.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ASW/ASW_one_pulse.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ASW_ASW_one_pulse.py: ASW inference - One pulse model =============================== This example implements inference for the ASW population under a one pulse model of admixture, using the tracts package. Inference is performed using autosomal and X chromosome data, allowing for the specification of sex-biased admixture. To implement this example, we use the following driver file: .. code-block:: yaml samples: directory: ./TrioPhased/ individual_names: [ "NA19625","NA19700","NA19701","NA19703","NA19704","NA19707","NA19711","NA19712","NA19713","NA19818","NA19819", "NA19834","NA19835","NA19900","NA19901","NA19904","NA19908","NA19909","NA19913","NA19914","NA19916","NA19917", "NA19920","NA19921","NA19922","NA19923","NA19982","NA19984","NA20126","NA20127","NA20274","NA20276","NA20278", "NA20281","NA20282","NA20287","NA20289","NA20291","NA20294","NA20296","NA20298","NA20299","NA20314","NA20317", "NA20318","NA20320","NA20321","NA20332","NA20334","NA20339","NA20340","NA20342","NA20346","NA20348","NA20351", "NA20355","NA20356","NA20357","NA20359","NA20362","NA20412"] male_names : [ "NA19700","NA19703","NA19711","NA19818","NA19834","NA19900","NA19904","NA19908","NA19916","NA19920", "NA19922","NA19982","NA19984","NA20126","NA20278","NA20281","NA20291","NA20298","NA20318","NA20340", "NA20342","NA20346","NA20348","NA20351","NA20356","NA20362"] #see Readme_dataprocessing.md for how this was generated filename_format: "{name}_{label}_final.bed" labels: [A, B] #If this field is omitted, 'A' and 'B' will be used by default chromosomes: 1-22 #The chromosomes to use for analysis. Can be specified as a list or a range allosomes: [X] output_filename_format: "ASW_test_output_{label}" log_filename: 'ASW_one_pulse.log' output_directory: ./output_one_pulse/ verbose_log: 1 verbose_screen: 30 log_scale : True start_params: t: 5:8 repetitions: 3 maximum_iterations: 1000 seed: 100 unknown_labels_for_smoothing: ["UNK", "centromere","miscall"] # segments with these labels will be smoother over, that is, will be filled with neighbouring ancestries up to their midpoints. exclude_tracts_below_cm: 2 npts : 50 #fix_parameters_from_ancestry_proportions: ['REUR', 'RNAT', 'REUR_sex_bias', 'RNAT_sex_bias'] output_directory: ./output_one_pulse/ ad_model_autosomes : M ad_model_allosomes: DC Complete results from this analysis are saved in the output directory specified in the driver file. Below, we display the optimal parameters estimated from this analysis, as well as the plots illustrating the inferred tract length distributions, compared to the observed histograms, for every source population and chromosome type (autosomes and X chromosome). Optimal parameters ------------------ .. csv-table:: Estimated optimal parameters :file: output_one_pulse/ASW_test_output_optimal_parameters.txt :header-rows: 1 :delim: tab Tract length histograms ----------------------- Autosomal admixture ^^^^^^^^^^^^^^^^^^^ .. image:: output_one_pulse/ASW_test_output_autosomes_all_populations.png :width: 700px :alt: African ancestry tract histogram X chromosome admixture in females ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. image:: output_one_pulse/ASW_test_output_female_allosomes_all_populations.png :width: 700px :alt: European ancestry tract histogram X chromosome admixture in males ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. image:: output_one_pulse/ASW_test_output_male_allosomes_all_populations.png :width: 700px :alt: Native American ancestry tract histogram .. GENERATED FROM PYTHON SOURCE LINES 89-109 .. rst-class:: sphx-glr-script-out .. code-block:: none ------------------------------------------------------------------------------------------------ Running tracts 2.0 with driver file: ASW_one_pulse.yaml Reading data, demographic model and driver specifications... ------------------------------------------------------------------------------------------------ excluding_tracts_below set to 2.0 cM. Ancestries: ['EUR', 'NAT', 'AFR'] Data autosome proportions: [0.19578862 0.03825495 0.76595643] Data allosome proportions: [0.16839124 0.03818939 0.79341937] Model parameters : ['REUR', 'REUR_sex_bias', 'RNAT', 'RNAT_sex_bias', 't'] Multiple starting parameters were generated. These will be converted to optimizer units and used for multiple optimization runs. Run | Starting parameters --------------------------------------------------- 1 | [0.8, 0.1, 0.1, 0.1, 6.051 ] 2 | [0.8, 0.1, 0.1, 0.1, 7.828 ] 3 | [0.8, 0.1, 0.1, 0.1, 7.308 ] --------------------------------------------------- Optimization run #1 ----------------------------------------------------------------------------------------- Admixture is modelled with the M model for autosomes and with the DC model for allosomes. Optimization is performed in two steps. Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RNAT', 't']. Iter. Log-likelihood Model parameters Transmission ----------------------------------------------------------------------------------------- 30 , -8783.43 , array([ 0.766851 , 0 , 0.0915407 , 0 , 5.38536 ]), Autosomes 60 , -6689.12 , array([ 0.716438 , 0 , 0.0834419 , 0 , 5.01123 ]), Autosomes 90 , -5048.93 , array([ 0.659646 , 0 , 0.0768037 , 0 , 4.64467 ]), Autosomes 120 , -3845.94 , array([ 0.593199 , 0 , 0.0717713 , 0 , 4.59802 ]), Autosomes 150 , -2917.74 , array([ 0.524202 , 0 , 0.0666869 , 0 , 4.6503 ]), Autosomes 180 , -2192.79 , array([ 0.454208 , 0 , 0.0619787 , 0 , 4.69761 ]), Autosomes 210 , -1622.15 , array([ 0.384954 , 0 , 0.058127 , 0 , 4.92339 ]), Autosomes 240 , -1177.56 , array([ 0.32578 , 0 , 0.0535765 , 0 , 5.36143 ]), Autosomes 270 , -861.203 , array([ 0.269123 , 0 , 0.0491301 , 0 , 5.75128 ]), Autosomes 300 , -678.289 , array([ 0.226608 , 0 , 0.0434688 , 0 , 6.31088 ]), Autosomes 330 , -599.738 , array([ 0.197439 , 0 , 0.0362033 , 0 , 6.71031 ]), Autosomes 360 , -590.05 , array([ 0.196257 , 0 , 0.032662 , 0 , 6.8269 ]), Autosomes 390 , -588.763 , array([ 0.195852 , 0 , 0.0313911 , 0 , 6.85613 ]), Autosomes 420 , -588.548 , array([ 0.195954 , 0 , 0.0310744 , 0 , 6.84569 ]), Autosomes 450 , -588.48 , array([ 0.195925 , 0 , 0.0308196 , 0 , 6.84602 ]), Autosomes 480 , -588.466 , array([ 0.195888 , 0 , 0.0307111 , 0 , 6.84943 ]), Autosomes Step 1 completed. ---------------------------------------------------------------------------------------------------------- Step 2 : Optimizing autosomal + allosomal likelihood over parameters : ['REUR_sex_bias', 'RNAT_sex_bias']. Non-sex-bias parameters fixed at values from previous optimization step. Iter. Log-likelihood Model parameters Transmission ---------------------------------------------------------------------------------------------------------- 510 , -210.568 , array([ 0.195846 , 0.0392888 , 0.0306803 , 0.0245961 , 6.84912 ]), Female allosomes 510 , -95.8795 , array([ 0.195846 , 0.0392888 , 0.0306803 , 0.0245961 , 6.84912 ]), Male allosomes 510 , -588.464 , array([ 0.195846 , 0.0392888 , 0.0306803 , 0.0245961 , 6.84912 ]), Autosomes 540 , -208.046 , array([ 0.195846 , 0.169352 , 0.0306803 , 0.0935103 , 6.84912 ]), Female allosomes 540 , -96.4478 , array([ 0.195846 , 0.169352 , 0.0306803 , 0.0935103 , 6.84912 ]), Male allosomes 540 , -588.464 , array([ 0.195846 , 0.169352 , 0.0306803 , 0.0935103 , 6.84912 ]), Autosomes 570 , -205.779 , array([ 0.195846 , 0.293332 , 0.0306803 , 0.164404 , 6.84912 ]), Female allosomes 570 , -97.081 , array([ 0.195846 , 0.293332 , 0.0306803 , 0.164404 , 6.84912 ]), Male allosomes 570 , -588.464 , array([ 0.195846 , 0.293332 , 0.0306803 , 0.164404 , 6.84912 ]), Autosomes 600 , -203.819 , array([ 0.195846 , 0.403457 , 0.0306803 , 0.242452 , 6.84912 ]), Female allosomes 600 , -97.7335 , array([ 0.195846 , 0.403457 , 0.0306803 , 0.242452 , 6.84912 ]), Male allosomes 600 , -588.464 , array([ 0.195846 , 0.403457 , 0.0306803 , 0.242452 , 6.84912 ]), Autosomes 630 , -202.161 , array([ 0.195846 , 0.497282 , 0.0306803 , 0.327043 , 6.84912 ]), Female allosomes 630 , -98.3684 , array([ 0.195846 , 0.497282 , 0.0306803 , 0.327043 , 6.84912 ]), Male allosomes 630 , -588.464 , array([ 0.195846 , 0.497282 , 0.0306803 , 0.327043 , 6.84912 ]), Autosomes 660 , -200.782 , array([ 0.195846 , 0.574544 , 0.0306803 , 0.415678 , 6.84912 ]), Female allosomes 660 , -98.9579 , array([ 0.195846 , 0.574544 , 0.0306803 , 0.415678 , 6.84912 ]), Male allosomes 660 , -588.464 , array([ 0.195846 , 0.574544 , 0.0306803 , 0.415678 , 6.84912 ]), Autosomes 690 , -199.648 , array([ 0.195846 , 0.63672 , 0.0306803 , 0.504418 , 6.84912 ]), Female allosomes 690 , -99.4863 , array([ 0.195846 , 0.63672 , 0.0306803 , 0.504418 , 6.84912 ]), Male allosomes 690 , -588.464 , array([ 0.195846 , 0.63672 , 0.0306803 , 0.504418 , 6.84912 ]), Autosomes 720 , -198.725 , array([ 0.195846 , 0.68615 , 0.0306803 , 0.589058 , 6.84912 ]), Female allosomes 720 , -99.9474 , array([ 0.195846 , 0.68615 , 0.0306803 , 0.589058 , 6.84912 ]), Male allosomes 720 , -588.464 , array([ 0.195846 , 0.68615 , 0.0306803 , 0.589058 , 6.84912 ]), Autosomes 750 , -197.981 , array([ 0.195846 , 0.72524 , 0.0306803 , 0.666226 , 6.84912 ]), Female allosomes 750 , -100.341 , array([ 0.195846 , 0.72524 , 0.0306803 , 0.666226 , 6.84912 ]), Male allosomes 750 , -588.464 , array([ 0.195846 , 0.72524 , 0.0306803 , 0.666226 , 6.84912 ]), Autosomes 780 , -197.389 , array([ 0.195846 , 0.756019 , 0.0306803 , 0.733874 , 6.84912 ]), Female allosomes 780 , -100.672 , array([ 0.195846 , 0.756019 , 0.0306803 , 0.733874 , 6.84912 ]), Male allosomes 780 , -588.464 , array([ 0.195846 , 0.756019 , 0.0306803 , 0.733874 , 6.84912 ]), Autosomes 810 , -196.99 , array([ 0.195846 , 0.776488 , 0.0306803 , 0.782897 , 6.84912 ]), Female allosomes 810 , -100.902 , array([ 0.195846 , 0.776488 , 0.0306803 , 0.782897 , 6.84912 ]), Male allosomes 810 , -588.464 , array([ 0.195846 , 0.776488 , 0.0306803 , 0.782897 , 6.84912 ]), Autosomes Step 2 completed. ---------------------------------------------------------------------------------------------------------- Optimization run #2 ----------------------------------------------------------------------------------------- Admixture is modelled with the M model for autosomes and with the DC model for allosomes. Optimization is performed in two steps. Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RNAT', 't']. Iter. Log-likelihood Model parameters Transmission ----------------------------------------------------------------------------------------- 30 , -10412.7 , array([ 0.775115 , 0 , 0.0926123 , 0 , 6.59048 ]), Autosomes 60 , -7616.56 , array([ 0.7386 , 0 , 0.0850206 , 0 , 5.52232 ]), Autosomes 90 , -5795.54 , array([ 0.686362 , 0 , 0.0779571 , 0 , 5.01961 ]), Autosomes 120 , -4389.2 , array([ 0.62694 , 0 , 0.0730759 , 0 , 4.6187 ]), Autosomes 150 , -3332.96 , array([ 0.557988 , 0 , 0.0684608 , 0 , 4.59829 ]), Autosomes 180 , -2505.49 , array([ 0.486429 , 0 , 0.0643787 , 0 , 4.70019 ]), Autosomes 210 , -1867.78 , array([ 0.4166 , 0 , 0.0603655 , 0 , 4.8819 ]), Autosomes 240 , -1409.54 , array([ 0.354496 , 0 , 0.0563356 , 0 , 5.02225 ]), Autosomes 270 , -1038.87 , array([ 0.302732 , 0 , 0.0522424 , 0 , 5.50553 ]), Autosomes 300 , -789.63 , array([ 0.252882 , 0 , 0.0471329 , 0 , 5.87739 ]), Autosomes 330 , -649.114 , array([ 0.220065 , 0 , 0.041178 , 0 , 6.43723 ]), Autosomes 360 , -594.211 , array([ 0.197269 , 0 , 0.0345642 , 0 , 6.79984 ]), Autosomes 390 , -589.814 , array([ 0.195126 , 0 , 0.0324256 , 0 , 6.81289 ]), Autosomes 420 , -588.91 , array([ 0.195836 , 0 , 0.0316839 , 0 , 6.84239 ]), Autosomes 450 , -588.531 , array([ 0.196004 , 0 , 0.0310264 , 0 , 6.84187 ]), Autosomes 480 , -588.475 , array([ 0.19595 , 0 , 0.0307788 , 0 , 6.85001 ]), Autosomes 510 , -588.464 , array([ 0.195898 , 0 , 0.030681 , 0 , 6.84984 ]), Autosomes Step 1 completed. ---------------------------------------------------------------------------------------------------------- Step 2 : Optimizing autosomal + allosomal likelihood over parameters : ['REUR_sex_bias', 'RNAT_sex_bias']. Non-sex-bias parameters fixed at values from previous optimization step. Iter. Log-likelihood Model parameters Transmission ---------------------------------------------------------------------------------------------------------- 540 , -210.891 , array([ 0.195857 , 0.0231546 , 0.0306393 , 0.0133796 , 6.851 ]), Female allosomes 540 , -95.8109 , array([ 0.195857 , 0.0231546 , 0.0306393 , 0.0133796 , 6.851 ]), Male allosomes 540 , -588.462 , array([ 0.195857 , 0.0231546 , 0.0306393 , 0.0133796 , 6.851 ]), Autosomes 570 , -208.334 , array([ 0.195857 , 0.155121 , 0.0306393 , 0.0799096 , 6.851 ]), Female allosomes 570 , -96.373 , array([ 0.195857 , 0.155121 , 0.0306393 , 0.0799096 , 6.851 ]), Male allosomes 570 , -588.462 , array([ 0.195857 , 0.155121 , 0.0306393 , 0.0799096 , 6.851 ]), Autosomes 600 , -206.032 , array([ 0.195857 , 0.280212 , 0.0306393 , 0.150485 , 6.851 ]), Female allosomes 600 , -97.0017 , array([ 0.195857 , 0.280212 , 0.0306393 , 0.150485 , 6.851 ]), Male allosomes 600 , -588.462 , array([ 0.195857 , 0.280212 , 0.0306393 , 0.150485 , 6.851 ]), Autosomes 630 , -204.034 , array([ 0.195857 , 0.391854 , 0.0306393 , 0.228229 , 6.851 ]), Female allosomes 630 , -97.6539 , array([ 0.195857 , 0.391854 , 0.0306393 , 0.228229 , 6.851 ]), Male allosomes 630 , -588.462 , array([ 0.195857 , 0.391854 , 0.0306393 , 0.228229 , 6.851 ]), Autosomes 660 , -202.341 , array([ 0.195857 , 0.487356 , 0.0306393 , 0.312745 , 6.851 ]), Female allosomes 660 , -98.2921 , array([ 0.195857 , 0.487356 , 0.0306393 , 0.312745 , 6.851 ]), Male allosomes 660 , -588.462 , array([ 0.195857 , 0.487356 , 0.0306393 , 0.312745 , 6.851 ]), Autosomes 690 , -200.928 , array([ 0.195857 , 0.566247 , 0.0306393 , 0.401703 , 6.851 ]), Female allosomes 690 , -98.8877 , array([ 0.195857 , 0.566247 , 0.0306393 , 0.401703 , 6.851 ]), Male allosomes 690 , -588.462 , array([ 0.195857 , 0.566247 , 0.0306393 , 0.401703 , 6.851 ]), Autosomes 720 , -199.765 , array([ 0.195857 , 0.629866 , 0.0306393 , 0.491234 , 6.851 ]), Female allosomes 720 , -99.4235 , array([ 0.195857 , 0.629866 , 0.0306393 , 0.491234 , 6.851 ]), Male allosomes 720 , -588.462 , array([ 0.195857 , 0.629866 , 0.0306393 , 0.491234 , 6.851 ]), Autosomes 750 , -198.817 , array([ 0.195857 , 0.680508 , 0.0306393 , 0.577075 , 6.851 ]), Female allosomes 750 , -99.8924 , array([ 0.195857 , 0.680508 , 0.0306393 , 0.577075 , 6.851 ]), Male allosomes 750 , -588.462 , array([ 0.195857 , 0.680508 , 0.0306393 , 0.577075 , 6.851 ]), Autosomes 780 , -198.051 , array([ 0.195857 , 0.720597 , 0.0306393 , 0.655707 , 6.851 ]), Female allosomes 780 , -100.294 , array([ 0.195857 , 0.720597 , 0.0306393 , 0.655707 , 6.851 ]), Male allosomes 780 , -588.462 , array([ 0.195857 , 0.720597 , 0.0306393 , 0.655707 , 6.851 ]), Autosomes 810 , -197.44 , array([ 0.195857 , 0.752202 , 0.0306393 , 0.724922 , 6.851 ]), Female allosomes 810 , -100.632 , array([ 0.195857 , 0.752202 , 0.0306393 , 0.724922 , 6.851 ]), Male allosomes 810 , -588.462 , array([ 0.195857 , 0.752202 , 0.0306393 , 0.724922 , 6.851 ]), Autosomes 840 , -196.981 , array([ 0.195857 , 0.776391 , 0.0306393 , 0.779926 , 6.851 ]), Female allosomes 840 , -100.9 , array([ 0.195857 , 0.776391 , 0.0306393 , 0.779926 , 6.851 ]), Male allosomes 840 , -588.462 , array([ 0.195857 , 0.776391 , 0.0306393 , 0.779926 , 6.851 ]), Autosomes Step 2 completed. ---------------------------------------------------------------------------------------------------------- Optimization run #3 ----------------------------------------------------------------------------------------- Admixture is modelled with the M model for autosomes and with the DC model for allosomes. Optimization is performed in two steps. Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RNAT', 't']. Iter. Log-likelihood Model parameters Transmission ----------------------------------------------------------------------------------------- 30 , -9937.44 , array([ 0.776072 , 0 , 0.0910664 , 0 , 6.1922 ]), Autosomes 60 , -7398.1 , array([ 0.737778 , 0 , 0.0825629 , 0 , 5.33587 ]), Autosomes 90 , -5633.63 , array([ 0.685075 , 0 , 0.0753631 , 0 , 4.8907 ]), Autosomes 120 , -4285.29 , array([ 0.623051 , 0 , 0.0705018 , 0 , 4.60474 ]), Autosomes 150 , -3254.54 , array([ 0.553734 , 0 , 0.0661967 , 0 , 4.60539 ]), Autosomes 180 , -2445.52 , array([ 0.482404 , 0 , 0.0619547 , 0 , 4.68537 ]), Autosomes 210 , -1817.46 , array([ 0.412159 , 0 , 0.0581488 , 0 , 4.85946 ]), Autosomes 240 , -1338.52 , array([ 0.348516 , 0 , 0.0556938 , 0 , 5.20333 ]), Autosomes 270 , -970.453 , array([ 0.290657 , 0 , 0.0511068 , 0 , 5.57969 ]), Autosomes 300 , -751.692 , array([ 0.243852 , 0 , 0.0460717 , 0 , 6.03623 ]), Autosomes 330 , -629.427 , array([ 0.211137 , 0 , 0.040258 , 0 , 6.49387 ]), Autosomes 360 , -595.382 , array([ 0.197204 , 0 , 0.035023 , 0 , 6.76729 ]), Autosomes 390 , -589.52 , array([ 0.19479 , 0 , 0.032207 , 0 , 6.82589 ]), Autosomes 420 , -588.589 , array([ 0.195728 , 0 , 0.0311791 , 0 , 6.84617 ]), Autosomes 450 , -588.463 , array([ 0.195868 , 0 , 0.0306499 , 0 , 6.85039 ]), Autosomes Step 1 completed. ---------------------------------------------------------------------------------------------------------- Step 2 : Optimizing autosomal + allosomal likelihood over parameters : ['REUR_sex_bias', 'RNAT_sex_bias']. Non-sex-bias parameters fixed at values from previous optimization step. Iter. Log-likelihood Model parameters Transmission ---------------------------------------------------------------------------------------------------------- 480 , -209.757 , array([ 0.195849 , 0.0798743 , 0.0306362 , 0.0429413 , 6.85142 ]), Female allosomes 480 , -96.0417 , array([ 0.195849 , 0.0798743 , 0.0306362 , 0.0429413 , 6.85142 ]), Male allosomes 480 , -588.462 , array([ 0.195849 , 0.0798743 , 0.0306362 , 0.0429413 , 6.85142 ]), Autosomes 510 , -207.294 , array([ 0.195849 , 0.210575 , 0.0306362 , 0.109903 , 6.85142 ]), Female allosomes 510 , -96.6395 , array([ 0.195849 , 0.210575 , 0.0306362 , 0.109903 , 6.85142 ]), Male allosomes 510 , -588.462 , array([ 0.195849 , 0.210575 , 0.0306362 , 0.109903 , 6.85142 ]), Autosomes 540 , -205.122 , array([ 0.195849 , 0.330292 , 0.0306362 , 0.183523 , 6.85142 ]), Female allosomes 540 , -97.2831 , array([ 0.195849 , 0.330292 , 0.0306362 , 0.183523 , 6.85142 ]), Male allosomes 540 , -588.462 , array([ 0.195849 , 0.330292 , 0.0306362 , 0.183523 , 6.85142 ]), Autosomes 570 , -203.258 , array([ 0.195849 , 0.435131 , 0.0306362 , 0.264343 , 6.85142 ]), Female allosomes 570 , -97.9335 , array([ 0.195849 , 0.435131 , 0.0306362 , 0.264343 , 6.85142 ]), Male allosomes 570 , -588.462 , array([ 0.195849 , 0.435131 , 0.0306362 , 0.264343 , 6.85142 ]), Autosomes 600 , -201.69 , array([ 0.195849 , 0.52337 , 0.0306362 , 0.35115 , 6.85142 ]), Female allosomes 600 , -98.5561 , array([ 0.195849 , 0.52337 , 0.0306362 , 0.35115 , 6.85142 ]), Male allosomes 600 , -588.462 , array([ 0.195849 , 0.52337 , 0.0306362 , 0.35115 , 6.85142 ]), Autosomes 630 , -200.389 , array([ 0.195849 , 0.595406 , 0.0306362 , 0.440849 , 6.85142 ]), Female allosomes 630 , -99.1272 , array([ 0.195849 , 0.595406 , 0.0306362 , 0.440849 , 6.85142 ]), Male allosomes 630 , -588.462 , array([ 0.195849 , 0.595406 , 0.0306362 , 0.440849 , 6.85142 ]), Autosomes 660 , -199.323 , array([ 0.195849 , 0.653104 , 0.0306362 , 0.529248 , 6.85142 ]), Female allosomes 660 , -99.6345 , array([ 0.195849 , 0.653104 , 0.0306362 , 0.529248 , 6.85142 ]), Male allosomes 660 , -588.462 , array([ 0.195849 , 0.653104 , 0.0306362 , 0.529248 , 6.85142 ]), Autosomes 690 , -198.458 , array([ 0.195849 , 0.698898 , 0.0306362 , 0.612295 , 6.85142 ]), Female allosomes 690 , -100.074 , array([ 0.195849 , 0.698898 , 0.0306362 , 0.612295 , 6.85142 ]), Male allosomes 690 , -588.462 , array([ 0.195849 , 0.698898 , 0.0306362 , 0.612295 , 6.85142 ]), Autosomes 720 , -197.762 , array([ 0.195849 , 0.735092 , 0.0306362 , 0.687009 , 6.85142 ]), Female allosomes 720 , -100.448 , array([ 0.195849 , 0.735092 , 0.0306362 , 0.687009 , 6.85142 ]), Male allosomes 720 , -588.462 , array([ 0.195849 , 0.735092 , 0.0306362 , 0.687009 , 6.85142 ]), Autosomes 750 , -197.21 , array([ 0.195849 , 0.763545 , 0.0306362 , 0.751776 , 6.85142 ]), Female allosomes 750 , -100.759 , array([ 0.195849 , 0.763545 , 0.0306362 , 0.751776 , 6.85142 ]), Male allosomes 750 , -588.462 , array([ 0.195849 , 0.763545 , 0.0306362 , 0.751776 , 6.85142 ]), Autosomes 780 , -196.969 , array([ 0.195849 , 0.776608 , 0.0306362 , 0.780294 , 6.85142 ]), Female allosomes 780 , -100.902 , array([ 0.195849 , 0.776608 , 0.0306362 , 0.780294 , 6.85142 ]), Male allosomes 780 , -588.462 , array([ 0.195849 , 0.776608 , 0.0306362 , 0.780294 , 6.85142 ]), Autosomes Step 2 completed. ---------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------- Results from multiple optimization runs with different starting parameters: ------------------------------------- Run | LogLik | Found parameters ------------------------------------- 1 | -886.353 | [0.1958, 0.7769, 0.03068, 0.784, 6.849] 2 | -886.339 | [0.1959, 0.7769, 0.03064, 0.781, 6.851] 3 | -886.334 | [0.1958, 0.7766, 0.03064, 0.7803, 6.851] ------------------------------------- Final parameters and corresponding likelihood: --------------------------------------------------------------------------------- LogLik | REUR REUR_sex_bias RNAT RNAT_sex_bias t --------------------------------------------------------------------------------- -886.334 | 0.1958 0.7766 0.03064 0.7803 6.851 --------------------------------------------------------------------------------- Results saved to : ./output_one_pulse/ {'destination_dir': PosixPath('/home/runner/work/tracts/tracts/docs/source/auto_examples/ASW/output_one_pulse'), 'table_file': PosixPath('/home/runner/work/tracts/tracts/docs/source/auto_examples/ASW/output_one_pulse/ASW_test_output_optimal_parameters.txt')} | .. code-block:: Python import sys from pathlib import Path sys.path.append('.') from tracts.driver import run_tracts script_path = Path(sys.argv[0]).resolve() driver_filename = "ASW_one_pulse.yaml" run_tracts(driver_filename = driver_filename, script_dir = script_path) # Don't run the code below: for documentation purposes only. from tracts.doc_utils import prepare_example_outputs_for_docs prepare_example_outputs_for_docs("output_one_pulse") .. rst-class:: sphx-glr-timing **Total running time of the script:** (11 minutes 24.197 seconds) .. _sphx_glr_download_auto_examples_ASW_ASW_one_pulse.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: ASW_one_pulse.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: ASW_one_pulse.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: ASW_one_pulse.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_