def financialplots(filename, plotkind):
try:
data = pd.read_csv(filename, index_col=0, parse_dates=True)
except (FileNotFoundError, IOError):
print('Wrong file or file path.')
return None
if plotkind == 'candlestick':
fig = FF.create_candlestick(data['Opening Price'],
data['Maximum Price'],
data['Minimum Price'],
data['Closing Price'],
dates=data.index)
elif plotkind == 'macd':
fig = data['Closing Price'].ta_plot(study='macd',
fast_period=12,
slow_period=26,
signal_period=9,
asFigure=True)
elif plotkind == 'boll':
fig = data['Closing Price'].ta_plot(study='boll', asFigure=True)
elif plotkind == 'ohlc':
fig = FF.create_ohlc(data['Opening Price'],
data['Maximum Price'],
data['Minimum Price'],
data['Closing Price'],
dates=data.index)
elif plotkind == 'sma':
fig = data['Closing Price'].ta_plot(study='sma', asFigure=True)
py.plot(fig,
filename='../../plots/' + filename[:-4] + plotkind,
validate=False,
auto_open=False)
def dist_plot(df,
groupby=None,
val=None,
bin_size=1,
title=None,
show_hist=True,
show_kde=True,
show_rug=True,
show_legend=True,
figsize=None,
outfile=None,
xlabel=None,
ylabel=None):
if groupby is None:
fig = FF.create_distplot([df[c] for c in df.columns],
df.columns.values.tolist(),
bin_size=bin_size,
show_rug=show_rug,
show_curve=show_kde)
else:
groups = sorted(df[groupby].unique().tolist(), reverse=True)
data = []
if val is None:
val = df.columns.drop(groupby)[0] # choose first non-groupby column
for group in groups:
mask = df[groupby] == group
data.append(df.loc[mask, val])
fig = FF.create_distplot(data,
groups,
bin_size=bin_size,
show_hist=show_hist,
show_rug=show_rug,
show_curve=show_kde)
fig['layout'].update(showlegend=show_legend)
if title:
fig['layout'].update(title=title)
if xlabel:
fig['layout'].update(xaxis=go.XAxis(title=xlabel))
if ylabel:
fig['layout'].update(yaxis=go.YAxis(title=ylabel))
if figsize and len(figsize) == 2:
fig['layout'].update(width=figsize[0])
fig['layout'].update(height=figsize[1])
ol.iplot(fig, show_link=False)
# write figure to HTML file
if outfile:
print('Exporting copy of figure to %s...' % outfile)
ol.plot(fig, auto_open=False, filename=outfile)
def dist_plot(df,
groupby=None,
val=None,
bin_size=1,
title=None,
show_hist=True,
show_kde=True,
show_rug=True,
show_legend=True,
figsize=None,
outfile=None,
xlabel=None,
ylabel=None):
if groupby is None:
fig = FF.create_distplot([df[c] for c in df.columns],
df.columns.values.tolist(),
bin_size=bin_size,
show_rug=show_rug,
show_curve=show_kde)
else:
groups = sorted(df[groupby].unique().tolist(), reverse=True)
data = []
if val is None:
val = df.columns.drop(groupby)[
0] # choose first non-groupby column
for group in groups:
mask = df[groupby] == group
data.append(df.loc[mask, val])
fig = FF.create_distplot(data,
groups,
bin_size=bin_size,
show_hist=show_hist,
show_rug=show_rug,
show_curve=show_kde)
fig['layout'].update(showlegend=show_legend)
if title:
fig['layout'].update(title=title)
if xlabel:
fig['layout'].update(xaxis=go.XAxis(title=xlabel))
if ylabel:
fig['layout'].update(yaxis=go.YAxis(title=ylabel))
if figsize and len(figsize) == 2:
fig['layout'].update(width=figsize[0])
fig['layout'].update(height=figsize[1])
ol.iplot(fig, show_link=False)
# write figure to HTML file
if outfile:
print('Exporting copy of figure to %s...' % outfile)
ol.plot(fig, auto_open=False, filename=outfile)
def createFigure(filename, isAutoOpen=True, isUploadToServer=False):
# ------------Custom Candlestick Colors------------
# Make increasing ohlc sticks and customize their color and name
opens = [datarow.open for datarow in dataRows]
highs = [datarow.high for datarow in dataRows]
lows = [datarow.low for datarow in dataRows]
closes = [datarow.close for datarow in dataRows]
fig_increasing = FigureFactory.create_candlestick(
opens,
highs,
lows,
closes,
dates=datetime_xAxis,
direction='increasing',
marker=go.Marker(color='rgb(61, 153, 112)'),
line=go.Line(color='rgb(61, 153, 112)'))
# Make decreasing ohlc sticks and customize their color and name
fig_decreasing = FigureFactory.create_candlestick(
opens,
highs,
lows,
closes,
dates=datetime_xAxis,
direction='decreasing',
marker=go.Marker(color='rgb(255, 65, 54)'),
line=go.Line(color='rgb(255, 65, 54)'))
# Initialize the figure
fig = fig_increasing
fig['data'].extend(fig_decreasing['data'])
# ------------Add extensions------------
for index, extensionList in enumerate(extensionLists):
addLineExtension(fig, [data[0] for data in extensionList],
[data[1]
for data in extensionList], extensionNames[index],
extensionLineTypes[index], extensionColors[index])
# ------------Update layout And Draw the chart------------
filename = chartTitle
fig['layout'].update(
title=filename,
xaxis=dict(ticktext=[datarow.date for datarow in dataRows],
tickvals=datetime_xAxis,
showticklabels=False,
showgrid=True,
showline=True),
)
plotly.offline.plot(fig, filename=filename + '.html', auto_open=isAutoOpen)
print "----------The chart has been generated----------\n"
def show(self, title):
figs = []
for ind_f in range(len(self.frames)):
(points, triangles, signals) = self.frames[ind_f]
points = array(points)
triangles = array(triangles)
signals = array(signals)
signals_per_triangle = list(
(signals[triangles[i, 0]] + signals[triangles[i, 1]] +
signals[triangles[i, 2]]) / 3
for i in range(triangles.shape[0]))
signals_per_triangle[0] += 0.001
# Validate colormap
my_colormap = FF._validate_colors("Portland", 'tuple')
newdata = FF._trisurf(x=points[:, 2],
y=points[:, 0],
z=points[:, 1],
colormap=my_colormap,
simplices=triangles,
color_func=signals_per_triangle,
plot_edges=False,
edges_color='rgb(50, 50, 50)',
show_colorbar=False,
data_list=True)
figs += newdata
axis = dict(showbackground=True,
backgroundcolor='rgb(230, 230, 230)',
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)')
xaxis = axis.copy()
xaxis['range'] = [-0.08, 0.09]
yaxis = axis.copy()
yaxis['range'] = [-0.11, 0.05]
zaxis = axis.copy()
zaxis['range'] = [0.02, 0.18]
aspectratio = dict(x=1, y=1, z=1)
layout = graph_objs.Layout(title=title,
width='100%',
height=800,
scene=graph_objs.Scene(
xaxis=graph_objs.XAxis(xaxis),
yaxis=graph_objs.YAxis(yaxis),
zaxis=graph_objs.ZAxis(zaxis),
aspectratio=dict(x=aspectratio['x'],
y=aspectratio['y'],
z=aspectratio['z']),
))
return my_iplot(graph_objs.Figure(data=figs, layout=layout))
def import_data(x, y):
data = pd.read_csv('data/overlapping/' + x + ',' + y + '-1,3,4,10,11,13.csv')
df = data[0:10]
table = FF.create_table(df)
plotly.offline.plot(table, filename='point_data_table')
return data
def __init__(self, sdf: ss.StockDataFrame):
self.StockDataFrame = sdf
self._fig = FF.create_candlestick(self.StockDataFrame.open,
self.StockDataFrame.high,
self.StockDataFrame.low,
self.StockDataFrame.close,
dates=self.StockDataFrame.index)
def scatter_matrix(self, dataframe, select_columns=None, index_colname=None,
diag_kind='scatter',
marker_size=10, height=800, width=1000, marker_outline_width=0,
marker_outline_color='black'):
"""
Create a scatter matrix plot from dataframes using Plotly.
Args:
dataframe: (array) array of the data with column headers
select_columns: (list) names/headers of columns to plot from the dataframe
index_colname: (str) name of the index column in data array
diag_kind: (str) sets the chart type for the main diagonal plots (default='scatter')
Choose from 'scatter'/'box'/'histogram'
marker_size: (float) sets the marker size (in px)
height: (int/float) sets the height of the chart
width: (int/float) sets the width of the chart
marker_outline_width: (int) thickness of marker outline (currently affects the outline of histograms too
when "diag_kind" = 'histogram')
marker_outline_color: (str/array) color of marker outline - accepts similar formats as other color variables
Returns: a Plotly scatter matrix plot
"""
df = dataframe[select_columns] if select_columns else dataframe
fig = FF.create_scatterplotmatrix(df, index=index_colname, diag=diag_kind, size=marker_size,
height=height, width=width)
# Add outline to markers
for trace in fig['data']:
trace['marker']['line'] = dict(width=marker_outline_width, color=marker_outline_color)
self.create_plot(fig)
def feature_scatterplot(fset_path, features_to_plot):
"""Create scatter plot of feature set.
Parameters
----------
fset_path : str
Path to feature set to be plotted.
features_to_plot : list of str
List of feature names to be plotted.
Returns
-------
(fig.data, fig.layout)
Returns (fig.data, fig.layout) where `fig` is an instance of
`plotly.tools.FigureFactory`.
"""
with featureset.from_netcdf(fset_path, engine=cfg['xr_engine']) as fset:
feat_df = fset.to_dataframe()
feat_df = feat_df[features_to_plot]
if 'target' in fset:
feat_df['target'] = fset.target.values
index = 'target'
else:
index = None
# TODO replace 'trace {i}' with class labels
fig = FF.create_scatterplotmatrix(feat_df, diag='box', index=index,
height=800, width=800)
py.plot(fig, auto_open=False, output_type='div')
return fig.data, fig.layout
def bitcoinchart():
'''
Use function from cryptofuncs.py to get rows and data frame to create
a candlestick chart using plotly module.
Creating the layout for the graph.
'''
rows = cf.bitcoin(
'https://coinmarketcap.com/currencies/bitcoin/historical-data/'
'?start=20180321&end=20190321')
data_frame = cf.data(rows)
fig = FF.create_candlestick(data_frame['open'],
data_frame['high'],
data_frame['low'],
data_frame['close'],
dates=data_frame['date'])
fig['layout'].update({
'title': 'Bitcoin Annual Price Development',
'yaxis': {
'title': 'Bitcoin in USD'
}
})
pyo.offline.plot(fig)
def plotBarsAndTableCount(HTList, plottype, plotTitle="Plot"):
index, counts = zip(*HTList)
index = list(index)
counts = list(counts)
data = [[plottype, 'count']] + HTList
figure = FF.create_table(data, height_constant=60)
bars = go.Bar(x=index,
y=counts,
xaxis='x2',
yaxis='y2',
marker=dict(color='#48C20C'),
name=plottype)
figure['data'].extend(go.Data([bars]))
figure.layout.yaxis.update({'domain': [0, .4]})
figure.layout.yaxis2.update({'domain': [.55, 1]})
figure.layout.xaxis.update({'domain': [.33, .66]})
figure.layout.yaxis2.update({'anchor': 'x2'})
figure.layout.xaxis2.update({'anchor': 'y2'})
figure.layout.margin.update({'t': 75, 'l': 50})
figure.layout.update({'title': plotTitle})
figure.layout.update({'height': 800})
# Make text size larger
for i in range(len(figure.layout.annotations)):
figure.layout.annotations[i].font.size = 20
return plotly.offline.plot(figure,
filename=plotTitle.lower().replace(" ", "_"))
def __init__(self, df, primaryKey="Close"):
super(Candlestick, self).__init__(
FF.create_candlestick(df.Open,
df.High,
df.Low,
df.Close,
dates=df.index))
self.df = df
if Candlestick.CUSTOM_MARKERS_ENABLED:
for subplot in self["data"]:
subplot["hoverinfo"] = "none"
self._addMarkers(primaryKey, df[primaryKey])
self._addMovingAverage(df[primaryKey], 50)
self._addMovingAverage(df[primaryKey], 14)
if Candlestick.VOLUME_ENABLED:
self._addVolumeBars(df["Volume"])
self["layout"].update = {
"paper_bgcolor": 'rgba(0,0,0,0)',
"plot_bgcolor": 'rgba(0,0,0,0)',
"yaxis1": {
"domain": [0, 1]
},
"yaxis2": {
"domain": [0, 0.2],
"anchor": "x1"
},
}
def statisticplots(filename, plotkind, columns):
try:
data = pd.read_csv(filename, index_col=0, parse_dates=True)
except (FileNotFoundError, IOError):
print('Wrong file or file path.')
return None
if columns is None or not columns:
columns = list(data.columns.values)
data = data.ix[:, columns]
if plotkind == 'scattermatrix':
fig = FF.create_scatterplotmatrix(
data,
diag='box',
title='Scattermatrix plot of {}'.format(filename[:-4]))
elif plotkind == 'line':
fig = data.iplot(theme='pearl',
kind='scatter',
title='Line plot of {}.'.format(filename[:-4]),
subplots=True,
asFigure=True)
elif plotkind == 'box':
fig = data.iplot(theme='pearl',
kind='box',
title='Box plot of {}.'.format(filename[:-4]),
asFigure=True)
py.plot(fig,
filename='../../plots/' + filename[:-4] + plotkind,
validate=False,
auto_open=False)
def table(percent_array,years):
mat = [years]
if not len(percent_array)==0:
ro = len(percent_array)
col = len(percent_array[0])
index = ["80","60","40","20","5"]
for i in range(col):
sel = []
sel.append(index[i])
for j in range(ro):
sel.append(percent_array[j,i])
mat.append(sel)
data_matrix = mat
table = FF.create_table(data_matrix)
plot_div = plot(table, output_type='div', include_plotlyjs=False)
return plot_div
def pretty_table(df, outfile=None):
"""
Display pandas dataframe as a nicely-formated HTML
Parameters
----------
outfile: filepath str
If provided, output to an HTML file at provided location
Example
-------
import pandas as pd
animals = pd.DataFrame([
['cat',10, 'housepet'],
['dog',20,'housepet'],
['fish',5,'housepet'],
['cat',20,'zooanimal'],
['dog',50,'zooanimal'],
['fish',20,'zooanimal'],], columns=['animal','value','group'])
pretty_table(animals)
"""
table = FF.create_table(df)
ol.iplot(table, show_link=False)
# write figure to HTML file
if outfile:
print('Exporting copy of figure to %s...' % outfile)
ol.plot(table, auto_open=False, filename=outfile)
def plot_Age(path, feature, type_):
feature = pd.DataFrame(feature)
feature = feature.reset_index()
data = folder_reader(path)
data.columns = ['temperature', 'wind', 'age', 'total']
aux = data[data.age < 1950]
aux = aux.groupby(type_)['total'].sum()
aux = aux.reset_index()
aux = pd.merge(aux, feature, how='left', left_index=type_, right_index="index")
number = aux[aux.columns[1]] * 100 / aux[aux.columns[3]]
Temp_1 = np.repeat(aux[aux.columns[0]].values, number.round(0).astype(int))
aux = data[data.age >= 1950]
aux = aux[aux.age < 1985]
aux = aux.groupby(type_)['total'].sum()
aux = aux.reset_index()
aux = pd.merge(aux, feature, how='left', left_index=type_, right_index="index")
number = aux[aux.columns[1]] * 100 / aux[aux.columns[3]]
Temp_2 = np.repeat(aux[aux.columns[0]].values, number.round(0).astype(int))
aux = data[data.age >= 1985]
aux = aux.groupby(type_)['total'].sum()
aux = aux.reset_index()
aux = pd.merge(aux, feature, how='left', left_index="wind", right_index="index")
number = aux[aux.columns[1]] * 100 / aux[aux.columns[3]]
Temp_3 = np.repeat(aux[aux.columns[0]].values, number.round(0).astype(int))
hist_data = [Temp_1, Temp_2, Temp_3]
group_labels = ['Older than 65', 'Betwen 30-65', 'Younger than 30']
colors = ['rgb(0, 0, 100)', 'rgb(0, 200, 200)', 'rgb(0, 300, 300)']
fig = FF.create_distplot(hist_data, group_labels, colors=colors)
plot_url = py.offline.plot(fig)
def get_isosurface(grid, limits, args): variable = grid[args.isosurface_variable].reshape(*args.resolution) #assume resolution matches grid if args.isosurface_value is None: vertices, simplices = measure.marching_cubes(variable,variable.mean()) else: vertices, simplices = measure.marching_cubes(variable,args.isosurface_value) x,y,z = zip(*vertices) ni, nj, nk = args.resolution x_ = np.linspace(*limits.x, num = ni) y_ = np.linspace(*limits.y, num = nj) z_ = np.linspace(*limits.z, num = nk) iso_x = np.interp(x, np.arange(ni), x_) iso_y = np.interp(y, np.arange(nj), y_) iso_z = np.interp(z, np.arange(nk), z_) colormap=['rgb(255,105,180)','rgb(255,255,51)','rgb(0,191,255)'] fig = FF.create_trisurf(x=iso_x, y=iso_y, z=iso_z, plot_edges=False, show_colorbar=False, colormap=colormap, simplices=simplices, title="Isosurface", ) trace = fig.data[0] trace.opacity = args.isosurface_opacity return [trace]
def add_surface(self, xyz, triangles, lighting=None, **kwargs):
"""Add a surface model to the plotly figure.
xyz : array, shape (n_vertices, 3)
An xyz array defining the position of each vertex in 3-D
triangles : array, shape (n_triangles, 3)
An ijk array defining triangles for the mesh. Each row
indexes 3 rows of in xyz, which together make a triangle.
lighting : None | dict
A dictionary specifying lighting parameters in plotly
"""
if lighting is None:
lighting = dict(ambient=.4, specular=1)
self.xyz = xyz
self.x = xyz.T[0]
self.y = xyz.T[1]
self.z = xyz.T[2]
self.triangles = triangles
self.xrange = np.array([xyz.min(0)[0], xyz.max(0)[0]])
self.yrange = np.array([xyz.min(0)[1], xyz.max(0)[1]])
self.zrange = np.array([xyz.min(0)[2], xyz.max(0)[2]])
colors = self.cmap(np.repeat(.5, len(triangles)))
colors = array_to_plotly(colors)
self.surfacedata = ff._trisurf(
x=self.x, y=self.y, z=self.z, simplices=self.triangles,
color_func=colors, **kwargs)
self.surfacedata[0]['lighting'] = lighting
self.facecolors = self.surfacedata[0]['facecolor'].copy()
self.tri_centroids = xyz[triangles].mean(1)
self.layout['scene'].update(dict(xaxis=dict(range=self.xrange),
yaxis=dict(range=self.yrange),
zaxis=dict(range=self.zrange)))
def plot_Gender(path, feature, type_):
feature = pd.DataFrame(feature)
feature = feature.reset_index()
data = folder_reader(path)
data.columns = ['temperature', 'wind', 'gender', 'total']
aux = data[data.gender == 1]
aux = aux.groupby(type_)['total'].sum()
aux = aux.reset_index()
aux = pd.merge(aux,
feature,
how='left',
left_index=type_,
right_index="index")
number = aux[aux.columns[1]] * 100 / aux[aux.columns[3]]
Temp_1 = np.repeat(aux[aux.columns[0]].values, number.round(0).astype(int))
aux = data[data.gender == 2]
aux = aux.groupby(type_)['total'].sum()
aux = aux.reset_index()
aux = pd.merge(aux,
feature,
how='left',
left_index=type_,
right_index="index")
number = aux[aux.columns[1]] / aux[aux.columns[3]]
Temp_2 = np.repeat(aux[aux.columns[0]].values, number.round(0).astype(int))
hist_data = [Temp_1, Temp_2]
group_labels = ['Man', 'Woman']
colors = ['rgb(0, 0, 100)', 'rgb(0, 200, 200)']
fig = FF.create_distplot(hist_data, group_labels, colors=colors)
plot_url = py.offline.plot(fig)
def pretty_table(df, outfile=None):
"""
Display pandas dataframe as a nicely-formated HTML
Parameters
----------
outfile: filepath str
If provided, output to an HTML file at provided location
Example
-------
import pandas as pd
animals = pd.DataFrame([
['cat',10, 'housepet'],
['dog',20,'housepet'],
['fish',5,'housepet'],
['cat',20,'zooanimal'],
['dog',50,'zooanimal'],
['fish',20,'zooanimal'],], columns=['animal','value','group'])
pretty_table(animals)
"""
table = FF.create_table(df)
ol.iplot(table, show_link=False)
# write figure to HTML file
if outfile:
print('Exporting copy of figure to %s...' % outfile)
ol.plot(table, auto_open=False, filename=outfile)
def plot_tables():
data_matrix_dem = [
['Feature', 'Coeff', 'Std Err', 'P Val'],
['State Average Turnout', 0.0048, 0.000, 0.000],
['Rural-urban Continuum Code', 0.0046, 0.000, 0.000],
['Perc Less than Highschool Diploma', 0.0006, 0.000, 0.000],
['Perc with Bachelor\'s', 0.0032, 8.54e-05, 0.000],
['Unemployment Rate', 0.0041, 0.000, 0.000],
['Religion NMF Feature 1', 0.0091, 0.001, 0.000],
['Religion NMF Feature 2', 0.0063, 0.000, 0.000],
['Campaign Expenditure', -4.084e-10, 5.09e-11, 0.000],
['Cook Index', 0.4752, 0.006, 0.000],
['Change in Vote Share 2008->2012', 0.2689, 0.024, 0.000],
['1 Field Office', 0.0088, 0.002, 0.000],
['2+ Field Offices', 0.0257, 0.004, 0.000],
['Field Office - Cook Index Interaction', 0.0348, 0.017, 0.041]
]
data_matrix_rep = [
['Feature', 'Coeff', 'Std Err', 'P Val'],
['State Average Turnout', 0.0060, 0.000, 0.000],
['Rural-urban Continuum Code', 0.0044, 0.000, 0.000],
['Perc Less than Highschool Diploma', -0.0024, 0.000, 0.000],
['Perc with Bachelor\'s', 0.0025, 0.000, 0.000],
['Unemployment Rate', 0.0054, 0.000, 0.000],
['Religion NMF Feature 1', 0.0003, 0.001, 0.700],
['Religion NMF Feature 2', 0.0072, 0.001, 0.000],
['Campaign Expenditure', -4.905e-10, 6.44e-11, 0.000],
['Cook Index', -0.5827, 0.008, 0.000],
['Change in Vote Share 2008->2012', -0.0543, 0.032, 0.000],
['1 Field Office', 0.0087, 0.004, 0.025],
['2+ Field Offices', 0.0143, 0.008, 0.080],
['Field Office - Cook Index Interaction', -.1054, 0.029, 0.000]
]
table_dem = FF.create_table(data_matrix_dem)
table_dem.layout.update(
{'title': 'Democratic Regression<br><i>Adj R2 0.978<i>'})
table_rep = FF.create_table(data_matrix_rep)
table_rep.layout.update(
{'title': 'Republican Regression<br><i>Adj R2 0.982<i>'})
# fig = tools.make_subplots(rows=1, cols=2, subplot_titles=('Democratic Regression<br><i>Adj R2 0.978<i>',
# 'Republican Regression<br><i>Adj R2 0.982<i>'))
plot_url = py.plot(table_dem, filename='Dem Regression Results')
plot_url = py.plot(table_rep, filename='Rep Regression Results')
def main(stock_name, day_in, future):
"""Control DataBase and make graph if have stock_name"""
year, month, day = 2015, 12, 12
open_data, close_data, high_data, low_data, values_data, dates = [], [], [], [], [], []
while day_in > 0:
file_name = "set-history_EOD_" + "%d-%02d-%02d.csv" % step_day(year, month, day)
try:
temporary = csv.reader(open(file_name, "r"))
bug = 1
for row in temporary:
if row[0] == stock_name:
bug = 0
open_data.append(float(row[2]))
high_data.append(float(row[3]))
low_data.append(float(row[4]))
close_data.append(float(row[5]))
values_data.append(float(row[6]) * float(row[5]))
dates.append(datetime(year=int(row[1][0:4]), month=int(row[1][4:6]), day=int(row[1][6:])))
if bug == 1:
it_bug ## Make error when that day haven't stock_name
day_in -= 1
except:
day_in -= 1 ## Count All day with have stock or no for can find graph of year from 365 day
year, month, day = step_day(year, month, day)
open_data, close_data, high_data, low_data, values_data, dates = (
open_data[::-1],
close_data[::-1],
high_data[::-1],
low_data[::-1],
values_data[::-1],
dates[::-1],
)
if len(open_data) == 0:
return "%s not found" % stock_name
data_sma, date_sma = make_sma(future, close_data, dates)
data_ema15, date_ema15 = make_ema(15, close_data, dates)
data_ema50, date_ema50 = make_ema(50, close_data, dates)
add_sma = Scatter(x=date_sma, y=data_sma, name="SMA%d" % (future), line=Line(color="rgb(166,212,64)"))
add_ema15 = Scatter(x=date_ema15, y=data_ema15, name="EMA15", line=Line(color="purple"))
add_ema50 = Scatter(x=date_ema50, y=data_ema50, name="EMA50", line=Line(color="blue"))
add_high = Scatter(x=dates, y=[max(high_data) for i in range(len(dates))], name="HIGH", line=Line(color="black"))
add_low = Scatter(x=dates, y=[min(low_data) for i in range(len(dates))], name="LOW", line=Line(color="black"))
fig = FF.create_candlestick(open_data, high_data, low_data, close_data, dates=dates)
fig["layout"].update({"title": stock_name, "yaxis": {"title": "Price"}, "xaxis": {"title": "Dates"}})
fig["data"].extend([add_high])
fig["data"].extend([add_low])
fig["data"].extend([add_sma])
fig["data"].extend([add_ema15])
fig["data"].extend([add_ema50])
plot_url = py.plot(fig, filename="candlestick", validate=False)
data = [go.Bar(x=dates, y=values_data)]
layout = go.Layout(title="Values of %s" % stock_name)
fig = go.Figure(data=data, layout=layout)
plot_url = py.plot(fig, filename="text-hover-bar")
return "Graph %s Success" % stock_name
def getCorrelationOfReturns(country):
dir = r"C:\\Users\\michael\\Desktop\\Test_output\\"
factors = ['Book Value', 'Div Pay Ratio (N)', 'Div Yld', 'Engs Yld', 'Cfl Yld',
'Sales to Pr', 'EBIT to EV (N)', 'EBITDA to Pr', 'RoA (N)', 'RoE', 'ROIC (N)',
'Sustainable GR', 'Asset Turnover (N)', 'Gross Prof to Assts (N)', 'Sales Gr',
'Inc to Sales', 'Op Prof Marg (N)', 'Gross Prof Marg (N)', 'Ass to Eq (N)']
nfactors = len(factors)
arr = []
for j in range(nfactors):
factor = factors[j].replace(" (N)","")
file = dir + "Michael Style 50-100 " + factor + " " + country + " MC M1 200611 to 201610 Dec.xlsx"
book=xlrd.open_workbook(file)
sheet=book.sheet_by_name('Return Data')
list = []
for i in range(5,123):
value = sheet.cell_value(12,i)
list.append(value)
arr.append([])
arr[j].append(list)
results = []
for k in range(nfactors):
results.append([])
for l in range(nfactors):
corr = np.corrcoef(arr[k],arr[l])
temp = corr[0,1]
temp2 = round(temp,2)
results[k].append(temp2)
fig = FF.create_annotated_heatmap(x = factors, y = factors, z=results)
fig['layout'].update(
title=country+' (Correlation)',
xaxis=dict(ticks='', ticksuffix='', side='bottom'),
width=450,
height=300,
margin=go.Margin(
l=180,
r=80,
b=100,
t=100,
pad=4
),
autosize=False
)
py.iplot(fig, filename=country+'-Correlation')
def financialplots(filename, plotkind):
try:
data = pd.read_csv(filename,index_col=0, parse_dates=True)
except(FileNotFoundError, IOError):
print('Wrong file or file path.')
return None
if plotkind == 'candlestick':
fig = FF.create_candlestick(data['Opening Price'], data['Maximum Price'], data['Minimum Price'], data['Closing Price'],dates=data.index)
elif plotkind == 'macd':
fig = data['Closing Price'].ta_plot(study='macd', fast_period=12, slow_period=26, signal_period=9, asFigure=True)
elif plotkind == 'boll':
fig = data['Closing Price'].ta_plot(study='boll',asFigure=True)
elif plotkind == 'ohlc':
fig = FF.create_ohlc(data['Opening Price'], data['Maximum Price'], data['Minimum Price'], data['Closing Price'],dates=data.index)
elif plotkind == 'sma':
fig = data['Closing Price'].ta_plot(study='sma', asFigure=True)
py.plot(fig,filename='../../plots/'+filename[:-4]+plotkind,validate=False,auto_open=False)
def plot2D(days=1):
df = studyUpdate(days)
fig = FF.create_ohlc(df.quoteOpen,
df.quoteHigh,
df.quoteLow,
df.quoteClose,
dates=df.index)
py.iplot(fig, filename='ubio-update-ohlc')
def slack_stock_price(msg: CommandMessage, config: Dict):
if len(msg.text) > 1:
try:
response = BarchartClient(config.get('api_key', '')) \
.get_history(msg.text.upper())
except:
# API request error
# Already logged in BarchartClient, so just return error message.
return "Something went wrong with %s" % msg.text
else:
try:
open_prices = []
high_prices = []
low_prices = []
close_prices = []
dates = []
volumes = []
for result in response['results']:
open_prices.append(result['open'])
high_prices.append(result['high'])
low_prices.append(result['low'])
close_prices.append(result['close'])
dates.append(result['tradingDay'])
volumes.append(result['volume'])
candle_graph_url = plotly.plot(
FigureFactory.create_candlestick(open_prices,
high_prices,
low_prices,
close_prices,
dates=dates),
filename="barchart/price_" + dates[-1],
vadlidate=False)
volume_graph_url = plotly.plot(
Data([Scatter(x=dates,
y=volumes)]))
attachments = [
MessageAttachment(fallback="stock price history",
title="stock price history",
title_link=candle_graph_url,
image_url=candle_graph_url + ".png"),
MessageAttachment(fallback="volume history",
title="volume history",
title_link=volume_graph_url,
image_url=volume_graph_url + ".png")]
return SlackMessage(
text="Stock price history for %s" % msg.text,
attachments=attachments)
except Exception as e:
# Response handling error
logging.error(e)
return "Something went wrong with %s" % msg.text
else:
return "Please enter ticker symbol."
def plot_tables():
data_matrix_dem = [
['Feature', 'Coeff', 'Std Err', 'P Val'],
['State Average Turnout', 0.0048, 0.000, 0.000],
['Rural-urban Continuum Code', 0.0046, 0.000, 0.000],
['Perc Less than Highschool Diploma', 0.0006, 0.000, 0.000],
['Perc with Bachelor\'s', 0.0032, 8.54e-05, 0.000],
['Unemployment Rate', 0.0041, 0.000, 0.000],
['Religion NMF Feature 1', 0.0091, 0.001, 0.000],
['Religion NMF Feature 2', 0.0063, 0.000, 0.000],
['Campaign Expenditure', -4.084e-10, 5.09e-11, 0.000],
['Cook Index', 0.4752, 0.006, 0.000],
['Change in Vote Share 2008->2012', 0.2689, 0.024, 0.000],
['1 Field Office', 0.0088, 0.002, 0.000],
['2+ Field Offices', 0.0257, 0.004, 0.000],
['Field Office - Cook Index Interaction', 0.0348, 0.017, 0.041]
]
data_matrix_rep =[
['Feature', 'Coeff', 'Std Err', 'P Val'],
['State Average Turnout', 0.0060, 0.000, 0.000],
['Rural-urban Continuum Code', 0.0044, 0.000, 0.000],
['Perc Less than Highschool Diploma', -0.0024, 0.000, 0.000],
['Perc with Bachelor\'s', 0.0025, 0.000, 0.000],
['Unemployment Rate', 0.0054, 0.000, 0.000],
['Religion NMF Feature 1', 0.0003, 0.001, 0.700],
['Religion NMF Feature 2', 0.0072, 0.001, 0.000],
['Campaign Expenditure', -4.905e-10, 6.44e-11, 0.000],
['Cook Index', -0.5827, 0.008, 0.000],
['Change in Vote Share 2008->2012', -0.0543, 0.032, 0.000],
['1 Field Office', 0.0087, 0.004, 0.025],
['2+ Field Offices', 0.0143, 0.008, 0.080],
['Field Office - Cook Index Interaction', -.1054, 0.029, 0.000]
]
table_dem = FF.create_table(data_matrix_dem)
table_dem.layout.update({'title': 'Democratic Regression<br><i>Adj R2 0.978<i>'})
table_rep = FF.create_table(data_matrix_rep)
table_rep.layout.update({'title': 'Republican Regression<br><i>Adj R2 0.982<i>'})
# fig = tools.make_subplots(rows=1, cols=2, subplot_titles=('Democratic Regression<br><i>Adj R2 0.978<i>',
# 'Republican Regression<br><i>Adj R2 0.982<i>'))
plot_url = py.plot(table_dem, filename='Dem Regression Results')
plot_url = py.plot(table_rep, filename='Rep Regression Results')
def filter_data():
# import data
data = pd.read_csv('data/ink-2.7mm-1m-length/0.01m-7.csv')
df = data[0:10]
table = FF.create_table(df)
#plotly.offline.plot(table, filename='data-sample')
# plot data
trace1 = go.Scatter(
x=np.arange(len(list(data['time']))),
y=list(data['value']),
mode='lines',
name='Test Data'
)
layout = go.Layout(
showlegend=True
)
trace_data = [trace1]
fig = go.Figure(data=trace_data, layout=layout)
#plotly.offline.plot(fig, filename='raw-data-plot')
# low-pass filter
fc = 0.1
b = 0.08
N = int(np.ceil((4 / b)))
if not N % 2: N += 1
n = np.arange(N)
sinc_func = np.sinc(2 * fc * (n - (N - 1) / 2.))
window = 0.42 - 0.5 * np.cos(2 * np.pi * n / (N - 1)) + 0.08 * np.cos(4 * np.pi * n / (N - 1))
sinc_func = sinc_func * window
sinc_func = sinc_func / np.sum(sinc_func)
s = list(data['value'])
new_signal = np.convolve(s, sinc_func)
trace1 = go.Scatter(
x=np.arange(len(new_signal)),
y=new_signal,
mode='lines',
name='Low-Pass Filter',
marker=dict(
color='#C54C82'
)
)
layout = go.Layout(
title='Low-Pass Filter',
showlegend=True
)
trace_data = [trace1]
fig = go.Figure(data=trace_data, layout=layout)
plotly.offline.plot(fig, filename='low-pass-filter')
def build_plotly_table(plotly_df):
plotly_df = plotly_df[['Passer_Name', 'Receiver_Name', 'Assists']]
plotly_df = plotly_df.sort_values(by='Assists', ascending=False)
plotly_df = plotly_df.head(25)
plotly_df.columns = ['Passer', 'Receiver', 'Assists']
plotly_df = plotly_df.as_matrix()
plotly_df = np.insert(plotly_df, 0, np.array(('Passer', 'Receiver', 'Assists')), 0)
table = FF.create_table(plotly_df)
py.iplot(table, filename='assist_pairs')
def makeCandlestick(self, filename):
self.table = self.importCSV(filename);
self.dates = self.makeDateList(0, self.table);
self.openPrice = self.makeList(2, self.table);
self.highPrice = self.makeList(3, self.table);
self.lowPrice = self.makeList(4, self.table);
self.closePrice = self.makeList(5, self.table);
fig = FF.create_candlestick(self.openPrice, self.highPrice, self.lowPrice, self.closePrice, dates=self.dates);
py.plot(fig, filename='finance/aapl-candlestick', validate=False)
def display_scatter_matrix(data):
'''
plot a scattter mtrix of data
Parameters
- data : Dataframe containing the data.
'''
fig = FF.create_scatterplotmatrix(data, height=800, width=800)
plotly.offline.iplot(fig)
return
def __init__(self, args):
self.graphs = []
self.sc = SparkContext(conf=SparkConf().setAppName('dashboard'))
self.sqlContext = SQLContext(self.sc)
self.parse_arguments(args)
self.months = self.get_months()
self.create_as_tables()
self.set_session_counts()
self.create_daily_event_graphs()
self.create_nav_only_graphs()
self.graphs.append(py.plot(FF.create_table(self.metric_table), filename="table", auto_open=False))
def draw(candles):
# build the OCHL graph
times = candles['time'].apply(parser.parse)
fig_increasing = ff.create_candlestick(candles['open'],
candles['high'],
candles['low'],
candles['close'],
dates=times,
direction='increasing',
marker=Marker(color='#ff6666'),
line=Line(color='#ff6666'))
fig_decreasing = ff.create_candlestick(candles['open'],
candles['high'],
candles['low'],
candles['close'],
dates=times,
direction='decreasing',
marker=Marker(color='#39ac73'),
line=Line(color='#39ac73'))
fig = fig_increasing
fig['data'].extend(fig_decreasing['data'])
# build the volume graph
volume_chart = Bar(x=times,
y=candles['volume'],
marker=Marker(color="#ff9933"))
g = tools.make_subplots(rows=2, cols=1, shared_xaxes=True)
g.append_trace(fig['data'][0], 1, 1)
g.append_trace(fig['data'][1], 1, 1)
g.append_trace(volume_chart, 2, 1)
g.layout['yaxis2']['domain'] = [0.0, 0.25]
g.layout['yaxis1']['domain'] = [0.3, 1.0]
g.layout['paper_bgcolor'] = '#1E2022'
g.layout['plot_bgcolor'] = "#3E3E3E"
g.layout['font'] = Font(color="#3E3E3E")
g.layout['xaxis1']['tickfont'] = Font(color="#B3A78C")
g.layout['yaxis1']['tickfont'] = Font(color="#B3A78C")
g.layout['yaxis2']['tickfont'] = Font(color="#B3A78C")
g.layout['showlegend'] = False
iplot(g)
def make_candlestick(gamelog, column, reduce_func=None):
base_series = gamelog.set_index("Date")[column]
if reduce_func:
series = reduce_func(base_series).dropna()
else:
series = base_series.cumsum().dropna()
weekdata = list(create_weekly(series))
weeks = pd.DataFrame(weekdata).dropna()
fig = FF.create_candlestick(weeks.Open, weeks.High, weeks.Low, weeks.Close,
dates=weeks.start_date)
fig['layout'].update(height=600, width=600, title=column)
return fig
def drawOHLCChart(df, ticker, start_date, end_date):
trace = go.Candlestick(x=df['Date'],
open=df['AAPL.Open'],
high=df['AAPL.High'],
low=df['AAPL.Low'],
close=df['AAPL.Close'])
data = [trace]
fig = FF.create_ohlc(df.Open, df.High, df.Low, df.Close, dates=df.index)
plotly.offline.plot(fig,
filename='OHLC/{}-ohlc-{}-{}.html'.format(
ticker, start_date, end_date))
def makeCandleStickGraph(stockSymbol, Webster): # Makes a Candle Stick Graph
old_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
fig = FF.create_candlestick(Webster.Open,
Webster.High,
Webster.Low,
Webster.Close,
dates=Webster.index) # Past Data
py.plot(fig, filename=stockSymbol + '_Candle', validate=False)
sys.stdout.close()
sys.stdout = old_stdout
def dr_cdl(df,
m_title='分时数据K线图',
ftg='tmp/tmp_plotly.html',
m_tkAng=-20,
m_dtick=5,
m_y2k=3):
fig = pyff.create_candlestick(df.open,
df.high,
df.low,
df.close,
dates=df.index)
fig['layout'].update(
title=m_title,
xaxis=pygo.XAxis(
autorange=True,
gridcolor='rgb(180, 180, 180)',
mirror='all',
showgrid=True,
showline=True,
ticks='outside',
tickangle=m_tkAng,
dtick=m_dtick,
type='category',
categoryarray=df.index,
),
yaxis=pygo.YAxis(
autorange=True,
gridcolor='rgb(180, 180, 180)',
),
yaxis2=pygo.YAxis(
side='right',
overlaying='y',
range=[0, max(df['volume']) * m_y2k],
),
) # fig.update
r_vol = pygo.Bar(
x=df.index, #df['xtim'],
y=df['volume'],
name='volume',
yaxis='y2',
opacity=0.6,
marker=dict(
color='rgb(158,202,225)',
line=dict(
color='rgb(8,48,107)',
width=1.5,
),
),
) #r_vol
#
fig['data'].extend([r_vol])
#
pyplt(fig, filename=ftg, show_link=False)
def plotly_3d(verts, faces):
x,y,z = zip(*verts)
colormap=['rgb(236, 236, 212)','rgb(236, 236, 212)']
fig = FF.create_trisurf(x=x,
y=y,
z=z,
plot_edges=False,
colormap=colormap,
simplices=faces,
backgroundcolor='rgb(64, 64, 64)',
title="Interactive Visualization")
iplot(fig)
def draw(name, open, close, max, min, dates):
py.get_credentials()
open = sql.search(name, "open")
close = sql.search(name, "close")
max = sql.search(name, "max")
min = sql.search(name, "min")
dates = sql.search(name, "date")
fig = FF.create_candlestick(open, max, min, close, dates)
py.iplot(
fig,
filename=name,
validate=False,
)
def create_plotly_candlestick(symbol, start_date, end_date, filename=None):
if filename is None:
filename = os.path.join(
CHARTS_DIR, '{}-{}-{}.html'.format(symbol, start_date.date(),
end_date.date()))
df = web.DataReader(symbol, 'yahoo', start_date, end_date)
fig = FF.create_candlestick(df.Open,
df.High,
df.Low,
df.Close,
dates=df.index)
plotly.offline.plot(fig, filename=filename)
return filename
def search(self, data_rows, key):
data = data_rows[0]
if key == 'searchUser':
data_matrix = [['Username', 'Count'],
[data[0], data[1]]]
elif key == 'searchSubred':
data_matrix = [['Subreddit', 'Count'],
[data[0], data[1]]]
colorscale = [[0, '#4d004c'], [.5, '#ffffff'], [1, '#f2e5ff']]
table = FF.create_table(data_matrix, colorscale=colorscale)
plotted = plotly.offline.plot(table, filename='../graphs.html')
return plotted
def scatter_matrix(self, dataframe, select_columns=None, index_colname=None, diag_kind='scatter',
marker_size=10, height=800, width=1000, marker_outline_width=0, marker_outline_color='black'):
"""
Create a scatter matrix plot from dataframes using Plotly.
Args:
dataframe: (array) array of the data with column headers
select_columns: (list) names/headers of columns to plot from the dataframe
index_colname: (str) name of the index column in data array
diag_kind: (str) sets the chart type for the main diagonal plots (default='scatter')
Choose from 'scatter'/'box'/'histogram'
marker_size: (float) sets the marker size (in px)
height: (int/float) sets the height of the chart
width: (int/float) sets the width of the chart
marker_outline_width: (int) thickness of marker outline (currently affects the outline of histograms too
when "diag_kind" = 'histogram')
marker_outline_color: (str/array) color of marker outline - accepts similar formats as other color variables
Returns: a Plotly scatter matrix plot
"""
df = dataframe[select_columns] if select_columns else dataframe
fig = FF.create_scatterplotmatrix(df, index=index_colname, diag=diag_kind, size=marker_size,
height=height, width=width)
# Add outline to markers
for trace in fig['data']:
trace['marker']['line'] = dict(width=marker_outline_width, color=marker_outline_color)
if self.plot_mode == 'offline':
if self.filename:
plotly.offline.plot(fig, filename=self.filename)
else:
plotly.offline.plot(fig)
elif self.plot_mode == 'notebook':
plotly.offline.init_notebook_mode() # run at the start of every notebook; version 1.9.4 required
plotly.offline.iplot(fig)
elif self.plot_mode == 'online':
plotly.tools.set_credentials_file(username=self.username, api_key=self.api_key)
if self.filename:
plotly.plotly.plot(fig, filename=self.filename, sharing='public')
else:
plotly.plotly.plot(fig, sharing='public')
elif self.plot_mode == 'static':
plotly.plotly.image.save_as(fig, filename=self.filename, height=self.height, width=self.width,
scale=self.scale)
def readAssetReturnsCSV(asset):
df = pandas.read_csv('../../Daily/' + asset + '.csv')
df['Index'] = pandas.to_datetime(df['Index']) # convert dates to Datetime objects
df = df.set_index('Index') # set Index
df = df.sort_index() # sort by date
# Will store the weekly data
df_calcs = pandas.DataFrame(columns=['period_ended', 'open', 'high', 'low', 'close', 'volume',
'adj.', 'weekly_return'])
# compute weekly returns only across full trading weeks (Monday -> Friday)
for index, row in df.iterrows():
date = pandas.to_datetime(index)
if date.weekday() == 4: # is Friday
thur = date + datetime.timedelta(days=-1)
wed = date + datetime.timedelta(days=-2)
tues = date + datetime.timedelta(days=-3)
mon = date + datetime.timedelta(days=-4)
# trading occurred on corresponding Friday
if thur in df.index and wed in df.index and tues in df.index and mon in df.index:
period_ended = date
open = df.loc[mon]['open']
close = row['close']
low = min(df.loc[mon]['low'], df.loc[tues]['low'], df.loc[wed]['low'], df.loc[thur]['low'],
row['close'])
high = max(df.loc[mon]['high'], df.loc[tues]['high'], df.loc[wed]['high'], df.loc[thur]['high'],
row['high'])
volume = df.loc[mon]['volume'] + df.loc[tues]['volume'] + df.loc[wed]['volume'] + \
df.loc[thur]['volume'] + row['volume']
adj = row['adj.']
weekly_return = (row['adj.'] - df.loc[mon]['adj.']) / df.loc[mon]['adj.']
#print(mon, '(', df.loc[mon]['adj.'], ') ->', date, '(', row['adj.'], '):', weekly_return*100, '%')
week = pandas.Series([period_ended, open, high, low, close, volume, adj, weekly_return])
week = week.rename({0: 'period_ended', 1: 'open', 2: 'high', 3: 'low', 4: 'close', 5: 'volume',
6: 'adj.', 7: 'weekly_return'})
df_calcs = df_calcs.append(week, ignore_index=True)
df_calcs = df_calcs.set_index('period_ended') # set index to period_ended
# standardize weekly returns
df_calcs['std_return'] = (df_calcs['weekly_return'] - df_calcs['weekly_return'].mean()) / \
df_calcs['weekly_return'].std()
# save OHLC data
ohlc = FF.create_ohlc(df_calcs.open, df_calcs.high, df_calcs.low, df_calcs.close, dates=df_calcs.index)
return Asset.Asset(asset, df_calcs, ohlc)
def make_plotly_figs():
from plotly.tools import FigureFactory as FF
import numpy as np
x1 = np.random.randn(200)
hist_data = [x1]
group_labels = ['Group 1']
# Create distplot with curve_type set to 'normal'
fig = FF.create_distplot(hist_data, group_labels, bin_size=.25, show_curve=False)
# Add title
fig['layout'].update(title='Plot')
return [fig]
def statisticplots(filename, plotkind,columns):
try:
data = pd.read_csv(filename,index_col=0, parse_dates=True)
except(FileNotFoundError, IOError):
print('Wrong file or file path.')
return None
if columns is None or not columns:
columns = list(data.columns.values)
data = data.ix[:,columns]
if plotkind == 'scattermatrix':
fig = FF.create_scatterplotmatrix(data,diag='box',title='Scattermatrix plot of {}'.format(filename[:-4]))
elif plotkind == 'line':
fig = data.iplot(theme='pearl',kind='scatter',title='Line plot of {}.'.format(filename[:-4]),subplots=True,asFigure=True)
elif plotkind == 'box':
fig = data.iplot(theme='pearl',kind='box',title='Box plot of {}.'.format(filename[:-4]),asFigure=True)
py.plot(fig,filename='../../plots/'+filename[:-4]+plotkind,validate=False,auto_open=False)
def make_test_plot():
# Add table data
table_data = [['Team', 'Wins', 'Losses', 'Ties'],
['Montreal<br>Canadiens', 18, 4, 0],
['Dallas Stars', 18, 5, 0],
['NY Rangers', 16, 5, 0],
['Boston<br>Bruins', 13, 8, 0],
['Chicago<br>Blackhawks', 13, 8, 0],
['Ottawa<br>Senators', 12, 5, 0]]
# Initialize a figure with FF.create_table(table_data)
figure = FF.create_table(table_data, height_constant=60)
# Add graph data
teams = ['Montreal Canadiens', 'Dallas Stars', 'NY Rangers',
'Boston Bruins', 'Chicago Blackhawks', 'Ottawa Senators']
GFPG = [3.54, 3.48, 3.0, 3.27, 2.83, 3.18]
GAPG = [2.17, 2.57, 2.0, 2.91, 2.57, 2.77]
# Make traces for graph
trace1 = go.Bar(x=teams, y=GFPG, xaxis='x2', yaxis='y2',
marker=dict(color='#0099ff'),
name='Goals For<br>Per Game')
trace2 = go.Bar(x=teams, y=GAPG, xaxis='x2', yaxis='y2',
marker=dict(color='#404040'),
name='Goals Against<br>Per Game')
# Add trace data to figure
figure['data'].extend(go.Data([trace1, trace2]))
# Edit layout for subplots
figure.layout.yaxis.update({'domain': [0, .45]})
figure.layout.yaxis2.update({'domain': [.6, 1]})
# The graph's yaxis2 MUST BE anchored to the graph's xaxis2 and vice versa
figure.layout.yaxis2.update({'anchor': 'x2'})
figure.layout.xaxis2.update({'anchor': 'y2'})
figure.layout.yaxis2.update({'title': 'Goals'})
# Update the margins to add a title and see graph x-labels.
figure.layout.margin.update({'t':75, 'l':50})
figure.layout.update({'title': '2016 Hockey Stats'})
# Update the height because adding a graph vertically will interact with
# the plot height calculated for the table
figure.layout.update({'height':800})
# Plot!
return plotly.offline.plot(figure, output_type='div')
def query_stock(stock, start='2011.01.01', end='today'):
stock+=".TW"
st = datetime.date(datetime.strptime(start, '%Y.%m.%d'))
if end == 'today':
en = datetime.date(datetime.today())
else:
en = datetime.date(datetime.strptime(end, '%Y.%m.%d'))
df = data.get_data_yahoo(stock, st, en)
fig = FF.create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index)
SMA20=moving_average(df.Close.values, 20)
add_line = Scatter(
x=df.index[-len(SMA20):],
y=SMA20,
name= '20SMA',
line=Line(color='rgb(200, 200, 250)')
)
fig['data'].extend([add_line])
plotly.offline.plot(fig, validate=False)
def plot(self):
fig = FF.create_candlestick(df.O, df.H, df.L, df.C, dates=df.index)
fig['layout'].update({'title': 'AMD',
'yaxis': {'title': 'Price'},
'xaxis': {'title': 'Date'},
'shapes': [{'x0': '2016-09-01', 'x1': '2016-09-01','y0':0,'y1':1,
'xref':'x','yref':'paper',
'line':{'color': 'rgb(60,60,60)','width':1}
}
]
})
sma5_line = Scatter(x=df.index, y=df.Sma5,name='SMA5',line={'shape':'spline','dash':'dot'})
sma10_line = Scatter(x=df.index, y=df.Sma10,name='SMA10', line={'shape':'spline','dash':'dot'})
sma15_line = Scatter(x=df.index, y=df.Sma15,name='SMA15', line={'shape':'spline','dash':'dot'})
volume = Bar(x = df.index, y = df.Volume/50000000.0, name='Volume')
fig['data'].extend([sma5_line,sma10_line,sma15_line,volume])
return fig
def generate():
# Add table data
global data
global internet_data_list
data = lp.all_running_process()
#print data
table_data = transform_program_data('table')
# Initialize a figure with FF.create_table(table_data)
figure = FF.create_table(table_data, height_constant=60, index=True)
pyChart1 = go.Pie(labels = ['tumblr', 'twitter', 'facebook', 'pinterest'],
values = internet_data_list,
name = 'Internet',
hole = .4,
domain = {'x':[0, .48], 'y':[1, .55]})
pyChart2 = go.Pie(labels = transform_program_data('pyName'),
values = transform_program_data('pyData'),
name = 'Programs',
hole = .4,
domain = {'x':[.52, 1], 'y':[1, .55]})
# Add trace data to figure
figure['data'].extend(go.Data([pyChart1]))
figure['data'].extend(go.Data([pyChart2]))
#Table goes at bottom of plot
figure.layout.yaxis.update({'domain': [0, .45]})
# Update the margins to add a title and see graph x-labels.
figure.layout.margin.update({'t':75, 'l':50})
figure.layout.update({'title': 'How I\'m Spending My Time'})
figure.layout.update({'height':800, 'width':1000})
# Plot!
plotly.offline.plot(figure, filename='pyChartWithTable.html', auto_open=False)
def distPlot(request):
df = cPickle.loads(str(request.session['dataframe']))
x0 = df[request.GET['x_axis']]
x1 = df[request.GET['y_axis']]
x2 = df[request.GET['z_axis']]
data=[x0,x1,x2]
labels = [request.GET['x_axis'],request.GET['y_axis'],request.GET['z_axis']]
colors = ['rgb(0, 0, 100)', 'rgb(0, 200, 200)','rgb(0, 200, 100)']
fig = FF.create_distplot(
data, labels, bin_size=.2, colors=colors)
fig['layout'].update(title='Distplot of {} vs {} vs {}'.format(request.GET['x_axis'], \
request.GET['y_axis'], \
request.GET['z_axis']))
url = py.plot(fig, filename='Distplot with Normal Curve', auto_open=False)
dashboard_json = {
"rows": [
[{"plot_url": url}]
],
"banner": {
"visible": False,
"backgroundcolor": "#2A3F54",
"textcolor": "white",
# "title": "{} group_by {}".format(request.GET['x_axis'], request.GET['y_axis']),
"links": []
},
"requireauth": False,
"auth": {
"username": "******",
"passphrase": ""
}
}
response = requests.post('https://dashboards.ly/publish',
data={'dashboard': json.dumps(dashboard_json)},
headers={'content-type': 'application/x-www-form-urlencoded'})
response.raise_for_status()
dashboard_url = response.json()['url']
return dashboard_url
def _plot_accuracy_distribution(dsets_kpis):
"""
Generate plotly histogram of accuracy distributions, overlaid
Return a plot structure
"""
data = []
for key in dsets_kpis.keys():
accuracy = dsets_kpis[key]['accuracy']
data.append(accuracy)
group_labels = dsets_kpis.keys()
fig = FF.create_distplot(data, group_labels, show_hist=False)
fig['layout']['xaxis'].update(title='Accuracy')
fig['layout']['xaxis']['tickfont'].update(size=24)
fig['layout']['xaxis']['titlefont'].update(size=24)
fig['layout']['yaxis'].update(title='density')
fig['layout']['yaxis']['tickfont'].update(size=24)
fig['layout']['yaxis']['titlefont'].update(size=24)
fig['layout']['font']['size'] = 20
return fig
def __init__(self, df, primaryKey="Close"):
super(Candlestick, self).__init__(
FF.create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index))
self.df = df
if Candlestick.CUSTOM_MARKERS_ENABLED:
for subplot in self["data"]:
subplot["hoverinfo"] = "none"
self._addMarkers(primaryKey, df[primaryKey])
self._addMovingAverage(df[primaryKey], 50)
self._addMovingAverage(df[primaryKey], 14)
if Candlestick.VOLUME_ENABLED:
self._addVolumeBars(df["Volume"])
self["layout"].update = {
"paper_bgcolor": 'rgba(0,0,0,0)',
"plot_bgcolor": 'rgba(0,0,0,0)',
"yaxis1": {"domain": [0, 1]},
"yaxis2": {"domain": [0, 0.2], "anchor": "x1"},
}
def plot_Gender(path, feature, type_):
feature = pd.DataFrame(feature)
feature = feature.reset_index()
data = folder_reader(path)
data.columns = ['temperature', 'wind', 'gender', 'total']
aux = data[data.gender == 1]
aux = aux.groupby(type_)['total'].sum()
aux = aux.reset_index()
aux = pd.merge(aux, feature, how='left', left_index=type_, right_index="index")
number = aux[aux.columns[1]] * 100 / aux[aux.columns[3]]
Temp_1 = np.repeat(aux[aux.columns[0]].values, number.round(0).astype(int))
aux = data[data.gender == 2]
aux = aux.groupby(type_)['total'].sum()
aux = aux.reset_index()
aux = pd.merge(aux, feature, how='left', left_index=type_, right_index="index")
number = aux[aux.columns[1]] / aux[aux.columns[3]]
Temp_2 = np.repeat(aux[aux.columns[0]].values, number.round(0).astype(int))
hist_data = [Temp_1, Temp_2]
group_labels = ['Man', 'Woman']
colors = ['rgb(0, 0, 100)', 'rgb(0, 200, 200)']
fig = FF.create_distplot(hist_data, group_labels, colors=colors)
plot_url = py.offline.plot(fig)
def isoSurface3d(image, level=1, fn='./plots/isosurf3d.html'): ''' wrapper function for plotly's isosurface 3d plots args: @a image: 3d array of numbers @a level: level at which to make an isocontour @a fn: string, filename to save plot as ''' vertices, simplices = measure.marching_cubes(image, level) x,y,z = zip(*vertices) fig = FF.create_trisurf( x=x, y=y, z=z, simplices=simplices ) py.offline.plot(fig, filename=fn)
import plotly
import plotly.plotly as py
from plotly.tools import FigureFactory as FF
import numpy as np
import pandas as pd
print(plotly.__version__)
dataframe = pd.DataFrame(np.random.randn(100, 3),
columns=['A', 'B', 'C'])
fig = FF.create_scatterplotmatrix(dataframe, diag='histogram', index='A',
colormap=['rgb(200, 150, 90)', '#3012CF', 'rgb(20, 124, 246)'],
colormap_type='seq', height=700, width=700)
py.plot(fig, filename = 'Customized Colormap')
def plotlyData(self, i_destDictKey, i_freq='d', i_debug=False, i_out=None):
l_data = self.m_data[i_destDictKey]['data'][i_freq]
l_data['Date_tmp'] = l_data['Date'].apply(lambda d: mdates.date2num(d.to_pydatetime()))
# idxLocalMins = self.m_data[i_destDictKey]['analysis'][i_freq]['localMins']
# idxLocalMaxs = self.m_data[i_destDictKey]['analysis'][i_freq]['localMaxs']
minIdx = self.m_data[i_destDictKey]['analysis'][i_freq]['imin']
maxIdx = self.m_data[i_destDictKey]['analysis'][i_freq]['imax']
fig = FF.create_ohlc(l_data['Open'], l_data['High'], l_data['Low'], l_data['Close'], dates=l_data['Date'],
line=Line(color='black'))
if self.m_data[i_destDictKey]['analysis'][i_freq]['trendType'] == 2:
trend = 'Up-Trend'
elif self.m_data[i_destDictKey]['analysis'][i_freq]['trendType'] == 1:
trend = 'Down-Trend'
else:
trend = 'None'
# Update the fig - all options here: https://plot.ly/python/reference/#Layout
fig['layout'].update({
# 'title': self.generalData['name'] + ' [' + trend + ']',
'title': i_destDictKey + ' [' + trend + ']',
'yaxis': {'title': 'Stock Price [$]'},
'xaxis': {'title': 'Date'},
# 'shapes': [{
# 'x0': '2008-09-15', 'x1': '2008-09-15', 'type': 'line',
# 'y0': 0, 'y1': 1, 'xref': 'x', 'yref': 'paper',
# 'line': {'color': 'rgb(40,40,40)', 'width': 0.5}
# }],
# 'annotations': [{
# 'text': "the fall of Lehman Brothers",
# 'x': '2008-09-15', 'y': 1.02,
# 'xref': 'x', 'yref': 'paper',
# 'showarrow': False, 'xanchor': 'left'
# }]
})
# plotly.offline.iplot(fig, filename='finance/aapl-recession-ohlc', validate=False)
if len(self.m_data[i_destDictKey]['analysis'][i_freq]['ema34']) > len(l_data['Date']):
idx = len(self.m_data[i_destDictKey]['analysis'][i_freq]['ema34']) - len(l_data['Date']) + 1
add_mins = Scatter(
x=l_data['Date'][minIdx],
y=l_data['Low'][minIdx],
name='min',
mode='markers')
add_maxs = Scatter(
x=l_data['Date'][maxIdx],
y=l_data['High'][maxIdx],
name='max',
mode='markers')
add_ema34 = Scatter(
x=l_data['Date'],
y=self.m_data[i_destDictKey]['analysis'][i_freq]['ema34'][idx:],
name='EMA-34',
mode='line')
add_ema14 = Scatter(
x=l_data['Date'],
y=self.m_data[i_destDictKey]['analysis'][i_freq]['ema14'][idx:],
name='EMA-14',
mode='line')
add_ema200 = Scatter(
x=l_data['Date'],
y=self.m_data[i_destDictKey]['analysis'][i_freq]['ema200'][idx:],
name='EMA-200',
mode='line')
add_ema50 = Scatter(
x=l_data['Date'],
y=self.m_data[i_destDictKey]['analysis'][i_freq]['ema50'][idx:],
name='EMA-50',
mode='line')
fig['data'].extend([add_mins, add_maxs, add_ema34, add_ema14, add_ema200, add_ema50])
plotly.offline.plot(fig, filename=str(i_destDictKey) + '.html')
width =1
),
opacity=0.8
)
)
dataList.append(name)
layout = go.Layout(scene = go.Scene(xaxis=dict(title='Heel Height (mm)'), yaxis=dict(title='Forefoot Height (mm)'),zaxis=dict(title ='Weight (oz)')),
margin=dict(l=0,r=0,b=0,t=0))
fig = go.Figure(data = dataList, layout=layout)
plot_url = py.plot(fig, filename="3d-scatter-test")
#%%
hist_data= []
for x in brands:
data = rwDF[(rwDF.Brand==x)]['Price']
data.dropna(inplace=True)
hist_data.append(data)
fig = FF.create_distplot(hist_data, brands, bin_size=20)
plot_url=py.plot(fig, filename="Multiple Hists")
#%%
# Average Price, Forefoot Height, Heel Height, Weight by different Runningwarehouse shoe type
rwDF.groupby('Shoe Type').mean().T.plot(kind = 'bar')
sns.pairplot(rwDF,vars=['Forefoot Height (mm)', 'Heel Height (mm)', 'Weight (oz)', 'Drop (mm)'], hue="Brand", palette="husl")
sns.pairplot(rwDF,vars=['Forefoot Height (mm)', 'Heel Height (mm)', 'Weight (oz)', 'Drop (mm)'], hue="Shoe Type")
def violin_plot(self, data, data_col=None, group_col=None, title=None, height=800, width=1000,
colors=None, use_colorscale=False, groups=None):
"""
Create a violin plot using Plotly.
Args:
data: (list/array) accepts either a list of numerical values, a list of dictionaries all with identical keys
and at least one column of numeric values, or a pandas dataframe with at least one column of numbers
data_col: (str) the header of the data column to be used from an inputted pandas dataframe. Not applicable
if 'data' is a list of numeric values
group_col: (str) applicable if grouping data by a variable. 'group_header' must be set to the name of the
grouping variable
title: (str) the title of the violin plot
height: (float) the height of the violin plot
width: (float) the width of the violin plot
colors: (str/tuple/list/dict) either a plotly scale name (Greys, YlGnBu, Greens, YlOrRd, Bluered, RdBu,
Reds, Blues, Picnic, Rainbow, Portland, Jet, Hot, Blackbody, Earth, Electric, Viridis), an rgb or hex
color, a color tuple, a list of colors or a dictionary. An rgb color is of the form 'rgb(x, y, z)'
where x, y and z belong to the interval [0, 255] and a color tuple is a tuple of the form (a, b, c)
where a, b and c belong to [0, 1]. If colors is a list, it must contain valid color types as its
members. If colors is a dictionary, its keys must represent group names, and corresponding values must
be valid color types (str).
use_colorscale: (bool) Only applicable if grouping by another variable. Will implement a colorscale based
on the first 2 colors of param colors. This means colors must be a list with at least 2 colors in it
(Plotly colorscales are accepted since they map to a list of two rgb colors)
groups: (list) list of group names (strings). This field is used when the same colorscale is required to be
used for all violins.
Returns: a Plotly violin plot
"""
if groups and isinstance(data, pd.DataFrame):
use_colorscale = True
group_stats = {}
groupby_data = data.groupby([group_col])
for group in groups:
data_from_group = groupby_data.get_group(group)[data_col]
stat = np.median(data_from_group)
group_stats[group] = stat
else:
group_stats = None
# Filter out groups from dataframe that have only 1 row.
if isinstance(data, pd.DataFrame):
group_value_counts = data[group_col].value_counts().to_dict()
for j in group_value_counts:
if group_value_counts[j] == 1:
data = data[data[group_col] != j]
warnings.warn('Omitting rows with group = ' + str(
j) + ' which have only one row in the dataframe.')
fig = FF.create_violin(data=data, data_header=data_col, group_header=group_col, title=title,
height=height,
width=width, colors=colors, use_colorscale=use_colorscale,
group_stats=group_stats)
# Cannot add x-axis title as the above object populates it with group names.
fig.update(dict(
layout=dict(
title=self.title,
titlefont=dict(size=self.textsize, family=self.fontfamily),
yaxis=dict(title=self.y_title,
titlefont=dict(size=self.textsize, family=self.fontfamily),
tickfont=dict(size=self.ticksize, family=self.fontfamily)),
)
))
# Change sizes in all x-axis
for item in fig['layout']:
if item.startswith('xaxis'):
fig['layout'][item].update(
dict(
titlefont=dict(size=self.textsize, family=self.fontfamily),
tickfont=dict(size=self.ticksize, family=self.fontfamily)
)
)
self.create_plot(fig)
