def read_file(self, filename):
print("Reading lines...")
# Read the file and split into lines
lines = open('data/%s.txt' % (filename), encoding='utf-8').\
read().strip().split('\n')
# Split every line into pairs and normalize
pairs = [[seq_utils.normalizeString(s) for s in l.split('\t')][:2]
for l in lines]
pairs = self.filterPairs(pairs)
source = Language()
target = Language()
for pair in pairs:
if self.is_valid_pair(pair[0], pair[1]):
source.addSentence(pair[0])
target.addSentence(pair[1])
print(f'Source language counted words: {source.n_words}')
print(f'Target language counted words: {target.n_words}')
return source, target, pairs
def setListItems(self):
self.DefaultLanguage = "English"
self.CurrentLanguage = xbmc.getLanguage()
self.FolderLanguage = self.settings["LanguagePath"]
self.DefaultFolderLanguage = "special://xbmc/language/"
if not os.path.exists(xbmc.translatePath(self.FolderLanguage)):
print "Folder language not exists! '%s'" % self.FolderLanguage
self.FolderLanguage = self.DefaultFolderLanguage
if not self.FolderLanguage.startswith("special"):
for folder in ["profile", "home", "xbmc"]:
special = "special://%s/" % folder
self.FolderLanguage = self.FolderLanguage.replace(
xbmc.translatePath(special), special)
if (xbmc.translatePath("special://skin/")
in xbmc.translatePath(self.FolderLanguage)):
self.FolderLanguage = "special://skin/language/"
self.FolderLanguage = self.FolderLanguage.replace("\\",
"/").rstrip("/")
self.setContainerProperties()
# get languages source
self.language = Language(self)
self.listitems = self.language.listitems
def main():
languages = Language()
requirements = Requirement(languages.language_dict)
while True:
language_name = languages.set_language_name()
languages.write_dict_language()
while True:
requirements.set_requirement_name(language_name)
requirements.write_requirements_name()
print('Czy chcesz wyjść? T/N')
c = input()
if c == 'T' or c == 't':
break
elif c == 'N' or c == 'n':
continue
else:
print('Coś poszło nie tak.')
print('Czy chcesz wyjść? T/N')
c = input()
if c == 'T' or c == 't':
break
elif c == 'N' or c == 'n':
continue
else:
print('Coś poszło nie tak.')
def NV_extract(self):
NList = []
VList = []
for i in range(1, self.s.nrows):
print i
noenc = self.delete_unnecc(i)
lan = Language(noenc)
if len(lan.str) > 4000:
continue
word = lan.getMorpheme()
tmpNoun = u""
NN = 0
for j, line in enumerate(lan.getMorpheme()):
if line[1] == u"動詞":
if line[7] == u"する":
VList.append(word[j - 1][0] + line[7])
else:
VList.append(line[7])
if line[1] == u"名詞" and j != len(word) - 1:
if word[j + 1][1] == u"名詞":
tmpNoun += line[0]
NN += 1
if NN > 3:
tmpNoun = u""
NN = 0
continue
elif word[j + 1][7] != u"する":
NList.append(tmpNoun + line[0])
tmpNoun = u""
NN = 0
return NList, VList
def test_isVerbSubjunctive(self):
self.h = Verb(Language() )
self.assertEqual(self.h.isVerbSubjunctive("xoxo"), False)
self.assertEqual(self.h.isVerbSubjunctive("xoxox"), False)
self.assertEqual(self.h.isVerbSubjunctive("oxooxo"), True)
self.assertEqual(self.h.isVerbSubjunctive("xxoxoxs"), False)
self.assertEqual(self.h.isVerbSubjunctive("oxoxoxo"), True)
def __init__(self):
print("Messenger Running...")
self.debug = True
self.username = "******"
self.password = "******"
# Poll frequency (in seconds)
self.timer = 8
self.language = Language()
def __init__(self):
self.language=Language()
self.box = Grid2D(50, 10)
self.dictionary = Random_Word_Generator()
self.dictionary.load_dictionary()
self.game = Game(self.box,self.language)
self.game.choose_generator(self.dictionary)
self.music=Music()
self.game.connect_music(self.music)
def __init__(self, world, color, capital):
self.world = world
self.color = color
self.capital = capital
self.settlements = [capital]
self.language = Language()
self.name = self.language.genName()
self.leader = self.genLeader()
def __init__(self, filename, erease=False, Language=Language()):
if filename is None:
logging.warning("Fichier de sauvegarde du dictionnaire invalide")
sys.exit()
self._filename = filename
logging.info("loading dictionary")
self._dico = AlphabeticTree(filename, erease)
logging.info("dictionary loaded")
self._Language = Language
def descr_from_content(content, var_name, language=Language('python')):
"""
Returns the description of the vector var_name with respect to the content
Parameters
----------
content : str
Content of the string ("xyz", "xy", "z", ...) (no rpY)
var_name : str
Name of the vector you want to describe
language : Language.Language
Language of the generated docstring (used for vector subscription)
Defaults to Language('python')
Returns
-------
description : str
Description of the vector
Examples
--------
>>> my_description = descr_from_content("xyz", "Xd")
>>> print(my_description)
This is a (3 x 1) numpy.ndarray where :
- Xd[0] is the x coordinate of Xd,
- Xd[1] is the y coordinate of Xd,
- Xd[2] is the z coordinate of Xd
>>> from Language import Language
>>> my_description = descr_from_content("xz", "Xd", Language('Julia'))
>>> print(my_description)
This is a (2 x 1) Matrix where :
- Xd[1] is the x coordinate of Xd,
- Xd[2] is the z coordinate of Xd
>>> from Language import Language
>>> my_description = descr_from_content("z", "Xd", Language('MATLAB'))
>>> print(my_description)
This is a (1 x 1) double where :
- Xd(1) is the z coordinate of Xd
"""
descr = f"This is a ({len(content)} x 1) {language.matrix_type} where :\n"
for i, c in enumerate(content):
qp = language.slice_mat(var_name, i, None, None, None)
coma = ",\n" if i < len(content) - 1 else ""
descr += f" - {qp} is the {c} coordinate of {var_name}{coma}"
return descr
def extract_terms(self, case_df):
Noun_comp = u""
wakachi = u""
preR_id = 1
terms = []
documents = []
for (Report_id, frame) in zip(
case_df[u"報告書_id"], case_df.
loc[:,
[u"主体", u"起点", u"対象", u"状況", u"着点", u"手段", u"関係", u"動詞"]].
values):
#if Report_id>100:break
if preR_id != Report_id:
documents.append(wakachi)
print Report_id
#print wakachi
wakachi = u""
if frame[7][-2:] != u"する":
wakachi += frame[7] + u" "
if frame[7] not in terms: terms.append(frame[7])
else:
wakachi += frame[7][:-2] + u" "
if frame[7][:-2] not in terms: terms.append(frame[7][:-2])
for i in range(0, 7):
if frame[i] == u' ':
continue
Lan = Language(frame[i])
outList = Lan.getMorpheme()
Mor_1 = [outList[i][1] for i in range(len(outList))]
# if (u"接続詞" in Mor_1) | (u"記号" in Mor_1):
# continue
for mi, Mor in enumerate(outList):
if Mor_1[mi] == u"名詞" and Mor[2] != u"形容動詞語幹":
Noun_comp += Mor[0]
if mi < len(Mor_1) - 1:
if Mor_1[mi + 1] != u"名詞":
wakachi += Noun_comp + u" "
if Noun_comp not in terms:
terms.append(Noun_comp)
Noun_comp = u""
else:
wakachi += Noun_comp + u" "
if Noun_comp not in terms: terms.append(Noun_comp)
Noun_comp = u""
elif Mor_1[mi] != u"助詞" and Mor_1[mi] != u"助動詞" and Mor[
5] != u"サ変・スル" and Mor[2] != u"接尾":
wakachi += Mor[0] + u" "
if Mor[0] not in terms: terms.append(Mor[0])
preR_id = Report_id
documents.append(wakachi)
return terms, documents
def robot_dof_descr(robot, var_name, language=Language('python')):
"""
Returns the description of the vector containing all the degrees of
freedom of the robot.
Parameters
----------
robot : robots.Robot
Robot you want to describe
var_name : str
Name of the vector variable
language : Language.Language
Language of the generated docstring (used for vector subscription)
Defaults to Language('python')
Returns
-------
description : str
Description of the vector
Examples
--------
>>> from robots import RobotURDF
>>> from URDF import URDF
>>> from Language import Language
>>> rob = RobotURDF(URDF("./Examples/example_0.urdf"))
>>> print(robot_dof_descr(rob, 'q', Language("julia")))
This is a (3 x 1) Matrix containing the state of all the joints where :
- q[1] = theta_joint1 :
Rotation value (in radians) around the joint1 joint axis.
- q[2] = theta_joint2 :
Rotation value (in radians) around the joint2 joint axis.
- q[3] = theta_joint3 :
Rotation value (in radians) around the joint3 joint axis.
"""
params = get_parameters(robot.dof)
descrq = (f"This is a ({len(robot.dof)} x 1) {language.matrix_type} "
f"containing the state of all the joints where :\n")
for i_p, param in enumerate(params):
qp = language.slice_mat(var_name, i_p, None, None, None)
descrq += f' - {qp} = {param["name"]}'
descrq += f' :\n {param["description"]}\n'
return descrq[:-1]
def tableGUI():
langPath=loadGUI()
try:
lang = Language(langPath)
except Exception:
showMessage("The Chosen file in not a language file , try again")
def submit():
Action(lang, int(numOfCluster.get()),(stream.get(),addDB.get(),cluster.get()))
window = Tk()
window.geometry("600x300")
window.resizable(0, 0)
window.title("NAC - Loaded News Articles")
mylabel = Label(window, text="Chosen language path :")
mylabel.place(x=0, y=0, height=20, width=600)
#Test
mylabel = Label(window, text="Current stored articles : {0}".format(len(lang.getArticles())))
mylabel.place(x=0, y=40, height=20, width=600)
Label(window, text=langPath).place(x=0, y=20, height=20, width=600)
stream = IntVar()
Checkbutton(window, text="Stream", variable=stream).place(x=0, y=60, height=20, width=600)
addDB = IntVar()
Checkbutton(window, text="Add To DataBase", variable=addDB).place(x=0, y=80, height=20, width=600)
cluster = IntVar()
Checkbutton(window, text="Cluster", variable=cluster).place(x=0, y=100, height=20, width=600)
Label(window, text="Number of clusters:").place(x=0, y=180, height=20, width=600)
numOfCluster = Entry(window)
numOfCluster.insert(10,"2")
numOfCluster.place(x=0, y=200, height=20, width=600)
Button(window, text='Quit', command=window.destroy).place(x=0, y=270, height=20, width=600)
Button(window, text='Submit', command=submit).place(x=0, y=250, height=20, width=600)
window.mainloop()
def main():
with open('datas/ZCloseness_centralities_results.txt', 'a') as result_file:
result_file.write('Language | original cc | random cc | p_value \n')
result_file.flush()
for language_file in os.listdir('datas')[:5]:
result_file.write(' {}'.format(language_file.split('_')[0]))
result_file.flush()
current_language = Language(language_file)
original_graph = current_language.graph()
#run the centrality calculation
monte_carlo_centrality(original_graph,
150,
'switching',
20,
'decrease',
result_file=result_file)
def to_class(self, Noun, Verb):
if Noun in self.Nclass.keys():
Nclasslist = self.Nclass[Noun]
else:
lan = Language(Noun)
word = lan.getMorpheme()
Noun_tail = word[len(word)-1][0]
if Noun_tail in self.Nclass.keys():
Nclasslist = self.Nclass[Noun_tail]
else:
Nclasslist = [u"未登録"]
if Verb in self.Vclass.keys():
Vclasslist = self.Vclass[Verb]
else:
Vclasslist =[u"未登録"]
#print Verb
NV = []
for NVclass in itertools.product(Nclasslist,Vclasslist):
NV.append(NVclass)
return NV
def __init__(self):
threading.Thread.__init__(self)
self.datafile = open("w_report.csv", 'r')
self.key = "http://api.openweathermap.org/data/2.5/weather?q=Kanchipuram&APPID=d500d89c5c31bf5d4e165dbaa8024895"
self.last_update = []
self.L = Language()
self.Structure = nx.Graph()
self.Structure.add_edges_from([('weather', 'description'),
('weather', 'haze'), ('wind', 'deg'),
('wind', 'speed'), ('main', 'humidity'),
('main', 'temp'), ('main', 'pressure'),
('main', 'temp_max'),
('main', 'temp_min'), ('obj', 'dt'),
('obj', 'main'), ('obj', 'weather'),
('obj', 'wind')])
self.Structure.add_edges_from([('sys', 'sunrise'), ('sys', 'sunset'),
('obj', 'sys'), ('obj', 'visibility'),
('obj', 'coord'), ('obj', 'clouds'),
('clouds', 'all'), ('obj', 'name'),
('weather', 'id'), ('weather', 'main')])
nx.draw_networkx(self.Structure, show_labels=True)
def test_isVerb(self):
self.h = Verb( Language() )
self.assertEqual(self.h.isVerb("xoxo"), False)
self.assertEqual(self.h.isVerb("xoxox"), False)
self.assertEqual(self.h.isVerb("xoxoxo"), True)
self.assertEqual(self.h.isVerb("xoxoxs"), False)
def test_isInLanguage(self):
self.h = Language()
self.assertEqual(self.h.isInLanguage("xoxox"), True)
def __init__(self):
self.lObj = Language()
self.vObj = Verb(self.lObj)
self.pObj = Preposition(self.lObj)
self.nObj = Numbers(self.vObj, self.pObj)
def __init__(self, n_len):
self.core = Language(path, n_len)
def TNoun_extract(self, tripleFrame, NV_class):
TW = TWordclass()
unify_particle= lambda x: TW.Particle_to.get(x, x)
tripleFrame[u"助詞"] = tripleFrame[u"助詞"].map(unify_particle)
triple_Treport = [[] for i in range(len(tripleFrame.columns) + 1)]
for R_id, S_id, V_id, noun, particle, verb in zip(tripleFrame[u"報告書_id"], tripleFrame[u"文_id"], tripleFrame[u"動詞_id"],
tripleFrame[u"名詞"], tripleFrame[u"助詞"], tripleFrame[u"動詞"],
):
#if id >300: break
#print noun, particle, verb
print "Extracting triple_Treport:", R_id, S_id, V_id
Lan = Language(noun)
outList = Lan.getMorpheme()
Mor_1 = [outList[i][1] for i in range(len(outList))]
Mor_2 = [outList[i][2] for i in range(len(outList))]
noun_comp_tmp = u""
noun_comp = []
noun_tail = []
noun_Pos1 = []
noun_Pos2 = []
for mi, Pos in enumerate(Mor_1):
if mi==len(Mor_1)-1:
noun_comp.append(noun_comp_tmp + outList[mi][0])
noun_tail.append(outList[mi][0])
noun_Pos1.append(outList[mi][1])
noun_Pos2.append(outList[mi][2])
break
if Pos==u"名詞":
if Mor_1[mi+1]==u"名詞":
noun_comp_tmp += outList[mi][0]
else:
noun_comp.append(noun_comp_tmp+outList[mi][0])
noun_tail.append(outList[mi][0])
noun_Pos1.append(outList[mi][1])
noun_Pos2.append(outList[mi][2])
noun_comp_tmp = u""
TNneed = False
TVneed = False
#トライボロジーに関係する名詞か判定
for cni, nounMor in enumerate(noun_comp):
if noun_Pos2[cni] == u"代名詞" :
TNneed = True
break
if nounMor in NV_class[0].keys():
noun_target = nounMor
elif noun_tail[cni] in NV_class[0].keys():
noun_target = noun_tail[cni]
else:
continue
for Nclass in NV_class[0][noun_target]:
if Nclass in TW.TNounclass_all:
TNneed = True
break
elif Nclass in TW.TNounclass_Nopart.keys():
TNneed = True
for TNoun_Nopart in TW.TNounclass_Nopart[Nclass]:
if TNoun_Nopart in noun_target:
TNneed = False
elif Nclass==u"様相" and noun_Pos2[cni]==u"形容動詞語幹":
TNneed = False
elif Nclass in TW.TNounclass_part.keys():
for TNoun_part in TW.TNounclass_part[Nclass]:
if TNoun_part in noun_target:
TNneed = True
break
else:
continue
#トライボロジーに関係する動詞か判定
if TNneed:
if verb in NV_class[1].keys():
for Vclass in NV_class[1][verb]:
if Vclass in TW.TVerbclass_all:
TVneed = True
break
elif Vclass in TW.TVerbclass_Nopart.keys():
TVneed = True
for TVerb_Nopart in TW.TVerbclass_Nopart[Vclass]:
if TVerb_Nopart in verb:
TVneed = False
elif Vclass in TW.TVerbclass_part.keys():
for TVerb_part in TW.TVerbclass_part[Vclass]:
if TVerb_part in verb:
TVneed = True
break
else:
continue
if TNneed and TVneed:
#並列している名詞の分解
Mor_connect = [[u"接続詞"],[u"読点", u"並立助詞", u"接続助詞"]]
if set(Mor_connect[0]).intersection(set(Mor_1)) or set(Mor_connect[1]).intersection(set(Mor_2)):
noun_con = u""
for oi, out in enumerate(outList):
if outList[oi][1] not in Mor_connect[0] and outList[oi][2] not in Mor_connect[1]:
if out[0]!=u"等":
noun_con += out[0]
else:
#print out[0], noun_con
triple_Treport[0].append(R_id)
triple_Treport[1].append(S_id)
triple_Treport[2].append(V_id)
triple_Treport[3].append(noun_con)
triple_Treport[4].append(particle)
triple_Treport[5].append(verb)
noun_con = u""
continue
if oi==len(outList)-1:
triple_Treport[0].append(R_id)
triple_Treport[1].append(S_id)
triple_Treport[2].append(V_id)
triple_Treport[3].append(noun_con)
triple_Treport[4].append(particle)
triple_Treport[5].append(verb)
noun_con = u""
else:
triple_Treport[0].append(R_id)
triple_Treport[1].append(S_id)
triple_Treport[2].append(V_id)
triple_Treport[3].append(noun)
triple_Treport[4].append(particle)
triple_Treport[5].append(verb)
triple_Treportdict = {
tripleFrame.columns[0]: triple_Treport[0],
tripleFrame.columns[1]: triple_Treport[1],
tripleFrame.columns[2]: triple_Treport[2],
tripleFrame.columns[3]: triple_Treport[3],
tripleFrame.columns[4]: triple_Treport[4],
tripleFrame.columns[5]: triple_Treport[5],
}
tripleFrame_Treport = DataFrame(triple_Treportdict,
columns=[i for i in tripleFrame.columns])
fvu = lambda x: TW.Verb_unify.get(x, x)
tripleFrame_Treport[u"動詞"] = tripleFrame_Treport[u"動詞"].map(fvu)
return tripleFrame_Treport
def bunrui_frame(self, case_df, terms, idf_Treport, dist_method, threshould_dist):
MorList = []
Noun_comp = u""
Noun_weight = 2.0
idf_Treport = Series(idf_Treport)
zero = min(idf_Treport)
if zero==0:
min_idf=1.0
for idf in idf_Treport:
if idf<min_idf and idf!=zero:
min_idf=idf
idf_Treport[idf_Treport == 0] = min_idf*0.5
for frame in case_df[u"事象"].drop_duplicates().values:
MorList_tmp = {}
for i, words in enumerate(frame.split(u" ")):
#print words, u":"
if i==len(frame.split(u" "))-1:
if words[-2:] != u"する":
MorList_tmp[words] = idf_Treport[terms.index(words)]
else:
MorList_tmp[words[:-2]] = idf_Treport[terms.index(words[:-2])]
#エラー回避用xm
Noun_comp = u""
else:
Lan = Language(words)
outList = Lan.getMorpheme()
Mor_1 = [outList[i][1] for i in range(len(outList))]
for mi, Mor in enumerate(outList):
if Mor_1[mi] == u"名詞" and Mor[2] != u"形容動詞語幹":
Noun_comp += Mor[0]
if mi < len(Mor_1) - 1:
if Mor_1[mi + 1] != u"名詞":
#print Noun_comp,
MorList_tmp[Noun_comp] = idf_Treport[terms.index(Noun_comp)] * Noun_weight
Noun_comp = u""
else:
#print Noun_comp
MorList_tmp[Noun_comp] = idf_Treport[terms.index(Noun_comp)] * Noun_weight
Noun_comp = u""
elif Mor_1[mi] != u"助詞" and Mor_1[mi] != u"助動詞" and Mor[5] != u"サ変・スル" and Mor[2] != u"接尾":
#print Mor[0]
MorList_tmp[Mor[0]] = idf_Treport[terms.index(Mor[0])]
MorList.append(MorList_tmp)
#caseFrame = case_df[u"主体"] + u" " + case_df[u"起点"] + u" " + case_df[u"対象"] + u" " + case_df[u"状況"] + u" " + case_df[
# u"着点"] + u" " + case_df[u"手段"] + u" " + case_df[u"関係"] + u" " + case_df[u"動詞"]
cf = [i for i in case_df[u"事象"].drop_duplicates()]
#Jaccard係数による統一辞書の作成
Wdist_index = []
Wdist_column = []
Wdist = []
unifyList = {}
Case_freq = Counter(case_df[u"事象"])
# 各文の動詞が反対語リストに含まれていれば分類しない
oppositeList = [u"良好", u"正常", u"低下"]
print len(cf), len(MorList)
for i, x in enumerate(MorList):
print u"calculating distance... ", i
x_keys_set = set(x.keys())
for j, y in enumerate(MorList[i + 1:]):
j = i + j + 1
y_keys_set = set(y.keys())
# print j
if bool(set(oppositeList).intersection(x_keys_set)) and not(bool(y_keys_set.issuperset(set(oppositeList).intersection(x_keys_set)))):
continue
elif bool(set(oppositeList).intersection(y_keys_set)) and not(bool(x_keys_set.issuperset(set(oppositeList).intersection(y_keys_set)))):
continue
if (x.keys() in unifyList.keys()) | (y.keys() in unifyList.keys()):
continue
if bool(x_keys_set.intersection(y_keys_set)) and cf[i] != cf[j]:
sym = x_keys_set.symmetric_difference(y_keys_set)
if len(sym)>3:
continue
#排他的論理和形態素に名詞が（２つ以上）含まれていてはいけない(時間かかる)
'''
Mor_sd = [(Language(sdm).getMorpheme().pop()[1], Language(sdm).getMorpheme().pop()[2]) for sdm in sym]
for Mor_set in [(u"名詞", u"サ変接続"), (u"名詞", u"形容動詞語幹")]:
while Mor_set in Mor_sd:
Mor_sd.remove(Mor_set)
if [ms[0] for ms in Mor_sd].count(u"名詞")<2:
'''
xy_set = dict(x.items() + y.items())
xy_insec = x_keys_set.intersection(y_keys_set)
w_all = 0.00
if dist_method == u"Jaccard":
#jaccard係数
for mor_val in xy_set.values():
w_all += mor_val
elif dist_method == u"Simpson":
#Simpthon係数
if len(x.keys())<len(y.keys()):
for mor_val in x.values():
w_all += mor_val
else:
for mor_val in y.values():
w_all += mor_val
w_insec = 0.00
for mor_val in xy_insec:
w_insec += xy_set[mor_val]
dist_str = w_insec / w_all
if dist_str >= threshould_dist:
'''
if dist_method==u"Jaccard":
#頻度が高い格フレームに統一
if Case_freq[cf[j]] <= Case_freq[cf[i]] and cf[i] not in unifyList.keys():
unifyList[cf[i]] = cf[j]
elif Case_freq[cf[j]] > Case_freq[cf[i]] and cf[j] not in unifyList.keys():
unifyList[cf[j]] = cf[i]
elif dist_method==u"Simpson":
'''
#形態素数が少ない格フレームに統一
if len(x_keys_set) < len(y_keys_set) and cf[j] not in unifyList.keys():
unifyList[cf[j]] = cf[i]
elif len(x_keys_set) > len(y_keys_set) and cf[i] not in unifyList.keys():
unifyList[cf[i]] = cf[j]
elif len(cf[i])<len(cf[j]) and cf[j] not in unifyList.keys():
unifyList[cf[j]] = cf[i]
elif len(cf[i])>=len(cf[j]) and cf[i] not in unifyList.keys():
unifyList[cf[i]] = cf[j]
#print "%d:%s" % (i, cf[i]), "%d:%s" % (j, cf[j]), dist_str, w_insec, w_all
#print outList[len(outList) - 1][0], outList[len(outList) - 1][1], outList[len(outList) - 1][2]
Wdist_index.append(cf[i])
Wdist_column.append(cf[j])
Wdist.append(dist_str)
Wdist = DataFrame(Wdist, index=[Wdist_index, Wdist_column], columns=[u"Similarity"])
fnc = lambda x: unifyList.get(x, x)
insecset = set()
while set(case_df[u"事象"]).intersection(set(unifyList.keys())):
if insecset== set(case_df[u"事象"]).intersection(set(unifyList.keys())):
break
case_df[u"事象"] = case_df[u"事象"].map(fnc)
insecset = set(case_df[u"事象"]).intersection(set(unifyList.keys()))
return case_df, Wdist
def table1():
print(" N | E | <k> | delta ")
for input_file in os.listdir("datas"):
current_language = Language(input_file)
print(current_language.characteristics())
def test_isNumber(self):
self.h = Numbers(Verb(Language() ),Preposition(Language()) )
self.assertEqual(self.h.isNumber("gxjrc"), True)
self.assertEqual(self.h.isNumber("pwdood"), False)
def Section_div(self, case_df, VC_Dc, thresold_perD):
Record_id = dict() # 文_id:レコード_id
Record_id[(case_df.ix[0, :][u"報告書_id"], case_df.ix[0, :][u"文_id"])] = 0
tail_key = -1
for Report_id in case_df[u"報告書_id"].drop_duplicates():
print u"Extracting Sec_id:", Report_id
Noun_pre = dict()
# print Report_id
case_df_perR = case_df[case_df[u"報告書_id"] == Report_id]
for first_Sen, Sentence_id in enumerate(case_df_perR[u"文_id"].drop_duplicates()):
for line in case_df_perR[case_df[u"文_id"] == Sentence_id].iterrows():
# print line[1][1]
# print line[1][3]
if line[1][1] not in Noun_pre.keys():
Noun_pre[line[1][1]] = [l for l in line[1][4:11].values if l != u" "]
else:
Noun_pre[line[1][1]] = Noun_pre[line[1][1]] + [l for l in line[1][4:11].values if l != u" "]
# 代名詞の補完
for di, l in enumerate(line[1][4:11].values):
if l != u" ":
Lan = Language(l)
outList = Lan.getMorpheme()
if set([u"代名詞"]).intersection(set([outList[i][2] for i in range(len(outList))])):
# '''
#代名詞の出力ベクトルと名詞の出力ベクトルのユークリッド距離が最小の名詞を選択
pronoun_vec = [np.array(out_perD[1]) for out_perD in self.Dc.predict(l, u"", line[1][3]) if
np.argmax(np.array(out_perD[1])) == di]
if len(pronoun_vec) == 0:
pronoun_vec = [np.array(out_perD[1]) for out_perD in self.Dc.predict(l, u"", line[1][3])]
Noun_out = [
{Np: [np.array(output[1]) for output in self.Dc.predict(Np, u"", line[1][3])] for Np in
Np_list if Np not in line[1][4:11].values} for Np_list in
[Noun_pre[line[1][1] - pre_i] for pre_i in range(0, 2) if
line[1][1] - pre_i in Noun_pre.keys()]]
if len(Noun_out[0]) == 0:
del Noun_out[0]
if len(Noun_out) == 0:
break
Neuclid_perS = [
{n: min([np.linalg.norm(pv - vec) for vec in No[n] for pv in pronoun_vec]) for n in
No.keys()} for No in Noun_out]
Neuclid_min_perS = [(perS.keys()[perS.values().index(min(perS.values()))], min(perS.values())) for perS in Neuclid_perS]
toNoun = [N_ed[0] for N_ed in Neuclid_min_perS if
min([nmp[1] for nmp in Neuclid_min_perS]) == N_ed[1]]
case_df.ix[line[0], u"事象"] = case_df.ix[line[0], u"事象"].replace(l, toNoun[0])
Noun_pre[line[1][1]][Noun_pre[line[1][1]].index(l)] = toNoun[0]
# '''
'''
#深層格における最大出力である名詞を選択
Noun_out = [{Np: max([output[1][di] for output in self.self.Dc.predict(Np, u"", line[1][3])]) for Np in Np_list if Np not in line[1][4:11].values} for Np_list in [Noun_pre[line[1][1] - pre_i] for pre_i in range(0, 2) if
line[1][1] - pre_i in Noun_pre.keys()]]
#del Noun_out[0][l]
if len(Noun_out[0])==0:
del Noun_out[0]
if len(Noun_out)==0:
break
MaxN_perS = [No[No.keys()[No.values().index(max(No.values()))]] for No in Noun_out]
SSen_rec = MaxN_perS.index(max(MaxN_perS))
toNoun = Noun_out[SSen_rec].keys()[Noun_out[SSen_rec].values().index(max(MaxN_perS))]
case_df.ix[line[0], u"事象"] = case_df.ix[line[0], u"事象"].replace(l, toNoun)
Noun_pre[line[1][1]][Noun_pre[line[1][1]].index(l)] = toNoun
'''
# 埋まっていない深層格（ゼロ代名詞）の補完
Deep_cand = []
for i in [VC_Dc[VC] for VC in self.Dc.NV_class[1][line[1][3]] if VC in VC_Dc]:
Deep_cand += i
Count_perD = [Deep_cand.count(d) for d in self.Dc.DeepCaseList]
Dc_toV = [Deep_cor for Deep_cor in
[d for d, Deep_cor in zip(self.Dc.DeepCaseList, Count_perD) if
sum(Count_perD) / float(len(Count_perD)) < Deep_cor]]
Noun_zero = dict()
for Dc_tmp in Dc_toV:
if line[1][Dc_tmp] == u" ":
Noun_out = [{Np: max([output[1][self.Dc.DeepCaseList.index(Dc_tmp)] for output in self.Dc.predict(Np, u"", line[1][3])]) for Np in Np_list if Np not in line[1][4:11].values} for Np_list in [Noun_pre[line[1][1] - pre_i] for pre_i in range(0, 2) if
line[1][1] - pre_i in Noun_pre.keys()]]
while {} in Noun_out:
Noun_out.remove({})
if len(Noun_out) == 0:
continue
MaxN_perS = [No[No.keys()[No.values().index(max(No.values()))]] for No in
Noun_out]
SSen_rec = MaxN_perS.index(max(MaxN_perS))
if max(MaxN_perS) > thresold_perD[self.Dc.DeepCaseList.index(Dc_tmp)]:
Noun_zero[Dc_tmp] = Noun_out[SSen_rec].keys()[Noun_out[SSen_rec].values().index(max(MaxN_perS))]
if len(Noun_zero.keys()) > 0:
case_zero = u""
for d, Noun_perD in zip(line[1][4:11].keys(), line[1][4:11].values):
if d in Noun_zero.keys():
case_zero += u" " + Noun_zero[d]
else:
case_zero += u" " + Noun_perD
case_zero += u" " + line[1][3]
case_zero = re.sub(r" +", u" ", case_zero.strip())
case_df.ix[line[0], u"事象"] = case_zero
for Noun_zero_tmp in Noun_zero.values():
Noun_pre[line[1][1]].append(Noun_zero_tmp)
# 前の文に含まれる名詞が含まれているか
if first_Sen == 0 and tail_key != -1:
Record_id[(Report_id, Sentence_id)] = Record_id[tail_key] + 1
continue
for pre_i in range(3, 0, -1):
if line[1][1] - pre_i in Noun_pre.keys():
if set(Noun_pre[line[1][1]]).intersection(set(Noun_pre[line[1][1] - pre_i])):
for pre_j in range(pre_i, -1, -1):
if line[1][1] - pre_j in Noun_pre.keys():
Record_id[(Report_id, Sentence_id - pre_j)] = Record_id[
(Report_id, line[1][1] - pre_i)]
break
else:
Record_id[(Report_id, Sentence_id)] = Record_id[(Report_id, line[1][1] - pre_i)] + 1
while (Report_id, Sentence_id) not in Record_id.keys():
pre_i += 1
if (Report_id, Sentence_id - pre_i) in Record_id.keys():
Record_id[(Report_id, Sentence_id)] = Record_id[(Report_id, line[1][1] - pre_i)] + 1
if first_Sen == len(case_df_perR[u"文_id"].drop_duplicates()) - 1:
tail_key = (Report_id, Sentence_id)
case_df[u"レコード_id"] = [(i, j) for i, j in zip(case_df[u"報告書_id"], case_df[u"文_id"])]
case_df[u"レコード_id"] = case_df[u"レコード_id"].map(lambda x: Record_id[x])
return case_df
class Treport:
def __init__(self, path):
book = xlrd.open_workbook(path)
sheets = book.sheets()
self.s = sheets[0]
def NV_class_load(self, NV_classpath):
file = open(NV_classpath.decode("shift-jis").encode("utf-8"))
NV_class = pickle.load(file)
return NV_class
def uniqword(seq):
seen = set()
seen_add = seen.add
return [x for x in seq if x not in seen and not seen_add(x)]
def delete_unnecc(self, i):
noenc = self.s.cell_value(i, 3).replace(u'－', u'-')
noenc = noenc.replace(u'～', u'~')
noenc = noenc.replace(u'', u'') #不要報告書
noenc = noenc.replace(u'Ⅰ', u'1')
noenc = noenc.replace(u'Ⅱ', u'2')
noenc = noenc.replace(u'Ⅲ', u'3')
noenc = noenc.replace(u'Ⅳ', u'4')
noenc = noenc.replace(u'Ⅴ', u'5')
noenc = noenc.replace(u'ⅰ', u'1')
noenc = noenc.replace(u'ⅱ', u'2')
noenc = noenc.replace(u'ⅲ', u'3')
noenc = noenc.replace(u'ⅳ', u'4')
noenc = noenc.replace(u'ⅴ', u'5')
noenc = noenc.replace(u'⑪', u'11')
noenc = noenc.replace(u'⑫', u'12')
noenc = noenc.replace(u'⑰', u'17')
noenc = noenc.replace(u'⑲', u'19')
noenc = noenc.replace(u'№', u'No.')
noenc = noenc.replace(u'㎎', u'mg')
noenc = noenc.replace(u'㎜', u'mm')
noenc = noenc.replace(u'㎡', u'm^2')
noenc = noenc.replace(u'㍑', u'リットル')
noenc = noenc.replace(u'槢', u'摺')
noenc = noenc.replace(u'<', u'＜')
noenc = noenc.replace(u'>', u'＞')
return noenc
def NV_extract(self):
NList = []
VList = []
for i in range(1, self.s.nrows):
print i
noenc = self.delete_unnecc(i)
lan = Language(noenc)
if len(lan.str) > 4000:
continue
word = lan.getMorpheme()
tmpNoun = u""
NN = 0
for j, line in enumerate(lan.getMorpheme()):
if line[1] == u"動詞":
if line[7] == u"する":
VList.append(word[j - 1][0] + line[7])
else:
VList.append(line[7])
if line[1] == u"名詞" and j != len(word) - 1:
if word[j + 1][1] == u"名詞":
tmpNoun += line[0]
NN += 1
if NN > 3:
tmpNoun = u""
NN = 0
continue
elif word[j + 1][7] != u"する":
NList.append(tmpNoun + line[0])
tmpNoun = u""
NN = 0
return NList, VList
if __name__ == '__main__':
path = u'D:/研究/データ/report_data_ver4_1.xlsx'
from Treport import Treport
TR = Treport(path)
NV_classpath = "C:/tmp/Evaluation/NV_class.Word"
NV_class = TR.NV_class_load(NV_classpath)
NList, VList = TR.NV_extract()
Wc = Word_class(NV_class)
fnc = lambda x: Wc.Nclass.get(x, "No entry")
Noun_uniq = uniqword(NList)
Noun_uniq2 = Series(Noun_uniq).map(fnc)
fvc = lambda x: Wc.Vclass.get(x, "No entry")
Verb_uniq = uniqword(VList)
Verb_uniq2 = Series(Verb_uniq).map(fvc)
Noun_uniq3 = []
for N in list(Series(Noun_uniq)[Noun_uniq2 == "No entry"]):
numin = False
alpha = False
N_uni = unicodedata.normalize('NFKC', N)
if re.search("[0-9]", N_uni):
numin = True
if re.search("[{-~]", N_uni) or re.search(
"[[-`]", N_uni) or re.search("[ -/]", N_uni) or re.search(
"[:-@]", N_uni):
alpha = True
if numin is False and alpha is False:
Noun_uniq3.append(N_uni)
Verb_uniq3 = []
for V in list(Series(Verb_uniq)[Verb_uniq2 == "No entry"]):
numin = False
alpha = False
V_uni = unicodedata.normalize('NFKC', V)
if re.search("[0-9]", V_uni):
numin = True
if re.search("[{-~]", V_uni) or re.search(
"[[-`]", V_uni) or re.search("[ -/]", V_uni) or re.search(
"[:-@]", V_uni):
alpha = True
if numin is False and alpha is False:
Verb_uniq3.append(V_uni)
Noun_tail = []
i = 0
for compN in Noun_uniq3:
print i
i += 1
lan = Language(compN)
word = lan.getMorpheme()
Noun_tail.append(word[len(word) - 1][0])
Noun_tail_uniq = uniqword(Noun_tail)
Noun_tail_uniq2 = Series(Noun_tail_uniq).map(fnc)
Noun_tail_uniq3 = []
for N in list(Series(Noun_tail_uniq)[Noun_tail_uniq2 == "No entry"]):
Noun_tail_uniq3.append(N)
def test_isPreposition(self):
self.h = Preposition(Language())
self.assertEqual(self.h.isPreposition("puxod"), False)
self.assertEqual(self.h.isPreposition("puxood"), False)
self.assertEqual(self.h.isPreposition("pwdood"), True)
def Triple_extract(self, path):
TR = Treport(path)
triplelist = {}
Mor_con = [[u"形容詞", u"助動詞", u"接続詞"], [u"連体化", u"並立助詞", u"読点", u"接続助詞"]]
for i in range(1, TR.s.nrows):
#if i>TR.s.nrows/2: break
if i>10: break
print i
noenc = TR.delete_unnecc(i)
#print TR.s.cell_value(i, 2).replace(u"-", u"")
#print noenc
for Sentence_id, perSen in enumerate(noenc.split(u"。")):
# TR.s.cell_value(i, 2)
Lan = Language(perSen)
cabocha_xml = Lan.cabocha_command()
chunkinfo, tokinfo, sentence_tok = Lan.chunk_structured(cabocha_xml)
#triple_perR = []
#id_perR = []
for chunk in chunkinfo:
compnoun_tail_id = -1
for tok_id, tokinfo_mor in enumerate(tokinfo[int(chunk[u"id"])]):
#print tok_id, compnoun_tail_id
if tok_id <= compnoun_tail_id:
continue
sentence_tok_set = sentence_tok[int(chunk[u"id"])]
if tokinfo_mor[0]==u"名詞":
Noun = sentence_tok_set[tok_id]
compnoun_tail_id = tok_id
for tok_id_noun in range(tok_id+1, len(tokinfo[int(chunk[u"id"])])-1):
if tokinfo[int(chunk[u"id"])][tok_id_noun][0]==u"名詞" :
if sentence_tok[int(chunk[u"id"])][tok_id_noun] == u"濃度":
continue
Noun += sentence_tok[int(chunk[u"id"])][tok_id_noun]
compnoun_tail_id = tok_id_noun
else:
break
if compnoun_tail_id+1 == len(tokinfo[int(chunk[u"id"])]):
continue
chunk_id_from = int(chunk[u"id"])
for i_from in reversed(range((int(chunk["id"])+1)*-1, 0)):
if int(chunkinfo[int(chunk[u"id"])+i_from]["link"])==chunk_id_from:
chunk_id_from -= 1
from_tail_tok = tokinfo[int(chunk[u"id"])+i_from][len(tokinfo[int(chunk[u"id"])+i_from])-1]
if from_tail_tok[0] in Mor_con[0] or from_tail_tok[1] in Mor_con[1]:
for sentence_tok_from in reversed(list(sentence_tok[int(chunkinfo[int(chunk[u"id"])+i_from]["id"])])):
Noun = sentence_tok_from + Noun
else:
break
else:
break
if tokinfo[int(chunk[u"id"])][compnoun_tail_id+1][0]==u"助詞" and tokinfo[int(chunk[u"id"])][compnoun_tail_id+1][1]!=u"接続助詞":
Particle = tokinfo[int(chunk[u"id"])][compnoun_tail_id+1][6]
Noun_suru = u""
for tok_id_link, tok_link_mor in enumerate(tokinfo[int(chunk[u"link"])]):
if tok_link_mor[0]==u"名詞" and tok_link_mor[1]!=u"形容動詞語幹":
Noun_suru += sentence_tok[int(chunk[u"link"])][tok_id_link]
continue
if tok_link_mor[0]==u"動詞" or tok_link_mor[0]==u"形容詞" or tok_link_mor[1]==u"形容動詞語幹":
if tok_link_mor[1]!=u"末尾":
Verb = u""
if tok_link_mor[6]==u"する" or tok_link_mor[6]==u"できる":
Verb = Noun_suru+u"する"
else:
Verb = tok_link_mor[6]
Verb_id = int(chunk[u"link"])
#数字以外を削除
if isinstance(TR.s.cell_value(i, 2), float):
id_tuple = (TR.s.cell_value(i, 2), Sentence_id, Verb_id)
else:
if re.search("[0-9]", TR.s.cell_value(i, 2)) is None:
id_tuple = (re.search("\d+[-]*\d+", TR.s.cell_value(i, 1)[re.search("\d+[-]*\d+", TR.s.cell_value(i, 1)).end():]).group(0).replace(u"-", u""), Sentence_id, Verb_id)
else:
id_tuple = (re.search("\d+[-]*\d+", TR.s.cell_value(i, 2)).group(0).replace(u"-", u""), Sentence_id, Verb_id)
if id_tuple not in triplelist.keys():
triplelist[id_tuple] = [(Noun, Particle, Verb)]
else:
triple_tmp = triplelist[id_tuple]
triple_tmp.append((Noun, Particle, Verb))
triplelist[id_tuple] = triple_tmp
#print Noun, Particle, Verb, TR.s.cell_value(i, 2).replace(u"-", u""), Sentence_id, Verb_id
break
return triplelist
def setupUi(self, mainWindow):
stylesheetFile = "Stylesheet.css" #styling
fh = open(stylesheetFile)
qstr = str(fh.read())
self.MainWindow = mainWindow #assign mainwindow
self.MainWindow.setStyleSheet(qstr)
self.MainWindow.setObjectName("MainWindow")
self.MainWindow.resize(1000, 650)
#setup basic variables
self.devices = []
self.currentDevice = None
self.setupLog()
self.mainQueue = Queue()
self.lang = Language(0)
#fill mainwindow
self.centralwidget = QtWidgets.QWidget(self.MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.verticalLayoutWidget = QtWidgets.QWidget(self.centralwidget)
self.verticalLayoutWidget.setGeometry(QtCore.QRect(0, 0, 90, 650))
self.verticalLayoutWidget.setObjectName("verticalLayoutWidget")
self.verticalLayout = QtWidgets.QVBoxLayout(self.verticalLayoutWidget)
self.verticalLayout.setContentsMargins(0, 0, 0, 0)
self.verticalLayout.setObjectName("buttonBar")
spacerItem = QtWidgets.QSpacerItem(20, 50, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)
self.verticalLayout.addItem(spacerItem)
self.addADevice = QtWidgets.QPushButton(self.verticalLayoutWidget)
self.addADevice.setObjectName("addADevice")
self.addADevice.setFixedSize(90, 90)
self.verticalLayout.addWidget(self.addADevice)
self.Manual = QtWidgets.QPushButton(self.verticalLayoutWidget)
self.Manual.setObjectName("Manual")
self.Manual.setFixedSize(90, 90)
self.verticalLayout.addWidget(self.Manual)
self.Graphs = QtWidgets.QPushButton(self.verticalLayoutWidget)
self.Graphs.setObjectName("Graphs")
self.Graphs.setFixedSize(90, 90)
self.verticalLayout.addWidget(self.Graphs)
self.Settings = QtWidgets.QPushButton(self.verticalLayoutWidget)
self.Settings.setObjectName("Settings")
self.Settings.setFixedSize(90, 90)
self.verticalLayout.addWidget(self.Settings)
spacerItem1 = QtWidgets.QSpacerItem(20, 40, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)
self.verticalLayout.addItem(spacerItem1)
self.Info = QtWidgets.QPushButton(self.verticalLayoutWidget)
self.Info.setObjectName("Info")
self.Info.setFixedSize(90, 90)
self.verticalLayout.addWidget(self.Info)
spacerItem2 = QtWidgets.QSpacerItem(20, 20, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)
self.verticalLayout.addItem(spacerItem2)
self.horizontalLayoutWidget = QtWidgets.QWidget(self.centralwidget)
self.horizontalLayoutWidget.setGeometry(QtCore.QRect(90, 0, 910, 50))
self.horizontalLayoutWidget.setObjectName("horizontalLayoutWidget")
self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget)
self.horizontalLayout_2.setContentsMargins(0, 0, 0, 0)
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
self.Logo = QtWidgets.QLabel(self.horizontalLayoutWidget)
self.Logo.setEnabled(True)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Expanding)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.Logo.sizePolicy().hasHeightForWidth())
self.Logo.setSizePolicy(sizePolicy)
self.Logo.setMinimumSize(QtCore.QSize(0, 0))
self.Logo.setMaximumSize(QtCore.QSize(250, 50))
font = QtGui.QFont()
font.setFamily("Calibri")
font.setPointSize(16)
font.setBold(True)
font.setWeight(75)
self.Logo.setFont(font)
self.Logo.setAutoFillBackground(True)
self.Logo.setFrameShape(QtWidgets.QFrame.Box)
self.Logo.setFrameShadow(QtWidgets.QFrame.Raised)
self.Logo.setObjectName("Logo")
pic = QPixmap('rsz_1aerosdev')
self.Logo.setPixmap(pic)
self.horizontalLayout_2.addWidget(self.Logo)
spacerItem3 = QtWidgets.QSpacerItem(20, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)
self.horizontalLayout_2.addItem(spacerItem3)
self.fSkyTemp = QtWidgets.QFrame(self.horizontalLayoutWidget)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Expanding)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.fSkyTemp.sizePolicy().hasHeightForWidth())
self.fSkyTemp.setSizePolicy(sizePolicy)
self.fSkyTemp.setMinimumSize(QtCore.QSize(180, 100))
self.fSkyTemp.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.fSkyTemp.setFrameShadow(QtWidgets.QFrame.Raised)
self.fSkyTemp.setObjectName("fSkyTemp")
self.Sky = QtWidgets.QLabel(self.fSkyTemp)
self.Sky.setGeometry(QtCore.QRect(10, 20, 75, 13))
self.Sky.setObjectName("Sky")
self.TempUp = QtWidgets.QLabel(self.fSkyTemp)
self.TempUp.setGeometry(QtCore.QRect(100, 20, 75, 13))
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Preferred)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.TempUp.sizePolicy().hasHeightForWidth())
self.TempUp.setSizePolicy(sizePolicy)
self.TempUp.setMinimumSize(QtCore.QSize(60, 0))
self.TempUp.setObjectName("TempUp")
self.horizontalLayout_2.addWidget(self.fSkyTemp)
spacerItem4 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum)
self.horizontalLayout_2.addItem(spacerItem4)
self.stackedWidget = QtWidgets.QStackedWidget(self.centralwidget)
self.stackedWidget.setGeometry(QtCore.QRect(90, 50, 910, 600))
# self.stackedWidget.setMinimumSize(QtCore.QSize(600, 600)) #400, 400
# self.stackedWidget.move(100,100)
# self.stackedWidget.setStyleSheet("background-color: black")
# sets up maingrid and adds it to stacked widget
self.page0 = QtWidgets.QWidget(self.MainWindow)
self.mainGrid = MainGrid(self.page0, self.devices)
self.stackedWidget.addWidget(self.mainGrid.page0)
#sets up pages
self.setupSettingsWindow()
self.setupEnterDevice()
self.setupGraphsWindow()
self.setupManual()
#sets starting page
self.stackedWidget.setCurrentIndex(0)
#binds functions to mainwindow buttons
self.addADevice.clicked.connect(lambda: self.setIndex(2))
self.Manual.clicked.connect(lambda: self.setIndex(4))
self.Graphs.clicked.connect(lambda: self.setIndex(3))
self.Settings.clicked.connect(lambda: self.setIndex(1))
self.Info.clicked.connect(self.showInfo)
QtCore.QMetaObject.connectSlotsByName(self.MainWindow)
self.MainWindow.setCentralWidget(self.centralwidget)
self.retranslateUi(0)
from Language import Language
for _ in range(10):
lang = Language()
for _ in range(5):
print(lang.name())
print('')
# s = ''
# for _ in range(10):
# s += lang.word() + ' '
# print(s.capitalize())
# s = ''
# for _ in range(10):
# s += lang.word() + ' '
# print(s.capitalize())
# print('')
# print(lang.name())
