From c889ccb6fd437f33d52462cdad7f9e5f944c3ef7 Mon Sep 17 00:00:00 2001 From: Unknown Date: Thu, 22 Oct 2020 14:08:43 +0200 Subject: [PATCH 1/3] Add PandasPdb.read_pdb_from_list method + unit test --- biopandas/pdb/pandas_pdb.py | 18 ++++++++++++++++++ biopandas/pdb/tests/test_read_pdb.py | 9 +++++++++ 2 files changed, 27 insertions(+) diff --git a/biopandas/pdb/pandas_pdb.py b/biopandas/pdb/pandas_pdb.py index 3e6e86e..38da1c1 100644 --- a/biopandas/pdb/pandas_pdb.py +++ b/biopandas/pdb/pandas_pdb.py @@ -92,6 +92,24 @@ def read_pdb(self, path): self.header, self.code = self._parse_header_code() return self + def read_pdb_from_list(self, pdb_lines): + """Reads PDB file from a list into DataFrames + + Attributes + ---------- + pdb_lines : list + A list of lines containing the pdb file contents. + + Returns + --------- + self + + """ + self.pdb_text = ''.join(pdb_lines) + self._df = self._construct_df(pdb_lines) + self.header, self.code = self._parse_header_code() + return self + def fetch_pdb(self, pdb_code): """Fetches PDB file contents from the Protein Databank at rcsb.org. diff --git a/biopandas/pdb/tests/test_read_pdb.py b/biopandas/pdb/tests/test_read_pdb.py index 7be9545..2e77464 100644 --- a/biopandas/pdb/tests/test_read_pdb.py +++ b/biopandas/pdb/tests/test_read_pdb.py @@ -111,6 +111,15 @@ def test_read_pdb(): assert ppdb.code == '3eiy', ppdb.code assert ppdb.pdb_path == TESTDATA_FILENAME +def test_read_pdb_from_list(): + """Test public read_pdb_from_list""" + + for pdb_text, code in zip([three_eiy, four_eiy], ['3eiy', '4eiy']): + ppdb = PandasPdb() + ppdb.read_pdb_from_list(pdb_text.splitlines(True)) + assert ppdb.pdb_text == pdb_text + assert ppdb.code == code + assert ppdb.pdb_path == '' def test_anisou_input_handling(): """Test public read_pdb""" From 9b4f1512d92cfa5c568dc769d2d439f486b59d39 Mon Sep 17 00:00:00 2001 From: Unknown Date: Thu, 22 Oct 2020 14:29:55 +0200 Subject: [PATCH 2/3] Apply flake8 to new code (no changes in existing code) --- biopandas/pdb/tests/test_read_pdb.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/biopandas/pdb/tests/test_read_pdb.py b/biopandas/pdb/tests/test_read_pdb.py index 2e77464..ff7fe27 100644 --- a/biopandas/pdb/tests/test_read_pdb.py +++ b/biopandas/pdb/tests/test_read_pdb.py @@ -111,6 +111,7 @@ def test_read_pdb(): assert ppdb.code == '3eiy', ppdb.code assert ppdb.pdb_path == TESTDATA_FILENAME + def test_read_pdb_from_list(): """Test public read_pdb_from_list""" @@ -121,6 +122,7 @@ def test_read_pdb_from_list(): assert ppdb.code == code assert ppdb.pdb_path == '' + def test_anisou_input_handling(): """Test public read_pdb""" ppdb = PandasPdb() From 50fd9c4ec6a07033600f65b5782775eb88d56068 Mon Sep 17 00:00:00 2001 From: rasbt Date: Thu, 22 Oct 2020 13:25:11 -0500 Subject: [PATCH 3/3] update tutorial examples --- .../sources/api_subpackages/biopandas.mol2.md | 2 +- docs/sources/api_subpackages/biopandas.pdb.md | 18 +- .../api_subpackages/biopandas.testutils.md | 2 +- ...g_with_MOL2_Structures_in_DataFrames.ipynb | 158 +++---- ...ng_with_PDB_Structures_in_DataFrames.ipynb | 430 +++++++++++------- docs/tutorials/data/3eiy_stripped.pdb.gz | Bin 37938 -> 37938 bytes docs/tutorials/data/selected_ids.txt | 3 - 7 files changed, 344 insertions(+), 269 deletions(-) diff --git a/docs/sources/api_subpackages/biopandas.mol2.md b/docs/sources/api_subpackages/biopandas.mol2.md index 5f22d8d..acce9e5 100644 --- a/docs/sources/api_subpackages/biopandas.mol2.md +++ b/docs/sources/api_subpackages/biopandas.mol2.md @@ -1,4 +1,4 @@ -biopandas version: 0.2.6 +biopandas version: 0.2.7 ## PandasMol2 *PandasMol2()* diff --git a/docs/sources/api_subpackages/biopandas.pdb.md b/docs/sources/api_subpackages/biopandas.pdb.md index d49aaed..5015be7 100644 --- a/docs/sources/api_subpackages/biopandas.pdb.md +++ b/docs/sources/api_subpackages/biopandas.pdb.md @@ -1,4 +1,4 @@ -biopandas version: 0.2.6 +biopandas version: 0.2.7 ## PandasPdb *PandasPdb()* @@ -229,6 +229,22 @@ self
+*read_pdb_from_list(pdb_lines)* + +Reads PDB file from a list into DataFrames + +**Attributes** + +- `pdb_lines` : list + + A list of lines containing the pdb file contents. + +**Returns** + +self + +
+ *rmsd(df1, df2, s=None, invert=False)* Compute the Root Mean Square Deviation between molecules. diff --git a/docs/sources/api_subpackages/biopandas.testutils.md b/docs/sources/api_subpackages/biopandas.testutils.md index 07bac6d..d140ff0 100644 --- a/docs/sources/api_subpackages/biopandas.testutils.md +++ b/docs/sources/api_subpackages/biopandas.testutils.md @@ -1,4 +1,4 @@ -biopandas version: 0.2.6 +biopandas version: 0.2.7 ## assert_raises *assert_raises(exception_type, message, func, *args, **kwargs)* diff --git a/docs/tutorials/Working_with_MOL2_Structures_in_DataFrames.ipynb b/docs/tutorials/Working_with_MOL2_Structures_in_DataFrames.ipynb index 7c04d63..f8222ef 100644 --- a/docs/tutorials/Working_with_MOL2_Structures_in_DataFrames.ipynb +++ b/docs/tutorials/Working_with_MOL2_Structures_in_DataFrames.ipynb @@ -21,10 +21,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "last updated: 2019-02-05 \n", + "last updated: 2020-10-22 \n", "\n", - "pandas 0.23.4\n", - "biopandas 0.2.4\n" + "pandas 1.1.3\n", + "biopandas 0.2.7\n" ] } ], @@ -243,10 +243,10 @@ "" ], "text/plain": [ - " atom_id atom_name x y ... atom_type subst_id subst_name charge\n", - "0 1 C1 -1.1786 2.7011 ... C.3 1 <0> -0.1537\n", - "1 2 C2 -1.2950 1.2442 ... C.3 1 <0> -0.1156\n", - "2 3 C3 -0.1742 0.4209 ... C.3 1 <0> -0.1141\n", + " atom_id atom_name x y ... atom_type subst_id subst_name charge\n", + "0 1 C1 -1.1786 2.7011 ... C.3 1 <0> -0.1537\n", + "1 2 C2 -1.2950 1.2442 ... C.3 1 <0> -0.1156\n", + "2 3 C3 -0.1742 0.4209 ... C.3 1 <0> -0.1141\n", "\n", "[3 rows x 9 columns]" ] @@ -501,17 +501,17 @@ "" ], "text/plain": [ - " atom_id atom_name x y ... atom_type subst_id subst_name charge\n", - "22 23 H3 15.8520 2.8983 ... H 1 <0> 0.0\n", - "23 24 H4 14.3405 3.3601 ... H 1 <0> 0.0\n", - "24 25 H5 15.3663 0.9351 ... H 1 <0> 0.0\n", - "25 26 H6 16.6681 1.6130 ... H 1 <0> 0.0\n", - "26 27 H7 15.3483 4.6961 ... H 1 <0> 0.0\n", - "27 28 H8 18.8490 1.8078 ... H 1 <0> 0.0\n", - "28 29 H9 17.8303 1.5497 ... H 1 <0> 0.0\n", - "29 30 H10 19.9527 7.4708 ... H 1 <0> 0.4\n", - "30 31 H11 18.5977 8.5756 ... H 1 <0> 0.4\n", - "31 32 H12 14.2530 1.0535 ... H 1 <0> 0.4\n", + " atom_id atom_name x y ... atom_type subst_id subst_name charge\n", + "22 23 H3 15.8520 2.8983 ... H 1 <0> 0.0\n", + "23 24 H4 14.3405 3.3601 ... H 1 <0> 0.0\n", + "24 25 H5 15.3663 0.9351 ... H 1 <0> 0.0\n", + "25 26 H6 16.6681 1.6130 ... H 1 <0> 0.0\n", + "26 27 H7 15.3483 4.6961 ... H 1 <0> 0.0\n", + "27 28 H8 18.8490 1.8078 ... H 1 <0> 0.0\n", + "28 29 H9 17.8303 1.5497 ... H 1 <0> 0.0\n", + "29 30 H10 19.9527 7.4708 ... H 1 <0> 0.4\n", + "30 31 H11 18.5977 8.5756 ... H 1 <0> 0.4\n", + "31 32 H12 14.2530 1.0535 ... H 1 <0> 0.4\n", "\n", "[10 rows x 9 columns]" ] @@ -707,17 +707,17 @@ "" ], "text/plain": [ - " atom_id atom_name x y ... atom_type subst_id subst_name charge\n", - "10 11 N2 16.8196 5.0644 ... N.am 1 <0> -0.4691\n", - "11 12 N3 19.0194 7.7275 ... N.pl3 1 <0> -0.8500\n", - "12 13 O1 18.7676 -2.3524 ... O.3 1 <0> -1.0333\n", - "13 14 O2 20.3972 -0.3812 ... O.3 1 <0> -1.0333\n", - "14 15 O3 15.0888 6.5824 ... O.2 1 <0> -0.5700\n", - "15 16 O4 18.9314 -0.7527 ... O.2 1 <0> -1.0333\n", - "16 17 O5 16.9690 3.4315 ... O.3 1 <0> -0.5600\n", - "17 18 O6 14.3223 1.8946 ... O.3 1 <0> -0.6800\n", - "18 19 O7 17.9091 -0.0135 ... O.3 1 <0> -0.5512\n", - "19 20 P1 19.0969 -0.9440 ... P.3 1 <0> 1.3712\n", + " atom_id atom_name x y ... atom_type subst_id subst_name charge\n", + "10 11 N2 16.8196 5.0644 ... N.am 1 <0> -0.4691\n", + "11 12 N3 19.0194 7.7275 ... N.pl3 1 <0> -0.8500\n", + "12 13 O1 18.7676 -2.3524 ... O.3 1 <0> -1.0333\n", + "13 14 O2 20.3972 -0.3812 ... O.3 1 <0> -1.0333\n", + "14 15 O3 15.0888 6.5824 ... O.2 1 <0> -0.5700\n", + "15 16 O4 18.9314 -0.7527 ... O.2 1 <0> -1.0333\n", + "16 17 O5 16.9690 3.4315 ... O.3 1 <0> -0.5600\n", + "17 18 O6 14.3223 1.8946 ... O.3 1 <0> -0.6800\n", + "18 19 O7 17.9091 -0.0135 ... O.3 1 <0> -0.5512\n", + "19 20 P1 19.0969 -0.9440 ... P.3 1 <0> 1.3712\n", "\n", "[10 rows x 9 columns]" ] @@ -815,9 +815,9 @@ "" ], "text/plain": [ - " atom_id atom_name x y ... atom_type subst_id subst_name charge\n", - "14 15 O3 15.0888 6.5824 ... O.2 1 <0> -0.5700\n", - "15 16 O4 18.9314 -0.7527 ... O.2 1 <0> -1.0333\n", + " atom_id atom_name x y ... atom_type subst_id subst_name charge\n", + "14 15 O3 15.0888 6.5824 ... O.2 1 <0> -0.5700\n", + "15 16 O4 18.9314 -0.7527 ... O.2 1 <0> -1.0333\n", "\n", "[2 rows x 9 columns]" ] @@ -951,14 +951,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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2Y0QcSjNSGCkRcX/gNcAhNKdQHpOZP69bVRWPioib2tergO3a5VU0v0jsWK+0xTEUtGiZuRF4fEQ8Gdirbf5cZn6lYlm1vBI4JyKOpAnIKeBxNKfRRiogI+Ik4Nk0T+4+IjNvqVxSNZm5unYNvfKJ5s2IiJuZGSFsD9zavl4xya8tJyL2BR5O8+fhu5n55col9V1E3An8Bridu46m/TuyAhkKkqTCJ5olSYWhIEkqDAUNjYg4PiLOql2HtJIZCtIWFhFT7bxRC61/UURc2M+apIUYCpKkwucUtKJExDjwXuCJwC3AP2fmqQtsuw9wMs38M1cDR2fmBe26C4ALgX2BRwL/CbwIOJVmYr8fAM/NzB+32z+03e9jgZ8Cb83MbNd9kGaqh93aur4HvCAzr4qIr7XlfLt90vnFmfmxWTU+DPg3YOuIuIXmts6nAp8FdpmeTC0i/qrd56Mj4nia50TuAJ4B/C9wRGZ+u9s+kuZypKAVIyK2Aj4DfBvYBdgPOCYinj7PtrsAnwP+Drg/8DrgkxHxgFmbPR84rP2s3wcuAs5ot19HO89VRNwbOB/4MLATcDDwLxHx8FmfdTDN3Fj3A64ETgDIzCe26x+VmTvMDoR2/TqaWTUvatffNzMvBf6PJhymHQp8aNbyQcDH21o/DPx7RGzdTR9J83GkoJXkccADMvMd7fIPI+L9NP+4f2HOtocC57XfAQ5wfkRcRvOb9fT8VWdMfwdERHyeZjKzL7XLH2dmIrcDgB9n5hnt8jcj4pPAc4Dvtm3nZOY32veeTTNC6cXa9hg+304h8XTgFbPWX56Zn2j3dzLwWmAfYBOL7yPpbgwFrSQPAsYj4sZZbatppimeb9vnRsTs73jYmuY00bSNs17/ap7lHWZ91p/M2e8Yd/3N/fpZr2+d9d6lOgtYFxE7AAF8PTOvm7X+mukXmXlnRKwHxmmeKF5sH0l3YyhoJbkG+FFmPmSR234oM1+6TPv9amY+dbNbLs18s61eGxEX0cyjdBjwr3M2eeD0i/aU0a7ABpprEovtI+luDAWtJN8AboqIN9JcEN4EPAzYrj0PP9tZNFN9Px34Es0oYR/gysxc3+V+PwucGBGH0UwHDfBo4Jb2msDmbAQeTHOtYaH1u0bENpm5aVb7mcCbaEYq5855z2Mj4tnAp4GjaOYeuhi4k8X3kXQ3XmjWipGZd9DcGfRo4EfAJHAa8FvzbHsNzcXYN9PcLXQN8HqW8Ge+/Wa1p9Gcl99Ac6roH4F7LfIjjgfWRsSNMf8E+1+huTZxfURMzmo/lzYQMnPuF9l8Cnge8HOakcSzM/O2bvpImo8T4kkDLCKuAl42fQG8bTse2CMzD61WmIaWIwVpQLXPJkzRjCSkvvCagjSA2ofr9gQOy8w7K5ejEeLpI0lS4ekjSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSp+H9q3MycWfy6wgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1065,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1122,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1174,7 +1168,7 @@ "15 18.9314 -0.7527 24.1606" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1197,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1222,7 +1216,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1324,17 +1318,17 @@ "" ], "text/plain": [ - " atom_id atom_name x y ... subst_id subst_name charge distances\n", - "0 1 C1 18.8934 5.5819 ... 1 <0> -0.1356 4.035144\n", - "1 2 C2 18.1301 4.7642 ... 1 <0> -0.0410 3.547712\n", - "2 3 C3 18.2645 6.8544 ... 1 <0> 0.4856 3.456969\n", - "3 4 C4 16.2520 6.2866 ... 1 <0> 0.8410 1.232313\n", - "4 5 C5 15.3820 3.0682 ... 1 <0> 0.0000 3.527546\n", + " atom_id atom_name x y ... subst_id subst_name charge distances\n", + "0 1 C1 18.8934 5.5819 ... 1 <0> -0.1356 4.035144\n", + "1 2 C2 18.1301 4.7642 ... 1 <0> -0.0410 3.547712\n", + "2 3 C3 18.2645 6.8544 ... 1 <0> 0.4856 3.456969\n", + "3 4 C4 16.2520 6.2866 ... 1 <0> 0.8410 1.232313\n", + "4 5 C5 15.3820 3.0682 ... 1 <0> 0.0000 3.527546\n", "\n", "[5 rows x 10 columns]" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1353,7 +1347,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1419,14 +1413,14 @@ "" ], "text/plain": [ - " atom_id atom_name x y ... subst_id subst_name charge distances\n", - "14 15 O3 15.0888 6.5824 ... 1 <0> -0.5700 0.000000\n", - "15 16 O4 18.9314 -0.7527 ... 1 <0> -1.0333 8.330738\n", + " atom_id atom_name x y ... subst_id subst_name charge distances\n", + "14 15 O3 15.0888 6.5824 ... 1 <0> -0.5700 0.000000\n", + "15 16 O4 18.9314 -0.7527 ... 1 <0> -1.0333 8.330738\n", "\n", "[2 rows x 10 columns]" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1458,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1560,17 +1554,17 @@ "" ], "text/plain": [ - " atom_id atom_name x y ... subst_id subst_name charge distances\n", - "7 8 C8 16.0764 4.1199 ... 1 <0> 0.5801 2.814490\n", - "9 10 N1 17.0289 7.1510 ... 1 <0> -0.6610 2.269690\n", - "10 11 N2 16.8196 5.0644 ... 1 <0> -0.4691 2.307553\n", - "14 15 O3 15.0888 6.5824 ... 1 <0> -0.5700 0.000000\n", - "26 27 H7 15.3483 4.6961 ... 1 <0> 0.0000 2.446817\n", + " atom_id atom_name x y ... subst_id subst_name charge distances\n", + "7 8 C8 16.0764 4.1199 ... 1 <0> 0.5801 2.814490\n", + "9 10 N1 17.0289 7.1510 ... 1 <0> -0.6610 2.269690\n", + "10 11 N2 16.8196 5.0644 ... 1 <0> -0.4691 2.307553\n", + "14 15 O3 15.0888 6.5824 ... 1 <0> -0.5700 0.000000\n", + "26 27 H7 15.3483 4.6961 ... 1 <0> 0.0000 2.446817\n", "\n", "[5 rows x 10 columns]" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1605,7 +1599,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1644,7 +1638,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1681,21 +1675,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'mputil'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmputil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlazy_imap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbiopandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmol2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPandasMol2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbiopandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmol2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msplit_multimol2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'mputil'" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "from mputil import lazy_imap\n", @@ -1782,7 +1764,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.8.2" }, "toc": { "base_numbering": 1, diff --git a/docs/tutorials/Working_with_PDB_Structures_in_DataFrames.ipynb b/docs/tutorials/Working_with_PDB_Structures_in_DataFrames.ipynb index 1cd8612..4012375 100644 --- a/docs/tutorials/Working_with_PDB_Structures_in_DataFrames.ipynb +++ b/docs/tutorials/Working_with_PDB_Structures_in_DataFrames.ipynb @@ -21,10 +21,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "last updated: 2020-08-03 \n", + "last updated: 2020-10-22 \n", "\n", - "pandas 1.0.1\n", - "biopandas 0.2.5\n" + "pandas 1.1.3\n", + "biopandas 0.2.7\n" ] } ], @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -131,7 +131,7 @@ " [666 rows x 3 columns]}" ] }, - "execution_count": 14, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -691,66 +691,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ATOM': record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - " 0 ATOM 1 N ... N NaN 609\n", - " 1 ATOM 2 CA ... C NaN 610\n", - " 2 ATOM 3 C ... C NaN 611\n", - " 3 ATOM 4 O ... O NaN 612\n", - " 4 ATOM 5 CB ... C NaN 613\n", - " ... ... ... ... ... ... ... ... ... ...\n", - " 1325 ATOM 1326 CG ... C NaN 1934\n", - " 1326 ATOM 1327 CD ... C NaN 1935\n", - " 1327 ATOM 1328 CE ... C NaN 1936\n", - " 1328 ATOM 1329 NZ ... N NaN 1937\n", - " 1329 ATOM 1330 OXT ... O NaN 1938\n", - " \n", - " [1330 rows x 21 columns],\n", - " 'HETATM': record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - " 0 HETATM 1332 K ... K NaN 1940\n", - " 1 HETATM 1333 NA ... NA NaN 1941\n", - " 2 HETATM 1334 NA ... NA NaN 1942\n", - " 3 HETATM 1335 P1 ... P NaN 1943\n", - " 4 HETATM 1336 O1 ... O NaN 1944\n", - " .. ... ... ... ... ... ... ... ... ...\n", - " 146 HETATM 1478 O ... O NaN 2086\n", - " 147 HETATM 1479 O ... O NaN 2087\n", - " 148 HETATM 1480 O ... O NaN 2088\n", - " 149 HETATM 1481 O ... O NaN 2089\n", - " 150 HETATM 1482 O ... O NaN 2090\n", - " \n", - " [151 rows x 21 columns],\n", - " 'ANISOU': Empty DataFrame\n", - " Columns: [record_name, atom_number, blank_1, atom_name, alt_loc, residue_name, blank_2, chain_id, residue_number, insertion, blank_3, U(1,1), U(2,2), U(3,3), U(1,2), U(1,3), U(2,3), blank_4, element_symbol, charge, line_idx]\n", - " Index: []\n", - " \n", - " [0 rows x 21 columns],\n", - " 'OTHERS': record_name entry line_idx\n", - " 0 HEADER HYDROLASE 17... 0\n", - " 1 TITLE CRYSTAL STRUCTURE OF INORGANIC PYROPHOSPHA... 1\n", - " 2 TITLE 2 PSEUDOMALLEI WITH BOUND PYROPHOSPHATE 2\n", - " 3 COMPND MOL_ID: 1; 3\n", - " 4 COMPND 2 MOLECULE: INORGANIC PYROPHOSPHATASE; 4\n", - " .. ... ... ...\n", - " 661 CONECT 1435 1334 2142\n", - " 662 CONECT 1445 1333 2143\n", - " 663 CONECT 1451 1334 2144\n", - " 664 MASTER 470 0 7 5 9 0 13 6 1... 2145\n", - " 665 END 2146\n", - " \n", - " [666 rows x 3 columns]}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from biopandas.pdb import PandasPdb\n", "\n", @@ -770,16 +713,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -806,16 +749,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -840,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -884,7 +827,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -962,15 +905,15 @@ "" ], "text/plain": [ - " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - "0 ATOM 1 N ... N NaN 609\n", - "1 ATOM 2 CA ... C NaN 610\n", - "2 ATOM 3 C ... C NaN 611\n", + " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", + "0 ATOM 1 N ... N NaN 609\n", + "1 ATOM 2 CA ... C NaN 610\n", + "2 ATOM 3 C ... C NaN 611\n", "\n", "[3 rows x 21 columns]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1063,7 +1006,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1072,7 +1015,7 @@ "dict_keys(['ATOM', 'HETATM', 'ANISOU', 'OTHERS'])" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1117,7 +1060,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1183,14 +1126,14 @@ "" ], "text/plain": [ - " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - "0 HETATM 1332 K ... K NaN 1940\n", - "1 HETATM 1333 NA ... NA NaN 1941\n", + " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", + "0 HETATM 1332 K ... K NaN 1940\n", + "1 HETATM 1333 NA ... NA NaN 1941\n", "\n", "[2 rows x 21 columns]" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1215,7 +1158,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1264,7 +1207,7 @@ "[0 rows x 21 columns]" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1282,7 +1225,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1291,7 +1234,7 @@ "True" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1309,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1318,7 +1261,7 @@ "Index(['record_name', 'atom_number', 'blank_1', 'atom_name', 'alt_loc', 'residue_name', 'blank_2', 'chain_id', 'residue_number', 'insertion', 'blank_3', 'U(1,1)', 'U(2,2)', 'U(3,3)', 'U(1,2)', 'U(1,3)', 'U(2,3)', 'blank_4', 'element_symbol', 'charge', 'line_idx'], dtype='object')" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1336,7 +1279,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1345,7 +1288,7 @@ "{'b_factor', 'occupancy', 'segment_id', 'x_coord', 'y_coord', 'z_coord'}" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1363,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1372,7 +1315,7 @@ "{'U(1,1)', 'U(1,2)', 'U(1,3)', 'U(2,2)', 'U(2,3)', 'U(3,3)'}" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1398,7 +1341,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1428,7 +1371,7 @@ "dtype: object" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1453,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1526,7 +1469,7 @@ "4 COMPND 2 MOLECULE: INORGANIC PYROPHOSPHATASE; 4" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1558,7 +1501,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1660,17 +1603,17 @@ "" ], "text/plain": [ - " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - "0 ATOM 1 N ... N NaN 609\n", - "1 ATOM 2 CA ... C NaN 610\n", - "2 ATOM 3 C ... C NaN 611\n", - "3 ATOM 4 O ... O NaN 612\n", - "4 ATOM 5 CB ... C NaN 613\n", + " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", + "0 ATOM 1 N ... N NaN 609\n", + "1 ATOM 2 CA ... C NaN 610\n", + "2 ATOM 3 C ... C NaN 611\n", + "3 ATOM 4 O ... O NaN 612\n", + "4 ATOM 5 CB ... C NaN 613\n", "\n", "[5 rows x 21 columns]" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1706,7 +1649,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1808,17 +1751,17 @@ "" ], "text/plain": [ - " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - "38 ATOM 39 N ... N NaN 647\n", - "39 ATOM 40 CA ... C NaN 648\n", - "40 ATOM 41 C ... C NaN 649\n", - "41 ATOM 42 O ... O NaN 650\n", - "42 ATOM 43 CB ... C NaN 651\n", + " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", + "38 ATOM 39 N ... N NaN 647\n", + "39 ATOM 40 CA ... C NaN 648\n", + "40 ATOM 41 C ... C NaN 649\n", + "41 ATOM 42 O ... O NaN 650\n", + "42 ATOM 43 CB ... C NaN 651\n", "\n", "[5 rows x 21 columns]" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1836,7 +1779,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1938,17 +1881,17 @@ "" ], "text/plain": [ - " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - "2 ATOM 3 C ... C NaN 611\n", - "8 ATOM 9 C ... C NaN 617\n", - "19 ATOM 20 C ... C NaN 628\n", - "25 ATOM 26 C ... C NaN 634\n", - "33 ATOM 34 C ... C NaN 642\n", + " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", + "2 ATOM 3 C ... C NaN 611\n", + "8 ATOM 9 C ... C NaN 617\n", + "19 ATOM 20 C ... C NaN 628\n", + "25 ATOM 26 C ... C NaN 634\n", + "33 ATOM 34 C ... C NaN 642\n", "\n", "[5 rows x 21 columns]" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1966,7 +1909,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -2068,17 +2011,17 @@ "" ], "text/plain": [ - " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - "0 ATOM 1 N ... N NaN 609\n", - "1 ATOM 2 CA ... C NaN 610\n", - "2 ATOM 3 C ... C NaN 611\n", - "3 ATOM 4 O ... O NaN 612\n", - "4 ATOM 5 CB ... C NaN 613\n", + " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", + "0 ATOM 1 N ... N NaN 609\n", + "1 ATOM 2 CA ... C NaN 610\n", + "2 ATOM 3 C ... C NaN 611\n", + "3 ATOM 4 O ... O NaN 612\n", + "4 ATOM 5 CB ... C NaN 613\n", "\n", "[5 rows x 21 columns]" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2096,7 +2039,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -2117,6 +2060,149 @@ "print('Average B-Factor [Main Chain]: %.2f' % bfact_mc_avg)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Loading PDB files from a Python List**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since biopandas 0.3.0, PDB files can also be loaded into a PandasPdb object from a Python list:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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record_nameatom_numberblank_1atom_name...segment_idelement_symbolchargeline_idx
0ATOM1N...NNaN609
1ATOM2CA...CNaN610
2ATOM3C...CNaN611
3ATOM4O...ONaN612
4ATOM5CB...CNaN613
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5 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", + "0 ATOM 1 N ... N NaN 609\n", + "1 ATOM 2 CA ... C NaN 610\n", + "2 ATOM 3 C ... C NaN 611\n", + "3 ATOM 4 O ... O NaN 612\n", + "4 ATOM 5 CB ... C NaN 613\n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('./data/3eiy.pdb', 'r') as f:\n", + " three_eiy = f.readlines()\n", + "\n", + "ppdb2 = PandasPdb()\n", + "ppdb2.read_pdb_from_list(three_eiy)\n", + "\n", + "ppdb2.df['ATOM'].head()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2133,7 +2219,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -2150,7 +2236,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -2162,19 +2248,17 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2188,19 +2272,17 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2214,19 +2296,17 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2275,7 +2355,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2305,7 +2385,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2324,7 +2404,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -2359,7 +2439,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2386,7 +2466,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -2427,7 +2507,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -2453,7 +2533,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -2467,7 +2547,7 @@ "dtype: float64" ] }, - "execution_count": 33, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -2485,7 +2565,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2587,17 +2667,17 @@ "" ], "text/plain": [ - " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", - "786 ATOM 787 CB ... C NaN 1395\n", - "787 ATOM 788 CG ... C NaN 1396\n", - "788 ATOM 789 CD1 ... C NaN 1397\n", - "789 ATOM 790 CD2 ... C NaN 1398\n", - "790 ATOM 791 N ... N NaN 1399\n", + " record_name atom_number blank_1 atom_name ... segment_id element_symbol charge line_idx\n", + "786 ATOM 787 CB ... C NaN 1395\n", + "787 ATOM 788 CG ... C NaN 1396\n", + "788 ATOM 789 CD1 ... C NaN 1397\n", + "789 ATOM 790 CD2 ... C NaN 1398\n", + "790 ATOM 791 N ... N NaN 1399\n", "\n", "[5 rows x 21 columns]" ] }, - "execution_count": 34, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -2632,7 +2712,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2699,7 +2779,7 @@ "1417 B T" ] }, - "execution_count": 35, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2720,7 +2800,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2729,7 +2809,7 @@ "['V', 'R', 'H', 'Y', 'T']" ] }, - "execution_count": 36, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2748,7 +2828,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2757,7 +2837,7 @@ "'SLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYT'" ] }, - "execution_count": 37, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2775,7 +2855,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -2820,7 +2900,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -2845,7 +2925,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -2871,7 +2951,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -2906,7 +2986,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.8.2" }, "toc": { "base_numbering": 1, diff --git a/docs/tutorials/data/3eiy_stripped.pdb.gz b/docs/tutorials/data/3eiy_stripped.pdb.gz index 52a41aa1a5a38a7bf6307dc6a03308562f48ffcc..065bbccce366a78e2234f643c07d156cad7a684e 100644 GIT binary patch delta 18 acmdnAf@#wVCU*I54vyS&6F0JJPXPcyoCduB delta 18 acmdnAf@#wVCU*I54vsIoG&ZtpPXPcz$Ogv% diff --git a/docs/tutorials/data/selected_ids.txt b/docs/tutorials/data/selected_ids.txt index 99e67a0..e69de29 100644 --- a/docs/tutorials/data/selected_ids.txt +++ b/docs/tutorials/data/selected_ids.txt @@ -1,3 +0,0 @@ -ZINC38611810 -ZINC39351796 -ZINC08789609