example_cicer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1aebfc19",
   "metadata": {},
   "source": [
    "# Chickpea example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54344942",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef7eb3f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from mtml_model import mtmlModel\n",
    "from dataset import Data, CVset\n",
    "from pandas import read_csv\n",
    "from optimisation import *\n",
    "from semopy.efa import explore_cfa_model\n",
    "from utils import *\n",
    "\n",
    "path_data = 'data_cicer/'\n",
    "file_phens = path_data + 'data_phens.txt'\n",
    "file_snps = path_data + 'snp_2579_renamed.txt'\n",
    "snp_pref = 'Ca'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bb241a",
   "metadata": {},
   "source": [
    "## Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "343fd934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Germ            0\n",
      "GBegF           0\n",
      "BegFEndF        0\n",
      "EndFBegM        0\n",
      "BegMEndM        0\n",
      "FloCol          0\n",
      "StemCol         0\n",
      "BushShape       0\n",
      "LeafSize        0\n",
      "FlowStemCol     0\n",
      "AscoRes         0\n",
      "StemBr          0\n",
      "StemL1ord       0\n",
      "StemBr1ord      0\n",
      "StemBr2ord      0\n",
      "PodD            0\n",
      "PodS            0\n",
      "Height          0\n",
      "Hlp             0\n",
      "NoPodsWeight    0\n",
      "PodsWeight      0\n",
      "PodsNumber      0\n",
      "SeedsNumber     0\n",
      "SeedsWeight     0\n",
      "PodShape        0\n",
      "PodLength       0\n",
      "PodWidth        0\n",
      "SeedShape       0\n",
      "SeedCol         0\n",
      "Seed1000W       0\n",
      "dtype: int64\n",
      "Imputation of phenotypes..\n",
      "Imputation of snps..\n",
      "Imputation is done.\n",
      "404\n"
     ]
    }
   ],
   "source": [
    "data_phens = read_csv(file_phens, sep='\\t', index_col=0)\n",
    "\n",
    "print(data_phens.isna().sum(axis=0))\n",
    "\n",
    "# Remove missing data\n",
    "data_phens = data_phens.loc[data_phens.isna().sum(axis=1) == 0, :]\n",
    "data_phens = data_phens.iloc[:, list(data_phens.nunique() > 2)]\n",
    "data_snps = read_csv(file_snps, sep='\\t', index_col=0)\n",
    "\n",
    "# Create data object\n",
    "data = Data(d_snps=data_snps, d_phens=data_phens)\n",
    "print(data.n_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e3485a",
   "metadata": {},
   "source": [
    "## Create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c91eff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEM model was not defined\n"
     ]
    }
   ],
   "source": [
    "model = mtmlModel(data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "532e150e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of the latent structure is unconnected\n",
      "======== show models ========\n",
      "# Model mod0\n",
      "F0 =~ PodsWeight + PodsNumber + SeedsNumber + SeedsWeight\n",
      "# Model mod1\n",
      "F1 =~ NoPodsWeight + PodShape + PodLength + PodWidth + Seed1000W\n",
      "# Model mod2\n",
      "F2 =~ FloCol + StemCol + SeedShape + SeedCol\n",
      "# Model mod3\n",
      "F3 =~ BegFEndF + EndFBegM\n",
      "# Model mod4\n",
      "F4 =~ Height + Hlp\n",
      "=============================\n"
     ]
    }
   ],
   "source": [
    "model.get_lat_struct()\n",
    "model.show_mod()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1250eac",
   "metadata": {},
   "source": [
    "## Add SNPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a786f11e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "Skip 0 SNPs\n",
      "2.102206266840767e-07\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "Skip 2099 SNPs\n",
      "1.0916242310088364e-07\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "Skip 2255 SNPs\n",
      "3.552713678800501e-15\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "Skip 2263 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5010749027387646e-10\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "Skip 1241 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.757344190167487e-07\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "Skip 2251 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1173286174634995e-09\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "PodsNumber ~ 0.1140120982914967*Ca7_35255679\n",
      "Skip 2264 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "PodsNumber ~ 0.1140120982914967*Ca7_35255679\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.1838516529633125e-09\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "PodsNumber ~ 0.1140120982914967*Ca7_35255679\n",
      "SeedsNumber ~ 0.264556267527295*Ca4_37047364\n",
      "Skip 946 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8.881784197001252e-16\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "PodsNumber ~ 0.1140120982914967*Ca7_35255679\n",
      "SeedsNumber ~ 0.264556267527295*Ca4_37047364\n",
      "SeedsNumber ~ 0.14441377413559975*Ca1_25737022\n",
      "Skip 2062 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "PodsNumber ~ 0.1140120982914967*Ca7_35255679\n",
      "SeedsNumber ~ 0.264556267527295*Ca4_37047364\n",
      "SeedsNumber ~ 0.14441377413559975*Ca1_25737022\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Fisher Information Matrix is not PD.Moore-Penrose inverse will be used instead of Cholesky decomposition. See 10.1109/TSP.2012.2208105.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.741604194194224e-09\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "PodsNumber ~ 0.1140120982914967*Ca7_35255679\n",
      "SeedsNumber ~ 0.264556267527295*Ca4_37047364\n",
      "SeedsNumber ~ 0.14441377413559975*Ca1_25737022\n",
      "PodsWeight ~ -0.43221833506470286*Ca6_35046173\n",
      "Skip 960 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.589733451775828e-10\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "PodsNumber ~ 0.1140120982914967*Ca7_35255679\n",
      "SeedsNumber ~ 0.264556267527295*Ca4_37047364\n",
      "SeedsNumber ~ 0.14441377413559975*Ca1_25737022\n",
      "PodsWeight ~ -0.43221833506470286*Ca6_35046173\n",
      "PodsWeight ~ 0.20533036278307198*Ca2_17161884\n",
      "Skip 2252 SNPs\n",
      "0.0\n",
      "F0 =~ 1.0 * PodsWeight\n",
      "F0 =~ 1.9931617663512406 * PodsNumber\n",
      "F0 =~ 1.8888180364762814 * SeedsNumber\n",
      "F0 =~ 1.9501172418497341 * SeedsWeight\n",
      "F0 ~ -0.18387218392912857*Ca5_39720969\n",
      "F0 ~ -0.15693182798338923*Ca7_30930779\n",
      "F0 ~ 0.146226707816762*Ca3_10062634\n",
      "SeedsWeight ~ -0.26238890488305144*Ca6_35046173\n",
      "SeedsWeight ~ -0.10945170786461976*Ca6_8894185\n",
      "PodsNumber ~ 0.1140120982914967*Ca7_35255679\n",
      "SeedsNumber ~ 0.264556267527295*Ca4_37047364\n",
      "SeedsNumber ~ 0.14441377413559975*Ca1_25737022\n",
      "PodsWeight ~ -0.43221833506470286*Ca6_35046173\n",
      "PodsWeight ~ 0.20533036278307198*Ca2_17161884\n",
      "PodsWeight ~ 0.2686354259388785*Ca7_21019002\n",
      "Skip 2269 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "Skip 0 SNPs\n",
      "0.0\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "Skip 1167 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "Skip 0 SNPs\n",
      "3.622260891233964e-08\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "Skip 1844 SNPs\n",
      "0.0\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "Skip 2178 SNPs\n",
      "-7.105427357601002e-15\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "Skip 2197 SNPs\n",
      "1.984584550029922e-07\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "Skip 2270 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.789298392258388e-08\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "Skip 1196 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Solver didnt converge, see SolverResult.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "Skip 1568 SNPs\n",
      "0.0\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "Skip 1933 SNPs\n",
      "1.2523235781713993e-08\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "Skip 2231 SNPs\n",
      "2.998193693315443e-09\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Skip 2270 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Fisher Information Matrix is not PD.Moore-Penrose inverse will be used instead of Cholesky decomposition. See 10.1109/TSP.2012.2208105.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.917889124238627e-08\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Skip 1843 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Skip 2109 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.866727217247899e-09\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Skip 2234 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.552713678800501e-15\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Skip 2260 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.513047796057435e-09\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Skip 2267 SNPs\n",
      "0.0\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "Skip 2274 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6115365809052946e-08\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "PodLength ~ 0.13807479876878986*Ca6_26457574\n",
      "Skip 2161 SNPs\n",
      "7.301457216613016e-10\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "PodLength ~ 0.13807479876878986*Ca6_26457574\n",
      "PodLength ~ 0.11346346518356523*Ca2_32039805\n",
      "Skip 2215 SNPs\n",
      "-6.217248937900877e-15\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "PodLength ~ 0.13807479876878986*Ca6_26457574\n",
      "PodLength ~ 0.11346346518356523*Ca2_32039805\n",
      "PodLength ~ 0.19418807818589723*Ca1_11229616\n",
      "Skip 2250 SNPs\n",
      "3.5781715368443656e-09\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "PodLength ~ 0.13807479876878986*Ca6_26457574\n",
      "PodLength ~ 0.11346346518356523*Ca2_32039805\n",
      "PodLength ~ 0.19418807818589723*Ca1_11229616\n",
      "PodLength ~ 0.14099509999596427*Ca3_9139541\n",
      "Skip 2269 SNPs\n",
      "1.7763568394002505e-15\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "PodLength ~ 0.13807479876878986*Ca6_26457574\n",
      "PodLength ~ 0.11346346518356523*Ca2_32039805\n",
      "PodLength ~ 0.19418807818589723*Ca1_11229616\n",
      "PodLength ~ 0.14099509999596427*Ca3_9139541\n",
      "PodLength ~ -0.1434431533982226*Ca3_23433956\n",
      "Skip 2273 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "PodLength ~ 0.13807479876878986*Ca6_26457574\n",
      "PodLength ~ 0.11346346518356523*Ca2_32039805\n",
      "PodLength ~ 0.19418807818589723*Ca1_11229616\n",
      "PodLength ~ 0.14099509999596427*Ca3_9139541\n",
      "PodLength ~ -0.1434431533982226*Ca3_23433956\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.217248937900877e-15\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "PodLength ~ 0.13807479876878986*Ca6_26457574\n",
      "PodLength ~ 0.11346346518356523*Ca2_32039805\n",
      "PodLength ~ 0.19418807818589723*Ca1_11229616\n",
      "PodLength ~ 0.14099509999596427*Ca3_9139541\n",
      "PodLength ~ -0.1434431533982226*Ca3_23433956\n",
      "PodWidth ~ -0.21955887891832274*Ca1_18381505\n",
      "Skip 2255 SNPs\n",
      "0.0\n",
      "F1 =~ 1.0 * NoPodsWeight\n",
      "F1 =~ -1.265611188745524 * PodShape\n",
      "F1 =~ 2.305261415004596 * PodLength\n",
      "F1 =~ 2.3254053132371832 * PodWidth\n",
      "F1 =~ 2.2771475573616518 * Seed1000W\n",
      "F1 ~ -0.2803945452306041*Ca7_5728003\n",
      "PodShape ~ 0.16172662036484148*Ca3_26879200\n",
      "PodShape ~ 0.22165872999827477*Ca5_24488052\n",
      "PodShape ~ 0.10413295512082515*Ca4_37300412\n",
      "PodShape ~ 0.2133082106998615*Ca6_47657340\n",
      "NoPodsWeight ~ -0.6319591283672416*Ca3_28094292\n",
      "NoPodsWeight ~ 0.26391792626360394*Ca8_10314452\n",
      "NoPodsWeight ~ -0.17692518874747545*Ca5_20853333\n",
      "NoPodsWeight ~ -0.29514029174597445*Ca4_8165224\n",
      "NoPodsWeight ~ -0.10200335231292726*Ca1_38884526\n",
      "Seed1000W ~ 0.2484565969675715*Ca3_3888778\n",
      "Seed1000W ~ -0.26606512732179094*Ca6_56158213\n",
      "Seed1000W ~ -0.12297133767188312*Ca1_44522192\n",
      "Seed1000W ~ -0.2437774849028187*Ca6_59115549\n",
      "Seed1000W ~ 0.16459835558288244*Ca7_12414609\n",
      "Seed1000W ~ 0.1891524979406283*Ca7_6324745\n",
      "PodLength ~ 0.13807479876878986*Ca6_26457574\n",
      "PodLength ~ 0.11346346518356523*Ca2_32039805\n",
      "PodLength ~ 0.19418807818589723*Ca1_11229616\n",
      "PodLength ~ 0.14099509999596427*Ca3_9139541\n",
      "PodLength ~ -0.1434431533982226*Ca3_23433956\n",
      "PodWidth ~ -0.21955887891832274*Ca1_18381505\n",
      "PodWidth ~ 0.11587629482944149*Ca4_8479518\n",
      "Skip 2270 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "Skip 0 SNPs\n",
      "1.725909726246755e-08\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "Skip 999 SNPs\n",
      "2.1316282072803006e-14\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "Skip 2267 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.512746715728099e-08\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "Skip 968 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.5330310999579524e-08\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "Skip 1953 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.4210854715202004e-14\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "Skip 2249 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "Skip 937 SNPs\n",
      "1.9294894570975885e-09\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "Skip 1246 SNPs\n",
      "0.0\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "Skip 1545 SNPs\n",
      "0.0\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "Skip 1662 SNPs\n",
      "1.0868339472835942e-07\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "Skip 1848 SNPs\n",
      "-8.881784197001252e-15\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "Skip 1983 SNPs\n",
      "1.2780047242699766e-07\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "Skip 2215 SNPs\n",
      "2.7045147987792006e-07\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "Skip 2236 SNPs\n",
      "0.0\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "Skip 2268 SNPs\n",
      "0.0\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "StemCol ~ -0.1440108463788633*Ca7_44317236\n",
      "Skip 2274 SNPs\n",
      "NO SNPs added\n",
      "-----------\n",
      "-----------\n",
      "-----------\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "StemCol ~ -0.1440108463788633*Ca7_44317236\n",
      "Skip 0 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Solver didnt converge, see SolverResult.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5612780280303014e-08\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "StemCol ~ -0.1440108463788633*Ca7_44317236\n",
      "SeedShape ~ -0.4247981074461703*Ca3_1871391\n",
      "Skip 1880 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n",
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2509720903608468e-07\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "StemCol ~ -0.1440108463788633*Ca7_44317236\n",
      "SeedShape ~ -0.4247981074461703*Ca3_1871391\n",
      "SeedShape ~ 0.20420634991500405*Ca7_30930779\n",
      "Skip 2089 SNPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Sample covariance matrix is not PD. It may indicate that data is bad. Also, it arises often when polychoric/polyserial correlations are used. semopy now will run nearPD subroutines.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9420620606401826e-08\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "StemCol ~ -0.1440108463788633*Ca7_44317236\n",
      "SeedShape ~ -0.4247981074461703*Ca3_1871391\n",
      "SeedShape ~ 0.20420634991500405*Ca7_30930779\n",
      "SeedShape ~ 0.34193069075328286*Ca2_2608533\n",
      "Skip 2143 SNPs\n",
      "0.0\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "StemCol ~ -0.1440108463788633*Ca7_44317236\n",
      "SeedShape ~ -0.4247981074461703*Ca3_1871391\n",
      "SeedShape ~ 0.20420634991500405*Ca7_30930779\n",
      "SeedShape ~ 0.34193069075328286*Ca2_2608533\n",
      "SeedShape ~ -0.16977466071276381*Ca4_6297533\n",
      "Skip 2171 SNPs\n",
      "-3.552713678800501e-15\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "StemCol ~ -0.1440108463788633*Ca7_44317236\n",
      "SeedShape ~ -0.4247981074461703*Ca3_1871391\n",
      "SeedShape ~ 0.20420634991500405*Ca7_30930779\n",
      "SeedShape ~ 0.34193069075328286*Ca2_2608533\n",
      "SeedShape ~ -0.16977466071276381*Ca4_6297533\n",
      "SeedShape ~ 0.1455878791482529*Ca1_47067099\n",
      "Skip 2200 SNPs\n",
      "9.13926534451548e-09\n",
      "F2 =~ 1.0 * FloCol\n",
      "F2 =~ 3.5387900779772496 * StemCol\n",
      "F2 =~ -1.044410480884952 * SeedShape\n",
      "F2 =~ 1.6297222619155287 * SeedCol\n",
      "F2 ~ 0.3474893461114271*Ca4_13593426\n",
      "F2 ~ 0.13223029505758094*Ca1_35502868\n",
      "FloCol ~ 0.2855795656417074*Ca6_48295748\n",
      "FloCol ~ 0.18302010063241464*Ca1_18015960\n",
      "FloCol ~ -0.19092497281965265*Ca4_3214214\n",
      "StemCol ~ -0.5978978137747183*Ca6_18802099\n",
      "StemCol ~ -0.5092526035212152*Ca1_1224263\n",
      "StemCol ~ 0.33473119868557816*Ca6_26354046\n",
      "StemCol ~ -0.5586486899010892*Ca8_11736056\n",
      "StemCol ~ 0.5944650694879928*Ca1_9331447\n",
      "StemCol ~ -0.28331751633081953*Ca1_2309179\n",
      "StemCol ~ 0.2516893415513162*Ca4_8101219\n",
      "StemCol ~ -0.3349848160581327*Ca5_30662807\n",
      "StemCol ~ -0.27767823902036*Ca2_32142844\n",
      "StemCol ~ -0.1440108463788633*Ca7_44317236\n",
      "SeedShape ~ -0.4247981074461703*Ca3_1871391\n",
      "SeedShape ~ 0.20420634991500405*Ca7_30930779\n",
      "SeedShape ~ 0.34193069075328286*Ca2_2608533\n",
      "SeedShape ~ -0.16977466071276381*Ca4_6297533\n",
      "SeedShape ~ 0.1455878791482529*Ca1_47067099\n",
      "SeedShape ~ -0.152615768773897*Ca5_39545825\n",
      "Skip 2228 SNPs\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_11356/1944132135.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_snps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnp_pref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msnp_pref\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~/Documents/Pr/mtmlsem/mtml_model.py\u001b[0m in \u001b[0;36madd_snps\u001b[0;34m(self, snp_pref, n_iter)\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgwas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\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[1;32m    151\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmods\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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--> 152\u001b[0;31m             self.mods[k], self.gwas[k], _ = add_snps(mod, self.data,\n\u001b[0m\u001b[1;32m    153\u001b[0m                                                   \u001b[0msnp_pref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msnp_pref\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m                                                   n_iter=n_iter)\n",
      "\u001b[0;32m~/Documents/Pr/mtmlsem/add_snps.py\u001b[0m in \u001b[0;36madd_snps\u001b[0;34m(mod, data, thresh_mlr, thresh_sign_snp, thresh_abs_param, snp_pref, n_iter)\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;31m# print(mod_init)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\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--> 197\u001b[0;31m         mod_init, gwas[variable], snps_added[variable] = add_snps_for_variable(mod_init, data, variable,\n\u001b[0m\u001b[1;32m    198\u001b[0m                                       \u001b[0mthresh_mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mthresh_mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m                                       \u001b[0mthresh_sign_snp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mthresh_sign_snp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Pr/mtmlsem/add_snps.py\u001b[0m in \u001b[0;36madd_snps_for_variable\u001b[0;34m(mod, data, variable, thresh_mlr, thresh_sign_snp, thresh_abs_param, snp_pref, n_iter)\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod_init\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0mmod_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnp_skip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnp_list\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m             one_snp_for_variable(mod_init, data, variable,\n\u001b[0m\u001b[1;32m    228\u001b[0m                                  \u001b[0msnp_skip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msnp_skip\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m                                  \u001b[0mthresh_mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mthresh_mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Pr/mtmlsem/add_snps.py\u001b[0m in \u001b[0;36mone_snp_for_variable\u001b[0;34m(mod_init, data, variable, snp_skip, thresh_mlr, thresh_sign_snp, thresh_abs_param, snp_pref, echo)\u001b[0m\n\u001b[1;32m    325\u001b[0m             \u001b[0mdata_tmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv_tmp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md_snps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msnp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msem_mod_tmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_tmp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclean_slate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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    328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mempty_mod\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/semopy/model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, cov, obj, solver, groups, clean_slate, regularization, n_samples, **kwargs)\u001b[0m\n\u001b[1;32m   1111\u001b[0m                         \u001b[0mconstrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstraints\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m                         bounds=self.get_bounds(), **kwargs)\n\u001b[0;32m-> 1113\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\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   1114\u001b[0m         \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1115\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_vals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/semopy/solver.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcustom_methods\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcustom_methods\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m]\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--> 101\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscipy_solve\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    102\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m             \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Solver didn'\u001b[0m\u001b[0;34m't converge, see SolverResult.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/semopy/solver.py\u001b[0m in \u001b[0;36mscipy_solve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    132\u001b[0m                        constraints=self.constraints, **t)\n\u001b[1;32m    133\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m             res = minimize(self.fun, self.start, jac=self.grad, bounds=self.bounds,\n\u001b[0m\u001b[1;32m    135\u001b[0m                            \u001b[0mconstraints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstraints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m                            method=self.method, **self.extra_options)\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_minimize_cobyla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'slsqp'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m         return _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[0m\u001b[1;32m    632\u001b[0m                                constraints, callback=callback, **options)\n\u001b[1;32m    633\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'trust-constr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/scipy/optimize/slsqp.py\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[0;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[1;32m    373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[0;31m# ScalarFunction provides function and gradient evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m     sf = _prepare_scalar_function(func, x, jac=jac, args=args, epsilon=eps,\n\u001b[0m\u001b[1;32m    376\u001b[0m                                   \u001b[0mfinite_diff_rel_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinite_diff_rel_step\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m                                   bounds=new_bounds)\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m_prepare_scalar_function\u001b[0;34m(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;31m# ScalarFunction caches. Reuse of fun(x) during grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m     \u001b[0;31m# calculation reduces overall function evaluations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m     sf = ScalarFunction(fun, x0, args, grad, hess,\n\u001b[0m\u001b[1;32m    262\u001b[0m                         finite_diff_rel_step, bounds, epsilon=epsilon)\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_grad_impl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_grad\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    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;31m# Hessian Evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36m_update_grad\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_update_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[1;32m    237\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg_updated\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_grad_impl\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    239\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg_updated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36mupdate_grad\u001b[0;34m()\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mupdate_grad\u001b[0m\u001b[0;34m(\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--> 149\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\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    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mFD_METHODS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36mgrad_wrapped\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mgrad_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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[1;32m    145\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mngev\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mupdate_grad\u001b[0m\u001b[0;34m(\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~/.local/lib/python3.9/site-packages/semopy/model.py\u001b[0m in \u001b[0;36mgrad_mlw\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1510\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1511\u001b[0m         \u001b[0msigma_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_sigma_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1512\u001b[0;31m         \u001b[0mcs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minv_sigma\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0minv_sigma\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmx_cov\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0minv_sigma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1513\u001b[0m         return np.array([np.einsum('ij,ji->', cs, g)\n\u001b[1;32m   1514\u001b[0m                          for g in sigma_grad])\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = model.add_snps(snp_pref=snp_pref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unique-illinois",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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