def update_market_data(attr, old, new):
"""update market data"""
label.text = "Loading Market Data..."
source_ind.data["color"] = ["red"]
(nodes, g_by_fuel, demand, inj, f_dc, t_first, t_last,
lines, dclines, lodf_matrix, n_0_flows, n_1_flows) = init_market_data(select_market_db.value)
## Update Data Sources
source_nodes_data = ColumnDataSource.from_df(nodes)
source_g_fuel_data.data = ColumnDataSource.from_df(g_by_fuel)
source_d_data.data = ColumnDataSource.from_df(demand)
source_inj_data.data = ColumnDataSource.from_df(inj)
source_f_dc_data.data = ColumnDataSource.from_df(f_dc)
source_n_0_flows_data.data = ColumnDataSource.from_df(n_0_flows)
source_n_1_flows_data.data = ColumnDataSource.from_df(n_1_flows)
source_lines_data.data = ColumnDataSource.from_df(lines)
source_dclines_data.data = ColumnDataSource.from_df(dclines)
source_lodf_data.data = ColumnDataSource.from_df(lodf_matrix)
### Update Data Sources for the plots
nodes_dict = create_nodes_source(nodes)
source_nodes.data = nodes_dict
line_dict = create_line_source(lines, nodes)
source_lines.data = line_dict
dcline_dict = create_dc_line_source(dclines, nodes)
source_dclines.data = dcline_dict
slider.start = t_first
slider.end = t_last
slider.value = t_first
label.text = "Data is Loaded!"
source_ind.data["color"] = ["green"]
def update_market_data(attr, old, new):
"""update market data"""
(nodes, g_by_fuel, demand, inj, f_dc, t_first, t_last, lines, dclines,
lodf_matrix, n_0_flows,
n_1_flows) = init_market_data(select_market_db.value)
## Update Data Sources
# source_nodes_data = ColumnDataSource.from_df(nodes)
source_g_fuel_data.data = ColumnDataSource.from_df(g_by_fuel)
source_d_data.data = ColumnDataSource.from_df(demand)
source_inj_data.data = ColumnDataSource.from_df(inj)
source_f_dc_data.data = ColumnDataSource.from_df(f_dc)
source_n_0_flows_data.data = ColumnDataSource.from_df(n_0_flows)
source_n_1_flows_data.data = ColumnDataSource.from_df(n_1_flows)
source_lines_data.data = ColumnDataSource.from_df(lines)
source_dclines_data.data = ColumnDataSource.from_df(dclines)
source_lodf_data.data = ColumnDataSource.from_df(lodf_matrix)
### Update Data Sources for the plots
nodes_dict = create_nodes_source(nodes)
source_nodes.data = nodes_dict
line_dict = create_line_source(lines, nodes)
source_lines.data = line_dict
dcline_dict = create_dc_line_source(dclines, nodes)
source_dclines.data = dcline_dict
slider.start = t_first
slider.end = t_last
slider.value = t_first
def calculate_mag_dec(attr, old, new, start_ie=None):
''' Is being call from the callback for the experiment chooser
loads selected experiment visualize in plot p1 and p2
gets experiment number from the slider
writes source_fid.data from the fid from the polymer object
writes source_mag_dec.data from the dataframe
'''
ie = experiment_slider.value #get expermient number from the slider
logger.debug('calculate mag_dec for ie={}'.format(ie))
fid, df = get_data_frames(ie)
source_fid.data=ColumnDataSource.from_df(fid) #convert fid to bokeh format
source_mag_dec.data = ColumnDataSource.from_df(df)
def update_market_data(attr, old, new):
"""update market data"""
label.text = "Loading..."
source_ind.data["color"] = ["red"]
g_by_fuel, demand, inj, f_dc, t_first, t_last = init_market_result(select_market_db.value)
source_g_fuel.data = ColumnDataSource.from_df(g_by_fuel)
source_d.data = ColumnDataSource.from_df(demand)
source_inj.data = ColumnDataSource.from_df(inj)
source_dc.data = ColumnDataSource.from_df(f_dc)
slider.start = t_first
slider.end = t_last
slider.value = t_first
label.text = "Data is Loaded!"
source_ind.data["color"] = ["green"]
def on_view_cluster(self, attr, old, new):
self.current_segment = new
self.current_segment_clients = (
self.df_rfm[self.cfc] == self.current_segment).sum()
cluster_column_kind = self.df_rfm[self.cfc].dtype.kind
if cluster_column_kind == 'i':
sel_val = int(self.current_segment)
elif cluster_column_kind == 'f':
sel_val = float(self.current_segment)
else:
sel_val = self.current_segment
self._logger("Viewing cluster selection {} (kind:{}) prep...".format(
sel_val, cluster_column_kind))
temp_df = self.df_rfm[self.df_rfm[self.cfc] == sel_val].head(
self.nr_shown_records)
self._logger("Viewed table head/tail:\n{}\n{}".format(
temp_df.head(3), temp_df.tail(3)))
self.cds_select.data = ColumnDataSource.from_df(temp_df)
self.update_texts()
if self.FULL_DEBUG:
self._logger("Done view cluster prep.")
return
def on_update_table(self, attr, old, new):
self.nr_shown_records = new
self._logger("Refreshing data for table view: {}".format(
self.df_rfm.head(self.nr_shown_records).shape))
self.cds_select.data = ColumnDataSource.from_df(
self.df_rfm.head(self.nr_shown_records))
return
def fit_mag_decay_all(polymer, par_df):
logger.debug('fitting all mag decay')
p3 = figure(plot_width=400, plot_height=400,
title='normalized phi vs normalized tau', webgl = True,
y_axis_type = 'log',
x_axis_type = 'linear')
nr_experiments = polymer.get_number_of_experiments()
r1=np.zeros(nr_experiments)
MANY_COLORS = 0
p3_line_glyph=[]
for i in range(1, nr_experiments):
try:
par=polymer.getparameter(i)
fid=polymer.getfid(i)
tau= polymer.get_tau_axis(i) # tau axis could be parameter created right after import
try:
startpoint=polymer.getparvalue(i,'fid_range')[0]
endpoint=polymer.getparvalue(i,'fid_range')[1]
except:
startpoint=int(0.05*polymer.getparvalue(i,'BS'))
endpoint = int(0.1*polymer.getparvalue(i,'BS'))
logger.warning('using preset range (from {startpoint} to {endpoint}) in experiment {i}'.format(**locals()))
polymer.addparameter(i,'fid_range',(startpoint,endpoint))
phi = get_mag_amplitude(fid, startpoint, endpoint,
polymer.getparameter(i)['NBLK'], polymer.getparameter(i)['BS'])
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized']=(df['phi'] - df['phi'].iloc[0] ) / (df['phi'].iloc[-1] - df['phi'].iloc[1] )
polymer.addparameter(i,'df_magnetization',df)
# initial values for the fit
p0 = [1, 2 * polymer.getparvalue(i,'T1MX'), 0]
df, popt = magnetization_fit(df,p0, fit_option=2)
polymer.addparameter(i,'amp',popt[0])
polymer.addparameter(i,'r1',popt[1])
polymer.addparameter(i,'noise',popt[2])
r1[i] = popt[1]
tau = popt[1]*df.tau
phi = popt[0]**-1*(df.phi_normalized - popt[2])
p3_df=pd.DataFrame(data=np.c_[ tau, phi ], columns=['tau', 'phi'])
source_p3=ColumnDataSource(data=ColumnDataSource.from_df(p3_df))
p3_line_glyph.append(p3.line('tau', 'phi', source=source_p3))
MANY_COLORS+=1
except KeyError:
logger.warning('no relaxation experiment found')
polymer.addparameter(i,'amp',float('NaN'))
polymer.addparameter(i,'r1',float('NaN'))
polymer.addparameter(i,'noise',float('NaN'))
COLORS=viridis(MANY_COLORS)
for ic in range(MANY_COLORS):
p3_line_glyph[ic].glyph.line_color=COLORS[ic]
par_df['r1']=r1
return p3
def candle_stix(dataframe):
'''
This function takes in dataframes and makes a candlesticks plot using bokeh
Parameters
----------
dataframe Stock dataframe
This frame has elements: Open, Volume, Close, High, Low, Symbol and Date
'''
# Develop Aspects and Calculations
mids = (dataframe["Open"] + dataframe["Close"])/2
spans = abs(dataframe.Close-dataframe.Open)
inc = dataframe.Close > dataframe.Open
dec = dataframe.Open > dataframe.Close
w = 12*60*60*1000 #width about a half day in width
# Define Hover tool tips
Gain = ColumnDataSource(ColumnDataSource.from_df(dataframe[inc]))
Loss = ColumnDataSource(ColumnDataSource.from_df(dataframe[dec]))
hover = HoverTool(
tooltips=[
("Price","$y"),
("High","@High"),
("Low","@Low"),
("Open","@Open"),
("Close","@Close"),
("Volume(M)","@Volume")
]
)
# Build Plot
output_file("candlestix.html", title="Candlestix")
TOOLS = "pan,wheel_zoom,box_zoom"
p = figure(x_axis_type="datetime",tools=[TOOLS,hover],plot_width=800,toolbar_location="left")
p.segment(dataframe.Time, dataframe.Low ,dataframe.Time, dataframe.High , color="black")
p.rect(dataframe.Time[inc], mids[inc], w, spans[inc], source=Gain, fill_color="white", line_color="black")
p.rect(dataframe.Time[dec], mids[dec], w, spans[dec], source=Loss, fill_color="black", line_color="black")
p.title = str(str(dataframe["Symbol"][1]))
show(p)
def create_scatter(df_orig, met, xaxis, **kwargs):
"""
Creates and returns a scatter plot as a bokeh figure object with circle glyphs
"""
if 'cato' in kwargs:
cato = kwargs['cato']
else:
cato = False
if 'col' in kwargs:
col = kwargs['colors']
else:
col = Set1[9]
if 'metric_attrib' in kwargs:
metric_attrib = kwargs['metric_attrib']
else:
metric_attrib = False
df = df_orig
p = scatter_template(df, met, xaxis, cato)
#Finds the unique list of the category and counts them
if cato:
uniq_cat = df[cato].unique()
cat_cnt = len(uniq_cat)
#Groups by the category and assigns each group to a ColumnDataSource
indv_cat = df.groupby(cato)
source = []
for item in indv_cat:
sub_source = ColumnDataSource(ColumnDataSource.from_df(item[1]))
source.append(sub_source)
i = 0
while i < cat_cnt:
p.circle(xaxis,
met,
size=10,
color=col[i % len(col)],
source=source[i])
i += 1
else:
source = ColumnDataSource(df)
p.circle(xaxis, met, size=10, source=source)
if metric_attrib:
p = create_limits(p, df, xaxis, metric_attrib)
return p
def _dict_from_da(da, unstack=None):
""""""
df = da.drop_vars([c for c in da.coords
if c not in da.dims]).to_dataframe()
if unstack is not None:
df = df.unstack(unstack)
# convert column multi-index levels to str
if isinstance(df.columns, pd.MultiIndex):
df.columns = df.columns.set_levels(
[l.astype(str) for l in df.columns.levels])
return ColumnDataSource.from_df(df)
def callback_load_more_data(attr,old,new):
''' callback for loading of data '''
# TODO: implement
logger.debug('callback for loading of data ')
logger.error('Not implemented!')
path=pathbox.value.strip()
file=filebox.value.strip()
if file=="*.sdf":
logger.info('callback for loading data. filename: {}'.format(file))
allsdf=filter(lambda x: x.endswith('.sdf'),os.listdir(path))
for f in allsdf:
sdf_list.append(sdf.StelarDataFile(f,path))
else:
sdf_list.append(sdf.StelarDataFile(file,path))
filenames=[x.file() for x in sdf_list]
filenames_df=pd.DataFrame(data=filenames,columns=['file'])
table_source.data=ColumnDataSource.from_df(filenames_df)
def make_figure3():
plot = figure(tools=TOOLS,
width=750,
height=450,
title='the Steep Climb',
x_axis_label='Percentage of Total Books',
y_axis_label='Percentage of Total Ratings')
plot.line(df_sorted['perc_books'],
df_sorted['cum_perc_ratings'],
color='Orange')
source = ColumnDataSource(ColumnDataSource.from_df(df_sorted))
hover = plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('Percentage of Total Books', '@perc_books'),
('Percentage of Total Ratings',
'@cum_perc_ratings')])
return plot
def make_figure1():
plot = figure(tools=TOOLS,
width=750,
height=450,
title='the Long Tail',
x_axis_label='Number of Books',
y_axis_label='Number of Ratings',
x_range=(-50, 1080))
plot.patch(
pd.Series([0]).append(df_sorted['cum_sum'].append(pd.Series([0]))),
pd.Series([0]).append(df_sorted['no_ratings'].append(pd.Series([0]))),
color="#99d8c9")
plot.line(df_sorted['cum_sum'], df_sorted['no_ratings'], color='Orange')
source = ColumnDataSource(ColumnDataSource.from_df(df_sorted))
hover = plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('Number of books', '@cum_sum'),
('Ratings', '@no_ratings')])
return plot
def _heatmap_summary(pvals, coefs, plot_width=1200, plot_height=400):
""" Plots heatmap of coefficients colored by pvalues
Parameters
----------
pvals : pd.DataFrame
Table of pvalues where rows are balances and columns are
covariates.
coefs : pd.DataFrame
Table of coefficients where rows are balances and columns are
covariates.
plot_width : int, optional
Width of plot.
plot_height : int, optional
Height of plot.
Returns
-------
bokeh.charts.Heatmap
Heatmap summarizing the regression statistics.
"""
c = coefs.reset_index()
c = c.rename(columns={'index': 'balance'})
# fix alpha in fdr to account for the number of covariates
def fdr(x):
return multipletests(x, method='fdr_bh',
alpha=0.05 / pvals.shape[1])[1]
cpvals = pvals.apply(fdr, axis=0)
# log scale for coloring
log_p = -np.log10(cpvals+1e-200)
log_p = log_p.reset_index()
log_p = log_p.rename(columns={'index': 'balance'})
p = pvals.reset_index()
p = p.rename(columns={'index': 'balance'})
cp = cpvals.reset_index()
cp = cp.rename(columns={'index': 'balance'})
cm = pd.melt(c, id_vars='balance', var_name='Covariate',
value_name='Coefficient')
pm = pd.melt(p, id_vars='balance', var_name='Covariate',
value_name='Pvalue')
cpm = pd.melt(cp, id_vars='balance', var_name='Covariate',
value_name='Corrected_Pvalue')
logpm = pd.melt(log_p, id_vars='balance', var_name='Covariate',
value_name='log_Pvalue')
m = pd.merge(cm, pm,
left_on=['balance', 'Covariate'],
right_on=['balance', 'Covariate'])
m = pd.merge(m, logpm,
left_on=['balance', 'Covariate'],
right_on=['balance', 'Covariate'])
m = pd.merge(m, cpm,
left_on=['balance', 'Covariate'],
right_on=['balance', 'Covariate'])
hover = HoverTool(
tooltips=[("Pvalue", "@Pvalue"),
("Corrected Pvalue", "@Corrected_Pvalue"),
("Coefficient", "@Coefficient")]
)
N, _min, _max = len(palette), m.log_Pvalue.min(), m.log_Pvalue.max()
X = pd.Series(np.arange(len(pvals.index)), index=pvals.index)
Y = pd.Series(np.arange(len(pvals.columns)), index=pvals.columns)
m['X'] = [X.loc[i] for i in m.balance]
m['Y'] = [Y.loc[i] for i in m.Covariate]
# fill in nans with zero. Sometimes the pvalue calculation fails.
m = m.fillna(0)
for i in m.index:
x = m.loc[i, 'log_Pvalue']
ind = int(np.floor((x - _min) / (_max - _min) * (N - 1)))
m.loc[i, 'color'] = palette[ind]
source = ColumnDataSource(ColumnDataSource.from_df(m))
hm = figure(title='Regression Coefficients Summary',
plot_width=1200, plot_height=400,
tools=[hover, PanTool(), BoxZoomTool(),
WheelZoomTool(), ResetTool(),
SaveTool()])
hm.rect(x='X', y='Y', width=1, height=1,
fill_color='color', line_color="white", source=source)
Xlabels = pd.Series(pvals.index, index=np.arange(len(pvals.index)))
Ylabels = pd.Series(pvals.columns, index=np.arange(len(pvals.columns)), )
hm.xaxis[0].ticker = FixedTicker(ticks=Xlabels.index)
hm.xaxis.formatter = FuncTickFormatter(code="""
var labels = %s;
return labels[tick];
""" % Xlabels.to_dict())
hm.yaxis[0].ticker = FixedTicker(ticks=Ylabels.index)
hm.yaxis.formatter = FuncTickFormatter(code="""
var labels = %s;
return labels[tick];
""" % Ylabels.to_dict())
return hm
def candle(ticker, days=365, zoom=20):
PATH = os.path.join(os.getcwd() + './Stocks/' + ticker + ".csv")
df = pd.read_csv(PATH).iloc[-days:]
df["Date"] = pd.to_datetime(df["Date"])
# Setting Axis
max_high = df.iloc[-zoom:].High.max()
min_low = df.iloc[-zoom:].Low.min()
inc = df.Close > df.Open
dec = df.Open > df.Close
shift = df["18EMA"] < df["50EMA"]
long_pivot = (df.Low < df.Low.shift(1)) & (df.Low < df.Low.shift(-1)) & (
df.High.shift(-1) > df.High.shift(1)) & (df.High.shift(-2) >
df.High.shift(1))
long_reversal = (df.Open > df["18EMA"]) & (df.Close > df["18EMA"]) & (
df.Low < df["18EMA"]) & (df.Low < df.Low.shift(1))
w = 24 * 60 * 60 * 1000 # full day in ms
sourceInc = ColumnDataSource.from_df(df.loc[inc])
sourceDec = ColumnDataSource.from_df(df.loc[dec])
hover = HoverTool(
names=["dot"],
tooltips=[
("open", "@Open"),
("close", "@Close"),
("high", "@High"),
("low", "@Low"),
("18EMA", "@18EMA"),
("50EMA", "@50EMA"),
("100EMA", "@100EMA"),
("200EMA", "@200EMA"),
],
)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
p = figure(
title=ticker,
x_axis_type="datetime",
tools=[TOOLS, hover],
plot_width=475,
plot_height=475,
background_fill_color="white",
y_range=(min_low - 5, max_high + 5),
x_range=(df.iloc[-zoom].Date, df.iloc[-1].Date),
)
p.xaxis.major_label_overrides = {
i: date.strftime("%b %d")
for i, date in enumerate(pd.to_datetime(df["Date"]))
}
p.xaxis.major_label_orientation = pi / 4
p.grid.grid_line_alpha = 0.3
p.segment(df.Date, df.High, df.Date, df.Low, color="black")
# p.vbar
p.vbar(
df.Date[inc],
w,
df.Open[inc],
df.Close[inc],
fill_color="green",
line_color="black",
)
p.vbar(
df.Date[dec],
w,
df.Open[dec],
df.Close[dec],
fill_color="red",
line_color="black",
)
p.vbar(
df.Date[shift],
w,
df.Open[shift],
df.Close[shift],
fill_color="purple",
line_color="black",
)
p.vbar(
df.Date[long_pivot],
w,
df.Open[long_pivot],
df.Close[long_pivot],
fill_color="orange",
line_color="black",
)
p.vbar(
df.Date[long_reversal],
w,
df.Open[long_reversal],
df.Close[long_reversal],
fill_color="blue",
line_color="black",
)
p.line(df.Date, df["18EMA"], line_width=2, line_color="red")
p.line(df.Date, df["50EMA"], line_width=2, line_color="green")
p.line(df.Date, df["100EMA"], line_width=2, line_color="blue")
p.line(df.Date, df["200EMA"], line_width=2, line_color="purple")
p.circle("Date",
"Close",
name="dot",
size=7,
source=sourceDec,
color="grey")
p.circle("Date",
"Close",
name="dot",
size=7,
source=sourceInc,
color="grey")
return p
def plot():
if request.method == 'POST':
symbol = request.form['symbol']
# Get Stock DataFrame
# msft = yf.Ticker("MSFT")
msft = yf.Ticker(symbol)
hist = msft.history(period='max')
# Define constants
W_PLOT = 1000
H_PLOT = 400
TOOLS = 'pan,wheel_zoom,hover,reset'
VBAR_WIDTH = 0.2
RED = Category20[7][6]
GREEN = Category20[5][4]
BLUE = Category20[3][0]
BLUE_LIGHT = Category20[3][1]
ORANGE = Category20[3][2]
PURPLE = Category20[9][8]
BROWN = Category20[11][10]
def get_symbol_df(symbol=None):
df = pd.DataFrame(hist)[-50:]
df.reset_index(inplace=True)
df["Date"] = pd.to_datetime(df["Date"])
return df
def plot_stock_price(stock):
p = figure(plot_width=W_PLOT,
plot_height=H_PLOT,
tools=TOOLS,
title="Stock price",
toolbar_location='above')
inc = stock.data['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {
i + int(stock.data['index'][0]): date.strftime('%b %d')
for i, date in enumerate(pd.to_datetime(stock.data["Date"]))
}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
p.segment(x0='index',
x1='index',
y0='Low',
y1='High',
color=RED,
source=stock,
view=view_inc)
p.segment(x0='index',
x1='index',
y0='Low',
y1='High',
color=GREEN,
source=stock,
view=view_dec)
p.vbar(x='index',
width=VBAR_WIDTH,
top='Open',
bottom='Close',
fill_color=BLUE,
line_color=BLUE,
source=stock,
view=view_inc,
name="price")
p.vbar(x='index',
width=VBAR_WIDTH,
top='Open',
bottom='Close',
fill_color=RED,
line_color=RED,
source=stock,
view=view_dec,
name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
p.x_range.range_padding = 0.05
p.xaxis.ticker.desired_num_ticks = 40
p.xaxis.major_label_orientation = 3.14 / 4
# Select specific tool for the plot
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),
("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters = {"Date": 'datetime'}
return p
stock = ColumnDataSource(
data=dict(Date=[], Open=[], Close=[], High=[], Low=[], index=[]))
#stock = AjaxDataSource(data=dict(Date=[], Open=[], Close=[], High=[], Low=[],index=[]),data_url='http://127.0.0.1:5000/plot',polling_interval=1000,mode='append')
#symbol = 'msft'
df = get_symbol_df(symbol)
stock.data = stock.from_df(df)
elements = list()
# update_plot()
p_stock = plot_stock_price(stock)
elements.append(p_stock)
curdoc().add_root(column(elements))
curdoc().title = 'Bokeh stocks historical prices'
#show(p_stock)
script, div = components(p_stock)
kwargs = {'script': script, 'div': div}
#kwargs['title'] = 'bokeh-with-flask'
return render_template('index.html', **kwargs)
#return redirect(url_for('index', **kwargs))
#return kwargs
#return json.dumps(json_item(p_stock,"myplot"))
#return redirect(url_for('index'))
return "OK"
def create_figure():
fig_df = df
fig_df = fig_df[-int(text.value):]
#Reverse lookup original column name if name if part of the conversion dict.
#This will not be needed in the general Bokeh_Select
if metric.value in conv.values():
metric_name = [k for (k, v) in conv.iteritems()
if v == metric.value][0]
else:
metric_name = metric.value
#Gets all the metric attributes from the xml file
metric_attrib = param_get(metric_name)
cycle_filt = lambda x: x['Cycles Completed'] >= int(metric_attrib['cycles']
)
fig_df = fig_df.loc[cycle_filt]
#Finds the unique list of the category and counts them
uniq_cat = list(set(fig_df[cat.value].dropna()))
color_cnt = len(uniq_cat)
#Groups by the category and assigns each group to a ColumnDataSource
indv_cat = fig_df.groupby(cat.value)
main_source = []
for item in indv_cat:
source = ColumnDataSource(ColumnDataSource.from_df(item[1]))
main_source.append(source)
#Creates the tooltip and assign plot parameters
hover = HoverTool(tooltips=[(
'Serial Number',
'@SerialNumber'), (cat.value, '@' +
cat.value), (metric.value, '@' +
metric.value), ('Date',
'@StartDateStr')])
p = figure(plot_height=625,
plot_width=950,
x_axis_type='datetime',
tools=['pan, wheel_zoom', 'reset', 'save', hover],
active_scroll='wheel_zoom',
title="{0} by {1}".format(metric.value, cat.value))
#Creates the specification and control limits
time_range = [min(fig_df['StartDate']), max(fig_df['StartDate'])]
if metric_attrib['usl'] != "NA":
usl = metric_attrib['usl']
p.line(time_range,
2 * [usl],
color='red',
legend='Upper Specification: ' + usl)
if metric_attrib['lsl'] != "NA":
lsl = metric_attrib['lsl']
p.line(time_range,
2 * [lsl],
color='red',
legend='Lower Specification: ' + lsl)
upl = metric_attrib['upl']
lpl = metric_attrib['lpl']
p.line(time_range,
2 * [upl],
color='orange',
legend='Upper Process Limit: ' + upl)
p.line(time_range,
2 * [lpl],
color='orange',
legend='Lower Process Limit: ' + lpl)
#Plot from each ColumnDataSource with a different color
i = 0
while i < color_cnt:
p.circle('StartDate',
metric.value,
size=10,
color=color_set[i % len(color_set)],
source=main_source[i])
i += 1
return p
def _heatmap_summary(pvals, coefs, plot_width=1200, plot_height=400):
""" Plots heatmap of coefficients colored by pvalues
Parameters
----------
pvals : pd.DataFrame
Table of pvalues where rows are balances and columns are
covariates.
coefs : pd.DataFrame
Table of coefficients where rows are balances and columns are
covariates.
plot_width : int, optional
Width of plot.
plot_height : int, optional
Height of plot.
Returns
-------
bokeh.charts.Heatmap
Heatmap summarizing the regression statistics.
"""
c = coefs.reset_index()
c = c.rename(columns={'index': 'balance'})
# fix alpha in fdr to account for the number of covariates
def fdr(x):
return multipletests(x, method='fdr_bh',
alpha=0.05 / pvals.shape[1])[1]
cpvals = pvals.apply(fdr, axis=0)
# log scale for coloring
log_p = -np.log10(cpvals + 1e-200)
log_p = log_p.reset_index()
log_p = log_p.rename(columns={'index': 'balance'})
p = pvals.reset_index()
p = p.rename(columns={'index': 'balance'})
cp = cpvals.reset_index()
cp = cp.rename(columns={'index': 'balance'})
cm = pd.melt(c,
id_vars='balance',
var_name='Covariate',
value_name='Coefficient')
pm = pd.melt(p,
id_vars='balance',
var_name='Covariate',
value_name='Pvalue')
cpm = pd.melt(cp,
id_vars='balance',
var_name='Covariate',
value_name='Corrected_Pvalue')
logpm = pd.melt(log_p,
id_vars='balance',
var_name='Covariate',
value_name='log_Pvalue')
m = pd.merge(cm,
pm,
left_on=['balance', 'Covariate'],
right_on=['balance', 'Covariate'])
m = pd.merge(m,
logpm,
left_on=['balance', 'Covariate'],
right_on=['balance', 'Covariate'])
m = pd.merge(m,
cpm,
left_on=['balance', 'Covariate'],
right_on=['balance', 'Covariate'])
hover = HoverTool(tooltips=[(
"Pvalue",
"@Pvalue"), ("Corrected Pvalue",
"@Corrected_Pvalue"), ("Coefficient", "@Coefficient")])
N, _min, _max = len(palette), m.log_Pvalue.min(), m.log_Pvalue.max()
X = pd.Series(np.arange(len(pvals.index)), index=pvals.index)
Y = pd.Series(np.arange(len(pvals.columns)), index=pvals.columns)
m['X'] = [X.loc[i] for i in m.balance]
m['Y'] = [Y.loc[i] for i in m.Covariate]
# fill in nans with zero. Sometimes the pvalue calculation fails.
m = m.fillna(0)
for i in m.index:
x = m.loc[i, 'log_Pvalue']
ind = int(np.floor((x - _min) / (_max - _min) * (N - 1)))
m.loc[i, 'color'] = palette[ind]
source = ColumnDataSource(ColumnDataSource.from_df(m))
hm = figure(title='Regression Coefficients Summary',
plot_width=1200,
plot_height=400,
tools=[
hover,
PanTool(),
BoxZoomTool(),
WheelZoomTool(),
ResetTool(),
SaveTool()
])
hm.rect(x='X',
y='Y',
width=1,
height=1,
fill_color='color',
line_color="white",
source=source)
Xlabels = pd.Series(pvals.index, index=np.arange(len(pvals.index)))
Ylabels = pd.Series(
pvals.columns,
index=np.arange(len(pvals.columns)),
)
hm.xaxis[0].ticker = FixedTicker(ticks=Xlabels.index)
hm.xaxis.formatter = FuncTickFormatter(code="""
var labels = %s;
return labels[tick];
""" % Xlabels.to_dict())
hm.yaxis[0].ticker = FixedTicker(ticks=Ylabels.index)
hm.yaxis.formatter = FuncTickFormatter(code="""
var labels = %s;
return labels[tick];
""" % Ylabels.to_dict())
return hm
elif symbol == 'EBAY':
lengthOfTime = '1y'
break
else:
print("Oops you typed it in wrong, try again... \n")
df = get_symbol_df(symbol, lengthOfTime)
stock = ColumnDataSource(data=dict(date=[],
Open=[],
Close=[],
high=[],
low=[],
volume=[],
volHeight=[],
index=[]))
stock.data = stock.from_df(df)
p_stock = plot_stock_price(stock, symbol, lengthOfTime)
while True:
fifty = input("An orange 50 period moving average? Y/N: ").upper()
if fifty == 'Y':
plot_fifty_ma(df, p_stock)
break
elif fifty == 'N':
break
else:
print("Oops you typed it in wrong, try again... \n")
while True:
bollinger = input(
def modify_doc(doc):
""" Contains the application, including all callbacks
TODO: could the callbacks be outsourced?
:param doc:
:type doc:
"""
logger.debug('modify_doc has been called')
def get_data_frames(ie,):
""" Called one time initially, and then every time the experiment number is changed by the slider
:param ie: experiment number
:type ie: int
:returns: dataframe from stella datafile and dataframe with tau and phi and fitted values
:rtype: list of 2 pandas dataframes
"""
logger.debug('get_dataframe with ie={}'.format(ie))
fid = polymer.getfid(ie) #read FID or series of FIDs for selected experiment
try:
tau = polymer.get_tau_axis(ie) #numpy array containing the taus for experiment ie
try:
startpoint=fid_slider.range[0] #lower integration bound
endpoint = fid_slider.range[1] #upper integration bound
except NameError:
# fid_slider not initialized for first plot. Use default values:
startpoint=int(0.05*polymer.getparvalue(ie,'BS'))
endpoint = int(0.1*polymer.getparvalue(ie,'BS'))
logger.debug('fid_slider not initialized for first plot. Use default values {} and {}.'.format(startpoint, endpoint))
polymer.addparameter(ie,'fid_range',(startpoint,endpoint)) #add integration range to parameters to make it accesible
phi = get_mag_amplitude(fid, startpoint, endpoint,
polymer.getparvalue(ie,'NBLK'),
polymer.getparvalue(ie,'BS')) # list containing averaged fid amplitudes (which is proportional to a magnetization phi)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi']) # DataFrames are nice
df['phi_normalized'] = (df['phi'] - df['phi'].iloc[0] ) / (df['phi'].iloc[-1] - df['phi'].iloc[1] ) #Normalize magnetization,
#Note: in the normalized magnetization the magnetization build-up curves and magnetization decay curves look alike
#Note: this makes it easier for fitting as everything looks like 1 * exp(-R/time) in first order
polymer.addparameter(ie,'df_magnetization',df) # make the magnetization dataframe accesible as parameter
fit_option = 2 #mono exponential, 3 parameter fit
p0=[1.0, polymer.getparvalue(ie,'T1MX')**-1*2, 0] #choose startparameters for fitting an exponential decay
df, popt = magnetization_fit(df, p0, fit_option) # use leastsq to find optimal parameters
polymer.addparameter(ie,'popt(mono_exp)',popt) # add fitting parameters for later access
logger.info('fitfunction(t) = {} * exp(- {} * t) + {}'.format(*popt)) # print the fitting parameters to console (for convenience)
except KeyError:
logger.warning('no relaxation experiment found')
tau=np.zeros(1)
phi=np.zeros(1)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized'] = np.zeros(1)
df['fit_phi'] = np.zeros(1)
return fid, df
def calculate_mag_dec(attr, old, new, start_ie=None):
''' Is being call from the callback for the experiment chooser
loads selected experiment visualize in plot p1 and p2
gets experiment number from the slider
writes source_fid.data from the fid from the polymer object
writes source_mag_dec.data from the dataframe
'''
ie = experiment_slider.value #get expermient number from the slider
logger.debug('calculate mag_dec for ie={}'.format(ie))
fid, df = get_data_frames(ie)
source_fid.data=ColumnDataSource.from_df(fid) #convert fid to bokeh format
source_mag_dec.data = ColumnDataSource.from_df(df)
def plot_par():
''' Creates plot for the parameters
Called with every update from the callback'''
logger.debug('creating plot for the parameters')
# read data due to selection of select_x/y
xs = par_df[select_xaxis.value ].values
ys = par_df[select_yaxis.value].values
# read titles due to name of select_x/y
x_title = select_xaxis.value.title()
y_title = select_yaxis.value.title()
# remark: many attributes in a bokeh plot cannot be modified after initialization
# for example p4.x_axis_type='datetime' does not work. keywords are a
# workaround to pass all optional arguments initially
# set optional keyword arguments, kw, for figure()
kw = dict() #initialize
if select_xaxis.value in discrete:
kw['x_range'] = sorted(set(xs))
if select_yaxis.value in discrete:
kw['y_range'] = sorted(set(ys))
if select_yaxis.value in time:
kw['y_axis_type'] = 'datetime'
if select_xaxis.value in time:
kw['x_axis_type'] = 'datetime'
kw['title']="%s vs %s" % (x_title, y_title)
# create figure using optional keywords kw
p4 = figure(plot_height=300, plot_width=600, tools='pan,box_zoom,reset',
**kw)
# set axis label
p4.xaxis.axis_label = x_title
p4.yaxis.axis_label = y_title
# strings at x axis ticks need a lot of space. solution: rotate label orientation
if select_xaxis.value in discrete:
p4.xaxis.major_label_orientation = pd.np.pi / 4 # rotates labels...
# standard size of symbols
sz = 9
# custom size of symbols due to select_size
if select_size.value != 'None':
groups = pd.qcut(pd.to_numeric(par_df[select_size.value].values), len(SIZES))
sz = [SIZES[xx] for xx in groups.codes]
# standard color
c = "#31AADE"
# custom color due to select_color
if select_color.value != 'None':
groups = pd.qcut(pd.to_numeric(par_df[select_color.value]).values, len(COLORS))
c = [COLORS[xx] for xx in groups.codes]
# create the plot using circles
p4.circle(x=xs, y=ys, color=c, size=sz, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)
return p4 #return the plot
def callback_update_plot_1(attr, old, new):
''' Callback for update of figure 1 in parameters tab '''
tabs.tabs[1].child.children[1] = plot_par()
print(tabs.tabs[1].child.children[1])
logger.debug('Parameter plot updated')
# p4 = plot_par()
def callback_update_p3():
logger.debug('update plot 3')
p3 = fit_mag_decay_all(polymer,par_df)
return p3
def callback_update_experiment(attr, old, new):
""" Callback for the experiment chooser
"""
ie = experiment_slider.value
logger.debug('Callback experiment update, ie={}'.format(ie))
fid_slider.end = polymer.getparvalue(ie,'BS')
try:
fid_slider.range=polymer.getparvalue(ie,'fid_range')
except:
startpoint = int(0.05 * polymer.getparvalue(ie,'BS'))
endpoint = int(0.1 * polymer.getparvalue(ie,'BS'))
fid_slider.range=(startpoint,endpoint)
calculate_mag_dec(attr,old,new)
def callback_load_more_data(attr,old,new):
''' callback for loading of data '''
# TODO: implement
logger.debug('callback for loading of data ')
logger.error('Not implemented!')
path=pathbox.value.strip()
file=filebox.value.strip()
if file=="*.sdf":
logger.info('callback for loading data. filename: {}'.format(file))
allsdf=filter(lambda x: x.endswith('.sdf'),os.listdir(path))
for f in allsdf:
sdf_list.append(sdf.StelarDataFile(f,path))
else:
sdf_list.append(sdf.StelarDataFile(file,path))
filenames=[x.file() for x in sdf_list]
filenames_df=pd.DataFrame(data=filenames,columns=['file'])
table_source.data=ColumnDataSource.from_df(filenames_df)
def callback_export_data(attr,old,new):
logger.debug('callback_export_data has been called ')
logger.error('Not implemented!')
pass
def callback_write_table_to_file(attr,old,new): ##FIXME
logger.debug('callback_write_table_to_file has been called ')
logger.error('Not implemented!')
pass
# path=export_text.value.strip()
# exportdata=export_source.data
# CustomJS(args=dict(source=export_source),
# code=open(join(dirname(__file__), "export_csv.js")).read())
def callback_update_parameters():
''' callback for button
function to call when button is clicked
for updates parameters of polymer, since they can change during evaluation '''
logger.debug('callback for button (update parameter).')
par_df, columns, discrete, continuous, time, quantileable = polymer.scan_parameters()
select_xaxis.options=columns
select_yaxis.options=columns
select_size.options=['None']+quantileable
select_color.options=['None']+quantileable
logger.info('Starting the script')
### This is the start of the script ###
### The callbacks are above ###
#load data:
# TODO: how to handle multiple datafiles?
# New Tab for each datafile?
# dropdown selection to choose datafile
# complete new start of process? (probably not prefered)
polymer = load_data('glyzerin_d3_300K.sdf')
nr_experiments = polymer.get_number_of_experiments()
start_ie = 1 # initially set ie = 1
par_df, columns, discrete, continuous, time, quantileable = polymer.scan_parameters(20)
# for the initial call get the dataframes without callback
# they are being updated in following callbacks
fid, df = get_data_frames(start_ie)
source_fid = ColumnDataSource(data=ColumnDataSource.from_df(fid))
source_mag_dec = ColumnDataSource(data=ColumnDataSource.from_df(df))
# initialy creates the plots p1 and p2
p1, p2 = create_plot_1_and_2(source_fid, source_mag_dec)
### initiates widgets, which will call the callback on change ###
# initiate slider to choose experiment
experiment_slider = Slider(start=1, end=nr_experiments, value=1, step=1,callback_policy='mouseup', width=800) #select experiment by value
# initiate slider for the range in which fid shall be calculated
# select the intervall from which magneitization is calculated from fid
fid_slider = RangeSlider(start=1,end=polymer.getparvalue(start_ie,'BS'),
range=polymer.getparvalue(start_ie,'fid_range'),
step=1,callback_policy='mouseup', width=400)
# fit magnetization decay for all experiments
p3 = fit_mag_decay_all(polymer, par_df)
# refit mag dec with updated ranges after button push
button_refit = Button(label='Update',button_type="success")
button_refit.on_click(callback_update_p3)
# initialize empty source for experiment slider
source = ColumnDataSource(data=dict(value=[]))
# 'data' is the attribute. it's a field in source, which is a ColumnDataSource
# initiate callback_update_experiment which is the callback
source.on_change('data',callback_update_experiment) #source for experiment_slider
experiment_slider.callback = CustomJS(args=dict(source=source),code="""
source.data = { value: [cb_obj.value] }
""")#unfortunately this customjs is needed to throttle the callback in current version of bokeh
# initialize empty source for fid slider, same as above
source2 = ColumnDataSource(data=dict(range=[], ie=[]))
source2.on_change('data',calculate_mag_dec)
fid_slider.callback=CustomJS(args=dict(source=source2),code="""
source.data = { range: cb_obj.range }
""")#unfortunately this customjs is needed to throttle the callback in current version of bokeh
# same for the update button
button_scan = Button(label='Scan Parameters',button_type="success")
button_scan.on_click(callback_update_parameters)
# here comes for callbacks for x, y, size, color
select_xaxis = Select(title='X-Axis', value='ZONE', options=columns)
select_xaxis.on_change('value', callback_update_plot_1)
select_yaxis = Select(title='Y-Axis', value='TIME', options=columns)
select_yaxis.on_change('value', callback_update_plot_1)
select_size = Select(title='Size', value='None', options=['None'] + quantileable)
select_size.on_change('value', callback_update_plot_1)
select_color = Select(title='Color', value='None', options=['None'] + quantileable)
select_color.on_change('value', callback_update_plot_1)
controls_p4 = widgetbox([button_scan, select_xaxis,select_yaxis,select_color,select_size], width=150)
#p4 = plot_par()
layout_p4 = row(controls_p4,plot_par())
logger.debug('layout for parameter plot created')
####
#### TODO: write file input
#### TODO: select files to import
#### TODO: discard imported files
####
# load more data:
table_source=ColumnDataSource(data=dict())
sdf_list=[polymer]
# TODO: This is current plan, to save the different dataframes in a list, right?
filenames=[x.file() for x in sdf_list]
files_df=pd.DataFrame(data=filenames,columns=['file'])
table_source.data=ColumnDataSource.from_df(files_df)
t_columns = [
TableColumn(field='file', title='Path / Filename'),
#TableColumn(field='file', title='Filename'),
]
table=DataTable(source=table_source,columns=t_columns)
pathbox=TextInput(title="Path",value=os.path.curdir)
filebox=TextInput(title="Filename",value="*.sdf")
pathbox.on_change('value',callback_load_more_data)
filebox.on_change('value',callback_load_more_data)
layout_input=column(pathbox,filebox,table)
# Data Out: export data from figures
# & export parameters
export_source=ColumnDataSource(data=dict())
export_columns=[]
output_table=DataTable(source=export_source,columns=export_columns)
export_slider = Slider(start=1, end=4, value=3, step=1,callback_policy='mouseup', width=200) #do we need mouseup on this?
export_slider.on_change('value',callback_export_data)
export_text = TextInput(title="Path",value=os.path.curdir)
export_button = Button(label='Export to csv',button_type="success") # FIXME Callback doesn't work yet
export_button.on_click(callback_write_table_to_file)
layout_output=row(column(export_slider,export_text,export_button),output_table)
print('after layout_output')
# set the layout of the tabs
layout_p1 = column(experiment_slider, p1,
row(
column(fid_slider,p2),
column(button_refit, p3)
),
)
tab_relaxation = Panel(child = layout_p1, title = 'Relaxation')
tab_parameters = Panel(child = layout_p4, title = 'Parameters')
tab_input = Panel(child = layout_input, title = 'Data In')
tab_output = Panel(child = layout_output, title = 'Data Out')
# initialize tabs object with 3 tabs
tabs = Tabs(tabs = [tab_relaxation, tab_parameters,
tab_input, tab_output])
print('tabs')
doc.add_root(tabs)
doc.add_root(source) # i need to add source to detect changes
doc.add_root(source2)
print('tab tab')
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),
("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters = {"Date": 'datetime'}
return p
stock = ColumnDataSource(
data=dict(Date=[], Open=[], Close=[], High=[], Low=[], index=[]))
myPortfolio = Portfolio(['CSCO', 'DTE.DE', 'NOK'], [1, 1, 2], [1, 1, 1],
[1, 1, 1])
sample_df = pd.DataFrame(data=myPortfolio.share_data['CSCO']).dropna()
sample_df.reset_index(inplace=True)
stock.data = stock.from_df(sample_df)
elements = list()
p_stock = plot_stock_price(stock)
elements.append(p_stock)
curdoc().add_root(column(elements))
curdoc().title = 'CSCO Data'
def dashboard(request):
current_user = request.user
user = Profile.objects.get(user = current_user)
if request.user.is_authenticated:
all_stocks = []
all_portfolios_of_current_users = Portfolio.objects.filter(user = user)
for indv in all_portfolios_of_current_users:
all_stocks.append(indv.stock)
else:
return redirect(landingpage)
if not all_stocks:
all_stocks = ["MMM", "ABT", "ABBV", "ABMD", "ACN", "ATVI", "AMZN", "FB", "GOOGL", "GOOG", "JNJ" ]
if request.method == 'POST':
symbol = request.POST.get('symbol')
# Get Stock DataFrame
# msft = yf.Ticker("MSFT")
msft = yf.Ticker(symbol)
hist = msft.history(period='max')
# Define constants
W_PLOT = 1000
H_PLOT = 400
TOOLS = 'pan,wheel_zoom,hover,reset'
VBAR_WIDTH = 0.2
RED = Category20[7][6]
GREEN = Category20[5][4]
BLUE = Category20[3][0]
BLUE_LIGHT = Category20[3][1]
ORANGE = Category20[3][2]
PURPLE = Category20[9][8]
BROWN = Category20[11][10]
def get_symbol_df(symbol=None):
df = pd.DataFrame(hist)[-50:]
df.reset_index(inplace=True)
df["Date"] = pd.to_datetime(df["Date"])
return df
def plot_stock_price(stock):
p = figure(plot_width=W_PLOT, plot_height=H_PLOT, tools=TOOLS,
title="Stock price", toolbar_location='above')
inc = stock.data['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {i+int(stock.data['index'][0]): date.strftime('%b %d') for i, date in enumerate(pd.to_datetime(stock.data["Date"]))}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
p.segment(x0='index', x1='index', y0='Low', y1='High', color=RED, source=stock, view=view_inc)
p.segment(x0='index', x1='index', y0='Low', y1='High', color=GREEN, source=stock, view=view_dec)
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=BLUE, line_color=BLUE,source=stock,view=view_inc, name="price")
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=RED, line_color=RED,source=stock,view=view_dec, name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
p.x_range.range_padding = 0.05
p.xaxis.ticker.desired_num_ticks = 40
p.xaxis.major_label_orientation = 3.14/4
# Select specific tool for the plot
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters={"Date": 'datetime'}
return p
stock = ColumnDataSource(data=dict(Date=[], Open=[], Close=[], High=[], Low=[],index=[]))
df = get_symbol_df(symbol)
stock.data = stock.from_df(df)
elements = list()
# update_plot()
p_stock = plot_stock_price(stock)
elements.append(p_stock)
curdoc().add_root(column(elements))
curdoc().title = 'Bokeh stocks historical prices'
#show(p_stock)
script, div = components(p_stock)
kwargs = {'script': script, 'div': div}
stock_list = all_stocks
er = EventRegistry(apiKey = '3a7a023b-6280-4476-bec0-5c9ff41770bd')
q = QueryArticlesIter(
keywords = QueryItems.OR(stock_list),
dataType = ["news"],
lang = 'eng'
)
news_list = q.execQuery(er, sortBy = ["rel","date","sourceImportance"], maxItems = 5, )
url = "https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/get-detail"
querystring = {"region":"US","lang":"en","symbol":symbol}
headers = {
'x-rapidapi-host': "apidojo-yahoo-finance-v1.p.rapidapi.com",
'x-rapidapi-key': "ff4cff81d5msh596a92309f2f43ap13412cjsn002bc2a74b4d"
}
response = requests.request("GET", url, headers=headers, params=querystring)
json_dict = json.loads(response.text)
for key in json_dict.keys():
if key == "summaryDetail":
try:
stock_close = json_dict[key]['previousClose']['raw']
except KeyError:
stock_close = ''
try:
stock_open = json_dict[key]['open']['raw']
except KeyError:
stock_open = ''
try:
stock_bid = json_dict[key]['bid']['raw']
except KeyError:
stock_bid = ''
try:
stock_ask = json_dict[key]['ask']['raw']
except KeyError:
stock_ask = ''
try:
stock_volume = json_dict[key]['volume']['fmt']
except KeyError:
stock_volume = ''
try:
stock_averageVolume = json_dict[key]['averageVolume']['fmt']
except KeyError:
stock_averageVolume = ''
try:
stock_marketCap = json_dict[key]['marketCap']['fmt']
except KeyError:
stock_marketCap = ''
if key == "defaultKeyStatistics":
try:
stock_weekChange = json_dict[key]['52WeekChange']['fmt']
except KeyError:
stock_weekChange = ''
try:
stock_beta = json_dict[key]['beta']['fmt']
except KeyError:
stock_beta = ''
try:
stock_EPS = json_dict[key]['trailingEps']['raw']
except KeyError:
stock_EPS = ''
try:
stock_PE = json_dict[key]['forwardPE']['fmt']
except KeyError:
stock_PE = ''
return render(request, 'dashboard.html', {**kwargs, 'news_list' : news_list,'stock_close':stock_close,'stock_open':stock_open,'stock_bid':stock_bid,'stock_ask':stock_ask,'stock_volume':stock_volume,'stock_averageVolume':stock_averageVolume,'stock_marketCap':stock_marketCap,'stock_weekChange':stock_weekChange,'stock_beta':stock_beta,'stock_EPS':stock_EPS,'stock_PE':stock_PE,'stock_list':stock_list,'symbol':symbol})
else:
symbol = all_stocks[0]
stock_list = all_stocks
msft = yf.Ticker(symbol)
hist = msft.history(period='max')
# Define constants
W_PLOT = 1000
H_PLOT = 400
TOOLS = 'pan,wheel_zoom,hover,reset'
VBAR_WIDTH = 0.2
RED = Category20[7][6]
GREEN = Category20[5][4]
BLUE = Category20[3][0]
BLUE_LIGHT = Category20[3][1]
ORANGE = Category20[3][2]
PURPLE = Category20[9][8]
BROWN = Category20[11][10]
def get_symbol_df(symbol=None):
df = pd.DataFrame(hist)[-50:]
df.reset_index(inplace=True)
df["Date"] = pd.to_datetime(df["Date"])
return df
def plot_stock_price(stock):
p = figure(plot_width=W_PLOT, plot_height=H_PLOT, tools=TOOLS,
title="Stock price", toolbar_location='above')
inc = stock.data['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {i+int(stock.data['index'][0]): date.strftime('%b %d') for i, date in enumerate(pd.to_datetime(stock.data["Date"]))}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
p.segment(x0='index', x1='index', y0='Low', y1='High', color=RED, source=stock, view=view_inc)
p.segment(x0='index', x1='index', y0='Low', y1='High', color=GREEN, source=stock, view=view_dec)
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=BLUE, line_color=BLUE,source=stock,view=view_inc, name="price")
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=RED, line_color=RED,source=stock,view=view_dec, name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
p.x_range.range_padding = 0.05
p.xaxis.ticker.desired_num_ticks = 40
p.xaxis.major_label_orientation = 3.14/4
# Select specific tool for the plot
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters={"Date": 'datetime'}
return p
stock = ColumnDataSource(data=dict(Date=[], Open=[], Close=[], High=[], Low=[],index=[]))
df = get_symbol_df(symbol)
stock.data = stock.from_df(df)
elements = list()
# update_plot()
p_stock = plot_stock_price(stock)
elements.append(p_stock)
curdoc().add_root(column(elements))
curdoc().title = 'Bokeh stocks historical prices'
#show(p_stock)
script, div = components(p_stock)
kwargs = {'script': script, 'div': div}
er = EventRegistry(apiKey = '3a7a023b-6280-4476-bec0-5c9ff41770bd')
q = QueryArticlesIter(
keywords = QueryItems.OR(stock_list),
dataType = ["news"],
lang = 'eng'
)
news_list = q.execQuery(er, sortBy = ["rel","date","sourceImportance"], maxItems = 5, )
url = "https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/get-detail"
querystring = {"region":"US","lang":"en","symbol":symbol}
headers = {
'x-rapidapi-host': "apidojo-yahoo-finance-v1.p.rapidapi.com",
'x-rapidapi-key': "ff4cff81d5msh596a92309f2f43ap13412cjsn002bc2a74b4d"
}
response = requests.request("GET", url, headers=headers, params=querystring)
json_dict = json.loads(response.text)
for key in json_dict.keys():
if key == "summaryDetail":
try:
stock_close = json_dict[key]['previousClose']['raw']
except KeyError:
stock_close = ''
try:
stock_open = json_dict[key]['open']['raw']
except KeyError:
stock_open = ''
try:
stock_bid = json_dict[key]['bid']['raw']
except KeyError:
stock_bid = ''
try:
stock_ask = json_dict[key]['ask']['raw']
except KeyError:
stock_ask = ''
try:
stock_volume = json_dict[key]['volume']['fmt']
except KeyError:
stock_volume = ''
try:
stock_averageVolume = json_dict[key]['averageVolume']['fmt']
except KeyError:
stock_averageVolume = ''
try:
stock_marketCap = json_dict[key]['marketCap']['fmt']
except KeyError:
stock_marketCap = ''
if key == "defaultKeyStatistics":
try:
stock_weekChange = json_dict[key]['52WeekChange']['fmt']
except KeyError:
stock_weekChange = ''
try:
stock_beta = json_dict[key]['beta']['fmt']
except KeyError:
stock_beta = ''
try:
stock_EPS = json_dict[key]['trailingEps']['raw']
except KeyError:
stock_EPS = ''
try:
stock_PE = json_dict[key]['forwardPE']['fmt']
except KeyError:
stock_PE = ''
return render(request, 'dashboard.html', {**kwargs, 'news_list' : news_list,'stock_close':stock_close,'stock_open':stock_open,'stock_bid':stock_bid,'stock_ask':stock_ask,'stock_volume':stock_volume,'stock_averageVolume':stock_averageVolume,'stock_marketCap':stock_marketCap,'stock_weekChange':stock_weekChange,'stock_beta':stock_beta,'stock_EPS':stock_EPS,'stock_PE':stock_PE,'stock_list':stock_list,'symbol':symbol})
