def test_axes_fill_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.fill(b)
assert_canvas_matches(canvas, "axes-fill-boundaries-masked-nan")
def test_axes_fill_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.fill(b)
assert_canvas_matches(canvas, "axes-fill-boundaries-masked-nan")
def test_axes_fill_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.fill(x, y, baseline="stacked")
assert_canvas_matches(canvas, "axes-fill-magnitudes-masked-nan")
def test_axes_fill_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.fill(x, y, baseline="stacked")
assert_canvas_matches(canvas, "axes-fill-magnitudes-masked-nan")
sample_sets=samples,
windows=np.linspace(
start=0,
stop=self.tree_sequence.sequence_length,
num=round(self.tree_sequence.sequence_length / window_size)
)
)
rep_values.append(values)
# get mean and std
means = np.array(rep_values).mean(axis=0)
stds = np.array(rep_values).mean(axis=0)
# draw canvas...
canvas, axes, mark = toyplot.fill(
means, height=300, width=500, opacity=0.5, margin=(60, 50, 50, 80)
)
# style axes
axes.x.ticks.show = True
axes.x.ticks.locator = toyplot.locator.Extended(only_inside=True)
axes.y.ticks.labels.angle = -90
axes.y.ticks.show = True
axes.y.ticks.locator = toyplot.locator.Extended(only_inside=True, count=5)
axes.label.offset = 20
axes.label.text = f"{stat} in {int(window_size / 1000)}kb windows"
return canvas, axes, mark
stop=self.tree_sequence.sequence_length,
num=round(self.tree_sequence.sequence_length /
window_size)))
rep_values.append(values)
# get mean and std
means = np.array(rep_values).mean(axis=0)
stds = np.array(rep_values).mean(axis=0)
# draw canvas...
style = {"fill": str(color)}
canvas, axes, mark = toyplot.fill(
means,
height=300,
width=500,
opacity=0.5,
margin=(60, 50, 50, 80),
style=style,
)
# style axes
axes.x.ticks.show = True
axes.x.ticks.locator = toyplot.locator.Extended(only_inside=True)
axes.y.ticks.labels.angle = -90
axes.y.ticks.show = True
axes.y.ticks.locator = toyplot.locator.Extended(only_inside=True,
count=5)
axes.label.offset = 20
axes.label.text = f"{stat} in {int(window_size / 1000)}kb windows"
return canvas, axes, mark
def ResourceMixPlotter(state):
cp = toyplot.color.Palette()
alt_cp = toyplot.color.brewer.palette("Accent")
cpdict = {
'Coal': alt_cp[7],
'Natural Gas': cp[6],
'Nuclear': cp[4],
'Geothermal': cp[1],
'Biomass': cp[3],
'Wind': cp[0],
'Solar': cp[5],
'Hydro': cp[2],
'Other': cp[7]
}
resources = {
'Coal': 'COW',
'Petroleum': 'PEL',
'Natural Gas': 'NG',
'Nuclear': 'NUC',
'Hydro': 'HYC',
'Geothermal': 'GEO',
'Biomass': 'WWW',
'Wind': 'WND',
'Small-Scale PV': 'DPV',
'PV': 'SPV',
'CSP': 'STH',
'Hydro PS': 'HPS',
'Other Bio': 'WAS',
'Other': 'OTH',
'Other Gas': 'OOG',
'Petroleum Coke': 'PC'
}
state_abrev = us_state_abbrev[state]
s_list = []
for key, value in resources.items():
j_ = requestmaker(api_url=f'ELEC.GEN.{value}-{state_abrev}-99.A',
source='EIA')
if 'series' in j_:
r = j_['series'][0]['data']
s_ = pd.Series([i[1] for i in r], index=[i[0] for i in r])
s_.name = key
s_list.append(s_)
df = pd.concat(s_list, axis=1)[-10:]
otherlist = [
'Petroleum Coke', 'Other Gas', 'Other Bio', 'Hydro PS', 'Petroleum',
'Small-Scale PV', 'PV', 'CSP'
]
for l in otherlist:
if l not in df.columns:
df[l] = 0
for l in ['Hydro']:
if l not in df.columns:
df[l] = 0
df['Other'] = df['Other'] + df['Other Gas'] + df['Petroleum Coke'] + df[
'Other Bio'] + df['Petroleum']
df['Hydro'] = df['Hydro'] + df['Hydro PS']
df['Solar'] = df['Small-Scale PV'] + df['PV'] + df['CSP']
df.drop(axis='columns', labels=otherlist, inplace=True)
df.dropna(how='all', inplace=True)
df.fillna(0, inplace=True)
df = df / 100
df.sort_values(by='2017', axis='columns', inplace=True, ascending=False)
resourcelist = df.columns
resourcecolorlist = [cpdict[i] for i in resourcelist]
dfout = df.values
canvas, axes, mark = toyplot.fill(dfout,
width=550,
height=350,
color=resourcecolorlist,
baseline='stacked',
margin=(40, 95, 40, 45),
label=f'{state} Resource Generation Mix',
ylabel='GWh')
legendlist = []
count = 0
for i in resourcelist:
l = (i, mark.markers[count])
legendlist.append(l)
count += 1
legendlist = list(reversed(legendlist))
canvas.legend(legendlist, corner=("right", 50, 40, 200))
axes.x.ticks.locator = toyplot.locator.Explicit(labels=df.index)
h = toyplot.html.tostring(canvas)
return h