def barh(labels, values):
df = pd.DataFrame(columns=['labels', 'values', 'color'])
df['labels'] = labels
df['values'] = values.astype(float)
df.loc[df['values'] >= 4, 'color'] = 'blue'
df.loc[(df['values'] >= 3) & (df['values'] < 4), 'color'] = 'green'
df.loc[(df['values'] >= 2) & (df['values'] < 3), 'color'] = 'purple'
df.loc[df['values'] < 2, 'color'] = 'red'
df['Metrics'] = df['labels'] + '(' + df['values'].astype(str) + ')'
df = df.set_index('Metrics')
fig = plt.figure(figsize=(14, 16))
ax = fig.add_subplot(111)
title = 'Areas doing well and areas needing attention'
df['values'].plot(kind='barh',
ax=ax,
alpha=0.6,
legend=False,
color=df.color,
edgecolor='w',
xlim=(0, max(df['values'])),
title=title)
#print(df)
file = 'Report_{}_{}.png'.format('barh', bl.getTimeStamp())
plt.savefig(file, bbox_inches="tight")
plt.close()
return file
def barRadar(sec, values, subsecs):
y_pos = np.arange(len(subsecs))
plt.bar(y_pos, values, color=['#ff6666', '#ffcc99', '#99ff99'])
plt.xticks(y_pos, subsecs)
plt.ylim(0, 5)
file = 'radar_{}_{}.png'.format(sec, bl.getTimeStamp())
plt.savefig(file, bbox_inches="tight")
plt.close()
return file
def genFullRadar(sectors, subsectors, subscores):
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot
################### PIE-DONUT-RADAR CHART ######################
x = subscores
labels = sectors
sizes = [
subscores[0] + subscores[4] + subscores[8],
subscores[1] + subscores[5] + subscores[9],
subscores[2] + subscores[6] + subscores[10],
subscores[3] + subscores[7] + subscores[11]
] # have to calculate from subscores
labels_sub = [
'Physical (score - {})'.format(x[0]), 'Organizational ({})'.format(
x[1]), 'Technical ({})'.format(x[2]), 'Physical ({})'.format(x[3]),
'Organizational ({})'.format(x[4]), 'Technical ({})'.format(x[5]),
'Physical ({})'.format(x[6]), 'Organizational ({})'.format(x[7]),
'Technical ({})'.format(x[8]), 'Physical ({})'.format(x[9]),
'Organizational ({})'.format(x[10]), 'Technical ({})'.format(x[11])
]
sizes_sub = x
colors = ['#ff6666', '#ffcc99', '#99ff99', '#66b3ff']
colors_sub = [
'#ff6666', '#ff6666', '#ff6666', '#ffcc99', '#ffcc99', '#ffcc99',
'#99ff99', '#99ff99', '#99ff99', '#66b3ff', '#66b3ff', '#66b3ff'
]
explode = (0.2, 0.2, 0.2, 0.2)
explode_sub = (0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1)
#Plot
plt.pie(sizes, startangle=90, colors=colors, radius=2)
plt.legend(labels, fontsize='large', fancybox=True)
plt.pie(sizes_sub,
explode=explode_sub,
labels=labels_sub,
startangle=90,
colors=colors_sub,
textprops={'fontsize': 20},
frame=True,
radius=4)
centre_circle = plt.Circle(
(0, 0), 2.5, color='black', fc='white',
linewidth=0) #### Chnage 2.75 to see diff variations
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
fig.set_size_inches(20, 15, forward=False)
plt.axis('equal')
#plt.tight_layout()
#plt.title('Overall Resilience Metrics Summary \n \n')
file = 'Report_{}.png'.format(bl.getTimeStamp())
plt.savefig(file, bbox_inches="tight")
plt.close()
return file
def questionsUpload():
survey = request.args.get('survey')
company = request.args.get('company')
#file = request.files['file']
file = 'C:\\Users\\bhara\\Desktop\\workspace\\questions.xlsx'
df = pd.read_excel(file)
#df.pop('category_id')
list = df['category_name'].unique()
cn = 1
print(df.columns)
for i in list:
df.loc[df['category_name'] == i, 'category_id'] = cn
cn += 1
df.category_id = df.category_id.astype(int)
#print(df)
#Getting timestamp
backup = bl.getTimeStamp()
#code pending for taking backup of this json
jFile = 'C:/Users/bhara/Desktop/workspace/db.json'
jsonFile = 'C:/Users/bhara/Desktop/workspace/db_{}.json'.format(backup)
copyfile(jFile, jsonFile)
#Parsing and divding into sector chunks of data from excel
df_R1 = df[df['sector'] == 'R1']
df_R2 = df[df['sector'] == 'R2']
df_R3 = df[df['sector'] == 'R3']
df_R4 = df[df['sector'] == 'R4']
#initialize Mongo DB details or can be taken from a config file in future
host = 'localhost'
database = 'BKBASE'
collection = 'questions'
user = '******'
pwd = 'Blackpearl'
#update the db.json file with related info
bl.parseDF(df_R1, df_R2, df_R3, df_R4, jsonFile, survey, company)
#push json to mongo
bl.pushMongoDB(host, database, collection, jsonFile, user, pwd, survey,
company)
#function to clean the workspace and close all handlers and files
#bl.cleanup()
print(df)
return 'Upload Done -- replace this with web page'
def genRadar(labels, values, clr, sector):
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot
from math import pi
N = len(values)
# What will be the angle of each axis in the plot? (we divide the plot / number of variable)
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]
cn = len(labels)
for i in range(cn):
labels[i] = labels[i] + ' (' + (str(values[i])) + ')'
# Initialise the spider plot
ax = plt.subplot(111, polar=True)
# Draw one axe per variable + add labels labels yet
plt.xticks(angles[:-1], labels, color='black', size=9)
# # Draw ylabels
ax.set_rlabel_position(0)
values += values[:1]
# Draw ylabels
plt.yticks([1, 2, 3, 4, 5], ["1", "2", "3", "4", "5"],
color="black",
size=8)
plt.ylim(0, 5)
# # # Plot data
ax.plot(angles, values, linewidth=1.5, color=clr, linestyle='solid')
# # # Fill area
ax.fill(angles, values, color=clr, alpha=1)
# Add a title
#plt.title('{} metrics radar display'.format(sector), size=11, color='k', y=1.1)
file = 'Report_{}_{}.png'.format(sector, bl.getTimeStamp())
plt.savefig(file, bbox_inches="tight")
plt.close()
return file
def usersUpload():
survey = request.args.get('survey')
company = request.args.get('company')
file = request.files['file']
#file = xlrd.open_workbook("/apps/surveyapp_python/user_details.xlsx")
df = pd.read_excel(file)
# Getting timestamp
backup = bl.getTimeStamp()
# code pending for taking backup of this json
jFile = './users.json'
jsonFile = './users_{}.json'.format(backup)
copyfile(jFile, jsonFile)
lFile = './login.json'
ljsonFile = './login_{}.json'.format(backup)
copyfile(lFile, ljsonFile)
# Parsing and divding into sector chunks of data from excel
#df_username = df[df['username']]
# initialize Mongo DB details or can be taken from a config file in future
host = 'localhost'
database = 'Surveyapp'
collection = 'userdetails'
user = '******'
pwd = 'Blackpearl'
# update the db.json file with related info
bl.parseDFusers(df, jsonFile, ljsonFile, survey, company)
# push json to mongo
bl.pushMongoDB(host, database, collection, jsonFile, user, pwd, survey,
company)
# function to clean the workspace and close all handlers and files
# bl.cleanup()
return 'Upload Done -- replace this with web page'
def questionsUpload():
survey = request.args.get('survey')
company = request.args.get('company')
file = request.files['file']
#file = xlrd.open_workbook("C:/Users/headway/PycharmProjects/untitled/questions.xlsx")
df = pd.read_excel(file)
n = 1
df1, n = bl.parse(df, 'R1', 'Physical', n)
df2, n = bl.parse(df, 'R1', 'Organizational', n)
df3, n = bl.parse(df, 'R1', 'Technical', n)
df4, n = bl.parse(df, 'R2', 'Physical', n)
df5, n = bl.parse(df, 'R2', 'Organizational', n)
df6, n = bl.parse(df, 'R2', 'Technical', n)
df7, n = bl.parse(df, 'R3', 'Physical', n)
df8, n = bl.parse(df, 'R3', 'Organizational', n)
df9, n = bl.parse(df, 'R3', 'Technical', n)
df10, n = bl.parse(df, 'R4', 'Physical', n)
df11, n = bl.parse(df, 'R4', 'Organizational', n)
df12, n = bl.parse(df, 'R4', 'Technical', n)
df = pd.concat(
[df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12])
print(df1[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df2[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df3[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df4[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df5[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df6[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df7[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df8[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df9[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df10[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df11[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print(df12[['sector', 'subsector', 'category_id', 'category_name', 'qid']])
print("MAINFRAME")
print(df[['sector', 'subsector', 'category_name', 'category_id', 'qid']])
fileName = 'dataframe_BK.xlsx'
wb = Workbook()
sheet1 = wb.add_sheet('Sheet 1')
wb.save(fileName)
writer = ExcelWriter(fileName)
df.to_excel(writer, 'Sheet1', index=False)
writer.save()
#df.pop('category_id')
#print(df)
#Getting timestamp
backup = bl.getTimeStamp()
#code pending for taking backup of this json
jFile = './db.json'
jsonFile = './db_{}.json'.format(backup)
copyfile(jFile, jsonFile)
#Parsing and divding into sector chunks of data from excel
df_R1 = df[df['sector'] == 'R1']
df_R2 = df[df['sector'] == 'R2']
df_R3 = df[df['sector'] == 'R3']
df_R4 = df[df['sector'] == 'R4']
#initialize Mongo DB details or can be taken from a config file in future
host = 'localhost'
database = 'Surveyapp'
collection = 'questions'
user = '******'
pwd = 'Blackpearl'
#update the db.json file with related info
bl.parseDF(df_R1, df_R2, df_R3, df_R4, jsonFile, survey, company)
#push json to mongo
bl.pushMongoDB(host, database, collection, jsonFile, user, pwd, survey,
company)
#function to clean the workspace and close all handlers and files
#bl.cleanup()
return 'Upload Done -- replace this with web page'
def genReport():
survey = request.args.get('survey')
company = request.args.get('company')
now = datetime.datetime.now()
curdate = now.strftime("%Y-%m-%d")
host, base, colection, dbuser, pwd = bl.mongoInit('users')
#file='C:\\toHDD\\toViggu\\surveyapp_python\\dataframe_1555184982.xlsx'
req = requests.get(
"http://localhost/backend/chartsAll?survey={}&company={}".format(
survey, company))
print(req.text)
file = req.text
r = requests.get(
"http://localhost/backend/getAllRadar?dataframe={}".format(file))
jsonres = r.json()
subscores = []
subscores.append(jsonres['Physical'][0])
subscores.append(jsonres['Organizational'][0])
subscores.append(jsonres['Technical'][0])
subscores.append(jsonres['Physical'][1])
subscores.append(jsonres['Organizational'][1])
subscores.append(jsonres['Technical'][1])
subscores.append(jsonres['Physical'][2])
subscores.append(jsonres['Organizational'][2])
subscores.append(jsonres['Technical'][2])
subscores.append(jsonres['Physical'][3])
subscores.append(jsonres['Organizational'][3])
subscores.append(jsonres['Technical'][3])
R0 = cl.genFullRadar(sectors, subsectors, subscores)
r = requests.get(
"http://localhost/backend/getAllRadarBySector?dataframe={}".format(
file))
jsonres = r.json()
R1_labels = jsonres[0]['labels']
R1_values = jsonres[0]['score']
R2_labels = jsonres[1]['labels']
R2_values = jsonres[1]['score']
R3_labels = jsonres[2]['labels']
R3_values = jsonres[2]['score']
R4_labels = jsonres[3]['labels']
R4_values = jsonres[3]['score']
R1 = cl.genRadar(R1_labels, R1_values, '#ff6666', 'Robustness')
R2 = cl.genRadar(R2_labels, R2_values, '#ffcc99', 'Redundancy')
R3 = cl.genRadar(R3_labels, R3_values, '#99ff99', 'Resourcefulness')
R4 = cl.genRadar(R4_labels, R4_values, '#66b3ff', 'Rapidity')
r = requests.get(
"http://localhost/backend/getAllRadarAllSectors?dataframe={}".format(
file))
jsonres = r.json()
labels = jsonres['category']
values = jsonres['scores']
print(values)
labels = pd.Series(labels)
values = pd.Series(values)
R5 = cl.barh(labels, values)
r = requests.get(
"http://localhost/backend/getAllRadar?dataframe={}".format(file))
jsonres = r.json()
phy = jsonres['Physical']
org = jsonres['Organizational']
tech = jsonres['Technical']
subsecs = ['Physical', 'Organizational', 'Technical']
bar1 = [phy[0], org[0], tech[0]]
bar2 = [phy[1], org[1], tech[1]]
bar3 = [phy[2], org[2], tech[2]]
bar4 = [phy[3], org[3], tech[3]]
R1_bar = cl.barRadar('R1', bar1, subsecs)
R2_bar = cl.barRadar('R2', bar2, subsecs)
R3_bar = cl.barRadar('R3', bar3, subsecs)
R4_bar = cl.barRadar('R4', bar4, subsecs)
lists = [R0, R1, R2, R3, R4, R5, R1_bar, R2_bar, R3_bar, R4_bar]
print(lists)
doc = DocxTemplate("sampleWord.docx")
s1 = doc.new_subdoc()
s1.add_picture(R1, width=Inches(7))
s2 = doc.new_subdoc()
s2.add_picture(R2, width=Inches(7))
s3 = doc.new_subdoc()
s3.add_picture(R3, width=Inches(7))
s4 = doc.new_subdoc()
s4.add_picture(R4, width=Inches(7))
s5 = doc.new_subdoc()
s5.add_picture(R0, width=Inches(7))
s6 = doc.new_subdoc()
s6.add_picture(R5, width=Inches(7))
s7 = doc.new_subdoc()
s7.add_picture(R1_bar, width=Inches(5))
s8 = doc.new_subdoc()
s8.add_picture(R2_bar, width=Inches(5))
s9 = doc.new_subdoc()
s9.add_picture(R3_bar, width=Inches(5))
s10 = doc.new_subdoc()
s10.add_picture(R4_bar, width=Inches(5))
req = requests.get(
"http://localhost/backend/surveysCount?survey={}&company={}".format(
survey, company))
count = req.text
context = {
'bar1': s7,
'bar2': s8,
'bar3': s9,
'bar4': s10,
'chart5': s6,
'radar1': s1,
'radar2': s2,
'radar3': s3,
'radar4': s4,
'complete': s5,
'company': company,
'survey': survey,
'date': curdate,
'number': count
}
doc.render(context)
filename = "Survey_Metrics_{}.docx".format(bl.getTimeStamp())
doc.save(filename)
outputFile = bl.convertToPDF(filename)
bl.sendReport(survey, company, outputFile)
return "Sent Report"
import pandas as pd
import businessLogic as bl
import sys, os
from shutil import copyfile
survey = sys.argv[1]
company = sys.argv[2]
xl = sys.argv[3]
#Needs to be updated from config file
path = 'C:/Users/bhara/Desktop/workspace'
#filepath=os.path.join(path, filename)
#Getting timestamp
backup = bl.getTimeStamp()
#code pending for taking backup of this json
jFile = 'C:/Users/bhara/Desktop/workspace/db.json'
jsonFile = 'C:/Users/bhara/Desktop/workspace/db_{}.json'.format(backup)
copyfile(jFile, jsonFile)
# Parsing excel data
#xl = pd.read_excel(path+'/'+filename)
#xl = pd.read_excel(filename)
#Parsing and divding into sector chunks of data from excel
df_R1 = xl[xl['sector'] == 'R1']
df_R2 = xl[xl['sector'] == 'R2']
df_R3 = xl[xl['sector'] == 'R3']
df_R4 = xl[xl['sector'] == 'R4']
