def load_file(self, df, _mean=['X', 'Y', 'LAT', 'LON', 'DEN', ],
_sum=['HAB', 'CC', 'MAS', 'PDC', 'VV'],
_first=['PAIS', 'REC', 'MUN', 'BOL']):
# agrupamos por recinto
_gr = df.groupby('ID_RECI')
rec_df = _gr[_mean].mean()
rec_df[_sum] = _gr[_sum].sum()
rec_df[_first] = _gr[_first].first()
rec_df['D_MAS_CC'] = rec_df['MAS'] - rec_df['CC']
rec_df['d_mas_cc'] = rec_df['D_MAS_CC'] / rec_df['VV'] * 100
rec_df['cc'] = rec_df['CC'] / rec_df['VV'] * 100
rec_df['mas'] = rec_df['MAS'] / rec_df['VV'] * 100
rec_df['creemos'] = rec_df['CREEMOS'] / rec_df['VV'] * 100
rec_df['fpv'] = rec_df['FPV'] / rec_df['VV'] * 100
rec_df['pan_bol'] = rec_df['PAN_BOL'] / rec_df['VV'] * 100
rec_df['r'] = np.sqrt(rec_df['HAB']) / self.RATIO_CIRCLE_CARTO
rec_df['r2'] = np.sqrt(
rec_df['HAB']) / self.RATIO_CIRCLE_MAP + self.MAP_CIRCLE_SIZE_OFFSET
res = ebu.lola_to_cart(rec_df['LON'].values, rec_df['LAT'].values)
rec_df['GX'] = res[0]
rec_df['GY'] = res[1]
rec_df = rec_df.sort_values('DEN', axis=0, ascending=True)
# remove nans
rec_df = rec_df.dropna(axis=0)
assert rec_df.isna().sum().sum() == 0
return rec_df
'DEN',
]
_sum = ['HAB', 'CC', 'MAS', 'PDC', 'VV']
_first = ['PAIS', 'REC', 'MUN', 'BOL']
#agrupamos por recinto
_gr = df.groupby('ID_RECI')
rec_df = _gr[_mean].mean()
rec_df[_sum] = _gr[_sum].sum()
rec_df[_first] = _gr[_first].first()
rec_df['D_MAS_CC'] = rec_df['MAS'] - rec_df['CC']
rec_df['d_mas_cc'] = rec_df['D_MAS_CC'] / rec_df['VV'] * 100
rec_df['r'] = np.sqrt(rec_df['HAB']) / 500
rec_df['r2'] = np.sqrt(rec_df['HAB']) / 7 + 5
res = ebu.lola_to_cart(rec_df['LON'].values, rec_df['LAT'].values)
rec_df['GX'] = res[0]
rec_df['GY'] = res[1]
needed_cols = [
'X', 'Y', 'd_mas_cc', 'r', 'LAT', 'LON', 'PAIS', 'REC', 'MUN', 'DEN'
'GX', 'GY'
]
# %%
# order by density
rec_df = rec_df.sort_values('DEN', axis=0, ascending=True)
# %%
# remove nans
rec_df = rec_df.dropna(axis=0)
def plot_carto_single(self, data, frente, palette, path=FILE_OUT,
name_file="", low=0, high=100, show_plot=True):
"""
:param data: df loaded by data_load
:param frente: string, name of "partido" lowercase: diff, mas, cc, creemos, fpv, pan_bol
:param palette: ej: P_GRAD_CC
:param name_file: default:test
:param low: cmap low limit: default: -80
:param high: cmap high limit: defauilt: +80.
:param path: file out
:return: df
"""
da_col = ['HAB','PAIS','MUN','REC','X','Y','LAT','LON','x','y',
'r','r2','GX','GY'
]
cart_init_val = self.CART_SLIDER_INIT # add slider
self.process_data(cart_init_val, data)
if frente == "diff":
low = self.C_BAR_LOW
high = self.C_BAR_HIGH
frente = "d_mas_cc"
f1 = 'mas_o_cc'
f2 = 'ad_mas_cc'
_p = 'mas'
_p1 = 'cc'
da_col.append(frente)
da_col.append(f1)
da_col.append(f2)
da_col.append(_p)
da_col.append(_p1)
if frente == "d_mas_creemos":
low = self.C_BAR_LOW
high = self.C_BAR_HIGH
f1 = 'mas_o_creemos'
f2 = 'ad_mas_creemos'
da_col.append(frente)
da_col.append(f1)
da_col.append(f2)
da_col.append('mas')
da_col.append('creemos')
da_col.append(frente)
cm = linear_cmap(frente, palette=palette, low=low, high=high)
data = data[da_col]
source_master = ColumnDataSource(data)
source_red_map = ColumnDataSource({'gx': [], 'gy': []})
# la, lo = ebu.get_la_lo_bolivia()
# source_bol = ColumnDataSource({'la': la, 'lo': lo})
# source_red_car = ColumnDataSource({'lo': [], 'la': []})
# JS CODE
code_draw_red_map = """
const data = {'gx': [], 'gy': []}
const indices = cb_data.index.indices
for (var i = 0; i < indices.length; i++ ) {
data['gx'].push(source_master.data.GX[indices[i]])
data['gy'].push(source_master.data.GY[indices[i]])
}
source_red_map.data = data
"""
code_slider = """
var data = source.data;
var f = cb_obj.value
var x = data['x']
var y = data['y']
var Y = data['Y']
var X = data['X']
var lat = data['LAT']
var lon = data['LON']
for (var i = 0; i < x.length; i++) {
y[i] = (1-f)*lat[i] + f*Y[i]
x[i] = (1-f)*lon[i] + f*X[i]
}
source.change.emit();
"""
# FIGURES
curr_time = ebu.get_bolivian_time(-3)
pw = self.FIG_WIDTH
callback_red_map = CustomJS(
args={'source_master': source_master,
'source_red_map': source_red_map, },
code=code_draw_red_map)
hover_cart = bokeh.models.HoverTool(
tooltips=self.TOOL_TIP_DIC[frente],
callback=callback_red_map,
# renderers = [red_scat_car]
)
cart_fig = Figure(plot_width=pw, plot_height=pw,
output_backend="webgl", )
cart_fig.background_fill_color = "grey"
cart_fig.background_fill_alpha = .5
cart_fig.scatter('x', 'y', source=source_master, radius='r', color=cm)
cart_fig.add_tools(hover_cart, )
title = "Última actualización: " + curr_time["datetime_val"].strftime(
"%Y-%m-%d %H:%M") + "BOT"
map_fig = Figure(plot_width=pw, plot_height=pw,
x_axis_type='mercator',
y_axis_type='mercator',
output_backend="webgl",
title=title,
)
# cb_fig = bokeh.plotting.Figure(plot_height=pw,plot_width=)
# cb_fig.toolbar.logo = None
# cb_fig.toolbar_location = None
# SCATTER
# noinspection PyUnresolvedReferences
# add tiles
tile_provider = bokeh.tile_providers.get_provider(
bokeh.tile_providers.Vendors.CARTODBPOSITRON)
map_fig.add_tile(tile_provider)
# scatter in map
map_fig.scatter(
'GX', 'GY', source=source_master, size='r2',
color=cm
)
# todo if we wont use map then we nee to delete the source
# cart_fig.line('lo', 'la', source=source_bol, color='black')
# noinspection PyUnusedLocal
red_scat_map = map_fig.circle_cross('gx', 'gy',
source=source_red_map,
fill_color=None,
size=20,
line_color="white",
line_width=4
)
# noinspection PyUnusedLocal
red_scat_map = map_fig.circle_cross('gx', 'gy',
source=source_red_map,
fill_color=None,
size=20,
line_color="red",
line_width=1
)
# red_scat_car = cart_fig.scatter('lo', 'la',
# source=source_red_car, color='green')
# add a hover tool that sets the link data for a hovered circle
# callbacks
# code = code_merged)
# callback_red_car = CustomJS(
# args={'source_master': source_master, 'source_red_car': source_red_car},
# code=code_draw_red_car)
# tools
hover_map = bokeh.models.HoverTool(
tooltips=self.TOOL_TIP_DIC[frente],
# callback=callback_red_car,
# renderers = [red_scat_map]
)
map_fig.add_tools(hover_map, )
# slider
callback_slider = CustomJS(args=dict(source=source_master),
code=code_slider)
slider = Slider(start=0, end=1, value=cart_init_val, step=.02,
title="carto")
slider.js_on_change('value', callback_slider)
# COLOR BAR
ml = {int(i): str(np.abs(i)) for i in np.arange(-80, 81, 20)}
cb = bokeh.models.ColorBar(
color_mapper=cm['transform'],
# width=int(.9 * 450),
width='auto',
location=(0, 0),
# title="DEN (N/km^2)",
# title=(BAR_TITLE),
# margin=0,padding=0,
title_standoff=10,
# ticker=bokeh.models.LogTicker(),
orientation='horizontal',
major_label_overrides=ml
)
cart_fig.add_layout(cb, 'above')
# cb.title_text_align = 'left'
cart_fig.title.text = self.BAR_TITLE_DIC[frente]
cart_fig.title.align = 'center'
# layout = row(column(slider, cart_f),map_f)
layout = bokeh.layouts.gridplot(
[[slider, None], [cart_fig, map_fig]], sizing_mode='scale_width',
merge_tools=False)
layout.max_width = 1400
# layout = bokeh.layouts.column([slider, cart_fig])
cart_fig.x_range.start = self.CXS
cart_fig.x_range.end = self.CXE
cart_fig.y_range.start = self.CYS
cart_fig.y_range.end = self.CYE
_ll = ebu.lola_to_cart(lo=[self.MXS, self.MXE], la=[self.MYS, self.MYE])
map_fig.x_range.start = _ll[0][0]
map_fig.x_range.end = _ll[0][1]
map_fig.y_range.start = _ll[1][0]
map_fig.y_range.end = _ll[1][1]
cart_fig.xaxis.major_tick_line_color = None
# turn off x-axis major ticks
cart_fig.xaxis.minor_tick_line_color = None
# turn off x-axis minor ticks
cart_fig.yaxis.major_tick_line_color = None
# turn off y-axis major ticks
cart_fig.yaxis.minor_tick_line_color = None
cart_fig.xaxis.major_label_text_font_size = '0pt'
# turn off x-axis tick labels
cart_fig.yaxis.major_label_text_font_size = '0pt'
# turn off y-axis tick labels
nam = 'z037_' + frente + '_' + name_file + '.html'
nam_lat = 'z037_' + frente + '_' + 'latest' + '.html'
nam1 = os.path.join(path, nam)
nam2 = os.path.join(os.path.dirname(ebu.DIR), 'docs',
'graficas_htmls',
nam_lat)
# bokeh.plotting.output_file(nam2)
if show_plot:
bokeh.plotting.show(layout)
bokeh.plotting.save(layout, nam1)
bokeh.plotting.save(layout, nam2)
return data
'z020_geopadron_recintos_2020_ALL_DEN.csv'),
# encoding='ISO-8859-1'
).set_index('ID_RECI')
df1 = pd.read_csv(os.path.join(ebu.DATA_PATH1_2020,
'z030_carto_xy.csv')).set_index('ID_RECI')
rec_df = pd.merge(df0, df1, left_index=True, right_index=True, validate='1:1')
# %%
len(rec_df)
# %%
rec_df['r'] = np.sqrt(rec_df['HAB']) / 10
res = ebu.lola_to_cart(rec_df['LON'].values, rec_df['LAT'].values)
rec_df['GX'] = res[0]
rec_df['GY'] = res[1]
needed_cols = [
'X', 'Y', 'd_mas_cc', 'r', 'LAT', 'LON', 'PAIS', 'REC', 'MUN', 'DEN'
'GX', 'GY'
]
# %%
len(rec_df)
# %%
# order by density
rec_df = rec_df.sort_values('DEN', axis=0, ascending=True)
def densidad_carto(width=500):
bokeh.plotting.reset_output()
WIDTH = width
CB_VALS = [0, 1, 2, 3]
CB_LIMS = ebu.DEN_LIMS
CB_LABS = {s: str(l) for s, l in enumerate(CB_LIMS[:])}
FILE_OUT = os.path.join(ebu.DIR,
'htlml_1_intermedios/2020/z040_densidad2020.html')
# bokeh.plotting.output_file(FILE_OUT)
df0 = pd.read_csv(
os.path.join(ebu.DATA_PATH1_2020,
'z020_geopadron_recintos_2020_ALL_DEN.csv'),
# encoding='ISO-8859-1'
).set_index('ID_RECI')
df1 = pd.read_csv(os.path.join(ebu.DATA_PATH1_2020,
'z030_carto_xy.csv')).set_index('ID_RECI')
rec_df = pd.merge(df0,
df1,
left_index=True,
right_index=True,
validate='1:1')
# %%
len(rec_df)
# %%
rec_df['r'] = np.sqrt(rec_df['HAB']) / 10
res = ebu.lola_to_cart(rec_df['LON'].values, rec_df['LAT'].values)
rec_df['GX'] = res[0]
rec_df['GY'] = res[1]
needed_cols = [
'X', 'Y', 'd_mas_cc', 'r', 'LAT', 'LON', 'PAIS', 'REC', 'MUN', 'DEN'
'GX', 'GY'
]
# %%
len(rec_df)
# %%
# order by density
rec_df = rec_df.sort_values('DEN', axis=0, ascending=True)
# %%
# remove nans
# rec_df = rec_df.dropna(axis=0)
# assert rec_df.isna().sum().sum() == 0
# %%
len(rec_df)
# %%
# cut = pd.IntervalIndex.from_tuples([(0, 50), (50, 500), (500, 1500),(1500,3000),(3000,4000),(4000,7000)])
# %%
# lab = ['B','M','X','A']
lab = CB_VALS
lims = CB_LIMS
NL = len(lims)
c = pd.cut(
rec_df['DEN'],
lims,
labels=lab,
# retbins=True
)
# %%
rec_df['DEN_CUT'] = c.astype(int)
# %%
# %% [markdown]
# ## Carto Densidad
# %% [markdown]
# ###### código
# %%
# output_file(os.path.join(ebu.DATA_FIG_OUT, "carto_map_mas_cc.html"))
# %%
# rec_df_spl = rec_df.sample(200).copy()
rec_df_spl = rec_df.copy()
# %%
# DATA
bokeh.plotting.output_notebook()
cart_init_val = .0
data = rec_df_spl.copy()
data['x'] = data['LON'] * (1 - cart_init_val) + data['X'] * cart_init_val
data['y'] = data['LAT'] * (1 - cart_init_val) + data['Y'] * cart_init_val
# %%
# COLOR
from bokeh.transform import linear_cmap
from bokeh.transform import log_cmap
# cm = linear_cmap('d_mas_cc', palette=ebu.P_DIF[::-1], low=-80, high=80)
# cm = log_cmap('DEN', palette=bokeh.palettes.Viridis11, low=1, high=10000)
cm = linear_cmap('DEN_CUT',
palette=bokeh.palettes.Viridis[NL - 1],
low=0,
high=NL - 1)
# %%
# SOURCES
source_master = ColumnDataSource(data)
source_red_map = ColumnDataSource({'gx': [], 'gy': []})
la, lo = ebu.get_la_lo_bolivia()
source_bol = ColumnDataSource({'la': la, 'lo': lo})
# source_red_car = ColumnDataSource({'lo': [], 'la': []})
# %%
# JS CODE
code_draw_red_map = """
const data = {'gx': [], 'gy': []}
const indices = cb_data.index.indices
for (var i = 0; i < indices.length; i++) {
data['gx'].push(source_master.data.GX[indices[i]])
data['gy'].push(source_master.data.GY[indices[i]])
}
source_red_map.data = data
"""
code_draw_red_car = """
const data = {'lo': [], 'la': []}
const indices = cb_data.index.indices
for (var i = 0; i < indices.length; i++) {
data['lo'].push(source_master.data.x[indices[i]])
data['la'].push(source_master.data.y[indices[i]])
}
source_red_car.data = data
"""
code_merged = """
const data_map = {'lo': [], 'la': []}
const data_car = {'gx': [], 'gy': []}
const indices = cb_data.index.indices
for (var i = 0; i < indices.length; i++) {
data_map['lo'].push(source_master.data.x[indices[i]])
data_map['la'].push(source_master.data.y[indices[i]])
data_car['gx'].push(source_master.data.GX[indices[i]])
data_car['gy'].push(source_master.data.GY[indices[i]])
}
source_red_car.data = data_car
source_red_map.data = data_map
"""
code_slider = """
var data = source.data;
var f = cb_obj.value
var x = data['x']
var y = data['y']
var Y = data['Y']
var X = data['X']
var lat = data['LAT']
var lon = data['LON']
for (var i = 0; i < x.length; i++) {
y[i] = (1-f)*lat[i] + f*Y[i]
x[i] = (1-f)*lon[i] + f*X[i]
}
source.change.emit();
"""
# %%
# FIGURES
pw = WIDTH
cart_fig = Figure(plot_width=pw + int(.2 * pw),
plot_height=pw,
output_backend="webgl")
# map_fig = Figure(plot_width=pw, plot_height=pw,
# x_axis_type='mercator',
# y_axis_type='mercator',
# output_backend="webgl",
# )
# cb_fig = bokeh.plotting.Figure(plot_height=pw,plot_width=)
# cb_fig.toolbar.logo = None
# cb_fig.toolbar_location = None
# %%
# SCATTER
# noinspection PyUnresolvedReferences
# add tiles
tile_provider = bokeh.tile_providers.get_provider(
bokeh.tile_providers.Vendors.CARTODBPOSITRON)
# map_fig.add_tile(tile_provider)
# scatter in map
# map_fig.scatter(
# 'GX', 'GY', source=source_master, size='r',
# color=cm
# )
# cart_fig.line('lo', 'la', source=source_bol, color='black')
cart_fig.scatter('x', 'y', source=source_master, size='r', color=cm)
# red_scat_map = map_fig.scatter('gx', 'gy',
# source=source_red_map, color='red',
# line_color='green',
# size=10
# )
# red_scat_car = cart_fig.scatter('lo', 'la',
# source=source_red_car, color='green')
# add a hover tool that sets the link data for a hovered circle
# callbacks
callback_red_map = CustomJS(
args={
'source_master': source_master,
'source_red_map': source_red_map,
# 'source_red_car':source_red_car
},
code=code_draw_red_map)
# code = code_merged)
# callback_red_car = CustomJS(
# args={'source_master': source_master, 'source_red_car': source_red_car},
# code=code_draw_red_car)
# tools
ebu.TOOL_TIPS1 = [('Inscritos', '@HAB'), ('País', '@PAIS'),
('Municipio', '@MUN'), ('Recinto', '@REC'),
('Votantes/km^2', '@DEN{0}'), ('--------', '------')
# ('PAIS', '@PAIS'),
]
hover_cart = bokeh.models.HoverTool(
tooltips=ebu.TOOL_TIPS1,
callback=callback_red_map,
# renderers = [red_scat_car]
)
cart_fig.add_tools(hover_cart, )
hover_map = bokeh.models.HoverTool(
tooltips=ebu.TOOL_TIPS1,
# callback=callback_red_car,
# renderers = [red_scat_map]
)
# map_fig.add_tools(hover_map, )
# slider
callback_slider = CustomJS(args=dict(source=source_master),
code=code_slider)
slider = Slider(start=0,
end=1,
value=cart_init_val,
step=.01,
title="carto")
slider.js_on_change('value', callback_slider)
# %%
# COLOR BAR
cb = bokeh.models.ColorBar(
color_mapper=cm['transform'],
width=30,
location=(0, 0),
title="Den. (V./km^2)",
# margin=0,padding=0,
title_standoff=10,
# ticker=bokeh.models.LogTicker(),
major_label_overrides=CB_LABS,
ticker=bokeh.models.FixedTicker(ticks=list(CB_LABS.keys())))
cart_fig.add_layout(cb, 'left')
# layout = row(column(slider, cart_f),map_f)
# layout = bokeh.layouts.gridplot(
# [[slider, None], [cart_fig, map_fig]]
# , merge_tools=False
# )
layout = bokeh.layouts.column([slider, cart_fig],
# sizing_mode='scale_width'
)
layout.width = width
cart_fig.x_range.start = -80
cart_fig.x_range.end = -45
cart_fig.y_range.start = -30
cart_fig.y_range.end = 0
_ll = ebu.lola_to_cart(lo=[-80, -45], la=[-30, 0])
# map_fig.x_range.start = _ll[0][0]
# map_fig.x_range.end = _ll[0][1]
# map_fig.y_range.start = _ll[1][0]
# map_fig.y_range.end = _ll[1][1]
# %% [markdown]
# ###### gráfica
# %% [markdown]
# En el mapa de abajo, cada punto corresponde un recinto electoral, su color está relacionado con la densidad de votantes, y su tamaño con la cantidad de votos.
# Mueve el slider (carto) para ver la deformación.
# %%
# %%
bokeh.plotting.show(layout)
_mean = ['X', 'Y', 'LAT', 'LON', 'DEN', ]
_sum = ['HAB', 'CC', 'MAS', 'VV']
_first = ['PAIS', 'REC', 'MUN', 'BOL']
# agrupamos por recinto
_gr = df2.groupby('ID_RECI')
rec_df = _gr[_mean].mean()
rec_df[_sum] = _gr[_sum].sum()
rec_df[_first] = _gr[_first].first()
rec_df['D_MAS_CC'] = rec_df['MAS'] - rec_df['CC']
rec_df['d_mas_cc'] = rec_df['D_MAS_CC'] / rec_df['VV'] * 100
rec_df['r'] = np.sqrt(rec_df['HAB']) / RATIO_CIRCLE_CARTO
rec_df['r2'] = np.sqrt(rec_df['HAB']) / RATIO_CIRCLE_MAP + MAP_CIRCLE_SIZE_OFFSET
res = ebu.lola_to_cart(rec_df['LON'].values, rec_df['LAT'].values)
rec_df['GX'] = res[0]
rec_df['GY'] = res[1]
needed_cols = ['X', 'Y', 'd_mas_cc', 'r', 'LAT', 'LON', 'PAIS', 'REC', 'MUN', 'DEN'
'GX', 'GY']
# %%
# order by density
rec_df = rec_df.sort_values('DEN', axis=0, ascending=True)
# %%
# remove nans
rec_df = rec_df.dropna(axis=0)
assert rec_df.isna().sum().sum() == 0
def plot_carto_single(self,
data,
frente,
palette,
name_file="test.html",
low=0,
high=100):
"""
:param data: df loaded by data_load
:param frente: string, name of "partido" lowercase: diff, mas, cc, creemos, fpv, panbol
:param palette: ej: P_GRAD_CC
:param name_file: default:test
:param low: cmap low limit: default: -80
:param high: cmap high limit: defauilt: +80.
:return: df
"""
if frente == "diff":
low = self.C_BAR_LOW
high = self.C_BAR_HIGH
frente = "d_mas_cc"
bokeh.plotting.output_file(self.FILE_OUT + '_' + frente + '_' +
name_file)
cart_init_val = self.CART_SLIDER_INIT # add slider
data['x'] = data['LON'] * (1 -
cart_init_val) + data['X'] * cart_init_val
data['y'] = data['LAT'] * (1 -
cart_init_val) + data['Y'] * cart_init_val
cm = linear_cmap(frente, palette=palette, low=low, high=high)
data['mas'] = data['MAS'] / data['VV'] * 100
data['cc'] = data['CC'] / data['VV'] * 100
data['pdc'] = data['PDC'] / data['VV'] * 100
#data['creemos'] = data['CREEMOS'] / data['VV'] * 100
#data['fpv'] = data['FPV'] / data['VV'] * 100
#data['panbol'] = data['PANBOL'] / data['VV'] * 100
data['ad_mas_cc'] = data['d_mas_cc'].abs()
data['mas_o_cc'] = 'n'
data.loc[data['d_mas_cc'] >= 0, 'mas_o_cc'] = 'MAS'
data.loc[data['d_mas_cc'] < 0, 'mas_o_cc'] = 'CC'
source_master = ColumnDataSource(data)
source_red_map = ColumnDataSource({'gx': [], 'gy': []})
la, lo = ebu.get_la_lo_bolivia()
source_bol = ColumnDataSource({'la': la, 'lo': lo})
# source_red_car = ColumnDataSource({'lo': [], 'la': []})
# JS CODE
code_draw_red_map = """
const data = {'gx': [], 'gy': []}
const indices = cb_data.index.indices
for (var i = 0; i < indices.length; i++ ) {
data['gx'].push(source_master.data.GX[indices[i]])
data['gy'].push(source_master.data.GY[indices[i]])
}
source_red_map.data = data
"""
code_draw_red_car = """
const data = {'lo': [], 'la': []}
const indices = cb_data.index.indices
for (var i = 0; i < indices.length; i++) {
data['lo'].push(source_master.data.x[indices[i]])
data['la'].push(source_master.data.y[indices[i]])
}
source_red_car.data = data
"""
code_merged = """
const data_map = {'lo': [], 'la': []}
const data_car = {'gx': [], 'gy': []}
const indices = cb_data.index.indices
for (var i = 0; i < indices.length; i++) {
data_map['lo'].push(source_master.data.x[indices[i]])
data_map['la'].push(source_master.data.y[indices[i]])
data_car['gx'].push(source_master.data.GX[indices[i]])
data_car['gy'].push(source_master.data.GY[indices[i]])
}
source_red_car.data = data_car
source_red_map.data = data_map
"""
code_slider = """
var data = source.data;
var f = cb_obj.value
var x = data['x']
var y = data['y']
var Y = data['Y']
var X = data['X']
var lat = data['LAT']
var lon = data['LON']
for (var i = 0; i < x.length; i++) {
y[i] = (1-f)*lat[i] + f*Y[i]
x[i] = (1-f)*lon[i] + f*X[i]
}
source.change.emit();
"""
# FIGURES
pw = self.FIG_WIDTH
cart_fig = Figure(plot_width=pw,
plot_height=pw,
output_backend="webgl")
map_fig = Figure(
plot_width=pw,
plot_height=pw,
x_axis_type='mercator',
y_axis_type='mercator',
output_backend="webgl",
)
cart_fig.background_fill_color = "grey"
cart_fig.background_fill_alpha = .5
# cb_fig = bokeh.plotting.Figure(plot_height=pw,plot_width=)
# cb_fig.toolbar.logo = None
# cb_fig.toolbar_location = None
# SCATTER
# noinspection PyUnresolvedReferences
# add tiles
tile_provider = bokeh.tile_providers.get_provider(
bokeh.tile_providers.Vendors.CARTODBPOSITRON)
map_fig.add_tile(tile_provider)
# scatter in map
map_fig.scatter('GX', 'GY', source=source_master, size='r2', color=cm)
# todo if we wont use map then we nee to delete the source
# cart_fig.line('lo', 'la', source=source_bol, color='black')
cart_fig.scatter('x', 'y', source=source_master, radius='r', color=cm)
red_scat_map = map_fig.circle_cross(
'gx',
'gy',
source=source_red_map,
# color='red',
fill_color=None,
# line_color='green',
size=20,
line_color="white",
line_width=4)
red_scat_map = map_fig.circle_cross(
'gx',
'gy',
source=source_red_map,
# color='red',
fill_color=None,
# line_color='green',
size=20,
line_color="red",
line_width=1)
# red_scat_car = cart_fig.scatter('lo', 'la',
# source=source_red_car, color='green')
# add a hover tool that sets the link data for a hovered circle
# callbacks
callback_red_map = CustomJS(
args={
'source_master': source_master,
'source_red_map': source_red_map,
# 'source_red_car':source_red_car
},
code=code_draw_red_map)
# code = code_merged)
# callback_red_car = CustomJS(
# args={'source_master': source_master, 'source_red_car': source_red_car},
# code=code_draw_red_car)
# tools
hover_cart = bokeh.models.HoverTool(
tooltips=self.TOOL_TIP_DIC[frente],
callback=callback_red_map,
# renderers = [red_scat_car]
)
cart_fig.add_tools(hover_cart, )
hover_map = bokeh.models.HoverTool(
tooltips=self.TOOL_TIP_DIC[frente],
# callback=callback_red_car,
# renderers = [red_scat_map]
)
map_fig.add_tools(hover_map, )
# slider
callback_slider = CustomJS(args=dict(source=source_master),
code=code_slider)
slider = Slider(start=0,
end=1,
value=cart_init_val,
step=.02,
title="carto")
slider.js_on_change('value', callback_slider)
# COLOR BAR
ml = {int(i): str(np.abs(i)) for i in np.arange(-80, 81, 20)}
cb = bokeh.models.ColorBar(
color_mapper=cm['transform'],
width=int(.9 * self.FIG_WIDTH),
location=(0, 0),
# title="DEN (N/km^2)",
# title=(BAR_TITLE),
# margin=0,padding=0,
title_standoff=10,
# ticker=bokeh.models.LogTicker(),
orientation='horizontal',
major_label_overrides=ml)
cart_fig.add_layout(cb, 'above')
# cb.title_text_align = 'left'
cart_fig.title.text = self.BAR_TITLE_DIC[frente]
cart_fig.title.align = 'center'
# layout = row(column(slider, cart_f),map_f)
layout = bokeh.layouts.gridplot([[slider, None], [cart_fig, map_fig]],
merge_tools=False)
# layout = bokeh.layouts.column([slider, cart_fig])
cart_fig.x_range.start = self.CXS
cart_fig.x_range.end = self.CXE
cart_fig.y_range.start = self.CYS
cart_fig.y_range.end = self.CYE
_ll = ebu.lola_to_cart(lo=[self.MXS, self.MXE],
la=[self.MYS, self.MYE])
map_fig.x_range.start = _ll[0][0]
map_fig.x_range.end = _ll[0][1]
map_fig.y_range.start = _ll[1][0]
map_fig.y_range.end = _ll[1][1]
cart_fig.xaxis.major_tick_line_color = None # turn off x-axis major ticks
cart_fig.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks
cart_fig.yaxis.major_tick_line_color = None # turn off y-axis major ticks
cart_fig.yaxis.minor_tick_line_color = None
cart_fig.xaxis.major_label_text_font_size = '0pt' # turn off x-axis tick labels
cart_fig.yaxis.major_label_text_font_size = '0pt' # turn off y-axis tick labels
bokeh.plotting.show(layout)
return data