def create_figure(y_column, x_column):
df_cars = load_data()
source = ColumnDataSource(data=df_cars)
hover = HoverTool(tooltips=[
("Modell", "@Modell"),
("price", "@price"),
])
p = figure(title='Cars', tools=[hover])
#x_column = 'Tillverkningsår'
#y_column = 'price'
markers = ['circle']
p.scatter(x=x_column,
y=y_column,
source=source,
legend="car type",
marker=factor_mark('car type',
markers=markers,
factors=df_cars['car type'].unique()),
color=factor_cmap('car type',
palette='Category10_3',
factors=df_cars['car type'].unique()),
size=8,
alpha=0.25)
p.xaxis.axis_label = x_column
p.yaxis.axis_label = y_column
return p
def test_defaults(self) -> None:
t = bt.factor_mark("foo", ["hex", "square"], ["foo", "bar"])
assert isinstance(t, Field)
assert t.field == "foo"
assert isinstance(t.transform, CategoricalMarkerMapper)
assert t.transform.markers == ["hex", "square"]
assert t.transform.factors == ["foo", "bar"]
assert t.transform.start == 0
assert t.transform.end is None
def test_defaults(self):
t = bt.factor_mark("foo", ["hex", "square"], ["foo", "bar"])
assert isinstance(t, dict)
assert set(t) == {"field", "transform"}
assert t['field'] == "foo"
assert isinstance(t['transform'], CategoricalMarkerMapper)
assert t['transform'].markers == ["hex", "square"]
assert t['transform'].factors == ["foo", "bar"]
assert t['transform'].start == 0
assert t['transform'].end is None
def test_basic(self) -> None:
t = bt.factor_mark("foo", ["hex", "square"], ["foo", "bar"], start=1, end=2)
assert isinstance(t, dict)
assert set(t) == {"field", "transform"}
assert t['field'] == "foo"
assert isinstance(t['transform'], CategoricalMarkerMapper)
assert t['transform'].markers == ["hex", "square"]
assert t['transform'].factors == ["foo", "bar"]
assert t['transform'].start == 1
assert t['transform'].end == 2
def test_defaults(self):
t = bt.factor_mark("foo", ["hex", "square"], ["foo", "bar"])
assert isinstance(t, dict)
assert set(t) == {"field", "transform"}
assert t['field'] == "foo"
assert isinstance(t['transform'], CategoricalMarkerMapper)
assert t['transform'].markers == ["hex", "square"]
assert t['transform'].factors == ["foo", "bar"]
assert t['transform'].start == 0
assert t['transform'].end is None
def test_basic(self) -> None:
t = bt.factor_mark("foo", ["hex", "square"], ["foo", "bar"],
start=1,
end=2)
assert isinstance(t, Field)
assert t.field == "foo"
assert isinstance(t.transform, CategoricalMarkerMapper)
assert t.transform.markers == ["hex", "square"]
assert t.transform.factors == ["foo", "bar"]
assert t.transform.start == 1
assert t.transform.end == 2
def mask_rendering_kwargs(self):
"""
Get the marks and colors to use for the various point masks
Returns
-------
dict : Returns a dict for bokeh that describes the markers and pallete
"""
return {
'marker': bt.factor_mark('mask', self.MARKERS, self.MASK_TYPE),
'color': bt.factor_cmap('mask', self.PALETTE, self.MASK_TYPE)
}
def mapping_marker():
SPECIES = ['setosa', 'versicolor', 'virginica']
MARKERS = ['hex', 'circle_x', 'triangle']
p = figure(title="Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Sepal Width'
p.scatter("petal_length",
"sepal_width",
source=flowers,
legend_field="species",
fill_alpha=0.4,
size=12,
marker=factor_mark('species', MARKERS, SPECIES),
color=factor_cmap('species', 'Category10_3', SPECIES))
show(p)
def create_sample_scatter(x_data,
y_data,
source,
label,
title='',
x_axis_title='',
y_axis_title=''):
result_plot = figure(title=title,
tools=tools_list,
tooltips=custom_tooltip)
result_plot.xaxis.axis_label = x_axis_title
result_plot.yaxis.axis_label = y_axis_title
for cat_filter in label['standard_label_list']:
index_list = []
for i in range(len(source.data[label['real_label_list']])):
if source.data[label['real_label_list']][i] == cat_filter:
index_list.append(i)
view = CDSView(source=source, filters=[IndexFilter(index_list)])
result_plot.scatter(
x_data,
y_data,
source=source,
fill_alpha=0.4,
size=8,
marker=factor_mark(label['real_label_list'], markers,
label['standard_label_list']),
color=factor_cmap(label['real_label_list'], 'Category10_8',
label['standard_label_list']),
# muted_color=factor_cmap(label['real_label_list'], 'Category10_8',
# label['standard_label_list']),
muted_alpha=0.1,
view=view,
legend=cat_filter)
result_plot.legend.click_policy = "mute"
# highlight x y axes
result_plot.renderers.extend([vline, hline])
return result_plot
def Electron_Energy_Graph(conn):
output_file(
"Electron_Energy_Graph2.html"
) #????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????
############################################################################
############################# USER INPUTS ##################################
# Decide what the default viewing option is going to be. (i.e. the fields to
# be plotted on the x and y axis when the graph is opened).
# NB: Have it set that if axis is 'adate' then will automatically update
# to plot datetime.
x_data1 = 'adate'
y_data1 = '6fwhm'
plot_title1 = 'Electron Energy'
x_axis_title1 = x_data1
y_axis_title1 = y_data1
plot_size_height1 = 450
plot_size_width1 = 800
legend_location = 'bottom_left'
hover_tool_fields = ['comments']
# Create a list of the plot parameters that will be used as input to a
# function later.
list_plot_parameters = [
x_data1, y_data1, plot_title1, x_axis_title1, y_axis_title1,
plot_size_height1, plot_size_width1, legend_location
]
# Define the fields that the legend will be based off. If there is only
# one field then put it in both columns.
color_column = 'machinename'
custom_color_boolean = False
custom_color_palette = []
marker_column = 'machinename'
custom_marker_boolean = False
custom_marker_palette = []
# From the legend defined above give the values that will be pre-ticked when
# the plot is opened. NB: Bokeh will throw an error if one of these lists is
# empty (i.e. =[]) If only using color or marker then set the color_to plot
# and then enter the command: marker_to_plot = color_to_plot.
color_to_plot = ['TrueBeam B', 'TrueBeam C']
marker_to_plot = ['option1', 'option2', 'option3']
marker_to_plot = color_to_plot
############################################################################
#################### CREATE THE DATA FOR THE GRAPH #########################
# Do this in a function so it can be used in an update callback later
def Create_df():
# Use the connection passed to the function to read the data into a
# dataframe via an SQL query.
df = pd.read_sql('SELECT * FROM [eEnergyICP]', conn)
# Delete empty rows where the data is very important to have
df = df.dropna(subset=['protocol id'], how='any')
# The format is complicated for this field but seems to be that the date is
# always the first element and the machine is always the last regardless of
# how many elements there are.
# Seperate on the first '_'
df_left = df['protocol id'].str.partition(sep='_')
# Seperate on the last '_'
df_right = df['protocol id'].str.rpartition(sep='_')
# From these sperated dataframes add the appropriate columns back into
# the main dataframe.
df.loc[:, 'adate'] = df_left[0]
df.loc[:, 'machinename'] = df_right[2]
# Turn 'adate' into datetime. An annoying factor in the database is a
# few entries with a different datetime format. In combination with the
# dayfirst=True parameter to override the American date default the
# to_datetime function seems to solve this. NB: Might be a little slow
# without feeding it a specific format but unlikely to be an issue given
# relatively small datasets. Possibly someway to feed multiple formats
# but currently more effort than it's worth.
df.loc[:, 'adate'] = pd.to_datetime(df.loc[:, 'adate'], dayfirst=True)
# Drop any rows that aren't related to the Truebeams (ditches the old
# uneeded data). Might be possible to put this in the SQL query but
# difficult as machinename is embedded in the protocol ID.
df = df[df['machinename'].isin(
['TrueBeam B', 'TrueBeam C', 'TrueBeam D', 'TrueBeam F'])]
# Drop any columns where there is no data (likely because of the
# dropping of the old linacs (e.g. data that used to be collected from
# them that is no longer collected for the Truebeams))
df = df.dropna(axis='columns', how='all')
return df
df = Create_df()
# Create a list of the fields using the dataframe. By doing it now before
# the extra legend fields are added it's easy to limit what is displayed in
# the select widgets.
TableFields = (list(df.columns))
############################################################################
############################################################################
############################################################################
################ CREATE THE DATAFRAME FOR THE TOLERANCES ###################
# If you want to add tolerances change the boolean to True and construct the
# dataframe in the correct format.
tolerance_boolean = True
# The format of the dataframe should be the first line being the x_axis
# (with some values taken from the main dataframe to get the right
# formatting). The subsequent columns are the tolerances [low, high].
# NB: column names should match those from the main dataframe.
if tolerance_boolean == True:
df_tol1 = pd.DataFrame({
'adate': [df['adate'].max(), df['adate'].max()],
'6fwhm': [6, 10],
'9fwhm': [9, 12]
})
df_tol1 = pd.read_sql('SELECT * FROM [ElectronFWHMLimits]', conn)
df_tol1 = df_tol1.set_index('class')
df_tol1 = pd.DataFrame({
'adate': [df['adate'].max(), df['adate'].max()],
'6fwhm':
[df_tol1.loc['TBUCLH', 'lower6'], df_tol1.loc['TBUCLH', 'upper6']],
'9fwhm':
[df_tol1.loc['TBUCLH', 'lower9'], df_tol1.loc['TBUCLH', 'upper9']],
'12fwhm': [
df_tol1.loc['TBUCLH', 'lower12'], df_tol1.loc['TBUCLH',
'upper12']
],
'15fwhm': [
df_tol1.loc['TBUCLH', 'lower15'], df_tol1.loc['TBUCLH',
'upper15']
]
})
############################################################################
############################################################################
############################################################################
############################################################################
'''
This is the end of the user input section. If you don't need to make any
other changes you can end here.
'''
##########################################################################
################### CREATE THE COLUMNS FOR THE LEGEND ######################
(color_list, color_palette, marker_list, marker_palette, df,
add_legend_to_df) = Create_Legend(df, color_column, custom_color_boolean,
custom_color_palette, marker_column,
custom_marker_boolean,
custom_marker_palette)
############################################################################
############################################################################
############################################################################
################## FORMATTING AND CREATING A BASIC PLOT ####################
######### Make Dataset:
# Run the Make_Dataset function to create a sub dataframe that the plot will
# be made from.
Sub_df1 = Make_Dataset(df, color_column, color_to_plot, marker_column,
marker_to_plot, x_data1, y_data1)
# Make the ColumnDataSource (when making convert dataframe to a dictionary,
# which is helpful for the callback).
src1 = ColumnDataSource(Sub_df1.to_dict(orient='list'))
######### Make Plot:
# Create an empty plot (plot parameters will be applied later in a way that
# can be manipulated in the callbacks)
p1 = figure()
p1.scatter(
source=src1,
x='x',
y='y',
fill_alpha=0.4,
size=12,
# NB: Always use legend_field for this not legend_group as the
# former acts on the javascript side but the latter the Python
# side. Therefore the former will update automatically when the
# plot is changed with no need for a callback.
legend_field='legend',
marker=factor_mark('marker1', marker_palette, marker_list),
color=factor_cmap('color1', color_palette, color_list))
######### Add plot parameters:
Define_Plot_Parameters(p1, list_plot_parameters)
############################################################################
############################################################################
############################################################################
############################ ADD TOLERANCES ################################
# We defined the tolerances further up and now want to add the correct ones
# to the plot. Only do this through if the boolean is set to True as
# otherwise the user doesn't want tolerances.
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(x_data1, y_data1, Sub_df1,
df_tol1)
src1_tol = ColumnDataSource(Sub_df1_tol1.to_dict(orient='list'))
p1.line(source=src1_tol, x='x', y='y_low', color='firebrick')
p1.line(source=src1_tol, x='x', y='y_high', color='firebrick')
############################################################################
############################################################################
############################################################################
################## ADD MORE COMPLEX TOOLS TO THE PLOT ######################
######## 1)
# Create a hover tool and add it to the plot
hover1 = HoverTool()
if len(hover_tool_fields) < 11:
kwargs = {}
i = 0
for x in hover_tool_fields:
i = i + 1
kwargs['Field' + str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, x_data1, y_data1, **kwargs)
p1.add_tools(hover1)
############################################################################
############################################################################
############################################################################
################# CREATE WIDGETS TO BE ADDED TO THE PLOT ###################
######## 1)
# This select funtion will be used to create dropdown lists to change the
# data plotted on the x/y-axis.
select_xaxis, select_yaxis = Create_Select_Axis(TableFields, x_axis_title1,
y_axis_title1)
######## 2)
# This select widget will be used to create dropdown lists to change the
# legend position.
select_legend = Create_Select_Legend(legend_location)
######## 3)
# These checkbox widgets will be used to create a tool to select the machine
# and energy that are being plotted.
checkbox_color, checkbox_marker = Create_Checkbox_Legend(
df, color_column, color_to_plot, marker_column, marker_to_plot)
######## 4)
# These checkbox widgets will be used to create a tool to select the machine
# and energy that are being plotted.
checkbox_hovertool = Create_Checkbox_HoverTool(TableFields,
hover_tool_fields)
######## 5)
# Make an 'Update Button' to requery the database and get up to date data.
update_button = Button(label='Update', button_type='success')
######## 6)
# Make a Range Button
range_button = Button(label='Range', button_type='primary')
######## 7)
# Make some titles for the checkboxes
color_title = Div(text='<b>Machine Choice</b>')
marker_title = Div(text='<b>Marker</b>')
hover_title = Div(text='<b>Hovertool Fields</b>')
############################################################################
############################################################################
############################################################################
########################### CREATE A LAYOUT ################################
# Create a layout to add widgets and arrange the display.
if color_column == marker_column:
layout_checkbox = column(
[color_title, checkbox_color, hover_title, checkbox_hovertool])
else:
layout_checkbox = column([
color_title, checkbox_color, marker_title, checkbox_marker,
hover_title, checkbox_hovertool
])
button_row = row([update_button, range_button])
layout_plots = column(
[button_row, select_xaxis, select_yaxis, select_legend, p1])
tab_layout = row([layout_plots, layout_checkbox])
############################################################################
############################################################################
############################################################################
####################### CREATE CALLBACK FUNCTIONS ##########################
# Create a big callback that does most stuff
def callback(attr, old, new):
# Want to acquire the current values of all of the checkboxes and select
# widgets to provide as inputs for the re-plot.
color_to_plot = [
checkbox_color.labels[i] for i in checkbox_color.active
]
if color_column != marker_column:
marker_to_plot = [
checkbox_marker.labels[i] for i in checkbox_marker.active
]
else:
marker_to_plot = color_to_plot
hovertool_to_plot = [
checkbox_hovertool.labels[i] for i in checkbox_hovertool.active
]
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
legend_location = select_legend.value
# Set the new axis titles
x_axis_title1 = plot1_xdata_to_plot
y_axis_title1 = plot1_ydata_to_plot
# Use the pre-defined Make_Dataset function with these new inputs to
# create new versions of the sub dataframes.
Sub_df1 = Make_Dataset(df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot,
plot1_ydata_to_plot)
# Use the pre-defined Define_Plot_Parameters function with these new
# inputs to update the plot parameters.
Define_Plot_Parameters(p1, [
plot1_xdata_to_plot, plot1_ydata_to_plot, plot_title1,
x_axis_title1, y_axis_title1, plot_size_height1, plot_size_width1,
legend_location
])
# Update the hovertool
if len(hovertool_to_plot) < 11:
kwargs = {}
i = 0
for x in hovertool_to_plot:
i = i + 1
kwargs['Field' + str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, plot1_xdata_to_plot, plot1_ydata_to_plot,
**kwargs)
# Use the pre-defined tolerances function with these new inputs to
# make a new version of the tolerances sub dataframe.
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(plot1_xdata_to_plot,
plot1_ydata_to_plot, Sub_df1,
df_tol1)
# Update the ColumnDataSources.
src1.data = Sub_df1.to_dict(orient='list')
if tolerance_boolean == True:
src1_tol.data = Sub_df1_tol1.to_dict(orient='list')
return
select_xaxis.on_change('value', callback)
select_yaxis.on_change('value', callback)
select_legend.on_change('value', callback)
checkbox_color.on_change('active', callback)
checkbox_marker.on_change('active', callback)
checkbox_hovertool.on_change('active', callback)
# Callback for the Update Button
def callback_update():
# Make a new version of the dataframe using the original Create_df
# function that connects to the database.
df = Create_df()
df = add_legend_to_df(df)
color_to_plot = [
checkbox_color.labels[i] for i in checkbox_color.active
]
if color_column != marker_column:
marker_to_plot = [
checkbox_marker.labels[i] for i in checkbox_marker.active
]
else:
marker_to_plot = color_to_plot
hovertool_to_plot = [
checkbox_hovertool.labels[i] for i in checkbox_hovertool.active
]
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
x_axis_title1 = plot1_xdata_to_plot
y_axis_title1 = plot1_ydata_to_plot
legend_location = select_legend.value
Sub_df1 = Make_Dataset(df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot,
plot1_ydata_to_plot)
Define_Plot_Parameters(p1, [
plot1_xdata_to_plot, plot1_ydata_to_plot, plot_title1,
x_axis_title1, y_axis_title1, plot_size_height1, plot_size_width1,
legend_location
])
if len(hovertool_to_plot) < 11:
kwargs = {}
i = 0
for x in hovertool_to_plot:
i = i + 1
kwargs['Field' + str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, plot1_xdata_to_plot, plot1_ydata_to_plot,
**kwargs)
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(plot1_xdata_to_plot,
plot1_ydata_to_plot, Sub_df1,
df_tol1)
src1_tol.data = Sub_df1_tol1.to_dict(orient='list')
src1.data = Sub_df1.to_dict(orient='list')
return
update_button.on_click(callback_update)
# Callback for the Range Button
def callback_range():
color_to_plot = [
checkbox_color.labels[i] for i in checkbox_color.active
]
if color_column != marker_column:
marker_to_plot = [
checkbox_marker.labels[i] for i in checkbox_marker.active
]
else:
marker_to_plot = color_to_plot
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
# Use the pre-defined Make_Dataset function with these new inputs to
# create new versions of the sub dataframes.
Sub_df1 = Make_Dataset(df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot,
plot1_ydata_to_plot)
x_data1 = select_xaxis.value
y_data1 = select_yaxis.value
if (x_data1 == 'adate') and ((y_data1 == '6fwhm') or
(y_data1 == '9fwhm') or
(y_data1 == '12fwhm') or
(y_data1 == '15fwhm') or
(y_data1 == '16fwhm')):
p1.x_range.start = Sub_df1['x'].max() - timedelta(weeks=53)
p1.x_range.end = Sub_df1['x'].max() + timedelta(weeks=2)
if y_data1 == '6fwhm':
p1.y_range.start = 9.6
p1.y_range.end = 10.3
elif y_data1 == '9fwhm':
p1.y_range.start = 12.6
p1.y_range.end = 13.32
elif y_data1 == '12fwhm':
p1.y_range.start = 16.25
p1.y_range.end = 17.01
elif y_data1 == '15fwhm':
p1.y_range.start = 19.4
p1.y_range.end = 20.16
elif y_data1 == '16fwhm':
p1.y_range.start = 19.5
p1.y_range.end = 19.9
return
range_button.on_click(callback_range)
############################################################################
############################################################################
############################################################################
####################### RETURN TO THE MAIN SCRIPT ##########################
return Panel(child=tab_layout, title='Electron Energy')
# SquareX, Triangle, X]
TOOLTIPS_SCATTER = [
("(Fare,AGE)", "$x, $y"),
]
# Set the Title
p = figure(title = "Titanic Passenger Age & Fare by Survial Type",
tooltips=TOOLTIPS_SCATTER)
# Construnct the colours
p.scatter("Fare", "Age", source=titanic_df, legend="Survived", fill_alpha=0.3, size=12,
marker=factor_mark('Survived', MARKERS, FATE),
color=factor_cmap('Survived', palette=['#3a6587', '#aeb3b7'], factors=FATE))
#Set the axis labels
p.xaxis.axis_label = 'Fare (In Pounds)'
p.yaxis.axis_label = 'Age (In Years)'
# Remove the Grid lines
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# change just some things about the x-axis
p.xaxis.axis_line_width = 2
p.xaxis.major_label_text_color = "black"
p.xaxis.axis_line_color = "#aeb3b7"
def main(assignment_dir: str, log_dirs: List[str], plotfile: str, csv: str):
"""This script generates a HTML file containing a plot of the coverage for each
sample after they have been through the QC pipeline.\n
LOG_DIRS: Director(y/ies) containing the subsampling log files. (Coverage is
extracted from these)
),
"""
logfiles = []
for d in log_dirs:
logfiles.extend(Path(d).rglob("*.log"))
data = defaultdict(dict)
for file in logfiles:
tech = "illumina" if "illumina" in file.parts[2] else "nanopore"
sample = file.with_suffix("").name
site = file.parts[-2]
covg = ripgrep_extract_covg(file)
data[sample].update({f"{tech}_covg": covg, "site": site})
assignment_files = Path(assignment_dir).rglob("*.csv")
for file in assignment_files:
fields = file.read_text().split("\n")[1].split(",")
sample = file.name.split(".")[0]
data[sample]["lineage"] = fields[1]
df = pd.DataFrame(data).T
df.index.name = "sample"
df.to_csv(csv)
cds = ColumnDataSource(df)
sites = list(set(df["site"]))
lineages = list(set(df["lineage"]))
tooltips = [
("index", "@sample"),
("Illumina", "@illumina_covg"),
("Nanopore", "@nanopore_covg"),
("site", "@site"),
("lineage", "@lineage"),
]
title = "Sample coverage for different technologies after quality control"
palette = Set2[len(lineages)]
legend_var = "lineage"
# inline effectively allows the plot to work offline
output_file(plotfile, title=title, mode="inline")
p = figure(
tools=TOOLS,
height=HEIGHT,
width=WIDTH,
tooltips=tooltips,
active_drag="box_zoom",
active_scroll="wheel_zoom",
title=title,
)
p.yaxis.axis_label = "Nanopore Coverage"
p.xaxis.axis_label = "Illumina Coverage"
legend = Legend(
click_policy="hide",
location="top_left",
title=legend_var.capitalize(),
background_fill_alpha=0.1,
)
p.add_layout(legend)
p.scatter(
source=cds,
y="nanopore_covg",
x="illumina_covg",
size=10,
fill_alpha=0.4,
marker=factor_mark("site", MARKERS, sites),
color=factor_cmap("lineage", palette, lineages),
legend_field=legend_var,
)
save(p)
def Electron_Energy_Graph_Old(conn):
############################################################################
#################### CREATE THE DATA FOR THE GRAPH #########################
output_file(
"Electron_Output_Graph.html"
) #????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????
# Use the connection passed to the function to read the data into a
# dataframe via an SQL query.
df = pd.read_sql('SELECT * FROM [eEnergyICP]', conn)
print(df)
# Delete cells where 'protocol id' is empty
df = df.dropna(subset=['protocol id'])
# With any luck this can be removed after chatting to AW and MB ?????????????????????????????????????????????????????????????????????????????????
# Get the date and machinename from the protocol id' field
# Seperate on the first '_'
df_left = df['protocol id'].str.partition(sep='_')
# Seperate on the last '_'
df_right = df['protocol id'].str.rpartition(sep='_')
# From these sperated dataframes add the appropriate columns back into the
# main dataframe.
df.loc[:, 'adate'] = df_left[0]
df.loc[:, 'machinename'] = df_right[2]
# Turn 'adate' into datetime. Problems with this function as it assumes american date formats over british. ?????????????????????????????????????????????????????????????????????????????????
# Talk to AW and MB about getting date from other tables in the database and pulling them into the query. ???????????????????????????????????????????????????????????????????????????????????
# This way the date should be in a set format that the datetime function can be told, which should resolve this issue. ??????????????????????????????????????????????????????????????????????
#
# Need to turn the date fields into a Dateime object (either 'adate'
# (protons) or the newly created 'adate' (photons)). The date field should
# always be named 'adate' for consistency.
df.loc[:, 'adate'] = pd.to_datetime(df.loc[:, 'adate'])
# When starting a new graph can be useful to print the dataframe after any
# manipulations to make sure the code has done what you expected.
print(df)
# Create a list of the fields using the dataframe
TableFields = (list(df.columns))
############################################################################
############################################################################
############################################################################
################ CREATE THE DATAFRAME FOR THE TOLERANCES ###################
# The purpose of this plot is generally to be as general as possible but
# there are only a few parameters that will have defined tolerances.
# Therefore the tolerance section can be a bit more specific and a dataframe
# containing tolereances can be manually created for many cases and
# extracted from the database in others (in a manner similar to above but
# calling from a different table/query with the SQL statement)
#
# The format of the dataframe should be the first line being the x_axis
# (with some values taken from the main dataframe to get the right
# formatting). The subsequent columns are the tolerances [low, high].
# NB: column names should match those from the main dataframe.
df_tol1 = pd.read_sql('SELECT * FROM [ElectronFWHMLimits]', conn)
print(df_tol1)
df_tol1 = df_tol1.set_index('class')
print(df_tol1)
df_tol_TB = pd.DataFrame({
'adate': [df['adate'].max(), df['adate'].max()],
'6fwhm':
[df_tol1.loc['TBUCLH', 'lower6'], df_tol1.loc['TBUCLH', 'upper6']],
'9fwhm':
[df_tol1.loc['TBUCLH', 'lower9'], df_tol1.loc['TBUCLH', 'upper9']],
'12fwhm':
[df_tol1.loc['TBUCLH', 'lower12'], df_tol1.loc['TBUCLH', 'upper12']],
'15fwhm':
[df_tol1.loc['TBUCLH', 'lower15'], df_tol1.loc['TBUCLH', 'upper15']]
})
print(df_tol_TB)
df_tol_Classic = pd.DataFrame({
'adate': [df['adate'].max(), df['adate'].max()],
'6fwhm':
[df_tol1.loc['Classic', 'lower6'], df_tol1.loc['Classic', 'upper6']],
'9fwhm':
[df_tol1.loc['Classic', 'lower9'], df_tol1.loc['Classic', 'upper9']],
'12fwhm':
[df_tol1.loc['Classic', 'lower12'], df_tol1.loc['Classic', 'upper12']],
'16fwhm':
[df_tol1.loc['Classic', 'lower16'], df_tol1.loc['Classic', 'upper16']],
'20fwhm':
[df_tol1.loc['Classic', 'lower20'], df_tol1.loc['Classic', 'upper20']]
})
print(df_tol_Classic)
############################################################################
############################################################################
##########################################################################
################### CREATE THE COLUMNS FOR THE LEGEND ######################
# NB: The following section has been designed to be as general as possible
# but in reality it might be preferable to more manually choose the markers
# and colors based on optimising the most likey things to be plotted.
#
# This code is a way of creating a legend with markers based on one
# parameter (e.g. machine name) and color on another parameter (e.g. energy)
######### Colors:
# Create a sorted list of the unique values in a dataframe column that the
# colors will be based on.
list_forcolor = sorted(df['machinename'].unique().tolist())
# If the length of the list is <9 then we can use the colorblind palette,
# which contains 8 colors. This should be the default for accessability
# reasons unless there are compeling reasons otherwise.
if len(list_forcolor) < 9:
color_palette = Colorblind[len(list_forcolor)]
# If not <9 then we can use the much larger Turbo palette which contains
# 256 colors. Will check if there are more than 256 options though and
# throw an error if so.
elif len(list_forcolor) > 256:
print( 'Error - Name of Function: >256 unique energies in database ' \
'causing failure of the turbo color palette function (only ' \
'256 availible colors.' )
exit()
# If it passes the check then use the built in turbo function that splits
# the turbo palette into roughly equal sections based on a supplied integer
# number.
else:
color_palette = turbo(len(list_forcolor))
######### Markers:
# Doesn't seem to be a way to create a simple list of all the Bokeh marker
# options so just do this manually. May want to re-order to improve
# visibility of commonly used options.
markers = [
'asterisk', 'circle', 'circle_cross', 'circle_x', 'cross', 'dash',
'diamond', 'diamond_cross', 'hex', 'inverted_triangle', 'square',
'square_cross', 'square_x', 'triangle', 'x'
]
# Create a sorted list of the unique values in a dataframe column that the
# markers will be based on.
list_formarker = sorted(df['machinename'].unique().tolist())
# Check that there are enough markers to give a unique marker to each option
# but otherwise throw an error.
if len(list_formarker) > len(markers):
print( 'Error - Name of Function: Not enough markers to assign a ' \
'unique marker to each option.' )
exit()
######### Legend Key:
# Create a function that will be used to run through the dataframe looking
# at the energy and machine column and creating a new column that will have
# values for both seperated by a '_', stored as a string.
def add_legend(row):
return str(str(row['machinename']))
# Run the function and also copy the other columns into new columns so that
# when ther are renamed to 'x' and 'y' later they are still availible for
# the legend if needed.
df.loc[:, 'legend'] = df.apply(lambda row: add_legend(row), axis=1)
df.loc[:, 'machinename1'] = df.loc[:, 'machinename']
print(df)
############################################################################
############################################################################
############################################################################
################## FORMATTING AND CREATING A BASIC PLOT ####################
############################################################################
############################# USER INPUTS ##################################
# Decide what the default viewing option is going to be. (i.e. the fields to
# be plotted on the x and y axis when the graph is opened, the plot size
# etc.).
# From the legend defined above give the values that will be pre-ticked when
# the plot is opened
color_to_plot = ['TrueBeam B', 'TrueBeam C']
marker_to_plot = color_to_plot
# Decide on what data to plot on the x/y axis when opened.
x_data1 = 'adate'
y_data1 = '6fwhm'
# Decide what the plot formatting will be, inluding the plot title, axis
# titles and the size of the plot.
plot_title1 = 'Electron Energy'
x_axis_title1 = x_data1
y_axis_title1 = y_data1
plot_size_height1 = 450
plot_size_width1 = 800
legend_location = 'bottom_left'
# Create a list of the plot parameters that will be used as input to a
# function later.
list_plot_parameters = [
x_data1, y_data1, plot_title1, x_axis_title1, y_axis_title1,
plot_size_height1, plot_size_width1, legend_location
]
############################################################################
############################################################################
############################################################################
########################### CREATE THE PLOT ################################
# Create the actual ploy. Generally it's a good idea to do this by defining
# functions as they can then be used in the callbacks later without having
# a lot of redundant very similar code.
######### Make Dataset:
# Define a make dataset function that can be used now but also called later
# in the callback functions to save re-writing similar code later.
def make_dataset(color_to_plot, marker_to_plot, x_data1, y_data1):
# Create a sub dataframe
Sub_df1 = df.copy()
# Delete any rows in the sub-dataframes that do not exist in the
# checkboxes/default user choices. (e.g. if you've selected 6MV in the
# checkbox this will remove any rows that have something other than 6MV)
Sub_df1 = Sub_df1[Sub_df1['machinename'].isin(color_to_plot)]
Sub_df1 = Sub_df1[Sub_df1['machinename'].isin(marker_to_plot)]
# Search for the columns with the x_data and y_data names and replace
# them with 'x' and 'y'. Unless plotting the same data on both in which
# case add an extra column for 'y' that's a copy of 'x'
if x_data1 == y_data1:
Sub_df1.rename(columns={x_data1: 'x'}, inplace=True)
Sub_df1.loc[:, 'y'] = Sub_df1.loc[:, 'x']
else:
Sub_df1.rename(columns={x_data1: 'x'}, inplace=True)
Sub_df1.rename(columns={y_data1: 'y'}, inplace=True)
# Return the newly created Sub_df1
return Sub_df1
# Run the make_dataset function to create a sub dataframe that the plot will
# be made from.
Sub_df1 = make_dataset(color_to_plot, marker_to_plot, x_data1, y_data1)
# Create a Column Data Source. This is important as it is the data format
# needed for Bokeh. When making this it is useful to convert the dataframe
# into a dictionary, which seems to help with the callback function (see
# 'Common Issues' for details).
src1 = ColumnDataSource(Sub_df1.to_dict(orient='list'))
######### Make Plot:
# Create an empty plot (plot parameters will be applied later in a way that
# can be manipulated in the callbacks)
p1 = figure()
# Create a scatter plot.
p1.scatter( # source = The ColumnDataSource made above.
source=src1,
# x/y = 'x'/'y' which are fields that were renamed as such in
# the make_dataset function
x='x',
y='y',
# Some general parameters about marker size. These seem like
# reasonable values but possible could alter these in a
# callback?
fill_alpha=0.4,
size=12,
# Create the legend using the created fields added in the legend
# section. Use the factor_mark and factor_cmap functions to
# match the colors/markers to the right lists.
# NB: Always use legend_field for this not legend_group as the
# former acts on the javascript side but the latter the Python
# side. Therefore the former will update automatically when the
# plot is changed with no need for a callback.
legend_field='legend',
marker=factor_mark('machinename1', markers, list_formarker),
color=factor_cmap('machinename1', color_palette, list_forcolor))
######### Add plot parameters:
# Define a define plot parameters factor that can be used now but also
# called later in the callback functions.
def define_plot_parameters(list):
# Input is a List of format:
# list_plot_parameters = [ x_data1, y_data1,
# plot_title1, x_axis_title1, y_axis_title1,
# plot_size_height1, plot_size_width1,
# legend_location ]
# The parameters have to be controlled like this in a callback to allow
# for them to be adjusted. Otherwise the plot parameters are not
# interactive.
# Yes! - p1.xaxis.axis_label = 'X_axis_title'
# No! - p1 = figure(x_axis_label = 'X_axis_title')
p1.title.text = list[2]
p1.xaxis.axis_label = list[3]
p1.yaxis.axis_label = list[4]
p1.plot_height = list[5]
p1.plot_width = list[6]
p1.legend.location = list[7]
# If the user wants to plot an axis as datetime then the axis needs to
# be reformatted. Will do this by checking if the x_data1/y_data1 is
# =='adate'.
# NB: This only works if 'adate' is used as the name for the date column
# and also that this is the only date column.
if list[0] == 'adate':
p1.xaxis.formatter = DatetimeTickFormatter(days=['%d/%m', '%a%d'])
else:
p1.xaxis.formatter = BasicTickFormatter()
if list[1] == 'adate':
p1.yaxis.formatter = DatetimeTickFormatter(days=['%d/%m', '%a%d'])
else:
p1.yaxis.formatter = BasicTickFormatter()
return
# Run the define_plot_parameters function to format the plot.
define_plot_parameters(list_plot_parameters)
############################################################################
############################################################################
############################################################################
############################################################################
############################################################################
############################ ADD TOLERANCES ################################
# We defined the tolerances further up and now want to add the correct ones
# to the plot (having created the plot above). Again this will be done with
# functions and in a way that the functions can be used in the callbacks
# later.
#
# NB: At the moment this is still a bit of a work in progress and shows the
# way to add line tolerances. Another option might be to add colorblocks
# using varea and/or varea_stack.
#
# NB: Also this funcion assumes that tolerances will all be against one
# x_axis value (e.g. date). This is probably the majority of use cases but
# probably relatively trivial to add further toleraces against other x_axis
# data.
# Create a function that will create a dataframe that can be used to supply
# a plot of two tolerance lines. This will including 'appearing' and
# 'disappearing' depending on whether tolerances are defined or not.
def tolerances(x_data1, y_data1, Sub_df1, df_tol1):
# Get a list of the column headers from the tolerance table defined
# earlier.
headers1 = df_tol1.columns.values.tolist()
# Check if the xdata is what is in the df_tol1 as the x_axis (if not no
# point plotting tolerances as all tolerances are vs this tolerance).
if x_data1 != headers1[0]:
# x_data1 doesn't match so going to output something that should
# basically just not plot but also won't throw the viewing range.
data = {
'x': [Sub_df1['x'].max(), Sub_df1['x'].max()],
'y_low': [Sub_df1['y'].max(), Sub_df1['y'].max()],
'y_high': [Sub_df1['y'].max(), Sub_df1['y'].max()]
}
Sub_df1_tol1 = pd.DataFrame(data)
return Sub_df1_tol1
# Otherwise we have the right x_data1 so now just check if it's datetime
# or not.
if x_data1 == 'adate':
# It has the format 'adate' so should be datetime. So find the max
# min dates in the Sub_df1 and add a couple of weeks either side so
# that it plots the full range (plus a little bit for visualisation
# reasons).
max_x = Sub_df1['x'].max() + pd.DateOffset(weeks=2)
min_x = Sub_df1['x'].min() + pd.DateOffset(weeks=-2)
else:
# If it's not datetime then just add about 5% of the range to
# either side to make the plot look nicer.
# NB: This has not been checked extensively as most tolerances are
# vs. time.
max_x = Sub_df1['x'].max()
min_x = Sub_df1['x'].min()
range = max_x - min_x
max_x = max_x + (range / 20)
min_x = min_x - (range / 20)
# Used the x part so now remove the element from the list. This will
# help for the small case where x_data1 == ydata1.
headers1.remove(x_data1)
if y_data1 in headers1:
# If y_data1 is in the list then loop through to find out where and
# get the data from the tolerance dataframe.
for x in headers1:
if y_data1 == x:
# When the loop has found where it is then can output a
# dataframe of the form:
# x = [far left of plot, far right of plot]
# y_low = [low_tolerance, low_tolerance]
# y_high = [high_tolerance, high_tolerance]
data = {
'x': [min_x, max_x],
'y_low': [df_tol1[x][0], df_tol1[x][0]],
'y_high': [df_tol1[x][1], df_tol1[x][1]]
}
Sub_df1_tol1 = pd.DataFrame(data)
else:
# If y_data1 is not in the headers1 list then there are no
# tolerances to plot so going to output something that should
# basically just not plot but also won't throw the viewing range.
data = {
'x': [Sub_df1['x'].max(), Sub_df1['x'].max()],
'y_low': [Sub_df1['y'].max(), Sub_df1['y'].max()],
'y_high': [Sub_df1['y'].max(), Sub_df1['y'].max()]
}
Sub_df1_tol1 = pd.DataFrame(data)
return Sub_df1_tol1
return Sub_df1_tol1
def choose_tolerances(x_data1, y_data1, Sub_df1, color_to_plot):
if any(item in color_to_plot for item in
['TrueBeam B', 'TrueBeam C', 'TrueBeam D', 'TrueBeam F']):
# If this is true then will need to run the df_tol_TB tolerances
Sub_df1_tol_TB = tolerances(x_data1, y_data1, Sub_df1, df_tol_TB)
else:
data = {
'x': [Sub_df1['x'].max(), Sub_df1['x'].max()],
'y_low': [Sub_df1['y'].max(), Sub_df1['y'].max()],
'y_high': [Sub_df1['y'].max(), Sub_df1['y'].max()]
}
Sub_df1_tol_TB = pd.DataFrame(data)
if any(item in color_to_plot
for item in ['Linac B', 'Linac C', 'Linac D', 'Linac E']):
# If this is true then will need to run the df_tol_TB tolerances
Sub_df1_tol_Classic = tolerances(x_data1, y_data1, Sub_df1,
df_tol_Classic)
else:
data = {
'x': [Sub_df1['x'].max(), Sub_df1['x'].max()],
'y_low': [Sub_df1['y'].max(), Sub_df1['y'].max()],
'y_high': [Sub_df1['y'].max(), Sub_df1['y'].max()]
}
Sub_df1_tol_Classic = pd.DataFrame(data)
return Sub_df1_tol_TB, Sub_df1_tol_Classic
# Run the tolerances function to output the new dataframe
Sub_df1_tol_TB, Sub_df1_tol_Classic = choose_tolerances(
x_data1, y_data1, Sub_df1, color_to_plot)
# Turn the dataframe into a new ColumnDataSource (again turning it into a
# dictionary)
src1_tol_TB = ColumnDataSource(Sub_df1_tol_TB.to_dict(orient='list'))
src1_tol_Classic = ColumnDataSource(
Sub_df1_tol_Classic.to_dict(orient='list'))
# Add two lines to the plot using the new ColumnDataSource as the source,
# one line for low tolerance and one line for high.
p1.line(source=src1_tol_TB, x='x', y='y_low', color='firebrick')
p1.line(source=src1_tol_TB, x='x', y='y_high', color='firebrick')
p1.line(source=src1_tol_Classic, x='x', y='y_low', color='hotpink')
p1.line(source=src1_tol_Classic, x='x', y='y_high', color='hotpink')
############################################################################
############################################################################
############################################################################
################## ADD MORE COMPLEX TOOLS TO THE PLOT ######################
# Create tools here that will allow for some manipulation or inspection of
# plotted data.
#
# As an example a 'HoverTool' will be added to the plot.
#
# Other useful tools and details of the syntax can be found here:
# https://docs.bokeh.org/en/latest/docs/user_guide/tools.html
# Create the hover tool (see website above for syntax/details).
# This example creates a hover tool that displays:
# Date: The value of the data-point as measued on the x-axis
# (formatted for datetime)
# Y-Axis: The value of the data-point as measued on the y-axis
# (x,y): The x and y co-ordinates in plot space
# Chamber Comb.: The data stored under the 'Chamber' column for that
# data-point.
# Comments: The data stored under the 'comments' column for that
# data-point.
hover = HoverTool(tooltips=[('Date', '@x{%F}'), ('Y-Axis', '@y'),
('(x,y)', '($x, $y)'),
('Chamber Comb.', '@Chamber'),
('Comments', '@comments')],
formatters={'x': 'datetime'})
# Add the newly created tool to the plot.
p1.add_tools(hover)
############################################################################
############################################################################
############################################################################
################# CREATE WIDGETS TO BE ADDED TO THE PLOT ###################
# Create widgets here that will allow for some manipulation of the plotted
# data. These widgets provide an interactive ability that can alter the data
# that is plotted, provide update fnctions and call other programmatic
# functions. This is done either using built in Bokeh functionality or
# using more powerful but complex python and javascript based callbacks.
#
# As an example some 'Select' widgets, 'Checkbox' widgets and 'RangeSliders'
# will be added to the plot.
#
# Other useful widgets and details of the syntax can be found here:
# https://docs.bokeh.org/en/latest/docs/user_guide/interaction/widgets.html
######## 1)
# Create the select widget (see website above for syntax/details). This
# widget will be used for the callback example later to change data plotted
# on the x/y-axis.
# This example creates a select tool that displays:
# Dropdown list containing a list of every field that was downloaded from
# the database.
# NB: When making a list it may be worth manually creating it to limit
# it to the fields that can be plotted (e.g. not including fields
# like 'Comments'). This will shorten the dropdown list but you
# should err on the side of inclusion to make the final plot as
# flexible as possible.
#
# Create a list of the availible options
menu_axis = []
for field in TableFields:
menu_axis.append(field)
menu_axis = sorted(menu_axis)
# Select tool needs inputs for the title, a starting value and the just
# created list to supply the available options.
select_xaxis = Select(title='X-Axis Fields Available:',
value=x_axis_title1,
options=menu_axis)
select_yaxis = Select(title='Y-Axis Fields Available:',
value=y_axis_title1,
options=menu_axis)
######## 2)
# This select widget will be made in the same way and used to create a
# dropdown list to change the legend position.
#
# Create a list of the availible options
menu_legend = [
'top_left', 'top_center', 'top_right', 'center_left', 'center',
'center_right', 'bottom_left', 'bottom_center', 'bottom_right'
]
# Create the select tool as above
select_legend = Select(title='Legend Position',
value=legend_location,
options=menu_legend)
######## 3)
# These checkbox widgets will be used to create a tool to select the
# values that are being plotted from the fields that the legend is based on.
#
# NB: There is some built in Bokeh functionality for interavtive legends
# that can fulfill some of the same goals where the number of options is
# limited to something that can display on a reasonably sized legend. May
# be a better and more robust solution where possible.
# Create a list of all unique names in the column chosen to be matched to
# markers (sorted).
options_marker = sorted(df['machinename'].unique().tolist())
# Create an index list for all of the values that should be pre-ticked.
index_marker = [
i for i in range(len(options_marker))
if options_marker[i] in marker_to_plot
]
# Create the checkbox, providing the list of availible options and a list
# of what should be active (pre-ticked).
checkbox_marker = CheckboxGroup(labels=options_marker,
active=index_marker,
visible=False)
# Do the same for the column that was matched to colors.
options_color = sorted(df['machinename'].unique().tolist())
index_color = [
i for i in range(len(options_color))
if options_color[i] in color_to_plot
]
checkbox_color = CheckboxGroup(labels=options_color, active=index_color)
######## 4)
# Make some range sliders that will be used to manipulate the x-axis and
# y-axis range.
# Most of the manipulation will be done using a later function but will need
# to create the bare minimum rangeslider first that can later be manipulated
# (This seems to be the minimum number of parameters needed to create these
# widgets). Note that a RangeSliders AND a DateRangeSlider needs to be
# created for each axis.
range_slider_x = RangeSlider(title='X-Axis Range',
start=0,
end=1,
value=(0, 1),
step=0.1)
range_slider_y = RangeSlider(title='Y-Axis Range',
start=0,
end=1,
value=(0, 1),
step=0.1)
range_slider_xdate = DateRangeSlider(title='X-Axis Range (Date)',
start=date(2017, 1, 1),
end=date(2017, 1, 2),
value=(date(2017, 1,
1), date(2017, 1, 2)),
step=1)
range_slider_ydate = DateRangeSlider(title='Y-Axis Range (Date)',
start=date(2017, 1, 1),
end=date(2017, 1, 2),
value=(date(2017, 1,
1), date(2017, 1, 2)),
step=1)
# Define the function that will be used now and also in the callbacks later.
# This will allow the range_sliders to adjust to match any changes in the
# data being plotted on the x/y axis.
def range_slider(x_data1, y_data1, Sub_df1):
# Start with the y-axis.
# First need to check if 'adate' and if so edit the date range slider
# but otherwise edit the normal slider.
if y_data1 == 'adate':
# Set the start, end and value fields to the full range.
range_slider_ydate.start = Sub_df1['y'].min()
range_slider_ydate.end = Sub_df1['y'].max()
range_slider_ydate.value = (Sub_df1['y'].min(), Sub_df1['y'].max())
# Step to 1 works for DateRangeSlider
range_slider_ydate.step = 1
# Make the DateRangeSlider visible and hide the normal RangeSlider
range_slider_ydate.visible = True
range_slider_y.visible = False
else:
# Set the start, end and value fields to the full range.
range_slider_y.start = Sub_df1['y'].min()
range_slider_y.end = Sub_df1['y'].max()
range_slider_y.value = (Sub_df1['y'].min(), Sub_df1['y'].max())
# Step to range/10000 should give sufficient granularity
range_slider_y.step = (Sub_df1['y'].max() -
Sub_df1['y'].min()) / 100000
# Make the normal RangeSlider visible and hide the DateRangeSlider
range_slider_y.visible = True
range_slider_ydate.visible = False
# Do the same for the x-axis
if x_data1 == 'adate':
range_slider_xdate.start = Sub_df1['x'].min()
range_slider_xdate.end = Sub_df1['x'].max()
range_slider_xdate.value = (Sub_df1['x'].min(), Sub_df1['x'].max())
range_slider_xdate.step = 1
range_slider_xdate.visible = True
range_slider_x.visible = False
else:
range_slider_x.start = Sub_df1['x'].min()
range_slider_x.end = Sub_df1['x'].max()
range_slider_x.value = (Sub_df1['x'].min(), Sub_df1['x'].max())
range_slider_x.step = (Sub_df1['x'].max() -
Sub_df1['x'].min()) / 100000
range_slider_x.visible = True
range_slider_xdate.visible = False
return
# Run the function.
range_slider(x_data1, y_data1, Sub_df1)
############################################################################
############################################################################
############################################################################
########################### CREATE A LAYOUT ################################
# Create a layout to add widgets and arrange the display. This simple layout
# displays the select widgets above the plot with the checkboxes to the
# right (one above the other).
#
# More details can be found at:
# https://docs.bokeh.org/en/latest/docs/user_guide/layout.html
#
# NB: More work to do here to make plots responsive to browser window size
# (e.g. using sizing_mode = scale_both) but need to invstigate with/without
# remote desktops.
layout_checkbox = column([checkbox_marker, checkbox_color])
layout_plots = column([
select_xaxis, select_yaxis, select_legend, range_slider_x,
range_slider_y, range_slider_xdate, range_slider_ydate, p1
])
tab_layout = row([layout_plots, layout_checkbox])
############################################################################
############################################################################
############################################################################
####################### CREATE CALLBACK FUNCTIONS ##########################
# CAVEAT: Callback functions are very complex and below is my (CB) rough
# understanding of how they function based mainly on experience/trial and
# error while writting these functions for other graphs. It should be taken
# as a starting point but not as a definitive user guide.
#
# Callback functions are very powerful and can be based off of javascript or
# python. The example presented here uses python but in future a javascript
# copy should also be added.
######## 1)
# This callback is designed to take inputs from the select and checkbox
# widgets update the graph to plot the new data requested by the user.
#
# Syntax:
# attr = The value passed from the on_change function before the callback
# was named (e.g. in this example attr = 'value')
# old = The value of the widget before it was changed (I.e. If a select
# widget is changed from 'Output' to 'T/P Correction', then
# old = 'Output'
# new = The value of the widget after it was changed (I.e. If a select
# widget is changed from 'Output' to 'T/P Correction', then
# old = 'T/P Correction'
#
# NB: In general seen little need to use these inputs as you can generally
# access the value of the widgets directly which seems to be more powerful
# and flexible
#
# First define the callback function.
def callback(attr, old, new):
# Want to acquire the current values of all of the checkboxes and select
# widgets to provide as inputs for the re-plot. For the checkboxes this
# means itterating through the active list and outputting the labels
# that are active
color_to_plot = [
checkbox_color.labels[i] for i in checkbox_color.active
]
marker_to_plot = color_to_plot
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
legend_location = select_legend.value
# Use the pre-defined make_dataset function with these new inputs to
# create a new version of the sub dataframe.
Sub_df1 = make_dataset(color_to_plot, marker_to_plot,
plot1_xdata_to_plot, plot1_ydata_to_plot)
# Use the pre-defined define_plot_parameters function with these new
# inputs to update the plot parameters.
x_axis_title1 = plot1_xdata_to_plot
y_axis_title1 = plot1_ydata_to_plot
define_plot_parameters([
plot1_xdata_to_plot, plot1_ydata_to_plot, plot_title1,
x_axis_title1, y_axis_title1, plot_size_height1, plot_size_width1,
legend_location
])
# Use the pre-defined tolerances function with these new inputs to
# make a new version of the tolerances sub dataframe.
Sub_df1_tol_TB, Sub_df1_tol_Classic = choose_tolerances(
plot1_xdata_to_plot, plot1_ydata_to_plot, Sub_df1, color_to_plot)
# Use the pre-defined range_slider function with these new inputs to
# update the range sliders (this will make sure that the range sliders
# start/end etc. match up with what's being plotted, as well as
# displaying/hiding the RangeSlider/DateRangeSlider as needed
range_slider(plot1_xdata_to_plot, plot1_ydata_to_plot, Sub_df1)
# Update the ColumnDataSources using the newly created dataframes. The
# plots look to these as the source so this changes what is being
# plotted.
src1.data = Sub_df1.to_dict(orient='list')
src1_tol_TB.data = Sub_df1_tol_TB.to_dict(orient='list')
src1_tol_Classic.data = Sub_df1_tol_Classic.to_dict(orient='list')
return
# Use the on_change function to call the now defined callback function
# whenever the user changes the value in the widget.
# NB: Other functions such as on_click are availible for other widgets.
# Syntax:
# First argument is passed to the callback as attr (see callback section
# above)
# Second argument is the name of the callback function to be called.
select_xaxis.on_change('value', callback)
select_yaxis.on_change('value', callback)
select_legend.on_change('value', callback)
checkbox_color.on_change('active', callback)
checkbox_marker.on_change('active', callback)
######## 2)
# This callback is designed to take inputs from the range sliders to change
# visible range
def callback_range(attr, old, new):
# Check what is currently being plotted. Need this to know whether to
# look for the values from the DateRangeSlider or the RangeSlider
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
# Start with the x-axis
if plot1_xdata_to_plot == 'adate':
# If it's 'adate' then need to look at the DateRangeSlider and
# update the start and end values of the range using the values from
# the slider.
# NB: range_slider.value = left_value, right_value
p1.x_range.start, p1.x_range.end = range_slider_xdate.value
else:
# If it's not 'adate' then need to look at the normal RangeSlider
p1.x_range.start, p1.x_range.end = range_slider_x.value
# Do the same for the y-axis
if plot1_ydata_to_plot == 'adate':
p1.y_range.start, p1.y_range.end = range_slider_ydate.value
else:
p1.y_range.start, p1.y_range.end = range_slider_y.value
return
# Use the on_change function to call the now defined callback function
# whenever the user changes the value in the widget.
range_slider_x.on_change('value', callback_range)
range_slider_y.on_change('value', callback_range)
range_slider_xdate.on_change('value', callback_range)
range_slider_ydate.on_change('value', callback_range)
############################################################################
############################################################################
############################################################################
####################### RETURN TO THE MAIN SCRIPT ##########################
# Now that the script is finished and the plot created we can return to the
# main script.
#
# To pass back the data for the tab we need to return a Panel with:
# child = layout (the one that we made earlier with the widget and plot)
# title = 'Something that makes sense as a tab label for the user'
return Panel(child=tab_layout, title='Electron Energy')
markers = ['hex', 'cross', 'triangle']
scatter_plot_subgroups = figure(plot_width=600,
plot_height=400,
title ='Iris',
x_axis_label='petalLength',
y_axis_label='petalWidth')
scatter_plot_subgroups.scatter(x='petalLength',
y='petalWidth',
source=iris,
legend='species',
fill_alpha=0.5,
size=15,
color=factor_cmap(field_name='species', palette='Dark2_3', factors=species),
marker=factor_mark('species', markers, species)
)
# move legend
scatter_plot_subgroups.legend.location = 'top_left'
show(scatter_plot_subgroups)
In [73]:
# scatter_plot_subgroups.scatter?
Subplots
In [74]:
from bokeh.layouts import gridplot
output_notebook()
# data
subplot_x1 = cars['Acceleration']; subplot_y1 = cars['Miles_per_Gallon']
def make_summary_plot(source, table_source, pars_dict):
tools = "pan,wheel_zoom,box_zoom,box_select,tap,hover,reset,crosshair"
pars = [
'M1_init', 'M2_init', 'P_init', 'q_init', 'product', 'stability',
'termination_code'
]
basic_tooltip = [(p, '@' + p) for p in pars]
PRODUCTS = ['HB', 'He-WD', 'CE', 'UK', 'failed', 'sdO', 'sdB', 'sdA'] + \
['stable', 'CE', 'contact', 'merger'] +\
['single-lined', 'composite']
MARKERS = ['square', 'triangle', 'asterisk', 'asterisk', 'diamond', 'circle', 'circle', 'circle'] + \
['circle', 'diamond', 'square', 'triangle', ] +\
['circle', 'circle']
COLORS = ['red', 'green', 'purple', 'purple', 'gray', 'green', 'blue', 'orange'] + \
['red', 'green', 'blue', 'gray'] +\
['gray', 'blue']
SIZES = [7, 7, 7, 7, 15, 7]
v_func = """
const norm = new Float64Array(xs.length)
for (let i = 0; i < xs.length; i++) {
if (xs[i] == 'sdB' || xs[i] == 'Fail' || xs[i] == 'CE') {
norm[i] = 15
} else {
norm[i] = 7
}
}
return norm
"""
size_transform = mpl.CustomJSTransform(v_func=v_func)
# Left Figure
p1 = figure(x_axis_label=pars_dict['x1'],
y_axis_label=pars_dict['y1'],
active_drag='box_select',
tools=tools,
tooltips=basic_tooltip)
p1.scatter(
x="x1",
y="y1",
source=source,
fill_alpha=0.4,
size=transform('z1', size_transform),
color=factor_cmap('z1', COLORS, PRODUCTS),
marker=factor_mark('z1', MARKERS, PRODUCTS),
) # legend_group="z1",
# Right Figure
p2 = figure(x_axis_label=pars_dict['x2'],
y_axis_label=pars_dict['y2'],
active_drag='box_select',
tools=tools,
tooltips=basic_tooltip)
p2.scatter(
x="x2",
y="y2",
source=source,
fill_alpha=0.4,
size=transform('z2', size_transform),
color=factor_cmap('z2', COLORS, PRODUCTS),
marker=factor_mark('z2', MARKERS, PRODUCTS),
) # legend_group="z2",
# color_bar2 = mpl.ColorBar(color_mapper=color_mapper, location=(0,0), title=pars_dict['color2'], title_text_font_size='12pt')
# p2.add_layout(color_bar2, 'right')
plot = gridplot([[p1, p2]])
# add interaction when selecting a model
callback = CustomJS(args=dict(summary_source=source,
table_source=table_source),
code="""
selected_indices = summary_source.selected.indices;
console.log(summary_source.selected.indices[0]);
console.log(summary_source);
if (summary_source.selected.indices.length > 0){
var data = summary_source.data;
var ind = summary_source.selected.indices[0];
var parameters = table_source.data['parameters']
var values = table_source.data['values']
parameters.forEach(function (par, index) {
values[index] = data[par][ind];
});
//table_source.data['parameters'] = x;
table_source.data['values'] = values;
table_source.change.emit();
}
""")
p1.js_on_event('tap', callback)
p2.js_on_event('tap', callback)
return plot, p1, p2
y_axis_type="log",
plot_height=400,
plot_width=700,
tools=[
HoverTool(tooltips=[(
'Country',
'@country'), ('Confirmed Cases',
'@total_cases'), ('Date', '@date_formatted')])
])
p.scatter("date",
"total_cases",
source=source,
fill_alpha=0.4,
size=6,
marker=factor_mark('country', MARKERS, COUNTRIES),
color=factor_cmap('country', 'Category10_5', COUNTRIES))
output_file("COVID19_1.html", title="Total Number of Confirmed COVID-19 Cases")
show(p)
# %%
#Plotting log-scale number of cases for the selected countries. Hover-tool and series-hiding on.
from bokeh.plotting import figure, show, output_file
from bokeh.models import ColumnDataSource, CDSView, GroupFilter, HoverTool
source = ColumnDataSource(covid)
source.add(covid['date'].apply(lambda d: d.strftime('%Y-%m-%d')),
'date_formatted')
x = 'x',
y = 'y',
# Some general parameters about marker size. These seem like
# reasonable values but possible could alter these in a
# callback?
fill_alpha = 0.4,
size = 12,
# Create the legend using the created fields added in the legend
# section. Use the factor_mark and factor_cmap functions to
# match the colors/markers to the right lists.
# NB: Always use legend_field for this not legend_group as the
# former acts on the javascript side but the latter the Python
# side. Therefore the former will update automatically when the
# plot is changed with no need for a callback.
legend_field = 'legend',
marker = factor_mark('marker1', marker_palette, marker_list),
color = factor_cmap('color1', color_palette, color_list)
)
######### Add plot parameters:
# Run the Define_Plot_Parameters function to format the plot. Takes the plot
# and the pre-defined list of default plot parameters as inputs.
Define_Plot_Parameters(p1, list_plot_parameters)
############################################################################
############################################################################
FATE = ['Died', 'Lived']
MARKERS = ['cross', 'circle']
# Set the Title a and size of plot
p = figure(plot_height=600,
plot_width=971,
title="Titanic Passenger Age & Fare by Survial Type")
# Construnct the colours
p.scatter("Fare",
"Age",
source=titanic_df,
legend="Survived",
fill_alpha=0.3,
size=12,
marker=factor_mark('Survived', MARKERS, FATE),
color=factor_cmap('Survived', ['#3a6587', '#aeb3b7'], FATE))
# Set axis labels
p.xaxis.axis_label = 'Fare (In Pounds)'
p.yaxis.axis_label = 'Age (In Years)'
# Remove the Grid lines
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# change just some things about the x-axis
p.xaxis.axis_line_width = 2
p.xaxis.major_label_text_color = "black"
p.xaxis.axis_line_color = "#aeb3b7"
from bokeh.plotting import figure, show, output_file
from bokeh.sampledata.iris import flowers
from bokeh.transform import factor_cmap, factor_mark
SPECIES = ['setosa', 'versicolor', 'virginica']
MARKERS = ['hex', 'circle_x', 'triangle']
p = figure(title = "Iris Morphology", background_fill_color="#fafafa")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Sepal Width'
p.scatter("petal_length", "sepal_width", source=flowers, legend="species", fill_alpha=0.4, size=12,
marker=factor_mark('species', MARKERS, SPECIES),
color=factor_cmap('species', 'Category10_3', SPECIES))
output_file("marker_map.html")
show(p)
def photometry_plot(obj_id, user, width=600, device="browser"):
"""Create object photometry scatter plot.
Parameters
----------
obj_id : str
ID of Obj to be plotted.
Returns
-------
dict
Returns Bokeh JSON embedding for the desired plot.
"""
data = pd.read_sql(
DBSession()
.query(
Photometry,
Telescope.nickname.label("telescope"),
Instrument.name.label("instrument"),
)
.join(Instrument, Instrument.id == Photometry.instrument_id)
.join(Telescope, Telescope.id == Instrument.telescope_id)
.filter(Photometry.obj_id == obj_id)
.filter(
Photometry.groups.any(Group.id.in_([g.id for g in user.accessible_groups]))
)
.statement,
DBSession().bind,
)
if data.empty:
return None, None, None
# get spectra to annotate on phot plots
spectra = (
Spectrum.query_records_accessible_by(user)
.filter(Spectrum.obj_id == obj_id)
.all()
)
data['color'] = [get_color(f) for f in data['filter']]
# get marker for each unique instrument
instruments = list(data.instrument.unique())
markers = []
for i, inst in enumerate(instruments):
markers.append(phot_markers[i % len(phot_markers)])
filters = list(set(data['filter']))
colors = [get_color(f) for f in filters]
color_mapper = CategoricalColorMapper(factors=filters, palette=colors)
color_dict = {'field': 'filter', 'transform': color_mapper}
labels = []
for i, datarow in data.iterrows():
label = f'{datarow["instrument"]}/{datarow["filter"]}'
if datarow['origin'] is not None:
label += f'/{datarow["origin"]}'
labels.append(label)
data['label'] = labels
data['zp'] = PHOT_ZP
data['magsys'] = 'ab'
data['alpha'] = 1.0
data['lim_mag'] = (
-2.5 * np.log10(data['fluxerr'] * PHOT_DETECTION_THRESHOLD) + data['zp']
)
# Passing a dictionary to a bokeh datasource causes the frontend to die,
# deleting the dictionary column fixes that
del data['original_user_data']
# keep track of things that are only upper limits
data['hasflux'] = ~data['flux'].isna()
# calculate the magnitudes - a photometry point is considered "significant"
# or "detected" (and thus can be represented by a magnitude) if its snr
# is above PHOT_DETECTION_THRESHOLD
obsind = data['hasflux'] & (
data['flux'].fillna(0.0) / data['fluxerr'] >= PHOT_DETECTION_THRESHOLD
)
data.loc[~obsind, 'mag'] = None
data.loc[obsind, 'mag'] = -2.5 * np.log10(data[obsind]['flux']) + PHOT_ZP
# calculate the magnitude errors using standard error propagation formulae
# https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Example_formulae
data.loc[~obsind, 'magerr'] = None
coeff = 2.5 / np.log(10)
magerrs = np.abs(coeff * data[obsind]['fluxerr'] / data[obsind]['flux'])
data.loc[obsind, 'magerr'] = magerrs
data['obs'] = obsind
data['stacked'] = False
split = data.groupby('label', sort=False)
finite = np.isfinite(data['flux'])
fdata = data[finite]
lower = np.min(fdata['flux']) * 0.95
upper = np.max(fdata['flux']) * 1.05
xmin = data['mjd'].min() - 2
xmax = data['mjd'].max() + 2
# Layout parameters based on device type
active_drag = None if "mobile" in device or "tablet" in device else "box_zoom"
tools = (
'box_zoom,pan,reset'
if "mobile" in device or "tablet" in device
else "box_zoom,wheel_zoom,pan,reset,save"
)
legend_loc = "below" if "mobile" in device or "tablet" in device else "right"
legend_orientation = (
"vertical" if device in ["browser", "mobile_portrait"] else "horizontal"
)
# Compute a plot component height based on rough number of legend rows added below the plot
# Values are based on default sizing of bokeh components and an estimate of how many
# legend items would fit on the average device screen. Note that the legend items per
# row is computed more exactly later once labels are extracted from the data (with the
# add_plot_legend() function).
#
# The height is manually computed like this instead of using built in aspect_ratio/sizing options
# because with the new Interactive Legend approach (instead of the legacy CheckboxLegendGroup), the
# Legend component is considered part of the plot and plays into the sizing computations. Since the
# number of items in the legend can alter the needed heights of the plot, using built-in Bokeh options
# for sizing does not allow for keeping the actual graph part of the plot at a consistent aspect ratio.
#
# For the frame width, by default we take the desired plot width minus 64 for the y-axis/label taking
# up horizontal space
frame_width = width - 64
if device == "mobile_portrait":
legend_items_per_row = 1
legend_row_height = 24
aspect_ratio = 1
elif device == "mobile_landscape":
legend_items_per_row = 4
legend_row_height = 50
aspect_ratio = 1.8
elif device == "tablet_portrait":
legend_items_per_row = 5
legend_row_height = 50
aspect_ratio = 1.5
elif device == "tablet_landscape":
legend_items_per_row = 7
legend_row_height = 50
aspect_ratio = 1.8
elif device == "browser":
# Width minus some base width for the legend, which is only a column to the right
# for browser mode
frame_width = width - 200
height = (
500
if device == "browser"
else math.floor(width / aspect_ratio)
+ legend_row_height * int(len(split) / legend_items_per_row)
+ 30 # 30 is the height of the toolbar
)
plot = figure(
frame_width=frame_width,
height=height,
active_drag=active_drag,
tools=tools,
toolbar_location='above',
toolbar_sticky=True,
y_range=(lower, upper),
min_border_right=16,
x_axis_location='above',
sizing_mode="stretch_width",
)
plot.xaxis.axis_label = 'MJD'
now = Time.now().mjd
plot.extra_x_ranges = {"Days Ago": Range1d(start=now - xmin, end=now - xmax)}
plot.add_layout(LinearAxis(x_range_name="Days Ago", axis_label="Days Ago"), 'below')
imhover = HoverTool(tooltips=tooltip_format)
imhover.renderers = []
plot.add_tools(imhover)
model_dict = {}
legend_items = []
for i, (label, sdf) in enumerate(split):
renderers = []
# for the flux plot, we only show things that have a flux value
df = sdf[sdf['hasflux']]
key = f'obs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='flux',
color='color',
marker=factor_mark('instrument', markers, instruments),
fill_color=color_dict,
alpha='alpha',
source=ColumnDataSource(df),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
key = f'bin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='flux',
color='color',
marker=factor_mark('instrument', markers, instruments),
fill_color=color_dict,
source=ColumnDataSource(
data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
stacked=[],
instrument=[],
)
),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
key = 'obserr' + str(i)
y_err_x = []
y_err_y = []
for d, ro in df.iterrows():
px = ro['mjd']
py = ro['flux']
err = ro['fluxerr']
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
source=ColumnDataSource(
data=dict(
xs=y_err_x, ys=y_err_y, color=df['color'], alpha=[1.0] * len(df)
)
),
)
renderers.append(model_dict[key])
key = f'binerr{i}'
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
# legend_label=label,
source=ColumnDataSource(data=dict(xs=[], ys=[], color=[])),
)
renderers.append(model_dict[key])
legend_items.append(LegendItem(label=label, renderers=renderers))
if device == "mobile_portrait":
plot.xaxis.ticker.desired_num_ticks = 5
plot.yaxis.axis_label = 'Flux (μJy)'
plot.toolbar.logo = None
add_plot_legend(plot, legend_items, width, legend_orientation, legend_loc)
slider = Slider(
start=0.0,
end=15.0,
value=0.0,
step=1.0,
title='Binsize (days)',
max_width=350,
margin=(4, 10, 0, 10),
)
callback = CustomJS(
args={'slider': slider, 'n_labels': len(split), **model_dict},
code=open(
os.path.join(os.path.dirname(__file__), '../static/js/plotjs', 'stackf.js')
)
.read()
.replace('default_zp', str(PHOT_ZP))
.replace('detect_thresh', str(PHOT_DETECTION_THRESHOLD)),
)
slider.js_on_change('value', callback)
# Mark the first and last detections
detection_dates = data[data['hasflux']]['mjd']
if len(detection_dates) > 0:
first = round(detection_dates.min(), 6)
last = round(detection_dates.max(), 6)
first_color = "#34b4eb"
last_color = "#8992f5"
midpoint = (upper + lower) / 2
line_top = 5 * upper - 4 * midpoint
line_bottom = 5 * lower - 4 * midpoint
y = np.linspace(line_bottom, line_top, num=5000)
first_r = plot.line(
x=np.full(5000, first),
y=y,
line_alpha=0.5,
line_color=first_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("First detection", f'{first}')],
renderers=[first_r],
)
)
last_r = plot.line(
x=np.full(5000, last),
y=y,
line_alpha=0.5,
line_color=last_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("Last detection", f'{last}')],
renderers=[last_r],
)
)
# Mark when spectra were taken
annotate_spec(plot, spectra, lower, upper)
layout = column(slider, plot, width=width, height=height)
p1 = Panel(child=layout, title='Flux')
# now make the mag light curve
ymax = (
np.nanmax(
(
np.nanmax(data.loc[obsind, 'mag']) if any(obsind) else np.nan,
np.nanmax(data.loc[~obsind, 'lim_mag']) if any(~obsind) else np.nan,
)
)
+ 0.1
)
ymin = (
np.nanmin(
(
np.nanmin(data.loc[obsind, 'mag']) if any(obsind) else np.nan,
np.nanmin(data.loc[~obsind, 'lim_mag']) if any(~obsind) else np.nan,
)
)
- 0.1
)
plot = figure(
frame_width=frame_width,
height=height,
active_drag=active_drag,
tools=tools,
y_range=(ymax, ymin),
x_range=(xmin, xmax),
toolbar_location='above',
toolbar_sticky=True,
x_axis_location='above',
sizing_mode="stretch_width",
)
plot.xaxis.axis_label = 'MJD'
now = Time.now().mjd
plot.extra_x_ranges = {"Days Ago": Range1d(start=now - xmin, end=now - xmax)}
plot.add_layout(LinearAxis(x_range_name="Days Ago", axis_label="Days Ago"), 'below')
obj = DBSession().query(Obj).get(obj_id)
if obj.dm is not None:
plot.extra_y_ranges = {
"Absolute Mag": Range1d(start=ymax - obj.dm, end=ymin - obj.dm)
}
plot.add_layout(
LinearAxis(y_range_name="Absolute Mag", axis_label="m - DM"), 'right'
)
# Mark the first and last detections again
detection_dates = data[obsind]['mjd']
if len(detection_dates) > 0:
first = round(detection_dates.min(), 6)
last = round(detection_dates.max(), 6)
midpoint = (ymax + ymin) / 2
line_top = 5 * ymax - 4 * midpoint
line_bottom = 5 * ymin - 4 * midpoint
y = np.linspace(line_bottom, line_top, num=5000)
first_r = plot.line(
x=np.full(5000, first),
y=y,
line_alpha=0.5,
line_color=first_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("First detection", f'{first}')],
renderers=[first_r],
)
)
last_r = plot.line(
x=np.full(5000, last),
y=y,
line_alpha=0.5,
line_color=last_color,
line_width=2,
)
plot.add_tools(
HoverTool(
tooltips=[("Last detection", f'{last}')],
renderers=[last_r],
point_policy='follow_mouse',
)
)
# Mark when spectra were taken
annotate_spec(plot, spectra, ymax, ymin)
imhover = HoverTool(tooltips=tooltip_format)
imhover.renderers = []
plot.add_tools(imhover)
model_dict = {}
# Legend items are individually stored instead of being applied
# directly when plotting so that they can be separated into multiple
# Legend() components if needed (to simulate horizontal row wrapping).
# This is necessary because Bokeh does not support row wrapping with
# horizontally-oriented legends out-of-the-box.
legend_items = []
for i, (label, df) in enumerate(split):
renderers = []
key = f'obs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='mag',
color='color',
marker=factor_mark('instrument', markers, instruments),
fill_color=color_dict,
alpha='alpha',
source=ColumnDataSource(df[df['obs']]),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
unobs_source = df[~df['obs']].copy()
unobs_source.loc[:, 'alpha'] = 0.8
key = f'unobs{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='lim_mag',
color=color_dict,
marker='inverted_triangle',
fill_color='white',
line_color='color',
alpha='alpha',
source=ColumnDataSource(unobs_source),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
key = f'bin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='mag',
color=color_dict,
marker=factor_mark('instrument', markers, instruments),
fill_color='color',
source=ColumnDataSource(
data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
instrument=[],
stacked=[],
)
),
)
renderers.append(model_dict[key])
imhover.renderers.append(model_dict[key])
key = 'obserr' + str(i)
y_err_x = []
y_err_y = []
for d, ro in df[df['obs']].iterrows():
px = ro['mjd']
py = ro['mag']
err = ro['magerr']
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
source=ColumnDataSource(
data=dict(
xs=y_err_x,
ys=y_err_y,
color=df[df['obs']]['color'],
alpha=[1.0] * len(df[df['obs']]),
)
),
)
renderers.append(model_dict[key])
key = f'binerr{i}'
model_dict[key] = plot.multi_line(
xs='xs',
ys='ys',
color='color',
source=ColumnDataSource(data=dict(xs=[], ys=[], color=[])),
)
renderers.append(model_dict[key])
key = f'unobsbin{i}'
model_dict[key] = plot.scatter(
x='mjd',
y='lim_mag',
color='color',
marker='inverted_triangle',
fill_color='white',
line_color=color_dict,
alpha=0.8,
source=ColumnDataSource(
data=dict(
mjd=[],
flux=[],
fluxerr=[],
filter=[],
color=[],
lim_mag=[],
mag=[],
magerr=[],
instrument=[],
stacked=[],
)
),
)
imhover.renderers.append(model_dict[key])
renderers.append(model_dict[key])
key = f'all{i}'
model_dict[key] = ColumnDataSource(df)
key = f'bold{i}'
model_dict[key] = ColumnDataSource(
df[
[
'mjd',
'flux',
'fluxerr',
'mag',
'magerr',
'filter',
'zp',
'magsys',
'lim_mag',
'stacked',
]
]
)
legend_items.append(LegendItem(label=label, renderers=renderers))
add_plot_legend(plot, legend_items, width, legend_orientation, legend_loc)
plot.yaxis.axis_label = 'AB mag'
plot.toolbar.logo = None
slider = Slider(
start=0.0,
end=15.0,
value=0.0,
step=1.0,
title='Binsize (days)',
max_width=350,
margin=(4, 10, 0, 10),
)
button = Button(label="Export Bold Light Curve to CSV")
button.js_on_click(
CustomJS(
args={'slider': slider, 'n_labels': len(split), **model_dict},
code=open(
os.path.join(
os.path.dirname(__file__), '../static/js/plotjs', "download.js"
)
)
.read()
.replace('objname', obj_id)
.replace('default_zp', str(PHOT_ZP)),
)
)
# Don't need to expose CSV download on mobile
top_layout = (
slider if "mobile" in device or "tablet" in device else row(slider, button)
)
callback = CustomJS(
args={'slider': slider, 'n_labels': len(split), **model_dict},
code=open(
os.path.join(os.path.dirname(__file__), '../static/js/plotjs', 'stackm.js')
)
.read()
.replace('default_zp', str(PHOT_ZP))
.replace('detect_thresh', str(PHOT_DETECTION_THRESHOLD)),
)
slider.js_on_change('value', callback)
layout = column(top_layout, plot, width=width, height=height)
p2 = Panel(child=layout, title='Mag')
# now make period plot
# get periods from annotations
annotation_list = obj.get_annotations_readable_by(user)
period_labels = []
period_list = []
for an in annotation_list:
if 'period' in an.data:
period_list.append(an.data['period'])
period_labels.append(an.origin + ": %.9f" % an.data['period'])
if len(period_list) > 0:
period = period_list[0]
else:
period = None
# don't generate if no period annotated
if period is not None:
# bokeh figure for period plotting
period_plot = figure(
frame_width=frame_width,
height=height,
active_drag=active_drag,
tools=tools,
y_range=(ymax, ymin),
x_range=(-0.01, 2.01), # initially one phase
toolbar_location='above',
toolbar_sticky=False,
x_axis_location='below',
sizing_mode="stretch_width",
)
# axis labels
period_plot.xaxis.axis_label = 'phase'
period_plot.yaxis.axis_label = 'mag'
period_plot.toolbar.logo = None
# do we have a distance modulus (dm)?
obj = DBSession().query(Obj).get(obj_id)
if obj.dm is not None:
period_plot.extra_y_ranges = {
"Absolute Mag": Range1d(start=ymax - obj.dm, end=ymin - obj.dm)
}
period_plot.add_layout(
LinearAxis(y_range_name="Absolute Mag", axis_label="m - DM"), 'right'
)
# initiate hover tool
period_imhover = HoverTool(tooltips=tooltip_format)
period_imhover.renderers = []
period_plot.add_tools(period_imhover)
# initiate period radio buttons
period_selection = RadioGroup(labels=period_labels, active=0)
phase_selection = RadioGroup(labels=["One phase", "Two phases"], active=1)
# store all the plot data
period_model_dict = {}
# iterate over each filter
legend_items = []
for i, (label, df) in enumerate(split):
renderers = []
# fold x-axis on period in days
df['mjd_folda'] = (df['mjd'] % period) / period
df['mjd_foldb'] = df['mjd_folda'] + 1.0
# phase plotting
for ph in ['a', 'b']:
key = 'fold' + ph + f'{i}'
period_model_dict[key] = period_plot.scatter(
x='mjd_fold' + ph,
y='mag',
color='color',
marker=factor_mark('instrument', markers, instruments),
fill_color=color_dict,
alpha='alpha',
# visible=('a' in ph),
source=ColumnDataSource(df[df['obs']]), # only visible data
)
# add to hover tool
period_imhover.renderers.append(period_model_dict[key])
renderers.append(period_model_dict[key])
# errorbars for phases
key = 'fold' + ph + f'err{i}'
y_err_x = []
y_err_y = []
# get each visible error value
for d, ro in df[df['obs']].iterrows():
px = ro['mjd_fold' + ph]
py = ro['mag']
err = ro['magerr']
# set up error tuples
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
# plot phase errors
period_model_dict[key] = period_plot.multi_line(
xs='xs',
ys='ys',
color='color',
alpha='alpha',
# visible=('a' in ph),
source=ColumnDataSource(
data=dict(
xs=y_err_x,
ys=y_err_y,
color=df[df['obs']]['color'],
alpha=[1.0] * len(df[df['obs']]),
)
),
)
renderers.append(period_model_dict[key])
legend_items.append(LegendItem(label=label, renderers=renderers))
add_plot_legend(
period_plot, legend_items, width, legend_orientation, legend_loc
)
# set up period adjustment text box
period_title = Div(text="Period (days): ")
period_textinput = TextInput(value=str(period if period is not None else 0.0))
period_textinput.js_on_change(
'value',
CustomJS(
args={
'textinput': period_textinput,
'numphases': phase_selection,
'n_labels': len(split),
'p': period_plot,
**period_model_dict,
},
code=open(
os.path.join(
os.path.dirname(__file__), '../static/js/plotjs', 'foldphase.js'
)
).read(),
),
)
# a way to modify the period
period_double_button = Button(label="*2", width=30)
period_double_button.js_on_click(
CustomJS(
args={'textinput': period_textinput},
code="""
const period = parseFloat(textinput.value);
textinput.value = parseFloat(2.*period).toFixed(9);
""",
)
)
period_halve_button = Button(label="/2", width=30)
period_halve_button.js_on_click(
CustomJS(
args={'textinput': period_textinput},
code="""
const period = parseFloat(textinput.value);
textinput.value = parseFloat(period/2.).toFixed(9);
""",
)
)
# a way to select the period
period_selection.js_on_click(
CustomJS(
args={'textinput': period_textinput, 'periods': period_list},
code="""
textinput.value = parseFloat(periods[this.active]).toFixed(9);
""",
)
)
phase_selection.js_on_click(
CustomJS(
args={
'textinput': period_textinput,
'numphases': phase_selection,
'n_labels': len(split),
'p': period_plot,
**period_model_dict,
},
code=open(
os.path.join(
os.path.dirname(__file__), '../static/js/plotjs', 'foldphase.js'
)
).read(),
)
)
# layout
if device == "mobile_portrait":
period_controls = column(
row(
period_title,
period_textinput,
period_double_button,
period_halve_button,
width=width,
sizing_mode="scale_width",
),
phase_selection,
period_selection,
width=width,
)
# Add extra height to plot based on period control components added
# 18 is the height of each period selection radio option (per default font size)
# and the 130 encompasses the other components which are consistent no matter
# the data size.
height += 130 + 18 * len(period_labels)
else:
period_controls = column(
row(
period_title,
period_textinput,
period_double_button,
period_halve_button,
phase_selection,
width=width,
sizing_mode="scale_width",
),
period_selection,
margin=10,
)
# Add extra height to plot based on period control components added
# Numbers are derived in similar manner to the "mobile_portrait" case above
height += 90 + 18 * len(period_labels)
period_layout = column(period_plot, period_controls, width=width, height=height)
# Period panel
p3 = Panel(child=period_layout, title='Period')
# tabs for mag, flux, period
tabs = Tabs(tabs=[p2, p1, p3], width=width, height=height, sizing_mode='fixed')
else:
# tabs for mag, flux
tabs = Tabs(tabs=[p2, p1], width=width, height=height + 90, sizing_mode='fixed')
return bokeh_embed.json_item(tabs)
("BowlerType", "@BowlerType"),
]
# Set the Title
p = figure(title = "Top Test Bowlers by Decade (1877-1919)",
tooltips=TOOLTIPS_SCATTER,
x_range=(0, 30),
y_range=(0, 200),
plot_width=1280, plot_height=800)
p.title.text_font_size = '24pt'
# Construnct the colours
p.scatter("Average", "Wickets", source=titanic_df, legend="RightHand", fill_alpha=0.9, size=24,
marker=factor_mark('RightHand', MARKERS, FATE),
color=factor_cmap('RightHand', palette=['#aeb3b7', '#3a6587'], factors=FATE))
#Set the axis labels
p.xaxis.axis_label = 'Career Bowling Average'
p.yaxis.axis_label = 'Total Career Wickets'
# Remove the Grid lines
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# change just some things about the x-axis
p.xaxis.axis_line_width = 2
p.xaxis.major_label_text_color = "black"
p.xaxis.axis_line_color = "#aeb3b7"
def get_total_infected_people_figure(colors):
with open(SOURCES_FILE_PATH) as f:
source_data = json.load(f)
df = pd.DataFrame(source_data['image'])
df = pd.DataFrame(df.stack(), columns=['rate']).reset_index()
mapper = LinearColorMapper(palette=colors,
low=df.rate.min(),
high=df.rate.max())
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
# Create a figure with a datetime type x-axis
fig = figure(title=source_data['name'],
plot_height=900,
plot_width=900,
x_axis_label=source_data['x'],
y_axis_label=source_data['y'],
x_minor_ticks=5,
y_range=(0, 50),
x_range=(0, 50),
y_minor_ticks=5,
y_scale=Scale(),
toolbar_location='below',
tools=TOOLS,
tooltips=[])
rects = fig.rect(x='level_0',
y='level_1',
width=1,
height=1,
source=df,
fill_color={
'field': 'rate',
'transform': mapper
},
line_color=None)
stars = fig.scatter("petal_length",
"sepal_width",
source=flowers,
legend_field="species",
fill_alpha=0.4,
size=12,
marker=factor_mark('species', MARKERS, SPECIES),
color=factor_cmap('species', 'Category10_3', SPECIES))
g1_hover = bkm.HoverTool(renderers=[rects], tooltips=[('rects', 'rects')])
g2_hover = bkm.HoverTool(renderers=[stars], tooltips=[('stars', 'stars')])
fig.add_tools(g1_hover, g2_hover)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_font_size="5pt",
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d%%"),
label_standoff=6,
border_line_color=None,
location=(0, 0))
tap_tool_1 = TapTool(behavior='inspect',
callback=CustomJS(args=dict(),
code=get_callback_function('red')),
renderers=[rects])
tap_tool_2 = TapTool(
behavior='inspect',
callback=CustomJS(args=dict(), code=get_callback_function('violet')),
renderers=[stars])
fig.add_tools(tap_tool_1, tap_tool_2)
fig.add_layout(color_bar, 'right')
return fig
def Gulmay_Output_Graph(conn):
output_file("PDD_Graph.html") #????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????
'''
In order to make a simple plot easy to create we're going to define all of
the user inputs up front. Then pull the data out of the database and
add a simple tolerance dataframe if necessary.
For a first plot this may be all that is needed but to make a more complex
plot the writer may need to add details further down.
Remember that:
* The dataframe will not have capital letters in the field names due
to a quirk of the read_sql function.
* The date field MUST be called 'adate' and of the format 'datetime'
in order for parts of the code to function correctly
'''
############################################################################
############################################################################
############################################################################
############################# USER INPUTS ##################################
# Decide what the default viewing option is going to be. (i.e. the fields to
# be plotted on the x and y axis when the graph is opened, the plot size
# etc.).
#
# Decide on what data to plot on the x/y axis when opened (enter the field
# names here)
x_data1 = 'adate'
y_data1 = 'output'
# Decide what the plot formatting will be, inluding the plot title, axis
# titles and the size of the plot. A good generic start point is to use the
# field names as the axis titles but future plots could potentially use a
# look up table to give more user friendly titles.
plot_title1 = 'Gulmay Output'
x_axis_title1 = x_data1
y_axis_title1 = y_data1
plot_size_height1 = 450
plot_size_width1 = 800
legend_location = 'bottom_left'
# Set the fields that will display in the hovertool in addition to the x and
# y fields (NB: Can have a maximum of 10 options here). (Useful example
# defaults would be the comments field or maybe chamber/electrometer?)
hover_tool_fields = ['comments', 'input by', 'checked by']
# Create a list of the plot parameters that will be used as input to a
# function later.
list_plot_parameters = [x_data1, y_data1, plot_title1, x_axis_title1,
y_axis_title1, plot_size_height1, plot_size_width1, legend_location]
# Define the fields that the legend will be based off. If there is only
# one field then put it in both columns.
#
# If the default options in the function are not acceptable then change the
# boolean to True and then set the palette to the color and marker palettes
# that you want (they will be mapped against a 'sorted' list of the unique
# values from the fields).
color_column = 'energy'
custom_color_boolean = False
custom_color_palette = []
marker_column = 'energy'
custom_marker_boolean = False
custom_marker_palette = []
# From the legend defined above give the values that will be pre-ticked when
# the plot is opened. NB: Bokeh will throw an error if one of these lists is
# empty (i.e. =[]) If only using color or marker then set the color_to plot
# and then enter the command: color_to_plot = marker_to_plot.
# color_to_plot = ['Gulmay']
marker_to_plot = ['100 kV', '150 kV', '220 kV']
color_to_plot = marker_to_plot
############################################################################
#################### CREATE THE DATA FOR THE GRAPH #########################
# Do this in a function so it can be used in an update callback later
def Create_df():
# Use the connection passed to the function to read the data into a
# dataframe via an SQL query.
df = pd.read_sql('select [gulmay session ID], [output], ' \
'[chamber and electrometer], [Chamber factor], [Dose rate], ' \
'[energy], [T/P factor], [Temp], [Press], [Comments], ' \
'[Input by], [Checked by] from [gulmay output]', conn)
# Delete empty rows where the data is very important to have
df = df.dropna(subset=['gulmay session id'])
df = df.dropna(subset=['energy'])
# The format is complicated for this field but seems to be that the date is
# always the first element and the machine is always the last regardless of
# how many elements there are.
# Seperate on the first '_'
df_left = df['gulmay session id'].str.partition(sep = '_')
# Seperate on the last '_'
df_right = df['gulmay session id'].str.rpartition(sep = '_')
# From these sperated dataframes add the appropriate columns back into
# the main dataframe.
df.loc[:,'adate'] = df_left[0]
df.loc[:,'machinename'] = df_right[2]
# Turn 'adate' into datetime.
df.loc[:,'adate'] = pd.to_datetime(df.loc[:,'adate'], dayfirst=True)
# Drop any columns where there is no data
df = df.dropna(axis='columns', how='all')
return df
df = Create_df()
# Create a list of the fields using the dataframe. By doing it now before
# the extra legend fields are added it's easy to limit what is displayed in
# the select widgets.
TableFields = (list(df.columns))
############################################################################
############################################################################
############################################################################
################ CREATE THE DATAFRAME FOR THE TOLERANCES ###################
# If you want to add tolerances change the boolean to True and construct the
# dataframe in the correct format.
tolerance_boolean = True
# The format of the dataframe should be the first line being the x_axis
# (with some values taken from the main dataframe to get the right
# formatting). The subsequent columns are the tolerances [low, high].
# NB: column names should match those from the main dataframe.
if tolerance_boolean == True:
df_tol1 = pd.DataFrame({'adate':[df['adate'].max(), df['adate'].max()],
'output':[97, +103]})
############################################################################
############################################################################
############################################################################
############################################################################
'''
This is the end of the user input section. If you don't need to make any
other changes you can end here.
'''
##########################################################################
################### CREATE THE COLUMNS FOR THE LEGEND ######################
(color_list, color_palette, marker_list, marker_palette, df,
add_legend_to_df) = Create_Legend(df, color_column,
custom_color_boolean, custom_color_palette, marker_column,
custom_marker_boolean, custom_marker_palette)
############################################################################
############################################################################
############################################################################
################## FORMATTING AND CREATING A BASIC PLOT ####################
######### Make Dataset:
# Run the Make_Dataset function to create a sub dataframe that the plot will
# be made from.
Sub_df1 = Make_Dataset( df, color_column, color_to_plot, marker_column,
marker_to_plot, x_data1, y_data1 )
# Make the ColumnDataSource (when making convert dataframe to a dictionary,
# which is helpful for the callback).
src1 = ColumnDataSource(Sub_df1.to_dict(orient='list'))
######### Make Plot:
# Create an empty plot (plot parameters will be applied later in a way that
# can be manipulated in the callbacks)
p1 = figure()
# Create a scatter plot.
p1.scatter( source = src1,
x = 'x',
y = 'y',
fill_alpha = 0.4,
size = 12,
# NB: Always use legend_field for this not legend_group as the
# former acts on the javascript side but the latter the Python
# side. Therefore the former will update automatically.
legend_field = 'legend',
marker = factor_mark('marker1', marker_palette, marker_list),
color = factor_cmap('color1', color_palette, color_list)
)
# Run the Define_Plot_Parameters function to format the plot.
Define_Plot_Parameters(p1, list_plot_parameters)
############################################################################
############################################################################
############################################################################
############################ ADD TOLERANCES ################################
# We defined the tolerances further up and now want to add the correct ones
# to the plot. Only do this through if the boolean is set to True as
# otherwise the user doesn't want tolerances.
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(x_data1, y_data1, Sub_df1,
df_tol1)
src1_tol = ColumnDataSource(Sub_df1_tol1.to_dict(orient='list'))
# Add two lines to the plot using the new ColumnDataSource as the
# source, one line for low tolerance and one line for high.
p1.line(source = src1_tol, x = 'x', y = 'y_low', color = 'firebrick')
p1.line(source = src1_tol, x = 'x', y = 'y_high', color = 'firebrick')
############################################################################
############################################################################
############################################################################
################## ADD MORE COMPLEX TOOLS TO THE PLOT ######################
######## 1)
# Create a hover tool and add it to the plot
hover1 = HoverTool()
if len(hover_tool_fields) < 11:
kwargs = {}
i = 0
for x in hover_tool_fields:
i = i+1
kwargs['Field'+str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, x_data1, y_data1, **kwargs)
p1.add_tools(hover1)
############################################################################
############################################################################
############################################################################
################# CREATE WIDGETS TO BE ADDED TO THE PLOT ###################
######## 1)
# This funtion from Universal.py will be used to create dropdown lists to
# change the data plotted on the x/y-axis.
select_xaxis, select_yaxis = Create_Select_Axis(TableFields, x_axis_title1,
y_axis_title1)
######## 2)
# This function from Universal.py will be used to create a dropdown list to
# change the legend position.
select_legend = Create_Select_Legend(legend_location)
######## 3)
# This funtion from Universal.py will be used to create checkbox widgets to
# change the data being plotted from the fields that the legend is based on.
checkbox_color, checkbox_marker = Create_Checkbox_Legend(df, color_column,
color_to_plot, marker_column, marker_to_plot)
######## 4)
# This funtion from Universal.py will be used to create a checkbox widget
# to change the fields included in the hovertool.
checkbox_hovertool = Create_Checkbox_HoverTool(TableFields,
hover_tool_fields)
######## 5)
# Make an 'Update Button' to requery the database and get up to date data.
update_button = Button(label='Update', button_type='success')
######## 6)
# Make a Range Button
range_button = Button(label='Range', button_type='primary')
######## 7)
# Make some titles for the checkboxes
color_title = Div(text='<b>Energy</b>')
marker_title = Div(text='<b>Energ</b>')
hover_title = Div(text='<b>Hovertool Fields</b>')
############################################################################
############################################################################
############################################################################
########################### CREATE A LAYOUT ################################
# Create a layout where the widgets will be added and any scaling applied.
if color_column == marker_column:
layout_checkbox = column([color_title, checkbox_color, hover_title,
checkbox_hovertool])
else:
layout_checkbox = column([color_title, checkbox_color, marker_title,
checkbox_marker, hover_title, checkbox_hovertool])
button_row = row([update_button, range_button])
layout_plots = column([ button_row, select_xaxis, select_yaxis,
select_legend,p1])
tab_layout = row([layout_plots, layout_checkbox])
############################################################################
############################################################################
############################################################################
####################### CREATE CALLBACK FUNCTIONS ##########################
# Create a big callback that does most stuff
def callback(attr, old, new):
# Want to acquire the current values of all of the checkboxes and select
# widgets to provide as inputs for the re-plot.
color_to_plot = [checkbox_color.labels[i] for i in
checkbox_color.active]
if color_column != marker_column:
marker_to_plot = [checkbox_marker.labels[i] for i in
checkbox_marker.active]
else:
marker_to_plot = color_to_plot
hovertool_to_plot = [checkbox_hovertool.labels[i] for i in
checkbox_hovertool.active]
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
legend_location = select_legend.value
# Set the new axis titles from the values just acquired.
x_axis_title1 = plot1_xdata_to_plot
y_axis_title1 = plot1_ydata_to_plot
# Use the pre-defined Make_Dataset function with these new inputs to
# create new versions of the sub dataframes.
Sub_df1 = Make_Dataset( df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot, plot1_ydata_to_plot)
# Use the pre-defined Define_Plot_Parameters function with these new
# inputs to update the plot parameters.
Define_Plot_Parameters(p1, [plot1_xdata_to_plot, plot1_ydata_to_plot,
plot_title1, x_axis_title1, y_axis_title1, plot_size_height1,
plot_size_width1, legend_location])
# Update the hovertool
if len(hovertool_to_plot) < 11:
kwargs = {}
i = 0
for x in hovertool_to_plot:
i = i+1
kwargs['Field'+str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, plot1_xdata_to_plot, plot1_ydata_to_plot,
**kwargs)
# Use the pre-defined tolerances function with these new inputs to
# make a new version of the tolerances sub dataframe.
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(plot1_xdata_to_plot,
plot1_ydata_to_plot, Sub_df1, df_tol1)
src1_tol.data = Sub_df1_tol1.to_dict(orient='list')
# Update the ColumnDataSources
src1.data = Sub_df1.to_dict(orient='list')
return
select_xaxis.on_change('value', callback)
select_yaxis.on_change('value', callback)
select_legend.on_change('value', callback)
checkbox_color.on_change('active', callback)
checkbox_marker.on_change('active', callback)
checkbox_hovertool.on_change('active', callback)
######## 2)
# This callback is designed to update the plotted data with new values from
# the database
def callback_update():
# Make a new version of the dataframe using the original Create_df
# function that connects to the database.
df = Create_df()
df = add_legend_to_df(df)
# The rest of this callback is a copy from the original callback above.
color_to_plot = [checkbox_color.labels[i] for i in
checkbox_color.active]
if color_column != marker_column:
marker_to_plot = [checkbox_marker.labels[i] for i in
checkbox_marker.active]
else:
marker_to_plot = color_to_plot
hovertool_to_plot = [checkbox_hovertool.labels[i] for i in
checkbox_hovertool.active]
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
legend_location = select_legend.value
# Set the new axis titles from the values just acquired.
x_axis_title1 = plot1_xdata_to_plot
y_axis_title1 = plot1_ydata_to_plot
Sub_df1 = Make_Dataset( df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot, plot1_ydata_to_plot)
Define_Plot_Parameters(p1, [plot1_xdata_to_plot, plot1_ydata_to_plot,
plot_title1, x_axis_title1, y_axis_title1, plot_size_height1,
plot_size_width1, legend_location])
if len(hovertool_to_plot) < 11:
kwargs = {}
i = 0
for x in hovertool_to_plot:
i = i+1
kwargs['Field'+str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, plot1_xdata_to_plot, plot1_ydata_to_plot,
**kwargs)
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(plot1_xdata_to_plot,
plot1_ydata_to_plot, Sub_df1, df_tol1)
src1_tol.data = Sub_df1_tol1.to_dict(orient='list')
src1.data = Sub_df1.to_dict(orient='list')
return
update_button.on_click(callback_update)
# Callback for the Range Button
def callback_range():
color_to_plot = [ checkbox_color.labels[i] for i in
checkbox_color.active]
if color_column != marker_column:
marker_to_plot = [ checkbox_marker.labels[i] for i in
checkbox_marker.active]
else:
marker_to_plot = color_to_plot
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
# Use the pre-defined Make_Dataset function with these new inputs to
# create new versions of the sub dataframes.
Sub_df1 = Make_Dataset( df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot, plot1_ydata_to_plot)
if (plot1_xdata_to_plot == 'adate') and (plot1_ydata_to_plot == 'output'):
p1.x_range.start = Sub_df1['x'].max() - timedelta(weeks=53)
p1.x_range.end = Sub_df1['x'].max() + timedelta(weeks=2)
if plot1_ydata_to_plot == 'output':
p1.y_range.start = 95
p1.y_range.end = 105
return
range_button.on_click(callback_range)
############################################################################
############################################################################
############################################################################
####################### RETURN TO THE MAIN SCRIPT ##########################
return Panel(child = tab_layout, title = 'Gulmay Output')
def plot_scatter(label=None,
water=None,
stakeholders=None,
costs=None,
potAll=None):
# needs some heavy refactoring...
data_path = os.path.join(os.getcwd(), '..', 'input_files', 'input',
'measures')
data_fname = 'stats_measures.csv'
df = pd.read_csv(os.path.join(data_path, data_fname))
fill_alpha = 0.7
line_width = 1
# Plain adding a column with marker sizes
marker_size = len(df) * [10]
df['marker_size'] = marker_size
# Add the user defined measure, in case
if not (label is None and water is None and stakeholders is None
and costs is None and potAll is None):
colour = 'gold'
marker = 'hex'
marker_size = 20
row = pd.DataFrame([[
label, water, colour, costs, potAll, stakeholders, marker,
marker_size
]],
columns=[
'labels', 'dwl_Qref', 'colour', 'cost_sum',
'FI', 'nr_stakeholders', 'marker', 'marker_size'
])
df = df.append(row)
subplot_width = 275
subplot_height = subplot_width
min_border = 0
delta_offset_left = 50
categories = df['labels']
markers = df['marker']
marker_sizes = df['marker_size']
pot_ymin = 60
pot_ymax = 180
colours = df['colour']
y = 'nr_stakeholders'
toolset = ['pan', 'box_zoom', 'wheel_zoom', 'zoom_in', 'zoom_out', 'reset']
subfig11 = figure(plot_width=subplot_width + delta_offset_left,
plot_height=subplot_height,
min_border_left=min_border,
min_border_bottom=min_border,
toolbar_location='above',
tools=toolset)
x = 'dwl_Qref'
v1 = 'dwl_Qref'
v2 = 'nr_stakeholders'
pp = pareto_points(df[[v1, v2]])
subfig11.line(pp[v1], pp[v2], line_width=20, color='gray', line_alpha=0.25)
scatter11 = subfig11.scatter(x,
y,
source=df,
size='marker_size',
marker=factor_mark('labels', markers,
categories),
color=factor_cmap('labels', colours,
categories),
fill_alpha=fill_alpha,
line_width=line_width)
subfig11.yaxis.axis_label = 'No. of stakeholders (-)'
subfig11.add_tools(
HoverTool(tooltips=[('', '@labels')], renderers=[scatter11]))
y = 'FI'
subfig21 = figure(plot_width=subplot_width + delta_offset_left,
plot_height=subplot_height,
min_border_left=min_border,
min_border_bottom=min_border,
tools=toolset,
toolbar_location=None,
x_range=subfig11.x_range)
x = 'dwl_Qref'
v1 = 'dwl_Qref'
v2 = 'FI'
pp = pareto_points(pd.concat([df[['dwl_Qref']], -df[['FI']]], axis=1))
subfig21.line(pp[v1],
-pp[v2],
line_width=20,
color='gray',
line_alpha=0.25)
scatter21 = subfig21.scatter(x,
y,
source=df,
size='marker_size',
marker=factor_mark('labels', markers,
categories),
color=factor_cmap('labels', colours,
categories),
fill_alpha=fill_alpha,
line_width=line_width)
subfig21.yaxis.axis_label = 'PotAll (-)'
subfig21.y_range = Range1d(pot_ymax, pot_ymin)
subfig21.add_tools(
HoverTool(tooltips=[('', '@labels')], renderers=[scatter21]))
subfig22 = figure(plot_width=subplot_width,
plot_height=subplot_height,
min_border_left=min_border,
min_border_bottom=min_border,
tools=toolset,
toolbar_location=None,
y_range=subfig21.y_range)
x = 'nr_stakeholders'
v1 = 'nr_stakeholders'
v2 = 'FI'
pp = pareto_points(
pd.concat([df[['nr_stakeholders']], -df[['FI']]], axis=1))
subfig22.line(pp[v1],
-pp[v2],
line_width=20,
color='gray',
line_alpha=0.25)
scatter22 = subfig22.scatter(x,
y,
source=df,
size='marker_size',
marker=factor_mark('labels', markers,
categories),
color=factor_cmap('labels', colours,
categories),
fill_alpha=fill_alpha,
line_width=line_width)
subfig22.yaxis.major_label_text_font_size = '0pt'
subfig22.add_tools(
HoverTool(tooltips=[('', '@labels')], renderers=[scatter22]))
y = 'cost_sum'
subfig31 = figure(plot_width=subplot_width + delta_offset_left,
plot_height=subplot_height,
min_border_left=min_border,
min_border_bottom=min_border,
tools=toolset,
toolbar_location=None,
x_range=subfig11.x_range)
x = 'dwl_Qref'
v1 = 'dwl_Qref'
v2 = 'cost_sum'
pp = pareto_points(df[[v1, v2]])
subfig31.line(pp[v1], pp[v2], line_width=20, color='gray', line_alpha=0.25)
scatter31 = subfig31.scatter(x,
y,
source=df,
size='marker_size',
marker=factor_mark('labels', markers,
categories),
color=factor_cmap('labels', colours,
categories),
fill_alpha=fill_alpha,
line_width=line_width)
subfig31.yaxis.axis_label = 'Implementation costs (\u20AC)'
subfig31.xaxis.axis_label = 'Water level lowering (m)'
subfig31.add_tools(
HoverTool(tooltips=[('', '@labels')], renderers=[scatter31]))
subfig32 = figure(plot_width=subplot_width,
plot_height=subplot_height,
min_border_left=min_border,
min_border_bottom=min_border,
tools=toolset,
toolbar_location=None,
x_range=subfig22.x_range,
y_range=subfig31.y_range)
x = 'nr_stakeholders'
v1 = 'nr_stakeholders'
v2 = 'cost_sum'
subfig32.circle(0,
0,
line_width=20,
fill_color='gray',
color='gray',
line_alpha=0.25)
scatter32 = subfig32.scatter(x,
y,
source=df,
size='marker_size',
marker=factor_mark('labels', markers,
categories),
color=factor_cmap('labels', colours,
categories),
fill_alpha=fill_alpha,
line_width=line_width)
subfig32.yaxis.major_label_text_font_size = '0pt'
subfig32.xaxis.axis_label = 'No. of stakeholders (-)'
subfig32.add_tools(
HoverTool(tooltips=[('', '@labels')], renderers=[scatter32]))
subfig33 = figure(plot_width=subplot_width,
plot_height=subplot_height,
min_border_left=min_border,
min_border_bottom=min_border,
tools=toolset,
toolbar_location=None,
y_range=subfig31.y_range)
x = 'FI'
v1 = 'FI'
v2 = 'cost_sum'
pp = pareto_points(
pd.concat([-df[[v1]], df[[v2]]],
axis=1)) # pd.concat([df[[v1], -df[[v2]]], axis=1))
#df[[v1, v2]])
subfig33.line(-pp[v1],
pp[v2],
line_width=20,
color='gray',
line_alpha=0.25)
scatter33 = subfig33.scatter(x,
y,
source=df,
size='marker_size',
marker=factor_mark('labels', markers,
categories),
color=factor_cmap('labels', colours,
categories),
fill_alpha=fill_alpha,
line_width=line_width)
subfig33.yaxis.major_label_text_font_size = '0pt'
subfig33.x_range = Range1d(pot_ymax, pot_ymin)
subfig33.xaxis.axis_label = 'PotAll (-)'
subfig33.add_tools(
HoverTool(tooltips=[('', '@labels')], renderers=[scatter33]))
matrix = gridplot([[subfig11, None, None], [subfig21, subfig22, None],
[subfig31, subfig32, subfig33]],
toolbar_location='above')
show(matrix)
items = []
cmap = viridis(len(species))
counter = 0
for index, animal in enumerate(species):
source = ColumnDataSource(
dataframe.loc[dataframe['AnimalGroupParent'] == animal])
scatters[animal] = m.scatter(x='x_mercator',
y='y_mercator',
source=source,
fill_alpha=0.3,
size=15,
color=cmap[index],
marker=factor_mark('AnimalGroupParent',
markers, species),
visible=False,
muted_alpha=0.00)
items.append((animal, [scatters[animal]]))
hover = m.select(dict(type=HoverTool))
hover.tooltips = [
("Animal", "@AnimalGroupParent"),
]
hover.mode = 'mouse'
legend = Legend(items=items)
m.add_layout(legend)
m.legend.location = 'top_left'
m.legend.click_policy = 'hide'
m.legend.label_text_font_size = "10px"
from bokeh.plotting import figure, show
from bokeh.sampledata.iris import flowers
from bokeh.transform import factor_cmap, factor_mark
SPECIES = ['setosa', 'versicolor', 'virginica']
MARKERS = ['hex', 'circle_x', 'triangle']
p = figure(title="Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Sepal Width'
p.scatter("petal_length",
"sepal_width",
source=flowers,
legend_field="species",
fill_alpha=0.4,
size=12,
marker=factor_mark('species', MARKERS, SPECIES),
color=factor_cmap('species', 'Category10_3', SPECIES))
show(p)
def make_Gaia_CM_diagram(source, table_source):
tools = "pan,wheel_zoom,box_zoom,box_select,tap,hover,reset,crosshair"
pars = [
'M1_init', 'M2_init', 'P_init', 'q_init', 'product', 'stability',
'termination_code'
]
basic_tooltip = [(p, '@' + p) for p in pars]
PRODUCTS = ['HB', 'He-WD', 'CE', 'UK', 'failed', 'sdB', 'sdA'] + \
['stable', 'CE', 'contact', 'merger']
MARKERS = ['square', 'triangle', 'asterisk', 'asterisk', 'diamond', 'circle', 'circle'] + \
['circle', 'diamond', 'square', 'triangle', ]
COLORS = ['red', 'green', 'purple', 'purple', 'gray', 'blue', 'orange'] + \
['red', 'green', 'blue', 'gray']
SIZES = [7, 7, 7, 7, 15, 7]
v_func = """
const norm = new Float64Array(xs.length)
for (let i = 0; i < xs.length; i++) {
if (xs[i] == 'sdB' || xs[i] == 'Fail' || xs[i] == 'CE') {
norm[i] = 15
} else {
norm[i] = 7
}
}
return norm
"""
size_transform = mpl.CustomJSTransform(v_func=v_func)
# Left Figure
booleans = [
True if val != 0 else False for val in source.data['G_HeCoreBurning']
]
view1 = CDSView(source=source, filters=[BooleanFilter(booleans)])
p1 = figure(x_axis_label='Gaia BP-RP',
y_axis_label='Gaia G mag',
active_drag='box_select',
tools=tools,
tooltips=basic_tooltip,
title="HeCoreBurning",
y_range=(6, -5))
p1.circle(hiparcos['bp_rp'], hiparcos['M_g'], size=1, color='gray')
p1.scatter(x="BP-RP_HeCoreBurning",
y="G_HeCoreBurning",
source=source,
fill_alpha=0.4,
size=transform('z1', size_transform),
color=factor_cmap('z1', COLORS, PRODUCTS),
marker=factor_mark('z1', MARKERS, PRODUCTS),
view=view1)
# Right Figure
booleans = [
True if val != 0 else False for val in source.data['G_MLstart']
]
view2 = CDSView(source=source, filters=[BooleanFilter(booleans)])
p2 = figure(x_axis_label='Gaia BP-RP',
y_axis_label='Gaia G mag',
active_drag='box_select',
tools=tools,
tooltips=basic_tooltip,
title="ML start",
y_range=(6, -5))
p2.circle(hiparcos['bp_rp'], hiparcos['M_g'], size=1, color='gray')
p2.scatter(x="BP-RP_MLstart",
y="G_MLstart",
source=source,
fill_alpha=0.4,
size=transform('z1', size_transform),
color=factor_cmap('z1', COLORS, PRODUCTS),
marker=factor_mark('z1', MARKERS, PRODUCTS),
view=view2)
plot = gridplot([[p1, p2]])
# add interaction when selecting a model
callback = CustomJS(args=dict(summary_source=source,
table_source=table_source),
code="""
selected_indices = summary_source.selected.indices;
console.log(summary_source.selected.indices[0]);
console.log(summary_source);
if (summary_source.selected.indices.length > 0){
var data = summary_source.data;
var ind = summary_source.selected.indices[0];
var parameters = table_source.data['parameters']
var values = table_source.data['values']
parameters.forEach(function (par, index) {
values[index] = data[par][ind];
});
//table_source.data['parameters'] = x;
table_source.data['values'] = values;
table_source.change.emit();
}
""")
p1.js_on_event('tap', callback)
p2.js_on_event('tap', callback)
return plot, p1, p2
def make_comparison_plot(source, pars_dict, titles=['', '']):
tools = "pan,wheel_zoom,box_zoom,box_select,tap,hover,reset,crosshair"
pars = [
'M1_init', 'M2_init', 'P_init', 'q_init', 'product', 'stability',
'termination_code'
]
basic_tooltip = [(p, '@' + p) for p in pars]
PRODUCTS = ['HB', 'He-WD', 'CE', 'UK', 'failed', 'sdB', 'sdA']
MARKERS = [
'square', 'triangle', 'asterisk', 'asterisk', 'diamond', 'circle',
'circle'
]
COLORS = ['red', 'green', 'purple', 'purple', 'gray', 'blue', 'orange']
SIZES = [7, 7, 7, 7, 15, 7]
v_func = """
const norm = new Float64Array(xs.length)
for (let i = 0; i < xs.length; i++) {
if (xs[i] == 'sdB' || xs[i] == 'Fail') {
norm[i] = 15
} else {
norm[i] = 7
}
}
return norm
"""
size_transform = mpl.CustomJSTransform(v_func=v_func)
# Left Figure
p1 = figure(x_axis_label=pars_dict['x1'],
y_axis_label=pars_dict['y1'],
active_drag='box_select',
tools=tools,
title=titles[0]) # , tooltips=basic_tooltip)
p1.scatter(
x="x1",
y="y1",
source=source,
fill_alpha=0.4,
size=transform('product_1', size_transform),
color=factor_cmap('product_1', COLORS, PRODUCTS),
marker=factor_mark('product_1', MARKERS, PRODUCTS),
)
# Right Figure
p2 = figure(x_axis_label=pars_dict['x2'],
y_axis_label=pars_dict['y2'],
active_drag='box_select',
tools=tools,
title=titles[1]) # , tooltips=basic_tooltip)
p2.scatter(
x="x2",
y="y2",
source=source,
fill_alpha=0.4,
size=transform('product_2', size_transform),
color=factor_cmap('product_2', COLORS, PRODUCTS),
marker=factor_mark('product_2', MARKERS, PRODUCTS),
)
# add interaction when selecting a model
callback = CustomJS(args=dict(summary_source=source),
code="""
selected_indices = summary_source.selected.indices;
""")
p1.js_on_event('tap', callback)
p2.js_on_event('tap', callback)
plot = gridplot([[p1, p2]])
return plot, p1, p2
def comps_tab(comps):
df = comps
df['Close Date'] = pd.to_datetime(df['Close Date'])
df = df.drop(columns=['index'])
df['Size'] = 24
df['Color'] = "#31AADE"
df['xs'] = 'Close Date'
df['ys'] = 'Finished Lot Value'
SIZES = list(range(12, 36, 3))
COLORS = Spectral6
N_SIZES = len(SIZES)
N_COLORS = len(COLORS)
MARKERSOURCE = list(df['Site Condition'].unique())
MARKERS = ['hex', 'circle_x', 'triangle', 'square']
source = ColumnDataSource(df)
columns = sorted(df.columns)
discrete = [x for x in columns if df[x].dtype == object]
continuous = [x for x in columns if x not in discrete]
x_axis_select = Select(title='X-Axis', value='Close Date', options=columns)
y_axis_select = Select(title='Y-Axis',
value='Finished Lot Value',
options=columns)
size = Select(title='Size', value='None', options=['None'] + continuous)
color = Select(title='Color', value='None', options=['None'] + continuous)
kw = dict()
x_title = x_axis_select.value.title()
y_title = y_axis_select.value.title()
df['xs'] = df[x_axis_select.value].values
df['ys'] = df[y_axis_select.value].values
x_title = x_axis_select.value.title()
y_title = y_axis_select.value.title()
kw = dict()
if x_axis_select.value in discrete:
kw['x_range'] = sorted(set(df['xs']))
if y_axis_select.value in discrete:
kw['y_range'] = sorted(set(df['ys']))
kw['title'] = "%s vs %s" % (x_title, y_title)
if x_axis_select.value in discrete:
p.xaxis.major_label_orientation = pd.np.pi / 4
df['Size'] = 24
if size.value != 'None':
if len(set(df[size.value])) > N_SIZES:
groups = pd.qcut(df[size.value].values, N_SIZES, duplicates='drop')
else:
groups = pd.Categorical(df[size.value])
df['Size'] = [SIZES[xx] for xx in groups.codes]
df['Color'] = "#31AADE"
if color.value != 'None':
if len(set(df[color.value])) > N_COLORS:
groups = pd.qcut(df[color.value].values,
N_COLORS,
duplicates='drop')
else:
groups = pd.Categorical(df[color.value])
df['Color'] = [COLORS[xx] for xx in groups.codes]
p = figure(plot_height=500,
plot_width=800,
tools='pan,box_zoom,hover,reset',
**kw)
p.xaxis.axis_label = x_title
p.yaxis.axis_label = y_title
map_options = GMapOptions(lat=df.Lat.mean(),
lng=df.Long.mean(),
map_type="roadmap",
zoom=8)
pmap = gmap(Google_API,
map_options,
plot_width=360,
plot_height=400,
title="CMA Map",
toolbar_location="above")
pmap.circle(x="Long",
y="Lat",
size=15,
fill_color='Color',
fill_alpha=0.25,
line_color='black',
line_width=.08,
source=source)
callback = CustomJS(code="""
var tooltips = document.getElementsByClassName("bk-tooltip");
for (var i = 0, len = tooltips.length; i < len; i ++) {
tooltips[i].style.top = "10px"; // unset what bokeh.js sets
tooltips[i].style.left = "800px";
tooltips[i].style.bottom = "";
tooltips[i].style.right = "";
}
""")
p.scatter(x='xs',
y='ys',
color='Color',
size='Size',
line_color="white",
alpha=0.6,
marker=factor_mark('Site Condition', MARKERS, MARKERSOURCE),
hover_color='white',
hover_alpha=0.5,
source=source)
hover = HoverTool(tooltips=[(x_title, '@xs'), (y_title, '@ys'),
('Name', '@Neighborhood'),
('FLV', '@{Finished Lot Value}{$0,0}'),
('Community Count', '@{Community Count}'),
('Market_Tier', '@{Market Tier}'),
('Close Date', '@DateString'),
('Lot Count', '@{Lot Count}'),
('Site_Condition', '@{Site Condition}'),
('Seller', '@Seller'),
('Entitlements', '@Entitlements'),
('Market', '@Market')],
callback=callback)
def select_df():
# filter df here based on widget inputs
selected = df
# (df['Square Footage (1)'] <= float(sf_slider.value[1]))
return selected
def update():
df = select_df()
df['xs'] = df[x_axis_select.value].values
df['ys'] = df[y_axis_select.value].values
x_title = x_axis_select.value.title()
y_title = y_axis_select.value.title()
kw = dict()
if x_axis_select.value in discrete:
kw['x_range'] = sorted(set(df['xs']))
if y_axis_select.value in discrete:
kw['y_range'] = sorted(set(df['ys']))
kw['title'] = "%s vs %s" % (x_title, y_title)
if x_axis_select.value in discrete:
p.xaxis.major_label_orientation = pd.np.pi / 4
df['Size'] = 24
if size.value != 'None':
if len(set(df[size.value])) > N_SIZES:
groups = pd.qcut(df[size.value].values,
N_SIZES,
duplicates='drop')
else:
groups = pd.Categorical(df[size.value])
df['Size'] = [SIZES[xx] for xx in groups.codes]
df['Color'] = "#31AADE"
if color.value != 'None':
if len(set(df[color.value])) > N_COLORS:
groups = pd.qcut(df[color.value].values,
N_COLORS,
duplicates='drop')
else:
groups = pd.Categorical(df[color.value])
df['Color'] = [COLORS[xx] for xx in groups.codes]
source = df
return source
controls = [x_axis_select, y_axis_select, size, color]
for control in controls:
control.on_change('value', lambda attr, old, new: update())
button = Button(label="Download", button_type="success")
button.callback = df.to_csv(
os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', 'data', 'Downloads',
'sales_comps_' + str(date) + '.csv')))
table = DataTable(source=source,
editable=True,
height=600,
width=1400,
fit_columns=True,
scroll_to_selection=True)
widgets = column([*controls, button], width=180, height=250)
widgets.sizing_mode = "fixed"
# Make a tab with the layout
p.add_tools(hover)
l = layout([[column([widgets]), p, pmap], table], sizing_mode='fixed')
# l = layout(table)
update()
tab = Panel(child=l, title='Comps')
return tab
def Photon_Output_Graph(conn):
output_file(
"Photon_Output_Graph.html"
) #????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????
############################################################################
############################# USER INPUTS ##################################
# Decide what the default viewing option is going to be. (i.e. the fields to
# be plotted on the x and y axis when the graph is opened).
# NB: Have it set that if axis is 'adate' then will automatically update
# to plot datetime.
x_data1 = 'adate'
y_data1 = 'graph % diff in output'
plot_title1 = 'Photon Output Results'
x_axis_title1 = x_data1
y_axis_title1 = y_data1
plot_size_height1 = 450
plot_size_width1 = 800
legend_location = 'bottom_left'
hover_tool_fields = ['chamber and electrometer', 'comments']
# Create a list of the plot parameters that will be used as input to a
# function later.
list_plot_parameters = [
x_data1, y_data1, plot_title1, x_axis_title1, y_axis_title1,
plot_size_height1, plot_size_width1, legend_location
]
# Define the fields that the legend will be based off. If there is only
# one field then put it in both columns.
color_column = 'energy'
custom_color_boolean = True
custom_color_palette = [
'#FF0000', 'black', 'yellow', 'purple', '#008F8F', '#FF00FF', 'white'
]
marker_column = 'machinename'
custom_marker_boolean = True
custom_marker_palette = [
'circle_x', 'square', 'square_x', 'diamond', 'hex', 'x',
'circle_cross', 'square_cross', 'diamond_cross', 'dash', 'cross',
'inverted_triangle', 'circle', 'triangle', 'asterisk'
]
# From the legend defined above give the values that will be pre-ticked when
# the plot is opened. NB: Bokeh will throw an error if one of these lists is
# empty (i.e. =[]) If only using color or marker then set the color_to plot
# and then enter the command: marker_to_plot = color_to_plot.
color_to_plot = ['6MV', '10MV']
marker_to_plot = ['TrueBeam B', 'TrueBeam C', 'TrueBeam D']
############################################################################
#################### CREATE THE DATA FOR THE GRAPH #########################
# Do this in a function so it can be used in an update callback later
def Create_df():
# Use the connection passed to the function to read the data into a
# dataframe via an SQL query.
df = pd.read_sql( 'SELECT [Protocol ID], [Energy], ' \
'[chamber and electrometer], [Chamber factor], ' \
'[Gantry angle], [Temp], [Press], [T/P factor], ' \
'[output], [QI], [Comments], ' \
'[Graph % Diff in output], [Graph % diff in QI] ' \
'FROM [phcal_Graph] ' \
, conn )
# Delete empty rows where the data is very important to have
df = df.dropna(subset=['protocol id'], how='any')
df = df.dropna(subset=['energy'], how='any')
# The format is complicated for this field but seems to be that the date is
# always the first element and the machine is always the last regardless of
# how many elements there are.
# Seperate on the first '_'
df_left = df['protocol id'].str.partition(sep='_')
# Seperate on the last '_'
df_right = df['protocol id'].str.rpartition(sep='_')
# From these sperated dataframes add the appropriate columns back into the
# main dataframe.
df.loc[:, 'adate'] = df_left[0]
df.loc[:, 'machinename'] = df_right[2]
# Turn 'adate' into datetime.
df.loc[:, 'adate'] = pd.to_datetime(df.loc[:, 'adate'], dayfirst=True)
# Drop any rows that aren't related to the Truebeams (ditches the old
# uneeded data). Might be possible to put this in the SQL query but
# difficult as machinename is embedded in the protocol ID.
df = df[df['machinename'].isin(
['TrueBeam B', 'TrueBeam C', 'TrueBeam D', 'TrueBeam F'])]
# Drop any columns where there is no data (likely because of the
# dropping of the old linacs (e.g. data that used to be collected from
# them that is no longer collected for the Truebeams))
df = df.dropna(axis='columns', how='all')
return df
df = Create_df()
# Create a list of the fields using the dataframe. By doing it now before
# the extra legend fields are added it's easy to limit what is displayed in
# the select widgets.
TableFields = (list(df.columns))
############################################################################
############################################################################
############################################################################
################ CREATE THE DATAFRAME FOR THE TOLERANCES ###################
# If you want to add tolerances change the boolean to True and construct the
# dataframe in the correct format.
tolerance_boolean = True
# The format of the dataframe should be the first line being the x_axis
# (with some values taken from the main dataframe to get the right
# formatting). The subsequent columns are the tolerances [low, high].
# NB: column names should match those from the main dataframe.
if tolerance_boolean == True:
df_tol1 = pd.DataFrame({
'adate': [df['adate'].max(), df['adate'].max()],
'output': [98, 102],
'graph % diff in output': [-2, 2]
})
# Added a seperate qi tolerance as multiple energes can appear
# simultaneously so need an special tolerance function to deal with
# this.
df_tol1_qi = pd.DataFrame({
'adate': [df['adate'].max(), df['adate'].max()],
'qi_6MV': [0.64, 0.68],
'qi_6XFFF': [0.61, 0.65],
'qi_10MV': [0.71, 0.75],
'qi_10XFFF': [0.68, 0.72]
})
def special_tolerance(color_to_plot, x_data1, y_data1, Sub_df1,
df_tol1_qi):
energy_list = ['6MV', '6XFFF', '10MV', '10XFFF']
data = {}
if (x_data1 != 'adate') or (y_data1 != 'qi'):
for x in range(0, len(energy_list)):
data.update({
'x_' + energy_list[x]:
[Sub_df1['x'].max(), Sub_df1['x'].max()],
'y_low_' + energy_list[x]:
[Sub_df1['y'].max(), Sub_df1['y'].max()],
'y_high_' + energy_list[x]:
[Sub_df1['y'].max(), Sub_df1['y'].max()]
})
else:
# Get a list of the column headers
headers1 = df_tol1_qi.columns.values.tolist()
# Check if the xdata is what is in the df_tol1 as the x_axis (if not no
# point plotting tolerances as all tolerances are vs this column).
max_x = Sub_df1['x'].max() + pd.DateOffset(weeks=2)
min_x = Sub_df1['x'].min() + pd.DateOffset(weeks=-2)
for x in range(0, len(energy_list)):
if energy_list[x] in color_to_plot:
data.update({
'x_' + energy_list[x]: [min_x, max_x],
'y_low_' + energy_list[x]: [
df_tol1_qi['qi_' + energy_list[x]][0],
df_tol1_qi['qi_' + energy_list[x]][0]
],
'y_high_' + energy_list[x]: [
df_tol1_qi['qi_' + energy_list[x]][1],
df_tol1_qi['qi_' + energy_list[x]][1]
]
})
else:
data.update({
'x_' + energy_list[x]:
[Sub_df1['x'].max(), Sub_df1['x'].max()],
'y_low_' + energy_list[x]:
[Sub_df1['y'].max(), Sub_df1['y'].max()],
'y_high_' + energy_list[x]:
[Sub_df1['y'].max(), Sub_df1['y'].max()]
})
Sub_df1_tol1_qi = pd.DataFrame(data)
return Sub_df1_tol1_qi
############################################################################
############################################################################
############################################################################
############################################################################
'''
This is the end of the user input section. If you don't need to make any
other changes you can end here.
'''
############################################################################
################### CREATE THE COLUMNS FOR THE LEGEND ######################
(color_list, color_palette, marker_list, marker_palette, df,
add_legend_to_df) = Create_Legend(df, color_column, custom_color_boolean,
custom_color_palette, marker_column,
custom_marker_boolean,
custom_marker_palette)
############################################################################
############################################################################
############################################################################
################## FORMATTING AND CREATING A BASIC PLOT ####################
######### Make Dataset:
# Run the Make_Dataset function to create two sub dataframs that the plots
# will be made from.
Sub_df1 = Make_Dataset(df, color_column, color_to_plot, marker_column,
marker_to_plot, x_data1, y_data1)
# Make the ColumnDataSource (when making convert dataframe to a dictionary,
# which is helpful for the callback).
src1 = ColumnDataSource(Sub_df1.to_dict(orient='list'))
######### Make Plot:
# Create an empty plot (plot parameters will be applied later in a way that
# can be manipulated in the callbacks)
p1 = figure()
p1.scatter(
source=src1,
x='x',
y='y',
fill_alpha=0.4,
size=12,
# NB: Always use legend_field for this not legend_group as the
# former acts on the javascript side but the latter the Python
# side. Therefore the former will update automatically.
legend_field='legend',
marker=factor_mark('marker1', marker_palette, marker_list),
color=factor_cmap('color1', color_palette, color_list))
# Run the Define_Plot_Parameters function to set the plot parameters
Define_Plot_Parameters(p1, list_plot_parameters)
############################################################################
############################################################################
############################################################################
############################ ADD TOLERANCES ################################
# We defined the tolerances further up and now want to add the correct ones
# to the plot. Done in such a way that they are updated with the callbacks
# later.
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(x_data1, y_data1, Sub_df1,
df_tol1)
src1_tol = ColumnDataSource(Sub_df1_tol1.to_dict(orient='list'))
p1.line(source=src1_tol, x='x', y='y_low', color='firebrick')
p1.line(source=src1_tol, x='x', y='y_high', color='firebrick')
Sub_df1_tol1_qi = special_tolerance(color_to_plot, x_data1, y_data1,
Sub_df1, df_tol1_qi)
src1_tol_qi = ColumnDataSource(Sub_df1_tol1_qi.to_dict(orient='list'))
p1.line(source=src1_tol_qi, x='x_6MV', y='y_low_6MV', color='yellow')
p1.line(source=src1_tol_qi, x='x_6MV', y='y_high_6MV', color='yellow')
p1.line(source=src1_tol_qi,
x='x_6XFFF',
y='y_low_6XFFF',
color='mediumorchid')
p1.line(source=src1_tol_qi,
x='x_6XFFF',
y='y_high_6XFFF',
color='mediumorchid')
p1.line(source=src1_tol_qi,
x='x_10MV',
y='y_low_10MV',
color='firebrick')
p1.line(source=src1_tol_qi,
x='x_10MV',
y='y_high_10MV',
color='firebrick')
p1.line(source=src1_tol_qi,
x='x_10XFFF',
y='y_low_10XFFF',
color='black')
p1.line(source=src1_tol_qi,
x='x_10XFFF',
y='y_high_10XFFF',
color='black')
############################################################################
############################################################################
############################################################################
################## ADD MORE COMPLEX TOOLS TO THE PLOT ######################
######## 1)
# Create a hover tool and add it to the plot
hover1 = HoverTool()
if len(hover_tool_fields) < 11:
kwargs = {}
i = 0
for x in hover_tool_fields:
i = i + 1
kwargs['Field' + str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, x_data1, y_data1, **kwargs)
p1.add_tools(hover1)
############################################################################
############################################################################
############################################################################
################# CREATE WIDGETS TO BE ADDED TO THE PLOT ###################
######## 1)
# This select funtion will be used to create dropdown lists to change the
# data plotted on the x/y-axis.
select_xaxis, select_yaxis = Create_Select_Axis(TableFields, x_axis_title1,
y_axis_title1)
######## 2)
# This select widget will be used to create dropdown lists to change the
# legend position.
select_legend = Create_Select_Legend(legend_location)
######## 3)
# These checkbox widgets will be used to create a tool to select the machine
# and energy that are being plotted.
checkbox_color, checkbox_marker = Create_Checkbox_Legend(
df, color_column, color_to_plot, marker_column, marker_to_plot)
######## 4)
# These checkbox widgets will be used to create a tool to select the machine
# and energy that are being plotted.
checkbox_hovertool = Create_Checkbox_HoverTool(TableFields,
hover_tool_fields)
######## 5)
# Make an 'Update Button' to requery the database and get up to date data.
update_button = Button(label='Update', button_type='success')
######## 6)
# Make a Range Button
range_button = Button(label='Range', button_type='primary')
######## 7)
# Make some titles for the checkboxes
color_title = Div(text='<b>Energy Choice</b>')
marker_title = Div(text='<b>Machine Choice</b>')
hover_title = Div(text='<b>Hovertool Fields</b>')
############################################################################
############################################################################
############################################################################
############################ CREATE A LAYOUT ###############################
# Create a layout where the widgets will be added and any scaling applied.
if color_column == marker_column:
layout_checkbox = column(
[color_title, checkbox_color, hover_title, checkbox_hovertool])
else:
layout_checkbox = column([
color_title, checkbox_color, marker_title, checkbox_marker,
hover_title, checkbox_hovertool
])
button_row = row([update_button, range_button])
layout_plots = column(
[button_row, select_xaxis, select_yaxis, select_legend, p1])
tab_layout = row([layout_plots, layout_checkbox])
############################################################################
############################################################################
############################################################################
####################### CREATE CALLBACK FUNCTIONS ##########################
# Create a big callback that does most stuff
def callback(attr, old, new):
# Want to acquire the current values of all of the checkboxes and select
# widgets to provide as inputs for the re-plot.
color_to_plot = [
checkbox_color.labels[i] for i in checkbox_color.active
]
if color_column != marker_column:
marker_to_plot = [
checkbox_marker.labels[i] for i in checkbox_marker.active
]
else:
marker_to_plot = color_to_plot
hovertool_to_plot = [
checkbox_hovertool.labels[i] for i in checkbox_hovertool.active
]
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
legend_location = select_legend.value
# Set the new axis titles
x_axis_title1 = plot1_xdata_to_plot
y_axis_title1 = plot1_ydata_to_plot
# Use the pre-defined Make_Dataset function with these new inputs to
# create new versions of the sub dataframes.
Sub_df1 = Make_Dataset(df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot,
plot1_ydata_to_plot)
# Use the pre-defined Define_Plot_Parameters function with these new
# inputs to update the plot.
Define_Plot_Parameters(p1, [
plot1_xdata_to_plot, plot1_ydata_to_plot, plot_title1,
x_axis_title1, y_axis_title1, plot_size_height1, plot_size_width1,
legend_location
])
# Update the hovertool
if len(hovertool_to_plot) < 11:
kwargs = {}
i = 0
for x in hovertool_to_plot:
i = i + 1
kwargs['Field' + str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, plot1_xdata_to_plot, plot1_ydata_to_plot,
**kwargs)
print(hover1.tooltips)
print(p1.hover.tooltips)
print(hover1)
print(p1.hover)
# Use the pre-defined Make_Dataset_Tolerance function with these new
# inputs to update the tolerances.
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(plot1_xdata_to_plot,
plot1_ydata_to_plot, Sub_df1,
df_tol1)
Sub_df1_tol1_qi = special_tolerance(color_to_plot,
plot1_xdata_to_plot,
plot1_ydata_to_plot, Sub_df1,
df_tol1_qi)
# Update the ColumnDataSources.
src1.data = Sub_df1.to_dict(orient='list')
if tolerance_boolean == True:
src1_tol.data = Sub_df1_tol1.to_dict(orient='list')
src1_tol_qi.data = Sub_df1_tol1_qi.to_dict(orient='list')
return
select_xaxis.on_change('value', callback)
select_yaxis.on_change('value', callback)
select_legend.on_change('value', callback)
checkbox_color.on_change('active', callback)
checkbox_marker.on_change('active', callback)
checkbox_hovertool.on_change('active', callback)
# Callback for the Update Button
def callback_update():
# Make a new version of the dataframe using the original Create_df
# function that connects to the database.
df = Create_df()
df = add_legend_to_df(df)
color_to_plot = [
checkbox_color.labels[i] for i in checkbox_color.active
]
if color_column != marker_column:
marker_to_plot = [
checkbox_marker.labels[i] for i in checkbox_marker.active
]
else:
marker_to_plot = color_to_plot
hovertool_to_plot = [
checkbox_hovertool.labels[i] for i in checkbox_hovertool.active
]
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
x_axis_title1 = plot1_xdata_to_plot
y_axis_title1 = plot1_ydata_to_plot
legend_location = select_legend.value
Sub_df1 = Make_Dataset(df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot,
plot1_ydata_to_plot)
Define_Plot_Parameters(p1, [
plot1_xdata_to_plot, plot1_ydata_to_plot, plot_title1,
x_axis_title1, y_axis_title1, plot_size_height1, plot_size_width1,
legend_location
])
if len(hovertool_to_plot) < 11:
kwargs = {}
i = 0
for x in hovertool_to_plot:
i = i + 1
kwargs['Field' + str(i)] = x
else:
kwargs = {}
msgbox('Too many fields selected to display on HoverTool ' \
'(Max = 10). Please reduce number of fields selected')
Update_HoverTool(hover1, plot1_xdata_to_plot, plot1_ydata_to_plot,
**kwargs)
if tolerance_boolean == True:
Sub_df1_tol1 = Make_Dataset_Tolerance(plot1_xdata_to_plot,
plot1_ydata_to_plot, Sub_df1,
df_tol1)
Sub_df1_tol1_qi = special_tolerance(color_to_plot,
plot1_xdata_to_plot,
plot1_ydata_to_plot, Sub_df1,
df_tol1_qi)
src1_tol.data = Sub_df1_tol1.to_dict(orient='list')
src1_tol_qi.data = Sub_df1_tol1_qi.to_dict(orient='list')
src1.data = Sub_df1.to_dict(orient='list')
return
update_button.on_click(callback_update)
# Callback for the Range Button
def callback_range():
color_to_plot = [
checkbox_color.labels[i] for i in checkbox_color.active
]
if color_column != marker_column:
marker_to_plot = [
checkbox_marker.labels[i] for i in checkbox_marker.active
]
else:
marker_to_plot = color_to_plot
plot1_xdata_to_plot = select_xaxis.value
plot1_ydata_to_plot = select_yaxis.value
# Use the pre-defined Make_Dataset function with these new inputs to
# create new versions of the sub dataframes.
Sub_df1 = Make_Dataset(df, color_column, color_to_plot, marker_column,
marker_to_plot, plot1_xdata_to_plot,
plot1_ydata_to_plot)
if (plot1_xdata_to_plot == 'adate') and (
(plot1_ydata_to_plot == 'graph % diff in output') or
(plot1_ydata_to_plot == 'output') or plot1_ydata_to_plot == 'qi'):
p1.x_range.start = Sub_df1['x'].max() - timedelta(weeks=53)
p1.x_range.end = Sub_df1['x'].max() + timedelta(weeks=2)
if plot1_ydata_to_plot == 'output':
p1.y_range.start = 97
p1.y_range.end = 103
elif plot1_ydata_to_plot == 'graph % diff in output':
p1.y_range.start = -3
p1.y_range.end = 3
elif plot1_ydata_to_plot == 'qi':
p1.y_range.start = 0.55
p1.y_range.end = 0.8
return
range_button.on_click(callback_range)
############################################################################
############################################################################
############################################################################
####################### RETURN TO THE MAIN SCRIPT ##########################
return Panel(child=tab_layout, title='Photon Output')
