def main(options, args):
logger = log.get_logger("ginga", options=options)
# create a new plot with default tools, using figure
fig = figure(x_range=[0,600], y_range=[0,600], plot_width=600, plot_height=600,
toolbar_location=None)
viewer = ib.CanvasView(logger)
viewer.set_figure(fig)
def load_file(path):
image = AstroImage(logger)
image.load_file(path)
viewer.set_image(image)
def load_file_cb(attr_name, old_val, new_val):
#print(attr_name, old_val, new_val)
load_file(new_val)
# add a entry widget and configure with the call back
dstdir = options.indir
path_w = TextInput(value=dstdir, title="File:")
path_w.on_change('value', load_file_cb)
if len(args) > 0:
load_file(args[0])
# put the path widget and viewer in a layout and add to the document
curdoc().add_root(vplot(fig, path_w))
def plot():
# Set up data
N = 200
x = np.linspace(0, 4*np.pi, N)
y = np.sin(x)
source = ColumnDataSource(data=dict(x=x, y=y))
# Set up plots
plot = Figure(plot_height=400, plot_width=400, title="my sine wave",
tools="crosshair,pan,reset,resize,save,wheel_zoom",
x_range=[0, 4*np.pi], y_range=[-2.5, 2.5])
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="title", value='my sine wave')
offset = Slider(title="offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="amplitude", value=1.0, start=-5.0, end=5.0)
phase = Slider(title="phase", value=0.0, start=0.0, end=2*np.pi)
freq = Slider(title="frequency", value=1.0, start=0.1, end=5.1)
# Set up callbacks
def update_title(attrname, old, new):
plot.title = text.value
text.on_change('value', update_title)
def update_data(attrname, old, new):
# Get the current slider values
a = amplitude.value
b = offset.value
w = phase.value
k = freq.value
# Generate the new curve
x = np.linspace(0, 4*np.pi, N)
y = a*np.sin(k*x + w) + b
source.data = dict(x=x, y=y)
for w in [offset, amplitude, phase, freq]:
w.on_change('value', update_data)
# Set up layouts and add to document
inputs = VBoxForm(children=[text, offset, amplitude, phase, freq])
fullformat = HBox(children=[inputs, plot], width=800)
return fullformat, []
def main(options, args):
logger = log.get_logger("ginga", options=options)
TOOLS = "pan,wheel_zoom,box_select,tap"
# create a new plot with default tools, using figure
fig = figure(x_range=[0,600], y_range=[0,600], plot_width=600, plot_height=600,
tools=TOOLS)
viewer = ib.CanvasView(logger)
viewer.set_figure(fig)
## box_select_tool = fig.select(dict(type=BoxSelectTool))
## box_select_tool.select_every_mousemove = True
#tap_tool = fig.select_one(TapTool).renderers = [cr]
# open a session to keep our local document in sync with server
session = push_session(curdoc())
#curdoc().add_periodic_callback(update, 50)
def load_file(path):
image = AstroImage(logger)
image.load_file(path)
viewer.set_image(image)
def load_file_cb(attr_name, old_val, new_val):
#print(attr_name, old_val, new_val)
load_file(new_val)
# add a entry widget and configure with the call back
dstdir = options.indir
path_w = TextInput(value=dstdir, title="File:")
path_w.on_change('value', load_file_cb)
curdoc().add_root(vplot(fig, path_w))
if len(args) > 0:
load_file(args[0])
# open the document in a browser
session.show()
# run forever
session.loop_until_closed()
# initialize controls
# choose between original and arc length parametrization
parametrization_input = CheckboxGroup(labels=['show original parametrization',
'show arc length parametrization'],
active=[0, 1])
parametrization_input.on_click(parametrization_change)
# slider controlling the current parameter t
t_value_input = Slider(title="parameter t", name='parameter t', value=arc_settings.t_value_init,
start=arc_settings.t_value_min, end=arc_settings.t_value_max,
step=arc_settings.t_value_step)
t_value_input.on_change('value', t_value_change)
# text input for the x component of the curve
x_component_input = TextInput(value=arc_settings.x_component_input_msg, title="curve x")
x_component_input.on_change('value', curve_change)
# text input for the y component of the curve
y_component_input = TextInput(value=arc_settings.y_component_input_msg, title="curve y")
y_component_input.on_change('value', curve_change)
# dropdown menu for selecting one of the sample curves
sample_curve_input = Dropdown(label="choose a sample function pair or enter one below",
menu=arc_settings.sample_curve_names)
sample_curve_input.on_click(sample_curve_change)
# initialize plot
toolset = "crosshair,pan,reset,resize,save,wheel_zoom"
# Generate a figure container
plot = Figure(plot_height=400, plot_width=400, tools=toolset,
title="Arc length parametrization",
x_range=[arc_settings.x_min_view, arc_settings.x_max_view],
def update(attr, old, new):
init_time = datetime.now()
main_doc.clear()
main_doc.add_root(layout)
#main_doc.theme = 'dark_minimal'
title = ticker0.value
print('-I- selected item is : ', title)
# sentiment = ticker1.value
reviews = reviews_ms1.copy()
reviews = reviews[reviews.title == title]
reviews = reviews.drop_duplicates()
reviews['id'] = range(len(reviews))
## sentence making
full_comments = []
full_title = []
l1 = int(len(reviews) / 2)
c1, t1, id1 = sentence_finder(reviews, 0, l1)
c2, t2, id2 = sentence_finder(reviews, l1, len(reviews))
full_comments = c1 + c2
full_title = t1 + t2
full_id = id1 + id2
reviews_old = reviews[['id', 'comments']]
reviews_old.columns = ['id', 'comments_full']
def stripper(s):
return s.strip()
reviews = pd.DataFrame()
reviews['comments'] = full_comments
reviews['comments'] = reviews['comments'].apply(stripper)
reviews['title'] = full_title
reviews['id'] = full_id
reviews = reviews[reviews.comments != '']
reviews = reviews.drop_duplicates(subset=['comments'])
reviews = reviews.merge(reviews_old, on='id', how='left')
title_words = spacy_tokenizer(reviews.title.iloc[0].replace("'",
"")).split()
def title_word_remover(s):
for t in title_words:
s = s.replace(t, '')
return s
## For finding bigrams
data = reviews.comments.values.tolist()
# Remove Emails
data = [re.sub('\S*@\S*\s?', '', sent) for sent in data]
# Remove new line characters
data = [re.sub('\s+', ' ', sent) for sent in data]
# Remove distracting single quotes
data = [re.sub("\'", "", sent) for sent in data]
data_words = list(sent_to_words(data))
# Build the bigram and trigram models
bigram = gensim.models.Phrases(
data_words, min_count=4,
threshold=50) # higher threshold fewer phrases.
# trigram = gensim.models.Phrases(bigram[data_words], threshold=50)
# Faster way to get a sentence clubbed as a trigram/bigram
bigram_mod = gensim.models.phrases.Phraser(bigram)
# trigram_mod = gensim.models.phrases.Phraser(trigram)
data_words1 = [' '.join(bigram_mod[d]) for d in data_words]
reviews['comments'] = data_words1
## steming lemmetising and stop word removal
reviews['cleaned_comments'] = reviews.comments.apply(spacy_tokenizer)
## sentiment finding
senti = sentimental_analysis.find_sentiment(reviews)
reviews['sentiment_pred'] = senti
## finding all nouns in the full reviews
all_nouns = []
for i in tqdm(range(len(reviews))):
all_nouns = all_nouns + noun_finder(reviews.cleaned_comments.iloc[i])
## Nouns and their count with weight
noun_df = pd.DataFrame(
pd.Series(all_nouns).astype('str').value_counts().reset_index())
noun_df.columns = ['noun', 'count']
noun_df['weight'] = 0
noun_df.head()
print('-I- finding the weight and updating it in df ---------------------')
## finding the weight and updating it in df
for text in tqdm(reviews.cleaned_comments):
doc = nlp(text)
noun_adj_pairs = []
for i, token in enumerate(doc):
bi_words = str(token).split('_')
if ((token.pos_ not in ('ADJ')) & (len(bi_words) == 1)):
continue
if ((len(bi_words) == 2)):
if ((nlp(bi_words[0])[0].pos_ == 'ADJ') &
(nlp(bi_words[1])[0].pos_ in ('NOUN', 'PROPN')) &
(~pd.Series(bi_words[1]).isin(title_words)[0])):
noun_adj_pairs.append((bi_words[0], bi_words[1]))
try:
noun_df.loc[noun_df.noun == str(bi_words[1]),
'weight'] = noun_df.loc[
noun_df.noun == str(bi_words[1]),
'weight'].iloc[0] + 1
except:
noun_df = noun_df.append(
pd.DataFrame(
{
'noun': [bi_words[1]],
'count': [1],
'weight': [1]
},
index=[len(noun_df)]))
elif ((token.pos_ in ('NOUN', 'PROPN')) &
(nlp(bi_words[0])[0].pos_ in ('NOUN', 'PROPN')) &
(nlp(bi_words[1])[0].pos_ in ('NOUN', 'PROPN')) &
(~pd.Series(bi_words[0]).isin(title_words)[0]) &
(~pd.Series(bi_words[1]).isin(title_words)[0])):
# elif((nlp(bi_words[0])[0].pos_ in ('NOUN','PROPN')) & (nlp(bi_words[1])[0].pos_ in ('NOUN','PROPN'))):
noun_df.loc[
noun_df.noun == str(token),
'weight'] = noun_df.loc[noun_df.noun == str(token),
'weight'].iloc[0] + 1
continue
if ((pd.Series([str(token)]).isin(positive)[0]) |
(pd.Series([str(token)]).isin(negative)[0])):
for j in range(i + 1, min(i + 6, len(doc))):
# if (doc[j].pos_ in ('NOUN','PROPN')):
if ((doc[j].pos_ in ('NOUN', 'PROPN')) &
(len(str(doc[j]).split('_')) != 2)):
noun_adj_pairs.append((token, doc[j]))
noun_df.loc[noun_df.noun == str(doc[j]),
'weight'] = noun_df.loc[
noun_df.noun == str(doc[j]),
'weight'].iloc[0] + 1
break
## removing words from noun which is in title to find top topics ( topic nouns )
noun_df = noun_df[~noun_df.noun.isin(
spacy_tokenizer(reviews.title.iloc[0].replace("'", "")).split())]
noun_df = noun_df.sort_values(by='weight', ascending=False)
noun_df = noun_df.iloc[0:20, ]
reviews.to_csv('./CRM_bokeh_app/static/temp.csv', index=False)
topic_df = pd.DataFrame()
topic_df['topics'] = noun_df['noun']
print('-I- topic sentimental distribution finding---')
pos_r = []
neg_r = []
neu_r = []
full_l = []
for t in tqdm(topic_df.topics):
temp = reviews.copy()
temp['item_presence'] = temp.apply(
lambda row: topic_presence(row['cleaned_comments'], t), axis=1)
temp = temp[temp.item_presence != -1]
full_l.append(len(temp))
pos_r.append(len(temp[temp.sentiment_pred == 'positive']))
neg_r.append(len(temp[temp.sentiment_pred == 'negative']))
neu_r.append(len(temp[temp.sentiment_pred == 'neutral']))
# topic_df['length'] = full_l
topic_df['positive'] = pos_r
topic_df['negative'] = neg_r
topic_df['neutral'] = neu_r
final_time = datetime.now()
print('-I- Time taken for update is = ', str(final_time - init_time))
global ticker2
global radio_button_group
global text_input
ticker2 = Select(title='Topic',
value='all',
options=['all'] + list(noun_df.noun))
ticker2.on_change('value', update2)
radio_button_group = RadioButtonGroup(
labels=['all', 'positive', 'negative', 'neutral'], active=0)
radio_button_group.on_change('active', update2)
text_input = TextInput(value="", title="Custom topic search:")
text_input.on_change("value", update2)
t_r = column([row([ticker2, text_input]), radio_button_group])
z = plot_senti(reviews, title)
z1 = plot_topic(noun_df, title)
z2 = column([
row([
plot_senti_stack(topic_df.sort_values(by='positive'), 1),
plot_senti_stack(topic_df.sort_values(by='negative'), 2)
]),
plot_senti_stack(topic_df.sort_values(by='neutral'), 3)
])
z = column([row([z, z1]), z2])
z1 = row([plot_senti(reviews, 'all topics'), summarizer(reviews)])
z2 = plot_rows(reviews)
z2 = column([z1, z2])
z3 = column(column([z, t_r], name='needed1'),
column([z2], name='review1'),
name='needed')
main_doc.add_root(z3)
def update_slider(attrname, old, new):
n_samples = int(samples_slider.value)
newdata = get_ends(n_samples)
source_cut.data = dict(newdata.to_dict('list'))
plot.x_range.factors = newdata['aspects'].tolist() # this was missing
plot.y_range.start = min(source_cut.data['importance'])
plot.y_range.end = max(source_cut.data['importance'])
data_table = DataTable(source=source_cut, columns=columns, width=700, height=500)
dataset_select.on_change('value', update_dataset)
exponent_slider.on_change('value', update_dataset)
ratings_box.on_change('value', update_dataset)
samples_slider.on_change('value', update_slider)
# Set up layout
selects = row(dataset_select)
inputs = column(selects, widgetbox(exponent_slider, ratings_box, samples_slider))
table = widgetbox(data_table)
tab1 = Panel(child=table, title="Data")
tab2 = Panel(child=plot, title="Bar Plot")
tabs = Tabs(tabs=[tab1, tab2])
lay = layout([[inputs,tabs],])
# Add to document
curdoc().add_root(lay)
curdoc().title = "Keyword Extraction"
def modify_doc(doc):
""" Contains the application, including all callbacks
TODO: could the callbacks be outsourced?
:param doc:
:type doc:
"""
logger.debug('modify_doc has been called')
def get_data_frames(ie,):
""" Called one time initially, and then every time the experiment number is changed by the slider
:param ie: experiment number
:type ie: int
:returns: dataframe from stella datafile and dataframe with tau and phi and fitted values
:rtype: list of 2 pandas dataframes
"""
logger.debug('get_dataframe with ie={}'.format(ie))
fid = polymer.getfid(ie) #read FID or series of FIDs for selected experiment
try:
tau = polymer.get_tau_axis(ie) #numpy array containing the taus for experiment ie
try:
startpoint=fid_slider.range[0] #lower integration bound
endpoint = fid_slider.range[1] #upper integration bound
except NameError:
# fid_slider not initialized for first plot. Use default values:
startpoint=int(0.05*polymer.getparvalue(ie,'BS'))
endpoint = int(0.1*polymer.getparvalue(ie,'BS'))
logger.debug('fid_slider not initialized for first plot. Use default values {} and {}.'.format(startpoint, endpoint))
polymer.addparameter(ie,'fid_range',(startpoint,endpoint)) #add integration range to parameters to make it accesible
phi = get_mag_amplitude(fid, startpoint, endpoint,
polymer.getparvalue(ie,'NBLK'),
polymer.getparvalue(ie,'BS')) # list containing averaged fid amplitudes (which is proportional to a magnetization phi)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi']) # DataFrames are nice
df['phi_normalized'] = (df['phi'] - df['phi'].iloc[0] ) / (df['phi'].iloc[-1] - df['phi'].iloc[1] ) #Normalize magnetization,
#Note: in the normalized magnetization the magnetization build-up curves and magnetization decay curves look alike
#Note: this makes it easier for fitting as everything looks like 1 * exp(-R/time) in first order
polymer.addparameter(ie,'df_magnetization',df) # make the magnetization dataframe accesible as parameter
fit_option = 2 #mono exponential, 3 parameter fit
p0=[1.0, polymer.getparvalue(ie,'T1MX')**-1*2, 0] #choose startparameters for fitting an exponential decay
df, popt = magnetization_fit(df, p0, fit_option) # use leastsq to find optimal parameters
polymer.addparameter(ie,'popt(mono_exp)',popt) # add fitting parameters for later access
logger.info('fitfunction(t) = {} * exp(- {} * t) + {}'.format(*popt)) # print the fitting parameters to console (for convenience)
except KeyError:
logger.warning('no relaxation experiment found')
tau=np.zeros(1)
phi=np.zeros(1)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized'] = np.zeros(1)
df['fit_phi'] = np.zeros(1)
return fid, df
def calculate_mag_dec(attr, old, new, start_ie=None):
''' Is being call from the callback for the experiment chooser
loads selected experiment visualize in plot p1 and p2
gets experiment number from the slider
writes source_fid.data from the fid from the polymer object
writes source_mag_dec.data from the dataframe
'''
ie = experiment_slider.value #get expermient number from the slider
logger.debug('calculate mag_dec for ie={}'.format(ie))
fid, df = get_data_frames(ie)
source_fid.data=ColumnDataSource.from_df(fid) #convert fid to bokeh format
source_mag_dec.data = ColumnDataSource.from_df(df)
def plot_par():
''' Creates plot for the parameters
Called with every update from the callback'''
logger.debug('creating plot for the parameters')
# read data due to selection of select_x/y
xs = par_df[select_xaxis.value ].values
ys = par_df[select_yaxis.value].values
# read titles due to name of select_x/y
x_title = select_xaxis.value.title()
y_title = select_yaxis.value.title()
# remark: many attributes in a bokeh plot cannot be modified after initialization
# for example p4.x_axis_type='datetime' does not work. keywords are a
# workaround to pass all optional arguments initially
# set optional keyword arguments, kw, for figure()
kw = dict() #initialize
if select_xaxis.value in discrete:
kw['x_range'] = sorted(set(xs))
if select_yaxis.value in discrete:
kw['y_range'] = sorted(set(ys))
if select_yaxis.value in time:
kw['y_axis_type'] = 'datetime'
if select_xaxis.value in time:
kw['x_axis_type'] = 'datetime'
kw['title']="%s vs %s" % (x_title, y_title)
# create figure using optional keywords kw
p4 = figure(plot_height=300, plot_width=600, tools='pan,box_zoom,reset',
**kw)
# set axis label
p4.xaxis.axis_label = x_title
p4.yaxis.axis_label = y_title
# strings at x axis ticks need a lot of space. solution: rotate label orientation
if select_xaxis.value in discrete:
p4.xaxis.major_label_orientation = pd.np.pi / 4 # rotates labels...
# standard size of symbols
sz = 9
# custom size of symbols due to select_size
if select_size.value != 'None':
groups = pd.qcut(pd.to_numeric(par_df[select_size.value].values), len(SIZES))
sz = [SIZES[xx] for xx in groups.codes]
# standard color
c = "#31AADE"
# custom color due to select_color
if select_color.value != 'None':
groups = pd.qcut(pd.to_numeric(par_df[select_color.value]).values, len(COLORS))
c = [COLORS[xx] for xx in groups.codes]
# create the plot using circles
p4.circle(x=xs, y=ys, color=c, size=sz, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)
return p4 #return the plot
def callback_update_plot_1(attr, old, new):
''' Callback for update of figure 1 in parameters tab '''
tabs.tabs[1].child.children[1] = plot_par()
print(tabs.tabs[1].child.children[1])
logger.debug('Parameter plot updated')
# p4 = plot_par()
def callback_update_p3():
logger.debug('update plot 3')
p3 = fit_mag_decay_all(polymer,par_df)
return p3
def callback_update_experiment(attr, old, new):
""" Callback for the experiment chooser
"""
ie = experiment_slider.value
logger.debug('Callback experiment update, ie={}'.format(ie))
fid_slider.end = polymer.getparvalue(ie,'BS')
try:
fid_slider.range=polymer.getparvalue(ie,'fid_range')
except:
startpoint = int(0.05 * polymer.getparvalue(ie,'BS'))
endpoint = int(0.1 * polymer.getparvalue(ie,'BS'))
fid_slider.range=(startpoint,endpoint)
calculate_mag_dec(attr,old,new)
def callback_load_more_data(attr,old,new):
''' callback for loading of data '''
# TODO: implement
logger.debug('callback for loading of data ')
logger.error('Not implemented!')
path=pathbox.value.strip()
file=filebox.value.strip()
if file=="*.sdf":
logger.info('callback for loading data. filename: {}'.format(file))
allsdf=filter(lambda x: x.endswith('.sdf'),os.listdir(path))
for f in allsdf:
sdf_list.append(sdf.StelarDataFile(f,path))
else:
sdf_list.append(sdf.StelarDataFile(file,path))
filenames=[x.file() for x in sdf_list]
filenames_df=pd.DataFrame(data=filenames,columns=['file'])
table_source.data=ColumnDataSource.from_df(filenames_df)
def callback_export_data(attr,old,new):
logger.debug('callback_export_data has been called ')
logger.error('Not implemented!')
pass
def callback_write_table_to_file(attr,old,new): ##FIXME
logger.debug('callback_write_table_to_file has been called ')
logger.error('Not implemented!')
pass
# path=export_text.value.strip()
# exportdata=export_source.data
# CustomJS(args=dict(source=export_source),
# code=open(join(dirname(__file__), "export_csv.js")).read())
def callback_update_parameters():
''' callback for button
function to call when button is clicked
for updates parameters of polymer, since they can change during evaluation '''
logger.debug('callback for button (update parameter).')
par_df, columns, discrete, continuous, time, quantileable = polymer.scan_parameters()
select_xaxis.options=columns
select_yaxis.options=columns
select_size.options=['None']+quantileable
select_color.options=['None']+quantileable
logger.info('Starting the script')
### This is the start of the script ###
### The callbacks are above ###
#load data:
# TODO: how to handle multiple datafiles?
# New Tab for each datafile?
# dropdown selection to choose datafile
# complete new start of process? (probably not prefered)
polymer = load_data('glyzerin_d3_300K.sdf')
nr_experiments = polymer.get_number_of_experiments()
start_ie = 1 # initially set ie = 1
par_df, columns, discrete, continuous, time, quantileable = polymer.scan_parameters(20)
# for the initial call get the dataframes without callback
# they are being updated in following callbacks
fid, df = get_data_frames(start_ie)
source_fid = ColumnDataSource(data=ColumnDataSource.from_df(fid))
source_mag_dec = ColumnDataSource(data=ColumnDataSource.from_df(df))
# initialy creates the plots p1 and p2
p1, p2 = create_plot_1_and_2(source_fid, source_mag_dec)
### initiates widgets, which will call the callback on change ###
# initiate slider to choose experiment
experiment_slider = Slider(start=1, end=nr_experiments, value=1, step=1,callback_policy='mouseup', width=800) #select experiment by value
# initiate slider for the range in which fid shall be calculated
# select the intervall from which magneitization is calculated from fid
fid_slider = RangeSlider(start=1,end=polymer.getparvalue(start_ie,'BS'),
range=polymer.getparvalue(start_ie,'fid_range'),
step=1,callback_policy='mouseup', width=400)
# fit magnetization decay for all experiments
p3 = fit_mag_decay_all(polymer, par_df)
# refit mag dec with updated ranges after button push
button_refit = Button(label='Update',button_type="success")
button_refit.on_click(callback_update_p3)
# initialize empty source for experiment slider
source = ColumnDataSource(data=dict(value=[]))
# 'data' is the attribute. it's a field in source, which is a ColumnDataSource
# initiate callback_update_experiment which is the callback
source.on_change('data',callback_update_experiment) #source for experiment_slider
experiment_slider.callback = CustomJS(args=dict(source=source),code="""
source.data = { value: [cb_obj.value] }
""")#unfortunately this customjs is needed to throttle the callback in current version of bokeh
# initialize empty source for fid slider, same as above
source2 = ColumnDataSource(data=dict(range=[], ie=[]))
source2.on_change('data',calculate_mag_dec)
fid_slider.callback=CustomJS(args=dict(source=source2),code="""
source.data = { range: cb_obj.range }
""")#unfortunately this customjs is needed to throttle the callback in current version of bokeh
# same for the update button
button_scan = Button(label='Scan Parameters',button_type="success")
button_scan.on_click(callback_update_parameters)
# here comes for callbacks for x, y, size, color
select_xaxis = Select(title='X-Axis', value='ZONE', options=columns)
select_xaxis.on_change('value', callback_update_plot_1)
select_yaxis = Select(title='Y-Axis', value='TIME', options=columns)
select_yaxis.on_change('value', callback_update_plot_1)
select_size = Select(title='Size', value='None', options=['None'] + quantileable)
select_size.on_change('value', callback_update_plot_1)
select_color = Select(title='Color', value='None', options=['None'] + quantileable)
select_color.on_change('value', callback_update_plot_1)
controls_p4 = widgetbox([button_scan, select_xaxis,select_yaxis,select_color,select_size], width=150)
#p4 = plot_par()
layout_p4 = row(controls_p4,plot_par())
logger.debug('layout for parameter plot created')
####
#### TODO: write file input
#### TODO: select files to import
#### TODO: discard imported files
####
# load more data:
table_source=ColumnDataSource(data=dict())
sdf_list=[polymer]
# TODO: This is current plan, to save the different dataframes in a list, right?
filenames=[x.file() for x in sdf_list]
files_df=pd.DataFrame(data=filenames,columns=['file'])
table_source.data=ColumnDataSource.from_df(files_df)
t_columns = [
TableColumn(field='file', title='Path / Filename'),
#TableColumn(field='file', title='Filename'),
]
table=DataTable(source=table_source,columns=t_columns)
pathbox=TextInput(title="Path",value=os.path.curdir)
filebox=TextInput(title="Filename",value="*.sdf")
pathbox.on_change('value',callback_load_more_data)
filebox.on_change('value',callback_load_more_data)
layout_input=column(pathbox,filebox,table)
# Data Out: export data from figures
# & export parameters
export_source=ColumnDataSource(data=dict())
export_columns=[]
output_table=DataTable(source=export_source,columns=export_columns)
export_slider = Slider(start=1, end=4, value=3, step=1,callback_policy='mouseup', width=200) #do we need mouseup on this?
export_slider.on_change('value',callback_export_data)
export_text = TextInput(title="Path",value=os.path.curdir)
export_button = Button(label='Export to csv',button_type="success") # FIXME Callback doesn't work yet
export_button.on_click(callback_write_table_to_file)
layout_output=row(column(export_slider,export_text,export_button),output_table)
print('after layout_output')
# set the layout of the tabs
layout_p1 = column(experiment_slider, p1,
row(
column(fid_slider,p2),
column(button_refit, p3)
),
)
tab_relaxation = Panel(child = layout_p1, title = 'Relaxation')
tab_parameters = Panel(child = layout_p4, title = 'Parameters')
tab_input = Panel(child = layout_input, title = 'Data In')
tab_output = Panel(child = layout_output, title = 'Data Out')
# initialize tabs object with 3 tabs
tabs = Tabs(tabs = [tab_relaxation, tab_parameters,
tab_input, tab_output])
print('tabs')
doc.add_root(tabs)
doc.add_root(source) # i need to add source to detect changes
doc.add_root(source2)
print('tab tab')
def plot():
# FIGURES AND X-AXIS
fig1 = Figure(title = 'Energy', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS)
timeticks = DatetimeTickFormatter(formats=dict(seconds =["%b%d %H:%M:%S"],
minutes =["%b%d %H:%M"],
hours =["%b%d %H:%M"],
days =["%b%d %H:%M"],
months=["%b%d %H:%M"],
years =["%b%d %H:%M %Y"]))
fig1.xaxis.formatter = timeticks
# INPUT WIDGETS
collection_list = CONN[DB].collection_names(include_system_collections=False)
gliders = sorted([platformID for platformID in collection_list if len(platformID)>2])
gliders = Select(title = 'PlatformID', value = gliders[0], options = gliders)
prev_glider = Button(label = '<')
next_glider = Button(label = '>')
glider_controlbox = HBox(children = [gliders, prev_glider, next_glider])
max_amphr = TextInput(title='Max AmpHrs', value='1040')
deadby_date = TextInput(title='Deadby Date', value='')
data_controlbox = HBox(max_amphr, deadby_date, width = 300)
control_box = HBox(glider_controlbox,
data_controlbox)
# DATA VARS
coulombs_raw = ColumnDataSource(dict(x=[],y=[]))
coulombs_ext = ColumnDataSource(dict(x=[],y=[]))
coulombs_per = ColumnDataSource(dict(x=[],y=[]))
# AXIS setup
fig1.yaxis.axis_label = 'Coulombs (AmpHr)'
fig1.extra_y_ranges = {'usage': Range1d(start=0, end=1200)}
# PLOT OBJECTS
fig1.line( 'x', 'y', source = coulombs_raw, legend = 'm_coulombs_amphr_total', color = 'blue')
fig1.circle('x', 'y', source = coulombs_raw, legend = 'm_coulombs_amphr_total', color = 'blue')
fig1.line( 'x', 'y', source = coulombs_ext, legend = 'projected', color = 'red')
#fig1.cross('x', 'y', source = coulombs_ext, legend = 'projected', size=10, color = 'red')
fig1.renderers.append(Span(name = 'maxamp_span', location = int(max_amphr.value), dimension = 'width', line_color= 'green', line_dash='dashed', line_width=2))
fig1.renderers.append(Span(name = 'maxamp_intersect', location = 1000*time.time(), dimension = 'height', line_color= 'green', line_dash='dashed', line_width=2))
fig1.legend[0].location = 'top_left'
fig1.legend[0].legend_padding = 30
# CALLBACK FUNCS
def update_coulombs(attrib,old,new):
g = gliders.value
coulombs_raw.data = load_sensor(g, 'm_coulomb_amphr_total')
#coulombs_per.data = moving_usage(coulombs_raw.data)
update_projection(None,None,None)
def update_projection(attrib,old,new):
g = gliders.value
try:
fig1.select('maxamp_span')[0].location = int(max_amphr.value)
coulombs_ext.data, deadby_date.value = calc_deadby_date(g, int(max_amphr.value))
fig1.select('maxamp_intersect')[0].location = coulombs_ext.data['x'][-1]
except Exception as e:
print('update_projection error',type(e),e)
#GLIDER SELECTS
def glider_buttons(increment):
ops = gliders.options
new_index = ops.index(gliders.value) + increment
if new_index >= len(ops):
new_index = 0
elif new_index < 0:
new_index = len(ops)-1
gliders.value = ops[new_index]
def next_glider_func():
glider_buttons(1)
def prev_glider_func():
glider_buttons(-1)
gliders.on_change('value', update_coulombs)
next_glider.on_click(next_glider_func)
prev_glider.on_click(prev_glider_func)
max_amphr.on_change('value', update_projection)
update_coulombs(None,None,None)
return vplot(control_box, fig1)
order = int(new)
update_data()
def on_text_value_change(attr, old, new):
try:
global expr
expr = sy.sympify(new, dict(x=xs))
except (sy.SympifyError, TypeError, ValueError) as exception:
dialog.content = str(exception)
dialog.visible = True
else:
update_data()
dialog = Dialog(title="Invalid expression")
slider = Slider(start=1, end=20, value=order, step=1, title="Order:")
slider.on_change('value', on_slider_value_change)
text = TextInput(value=str(expr), title="Expression:")
text.on_change('value', on_text_value_change)
inputs = HBox(children=[slider, text])
layout = VBox(children=[inputs, plot, dialog])
update_data()
document.add_root(layout)
session.show(layout)
if __name__ == "__main__":
print("\npress ctrl-C to exit")
session.loop_until_closed()
reset_button.on_click(lambda x: reset_ButtonClick())
autoscale_button.on_click(lambda x: autoscale_ButtonClick())
channel_button[0].on_click(lambda x: channel_ButtonClick(1))
channel_button[1].on_click(lambda x: channel_ButtonClick(2))
channel_button[2].on_click(lambda x: channel_ButtonClick(3))
channel_button[3].on_click(lambda x: channel_ButtonClick(4))
pulse_capture_button.on_click(lambda x: pulseCapture_ButtonClick())
auto_save_button.on_click(lambda x: autoSave_ButtonClick())
load_button.on_click(lambda x: load_ButtonClick())
force_save_button.on_click(lambda x: forceSave_ButtonClick())
save_PC_button.on_click(lambda x: savePC_ButtonClick())
acq_period_select.on_change("value", acqPeriod_SelectChange)
channel_name_select1.on_change("value", chanNameSelect1_SelectChange)
force_save_filename_input.on_change("value", forceSaveFilename_InputChange)
channel_name_input.on_change("value", channelName_InputChange)
time_range_input.on_change("value", timeRange_InputChange)
trigger_level_input.on_change("value", triggerLevel_InputChange)
save_filepath_input.on_change("value", saveFilepath_InputChange)
load_file_input.on_change("value", loadFile_InputChange)
save_filepath_PC_input.on_change("value", saveFilepath_InputChange) #
save_filename_PC_input.on_change("value", forceSaveFilename_InputChange) #reusing callbacks
#set up layout
doc.title = "Intensity Tracker"
grid = layout([
[channel_button[0],channel_button[1],channel_button[2],channel_button[3]],
[all_on_button, all_off_button, reset_button, autoscale_button],
class MetaModelVisualization(object):
"""
Top-level container for the Meta Model Visualization.
Attributes
----------
prob : Problem
Name of variable corresponding to Problem Component
meta_model : MetaModel
Name of empty Meta Model Component object reference
resolution : int
Number used to calculate width and height of contour plot
is_structured_meta_model : Bool
Boolean used to signal whether the meta model is structured or unstructured
slider_source : ColumnDataSource
Data source containing dictionary of sliders
contour_training_data_source : ColumnDataSource
Data source containing dictionary of training data points
bottom_plot_source : ColumnDataSource
Data source containing data for the bottom subplot
bottom_plot_scatter_source : ColumnDataSource
Data source containing scatter point data for the bottom subplot
right_plot_source : ColumnDataSource
Data source containing data for the right subplot
right_plot_scatter_source : ColumnDataSource
Data source containing scatter point data for the right subplot
contour_plot_source : ColumnDataSource
Data source containing data for the contour plot
input_names : list
List of input data titles as strings
output_names : list
List of output data titles as strings
training_inputs : dict
Dictionary of input training data
x_input_select : Select
Bokeh Select object containing a list of inputs for the x axis
y_input_select : Select
Bokeh Select object containing a list of inputs for the y axis
output_select : Select
Bokeh Select object containing a list of inputs for the outputs
x_input_slider : Slider
Bokeh Slider object containing a list of input values for the x axis
y_input_slider : Slider
Bokeh Slider object containing a list of input values for the y axis
slider_dict : dict
Dictionary of slider names and their respective slider objects
predict_inputs : dict
Dictionary containing training data points to predict at.
num_inputs : int
Number of inputs
num_outputs : int
Number of outputs
limit_range : array
Array containing the range of each input
scatter_distance : TextInput
Text input for user to enter custom value to calculate distance of training points around
slice line
right_alphas : array
Array of points containing alpha values for right plot
bottom_alphas : array
Array of points containing alpha values for bottom plot
dist_range : float
Value taken from scatter_distance used for calculating distance of training points around
slice line
x_index : int
Value of x axis column
y_index : int
Value of y axis column
output_variable : int
Value of output axis column
sliders_and_selects : layout
Layout containing the sliders and select elements
doc_layout : layout
Contains first row of plots
doc_layout2 : layout
Contains second row of plots
Z : array
A 2D array containing contour plot data
"""
def __init__(self, model, resolution=50, doc=None):
"""
Initialize parameters.
Parameters
----------
model : MetaModelComponent
Reference to meta model component
resolution : int
Value used to calculate the size of contour plot meshgrid
doc : Document
The bokeh document to build.
"""
self.prob = Problem()
self.resolution = resolution
logging.getLogger("bokeh").setLevel(logging.ERROR)
# If the surrogate model coming in is structured
if isinstance(model, MetaModelUnStructuredComp):
self.is_structured_meta_model = False
# Create list of input names, check if it has more than one input, then create list
# of outputs
self.input_names = [name[0] for name in model._surrogate_input_names]
if len(self.input_names) < 2:
raise ValueError('Must have more than one input value')
self.output_names = [name[0] for name in model._surrogate_output_names]
# Create reference for untructured component
self.meta_model = MetaModelUnStructuredComp(
default_surrogate=model.options['default_surrogate'])
# If the surrogate model coming in is unstructured
elif isinstance(model, MetaModelStructuredComp):
self.is_structured_meta_model = True
self.input_names = [name for name in model._var_rel_names['input']]
if len(self.input_names) < 2:
raise ValueError('Must have more than one input value')
self.output_names = [name for name in model._var_rel_names['output']]
self.meta_model = MetaModelStructuredComp(
distributed=model.options['distributed'],
extrapolate=model.options['extrapolate'],
method=model.options['method'],
training_data_gradients=model.options['training_data_gradients'],
vec_size=1)
# Pair input list names with their respective data
self.training_inputs = {}
self._setup_empty_prob_comp(model)
# Setup dropdown menus for x/y inputs and the output value
self.x_input_select = Select(title="X Input:", value=[x for x in self.input_names][0],
options=[x for x in self.input_names])
self.x_input_select.on_change('value', self._x_input_update)
self.y_input_select = Select(title="Y Input:", value=[x for x in self.input_names][1],
options=[x for x in self.input_names])
self.y_input_select.on_change('value', self._y_input_update)
self.output_select = Select(title="Output:", value=[x for x in self.output_names][0],
options=[x for x in self.output_names])
self.output_select.on_change('value', self._output_value_update)
# Create sliders for each input
self.slider_dict = {}
self.predict_inputs = {}
for title, values in self.training_inputs.items():
slider_data = np.linspace(min(values), max(values), self.resolution)
self.predict_inputs[title] = slider_data
# Calculates the distance between slider ticks
slider_step = slider_data[1] - slider_data[0]
slider_object = Slider(start=min(values), end=max(values), value=min(values),
step=slider_step, title=str(title))
self.slider_dict[title] = slider_object
self._slider_attrs()
# Length of inputs and outputs
self.num_inputs = len(self.input_names)
self.num_outputs = len(self.output_names)
# Precalculate the problem bounds.
limits = np.array([[min(value), max(value)] for value in self.training_inputs.values()])
self.limit_range = limits[:, 1] - limits[:, 0]
# Positional indicies
self.x_index = 0
self.y_index = 1
self.output_variable = self.output_names.index(self.output_select.value)
# Data sources are filled with initial values
# Slider Column Data Source
self.slider_source = ColumnDataSource(data=self.predict_inputs)
# Contour plot Column Data Source
self.contour_plot_source = ColumnDataSource(data=dict(
z=np.random.rand(self.resolution, self.resolution)))
self.contour_training_data_source = ColumnDataSource(
data=dict(x=np.repeat(0, self.resolution), y=np.repeat(0, self.resolution)))
# Bottom plot Column Data Source
self.bottom_plot_source = ColumnDataSource(data=dict(
x=np.repeat(0, self.resolution), y=np.repeat(0, self.resolution)))
self.bottom_plot_scatter_source = ColumnDataSource(data=dict(
bot_slice_x=np.repeat(0, self.resolution), bot_slice_y=np.repeat(0, self.resolution)))
# Right plot Column Data Source
self.right_plot_source = ColumnDataSource(data=dict(
x=np.repeat(0, self.resolution), y=np.repeat(0, self.resolution)))
self.right_plot_scatter_source = ColumnDataSource(data=dict(
right_slice_x=np.repeat(0, self.resolution),
right_slice_y=np.repeat(0, self.resolution)))
# Text input to change the distance of reach when searching for nearest data points
self.scatter_distance = TextInput(value="0.1", title="Scatter Distance")
self.scatter_distance.on_change('value', self._scatter_input)
self.dist_range = float(self.scatter_distance.value)
# Grouping all of the sliders and dropdowns into one column
sliders = [value for value in self.slider_dict.values()]
sliders.extend(
[self.x_input_select, self.y_input_select, self.output_select, self.scatter_distance])
self.sliders_and_selects = row(
column(*sliders))
# Layout creation
self.doc_layout = row(self._contour_data(), self._right_plot(), self.sliders_and_selects)
self.doc_layout2 = row(self._bottom_plot())
if doc is None:
doc = curdoc()
doc.add_root(self.doc_layout)
doc.add_root(self.doc_layout2)
doc.title = 'Meta Model Visualization'
def _setup_empty_prob_comp(self, metamodel):
"""
Take data from surrogate ref and pass it into new surrogate model with empty Problem model.
Parameters
----------
metamodel : MetaModelComponent
Reference to meta model component
"""
# Check for structured or unstructured
if self.is_structured_meta_model:
# Loop through the input names
for idx, name in enumerate(self.input_names):
# Check for no training data
try:
# Append the input data/titles to a dictionary
self.training_inputs[name] = metamodel.inputs[idx]
# Also, append the data as an 'add_input' to the model reference
self.meta_model.add_input(name, 0.,
training_data=metamodel.inputs[idx])
except TypeError:
msg = "No training data present for one or more parameters"
raise TypeError(msg)
# Add the outputs to the model reference
for idx, name in enumerate(self.output_names):
self.meta_model.add_output(
name, 0.,
training_data=metamodel.training_outputs[name])
else:
for name in self.input_names:
try:
self.training_inputs[name] = {
title for title in metamodel.options['train:' + str(name)]}
self.meta_model.add_input(
name, 0.,
training_data=[
title for title in metamodel.options['train:' + str(name)]])
except TypeError:
msg = "No training data present for one or more parameters"
raise TypeError(msg)
for name in self.output_names:
self.meta_model.add_output(
name, 0.,
training_data=[
title for title in metamodel.options['train:' + str(name)]])
# Add the subsystem and setup
self.prob.model.add_subsystem('interp', self.meta_model)
self.prob.setup()
def _slider_attrs(self):
"""
Assign data to slider objects and callback functions.
Parameters
----------
None
"""
for name, slider_object in self.slider_dict.items():
# Checks if there is a callback previously assigned and then clears it
if len(slider_object._callbacks) == 1:
slider_object._callbacks.clear()
# Check if the name matches the 'x input' title
if name == self.x_input_select.value:
# Set the object and add an event handler
self.x_input_slider = slider_object
self.x_input_slider.on_change('value', self._scatter_plots_update)
# Check if the name matches the 'y input' title
elif name == self.y_input_select.value:
# Set the object and add an event handler
self.y_input_slider = slider_object
self.y_input_slider.on_change('value', self._scatter_plots_update)
else:
# If it is not an x or y input then just assign it the event handler
slider_object.on_change('value', self._update)
def _make_predictions(self, data):
"""
Run the data parameter through the surrogate model which is given in prob.
Parameters
----------
data : dict
Dictionary containing training points.
Returns
-------
array
np.stack of predicted points.
"""
# Create dictionary with an empty list
outputs = {name: [] for name in self.output_names}
# Parse dict into shape [n**2, number of inputs] list
inputs = np.empty([self.resolution**2, self.num_inputs])
for idx, values in enumerate(data.values()):
inputs[:, idx] = values.flatten()
# Check for structured or unstructured
if self.is_structured_meta_model:
# Assign each row of the data coming in to a tuple. Loop through the tuple, and append
# the name of the input and value.
for idx, tup in enumerate(inputs):
for name, val in zip(data.keys(), tup):
self.prob[self.meta_model.name + '.' + name] = val
self.prob.run_model()
# Append the predicted value(s)
for title in self.output_names:
outputs[title].append(
np.array(self.prob[self.meta_model.name + '.' + title]))
else:
for idx, tup in enumerate(inputs):
for name, val in zip(data.keys(), tup):
self.prob[self.meta_model.name + '.' + name] = val
self.prob.run_model()
for title in self.output_names:
outputs[title].append(
float(self.prob[self.meta_model.name + '.' + title]))
return stack_outputs(outputs)
def _contour_data_calcs(self):
"""
Parse input data into a dictionary to be predicted at.
Parameters
----------
None
Returns
-------
dict
Dictionary of training data to be predicted at.
"""
# Create initial data array of training points
resolution = self.resolution
x_data = np.zeros((resolution, resolution, self.num_inputs))
self._slider_attrs()
# Broadcast the inputs to every row of x_data array
x_data[:, :, :] = np.array(self.input_point_list)
# Find the x/y input titles and match their index positions
for idx, (title, values) in enumerate(self.slider_source.data.items()):
if title == self.x_input_select.value:
self.xlins_mesh = values
x_index_position = idx
if title == self.y_input_select.value:
self.ylins_mesh = values
y_index_position = idx
# Make meshgrid from the x/y inputs to be plotted
X, Y = np.meshgrid(self.xlins_mesh, self.ylins_mesh)
# Move the x/y inputs to their respective positions in x_data
x_data[:, :, x_index_position] = X
x_data[:, :, y_index_position] = Y
pred_dict = {}
for idx, title in enumerate(self.slider_source.data):
pred_dict.update({title: x_data[:, :, idx]})
return pred_dict
def _contour_data(self):
"""
Create a contour plot.
Parameters
----------
None
Returns
-------
Bokeh Image Plot
"""
resolution = self.resolution
# Output data array initialization
y_data = np.zeros((resolution, resolution, self.num_outputs))
self.input_point_list = [point.value for point in self.slider_dict.values()]
# Pass the dict to make predictions and then reshape the output to
# (resolution, resolution, number of outputs)
y_data[:, :, :] = self._make_predictions(self._contour_data_calcs()).reshape(
(resolution, resolution, self.num_outputs))
# Use the output variable to pull the correct column of data from the predicted
# data (y_data)
self.Z = y_data[:, :, self.output_variable]
# Reshape it to be 2D
self.Z = self.Z.reshape(resolution, resolution)
# Update the data source with new data
self.contour_plot_source.data = dict(z=[self.Z])
# Min to max of training data
self.contour_x_range = xlins = self.xlins_mesh
self.contour_y_range = ylins = self.ylins_mesh
# Color bar formatting
color_mapper = LinearColorMapper(
palette="Viridis11", low=np.amin(self.Z), high=np.amax(self.Z))
color_bar = ColorBar(color_mapper=color_mapper, ticker=BasicTicker(), label_standoff=12,
location=(0, 0))
# Contour Plot
self.contour_plot = contour_plot = figure(
match_aspect=False,
tooltips=[(self.x_input_select.value, "$x"), (self.y_input_select.value, "$y"),
(self.output_select.value, "@z")], tools='')
contour_plot.x_range.range_padding = 0
contour_plot.y_range.range_padding = 0
contour_plot.plot_width = 600
contour_plot.plot_height = 500
contour_plot.xaxis.axis_label = self.x_input_select.value
contour_plot.yaxis.axis_label = self.y_input_select.value
contour_plot.min_border_left = 0
contour_plot.add_layout(color_bar, 'right')
contour_plot.x_range = Range1d(min(xlins), max(xlins))
contour_plot.y_range = Range1d(min(ylins), max(ylins))
contour_plot.image(image='z', source=self.contour_plot_source, x=min(xlins), y=min(ylins),
dh=(max(ylins) - min(ylins)), dw=(max(xlins) - min(xlins)),
palette="Viridis11")
# Adding training data points overlay to contour plot
if self.is_structured_meta_model:
data = self._structured_training_points()
else:
data = self._unstructured_training_points()
if len(data):
# Add training data points overlay to contour plot
data = np.array(data)
if self.is_structured_meta_model:
self.contour_training_data_source.data = dict(x=data[:, 0], y=data[:, 1],
z=self.meta_model.training_outputs[
self.output_select.value].flatten())
else:
self.contour_training_data_source.data = dict(x=data[:, 0], y=data[:, 1],
z=self.meta_model._training_output[
self.output_select.value])
training_data_renderer = self.contour_plot.circle(
x='x', y='y', source=self.contour_training_data_source,
size=5, color='white', alpha=0.50)
self.contour_plot.add_tools(HoverTool(renderers=[training_data_renderer], tooltips=[
(self.x_input_select.value + " (train)", '@x'),
(self.y_input_select.value + " (train)", '@y'),
(self.output_select.value + " (train)", '@z'), ]))
return self.contour_plot
def _right_plot(self):
"""
Create the right side subplot to view the projected slice.
Parameters
----------
None
Returns
-------
Bokeh figure
"""
# List of the current positions of the sliders
self.input_point_list = [point.value for point in self.slider_dict.values()]
# Find the title of the y input and match it with the data
y_idx = self.y_input_select.value
y_data = self.predict_inputs[y_idx]
# Find the position of the x_input slider
x_value = self.x_input_slider.value
# Rounds the x_data to match the predict_inputs value
subplot_value_index = np.where(
np.around(self.predict_inputs[self.x_input_select.value], 5) ==
np.around(x_value, 5))[0]
# Make slice in Z data at the point calculated before and add it to the data source
z_data = self.Z[:, subplot_value_index].flatten()
x = z_data
y = self.slider_source.data[y_idx]
# Update the data source with new data
self.right_plot_source.data = dict(x=x, y=y)
# Create and format figure
self.right_plot_fig = right_plot_fig = figure(
plot_width=250, plot_height=500,
title="{} vs {}".format(y_idx, self.output_select.value), tools="pan")
right_plot_fig.xaxis.axis_label = self.output_select.value
right_plot_fig.yaxis.axis_label = y_idx
right_plot_fig.xaxis.major_label_orientation = math.pi / 9
right_plot_fig.line(x='x', y='y', source=self.right_plot_source)
right_plot_fig.x_range.range_padding = 0.1
right_plot_fig.y_range.range_padding = 0.02
# Determine distance and alpha opacity of training points
if self.is_structured_meta_model:
data = self._structured_training_points(compute_distance=True, source='right')
else:
data = self._unstructured_training_points(compute_distance=True, source='right')
self.right_alphas = 1.0 - data[:, 2] / self.dist_range
# Training data scatter plot
scatter_renderer = right_plot_fig.scatter(x=data[:, 3], y=data[:, 1], line_color=None,
fill_color='#000000',
fill_alpha=self.right_alphas.tolist())
right_plot_fig.add_tools(HoverTool(renderers=[scatter_renderer], tooltips=[
(self.output_select.value + " (train)", '@x'),
(y_idx + " (train)", '@y'),
]))
right_plot_fig.scatter(x=data[:, 3], y=data[:, 1], line_color=None, fill_color='#000000',
fill_alpha=self.right_alphas.tolist())
span_width = self.dist_range * (max(y_data) - min(y_data))
# Set the right_plot data source to new values
self.right_plot_scatter_source.data = dict(
right_slice_x=np.repeat(x_value, self.resolution), right_slice_y=y_data,
left_dashed=[i - span_width for i in np.repeat(x_value, self.resolution)],
right_dashed=[i + span_width for i in np.repeat(x_value, self.resolution)])
self.contour_plot.line(
'right_slice_x', 'right_slice_y', source=self.right_plot_scatter_source,
color='black', line_width=2)
self.contour_plot.line(
'left_dashed', 'right_slice_y', line_dash='dashed',
source=self.right_plot_scatter_source, color='black', line_width=2)
self.contour_plot.line(
'right_dashed', 'right_slice_y', line_dash='dashed',
source=self.right_plot_scatter_source, color='black', line_width=2)
return self.right_plot_fig
def _bottom_plot(self):
"""
Create the bottom subplot to view the projected slice.
Parameters
----------
None
Returns
-------
Bokeh figure
"""
# List of the current positions of the sliders
self.input_point_list = [point.value for point in self.slider_dict.values()]
# Find the title of the x input and match it with the data
x_idx = self.x_input_select.value
x_data = self.predict_inputs[x_idx]
# Find the position of the y_input slider
y_value = self.y_input_slider.value
# Rounds the y_data to match the predict_inputs value
subplot_value_index = np.where(
np.around(self.predict_inputs[self.y_input_select.value], 5) ==
np.around(y_value, 5))[0]
# Make slice in Z data at the point calculated before and add it to the data source
z_data = self.Z[subplot_value_index, :].flatten()
x = self.slider_source.data[x_idx]
y = z_data
# Update the data source with new data
self.bottom_plot_source.data = dict(x=x, y=y)
# Create and format figure
self.bottom_plot_fig = bottom_plot_fig = figure(
plot_width=550, plot_height=250,
title="{} vs {}".format(x_idx, self.output_select.value), tools="")
bottom_plot_fig.xaxis.axis_label = x_idx
bottom_plot_fig.yaxis.axis_label = self.output_select.value
bottom_plot_fig.line(x='x', y='y', source=self.bottom_plot_source)
bottom_plot_fig.x_range.range_padding = 0.02
bottom_plot_fig.y_range.range_padding = 0.1
# Determine distance and alpha opacity of training points
if self.is_structured_meta_model:
data = self._structured_training_points(compute_distance=True)
else:
data = self._unstructured_training_points(compute_distance=True)
self.bottom_alphas = 1.0 - data[:, 2] / self.dist_range
# Training data scatter plot
scatter_renderer = bottom_plot_fig.scatter(x=data[:, 0], y=data[:, 3], line_color=None,
fill_color='#000000',
fill_alpha=self.bottom_alphas.tolist())
bottom_plot_fig.add_tools(HoverTool(renderers=[scatter_renderer], tooltips=[
(x_idx + " (train)", '@x'),
(self.output_select.value + " (train)", '@y'),
]))
span_width = self.dist_range * (max(x_data) - min(x_data))
# Set the right_plot data source to new values
self.bottom_plot_scatter_source.data = dict(
bot_slice_x=x_data, bot_slice_y=np.repeat(y_value, self.resolution),
upper_dashed=[i + span_width for i in np.repeat(y_value, self.resolution)],
lower_dashed=[i - span_width for i in np.repeat(y_value, self.resolution)])
self.contour_plot.line(
'bot_slice_x', 'bot_slice_y', source=self.bottom_plot_scatter_source, color='black',
line_width=2)
self.contour_plot.line(
'bot_slice_x', 'upper_dashed', line_dash='dashed',
source=self.bottom_plot_scatter_source, color='black', line_width=2)
self.contour_plot.line(
'bot_slice_x', 'lower_dashed', line_dash='dashed',
source=self.bottom_plot_scatter_source, color='black', line_width=2)
return self.bottom_plot_fig
def _unstructured_training_points(self, compute_distance=False, source='bottom'):
"""
Calculate the training points and returns and array containing the position and alpha.
Parameters
----------
compute_distance : bool
If true, compute the distance of training points from surrogate line.
source : str
Which subplot the method is being called from.
Returns
-------
array
The array of training points and their alpha opacity with respect to the surrogate line
"""
# Input training data and output training data
x_training = self.meta_model._training_input
training_output = np.squeeze(stack_outputs(self.meta_model._training_output), axis=1)
# Index of input/output variables
x_index = self.x_input_select.options.index(self.x_input_select.value)
y_index = self.y_input_select.options.index(self.y_input_select.value)
output_variable = self.output_names.index(self.output_select.value)
# Vertically stack the x/y inputs and then transpose them
infos = np.vstack((x_training[:, x_index], x_training[:, y_index])).transpose()
if not compute_distance:
return infos
points = x_training.copy()
# Normalize so each dimension spans [0, 1]
points = np.divide(points, self.limit_range)
dist_limit = np.linalg.norm(self.dist_range * self.limit_range)
scaled_x0 = np.divide(self.input_point_list, self.limit_range)
# Query the nearest neighbors tree for the closest points to the scaled x0 array
# Nearest points to x slice
if x_training.shape[1] < 3:
tree = cKDTree(points)
# Query the nearest neighbors tree for the closest points to the scaled x0 array
dists, idxs = tree.query(
scaled_x0, k=len(x_training), distance_upper_bound=self.dist_range)
# kdtree query always returns requested k even if there are not enough valid points
idx_finite = np.where(np.isfinite(dists))
dists = dists[idx_finite]
idxs = idxs[idx_finite]
else:
dists, idxs = self._multidimension_input(scaled_x0, points, source=source)
# data contains:
# [x_value, y_value, ND-distance, func_value]
data = np.zeros((len(idxs), 4))
for dist_index, j in enumerate(idxs):
data[dist_index, 0:2] = infos[j, :]
data[dist_index, 2] = dists[dist_index]
data[dist_index, 3] = training_output[j, output_variable]
return data
def _structured_training_points(self, compute_distance=False, source='bottom'):
"""
Calculate the training points and return an array containing the position and alpha.
Parameters
----------
compute_distance : bool
If true, compute the distance of training points from surrogate line.
source : str
Which subplot the method is being called from.
Returns
-------
array
The array of training points and their alpha opacity with respect to the surrogate line
"""
# Create tuple of the input parameters
input_dimensions = tuple(self.meta_model.inputs)
# Input training data and output training data
x_training = np.array([z for z in product(*input_dimensions)])
training_output = self.meta_model.training_outputs[self.output_select.value].flatten()
# Index of input/output variables
x_index = self.x_input_select.options.index(self.x_input_select.value)
y_index = self.y_input_select.options.index(self.y_input_select.value)
# Vertically stack the x/y inputs and then transpose them
infos = np.vstack((x_training[:, x_index], x_training[:, y_index])).transpose()
if not compute_distance:
return infos
points = x_training.copy()
# Normalize so each dimension spans [0, 1]
points = np.divide(points, self.limit_range)
self.dist_limit = np.linalg.norm(self.dist_range * self.limit_range)
scaled_x0 = np.divide(self.input_point_list, self.limit_range)
# Query the nearest neighbors tree for the closest points to the scaled x0 array
# Nearest points to x slice
if x_training.shape[1] < 3:
x_tree, x_idx = self._two_dimension_input(scaled_x0, points, source=source)
else:
x_tree, x_idx = self._multidimension_input(scaled_x0, points, source=source)
# format for 'data'
# [x_value, y_value, ND-distance_(x or y), func_value]
n = len(x_tree)
data = np.zeros((n, 4))
for dist_index, j in enumerate(x_idx):
data[dist_index, 0:2] = infos[j, :]
data[dist_index, 2] = x_tree[dist_index]
data[dist_index, 3] = training_output[j]
return data
def _two_dimension_input(self, scaled_points, training_points, source='bottom'):
"""
Calculate the distance of training points to the surrogate line.
Parameters
----------
scaled_points : array
Array of normalized slider positions.
training_points : array
Array of input training data.
source : str
Which subplot the method is being called from.
Returns
-------
idxs : array
Index of closest points that are within the dist range.
x_tree : array
One dimentional array of points that are within the dist range.
"""
# Column of the input
if source == 'right':
col_idx = self.y_input_select.options.index(self.y_input_select.value)
else:
col_idx = self.x_input_select.options.index(self.x_input_select.value)
# Delete the axis of input from source to predicted 1D distance
x = np.delete(scaled_points, col_idx, axis=0)
x_training_points = np.delete(training_points, col_idx, axis=1).flatten()
# Tree of point distances
x_tree = np.abs(x - x_training_points)
# Only return points that are within our distance-viewing paramter.
idx = np.where(x_tree <= self.dist_range)
x_tree = x_tree[idx]
return x_tree, idx[0]
def _multidimension_input(self, scaled_points, training_points, source='bottom'):
"""
Calculate the distance of training points to the surrogate line.
Parameters
----------
scaled_points : array
Array of normalized slider positions.
training_points : array
Array of input training data.
source : str
Which subplot the method is being called from.
Returns
-------
idxs : array
Index of closest points that are within the dist range.
x_tree : array
Array of points that are within the dist range.
"""
# Column of the input
if source == 'right':
col_idx = self.y_input_select.options.index(self.y_input_select.value)
else:
col_idx = self.x_input_select.options.index(self.x_input_select.value)
# Delete the axis of input from source to predicted distance
x = np.delete(scaled_points, col_idx, axis=0)
x_training_points = np.delete(training_points, col_idx, axis=1)
# Tree of point distances
x_tree = cKDTree(x_training_points)
# Query the nearest neighbors tree for the closest points to the scaled array
dists, idx = x_tree.query(x, k=len(x_training_points),
distance_upper_bound=self.dist_range)
# kdtree query always returns requested k even if there are not enough valid points
idx_finite = np.where(np.isfinite(dists))
dists_finite = dists[idx_finite]
idx = idx[idx_finite]
return dists_finite, idx
# Event handler functions
def _update_all_plots(self):
self.doc_layout.children[0] = self._contour_data()
self.doc_layout.children[1] = self._right_plot()
self.doc_layout2.children[0] = self._bottom_plot()
def _update_subplots(self):
self.doc_layout.children[1] = self._right_plot()
self.doc_layout2.children[0] = self._bottom_plot()
def _update(self, attr, old, new):
self._update_all_plots()
def _scatter_plots_update(self, attr, old, new):
self._update_subplots()
def _scatter_input(self, attr, old, new):
# Text input update function of dist range value
self.dist_range = float(new)
self._update_all_plots()
def _x_input_update(self, attr, old, new):
# Checks that x and y inputs are not equal to each other
if new == self.y_input_select.value:
raise ValueError("Inputs should not equal each other")
else:
self.x_input_select.value = new
self._update_all_plots()
def _y_input_update(self, attr, old, new):
# Checks that x and y inputs are not equal to each other
if new == self.x_input_select.value:
raise ValueError("Inputs should not equal each other")
else:
self.y_input_select.value = new
self._update_all_plots()
def _output_value_update(self, attr, old, new):
self.output_variable = self.output_names.index(new)
self._update_all_plots()
# initialize controls
# buttons for choosing a sample function
sample_function_type = RadioButtonGroup(labels=fs.function_names, active=fs.function_init)
# here one can choose arbitrary input function
default_function_input = TextInput(value=fs.function_input_init)
default_function_period_start = TextInput(title='period start', value=fs.timeinterval_start_init)
default_function_period_end = TextInput(title='period end', value=fs.timeinterval_end_init)
# slider controlling degree of the fourier series
degree = Slider(title="degree", name='degree', value=fs.degree_init, start=fs.degree_min,
end=fs.degree_max, step=fs.degree_step)
# initialize callback behaviour
degree.on_change('value', degree_change)
default_function_input.on_change('value',
type_input_change) # todo write default functions for any callback, like above
default_function_period_start.on_change('value', type_input_change)
default_function_period_end.on_change('value', type_input_change)
sample_function_type.on_change('active', type_input_change)
# initialize plot
toolset = "crosshair,pan,reset,resize,save,wheel_zoom"
# Generate a figure container
plot = Figure(plot_height=fs.resolution,
plot_width=fs.resolution,
tools=toolset,
title="Fourier Series Approximation",
x_range=[fs.x_min, fs.x_max],
y_range=[fs.y_min, fs.y_max]
)
# Plot the line by the x,y values in the source property
def plotting(self):
#Tools = [hover, TapTool(), BoxZoomTool(), BoxSelectTool(), PreviewSaveTool(), ResetTool()]
TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave"
tab_plots = []
#output_file("test.html")
self.all_elements = []
self.elements_comparison = []
for attr_id, i in zip(self.attribute_ids, range(len(self.attribute_ids))):
"""
create plots for each datafile and put them in a tab.
"""
list_of_datasets = getattr(self, attr_id)
y_axis_units = [x["y_unit"] for x in list_of_datasets]
x_axis_units = [x["x_unit"] for x in list_of_datasets]
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log",
title = attr_id, tools = TOOLS)
#figure_obj.axes.major_label_text_font_size("12pt")
#figure_obj.major_label_text_font_size("12pt")
setattr(self, attr_id+"_"+"figure_obj",figure_obj)
figure_obj.yaxis.axis_label = y_axis_units[0]
figure_obj.xaxis.axis_label = x_axis_units[0]
if not all(x == y_axis_units[0] for x in y_axis_units):
for unit, data in zip(y_axis_units, list_of_datasets):
if not unit == y_axis_units[0]:
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(data["data"]["y"]),
end = np.amax(data["data"]["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[0] for x in x_axis_units):
for unit, data in zip(x_axis_units, list_of_datasets):
if not unit == x_axis_units[0]:
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(data["data"]["x"]),
end = np.amax(data["data"]["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
figure_obj.xaxis.axis_label = list_of_datasets[0]["x_unit"]
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
list_of_elements = []
for dataset, color_index in zip(list_of_datasets, colour_indices):
self.all_elements.append(dataset["sample element"]) #strip isotope number
color = colour_list[color_index]
source = ColumnDataSource(data = dataset["data"]) #Datastructure for source of plotting
setattr(self, attr_id+"_"+dataset["sample element"]+"_source", source) #Source element generalized for all plotting
list_of_elements.append(dataset["sample element"])
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]
+"_source"), line_width = 2, line_color = color,
legend = dataset["sample element"], name = dataset["sample element"],
)
hover = figure_obj.select_one(HoverTool).tooltips = [("element", "@element"), ("(x,y)", "($x, $y)")]
radio_group = RadioGroup(labels = list_of_elements, active=0)
"""
Need to fetch default variables from input file and replace DEFAULT
Block of code produces the layout of buttons and callbacks
"""
#Calculations on the dataset
text_input_rsf = TextInput(value = "default", title = "RSF (at/cm^3): ")
do_integral_button = Button(label = "Calibration Integral")
smoothing_button = Button(label = "Smoothing on selected curve")
text_input_sputter = TextInput(value = "default", title = "Sputter speed: float unit")
text_input_crater_depth = TextInput(value = "default", title = "Depth of crater in: float")
radio_group.on_change("active", lambda attr, old, new: None)
text_input_xval_integral = TextInput(value = "0", title = "x-value for calibration integral ")
text_input_yval_integral = TextInput(value = "0", title = "y-value for calibration integral ")
#Save files for later use
save_flexDPE_button = Button(label = "Save element for FlexPDE")
save_all_flexDPE_button = Button(label = "Save all elements for FlexPDE")
#Pointers to methods on click / change handlers
do_integral_button.on_click(lambda identity = self.attribute_ids[i], radio = radio_group,
x_box = text_input_xval_integral, y_box = text_input_yval_integral:
self.integrate(identity, radio, x_box, y_box))
smoothing_button.on_click(lambda identity = self.attribute_ids[i], radio = radio_group:
self.smoothing(identity, radio) )
save_flexDPE_button.on_click(lambda identity = self.attribute_ids[i], radio = radio_group:
self.write_to_flexPDE(identity, radio))
save_all_flexDPE_button.on_click(lambda identity = self.attribute_ids[i], radio = radio_group:
self.write_all_to_flexPDE(identity, radio))
text_input_rsf.on_change("value", lambda attr, old, new, radio = radio_group,
identity = self.attribute_ids[i], text_input = text_input_rsf, which = "rsf":
self.update_data(identity, radio, text_input, new, which))
text_input_sputter.on_change("value", lambda attr, old, new, radio = radio_group,
identity = self.attribute_ids[i], text_input = text_input_sputter, which = "sputter":
self.update_data(identity, radio, text_input, new, which))
text_input_crater_depth.on_change("value", lambda attr, old, new, radio = radio_group,
identity = self.attribute_ids[i], text_input = text_input_crater_depth, which = "crater_depth":
self.update_data(identity, radio, text_input, new, which))
#Initialization of actual plotting.
tab_plots.append(Panel(child = hplot(figure_obj,
vform(radio_group, save_flexDPE_button, save_all_flexDPE_button),
vform(text_input_rsf, smoothing_button, text_input_sputter, text_input_crater_depth),
vform(text_input_xval_integral, text_input_yval_integral, do_integral_button)),
title = attr_id))
"""
Check to see if one or more element exists in the samples and creat a comparison plot for each
of those elements.
"""
for element in self.all_elements:
checkers = list(self.all_elements)
checkers.remove(element)
if element in checkers and not element in self.elements_comparison:
self.elements_comparison.append(element)
"""create plots for each element that is to be compared """
for comparison_element in self.elements_comparison:
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log", title = comparison_element, tools = TOOLS)
#figure_obj.xaxis.major_label_text_font_size("12pt")
#figure_obj.yaxis.major_label_text_font_size("12pt")
y_axis_units = []
x_axis_units = []
comparison_datasets = []
for attr_id, color_index in zip(self.attribute_ids, colour_indices):
list_of_datasets = getattr(self, attr_id)
for dataset in list_of_datasets:
if dataset["sample element"] == comparison_element:
comparison_datasets.append(dataset)
y_axis_units.append(dataset["y_unit"])
x_axis_units.append(dataset["x_unit"])
figure_obj.xaxis.axis_label = comparison_datasets[-1]["x_unit"]
figure_obj.yaxis.axis_label = comparison_datasets[-1]["y_unit"]
if not all(x == y_axis_units[-1] for x in y_axis_units):
for unit, data in zip(y_axis_units, comparison_datasets):
if not unit == y_axis_units[-1]:
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(data["data"]["y"]),
end = np.amax(data["data"]["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[-1] for x in x_axis_units):
for unit, data in zip(x_axis_units, comparison_datasets):
if not unit == x_axis_units[-1]:
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(data["data"]["x"]),
end = np.amax(data["data"]["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
for attr_id, color_index in zip(self.attribute_ids, colour_indices):
list_of_datasets = getattr(self, attr_id)
for dataset in list_of_datasets:
if dataset["sample element"] == comparison_element:
color = colour_list[color_index]
"""
Logic that ensures that plots get put with correspoinding axes.
"""
if dataset["x_unit"] != x_axis_units[-1] or dataset["y_unit"] != y_axis_units[-1]:
if dataset["x_unit"] != x_axis_units[-1] and dataset["y_unit"] != y_axis_units[-1]:
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]+"_source"), line_width = 2,
line_color = color, legend = attr_id, x_range_name = "bar", y_range_name = "foo")
elif dataset["x_unit"] != x_axis_units[-1]:
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]+"_source"), line_width = 2,
line_color = color, legend = attr_id, x_range_name = "bar")
else:
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]+"_source"), line_width = 2,
line_color = color, legend = attr_id, y_range_name = "foo")
else:
figure_obj.line("x", "y", source = getattr(self, attr_id+"_"+dataset["sample element"]+"_source"), line_width = 2,
line_color = color, legend = attr_id)
tab_plots.append(Panel(child = figure_obj, title = comparison_element))
tabs = Tabs(tabs = tab_plots)
session = push_session(curdoc())
session.show()
session.loop_until_closed()
"PARAMS: offset: %s amplitude: %s", offset.value,
amplitude.value
)
source.data = dict(x=x, y=y)
update_data()
def input_change(attrname, old, new):
"""Executes whenever the input form changes.
It is responsible for updating the plot, or anything else you want.
Args:
attrname : the attr that changed
old : old value of attr
new : new value of attr
"""
update_data()
plot.title = text.value
# Text box event registration
text.on_change('value', input_change)
# Slider event registration
for w in [offset, amplitude, phase, freq]:
w.on_change('value', input_change)
# put ourselves in the document
curdoc().add(hbox)
# other widgets (not all are used yet)
phase = Slider(title="phase", value=0.0, start=0.0, end=5.0, step=0.1)
points = Slider(title="data points", value=20, start=0, end=500, step=1)
statsA = Paragraph(text="100", width=400, height=40)
statsB = Paragraph(text="100", width=400, height=40)
g2 = Paragraph(text="100", width=400, height=80)
g2_2d = Paragraph(text="100", width=400, height=40)
# Set up callbacks
def send_command(attrname, old, new):
# not implemented yet
# TODO turn into a raw command area for sending any device command
plot.title.text = command.value
command.on_change('value', send_command)
last_time = time.time()
# start out keeping 20 data points
datapoints = 20
def update_data():
# TODO: store data in a stream for charting vs time
# this function is called every 100 ms (set below if you want to change it)
# keep track of time interval for accurate counting calculations
global last_time
T = time.time() - last_time
last_time = time.time()
#print(T)
text = TextInput(title="Map Name", value="NicheLife Map")
feature1 = Slider(title="Subway Accessibility", value=0.5, start=0, end=1, step=0.1)
feature2 = Slider(title="Safety", value=0.5, start=0, end=1, step=0.1)
feature3 = Slider(title="Public Satisfaction", value=0.5, start=0, end=1, step=0.1)
feature4 = Slider(title="Restaurants", value=0.5, start=0, end=1, step=0.1)
feature5 = Slider(title="Grocery Stores", value=0.5, start=0, end=1, step=0.1)
feature6 = Slider(title="Nightlife", value=0.5, start=0, end=1, step=0.1)
price = Select(title="Show Affordability", options=["Yes", "No"])
# Set up callbacks
def update_title(attrname, old, new):
p.title = text.value
text.on_change("value", update_title)
def update_data(attrname, old, new):
# Get the current slider values
f1user = feature1.value
f2user = feature2.value
f3user = feature3.value
f4user = feature4.value
f5user = feature5.value
f6user = feature6.value
showprice = price.value
# Calculate score based on user input
qivals = getscore([f1user, f2user, f3user, f4user, f5user, f6user])
value=10,
step=1)
salary_slider.on_change('value', lambda attr, old, new: update())
city = Select(title="Location",
value="All",
options=open('city.txt').read().split('\n'))
city.on_change('value', lambda attr, old, new: update())
company = Select(title="Company",
value="All",
options=open('company.txt').read().split('\n'))
company.on_change('value', lambda attr, old, new: update())
keyword = TextInput(title="Keywords")
keyword.on_change('value', lambda attr, old, new: update())
def select_jobs():
company_val = str(company.value)
city_val = str(city.value)
kwd_val = str(keyword.value)
selected = data[data.Salary_Upper_Bound >= salary_slider.value]
if (company_val != "All"):
selected = selected[selected.Company.str.contains(company_val) == True]
if (city_val != "All"):
selected = selected[selected.City.str.contains(city_val) == True]
if (keyword != ""):
selected = selected[selected.Description.str.contains(kwd_val) == True]
# 绝对值曲线
source_absolute.data = source_absolute.from_df(
pd.DataFrame({'ts': dataframe['absolute']}))
# 词表格
data = pd.read_csv("%s/%s_%s_words.csv" % (DATA_DIR, word, threshold))
source_table.data = {'word': data['word'], 'distance': data['distance']}
years_selections = [str(year) for year in range(2010, 2018)]
year_select = Select(value="2013",
title="开始年份",
width=200,
options=years_selections)
year_select.on_change("value", lambda attr, old, new: update_data())
slider = TextInput(title="阈值", value="0.3")
# slider = Slider(title="阈值", start=0.0, end=1.0, value=0.3, step=0.1)
slider.on_change('value', lambda attr, old, new: update_data())
text = TextInput(title="关键词（例如：MPA、房地产、通胀）", value=u'楼市')
text.on_change('value', lambda attr, old, new: update_data())
update_data()
# Set up layouts and add to document
inputs = widgetbox(text, slider, year_select)
table = widgetbox(data_table)
curdoc().add_root(
row(inputs, table, plot_absolute, plot, plot_weighted, width=800))
curdoc().title = u"关键词历史热度"
class Market():
###########################################################################
# PLOT WIDGET
###########################################################################
# +----------------------------+
# | +-----------+ +----------+ |
# | | start_btn | | end_btn | |
# | +-----------+ +----------+ |
# +----------------------------+
# | |
# | plot |
# | |
# +----------------------------+
###########################################################################
def __init__(self,
start=(date.today() - relativedelta(years=3)),
end=date.today()):
self.start = str(start)
self.end = str(end)
self.sp500 = core.market.SP500('dataset/tickers.h5')
symbols = self.sp500.store['info']['symbol'].values.tolist()
# log.info("{}".format(symbols))
# Select tick button
title = "Company tick"
self.select_tick = Select(title=title,
value=symbols[0],
options=symbols)
# Inputs buttons
self.start_date = TextInput(value=self.start, title='start')
self.end_date = TextInput(value=self.end, title='end')
# layout
self.plot_layout = self.candle_plot(symbols[0])
self.start_date.on_change('value', self.on_start_date_change)
self.end_date.on_change('value', self.on_end_date_change)
self.select_tick.on_change('value', self.on_tick_selection_change)
self.layout = layout(
[[self.select_tick, self.start_date, self.end_date],
[self.plot_layout]])
def candle_plot(self, ticker, index_name='date'):
self.df = self.sp500.get_ticker_stocks(ticker, self.start, self.end)
self.df = plot.normalize_name(self.df)
self.df = self.df.set_index(index_name)
index = self.df.index.get_level_values(index_name)
stock_data = {index_name: index}
for val in self.df.columns.values:
stock_data[val] = self.df[val]
# log.info(stock_data)
source = ColumnDataSource(data=dict(stock_data))
hover = HoverTool(tooltips=[('date', '@date{%F}'),
('adj close', '$@adj_close{%0.2f}'),
('adj open', '$@adj_open{%0.2f}'),
('volume', '@volume{0.00 a}'),
('open', '@open{%0.2f}'),
('close', '@close{%0.2f}')],
formatters={
'date': 'datetime',
'adj_close': 'printf',
'adj_open': 'printf',
'open': 'printf',
'close': 'printf'
},
mode='vline')
inc = self.df['close'] > self.df['open']
dec = self.df['open'] > self.df['close']
w = 12 * 60 * 60 * 1000 # half day in ms
p = figure(x_axis_type="datetime",
plot_width=1600,
title=ticker,
tools="xwheel_zoom, xpan, reset, save",
active_drag="xpan")
p.add_tools(hover)
p.toolbar.logo = None
p.grid.grid_line_alpha = 0.3
p.xaxis.major_label_orientation = pi / 4
p.xaxis.axis_label = 'Date'
p.yaxis.axis_label = 'Price'
p.line(index_name, 'adj_close', color='#A6CEE3', source=source)
p.line(index_name, 'adj_open', color='#FB9A99', source=source)
p.segment(index_name,
'high',
index_name,
'low',
color="white",
source=source)
p.vbar(index[inc],
w,
self.df['open'][inc],
self.df['close'][inc],
fill_color="#D5E1DD",
line_color="white")
p.vbar(index[dec],
w,
self.df['open'][dec],
self.df['close'][dec],
fill_color="#F2583E",
line_color="white")
p.legend.location = "top_left"
p.background_fill_color = "black"
columns = [
TableColumn(field="date", title="date", formatter=DateFormatter())
]
for key in stock_data.keys():
if key != 'date':
columns.append(TableColumn(field=key, title=key))
# Layout
return column(p, DataTable(source=source, columns=columns, width=1600))
# Callbacks
def on_tick_selection_change(self, attr, old, new):
log.debug('VALUE: old {} | new {}'.format(old, new))
self.layout.children[1] = self.candle_plot(new)
def on_start_date_change(self, attr, old, new):
log.debug('VALUE: old {} | new {}'.format(old, new))
self.start = new
self.layout.children[1] = self.candle_plot(self.select_tick.value)
def on_end_date_change(self, attr, old, new):
log.debug('VALUE: old {} | new {}'.format(old, new))
self.end = new
self.layout.children[1] = self.candle_plot(self.select_tick.value)
# Filtering both DataFrames and creating the CDSs in this callback
cb_cds = ColumnDataSource(df[μ].copy())
# Updating old CDSs data with new CDSs data
cds.data.update(cb_cds.data)
# Creating title with the number filtered-in trees of the given species
fig.title.text = f'There are {trees} {species} in Barcelona.'
# %% ATTACHING BOKEH CALLBACKS ================================================
# =============================================================================
# Attaching the Text Input widget to the Text Input callback
ti_wg.on_change('value', ti_callback)
# %% CREATING BOKEH LAYOUT ====================================================
# =============================================================================
# Creating a layout combining the slider and the figure
layout = column(ti_wg, fig)
# Adding the layout to the current document
curdoc().add_root(layout)
# Defining a title for our current document
curdoc().title = 'pyTree dashboard'
# %% INVOKING BOKEH SERVER ====================================================
# =============================================================================
p.y_range.start = max(nys) + y_buf
p.y_range.end = min(nys) - y_buf
mld = {}
mldf = {}
for nd in nds:
mld.setdefault('xs',[]).append(nd['x'] if len(nd['x']) else [0.0])
mld.setdefault('ys',[]).append(nd['y'] if len(nd['y']) else [0.0])
mld.setdefault('colors',[]).append(plotting.bandcolorf(nd['band']) if len(nd['x']) else 'white')
mld.setdefault('lws',[]).append(2)
mldf.setdefault('xs',[]).extend(nd['x'] if len(nd['x']) else [])
mldf.setdefault('ys',[]).extend(nd['y'] if len(nd['y']) else [])
mldf.setdefault('colors',[]).extend([plotting.bandcolorf(nd['band']) for x in range(len(nd['x']))])
mlobs.data_source.data = mld
circobs.data_source.data = mldf
def bandcb(attrname, old, new):
callback()
def namecb(attrname, old, new):
callback()
namefield.on_change('value', namecb)
bandfield.on_change('value', bandcb)
button.on_click(callback)
# put the button and plot in a layout and add to the document
curdoc().add_root(column(p, row(namefield, bandfield), button))
toolset = "box_zoom,resize,pan,reset,save,xwheel_zoom"
plot_zoomed = Figure(plot_width=1000, plot_height=500, x_axis_type="datetime",tools=toolset, lod_factor=100,lod_interval=1000)
plot_zoomed.line('x', 'y',source=source_zoomed, color='navy', alpha=0.5)
plot = Figure(plot_width=PLOT_WIDTH, plot_height=250, x_axis_type="datetime",toolbar_location=None,lod_factor=100,lod_interval=1000)
plot.line('x', 'y',source=source_small, color='navy', alpha=0.5)
plot.y_range.start=Min_Y
plot.y_range.end=Max_Y
glyph = Quad(left="left", right="right", top="top", bottom="bottom", fill_color="#b3de69", fill_alpha=0.1)
plot.add_glyph(sourceQuad, glyph)
RangeStartX=0
RangeEndX=1
text_start_x = TextInput(title="X range start", name='x_range_start', value="0")
text_end_x = TextInput(title="X range end", name='x_range_end', value="1")
text_start_x.on_change('value', update_data)
text_end_x.on_change('value', update_data)
toolset = "box_zoom,resize,pan,reset,save,x_wheel_zoom"
plot_zoomed.x_range.callback = CustomJS(args=dict(xrange=plot_zoomed.x_range,start_x=text_start_x,end_x=text_end_x),code="""
var start = xrange.get("start");
var end = xrange.get("end");
start_x.set("value",start.toString());
end_x.set("value",end.toString());
start_x.trigger('change');
end_x.trigger('change');
""")
curdoc().add_root(VBoxForm(children=[plot_zoomed,plot], width=1000))
def plotting(self):
if self.debug:
self.debug_file = open("debug_output.txt", "w")
self.debug_file.write("Initialized plotting subroutine \n")
TOOLS="pan,wheel_zoom,box_zoom,reset,hover,previewsave"
tab_plots = []
self.all_elements = []
self.elements_comparison = []
for filename in self.filenames:
if "ITO" in filename:
tab_plots.append(self.mass_plotting(filename))
continue
data_dict = self.data_generation(filename)
self.data_test(data_dict)
name_check = data_dict["gen_info"]["DATA FILES"]
attr_id = name_check[1][4][:-3] + "_" + name_check[2][2]
self.attribute_ids.append(attr_id)
attr_extra_y_ranges = False
attr_extra_x_ranges = False
local_source_line = []
"""
create plots for each datafile and put them in a tab.
"""
y_axis_units = [x["y_unit"] for x in data_dict["data"]]
x_axis_units = [x["x_unit"] for x in data_dict["data"]]
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log",
title = attr_id, tools = TOOLS)
#figure_obj.axes.major_label_text_font_size("12pt")
#figure_obj.major_label_text_font_size("12pt")
hover = figure_obj.select(dict(type = HoverTool))
hover.tooltips = [
("Element:", "@element"),
("(x, y):", "($x, $y)")]
self.figure_data.append((figure_obj, data_dict))
figure_obj.yaxis.axis_label = y_axis_units[0]
figure_obj.xaxis.axis_label = x_axis_units[0]
if not all(x == y_axis_units[0] for x in y_axis_units):
for unit, dataset in zip(y_axis_units, data_dict["data"]):
if not unit == y_axis_units[0]:
extra_y_ranges_exists = attr_extra_y_ranges
extra_y_ranges_exists = True
if self.debug:
self.debug_file.write("Added extra y-axis for file_id: %s, element: %s | New length %g \n"
%(attr_id, dataset["sample_element"], len(figure_obj.yaxis)))
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(dataset["y"]),
end = np.amax(dataset["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[0] for x in x_axis_units):
for unit, dataset in zip(x_axis_units, data_dict["data"]):
if not unit == x_axis_units[0]:
extra_x_ranges_exists = attr_extra_x_ranges
extra_x_ranges_exists = True
if self.debug:
self.debug_file.write("Added extra x-axis for file_id: %s, element: %s. | New length %g \n"
%(attr_id, dataset["sample_element"], len(figure_obj.yaxis)))
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(dataset["x"]),
end = np.amax(dataset["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
figure_obj.xaxis.axis_label = x_axis_units[0]
colour_list = Spectral11 + RdPu9 + Oranges9
colour_indices = [0, 2, 8, 10, 12, 14, 20, 22, 1, 3, 9, 11, 13, 15]
list_of_elements = []
source_list = []
line_list = []
for dataset, color_index in zip(data_dict["data"], colour_indices):
self.all_elements.append(dataset["sample_element"]) #strip isotope number
color = colour_list[color_index]
source = ColumnDataSource(data = dataset) #Datastructure for source of plotting
self.source_test(source)
list_of_elements.append(dataset["sample_element"])
line_glyph = figure_obj.line("x", "y", source = source,
line_width = 2,
line_color = color,
legend = dataset["sample_element"])
if self.debug:
self.debug_file.write("Create line object on figure %s at %s \n" %(id(figure_obj), id(line_glyph)))
line_list.append(line_glyph)
source_list.append(source)
local_source_line.append([[source, line] for source, line in zip(source_list, line_list)])
self.source_line.append(local_source_line)
#Calculations on the dataset
text_input_rsf = TextInput(value = "default", title = "RSF or SF (at/cm^3): ")
do_integral_button = Button(label = "Calibration integral")
smoothing_button = Button(label = "smth selct elem")
matplot_button = Button(label = "Create matplotlib fig")
text_input_sputter = TextInput(value = "default", title = "Sputter speed: number unit")
text_input_crater_depth = TextInput(value = "default", title = "Depth of crater in: number unit")
radio_group = RadioGroup(labels = list_of_elements, active=0)
text_input_xval_integral = TextInput(value = "0", title = "x-delimiter ")
text_input_dose = TextInput(value = "0", title = "Dose[cm^-2] ")
#Save files for later use
save_flexDPE_button = Button(label = "Save element for FlexPDE")
save_all_flexDPE_button = Button(label = "Save all elements for FlexPDE")
save_textfile_button = Button(label = "Sava Data in textfile")
#Pointers to methods on click / change handlers
radio_group.on_change("active", lambda attr, old, new: None)
matplot_button.on_click(lambda source_list = source_list:
self.matplotlib_export(source_list))
do_integral_button.on_click(lambda
source_list = source_list,
line_list = line_list,
source_line = self.source_line,
figure_data = self.figure_data,
data_dict = data_dict,
radio = radio_group,
x_box = text_input_xval_integral,
dose = text_input_dose,
extra_y_ranges = attr_extra_y_ranges:
self.integrate(data_dict, source_list, line_list, source_line, figure_data, radio, x_box, dose, extra_y_ranges))
smoothing_button.on_click(lambda
source_list = source_list,
radio = radio_group,
data_dict = data_dict,
x_box = text_input_xval_integral:
self.smoothing(source_list, data_dict, radio, x_box) )
save_flexDPE_button.on_click(lambda
source_list = source_list,
attrname = attr_id,
radio = radio_group:
self.write_to_flexPDE(source_list, attrname, radio))
save_all_flexDPE_button.on_click(lambda
source_list = source_list,
attrname = attr_id:
self.write_all_to_flexPDE(source_list, attrname))
save_textfile_button.on_click(lambda
data_dict = data_dict,
source_list = source_list,
attrname = attr_id,
radio = radio_group:
self.write_new_datafile(data_dict, source_list, attrname,radio))
text_input_rsf.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
figure = figure_obj,
source_list = source_list,
text_input = text_input_rsf,
line_list = line_list,
which = "rsf":
self.update_data(line_list, data_dict, source_list, figure, radio, text_input, new, which))
text_input_sputter.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
figure = figure_obj,
source_list = source_list,
text_input = text_input_sputter,
which = "sputter":
self.update_data(data_dict, source_list, figure, radio, text_input, new, which))
text_input_crater_depth.on_change("value", lambda attr, old, new,
radio = radio_group,
data_dict = data_dict,
source_list = source_list,
figure = figure_obj,
text_input = text_input_crater_depth,
which = "crater_depth":
self.update_data(data_dict, source_list, figure, radio, text_input, new, which))
#Initialization of actual plotting.
tab_plots.append(Panel(child = hplot(figure_obj,
vform(
vform(radio_group, save_flexDPE_button, save_all_flexDPE_button, save_textfile_button, matplot_button),
vform(text_input_rsf, smoothing_button, text_input_sputter, text_input_crater_depth)
),
vform(text_input_xval_integral, text_input_dose, do_integral_button)),
title = attr_id))
"""
Check to see if one or more element exists in the samples and creat a comparison plot for each
of those elements.
"""
for element in self.all_elements:
checkers = list(self.all_elements)
checkers.remove(element)
if element in checkers and not element in self.elements_comparison:
self.elements_comparison.append(element)
"""create plots for each element that is to be compared """
for comparison_element in self.elements_comparison:
figure_obj = figure(plot_width = 1000, plot_height = 800, y_axis_type = "log", title = comparison_element, tools = TOOLS)
#figure_obj.xaxis.major_label_text_font_size("12pt")
#figure_obj.yaxis.major_label_text_font_size("12pt")
y_axis_units = []
x_axis_units = []
comparison_datasets = []
for data_dict_iter in self.column(self.figure_data, 1):
for dataset in data_dict_iter["data"]:
if dataset["sample_element"] == comparison_element:
comparison_datasets.append(dataset)
y_axis_units.append(dataset["y_unit"])
x_axis_units.append(dataset["x_unit"])
figure_obj.xaxis.axis_label = comparison_datasets[-1]["x_unit"]
figure_obj.yaxis.axis_label = comparison_datasets[-1]["y_unit"]
if not all(x == y_axis_units[-1] for x in y_axis_units):
for unit, data in zip(y_axis_units, comparison_datasets):
if not unit == y_axis_units[-1]:
figure_obj.extra_y_ranges = {"foo": Range1d(start = np.amin(data["y"]),
end = np.amax(data["y"]))}
figure_obj.add_layout(LogAxis(y_range_name = "foo", axis_label = unit), "right")
break
if not all(x == x_axis_units[-1] for x in x_axis_units):
for unit, data in zip(x_axis_units, comparison_datasets):
if not unit == x_axis_units[-1]:
figure_obj.extra_x_ranges = {"bar": Range1d(start = np.amin(data["x"]),
end = np.amax(data["x"]))}
figure_obj.add_layout(LinearAxis(x_range_name = "bar", axis_label = unit), "above")
break
active_sources = []
for data_dict, source_line_nested, attr_id, color_index in zip(self.column(self.figure_data, 1), self.source_line, self.attribute_ids, colour_indices):
for dataset, source_lis_coup, in zip(data_dict["data"], source_line_nested[0]):
source_local = source_lis_coup[0]
active_sources.append(source_local)
self.source_test(source_local)
self.source_dataset_test(source_local, dataset)
if dataset["sample_element"] == comparison_element:
color = colour_list[color_index]
"""
Logic that ensures that plots get put with correspoinding axes.
"""
if dataset["x_unit"] != x_axis_units[-1] or dataset["y_unit"] != y_axis_units[-1]:
if dataset["x_unit"] != x_axis_units[-1] and dataset["y_unit"] != y_axis_units[-1]:
name_check = data_dict["gen_info"]["DATA FILES"]
attr_id = name_check[1][4][:-3] + "_" + name_check[2][2]
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
x_range_name = "bar",
y_range_name = "foo")
elif dataset["x_unit"] != x_axis_units[-1]:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
x_range_name = "bar")
else:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id,
y_range_name = "foo")
else:
figure_obj.line("x", "y", source = source_local,
line_width = 2,
line_color = color,
legend = attr_id)
matplot_button = Button(label = "Create matplotlib fig")
save_all_flexDPE_button = Button(label = "Save all elements for FlexPDE")
matplot_button.on_click(lambda source_list = active_sources:
self.matplotlib_export(source_list))
save_all_flexDPE_button.on_click(lambda
source_list = active_sources,
attrname = comparison_element:
self.write_all_to_flexPDE(source_list, attrname))
tab_plots.append(Panel(child = hplot(figure_obj, vform(save_all_flexDPE_button, matplot_button)),
title = comparison_element))
tabs = Tabs(tabs = tab_plots)
#curdoc().add_root(tabs)
session = push_session(curdoc())
session.show()
session.loop_until_closed()
geneSource = ColumnDataSource(data=dict(Gene=[], Cluster=[]))
df = pd.DataFrame()
boundaryDF = pd.DataFrame()
colorDF = pd.DataFrame()
outDF = pd.DataFrame()
Console = PreText(
text="Console:\nStart visualize by entering \nannotations, pickle file and gene.\nPress Enter to submit.\n",
width=250,
height=100,
)
p = createPlot(df, boundaryDF)
Gene.on_change("value", updateGene)
Full.on_change("value", updateFP)
Partial.on_change("value", updateFP)
Alpha.on_change("value", updateFP)
Cluster.on_change("value", updateGroup)
Save.on_change("value", saveFasta)
Width.on_change("value", updateWidth)
dataColumns = [TableColumn(field="Gene", title="Gene"), TableColumn(field="Cluster", title="Cluster")]
data_table = DataTable(source=geneSource, columns=dataColumns, width=200, height=1200)
paramSource = ColumnDataSource(
data=dict(
Parameter=[
"annotation",
"format",
here=np.loadtxt('here5.csv',str,delimiter='\n')
here=list(here)
here_height=df_counts['numbers']
y_here_height=here_height/2.0
source.data=dict(
x=here[:10],
y=y_here_height[:10],
height=here_height[:10]
)
plot.tools=[hover]
plot.xaxis.major_label_orientation=np.pi/3
plot.x_range.factors=here[:10]
new_height=here_height[:10].values
plot.y_range.end=new_height[0]+200
#plot.rect(x=axis_map[x_axis.value],y=height_map[x_axis.value],height=height_map[x_axis.value]*2.0)
#plot.x_range.on_change('_bounds_as_factors',update)
x_axis.on_change('value',update)
cities.on_change('value',update)
companies.on_change('value',update)
industries.on_change('value',update)
#filters=VBox(x_axis)
#tot=HBox(filters,plot)
#show(tot)
curdoc().add_root(HBox(inputs,plot))
# initialize data source
source_curve = ColumnDataSource(data=dict(x=[], y=[]))
source_point = ColumnDataSource(data=dict(x=[], y=[]))
source_sector = ColumnDataSource(data=dict(x=[], y=[]))
source_lines = ColumnDataSource(data=dict(x_start=[], y_start=[], x_end=[], y_end=[]))
source_text = ColumnDataSource(data=dict(area=[]))
# initialize controls
# slider controlling the current parameter t
t_value_input = Slider(title="parameter t", name='parameter t', value=leibnitz_settings.t_value_init,
start=leibnitz_settings.t_value_min, end=leibnitz_settings.t_value_max,
step=leibnitz_settings.t_value_step)
t_value_input.on_change('value', t_value_change)
# text input for the x component of the curve
x_component_input = TextInput(value=leibnitz_settings.x_component_input_msg, title="curve x")
x_component_input.on_change('value', curve_change)
# text input for the y component of the curve
y_component_input = TextInput(value=leibnitz_settings.y_component_input_msg, title="curve y")
y_component_input.on_change('value', curve_change)
# dropdown menu for selecting one of the sample curves
sample_curve_input = Dropdown(label="choose a sample function pair or enter one below",
menu=leibnitz_settings.sample_curve_names)
sample_curve_input.on_change('value', sample_curve_change)
# initialize plot
toolset = "crosshair,pan,reset,save,wheel_zoom"
# Generate a figure container
plot = Figure(plot_height=400, plot_width=400, tools=toolset,
title="Leibnitz sector formula",
x_range=[leibnitz_settings.x_min_view, leibnitz_settings.x_max_view],
y_range=[leibnitz_settings.y_min_view, leibnitz_settings.y_max_view])
def plot():
# FIGURES AND X-AXIS
fig1 = Figure(title = 'Dive Profile', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS)
fig2 = Figure(title = 'Dive Controls', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS, x_range=fig1.x_range)
fig3 = Figure(title = 'Attitude', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS, x_range=fig1.x_range)
figs = gridplot([[fig1],[fig2],[fig3]])
# Formatting x-axis
timeticks = DatetimeTickFormatter(formats=dict(seconds =["%b%d %H:%M:%S"],
minutes =["%b%d %H:%M"],
hourmin =["%b%d %H:%M"],
hours =["%b%d %H:%M"],
days =["%b%d %H:%M"],
months=["%b%d %H:%M"],
years =["%b%d %H:%M %Y"]))
fig1.xaxis.formatter = timeticks
fig2.xaxis.formatter = timeticks
fig3.xaxis.formatter = timeticks
# removing gridlines
fig1.xgrid.grid_line_color = None
fig1.ygrid.grid_line_color = None
fig2.xgrid.grid_line_color = None
fig2.ygrid.grid_line_color = None
fig3.xgrid.grid_line_color = None
fig3.ygrid.grid_line_color = None
# INPUT WIDGETS
collection_list = CONN[DB].collection_names(include_system_collections=False)
gliders = sorted([platformID for platformID in collection_list if len(platformID)>2])
gliders = Select(title = 'PlatformID', value = gliders[0], options = gliders)
prev_glider = Button(label = '<')
next_glider = Button(label = '>')
glider_controlbox = HBox(children = [gliders, prev_glider, next_glider], height=80)
chunkations = Select(title = 'Chunkation', value = 'segment', options = ['segment', '24hr', '30days', '-ALL-'])
chunk_indicator = TextInput(title = 'index', value = '0')
prev_chunk = Button(label = '<')
next_chunk = Button(label = '>')
chunk_ID = PreText(height=80)
chunk_controlbox = HBox(chunkations,
HBox(chunk_indicator, width=25),
prev_chunk, next_chunk,
chunk_ID,
height = 80)
control_box = HBox(glider_controlbox,
chunk_controlbox)
# DATA VARS
deadby_date = ''
depth = ColumnDataSource(dict(x=[],y=[]))
vert_vel = ColumnDataSource(dict(x=[],y=[]))
mbpump = ColumnDataSource(dict(x=[],y=[]))
battpos = ColumnDataSource(dict(x=[],y=[]))
pitch = ColumnDataSource(dict(x=[],y=[]))
mfin = ColumnDataSource(dict(x=[],y=[]))
cfin = ColumnDataSource(dict(x=[],y=[]))
mroll = ColumnDataSource(dict(x=[],y=[]))
mheading = ColumnDataSource(dict(x=[],y=[]))
cheading = ColumnDataSource(dict(x=[],y=[]))
# AXIS setup
colors = COLORS[:]
fig1.y_range.flipped = True
fig1.yaxis.axis_label = 'm_depth (m)'
fig1.extra_y_ranges = {'vert_vel': Range1d(start=-50, end=50),
'dummy': Range1d(start=0, end=100)}
fig1.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'vert_vel',
axis_label = 'vertical velocity (cm/s)'))
fig1.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'dummy',
axis_label = ' '))
fig1.yaxis[1].visible = False
fig1.yaxis[1].axis_line_alpha = 0
fig1.yaxis[1].major_label_text_alpha = 0
fig1.yaxis[1].major_tick_line_alpha = 0
fig1.yaxis[1].minor_tick_line_alpha = 0
fig2.yaxis.axis_label = 'pitch (deg)'
fig2.y_range.start, fig2.y_range.end = -40,40
fig2.extra_y_ranges = {'battpos': Range1d(start=-1, end = 1),
'bpump': Range1d(start=-275, end=275)}
fig2.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'battpos',
axis_label = 'battpos (in)'))
fig2.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'bpump',
axis_label = 'bpump (cc)'))
fig2.yaxis[1].visible = False # necessary for spacing. later gets set to true
fig3.yaxis.axis_label = 'fin/roll (deg)'
fig3.y_range.start, fig3.y_range.end = -30, 30
fig3.extra_y_ranges = {'heading': Range1d(start=0, end=360), #TODO dynamic avg centering
'dummy': Range1d(start=0, end=100)}
fig3.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'heading',
axis_label = 'headings (deg)'))
fig3.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'dummy',
axis_label = ' '))
fig3.yaxis[1].visible = False
fig3.yaxis[1].axis_line_alpha = 0
fig3.yaxis[1].major_label_text_alpha = 0
fig3.yaxis[1].major_tick_line_alpha = 0
fig3.yaxis[1].minor_tick_line_alpha = 0
# PLOT OBJECTS
fig1.line( 'x', 'y', source = depth, legend = 'm_depth', color = 'red')
fig1.circle('x', 'y', source = depth, legend = 'm_depth', color = 'red')
fig1.line( 'x', 'y', source = vert_vel, legend = 'vert_vel', color = 'green', y_range_name = 'vert_vel')
fig1.circle('x', 'y', source = vert_vel, legend = 'vert_vel', color = 'green', y_range_name = 'vert_vel')
fig1.renderers.append(Span(location = 0, dimension = 'width', y_range_name = 'vert_vel',
line_color= 'green', line_dash='dashed', line_width=1))
fig2.line( 'x', 'y', source = pitch, legend = "m_pitch", color = 'indigo')
fig2.circle('x', 'y', source = pitch, legend = "m_pitch", color = 'indigo')
fig2.line( 'x', 'y', source = battpos, legend = 'm_battpos', color = 'magenta', y_range_name = 'battpos')
fig2.circle('x', 'y', source = battpos, legend = 'm_battpos', color = 'magenta', y_range_name = 'battpos')
fig2.line( 'x', 'y', source = mbpump, legend = "m_'bpump'", color = 'blue', y_range_name = 'bpump')
fig2.circle('x', 'y', source = mbpump, legend = "m_'bpump'", color = 'blue', y_range_name = 'bpump')
fig2.renderers.append(Span(location = 0, dimension = 'width',
line_color= 'black', line_dash='dashed', line_width=1))
fig3.line( 'x', 'y', source = mfin, legend = 'm_fin', color = 'cyan')
fig3.circle('x', 'y', source = mfin, legend = 'm_fin', color = 'cyan')
fig3.line( 'x', 'y', source = cfin, legend = 'c_fin', color = 'orange')
fig3.circle('x', 'y', source = cfin, legend = 'c_fin', color = 'orange')
fig3.line( 'x', 'y', source = mroll, legend = 'm_roll', color = 'magenta')
fig3.circle('x', 'y', source = mroll, legend = 'm_roll', color = 'magenta')
fig3.line( 'x', 'y', source = mheading, legend = 'm_heading', color = 'blue', y_range_name = 'heading')
fig3.circle('x', 'y', source = mheading, legend = 'm_heading', color = 'blue', y_range_name = 'heading')
fig3.line( 'x', 'y', source = cheading, legend = 'c_heading', color = 'indigo', y_range_name = 'heading')
fig3.circle('x', 'y', source = cheading, legend = 'c_heading', color = 'indigo', y_range_name = 'heading')
fig3.renderers.append(Span(location = 0, dimension = 'width', y_range_name = 'default',
line_color= 'black', line_dash='dashed', line_width=1))
# CALLBACK FUNCS
def update_data(attrib,old,new):
g = gliders.value
chnk = chunkations.value
chindex = abs(int(chunk_indicator.value))
depth.data = dict(x=[],y=[])
vert_vel.data = dict(x=[],y=[])
mbpump.data = dict(x=[],y=[])
battpos.data = dict(x=[],y=[])
pitch.data = dict(x=[],y=[])
mfin.data = dict(x=[],y=[])
cfin.data = dict(x=[],y=[])
mroll.data = dict(x=[],y=[])
mheading.data = dict(x=[],y=[])
cheading.data = dict(x=[],y=[])
depth.data,startend = load_sensor(g, 'm_depth', chnk, chindex)
if chnk == 'segment':
xbd = startend[2]
chunk_ID.text = '{} {} \n{} ({}) \nSTART: {} \nEND: {}'.format(g, xbd['mission'],
xbd['onboard_filename'], xbd['the8x3_filename'],
e2ts(xbd['start']), e2ts(xbd['end']))
if len(set(depth.data['x']))<=1 and attrib == 'chunk':
if old > new:
next_chunk.clicks += 1
else:
prev_chunk.clicks += 1
return
elif len(set(depth.data['x']))<=1 and chunk_indicator.value == 0:
chunk_indicator.value = 1
elif chnk in ['24hr', '30days']:
chunk_ID.text = '{} \nSTART: {} \nEND: {}'.format(g, e2ts(startend[0]), e2ts(startend[1]))
elif chnk == '-ALL-':
chunk_ID.text = '{} \nSTART: {} \nEND: {}'.format(g,e2ts(depth.data['x'][0] /1000),
e2ts(depth.data['x'][-1]/1000))
vert_vel.data = calc_vert_vel(depth.data)
mbpump.data,_ = load_sensor(g, 'm_de_oil_vol', chnk, chindex)
if len(mbpump.data['x']) > 1:
#for yax in fig2.select('mbpump'):
# yax.legend = 'm_de_oil_vol'
pass
else:
mbpump.data,_ = load_sensor(g, 'm_ballast_pumped', chnk, chindex)
#for yax in fig2.select('mbpump'):
# yax.legend = 'm_ballast_pumped'
battpos.data,_ = load_sensor(g, 'm_battpos', chnk, chindex)
pitch.data,_ = load_sensor(g, 'm_pitch', chnk, chindex)
pitch.data['y'] = [math.degrees(y) for y in pitch.data['y']]
mfin.data,_ = load_sensor(g, 'm_fin', chnk, chindex)
cfin.data,_ = load_sensor(g, 'c_fin', chnk, chindex)
mroll.data,_ = load_sensor(g, 'm_roll', chnk, chindex)
mheading.data,_ = load_sensor(g, 'm_heading', chnk, chindex)
cheading.data,_ = load_sensor(g, 'c_heading', chnk, chindex)
mfin.data['y'] = [math.degrees(y) for y in mfin.data['y']]
cfin.data['y'] = [math.degrees(y) for y in cfin.data['y']]
mheading.data['y'] = [math.degrees(y) for y in mheading.data['y']]
cheading.data['y'] = [math.degrees(y) for y in cheading.data['y']]
mroll.data['y'] = [math.degrees(y) for y in mroll.data['y']]
fig1.yaxis[1].visible = True
fig2.yaxis[1].visible = True
fig3.yaxis[1].visible = True
#GLIDER SELECTS
def glider_buttons(increment):
ops = gliders.options
new_index = ops.index(gliders.value) + increment
if new_index >= len(ops):
new_index = 0
elif new_index < 0:
new_index = len(ops)-1
gliders.value = ops[new_index]
chunkation_update(None, None, None) #reset chunk indicator and clicks
def next_glider_func():
glider_buttons(1)
def prev_glider_func():
glider_buttons(-1)
def update_glider(attrib,old,new):
chunk_indicator.value = '0'
#update_data(None,None,None)
gliders.on_change('value', update_glider)
next_glider.on_click(next_glider_func)
prev_glider.on_click(prev_glider_func)
#CHUNK SELECTS
def chunkation_update(attrib,old,new):
chunk_indicator.value = '0'
prev_chunk.clicks = 0
next_chunk.clicks = 0
update_data(None,None,None)
if new == '-ALL-':
chunk_indicator.value = '-'
def chunk_func():
chunkdiff = prev_chunk.clicks - next_chunk.clicks
if chunkdiff < 0:
prev_chunk.clicks = 0
next_chunk.clicks = 0
chunkdiff = 0
print (chunkdiff)
chunk_indicator.value = str(chunkdiff)
def chunk_indicator_update(attrib,old,new):
try:
if abs(int(old)-int(new))>1: #manual update, triggers new non-manual indicator update, ie else clause below
prev_chunk.clicks = int(new)
next_chunk.clicks = 0
else:
update_data('chunk',int(old),int(new))
print("UPDATE", old, new)
except Exception as e:
print(type(e),e, old, new)
chunkations.on_change('value', chunkation_update)
chunk_indicator.on_change('value', chunk_indicator_update)
next_chunk.on_click(chunk_func)
prev_chunk.on_click(chunk_func)
update_data(None,None,None)
return vplot(control_box, figs)
solution_select.value))
data_table_source.data = new_data_table_source.data
store_now.data = new_store_now.data
store_future.data = new_store_future.data
ecom_zipcode_all.data = new_ecom_zipcode_all.data
# Bokeh Widgets
from bokeh.io import output_file, show
from bokeh.layouts import widgetbox
from bokeh.models.widgets import TextInput, Slider, CheckboxGroup, DatePicker, DataTable, Div
state_choose = TextInput(value="MA", title="Choose State")
state_choose.on_change('value', update)
radius_select = TextInput(value='5', title='Select Radius (Miles)')
radius_select.on_change('value', update)
solution_select = TextInput(value='0', title='Select Solution')
solution_select.on_change('value', update)
tran_type_select = CheckboxGroup(labels=['Sale', 'Return'],
active=[0, 1],
name='Transaction Type')
tran_type_select.on_change('active', update)
min_date_select = DatePicker(title='Start Date',
value=min_date1,
min_date=min_date1,
def on_text_value_change(attr, old, new):
try:
global expr
expr = sy.sympify(new, dict(x=xs))
except (sy.SympifyError, TypeError, ValueError) as exception:
dialog.content = str(exception)
dialog.visible = True
else:
update_data()
dialog = Dialog(title="Invalid expression")
slider = Slider(start=1, end=20, value=order, step=1, title="Order", callback_policy="mouseup")
slider.on_change("value", on_slider_value_change)
text = TextInput(value=str(expr), title="Expression:")
text.on_change("value", on_text_value_change)
inputs = WidgetBox(children=[slider, text], width=400)
layout = Column(children=[inputs, plot, dialog])
update_data()
document.add_root(layout)
session.show(layout)
if __name__ == "__main__":
print("\npress ctrl-C to exit")
session.loop_until_closed()
R = float(new)
x = R * np.cos(t)
y = R * np.sin(t)
source.data = dict(x=x, y=y)
def x_input_handler(attr, old, new):
x = R * np.cos(t) + float(new)
y = R * np.sin(t)
source.data = dict(x=x, y=y)
def y_input_handler(attr, old, new):
x = R * np.cos(t)
y = R * np.sin(t) + float(new)
source.data = dict(x=x, y=y)
R_input = TextInput(value="1", title="R:")
x_input = TextInput(value="0", title="x:")
y_input = TextInput(value="0", title="y:")
R_input.on_change("value", R_input_handler)
x_input.on_change("value", x_input_handler)
y_input.on_change("value", y_input_handler)
inputs = column(R_input, x_input, y_input)
curdoc().add_root(row(inputs, plot, width=800))
curdoc().title = "circle"
def _create_ui_components() -> (Figure, ColumnDataSource): # pylint: disable=too-many-statements
global asp_table_source, asp_filter_src, op_table_source, op_filter_src
global stats, aspects, tabs, lexicons_dropdown
stats = pd.DataFrame(columns=["Quantity", "Score"])
aspects = pd.Series([])
def new_col_data_src():
return ColumnDataSource({"file_contents": [], "file_name": []})
large_text = HTMLTemplateFormatter(template="""<div><%= value %></div>""")
def data_column(title):
return TableColumn(field=title,
title='<span class="header">' + title + "</span>",
formatter=large_text)
asp_table_columns = [
data_column("Term"),
data_column("Alias1"),
data_column("Alias2"),
data_column("Alias3"),
]
op_table_columns = [
data_column("Term"),
data_column("Score"),
data_column("Polarity")
]
asp_table_source = empty_table("Term", "Alias1", "Alias2", "Alias3")
asp_filter_src = empty_table("Term", "Alias1", "Alias2", "Alias3")
asp_src = new_col_data_src()
op_table_source = empty_table("Term", "Score", "Polarity", "Polarity")
op_filter_src = empty_table("Term", "Score", "Polarity", "Polarity")
op_src = new_col_data_src()
asp_table = DataTable(
source=asp_table_source,
selectable="checkbox",
columns=asp_table_columns,
editable=True,
width=600,
height=500,
)
op_table = DataTable(
source=op_table_source,
selectable="checkbox",
columns=op_table_columns,
editable=True,
width=600,
height=500,
)
asp_examples_box = _create_examples_table()
op_examples_box = _create_examples_table()
asp_layout = layout([[asp_table, asp_examples_box]])
op_layout = layout([[op_table, op_examples_box]])
asp_tab = Panel(child=asp_layout, title="Aspect Lexicon")
op_tab = Panel(child=op_layout, title="Opinion Lexicon")
tabs = Tabs(tabs=[asp_tab, op_tab], width=700, css_classes=["mytab"])
lexicons_menu = [("Open", "open"), ("Save", "save")]
lexicons_dropdown = Dropdown(
label="Edit Lexicons",
button_type="success",
menu=lexicons_menu,
width=140,
height=31,
css_classes=["mybutton"],
)
train_menu = [("Parsed Data", "parsed"), ("Raw Data", "raw")]
train_dropdown = Dropdown(
label="Extract Lexicons",
button_type="success",
menu=train_menu,
width=162,
height=31,
css_classes=["mybutton"],
)
inference_menu = [("Parsed Data", "parsed"), ("Raw Data", "raw")]
inference_dropdown = Dropdown(
label="Classify",
button_type="success",
menu=inference_menu,
width=140,
height=31,
css_classes=["mybutton"],
)
text_status = TextInput(value="Select training data",
title="Train Run Status:",
css_classes=["statusText"])
text_status.visible = False
train_src = new_col_data_src()
infer_src = new_col_data_src()
with open(join(SOLUTION_DIR, "dropdown.js")) as f:
args = dict(
clicked=lexicons_dropdown,
asp_filter=asp_filter_src,
op_filter=op_filter_src,
asp_src=asp_src,
op_src=op_src,
tabs=tabs,
text_status=text_status,
train_src=train_src,
infer_src=infer_src,
train_clicked=train_dropdown,
infer_clicked=inference_dropdown,
opinion_lex_generic="",
)
code = f.read()
args["train_clicked"] = train_dropdown
train_dropdown.js_on_change("value", CustomJS(args=args, code=code))
args["train_clicked"] = inference_dropdown
inference_dropdown.js_on_change("value", CustomJS(args=args, code=code))
args["clicked"] = lexicons_dropdown
lexicons_dropdown.js_on_change("value", CustomJS(args=args, code=code))
def update_filter_source(table_source, filter_source):
df = table_source.to_df()
sel_inx = sorted(table_source.selected.indices)
df = df.iloc[sel_inx, 1:]
new_source = ColumnDataSource(df)
filter_source.data = new_source.data
def update_examples_box(data, examples_box, old, new):
examples_box.source.data = {"Examples": []}
unselected = list(set(old) - set(new))
selected = list(set(new) - set(old))
if len(selected) <= 1 and len(unselected) <= 1:
examples_box.source.data.update({
"Examples":
[str(data.iloc[unselected[0], i])
for i in range(4, 24)] if len(unselected) != 0 else
[str(data.iloc[selected[0], i]) for i in range(4, 24)]
})
def asp_selected_change(_, old, new):
global asp_filter_src, asp_table_source, aspects_data
update_filter_source(asp_table_source, asp_filter_src)
update_examples_box(aspects_data, asp_examples_box, old, new)
def op_selected_change(_, old, new):
global op_filter_src, op_table_source, opinions_data
update_filter_source(op_table_source, op_filter_src)
update_examples_box(opinions_data, op_examples_box, old, new)
def read_csv(file_src, headers=False, index_cols=False, readCSV=True):
if readCSV:
raw_contents = file_src.data["file_contents"][0]
if len(raw_contents.split(",")) == 1:
b64_contents = raw_contents
else:
# remove the prefix that JS adds
b64_contents = raw_contents.split(",", 1)[1]
file_contents = base64.b64decode(b64_contents)
return pd.read_csv(
io.BytesIO(file_contents),
encoding="ISO-8859-1",
keep_default_na=False,
na_values={None},
engine="python",
index_col=index_cols,
header=0 if headers else None,
)
return file_src
def read_parsed_files(file_content, file_name):
try:
# remove the prefix that JS adds
b64_contents = file_content.split(",", 1)[1]
file_content = base64.b64decode(b64_contents)
with open(SENTIMENT_OUT / file_name, "w") as json_file:
data_dict = json.loads(file_content.decode("utf-8"))
json.dump(data_dict, json_file)
except Exception as e:
print(str(e))
# pylint: disable=unused-argument
def train_file_callback(attr, old, new):
global train_data
SENTIMENT_OUT.mkdir(parents=True, exist_ok=True)
train = TrainSentiment(parse=True, rerank_model=None)
if len(train_src.data["file_contents"]) == 1:
train_data = read_csv(train_src, index_cols=0)
file_name = train_src.data["file_name"][0]
raw_data_path = SENTIMENT_OUT / file_name
train_data.to_csv(raw_data_path, header=False)
print("Running_SentimentTraining on data...")
train.run(data=raw_data_path)
else:
f_contents = train_src.data["file_contents"]
f_names = train_src.data["file_name"]
raw_data_path = SENTIMENT_OUT / train_src.data["file_name"][
0].split("/")[0]
if not os.path.exists(raw_data_path):
os.makedirs(raw_data_path)
for f_content, f_name in zip(f_contents, f_names):
read_parsed_files(f_content, f_name)
print("Running_SentimentTraining on data...")
train.run(parsed_data=raw_data_path)
text_status.value = "Lexicon extraction completed"
with io.open(AcquireTerms.acquired_aspect_terms_path, "r") as fp:
aspect_data_csv = fp.read()
file_data = base64.b64encode(str.encode(aspect_data_csv))
file_data = file_data.decode("utf-8")
asp_src.data = {
"file_contents": [file_data],
"file_name": ["nameFile.csv"]
}
out_path = LEXICONS_OUT / "generated_opinion_lex_reranked.csv"
with io.open(out_path, "r") as fp:
opinion_data_csv = fp.read()
file_data = base64.b64encode(str.encode(opinion_data_csv))
file_data = file_data.decode("utf-8")
op_src.data = {
"file_contents": [file_data],
"file_name": ["nameFile.csv"]
}
def show_analysis() -> None:
global stats, aspects, plot, source, tabs
plot, source = _create_plot()
events_table = _create_events_table()
# pylint: disable=unused-argument
def _events_handler(attr, old, new):
_update_events(events_table, events_type.active)
# Toggle display of in-domain / All aspect mentions
events_type = RadioButtonGroup(
labels=["All Events", "In-Domain Events"], active=0)
analysis_layout = layout([[plot], [events_table]])
# events_type display toggle disabled
# analysis_layout = layout([[plot],[events_type],[events_table]])
analysis_tab = Panel(child=analysis_layout, title="Analysis")
tabs.tabs.insert(2, analysis_tab)
tabs.active = 2
events_type.on_change("active", _events_handler)
source.selected.on_change("indices", _events_handler) # pylint: disable=no-member
# pylint: disable=unused-argument
def infer_file_callback(attr, old, new):
# run inference on input data and current aspect/opinion lexicons in view
global infer_data, stats, aspects
SENTIMENT_OUT.mkdir(parents=True, exist_ok=True)
df_aspect = pd.DataFrame.from_dict(asp_filter_src.data)
aspect_col_list = ["Term", "Alias1", "Alias2", "Alias3"]
df_aspect = df_aspect[aspect_col_list]
df_aspect.to_csv(SENTIMENT_OUT / "aspects.csv",
index=False,
na_rep="NaN")
df_opinion = pd.DataFrame.from_dict(op_filter_src.data)
opinion_col_list = ["Term", "Score", "Polarity", "isAcquired"]
df_opinion = df_opinion[opinion_col_list]
df_opinion.to_csv(SENTIMENT_OUT / "opinions.csv",
index=False,
na_rep="NaN")
solution = SentimentSolution()
if len(infer_src.data["file_contents"]) == 1:
infer_data = read_csv(infer_src, index_cols=0)
file_name = infer_src.data["file_name"][0]
raw_data_path = SENTIMENT_OUT / file_name
infer_data.to_csv(raw_data_path, header=False)
print("Running_SentimentInference on data...")
text_status.value = "Running classification on data..."
stats = solution.run(
data=raw_data_path,
aspect_lex=SENTIMENT_OUT / "aspects.csv",
opinion_lex=SENTIMENT_OUT / "opinions.csv",
)
else:
f_contents = infer_src.data["file_contents"]
f_names = infer_src.data["file_name"]
raw_data_path = SENTIMENT_OUT / infer_src.data["file_name"][
0].split("/")[0]
if not os.path.exists(raw_data_path):
os.makedirs(raw_data_path)
for f_content, f_name in zip(f_contents, f_names):
read_parsed_files(f_content, f_name)
print("Running_SentimentInference on data...")
text_status.value = "Running classification on data..."
stats = solution.run(
parsed_data=raw_data_path,
aspect_lex=SENTIMENT_OUT / "aspects.csv",
opinion_lex=SENTIMENT_OUT / "opinions.csv",
)
aspects = pd.read_csv(SENTIMENT_OUT / "aspects.csv",
encoding="utf-8")["Term"]
text_status.value = "Classification completed"
show_analysis()
# pylint: disable=unused-argument
def asp_file_callback(attr, old, new):
global aspects_data, asp_table_source
aspects_data = read_csv(asp_src, headers=True)
# Replaces None values by empty string
aspects_data = aspects_data.fillna("")
new_source = ColumnDataSource(aspects_data)
asp_table_source.data = new_source.data
asp_table_source.selected.indices = list(range(len(aspects_data)))
# pylint: disable=unused-argument
def op_file_callback(attr, old, new):
global opinions_data, op_table_source, lexicons_dropdown, df_opinion_generic
df = read_csv(op_src, headers=True)
# Replaces None values by empty string
df = df.fillna("")
# Placeholder for generic opinion lexicons from the given csv file
df_opinion_generic = df[df["isAcquired"] == "N"]
# Update the argument value for the callback customJS
lexicons_dropdown.js_property_callbacks.get("change:value")[0].args[
"opinion_lex_generic"] = df_opinion_generic.to_dict(orient="list")
opinions_data = df[df["isAcquired"] == "Y"]
new_source = ColumnDataSource(opinions_data)
op_table_source.data = new_source.data
op_table_source.selected.indices = list(range(len(opinions_data)))
# pylint: disable=unused-argument
def txt_status_callback(attr, old, new):
print("Previous label: " + old)
print("Updated label: " + new)
text_status.on_change("value", txt_status_callback)
asp_src.on_change("data", asp_file_callback)
# pylint: disable=no-member
asp_table_source.selected.on_change("indices", asp_selected_change)
op_src.on_change("data", op_file_callback)
op_table_source.selected.on_change("indices", op_selected_change) # pylint: disable=no-member
train_src.on_change("data", train_file_callback)
infer_src.on_change("data", infer_file_callback)
return layout(
[[_create_header(train_dropdown, inference_dropdown, text_status)],
[tabs]])
def date_of_file(filename):
day = filename[11:19]
datetime_format = date(int(day[0:4]), int(day[4:6]), int(
day[6:])) + timedelta(days=1)
return datetime_format
with open("two_reds/column_names.txt") as f:
content = f.readlines()
content = content[0].split(',')
text_dsn = TextInput(title="DSN:")
dsn_button = Button(label="Update DSN", button_type="success")
curdoc().add_root(row(text_dsn))
def dsn_text_update(attrname, old, new):
all_dates = get_file_names(text_dsn.value)
most_recent = all_dates[-1] # is it already sorted???
df = dsn_date(most_recent)
# df must not be empty
p, source, source1, source2, source3, source4, source5 = plot_data(
df, text_dsn.value)
text_dsn.remove_on_change('value', dsn_text_update)
set_up_widgets(p, source, source1, source2, source3, source4, source5, df,
all_dates, text_dsn)
text_dsn.on_change('value', dsn_text_update)
u_val = u_fun(x_val, y_val)
v_val = v_fun(x_val, y_val)
return x_val, y_val, u_val, v_val, hx
# initialize controls
# text input for input of the vector function [fx(x,y),fy(x,y)]
u_input = TextInput(value=curveintegral_settings.sample_functions[
curveintegral_settings.init_fun_key][0],
title="fx(x,y):")
v_input = TextInput(value=curveintegral_settings.sample_functions[
curveintegral_settings.init_fun_key][1],
title="fy(x,y):")
u_input.on_change('value', function_change)
v_input.on_change('value', function_change)
# text input for input of the parametrized curve [cx(t),cy(t)]
cx_input = TextInput(value=curveintegral_settings.sample_curves[
curveintegral_settings.init_curve_key][0],
title="cx(t):")
cy_input = TextInput(value=curveintegral_settings.sample_curves[
curveintegral_settings.init_curve_key][1],
title="cy(t):")
# slider controlling the parameter t
parameter_input = Slider(title="t",
value=curveintegral_settings.parameter_input_init,
start=curveintegral_settings.parameter_min,
end=curveintegral_settings.parameter_max,
step=curveintegral_settings.parameter_step)
def set_up_widgets(p, source, source1, source2, source3, source4, source5, df,
all_dates, text_dsn):
'''Set up widgets needed after an initial dsn is entered'''
dsn = text_dsn.value
# Set up widgets
text_title = TextInput(title="Title:", value="{} Data".format(dsn))
text_save = TextInput(title="Save As:", value=dsn)
max_for_dsn = df.timestamp.max()
min_day = date_of_file(
all_dates[0]) # works if sorted, needs to be formatted...
max_day = date_of_file(all_dates[-1])
plus_one = max_for_dsn + timedelta(days=1)
calendar = DatePicker(title="Day:",
value=date(plus_one.year, plus_one.month,
plus_one.day),
max_date=max_day,
min_date=min_day)
button = Button(label="Update", button_type="success")
columns = [
TableColumn(field="day",
title="Date",
formatter=DateFormatter(format="%m/%d/%Y")),
TableColumn(field="hr",
title="Avg HR",
formatter=NumberFormatter(format="0.0")),
TableColumn(field="o2",
title="Avg O2",
formatter=NumberFormatter(format="0.0")),
TableColumn(field="temp",
title="Avg Temp",
formatter=NumberFormatter(format="0.0"))
]
# table_title = Div(text="""Daily Averages:""", width=200)
# daily_avg = get_daily_avgs(df_full)
# data = {
# 'day' : daily_avg.index,
# 'hr' : daily_avg.heart_rate_avg,
# 'o2' : daily_avg.oxygen_avg,
# 'temp' : daily_avg.skin_temperature
# }
# table_source = ColumnDataSource(data=data)
# data_table = DataTable(source=table_source, columns=columns, width=280, height=180, index_position=None)
# export_png(data_table, filename="table.png")
# save_table = Button(label='Save Daily Averages Table', button_type="primary")
# Set up callbacks
def update_title(attrname, old, new):
p.title.text = text_title.value
def update_save(attrname, old, new):
p.tools[0].save_name = text_save.value
def update():
text_dsn.value = text_dsn.value.strip(" ") # Get rid of extra space
# Make sure time is valid
update_data(p,
source,
source1,
source2,
source3,
source4,
source5,
df,
text_dsn.value,
all_dates,
date=str(calendar.value))
# Title/save update dsn
text_title.value = text_dsn.value + " Data"
text_save.value = text_dsn.value
def save():
export_png(data_table,
filename="{}_averages.png".format(text_dsn.value))
text_title.on_change('value', update_title)
text_save.on_change('value', update_save)
button.on_click(update)
button.js_on_click(CustomJS(args=dict(p=p), code="""p.reset.emit()"""))
# save_table.on_click(save)
# Set up layouts and add to document
inputs = widgetbox(text_title, text_save, calendar,
button) #, table_title, data_table, save_table)
curdoc().add_root(row(inputs, p, width=1300))
P2_experiment.append(float(j[3]))
P0_experiment_source.data = dict(x=t, y=P0_experiment)
P1_experiment_source.data = dict(x=t, y=P1_experiment)
P2_experiment_source.data = dict(x=t, y=P2_experiment)
except:
print "could not open file, doesnt exist"
file_name.on_change('value', update_exp_data)
path.on_change('value', update_exp_data)
def update_data(attrname, old, new):
global t
delta = 2 * math.pi * 17.5 * 10 ** 3
# Get the current slider values
eta = etta.value
pi_time = Pi_time.value
nbar = n_bar.value
omega = math.pi / (pi_time * 10 ** (-6))
lamda = 1.0*(eta*omega)**2/delta
kai = 1.0*(eta*omega/delta)**2
class SlidersApp(HBox):
"""The main app, where parameters and controllers are defined."""
extra_generated_classes = [["SlidersApp", "SlidersApp", "HBox"]]
## read the BTC price data
raw_price = pd.read_csv(os.path.join(input_folder, "price.csv"), names=['time', 'price'],
index_col='time', parse_dates=[0],
date_parser=lambda x: datetime.strptime(x, "%Y-%m-%dT%H:%M:%S"))
raw_price['time_index'] = raw_price.index
raw_price.drop_duplicates(subset='time_index', take_last=True, inplace=True)
del raw_price['time_index']
## downsample to 12h data
price_data = pd.DataFrame(raw_price.resample('12h', how='ohlc').ix[:, 3])
price_data.columns = ['price']
## data source
source = Instance(ColumnDataSource)
## inputs
inputs = Instance(VBoxForm)
text = Instance(TextInput)
threshold = Instance(Slider)
## plots
plot = Instance(Plot)
@classmethod
def create(cls):
"""One-time creation of app's objects.
This function is called once, and is responsible for
creating all objects (plots, datasources, etc)
"""
## create the obj of the app
obj = cls()
obj.inputs = VBoxForm()
## create input widgets
obj.make_inputs()
## create data source
obj.make_source()
##
obj.make_plots()
## layout
obj.set_children()
return obj
def make_inputs(self):
self.text = TextInput(
title="Title", name='title', value='BTC chart with news tag'
)
self.threshold = Slider(
title="Threshold", name='threshold',
value=0.0, start=-1.0, end=1.0, step=0.1
)
def make_source(self):
self.source = ColumnDataSource(data=self.df)
def make_plots(self):
toolset = "crosshair,pan,reset,resize,save,wheel_zoom"
## fixed time index limit (epoch)
start_time = (datetime.strptime("1/1/12 16:30", "%d/%m/%y %H:%M") - datetime(1970,1,1)).total_seconds()*1000
end_time = (datetime.strptime("1/5/15 16:30", "%d/%m/%y %H:%M") - datetime(1970,1,1)).total_seconds()*1000
## Generate a figure container
plot = figure(title_text_font_size="12pt",
plot_height=600,
plot_width=1100,
tools=toolset,
title=self.text.value,
# title="BTC chart with news",
x_axis_type="datetime",
x_range=[start_time, end_time],
y_range=[0, 1300]
)
plot.below[0].formatter.formats = dict(years=['%Y'], months=['%b %Y'], days=['%d %b %Y'])
## the price line plot
plot.line(
self.price_data.index, self.price_data['price'],
# color='#A6CEE3',
legend='BTC Price'
)
## the news tag plot
plot.circle('time', 'price',
source=self.source,
fill_color=self.source.data['color'],
legend="News",
size=8,
)
self.plot = plot
def set_children(self):
self.children = [self.inputs, self.plot]
self.inputs.children = [self.text, self.threshold]
def setup_events(self):
"""Attaches the on_change event to the value property of the widget.
The callback is set to the input_change method of this app.
"""
super(SlidersApp, self).setup_events()
# if not self.text:
# return
# Text box event registration
if self.text:
self.text.on_change('value', self, 'input_change')
# Slider event registration
if self.threshold:
self.threshold.on_change('value', self, 'input_change')
def input_change(self, obj, attrname, old, new):
"""Executes whenever the input form changes.
It is responsible for updating the plot, or anything else you want.
Args:
obj : the object that changed
attrname : the attr that changed
old : old value of attr
new : new value of attr
"""
self.update_data()
curdoc().add(self)
def update_data(self):
"""Called each time that any watched property changes.
This updates the sin wave data with the most recent values of the
sliders. This is stored as two numpy arrays in a dict into the app's
data source property.
"""
self.make_source()
self.make_plots()
self.set_children()
# x = news_price.index
# y = news_price['price']
# logging.debug(
# "Threshold: %f" % self.threshold.value
# )
## plug back to source of the obj
# self.source.data = dict(x=x, y=y, color=news_price['color'])
@property
def df(self):
return get_data(self.threshold.value)
current = df[df.content.str.contains(
search_text.value.strip()) == True] # df[""]
source.data = {
'group_name': current.group_name,
'group_user_name': current.group_user_name,
'content': current.content,
'user_img': current.user_img,
'createdAt': current.createdAt,
}
search_text = TextInput(title="search")
#search_text_val = search_text.value.strip()
#slider = Slider(title="开始时间", start=10000, end=250000, value=150000, step=1000)
#slider.on_change('value', lambda attr, old, new: update())
search_text.on_change('value', lambda attr, old, new: update())
'''
button = Button(label=u"搜索", button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=open(join(dirname(__file__), "download.js")).read())
'''
columns = [
TableColumn(field="group_name", title="Group Name"),
TableColumn(field="group_user_name", title="Group User Name"),
TableColumn(field="content", title="Content"),
TableColumn(field="user_img", title="User Img"),
TableColumn(field="createdAt", title="createdAt"),
#TableColumn(field="salary", title="Income", formatter=NumberFormatter(format="$0,0.00")),
]
])
df = df[1:]
df['failure'] = [1 if b == 1 or b == '1' else 0 for b in df.failure]
y = df.failure
x = df.drop('failure', axis=1)
x_train_original, x_test_original, y_train_original, y_test_original = train_test_split(
x, y, test_size=N)
clf = svm.LinearSVC(loss='l2', penalty='l1', dual=False)
clf.fit(x_train_original, y_train_original)
predictions = clf.predict(x_test_original)
print("Accuracy =", accuracy_score(y_test_original, predictions))
print(np.unique(predictions))
tn, fp, fn, tp = confusion_matrix(y_test_original, predictions).ravel()
print("True Negative:", tn)
print("False Positive:", fp)
print("False Negative:", fn)
print("True Positive:", tp)
#newdict = {'values':[tn,fp,tn,fn], 'names':['TP','FP','TN',"FN"]
newdict = {'values': ['TP', 'FN', 'TN', 'FP'], 'timing': [tp, fp, fn, tn]}
data.update(newdict)
print(data)
input.on_change('value', update_points)
layout = column(row(input, width=400), row(bar))
#show(layout)
curdoc().add_root(layout)
def main():
df = pd.read_csv("nlp_entities.csv")
per_df = pd.read_csv("Person.csv")
pla_df = pd.read_csv("Place.csv")
org_df = pd.read_csv("Org.csv")
entities = ["Person", "Place", "Org"]
relations = [["Person_Org","Person_Place","Person_Person"],["Place_Person","Place_Org","Place_Place"],["Org_Person","Org_Place","Org_Org"]]
dfs = [per_df, pla_df,org_df]
button_group = RadioButtonGroup(labels=entities, active=0)
slider = Slider(title="by Index", value=0, start=0, end=len(per_df.index)-1, step=1)
text = TextInput(title="by Name:", value='')
select1 = Select(title="y axis:", value="Person", options=["Person", "Place", "Org"])
files = per_df.iloc[0]["Documents"]
d = []
y_n = []
headlines = []
datestr = []
if files == "None":
files = []
else:
files = ast.literal_eval(files)
for f in files:
v_d = df.loc[df["filename"] == f]["date"].values
datestr.append(v_d[0])
d.append(datetime.strptime(v_d[0],"%m/%d/%Y"))
v_p = df.loc[df["filename"] == f]["Person"].values
y_n.append(len(ast.literal_eval(v_p[0])))
v_h = df.loc[df["filename"] == f]["headlines"].values
headlines.append(v_h[0])
dates = np.array(d)
n = np.array(y_n)
headlines = np.array(headlines)
name = per_df.iloc[0]["Person"]
# create the timeline plot for the searched entity
s_source = ColumnDataSource(data=dict(x = dates, y = n, f = files, h =headlines, d = datestr))
TOOLS = "box_select,pan,box_zoom,reset"
f1 = figure(tools=TOOLS, plot_width=600, plot_height=450, title="Timeline for '" + name + "'",
x_axis_type='datetime', x_axis_label='Time', y_range=[-50,250],
y_axis_label='number in total', output_backend="webgl")
c1 = f1.circle('x', 'y', source=s_source, size=10, color="red", alpha=0.5)
hover1 = HoverTool(
tooltips=[
("document: ", "@f"),
("date", "@d"),
("headline: ", "@h")
]
)
f1.add_tools(hover1)
f1.x_range = Range1d(datetime.strptime('01/01/2002',"%m/%d/%Y"),datetime.strptime('12/31/2004',"%m/%d/%Y"))
def update_y(attr,old,new):
y = select1.value
new_y = []
for f in s_source.data["f"]:
v = df.loc[df["filename"] == f][y].values
new_y.append(len(ast.literal_eval(v[0])))
new_y = np.array(new_y)
s_source.data["y"] = new_y
return
select1.on_change("value",update_y)
# create interactive network graph for timeline plot
s1 = ColumnDataSource(data=dict(xs=[], ys=[]))
s2 = ColumnDataSource(data=dict(vx=[], vy=[], labels=[], color=[]))
n1 = figure(plot_width=500, plot_height=500, x_axis_type=None, y_axis_type=None,
outline_line_color=None, tools="pan,box_zoom,reset,save",title="Related Entities of Selected Files",
output_backend="webgl")
n1.multi_line('xs', 'ys', line_color="blue", source=s1, alpha=0.3)
c2 = n1.circle('vx', 'vy', size=20, line_color="black", fill_color='color', source=s2, alpha=0.5)
n1.text('vx', 'vy', text='labels', text_color="black", text_font_size="10px", text_align="center",
text_baseline="middle", source=s2)
# update network graph when the files are selected
def update_network1(attr, old, new):
inds = new['1d']['indices']
nodes = []
edges = []
t_t_edges = []
selected = []
if inds == []:
s1.data = dict(xs=[], ys=[])
s2.data = dict(vx=[], vy=[], labels=[], color=[])
return
for i in inds:
f = s_source.data["f"][i]
v = df.loc[df["filename"] == f]["all_nodes"].values
n = ast.literal_eval(v[0])
selected.append(f)
nodes = list(set(nodes).union(n))
e = ast.literal_eval(df.loc[df["filename"] == f]["all_edges"].values[0])
edges = list(set(edges).union(e))
t_t_e = ast.literal_eval(df.loc[df["filename"] == f]["file_neighbors_edge"].values[0])
t_t_edges = list(set(t_t_edges).union(t_t_e))
t_t = list(itertools.combinations(selected, 2))
for t in t_t:
if (t in t_t_edges) or ((t[1],t[0])in t_t_edges):
edges.append(t)
new_dict = create_graph(nodes, edges, selected)
s1.data = new_dict["s1"]
s2.data = new_dict["s2"]
return
c1.data_source.on_change("selected", update_network1)
# create person-place network graph
v1 = per_df.at[0, "Person_Place"]
edges_1 = ast.literal_eval(v1)
nodes_1 = [name]
for e in edges_1:
nodes_1.append(e[1])
g1 = create_graph(nodes_1,edges_1,[name])
s3 = ColumnDataSource(data=g1["s1"])
s4 = ColumnDataSource(data=g1["s2"])
n2 = figure(plot_width=400, plot_height=400, x_axis_type=None, y_axis_type=None,
outline_line_color=None, tools="pan,box_zoom,reset,save", title="Person_Place Relationships for Person: " + name,
output_backend="webgl")
n2.multi_line('xs', 'ys', line_color="blue", source=s3, alpha=0.3)
c3 = n2.circle('vx', 'vy', size=20, line_color="black", fill_color='color', source=s4, alpha=0.5)
n2.text('vx', 'vy', text='labels', text_color="black", text_font_size="8px", text_align="center",
text_baseline="middle", source=s4)
# create person-org network graph
v2 = per_df.at[0, "Person_Org"]
edges_2 = ast.literal_eval(v2)
nodes_2 = [name]
for e in edges_2:
nodes_2.append(e[1])
g2 = create_graph(nodes_2, edges_2, [name])
s5 = ColumnDataSource(data=g2["s1"])
s6 = ColumnDataSource(data=g2["s2"])
n3 = figure(plot_width=400, plot_height=400, x_axis_type=None, y_axis_type=None,
outline_line_color=None, tools="pan,box_zoom,reset,save", title="Person_Org Relationships for Person: " + name,
output_backend="webgl")
n3.multi_line('xs', 'ys', line_color="blue", source=s5, alpha=0.3)
c4 = n3.circle('vx', 'vy', size=20, line_color="black", fill_color='color', source=s6, alpha=0.5)
n3.text('vx', 'vy', text='labels', text_color="black", text_font_size="8px", text_align="center",
text_baseline="middle", source=s6)
# create person-org network graph
v3 = per_df.at[0, "Person_Person"]
edges_3 = ast.literal_eval(v3)
nodes_3 = [name]
for e in edges_3:
nodes_3.append(e[1])
g3 = create_graph(nodes_3, edges_3, [name])
s7 = ColumnDataSource(data=g3["s1"])
s8 = ColumnDataSource(data=g3["s2"])
n4 = figure(plot_width=400, plot_height=400, x_axis_type=None, y_axis_type=None,
outline_line_color=None, tools="pan,box_zoom,reset,save",
title="Person_Person Relationships for Person: " + name,
output_backend="webgl")
n4.multi_line('xs', 'ys', line_color="blue", source=s7, alpha=0.3)
c5 = n4.circle('vx', 'vy', size=20, line_color="black", fill_color='color', source=s8, alpha=0.5)
n4.text('vx', 'vy', text='labels', text_color="black", text_font_size="8px", text_align="center",
text_baseline="middle", source=s8)
#update visualizations when button group changes
def button_group_update(attr, old, new):
b = button_group.active
entity = entities[b]
d = dfs[b]
t = text.value
#change slider
slider.end = len(d.index) - 1
if (slider.value>slider.end):
slider.value = slider.end
if t == '':
slider_update(attr, old, new)
else:
text_update(attr, old, new)
button_group.on_change("active", button_group_update)
def slider_update(attr,old,new):
text.value = ''
b = button_group.active
entity = entities[b]
d = dfs[b]
s = slider.value
# clear the visualizations
sources = [[s1, s2], [s3, s4], [s5, s6], [s7, s8]]
networks = [n1, n2, n3, n4]
s_source.data = dict(x=[], y=[], f=[], h=[], d=[])
for i in range(4):
sources[i][0].data = dict(xs=[], ys=[])
sources[i][1].data = dict(vx=[], vy=[], labels=[], color=[])
name = d.iloc[s][entity]
# update cooccurence relationships
for i in range(3):
v = d.at[s, relations[b][i]]
edges = ast.literal_eval(v)
nodes = [name]
for e in edges:
nodes.append(e[1])
g = create_graph(nodes, edges, [name])
sources[i + 1][0].data = g["s1"]
sources[i + 1][1].data = g["s2"]
networks[i + 1].title.text = relations[b][i] + " Relationships for " + entity + ": " + name
#update timeline plot
files = d.iloc[s]["Documents"]
dates = []
y_n = []
headlines = []
datestr = []
select = select1.value
if files == "None":
files = []
f1.title.text = "No files found for '" + name + "'"
else:
files = ast.literal_eval(files)
for f in files:
v_d = df.loc[df["filename"] == f]["date"].values
datestr.append(v_d[0])
dates.append(datetime.strptime(v_d[0], "%m/%d/%Y"))
v_p = df.loc[df["filename"] == f][select].values
y_n.append(len(ast.literal_eval(v_p[0])))
v_h = df.loc[df["filename"] == f]["headlines"].values
headlines.append(v_h[0])
dates = np.array(dates)
n = np.array(y_n)
headlines = np.array(headlines)
s_source.data = dict(x=dates, y=n, f=files, h=headlines, d=datestr)
f1.title.text = "Timeline for '" + name + "'"
return
slider.on_change("value", slider_update)
def text_update(attr,old,new):
b = button_group.active
entity = entities[b]
print(entity)
d = dfs[b]
t = text.value
slider.value = 0
# clear the visualizations
sources = [[s1, s2], [s3, s4], [s5, s6], [s7, s8]]
networks = [n1, n2, n3, n4]
s_source.data = dict(x=[], y=[], f=[], h=[], d=[])
for i in range(4):
sources[i][0].data = dict(xs=[], ys=[])
sources[i][1].data = dict(vx=[], vy=[], labels=[], color=[])
name = t
if t in np.array(d[entity]):
s = d[d[entity] == t].index.tolist()[0]
else:
f1.title.text = "No such " + entity + " in the datasets"
for x in networks:
x.title.text = "No such " + entity + " in the datasets"
return
# update cooccurence relationships
for i in range(3):
v = d.at[s, relations[b][i]]
edges = ast.literal_eval(v)
nodes = [name]
for e in edges:
nodes.append(e[1])
g = create_graph(nodes, edges, [name])
sources[i + 1][0].data = g["s1"]
sources[i + 1][1].data = g["s2"]
networks[i + 1].title.text = relations[b][i] + " Relationships for " + entity + ": " + name
# update timeline plot
files = d.iloc[s]["Documents"]
dates = []
y_n = []
headlines = []
datestr = []
select = select1.value
if files == "None":
files = []
f1.title.text = "No files found for '" + name + "'"
else:
files = ast.literal_eval(files)
for f in files:
v_d = df.loc[df["filename"] == f]["date"].values
datestr.append(v_d[0])
dates.append(datetime.strptime(v_d[0], "%m/%d/%Y"))
v_p = df.loc[df["filename"] == f][select].values
y_n.append(len(ast.literal_eval(v_p[0])))
v_h = df.loc[df["filename"] == f]["headlines"].values
headlines.append(v_h[0])
dates = np.array(dates)
n = np.array(y_n)
headlines = np.array(headlines)
s_source.data = dict(x=dates, y=n, f=files, h=headlines, d=datestr)
f1.title.text = "Timeline for '" + name + "'"
return
text.on_change("value",text_update)
widgets = widgetbox(button_group,slider,text)
layout = column(widgets,row(n2,n3,n4),select1,row(f1,n1))
curdoc().add_root(layout)
show(layout)
clf = KNeighborsClassifier(n_neighbors=Kn.value, p=Kd.value)
n_feats = f.shape[1]
y = []
for i in range(n_feats):
b_trs = b_tr.drop([i], axis=1)
b_tss = b_ts.drop([i], axis=1)
clf.fit(b_trs, y_tr)
y.append(knn.score(b_tss, y_ts))
source.data = dict(x=x, y=y)
controls = [Test, Plot, model, Kn, Kd, Rn, Rmd, Rmf]
for control in controls[1:len(controls)]:
control.on_change('value', lambda attr, old, new: update(f))
Test.on_change('value', lambda attr, old, new: call())
inputs = column(*controls, width=320, height=1000)
inputs.sizing_mode = "fixed"
l = layout([
[desc],
[inputs, plot],
], sizing_mode="scale_both")
#plot = figure(y_range=(0, 2),plot_width=400, plot_height=400)
#plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
#layout=column(plot,n)
#n.on_change('value', lambda attr, old, new: call())
call()
update(f)
curdoc().add_root(l)
)
T_select = Slider(start=0.0,
end=1000.0,
step=1.0,
value=10.0,
title='T'
)
r0_select = TextInput(value='1.00', title='r0')
drdt0_select = TextInput(value='0.00', title='dr/dt_0')
theta0_select = TextInput(value='0.00', title='theta0')
dthetadt0_select = TextInput(value='1.00', title='dtheta/dt_0')
# Update the plot when yields are changed
mass_select.on_change('value', update)
mass_attractive_center_select.on_change('value', update)
mass_attractive_center_select.on_change('value', update)
exponent_select.on_change('value', update)
n_steps_ode_select.on_change('value', update)
solver_method_select.on_change('value', update)
T_select.on_change('value', update)
r0_select.on_change('value', update)
drdt0_select.on_change('value', update)
theta0_select.on_change('value', update)
dthetadt0_select.on_change('value', update)
div = Div(text='', width=150, height=50)
size=25,
color="red",
alpha=2.0)
p = gridplot([[p1, p2, p6], [p3, p4, p5], [p7]])
return row(
p,
column(widgetbox(button),
widgetbox(table_values(ulirg, x_plot, y_plot)), widgetbox(pre)))
def update(attr, old, new):
layout.children[1] = row(create_figure())
ulirg = Select(title='Select ULIRG:', value="1", options=ulirg_num)
ulirg.on_change('value', update)
x_plot = TextInput(value="60", title="x_value:")
x_plot.on_change('value', update)
y_plot = TextInput(value="75", title="y_value:")
y_plot.on_change('value', update)
controls = column(widgetbox([ulirg, x_plot, y_plot]), width=250)
layout = row(controls, create_figure())
curdoc().add_root(layout)
curdoc().title = "LYA ULIRG plotting "
scalemax = Slider(title="Singles Scale maximum",
value=10.0,
start=-100.0,
end=100.0,
step=1)
statsA = Paragraph(text="100", width=400, height=40)
statsB = Paragraph(text="100", width=400, height=40)
# Set up callbacks
def send_command(attrname, old, new):
#TODO turn into a raw command area for sending any device command
plot.title.text = command.value
command.on_change('value', send_command)
last_time = time.time()
def update_data():
# TODO: store data in a stream for charting vs time
global last_time
T = time.time() - last_time
last_time = time.time()
#print(T)
# get data:
if realInstrument:
data = inst.get_data("CH2") # select channel later on
class StockApp(VBox):
extra_generated_classes = [["StockApp", "StockApp", "VBox"]]
jsmodel = "VBox"
Y = Instance(ColumnDataSource)
# plots
hist1 = Instance(Plot)
hist2 = Instance(Plot)
hist3 = Instance(Plot)
# data source
source = Instance(ColumnDataSource)
risk_source = Instance(ColumnDataSource)
# layout boxes
mainrow = Instance(HBox)
ticker1_box = Instance(HBox)
ticker2_box = Instance(HBox)
ticker3_box = Instance(HBox)
ticker4_box = Instance(HBox)
ticker5_box = Instance(HBox)
second_row = Instance(HBox)
histrow = Instance(HBox)
# inputs
ticker1 = String(default="1.2*(1.1-x)")
ticker1p = String(default="-1.2")
ticker2 = String(default="4.0")
ticker2p = String(default="0.0")
ticker3 = String(default="500")
ticker3_1 = String(default="252")
ticker3_2 = String(default="0.01")
ticker4 = String(default="100")
ticker4_1 = String(default="1.01")
ticker4_2 = String(default="Milstein")
button = String(default="")
ticker1_select = Instance(TextInput)
ticker1p_select = Instance(TextInput)
ticker2_select = Instance(TextInput)
ticker2p_select = Instance(TextInput)
ticker3_select = Instance(TextInput)
ticker3_1_select = Instance(TextInput)
ticker3_2_select = Instance(TextInput)
ticker4_select = Instance(TextInput)
ticker4_1_select = Instance(TextInput)
ticker4_2_select = Instance(Select)
button_select = Instance(TextInput)
input_box = Instance(VBoxForm)
def __init__(self, *args, **kwargs):
super(StockApp, self).__init__(*args, **kwargs)
self._dfs = {}
@classmethod
def create(cls):
"""
This function is called once, and is responsible for
creating all objects (plots, datasources, etc)
"""
# create layout widgets
obj = cls()
obj.mainrow = HBox()
obj.ticker1_box = HBox(width=500)
obj.ticker2_box = HBox(width=500)
obj.ticker3_box = HBox(width=467)
obj.ticker4_box = HBox(width=500)
obj.ticker5_box = HBox(width=500)
obj.second_row = HBox()
obj.histrow = HBox()
obj.input_box = VBoxForm(width=600)
# create input widgets
obj.make_inputs()
# outputs
#obj.make_source()
obj.main_mc(252,500,0.01,'Milstein',1.01)
obj.make_plots()
# layout
obj.set_children()
return obj
def make_inputs(self):
self.ticker1_select = TextInput(
name='ticker1',
title='Drift Function:',
value='1.2*(1.1-x)',
)
self.ticker1p_select = TextInput(
name='ticker1p',
title='Drift Derivative:',
value='-1.2',
)
self.ticker2_select = TextInput(
name='ticker2',
title='Volatility Function:',
value='4.0',
)
self.ticker2p_select = TextInput(
name='ticker2p',
title='Volatility Derivative:',
value='0.0',
)
self.ticker3_select = TextInput(
name='ticker3',
title='Number of Paths:',
value='500'
)
self.ticker3_1_select = TextInput(
name='ticker3_1',
title='Number of Points:',
value='252'
)
self.ticker3_2_select = TextInput(
name='ticker3_2',
title='Time Step:',
value='0.01'
)
self.ticker4_select = TextInput(
name='ticker4',
title='Histogram Line:',
value='100'
)
self.ticker4_1_select = TextInput(
name='ticker4_1',
title='Initial Value:',
value='1.01'
)
self.ticker4_2_select = Select(
name='ticker4_2',
title='MC Scheme:',
value='Milstein',
options=['Euler','Milstein', 'Pred/Corr']
)
self.button_select = TextInput(
name='button',
title='Type any word containing "run" to run Simulation ',
value = ''
)
def make_source(self):
self.source = ColumnDataSource(data=self.Y)
def main_mc(self,num_pts,num_paths, delta_t, method, Y0):
def a(x):
return eval(self.ticker1)
def ap(x):
return eval(self.ticker1p)
def b(x):
return eval(self.ticker2)
def bp(x):
return eval(self.ticker2p)
rpaths = np.random.normal(0, delta_t, size=(num_pts,num_paths))
Y = np.array([[Y0]*num_paths])
dt_vec = np.array([delta_t]*num_paths)
if method == 'Milstein':
for i in xrange(0,num_pts):
tY = Y[-1,:]
dW = rpaths[i,:]
Y = np.vstack([Y, tY + a(tY)*dt_vec + b(tY)*dW + 0.5*b(tY)*bp(tY)*(dW*dW-dt_vec)])
elif method == 'Pred/Corr':
# Predictor corrector method is taken from equation 2.6 in this paper:
# http://www.qfrc.uts.edu.au/research/research_papers/rp222.pdf
rpaths2 = np.random.normal(0, delta_t, size=(num_pts,num_paths))
for i in xrange(0,num_pts):
tY = Y[-1,:]
Ybar = tY + a(tY)*dt_vec + b(tY)*rpaths[i,:]
abar_before = a(tY) - 0.5*b(tY)*bp(tY)
abar_after = a(Ybar) - 0.5*b(Ybar)*bp(Ybar)
Y = np.vstack([Y, tY + 0.5*(abar_before + abar_after)*dt_vec + 0.5*(b(tY)+b(Ybar))*rpaths2[i,:]])
else: # default to Euler Scheme
for i in xrange(0,num_pts):
tY = Y[-1,:]
Y = np.vstack([Y, tY + a(tY)*dt_vec + b(tY)*rpaths[i,:]])
return Y # return simulated paths
def path_plot(self):
num_paths_plot = min(50,int(self.ticker3))
hist_point = int(self.ticker4)
#print 'Hist Point ', hist_point
Y = self.Y.as_matrix()
pltdat = Y[:,0:num_paths_plot]
mY, MY = min(Y[hist_point,:]), max(Y[hist_point,:])
plt.plot(pltdat, alpha=0.1, linewidth=1.8)
sns.tsplot(pltdat.T,err_style='ci_band', ci=[68,95,99], alpha=1, \
linewidth = 2.5, color='indianred')
#sns.tsplot(pltdat.T,err_style='ci_band', ci=[68,95,99,99.99999], alpha=1, \
# linewidth = 2.5, condition='Mean Path', color='indianred')
plt.plot([hist_point, hist_point], [0.99*mY, 1.01*MY], 'k-',label='Time Series Histogram')
plt.xlabel('Time Step')
plt.ylabel('Price')
#plt.legend()
p = mpl.to_bokeh()
p.title = 'Mean Path (Red), MC Paths (Background) and Density Line (Black)'
p.title_text_font_size= str(TITLE_SIZE)+'pt'
return p
def hist_den_plot(self):
Y = self.Y.as_matrix()
hist_point = int(self.ticker4)
delta_t = float(self.ticker3_2)
data = Y[hist_point,:]
sns.distplot(data, color='k', hist_kws={"color":"b"}, norm_hist=True)
#sns.distplot(data, color='k', hist_kws={"color":"b"})
plt.hist(data)
plt.title('Distribution at T = ' + str(np.round(delta_t*hist_point,4)) + ' with Mean: ' +str(np.round(np.mean(data),4)) + ' and Std Dev: ' + str(np.round(np.std(data),4)))
plt.xlabel('Price Bins')
plt.ylabel('Bin Count')
p = mpl.to_bokeh()
p.title_text_font_size= str(TITLE_SIZE)+'pt'
return p
def mc_results(self):
# Compute Monte Carlo results
Y = self.Y.as_matrix()
Y0 = float(self.ticker4_1)
hist_point = int(self.ticker4)
num_paths = int(self.ticker3)
center_point = np.mean(Y[hist_point,:])
stkgrid = np.linspace(0.5*center_point,1.5*center_point,100)
meanlst = np.array([])
stdlst = np.array([])
paylst = np.array([])
for stk in stkgrid:
meanlst = np.append(meanlst, np.mean(payoff(Y[hist_point,:],stk)))
stdlst = np.append(stdlst,np.std(payoff(Y[hist_point,:],stk))/np.sqrt(num_paths))
plt.plot(stkgrid,meanlst+2*stdlst, 'g-')
plt.plot(stkgrid,meanlst-2*stdlst,'g-',label='2-Sig Error')
plt.plot(stkgrid,meanlst+stdlst,'r-')
plt.plot(stkgrid,meanlst-stdlst,'r-',label='1-Sig Error')
plt.plot(stkgrid,meanlst,'b',label='Mean')
plt.title('MC Option Price (Blue) with 1-Sig (Red) and 2-Sig (Green) Errors')
plt.xlabel('Strike')
plt.ylabel('Value')
p = mpl.to_bokeh()
p.title_text_font_size= str(TITLE_SIZE)+'pt'
return p
def hist_plot(self):
histdf = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))
#pltdf = self.source.to_df().set_index('date').dropna()
#qlow, qhigh = mquantiles(pltdf[ticker],prob=[0.01,0.99])
#tdf = pltdf[ticker]
#histdf = tdf[((tdf > qlow) & (tdf < qhigh))]
hist, bins = np.histogram(histdf, bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
start = bins.min()
end = bins.max()
top = hist.max()
p = figure(
title=' Histogram',
plot_width=600, plot_height=400,
tools="",
title_text_font_size="16pt",
x_range=[start, end],
y_range=[0, top],
x_axis_label = ' Bins',
y_axis_label = 'Bin Count'
)
p.rect(center, hist / 2.0, width, hist)
return p
def make_plots(self):
self.hist_plots()
def hist_plots(self):
self.hist1 = self.path_plot()
self.hist2 = self.hist_den_plot()
self.hist3 = self.mc_results()
def set_children(self):
self.children = [self.mainrow, self.second_row]
self.mainrow.children = [self.input_box, self.hist1]
self.second_row.children = [self.hist2, self.hist3]
self.input_box.children = [self.ticker1_box, self.ticker2_box, self.ticker3_box,self.ticker4_box,self.ticker5_box]
self.ticker1_box.children =[self.ticker1_select, self.ticker1p_select]
self.ticker2_box.children =[self.ticker2_select, self.ticker2p_select]
self.ticker3_box.children =[self.ticker3_select, self.ticker3_1_select, self.ticker3_2_select]
self.ticker4_box.children =[self.ticker4_select, self.ticker4_1_select, self.ticker4_2_select]
self.ticker5_box.children =[self.button_select]
def input_change(self, obj, attrname, old, new):
if obj == self.ticker4_2_select:
self.ticker4_2 = new
if obj == self.ticker4_1_select:
self.ticker4_1 = new
if obj == self.ticker4_select:
self.ticker4 = new
if obj == self.ticker3_2_select:
self.ticker3_2 = new
if obj == self.ticker3_1_select:
self.ticker3_1 = new
if obj == self.ticker3_select:
self.ticker3 = new
if obj == self.ticker2p_select:
self.ticker2p = new
if obj == self.ticker2_select:
self.ticker2 = new
if obj == self.ticker1p_select:
self.ticker1p = new
if obj == self.ticker1_select:
self.ticker1 = new
if obj == self.button_select:
self.button = new
if 'run' in self.button:
self.make_source()
self.make_plots()
self.set_children()
curdoc().add(self)
#self.make_source()
#self.make_plots()
#self.set_children()
#curdoc().add(self)
def setup_events(self):
super(StockApp, self).setup_events()
if self.ticker1_select:
self.ticker1_select.on_change('value', self, 'input_change')
if self.ticker1p_select:
self.ticker1p_select.on_change('value', self, 'input_change')
if self.ticker2_select:
self.ticker2_select.on_change('value', self, 'input_change')
if self.ticker2p_select:
self.ticker2p_select.on_change('value', self, 'input_change')
if self.ticker3_select:
self.ticker3_select.on_change('value', self, 'input_change')
if self.ticker3_1_select:
self.ticker3_1_select.on_change('value', self, 'input_change')
if self.ticker3_2_select:
self.ticker3_2_select.on_change('value', self, 'input_change')
if self.ticker4_select:
self.ticker4_select.on_change('value', self, 'input_change')
if self.ticker4_1_select:
self.ticker4_1_select.on_change('value', self, 'input_change')
if self.ticker4_2_select:
self.ticker4_2_select.on_change('value', self, 'input_change')
if self.button_select:
self.button_select.on_change('value',self, 'input_change')
@property
def Y(self):
tmpdf = pd.DataFrame(self.main_mc(int(self.ticker3_1),int(self.ticker3),float(self.ticker3_2),self.ticker4_2,float(self.ticker4_1)))
tmpdf.columns = ['Col_' + str(i) for i in xrange(len(tmpdf.columns))]
#print tmpdf
return tmpdf
Speed = Slider(title="Speed", value=250, start=100, end=250)
Delay = Slider(title="Delay", value=1, start=1, end=100)
CurveList = [("Sin", "C1"), ("Poly", "C2"), ("Abs", "C3"), ("inv/inf", "C5"),
("Frq", "C6"), ("Custom", "C4")]
dropdown = Dropdown(label="Curve Lists", button_type="warning", menu=CurveList)
button = Button(label="Run ", button_type="success")
def update_title(attrname, old, new):
plot.title.text = text.value
x = np.linspace(-4 * np.pi, 4 * np.pi, N)
text.on_change('value', update_title)
div = Div(width=1000)
def change_output(attr, old, new):
global Del
global Sp
Del = Delay.value
Sp = Speed.value
def display_event(div):
return CustomJS(args=dict(div=div),
code="""
console.log("run")
def load_page(plate):
'''
Load new page
'''
global well_id
well_id = (0, 0)
global sample
sample = plate[well_id]
# Button to upload local file
global file_source
file_source = ColumnDataSource(data=dict(file_contents = [], file_name = []))
file_source.on_change('data', file_callback)
try:
output_file_name = file_source.data['file_name'] + '-out.csv'
except:
output_filename = 'output.csv'
global upload_button
upload_button = Button(label="Upload local file", button_type="success", width=200, height=30)
upload_button.js_on_click(CustomJS(args=dict(file_source=file_source),
code=open(join(dirname(__file__), "upload.js")).read()))
# Text boxes for setting fit parameters
global bottom_set_text
bottom_set_text = TextInput(value='', title="Set initial value for Fmin", width=200, height=50)
bottom_set_text.on_change('value', parameter_set_callback)
global top_set_text
top_set_text = TextInput(value='', title="Set initial value for Fmax", width=200, height=50)
top_set_text.on_change('value', parameter_set_callback)
global slope_set_text
slope_set_text = TextInput(value='', title="Set initial value for a", width=200, height=50)
slope_set_text.on_change('value', parameter_set_callback)
# Radio button group for setting plate type
global plate_type_buttons
global plate_type
plate_type_buttons = RadioButtonGroup(labels=['96 well', '384 well'],
width=200, height=25, active=plate_type)
plate_type_buttons.on_change('active', plate_type_callback)
# Radio button group for setting data layout
global plate_layout_buttons
global plate_layout
plate_layout_buttons = RadioButtonGroup(labels=['by row', 'by column'],
width=200, height=25, active=plate_layout)
plate_layout_buttons.on_change('active', plate_layout_callback)
# Checkbox groups for fixing fit parameters
global fix_bottom_checkbox
fix_bottom_checkbox = CheckboxButtonGroup(labels=['Fix min fluoresence (Fmin)'],
width=200, height=30)
fix_bottom_checkbox.on_change('active', parameter_set_callback)
global fix_top_checkbox
fix_top_checkbox = CheckboxButtonGroup(labels=['Fix max fluorescence (Fmax)'], width=200, height=30)
fix_top_checkbox.on_change('active', parameter_set_callback)
global fix_slope_checkbox
fix_slope_checkbox = CheckboxButtonGroup(labels=['Fix curve shape parameter (a)'], width=200, height=30)
fix_slope_checkbox.on_change('active', parameter_set_callback)
# Slider for selecting data to fit
global df
xmin = df[df.columns[0]].values[0]
xstep = df[df.columns[0]].values[1] - xmin
xstart = sample.data['x'].values[0]
xend = sample.data['x'].values[-1]
xmax = df[df.columns[0]].values[-1]
global range_slider
range_slider = RangeSlider(start=xmin, end=xmax, value=(xstart, xend),
step=xstep,
title='Fine tune temperature range', width=550)
range_slider.on_change('value', slider_callback)
# Scatter plot for fitting individual samples
global sample_source
sample_source = ColumnDataSource(data=dict(x=sample.data.x, y=sample.data.y,
fit=sample.y_fit, residuals=sample.residuals))
global sample_scatter
plot_tools = 'wheel_zoom, pan, reset, save'
sample_scatter = figure(title="Boltzman sigmoidal fit", x_axis_label='Temperature ('+degree_sign+'C)',
y_axis_label="Fluoresence intensity", plot_width=600,
plot_height=300, tools=plot_tools)
sample_scatter.circle(x='x', y='y', color='grey', size=8, alpha=0.6, source=sample_source)
sample_scatter.line(x='x', y='fit', color='black', line_width=2,
alpha=1.0, source=sample_source)
sample_scatter.title.text = sample.name + ' fit'
# Scatter plot for residuals of individual sample fit
global residual_scatter
residual_scatter = figure(title="Fit residuals", x_axis_label='Temperature ('+degree_sign+'C)',
y_axis_label="Residual", plot_width=600,
plot_height=200, tools='wheel_zoom,pan,reset')
residual_scatter.yaxis.formatter = BasicTickFormatter(precision=2, use_scientific=True)
residual_scatter.circle('x', 'residuals', size=8, source=sample_source,
color='grey', alpha=0.6)
# Heatmap for displaying all Tm values in dataset
global plate_source
letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P']
w, n, t, e = [], [], [], []
if plate_type_buttons.active == 1:
rows = 16
columns = 24
else:
rows = 8
columns = 12
for i in range(rows):
for j in range(columns):
w.append(letters[i]+str(j+1))
try:
n.append(plate[(i, j)].name)
t.append(plate[(i, j)].v50_fit)
e.append(plate[(i, j)].v50_err)
except:
n.append('')
t.append(np.nan)
e.append(np.nan)
xname = [x[1:] for x in w]
yname = [y[0] for y in w]
plate_source = ColumnDataSource(dict(w=w, n=n, t=t, e=e, xname=xname, yname=yname))
plate_columns = [
TableColumn(field='w', title='Well ID'),
TableColumn(field='n', title='Sample name'),
TableColumn(field='t', title='Tm ('+degree_sign+'C)'),
TableColumn(field='e', title='Error ('+degree_sign+'C)'),
]
plate_map_hover = HoverTool(tooltips="""
<div>
<div>
<span style="font-size: 14px; font-weight: bold; ">@n:</span>
<span style="font-size: 14px; font-weight: bold; ">@t</span>
</div>
</div>
"""
)
if plate_type_buttons.active == 1:
plate_map = figure(title="Plate view", x_axis_location="above", height=400, width=620,
tools=["save, tap, reset", plate_map_hover],
x_range=[str(x+1) for x in range(0, columns)]+['', 'Tm ('+degree_sign+'C)'],
y_range=letters[:rows][::-1])
else:
plate_map = figure(title="Plate view", x_axis_location="above", height=400, width=620,
tools=["save, tap, reset", plate_map_hover],
x_range=[str(x+1) for x in range(0, columns)]+['Tm ('+degree_sign+'C)'],
y_range=letters[:rows][::-1])
taptool = plate_map.select(type=TapTool)
plate_map.on_event(Tap, plate_select)
global mapper
mapper = linear_cmap(field_name='t', palette=RdBu[8], low=min(t), high=max(t))
global color_bar
color_bar = ColorBar(color_mapper=mapper['transform'], width=10, height=250, name='Tm ('+degree_sign+'C)')
plate_map.add_layout(color_bar, 'right')
plate_map.grid.grid_line_color = None
plate_map.axis.axis_line_color = None
plate_map.axis.major_tick_line_color = None
plate_map.axis.major_label_text_font_size = "10pt"
plate_map.axis.major_label_standoff = 0
plate_map.rect('xname', 'yname', .95, .95, source=plate_source,
color=mapper, line_color='black', line_width=1)
# Table listing all Tm values in dataset
global plate_table
plate_table = DataTable(source=plate_source, columns=plate_columns, width=500,
height=500, selectable=True, editable=True)
plate_table.source.selected.on_change('indices', table_select)
# Table showing fitting parameters for current sample
global sample_table_source
sample_table_source = ColumnDataSource(data=dict(l=['Fit value', 'Std. error'],
b=[sample.bottom_fit, sample.bottom_err],
t=[sample.top_fit, sample.top_err],
v=[sample.v50_fit, sample.v50_err],
s=[sample.slope_fit, sample.slope_err]))
sample_table_columns = [
TableColumn(field='l', title=''),
TableColumn(field='b', title='Fmin'),
TableColumn(field='t', title='Fmax'),
TableColumn(field='v', title='Tm ('+degree_sign+'C)'),
TableColumn(field='s', title='a')
]
global sample_table
sample_table = DataTable(source=sample_table_source, columns=sample_table_columns, width=600,
height=200, selectable=False, editable=False)
# Button to re-fit all with current parameter settings
global refit_all_button
refit_all_button = Button(label="Re-fit all samples",
button_type='danger', width=200, height=30)
refit_all_button.on_click(refit_all_callback)
# Button to download Tm table to csv file
global download_button
download_button = Button(label="Download table to CSV",
button_type="primary", width=200, height=30)
download_button.js_on_click(CustomJS(args=dict(source=plate_source, file_name=output_filename),
code=open(join(dirname(__file__), "download.js")).read()))
# Button to copy Tm table to clipboard
global copy_button
copy_button = Button(label="Copy table to clipboard", button_type="primary",
width=200, height=30)
copy_button.js_on_click(CustomJS(args=dict(source=plate_source),
code=open(join(dirname(__file__), "copy.js")).read()))
# page formatting
desc = Div(text=open(join(dirname(__file__), "description.html")).read(), width=1200)
main_row = row(column(plate_type_buttons, plate_layout_buttons, upload_button,
fix_bottom_checkbox, bottom_set_text, fix_top_checkbox,
top_set_text, fix_slope_checkbox, slope_set_text, refit_all_button,
download_button, copy_button),
column(sample_scatter, residual_scatter, range_slider, sample_table),
column(plate_map, plate_table))
sizing_mode = 'scale_width'
l = layout([
[desc],
[main_row]
], sizing_mode=sizing_mode)
update()
curdoc().clear()
curdoc().add_root(l)
curdoc().title = "DSF"
order = int(new)
update_data()
def on_text_value_change(attr, old, new):
try:
global expr
expr = sy.sympify(new, dict(x=xs))
except (sy.SympifyError, TypeError, ValueError) as exception:
dialog.content = str(exception)
dialog.visible = True
else:
update_data()
dialog = Dialog(title="Invalid expression")
slider = Slider(start=1, end=20, value=order, step=1, title="Order",callback_policy='mouseup')
slider.on_change('value', on_slider_value_change)
text = TextInput(value=str(expr), title="Expression:")
text.on_change('value', on_text_value_change)
inputs = WidgetBox(children=[slider, text],width=400)
layout = Column(children=[inputs, plot, dialog])
update_data()
document.add_root(layout)
session.show(layout)
if __name__ == "__main__":
print("\npress ctrl-C to exit")
session.loop_until_closed()
TableColumn(field="name", title=u"组合"),
TableColumn(field="net value", title=u"单位净值"),
TableColumn(field="year return", title=u"今年以来业绩"),
TableColumn(field="total return", title=u"成立以来业绩"),
TableColumn(field="max drawdown", title=u"最大回撤"),
TableColumn(field="sharpe", title=u"夏普率"),
TableColumn(field="volatility", title=u"波动率")
]
data_table = DataTable(source=source, columns=columns, width=900, height=500)
today = datetime.datetime.now()
yesterday = today - datetime.timedelta(1)
yesterday = yesterday.strftime("%Y-%m-%d")
time_text = TextInput(value=yesterday, title="提取时间", width=300)
time_text.on_change('value', lambda attr, old, new: update_excel())
portfolio_select = Select(value=portfolio_selection[0],
title="组合",
width=300,
options=portfolio_selection)
portfolio_select.on_change('value', lambda attr, old, new: update_plot())
update_excel()
update_plot()
update_time_text = TextInput(value=today.strftime("%Y-%m-%d"),
title=u"更新日期",
width=300)
update_button = Button(label=u"更新数据", width=300, button_type="success")
update_row = row(update_time_text, update_button)
contour_g = my_bokeh_utils.Contour(plot, line_color='red', line_width=2, legend='g(x,y) = 0')
# Plot corresponding tangent vector
quiver_isolevel = my_bokeh_utils.Quiver(plot, fix_at_middle=False, line_width=2, color='black')
# Plot corresponding tangent vector
quiver_constraint = my_bokeh_utils.Quiver(plot, fix_at_middle=False, line_width=2, color='red')
# Plot mark at position on constraint function
plot.cross(x='x', y='y', color='red', size=10, line_width=2, source=source_mark)
# object that detects, if a position in the plot is clicked on
interactor = my_bokeh_utils.Interactor(plot)
# adds callback function to interactor, if position in plot is clicked, call on_selection_change
interactor.on_click(on_selection_change)
# text input window for objective function f(x,y) to be optimized
f_input = TextInput(value=lagrange_settings.f_init, title="f(x,y):")
f_input.on_change('value', f_changed)
# dropdown menu for selecting one of the sample functions
sample_fun_input_f = Dropdown(label="choose a sample function f(x,y) or enter one below",
menu=lagrange_settings.sample_f_names)
sample_fun_input_f.on_click(sample_fun_input_f_changed)
# text input window for side condition g(x,y)=0
g_input = TextInput(value=lagrange_settings.g_init, title="g(x,y):")
g_input.on_change('value', g_changed)
# dropdown menu for selecting one of the sample functions
sample_fun_input_g = Dropdown(label="choose a sample function g(x,y) or enter one below",
menu=lagrange_settings.sample_g_names)
sample_fun_input_g.on_click(sample_fun_input_g_changed)
#for mytool in Tools:
# mytool.plot = figure_obj
#figure_obj.tools = Tools
figure_obj.line("x",
"y",
source=two[0],
line_width=2,
line_color=colour_list[3])
figure_obj.line("x",
"y",
source=two[1],
line_width=2,
line_color=colour_list[1])
text = TextInput(title="title", value='my sine wave')
radio = RadioGroup(labels=["0", "1"], active=0)
text.on_change('value',
lambda attr, old, new, radio=radio, sources=two:
update_title(new, radio, sources))
tabs.append(
Panel(child=hplot(figure_obj, vform(text, radio)), title="two by two"))
tabs = Tabs(tabs=tabs)
session = push_session(curdoc())
session.show()
session.loop_until_closed()
x_val, y_val = np.meshgrid(xx, yy)
# evaluate function at sample points
u_val = u_fun(x_val, y_val)
v_val = v_fun(x_val, y_val)
return x_val, y_val, u_val, v_val, hx
# initialize controls
# text input for input of the vector function [fx(x,y),fy(x,y)]
u_input = TextInput(value=curveintegral_settings.sample_functions[curveintegral_settings.init_fun_key][0],
title="fx(x,y):")
v_input = TextInput(value=curveintegral_settings.sample_functions[curveintegral_settings.init_fun_key][1],
title="fy(x,y):")
u_input.on_change('value', function_change)
v_input.on_change('value', function_change)
# text input for input of the parametrized curve [cx(t),cy(t)]
cx_input = TextInput(value=curveintegral_settings.sample_curves[curveintegral_settings.init_curve_key][0],
title="cx(t):")
cy_input = TextInput(value=curveintegral_settings.sample_curves[curveintegral_settings.init_curve_key][1],
title="cy(t):")
# slider controlling the parameter t
parameter_input = Slider(title="t",
value=curveintegral_settings.parameter_input_init,
start=curveintegral_settings.parameter_min,
end=curveintegral_settings.parameter_max,
step=curveintegral_settings.parameter_step)
cx_input.on_change('value', curve_change)
def country_tab(list_sp_objs):
def make_data_set(speeches, country, type_display):
overall_counter = Counter()
word_counter = dict()
dict_of_selected_counters_inp = search_mentions(country)
dict_of_selected_counters_out = search_is_mentioned_by(country)
tot_mentions = Counter()
tot_mentioned_by = Counter()
for k, val in dict_of_selected_counters_inp.items():
tot_mentions += Counter(dict(dict_of_selected_counters_inp[k]))
for k, val in dict_of_selected_counters_inp.items():
tot_mentioned_by += Counter(dict(dict_of_selected_counters_out[k]))
sp_country = []
for s in speeches:
if s.country == country:
sp_country.append(s)
# sp_country = speeches[idx]
# counts = defaultdict(int)
for sp in list(sp_country):
overall_counter += sp.word_frequency
most_common_words = list(dict(overall_counter.most_common(10)).keys())
most_common_counter = Counter()
for mcw in most_common_words:
most_common_counter[mcw] = overall_counter[mcw]
word = list(dict(most_common_counter).keys())
counts = list(dict(most_common_counter).values())
for w in most_common_words:
word_counter[w] = Counter()
for sp in sp_country:
if sp.word_frequency[w]:
add = sp.word_frequency[w]
else:
add = 0
word_counter[w][sp.year] += add
years = range(1970, 2016, 1)
selected_data = dict()
for w, cnter in word_counter.items():
selected_data[w] = []
for yr in years:
if yr in cnter:
count = cnter[yr]
else:
count = float('nan')
selected_data[w].append(count)
# print(selected_data)
multi_counts = [val for val in selected_data.values()]
multi_years = [list(years)] * len(multi_counts)
colors = word_colors[:len(multi_counts)]
labels = most_common_words
data = {
'counts': multi_counts,
'years': multi_years,
'colors': colors,
'labels': labels
}
if type_display == 'mentions':
prepared_map_data = make_map_data(tot_mentions)
else:
prepared_map_data = make_map_data(tot_mentioned_by)
return ColumnDataSource(data), ColumnDataSource(prepared_map_data)
def update(attr, old, new):
country_code = list(country_dic.keys())[list(
country_dic.values()).index(country_input.value)]
# print('updating ', country_input.value, country_code, dropdown.value)
(word_frequency_to_plot,
map_data) = make_data_set(list_sp_objs, country_code, dropdown.value)
# print(country_input.value, word_frequency_to_plot)
src.data.update(word_frequency_to_plot.data)
map_src.data.update(map_data.data)
def search_mentions(input_country, output_countries=list_country_codes):
specific_mentions_dict = yearwise_data(input_country, 'mentions')
return specific_mentions_dict
def search_is_mentioned_by(input_country,
output_countries=list_country_codes):
specific_mentioned_by_dict = yearwise_data(input_country,
'is_mentioned_by')
return specific_mentioned_by_dict
def yearwise_data(inp_country, m):
'''Function to get yearly data on either mentions or is mentioned by
data for a specific country.
@param inp_country: country input.
@param out_country: country of interest
@param m: 'mentions' or 'is_mentioned_by'
Example: After receiving an input country 'IND', to find the number of
mentions of 'USA' in speeches by 'IND'.
-Here, dict_of_interest is the mentions dict of IND
>>>yearwise_data('IND','USA','mentions')
<returns two arrays: years and mentions
>>>[1971,1975,1995,2000],[1,2,2,1]
The same thing can be applied to an is_mentioned_by dict
'''
# [x for x in dict_searched_country[1989] if x[0]=='COL']
# print(inp_country,out_country,m)
input_file = Path.cwd().joinpath(f'data/{m}.pickle')
# Path.cwd().joinpath('data/members_dic.pkl')
with input_file.open('rb') as pkl_file:
dict = pickle.load(pkl_file)
try:
dict_of_int = dict[inp_country]
except KeyError:
print("Check the country again!")
return None, None
return dict_of_int
def make_plot(src):
p = figure(plot_height=600, title='Most used words')
# print('SRC', src['years'], src['counts'])
# print(src.daa['labels'])
p.multi_line('years',
'counts',
color='colors',
legend='labels',
source=src)
p.xaxis.axis_label = 'Year'
p.yaxis.axis_label = 'Word count'
# print(selected_countries)
# for country in selected_countries:
# p.line('years', country, source=src)
return p
def make_map_data(country_counter1):
# 1 A mentions B , 2 A is mentioned by B
unzipped = list(zip(*country_counter1))
countries = list(dict(country_counter1).keys())
country_counts = list(dict(country_counter1).values())
data = dict()
data['country'] = countries
data['counts'] = country_counts
k = list(country_shapes.keys())
country_xs = [country_shapes[i]['lats'] for i in k]
country_ys = [country_shapes[i]['lons'] for i in k]
country_names = [country_shapes[i]['name'] for i in k]
# country_rates = list(range(len(country_names)))
country_rates = [float('NaN')] * len(country_names)
country_inds = {country_shapes[j]['ID']: i for i, j in enumerate(k)}
for i in range(len(data['country'])):
try:
country_rates[country_inds[data['country']
[i]]] = data['counts'][i]
except:
pass
src_map = dict(
x=country_xs,
y=country_ys,
name=country_names,
rate=country_rates,
)
return src_map
def make_map(src_map, country, type_display):
color_mapper = LogColorMapper(palette=palette)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
plot_width=1150,
plot_height=800,
title='World Map',
tools=TOOLS,
x_axis_location=None,
y_axis_location=None,
tooltips=[
("Name", "@name"),
("Mentions", "@rate") # ,
# ("(Long, Lat)", "($x, $y)")
])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.x_range = Range1d(start=-180, end=180)
p.y_range = Range1d(start=-90, end=90)
p.grid.grid_line_color = None
p.patches('x',
'y',
source=src_map,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
return (p)
country_input = TextInput(value="India", title="Label:")
country_code = list(country_dic.keys())[list(country_dic.values()).index(
country_input.value)]
country_input.on_change('value', update)
# For the dropdown
menu = [("Mentions", "mentions"), ("Is mentioned by", "is_mentioned_by")]
dropdown = Dropdown(label="Type of display",
button_type="primary",
value='mentions',
menu=menu)
dropdown.on_change('value', update)
word_colors = Category20_16
word_colors.sort()
src, map_src = make_data_set(list_sp_objs, country_code, dropdown.value)
p = make_plot(src)
map = make_map(map_src, country_input, dropdown.value)
# Put controls in a single element
controls = widgetbox(country_input)
# Create a row layout
layout = row(
column(controls, dropdown, p),
map,
)
# Make a tab with the layout
tab = Panel(child=layout, title='Country referencing')
return tab
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="title", value='my sine wave')
offset = Slider(title="offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="amplitude", value=1.0, start=-5.0, end=5.0)
phase = Slider(title="phase", value=0.0, start=0.0, end=2*np.pi)
freq = Slider(title="frequency", value=1.0, start=0.1, end=5.1)
# Set up callbacks
def update_title(attrname, old, new):
plot.title.text = text.value
text.on_change('value', update_title)
def update_data(attrname, old, new):
# Get the current slider values
a = amplitude.value
b = offset.value
w = phase.value
k = freq.value
# Generate the new curve
x = np.linspace(0, 4*np.pi, N)
y = a*np.sin(k*x + w) + b
source.data = dict(x=x, y=y)
def correlations_tab(ts):
# Making dataset using the autocorrelation function,
# input is a timeseries and default maximum lag of 10
def make_dataset_autocorr(ts, col, lag):
df_to_plot_autocorr = auto_corr(ts[col], lag)
return ColumnDataSource(df_to_plot_autocorr)
# Making autocorrelation plot
def make_autocorrplot(source):
ttp = [("Lag", "$x"), ("Autocorrelation", "$y")]
plot_autocorr = figure(plot_height=400, plot_width=600, tooltips=ttp,
title="Autocorrelation",
x_axis_label="Lag",
y_axis_label="Autocorrelation",
tools="hover, pan, zoom_in, zoom_out, \
reset, save")
plot_autocorr.circle('lags', 'auto_corrs', source=source)
plot_autocorr.line('lags', 'auto_corrs', source=source, line_width=0.5)
plot_autocorr.title.text_font_size = '14pt'
plot_autocorr.xaxis.axis_label_text_font_size = '12pt'
plot_autocorr.xaxis.axis_label_text_font_style = 'bold'
plot_autocorr.yaxis.axis_label_text_font_size = '12pt'
plot_autocorr.yaxis.axis_label_text_font_style = 'bold'
return plot_autocorr
# Set up callbacks to interactively update data when different
# time series and lag values are selected, and letting the user
# set the title
def update_title(attrname, old, new):
plot_autocorr.title.text = text_autocorr.value
def update_data_autocorr(attrname, old, new):
new_source_autocorr = make_dataset_autocorr(ts,
ts_select_autocorr.value,
lag=lag_select_autocorr.
value)
source_autocorr.data.update(new_source_autocorr.data)
# Set up widgets.
# Input for plot titles
text_autocorr = TextInput(title="Title", value='Autocorrelation')
text_autocorr.on_change('value', update_title)
# Select time series for Autocorrelation
ts_available = ts.columns.tolist()
print(ts_available)
ts_available.remove('time')
ts_select_autocorr = Select(value=ts_available[0], title='Time Series',
options=ts_available)
ts_select_autocorr.on_change('value', update_data_autocorr)
# Select lag value up to a max of
# the length of the time series
max_lag = ts.shape[0]
lag_select_autocorr = Slider(start=10, end=max_lag, step=1, value=10,
title='Lag')
lag_select_autocorr.on_change('value', update_data_autocorr)
# Initial state and plotting.
source_autocorr = make_dataset_autocorr(ts, ts_available[0], 10)
plot_autocorr = make_autocorrplot(source_autocorr)
# Set up layouts and add to document.
# Put controls in a single element.
controls_autocorr = WidgetBox(text_autocorr, ts_select_autocorr,
lag_select_autocorr)
# making dataset using the correlation function
# inputs are two different timeseries and default maximum lag of 10
def make_dataset_corr(ts, col1, col2, lag):
df_to_plot_corr = corr(ts[col1], ts[col2], lag)
return ColumnDataSource(df_to_plot_corr)
# Make line plot for correlation
def make_corrplot(source):
ttp = [("Lag", "$x"), ("Autocorrelation", "$y")]
plot_corr = figure(plot_height=400, plot_width=600, tooltips=ttp,
title="Correlation",
x_axis_label="Lag", y_axis_label="Correlation",
tools="hover, pan, zoom_in, zoom_out, reset, save")
plot_corr.circle('lags', 'corrs', source=source)
plot_corr.line('lags', 'corrs', source=source, line_width=0.5)
plot_corr.title.text_font_size = '14pt'
plot_corr.xaxis.axis_label_text_font_size = '12pt'
plot_corr.xaxis.axis_label_text_font_style = 'bold'
plot_corr.yaxis.axis_label_text_font_size = '12pt'
plot_corr.yaxis.axis_label_text_font_style = 'bold'
return plot_corr
# Set up callbacks to interactively update data when different
# time series and lag values are selected, and letting the user
# set the title
def update_title(attrname, old, new):
plot_corr.title.text = text_corr.value
def update_data_corr(attrname, old, new):
new_source_corr = make_dataset_corr(ts, ts_select_corr.value,
ts_select2_corr.value,
lag=lag_select_corr.value)
source_corr.data.update(new_source_corr.data)
# Set up widgets.
# Input for plot titles
text_corr = TextInput(title="Title", value='Correlation')
text_corr.on_change('value', update_title)
# Select time series for Autocorrelation
ts_available = ts.columns.tolist()
ts_available.remove('time')
ts_select_corr = Select(value=ts_available[0], title='Time Series 1',
options=ts_available)
ts_select_corr.on_change('value', update_data_corr)
ts_select2_corr = Select(value=ts_available[0],
title='Time Series 2 (shifted)',
options=ts_available)
ts_select2_corr.on_change('value', update_data_corr)
# Select lag value up to a max of
# the length of the time series
max_lag = ts.shape[0]
lag_select_corr = Slider(start=10, end=max_lag, step=1, value=10,
title='Lag')
lag_select_corr.on_change('value', update_data_corr)
# Initial state and plotting.
source_corr = make_dataset_corr(ts, ts_available[0], ts_available[0], 10)
plot_corr = make_corrplot(source_corr)
# Set up layouts and add to document.
# Put controls in a single element.
controls_corr = WidgetBox(text_corr, ts_select_corr,
ts_select2_corr, lag_select_corr)
# Create a row layout
grid = gridplot([[controls_autocorr, plot_autocorr],
[controls_corr, plot_corr]],
plot_width=500, plot_height=500)
# Make a tab with the layout.
tab = Panel(child=grid, title='Autocorrelation and Correlation')
return tab
from bokeh.models.widgets import Button, TextInput
label_saccade_button = Button(label='saccade')
label_saccade_button.on_click(label_saccade_cb)
label_pursuit_button = Button(label='pursuit')
label_pursuit_button.on_click(label_pursuit_cb)
label_fixation_button = Button(label='fixation')
label_fixation_button.on_click(label_fixation_cb)
remove_button = Button(label='remove')
remove_button.on_click(remove_cb)
trial_text = TextInput(value=str(trialNum))
trial_text.on_change('value', trial_text_cb)
nextTrial_button = Button(label='+trial')
nextTrial_button .on_click(next_trial_cb)
prevTrial_button= Button(label='-trial')
prevTrial_button.on_click(prev_trial_cb)
from bokeh.layouts import row
buttonBox = row(label_saccade_button, label_pursuit_button, label_fixation_button, remove_button)
trialSelectBox = row(prevTrial_button, trial_text, nextTrial_button)
#, sizing_mode='scale_width'
###########################################################################
# Get the HTML description header
from os.path import dirname, join
class StockApp(VBox):
extra_generated_classes = [["StockApp", "StockApp", "VBox"]]
jsmodel = "VBox"
# text statistics
pretext = Instance(DataTable)
# plots
line_plot1 = Instance(Plot)
hist1 = Instance(Plot)
# data source
source = Instance(ColumnDataSource)
risk_source = Instance(ColumnDataSource)
# layout boxes
mainrow = Instance(HBox)
histrow = Instance(HBox)
statsbox = Instance(VBox)
# inputs
ticker1 = String(default="INTC")
ticker2 = String(default="Daily Prices")
ticker3 = String(default='63')
ticker4 = String(default='2010-01-01')
ticker5 = String(default='2015-08-01')
ticker1_select = Instance(Select)
ticker2_select = Instance(Select)
ticker3_select = Instance(TextInput)
ticker4_select = Instance(TextInput)
ticker5_select = Instance(TextInput)
input_box = Instance(VBoxForm)
def __init__(self, *args, **kwargs):
super(StockApp, self).__init__(*args, **kwargs)
self._dfs = {}
@classmethod
def create(cls):
"""
This function is called once, and is responsible for
creating all objects (plots, datasources, etc)
"""
# create layout widgets
obj = cls()
obj.mainrow = HBox()
obj.histrow = HBox()
obj.statsbox = VBox()
obj.input_box = VBoxForm()
# create input widgets
obj.make_inputs()
# outputs
#obj.pretext = PreText(text="", width=300)
obj.pretext = DataTable(width=300)
obj.make_source()
obj.make_plots()
obj.make_stats()
# layout
obj.set_children()
return obj
def make_inputs(self):
self.ticker1_select = Select(
name='ticker1',
title='Portfolio:',
value='MSFT',
options = ['INTC', 'Tech Basket', 'IBB', 'IGOV']
)
self.ticker2_select = Select(
name='ticker2',
title='Risk/Performance Metric:',
value='Price',
options=['Daily Prices', 'Daily Returns', 'Daily Cum Returns', 'Max DD Percentage', 'Percentage Up Days', 'Rolling 95% VaR', 'Rolling Ann. Volatility', 'Rolling Worst Dly. Loss', 'Ann. Sharpe Ratio']
)
self.ticker3_select = TextInput(
name='ticker3',
title='Window Size:',
value='63'
)
self.ticker4_select = TextInput(
name='ticker4',
title='Start Date:',
value='2010-01-01'
)
self.ticker5_select = TextInput(
name='ticker5',
title='End Date:',
value='2015-08-01'
)
@property
def selected_df(self):
pandas_df = self.df
selected = self.source.selected['1d']['indices']
if selected:
pandas_df = pandas_df.iloc[selected, :]
return pandas_df
def make_source(self):
if self.ticker2 == 'Daily Prices':
self.source = ColumnDataSource(data=self.df)
elif self.ticker2 == 'Daily Returns':
self.source = ColumnDataSource(data=daily_returns_df(self.df))
elif self.ticker2 == 'Daily Cum Returns':
self.source = ColumnDataSource(data=daily_cum_returns_df(daily_returns_df(self.df),int(self.ticker3)))
elif self.ticker2 == 'Rolling Ann. Volatility':
self.source = ColumnDataSource(data=annualized_volatility_df(self.df,int(self.ticker3)))
elif self.ticker2 == 'Rolling Worst Dly. Loss':
self.source = ColumnDataSource(data=worst_daily_loss_df(self.df,int(self.ticker3)))
elif self.ticker2 == 'Rolling 95% VaR':
self.source = ColumnDataSource(data=VaR_df(self.df,int(self.ticker3),0.95))
elif self.ticker2 == 'Ann. Sharpe Ratio':
self.source = ColumnDataSource(data=sharpe_ratio_df(self.df,int(self.ticker3)))
elif self.ticker2 == 'Max DD Percentage':
self.source = ColumnDataSource(data=draw_down_df(self.df,int(self.ticker3)))
elif self.ticker2 == 'Percentage Up Days':
self.source = ColumnDataSource(data=per_up_days_df(self.df,int(self.ticker3)))
else:
self.source = ColumnDataSource(data=self.df)
def line_plot(self, ticker, x_range=None):
if self.ticker2 in ['Daily Prices', 'Daily Returns']:
tltstr = self.ticker1 + ' ' + self.ticker2
else:
tltstr=self.ticker1 + ' ' +self.ticker2 + ' with ' + self.ticker3 + ' Day Trailing Window'
p = figure(
title=tltstr,
x_range=x_range,
x_axis_type='datetime',
plot_width=1200, plot_height=400,
title_text_font_size="16pt",
tools="pan,box_zoom,wheel_zoom,reset",
x_axis_label = 'Date',
y_axis_label = self.ticker2
)
p.line(
'date', ticker,
line_width=2,
line_join='bevel',
source=self.source,
nonselection_alpha=0.02
)
return p
def hist_plot(self, ticker):
pltdf = self.source.to_df().set_index('date').dropna()
qlow, qhigh = mquantiles(pltdf[ticker],prob=[0.01,0.99])
tdf = pltdf[ticker]
histdf = tdf[((tdf > qlow) & (tdf < qhigh))]
hist, bins = np.histogram(histdf, bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
start = bins.min()
end = bins.max()
top = hist.max()
p = figure(
title=self.ticker1 + ' ' + self.ticker2 + ' Histogram',
plot_width=600, plot_height=400,
tools="",
title_text_font_size="16pt",
x_range=[start, end],
y_range=[0, top],
x_axis_label = self.ticker2 + ' Bins',
y_axis_label = 'Bin Count'
)
p.rect(center, hist / 2.0, width, hist)
return p
def make_plots(self):
ticker1 = self.ticker1
ticker2 = self.ticker2
self.line_plot1 = self.line_plot(ticker1)
self.hist_plots()
def hist_plots(self):
ticker1 = self.ticker1
ticker2 = self.ticker2
self.hist1 = self.hist_plot(ticker1)
#self.hist2 = self.hist_plot(ticker2)
def set_children(self):
self.children = [self.mainrow, self.line_plot1]
self.mainrow.children = [self.input_box, self.hist1, self.pretext]
self.input_box.children = [self.ticker1_select, self.ticker2_select, self.ticker3_select, self.ticker4_select, self.ticker5_select]
#self.statsbox.children = [self.pretext]
def input_change(self, obj, attrname, old, new):
if obj == self.ticker5_select:
self.ticker5 = new
if obj == self.ticker4_select:
self.ticker4 = new
if obj == self.ticker3_select:
self.ticker3 = new
if obj == self.ticker2_select:
self.ticker2 = new
if obj == self.ticker1_select:
self.ticker1 = new
self.make_source()
self.make_stats()
self.make_plots()
self.set_children()
curdoc().add(self)
def setup_events(self):
super(StockApp, self).setup_events()
if self.source:
self.source.on_change('selected', self, 'selection_change')
if self.ticker1_select:
self.ticker1_select.on_change('value', self, 'input_change')
if self.ticker2_select:
self.ticker2_select.on_change('value', self, 'input_change')
if self.ticker3_select:
self.ticker3_select.on_change('value', self, 'input_change')
if self.ticker4_select:
self.ticker4_select.on_change('value', self, 'input_change')
if self.ticker5_select:
self.ticker5_select.on_change('value', self, 'input_change')
def make_stats(self):
## Build up a list of Summary stats for the time series
statsdf = self.source.to_df().dropna()
stats = statsdf.describe()
stats.index = ['Data Point Count', 'Mean', 'Standard Deviation', 'Minimum', '25%-ile',
'50%-ile', '75%-ile', 'Maximum']
stats.loc['Skewness',:] = my_round(my_skewness(statsdf[self.ticker1].values),4)
stats.loc['Excess Kurtosis',:] = my_round(my_ex_kurtosis(statsdf[self.ticker1].values),4)
stats = stats.fillna(-10)
stats.index.name = "stats"
columns = [TableColumn(field='stats', title='Time Series Statistic'),
TableColumn(field=self.ticker1, title="Value",
formatter=NumberFormatter(format='0.0000'))]
self.pretext.columns = columns
self.pretext.source = ColumnDataSource(data=stats)
def selection_change(self, obj, attrname, old, new):
self.make_stats()
self.hist_plots()
self.set_children()
curdoc().add(self)
@property
def df(self):
return get_data(self.ticker1).truncate(before=self.ticker4, after=self.ticker5)