From afa95c7ba7b9edda165a61f85fcae23f8f184ab8 Mon Sep 17 00:00:00 2001 From: aevans1 Date: Thu, 14 Sep 2023 11:16:21 -0400 Subject: [PATCH] hsp90 reweighting tests --- Lukes_folder/cryoBIFE_test.ipynb | 21 +- .../hsp90_compute_posterior_matrix.ipynb | 57 +- Lukes_folder/hsp90_neg_log_posterior_mat.npz | Bin 0 -> 1600296 bytes Lukes_folder/hsp90_neg_log_posterior_mat.txt | 10000 ++++++++++++++++ Lukes_folder/hsp90_posterior_mat.npz | Bin 1600280 -> 1600280 bytes Lukes_folder/hsp90_posterior_mat.txt | 10000 ++++++++++++++++ crybife_pdf.png | Bin 0 -> 27090 bytes ensemble_reweighting_pdf.png | Bin 0 -> 26969 bytes 8 files changed, 20056 insertions(+), 22 deletions(-) create mode 100644 Lukes_folder/hsp90_neg_log_posterior_mat.npz create mode 100644 Lukes_folder/hsp90_neg_log_posterior_mat.txt create mode 100644 Lukes_folder/hsp90_posterior_mat.txt create mode 100644 crybife_pdf.png create mode 100644 ensemble_reweighting_pdf.png diff --git a/Lukes_folder/cryoBIFE_test.ipynb b/Lukes_folder/cryoBIFE_test.ipynb index b8031bf..b5d770d 100644 --- a/Lukes_folder/cryoBIFE_test.ipynb +++ b/Lukes_folder/cryoBIFE_test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -50,14 +50,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s_vals = np.linspace(0, 1, 20)\n", "P_vals = np.zeros_like(s_vals)\n", "for idx in range(s_vals.shape[0]):\n", - " P_vals[idx] = P(s_vals[idx])" + " P_vals[idx] = P(s_vals[idx])\n", + "P_vals/=np.sum(P_vals)" ] }, { @@ -76,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -135,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -193,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -340,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { diff --git a/Lukes_folder/hsp90_compute_posterior_matrix.ipynb b/Lukes_folder/hsp90_compute_posterior_matrix.ipynb index e32cd2e..5b7bdfa 100644 --- a/Lukes_folder/hsp90_compute_posterior_matrix.ipynb +++ b/Lukes_folder/hsp90_compute_posterior_matrix.ipynb @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -128,13 +128,13 @@ "Text(0.5, 0, '$\\\\theta$')" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -159,9 +159,18 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/home/levans/ceph/cryo_em_SBI/.venv/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\n", + " return F.conv2d(input, weight, bias, self.stride,\n" + ] + } + ], "source": [ "indices = torch.arange(0, sim.max_index + 1, 1, dtype=torch.float32).reshape(-1, 1).to(device)\n", "norm_indices = estimator.standardize(indices)\n", @@ -174,25 +183,49 @@ " logprobs = flow_at_image.log_prob(norm_indices).to(device)\n", " probs = torch.exp(logprobs) / 10\n", " posterior_mat[i, :] = probs.detach().cpu().numpy()\n", - "fname = \"hsp90_posterior_mat.npz\" \n", - "np.savez(fname, posterior_mat=posterior_mat) " + "\n", + "neg_log_posterior_mat = -np.log(posterior_mat)\n", + "fname = \"hsp90_neg_log_posterior_mat.npz\" \n", + "np.savez(fname, neg_log_posterior_mat=neg_log_posterior_mat) \n", + "np.savetxt(\"hsp90_neg_log_posterior_mat.txt\", neg_log_posterior_mat)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.95002456 0.95723009 1.04056791 ... 0.94908274 0.95177491 0.90810306]\n" + ] + } + ], + "source": [ + "print(np.sum(np.exp(-neg_log_posterior_mat), axis=1))" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.94847241 0.95781448 0.94858524 ... 0.95188757 0.95072114 0.94971349]\n" + "[3.22652577 2.41179399 2.32536547 2.2323031 2.28799024 2.34901498\n", + " 2.39948761 2.52841513 2.74808945 2.98477799 3.2261906 3.47986705\n", + " 3.75672259 4.10466158 4.56358501 5.01818457 5.40955015 5.49491498\n", + " 5.51266906 7.21653122]\n" ] } ], "source": [ - "print(np.sum(posterior_mat, axis=1))" + "file = np.loadtxt(\"hsp90_neg_log_posterior_mat.txt\")\n", + "print(file[1, :])" ] } ], diff --git a/Lukes_folder/hsp90_neg_log_posterior_mat.npz b/Lukes_folder/hsp90_neg_log_posterior_mat.npz new file mode 100644 index 0000000000000000000000000000000000000000..2d6415a88ba637ff11d2abd39461969b9f200126 GIT binary patch literal 1600296 zcmbT7g;!LM7wyRzYKHEvnPG}h3`Fk6R#FT~K}A4G2}MQ37LgDG1O*dCNefW~4E)k1 z*n(mqlG4q)zxV!wH*2wG!2}ccp0m$q@8f7AETSOr|1P=$OH8fn5BaGG{O?CqKtUkL z+t)K7*w=Gca9Fr^=+59!&%mwW{GeU?1O;UN@16el!2jL(f0uLi8|)li1qJp9L^8K~ zhk1oEEp(WcJ`0#!9i~rkXn5$>AkW~??cV?U`--grVcz85!**=lf`KL~_S}m+d&W!{&r+^uOYRjwxa+X;Pmx(dy0418ByN^y+*lYBCLsu8AF7xF zUl=@3uSey&6a;(y6n`}?j$QK~{K-B*$ND7EwNF3Df?m_R=$D8BIxy$Iz>SJ9pOVC! z51J5jeqPeuAdY!YT{GQmgt0|FW1ef85WG(^pT77kh-Ftdw$#}P;fb!x=E`azD0`hr zWRS1x%20Blsf7sQBk!zLni7So+^dz%->6u>VyDPr^7+!*pT(5Flf;X;OU{neg3cG6&;L(RgN*_b|J@beBBh9DPe1N6 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