Merge pull request #92 from MannLabs/development

Development

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.0.1
+current_version = 1.0.2
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?

CHANGELOG.md

@@ -2,6 +2,12 @@
 
 Follow the changelog format from https://keepachangelog.com/en/1.0.0/.
 
+## 1.0.2
+
+### Added
+
+- Enable `speclib_tsv` for transfer learning in CLI and GUI.
+
 ## 1.0.1 - 2023.01.20
 
 ### Fixed

docs/conf.py

@@ -23,7 +23,7 @@
 copyright = '2022, Mann Labs, MPIB'
 author = 'Mann Labs, MPIB'
 
-release = "1.0.1"
+release = "1.0.2"
 
 # -- General configuration ---------------------------------------------------
 

nbdev_nbs/test_ui.ipynb

@@ -11,55 +11,25 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022-07-22 09:09:17.053 INFO    numexpr.utils: NumExpr defaulting to 8 threads.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-07-22 09:09:42> \n",
-      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
-      "  command:\n",
-      "\n",
-      "    streamlit run /Users/zengwenfeng/opt/anaconda3/lib/python3.8/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2022-07-22 09:09:42.894 \n",
-      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
-      "  command:\n",
-      "\n",
-      "    streamlit run /Users/zengwenfeng/opt/anaconda3/lib/python3.8/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import os\n",
-    "__file__ = os.path.abspath('./test_ui.py')\n",
-    "import peptdeep.webui.library_ui\n",
-    "import peptdeep.webui.main_ui\n",
-    "import peptdeep.webui.model_ui\n",
-    "import peptdeep.webui.rescore_ui\n",
-    "import peptdeep.webui.startpage\n",
-    "import peptdeep.webui.transfer_ui\n",
-    "import peptdeep.webui.settings_ui\n",
-    "# import peptdeep.webui.server_ui\n",
+    "# import os\n",
+    "# __file__ = os.path.abspath('./test_ui.py')\n",
+    "# import peptdeep.webui.library_ui\n",
+    "# import peptdeep.webui.main_ui\n",
+    "# import peptdeep.webui.model_ui\n",
+    "# import peptdeep.webui.rescore_ui\n",
+    "# import peptdeep.webui.startpage\n",
+    "# import peptdeep.webui.transfer_ui\n",
+    "# import peptdeep.webui.settings_ui\n",
+    "# # import peptdeep.webui.server_ui\n",
     "\n",
-    "peptdeep.webui.library_ui.show()\n",
-    "peptdeep.webui.model_ui.show()\n",
-    "peptdeep.webui.rescore_ui.show()\n",
-    "peptdeep.webui.transfer_ui.show()\n",
-    "peptdeep.webui.settings_ui.show()\n",
-    "# peptdeep.webui.server_ui.show()"
+    "# peptdeep.webui.library_ui.show()\n",
+    "# peptdeep.webui.model_ui.show()\n",
+    "# peptdeep.webui.rescore_ui.show()\n",
+    "# peptdeep.webui.transfer_ui.show()\n",
+    "# peptdeep.webui.settings_ui.show()\n",
+    "# # peptdeep.webui.server_ui.show()"
    ]
   },
   {
@@ -72,7 +42,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.3 ('base')",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   }

nbdev_nbs/utils.ipynb

@@ -64,9 +64,9 @@
        "  'device': 'cuda'},\n",
        " 'cuda': {'is_available': <function torch.cuda.is_available() -> bool>,\n",
        "  'device': 'cuda'},\n",
-       " 'mps': {'is_available': <function __main__._is_mps_available() -> bool>,\n",
+       " 'mps': {'is_available': <function peptdeep.utils._is_mps_available() -> bool>,\n",
        "  'device': 'mps'},\n",
-       " 'm1': {'is_available': <function __main__._is_mps_available() -> bool>,\n",
+       " 'm1': {'is_available': <function peptdeep.utils._is_mps_available() -> bool>,\n",
        "  'device': 'mps'}}"
       ]
      },
@@ -90,40 +90,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='rt_pred', ylabel='rt_norm'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEICAYAAAC3Y/QeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAn2UlEQVR4nO3dd3Rc5bnv8e9jFduyhC0Xmo0xceg1RJfQk0AAUwKEFiD04hCbZsI99+Scc9dZJ3dlnZxzCGAgOJhOcEIxhBBIIIEQCDUudDDFVBsbjOUiybaK9d4/nhnvGWlGGtka7Sm/z1pZtt7Ze/RGiX6z/e5nP6+FEBARkfIyKO4JiIjIwFP4i4iUIYW/iEgZUviLiJQhhb+ISBlS+IuIlKHKfL65md0GHAN8EULYLTE2ErgXmAB8BJwSQljR0/uMHj06TJgwIZ9TFREpOfPmzfsyhDAm02uWzzp/MzsYaAbuSgn//wYaQwg/N7N/BupDCP+np/dpaGgIc+fOzds8RURKkZnNCyE0ZHotr8s+IYRngMYuw8cBdyb+fidwfD7nICIi3cWx5r9FCGFJ4u9LgS1imIOISFmL9YZv8DWnjOtOZjbZzOaa2dxly5YN8MxEREpbHOH/uZltBZD484tMB4UQZoYQGkIIDWPGZLxfISIiGymO8H8YODvx97OB38cwBxGRspbX8Dez3wIvADua2SIzOx/4OXCYmb0HfCfxtYiIDKC81vmHEE7L8tKh+fy+IiLSMz3hKyJShhT+IiKlaPXqHl/O67KPiIgMsHXr4Isv/M8eKPxFREpBRwd8+WWvV/xJCn8RkWIWAqxYAY2N0NmZ82kKfxGRYtXU5Ff77e19PlXhLyJSbFpbfV1/7dqNfguFv4hIsVi/3q/0V63a5LdS+IuIFLoQYOVKWL68T+v6PVH4i4gUsuZmv9pva+vXt1X4i4gUorY2X9dfs2bjzu9l90OFv4hIIVm/3pd3Vq3y5Z6+eucduOYaeOqpHg9T+IuIFIIQPPCXL/cPgL5atAiuuw4efjinDw319hERiduaNfDxx77M09fgb2yEn/0MJk2C3//eg3+77eD664GKrBmvK38Rkbi0t3vgt7T0/dzmZrjjDrjttuj8LbaASy6B732P5nWVwNitsp2u8BcRGWidnb68s3Jl39f129rgnntgxgy/6gcYPhwmT4YzzoAhQwCorQVYvCTb2yj8RUQG0qpVXrrZ1+Wd9evhD3/wdf3Fi31syBA46yy48ELYbLNMJ2V9KEDhLyIyENau9SWe1ta+nRcCPP00/OIX8O67PlZRASedBFOn+lLPRlD4i4jkU3s7LFvma/R9NX++h35qzf6RR8Lll8OECdnPGzQI6ut7fGuFv4hIPnR2+pr8ihV9X9d/7z24+mr461+jsQMOgCuugN12y37eoEEwYoQHf0VFj99C4S8i0t9Wr/Z1/Y6Ovp23eLGXaD70UPSBsdtucOWVsN9+2c/rQ+gnKfxFRPpLjlsodtPYCDfdBLNmRb35J0yAadPgiCPALPN5Zh76I0fmHPpJCn8RkU3Vxy0UN2hp8Vr9W2+NavU339xr9U84ASqzRLSZl3eOHJn9mF4o/EVENtZGbqFIWxvcfz/ceKN/aICXal54IZx5Jgwdmvk8Mz9u1KiNDv0khb+IyMZobvYqnr5sodjZCY88AtOney8egMGDo1r94cOzn5sM/aqqTZt3gsJfRKQvWls99PvSajkEeOYZr+BZsMDHKirgxBPh4ot7rtWvq/PQr67etHl3ofAXEcnFxm6h+MorcNVVMGdONHbEEV6r/5WvZD9v2DAYPdr/ZZAHCn8RkZ5s7BaKCxd6X/2//CUa23df+PGPYY89sp9XU+Ohn+jRky8KfxGRbFpafImnL1soLlni/Xceeij6sNh1Vw/9Aw7Ift6QIR76NTWbNOVcxRb+ZjYNuAAIwOvAuSGEPhbHiojkQVubh35fWi2vWAEzZ8Ldd0cfFttu68s7kyb5g1iZDB7soT9s2CZPuy9iCX8zGwtcCuwSQlhrZvcBpwJ3xDEfERFg41otr1kDd94Jt9wS9e8ZM8Zv5J54YvbqnOpqv5FbV9cvU++rOJd9KoGhZtYO1ACfxTgXESl3fW213N4O992XXqtfV+d99Xuq1a+q8tDP2IJ54MQS/iGExWZ2FfAJsBb4cwjhz3HMRUTKXF9bLXd2wp/+BNdeC5984mODB/tGKpMne7uFhObm5KYq+ENZydDP1q5hAMW17FMPHAdsB6wE7jezM0IId6ccMxmYDDB+/Pg4pikipay93a/Ym5pyOz4EePZZr9V/6y0fS9bqT50KW26ZdnhzM/zqV3DR1Apqt6n3pmsFEPpJFvraarQ/vqnZycCkEML5ia/PAvYNIUzJdHxDQ0OYm9rPWkRkY21Mq+VXX/W++i+9FI0dcQRcdhlMnJj5nEGDaK4cQe34kdlv9uaZmc0LITRkei2uNf9PgH3NrAZf9jkUULqLSH71tdXywoW+vPPnlFXpb3zDWyxnq9VP6bRZ28dOmwMprjX/l8xsNjAf6ABeBmbGMRcRKQN9bbW8dKn31X/wwahWf5ddfDOVAw/MvHzTD502B1JsMwwh/Dvw73F9fxEpA31ttbxyZVSrn7wBPH681+ofeWT25Zt+bro2EAr/40lEpK/62mp5zRr49a/h5pujG8BjxsCUKXDyydlDPU9N1waCwl9ESktfWi23t8Ps2fDLX/o54LWZF17obZaztVqorfXQz1PTtYGg8BeR0tDe7uv6ubRk6OyExx7zm7kff+xj1dVRrX59febzBqjp2kBQ+ItIcQvBWzLkUroZAjz/vJdtvvmmjw0aBMcf71snbr115vOGDvXQz/bUbhFS+ItI8Wpp8av9XJZ4XnvNQ//FF6Oxww7zm7lf/Wrmcwa40+ZAUviLSPHpyxLPBx/48s7jj0dj++zjLZb32ivzOTF12hxICn8RKR7JKp7ly3tf4vn8c7jhBnjggahZ2047eegfdFDmWv2YO20OJIW/iBSHXKt4Vq3yks277opq9ceN8+Wdo4/OXKtfIJ02B5LCX0QKW64bpq9dG9XqJx/qGjXKm66dfHLmWvwC67Q5kBT+IlKYct0wvb3d2zDccIPfBwBfq7/gAjj77Mzr9hUV3oZhxIiyC/0khb+IFJZcn84NIarV/+gjH6uqgh/8AH74Qw/3rgYNikI/pk6bhULhLyKFo6nJr/Z7W9d//nm46qrutfoXXwxjx3Y/ftCgDZ02yz30kxT+IhK/1lZfslm7tufj3njDa/Wffz4aO+QQ77a5/fbdj09pr0wBt1eOg8JfRAZcU1OimrKzM1rX76l088MPYfp03z4xqaHByzb33rv78UXWXjkO+qmIyIBqaoKf/Qz+9ZLV1K1b1vOG6Z9/7k3XZs+OjttxRw/9gw/OfLO2CNsrx0HhLyIDqq66lX87+wtqW3pY4lm9OqrVT27AMnasb5v43e9mXrcv4vbKcVD4i8jAWL/en8xduZLabMvv69ZFtfrJEs+RI72v/ve/nznYS6C9chwU/iKSf6tW+dp+tiWejg6v1b/++qhWv6YGzj8fzjnHA76rYcM89EugvXIcFP4ikj+97Z0bgm+Ofs01flMXfK3+tNPgRz/KXKtfU+OhX0LtleOg8BeR/pfL07kvvOBlm6+/7l+bwXHHeV/9ceO6Hz90qId+CbZXjoPCX0T6V29LPG+95aH/7LPR2Le/7bX6O+zQ/fgS7qkfJ4W/iPSP1lYvzcy2xPPxx16r/+ij0djee3vZZkND9+OHDPEr/RLuqR8nhb+IbJr1670Pz4oVmV9ftsxr9e+/32/sgl/hX3EFfOtb3Wv1Bw/20M90k1f6jcJfRDZObw3Ymprgllvgzjujtg1jx8Kll3qtftd2C2W0kUohUPiLSN+tXu3r+skr+VStrTBrFtx0E6xc6WP19V69c9pp3Wv1y3AjlUKg8BeR3K1d68s4mdb1OzrgoYe8Vn/pUh+rqYFzz4Xzzuu+jFNZ6aWcw4eXbU/9OCn8RaR37e1+pd/U1P21EOCJJ7xWf+FCH6uqglNP9av9UaPSj0/21K+vV+jHSOEvItklb+auXJm56+ZLL3nZ5quv+tdmcOyxXqu/zTbpxyZ76tfXq71yAVD4i0h3IXjgNzZmrtd/+20P/b//PRr71rdg2jTYaaf0Y9VTvyAp/EUkXU+7aX3yidfqP/JINPa1r8GVV3av1U/21B81SqFfgGILfzMbAdwC7AYE4LwQwgtxzUek7PW0m9ayZTBjBtx7b1Ths/32fqV/yCHpa/dmUU99baRSsOL8X2Y68FgI4SQzqwb07LZIHHrqw9PUBLfeCnfcEX0obL211+ofe2z3K3ptpFI0Ygl/MxsOHAycAxBCaAPa4piLSDnYsG1iqp4e0mpthd/8Bn71q6hWf8SIqFa/a+98baRSdOK68t8OWAbcbmZ7AvOAy0IILckDzGwyMBlg/PjxsUxSpBRs2DbxX1M+ALI9pLV+fVSrv2SJj/VUq19T403X1FO/6FjoadPkfH1TswbgReCAEMJLZjYdWB1C+L+Zjm9oaAhz584d0DmKlJINV/7ZHtIKAZ580mv133/fx6qqfPesH/3IAz6VOm0WBTObF0LI0DUvviv/RcCiEMJLia9nA/8c01xESl7d4Db47Etobu7+4pw5Xrb58svR2DHHwOWXd6/VHzzYQ1+dNoteLOEfQlhqZp+a2Y4hhHeAQ4G34piLSElL7pu7alX3h7QWLICrr4ann47GDj7Yu23uvHP6sdXVHvrqtFky4qz2uQSYlaj0+QA4N8a5iJSWnm7mfvopXHcd/OEP0QfCXnt5X/199kk/Vk3XSlZs4R9CeAXIuBYlIpsg283cL7+MavWTD3BNnOhX+oceml6rX1kZhb7675QkPYEhUirWrPGbua2t6ePNzVGt/po1PrbVVt5/57jj0h/EqqjwNgwjRij0S5zCX6TYtbV56Le0dB9P1uond9kaMQIuughOPz29Vj/ZaXPECP+7lDyFv0ix6uiIbuamWr8eHn7Y1/U/+8zHhg6Fs8+GCy5If9pLnTbLlsJfpNh0dkZ75qZW8IQATz3ltfrvvutjlZVwyikwZQqMGRMdq06bZU/hL1JMVq3yG7dd2yzPneu1+vPnR2PHHOM9eLbdNhpT0zVJ0P/6IsUg283cd97xK/2nnorGDjzQyzZ32SX92Lo6r9VX0zUhx/A3s3pgm9TjQwjzs58hIv2irc2v9Ls+mbtoka/pP/xwtPSzxx4e+vvum37ssGEe+l2bsUlZ6zX8zez/4d03F+J990n8eUj+piVS5rJtn7h8udfq33NPVKu/3XZeq3/YYenlmUOG+Dr/0KEDOnUpDrlc+Z8CTEy0XRaRfMq2fWJzM9x+O9x2W1Srv8UWXqv/ve+lr9+rFYPkIJfwfwMYAXyR36mIlLnmZl/XT90+sa3Nr/JnzPAPBPCtESdPhjPOSG+lXFnpoa9WDJKDXML/P4GXzewNYMPdphDCsXmblUg5WbPG1/VT2yyvX++9d667DhYv9rEhQ6Ja/dSA11O5shFyCf87gf8CXgc6ezlWRHLV2upX+sllHPBln6ef9m6b77zjYxUVcPLJMHUqbL55dOygQf5wVn29nsqVPssl/NeEEK7L+0xESlS3LRQ7OvxKf/Xq9APnz/da/dSNi446Ci67DCZMiMaST+WOHKnQl42WS/j/3cz+E3iY9GUflXqK9CJtC8VhWZ7Mffddv9LvWqs/bRrstls0Zubr/aNG6alc2WS5hP/XEn+mFg+r1FMkB3V1ieAPq+GjLm2WFy/2vXIfeij6MNh9d6/V32+/7m+kB7SkH/UY/mZWATwcQrhmgOYjUlrWrqVuRZc9cxsbvdPmb34TVfZMmOC1+ocfnn7TVhukS570GP4hhPVmdhqg8Bfpi/Z2X9dvaorGWlq8p/6tt0btlzff3PvvdK3V1wbpkme5LPs8Z2Y3APcCGxqGa81fJINMHTfb2uC+++DGG/0JXfBSzcmT4cwz06/qq6t9TT/tDrFI/8sl/PdK/PnTlDGt+YukCsE7bi5fHj2Z29kJjzwC06d7Lx7woD/rLK/VHz48Oj+5bWLqmEge9Rr+IYRvD8RERIpWU5OHfluiA0oI8MwzXraZWqt/0kleq7/FFtG5ekBLYpJLY7fhwL8DByeGngZ+GkJYlf0skTKwdq0/pJV6M/fllz3058yJxiZNgssv9wZsSXpAS2KWy7LPbXh/n1MSX58J3A6ckK9JiRS01la/mZu6Z+7773ut/pNPRmP77+8VPLvvHo2pVl8KRC7hPzGEcGLK1/9hZq/kaT4ihautzZd3Uit4PvssqtXvTHQ/2XVXr9U/4ID085M7aKlWXwpALuG/1swODCE8C2BmBwBr8zstkQKSaaP0FSvgpptg1qxorX/CBF/eOeKI9KUcbaYiBSiX8L8IuCux9m9AI765i0hpy7Shypo1Ua1+cnetMWPg4ovhxBPTr+qHDvUrfdXqSwHKpdrnVWBPM9ss8fXqXk4RKW4h+JV9Y2O0lNPWBvff77X6X37pY3V1Ua1+6m5Zgwf7lf6wYQM/d5Ec5VLtMxg4EZgAVFqiHC2E8NMeThMpPiF4p83ly6MePJ2d8Mc/wrXXwqef+tjgwR74F17oJZpJ2kxFikguyz6/B1YB80jp6ilSUjLV6v/9717B8/bbPlZR4Us7U6fClltG56pWX4pQLuE/LoQwKe8zEYlDS4sv47SmXNe88orX6v/jH9HYEUd4X/2JE6Mx1epLEcsl/J83s91DCK/35zdOdAydCywOIRzTn+8t0qtMWycuXAjXXAN/+Us0tu++Xra5xx7RmFm0mYpq9aVI5RL+BwLnmNmH+LKPASGEsEfPp/XqMuBtQAukMnDWrfOnctemVCsvWQI33AAPPhjd4N1ll6hWP3UpZ7PNfF2/MpdfHZHClcv/g4/s6UUzqw8hrOjLNzWzccDRwM+AK/pyrshGaW31Nf1keSZ4CedNN8Hdd0dr/ePHe63+kUd2r9UfM8a7bvZBty0cRQpELqWeH/dyyJPA3n38vtcC/wRk/bUws8nAZIDx48f38e1FEjLtl7tmDdx1F9xyS/S07pgxMGWKb5TetVZ/9Oj0Us4cpW3hqA8AKTD98W/XPpU3mNkxwBchhHlm9q1sx4UQZgIzARoaGkK240QyyvSAVns7zJ4Nv/ylL/0A1NZ6yeZZZ6U/jFVd7aFfW7vRU9iwhaOCXwpQf4R/X4P5AOBYMzsKGAJsZmZ3hxDO6Ie5SLnr7PTAT31Aq7MTHnvMa/U/TvxDtroazjjDH9Kqr4/O7+e++gp+KVQDftcqhPAT4CcAiSv/KxX8sskybaYSAjz3nNfqv/mmjw0a5FsmXnIJbLVVdP6gQV69U1+vWn0pCwO+7CPS75Khn3wqF+C117xW/8UXo7HDDoNp09Jr9VW2KWUql/YOvw4hnNnD2KEb+81DCH8D/rax50uZ6/pULsAHH/jyzuOPR2P77ANXXgl77pl+vlosSxnL5cp/19QvEg9nfT35dQihsb8nJdKjTE/lLl0a1eonl3123tk3UznooPSlHLVYFske/mb2E+DfgCFmlqyTM6CNRBWOyIDKtG3iypUwc6bX6ic/DLbZxmv1jzoqvVZ/yBAPfbVYFske/iGE/zSznwPvhRC+OoBzEkmXadvEtWvh17/24E/W6o8eHdXqpz6MVV3tyzsqvRHZoMdlnxBCMLPnzOx/hRDm9HSsSL9rb/fQT902sb0dHnjAl3hSa/UvuMBr9VN76FdW+o3c4cNVwSPSRS5r/t8AfmBmHwMt9F9vH5HMktsmrl4dPaAVQlSr/9FHPlZVFdXqjxwZna9umyK9yiX8j8j7LEQg81O5AC+8AP/zP+m1+scf77X6W28dHWfmV/mjRqlsU6QX/dHbR2TTdHb6tokrVkRP5QK88YbX6j//fDR26KFeq7/99unvUVfna/4q2xTJifrSSnxCiFoxJMszAT780Jd3HnssGmto8BbLe3fpIVhT403ZVLYp0icKfxl4yVYMjY3pT+V+/rk3XZs9O/ow2HFHD/2DD06/aauyTZFNovCXgZNpg3TwD4JbbvE2y8ka/nHjfNvEY45Jv2lbVeWhr7JNkU2i8JeBkQz99vZobN06r9W/+Wb/AAC/WTtlCpxySnqtvjZJF+lXCn/Jr0z9dzo64He/g+uv96Ue8Pr8886Dc85J76GfbLw2apTKNkX6kcJf8qO52UM/tf9OCPDnP/sm6R9+6ENVVdjpp8NFF6XX6ptFjde0X65Iv9NvlfSvNWv8qdzU/jvgtfq/+AW8/rp/bUb70cdze+0lnH7p2PQNs1S2KZJ3Cn/pH2vXeuivXZs+/uabHvrPPReNHXIITJtG1Q47cHpzyipPTY2H/pAhAzZtkXKl8JdNk6npGngLhmuvhT/9KRr7+te9bPPrGzqCe/APHuy1+irbFBkwCn/ZOO3tUf+dVF98EdXqJ8s5d9jBQ/+b30yv1FHZpkhsFP7SN9n676xeDbfeCnfeGS39jB0Ll14K3/1ueq8dlW2KxE7hL7np7IxaMaT232lt9Y1UZs7018G7af7oR3Daaem1+mb+2siRKtsUiZnCX3qWbMWwfHl6/52ODnjoIa/VX7rUx2pqvFb/3HPTa/XByzZHj1bZpkiB0G+iZJfpqdwQ4IknvFZ/4UIfq6qCU0/1q/1Ro9LfQ43XRAqSwl+6a272Cp7Up3IBXnrJyzZffdW/NoNjj/V1/XHj0o9V4zWRgqbwl0hLi1/pd31A6623PPSffTYa+/a3fZP0nXZKP1YVPCJFQeEv2R/Q+uQTr9V/9NFobO+9vWyzoSH92EGD/EZufb0qeESKgMK/nGV7QGvZMrjxRrjvvqhWf/vt4Yor/Iq/a7gPH+5X+9o6UaRoKPzLUVubL+80NaWPNzV5X/3UWv2tt/Y1/WOP7R7uupkrUrQU/uUk21O5ra3wm9/Ar36VXqt/0UVw+unptfrgX48e3b2cU0SKhsK/HHR0+MNZq1alP5Xb0QG//73X6i9Z4mM1NV6nf9553cO9osJLOYcP17q+SJFT+JeybK0YQoAnn4Srr+5eq3/RRX5Vn0pP5oqUnFjC38y2Ae4CtgACMDOEMD2OuZSkzk5YscL/k9qKAWDOHC/bfPll/9oMjj7ayza32Sb9WG2oIlKy4vqN7gB+HEKYb2Z1wDwz+0sI4a2Y5lMaQoj676S2YgBYsMBD/5lnorFvftMreLrW6oMv+Ywe3X29X0RKQizhH0JYAixJ/L3JzN4GxgIK/42R7L/T2BiVZiZ9+ilMnw6PPBIt/ey1l9fq77NP2qHNzVA7eohX8AwdOjBzF5FYxP5veTObAHwNeKnL+GRgMsD48eMHfmLFIISo/07X0P/yS5gxA+69N+rNM3GiX+kfemi3G7bNbdX8992j+d//UUudcl+k5FlIvRE40N/crBZ4GvhZCOHBbMc1NDSEuXPnDtzECl0y9Bsb05uugV++33or3HGH76cLsNVWcMklcPzx3Wv1Uyp4mppNXRlESoiZzQshNGR6LbYrfzOrAh4AZvUU/NJFpk6b4A9uJWv1V6zwsREjolr9rg9imfnro0ZtqOBR8IuUj7iqfQy4FXg7hHB1HHMoOtk6ba5fDw8/DNddB5995mNDh8I558D552dO9NpaX9evqsr7tEWkMMV15X8AcCbwupm9khj7lxDCH2OaT+HK1mkzBPjrX72v/nvv+VhlJXz/+95Xf8yY7u+ljdJFJCGuap9nAT0i2pNsnTYB5s6Fq66KavUBjjkGLrsMMt0cT67rjxiRt+mKSHGJvdpHuli3zkM/ebM21YIFfqX/t79FYwcd5GWbO+/c/Xg9mSsiWSj8C0W29soAixZ5rf4f/hDV6u+xB1x5JXzjG5nfT3vmikgPlAxxa231Nf3m5u6vLV/u1Tu//W1U3fOVr3it/ne+k7m52rBhHvpqsywiPVD4xyVbT33wD4Lbb4fbbouWf7bYwmv1v/e9zFfzupkrIn2g8B9obW3+cFbXnvrJ1+65x5/MbWz0seHD4Yc/hB/8wDdF76qy0q/0N9ssv/MWkZKi8B8o7e1RT/2u1q/39fzrroPFi31syJCoVj9TsGvPXBHZBAr/fMu2kQr413/7m/fVf/ddH6ushJNPhilTYPPNM7+n9swVkU2k8M+X9eujnvqZ+ifNn++1+vPmRWNHHeV99bfdNvN76mauiPQThX9/62kjFfAr/KuvhqeeisYOPNAreHbdNfN7Dh7soT9sWH7mLCJlR+HfX3raSAV8Lf+663zP3OS/BHbf3R/Q2m+/zO9ZUeGhP3x43qYtIuVJ4b+peuqpD/5hMGNGeq3+dtvBtGlw+OGZb9bqyVwRyTOF/6ZoavKncru2Vwav1b/jDu+tn1qrf/HFcMIJ2Z+81ZO5IjIAlDAbo6XFQ7+1tftrbW2+e9aMGf6vAfBAnzwZzjwThgzx7RJru5w3dKg/pJWpll9EpJ8p/Pti3TpYtixzp83OTq/Vnz49vVb/rLPgggs2rNs3N3vHhosuSnwAVFV56Hf7NBARyR+Ffy7a2vxKP1P/nRDg6ae9guedd3ysogJOOgmmTvWlnhS1tYng32xQ1GZZD2mJyABT+Peko8NDP1MrBvB++r/4BcyZE41NmuR99b/ylcznmFG7zQi/mauHtEQkJgr/TNav9yqdlSszP6D1/vt+pf/kk9HY/vt7rf7uu2d/X22fKCIFQuGfqrcHtD77DK6/Hh56KHp91129r/7++2d/3+pqb9WgjpsiUiAU/uBX98nQz/SAVmMjzJwJs2ZFG6hPmOCtGCZNyr5mX1Hhyzta1xeRAlPe4R+CN1xrbMz8gFZLC9x5p9fqJ2/2jhnjffVPOCH78o0e0hKRAlee4d/bU7ltbXD//XDjjX7DF6CuLqrVHzo0+3vrIS0RKQLll1DJ0M/0VG5nJzz6qNfqf/qpjw0e7IF/4YW+fJPN4MG+rt/TB4OISIEon/BvavLQT67ZpwoBnnnGK3gWLPCxigo48USv1d9yy+zvW1ER1euLiBSJ0g//5mZfuskU+gCvvOK1+v/4RzR2xBF+MzdbrT74uv7w4R78qtcXkSJTuuHfU/8dgIUL/Ur/iSeisX339RbLe+zR83vX1PgST3V1/81XRGQAlV74r1njob9uXebXly71vvq/+116rf6Pf+y1+j2VZKoPj4iUiNIJ/7VrPfQzNV0Dr+GfORPuvjtaAtp226hWv6eSTG2WLiIlpvjDf906D/1kz/yu1qyBu+6Cm29Or9WfOtWbr/XWaiG5rq/STREpIcWbaK2tHvotLZlfb2+PavWXLfOxujov2TzzzN5bLQwZ4h05tVm6iJSg2MLfzCYB04EK4JYQws9zOrGtzUs2m5oyv97ZCY89BtdeCx9/7GPV1VGtfn19z++vfXNFpAzEEv5mVgH8EjgMWATMMbOHQwhvZT2pvd1DP1t75RDguee8gufNN31s0CBvw3DJJT3X6ieNGKHSTREpC3Fd+e8DvB9C+ADAzO4BjgMyh39HB3z0Ueb2ygCvvea1+i++GI0dfrjfzJ04sffZDB3qpZta4hGRMhFX+I8FPk35ehHwjWwHd3Z0Zg7+hQu9FcPjj0dj++zjLZb33LP3WVRW+s3furqcJy4iUgoK9oavmU0GJgOMqB2bvun50qVwww3wwANRrf7OO/tmKgcd1Hs5prpuikiZiyv8FwPbpHw9LjG2QQhhJjATYO/d9gi1tfjOWsla/eSTu+PH+/LOkUfmFuTaTUtEJLbwnwNsb2bb4aF/KnB6toMH0Qk33eS1+skqn9GjYcoUOPnk3NosDB7soa/dtERE4gn/EEKHmV0MPI6Xet4WQngz6wnvvBNV8NTWwvnnw9lnw7BhvX8zlW6KiHQT25p/COGPwB9zOrijw6/uzzjDN1TprVYftK4vItKDgr3hm6a+Hh58ELbeOrfjhw3z0k2t64uIZFQc4T9uXG7BX1XloZ/LcpCISBkrjvDvjZk/mauumyIiOSn+8FfppohInxVv+FdXe+hriUdEpM+KL/y1sYqIyCYrrvCvq/OrfW2sIiKySYojRc284kdP54qI9IviePqpqkrBLyLSj4oj/EVEpF8p/EVEypDCX0SkDCn8RUTKkMJfRKQMKfxFRMqQwl9EpAwp/EVEypDCX0SkDFkIIe459MrMlgEf93DIaODLAZpOsdDPpDv9TDLTz6W7UvmZbBtCGJPphaII/96Y2dwQQkPc8ygk+pl0p59JZvq5dFcOPxMt+4iIlCGFv4hIGSqV8J8Z9wQKkH4m3elnkpl+Lt2V/M+kJNb8RUSkb0rlyl9ERPqgqMPfzCaZ2Ttm9r6Z/XPc84mbmW1jZk+Z2Vtm9qaZXRb3nAqJmVWY2ctm9kjccykEZjbCzGab2QIze9vM9ot7TnEzs2mJ3503zOy3ZjYk7jnlS9GGv5lVAL8EjgR2AU4zs13inVXsOoAfhxB2AfYFpupnkuYy4O24J1FApgOPhRB2AvakzH82ZjYWuBRoCCHsBlQAp8Y7q/wp2vAH9gHeDyF8EEJoA+4Bjot5TrEKISwJIcxP/L0J/2UeG++sCoOZjQOOBm6Jey6FwMyGAwcDtwKEENpCCCtjnVRhqASGmlklUAN8FvN88qaYw38s8GnK14tQ0G1gZhOArwEvxTyVQnEt8E9AZ8zzKBTbAcuA2xNLYbeY2bC4JxWnEMJi4CrgE2AJsCqE8Od4Z5U/xRz+koWZ1QIPAJeHEFbHPZ+4mdkxwBchhHlxz6WAVAJ7AzNCCF8DWoCyvm9mZvX46sF2wNbAMDM7I95Z5U8xh/9iYJuUr8clxsqamVXhwT8rhPBg3PMpEAcAx5rZR/jy4CFmdne8U4rdImBRCCH5L8PZ+IdBOfsO8GEIYVkIoR14ENg/5jnlTTGH/xxgezPbzsyq8RszD8c8p1iZmeFruG+HEK6Oez6FIoTwkxDCuBDCBPz/J38NIZTsFV0uQghLgU/NbMfE0KHAWzFOqRB8AuxrZjWJ36VDKeGb4JVxT2BjhRA6zOxi4HH8rvxtIYQ3Y55W3A4AzgReN7NXEmP/EkL4Y3xTkgJ2CTArcfH0AXBuzPOJVQjhJTObDczHK+depoSf9NUTviIiZaiYl31ERGQjKfxFRMqQwl9EpAwp/EVEypDCX0SkDCn8RUTKkMJfJIWZ/csAf79vqcW0xEHhL5KQeKrz3/rpvSr6431E8kXhL2XNzCYkNgS6C3gfb+f7ipnN6uH4BWY2K7EBymwzq0m89pGZ/ZeZzQdONrPDzewFM5tvZvcnGu4lNyFakDjuhIH67yqSSuEvAtsDN4YQJgItIYS9Qgg/6OH4HRPH7wysBqakvLY8hLA38AT+r4jvJL6eC1yR2BnqZuC7wNeBLfv/v45I7xT+IvBxCOHFPhz/aQjhucTf7wYOTHnt3sSf++I7zD2X6LN0NrAtsBPeOfK94L1Vyr27qMSkaBu7ifSjlj4e37UhVurXyfcy4C8hhNNSDzSzvfr4vUTyQlf+IunaE3si9GR8ymbnpwPPZjjmReAAM/sqgJkNM7MdgAXABDObmDjutAzniuSdwl8k3UzgtWw3fBPeAaaa2dtAPTCj6wEhhGXAOcBvzew14AVgpxDCOmAy8Gjihu8X/Tx/kZyopbNIHyT2Rn4khLBb3HMR2RS68hcRKUO68hfJwMxGAU9meOnQEMLygZ6PSH9T+IuIlCEt+4iIlCGFv4hIGVL4i4iUIYW/iEgZUviLiJSh/w9tYK98oxr6HwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "#| hide\n",
-    "df = pd.DataFrame({'rt_norm':np.arange(10), 'rt_pred':np.arange(10)+np.random.normal(0,0.3,10)})\n",
-    "evaluate_linear_regression_plot(df)"
+    "from peptdeep.utils import parse_ms_file_names_to_dict\n",
+    "\n",
+    "d = parse_ms_file_names_to_dict([\n",
+    "    'a.raw', 'b.ms_data.hdf','c_HCDFT.mgf'\n",
+    "])\n",
+    "assert 'a' in d\n",
+    "assert 'b' in d\n",
+    "assert 'c' in d"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.3 ('base')",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   }

nbs_tests/test_multi_target.ipynb

@@ -32,6 +32,7 @@
     "    ModelInterface_for_Generic_AASeq_Regression,\n",
     ")\n",
     "import torch\n",
+    "import numpy as np\n",
     "\n",
     "class ModelInterface_MultiTarget(ModelInterface_for_Generic_AASeq_BinaryClassification):\n",
     "    def __init__(self):\n",
@@ -45,7 +46,7 @@
     "\n",
     "    def _get_targets_from_batch_df(self, batch_df, **kwargs):\n",
     "        return self._as_tensor(\n",
-    "            list(batch_df[self.target_column_to_train].values), \n",
+    "            np.stack(batch_df[self.target_column_to_train].values), \n",
     "            dtype=torch.float32\n",
     "        )\n",
     "\n",

peptdeep/__init__.py

@@ -11,9 +11,9 @@
     pass
 
 __project__ = "peptdeep"
-__version__ = "1.0.1"
+__version__ = "1.0.2"
 __license__ = "Apache 2.0"
-__description__ = "The AlphaX Deep (PeptDeep) Learning Platform for Proteomics"
+__description__ = "The AlphaX deep learning framework for Proteomics"
 __author__ = "Mann Labs"
 __author_email__ = "[email protected]"
 __github__ = "https://github.com/MannLabs/peptdeep"