def calculator(a, b, c, d):
df3 = pd.DataFrame(a, columns=[i['name'] for i in b])
df4 = pd.DataFrame(c, columns=[i['name'] for i in d])
luas = np.random.normal(int(df3.iloc[2, 1]), int(df3.iloc[3, 1]),
int(df4.iloc[0, 0]))
luas2 = luas.clip(int(df3.iloc[0, 1]), int(df3.iloc[1, 1]))
ketebalan = np.random.normal(int(df3.iloc[2, 2]), int(df3.iloc[3, 2]),
int(df4.iloc[0, 0]))
ketebalan2 = ketebalan.clip(int(df3.iloc[0, 2]), int(df3.iloc[1, 2]))
ntg = np.random.normal(int(df3.iloc[2, 3]), int(df3.iloc[3, 3]),
int(df4.iloc[0, 0]))
ntg2 = ntg.clip(int(df3.iloc[0, 3]), int(df3.iloc[1, 3]))
por = np.random.normal(int(df3.iloc[2, 4]), int(df3.iloc[3, 4]),
int(df4.iloc[0, 0]))
por2 = por.clip(int(df3.iloc[0, 4]), int(df3.iloc[1, 4]))
sw = np.random.normal(int(df3.iloc[2, 5]), int(df3.iloc[3, 5]),
int(df4.iloc[0, 0]))
sw2 = sw.clip(int(df3.iloc[0, 5]), int(df3.iloc[1, 5]))
bo = np.random.uniform(int(df3.iloc[0, 6]), int(df3.iloc[1, 6]),
int(df4.iloc[0, 0]))
cadangan = 7758 * (luas2 * 0.000247105) * (ketebalan2 * 3.28084) * (
ntg2 / 100) * ((100 - sw2) / 100) * (por2 / 100) / bo
total = pd.DataFrame(cadangan, columns=[
'OOIP'
]).describe(percentiles=[.1, .2, .25, .5, .6, .75, .9]).round().T
fig = go.Figure()
fig.add_trace(go.Histogram(x=cadangan, cumulative_enabled=True))
fig.update_layout(
title_text='OOIP Iteration ' + str(df4.iloc[0, 0]), # title of plot
xaxis_title_text='Barrel Oil', # xaxis label
yaxis_title_text='Count', # yaxis label
)
des = [
dash_table.DataTable(
id='tes',
columns=[{
"name": i,
"id": i
} for i in total.columns],
data=total.to_dict('records'),
)
]
return (des, fig)
def make_hist(ys: Iterable[float], name: str):
fig = go.Figure()
fig.add_trace(go.Histogram(
x=ys,
xbins=dict(size=0.1)
))
fig.update_layout(
title_text=f'{name} Score Distribution',
xaxis_title_text='Docking Score',
yaxis_title_text='Count'
)
return fig.write_image(f'{name}_clusters_scores_histogram.pdf')
def plot_hours(df):
"""Plots the hours required to work in an Histogram.
Parameters
----------
df : pandas.DataFrame
A pandas DataFrame containing job offers data.
"""
fig = go.Figure()
fig.add_traces(go.Histogram(x=df["hours_worked"], marker_color="#ffa000"))
fig.update_xaxes(title="Hours Required",
ticks="outside",
ticklen=10,
gridwidth=0.5,
tickcolor="#FFFFFF",
linewidth=2,
showline=True,
mirror=True,
nticks=35,
title_standoff=20)
fig.update_yaxes(title="Number of Job Offers",
ticks="outside",
ticklen=10,
separatethousands=True,
tickcolor="#FFFFFF",
linewidth=2,
showline=True,
mirror=True,
nticks=18,
gridwidth=0.5,
title_standoff=5)
# Add final customizations.
fig.update_layout(showlegend=False,
width=1200,
height=800,
font_color="#FFFFFF",
font_size=18,
title_text="Labour Hours Distribution",
title_x=0.5,
title_y=0.93,
margin_l=120,
margin_b=120,
title_font_size=30,
paper_bgcolor="#37474f",
plot_bgcolor="#263238")
fig.write_image("7.png")
def q3Plotter(productCD, width=800, height=300): enable_plotly_in_cell() fig = make_subplots(rows=1, cols=2, subplot_titles=['Histogram', 'Violin Plot'], horizontal_spacing=0.1) fig.append_trace(go.Histogram(x = mergeData[mergeData.ProductCD==productCD].TransactionAmt), row=1, col=1) fig.append_trace(go.Violin(x = mergeData[mergeData.ProductCD==productCD].TransactionAmt, box_visible=True), row=1, col=2) #trace = go.Histogram(x = mergeData[mergeData.ProductCD==i].TransactionAmt) #data = [trace] #layout = go.Layout(title=go.layout.Title(text='Distribution of TransactionAmt for ProductCD: '+i), # xaxis=go.layout.XAxis(title=go.layout.xaxis.Title(text="$"))) #fig = go.Figure(data=data, layout=layout) fig.update_layout(height = height, width = width, showlegend=False, title_text="Distribution of TransactionAmt for ProductCD: "+productCD) iplot(fig)
def histBox(df, col):
fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Histogram(x=df[col], name='Hitrogram'), row=1, col=1)
fig.add_trace(go.Box(y=df[col], name='Boxplot'), row=1, col=2)
fig.update_layout(height=600,
width=800,
title_text=f'Distribution of {col}')
return fig
def histogram(vals, thresholds):
nbins = int(np.sqrt(vals.size))
h_up = np.histogram(vals, bins=nbins)[0].max()
hist = go.Histogram(x=vals, nbinsx=nbins)
fig = go.Figure(data=[hist] + [
go.Scatter(x=[x, x],
y=[0, h_up],
mode='lines',
name='Threshold {0}'.format(idx + 1))
for idx, x in enumerate(thresholds)
])
return fig
def plot_hist(kernel_name_1, bin_size_1, kernel_name_1000, bin_size_1000, layer_dfs):
fig1 = go.Figure()
fig1000 = go.Figure()
# BatchSize=1
fig1.add_trace(go.Histogram(x=layer_dfs['l1_df'].query('Name==\'%s\''%kernel_name_1).Duration, name='L1: '+kernel_name_1[:15], xbins=dict(size=bin_size_1), marker_color='lightblue'))
fig1.add_trace(go.Histogram(x=layer_dfs['l2_df'].query('Name==\'%s\''%kernel_name_1).Duration, name='L2: '+kernel_name_1[:15], xbins=dict(size=bin_size_1), marker_color='blue'))
fig1.add_trace(go.Histogram(x=layer_dfs['l3_df'].query('Name==\'%s\''%kernel_name_1).Duration, name='L3: '+kernel_name_1[:15], xbins=dict(size=bin_size_1), marker_color='darkblue'))
# BatchSize=1000
fig1000.add_trace(go.Histogram(x=layer_dfs['l1_df_1000'].query('Name==\'%s\''%kernel_name_1000).Duration, name='L1: '+kernel_name_1000[:15], xbins=dict(size=bin_size_1000), marker_color='lightgreen'))
fig1000.add_trace(go.Histogram(x=layer_dfs['l2_df_1000'].query('Name==\'%s\''%kernel_name_1000).Duration, name='L2: '+kernel_name_1000[:15], xbins=dict(size=bin_size_1000), marker_color='green'))
fig1000.add_trace(go.Histogram(x=layer_dfs['l3_df_1000'].query('Name==\'%s\''%kernel_name_1000).Duration, name='L3: '+kernel_name_1000[:15], xbins=dict(size=bin_size_1000), marker_color='darkgreen'))
# Additional formatting and titles
fig1.update_layout(barmode='overlay',
title_text='BatchSize=1 Kernel Runtime Variations',
xaxis_title_text='Kernel Duration (us)',
yaxis_title_text='Number of Occurances (out of 50 trials)'
)
fig1.update_traces(opacity=.75)
fig1000.update_layout(barmode='overlay',
title_text='BatchSize=1 Kernel Runtime Variations',
xaxis_title_text='Kernel Duration (us)',
yaxis_title_text='Number of Occurances (out of 50 trials)'
)
fig1000.update_traces(opacity=.75)
fig1.show()
fig1000.show()
def plot_gain(df):
'''
Input: df - Dataframe of the stock
Output: Histograms of the daily returns and the daily change in percentage of the stock
'''
xDR = np.arange(min(df['Daily Return'].dropna().tolist()),
max(df['Daily Return'].dropna().tolist()),
len(df['Daily Return'].dropna()))
xC = np.arange(min(df['Change %'].dropna().tolist()),
max(df['Change %'].dropna().tolist()),
len(df['Change %'].dropna()))
fig = make_subplots(rows=1, cols=2,
subplot_titles=(f'Daily Return of stock "{df["Company stock name"][0]}"',
f'Daily change in % of stock "{df["Company stock name"][0]}"'))
fig.add_trace(go.Histogram(x=df['Daily Return'].dropna(), marker_color='#330C73', opacity=0.8), row=1, col=1)
fig.add_trace(go.Scatter(x=xDR, y=df['Daily Return'].dropna(), mode='lines', line_color='#330C73'), row=1, col=1)
fig.add_trace(go.Histogram(x=df['Change %'].dropna(), marker_color='#330C73', opacity=0.8), row=1, col=2)
fig.add_trace(go.Scatter(x=xC, y=df['Change %'].dropna(), mode='lines', line_width=5, line_color='#330C73'), row=1,
col=2)
fig.update_layout(showlegend=False)
fig.update_xaxes(title_text="Price USD ($)", row=1, col=1)
fig.update_xaxes(title_text="Percentage %", row=1, col=2)
fig.update_yaxes(title_text="Counts", row=1, col=1)
fig.update_yaxes(title_text="Counts", row=1, col=2)
fig.update_layout(
bargap=0.1,
bargroupgap=0.1
)
return fig
def generate_histogram(n_clicks, decision, range, table_data):
if n_clicks > 0 and decision and range:
df = pd.DataFrame(table_data)
data = df[decision]
min_val = data.min()
max_val = data.max()
size = (max_val - min_val) / int(range)
return {
'data': [go.Histogram(x=df[decision], xbins=dict(
start=min_val,
end=max_val,
size=size),
autobinx=False)],
'layout': go.Layout(
xaxis={'title': decision},
yaxis={'title': 'Quantity'},
margin={'l': 40, 'b': 40, 't': 30, 'r': 0},
clickmode='event+select',
title='histogram'
)
}
elif n_clicks > 0 and decision:
df = pd.DataFrame(table_data)
return {
'data': [go.Histogram(x=df[decision])],
'layout': go.Layout(
xaxis={'title': decision},
yaxis={'title': 'Ilość'},
margin={'l': 40, 'b': 40, 't': 30, 'r': 0},
clickmode='event+select',
title='histogram'
)
}
else:
return {
'data': [],
'layout': go.Layout(title='histogram'),
}
def plot_distributions(self):
df = self.normalised_df[self.col_types['int']].set_index(
self.y_cols[0])
if df.shape[0] > 1000:
df = df.sample(1000)
fig = go.Figure()
def get_kde(col, var):
fig_temp = ff.create_distplot(
[df[col]], ['distplot']) # TODO: check curve_type='normal'
x = fig_temp['data'][1].x
y = fig_temp['data'][1].y
if var == 'x':
return x
elif var == 'y':
return y
fig_temp = ff.create_distplot([df[df.columns[0]]], ['distplot'])
fig.add_trace(
go.Histogram(x=df[df.columns[0]],
visible=True,
histnorm='probability density',
marker_color='#2D3949',
opacity=0.75))
fig.add_trace(
go.Scatter(x=fig_temp['data'][1].x,
y=fig_temp['data'][1].y,
visible=True,
marker_color='#ff9900'))
buttons = []
for col in df.columns:
buttons.append(
dict(
method='restyle',
label=col,
visible=True,
args=[{
'y': [None, get_kde(col, 'y')],
'x': [df[col], get_kde(col, 'x')],
'type': ['histogram', 'scatter']
}],
))
updatemenu = set_up_buttons(buttons)
fig.update_layout(
showlegend=False,
updatemenus=updatemenu,
title_text=f"Distribution",
template="plotly_white_custom",
)
return opy.plot(fig, auto_open=False, output_type='div')
def histogram(df,
title='Histogram',
out_path=None,
max_col=2,
layout_kwargs={},
to_image=False,
bin_algo='default',
str_length=None,
hidden_char='..'):
bin_algos = ['default', 'count', 'width']
assert bin_algo in bin_algos, f'bin_algo not in valid list: {bin_algos}'
data = []
colors = DEFAULT_PLOTLY_COLORS
columns = df.columns
for column in columns:
try:
# https://www.qimacros.com/histogram-excel/how-to-determine-histogram-bin-interval/
n_data = len(np.unique(df[column]))
n_bins = int(np.ceil(np.sqrt(n_data)))
width = (df[column].max() - df[column].min()) / n_bins
nbinsx = n_bins if bin_algo == 'count' else None
xbins = {'size': width} if bin_algo == 'width' else None
except TypeError:
nbinsx = None
xbins = None
x = df[column].copy()
if x.dtype == object:
x = x.sort_values()
x = np.where(
x.str.len() > str_length,
x.str.slice(stop=str_length).str.strip() + hidden_char, x)
data.append(
go.Histogram(x=x,
name=column,
showlegend=False,
nbinsx=nbinsx,
xbins=xbins,
marker={'color': colors[0]}))
plot_subplots(data,
max_col=max_col,
title=title,
out_path=out_path,
layout_kwargs=layout_kwargs,
to_image=to_image)
def create_histogram(df: pd.DataFrame, column: str) -> dcc.Graph:
"""Create Histogram for dataframe and column"""
return dcc.Graph(
id="graph-{:s}".format(column),
figure={
"data": [go.Histogram(x=df[column])],
"layout": {
"title": column.title(),
"xaxis": {"title": column.title()},
"yaxis": {"title": "Frequency"},
}
}
)
def plot_hit_miss(data, min_value, max_value, step):
fig = go.Figure()
fig.add_trace(go.Histogram(
x=data['hits'],
name='hits',
xbins=dict(
start=min_value,
end=max_value,
size=step
),
))
fig.add_trace(go.Histogram(
x=data['misses'],
name='misses',
xbins=dict(
start=min_value,
end=max_value,
size=step
),
))
fig.layout.xaxis.update(range=[min_value,max_value])
fig.show()
def plot_histogram(X,opt):
if opt == 'ambos':
#Dataset completo
x = df[X]
elif opt == 'crash':
#Dataset filtrado por coque
x = df[df[trg[0]]==1][X]
elif opt == 'libre':
#Dataset filtrado por libre de accidente
x = df[df[trg[0]]==0][X]
fig = go.Figure(data=[go.Histogram(x=x)])
fig.update_layout(title_text=X)
return fig
def get_unit_tab_content(self, unit_idx, active_tab):
""" Get the content of the selected tab """
w = self.weights[:, unit_idx]
if active_tab == 'info':
return self._get_unit_info(unit_idx, len(w)), None
elif active_tab == 'weights':
fig = go.Figure(data=[
go.Histogram(x=w,
marker=self.theme.gradient_color_scale.as_dict(w))
])
fig.update_layout(
margin=self.theme.bottom_figure_margins,
title=dict(text='Weight histogram', font=dict(size=14)),
xaxis_title_text='Amplitude',
bargap=0.2, # gap between bars of adjacent location coordinates)
template=self.theme.plotly)
return [], fig
elif active_tab == 'grads':
if self.grads is None:
return html.P("No gradients available"), None
g = self.grads[:, unit_idx]
fig = go.Figure(data=[
go.Histogram(x=g,
marker=self.theme.gradient_color_scale.as_dict(g))
])
fig.update_layout(
margin=self.theme.bottom_figure_margins,
title=dict(text='Gradients histogram', font=dict(size=14)),
xaxis_title_text='Amplitude',
# yaxis_title_text='Count',
bargap=0.2, # gap between bars of adjacent location coordinates)
template=self.theme.plotly)
return [], fig
return AbstractLayer.get_layer_tab_content(self, active_tab)
def trimPlot(data, out_pre):
k = list(data.keys())
k.sort()
y1 = [data[y][READS_W_ADAPTERS] for y in k]
y2 = [data[y][READS_WRITTEN] for y in k]
fig = go.Figure()
fig.add_trace(go.Histogram(histfunc="sum", y=y1, x=k,
name="reads written"))
fig.update_layout(autosize=True, height=700)
fig.update_xaxes(title_text='File Name')
fig.update_yaxes(title_text='Percent Trimmed with 3\' Adapter Found')
fig.write_html(str(out_pre) + "/trim_plot.html")
def view_data(state):
# Get data...
mvals = df[(df['State_Name'].str.match(state)) & (df['Mean'] != 0)]['Mean']
# Print Histogram...
fig = go.Figure(data=[go.Histogram(x=mvals)])
fig.update_layout(title_text='{} Mean Household Income'.format(state),
xaxis_title_text='Household Income',
yaxis_title_text='Count',
bargap=0.2,
bargroupgap=0.1)
fig.show()
# Print Probability Plot...
stats.probplot(mvals, plot=plt)
plt.show()
# Log Transformation...
mvals_log = df[(df['State_Name'].str.contains(state))
& (df['Mean'] != 0)]['Mean'].apply(lambda x: np.log(x))
# Log Transformation Histogram...
fig = go.Figure(data=[go.Histogram(x=mvals_log)])
fig.update_layout(
title_text='{} Mean Household Income (Log Transformation)'.format(
state),
xaxis_title_text='Household Income',
yaxis_title_text='Count',
bargap=0.2,
bargroupgap=0.1)
fig.show()
# Log Transforamtion Probability Plot
stats.probplot(mvals_log, plot=plt)
plt.show()
def histogram(column, telcom):
# Separating churn and non churn customers
churn = telcom[telcom["Churn"] == "Yes"]
not_churn = telcom[telcom["Churn"] == "No"]
trace1 = go.Histogram(x=churn[column],
histnorm="percent",
name="Churn Customers",
marker=dict(line=dict(width=.5, color="black")),
opacity=.9)
x = np.random.randn(500)
fig = go.Figure(data=[go.Histogram(x=x, histnorm='probability')])
fig.show()
trace2 = go.Histogram(x=not_churn[column],
histnorm="percent",
name="Non churn customers",
marker=dict(line=dict(width=.5, color="black")),
opacity=.9)
data = [trace1, trace2]
layout = go.Layout(
dict(
title=column + " distribution in customer attrition ",
plot_bgcolor="rgb(243,243,243)",
paper_bgcolor="rgb(243,243,243)",
xaxis=dict(gridcolor='rgb(255, 255, 255)',
title=column,
zerolinewidth=1,
ticklen=5,
gridwidth=2),
yaxis=dict(gridcolor='rgb(255, 255, 255)',
title="percent",
zerolinewidth=1,
ticklen=5,
gridwidth=2),
))
fig = go.Figure(data=data, layout=layout)
py.plot(fig)
def GetTotalsPlot():
# get the data
# later must filter by active movies
master_ls = list(TB.MasterMovie.objects.values())
year_distrib = {'netflix': [], 'amazon': [], 'hulu': []}
for mov in master_ls:
year = int(mov['Year'])
index_dx = json.loads(mov['Indeces'])
for key, val in index_dx.items():
if key in year_distrib and year >= 1980:
year_distrib[key].append(year)
# create the plot
fig = GO.Figure()
fig.add_trace(GO.Histogram(x= year_distrib['amazon'], xbins={'size': 1}, name='Amazon',
marker_color='darkblue', opacity=0.6))
fig.add_trace(GO.Histogram(x= year_distrib['netflix'], xbins={'size': 1}, name='Netflix',
marker_color='crimson', opacity=0.7))
fig.add_trace(GO.Histogram(x= year_distrib['hulu'], xbins={'size': 1}, name='Hulu',
marker_color='green', opacity=0.8))
fig.update_layout(
title="Total Movie Count for Each Service",
xaxis_title="Release Year",
yaxis_title="Movie Count",
width=600,
height=400,
margin=GO.layout.Margin(t=50, r=20, b=50, l=70, pad=0),
paper_bgcolor="LightSteelBlue",
barmode='overlay',
)
# format and send to frontend
return UT.ConvertFigureToJson(fig)
def plot_distribution(metric, start, end, size):
fig = go.Figure()
fig.add_trace(
go.Histogram(x=df_stat[metric],
name=metric,
xbins=dict(start=start, end=end, size=size)))
fig.update_layout(
barmode='overlay',
title_text=metric + ' distribution',
xaxis_title_text='Value', # xaxis label
yaxis_title_text='Count', # yaxis label
)
return fig
def histogram(selected_patient, selector, value):
x = histogram_soz(selected_patient, selector, value)
data = []
for e in x:
d = go.Histogram(x=e['d'], name=e[0], opacity=0.75, bingroup=1)
data.append(d)
layout = go.Layout(title_text="Histogram prove",
showlegend=True,
xaxis_title_text=value,
yaxis_title_text='Count',
barmode='overlay')
fig = go.Figure(data=data, layout=layout)
return fig
def plot_user_analysis_chart(df, search_type):
if (df is None) or (search_type is None):
raise PreventUpdate
subplot_titles = [
'Followers Count', 'Statuses Count', 'Friends Count',
'Favourites Count', 'Verified', 'Tweet Source', 'Lang',
'User Created At'
]
df = pd.DataFrame(df).drop_duplicates('user_screen_name')
fig = make_subplots(rows=2, cols=4, subplot_titles=subplot_titles)
for i, col in enumerate(subplot_titles[:4], start=1):
col = ('user_' + col).replace(' ', '_').lower()
fig.append_trace(go.Histogram(x=df[col], nbinsx=30, name='Users'), 1,
i)
for i, col in enumerate(subplot_titles[4:7], start=5):
if (i == 6) and (search_type == 'Search Users'):
continue
if col == 'Tweet Source':
col = 'tweet_source'
else:
col = ('user_' + col).replace(' ', '_').lower()
fig.append_trace(
go.Bar(x=df[col].value_counts().index[:14],
width=0.9,
y=df[col].value_counts().values[:14],
name='Users'), 2, i - 4)
fig.append_trace(
go.Histogram(
x=df['user_created_at'],
name='Users',
nbinsx=30,
), 2, 4)
fig['layout'].update(height=600,
plot_bgcolor='#878787',
paper_bgcolor='#878787',
showlegend=False)
return fig
def drawHistogram(pos,neg,t):
x = []
y = []
xn = []
yn = []
for keys, values in pos.items():
x.append(keys)
y.append(values)
for keysn,valuesn in neg.items():
xn.append(keysn)
yn.append(valuesn)
fig = go.Figure()
fig.add_trace(go.Histogram(histfunc="sum", y=y, x=x, name="positive"))
fig.add_trace(go.Histogram(histfunc="sum", y=yn, x=xn, name="negative"))
fig.update_layout(
title = t,
xaxis_title="Words",
yaxis_title="Frequency",
)
fig.show()
def plotHistogram(datapoints, title, xlabel, ylabel):
layout = go.Layout(title=title,
xaxis=dict(title=xlabel),
yaxis=dict(title=ylabel))
fig = go.Figure(layout=layout)
fig.update_layout(template="ggplot2")
fig.add_trace(
go.Histogram(x=datapoints.values,
# xbins = {'start': 1, 'size': 0.1, 'end' : 5}
))
return fig
def hist_by(df:pd.DataFrame, column:str, by:str, alias_column:str=None,
alias_by:str=None, colors:list=None,
nbins:int=10, func:callable=np.sum) -> go.Figure:
'''
This function excludes null values on "column" and "by" doing a simple & functions
df -> Pandas DataFrame
column -> Column to analyze
by -> Column respect to analyze "column"
alias_column -> String to show in texts (legend, titles, axis)
alias_by -> String to show in texts (legend, titles, axis)
func -> Criteria for the histogram. Frequency by defaul
colors -> list of colors wich the plots will take. This colors should be representated
in format:
hexa -> '#F33DF5'
rgb -> 'rgb(200, 56, 13)'
rgba -> 'rgba(20, 189, 12, 90)'
string -> 'green'
Thes list can contain mix format.
colors=['lightgreen', 'rgba(1,200,3,10)', '#FF0034']
colors=['green', 'lightgreen', 'darkgreen']
This list must be at least teh same lenght of unique values to plot
nibins -> Number of bins for the histograms
return plotly figure or None
'''
if not alias_column:
alias_column = column
if not alias_by:
alias_by = by
new_df = not_nulls(df, column, by)
uniques = uniques_values(new_df, by)
if not colors:
colors = ['#7ED388', '#2F668C', '#ABB392', '#EA7251', '#8501BA', '#09993E', '#BB60C4', '#65F587', '#13CEB7', '#C17B62', '#73A12E', '#344620', '#9B355F', '#B02D36', '#22B407', '#B3391C', '#408C09', '#7846C3', '#548FE2', '#290AFC', '#7C01A8', '#0D8911', '#DEC5CB', '#741BE5', '#5E11F9', '#1C4D07', '#7827A1', '#BFA409', '#F6CB1C', '#DC7AE4', '#97F7ED', '#492CDE', '#CEE30D', '#FAD149', '#BE98C9', '#04D364', '#3D4FF6', '#BB326A', '#4F50A5', '#D02CE0', '#11C68F', '#1863DD', '#0BC224', '#4F2000', '#A38BBC', '#06C5B2']*5
fig = make_subplots(
rows=len(uniques),
subplot_titles=list(uniques))
for i, unique_ in enumerate(uniques):
filter_ = new_df[by].apply(contain_substr, args=(unique_,)) # get certain value even on multimple values rows
filtered_df = new_df[filter_] # filtered dataframe
fig.add_trace(
go.Histogram(x=filtered_df[column], name=unique_,
marker_color=colors[i], nbinsx=nbins
), row=i+1, col=1
)
fig.update_xaxes(title_text=alias_column, row=i + 1, col=1)
fig.update_yaxes(title_text=alias_by, row=i + 1, col=1)
# configurating size, titles and legend
fig.update_layout(height=350*len(uniques), width=800,
title_text=alias_column + ' by ' + alias_by,
showlegend=False)
return fig
def histogram_plot(performance_data, ticker):
returns = performance_data.diff(axis=0) / performance_data
returns = performance_data.diff(axis=0) / performance_data
trace_open = go.Histogram(x=returns['Open'],
marker={'colorscale': 'Viridis'},
name='Open')
trace_close = go.Histogram(x=returns['Open'],
marker={'colorscale': 'Viridis'},
name='Close')
data = [trace_open, trace_close]
layout = {
'title': f'{ticker} Histogram'
# 'yaxis':{'type': 'lin'}
}
fig_histogram = dict(data=data, layout=layout)
return fig_histogram
def create_graph(self):
self.menu._close()
#fig = go.Figure(data=go.Scatter(x=self.game.time, y=self.game.life))
#fig.show()
fig = go.Figure(data=[go.Histogram(x=self.game.life)])
# Documentazione https://plotly.com/python/histograms/
fig.update_layout(
title_text='Tempi percorrenza', # title of plot
xaxis_title_text='Value', # xaxis label
yaxis_title_text='Count', # yaxis label
#bargap=0.2, # gap between bars of adjacent location coordinates
bargroupgap=0.1 # gap between bars of the same location coordinates
)
fig.write_html('tmp.html', auto_open=True)
def plot_distribution(random_sample, bins):
fig = go.Figure(data=[go.Histogram(x=random_sample, nbinsx=bins)])
fig.update_layout(title_text="Гистограмма")
st.write(fig)
fig = ff.create_distplot([random_sample], [""],
bin_size=.2,
show_hist=False)
fig.update_layout(title_text="Плотность")
st.write(fig)
fig = go.Figure(go.Histogram2dContour(x=random_sample, y=random_sample))
fig.update_layout(title_text="Контуры плотности")
st.write(fig)
def normal_histogram(tweets: List[Tweet]) -> None:
"""Displays a normal histogram in plotly containing the compound values of each tweet
in tweets.
Precondition:
- all(t.sentiment is not None for t in tweets)
"""
# Retrieves the compound scores of each tweet
compound_values = [tweet.sentiment['compound'] for tweet in tweets]
# Compute the summary statistics of the compound values
summary_data = summary(compound_values)
# Store the summary statistics of the compound values in text form to add as annotation
lines = [
'Mean :' + str(summary_data['mean']),
'Median :' + str(summary_data['median']),
'Standard Deviation :' + str(summary_data['stdev']),
'Range :' + str(summary_data['range'])
]
text = '<br>'.join(lines) # HTML '<br>' sequence gives newlines
# Adds the annotation that displays the summary statistics in a box below the graph.
layout = go.Layout(
height=800,
width=800,
yaxis=go.layout.YAxis(domain=[0.5, 1]),
annotations=[
go.layout.Annotation(
bordercolor='black',
align='left',
yanchor='top', # Align text box's top edge with y axis
text=text,
showarrow=False,
width=650,
xref='paper', # Place relative to figure, not axes
yref='paper',
font={'family': 'Courier'},
x=0, # Coordinates start from top left corner
y=0.4)
])
# Creates the figure object that draws the histogram and takes in the annotation created above
fig = go.Figure(
data=[go.Histogram(x=compound_values, histnorm='probability')],
layout=layout)
fig.update_traces(xbins_size=0.01, selector=dict(type='histogram'))
fig.update_layout(
title='Percentage of frequency of tweets against compound value range',
xaxis_title='Compound value range',
yaxis_title='Percentage of occurrence')
# Shows the figure object
fig.show()
def alignPlot(data, out_pre):
k = list(data.keys())
k.sort()
y = [data[y] for y in k]
fig = go.Figure()
fig.add_trace(
go.Histogram(histfunc="sum",
y=y,
x=k,
name="percent aligned to miR reference"))
fig.update_layout(autosize=True, height=700)
fig.update_xaxes(title_text='File Name')
fig.update_yaxes(title_text='Percent of Trimmed Reads Aligned')
fig.write_html(str(out_pre) + "/align_plot.html")