diff --git a/examples/notebook_api_functionality.ipynb b/examples/notebook_api_functionality.ipynb
index d81c31711bfc5d6de3159fd8958df96dd145ded0..8d454880c82dba172d33a95532c47bc95f24ff6a 100644
--- a/examples/notebook_api_functionality.ipynb
+++ b/examples/notebook_api_functionality.ipynb
@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "ded7eaf6-e1ba-46c2-9f6f-9ebcb14a264d",
"metadata": {},
"outputs": [
@@ -30,37 +30,69 @@
"\n",
"FUNCTIONS\n",
" free_persistent_data_memaddr_and_return_json_output(...)\n",
- " Frees the persistent_data memory and returns the json output\n",
+ " Frees the persistent_data memory and returns the json output.\n",
+ " \n",
+ " Arguments:\n",
+ " store capsule: capsule containing the memory adress of the persistent data object (contains the ensemble)\n",
" \n",
" free_store_memaddr(...)\n",
- " Frees the store memaddr\n",
+ " Frees the store memaddr.\n",
+ " \n",
+ " Arguments:\n",
+ " store capsule: capsule containing the memory adress of the store object\n",
" \n",
" return_arglines(...)\n",
" Return the default args for a binary_c system\n",
+ " \n",
+ " Arguments:\n",
+ " No arguments.\n",
" \n",
" return_help(...)\n",
" Return the help info for a given parameter\n",
+ " \n",
+ " Arguments:\n",
+ " parameter: parameter name.\n",
" \n",
" return_help_all(...)\n",
" Return an overview of all the parameters, their description, categorized in sections\n",
+ " \n",
+ " Arguments:\n",
+ " No arguments.\n",
" \n",
" return_maximum_mass_ratio_for_RLOF(...)\n",
- " Returns a string containing the maximum mass ratio for which a binary system does not RLOF at zams. Optionally accepts a store_capsule. Please use the wrapper functions in utils for this except when you know what you're doing\n",
+ " Returns a string containing the maximum mass ratio for which a binary system does not RLOF at ZAMS. Please use the wrapper functions in utils for this except when you know what you're doing.\n",
+ " \n",
+ " Arguments:\n",
+ " argstring: argument string for binary_c\n",
+ " (opt) store_capsule: capsule containing memory adress for the store object.unction. Default = Null\n",
" \n",
" return_minimum_orbit_for_RLOF(...)\n",
- " Returns a string containing the minimum orbit and separation for which a binary system does not RLOF at zams. Please use the wrapper functions in utils for this except when you know what you're doing\n",
+ " Returns a string containing the minimum orbit and separation for which a binary system does not RLOF at ZAMS. Please use the wrapper functions in utils for this except when you know what you're doing.\n",
+ " \n",
+ " Arguments:\n",
+ " argstring: argument string for binary_c\n",
+ " (opt) store_capsule: capsule containing memory adress for the store object.unction. Default = Null\n",
" \n",
" return_persistent_data_memaddr(...)\n",
" Return the store memory adress that will be passed to run_population\n",
+ " \n",
+ " Arguments:\n",
+ " No arguments.\n",
" \n",
" return_store_memaddr(...)\n",
" Return the store memory adress that will be passed to run_population\n",
+ " \n",
+ " Arguments:\n",
+ " No arguments.\n",
" \n",
" return_version_info(...)\n",
" Return the version information of the used binary_c build\n",
+ " \n",
+ " Arguments:\n",
+ " No arguments.\n",
" \n",
" run_system(...)\n",
- " Function to run a system. This is a general function that will be able to handle different kinds of situations: single system run with different settings, population run with different settings, etc. To avoid having too many functions doing slightly different things. \n",
+ " Function to run a system. This is a general function that will be able to handle different kinds of situations: single system run with different settings, population run with different settings, etc. To avoid having too many functions doing slightly different things.\n",
" \n",
" Arguments:\n",
" argstring: argument string for binary_c\n",
@@ -126,7 +158,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 2,
"id": "10a74d5a-a3d5-4543-a5bc-20d1fe885bb4",
"metadata": {},
"outputs": [
@@ -134,8 +166,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "<capsule object \"STORE\" at 0x7f163859d0c0>\n",
- "SINGLE_STAR_LIFETIME 10 27.7358\n",
+ "<capsule object \"STORE\" at 0x7fa6a45ed180>\n",
+ "SINGLE_STAR_LIFETIME 10 28.4838\n",
"\n"
]
}
@@ -183,7 +215,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 3,
"id": "318874f6-7acf-49bb-9786-299d4dffc0b3",
"metadata": {},
"outputs": [
@@ -217,7 +249,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 4,
"id": "d7e757ae-579c-42a2-a310-f0401b7800e8",
"metadata": {
"scrolled": true,
@@ -291,6 +323,9 @@
"opacity_algorithm : Set the opacity algorithm. 0 = Paczynski, 1 = Ferguson/Opal. : (null)\n",
"wind_mass_loss : Defines the algorithm used for stellar winds. 0 = none, 1 = Hurley et al. (2002), 2 = Schneider (2018). : 0\n",
"gbwind : Wind prescription for first red giant branch stars. 0=Reimers (Hurley et al 2000/2002; choose gb_reimers_eta=0.5 for their mass loss rate), 1=Schroeder+Cuntz 2005 (set gb_reimers_eta=1.0 for their mass loss rate). : (null)\n",
+ "postagbwind : Apply special post-(A)GB prescription. Default is POSTAGB_WIND_USE_GIANT which means we just use whatever is prescribed on the giant branch. Other options include: POSTAGB_WIND_NONE = 1 (no wind on the post (A)GB), POSTAGB_WIND_KRTICKA2020 = 2 which uses Krticka, Kubát and Krticková (2020, A&A 635, A173). : (null)\n",
+ "Teff_postAGB_min : The minimum temperature for which we apply post-(A)GB winds. See also Teff_postAGB_max. (6000 K) : (null)\n",
+ "Teff_postAGB_max : The maximum temperature for which we apply post-(A)GB winds. See also Teff_postAGB_min. (120000 K) : (null)\n",
"mattsson_Orich_tpagbwind : Experimental : turns on Mattsson's TPAGB wind when the star is oxygen rich. Requires MATTSSON_MASS_LOSS. : (null)\n",
"magnetic_braking_factor : Multiplier for the magnetic braking angular momentum loss rate. : (null)\n",
"magnetic_braking_gamma : gamma factor in Rappaport style magnetic braking expression. : (null)\n",
@@ -310,7 +345,9 @@
"vw93_mira_shift : In the Vassiliadis and Wood (1993) AGB wind prescription, the wind loss rate depends on the Mira period plus this offset. Requires VW93_MIRA_SHIFT. : (null)\n",
"vw93_multiplier : In the Vassiliadis and Wood (1993) AGB wind prescription, the wind loss rate is multiplied by this factor. Requires VW93_MULTIPLIER. : (null)\n",
"tpagb_reimers_eta : TPAGB Reimers wind multiplication factor, cf. eta in Reimers' mass loss formula. (This multiplies the 4e-13 in Reimers' formula, or the 8e-14 in Schroeder and Cuntz.) Note that Reimers is not the default TPAGB wind prescription. See also tpagbwind. : (null)\n",
+ "Tout_Pringle_1992_multiplier : Multiplier for the Tout & Pringle (1992) magnetic wind. (0.0) : (null)\n",
"artificial_mass_accretion_rate%d : Constant mass accretion rate for star <n>. : (null)\n",
+ "artificial_mass_accretion_rate_by_stellar_type%d : Constant mass accretion rate for stellar type <n>. : (null)\n",
"artificial_angular_momentum_accretion_rate%d : Constant angular momentum accretion for star <n>. : (null)\n",
"artificial_orbital_angular_momentum_accretion_rate : Constant angular momentum accretion rate on the orbit. : (null)\n",
"artificial_accretion_start_time : Time at which artificial accretion stars. Ignored if <0 (default is -1). : (null)\n",
@@ -318,8 +355,7 @@
"wr_wind : Massive-star (WR) wind prescription. 0 = Hurley et al 2000/2002, 1=Maeder and Meynet, 2=Nugis and Lamers, 3=John Eldridge's version of Vink's early-2000s wind (See Lynnette Dray's thesis, or John Eldridge's thesis) : (null)\n",
"wr_wind_fac : Massive-star (WR) wind multiplication factor. : (null)\n",
"wrwindfac : Massive-star (WR) wind multiplication factor. Synonymous with wr_wind_fac (which you should use instead). : (null)\n",
- "BH_prescription : Black hole mass prescrition: relates the mass of a newly formed black hole to its progenitor's (CO) core mass. 0=Hurley et al 2000/2002, 1=Belczynski (early 2000s). : (null)\n",
- "PPISN_prescription : (Pulsational) Pair-Instability Supernova prescription: Relates initial helium core mass of star to whether the star undergoes PPISN or PISN. Requires PPISN flag to be True (see binary_c_parameters.h). 0=no ppisn, 1=Farmer et al 2019. : Ignore\n",
+ "BH_prescription : Black hole mass prescrition: relates the mass of a newly formed black hole to its progenitor's (CO) core mass. BH_HURLEY2002 = 0 = Hurley et al 2000/2002, BH_BELCZYNSKI = 1 = Belczynski (early 2000s), BH_SPERA2015 = Spera+ 2015, BH_FRYER12_DELAYED = 3 = Fryer et al. (2012) delayed prescription, BH_FRYER12_RAPID = 4 = Fryer et al. (2012) rapid prescription, BH_FRYER12_STARTRACK = 5 = Fryer et al. (2012) startrack prescription. : (null)\n",
"sn_kick_distribution_II : Set the distribution of speeds applied to kick type II core collapse supernova systems. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_distribution_ECAP : Set the distribution of speeds applied to the remnants of electron-capture supernovae. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_distribution_NS_NS : Set the distribution of speeds applied to kick neutron stars and black holes that survive a NS-NS merger. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
@@ -331,9 +367,6 @@
"sn_kick_distribution_BH_NS : Set the distribution of speeds applied to black holes formed by the merger of a neutron star and a black holes. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_distribution_IA_Hybrid_HeCOWD : Set the distribution of speeds applied to any survivor of a hybrid He-COWD SNIa explosion. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_distribution_IA_Hybrid_HeCOWD_subluminous : Set the distribution of speeds applied to any survivor of a subluminous hybrid He-COWD SNIa explosion. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
- "sn_kick_distribution_PPISN : Set the distribution of speeds applied to PPISN supernovae. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
- "sn_kick_distribution_PISN : Set the distribution of speeds applied to PISN supernovae. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
- "sn_kick_distribution_PHDIS : Set the distribution of speeds applied to PHDIS supernovae. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_dispersion_II : Set the dispersion of speeds applied to kick type II core collapse supernova systems. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_dispersion_ECAP : Set the dispersion of speeds applied to the remnants of electron-capture supernovae. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_dispersion_NS_NS : Set the dispersion of speeds applied to kick neutron stars and black holes that survive a NS-NS merger. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
@@ -345,9 +378,6 @@
"sn_kick_dispersion_BH_NS : Set the dispersion of speeds applied to black holes formed by the merger of a neutron star and a black holes. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_dispersion_IA_Hybrid_HeCOWD : Set the dispersion of speeds applied to the survivor of a SNIa explosion of a hybrid He-COWD. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_dispersion_IA_Hybrid_HeCOWD_subluminous : Set the dispersion of speeds applied to the survivor of a subluminous SNIa explosion of a hybrid He-COWD. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
- "sn_kick_dispersion_PPISN : Set the dispersion of speeds applied to the survivor of a PPISN supernova. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
- "sn_kick_dispersion_PISN : Set the dispersion of speeds applied to the survivor of a PISN supernova. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
- "sn_kick_dispersion_PHDIS : Set the dispersion of speeds applied to the survivor of a PHDIS supernova. 0=fixed, 1=maxwellian (hurley/BSE), 2=custom function (see monte_carlo_kicks.c). : (null)\n",
"sn_kick_companion_IA_He : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the, kick on the companion when a Ia He supernova occurs. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
"sn_kick_companion_IA_ELD : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the, kick on the companion when a Ia ELD (sub-Mch) supernova occurs. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
"sn_kick_companion_IA_CHAND : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the, kick on the companion when a Ia Mch supernova occurs. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
@@ -368,9 +398,6 @@
"sn_kick_companion_BH_NS : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the, kick on the companion when a black hole merges with a neutron star. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
"sn_kick_companion_IA_Hybrid_HeCOWD : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the kick on the companion, if it survives, in a hybrid He-COWD type Ia explosion. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
"sn_kick_companion_IA_Hybrid_HeCOWD_subluminous : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the kick on the companion, if it survives, in a subluminous hybrid He-COWD type Ia explosion. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
- "sn_kick_companion_PPISN : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the kick on the companion, if it survives, in a PPISN supernova. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
- "sn_kick_companion_PISN : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the kick on the companion, if it survives, in a PISN supernova. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
- "sn_kick_companion_PHDIS : Set the speed (if >=0) of, or the algothim (if <0) used to calculate the kick on the companion, if it survives, in a PHDIS supernova. 0 = none, 1 = Liu+2015, 2 = Wheeler+ 1975. : (null)\n",
"wd_sigma : Set the speed at which white dwarfs are kicked when they form, in km/s. Default is zero (i.e. no kick). Requires WD_KICKS. : (null)\n",
"wd_kick_direction : Set the direction of white dwarf kicks. 0 = random, 1 = up, 2 = forward, 3 = backward, 4 = inward, 5 = outward. Requires WD_KICKS. : (null)\n",
"wd_kick_when : Decide when to kick a white dwarf. 0=at birth, 1=at first RLOF, 2=at given pulse number (see wd_kick_pulse_number), 3 at every pulse Requires WD_KICKS. : (null)\n",
@@ -443,6 +470,11 @@
"HeWD_HeWD_ignition_mass : HeWD-HeWD mergers above this mass reignite helium. (0.3) : (null)\n",
"wind_Nieuwenhuijzen_luminosity_lower_limit : Above this luminosity we activate the Nieuwenhuijzen and de Jager wind (4e3 Lsun). : (null)\n",
"wind_LBV_luminosity_lower_limit : Above this luminosity we activate the LBV wind (6e5 Lsun). : (null)\n",
+ "colour%d : Sets colour %d (0 to NUM_ANSI_COLOURS-1) to the extended ANSI set colour you choose (1-255, 0 means ignore). The colour numbers are defined in src/logging/ansi_colours.h : (null)\n",
+ "apply_Darwin_Radau_correction : Apply Darwin-Radau correction to the moment of inertia to take rotation into account? : (null)\n",
+ "degenerate_core_merger_nucsyn : If TRUE, assume that in a degnerate core merger, energy is generated from nucleosynthesis of the whole core, and that this can disrupt the core. The BSE algorithm (Hurley et al. 2002) assumes this to be TRUE, but binary_c assumes FALSE by default. (FALSE) : (null)\n",
+ "degenerate_core_helium_merger_ignition : If TRUE, assume that when there is a degenerate helium core merger, the star reignites helium. This is required to make R-type carbon stars. (TRUE) : (null)\n",
+ "degenerate_core_merger_dredgeup_fraction : If non-zero, mix this fraction of the degenerate core during a merger.(0.0). : (null)\n",
"\n",
"############################################################\n",
"##### Section Binary\n",
@@ -709,7 +741,6 @@
"############################################################\n",
"##### Section Output\n",
"############################################################\n",
- "david_logging_function : Function to choose which kind of information gets logged Requires DAVID. Choices are: 0= None, >0 for custom logging functions : Ignore\n",
"cf_amanda_log : Enable logging to compare to Amanda's models. : (null)\n",
"float_overflow_checks : Turn on to enable floating-point overflow checks at the end of each timestep, if they are available. 0=off, 1=warn (stderr) on failure, 2=exit on failure (0) : (null)\n",
"save_pre_events_stardata : Enable this to save a copy of stardata to stardata->pre_events_stardata just before an event. : (null)\n",
@@ -739,6 +770,7 @@
"escape_fraction : A parameter used in constructing galactic chemical evolution (GCE) models. If the stellar wind velocity exceeds this value, any chemical yield from the wind is ignored, i.e. assumed lost. (km/s) Requires NUCSYN_GCE_OUTFLOW_CHECKS. Default 0.0. See also escape_velocity. : (null)\n",
"colour_log : If set to True, thelog is coloured with ANSI colour formatting. Requires FILE_LOG to be defined. : \n",
"log_filename : Location of the output logging filename. If set to \"/dev/null\" then there is no logging. : \n",
+ "log_arrows : Add arrows to the output log to show whether values are increasing or decreasing. : \n",
"stopfile : File which, when it exists, will stop the current binary_c repeat run. : \n",
"stardata_dump_filename : Location of the stardata dump file. : \n",
"stardata_load_filename : Location of the stardata file to load. : \n",
@@ -763,8 +795,12 @@
"MINT_MS_rejuvenation : Turn on or off (hydrogen) main-sequence rejuvenation. : \n",
"MINT_remesh : Turn on or off MINT's remeshing. : \n",
"MINT_use_ZAMS_profiles : Use chemical profiles at the ZAMS if MINT_use_ZAMS_profiles is TRUE, otherwise set homogeneous abundances. (Default is TRUE, so we use the profiles if they are available.) : \n",
+ "MINT_fallback_to_test_data : If TRUE, use the MINT test_data directory as a fallback when data is unavailable. (FALSE) : \n",
"MINT_disable_grid_load_warnings : Use this to explicitly disable MINT's warnings when loading a grid with, e.g., missing or too much data. : \n",
"MINT_Kippenhahn : Turn on or off MINT's Kippenhahn diagrams. If 0, off, if 1, output star 1 (index 0), if 2 output star 2 (index 1). Default 0. : \n",
+ "MINT_nshells : Set the initial number of shells MINT uses in each star when doing nuclear burning. Note: remeshing can change this. If MINT_nshells is 0, shellular burning and other routines that require shells will not be available. (200) : \n",
+ "MINT_maximum_nshells : Set the maximum number of shells MINT uses in each star when doing nuclear burning. Note that this will be limited to MINT_HARD_MAX_NSHELLS. (1000) : \n",
+ "MINT_minimum_nshells : Set the minimum number of shells MINT uses in each star when doing nuclear burning. Note that this will be greater than or equal to MINT_HARD_MIN_NSHELLS, which is 0 by default. (0) : \n",
"MINT_Kippenhahn_stellar_type : Stellar type selector for Kippenhahn plots. Set to -1 to ignore, otherwise the stellar type number for which Kippenhahn plot data should be output. : \n",
"MINT_Kippenhahn_companion_stellar_type : Companion stellar type selector for Kippenhahn plots. Set to -1 to ignore, otherwise the stellar type number for the companion for which Kippenhahn plot data should be output. : \n",
"MINT_nuclear_burning : Turn on or off MINT's nuclear burning algorithm. : \n",
@@ -825,7 +861,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 5,
"id": "3d29ca9d-ac66-4f9e-81cf-2edd14a98b79",
"metadata": {},
"outputs": [
@@ -854,7 +890,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"id": "e517f561-09c6-419d-ba89-d9cb61e6ebab",
"metadata": {},
"outputs": [
@@ -887,7 +923,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 7,
"id": "7da75a95-8831-4346-a584-e042ced75249",
"metadata": {},
"outputs": [
diff --git a/examples/notebook_custom_logging.ipynb b/examples/notebook_custom_logging.ipynb
index 05ffbccfc23f0b08e85abed0d467233385520a4b..84e41e6bcee06490f5b4dc61fb616cc213024aba 100644
--- a/examples/notebook_custom_logging.ipynb
+++ b/examples/notebook_custom_logging.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 1,
"id": "696ecbb9-1efd-48f4-a57e-2cf6dfe416f1",
"metadata": {},
"outputs": [],
@@ -65,7 +65,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 2,
"id": "236cf821-09ac-4237-9b8f-6e36d2edf446",
"metadata": {},
"outputs": [
@@ -90,7 +90,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 3,
"id": "feb423d5-5cc3-433c-9801-f8017abbc03a",
"metadata": {},
"outputs": [
@@ -110,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 4,
"id": "2f5defbf-c623-49ed-a238-fba52a563a58",
"metadata": {},
"outputs": [
@@ -155,7 +155,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 5,
"id": "dcd74bbc-478b-43e4-b495-8c456e8d1d88",
"metadata": {},
"outputs": [
@@ -195,7 +195,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 6,
"id": "77bd09b0-1a94-499d-97db-a1f991c67c12",
"metadata": {},
"outputs": [
@@ -203,10 +203,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "EXAMPLE_ABOVE_MS 1.041660877905e+02 4.99198 4.99198 6.1357 6.1357 2 1\n",
- "EXAMPLE_ABOVE_MS 1.041662558619e+02 4.99198 4.99198 6.14057 6.1357 2 2\n",
- "EXAMPLE_ABOVE_MS 1.041662560111e+02 4.99198 4.99198 6.14057 6.14057 2 2\n",
- "EXAMPLE_ABOVE_MS 1.041662564579e+02 4.99198 4.99198 6.14059 6.14057 2 2\n"
+ "EXAMPLE_ABOVE_MS 1.044142002936e+02 4.99194 4.99194 6.13567 6.13567 2 1\n",
+ "EXAMPLE_ABOVE_MS 1.044572277695e+02 4.99192 4.99194 7.51803 6.13567 2 2\n",
+ "EXAMPLE_ABOVE_MS 1.044654032097e+02 4.99192 4.99192 7.81395 7.51803 2 2\n",
+ "EXAMPLE_ABOVE_MS 1.045084306856e+02 4.99191 4.99192 9.57443 7.81395 2 2\n"
]
}
],
@@ -260,7 +260,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 7,
"id": "30142286-34ce-433e-82c8-565e2160ff5b",
"metadata": {},
"outputs": [
@@ -336,7 +336,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 8,
"id": "6f0edc65-a788-4706-a0c5-2ace030765ec",
"metadata": {},
"outputs": [
@@ -344,8 +344,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "SINGLE_STAR_LIFETIME 10 27.7358\n",
- "EXAMPLE_LOG_CO 2.773581245005e+01 1.33524 9.19314 1.72498e-05 730.446 13 5\n"
+ "SINGLE_STAR_LIFETIME 10 28.4838\n",
+ "EXAMPLE_LOG_CO 2.848380621869e+01 1.33469 9.1865 1.72498e-05 724.338 13 5\n"
]
}
],
@@ -395,7 +395,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 10,
"id": "8f58fdf9-3e76-4c18-a1c5-eed0980d4133",
"metadata": {},
"outputs": [
@@ -403,8 +403,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "EXAMPLE_MASSLOSS 9.878236827680e+00 1.61349 8.38063 20 13 1\n",
- "EXAMPLE_SN 9.878236827680e+00 1.61349 8.38063 20 12 13 5 1 6.74037 4.92267 6.74037 0 0\n"
+ "EXAMPLE_MASSLOSS 1.050651207308e+01 1.59452 9.34213 20 13 1\n",
+ "EXAMPLE_SN 1.050651207308e+01 1.59452 9.34213 20 12 13 5 1 6.55458 4.71662 6.55458\n"
]
}
],
@@ -424,58 +424,27 @@
"{\n",
" if (stardata->model.time < stardata->model.max_evolution_time)\n",
" {\n",
- " if(stardata->pre_events_stardata != NULL)\n",
- " {\n",
- " Printf(\"EXAMPLE_SN %30.12e \" // 1\n",
- " \"%g %g %g %d \" // 2-5\n",
- " \"%d %d %g %g \" // 6-9\n",
- " \"%g %g %g %g\\\\n\", // 10-13\n",
- "\n",
- " // \n",
- " stardata->model.time, // 1\n",
- "\n",
- " stardata->star[0].mass, //2\n",
- " stardata->pre_events_stardata->star[0].mass, //3\n",
- " stardata->common.zero_age.mass[0], //4\n",
- " stardata->star[0].SN_type, //5\n",
+ " Printf(\"EXAMPLE_SN %30.12e \" // 1\n",
+ " \"%g %g %g %d \" // 2-5\n",
+ " \"%d %d %g %g \" // 6-9\n",
+ " \"%g %g\\\\n\", // 10-13\n",
"\n",
- " stardata->star[0].stellar_type, //6\n",
- " stardata->pre_events_stardata->star[0].stellar_type, //7\n",
- " stardata->model.probability, //8\n",
- " stardata->pre_events_stardata->star[0].core_mass[ID_core(stardata->pre_events_stardata->star[0].stellar_type)], // 9\n",
- "\n",
- " stardata->pre_events_stardata->star[0].core_mass[CORE_CO], // 10\n",
- " stardata->pre_events_stardata->star[0].core_mass[CORE_He], // 11\n",
- " stardata->star[0].fallback, // 12\n",
- " stardata->star[0].fallback_mass // 13\n",
- " );\n",
- " }\n",
- " else\n",
- " {\n",
- " Printf(\"EXAMPLE_SN %30.12e \" // 1\n",
- " \"%g %g %g %d \" // 2-5\n",
- " \"%d %d %g %g \" // 6-9\n",
- " \"%g %g %g %g\\\\n\", // 10-13\n",
- "\n",
- " // \n",
- " stardata->model.time, // 1\n",
+ " // \n",
+ " stardata->model.time, // 1\n",
"\n",
- " stardata->star[0].mass, //2\n",
- " stardata->previous_stardata->star[0].mass, //3\n",
- " stardata->common.zero_age.mass[0], //4\n",
- " stardata->star[0].SN_type, //5\n",
+ " stardata->star[0].mass, //2\n",
+ " stardata->previous_stardata->star[0].mass, //3\n",
+ " stardata->common.zero_age.mass[0], //4\n",
+ " stardata->star[0].SN_type, //5\n",
"\n",
- " stardata->star[0].stellar_type, //6\n",
- " stardata->previous_stardata->star[0].stellar_type, //7\n",
- " stardata->model.probability, //8\n",
- " stardata->previous_stardata->star[0].core_mass[ID_core(stardata->previous_stardata->star[0].stellar_type)], // 9\n",
+ " stardata->star[0].stellar_type, //6\n",
+ " stardata->previous_stardata->star[0].stellar_type, //7\n",
+ " stardata->model.probability, //8\n",
+ " stardata->previous_stardata->star[0].core_mass[ID_core(stardata->previous_stardata->star[0].stellar_type)], // 9\n",
"\n",
- " stardata->previous_stardata->star[0].core_mass[CORE_CO], // 10\n",
- " stardata->previous_stardata->star[0].core_mass[CORE_He], // 11\n",
- " stardata->star[0].fallback, // 12\n",
- " stardata->star[0].fallback_mass // 13\n",
- " );\n",
- " }\n",
+ " stardata->previous_stardata->star[0].core_mass[CORE_CO], // 10\n",
+ " stardata->previous_stardata->star[0].core_mass[CORE_He] // 11\n",
+ " );\n",
" };\n",
" /* Kill the simulation to save time */\n",
" stardata->model.max_evolution_time = stardata->model.time - stardata->model.dtm;\n",
@@ -491,6 +460,14 @@
"# print (abridged) output\n",
"print(\"\\n\".join(output.splitlines()[-2:]))"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "484297c0-accb-4efc-a9c8-dbd2f32b89a6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/examples/notebook_luminosity_function_binaries.ipynb b/examples/notebook_luminosity_function_binaries.ipynb
index eecc6b199910c2a20517d05f3110e8500caa94eb..fb64dbcc9754aa9dc823a41ae3c52223cb2a8d9a 100644
--- a/examples/notebook_luminosity_function_binaries.ipynb
+++ b/examples/notebook_luminosity_function_binaries.ipynb
@@ -379,6 +379,10 @@
"execution_count": 9,
"id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
"metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
"tags": []
},
"outputs": [
@@ -395,8 +399,8 @@
"Constructing/adding: q\n",
"Constructing/adding: log10per\n",
"Saving grid code to grid_options\n",
- "Writing grid code to /tmp/binary_c_python/binary_c_grid_cd4b14dc28814364b94a8608b70990fd.py\n",
- "Loading grid code function from /tmp/binary_c_python/binary_c_grid_cd4b14dc28814364b94a8608b70990fd.py\n",
+ "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+ "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
"Grid code loaded\n",
"Grid has handled 2000 stars\n",
"with a total probability of 0.6495098935846658\n",
@@ -407,79 +411,208 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "[2021-09-09 11:46:07,695 DEBUG Process-2] --- Setting up processor: process-0\n",
- "[2021-09-09 11:46:07,699 DEBUG Process-3] --- Setting up processor: process-1\n",
- "[2021-09-09 11:46:07,703 DEBUG Process-4] --- Setting up processor: process-2\n",
- "[2021-09-09 11:46:07,706 DEBUG MainProcess] --- setting up the system_queue_filler now\n",
- "[2021-09-09 11:46:07,708 DEBUG Process-5] --- Setting up processor: process-3\n"
+ "[2021-09-10 15:14:08,077 DEBUG Process-2] --- Setting up processor: process-0[2021-09-10 15:14:08,080 DEBUG Process-3] --- Setting up processor: process-1[2021-09-10 15:14:08,086 DEBUG MainProcess] --- setting up the system_queue_filler now\n",
+ "\n",
+ "[2021-09-10 15:14:08,084 DEBUG Process-4] --- Setting up processor: process-2\n",
+ "\n",
+ "[2021-09-10 15:14:08,117 DEBUG Process-5] --- Setting up processor: process-3"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Process 1 started at 2021-09-10T15:14:08.119437.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>Process 0 started at 2021-09-10T15:14:08.126435.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>\n",
+ "Process 2 started at 2021-09-10T15:14:08.138353.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Process 0 started at 2021-09-09T11:46:07.718767.\tUsing store memaddr <capsule object \"STORE\" at 0x149cd4efb720>\n",
- "Process 1 started at 2021-09-09T11:46:07.722569.\tUsing store memaddr <capsule object \"STORE\" at 0x149cd4efb630>\n",
- "Process 2 started at 2021-09-09T11:46:07.727251.\tUsing store memaddr <capsule object \"STORE\" at 0x149cd4efb0c0>\n",
- "Process 3 started at 2021-09-09T11:46:07.731045.\tUsing store memaddr <capsule object \"STORE\" at 0x149cd4efb180>\n",
+ "\n",
+ "\n",
+ "Process 3 started at 2021-09-10T15:14:08.186492.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>\n",
"Generating grid code\n",
"Generating grid code\n",
"Constructing/adding: lnm1\n",
"Constructing/adding: q\n",
"Constructing/adding: log10per\n",
"Saving grid code to grid_options\n",
- "Writing grid code to /tmp/binary_c_python/binary_c_grid_cd4b14dc28814364b94a8608b70990fd.py\n",
- "Loading grid code function from /tmp/binary_c_python/binary_c_grid_cd4b14dc28814364b94a8608b70990fd.py\n",
+ "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+ "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
"Grid code loaded\n",
- "748/2000 37.4% complete 11:46:12 ETA= 8.4s tpr=6.70e-03 ETF=11:46:20 mem:509.0MB\n",
- "819/2000 41.0% complete 11:46:17 ETA= 1.4m tpr=7.16e-02 ETF=11:47:41 mem:460.4MB\n",
- "886/2000 44.3% complete 11:46:22 ETA= 1.4m tpr=7.61e-02 ETF=11:47:46 mem:462.7MB\n",
- "945/2000 47.2% complete 11:46:27 ETA= 1.5m tpr=8.61e-02 ETF=11:47:58 mem:463.5MB\n",
- "981/2000 49.0% complete 11:46:32 ETA= 2.4m tpr=1.42e-01 ETF=11:48:57 mem:463.5MB\n"
+ "624/2000 31.2% complete 15:14:12 ETA= 11.1s tpr=8.05e-03 ETF=15:14:23 mem:800.5MB625/2000 31.2% complete 15:14:12 ETA= 11.1s tpr=8.04e-03 ETF=15:14:23 mem:800.5MB\n",
+ "626/2000 31.3% complete 15:14:12 ETA= 11.1s tpr=8.05e-03 ETF=15:14:23 mem:800.5MB\n",
+ "\n",
+ "713/2000 35.6% complete 15:14:17 ETA= 1.3m tpr=6.00e-02 ETF=15:15:34 mem:547.8MB\n",
+ "728/2000 36.4% complete 15:14:22 ETA= 7.1m tpr=3.37e-01 ETF=15:21:30 mem:548.1MB\n",
+ "743/2000 37.1% complete 15:14:27 ETA= 7.0m tpr=3.34e-01 ETF=15:21:26 mem:549.5MB\n",
+ "759/2000 38.0% complete 15:14:33 ETA= 7.7m tpr=3.73e-01 ETF=15:22:16 mem:550.5MB\n",
+ "774/2000 38.7% complete 15:14:38 ETA= 6.9m tpr=3.35e-01 ETF=15:21:29 mem:551.1MB\n",
+ "787/2000 39.4% complete 15:14:43 ETA= 7.8m tpr=3.88e-01 ETF=15:22:33 mem:551.1MB\n",
+ "799/2000 40.0% complete 15:14:48 ETA= 8.5m tpr=4.24e-01 ETF=15:23:17 mem:552.5MB\n",
+ "812/2000 40.6% complete 15:14:54 ETA= 8.4m tpr=4.23e-01 ETF=15:23:16 mem:554.8MB\n",
+ "830/2000 41.5% complete 15:14:59 ETA= 5.5m tpr=2.80e-01 ETF=15:20:26 mem:555.2MB\n",
+ "847/2000 42.4% complete 15:15:05 ETA= 6.8m tpr=3.52e-01 ETF=15:21:50 mem:555.2MB\n",
+ "864/2000 43.2% complete 15:15:10 ETA= 6.2m tpr=3.28e-01 ETF=15:21:23 mem:557.0MB\n",
+ "876/2000 43.8% complete 15:15:15 ETA= 8.2m tpr=4.38e-01 ETF=15:23:27 mem:559.7MB\n",
+ "887/2000 44.4% complete 15:15:21 ETA= 9.2m tpr=4.95e-01 ETF=15:24:32 mem:560.5MB\n",
+ "898/2000 44.9% complete 15:15:26 ETA= 9.2m tpr=4.99e-01 ETF=15:24:37 mem:560.5MB\n",
+ "908/2000 45.4% complete 15:15:32 ETA= 9.5m tpr=5.23e-01 ETF=15:25:03 mem:560.5MB\n",
+ "919/2000 46.0% complete 15:15:37 ETA= 8.3m tpr=4.60e-01 ETF=15:23:54 mem:560.9MB\n",
+ "934/2000 46.7% complete 15:15:42 ETA= 6.4m tpr=3.60e-01 ETF=15:22:06 mem:561.7MB\n",
+ "947/2000 47.4% complete 15:15:47 ETA= 7.2m tpr=4.08e-01 ETF=15:22:57 mem:561.7MB\n",
+ "956/2000 47.8% complete 15:15:53 ETA= 11.1m tpr=6.39e-01 ETF=15:27:01 mem:561.7MB\n",
+ "963/2000 48.1% complete 15:15:58 ETA= 12.6m tpr=7.30e-01 ETF=15:28:35 mem:561.7MB\n",
+ "969/2000 48.5% complete 15:16:04 ETA= 15.2m tpr=8.85e-01 ETF=15:31:16 mem:561.9MB\n",
+ "979/2000 49.0% complete 15:16:11 ETA= 11.9m tpr=7.01e-01 ETF=15:28:06 mem:562.0MB\n",
+ "988/2000 49.4% complete 15:16:16 ETA= 9.7m tpr=5.76e-01 ETF=15:25:59 mem:562.0MB\n",
+ "995/2000 49.8% complete 15:16:21 ETA= 12.3m tpr=7.37e-01 ETF=15:28:42 mem:562.2MB\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "[2021-09-09 11:46:34,956 DEBUG MainProcess] --- Signaling stop to processes\n"
+ "[2021-09-10 15:16:25,175 DEBUG MainProcess] --- Signaling stop to processes\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "1026/2000 51.3% complete 11:46:37 ETA= 1.8m tpr=1.11e-01 ETF=11:48:26 mem:464.5MB\n",
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+ "1003/2000 50.1% complete 15:16:26 ETA= 11.2m tpr=6.76e-01 ETF=15:27:40 mem:563.0MB\n",
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+ "1148/2000 57.4% complete 15:17:59 ETA= 8.8m tpr=6.20e-01 ETF=15:26:47 mem:567.4MB\n",
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+ "1162/2000 58.1% complete 15:18:11 ETA= 14.3m tpr=1.02e+00 ETF=15:32:28 mem:567.6MB\n",
+ "1168/2000 58.4% complete 15:18:17 ETA= 15.2m tpr=1.09e+00 ETF=15:33:27 mem:568.6MB\n",
+ "1177/2000 58.9% complete 15:18:23 ETA= 8.8m tpr=6.45e-01 ETF=15:27:14 mem:568.6MB\n",
+ "1181/2000 59.0% complete 15:18:28 ETA= 17.8m tpr=1.30e+00 ETF=15:36:16 mem:568.7MB\n",
+ "1187/2000 59.4% complete 15:18:34 ETA= 12.1m tpr=8.93e-01 ETF=15:30:40 mem:568.7MB\n",
+ "1194/2000 59.7% complete 15:18:39 ETA= 9.8m tpr=7.29e-01 ETF=15:28:26 mem:568.8MB\n",
+ "1202/2000 60.1% complete 15:18:44 ETA= 9.5m tpr=7.12e-01 ETF=15:28:12 mem:568.8MB\n",
+ "1219/2000 61.0% complete 15:18:51 ETA= 5.3m tpr=4.07e-01 ETF=15:24:09 mem:569.7MB\n",
+ "1228/2000 61.4% complete 15:18:57 ETA= 7.4m tpr=5.76e-01 ETF=15:26:21 mem:569.7MB\n",
+ "1234/2000 61.7% complete 15:19:02 ETA= 11.8m tpr=9.22e-01 ETF=15:30:48 mem:571.7MB1235/2000 61.8% complete 15:19:02 ETA= 10.1m tpr=7.92e-01 ETF=15:29:08 mem:571.7MB\n",
+ "\n",
+ "1243/2000 62.1% complete 15:19:07 ETA= 7.3m tpr=5.79e-01 ETF=15:26:26 mem:573.4MB\n",
+ "1251/2000 62.5% complete 15:19:13 ETA= 8.3m tpr=6.68e-01 ETF=15:27:33 mem:575.4MB\n",
+ "1260/2000 63.0% complete 15:19:19 ETA= 8.2m tpr=6.65e-01 ETF=15:27:31 mem:575.4MB\n",
+ "1268/2000 63.4% complete 15:19:24 ETA= 7.8m tpr=6.41e-01 ETF=15:27:13 mem:576.8MB\n",
+ "1276/2000 63.8% complete 15:19:29 ETA= 7.6m tpr=6.30e-01 ETF=15:27:05 mem:577.0MB\n",
+ "1282/2000 64.1% complete 15:19:34 ETA= 10.1m tpr=8.44e-01 ETF=15:29:40 mem:578.0MB\n",
+ "1289/2000 64.5% complete 15:19:40 ETA= 10.8m tpr=9.08e-01 ETF=15:30:26 mem:578.0MB\n",
+ "1295/2000 64.8% complete 15:19:46 ETA= 10.5m tpr=8.95e-01 ETF=15:30:16 mem:578.1MB\n",
+ "1309/2000 65.5% complete 15:19:51 ETA= 4.3m tpr=3.70e-01 ETF=15:24:06 mem:578.1MB\n",
+ "1323/2000 66.2% complete 15:19:58 ETA= 6.1m tpr=5.45e-01 ETF=15:26:07 mem:579.2MB\n",
+ "1332/2000 66.6% complete 15:20:03 ETA= 6.2m tpr=5.58e-01 ETF=15:26:16 mem:579.3MB\n",
+ "1338/2000 66.9% complete 15:20:09 ETA= 10.1m tpr=9.11e-01 ETF=15:30:12 mem:579.3MB\n",
+ "1346/2000 67.3% complete 15:20:18 ETA= 12.5m tpr=1.14e+00 ETF=15:32:46 mem:581.5MB\n",
+ "1355/2000 67.8% complete 15:20:25 ETA= 8.5m tpr=7.90e-01 ETF=15:28:54 mem:581.6MB\n",
+ "1359/2000 68.0% complete 15:20:30 ETA= 13.9m tpr=1.30e+00 ETF=15:34:26 mem:581.6MB\n",
+ "1366/2000 68.3% complete 15:20:38 ETA= 11.7m tpr=1.10e+00 ETF=15:32:18 mem:581.7MB\n",
+ "1376/2000 68.8% complete 15:20:44 ETA= 6.1m tpr=5.89e-01 ETF=15:26:51 mem:581.7MB\n",
+ "1384/2000 69.2% complete 15:20:49 ETA= 6.9m tpr=6.76e-01 ETF=15:27:46 mem:581.7MB\n",
+ "1393/2000 69.7% complete 15:20:55 ETA= 6.2m tpr=6.13e-01 ETF=15:27:07 mem:581.8MB1394/2000 69.7% complete 15:20:55 ETA= 5.6m tpr=5.52e-01 ETF=15:26:29 mem:581.8MB\n",
+ "\n",
+ "1423/2000 71.2% complete 15:21:00 ETA= 1.6m tpr=1.69e-01 ETF=15:22:37 mem:581.9MB\n",
+ "1435/2000 71.8% complete 15:21:07 ETA= 5.6m tpr=5.92e-01 ETF=15:26:42 mem:582.3MB\n",
+ "1443/2000 72.2% complete 15:21:12 ETA= 6.1m tpr=6.54e-01 ETF=15:27:17 mem:582.5MB\n",
+ "1445/2000 72.2% complete 15:21:18 ETA= 28.2m tpr=3.05e+00 ETF=15:49:28 mem:582.6MB\n",
+ "1448/2000 72.4% complete 15:21:25 ETA= 20.0m tpr=2.18e+00 ETF=15:41:27 mem:582.6MB\n",
+ "1454/2000 72.7% complete 15:21:31 ETA= 8.6m tpr=9.49e-01 ETF=15:30:09 mem:583.0MB\n",
+ "1455/2000 72.8% complete 15:21:37 ETA= 54.9m tpr=6.05e+00 ETF=16:16:32 mem:583.0MB\n",
+ "1459/2000 73.0% complete 15:21:43 ETA= 13.5m tpr=1.50e+00 ETF=15:35:12 mem:583.0MB\n",
+ "1465/2000 73.2% complete 15:21:48 ETA= 8.6m tpr=9.65e-01 ETF=15:30:25 mem:583.0MB\n",
+ "1474/2000 73.7% complete 15:21:54 ETA= 5.6m tpr=6.38e-01 ETF=15:27:30 mem:583.0MB\n",
+ "1482/2000 74.1% complete 15:21:59 ETA= 5.4m tpr=6.30e-01 ETF=15:27:26 mem:583.0MB\n",
+ "1485/2000 74.2% complete 15:22:04 ETA= 14.8m tpr=1.73e+00 ETF=15:36:54 mem:583.5MB\n",
+ "1487/2000 74.3% complete 15:22:10 ETA= 24.9m tpr=2.91e+00 ETF=15:47:02 mem:583.5MB\n",
+ "1496/2000 74.8% complete 15:22:16 ETA= 5.0m tpr=5.91e-01 ETF=15:27:13 mem:583.7MB\n",
+ "1509/2000 75.5% complete 15:22:21 ETA= 3.6m tpr=4.40e-01 ETF=15:25:57 mem:583.9MB\n",
+ "1523/2000 76.2% complete 15:22:27 ETA= 3.0m tpr=3.80e-01 ETF=15:25:28 mem:583.9MB\n",
+ "1531/2000 76.5% complete 15:22:33 ETA= 5.9m tpr=7.60e-01 ETF=15:28:29 mem:583.9MB\n",
+ "1537/2000 76.8% complete 15:22:38 ETA= 6.7m tpr=8.71e-01 ETF=15:29:21 mem:583.9MB\n",
+ "1545/2000 77.2% complete 15:22:44 ETA= 5.4m tpr=7.14e-01 ETF=15:28:08 mem:584.0MB\n",
+ "1555/2000 77.8% complete 15:22:49 ETA= 4.1m tpr=5.52e-01 ETF=15:26:55 mem:584.2MB\n",
+ "1564/2000 78.2% complete 15:22:54 ETA= 4.2m tpr=5.78e-01 ETF=15:27:06 mem:584.2MB\n",
+ "1574/2000 78.7% complete 15:23:00 ETA= 4.4m tpr=6.16e-01 ETF=15:27:23 mem:584.4MB\n",
+ "1584/2000 79.2% complete 15:23:07 ETA= 4.4m tpr=6.28e-01 ETF=15:27:28 mem:584.8MB\n",
+ "1594/2000 79.7% complete 15:23:12 ETA= 3.8m tpr=5.66e-01 ETF=15:27:02 mem:584.9MB\n",
+ "1607/2000 80.3% complete 15:23:17 ETA= 2.5m tpr=3.86e-01 ETF=15:25:49 mem:585.0MB\n",
+ "1618/2000 80.9% complete 15:23:24 ETA= 3.8m tpr=5.97e-01 ETF=15:27:12 mem:585.4MB\n",
+ "1628/2000 81.4% complete 15:23:29 ETA= 3.3m tpr=5.28e-01 ETF=15:26:46 mem:585.5MB\n",
+ "1635/2000 81.8% complete 15:23:34 ETA= 4.4m tpr=7.30e-01 ETF=15:28:01 mem:585.9MB\n",
+ "1645/2000 82.2% complete 15:23:40 ETA= 3.4m tpr=5.81e-01 ETF=15:27:06 mem:585.9MB\n",
+ "1655/2000 82.8% complete 15:23:47 ETA= 4.0m tpr=7.02e-01 ETF=15:27:49 mem:586.0MB1656/2000 82.8% complete 15:23:47 ETA= 3.7m tpr=6.39e-01 ETF=15:27:27 mem:586.0MB\n",
+ "\n",
+ "1664/2000 83.2% complete 15:23:54 ETA= 4.5m tpr=8.01e-01 ETF=15:28:23 mem:586.1MB\n",
+ "1674/2000 83.7% complete 15:24:02 ETA= 4.5m tpr=8.27e-01 ETF=15:28:31 mem:586.2MB\n",
+ "1684/2000 84.2% complete 15:24:07 ETA= 2.9m tpr=5.55e-01 ETF=15:27:03 mem:586.2MB\n",
+ "1691/2000 84.5% complete 15:24:13 ETA= 4.2m tpr=8.21e-01 ETF=15:28:27 mem:586.5MB\n",
+ "1699/2000 85.0% complete 15:24:19 ETA= 3.4m tpr=6.75e-01 ETF=15:27:42 mem:586.5MB\n",
+ "1713/2000 85.7% complete 15:24:24 ETA= 1.9m tpr=4.07e-01 ETF=15:26:21 mem:586.6MB\n",
+ "1725/2000 86.2% complete 15:24:31 ETA= 2.6m tpr=5.57e-01 ETF=15:27:04 mem:586.7MB\n",
+ "1735/2000 86.8% complete 15:24:38 ETA= 3.0m tpr=6.76e-01 ETF=15:27:37 mem:586.7MB\n",
+ "1745/2000 87.2% complete 15:24:44 ETA= 2.7m tpr=6.40e-01 ETF=15:27:27 mem:586.9MB\n",
+ "1755/2000 87.8% complete 15:24:51 ETA= 2.8m tpr=6.88e-01 ETF=15:27:40 mem:586.9MB\n",
+ "1763/2000 88.2% complete 15:24:56 ETA= 2.6m tpr=6.59e-01 ETF=15:27:32 mem:586.9MB\n",
+ "1767/2000 88.3% complete 15:25:02 ETA= 5.3m tpr=1.36e+00 ETF=15:30:18 mem:586.9MB\n",
+ "1776/2000 88.8% complete 15:25:09 ETA= 2.9m tpr=7.71e-01 ETF=15:28:01 mem:586.9MB\n",
+ "1785/2000 89.2% complete 15:25:14 ETA= 2.1m tpr=5.90e-01 ETF=15:27:21 mem:586.9MB\n",
+ "1793/2000 89.7% complete 15:25:19 ETA= 2.2m tpr=6.29e-01 ETF=15:27:29 mem:587.1MB\n",
+ "1801/2000 90.0% complete 15:25:24 ETA= 2.2m tpr=6.59e-01 ETF=15:27:35 mem:587.1MB\n",
+ "1812/2000 90.6% complete 15:25:29 ETA= 1.5m tpr=4.68e-01 ETF=15:26:57 mem:587.1MB\n",
+ "1822/2000 91.1% complete 15:25:35 ETA= 1.6m tpr=5.54e-01 ETF=15:27:14 mem:587.4MB\n",
+ "1830/2000 91.5% complete 15:25:41 ETA= 2.1m tpr=7.49e-01 ETF=15:27:48 mem:587.4MB\n",
+ "1839/2000 92.0% complete 15:25:47 ETA= 1.7m tpr=6.21e-01 ETF=15:27:27 mem:587.4MB\n",
+ "1847/2000 92.3% complete 15:25:52 ETA= 1.8m tpr=7.10e-01 ETF=15:27:41 mem:587.4MB\n",
+ "1855/2000 92.8% complete 15:25:59 ETA= 2.0m tpr=8.17e-01 ETF=15:27:57 mem:587.6MB\n",
+ "1864/2000 93.2% complete 15:26:05 ETA= 1.5m tpr=6.79e-01 ETF=15:27:37 mem:587.8MB\n",
+ "1873/2000 93.7% complete 15:26:10 ETA= 1.3m tpr=6.07e-01 ETF=15:27:27 mem:588.0MB\n",
+ "1884/2000 94.2% complete 15:26:16 ETA= 57.0s tpr=4.91e-01 ETF=15:27:13 mem:588.1MB\n",
+ "1895/2000 94.8% complete 15:26:21 ETA= 48.7s tpr=4.63e-01 ETF=15:27:09 mem:588.8MB\n",
+ "1907/2000 95.3% complete 15:26:27 ETA= 45.6s tpr=4.91e-01 ETF=15:27:12 mem:588.9MB\n",
+ "1916/2000 95.8% complete 15:26:33 ETA= 57.5s tpr=6.84e-01 ETF=15:27:30 mem:589.1MB\n",
+ "1926/2000 96.3% complete 15:26:39 ETA= 46.5s tpr=6.28e-01 ETF=15:27:26 mem:589.1MB\n",
+ "1936/2000 96.8% complete 15:26:46 ETA= 42.0s tpr=6.57e-01 ETF=15:27:28 mem:589.1MB\n",
+ "1946/2000 97.3% complete 15:26:53 ETA= 40.1s tpr=7.42e-01 ETF=15:27:33 mem:589.2MB\n",
+ "1956/2000 97.8% complete 15:26:59 ETA= 25.1s tpr=5.70e-01 ETF=15:27:24 mem:589.2MB\n",
+ "1966/2000 98.3% complete 15:27:04 ETA= 19.1s tpr=5.62e-01 ETF=15:27:24 mem:589.5MB\n",
+ "1976/2000 98.8% complete 15:27:10 ETA= 14.4s tpr=6.01e-01 ETF=15:27:25 mem:589.5MB\n",
+ "1987/2000 99.3% complete 15:27:16 ETA= 6.4s tpr=4.92e-01 ETF=15:27:22 mem:589.5MB\n",
+ "1998/2000 99.9% complete 15:27:21 ETA= 1.0s tpr=4.85e-01 ETF=15:27:22 mem:589.6MB\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "[2021-09-09 11:48:39,930 DEBUG Process-5] --- Process-3 is finishing.\n"
+ "[2021-09-10 15:27:22,382 DEBUG Process-5] --- Process-3 is finishing.\n"
]
},
{
@@ -487,8 +620,8 @@
"output_type": "stream",
"text": [
"Process 3 finished:\n",
- "\tgenerator started at 2021-09-09T11:46:07.708030, done at 2021-09-09T11:48:39.932656 (total: 152.224626s of which 151.9391267299652s interfacing with binary_c).\n",
- "\tRan 495 systems with a total probability of 0.15751390762579187.\n",
+ "\tgenerator started at 2021-09-10T15:14:08.117391, done at 2021-09-10T15:27:22.400722 (total: 794.283331s of which 792.6935975551605s interfacing with binary_c).\n",
+ "\tRan 499 systems with a total probability of 0.17005450973840136.\n",
"\tThis thread had 0 failing systems with a total probability of 0.\n",
"\tSkipped a total of 0 systems because they had 0 probability\n"
]
@@ -497,8 +630,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "[2021-09-09 11:48:39,935 DEBUG Process-5] --- Process-3 is finished.\n",
- "[2021-09-09 11:48:39,969 DEBUG Process-3] --- Process-1 is finishing.\n"
+ "[2021-09-10 15:27:22,435 DEBUG Process-5] --- Process-3 is finished.\n",
+ "[2021-09-10 15:27:22,480 DEBUG Process-3] --- Process-1 is finishing.\n"
]
},
{
@@ -506,8 +639,8 @@
"output_type": "stream",
"text": [
"Process 1 finished:\n",
- "\tgenerator started at 2021-09-09T11:46:07.699120, done at 2021-09-09T11:48:39.971885 (total: 152.272765s of which 151.99865984916687s interfacing with binary_c).\n",
- "\tRan 512 systems with a total probability of 0.177316124969565.\n",
+ "\tgenerator started at 2021-09-10T15:14:08.080367, done at 2021-09-10T15:27:22.505288 (total: 794.424921s of which 793.1943278312683s interfacing with binary_c).\n",
+ "\tRan 474 systems with a total probability of 0.15740832333567983.\n",
"\tThis thread had 0 failing systems with a total probability of 0.\n",
"\tSkipped a total of 0 systems because they had 0 probability\n"
]
@@ -516,8 +649,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "[2021-09-09 11:48:39,974 DEBUG Process-3] --- Process-1 is finished.\n",
- "[2021-09-09 11:48:40,110 DEBUG Process-2] --- Process-0 is finishing.\n"
+ "[2021-09-10 15:27:22,531 DEBUG Process-3] --- Process-1 is finished.\n",
+ "[2021-09-10 15:27:22,846 DEBUG Process-2] --- Process-0 is finishing.\n"
]
},
{
@@ -525,8 +658,8 @@
"output_type": "stream",
"text": [
"Process 0 finished:\n",
- "\tgenerator started at 2021-09-09T11:46:07.695606, done at 2021-09-09T11:48:40.113158 (total: 152.417552s of which 152.14919590950012s interfacing with binary_c).\n",
- "\tRan 512 systems with a total probability of 0.1629988228713039.\n",
+ "\tgenerator started at 2021-09-10T15:14:08.077117, done at 2021-09-10T15:27:22.851971 (total: 794.774854s of which 793.4976091384888s interfacing with binary_c).\n",
+ "\tRan 507 systems with a total probability of 0.16018641159091498.\n",
"\tThis thread had 0 failing systems with a total probability of 0.\n",
"\tSkipped a total of 0 systems because they had 0 probability\n"
]
@@ -535,8 +668,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "[2021-09-09 11:48:40,115 DEBUG Process-2] --- Process-0 is finished.\n",
- "[2021-09-09 11:48:40,145 DEBUG Process-4] --- Process-2 is finishing.\n"
+ "[2021-09-10 15:27:22,872 DEBUG Process-2] --- Process-0 is finished.\n",
+ "[2021-09-10 15:27:22,976 DEBUG Process-4] --- Process-2 is finishing.\n"
]
},
{
@@ -544,8 +677,8 @@
"output_type": "stream",
"text": [
"Process 2 finished:\n",
- "\tgenerator started at 2021-09-09T11:46:07.702814, done at 2021-09-09T11:48:40.148157 (total: 152.445343s of which 152.1856849193573s interfacing with binary_c).\n",
- "\tRan 481 systems with a total probability of 0.1516810381180079.\n",
+ "\tgenerator started at 2021-09-10T15:14:08.084369, done at 2021-09-10T15:27:22.981706 (total: 794.897337s of which 793.4600214958191s interfacing with binary_c).\n",
+ "\tRan 520 systems with a total probability of 0.1618606489196724.\n",
"\tThis thread had 0 failing systems with a total probability of 0.\n",
"\tSkipped a total of 0 systems because they had 0 probability\n"
]
@@ -554,14 +687,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "[2021-09-09 11:48:40,150 DEBUG Process-4] --- Process-2 is finished.\n"
+ "[2021-09-10 15:27:22,986 DEBUG Process-4] --- Process-2 is finished.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Population-cd4b14dc28814364b94a8608b70990fd finished! The total probability was: 0.6495098935846687. It took a total of 152.67183566093445s to run 2000 systems on 4 cores\n",
+ "Population-0fa295ee5c76444bace8fd0ee17a3e11 finished! The total probability was: 0.6495098935846686. It took a total of 795.1383104324341s to run 2000 systems on 4 cores\n",
"There were no errors found in this run.\n",
"Done population run!\n"
]
@@ -595,7 +728,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 12,
"id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
"metadata": {},
"outputs": [
@@ -603,7 +736,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "{'population_name': 'cd4b14dc28814364b94a8608b70990fd', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6495098935846687, 'total_count': 2000, 'start_timestamp': 1631180767.6636574, 'end_timestamp': 1631180920.335493, 'total_mass_run': 41112.22096439228, 'total_probability_weighted_mass_run': 0.6452116023479679, 'zero_prob_stars_skipped': 0}\n"
+ "{'population_name': '0fa295ee5c76444bace8fd0ee17a3e11', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6495098935846686, 'total_count': 2000, 'start_timestamp': 1631283248.057525, 'end_timestamp': 1631284043.1958354, 'total_mass_run': 41112.220964392276, 'total_probability_weighted_mass_run': 0.6452116023479681, 'zero_prob_stars_skipped': 0}\n"
]
}
],
@@ -613,7 +746,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
"metadata": {},
"outputs": [
@@ -623,13 +756,13 @@
"[None]"
]
},
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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+ "image/png": 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"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
@@ -673,40 +806,10 @@
"p.set(yscale=\"log\")"
]
},
- {
- "cell_type": "markdown",
- "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "44586e42-b7cb-4a55-be0a-330b98b20de4",
- "metadata": {},
- "source": [
- "## "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "29c6588b-078d-42ec-9b07-63278320ca9c",
- "metadata": {},
- "source": [
- "## "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "99e25a72-54e6-4826-b0e5-4a02460b857d",
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "code",
"execution_count": null,
- "id": "fe84d2dd-5e5f-4eaa-a09e-2bfb7559b70b",
+ "id": "e7541ebf-fe9a-4fb0-a88e-bb318d06f9eb",
"metadata": {},
"outputs": [],
"source": []
@@ -714,7 +817,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -728,7 +831,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.6.4"
}
},
"nbformat": 4,