def plt_barchart_grupped(data, param_dict, mode='object', output_path=None):
'''
data: rows = list of dicts(): keywords with neat naming straight to put in charts
param_dict: dictionary with settings as follows:
{'x':'name of column to put on x axis',
'y':'name of column to put in y axis',
'agg':'name of aggregate function: 'sum'/'count' /etc',
'color':'name of name of column to create siloses by/to apply aggr funct',
'tooltips':['list() of strings with column names to put in tooltip cloud']}
mode: 'object'/'save'/'show'/'embed' - either returns plot object / saves to path + returns True / saves to path and shows plot
output_path: string with html ext to save chart to. If None then emmbed is returned
returns: True(saved to file) or emmbed data code
'''
# tooltips=[
# ("Date", "$x"),
# ("version", "@HVAC_version")]
# p = Bar(data, label='Date', values='HVAC_version', agg='count', group='HVAC_version',
# title="Median MPG by YR, grouped by ORIGIN", legend='bottom_right', bar_width=3.0,
# plot_width=1000, plot_height=400, tooltips = tooltips)
# p.legend.background_fill_alpha = 0.8
# output_file("bar.html")
# show(p)
#to_date(data,'%Y-%m-01')
tooltips = create_tooltips(param_dict)
title = "{} of {} by {}, grouped by {}".format(param_dict['agg'],
param_dict['y'].upper(),
param_dict['x'].upper(),
param_dict['color'].upper())
p = Bar(data=data,
label=param_dict['x'],
values=param_dict['y'],
agg=param_dict['agg'],
group=param_dict['color'],
title=title,
legend='bottom_right',
bar_width=3.0,
plot_width=1000,
plot_height=600,
tooltips=tooltips)
#legend_sort_field = param_dict['group'])
p.legend.background_fill_alpha = 0.8
if type(data[0][param_dict['x']]) == str:
x_rng = sort_axis(data, param_dict['x'], False, True)
p.x_range = x_rng
if type(data[0][param_dict['y']]) == str:
y_rng = sort_axis(data, param_dict['y'], False, True)
p.y_range = y_rng
return do_output(p, mode, output_path)
def leagues():
grouped = df_squads.groupby('year')
club_country = df_squads.club_country.unique()
club_country.sort()
df_clb_ctry = pd.DataFrame({"club_country":club_country})
for year in df_squads.year.unique():
country_to_club = grouped.club_country.value_counts()[year] / grouped.team.value_counts()[year]
df_clb_ctry = df_clb_ctry.join(country_to_club.to_frame(year))
df_clb_ctry = df_clb_ctry.fillna(0) # If NaN, country league not represented at the world cup
df_clb_ctry = df_clb_ctry.T
plot_nat_leagues = figure(tools=TOOLS,
x_axis_label='World Cup Year',
toolbar_location=None
)
# take the top 11 countries and plot
for num, country in enumerate(df_clb_ctry.sum().sort_values(ascending=False).head(11).index):
plot_nat_leagues.scatter(df_clb_ctry.index, df_clb_ctry[country],legend=country,color=RdBu11[num],size=8, alpha=0.5)
plot_nat_leagues.legend.location = "top_left"
df_expats = df_league_data[df_league_data.nation!=df_league_data.nationality]
seasons = df_expats.season.unique()
min = int(seasons[0].split('-')[0])
max = int(seasons[-1].split('-')[0])
wc_year_slider = Slider(start=min, end=max, value=max, step=1, title="Soccer Season")
for season in seasons:
nationality_expats = df_expats.groupby('season').nationality.value_counts()[season].head(5)
nation_expats = df_expats.groupby('season').nation.value_counts()[season]
plot_expats = Bar(nationality_expats, toolbar_location=None)
plot_expats.x_range = FactorRange(factors=nationality_expats.index.tolist())
plot_dict = {"nations":plot_nat_leagues, "expats":plot_expats}
script, div = components(plot_dict)
return render_template('league.html', js_resources=js_resources, script=script, div=div)
def bar_plot_table(table, **kwargs):
"""Plot a tabular DataFrame with an index and multiple columns representing
categories using a Bokeh bar chart.
In addition to the keyword parameters accepted by bokeh.charts.Bar, this
function accepts the following additional keyword arguments:
number_of_categories: integer
The number of categories ranked by total value to use.
xaxis_formatter: TickFormatter
The formatter to use for x axis values.
yaxis_formatter: TickFormatter
The formatter to use for y axis values.
x_range: Range1d
A range to use for the X-axis.
y_range: Range1d
A range to use for the Y-axis.
"""
if kwargs.has_key('number_of_categories'):
revenue_table = analysis.select_top_n_columns(
table, kwargs['number_of_categories'])
else:
revenue_table = table
revenue_stacked = revenue_table.stack().reset_index()
revenue_stacked.columns = ['year', 'commodity_desc', 'value']
revenue_plot = Bar(
revenue_stacked,
label='year',
stack='commodity_desc',
values='value',
**_remove_custom_keys(kwargs)
)
if kwargs.has_key('x_range'):
revenue_plot.x_range = kwargs['x_range']
if kwargs.has_key('y_range'):
revenue_plot.y_range = kwargs['y_range']
else:
revenue_plot.y_range = Range1d(0, revenue_table.max().sum())
if kwargs.has_key('xaxis_formatter'):
revenue_plot._xaxis.formatter = kwargs['xaxis_formatter']
if kwargs.has_key('yaxis_formatter'):
revenue_plot._yaxis.formatter = kwargs['yaxis_formatter']
return revenue_plot
def generate_chart(year):
data = get_data(year)
plot_data = {
'country': data.index.tolist(),
'count': [float(i) for i in data.values.tolist()]
}
barchart = Bar(plot_data,
label='country',
values='count',
color="red",
xgrid=False,
ygrid=False,
plot_width=800,
plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + year + " Countries"
barchart.x_range = FactorRange(factors=data.index.tolist())
barchart._xaxis.axis_label = "Country"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Number of Teams"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Country", '@x'), ("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def bar_plot_table(table, **kwargs):
"""Plot a tabular DataFrame with an index and multiple columns representing
categories using a Bokeh bar chart.
In addition to the keyword parameters accepted by bokeh.charts.Bar, this
function accepts the following additional keyword arguments:
number_of_categories: integer
The number of categories ranked by total value to use.
xaxis_formatter: TickFormatter
The formatter to use for x axis values.
yaxis_formatter: TickFormatter
The formatter to use for y axis values.
x_range: Range1d
A range to use for the X-axis.
y_range: Range1d
A range to use for the Y-axis.
"""
if kwargs.has_key('number_of_categories'):
revenue_table = analysis.select_top_n_columns(
table, kwargs['number_of_categories'])
else:
revenue_table = table
revenue_stacked = revenue_table.stack().reset_index()
revenue_stacked.columns = ['year', 'commodity_desc', 'value']
revenue_plot = Bar(revenue_stacked,
label='year',
stack='commodity_desc',
values='value',
**_remove_custom_keys(kwargs))
if kwargs.has_key('x_range'):
revenue_plot.x_range = kwargs['x_range']
if kwargs.has_key('y_range'):
revenue_plot.y_range = kwargs['y_range']
else:
revenue_plot.y_range = Range1d(0, revenue_table.max().sum())
if kwargs.has_key('xaxis_formatter'):
revenue_plot._xaxis.formatter = kwargs['xaxis_formatter']
if kwargs.has_key('yaxis_formatter'):
revenue_plot._yaxis.formatter = kwargs['yaxis_formatter']
return revenue_plot
def generate_chart(year):
data = get_data(year)
plot_data = {'country': data.index.tolist(),
'count': [float(i) for i in data.values.tolist()]}
barchart = Bar(plot_data,
label='country',
values='count',
color="red",
xgrid=False, ygrid=False,
plot_width=800, plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + year + " Countries"
barchart.x_range = FactorRange(factors=data.index.tolist())
barchart._xaxis.axis_label = "Country"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Number of Teams"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Country", '@x'),
("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def fun():
plot_transfer_fee = figure(tools=TOOLS,
title='World Record Soccer Transfer Fee',
x_axis_label='Year',
y_axis_label=u"Fee (\u00A3)",
toolbar_location=None
)
plot_transfer_fee.line(df_tranfer['year'], df_tranfer['fee_pounds'],color=RdBu11[0],line_width=4)
month_map = {k: v for k,v in enumerate(calendar.month_abbr)}
df_mth = df_squads.birth_month.value_counts().sort_index()
df_mth.index = df_mth.index.map(lambda x: month_map[x])
plot_mth_of_birth = Bar(df_mth, toolbar_location=None)
plot_mth_of_birth.x_range = FactorRange(factors=df_mth.index.tolist())
plots = {"plt_trans_fee": plot_transfer_fee, "plt_mth_birth": plot_mth_of_birth}
script, div = components(plots)
return render_template('interesting.html', js_resources=js_resources, script=script, div=div)
# Display just the given columns in the tooltips
hover.tooltips = [(c, '@' + c) for c in cols]
hover.tooltips.append(('index', '$index'))
# Finally add/enable the tool
fig.add_tools(hover)
return fig
p2 = scatter_with_hover(test2, 'abs_third_less_adv_simple', 'abs_third_less_adv', marker='circle', fig_width=800, fig_height=800, cols=['bea_code', 'date', 'abs_third_less_adv_simple', 'abs_third_less_adv'], size=20)
p2.title ="Offsetting revisions"
p2.xaxis.axis_label="Absolute (third less adv)"
p2.yaxis.axis_label="Aggregate absolute (third less adv)"
p2.x_range= Range1d(0,test2['abs_third_less_adv_simple'].max()*1.1)
p2.y_range= Range1d(0,test2['abs_third_less_adv'].max()*1.1)
sum_rev = test2['abs_third_less_adv']
simple = test2['abs_third_less_adv_simple']
regression = np.polyfit(simple, sum_rev, 1)
r_x, r_y = zip(*((i, i*regression[0] + regression[1]) for i in range(len(test2['abs_third_less_adv']))))
p2.line(r_x, r_y, color="red", line_width=6)
output_file("regression.html")
show(p2)
second = elem[1]
list_of_words.append(first)
list_of_num.append(second)
return list_of_words, list_of_num
a = [(u'colada', 1.06465), (u'mmmmmmmmmmmmmmmmmmmmm', 0.85172), (u'brunched', 0.85172), (u'combinacion', 0.85172), (u'altar', 0.85172), (u'heckled', 0.70977), (u'guitarist', 0.70977), (u'benched', 0.60837), (u'suprisingly', 0.60837), (u'jombot', 0.60837), (u'cpt', 0.53232), (u'pubgrub', 0.53232), (u'prepaired', 0.53232), (u'bestpapersonaltrainercom', 0.53232), (u'touristic', 0.47318), (u'delucas', 0.47318), (u'stickler', 0.47318), (u'parfaits', 0.47318), (u'mochas', 0.45543), (u'bearclaw', 0.42586), (u'isntead', 0.42586), (u'portapottys', 0.42586), (u'centerpieces', 0.42586), (u'newcastle', 0.42586), (u'anf', 0.42586), (u'hollywoodlanes', 0.42586), (u'hideaway', 0.38715), (u'haiku', 0.38715), (u'muffaletta', 0.38715), (u'anazing', 0.38715),(u'balboa', 0.06347), (u'novak', 0.06342), (u'aamco', 0.06337), (u'faded', 0.06317), (u'jojo', 0.06312), (u'unos', 0.06309), (u'proves', 0.06309), (u'refurbish', 0.06309), (u'fleet', 0.06309), (u'koh', 0.06309)]
list_of_words, list_of_num = parse_elements(a)
model = {'TF-IDF': list_of_num, 'Words': list_of_words}
df1 = df(data = model, columns = ['TF-IDF','Words'])
graph1 = Bar(df1, 'Words', values = 'TF-IDF', title = "TF-IDF of top positive words", ylabel = 'TF-IDF scores', width = 800, height = 400, color = 'blue')
graph1.left[0].formatter.use_scientific= False
graph1.x_range = FactorRange(factors=df1['Words'].tolist())
c = [(u'hoofah', 4.2586), (u'ewwww', 4.2586), (u'disconnected', 0.70977), (u'precooked', 0.70977), (u'crimes', 0.60837), (u'greasiest', 0.60837), (u'redevelopment', 0.60837), (u'sloooooow', 0.53232), (u'yucky', 0.53232), (u'horrifying', 0.53232), (u'rancho', 0.47318), (u'crisco', 0.47318), (u'grabn', 0.42586), (u'gnocci', 0.42586), (u'strictest', 0.38715), (u'clout', 0.38715), (u'unenjoyable', 0.38715), (u't3', 0.36502), (u'terriable', 0.32758), (u'foe', 0.32758), (u'pararell', 0.32758), (u'thrifters', 0.32758), (u'society', 0.30419), (u'cremation', 0.28391), (u'fabricated', 0.28391), (u'yiengling', 0.28391), (u'delish', 0.27475), (u'uncover', 0.26616), (u'm8n', 0.26616), (u'pepparoni', 0.26616),(u'drops', 0.03097), (u'scheduling', 0.03097), (u'towed', 0.03089), (u'stark', 0.03086), (u'bjork', 0.03086), (u'arrogance', 0.03086), (u'maitre', 0.03086), (u'electronica', 0.03086), (u'basing', 0.03086), (u'caters', 0.03086)]
list_of_words2, list_of_num2 = parse_elements(c)
model2 = {'TF-IDF': list_of_num2, 'Words': list_of_words2}
df2 = df(data = model2, columns = ['TF-IDF','Words'])
graph2 = Bar(df2, 'Words', values = 'TF-IDF', title = "TF-IDF of top negative words", ylabel = 'TF-IDF scores', width = 800, height = 400, color = 'red')
graph2.left[0].formatter.use_scientific= False
graph2.x_range = FactorRange(factors=df2['Words'].tolist())
output_file("swag1.html")
total = vplot(graph1, graph2)
show(total)
def templateCoefRegression(c='c00001'):
data = pd.read_csv(DATA + 'LogisticRegressionCoef_' + c + '_true.csv', header=0)
data_month = data.ix[12:23, ]
data_month = data_month.reindex([16, 15, 19, 12, 20, 18, 17, 13, 23, 22, 21, 14])
data_empl = data.ix[0:11, ]
data_empl = data_empl.reindex([10, 0, 2, 3, 4, 5, 6, 7, 8, 9, 1, 11])
data_state = data.ix[24:74, ]
data_other = data.ix[75:76, ]
pMonth = Bar(data_month, 'coef', values='val', legend=False, tools='hover')
pMonth.x_range = FactorRange(factors=data_month['coef'].tolist())
pMonth.xaxis.axis_label = 'Coefficients'
pMonth.yaxis.axis_label = 'Value'
hover = pMonth.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Coef: ", "@coef"),
("Value: ", "@height")
]
# show(pMonth)
pEmpl = Bar(data_empl, 'coef', values='val', legend=False, tools='hover')
pEmpl.x_range = FactorRange(factors=data_empl['coef'].tolist())
pEmpl.xaxis.axis_label = 'Coefficients'
pEmpl.yaxis.axis_label = 'Value'
hover = pEmpl.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Coef: ", "@coef"),
("Value: ", "@height")
]
# show(pEmpl)
pState = Bar(data_state, 'coef', values='val', legend=False, tools='hover', width=1200)
# # pState.x_range = FactorRange(factors=dm['coef'].tolist())
pState.xaxis.axis_label = 'Coefficients'
pState.yaxis.axis_label = 'Value'
hover = pState.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Coef: ", "@coef"),
("Value: ", "@height")
]
# show(pState)
pOther = Bar(data_other, 'coef', values='val', legend=False, tools='hover')
pOther.xaxis.axis_label = 'Coefficients'
pOther.yaxis.axis_label = 'Value'
# pOther.x_range = FactorRange(factors=data_other['coef'].tolist())
hover = pOther.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Coef: ", "@coef"),
("Value: ", "@height")
]
# show(pOther)
scriptM, divM = components(pMonth)
scriptE, divE = components(pEmpl)
scriptS, divS = components(pState)
scriptO, divO = components(pOther)
dic_r = {'month': [scriptM, divM], 'empl': [scriptE, divE], 'state': [scriptS, divS], 'other': [scriptO, divO]}
return dic_r
from bokeh.charts import Bar, output_file, show, vplot
from numpy.random import rand
from pandas import DataFrame
N = 10
data = DataFrame({'A': rand(N), 'B': rand(N), 'C': rand(N)})
# Stack columns A,B,C and convert the multiindices to columns
sdata = data.stack().reset_index()
sdata.columns = ['labels', 'stack', 'values']
bar = Bar(sdata, values='values', label='labels', stack='stack', legend='top_right')
bar2 = Bar(sdata, values='values', label='labels', stack='stack', legend='top_right')
bar2.x_range = bar.x_range # Link the x axes
output_file("stacked_bar.html")
show(vplot(bar, bar2))
def predict():
def weighted_choice(choices):
total = sum(w for c, w in choices)
r = random.uniform(0, total)
upto = 0
for c, w in choices:
if upto + w >= r:
return c
upto += w
assert False, "Shouldn't get here"
df_fifa_latest_rank
z = np.polyfit(df_prob_win_fifa_rank.rank_diff, df_prob_win_fifa_rank.prob, 4)
p_win = np.poly1d(z)
# p_win(_some_rank)
z = np.polyfit(df_prob_draw_fifa_rank.rank_diff.values, df_prob_draw_fifa_rank.prob.values, 3)
p_draw = np.poly1d(z)
country_codes = {
"Albania": "ALB",
"Austria": "AUT",
"Belgium": "BEL",
"Croatia": "CRO",
"Czech Republic": "CZE",
"England": "ENG",
"France": "FRA",
"Germany": "GER",
"Hungary": "HUN",
"Iceland": "ISL",
"Italy": "ITA",
"Northern Ireland": "NIR",
"Poland": "POL",
"Portugal": "POR",
"Republic of Ireland": "IRL",
"Romania": "ROU",
"Russia": "RUS",
"Slovakia": "SVK",
"Spain": "ESP",
"Sweden": "SWE",
"Switzerland": "SUI",
"Turkey": "TUR",
"Ukraine": "UKR",
"Wales": "WAL",
}
country_from_codes = {
"ALB": "Albania",
"AUT": "Austria",
"BEL": "Belgium",
"CRO": "Croatia",
"CZE": "Czech Republic",
"ENG": "England",
"FRA": "France",
"GER": "Germany",
"HUN": "Hungary",
"ISL": "Iceland",
"ITA": "Italy",
"NIR": "Northern Ireland",
"POL": "Poland",
"POR": "Portugal",
"IRL": "Republic of Ireland",
"ROU": "Romania",
"RUS": "Russia",
"SVK": "Slovakia",
"ESP": "Spain",
"SWE": "Sweden",
"SUI": "Switzerland",
"TUR": "Turkey",
"UKR": "Ukraine",
"WAL": "Wales",
}
grp_A_matches = [
['2016-06-10', 'France', 'Romania'],
['2016-06-11', 'Albania', 'Switzerland'],
['2016-06-15', 'Romania', 'Switzerland'],
['2016-06-15', 'France', 'Albania'],
['2016-06-19', 'Switzerland', 'France'],
['2016-06-19', 'Romania', 'Albania'],
]
grp_B_matches = [
['2016-06-11', 'Wales', 'Slovakia'],
['2016-06-11', 'England', 'Russia'],
['2016-06-15', 'Russia', 'Slovakia'],
['2016-06-16', 'England', 'Wales'],
['2016-06-20', 'Slovakia', 'England'],
['2016-06-20', 'Russia', 'Wales'],
]
grp_C_matches = [
['2016-06-12', 'Poland', 'Northern Ireland'],
['2016-06-12', 'Germany', 'Ukraine'],
['2016-06-16', 'Ukraine', 'Northern Ireland'],
['2016-06-16', 'Germany', 'Poland'],
['2016-06-21', 'Northern Ireland', 'Germany'],
['2016-06-21', 'Ukraine', 'Poland'],
]
grp_D_matches = [
['2016-06-12', 'Turkey', 'Croatia'],
['2016-06-13', 'Spain', 'Czech Republic'],
['2016-06-17', 'Czech Republic', 'Croatia'],
['2016-06-17', 'Spain', 'Turkey'],
['2016-06-21', 'Croatia', 'Spain'],
['2016-06-21', 'Czech Republic', 'Turkey'],
]
grp_E_matches = [
['2016-06-13', 'Republic of Ireland', 'Sweden'],
['2016-06-13', 'Belgium', 'Italy'],
['2016-06-17', 'Italy', 'Sweden'],
['2016-06-18', 'Belgium', 'Republic of Ireland'],
['2016-06-22', 'Sweden', 'Belgium'],
['2016-06-22', 'Italy', 'Republic of Ireland'],
]
grp_F_matches = [
['2016-06-14', 'Austria', 'Hungary'],
['2016-06-14', 'Portugal', 'Iceland'],
['2016-06-18', 'Iceland', 'Hungary'],
['2016-06-18', 'Portugal', 'Austria'],
['2016-06-22', 'Iceland', 'Austria'],
['2016-06-22', 'Hungary', 'Portugal'],
]
grp_matchs = grp_A_matches + grp_B_matches + grp_C_matches + grp_D_matches + grp_E_matches + grp_F_matches
df_ctry_codes = df_fifa_latest_rank.country_code
probailities = []
plot_dict = {}
prediction_dict = {}
results = {}
for k, match in enumerate(grp_matchs,1):
rank_diff = df_fifa_latest_rank[df_ctry_codes==country_codes[match[1]]].ranking.values - df_fifa_latest_rank[df_ctry_codes==country_codes[match[2]]].ranking.values
probs = [p_win(rank_diff*-1)[0], p_draw(abs(rank_diff))[0], p_win(rank_diff)[0]]
probs = [float(i)/sum(probs) for i in probs]
probailities = [country_codes[match[1]], 'DRAW', country_codes[match[2]]] + probs
choices = [(country_codes[match[1]], probs[0]), ('DRAW', probs[1]), (country_codes[match[2]], probs[2])]
prediction = weighted_choice(choices)
if prediction=='DRAW':
prediction_dict["match_{0}".format(k)] = prediction
else:
prediction_dict["match_{0}".format(k)] = country_from_codes[prediction]
df = pd.DataFrame(probailities[3:], probailities[:3])
df.columns = ['probability']
plot_probs = Bar(df, toolbar_location=None, plot_width=200, plot_height=200, agg='mean')
plot_probs.x_range = FactorRange(factors=probailities[:3])
plot_dict["match_{0}".format(k)] = plot_probs
gp_A = dict.fromkeys(["France","Romania","Albania","Switzerland"], 0)
gp_B = dict.fromkeys(["England","Russia","Wales","Slovakia"], 0)
gp_C = dict.fromkeys(["Germany","Ukraine","Poland","Northern Ireland"], 0)
gp_D = dict.fromkeys(["Spain","Czech Republic","Turkey","Croatia"], 0)
gp_E = dict.fromkeys(["Belgium","Italy","Republic of Ireland","Sweden"], 0)
gp_F = dict.fromkeys(["Portugal","Iceland","Austria","Hungary"], 0)
for k, match in enumerate(grp_A_matches,1):
result = prediction_dict["match_{0}".format(k)]
if result == 'DRAW':
gp_A[match[1]] += 1
gp_A[match[2]] += 1
else:
gp_A[result] += 3
for k, match in enumerate(grp_B_matches,7):
result = prediction_dict["match_{0}".format(k)]
if result == 'DRAW':
gp_B[match[1]] += 1
gp_B[match[2]] += 1
else:
gp_B[result] += 3
for k, match in enumerate(grp_C_matches,13):
result = prediction_dict["match_{0}".format(k)]
if result == 'DRAW':
gp_C[match[1]] += 1
gp_C[match[2]] += 1
else:
gp_C[result] += 3
for k, match in enumerate(grp_D_matches,19):
result = prediction_dict["match_{0}".format(k)]
if result == 'DRAW':
gp_D[match[1]] += 1
gp_D[match[2]] += 1
else:
gp_D[result] += 3
for k, match in enumerate(grp_E_matches,25):
result = prediction_dict["match_{0}".format(k)]
if result == 'DRAW':
gp_E[match[1]] += 1
gp_E[match[2]] += 1
else:
gp_E[result] += 3
for k, match in enumerate(grp_F_matches,31):
result = prediction_dict["match_{0}".format(k)]
if result == 'DRAW':
gp_F[match[1]] += 1
gp_F[match[2]] += 1
else:
gp_F[result] += 3
all_groups = [gp_A,gp_B,gp_C,gp_D,gp_E,gp_F]
gp_labels = {1:"A",2:"B",3:"C",4:"D",5:"E",6:"F"}
table_html = {}
for k, group in enumerate(all_groups,1):
table_string = [' <table style="width:100%">']
group_standings = {'team':[], 'points':[]}
for j in xrange(1,len(group)+1):
table_string.append(' <tr>')
top = max(group, key=group.get)
points = group.get(top)
group_standings['team'].append(top)
group_standings['points'].append(points)
group.pop(top)
table_string.append(' <td>{0}</td>'.format(j))
table_string.append(' <td>{0}</td>'.format(top))
table_string.append(' <td>{0}</td>'.format(points))
table_string.append(' </tr>')
table_string.append(' </table>')
table_html["gp_{0}".format(gp_labels[k])] = '\n'.join(table_string)
script, div = components(plot_dict)
return render_template('euro.html', js_resources=js_resources, script=script, div=div, pred=prediction_dict, table=table_html)
