CSS + test query iniziato

site2/css/mystyle.css

@@ -177,3 +177,24 @@ footer img {
   .select-items div:hover, .same-as-selected {
     background-color: rgba(0, 0, 0, 0.1);
   }
+
+  .italic {
+    font-style: italic;
+}
+
+.bold {
+    font-weight: bold;
+}
+
+.underline {
+    text-decoration-line: underline;
+}
+
+.linethrough {
+    text-decoration-line: line-through;
+}
+
+/* Ugly workaround */
+.underlinethrough {
+    text-decoration-line: underline line-through;
+}

test_suite/tests_kora_misc/Query_speed/decoding/decoding.py

@@ -0,0 +1,46 @@
+# %%
+import csv
+
+def getVettSpec(path):
+    with open(path + "vettSpec.csv", 'r') as file1:
+        reader = csv.DictReader(file1)
+        vettSpec = [row for row in reader]
+        return vettSpec
+
+def isColumnToDecode(col):
+    columns = ['forma', 'lemma', 'cat_gr', 'disambiguatore']
+    if col in columns or col.startswith('highlight'):
+        return True
+    return False
+
+def db_results_decode_pandas(df, vettSpec):
+    for col in df.columns:
+        if isColumnToDecode(col):
+            df[col] = df[col].apply( lambda el: db_decode(vettSpec, el) )
+    return df
+
+def db_results_decode(result, vettSpec):
+    for row in result:
+        for key, value in row.items():
+            if isColumnToDecode(key):
+                row[key] = db_decode(vettSpec, value)
+    return result
+
+def db_results_decode_nodict(result, vettSpec):
+    out = []
+    for row in result:
+        out.append( (db_decode(vettSpec, row[0]), db_decode(vettSpec, row[1]), db_decode(vettSpec, row[2]), row[3], row[4]) )
+    return out
+
+
+def db_decode(vettSpec, string0):
+    res = ""
+    for char0 in string0:
+        #1
+        char0Dec = ord(char0) # Dal carattere al codice Unicode DECIMALE corrispondente
+        #2
+        char0ConvDec = next((el['unicode'] for el in vettSpec if el['intcode'] == str(char0Dec)), None) # Il codice DECIMALE (passato come stringa) viene ricercato in vettSpec, ritornando l'Unicode ESADECIMALE del carattere decriptato o None se non c'è riscontro -- il che non DOVREBBE succedere.
+        #3
+        res += chr(int(char0ConvDec, 16)) # Si converte il codice esadecimale a decimale e si usa la built-in chr per recuperare il carattere
+    return res
+# %%

test_suite/tests_kora_misc/Query_speed/decoding/vettSpec.csv

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test_suite/tests_kora_misc/Query_speed/queries.py

@@ -0,0 +1,71 @@
+# %%
+import json
+import sqlite3
+import pandas as pd
+import time
+
+from decoding.decoding import getVettSpec, db_results_decode_pandas, db_results_decode, db_results_decode_nodict
+# %%
+#
+# Ricerca: cooccorrenze, 'c*' come Lemma (+ forme non lemmatizzate) e 'pazzo' come Forma
+
+# Il DB (copiato in locale)
+dbFile = 'corpus.db'
+
+## First query (notare che il DB è codificato)
+firstQuery = "SELECT spec AS lemma, cat AS cat_gr, omo AS disambiguatore, nocc AS occ, cod FROM lem WHERE spec LIKE 'Г%' ORDER BY idlem"
+
+def secondQuery(strlist):
+    return f"SELECT DISTINCT lemma as codLemma, forma as codForma FROM pfl WHERE lemma IN ({strlist})"
+
+# The BAD query
+def theQuery(LIST1, LIST2):
+    return f'SELECT \
+    tab.cod, tab.ntx, tab.pitxt, tab.elemlen, tab.mappa, tab.numperiod, tab.links, tab.numorg, intbib.sigla, tab.vol, tab.pag, tab.riga, tab.col, tab.tipostanza, tab.stanza, tab.verso, tab.numbrano, lem.spec AS lemma, lem.cat AS cat_gr, lem.omo AS disambiguatore, prev_tab.pitxt AS piniz, next_tab.pitxt AS pfin, periodi.piniz AS backup_piniz, periodi.pfin AS backup_pfin \
+    FROM Occ00001 AS tab INNER JOIN intbib ON tab.ntx = intbib.ntx INNER JOIN lem ON tab.indlem = lem.cod LEFT JOIN Occ00001 AS prev_tab ON (tab.ntx = prev_tab.ntx AND tab.mappa = prev_tab.mappa+15) LEFT JOIN Occ00001 AS next_tab ON (tab.ntx = next_tab.ntx AND tab.mappa = next_tab.mappa-15) LEFT JOIN periodi ON (tab.ntx = periodi.ntx AND tab.numperiod = periodi.numperiod) WHERE tab.indlem IN ({LIST1})  OR (tab.indlem = 0 AND tab.cod IN ({LIST2}))'
+
+# LIST1, LIST2 sono forniti in degli array in due file json in questo folder
+
+
+# Dict factory non-Pandas queries
+def dict_factory(cursor, row):
+    fields = [column[0] for column in cursor.description]
+    return {key: value for key, value in zip(fields, row)}
+
+# VettSpec for decoding
+vettSpec = getVettSpec('decoding/')
+# %%
+timestamp0 = time.time()
+with sqlite3.connect(f"file:{dbFile}?mode=ro", uri=True) as connection:
+    results = pd.read_sql(firstQuery, connection)
+    timestamp1 = time.time()
+    resultsb = db_results_decode_pandas(results, vettSpec)
+
+print(time.time() - timestamp0)
+print(timestamp1 - timestamp0)
+# %%
+timestamp0 = time.time()
+with sqlite3.connect(f"file:{dbFile}?mode=ro", uri=True) as connection:
+    connection.row_factory = dict_factory
+    queryReponse = connection.cursor().execute(firstQuery)
+    results2 = queryReponse.fetchall()
+    timestamp1 = time.time()
+    results2b = db_results_decode(results2, vettSpec)
+
+print(time.time() - timestamp0)
+print(timestamp1 - timestamp0)
+# %%
+timestamp0 = time.time()
+with sqlite3.connect(f"file:{dbFile}?mode=ro", uri=True) as connection:
+    queryReponse = connection.cursor().execute(firstQuery)
+    results3 = queryReponse.fetchall()
+    timestamp1 = time.time()
+    results3b = db_results_decode_nodict(results3, vettSpec)
+
+print(time.time() - timestamp0)
+print(timestamp1 - timestamp0)
+# %%
+results2
+# %%
+results3b
+# %%