\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['[OII]', 'HeI', 'HI', '[NeIII]', '[NeIII]/HI', '[SII]', '[OIII]',\n", " 'HeII', 'HeI/[ArIV]', '[ArIV]', '[NII]', '[OI]', '[SIII]', '[ArV]',\n", " '[ArIII]'], dtype=object)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ft.get_ions()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3805, 3889, 4471, 5876, 6678, 6891, 7065])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ft.get_wavelengths_by_ion('HeI')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " name ion wl flux rms I\n", "0 PN3m [OII] 3727 373.9 58.6 517.3\n", "1 PN3m HeI 3805 0.0 0.0 0.0\n", "2 PN3m HI 3835 0.0 0.0 0.0\n", "3 PN3m [NeIII] 3869 0.0 0.0 0.0\n", "4 PN3m HeI 3889 0.0 0.0 0.0" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ft.df.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameionwlfluxrmsI
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" ], "text/plain": [ " name ion wl flux rms I\n", "0 PN3m [OII] 3727 373.9 58.6 517.3\n", "8 PN3m HI 4340 50.0 3.5 58.0\n", "15 PN3m HI 4861 100.0 4.5 100.0\n", "16 PN3m [OIII] 4959 35.4 3.8 34.4\n", "17 PN3m [OIII] 5007 104.2 5.2 99.9\n", "19 PN3m [NII] 5755 1.3 0.3 1.1\n", "20 PN3m HeI 5876 9.1 0.3 7.2\n", "24 PN3m [NII] 6548 59.8 3.5 42.2\n", "25 PN3m HI 6563 412.0 6.9 290.1\n", "26 PN3m [NII] 6584 142.5 4.5 100.0\n", "28 PN3m [SII] 6717 60.3 3.8 41.4\n", "29 PN3m [SII] 6731 44.3 3.4 30.4" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ft.select_object('PN3m')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bonus: (+1) Problem 1.7 \n", "\n", "Finally, let's add one final method to our class. This method will be called `query`, and it will act as a bit of a \"catch all\", allowing the user to query for certain conditions as desired. \n", "\n", "Your `query` method should take as it's primary argument a string containing a comma separated list of desired columns. It should then have optional arguments for `name`, `ion`, and `wl`, which are by default set to `None`. For name and ion, the goal is to allow the user to specify specific ones. For `wl`, we'll go one step further and allow either a specific wavelength, or a range of wavelengths input as a string of the form `>4343` or `<3050` or `2010-5000`. \n", "\n", "The usage of this method will look something like \n", "\n", "```\n", "ft.query('name,flux',ion='[OII]',wl='3000-5000')\n", "```\n", "\n", "You will of course need to do some string checking (particularly with wl) to figure out what the user wants, and then you can use your filtering methods you already wrote to successfully construct a result dataframe to return." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 2\n", "\n", "In this problem, we're going to use the [3DHST catalog](https://archive.stsci.edu/prepds/3d-hst/), which contains numerous measurements of galaxies at high redshift taken with HST. This program was led at Yale(!) and the \"3D\" refers to the fact that beyond just imaging, spectroscopic information was also obtained. \n", "\n", "We'll be using a subset of the catalog set up for the GOODS-South field, a well studied patch of the sky. The data are split across four `fits` files -- but we'll be using `pandas` to join them together! \n", "\n", "- the `.cat` file contains the primary catalog \n", "- the `.fout` file contains the output of `FAST`, a quick template fitting program for galaxies. \n", "- the `.RF` file contains the Rest Frame (de-redshifted) colors of the galaxies in common bands\n", "- the `.zout` file contains redshift estimates for all galaxies (either spec-z or photo-z) made using the EAZY redshift fitting code (also Yale!) \n", "\n", "### Problem 2.1\n", "\n", "**Load the four datasets into Python, and create dataframes for each.** For ease of following the solutions, we suggest you name these\n", "\n", "- `cat_df` for the catalog\n", "- `fast_df` for the fast output\n", "- `rf_df` for the RF file \n", "- `z_df` for the redshifts\n", "\n", "**Examine each of these dataframes to see what types of columns they have.**\n", "\n", "```{hint}\n", "Remember that the default extension for tabular data (as this is) will be 1, not 0 as we are used to for images. You can run pd.DataFrame() directly on the data attribute of this extension\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from astropy.io import fits" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "with fits.open('goodss_3dhst.v4.1.cat.FITS') as hdu:\n", " cat_df=pd.DataFrame(np.array(hdu[1].data).byteswap().newbyteorder()) \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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50507 rows × 47 columns

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" ], "text/plain": [ " id z dm l153 n_153 l154 n_154 l155 \\\n", "0 1 0.50411 41.84 6.834770 24 18.878300 26 37.766000 \n", "1 2 2.89389 45.46 0.974816 16 0.898770 8 1.415800 \n", "2 3 2.59984 45.26 1.947970 15 3.226570 8 3.760400 \n", "3 4 0.14724 39.07 3.020890 15 6.760810 20 10.564000 \n", "4 5 2.83163 45.42 1.229800 14 1.707370 8 1.783730 \n", "... ... ... ... ... ... ... ... ... \n", "50502 50503 1.86680 44.63 0.055699 23 0.091228 14 0.127892 \n", "50503 50504 3.90205 45.98 0.028043 8 0.042124 5 0.036900 \n", "50504 50505 1.02180 43.40 0.314315 26 0.460368 23 0.499178 \n", "50505 50506 0.22070 39.98 0.089649 19 0.196616 22 0.293911 \n", "50506 50507 3.66905 45.88 0.000228 8 0.000279 8 0.000197 \n", "\n", " n_155 l161 ... l271 n_271 l272 n_272 l273 \\\n", "0 19 191.174000 ... 0.614206 8 0.787911 14 1.535420 \n", "1 4 2.605140 ... 1.533340 31 1.337740 33 1.421140 \n", "2 5 10.163800 ... 2.110700 31 1.932050 32 2.122260 \n", "3 24 25.685900 ... 0.893579 2 0.708750 6 1.095480 \n", "4 5 2.731230 ... 1.142590 31 1.049750 33 1.130200 \n", "... ... ... ... ... ... ... ... ... \n", "50502 8 0.083159 ... 0.013670 28 0.018509 28 0.027219 \n", "50503 3 0.000014 ... 0.010071 34 0.014247 34 0.020465 \n", "50504 14 0.628124 ... 0.205580 19 0.192318 24 0.226305 \n", "50505 24 0.534657 ... 0.026062 3 0.025747 6 0.038494 \n", "50506 3 0.000004 ... 0.000264 33 0.000193 34 0.000203 \n", "\n", " n_273 l274 n_274 l275 n_275 \n", "0 19 1.706270 19 38.879100 19 \n", "1 32 1.348220 29 1.454440 4 \n", "2 32 2.116470 32 3.824160 5 \n", "3 8 1.204680 9 10.841700 24 \n", "4 32 1.122430 29 1.778560 5 \n", "... ... ... ... ... ... \n", "50502 31 0.028836 31 0.131533 8 \n", "50503 28 0.021406 25 0.036202 3 \n", "50504 28 0.230881 28 0.496287 14 \n", "50505 10 0.040717 11 0.300814 24 \n", "50506 29 0.000202 27 0.000193 3 \n", "\n", "[50507 rows x 47 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf_df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "with fits.open('goodss_3dhst.v4.1.zout.FITS') as hdu:\n", " z_df = pd.DataFrame(np.array(hdu[1].data).byteswap().newbyteorder()) " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idz_specz_az_m1chi_az_pchi_pz_m2oddsl68u68l95u95l99u99nfiltq_zz_peakpeak_probz_mc
01-1.00.5040.504421.7551000.504421.7551000.5041.0000.4860.5220.4820.5260.4820.541330.8382900.50410.9990.5016
12-1.02.9092.8972560.6270002.9092560.6270002.8971.0002.8422.9552.8172.9932.8023.0062622.7308002.89390.9362.8483
23-1.02.6102.6001078.6100002.6101078.6100002.6001.0002.5582.6492.5272.6622.5212.691258.3025802.59980.9952.6220
34-1.00.1490.147249.7079000.149249.7079000.1471.0000.1310.1640.1220.1750.1120.178290.6351660.14720.9990.1478
45-1.02.8322.832445.0474002.832445.0474002.8321.0002.7872.8772.7772.8882.7702.909292.3814302.83160.9972.8648
...............................................................
5050250503-1.00.2081.41635.1536700.20835.1536701.8710.4440.4392.7850.0733.7210.0124.1272814.7374001.86680.9980.3553
5050350504-1.00.0103.01739.1154500.01039.1154504.0100.3361.7365.7750.2225.9460.0115.9602829.6910003.90200.9511.5299
5050450505-1.00.8170.96835.2441500.81735.2441501.0210.8100.7611.5390.6691.8930.3852.145293.4546901.02180.9980.7126
5050550506-1.00.2820.21448.7560500.28248.7560500.2360.9870.0920.3260.0270.4660.0113.367345.4757400.22070.9910.3037
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50507 rows × 20 columns

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" ], "text/plain": [ " id z_spec z_a z_m1 chi_a z_p chi_p z_m2 \\\n", "0 1 -1.0 0.504 0.504 421.755100 0.504 421.755100 0.504 \n", "1 2 -1.0 2.909 2.897 2560.627000 2.909 2560.627000 2.897 \n", "2 3 -1.0 2.610 2.600 1078.610000 2.610 1078.610000 2.600 \n", "3 4 -1.0 0.149 0.147 249.707900 0.149 249.707900 0.147 \n", "4 5 -1.0 2.832 2.832 445.047400 2.832 445.047400 2.832 \n", "... ... ... ... ... ... ... ... ... \n", "50502 50503 -1.0 0.208 1.416 35.153670 0.208 35.153670 1.871 \n", "50503 50504 -1.0 0.010 3.017 39.115450 0.010 39.115450 4.010 \n", "50504 50505 -1.0 0.817 0.968 35.244150 0.817 35.244150 1.021 \n", "50505 50506 -1.0 0.282 0.214 48.756050 0.282 48.756050 0.236 \n", "50506 50507 -1.0 5.961 2.987 9.270822 5.961 9.270822 3.715 \n", "\n", " odds l68 u68 l95 u95 l99 u99 nfilt q_z \\\n", "0 1.000 0.486 0.522 0.482 0.526 0.482 0.541 33 0.838290 \n", "1 1.000 2.842 2.955 2.817 2.993 2.802 3.006 26 22.730800 \n", "2 1.000 2.558 2.649 2.527 2.662 2.521 2.691 25 8.302580 \n", "3 1.000 0.131 0.164 0.122 0.175 0.112 0.178 29 0.635166 \n", "4 1.000 2.787 2.877 2.777 2.888 2.770 2.909 29 2.381430 \n", "... ... ... ... ... ... ... ... ... ... \n", "50502 0.444 0.439 2.785 0.073 3.721 0.012 4.127 28 14.737400 \n", "50503 0.336 1.736 5.775 0.222 5.946 0.011 5.960 28 29.691000 \n", "50504 0.810 0.761 1.539 0.669 1.893 0.385 2.145 29 3.454690 \n", "50505 0.987 0.092 0.326 0.027 0.466 0.011 3.367 34 5.475740 \n", "50506 0.384 1.868 5.436 0.359 5.918 0.011 5.959 8 28.822300 \n", "\n", " z_peak peak_prob z_mc \n", "0 0.5041 0.999 0.5016 \n", "1 2.8939 0.936 2.8483 \n", "2 2.5998 0.995 2.6220 \n", "3 0.1472 0.999 0.1478 \n", "4 2.8316 0.997 2.8648 \n", "... ... ... ... \n", "50502 1.8668 0.998 0.3553 \n", "50503 3.9020 0.951 1.5299 \n", "50504 1.0218 0.998 0.7126 \n", "50505 0.2207 0.991 0.3037 \n", "50506 3.6691 0.984 2.4819 \n", "\n", "[50507 rows x 20 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 2.2 \n", "\n", "You should notice that every one of these tables has 50507 rows. This is a relief! It means the creators were consistent, and that each object has a row in each table. \n", "\n", "You should also notice one column, `id`, is a unique identifier for each row in each table (and is consistent across tables). Pandas has assigned its own index (it's off by 1 from the id column), but we might as well use `id`. \n", "\n", "**For each of your four dataframes, set 'id' to be the index column.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cat_df = cat_df.set_index('id')\n", "fast_df = fast_df.set_index('id')\n", "rf_df = rf_df.set_index('id')\n", "z_df = z_df.set_index('id')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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zdml153n_153l154n_154l155n_155l161n_161...l271n_271l272n_272l273n_273l274n_274l275n_275
id
10.5041141.846.8347702418.878302637.7660019191.174004...0.61420680.787911141.53542191.706271938.8791019
22.8938945.460.974816160.8987781.4158042.605142...1.533340311.337740331.42114321.34822291.454444
32.5998445.261.947970153.2265783.76040510.163801...2.110700311.932050322.12226322.11647323.824165
40.1472439.073.020890156.760812010.564002425.685906...0.89357920.70875061.0954881.20468910.8417024
52.8316345.421.229800141.7073781.7837352.731231...1.142590311.049750331.13020321.12243291.778565
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5 rows × 46 columns

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" ], "text/plain": [ " z dm l153 n_153 l154 n_154 l155 n_155 \\\n", "id \n", "1 0.50411 41.84 6.834770 24 18.87830 26 37.76600 19 \n", "2 2.89389 45.46 0.974816 16 0.89877 8 1.41580 4 \n", "3 2.59984 45.26 1.947970 15 3.22657 8 3.76040 5 \n", "4 0.14724 39.07 3.020890 15 6.76081 20 10.56400 24 \n", "5 2.83163 45.42 1.229800 14 1.70737 8 1.78373 5 \n", "\n", " l161 n_161 ... l271 n_271 l272 n_272 l273 n_273 \\\n", "id ... \n", "1 191.17400 4 ... 0.614206 8 0.787911 14 1.53542 19 \n", "2 2.60514 2 ... 1.533340 31 1.337740 33 1.42114 32 \n", "3 10.16380 1 ... 2.110700 31 1.932050 32 2.12226 32 \n", "4 25.68590 6 ... 0.893579 2 0.708750 6 1.09548 8 \n", "5 2.73123 1 ... 1.142590 31 1.049750 33 1.13020 32 \n", "\n", " l274 n_274 l275 n_275 \n", "id \n", "1 1.70627 19 38.87910 19 \n", "2 1.34822 29 1.45444 4 \n", "3 2.11647 32 3.82416 5 \n", "4 1.20468 9 10.84170 24 \n", "5 1.12243 29 1.77856 5 \n", "\n", "[5 rows x 46 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 2.3 \n", "\n", "Instead of working with these four dataframes separately, let's join them all into one MEGA DATAFRAME. \n", "\n", "By setting 'id' as the index for each, we'll be able to merge the dataframes using `pd.merge` with the `left_index` and `right_index` parameters set to true. \n", "\n", "**Create your mega dataframe, which we'll simply call `df`, by consecutively merging the first two, then third and fourth dataframes.**\n", "\n", "```{hint}\n", "You should end up with 215 columns in your final dataframe.\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df = cat_df.merge(fast_df,how='outer',left_index=True,right_index=True)\n", "df = df.merge(rf_df,how='outer',left_index=True,right_index=True)\n", "df = df.merge(z_df,how='outer',left_index=True,right_index=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "tmp_df = pd.merge(cat_df, fast_df, left_index=True,right_index=True)\n", "tmp2_df = pd.merge(tmp_df, rf_df, left_index=True,right_index=True)\n", "mega_df = pd.merge(tmp2_df, z_df, left_index=True,right_index=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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xyradecfaper_f160weaper_f160wfaper_f140weaper_f140wf_f160we_f160w...u68l95u95l99u99nfiltq_zz_peakpeak_probz_mc
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212056.7151321.05553.089613-27.9597420.5300630.077372-99.000000-99.0000000.6383940.093185...2.9552.8172.9932.8023.0062622.7308002.89390.9362.8483
311351.8751327.24453.102913-27.9596420.4677910.200590-99.000000-99.0000000.7143550.378915...2.6492.5272.6622.5212.691258.3025802.59980.9952.6220
411415.6811396.83653.101709-27.95848112.4973840.086093-99.000000-99.00000027.2702850.403132...0.1640.1220.1750.1120.178290.6351660.14720.9990.1478
511385.5701384.72953.102277-27.9586831.1017400.087183-99.000000-99.0000001.4129120.168798...2.8772.7772.8882.7702.909292.3814302.83160.9972.8648
..................................................................
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505057634.09118915.90853.172928-27.6664900.3030360.024853-99.000000-99.0000000.5550120.069866...1.5390.6691.8930.3852.145293.4546901.02180.9980.7126
505068669.85918840.10053.153437-27.6677590.4164490.024596-99.000000-99.0000000.5262320.049303...0.3260.0270.4660.0113.367345.4757400.22070.9910.3037
505073041.90318822.67053.259346-27.6679860.0301830.0175990.0111910.0841540.0363520.021195...5.4360.3595.9180.0115.959828.8223003.66910.9842.4819
\n", "

50507 rows × 215 columns

\n", "
" ], "text/plain": [ " x y ra dec faper_f160w eaper_f160w \\\n", "id \n", "1 11876.639 1632.890 53.093012 -27.954546 55.142755 0.046190 \n", "2 12056.715 1321.055 53.089613 -27.959742 0.530063 0.077372 \n", "3 11351.875 1327.244 53.102913 -27.959642 0.467791 0.200590 \n", "4 11415.681 1396.836 53.101709 -27.958481 12.497384 0.086093 \n", "5 11385.570 1384.729 53.102277 -27.958683 1.101740 0.087183 \n", "... ... ... ... ... ... ... \n", "50503 3207.811 18767.998 53.256225 -27.668900 0.083831 0.017599 \n", "50504 3319.077 18889.404 53.254129 -27.666879 0.030584 0.017599 \n", "50505 7634.091 18915.908 53.172928 -27.666490 0.303036 0.024853 \n", "50506 8669.859 18840.100 53.153437 -27.667759 0.416449 0.024596 \n", "50507 3041.903 18822.670 53.259346 -27.667986 0.030183 0.017599 \n", "\n", " faper_f140w eaper_f140w f_f160w e_f160w ... u68 l95 \\\n", "id ... \n", "1 -99.000000 -99.000000 152.454867 0.566142 ... 0.522 0.482 \n", "2 -99.000000 -99.000000 0.638394 0.093185 ... 2.955 2.817 \n", "3 -99.000000 -99.000000 0.714355 0.378915 ... 2.649 2.527 \n", "4 -99.000000 -99.000000 27.270285 0.403132 ... 0.164 0.122 \n", "5 -99.000000 -99.000000 1.412912 0.168798 ... 2.877 2.777 \n", "... ... ... ... ... ... ... ... \n", "50503 0.009053 0.083001 0.151608 0.047542 ... 2.785 0.073 \n", "50504 0.079141 0.082396 0.033300 0.027409 ... 5.775 0.222 \n", "50505 -99.000000 -99.000000 0.555012 0.069866 ... 1.539 0.669 \n", "50506 -99.000000 -99.000000 0.526232 0.049303 ... 0.326 0.027 \n", "50507 0.011191 0.084154 0.036352 0.021195 ... 5.436 0.359 \n", "\n", " u95 l99 u99 nfilt q_z z_peak peak_prob z_mc \n", "id \n", "1 0.526 0.482 0.541 33 0.838290 0.5041 0.999 0.5016 \n", "2 2.993 2.802 3.006 26 22.730800 2.8939 0.936 2.8483 \n", "3 2.662 2.521 2.691 25 8.302580 2.5998 0.995 2.6220 \n", "4 0.175 0.112 0.178 29 0.635166 0.1472 0.999 0.1478 \n", "5 2.888 2.770 2.909 29 2.381430 2.8316 0.997 2.8648 \n", "... ... ... ... ... ... ... ... ... \n", "50503 3.721 0.012 4.127 28 14.737400 1.8668 0.998 0.3553 \n", "50504 5.946 0.011 5.960 28 29.691000 3.9020 0.951 1.5299 \n", "50505 1.893 0.385 2.145 29 3.454690 1.0218 0.998 0.7126 \n", "50506 0.466 0.011 3.367 34 5.475740 0.2207 0.991 0.3037 \n", "50507 5.918 0.011 5.959 8 28.822300 3.6691 0.984 2.4819 \n", "\n", "[50507 rows x 215 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mega_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 2.4 \n", "\n", "Let's take a look at the redshift distribution of sources in the catalog.\n", "\n", "There are several estimates of the photometric redshift, the one we want to use is `z_peak` for photometry, or, if available, `z_spec_x`, from spectroscopy. \n", "\n", "**What percentage of the catalog have measured spectroscopic redshifts? The `z_spec_x` column is set to `-1` if no spectroscopic redshift exits.**" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.86692933652761" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(len(df.loc[df.z_spec_x!=-1,'z_spec_x']) / len(df))*100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 2.5 \n", "\n", "Write a function `get_redshift()` which takes in an object ID, and returns `z_spec_x` for that source if it's not -1, otherwise returns `z_peak`. Because `id` is a special word in python, we suggest using `objid` as the input, and setting df=df as an optional argument. You can make this a memory lite function by using `df.loc[]` to pull the row and only the two columns you need for this.\n", "\n", "There are two additional \"flagged\" values: -99.0 and -99.9 -- Have your function output np.nan if this is the value in the table. \n", "\n", "Your function should return the redshift as well as a flag (string) 's' or 'p', or 'f' for spectroscopic or photometric (or fail, if you're returning nan). " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def get_redshift(objid,df=df):\n", " options = df.loc[objid,['z_spec_x','z_peak']]\n", " if options.z_spec_x != -1:\n", " if options.z_spec_x != -99.0 and options.z_spec_x != -99.9:\n", " return options.z_spec_x, 's'\n", " else:\n", " return np.nan,'f'\n", " else:\n", " if options.z_peak != -99.0 and options.z_peak != -99.9:\n", " return options.z_peak, 'p'\n", " else:\n", " return np.nan, 'f'" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1.6689, 'p')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_redshift(150)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 2.6 \n", "\n", "Now that we can get the best redshift for each row, use a list comprehension to grab these values for every object. You can index the output tuple of your function at 0 to drop the flag for now. \n", "\n", "Once you have this, plot a histogram of the redshifts, using `fig, ax = plt.subplots`. Make your plot nice!\n", "\n", "```{note}\n", "My list comprehension takes ~15 seconds to run. It's a lot of data! If you wish, you may try to find a more optimized solution built on a full column operation rather than a loop. One possibility is to take the spec-z column, mask any bad values, and then replace those entries in the z-phot column and plot that... \n", "```" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [], "source": [ "all_z = [get_redshift(i)[0] for i in df.index]" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "all_z = np.array(all_z)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9,9))\n", "ax.hist(all_z[~np.isnan(all_z)],bins=50)\n", "ax.set_xlabel('redshift',fontsize=15)\n", "ax.set_ylabel('Number of galaxies',fontsize=15)\n", "ax.tick_params(direction='in',top=True,right=True,length=8);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 2.7\n", "\n", "Now do the same, but separately plot the distributions of redshift for those with spectroscopic redshifts and those that only have photometric. For this, you'll want to set `density=True` in your `hist`, and play with the linestyles and opacities so that both are visible. \n", "\n", "**Bonus (+0.5): Use KDE from seaborn to smoothly represent the distribution**. " ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [], "source": [ "spec_z = [get_redshift(i)[0] for i in df.index if get_redshift(i)[1]=='s']; spec_z = np.array(spec_z)" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [], "source": [ "phot_z = [get_redshift(i)[0] for i in df.index if get_redshift(i)[1]=='p']; phot_z = np.array(phot_z)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZZ/LnP/+Zp59+ml69ejFlyhR23HFHjj76aE444QQABg4cmPn9dSZPnsw///lPLrzwQj7++GO+853v8JWvfIWNNtqIjTfemNtuu43Jkydz/vnnM2TIEE4++WQA3nvvPb74xS+yaNEiampqGDx4MPfffz8nnXQSH330EaeccgrHHXccs2bN4tprr+Wxxx6ja9euy9yfCy64gO23357a2lo+/fRTVl555Sbv4/e//30uvvhijj32WCZMmECXLl2YOnUqr732GjvttFPJ/r5KwbAiScq1Tz/9lMWLF7NgwQJuv/127rzzTr70pS+xyiqrAIVf8n/9619Za621AFhnnXXYfvvtmThxIl/72tf49NNPOfvss9l999257bbb6q+72Wabscsuu3Ddddfx7W9/my984QsADBgwoP77us/P8v4677//Pvfddx9rrLEGAHPmzOHUU09lxIgRXHTRRQDsvffe3Hvvvdxxxx31YeXyyy9n5syZ/OMf/2CTTTYBYK+99uLdd99lwoQJnHTSSQwcOLA+QO2www6stNKyv8b79+/PXXfdVd/j0pTp06dz6aWXctppp3HJJZfUH99///2X+/dRDQ4DSZJybbPNNqNbt26svfbanHzyyXz961/nuuuuqz+/44471gcVgK222gqA1157DYAXX3yRuXPn8vWvf32p6+68885suOGGPProoy1+fmvfv+OOO9YHlbr6AfbZZ59lfq7XX3+9/vV9993HDjvswODBg1m8eHH91z777MM777zDc88912KddQ466KAWgwrAgw8+yJIlSxg7dmyma1abPSuSpFy76667GDhwIKutthobbrjhMsMaa6+99lKve/ToAcCHH34IwPz58wGaXAmzzjrr1J9vTmvf3zA4AXTv3r3Z43U1AsydO5fp06fTrVu3Jut45513WqyzTpYVP3XXquulyTvDiiQp17bccsv61UBtURdm5syZs8y5OXPmMHz48LK+P6vevXvTr18/Lr/88ibPb7rpppmus7xeFYA+ffoAMHv27MzXrSaHgSRJHdqmm25K//79l5pvAvD4448zc+ZMdtttt/pj3bt3Z9GiRW1+/4oYPXo0L7zwAhtssAHDhw9f5mu11VYDPus5alxna+y111506dJlqZVGeWbPiiSpQ+vatSvnnnsuJ5xwAkcccQRHHHEEs2fP5qyzzmKTTTZZapfcLbbYgnvvvZfRo0ez1lprsd5667Heeutlfv+KOO200/jNb37DLrvswmmnncamm27KBx98wAsvvMAf//hH7r777voaAS6++GL23Xdfunbt2urenSFDhtRPrl2wYAEHHnggXbt25YknnmCzzTbjsMMOK8nPVCqGFUnqyFZgu/uOZOzYsayyyir89Kc/ZcyYMfTq1Yv99tuPn/zkJ0stO/75z3/Ot7/9bb70pS/x0Ucfcc4551BTU5P5/StijTXW4PHHH+fcc8/lwgsvZPbs2ay55ppsuummfOUrX6lvd8ABB3DyySdz5ZVXcu6555JSIqXU6s+76KKL2Hjjjbnyyiu58cYbWXXVVRk2bBijRo0qyc9TStGWHzAPhg8fnqZNm1btMqqmpqaGqye/AsCAkUe12Hb2Qzdx/K4b1b9PUsfz/PPPs/nmm1e7DKlZy/tvNCKeSik12UXknBVJkpRrFQ0rEXFdRMyNiGeaOR8R8bOImB4Rf4+I7SpZnyRJyp9K96zcAIxu4fy+wCbFr7HALypQkyRJyrGKhpWU0mSgpd13xgA3pYKpwJoR0T6fZy1Jkkoib3NWBgCvN3g9q3hMkiR1UnlbutzUtntNLleaN29ei+vKx44d226eeSBJpZBSyrR7qVRpKSUWLFiwvP1g+jR3Im9hZRawfoPXA4E3mmrYt29fOvPSZUlqqFu3bixatKj+ScRSnixatIi11167xd/bEfF2c+fyNgx0D3BUcVXQF4B/p5TerHZRkpR3/fr1Y/bs2SxcuLBNG4RJ5ZBSYuHChcyePZt+/fq1+ToV7VmJiF8DuwN9ImIWcA7QDSCldBUwEdgPmA4sBEqzh7EkdXCrr746AG+88QaffPJJlauRPtOtWzf69+9f/99oW1Q0rKSUDl/O+QR8s0LlSFKHsvrqq6/QLwQpr/I2DCRJkrQUw4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okSco1w4okScq1lapdgCqrpqampO0kSSo3w0oncfXkVzK1O37XjcpciSRJreMwkCRJyjV7VjqBASOPytRu9kM3lbkSSZJaz54VSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUa4YVSZKUaytVuwC1TzU1NRV9nySp8zKsqM2unvxK5rbH77pRGSuRJHVkDgNJkqRcs2dFK2zAyKNaPD/7oZsqVIkkqSOyZ0WSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOVaxcNKRIyOiBcjYnpEnNHE+TUi4v9FxN8i4tmIOKbSNUqSpPyoaFiJiK7AFcC+wBbA4RGxRaNm3wSeSyltDewOXBwR3StZpyRJyo9K96yMAKanlF5JKX0M3AaMadQmAatFRAC9gPnA4sqWKUmS8mKlCn/eAOD1Bq9nATs0avNz4B7gDWA14LCU0pLGF5o3bx7Dhw9v9oPGjh3L2LFjV7hgSZK04mpra6mtrW2pSZ/mTlQ6rEQTx1Kj1/sAfwX2BIYAD0TEH1NK7zVs1LdvX6ZNm1aWIiVJUmktrxMhIt5u7lylh4FmAes3eD2QQg9KQ8cAd6aC6cCrwGYVqk+SJOVMpcPKk8AmETG4OGn2qxSGfBp6DRgJEBH9gU2BVypapSRJyo2KDgOllBZHxLeA+4GuwHUppWcj4sTi+auA84AbIuIfFIaNxqWUmu0akiRJHVul56yQUpoITGx07KoG378BjKp0XZIkKZ/cwVaSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOXaSlkaRcRKQNeU0kcNjo0CtgAmp5SeLlN9kiSpk8sUVoDfAP8G/hMgIr4NXAZ8BHSNiINTSr8vS4WSJKlTyzoM9AVgYoPX/wVcnFLqCVwDnFXqwiRJkiB7WOkNzAGIiK2A9YCriufuoDAcJEmSVHJZw8pbwKDi96OBmSmlfxZf9wSWlLguSZIkIPuclTuACyNia+AY4OcNzm0LvFzqwiRJkiB7WDkDeA/YHvgF8KMG5z5PYQKuJElSyWUKKymlxcC5zZw7uKQVSZIkNdCqTeEiYt+IODsiaiNig+KxXSNivfKUJ0mSOrusm8L1B+6hMOQzAxhMYTXQaxTmsHwInFSeEiVJUmeWtWflf4BewGbFr2hw7kFgZInrkiRJArJPsB0NfCOlND0iujY6NwsYUNqyJEmSClozZ+XTZo73ARaVoBZJkqRlZA0rfwROadSrkop//ifwcEmrkiRJKso6DDQOeAx4BriLQlA5PiK2BLak8OwgSZKkksvUs5JSeobCSqBpwNEUhoQOBl4HdkgpvVSuAiVJUueWtWeF4rOAjixjLZIkScto1aZwkiRJldZsz0pE3A78d0rpn8XvW5JSSoeVtjRJkqSWh4H6At2K3/fjs9U/kiRJFdNsWEkp7dHg+90rUo0kSVIjmeasRMTKyznvgwwlSVJZZJ1g+9eI2KGpExHxDQr7r0iSJJVc1qXLLwGPRcRPgXNSSp9ERD+gFjgAuKRcBVZaTU1NRd4jSZKyyRRWUkoHRsQxFELJ/hFxHTAe+BewS0ppShlrrLirJ7+Sqd3xu25U5kokSVJrNoW7PiL+AkyhEFr+AuycUvqwXMVJkiRlDisRcSDwS+AN4PfAScBvIuL4lNLcMtVXVQNGHtXsudkP3VTBSiRJ6ryyrga6EfgdcDcwLKV0KrATsAnwXES4IZwkSSqLrKuB9gRGp5ROTCl9AJBSmgZsC9wA3FKe8iRJUmeXdRhoy5TSvxsfTCl9BHw/Iu4sbVmSJEkFmXpWmgoqjc4/XppyJEmSltaaCbaDgCOAocAyO9qmlA4tXVmSJEkFmcJKRHweeBR4nUJY+TuwBjAImAVML1N9kiSpk8s6wfanwG+BLYEAjk0pbQTsTOFpzD8pT3mSJKmzyxpWtgFuBZYUX68M9XNVJgA/LnllkiRJZA8rCfg4pZSAucCGDc69TmG/FUmSpJLLGlaeA4YUv58CnBYRm0TEhsDpwD/LUZwkSVLW1UC1fNabcibwB+CF4usPgENKXJckSRKQ/anLNzf4/vmI2BzYEegJTO2ozwaSJEnVl3mflYZSSu8DD5S4FkmSpGU0G1YiYr/WXCilNHHFy5EkSVpaSz0rv6ewCigyXCcBXUtSkSRJUgMthZXBFatCkiSpGc2GlZTSzEoWIkmS1JRWTbCNiJWADWj6QYbPlaooSZKkOlkfZNgN+BnwDaBHM82csyJJkkou6w62PwAOAI6lMOH2W8AxwEPADOBL5ShOkiQpa1g5FKgBbi++fiKldFNKaRTwGDCmDLVJkiRlDivrAy+llD4FPgTWanDuV8BXSl2YJEkSZA8rbwJrFr9/Fdi1wbkhy7SWJEkqkayrgSYBuwD/D7gauCgiNgY+Ag4Dfl2W6iRJUqeXNaycBfQBSCldFhFB4UnLPYH/Ac4tT3mSJKmzy/rU5TnAnAavLwUuLVdRkiRJdbLOWSmZiBgdES9GxPSIOKOZNrtHxF8j4tmIeLTSNUqSpPzIuincqxQeVtiUJcB7wN+An6eUnmrhOl2BK4C9gVnAkxFxT8PdbyNiTeBKYHRK6bWI6JelRkmS1DFl7Vn5LYVgsxrwZwpPZP4zsDrQDZgGfAGYGhH7tHCdEcD0lNIrKaWPgdtYdo+WrwF3ppReA0gpzc1YoyRJ6oCyTrCdC7wEHJBS+rDuYET0pLBC6DVgS+AeYAJwfzPXGQC83uD1LGCHRm2GAt0iYhKFcHR5SummjHVKkqQOJmtY+TYwtmFQAUgpLYqIS4FrUkrnR8TVFDaJa040cazx8NJKwOeBkRRWG02JiKkppZcaNpo3bx7Dhw9v9oPGjh3L2LFjWyhFkiRVSm1tLbW1tS016dPciaxhZU2gfzPn+gO9it//G/i0hevMorAbbp2BwBtNtHk7pfQB8EFETAa2ptCzU69v375MmzYtU/GSJKm6lteJEBFvN3cu65yV3wM/iYiDI6J78aLdI+IQ4CfF8wBbAf9s4TpPAptExODidb5KYeioobuBXSJipYhYhcIw0fMZ65QkSR1M1p6VE4Ebgf8FUkQsoDCfJCjMWTmp2O4N4MzmLpJSWhwR36Iwp6UrcF1K6dmIOLF4/qqU0vMRcR/wdworja5JKT3T+h9NkiR1BFk3hXsXGBMRnwOGA+tQ2CRuWkrp2Qbt/jfDtSYCExsdu6rR658CP81SmyRJ6tiy9qwAUAwmzy63oSRJUolUfAdbSZKk1jCsSJKkXDOsSJKkXGs2rETEBhHRrZLFSJIkNdZSz8qrwLYAEfFwRGxWmZIkSZI+01JYWQSsUvx+dwoPLZQkSaqolpYu/wW4PCIeKL4+JSLebKZtSimNK21pkiRJLYeV4ylszDaGwsMGRwIfNdM2AYaVFVRTU1PtEiRJyp1mw0pK6QXgSwARsQQ4KKX0RKUK66yunvxKtUuQJClXsu5gOxhobghIkiSpbLI+G2hm8SnIhwE7A2sD84E/AnemlBaXscZOacDIo6pdgiRJuZAprEREP+APwDBgBvAWsCPwTeBvETEqpTSvXEVKkqTOK+sOtpcAvYEdUkobpZR2TCltBOxQPH5JuQqUJEmdW9awsh8wLqX0ZMODxdf/Dexf6sIkSZIge1jpASxo5twCoHtpypEkSVpa1rAyFRgXEas2PFh8Pa54XpIkqeSyLl3+HvAI8HpE/IHCBNt+wD5AUNiOX6o3+6Gb6r9/79W/MWnJa0DzG9+5IZ4kqTlZly7/NSI2Ab4PbE9hVdCbwFXAJSmlt8tXotqrPZZMAeDv6R2Y91bh4HP/XrrRFmMqXJUkqb3J2rNCMZCcUcZaJEmSlpE5rEhtNWxQbzbu1wuAmkO3qz9ec/vT1SpJktSOZJ1gK0mSVBWGFUmSlGuGFUmSlGvLDSsR0SMizoqIrStRkCRJUkPLDSsppY+As4A1y16NJElSI1mHgf4MfL6chUiSJDUl69Ll04FbI+JjYCKFHWxTwwYppYUlrk2dSNYdbN3pVpI6n6xh5c/FP38GXN5Mm64rXo46nefuztbOnW4lqdPKGlb+k0Y9KZIkSZWQ9dlAN5S5DnVCDXezbbGdO91KUqfWqu32I2ILChNt1weuSynNiYiNgbdSSgvKUaDyp6amhkmTJvHezH8VDjR4wrIkSaWWKaxERC/gOuAQ4JPi++4D5gA/BF6j8ERmdRbzXmBw+hiAYUuyz63O2kuStddFktTxZe1ZuQTYCRgJ/An4sMG5iRSCimFFzZo+9/1M7eoeeChJUp2sYeVg4NSU0iMR0XjVz0xgw9KWpfZi2KDeHLnH0Bbb3PzISxWqRpLUEWUNKz2Bd5o5txrwaWnKUUe0vDBTx1AjSWpK1h1snwSOaubcIcDjpSlHkiRpaVl7VsYDD0bEg8AdFPZc2S8iTqMQVnYtU32SJKmTy9SzklJ6jMLk2h7Az4EAJgAbAXullJ4sW4WSJKlTy7zPSkrpT8AuEdETWAt41+cBSZKkcss6Z6WhDynstbKoxLVIkiQtI3NYiYj9IuJxCmFlDvBhRDweEfuXrTpJktTpZQorEXEC8P+A94FTgf8o/vk+cE/xvCRJUsllnbNyJlCbUjqp0fGrIuIq4CzglyWtTJIkiezDQL2BO5s591tg7dKUI0mStLSsYeURYLdmzu0GTC5NOZIkSUtrdhgoIrZo8PJnwDUR0Rv4HTAX6Ad8GdgXOK6MNUqSpE6spTkrz1DYqbZOACcUv1LxdZ37gMYPOJQkSVphLYWVPSpWhSRJUjOaDSsppUcrWYgkSVJTMm+3XyciVgK6Nz7u1vuSJKkcsm4Kt0ZEXBkRb1LYwXZBE1+SJEkll7Vn5QYKS5SvBqYDH5erIEmSpIayhpWRwAkppV+XsxhJkqTGsoaV1wDnpLRRTU1NtUuQJKndyhpWTgcmRMRfUkqvlbOgjurqya9Uu4RMJk2aBBiwJEn5kSmspJQmRsRewPSImAG820SbEaUtTVUz7wV47t/VrkKSJCBjWImIi4DvAE/iBNsVMmDkUdUuQZKkdiXrMNBxwFkppR+VsxjlS82h2zV/7vanmT73/QpWI0nqrLKGlYXAU+UsRMoiy1wa59tIUseSNaxcDoyNiAdSSmm5raVSe+7u5bfZYkyLp1sbYgw9kpQPWcNKH2AH4MWImMSyE2xTSmlcCeuSyqMEoUeSVFlZw8ohwGKgG7B3E+cTYFhRWbQ0d6a+ze1Pf/a9PSKS1KFkXbo8uNyFSCssS69JUXMBqGHokSTlQ6YHGUqSJFVL1n1WTl5em5TSlStejtQ2WYaKJEntU9Y5Kz9v4Vzd6iDDiiRJKrlMw0AppS6Nv4C1gcOBvwFblLNISZLUeWXtWVlGSuld4DcRsQbwS2D3EtUkSZJUrxQTbF8FhpfgOpIkSctYobASEesC36MQWCRJkkou62qgeXw2kbZOd2A14EPg4BLXpSqb9Oyb7jkiScqFrHNWrmDZsPIhMAu4L6X0TkmrUtU8NfNfDE4fA/hUZUlSLmTdwbamzHVIkiQ1qc2rgdTxDBh5FDx0E8OWLATgyD2GVrkiSZJaCCsR8XArrpNSSiNLUI8kSdJSWupZyTIPZV1gJ5adzyJJklQSzYaVlNJ/NHcuIjYAxgEHAG8Dl5a+NEmSpFbusxIRG0fEtcDLwIHAfwMbppR+1IprjI6IFyNiekSc0UK77SPi04g4pDU1SpKkjiVTWImIz0XErcDzwB7AqcCQlNJlKaVFWT8sIrpSWAa9L4XnCR0eEcs8V6jY7kLg/qzXliRJHVOLYSUiPh8RdwJ/B7YFjgM2SSldlVJxM47WGQFMTym9Unz/bcCYJtqdAvwWmNuGz5AkSR1Is2ElIv4PeAIYDHw1pbR5SunGlNKnK/B5A4DXG7yeVTzW8HMHAF8GrlqBz5EkSR1ES6uB9in+uT5wRURc0dKFUkr9MnxeNPXWRq8vA8allD6NaKp5wbx58xg+vPnnJ44dO5axY8dmKEmSJJVbbW0ttbW1LTXp09yJlsLKhDZX1LxZFMJPnYHAG43aDAduKwaVPsB+EbE4pfS7ho369u3LtGnTylCiJEkqteV1IkTE282da2npcjnCypPAJhExGJgNfBX4WqPPHVz3fUTcAPy+cVBR28x+6KZqlyBJUqtVdLv9lNLiiPgWhVU+XYHrUkrPRsSJxfPOUymzPZZMqXYJkiS1SsWfDZRSmghMbHSsyZCSUjq6EjVJkqT88kGGnZQPKZQktRet2sFWkiSp0gwrkiQp1wwrkiQp1wwrkiQp1wwrkiQp1wwrkiQp1wwrkiQp19xnRblUc/vT2dseul0ZK5EkVZthRbkyfe77mdtu3K9XGSuRJOWFw0CSJCnX7FlRbrTmEQA3P/JSGSuRJOWJPSuSJCnXDCuSJCnXDCuSJCnXDCuSJCnXnGDbQcx+6KZqlyBJUlkYVjqQPZZMqXYJkiSVnMNAkiQp1+xZ6YBas1+JJEl5Z8+KJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKNcOKJEnKtZWqXYCUVzU1NSVpI0laMYYVqbHn7l5+my3GlL8OSRJgWGkXZj90U7VLkCSpagwr7cQeS6ZUu4ROoebQ7Zbf5vanK1CJJKmOE2wlSVKu2bPSzhy5x9BqlyBJUkXZsyJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNsCJJknLNpy6XQE1NTbVLkCSpwzKsrICrJ79S7RIkSerwHAaSJEm5Zs9KGw0YeVS1S5AkqVOwZ0WSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWaYUWSJOWazwaqstkP3VTtEiRJyjXDSg7ssWRKtUuQJCm3HAaSJEm5Zs9Kjhy5x9BqlyBJUu7YsyJJknLNsCJJknKt4mElIkZHxIsRMT0izmji/Ncj4u/Fr8cjYutK1yhJkvKjomElIroCVwD7AlsAh0fEFo2avQrsllIaBpwH1FayRkmSlC+VnmA7ApieUnoFICJuA8YAz9U1SCk93qD9VGBgJQucNGkS7838V+FFhj1QBow8qswVSZLUuVU6rAwAXm/wehawQwvtjwX+r6wVNWFwKpQ4bMnCZts80mXH5V7HDd8kSVpxlQ4r0cSx1GTDiD0ohJWdmzo/b948hg8f3uwHjR07lrFjx7alxpJywzdJkqC2tpba2hZndvRp7kSlw8osYP0GrwcCbzRuFBHDgGuAfVNK7zR1ob59+zJt2rSyFFmnuX1Pbn7kpbJ+riRJHc3yOhEi4u3mzlU6rDwJbBIRg4HZwFeBrzVsEBEbAHcCR6aUOkwqcMM3SZLapqJhJaW0OCK+BdwPdAWuSyk9GxEnFs9fBfwA6A1cGREAi1NKzY/3SCqZmpqasraXpLao+Hb7KaWJwMRGx65q8P1xwHGVrktS0XN3L7/NFmPKX4ckFbmDrSRJyjUfZLiCXJ6sjqrm0O2aPn770xWuRFJnZ1hpI5ckS5JUGQ4DSZKkXLNnpQ1chqw6WVbDuGJGklaMYUVqC1fMSFLFOAwkSZJyzZ4VqZWaWyWzVJtOsmLGYTBJlWBYkdq5toSBFQ4QDoNJqiDDitQRZAkPYICQ1C4ZViS1isNgkirNsCJ1IC0FCQOEpPbK1UCSJCnXDCuSJCnXDCuSJCnXDCuSJCnXnGCrDq8tE0uzrHiRJFWGYUWdwvS572dqt3G/XmWupHXKtfuru8pKak8MK1LeZd3wrdLXkqQKMayoUzlyj6HNnrv5kZcqWIkkKSvDitROrOg8GufhSGqvXA0kSZJyzZ4VqcyyTGZ1wqskNc+wIpVLlsmsPgVZkpbLYSBJkpRr9qxIZZBlMqtPQZakbOxZkSRJuWZYkSRJueYwkJQDrgaSpOYZVtTuteu5H25/L0nLZVhRu5X14YSSpPbNsCJVSWfZ/r49bYrX2jryUrfU0RlW1C619EBC5UR73RSvBHUbeqTSMqxIUjm017Am5ZBhRVLJdYRN8Zr7GRrWbY+IVBmGFUlV157mtQCtWsWVJfRIaplhRVL1OFQiKQPDiiS1QmdZxSXliWFFUlW0dl5LqYaBcjWcJCkTw4qkfCvVLr/F4STDitT+GFYkdQ45frRBu5tgLFWYYUVSbpVqfkhuV944wVjKxLAiqcNzUqzUvhlWJKkKOsLGeVKlGFakdqitv8TsYZDUHhlWpHZq+tz3M7fduF+vMlYiSeVlWJHKrC29IPaASNJnDCtSBWTtBWlLD8iRewxt8fzNj7zU6mtKUp50qXYBkiRJLbFnRaqglnpB7AGRpKYZViQBrjCSlF+GFUn1XGGUX27Jr87MsCKprFwNtYLckl8yrEhaVqlXGJVzNZQ+Y++LOirDitQGboPe8eRxzk7mLflb0ftioFF7ZFiR2qg18ztUkPfVUB16zo7DSWrHDCuS1I5V8oGIbelxsZdGpWBYkVbQ8uZ35InDV9l0tF2BWxtoWgwYWXpoYLm9NK0NMYaezs2wInUCDlkpk6xBpBVWOPg4NCUMK5KkNmiptybzpF8pI8OK1MG1p2EqVU8197Zp7rMdtlQdw4rUBP9PUmo7N/VTqRlWpAac2yHlk/vDdG6GFSmH2lPPTnuqtdLyuNFcu+OGd8KwItXLw9yO9tSz055qraYOvdFcXriqqMMzrEhqd+yx6BwqueGd8s2wIuVEHnp2sspDre2tx6KjbTSXFwaazsGwIkkrwF+E7Ut7mtfiLr+fMaxIateq1WPhnJ12pgTzWqoSHpyPAxhWJEnKrsSrk5SNYUVS7uR9aCUPc3aUXUkf5JhFOZ6xlGGX3/Y0xNVahhVJueHQiqqqFSEjV48I6ARDRYYVSZ1G3nts1P65PL48DCtNeGfBR/RerUe1y2g3Jk57jf2Gb1DtMtoV79myWhpaWdH71Rl7bGofeIGxe29W7TLajfXWWqVd3q9yDHFlHSqqra1l7NixmdquqC4V+ZR2Zv6CD6tdQrvyf0+9Vu0S2h3vWet4v1qv9sEXq11Cu9Lh79dzdy//q5Vqa2vLUGjT7FmR1KE5GVZq/wwrktRB+VgCQcfY5bfiYSUiRgOXA12Ba1JKP250Porn9wMWAkenlPJ9FyUpp9rbYwmkplQ0rEREV+AKYG9gFvBkRNyTUnquQbN9gU2KXzsAvyj+KUnKgbb8K9zempbZC9aySvesjACmp5ReAYiI24AxQMOwMga4KaWUgKkRsWZErJtSerPCtUpSh1HqxxJk7bGxtya7vPSC5XFzuUqHlQHA6w1ez2LZXpOm2gwADCuSOoVSzB94Y/7Cqn5+Ja5ZSm/MX5j7Gssux5vLRaEDo0IfFvEfwD4ppeOKr48ERqSUTmnQ5l7gRymlx4qvHwJOTyk91ehaC2h56fU84O02ltpnBd7bGXm/Ws971jrer9bznrWO96v1WnvP+gB9Wzi/JKW0WlMnKt2zMgtYv8HrgcAbbWhDcz+QJEnqWCq9KdyTwCYRMTgiugNfBe5p1OYe4Kgo+ALwb+erSJLUeVW0ZyWltDgivgXcT2Hp8nUppWcj4sTi+auAiRSWLU+nsHT5mErWKEmS8qWic1bybnl7wGhpEXEdcAAwN6W0ZbXrybuIWB+4CVgHWALUppQur25V+RYRKwOTgR4U/nH1vymlc6pbVf4Vt4mYBsxOKR1Q7XryLiJmAAuAT4HFKaXh1a0o/yJiTeAaYEsgAf+ZUppSts8zrBQU/8f9Eg32gAEOb7QHjBqIiF2B9yksNTesLEdErAusm1J6OiJWA54CDvK/seYVN4lcNaX0fkR0Ax4DTk0pTa1yabkWEd8FhgOrG1aWrxhWhqeUnGCbUUTcCPwxpXRNcVrHKimld8v1eT7I8DP1e8CklD4G6vaAUTNSSpOB+dWuo71IKb1ZtxtzSmkB8DyFZflqRiqo23yiW/HLf2G1ICIGAvtT+FevVHIRsTqwK3AtQErp43IGFTCsNNTc/i5SyUXEIGBb4M9VLiX3IqJrRPwVmAs8kFLynrXsMuB0CkONyiYBf4iIpyJibLWLaQc2orA9yPUR8ZeIuCYiVi3nBxpWPhNNHPNfcCq5iOgF/Bb4TkrpvWrXk3cppU9TSttQ2MZgREQ45NiMiKibQ/bUchuroS+mlLaj8LiXbxaHuNW8lYDtgF+klLYFPgDOKOcHGlY+k2l/F2lFFOdd/Bb4VUrpzmrX054Uu5knAaOrW0mufRE4sDgH4zZgz4i4pbol5V9K6Y3in3OBuyhMC1DzZgGzGvRy/i+F8FI2hpXPZNkDRmqz4mTRa4HnU0qXVLue9iAi+hZXHRARPYG9gBeqWlSOpZT+O6U0MKU0iML/hz2cUjqiymXlWkSsWpzwTnEoYxTwTHWryreU0hzg9YjYtHhoJEs/46/kKr2DbW41twdMlcvKtYj4NbA70CciZgHnpJSurW5VufZF4EjgH8U5GABnppQmVq+k3FsXuLG4Wq8LcHtK6fdVrkkdS3/grsK/JVgJuDWldF91S2oXTgF+VfzH/SuUeU80ly5LkqRccxhIkiTlmmFFkiTlmmFFkiTlmmFFkiTlmmFFkiTlmmFFUtVERK+ISBFxdCvek4rbDLTU5uhiu14Njm0eEX+MiA+K54ZGRE1EbNP2n0BSJbjPiqSO6F5gR2Bhg2M/BdYEDqSwPfhbwDnADOCvFa1OUqsYViStkOKGbV2LTyvPhZTSPAoPWmtoM+CelNJDUP+MJkntgMNAklolIm6IiGkRcVBEPAt8COwQEWOKxz+MiDkR8ZPis5AavvcrEfFSRCyKiMkUAkTj6x9YfPrtBxHxr4j4c0Ts1qhZ14j4YUTMi4i5EXFFRPRocI36YaCIGBQRCRgCnFY8PglYUGx+ffFYKj4NW1LO2LMiqS0GAT8BzqUwnDIYuB74JXAmhWDwIwr/IPo+QERsB/yGwoPiTgU+B9ze8KIRMYTCQ9EuB/4LWBn4PLB2o8//HvAwcAQwrPhZM4s1NfYmhSGhu4rv+R/gPQrbrD8MnE9h2KiuraScMaxIaovewF4ppb8WH9A4A7gppXRyXYOI+Ai4IiJ+lFJ6h8Ij5F8CDk2F53z8X7E35PwG190WWJBS+q8Gx5p6dtKMlNLRxe/vj4gvAgfTRFhJKX0ETC3W82ZKaWqxvteKTf5Zd0xSPjkMJKktZqeU/lr8fiiwAXB7RKxU90Wh12JlYMtiuxEU5ow0fCDZnY2u+w9gjYi4MSJGFZ+C25Q/NHr9HDCwjT+LpJwzrEhqi7cafN+n+OdE4JMGX68Wj69f/HMdYG6j6yz1OqX0IjAG2Kh4vbcj4taI6Nvofe82ev0xhWAkqQNyGEhSWzTsHZlf/HMs8Jcm2taFljlAv0bnGr8mpXQvcG9ErAHsD1xGYZ7JV1egXkntmGFF0op6EZgNDEopXd1CuyeBAyPivxsMBR3cXOOU0r+BW4srgXYsWbWfqVtqbY+MlHOGFUkrJKW0JCK+B9wcEasD/0chCGwEHAQcklJaCFwI/JnC3JZrKcxlObbhtSLiBArB5D7gDWAT4D+Am8pQ98cR8SpwaEQ8Q2EJ9t/ztF+MpALnrEhaYSml31CYa7INcAeFibMnA09T7MFIKU2jMJSzLfA7CkHmsEaX+jvQF7iEwiTa8cDVwLgylX4ihTk3D1Lo+VmvTJ8jaQXE0hPzJUmS8sWeFUmSlGuGFUmSlGuGFUmSlGuGFUmSlGuGFUmSlGuGFUmSlGuGFUmSlGuGFUmSlGuGFUmSlGv/H4UJRNX//B7zAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9,9))\n", "ax.hist(spec_z,bins=40,density=True,alpha=0.9,label='Spectroscopic')\n", "ax.hist(spec_z,bins=40,density=True,alpha=0.5,histtype='step',color='k',lw=3)\n", "\n", "ax.hist(phot_z,bins=50,density=True,alpha=0.65,histtype='stepfilled',label='Photometric')\n", "ax.hist(phot_z,bins=50,density=True,alpha=0.5,histtype='step',color='k',lw=3)\n", "\n", "ax.set_xlabel('redshift',fontsize=15)\n", "ax.set_ylabel('Number of galaxies',fontsize=15)\n", "ax.legend(prop={'size': 16})\n", "ax.tick_params(direction='in',top=True,right=True,length=8);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Do the differences between the two distributions make sense? Why or why not?**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{note}\n", "Yes! We can see that the spectroscopic redshifts are clustered toward low redshift, while the tail of the photometric redshifts extends to higher redshift. This is because obtaining a spectrum is many times more observationally expensive than obtaining photometry, especially for many objects, and objects far enough away become too faint to measure in a survey of a set depth. \n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 3\n", "\n", "The \"UVJ diagram\" is a useful diagnostic tool in extragalactic astronomy which allows one to relatively accurately diagnose a galaxy as star forming or quiescent, even in the presence of dust. It is composed by plotting the \"U-V\" color of the galaxy against the \"V-J\" colors. You'll likely know U and V (these are from the Johnsons-Cousin's filter system). J is a filter in the near infrared. \n", "\n", "In this problem, we're going to write a function that can create a UVJ diagram for subsets of our data, cutting on mass and redshift. \n", "\n", "You'll need to access the following columns in the data (besides redshift, which you've already handled):\n", "\n", "- stellar mass: the mass of all the stars in the galaxy. (column: `lmass`, flagged value of -1)\n", "- star formation rate: rate at which galaxy is forming stars. (column: `lsfr`, flagged value of -99.0)\n", "- U band flux density (column: `l153`, flagged value of -99.0)\n", "- V band flux density (column: `l155`, flagged value of -99.0)\n", "- J band flux density (column: `l161`, flagged value of -99.0)\n", "\n", "### Problem 3.1 \n", "\n", "For step one, we need to be able to filter our dataframe for particular mass and redshift ranges. \n", "\n", "Write a function, `select_galaxies()`, which takes as arguments `M, delta_M, z, delta_z` (and our dataframe). \n", "It should then return a df of only systems between M-deltaM to M+deltaM, and z-deltaz to z+deltaz. The columns it should return are the ones specified above.\n", "\n", "There is actually a column in `rf_df` called `z`, that contains the spec_z if available or the peak z if not. At the time of writing, I cannot determine why this column was not included in the merge. In any case, set `df['z']` equal to `rf_df.z` before continuing, as you'll use it below.\n", "\n", "\n", "```{note}\n", "All masses and sfrs are in log units. \n", "```\n", "\n", "Try your function out using a mass of 10, delta M of 0.5 (i.e., a bin from 9.5 - 10.5), a redshift of 1, and a delta z of 0.25." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "df['z'] = rf_df['z']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def select_galaxies(M,delta_M,z,delta_z,df=df):\n", " Mmin = M-delta_M\n", " Mmax = M+delta_M\n", " zmin = z-delta_z\n", " zmax = z+delta_z\n", " return df.loc[((df.lmass>Mmin)&(df.lmasszmin)&(df.z\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
lmasslsfrl153l155l161
id
3110.101.288.2180419.4570047.12630
5610.001.325.8547412.9406029.94510
9010.02-1.154.8538313.2964032.60070
17810.36-1.901.947218.6058549.25090
1809.600.402.343704.871967.86611
..................
500009.840.239.2587221.8622052.29720
500439.710.071.902775.0974610.16970
5012710.22-0.0817.2179033.7947070.21750
501809.96-0.761.324086.0307915.46360
503239.98-2.841.740686.5979614.86660
\n", "

690 rows × 5 columns

\n", "" ], "text/plain": [ " lmass lsfr l153 l155 l161\n", "id \n", "31 10.10 1.28 8.21804 19.45700 47.12630\n", "56 10.00 1.32 5.85474 12.94060 29.94510\n", "90 10.02 -1.15 4.85383 13.29640 32.60070\n", "178 10.36 -1.90 1.94721 8.60585 49.25090\n", "180 9.60 0.40 2.34370 4.87196 7.86611\n", "... ... ... ... ... ...\n", "50000 9.84 0.23 9.25872 21.86220 52.29720\n", "50043 9.71 0.07 1.90277 5.09746 10.16970\n", "50127 10.22 -0.08 17.21790 33.79470 70.21750\n", "50180 9.96 -0.76 1.32408 6.03079 15.46360\n", "50323 9.98 -2.84 1.74068 6.59796 14.86660\n", "\n", "[690 rows x 5 columns]" ] }, "execution_count": 215, "metadata": {}, "output_type": "execute_result" } ], "source": [ "select_galaxies(10,0.5,1,0.25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 3.2\n", "\n", "Great, we can now get subsamples in mass/redshift bins. This is important because the UVJ diagram actually changes as a function of mass and redshift. \n", "\n", "Next we need to get the colors out. Write a function `get_colors()` which takes the same arguments as your `select_galaxies()` function above. Inside, it should run `select_galaxies` passing through the arguments, and then from the resulting data frame, calculate U-V and U-J (see below). Add these as columns to said dataframe with names 'U-V' and 'V-J' and return it. \n", "\n", "Run this function with the same mass/redshift bin from above and look at it.\n", "\n", "```{warning}\n", "As noted above, the U,V, and J band data are in Fnu (flux densities). Thus, a color is computed via -2.5*log10(Lfilter1/Lfilter2)\n", "```\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def get_colors(M,delta_M,z,delta_z,df=df):\n", " minidf = select_galaxies(M,delta_M,z,delta_z,df=df)\n", " minidf['U-V'] = -2.5*np.log10(minidf.l153/minidf.l155)\n", " minidf['V-J'] = -2.5*np.log10(minidf.l155/minidf.l161)\n", " return minidf" ] }, { "cell_type": "code", "execution_count": 219, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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lmasslsfrl153l155l161U-VV-J
id
3110.101.288.2180419.4570047.126300.9357690.960469
5610.001.325.8547412.9406029.945100.8611170.910928
9010.02-1.154.8538313.2964032.600701.0941240.973732
17810.36-1.901.947218.6058549.250901.6134521.894051
1809.600.402.343704.871967.866110.7945040.520141
........................
500009.840.239.2587221.8622052.297200.9328570.946961
500439.710.071.902775.0974610.169701.0699190.749886
5012710.22-0.0817.2179033.7947070.217500.7321710.793992
501809.96-0.761.324086.0307915.463601.6461501.022341
503239.98-2.841.740686.5979614.866601.4467270.882005
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690 rows × 7 columns

\n", "
" ], "text/plain": [ " lmass lsfr l153 l155 l161 U-V V-J\n", "id \n", "31 10.10 1.28 8.21804 19.45700 47.12630 0.935769 0.960469\n", "56 10.00 1.32 5.85474 12.94060 29.94510 0.861117 0.910928\n", "90 10.02 -1.15 4.85383 13.29640 32.60070 1.094124 0.973732\n", "178 10.36 -1.90 1.94721 8.60585 49.25090 1.613452 1.894051\n", "180 9.60 0.40 2.34370 4.87196 7.86611 0.794504 0.520141\n", "... ... ... ... ... ... ... ...\n", "50000 9.84 0.23 9.25872 21.86220 52.29720 0.932857 0.946961\n", "50043 9.71 0.07 1.90277 5.09746 10.16970 1.069919 0.749886\n", "50127 10.22 -0.08 17.21790 33.79470 70.21750 0.732171 0.793992\n", "50180 9.96 -0.76 1.32408 6.03079 15.46360 1.646150 1.022341\n", "50323 9.98 -2.84 1.74068 6.59796 14.86660 1.446727 0.882005\n", "\n", "[690 rows x 7 columns]" ] }, "execution_count": 219, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_colors(10,0.5,0.75,0.25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 3.3 \n", "\n", "Now that we can easily grab U-V and V-J colors for subsamples, we're ready to plot! \n", "\n", "Next, set your xlim and ylim to (0,2) in V-J (x axis) and (0,2.8) in U-V (y axis).\n", "\n", "Once you have the distribution plotted nicely, use the definitions of the bounding box provided in [Whitaker et al. 2011](https://iopscience.iop.org/article/10.1088/0004-637X/735/2/86/pdf) (Eqns 15, Fig 17 for example) to draw the box where quiescent galaxies sit. " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'U - V color')" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9,9))\n", "ax.set_box_aspect(1)\n", "ob = get_colors(9.5,2,1.0,0.5)\n", "ax.plot(ob['V-J'],ob['U-V'],'.',color='k',alpha=0.1);\n", "ax.plot([0,0.7],[1.3,1.3],'r',lw=3)\n", "ax.plot([1.6,1.6],[2.0,2.8],'r',lw=3)\n", "ax.plot([0.7,1.6],[1.3,2.0],'r',lw=3)\n", "ax.text(0.05,0.95,'Quiescent',ha='left',va='top',transform=ax.transAxes,fontsize=20)\n", "ax.set_xlim(0,2)\n", "ax.set_ylim(0,2.8)\n", "ax.set_xlabel('V - J color',fontsize=20)\n", "ax.set_ylabel('U - V color',fontsize=20)\n", "ax.tick_params(direction )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bonus (+2) \n", "\n", "(+1) Now that you can easily plot up a UVJ diagram for a given mass/redshift bin, make a plot with 6 panels. Across the 3, plot the UVJ diagram in the redshift bins 0-0.5, 0.5-1.0, 1.0-2.0. In the top panel, include galaxies in the mass range 8-9.5, and in the bottom, 9.5-11. \n", "\n", "(+1) Feeling extra fancy? Use the conditions on the UVJ quiescent box to color the quiescent galaxies red and the star forming galaxies blue in you plots. \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "a330", "language": "python", "name": "a330" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.11" } }, "nbformat": 4, "nbformat_minor": 4 }