Hi arham, thanks to your great help I’m almost finished with this (for me) difficult problem. I only need the pdf to adopt the css.
I used this template to test your button
I added an explicit reference to the assets folder on line 23 and your dash snippet in line 82. Thereafter I added your code (adjusting the element id in the printData function from main_container to mainContainer). This worked! I now have a code that has a print button that will allow me to print the dash in pdf…but the styling is entirely off. It does not seem like the print pdf function incorporates the css.
Do you have any recommendations on this?
For more clarity:
-
here a screenshot so you can see how it looks when i run app.py
-
a screenshot that shows that the pdf print does not incorporate the css.
-
see below the adjusted app.py code (with explicit reference to assets folder and the button code you provided):
# Import required libraries
import pickle
import copy
import pathlib
import dash
import math
import datetime as dt
import pandas as pd
from dash.dependencies import Input, Output, State, ClientsideFunction
import dash_core_components as dcc
import dash_html_components as html
# Multi-dropdown options
from controls import COUNTIES, WELL_STATUSES, WELL_TYPES, WELL_COLORS
# get relative data folder
PATH = pathlib.Path(__file__).parent
DATA_PATH = PATH.joinpath("data").resolve()
app = dash.Dash(
__name__
, meta_tags=[{"name": "viewport", "content": "width=device-width"}]
,assets_folder = str(PATH.joinpath("assets").resolve())
)
server = app.server
# Create controls
county_options = [
{"label": str(COUNTIES[county]), "value": str(county)} for county in COUNTIES
]
well_status_options = [
{"label": str(WELL_STATUSES[well_status]), "value": str(well_status)}
for well_status in WELL_STATUSES
]
well_type_options = [
{"label": str(WELL_TYPES[well_type]), "value": str(well_type)}
for well_type in WELL_TYPES
]
# Load data
df = pd.read_csv(DATA_PATH.joinpath("wellspublic.csv"), low_memory=False)
df["Date_Well_Completed"] = pd.to_datetime(df["Date_Well_Completed"])
df = df[df["Date_Well_Completed"] > dt.datetime(1960, 1, 1)]
trim = df[["API_WellNo", "Well_Type", "Well_Name"]]
trim.index = trim["API_WellNo"]
dataset = trim.to_dict(orient="index")
points = pickle.load(open(DATA_PATH.joinpath("points.pkl"), "rb"))
# Create global chart template
mapbox_access_token = "pk.eyJ1IjoicGxvdGx5bWFwYm94IiwiYSI6ImNrOWJqb2F4djBnMjEzbG50amg0dnJieG4ifQ.Zme1-Uzoi75IaFbieBDl3A"
layout = dict(
autosize=True,
automargin=True,
margin=dict(l=30, r=30, b=20, t=40),
hovermode="closest",
plot_bgcolor="#F9F9F9",
paper_bgcolor="#F9F9F9",
legend=dict(font=dict(size=10), orientation="h"),
title="Satellite Overview",
mapbox=dict(
accesstoken=mapbox_access_token,
style="light",
center=dict(lon=-78.05, lat=42.54),
zoom=7,
),
)
# Create app layout
app.layout = html.Div(
[
dcc.Store(id="aggregate_data"),
# empty Div to trigger javascript file for graph resizing
html.Div(id="output-clientside"),
html.Div([
html.Button('click', id='run')
]),
html.Div(
[
html.Div(
[
html.Img(
src=app.get_asset_url("dash-logo.png"),
id="plotly-image",
style={
"height": "60px",
"width": "auto",
"margin-bottom": "25px",
},
)
],
className="one-third column",
),
html.Div(
[
html.Div(
[
html.H3(
"New York Oil and Gas",
style={"margin-bottom": "0px"},
),
html.H5(
"Production Overview", style={"margin-top": "0px"}
),
]
)
],
className="one-half column",
id="title",
),
html.Div(
[
html.A(
html.Button("Learn More", id="learn-more-button"),
href="https://plot.ly/dash/pricing/",
)
],
className="one-third column",
id="button",
),
],
id="header",
className="row flex-display",
style={"margin-bottom": "25px"},
),
html.Div(
[
html.Div(
[
html.P(
"Filter by construction date (or select range in histogram):",
className="control_label",
),
dcc.RangeSlider(
id="year_slider",
min=1960,
max=2017,
value=[1990, 2010],
className="dcc_control",
),
html.P("Filter by well status:", className="control_label"),
dcc.RadioItems(
id="well_status_selector",
options=[
{"label": "All ", "value": "all"},
{"label": "Active only ", "value": "active"},
{"label": "Customize ", "value": "custom"},
],
value="active",
labelStyle={"display": "inline-block"},
className="dcc_control",
),
dcc.Dropdown(
id="well_statuses",
options=well_status_options,
multi=True,
value=list(WELL_STATUSES.keys()),
className="dcc_control",
),
dcc.Checklist(
id="lock_selector",
options=[{"label": "Lock camera", "value": "locked"}],
className="dcc_control",
value=[],
),
html.P("Filter by well type:", className="control_label"),
dcc.RadioItems(
id="well_type_selector",
options=[
{"label": "All ", "value": "all"},
{"label": "Productive only ", "value": "productive"},
{"label": "Customize ", "value": "custom"},
],
value="productive",
labelStyle={"display": "inline-block"},
className="dcc_control",
),
dcc.Dropdown(
id="well_types",
options=well_type_options,
multi=True,
value=list(WELL_TYPES.keys()),
className="dcc_control",
),
],
className="pretty_container four columns",
id="cross-filter-options",
),
html.Div(
[
html.Div(
[
html.Div(
[html.H6(id="well_text"), html.P("No. of Wells")],
id="wells",
className="mini_container",
),
html.Div(
[html.H6(id="gasText"), html.P("Gas")],
id="gas",
className="mini_container",
),
html.Div(
[html.H6(id="oilText"), html.P("Oil")],
id="oil",
className="mini_container",
),
html.Div(
[html.H6(id="waterText"), html.P("Water")],
id="water",
className="mini_container",
),
],
id="info-container",
className="row container-display",
),
html.Div(
[dcc.Graph(id="count_graph")],
id="countGraphContainer",
className="pretty_container",
),
],
id="right-column",
className="eight columns",
),
],
className="row flex-display",
),
html.Div(
[
html.Div(
[dcc.Graph(id="main_graph")],
className="pretty_container seven columns",
),
html.Div(
[dcc.Graph(id="individual_graph")],
className="pretty_container five columns",
),
],
className="row flex-display",
),
html.Div(
[
html.Div(
[dcc.Graph(id="pie_graph")],
className="pretty_container seven columns",
),
html.Div(
[dcc.Graph(id="aggregate_graph")],
className="pretty_container five columns",
),
],
className="row flex-display",
),
],
id="mainContainer",
style={"display": "flex", "flex-direction": "column"},
)
# Helper functions
def human_format(num):
if num == 0:
return "0"
magnitude = int(math.log(num, 1000))
mantissa = str(int(num / (1000 ** magnitude)))
return mantissa + ["", "K", "M", "G", "T", "P"][magnitude]
def filter_dataframe(df, well_statuses, well_types, year_slider):
dff = df[
df["Well_Status"].isin(well_statuses)
& df["Well_Type"].isin(well_types)
& (df["Date_Well_Completed"] > dt.datetime(year_slider[0], 1, 1))
& (df["Date_Well_Completed"] < dt.datetime(year_slider[1], 1, 1))
]
return dff
def produce_individual(api_well_num):
try:
points[api_well_num]
except:
return None, None, None, None
index = list(
range(min(points[api_well_num].keys()), max(points[api_well_num].keys()) + 1)
)
gas = []
oil = []
water = []
for year in index:
try:
gas.append(points[api_well_num][year]["Gas Produced, MCF"])
except:
gas.append(0)
try:
oil.append(points[api_well_num][year]["Oil Produced, bbl"])
except:
oil.append(0)
try:
water.append(points[api_well_num][year]["Water Produced, bbl"])
except:
water.append(0)
return index, gas, oil, water
def produce_aggregate(selected, year_slider):
index = list(range(max(year_slider[0], 1985), 2016))
gas = []
oil = []
water = []
for year in index:
count_gas = 0
count_oil = 0
count_water = 0
for api_well_num in selected:
try:
count_gas += points[api_well_num][year]["Gas Produced, MCF"]
except:
pass
try:
count_oil += points[api_well_num][year]["Oil Produced, bbl"]
except:
pass
try:
count_water += points[api_well_num][year]["Water Produced, bbl"]
except:
pass
gas.append(count_gas)
oil.append(count_oil)
water.append(count_water)
return index, gas, oil, water
# Create callbacks
app.clientside_callback(
ClientsideFunction(namespace="clientside", function_name="resize"),
Output("output-clientside", "children"),
[Input("count_graph", "figure")],
)
@app.callback(
Output("aggregate_data", "data"),
[
Input("well_statuses", "value"),
Input("well_types", "value"),
Input("year_slider", "value"),
],
)
def update_production_text(well_statuses, well_types, year_slider):
dff = filter_dataframe(df, well_statuses, well_types, year_slider)
selected = dff["API_WellNo"].values
index, gas, oil, water = produce_aggregate(selected, year_slider)
return [human_format(sum(gas)), human_format(sum(oil)), human_format(sum(water))]
# Radio -> multi
@app.callback(
Output("well_statuses", "value"), [Input("well_status_selector", "value")]
)
def display_status(selector):
if selector == "all":
return list(WELL_STATUSES.keys())
elif selector == "active":
return ["AC"]
return []
# Radio -> multi
@app.callback(Output("well_types", "value"), [Input("well_type_selector", "value")])
def display_type(selector):
if selector == "all":
return list(WELL_TYPES.keys())
elif selector == "productive":
return ["GD", "GE", "GW", "IG", "IW", "OD", "OE", "OW"]
return []
# Slider -> count graph
@app.callback(Output("year_slider", "value"), [Input("count_graph", "selectedData")])
def update_year_slider(count_graph_selected):
if count_graph_selected is None:
return [1990, 2010]
nums = [int(point["pointNumber"]) for point in count_graph_selected["points"]]
return [min(nums) + 1960, max(nums) + 1961]
# Selectors -> well text
@app.callback(
Output("well_text", "children"),
[
Input("well_statuses", "value"),
Input("well_types", "value"),
Input("year_slider", "value"),
],
)
def update_well_text(well_statuses, well_types, year_slider):
dff = filter_dataframe(df, well_statuses, well_types, year_slider)
return dff.shape[0]
@app.callback(
[
Output("gasText", "children"),
Output("oilText", "children"),
Output("waterText", "children"),
],
[Input("aggregate_data", "data")],
)
def update_text(data):
return data[0] + " mcf", data[1] + " bbl", data[2] + " bbl"
# Selectors -> main graph
@app.callback(
Output("main_graph", "figure"),
[
Input("well_statuses", "value"),
Input("well_types", "value"),
Input("year_slider", "value"),
],
[State("lock_selector", "value"), State("main_graph", "relayoutData")],
)
def make_main_figure(
well_statuses, well_types, year_slider, selector, main_graph_layout
):
dff = filter_dataframe(df, well_statuses, well_types, year_slider)
traces = []
for well_type, dfff in dff.groupby("Well_Type"):
trace = dict(
type="scattermapbox",
lon=dfff["Surface_Longitude"],
lat=dfff["Surface_latitude"],
text=dfff["Well_Name"],
customdata=dfff["API_WellNo"],
name=WELL_TYPES[well_type],
marker=dict(size=4, opacity=0.6),
)
traces.append(trace)
# relayoutData is None by default, and {'autosize': True} without relayout action
if main_graph_layout is not None and selector is not None and "locked" in selector:
if "mapbox.center" in main_graph_layout.keys():
lon = float(main_graph_layout["mapbox.center"]["lon"])
lat = float(main_graph_layout["mapbox.center"]["lat"])
zoom = float(main_graph_layout["mapbox.zoom"])
layout["mapbox"]["center"]["lon"] = lon
layout["mapbox"]["center"]["lat"] = lat
layout["mapbox"]["zoom"] = zoom
figure = dict(data=traces, layout=layout)
return figure
# Main graph -> individual graph
@app.callback(Output("individual_graph", "figure"), [Input("main_graph", "hoverData")])
def make_individual_figure(main_graph_hover):
layout_individual = copy.deepcopy(layout)
if main_graph_hover is None:
main_graph_hover = {
"points": [
{"curveNumber": 4, "pointNumber": 569, "customdata": 31101173130000}
]
}
chosen = [point["customdata"] for point in main_graph_hover["points"]]
index, gas, oil, water = produce_individual(chosen[0])
if index is None:
annotation = dict(
text="No data available",
x=0.5,
y=0.5,
align="center",
showarrow=False,
xref="paper",
yref="paper",
)
layout_individual["annotations"] = [annotation]
data = []
else:
data = [
dict(
type="scatter",
mode="lines+markers",
name="Gas Produced (mcf)",
x=index,
y=gas,
line=dict(shape="spline", smoothing=2, width=1, color="#fac1b7"),
marker=dict(symbol="diamond-open"),
),
dict(
type="scatter",
mode="lines+markers",
name="Oil Produced (bbl)",
x=index,
y=oil,
line=dict(shape="spline", smoothing=2, width=1, color="#a9bb95"),
marker=dict(symbol="diamond-open"),
),
dict(
type="scatter",
mode="lines+markers",
name="Water Produced (bbl)",
x=index,
y=water,
line=dict(shape="spline", smoothing=2, width=1, color="#92d8d8"),
marker=dict(symbol="diamond-open"),
),
]
layout_individual["title"] = dataset[chosen[0]]["Well_Name"]
figure = dict(data=data, layout=layout_individual)
return figure
# Selectors, main graph -> aggregate graph
@app.callback(
Output("aggregate_graph", "figure"),
[
Input("well_statuses", "value"),
Input("well_types", "value"),
Input("year_slider", "value"),
Input("main_graph", "hoverData"),
],
)
def make_aggregate_figure(well_statuses, well_types, year_slider, main_graph_hover):
layout_aggregate = copy.deepcopy(layout)
if main_graph_hover is None:
main_graph_hover = {
"points": [
{"curveNumber": 4, "pointNumber": 569, "customdata": 31101173130000}
]
}
chosen = [point["customdata"] for point in main_graph_hover["points"]]
well_type = dataset[chosen[0]]["Well_Type"]
dff = filter_dataframe(df, well_statuses, well_types, year_slider)
selected = dff[dff["Well_Type"] == well_type]["API_WellNo"].values
index, gas, oil, water = produce_aggregate(selected, year_slider)
data = [
dict(
type="scatter",
mode="lines",
name="Gas Produced (mcf)",
x=index,
y=gas,
line=dict(shape="spline", smoothing="2", color="#F9ADA0"),
),
dict(
type="scatter",
mode="lines",
name="Oil Produced (bbl)",
x=index,
y=oil,
line=dict(shape="spline", smoothing="2", color="#849E68"),
),
dict(
type="scatter",
mode="lines",
name="Water Produced (bbl)",
x=index,
y=water,
line=dict(shape="spline", smoothing="2", color="#59C3C3"),
),
]
layout_aggregate["title"] = "Aggregate: " + WELL_TYPES[well_type]
figure = dict(data=data, layout=layout_aggregate)
return figure
# Selectors, main graph -> pie graph
@app.callback(
Output("pie_graph", "figure"),
[
Input("well_statuses", "value"),
Input("well_types", "value"),
Input("year_slider", "value"),
],
)
def make_pie_figure(well_statuses, well_types, year_slider):
layout_pie = copy.deepcopy(layout)
dff = filter_dataframe(df, well_statuses, well_types, year_slider)
selected = dff["API_WellNo"].values
index, gas, oil, water = produce_aggregate(selected, year_slider)
aggregate = dff.groupby(["Well_Type"]).count()
data = [
dict(
type="pie",
labels=["Gas", "Oil", "Water"],
values=[sum(gas), sum(oil), sum(water)],
name="Production Breakdown",
text=[
"Total Gas Produced (mcf)",
"Total Oil Produced (bbl)",
"Total Water Produced (bbl)",
],
hoverinfo="text+value+percent",
textinfo="label+percent+name",
hole=0.5,
marker=dict(colors=["#fac1b7", "#a9bb95", "#92d8d8"]),
domain={"x": [0, 0.45], "y": [0.2, 0.8]},
),
dict(
type="pie",
labels=[WELL_TYPES[i] for i in aggregate.index],
values=aggregate["API_WellNo"],
name="Well Type Breakdown",
hoverinfo="label+text+value+percent",
textinfo="label+percent+name",
hole=0.5,
marker=dict(colors=[WELL_COLORS[i] for i in aggregate.index]),
domain={"x": [0.55, 1], "y": [0.2, 0.8]},
),
]
layout_pie["title"] = "Production Summary: {} to {}".format(
year_slider[0], year_slider[1]
)
layout_pie["font"] = dict(color="#777777")
layout_pie["legend"] = dict(
font=dict(color="#CCCCCC", size="10"), orientation="h", bgcolor="rgba(0,0,0,0)"
)
figure = dict(data=data, layout=layout_pie)
return figure
# Selectors -> count graph
@app.callback(
Output("count_graph", "figure"),
[
Input("well_statuses", "value"),
Input("well_types", "value"),
Input("year_slider", "value"),
],
)
def make_count_figure(well_statuses, well_types, year_slider):
layout_count = copy.deepcopy(layout)
dff = filter_dataframe(df, well_statuses, well_types, [1960, 2017])
g = dff[["API_WellNo", "Date_Well_Completed"]]
g.index = g["Date_Well_Completed"]
g = g.resample("A").count()
colors = []
for i in range(1960, 2018):
if i >= int(year_slider[0]) and i < int(year_slider[1]):
colors.append("rgb(123, 199, 255)")
else:
colors.append("rgba(123, 199, 255, 0.2)")
data = [
dict(
type="scatter",
mode="markers",
x=g.index,
y=g["API_WellNo"] / 2,
name="All Wells",
opacity=0,
hoverinfo="skip",
),
dict(
type="bar",
x=g.index,
y=g["API_WellNo"],
name="All Wells",
marker=dict(color=colors),
),
]
layout_count["title"] = "Completed Wells/Year"
layout_count["dragmode"] = "select"
layout_count["showlegend"] = False
layout_count["autosize"] = True
figure = dict(data=data, layout=layout_count)
return figure
# Main
if __name__ == "__main__":
app.run_server(debug=False)
