def run():
print "Loading test County Dataset"
# clean up if necessary
cat = Category.objects.filter(name='Test')
if not cat:
# create the test Category
cat = Category(name="Test")
cat.save()
print "Created Category"
cat = Category.objects.filter(name='Test')
# clean up if necessary
dat = Dataset.objects.filter(name='TestCounties')
if dat:
dat.delete()
print "Deleted Dataset"
# create a test Dataset
dat = Dataset(category=cat[0], name="TestCounties", description="Counties Test Dataset", legend="Empty")
dat.save()
print "Created Test County Dataset"
reg = Region.objects.filter(state='MA')
# populate datasets
for r in reg:
if r.fips != None and len(r.fips) == 5:
row = Datarow(dataset=dat, region=r, value=random.randrange(0,100))
row.save()
print("%s County rows created" % len(reg))
def run():
print "Loading empty Dataset"
# create the empty Category
cat = Category(name="Empty")
cat.save()
# clean up if necessary
dat = Dataset.objects.filter(name='Empty')
if not dat:
# create the empty Dataset
dat = Dataset(category=cat, name="Empty", description="Empty Dataset", legend="Empty")
dat.save()
dat = Dataset.objects.filter(name='Empty')
# create empty ranges
range = Range(dataset=dat[0], name="Low", low=0, high=10, color="#FFFAFA")
range.save()
range = Range(dataset=dat[0], name="Low-Mid", low=10, high=20, color="#F2F2F2")
range.save()
range = Range(dataset=dat[0], name="Mid", low=20, high=30, color="#D0CFCF")
range.save()
range = Range(dataset=dat[0], name="Mid-High", low=30, high=40, color="#ADACAC")
range.save()
range = Range(dataset=dat[0], name="High", low=40, high=50, color="#8B8989")
range.save()
range = Range(dataset=dat[0], name="Very High", low=50, high=60, color="#596C56")
range.save()
row = Datarow.objects.filter(dataset=dat[0])
if row:
row.delete()
# use states.xml to iterate through States
xmldoc = minidom.parse('HealthMap/scripts/states.xml')
cNodes = xmldoc.childNodes
sList = cNodes[0].getElementsByTagName("state") # top level in DOM is States
for state in sList: # iterate through each State
# create the State record in Datarow table with one zero value
print ("%s (%s)" % (state.getAttribute('name'), state.getAttribute('abbrev')))
reg = Region.objects.filter(state=state.getAttribute('abbrev'))
if len(reg)==1:
row = Datarow(dataset=dat[0], region=reg[0], value=0)
row.save()
print ("... %s values" % len(sList))
def run():
col = [None] * 60
cat = [None] * 60
desc = [None] * 60
source = [None] * 60
col[0] = "FIPS"
col[1] = "State"
col[2] = "County"
col[3] = "Years of Potential Life Lost Rate"
cat[3] = "Health"
desc[3] = "Age-adjusted years of potential life lost rate per 100,000"
source[3] = "National Center for Health Statistics (NCHS) 2006-2008"
col[4] = "Fair/Poor Health"
cat[4] = "Health"
desc[4] = "Percent of adults that report fair or poor health (age-adjusted)"
source[4] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[5] = "Physically Unhealthy Days"
cat[5] = "Health"
desc[5] = "Average number of reported physically unhealthy days per month"
source[5] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[6] = "Mentally Unhealthy Days"
cat[6] = "Health"
desc[6] = "Average number of reported mentally unhealthy days per month"
source[6] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[7] = "Low Birth Weight"
cat[7] = "Health"
desc[7] = "Percent of births with low birth weight (<2,500g)"
source[7] = "Vital Statistics, National Center for Health Statistics (NCHS) 2002-2008"
col[8] = "Smoking"
cat[8] = "Health"
desc[8] = "Percent of adults that reported currently smoking"
source[8] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[9] = "Obesity"
cat[9] = "Health"
desc[9] = "Percent of adults that report BMI >= 30"
source[9] = "National Center for Chronic Disease Prevention and Health 2009"
col[10] = "Physically Inactive"
cat[10] = "Health"
desc[10] = "Percent of adults that report no leisure time physical activity"
source[10] = "National Center for Chronic Disease Prevention and Health 2009"
col[11] = "Excessive Drinking"
cat[11] = "Health"
desc[11] = "Percent of adults that report excessive drinking"
source[11] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[12] = "Motor Vehicle Mortality Rate"
cat[12] = "Safety"
desc[12] = "Crude motor-vehicle related mortality rate per 100,000 population"
source[12] = "Vital Statistics, National Center for Health Statistics (NCHS) 2002-2008"
col[13] = "Sexually Transmitted Infections"
cat[13] = "Health"
desc[13] = "Number of chlamydia cases"
source[13] = "CDC National Center for Hepatitis, HIV, STD 2009"
col[14] = "Teen Birth Rate"
cat[14] = "Health"
desc[14] = "Teen birth count, ages 15-19"
source[14] = "Vital Statistics, National Center for Health Statistics (NCHS) 2002-2008"
col[15] = "Uninsured under 65"
cat[15] = "Economic"
desc[15] = "Total number of people under age 65 without insurance"
source[15] = "Census/American Community Survey (ACS) 2009"
col[17] = "Primary Care Physicians"
cat[17] = "Environment"
desc[17] = "Number of primary care physicians in patient care"
source[17] = "Health Resources and Services Administration 2009"
col[18] = "ACSC Medicare Preventable Hospital Stays"
cat[18] = "Health"
desc[18] = "Discharges for ambulatory care sensitive conditions/Medicare enrollees"
source[18] = "Dartmouth Atlas 2009"
col[19] = "Medicare Enrollees Diabetic Screening"
cat[19] = "Health"
desc[19] = "Percent of Diabetic Medicare enrollees receiving HbA1c test"
source[19] = "Dartmouth Atlas 2009"
col[20] = "Medicare Enrollees Mammography Screening"
cat[20] = "Health"
desc[20] = "Percent of female Medicare enrollees having at least 1 mammogram in 2 yrs (age 67-69)"
source[20] = "Dartmouth Atlas 2009"
col[21] = "AFGR High School Graduation Rates"
cat[21] = "Environment"
desc[21] = "Calculated average freshman graduation rate"
source[21] = "Sate sources"
col[22] = "PSED Post-Secondary Education"
cat[22] = "Environment"
desc[22] = "Adults age 25-44 with some post-secondary education"
source[22] = "Census/American Community Survey (ACS) 2006-2010"
col[23] = "Unemployed"
cat[23] = "Economic"
desc[23] = "Number of people age 16+ unemployed and looking for work"
source[23] = "Unemployment Statistics, Bureau of Labor Statistics 2010"
col[24] = "Child Poverty"
cat[24] = "Economic"
desc[24] = "Percent of children (under age 18) living in poverty"
source[24] = "Census/CPS 2010"
col[25] = "Social-Emotional Support Inadequate"
cat[25] = "Health"
desc[25] = "Percent of adults that report not getting social/emotional support"
source[25] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[26] = "Single Parent Households"
cat[26] = "Health"
desc[26] = "Number of children that live in single parent households"
source[26] = "Census/American Community Survey (ACS) 2006-2010"
col[27] = "Violent Crime Rate"
cat[27] = "Environment"
desc[27] = "Sum of violent crimes"
source[27] = "Federal Bureau of Investigation 2007-2009"
col[28] = "Air Pollution Particular Matter Days"
cat[28] = "Environment"
desc[28] = "Number of days that air quality was unhealthy due to fine particulate matter"
source[28] = "CDC Environmental Protection Agency (EPA) 2007"
col[29] = "Air Pollution Ozone Unhealthy Days"
cat[29] = "Environment"
desc[29] = "Number of days that air quality was unhealthy due to ozone"
source[29] = "CDC Environmental Protection Agency (EPA) 2007"
col[30] = "Recreational Facilities Access"
cat[30] = "Environment"
desc[30] = "Total recreational facilities"
source[30] = "Census County Business Patterns 2009"
col[31] = "Health Foods Limited Access"
cat[31] = "Environment"
desc[31] = "Total number of people with limited access to health foods"
source[31] = "Census Zip Code Business Patterns 2009"
col[32] = "Fast Food Restaurants"
cat[32] = "Environment"
desc[32] = "Number of zip codes with a healthy food outlet"
source[32] = "Census County Business Patterns 2009"
# category: Demographics
col[33] = "<18 Population"
cat[33] = "Demographic"
desc[33] = "Percent"
source[33] = "US Census Bureau 2009"
col[34] = "65+ Population"
cat[34] = "Demographic"
desc[34] = "Percent"
source[34] = "US Census Bureau 2009"
col[35] = "African American Population"
cat[35] = "Demographic"
desc[35] = "Percent"
source[35] = "US Census Bureau 2009"
col[36] = "American Indian/Alaskan Native Population"
cat[36] = "Demographic"
desc[36] = "Percent"
source[36] = "US Census Bureau 2009"
col[37] = "Asian Population"
cat[37] = "Demographic"
desc[37] = "Percent"
source[37] = "US Census Bureau 2009"
col[38] = "Native Hawaiian/Other Pacific Population"
cat[38] = "Demographic"
desc[38] = "Percent"
source[38] = "US Census Bureau 2009"
col[39] = "Hispanic Population"
cat[39] = "Demographic"
desc[39] = "Percent"
source[39] = "US Census Bureau 2009"
col[40] = "Not Proficient in English"
cat[40] = "Demographic"
desc[40] = "Percent"
source[40] = "US Census Bureau 2009"
col[41] = "Female Population"
cat[41] = "Demographic"
desc[41] = "Percent"
source[41] = "US Census Bureau 2009"
col[42] = "Rural Population"
cat[42] = "Demographic"
desc[42] = "Percent"
source[42] = "US Census Bureau 2009"
col[43] = "Diabetics"
cat[43] = "Demographic"
desc[43] = "Percent"
source[43] = "Centers for Disease Control (CDC) 2009"
col[44] = "HIV Cases"
cat[44] = "Demographic"
desc[44] = "Rate of HIV cases"
source[44] = "National Center for Hepatitis, HIC, STD 20008"
col[46] = "Primary Care Physicians Ratio"
cat[46] = "Demographic"
desc[46] = "Number of people per PCP"
source[46] = "Health Resources & Services Administration (HRSA) 2007"
col[47] = "Uninsured Adults"
cat[47] = "Demographic"
desc[47] = "Percent"
source[47] = "Small Area Health Insurance Estimates (SAHIE 2009"
col[48] = "Could Not Access Physician Due to Cost"
cat[48] = "Demographic"
desc[48] = "Percent"
source[48] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[50] = "Dentist Ratio"
cat[50] = "Demographic"
desc[50] = "Number of people per dentist"
source[50] = "Health Resources & Services Administration (HRSA) 2007"
col[51] = "Household Income"
cat[51] = "Demographic"
desc[51] = "Median household imcome"
source[51] = "Small Area Health Insurance Estimates (SAHIE 2009"
col[52] = "High Housing Costs"
cat[52] = "Demographic"
desc[52] = "Percent"
source[52] = "ACS Estimates 2006"
col[53] = "Illiteracy"
cat[53] = "Demographic"
desc[53] = "Percent"
source[53] = "National Center for Education Statistics 2003"
col[54] = "Homicides"
cat[54] = "Demographic"
desc[54] = "Total number of homicides"
source[54] = "National Center for Health Statistics 2002-2008"
col[55] = "Access to Health Foods"
cat[55] = "Demographic"
desc[55] = "Percent of zip codes with healthy food outlets"
source[55] = "Census Zip Code Business Patterns 2009"
# clean up if necessary
cate = Category.objects.filter(name__in=['Health','Safety','Environment','Economic','Demographic'])
if cate:
cate.delete()
# create the Categories
cate = Category(name="Health")
cate.save()
cate = Category(name="Safety")
cate.save()
cate = Category(name="Environment")
cate.save()
cate = Category(name="Economic")
cate.save()
cate = Category(name="Demographic")
cate.save()
print "Created Categories"
print "Loading CHR Datasets"
hidden_cols = [0, 1, 2, 16, 45, 49]
book = open_workbook('HealthMap/scripts/data.xls')
sheet = book.sheet_by_index(1)
# delete existing Datasets
dat = Dataset.objects.filter(name__in=col)
if dat:
dat.delete()
for row_index in range(sheet.nrows):
state = sheet.cell(row_index,1).value
county = sheet.cell(row_index,2).value
if len(state)>1 and len(county)<1: # state data row
# print state
for col_index in range(sheet.ncols):
if col_index not in hidden_cols:
# find Region
reg = Region.objects.filter(stateName=state.strip())
if not reg:
print("Missing State: %s" % state)
# create Dataset
cate = Category.objects.filter(name=cat[col_index])
if not cate:
print("Missing Category: %s" % cat[col_index])
if not Dataset.objects.filter(name=col[col_index]):
print("Creating Dataset: %s" % col[col_index])
dat = Dataset(category=cate[0],
name=col[col_index],
description=desc[col_index],
citations=source[col_index])
dat.save()
dat = Dataset.objects.filter(name=col[col_index])
val = sheet.cell(row_index,col_index).value
print("data for Region:%s, [%s] (%s) Dataset:%s" % (reg[0].state, col_index, str(val).strip(), col[col_index]))
# create Datarow if there is value
if len(str(val).strip())>0:
row = Datarow(dataset=dat[0], region=reg[0], value=val)
row.save()
def detailRegion(region, region_name):
col = [None] * 60
cat = [None] * 60
desc = [None] * 60
source = [None] * 60
col[0] = "FIPS"
col[1] = "State"
col[2] = "County"
col[3] = "Years of Potential Life Lost Rate : "
cat[3] = "Health"
desc[3] = "Age-adjusted years of potential life lost rate per 100,000"
source[3] = "National Center for Health Statistics (NCHS) 2006-2008"
col[4] = "Fair/Poor Health : "
cat[4] = "Health"
desc[
4] = "Percent of adults that report fair or poor health (age-adjusted)"
source[4] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[5] = "Physically Unhealthy Days : "
cat[5] = "Health"
desc[5] = "Average number of reported physically unhealthy days per month"
source[5] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[6] = "Mentally Unhealthy Days : "
cat[6] = "Health"
desc[6] = "Average number of reported mentally unhealthy days per month"
source[6] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[7] = "Low Birth Weight : "
cat[7] = "Health"
desc[7] = "Percent of births with low birth weight (<2,500g)"
source[
7] = "Vital Statistics, National Center for Health Statistics (NCHS) 2002-2008"
col[8] = "Smoking : "
cat[8] = "Health"
desc[8] = "Percent of adults that reported currently smoking"
source[8] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[9] = "Obesity : "
cat[9] = "Health"
desc[9] = "Percent of adults that report BMI >= 30"
source[
9] = "National Center for Chronic Disease Prevention and Health 2009"
col[10] = "Physically Inactive : "
cat[10] = "Health"
desc[
10] = "Percent of adults that report no leisure time physical activity"
source[
10] = "National Center for Chronic Disease Prevention and Health 2009"
col[11] = "Excessive Drinking : "
cat[11] = "Health"
desc[11] = "Percent of adults that report excessive drinking"
source[11] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[12] = "Motor Vehicle Mortality Rate : "
cat[12] = "Safety"
desc[
12] = "Crude motor-vehicle related mortality rate per 100,000 population"
source[
12] = "Vital Statistics, National Center for Health Statistics (NCHS) 2002-2008"
col[13] = "Sexually Transmitted Infections : "
cat[13] = "Health"
desc[13] = "Number of chlamydia cases"
source[13] = "CDC National Center for Hepatitis, HIV, STD 2009"
col[14] = "Teen Birth Rate : "
cat[14] = "Health"
desc[14] = "Teen birth count, ages 15-19"
source[
14] = "Vital Statistics, National Center for Health Statistics (NCHS) 2002-2008"
col[15] = "Uninsured under 65 : "
cat[15] = "Economic"
desc[15] = "Total number of people under age 65 without insurance"
source[15] = "Census/American Community Survey (ACS) 2009"
col[17] = "Primary Care Physicians : "
cat[17] = "Environment"
desc[17] = "Number of primary care physicians in patient care"
source[17] = "Health Resources and Services Administration 2009"
col[18] = "ACSC Medicare Preventable Hospital Stays : "
cat[18] = "Health"
desc[
18] = "Discharges for ambulatory care sensitive conditions/Medicare enrollees"
source[18] = "Dartmouth Atlas 2009"
col[19] = "Medicare Enrollees Diabetic Screening : "
cat[19] = "Health"
desc[19] = "Percent of Diabetic Medicare enrollees receiving HbA1c test"
source[19] = "Dartmouth Atlas 2009"
col[20] = "Medicare Enrollees Mammography Screening : "
cat[20] = "Health"
desc[
20] = "Percent of female Medicare enrollees having at least 1 mammogram in 2 yrs (age 67-69)"
source[20] = "Dartmouth Atlas 2009"
col[21] = "AFGR High School Graduation Rates : "
cat[21] = "Environment"
desc[21] = "Calculated average freshman graduation rate"
source[21] = "Sate sources"
col[22] = "PSED Post-Secondary Education : "
cat[22] = "Environment"
desc[22] = "Adults age 25-44 with some post-secondary education"
source[22] = "Census/American Community Survey (ACS) 2006-2010"
col[23] = "Unemployed : "
cat[23] = "Economic"
desc[23] = "Number of people age 16+ unemployed and looking for work"
source[23] = "Unemployment Statistics, Bureau of Labor Statistics 2010"
col[24] = "Child Poverty : "
cat[24] = "Economic"
desc[24] = "Percent of children (under age 18) living in poverty"
source[24] = "Census/CPS 2010"
col[25] = "Social-Emotional Support Inadequate : "
cat[25] = "Health"
desc[
25] = "Percent of adults that report not getting social/emotional support"
source[25] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[26] = "Single Parent Households : "
cat[26] = "Health"
desc[26] = "Number of children that live in single parent households"
source[26] = "Census/American Community Survey (ACS) 2006-2010"
col[27] = "Violent Crime Rate : "
cat[27] = "Environment"
desc[27] = "Sum of violent crimes"
source[27] = "Federal Bureau of Investigation 2007-2009"
col[28] = "Air Pollution Particular Matter Days : "
cat[28] = "Environment"
desc[
28] = "Number of days that air quality was unhealthy due to fine particulate matter"
source[28] = "CDC Environmental Protection Agency (EPA) 2007"
col[29] = "Air Pollution Ozone Unhealthy Days : "
cat[29] = "Environment"
desc[29] = "Number of days that air quality was unhealthy due to ozone"
source[29] = "CDC Environmental Protection Agency (EPA) 2007"
col[30] = "Recreational Facilities Access : "
cat[30] = "Environment"
desc[30] = "Total recreational facilities"
source[30] = "Census County Business Patterns 2009"
col[31] = "Health Foods Limited Access : "
cat[31] = "Environment"
desc[31] = "Total number of people with limited access to health foods"
source[31] = "Census Zip Code Business Patterns 2009"
col[32] = "Fast Food Restaurants : "
cat[32] = "Environment"
desc[32] = "Number of zip codes with a healthy food outlet"
source[32] = "Census County Business Patterns 2009"
# category: Demographics
col[33] = "<18 Population : "
cat[33] = "Demographic"
desc[33] = ""
source[33] = "US Census Bureau 2009"
col[34] = "65+ Population : "
cat[34] = "Demographic"
desc[34] = ""
source[34] = "US Census Bureau 2009"
col[35] = "African American Population : "
cat[35] = "Demographic"
desc[35] = ""
source[35] = "US Census Bureau 2009"
col[36] = "American Indian/Alaskan Native Population : "
cat[36] = "Demographic"
desc[36] = ""
source[36] = "US Census Bureau 2009"
col[37] = "Asian Population : "
cat[37] = "Demographic"
desc[37] = ""
source[37] = "US Census Bureau 2009"
col[38] = "Native Hawaiian/Other Pacific Population : "
cat[38] = "Demographic"
desc[38] = ""
source[38] = "US Census Bureau 2009"
col[39] = "Hispanic Population : "
cat[39] = "Demographic"
desc[39] = ""
source[39] = "US Census Bureau 2009"
col[40] = "Not Proficient in English : "
cat[40] = "Demographic"
desc[40] = ""
source[40] = "US Census Bureau 2009"
col[41] = "Female Population : "
cat[41] = "Demographic"
desc[41] = ""
source[41] = "US Census Bureau 2009"
col[42] = "Rural Population : "
cat[42] = "Demographic"
desc[42] = ""
source[42] = "US Census Bureau 2009"
col[43] = "Diabetics : "
cat[43] = "Demographic"
desc[43] = ""
source[43] = "Centers for Disease Control (CDC) 2009"
col[44] = "HIV Cases : "
cat[44] = "Demographic"
desc[44] = "Rate of HIV cases"
source[44] = "National Center for Hepatitis, HIC, STD 20008"
col[46] = "Primary Care Physicians Ratio : "
cat[46] = "Demographic"
desc[46] = "Number of people per PCP"
source[46] = "Health Resources & Services Administration (HRSA) 2007"
col[47] = "Uninsured Adults : "
cat[47] = "Demographic"
desc[47] = ""
source[47] = "Small Area Health Insurance Estimates (SAHIE 2009"
col[48] = "Could Not Access Physician Due to Cost : "
cat[48] = "Demographic"
desc[48] = ""
source[48] = "Behavioral Risk Factor Surveillance System (BRFSS) 2004-2010"
col[50] = "Dentist Ratio : "
cat[50] = "Demographic"
desc[50] = "Number of people per dentist"
source[50] = "Health Resources & Services Administration (HRSA) 2007"
col[51] = "Household Income : "
cat[51] = "Demographic"
desc[51] = "Median household imcome"
source[51] = "Small Area Health Insurance Estimates (SAHIE 2009"
col[52] = "High Housing Costs : "
cat[52] = "Demographic"
desc[52] = ""
source[52] = "ACS Estimates 2006"
col[53] = "Illiteracy : "
cat[53] = "Demographic"
desc[53] = ""
source[53] = "National Center for Education Statistics 2003"
col[54] = "Homicides : "
cat[54] = "Demographic"
desc[54] = "Total number of homicides"
source[54] = "National Center for Health Statistics 2002-2008"
col[55] = "Access to Health Foods : "
cat[55] = "Demographic"
desc[55] = "Percent of zip codes with healthy food outlets"
source[55] = "Census Zip Code Business Patterns 2009"
print "Loading CHR Detailed Datasets"
hidden_cols = [0, 1, 2, 16, 45, 49]
book = open_workbook('HealthMap/scripts/data.xls')
sheet = book.sheet_by_index(1)
for row_index in range(sheet.nrows):
state = sheet.cell(row_index, 1).value
fips = sheet.cell(row_index, 0).value
county = sheet.cell(row_index, 2).value
if len(state) > 1 and len(county) < 1: # state data row
print state
elif state in region:
print("Processing %s" % state)
for col_index in range(sheet.ncols):
if col_index not in hidden_cols:
# find Region
reg = Region.objects.filter(stateName=state.strip(),
fips=fips)
proceed = True
if not reg:
print("Missing State: %s County:%s" % (state, county))
proceed = False
# create Dataset
cate = Category.objects.filter(name=cat[col_index])
if not cate:
print("Missing Category: %s" % cat[col_index])
proceed = False
dataset_name = col[col_index] + region_name
if proceed:
if not Dataset.objects.filter(name=dataset_name):
print("Creating Dataset: %s" % dataset_name)
dat = Dataset(category=cate[0],
name=dataset_name,
description=desc[col_index],
citations=source[col_index])
dat.save()
dat = Dataset.objects.filter(name=dataset_name)
val = sheet.cell(row_index, col_index).value
# print("data for Region:%s/%s, (%s) Dataset:%s" % (reg[0].state, reg[0].county, str(val).strip(), dataset_name))
row = Datarow.objects.filter(dataset=dat[0],
region=reg[0])
if row: # cleanup if necessary
row.delete()
# create Datarow if there is value
if len(str(val).strip()) > 0:
row = Datarow(dataset=dat[0],
region=reg[0],
value=val)
row.save()
def run():
# colors:
DARK_GREEN="#006600"
GREEN="#009900"
LIGHT_GREEN="#33FF66"
LIGHT_YELLOW="#FFFF66"
YELLOW="#FFFF00"
ORANGE="#FFFC00"
LIGHT_RED="#FFF900"
RED="#FFF000"
DARK_RED="#CC0000"
DARK="#333333"
print "Loading test Datasets"
# clean up if necessary
cat = Category.objects.filter(name__startswith='Test')
if cat:
cat.delete()
# create the test Category
cat = Category(name="Test")
cat.save()
print "Created Category"
# clean up if necessary
dat = Dataset.objects.filter(name__startswith='Test')
if dat:
dat.delete()
# create 3 test Datasets
dat1 = Dataset(category=cat, name="Test1", description="1st Test Dataset", legend="Empty")
dat1.save()
dat2 = Dataset(category=cat, name="Test2", description="2nd Test Dataset", legend="Empty")
dat2.save()
dat3 = Dataset(category=cat, name="Test3", description="3rd Test Dataset", legend="Empty")
dat3.save()
print "Created Test Datasets"
# clean up if necessary
ran = Range.objects.filter(name__startswith='Test')
if ran:
ran.delete()
# create the test Ranges
ran = Range(dataset=dat1, name="Test Low Range", low=0, high=9, color=GREEN)
ran.save()
ran = Range(dataset=dat1, name="Test Mid Range", low=10, high=19, color=YELLOW)
ran.save()
ran = Range(dataset=dat1, name="Test High Range", low=20, high=29, color=RED)
ran.save()
ran = Range(dataset=dat2, name="Test Low Range", low=0, high=9, color=GREEN)
ran.save()
ran = Range(dataset=dat2, name="Test Mid Range", low=10, high=19, color=YELLOW)
ran.save()
ran = Range(dataset=dat2, name="Test High Range", low=20, high=29, color=RED)
ran.save()
ran = Range(dataset=dat3, name="Test Low Range", low=0, high=9, color=GREEN)
ran.save()
ran = Range(dataset=dat3, name="Test Mid Range", low=10, high=19, color=YELLOW)
ran.save()
ran = Range(dataset=dat3, name="Test High Range", low=20, high=29, color=RED)
ran.save()
print "Created Ranges"
# use states.xml to iterate through States
xmldoc = minidom.parse('HealthMap/scripts/states.xml')
cNodes = xmldoc.childNodes
sList = cNodes[0].getElementsByTagName("state") # top level in DOM is States
# populate datasets
for state in sList: # iterate through each State
# create the State record in Datarow table with one zero value
# print ("%s (%s)" % (state.getAttribute('name'), state.getAttribute('abbrev')))
reg = Region.objects.filter(state=state.getAttribute('abbrev'))
if len(reg)==1:
row = Datarow(dataset=dat1, region=reg[0], value=random.randrange(0,29))
row.save()
row = Datarow(dataset=dat2, region=reg[0], value=random.randrange(0,29))
row.save()
row = Datarow(dataset=dat3, region=reg[0], value=random.randrange(0,29))
row.save()
print ("... %s values" % len(sList))
def run():
print "Loading empty Dataset"
# create the empty Category
cat = Category(name="Empty")
cat.save()
# clean up if necessary
dat = Dataset.objects.filter(name='Empty')
if not dat:
# create the empty Dataset
dat = Dataset(category=cat,
name="Empty",
description="Empty Dataset",
legend="Empty")
dat.save()
dat = Dataset.objects.filter(name='Empty')
# create empty ranges
range = Range(dataset=dat[0], name="Low", low=0, high=10, color="#FFFAFA")
range.save()
range = Range(dataset=dat[0],
name="Low-Mid",
low=10,
high=20,
color="#F2F2F2")
range.save()
range = Range(dataset=dat[0], name="Mid", low=20, high=30, color="#D0CFCF")
range.save()
range = Range(dataset=dat[0],
name="Mid-High",
low=30,
high=40,
color="#ADACAC")
range.save()
range = Range(dataset=dat[0],
name="High",
low=40,
high=50,
color="#8B8989")
range.save()
range = Range(dataset=dat[0],
name="Very High",
low=50,
high=60,
color="#596C56")
range.save()
row = Datarow.objects.filter(dataset=dat[0])
if row:
row.delete()
# use states.xml to iterate through States
xmldoc = minidom.parse('HealthMap/scripts/states.xml')
cNodes = xmldoc.childNodes
sList = cNodes[0].getElementsByTagName(
"state") # top level in DOM is States
for state in sList: # iterate through each State
# create the State record in Datarow table with one zero value
print("%s (%s)" %
(state.getAttribute('name'), state.getAttribute('abbrev')))
reg = Region.objects.filter(state=state.getAttribute('abbrev'))
if len(reg) == 1:
row = Datarow(dataset=dat[0], region=reg[0], value=0)
row.save()
print("... %s values" % len(sList))
def run():
# colors:
DARK_GREEN = "#006600"
GREEN = "#009900"
LIGHT_GREEN = "#33FF66"
LIGHT_YELLOW = "#FFFF66"
YELLOW = "#FFFF00"
ORANGE = "#FFFC00"
LIGHT_RED = "#FFF900"
RED = "#FFF000"
DARK_RED = "#CC0000"
DARK = "#333333"
print "Loading test Datasets"
# clean up if necessary
cat = Category.objects.filter(name__startswith='Test')
if cat:
cat.delete()
# create the test Category
cat = Category(name="Test")
cat.save()
print "Created Category"
# clean up if necessary
dat = Dataset.objects.filter(name__startswith='Test')
if dat:
dat.delete()
# create 3 test Datasets
dat1 = Dataset(category=cat,
name="Test1",
description="1st Test Dataset",
legend="Empty")
dat1.save()
dat2 = Dataset(category=cat,
name="Test2",
description="2nd Test Dataset",
legend="Empty")
dat2.save()
dat3 = Dataset(category=cat,
name="Test3",
description="3rd Test Dataset",
legend="Empty")
dat3.save()
print "Created Test Datasets"
# clean up if necessary
ran = Range.objects.filter(name__startswith='Test')
if ran:
ran.delete()
# create the test Ranges
ran = Range(dataset=dat1,
name="Test Low Range",
low=0,
high=9,
color=GREEN)
ran.save()
ran = Range(dataset=dat1,
name="Test Mid Range",
low=10,
high=19,
color=YELLOW)
ran.save()
ran = Range(dataset=dat1,
name="Test High Range",
low=20,
high=29,
color=RED)
ran.save()
ran = Range(dataset=dat2,
name="Test Low Range",
low=0,
high=9,
color=GREEN)
ran.save()
ran = Range(dataset=dat2,
name="Test Mid Range",
low=10,
high=19,
color=YELLOW)
ran.save()
ran = Range(dataset=dat2,
name="Test High Range",
low=20,
high=29,
color=RED)
ran.save()
ran = Range(dataset=dat3,
name="Test Low Range",
low=0,
high=9,
color=GREEN)
ran.save()
ran = Range(dataset=dat3,
name="Test Mid Range",
low=10,
high=19,
color=YELLOW)
ran.save()
ran = Range(dataset=dat3,
name="Test High Range",
low=20,
high=29,
color=RED)
ran.save()
print "Created Ranges"
# use states.xml to iterate through States
xmldoc = minidom.parse('HealthMap/scripts/states.xml')
cNodes = xmldoc.childNodes
sList = cNodes[0].getElementsByTagName(
"state") # top level in DOM is States
# populate datasets
for state in sList: # iterate through each State
# create the State record in Datarow table with one zero value
# print ("%s (%s)" % (state.getAttribute('name'), state.getAttribute('abbrev')))
reg = Region.objects.filter(state=state.getAttribute('abbrev'))
if len(reg) == 1:
row = Datarow(dataset=dat1,
region=reg[0],
value=random.randrange(0, 29))
row.save()
row = Datarow(dataset=dat2,
region=reg[0],
value=random.randrange(0, 29))
row.save()
row = Datarow(dataset=dat3,
region=reg[0],
value=random.randrange(0, 29))
row.save()
print("... %s values" % len(sList))