def temp_graph(xdata, ydata, w, h):
'''A helper function to plot data
Args
Returns
'''
plt = figure(plot_width=w, plot_height=h)
plt.line(xdata, ydata, line_width=2)
plt.circle(xdata, ydata, fill_color="white", size=8)
return plt
def generate_rec_prec_html(mrec, mprec, scores, class_name, ap):
"""
generate dynamic P-R curve HTML page for each class
"""
# bypass invalid class
if len(mrec) == 0 or len(mprec) == 0 or len(scores) == 0:
return
rec_prec_plot_path = os.path.join('result', 'classes')
os.makedirs(rec_prec_plot_path, exist_ok=True)
bokeh_io.output_file(os.path.join(rec_prec_plot_path,
class_name + '.html'),
title='P-R curve for ' + class_name)
# prepare curve data
area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]]
area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]]
score_on_curve = [0.0] + scores[:-1] + [0.0] + [scores[-1]] + [1.0]
source = bokeh.models.ColumnDataSource(
data={
'rec': area_under_curve_x,
'prec': area_under_curve_y,
'score': score_on_curve,
})
# prepare plot figure
plt_title = 'class: ' + class_name + ' AP = {}%'.format(ap * 100)
plt = bokeh_plotting.figure(plot_height=200,
plot_width=200,
tools="",
toolbar_location=None,
title=plt_title,
sizing_mode="scale_width")
plt.background_fill_color = "#f5f5f5"
plt.grid.grid_line_color = "white"
plt.xaxis.axis_label = 'Recall'
plt.yaxis.axis_label = 'Precision'
plt.axis.axis_line_color = None
# draw curve data
plt.line(x='rec', y='prec', line_width=2, color='#ebbd5b', source=source)
plt.add_tools(
bokeh.models.HoverTool(tooltips=[
('score', '@score{0.0000 a}'),
('Prec', '@prec'),
('Recall', '@rec'),
],
formatters={
'rec': 'printf',
'prec': 'printf',
},
mode='vline'))
bokeh_io.save(plt)
return
def get_chart(chart_type, data, **kwargs):
plt.switch_backend('AGG')
fig = plt.figure(figsize=(10, 4))
if chart_type == "#1":
print("bar chart")
plt.bar(data['transaction_id'], data['price'])
elif chart_type == "#2":
labels = kwargs.get('labels')
plt.pie(data=data, x='price', labels=labels)
print("pie chart")
elif chart_type == "#3":
plt.line(data['transaction_id'], data['price'])
print("line chart")
else:
print("chart generation failed")
plt.tight_layout()
chart = get_graph()
return chart
def main():
N = 4
localnetMeans = (20, 35, 30, 35) #LAN length of outage (mins)
wanMeans = (25, 32, 34, 20) #WAN length of outage (min)
ind = np.arange(N) # the x locations for the groups
# the width of the bars: can also be len(x) sequence
width = 0.35
# describe where to display p
# stack p2 on top of p1
p1 = plt.line(ind, localnetMeans, width)
p2 = plt.line(ind, wanMeans, width, bottom=localnetMeans)
# Describe the table metadata
plt.ylabel("Length of Outage (mins)")
plt.title("2018 Network Summary")
plt.xticks(ind, ("Q1", "Q2", "Q3", "Q4"))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ("LAN", "WAN"))
# display the graph
# plt.show() # you can try this on a Python IDE with a GUI if you'd like
plt.savefig("/home/student/mycode/graphing/2018summary.pdf")
def Stock_intchart(ticker, ndays=180):
today = dt.date.today()
data = yfs.get_data(ticker, start_date=today - dt.timedelta(days=ndays), end_date=today)
fig = plt.line(data, y='close', title=ticker)
fig.update_xaxes(rangeslider_visible=True,rangeselector=dict(
buttons=list([
dict(count=15, label="15d", step="day", stepmode="backward"),
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=3, label="3m", step="year", stepmode="backward"),
dict(step="all")
])
)
)
fig.show()
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 6 16:30:55 2020
@author: pulki
"""
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize']=(20.0,10.0)
data=pd.read_csv('E:/analytics/datasets/headbrain.csv')
data.shape
data.head()
X=data['Head Size(cm^3)'].values
Y=data['Brain Weight(grams)'].values
ax=plt.scatter(X,Y)
plt.line(X,Y)
plt.title('Linear Regression')
plt.xlabel("Head Size")
plt.ylabel('Brain Weight')
def plot_powspec_binned_bokeh(data: Dict,
lmax: Dict,
title_string: str,
truthfile=None,
truth_label: str = None) -> None:
"""Plotting
Args:
data (Dict): powerspectra with spectrum and frequency-combinations in the columns
plotsubtitle (str, optional): Add characters to the title. Defaults to 'default'.
plotfilename (str, optional): Add characters to the filename. Defaults to 'default'
"""
# koi = next(iter(data.keys()))
# specs = list(data[koi].keys())
bins = np.logspace(np.log10(1), np.log10(lmax + 1), 250)
bl = bins[:-1]
br = bins[1:]
from bokeh.plotting import figure as bfigure
plt = bfigure(title=title_string,
y_axis_type="log",
x_axis_type="log",
x_range=(10, 4000),
y_range=(1e-3, 1e6),
background_fill_color="#fafafa")
# plt.xlabel("Multipole l")
# plt.ylabel("Powerspectrum")
idx = 0
idx_max = 8
from bokeh.palettes import inferno
for freqc, val in data.items():
col = inferno(idx_max)
freqs = freqc.split("-")
if freqs[0] == freqs[1]:
binmean, binerr, _ = _std_dev_binned(data[freqc])
binerr_low = np.array([
binmean[n] * 0.01 if binerr[n] > binmean[n] else binerr[n]
for n in range(len(binerr))
])
plt.line(
0.5 * bl + 0.5 * br,
np.nan_to_num(binmean),
legend_label=freqc + " Channel",
# yerr=(binerr_low, binerr),
# # 0.5 * bl + 0.5 * br,
# # binmean,
# # yerr=binerr,
# label=freqc,
# capsize=2,
# elinewidth=1,
# fmt='x',
# # ls='-',
# ms=4,
# alpha=(2*idx_max-idx)/(2*idx_max)
color=col[idx],
line_width=3,
muted_alpha=0.2)
plt.multi_line( #[(bm, bm) for bm in np.nan_to_num(binmean)]
[(bx, bx) for bx in 0.5 * bl + 0.5 * br],
[(bm - br, bm + br) for bm, br in zip(np.nan_to_num(binmean),
np.nan_to_num(binerr))],
legend_label=freqc + " Channel",
# yerr=(binerr_low, binerr),
# # 0.5 * bl + 0.5 * br,
# # binmean,
# # yerr=binerr,
# label=freqc,
# capsize=2,
# elinewidth=1,
# fmt='x',
# # ls='-',
# ms=4,
# alpha=(2*idx_max-idx)/(2*idx_max)
line_color=col[idx],
line_width=2,
muted_alpha=0.2)
idx += 1
plt.xaxis.axis_label = "Multipole l"
plt.yaxis.axis_label = "Powerspectrum"
plt.line(np.arange(0, 4000, 1),
truthfile,
color='red',
line_width=4,
legend_label="Best Planck EE",
muted_alpha=0.2)
# label = truth_label,
# ls='-', marker='.',
# ms=0,
# lw=3)
plt.legend.location = "top_left"
plt.legend.click_policy = "mute"
return plt
sales = sales.set_index('date')
print(sales.head(5))
y = sales['item_cnt_day'].resample('MS').sum()
y = y.diff()
plt.style.use('fivethirtyeight')
y.plot(figsize=(15, 6))
plt.show()
plt.style.use('ggplot')
decomposition = sm.tsa.seasonal_decompose(y, model='additive')
fig = decomposition.plot()
plt.show()
plt.line()
decomposition = sm.tsa.seasonal_decompose(y, model='multiplicative')
fig = decomposition.plot()
plt.show()
print(sales.info())
monthly_sales = sales.groupby(["date_block_num", "shop_id",
"item_id"])["date", "item_price",
"item_cnt_day"].agg({
"date": ["min", 'max'],
"item_price":
"mean",
"item_cnt_day":
"sum"
level='glyph',
x_offset=-17.5,
y_offset=0,
source=source,
text_font_size='14pt',
render_mode='canvas')
plt.add_layout(labels)
# plot
a = plt.vbar(x='kod', top='cases', width=0.8, source=source)
a.glyph.fill_color = '#084594'
r = {}
r['in_pop'] = plt.line(x='kod',
y='in_pop',
color='red',
source=source,
y_range_name='procenty',
legend_label='share in the population')
r['increment'] = plt.circle(x='kod',
y='increment',
size=20,
color='green',
source=source,
y_range_name='procenty',
legend_label='daily increment ')
# legenda
plt.legend.location = 'top_right'
plt.legend.click_policy = 'hide'
# tabela do layout'u
def cum_sum(Y):
Y['cum_sum'] = Y['spot_counts'].cumsum()
Y.reset_index(inplace=True)
return Y
# Z=group.apply(cum_sum)
group = good_check.groupby([
'market', 'station_name', 'daypart_name', 'invcode_name', 'air_week',
'air_year'
])
bad_check = Bad_Data[Bad_Data.invcode_name == 'Today Show 7-8a']
plt.line('')
normal_pile = data[data['sport_olympic_flag'] == 'normal']
sport_pile = data[data['sport_olympic_flag'] == 'sports']
olympic_pile = data[data['sport_olympic_flag'] == 'olympic']
# Good Pile
Fit_TS = normal_pile[(normal_pile['time_series_flag'] == 'fit')]
Good_Data = Fit_TS[Fit_TS['spot_flag'] == 'good']
# Bad_Pile
Unfit_TS = normal_pile[(normal_pile['time_series_flag'] == 'unfit')]
Fit_Bad_Spot = Fit_TS[Fit_TS['spot_flag'] == 'bad']
Bad_Data = Unfit_TS.append(Fit_Bad_Spot)
import matplotlib.pyplot as pt #importing matplotlib pt is an alias of pyplot
import pandas as pd
#importing pandas , pd is an elias of pandas
#If we want to read something from dataset or csv file we use read_csv() method.
data = pd.read_csv("../../matplotlib/cgpa.csv")
data = data.head(40)
# head() method is used to select some elements of dataset
#here i am plotting rollno vs cgpa so x-axis will be rollno and y-axis will be cgpa
#color of scatter is blue and we also include label as scatter.
#scatter() method is used to plot the scatter
pt.line(data["rollno"], data["cgpa"], color="blue", label="scatter")
pt.xlabel("RollNo", color="green") #xlabel() defines the label of x-axis
pt.ylabel("CGPA", color="blue") #ylabel() defines the label of x-axis
pt.title("CGPA vs Roll No",
color="green") #title() is used to give title of this scatter plot
pt.show()