def update_triplot(tabs, dp1, dp2, dp3, dp4, nameform, contents):
import plotly.express as px
if tabs == 'graphic-table':
if contents == None:
return
relative_filename = nameform
df = pd.read_excel(relative_filename)
if 'Total' in df.columns:
fig = px.scatter_ternary(df,
a=df[dp1],
b=df[dp2],
c=df[dp3],
size_max=15,
color=dp4,
hover_name=df['PREDICTED MINERAL'],
size=df['Total'])
else:
fig = px.scatter_ternary(df,
a=df[dp1],
b=df[dp2],
c=df[dp3],
size_max=15,
color=dp4,
hover_name=df['PREDICTED MINERAL'])
return html.Div([dcc.Graph(figure=fig)])
else:
return None
def plot_3d(self, path):
colsets = list(it.combinations(range(8), 3))
for colset in colsets:
colnames = dict(zip(['a','b','c'], ['C_{}'.format(x) for x in colset]))
ppc = pd.DataFrame(self.generate_posterior_predictive_simplex_3d(colset),
columns = [colnames[x] for x in ['a','b','c']])
ppc['source'] = 'PostPred'
emc = pd.DataFrame(self.data.Yl[:,colset],
columns = [colnames[x] for x in ['a','b','c']])
emc['source'] = 'Empirical'
out_df = pd.concat([emc, ppc], axis = 0)
fig = px.scatter_ternary(out_df, color = 'source', **colnames)
fig.write_image(os.path.join(path, self.png_name.format(*colset)))
del(fig)
del(emc)
del(ppc)
return
def plot_sentiments_with_ternary(tweets: List[TweetData],
show: bool = False,
group: str = ""):
# draw ternary plot
negative_count = []
neutral_count = []
positive_count = []
# This is really slow to instance, need to define outside of tweet class
analyser = SentimentIntensityAnalyzer()
sentiments = {
"NEGATIVE": [],
"NEUTRAL": [],
"POSITIVE": [],
}
for tweet in tweets:
sentiment = tweet.set_sentiment_value(analyser)
if sentiment is Sentiment.NEGATIVE:
negative_count.append(tweet.sentiment_value)
elif sentiment is Sentiment.POSITIVE:
positive_count.append(tweet.sentiment_value)
elif sentiment is Sentiment.NEUTRAL:
neutral_count.append(tweet.sentiment_value)
sentiments["NEGATIVE"].append(tweet.sentiment_value.get("neg"))
sentiments["NEUTRAL"].append(tweet.sentiment_value.get("neu"))
sentiments["POSITIVE"].append(tweet.sentiment_value.get("pos"))
GLOBAL_LOGGER.debug("Sentiment calculation done.")
GLOBAL_LOGGER.info(f"Length of negative: {len(negative_count)}")
GLOBAL_LOGGER.info(f"Length of neutral: {len(neutral_count)}")
GLOBAL_LOGGER.info(f"Length of positive: {len(positive_count)}")
df = pandas.DataFrame(sentiments)
fig = px.scatter_ternary(df, b="NEGATIVE", a="NEUTRAL", c="POSITIVE")
name = group if group else "all"
path = f"article/figures/sentiment_ternary_{name}.png"
GLOBAL_LOGGER.info(
f"Writing ternary plot of sentiment analysis into path {path}")
fig.write_image(path)
if show:
fig.show()
img = data.camera()
fig = px.imshow(img, color_continuous_scale='gray')
fig.add_trace(
go.Contour(z=img,
showscale=False,
contours=dict(start=0, end=70, size=70, coloring='lines'),
line_width=2))
fig.add_trace(go.Scatter(x=[230], y=[100], marker=dict(color='red', size=16)))
fig.show()
# In[ ]:
import plotly.express as px
df = px.data.election()
fig = px.scatter_ternary(df, a="Joly", b="Coderre", c="Bergeron")
fig.show()
# In[ ]:
import plotly.express as px
df = px.data.election()
fig = px.scatter_ternary(df,
a="Joly",
b="Coderre",
c="Bergeron",
hover_name="district",
color="winner",
size="total",
size_max=15,
def fig_14():
fig = px.scatter_ternary(df_MPG,
a="SAT (%)",
b="ARO (%)",
c="POL (%)",
color="Stratigraphic unit",
symbol="Facies",
size="Locality_size",
size_max=8,
template="simple_white",
color_discrete_map={
"Lower-Middle Muen Mb": '#F0E442',
'Upper Muen Mb': "#E69F00",
"Lower Blanknuten Mb": "#56B4E9",
"Middle Blanknuten Mb": "#009E73",
"Upper Blanknuten Mb": "#0072B2",
"Tschermakfjellet Fm": "#CC79A7",
})
fig.update_traces(marker=dict(opacity=0.9,
line=dict(width=0.5, color='black')),
selector=dict(mode='markers'))
def makeAxis(title, tickangle):
return {
'title': title,
'titlefont': {
'size': 20
},
'tickangle': tickangle,
'tickfont': {
'size': 10
},
'tickcolor': 'rgba(0,0,0,0)',
'ticklen': 5,
'showline': True,
# 'showgrid': True
}
fig.update_layout({
'ternary': {
'sum': 100,
#'aaxis': makeAxis('SAT %', 0),
#'baxis': makeAxis('<br>ARO %', 45),
#'caxis': makeAxis('<br>POL %', -45)
},
'annotations': [{
'showarrow': False,
'text': 'Simple Ternary Plot with Markers',
'x': 0.5,
'y': 1.3,
'font': {
'size': 15
}
}]
})
fig.update_ternaries(caxis_min=45)
fig.show()
fig.write_image("Fig_14_UNEDITED.pdf")
def processAlgorithm(self, parameters, context, feedback):
Grid = self.parameterAsVectorLayer(parameters, self.Grid, context)
try:
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
import chart_studio.plotly as py
except Exception:
feedback.reportError(QCoreApplication.translate('Error','%s'%(e)))
feedback.reportError(QCoreApplication.translate('Error',' '))
feedback.reportError(QCoreApplication.translate('Error','Error loading modules - please install the necessary dependencies'))
return {}
X = self.parameterAsString(parameters, self.X, context)
Y = self.parameterAsString(parameters, self.Y, context)
Z = self.parameterAsString(parameters, self.Z, context)
S = self.parameterAsString(parameters, self.Size, context)
T = self.parameterAsString(parameters, self.Time, context)
G = self.parameterAsString(parameters, self.Grp, context)
speed = parameters[self.Speed]
A = parameters[self.A]
if A and T == '':
feedback.reportError(QCoreApplication.translate('Error','Error - Please select a Time Field for animations.'))
return {}
names = [X,Y,Z,S,T,G]
vNames = {}
for n in names:
if n != '':
vNames[n] = []
features = Grid.selectedFeatures()
if len(features) == 0:
features = Grid.getFeatures()
total = Grid.featureCount()
else:
total = len(features)
total = 100.0/total
for enum,feature in enumerate(features):
if total != -1:
feedback.setProgress(int(enum*total))
for name in vNames.keys():
try:
v = feature[name]
if type(v) == PyQt5.QtCore.QDateTime:
v = v.toPyDateTime().strftime('%d/%m/%Y, %H:%M:%S')
vNames[name].append(v)
except Exception:
vNames[name].append(0)
continue
df = pd.DataFrame(vNames)
if S == '':
df['Size'] = 10
S = 'Size'
if G == '':
df['Trace'] = '1'
G = 'Trace'
df[G] = df[G].astype(str)
if A:
df.sort_values(by=[T], inplace=True)
if Y:
if Z:
fig = px.scatter_ternary(df, a=X, b=Y, c=Z, animation_frame=T, color=G,size=S)
else:
xRange = [df[X].min()*0.9,df[X].max()*1.1]
yRange = [df[Y].min()*0.9,df[Y].max()*1.1]
fig = px.scatter(df, x=X, y=Y, animation_frame=T, color=G,size=S,range_x=xRange,range_y=yRange)
else:
if G == '':
fig = px.histogram(df, x=X, animation_frame=T)
else:
fig = px.histogram(df, x=X, animation_frame=T, color=G)
fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = speed
else:
if T != '':
df.sort_values(by=[T], inplace=True)
if Y:
if Z:
fig = px.scatter_ternary(df, a=X, b=Y, c=Z, color=G,size=S)
else:
xRange = [df[X].min()*0.9,df[X].max()*1.1]
yRange = [df[Y].min()*0.9,df[Y].max()*1.1]
if T == '':
fig = px.scatter(df, x=X, y=Y, color=G,size=S,range_x=xRange,range_y=yRange)
else:
fig = px.line(df, x=X, y=Y, color=G,range_x=xRange,range_y=yRange)
else:
if G == '':
fig = px.histogram(df, x=X)
else:
fig = px.histogram(df, x=X, color=G)
try:
py.plot(fig, filename='Animation', auto_open=True)
except Exception as e:
fig.show()
return {}
import plotly.express as px
df = {
'Информатика': [3.3, 4.4, 3.5, 5, 2],
'Физика': [3.2, 4.5, 5, 5, 5],
'Математика': [3.4, 3.8, 4.2, 2, 5]
}
fig = px.scatter_ternary(df,
a="Информатика",
b="Физика",
c="Математика",
color="Математика",
size="Математика",
hover_name="Математика",
size_max=20,
template='presentation')
fig.show()
IO_Path(
empirical_path.format(*col_set),
postpred_path.format(*col_set),
out_path.format(*col_set),
{
'a': 'C_{}'.format(col_set[0]),
'b': 'C_{}'.format(col_set[1]),
'c': 'C_{}'.format(col_set[2])
},
))
for path in paths:
emp = pd.DataFrame(polar_to_simplex(pd.read_csv(path.emp).values),
columns=[path.colnames[x] for x in ['a', 'b', 'c']])
emp['source'] = 'Empirical'
pp = pd.DataFrame(polar_to_simplex(pd.read_csv(path.pp).values),
columns=[path.colnames[x] for x in ['a', 'b', 'c']])
pp['source'] = 'PostPred'
selected_rows = sample(range(pp.shape[0]), emp.shape[0])
out_df = pd.concat([emp, pp.iloc[selected_rows, ]], axis=0)
fig = px.scatter_ternary(out_df, color='source', **path.colnames)
fig.write_image(path.out)
del (fig)
del (emp)
del (pp)
# EOF
df_wind = px.data.wind()
px.line_polar(df_wind, r= 'frequency',theta = 'direction',color='strength',line_close=True,template="plotly_dark")
# In[85]:
#ternary plot
# In[87]:
df_exp = px.data.experiment()
px.scatter_ternary(df_exp, a='experiment_1',b ='experiment_2',c='experiment_3',hover_name='group',color="gender")
# In[88]:
#facets
# In[91]:
df_tips = px.data.tips()
px.scatter(df_tips,x='total_bill',y='tip',color='smoker',facet_col='sex')
px.histogram(df_tips,x = 'total_bill', y ='tip',color='sex',facet_row='time',facet_col='day',category_orders={'day':["Thur","fri","Sat","Sun"],"time":["Lunch","Dinner"]})
figthhhh = px.scatter(dmtrial, x="Hour", y="Comments",
color= 'Hour', size='Upvotes', hover_name = "Title",
title="Number of comments on r/Ask Subreddits with Time of Day, with Size of Dots indicating Upvotes")
figthhhh.show()
figthhhh.write_html("Fig13.html")
#fig321 = px.scatter(dmhours, x="Hour", y="Upvotes", color= 'Upvote Ratio', title="//")
#fig321.show()
figtt = px.scatter_ternary(dmhours, a = 'Downvotes', b = 'Comments', c = 'Upvotes',
hover_name = 'Title', color = 'Upvote Ratio',
title="Ternary Plot of Relative Upvotes, Downvotes, and Comments on r/Ask Subreddits")
figtt.show()
figtt.write_html("Fig14.html")
#figtta = px.scatter_ternary(dmhours, a = 'Upvotes', b = 'Comments', c = 'Comment/Upvote Ratio')
#figtta.show()
def ternary(df):
import numpy as np
features = df.columns.to_list()
clean_features = []
for name in features:
if df[name].dtypes == np.float or df[name].dtypes == np.int64:
clean_features.append(name)
fig = px.scatter_ternary(df,
a=clean_features[2],
b=clean_features[3],
c=clean_features[4],
size_max=15,
color=df['MINERAL PREDICTED'])
return html.Div([
html.Div([
dcc.Dropdown(id='dropdown1',
options=[{
'label': i,
'value': i
} for i in clean_features],
value=clean_features[2])
],
style={
'width': '24%',
'display': 'inline-block'
}),
html.Div([
dcc.Dropdown(id='dropdown2',
options=[{
'label': i,
'value': i
} for i in clean_features],
value=clean_features[3])
],
style={
'width': '24%',
'display': 'inline-block'
}),
html.Div([
dcc.Dropdown(id='dropdown3',
options=[{
'label': i,
'value': i
} for i in clean_features],
value=clean_features[4])
],
style={
'width': '24%',
'display': 'inline-block'
}),
html.Div([
dcc.Dropdown(id='dropdown4',
options=[{
'label': 'MINERAL PREDICTED',
'value': 'MINERAL PREDICTED'
}, {
'label': 'GROUP PREDICTED',
'value': 'GROUP PREDICTED'
}],
value='MINERAL PREDICTED')
],
style={
'width': '24%',
'display': 'inline-block'
}),
html.Div(id='dd-output-container'),
dcc.Graph(id='ternary-graph', figure=fig)
],
style={'passing': 10})
import plotly.graph_objects as go
import plotly.express as px
import statsmodels.api as sm
df = pd.read_csv('/Users/william/Desktop/Python Website/output4.csv')
fig = px.scatter_ternary(
df,
a="Relative Upvotes",
b="Relative Comments",
c="Relative Downvotes",
hover_name="Title",
opacity=0.7,
color="Topic",
size="Upvotes",
size_max=9,
title=
'Post reception within news Subreddits by category (2020) — Normalized Upvotes, Comments, and Downvotes',
color_discrete_map={
"Donald Trump": "darkred",
"Joe Biden": "blue",
"Racial Justice Protests": "orange",
"COVID-19": "black",
"The Economy": "green"
})
'''trace_a = go.Scatterternary({
'mode': 'markers',
'name': 'Donald Trump',
'a': df.loc[df['Topic'] == 'Donald Trump']['Relative Upvotes'],
'b': df.loc[df['Topic'] == 'Donald Trump']['Relative Comments'],
'c': df.loc[df['Topic'] == 'Donald Trump']['Relative Downvotes'],
def generate_ternary(data, x, y, z, data_format="long", **kwargs):
if data_format == "long":
return px.scatter_ternary(data, a=x[0], b=y[0], c=z[0], **kwargs)
