def build_message_length_box(self):
"""
Creates a bar plot of the total number of messages in each category
:return: Object defining the plot, in Plotly format
"""
boxes = []
for genre in self.df["genre"].unique():
category_message_lengths = self.df[
self.df['genre'] == genre]['message'].apply(len).values
boxes.append(Box(x=category_message_lengths, name=genre))
return {
'data': boxes,
'layout': {
'title': 'Distribution of Message Lengths per Genre',
'xaxis': {
'title': "Message Length"
}
}
}
def tab4_chart2(clicked_bandit, window, pradio, data):
print('tab4 chart2')
if data:
proxies = data['proxies']
if pradio == 'all':
proxy_data = [{
'y': proxy['response_times']
} for proxy in proxies if proxy['bandit'][-1] == clicked_bandit
and len(proxy['response_times']) > 0]
elif pradio == 'window':
window = int(window)
proxy_data = [{
'y': proxy['response_times'][-window:]
} for proxy in proxies if proxy['bandit'][-1] == clicked_bandit
and len(proxy['response_times']) > 0]
data = [
Box(y=proxy['y'], name='p' + str(i))
for i, proxy in enumerate(proxy_data)
]
data0 = Scatter(x=['p' + str(i) for i in range(0, len(proxy_data))],
y=[len(i['y']) for i in proxy_data],
yaxis='y2',
line=dict(width=1),
opacity=0.5)
data.append(data0)
layout = dict(showlegend=False,
title='Proxies response times in bandit ' +
str(clicked_bandit),
xaxis=dict(title='Proxy'),
yaxis=dict(title='Proxies reposne times [s]'),
yaxis2=dict(overlaying='y',
side='right',
title='Nr. of times used'),
margin=dict(l=50, r=80, t=80, b=50))
return Figure(data=data, layout=layout)
def boxplot_min_energy_errors( schedule_names, experiment_outputs, title='', fsave='', x_axis='' ):
n_exps = len( experiment_outputs[0].final_errors() )
n_schedules = len( schedule_names )
data = []
for i in range(n_schedules):
schedule_name = schedule_names[i]
errors = experiment_outputs[i].min_energy_errors()
data.append( Box(y=errors, boxpoints=False, name=schedule_name))
layout = go.Layout( autosize=False, width=900, height=600,
margin=go.layout.Margin( l=60, r=75, b=100, t=100, pad=4 ),
title=title, showlegend=True,
font = dict( color = "black",
size = 10),
yaxis=dict(title='Error'),
xaxis=dict(title=x_axis))
fig = Figure(data, layout)
iplot(fig, filename=fsave)
if len(fsave) > 0:
pio.write_image(fig, fsave + '.pdf')
def get_mch_box(species, gene, level, outliers):
"""Generate gene body mCH box plot.
Traces are grouped by cluster.
Arguments:
species (str): Name of species.
gene (str): Ensembl ID of gene for that species.
level (str): Type of mCH data. Should be "original" or "normalized".
outliers (bool): Whether if outliers should be displayed.
Returns:
str: HTML generated by Plot.ly.
"""
points = get_gene_mch(species, gene, outliers)
if not points:
raise FailToGraphException
traces = OrderedDict()
max_cluster = int(
max(points,
key=lambda x: int(x['cluster_ordered']))['cluster_ordered']) + 1
if species == 'mmu':
max_cluster = 16
colors = generate_cluster_colors(max_cluster)
for point in points:
trace = traces.setdefault(
int(point['cluster_ordered']),
Box(
y=list(),
name=point['cluster_name'],
marker={
'color':
colors[(int(point['cluster_ordered']) - 1) % len(colors)],
'outliercolor':
colors[(int(point['cluster_ordered']) - 1) % len(colors)],
'size':
6
},
boxpoints='suspectedoutliers',
visible=True,
))
if level == 'normalized':
trace['y'].append(point['normalized'])
else:
trace['y'].append(point['original'])
if species == 'mmu':
for i in range(17, 23, 1):
traces[i]['marker']['color'] = 'black'
traces[i]['marker']['outliercolor'] = 'black'
traces[i]['visible'] = "legendonly"
geneName = gene_id_to_name(species, gene)
geneName = geneName['geneName']
layout = Layout(
autosize=False,
width=950,
height=700,
title='Gene body mCH in each cluster: ' + geneName,
titlefont={
'color': 'rgba(1,2,2,1)',
'size': 20
},
legend={
'orientation': 'h',
'y': -0.3,
'traceorder': 'normal',
},
xaxis={
'title': 'Cluster',
'titlefont': {
'size': 17
},
'type': 'category',
'anchor': 'y',
'ticks': 'outside',
'ticklen': 4,
'tickangle': -45,
'tickwidth': 0.5,
'showticklabels': True,
'tickfont': {
'size': 12
},
'showline': True,
'zeroline': False,
'showgrid': True,
'linewidth': 1,
'mirror': True,
},
yaxis={
'title': geneName + ' ' + level.capitalize() + ' mCH',
'titlefont': {
'size': 15
},
'type': 'linear',
'anchor': 'x',
'ticks': 'outside',
# 'tickcolor': 'white',
'ticklen': 4,
'tickwidth': 0.5,
'showticklabels': True,
'tickfont': {
'size': 12
},
'showline': True,
'zeroline': False,
'showgrid': True,
'linewidth': 1,
'mirror': True,
},
)
return plotly.offline.plot(
{
'data': list(traces.values()),
'layout': layout
},
output_type='div',
show_link=False,
include_plotlyjs=False)
def graph_03(self):
legend = {
"title":
"Variação de notas dos estudantes por cluster de navegação",
"xaxis": "",
"yaxis": "Notas",
'hovertext': 'Nota'
}
if (self._language == "en"):
legend = {
"title": "Student grades variation by navigate cluster",
"xaxis": "",
"yaxis": "Grades",
'hovertext': 'Grade'
}
df = self.DATASET.sort_values(by=self.DATASET.columns[1])
Clusters = df[df.columns[len(df.columns) - 1]].unique()
color = ["rgb(198, 218, 32)", "rgb(121,64,64)", "rgb(0,0,204)"]
color = ["rgb(255,0,0)", "rgb(0,0,255)", "rgb(0,255,0)"]
# print(Clusters)
trace = []
for i in range(0, len(Clusters)):
lst_grades = df[df.columns[1]].loc[df[df.columns[
len(df.columns) - 1]] == Clusters[i]].values.tolist() #Grades
lst_names = df[df.columns[0]].loc[df[df.columns[
len(df.columns) - 1]] == Clusters[i]].values.tolist()
trace.append(
Box(
y=lst_grades,
# y=df[df.columns[1]].loc[df[df.columns[len(df.columns)-1]]==Clusters[i]].values.tolist(), #Access
name="Cluster " + str(i + 1),
# text=df[df.columns[0]].loc[df[df.columns[len(df.columns)-1]]==Clusters[i]].values.tolist(),
text=[
'<b>' + lst_names[j] + '</b><br>' +
legend['hovertext'] + ": " + str(lst_grades[j]) +
'<br>Cluster ' + str(i + 1)
for j in range(len(lst_names))
],
boxpoints='all',
marker=dict(color=color[i], line=dict(width=1)),
boxmean=True))
layout = Layout(
title=legend['title'],
# hovermode = "closest",
showlegend=True,
xaxis=dict(
title=legend["xaxis"],
titlefont=dict(
# family='Arial, sans-serif',
# size=18,
color='rgb(180,180,180)', ),
),
yaxis=dict(
title=legend["yaxis"],
titlefont=dict(
# family='Arial, sans-serif',
# size=18,
color='rgb(180,180,180)', ),
fixedrange=False,
range=[-1, self.DATASET[self.DATASET.columns[1]].max() + 10],
rangemode="normal",
# showline = True,
zeroline=False,
))
data = trace
fig = Figure(data=data, layout=layout)
if self._type_result == "jupyter-notebook":
iplot(fig, filename='Box')
elif self._type_result == "dash":
return dcc.Graph(id='V011@3', figure=fig)
elif self._type_result == "flask":
modeBarButtonsToRemove = [
'toImage', 'sendDataToCloud', 'hoverCompareCartesian',
'lasso2d', 'hoverClosestCartesian', 'toggleHover',
'hoverClosest3d', 'hoverClosestGeo', 'hoverClosestGl2d',
'hoverClosestPie'
]
config = {
"displaylogo": False,
"responsive": True,
"displayModeBar": True,
"modeBarButtonsToRemove": modeBarButtonsToRemove
}
return {
"id":
"V011@3",
"layout":
json.dumps({
"data": data,
"layout": layout,
"config": config
},
cls=PlotlyJSONEncoder)
}
def graph_mean_in_tissues(title, column_names, summary):
"""
Compute plotly graph with mean value in tissue masks
Parameters
----------
title : string
Title of the graph.
column_names : array of strings
Name of the columns in the summary DataFrame.
summary : DataFrame
DataFrame containing the mean stats.
Returns
-------
div : html div (string)
Graph as a HTML div.
"""
means_wm = []
means_gm = []
means_csf = []
np.random.seed(1)
metric = np.concatenate(summary.index)
means_wm = np.array(summary[column_names[0]])
means_gm = np.array(summary[column_names[1]])
means_csf = np.array(summary[column_names[2]])
wm = Box(
name="WM",
y=means_wm,
boxpoints='all',
jitter=0.3,
text=metric,
pointpos=-1.8,
hoverinfo="y+text"
)
gm = Box(
name="GM",
y=means_gm,
boxpoints='all',
jitter=0.3,
text=metric,
pointpos=-1.8,
hoverinfo="y+text"
)
csf = Box(
name="CSF",
y=means_csf,
boxpoints='all',
jitter=0.3,
text=metric,
pointpos=-1.8,
hoverinfo="y+text",
)
data = [wm, gm, csf]
fig = Figure(data=data)
range_yaxis = [0, np.max(means_wm) + 2 * np.max(means_wm)]
fig['layout']['yaxis'].update(range=range_yaxis)
fig['layout'].update(title=title)
fig['layout'].update(width=500, height=500)
div = off.plot(fig, show_link=False, output_type='div')
div = div.replace("<div>", "<div style=\"display:inline-block\">")
return div
#mode = 'markers',
name=op_type(key) + '-' + compression_type(key) + '-' + link_type(key),
boxmean='sd',
boxpoints='all'
)
)
'''
# Grouped Plot
for key, value in xxx.iteritems():
if True:
trace0.append(
Box(
x=value,
y=yyy[key],
#mode = 'markers',
name=key,
boxmean='sd',
boxpoints='all'))
plotly.offline.plot({
"data":
sorted(trace0),
"layout":
Layout(title="Operations Per Second Sensitivity, Wired v. Wireless",
yaxis=dict(title='operations per second'),
boxmode='group')
})
# or plot with: plot_url = py.plot(data, filename='basic-line')
def showPlotly(self):
"""
:return:
"""
title = " vs ".join(self.prefixes)
imagefile = join(self.outputdir, "_".join(self.prefixes) + '.html')
linewidth = 1.5
markerdiam = 4
errbarw = 3
colors = {
self.prefixes[0]: 'rgb(255,140,0)',
self.prefixes[1]: 'rgb(70,130,180)'
}
# HistoAvg
trace1 = []
df = self.histodata
for prefix in self.prefixes:
meancol = 'MEAN_' + prefix
semcol = 'SEM_' + prefix
trace1.append(
Scatter(x=df['bins'],
y=df[meancol],
name=prefix,
showlegend=False,
line=dict(width=linewidth, color=colors[prefix]),
marker=dict(color=colors[prefix], size=markerdiam),
error_y=dict(array=df[semcol],
type='data',
symmetric=True,
color=colors[prefix],
thickness=linewidth,
width=errbarw)))
# MSDAvg
trace2 = []
x = [str(x) for x in range(1, self.msdpoints + 1)]
xi = [x * self.timeint
for x in range(1, self.msdpoints + 1)] # convert to times
i = 0
for file in self.msddatafiles:
df = pd.read_csv(file)
all = df.groupby('Cell').get_group('ALL')
allmeans = all.groupby('Stats').get_group('Mean')
allsems = all.groupby('Stats').get_group('SEM')
trace2.append(
Scatter(y=allmeans[x].iloc[0],
x=xi,
name=self.prefixes[i],
mode='lines+markers',
showlegend=False,
line=dict(color=colors[self.prefixes[i]],
width=linewidth),
marker=dict(color=colors[self.prefixes[i]],
size=markerdiam),
error_y=dict(array=allsems[x].iloc[0],
type='data',
symmetric=True,
color=colors[self.prefixes[i]],
thickness=linewidth,
width=errbarw)))
i += 1
# Ratios
trace3 = []
df = self.ratiodata
for prefix in self.prefixes:
cols = 'Ratio_' + prefix
trace3.append(
Box(y=df[cols],
boxpoints='all',
showlegend=False,
jitter=0.3,
pointpos=-1.8,
name=prefix,
marker=dict(color=colors[prefix])))
# Areas
trace4 = []
df = self.areadata[self.areadata['Cell'] != 'ALL']
for prefix in self.prefixes:
cols = 'MSD Area_' + prefix
trace4.append(
Box(y=df[cols],
boxpoints='all',
jitter=0.3,
pointpos=-1.8,
name=prefix,
marker=dict(color=colors[prefix])))
fig = tools.make_subplots(
rows=2,
cols=2,
subplot_titles=('Mean D with SEM', 'Mean MSD with SEM',
'Log10(D) Ratios', 'MSD Areas'))
fig.append_trace(trace1[0], 1, 1)
fig.append_trace(trace1[1], 1, 1)
fig.append_trace(trace2[0], 1, 2)
fig.append_trace(trace2[1], 1, 2)
fig.append_trace(trace3[0], 2, 1)
fig.append_trace(trace3[1], 2, 1)
fig.append_trace(trace4[0], 2, 2)
fig.append_trace(trace4[1], 2, 2)
fig['layout'].update(height=800, width=800, title=title)
fig['layout']['xaxis1'].update(title='Log<sub>10</sub>(D)')
fig['layout']['yaxis1'].update(title='Relative Frequency')
fig['layout']['xaxis2'].update(title='Time (s)')
fig['layout']['yaxis2'].update(title='MSD (μm<sup>2</sup>)')
# fig['layout']['xaxis3'].update(title='Group')
fig['layout']['yaxis3'].update(title='Mobile/Immobile Ratio')
# fig['layout']['xaxis4'].update(title='Group')
fig['layout']['yaxis4'].update(
title='Area under curve (μm<sup>2</sup>)')
# py.plot(fig, filename=imagefile)
offline.plot(fig, filename=imagefile)
def plot(yaxis_values, positions, yaxis_title,
xaxis_title, plot_title, box_name):
"""
Plot nba data
:ages: list of the ages of players
:positions: list of the positions
:yaxis_title: title of the yaxis
:xaxis_title: title of the xaxis
:plot_title: title of the plot
:box_name: name of the box
:return: None, data sent to plotly via API
"""
data = Data([
Box(
y=yaxis_values,
x=positions,
name=box_name,
boxmean=True,
boxpoints='all',
jitter=0.5,
whiskerwidth=0.5,
fillcolor='rgb(106, 168, 79)',
marker=Marker(
color='rgba(7, 55, 99, 0.5)',
size=4,
symbol='circle',
opacity=0.7
),
line=Line(
color='rgba(7, 55, 99, 0.5)',
width=2
),
opacity=1,
showlegend=False
)
])
layout = Layout(
title=plot_title,
showlegend=False,
autosize=True,
width=792,
height=469,
xaxis=XAxis(
title=xaxis_title,
range=[-0.6799999999999999, 6.5],
type='category',
autorange=True,
showexponent='all',
side='bottom'
),
yaxis=YAxis(
title=yaxis_title,
range=[17.944444444444443, 39.05555555555556],
type='linear',
autorange=True,
showexponent='all'
),
paper_bgcolor='rgb(255, 255, 255)',
plot_bgcolor='rgb(217, 217, 217)',
hovermode='closest',
boxmode='overlay',
boxgap=0.4,
boxgroupgap=0.4
)
fig = Figure(data=data, layout=layout)
py.plot(fig)
Created on Thu Jul 20 15:03:07 2017
@author: Sayali Sonawane
"""
# Import packages
import numpy as np
import plotly
from plotly.graph_objs import Layout, Box
# Create data
y0 = np.random.randn(50) - 1
y1 = np.random.randn(50) + 1
# Define trace
trace1 = Box(y=y0,
showlegend=True,
marker=dict(color='rgb(214, 12, 140)', ),
name='Trace_y0')
trace2 = Box(y=y1,
showlegend=True,
marker=dict(color='rgb(0, 128, 128)', ),
name='Trace_y1')
# Create chart
# Output will be stored as a html file.
# Whenever we will open output html file, one popup option will ask us about if want to save it in jpeg format.
# Font family can be used in a layout to define font type, font size and font color for title
plotly.offline.plot(
{
"data": [trace1, trace2],
"layout":
Layout(title="Colored Box Chart",
def index():
# extract data needed for visuals
# TODO: Below is an example - modify to extract data for your own visuals
genre_counts = df.groupby('genre').count()['message']
genre_names = list(genre_counts.index)
labels_list, num_obs_labels = _get_num_obs_per_label(df)
df['words_count'] = df['message'].apply(lambda x: len(word_tokenize(x)))
words_count_list = \
list(df.words_count)
words_count_list_filtered = list(df[df['words_count'] < 61].words_count)
# create visuals
# TODO: Below is an example - modify to create your own visuals
graphs = [{
'data': [Bar(x=genre_names, y=genre_counts)],
'layout': {
'title': 'Distribution of Message Genres',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Genre"
}
}
}, {
'data': [Bar(x=num_obs_labels, y=labels_list, orientation='h')],
'layout': {
'title': 'Number of observations per label',
'xaxis': {
'title': "Label"
}
}
}, {
'data': [Histogram(x=words_count_list)],
'layout': {
'title': 'Histogram number of words per message',
'xaxis': {
'title': "Number of words"
}
}
}, {
'data': [Box(x=words_count_list)],
'layout': {
'title': 'Box Plot number of words per message'
}
}, {
'data': [
Histogram(x=words_count_list_filtered,
xbins=dict(size=2),
autobinx=False)
],
'layout': {
'title': 'Histogram number of words per message filtering only'
' messages with less then 61 words',
'xaxis': {
'title': "Number of words"
}
}
}]
# encode plotly graphs in JSON
ids = ["graph-{}".format(i) for i, _ in enumerate(graphs)]
graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
# render web page with plotly graphs
return render_template('master.html', ids=ids, graphJSON=graphJSON)
def append_bars_and_lines(data1, data2, data3, measure_results, table_rows):
is_compare = measure_results[0].is_compare()
results = {}
versions = []
webengine_compare = {}
webkit_compare = {}
webkit_labels = []
for result in sorted(measure_results):
if not is_compare:
one_table_row = []
if result.is_webengine():
if result.get_engine() not in results:
results[result.get_engine()] = {}
versions.append(result.get_version())
results[result.get_engine()][
result.get_version()] = result.get_result()
full_name = result.get_engine() + "-" + result.get_version()
one_table_row.append(result.get_version())
else:
if re.match(".*QtWebkit-Annulen.*", result.get_engine()):
one_table_row.append("WK-A")
else:
one_table_row.append("WK")
if result.get_engine() not in results:
results[result.get_engine()] = []
for i in range(0, 5):
results[result.get_engine()].append(result.get_result())
full_name = result.get_engine()
memories = result.get_memories()
for memory in memories:
one_table_row.append(memory)
table_rows.append(one_table_row)
data3.append(
Box(y=memories, name=full_name, xaxis='x2', yaxis='y2'))
else:
if result.is_webengine():
if result.get_gpu() not in webengine_compare:
webengine_compare[result.get_gpu()] = {}
if not result.get_version() in versions:
versions.append(result.get_version())
webengine_compare[result.get_gpu()][
result.get_version()] = result.get_result()
else:
if result.get_gpu() not in webkit_compare:
webkit_compare[result.get_gpu()] = {}
webkit_compare[result.get_gpu()][
result.get_engine()] = result.get_result()
versions = sorted(versions)
if not is_compare:
for key, values in sorted(results.items()):
values_to_append = []
if key == "QtWebEngine":
for inner_key, inner_value in sorted(values.items()):
values_to_append.append(inner_value)
else:
values_to_append = values
data2.append(
Scatter(x=versions,
y=values_to_append,
mode='lines+markers+text',
name=key,
text=values_to_append,
textposition='top'))
data1.append(
Bar(x=versions,
y=values_to_append,
name=key,
text=values_to_append,
textposition='auto'))
else:
for key, values in webengine_compare.items():
values_to_append = []
for inner_key, inner_value in sorted(values.items()):
values_to_append.append(inner_value)
data1.append(
Bar(x=versions,
y=values_to_append,
name=key,
text=values_to_append,
textposition='auto'))
for key, values in sorted(webkit_compare.items()):
values_to_append = []
for inner_key, inner_value in sorted(values.items()):
webkit_labels.append(inner_key)
values_to_append.append(inner_value)
data2.append(
Bar(x=webkit_labels,
y=values_to_append,
name=key,
text=values_to_append,
textposition='auto'))
def index():
# extract data needed for visuals
# TODO: Below is an example - modify to extract data for your own visuals
genre_counts = df.groupby('genre').count()['message']
genre_names = list(genre_counts.index)
classes_count = df.drop(columns = ['id','message','genre']).sum()
classes_names = list(classes_count.index)
text_length = df['message'].str.len()
# create visuals
# TODO: Below is an example - modify to create your own visuals
graphs = [
{
'data': [
Bar(
x=genre_names,
y=genre_counts
)
],
'layout': {
'title': 'Distribution of Message Genres',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Genre"
},
}
},
{
'data': [
Bar(
x=classes_names,
y=classes_count
)
],
'layout': {
'title': 'Distribution of Classes in Training Data',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Classes"
}
}
},
{
'data': [
Box(
x = text_length,
name = ''
)
],
'layout': {
'title': 'Box Plot of Text Length',
'xaxis': {
'title': "Text length"
}
}
}
]
# encode plotly graphs in JSON
ids = ["graph-{}".format(i) for i, _ in enumerate(graphs)]
graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
# render web page with plotly graphs
return render_template('master.html', ids=ids, graphJSON=graphJSON)
def get_mch_box_two_species(species, gene_mmu, gene_hsa, level, outliers):
"""Generate gene body mCH box plot for two species.
Traces are grouped by cluster and ordered by mm_hs_homologous_cluster.txt.
Mouse clusters red, human clusters black.
Arguments:
gene_mmu (str): Ensembl ID of gene for that species for mouse.
gene_hsa (str): Ensembl ID of gene for that species for human.
level (str): Type of mCH data. Should be "original" or "normalized".
outliers (bool): Whether if outliers should be displayed.
Returns:
str: HTML generated by Plot.ly.
"""
points_mmu = get_gene_mch('mouse_published', gene_mmu, outliers)
points_hsa = get_gene_mch(species, gene_hsa, outliers)
cluster_order = get_ortholog_cluster_order()
if not points_mmu or not points_hsa or not cluster_order:
raise FailToGraphException
geneName = gene_id_to_name('mouse_published', gene_mmu)
geneName = geneName['geneName']
# EAM - This organizes the box plot into groups
traces_mmu = Box(y=list(
i.get(level) for i in points_mmu if i.get('cluster_ortholog')),
x=list(
i.get('cluster_ortholog') for i in points_mmu
if i.get('cluster_ortholog')),
marker={
'color': 'red',
'outliercolor': 'red'
},
boxpoints='suspectedoutliers',
hoverinfo='text')
traces_hsa = Box(y=list(
i.get(level) for i in points_hsa if i.get('cluster_ortholog')),
x=list(
i.get('cluster_ortholog') for i in points_hsa
if i.get('cluster_ortholog')),
marker={
'color': 'black',
'outliercolor': 'black'
},
boxpoints='suspectedoutliers')
traces_combined = [traces_mmu, traces_hsa]
layout = Layout(
boxmode='group',
autosize=False,
showlegend=False,
width=900,
height=700,
title='Gene body mCH in each cluster: ' + geneName,
titlefont={
'color': 'rgba(1,2,2,1)',
'size': 20
},
# legend={
# 'orientation': 'h',
# 'x': -0.1,
# 'y': -0.6,
# 'traceorder': 'normal',
# },
xaxis={
'title': '',
'titlefont': {
'size': 14
},
'type': 'category',
'anchor': 'y',
'ticks': 'outside',
'tickcolor': 'rgba(51,51,51,1)',
'ticklen': 4,
'tickwidth': 0.5,
'tickangle': -35,
'showticklabels': True,
'tickfont': {
'size': 12
},
'showline': False,
'zeroline': False,
'showgrid': True,
},
yaxis={
'title': geneName + ' ' + level.capitalize() + ' mCH',
'titlefont': {
'size': 15
},
'type': 'linear',
'anchor': 'x',
'ticks': 'outside',
'tickcolor': 'rgba(51,51,51,1)',
'ticklen': 4,
'tickwidth': 0.5,
'showticklabels': True,
'tickfont': {
'size': 12
},
'showline': False,
'zeroline': False,
'showgrid': True,
},
shapes=[
{
'type': 'rect',
'fillcolor': 'transparent',
'line': {
'color': 'rgba(115, 115, 115, 1)',
'width': 1,
'dash': False
},
'yref': 'paper',
'xref': 'paper',
'x0': 0,
'x1': 1,
'y0': 0,
'y1': 1
},
],
annotations=[{
'text': '<b>■</b> Mouse',
'x': 0.4,
'y': 1.02,
'ax': 0,
'ay': 0,
'showarrow': False,
'font': {
'color': 'red',
'size': 12
},
'xref': 'paper',
'yref': 'paper',
'xanchor': 'left',
'yanchor': 'bottom',
'textangle': 0,
}, {
'text': '<b>■</b> Human',
'x': 0.5,
'y': 1.02,
'ax': 0,
'ay': 0,
'showarrow': False,
'font': {
'color': 'Black',
'size': 12
},
'xref': 'paper',
'yref': 'paper',
'xanchor': 'left',
'yanchor': 'bottom',
'textangle': 0,
}])
return plotly.offline.plot({
'data': traces_combined,
'layout': layout
},
output_type='div',
show_link=False,
include_plotlyjs=False)
def jobsanalysis():
with orm.db_session:
countriesset = set(orm.select((c.country) for c in Stat)[:])
keywords = orm.select((c.keyword) for c in Stat)[:]
countries = [(k, allcountries[k]) for k in countriesset]
#form.country.choices = countries
#form.keyword.choices = [(x, x) for x in keywords]
if request.method == 'GET':
dataseries = []
popularity = []
plotly.tools.set_credentials_file(username='******',
api_key='mYs3EJORl98E0vxkWvGr')
keyword = ''
countrytext = ''
c = 0
country = request.values.get('c', '')
if country in allcountries:
countrytext = allcountries[country]
with orm.db_session:
localarr = orm.select(
(c.keyword) for c in JobEntity if c.country == country)[:]
for key in localarr:
yr = orm.select(
(c.minusdsalary) for c in JobEntity
if c.country == country and c.period == 'annum'
and c.keyword == key)[:]
dataseries.append(
Box(y=yr,
boxpoints=False,
name='Start %s' % key,
marker=dict(color=colors0[c])))
yr = orm.select(
(c.maxusdsalary) for c in JobEntity
if c.country == country and c.period == 'annum'
and c.keyword == key)[:]
dataseries.append(
Box(y=yr,
boxpoints=False,
name='Top %s' % key,
marker=dict(color=colors1[c])))
title = 'Salaries in %s for known keywords.' % countrytext
vacancies, days = orm.select(
(c.total, c.days) for c in Stat if c.country == country
and c.period == 'annum' and c.keyword == key)[:][0]
popularity.append(int(int(vacancies) / int(days)))
c += 1
else:
keyword = request.values.get('k', '')
with orm.db_session:
localarr = orm.select(
(c.country) for c in JobEntity if c.keyword == keyword)[:]
for cnt in localarr:
yr = orm.select(
(c.minusdsalary) for c in JobEntity
if c.keyword == keyword and c.period == 'annum'
and c.country == cnt)[:]
dataseries.append(
Box(y=yr,
boxpoints=False,
name='Start %s' % cnt,
marker=dict(color=colors0[c])))
yr = orm.select(
(c.maxusdsalary) for c in JobEntity
if c.keyword == keyword and c.period == 'annum'
and c.country == cnt)[:]
dataseries.append(
Box(y=yr,
boxpoints=False,
name='Top %s' % cnt,
marker=dict(color=colors1[c])))
title = 'Salaries for jobs with keyword "%s".' % keyword
vacancies, days = orm.select(
(c.total, c.days) for c in Stat if c.country == cnt
and c.period == 'annum' and c.keyword == keyword)[:][0]
popularity.append(int(int(vacancies) / int(days)))
c += 1
graph = getplotly(dataseries, Layout(title=title))
dataseries_pop = [Bar(x=localarr, y=popularity)]
graphp = getplotly(dataseries_pop,
Layout(title='Relevance, vacancies per day'))
return render_template('parsers/graph.jade',
countries=countries,
keywords=keywords,
country=countrytext,
keyword=keyword,
graph=graph,
graphp=graphp)
# json_error_list = json.dump(error_data, error_list_file)
# open error list from disk
with open(error_list_path, "r") as error_list_file:
json_error_list = json.loads(error_list_file.read())
px_error_list = json_error_list["px"]
py_error_list = json_error_list["py"]
pz_error_list = json_error_list["pz"]
roll_error_list = json_error_list["roll"]
pitch_error_list = json_error_list["pitch"]
yaw_error_list = json_error_list["yaw"]
# prepare the data for the box plot for plotly to plot
# box plot taken here https://plot.ly/python/box-plots/
px_errors = Box(x=1, y=px_error_list, name='px')
py_errors = Box(x=2, y=py_error_list, name='py')
pz_errors = Box(x=3, y=pz_error_list, name='pz')
roll_errors = Box(x=4, y=roll_error_list, name='roll')
pitch_errors = Box(x=5, y=pitch_error_list, name='pitch')
yaw_errors = Box(x=6, y=yaw_error_list, name='yaw')
# data = [px_errors, py_errors, pz_errors, roll_errors, pitch_errors, yaw_errors]
# plot({"data": data})
px_errors = Histogram(x=px_error_list, name='px')
py_errors = Histogram(x=py_error_list, name='py')
pz_errors = Histogram(x=pz_error_list, name='pz')
roll_errors = Histogram(x=roll_error_list, name='roll')
pitch_errors = Histogram(x=pitch_error_list, name='pitch')
yaw_errors = Histogram(x=yaw_error_list, name='yaw')
def index():
# extract data needed for visuals
# TODO: Below is an example - modify to extract data for your own visuals
genre_counts = df.groupby('genre').count()['message']
genre_names = list(genre_counts.index)
cat_names = df.columns.tolist()
for x in ['id', 'message', 'original', 'genre']:
cat_names.remove(x)
cat_counts = df[cat_names].sum()
df['length'] = df['message'].str.len()
name_len_val = []
for col in cat_names:
name_len_val.append(df[df[col] == 1]['length'].values)
data3 = []
for i, name in enumerate(cat_names):
data3.append(Box(y=name_len_val[i], name=name, boxpoints='outliers'))
# create visuals
# TODO: Below is an example - modify to create your own visuals
graphs = [
####################### PLOT 1 ##########################
{
'data': [Bar(x=genre_names, y=genre_counts)],
'layout': {
'title': 'Distribution of Message Genres',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Genre"
}
}
},
####################### PLOT 2 ##########################
{
'data': [Bar(x=cat_names, y=cat_counts)],
'layout': {
'title': 'Distribution of Tagged Categories',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Cat"
}
}
},
####################### PLOT 3 ##########################
{
'data': data3,
'layout': {
'title': "Box Plot Styling Outliers",
'yaxis': {
'type': 'log',
'autorange': 'True'
}
}
}
]
# encode plotly graphs in JSON
ids = ["graph-{}".format(i) for i, _ in enumerate(graphs)]
graph1JSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
# render web page with plotly graphs
return render_template('master.html', ids=ids, graphJSON=graph1JSON)
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 20 15:03:07 2017
@author: Sayali Sonawane
"""
# Import packages
import numpy as np
import plotly
from plotly.graph_objs import Layout,Box
# Create data
y0 = np.random.randn(50)-1
y1 = np.random.randn(50)+1
# Define trace
trace1 =Box(x=y0,showlegend=True,name='Trace_y0')
trace2 =Box(x=y1,showlegend=True,name='Trace_y1')
# Create chart
# Output will be stored as a html file.
# Whenever we will open output html file, one popup option will ask us about if want to save it in jpeg format.
# Font family can be used in a layout to define font type, font size and font color for title
plotly.offline.plot({
"data": [
trace1,trace2
],
"layout": Layout(title="Basic Horizontal Box Chart",font=dict(family='Courier New, monospace', size=18, color='rgb(0,0,0)'))
},filename='basic_horizontal_box_chart.html',image='jpeg')
def plotBoxplot(db, table, **xargs):
data = read(db, table)
return _plotly(
[Box(x=v.get("x", None), y=v["y"], name=k) for k, v in data.items()],
table, **xargs)
