Removing data files from the project

pyFTS/data/AirPassengers.csv

@@ -1,145 +0,0 @@
-Month,Passengers
-1949-01,112
-1949-02,118
-1949-03,132
-1949-04,129
-1949-05,121
-1949-06,135
-1949-07,148
-1949-08,148
-1949-09,136
-1949-10,119
-1949-11,104
-1949-12,118
-1950-01,115
-1950-02,126
-1950-03,141
-1950-04,135
-1950-05,125
-1950-06,149
-1950-07,170
-1950-08,170
-1950-09,158
-1950-10,133
-1950-11,114
-1950-12,140
-1951-01,145
-1951-02,150
-1951-03,178
-1951-04,163
-1951-05,172
-1951-06,178
-1951-07,199
-1951-08,199
-1951-09,184
-1951-10,162
-1951-11,146
-1951-12,166
-1952-01,171
-1952-02,180
-1952-03,193
-1952-04,181
-1952-05,183
-1952-06,218
-1952-07,230
-1952-08,242
-1952-09,209
-1952-10,191
-1952-11,172
-1952-12,194
-1953-01,196
-1953-02,196
-1953-03,236
-1953-04,235
-1953-05,229
-1953-06,243
-1953-07,264
-1953-08,272
-1953-09,237
-1953-10,211
-1953-11,180
-1953-12,201
-1954-01,204
-1954-02,188
-1954-03,235
-1954-04,227
-1954-05,234
-1954-06,264
-1954-07,302
-1954-08,293
-1954-09,259
-1954-10,229
-1954-11,203
-1954-12,229
-1955-01,242
-1955-02,233
-1955-03,267
-1955-04,269
-1955-05,270
-1955-06,315
-1955-07,364
-1955-08,347
-1955-09,312
-1955-10,274
-1955-11,237
-1955-12,278
-1956-01,284
-1956-02,277
-1956-03,317
-1956-04,313
-1956-05,318
-1956-06,374
-1956-07,413
-1956-08,405
-1956-09,355
-1956-10,306
-1956-11,271
-1956-12,306
-1957-01,315
-1957-02,301
-1957-03,356
-1957-04,348
-1957-05,355
-1957-06,422
-1957-07,465
-1957-08,467
-1957-09,404
-1957-10,347
-1957-11,305
-1957-12,336
-1958-01,340
-1958-02,318
-1958-03,362
-1958-04,348
-1958-05,363
-1958-06,435
-1958-07,491
-1958-08,505
-1958-09,404
-1958-10,359
-1958-11,310
-1958-12,337
-1959-01,360
-1959-02,342
-1959-03,406
-1959-04,396
-1959-05,420
-1959-06,472
-1959-07,548
-1959-08,559
-1959-09,463
-1959-10,407
-1959-11,362
-1959-12,405
-1960-01,417
-1960-02,391
-1960-03,419
-1960-04,461
-1960-05,472
-1960-06,535
-1960-07,622
-1960-08,606
-1960-09,508
-1960-10,461
-1960-11,390
-1960-12,432

pyFTS/data/Enrollments.csv

@@ -1,23 +0,0 @@
-Year;Enrollments
-1971;13055
-1972;13563
-1973;13867
-1974;14696
-1975;15460
-1976;15311
-1977;15603
-1978;15861
-1979;16807
-1980;16919
-1981;16388
-1982;15433
-1983;15497
-1984;15145
-1985;15163
-1986;15984
-1987;16859
-1988;18150
-1989;18970
-1990;19328
-1991;19337
-1992;18876

pyFTS/data/INMET.py

@@ -13,14 +13,10 @@
 
 # http://www.inmet.gov.br
 
+from pyFTS.data import common
 import pandas as pd
-import numpy as np
-import os
-import pkg_resources
-
 
 def get_dataframe():
-    filename = pkg_resources.resource_filename('pyFTS', 'data/INMET.csv.bz2')
+    dat = common.get_dataframe('data/INMET.csv.bz2',sep=";", compression='bz2')
-    dat = pd.read_csv(filename, sep=";", compression='bz2')
     dat["DataHora"] = pd.to_datetime(dat["DataHora"], format='%d/%m/%Y %H:%M')
     return dat

pyFTS/data/common.py

@@ -0,0 +1,19 @@
+import pandas as pd
+import numpy as np
+import os
+import pkg_resources
+from pathlib import Path
+from urllib import request
+
+
+def get_dataframe(path, url, sep=";", compression='infer'):
+    filename = pkg_resources.resource_filename('pyFTS', path)
+    tmp_file = Path(filename)
+
+    if tmp_file.is_file():
+        return pd.read_csv(filename, sep=sep, compression=compression)
+    else:
+        request.urlretrieve(url, filename)
+        return pd.read_csv(filename, sep=sep, compression=compression)
+
+

pyFTS/data/sunspots.csv

@@ -1,310 +0,0 @@
-YEAR,SUNACTIVITY
-1700,5
-1701,11
-1702,16
-1703,23
-1704,36
-1705,58
-1706,29
-1707,20
-1708,10
-1709,8
-1710,3
-1711,0
-1712,0
-1713,2
-1714,11
-1715,27
-1716,47
-1717,63
-1718,60
-1719,39
-1720,28
-1721,26
-1722,22
-1723,11
-1724,21
-1725,40
-1726,78
-1727,122
-1728,103
-1729,73
-1730,47
-1731,35
-1732,11
-1733,5
-1734,16
-1735,34
-1736,70
-1737,81
-1738,111
-1739,101
-1740,73
-1741,40
-1742,20
-1743,16
-1744,5
-1745,11
-1746,22
-1747,40
-1748,60
-1749,80.9
-1750,83.4
-1751,47.7
-1752,47.8
-1753,30.7
-1754,12.2
-1755,9.6
-1756,10.2
-1757,32.4
-1758,47.6
-1759,54
-1760,62.9
-1761,85.9
-1762,61.2
-1763,45.1
-1764,36.4
-1765,20.9
-1766,11.4
-1767,37.8
-1768,69.8
-1769,106.1
-1770,100.8
-1771,81.6
-1772,66.5
-1773,34.8
-1774,30.6
-1775,7
-1776,19.8
-1777,92.5
-1778,154.4
-1779,125.9
-1780,84.8
-1781,68.1
-1782,38.5
-1783,22.8
-1784,10.2
-1785,24.1
-1786,82.9
-1787,132
-1788,130.9
-1789,118.1
-1790,89.9
-1791,66.6
-1792,60
-1793,46.9
-1794,41
-1795,21.3
-1796,16
-1797,6.4
-1798,4.1
-1799,6.8
-1800,14.5
-1801,34
-1802,45
-1803,43.1
-1804,47.5
-1805,42.2
-1806,28.1
-1807,10.1
-1808,8.1
-1809,2.5
-1810,0
-1811,1.4
-1812,5
-1813,12.2
-1814,13.9
-1815,35.4
-1816,45.8
-1817,41.1
-1818,30.1
-1819,23.9
-1820,15.6
-1821,6.6
-1822,4
-1823,1.8
-1824,8.5
-1825,16.6
-1826,36.3
-1827,49.6
-1828,64.2
-1829,67
-1830,70.9
-1831,47.8
-1832,27.5
-1833,8.5
-1834,13.2
-1835,56.9
-1836,121.5
-1837,138.3
-1838,103.2
-1839,85.7
-1840,64.6
-1841,36.7
-1842,24.2
-1843,10.7
-1844,15
-1845,40.1
-1846,61.5
-1847,98.5
-1848,124.7
-1849,96.3
-1850,66.6
-1851,64.5
-1852,54.1
-1853,39
-1854,20.6
-1855,6.7
-1856,4.3
-1857,22.7
-1858,54.8
-1859,93.8
-1860,95.8
-1861,77.2
-1862,59.1
-1863,44
-1864,47
-1865,30.5
-1866,16.3
-1867,7.3
-1868,37.6
-1869,74
-1870,139
-1871,111.2
-1872,101.6
-1873,66.2
-1874,44.7
-1875,17
-1876,11.3
-1877,12.4
-1878,3.4
-1879,6
-1880,32.3
-1881,54.3
-1882,59.7
-1883,63.7
-1884,63.5
-1885,52.2
-1886,25.4
-1887,13.1
-1888,6.8
-1889,6.3
-1890,7.1
-1891,35.6
-1892,73
-1893,85.1
-1894,78
-1895,64
-1896,41.8
-1897,26.2
-1898,26.7
-1899,12.1
-1900,9.5
-1901,2.7
-1902,5
-1903,24.4
-1904,42
-1905,63.5
-1906,53.8
-1907,62
-1908,48.5
-1909,43.9
-1910,18.6
-1911,5.7
-1912,3.6
-1913,1.4
-1914,9.6
-1915,47.4
-1916,57.1
-1917,103.9
-1918,80.6
-1919,63.6
-1920,37.6
-1921,26.1
-1922,14.2
-1923,5.8
-1924,16.7
-1925,44.3
-1926,63.9
-1927,69
-1928,77.8
-1929,64.9
-1930,35.7
-1931,21.2
-1932,11.1
-1933,5.7
-1934,8.7
-1935,36.1
-1936,79.7
-1937,114.4
-1938,109.6
-1939,88.8
-1940,67.8
-1941,47.5
-1942,30.6
-1943,16.3
-1944,9.6
-1945,33.2
-1946,92.6
-1947,151.6
-1948,136.3
-1949,134.7
-1950,83.9
-1951,69.4
-1952,31.5
-1953,13.9
-1954,4.4
-1955,38
-1956,141.7
-1957,190.2
-1958,184.8
-1959,159
-1960,112.3
-1961,53.9
-1962,37.6
-1963,27.9
-1964,10.2
-1965,15.1
-1966,47
-1967,93.8
-1968,105.9
-1969,105.5
-1970,104.5
-1971,66.6
-1972,68.9
-1973,38
-1974,34.5
-1975,15.5
-1976,12.6
-1977,27.5
-1978,92.5
-1979,155.4
-1980,154.6
-1981,140.4
-1982,115.9
-1983,66.6
-1984,45.9
-1985,17.9
-1986,13.4
-1987,29.4
-1988,100.2
-1989,157.6
-1990,142.6
-1991,145.7
-1992,94.3
-1993,54.6
-1994,29.9
-1995,17.5
-1996,8.6
-1997,21.5
-1998,64.3
-1999,93.3
-2000,119.6
-2001,111
-2002,104
-2003,63.7
-2004,40.4
-2005,29.8
-2006,15.2
-2007,7.5
-2008,2.9

pyFTS/tests/multivariate.py

@@ -17,7 +17,7 @@ from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
 
 bc = Transformations.BoxCox(0)
-diff = Transformations.Differential(1)
+tdiff = Transformations.Differential(1)
 
 np = 10
 
@@ -38,10 +38,12 @@ vhour = variable.Variable("Hour", data_label="datahora", partitioner=seasonal.Ti
                           data=train, partitioner_specific=sp)
 model.append_variable(vhour)
 
-vhumid = variable.Variable("Humidity", data_label="humid", partitioner=Grid.GridPartitioner, npart=np, data=train)
+vhumid = variable.Variable("Humidity", data_label="humid", partitioner=Grid.GridPartitioner, npart=np, data=train,
+                           transformation=tdiff)
 model.append_variable(vhumid)
 
-vpress = variable.Variable("AtmPress", data_label="press", partitioner=Grid.GridPartitioner, npart=np, data=train)
+vpress = variable.Variable("AtmPress", data_label="press", partitioner=Grid.GridPartitioner, npart=np, data=train,
+                           transformation=tdiff)
 model.append_variable(vpress)
 
 vrain = variable.Variable("Rain", data_label="rain", partitioner=Grid.GridPartitioner, npart=20, data=train)#train)
@@ -53,7 +55,7 @@ model.target_variable = vrain
 #model.fit(train, num_batches=60, save=True, batch_save=True, file_path='mvfts_sonda')
 
 model.fit(train, num_batches=200, save=True, batch_save=True, file_path='mvfts_sonda', distributed=True,
-          nodes=['192.168.0.110','192.168.0.106'], batch_save_interval=10)
+          nodes=['192.168.1.22'], batch_save_interval=10)
 
 
 #model = Util.load_obj('mvfts_sonda')