def contam(cube, instrument, targetName='noName', paRange=[0, 360],
badPAs=np.asarray([]), tmpDir="", fig='', to_html=True):
# Get data from FITS file
if isinstance(cube, str):
hdu = fits.open(cubeName)
cube = hdu[0].data
hdu.close()
if instrument != 'NIRISS':
trace1 = cube[0, :, :] # target star order 1 trace
cube = cube[1:, :, :] # neighbor star order 1 and 2 traces in all the angles
elif instrument=='NIRISS':
trace1 = cube[0, :, :]
trace2 = cube[1, :, :]
cube = cube[2:, :, :]
plotPAmin, plotPAmax = paRange
# start calculations
if not TRACES_PATH:
return None
lam_file = os.path.join(TRACES_PATH, 'NIRISS', 'lambda_order1-2.txt')
ypix, lamO1, lamO2 = np.loadtxt(lam_file, unpack=True)
nPA = cube.shape[0]
rows = cube.shape[1]
cols = cube.shape[2]
print('cols ', cols)
dPA = 360//nPA
PA = np.arange(nPA)*dPA
contamO1 = np.zeros([rows, nPA])
if instrument=='NIRISS':
contamO2 = np.zeros([rows, nPA])
for row in np.arange(rows):
i = np.argmax(trace1[row, :])
#tr = trace1[row, i-20:i+41]
tr = trace1[row, i-20:i+41]
w = tr/np.sum(tr**2)
ww = np.tile(w, nPA).reshape([nPA, tr.size])
contamO1[row, :] = np.sum(cube[:, row, i-20:i+41]*ww, axis=1)
if instrument=='NIRISS':
if lamO2[row] < 0.6:
continue
i = np.argmax(trace2[row, :])
tr = trace2[row, i-20:i+41]
w = tr/np.sum(tr**2)
ww = np.tile(w, nPA).reshape([nPA, tr.size])
contamO2[row, :] = np.sum(cube[:, row, i-20:i+41]*ww, axis=1)
TOOLS = 'pan, box_zoom, crosshair, reset, hover'
y = np.array([0., 0.])
y1 = 0.07
y2 = 0.12
y3 = 0.17
y4 = 0.23
bad_PA_color = '#dddddd'
bad_PA_alpha = 0.7
# Order 1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Contam plot
if instrument == 'NIRISS':
xlim0 = lamO1.min()
xlim1 = lamO1.max()
elif instrument == 'NIRCam F322W2':
xlim0 = lam0_nircam322w2
xlim1 = lam1_nircam322w2
elif instrument == 'NIRCam F444W':
xlim0 = lam0_nircam444w
xlim1 = lam1_nircam444w
elif instrument == 'MIRI':
xlim0 = 5
xlim1 = 12
ylim0 = PA.min()-0.5*dPA
ylim1 = PA.max()+0.5*dPA
color_mapper = LinearColorMapper(palette=inferno(8)[::-1],
low=-4, high=1)
color_mapper.low_color = 'white'
color_mapper.high_color = 'black'
s2 = figure(tools=TOOLS, width=500, height=500,
title='Order 1 {} Contamination with {}'.format(targetName, instrument),
x_range=Range1d(xlim0, xlim1),
y_range=Range1d(ylim0, ylim1))
fig_data = np.log10(np.clip(contamO1.T, 1.e-10, 1.))
s2.image([fig_data], x=xlim0, y=ylim0, dw=xlim1-xlim0, dh=ylim1-ylim0,
color_mapper=color_mapper)
s2.xaxis.axis_label = 'Wavelength (um)'
if instrument != 'NIRISS':
s2.yaxis.axis_label = 'Aperture Position Angle (degrees)'
# Add bad PAs
bad_PA_color = '#555555'
bad_PA_alpha = 0.6
#for ybad0, ybad1 in badPA:
if len(badPAs)>0:
tops, bottoms, lefts, rights = [], [], [], []
for idx in range(0, len(badPAs)):
PAgroup = badPAs[idx]
top_idx = np.max(PAgroup)
bot_idx = np.min(PAgroup)
tops.append(top_idx)
bottoms.append(bot_idx)
lefts.append(xlim0)
rights.append(xlim1)
s2.quad(top=tops, bottom=bottoms,
left=lefts, right=rights,
color=bad_PA_color, alpha=bad_PA_alpha)
# Line plot
s3 = figure(tools=TOOLS, width=150, height=500,
x_range=Range1d(0, 100), y_range=s2.y_range, title=None)
s3.line(100*np.sum(contamO1 >= 0.001, axis=0)/rows, PA-dPA/2,
line_color='blue', legend='> 0.001')
s3.line(100*np.sum(contamO1 >= 0.01, axis=0)/rows, PA-dPA/2,
line_color='green', legend='> 0.01')
s3.xaxis.axis_label = '% channels contam.'
s3.yaxis.major_label_text_font_size = '0pt'
# ~~~~~~ Order 2 ~~~~~~
# Contam plot
if instrument=='NIRISS':
xlim0 = lamO2.min()
xlim1 = lamO2.max()
ylim0 = PA.min()-0.5*dPA
ylim1 = PA.max()+0.5*dPA
xlim0 = 0.614
s5 = figure(tools=TOOLS, width=500, height=500,
title='Order 2 {} Contamination with {}'.format(targetName, instrument),
x_range=Range1d(xlim0, xlim1), y_range=s2.y_range)
fig_data = np.log10(np.clip(contamO2.T, 1.e-10, 1.))[:, 300:]
s5.image([fig_data], x=xlim0, y=ylim0, dw=xlim1-xlim0, dh=ylim1-ylim0,
color_mapper=color_mapper)
#s5.yaxis.major_label_text_font_size = '0pt'
s5.xaxis.axis_label = 'Wavelength (um)'
s5.yaxis.axis_label = 'Aperture Position Angle (degrees)'
if len(badPAs)>0:
tops, bottoms, lefts, rights = [], [], [], []
for idx in range(0, len(badPAs)):
PAgroup = badPAs[idx]
top_idx = np.max(PAgroup)
bot_idx = np.min(PAgroup)
tops.append(top_idx)
bottoms.append(bot_idx)
lefts.append(xlim0)
rights.append(xlim1)
s5.quad(top=tops, bottom=bottoms,
left=lefts, right=rights,
color=bad_PA_color, alpha=bad_PA_alpha)
# Line plot
s6 = figure(tools=TOOLS, width=150, height=500, y_range=s2.y_range,
x_range=Range1d(100, 0), title=None)
s6.line(100*np.sum(contamO2 >= 0.001, axis=0)/rows, PA-dPA/2,
line_color='blue', legend='> 0.001')
s6.line(100*np.sum(contamO2 >= 0.01, axis=0)/rows, PA-dPA/2,
line_color='green', legend='> 0.01')
s6.xaxis.axis_label = '% channels contam.'
s6.yaxis.major_label_text_font_size = '0pt'
if len(badPAs)>0:
tops, bottoms, lefts, rights = [], [], [], []
for idx in range(0, len(badPAs)):
PAgroup = badPAs[idx]
top_idx = np.max(PAgroup)
bot_idx = np.min(PAgroup)
tops.append(top_idx)
bottoms.append(bot_idx)
lefts.append(0)
rights.append(100)
s3.quad(top=tops, bottom=bottoms,
left=lefts, right=rights,
color=bad_PA_color, alpha=bad_PA_alpha)
if instrument=='NIRISS':
s6.quad(top=tops, bottom=bottoms,
left=rights, right=lefts,
color=bad_PA_color, alpha=bad_PA_alpha)
# ~~~~~~ Plotting ~~~~~~
if instrument!='NIRISS':
fig = gridplot(children=[[s2, s3]])
else:
fig = gridplot(children=[[s6, s5, s2, s3]])
return fig
def contam(cube,
targetName='noName',
paRange=[0, 360],
badPA=[],
tmpDir="",
fig='',
to_html=True):
"""
Generate the contamination plot.
Parameters
----------
cube: array-like, str
The data cube or FITS filename containing the data.
targetName: str
The name of the target.
paRange: sequence
The position angle range to consider.
badPA: sequence
Position angles to exclude.
tmpDir: str
A directory to write the files to.
fig: matplotlib.figure, bokeh.figure
A figure to add the plots to.
to_html: bool
Return the image as bytes for HTML.
Returns
-------
fig : matplotlib.pyplot or bokeh object
The populated matplotlib or bokeh plot.
"""
# Get data from FITS file
if isinstance(cube, str):
# hdu = fits.open('cube_'+target+'.fits')
hdu = fits.open(cubeName)
cube = hdu[0].data
hdu.close()
trace2dO1 = cube[0, :, :] # order 1
trace2dO2 = cube[1, :, :] # order 2
cube = cube[2:, :, :] # all the angles
plotPAmin, plotPAmax = paRange
# start calculations
loc = 'data/contam_visibility/lambda_order1-2.txt'
lam_file = pkg_resources.resource_filename('exoctk', loc)
ypix, lamO1, lamO2 = np.loadtxt(lam_file, unpack=True)
ny = trace2dO1.shape[0]
nPA = cube.shape[0]
dPA = 360 // nPA
PA = np.arange(nPA) * dPA
contamO1 = np.zeros([ny, nPA])
contamO2 = np.zeros([ny, nPA])
for y in np.arange(ny):
i = np.argmax(trace2dO1[y, :])
tr = trace2dO1[y, i - 20:i + 41]
w = tr / np.sum(tr**2)
ww = np.tile(w, nPA).reshape([nPA, tr.size])
contamO1[y, :] = np.sum(cube[:, y, i - 20:i + 41] * ww, axis=1)
if lamO2[y] < 0.6:
continue
i = np.argmax(trace2dO2[y, :])
tr = trace2dO2[y, i - 20:i + 41]
w = tr / np.sum(tr**2)
ww = np.tile(w, nPA).reshape([nPA, tr.size])
contamO2[y, :] = np.sum(cube[:, y, i - 20:i + 41] * ww, axis=1)
# Otherwise, it's a Bokeh plot
if fig:
TOOLS = 'pan, box_zoom, crosshair, reset, hover, save'
y = np.array([0., 0.])
y1 = 0.07
y2 = 0.12
y3 = 0.17
y4 = 0.23
bad_PA_color = '#dddddd'
bad_PA_alpha = 0.7
# Order 1
# Contam plot
xlim0 = lamO1.min()
xlim1 = lamO1.max()
ylim0 = PA.min() - 0.5 * dPA
ylim1 = PA.max() + 0.5 * dPA
color_mapper = LinearColorMapper(palette=inferno(8)[::-1],
low=-4,
high=1)
color_mapper.low_color = 'white'
color_mapper.high_color = 'black'
s2 = figure(tools=TOOLS,
width=500,
height=500,
title=None,
x_range=Range1d(xlim0, xlim1),
y_range=Range1d(ylim0, ylim1))
fig_data = np.log10(np.clip(contamO1.T, 1.e-10, 1.))
s2.image([fig_data],
x=xlim0,
y=ylim0,
dw=xlim1 - xlim0,
dh=ylim1 - ylim0,
color_mapper=color_mapper)
s2.xaxis.axis_label = 'Wavelength (um)'
s2.yaxis.axis_label = 'Position Angle (degrees)'
# Line plot
s3 = figure(tools=TOOLS,
width=150,
height=500,
x_range=Range1d(0, 100),
y_range=s2.y_range,
title=None)
s3.line(100 * np.sum(contamO1 >= 0.001, axis=0) / ny,
PA - dPA / 2,
line_color='blue',
legend='> 0.001')
s3.line(100 * np.sum(contamO1 >= 0.01, axis=0) / ny,
PA - dPA / 2,
line_color='green',
legend='> 0.01')
s3.xaxis.axis_label = '% channels contam.'
s3.yaxis.major_label_text_font_size = '0pt'
# Add bad PAs
for ybad0, ybad1 in badPA:
s2.patch([xlim0, xlim1, xlim1, xlim0],
[ybad1, ybad1, ybad0, ybad0],
color=bad_PA_color,
alpha=bad_PA_alpha)
s3.patch([0, 100, 100, 0], [ybad1, ybad1, ybad0, ybad0],
color=bad_PA_color,
alpha=bad_PA_alpha,
legend='Bad PA')
# Line list
s1 = figure(tools=TOOLS,
width=500,
plot_height=100,
x_range=s2.x_range,
title=None)
l = np.array([0.89, 0.99])
s1.line(l, y + y1, line_color='black', line_width=1.5)
s1.add_layout(
Label(x=l.mean(),
y=y1,
x_units='data',
y_units='data',
text='H2O',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.09, 1.2])
s1.line(l, y + y1, line_color='black', line_width=1.5)
s1.add_layout(
Label(x=l.mean(),
y=y1,
x_units='data',
y_units='data',
text='H2O',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.1, 1.24])
s1.line(l, y + y2, line_color='black', line_width=1.5)
s1.add_layout(
Label(x=l.mean(),
y=y2,
x_units='data',
y_units='data',
text='CH4',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.3, 1.51])
s1.line(l, y + y1, line_color='black', line_width=1.5)
s1.add_layout(
Label(x=l.mean(),
y=y1,
x_units='data',
y_units='data',
text='H2O',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.6, 1.8])
s1.line(l, y + y2, line_color='black', line_width=1.5)
s1.add_layout(
Label(x=l.mean(),
y=y2,
x_units='data',
y_units='data',
text='CH4',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.75, 2.05])
s1.line(l, y + y1, line_color='black', line_width=1.5)
s1.add_layout(
Label(x=l.mean(),
y=y1,
x_units='data',
y_units='data',
text='H2O',
render_mode='css',
text_font_size='8pt'))
l = np.array([2.3, lamO1.max()])
s1.line(l, y + y1, line_color='black', line_width=1.5)
s1.add_layout(
Label(x=l.mean(),
y=y1,
x_units='data',
y_units='data',
text='H2O',
render_mode='css',
text_font_size='8pt'))
l = np.array([2.15, 2.5])
s1.line(l, y + y2, line_color='black', line_width=1.5)
s1.add_layout(
Label(x=l.mean(),
y=y2,
x_units='data',
y_units='data',
text='CH4',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.1692, 1.1778])
s1.line(l[0], [y3, y3 + 0.02], line_color='black')
s1.line(l[1], [y3, y3 + 0.02], line_color='black')
s1.add_layout(
Label(x=l.mean(),
y=y3 + 0.02,
x_units='data',
y_units='data',
text='K',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.2437, 1.2529])
s1.line(l[0], [y3, y3 + 0.02], line_color='black')
s1.line(l[1], [y3, y3 + 0.02], line_color='black')
s1.add_layout(
Label(x=l.mean(),
y=y3 + 0.02,
x_units='data',
y_units='data',
text='K',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.5168])
s1.line(l[0], [y3, y3 + 0.02], line_color='black')
s1.add_layout(
Label(x=l.mean(),
y=y3 + 0.02,
x_units='data',
y_units='data',
text='K',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.1384, 1.1409])
s1.line(l[0], [y4, y4 + 0.02], line_color='black')
s1.line(l[1], [y4, y4 + 0.02], line_color='black')
s1.add_layout(
Label(x=l.mean(),
y=y4 + 0.02,
x_units='data',
y_units='data',
text='Na',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.2682])
s1.line(l[0], [y4, y4 + 0.02], line_color='black')
s1.add_layout(
Label(x=l.mean(),
y=y4 + 0.02,
x_units='data',
y_units='data',
text='Na',
render_mode='css',
text_font_size='8pt'))
l = np.array([2.2063, 2.2090])
s1.line(l[0], [y4, y4 + 0.02], line_color='black')
s1.line(l[1], [y4, y4 + 0.02], line_color='black')
s1.add_layout(
Label(x=l.mean(),
y=y4 + 0.02,
x_units='data',
y_units='data',
text='Na',
render_mode='css',
text_font_size='8pt'))
l = np.array([2.2935, 2.3227, 2.3525, 2.3830, 2.4141])
s1.line(l[0], [y3, y3 + 0.02], line_color='black')
s1.line(l[1], [y3, y3 + 0.02], line_color='black')
s1.line(l[2], [y3, y3 + 0.02], line_color='black')
s1.line(l[3], [y3, y3 + 0.02], line_color='black')
s1.line(l[4], [y3, y3 + 0.02], line_color='black')
s1.line(l[[0, -1]], y + y3 + 0.02, line_color='black', line_width=1)
s1.add_layout(
Label(x=l[[0, -1]].mean(),
y=y3 + 0.02,
x_units='data',
y_units='data',
text='CO',
render_mode='css',
text_font_size='8pt'))
s1.xaxis.major_label_text_font_size = '0pt'
s1.yaxis.major_label_text_font_size = '0pt'
# Order 2
# Contam plot
xlim0 = lamO2.min()
xlim1 = lamO2.max()
ylim0 = PA.min() - 0.5 * dPA
ylim1 = PA.max() + 0.5 * dPA
xlim0 = 0.614
s5 = figure(tools=TOOLS,
width=250,
height=500,
title=None,
x_range=Range1d(xlim0, xlim1),
y_range=s2.y_range)
fig_data = np.log10(np.clip(contamO2.T, 1.e-10, 1.))[:, 300:]
s5.image([fig_data],
x=xlim0,
y=ylim0,
dw=xlim1 - xlim0,
dh=ylim1 - ylim0,
color_mapper=color_mapper)
s5.yaxis.major_label_text_font_size = '0pt'
s5.xaxis.axis_label = 'Wavelength (um)'
# Line plot
s6 = figure(tools=TOOLS,
width=150,
height=500,
y_range=s2.y_range,
x_range=Range1d(100, 0),
title=None)
s6.line(100 * np.sum(contamO2 >= 0.001, axis=0) / ny,
PA - dPA / 2,
line_color='blue',
legend='> 0.001')
s6.line(100 * np.sum(contamO2 >= 0.01, axis=0) / ny,
PA - dPA / 2,
line_color='green',
legend='> 0.01')
s6.xaxis.axis_label = '% channels contam.'
s6.yaxis.major_label_text_font_size = '0pt'
# Dummy plots for nice spacing
s0 = figure(tools=TOOLS, width=150, plot_height=100, title=None)
s0.outline_line_color = "white"
s7 = figure(tools=TOOLS, width=150, plot_height=100, title=targetName)
s7.outline_line_color = "white"
# Add bad PAs
for ybad0, ybad1 in badPA:
s5.patch([xlim0, xlim1, xlim1, xlim0],
[ybad1, ybad1, ybad0, ybad0],
color=bad_PA_color,
alpha=bad_PA_alpha)
s6.patch([0, 100, 100, 0], [ybad1, ybad1, ybad0, ybad0],
color=bad_PA_color,
alpha=bad_PA_alpha,
legend='Bad PA')
# Line list
s4 = figure(tools=TOOLS,
width=250,
plot_height=100,
x_range=s5.x_range,
title=None)
l = np.array([0.89, 0.99])
s4.line(l, y + y1, line_color='black', line_width=1.5)
s4.add_layout(
Label(x=l.mean(),
y=y1,
x_units='data',
y_units='data',
text='H2O',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.09, 1.2])
s4.line(l, y + y1, line_color='black', line_width=1.5)
s4.add_layout(
Label(x=l.mean(),
y=y1,
x_units='data',
y_units='data',
text='H2O',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.1, 1.24])
s4.line(l, y + y2, line_color='black', line_width=1.5)
s4.add_layout(
Label(x=l.mean(),
y=y2,
x_units='data',
y_units='data',
text='CH4',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.3, lamO2.max()])
s4.line(l, y + y1, line_color='black', line_width=1.5)
s4.add_layout(
Label(x=l.mean(),
y=y1,
x_units='data',
y_units='data',
text='H2O',
render_mode='css',
text_font_size='8pt'))
l = np.array([0.7665, 0.7699])
s4.line(l[0], [y3, y3 + 0.02], line_color='black')
s4.line(l[1], [y3, y3 + 0.02], line_color='black')
s4.add_layout(
Label(x=l.mean(),
y=y3 + 0.02,
x_units='data',
y_units='data',
text='K',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.1692, 1.1778])
s4.line(l[0], [y3, y3 + 0.02], line_color='black')
s4.line(l[1], [y3, y3 + 0.02], line_color='black')
s4.add_layout(
Label(x=l.mean(),
y=y3 + 0.02,
x_units='data',
y_units='data',
text='K',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.2437, 1.2529])
s4.line(l[0], [y3, y3 + 0.02], line_color='black')
s4.line(l[1], [y3, y3 + 0.02], line_color='black')
s4.add_layout(
Label(x=l.mean(),
y=y3 + 0.02,
x_units='data',
y_units='data',
text='K',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.1384, 1.1409])
s4.line(l[0], [y4, y4 + 0.02], line_color='black')
s4.line(l[1], [y4, y4 + 0.02], line_color='black')
s4.add_layout(
Label(x=l.mean(),
y=y4 + 0.02,
x_units='data',
y_units='data',
text='Na',
render_mode='css',
text_font_size='8pt'))
l = np.array([1.2682])
s4.line(l[0], [y4, y4 + 0.02], line_color='black')
s4.add_layout(
Label(x=l.mean(),
y=y4 + 0.02,
x_units='data',
y_units='data',
text='Na',
render_mode='css',
text_font_size='8pt'))
s4.xaxis.major_label_text_font_size = '0pt'
s4.yaxis.major_label_text_font_size = '0pt'
# put all the plots in a grid layout
fig = gridplot(children=[[s7, s4, s1, s0], [s6, s5, s2, s3]])
return fig
# create figure for bar chart
p = figure(x_range=Maj_mat,
plot_width=1000,
plot_height=600,
toolbar_location=None)
p.y_range.start = 0
p.xaxis.major_label_orientation = pi / 4
hover = HoverTool(tooltips=[('# Graduated',
'@tooltip1'), ('# Left Institution',
'@tooltip2')])
mapper = LinearColorMapper(palette=p_colorbartot, low=0, high=1)
mapper.low_color = '#0000ff'
mapper.high_color = code_maxtot
ff = p.vbar(x='CIP_matf',
top='dec_sumf',
width=0.9,
source=sourcef,
fill_color=factor_cmap('CIP_matf',
palette=palettef,
factors=Maj_mat))
mm = p.vbar(x='CIP_matm',
top='dec_summ',
width=0.9,
source=sourcem,
fill_color=factor_cmap('CIP_matm',
palette=palettem,
factors=Maj_mat))
def contam(cube, instrument, targetName='noName', paRange=[0, 360],
badPAs=np.asarray([]), tmpDir="", fig='', to_html=True):
lam_file = os.path.join(TRACES_PATH, 'NIRISS', 'lambda_order1-2.txt')
ypix, lamO1, lamO2 = np.loadtxt(lam_file, unpack=True)
rows, cols = cube.shape[1], cube.shape[2]
PAmin, PAmax = paRange[0], paRange[1]
PA = np.arange(PAmin, PAmax, 1)
# Generate the contam figure
if instrument == 'NIRISS':
contamO1, contamO2 = nirissContam(cube)
elif (instrument == 'NIRCam F322W2') or (instrument == 'NIRCam F444W'):
contamO1 = nircamContam(cube, instrument)
elif instrument == 'MIRI':
contamO1 = miriContam(cube)
TOOLS = 'pan, box_zoom, crosshair, reset, hover'
bad_PA_color = '#dddddd'
bad_PA_alpha = 0.7
dPA = 1
# Order 1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Contam plot
if instrument == 'NIRISS':
xlim0 = lamO1.min()
xlim1 = lamO1.max()
elif instrument == 'NIRCam F322W2':
xlim0 = LAM0_NIRCAM322W2
xlim1 = LAM1_NIRCAM322W2
elif instrument == 'NIRCam F444W':
xlim0 = LAM0_NIRCAM444W
xlim1 = LAM1_NIRCAM444W
elif instrument == 'MIRI':
xlim0 = 5
xlim1 = 12
ylim0 = PAmin - 0.5
ylim1 = PAmax + 0.5
color_mapper = LinearColorMapper(palette=PuBu[8][::-1][2:],
low=-4, high=1)
color_mapper.low_color = 'white'
color_mapper.high_color = 'black'
orders = 'Orders 1 & 2' if instrument == 'NIRCam' else 'Order 1'
s2 = figure(
tools=TOOLS, width=500, height=500, title='{} {} Contamination with {}'.format(
orders, targetName, instrument), x_range=Range1d(
xlim0, xlim1), y_range=Range1d(
ylim0, ylim1))
contamO1 = contamO1 if 'NIRCam' in instrument else contamO1.T
contamO1 = np.fliplr(contamO1) if (
instrument == 'MIRI') or (
instrument == 'NIRCam F322W2') else contamO1
fig_data = np.log10(np.clip(contamO1, 1.e-10, 1.)
) # [:, :361] # might this
# index have somethig to
# do w the choppiness
# of o1 in all instruments
# Begin plotting ~~~~~~~~~~~~~~~~~~~~~~~~
s2.image([fig_data], x=xlim0, y=ylim0, dw=xlim1 - xlim0, dh=ylim1 - ylim0,
color_mapper=color_mapper)
s2.xaxis.axis_label = 'Wavelength (um)'
if instrument != 'NIRISS':
s2.yaxis.axis_label = 'Aperture Position Angle (degrees)'
# Add bad PAs
bad_PA_color = '#555555'
bad_PA_alpha = 0.6
# for ybad0, ybad1 in badPA:
if len(badPAs) > 0:
tops, bottoms, lefts, rights = [], [], [], []
for idx in range(0, len(badPAs)):
PAgroup = badPAs[idx]
top_idx = np.max(PAgroup)
bot_idx = np.min(PAgroup)
tops.append(top_idx)
bottoms.append(bot_idx)
lefts.append(xlim0)
rights.append(xlim1)
s2.quad(top=tops, bottom=bottoms,
left=lefts, right=rights,
color=bad_PA_color, alpha=bad_PA_alpha)
# Line plot
# ax = 1 if 'NIRCam' in instrument else 0
channels = cols if 'NIRCam' in instrument else rows
s3 = figure(tools=TOOLS, width=150, height=500,
x_range=Range1d(0, 100), y_range=s2.y_range, title=None)
try:
s3.line(100 * np.sum(contamO1 >= 0.001, axis=1) / channels, PA - dPA / 2,
line_color='blue', legend_label='> 0.001')
s3.line(100 * np.sum(contamO1 >= 0.01, axis=1) / channels, PA - dPA / 2,
line_color='green', legend_label='> 0.01')
except AttributeError:
s3.line(100 * np.sum(contamO1 >= 0.001, axis=1) / channels, PA - dPA / 2,
line_color='blue', legend='> 0.001')
s3.line(100 * np.sum(contamO1 >= 0.01, axis=1) / channels, PA - dPA / 2,
line_color='green', legend='> 0.01')
s3.xaxis.axis_label = '% channels contam.'
s3.yaxis.major_label_text_font_size = '0pt'
# ~~~~~~ Order 2 ~~~~~~
# Contam plot
if instrument == 'NIRISS':
xlim0 = lamO2.min()
xlim1 = lamO2.max()
ylim0 = PA.min() - 0.5 * dPA
ylim1 = PA.max() + 0.5 * dPA
xlim0 = 0.614
s5 = figure(
tools=TOOLS,
width=500,
height=500,
title='Order 2 {} Contamination with {}'.format(
targetName,
instrument),
x_range=Range1d(
xlim0,
xlim1),
y_range=s2.y_range)
fig_data = np.log10(np.clip(contamO2.T, 1.e-10, 1.))[:, 300:]
s5.image(
[fig_data],
x=xlim0,
y=ylim0,
dw=xlim1 - xlim0,
dh=ylim1 - ylim0,
color_mapper=color_mapper)
# s5.yaxis.major_label_text_font_size = '0pt'
s5.xaxis.axis_label = 'Wavelength (um)'
s5.yaxis.axis_label = 'Aperture Position Angle (degrees)'
if len(badPAs) > 0:
tops, bottoms, lefts, rights = [], [], [], []
for idx in range(0, len(badPAs)):
PAgroup = badPAs[idx]
top_idx = np.max(PAgroup)
bot_idx = np.min(PAgroup)
tops.append(top_idx)
bottoms.append(bot_idx)
lefts.append(xlim0)
rights.append(xlim1)
s5.quad(top=tops, bottom=bottoms,
left=lefts, right=rights,
color=bad_PA_color, alpha=bad_PA_alpha)
# Line plot
s6 = figure(tools=TOOLS, width=150, height=500, y_range=s2.y_range,
x_range=Range1d(100, 0), title=None)
try:
s6.line(100 * np.sum(contamO2 >= 0.001, axis=0) / rows, PA - dPA / 2,
line_color='blue', legend_label='> 0.001')
s6.line(100 * np.sum(contamO2 >= 0.01, axis=0) / rows, PA - dPA / 2,
line_color='green', legend_label='> 0.01')
except AttributeError:
s6.line(100 * np.sum(contamO2 >= 0.001, axis=0) / rows, PA - dPA / 2,
line_color='blue', legend='> 0.001')
s6.line(100 * np.sum(contamO2 >= 0.01, axis=0) / rows, PA - dPA / 2,
line_color='green', legend='> 0.01')
s6.xaxis.axis_label = '% channels contam.'
s6.yaxis.major_label_text_font_size = '0pt'
if len(badPAs) > 0:
tops, bottoms, lefts, rights = [], [], [], []
for idx in range(0, len(badPAs)):
PAgroup = badPAs[idx]
top_idx = np.max(PAgroup)
bot_idx = np.min(PAgroup)
tops.append(top_idx)
bottoms.append(bot_idx)
lefts.append(0)
rights.append(100)
s3.quad(top=tops, bottom=bottoms,
left=lefts, right=rights,
color=bad_PA_color, alpha=bad_PA_alpha)
if instrument == 'NIRISS':
s6.quad(top=tops, bottom=bottoms,
left=rights, right=lefts,
color=bad_PA_color, alpha=bad_PA_alpha)
# ~~~~~~ Plotting ~~~~~~
if instrument != 'NIRISS':
fig = gridplot(children=[[s2, s3]])
else:
fig = gridplot(children=[[s6, s5, s2, s3]])
return fig # , contamO1
def plot_states(state_dict, state_types):
palette.reverse()
output_file("fatalities_per_state_rel.html")
# hawaii en alaska weggehaald want dat fockte mn kaart op
del us_states["HI"]
del us_states["AK"]
# vind long/lat van staten
state_lons = [us_states[code]["lons"] for code in us_states]
state_lats = [us_states[code]["lats"] for code in us_states]
state_names = [us_states[code]["name"] for code in us_states]
state_fatalities = []
for state_name in state_names:
amount = state_dict[state_name]
state_fatalities.append(amount)
state_killer = []
for state_name in state_names:
state_killer.append(
max(state_types[state_name], key=state_types[state_name].get))
color_mapper = {}
color_mapper = LinearColorMapper(palette=palette, low=5, high=50)
t = sorted(
zip(state_fatalities, state_killer, state_lons, state_lats,
state_names))
state_lons = [x for _, _, x, _, _ in t]
state_lats = [x for _, _, _, x, _ in t]
state_names = [x for _, _, _, _, x in t]
state_fatalities = [x for x, _, _, _, _ in t]
state_killer = [x for _, x, _, _, _ in t]
data = dict(x=state_lons,
y=state_lats,
name=state_names,
rate=state_fatalities,
cause=state_killer)
color_mapper.low_color = 'blue'
color_mapper.high_color = 'red'
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="Fatalities per 100,000 inhabitants",
tools=TOOLS,
toolbar_location="left",
x_axis_location=None,
y_axis_location=None,
plot_width=800,
plot_height=600,
)
p.grid.grid_line_color = None
color_bar = ColorBar(color_mapper=color_mapper, location=(0, 0))
p.add_layout(color_bar, 'right')
p.grid.grid_line_color = None
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("State", "@name"), ("Gun deaths per 100k", "@rate"),
("Main cause of death:", "@cause")]
hover.point_policy = "follow_mouse"
p.patches(
'x',
'y',
source=data,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5,
line_alpha=0.3,
)
return (p)
