def make_table_layout():
filename = "../examples/datatables/project/static/data.json"
df = get_data(filename)
ui = UILayout("SimpleChart")
ui.add_chart(create_datatable(df))
return ui
def make_mg_layout():
# load a dataframe
filename = "../examples/metricsgraphics/project/fitbit_data.csv"
df = pd.read_csv(filename)
# Make a UI
ui = UILayout("FilterChart")
# Make a Button
cols = [c for c in df.columns if c != "Date"]
btn = SelectButton("Data", cols, "Data", "Steps")
# add the button to the UI
ui.add_filter(btn)
# stack the dataframe
_stack = df.set_index("Date").stack().reset_index()
_stack = _stack.rename(columns={"level_1": "Data", 0: "value"})
# Make a Figure, add some settings, make a line plot
ui.add_chart(create_line_plot(_stack))
ui.add_chart(create_histogram(_stack))
ui.add_chart(create_scatterplot(df))
return ui
def build_filter_props(buttons):
""" build filter props
return props for a list of buttons.
Args:
buttons (list): list of pyxley.Filter components.
"""
ui = UILayout("PyxleyChart")
for b in buttons:
ui.add_filter(b)
return ui.build_props()
def make_nv_layout(filename):
# load the data
df = pd.read_csv(filename)
ui = UILayout("FilterChart")
# Make a button
choices = ["Heart Rate", "Pace", "Distance"]
btn = SelectButton("Data", choices, "Data", "Heart Rate")
ui.add_filter(btn)
# Add the chart
ui.add_chart(two_axis_focus(df))
ui.add_chart(pie_chart(df))
return ui
def make_nv_layout():
# load the data
filename = "../examples/nvd3/project/static/formatted_run.csv"
df = pd.read_csv(filename)
ui = UILayout("FilterChart")
# Make a button
choices = ["Heart Rate", "Pace", "Distance"]
btn = SelectButton("Data", choices, "Data", "Heart Rate")
ui.add_filter(btn)
# Add the chart
ui.add_chart(two_axis_focus(df))
ui.add_chart(pie_chart(df))
return ui
def make_datamaps_ui(filename):
# Make a UI
ui = UILayout("SimpleChart")
df = pd.read_csv(filename)
df.month = df.month.astype('str')
sldr = SliderInput("Month", 1, 13, "month", "1")
ui.add_filter(sldr)
dm = DatamapUSA("/data_map/",
"mapid",
df,
"state",
"color1",
init_params={"month": "1"})
ui.add_chart(dm)
return ui
def make_datamaps_ui():
# Make a UI
ui = UILayout("PyxleyChart")
datafile = "../examples/datamaps/project/Colorful_State.csv"
df = pd.read_csv(datafile)
df.month = df.month.astype('str')
sldr = SliderInput("Month", 1, 13, "month", "1")
ui.add_filter(sldr)
dm = DatamapUSA("/data_map/",
"mapid",
df,
"state",
"color1",
init_params={"month": "1"})
ui.add_chart(dm)
return ui
def make_ui(datafile):
# Make a UI
ui = UILayout("FilterChart", "pyxley", "component_id", filter_style="''")
df = pd.read_csv(datafile)
# Read in the data and stack it, so that we can filter on columns
sf = df.set_index("Date").stack().reset_index()
sf = sf.rename(columns={"level_1": "Data", 0: "value"})
# Make a Button
cols = [c for c in df.columns if c != "Date"]
btn = SelectButton("Data", cols, "Data", "Steps")
# Make a FilterFrame and add the button to the UI
ui.add_filter(btn)
dm = PlotlyLines([["Date", "value"]], sf, init_params={"Data": "Steps"})
ui.add_chart(dm)
return ui
def make_plotly_ui():
filename = "../examples/metricsgraphics/project/fitbit_data.csv"
df = pd.read_csv(filename)
# Make a UI
ui = UILayout("PyxleyChart")
# Read in the data and stack it, so that we can filter on columns
_stack = df.set_index("Date").stack().reset_index()
_stack = _stack.rename(columns={"level_1": "Data", 0: "value"})
# Make a Button
cols = [c for c in df.columns if c != "Date"]
btn = SelectButton("Data", cols, "Data", "Steps")
# add the button to the UI
ui.add_filter(btn)
init_params = {"Data": "Steps"}
def get_data():
args = {}
for c in init_params:
if request.args.get(c):
args[c] = request.args[c]
else:
args[c] = init_params[c]
return jsonify(
PlotlyAPI.line_plot(
PlotlyAPI.apply_filters(_stack, args),
[("Date", "value")],
"lines+markers",
{}
))
_plot = PlotlyAPI(
"plotly_chart",
"/api/plotly_line_plot/",
get_data,
init_params=init_params
)
ui.add_chart(_plot)
return ui
def build_ui(self, save):
self.ui = UILayout("FilterChart", "./jsx/SimpleChart", "component_id")
sldr = SliderInput("play_index", 1,
int(self.on_field["play_index"].max()),
"play_index", "1")
self.ui.add_filter(sldr)
# Add a plotly chart
self.ui.add_chart(add_plotly_chart(self.locations, self.init_params))
# Add the image panel
self.ui.add_chart(
add_image_panel(self.on_field, self.off_field, self.init_params))
if save:
self.ui.render_layout(self.mod,
self.static_ + "/layout.js",
alias="Main")
else:
self.ui.assign_routes(self.mod)
def make_mg_layout(filename):
# load a dataframe
df = pd.read_csv(filename)
# Make a UI
ui = UILayout("FilterChart")
# Make a Button
cols = [c for c in df.columns if c != "Year"]
btn = SelectButton("Abschlussprüfungsleistungen", cols, "Data",
"Abschlussprüfungsleistungen")
# add the button to the UI
ui.add_filter(btn)
# stack the dataframe
_stack = df.set_index("Year").stack().reset_index()
_stack = _stack.rename(columns={"level_1": "Data", 0: "value"})
# Make a Figure, add some settings, make a line plot
ui.add_chart(
create_line_plot(_stack,
i=0,
name="Abschlussprüfungsleistungen",
n=3,
titel="KPMG Umsätze"))
here = path.abspath(path.dirname(__file__))
df1 = pd.read_csv(here + "/file")
# Make a Button
cols1 = [c for c in df1.columns if c != "Year"]
btn1 = SelectButton("Professionals", cols1, "Data", "Professionals")
# add the button to the UI
ui.add_filter(btn1)
_stack1 = df1.set_index("Year").stack().reset_index()
_stack1 = _stack1.rename(columns={"level_1": "Data", 0: "value"})
# Make a Figure, add some settings, make a line plot
ui.add_chart(
create_line_plot(
_stack1,
i=1,
name="Professionals",
n=8,
titel="Employees of KPMG worldwide by position 2010-2017"))
# stack the dataframe
df2 = pd.read_csv(here + "/file1")
cols2 = [c for c in df2.columns if c != "Year"]
btn2 = SelectButton("Revenue in billion $", cols2, "Data",
"Revenue in billion $")
btn2.__setattr__("margin", 50)
ui.add_filter(btn2)
# Make a Button
_stack2 = df2.set_index("Year").stack().reset_index()
_stack2 = _stack2.rename(columns={"level_1": "Data", 0: "value"})
# Make a Figure, add some settings, make a line plot
ui.add_chart(
create_line_plot(
_stack2,
i=2,
name="Revenue in billion U.S. dollars",
n=6,
titel="Big data and business analytics revenue worldwide 2015-2020"
))
return ui
"./bower_components/react/react.js",
"./bower_components/react-bootstrap/react-bootstrap.min.js",
"./bower_components/pyxley/build/pyxley.js",
]
css = [
"./bower_components/bootstrap/dist/css/bootstrap.min.css",
"./bower_components/metrics-graphics/dist/metricsgraphics.css",
"./bower_components/datatables/media/css/jquery.dataTables.min.css",
"./css/main.css"
]
# Make a UI
ui = UILayout(
"FilterChart",
"./static/bower_components/pyxley/build/pyxley.js",
"component_id",
filter_style="''")
# Read in the data and stack it, so that we can filter on columns
df = pd.read_csv("fitbit_data.csv")
sf = df.set_index("Date").stack().reset_index()
sf = sf.rename(columns={"level_1": "Data", 0: "value"})
# Make a Button
cols = [c for c in df.columns if c != "Date"]
btn = SelectButton("Data", cols, "Data", "Steps")
# Make a FilterFrame and add the button to the UI
ui.add_filter(btn)
from pyxley.charts.mg import LineChart, Figure, ScatterPlot, Histogram
from pyxley.charts.datatables import DataTable
from collections import OrderedDict
parser = argparse.ArgumentParser(description="Flask Template")
parser.add_argument("--env", help="production or local", default="local")
args = parser.parse_args()
TITLE = "Pyxley"
scripts = []
css = ["./css/main.css"]
# Make a UI
ui = UILayout("FilterChart", "pyxley", "component_id", filter_style="''")
# Read in the data and stack it, so that we can filter on columns
df = pd.read_csv("project/fitbit_data.csv")
sf = df.set_index("Date").stack().reset_index()
sf = sf.rename(columns={"level_1": "Data", 0: "value"})
# Make a Button
cols = [c for c in df.columns if c != "Date"]
btn = SelectButton("Data", cols, "Data", "Steps")
# Make a FilterFrame and add the button to the UI
ui.add_filter(btn)
# Make a Figure, add some settings, make a line plot
fig = Figure("/mgchart/", "mychart")
]
css = [
"./bower_components/bootstrap/dist/css/bootstrap.min.css",
"./bower_components/nvd3/build/nv.d3.min.css", "./css/main.css"
]
transformer = jsx.JSXTransformer()
jsx_input = "static/jsx/"
jsx_output = "static/js/"
for f in glob.glob(jsx_input + "*.js"):
transformer.transform(f, js_path=jsx_output + f.split('/')[-1])
# Make a UI
ui = UILayout("RunLayout", "./static/js/custom.js", "component_id")
# Read in the data and stack it, so that we can filter on columns
df = pd.read_csv("./static/formatted_run.csv")
# Make a Button
choices = ["Heart Rate", "Pace", "Distance"]
btn = SelectButton("Data", choices, "Data", "Heart Rate")
ui.add_filter(btn)
# Add our new chart
colors = ["#847c77", "#ff5c61"]
nc = NewChart("Seconds",
"value",
"Altitude",
df,
parser.add_argument("--env", help="production or local", default="local")
args = parser.parse_args()
TITLE = "Pyxley"
scripts = [
"./chartfunc.js"
]
css = [
"./css/main.css"
]
# Make a UI
ui = UILayout(
"RunLayout",
"./jsx/custom",
"component_id")
# Read in the data and stack it, so that we can filter on columns
df = pd.read_csv("./project/static/formatted_run.csv")
# Make a Button
choices = ["Heart Rate", "Pace", "Distance"]
btn = SelectButton("Data", choices, "Data", "Heart Rate")
ui.add_filter(btn)
# Add our new chart
colors = ["#847c77", "#ff5c61"];
nc = NewChart("Seconds", "value", "Altitude", df,
init_params={"Data": "Heart Rate"}, colors=colors)
ui.add_chart(nc)
def make_table_layout(filename):
df = get_data(filename)
ui = UILayout("SimpleChart")
ui.add_chart(create_datatable(df))
return ui
def uploaded_file(filename):
extension = get_extension(filename)
path = os.path.join(app.config['UPLOAD_FOLDER'], extension, filename)
txt_path = os.path.join(app.config['UPLOAD_FOLDER'], 'txt', filename)
if extension == "pdf":
txt_path += '.txt'
if not os.path.isfile(txt_path):
#Layout preservation crucial to preserve clues about tabular data
cmd = "pdftotext -layout %s %s" % (path, txt_path)
os.system(cmd)
min_columns = request.args.get('min_columns')
tables = return_tables(txt_path)
#Construct histogram
lines_per_page = 80
nr_data_rows = []
for b, t in tables.iteritems():
e = t['end_line']
#print b, e
for l in range(b, e):
page = l / lines_per_page
if len(nr_data_rows) <= page:
nr_data_rows += ([0] * (page - len(nr_data_rows) + 1))
nr_data_rows[page] += 1
dr = pd.DataFrame()
dr['value'] = nr_data_rows
dr['page'] = range(0, len(dr))
js_layout = filename + ".js"
ui_show = UILayout("FilterChart",
"../static/bower_components/pyxley/build/pyxley.js",
"component_id",
filter_style="''")
if filename in all_charts:
print "old/update ui", filename
path_to_fig = '/show/line/' + filename
#del all_charts[filename]
#hFig = Figure(path_to_fig, "line")
#bc = LineChart(dr, hFig, "page", ["page"], "Rows containing Data per Page")
elif True:
print "new ui", filename
# Make a Button
cols = ["page"]
btn = SelectButton("Data", cols, "Data", "Data Rows per Page")
# Make a FilterFrame and add the button to the UI
ui_show.add_filter(btn)
# Now make a FilterFrame for the histogram
path_to_fig = '/show/line/' + filename
hFig = Figure(path_to_fig, "line")
hFig.layout.set_size(width=1000, height=300)
hFig.layout.set_margin(left=80, right=80)
#hFig.graphics.animate_on_load()
bc = LineChart(dr, hFig, "page", ["value"],
"Rows containing Data per Page")
ui_show.add_chart(bc)
all_charts[filename] = bc
sb = ui_show.render_layout(app, "./static/ug/" + js_layout)
_scripts = ["ug/" + js_layout]
notices = ['Extraction Results for ' + filename, 'Ordered by lines']
dfs = (table_to_df(table).to_html() for table in tables.values())
headers = []
for t in tables.values():
if 'header' in t:
headers.append(t['header'])
else:
headers.append('-')
line_nrs = [('line %i-%i' % (t['begin_line'], t['end_line']))
for t in tables.values()]
#headers = ['aslkdfjas', ' alsdkfjasoedf']
return render_template('index.html',
title=TITLE + ' - ' + filename,
base_scripts=scripts,
page_scripts=_scripts,
css=css,
notices=notices,
tables=dfs,
headers=headers,
line_nrs=line_nrs)
