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ea45e30b · Julien Cornut · ea45e30b · ea45e30b · ea45e30b · ea45e30b
Commit ea45e30b authored 9 years ago by Julien Cornut
--- a/.ipynb_checkpoints/Complementing a Strand of DNA-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Complementing a Strand of DNA-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Complementing a Strand of DNA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...ACTCAAGGAG\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('Complementing a Strand of DNA Data.txt') as f:\n",
+    "    dnastr = f.readline()\n",
+    "    \n",
+    "print(\"...\"+dnastr[-10:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GAGGAACTCA...\n"
+     ]
+    }
+   ],
+   "source": [
+    "rtsand = dnastr[::-1]\n",
+    "print(rtsand[:10]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CTCCTTGAGTGGATGGGAGGTCATTGCTGCCACCCAGCGCCCCGCTCCTCGGTCCAGTTCTTCACGTCATCGCGGTGTGACGCATGAAGAGTAATGCCTTGTGCAAGAATCTTCTAGGCAAACGCACGGCGTAATTGATTTTGGACAACTTTTTCATCGAGCCGTGCGCATCATAAGGAGTGAGCTTTCTCTTTATGGATTAGGTTTGCGGATACATTGCACTGCGCCTCACCGCCGTATAACCAATGCCAAAGCGCGATTTAACAGCGGGAGTCCTTTTCGTAGTGCACGGATAAAAAAGAGACCAGTCGAAAGGCGTATTTAAATCTCATAGTCCCTGCAACCTAGCCGGCTCGGCGAACGCGGAAAGGCTGGGATCCCCGTGTATCGGGTATCCCGTCTACTACATCCAATTATCCCTTGTTTAGATGTGACCTCTTGCCCTATCTTCCGTACGAATCCTATCAAATGTGTAAGCGCAGACGGTAGTAGGAGGCCATTGGGAGTTCAAGCATTTGGAGAACTTGATTACAAAGGGTAGGCTACTCTTACGGTCCTTCGCCTTCCCTGATCGGCTCCAGGTGGGCGGTGGAGACGGAAAAAGGAACGGCAGTATTCGACGTGTTTAGATCGCTGGGCTCGACTTGGCGTCGCCTCGTAGAGAACTGCACCACAGTACGGGGGGGGCCAATTCCGTGTCTGGTGCACTCACACCTGTCACATCACGATTTCCCCGCCCGCAACCCAGAATCGATCCTAACTTTTTCCAGATTCACAATACCGTCCTACCTTAAACGAGGTGACAGATAGGTTGATTGTCTCATTTGTGGTAGGCTTTCCCATGACTAAATATCAGTCACAGTGACGTAT\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(rtsand)\n",
+    "for i,x in enumerate(lst):\n",
+    "    if (x=='A'): lst[i]='T'\n",
+    "    elif (x=='C'): lst[i]='G'\n",
+    "    elif (x=='G'): lst[i]='C'\n",
+    "    elif (x=='T'): lst[i]='A'\n",
+    "    \n",
+    "compl = ''.join(lst)\n",
+    "\n",
+    "print(compl)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Complementing a Strand of DNA
+%% Cell type:code id: tags:
+``` python
+with open('Complementing a Strand of DNA Data.txt') as f:
+    dnastr = f.readline()
+print("..."+dnastr[-10:])
+```
+%% Output
+    ...ACTCAAGGAG
+%% Cell type:code id: tags:
+``` python
+rtsand = dnastr[::-1]
+print(rtsand[:10]+"...")
+```
+%% Output
+    GAGGAACTCA...
+%% Cell type:code id: tags:
+``` python
+lst = list(rtsand)
+for i,x in enumerate(lst):
+    if (x=='A'): lst[i]='T'
+    elif (x=='C'): lst[i]='G'
+    elif (x=='G'): lst[i]='C'
+    elif (x=='T'): lst[i]='A'
+compl = ''.join(lst)
+print(compl)
+```
+%% Output
+    CTCCTTGAGTGGATGGGAGGTCATTGCTGCCACCCAGCGCCCCGCTCCTCGGTCCAGTTCTTCACGTCATCGCGGTGTGACGCATGAAGAGTAATGCCTTGTGCAAGAATCTTCTAGGCAAACGCACGGCGTAATTGATTTTGGACAACTTTTTCATCGAGCCGTGCGCATCATAAGGAGTGAGCTTTCTCTTTATGGATTAGGTTTGCGGATACATTGCACTGCGCCTCACCGCCGTATAACCAATGCCAAAGCGCGATTTAACAGCGGGAGTCCTTTTCGTAGTGCACGGATAAAAAAGAGACCAGTCGAAAGGCGTATTTAAATCTCATAGTCCCTGCAACCTAGCCGGCTCGGCGAACGCGGAAAGGCTGGGATCCCCGTGTATCGGGTATCCCGTCTACTACATCCAATTATCCCTTGTTTAGATGTGACCTCTTGCCCTATCTTCCGTACGAATCCTATCAAATGTGTAAGCGCAGACGGTAGTAGGAGGCCATTGGGAGTTCAAGCATTTGGAGAACTTGATTACAAAGGGTAGGCTACTCTTACGGTCCTTCGCCTTCCCTGATCGGCTCCAGGTGGGCGGTGGAGACGGAAAAAGGAACGGCAGTATTCGACGTGTTTAGATCGCTGGGCTCGACTTGGCGTCGCCTCGTAGAGAACTGCACCACAGTACGGGGGGGGCCAATTCCGTGTCTGGTGCACTCACACCTGTCACATCACGATTTCCCCGCCCGCAACCCAGAATCGATCCTAACTTTTTCCAGATTCACAATACCGTCCTACCTTAAACGAGGTGACAGATAGGTTGATTGTCTCATTTGTGGTAGGCTTTCCCATGACTAAATATCAGTCACAGTGACGTAT
--- a/.ipynb_checkpoints/Counting DNA Nucleotides-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Counting DNA Nucleotides-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rosalind Challenges"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Counting DNA Nucleotides"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "with open('Counting DNA Nucleotides Data.txt') as f:\n",
+    "    dnastr = f.readline()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 4 5 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "A=0;T=0;G=0;C=0;\n",
+    "for symb in dnastr:\n",
+    "    if(symb=='A'): A += 1\n",
+    "    if(symb=='T'): T += 1\n",
+    "    if(symb=='G'): G += 1\n",
+    "    if(symb=='C'): C += 1\n",
+    "\n",
+    "print(A,C,G,T)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Rosalind Challenges
+%% Cell type:markdown id: tags:
+## Counting DNA Nucleotides
+%% Cell type:code id: tags:
+``` python
+with open('Counting DNA Nucleotides Data.txt') as f:
+    dnastr = f.readline()
+```
+%% Cell type:code id: tags:
+``` python
+A=0;T=0;G=0;C=0;
+for symb in dnastr:
+    if(symb=='A'): A += 1
+    if(symb=='T'): T += 1
+    if(symb=='G'): G += 1
+    if(symb=='C'): C += 1
+print(A,C,G,T)
+```
+%% Output
+    5 4 5 3
--- a/.ipynb_checkpoints/Counting Point Mutations-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Counting Point Mutations-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Counting Point Mutations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "with open('Counting Point Mutations Data.txt') as f:\n",
+    "    str1 = f.readline()\n",
+    "    # ! Need a line termination character after last string\n",
+    "    str2 = f.readline()\n",
+    "\n",
+    "lst1 = list(str1)[:-1] # rip \\n\n",
+    "lst2 = list(str2)[:-1] # rip \\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The hamming distance is 474\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Comprehension list for matchin tuples in lists' zip\n",
+    "\n",
+    "hamming_d = len([i for i,j in zip(lst1,lst2) if i!=j])\n",
+    "\n",
+    "print(\"The hamming distance is {0}\".format(hamming_d))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Counting Point Mutations
+%% Cell type:code id: tags:
+``` python
+with open('Counting Point Mutations Data.txt') as f:
+    str1 = f.readline()
+    # ! Need a line termination character after last string
+    str2 = f.readline()
+lst1 = list(str1)[:-1] # rip \n
+lst2 = list(str2)[:-1] # rip \n
+```
+%% Cell type:code id: tags:
+``` python
+#Comprehension list for matchin tuples in lists' zip
+hamming_d = len([i for i,j in zip(lst1,lst2) if i!=j])
+print("The hamming distance is {0}".format(hamming_d))
+```
+%% Output
+    The hamming distance is 474
--- a/.ipynb_checkpoints/Mendel's First Law-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Mendel's First Law-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mendel's First Law"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29 26 16\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"Mendel's First Law Data.txt\") as f:\n",
+    "    line = f.readline()\n",
+    "print(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([29, 26, 16])"
+      ]
+     },
+     "execution_count": 315,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop = np.fromstring(line, dtype=int, sep=' ')\n",
+    "pop\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "71\n",
+      "29 26 16\n"
+     ]
+    }
+   ],
+   "source": [
+    "tot = sum(pop)\n",
+    "tot\n",
+    "print(tot)\n",
+    "k = pop[0]\n",
+    "m = pop[1]\n",
+    "n = pop[2]\n",
+    "print(k,m,n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B1 = k/tot\n",
+    "B2 = m/tot\n",
+    "B3 = n/tot\n",
+    "B1+B2+B3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4\n",
+      "0.371428571429\n",
+      "0.228571428571\n",
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "B11 = ((k-1)/(tot-1)) * 1\n",
+    "B12 = (m/(tot-1)) * 1\n",
+    "B13 = (n/(tot-1)) * 1\n",
+    "print(B11)\n",
+    "print(B12)\n",
+    "print(B13)\n",
+    "print((B11+B12+B13))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.414285714286\n",
+      "0.267857142857\n",
+      "0.114285714286\n",
+      "0.796428571429\n"
+     ]
+    }
+   ],
+   "source": [
+    "B21 = (k/(tot-1)) * 1\n",
+    "B22 = ((m-1)/(tot-1)) * (3/4)\n",
+    "B23 = (n/(tot-1)) * (2/4)\n",
+    "print(B21)\n",
+    "print(B22)\n",
+    "print(B23)\n",
+    "print(B21+B22+B23)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.414285714286\n",
+      "0.185714285714\n",
+      "0.0\n",
+      "0.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "B31 = (k/(tot-1)) * 1\n",
+    "B32 = (m/(tot-1)) * (2/4)\n",
+    "B33 = ((n-1)/(tot-1)) * 0\n",
+    "print(B31)\n",
+    "print(B32)\n",
+    "print(B33)\n",
+    "print(B31+B32+B33)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.835311871227\n"
+     ]
+    }
+   ],
+   "source": [
+    "B1 = (B11 + B12 + B13) * B1\n",
+    "B2 = (B21 + B22 + B23) * B2\n",
+    "B3 = (B31 + B32 + B33) * B3\n",
+    "print(B1+B2+B3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.835311871227\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(B1+B2+B3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# StackOverflow Solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.misc\n",
+    "\n",
+    "pop_total = 4*scipy.misc.comb(k+m+n,2)\n",
+    "\n",
+    "hom = k\n",
+    "het = m\n",
+    "rec = n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total of organisms with dominant allele : 8303.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "dom_total = 4*scipy.misc.comb(hom,2) + 4*hom*het + 4*hom*rec + 3*scipy.misc.comb(het,2) + 2*het*rec\n",
+    "print(\"Total of organisms with dominant allele : {0}\".format(dom_total))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of dominant : 0.8353118712273642\n"
+     ]
+    }
+   ],
+   "source": [
+    "phom = dom_total/pop_total\n",
+    "print(\"Probability of dominant : {0}\".format(phom))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of recessive : 0.16468812877263583\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Probability of recessive : {0}\".format(1-phom))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Mendel's First Law
+%% Cell type:code id: tags:
+``` python
+import numpy as np
+```
+%% Cell type:code id: tags:
+``` python
+with open("Mendel's First Law Data.txt") as f:
+    line = f.readline()
+print(line)
+```
+%% Output
+    29 26 16
+%% Cell type:code id: tags:
+``` python
+pop = np.fromstring(line, dtype=int, sep=' ')
+pop
+```
+%% Output
+    array([29, 26, 16])
+%% Cell type:code id: tags:
+``` python
+tot = sum(pop)
+tot
+print(tot)
+k = pop[0]
+m = pop[1]
+n = pop[2]
+print(k,m,n)
+```
+%% Output
+    71
+    29 26 16
+%% Cell type:code id: tags:
+``` python
+B1 = k/tot
+B2 = m/tot
+B3 = n/tot
+B1+B2+B3
+```
+%% Output
+    1.0
+%% Cell type:code id: tags:
+``` python
+B11 = ((k-1)/(tot-1)) * 1
+B12 = (m/(tot-1)) * 1
+B13 = (n/(tot-1)) * 1
+print(B11)
+print(B12)
+print(B13)
+print((B11+B12+B13))
+```
+%% Output
+    0.4
+    0.371428571429
+    0.228571428571
+    1.0
+%% Cell type:code id: tags:
+``` python
+B21 = (k/(tot-1)) * 1
+B22 = ((m-1)/(tot-1)) * (3/4)
+B23 = (n/(tot-1)) * (2/4)
+print(B21)
+print(B22)
+print(B23)
+print(B21+B22+B23)
+```
+%% Output
+    0.414285714286
+    0.267857142857
+    0.114285714286
+    0.796428571429
+%% Cell type:code id: tags:
+``` python
+B31 = (k/(tot-1)) * 1
+B32 = (m/(tot-1)) * (2/4)
+B33 = ((n-1)/(tot-1)) * 0
+print(B31)
+print(B32)
+print(B33)
+print(B31+B32+B33)
+```
+%% Output
+    0.414285714286
+    0.185714285714
+    0.0
+    0.6
+%% Cell type:code id: tags:
+``` python
+B1 = (B11 + B12 + B13) * B1
+B2 = (B21 + B22 + B23) * B2
+B3 = (B31 + B32 + B33) * B3
+print(B1+B2+B3)
+```
+%% Output
+    0.835311871227
+%% Cell type:markdown id: tags:
+# Result
+%% Cell type:code id: tags:
+``` python
+print(B1+B2+B3)
+```
+%% Output
+    0.835311871227
+%% Cell type:markdown id: tags:
+# StackOverflow Solution
+%% Cell type:code id: tags:
+``` python
+import scipy.misc
+pop_total = 4*scipy.misc.comb(k+m+n,2)
+hom = k
+het = m
+rec = n
+```
+%% Cell type:code id: tags:
+``` python
+dom_total = 4*scipy.misc.comb(hom,2) + 4*hom*het + 4*hom*rec + 3*scipy.misc.comb(het,2) + 2*het*rec
+print("Total of organisms with dominant allele : {0}".format(dom_total))
+```
+%% Output
+    Total of organisms with dominant allele : 8303.0
+%% Cell type:code id: tags:
+``` python
+phom = dom_total/pop_total
+print("Probability of dominant : {0}".format(phom))
+```
+%% Output
+    Probability of dominant : 0.8353118712273642
+%% Cell type:code id: tags:
+``` python
+print("Probability of recessive : {0}".format(1-phom))
+```
+%% Output
+    Probability of recessive : 0.16468812877263583
--- a/.ipynb_checkpoints/Rabbits and Recurrence Relations-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Rabbits and Recurrence Relations-checkpoint.ipynb
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
--- a/.ipynb_checkpoints/Transcribing DNA into RNA-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Transcribing DNA into RNA-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transcribing DNA into RNA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GATGGAACTTGACTACGTAAATT\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('Transcribing DNA into RNA Data.txt') as f:\n",
+    "    dnastr = f.readline()\n",
+    "    \n",
+    "print(dnastr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GAUGGAACUUGACUACGUAAAUU\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(dnastr)\n",
+    "\n",
+    "for i,x in enumerate(lst):\n",
+    "    if(x=='T'): lst[i]='U'\n",
+    "      \n",
+    "rnastr = ''.join(lst)\n",
+    "print(rnastr)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Transcribing DNA into RNA
+%% Cell type:code id: tags:
+``` python
+with open('Transcribing DNA into RNA Data.txt') as f:
+    dnastr = f.readline()
+print(dnastr)
+```
+%% Output
+    GATGGAACTTGACTACGTAAATT
+%% Cell type:code id: tags:
+``` python
+lst = list(dnastr)
+for i,x in enumerate(lst):
+    if(x=='T'): lst[i]='U'
+rnastr = ''.join(lst)
+print(rnastr)
+```
+%% Output
+    GAUGGAACUUGACUACGUAAAUU
--- a/Complementing a Strand of DNA Data.txt
+++ b/Complementing a Strand of DNA Data.txt
+ATACGTCACTGTGACTGATATTTAGTCATGGGAAAGCCTACCACAAATGAGACAATCAACCTATCTGTCACCTCGTTTAAGGTAGGACGGTATTGTGAATCTGGAAAAAGTTAGGATCGATTCTGGGTTGCGGGCGGGGAAATCGTGATGTGACAGGTGTGAGTGCACCAGACACGGAATTGGCCCCCCCCGTACTGTGGTGCAGTTCTCTACGAGGCGACGCCAAGTCGAGCCCAGCGATCTAAACACGTCGAATACTGCCGTTCCTTTTTCCGTCTCCACCGCCCACCTGGAGCCGATCAGGGAAGGCGAAGGACCGTAAGAGTAGCCTACCCTTTGTAATCAAGTTCTCCAAATGCTTGAACTCCCAATGGCCTCCTACTACCGTCTGCGCTTACACATTTGATAGGATTCGTACGGAAGATAGGGCAAGAGGTCACATCTAAACAAGGGATAATTGGATGTAGTAGACGGGATACCCGATACACGGGGATCCCAGCCTTTCCGCGTTCGCCGAGCCGGCTAGGTTGCAGGGACTATGAGATTTAAATACGCCTTTCGACTGGTCTCTTTTTTATCCGTGCACTACGAAAAGGACTCCCGCTGTTAAATCGCGCTTTGGCATTGGTTATACGGCGGTGAGGCGCAGTGCAATGTATCCGCAAACCTAATCCATAAAGAGAAAGCTCACTCCTTATGATGCGCACGGCTCGATGAAAAAGTTGTCCAAAATCAATTACGCCGTGCGTTTGCCTAGAAGATTCTTGCACAAGGCATTACTCTTCATGCGTCACACCGCGATGACGTGAAGAACTGGACCGAGGAGCGGGGCGCTGGGTGGCAGCAATGACCTCCCATCCACTCAAGGAG
\ No newline at end of file
--- a/Complementing a Strand of DNA.ipynb
+++ b/Complementing a Strand of DNA.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Complementing a Strand of DNA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...ACTCAAGGAG\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('Complementing a Strand of DNA Data.txt') as f:\n",
+    "    dnastr = f.readline()\n",
+    "    \n",
+    "print(\"...\"+dnastr[-10:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GAGGAACTCA...\n"
+     ]
+    }
+   ],
+   "source": [
+    "rtsand = dnastr[::-1]\n",
+    "print(rtsand[:10]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CTCCTTGAGTGGATGGGAGGTCATTGCTGCCACCCAGCGCCCCGCTCCTCGGTCCAGTTCTTCACGTCATCGCGGTGTGACGCATGAAGAGTAATGCCTTGTGCAAGAATCTTCTAGGCAAACGCACGGCGTAATTGATTTTGGACAACTTTTTCATCGAGCCGTGCGCATCATAAGGAGTGAGCTTTCTCTTTATGGATTAGGTTTGCGGATACATTGCACTGCGCCTCACCGCCGTATAACCAATGCCAAAGCGCGATTTAACAGCGGGAGTCCTTTTCGTAGTGCACGGATAAAAAAGAGACCAGTCGAAAGGCGTATTTAAATCTCATAGTCCCTGCAACCTAGCCGGCTCGGCGAACGCGGAAAGGCTGGGATCCCCGTGTATCGGGTATCCCGTCTACTACATCCAATTATCCCTTGTTTAGATGTGACCTCTTGCCCTATCTTCCGTACGAATCCTATCAAATGTGTAAGCGCAGACGGTAGTAGGAGGCCATTGGGAGTTCAAGCATTTGGAGAACTTGATTACAAAGGGTAGGCTACTCTTACGGTCCTTCGCCTTCCCTGATCGGCTCCAGGTGGGCGGTGGAGACGGAAAAAGGAACGGCAGTATTCGACGTGTTTAGATCGCTGGGCTCGACTTGGCGTCGCCTCGTAGAGAACTGCACCACAGTACGGGGGGGGCCAATTCCGTGTCTGGTGCACTCACACCTGTCACATCACGATTTCCCCGCCCGCAACCCAGAATCGATCCTAACTTTTTCCAGATTCACAATACCGTCCTACCTTAAACGAGGTGACAGATAGGTTGATTGTCTCATTTGTGGTAGGCTTTCCCATGACTAAATATCAGTCACAGTGACGTAT\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(rtsand)\n",
+    "for i,x in enumerate(lst):\n",
+    "    if (x=='A'): lst[i]='T'\n",
+    "    elif (x=='C'): lst[i]='G'\n",
+    "    elif (x=='G'): lst[i]='C'\n",
+    "    elif (x=='T'): lst[i]='A'\n",
+    "    \n",
+    "compl = ''.join(lst)\n",
+    "\n",
+    "print(compl)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Complementing a Strand of DNA
+%% Cell type:code id: tags:
+``` python
+with open('Complementing a Strand of DNA Data.txt') as f:
+    dnastr = f.readline()
+print("..."+dnastr[-10:])
+```
+%% Output
+    ...ACTCAAGGAG
+%% Cell type:code id: tags:
+``` python
+rtsand = dnastr[::-1]
+print(rtsand[:10]+"...")
+```
+%% Output
+    GAGGAACTCA...
+%% Cell type:code id: tags:
+``` python
+lst = list(rtsand)
+for i,x in enumerate(lst):
+    if (x=='A'): lst[i]='T'
+    elif (x=='C'): lst[i]='G'
+    elif (x=='G'): lst[i]='C'
+    elif (x=='T'): lst[i]='A'
+compl = ''.join(lst)
+print(compl)
+```
+%% Output
+    CTCCTTGAGTGGATGGGAGGTCATTGCTGCCACCCAGCGCCCCGCTCCTCGGTCCAGTTCTTCACGTCATCGCGGTGTGACGCATGAAGAGTAATGCCTTGTGCAAGAATCTTCTAGGCAAACGCACGGCGTAATTGATTTTGGACAACTTTTTCATCGAGCCGTGCGCATCATAAGGAGTGAGCTTTCTCTTTATGGATTAGGTTTGCGGATACATTGCACTGCGCCTCACCGCCGTATAACCAATGCCAAAGCGCGATTTAACAGCGGGAGTCCTTTTCGTAGTGCACGGATAAAAAAGAGACCAGTCGAAAGGCGTATTTAAATCTCATAGTCCCTGCAACCTAGCCGGCTCGGCGAACGCGGAAAGGCTGGGATCCCCGTGTATCGGGTATCCCGTCTACTACATCCAATTATCCCTTGTTTAGATGTGACCTCTTGCCCTATCTTCCGTACGAATCCTATCAAATGTGTAAGCGCAGACGGTAGTAGGAGGCCATTGGGAGTTCAAGCATTTGGAGAACTTGATTACAAAGGGTAGGCTACTCTTACGGTCCTTCGCCTTCCCTGATCGGCTCCAGGTGGGCGGTGGAGACGGAAAAAGGAACGGCAGTATTCGACGTGTTTAGATCGCTGGGCTCGACTTGGCGTCGCCTCGTAGAGAACTGCACCACAGTACGGGGGGGGCCAATTCCGTGTCTGGTGCACTCACACCTGTCACATCACGATTTCCCCGCCCGCAACCCAGAATCGATCCTAACTTTTTCCAGATTCACAATACCGTCCTACCTTAAACGAGGTGACAGATAGGTTGATTGTCTCATTTGTGGTAGGCTTTCCCATGACTAAATATCAGTCACAGTGACGTAT
--- a/Counting DNA Nucleotides Data.txt
+++ b/Counting DNA Nucleotides Data.txt
+GAGCCTACTAACGGGAT
+CATCGTAATGACGGCCT
\ No newline at end of file
--- a/Counting DNA Nucleotides.ipynb
+++ b/Counting DNA Nucleotides.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rosalind Challenges"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Counting DNA Nucleotides"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "with open('Counting DNA Nucleotides Data.txt') as f:\n",
+    "    dnastr = f.readline()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 4 5 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "A=0;T=0;G=0;C=0;\n",
+    "for symb in dnastr:\n",
+    "    if(symb=='A'): A += 1\n",
+    "    if(symb=='T'): T += 1\n",
+    "    if(symb=='G'): G += 1\n",
+    "    if(symb=='C'): C += 1\n",
+    "\n",
+    "print(A,C,G,T)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Rosalind Challenges
+%% Cell type:markdown id: tags:
+## Counting DNA Nucleotides
+%% Cell type:code id: tags:
+``` python
+with open('Counting DNA Nucleotides Data.txt') as f:
+    dnastr = f.readline()
+```
+%% Cell type:code id: tags:
+``` python
+A=0;T=0;G=0;C=0;
+for symb in dnastr:
+    if(symb=='A'): A += 1
+    if(symb=='T'): T += 1
+    if(symb=='G'): G += 1
+    if(symb=='C'): C += 1
+print(A,C,G,T)
+```
+%% Output
+    5 4 5 3
--- a/Counting Point Mutations Data.txt
+++ b/Counting Point Mutations Data.txt
+CCGCACTATCAGCGTGGGGGTCTGCACCGTGTGTCTCATCAACGTCTGATCGGACGGTAATTCGGTGTCGCCATACGCCCCCTAGTTGTTACGACCAGCCTTCCGGAAAGTTCCGCGAAACCTTTCTATGCTGGTGCAACAGAGGCACCTGTAGTAAGAGTGACGGGGTAGGAAGGTAGTCTCAACTAGTATCCCTAATGCCAGACCACAAGAACGTAGGGGTCCCGCTCGCAGGCTTCCTGGTGGAACTGACTCCGTTTGCTGTCGTGTAAGAAGAAGTTTGTTCAGCATGTGTGCATTCAGTCTCGTCATGTGATATTAGTATCACCGCTCGAAGGACAGTCTCATAAGAAACTGCGTTGGGTTAACAGCTATTCACATTTATTCTAGCAGAGCCAATTCAGCTGAAATAAACTCTGATGGGGTCGTCTGCGACTAATCGTGCCACGCGTGGGGTCGTTGATACCATACCAGTAACTGAAAGGGTTAAACCAAACCTAATCGCGAACGGTTTTCAGAATCTCTATGTGATACTAGAGACCTCGATCAGGCCTTGGGGCGAAAGCATCAGAGATTGTAAATTGAAGATCATGGACTGCTAATGTGCCACGGCCTCTACACCGATTATAGAATGCGGCGACGGCGCAAGTCGGTACGTCGTGAGTGACCCTATCGACTTAACGAGGTACCAGTTCAGGCTGGCCGACCCACGATTGCAGTCGTCTCCTCTCGGCGCATTAGGCGCTTTTCTGAATAGTACTGAGAAGCCTATTCCAGCCTGGTACAAATAAGAGCAGGCCCTAACAAGACCATGACCGGAAAACTTTCCAGCTTCTTAAAGAGGTAATCTAAGATAAAAATTCCCTACTCTCACGCTCGCCGGACGAATGGTACGACCTTCCAAGACCACCCTTGGATTCCAGGTAGCAGA
+GCGGTGAAGCATGGTGATTAGGTGAACCCGCGGTGTCGATTACTCAGAATTTGTCCGCACGGGGCAATCGACAGAGTCCCCTTGACCAAGCCCTTCAGCCTTCCAGGTAACAGCTAGGAAGAACAATATACTCGAGAATTAGGAGACGCTCTAGCAAAATTGACGTAGCAAGACAGTGTTCCCAACTCGGAGCAATAATTTCTCTGTTCTAGCAACCTGGACACGAGCTCGGATGCGTGCCTCGGGAGCGTACACGGTTGGAGGTCGTGTATGAGGACCGTTATTTCGCATGTGAGCGAGCCGAATCGTAATGGGGTTGTTCCATCGCTCAAGAAGGGAGAGTCGCCTGCCAAGGTTGTTCAGCATCTAAACTCGGCACCTTCGTTCAGAAAGAGCGACTTGGGCTGCGAGGAACCGAAAGAATGTTATCTCCCACTGATTATGCGTCACGCGCGAATGTAGTATACCAACCTGAAGAATCAAGGGTCTCCCCTAGGATATGTACAAACGTATCTGACCAACGCTAAGTCAGTCTAGATATCTCGATCAGCACTCGGGTAAACTGACTCAGAGATTCAATTGTATATCTGATTCTTTCCATAGATCTTTGATCTCGCACATCAAACAGCAAGTTCGAATACACCGGGTAATGCCACCTAGTTCGAGCTCGCCGCTTGTCCAATGTGGACCAGCTTCGACACTTCATTCACCGATGTCGGTCGTCTCTTCTCAGCGCATATGGCCTCAACCTCGAGGCCTTTGCGGCGCGGCCTGCGGCATCTTTCATCTCAGAGTTACTCCTTTCGCGCCAACGCCCCGAGGACATACCAGGTACTGTCCAACATCAAATAAGAAAAGTGCGCTAAACGTACATATCGGGCCCCCCAATGATGCGACGGCCCGAGGCTATCCTCGGGTAATGGACCGCACC
--- a/Counting Point Mutations.ipynb
+++ b/Counting Point Mutations.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Counting Point Mutations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "with open('Counting Point Mutations Data.txt') as f:\n",
+    "    str1 = f.readline()\n",
+    "    # ! Need a line termination character after last string\n",
+    "    str2 = f.readline()\n",
+    "\n",
+    "lst1 = list(str1)[:-1] # rip \\n\n",
+    "lst2 = list(str2)[:-1] # rip \\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The hamming distance is 474\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Comprehension list for matchin tuples in lists' zip\n",
+    "\n",
+    "hamming_d = len([i for i,j in zip(lst1,lst2) if i!=j])\n",
+    "\n",
+    "print(\"The hamming distance is {0}\".format(hamming_d))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Counting Point Mutations
+%% Cell type:code id: tags:
+``` python
+with open('Counting Point Mutations Data.txt') as f:
+    str1 = f.readline()
+    # ! Need a line termination character after last string
+    str2 = f.readline()
+lst1 = list(str1)[:-1] # rip \n
+lst2 = list(str2)[:-1] # rip \n
+```
+%% Cell type:code id: tags:
+``` python
+#Comprehension list for matchin tuples in lists' zip
+hamming_d = len([i for i,j in zip(lst1,lst2) if i!=j])
+print("The hamming distance is {0}".format(hamming_d))
+```
+%% Output
+    The hamming distance is 474
--- a/Mendel's First Law Data.txt
+++ b/Mendel's First Law Data.txt
+29 26 16
\ No newline at end of file
--- a/Mendel's First Law.ipynb
+++ b/Mendel's First Law.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mendel's First Law"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29 26 16\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"Mendel's First Law Data.txt\") as f:\n",
+    "    line = f.readline()\n",
+    "print(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([29, 26, 16])"
+      ]
+     },
+     "execution_count": 315,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop = np.fromstring(line, dtype=int, sep=' ')\n",
+    "pop\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "71\n",
+      "29 26 16\n"
+     ]
+    }
+   ],
+   "source": [
+    "tot = sum(pop)\n",
+    "tot\n",
+    "print(tot)\n",
+    "k = pop[0]\n",
+    "m = pop[1]\n",
+    "n = pop[2]\n",
+    "print(k,m,n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B1 = k/tot\n",
+    "B2 = m/tot\n",
+    "B3 = n/tot\n",
+    "B1+B2+B3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4\n",
+      "0.371428571429\n",
+      "0.228571428571\n",
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "B11 = ((k-1)/(tot-1)) * 1\n",
+    "B12 = (m/(tot-1)) * 1\n",
+    "B13 = (n/(tot-1)) * 1\n",
+    "print(B11)\n",
+    "print(B12)\n",
+    "print(B13)\n",
+    "print((B11+B12+B13))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.414285714286\n",
+      "0.267857142857\n",
+      "0.114285714286\n",
+      "0.796428571429\n"
+     ]
+    }
+   ],
+   "source": [
+    "B21 = (k/(tot-1)) * 1\n",
+    "B22 = ((m-1)/(tot-1)) * (3/4)\n",
+    "B23 = (n/(tot-1)) * (2/4)\n",
+    "print(B21)\n",
+    "print(B22)\n",
+    "print(B23)\n",
+    "print(B21+B22+B23)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.414285714286\n",
+      "0.185714285714\n",
+      "0.0\n",
+      "0.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "B31 = (k/(tot-1)) * 1\n",
+    "B32 = (m/(tot-1)) * (2/4)\n",
+    "B33 = ((n-1)/(tot-1)) * 0\n",
+    "print(B31)\n",
+    "print(B32)\n",
+    "print(B33)\n",
+    "print(B31+B32+B33)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.835311871227\n"
+     ]
+    }
+   ],
+   "source": [
+    "B1 = (B11 + B12 + B13) * B1\n",
+    "B2 = (B21 + B22 + B23) * B2\n",
+    "B3 = (B31 + B32 + B33) * B3\n",
+    "print(B1+B2+B3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.835311871227\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(B1+B2+B3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# StackOverflow Solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.misc\n",
+    "\n",
+    "pop_total = 4*scipy.misc.comb(k+m+n,2)\n",
+    "\n",
+    "hom = k\n",
+    "het = m\n",
+    "rec = n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total of organisms with dominant allele : 8303.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "dom_total = 4*scipy.misc.comb(hom,2) + 4*hom*het + 4*hom*rec + 3*scipy.misc.comb(het,2) + 2*het*rec\n",
+    "print(\"Total of organisms with dominant allele : {0}\".format(dom_total))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of dominant : 0.8353118712273642\n"
+     ]
+    }
+   ],
+   "source": [
+    "phom = dom_total/pop_total\n",
+    "print(\"Probability of dominant : {0}\".format(phom))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of recessive : 0.16468812877263583\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Probability of recessive : {0}\".format(1-phom))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Mendel's First Law
+%% Cell type:code id: tags:
+``` python
+import numpy as np
+```
+%% Cell type:code id: tags:
+``` python
+with open("Mendel's First Law Data.txt") as f:
+    line = f.readline()
+print(line)
+```
+%% Output
+    29 26 16
+%% Cell type:code id: tags:
+``` python
+pop = np.fromstring(line, dtype=int, sep=' ')
+pop
+```
+%% Output
+    array([29, 26, 16])
+%% Cell type:code id: tags:
+``` python
+tot = sum(pop)
+tot
+print(tot)
+k = pop[0]
+m = pop[1]
+n = pop[2]
+print(k,m,n)
+```
+%% Output
+    71
+    29 26 16
+%% Cell type:code id: tags:
+``` python
+B1 = k/tot
+B2 = m/tot
+B3 = n/tot
+B1+B2+B3
+```
+%% Output
+    1.0
+%% Cell type:code id: tags:
+``` python
+B11 = ((k-1)/(tot-1)) * 1
+B12 = (m/(tot-1)) * 1
+B13 = (n/(tot-1)) * 1
+print(B11)
+print(B12)
+print(B13)
+print((B11+B12+B13))
+```
+%% Output
+    0.4
+    0.371428571429
+    0.228571428571
+    1.0
+%% Cell type:code id: tags:
+``` python
+B21 = (k/(tot-1)) * 1
+B22 = ((m-1)/(tot-1)) * (3/4)
+B23 = (n/(tot-1)) * (2/4)
+print(B21)
+print(B22)
+print(B23)
+print(B21+B22+B23)
+```
+%% Output
+    0.414285714286
+    0.267857142857
+    0.114285714286
+    0.796428571429
+%% Cell type:code id: tags:
+``` python
+B31 = (k/(tot-1)) * 1
+B32 = (m/(tot-1)) * (2/4)
+B33 = ((n-1)/(tot-1)) * 0
+print(B31)
+print(B32)
+print(B33)
+print(B31+B32+B33)
+```
+%% Output
+    0.414285714286
+    0.185714285714
+    0.0
+    0.6
+%% Cell type:code id: tags:
+``` python
+B1 = (B11 + B12 + B13) * B1
+B2 = (B21 + B22 + B23) * B2
+B3 = (B31 + B32 + B33) * B3
+print(B1+B2+B3)
+```
+%% Output
+    0.835311871227
+%% Cell type:markdown id: tags:
+# Result
+%% Cell type:code id: tags:
+``` python
+print(B1+B2+B3)
+```
+%% Output
+    0.835311871227
+%% Cell type:markdown id: tags:
+# StackOverflow Solution
+%% Cell type:code id: tags:
+``` python
+import scipy.misc
+pop_total = 4*scipy.misc.comb(k+m+n,2)
+hom = k
+het = m
+rec = n
+```
+%% Cell type:code id: tags:
+``` python
+dom_total = 4*scipy.misc.comb(hom,2) + 4*hom*het + 4*hom*rec + 3*scipy.misc.comb(het,2) + 2*het*rec
+print("Total of organisms with dominant allele : {0}".format(dom_total))
+```
+%% Output
+    Total of organisms with dominant allele : 8303.0
+%% Cell type:code id: tags:
+``` python
+phom = dom_total/pop_total
+print("Probability of dominant : {0}".format(phom))
+```
+%% Output
+    Probability of dominant : 0.8353118712273642
+%% Cell type:code id: tags:
+``` python
+print("Probability of recessive : {0}".format(1-phom))
+```
+%% Output
+    Probability of recessive : 0.16468812877263583
--- a/Rabbits and Recurrence Relations Data.txt
+++ b/Rabbits and Recurrence Relations Data.txt
--- a/Rabbits and Recurrence Relations.ipynb
+++ b/Rabbits and Recurrence Relations.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rabbits and Recurrence Relations"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Rabbits and Recurrence Relations
--- a/Transcribing DNA into RNA Data.txt
+++ b/Transcribing DNA into RNA Data.txt
+GCCGAGCTTGATTACTTGACCCGAATTCGATTTACCCGACGACTCGAGCGGGAACGGAAATTCTTGTGGACCGCTATAGCTATCTCTAGGGAAATTACAGGAACGTCTATGACTTTGAACTGAGGCAACGGCAGCGCAAACTATGAGAGAACAGGAGGCCCAATCTAGAGAAAAACTCTCTCTTCGAAGTTACAGCCAGTTGACTATTGAGTAGGTTCAGCTTTCGAGTAAGCCGGCGAAACCAATAGGGCCCAGGACATAACTGTCTGGCCTTGAGTCTCTATGTCGGAGGCCAGGGTCGCCGCTAGTTCACAAGCCTATGGCGTAATACACAGCCATTACAAACAGACCTTATAAGCCCAACTCGAGGGCACATGGGGACAGCTTAGTACGCCGGCGTCGGGGGGTGTGTGGTGGGCCGTTCCGACTGACGGCCCATTATTGATCATGCGCAAACGTTTAATTCTGCAGGGCCGACCAGGCTTTAGCTTAATAAATTCTTAAAGACGTGAACGACCGTCAGTTATTTTCGTACCATTTACCTTTGGTCCCCACCCAAATTTTCTTTGAGGAGAATCGCTAGCCTTGTAGCCGCCGTTATTCTATAATGGGTTAACCAAGAAGTATCCTTCTCTTGAGAATCTTGCGCAAATATATAACAACCGCCCGTCGCTCTTCTTAAAAGGCGAGACGGCCCACTAACCATCATGTGAGGAGTAGGGCCGTGTAGATTCAGACATAACCTATTTATGAATTTTCTGCGAGTTAAGCCACTTGTTTGCCGCATTCCAGTCCCTGGGACTCTTTCTTTGAGGGCGAACGCGAAACCTCAGTTCTCTCCATTAAATGCTGCGCTTCTAGACCGCAGTCATACCTCACTACGTGAACCCGTTCGTAGAACGGATTCGACGAACAGGTAAAGATCCGCAAGCAGCACGAGGCAC
\ No newline at end of file
--- a/Transcribing DNA into RNA.ipynb
+++ b/Transcribing DNA into RNA.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transcribing DNA into RNA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GCCGAGCTTGATTACTTGACCCGAATTCGATTTACCCGACGACTCGAGCGGGAACGGAAATTCTTGTGGACCGCTATAGCTATCTCTAGGGAAATTACAGGAACGTCTATGACTTTGAACTGAGGCAACGGCAGCGCAAACTATGAGAGAACAGGAGGCCCAATCTAGAGAAAAACTCTCTCTTCGAAGTTACAGCCAGTTGACTATTGAGTAGGTTCAGCTTTCGAGTAAGCCGGCGAAACCAATAGGGCCCAGGACATAACTGTCTGGCCTTGAGTCTCTATGTCGGAGGCCAGGGTCGCCGCTAGTTCACAAGCCTATGGCGTAATACACAGCCATTACAAACAGACCTTATAAGCCCAACTCGAGGGCACATGGGGACAGCTTAGTACGCCGGCGTCGGGGGGTGTGTGGTGGGCCGTTCCGACTGACGGCCCATTATTGATCATGCGCAAACGTTTAATTCTGCAGGGCCGACCAGGCTTTAGCTTAATAAATTCTTAAAGACGTGAACGACCGTCAGTTATTTTCGTACCATTTACCTTTGGTCCCCACCCAAATTTTCTTTGAGGAGAATCGCTAGCCTTGTAGCCGCCGTTATTCTATAATGGGTTAACCAAGAAGTATCCTTCTCTTGAGAATCTTGCGCAAATATATAACAACCGCCCGTCGCTCTTCTTAAAAGGCGAGACGGCCCACTAACCATCATGTGAGGAGTAGGGCCGTGTAGATTCAGACATAACCTATTTATGAATTTTCTGCGAGTTAAGCCACTTGTTTGCCGCATTCCAGTCCCTGGGACTCTTTCTTTGAGGGCGAACGCGAAACCTCAGTTCTCTCCATTAAATGCTGCGCTTCTAGACCGCAGTCATACCTCACTACGTGAACCCGTTCGTAGAACGGATTCGACGAACAGGTAAAGATCCGCAAGCAGCACGAGGCAC\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('Transcribing DNA into RNA Data.txt') as f:\n",
+    "    dnastr = f.readline()\n",
+    "    \n",
+    "print(dnastr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GCCGAGCUUGAUUACUUGACCCGAAUUCGAUUUACCCGACGACUCGAGCGGGAACGGAAAUUCUUGUGGACCGCUAUAGCUAUCUCUAGGGAAAUUACAGGAACGUCUAUGACUUUGAACUGAGGCAACGGCAGCGCAAACUAUGAGAGAACAGGAGGCCCAAUCUAGAGAAAAACUCUCUCUUCGAAGUUACAGCCAGUUGACUAUUGAGUAGGUUCAGCUUUCGAGUAAGCCGGCGAAACCAAUAGGGCCCAGGACAUAACUGUCUGGCCUUGAGUCUCUAUGUCGGAGGCCAGGGUCGCCGCUAGUUCACAAGCCUAUGGCGUAAUACACAGCCAUUACAAACAGACCUUAUAAGCCCAACUCGAGGGCACAUGGGGACAGCUUAGUACGCCGGCGUCGGGGGGUGUGUGGUGGGCCGUUCCGACUGACGGCCCAUUAUUGAUCAUGCGCAAACGUUUAAUUCUGCAGGGCCGACCAGGCUUUAGCUUAAUAAAUUCUUAAAGACGUGAACGACCGUCAGUUAUUUUCGUACCAUUUACCUUUGGUCCCCACCCAAAUUUUCUUUGAGGAGAAUCGCUAGCCUUGUAGCCGCCGUUAUUCUAUAAUGGGUUAACCAAGAAGUAUCCUUCUCUUGAGAAUCUUGCGCAAAUAUAUAACAACCGCCCGUCGCUCUUCUUAAAAGGCGAGACGGCCCACUAACCAUCAUGUGAGGAGUAGGGCCGUGUAGAUUCAGACAUAACCUAUUUAUGAAUUUUCUGCGAGUUAAGCCACUUGUUUGCCGCAUUCCAGUCCCUGGGACUCUUUCUUUGAGGGCGAACGCGAAACCUCAGUUCUCUCCAUUAAAUGCUGCGCUUCUAGACCGCAGUCAUACCUCACUACGUGAACCCGUUCGUAGAACGGAUUCGACGAACAGGUAAAGAUCCGCAAGCAGCACGAGGCAC\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(dnastr)\n",
+    "\n",
+    "for i,x in enumerate(lst):\n",
+    "    if(x=='T'): lst[i]='U'\n",
+    "      \n",
+    "rnastr = ''.join(lst)\n",
+    "print(rnastr)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
+%% Cell type:markdown id: tags:
+# Transcribing DNA into RNA
+%% Cell type:code id: tags:
+``` python
+with open('Transcribing DNA into RNA Data.txt') as f:
+    dnastr = f.readline()
+print(dnastr)
+```
+%% Output
+    GCCGAGCTTGATTACTTGACCCGAATTCGATTTACCCGACGACTCGAGCGGGAACGGAAATTCTTGTGGACCGCTATAGCTATCTCTAGGGAAATTACAGGAACGTCTATGACTTTGAACTGAGGCAACGGCAGCGCAAACTATGAGAGAACAGGAGGCCCAATCTAGAGAAAAACTCTCTCTTCGAAGTTACAGCCAGTTGACTATTGAGTAGGTTCAGCTTTCGAGTAAGCCGGCGAAACCAATAGGGCCCAGGACATAACTGTCTGGCCTTGAGTCTCTATGTCGGAGGCCAGGGTCGCCGCTAGTTCACAAGCCTATGGCGTAATACACAGCCATTACAAACAGACCTTATAAGCCCAACTCGAGGGCACATGGGGACAGCTTAGTACGCCGGCGTCGGGGGGTGTGTGGTGGGCCGTTCCGACTGACGGCCCATTATTGATCATGCGCAAACGTTTAATTCTGCAGGGCCGACCAGGCTTTAGCTTAATAAATTCTTAAAGACGTGAACGACCGTCAGTTATTTTCGTACCATTTACCTTTGGTCCCCACCCAAATTTTCTTTGAGGAGAATCGCTAGCCTTGTAGCCGCCGTTATTCTATAATGGGTTAACCAAGAAGTATCCTTCTCTTGAGAATCTTGCGCAAATATATAACAACCGCCCGTCGCTCTTCTTAAAAGGCGAGACGGCCCACTAACCATCATGTGAGGAGTAGGGCCGTGTAGATTCAGACATAACCTATTTATGAATTTTCTGCGAGTTAAGCCACTTGTTTGCCGCATTCCAGTCCCTGGGACTCTTTCTTTGAGGGCGAACGCGAAACCTCAGTTCTCTCCATTAAATGCTGCGCTTCTAGACCGCAGTCATACCTCACTACGTGAACCCGTTCGTAGAACGGATTCGACGAACAGGTAAAGATCCGCAAGCAGCACGAGGCAC
+%% Cell type:code id: tags:
+``` python
+lst = list(dnastr)
+for i,x in enumerate(lst):
+    if(x=='T'): lst[i]='U'
+rnastr = ''.join(lst)
+print(rnastr)
+```
+%% Output
+    GCCGAGCUUGAUUACUUGACCCGAAUUCGAUUUACCCGACGACUCGAGCGGGAACGGAAAUUCUUGUGGACCGCUAUAGCUAUCUCUAGGGAAAUUACAGGAACGUCUAUGACUUUGAACUGAGGCAACGGCAGCGCAAACUAUGAGAGAACAGGAGGCCCAAUCUAGAGAAAAACUCUCUCUUCGAAGUUACAGCCAGUUGACUAUUGAGUAGGUUCAGCUUUCGAGUAAGCCGGCGAAACCAAUAGGGCCCAGGACAUAACUGUCUGGCCUUGAGUCUCUAUGUCGGAGGCCAGGGUCGCCGCUAGUUCACAAGCCUAUGGCGUAAUACACAGCCAUUACAAACAGACCUUAUAAGCCCAACUCGAGGGCACAUGGGGACAGCUUAGUACGCCGGCGUCGGGGGGUGUGUGGUGGGCCGUUCCGACUGACGGCCCAUUAUUGAUCAUGCGCAAACGUUUAAUUCUGCAGGGCCGACCAGGCUUUAGCUUAAUAAAUUCUUAAAGACGUGAACGACCGUCAGUUAUUUUCGUACCAUUUACCUUUGGUCCCCACCCAAAUUUUCUUUGAGGAGAAUCGCUAGCCUUGUAGCCGCCGUUAUUCUAUAAUGGGUUAACCAAGAAGUAUCCUUCUCUUGAGAAUCUUGCGCAAAUAUAUAACAACCGCCCGUCGCUCUUCUUAAAAGGCGAGACGGCCCACUAACCAUCAUGUGAGGAGUAGGGCCGUGUAGAUUCAGACAUAACCUAUUUAUGAAUUUUCUGCGAGUUAAGCCACUUGUUUGCCGCAUUCCAGUCCCUGGGACUCUUUCUUUGAGGGCGAACGCGAAACCUCAGUUCUCUCCAUUAAAUGCUGCGCUUCUAGACCGCAGUCAUACCUCACUACGUGAACCCGUUCGUAGAACGGAUUCGACGAACAGGUAAAGAUCCGCAAGCAGCACGAGGCAC