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Add output of notebook cells
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‎notebooks/CreatingDecodingQuantities.ipynb‎

Lines changed: 148 additions & 15 deletions
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"id": "42f85552",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"id": "56e8e52f",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Demography set to file /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_CEU.demo\n",
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"Discretization file set to file /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_30-100-2000.disc\n",
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"Frequencies file set to /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_UKBB.frq\n",
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"New CSFS will be calculated\n",
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"Will read discretization intervals from /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_30-100-2000.disc ...\n",
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"smcpp: init_cache() successful\n",
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"smcpp: storing cache: /tmp/smcpp_matrices.dat\n",
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"smcpp: store_cache() successful\n",
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"Will use minor allele frequencies from /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_UKBB.frq ...\n",
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"Will use mutation rate mu = 1.65e-08.\n",
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"Number of samples in CSFS calculations: 50.\n",
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"Verified 69 CSFS entries.\n",
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"Probability of a site being monomorphic due to subsampling: 0.222911939663710\n",
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"\n",
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"Building decoding quantities...\n",
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"Genetic distances progress: 100%\t\n",
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"Physical distances progress: 100%\t\n",
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"Demography set to file /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_CEU.demo\n",
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"Frequencies file set to /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_UKBB.frq\n",
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"New CSFS will be calculated\n",
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"Using the following pre-specified discretization intervals: {0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, 390, 420, 450, 550, 650, 750, 850, 950, 1050, 1150, 1250, 1350, 1450, 1550, 1650, 1750, 1850, 1950}\n",
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" and calculating 39 additional intervals from coalescent distribution.\n",
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"smcpp: init_cache() successful\n",
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"Will use minor allele frequencies from /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_UKBB.frq ...\n",
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"Will use mutation rate mu = 1.65e-08.\n",
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"Number of samples in CSFS calculations: 50.\n",
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"Verified 70 CSFS entries.\n",
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"Probability of a site being monomorphic due to subsampling: 0.222911939663710\n",
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"\n",
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"Building decoding quantities...\n",
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"Genetic distances progress: 100%\t\n",
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"Physical distances progress: 100%\t\n",
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"Demography set to built-in CEU\n",
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"Frequencies file set to /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_UKBB.frq\n",
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"New CSFS will be calculated\n",
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"Using the following pre-specified discretization intervals: {0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, 390, 420, 450, 550, 650, 750, 850, 950, 1050, 1150, 1250, 1350, 1450, 1550, 1650, 1750, 1850, 1950}\n",
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" and calculating 39 additional intervals from coalescent distribution.\n",
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"smcpp: init_cache() successful\n",
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"Will use minor allele frequencies from /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/input_UKBB.frq ...\n",
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"Will use mutation rate mu = 1.65e-08.\n",
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"Number of samples in CSFS calculations: 50.\n",
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"Verified 70 CSFS entries.\n",
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"Probability of a site being monomorphic due to subsampling: 0.222911939663710\n",
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"\n",
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"Building decoding quantities...\n",
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"Genetic distances progress: 100%\t\n",
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"Physical distances progress: 100%\t\n",
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"Demography set to built-in CEU\n",
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"Frequencies set to built-in UKBB with 50 samples\n",
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"New CSFS will be calculated\n",
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"Using the following pre-specified discretization intervals: {0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, 390, 420, 450, 550, 650, 750, 850, 950, 1050, 1150, 1250, 1350, 1450, 1550, 1650, 1750, 1850, 1950}\n",
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" and calculating 39 additional intervals from coalescent distribution.\n",
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"smcpp: init_cache() successful\n",
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"Using built-in frequency information from UKBB ...\n",
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"Will use mutation rate mu = 1.65e-08.\n",
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"Number of samples in CSFS calculations: 50.\n",
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"Verified 70 CSFS entries.\n",
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"\n",
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"Building decoding quantities...\n",
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"Genetic distances progress: 100%\t\n",
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"Physical distances progress: 100%\t"
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]
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}
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],
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"source": [
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"files_dir = (pathlib.Path('..') / 'test' / 'regression').resolve()\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"id": "e1d704d3",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"id": "84e495bb",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Demography set to built-in CEU\n",
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"Frequencies set to built-in UKBB with 50 samples\n",
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"Precomputed CSFS will be loaded from file: /home/fcooper/GitRepos/PalamaraLab/PrepareDecoding/test/regression/output.csfs\n",
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"Using the following pre-specified discretization intervals: {0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, 390, 420, 450, 550, 650, 750, 850, 950, 1050, 1150, 1250, 1350, 1450, 1550, 1650, 1750, 1850, 1950}\n",
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" and calculating 39 additional intervals from coalescent distribution.\n",
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"Using built-in frequency information from UKBB ...\n",
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"Will use mutation rate mu = 1.65e-08.\n",
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"Number of samples in CSFS calculations: 50.\n",
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"Verified 70 CSFS entries.\n",
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"\n",
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"Building decoding quantities...\n",
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"Genetic distances progress: 100%\t\n",
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"Physical distances progress: 100%\t"
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]
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}
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],
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"source": [
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"dq = prepare_decoding(\n",
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" csfs_file=str(files_dir / 'output.csfs'),\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"id": "6ecc7fd7",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'states': 70, 'samples': 50, 'mu': 1.65e-08}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"{\"states\": dq.states, \"samples\": dq.samples, \"mu\": dq.mu}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 6,
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"id": "fad47cb3",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(numpy.ndarray, (2, 70))"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X = dq.compressedEmission\n",
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"type(X), X.shape"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 7,
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"id": "aacfc6cd",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"70"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len([x for x in dq.CSFS])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 8,
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"id": "2a1689b4",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(3, 49)"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"c0 = dq.CSFS[0]\n",
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"{\"mu\": c0.mu, \"from\": c0.csfsFrom, \"to\": c0.csfsTo, \"samples\": c0.samples}\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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"version": "3.11.11"
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}
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},
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"nbformat": 4,

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