def song_analysis_get(id):
""" Get the analysis for a single song endpoint """
# Get the song from the database
song = session.query(models.Song).get(id)
# Check whether the song exists
# If not return a 404 with a helpful message
if not song:
message = "Could not find song with id {}".format(id)
data = json.dumps({"message": message})
return Response(data, 404, mimetype="application/json")
# Get the filename
files = session.query(models.File).all()
file1id = files[song.column-1].id
#file1name = session.query(models.File).get(filename)
#songname = file[song.column].filename
songname = files[file1id-1].filename
analysis = analyse(songname)
data = json.dumps(analysis)
return Response(data, 200, mimetype="application/json")
def display_state(self, state, cols=[]):
"""Display state data for specified or default columns"""
if len(cols) == 0:
cols = self._default_columns
state_data = self._data[self._data["state"] == state]
state_freq_data = [an.analyse(state_data, col) for col in cols]
graphs.display_freq(state_freq_data, filter=f"state: {state}")
def analyze_song(id):
song = session.query(models.Song).get(id)
if not song:
data = {"message": "Could not find song with id {}".format(id)}
return Response(json.dumps(data), 422, mimetype="application/json")
filename = song.file.filename
data = json.dumps(analysis.analyse(upload_path(filename)))
return Response(data, 200, mimetype="application/json")
def output_results(self):
"""Generate a report of the simulation"""
rg = HtmlReportGenerator(self.args)
add_plots(self, rg) # change add_plots to show different plots!
rg.variables.update(analyse(self))
rg.generate()
rg.save()
print("Report saved to {0}".format(self.args["filename_out"]))
def analyze_song(id):
song = session.query(models.Song).get(id)
if not song:
data = {"message": "Could not find song with id {}".format(id)}
return Response(json.dumps(data), 422, mimetype="application/json")
filename = song.file.filename
data = json.dumps(analysis.analyse(upload_path(filename)))
return Response(data, 200, mimetype="application/json")
def song_analysis(id):
song = session.query(models.Song).get(id)
if not song:
data = {"message": "Could not find song with id {}".format(id)}
return Response(json.dumps(data), 404, mimetype="application/json")
path = upload_path(song.file.filename)
data = analysis.analyse(path)
return Response(json.dumps(data), 200, mimetype="application/json")
def graph_usa(self, cols=[], show=True, export=False):
"""Graphs the specified list of columns"""
if len(cols) == 0:
cols = self._default_columns
freq_data = [an.analyse(self._data, col) for col in cols]
graphs.graph_freq(freq_data,
title="Frequency of Digits for US",
export=export,
show=show,
export_path="USA")
def graph_state(self, state, cols=[], show=True, export=False):
"""Graph state data for specified or default columns"""
if len(cols) == 0:
cols = self._default_columns
state_data = self._data[self._data["state"] == state]
state_freq_data = [an.analyse(state_data, col) for col in cols]
graphs.graph_freq(state_freq_data,
title=f"Frequency of Digits for {state}",
export=export,
show=show,
export_path=state)
def cumulative(data, path):
cols = [' New_cases', ' Cumulative_cases', ' New_deaths', ' Cumulative_deaths']
analysis = [an.analyse(data, col) for col in cols]
with pd.ExcelWriter(path) as writer:
pd.DataFrame().to_excel(writer)
with pd.ExcelWriter(path, mode='a') as writer:
for col, freq in analysis:
header = pd.DataFrame()
header["title"] = [col]
if len(freq["digit"] > 0):
header.to_excel(writer, index=False, sheet_name=col, startcol=0)
freq.to_excel(writer, sheet_name=col, startcol=1)
def __init__(self, block_sizeGB=1, swap=False, pool_dtype='float32'):
# Default RAM based data array:
self.data = data_array(dtype='float32',
block_sizeGB=block_sizeGB,
swap=swap,
pool_dtype=pool_dtype)
# Common classes for the cvolume and for the projections:
self.io = io(self)
self.display = display(self)
self.analyse = analyse(self)
# Specific to the projection data or the volume data:
self.process = None
self.meta = None
def song_analyze(id):
#check wether a song with the correct ID exists
song = session.query(models.Song).get(id)
if not song:
message = "Could not find song with id {}".format(id)
data = json.dumps({"message": message})
return Response(data, 404, mimetype="application/json")
#get the filename of the song from the database
song_file = session.query(models.File).filter_by(id=song.file_id).first()
#save file to an upload folder using upload_path() function
song_file_path = upload_path(song_file.filename)
#call analyse function, passing in path of the uploaded file
file_analysis = analysis.analyse(song_file_path)
data = json.dumps(file_analysis)
#return results of analysis function as a JSON object
return Response(data, 201, mimetype="application/json")
def song_analyze(id):
#check wether a song with the correct ID exists
song = session.query(models.Song).get(id)
if not song:
message = "Could not find song with id {}".format(id)
data = json.dumps({"message": message})
return Response(data, 404, mimetype="application/json")
#get the filename of the song from the database
song_file = session.query(models.File).filter_by(id=song.file_id).first()
#save file to an upload folder using upload_path() function
song_file_path = upload_path(song_file.filename)
#call analyse function, passing in path of the uploaded file
file_analysis = analysis.analyse(song_file_path)
data = json.dumps(file_analysis)
#return results of analysis function as a JSON object
return Response(data, 201, mimetype="application/json")
def translate(sentence, model, log = None):
tokens = tokenize(sentence)
if log:
print(' tokens = {}\n'.format(tokens), file = log)
analysis = analyse(tokens, model)
if log:
print(' analysis = {}\n'.format(analysis), file = log)
chuncks = chuncker(analysis[0])
if log:
print(' chuncks = {}\n'.format(chuncks), file = log)
translation = to_arabic(chuncks)
if log:
print(' translation = {}\n'.format(translation), file = log)
return translation
def indiv_countries(data, path):
country_col = " Country_code"
countries = data[country_col].unique()
cols = [' New_cases', ' Cumulative_cases', ' New_deaths', ' Cumulative_deaths']
with pd.ExcelWriter(path) as writer:
pd.DataFrame().to_excel(writer)
progress = 0
for country in countries:
country_data = data[data[country_col] == country]
analysis = [an.analyse(country_data, col) for col in cols]
i = 0
with pd.ExcelWriter(path, mode='a') as writer:
for col, freq in analysis:
header = pd.DataFrame()
header["title"] = [col]
if len(freq["digit"] > 0):
header.to_excel(writer, index=False, sheet_name=country, startcol=0, startrow=i)
freq.to_excel(writer, sheet_name=country, startcol=1, startrow=i)
i += 10
progress += 1
print(f"Progress: {( progress / len(countries) * 100 ):.2f}%\t\tCountries: {progress} / {len(countries)}")
def task(quarter, k):
"""
quarter : pandas.DataFrame
k : int
"""
quarter_normalized = pandas.DataFrame(minmax_scale(
quarter.values.astype(numpy.float64)),
columns=quarter.columns)
quarter_normalized = reweight(quarter_normalized)
points = quarter_normalized.values
kdtree = KDTree(points)
dist, ind = kdtree.query(points, k=k + 1)
dist = dist[:, 1:]
ind = ind[:, 1:]
results = []
for main_i, indices in enumerate(ind):
indices = numpy.append([main_i], indices)
knearest = pandas.DataFrame(quarter.values[indices],
columns=quarter.columns)
results.append(analyse(knearest))
return results
def main(Scraper):
# Scraper = Scraper()
# reddit = Scraper.make_reddit_obj()
# Scraper.scrape("emojipasta", limit = 1000)
analyse(posts_dir=r"../posts/", results_dir="../results/", chunk_size=None)
return
def run(main_folder, tstart, teind, weight=10, growth=2, shrink=2):
"""
Main function that controls the circle analysis and passes the resluts to
the analysis file.
"""
import time
t1 = time.time()
directory = f"{main_folder}_analysed"
path = os.path.join(main_folder, directory)
os.makedirs(path, exist_ok=True)
output_name = f"{main_folder}_results.xlsx"
output_path = os.path.join(path, output_name)
import xlsxwriter
export = xlsxwriter.Workbook(f"{output_path}")
bold = export.add_format({"bold": True})
red = export.add_format({"font_color": "red"})
export_sheet = export.add_worksheet("Surface area")
export_sheet_2 = export.add_worksheet("Slope")
column = 0
import imp
import analysis
imp.reload(analysis)
print("reloaded analysis")
for i, sub_folder in enumerate(os.listdir(main_folder)): # open main folder
img_folder = f"Circles_{sub_folder}"
img_path = os.path.join(path, img_folder)
os.makedirs(img_path, exist_ok=True)
route = os.path.join(main_folder, sub_folder)
results = files(route, img_path)
_, stepsize = np.linspace(tstart[i], teind[i], len(results), retstep=True)
x = [
e - stepsize
for e in np.linspace(tstart[i], teind[i] + stepsize, len(results))
]
if x[0] != tstart[i]:
x = [
e - 2 * stepsize
for e in np.linspace(tstart[i], teind[i] + 2 * stepsize, len(results))
]
export_sheet.write(column + 2, 0, "Removed data points [hour]", bold)
# for ind, e in enumerate(removed):
# export_sheet.write(column+2, ind+1, np.around(x[e-1], decimals=1), red)
analysis.analyse(
results,
export,
export_sheet,
export_sheet_2,
column,
sub_folder,
weight,
x,
path,
)
column += 4
export.close()
t2 = time.time()
dt = t2 - t1
print("Deze analyse duurde totaal %.3f minuten" % (dt / 60))
XXX, yyy = import_wine_review(nbr_class=5, subset_size=10000)
X_train, X_test, y_train, y_test = train_test_split(XXX,
yyy,
test_size=0.15,
random_state=0)
fold = 10
print("----------Done loading dataset----------")
#endregion
#region[purple] DECISION TREE
#%%
clf = tree.DecisionTreeClassifier(criterion='entropy',
max_leaf_nodes=200,
max_depth=10)
clf = analyse(clf, fold, XXX, yyy, X_train, y_train, X_test, y_test)
# print("The depth is : {}".format(clf.get_depth()))
# print("The number of leaves is : {}".format(clf.get_n_leaves()))
# dot_data = io.StringIO()
# tree.export_graphviz(clf, out_file=dot_data, filled=True)
# graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
# graph.write_pdf("DT_WQ_PRUNED.pdf")
#%%
if (verbose): print("\n\n----------Maximum depth----------")
max_depths = [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 16, 20, 25, 30]
acc_min = []
acc_avg = []
def main():
"""The brains of the bot
Finds all the races
"""
betfairClient = bfclient.BfClient()
eventResponse = None
race_list = None
cardGenTime = 0
login_timer = 0
while True:
login_rsp = auth.login(betfairClient)
# Generate the list of available races. Limit the result to 100
(race_list, cardGenTime) = gen_race_list(cardGenTime, race_list, betfairClient)
# get the exchangeId, marketId for the next race and race_queue
# refactor - turn into class, return object.
(exchangeId, marketId, race_queue, race_list) = get_next_race(race_list)
# determine race status, proceed if it is active.
market_price = qwertymarket.getMarketPricesCompressed(betfairClient,
exchangeId,
marketId)
market_status = qwertymarket.marketStatus(market_price)
if market_status == 'CLOSED':
logger.debug('Oops market is closed, finding the next race/market')
time.sleep(2)
continue
elif market_status == 'SUSPENDED':
logger.debug('Market is now suspended, finding the next race/market')
time.sleep(5)
continue
# Great the next race is active, lets find out when it starts
market_info = qwertymarket.getMarketInfo(betfairClient,
exchangeId,
marketId)
TTL = qwertymarket.timeTillRaceStarts(market_info)
logger.info('Time till live: %s', TTL)
if TTL > 240: #if time till race is more than 5 minutes away
handle_long_ttl(cardGenTime, race_list, market_info)
elif market_price.marketPrices.delay > 0:
logger.info("-------------Market is in-play---------------")
time.sleep(10)
continue
# nested while true, why? Because we don't want it to change races if it's active.
while True:
try:
collectData.collectData(betfairClient, exchangeId,
marketId)
timeId = collectData.Datastore[marketId]['timeId']
IDgap = collectData.Datastore[marketId]['IDgap']
if IDgap == 0:
time.sleep(2)
logger.debug('IDgap is zero')
continue #don't run anything else
except IndexError:
if handle_IndexError(betfairClient, exchangeId, marketId):
continue
else:
break
except ZeroDivisionError:
logger.warning('Zero Division Exception caught.')
break
except bfpy.bferror.BfNetworkError:
logger.error("Network timed out...retrying...")
continue
except Exception, e:
logger.exception('Collecting data at timeId: %s', timeId)
raise e
try:
play.inPlay(betfairClient, exchangeId, marketId, timeId)
play.pastPlay(betfairClient, exchangeId, marketId, timeId)
except play.InPlayClause as e:
logger.debug('In Play is greater than 30 seconds, look for the next race')
break
except Exception as e:
logger.exception('Running inPlay or pastPlay')
raise e
try:
for fillInId in range(timeId-IDgap+1, timeId+1):
for selectionId in collectData.Datastore[marketId]['runnerKeys']:
if fillInId != timeId:
collectData.fillInMissingData(marketId, selectionId,
fillInId, timeId)
if fillInId >= 30:
selection.calculateTPS(marketId, selectionId, fillInId)
except IndexError:
if handle_IndexError(betfairClient, exchangeId, marketId):
continue
else:
break
except ZeroDivisionError:
logger.warning('Zero Division Exception caught.')
break
except Exception, e:
logger.exception('Filling missing data or calculating TPS at timeId: %s', timeId)
raise e
try:
if timeId > 1: selection.selectionCriteria(marketId, timeId)
for selectionId in collectData.Datastore[marketId]['selectedRunnerIds']:
if 'max30BP' not in collectData.Datastore[marketId][selectionId][0].keys():
for backCalc in range(0, timeId - IDgap+1):
analysis.analyse(marketId, selectionId, backCalc)
if backCalc == timeId - IDgap:
logger.info('Successful back analysis of %s to timeId %s',
collectData.Datastore[marketId][selectionId]['HorseName'],
backCalc)
collectData.Datastore[marketId][selectionId]['backCalc'] = backCalc
for fillInId in range(timeId-IDgap+1, timeId+1):
analysis.analyse(marketId, selectionId, fillInId)
except IndexError:
if handle_IndexError(betfairClient, exchangeId, marketId):
continue
else:
break
except ZeroDivisionError:
logger.warning('Zero Division Exception caught.')
break
except Exception, e:
logger.exception('Error: Running Analysis or selecting horses at timeId: %s',
timeId)
logger.exception("timeId: %s", timeId)
raise e
def display_usa(self, cols=[]):
"""Display USA data for specified or default columns"""
if len(cols) == 0:
cols = self._default_columns
freq_data = [an.analyse(self._data, col) for col in cols]
graphs.display_freq(freq_data)
if print_trees:
print >> log_out, source
print >> log_out, target
use, auto_ptb, auto_schema = (False, None, None)
if 'method' in args:
method_name = args.split('method=')[1].split()[0]
ans = methods[method_name](source, sys.argv, log_out)
use, auto_ptb, auto_schema = ans
else:
ans = trivial.convert(source, sys.argv, log_out)
use, auto_ptb, auto_schema = ans
if not use:
print >> log_out, "Not being included"
if auto_schema is not None:
analysis.analyse(source, target, auto_ptb, auto_schema,
analysis_out)
if tree_out is not None:
if use:
print >> gold_out, target.one_line_repr()
print >> tree_out, auto_ptb.one_line_repr()
elif not only_parsed:
print >> gold_out, target.one_line_repr()
print >> tree_out
if print_trees:
print >> log_out, auto_ptb
if colour_out is not None:
print >> colour_out, source
print >> colour_out, auto_ptb.repr_with_corrections(target)
scores = score_count(target, auto_ptb)
def submit():
global color
global bar_color
depression_mark = 0
name = request.form["name"]
day = request.form["day"]
previousDay = request.form["previousDay"]
time = request.form["time"]
future = request.form["future"]
answers = [name, day, previousDay, time, future]
answers = [x.strip() for x in answers if x.strip()]
print(answers)
file = open("./answers.txt", "w")
file.writelines("\n".join(answers))
file.close()
absolute_self_centrism, rel_self_centrism, rel_swear_counter, \
rel_absolute_counter, final_afinn_score, sentiment = analyse("answers.txt")
first_person = "You referenced to yourself " + str(
rel_self_centrism * 100) + "% of time. "
if rel_self_centrism > 0.05:
first_person += "So, you are quite focused on yourself."
depression_mark += 1
else:
first_person += "Good news: that corresponds to the average use of first person pronouns."
absolute = str(round(rel_absolute_counter * 100,
2)) + "% of words were absolute. "
# Threshold taken from al-Mosaiwi(2018)
if rel_absolute_counter > 0.012:
absolute = absolute + "You seem to interpret your experiences in a very black or white kind of way. " \
"It is possible that you see all your experiences as either perfect or terrible. "
depression_mark += 1
else:
absolute = absolute + "Good news: that corresponds to the average use of absolute words."
swearSentence = str(round(rel_swear_counter * 100,
2)) + "% of words were swear words. "
if rel_swear_counter > 0.002:
swearSentence += "You are cursing much more than an average person. " \
"Perhaps you are in a negative state of mind or angry at something."
depression_mark += 1
else:
swearSentence += "Good news: you are cursing not very much."
if final_afinn_score <= 0:
afinnString = "The afinn score is " + str(final_afinn_score) + \
". That means that you seem to be thinking very negatively."
depression_mark += 1
else:
afinnString = "The afinn score is " + str(final_afinn_score) + \
". You're seeing your experiences in a neutral to positive view."
SentimentString = "The sentiment analysis score is " + str(sentiment)
if sentiment > 0.5:
SentimentString += ". Good news: you are thinking rather positively."
else:
SentimentString += ". That means you are thinking rather negatively."
depression_mark += 1
mark = "Your overall depression mark is " + str(depression_mark)
depr_sentence = "The greater is your overall depression mark, " \
"the more likely you have depression symptoms. " \
"0 means you have none. 5 means most probably you should go to the therapist."
session['data'] = [
final_afinn_score, sentiment, rel_swear_counter, rel_absolute_counter,
rel_self_centrism, depression_mark
]
relation = depression_mark / 5 * 100
if depression_mark >= 3:
color = "#726E6D"
bar_color = "#8C001A"
else:
color = "#6AFB92"
bar_color = "#FA69D1"
return render_template("results.html",
sentimen_analysis=SentimentString,
absolute=absolute,
afinn=afinnString,
swear=swearSentence,
first_person_pronouns=first_person,
speaker_name=name,
color=color,
depression_mark=depression_mark,
relation=relation,
bar_color=bar_color)
X_clust = np.concatenate((X_clust,labs),axis=1)
#endregion
#%% region[green] EXPECTATION MAXIMIZATION
km = KMeans(n_clusters=10, random_state=0).fit(X_clust)
labs = np.array([km.labels_]).transpose()
scaler = preprocessing.StandardScaler()
labs = scaler.fit_transform(labs)
X_clust = np.concatenate((X_clust,labs),axis=1)
#endregion
# %% region[black] NEURAL NETWORK
X_clust_train, X_clust_test, y_clust_train, y_clust_test = train_test_split(X_clust, yyy, test_size=0.1, random_state=0)
network = (12,12,12)
print("For the network {}".format(network))
nn = MLPClassifier(hidden_layer_sizes=network, activation='relu', verbose=False, max_iter=4000)
nn = analyse(nn, fold, XXX, yyy, X_clust_train, y_clust_train, X_clust_test, y_clust_test)
winsound.Beep(frequency, duration)
#endregion
# %%
if print_trees:
print >> log_out, source
print >> log_out, target
use, auto_ptb, auto_schema = (False, None, None)
if 'method' in args:
method_name = args.split('method=')[1].split()[0]
ans = methods[method_name](source, sys.argv, log_out)
use, auto_ptb, auto_schema = ans
else:
ans = trivial.convert(source, sys.argv, log_out)
use, auto_ptb, auto_schema = ans
if not use:
print >> log_out, "Not being included"
if auto_schema is not None:
analysis.analyse(source, target, auto_ptb, auto_schema, analysis_out)
if tree_out is not None:
if use:
print >> gold_out, target.one_line_repr()
print >> tree_out, auto_ptb.one_line_repr()
elif not only_parsed:
print >> gold_out, target.one_line_repr()
print >> tree_out
if print_trees:
print >> log_out, auto_ptb
if colour_out is not None:
print >> colour_out, source
print >> colour_out, auto_ptb.repr_with_corrections(target)
scores = score_count(target, auto_ptb)
random_state=None,
schedule=mlr.ExpDecay())
nn_model1.fit(X_train_scaled, y_train)
y_train_pred = nn_model1.predict(X_train_scaled)
y_train_accuracy = accuracy_score(y_train, y_train_pred)
print(y_train_accuracy)
y_test_pred = nn_model1.predict(X_test_scaled)
y_test_accuracy = accuracy_score(y_test, y_test_pred)
print(y_test_accuracy)
winsound.Beep(frequency, duration)
analyse(nn_model1, fold, XXX, yyy, X_train, y_train, X_test, y_test)
winsound.Beep(frequency, duration)
#endregion
#%% #region[black] NN OPTIMIZATION WITH GA
nn_model1 = mlr.NeuralNetwork(hidden_nodes=[100],
activation='relu',
algorithm='genetic_alg',
max_iters=4000,
bias=True,
is_classifier=True,
learning_rate=0.75,
early_stopping=True,
clip_max=5,
current_resigning[0].to_csv("../results/" + str(month_no) +
"/Resigning_Billable.csv",
index=False)
current_resigning[1].to_csv("../results/" + str(month_no) +
"/Resigning_Bench.csv",
index=False)
billable.to_csv("../results/" + str(month_no) + "/Billable_Resources.csv",
index=False)
bench.to_csv("../results/" + str(month_no) + "/Benched_Resources.csv",
index=False)
new_hires[month_no + 2].to_csv("../results/" + str(month_no) +
"/SkillLists_To_Hire.csv",
index=False)
analysis = analyse(details, revenue_per_billable_r, cost_per_r)
print("-" * 25)
print("Details")
for key in details.keys():
print(key, ":", details[key])
print("-" * 25)
print("Analysis")
for key in analysis.keys():
print(key, ":", analysis[key])
details['Month'] = month_no
analysis['Month'] = month_no
year_details = year_details.append(details, ignore_index=True)
"scorecards-actors",
args.start_date,
args.end_date)
if not os.path.exists("scorecards-ransomware"):
os.makedirs("scorecards-ransomware")
print("Generating Ransomware scorecards")
scorecards.generate_ransomware_scorecards(misp_data,
"scorecards-ransomware",
args.start_date,
args.end_date)
elif args.analyse:
# Perform some basic analysis on the MISP data, which can be useful
# for learning what is present in the data
analysis.analyse(misp_data)
elif args.list_actors:
# List the threat actors present in the data
#
threat_actors = utility.identify_threat_actors(misp_data, initial={})
for actor in threat_actors:
print(actor)
else:
# Generate the desired heat maps
#
if not os.path.exists("heatmaps"):
os.makedirs("heatmaps")
if args.num_days != 0 and args.bin_size != 0:
print("Generating custom heatmaps")
