From e054342d0e2713708d3e39c213728ac6bd329144 Mon Sep 17 00:00:00 2001
From: dh00601 <dh00601@surrey.ac.uk>
Date: Sat, 18 Jun 2022 14:20:23 +0100
Subject: [PATCH] updated notebook for common envelope evolution

---
 .../notebook_common_envelope_evolution.ipynb  | 474 ++----------------
 1 file changed, 54 insertions(+), 420 deletions(-)

diff --git a/examples/notebook_common_envelope_evolution.ipynb b/examples/notebook_common_envelope_evolution.ipynb
index b543957d6..0a31b229f 100644
--- a/examples/notebook_common_envelope_evolution.ipynb
+++ b/examples/notebook_common_envelope_evolution.ipynb
@@ -26,7 +26,9 @@
     "import matplotlib.pyplot as plt\n",
     "from binarycpython.utils.functions import temp_dir\n",
     "from binarycpython.utils.grid import Population\n",
-    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_comenv\")"
+    "\n",
+    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_comenv\")\n",
+    "VERBOSITY = 0"
    ]
   },
   {
@@ -45,27 +47,15 @@
    "execution_count": 2,
    "id": "79ab50b7-591f-4883-af09-116d1835a751",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "adding: log_dt=10 to grid_options\n",
-      "adding: max_evolution_time=13700 to BSE_options\n",
-      "adding: metallicity=0.02 to BSE_options\n",
-      "adding: alpha_ce=1.0 to BSE_options\n",
-      "adding: lambda_ce=0.5 to BSE_options\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create population object\n",
     "population = Population()\n",
     "population.set(\n",
     "    # grid options\n",
     "    tmp_dir = TMP_DIR,\n",
-    "    verbosity = 1,\n",
-    "    log_dt = 10, # log every 10 seconds\n",
+    "    verbosity = VERBOSITY,\n",
+    "    log_dt = 10, # log progress every 10 seconds\n",
     "\n",
     "    # binary-star evolution options\n",
     "    max_evolution_time=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
@@ -89,79 +79,8 @@
    "execution_count": 3,
    "id": "47979841-2c26-4b26-8945-603d013dc93a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Added grid variable: {\n",
-      "    \"name\": \"lnm1\",\n",
-      "    \"parameter_name\": \"M_1\",\n",
-      "    \"longname\": \"Primary mass\",\n",
-      "    \"valuerange\": [\n",
-      "        1,\n",
-      "        6\n",
-      "    ],\n",
-      "    \"samplerfunc\": \"const(math.log(1), math.log(6), 10)\",\n",
-      "    \"precode\": \"M_1=math.exp(lnm1)\",\n",
-      "    \"postcode\": null,\n",
-      "    \"probdist\": \"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
-      "    \"dphasevol\": \"dlnm1\",\n",
-      "    \"condition\": \"\",\n",
-      "    \"gridtype\": \"centred\",\n",
-      "    \"branchpoint\": 0,\n",
-      "    \"branchcode\": null,\n",
-      "    \"topcode\": null,\n",
-      "    \"bottomcode\": null,\n",
-      "    \"grid_variable_number\": 0\n",
-      "}\n",
-      "Added grid variable: {\n",
-      "    \"name\": \"q\",\n",
-      "    \"parameter_name\": \"M_2\",\n",
-      "    \"longname\": \"Mass ratio\",\n",
-      "    \"valuerange\": [\n",
-      "        \"0.1/M_1\",\n",
-      "        1\n",
-      "    ],\n",
-      "    \"samplerfunc\": \"const(1/M_1, 1, 10)\",\n",
-      "    \"precode\": \"M_2 = q * M_1\",\n",
-      "    \"postcode\": null,\n",
-      "    \"probdist\": \"flatsections(q, [{'min': 1/M_1, 'max': 1.0, 'height': 1}])\",\n",
-      "    \"dphasevol\": \"dq\",\n",
-      "    \"condition\": \"\",\n",
-      "    \"gridtype\": \"centred\",\n",
-      "    \"branchpoint\": 0,\n",
-      "    \"branchcode\": null,\n",
-      "    \"topcode\": null,\n",
-      "    \"bottomcode\": null,\n",
-      "    \"grid_variable_number\": 1\n",
-      "}\n",
-      "Added grid variable: {\n",
-      "    \"name\": \"log10per\",\n",
-      "    \"parameter_name\": \"orbital_period\",\n",
-      "    \"longname\": \"log10(Orbital_Period)\",\n",
-      "    \"valuerange\": [\n",
-      "        0.15,\n",
-      "        5.5\n",
-      "    ],\n",
-      "    \"samplerfunc\": \"const(0.15, 4, 10)\",\n",
-      "    \"precode\": \"orbital_period = 10.0 ** log10per\\nsep = calc_sep_from_period(M_1, M_2, orbital_period)\\nsep_min = calc_sep_from_period(M_1, M_2, 10**0.15)\\nsep_max = calc_sep_from_period(M_1, M_2, 10**4)\",\n",
-      "    \"postcode\": null,\n",
-      "    \"probdist\": \"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**0.15), math.log10(10**4), -0.55)\",\n",
-      "    \"dphasevol\": \"dlog10per\",\n",
-      "    \"condition\": null,\n",
-      "    \"gridtype\": \"centred\",\n",
-      "    \"branchpoint\": 0,\n",
-      "    \"branchcode\": null,\n",
-      "    \"topcode\": null,\n",
-      "    \"bottomcode\": null,\n",
-      "    \"grid_variable_number\": 2\n",
-      "}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import binarycpython.utils.distribution_functions\n",
     "# Set resolution and mass range that we simulate\n",
     "resolution = {\"M_1\": 10, \"q\" : 10, \"per\": 10} \n",
     "massrange = [1, 6] \n",
@@ -171,9 +90,9 @@
     "    name=\"lnm1\",\n",
     "    longname=\"Primary mass\",\n",
     "    valuerange=massrange,\n",
-    "    samplerfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
+    "    samplerfunc=\"self.const_linear(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
     "    precode=\"M_1=math.exp(lnm1)\",\n",
-    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+    "    probdist=\"self.three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
     "    dphasevol=\"dlnm1\",\n",
     "    parameter_name=\"M_1\",\n",
     "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
@@ -184,28 +103,28 @@
     "     name=\"q\",\n",
     "     longname=\"Mass ratio\",\n",
     "     valuerange=[\"0.1/M_1\", 1],\n",
-    "     samplerfunc=\"const({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
-    "     probdist=\"flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
+    "     samplerfunc=\"self.const_linear({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
+    "     probdist=\"self.flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
     "     dphasevol=\"dq\",\n",
     "     precode=\"M_2 = q * M_1\",\n",
     "     parameter_name=\"M_2\",\n",
     "     condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
-    " )\n",
+    ")\n",
     "\n",
     "# Orbital period\n",
     "population.add_grid_variable(\n",
     "    name=\"log10per\", # in days\n",
     "    longname=\"log10(Orbital_Period)\",\n",
     "    valuerange=[0.15, 5.5],\n",
-    "    samplerfunc=\"const({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
+    "    samplerfunc=\"self.const_linear({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
     "    precode=\"\"\"orbital_period = 10.0 ** log10per\n",
     "sep = calc_sep_from_period(M_1, M_2, orbital_period)\n",
     "sep_min = calc_sep_from_period(M_1, M_2, 10**{})\n",
     "sep_max = calc_sep_from_period(M_1, M_2, 10**{})\"\"\".format(logperrange[0],logperrange[1]),\n",
-    "    probdist=\"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
+    "    probdist=\"self.sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
     "    parameter_name=\"orbital_period\",\n",
     "    dphasevol=\"dlog10per\",\n",
-    " )"
+    ")"
    ]
   },
   {
@@ -223,42 +142,7 @@
    "execution_count": 4,
    "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "adding: C_logging_code=\n",
-      "\n",
-      "/*\n",
-      " * Detect when the comenv_count increased \n",
-      " */\n",
-      "if(stardata->model.comenv_count == 1 && \n",
-      "   stardata->previous_stardata->model.comenv_count == 0)\n",
-      "{\n",
-      "   /*\n",
-      "    * We just had this system's first common envelope:\n",
-      "    * output the time at which this happens, \n",
-      "    * the system's probability (proportional to the number of stars),\n",
-      "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
-      "    * the current timestep (post-comenv) orbital period (days)\n",
-      "    */\n",
-      "    Printf(\"COMENV %g %g %g %g\\n\",\n",
-      "           stardata->model.time,\n",
-      "           stardata->model.probability,\n",
-      "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
-      "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
-      "           \n",
-      "    /*\n",
-      "     * We should waste no more CPU time on this system now we have the\n",
-      "     * data we want.\n",
-      "     */\n",
-      "    stardata->model.evolution_stop = TRUE;\n",
-      "}\n",
-      " to grid_options\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "custom_logging_statement = \"\"\"\n",
     "\n",
@@ -307,17 +191,9 @@
    "execution_count": 5,
    "id": "fd197154-a8ce-4865-8929-008d3483101a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "adding: parse_function=<function parse_function at 0x149c95c56c10> to grid_options\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from binarycpython.utils.functions import bin_data,datalinedict\n",
+    "from binarycpython.utils.functions import bin_data, datalinedict\n",
     "import re\n",
     "\n",
     "# log-period distribution bin width (dex)\n",
@@ -336,7 +212,7 @@
     "        \n",
     "        # obtain the line of data in dictionary form \n",
     "        linedata = datalinedict(line,parameters)\n",
-    "            \n",
+    "\n",
     "        # choose COMENV lines of output\n",
     "        if linedata[\"header\"] == \"COMENV\":\n",
     "            # bin the pre- and post-comenv log10-orbital-periods to nearest 0.5dex\n",
@@ -353,7 +229,7 @@
     "            # make the \"histograms\"\n",
     "            self.grid_results['pre'][binned_pre_period] += linedata[\"probability\"]\n",
     "            self.grid_results['post'][binned_post_period] += linedata[\"probability\"]\n",
-    "\n",
+    "            \n",
     "    # verbose reporting\n",
     "    #print(\"parse out results_dictionary=\",self.grid_results)\n",
     "    \n",
@@ -384,265 +260,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "adding: num_cores=4 to grid_options\n",
-      "Creating and loading custom logging functionality\n",
+      "Do dry run? True\n",
       "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Generating grid code\n",
-      "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python-izzard/notebooks/notebook_comenv/binary_c_grid_2b66f805db424c48a1d29c45092b6e3c.py [dry_run = True]\n",
-      "Symlinked grid code to /tmp/binary_c_python-izzard/notebooks/notebook_comenv/binary_c_grid-latest0 \n",
-      "Loading grid code function from /tmp/binary_c_python-izzard/notebooks/notebook_comenv/binary_c_grid_2b66f805db424c48a1d29c45092b6e3c.py\n",
-      "Grid code loaded\n",
-      "Dry run of the grid\n",
-      "Grid has handled 1000 stars with a total probability of 0.0645906\n",
-      "****************************************\n",
-      "* Total starcount for this run is 1000 *\n",
-      "*    Total probability is 0.0645906    *\n",
-      "****************************************\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:50:56,156 DEBUG    Process-2] --- Setting up processor: process-0\n",
-      "[2021-11-01 09:50:56,159 DEBUG    Process-3] --- Setting up processor: process-1\n",
-      "[2021-11-01 09:50:56,163 DEBUG    Process-4] --- Setting up processor: process-2\n",
-      "[2021-11-01 09:50:56,166 DEBUG    MainProcess] --- setting up the system_queue_filler now\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generating grid code\n",
-      "Generating grid code\n",
-      "Saving grid code to grid_options"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:50:56,168 DEBUG    Process-5] --- Setting up processor: process-3\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Grid has handled 729 stars with a total probability of 0.0645564\n",
+      "**********************************\n",
+      "*             Dry run            *\n",
+      "*     Total starcount is 729     *\n",
+      "* Total probability is 0.0645564 *\n",
+      "**********************************\n",
       "\n",
-      "Writing grid code to /tmp/binary_c_python-izzard/notebooks/notebook_comenv/binary_c_grid_2b66f805db424c48a1d29c45092b6e3c.py [dry_run = False]\n",
-      "Symlinked grid code to /tmp/binary_c_python-izzard/notebooks/notebook_comenv/binary_c_grid-latest1 \n",
-      "Loading grid code function from /tmp/binary_c_python-izzard/notebooks/notebook_comenv/binary_c_grid_2b66f805db424c48a1d29c45092b6e3c.py\n",
-      "Grid code loaded\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:50:56,258 DEBUG    MainProcess] --- Signaling stop to processes\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "177/1000  17.7% complete 09:51:05 ETA=   46.9s tpr=2.28e-01 ETF=09:51:52 mem:941.1MB M1=1.3 M2=1.2 P=1.1e+3\n",
-      "348/1000  34.8% complete 09:51:15 ETA=   38.1s tpr=2.34e-01 ETF=09:51:53 mem:679.5MB M1=1.9 M2=1.4 P=2.6e+3\n",
-      "451/1000  45.1% complete 09:51:25 ETA=   32.1s tpr=2.34e-01 ETF=09:51:57 mem:682.6MB M1=2.2 M2=1.7 P=5.3\n",
-      "555/1000  55.5% complete 09:51:35 ETA=   32.8s tpr=2.95e-01 ETF=09:52:08 mem:684.4MB M1=2.7 M2=1.9 P=1.9e+2\n",
-      "631/1000  63.1% complete 09:51:46 ETA=   29.7s tpr=3.22e-01 ETF=09:52:15 mem:685.3MB M1=3.2 M2=1.8 P=5.3\n",
-      "682/1000  68.2% complete 09:51:56 ETA=   28.4s tpr=3.58e-01 ETF=09:52:24 mem:687.5MB M1=3.2 M2=2.9 P=13\n",
-      "757/1000  75.7% complete 09:52:06 ETA=   24.4s tpr=4.02e-01 ETF=09:52:30 mem:687.9MB M1=3.8 M2=2.6 P=1.1e+3\n",
-      "810/1000  81.0% complete 09:52:16 ETA=   19.9s tpr=4.20e-01 ETF=09:52:36 mem:688.0MB M1=4.6 M2=1.5 P=2.2\n",
-      "863/1000  86.3% complete 09:52:27 ETA=   15.3s tpr=4.48e-01 ETF=09:52:42 mem:691.9MB M1=4.6 M2=3.3 P=31\n",
-      "915/1000  91.5% complete 09:52:37 ETA=   10.1s tpr=4.76e-01 ETF=09:52:47 mem:693.2MB M1=5.5 M2=1.7 P=1.9e+2\n",
-      "963/1000  96.3% complete 09:52:47 ETA=    4.6s tpr=4.97e-01 ETF=09:52:52 mem:694.2MB M1=5.5 M2=3.9 P=31\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:52:56,316 DEBUG    Process-5] --- Process-3 is finishing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "process 3 free memory and return \n",
-      "****************************************************\n",
-      "*                Process 3 finished:               *\n",
-      "*  generator started at 2021-11-01T09:50:56.168379 *\n",
-      "* generator finished at 2021-11-01T09:52:56.323359 *\n",
-      "*                  total: 2m 0.15s                 *\n",
-      "*          of which 2m 0.03s with binary_c         *\n",
-      "*                  Ran 251 systems                 *\n",
-      "*       with a total probability of 0.0163656      *\n",
-      "*         This thread had 0 failing systems        *\n",
-      "*       with a total failed probability of 0       *\n",
-      "*   Skipped a total of 0 zero-probability systems  *\n",
-      "*                                                  *\n",
-      "****************************************************\n",
+      "Do join of subprocesses ...\n",
+      "Joined subprocesses.\n",
+      "************************************************************\n",
+      "*   Population-e7857f8149e949a286f9bfe35157f91f finished!  *\n",
+      "*            The total probability is 0.0645564.           *\n",
+      "*  It took a total of 25.66s to run 729 systems on 4 cores *\n",
+      "*             = 1 minute and 42.63s of CPU time.           *\n",
+      "*               Maximum memory use 668.594 MB              *\n",
+      "************************************************************\n",
       "\n",
-      "process 3 queue put output_dict \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:52:56,329 DEBUG    Process-5] --- Process-3 is finished.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "process 3 return \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:52:56,986 DEBUG    Process-2] --- Process-0 is finishing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "process 0 free memory and return \n",
-      "****************************************************\n",
-      "*                Process 0 finished:               *\n",
-      "*  generator started at 2021-11-01T09:50:56.155678 *\n",
-      "* generator finished at 2021-11-01T09:52:56.991657 *\n",
-      "*                  total: 2m 0.84s                 *\n",
-      "*          of which 2m 0.70s with binary_c         *\n",
-      "*                  Ran 267 systems                 *\n",
-      "*       with a total probability of 0.0175264      *\n",
-      "*         This thread had 0 failing systems        *\n",
-      "*       with a total failed probability of 0       *\n",
-      "*   Skipped a total of 0 zero-probability systems  *\n",
-      "*                                                  *\n",
-      "****************************************************\n",
-      "\n",
-      "process 0 queue put output_dict \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:52:56,996 DEBUG    Process-2] --- Process-0 is finished.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "process 0 return \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:52:57,094 DEBUG    Process-3] --- Process-1 is finishing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "process 1 free memory and return \n",
-      "****************************************************\n",
-      "*                Process 1 finished:               *\n",
-      "*  generator started at 2021-11-01T09:50:56.158640 *\n",
-      "* generator finished at 2021-11-01T09:52:57.099417 *\n",
-      "*                  total: 2m 0.94s                 *\n",
-      "*          of which 2m 0.83s with binary_c         *\n",
-      "*                  Ran 234 systems                 *\n",
-      "*       with a total probability of 0.0143896      *\n",
-      "*         This thread had 0 failing systems        *\n",
-      "*       with a total failed probability of 0       *\n",
-      "*   Skipped a total of 0 zero-probability systems  *\n",
-      "*                                                  *\n",
-      "****************************************************\n",
-      "\n",
-      "process 1 queue put output_dict \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:52:57,104 DEBUG    Process-3] --- Process-1 is finished.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "process 1 return \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:52:57,728 DEBUG    Process-4] --- Process-2 is finishing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "process 2 free memory and return \n",
-      "****************************************************\n",
-      "*                Process 2 finished:               *\n",
-      "*  generator started at 2021-11-01T09:50:56.163481 *\n",
-      "* generator finished at 2021-11-01T09:52:57.732244 *\n",
-      "*                  total: 2m 1.57s                 *\n",
-      "*          of which 2m 1.45s with binary_c         *\n",
-      "*                  Ran 248 systems                 *\n",
-      "*       with a total probability of 0.016309       *\n",
-      "*         This thread had 0 failing systems        *\n",
-      "*       with a total failed probability of 0       *\n",
-      "*   Skipped a total of 0 zero-probability systems  *\n",
-      "*                                                  *\n",
-      "****************************************************\n",
-      "\n",
-      "process 2 queue put output_dict \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-11-01 09:52:57,736 DEBUG    Process-4] --- Process-2 is finished.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "process 2 return \n",
-      "**************************************************************\n",
-      "*    Population-2b66f805db424c48a1d29c45092b6e3c finished!   *\n",
-      "*             The total probability is 0.0645906.            *\n",
-      "* It took a total of 2m 1.85s to run 1000 systems on 4 cores *\n",
-      "*                   = 8m 7.40s of CPU time.                  *\n",
-      "*                Maximum memory use 941.109 MB               *\n",
-      "**************************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
+      "No failed systems were found in this run.\n",
+      "Do analytics\n",
+      "Added analytics to metadata\n"
      ]
     }
    ],
@@ -651,7 +290,8 @@
     "population.set(\n",
     "    # set number of threads (i.e. number of CPU cores we use)\n",
     "    num_cores=4,\n",
-    "    )\n",
+    "    verbosity=VERBOSITY\n",
+    ")\n",
     "\n",
     "# Evolve the population - this is the slow, number-crunching step\n",
     "analytics = population.evolve()  \n",
@@ -678,7 +318,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '2b66f805db424c48a1d29c45092b6e3c', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.06459059967730083, 'total_count': 1000, 'start_timestamp': 1635760256.1239555, 'end_timestamp': 1635760377.9739752, 'total_mass_run': 4680.235689312423, 'total_probability_weighted_mass_run': 0.22611318083528548, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_id': 'e7857f8149e949a286f9bfe35157f91f', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.06455639233064192, 'total_count': 729, 'start_timestamp': 1655558353.8189669, 'end_timestamp': 1655558379.477376, 'time_elapsed': 25.658409118652344, 'total_mass_run': 3410.9363465845586, 'total_probability_weighted_mass_run': 0.2260906041851175, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -692,13 +332,6 @@
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'merged': 0.03502960360000004, 'unmerged': 0.019715467199999996}\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -711,12 +344,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -725,17 +360,18 @@
     "import seaborn as sns\n",
     "import pandas as pd\n",
     "import copy\n",
-    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "\n",
     "# set up seaborn for use in the notebook\n",
     "sns.set(rc={'figure.figsize':(20,10)})\n",
     "sns.set_context(\"notebook\",\n",
     "                font_scale=1.5,\n",
     "                rc={\"lines.linewidth\":2.5})\n",
     "\n",
-    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
-    "\n",
     "# remove the merged objects\n",
     "probability = { \"merged\" : 0.0, \"unmerged\" : 0.0}\n",
     "\n",
@@ -752,7 +388,6 @@
     "        else:\n",
     "            # unmerged system\n",
     "            probability[\"unmerged\"] += dprob\n",
-    "print(probability)\n",
     "    \n",
     "# pad the final distribution with zero\n",
     "for distribution in population.grid_results:    \n",
@@ -765,8 +400,7 @@
     "# make the plot\n",
     "p = sns.lineplot(data=plot_data)\n",
     "p.set_xlabel(\"$\\log_{10} (P_\\mathrm{orb} / \\mathrm{day})$\")\n",
-    "p.set_ylabel(\"Number of stars\")\n",
-    "#p.set(xlim=(-5,5)) # might be necessary?\n"
+    "p.set_ylabel(\"Number of stars\")"
    ]
   },
   {
@@ -797,7 +431,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -811,7 +445,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,
-- 
GitLab