def update(attr, old, new):
if old == new:
return
t0 = time.time()
timer = Paragraph()
timer.text = '(Executing query...)'
test_name_para = Paragraph()
test_name_para.text = 'Metrics for: {}'.format(test_select.value)
curdoc().clear()
base_rows = [row(test_name_para), row(test_select, metric_select, timer)]
curdoc().add_root(column(children=base_rows, ))
test_name = test_select.value
if test_name not in test_names:
timer.text = 'Invalid test_name: {}'.format(test_name)
return
metric_substr = metric_select.value
cutoff_timestamp = (
datetime.datetime.now() -
datetime.timedelta(days=30)).strftime('%Y-%m-%d %H:%M:%S UTC')
data = metric_history.fetch_data(test_name, cutoff_timestamp)
plots = metric_history.make_plots(test_name, metric_substr, data)
plot_rows = [row(p) for p in plots] if plots else []
curdoc().clear()
curdoc().add_root(column(children=base_rows + plot_rows, ))
t1 = time.time()
timer.text = '(Execution time: %s seconds)' % round(t1 - t0, 4)
def create_plots():
t0 = time.time()
timer = Paragraph()
test_names = [x for x in test_select.value.split(',') if x]
logging.info('test_names: `{}`'.format(test_names))
metric_names = [x for x in metric_select.value.split(',') if x]
logging.info('metric_names: `{}`'.format(metric_names))
if not test_names or not metric_names:
timer.text = 'Neither test_names nor metric_names can be blank.'
draw_base_ui(timer, test_select, metric_select)
return
data = metric_compare.fetch_data(test_names, metric_names)
if data.empty:
timer.text = 'No data found. Double check metric/test names.'
draw_base_ui(timer, test_select, metric_select)
return
plots = metric_compare.make_plots(test_names, metric_names, data)
plot_rows = [row(p) for p in plots] if plots else []
base_rows = [row(timer), row(test_select), row(metric_select)]
curdoc().clear()
curdoc().add_root(
column(
children=base_rows + plot_rows,
)
)
t1 = time.time()
timer.text = '(Execution time: %s seconds)' % round(t1 - t0, 4)
def update(attr, old, new):
if old == new:
return
t0 = time.time()
timer = Paragraph()
timer.text = '(Executing query...)'
test_name_para = Paragraph()
test_name_para.text = 'Metrics for: {}'.format(test_select.value)
curdoc().clear()
base_rows = [row(test_name_para), row(test_select, metric_select, timer)]
curdoc().add_root(column(children=base_rows, ))
test_name = test_select.value
metric_substr = metric_select.value
data = metric_history.fetch_data(test_name)
plots = metric_history.make_plots(test_name, metric_substr, data)
plot_rows = [row(p) for p in plots] if plots else []
curdoc().clear()
curdoc().add_root(column(children=base_rows + plot_rows, ))
t1 = time.time()
timer.text = '(Execution time: %s seconds)' % round(t1 - t0, 4)
def forecast_tab(data, ticker):
pg_title = Paragraph()
pg_title.text = '[ ' + ticker + ' ] CLOSE PRICE FORECAST'
# Create a row layout
layout = row(pg_title)
# Make a tab with the layout
tab = Panel(child=layout, title='Forecast')
return tab
def update_reviews(attr, old, new):
data_set = pd.read_csv('Outputs/data_set.csv')
i = int(star_rating.value)
#output_review_list = extract_ngrams(str(data_set[(data_set['Star_count'] == i)]['Review'].values),2,3)
data_set_blob = data_set.copy()
data_set_blob['Noun_sentences'] = data_set_blob['Review'].apply(lambda x:get_nouns(x))
n_gram_blob = TextBlob(str(data_set_blob[(data_set_blob['Star_count'] == i)]['Noun_sentences'].values))
#Styling the paragraph element
text1 = Paragraph(style={'font-variant': 'small-caps','font-family': "Tahoma"})
text1.text=""
#review1 = text_cleaner(str(n_gram_blob.ngrams(1)[0]))
#review2 = text_cleaner(str(n_gram_blob.ngrams(1)[1]))
review1 = text_cleaner(n_gram_blob.ngrams(1)[1][0])
review2 = text_cleaner(n_gram_blob.ngrams(1)[2][0])
text1.text = "Top "+str(i)+" star reviews feel: "+review1+", followed by "+review2
curdoc().add_root(Row(text1))
lattice=Select(title="Lattice:", value="Triangular", options=["Rectangular", "Triangular"])
dxin=TextInput(title='dx (cm)', value='5.74')
dyin=TextInput(title='dy (cm)', value='7.13')
tiltin=TextInput(title='Tilt (degrees)', value='22.5')
scanazin=TextInput(title='Azimuth Scan (degrees)', value='-60 60')
scanelin=TextInput(title='Elevation Scan (degrees)', value='0 45')
kgxout=Paragraph(width=200, height=15)
kgyout=Paragraph(width=200, height=15)
wlout=Paragraph(width=200, height=15)
p=Figure(match_aspect=True)
tilt=float(tiltin.value)*np.pi/180
wl=2.997e4/float(freqin.value)
wlout.text='Wavelength = '+str(wl)+' cm'
kgy=wl/float(dyin.value)
if lattice.value=='Triangular':
kgx=2*wl/float(dxin.value)
kmult=0.5
else:
kgx=wl/float(dxin.value)
kmult=1
kgxout.text='kgx = '+str(kgx)
kgyout.text='kgy = '+str(kgy)
newscanazstr = ''.join((ch if ch in '0123456789.-e' else ' ') for ch in scanazin.value)
newscanelstr = ''.join((ch if ch in '0123456789.-e' else ' ') for ch in scanelin.value)
scanaz = [float(i) for i in newscanazstr.split()]
scanel = [float(i) for i in newscanelstr.split()]
]
x_select = Select(options=option_list,
value='length',
title='Select the x-axis data')
# Create a dropdown Select widget for the y data: y_select
y_select = Select(options=option_list,
value='price',
title='Select the y-axis data')
# create some widgets like adding text
#button = Button(label="Get the Pearson correlation (Cor) and the P-value between the selected variables")
output1 = Paragraph()
output2 = Paragraph()
pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])
output1.text = "Pearson Correlation = " + str(pearson_coef)
output2.text = "P-value = " + str(p_value)
#Define the callback: update_plot
def callback(attr, old, new):
# Read the current values 2 dropdowns: x, y
new_data_dict = {
'x': df[x_select.value],
'y': df[y_select.value],
'drive_wheels': df['drive_wheels']
}
source.data = new_data_dict
pearson_coef, p_value = stats.pearsonr(df[x_select.value],
df[y_select.value])
def update(attr, old, new): timer = Paragraph() timer.text = '(Executing query...)' draw_base_ui(timer, test_select, metric_select) curdoc().add_next_tick_callback(create_plots)
print(authors)
print(abstract)
print(paper)
# SVM
# kNN
model.fit()
model.predict()
return (5, 10, 15)
def get_paper_string():
data = str(b64decode(file_input.value))
return data
y1.text = " - "
def update_output():
title = title_input.value
authors = author_number.value
abstract = abstract_input.value
paper = get_paper_string()
if check_for_errors():
y1.text = "-"
y5.text = "-"
y10.text = "-"
else:
y1.text = str(calculate(title, authors, abstract, paper)[0])
y5.text = str(calculate(title, authors, abstract, paper)[1])
y10.text = str(calculate(title, authors, abstract, paper)[2])
