From 8ed64be381531306152edf0aa7c57711fff4b293 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lu=C3=A3=20B=2E=20Vacaro?= Date: Tue, 3 Dec 2024 09:20:29 -0300 Subject: [PATCH] feat(package): bump version to 0.15.0 (#216) --- docs/source/databases/CNES.ipynb | 4 +- docs/source/databases/SIA.ipynb | 4 +- docs/source/databases/SIH.ipynb | 4 +- docs/source/databases/SIM.ipynb | 4 +- docs/source/databases/SINAN.ipynb | 2 +- docs/source/databases/SINASC.ipynb | 4 +- docs/source/databases/territory.ipynb | 43 +- docs/source/tutorials/Chikungunya.ipynb | 380 +++++++++--------- docs/source/tutorials/Dengue.ipynb | 4 +- docs/source/tutorials/Infodengue.ipynb | 2 +- docs/source/tutorials/Infogripe.ipynb | 2 +- .../Preprocessing SIM with municipality.ipynb | 2 +- docs/source/tutorials/Preprocessing SIM.ipynb | 2 +- docs/source/tutorials/Zika.ipynb | 4 +- pysus/__init__.py | 2 + pysus/ftp/databases/__init__.py | 9 + pysus/ftp/databases/ciha.py | 2 + pysus/ftp/databases/cnes.py | 2 + pysus/ftp/databases/ibge_datasus.py | 2 + pysus/ftp/databases/pni.py | 2 + pysus/ftp/databases/sia.py | 2 + pysus/ftp/databases/sih.py | 2 + pysus/ftp/databases/sim.py | 2 + pysus/ftp/databases/sinan.py | 2 + pysus/ftp/databases/sinasc.py | 2 + 25 files changed, 268 insertions(+), 222 deletions(-) diff --git a/docs/source/databases/CNES.ipynb b/docs/source/databases/CNES.ipynb index c8f080d9..2a00576f 100644 --- a/docs/source/databases/CNES.ipynb +++ b/docs/source/databases/CNES.ipynb @@ -18,7 +18,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pysus.ftp.databases.cnes import CNES\n", + "from pysus import CNES\n", "cnes = CNES()" ] }, @@ -887,7 +887,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/docs/source/databases/SIA.ipynb b/docs/source/databases/SIA.ipynb index dac1ae2f..d201580a 100644 --- a/docs/source/databases/SIA.ipynb +++ b/docs/source/databases/SIA.ipynb @@ -16,7 +16,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pysus.ftp.databases.sia import SIA\n", + "from pysus import SIA\n", "sia = SIA().load() # Loads the files from DATASUS" ] }, @@ -686,7 +686,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/docs/source/databases/SIH.ipynb b/docs/source/databases/SIH.ipynb index 44dd0178..c86615cb 100644 --- a/docs/source/databases/SIH.ipynb +++ b/docs/source/databases/SIH.ipynb @@ -16,7 +16,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pysus.ftp.databases.sih import SIH\n", + "from pysus import SIH\n", "sih = SIH().load() # Loads the files from DATASUS" ] }, @@ -677,7 +677,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/docs/source/databases/SIM.ipynb b/docs/source/databases/SIM.ipynb index 007ae26c..84c00eda 100644 --- a/docs/source/databases/SIM.ipynb +++ b/docs/source/databases/SIM.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "from pysus.ftp.databases.sim import SIM\n", + "from pysus import SIM\n", "sim = SIM().load() # Loads the files from DATASUS" ] }, @@ -692,7 +692,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "vscode": { "interpreter": { diff --git a/docs/source/databases/SINAN.ipynb b/docs/source/databases/SINAN.ipynb index d94abe23..48318721 100644 --- a/docs/source/databases/SINAN.ipynb +++ b/docs/source/databases/SINAN.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "from pysus.ftp.databases.sinan import SINAN\n", + "from pysus import SINAN\n", "sinan = SINAN().load() # Loads the files from DATASUS" ] }, diff --git a/docs/source/databases/SINASC.ipynb b/docs/source/databases/SINASC.ipynb index fb78cc02..097e9c40 100644 --- a/docs/source/databases/SINASC.ipynb +++ b/docs/source/databases/SINASC.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "from pysus.ftp.databases.sinasc import SINASC\n", + "from pysus import SINASC\n", "sinasc = SINASC().load() # Loads the files from DATASUS" ] }, @@ -680,7 +680,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "vscode": { "interpreter": { diff --git a/docs/source/databases/territory.ipynb b/docs/source/databases/territory.ipynb index 2acda759..253de5a5 100644 --- a/docs/source/databases/territory.ipynb +++ b/docs/source/databases/territory.ipynb @@ -34,6 +34,7 @@ " 08-base_territorial_ago24.zip,\n", " 09-base_territorial_set24.zip,\n", " 10-base_territorial_out24.zip,\n", + " 11-base territorial_nov24.zip,\n", " base_territorial_2023.zip]" ] }, @@ -166,13 +167,13 @@ " sp_mapas_2001.zip,\n", " sp_mapas_2005.zip,\n", " sp_mapas_2013.zip,\n", - " to_mapas_2013.zip,\n", " todos_mapas_1991.zip,\n", " todos_mapas_1994.zip,\n", " todos_mapas_1997.zip,\n", " todos_mapas_2001.zip,\n", " todos_mapas_2005.zip,\n", - " todos_mapas_2013.zip]" + " todos_mapas_2013.zip,\n", + " to_mapas_2013.zip]" ] }, "execution_count": 3, @@ -190,11 +191,14 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/20 [00:002\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", - " 29\n", - " 292630\n", - " 1381\n", - " 2015-09-05\n", + " 28\n", + " 280460\n", + " 2061\n", + " 2015-01-01\n", " ...\n", " 1\n", - " 292630\n", - " \n", - " \n", + " 280460\n", + " 2\n", + " 1\n", " \n", - " 20151009\n", + " 20151217\n", " 0\n", " 2\n", " 1\n", @@ -132,20 +132,20 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", - " 29\n", - " 291360\n", - " 1385\n", - " 2015-08-28\n", + " 24\n", + " 240310\n", + " 1414\n", + " 2015-01-01\n", " ...\n", - " 1\n", - " 291360\n", + " 0\n", " \n", " \n", " \n", - " 20151228\n", + " \n", + " 20150309\n", " 0\n", " 2\n", " 1\n", @@ -156,23 +156,23 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", - " 29\n", - " 292740\n", - " 1380\n", - " 2015-09-01\n", + " 26\n", + " 260890\n", + " 1507\n", + " 2015-01-01\n", " ...\n", " 0\n", " \n", " \n", " \n", " \n", - " 20160111\n", + " \n", " 0\n", + " \n", " 2\n", - " 1\n", " \n", " \n", " \n", @@ -180,20 +180,20 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", " 29\n", - " 292895\n", + " 292630\n", " 1381\n", - " 2015-09-04\n", + " 2015-01-01\n", " ...\n", - " 0\n", - " \n", - " \n", - " \n", + " 1\n", + " 292630\n", + " 9\n", + " 9\n", " \n", - " 20151111\n", + " 20150113\n", " 0\n", " 2\n", " 1\n", @@ -204,20 +204,20 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", " 29\n", - " 292895\n", + " 292630\n", " 1381\n", - " 2015-09-05\n", + " 2014-12-27\n", " ...\n", - " 0\n", - " \n", - " \n", - " \n", + " 1\n", + " 292630\n", + " 9\n", + " 9\n", " \n", - " 20151111\n", + " 20150113\n", " 0\n", " 2\n", " 1\n", @@ -252,23 +252,23 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-12-31\n", + " 201552\n", " 2015\n", - " 29\n", - " 292630\n", - " 1381\n", - " 2015-09-06\n", + " 26\n", + " 260890\n", + " 1507\n", + " 2015-12-31\n", " ...\n", - " 1\n", - " 292630\n", + " 0\n", + " \n", " \n", " \n", " \n", - " 20151009\n", + " 20151231\n", " 0\n", + " \n", " 2\n", - " 1\n", " \n", " \n", " \n", @@ -276,23 +276,23 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-12-31\n", + " 201552\n", " 2015\n", - " 29\n", - " 292630\n", - " 1381\n", - " 2015-09-07\n", + " 26\n", + " 260410\n", + " 1499\n", + " 2015-12-26\n", " ...\n", - " 1\n", - " 292630\n", + " 0\n", " \n", " \n", + " 1\n", " \n", - " 20151009\n", + " 20160816\n", " 0\n", + " \n", " 2\n", - " 1\n", " \n", " \n", " \n", @@ -300,23 +300,23 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-12-31\n", + " 201552\n", " 2015\n", - " 29\n", - " 292630\n", - " 1381\n", - " 2015-09-06\n", + " 26\n", + " 261160\n", + " 1497\n", + " 2015-12-26\n", " ...\n", - " 1\n", - " 292630\n", + " 0\n", + " \n", + " \n", " \n", " \n", " \n", - " 20151009\n", " 0\n", + " \n", " 2\n", - " 1\n", " \n", " \n", " \n", @@ -324,47 +324,47 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-12-31\n", + " 201552\n", " 2015\n", - " 29\n", - " 292630\n", - " 1381\n", - " 2015-09-07\n", + " 26\n", + " 261160\n", + " 1497\n", + " 2015-12-29\n", " ...\n", - " 1\n", - " 292630\n", + " 0\n", " \n", " \n", + " 1\n", " \n", - " 20151009\n", - " 0\n", - " 2\n", + " 20160811\n", " 1\n", " \n", + " 2\n", + " \n", " \n", " \n", " 53270\n", " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-12-31\n", + " 201552\n", " 2015\n", - " 29\n", - " 292630\n", - " 1381\n", - " 2015-09-05\n", + " 26\n", + " 261160\n", + " 1497\n", + " 2015-12-30\n", " ...\n", - " 1\n", - " 292630\n", + " 0\n", + " \n", + " \n", " \n", " \n", " \n", - " 20151009\n", " 0\n", + " \n", " 2\n", - " 1\n", " \n", " \n", " \n", @@ -374,43 +374,43 @@ ], "text/plain": [ " TP_NOT ID_AGRAVO CS_SUSPEIT DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT \\\n", - "0 2 A920 2015-09-08 201536 2015 29 \n", - "1 2 A920 2015-09-08 201536 2015 29 \n", - "2 2 A920 2015-09-08 201536 2015 29 \n", - "3 2 A920 2015-09-08 201536 2015 29 \n", - "4 2 A920 2015-09-08 201536 2015 29 \n", + "0 2 A920 2015-01-01 201453 2015 28 \n", + "1 2 A920 2015-01-01 201453 2015 24 \n", + "2 2 A920 2015-01-01 201453 2015 26 \n", + "3 2 A920 2015-01-01 201453 2015 29 \n", + "4 2 A920 2015-01-01 201453 2015 29 \n", "... ... ... ... ... ... ... ... \n", - "53266 2 A920 2015-09-08 201536 2015 29 \n", - "53267 2 A920 2015-09-08 201536 2015 29 \n", - "53268 2 A920 2015-09-08 201536 2015 29 \n", - "53269 2 A920 2015-09-08 201536 2015 29 \n", - "53270 2 A920 2015-09-08 201536 2015 29 \n", + "53266 2 A920 2015-12-31 201552 2015 26 \n", + "53267 2 A920 2015-12-31 201552 2015 26 \n", + "53268 2 A920 2015-12-31 201552 2015 26 \n", + "53269 2 A920 2015-12-31 201552 2015 26 \n", + "53270 2 A920 2015-12-31 201552 2015 26 \n", "\n", " ID_MUNICIP ID_REGIONA DT_SIN_PRI ... COPAISINF COMUNINF DOENCA_TRA \\\n", - "0 292630 1381 2015-09-05 ... 1 292630 \n", - "1 291360 1385 2015-08-28 ... 1 291360 \n", - "2 292740 1380 2015-09-01 ... 0 \n", - "3 292895 1381 2015-09-04 ... 0 \n", - "4 292895 1381 2015-09-05 ... 0 \n", + "0 280460 2061 2015-01-01 ... 1 280460 2 \n", + "1 240310 1414 2015-01-01 ... 0 \n", + "2 260890 1507 2015-01-01 ... 0 \n", + "3 292630 1381 2015-01-01 ... 1 292630 9 \n", + "4 292630 1381 2014-12-27 ... 1 292630 9 \n", "... ... ... ... ... ... ... ... \n", - "53266 292630 1381 2015-09-06 ... 1 292630 \n", - "53267 292630 1381 2015-09-07 ... 1 292630 \n", - "53268 292630 1381 2015-09-06 ... 1 292630 \n", - "53269 292630 1381 2015-09-07 ... 1 292630 \n", - "53270 292630 1381 2015-09-05 ... 1 292630 \n", + "53266 260890 1507 2015-12-31 ... 0 \n", + "53267 260410 1499 2015-12-26 ... 0 \n", + "53268 261160 1497 2015-12-26 ... 0 \n", + "53269 261160 1497 2015-12-29 ... 0 \n", + "53270 261160 1497 2015-12-30 ... 0 \n", "\n", " EVOLUCAO DT_OBITO DT_ENCERRA CS_FLXRET FLXRECEBI TP_SISTEMA TPUNINOT \n", - "0 20151009 0 2 1 \n", - "1 20151228 0 2 1 \n", - "2 20160111 0 2 1 \n", - "3 20151111 0 2 1 \n", - "4 20151111 0 2 1 \n", + "0 1 20151217 0 2 1 \n", + "1 20150309 0 2 1 \n", + "2 0 2 \n", + "3 9 20150113 0 2 1 \n", + "4 9 20150113 0 2 1 \n", "... ... ... ... ... ... ... ... \n", - "53266 20151009 0 2 1 \n", - "53267 20151009 0 2 1 \n", - "53268 20151009 0 2 1 \n", - "53269 20151009 0 2 1 \n", - "53270 20151009 0 2 1 \n", + "53266 20151231 0 2 \n", + "53267 1 20160816 0 2 \n", + "53268 0 2 \n", + "53269 1 20160811 1 2 \n", + "53270 0 2 \n", "\n", "[53271 rows x 38 columns]" ] @@ -479,20 +479,20 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", - " 29\n", - " 292630\n", - " 1381\n", - " 2015-09-05\n", + " 28\n", + " 280460\n", + " 2061\n", + " 2015-01-01\n", " ...\n", " 1\n", - " 292630\n", - " \n", - " \n", + " 280460\n", + " 2\n", + " 1\n", " \n", - " 20151009\n", + " 20151217\n", " 0\n", " 2\n", " 1\n", @@ -503,20 +503,20 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", - " 29\n", - " 291360\n", - " 1385\n", - " 2015-08-28\n", + " 24\n", + " 240310\n", + " 1414\n", + " 2015-01-01\n", " ...\n", - " 1\n", - " 291360\n", + " 0\n", + " \n", " \n", " \n", " \n", - " 20151228\n", + " 20150309\n", " 0\n", " 2\n", " 1\n", @@ -527,23 +527,23 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", - " 29\n", - " 292740\n", - " 1380\n", - " 2015-09-01\n", + " 26\n", + " 260890\n", + " 1507\n", + " 2015-01-01\n", " ...\n", " 0\n", " \n", " \n", " \n", " \n", - " 20160111\n", + " \n", " 0\n", + " \n", " 2\n", - " 1\n", " \n", " \n", " \n", @@ -551,20 +551,20 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", " 29\n", - " 292895\n", + " 292630\n", " 1381\n", - " 2015-09-04\n", + " 2015-01-01\n", " ...\n", - " 0\n", - " \n", - " \n", - " \n", + " 1\n", + " 292630\n", + " 9\n", + " 9\n", " \n", - " 20151111\n", + " 20150113\n", " 0\n", " 2\n", " 1\n", @@ -575,20 +575,20 @@ " 2\n", " A920\n", " \n", - " 2015-09-08\n", - " 201536\n", + " 2015-01-01\n", + " 201453\n", " 2015\n", " 29\n", - " 292895\n", + " 292630\n", " 1381\n", - " 2015-09-05\n", + " 2014-12-27\n", " ...\n", - " 0\n", - " \n", - " \n", - " \n", + " 1\n", + " 292630\n", + " 9\n", + " 9\n", " \n", - " 20151111\n", + " 20150113\n", " 0\n", " 2\n", " 1\n", @@ -601,25 +601,25 @@ ], "text/plain": [ " TP_NOT ID_AGRAVO CS_SUSPEIT DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP \\\n", - "0 2 A920 2015-09-08 201536 2015 29 292630 \n", - "1 2 A920 2015-09-08 201536 2015 29 291360 \n", - "2 2 A920 2015-09-08 201536 2015 29 292740 \n", - "3 2 A920 2015-09-08 201536 2015 29 292895 \n", - "4 2 A920 2015-09-08 201536 2015 29 292895 \n", + "0 2 A920 2015-01-01 201453 2015 28 280460 \n", + "1 2 A920 2015-01-01 201453 2015 24 240310 \n", + "2 2 A920 2015-01-01 201453 2015 26 260890 \n", + "3 2 A920 2015-01-01 201453 2015 29 292630 \n", + "4 2 A920 2015-01-01 201453 2015 29 292630 \n", "\n", " ID_REGIONA DT_SIN_PRI ... COPAISINF COMUNINF DOENCA_TRA EVOLUCAO DT_OBITO \\\n", - "0 1381 2015-09-05 ... 1 292630 \n", - "1 1385 2015-08-28 ... 1 291360 \n", - "2 1380 2015-09-01 ... 0 \n", - "3 1381 2015-09-04 ... 0 \n", - "4 1381 2015-09-05 ... 0 \n", + "0 2061 2015-01-01 ... 1 280460 2 1 \n", + "1 1414 2015-01-01 ... 0 \n", + "2 1507 2015-01-01 ... 0 \n", + "3 1381 2015-01-01 ... 1 292630 9 9 \n", + "4 1381 2014-12-27 ... 1 292630 9 9 \n", "\n", " DT_ENCERRA CS_FLXRET FLXRECEBI TP_SISTEMA TPUNINOT \n", - "0 20151009 0 2 1 \n", - "1 20151228 0 2 1 \n", - "2 20160111 0 2 1 \n", - "3 20151111 0 2 1 \n", - "4 20151111 0 2 1 \n", + "0 20151217 0 2 1 \n", + "1 20150309 0 2 1 \n", + "2 0 2 \n", + "3 20150113 0 2 1 \n", + "4 20150113 0 2 1 \n", "\n", "[5 rows x 38 columns]" ] @@ -660,7 +660,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -690,7 +690,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "vscode": { "interpreter": { diff --git a/docs/source/tutorials/Dengue.ipynb b/docs/source/tutorials/Dengue.ipynb index b4e85f10..1dd8df7d 100644 --- a/docs/source/tutorials/Dengue.ipynb +++ b/docs/source/tutorials/Dengue.ipynb @@ -29,7 +29,7 @@ } ], "source": [ - "from pysus.ftp.databases.sinan import SINAN\n", + "from pysus import SINAN\n", "import pandas as pd\n", "import geopandas as gpd\n", "%pylab inline\n", @@ -424,7 +424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/docs/source/tutorials/Infodengue.ipynb b/docs/source/tutorials/Infodengue.ipynb index 88501b65..99f8782a 100644 --- a/docs/source/tutorials/Infodengue.ipynb +++ b/docs/source/tutorials/Infodengue.ipynb @@ -487,7 +487,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "vscode": { "interpreter": { diff --git a/docs/source/tutorials/Infogripe.ipynb b/docs/source/tutorials/Infogripe.ipynb index 14e7309a..6840b913 100644 --- a/docs/source/tutorials/Infogripe.ipynb +++ b/docs/source/tutorials/Infogripe.ipynb @@ -1513,7 +1513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "vscode": { "interpreter": { diff --git a/docs/source/tutorials/Preprocessing SIM with municipality.ipynb b/docs/source/tutorials/Preprocessing SIM with municipality.ipynb index 7b87af1d..4129af95 100644 --- a/docs/source/tutorials/Preprocessing SIM with municipality.ipynb +++ b/docs/source/tutorials/Preprocessing SIM with municipality.ipynb @@ -160,7 +160,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "vscode": { "interpreter": { diff --git a/docs/source/tutorials/Preprocessing SIM.ipynb b/docs/source/tutorials/Preprocessing SIM.ipynb index 173bbe25..23de1f64 100644 --- a/docs/source/tutorials/Preprocessing SIM.ipynb +++ b/docs/source/tutorials/Preprocessing SIM.ipynb @@ -1700,7 +1700,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "vscode": { "interpreter": { diff --git a/docs/source/tutorials/Zika.ipynb b/docs/source/tutorials/Zika.ipynb index bf35fdd2..cc3728f5 100644 --- a/docs/source/tutorials/Zika.ipynb +++ b/docs/source/tutorials/Zika.ipynb @@ -20,7 +20,7 @@ }, "outputs": [], "source": [ - "from pysus.ftp.databases.sinan import SINAN\n", + "from pysus import SINAN\n", "\n", "sinan = SINAN().load()" ] @@ -424,7 +424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/pysus/__init__.py b/pysus/__init__.py index 0ba1d689..aa1a340b 100644 --- a/pysus/__init__.py +++ b/pysus/__init__.py @@ -3,6 +3,8 @@ from importlib import metadata as importlib_metadata +from pysus.ftp.databases import * # noqa + def get_version() -> str: try: diff --git a/pysus/ftp/databases/__init__.py b/pysus/ftp/databases/__init__.py index e69de29b..b053c1a6 100644 --- a/pysus/ftp/databases/__init__.py +++ b/pysus/ftp/databases/__init__.py @@ -0,0 +1,9 @@ +from .ciha import * # noqa +from .cnes import * # noqa +from .ibge_datasus import * # noqa +from .pni import * # noqa +from .sia import * # noqa +from .sih import * # noqa +from .sim import * # noqa +from .sinan import * # noqa +from .sinasc import * # noqa diff --git a/pysus/ftp/databases/ciha.py b/pysus/ftp/databases/ciha.py index 7e7ac384..5c8c43c4 100644 --- a/pysus/ftp/databases/ciha.py +++ b/pysus/ftp/databases/ciha.py @@ -1,3 +1,5 @@ +__all__ = ["CIHA"] + from typing import List, Optional, Union from pysus.ftp import Database, Directory, File diff --git a/pysus/ftp/databases/cnes.py b/pysus/ftp/databases/cnes.py index e6e64272..1e070be7 100644 --- a/pysus/ftp/databases/cnes.py +++ b/pysus/ftp/databases/cnes.py @@ -1,3 +1,5 @@ +__all__ = ["CNES"] + from typing import List, Optional, Union from pysus.ftp import Database, Directory, File diff --git a/pysus/ftp/databases/ibge_datasus.py b/pysus/ftp/databases/ibge_datasus.py index e7b27327..d1547ae5 100644 --- a/pysus/ftp/databases/ibge_datasus.py +++ b/pysus/ftp/databases/ibge_datasus.py @@ -1,3 +1,5 @@ +__all__ = ["IBGEDATASUS"] + from typing import List, Literal, Optional, Union from pysus.ftp import Database, Directory, File diff --git a/pysus/ftp/databases/pni.py b/pysus/ftp/databases/pni.py index 6fa7d113..37cf8484 100644 --- a/pysus/ftp/databases/pni.py +++ b/pysus/ftp/databases/pni.py @@ -1,3 +1,5 @@ +__all__ = ["PNI"] + from typing import List, Literal, Optional, Union from pysus.ftp import Database, Directory, File diff --git a/pysus/ftp/databases/sia.py b/pysus/ftp/databases/sia.py index d3873e97..76b5dd7b 100644 --- a/pysus/ftp/databases/sia.py +++ b/pysus/ftp/databases/sia.py @@ -1,3 +1,5 @@ +__all__ = ["SIA"] + from typing import List, Optional, Union from pysus.ftp import Database, Directory, File diff --git a/pysus/ftp/databases/sih.py b/pysus/ftp/databases/sih.py index e0289e23..97757d8c 100644 --- a/pysus/ftp/databases/sih.py +++ b/pysus/ftp/databases/sih.py @@ -1,3 +1,5 @@ +__all__ = ["SIH"] + from typing import List, Optional, Union from pysus.ftp import Database, Directory, File diff --git a/pysus/ftp/databases/sim.py b/pysus/ftp/databases/sim.py index ccb1bc2d..83134a49 100644 --- a/pysus/ftp/databases/sim.py +++ b/pysus/ftp/databases/sim.py @@ -1,3 +1,5 @@ +__all__ = ["SIM"] + from typing import List, Optional, Union from pysus.ftp import Database, Directory, File diff --git a/pysus/ftp/databases/sinan.py b/pysus/ftp/databases/sinan.py index 2c664e2b..ccc3ae80 100644 --- a/pysus/ftp/databases/sinan.py +++ b/pysus/ftp/databases/sinan.py @@ -1,3 +1,5 @@ +__all__ = ["SINAN"] + from typing import List, Optional, Union from pysus.ftp import Database, Directory, File diff --git a/pysus/ftp/databases/sinasc.py b/pysus/ftp/databases/sinasc.py index c365960d..aaac7b63 100644 --- a/pysus/ftp/databases/sinasc.py +++ b/pysus/ftp/databases/sinasc.py @@ -1,3 +1,5 @@ +__all__ = ["SINASC"] + from typing import List, Optional, Union from pysus.ftp import Database, Directory, File