def test_bars_one_magnitude():
numpy.random.seed(1234)
observations = numpy.random.normal(loc=1, size=(25, 100))
y = numpy.mean(observations, axis=1)
canvas, axes, mark = toyplot.bars(y)
assert_canvas_matches(canvas, "bars-one-magnitude")
def test_bars_one_magnitude():
numpy.random.seed(1234)
observations = numpy.random.normal(loc=1, size=(25, 100))
y = numpy.mean(observations, axis=1)
canvas, axes, mark = toyplot.bars(y)
assert_canvas_matches(canvas, "bars-one-magnitude")
def test_axes_bars_boundaries_masked_nan():
numpy.random.seed(1234)
observations = numpy.random.normal(size=(50, 50))
b = numpy.ma.column_stack((numpy.min(observations, axis=1), numpy.median(
observations, axis=1), numpy.max(observations, axis=1)))
b[10:20, 0] = numpy.nan
b[30:40, 1] = numpy.ma.masked
b[20:30, 2] = numpy.nan
canvas, axes, mark = toyplot.bars(b, baseline=None)
assert_canvas_matches(canvas, "axes-bars-boundaries-masked-nan")
def test_axes_bars_boundaries_masked_nan():
numpy.random.seed(1234)
observations = numpy.random.normal(size=(50, 50))
b = numpy.ma.column_stack((numpy.min(observations, axis=1), numpy.median(
observations, axis=1), numpy.max(observations, axis=1)))
b[10:20, 0] = numpy.nan
b[30:40, 1] = numpy.ma.masked
b[20:30, 2] = numpy.nan
canvas, axes, mark = toyplot.bars(b, baseline=None)
assert_canvas_matches(canvas, "axes-bars-boundaries-masked-nan")
def test_axes_bars_magnitudes_masked_nan():
x = numpy.linspace(0, 2 * numpy.pi, 51)
y = numpy.ma.column_stack((
1 + 0.5 * numpy.sin(x),
1 + 0.5 * numpy.cos(x),
1 + 0.2 * numpy.sin(2 * x),
))
y[8:18, 0] = numpy.nan
y[33:43, 1] = numpy.ma.masked
canvas, axes, mark = toyplot.bars(x, y)
assert_canvas_matches(canvas, "axes-bars-magnitudes-masked-nan")
def test_axes_bars_magnitudes_masked_nan():
x = numpy.linspace(0, 2 * numpy.pi, 51)
y = numpy.ma.column_stack((
1 + 0.5 * numpy.sin(x),
1 + 0.5 * numpy.cos(x),
1 + 0.2 * numpy.sin(2 * x),
))
y[8:18, 0] = numpy.nan
y[33:43, 1] = numpy.ma.masked
canvas, axes, mark = toyplot.bars(x, y)
assert_canvas_matches(canvas, "axes-bars-magnitudes-masked-nan")
def ElectricityorGasPlotter(state, resource, zipcode=None):
cp = toyplot.color.Palette()
cpalpha = []
for i in cp:
i['a'] = 0.4
cpalpha.append(i)
colors = [cp[0], cpalpha[0], cp[1], cpalpha[1], cp[2], cpalpha[2]]
if len(zipcode) != 5:
zipcode = None
if zipcode != None:
j = requestmaker(
api_url='cleap/v1/energy_expenditures_and_ghg_by_sector',
source='NREL',
zipcode=zipcode)
geo = zipcode
elif zipcode == None:
state_abrev = us_state_abbrev[state]
geo = state_abrev
j = requestmaker(
api_url='cleap/v1/energy_expenditures_and_ghg_by_sector',
source='NREL',
state=state_abrev)
r = j['result'][list(j['result'].keys())[0]]
labels = [
f'{geo} Res.', 'US Res.', f'{geo} Com.', 'US Com.', f'{geo} Ind.',
'US Ind.'
]
if resource == 'Elec':
i_res_elec = r['residential']['elec_mwh'] / r['residential'][
'total_pop']
i_com_elec = r['commercial']['elec_mwh'] / r['residential']['total_pop']
i_ind_elec = r['industrial']['elec_mwh'] / r['residential']['total_pop']
us_res_elec = 3.64
us_com_elec = 3.29
us_ind_elec = 2.62
values_elec = [
i_res_elec, us_res_elec, i_com_elec, us_com_elec, i_ind_elec,
us_ind_elec
]
canvas_elec, axes_elec, mark_elec = toyplot.bars(
values_elec,
width=550,
height=350,
color=colors,
label=f'{geo} Electricity Consumption Per Capita',
ylabel='MWh')
axes_elec.x.ticks.locator = toyplot.locator.Explicit(labels=labels)
h_elec = toyplot.html.tostring(canvas_elec)
return h_elec
elif resource == 'Gas':
i_res_gas = r['residential']['gas_mcf'] / r['residential']['total_pop']
i_com_gas = r['commercial']['gas_mcf'] / r['residential']['total_pop']
i_ind_gas = r['industrial']['gas_mcf'] / r['residential']['total_pop']
us_res_gas = 15.42
us_com_gas = 7.96
us_ind_gas = 19.8
values_gas = [
i_res_gas, us_res_gas, i_com_gas, us_com_gas, i_ind_gas, us_ind_gas
]
canvas_gas, axes_gas, mark_gas = toyplot.bars(
values_gas,
width=550,
height=350,
color=colors,
label=f'{geo} Natural Gas Consumption Per Capita',
ylabel='MCF')
axes_gas.x.ticks.locator = toyplot.locator.Explicit(labels=labels)
h_gas = toyplot.html.tostring(canvas_gas)
return h_gas
