def test_plot_if_title_is_converted_from_string_to_Title() -> None:
plot = Plot()
plot.title = "A title"
plot.title.text_color = "olive"
assert isinstance(plot.title, Title)
assert plot.title.text == "A title"
assert plot.title.text_color == "olive"
def get_weather_plot(time_int=None, t_min=None, t_max=None):
plot_height = cs.plot_height
plot_width = cs.plot_width
border_left = cs.border_left
column_names = cs.column_names_weewx
column_time = cs.time_column_name_weewx
if t_max == None or t_min == None:
t_max, t_min = get_first_time_interval()
t_span = t_max - t_min
if time_int == None:
time_int = cs.time_int
data_seconds = sql_con.get_data_ws(time_int=time_int,
t_min=t_min,
t_max=t_max)
data_seconds.iloc[:, 0] = data_seconds.iloc[:, 0] * 1000
plot = Plot(
x_range=DataRange1d(start=(t_min - t_span * .1) * 1000,
end=(t_max + t_span * .1) * 1000),
y_range=DataRange1d(),
plot_width=plot_width,
plot_height=plot_height,
# x_axis_type="datetime",
min_border_left=border_left,
toolbar_location="right")
add_glyphs_to_plot(column_names, column_time, data_seconds, plot,
'source_weather')
plot.add_layout(DatetimeAxis(name="date_time_axis"), 'below')
plot.add_tools(
PanTool(),
WheelZoomTool(),
# ResizeTool(),
CrosshairTool(),
PreviewSaveTool())
Grid(plot=plot,
dimension=0,
ticker=plot.select('date_time_axis')[0].ticker)
Grid(plot=plot,
dimension=1,
ticker=plot.select(type=LinearAxis, name=column_names[0])[0].ticker)
set_legends(plot)
plot.title = cs.plot_title_weewx + ' averaged to {} seconds'.format(
time_int)
return plot
def get_weather_plot(time_int=None,t_min=None,t_max=None):
plot_height = cs.plot_height
plot_width = cs.plot_width
border_left = cs.border_left
column_names = cs.column_names_weewx
column_time = cs.time_column_name_weewx
if t_max == None or t_min == None:
t_max, t_min = get_first_time_interval()
t_span = t_max - t_min
if time_int == None:
time_int = cs.time_int
data_seconds = sql_con.get_data_ws(time_int=time_int, t_min=t_min, t_max=t_max)
data_seconds.iloc[:, 0] = data_seconds.iloc[:, 0] * 1000
plot = Plot(x_range=DataRange1d(start=(t_min-t_span*.1)*1000,
end=(t_max+t_span*.1)*1000),
y_range=DataRange1d(),
plot_width=plot_width,
plot_height=plot_height,
# x_axis_type="datetime",
min_border_left=border_left,
toolbar_location="right")
add_glyphs_to_plot(column_names, column_time, data_seconds, plot,'source_weather')
plot.add_layout(DatetimeAxis(name="date_time_axis"), 'below')
plot.add_tools(PanTool(),
WheelZoomTool(),
# ResizeTool(),
CrosshairTool(),
PreviewSaveTool()
)
Grid(plot=plot,dimension=0,ticker=plot.select('date_time_axis')[0].ticker)
Grid(plot=plot,
dimension=1,
ticker=plot.select(type=LinearAxis,name=column_names[0])[0].ticker
)
set_legends(plot)
plot.title = cs.plot_title_weewx + ' averaged to {} seconds'.format(time_int)
return plot
def __init__(self, total: int, title: str, color: str,
bokeh_session: object):
"""
Initialize a ProgressBar instance.
:param total: number of steps the progress bar is divided into. In other words, the number of times update should be called.
:param title: the title of the progress bar
:param color: the color of the progress bar
:param bokeh_session: bokeh server session
"""
self.color = color
self.title = title
self.total = total
self.current_source_index = 0
self.source = self.create_column_data_source()
plot = Plot(plot_width=1000,
plot_height=50,
min_border=0,
toolbar_location=None,
outline_line_color=None)
glyph = Rect(x="x_coordinate",
y=0,
width=1,
height=1,
angle=-0.0,
fill_color="color",
line_color="color",
line_alpha=0.3,
fill_alpha=0.3)
plot.add_glyph(self.source, glyph)
self.title_object = Title()
self.title_object.text = " " + self.title
plot.title = self.title_object
bokeh_session.document.add_root(plot)
x = np.linspace(1, 5, 100)
y = x + np.sin((x - 1) * np.pi)
x2 = np.linspace(1.5, 5.5, 5)
z = x2 + 2 * np.cos((x2 - 1) * np.pi)
source1 = ColumnDataSource({
"x": [1, 2, 3, 4, 5],
"y": [1, 2, 3, 4, 5],
"who": ["a", "b", "c", "d", "e"]
})
source2 = ColumnDataSource({"x": x, "y": y})
source3 = ColumnDataSource({"x": x2, "y": z})
source4 = ColumnDataSource({"y": [2.5], "x": [0.5]})
plot = Plot(width=300, height=300)
plot.title = Title(text="Themed glyphs")
xaxis = LinearAxis(ticker=BasicTicker(), formatter=BasicTickFormatter())
yaxis = LinearAxis(ticker=BasicTicker(), formatter=BasicTickFormatter())
plot.add_layout(xaxis, "below")
plot.add_layout(yaxis, "left")
plot.add_glyph(source1, Scatter(x="x", y="y", marker="diamond", size=20))
plot.add_glyph(source1, Text(x=dodge("x", -0.2), y=dodge("y", 0.1),
text="who"))
plot.add_glyph(source2, Line(x="x", y="y"))
plot.add_glyph(source3, Ellipse(x="x",
y="y",
width=0.2,
height=0.3,
angle=-0.7))
def tweets_per_hour_plot(self, data_path, emotion=None, color="#b3de69"):
#data_path = os.path.dirname(__file__) + "/../data/pbFollowers/merged/"
data_path = data_path + "merged/"
dir_files = os.listdir(data_path)
counts_of_tweets = {}
for filename in dir_files:
#df = pd.read_json(data_path + filename)
df = self.get_flattened_data(data_path + filename, 'tones',
['user', 'created_at'])
if emotion is not None:
try:
df = df[df.tone_name == emotion]
except AttributeError:
continue
try:
df['created_at'] = df['created_at'] / 1000
except TypeError:
print("\'Created_at\' error where it is somehow a dict")
continue
offset = 0
try:
offset = df['user'][0]['utc_offset']
except (IndexError, KeyError):
offset = 0
if offset is None:
offset = 0
#df['std_time'] = df['created_at'] + pd.TimedeltaIndex(df['offset'], unit='s')
df['std_time'] = df['created_at'] + offset
df['std_time'] = [
datetime.datetime.fromtimestamp(x) for x in df['std_time']
]
df = df.set_index(df['std_time'])
temp = pd.DatetimeIndex(df['std_time'])
df['hour'] = temp.hour
p = df.groupby(df['hour'])
freq_of_tweets = p['std_time'].count()
freq_dict = freq_of_tweets.to_dict()
for key, value in freq_dict.items():
try:
counts_of_tweets[key] += value
except KeyError:
counts_of_tweets[key] = value
hours = np.fromiter(counts_of_tweets.keys(), dtype=float)
hours += 1
numTweets = np.fromiter(counts_of_tweets.values(), dtype=float)
# output to static HTML file
if emotion is None:
hourly_freq_plot_path = data_path + "../plots/Hourly_freq_plot.html"
output_file(hourly_freq_plot_path)
source = ColumnDataSource(data=dict(hours=hours, numTweets=numTweets))
hover = HoverTool(tooltips=[
('hours', '@hours'),
('numTweets', '@numTweets'),
])
xdr = DataRange1d()
ydr = DataRange1d()
plot = Plot(x_range=xdr,
y_range=ydr,
plot_width=500,
plot_height=500,
h_symmetry=False,
v_symmetry=False,
min_border=0,
tools=[hover])
glyph = VBar(x="hours",
top="numTweets",
bottom=0,
width=0.5,
fill_color=color)
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis, 'left')
t = Title()
if emotion is not None:
t.text = "(" + emotion + ") Number of Tweets by Hour"
else:
t.text = "(all emotions) Number of Tweets by Hour"
plot.title = t
plot.xaxis.axis_label = 'Hours'
plot.yaxis.axis_label = 'Number of Tweets'
if emotion is not None:
return plot
else:
show(plot)
return plot
