From 4900a5476f71c122ce546de47937681b44d95a09 Mon Sep 17 00:00:00 2001
From: artur <me@artur.rocks>
Date: Sun, 4 Feb 2024 18:25:59 +0100
Subject: [PATCH] clean-up

---
 jupyter/crh.py                        |   150 -
 jupyter/data/weather_data_set.txt     | 16038 ------------------------
 jupyter/data/weather_ground_truth.txt |  1739 ---
 jupyter/notebook.ipynb                |   478 -
 prototype/code.py                     |   152 -
 prototype/weather_data_set.txt        | 16038 ------------------------
 6 files changed, 34595 deletions(-)
 delete mode 100644 jupyter/crh.py
 delete mode 100644 jupyter/data/weather_data_set.txt
 delete mode 100644 jupyter/data/weather_ground_truth.txt
 delete mode 100644 jupyter/notebook.ipynb
 delete mode 100644 prototype/code.py
 delete mode 100644 prototype/weather_data_set.txt

diff --git a/jupyter/crh.py b/jupyter/crh.py
deleted file mode 100644
index 6e490ee..0000000
--- a/jupyter/crh.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import pandas as pd
-import numpy as np
-
-def weighted_median1(values, weights):
-    sorted_indices = np.argsort(values)
-    sorted_values = sorted(values)
-    sorted_weights = sorted(weights)
-
-    cumulative_weights = np.cumsum(sorted_weights)
-    total_weight = np.sum(sorted_weights)
-
-    # Find the median index
-    median_index = np.searchsorted(cumulative_weights, total_weight / 2.0)
-
-    return sorted_values[median_index]
-
-
-def execute(dataset, iti):
-    dataset.sort_values(by=[0], inplace=True)
-    
-    # Calculate statistics about the data
-    nof = len(dataset)
-    # Get the entry list
-    list_entry = dataset[0].unique()
-    # Get the source list
-    list_source = dataset[2].unique()
-    # Number of sources
-    nos = len(list_source)
-    # Number of entries
-    noe = len(list_entry)
-    
-    # Modify entry_ia so that the following works
-    entry_ia = np.append(np.arange(0, noe), nof)
-    
-    # Initialization
-    ini_truth = []
-    weight = np.ones(nos) / nos
-    
-    weight_vector = weight[dataset[2].values-1]
-    weight_matrix = pd.DataFrame(weight_vector, columns=['weight'],index = dataset.index)
-    # Calculate initial truth entry by entry
-    
-    stand_error=[]
-    
-    
-    for entry_id in list_entry:
-        val = dataset[dataset[0] == entry_id].values[0,1]
-     
-        if not val.lstrip('-+').isdigit():  # Categorical data
-            same_entry = dataset[dataset[0]==entry_id]
-            tempvalue = same_entry[1].values
-            # Get unique value list on the same entry
-            temp_list = same_entry[1].unique()
-            wv = []
-    
-            # Calculate the weighted occurrence for this entry
-            for k in range(len(temp_list)):
-                wv.append(np.sum(((tempvalue == temp_list[k]) * weight_matrix.loc[same_entry.index].values)))
-    
-            # Get the voting result
-            I = np.argmax(wv)
-            ini_truth.append([entry_id, temp_list[I]])
-    
-        else:  # Numerical data
-            
-            same_entry = dataset[dataset[0]==entry_id]
-            
-            tempvalue = same_entry[1].values
-            tempvalue_int = [int(x) for x in tempvalue]
-            median = np.median(tempvalue_int)
-            
-            # Calculate median for this entry
-            ini_truth.append([entry_id, median])
-            stand_error.append([entry_id,np.std(tempvalue_int) ])
-    index_truth = [] #ini_truth.copy()
-    truth_matrix = pd.DataFrame(ini_truth, columns=['entry_id', 'truth'])
-    std_matrix = pd.DataFrame(stand_error, columns=['entry_id', 'error'])
-    
-    # Initialize other parameters
-    cat_count = np.zeros(nos)  # Count of categorical data for each source
-    con_count = np.zeros(nos)  # Count of continuous data for each source
-    
-    for i in range(iti):
-        index_truth = []
-        score1 = np.zeros(nos)
-        score2 = np.zeros(nos)
-    
-        # Update weight
-        
-        for j in range(len(dataset)):
-            val = dataset.loc[j].values[1]
-            entry_id = dataset.loc[j].values[0]
-            source_id = dataset.loc[j].values[2]
-            true_val=truth_matrix[truth_matrix['entry_id'] == entry_id].values[0,1]
-            if not val.lstrip('-+').isdigit():
-                true_val=truth_matrix[truth_matrix['entry_id'] == entry_id].values[0,1]
-                score1[source_id-1] += int(true_val != val)  #we count non-matching TypeError: weighted_median() missing 1 required positional argument: 'weight'values
-                #if i == 2:
-                cat_count[source_id-1] += 1
-            else:  # Numerical data
-                error=std_matrix[std_matrix['entry_id'] == entry_id].values[0,1]
-                if (error != 0 ):
-                    score2[source_id-1] += np.abs( int(val) - int(true_val)) / error
-    
-                #if i == 2:
-                con_count[source_id-1] += 1
-        score1 /= cat_count
-        score2 /= con_count
-        score1 /= sum(score1)
-        score2 /= sum(score2)
-    
-        score = score1 + score2
-        norm_score = np.max(score)
-        w = score / norm_score
-        weight = -np.log(w) + 0.00001
-    
-        weight_vector = weight[dataset[2].values-1]
-        weight_matrix = pd.DataFrame(weight_vector, columns=['weight'],index = dataset.index)
-        for entry_id in list_entry:
-            val = dataset[dataset[0] == entry_id].values[0,1]
-            if not val.lstrip('-+').isdigit():  # Categorical data
-                same_entry = dataset[dataset[0]==entry_id]
-                tempvalue = same_entry[1].values
-                temp_list = same_entry[1].unique()
-                wv = []
-                for k in range(len(temp_list)):
-                    wv.append(np.sum(((tempvalue == temp_list[k]) * weight_matrix.loc[same_entry.index].values)))
-                I = np.argmax(wv)
-                index_truth.append([entry_id, temp_list[I]])
-            else:  # Numerical data
-                same_entry = dataset[dataset[0]==entry_id]
-                tempvalue = same_entry[1].values
-                tempvalue_int = [int(x) for x in tempvalue]
-                
-                tempweight=weight_matrix.loc[same_entry.index].values
-                wm = weighted_median1(tempvalue_int, tempweight)
-                index_truth.append([entry_id, wm])
-    
-        print(len(index_truth))
-        truth_matrix = pd.DataFrame(index_truth, columns=['entry_id', 'truth'])
-
-
-dataset = pd.read_csv('data/weather_data_set.txt', sep="\t",  header=None)
-groundtruth = pd.read_csv('data/weather_ground_truth.txt', sep="\t")
-iti = 3
-
-
-execute(dataset, iti)
-       
- 
\ No newline at end of file
diff --git a/jupyter/data/weather_data_set.txt b/jupyter/data/weather_data_set.txt
deleted file mode 100644
index e0c51bb..0000000
--- a/jupyter/data/weather_data_set.txt
+++ /dev/null
@@ -1,16038 +0,0 @@
-10	w-7	1
-11	61	1
-12	45	1
-13	w-1	2
-14	63	2
-15	41	2
-16	w-1	3
-17	63	3
-18	41	3
-31	w-1	1
-32	19	1
-33	14	1
-34	w-1	2
-35	21	2
-36	10	2
-37	w-1	3
-38	23	3
-39	12	3
-52	w-1	1
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-55	w-1	2
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-81	50	3
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-100	w-7	3
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-115	w-1	1
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-120	55	2
-121	w-1	3
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-201	45	1
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-220	w-2	1
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-356	52	3
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-1636	w-7	7
-1637	56	7
-1638	50	7
-1696	w-1	8
-1697	59	8
-1698	52	8
-1756	w-2	9
-1757	61	9
-1758	50	9
-1639	w-7	7
-1640	57	7
-1641	52	7
-1699	w-2	8
-1700	60	8
-1701	51	8
-1759	w-2	9
-1760	62	9
-1761	49	9
-1642	w-1	7
-1643	39	7
-1644	33	7
-1702	w-2	8
-1703	39	8
-1704	39	8
-1762	w-7	9
-1763	40	9
-1764	37	9
-1645	w-1	7
-1646	48	7
-1647	42	7
-1705	w-2	8
-1706	49	8
-1707	46	8
-1765	w-2	9
-1766	49	9
-1767	48	9
-1648	w-1	7
-1649	50	7
-1650	42	7
-1708	w-2	8
-1709	50	8
-1710	49	8
-1768	w-1	9
-1769	52	9
-1770	46	9
-1651	w-2	7
-1652	69	7
-1653	63	7
-1711	w-2	8
-1712	72	8
-1713	66	8
-1771	w-2	9
-1772	69	9
-1773	50	9
-1654	w-2	7
-1655	79	7
-1656	70	7
-1714	w-1	8
-1715	79	8
-1716	71	8
-1774	w-7	9
-1775	80	9
-1776	71	9
-1657	w-2	7
-1658	76	7
-1659	63	7
-1717	w-7	8
-1718	77	8
-1719	63	8
-1777	w-2	9
-1778	77	9
-1779	65	9
-1660	w-2	7
-1661	60	7
-1662	52	7
-1720	w-2	8
-1721	61	8
-1722	57	8
-1780	w-7	9
-1781	65	9
-1782	43	9
-1663	w-1	7
-1664	81	7
-1665	70	7
-1723	w-1	8
-1724	80	8
-1725	74	8
-1783	w-7	9
-1784	78	9
-1785	74	9
-1666	w-2	7
-1667	33	7
-1668	23	7
-1726	w-9	8
-1727	27	8
-1728	23	8
-1786	w-9	9
-1787	27	9
-1788	23	9
-1669	w-2	7
-1670	53	7
-1671	40	7
-1729	w-2	8
-1730	53	8
-1731	34	8
-1789	w-2	9
-1790	40	9
-1791	31	9
-1672	w-2	7
-1673	56	7
-1674	45	7
-1732	w-2	8
-1733	51	8
-1734	32	8
-1792	w-2	9
-1793	39	9
-1794	30	9
-1675	w-7	7
-1676	50	7
-1677	46	7
-1735	w-1	8
-1736	51	8
-1737	34	8
-1795	w-2	9
-1796	42	9
-1797	29	9
-1678	w-2	7
-1679	52	7
-1680	43	7
-1738	w-2	8
-1739	44	8
-1740	29	8
-1798	w-1	9
-1799	35	9
-1800	27	9
-1861	w-2	1
-1862	54	1
-1863	37	1
-1921	w-2	2
-1922	57	2
-1923	36	2
-1981	w-7	3
-1982	59	3
-1983	36	3
-1864	w-9	1
-1865	18	1
-1866	5	1
-1924	w-9	2
-1925	7	2
-1926	-2	2
-1984	w-9	3
-1985	10	3
-1986	0	3
-1867	w-2	1
-1868	66	1
-1869	48	1
-1927	w-7	2
-1928	63	2
-1929	46	2
-1987	w-1	3
-1988	64	3
-1989	48	3
-1870	w-1	1
-1871	57	1
-1872	43	1
-1930	w-2	2
-1931	61	2
-1932	43	2
-1990	w-10	3
-1991	55	3
-1992	36	3
-1873	w-7	1
-1874	50	1
-1875	32	1
-1933	w-2	2
-1934	50	2
-1935	34	2
-1993	w-2	3
-1994	52	3
-1995	34	3
-1876	w-7	1
-1877	61	1
-1878	48	1
-1936	w-1	2
-1937	59	2
-1938	43	2
-1996	w-1	3
-1997	64	3
-1998	48	3
-1879	w-7	1
-1880	57	1
-1881	46	1
-1939	w-1	2
-1940	55	2
-1941	43	2
-1999	w-1	3
-2000	61	3
-2001	48	3
-1882	w-7	1
-1883	45	1
-1884	36	1
-1942	w-2	2
-1943	43	2
-1944	30	2
-2002	w-2	3
-2003	39	3
-2004	30	3
-1885	w-7	1
-1886	50	1
-1887	37	1
-1945	w-7	2
-1946	46	2
-1947	34	2
-2005	w-2	3
-2006	45	3
-2007	34	3
-1888	w-7	1
-1889	52	1
-1890	39	1
-1948	w-2	2
-1949	48	2
-1950	36	2
-2008	w-7	3
-2009	45	3
-2010	34	3
-1891	w-1	1
-1892	66	1
-1893	52	1
-1951	w-1	2
-1952	66	2
-1953	52	2
-2011	w-10	3
-2012	68	3
-2013	55	3
-1894	w-7	1
-1895	79	1
-1896	61	1
-1954	w-7	2
-1955	73	2
-1956	59	2
-2014	w-2	3
-2015	77	3
-2016	63	3
-1897	w-7	1
-1898	72	1
-1899	54	1
-1957	w-1	2
-1958	72	2
-1959	52	2
-2017	w-1	3
-2018	72	3
-2019	54	3
-1900	w-7	1
-1901	55	1
-1902	37	1
-1960	w-1	2
-1961	57	2
-1962	36	2
-2020	w-7	3
-2021	57	3
-2022	41	3
-1903	w-7	1
-1904	81	1
-1905	72	1
-1963	w-7	2
-1964	81	2
-1965	72	2
-2023	w-7	3
-2024	81	3
-2025	72	3
-1906	w-9	1
-1907	25	1
-1908	12	1
-1966	w-9	2
-1967	21	2
-1968	-4	2
-2026	w-9	3
-2027	21	3
-2028	0	3
-1909	w-2	1
-1910	39	1
-1911	34	1
-1969	w-2	2
-1970	43	2
-1971	36	2
-2029	w-2	3
-2030	39	3
-2031	28	3
-1912	w-2	1
-1913	39	1
-1914	30	1
-1972	w-2	2
-1973	43	2
-1974	32	2
-2032	w-7	3
-2033	39	3
-2034	28	3
-1915	w-9	1
-1916	39	1
-1917	32	1
-1975	w-2	2
-1976	43	2
-1977	32	2
-2035	w-7	3
-2036	43	3
-2037	28	3
-1918	w-2	1
-1919	37	1
-1920	30	1
-1978	w-2	2
-1979	41	2
-1980	32	2
-2038	w-9	3
-2039	36	3
-2040	23	3
-1681	w-7	4
-1682	68	4
-1683	54	4
-1741	w-7	5
-1742	66	5
-1743	39	5
-1801	w-1	6
-1802	59	6
-1803	39	6
-1684	w-2	4
-1685	3	4
-1686	-4	4
-1744	w-2	5
-1745	10	5
-1746	9	5
-1804	w-2	6
-1805	21	6
-1806	3	6
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-1688	57	4
-1689	43	4
-1747	w-2	5
-1748	59	5
-1749	43	5
-1807	w-2	6
-1808	64	6
-1809	50	6
-1690	w-2	4
-1691	70	4
-1692	45	4
-1750	w-2	5
-1751	59	5
-1752	36	5
-1810	w-1	6
-1811	63	6
-1812	41	6
-1693	w-7	4
-1694	54	4
-1695	46	4
-1753	w-7	5
-1754	55	5
-1755	41	5
-1813	w-2	6
-1814	55	6
-1815	34	6
-1696	w-7	4
-1697	61	4
-1698	50	4
-1756	w-2	5
-1757	59	5
-1758	52	5
-1816	w-7	6
-1817	61	6
-1818	39	6
-1699	w-7	4
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-1759	w-2	5
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-1702	w-9	4
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-1822	w-7	6
-1823	45	6
-1824	36	6
-1705	w-7	4
-1706	52	4
-1707	48	4
-1765	w-7	5
-1766	61	5
-1767	45	5
-1825	w-7	6
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-1708	w-7	4
-1709	54	4
-1710	48	4
-1768	w-7	5
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-1828	w-7	6
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-1830	34	6
-1711	w-7	4
-1712	72	4
-1713	64	4
-1771	w-7	5
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-1717	w-2	4
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-1778	79	5
-1779	63	5
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-1780	w-7	5
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-1841	55	6
-1842	39	6
-1723	w-7	4
-1724	77	4
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-1783	w-7	5
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-1789	w-7	5
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-1791	30	5
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-1732	w-2	4
-1733	54	4
-1734	37	4
-1792	w-7	5
-1793	43	5
-1794	32	5
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-1853	43	6
-1854	25	6
-1735	w-7	4
-1736	61	4
-1737	37	4
-1795	w-7	5
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-1797	34	5
-1855	w-2	6
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-1738	w-7	4
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-1860	23	6
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-1741	w-1	8
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-1806	15	9
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-1689	44	7
-1747	w-1	8
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-1749	50	8
-1807	w-1	9
-1808	58	9
-1809	51	9
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-1816	w-1	9
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-1701	52	7
-1759	w-1	8
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-1819	w-1	9
-1820	60	9
-1821	45	9
-1702	w-2	7
-1703	39	7
-1704	35	7
-1762	w-2	8
-1763	39	8
-1764	38	8
-1822	w-2	9
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-1705	w-2	7
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-1707	49	7
-1765	w-2	8
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-1708	w-7	7
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-1768	w-2	8
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-1770	49	8
-1828	w-1	9
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-1711	w-2	7
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-1713	66	7
-1771	w-2	8
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-1773	52	8
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-1833	50	9
-1714	w-7	7
-1715	79	7
-1716	71	7
-1774	w-7	8
-1775	79	8
-1776	71	8
-1834	w-1	9
-1835	82	9
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-1719	65	7
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-1838	65	9
-1839	48	9
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-1722	56	7
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-1840	w-1	9
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-1842	40	9
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-1785	73	8
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-1844	77	9
-1845	73	9
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-1727	27	7
-1728	24	7
-1786	w-9	8
-1787	28	8
-1788	24	8
-1846	w-9	9
-1847	28	9
-1848	18	9
-1729	w-7	7
-1730	52	7
-1731	33	7
-1789	w-2	8
-1790	43	8
-1791	29	8
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-1851	29	9
-1732	w-2	7
-1733	51	7
-1734	38	7
-1792	w-1	8
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-1794	31	8
-1852	w-1	9
-1853	41	9
-1854	33	9
-1735	w-7	7
-1736	55	7
-1737	42	7
-1795	w-2	8
-1796	49	8
-1797	31	8
-1855	w-2	9
-1856	38	9
-1857	31	9
-1738	w-2	7
-1739	45	7
-1740	33	7
-1798	w-2	8
-1799	37	8
-1800	28	8
-1858	w-1	9
-1859	35	9
-1860	29	9
-1921	w-2	1
-1922	57	1
-1923	37	1
-1981	w-7	2
-1982	54	2
-1983	30	2
-2041	w-1	3
-2042	43	3
-2043	28	3
-1924	w-9	1
-1925	9	1
-1926	-2	1
-1984	w-9	2
-1985	7	2
-1986	0	2
-2044	w-9	3
-2045	10	3
-2046	1	3
-1927	w-7	1
-1928	59	1
-1929	43	1
-1987	w-1	2
-1988	61	2
-1989	45	2
-2047	w-1	3
-2048	66	3
-2049	46	3
-1930	w-2	1
-1931	61	1
-1932	41	1
-1990	w-2	2
-1991	55	2
-1992	32	2
-2050	w-1	3
-2051	50	3
-2052	32	3
-1933	w-2	1
-1934	48	1
-1935	36	1
-1993	w-7	2
-1994	46	2
-1995	39	2
-2053	w-7	3
-2054	50	3
-2055	43	3
-1936	w-1	1
-1937	59	1
-1938	46	1
-1996	w-1	2
-1997	63	2
-1998	48	2
-2056	w-1	3
-2057	64	3
-2058	50	3
-1939	w-1	1
-1940	57	1
-1941	46	1
-1999	w-1	2
-2000	61	2
-2001	48	2
-2059	w-2	3
-2060	63	3
-2061	50	3
-1942	w-2	1
-1943	41	1
-1944	32	1
-2002	w-2	2
-2003	41	2
-2004	30	2
-2062	w-2	3
-2063	39	3
-2064	30	3
-1945	w-1	1
-1946	46	1
-1947	34	1
-2005	w-7	2
-2006	43	2
-2007	34	2
-2065	w-7	3
-2066	46	3
-2067	32	3
-1948	w-1	1
-1949	50	1
-1950	36	1
-2008	w-7	2
-2009	45	2
-2010	36	2
-2068	w-7	3
-2069	46	3
-2070	32	3
-1951	w-1	1
-1952	64	1
-1953	46	1
-2011	w-10	2
-2012	66	2
-2013	39	2
-2071	w-1	3
-2072	55	3
-2073	39	3
-1954	w-1	1
-1955	75	1
-1956	63	1
-2014	w-1	2
-2015	79	2
-2016	66	2
-2074	w-7	3
-2075	77	3
-2076	63	3
-1957	w-1	1
-1958	72	1
-1959	52	1
-2017	w-1	2
-2018	75	2
-2019	55	2
-2077	w-7	3
-2078	72	3
-2079	54	3
-1960	w-1	1
-1961	57	1
-1962	37	1
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-2021	61	2
-2022	43	2
-2080	w-1	3
-2081	48	3
-2082	30	3
-1963	w-7	1
-1964	81	1
-1965	70	1
-2023	w-7	2
-2024	81	2
-2025	68	2
-2083	w-7	3
-2084	79	3
-2085	68	3
-1966	w-9	1
-1967	14	1
-1968	-2	1
-2026	w-2	2
-2027	18	2
-2028	1	2
-2086	w-2	3
-2087	23	3
-2088	9	3
-1969	w-2	1
-1970	41	1
-1971	34	1
-2029	w-2	2
-2030	39	2
-2031	27	2
-2089	w-9	3
-2090	28	3
-2091	21	3
-1972	w-2	1
-1973	45	1
-1974	34	1
-2032	w-2	2
-2033	41	2
-2034	28	2
-2092	w-1	3
-2093	30	3
-2094	21	3
-1975	w-2	1
-1976	45	1
-1977	34	1
-2035	w-7	2
-2036	45	2
-2037	30	2
-2095	w-2	3
-2096	30	3
-2097	23	3
-1978	w-2	1
-1979	41	1
-1980	30	1
-2038	w-9	2
-2039	34	2
-2040	21	2
-2098	w-1	3
-2099	23	3
-2100	14	3
-1741	w-7	4
-1742	70	4
-1743	36	4
-1801	w-2	5
-1802	61	5
-1803	41	5
-1861	w-2	6
-1862	66	6
-1863	52	6
-1744	w-2	4
-1745	10	4
-1746	5	4
-1804	w-9	5
-1805	16	5
-1806	1	5
-1864	w-2	6
-1865	9	6
-1866	-2	6
-1747	w-2	4
-1748	61	4
-1749	43	4
-1807	w-7	5
-1808	63	5
-1809	46	5
-1867	w-2	6
-1868	55	6
-1869	39	6
-1750	w-2	4
-1751	63	4
-1752	37	4
-1810	w-1	5
-1811	63	5
-1812	46	5
-1870	w-7	6
-1871	64	6
-1872	37	6
-1753	w-7	4
-1754	57	4
-1755	41	4
-1813	w-7	5
-1814	52	5
-1815	34	5
-1873	w-2	6
-1874	52	6
-1875	39	6
-1756	w-7	4
-1757	64	4
-1758	54	4
-1816	w-7	5
-1817	59	5
-1818	45	5
-1876	w-1	6
-1877	57	6
-1878	41	6
-1759	w-7	4
-1760	63	4
-1761	54	4
-1819	w-7	5
-1820	57	5
-1821	43	5
-1879	w-1	6
-1880	57	6
-1881	45	6
-1762	w-7	4
-1763	41	4
-1764	41	4
-1822	w-7	5
-1823	50	5
-1824	32	5
-1882	w-1	6
-1883	41	6
-1884	28	6
-1765	w-7	4
-1766	59	4
-1767	54	4
-1825	w-7	5
-1826	57	5
-1827	37	5
-1885	w-2	6
-1886	50	6
-1887	39	6
-1768	w-7	4
-1769	59	4
-1770	54	4
-1828	w-7	5
-1829	57	5
-1830	41	5
-1888	w-2	6
-1889	55	6
-1890	43	6
-1771	w-7	4
-1772	70	4
-1773	46	4
-1831	w-1	5
-1832	68	5
-1833	45	5
-1891	w-2	6
-1892	70	6
-1893	55	6
-1774	w-7	4
-1775	84	4
-1776	68	4
-1834	w-7	5
-1835	88	5
-1836	63	5
-1894	w-2	6
-1895	84	6
-1896	66	6
-1777	w-7	4
-1778	79	4
-1779	66	4
-1837	w-7	5
-1838	75	5
-1839	54	5
-1897	w-2	6
-1898	75	6
-1899	61	6
-1780	w-7	4
-1781	70	4
-1782	41	4
-1840	w-2	5
-1841	55	5
-1842	37	5
-1900	w-2	6
-1901	63	6
-1902	46	6
-1783	w-7	4
-1784	75	4
-1785	70	4
-1843	w-7	5
-1844	75	5
-1845	70	5
-1903	w-1	6
-1904	77	6
-1905	70	6
-1786	w-9	4
-1787	37	4
-1788	14	4
-1846	w-9	5
-1847	25	5
-1848	10	5
-1906	w-9	6
-1907	19	6
-1908	10	6
-1789	w-7	4
-1790	41	4
-1791	34	4
-1849	w-7	5
-1850	45	5
-1851	36	5
-1909	w-2	6
-1910	48	6
-1911	41	6
-1792	w-7	4
-1793	45	4
-1794	28	4
-1852	w-2	5
-1853	54	5
-1854	32	5
-1912	w-2	6
-1913	50	6
-1914	36	6
-1795	w-7	4
-1796	50	4
-1797	34	4
-1855	w-2	5
-1856	52	5
-1857	30	5
-1915	w-2	6
-1916	54	6
-1917	46	6
-1798	w-9	4
-1799	39	4
-1800	28	4
-1858	w-9	5
-1859	45	5
-1860	25	5
-1918	w-2	6
-1919	45	6
-1920	25	6
-1741	w-2	7
-1742	65	7
-1743	43	7
-1801	w-1	8
-1802	57	8
-1803	38	8
-1861	w-1	9
-1862	57	9
-1863	43	9
-1744	w-1	7
-1745	5	7
-1746	3	7
-1804	w-2	8
-1805	6	8
-1806	15	8
-1864	w-1	9
-1865	6	9
-1866	-2	9
-1747	w-1	7
-1748	59	7
-1749	42	7
-1807	w-1	8
-1808	60	8
-1809	54	8
-1867	w-1	9
-1868	58	9
-1869	40	9
-1750	w-1	7
-1751	63	7
-1752	43	7
-1810	w-1	8
-1811	62	8
-1812	43	8
-1870	w-1	9
-1871	64	9
-1872	58	9
-1753	w-2	7
-1754	53	7
-1755	39	7
-1813	w-2	8
-1814	50	8
-1815	34	8
-1873	w-1	9
-1874	45	9
-1875	33	9
-1756	w-7	7
-1757	60	7
-1758	52	7
-1816	w-2	8
-1817	61	8
-1818	41	8
-1876	w-1	9
-1877	58	9
-1878	41	9
-1759	w-2	7
-1760	61	7
-1761	54	7
-1819	w-2	8
-1820	60	8
-1821	39	8
-1879	w-1	9
-1880	59	9
-1881	39	9
-1762	w-2	7
-1763	44	7
-1764	40	7
-1822	w-2	8
-1823	44	8
-1824	37	8
-1882	w-2	9
-1883	44	9
-1884	33	9
-1765	w-2	7
-1766	56	7
-1767	53	7
-1825	w-7	8
-1826	58	8
-1827	36	8
-1885	w-1	9
-1886	44	9
-1887	35	9
-1768	w-2	7
-1769	56	7
-1770	52	7
-1828	w-7	8
-1829	56	8
-1830	36	8
-1888	w-1	9
-1889	47	9
-1890	34	9
-1771	w-7	7
-1772	70	7
-1773	55	7
-1831	w-1	8
-1832	65	8
-1833	50	8
-1891	w-1	9
-1892	66	9
-1893	61	9
-1774	w-2	7
-1775	80	7
-1776	70	7
-1834	w-7	8
-1835	79	8
-1836	62	8
-1894	w-1	9
-1895	73	9
-1896	69	9
-1777	w-1	7
-1778	79	7
-1779	66	7
-1837	w-1	8
-1838	75	8
-1839	51	8
-1897	w-1	9
-1898	74	9
-1899	60	9
-1780	w-7	7
-1781	66	7
-1782	48	7
-1840	w-1	8
-1841	58	8
-1842	40	8
-1900	w-1	9
-1901	59	9
-1902	44	9
-1783	w-7	7
-1784	80	7
-1785	71	7
-1843	w-7	8
-1844	81	8
-1845	73	8
-1903	w-7	9
-1904	78	9
-1905	73	9
-1786	w-9	7
-1787	30	7
-1788	19	7
-1846	w-9	8
-1847	23	8
-1848	7	8
-1906	w-2	9
-1907	13	9
-1908	-12	9
-1789	w-2	7
-1790	41	7
-1791	31	7
-1849	w-2	8
-1850	42	8
-1851	32	8
-1909	w-2	9
-1910	38	9
-1911	34	9
-1792	w-2	7
-1793	45	7
-1794	35	7
-1852	w-1	8
-1853	45	8
-1854	32	8
-1912	w-2	9
-1913	43	9
-1914	42	9
-1795	w-2	7
-1796	47	7
-1797	36	7
-1855	w-2	8
-1856	47	8
-1857	31	8
-1915	w-1	9
-1916	42	9
-1917	38	9
-1798	w-2	7
-1799	39	7
-1800	31	7
-1858	w-2	8
-1859	39	8
-1860	29	8
-1918	w-2	9
-1919	39	9
-1920	35	9
diff --git a/jupyter/data/weather_ground_truth.txt b/jupyter/data/weather_ground_truth.txt
deleted file mode 100644
index a77ab49..0000000
--- a/jupyter/data/weather_ground_truth.txt
+++ /dev/null
@@ -1,1739 +0,0 @@
-1	w-1
-2	70.0
-3	30
-22	w-2
-23	21.5
-24	12
-43	w-2
-44	79
-45	49
-64	w-2
-65	64
-66	34
-85	w-7
-86	63
-87	47.4
-106	w-7
-107	70
-108	58.6
-127	w-7
-128	69
-129	59
-148	w-2
-149	50.2
-150	24.3
-169	w-2
-170	54
-171	36
-190	w-2
-191	54
-192	41
-211	w-2
-212	70
-213	46
-232	w-7
-233	81.5
-234	67.2
-253	w-2
-254	72
-255	61.1
-274	w-2
-275	64
-276	39
-295	w-7
-296	86.0
-297	70
-316	w-9
-317	52
-318	31
-337	w-2
-338	59
-339	30
-358	w-2
-359	61
-360	27.7
-379	w-2
-380	64
-381	30
-400	w-2
-401	59
-402	27
-4	w-2
-5	70.5
-6	32
-25	w-2
-26	22.0
-27	7
-46	w-2
-47	79
-48	52
-67	w-2
-68	64.2
-69	43
-88	w-2
-89	68.4
-109	w-7
-110	70
-111	52
-130	w-2
-131	70
-132	52
-151	w-2
-152	48
-153	27.5
-172	w-2
-173	50.5
-174	39
-193	w-2
-194	55.4
-195	46
-214	w-2
-215	73
-216	51.6
-235	w-2
-236	79.7
-237	63
-256	w-2
-257	63
-258	55
-277	w-2
-278	61
-279	37
-298	w-7
-299	87.8
-300	64
-319	w-9
-320	54
-321	23
-340	w-2
-341	59
-342	30
-361	w-7
-362	56.5
-363	37.8
-382	w-2
-383	62
-384	33.4
-403	w-2
-404	55
-405	39
-7	w-2
-8	70.9
-9	39
-28	w-2
-29	17.8
-30	1
-49	w-2
-50	82
-51	48
-70	w-2
-71	73
-72	46
-91	w-2
-92	68.9
-93	53.5
-112	w-7
-113	72
-114	51.3
-133	w-7
-134	72
-135	51
-154	w-2
-155	48.2
-156	24.1
-175	w-2
-176	52.1
-177	41
-196	w-2
-197	57
-198	45
-217	w-2
-218	72
-219	46
-238	w-2
-239	70
-240	61
-259	w-2
-260	72
-261	55
-280	w-2
-281	68
-282	47
-301	w-7
-302	82.2
-303	69.4
-322	w-7
-323	52
-324	20
-343	w-7
-344	57
-345	48
-364	w-2
-365	71.4
-366	46
-385	w-7
-386	59
-387	42
-406	w-2
-407	61
-408	37
-10	w-2
-11	73.4
-12	39
-31	w-2
-32	14
-33	0
-52	w-2
-53	83.4
-54	49
-73	w-2
-74	70
-75	45
-94	w-7
-95	66
-96	55
-115	w-7
-116	68
-117	50.8
-136	w-2
-137	69
-138	52
-157	w-1
-158	52
-159	31.2
-178	w-2
-179	55.0
-180	34
-199	w-2
-200	57
-201	39
-220	w-2
-221	75
-222	46
-241	w-2
-242	78.7
-243	61
-262	w-2
-263	73
-264	55
-283	w-2
-284	70
-285	39
-304	w-2
-305	84.4
-306	64
-325	w-2
-326	57
-327	26
-346	w-2
-347	59
-348	37
-367	w-2
-368	59
-369	41.7
-388	w-2
-389	63
-390	37
-409	w-2
-410	63
-411	36
-13	w-2
-14	75.7
-15	34
-34	w-2
-35	16.3
-36	1
-55	w-2
-56	81
-57	48
-76	w-2
-77	70
-78	43
-97	w-2
-98	64.3
-99	46
-118	w-2
-119	72
-120	53
-139	w-2
-140	73
-141	52
-160	w-2
-161	54
-162	26.7
-181	w-1
-182	56.5
-183	30
-202	w-1
-203	59
-204	36
-223	w-1
-224	75
-225	52
-244	w-2
-245	77.9
-246	57
-265	w-1
-266	70
-267	53
-286	w-2
-287	70
-288	37
-307	w-2
-308	83.8
-309	68
-328	w-7
-329	50
-330	18
-349	w-7
-350	64
-351	43
-370	w-2
-371	66
-372	46.4
-391	w-2
-392	63
-393	34
-412	w-7
-413	63
-414	48
-16	w-2
-17	59.7
-18	37
-37	w-2
-38	22.8
-39	3
-58	w-2
-59	85.5
-60	44
-79	w-7
-80	64
-81	49.6
-100	w-2
-101	65.4
-102	39
-121	w-7
-122	75
-123	48
-142	w-2
-143	80
-144	48
-163	w-2
-164	57.2
-165	28.0
-184	w-2
-185	60.2
-186	33.5
-205	w-2
-206	61
-207	37
-226	w-2
-227	76.8
-228	54
-247	w-2
-248	81.3
-249	56.9
-268	w-1
-269	72
-270	46
-289	w-2
-290	72
-291	37
-310	w-7
-311	86.5
-312	66
-331	w-1
-332	46
-333	12
-352	w-7
-353	59
-354	27.0
-373	w-2
-374	63
-375	32.2
-394	w-7
-395	59
-396	33
-415	w-2
-416	59
-417	28
-19	w-1
-20	52.2
-21	28
-40	w-2
-41	22.2
-42	3
-61	w-1
-62	84
-63	55
-82	w-2
-83	57
-84	30
-103	w-2
-104	66.4
-105	39
-124	w-7
-125	84
-126	49.3
-145	w-7
-146	85
-147	50
-166	w-7
-167	53.8
-168	35.8
-187	w-7
-188	52.4
-189	39
-208	w-2
-209	58.4
-210	37
-229	w-1
-230	64
-231	46
-250	w-2
-251	77.8
-252	52.9
-271	w-2
-272	72.4
-273	52
-292	w-2
-293	61
-294	36.7
-313	w-7
-314	84.7
-315	63
-334	w-1
-335	52
-336	20
-355	w-2
-356	34.0
-357	24.9
-376	w-2
-377	37.4
-378	21.7
-397	w-2
-398	34
-399	25
-418	w-2
-419	32
-420	19
-421	w-1
-422	64.4
-423	25
-424	w-2
-425	19
-426	3
-427	w-1
-428	82.5
-429	47
-430	w-2
-431	63
-432	28
-433	w-2
-434	66.5
-435	37
-436	w-7
-437	70
-438	52
-439	w-2
-440	74
-441	52
-442	w-2
-443	40.3
-444	27.3
-445	w-2
-446	41
-447	32
-448	w-2
-449	43
-450	34.9
-451	w-1
-452	61.9
-453	36
-454	w-2
-455	80.0
-456	54.9
-457	w-1
-458	70
-459	46
-460	w-1
-461	61
-462	28
-463	w-2
-464	84
-465	66
-466	w-9
-467	48
-468	26
-469	w-2
-470	46
-471	28
-472	w-2
-473	53.4
-474	31.1
-475	w-2
-476	46
-477	25
-478	w-2
-479	43
-480	27
-481	w-7
-482	67.5
-483	32
-484	w-1
-485	16.8
-486	1
-487	w-2
-488	81.2
-489	47
-490	w-7
-491	59
-492	43
-493	w-2
-494	64.6
-495	39
-496	w-2
-497	70
-498	57
-499	w-2
-500	70
-501	57
-502	w-2
-503	46
-504	30.0
-505	w-2
-506	52
-507	26.6
-508	w-2
-509	55.6
-510	34
-511	w-2
-512	68.7
-513	44.8
-514	w-2
-515	79.6
-516	57.7
-517	w-2
-518	77
-519	48
-520	w-2
-521	66
-522	28
-523	w-7
-524	81
-525	66.9
-526	w-2
-527	45
-528	17
-529	w-2
-530	39
-531	27
-532	w-2
-533	46
-534	28.0
-535	w-2
-536	51
-537	30
-538	w-2
-539	38
-540	28.9
-541	w-7
-542	63
-543	41
-544	w-2
-545	16.8
-546	-2
-547	w-2
-548	82
-549	45
-550	w-7
-551	55
-552	37
-553	w-2
-554	64
-555	39
-556	w-7
-557	70
-558	51
-559	w-2
-560	70
-561	52
-562	w-9
-563	39
-564	26.0
-565	w-7
-566	46
-567	33.2
-568	w-7
-569	47.2
-570	39
-571	w-7
-572	66
-573	59.5
-574	w-7
-575	81
-576	67.4
-577	w-2
-578	79
-579	55
-580	w-7
-581	61
-582	48
-583	w-2
-584	81.0
-585	68
-586	w-2
-587	48
-588	14
-589	w-2
-590	72
-591	19
-592	w-2
-593	40
-594	19
-595	w-9
-596	42
-597	26
-598	w-2
-599	34
-600	12
-601	w-2
-602	59.2
-603	34
-604	w-2
-605	16.9
-606	-2
-607	w-2
-608	81.7
-609	49
-610	w-2
-611	55.2
-612	30
-613	w-7
-614	61
-615	48
-616	w-2
-617	67
-618	55
-619	w-2
-620	67
-621	55
-622	w-2
-623	43
-624	31.5
-625	w-2
-626	43.6
-627	33.9
-628	w-2
-629	48
-630	37
-631	w-2
-632	63
-633	48
-634	w-2
-635	82.2
-636	65.0
-637	w-2
-638	70
-639	60
-640	w-2
-641	59
-642	35
-643	w-2
-644	86.4
-645	63
-646	w-2
-647	51
-648	21
-649	w-2
-650	46
-651	28
-652	w-2
-653	52
-654	27
-655	w-2
-656	45
-657	23
-658	w-2
-659	47
-660	19
-661	w-1
-662	66.4
-663	27
-664	w-2
-665	17.1
-666	-6
-667	w-2
-668	79
-669	48
-670	w-2
-671	63
-672	32
-673	w-7
-674	63
-675	53.5
-676	w-7
-677	65
-678	57
-679	w-7
-680	65
-681	57
-682	w-2
-683	45
-684	23.3
-685	w-1
-686	47.8
-687	26.3
-688	w-2
-689	50
-690	34
-691	w-2
-692	64.2
-693	44.1
-694	w-7
-695	79
-696	61.9
-697	w-2
-698	75
-699	54
-700	w-2
-701	61
-702	28
-703	w-2
-704	80.4
-705	63
-706	w-9
-707	55
-708	30
-709	w-2
-710	52
-711	28
-712	w-1
-713	57
-714	30.0
-715	w-2
-716	50
-717	24
-718	w-1
-719	55
-720	28
-721	w-2
-722	69.6
-723	30
-724	w-1
-725	24.8
-726	-6
-727	w-2
-728	76.3
-729	48
-730	w-2
-731	66
-732	36
-733	w-7
-734	61.4
-735	58.2
-736	w-7
-737	64.4
-738	61
-739	w-7
-740	64
-741	60
-742	w-9
-743	41
-744	27.9
-745	w-2
-746	51.7
-747	27
-748	w-2
-749	54
-750	34
-751	w-2
-752	70
-753	50
-754	w-7
-755	79
-756	69.3
-757	w-2
-758	76
-759	55
-760	w-2
-761	64
-762	34
-763	w-2
-764	83.3
-765	63
-766	w-7
-767	54
-768	27
-769	w-2
-770	194
-771	38
-772	w-2
-773	63
-774	40.6
-775	w-2
-776	59
-777	32
-778	w-2
-779	54
-780	28
-781	w-2
-782	74.1
-783	36
-784	w-2
-785	27
-786	-1
-787	w-2
-788	75.6
-789	48
-790	w-7
-791	75
-792	54
-793	w-7
-794	65.6
-795	55
-796	w-7
-797	66
-798	58.6
-799	w-7
-800	67
-801	59
-802	w-9
-803	34
-804	28.7
-805	w-2
-806	56.7
-807	32
-808	w-2
-809	52
-810	37
-811	w-2
-812	73.6
-813	56.3
-814	w-7
-815	79
-816	69.9
-817	w-7
-818	75
-819	55.0
-820	w-2
-821	68
-822	37
-823	w-2
-824	87.1
-825	63
-826	w-9
-827	54
-828	33
-829	w-2
-830	61
-831	39
-832	w-2
-833	66
-834	39.6
-835	w-2
-836	65
-837	33
-838	w-2
-839	59
-840	28
-841	w-2
-842	72.9
-843	45
-844	w-1
-845	16.1
-846	-6
-847	w-2
-848	78.6
-849	48
-850	w-2
-851	73
-852	62.4
-853	w-7
-854	64.7
-855	55
-856	w-7
-857	66
-858	59.3
-859	w-7
-860	66
-861	59.6
-862	w-2
-863	48
-864	30
-865	w-2
-866	61
-867	39
-868	w-7
-869	56.5
-870	37
-871	w-2
-872	73.9
-873	54
-874	w-7
-875	81
-876	70.4
-877	w-7
-878	79
-879	58.0
-880	w-2
-881	72
-882	43
-883	w-7
-884	85.1
-885	68
-886	w-7
-887	55
-888	27
-889	w-7
-890	64
-891	52
-892	w-2
-893	75.0
-894	56.1
-895	w-7
-896	62
-897	55
-898	w-2
-899	61
-900	48
-901	w-2
-902	76.8
-903	43
-904	w-2
-905	15.6
-906	-6
-907	w-2
-908	76.6
-909	50
-910	w-2
-911	75
-912	61.3
-913	w-2
-914	63
-915	39
-916	w-7
-917	68
-918	60.0
-919	w-7
-920	69
-921	59
-922	w-7
-923	57
-924	38.6
-925	w-2
-926	68.7
-927	48
-928	w-2
-929	70
-930	46
-931	w-7
-932	75.7
-933	57
-934	w-7
-935	79.3
-936	70
-937	w-2
-938	77.5
-939	63
-940	w-2
-941	72
-942	41
-943	w-2
-944	84.7
-945	72
-946	w-9
-947	52
-948	20
-949	w-2
-950	72
-951	42
-952	w-2
-953	73
-954	53.4
-955	w-7
-956	70
-957	54
-958	w-2
-959	70
-960	43
-961	w-7
-962	72
-963	52
-964	w-2
-965	14.0
-966	3
-967	w-2
-968	77.7
-969	49
-970	w-7
-971	72
-972	57.7
-973	w-7
-974	60.6
-975	52
-976	w-7
-977	66
-978	54.8
-979	w-2
-980	67
-981	55
-982	w-7
-983	48
-984	30
-985	w-2
-986	72
-987	48
-988	w-2
-989	71.3
-990	48
-991	w-2
-992	85.8
-993	60.3
-994	w-2
-995	78.7
-996	66
-997	w-2
-998	79
-999	55
-1000	w-2
-1001	72
-1002	43
-1003	w-2
-1004	86.7
-1005	64
-1006	w-2
-1007	43
-1008	16
-1009	w-7
-1010	70
-1011	49
-1012	w-7
-1013	66
-1014	47.4
-1015	w-7
-1016	67
-1017	45
-1018	w-7
-1019	68
-1020	37
-1021	w-7
-1022	68
-1023	52
-1024	w-2
-1025	5
-1026	-11
-1027	w-2
-1028	81
-1029	47
-1030	w-2
-1031	63
-1032	45.1
-1033	w-7
-1034	59.1
-1035	53.2
-1036	w-7
-1037	66
-1038	50
-1039	w-2
-1040	67
-1041	50
-1042	w-7
-1043	57
-1044	37.9
-1045	w-7
-1046	61.8
-1047	48
-1048	w-7
-1049	65.6
-1050	50.0
-1051	w-7
-1052	75.9
-1053	59
-1054	w-2
-1055	79
-1056	70
-1057	w-2
-1058	76
-1059	57
-1060	w-2
-1061	68
-1062	52
-1063	w-7
-1064	82.6
-1065	68
-1066	w-9
-1067	46
-1068	27
-1069	w-2
-1070	54
-1071	30
-1072	w-1
-1073	50
-1074	32.0
-1075	w-7
-1076	50
-1077	35
-1078	w-1
-1079	49
-1080	27
-1081	w-2
-1082	72.1
-1083	45
-1084	w-2
-1085	5.7
-1086	-10
-1087	w-2
-1088	81
-1089	47
-1090	w-2
-1091	64
-1092	52.9
-1093	w-2
-1094	59.4
-1095	53.3
-1096	w-2
-1097	66
-1098	55.8
-1099	w-2
-1100	68
-1101	57
-1102	w-2
-1103	41
-1104	27.0
-1105	w-1
-1106	48
-1107	27
-1108	w-2
-1109	54
-1110	34.1
-1111	w-2
-1112	75.6
-1113	57
-1114	w-2
-1115	81.9
-1116	67.4
-1117	w-7
-1118	79
-1119	62
-1120	w-2
-1121	64
-1122	55
-1123	w-2
-1124	84.2
-1125	63
-1126	w-9
-1127	45
-1128	29
-1129	w-2
-1130	46.2
-1131	32
-1132	w-2
-1133	54
-1134	34.7
-1135	w-2
-1136	49
-1137	28
-1138	w-2
-1139	52
-1140	34
-1141	w-2
-1142	68
-1143	52
-1144	w-9
-1145	5.5
-1146	-6
-1147	w-2
-1148	75.1
-1149	46
-1150	w-2
-1151	63
-1152	55
-1153	w-2
-1154	63.1
-1155	39
-1156	w-7
-1157	66
-1158	52
-1159	w-2
-1160	69
-1161	51
-1162	w-7
-1163	41.2
-1164	21.6
-1165	w-7
-1166	48.5
-1167	29.1
-1168	w-7
-1169	51.3
-1170	37
-1171	w-2
-1172	70
-1173	61
-1174	w-7
-1175	80.0
-1176	67.3
-1177	w-2
-1178	77
-1179	63
-1180	w-2
-1181	64
-1182	47.2
-1183	w-2
-1184	88.2
-1185	69.6
-1186	w-9
-1187	43
-1188	25
-1189	w-7
-1190	48
-1191	41
-1192	w-7
-1193	52
-1194	44.2
-1195	w-7
-1196	50
-1197	43
-1198	w-7
-1199	46
-1200	39
-1201	w-2
-1202	73.2
-1203	52
-1204	w-2
-1205	16.6
-1206	-2
-1207	w-2
-1208	74.2
-1209	46
-1210	w-2
-1211	68
-1212	55
-1213	w-2
-1214	61
-1215	43
-1216	w-7
-1217	64
-1218	50
-1219	w-7
-1220	67
-1221	48
-1222	w-7
-1223	45
-1224	41.0
-1225	w-7
-1226	54
-1227	48
-1228	w-7
-1229	54.3
-1230	48
-1231	w-7
-1232	75.4
-1233	64
-1234	w-2
-1235	81.4
-1236	72
-1237	w-2
-1238	81
-1239	62
-1240	w-7
-1241	68
-1242	50
-1243	w-2
-1244	82.9
-1245	66
-1246	w-9
-1247	39
-1248	18
-1249	w-7
-1250	45
-1251	38.5
-1252	w-7
-1253	50
-1254	40.8
-1255	w-7
-1256	51
-1257	44
-1258	w-9
-1259	41.2
-1260	32.9
-1261	w-2
-1262	73.9
-1263	59.7
-1264	w-9
-1265	27
-1266	16
-1267	w-2
-1268	72
-1269	41
-1270	w-7
-1271	72
-1272	59.7
-1273	w-2
-1274	63
-1275	37
-1276	w-2
-1277	68
-1278	54.3
-1279	w-2
-1280	70
-1281	55
-1282	w-7
-1283	46
-1284	32.0
-1285	w-7
-1286	52
-1287	46
-1288	w-7
-1289	54.0
-1290	47.9
-1291	w-7
-1292	76.5
-1293	57
-1294	w-2
-1295	83.5
-1296	71.4
-1297	w-7
-1298	81.6
-1299	66
-1300	w-7
-1301	70
-1302	54
-1303	w-7
-1304	82
-1305	70
-1306	w-9
-1307	28
-1308	0
-1309	w-7
-1310	43
-1311	37
-1312	w-7
-1313	50
-1314	39.0
-1315	w-7
-1316	55
-1317	41
-1318	w-7
-1319	39
-1320	35.6
-1321	w-7
-1322	67.8
-1323	43
-1324	w-9
-1325	29
-1326	24
-1327	w-2
-1328	70
-1329	45
-1330	w-7
-1331	68
-1332	37
-1333	w-2
-1334	63.0
-1335	39
-1336	w-7
-1337	73
-1338	48
-1339	w-2
-1340	74
-1341	49
-1342	w-7
-1343	55
-1344	39
-1345	w-7
-1346	66
-1347	46
-1348	w-7
-1349	64.3
-1350	48
-1351	w-2
-1352	79
-1353	67.5
-1354	w-2
-1355	83.8
-1356	72.0
-1357	w-7
-1358	81
-1359	64
-1360	w-7
-1361	68
-1362	54
-1363	w-7
-1364	84
-1365	69.4
-1366	w-9
-1367	30
-1368	7
-1369	w-9
-1370	43
-1371	30
-1372	w-9
-1373	46
-1374	26.7
-1375	w-9
-1376	55
-1377	30
-1378	w-2
-1379	39
-1380	23.5
-1381	w-7
-1382	48
-1383	32
-1384	w-9
-1385	28
-1386	16
-1387	w-2
-1388	68.6
-1389	37
-1390	w-2
-1391	45
-1392	27
-1393	w-2
-1394	61.9
-1395	37
-1396	w-7
-1397	72
-1398	48
-1399	w-2
-1400	73
-1401	48
-1402	w-7
-1403	55
-1404	36.9
-1405	w-7
-1406	63
-1407	45
-1408	w-7
-1409	63
-1410	46
-1411	w-7
-1412	70
-1413	45
-1414	w-7
-1415	85.8
-1416	72
-1417	w-7
-1418	81.3
-1419	68
-1420	w-2
-1421	61
-1422	36
-1423	w-2
-1424	83.8
-1425	71.6
-1426	w-9
-1427	41
-1428	9
-1429	w-9
-1430	36
-1431	25
-1432	w-9
-1433	39
-1434	20.3
-1435	w-9
-1436	37
-1437	28
-1438	w-2
-1439	38.3
-1440	18
-1441	w-7
-1442	51.6
-1443	30
-1444	w-9
-1445	28
-1446	21
-1447	w-2
-1448	72
-1449	37
-1450	w-2
-1451	52
-1452	27
-1453	w-7
-1454	61
-1455	46
-1456	w-7
-1457	66
-1458	53.3
-1459	w-2
-1460	70
-1461	55
-1462	w-2
-1463	55
-1464	25.0
-1465	w-2
-1466	63
-1467	32
-1468	w-2
-1469	63
-1470	39
-1471	w-7
-1472	68
-1473	42.3
-1474	w-2
-1475	83.3
-1476	75
-1477	w-7
-1478	79.2
-1479	69.6
-1480	w-2
-1481	60
-1482	36
-1483	w-2
-1484	82
-1485	68.7
-1486	w-9
-1487	46
-1488	13
-1489	w-2
-1490	46
-1491	26.0
-1492	w-9
-1493	48
-1494	23.7
-1495	w-2
-1496	47
-1497	22
-1498	w-2
-1499	48
-1500	27
-1501	w-1
-1502	63.0
-1503	27
-1504	w-9
-1505	36
-1506	25.7
-1507	w-2
-1508	72
-1509	39
-1510	w-2
-1511	55.2
-1512	28
-1513	w-2
-1514	55
-1515	37
-1516	w-7
-1517	61
-1518	52.7
-1519	w-7
-1520	61
-1521	53
-1522	w-2
-1523	45
-1524	23.3
-1525	w-1
-1526	48
-1527	34
-1528	w-2
-1529	48
-1530	37
-1531	w-7
-1532	66.7
-1533	39
-1534	w-2
-1535	83.0
-1536	70.3
-1537	w-7
-1538	72
-1539	58
-1540	w-7
-1541	58
-1542	39
-1543	w-7
-1544	81
-1545	68.0
-1546	w-9
-1547	48
-1548	16
-1549	w-2
-1550	48
-1551	29
-1552	w-9
-1553	48
-1554	29.3
-1555	w-2
-1556	50
-1557	23
-1558	w-1
-1559	49
-1560	34
-1561	w-1
-1562	63.7
-1563	28
-1564	w-2
-1565	36
-1566	19
-1567	w-7
-1568	68
-1569	50
-1570	w-2
-1571	61
-1572	30
-1573	w-2
-1574	52.4
-1575	34
-1576	w-2
-1577	59
-1578	43.4
-1579	w-2
-1580	60
-1581	44
-1582	w-2
-1583	48
-1584	22.2
-1585	w-1
-1586	52
-1587	24.9
-1588	w-2
-1589	53.9
-1590	30
-1591	w-2
-1592	52
-1593	43
-1594	w-2
-1595	79.6
-1596	63.8
-1597	w-2
-1598	70
-1599	55
-1600	w-2
-1601	61
-1602	30
-1603	w-7
-1604	81
-1605	67.5
-1606	w-9
-1607	45
-1608	13
-1609	w-2
-1610	50
-1611	30
-1612	w-2
-1613	55
-1614	31.6
-1615	w-2
-1616	54
-1617	27
-1618	w-1
-1619	52
-1620	21
-1621	w-7
-1622	64.6
-1623	43
-1624	w-2
-1625	22
-1626	2.6
-1627	w-7
-1628	64
-1629	48.6
-1630	w-7
-1631	70
-1632	48
-1633	w-7
-1634	48
-1635	30
-1636	w-7
-1637	61
-1638	45
-1639	w-7
-1640	63
-1641	45
-1642	w-2
-1643	45
-1644	25.9
-1645	w-2
-1646	50
-1647	31.4
-1648	w-2
-1649	54
-1650	37
-1651	w-2
-1652	68
-1653	43
-1654	w-7
-1655	79
-1656	66
-1657	w-2
-1658	79
-1659	57
-1660	w-2
-1661	59
-1662	37
-1663	w-2
-1664	82
-1665	67.8
-1666	w-9
-1667	36
-1668	21
-1669	w-7
-1670	49.4
-1671	38.0
-1672	w-7
-1673	54
-1674	43
-1675	w-7
-1676	51
-1677	39
-1678	w-7
-1679	50
-1680	40
-1681	w-7
-1682	61.5
-1683	55
-1684	w-2
-1685	10
-1686	-7.3
-1687	w-7
-1688	60.0
-1689	43
-1690	w-2
-1691	66
-1692	48
-1693	w-7
-1694	54
-1695	39
-1696	w-7
-1697	59
-1698	52
-1699	w-7
-1700	60
-1701	52
-1702	w-7
-1703	37
-1704	30
-1705	w-7
-1706	52.8
-1707	43
-1708	w-7
-1709	51.0
-1710	46
-1711	w-7
-1712	77.0
-1713	62.4
-1714	w-7
-1715	81.0
-1716	68
-1717	w-2
-1718	79.1
-1719	60
-1720	w-7
-1721	57
-1722	52
-1723	w-7
-1724	82
-1725	67.5
-1726	w-9
-1727	34
-1728	19
-1729	w-7
-1730	52
-1731	39
-1732	w-2
-1733	54
-1734	41.0
-1735	w-7
-1736	57
-1737	46
-1738	w-2
-1739	48
-1740	37
diff --git a/jupyter/notebook.ipynb b/jupyter/notebook.ipynb
deleted file mode 100644
index cefa1fc..0000000
--- a/jupyter/notebook.ipynb
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def weighted_median1(values, weights):\n",
-    "    sorted_indices = np.argsort(values)\n",
-    "    sorted_values = sorted(values)\n",
-    "    sorted_weights = sorted(weights)\n",
-    "\n",
-    "    cumulative_weights = np.cumsum(sorted_weights)\n",
-    "    total_weight = np.sum(sorted_weights)\n",
-    "\n",
-    "    # Find the median index\n",
-    "    median_index = np.searchsorted(cumulative_weights, total_weight / 2.0)\n",
-    "\n",
-    "    return sorted_values[median_index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#def weighted_median(df, val, weight):\n",
-    "#    df_sorted = df.sort_values(val)\n",
-    "#    cumsum = df_sorted[weight].cumsum()\n",
-    "#    cutoff = df_sorted[weight].sum() / 2.\n",
-    "#    return df_sorted[cumsum >= cutoff][val].iloc[0]\n",
-    "\n",
-    "#weighted_median(df, 'impwealth', 'indweight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = pd.read_csv('data/weather_data_set.txt', sep=\"\\t\",  header=None)\n",
-    "groundtruth = pd.read_csv('data/weather_ground_truth.txt', sep=\"\\t\")\n",
-    "iti = 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Main CRH Method"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.sort_values(by=[0], inplace=True)\n",
-    "\n",
-    "# Calculate statistics about the data\n",
-    "nof = len(dataset)\n",
-    "# Get the entry list\n",
-    "list_entry = dataset[0].unique()\n",
-    "# Get the source list\n",
-    "list_source = dataset[2].unique()\n",
-    "# Number of sources\n",
-    "nos = len(list_source)\n",
-    "# Number of entries\n",
-    "noe = len(list_entry)\n",
-    "\n",
-    "# Modify entry_ia so that the following works\n",
-    "entry_ia = np.append(np.arange(0, noe), nof)\n",
-    "\n",
-    "# Initialization\n",
-    "ini_truth = []\n",
-    "weight = np.ones(nos) / nos\n",
-    "\n",
-    "weight_vector = weight[dataset[2].values-1]\n",
-    "weight_matrix = pd.DataFrame(weight_vector, columns=['weight'],index = dataset.index)\n",
-    "# Calculate initial truth entry by entry\n",
-    "\n",
-    "stand_error=[]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>333</th>\n",
-       "      <td>0.111111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>153</th>\n",
-       "      <td>0.111111</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       weight\n",
-       "333  0.111111\n",
-       "153  0.111111"
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weight_matrix.loc[ [333, 153]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index([  333,   153,   334,   154,   335,   155,   336,   675,   855,   156,\n",
-       "       ...\n",
-       "       15650, 15657, 15658, 15659, 15666, 15667, 15668, 15675, 15676, 15677],\n",
-       "      dtype='int64', length=16038)"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weight_matrix.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for entry_id in list_entry:\n",
-    "    val = dataset[dataset[0] == entry_id].values[0,1]\n",
-    " \n",
-    "    if not val.lstrip('-+').isdigit():  # Categorical data\n",
-    "        same_entry = dataset[dataset[0]==entry_id]\n",
-    "        tempvalue = same_entry[1].values\n",
-    "        # Get unique value list on the same entry\n",
-    "        temp_list = same_entry[1].unique()\n",
-    "        wv = []\n",
-    "\n",
-    "        # Calculate the weighted occurrence for this entry\n",
-    "        for k in range(len(temp_list)):\n",
-    "            wv.append(np.sum(((tempvalue == temp_list[k]) * weight_matrix.loc[same_entry.index].values)))\n",
-    "\n",
-    "        # Get the voting result\n",
-    "        I = np.argmax(wv)\n",
-    "        ini_truth.append([entry_id, temp_list[I]])\n",
-    "\n",
-    "    else:  # Numerical data\n",
-    "        \n",
-    "        same_entry = dataset[dataset[0]==entry_id]\n",
-    "        \n",
-    "        tempvalue = same_entry[1].values\n",
-    "        tempvalue_int = [int(x) for x in tempvalue]\n",
-    "        median = np.median(tempvalue_int)\n",
-    "        \n",
-    "        # Calculate median for this entry\n",
-    "        ini_truth.append([entry_id, median])\n",
-    "        stand_error.append([entry_id,np.std(tempvalue_int) ])\n",
-    "index_truth = [] #ini_truth.copy()\n",
-    "truth_matrix = pd.DataFrame(ini_truth, columns=['entry_id', 'truth'])\n",
-    "std_matrix = pd.DataFrame(stand_error, columns=['entry_id', 'error'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Initialize other parameters\n",
-    "cat_count = np.zeros(nos)  # Count of categorical data for each source\n",
-    "con_count = np.zeros(nos)  # Count of continuous data for each source"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2100\n",
-      "2100\n",
-      "2100\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(iti):\n",
-    "    index_truth = []\n",
-    "    score1 = np.zeros(nos)\n",
-    "    score2 = np.zeros(nos)\n",
-    "\n",
-    "    # Update weight\n",
-    "    \n",
-    "    for j in range(len(dataset)):\n",
-    "        val = dataset.loc[j].values[1]\n",
-    "        entry_id = dataset.loc[j].values[0]\n",
-    "        source_id = dataset.loc[j].values[2]\n",
-    "        true_val=truth_matrix[truth_matrix['entry_id'] == entry_id].values[0,1]\n",
-    "        if not val.lstrip('-+').isdigit():\n",
-    "            true_val=truth_matrix[truth_matrix['entry_id'] == entry_id].values[0,1]\n",
-    "            score1[source_id-1] += int(true_val != val)  #we count non-matching TypeError: weighted_median() missing 1 required positional argument: 'weight'values\n",
-    "            #if i == 2:\n",
-    "            cat_count[source_id-1] += 1\n",
-    "        else:  # Numerical data\n",
-    "            error=std_matrix[std_matrix['entry_id'] == entry_id].values[0,1]\n",
-    "            if (error != 0 ):\n",
-    "                score2[source_id-1] += np.abs( int(val) - int(true_val)) / error\n",
-    "\n",
-    "            #if i == 2:\n",
-    "            con_count[source_id-1] += 1\n",
-    "    score1 /= cat_count\n",
-    "    score2 /= con_count\n",
-    "    score1 /= sum(score1)\n",
-    "    score2 /= sum(score2)\n",
-    "\n",
-    "    score = score1 + score2\n",
-    "    norm_score = np.max(score)\n",
-    "    w = score / norm_score\n",
-    "    weight = -np.log(w) + 0.00001\n",
-    "\n",
-    "    weight_vector = weight[dataset[2].values-1]\n",
-    "    weight_matrix = pd.DataFrame(weight_vector, columns=['weight'],index = dataset.index)\n",
-    "    for entry_id in list_entry:\n",
-    "        val = dataset[dataset[0] == entry_id].values[0,1]\n",
-    "        if not val.lstrip('-+').isdigit():  # Categorical data\n",
-    "            same_entry = dataset[dataset[0]==entry_id]\n",
-    "            tempvalue = same_entry[1].values\n",
-    "            temp_list = same_entry[1].unique()\n",
-    "            wv = []\n",
-    "            for k in range(len(temp_list)):\n",
-    "                wv.append(np.sum(((tempvalue == temp_list[k]) * weight_matrix.loc[same_entry.index].values)))\n",
-    "            I = np.argmax(wv)\n",
-    "            index_truth.append([entry_id, temp_list[I]])\n",
-    "        else:  # Numerical data\n",
-    "            same_entry = dataset[dataset[0]==entry_id]\n",
-    "            tempvalue = same_entry[1].values\n",
-    "            tempvalue_int = [int(x) for x in tempvalue]\n",
-    "            \n",
-    "            tempweight=weight_matrix.loc[same_entry.index].values\n",
-    "            wm = weighted_median1(tempvalue_int, tempweight)\n",
-    "            index_truth.append([entry_id, wm])\n",
-    "\n",
-    "    print(len(index_truth))\n",
-    "    truth_matrix = pd.DataFrame(index_truth, columns=['entry_id', 'truth'])\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "        \n",
-    "       \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>entry_id</th>\n",
-       "      <th>truth</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>w-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>w-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2095</th>\n",
-       "      <td>2096</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2096</th>\n",
-       "      <td>2097</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2097</th>\n",
-       "      <td>2098</td>\n",
-       "      <td>w-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2098</th>\n",
-       "      <td>2099</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2099</th>\n",
-       "      <td>2100</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2100 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      entry_id truth\n",
-       "0            1   w-1\n",
-       "1            2    64\n",
-       "2            3    42\n",
-       "3            4   w-2\n",
-       "4            5    66\n",
-       "...        ...   ...\n",
-       "2095      2096    30\n",
-       "2096      2097    23\n",
-       "2097      2098   w-1\n",
-       "2098      2099    23\n",
-       "2099      2100    14\n",
-       "\n",
-       "[2100 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "truth_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/prototype/code.py b/prototype/code.py
deleted file mode 100644
index 31c51d4..0000000
--- a/prototype/code.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import pandas as pd
-import numpy as np
-
-def weighted_median(values, weights):
-    sorted_indices = np.argsort(values)
-    sorted_values = values[sorted_indices]
-    sorted_weights = weights[sorted_indices]
-
-    cumulative_weights = np.cumsum(sorted_weights)
-    total_weight = np.sum(sorted_weights)
-
-    # Find the median index
-    median_index = np.searchsorted(cumulative_weights, total_weight / 2.0)
-
-    return sorted_values[median_index]
-
-def crh_weather(dataset, iti):
-    # Prepare data
-    dataset.sort_values(by=[0], inplace=True)
-
-    # Calculate statistics about the data
-    nof = len(dataset)
-    # Get the entry list
-    list_entry = dataset[0].unique()
-
-    # Get the source list
-    list_source = dataset[2].unique()
-
-    # Number of sources
-    nos = len(list_source)
-
-    # Number of entries
-    noe = len(list_entry)
-
-    # Modify entry_ia so that the following works
-    entry_ia = np.append(np.arange(0, noe), nof)
-    
-    # Initialization
-    ini_truth = []
-    weight = np.ones(nos) / nos
-
-    weight_matrix = weight[dataset[2].values-1]
-    print(weight_matrix[0:5])
-    # Calculate initial truth entry by entry
-
-    stand_error=[]
-    
-    for entry_id in list_entry:
-        val = dataset[dataset[0] == entry_id].values[0,1]
-        if not val.isdigit():  # Categorical data
-            same_entry = dataset[dataset[0]==entry_id]
-            tempvalue = same_entry[1].values
-            # Get unique value list on the same entry
-            temp_list = same_entry[1].unique()
-            wv = []
-
-            # Calculate the weighted occurrence for this entry
-            for k in range(len(temp_list)):
-                wv.append(np.sum(((tempvalue == temp_list[k]) * weight_matrix[entry_id])))
-
-            # Get the voting result
-            I = np.argmax(wv)
-            ini_truth.append([entry_id, temp_list[I]])
-
-        else:  # Numerical data
-            same_entry = dataset[dataset[0]==entry_id]
-            tempvalue = same_entry[1].values
-            tempvalue_int = [int(x) for x in tempvalue]
-            median = np.median(tempvalue_int)
-            # Calculate median for this entry
-            ini_truth.append([entry_id, median])
-            stand_error.append([entry_id,np.std(tempvalue_int) ])
-    index_truth = ini_truth.copy()
-    truth_matrix = pd.DataFrame(ini_truth, columns=['entry_id', 'truth'])
-    std_matrix = pd.DataFrame(stand_error, columns=['entry_id', 'error'])
-
-    # Initialize other parameters
-    cat_count = np.zeros(nos)  # Count of categorical data for each source
-    con_count = np.zeros(nos)  # Count of continuous data for each source
-
-    # CRH iteration
-    for i in range(iti):
-        score1 = np.zeros(nos)
-        score2 = np.zeros(nos)
-
-        # Update weight
-        
-        for j in range(len(dataset)):
-            print(dataset.loc[j])
-            val = dataset.loc[j].values[1]
-            if not val.isdigit():
-           # if pd.api.types.is_string_dtype(dataset.at[j, 1]):  # Categorical data
-                score1[dataset.at[j, 2]] += int(truth_matrix[j] != dataset.at[j, 1])
-
-                if i == 2:
-                    cat_count[dataset.at[j, 2]] += 1
-            else:  # Numerical data
-                score2[dataset.at[j, 2]] += np.abs((pd.to_numeric(dataset.at[j, 1]) - truth_matrix[j]) / std_matrix[j])
-
-                if i == 2:
-                    con_count[dataset.at[j, 2]] += 1
-
-        score1 /= cat_count
-        score2 /= con_count
-        score1 /= np.sum(score1)
-        score2 /= np.sum(score2)
-
-        # Sum up the distance for categorical and continuous data
-        score = score1 + score2
-
-        # Calculate weight for sources
-        norm_score = np.max(score)
-        w = score / norm_score
-        weight = -np.log(w) + 0.00001
-        weight_matrix = weight[dataset[2].values]
-
-        # Update truth
-        for j in range(noe):
-            if pd.api.types.is_string_dtype(dataset.at[entry_ia[j], 1]):  # Categorical data
-                tempvalue = dataset.loc[entry_ia[j]:entry_ia[j + 1] - 1, 1]
-                temp_list = tempvalue.unique()
-                length_list = len(temp_list)
-
-                wv = []
-                for k in range(length_list):
-                    wv.append(np.sum((tempvalue == temp_list[k]) * weight_matrix[entry_ia[j]:entry_ia[j + 1]]))
-
-                I = np.argmax(wv)
-                index_truth.at[j + 1, 2] = temp_list[I]
-
-            else:  # Numerical data
-                tempvalue = pd.to_numeric(dataset.loc[entry_ia[j]:entry_ia[j + 1] - 1, 1])
-                tempweight = weight_matrix[entry_ia[j]:entry_ia[j + 1]]
-
-                # Update truth by weighted median
-                index_truth.at[j + 1, 2] = weighted_median(tempvalue.values, tempweight)
-
-        truth_matrix = index_truth.loc[dataset[0].values].reset_index(drop=True)[2]
-
-    weight = pd.DataFrame({'Source ID': list_source, 'Weight': weight})
-
-    return index_truth, weight, ini_truth
-
-# Example usage:
-# Replace 'your_dataset.csv' with the actual path to your dataset CSV file
-dataset = pd.read_csv('/Users/artur/rnd/git/fitsinn/prototype/weather_data_set.txt', header=None, sep="\t")
-#print(dataset.head())
-iti = 3
-result_index_truth, result_weight, result_ini_truth = crh_weather(dataset, iti)
-print(result_index_truth)
-print(result_weight)
-print(result_ini_truth)
diff --git a/prototype/weather_data_set.txt b/prototype/weather_data_set.txt
deleted file mode 100644
index d6e7931..0000000
--- a/prototype/weather_data_set.txt
+++ /dev/null
@@ -1,16038 +0,0 @@
-10	w-7	1
-11	61	1
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-81	50	3
-94	w-7	1
-95	63	1
-96	50	1
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-101	63	3
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-115	w-1	1
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-51	54	5
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-53	79	6
-54	55	6
-67	w-7	4
-68	64	4
-69	52	4
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-1705	w-7	9
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-1707	42	9
-1588	w-1	7
-1589	51	7
-1590	38	7
-1648	w-1	8
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-1708	w-2	9
-1709	51	9
-1710	42	9
-1591	w-1	7
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-1653	60	8
-1711	w-2	9
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-1713	64	9
-1594	w-2	7
-1595	78	7
-1596	69	7
-1654	w-1	8
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-1714	w-1	9
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-1597	w-2	7
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-1659	60	8
-1717	w-1	9
-1718	78	9
-1719	62	9
-1600	w-1	7
-1601	58	7
-1602	43	7
-1660	w-1	8
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-1662	53	8
-1720	w-2	9
-1721	55	9
-1722	54	9
-1603	w-2	7
-1604	80	7
-1605	71	7
-1663	w-1	8
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-1723	w-1	9
-1724	79	9
-1725	73	9
-1606	w-9	7
-1607	34	7
-1608	23	7
-1666	w-2	8
-1667	32	8
-1668	24	8
-1726	w-9	9
-1727	28	9
-1728	12	9
-1609	w-1	7
-1610	47	7
-1611	39	7
-1669	w-2	8
-1670	49	8
-1671	37	8
-1729	w-2	9
-1730	40	9
-1731	38	9
-1612	w-1	7
-1613	48	7
-1614	43	7
-1672	w-7	8
-1673	50	8
-1674	37	8
-1732	w-2	9
-1733	44	9
-1734	38	9
-1615	w-1	7
-1616	46	7
-1617	38	7
-1675	w-7	8
-1676	48	8
-1677	40	8
-1735	w-2	9
-1736	47	9
-1737	39	9
-1618	w-1	7
-1619	46	7
-1620	39	7
-1678	w-2	8
-1679	49	8
-1680	33	8
-1738	w-2	9
-1739	39	9
-1740	33	9
-1801	w-7	1
-1802	59	1
-1803	41	1
-1861	w-7	2
-1862	50	2
-1863	34	2
-1921	w-1	3
-1922	55	3
-1923	34	3
-1804	w-2	1
-1805	12	1
-1806	7	1
-1864	w-9	2
-1865	16	2
-1866	-2	2
-1924	w-9	3
-1925	3	3
-1926	-2	3
-1807	w-1	1
-1808	63	1
-1809	45	1
-1867	w-1	2
-1868	66	2
-1869	46	2
-1927	w-2	3
-1928	66	3
-1929	48	3
-1810	w-2	1
-1811	54	1
-1812	41	1
-1870	w-1	2
-1871	52	2
-1872	39	2
-1930	w-2	3
-1931	54	3
-1932	41	3
-1813	w-7	1
-1814	54	1
-1815	45	1
-1873	w-7	2
-1874	54	2
-1875	39	2
-1933	w-7	3
-1934	52	3
-1935	41	3
-1816	w-2	1
-1817	64	1
-1818	52	1
-1876	w-7	2
-1877	63	2
-1878	46	2
-1936	w-2	3
-1937	63	3
-1938	50	3
-1819	w-2	1
-1820	63	1
-1821	52	1
-1879	w-7	2
-1880	61	2
-1881	46	2
-1939	w-2	3
-1940	61	3
-1941	50	3
-1822	w-7	1
-1823	37	1
-1824	36	1
-1882	w-7	2
-1883	39	2
-1884	36	2
-1942	w-2	3
-1943	37	3
-1944	32	3
-1825	w-7	1
-1826	52	1
-1827	41	1
-1885	w-7	2
-1886	48	2
-1887	39	2
-1945	w-7	3
-1946	46	3
-1947	36	3
-1828	w-7	1
-1829	55	1
-1830	43	1
-1888	w-7	2
-1889	48	2
-1890	41	2
-1948	w-7	3
-1949	45	3
-1950	36	3
-1831	w-7	1
-1832	72	1
-1833	55	1
-1891	w-1	2
-1892	66	2
-1893	50	2
-1951	w-1	3
-1952	66	3
-1953	48	3
-1834	w-2	1
-1835	81	1
-1836	70	1
-1894	w-7	2
-1895	81	2
-1896	63	2
-1954	w-2	3
-1955	73	3
-1956	61	3
-1837	w-7	1
-1838	79	1
-1839	61	1
-1897	w-7	2
-1898	73	2
-1899	54	2
-1957	w-1	3
-1958	72	3
-1959	52	3
-1840	w-7	1
-1841	61	1
-1842	43	1
-1900	w-7	2
-1901	54	2
-1902	39	2
-1960	w-7	3
-1961	52	3
-1962	37	3
-1843	w-7	1
-1844	82	1
-1845	72	1
-1903	w-7	2
-1904	81	2
-1905	72	2
-1963	w-7	3
-1964	81	3
-1965	72	3
-1846	w-9	1
-1847	28	1
-1848	21	1
-1906	w-9	2
-1907	28	2
-1908	12	2
-1966	w-9	3
-1967	21	3
-1968	14	3
-1849	w-2	1
-1850	39	1
-1851	32	1
-1909	w-2	2
-1910	37	2
-1911	32	2
-1969	w-2	3
-1970	41	3
-1971	34	3
-1852	w-2	1
-1853	37	1
-1854	32	1
-1912	w-2	2
-1913	39	2
-1914	32	2
-1972	w-2	3
-1973	39	3
-1974	30	3
-1855	w-2	1
-1856	41	1
-1857	34	1
-1915	w-2	2
-1916	39	2
-1917	32	2
-1975	w-2	3
-1976	39	3
-1977	30	3
-1858	w-2	1
-1859	37	1
-1860	30	1
-1918	w-2	2
-1919	37	2
-1920	28	2
-1978	w-2	3
-1979	39	3
-1980	28	3
-1621	w-7	4
-1622	66	4
-1623	54	4
-1681	w-7	5
-1682	70	5
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-1624	w-2	4
-1625	9	4
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-1696	w-7	5
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-1759	w-2	6
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-1702	w-9	5
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-1740	32	5
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-1622	64	7
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-1635	41	7
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-1718	77	8
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-1723	w-1	8
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-1731	34	8
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-1672	w-2	7
-1673	56	7
-1674	45	7
-1732	w-2	8
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-1734	32	8
-1792	w-2	9
-1793	39	9
-1794	30	9
-1675	w-7	7
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-1677	46	7
-1735	w-1	8
-1736	51	8
-1737	34	8
-1795	w-2	9
-1796	42	9
-1797	29	9
-1678	w-2	7
-1679	52	7
-1680	43	7
-1738	w-2	8
-1739	44	8
-1740	29	8
-1798	w-1	9
-1799	35	9
-1800	27	9
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-1862	54	1
-1863	37	1
-1921	w-2	2
-1922	57	2
-1923	36	2
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-1982	59	3
-1983	36	3
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-1865	18	1
-1866	5	1
-1924	w-9	2
-1925	7	2
-1926	-2	2
-1984	w-9	3
-1985	10	3
-1986	0	3
-1867	w-2	1
-1868	66	1
-1869	48	1
-1927	w-7	2
-1928	63	2
-1929	46	2
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-1988	64	3
-1989	48	3
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-1871	57	1
-1872	43	1
-1930	w-2	2
-1931	61	2
-1932	43	2
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-1992	36	3
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-1874	50	1
-1875	32	1
-1933	w-2	2
-1934	50	2
-1935	34	2
-1993	w-2	3
-1994	52	3
-1995	34	3
-1876	w-7	1
-1877	61	1
-1878	48	1
-1936	w-1	2
-1937	59	2
-1938	43	2
-1996	w-1	3
-1997	64	3
-1998	48	3
-1879	w-7	1
-1880	57	1
-1881	46	1
-1939	w-1	2
-1940	55	2
-1941	43	2
-1999	w-1	3
-2000	61	3
-2001	48	3
-1882	w-7	1
-1883	45	1
-1884	36	1
-1942	w-2	2
-1943	43	2
-1944	30	2
-2002	w-2	3
-2003	39	3
-2004	30	3
-1885	w-7	1
-1886	50	1
-1887	37	1
-1945	w-7	2
-1946	46	2
-1947	34	2
-2005	w-2	3
-2006	45	3
-2007	34	3
-1888	w-7	1
-1889	52	1
-1890	39	1
-1948	w-2	2
-1949	48	2
-1950	36	2
-2008	w-7	3
-2009	45	3
-2010	34	3
-1891	w-1	1
-1892	66	1
-1893	52	1
-1951	w-1	2
-1952	66	2
-1953	52	2
-2011	w-10	3
-2012	68	3
-2013	55	3
-1894	w-7	1
-1895	79	1
-1896	61	1
-1954	w-7	2
-1955	73	2
-1956	59	2
-2014	w-2	3
-2015	77	3
-2016	63	3
-1897	w-7	1
-1898	72	1
-1899	54	1
-1957	w-1	2
-1958	72	2
-1959	52	2
-2017	w-1	3
-2018	72	3
-2019	54	3
-1900	w-7	1
-1901	55	1
-1902	37	1
-1960	w-1	2
-1961	57	2
-1962	36	2
-2020	w-7	3
-2021	57	3
-2022	41	3
-1903	w-7	1
-1904	81	1
-1905	72	1
-1963	w-7	2
-1964	81	2
-1965	72	2
-2023	w-7	3
-2024	81	3
-2025	72	3
-1906	w-9	1
-1907	25	1
-1908	12	1
-1966	w-9	2
-1967	21	2
-1968	-4	2
-2026	w-9	3
-2027	21	3
-2028	0	3
-1909	w-2	1
-1910	39	1
-1911	34	1
-1969	w-2	2
-1970	43	2
-1971	36	2
-2029	w-2	3
-2030	39	3
-2031	28	3
-1912	w-2	1
-1913	39	1
-1914	30	1
-1972	w-2	2
-1973	43	2
-1974	32	2
-2032	w-7	3
-2033	39	3
-2034	28	3
-1915	w-9	1
-1916	39	1
-1917	32	1
-1975	w-2	2
-1976	43	2
-1977	32	2
-2035	w-7	3
-2036	43	3
-2037	28	3
-1918	w-2	1
-1919	37	1
-1920	30	1
-1978	w-2	2
-1979	41	2
-1980	32	2
-2038	w-9	3
-2039	36	3
-2040	23	3
-1681	w-7	4
-1682	68	4
-1683	54	4
-1741	w-7	5
-1742	66	5
-1743	39	5
-1801	w-1	6
-1802	59	6
-1803	39	6
-1684	w-2	4
-1685	3	4
-1686	-4	4
-1744	w-2	5
-1745	10	5
-1746	9	5
-1804	w-2	6
-1805	21	6
-1806	3	6
-1687	w-2	4
-1688	57	4
-1689	43	4
-1747	w-2	5
-1748	59	5
-1749	43	5
-1807	w-2	6
-1808	64	6
-1809	50	6
-1690	w-2	4
-1691	70	4
-1692	45	4
-1750	w-2	5
-1751	59	5
-1752	36	5
-1810	w-1	6
-1811	63	6
-1812	41	6
-1693	w-7	4
-1694	54	4
-1695	46	4
-1753	w-7	5
-1754	55	5
-1755	41	5
-1813	w-2	6
-1814	55	6
-1815	34	6
-1696	w-7	4
-1697	61	4
-1698	50	4
-1756	w-2	5
-1757	59	5
-1758	52	5
-1816	w-7	6
-1817	61	6
-1818	39	6
-1699	w-7	4
-1700	61	4
-1701	50	4
-1759	w-2	5
-1760	59	5
-1761	52	5
-1819	w-7	6
-1820	59	6
-1821	37	6
-1702	w-9	4
-1703	37	4
-1704	36	4
-1762	w-7	5
-1763	43	5
-1764	36	5
-1822	w-7	6
-1823	45	6
-1824	36	6
-1705	w-7	4
-1706	52	4
-1707	48	4
-1765	w-7	5
-1766	61	5
-1767	45	5
-1825	w-7	6
-1826	55	6
-1827	34	6
-1708	w-7	4
-1709	54	4
-1710	48	4
-1768	w-7	5
-1769	61	5
-1770	52	5
-1828	w-7	6
-1829	55	6
-1830	34	6
-1711	w-7	4
-1712	72	4
-1713	64	4
-1771	w-7	5
-1772	70	5
-1773	48	5
-1831	w-1	6
-1832	63	6
-1833	45	6
-1714	w-2	4
-1715	82	4
-1716	72	4
-1774	w-7	5
-1775	84	5
-1776	68	5
-1834	w-2	6
-1835	84	6
-1836	61	6
-1717	w-2	4
-1718	79	4
-1719	64	4
-1777	w-7	5
-1778	79	5
-1779	63	5
-1837	w-2	6
-1838	72	6
-1839	50	6
-1720	w-7	4
-1721	63	4
-1722	52	4
-1780	w-7	5
-1781	64	5
-1782	41	5
-1840	w-1	6
-1841	55	6
-1842	39	6
-1723	w-7	4
-1724	77	4
-1725	70	4
-1783	w-7	5
-1784	75	5
-1785	70	5
-1843	w-7	6
-1844	75	6
-1845	68	6
-1726	w-9	4
-1727	32	4
-1728	25	4
-1786	w-9	5
-1787	39	5
-1788	19	5
-1846	w-9	6
-1847	28	6
-1848	12	6
-1729	w-7	4
-1730	50	4
-1731	37	4
-1789	w-7	5
-1790	41	5
-1791	30	5
-1849	w-2	6
-1850	41	6
-1851	30	6
-1732	w-2	4
-1733	54	4
-1734	37	4
-1792	w-7	5
-1793	43	5
-1794	32	5
-1852	w-1	6
-1853	43	6
-1854	25	6
-1735	w-7	4
-1736	61	4
-1737	37	4
-1795	w-7	5
-1796	52	5
-1797	34	5
-1855	w-2	6
-1856	41	6
-1857	27	6
-1738	w-7	4
-1739	41	4
-1740	30	4
-1798	w-9	5
-1799	37	5
-1800	21	5
-1858	w-2	6
-1859	34	6
-1860	23	6
-1681	w-2	7
-1682	64	7
-1683	58	7
-1741	w-1	8
-1742	65	8
-1743	39	8
-1801	w-1	9
-1802	49	9
-1803	36	9
-1684	w-1	7
-1685	6	7
-1686	-3	7
-1744	w-2	8
-1745	6	8
-1746	11	8
-1804	w-2	9
-1805	6	9
-1806	15	9
-1687	w-1	7
-1688	56	7
-1689	44	7
-1747	w-1	8
-1748	56	8
-1749	50	8
-1807	w-1	9
-1808	58	9
-1809	51	9
-1690	w-1	7
-1691	68	7
-1692	50	7
-1750	w-2	8
-1751	60	8
-1752	42	8
-1810	w-1	9
-1811	60	9
-1812	42	9
-1693	w-2	7
-1694	50	7
-1695	46	7
-1753	w-2	8
-1754	53	8
-1755	51	8
-1813	w-7	9
-1814	50	9
-1815	34	9
-1696	w-2	7
-1697	60	7
-1698	51	7
-1756	w-1	8
-1757	62	8
-1758	49	8
-1816	w-1	9
-1817	61	9
-1818	47	9
-1699	w-2	7
-1700	62	7
-1701	52	7
-1759	w-1	8
-1760	63	8
-1761	49	8
-1819	w-1	9
-1820	60	9
-1821	45	9
-1702	w-2	7
-1703	39	7
-1704	35	7
-1762	w-2	8
-1763	39	8
-1764	38	8
-1822	w-2	9
-1823	40	9
-1824	32	9
-1705	w-2	7
-1706	53	7
-1707	49	7
-1765	w-2	8
-1766	56	8
-1767	47	8
-1825	w-7	9
-1826	50	9
-1827	34	9
-1708	w-7	7
-1709	52	7
-1710	49	7
-1768	w-2	8
-1769	53	8
-1770	49	8
-1828	w-1	9
-1829	52	9
-1830	33	9
-1711	w-2	7
-1712	69	7
-1713	66	7
-1771	w-2	8
-1772	71	8
-1773	52	8
-1831	w-1	9
-1832	64	9
-1833	50	9
-1714	w-7	7
-1715	79	7
-1716	71	7
-1774	w-7	8
-1775	79	8
-1776	71	8
-1834	w-1	9
-1835	82	9
-1836	60	9
-1717	w-2	7
-1718	78	7
-1719	65	7
-1777	w-2	8
-1778	79	8
-1779	65	8
-1837	w-7	9
-1838	65	9
-1839	48	9
-1720	w-2	7
-1721	59	7
-1722	56	7
-1780	w-2	8
-1781	60	8
-1782	43	8
-1840	w-1	9
-1841	53	9
-1842	40	9
-1723	w-1	7
-1724	80	7
-1725	73	7
-1783	w-7	8
-1784	79	8
-1785	73	8
-1843	w-7	9
-1844	77	9
-1845	73	9
-1726	w-9	7
-1727	27	7
-1728	24	7
-1786	w-9	8
-1787	28	8
-1788	24	8
-1846	w-9	9
-1847	28	9
-1848	18	9
-1729	w-7	7
-1730	52	7
-1731	33	7
-1789	w-2	8
-1790	43	8
-1791	29	8
-1849	w-1	9
-1850	35	9
-1851	29	9
-1732	w-2	7
-1733	51	7
-1734	38	7
-1792	w-1	8
-1793	42	8
-1794	31	8
-1852	w-1	9
-1853	41	9
-1854	33	9
-1735	w-7	7
-1736	55	7
-1737	42	7
-1795	w-2	8
-1796	49	8
-1797	31	8
-1855	w-2	9
-1856	38	9
-1857	31	9
-1738	w-2	7
-1739	45	7
-1740	33	7
-1798	w-2	8
-1799	37	8
-1800	28	8
-1858	w-1	9
-1859	35	9
-1860	29	9
-1921	w-2	1
-1922	57	1
-1923	37	1
-1981	w-7	2
-1982	54	2
-1983	30	2
-2041	w-1	3
-2042	43	3
-2043	28	3
-1924	w-9	1
-1925	9	1
-1926	-2	1
-1984	w-9	2
-1985	7	2
-1986	0	2
-2044	w-9	3
-2045	10	3
-2046	1	3
-1927	w-7	1
-1928	59	1
-1929	43	1
-1987	w-1	2
-1988	61	2
-1989	45	2
-2047	w-1	3
-2048	66	3
-2049	46	3
-1930	w-2	1
-1931	61	1
-1932	41	1
-1990	w-2	2
-1991	55	2
-1992	32	2
-2050	w-1	3
-2051	50	3
-2052	32	3
-1933	w-2	1
-1934	48	1
-1935	36	1
-1993	w-7	2
-1994	46	2
-1995	39	2
-2053	w-7	3
-2054	50	3
-2055	43	3
-1936	w-1	1
-1937	59	1
-1938	46	1
-1996	w-1	2
-1997	63	2
-1998	48	2
-2056	w-1	3
-2057	64	3
-2058	50	3
-1939	w-1	1
-1940	57	1
-1941	46	1
-1999	w-1	2
-2000	61	2
-2001	48	2
-2059	w-2	3
-2060	63	3
-2061	50	3
-1942	w-2	1
-1943	41	1
-1944	32	1
-2002	w-2	2
-2003	41	2
-2004	30	2
-2062	w-2	3
-2063	39	3
-2064	30	3
-1945	w-1	1
-1946	46	1
-1947	34	1
-2005	w-7	2
-2006	43	2
-2007	34	2
-2065	w-7	3
-2066	46	3
-2067	32	3
-1948	w-1	1
-1949	50	1
-1950	36	1
-2008	w-7	2
-2009	45	2
-2010	36	2
-2068	w-7	3
-2069	46	3
-2070	32	3
-1951	w-1	1
-1952	64	1
-1953	46	1
-2011	w-10	2
-2012	66	2
-2013	39	2
-2071	w-1	3
-2072	55	3
-2073	39	3
-1954	w-1	1
-1955	75	1
-1956	63	1
-2014	w-1	2
-2015	79	2
-2016	66	2
-2074	w-7	3
-2075	77	3
-2076	63	3
-1957	w-1	1
-1958	72	1
-1959	52	1
-2017	w-1	2
-2018	75	2
-2019	55	2
-2077	w-7	3
-2078	72	3
-2079	54	3
-1960	w-1	1
-1961	57	1
-1962	37	1
-2020	w-7	2
-2021	61	2
-2022	43	2
-2080	w-1	3
-2081	48	3
-2082	30	3
-1963	w-7	1
-1964	81	1
-1965	70	1
-2023	w-7	2
-2024	81	2
-2025	68	2
-2083	w-7	3
-2084	79	3
-2085	68	3
-1966	w-9	1
-1967	14	1
-1968	-2	1
-2026	w-2	2
-2027	18	2
-2028	1	2
-2086	w-2	3
-2087	23	3
-2088	9	3
-1969	w-2	1
-1970	41	1
-1971	34	1
-2029	w-2	2
-2030	39	2
-2031	27	2
-2089	w-9	3
-2090	28	3
-2091	21	3
-1972	w-2	1
-1973	45	1
-1974	34	1
-2032	w-2	2
-2033	41	2
-2034	28	2
-2092	w-1	3
-2093	30	3
-2094	21	3
-1975	w-2	1
-1976	45	1
-1977	34	1
-2035	w-7	2
-2036	45	2
-2037	30	2
-2095	w-2	3
-2096	30	3
-2097	23	3
-1978	w-2	1
-1979	41	1
-1980	30	1
-2038	w-9	2
-2039	34	2
-2040	21	2
-2098	w-1	3
-2099	23	3
-2100	14	3
-1741	w-7	4
-1742	70	4
-1743	36	4
-1801	w-2	5
-1802	61	5
-1803	41	5
-1861	w-2	6
-1862	66	6
-1863	52	6
-1744	w-2	4
-1745	10	4
-1746	5	4
-1804	w-9	5
-1805	16	5
-1806	1	5
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-1865	9	6
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-1749	43	4
-1807	w-7	5
-1808	63	5
-1809	46	5
-1867	w-2	6
-1868	55	6
-1869	39	6
-1750	w-2	4
-1751	63	4
-1752	37	4
-1810	w-1	5
-1811	63	5
-1812	46	5
-1870	w-7	6
-1871	64	6
-1872	37	6
-1753	w-7	4
-1754	57	4
-1755	41	4
-1813	w-7	5
-1814	52	5
-1815	34	5
-1873	w-2	6
-1874	52	6
-1875	39	6
-1756	w-7	4
-1757	64	4
-1758	54	4
-1816	w-7	5
-1817	59	5
-1818	45	5
-1876	w-1	6
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-1878	41	6
-1759	w-7	4
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-1819	w-7	5
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-1821	43	5
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-1881	45	6
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-1764	41	4
-1822	w-7	5
-1823	50	5
-1824	32	5
-1882	w-1	6
-1883	41	6
-1884	28	6
-1765	w-7	4
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-1825	w-7	5
-1826	57	5
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-1885	w-2	6
-1886	50	6
-1887	39	6
-1768	w-7	4
-1769	59	4
-1770	54	4
-1828	w-7	5
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-1830	41	5
-1888	w-2	6
-1889	55	6
-1890	43	6
-1771	w-7	4
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-1831	w-1	5
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-1777	w-7	4
-1778	79	4
-1779	66	4
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-1839	54	5
-1897	w-2	6
-1898	75	6
-1899	61	6
-1780	w-7	4
-1781	70	4
-1782	41	4
-1840	w-2	5
-1841	55	5
-1842	37	5
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-1901	63	6
-1902	46	6
-1783	w-7	4
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-1843	w-7	5
-1844	75	5
-1845	70	5
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-1904	77	6
-1905	70	6
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-1908	10	6
-1789	w-7	4
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-1791	34	4
-1849	w-7	5
-1850	45	5
-1851	36	5
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-1910	48	6
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-1794	28	4
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-1853	54	5
-1854	32	5
-1912	w-2	6
-1913	50	6
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-1916	54	6
-1917	46	6
-1798	w-9	4
-1799	39	4
-1800	28	4
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-1919	45	6
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-1801	w-1	8
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-1807	w-1	8
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-1813	w-2	8
-1814	50	8
-1815	34	8
-1873	w-1	9
-1874	45	9
-1875	33	9
-1756	w-7	7
-1757	60	7
-1758	52	7
-1816	w-2	8
-1817	61	8
-1818	41	8
-1876	w-1	9
-1877	58	9
-1878	41	9
-1759	w-2	7
-1760	61	7
-1761	54	7
-1819	w-2	8
-1820	60	8
-1821	39	8
-1879	w-1	9
-1880	59	9
-1881	39	9
-1762	w-2	7
-1763	44	7
-1764	40	7
-1822	w-2	8
-1823	44	8
-1824	37	8
-1882	w-2	9
-1883	44	9
-1884	33	9
-1765	w-2	7
-1766	56	7
-1767	53	7
-1825	w-7	8
-1826	58	8
-1827	36	8
-1885	w-1	9
-1886	44	9
-1887	35	9
-1768	w-2	7
-1769	56	7
-1770	52	7
-1828	w-7	8
-1829	56	8
-1830	36	8
-1888	w-1	9
-1889	47	9
-1890	34	9
-1771	w-7	7
-1772	70	7
-1773	55	7
-1831	w-1	8
-1832	65	8
-1833	50	8
-1891	w-1	9
-1892	66	9
-1893	61	9
-1774	w-2	7
-1775	80	7
-1776	70	7
-1834	w-7	8
-1835	79	8
-1836	62	8
-1894	w-1	9
-1895	73	9
-1896	69	9
-1777	w-1	7
-1778	79	7
-1779	66	7
-1837	w-1	8
-1838	75	8
-1839	51	8
-1897	w-1	9
-1898	74	9
-1899	60	9
-1780	w-7	7
-1781	66	7
-1782	48	7
-1840	w-1	8
-1841	58	8
-1842	40	8
-1900	w-1	9
-1901	59	9
-1902	44	9
-1783	w-7	7
-1784	80	7
-1785	71	7
-1843	w-7	8
-1844	81	8
-1845	73	8
-1903	w-7	9
-1904	78	9
-1905	73	9
-1786	w-9	7
-1787	30	7
-1788	19	7
-1846	w-9	8
-1847	23	8
-1848	7	8
-1906	w-2	9
-1907	13	9
-1908	-12	9
-1789	w-2	7
-1790	41	7
-1791	31	7
-1849	w-2	8
-1850	42	8
-1851	32	8
-1909	w-2	9
-1910	38	9
-1911	34	9
-1792	w-2	7
-1793	45	7
-1794	35	7
-1852	w-1	8
-1853	45	8
-1854	32	8
-1912	w-2	9
-1913	43	9
-1914	42	9
-1795	w-2	7
-1796	47	7
-1797	36	7
-1855	w-2	8
-1856	47	8
-1857	31	8
-1915	w-1	9
-1916	42	9
-1917	38	9
-1798	w-2	7
-1799	39	7
-1800	31	7
-1858	w-2	8
-1859	39	8
-1860	29	8
-1918	w-2	9
-1919	39	9
-1920	35	9