def scoreUpdate(self, **kw): print "-------------" print kw id = kw['id'] value = kw['value'] score = Score.get(int(id)) crit = score.criterion assessment = score.assessment category = crit.category score.value = value user = identity.current.user eh = EditHistory.selectBy(assessmentID=assessment.id, category=category) if eh.count() > 0: eh[0].editor = user.firstlast() eh[0].program = user.program.name else: EditHistory(assessmentID=assessment.id, category=category, editor=user.firstlast(), program=user.program.name) if crit.prefix in ['Y','Z']: st_crit = Criterion.selectBy(level=assessment.level, category=category, prefix="Z")[0] na_crit = Criterion.selectBy(level=assessment.level, category=category, prefix="Y")[0] possible = util.scorePossible(category, assessment.level) subtotal = Score.selectBy(criterionID=st_crit.id, assessmentID=assessment.id)[0].value na = Score.selectBy(criterionID=na_crit.id, assessmentID=assessment.id)[0].value return str(util.percent(subtotal,possible,na)) return dict()
def move_events():
db = DbStore()
total = util.get_spotify_db().events.find().count()
for i, event in enumerate(util.get_spotify_db().events.find()):
if i % 100 == 0:
print("Moving events {}%".format(util.percent(i, total)))
db.add_event(event)
db.commit()
def main():
trader_list = [
{'name': 'Tom', 'krw': 10000000, 'algo': 'random', 'currency': 'xrp'},
{'name': 'Ian', 'krw': 10000000, 'algo': 'type1', 'currency': 'xrp'},
{'name': 'JDY', 'krw': 10000000, 'algo': 'type2', 'currency': 'xrp'},
{'name': 'YKK', 'krw': 10000000, 'algo': 'type3', 'currency': 'xrp'}]
traders = [Trader(x) for x in trader_list]
for trader in traders:
trader.think()
price = price_gen(1000, 24*360*1000)
queue = deque()
# Collect for one day
for _ in range(360*24):
queue.append(int(next(price)))
for i in price:
queue.append(int(next(price)))
queue.popleft()
analytics = {
'price': queue[-1],
'change': {
'day': queue[-1] - queue[0],
'pc_day': percent(queue[0], queue[-1]),
'hour': queue[-1] - queue[-360],
'pc_hour' : percent(queue[-360], queue[-1]),
'5min': queue[-1] - queue[-30],
'pc_5min': percent(queue[-30], queue[-1])
}
}
for trader in traders:
trader.make_decesion(analytics)
for trader in traders:
trader.log('completed')
ioloop.stop()
def main():
spotify = util.get_spotify_db()
all = spotify.tracks.find({})
n = all.count()
print("Processing {} docs".format(n))
i = 0
for track in all:
i = i + 1
clean = clean_track(track["track"])
spotify.tracks.delete_one({"_id": track["_id"]})
track["track"] = clean
spotify.tracks.insert(track)
if i % 100 == 0:
print("[{}%] done".format(util.percent(i, n)))
def create_image(time: str):
"""
Создает картинку с временем из строки полученной из параметра time.
"""
image = Image.new('RGB', (500, 500), color='white')
W, H = image.size # ширина, высота фото
draw = ImageDraw.Draw(image)
clock_font = ImageFont.truetype(font='fonts/digital_num.ttf', size=215)
progressbar_font = ImageFont.truetype(font='fonts/progressbar.ttf',
size=22)
percent_font = ImageFont.truetype(font='fonts/num.ttf', size=25)
wt, ht = draw.textsize(time, font=clock_font)
draw.text(((W - wt) / 2, ((H - ht) / 2) - 60),
time,
font=clock_font,
fill='black') # write main clock in photo
draw.text((6, 355), progressbar(time), font=progressbar_font,
fill='black') # write progressbar in photo
draw.text((410, 350), percent(time), font=percent_font,
fill='black') # write percent in photo
image.save("clock.png")
def compute_rcm_and_module_tree(blast_path,
tree_path,
tree_diagram_path,
protLenDict,
protNidDict,
printProgress=True,
progressHeader="",
progressFooter=""):
util.openfiles([tree_path, tree_diagram_path])
#print blast_path
if os.path.exists(tree_path):
processed = set()
with open(tree_path) as f:
for line in f:
processed.add(line.split()[0])
tree_file_handle = open(tree_path, 'a')
tree_diagram_file_handle = open(tree_diagram_path, 'a')
else:
processed = set()
tree_file_handle = open(tree_path, 'w')
tree_diagram_file_handle = open(tree_diagram_path, 'w')
cnt = 0
with open(blast_path, 'r') as f:
inputStr = f.read()
# for blastxmlpath in os.listdir(eco_blast_brief_path):
lines = inputStr.split("\n")
numlines = len(lines)
currentGi = 0
rcm = ""
rcmNotNull = False
# these varaiables will contain the output temporarily for speeding up the runtime
# writeDia=""
writeTre = ""
hspsCount = 0
for i, line in enumerate(lines):
if printProgress:
util.percent(i,
numlines,
50,
header=progressHeader,
footer=progressFooter)
if len(line) > 0:
# query_length=500
(query_gi, hsps) = parse_blast(line)
# print query_gi
# print protNidDict
query_length = protLenDict[protNidDict[int(query_gi)]]
testing = True
if query_gi != None and hsps != None: # this is true if it passes through the filters in parse_blast
n = query_length
hspsCount += len(hsps)
if currentGi != query_gi: # this is a new protein so make a new rcm
if testing:
outstring = ""
for row in rcm:
for entry in row:
outstring += str(entry) + ","
outstring += "\n"
# with open("rcms.txt","a") as f:
# f.write(outstring+"\n\n\n")
# if(rcm!=""):
if rcmNotNull:
# print rcm
# np.savetxt(os.path.join("rcmTest", str(currentGi)+'.csv'), rcm, delimiter=',', fmt='%d')
tree_root = split_rcm(rcm)
# if len(writeDia)>300:
if len(writeTre) > 30000:
# print len(writeDia)
# tree_diagram_file_handle.write(writeDia)
tree_diagram_file_handle.flush()
tree_file_handle.write(writeTre)
tree_file_handle.flush()
writeTre = ""
# writeDia=""
# writeDia=writeDia+('%d (%d hsps)\n' % (cnt, hspsCount))
# writeDia=writeDia+('%s\n\n' % tree_root)
hspsCount = 0
# print len(writeDia)
writeTre = writeTre + adda_tree_string(
currentGi, tree_root)
rcm = np.zeros((n, n), dtype=np.int)
rcmNotNull = True
cnt += 1
currentGi = query_gi
sum_l = 0
for hsp in hsps:
sum_l += len(hsp.query_span)
for i in xrange(hsp.query_span.start,
hsp.query_span.end + 1):
rcm[i - 1,
range(hsp.query_span.start -
1, hsp.query_span.end)] += 1
# tree_diagram_file_handle.write('%d (%d hsps)\n' % (cnt, len(hsps)))
# tree_diagram_file_handle.flush()
# tree_diagram_file_handle.write('%s\n%s\n\n' % (gi, tree_root))
# os.fsync()
tree_root = split_rcm(rcm)
# writeDia=writeDia+('%s\n\n' % tree_root)
writeTre = writeTre + adda_tree_string(currentGi, tree_root)
# tree_diagram_file_handle.write(writeDia)
tree_diagram_file_handle.flush()
tree_file_handle.write(writeTre)
tree_file_handle.flush()
def create_spreadsheet(finances: Finances, output_filepath: str):
wb = Workbook()
summaryws = wb.active
# Create year summary tab
period = finances.whole_finances
summaryws.title = "Period Summary"
summaryws.append(["Since", f"{str(period.cutoff_date)}"])
summaryws.append([])
summaryws.append(["Income", f"{USD(period.income.income_sum)}"])
summaryws.append(["Work Income", f"{USD(period.income.workincomesum)}"])
summaryws.append(
["Non-Work Income", f"{USD(period.income.nonworkincomesum)}"])
tab_append_top_trans(finances, summaryws, period.income.nonworkincome)
summaryws.append([])
summaryws.append(
["Returns Income", f"{USD(period.income.returnsincomesum)}"])
summaryws.append(["Expenses", f"{USD(period.expense.expensessum)}"])
summaryws.append([])
summaryws.append(
["Gross Savings", f"{USD(period.gross_savings.amount_saved)}"])
summaryws.append([
"Gross Savings Percentage",
f"{percent(period.gross_savings.saving_ratio)}"
])
summaryws.append([
"Gross Avg Monthly Savings",
f"{USD(period.avg_monthly_savings.amount_saved)}"
])
summaryws.append([
f"Gross Avg Monthly Savings Goals for {period.period_in_months} months"
])
summaryws.append(["Goal", "Goal Amount", "Leftover"])
sav = finances.whole_finances.avg_monthly_savings
for i in range(len(sav.savings_goals)):
summaryws.append([
percent(sav.savings_goals[i]),
USD(sav.savings_goal_amts[i]),
USD(sav.savings_goal_leftover[i])
])
summaryws.append([])
summaryws.append(["Investments", f"{USD(period.investments_sum)}"])
summaryws.append([])
summaryws.append(["Category", f"% of Expenses", "Amount"])
egc = period.expense.expensegroupcoll
for eg_i in egc.group_sums_sorted[:20]:
summaryws.append([
egc.group_keys[eg_i],
percent(egc.group_percentages[eg_i]),
USD(egc.group_sums[eg_i])
])
summaryws.append([])
# Period Income tab
tab = wb.create_sheet()
tab.title = "Period Income"
tab_append_data(finances, tab, period.income.income)
# Period Work Income tab
tab = wb.create_sheet()
tab.title = "Period Work Income"
tab_append_data(finances, tab, period.income.workincome)
# Period Non-work Income tab
tab = wb.create_sheet()
tab.title = "Period Non-work Income"
tab_append_data(finances, tab, period.income.nonworkincome)
# Period Returns Income tab
tab = wb.create_sheet()
tab.title = "Period Returns Income"
tab_append_data(finances, tab, period.income.returnsincome)
# Period Expenses tab
tab = wb.create_sheet()
tab.title = "Period Expenses"
tab_append_data(finances, tab, period.expense.expenses)
# Period Investments tab
tab = wb.create_sheet()
tab.title = "Period Investments"
tab_append_data(finances, tab, period.investments)
# Monthly Tabs
sorted_month_keys = sorted(finances.monthly_finances.keys(), reverse=True)
for month_key in sorted_month_keys:
month = finances.monthly_finances[month_key]
# Month Summary
tab = wb.create_sheet()
tab.title = f"{str(month_key)} Summary"
tab.append(["Income", USD(month.income.income_sum)])
tab.append(["Work Income", USD(month.income.workincomesum)])
tab_append_top_trans(finances, tab, month.income.workincome)
tab.append([])
tab.append(["Returns Income", USD(month.income.returnsincomesum)])
tab.append(["Expenses", USD(month.expense.expensessum)])
tab.append(["Gross Savings", USD(month.savings.amount_saved)])
tab.append(
["Gross Savings Percentage",
percent(month.savings.saving_ratio)])
tab.append(["Top expenses"])
tab_append_top_trans(finances, tab, month.expense.expenses, 10)
tab.append([])
tab.append(["Top expense groups"])
egc = month.expense.expensegroupcoll
for eg_i in egc.group_sums_sorted[:5]:
tab.append([egc.group_keys[eg_i], USD(egc.group_sums[eg_i])])
# Month Data
tab = wb.create_sheet()
tab.title = f"{str(month_key)} Data"
tab_append_data(finances, tab, month.source)
# Ignored Transactions tab
tab = wb.create_sheet()
tab.title = "Ignored Transactions"
tab_append_data(finances, tab, finances.ignored)
# Source Data tab
tab = wb.create_sheet()
tab.title = "Period Source Data"
tab_append_trans(tab, finances.source)
wb.save(output_filepath)
def main(inputFilename, printProgress=True):
# import configurations from the config file
EvalueCutoff = conf.EvalueCutoff
inputFolder = conf.blastAlltoAllInput
processedFolder = conf.addaProcessedInput
tableFolder = conf.proteinToNidTables
nidListFolder = conf.nidListDir
processedAppend = conf.addaAppend
tableAppend = conf.tableAppend
nidListAppend = conf.nidAppend
# generate the output folders
util.generateDirectoriesMult([tableFolder, nidListFolder, processedFolder, conf.NidToProteinTables])
inputFilenames = os.listdir(inputFolder)
# for j, inputFilename in enumerate(inputFilenames):
# the counter to determine which id we give to each of the protein names.
# ADDA only uses id for proteins, not strings
nidCounter = 1
# The dictionary that stores the information on which nids associate with which proteins
# key:protein names (string)
# val:nids (int)
protToNid = {}
# The dictionary that stores the information on which nids associate with which proteins
# key:nids (int)
# val:protein names (string)
NidToProt = {}
# initiate the filenames for generated files
# fileRootReplace="."+inputFilename.split(".")[-1]
fileRootReplace = conf.alltoallExt
# find the file Directories
processedDir = os.path.join(processedFolder, inputFilename.replace(fileRootReplace, processedAppend))
nidDir = os.path.join(nidListFolder, inputFilename.replace(fileRootReplace, nidListAppend))
tableDir = os.path.join(tableFolder, inputFilename.replace(fileRootReplace, tableAppend))
# open the files to clear the files
util.openfiles([processedDir, tableDir, nidDir])
# read the inputfile
read = ""
with open(os.path.join(inputFolder, inputFilename.replace(conf.seqExt, conf.alltoallExt)), 'r') as f:
read = f.read()
lines = read.split("\n")
numLines = len(lines)
write = ""
writeNid = ""
# input file format:
# 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11
# query, subject, %id, alignlen, mismatch, gapopen, qst, qend, sst, send, Evalue, bitscore
# output file format:
# 0 1 2 3 4 5 6 7 8
# nid1 nid2 %id from1 to1 alignlen from2 to2 Evalue na na
# header=str(j)+"/"+str(len(inputFilenames))+"\n"
# footer="\n"
for i, line in enumerate(lines):
# print percent to keep progress
if printProgress:
util.percent(i, numLines, 25, percentRange=(0, 80))
inArr = line.split("\t")
query = inArr[0]
if query not in protToNid.keys():
nid1 = nidCounter
protToNid[query] = nid1
NidToProt[nid1] = query
writeLine = str(nidCounter) + "\t" + query + "\n"
write = write + writeLine
writeNid = writeNid + str(nidCounter) + "\n"
# compile the writing in to memery so we can write it down in bulk (this saves runtime)
if len(write) > 1000000:
with open(tableDir, "a") as f:
f.write(write)
with open(nidDir, "a") as f:
f.write(writeNid)
nidCounter += 1
# write the left over string into the file
with open(tableDir, "a") as f:
f.write(write)
with open(nidDir, "a") as f:
f.write(writeNid)
dictpath = os.path.join(conf.NidToProteinTables, inputFilename.replace(fileRootReplace, conf.NidToProteinAppend))
with open(dictpath, "wb") as f:
dump(NidToProt, f)
write = ""
for i, line in enumerate(lines):
if len(line) > 0:
# print percent to keep progress
if printProgress:
util.percent(i, numLines, 5, percentRange=(80, 100))
inArr = line.split("\t")
query = inArr[0]
subject = inArr[1]
identity = inArr[2]
alignlen = inArr[3]
from1 = inArr[6]
to1 = inArr[7]
from2 = inArr[8]
to2 = inArr[9]
Evalue = inArr[10]
repeat = (query == subject and ((from1 == from2 and to1 == to2) or (from1 == to2 and to1 == from2)))
if float(Evalue) < EvalueCutoff and repeat == False:
nid1 = protToNid[query]
nid2 = protToNid[subject]
writeLine = str(nid1) + "\t" + str(nid2) + "\t" + str(
identity) + "\t" + from1 + "\t" + to1 + "\t" + str(
alignlen) + "\t" + from2 + "\t" + to2 + "\t" + str(Evalue) + "\t" + "na" + "\t" + "na" + "\n"
if len(write) < 1000000000:
write = write + writeLine
else:
with open(processedDir, "a") as f:
write = write + writeLine
f.write(write)
write = ""
with open(processedDir, "a") as f:
f.write(write)
def do_custom(cutoff: int):
finances: Finances = standard_compilation(cutoff)
period_in_months = finances.whole_finances.period_in_months
print(f"Past {cutoff} Months")
table = []
headers = [
"Category", "Average Monthly Amount", f"% of Work Income",
f"% of Expenses"
]
work_income = abs(finances.whole_finances.income.workincomesum)
avg_monthly_work_income = work_income / period_in_months
expenses = abs(finances.whole_finances.expense.expensessum)
avg_monthly_expenses = expenses / period_in_months
rent_utilities_classes = ["Housing", "Utilities"]
rent_utilities = filter(
lambda t: t.classification in rent_utilities_classes, finances.source)
avg_monthly_rent_utilites = sum(map(lambda t: abs(t.amt),
rent_utilities)) / period_in_months
rent_utilities_work_income_percent = avg_monthly_rent_utilites / avg_monthly_work_income
rent_utilities_expenses_percent = avg_monthly_rent_utilites / avg_monthly_expenses
table.append([
"Rent & Utilities",
USD(avg_monthly_rent_utilites),
percent(rent_utilities_work_income_percent),
percent(rent_utilities_expenses_percent)
])
# print(f"Avg Monthly Rent & Utilities:\t{USD(avg_monthly_rent_utilites)}\t"
# + f"% of Work Income:\t{percent(rent_utilities_work_income_percent)}\t"
# + f"% of Expenses:\t{percent(rent_utilities_expenses_percent)}")
transportation_classes = ["Transportation"]
transportation = filter(
lambda t: t.classification in transportation_classes, finances.source)
avg_monthly_transportation = sum(map(lambda t: abs(t.amt),
transportation)) / period_in_months
transportation_work_income_percent = avg_monthly_transportation / avg_monthly_work_income
transportation_expenses_percent = avg_monthly_transportation / avg_monthly_expenses
table.append([
"Transportation",
USD(avg_monthly_transportation),
percent(transportation_work_income_percent),
percent(transportation_expenses_percent)
])
# print(f"Avg Monthly Transportation:\t{USD(avg_monthly_transportation)}\t"
# + f"% of Work Income:\t{percent(transportation_work_income_percent)}\t"
# + f"% of Expenses:\t{percent(transportation_expenses_percent)}")
groceries_cat_classes = ["Pet Expenses", "Groceries"]
groceries_cat = filter(lambda t: t.classification in groceries_cat_classes,
finances.source)
avg_monthly_groceries_cat = sum(map(lambda t: abs(t.amt),
groceries_cat)) / period_in_months
groceries_cat_work_income_percent = avg_monthly_groceries_cat / avg_monthly_work_income
groceries_cat_expenses_percent = avg_monthly_groceries_cat / avg_monthly_expenses
table.append([
"Groceries & Cat Expenses",
USD(avg_monthly_groceries_cat),
percent(groceries_cat_work_income_percent),
percent(groceries_cat_expenses_percent)
])
# print(f"Avg Monthly Groceries & Cat Expenses:\t{USD(avg_monthly_groceries_cat)}\t"
# + f"% of Work Income:\t{percent(groceries_cat_work_income_percent)}\t"
# + f"% of Expenses:\t{percent(groceries_cat_expenses_percent)}")
print(tabulate.tabulate(table, headers=headers))
def index1(self, id=None, category=None, osep='None', print_area='false'):
if identity.current.user.basketmode:
raise redirect(url("index2",id=id,category=category,osep=osep,print_area=print_area))
osep = True
if print_area == 'true':
print_area = True
else:
print_area = False
id = util.session('current_assessment', 0, id)
category = util.session('current_category', 'Fine Motor', category)
osep = util.session('osep', False, osep)
child = util.session('current_child', 0)
if id == 0: # no id given
raise redirect("/")
try:
current_assessment = Assessment.get(id)
except SQLObjectNotFound: # Child with given id does not exist
raise redirect("/")
current_child = current_assessment.child
all_assessments = current_child.assessments
sorted_assessments = Assessment.select(AND(Assessment.q.childID==current_assessment.childID,
Assessment.q.id!=id,
Assessment.q.level==current_assessment.level), orderBy='-dateEntered')
old = list(sorted_assessments[:7])
if osep:
criteria = Criterion.selectBy(category=category, level=current_assessment.level, osep=True)
else:
criteria = Criterion.selectBy(category=category, level=current_assessment.level)
criteria = criteria.orderBy('sort_order')
crit_ids = [c.id for c in criteria]
sums = {}
for t in old:
sum = 0;
for s in t.scores:
if s.criterionID in crit_ids and s.criterion.rank != "1":
try:
sum += int(s.value)
except:
pass
sums[t.id] = sum
sum = 0;
for s in current_assessment.scores:
if s.criterionID in crit_ids and s.criterion.rank != "1":
try:
sum += int(s.value)
except:
pass
sums[current_assessment.id] = sum
util.session('fill', 'missing')
if len(old) == 0:
temp = util.session('values', 'zeros')
if temp == 'previous':
cherrypy.session['values'] = 'zeros'
else:
util.session('values', 'previous')
subtotal = Criterion.selectBy(category=category, level=current_assessment.level, prefix='Z')[0]
_na = Criterion.selectBy(category=category, level=current_assessment.level, prefix='Y')[0]
oldtotals = {}
oldna = {}
percents = {}
for t in old:
stscore = Score.selectBy(assessmentID=t.id, criterionID=subtotal.id)
if stscore.count() > 0:
stscore = stscore[0]
else:
stscore = Score(assessmentID=t.id, criterionID=subtotal.id, value="0", type='subtotal')
oldtotals[t.id] = stscore.value
nascore = Score.selectBy(assessmentID=t.id, criterionID=_na.id)
if nascore.count() > 0:
nascore = nascore[0]
else:
nascore = Score(assessmentID=t.id, criterionID=_na.id, value="0", type='na')
oldna[t.id] = nascore.value
percents[t.id] = util.percent(stscore.value, util.scorePossible(category, current_assessment.level), nascore.value)
stscore = Score.selectBy(assessmentID=id, criterionID=subtotal.id)
nascore = Score.selectBy(assessmentID=id, criterionID=_na.id)
if stscore.count() > 0:
stscore = stscore[0]
else:
stscore = Score(assessmentID=id, criterionID=subtotal.id, value="0", type='subtotal')
if nascore.count() > 0:
nascore = nascore[0]
else:
nascore = Score(assessmentID=id, criterionID=_na.id, value="0", type='na')
percent = util.percent(stscore.value, util.scorePossible(category, current_assessment.level), nascore.value)
return dict(child=current_child, id=id, a=current_assessment, old=old, oldtotals=oldtotals, \
stc=subtotal, subtotal=stscore, criteria=criteria, osep=osep, sums=sums, \
catlist=self.category_list, category=category, nextCategory=self.nextCategory, \
print_area=print_area, getEditor=self.getEditor, pcheck=self.persistCheck, \
shaded=util.shaded, scoreclass=util.color, scorePossible=util.scorePossible, \
nac=_na, na=nascore, oldna=oldna, percents=percents, percent=percent)
def index2(self, id=None, category=None, osep='None', print_area='false'):
if not identity.current.user.basketmode:
raise redirect(url("index1",id=id,category=category,osep=osep,print_area=print_area))
osep = True
if print_area == 'true':
print_area = True
else:
print_area = False
id = util.session('current_assessment', 0, id)
child = util.session('current_child', 0)
if id == 0: # no id given
raise redirect("/")
try:
current_assessment = Assessment.get(id)
except SQLObjectNotFound: # Child with given id does not exist
raise redirect("/")
current_child = current_assessment.child
all_assessments = current_child.assessments
sorted_assessments = Assessment.select(AND(Assessment.q.childID==current_assessment.childID,
Assessment.q.id!=id,
Assessment.q.level==current_assessment.level), orderBy='-dateEntered')
old = list(sorted_assessments[:7])
categories = self.category_list
data = {}
for c in categories:
cdata = {}
subtotal = Criterion.selectBy(category=c, level=current_assessment.level, prefix='Z')[0]
_na = Criterion.selectBy(category=c, level=current_assessment.level, prefix='Y')[0]
cdata['subtotal'] = subtotal
cdata['na'] = _na
oldtotals = {}
oldna = {}
percents = {}
for t in old:
stscore = Score.selectBy(assessmentID=t.id, criterionID=subtotal.id)
if stscore.count() > 0:
stscore = stscore[0]
else:
stscore = Score(assessmentID=t.id, criterionID=subtotal.id, value="0", type='subtotal')
oldtotals[t.id] = stscore.value
nascore = Score.selectBy(assessmentID=t.id, criterionID=_na.id)
if nascore.count() > 0:
nascore = nascore[0]
else:
nascore = Score(assessmentID=t.id, criterionID=_na.id, value="0", type='na')
oldna[t.id] = nascore.value
percents[t.id] = util.percent(stscore.value, util.scorePossible(c, current_assessment.level), nascore.value)
cdata['oldtotals'] = oldtotals
cdata['oldna'] = oldna
cdata['percents'] = percents
stscore = Score.selectBy(assessmentID=id, criterionID=subtotal.id)
nascore = Score.selectBy(assessmentID=id, criterionID=_na.id)
if stscore.count() > 0:
stscore = stscore[0]
else:
stscore = Score(assessmentID=id, criterionID=subtotal.id, value="0", type='subtotal')
if nascore.count() > 0:
nascore = nascore[0]
else:
nascore = Score(assessmentID=id, criterionID=_na.id, value="0", type='na')
percent = util.percent(stscore.value, util.scorePossible(c, current_assessment.level), nascore.value)
cdata['stscore'] = stscore
cdata['nascore'] = nascore
cdata['percent'] = percent
data[c] = cdata
baskets = []
for i in [1,2,3]:
bdata = {}
prefix = "B" + str(i)
b = Criterion.selectBy(level=current_assessment.level, prefix=prefix)[0]
oldb = {}
for t in old:
bscore = Score.selectBy(assessmentID=t.id, criterionID=b.id)
if bscore.count() > 0:
bscore = bscore[0]
else:
bscore = Score(assessmentID=t.id, criterionID=b.id, value="0", type='basket')
oldb[t.id] = bscore
bdata['old'] = oldb
bscore = Score.selectBy(assessmentID=id, criterionID=b.id)
if bscore.count() > 0:
bscore = bscore[0]
else:
bscore = Score(assessmentID=id, criterionID=b.id, value="0", type='basket')
bdata['val'] = bscore
baskets.append(bdata)
return dict(child=current_child, id=id, a=current_assessment, old=old, categories=categories, \
print_area=print_area, getEditor=self.getEditor, pcheck=self.persistCheck, \
shaded=util.shaded, scoreclass=util.color, scorePossible=util.scorePossible, \
data=data, baskets=baskets)
def processa_estados():
enf_uf, med_uf, hosp_uf, leitos_uf, uti_uf, uti_sus_uf, ibge_uf = util.read_uf_reg()
# troca "-" pelo valor 0 nas celulas em que aparece
util.dash_to_zero(enf_uf)
util.dash_to_zero(med_uf)
util.dash_to_zero(hosp_uf)
util.dash_to_zero(leitos_uf)
util.dash_to_zero(uti_uf)
util.dash_to_zero(uti_sus_uf)
# converte strings para valores numericos quando pertinente
util.str_to_int(enf_uf)
util.str_to_int(med_uf)
util.str_to_int(hosp_uf)
util.str_to_int(leitos_uf)
util.str_to_int(uti_uf)
util.str_to_int(uti_sus_uf)
# retira linhas com informacoes desnecessarias
util.drop(enf_uf, [-1, -1, -1])
util.drop(med_uf, [-1, -1, -1])
util.drop(hosp_uf, [-1, -1])
util.drop(leitos_uf, [-1, -1])
util.drop(uti_uf, [-1, -1])
util.drop(uti_sus_uf, [-1, -1, -1])
# cada bloco a seguir cria um atributo da tabela final
nome = enf_uf["Região/Unidade da Federação"]
# remove caracteres desnecessarios
for i in range(len(nome)):
nome[i] = nome[i].strip(". ")
pop = []
for item in ibge_uf["EST_POP_19"]:
pop.append(item)
# adiciona populacoes de cada regiao
pop.insert(0, sum(ibge_uf["EST_POP_19"][0:7])) # norte
pop.insert(8, sum(ibge_uf["EST_POP_19"][7:16])) # nordeste
pop.insert(18, sum(ibge_uf["EST_POP_19"][16:20])) # sudeste
pop.insert(23, sum(ibge_uf["EST_POP_19"][20:23])) # sul
pop.insert(27, sum(ibge_uf["EST_POP_19"][23:27])) # centro-oeste
enf_sus = enf_uf["Sim"]
enf_nsus = enf_uf["Não"]
enf_total = enf_uf["Total"]
enf_sus_percent = util.percent(enf_sus, enf_total)
enf_10k = util.pop_ratio(enf_total, 10000, pop)
med_sus = med_uf["Sim"]
med_nsus = med_uf["Não"]
med_total = med_uf["Total"]
med_sus_percent = util.percent(med_sus, med_total)
med_10k = util.pop_ratio(med_total, 10000, pop)
hosp_publ = []
for i in range(len(hosp_uf)):
hosp_publ.append(int(sum([hosp_uf["Administração Pública Federal"][i],
hosp_uf["Administração Pública Estadual ou Distrito Federal"][i],
hosp_uf["Administração Pública Municipal"][i],
hosp_uf["Administração Pública - Outros"][i],
hosp_uf["Empresa Pública ou Sociedade de Economia Mista"][i]])))
hosp_npubl = []
for i in range(len(hosp_uf)):
hosp_npubl.append(int(sum([hosp_uf["Demais Entidades Empresariais"][i],
hosp_uf["Entidades sem Fins Lucrativos"][i]])))
hosp_total = []
for i in range(len(hosp_uf)):
hosp_total.append(int(hosp_publ[i] + hosp_npubl[i]))
hosp_publ_percent = util.percent(hosp_publ, hosp_total)
hosp_100k = util.pop_ratio(hosp_total, 100000, pop)
leitos_sus = []
for i in range(len(leitos_uf)):
leitos_sus.append(int(leitos_uf["Quantidade SUS"][i]))
leitos_nsus = []
for i in range(len(leitos_uf)):
leitos_nsus.append(int(leitos_uf["Quantidade Não SUS"][i]))
leitos_total = []
for i in range(len(leitos_uf)):
leitos_total.append(int(leitos_sus[i] + leitos_nsus[i]))
leitos_sus_percent = util.percent(leitos_sus, leitos_total)
leitos_10k = util.pop_ratio(leitos_total, 10000, pop)
leitos_uti_total = []
for i in range(len(uti_uf)):
leitos_uti_total.append(int(uti_uf["Total"][i]))
leitos_uti_sus = uti_sus_uf["Total"]
leitos_uti_nsus = []
for i in range(len(leitos_uti_total)):
leitos_uti_nsus.append(int(leitos_uti_total[i] - leitos_uti_sus[i]))
leitos_uti_sus_percent = util.percent(leitos_uti_sus, leitos_uti_total)
leitos_uti_10k = util.pop_ratio(leitos_uti_total, 10000, pop)
leitos_covid_total = []
for i in range(len(uti_uf)):
leitos_covid_total.append(int(uti_uf["UTI adulto II COVID-19"][i] +
uti_uf["UTI pediátrica II COVID-19"][i]))
leitos_covid_sus = []
for i in range(len(uti_sus_uf)):
leitos_covid_sus.append(int(uti_sus_uf["UTI adulto II COVID-19"][i] +
uti_sus_uf["UTI pediátrica II COVID-19"][i]))
leitos_covid_nsus = []
for i in range(len(leitos_covid_total)):
leitos_covid_nsus.append(int(leitos_covid_total[i] - leitos_covid_sus[i]))
leitos_covid_sus_percent = util.percent(leitos_covid_sus, leitos_covid_total)
leitos_covid_10k = util.pop_ratio(leitos_covid_total, 10000, pop)
# deficit ou excesso de profissionais de saude no estado ou regiao
# em relacao a densidade populacional
# considerando referencia oms (health workforce requirements, 2016)
# recomendado = 44.5 medicos/enfs a cada 10k habitantes
rel_prof_saude_recomend = []
recomend = 44.5
for i in range(len(enf_total)):
med_e_enf_10k = med_10k[i] + enf_10k[i]
dfca = med_e_enf_10k - recomend # valor pos indica excesso; valor neg indica deficit
rel_prof_saude_recomend.append(round((dfca*pop[i])/10000, 2))
dados = {'Região ou UF': nome,
'População': pop,
'Enfermeiros SUS': enf_sus,
'Enfermeiros não-SUS': enf_nsus,
'Enfermeiros - Total': enf_total,
'%' + ' de enfermeiros SUS': enf_sus_percent,
'Enfermeiros a cada 10k habitantes': enf_10k,
'Médicos SUS': med_sus,
'Médicos não-SUS': med_nsus,
'Médicos - Total': med_total,
'%' + ' de médicos SUS': med_sus_percent,
'Médicos a cada 10k habitantes': med_10k,
'Déficit ou excesso de médicos/enfermeiros': rel_prof_saude_recomend,
'Hospitais públicos': hosp_publ,
'Hospitais não-públicos': hosp_npubl,
'Hospitais - Total': hosp_total,
'%' + ' de hospitais públicos': hosp_publ_percent,
'Hospitais a cada 100k habitantes': hosp_100k,
'Leitos (internação) SUS': leitos_sus,
'Leitos (internação) não-SUS': leitos_nsus,
'Leitos (internação) - Total': leitos_total,
'%' + ' de leitos (internação) SUS': leitos_sus_percent,
'Leitos (internação) a cada 10k habitantes': leitos_10k,
'Leitos UTI SUS': leitos_uti_sus,
'Leitos UTI não-SUS': leitos_uti_nsus,
'Leitos UTI - Total': leitos_uti_total,
'%' + ' de leitos UTI SUS': leitos_uti_sus_percent,
'Leitos UTI a cada 10k habitantes': leitos_uti_10k,
'Leitos UTI COVID-19 SUS': leitos_covid_sus,
'Leitos UTI COVID-19 não-SUS': leitos_covid_nsus,
'Leitos UTI COVID-19 - Total': leitos_covid_total,
'%' + ' de leitos UTI COVID-19 SUS': leitos_covid_sus_percent,
'Leitos UTI COVID-19 a cada 10k habitantes': leitos_covid_10k}
df = pd.DataFrame(data=dados)
util.write_to_csv(df, "Brasil - UFs e Regiões")
stats.stats_estados(pd.read_csv("../data/processed/Brasil - UFs e Regiões.csv"))
def processa_municipios():
enf_mun, med_mun, hosp_mun, leitos_mun, uti_mun, uti_sus_mun, ibge_mun = util.read_mun()
# troca "-" pelo valor 0 nas celulas em que aparece
util.dash_to_zero(enf_mun)
util.dash_to_zero(med_mun)
util.dash_to_zero(hosp_mun)
util.dash_to_zero(leitos_mun)
util.dash_to_zero(uti_mun)
util.dash_to_zero(uti_sus_mun)
# converte strings para valores numericos quando pertinente
util.str_to_int(enf_mun)
util.str_to_int(med_mun)
util.str_to_int(hosp_mun)
util.str_to_int(leitos_mun)
util.str_to_int(uti_mun)
util.str_to_int(uti_sus_mun)
# retira linhas com informacoes desnecessarias
util.drop(enf_mun, [-1, -1, -1])
util.drop(med_mun, [-1, -1, -1])
util.drop(hosp_mun, [-1, -1, -1])
util.drop(leitos_mun, [-1, -1, -1])
util.drop(uti_mun, [-1, -1, -1])
util.drop(uti_sus_mun, [-1, -1, -1])
util.drop_mun(enf_mun)
util.drop_mun(med_mun)
util.drop_mun(hosp_mun)
util.drop_mun(leitos_mun)
util.drop_mun(uti_mun)
util.drop_mun(uti_sus_mun)
# adiciona informacao sobre sigla correspondente a cada codigo uf
siglas_uf = util.cod_to_uf(ibge_mun)
ibge_mun["NM_UF"] = siglas_uf
# remove ultimo digito do codigo municipal do dataset do ibge
# o objetivo eh igualar ao codigo municipal dos datasets do datasus
for i in range(len(ibge_mun)):
ibge_mun.at[i, "CD_MUN"] = ibge_mun["CD_MUN"][i]//10
# cada bloco a seguir cria um atributo da tabela final
cod = []
for i in range(len(enf_mun)):
cod.append(int(enf_mun["Município"][i][0:7])) # primeiros seis digitos
nome = []
for i in range(len(enf_mun)):
nome.append(enf_mun["Município"][i][7:]) # todos os digitos menos 7 primeiros (6 do codigo + espaco)
pop = []
# insere na ordem dos datasets do datasus
for i in range(len(cod)):
# pop_cod = populacao registrada para a linha em que cd_mun == cod[i]
pop_cod = ibge_mun["EST_POP_19"][ibge_mun["CD_MUN"] == cod[i]].iloc[0]
pop.append(pop_cod)
uf = []
# insere na ordem dos datasets do datasus
for i in range(len(cod)):
# uf_cod = sigla da uf registrada para a linha em que cd_mun == cod[i]
uf_cod = ibge_mun["NM_UF"][ibge_mun["CD_MUN"] == cod[i]].iloc[0]
uf.append(uf_cod)
enf_sus = enf_mun["Sim"]
enf_nsus = enf_mun["Não"]
enf_total = enf_mun["Total"]
enf_sus_percent = util.percent(enf_sus, enf_total)
enf_10k = util.pop_ratio(enf_total, 10000, pop)
med_sus = med_mun["Sim"]
med_nsus = med_mun["Não"]
med_total = med_mun["Total"]
med_sus_percent = util.percent(med_sus, med_total)
med_10k = util.pop_ratio(med_total, 10000, pop)
hosp_publ = []
for i in range(len(hosp_mun)):
hosp_publ.append(int(sum([hosp_mun["Administração Pública Federal"][i],
hosp_mun["Administração Pública Estadual ou Distrito Federal"][i],
hosp_mun["Administração Pública Municipal"][i],
hosp_mun["Administração Pública - Outros"][i],
hosp_mun["Empresa Pública ou Sociedade de Economia Mista"][i]])))
hosp_npubl = []
for i in range(len(hosp_mun)):
hosp_npubl.append(int(sum([hosp_mun["Demais Entidades Empresariais"][i],
hosp_mun["Entidades sem Fins Lucrativos"][i]])))
hosp_total = []
for i in range(len(hosp_mun)):
hosp_total.append(int(hosp_publ[i] + hosp_npubl[i]))
hosp_publ_percent = util.percent(hosp_publ, hosp_total)
hosp_100k = util.pop_ratio(hosp_total, 100000, pop)
leitos_sus = leitos_mun["Quantidade SUS"]
leitos_nsus = leitos_mun["Quantidade Não SUS"]
leitos_total = []
for i in range(len(leitos_mun)):
leitos_total.append(int(leitos_sus[i] + leitos_nsus[i]))
leitos_sus_percent = util.percent(leitos_sus, leitos_total)
leitos_10k = util.pop_ratio(leitos_total, 10000, pop)
leitos_uti_total = uti_mun["Total"]
leitos_uti_sus = uti_sus_mun["Total"]
leitos_uti_nsus = []
for i in range(len(leitos_uti_total)):
leitos_uti_nsus.append(int(leitos_uti_total[i] - leitos_uti_sus[i]))
leitos_uti_sus_percent = util.percent(leitos_uti_sus, leitos_uti_total)
leitos_uti_10k = util.pop_ratio(leitos_uti_total, 10000, pop)
leitos_covid_total = []
for i in range(len(uti_mun)):
leitos_covid_total.append(int(uti_mun["UTI adulto II COVID-19"][i] +
uti_mun["UTI pediátrica II COVID-19"][i]))
leitos_covid_sus = []
for i in range(len(uti_sus_mun)):
leitos_covid_sus.append(int(uti_sus_mun["UTI adulto II COVID-19"][i] +
uti_sus_mun["UTI pediátrica II COVID-19"][i]))
leitos_covid_nsus = []
for i in range(len(leitos_covid_total)):
leitos_covid_nsus.append(int(leitos_covid_total[i] - leitos_covid_sus[i]))
leitos_covid_sus_percent = util.percent(leitos_covid_sus, leitos_covid_total)
leitos_covid_10k = util.pop_ratio(leitos_covid_total, 10000, pop)
# deficit ou excesso de profissionais de saude no municipio
# em relacao a densidade populacional
# considerando referencia oms (health workforce requirements, 2016)
# recomendado = 44.5 medicos/enfs a cada 10k habitantes
rel_prof_saude_recomend = []
recomend = 44.5
for i in range(len(enf_total)):
med_e_enf_10k = med_10k[i] + enf_10k[i]
dfca = med_e_enf_10k - recomend # valor pos indica excesso; valor neg indica deficit
rel_prof_saude_recomend.append(round((dfca*pop[i])/10000, 2))
dados = {'Código Municipal': cod,
'Município': nome,
'População': pop,
'UF': uf,
'Enfermeiros SUS': enf_sus,
'Enfermeiros não-SUS': enf_nsus,
'Enfermeiros - Total': enf_total,
'%' + ' de enfermeiros SUS': enf_sus_percent,
'Enfermeiros a cada 10k habitantes': enf_10k,
'Médicos SUS': med_sus,
'Médicos não-SUS': med_nsus,
'Médicos - Total': med_total,
'%' + ' de médicos SUS': med_sus_percent,
'Médicos a cada 10k habitantes': med_10k,
'Déficit ou excesso de médicos/enfermeiros': rel_prof_saude_recomend,
'Hospitais públicos': hosp_publ,
'Hospitais não-públicos': hosp_npubl,
'Hospitais - Total': hosp_total,
'%' + ' de hospitais públicos': hosp_publ_percent,
'Hospitais a cada 100k habitantes': hosp_100k,
'Leitos (internação) SUS': leitos_sus,
'Leitos (internação) não-SUS': leitos_nsus,
'Leitos (internação) - Total': leitos_total,
'%' + ' de leitos (internação) SUS': leitos_sus_percent,
'Leitos (internação) a cada 10k habitantes': leitos_10k,
'Leitos UTI SUS': leitos_uti_sus,
'Leitos UTI não-SUS': leitos_uti_nsus,
'Leitos UTI - Total': leitos_uti_total,
'%' + ' de leitos UTI SUS': leitos_uti_sus_percent,
'Leitos UTI a cada 10k habitantes': leitos_uti_10k,
'Leitos UTI COVID-19 SUS': leitos_covid_sus,
'Leitos UTI COVID-19 não-SUS': leitos_covid_nsus,
'Leitos UTI COVID-19 - Total': leitos_covid_total,
'%' + ' de leitos UTI COVID-19 SUS': leitos_covid_sus_percent,
'Leitos UTI COVID-19 a cada 10k habitantes': leitos_covid_10k}
df = pd.DataFrame(data=dados)
util.write_to_csv(df, "Brasil - Municípios")
stats.stats_municipios(pd.read_csv("../data/processed/Brasil - Municípios.csv"))
