def get_quake_desc(event,lat,lon,isMainEvent):
ndeaths = event['TotalDeaths']
#summarize the exposure values
exposures = np.array([event['MMI1'],event['MMI2'],event['MMI3'],event['MMI4'],event['MMI5'],
event['MMI6'],event['MMI7'],event['MMI8'],event['MMI9+']])
exposures = np.array([round_to_nearest(exp,1000) for exp in exposures])
#get the highest two exposures greater than zero
iexp = np.where(exposures > 0)[0][::-1]
romans = ['I', 'II', 'III', 'IV', 'V', 'VI', 'VII', 'VIII', 'IX or greater']
if len(iexp) >= 2:
exposures = [exposures[iexp[1]],exposures[iexp[0]]]
ilevels = [romans[iexp[1]],romans[iexp[0]]]
expfmt = ', with estimated population exposures of %s at intensity'
expfmt = expfmt + ' %s and %s at intensity %s'
exptxt = expfmt % (commify(int(exposures[0])),ilevels[0],commify(int(exposures[1])),ilevels[1])
else:
exptxt = ''
#create string describing this most impactful event
dfmt = 'A magnitude %.1f earthquake %i km %s of this event struck %s on %s (UTC)%s'
mag = event['Magnitude']
etime = event['Time'].strftime('%B %d, %Y')
etime = re.sub(' 0',' ',etime)
country = Country()
if not event['Name']:
if event['CountryCode'] == 'UM' and event['Latitude'] > 40: #hack for persistent error in expocat
cdict = country.getCountryCode('US')
else:
cdict = country.getCountryCode(event['CountryCode'])
if cdict:
cname = cdict['Name']
else:
cname = 'in the open ocean'
else:
cname = event['Name'].replace('"','')
cdist = round(geodetic_distance(event['Lat'],event['Lon'],lat,lon))
cdir = get_compass_dir(lat,lon,event['Lat'],event['Lon'],format='long').lower()
if ndeaths and str(ndeaths) != "nan":
dfmt = dfmt + ', resulting in a reported %s %s.'
if ndeaths > 1:
dstr = 'fatalities'
else:
dstr = 'fatality'
ndeathstr = commify(int(ndeaths))
eqdesc = dfmt % (mag,cdist,cdir,cname,etime,exptxt,ndeathstr,dstr)
else:
dfmt = dfmt + ', with no reported fatalities.'
eqdesc = dfmt % (mag,cdist,cdir,cname,etime,exptxt)
return eqdesc
def createDebrisTable(self):
# values are in thousands
wood = self._dataframe['DebrisW'].sum() / 1e3
steel = self._dataframe['DebrisS'].sum() / 1e3
wood_total = '%.3f' % wood
steel_total = '%.3f' % steel
debris_total = '%.3f' % (wood + steel)
table_lines = [
'\\begin{tabularx}{\\barwidth}{l*{1}{>{\\raggedleft\\arraybackslash}X}}'
]
table_lines.append('\\hline')
table_lines.append('\\ & \\textbf{Tons} \\\\')
table_lines.append('\\textbf{Category} & \\textbf{(millions)} \\\\')
table_lines.append('\\hline')
table_lines.append('Brick / Wood & %s \\\\' % wood_total)
table_lines.append('Reinforced Concrete / Steel & %s \\\\' %
steel_total)
table_lines.append('\\textbf{Total} & \\textbf{%s} \\' % debris_total)
table_lines.append('& \\\\')
table_lines.append('& \\\\')
trucks = commify(int(round(((wood + steel) * 1e6) / 25)))
fmt = '\\textbf{Truck Loads (@25 tons/truck)} & \\textbf{%s} \\'
line = fmt % trucks
table_lines.append(line)
table_lines.append('\\end{tabularx}')
table_text = '\n'.join(table_lines)
return table_text
def createDebrisTable(self):
# values are in thousands
wood = self._dataframe["DebrisW"].sum() / 1e3
steel = self._dataframe["DebrisS"].sum() / 1e3
wood_total = "%.3f" % wood
steel_total = "%.3f" % steel
debris_total = "%.3f" % (wood + steel)
table_lines = [
"\\begin{tabularx}{\\barwidth}{l*{1}{>{\\raggedleft\\arraybackslash}X}}"
]
table_lines.append("\\hline")
table_lines.append("\\ & \\textbf{Tons} \\\\")
table_lines.append("\\textbf{Category} & \\textbf{(millions)} \\\\")
table_lines.append("\\hline")
table_lines.append("Brick / Wood & %s \\\\" % wood_total)
table_lines.append("Reinforced Concrete / Steel & %s \\\\" %
steel_total)
table_lines.append("\\textbf{Total} & \\textbf{%s} \\\\" %
debris_total)
table_lines.append("& \\\\")
table_lines.append("& \\\\")
trucks = commify(int(round(((wood + steel) * 1e6) / 25)))
fmt = "\\textbf{Truck Loads (@25 tons/truck)} & \\textbf{%s} \\\\"
line = fmt % trucks
table_lines.append(line)
table_lines.append("\\end{tabularx}")
table_text = "\n".join(table_lines)
return table_text
def format_city_table(cities):
"""Abbreviate a Pandas dataframe of city information
Output should look like:
MMI City Population
IV Muisne 13,393
IV Rosa Zarate 42,121
III Pedernales 5,983
Input will a dataframe with columns: ccode iscap lat lon mmi name on_map pop
:param cities:
Pandas dataframe.
:returns:
String of city table info abbreviated for email delivery.
"""
#name, mmi,pop
fmt = '{mmi:5s} {city:30s} {pop:<10s}\n'
city_table = ''
if len(cities):
city_table += fmt.format(mmi='MMI', city='City', pop='Population')
for idx, city in cities.iterrows():
mmiroman = dec_to_roman(city['mmi'])
if city['pop'] == 0:
citypop = '<1k'
else:
citypop = commify(city['pop'])
city_table += fmt.format(mmi=mmiroman,
city=city['name'],
pop=citypop)
#city_table = dedent(city_table)
return city_table
def test():
print('Testing decimal to roman number conversion...')
assert text.dec_to_roman(10) == 'X'
print('Passed decimal to roman number conversion...')
print('Testing setting number precision...')
assert text.set_num_precision(7642, 2) == 7600
assert text.set_num_precision(321, 2) == 320
print('Passed setting number precision...')
print('Testing rounding population value...')
assert text.pop_round(7642) == '8,000'
print('Passed rounding population value...')
print('Testing rounding dollar value...')
assert text.dollar_round(1.234e9, digits=2, mode='short') == '$1.2B'
assert text.dollar_round(1.234e9, digits=2, mode='long') == '$1.2 billion'
print('Passed rounding population value...')
print('Testing abbreviating population value...')
assert text.pop_round_short(1024125) == '1,024k'
print('Passed abbreviating population value...')
print('Testing rounding to nearest integer value...')
assert text.round_to_nearest(998, round_value=1000) == 1000
assert text.round_to_nearest(78, round_value=100) == 100
print('Passed rounding population value...')
print('Testing flooring to nearest integer value...')
assert text.floor_to_nearest(1501, floor_value=1000) == 1000
assert text.floor_to_nearest(51, floor_value=100) == 0
print('Passed flooring population value...')
print('Testing ceiling to nearest integer value...')
assert text.ceil_to_nearest(1001, ceil_value=1000) == 2000
assert text.ceil_to_nearest(49, ceil_value=100) == 100
print('Passed ceiling population value...')
print('Testing commify...')
assert text.commify(1234567) == '1,234,567'
print('Passed commify...')
assert text.floor_to_nearest(0.56, floor_value=0.1) == 0.5
assert text.ceil_to_nearest(0.44, ceil_value=0.1) == 0.5
assert text.round_to_nearest(0.48, round_value=0.1) == 0.5
assert text.pop_round_short(125) == '125'
assert text.pop_round_short(1.23e6, usemillion=True) == '1m'
assert text.pop_round_short(0) == '0'
assert text.dollar_round(1.23, digits=2, mode='short') == '$1'
assert text.dollar_round(1.23e3, digits=2, mode='short') == '$1.2K'
assert text.dollar_round(1.23e3, digits=2, mode='long') == '$1.2 thousand'
assert text.dollar_round(1.23e6, digits=2, mode='short') == '$1.2M'
assert text.dollar_round(1.23e6, digits=2, mode='long') == '$1.2 million'
assert text.dec_to_roman(10) == 'X'
def test_historical():
clat = 0.37
clon = -79.94
expodict = {'EC': [0, 0, 115000, 5238000, 5971000, 2085000, 1760000, 103000, 0, 0],
'TotalExposure': [0, 0, 115000, 5238000, 5971000, 2085000, 1760000, 103000, 0, 0]}
fatdict = {'EC': 98,
'TotalDeaths': 98}
ccode = 'EC'
histcomment = get_historical_comment(clat, clon, 7.8, expodict, fatdict)
expocat = ExpoCat.fromDefault()
minicat = expocat.selectByRadius(clat, clon, SEARCH_RADIUS)
df = minicat.getDataFrame()
df = df.sort_values(
['TotalDeaths', 'MaxMMI', 'NumMaxMMI'], ascending=False)
assert histcomment.find(commify(int(df.iloc[0]['TotalDeaths']))) > -1
def get_quake_desc(event, lat, lon, isMainEvent):
ndeaths = event["TotalDeaths"]
# summarize the exposure values
exposures = np.array([
event["MMI1"],
event["MMI2"],
event["MMI3"],
event["MMI4"],
event["MMI5"],
event["MMI6"],
event["MMI7"],
event["MMI8"],
event["MMI9+"],
])
exposures = np.array([round_to_nearest(exp, 1000) for exp in exposures])
# get the highest two exposures greater than zero
iexp = np.where(exposures > 0)[0][::-1]
romans = [
"I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX or greater"
]
if len(iexp) >= 2:
exposures = [exposures[iexp[1]], exposures[iexp[0]]]
ilevels = [romans[iexp[1]], romans[iexp[0]]]
expfmt = ", with estimated population exposures of %s at intensity"
expfmt = expfmt + " %s and %s at intensity %s"
exptxt = expfmt % (
commify(int(exposures[0])),
ilevels[0],
commify(int(exposures[1])),
ilevels[1],
)
else:
exptxt = ""
# create string describing this most impactful event
dfmt = "A magnitude %.1f earthquake %i km %s of this event struck %s on %s (UTC)%s"
mag = event["Magnitude"]
etime = pd.Timestamp(event["Time"])
etime = etime.strftime("%B %d, %Y")
etime = re.sub(" 0", " ", etime)
country = Country()
if pd.isnull(event["Name"]):
# hack for persistent error in expocat
if event["CountryCode"] == "UM" and event["Lat"] > 40:
cdict = country.getCountry("US")
else:
cdict = country.getCountry(event["CountryCode"])
if cdict:
cname = cdict["Name"]
else:
cname = "in the open ocean"
else:
cname = event["Name"].replace('"', "")
cdist = round(geodetic_distance(event["Lat"], event["Lon"], lat, lon))
cdir = get_compass_dir(lat, lon, event["Lat"], event["Lon"],
format="long").lower()
if ndeaths and str(ndeaths) != "nan":
dfmt = dfmt + ", resulting in a reported %s %s."
if ndeaths > 1:
dstr = "fatalities"
else:
dstr = "fatality"
ndeathstr = commify(int(ndeaths))
eqdesc = dfmt % (mag, cdist, cdir, cname, etime, exptxt, ndeathstr,
dstr)
else:
dfmt = dfmt + ", with no reported fatalities."
eqdesc = dfmt % (mag, cdist, cdir, cname, etime, exptxt)
return eqdesc
