def metrics(M, metrics_labels, types, filename='metrics.html'):
rows = [(metrics_labels[i], types[j], types[k], m)
for (i, j, k), m in np.ndenumerate(M)]
columns = ['m', 't1', 't2', 'v']
df = pd.DataFrame.from_records(rows, columns=columns)
rating_radio = alt.binding_radio(options=metrics_labels)
rating_select = alt.selection_single(fields=['m'],
bind=rating_radio,
name="Metric",
empty='none',
init={'m': metrics_labels[0]})
heatmap = alt.Chart(df).encode(
alt.X('t2:N'),
alt.Y('t1:N'),
).mark_rect().encode(color='v:Q')
text = alt.Chart(df).encode(
alt.X('t2:N'),
alt.Y('t1:N'),
).mark_text(baseline='middle').encode(
text=alt.Text('v:Q', format='.3f'),
color=alt.value('black'),
# color=alt.condition(
# alt.datum.v < 0.8,
# alt.value('black'),
# alt.value('white')
# )
)
p = (heatmap + text).add_selection(rating_select).transform_filter(
rating_select).properties(width=400, height=400)
p.save(filename)
return p
def plot_tsne(source, x_col, y_col, category, perplexities, img_name):
cats = list(source[category].unique())
# Base
base = alt.Chart(source).mark_point(filled=True).encode(x=x_col + ':Q',
y=y_col + ':Q',
tooltip=img_name +
':N')
# A slider filter
perp_slider = alt.binding_range(min=10, max=40, step=10)
slider_selection = alt.selection_single(bind=perp_slider,
fields=[perplexities],
name="Change")
# Color changing marks
rating_radio = alt.binding_radio(options=cats)
rating_select = alt.selection_single(fields=[category],
bind=rating_radio,
name="Filter")
rating_color_condition = alt.condition(
rating_select, alt.Color(category + ':N', legend=None),
alt.value('lightgray'))
highlight_ratings = base.add_selection(
rating_select, slider_selection).encode(
color=rating_color_condition).transform_filter(
slider_selection).properties(title="tSNE Scatter Plot")
return highlight_ratings.properties(width=800, height=300)
def plot_normalization_comparison():
normalized = pd.read_csv(
'https://raw.githubusercontent.com/jamescoller/multilayer_design_network_tool/master/results/id_normalized.csv',
names=['NodeID', 'id'])
not_normal = pd.read_csv(
'https://raw.githubusercontent.com/jamescoller/multilayer_design_network_tool/master/results/id_not_normalized.csv',
names=['NodeID', 'id'])
# normalization = pd.DataFrame()
# normalization['NodeID'] = normalized['NodeID']
# normalization['normal'] = normalized['id']
# normalization['not_normal'] = not_normal['id']
# normalization
normalized['normal'] = 1
not_normal['normal'] = 0
norm2 = pd.concat([normalized, not_normal])
radial_input = alt.binding_radio(options=[1, 0])
norm_choice = alt.selection_single(fields=['normal'],
bind=radial_input,
name='Normalized?')
norm_comparison = alt.Chart(norm2).mark_bar().encode(
x=alt.X('id:Q',
bin=alt.Bin(maxbins=20),
title='Interdependency Rating'),
y=alt.Y('count()', title='Number of Nodes')).add_selection(
norm_choice).transform_filter(norm_choice).transform_filter(
alt.datum.NodeID != 64)
norm_comparison.serve()
def other_viz3(result):
#result = load5()
types = [
'Action Fires/Supressed Fires', 'Natural Out',
'Support Action/Assist Fire', 'Fire Management/Perscribed',
'False Alarm', 'Severe'
]
firetype_df2 = result.groupby(['YEAR_', 'FIRETYPE'
]).size().reset_index(name="Firetype Count")
firetype_df2['FIRETYPE'] = firetype_df2['FIRETYPE'].replace(
0, 'Action Fires/Supressed Fires')
firetype_df2['FIRETYPE'] = firetype_df2['FIRETYPE'].replace(
1, 'Natural Out')
firetype_df2['FIRETYPE'] = firetype_df2['FIRETYPE'].replace(
2, 'Support Action/Assist Fire')
firetype_df2['FIRETYPE'] = firetype_df2['FIRETYPE'].replace(
3, 'Fire Management/Perscribed')
firetype_df2['FIRETYPE'] = firetype_df2['FIRETYPE'].replace(
4, 'False Alarm')
firetype_df2['FIRETYPE'] = firetype_df2['FIRETYPE'].replace(5, 'Severe')
base = alt.Chart(firetype_df2, width=600,
height=400).mark_point(filled=True).encode(
x=alt.X('YEAR_:N',
axis=alt.Axis(labelAngle=360,
values=[
1980, 1985, 1990, 1995,
2000, 2005, 2010, 2016
]),
title='Year'),
y='Firetype Count:Q',
tooltip=["FIRETYPE:N", 'YEAR_:N'])
type_radio = alt.binding_radio(options=types)
type_select = alt.selection_single(fields=['FIRETYPE'],
bind=type_radio,
name='Pick a')
type_color_condition = alt.condition(type_select, alt.Color('FIRETYPE:N'),
alt.value('lightgray'))
highlight_types = base.add_selection(type_select).encode(
color=type_color_condition).properties(
title="U.S. Number of Fires (1980-2016)")
highlight_types
name="Release Year_")
filter_year = base.add_selection(slider_selection).transform_filter(
slider_selection).properties(title="Slider Filtering")
# A dropdown filter
genre_dropdown = alt.binding_select(options=genres)
genre_select = alt.selection_single(fields=['Major_Genre'],
bind=genre_dropdown,
name="Genre")
filter_genres = base.add_selection(genre_select).transform_filter(
genre_select).properties(title="Dropdown Filtering")
#color changing marks
rating_radio = alt.binding_radio(options=ratings)
rating_select = alt.selection_single(fields=['MPAA_Rating'],
bind=rating_radio,
name="Rating")
rating_color_condition = alt.condition(rating_select,
alt.Color('MPAA_Rating:N', legend=None),
alt.value('lightgray'))
highlight_ratings = base.add_selection(rating_select).encode(
color=rating_color_condition).properties(title="Radio Button Highlighting")
# Boolean selection for format changes
input_checkbox = alt.binding_checkbox()
checkbox_selection = alt.selection_single(bind=input_checkbox,
name="Big Budget Films")
def chart_altair(df, system_ini='peninsular'):
'''
Create an altair chart with the average of last 7 days of electric generation of total enery, renewable energy, solar
photovoltaic energy and wind powered energy by date, the chart can be filtered by year and electric system.
also add a vertical line to show the values where put the mouse in the chart.
:param df: Pandas Dataframe with de average of last 7 days of electric generation by electric system, date, year and technology
:param system_ini: Initial system to show in the chart, by default 'peninsular
:return: altair layered chart
'''
# labels of X axis to show in the chart, every first day of month from 2016-01 to 2021-12.
x_labels = [
'2016-01-01', '2016-02-01', '2016-03-01', '2016-04-01', '2016-05-01',
'2016-06-01', '2016-07-01', '2016-08-01', '2016-09-01', '2016-10-01',
'2016-11-01', '2016-12-01', '2016-12-31', '2017-01-01', '2017-02-01',
'2017-03-01', '2017-04-01', '2017-05-01', '2017-06-01', '2017-07-01',
'2017-08-01', '2017-09-01', '2017-10-01', '2017-11-01', '2017-12-01',
'2017-12-31', '2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',
'2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01', '2018-09-01',
'2018-10-01', '2018-11-01', '2018-12-01', '2018-12-31', '2019-01-01',
'2019-02-01', '2019-03-01', '2019-04-01', '2019-05-01', '2019-06-01',
'2019-07-01', '2019-08-01', '2019-09-01', '2019-10-01', '2019-11-01',
'2019-12-01', '2019-12-31', '2020-01-01', '2020-02-01', '2020-03-01',
'2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',
'2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01', '2020-12-31',
'2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01', '2021-05-01',
'2021-06-01', '2021-07-01', '2021-08-01', '2021-09-01', '2021-10-01',
'2021-11-01', '2021-12-01', '2021-12-31'
]
# list of elements in Color to be plotted
domain = ['Generación total', 'Renovable', 'Solar fotovoltaica', 'Eólica']
# colors in hexadecimal, for each element in domain list
range_ = ['#85C1E9', '#239B56', '#D35400', '#F7DC6F']
# set a select box to select the system to show in the chart
select_box_sys = alt.binding_select(options=list(df['system'].unique()))
selection_sys = alt.selection_single(name='REE',
fields=['system'],
bind=select_box_sys,
init={'system': system_ini})
# set a radio selector to select the year to show in the chart
select_radio_year = alt.binding_radio(options=list(df['year'].unique()))
selection_year = alt.selection_single(name='Choose',
fields=['year'],
bind=select_radio_year,
init={'year': max(df['year'])})
# create a markpoint with variable fecha as X axis
# with a selection that works over the variable fecha showing the nearest value where the mouse is over.
nearest = alt.selection(type='single',
nearest=True,
on='mouseover',
fields=['fecha'],
empty='none')
selectors = alt.Chart(df).mark_point().encode(
alt.X('fecha'),
opacity=alt.value(0)).add_selection(nearest).transform_filter(
selection_sys).transform_filter(selection_year)
# Create the main chart, with the electric generation by date, and add the selectors of year and systems
bar = alt.Chart(df[df['Renov_norenov'] == 'Generación total']).mark_area(
color='#85C1E9').encode(
alt.X('fecha', axis=alt.Axis(values=x_labels, labelAngle=0)),
alt.Y('Generacion_Mwh:Q')).add_selection(
selection_sys, selection_year).transform_filter(
selection_sys).transform_filter(selection_year).properties(
width=1400, height=450)
# Create the chart of renewable energy by fecha, also add the color with the list domain and his colors in list range_
# also add the transformers of the main chart
bar_renov = alt.Chart(
df[df['Tecnologia'] == 'Renovable']).mark_area().encode(
alt.X('fecha'),
alt.Y('Generacion_Mwh:Q'),
color=alt.Color('Tecnologia',
scale=alt.Scale(domain=domain, range=range_))
).transform_filter(selection_sys).transform_filter(selection_year)
# add a text chart to show the value of the bar_renov chart
text_renov = bar_renov.mark_text(
align='left', dx=3, dy=-20, color='#212F3C').encode(
text=alt.condition(nearest, 'Generacion_Mwh', alt.value(' ')))
rules = alt.Chart(df).mark_rule(color='gray').encode(
x='fecha', ).transform_filter(nearest)
# Create the chart of Solar photovoltaic by fecha, also add the transformers of the main chart
bar_solar = alt.Chart(
df[df['Tecnologia'] == 'Solar fotovoltaica']).mark_area(
opacity=.8, color='#D35400').encode(
alt.X('fecha'), alt.Y('Generacion_Mwh:Q')).transform_filter(
selection_sys).transform_filter(selection_year)
# add a text chart to show the value of the bar_renov chart
text_solar = bar_solar.mark_text(
align='left', dx=5, dy=-5, color='#212F3C').encode(
text=alt.condition(nearest, 'Generacion_Mwh', alt.value(' ')))
# Create the chart of wind power by fecha, also add the transformers of the main chart
bar_eolica = alt.Chart(
df[df['Tecnologia'] == 'Eólica']).mark_area(color='#F7DC6F').encode(
alt.X('fecha'), alt.Y('Generacion_Mwh:Q')).transform_filter(
selection_sys).transform_filter(selection_year)
# add a text chart to show the value of the bar_renov chart
text_eolica = bar_eolica.mark_text(
align='left', dx=5, dy=-5, color='#212F3C').encode(
text=alt.condition(nearest, 'Generacion_Mwh', alt.value(' ')))
# retrun a altair layered chart with all the elements created in the function
return alt.layer(
bar, bar_renov, bar_eolica, bar_solar, selectors, rules, text_renov,
text_eolica, text_solar).configure_axis(
labelFontSize=13, titleFontSize=14).configure_text(
fill='#212F3C',
fontSize=13).configure_legend(labelFontSize=14).interactive()
st.write(base_peak)
if 1 == 1:
base = alt.Chart(data).mark_circle(size=60).encode(
x=alt.X('phi', type='ordinal'),
y=alt.Y(option_FOM,
type='quantitative',
scale=alt.Scale(type='log')),
facet=alt.Facet('bf_id_s:N',
columns=len(unq_bf),
spacing=-.5),
color='np_id_s:N',
size='a_33:O',
).properties(width=180).interactive()
p_radio = alt.binding_radio(options=unq_p.tolist())
p_select = alt.selection_single(fields=['p'],
bind=p_radio,
name="Aspect ratio, p")
p_color_condition = alt.condition(
p_select, alt.Color('p:N', legend=None),
alt.value('lightgray'))
np_dropdown = alt.binding_select(options=unq_np.tolist())
np_select = alt.selection_single(fields=['np_id_s'],
bind=np_dropdown,
name="Nanoparticle")
radio_p = base.add_selection(p_select).encode(
color=p_color_condition, ).add_selection(
np_select).transform_filter(np_select).properties(
def make_figure(ranks_pd: pd.DataFrame, o_ranks_explored: str,
p_pair_number: int, p_color_palette: str, omic1_column: str,
omic2_column: str, omic1: str, omic2: str, p_omic1_filt: str,
p_omic1_value: str, p_omic2_filt: str, p_omic2_value: str,
p_omic1_max: int, p_omic2_max: int, p_color_top: str) -> None:
conditionals_1 = 'conditionals_per_%s' % omic1
conditionals_2 = 'conditionals_per_%s' % omic2
if p_omic1_max:
ranks_pd = ranks_pd.loc[ranks_pd[conditionals_1] <= p_omic1_max, :]
if p_omic2_max:
ranks_pd = ranks_pd.loc[ranks_pd[conditionals_2] <= p_omic2_max, :]
ranks_pd2merge = ranks_pd[[omic1, omic2, conditionals_1, conditionals_2]]
ranks_st = get_stacked(ranks_pd, omic1_column, omic2_column, omic1, omic2)
ranks_st = ranks_st.merge(ranks_pd2merge, on=[omic1, omic2], how='left')
text = [
"\n\nCo-occurrence matrix exploration tool (mmvec)",
"%s (%s features) vs. %s (%s features)" %
(omic1, ranks_st[omic1].unique().size, omic2,
ranks_st[omic2].unique().size)
]
subtext = []
if p_omic1_filt and p_omic1_value:
subtext.append("Subset %s for '%s' in '%s'" %
(omic1, ', '.join(list(p_omic1_value)), p_omic1_filt))
if p_omic2_filt and p_omic2_value:
subtext.append("Subset %s for '%s' in '%s'" %
(omic2, ', '.join(list(p_omic1_value)), p_omic2_filt))
conditionals = [
'conditionals', 'ranked_conditionals', conditionals_1, conditionals_2
]
conditionals_radio = alt.binding_radio(options=conditionals,
name="Co-occurrence measure")
conditionals_select = alt.selection_single(
fields=['conditional'],
bind=conditionals_radio,
init={'conditional': 'conditionals'})
max_rank1 = int(ranks_st[conditionals_1].max())
init1 = p_pair_number
if p_pair_number > max_rank1:
init1 = max_rank1
slider1 = alt.binding_range(min=1,
max=max_rank1,
step=1,
name='Maximum number of %s for each %s:' %
(omic2, omic1))
selector1 = alt.selection_single(name="cutoff1",
fields=['cutoff1'],
bind=slider1,
init={'cutoff1': init1})
max_rank2 = int(ranks_st[conditionals_2].max())
init2 = p_pair_number
if p_pair_number > max_rank2:
init2 = max_rank2
slider2 = alt.binding_range(min=1,
max=max_rank2,
step=1,
name='Maximum number of %s for each %s:' %
(omic1, omic2))
selector2 = alt.selection_single(name="cutoff2",
fields=['cutoff2'],
bind=slider2,
init={'cutoff2': init2})
mlt1 = alt.selection_multi(fields=[omic1], toggle=True)
mlt2 = alt.selection_multi(fields=[omic2], toggle=True)
slider_label1 = alt.binding_range(min=0,
max=max_rank1,
step=1,
name='Label this much top %s per %s:' %
(omic2, omic1))
label1 = alt.selection_single(name="cutoff_label1",
fields=['cutoff_label1'],
bind=slider_label1,
init={'cutoff_label1': 0})
slider_label2 = alt.binding_range(min=0,
max=max_rank2,
step=1,
name='Label this much top %s per %s:' %
(omic1, omic2))
label2 = alt.selection_single(name="cutoff_label2",
fields=['cutoff_label2'],
bind=slider_label2,
init={'cutoff_label2': 0})
sorted_omic1 = get_sorted(ranks_st, omic1_column, omic1)
sorted_omic2 = get_sorted(ranks_st, omic2_column, omic2)
tooltips = [
omic1, omic2, 'conditional', 'rank', conditionals_1, conditionals_2
]
if omic1_column:
tooltips.append(omic1_column)
if omic2_column:
tooltips.append(omic2_column)
x_size = len(sorted_omic1) * 6
y_size = len(sorted_omic2) * 6
rect = alt.Chart(ranks_st).mark_rect().encode(
x=alt.X('%s:O' % omic1,
sort=sorted_omic1,
axis=alt.Axis(labelOverlap=False,
labelFontSize=6,
labelLimit=500,
orient='top',
labelAngle=45,
titleFontSize=0)),
y=alt.Y('%s:O' % omic2,
sort=sorted_omic2,
axis=alt.Axis(labelOverlap=False,
labelFontSize=6,
titleFontSize=0,
labelLimit=500)),
color=alt.Color('rank:Q',
legend=alt.Legend(orient='left'),
sort="descending",
scale=alt.Scale(scheme=p_color_palette)),
tooltip=tooltips).add_selection(
conditionals_select, mlt1, mlt2, selector1,
selector2).transform_filter(conditionals_select).transform_filter(
mlt1).transform_filter(mlt2).transform_filter(
alt.datum[conditionals_1] <= selector1.cutoff1
).transform_filter(
alt.datum[conditionals_2] <= selector2.cutoff2).properties(
width=x_size,
height=y_size,
)
circ = alt.Chart(ranks_st).mark_point(size=5, shape='diamond').encode(
x=alt.X('%s:O' % omic1,
sort=sorted_omic1,
axis=alt.Axis(labelOverlap=False,
labelFontSize=6,
orient='top',
labelAngle=45,
titleFontSize=0)),
y=alt.Y('%s:O' % omic2,
sort=sorted_omic2,
axis=alt.Axis(labelOverlap=False,
labelFontSize=6,
titleFontSize=0)),
color=alt.ColorValue(p_color_top),
tooltip=tooltips).add_selection(label1, label2).transform_filter(
conditionals_select
).transform_filter(mlt1).transform_filter(mlt2).transform_filter(
alt.datum[conditionals_1] <= selector1.cutoff1).transform_filter(
alt.datum[conditionals_2] <= selector2.cutoff2
).transform_filter(
alt.datum[conditionals_1] <= label1.cutoff_label1
).transform_filter(
alt.datum[conditionals_2] <= label2.cutoff_label2).properties(
width=x_size, height=y_size)
bar_omic1 = get_bar_chart(ranks_st, sorted_omic1, conditionals_1,
conditionals_2, omic1_column, omic1, omic1,
omic2, x_size, y_size, mlt1, selector1,
selector2)
bar_omic2 = get_bar_chart(ranks_st, sorted_omic2, conditionals_1,
conditionals_2, omic2_column, omic2, omic1,
omic2, x_size, y_size, mlt2, selector1,
selector2)
chart = alt.vconcat(alt.hconcat((rect + circ), bar_omic2), bar_omic1)
chart.resolve_legend(
color="independent", size="independent").configure_axis(
labelLimit=300,
labelFontSize=8,
).configure_legend(labelLimit=1000,
labelFontSize=8,
titleFontSize=8,
symbolSize=12,
columns=3).properties(
title={
"text": text,
"subtitle": (subtext +
["(based on altair)"]),
"color": "black",
"subtitleColor": "grey"
})
if not isdir(dirname(o_ranks_explored)):
os.makedirs(dirname(o_ranks_explored))
chart.save(o_ranks_explored)
print('-> Written:', o_ranks_explored)