def start_analysis(self):
if not self.lineEdit.text():
QMessageBox.critical(self, "严重错误", "文件夹路径为空")
return
if self.label_analysis_status.text() != "分析中":
self.label_analysis_status.setText("分析中")
QMessageBox.information(self, "消息", "开始分析...")
analysis(self.lineEdit.text(), self.comboBox.currentText())
self.label_analysis_status.setText("完 成")
def __init__(self):
self.services = []
self.resources = []
self.total_req = 0
self.timeout = 100000
self.plat = kube()
self.analysis = analysis()
self.vm_types = list()
self.vm_types.append(vm_type(2, 16000, 30))
self.vm_types.append(vm_type(4, 16000, 60))
self.vm_types.append(vm_type(4, 32000, 80))
def call_graph(self,graph_type,params):
self.m = mongo('10.0.0.1', 27017,'rktest','sensors')
self.a = analysis()
self.p = plotting()
self.t = truth('test.txt')
self.z = zone('zone.txt')
print params
dates = params[4][1].split("/")[0].split("-")
times = params[4][1].split("/")[1].split(":")
start = datetime.datetime(int(dates[2]),int(dates[1]),int(dates[0]),int(times[0]),int(times[1]),int(times[2]));
dates = params[5][1].split("/")[0].split("-")
times = params[5][1].split("/")[1].split(":")
end = datetime.datetime(int(dates[2]),int(dates[1]),int(dates[0]),int(times[0]),int(times[1]),int(times[2]));
start = time.mktime(start.timetuple())-36000
end = time.mktime(end.timetuple())-36000
self.start = start
self.end = end
print start
print end
self.m_time = self.m.get_array_time(start, end,self.m.get_array())
if(graph_type == "occupancy"):
self.p.new(self.fig)
try:
c = self.a.room_occupency(self.m, 0,start,end)
self.p.add_line(c[0], c[1], 'b')
self.p.show_legend()
except:
a = 1
self.p.show()
if(graph_type == "average"):
self.p.new(self.fig)
self.average(params)
self.p.show_legend()
self.p.show()
if(graph_type == "weighted"):
self.p.new(self.fig)
self.weighted_vote(params)
self.p.show_legend()
self.p.show()
if(graph_type == "joined"):
self.p.new(self.fig)
self.average(params)
self.weighted_vote(params)
self.p.show_legend()
self.p.show()
if(graph_type == "heat"):
self.heatmap(params)
self.fig = self.fig+1
def main():
my_data = acquisition()
my_data = cleaning(my_data)
my_data = analysis(my_data)
from mongo import *
from truth import *
from analysis import *
from plotting import *
import pylab
t = truth('test.txt')
m = mongo('10.0.0.1', 27017,'rktest','sensors')
a = analysis()
p = plotting()
master = m.get_array()
#print m.get_nodes()
end_time = a.get_time_bounds(master)[1]
start_time = end_time-800
master = m.get_array_time(start_time,end_time,master)
#print m.get_array_time(0,end_time,master)
#print m.get_sensortype(master)
#print m.get_sensortype_activation('VC',master)
#print m.get_sensortype_activation('ACCL',master)
#print t.get_table()
tmaster = t.parse_raw(master)
i = 0
for d in m.get_nodes():
print d
col = a.collective_average(m,t,{d:master[d]})
wv = a.weighted_vote(m,t,{d:master[d]})
#print col
#print wv
pos = []
neg = []
for f in mf_names:
df = pd.read_csv(f + ".csv")
df['TG'] = df['FT'].map(total_goals_scored) # Total goals.
df['GD'] = df['FT'].map(goal_difference) # Goal difference.
samples.append(df)
samples_min_length = min([len(s) for s in samples]) # We find the minimum length among all samples.
samples = [s.head(samples_min_length) for s in samples] # We truncate all samples.
samples = [{'title': pair[0], 'df': pair[1]} for pair in zip(samples_names, samples)]
# Now we'll plot some histograms.
for i in range(len(samples)):
plot_histogram(samples[i]['df'], mf_names[i] + ".png")
# Finally, run a proper analysis.
statistics = descriptive_statistics(samples)
anova_table, tukey_hsd_table = analysis(samples)
# Print the results
if store_results:
with open(file, 'w') as f:
f.write(statistics)
f.write(anova_table)
f.write(tukey_hsd_table)
else:
print statistics
print anova_table
print tukey_hsd_table
while True:
# keep reading year from user input until input == 'finish'
try:
year_input = input("Enter year or 'finish' to exit:")
if year_input == 'finish':
break
else:
try:
year = int(year_input)
if year < 1800 or year > 2012:
raise InvalidInput
income_distribution(year)
except InvalidInput:
print('Value out of range!')
continue
except ValueError:
print('Invalid Input!')
except KeyboardInterrupt:
print('User Interrupt')
except EOFError:
print('Input Error')
for year in range(2007,2013):
merged = merge_by_year(year)
analysis_year = analysis(merged, year)
analysis_year.histogram_by_region()
analysis_year.boxplot_by_region()
from config import * from utils import * from pull_csk_data import * from pull_utd_data import * from combine_files import * from clean_csvs import * from unify import * from get_result import * from played_first import * from analysis import * pull_csk_data() pull_utd_data() combine_files() clean_csvs() unify_data() get_match_result() set_played_first() analysis()
income = income.drop(['gdp pc test'],axis=0)
print('The head of income dataset:')
print(income.head())
'''
The program asks the user to enter a year, then display the distribution of income per person for that year
until 'finish' is typed.
Then, use the class analysis to generate graphs for the years 2007-2012
'''
while True:
try:
year_input = input('Enter a year(type finish to quit):')
if year_input.lower() == 'finish':
for year in range(2007,2013):
merged_df = merge_by_year(year,income,countries)
plot = analysis(merged_df,year)
plot.histogram()
plot.boxplot()
sys.exit()
if int(year_input) not in income.index:
raise InvalidInput
else:
year = int(year_input)
distr_income(income, year)
except KeyboardInterrupt:
sys.exit(0)
except EOFError:
sys.exit(0)
def selection(self):
""" KC - selection according to selection criteria """
self.best_model = self.blank_model
# #KC# loop with arbitrary number
for step in xrange(self.N_models):
self.actions = []
# #CG# random choice of restraints which will be done
for donor in self.list_donor:
donor_idx = self.list_donor.index(donor)
acceptor_idx = self.list_acceptor.index(acceptor)
acceptors = [
acceptor for acceptor in self.list_acceptor
if self.contact_matrix_probas[donor_idx][acceptor_idx] > 0
]
for acceptor in rd.sample(acceptors, len(acceptors) / 2):
random_number = int(rd.random() * 100)
if random_number >= 0 and random_number < \
self.contact_matrix_probas[donor_idx][acceptor_idx]:
self.actions.append([
"restraint", "upperdistance", donor, acceptor, 6, 3
])
self.actions.append(["modelisation", "selection" + str(step + 1)])
self.set_actions()
self.modelisation_run("selection")
for model in self.modeling.models:
model.interactions.Hbonds_dist_model(self.list_donor, step + 1)
if self.best_model != self.blank_model:
print "\n Best model :"
print "filename : " + str(self.best_model.filename)
print "GA341 score : " + str(self.best_model.GA341[0])
print "molpdf score : " + str(self.best_model.molpdf)
print "DOPE score : " + str(self.best_model.DOPE)
print self.selection_criteria + " : " + str(
self.best_selection_criteria)
for restraint in self.best_modelisation.restraints:
if [restraint[1], restraint[2]] in \
self.best_model.interactions.get_bonds(self.opt):
donor_idx = self.list_donor.index(restraint[1])
acceptor_idx = self.list_acceptor.index(restraint[2])
self.contact_matrix_best_model[donor_idx][acceptor_idx] = 1
save_matrix(self.contact_matrix_best_model,
"contact_matrix_best_model", self.list_acceptor,
self.list_donor)
analysis(None, None, None, None, self.query_structure_model,
self.list_donor, self.list_acceptor, None, None, None,
self.opt, None, None, None, None, None, None, None, True,
self.best_model, "final")
self.best_model.structure_alignement(self.query_structure_model)
self.best_model.interactions.network_distribution("Hbond")
self.best_model.interactions.accessibility_VS_Hbond()
else:
print "No better model than blank found !"
print "filename : " + str(self.blank_model.filename)
print "GA341 score : " + str(self.blank_model.GA341[0])
print "molpdf score : " + str(self.blank_model.molpdf)
print "DOPE score : " + str(self.blank_model.DOPE)
print self.selection_criteria + " : " + \
str(-(self.best_selection_criteria))
analysis(None, None, None, None, self.query_structure_model,
self.list_donor, self.list_acceptor, None, None, None,
self.opt, None, None, None, None, None, None, None, True,
self.blank_model, "final")
os.mkdir("selection")
os.system("mv *matrix* sel* Hbonds_dist* selection/. > /dev/null 2>&1")
otpion1=input("Do you want to see the current functionality? Yes or No ")
if(otpion1.lower()=="yes"):
functionality()
while(1):
while(1):
print()
print("1 for Voice Input")
print("2 for Text Input")
print("3 to create own functionality")
option=input("Enter Option: ")
if(option=="1"):
command=speech_recog().lower()
break
elif(option=="3"):
define_usr_fun()
continue
else:
command=input("What do you want to do? ")
break
with open('usr_triggers.json', 'r') as fp:
usr_triggers= json.load(fp)
if(command in usr_triggers):
run_usr_fun(command)
continue
command,command_sentence=run_classifier(command)
analysis(command,command_sentence)
temp_var_1=input("Do you want to continue? yes or no").lower()
if(temp_var_1=="no"):
break
from analysis import *
import optparse
parser = optparse.OptionParser()
parser.add_option('-i', '--inFileName', dest="inFileName", default="", help="")
parser.add_option('-o', '--outFileName', dest="outFileName", default="", help="")
parser.add_option('-n', '--nEvents', dest="nEvents", default=None, help="Number of events to process")
parser.add_option('-d', '--debug', dest="debug", action="store_true", default=False, help="Debug")
o, a = parser.parse_args()
f=ROOT.TFile(o.inFileName)
tree=f.Get("LHEF")
a = analysis(tree, o.outFileName, o.debug)
a.lumi = 24.3e3
a.kFactor = 1
if "ZH" in o.inFileName:
a.kFactor = 1.5
if "ZZ" in o.inFileName:
a.kFactor = 1.6
if o.nEvents: a.eventLoop(range(int(o.nEvents))) #loop over a subset of events
else: a.eventLoop() #loop over all events in o.inFileName
f.Close()
a.Write()
"/Users/zoem/Documents/ds1007/assignment10/assignment9/indicator_gapminder_gdp_per_capita_ppp.xlsx"
)
income = income.transpose()
print("head: \n")
print(income.head(5))
while True:
try:
input_year = input(
"Please enter a year to begin, enter finish if you want to quit: ")
if input_year.lower() == 'finish':
for year in range(2007, 2013):
merged_df = merge_by_year(year, countries, income)
anal = analysis(merged_df, year)
anal.hist_plot()
anal.box_plot()
sys.exit()
if int(input_year) not in income.index:
raise ValueError(
"Invalid input. Please enter an integer from 1800 to 2012")
else:
year = int(input_year)
income_distribution(income, year)
except KeyboardInterrupt:
sys.exit(0)
except EOFError:
income = pd.read_excel(
'indicator gapminder gdp_per_capita_ppp.xlsx').transpose()
income.columns = [income.ix[0]]
income = income.drop(['gdp pc test'], axis=0)
print('The head of income dataset:')
print(income.head())
'''
The program asks the user to enter a year, then display the distribution of income per person for that year
until 'finish' is typed.
Then, use the class analysis to generate graphs for the years 2007-2012
'''
while True:
try:
year_input = input('Enter a year(type finish to quit):')
if year_input.lower() == 'finish':
for year in range(2007, 2013):
merged_df = merge_by_year(year, income, countries)
plot = analysis(merged_df, year)
plot.histogram()
plot.boxplot()
sys.exit()
if int(year_input) not in income.index:
raise InvalidInput
else:
year = int(year_input)
distr_income(income, year)
except KeyboardInterrupt:
sys.exit(0)
except EOFError:
sys.exit(0)
def test_vm_type(self):
analysis_test = analysis()
limits = analysis_test.get_limits(self.service, "max_strategy")
vm_type_t = analysis_test.get_vm_type([self.service], self.vm_types)
self.assertTrue(vm_type_t.limits["cpu"] == 2
and vm_type_t.limits["mem"] == 16)
def test_calculate_limits(self):
analysis_test = analysis()
limits = analysis_test.get_limits(self.service, "max_strategy")
self.assertTrue(limits["cpu"] == 2)
self.assertTrue(limits["mem"] == 8)
per person in relation to geographical region for that year.
"""
while True:
try:
year_input = input(
"Enter a year between 1800 and 2012 to display graphs: ")
if year_input == "finish":
break
else:
try:
year = int(year_input)
if year < 1800 or year > 2012:
raise InvalidInput
income_dist(year)
except InvalidInput:
print("Value is not between 1800 - 2012.")
continue
except ValueError:
print('Invalid Input')
except EOFError:
print('Input Error')
except KeyboardInterrupt:
print('Keyboard Interrupt')
for y in range(2007, 2013):
merged = merge_by_year(y)
analysis_by_year = analysis(merged, y)
analysis_by_year.histogram_region()
analysis_by_year.boxplot_region()
from data import *
from exceptions import *
while True:
# keep reading year from user input until input == 'finish'
try:
year_input = input("Enter year or 'finish' to exit:")
if year_input == 'finish':
break
else:
try:
year = int(year_input)
if year < 1800 or year > 2012:
raise InvalidInput
income_distribution(year)
except InvalidInput:
print('Value out of range!')
continue
except ValueError:
print('Invalid Input!')
except KeyboardInterrupt:
print('User Interrupt')
except EOFError:
print('Input Error')
for year in range(2007, 2013):
merged = merge_by_year(year)
analysis_year = analysis(merged, year)
analysis_year.histogram_by_region()
analysis_year.boxplot_by_region()
def optimization(self):
""" KC - optimization of number of core hydrogen bonds """
# #KC# list of donors/acceptors with parametres.py file
if self.opt == "H_bonds":
self.list_donor = [(donor.get_full_id()[4][0]) + ":" +
str(donor.get_full_id()[3][1]) + ":" +
str(donor.get_full_id()[2])
for donor in self.blank_model.get_donors()]
self.list_acceptor = [
(acceptor.get_full_id()[4][0] + ":" +
str(acceptor.get_full_id()[3][1]) + ":" +
str(acceptor.get_full_id()[2]))
for acceptor in self.blank_model.get_acceptors()
]
elif self.opt == "ionic_bonds":
self.list_donor = [
(donor.get_full_id()[4][0]) + ":" +
str(donor.get_full_id()[3][1]) + ":" +
str(donor.get_full_id()[2])
for donor in self.blank_model.get_ionic_donors()
if donor.get_full_id()[4][0] != "N"
]
self.list_acceptor = [
(acceptor.get_full_id()[4][0] + ":" +
str(acceptor.get_full_id()[3][1]) + ":" +
str(acceptor.get_full_id()[2]))
for acceptor in self.blank_model.get_ionic_acceptors()
if acceptor.get_full_id()[4][0] != "O"
]
# #KC# matrix initialization
self.contact_matrix_probas = [[
self.p_min for i in xrange(len(self.list_acceptor))
] for j in xrange(len(self.list_donor))]
self.contact_matrix_blank = [[
0 for i in xrange(len(self.list_acceptor))
] for j in xrange(len(self.list_donor))]
self.contact_matrix_query = [[
0 for i in xrange(len(self.list_acceptor))
] for j in xrange(len(self.list_donor))]
self.contact_matrix_best_model = [[
0 for i in xrange(len(self.list_acceptor))
] for j in xrange(len(self.list_donor))]
# #KC# negative weight applied in contact matrix
self.zones_constraints()
for (atom1,
atom2) in self.blank_model.interactions.get_bonds(self.opt):
atom1_idx = self.list_donor.index(atom1)
atom2_idx = self.list_acceptor.index(atom2)
self.contact_matrix_blank[atom1_idx][atom2_idx] = 1
save_matrix(self.contact_matrix_blank, "contact_matrix_blank",
self.list_acceptor, self.list_donor)
# self.num_query, self.num_model = get_aligned_residues('structure_sequence.ali')
# dictionary = dict(zip(self.num_query, self.num_model))
# for (atom1, atom2) in self.query_structure_model.interactions.get_bonds(self.opt):
# atom1 = atom1.split(":")
# atom2 = atom2.split(":")
# chain1 = [res.get_full_id()[2]
# for res in self.blank_model.res if res.get_full_id()[3][1] ==
# dictionary[int(atom1[1])]]
# chain2 = [res.get_full_id()[2]
# for res in self.blank_model.res if res.get_full_id()[3][1] ==
# dictionary[int(atom2[1])]]
# atom1 = atom1[0] + ":" + str(dictionary[int(atom1[1])]) + ":" + chain1[0]
# atom2 = atom2[0] + ":" + str(dictionary[int(atom2[1])]) + ":" + str(chain2[0])
# self.contact_matrix_query[self.list_donor.index(atom1)][self.list_acceptor.index(atom2)] = 1
save_matrix(self.contact_matrix_query, "contact_matrix_query",
self.list_acceptor, self.list_donor)
self.all_models = []
self.list_tanimoto = []
self.sum_probas = []
self.sum_Hbonds = []
self.sum_restraints = []
self.blank_Hbonds_number = \
len(self.blank_model.interactions.get_bonds(self.opt))
for step in xrange(3):
self.actions = []
for donor in rd.sample(self.list_donor, len(self.list_donor) / 3):
acceptors = []
for acceptor in self.list_acceptor:
donor_idx = self.list_donor.index(donor)
acceptor_idx = self.list_acceptor.index(acceptor)
if self.contact_matrix_probas[donor_idx][acceptor_idx] > 0:
acceptors.append(acceptor)
for acceptor in rd.sample(acceptors, len(acceptors) / 2):
donor_idx = self.list_donor.index(donor)
acceptor_idx = self.list_acceptor.index(acceptor)
random_number = int(rd.random() * 100)
if random_number >= 0 and random_number < \
self.contact_matrix_probas[donor_idx][acceptor_idx]:
self.actions.append([
"restraint", "upperdistance", donor, acceptor, 6, 3
])
self.actions.append(
["modelisation", "optimization" + str(step + 1)])
self.set_actions()
self.modelisation_run("optimization")
if (step + 1) in [1, 50, 100, 200, 300, 500]:
for model in self.modeling.models:
model.interactions.Hbonds_dist_model(
self.list_donor, step + 1)
if self.query_structure_model is not None:
for model in self.modeling.models:
analysis(None, None, None, None,
self.query_structure_model, self.list_donor,
self.list_acceptor, None, None, None,
self.opt, None, None, None, None, None, None,
None, True, model, step + 1)
save_matrix(self.contact_matrix_probas, "contact_matrix_probas",
self.list_acceptor, self.list_donor)
# print self.contact_matrix_probas
# #KC# models analysis
analysis(self.all_models, self.n_models, self.blank_model,
self.template_model, self.query_structure_model,
self.list_donor, self.list_acceptor,
self.contact_matrix_probas, self.contact_matrix_best_model,
self.contact_matrix_query, self.opt, self.p_min, self.p_max,
self.p_step, self.list_tanimoto, self.sum_probas,
self.sum_Hbonds, self.sum_restraints, False, None, None)
os.mkdir("optimization")
os.system("mv *matrix* opt* models* probas_dist*" +
"Hbonds_dist* optimization/. > /dev/null 2>&1")
"/Users/zoem/Documents/ds1007/assignment10/assignment9/indicator_gapminder_gdp_per_capita_ppp.xlsx"
)
income = income.transpose()
print("head: \n")
print(income.head(5))
while True:
try:
input_year = input("Please enter a year to begin, enter finish if you want to quit: ")
if input_year.lower() == "finish":
for year in range(2007, 2013):
merged_df = merge_by_year(year, countries, income)
anal = analysis(merged_df, year)
anal.hist_plot()
anal.box_plot()
sys.exit()
if int(input_year) not in income.index:
raise ValueError("Invalid input. Please enter an integer from 1800 to 2012")
else:
year = int(input_year)
income_distribution(income, year)
except KeyboardInterrupt:
sys.exit(0)
except EOFError:
sys.exit(0)
