protamine_sequence_order_parameter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################\n",
      "#            Alignments             #\n",
      "#                                   #\n",
      "# Conserved positions marked with * #\n",
      "#####################################\n",
      "Eutherian P1 Sperm Protamines\n",
      "                         10         20         30         40         50         60\n",
      "                 *    **          *            *        **           **          *     \n",
      "HSP1_CAPHI       MARYRCCLTH --SRSRCR-R ---RRRRRCR -RRRRRFGRR --RRR-RVCC RRY--TVVRC TRQ- \n",
      "HSP1_SHEEP       MARYRCCLTH --SRSRCR-R ---RRRRRCR -RRRRRFGRR --RRR-RVCC RRY--TVVRC TRQ- \n",
      "HSP1_BOVIN       MARYRCCLTH --SGSRCR-R ---RRRRRCR -RRRRRFGRR --RRR-RVCC RRY--TVIRC TRQ- \n",
      "HSP1_PIG         MARYRCCRSH --SRSRCR-P ---R-RRRCR -RRRRRCCPR --RRR-AVCC RRY--TVIRC RRC- \n",
      "HSP1_HORSE       MARYRCCRSQ --SQSRCR-R ---RRRRRCR -RRRRRSVRQ --RR---VCC RRY--TVLRC RRRR \n",
      "HSP1_ORCOR       MARNR-CRSP --SQSRCR-R ---P-RRRCR --RRIRCCRR --QR--RVCC RRY--TTTRC ARQ- \n",
      "HSP1_MOUSE       MARYRCCRSK --SRSRCR-R ---R-RRRCR -RRRRRCCRR --RR--RRCC RRRRSYTIRC KKY- \n",
      "HSP1_RAT         MARYRCCRSK --SRSRCR-R ---R-RRRCR -RRRRRCCRR --RR--RRCC RRRRSYTFRC KRY- \n",
      "HSP1_HUMAN       MARYRCCRSQ --SRSRYY-R ---Q-RQRSR -RRRRRSCQT --RRRAMRCC RPR--YRPRC RRH- \n",
      "HSP1_GORGO       MARYRCCRSQ --SRSRCY-R ---Q-RQTSR -RRRRRSCQT --QRRAMRCC RRR--NRLRR RKH- \n",
      "\n",
      "Eutherian P2 Sperm Protamines\n",
      "                         10         20         30         40         50         60         70\n",
      "                 *  *          *    *       *        *          *  **     **    *         *   \n",
      "PRM2_RATTU       MVRYRMRSPS ESPHQGPGQD HESEEQG--- -QGQELNPER VEDYGRTHRG --HHRHRRCS RKRLHRIHKR \n",
      "PRM2_MOUSE       MVRYRMRSPS EGPHQGPGQD HEREEQG--- -QGQGLSPER VEDYGRTHRG HHHHRHRRCS RKRLHRIHKR \n",
      "PRM2_RATFU       MVRYRMRSPS EGPHQGPGQD HEREEQG--- -QGQELSPER VEDYGRTHRG --HHRHRRCS RKRLHRIHKR \n",
      "PRM2_ALOSE       MVRYHVRSPS ERPHREYRQL VNGQEQGRHG QEEQGMSAEG VEGYGRTHQG CYGYRRRLCS RRRLYRVHRR \n",
      "PRM2_CALJA       MVRYRVRSPS ERPHEEYRQL VNWQEQGRNG QEEQGLSAEG GEVYGRTHQG YSSYRRRRCS RRRRYRIHRR \n",
      "PRM2_SEMEN       MVRYRMRSLS ERPHEVHGQQ VYGQEQGHNG QEEQGLSPEH VEVYERTHQG YSHHRRRRCS RRRLYRIHRR \n",
      "PRM2_ERYPA       MVRYRTRSLS ERPHEVHGQQ VHGQDQGHNG QEEQGLSPEH VEVYERTHQG HSHHRRRRCS QRRLHRIHRR \n",
      "PRM2_MACNE       MVRYRMRSLS ERPHEVHGQQ VHGQDQGHNG QEEQGLNPEH VEVYERTHRG HSHHRRRRCS RRRLHRIHRR \n",
      "PRM2_MACFU       MVRYRMRSLS ERPHEVHGQQ VHGQDQGHNG QEEQGLNPEH VEVYERTHRG HSHHRRRRCS RRRLHRIHRR \n",
      "PRM2_MACMU       MVRYRMRSLS ERSHEVHGQQ VHGQDQGHNG QEEQGLNPEH VEVYERTH-G HSHYRRRHCS RRRLHRIHRR \n",
      "PRM2_GORGO       MVRCRVRSPS ERSHEVYRQQ LHGQEQGHHG QEEQGLSPEH VEVYERTH-G HSHYRRRHCS RRRLRRIHRQ \n",
      "PRM2_PANPA       MVRYRVRSPS EPSHEVYRQQ LHGQEQGHHG QEEQGLSPEH VEVYERTH-G HSHYRRRHCS RRRLRRIHRQ \n",
      "PRM2_PANTR       MVRYRVRSPS EPSHEVYRQQ LHGQEQGHHG QEEQGLSPEH VEVYERTH-G HSHYRRRHCS RRRLRRIHRQ \n",
      "PRM2_HUMAN       MVRYRVRSLS ERSHEVYRQQ LHGQEQGHHG QEEQGLSPEH VEVYERTH-G QSHYRRRHCS RRRLHRIHRR \n",
      "PRM2_PONPY       MVRYCVRSLS ERSHEVYGQQ LHGQEQGHHD QEEQGLSPEQ VEVYERTQ-G HSHYRRRHCS RRRLHRIHRQ \n",
      "PRM2_HYLLA       MVRYCVRSLS ERSHEVYGQQ LRGQEQGHHG QEEQGLSPED VEVYERTH-G HSHYRRRHCS RRRLHRIHRQ \n",
      "                         80         90        100        110\n",
      "                     *        * *   *    *              *  *   \n",
      "PRM2_RATTU       R-RSCRRRRR HSCCHRRRHR RGCRRSRRRR RCRCRKCRRQ CH        \n",
      "PRM2_MOUSE       R-RSCRRRRR HSCRHRRRHR RGCRRSRRRR RCRCRKCRRH HH        \n",
      "PRM2_RATFU       R-RSCRRRRR HSCCHRRRHR RGCRRSRRRR RCKCRKCRRH CH        \n",
      "PRM2_ALOSE       QRRSCRRRC- --CRYRRRNR RGCRT-RRRT ------CRRH --        \n",
      "PRM2_CALJA       RSRSCRRRRR RSCRYRRRPR RGCRSRRRRR ------CRRY --        \n",
      "PRM2_SEMEN       RHRSCRRRRR RSCRHRRRHR RGCRT-RRRR ------CRRY --        \n",
      "PRM2_ERYPA       RHRSCRRRRR RSCRHRRRHR RGCRT-RRRR ------CRRY --        \n",
      "PRM2_MACNE       RHRSCRRRRR RSCRHRRRHR RGCRT-RRRR ------CRRH --        \n",
      "PRM2_MACFU       RHRSCRRRRR RSCRHRRRHR RGCRT-RRRR ------CRRH --        \n",
      "PRM2_MACMU       RHRSCRRRRR RSCRHRRRHR RGCRT-RRRR ------CRRH --        \n",
      "PRM2_GORGO       QHRSCRRRKR RSCRHRRRHR KGCRT-RRRT ------CRRH --        \n",
      "PRM2_PANPA       QHRSCRRRKR RSCRHRRRHR RGCRT-RRRT ------CRKH --        \n",
      "PRM2_PANTR       QHRSCRRRKR RSCRHRRKHR RGCRT-RRRT ------CRRH --        \n",
      "PRM2_HUMAN       QHRSCRRRKR RSCRHRRRHR RGCRT-RKRT ------CRRH --        \n",
      "PRM2_PONPY       QHRSCKRRRR HSCRHRRKHR RGCRT-RRRT ------CRRH --        \n",
      "PRM2_HYLLA       QHRSCGRRRR RSCRQRRRHR RGCRT-RRRR ------CRRH --        \n",
      "\n",
      "Truncated Eutherian P2 Sperm Protamines\n",
      "                         58         68         78         88         98        108        118\n",
      "                     **     *        *       *        * *    *   *              *   *  \n",
      "PRM2_RATTU       RG--HHRHRR CSRKRLHRIH KRR-RSCRRR RRHSCCHRRR HRRGCRRSRR RRRCRCRKCR RQCH      \n",
      "PRM2_MOUSE       RGHHHHRHRR CSRKRLHRIH KRR-RSCRRR RRHSCRHRRR HRRGCRRSRR RRRCRCRKCR RHHH      \n",
      "PRM2_RATFU       RG--HHRHRR CSRKRLHRIH KRR-RSCRRR RRHSCCHRRR HRRGCRRSRR RRRCKCRKCR RHCH      \n",
      "PRM2_ALOSE       QGCYGYRRRL CSRRRLYRVH RRQRRSCRRR C---CRYRRR NRRGCRT-RR RT------CR RH--      \n",
      "PRM2_CALJA       QGYSSYRRRR CSRRRRYRIH RRRSRSCRRR RRRSCRYRRR PRRGCRSRRR RR------CR RY--      \n",
      "PRM2_SEMEN       QGYSHHRRRR CSRRRLYRIH RRRHRSCRRR RRRSCRHRRR HRRGCRT-RR RR------CR RY--      \n",
      "PRM2_ERYPA       QGHSHHRRRR CSQRRLHRIH RRRHRSCRRR RRRSCRHRRR HRRGCRT-RR RR------CR RY--      \n",
      "PRM2_MACNE       RGHSHHRRRR CSRRRLHRIH RRRHRSCRRR RRRSCRHRRR HRRGCRT-RR RR------CR RH--      \n",
      "PRM2_MACFU       RGHSHHRRRR CSRRRLHRIH RRRHRSCRRR RRRSCRHRRR HRRGCRT-RR RR------CR RH--      \n",
      "PRM2_MACMU       -GHSHYRRRH CSRRRLHRIH RRRHRSCRRR RRRSCRHRRR HRRGCRT-RR RR------CR RH--      \n",
      "PRM2_GORGO       -GHSHYRRRH CSRRRLRRIH RQQHRSCRRR KRRSCRHRRR HRKGCRT-RR RT------CR RH--      \n",
      "PRM2_PANPA       -GHSHYRRRH CSRRRLRRIH RQQHRSCRRR KRRSCRHRRR HRRGCRT-RR RT------CR KH--      \n",
      "PRM2_PANTR       -GHSHYRRRH CSRRRLRRIH RQQHRSCRRR KRRSCRHRRK HRRGCRT-RR RT------CR RH--      \n",
      "PRM2_HUMAN       -GQSHYRRRH CSRRRLHRIH RRQHRSCRRR KRRSCRHRRR HRRGCRT-RK RT------CR RH--      \n",
      "PRM2_PONPY       -GHSHYRRRH CSRRRLHRIH RQQHRSCKRR RRHSCRHRRK HRRGCRT-RR RT------CR RH--      \n",
      "PRM2_HYLLA       -GHSHYRRRH CSRRRLHRIH RQQHRSCGRR RRRSCRQRRR HRRGCRT-RR RR------CR RH--      \n",
      "\n",
      "Metatherian P1 Sperm Protamines\n",
      "                         10         20         30         40         50         60         70\n",
      "                 *  *  *         *                   *                        *     *         \n",
      "HSP1_MONDO       MARYRRRSRS RSRSRYGRRR RRSRS----R RRRSRRRRR- ---RRGRR-- ---GRGYHRR SPHRR---RR \n",
      "HSP1_DIDVI       MARYRRRSRS RSRSRYGRRR RRSRS----R RRRSRRRRR- ---RRGRR-- ---GRGYHRR SPHRR---RR \n",
      "HSP1_PHACI       MARYR-HSRS RSRSRY-QRR RRRRSRYRSQ RRRYRRRRGS RRRRRRGR-- ---RRGY-RR RYS-----RR \n",
      "HSP1_PSECU       MARYR-HSRS RSRSRYRRRR RRRRSRYRGR RRRYRRSRR- RRRRGRRRGN CLGRRGYRRR RYS-----RR \n",
      "HSP1_MACRU       MARYR-HSRS RSRSRY-RRR RRRRSRYRSQ RRRYRGRRR- -RRSRRGR-- ---RRGYSRR RYS------R \n",
      "HSP1_MACGI       MARYR-HSRS RSRSRY-RRR RRRRSRYRSR RRRYRGRRR- RRSRRGRR-- ---RRGYSRR RYS------R \n",
      "HSP1_WALBI       MARYR-HSRS RSRSRY-RRR RRRRSRYRSR RRRYRGRRR- RRSRRGRR-- ---RRGYSRR RYS-----RR \n",
      "HSP1_THYCY       MARYRRHSRS RSRSRY-RRR RRRRSRHHNR RRTYRRSRR- HSRRRRGR-- ---RRGYSRR RYS-----RR \n",
      "HSP1_PLAMS       MARCRRHSRS RSRSRN-QCQ RRRRRRY-NR RRTYRRSRR- HSRRRRGR-- ---RRGCSRR RYS-----RR \n",
      "HSP1_PLAIN       MARSRRHSRS RSRSR--NQC QRRRRRTYNR RRTMREKPR- HSRRRRVR-- ---RRGCSCR RCS-----RR \n",
      "                         80\n",
      "                     *       \n",
      "HSP1_MONDO       RRRR------ -        \n",
      "HSP1_DIDVI       RRRR------ -        \n",
      "HSP1_PHACI       --RRY----- -        \n",
      "HSP1_PSECU       RRRRYY---- -        \n",
      "HSP1_MACRU       RRRRY----- -        \n",
      "HSP1_MACGI       RRRRY----- -        \n",
      "HSP1_WALBI       RRRRY----- -        \n",
      "HSP1_THYCY       GRRRY----- -        \n",
      "HSP1_PLAMS       GRRRY----- -        \n",
      "HSP1_PLAIN       RRRRC----- -        \n",
      "\n",
      "Fish Protamines\n",
      "                         10         20         30         40\n",
      "                                                        \n",
      "PRTA_ACIST       -ARRRRRHAS TKLKRRR--- -----RRRRH GKKSHK \n",
      "PRT_ORYLA        ----MRRQAS LPARRRRRVR RTRVVRRRRR VGRRRH \n",
      "PRT_PERFV        --PRRRRHAA RPVRRRRRTR RSSRVHRRRR AVRRRR \n",
      "PRTB_MUGCE       --PRRRRETS RPIRRRRRAR RAPI-RRRRR VVRRRR \n",
      "PRT1_SCOSC       -MPRRRRRAS RPVRRRRRAR RSTAVRRRRR VVRRRR \n",
      "PRT2_SCOSC       -MPRRRRRAS RPIRRRRRAR RSTAVRRRRR VVRRRR \n",
      "PRT_DICLA        --PRRRRQAS RPVRRRRRTR RSTAERRRRR VVRRRR \n",
      "PRTY_THUTH       --PRRRRQAS RPVRRRRRYR RSTAARRRRR VVRRRR \n",
      "PRTZ_THUTH       --PRRRRRSS RPVRRRRRYR RSTVARRRRR VVRRRR \n",
      "PRTZ1_SAROR      --PRRRRRSS RPVRRRRRYR RSTAARRRRR VVRRRR \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRTB_ACIGU       --ARRRRRSS RPQRRRRR-- -----RRHGR RRRGRR \n",
      "PRTB_ACIST       --ARRRRRSS RPQRRRRR-- -----RRHGR RRRGRR \n",
      "PRTY1_CLUHA      --ARRRRSSS RPIRRRRPRR RT---TRRRR AGRRRR \n",
      "PRTY1_CLUPA      --ARRRRSSS RPIRRRRPRR RT---TRRRR AGRRRR \n",
      "PRT1_ESOLU       -PRRRRASSG RPVRRRRRP- KMS--RRRRR GGRRRR \n",
      "PRTZ_CLUPA       ARRRRSRRAS RPVRRRRPR- RVS--RRRR- -ARRRR \n",
      "PRTZ_CLUHA       ARRRRSRRAS RPVRRRRPR- RVS--RRRR- -ARRRR \n",
      "PRT4_ONCMY       -MPRRRR-AS RRIRRRRRP- RVS--RRRRG GRRRRR \n",
      "PRTY2_CLUPA      -PRRRTRRAS RPVRRRRPR- RVS--RRRR- -ARRRR \n",
      "PRTY2_CLUHA      -PRRRTRRAS RPVRRRRPR- RVS--RRRR- -ARRRR \n",
      "PRT3A_ONCMY      -PRRRRRSSS RPIRRRRRP- RVS--RRRRR GGRRRR \n",
      "PRTIB_ONCMY      PRRRRRRSSS RPIRRRRPR- RVS--RRRRR GGRRRR \n",
      "PRT1B_ONCMY      -MPRRRR-AS RRIRRRRRP- RVS--RRRRR GGRRRR \n",
      "PRT1A_ONCMY      -MPRRRR-AS RRVRRRRRP- RVS--RRRRR GGRRRR \n",
      "PRTC3_ONCMY      -MPRRRR-AS RPVRRRRRP- RVS--RRRRR GGRRRR \n",
      "PRT2C_ONCMY      -MPRRRRSSR RPVRRRRRP- RVSR-RRRRR GGRRRR \n",
      "PRT14_ONCMY      -MPRRRRSSR PPVRRRRRP- RVSR-RRRRR GGRRRR \n",
      "PRT2_ONCMY       --PRRRRSSS RPVRRRRAR- RVSR-RRRRR GGRRRR \n",
      "PRT2A_ONCMY      --PRRRRSSS RPVRRRRRA- RVSR-RRRRR GGRRRR \n",
      "PRTIA_ONCMY      --PRRRRSSS RPVRRRRRPR RVSR-RRRRR GGRRRR \n",
      "PRT1_ONCKE       -MPRRRRSSS RPVRRRRRP- RVSR-RRRRR GGRRRR \n",
      "PRT2B_ONCMY      -MPRRRRSSS RPVRRRRRP- RVSR-RRRRR GGRRRR \n",
      "PRT16_ONCMY      -MPRRRRSSS RPVRRRRRA- RVSR-RRRRR GRRRRR \n",
      "PRT17_ONCMY      -MPRRRRSSS RPVRRRRRP- RVSR-RRRRR GRRRRR \n",
      "\n",
      "##############################\n",
      "# Conserved Residue Rankings #\n",
      "##############################\n",
      "\n",
      "Eutherian P1 Sperm Protamines\n",
      "┌──────┬──────────┬─────────┬────────────┐\n",
      "│Rank  │Position  │Relative │Represented │\n",
      "│      │in        │Entropy  │Residues    │\n",
      "│      │Alignment │(bit)    │            │\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     1│         7│   6.1571│  C:   98.6%│\n",
      "│      │          │         │  R:    1.4%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     2│        49│   6.1198│  C:  97.92%│\n",
      "│      │          │         │  G:   1.39%│\n",
      "│      │          │         │  S:   0.69%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     3│        50│   5.8701│  C:  95.14%│\n",
      "│      │          │         │  R:   2.08%│\n",
      "│      │          │         │  Y:   1.39%│\n",
      "│      │          │         │  G:   0.69%│\n",
      "│      │          │         │  S:   0.69%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     4│        60│   5.4386│  C:  89.51%│\n",
      "│      │          │         │  R:    4.9%│\n",
      "│      │          │         │  S:    4.2%│\n",
      "│      │          │         │  Y:    1.4%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     5│        38│   5.1866│  C:   86.9%│\n",
      "│      │          │         │  G:   4.14%│\n",
      "│      │          │         │  Y:   2.76%│\n",
      "│      │          │         │  R:   1.38%│\n",
      "│      │          │         │  S:   1.38%│\n",
      "│      │          │         │  V:   1.38%│\n",
      "│      │          │         │  A:   0.69%│\n",
      "│      │          │         │  F:   0.69%│\n",
      "│      │          │         │  H:   0.69%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     6│        17│   4.8853│  C:  79.31%│\n",
      "│      │          │         │  R:   13.1%│\n",
      "│      │          │         │  G:   5.52%│\n",
      "│      │          │         │  S:   1.38%│\n",
      "│      │          │         │  Y:   0.69%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     7│        29│   4.8128│  C:  77.24%│\n",
      "│      │          │         │  Y:   13.1%│\n",
      "│      │          │         │  S:   5.52%│\n",
      "│      │          │         │  G:   1.38%│\n",
      "│      │          │         │  R:   1.38%│\n",
      "│      │          │         │  F:   0.69%│\n",
      "│      │          │         │  N:   0.69%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     8│         6│   4.6044│  C:   95.5%│\n",
      "│      │          │         │  R:    4.5%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     9│        37│   4.3298│  C:  68.97%│\n",
      "│      │          │         │  S:  19.31%│\n",
      "│      │          │         │  F:   5.52%│\n",
      "│      │          │         │  R:   5.52%│\n",
      "│      │          │         │  T:   0.69%│\n",
      "└──────┴──────────┴─────────┴────────────┘\n",
      "\n",
      "Eutherian P2 Sperm Protamines\n",
      "┌──────┬──────────┬─────────┬────────────┐\n",
      "│Rank  │Position  │Relative │Represented │\n",
      "│      │in        │Entropy  │Residues    │\n",
      "│      │Alignment │(bit)    │            │\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     1│        59│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     2│        75│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     3│        83│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     4│        93│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     5│       107│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     6│        14│   5.5129│  H:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     7│        68│   5.5129│  H:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     8│        48│   5.1264│  H:  93.75%│\n",
      "│      │          │         │  Q:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     9│        44│    5.093│  Y:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    10│         4│   4.8362│  Y:  93.75%│\n",
      "│      │          │         │  C:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    11│        19│   4.7255│  Q:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    12│        26│   4.7255│  Q:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    13│        34│   4.7255│  Q:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    14│        89│   4.7208│  H:   87.5%│\n",
      "│      │          │         │  N:   6.25%│\n",
      "│      │          │         │  P:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    15│        53│   4.6355│  H:   87.5%│\n",
      "│      │          │         │  G:   6.25%│\n",
      "│      │          │         │  S:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    16│        85│   4.5428│  H:  81.25%│\n",
      "│      │          │         │  Y:   12.5%│\n",
      "│      │          │         │  Q:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    17│       110│   4.3709│  H:   75.0%│\n",
      "│      │          │         │  Y:  18.75%│\n",
      "│      │          │         │  Q:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    18│        54│    4.288│  Y:  56.25%│\n",
      "│      │          │         │  H:  43.75%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    19│        47│   4.1714│  T:  100.0%│\n",
      "└──────┴──────────┴─────────┴────────────┘\n",
      "\n",
      "Truncated Eutherian P2 Sperm Protamines\n",
      "┌──────┬──────────┬─────────┬────────────┐\n",
      "│Rank  │Position  │Relative │Represented │\n",
      "│      │in        │Entropy  │Residues    │\n",
      "│      │Alignment │(bit)    │            │\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     1│        59│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     2│        75│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     3│        83│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     4│        93│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     5│       107│   6.3808│  C:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     6│        68│   5.5129│  H:  100.0%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     7│        89│   4.7208│  H:   87.5%│\n",
      "│      │          │         │  N:   6.25%│\n",
      "│      │          │         │  P:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     8│        53│   4.6355│  H:   87.5%│\n",
      "│      │          │         │  G:   6.25%│\n",
      "│      │          │         │  S:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     9│        85│   4.5428│  H:  81.25%│\n",
      "│      │          │         │  Y:   12.5%│\n",
      "│      │          │         │  Q:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    10│       110│   4.3709│  H:   75.0%│\n",
      "│      │          │         │  Y:  18.75%│\n",
      "│      │          │         │  Q:   6.25%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│    11│        54│    4.288│  Y:  56.25%│\n",
      "│      │          │         │  H:  43.75%│\n",
      "└──────┴──────────┴─────────┴────────────┘\n",
      "\n",
      "Metatherian P1 Sperm Protamines\n",
      "┌──────┬──────────┬─────────┬────────────┐\n",
      "│Rank  │Position  │Relative │Represented │\n",
      "│      │in        │Entropy  │Residues    │\n",
      "│      │Alignment │(bit)    │            │\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     1│         7│   4.9798│  H:  92.63%│\n",
      "│      │          │         │  R:   5.26%│\n",
      "│      │          │         │  N:   2.11%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     2│         4│   4.8351│  Y:  95.79%│\n",
      "│      │          │         │  C:   3.16%│\n",
      "│      │          │         │  S:   1.05%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     3│        57│    4.733│  Y:  93.68%│\n",
      "│      │          │         │  C:   4.21%│\n",
      "│      │          │         │  K:   2.11%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     4│        16│   4.6696│  Y:  94.68%│\n",
      "│      │          │         │  N:   3.19%│\n",
      "│      │          │         │  F:   2.13%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     5│        62│   4.5991│  Y:  92.63%│\n",
      "│      │          │         │  C:   3.16%│\n",
      "│      │          │         │  P:   2.11%│\n",
      "│      │          │         │  S:   2.11%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     6│        75│   4.5653│  Y:  94.51%│\n",
      "│      │          │         │  C:    3.3%│\n",
      "│      │          │         │  N:    2.2%│\n",
      "├──────┼──────────┼─────────┼────────────┤\n",
      "│     7│        34│   4.5358│  Y:  91.58%│\n",
      "│      │          │         │  S:   4.21%│\n",
      "│      │          │         │  Q:   2.11%│\n",
      "│      │          │         │  M:   1.05%│\n",
      "│      │          │         │  X:   1.05%│\n",
      "└──────┴──────────┴─────────┴────────────┘\n",
      "\n",
      "#################################\n",
      "# Charged Residue Quartile Data #\n",
      "#################################\n",
      "\n",
      "┌────────────────┬──────┬──────┬──────┬──────┬──────┐\n",
      "│Name            │Min   │Q1    │Median│Q3    │Max   │\n",
      "├────────────────┼──────┼──────┼──────┼──────┼──────┤\n",
      "│Eutherian P1    │ 0.429│ 0.471│  0.52│  0.58│ 0.652│\n",
      "├────────────────┼──────┼──────┼──────┼──────┼──────┤\n",
      "│Eutherian P2    │ 0.294│ 0.328│ 0.341│ 0.362│ 0.385│\n",
      "├────────────────┼──────┼──────┼──────┼──────┼──────┤\n",
      "│Eutherian P2 T. │ 0.473│ 0.509│ 0.545│ 0.559│ 0.589│\n",
      "├────────────────┼──────┼──────┼──────┼──────┼──────┤\n",
      "│Metatherian     │ 0.478│ 0.587│   0.6│ 0.624│ 0.656│\n",
      "├────────────────┼──────┼──────┼──────┼──────┼──────┤\n",
      "│Fish            │ 0.562│ 0.636│ 0.652│ 0.667│ 0.704│\n",
      "└────────────────┴──────┴──────┴──────┴──────┴──────┘\n",
      "\n",
      "┌────────────────┬──────┬──────┬──────┬──────┬──────┐\n",
      "│Name            │Min   │Q1    │Median│Q3    │Max   │\n",
      "├────────────────┼──────┼──────┼──────┼──────┼──────┤\n",
      "│Eutherian P1 DNA│ 0.478│ 0.636│ 0.714│ 0.773│ 0.857│\n",
      "├────────────────┼──────┼──────┼──────┼──────┼──────┤\n",
      "│Metatherian DNA │ 0.529│ 0.667│ 0.697│ 0.719│ 0.781│\n",
      "└────────────────┴──────┴──────┴──────┴──────┴──────┘\n",
      "\n",
      "###################################\n",
      "# t-Tests for DNA Binding Regions #\n",
      "###################################\n",
      "\n",
      "ep1-mp1  2.184e-02\n",
      "ep1-fish 4.762e-11\n",
      "mp1-fish 4.987e-08\n",
      "\n",
      "\n",
      "########################\n",
      "# Variability Analysis #\n",
      "########################\n",
      "\n",
      "Eutherian P1 Sperm Protamines\n",
      "┌──────┬──────┬──────┬──────┬──────┐\n",
      "│Rank  │AA    │Range │AA    │std   │\n",
      "├──────┼──────┼──────┼──────┼──────┤\n",
      "│     1│     Q│ 0.182│     Q│0.0335│\n",
      "│     2│     K│ 0.224│     P│ 0.044│\n",
      "│     3│     T│ 0.224│     T│0.0456│\n",
      "│     4│     S│ 0.242│     N│0.0463│\n",
      "│     5│     N│ 0.244│     A│0.0489│\n",
      "└──────┴──────┴──────┴──────┴──────┘\n",
      "\n",
      "Eutherian P2 Sperm Protamines\n",
      "┌──────┬──────┬──────┬──────┬──────┐\n",
      "│Rank  │AA    │Range │AA    │std   │\n",
      "├──────┼──────┼──────┼──────┼──────┤\n",
      "│     1│     Q│0.0698│     Q│0.0169│\n",
      "│     2│     L│0.0901│     T│0.0217│\n",
      "│     3│     T│0.0903│     L│0.0227│\n",
      "│     4│     K│0.0905│     E│0.0238│\n",
      "│     5│     C│0.0994│     V│0.0249│\n",
      "└──────┴──────┴──────┴──────┴──────┘\n",
      "\n",
      "Truncated Eutherian P2 Sperm Protamines\n",
      "┌──────┬──────┬──────┬──────┬──────┐\n",
      "│Rank  │AA    │Range │AA    │std   │\n",
      "├──────┼──────┼──────┼──────┼──────┤\n",
      "│     1│     Q│ 0.116│     K│0.0349│\n",
      "│     2│     K│ 0.117│     Q│0.0365│\n",
      "│     3│     T│ 0.127│     T│0.0401│\n",
      "│     4│     L│ 0.145│     L│ 0.044│\n",
      "│     5│     C│ 0.151│     G│0.0454│\n",
      "└──────┴──────┴──────┴──────┴──────┘\n",
      "\n",
      "Metatherian P1 Sperm Protamines\n",
      "┌──────┬──────┬──────┬──────┬──────┐\n",
      "│Rank  │AA    │Range │AA    │std   │\n",
      "├──────┼──────┼──────┼──────┼──────┤\n",
      "│     1│     K│ 0.177│     N│0.0273│\n",
      "│     2│     N│ 0.179│     K│0.0305│\n",
      "│     3│     G│ 0.202│     T│0.0349│\n",
      "│     4│     A│ 0.223│     A│0.0349│\n",
      "│     5│     E│ 0.235│     Q│0.0354│\n",
      "└──────┴──────┴──────┴──────┴──────┘\n",
      "\n",
      "Fish Protamines\n",
      "┌──────┬──────┬──────┬──────┬──────┐\n",
      "│Rank  │AA    │Range │AA    │std   │\n",
      "├──────┼──────┼──────┼──────┼──────┤\n",
      "│     1│     K│ 0.141│     K│0.0272│\n",
      "│     2│     H│ 0.185│     H│0.0351│\n",
      "│     3│     L│ 0.222│     L│0.0387│\n",
      "│     4│     T│ 0.228│     T│0.0411│\n",
      "│     5│     A│ 0.256│     E│0.0448│\n",
      "└──────┴──────┴──────┴──────┴──────┘\n",
      "\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/usr/bin/muscle3.8.31_i86linux64': '/usr/bin/muscle3.8.31_i86linux64'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-d057bc67ae63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtermcolor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ProtamineAnalysis.py'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GenerateDatasets.py'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/protamine_simulation/jupyter_nb/ProtamineAnalysis/alignment_tools.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, protein_list, back_freq_dict)\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotein_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprotein_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mback_freq_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mback_freq_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfasta_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmuscle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfasta\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpro\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotein_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malignment_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alignment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_dicts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_count_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/protamine_simulation/jupyter_nb/ProtamineAnalysis/muscle_tools.py\u001b[0m in \u001b[0;36malign\u001b[0;34m(fasta_str)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         muscle_cline = subprocess.Popen(['/usr/bin/muscle3.8.31_i86linux64', '-in', tmp_file.name,\n\u001b[0;32m---> 27\u001b[0;31m                                          '-quiet'], cwd='/tmp/', stdout=subprocess.PIPE)\n\u001b[0m\u001b[1;32m     28\u001b[0m         \u001b[0maligned_fasta_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmuscle_cline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/public/apps/python/3.7.0/lib/python3.7/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors, text)\u001b[0m\n\u001b[1;32m    754\u001b[0m                                 \u001b[0mc2pread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc2pwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    755\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 756\u001b[0;31m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[1;32m    757\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;31m# Cleanup if the child failed starting.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/public/apps/python/3.7.0/lib/python3.7/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, start_new_session)\u001b[0m\n\u001b[1;32m   1497\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0merrno_num\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1498\u001b[0m                             \u001b[0merr_msg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m': '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1499\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1500\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/usr/bin/muscle3.8.31_i86linux64': '/usr/bin/muscle3.8.31_i86linux64'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import termcolor\n",
    "exec(open('ProtamineAnalysis.py').read())\n",
    "exec(open('GenerateDatasets.py').read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(e_p1_list[1]['_ID'],e_p1_list[1]['_SQ'])\n",
    "#AAABBBBBBBAA\n",
    "b_count=7\n",
    "e_p1_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ep1_align.print_alignment_2(line_len=80)\n",
    "def remove_position_with_excess_dash(protamine_sequence):\n",
    "#remove positions where at least 40% is just dash (-), so set below variable at 0.4\n",
    "#this is for ALIGNED sequences\n",
    "    removal_fraction=0.4\n",
    "    aligned_sequence_array=np.array([])\n",
    "    j=0\n",
    "    for sequence in protamine_sequence:\n",
    "        temp_sequence_array=np.array([])\n",
    "        for position in sequence:\n",
    "            if position == '-':\n",
    "                temp_sequence_array=np.append(temp_sequence_array,1)\n",
    "            else:\n",
    "                temp_sequence_array=np.append(temp_sequence_array,0)\n",
    "        if j==0:\n",
    "            aligned_sequence_array=temp_sequence_array\n",
    "        elif j!=0:\n",
    "            aligned_sequence_array=np.vstack((aligned_sequence_array,temp_sequence_array))\n",
    "        j+=1\n",
    "    positions_for_removal_bool=np.sum(aligned_sequence_array,axis=0)/len(aligned_sequence_array)>=removal_fraction\n",
    "    j=0\n",
    "    for sequence in protamine_sequence:\n",
    "        modified_sequence=np.array(sequence)[[~(positions_for_removal_bool)]]\n",
    "        if j==0:\n",
    "            sequence_list_with_positions_removed=modified_sequence\n",
    "        else:\n",
    "            sequence_list_with_positions_removed=np.vstack((sequence_list_with_positions_removed,modified_sequence))\n",
    "        j+=1\n",
    "    return sequence_list_with_positions_removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count_chars_arg misses terminal arginine\n",
    "def count_chars_arg(txt):\n",
    "    result = 0\n",
    "    for i,char in enumerate(txt):\n",
    "        if char=='R' and i<len(txt)-1:\n",
    "            result += 1    \n",
    "    return result\n",
    "def count_chars_nonarg(txt):\n",
    "    result = 0\n",
    "    for i,char in enumerate(txt):\n",
    "        if char!='R' and i<len(txt)-1:\n",
    "            result += 1    \n",
    "    return result\n",
    "def aa_count(txt):\n",
    "    result=0\n",
    "    i=0\n",
    "    j=0\n",
    "    for i,char in enumerate(txt):\n",
    "        if char=='R' and i<len(txt)-1:\n",
    "            if txt[i+1]=='R':\n",
    "                result+=1\n",
    "    return result\n",
    "def aa_count_rk(txt):\n",
    "    result=0\n",
    "    i=0\n",
    "    j=0\n",
    "    for i,char in enumerate(txt):\n",
    "        if (char=='R' or char=='K') and i<len(txt)-1:\n",
    "            if txt[i+1]=='R' or txt[i+1]=='K':\n",
    "                result+=1\n",
    "    return result\n",
    "def bb_count(txt):\n",
    "    result=0\n",
    "    i=0\n",
    "    j=0\n",
    "    for i,char in enumerate(txt):\n",
    "        if char!='R' and i<len(txt)-1:\n",
    "            if txt[i+1]!='R':\n",
    "                result+=1\n",
    "    return result\n",
    "def order_parameter(txt):\n",
    "    aa=aa_count(txt)\n",
    "    bb=bb_count(txt)\n",
    "    a_count=count_chars_arg(txt)\n",
    "    b_count=count_chars_nonarg(txt)     \n",
    "    Paa=aa/a_count\n",
    "    Pbb=bb/b_count\n",
    "    lambda_order_parameter=Paa+Pbb-1\n",
    "    return lambda_order_parameter\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aa= 15\n",
      "bb= 6\n",
      "a_count= 20\n",
      "b_count= 12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salmon='MPRRRRSSSRPVRRRRRPRVSRRRRRRGGRRRR'\n",
    "order_parameter(salmon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_charge(txt):\n",
    "    result = 0\n",
    "    for char in txt:\n",
    "        if char=='K' or char=='R' or char=='H':\n",
    "            result += 1\n",
    "        if char=='D' or char=='E':\n",
    "            result = result - 1\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all sequences\n",
    "def sequence_order_parameter(protamine_sequence):\n",
    "    order_p=list()\n",
    "    for sequence in range(0,len(protamine_sequence)):\n",
    "        order_p.append(order_parameter(protamine_sequence[sequence]['_SQ']))\n",
    "    return order_p\n",
    "def sequence_order_parameter_bindingregion(protamine_sequence,list_of_aligned_sequence):\n",
    "#binding region according to christian powell paper is positions 17 to 46 for ALIGNED eutherian p1 sequences\n",
    "#ALIGNED sequences are each 64 positions total\n",
    "#i apply below function to non-aligned sequences, so i set my upper bound and lower bound for binding region,-\n",
    "# - by taking two fractions 17/64 and 46/64 and applying these as lower & upper bound for non-aligned sequence\n",
    "    order_p=list()\n",
    "    powell_lower_bound_for_aligned_sequence=18\n",
    "    powell_upper_bound_for_aligned_sequence=50\n",
    "#below variable (powell_total_length_aligned_sequence) is made\n",
    "#by taking length of a single sequence in list of aligned sequences with unnecessary positions removed\n",
    "    powell_total_length_of_aligned_sequence=len(list_of_aligned_sequence[0])\n",
    "    powell_lower_fraction=powell_lower_bound_for_aligned_sequence/powell_total_length_of_aligned_sequence\n",
    "    powell_upper_fraction=powell_upper_bound_for_aligned_sequence/powell_total_length_of_aligned_sequence\n",
    "    for sequence in range(0,len(protamine_sequence)):\n",
    "        lower_bound=round(powell_lower_fraction*len(protamine_sequence[sequence]['_SQ']))\n",
    "        upper_bound=round(powell_upper_fraction*len(protamine_sequence[sequence]['_SQ']))\n",
    "#        print(e_p1_list[sequence]['_SQ'][0:lower_bound]+termcolor.colored(e_p1_list[sequence]['_SQ'][lower_bound:(upper_bound+1)],color='yellow',on_color='on_grey')+e_p1_list[sequence]['_SQ'][(upper_bound+1):len(e_p1_list[sequence]['_SQ'])])\n",
    "        order_p.append(order_parameter(protamine_sequence[sequence]['_SQ'][lower_bound:upper_bound]))\n",
    "    return order_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average for whole fish is 0.21017050544914323\n",
      "The average for binding region of p1 is 0.13773729689524494\n",
      "The average for whole eutherian p1 sequences is 0.0015294459327693738\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(nrows=2,ncols=2,figsize=(6,6))\n",
    "p1_sequence_order_parameter_bindingregion=sequence_order_parameter_bindingregion(e_p1_list,ep1_align.alignment_matrix)\n",
    "fish_sequence_order_parameter=sequence_order_parameter(fish_list)\n",
    "p1_sequence_order_parameter=sequence_order_parameter(e_p1_list)\n",
    "x_lim_min = min([min(fish_sequence_order_parameter),min(p1_sequence_order_parameter),min(p1_sequence_order_parameter_bindingregion)])\n",
    "x_lim_max = max([max(fish_sequence_order_parameter),max(p1_sequence_order_parameter),max(p1_sequence_order_parameter_bindingregion)])\n",
    "ax[0,0].hist(fish_sequence_order_parameter,\n",
    "                color='blue',\n",
    "                    alpha=0.5,\n",
    "                edgecolor='black',density=False,\n",
    "        weights=np.ones(len(fish_sequence_order_parameter)) / len(fish_sequence_order_parameter),label='Fish')\n",
    "ax[0,0].set_xlabel('Sequence Order Parameter',fontsize=10)\n",
    "ax[0,0].set_ylabel('Fraction of Sequences',fontsize=10)\n",
    "plt.setp(ax[0,0].get_xticklabels(),fontsize=9.5)\n",
    "plt.setp(ax[0,0].get_yticklabels(),fontsize=9.5)\n",
    "#ax[0,0].text(.92,.93,'Fish',fontsize=9,\n",
    "#        horizontalalignment='center',\n",
    "#        transform=ax[0,0].transAxes)\n",
    "ax[0,0].set_xlim(x_lim_min,x_lim_max)\n",
    "ax[0,0].set_title('Fish')\n",
    "ax[1,0].hist(p1_sequence_order_parameter_bindingregion,\n",
    "                color='blue',\n",
    "                    alpha=0.5,\n",
    "                edgecolor='black',density=False,\n",
    "         weights=np.ones(len(p1_sequence_order_parameter_bindingregion)) / len(p1_sequence_order_parameter_bindingregion),label='Eutherian P1')\n",
    "ax[1,0].set_xlabel('Sequence Order Parameter',fontsize=10)\n",
    "ax[1,0].set_ylabel('Fraction of Sequences',fontsize=10)\n",
    "plt.setp(ax[1,0].get_xticklabels(),fontsize=9.5)\n",
    "plt.setp(ax[1,0].get_yticklabels(),fontsize=9.5)\n",
    "#ax[1,0].text(.8,.93,'Eutherian P1',fontsize=9,\n",
    "#        horizontalalignment='center',\n",
    "#        transform=ax[1,0].transAxes)\n",
    "#ax[1,0].text(.76,.83,'Binding Region',fontsize=9,\n",
    "#        horizontalalignment='center',\n",
    "#        transform=ax[1,0].transAxes)\n",
    "ax[1,0].set_title('P1: Binding Region')\n",
    "ax[1,0].set_xlim(x_lim_min,x_lim_max)\n",
    "ax[1,1].hist(p1_sequence_order_parameter,\n",
    "                color='blue',\n",
    "                    alpha=0.5,\n",
    "                edgecolor='black',density=False,\n",
    "         weights=np.ones(len(p1_sequence_order_parameter)) / len(p1_sequence_order_parameter),label='Eutherian P1')\n",
    "ax[1,1].set_xlabel('Sequence Order Parameter',fontsize=10)\n",
    "ax[1,1].set_ylabel('Fraction of Sequences',fontsize=10)\n",
    "plt.setp(ax[1,1].get_xticklabels(),fontsize=9.5)\n",
    "plt.setp(ax[1,1].get_yticklabels(),fontsize=9.5)\n",
    "#ax[1,1].text(.8,.93,'Eutherian P1',fontsize=9,\n",
    "#        horizontalalignment='center',\n",
    "#        transform=ax[1,1].transAxes)\n",
    "#ax[1,1].text(.83,.83,'Whole Seq',fontsize=9,\n",
    "#        horizontalalignment='center',\n",
    "#        transform=ax[1,1].transAxes)\n",
    "ax[1,1].set_title('P1: whole sequence')\n",
    "ax[1,1].set_xlim(x_lim_min,x_lim_max)\n",
    "print('The average for whole fish is',\n",
    "      sum(fish_sequence_order_parameter)/len(fish_sequence_order_parameter))\n",
    "print('The average for binding region of p1 is',\n",
    "      sum(p1_sequence_order_parameter_bindingregion)/len(p1_sequence_order_parameter_bindingregion))\n",
    "print('The average for whole eutherian p1 sequences is',\n",
    "      sum(p1_sequence_order_parameter)/len(p1_sequence_order_parameter))\n",
    "ax[0,1].remove()\n",
    "fig.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MARHRCCRSR--SQSRS\u001b[40m\u001b[33mR-R--DRQKRRCR-TPRRRSCRR--RTA-R\u001b[0mRCGRRR--YKPRCRRN-\n",
      "MARYSCCRSH--SRSRS\u001b[40m\u001b[33mR-R---R-RQRCR-RRRRRSCGR--RR--R\u001b[0mACYRRYTVRYRRRRRRR\n",
      "MARYRCCRSH--SRSRC\u001b[40m\u001b[33mR-R---R-RRRSR-RRRRRSCGR--RR--R\u001b[0mAGYRRY---TVRYRRRR\n",
      "MARYRCCLTH--SRSRC\u001b[40m\u001b[33mRPR---RRRRRCR-KLRRRFCRR-PRR--R\u001b[0mVCCRRY--TAIRCTR--\n",
      "MARYRCRLTH--SRSGC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRFCRR--RRR-R\u001b[0mVCCRRY--TVVRCTRQ-\n",
      "MARYRCCLTH--SRSRC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRFGRR--RRR-R\u001b[0mVCCRRY--TVVRCTRQ-\n",
      "MARYRCCLTH--SRSRC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRFGRR--RRR-R\u001b[0mVCCRRY--TVVRCTRQ-\n",
      "MARYRCCLTH--SRSRC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRFGRR--RRR-R\u001b[0mVCCRRY--TVVRCTRQ-\n",
      "MARYRCCLTH--SGSRC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRFGRR--RRR-R\u001b[0mVCCRRY--TVIRCTRQ-\n",
      "MARYRCCLTH--SGSRC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRFGRR--RRR-R\u001b[0mVCCRRY--TVIRCTRQ-\n",
      "MARYRCCLTH--SGSRC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRFGRR--RR---\u001b[0m----RR-----------\n",
      "MARNRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---P-KRGCR-SRRRRCYQR--RR--R\u001b[0mVCCRRY--TTIRCARQ-\n",
      "MARNRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---P-RRRCR-SPRRRRYQR--RR--R\u001b[0mVCCRRY--TVTRCARQ-\n",
      "MARNRCCRSQ--SRGRC\u001b[40m\u001b[33mR-R---P-RRRCR-SPKRRRYQR--RR--R\u001b[0mVCCRRS--ATMRCASQ-\n",
      "MARNRCCRSQ--SRGRC\u001b[40m\u001b[33mR-R---P-RRRYR-SPRRRRYQR--RR--R\u001b[0mVCCRRS--TTMRCASQ-\n",
      "MARYRCCRSH--SRSRC\u001b[40m\u001b[33mR-P---R-RRRCR-RRRRRCCPR--RRR-A\u001b[0mVCCRRY--TVIRCRRC-\n",
      "MARYRCCRSH--SRSRC\u001b[40m\u001b[33mR-P---R-RRRCR-RRRRRCCPR--RRR-A\u001b[0mVCCRRY--TVIRCRRC-\n",
      "MARYRCCRSH--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR-PRK--R\u001b[0mVCSRRY--RVGRCRRR-\n",
      "MARYRCCRSR--SRSRC\u001b[40m\u001b[33mR-R---R-RRKCY-RRRRRCSRK--RRR-R\u001b[0mVCCRRY--TVMRCRRR-\n",
      "MARYRCCRSP--SRSRC\u001b[40m\u001b[33mR-R---Q-RRRCR-RRRRRCCRQ--RRR-R\u001b[0mVCCRRY--TMVRCTRQ-\n",
      "MARYRCCRSP--SRSRC\u001b[40m\u001b[33mR-R---Q-RRRCR-RRRRRCCRQ--RRR-R\u001b[0mVCCRRY--TMVRCTRQ-\n",
      "MARYRCCRSQ--SQSRC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRCVRR--RR---\u001b[0mVCCRRY--TVLRCRRRR\n",
      "MARYRCCRSQ--SQSRC\u001b[40m\u001b[33mR-R---RRRRRCR-RRRRRSVRQ--RR---\u001b[0mVCCRRY--TVLRCRRRR\n",
      "MARYRRCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRRCH-RRRRRCCRR--RRRRR\u001b[0mACCRRY-----RCRRR-\n",
      "MARYRRCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRRCH-RRRRRCCRR--RRRRR\u001b[0mACCRRY-----RCRRR-\n",
      "MARYRCCRSR--SRSRC\u001b[40m\u001b[33mRPR-----RRRCR-RRRRRCCRR--RR--R\u001b[0mVCCRRY---SARCRRRR\n",
      "MARYRCCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRRCH-RRRRRCSRR--RRR-R\u001b[0mVCCRRY--TVIRCRRR-\n",
      "MARYRCCRSP--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mVCCRRY---TVRCRRR-\n",
      "MARYRCCRSP--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mVCCRRY---TVRCRRR-\n",
      "MARYRCCRSP--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mVCCRRY---TVRCRRR-\n",
      "MARYRCCRSP--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRSCRR--RR--R\u001b[0mVCCRRY---TVRCRRR-\n",
      "MARYRCCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRRCY-RRRRRCCRR--RRRRR\u001b[0mVCCRRY----SRCRRR-\n",
      "MARYRCCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRRCY-RRRRRCCRR--RRRRR\u001b[0mVCCRRY----SRCRRR-\n",
      "MARYRCCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRRSY-RRRRRCCRR--RRR-R\u001b[0mVCCRRY----VRCRRR-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mR-P---R-RRRCR-TRRRRCCRR-RRR--R\u001b[0mVCCRRY--TVVRCRRR-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-TRRRRCCRR-RRR--R\u001b[0mVCCRRY--TVVRCRRR-\n",
      "MARYRCCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRRCY-RRRRRCCRR-RRR--R\u001b[0mVCCRRY--TVIRCRRR-\n",
      "MARYRCCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRRCY-RRRRRCCRR-RRR--R\u001b[0mVCCRRY--TVIRCRRR-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCY-RRRRRCCRR--RRR-R\u001b[0mVCCRRY----TRYRRR-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCY-RRRRRCCRR-RRR--R\u001b[0mVCCRRY--TVVRCRRR-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mVCCRRY---TVRCRRRR\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRRRRR---R\u001b[0mVCCRRY--TVVRCRRR-\n",
      "MARNRCCRSQ--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARR-\n",
      "MARNRCCRSQ--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARR-\n",
      "MARNRCCRSQ--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARR-\n",
      "MARNRCCRSQ--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARR-\n",
      "MARNRCCRSQ--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARQ-\n",
      "MARNSCCRSP--SRSRR\u001b[40m\u001b[33mR-R---P-RQRRR-SQRRRCCQR--RR--R\u001b[0mVCCRRY--TTVRCARQ-\n",
      "MARNSCCRSP--SRSRR\u001b[40m\u001b[33mR-R---P-RQRRR-SQRRRCCQR--RR--R\u001b[0mVCCRRY--TTVRCARQ-\n",
      "MARNSCCRSP--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCAGQ-\n",
      "MARNSCCRSP--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCAGQ-\n",
      "MARNSCCRSP--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARQ-\n",
      "MARNSCCRSP--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARQ-\n",
      "MARNSCCRSP--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARQ-\n",
      "MARNTCCRSP--SRSRR\u001b[40m\u001b[33mR-R---P-RQRCR-SQRRRCCRR--RR--R\u001b[0mVCCRRY--TTVRCARQ-\n",
      "MARNRCCRSQ--SQSRR\u001b[40m\u001b[33mR-R---P-RRRNR-SRRRQCCQR--RR--R\u001b[0mVCCRRY--TAIRCARQ-\n",
      "MARNRCCRGQ--SQSRR\u001b[40m\u001b[33mR-R---P-RRRYR-SRRRQCCQK--RR--R\u001b[0mVCCRRY--TATRCARQ-\n",
      "MARNTCCRSQ--SQSRR\u001b[40m\u001b[33mR-R---P-RRRYR-SRRKQCCQK--RR--R\u001b[0mVCCRRY--TAIRCARQ-\n",
      "MARNRCCRSQ--SQSRR\u001b[40m\u001b[33mR-R---P-RRRYR-SRRRQCCQK--RR--R\u001b[0mVCCRRY--TAIRCARQ-\n",
      "MARNRCCRSQ--SQSRR\u001b[40m\u001b[33mR-R---P-RRRYR-SRRRQCCQK--RR--R\u001b[0mVCCRRY--TAIRCARQ-\n",
      "MARNRCCRSQ--SQSRR\u001b[40m\u001b[33mR-R---P-RRRYR-SRRKQCCQK--RR--R\u001b[0mVCCRRY--TAIRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRIRCCRR--QR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRYR--RRRQCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRYR--RRRQCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRYR--RRRQCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRYR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRYR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCL--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--ATTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCASQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRLCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRLCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRLCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQSRC\u001b[40m\u001b[33mR-R---P-RRRCR--RRRRCCRR--RR--R\u001b[0mVCCRRY--TTTRCAR--\n",
      "MARNR-CRSP--SQSRG\u001b[40m\u001b[33mR-C---P-RRRYR-SKRRRCCQR--RR--R\u001b[0mVCRRRY---TRRCARQ-\n",
      "MARNR-CRSP--SQSRG\u001b[40m\u001b[33mR-C---P-RRRYR-SKRRRCCQR--RR--R\u001b[0mVCRRRY---TRRCARQ-\n",
      "MARNR-CRSP--SQSRG\u001b[40m\u001b[33mR-C---P-RRRYR-SKRRRCCQR--RR--R\u001b[0mVCRRRY---TRRCARQ-\n",
      "MARNR-CRSP--SQSRG\u001b[40m\u001b[33mR-R---P-RRKYR-SRRRRCCQR--RR--R\u001b[0mVCCRRY--TTMRCAKQ-\n",
      "MARNR-CRSP--SQSRG\u001b[40m\u001b[33mR-R---P-RRRYR-SKRRRCCQR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARTR-CRSP--SQSRG\u001b[40m\u001b[33mR-R---P-RRRYR-SKRRRCCQR--RR--R\u001b[0mVCCRRY--TTTRCARQ-\n",
      "MARNR-CRSP--SQNRG\u001b[40m\u001b[33mR-R---P-RRRYR-SRRRRCCQR--RR--R\u001b[0mVCCRRY--TSVRCARQ-\n",
      "MARNR-CRSP--SQSRG\u001b[40m\u001b[33mR-R---P-RRRYR-SRRRRCCQR--RR--R\u001b[0mVCCRRY--TTVRCARQ-\n",
      "MARYRCCRSR--SRSRC\u001b[40m\u001b[33mRSR---R-RRRSH-RRRRRCARR--RRRTR\u001b[0mRGCRRR--YSLRRRRY-\n",
      "MARYRCCRSQ--SQSRC\u001b[40m\u001b[33mC-R---R-RQRCH-RRRRRCCQT--RRRAM\u001b[0mRCCRRR--YRLRCRRH-\n",
      "MARYRCCRSQ--SQSRC\u001b[40m\u001b[33mC-R---R-RQRCH-RRRRRCCQT--RRRAM\u001b[0mRCCRRR--YRLRCRRH-\n",
      "MARYRCCR----SRSRC\u001b[40m\u001b[33mR-R---R-RRSCR--RRRRCRRR--RA--R\u001b[0mRSCRRR--YSLRCCRRY\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mY-R---Q-RRRGR-RRRRRTCRR--RRA-S\u001b[0mRCCRRR--YKLTCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mY-R---Q-RRRGR-RRRRRSCRR--RRA-S\u001b[0mRCCRRR--YKLPCRRY-\n",
      "MARYRCCRSRSLSRSRC\u001b[40m\u001b[33mY-R---Q-RPRCR-RRRRRSCRR-PRA--S\u001b[0mRCCRRR--YRLRRRRY-\n",
      "MARYRCCRSR--SRSRC\u001b[40m\u001b[33mY-R---R-RRRCR-TRRRRCCRR--RRA-R\u001b[0mRCCRRR--YKLRCRRY-\n",
      "MARYRCCRSR--SRSRC\u001b[40m\u001b[33mY-R---R-RRRCR-TRRRRCCRR--RRA-R\u001b[0mRCCRRR--YKLRCRRY-\n",
      "MARYRCCRSR--SRSRC\u001b[40m\u001b[33mY-R---Q-RRRCR-RRRRRCRSR--RA--R\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MVRYRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCQR--RRV-R\u001b[0mKCCRRT--YTLRCRRY-\n",
      "MARYRCCRSP--SRSRC\u001b[40m\u001b[33mR-R---R-RRRFY-RRRRRCHRR--RR---\u001b[0mRCCRRR--YTRRCKRY-\n",
      "MARYRCRRSQ--SRSRC\u001b[40m\u001b[33mC-R---Q-RRRCR-RRRRQRFRA--RKRAM\u001b[0mRCCHRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mS-RRRYR-RRRCR-RRRRRSCRR--RRRR-\u001b[0mACCRYR--Y----RRY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCQR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--Q\u001b[0mRCCRRRRSYTIRCKKY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTFRCKRY-\n",
      "MARYRCCRSK--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCRSRRRRRCCRR--RR--R\u001b[0mRCCRRRRSYTIRCKRY-\n",
      "MAKSRCCGSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RQRCR-RRRRRSCRA--RRRAM\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRRCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRQSCRA--RRRAT\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRL-SRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRL-SRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRSRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRSRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRSRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRSRRY-\n",
      "MARYRRCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR--RRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRRCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR--RRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR-RRRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRCRRY-\n",
      "-------RSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR--RRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRCRRY-\n",
      "-------RSQ--SRSRC\u001b[40m\u001b[33mC-R---P-RRRCR--RRRRSCRA--RRRAT\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mR-R---R-RRRCR-RRRRRCCRR--RR--R\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---R-RRRCR-RRRRQRCRA--RRTAM\u001b[0mRCCRRR--YRRRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---R-RRRCR-RRRRRRCRA--RRRAM\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---R-RRRCR-RRRRRRCRA--RRRAM\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mC-R---Q-RRRCR-RRRRRRCRA--RRRAM\u001b[0mRCCRRR--YRLRCRRY-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mY-R---R-GQRSR-RRRRRSCQT--RRRAM\u001b[0mRCCRPR--YRLRRRRH-\n",
      "MARYRCCRSQ--SRSRY\u001b[40m\u001b[33mY-R---Q-RQRSR-RRRRRSCQT--RRRAM\u001b[0mRCCRPR--YRPRCRRH-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mY-R---Q-RQTSR-RRRRRSCQT--QRRAM\u001b[0mRCCRRR--NRLRRRKH-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mY-R---Q-RQRSR-RRKRQSCQT--QRRAM\u001b[0mRCCRRR--SRMRRRRH-\n",
      "MARYRCCRSQ--SRSRC\u001b[40m\u001b[33mY-R-----QRRSR-RRKRQSCQT--QRRAM\u001b[0mRCCRRR--SRLRRRRH-\n"
     ]
    }
   ],
   "source": [
    "#this is for eutherian P1 sequences only, based on aligned eutherian p1 on powell paper\n",
    "lower_bound_aligned=17\n",
    "upper_bound_aligned=46\n",
    "for sequence in ep1_align.alignment_matrix:\n",
    "    sequence=''.join(sequence)\n",
    "    print(sequence[0:lower_bound_aligned]+termcolor.colored(sequence[lower_bound_aligned:(upper_bound_aligned+1)],color='yellow',on_color='on_grey')+sequence[(upper_bound_aligned+1):len(sequence)])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average is 0.0015294459327693738\n"
     ]
    }
   ],
   "source": [
    "p1_sequence_order_parameter=sequence_order_parameter(e_p1_list)\n",
    "plt.hist(p1_sequence_order_parameter,\n",
    "                color='violet',\n",
    "                    alpha=0.5,\n",
    "                edgecolor='black',density=False)\n",
    "plt.title('Eutherian P1',fontsize=25)\n",
    "plt.xlabel('Sequence Order Parameter',fontsize=20)\n",
    "plt.ylabel('Frequency',fontsize=20)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "print('The average is',sum(p1_sequence_order_parameter)/len(p1_sequence_order_parameter))\n",
    "plt.savefig('figures_for_publication/p1_seq_order_param_frequency.png',dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average is 0.0015294459327693738\n"
     ]
    },
    {
     "data": {
      "image/png": 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B1LMk/TrtmEol6dwQ/1OS7pfUK0abKZIaE77fTElzwvd1dziBX3WSRkiqT+O9k/AkLOwEYGczGxgG/16adkClkDQI+Dow3MyGAD8nz5AySd068B752p4Zvq+NwBkJ19FRI4DYSVihGGLz0miFbQIOknQY8IqZrQOQ9BngZ8BeRGNQLzSzJZLOBMYCy4jGJ14LLAemmNnw0HaJmR0oqTvwU+ALQHfgMjP7o6SpRAO29wvrP8XM3pT0D8B3gc3ANDP7YXi/74QYHmu9c12Oc4EbzGwzgJn9XtLVkj4HfAU4Obz305JWtImdEO+lRFed7AzcYWZTJDXltgV+XOD7mw/0lvRDoD/wGeA2M5scetoriUYuLZX0PPAN4BPAi8A3zMwktQDTgGOA+4hG5RwLzDWzS/J9j0TJfyJwZPi+z+zg56g8M/NHgQdRTzKD6Mc5Osy7CRgZpo8EfgN0A/4EfJroH/UloBFoAKbnrG9J+HsxMCZM9wKeCdNTc97nKuDbRMm4GNg9zO8G9CAaftU9zHsAOLxN7I8Ah7aZdw9RQjSF11Uk9sOAh3OWmR1i2dY2z/c1E9g/tPkN8Hc5ce8a3qd7WP/inPh3z1nHvcDgML0Z+Gxoux44KsxfAOzZzvd4XJgu+XNU++E9YRFmdidwp6Q9iHqMB4HDgSGSLg6LbSG6h+UaM/sLgKT5ratos8rWqzcOBwZIOjE8/0zOMvPC3xai/+G/ADxvZptCTFslHQj0Bh6XBFHv1ZuPX8e3imiT7JWcefVh/l8Dz5qZSSoUe5+w3IzwfA/ggDD9rIVfeB73Ax8QXc70MHC5pBFEQ8Y+Gx4Q9WYfhunBkr5HlCS9gQfD/NVm9maIay1R8rV+th4U/x5bJf0cVeNJWEAY6L3RzDYAfyHazBHR5tIcM3sgLLcL0Q+sl6RPAe8BfcNq3gH2U5QpvYDPhfkvEvWKE3PW0Sr3RyFgCXC4pN3MbLOknYDXwvzhZrYlzGt7edavgGskzQrtTgQ2m9mqkLit1yiuLRD7y0Q/+r8PydrdzD6U1Jfi1zeeaWYrw+fqQXRR8hFEPeCrOXHmruMm4EQz+7Oke3OW+ViCtEmY1n+LfN/jB2z/bSf9HFXjSVjY/sBESR8RfU8PmdlSSdcDt4X9DAEPm9m/SRoHNBNtuq4CMLMN4aqCOcAfgTVh3T8DbpXU+r/zXOB7+YIws3WSbgBmSvoftu8TTgKelLSVaD/yXOCNnHZPSfoVMF3SFqJkG5Vn/VsLxP6CpOnAU+E9Nks6pcTvcD3R5m0zUTK8XWC5XxL16q8UeL2QQt/j74AJkl42s4vK8DkqygdwV4CkKcBdZjYz7Vhc5+enKJxLmfeEzqWsJvcJe/bsaQ0NDWmH4Vxs8+bNW2udoCpT2TQ0NDB37ty0w3AuNkmvF3rN9wmdS1nFklAl3Fla0nhJi8IA4MsqFZNLz/Lly+nRoweNjY00NjZy/PHHF11+6tSpbNiwYVvb4cOT39D7nHPOSdwWYObMmey77740NjbSv39/pk+fDsDVV19N7969OxQbUJlha8BRwM/C9E+BY3Je+3z4exDw/8L0eKITz7HWf/TRR5urLcuWLbPjjz8+9vJDhgyxFStWJGqba+vWrYna5ZoxY4adf/75Zmb2+uuvW58+fczMbPXq1bZ06dJYsRGNEMr7e65UT5jkztI/lDQ9jGRwGdHU1ERzc3Rb1bvuuovx48fz5JNPsnDhQs4880wuvTS6eOWdd95h1KhRHHXUUUyaNAmAd999l7POOovjjz+eYcOGsWTJEgAaGxu5/PLLOeGEE1iyZAkHHnggADNmzGDo0KEMGjSIU089lffeew+AAw88kCuuuIIhQ4YwcuTIovHW19ezceNGAPbdd1922qnjKVSpJKyjtDtL32JmRwPfJLrV3w4kXShprqS5b731VnmjdVUxb968bZujxTYRhw0bRt++fbn//vu59dbo5/DnP/+ZyZMnM3v2bG6+OdrDufHGGzn99NN54oknmDhxImPGjNm2jn79+vHoo49y8MEHb5vXv39/ZsyYwaxZszj00EO57777ANiyZQtnn302Tz31FOvWreOFF14oGNvChQvZe++8BzkTq9TR0ZLuLG3hMiEz++8wrnEHllOzvl+/fn5yswLGjZtES8v6xO3r6+uYMGF0wdePPvrobftTrXL/va3IOevDDjuMT37ykwB06xZd/rd48WKeeuopbrvtNgB23nn7z3nAgAE7rOPFF1/k+9//Pu+//z5r1qxhjz322Naub9++4TPU8/bbO46ue/jhhxk6dCi77rorkyeXt75opZJwDnAR0TVgw4kuLQE+dmfpf8iZt4dF4yx7VjAm146WlvU0NIxP3H758tLb7rnnnqxcuRKIesq6ujoAdtllF7Zs2bJtuXz/OX/xi1/k2GOP5bTTTgPggw8+2PZaa6Lmuv7667n22ms59thj+dd//deCSZ9v/sknn8yUKVPif7ASVGRz1Eq/s/SPJD0DPASM2WGFrkvI3RxtbGxk48aNXHDBBdx0002ccsop246GApx++umcf/75XH311QXXN3bsWO677z6GDRvG0KFDueWW4nVzRo4cyfnnn89pp53Gm2++2eHP85Of/IRRo0axYMEChg8fztKlSxOtpyaHrfXr18/8ZH35NTWN73BPOHVq8vZdmaR5ZtYv32t+st65lHkSOpcyT0LnUuZJ6FzKPAmdS5knoXMp8yR0LmWehM6lzJPQuZR5EjqXMk9C51LmSehcyjwJnUuZJ6FzKWs3CUPhEyQNDLeX+KfKh+VcdsTpCUeEvxcBpxIVZnTOlUmcJNxd0u7AFjNbBbxf4Zicy5Q4SXgvUZXTKZJ2I6pz55wrk3aT0Mwmmll/4BUz22xmZ1QhLucyo907m0k6CbgB6C7pSOAOM/ODM51UR25buGDBS3ixq+qLc3vBsUR30P69RfXRD4izYkkTgX7AfDP755z5twN9iOqRf8vMng/14e8CPgGMM7Pp+dbp2teR2xY2N48oaywunjj7hB+Y2WaipIGoTntRko4CPmVmg4BdJB2T8/JNZjYQOA+4JswbA1wNfBX4ftzgnesK4iThHEn/AewTerfmGG1KrUVxODDbzDYCf5HUevdu57q8djdHzewqSScArwEvmtkjMdZbF5aH6Jb4X8yzTG4tim62/QaorbUrNuQuLOlC4EKIblXuXFcRZ8TMV4DHzOzfgGnheXtKqkUBfJTz8g7LQ1SLwsz6mVm/chfkcC5NcTZHx7T2UuHvFTHazAFaq0AOB55tfSGnFsV1Ocs/L+nYMChgDzP7WC/oXFcWJwl3a/P8k+01SFCL4n8D1xPtP94QJ3Dnuoo4pyielnQX0aiZRmBWnBXnnpYIrg/zD8mz7EpgWJz1OtfVxDkwM0bS3xKd27s75oEZ51xMca8nfJ1ov26TpMEVjMe5zIkzbO1uYE9gRZhlwNOVDMq5LImzT7ifmTVWOhDnsiruiJnDKx6JcxkVpyc8AxgpqbWAuJnZwRWMyblMiXN09KBqBOJcVsUZtraXpB9JukPSzpL+sRqBOZcVcfYJpxKdoD/IzLYAX69oRM5lTKxha2b2INC6T9ju9YTOufjiJOF7kvoDhNtbbKxsSM5lS5yjoxcDPwI+C1wJXFLRiFzNWrBgEU1N4xO3r6+vY8KE0WWLp1bEOTq6Eji7CrG4GrdpkyW+vw3A8uXJ29ayOMPWZrD9/jIAmJlf8eBcmcTZHB0V/oro7ml/U7lwnMueOJujq3KerpTktSicK6M4m6Nj2b45egDQvaIROZcxcTZHV4a/BjwPjK5YNM5lUJzN0V9UIxDnsirO5ujrwD7AGqAXsJpo9IxfTeFcGcQZMfMEcLCZ1QMHAY+b2UGegM6VR5wkPMTMXgcwsxby3017B5ImSpol6eY288dKWi3pupx5UyX9QdJMv0rDZU2cAzOzJf0eeA44mujGvkXlFoSR9FNJx5jZc+HlKcBstt8cuNU5ZrakhNid6xLiHJj5nqS+wMHAf5rZghjrzVcQ5rmwvjWSDmv7NsAvJb0NfLu1583ltShcVxXnot5dgYFE9x1dLKltD5ZPHdsLurQWeCnmcjMbAPwQ+Pd8C3gtCtdVxdkn/AXQDRgeLuq9MkabogVh2jKzdeFvM9GRWOcyI04S9jSzW4D3w/M4F/UWLAiTT2s9QkmH0E7COtfVxDkwY5L2D397ERX3LN7AbL6k1oIwCwkFYczseknnA98C9pTUw8wuAX4tqQfRvuE3E38a52pQnCT8LvBrolMT9wDfjrPiIgVh7gDuaLPs1+Ks07muKM7R0ReAIVWIxblMKrhPKGl02ERE0lBJr0h6SdKJ1QvPua6v2IGZM83snTA9Cfga0amK71c6KOeypNjm6PsAkvYBtprZf4fn7R6Ycc7FVzQJJZ1DdDuLBwEk7USMctnOufiKbY42AYcDbwE3hXmfB24v1MA5V7qCPaGZrQHGtJm3FFha6aCcy5K45bKdcxVS7BRFnBP5zrkOKtYTPgogaXKVYnEuk4r1drtJugY4XtJVuS+Y2Q2VDcu57CiWhKcBQ4EPgFVFlnPOdUB7R0fvkfS0ma2W1NPM1lYxNucyIc7R0b6SXgUeCONHT6p0UM5lSZwjoFcD/c3sXUl1wDTg9xWNyrkMidMTmpm9GybW06ZMmnOuY+L0hNMkPUZ0i4ovE05dOOfKI85FvRNCrfpDgN+a2cKKR+UyKavltmONijGzRcCiCsfiMi6r5bZ97KhzKatYEpZYi6KPpGZJz0g6olIxOdcZxSmN1hM4C9iLcM9RM5vQTptSa1H8ADgb+Aj4D+DUUj9IZzFu3CRaWtYnbl+r+zUuuTj7hA8CDwGvlbDeUmtR9DCzFQDhXGTNamlZn8n9GpdcnCRcZ2Y3lrjeOrYn7bu0X04td7M47x2+vSCM66ri7BOulvQdSQMlDZA0IEabkmpR8PEBAB/lXcALwrguKk5P2B34UnhAlDCz22kzB7gIuI+oFsXUdpZfF261/xHbqzllUkfPlS1Y8BINDWULx1VBnJP155W60gS1KK4B7g3NLyn1/bqSjp4ra24eUbZYXHXEOTraD/g/RPt564mKeD5XrA2UXIvieaIbCzuXOXE2RycBI81smaS/An6FJ4xzZRP3KoplYeI1Chw4cc4lE6cnfFFS6wn2gcDLlQ3JuWyJc2DmYkmnEl1F8WB4OOfKpGASStrLzN6WtB/RaJfWgzH7AqurEZxzWVCsJxwLXEZUpbf1ZLrC9LAKx+VcZhS729plYfIcM9vW84W69c65MolzdPSuNs9vrUQgzmVVsX3CQcBgoHfOHbi7A5+rRmDOZUWxfcINwEpgI9vvwP0B8PNKB+VclhTbJ1wELJI0DXjTzEySAL+EwbkyirNPeLeZGURDZ4C7KxuSc9kSJwm7tXnudQudK6M4CbVW0jeAJ4mqNK2rbEjOZUucnvAC4EDgFuAg4OsVjci5jIkzdnQ9cEXlQ3Eum9rtCSV9TdJiSe9KekfSsmoE5lxWxNkcvQYYAswn2iz9r4pG5FzGxEnCDWa2DuhmZm8DR1Y4JucyJU4StkjaDZgj6RFgS4Vjci5TiiahpJ2AO81sMzAGuBI4Jc6Ki9Si2KHuhKSpkv4gaaakf0z2UZyrTUWT0Mw+Av4lTJuZLQwJWVRuLQpgF0nH5LzcWnfirDDd6hwzazQzH5HjMiXO5ugqSVdIGlTCHbjz1aJo1cPMVpjZKqLbKEJ0ofAvJT0kqXfc4J3rCuKMmPkEcGh4QLw7cNdRuBZFvroTl5vZOknHAf8OnNF2hV6LwnVVBXtCSZfDtjtw32lm54VHnBEzxWpR7FB3Ihx9xcyagX3yrdBrUbiuqtjm6Mk509eWuN45bK8/OBx4Nue1dZL2DzeQ2gAgaY/w9xDaLx7jXJdSkSsiitWiIH/diV9L6kHUS36zEjE511kVS8K+kh4j2m/LnTYz+2p7Ky5Si2KHuhNm9rWSonauCymWhF8q8ppzrkyK3d7i9WoG4lxWxTlP6JyrIE9C51LmSehcyjwJnUuZJ6FzKfMkdC5lfg/RPMaNm0RLy/pEbRcseImGhrKG47o4T8I8WlrW09AwPlHb5uYRZY3FdX2+OepcyjwJnUuZJ6FzKfMkdC5lnoTOpcyT0LmUdclTFB05zwd+rq9WLViwiKam8Yna1tfXMWHC6LLGE1eXTMKOnOcDP9dXqzZtssT/7suXJ2tXDr456lzKPAmdS1mX3Bx1rlQd2Z+Eju1TViwJJU0E+gHzc++8JqkPcBvRndu+aWbP55tXqbicy6cj+5PQsX3KimyOJigIU6hIjHNdXqV6wnwFYZ4Lz3uY2QoASXVF5jmXCTKz9pcqdaXSVUSbodMkDQcGmNmE8NrTZjY4dzrfvDzr3FYQBjgEeBXoCawt+weojFqJtVbihNqKtbeZ5S2iUqmesKSCMAXmfYyZTQYm586TNNfM+nUo0iqplVhrJU6orViLqdQpipIKwhSY51wmdJaCMPnmOZcJFdknrBZJF4bN1E6vVmKtlTihtmItpqaT0LmuwIetOZeymktCSZ+W9JCkZySdW2CZoySZpFSH5RWLNbz2hKSnJf1O0qdTinGipFmSbm4zv4+k5hD7EWnE1laRWG8PcTZ3llhLUXNJCHwDuAcYDFwgaZc8y3wLmF/VqPIrFuuHwKhwTvS/gKZqB5dgZFNq2on1JjMbCJxHdJCvptRiEn4ZeNzMtgKLgENzX5T0RWAl8JcUYmurYKxm9p6Z/Tk8/RDYmlZ8Ybp1ZFOrHma2wsxWAXXVDiyPgrGa2bIwmdb32CG1mIR1bD+X+C47/kBGAz+pXjhF1VE8ViR9CrgIuLtqUW1XR+H4cn8bqlI8xdTRzncJ3AjcUqV4yqbTXsokaR+iTblcb7B9NM57tBmNI+kgYIOZrZWq97tJEmtoJ+BOYKyZraf6Sh3ZlKZisSJpNPCSmTVXN6yO67Q9oZm9YWaNbR4jCaNxJHUD+gKv5DQ7HDhG0jTgCKLLozprrAATgGfM7MlqxJlHqSOb0lQwVklfBQYA16UQV8eZWU09iP4X/B0wG2gK804ETm6z3Exg584aK7Af8EGIcybRdZRpxHgzMAu4FdiHqFeG6D+xZ8Kjb9r/7u3E+irRVTozgdvTjrPUh5+sdy5lnXZz1Lms8CR0LmWehM6lzJPQuZR5EjqXMk/CAiTtlDMweJakX6cdU6kknRvif0rS/ZJ6xWgzRVJjwvebKWlO+L7uTmsAvaQRkurTeO8kPAkLO4HoPONAiwYNX5p2QKWQNAj4OjDczIYAPwfuyrNctw68R762Z4bvayNwRsJ1dNQIIHYSViiG2DrtsLVOYBNwkKTDgFfMbB2ApM8APwP2IhpTeaGZLZF0JjAWWEY0rvFaYDkwxcyGh7ZLzOxASd2BnwJfALoDl5nZHyVNJRqEvF9Y/ylm9qakfwC+C2wGppnZD8P7fSfE8JiFu9nlOBe4wcw2A5jZ7yVdLelzwFeIBgx0B56WtKJN7IR4LyW6imJn4A4zmyKpKbct8OMC3998oLekHwL9gc8At5nZ5NDTXkk0EmeppOeJrjj5BPAi8A0zM0ktwDTgGOA+oBfRwO25ZnZJvu+RKPlPBI4M3/eZHfwclZf2aIHO/CDqSWYQ/ThHh3k3ASPD9JHAb4BuwJ+ATxP9o74ENAINwPSc9S0Jfy8GxoTpXkRD1wCm5rzPVcC3iZJxMbB7mN8N6EE0bKt7mPcAcHib2B8BDm0z7x6ihGgKr6tI7IcBD+csMzvEsq1tnu9rJrB/aPMb4O9y4t41vE/3sP7FOfHvnrOOe4HBYXoz8NnQdj1wVJi/ANizne/xuDBd8ueo9sN7wiLM7E7gTkl7EPUYDxKNTx0i6eKw2Bai+1+uMbO/AEhqvZax7XCk1lHlhwMDJJ0Ynn8mZ5l54W8L0f/wXwCeN7NNIaatkg4EegOPh4HqdeH54pz1rCLaJMsdr1of5v818KyZmaRCsfcJy80Iz/cADgjTz1r4hedxP9FwvGeIfvyXSxpBdInRZ8MDot7swzA9WNL3iJKkN/BgmL/azN4Mca0lSr7Wz9aD4t9jq6Sfo2o8CQsIA5c3mtkGomsTNxIl0YvAHDN7ICy3C9EPrFe4LOk9osHaAO8A+4WrJXoBnwvzXyTqFSfmrKNV7o9CwBLgcEm7mdlmSTsBr4X5w81sS5jX9rKRXwHXSJoV2p0IbDazVSFxW6+7W1sg9peJfvR/H5K1u5l9KKkvxa/ZO9PMVobP1YPoQtsjiHrAV3PizF3HTcCJZvZnSffmLPOxBGmTMK3/Fvm+xw/Y/ttO+jmqxpOwsP2BiZI+IvqeHjKzpZKuB24L+xkCHjazf5M0Dmgm2nRdBWBmG8IVHXOAPwJrwrp/BtwqqfV/57nA9/IFYWbrJN0AzJT0P2zfJ5wEPClpK9F+5LlEl0+1tntK0q+A6ZK2ECXbqDzr31og9hckTQeeCu+xWdIpJX6H64k2b5uJkuHtAsv9kqhXb3uVSXsKfY+/AyZIetnMLirD56goH8BdAZKmAHeZ2cy0Y3Gdn5+icC5l3hM6lzLvCZ1LmSehcynzJHQuZZ6EzqXMk9C5lHkSOpey/w8gU51wbMSiEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 223.2x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(nrows=2,ncols=1,figsize=(3.1,6))\n",
    "p1_sequence_order_parameter=sequence_order_parameter(e_p1_list)\n",
    "fish_sequence_order_parameter=sequence_order_parameter(fish_list)\n",
    "ax[0].hist(fish_sequence_order_parameter,\n",
    "                color='blue',\n",
    "                    alpha=0.5,\n",
    "                edgecolor='black',density=False,\n",
    "        weights=np.ones(len(fish_sequence_order_parameter)) / len(fish_sequence_order_parameter),label='Fish')\n",
    "ax[0].set_xlabel('Sequence Order Parameter',fontsize=9)\n",
    "ax[0].set_ylabel('Fraction of Sequences',fontsize=9)\n",
    "plt.setp(ax[0].get_xticklabels(),fontsize=8)\n",
    "plt.setp(ax[0].get_yticklabels(),fontsize=8)\n",
    "ax[0].text(.92,.93,'Fish',fontsize=9,\n",
    "        horizontalalignment='center',\n",
    "        transform=ax[0].transAxes)\n",
    "\n",
    "ax[1].hist(p1_sequence_order_parameter,\n",
    "                color='blue',\n",
    "                    alpha=0.5,\n",
    "                edgecolor='black',density=False,\n",
    "         weights=np.ones(len(p1_sequence_order_parameter)) / len(p1_sequence_order_parameter),label='Eutherian P1')\n",
    "ax[1].set_xlabel('Sequence Order Parameter',fontsize=9)\n",
    "ax[1].set_ylabel('Fraction of Sequences',fontsize=9)\n",
    "plt.setp(ax[1].get_xticklabels(),fontsize=8)\n",
    "plt.setp(ax[1].get_yticklabels(),fontsize=8)\n",
    "ax[1].text(.8,.93,'Eutherian P1',fontsize=9,\n",
    "        horizontalalignment='center',\n",
    "        transform=ax[1].transAxes)\n",
    "\n",
    "print('The average is',sum(p1_sequence_order_parameter)/len(p1_sequence_order_parameter))\n",
    "#plt.savefig('figures_for_publication/overall_seq_order_param.png',dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/public/apps/python/3.7.0/lib/python3.7/site-packages/ipykernel_launcher.py:23: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([['M', 'A', 'R', ..., 'R', 'R', 'N'],\n",
       "       ['M', 'A', 'R', ..., 'R', 'R', 'R'],\n",
       "       ['M', 'A', 'R', ..., 'R', 'R', 'R'],\n",
       "       ...,\n",
       "       ['M', 'A', 'R', ..., 'R', 'K', 'H'],\n",
       "       ['M', 'A', 'R', ..., 'R', 'R', 'H'],\n",
       "       ['M', 'A', 'R', ..., 'R', 'R', 'H']], dtype='<U1')"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_position_with_excess_dash(ep1_align.alignment_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36190476190476195"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array(p1_sequence_order_parameter).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average is 0.21017050544914323\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fish_sequence_order_parameter=sequence_order_parameter(fish_list)\n",
    "plt.hist(fish_sequence_order_parameter,\n",
    "                color='violet',\n",
    "                    alpha=0.5,\n",
    "                edgecolor='black',density=False)\n",
    "plt.title('Fish',fontsize=25)\n",
    "plt.xlabel('Sequence Order Parameter',fontsize=20)\n",
    "plt.ylabel('Frequency',fontsize=20)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "print('The average is',sum(fish_sequence_order_parameter)/len(fish_sequence_order_parameter))\n",
    "plt.savefig('figures_for_publication/fish_seq_order_param.png',dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average is 0.21017050544914323\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fish_sequence_order_parameter=sequence_order_parameter(fish_list)\n",
    "plt.hist(fish_sequence_order_parameter,\n",
    "                color='blue',\n",
    "                    alpha=0.5,\n",
    "                edgecolor='black',density=False,\n",
    "        weights=np.ones(len(fish_sequence_order_parameter)) / len(fish_sequence_order_parameter))\n",
    "plt.title('Fish',fontsize=25)\n",
    "plt.xlabel('Sequence Order Parameter',fontsize=20)\n",
    "plt.ylabel('Fraction of Sequences',fontsize=20)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "print('The average is',sum(fish_sequence_order_parameter)/len(fish_sequence_order_parameter))\n",
    "plt.savefig('figures_for_publication/fish_seq_order_param.png',dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6904761904761905"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(fish_sequence_order_parameter).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence Order parameters:\n",
      "salmon   = 0.25 \n",
      "human P1 = -0.31 \n",
      "bull P1  = 0.28\n",
      "Number of arginines:\n",
      "salmon   = 20 \n",
      "human P1 = 24 \n",
      "bull P1  = 26\n"
     ]
    }
   ],
   "source": [
    "human_p1='ARYRCCRSQSRSRYYRQRQRSRRRRRRSCQTRRRAMRCCRPRYRPRCRRH'\n",
    "bull_p1='ARYRCCLTHSGSRCRRRRRRRCRRRRRRFGRRRRRRVCCRRYTVIRCRR'\n",
    "salmon='MPRRRRSSSRPVRRRRRPRVSRRRRRRGGRRRR'\n",
    "print('Sequence Order parameters:')\n",
    "print('salmon   =',order_parameter(salmon),'\\nhuman P1 =',format(order_parameter(human_p1),'0.2f'),'\\nbull P1  =',format(order_parameter(bull_p1),'0.2f'))\n",
    "print('Number of arginines:')\n",
    "print('salmon   =',count_chars_arg(salmon),'\\nhuman P1 =',count_chars_arg(human_p1),'\\nbull P1  =',count_chars_arg(bull_p1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5, 16)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_block_length_number(human_p1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence Order parameters:\tNumber of arginines:\n",
      "salmon   = 0.25 \t\t salmon   = 20 \n",
      "human P1 = -0.31 \t\t human P1 = 24 \n",
      "bull P1  = 0.28 \t\t bull P1  = 26\n"
     ]
    }
   ],
   "source": [
    "print('Sequence Order parameters:\\tNumber of arginines:')\n",
    "print('salmon   =',order_parameter(salmon),'\\t\\t','salmon   =',count_chars_arg(salmon),'\\nhuman P1 =',format(order_parameter(human_p1),'0.2f'),'\\t\\t','human P1 =',count_chars_arg(human_p1),'\\nbull P1  =',format(order_parameter(bull_p1),'0.2f'),'\\t\\t','bull P1  =',count_chars_arg(bull_p1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of arginines in human P1 is 24\n",
      "The number of arginines in bull P1 is 26\n",
      "The number of arginines in salmon is 20\n"
     ]
    }
   ],
   "source": [
    "print('The number of arginines in human P1 is',count_chars_arg('ARYRCCRSQSRSRYYRQRQRSRRRRRRSCQTRRRAMRCCRPRYRPRCRRH'))\n",
    "print('The number of arginines in bull P1 is',count_chars_arg('MARYRCCLTHSGSRCRRRRRRRCRRRRRRFGRRRRRRVCCRRYTVIRCTRQ'))\n",
    "print('The number of arginines in salmon is',count_chars_arg('MPRRRRSSSRPVRRRRRPRVSRRRRRRGGRRRR'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The charge density in human P1 is 0.5\n",
      "The charge density in bull P1 is 0.5714285714285714\n",
      "The charge density in salmon is 0.6363636363636364\n"
     ]
    }
   ],
   "source": [
    "print('The charge density in human P1 is',count_charge('ARYRCCRSQSRSRYYRQRQRSRRRRRRSCQTRRRAMRCCRPRYRPRCRRH')/len('ARYRCCRSQSRSRYYRQRQRSRRRRRRSCQTRRRAMRCCRPRYRPRCRRH'))\n",
    "print('The charge density in bull P1 is',count_charge('ARYRCCLTHSGSRCRRRRRRRCRRRRRRFGRRRRRRVCCRRYTVIRCRR')/len('ARYRCCLTHSGSRCRRRRRRRCRRRRRRFGRRRRRRVCCRRYTVIRCRR'))\n",
    "print('The charge density in salmon is',count_charge('MPRRRRSSSRPVRRRRRPRVSRRRRRRGGRRRR')/len('MPRRRRSSSRPVRRRRRPRVSRRRRRRGGRRRR'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of blocks in human P1 is [0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 3, 3, 3, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 2, 2, 0]\n",
      "The number of blocks in bull P1 is [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 7, 7, 7, 7, 7, 7, 7, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 2, 2, 0, 0, 0, 0, 1, 0, 0, 1, 0]\n",
      "The number of blocks in salmon is [0, 0, 4, 4, 4, 4, 0, 0, 0, 1, 0, 0, 5, 5, 5, 5, 5, 0, 1, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 4, 4, 4, 4]\n"
     ]
    }
   ],
   "source": [
    "print('The number of blocks in human P1 is',count_arg_blocks('ARYRCCRSQSRSRYYRQRQRSRRRRRRSCQTRRRAMRCCRPRYRPRCRRH'))\n",
    "print('The number of blocks in bull P1 is',count_arg_blocks('MARYRCCLTHSGSRCRRRRRRRCRRRRRRFGRRRRRRVCCRRYTVIRCTRQ'))\n",
    "print('The number of blocks in salmon is',count_arg_blocks('MPRRRRSSSRPVRRRRRPRVSRRRRRRGGRRRR'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence Order parameters:\n",
      "P2a=  -0.03831417624521072\n",
      "Number of arginines:\n",
      "P2a=  27\n"
     ]
    }
   ],
   "source": [
    "p2a='RTHGQSHYRRRHCSRRRLHRIHRRQHRSCRRRKRRSCRHRRRHRRGCRTRKRTCRRH'\n",
    "print('Sequence Order parameters:')\n",
    "print('P2a= ', order_parameter(p2a))\n",
    "print('Number of arginines:')\n",
    "print('P2a= ',count_chars_arg(p2a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#protamine_sequence='MARYRCCRSRSRCRRRRRRCYRRRRRCCRRRRRRVCCRRYTVIRCRRR'\n",
    "#look at example in above line. protamine_sequence is a single sequence. \n",
    "def count_arg_blocks(protamine_sequence):\n",
    "    import numpy as np\n",
    "    arg_length=[]\n",
    "    i=0\n",
    "    j=0\n",
    "    k=1\n",
    "    while j<=len(protamine_sequence)-2 and k<=len(protamine_sequence):\n",
    "        if protamine_sequence[j]=='R' and protamine_sequence[j+1]=='R':\n",
    "            count_arg=0\n",
    "            i=j\n",
    "            while protamine_sequence[i]=='R':\n",
    "                count_arg=count_arg+1\n",
    "                i=i+1\n",
    "                if i>(len(protamine_sequence)-1):\n",
    "                        break\n",
    "            arg_length.extend([count_arg]*count_arg)\n",
    "            j=i\n",
    "        elif protamine_sequence[j]=='R' and protamine_sequence[j+1]!='R':\n",
    "            arg_length.append(1)\n",
    "            j=j+1\n",
    "        elif protamine_sequence[j]=='-':\n",
    "            arg_length.append(np.nan)\n",
    "            j=j+1\n",
    "        else:\n",
    "            arg_length.append(0)\n",
    "            j=j+1\n",
    "        k=k+1\n",
    "    if protamine_sequence[len(protamine_sequence)-1]=='R' and protamine_sequence[len(protamine_sequence)-2]!='R':\n",
    "        arg_length.append(1)\n",
    "#    elif protamine_sequence[len(protamine_sequence)-1]=='-' and protamine_sequence[len(protamine_sequence)-2]!='R':\n",
    "#        arg_length.append(np.nan)\n",
    "#    elif protamine_sequence[len(protamine_sequence)-1]=='-' and protamine_sequence[len(protamine_sequence)-2]=='R':\n",
    "#        arg_length.append(np.nan)\n",
    "#    elif protamine_sequence[len(protamine_sequence)-1]!='R' and protamine_sequence[len(protamine_sequence)-2]!='R' and protamine_sequence[len(protamine_sequence)-1]!='-':\n",
    "#        arg_length.append(0)\n",
    "    elif protamine_sequence[len(protamine_sequence)-1]=='-':\n",
    "        arg_length.append(np.nan)\n",
    "    elif protamine_sequence[len(protamine_sequence)-1]!='R':\n",
    "        arg_length.append(0)\n",
    "    return arg_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_block_length_number(protamine_sequence):\n",
    "    unique_nos=[]\n",
    "    k=0\n",
    "    for i in count_arg_blocks(protamine_sequence):\n",
    "        if i==1:\n",
    "            unique_nos.append(1)\n",
    "        elif i!=0 and i!=1 and k>0 and count_arg_blocks(protamine_sequence)[k-1]==0:\n",
    "            unique_nos.append(i)\n",
    "        elif i!=0 and i!=1 and k==0:\n",
    "            unique_nos.append(i)\n",
    "        k+=1\n",
    "    average_block_length=sum(unique_nos)/len(unique_nos)\n",
    "    total_no_of_blocks=len(unique_nos)\n",
    "    return average_block_length, total_no_of_blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In my human P1 sequence, the average block length: 1.5 and total # of blocks 16\n",
      "In my   salmon sequence, the average block length: 3.5 and total # of blocks 6\n",
      "In my  bull P1 sequence, the average block length: 3.0 and total # of blocks 9\n"
     ]
    }
   ],
   "source": [
    "print('In my human P1 sequence, the average block length:',count_block_length_number(human_p1)[0],'and total # of blocks',count_block_length_number(human_p1)[1])\n",
    "print('In my   salmon sequence, the average block length:',count_block_length_number(salmon)[0],'and total # of blocks',count_block_length_number(salmon)[1])\n",
    "print('In my  bull P1 sequence, the average block length:',format(count_block_length_number(bull_p1)[0],'0.1f'),'and total # of blocks',count_block_length_number(bull_p1)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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