def handle(self, *arg, **options):
# get all deviations
deviations = Deviation.objects.all()
# find unique deviations
categories = set()
for deviation in deviations:
categories.add(deviation.category)
# # check to see if deviation is in category2group mapping
for category in categories:
try:
deviation_category2group(category)
# if not found, print
except KeyError:
print category
def employee_profile(request, badge_number):
# Get the employee.
employee = Employee.objects.get(badge_number=badge_number)
# Crunch approx. number of years since employee started at CTA.
employee.years_since_start = years_since(employee.start_date)
# DEVIATION SECTION
# Get employee's deviations.
employee_deviations = Deviation.objects.filter(employee__badge_number=badge_number)
# Get employee's FAST garage name from that employee's first deviation.
# Replace this with garage data from payroll or FAST eventually.
deviation_list = list(employee_deviations)
if deviation_list:
employee.deviation_garage_name = deviation_list[0].garage.name
# Build d3 deviation calendar data.
calendar_deviations = {}
deviation_categories = []
for deviation in employee_deviations:
# Parse employee's deviations into deviation groups and categories
# keyed on the date to display on D3 deviations calendar.
clean_deviation_date = str(deviation.date).encode('utf8')
calendar_deviations[clean_deviation_date] = [ deviation_category2group(deviation.category), deviation.category ]
# Add employee's deviation categories to list to find their count below and display it on d3 badges.
deviation_categories.append(deviation.category)
calendar_deviations_json = json.dumps(calendar_deviations)
# Build d3 deviation badges data.
# Count employee's deviations by category.
deviation_category_count = {}
for category in deviation_categories:
try:
deviation_category_count[category] += 1
except KeyError:
deviation_category_count[category] = 1
deviation_count_list = [(count, category) for (category, count) in deviation_category_count.items()]
deviation_count_list.sort(reverse=True)
deviation_badge_data = []
for deviation_count in deviation_count_list:
deviation_category = deviation_count[1]
deviation_group = deviation_category2group(deviation_category)
deviation_badge_data.append((deviation_count[0], deviation_count[1], deviation_group))
deviation_badge_data_json = json.dumps(deviation_badge_data)
# DRIVING PERFORMANCE SECTION
# Get employee's daily average driving ranks.
employee_driving_performance = DrivingRank.objects.filter(employee__badge_number=badge_number)
# Parse employee's daily ranks into a rank dictionary keyed on the date to display on D3 driving calendar.
calendar_driving_otp = {}
calendar_driving_ranks = {}
for driving_performance in employee_driving_performance:
date = str(driving_performance.date).encode('utf8')
calendar_driving_otp[date] = driving_performance.otp
calendar_driving_ranks[date] = driving_performance.rank
calendar_driving_otp_json = json.dumps(calendar_driving_otp)
calendar_driving_ranks_json = json.dumps(calendar_driving_ranks)
# Build data for d3 otp badges: count employee's daily otp values by quintile.
# First, convert daily otp values to bin indexes.
otp_list = [opt for opt in calendar_driving_otp.itervalues()]
x = np.array(otp_list)
# Define the bins.
otp_quintiles = np.array([0.0, 0.5, 0.60, 0.69, 0.78, 1.1])
# Return the index of the quintile bin each daily otp value falls into.
# Catch drivers with no driving data. digitize() throws an error if x is missing.
try:
inds = np.digitize(x, otp_quintiles)
# Count the number of otp days per bin.
otp_quintile_count = {}
inds_to_otp_quintile_labels = {1: '0 - 50%', 2: '50 - 60%', 3: '60 - 69%', 4: '69 - 78%', 5: '78 - 100%'}
for ind in inds:
# Translate quintile bin indexes to quintile labels to display on badges.
otp_quintile_label = inds_to_otp_quintile_labels[ind]
try:
otp_quintile_count[otp_quintile_label] += 1
except KeyError:
otp_quintile_count[otp_quintile_label] = 1
except ValueError:
otp_quintile_count = {'0 - 50%': 0, '50 - 60%': 0, '60 - 69%': 0, '69 - 78%': 0, '78 - 100%': 0}
# Sort quintiles in ascending order to match css color classes.
otp_badge_data = [(otp_quintile_label, count) for (otp_quintile_label, count) in otp_quintile_count.iteritems()]
otp_badge_data.sort()
otp_badge_data_json = json.dumps(otp_badge_data)
# Build d3 rank badges data: count employee's daily rank scores by quintile.
rank_list = [rank for rank in calendar_driving_ranks.itervalues()]
# Convert rank days to bin indexes.
x = np.array(rank_list)
# Define the bins.
rank_quintiles = np.array([0, 2.718, 2.896, 3.06, 3.270, 5.1])
# For each rank day, returns the index of the quintile bin it falls into.
# Catch drivers with no driving data. digitize() throws an error if x is missing.
try:
inds = np.digitize(x, rank_quintiles)
# Count number of rank days per bin.
rank_quintile_count = {}
# Tuples sort the quintile labels and count in the red-to-green order on the page.
inds_to_rank_quintile_labels = {1: (5, 'Better than peers'), 2: (4, '+'), 3: (3, 'Same as peers'), 4: (2, '-'), 5: (1, 'Worse than peers') }
for ind in inds:
# Translate quintile bin indexes to quintile labels to display on badges.
rank_quintile_label = inds_to_rank_quintile_labels[ind]
try:
rank_quintile_count[rank_quintile_label] += 1
except KeyError:
rank_quintile_count[rank_quintile_label] = 1
except ValueError:
rank_quintile_count = {(4, '+'): 0, (1, 'Worse than peers'): 0, (3, 'Same as peers'): 0, (5, 'Better than peers'): 0, (2, '-'): 0}
# Sort quintiles in ascending order to match css color classes.
rank_badge_data = [(rank_quintile_label, count) for (rank_quintile_label, count) in rank_quintile_count.iteritems()]
rank_badge_data.sort()
rank_badge_data_json = json.dumps(rank_badge_data)
# Pass data to the template.
return render_to_response('main/employee_profile.haml', {
'employee': employee,
'employee_deviations': employee_deviations,
'calendar_deviations_json': calendar_deviations_json,
'deviation_badge_data_json': deviation_badge_data_json,
'calendar_driving_otp_json': calendar_driving_otp_json,
'otp_badge_data_json': otp_badge_data_json,
'calendar_driving_ranks_json': calendar_driving_ranks_json,
'rank_badge_data_json': rank_badge_data_json,
},
context_instance=RequestContext(request),
)
