def BP_display(df):
DF = df
DF['bp_cat'] = ""
for i in range(len(DF)):
if DF.ap_lo[i] <= 60 and DF.ap_hi[i] <= 100:
DF.bp_cat[i] = 'low'
elif (DF.ap_lo[i] > 60
and DF.ap_lo[i] <= 80) or (DF.ap_hi[i] > 100
and DF.ap_hi[i] <= 120):
DF.bp_cat[i] = 'normal'
elif (DF.ap_lo[i] > 80) or (DF.ap_hi[i] > 120):
DF.bp_cat[i] = 'high'
DF['bp_sum'] = ""
for i in range(len(DF)):
DF.bp_sum[i] = float(0.25 * (DF.ap_lo[i] + DF.ap_hi[i]))
DF.bp_sum = pd.to_numeric(DF.bp_sum)
fig = px.scatter_3d(DF,
x='ap_lo',
y='ap_hi',
z='date',
color='bp_cat',
size='bp_sum',
size_max=100,
hover_name='bp_cat')
py.plot(fig, filename='bp_graph', auto_open=True)
def Real_time_plot1(request):
######### Phase 1:- Data aloocating and defining
import plotly.graph_objects as go
import plotly
import numpy as np
import plotly.express as px
########### phase :: Accessing the downloaded file (Data1.csv)
import os
from smartfasal_project.settings import BASE_DIR
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
path = STATIC_ROOT
os.chdir(path)
import pandas as pd
filename = "data1.csv"
#Local_data_smartfasal = pd.read_csv(filename, names=['Timestamp', 'S_M_10cm','S_M_45cm','S_M_80cm', 'Temperature', 'Humidity', 'Pressure', 'Luxes'])
#Local_data_smartfasal = pd.read_csv(filename)
import plotly.express as px
df = pd.read_csv('Last_rows.csv')
x_Time = df['Time']
y0 = df['SM10']
y1 = df['SM45']
y2 = df['SM80']
y3 = df['Temp']
y4 = df['Humd']
y5 = df['LMNS']
y6 = df['PRSR']
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_Time, y=y0, mode='lines', name='SM @ 10 cm',
marker = dict(color = 'rgba(255,0,0,0.8)')))
fig.add_trace(go.Scatter(x=x_Time, y=y1, mode='lines', name='SM @ 45 cm',
marker = dict(color = 'rgba(0,255,0,0.8)')))
fig.add_trace(go.Scatter(x=x_Time, y=y2, mode='lines', name='SM @ 80 cm',
marker = dict(color = 'rgba((26, 102, 255,0.8)')))
fig.add_trace(go.Scatter(x=x_Time, y=y3, mode='lines', name='Temperature',
marker = dict(color = 'rgba(204, 0, 204, 0.8)')))
fig.add_trace(go.Scatter(x=x_Time, y=y4, mode='lines', name='Humidity',
marker = dict(color = 'rgba(0, 153, 51, 0.8)')))
fig.add_trace(go.Scatter(x=x_Time, y=y5, mode='lines', name='Luminisity',
marker = dict(color = 'rgba(0, 0, 204, 0.8)')))
fig.add_trace(go.Scatter(x=x_Time, y=y6, mode='lines', name='Pressure',
marker = dict(color = 'rgba(80, 26, 80, 0.8)')))
fig.show()
import chart_studio
username = '******' # your usernam
api_key = 'VQ5pvk3TMJi50tDGdWne' # your api key - go to profile > settings > regenerate keychart_studio.tools.set_credentials_file(username=username, api_key=api_key)
chart_studio.tools.set_credentials_file(username=username, api_key=api_key)
import chart_studio.plotly as csp
csp.plot(fig, showLink= 'false');
#Plotly.plot(divid, data, layout, {showLink: false})
#plt_div = plot(fig, output_type='div', include_plotlyjs=False)
#return render(request,'www.http://smartfasal.in/wp/?page_id=277')
return HttpResponse("Prcoessed completed")
def investor_vs_category(self, cat_inv_list, plot_type='plotly'):
# group by category
labels = []
sizes = []
texts = []
sorted_list = sorted(cat_inv_list, key=lambda row: (row[0]) if len(row)>0 else (' '))
cat_idx = 0
for category, rows in groupby(sorted_list, lambda x: x[0] if len(x)>0 else (' ')):
count = 0
for row in rows:
for idx in range(1, len(row)):
count += len(row[idx].split(','))
labels.append(cat_idx)
sizes.append(count)
texts.append(category)
print(cat_idx, ":: ", category)
cat_idx += 1
# plot data
if plot_type == 'plotly':
fig = go.Figure(data=[go.Pie(labels=labels, values=sizes,
name='Category vs Investor', text=texts)])
fig.update_layout(title=go.layout.Title(text="Category vs Investor"))
py.plot(fig, filename = 'Category vs Investor', auto_open=True)
# fig.show()
elif plot_type == 'matplotlib':
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels)
ax1.axis('equal')
fig1.savefig('Category vs Investor.png')
pass
def funding_pie(self, funding_list, plot_type='plotly'):
funding_list = funding_list[0]+funding_list[1]
funding_list = [[fund.strip() for fund in funding_string.split(',')] for funding_string in funding_list]
funding_count = Counter(list(chain.from_iterable(funding_list))) # {'blue': 3, 'red': 2, 'yellow': 1}
print(funding_count)
print()
labels = []
sizes = []
texts = []
for idx, (funding, count) in enumerate(funding_count.items()):
if funding != 'n/a' and funding != '' and funding != '--':
# if len(funding) > 12:
# # print([fund.strip() for fund in funding.split(',')])
# labels.append([fund.strip() for fund in funding.split(',')])
# else:
labels.append(idx)
texts.append(funding)
sizes.append(count)
if plot_type == 'plotly':
fig = go.Figure(data=[go.Pie(labels=labels, values=sizes,
name='Volume of Each Investor', text=texts)])
fig.update_layout(title=go.layout.Title(text="Volume of Each Investor"))
py.plot(fig, filename = 'Volume of Each Investor', auto_open=True)
# fig.show()
elif plot_type == 'matplotlib':
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels)
ax1.axis('equal')
fig1.savefig('Volume of Each Investor.png')
pass
def skuRevenu(self):
sku = []
#get sku list
for order in self.Collection.find({"orderStatus":{"$in":["Shipped","Pending"]}}):
if order["sku"] not in sku:
sku.append(order["sku"])
#for each sku get it curve and add to trace
plotData = []
for targetSku in sku:
orderDates = {}
for order in self.Collection.find({"orderStatus":{"$in":["Shipped","Pending"]},"sku":targetSku}).sort("purchaseDate",pymongo.ASCENDING):
if order["purchaseDate"] not in orderDates.keys():
try:
orderDates[order["purchaseDate"]] = float(order["itemPrice"])
except ValueError:
print(order["itemPrice"])
else:
orderDates[order["purchaseDate"]] += float(order["itemPrice"])
x = []
y = []
for date in orderDates.keys():
x.append(date)
y.append(orderDates[date])
trace = go.Scatter(
x = x,
y = y,
name = targetSku
)
plotData.append(trace)
#plot(plotData,filename="skuRevenu.html")
py.plot(plotData, filename="skuRevenue", auto_open=True)
def subtag_pie(self, subtag_list, plot_type='plotly'):
data = []
for subtag_col in subtag_list:
data = data + subtag_col
subtag_list = np.array(data)
subtag_count = Counter(
subtag_list) # {'blue': 3, 'red': 2, 'yellow': 1}
print(subtag_count)
print()
labels = []
sizes = []
texts = []
for idx, (subtag, count) in enumerate(subtag_count.items()):
if subtag:
labels.append(idx)
texts.append(subtag)
sizes.append(count)
print(subtag, ": ", idx)
if plot_type == 'plotly':
fig = go.Figure(
data=[go.Pie(labels=labels, values=sizes, text=texts)])
fig.update_layout(title=go.layout.Title(text="Subtag Prevalence"))
py.plot(fig, filename='Subtag Prevalence', auto_open=True)
# fig.show()
elif plot_type == 'matplotlib':
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels)
ax1.axis('equal')
fig1.savefig('subtag_prevalence.png')
pass
def draw_gross_rating():
"""
The function plots total rating against average weekly gross
"""
# read in data
gs = pd.read_csv("part2cleanedGrosses.csv", sep=',', encoding='latin1')
rt = pd.read_csv("Musical_ratings-withoutNa-cleaned.csv")
# prepare data
# limit the time scope to recent 5 years
testData2 = gs[gs['year'] >= 2015]
testData2 = testData2[['show', 'year', 'month', 'this_week_gross']]
# calculate avg weekly grosses mean (by show)
testData2['avg_weekly_gross'] = testData2.groupby('show')['this_week_gross'].transform('mean')
testData2_1 = pd.merge(testData2, rt, on='show')
# select distinct show
testData2_1 = testData2_1.drop_duplicates('show')
# Select relevant columns
testData2_1 = testData2_1[['show', 'avg_weekly_gross', 'total_rating']]
fig = px.scatter(testData2_1, x="avg_weekly_gross", y="total_rating", color="total_rating", hover_name="show",
labels=dict(avg_weekly_gross="Average Weekly Grosses (in USD)", total_rating="Ratings"),
color_continuous_scale=px.colors.colorbrewer.RdYlGn,
title="Ratings vs. Average weekly grosses of recent 5 years")
py.plot(fig, "gross-rating", auto_open=True)
def plot_work_hours(rows):
daily = work_hours_daily(rows)
weekly = work_hours_weekly(daily)
monthly = work_hours_monthly(daily)
data = [
go.Scatter(
x=daily.Date,
y=daily.Hours,
mode='markers',
name='Days',
),
go.Scatter(
x=weekly.index,
y=weekly.values,
mode='lines+markers',
name='Weeks',
),
go.Scatter(
x=monthly.index,
y=monthly.values,
mode='lines',
name='Months',
line={
'dash': 'dash',
'color': 'firebrick',
},
),
]
plotly_setup()
py.plot(data,
filename=f'woкk_hours_{datetime.now().date()}',
sharing='public')
def review_overall_hist(yelp):
# plot a histogram of the review count
figure9 = px.histogram(yelp, x="Review Count")
# add title and axis label
figure9.update_layout(title_text='Histogram of Review Count')
# Display the plot
py.plot(figure9, auto_open=True)
def price_overall_hist(yelp):
# plot a histogram of the rating
figure8 = px.histogram(yelp, x="Price Level")
# add title and axis label
figure8.update_layout(title_text='Histogram of Price Level')
# Display the plot
py.plot(figure8, auto_open=True)
def rating_overall_hist(yelp):
# plot a histogram of the review count
figure10 = px.histogram(yelp, x="Rating")
# add title and axis label
figure10.update_layout(title_text='Histogram of Rating')
# Display the plot
py.plot(figure10, auto_open=True)
def create_candlestick(self):
r = requests.get(
f'https://financialmodelingprep.com/api/v3/historical-price-full/{self.quote}?timeseries={self.days}'
)
r = r.json()
stockdata = r['historical']
stockdata_df = pd.DataFrame(stockdata)
fig = go.Figure(data=[
go.Candlestick(x=stockdata_df['date'],
open=stockdata_df['open'],
high=stockdata_df['high'],
low=stockdata_df['low'],
close=stockdata_df['close'])
])
fig.update_layout(title={
'text': self.quote,
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
font=dict(family="Courier New, monospace",
size=20,
color="#7f7f7f"))
fig.show()
py.plot(fig, filename='data', auto_open=True)
def plotMap(draftClassData, latitudeData, longitudeData, colourList):
data = []
for i in range(len(playerNames)):
trace = go.Scattermapbox(lat=[sitelat[i]],
lon=[sitelon[i]],
name=playerNames[i],
mode='markers',
marker=dict(size=draftClassData[i],
color='red',
opacity=0.7),
text=playerNames[i],
hoverinfo='text')
data.append(trace)
layout = go.Layout(
title='Popular Cities',
autosize=True,
hovermode='closest',
showlegend=False,
mapbox=dict(accesstoken=mapbox_access_token,
bearing=0,
center=dict(lat=38, lon=-94),
pitch=0,
zoom=3,
style='light'),
)
fig = dict(data=data, layout=layout)
py.plot(fig, filename='Popular Cities')
def createGraph(listOfYears, proportionsForGenreAll, proportionsForGenreAction,
proportionsForOverview):
# will prepare data for a t-test / correlation
# set up so that it will save to plotly account
chart_studio.tools.set_credentials_file(username='******',
api_key='quWnfkS81TFD5CzuIDYU')
# the listToCheck is the list that will define the binary violent column
fig = go.Figure(data=[
go.Bar(name='All Violent Genres',
x=listOfYears,
y=proportionsForGenreAll),
go.Bar(
name='Action Movies', x=listOfYears, y=proportionsForGenreAction),
go.Bar(name='Violent Movie Descriptions',
x=listOfYears,
y=proportionsForOverview)
])
# Change the bar mode
fig.update_layout(
title='proportion of violent movies through the years',
barmode='group',
yaxis=dict(title='proportion of violent movies in a given year', ),
xaxis=dict(title='year', ),
)
# save graph to account
py.plot(fig, filename="violent_movies_over_time.html", auto_open=True)
fig.show()
def nat_bar_plot(df, file_name):
plotly_fig = go.Figure(data=[
go.Bar(name='USAU Members',
x=df['Race/Ethnicity'],
y=df['member_percent_rounded'],
text=df['member_percent_rounded'],
textposition='outside',
hoverinfo='skip'),
go.Bar(name='US Census Population',
x=df['Race/Ethnicity'],
y=df['us_pop_percent_rounded'],
text=df['us_pop_percent_rounded'],
textposition='outside',
hoverinfo='skip')
])
# Change the bar mode
plotly_fig.update_layout(
barmode='group',
legend=dict(orientation="h", y=1.1),
xaxis_title="",
yaxis_title="Percent of Group",
# width=1000,
# height=2000,
)
# plot(plotly_fig)
py.plot(plotly_fig, filename=file_name, auto_open=True)
def makeHeatMap(presyn_connectDict):
df = pd.DataFrame.from_dict(presyn_connectDict)
#g = sns.clustermap(df, method='ward', metric='euclidean')
g = sns.clustermap(df, metric='euclidean')
mask = g.mask
colList = list(mask.columns)
indList = list(mask.index)
df = df.reindex(columns=colList)
df = df.reindex(indList)
x = list(df.columns)
y = list(df.index)
colorscale = [[0.0, '#000000'], [.000001, '#6D00D1'], [.0002, '#1600FF'],
[.0003, '#00FF2C'], [.001, '#DFFF0A'], [.0015, '#FF8A0A'],
[.05, '#ff0f0a'], [1, '#BB0000']]
heatmap = go.Heatmap(z=df, x=x, y=y, colorscale=colorscale)
layout = go.Layout(title="GF Input Type Interconnectivity",
xaxis=dict(title="Input", tickmode='linear'),
yaxis=dict(title="Output", tickmode='linear'))
data = [heatmap]
fig = go.Figure(data=data, layout=layout)
py.plot(
fig,
filename='GFInput_Interconnectivity: yaxis = Output; xaxis = Input')
return
def plotly_graph():
"""
Create interactive plotly graph
:return:
"""
import plotly.graph_objects as go
import chart_studio.plotly as py
d = Dataset()
vocabulary = [f'topic.{i}' for i in range(1, 71)]
d_dtm = d.get_document_topic_matrix()
male = d.copy().filter(author_gender='male')
female = d.copy().filter(author_gender='female')
c1_dtm = female.get_document_topic_matrix() * 300
c2_dtm = male.get_document_topic_matrix() * 300
s = StatisticalAnalysis(d_dtm, c1_dtm, c2_dtm, vocabulary)
correlated_terms = s.correlation_coefficient(
return_correlation_matrix=True)
for i in range(70):
correlated_terms[i, i] = 0
l = [TOPICS[i]['name'] for i in range(1, 71)]
# interactive viz with plotly
fig = go.Figure(data=go.Heatmap(z=correlated_terms, x=l, y=l))
fig.show()
# online with plotly
data = go.Heatmap(z=correlated_terms, x=l, y=l)
py.plot([data], filename='topic_cors')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--csv_file_path')
args = parser.parse_args()
st = TokyoCovid19Stat(args.csv_file_path)
cases_by_age = melt(st.cases_by_age,
value_columns=TokyoCovid19Stat.AGECOLNAMES,
var_name='Age')
sma_by_age = melt(sma(st.cases_by_age),
value_columns=TokyoCovid19Stat.AGECOLNAMES,
var_name='Age')
title = 'Tokyo Covid-19 New Cases By Age'
fig = px.area(cases_by_age, x='Date', y='Cases', color='Age', title=title)
py.plot(fig, filename=title, auto_open=False)
title = 'Tokyo Covid-19 New Cases 7-day Moving Average By Age'
fig = px.line(sma_by_age, x='Date', y='Cases', color='Age', title=title)
fig.add_bar(x=st.cases.index,
y=st.cases['Cases'],
name='Raw Total',
marker=dict(color='#dddddd'))
py.plot(fig, filename=title, auto_open=False)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--csv_file_path')
args = parser.parse_args()
if args.csv_file_path is None:
return
st = TokyoCovid19Stat(args.csv_file_path)
cases_by_area = melt(st.cases_by_area,
value_columns=st.area_list,
var_name='Area')
sma_by_area = melt(sma(st.cases_by_area),
value_columns=st.area_list,
var_name='Area')
# title = 'Tokyo Covid-19 New Cases By Area'
# fig = px.area(cases_by_area, x='Date', y='Cases', color='Area', title=title)
# py.plot(fig, filename=title, auto_open=False)
title = '[TEST] Tokyo Covid-19 New Cases 7-day Moving Average By Area'
fig = px.line(sma_by_area, x='Date', y='Cases', color='Area', title=title)
fig.add_bar(x=st.cases.index,
y=st.cases['Cases'],
name='Raw Total',
marker=dict(color='#dddddd'))
py.plot(fig, filename=title, auto_open=False)
def createFigure(myDataFrame):
myDataFrame['text'] = myDataFrame['case'] + \
'<br>Total Victims: ' + (myDataFrame['total_victims']).astype(str)
limits = [(0, 6), (7, 15), (16, 605)]
colors = ["royalblue", "crimson", "lightseagreen"]
fig = go.Figure()
fig.add_trace(
go.Scattergeo(locationmode='USA-states',
lon=myDataFrame['longitude'],
lat=myDataFrame['latitude'],
text=myDataFrame['text'],
marker=dict(size=myDataFrame['total_victims'],
color=colors[0],
line_color='rgb(40,40,40)',
line_width=0.5,
sizemode='area'),
name="mass shootings by location and number of victims"))
fig.update_layout(
title_text=
'Mass Shootings Corresponding to Total Victims Across the United States',
showlegend=True,
geo=dict(
scope='usa',
landcolor='rgb(217, 217, 217)',
),
)
py.plot(fig, filename="map_of_mass_shootings.html", auto_open=True)
fig.show()
def plotly_graph(deaths_array, cases_array):
"""
WHAT IT DOES: creates a Plotly graph containing data from the last 14 days
PARAMETERS: passes in a deaths and cases array
RETURNS: a graph
"""
length = len(deaths_array)
i = 1
pd = []
pc = []
while i < 15:
pd.append(deaths_array[length - i])
pc.append(cases_array[length - i])
i = i + 1
deaths = go.Scatter(x=[14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
y=[
pd[0], pd[1], pd[2], pd[3], pd[4], pd[5], pd[6],
pd[7], pd[8], pd[9], pd[10], pd[11], pd[12], pd[13]
])
cases = go.Scatter(x=[14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
y=[
pc[0], pc[1], pc[2], pc[3], pc[4], pc[5], pc[6],
pc[7], pc[8], pc[9], pc[10], pc[11], pc[12], pc[13]
])
data = [deaths, cases]
py.plot(data, filename='basic-line', auto_open=True)
def onlyFindSoma(DN_DF):
FindSoma_DF = DN_DF[DN_DF['Soma Hemisphere'] == "FindSoma"]
fig = px.sunburst(FindSoma_DF,
path=['DT Hemisphere', 'Soma Hemisphere', 'Status'],
title="DN Tract - FindSoma - Status")
py.plot(fig, filename="DN Tract - FindSoma - Status")
return
def vis4(permits2018):
# Get all rows where permit type is Construction
constPermits = permits2018.loc[permits2018['PERMIT_TYPE_NAME'] == 'CONSTRUCTION']
# The labels will be the cluser numbers (1-39)
clusterLabels = []
# The values will be the count permits in each cluster
clusterValues = []
for x in range(1,40):
# Get all rows in permits2010 dataframe where the cluster is equal to x
clusterRows = constPermits.loc[constPermits['NEIGHBORHOODCLUSTER'] == x]
# Count how many permits were filled in cluster x
rowCount = clusterRows.shape[0]
# Add it to the values list
clusterValues.append(rowCount)
# Add value of x to labels list
clusterLabels.append(x)
# Add title
myLayout = go.Layout(
title = "Percentage of Construction Permits Filed in DC Clusters in 2018",
)
# Assign data to appropriate axis on heatmap
myData = go.Pie(
labels = clusterLabels,
values = clusterValues
)
# Setup figure
myFigure = go.Figure(data=myData, layout=myLayout)
# Display the scatterplot
py.plot(myFigure, filename='2018 Permits Pie Chart')
def vis6(census2010):
# Create trace with x-axis as % white and y-axis as median income
trace = go.Scatter(
x = census2010['%WHITE'],
y = census2010['FAGI_MEDIAN_2015'],
hovertext = census2010['NEIGHBORHOODCLUSTER'],
mode = 'markers'
)
# Assign it to an trace object named myData
myData = [trace]
# Add axis and title
myLayout = go.Layout(
title = "Relationship Between Population of White People and Income in 2015",
xaxis=dict(
title = 'Population of White People in Tract'
),
yaxis=dict(
title = 'Median Income (USD)'
)
)
# Setup figure
myFigure = go.Figure(data=myData, layout=myLayout)
# Display the scatterplot
py.plot(myFigure, filename='2015 ScatterPlot')
def plotMap(latitudeData, longitudeData, draftYears, colourList,
draftClassData):
data = []
for i in range(len(colourList)):
roundNumber = str(i)
trace = go.Scattermapbox(lat=latitudeData[i],
lon=longitudeData[i],
name=str(draftYears[i]) + ' Class',
mode='markers',
marker=dict(size=(i + 10) / 2,
color=colourList[i],
opacity=0.7),
text=draftClassData[i],
hoverinfo='text')
data.append(trace)
layout = go.Layout(
title='1996 to 2017 NBA Draft',
autosize=True,
hovermode='closest',
showlegend=True,
#scope='usa',
mapbox=dict(accesstoken=mapbox_access_token,
bearing=0,
center=dict(lat=38, lon=-94),
pitch=0,
zoom=3,
style='light'),
legend=dict(traceorder='reversed'))
fig = dict(data=data, layout=layout)
py.plot(fig, filename='1996 to 2017 NBA Player Drafts')
def api(key, fig):
username = '******'
api_key = str(key)
chart_studio.tools.set_credentials_file(username=username, api_key=api_key)
py.plot(fig, filename='nlp_protein_papers', auto_open=False)
print(
tls.get_embed('https://plotly.com/~tadorfer/1/')) #change to your url
def graph_OHLC(self):
#not quite there, but the other one works, which is what i really care about
OHLC_trace = go.Ohlc(x=self.OHLC_data.Date_Time,
open=self.OHLC_data.Open,
high=self.OHLC_data.High,
low=self.OHLC_data.Low,
close=self.OHLC_data.Close,
name="OHLC Data",
increasing=dict(line=dict(color='#408e4a')),
decreasing=dict(line=dict(color='#cc2718')))
swing_data = pd.read_csv(
self.swing_file,
names=['Date_Time', 'Price', 'Direction', 'Row'],
parse_dates=True)
swing_trace = go.Scatter(x=swing_data.Date_Time,
y=swing_data.Price,
mode='lines+markers',
name='Swings',
line=dict(color=('rgb(111, 126, 130)'),
width=3))
data = [OHLC_trace, swing_trace]
layout = go.Layout(xaxis=dict(rangeslider=dict(visible=False)),
title=self.data_file[:-4])
fig = go.Figure(data=data, layout=layout)
py.plot(fig, filename=self.data_file + ".html", output_type='file')
def maturity_vs_tag(self, mat_tag_list, plot_type='plotly'):
# group by tag
data = {}
labels = []
sizes = []
subplot_titles = []
sorted_list = sorted(mat_tag_list, key=lambda row: (row[4]) if len(row)>4 else (' '))
for tag1, tag1_rows in groupby(sorted_list, lambda x: x[4] if len(x)>4 else ' '):
if tag1 != '' and tag1 != ' ':
for maturity, mat_rows in groupby(list(tag1_rows), lambda x: x[0] if len(x)>0 else ' '):
if maturity != ' ' and maturity != '':
sizes.append(len(list(mat_rows)))
labels.append(maturity)
print(":: ", tag1)
data[tag1] = [sizes[:], labels[:]]
subplot_titles.append(tag1)
sorted_list = sorted(mat_tag_list, key=lambda row: (row[5]) if len(row)>5 else (' '))
for tag2, tag2_rows in groupby(sorted_list, lambda x: x[5] if len(x)>5 else ' '):
if tag2 != ' ' and tag2 != '':
for maturity, mat_rows in groupby(list(tag2_rows), lambda x: x[0] if len(x)>0 else ' '):
if maturity != ' ' and maturity != '':
sizes.append(len(list(mat_rows)))
labels.append(maturity)
if tag2 in list(data.keys()):
for new_idx, label in enumerate(labels):
if label in data[tag2][1]:
old_idx = data[tag2][1].index(label)
data[tag2][0][old_idx] += sizes[new_idx]
else:
data[tag2].append([sizes[new_idx], label])
else:
print("&& ", tag2)
data[tag2] = [sizes[:], labels[:]]
subplot_titles.append(tag2)
# plot data
if plot_type == 'plotly':
domain = [[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}],
[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}]]
fig = make_subplots(rows=2, cols=3, specs=domain, subplot_titles=subplot_titles[:7])
print("Subplot Title: ", subplot_titles)
for idx, (key, value) in enumerate(data.items()):
if idx+1<4:
fig.add_trace(go.Pie(labels=value[1], values=value[0], name=key, text=value[1]),
1,idx+1)
elif idx+1<7:
fig.add_trace(go.Pie(labels=value[1], values=value[0], name=key, text=value[1]),
2,idx-2)
fig.update_layout(title_text="Maturity vs Tag")
py.plot(fig, filename = 'Maturity vs Tag', auto_open=True)
# fig.show()
elif plot_type == 'matplotlib':
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels)
ax1.axis('equal')
fig1.savefig('Maturity vs Tag.png')
pass
def review_city_box(yelp):
# second plot
# it will be a boxplot
figure1 = px.box(yelp, x="City", y="Review Count")
figure1.update_layout(title_text="Boxplot of Review Count in Each City")
# Display the plot
py.plot(figure1, auto_open=True)
def SankeyItemTrees(Com_dfs=[], Labels=[], Projection='Item'):
"""Create Sankey diagrams illustrating shifts in community belonging with variable number of timepoints.
Keyword arguments:
Coms_dfs -- list of dataframes of community belonging by item from either FindCommunities or IterativeCommunityFinding
Labels -- list of labels to associate with each community dataframe
Projection -- either Item or Students, which level the networks have been projected
"""
assert (len(Com_dfs) == len(Labels))
Colors = cl.scales[str(max(len(Com_dfs), 3))]['div']['RdYlBu']
Source = []
Target = []
Value = []
for df in Com_dfs:
df['Item'] = df.index
for i, df in enumerate(Com_dfs):
if (i == len(Com_dfs) - 1):
break
Com_dfs[i + 1].loc[:, 'Community'] = Com_dfs[
i + 1].loc[:, 'Community'] + Com_dfs[i].loc[:,
'Community'].max() + 1
for c_pre in range(Com_dfs[i].loc[:, 'Community'].max() + 1):
for c_post in range(Com_dfs[i + 1].loc[:, 'Community'].min(),
Com_dfs[i + 1].loc[:, 'Community'].max() + 1):
Link = sum(Com_dfs[i + 1].loc[
Com_dfs[i + 1].loc[:, 'Community'] == c_post,
Projection].isin(
Com_dfs[i].loc[Com_dfs[i].loc[:, 'Community'] == c_pre,
Projection]))
Source.append(c_pre)
Target.append(c_post)
Value.append(Link)
data = dict(
type='sankey',
node=dict(pad=15,
thickness=20,
line=dict(color="black", width=0.5),
label=[
Labels[i] + str(c) for i in range(len(Com_dfs))
for c in range(Com_dfs[i].loc[:, 'Community'].max() -
Com_dfs[i].loc[:, 'Community'].min() + 1)
],
color=[
Colors[i] for i in range(len(Com_dfs))
for c in range(Com_dfs[i].loc[:, 'Community'].max() -
Com_dfs[i].loc[:, 'Community'].min() + 1)
]),
link=dict(source=Source, target=Target, value=Value))
layout = dict(title="Response choice community Shifts", font=dict(size=20))
fig = dict(data=[data], layout=layout)
py.plot(fig, validate=False)
def test_initialize_stream_plot(self):
py.sign_in(un, ak)
stream = Stream(token=tk, maxpoints=50)
url = py.plot([Scatter(x=[], y=[], mode='markers', stream=stream)],
auto_open=False,
world_readable=True,
filename='stream-test')
assert url == 'https://plot.ly/~PythonAPI/461'
time.sleep(.5)
def test_stream_multiple_points(self):
py.sign_in(un, ak)
stream = Stream(token=tk, maxpoints=50)
url = py.plot([Scatter(x=[], y=[], mode='markers', stream=stream)],
auto_open=False,
world_readable=True,
filename='stream-test')
time.sleep(.5)
my_stream = py.Stream(tk)
my_stream.open()
my_stream.write(Scatter(x=[1, 2, 3, 4], y=[2, 1, 2, 5]))
time.sleep(.5)
my_stream.close()
def test_stream_layout(self):
py.sign_in(un, ak)
stream = Stream(token=tk, maxpoints=50)
url = py.plot([Scatter(x=[], y=[], mode='markers', stream=stream)],
auto_open=False,
world_readable=True,
filename='stream-test')
time.sleep(.5)
title_0 = "some title i picked first"
title_1 = "this other title i picked second"
my_stream = py.Stream(tk)
my_stream.open()
my_stream.write(Scatter(x=[1], y=[10]), layout=Layout(title=title_0))
time.sleep(.5)
my_stream.close()
my_stream.open()
my_stream.write(Scatter(x=[1], y=[10]), layout=Layout(title=title_1))
my_stream.close()
def _handle_msg(self, message):
"""Handle a msg from the front-end.
Args:
content (dict): Content of the msg.
"""
content = message['content']['data']['content']
if content.get('event', '') == 'pong':
self._graphId = content['graphId']
# ready to recieve - pop out all of the items in the deque
while self._clientMessages:
_message = self._clientMessages.popleft()
_message['graphId'] = self._graphId
_message = _json.dumps(_message)
self._message = _message
if content.get('event', '') in ['click', 'hover', 'zoom']:
# De-nest the message
if content['event'] == 'click' or content['event'] == 'hover':
message = content['message']['points']
elif content['event'] == 'zoom':
message = content['message']['ranges']
self._event_handlers[content['event']](self, message)
if content.get('event', '') == 'getAttributes':
self._attributes = content.get('response', {})
# there might be a save pending, use the plotly module to save
if self._flags['save_pending']:
self._flags['save_pending'] = False
url = py.plot(self._attributes, auto_open=False,
filename=self._filename, validate=False)
self._new_url = url
self._fade_to('slow', 1)
