def displayPlot(self):
"""Displays Visualisation of Data using MatPlotLib"""
try:
# retrieve combobox values
graphtype = str(self.typeBox.currentText())
xaxis = str(self.xBox.currentText())
yaxis = str(self.yBox.currentText())
plotType = str(self.plotBox.currentText())
xval = int(xaxis[0])
yval = int(yaxis[0])
plotNone = None
def defineplot(x):
if x == "None":
return plotNone
else:
a = int(x[0])
return a
plotval = defineplot(plotType)
# generates the plot (plot func is under modules)
mod.plot(self.table, graphtype, xval, yval, plotval)
plt.show()
except Exception as e:
mod.errorGUI(str(e))
print e
def importCSV(self):
"""Imports CSV and displays as table in GUI"""
try:
fileName = mod.openFileLocation(self)
self.table = mod.fileUpload(fileName)
self.csvTable.setSortingEnabled(True)
# self.csvTable.setHorizontalHeaderLabels()
self.csvTable.setModel(PandasModel(self.table))
self.csvTable.resizeColumnsToContents()
self.csvTable.show()
# clears comboboxes to avoid repeat values when another CSV is imported
self.xBox.clear()
self.yBox.clear()
self.plotBox.clear()
# populate combobox values
colVal = 0
self.plotBox.addItem("None")
for i in self.table.columns:
colName = str(colVal) + ". " + i
self.xBox.addItem(colName)
self.yBox.addItem(colName)
self.plotBox.addItem(colName)
colVal += 1
except Exception as e:
# Display error message
mod.errorGUI(str(e))
def mergeCSV(self):
"""Merges another CSV with the currently open Pandas Dataframe"""
try:
fileName = mod.openFileLocation(self)
table2 = mod.fileUpload(fileName)
if collections.Counter(
self.table.columns.values) == collections.Counter(
table2.columns.values):
newTable = pd.concat([self.table, table2])
newTable = newTable.reset_index(drop=True)
else:
return ValueError("No matching columns found!")
# newTable = pd.concat([self.table, table2], axis=1)
self.table = newTable
self.csvTable.setSortingEnabled(True)
self.csvTable.setModel(PandasModel(self.table))
self.csvTable.resizeColumnsToContents()
self.csvTable.show()
except Exception as e:
# Display error message
mod.errorGUI(str(e))
def exportPDF(self):
"""Exports the current Pandas Dataframe as a PDF File (No modifications is made)"""
dateTimeFormat = time.strftime(
"%Y%m%d-%H%M%S") # Date and Time Format (YYYYMMDD-HHMMSS)
config = pdf.configuration(
wkhtmltopdf="wkhtmltopdf\\bin\\wkhtmltopdf.exe"
) # where the config file is located at
try:
if self.table.empty:
raise ValueError("There is no data!")
else:
self.table.to_html(
'exportPDF.html') # convert table data to html
pdfFileName = 'tablePDF-' + dateTimeFormat + ".pdf" # name file of the pdf file
pdf.from_file('exportPDF.html',
pdfFileName,
configuration=config) # convert html to pdf file
os.remove('exportPDF.html') # delete the html file
mod.successGUI() # prompt file downloaded
except Exception as e:
mod.errorGUI(str(e))
def importCSV(self):
"""Imports CSV and displays as table in GUI"""
try:
#prompts user for file location
fileName = mod.openFileLocation(self)
#populates dataframe with CSV data
self.table = mod.fileUpload(fileName)
#enables sorting for table by pressing the headers
self.csvTable.setSortingEnabled(True)
#sets and shows the table in GUI
self.csvTable.setModel(PandasModel(self.table))
self.csvTable.resizeColumnsToContents()
self.csvTable.show()
# clears comboboxes to avoid repeat values when another CSV is imported
self.xBox.clear()
self.yBox.clear()
self.plotBox.clear()
# populate combobox values in visualisation
colVal = 0
self.plotBox.addItem("None")
for i in self.table.columns:
colName = str(colVal) + ". " + i
self.xBox.addItem(colName)
self.yBox.addItem(colName)
self.plotBox.addItem(colName)
colVal += 1
except Exception as e:
# Display error message
mod.errorGUI(str(e))
def mergeCSV(self):
"""Merges another CSV with the currently open Pandas Dataframe"""
try:
#prompts user for 2nd dataset and stores it as a separate dataframe
fileName = mod.openFileLocation(self)
table2 = mod.fileUpload(fileName)
#if same columns are present, then it will merge with the existing dataframe. otherwise will not allow merging
if collections.Counter(
self.table.columns.values) == collections.Counter(
table2.columns.values):
newTable = pd.concat([self.table, table2])
newTable = newTable.reset_index(drop=True)
else:
return ValueError("No matching columns found!")
#initialises table
self.table = newTable
self.csvTable.setSortingEnabled(True)
self.csvTable.setModel(PandasModel(self.table))
self.csvTable.resizeColumnsToContents()
self.csvTable.show()
except Exception as e:
# Display error message
mod.errorGUI(str(e))
def exportCSV(self):
"""Exports the current Pandas Dataframe (after merges and modifications) as a CSV File"""
try:
fileName = mod.saveFileLocation(self)
self.table.to_csv(fileName, encoding='utf-8', index=False)
except Exception as e:
# Display error message
mod.errorGUI(str(e))
def searchTable(self):
"""Searches the table and presents the search results as a view"""
try:
self.view = self.view.iloc[0:0]
# search table and generates view
searchQuery = str(self.search.text())
#throws an error if there is no search value
if searchQuery == "":
raise ValueError("Please enter a search query!")
# creates dictionary to hold the float, int and str values of the query
queryDict = {'string': searchQuery}
#if value can be an integer or float, it will convert accordingly. else it will set a default value of 0.
try:
queryDict['int'] = int(searchQuery)
queryDict['float'] = float(searchQuery)
except:
queryDict['int'] = 0
queryDict['float'] = 0.0
# initialises the view dataframe
for i in self.table.columns:
loopQ = ""
# sets the query datatype according to the type in the column
if is_numeric_dtype(self.table[i].dtype):
loopQ = queryDict['int']
queryBool = self.table[i] == loopQ
elif is_string_dtype(self.table[i].dtype):
loopQ = queryDict['string']
queryBool = self.table[i].str.contains(loopQ,
case=False,
regex=True)
elif is_float_dtype(self.table[i].dtype):
loopQ = queryDict['float']
queryBool = self.table[i] == loopQ
# if there are matches, add the rows to the view otherwise will show no result.
if self.table[queryBool].empty is False:
self.view = pd.concat(
[self.view, self.table.loc[queryBool]])
else:
continue
# displays view
if not self.view.empty:
self.csvTable.setSortingEnabled(True)
self.csvTable.setModel(PandasModel(self.view))
self.csvTable.resizeColumnsToContents()
self.csvTable.show()
else:
raise ValueError("No Value Found!")
except Exception as e:
mod.errorGUI(str(e))
def exportTXT(self):
dateTimeFormat = time.strftime(
"%Y%m%d-%H%M%S") # Date and Time Format (YYYYMMDD-HHMMSS)
try:
if self.table.empty:
raise ValueError("There is no data!")
else:
self.table.to_csv('tableTXT-' + dateTimeFormat + '.TXT',
sep='\t') # export data to TXT file
mod.successGUI() # prompt file downloaded
except Exception as e:
mod.errorGUI(str(e))
def exportCSV(self):
"""Exports the current Pandas Dataframe (after merges and modifications) as a CSV File"""
try:
fileName = mod.saveFileLocation(
self) # if search is valid export with the latest version
if not self.view.empty:
self.view.to_csv(fileName, encoding='utf-8', index=False)
mod.successGUI()
else:
self.table.to_csv(fileName, encoding='utf-8', index=False)
mod.successGUI()
except Exception as e:
# Display error message
mod.errorGUI(str(e))
def displayLr(self):
"""Machine Learning - Linear Regression"""
try:
# retrieve combobox values
xaxis = str(self.xBox.currentText())
yaxis = str(self.yBox.currentText())
plotType = str(self.plotBox.currentText())
predictions = int(self.predictBox.currentText())
x = int(xaxis[0])
y = int(yaxis[0])
z = str(self.plotBox_2.currentText())
# generates the plot
mod.plot_lr(self.table, x, y, z, plotType, predictions)
except Exception as e:
mod.errorGUI(str(e))
print "mell"
def displayPlot(self):
"""Displays Visualisation of Data using MatPlotLib"""
try:
# retrieve combobox values
graphtype = str(self.typeBox.currentText())
xaxis = str(self.xBox.currentText())
yaxis = str(self.yBox.currentText())
plotType = str(self.plotBox.currentText())
if xaxis == yaxis:
raise ValueError("X and Y axis are the same")
else:
xval = int(xaxis[0])
yval = int(yaxis[0])
# set the plot value to be None if no columns are selected
plotval = mod.defineplot(plotType)
# generates the plot
mod.plot(self.table, graphtype, xval, yval, plotval)
#machine learning
#Split the different graphs into section
try:
for i in yaxis:
if is_numeric_dtype(i):
if xaxis != yaxis:
self.plotBox_2.clear()
sections = []
for n in self.table[plotType[3:]]:
if n in sections:
continue
else:
sections.append(n)
self.plotBox_2.addItem(n)
#load prediction options
x = self.table[self.table.columns[xval]].tail(1)
for a in range(1, 11):
next = int(x) + a
self.predictBox.addItem(str(next))
except Exception as e:
mod.errorGUI(
str(e) +
"\nMachine Learning will not work for this graph!")
print e
except Exception as e:
mod.errorGUI(str(e))
print e
def exportIMG_filter(self):
"""Exports the current Pandas Dataframe (after merges and modifications) as a IMG(.png) File"""
dateTimeFormat = time.strftime(
"%Y%m%d-%H%M%S") # Date and Time Format (YYYYMMDD-HHMMSS)
config = img.config(
wkhtmltoimage="wkhtmltopdf\\bin\\wkhtmltoimage.exe")
try:
if self.view.empty:
raise ValueError("There is no search data!")
else:
self.view.to_html('exportIMG_filter.html')
img.from_file(
"exportIMG_filter.html",
"table_image_filter-" + dateTimeFormat + ".png",
config=config) # from html change to img(.png) file
os.remove('exportIMG_filter.html') # delete html file
mod.successGUI() # prompt file downloaded
except Exception as e:
mod.errorGUI(str(e))
