def plot_graph_wrapper(df, title='', xTitle='', yTitle='',
filename='output.html'):
"""Cufflink offline plot for easy viewing"""
cf.set_config_file(offline=True, world_readable=True, theme='ggplot')
cf_output = df.iplot(kind='scatter', title=title, xTitle=xTitle,
online=False, asFigure=True)
py.offline.plot(cf_output, filename=filename)
def interactive_charts(df: pd.DataFrame, x: str, y: str, kind: str):
# https://github.com/santosjorge/cufflinks/blob/master/Cufflinks%20Tutorial%20-%20Pandas%20Like.ipynb
import plotly.offline
import cufflinks as cf
cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)
df.iplot(x=x, y=y, kind=kind)
def get_data_view(limit):
data = pd.read_csv('forbes.csv')
regions_available = list(data.values)
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
N = pd.read_csv('forbes.csv',
encoding="utf8",
keep_default_na=False,
na_values='na_rep')
N.head()
# Count crime numbers in each neighborhood
limit = 100
data5 = N.iloc[0:limit, :]
print(data5)
input()
# disdata5 = pd.DataFrame(data['行业'].value_counts())
# disdata5.reset_index(inplace=True)
# disdata5.rename(
# columns={'index': '分类', 'PdDistrict': 'Count'}, inplace=True)
# print(disdata5)
# input()
limit = 20
data4 = N.iloc[0:limit, :]
print(data4)
input()
def IV_final_2020():
Locality_available = list(df.Year.dropna().unique())
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
data_str = df.to_html()
return render_template('results3.html',
the_res = data_str,
the_select_region=Locality_available)
def area_situation():
Year_available = list(df.Year.dropna().unique())
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
data_str = df[:30].to_html()
return render_template('area_situation.html',
the_res = data_str,
the_select_Year=Year_available)
def zm_qwm():
df4 = pd.read_csv('G.csv', encoding='gbk')
zb_available = list(df4.指标.dropna().unique())
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
data_str = df4.to_html()
return render_template('zm_qwm.html',
the_res=data_str,
the_select_year=zb_available)
def ge_data():
df2 = pd.read_csv('F.csv', encoding='gbk')
year_available = list(df2.年份.dropna().unique())
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
data_str = df2.to_html()
return render_template('ge_data.html',
the_res=data_str,
the_select_year=year_available)
def histog(data):
cf.set_config_file(offline=False, world_readable=True, theme='pearl')
df = pd.DataFrame({'Distances': data})
df.head(2)
df.iplot(kind='histogram', subplots=True, shape=(3, 1), filename='cufflinks/histogram-subplots')
def zd_jr():
df = pd.read_csv('junfeizhanbi.csv', encoding='gbk')
jf_available = ['高收入国家','低收入国家'] # 列表下拉值赋予给regions_available
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
data_str = df[:40].to_html()
return render_template('zd_jf.html',
the_select_jf=jf_available,
the_res = data_str)
def plot_timeseries_with_bound(df,
val_col,
bound_col,
true_col,
xTitle='',
yTitle='',
title='',
filename='LSTM_output_bounds.html'):
cf.set_config_file(offline=True, world_readable=True, theme='ggplot')
# cf_output = df.iplot(kind='scatter', title=title, xTitle=xTitle,
# online=False, asFigure=True)
upper_bound = go.Scatter(
name='Upper Bound',
#x=df['Time'],
y=df[val_col] + df[bound_col],
mode='lines',
marker=dict(color="444"),
line=dict(width=0),
fillcolor='rgba(68, 68, 68, 0.3)',
fill='tonexty')
trace = go.Scatter(
name='Measurement',
#x=df['Time'],
y=df[val_col],
mode='lines',
line=dict(color='rgb(31, 119, 180)'),
fillcolor='rgba(68, 68, 68, 0.3)',
fill='tonexty')
lower_bound = go.Scatter(
name='Lower Bound',
#x=df['Time'],
y=df[val_col] - df[bound_col],
marker=dict(color="444"),
line=dict(width=0),
mode='lines')
true_line = go.Scatter(
name='Actual',
#x=df['Time'],
y=df[true_col],
mode='lines',
line=dict(color='rgb(180, 119, 180)'),
fillcolor='rgba(68, 68, 68, 0.3)',
fill='lines')
# Trace order can be important
# with continuous error bars
data = [lower_bound, trace, upper_bound, true_line]
layout = go.Layout(yaxis=dict(title=yTitle), title=title, showlegend=True)
fig = go.Figure(data=data, layout=layout)
py.offline.plot(fig, filename=filename)
def map1() -> 'html':
Year_available = list(df.Year.dropna().unique())
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
the_Year = request.form["the_Year_selected"]
dfs = df.query("Year=='{}'".format(the_Year))
State = list(dfs['State'].unique())
State.remove('United States')
p = []
z = []
for st in State:
df4 = dfs[dfs.State == st]
z.append(sum(df4['Death_Rate']))
p.append(sum(df4['Population']))
fig = go.Figure(data=go.Choropleth(
locations=State, # Spatial coordinates
z=z, # Data to be color-coded
locationmode='USA-states', # set of locations match entries in `locations`
colorscale='Reds',
colorbar_title="人数",
))
fig.update_layout(
title_text='1999-2017年美国各州的药物中毒人数地图可视化',
geo_scope='usa', # limite map scope to USA
)
py.offline.plot(fig, filename="us.html", auto_open=False)
with open("us.html", encoding="utf8", mode="r") as f:
plot_all2 = "".join(f.readlines())
fig = go.Figure(data=go.Choropleth(
locations=State, # Spatial coordinates
z=p, # Data to be color-coded
locationmode='USA-states', # set of locations match entries in `locations`
colorscale='Reds',
colorbar_title="人数",
))
fig.update_layout(
title_text='1999-2017年美国各州的人数地图可视化',
geo_scope='usa', # limite map scope to USA
)
py.offline.plot(fig, filename="us.html", auto_open=False)
with open("us.html", encoding="utf8", mode="r") as f:
plot_all3 = "".join(f.readlines())
data_str = dfs[:30].to_html()
return render_template('area_situation.html',
the_plot_all2 = plot_all2,
the_plot_all3 = plot_all3,
the_res = data_str,
the_select_Year=Year_available,
)
def hr():
df = pd.read_csv('hurun.csv', encoding='utf-8', delimiter="\t")
# df = df.set_index('Index')
regions_available = list(df.region.dropna().unique())
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
data_str = df.to_html(classes='table align-items-center table-flush')
return render_template('layouts/default.html',
content=render_template('pyecharts/index.html',
the_res=data_str))
def show_graph(self):
cf.set_config_file(sharing='public', theme='pearl', offline=False)
cf.go_offline
init_notebook_mode(connected=True)
df = pd.DataFrame({'Activity': self.graphActivityData},
index=self.graphTimeData)
#df.head()
print(df)
print("bfr")
df.plot()
print("afr")
def trend_situation():
Race_available = list(df.Race.dropna().unique()) # 列表下拉值赋予给regions_available
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
data_str = df[:30].to_html()
def line_markline2() -> Line:
df = pd.read_csv('Mortality_States.csv')
time = list(df['Year'].unique())
times = []
for t in list(time):
times.append(str(t))
Race1 = list(df['Race'].unique())
r2 = []
for r in Race1:
df5 = df[df.Race == r]
r1 = []
r2.append(r1)
for t in time:
df6 = df5[df5.Year == t]
r1.append(int(sum(df6['Death_Rate'])))
c = (
Line()
.add_xaxis(times)
.add_yaxis(
"All Races-All Origins",
r2[0],
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.add_yaxis(
"Hispanic",
r2[1],
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.add_yaxis(
"Non-Hispanic Black",
r2[2],
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.add_yaxis(
"Non-Hispanic White",
r2[3],
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.set_global_opts(title_opts=opts.TitleOpts(title="种族药物中毒死亡率"))
)
return c
line_markline2().render()
with open("render.html", encoding="utf8", mode="r") as f:
plot_all2 = "".join(f.readlines())
return render_template('trend_situation.html',
the_plot_all2=plot_all2,
the_res = data_str,
the_select_Race=Race_available)
def __init__(self, upid):
cf.set_config_file(world_readable=True,
theme='henanigans',
offline=True)
cf.go_offline()
self.mongo_persist = MongoPersist()
self.upid = upid
data_dict = self.mongo_persist.read("up_video_list", {"mid": upid})
self.df_data = pd.DataFrame(data_dict)
self.df_data['created'] = pd.to_datetime(self.df_data['created'],
unit='s')
def __init__(self, db):
self.db = db
if isinstance(self.db, dict):
self.materials = RecursiveDict()
self.compositions = RecursiveDict()
else:
import plotly.plotly as py
import cufflinks
cufflinks.set_config_file(world_readable=True, theme='pearl')
opts = bson.CodecOptions(document_class=bson.SON)
self.contributions = self.db.contributions.with_options(codec_options=opts)
self.materials = self.db.materials.with_options(codec_options=opts)
self.compositions = self.db.compositions.with_options(codec_options=opts)
def plot_chart(path_to_png, path_to_log_list):
print(path_to_png[0:-4] + ".html")
output_file(path_to_png[0:-4] + ".html")
TOOLS = 'pan,box_zoom,wheel_zoom,save,hover,reset,resize'
p = figure(title="TrainLoss(smoothed) vs NumIters",
x_axis_label='NumIters')
fig, ax1 = plt.subplots()
cf.set_config_file(offline=False, world_readable=True, theme='ggplot')
for path_to_log in path_to_log_list:
os.system('%s %s ./ ' % (get_log_parsing_script(), path_to_log))
data_file = get_data_file(6, path_to_log)
pddata = pd.read_csv(data_file)
standard = ['NumIters', 'Seconds', 'LearningRate']
labels = [label for label in pddata if label not in standard]
sp = 500 if len(pddata['NumIters']) > 2000 else 5
skip = 5 if len(pddata['NumIters']) > 2000 else 0
ylims = [100, -100]
data = []
for label, i in zip(labels, range(len(labels))):
Y = pddata[label].ewm(span=sp, adjust=True).mean()
plt.plot(pddata["NumIters"], Y, alpha=0.4)
ylims[0] = min(ylims[0], Y[skip:].min())
ylims[1] = max(ylims[1], Y[skip:].max())
data += [go.Scattergl(x=pddata["NumIters"], y=Y, name=label)]
# p.line(pddata["NumIters"], Y, legend=label,
# color=Spectral11[i%11], line_width=2)
print(int(pddata["NumIters"].iloc[-1]))
plt.legend()
ax1.set_ylim(ylims)
# p.extra_y_ranges = {"LearningRate": Range1d(start=0, end=pddata['LearningRate'].max()*1.1)}
# p.add_layout(LinearAxis(y_range_name="LearningRate"), 'right')
# p.line(pddata["NumIters"],
# pddata['LearningRate'],
# legend='LearningRate',
# color='navy', y_range_name="LearningRate")
# p.add_layout(LinearAxis(y_range_name="LearningRate"), 'left')
# save(p)
# ax2 = ax1.twinx()
# ax2.plot(pddata["NumIters"], pddata['LearningRate'], alpha=0.4)
plt.title('TrainLoss vs NumIters')
# ax2.set_ylabel('Learning Rate', color='r')
plt.xlabel('NumIters')
plt.savefig(path_to_png)
url = py.plot(data, filename=path_to_png[0:-4], fileopt='overwrite')
print(url)
def plot_hist(ds, user_id: str, x_axis_column=None):
"""
histogram plot of timeseries data
Args:
ds (DataStream):
user_id (str): uuid of a user
x_axis_column (str): x axis column of the plot
"""
pdf = ds_to_pdf(ds, user_id)
cf.set_config_file(offline=True, world_readable=True, theme='ggplot')
init_notebook_mode(connected=True)
pdf = _remove_cols(pdf)
if x_axis_column:
data = [go.Histogram(x=pdf[str(x_axis_column)])]
iplot(data, filename='basic histogram')
else:
pdf.iplot(kind='histogram', filename='basic histogram')
def plot(self, traces=None):
try:
import cufflinks as cf
cf.set_config_file(offline=True, theme='white')
if traces is None:
traces = self.data.columns.drop(TIMESTAMP).tolist()
if not isinstance(traces, Iterable):
traces = [traces]
self.data.iplot(x=TIMESTAMP,
y=traces,
mode='markers+lines',
symbol="x",
size=5,
width=.5,
zerolinecolor="black")
except ImportError as exc:
logger.exception("It appears there was a problem during plotting.")
def map() -> 'html':
Locality_available = list(df.Year.dropna().unique())
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
the_region = request.form["the_region_selected"]
dfs = df.query("Year=='{}'".format(the_region))
State = list(dfs['State'].unique())
State.remove('United States')
z = []
for st in State:
df4 = dfs[dfs.State == st]
z.append(sum(df4['Potentially Excess Deaths'][(df['Locality'] == 'All')][(df['Cause of Death'] == 'Stroke')]))
fig = go.Figure(data=go.Choropleth(
locations=State, # Spatial coordinates
z=z, # Data to be color-coded
locationmode='USA-states', # set of locations match entries in `locations`
colorscale='Reds',
colorbar_title="人数",
))
fig.update_layout(
title_text='2005-2015年美国各州中风潜在死亡人数',
geo_scope='usa', # limite map scope to USA
)
py.offline.plot(fig, filename="us.html", auto_open=False)
with open("us.html", encoding="utf8", mode="r") as f:
plot_all2 = "".join(f.readlines())
#with open("render1.html", encoding="utf8", mode="r") as f:
#plot_all3 = "".join(f.readlines())
data_str = df.to_html()
return render_template('results3.html',
the_plot_all2 = plot_all2,
the_res = data_str,
the_select_region=Locality_available,
)
def interactive_results(y_train, y_train_pred, y_test, y_test_pred):
pio.renderers.default = 'colab'
cf.set_config_file(theme='pearl')
fig = make_subplots(rows=2, cols=1, shared_xaxes=False)
enum_x_train = list(range(len(y_train)))
enum_x_test = list(range(len(y_test)))
fig.append_trace(
{
'x': enum_x_train,
'y': y_train,
'type': 'scatter',
'name': 'Real | Train'
}, 1, 1)
fig.append_trace(
{
'x': enum_x_train,
'y': y_train_pred,
'type': 'scatter',
'opacity': 0.7,
'name': 'Predicted | Train'
}, 1, 1)
fig.append_trace(
{
'x': enum_x_test,
'y': y_test,
'type': 'scatter',
'name': 'Real | Test'
}, 2, 1)
fig.append_trace(
{
'x': enum_x_test,
'y': y_test_pred,
'type': 'scatter',
'opacity': 0.7,
'name': 'Predicted | Test'
}, 2, 1)
#fig['layout'].update(title=title+ ' | RMSE: '+str(RMSE))
iplot(fig)
def plot_plotly(df):
"""
pip install plotly # Plotly is a pre-requisite before installing cufflinks
pip install cufflinks
#importing Pandas
import pandas as pd
#importing plotly and cufflinks in offline mode
import cufflinks as cf
import plotly.offline
cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)
:param df:
:return:
"""
import cufflinks as cf
import plotly.offline
cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)
df.iplot()
def plot_timeseries(ds: DataStream, user_id: str, y_axis_column: str = None):
"""
line plot of timeseries data
Args:
ds (DataStream):
user_id (str): uuid of a user
y_axis_column (str): x axis column is hard coded as timestamp column. only y-axis can be passed as a param
"""
pdf = ds_to_pdf(ds, user_id)
cf.set_config_file(offline=True, world_readable=True, theme='ggplot')
init_notebook_mode(connected=True)
ts = pdf['timestamp']
pdf = _remove_cols(pdf)
if y_axis_column:
data = [go.Scatter(x=ts, y=pdf[str(y_axis_column)])]
iplot(data, filename='time-series-plot')
else:
iplot([{
'x': ts,
'y': pdf[col],
'name': col
} for col in pdf.columns],
filename='time-series-plot')
def zd_jr_response() -> 'html':
jf_available = ['高收入国家','低收入国家'] # 列表下拉值赋予给regions_available
cf.set_config_file(offline=True, theme="ggplot")
py.offline.init_notebook_mode()
the_jf = request.form["the_jf_selected"]
dfs = df.query("Country=='{}'".format(the_jf))
# 数据循环模块
def line_markline() -> Line:
a = []
times = []
for i in range(1960, 2018):
a.append(int(int(dfs[str(i)]) / 100000000))
times.append(str(i))
c = (
Line()
.add_xaxis(times)
.add_yaxis(
"{}".format(the_jf),
a,
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.set_global_opts(title_opts=opts.TitleOpts(title="{}军费占比趋势".format(the_jf)))
)
return c
line_markline().render()
with open("render.html", encoding="utf8", mode="r") as f:
plot_all = "".join(f.readlines())
data_str = dfs[:40].to_html()
return render_template('zd_jf.html',
the_plot_all=plot_all,
the_res = data_str,
the_select_jf=jf_available,
)
git show HEAD^:presentation.pptx > temp.pptx #ひとつ前のコミットを別ファイルとして取りだし+-
git add -A 全ての変更をステージング
git commit -m "コメント" ステージングした変更をコミット
git push リモートに反映
git reset --soft "HEAD^" コミットの削除 --softは変更はそのままにするという意味
"""
#---------------------------辞書 dict ------------------------------------
d = {}
d['k3'] = 3
#---------------------------import lib library------------------------------------
%matplotlib inline
import pandas as pd
import cufflinks as cf
cf.set_config_file(offline=True, theme="white", offline_show_link=False)
cf.go_offline()
"""
ライブラリのリロード
"""
import importlib
importlib.reload(foo)
"""
inf弾く
"""
import numpy as np
df.replace([np.inf, -np.inf], np.nan)
"""
# from jupyter_dash import JupyterDash
# except:
# import os
# os.system("conda install -c conda-forge -c plotly jupyter-dash")
# from jupyter_dash import JupyterDash
## PLOTLY IMPORTS/PARAMS
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
pio.templates.default = "plotly_dark"
## Acitvating Cufflinks
import cufflinks as cf
cf.go_offline()
cf.set_config_file(sharing='public', theme='solar', offline=True)
## Importing Dash
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
## Load Functions and Data
from functions import CoronaData, plot_states, get_state_ts, plot_group_ts
## LOAD DATA AND SAVE DFs
corona_data = CoronaData(verbose=False, run_workflow=True)
df = corona_data.df_us.copy()
df_world = corona_data.df.copy()
hc.fit(mat) labels = hc.labels_ results = pd.DataFrame([dfdif.T.index,labels]) display(results.loc[:0,results.iloc[1]==0]) display(results.loc[:0,results.iloc[1]==1]) display(results.loc[:0,results.iloc[1]==2]) # My controls are # * WT: I12, J12 # * KO: I13, J13 # * HET: I14, J14 # # So you can identify your genotyping results by looking at: to which control they cluster. # Ploting with plot.ly, so you can look at individual lines for better pattern recognition # In[ ]: import plotly.plotly as py import cufflinks as cf import plotly.graph_objs as go cf.set_config_file(offline=False, world_readable=True, theme='ggplot') dfpy = dfdif.set_index(df_melt.iloc[:,0]) # Plot and embed in ipython notebook! dfpy.iplot(kind='scatter', filename='pyHRM')
import cufflinks as cf
import numpy as np
import pandas as pd
import plotly
import plotly.graph_objs as go
from plotly import tools
from UCB_MIDS_W205.Project.api.great_schools import GreatSchools
from UCB_MIDS_W205.Project.api.population import Population
from UCB_MIDS_W205.Project.data_models import Datamodel
from UCB_MIDS_W205.Project.mission_control import MissionControl
from UCB_MIDS_W205.Project.postgresql_handler import Postgresql
print(plotly.__version__) # version 1.9.x required
plotly.offline.init_notebook_mode() # run at the start of every notebook
cf.set_config_file(offline=True, world_readable=False)
class Plotter:
def __init__(self, min_price=150000, max_price=300000, top_percentage=0.25, top_max_num_entries=30):
self.MIN_PRICE = min_price
self.MAX_PRICE = max_price
self.TOP_PERCENTAGE = top_percentage
self.TOP_MAX_NUM_ENTRIES = top_max_num_entries
datamodel = Datamodel()
self.time_series_postgres = self._initialize_postgres(datamodel.zipcode_timeseries())
self.population_postgres = self._initialize_postgres(datamodel.population())
self.great_schools_postgres = self._initialize_postgres(datamodel.great_schools())
self.zipcode_timeseries = None
import plotly as plty
import plotly.graph_objs as go
import cufflinks as cf
# Before using this, the following changes should be made to the offline.py file in the environment's plotly installation:
# * To get to plotly offline install, eg /Users/eczech/anaconda/envs/research3.5/lib/python3.5/site-packages/plotly/offline
# - Add the following lines to offline.py in _plot_html after the line "config = {}":
# config['modeBarButtonsToRemove'] = ['sendDataToCloud'] # Added manually to remove save icon
# config['displaylogo'] = False # Added manually to remove plotly icon
#
# After doing this, the extra cleaning logic in "plotly_clean_html" is no longer necessary but it is still left
# here because it is harmless otherwise and at least strips save icons from serialized plots if the manual edits
# above have not been made.
# Initialize Jupyter notebook mode
cf.set_config_file(offline=True, theme='white', offline_link_text=None, offline_show_link=False)
def plotly_clean_html(plotly_filename, auto_open=True):
""" Strips Save icon from plot.ly html visualizations """
# Read in source html for viz
with open(plotly_filename, 'r') as of:
html = of.read()
# Replace the target strings (removes "Save" icon)
html = html.replace('displaylogo:!0', 'displaylogo:!1')
html = html.replace('modeBarButtonsToRemove:[]', 'modeBarButtonsToRemove:["sendDataToCloud"]')
# Re-write source html
with open(plotly_filename, 'w') as of:
of.write(html)
from plotly import tools
from plotly.offline.offline import _plot_html
import cufflinks as cf
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import colorlover as cl
# local
from .episem import episem, lastepiday
cf.set_config_file(theme='white')
def ethio_ts(df=pd.DataFrame, scale_id=int, year=int):
cols = [
'Testes positivos',
'Influenza A',
'Influenza B',
'VSR',
'Adenovirus',
'Parainfluenza 1',
'Parainfluenza 2',
'Parainfluenza 3',
]
trace = []
if scale_id == 2:
ytitle = 'Casos'
import pandas as pd
import numpy as np
import csv
import os
import matplotlib.pyplot as plt
import cufflinks as cf
import plotly
import plotly.offline as py
from plotly.offline.offline import _plot_html
import plotly.graph_objs as go
from plotly.tools import FigureFactory as FF
cf.set_config_file(world_readable=False,offline=True)
plt.style.use('ggplot')
def plot(name, *, cols=[], plot_kind=None, start_date=None, end_date=None):
""" Plots selected financial data of selected company which ranges over specified
date range[start_date:end_date]. The plot is as specified by the plot_kind parameter.
:param
name: company's ticker
cols: list of columns specifying data fields to plot.
kind: type of plot. One of 'line', 'box', 'hexbin','scatter_matrix'.
start_date: The data is indexed by the Date column. starting date specifies
the first date index row to be plotted.
end_date: end_date specifies the last date index row to be plotted.
"""
header = ['Date','Total Transactions','Traded Shares','TotalTraded Amount',
'Maximum Price','Minimum Price','Closing Price']
plottypes = ['line', 'box', 'hexbin','scatter_matrix']
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
import plotly.io as pio
pio.orca.config.executable = '/u/fs1/vatj2/.local/bin//orca'
pio.orca.config.save()
import pandas as pd
import numpy as np
from plotly.colors import DEFAULT_PLOTLY_COLORS
import cufflinks as cf
import itertools
# In[ ]:
init_notebook_mode(connected=True)
cf.set_config_file(offline=True)
figure_dir = os.path.join(os.getcwd(), '..', 'figures')
if not os.path.exists(figure_dir):
os.mkdir(figure_dir)
data_dir = os.path.join(os.getcwd(), "..", "data", "V" + ipa.__version__)
if not os.path.exists(data_dir):
os.mkdir(data_dir)
# In[ ]:
p = dict()
p["n_genes"] = 3
p["low_colour"] = 0
import html
from flask import Flask, render_template, request
import pandas as pd
import cufflinks as cf
import plotly as py
import plotly.graph_objs as go
app = Flask(__name__)
# 准备工作
df = pd.read_csv('douban_movie.csv', encoding='utf-8', delimiter="\t")
cf.set_config_file(offline=True, theme="ggplot")
regions_available = list(df.concluding.dropna().unique())
print(regions_available) # 读取数据确认
@app.route('/',methods=['GET'])
def douban_2019():
data_str = df.to_html()
return render_template('results.html',
the_res = data_str,
the_select_region=regions_available,
)
# 初始表格置入选择
def plot_model(model, plot='2d'):
"""
Description:
------------
This function takes a model dataframe returned by create_model() function.
'2d' and '3d' plots are available.
Example:
--------
rule1 = create_model(metric='confidence', threshold=0.7, min_support=0.05)
plot_model(rule1, plot='2d')
plot_model(rule1, plot='3d')
Parameters
----------
model : DataFrame, default = none
DataFrame returned by trained model using create_model().
plot : string, default = '2d'
Enter abbreviation of type of plot. The current list of plots supported are:
Name Abbreviated String
--------- ------------------
Support, Confidence and Lift (2d) '2d'
Support, Confidence and Lift (3d) '3d'
Returns:
--------
Visual Plot: Prints the visual plot.
------------
"""
#loading libraries
import numpy as np
import pandas as pd
import plotly.express as px
from IPython.display import display, HTML, clear_output, update_display
#import cufflinks
import cufflinks as cf
cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)
#copy dataframe
data_ = model.copy()
antecedents = []
for i in data_['antecedents']:
i = str(i)
a = i.split(sep="'")
a = a[1]
antecedents.append(a)
data_['antecedents'] = antecedents
antecedents_short = []
for i in antecedents:
a = i[:10]
antecedents_short.append(a)
data_['antecedents_short'] = antecedents_short
consequents = []
for i in data_['consequents']:
i = str(i)
a = i.split(sep="'")
a = a[1]
consequents.append(a)
data_['consequents'] = consequents
if plot == '2d':
fig = px.scatter(
data_,
x="support",
y="confidence",
text="antecedents_short",
log_x=True,
size_max=600,
color='lift',
hover_data=['antecedents', 'consequents'],
opacity=0.5,
)
fig.update_traces(textposition='top center')
fig.update_layout(plot_bgcolor='rgb(240,240,240)')
fig.update_layout(height=800,
title_text='2D Plot of Support, Confidence and Lift')
fig.show()
if plot == '3d':
fig = px.scatter_3d(data_,
x='support',
y='confidence',
z='lift',
color='antecedent support',
title='3d Plot for Rule Mining',
opacity=0.7,
width=900,
height=800,
hover_data=['antecedents', 'consequents'])
fig.show()
"""
If you want to make a plot, change the folder name
after installing plotly,cufflink and run the following code.
folder_name='new_results'
"""
import pandas as pd
import os, time
import glob
import cufflinks as cf
import plotly.offline
cf.go_offline()
cf.set_config_file(offline=True, world_readable=True)
import torchvision.models as models
MODEL_LIST = {
'mnasnet': models.mnasnet.__all__[1:],
'resnet': models.resnet.__all__[1:],
'densenet': models.densenet.__all__[1:],
'squeezenet': models.squeezenet.__all__[1:],
'vgg': models.vgg.__all__[1:],
#'mobilenet':[m for m in models.mobilenet.__all__[1:] if m.islower()],
'mobilenet': ['mobilenet_v2', 'mobilenet_v3_large', 'mobilenet_v3_small'],
'shufflenetv2': models.shufflenetv2.__all__[1:]
}
setattr(plotly.offline, "__PLOTLY_OFFLINE_INITIALIZED", True)
folder_name = 'result/'
csv_list = glob.glob(folder_name + '/*.csv')
columes = []
for key, values in MODEL_LIST.items():
for i in values:
columes.append((key, i))
for csv in csv_list:
from plotly.offline.offline import _plot_html
import cufflinks as cf
import pandas as pd
cf.set_config_file(theme='pearl', offline=True)
class ReportCityCharts:
@classmethod
def create_incidence_chart(
cls, df: pd.DataFrame, year_week: int, threshold_pre_epidemic: float,
threshold_pos_epidemic: float, threshold_epidemic: float
) -> 'Plotly_HTML':
"""
@see: https://stackoverflow.com/questions/45526734/
hide-legend-entries-in-a-plotly-figure
:param df:
:param year_week:
:param threshold_pre_epidemic: float,
:param threshold_pos_epidemic: float
:param threshold_epidemic: float
:return:
"""
df = df.reset_index()[[
'SE', 'incidência', 'casos notif.', 'level_code'
]]
# 200 = 2 years