def FeatureExtraction(Sample,Feature,Data_Array):
# Append the data
Data_Array = np.append(Data_Array,Sample,axis=0)
# Common Average Reference
Sample[:,range(1,9)] = Process.Common_Average_Reference(Sample[:,range(1,9)])
# Feature Extraction
if ALPHA:
Feature = np.append(Feature,Process.AlphaDifference(Sample,([5,6],[7,8]),250))
else:
Feature = np.append(Feature,Process.PowerExtraction(Sample[:,3],[8,12],250))
return (Feature,Data_Array)
def main():
trainData = pd.read_csv('CrimeClassification/Dataset/train-2.csv')
classesMap = dm.mapClasses(trainData)
print trainData.info()
print(classesMap)
cleanedTrainData,normalizationValues = dm.cleanTrainData(trainData,classesMap)
print(cleanedTrainData.info())
[Xtrain, Ytrain, Xtest,Ytest]=splitData(cleanedTrainData.values)
model = trainModel(Xtrain,Ytrain)
Ypred = testModel(model,Xtest)
confMatrix = da.confusionMatrix(Ypred,Ytest)
titleCM = da.orderClassesMapKeys(classesMap)
da.plotConfusionMatrix(confMatrix,titleCM)
print (da.f1Score(Ypred,Ytest))
def saveTrial(trialDataArray, previousITI):
global trialsSaved
#If we stop the session prematurely, we need to know how many trials are in the saved session
trialsSaved += 1
#Convert trial data from numpy array to python list
trialDataList = trialDataArray.tolist()
#Create new trial (singular) object
trialObject = {
"trialNumber":
trialsSaved,
"previousITI":
previousITI,
"stats":
da.getTrialStats(ts.currentSession.minVoltage,
ts.currentSession.thresholdSD,
ts.currentSession.thresholdMinDuration),
"samples":
trialDataList
}
#print(str(trialObject["previousITI"]))
#Append trial (singular) object to trials (plural) array
jsonObject["trials"].append(trialObject)
def plotPolyRegression(self):
x = str(self.dialog.featureX.currentText())
y = str(self.dialog.featureY.currentText())
order = int(self.dialog.order.currentText())
descriptions = getDescriptions()
if (x not in descriptions) or (y not in descriptions):
self.errorLabel.setText("One or more feature names were invalid")
self.dialog.close()
self.dialog = None
return
try:
self.coefs = da.polynomialRegression(x, y, order)
except:
self.errorLabel.setText(
"An error occurred while loading or analyzing the data.")
self.dialog.close()
self.dialog = None
if (type(self.coefs) == str):
self.errorLabel.setText(self.coefs)
self.dialog.close()
self.dialog = None
else:
self.mode_linear = False
self.mode_poly = True
self.data = self.coefs[0]
self.errorLabel.setText("")
self.sc.update_figure(self.coefs, x, y, Poly=True)
self.updateDataDisplay()
self.dialog.close()
self.dialog = None
def post(self):
try:
data = json_decode(self.request.body)
# print(data)
da = DataAnalysis.Vectorization('D:\\VSCodeTest\\Show\\static')
# da = DataAnalysis.Vectorization('C:\\Users\\Administrator\\Desktop\\Dioxin_tornado_80\\static')
format_matrix = da.get_format_matrix(data['key_string'])
sim_dict = da.get_similarity_vector(format_matrix)
case_description = da.get_case(sim_dict['max_sim'])
img_dict = da.get_img(sim_dict['max_sim'])
suggest_dict = da.get_suggest(sim_dict['max_sim'])
order_str = da.get_order()
format_dict = self.formatmatrix_to_dict(format_matrix)
json_str = json.dumps({
'format_dict': format_dict,
'sim_dict': sim_dict,
'case_description': case_description,
'img_dict': img_dict,
'suggest_dict': suggest_dict,
'order_str': order_str,
'status_code': str(200),
'status_msg': '(^_^)'
})
time.sleep(1)
assert len(data['key_string']) >= 50
self.write(json_str)
except Exception as e:
print(e)
rt_dict = {
'status_code': str(404),
'status_msg': 'AjaxHandler Post Error'
}
self.write(json.dumps(rt_dict))
def buildMLModel(nameCSV):
X_full = da.readCSV(nameCSV)
y = X_full.Diabetes
X = X_full.drop(['Diabetes'], axis=1)
beside_list = ['Pregnancies']
#ca.null_to_NaN(X, beside_list)
# si=SimpleImputer(strategy='most_frequent')
# si.fit(X,y)
X_train, X_valid, y_train, y_valid = train_test_split(X,
y,
train_size=0.9,
test_size=0.1,
random_state=1)
# results = {}
# for i in range(1, 10):
# results[25 * i] = get_score(25 * i, X_train, y_train)
# n_estimators_best = min(results, key=results.get)
# print(n_estimators_best)
n_estimators_best = 70
print(get_score(n_estimators_best, X_train, y_train, X_valid, y_valid))
def testhub(self):
data = open('data_test.json').read()
data = json.loads(data)
result = DataAnalysis.findHub(data)
print(result)
self.assertEqual(result[0][0], 'Olivia Colman')
self.assertEqual(result[0][1], 11)
def __init__(self, master=None):
"""
Constructor of the Window object
"""
# Initialize the database
cwd = os.path.dirname(os.path.realpath(__file__))
pathfile = os.path.join(cwd, '_path_to_folders')
if not os.path.exists(pathfile):
path = tkFileDialog.askdirectory()
with open(pathfile, 'w') as f:
f.write(path)
else:
with open(pathfile, 'r') as f:
for l in f:
path = l.strip()
self.qd = da.DataAnalysis(path=path)
self.qd.slct_tags = tag_list
#Initialize the filter dictionaries
self.include = {} # dictionary store including VARs
self.exclude = {} # dictionary store excluding VARs
self.include_boxes = {} # dictionary store excluding buttons
self.exclude_boxes = {} # dictionary store excluding buttons
self.subwindows = [] # list of filter results window
for tag in self.qd.slct_tags:
self.include[tag] = []
self.exclude[tag] = []
self.include_boxes[tag] = []
self.exclude_boxes[tag] = []
# Initialize the frame and master
Frame.__init__(self, master)
self.master = master
# Create a frame for the Chapter selection
self.chp_frame = Frame(master)
self.chp_frame.pack(fill=X)
# Create a frame for key selections
self.tags_frame = Frame(master)
self.mycanvas1 = MyCanvas(self.tags_frame, 320)
self.tags_frame.pack(fill=X)
# Create a frame for score range entries
self.score_frame = Frame(master)
self.score_frame.pack(fill=BOTH, expand=True, anchor=W)
# Create a frame for the Display messages
self.display_frame = Frame(master)
self.mycanvas2 = MyCanvas(self.display_frame, 100, 'lightgreen')
self.display_frame.pack(fill=BOTH, anchor=W, expand=True)
# Create a frame for control buttons
self.button_frame = Frame(master)
self.button_frame.pack(fill=BOTH, expand=True, anchor=W)
# Initilize the window
self.init_window()
def initialize_parameters_from_config_file(self, dir, config):
# TODO: Should I access the config directly? Or store variables here.
self.config = config
# Variables for storing data
self.BEST_INDIVIDUAL_FILE = "elite"
self.POPULATION_FILE = "pop"
self.SAVE_FILE_DIRECTORY = os.path.join(dir, 's_')
self.CHECKPOINT_FREQUENCY = int(
config['experiment']['checkpoint_frequency'])
# Keeping track of evolutionary progression
self.EVALUATION_NR = 0
self.POPULATION_SIZE = int(config['ea']['batch_size'])
# Mutation rates
self.MUTATION_RATE = float(config['ea']['mutation_prob'])
self.MORPH_MUTATION_RATE = float(config['ea']['morphmutation_prob'])
self.MUT_SIGMA = float(config['ea']['mutation_sigma'])
self.TREE_DEPTH = int(config['morphology']['max_depth'])
#
print("Mutation rates - ", " control: ", self.MUTATION_RATE,
", morphology: ", self.MORPH_MUTATION_RATE, ", sigma: ",
self.MUT_SIGMA)
# Wall of death speed
self.WOD_SPEED = float(config['evaluation']['wod_speed'])
# This parameter is used for showing the best individual every generation.
# NOTE: this apparently doesn't work when headlessly simulating the rest
self.show_best = False
if (int(config['ea']['show_best']) == 1):
self.show_best = True
self.headless = False
if (int(config['ea']['headless']) == 1):
self.headless = True
self.load_best = False
if (int(config['ea']['load_best']) == 1):
self.load_best = True
# plots the virtual creates at every <interval> frames
self.interval = int(config['ea']['interval'])
# Elements for visualization
# plot fitness over time
self.PLOT_FITNESS = False
if (int(config['visualization']['v_progression']) == 1):
self.PLOT_FITNESS = True
self.plotter = da.Plotter()
# plot tree structure of current individual being evaluated (for debugging)
self.PLOT_TREE = False
if (int(config['visualization']['v_tree']) == 1):
""" Deprecated debug function """
print(
"Note: visualization of the tree structure was set to true, this is not functional in this version."
)
self.PLOT_TREE = False
def filterData(self):
threshs = []
feat1s = []
feat2s = []
logics = []
numfilters = self.dialog.counter + 1
for i in range(numfilters):
threshs.append(self.dialog.threshold[i].text())
feat1s.append(self.dialog.feature1[i].currentText())
feat2s.append(self.dialog.feature2[i].currentText())
logics.append(self.dialog.logic[i].currentText())
for i in range(numfilters):
try:
threshs[i] = float(str(threshs[i]))
except:
threshs[i] = str(threshs[i])
feat1s[i] = str(feat1s[i])
feat2s[i] = str(feat2s[i])
logics[i] = str(logics[i])
descriptions = getDescriptions()
for i in range(numfilters):
if (feat2s[i] not in descriptions) \
or ((feat1s[i] not in descriptions) and (i == 0)):
self.errorLabel.setText(
"One or more of the feature names were invalid.")
self.dialog.close()
self.dialog = None
return
if type(threshs[i]) is str:
if threshs[i] == "":
self.errorLabel.setText(
"One or more threshold value was empty")
self.dialog.close()
self.dialog = None
return
try:
filterResult = da.filtering(
feat1s[0], feat2s, logics, threshs,
[feat1s[i] for i in range(1, numfilters)])
except:
self.errorLabel.setText(
"An error occurred while loading or analyzing the data.")
self.dialog.close()
self.dialog = None
if type(filterResult) == str:
self.errorLabel.setText(filterResult)
self.dialog.close()
self.dialog = None
else:
self.mode_linear = False
self.mode_poly = False
self.sc.clear_figure()
self.errorLabel.setText("")
self.data = filterResult[1]
self.updateDataDisplay()
self.dialog.close()
self.dialog = None
def main():
# clearing console screen and printing info
clear()
info()
# getting info from data.csv
expert_count = FileReader.get_expert_count()
csv_data, line_count = FileReader.get_data_and_line_count()
# deleting commentaries and non-criteria info from dictionaries
criteria_count, criteria_dict = DataAnalysis.get_criteria_dict_and_criteria_count(
csv_data)
# splitting criterias to blocks and counting concordance for blocks
blocks_dict = DataAnalysis.split_blocks(criteria_dict)
concordance_by_block = {}
for block in blocks_dict:
sum_on_criteria_dict = DataAnalysis.get_sum_on_criteria_dict(
blocks_dict[block])
overall_average_mark = DataAnalysis.get_overall_average_mark(
sum_on_criteria_dict, len(blocks_dict[block]))
squared_difference_sum = DataAnalysis.get_squared_difference_sum(
sum_on_criteria_dict, overall_average_mark)
concordance_by_block[block] = float(
DataAnalysis.get_concordance(squared_difference_sum, expert_count,
len(blocks_dict[block])))
# counting gaussian_distribution
gaussian_distribution_for_criteria = {}
mathematical_expectation = DataAnalysis.get_average_on_criteria(
criteria_dict, expert_count)
for criteria in criteria_dict:
avg_squared_diff, dispersion = DataAnalysis.get_avg_squared_diff_and_dispersion(
criteria_dict[criteria], mathematical_expectation[criteria],
expert_count)
gaussian_distribution_for_criteria[
criteria] = DataAnalysis.get_gaussian_distribution(
dispersion, avg_squared_diff,
mathematical_expectation[criteria], criteria_dict[criteria])
# putting data into output files
FileReader.write_dict_to_csv(concordance_by_block,
'concordance_output.csv')
FileReader.write_dict_to_csv(gaussian_distribution_for_criteria,
'gaussian_distribution_output.csv')
print("Done. Check files.")
def main() -> None:
option = ' '
while option != '0':
print(''' Select the option:
1 - Register
2 - Report
3 - Data Analysis
0 - Exit ''')
option = input('option: ').strip()
if option == '0':
continue
elif option == '1':
menuRegister.menu()
elif option == '2':
Report.menu()
elif option == '3':
DataAnalysis.menu()
else:
print('\33[1;31mInvalid option\33[m')
print('Thanks')
def buildMLModel(nameCSV):
X_full = da.readCSV(nameCSV)
beside_list = ['Pregnancies']
y = X_full.Diabetes
X = ca.null_to_NaN(X_full.drop(['Diabetes'], axis=1), beside_list)
X_train, X_valid, y_train, y_valid = train_test_split(X,
y,
train_size=0.85,
test_size=0.15)
my_model = XGBRegressor(n_estimators=1000, learning_rate=0.05)
my_model.fit(X_train,
y_train,
early_stopping_rounds=5,
eval_set=[(X_valid, y_valid)],
verbose=False)
predictions = my_model.predict(X_valid)
print("Mean Absolute Error: " +
str(mean_absolute_error(predictions, y_valid)))
def write_pot_file(filename, derivative, potential, numofbins, DeltaR, ii, iii, index, offset=0, smoothing="yes", selection="yes"):
print "WRITING POT FILE AT:", filename
OUTPUT_FILE_POT = open (filename, 'w')
# outputting the updated potential and forces to a new LAMMPS potential file
OUTPUT_FILE_POT.write("# POTENTIAL FOR type %d and type %d\n\nTABLE_%d.%d\nN %d\n\n" % (ii, iii, ii, iii, index))
#OUTPUT_FILE_POT.write("# POTENTIAL WITH NAME %s with %d bins.\n\n\n\n\n" % (filename, numofbins))
index = 0
filtered_derivative = np.append(derivative,[0])
filtered_potential = potential
if smoothing=="yes":
#filtered_derivative = dm.smooth_data(np.append(derivative,[0]))
#workaround to improve smoothing
potential[0] = potential[1]
#filtered_potential = dm.smooth_data(filtered_potential,window_len=7,window='hamming') #Temporary
filtered_potential = dm.smooth_data(filtered_potential)
# add thresholding to avoid smoothing of charged particles at low distances
#for i in xrange(0, len(filtered_potential)):
# if np.abs(filtered_potential[i]) > 10.0:
# blend_factor = np.log(10.0)/np.log(np.abs(filtered_potential[i]))
# filtered_potential[i] = filtered_potential[i]*blend_factor + potential[i]*(1.0-blend_factor)
filtered_derivative = da.derivatives(np.arange(offset, offset+(numofbins+1)*DeltaR, DeltaR),filtered_potential)
print "LEN DERIVATIVE", len(filtered_derivative), len(filtered_potential), "smoothing: ", smoothing
for i in xrange(0, numofbins+1):
# if i>340:
# print i*DeltaR, derivative[ii][iii][i], len(dy)
if selection=="yes" and len(derivative) > i:
if abs(derivative[i]) > 0:
# print i, index, i*DeltaR, len(derivative[ii][iii]), numofbins, len(filtered_potential), len(filtered_derivative)
index += 1
OUTPUT_FILE_POT.write("%d\t%f\t%f\t%f \n" % (index, i*DeltaR+offset, filtered_potential[i], filtered_derivative[i]*-1))
if selection=="no" and i < numofbins:
index += 1
OUTPUT_FILE_POT.write("%d\t%f\t%f\t%f \n" % (index, i*DeltaR+offset, filtered_potential[i], filtered_derivative[i]*-1))
OUTPUT_FILE_POT.close()
def __init__(self, config, dir):
print(config.items())
self.start_time = datetime.datetime.now()
self.time = datetime.datetime.now()
self.initialize_parameters_from_config_file(dir,config)
self.fitnessData = da.FitnessData() # stores data of the progression
# TODO take from configuration file
self.EVALUATION_STEPS = 10000
self.TOTAL_EVALUATIONS = 50000
self.SAVEDATA = True
# Initializing modules
self.moduleList = get_module_list() # stores which module types to select from. This list is mutated using the L-System
if self.load_best:
print("Loading best")
individual = pickle.load(open(self.SAVE_FILE_DIRECTORY + self.BEST_INDIVIDUAL_FILE,"rb"))
for i in range(20):
evaluate(individual,HEADLESS = False,INTERVAL =2 + i)
def plotLinearRegression(self):
x = str(self.dialog.featureX.currentText())
y = str(self.dialog.featureY.currentText())
descriptions = getDescriptions()
if (x not in descriptions) or (y not in descriptions):
self.errorLabel.setText("One or more feature names were invalid")
self.dialog.close()
self.dialog = None
return
try:
self.coefs = da.linearRegression(x, y)
except:
self.errorLabel.setText(
"An error occurred while loading or analyzing the data.")
self.dialog.close()
self.dialog = None
self.checkyint = self.dialog.yIntercept.isChecked()
self.checkslope = self.dialog.slopeCheck.isChecked()
self.checkrsquare = self.dialog.rSquared.isChecked()
if (type(self.coefs) == str):
self.errorLabel.setText(self.coefs)
self.dialog.close()
self.dialog = None
else:
self.mode_linear = True
self.mode_poly = False
self.data = self.coefs[0]
self.errorLabel.setText("")
self.sc.update_figure(self.coefs,
x,
y,
Linear=True,
yint=self.dialog.yIntercept.isChecked(),
slope=self.dialog.slopeCheck.isChecked(),
rsquare=self.dialog.rSquared.isChecked())
self.updateDataDisplay()
self.dialog.close()
self.dialog = None
def __init__(self, filename, parent=None):
super(MapDisplay, self).__init__(parent)
print('Loading Data...')
self.map = DataAnalysis.map(filename)
print('Total {} nodes, {} ways.'.format(len(self.map.cross_list.nodes),
len(self.map.ways)))
self.size_x = 680
self.size_y = 830
self.show_path = False
self.zoom = [0, 0] # 第一个代表鼠标滚动之前（过去状态），第二个代表鼠标滚动之后（当前状态）
self.max_zoom = 50
self.zoom_ratio = 1.1
self.mouse = QPoint(0, 0) # 发生事件时鼠标在屏幕上的位置（相对窗口左上角）
self.top_left = node('5', lat_min, lon_min) # 图片左上角坐标
self.top_left.cartesian_coordinate(self.map.cross_list.origin)
self.is_zoom = 0 # 缩放事件标记
self.is_press = 0 # 鼠标按下事件标记
self.before_drag = QPoint(0, 0)
self.road_list = None
self.min_distance = None
self.intro = ""
self.initUI()
def get_filtered_data():
df, returnStr = DataAnalysis.get_filtered_data()
print(df.head())
returnStr += str(df.head())
# create a new feature
print(df.head())
df['CalcFeature'] = df['petal_length'] * df['petal_width']
df = df.drop(['petal_length', 'petal_width'], axis=1)
print(df.head())
# get a dataframe without the species column
df_feat = df.drop(columns=['species'])
# split dataset into training and testing sets
X = df_feat
y = df['species']
# random_state = seed for random values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
return X_train, X_test, y_train, y_test, returnStr
def MainPage(root, photo):
root.title('Consumer Complaint Analysis - Main Page')
MainPageFrame = Frame(root)
MainPageFrame.pack(side=LEFT)
# Side Image for MainPage
ImageFrame = Frame(root)
ImageFrame.pack(side=RIGHT)
ImageLabel = Label(ImageFrame, image=photo)
ImageLabel.pack(side=TOP)
# Functions
# Fetch/Update Data Button Function
###################################Fetch Data Starts####################################################
def FetchData(root, ImageFrame, ImageLabel, photo, MainPageFrame,
FetchDataButton, ViewLogButton):
#Start the Fetching Process
InitialParameters = Initialize.getParam()
LoadFileName = InitialParameters[15]
#Disable the Button and destroy the Image
FetchDataButton.config(state=DISABLED)
ViewLogButton.config(state=DISABLED)
ImageLabel.destroy()
#Create Top and Bottom Frame
TopFetchFrame = Frame(ImageFrame)
TopFetchFrame.pack(side=TOP)
BottopFetchFrame = Frame(ImageFrame)
BottopFetchFrame.pack(side=BOTTOM)
#Label to Display Log with Scroll Bar
#Function for Scroll Bar
def ScrollLogFunc(event):
FetchCanvas.configure(scrollregion=FetchCanvas.bbox("all"),
height=370,
width=700)
#canvas to Implement Scroll Bar for Log
FetchCanvas = Canvas(TopFetchFrame)
CanvasFrame = Frame(FetchCanvas)
ScrollLogY = Scrollbar(TopFetchFrame,
orient="vertical",
command=FetchCanvas.yview)
FetchCanvas.configure(yscrollcommand=ScrollLogY.set)
FetchCanvas.pack(side=LEFT)
ScrollLogY.pack(side=RIGHT, fill=Y)
FetchCanvas.create_window((0, 0), window=CanvasFrame, anchor=NW)
#Label to display the Log Details
LogContentDisplay = Label(CanvasFrame,
text='Loading Log Details . . ..',
anchor=NW,
justify=LEFT,
wraplength=650)
CanvasFrame.bind("<Configure>", ScrollLogFunc)
LogContentDisplay.pack()
#Function to Fetch Log Details from Log File - continuesly
def DisplayLogContent():
#global LogValue
CurrentLogValue = ''
InitialParameters = Initialize.getParam()
LogFileName = InitialParameters[14]
try:
LogFile = open(LogFileName, 'r')
except Exception as e:
#print('Log File Error : ',e)
CurrentLogValue = 'Unable to Display Log.Please Wait . . .'
else:
CurrentLogValue = LogFile.read()
LogContentDisplay.config(text=CurrentLogValue)
LogContentDisplay.after(200, DisplayLogContent)
#run the Fetch Log function
DisplayLogContent()
#Clear the Fetch Data Screen and revert to Default Main Page
def DisplayLogClear(root, photo, ImageFrame, MainPageFrame):
ImageFrame.destroy()
MainPageFrame.destroy()
MainPage(root, photo)
#Button to Clear the Screen and revert back
BackButton = ttk.Button(BottopFetchFrame,
text='Back',
width=20,
command=lambda: DisplayLogClear(
root, photo, ImageFrame, MainPageFrame))
BackButton.pack(side=LEFT, padx=50)
#Runthe Update Process using thread
if os.path.isfile(LoadFileName) == True:
popupmsg('Update Already in Progress !!!')
else:
Choice = messagebox.askquestion(
"Update",
"Are You sure you want to update the Main DB Tables?",
icon='warning')
if Choice == 'yes':
threading.Thread(target=FD.main).start()
else:
DisplayLogClear(root, photo, ImageFrame, MainPageFrame)
###################################Fetch Data Ended####################################################
###################################View Log Starts####################################################
def ViewLog(root, ImageFrame, ImageLabel, photo, MainPageFrame,
ViewLogButton, FetchDataButton):
#Disable the Button and destroy the Image
ViewLogButton.config(state=DISABLED)
FetchDataButton.config(state=DISABLED)
ImageLabel.destroy()
#Create Top and Bottom Frame
TopViewFrame = Frame(ImageFrame)
TopViewFrame.pack(side=TOP)
BottopViewFrame = Frame(ImageFrame)
BottopViewFrame.pack(side=BOTTOM)
#Label to Display Log with Scroll Bar
#Function for Scroll Bar
def ScrollLogFunc(event):
ViewCanvas.configure(scrollregion=ViewCanvas.bbox("all"),
height=370,
width=800)
#canvas to Implement Scroll Bar for Log
ViewCanvas = Canvas(TopViewFrame)
CanvasFrame = Frame(ViewCanvas)
ScrollLogY = Scrollbar(TopViewFrame,
orient="vertical",
command=ViewCanvas.yview)
ViewCanvas.configure(yscrollcommand=ScrollLogY.set)
ViewCanvas.pack(side=LEFT)
ScrollLogY.pack(side=RIGHT, fill=Y)
ViewCanvas.create_window((0, 0), window=CanvasFrame, anchor=NW)
#Label to display the Log Details
ViewLogDisplay = Label(CanvasFrame,
text='Please Select a Log Category.',
anchor=NW,
justify=LEFT,
wraplength=700)
CanvasFrame.bind("<Configure>", ScrollLogFunc)
ViewLogDisplay.pack(side=BOTTOM)
#DropDown Button For Archive Log
var = StringVar()
var.set('')
InitialParameters = Initialize.getParam()
LogPath = InitialParameters[19]
#LogPath=r'C:\Users\Priyank\Desktop\MIS\Scripting Languages\DataAnalyst-Project(Python)\Coding\Logs'
LogList = []
for filename in os.listdir(LogPath):
#if filename.split('.')[-1]=='log' and filename.split('_')[0]=='DataUpdateLog':
LogList.append(filename)
def func(value):
LogFile = LogPath + '\\' + str(value)
Log = open(LogFile, 'r')
LogValue = Log.read()
ViewLogDisplay.config(text=LogValue)
ChoiceLabel.config(text='Archived Process Logs -')
ChoiceLabel = Label(CanvasFrame, text='LOGS !!!')
ChoiceLabel.pack(side=LEFT)
ChoiceDropDown = OptionMenu(CanvasFrame, var, *LogList, command=func)
ChoiceDropDown.pack(side=RIGHT)
#Function to Display the Logs based on selection
#APi and Source file info from Log table
def APILog(ChoiceLabel):
ChoiceLabel.config(text='Source File and API Logs -')
var.set('')
APLDataFrame = FirebirdDB.DisplayAPILog()
APLDataFrame['FileLen'] = APLDataFrame.FileName.map(len)
MaxLength = 0
for i in range(0, len(APLDataFrame)):
if APLDataFrame.loc[i, 'FileLen'] > MaxLength:
MaxLength = APLDataFrame.loc[i, 'FileLen']
for i in range(0, len(APLDataFrame)):
APLDataFrame.loc[i, 'FileName'] = str(
APLDataFrame.loc[i, 'FileName']).ljust(MaxLength, '_')
m, s = divmod(APLDataFrame.loc[i, 'Duration'], 60)
h, m = divmod(m, 60)
DurationTime = str(int(h)).rjust(2, '0') + ':' + str(
int(m)).rjust(2, '0') + ':' + str(int(s)).rjust(2, '0')
APLDataFrame.loc[i, 'Duration'] = DurationTime
#print(APLDataFrame.loc[i,'LoadStartDTTM'])
APLDataFrame.loc[
i, 'LoadStartDTTM'] = datetime.datetime.strptime(
APLDataFrame.loc[i, 'LoadStartDTTM'],
"%Y-%m-%d %H:%M:%S.%f").strftime("%d %b %y %H:%M:%S")
APLDataFrame.loc[i, 'LoadStartDTTM'] = str(
APLDataFrame.loc[i, 'LoadStartDTTM']).ljust(30,
' ').upper()
#print(datetime.datetime.strptime(APLDataFrame.loc[i,'LoadStartDTTM']),"%d/%m/%Y").strftime("%d%b%y"))
APLDataFrame.loc[i, 'LoadIndex'] = str(
int(APLDataFrame.loc[i, 'LoadIndex']))
APLDataFrame.loc[i, 'LoadOrder'] = str(
int(APLDataFrame.loc[i, 'LoadOrder']))
APLDataFrame.loc[i, 'RecordCount'] = str(
int(APLDataFrame.loc[i, 'RecordCount']))
ViewLogDisplay.config(
text=
'--------------------------------------------------------------------------------'
'-----------------------------------------------\n'
'Index\tOrder\t' + 'FileName'.ljust(MaxLength, '_') +
'\tCount\tStartTime\t\tDuration\n' +
'--------------------------------------------------------------------------------'
'-----------------------------------------------\n' +
APLDataFrame.to_csv(
sep='\t',
index=False,
header=False,
columns=[
'LoadIndex', 'LoadOrder', 'FileName', 'RecordCount',
'LoadStartDTTM', 'Duration'
]) +
'--------------------------------------------------------------------------------'
'-----------------------------------------------')
#Load Step info from Main Log
def LoadStep(ChoiceLabel):
ChoiceLabel.config(text='Load Step Logs -')
var.set('')
LoadStepDataFrame = FirebirdDB.DisplayLoadStepLog()
LoadStepDataFrame['StatusLen'] = LoadStepDataFrame.Status.map(len)
MaxStatus = 0
for i in range(0, len(LoadStepDataFrame)):
if LoadStepDataFrame.loc[i, 'StatusLen'] > MaxStatus:
MaxStatus = LoadStepDataFrame.loc[i, 'StatusLen']
for i in range(0, len(LoadStepDataFrame)):
LoadStepDataFrame.loc[i, 'Status'] = str(
LoadStepDataFrame.loc[i, 'Status']).ljust(MaxStatus, '_')
LoadStepDataFrame.loc[i, 'LoadIndex'] = str(
int(LoadStepDataFrame.loc[i, 'LoadIndex']))
LoadStepDataFrame.loc[i, 'LoadOrder'] = str(
int(LoadStepDataFrame.loc[i, 'LoadOrder']))
LoadStepDataFrame.loc[i, 'RecordCount'] = str(
int(LoadStepDataFrame.loc[i, 'RecordCount']))
LoadStepDataFrame.loc[
i, 'LoadStartDTTM'] = datetime.datetime.strptime(
LoadStepDataFrame.loc[i, 'LoadStartDTTM'],
"%Y-%m-%d %H:%M:%S.%f").strftime("%d %b %y %H:%M:%S")
LoadStepDataFrame.loc[i, 'LoadStartDTTM'] = str(
LoadStepDataFrame.loc[i,
'LoadStartDTTM']).ljust(30,
' ').upper()
m, s = divmod(LoadStepDataFrame.loc[i, 'Duration'], 60)
h, m = divmod(m, 60)
DurationTime = str(int(h)).rjust(2, '0') + ':' + str(
int(m)).rjust(2, '0') + ':' + str(int(s)).rjust(2, '0')
LoadStepDataFrame.loc[i, 'Duration'] = DurationTime
ViewLogDisplay.config(
text=
'--------------------------------------------------------------------------------'
'----------------------------------------------------------\n'
'Index\tOrder\t' + 'Status'.ljust(MaxStatus, '_') +
'\tCount\tStartTime\t\tDuration\n' +
'--------------------------------------------------------------------------------'
'----------------------------------------------------------\n'
+ LoadStepDataFrame.to_csv(
sep='\t',
index=False,
header=False,
columns=[
'LoadIndex', 'LoadOrder', 'Status', 'RecordCount',
'LoadStartDTTM', 'Duration'
]) +
'--------------------------------------------------------------------------------'
'----------------------------------------------------------')
#Display Log details from Archived files/Rejects
def ProcessLog():
ChoiceLabel.config(text='Archived Process Logs -')
ViewLogDisplay.config(text='Please Select an Archive File !!!')
var.set('')
#Clear the View Log Screen and revert to Default Main Page
def DisplayLogClear(root, photo, ImageFrame, MainPageFrame):
ImageFrame.destroy()
MainPageFrame.destroy()
MainPage(root, photo)
#Buttions to choose log type
APILogButton = ttk.Button(BottopViewFrame,
text='API Logs',
width=20,
command=lambda: APILog(ChoiceLabel))
LoadStepButton = ttk.Button(BottopViewFrame,
text='Load Step Logs',
width=20,
command=lambda: LoadStep(ChoiceLabel))
ProcessLogButton = ttk.Button(BottopViewFrame,
text='Process Logs/Rejects',
width=20,
command=ProcessLog)
BackButton = ttk.Button(BottopViewFrame,
text='Back',
width=20,
command=lambda: DisplayLogClear(
root, photo, ImageFrame, MainPageFrame))
#pack Buttons
APILogButton.pack(side=LEFT, padx=20)
LoadStepButton.pack(side=LEFT, padx=20)
ProcessLogButton.pack(side=LEFT, padx=20)
BackButton.pack(side=LEFT, padx=20)
###################################View Log Ended####################################################
# Create Buttions to Perform Tasks
FetchDataButton = ttk.Button(
MainPageFrame,
text='Fetch/Update Database',
width=40,
command=lambda: FetchData(root, ImageFrame, ImageLabel, photo,
MainPageFrame, FetchDataButton, ViewLogButton
))
ViewLogButton = ttk.Button(
MainPageFrame,
text='View Log History',
width=40,
command=lambda: ViewLog(root, ImageFrame, ImageLabel, photo,
MainPageFrame, ViewLogButton, FetchDataButton))
DataAnalystButton = ttk.Button(MainPageFrame,
text='Data Analyst(Consumer Complaint)',
command=lambda: DataAnalysis.AnalystMain(
root, ImageFrame, MainPageFrame),
width=40)
MainExitButton = ttk.Button(MainPageFrame,
text='Exit',
width=40,
command=root.quit)
SpaceLabel0 = Label(MainPageFrame, text='', height=3)
SpaceLabel1 = Label(MainPageFrame, text='', height=3)
SpaceLabel2 = Label(MainPageFrame, text='', height=3)
SpaceLabel3 = Label(MainPageFrame, text='', height=3)
SpaceLabel4 = Label(MainPageFrame, text='', height=3)
#Clock
clock = Label(MainPageFrame, font=CLOCK_FONT, anchor=W)
clock.pack(side=TOP, fill=BOTH, expand=1)
def tick():
global TIMER
#CurrTime = time.strftime('%H:%M:%S')
CurrTime = time.strftime('%d %b %Y %X')
if CurrTime != TIMER:
Timer = CurrTime
clock.config(text=CurrTime)
clock.after(200, tick)
#run Clock
tick()
# Display the Buttons on Main Page
SpaceLabel0.pack()
FetchDataButton.pack()
SpaceLabel1.pack()
ViewLogButton.pack()
SpaceLabel2.pack()
DataAnalystButton.pack()
SpaceLabel3.pack()
MainExitButton.pack()
SpaceLabel4.pack()
# Status Bar Functions
def NormStatus(event):
status.config(text='Consumer Complaint Analysis', anchor=NE)
def FetchStatus(event):
status.config(
text=
'Fetch Consumer Complaint Data from http://data.consumerfinance.gov/ site and Update the Database Tables.',
anchor=NW,
justify=LEFT,
wraplength=280)
def ViewLogStatus(event):
status.config(
text=
'View Log History of all the All Database updates from http://data.consumerfinance.gov/ site.',
anchor=NW,
justify=LEFT,
wraplength=280)
def DataAnalystStatus(event):
status.config(
text=
'Click to Analyse in Detail the Comsumer Complaint Data ,fetch Reports and Visualize Data.',
anchor=NW,
justify=LEFT,
wraplength=280)
def ExitStatus(event):
status.config(text='Exit the Consumer Complaint Analyst Program',
anchor=NW,
justify=LEFT,
wraplength=280)
#status Bar Bottom
status = Label(MainPageFrame,
text='Consumer Complaint Analysis',
bd=1,
relief=SUNKEN,
anchor=NE,
height=4,
width=40)
status.pack(side=BOTTOM, fill=X)
#Binding Status Functions
FetchDataButton.bind('<Enter>', FetchStatus)
FetchDataButton.bind('<Leave>', NormStatus)
ViewLogButton.bind('<Enter>', ViewLogStatus)
ViewLogButton.bind('<Leave>', NormStatus)
DataAnalystButton.bind('<Enter>', DataAnalystStatus)
DataAnalystButton.bind('<Leave>', NormStatus)
MainExitButton.bind('<Enter>', ExitStatus)
MainExitButton.bind('<Leave>', NormStatus)
detector_dist = 3289.95 # pixels
true_center = [1020.67, 1024.61] # [row, column] of detector image center in pixels (shifted by 1 for python index)
e_rng = [-0.012, 0.012] # elastic strain range
p_rng = [-0.024, 0.024] # plastic strain range
t_rng = [-0.036, 0.036] # total strain range
E, G, v = 71.7, 26.9, 0.33 # elastic modulus (GPa), shear modulus (GPa), poisson's ratio
ring_name = 'al_311'
radius = 718 # ring radius in pixels
dr = 30 # half of ring width in pixels
min_amp = 25 # minimum acceptable peak amplitude
vec_frac = 0.25 # fraction of peaks that must be acceptable
sample = DA.Specimen(specimen_name, data_dir, out_dir,
step_names, dic_files, dark_dirs,
init_dirs, dic_center,
x_range,x_num, y_range, y_num,
detector_dist, true_center,
e_rng, p_rng, t_rng, E, G, v)
#%% Lasso fitting for each ring
# Specify image data
load_step_list = [3]
img_num_list = range(23,24)
num_theta = 2400
dtheta = 2*np.pi/num_theta
num_var = 100
var_domain = np.linspace((dtheta),(np.pi/16),num_var)**2
basis_path = os.path.join('basis_radial_sum','gaus_basis_shift_')
def __init__(self):
self.dbController = DBController.DBController()
self.dataAnalysis = DataAnalysis.DataAnalysis()
Title: Analyze user patterns using Data(instacart-market-basket-analysis/Kaggle)
'''
# install Library List
## pip3 install pandas
## pip3 install numpy
## pip3 install matplotlib
# Import Library
## Data Preprocessing Classes
import Preprocessing
## Classes related to analysis and visualization
import DataAnalysis
# Class declaration
prepro = Preprocessing.Preprocessing()
analysis = DataAnalysis.DataAnalysis()
# Running Process
while True:
print('Run Data PreProcessing ▶ 1')
print('Run Analysis and Visualization ▶ 2')
choice = int(input('Choose the Job:'))
if choice == 1:
# Run Data Preprocessing
## num: PreProcessing(-1), PreProcessing & Save(0), Read Sample Data(1)
## user: Specify user counts when saving
n = int(
input(
'PreProcessing(-1), PreProcessing & Save(0), Read Sample Data(1):'
))
#Set path for libaries on server #import sys #sys.path.insert(0, "/home/student/anaconda3/lib/python3.4/site-packages") import MakeLogDataDB import makeSurveyDB import makeTimeTableDB import glob, os import wicount import db import DataAnalysis #Connect to the database con = db.get_connection() c = con.cursor() # Create all the database tables wicount.SetUpDatabase() MakeLogDataDB.main() makeSurveyDB.main() makeTimeTableDB.main() #Run the analytics algorithm DataAnalysis.main() #Close the database connection con.close()
import CoreActions as ca
import DataAnalysis as da
import LearningModel as lm
import PrepareDataset as pds
if __name__ == '__main__':
nameXLS = 'Diabetes.xls'
nameCSV = 'csvData.csv'
ca.convertXLSToCSV(nameCSV, nameXLS)
da.describeData(nameCSV)
#da.detectNullVal(nameCSV)
pds.buildMLModel(nameCSV)
#lm.buildMLModel(nameCSV)
ring_name = 'al_111'
left = [372, 432]
right = [1623, 1683]
top = []
bottom = []
lambda_sel = 2.0
#%%
ring_name = 'al_311'
left = [277, 337]
right = [1713, 1773]
top = [274, 334]
bottom = [1709, 1769]
lambda_sel = 2.0
#%%
sample = DataAnalysis.Specimen(specimen_name, data_dir, out_dir, x_range,
x_num, z_range, z_num, step_names, dark_dirs,
init_dirs)
ring = DataAnalysis.Ring(ring_name, sample, left, right, top, bottom,
lambda_sel)
if specimen_name == 'ti64_notched' or specimen_name == 'ti64_plain':
xa = np.linspace(sample.x_range[1],
sample.x_range[0],
num=x_num,
endpoint=True)
za = np.linspace(sample.z_range[1],
sample.z_range[0],
num=z_num,
endpoint=True)
z2d, x2d = np.meshgrid(za, xa)
if specimen_name == 'al7075_plain':
""" Kaggle Titanic Competition -- 2020-02-02 kaggle/mimocha github/mimocha Chawit Leosrisook """ """ =========================================================================================== """ """ ======================================= MAIN SCRIPT ======================================= """ """ =========================================================================================== """ import DataAnalysis import DataHandler filename = './data/train.csv' rawData = DataHandler.ReadCSV(filename) # Preliminary Data Analysis DataAnalysis.Preliminary(rawData)
t = numpy.linspace(0, sim_time, nsamps)
freqs = [0.1, 0.5, 1, 4]
x = 0
for i in range(len(freqs)):
x += numpy.cos(2*math.pi*freqs[i]*t)
time_dom = fig.add_subplot(232)
fig.add_subplot(233)
pyplot.plot(t, x)
# pyplot.ylim([-5,5])
pyplot.title('Filter Input - Time Domain')
pyplot.grid(True)
f = h5py.File("test.hdf5", "w")
dset = f.create_dataset("test", data= x)
f.flush()
f.close()
da = DataAnalysis.data_analysis()
da.load_hdf5("test", "test", dataset_rename="test")
da.high_pass_filter(samp_rate, order= 7)
fig.add_subplot(236)
pyplot.plot(t, da.data)
pyplot.title('Filter Output - Time Domain')
pyplot.grid(True)
pyplot.show()
x1d, z1d = x2d.flatten(), z2d.flatten()
#%% read peak diameters if they have been fit, if not fit here
orient = 'h'
try:
x, z, diams = [], [], []
for i_step in range(sample.n_load_step):
txt_data = DataReader.read_data_from_text(ring.out_dir+sample.step_names[i_step]+'_diams_'+orient+'.txt')
x.append(txt_data[:, 0]), z.append(txt_data[:, 1]), diams.append(txt_data[:, 2])
except:
l_centers, l_errs = np.zeros((sample.n_load_step, sample.n_data_pt)), np.zeros((sample.n_load_step, sample.n_data_pt))
u_centers, u_errs = np.zeros((sample.n_load_step, sample.n_data_pt)), np.zeros((sample.n_load_step, sample.n_data_pt))
diams = np.zeros((sample.n_load_step, sample.n_data_pt))
for i_step in range(sample.n_load_step):
l_centers[i_step,:], l_errs[i_step,:], u_centers[i_step,:], u_errs[i_step,:], diams[i_step,:] = DataAnalysis.write_scan_diameters(sample, ring, x1d, z1d, i_step, orient)
#%%
# total variation filtering
fits, coords = [], []
for i_step in range(sample.n_load_step):
s_fits, s_coords = [], []
xvals = np.unique(x[i_step])
for xval in xvals:
x_col = x[i_step][x[i_step]==xval]
z_col = z[i_step][x[i_step]==xval]
diam_col = diams[i_step][x[i_step]==xval]
fit = DataAnalysis.total_variation(diam_col, ring.lambda_sel)
path = ring.out_dir+sample.step_names[i_step]+'_x_'+str(xval)+orient+'.tiff'
def testAge(self):
data = open('data_test.json').read()
data = json.loads(data)
result = DataAnalysis.getAgeGross(data)
self.assertEqual(result[0][0], 19)
self.assertEqual(result[1][0], 22)
#Set path for libaries on server #import sys #sys.path.insert(0, "/home/student/anaconda3/lib/python3.4/site-packages") import MakeLogDataDB import makeSurveyDB import makeTimeTableDB import glob, os import wicount import db import DataAnalysis #Connect to the database con = db.get_connection() c=con.cursor() # Create all the database tables wicount.SetUpDatabase() MakeLogDataDB.main() makeSurveyDB.main() makeTimeTableDB.main() #Run the analytics algorithm DataAnalysis.main() #Close the database connection con.close()
detector_dist = 3293.09 # pixels
true_center = [1027, 1027] # [row, column] of center in pixels
e_rng = [-0.012, 0.012]
p_rng = [-0.024, 0.024]
t_rng = [-0.036, 0.036]
ring_name = 'al_311'
left = [278, 338]
right = [1713, 1773]
top = [274, 334]
bottom = [1709, 1769]
lambda_h = 2.0
lambda_v = 0.8
#%%
# create sample and ring objects
sample = DA.Specimen(specimen_name, data_dir, out_dir, x_range, x_num, z_range, z_num, step_names, dark_dirs, init_dirs, detector_dist, true_center)
ring = DA.Ring(ring_name, sample, left, right, top, bottom, lambda_h, lambda_v)
# create appropriate coordinate arrays
if sample.name == 'ti64_notched' or specimen_name=='ti64_plain':
xa = np.linspace(sample.x_range[1], sample.x_range[0], num=x_num, endpoint=True)
za = np.linspace(sample.z_range[1], sample.z_range[0], num=z_num, endpoint=True)
z2d, x2d = np.meshgrid(za, xa)
if sample.name == 'al7075_plain':
xa = np.linspace(x_range[1], x_range[0], num=x_num, endpoint=True)
za = np.linspace(z_range[1], z_range[0], num=z_num, endpoint=True)
x2d, z2d = np.meshgrid(xa, za)
if sample.name == 'al7075_mlf':
xa = np.linspace(x_range[0], x_range[1], num=x_num, endpoint=True)
za = np.linspace(z_range[0], z_range[1], num=z_num, endpoint=True)
def main(argv=None):
if argv is None:
argv = sys.argv
run_importer = True
run_formatter = True
run_analysis = True
try:
try:
opts, args = getopt.getopt(argv[1:], "h", ["help", "skip-importer", "skip-formatter", "skip-analysis"])
print "got args"
except getopt.error, msg:
raise Usage(msg)
for option, data in opts:
if('-h' == option or '--help' == option):
print "LSCE test script. Usage: \"python testscript.py [--skip-importer] [--skip-formatter] [--skip-analysis]" +\
" mat_source hdf5_dest" +\
"\"\n\nSupply the following arguments to run pipeline:\n\n\tmat_source: " +\
"The path to the raw .mat files to be imported.\n\thdf5_dest: the name to save hdf5 output file under" +\
"\n\nAvailable modes:\n\t--skip-importer: skip importation step. Formatter wil still run using" +\
" mat_source as src directory." +\
"\n\t--skip-formatter: skip formatting step. Importer will use mat_source as usual. \n\t\t\t Analysis will" +\
" use hdf5_dest if it exists." + \
"\n\t--skip-analysis: skip computation of analysis data. Formatter will still output to hdf5_dest. "
return
if('--skip-importer' == option):
run_importer = False
if('--skip-formatter' == option):
run_formatter = False
if('--skip-analysis' == option):
run_analysis = False
if(len(args) < 2):
raise Usage("Insufficient arguments supplied.")
else:
print args.__repr__()
print "Welcome to LSCE test script.\nThis script will perform a " + \
"complete iteration of our pipeline, starting with the data importer."
if(run_importer):
print "Importing data from directory "+args[0]
Importer.loadFromMat(args[0])
else:
print "Skipped importing data."
if(run_formatter):
print "Formatting data as hdf5 in "+args[1]+".hdf5"
DataFormatter.formatData(args[0], args[1])
else:
print "Skipped formatting data."
os.system("PAUSE")
testing = None
raw_data = None
if(run_analysis):
dtool = DataAnalysis.data_analysis()
dtool.load_hdf5(args[1], dataset_name="Electrode_12_master", group_name="raw_data")
dtool.sampling_rate = 1000
testing = dtool.high_demo_filter(20)
raw_data = dtool.f["raw_data"]["Electrode_12_master"]
else:
print "Skipped data analysis.\nPlaceholder groups " + \
"\"/data_analysis/demo_filter_results\" and \"/raw_data/Electrode_12_master\" will be used."
hdfile = h5py.File(args[1]+".hdf5", "r+")
if("data_analysis" not in hdfile or "demo_filter_results" not in hdfile["data_analysis"]):
print "Skipping graphs..."
return
testing = hdfile["data_analysis"]["demo_filter_results"]
raw_data = hdfile["raw_data"]["Electrode_12_master"]
os.system("PAUSE")
plt.subplot(2, 1, 1)
plt.plot(testing)
plt.subplot(2, 1, 2)
plt.plot(raw_data)
plt.show()
if(run_analysis):
dtool.close()
#!/usr/bin/python2.7
# -*- coding:utf-8 -*-
from ***.data_analysis import DataAnalysis
import ***
import sys
reload(sys)
sys.setdefaultencoding('utf8')
filename = 'data.txt'
analysis=DataAnalysis(filename)
I=analysis.plotXY()
subseq = analysis.findMaxSubSequence()
if subseq == [1, 2, 8, 9, 10, 11, 13]:
print('PASS')
else:
print('FAILURE')
sys.stdout.write(str(subseq))
SamplingData = Random_Data['GenericData']
Board = gen.Generic_Generator(Queue=Data_Queue,
binSize=50,
Data=SamplingData)
Board.start()
while len(Feature) < 5 * TaskSetting['Initiation']:
if not Data_Queue.empty():
Sample = Data_Queue.get()
if not EEG_Recording.any():
EEG_Recording = Sample
else:
EEG_Recording = np.append(EEG_Recording, Sample, axis=0)
# Common Average Reference
Sample[:, range(1, 9)] = Process.Common_Average_Reference(
Sample[:, range(1, 9)])
# Feature Extraction
if ALPHA:
Feature = np.append(
Feature, Process.AlphaDifference(Sample, ([5, 6], [7, 8]),
250))
else:
Feature = np.append(
Feature, Process.PowerExtraction(Sample[:, 3], [8, 12], 250))
FIFO.Rewrite(Trigger_Log, "Calibration On")
Trigger = np.array([[len(Feature), CALIBRATION_START]])
print "Calibration Stage 1"
Current_Index = len(Feature)
# distribution of GDP per person. If the input is finish, then it will plot the
# histogram and boxplot from 2007 to 2012.
import pandas as pd
from DataAnalysis import *
from Methods import *
from InvalidInputException import *
import sys
if __name__ == "__main__":
countries = pd.read_csv("countries.csv")
income = pd.read_excel("indicator gapminder gdp_per_capita_ppp.xlsx", index_col=0).T
print(income.head())
while True:
try:
year_string = input("Enter a year between 1800 and 2012 to display the distribution, or 'finish' to exit")
if year_string.isdigit() and int(year_string) >= 1800 and int(year_string) <= 2012:
year = int(year_string)
displayIncome(year, income)
elif year_string == "finish":
for i in range(2007, 2013):
cur_data = merge_by_year(i, income, countries)
cur_data_analysis = DataAnalysis(i)
cur_data_analysis.histogram(cur_data)
cur_data_analysis.boxplot(cur_data)
break
else:
raise InvalidInputException()
except KeyboardInterrupt:
sys.exit()
#da.__testInialization__()
#da.__printDataset__()
#------------ Simple Test 1 ------------
# *** Please refer to "testInialization" for the feature name(s)
#linearRegressionOutput = DA.linearRegression('Feature1', 'Feature2')
#filteringOutput = DA.filtering2("Feature5", "Feature5", ">=", 0)
#filteringOutput = DA.filtering("Feature5", ["Feature5"], [">="], [0], [])
#filteringOutput = DA.filtering("Feature5", ["Feature5", "Feature5"], [">=", "<="], [0, 5], ["AND"])
#filteringOutput = DA.filtering("Feature5", ["Feature5", "Feature5"], [">=", "<="], [0, 3], ["OR"])
#filteringOutput = DA.filtering("Feature5", ["Feature5", "Feature5", "Feature5"], [">=", "<=", "<="], [0, 5, 3], ["AND", "AND"])
#filteringOutput = DA.filtering("Feature5", ["Feature5", "Feature5", "Feature5","Feature5","Feature5","Feature5"], ["<=", "<=", "<=","<=","<=","<="], [10, 9, 8,7,6,5], ["AND", "AND","AND", "AND","AND"])
filteringOutput = DA.filtering("Feature2", ["Feature2"], ["Does Not Contain"], ["HI"], [])
#filteringOutput = DA.filtering("Feature1", ["Feature2","Feature2"], ["Contains","Does Not Contain"], ["hi","hi"], ["AND"])
#filteringOutput = DA.filtering("Feature1", ["Feature2","Feature2"], ["Contains","Does Not Contain"], ["hi","hi"], ["OR"])
# 1. output of linearRegression
#if type(linearRegressionOutput) == str:
# print (linearRegressionOutput)
#print (linearRegressionOutput[0])
#print ('\nCoefficients:', linearRegressionOutput[1])
#print ('Y-intecept:', linearRegressionOutput[2])
#print ('R^2:', linearRegressionOutput[3])
# 2. output of filtering
if type(filteringOutput) == str:
print (filteringOutput)
