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hvsrweb.py
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hvsrweb.py
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import io
import base64
import numpy as np
import dash
from dash import dcc, html
import dash_bootstrap_components as dbc
from dash.exceptions import PreventUpdate
from dash.dependencies import Input, Output, State
from flask import Flask
import pandas as pd
import xgboost as xgb
from sklearn import cluster
import plotly
import plotly.subplots
import plotly.graph_objs as go
import hvsrpy
# Style Settings
default_span_style = {"cursor": "context-menu",
"padding": "1em", "margin-top": "0em"}
default_p_style = {"margin-top": "0.5em", "margin-bottom": "0em"}
default_cardbody_style = {"min-height": "110vh"}
COLORS = {
"error": "#f2190a",
"primary": "#0d6efd",
"link": "#495057"
}
HIDE_CONTAINER = dict(display="none")
DISPLAY_CONTAINER = dict(display="block")
intro_tab = dbc.Card(
dbc.CardBody([
dcc.Markdown("""
##### Welcome to HVSRweb
HVSRweb is a web application for horizontal-to-vertical
spectral ratio (HVSR) processing (Vantassel et al., 2021).
HVSRweb utilizes _hvsrpy_ (Vantassel, 2020) behind Dash
(Plotly, 2017) to offer a cloud-based tool for HVSR
processing. HVSRweb is hosted on computing resources
made available through the DesignSafe-CI
(Rathje et al., 2017).
##### Citation
If you use HVSRweb in your research or consulting we ask you
please cite the following:
Vantassel, J.P., Cox, B.R., & Brannon, D.M. (2021). HVSRweb:
An Open-Source, Web-Based Application for
Horizontal-to-Vertical Spectral Ratio Processing. IFCEE
2021. https://doi.org/10.1061/9780784483428.005.
##### References
Background information concerning the HVSR statistics can be
found in the following references:
Cox, B. R., Cheng, T., Vantassel, J. P., & Manuel, L.
(2020). A statistical representation and frequency-domain
window-rejection algorithm for single-station HVSR
measurements. Geophysical Journal International, 221(3),
2170-2183. https://doi.org/10.1093/gji/ggaa119.
Cheng, T., Cox, B. R., Vantassel, J. P., & Manuel, L.
(2020). A statistical approach to account for azimuthal
variability in single-station HVSR measurements. Geophysical
Journal International, 223(2), 1040-1053.
https://doi.org/10.1093/gji/ggaa342.
##### Getting Started
1. Progress to the __Data__ tab to upload your data,
HVSRweb supports many different file formats
(e.g., miniSEED, SAF, SAC, etc.).
2. Make your __PreProcessing__ and __Processing__ selections
in the associated tabs.
3. After processing review the results of your HVSR analysis
in the __Results__, __HVSR__, and __HVSR-3D__ tabs as
applicable.
##### More Information
"""),
dbc.Row([
html.P("Looking for a previous version of HVSRweb?", style=default_p_style),
html.Div([
html.P(" Refer to the ",
style={**default_p_style, "display": "inline"}),
html.A("HVSRweb GitHub.", href="https://github.com/jpvantassel/hvsrweb",
style={"display": "inline"})
], style={"padding-left": "2em"})
]),
dbc.Row([
html.P("Looking for more information about the HVSR calculations?", style=default_p_style),
html.Div([
html.P("See the ",
style={**default_p_style, "display": "inline"}),
html.A("hvsrpy GitHub.", href="https://github.com/jpvantassel/hvsrpy",
style={"display": "inline"})
], style={"padding-left": "2em"})
]),
],
style=default_cardbody_style),
className="mt-3 md-4",
)
data_tab = dbc.Card(
dbc.CardBody(
[
html.P([
html.Span(
"Data Upload:",
style=default_span_style,
),
], style=default_p_style),
dcc.Upload(
children=html.Div(children=html.P("Drag & drop or Click to select file(s)",
style={"font-size": "1em"})),
id="upload-bar",
style={
"height": "4em",
"lineHeight": "4em",
"textAlign": "center",
"cursor": "pointer",
"background-color": "white",
"color": "black",
"border": "0.1em solid #dedede",
"border-radius": "0.5em",
# "padding":"1em",
},
multiple=True,
),
html.P([
html.Span(
"OR",
style=default_span_style,
),
], style={**default_p_style, "text-align": "center"}),
dbc.Row(children=[
dbc.Button(children="Load Demo Data", id="demo-button",
color="primary", size="lg", style={"padding": "1em"}),
dbc.Tooltip(children="Load ambient noise data provided by us.",
id="demo-button-tooltip",
target="demo-button"),
],
style={"text-align": "center", "margin": "1em"}),
dbc.Row([
html.H6(children="",
id="data-continue-instructions",
style={"display": "inline", "margin-top": "2em", "margin-bottom": "2em", "text-align": "center"}),
]),
], style=default_cardbody_style),
className="mt-3 md-4",
)
preprocess_tab = dbc.Card(
dbc.CardBody(
[
# processing-workflow
html.P([
html.Span(
"Processing Workflow:",
id="processing-workflow-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Workflow for processing the HVSR data.
""",
target="processing-workflow-target",
),
dbc.Select(id="processing-workflow",
options=[
dict(label="Manual", value="manual"),
dict(label="AutoHVSR", value="autohvsr"),
],
),
# preprocess-default-container (start)
dbc.Container([
# trim
html.P([
html.Span(
"Start & End Times (s):",
id="new-start-and-end-time-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
New start and end times for the time-domain
recording in seconds.
""",
target="new-start-and-end-time-tooltip-target",
),
dbc.Row([dbc.Col([dbc.Input(id="new-start-time", type="number",
value=0, min=0, max=99999, step=1),]),
dbc.Col([dbc.Input(id="new-end-time", type="number",
value=0, min=0, max=99999, step=1),])
]),
dbc.Tooltip("New start time in seconds.",
target="new-start-time"),
dbc.Tooltip("New end time in seconds.",
target="new-end-time"),
# orient-to-degrees-from-north
html.P([
html.Span(
"Rotate Sensor (degree):",
id="orient-to-degrees-from-north-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Rotate sensor to new orientation. The sensor's
new orientation is defined in degrees from north
(clockwise positive). The sensor's north
component will be oriented such that it is
aligned with the defined orientation.
""",
target="orient-to-degrees-from-north-tooltip-target",
),
dbc.Input(id="orient-to-degrees-from-north", type="number",
value=0, min=0, max=360, step=1),
# butterworth-filter
html.P([
html.Span(
"Butterworth Filter",
id="butterworth-filter-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Select type of Butterworth filter to be applied
to the time-domain recording.
""",
target="butterworth-filter-tooltip-target",
),
dbc.Select(
id="butterworth-filter",
options=[
dict(label="Bandpass", value="bandpass"),
dict(label="Lowpass", value="lowpass"),
dict(label="Highpass", value="highpass"),
dict(label="None", value="none"),
], value="none"),
# butterworth-filter-lower-frequency
dbc.Container([
html.P([
html.Span(
"Low-cut Frequency (Hz):",
id="butterworth-filter-lower-frequency-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip(
"Frequencies below that specified will be filtered.",
target="butterworth-filter-lower-frequency-tooltip-target",
),
dbc.Input(id="butterworth-filter-lower-frequency", type="number",
value=0.1, min=0, max=100, step=0.01),
], className="ml-2 mr-0", id="butterworth-filter-lower-frequency-container"),
# butterworth-filter-upper-frequency
dbc.Container([
html.P([
html.Span(
"High-cut Frequency (Hz):",
id="butterworth-filter-upper-frequency-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip(
"Frequencies above that specified will be filtered.",
target="butterworth-filter-upper-frequency-tooltip-target",
),
dbc.Input(id="butterworth-filter-upper-frequency", type="number",
value=30, min=0, max=100, step=0.01),
], className="ml-2 mr-0", id="butterworth-filter-upper-frequency-container"),
], id="preprocess-default-container", style=HIDE_CONTAINER),
# preprocess-default-container (end)
# traditional-container (start)
dbc.Container([
# window-length
html.P([
html.Span(
"Window Length (s):",
id="window-length-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Length of each time window in seconds.
SESAME (2004) recommends 10 significant cycles
per window.
""",
target="window-length-tooltip-target",
),
dbc.Input(id="window-length", type="number",
value=100, min=0, max=600, step=1),
], id="traditional-container", style=HIDE_CONTAINER),
# traditional-container (end)
# preprocess-default-container-continued (start)
dbc.Container([
# detrend
html.P([
html.Span(
"Detrend:",
id="detrend-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Type of detrend to be performed.
""",
target="detrend-tooltip-target",
),
dbc.Select(id="detrend",
options=[
dict(label="Linear", value="linear"),
dict(label="Constant", value="constant"),
dict(label="None", value="none"),
],
value="linear"),
], id="preprocess-default-container-continued", style=HIDE_CONTAINER),
# preprocess-default-container-continued (end)
# preprocess-button
dbc.Row(
children=[
dbc.Button(children="Preprocess", id="preprocess-button",
color="primary", size="lg", style={"padding": "1em"}),
dbc.Tooltip(children="Apply preprocessing settings to time-domain data.",
target="preprocess-button"),
], style={"text-align": "center", "margin": "1em"}
),
# preprocess-continue-instructions
dbc.Row([
html.H6(children="",
id="preprocess-continue-instructions",
style={"display": "inline", "margin-top": "2em", "margin-bottom": "2em", "text-align": "center"}),
]),
], style=default_cardbody_style),
className="mt-3",
)
process_tab = dbc.Card(
dbc.CardBody(
[
# process-method
html.P([
html.Span(
"Processing Method:",
id="process-method-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Select method for HVSR processing.
""",
target="process-method-tooltip-target",
),
dbc.Select(id="process-method",
options=[
dict(label="Traditional", value="traditional"),
dict(label="Azimuthal", value="azimuthal"),
dict(label="Diffuse Field", value="diffuse"),
],
),
# combine-horizontals-container (start)
dbc.Container([
html.P([
html.Span(
"Method to Combine Horizontals:",
id="combine-horizontals-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Select a method to combine the horizontal
components.
""",
target="combine-horizontals-tooltip-target",
),
dbc.Select(id="combine-horizontals-select",
options=[
dict(label="Geometric Mean", value="geometric_mean"),
dict(label="Single Azimuth", value="single_azimuth"),
dict(label="RotDpp", value="rotdpp"),
dict(label="Arithmetic Mean", value="arithmetic_mean"),
dict(label="Squared Average", value="squared_average"),
dict(label="Quadratic Mean", value="quadratic_mean"),
dict(label="Total Horizontal Energy",
value="total_horizontal_energy"),
dict(label="Maximum Horizontal Value",
value="maximum_horizontal_value"),
],
),
], id="combine-horizontals-container", style=HIDE_CONTAINER),
# combine-horizontals-container (end)
# process-base-container (start)
dbc.Container([
# window-type-and-width
html.P([
html.Span(
"Tapering Window:",
id="window-type-and-width-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Window function and associated width applied
to each window.
""",
target="window-type-and-width-tooltip-target",
),
dbc.Row([
dbc.Col([
dbc.Select(id="window-type",
options=[dict(label="Tukey", value="tukey")],
value="tukey"
),
], md=8),
dbc.Col([
dbc.Input(id="window-width", type="number",
value=0.1, min=0., max=1.0, step=0.1),
], md=4)
]),
# smoothing-operator-and-bandwidth
html.P([
html.Span(
"Smoothing Operator:",
id="smoothing-operator-and-bandwidth-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Smoothing operator and associated bandwidth
to be used on the raw horizontal and
vertical Fourier spectra.
""",
target="smoothing-operator-and-bandwidth-tooltip-target",
),
dbc.Row([
dbc.Col([
dbc.Select(id="smoothing-operator",
options=[
dict(label="Konno and Ohmachi", value="konno_and_ohmachi"),
dict(label="Parzen", value="parzen"),
dict(label="Savitzky and Golay", value="savitzky_and_golay"),
dict(label="Linear Rectangular", value="linear_rectangular"),
dict(label="Log Rectangular", value="log_rectangular"),
dict(label="Linear Triangular", value="linear_triangular"),
dict(label="Log Triangular", value="log_triangular"),
],
value="konno_and_ohmachi"
),
], md=8),
dbc.Col([
dbc.Input(id="smoothing-bandwidth", type="number",
value=40., min=0., max=100, step=0.01),
], md=4)
]),
], id="process-base-container", style=HIDE_CONTAINER),
# process-base-container (end)
# frequency-sampling-container (start)
dbc.Container([
# frequency-sampling
html.P([
html.Span(
"Frequency Sampling:",
style=default_span_style,
),
], style=default_p_style),
dbc.Row([
dbc.Col([
dbc.Input(id="minimum-frequency", type="number",
value=0.2, min=0.001, max=999, step=0.001),
]),
dbc.Col([
dbc.Input(id="maximum-frequency", type="number",
value=20, min=1, max=100, step=1),
]),
dbc.Col([
dbc.Input(id="n-frequency", type="number",
value=256, min=32, max=4096, step=1),
]),
]),
dbc.Row([
dbc.Col([
dbc.Select(id="sampling-type-frequency",
options=[
{"label": "Logarithmic", "value": "log"},
{"label": "Linear", "value": "linear"},
],
value="log",
),
]),
]),
dbc.Tooltip(
"Minimum frequency considered when resampling in Hz.",
target="minimum-frequency",
),
dbc.Tooltip(
"Maximum frequency considered when resampling in Hz.",
target="maximum-frequency",
),
dbc.Tooltip(
"Number of frequency points to consider when resampling.",
target="n-frequency",
),
dbc.Tooltip(
"Distribution of frequency samples.",
target="sampling-type-frequency",
),
], id="frequency-sampling-container", style=HIDE_CONTAINER),
# frequency-sampling-container (end)
# traditional-traditional (start)
dbc.Container([
], id="traditional-traditional", style=HIDE_CONTAINER),
# traditional-traditional (end)
# traditional-single-azimuthal (start)
dbc.Container([
html.P([
html.Span(
"Azimuth (degree):",
id="traditional-single-azimuthal-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Azimuth direction for HVSR computation.
""",
target="traditional-single-azimuthal-tooltip-target",
),
dbc.Input(id="single-azimuth", type="number",
value=0, min=0., max=360., step=1.),
], id="traditional-single-azimuth", style=HIDE_CONTAINER),
# traditional-single-azimuthal (end)
# traditional-rotdpp (start)
dbc.Container([
# rotdpp-azimuthal-interval
html.P([
html.Span(
"Azimuthal Interval (degree):",
id="rotdpp-azimuthal-interval-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Interval azimuth between measurements for HVSR.
An azimuthal interval of 5 indicates that HVSR
will be computed every 5 degrees between 0
and 180 for the determination of RotDpp.
""",
target="rotdpp-azimuthal-interval-tooltip-target",
),
dbc.Input(id="rotdpp-azimuthal-interval", type="number",
value=5, min=1., max=180., step=1.),
# rotdpp-azimuthal-ppth-percentile
html.P([
html.Span(
"ppth Percential (%):",
id="rotdpp-azimuthal-ppth-percentile-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Percentile for RotDpp computation. A value
of 50 would result in the computation of RotD50.
""",
target="rotdpp-azimuthal-ppth-percentile-tooltip-target",
),
dbc.Input(id="rotdpp-azimuthal-ppth-percentile", type="number",
value=50, min=0, max=100, step=1.),
], id="traditional-rotdpp", style=HIDE_CONTAINER),
# traditional-rotdpp (end)
# azimuthal (start)
dbc.Container([
# azimuthal-interval
html.P([
html.Span(
"Azimuthal Interval (degree):",
id="azimuthal-interval-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Interval between azimuths for HVSR computation.
An azimuthal interval of 5 indicates that HVSR
will be computed every 5 degrees between 0
and 180.
""",
target="azimuthal-interval-tooltip-target",
),
dbc.Input(id="azimuthal-interval", type="number",
value=5, min=1., max=180., step=1.),
], id="azimuthal", style=HIDE_CONTAINER),
# azimuthal (end)
# statistics-container (start)
dbc.Container([
# distribution-resonance
html.P([
html.Span(
"Distribution of Resonance:",
id="distribution-resonance-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Distribution of the HVSR resonance(s)
identified during processing. Applies in
terms of both frequency and amplitude.
""",
target="distribution-resonance-tooltip-target",
),
dbc.Select(id="distribution-resonance",
options=[
dict(label="Lognormal", value="lognormal"),
dict(label="Normal", value="normal"),
],
value="lognormal"
),
# distribution-mean-curve
html.P([
html.Span(
"Distribution of Mean Curve:",
id="distribution-mean-curve-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Distribution of the mean curve.
""",
target="distribution-mean-curve-tooltip-target",
),
dbc.Select(id="distribution-mean-curve",
options=[
dict(label="Lognormal", value="lognormal"),
dict(label="Normal", value="normal"),
],
value="lognormal"
),
], id="statistics-container", style=HIDE_CONTAINER),
# statistics-container (start)
# resonance-search-range-container (start)
dbc.Container([
# resonance-search-range
html.P([
html.Span(
"Resonance Search Range:",
style=default_span_style,
),
], style=default_p_style),
dbc.Row([
dbc.Col([
dbc.Input(id="minimum-search-frequency", type="number",
value=0, min=0, max=999, step=0.001),
]),
dbc.Col([
dbc.Input(id="maximum-search-frequency", type="number",
value=100, min=0, max=999, step=0.001),
]),
]),
dbc.Tooltip(
"""
Lowest frequency in Hz considered when searching
for peaks in the HVSR curve.
""",
target="minimum-search-frequency",
),
dbc.Tooltip(
"""
Highest frequency in Hz considered when searching
for peaks in the HVSR curve.
""",
target="maximum-search-frequency",
)], id="resonance-search-range-container", style=HIDE_CONTAINER),
# resonance-search-range-container (end)
# rejection (start)
dbc.Container([
# rejection-select
html.P([
html.Span(
"Apply Window Rejection Algorithm:",
id="rejection-select-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Select which (if any) window rejection
algorithm will be applied during the HVSR
computation.
""",
target="rejection-select-tooltip-target",
),
dbc.Select(id="rejection-select",
options=[
dict(label="No", value="False"),
dict(
label="Frequency-Domain Window Rejection Algorithm (Cox et al., 2020)", value="fdwra"),
],
value="fdwra"
),
# fdwra (start)
dbc.Container([
# fdwra-n
html.P([
html.Span(
"Number of Standard Deviations:",
id="fdwra-n-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Number of standard deviations to consider during
rejection. Smaller values will tend to reject more
windows than larger values.
""",
target="traditional-single-azimuthal-tooltip-target",
),
dbc.Input(id="fdwra-n", type="number",
value=2.5, min=0.5, max=4, step=0.5),
# fdwra-max-iteration
html.P([
html.Span(
"Maximum Number of Allowed Iterations:",
id="fdwra-max-iteration-tooltip-target",
style=default_span_style,
),
], style=default_p_style),
dbc.Tooltip("""
Maximum number of iterations allowed for the
algorithm to converge.
""",
target="fdwra-max-iteration-tooltip-target",
),
dbc.Input(id="fdwra-max-iteration", type="number",
value=50, min=5., max=75., step=5.),
], id="fdwra"),
# fdwra (end)
], id="rejection", style=HIDE_CONTAINER),
# rejection (end)
# diffuse (start)
dbc.Container([
], id="diffuse", style=HIDE_CONTAINER),
# diffuse (end)
# process-button
dbc.Row(
children=[
dbc.Button(children="Process", id="process-button",
color="primary", size="lg", style={"padding-left": "2em", "padding-right": "2em"}),
dbc.Tooltip(children="Apply processing settings to compute HVSR.",
target="process-button"),
], style={"padding": "2em"}
),
# process-continue-instructions
dbc.Row([
html.H6(children="",
id="process-continue-instructions",
style={"display": "inline", "margin-top": "2em", "margin-bottom": "2em", "text-align": "center"}),
]),
], style=default_cardbody_style),
className="mt-3 md-4",
)
results_tab = dbc.Card(
dbc.CardBody(
[
html.P([
html.Span("General Summary:",
style=default_span_style),
], style=default_p_style),
dbc.Row(dbc.Col(id="general-summary-table")),
html.P([
html.Span("Resonance Summary:",
style=default_span_style),
], style=default_p_style),
dbc.Row(dbc.Col(id="results-table")),
dbc.Container([
html.P([
html.Span("2nd Resonance Summary:",
style=default_span_style),
], style=default_p_style),
dbc.Row(dbc.Col(id="results-table-1")),
], id="results-table-1-container", style=HIDE_CONTAINER),
dbc.Container([
html.P([
html.Span("3rd Resonance Summary:",
style=default_span_style),
], style=default_p_style),
dbc.Row(dbc.Col(id="results-table-2")),
], id="results-table-2-container", style=HIDE_CONTAINER),
dbc.Container([
html.P([
html.Span("4th Resonance Summary:",
style=default_span_style),
], style=default_p_style),
dbc.Row(dbc.Col(id="results-table-3")),
], id="results-table-3-container", style=HIDE_CONTAINER),
dbc.Container([
html.P([
html.Span("5th Resonance Summary:",
style=default_span_style),
], style=default_p_style),
dbc.Row(dbc.Col(id="results-table-4")),
], id="results-table-4-container", style=HIDE_CONTAINER),
dbc.Container([
html.P([
html.Span("6th Resonance Summary:",
style=default_span_style),
], style=default_p_style),
dbc.Row(dbc.Col(id="results-table-5")),
], id="results-table-5-container", style=HIDE_CONTAINER),
# html.P([
# html.Span("Download Results:",
# style=default_span_style),
# ], style=default_p_style),
dbc.Row([
# dbc.Col([
html.A(
dbc.Button(children="Download Results", id="button-save-hvsrpy",
color="primary", size="lg", style={"width": "100%", "padding-left": "2em", "padding-right": "2em", "margin": "0"}),
id="hvsrpy-download", download="", href="", target="_blank", style={"width": "100%", "padding": "0", "margin": "0"}),
# style={"padding-left": "2em", "padding-right": "2em"}
# ]),
# TODO(jpv): Skip for now
# dbc.Col([
# html.A(
# dbc.Button("Save as geopsy", color="primary", size="md",
# id="button-save-geopsy", style=dict(width="80%")),
# id="geopsy-download", download="", href="", target="_blank"),
# ]),
], style={"padding-left": "2em", "padding-right": "2em", "padding-top": "0.5em", "padding-bottom": "1em"}),
# dbc.Tooltip(
# "Save results as a .",
# target="button-save-hvsrpy"),
# TODO(jpv): Skip for now
# dbc.Tooltip(
# "Save results in the geopsy-style text format.",
# target="button-save-geopsy"),
dbc.Row([
html.P("Looking for more information?", style=default_p_style),
html.Div([
html.P("Refer to the references back in the ",
style={**default_p_style, "display": "inline"}),
html.P("Intro ",
style={**default_p_style, "display": "inline", "font-weight":"bold"}),
html.P(" tab.",
style={**default_p_style, "display": "inline"}),
], style={"padding-left": "2em"})
]),
dbc.Row([
html.P("Looking for more functionality?", style=default_p_style),
html.Div([
html.P("Checkout ",
style={**default_p_style, "display": "inline"}),
html.A("hvsrpy.", href="https://github.com/jpvantassel/hvsrpy",
style={"display": "inline"})
], style={"padding-left": "2em"})
]),
dbc.Row([
html.P("Looking for an earlier version of HVSRweb?", style=default_p_style),
html.Div([
html.P("Find instructions on the ",
style={**default_p_style, "display": "inline"}),
html.A("HVSRweb GitHub.",
href="https://github.com/jpvantassel/hvsrweb", style={"display": "inline"})
], style={"padding-left": "2em"})
]),
], style=default_cardbody_style),
className="mt-3 md-4",
)
application = Flask(__name__)
app = dash.Dash(server=application, external_stylesheets=[dbc.themes.BOOTSTRAP])
tab_label_style = dict(padding="0.5em")
app.title = "HVSRweb: A web application for HVSR processsing"
app.layout = html.Div(
[
html.Div(
id="banner",
className="banner",
children=[html.Img(src=app.get_asset_url("hvsrweb_logo.png")),
html.H4("HVSRweb: A web application for HVSR processing")]
),
dbc.Container([
dbc.Row([
dbc.Col(
children=[
dbc.Tabs(children=[
dbc.Tab(intro_tab, id="intro-tab",
label="Intro", label_style=tab_label_style),
dbc.Tab(data_tab, id="data-tab",
label="Data", label_style=tab_label_style),
dbc.Tab(preprocess_tab, id="preprocess-tab",
label="Preprocess", disabled=True, label_style=tab_label_style),
dbc.Tab(process_tab, id="process-tab",
label="Process", disabled=True, label_style=tab_label_style),
dbc.Tab(results_tab, id="results-tab",
label="Results", disabled=True, label_style=tab_label_style),
]),
],
md=4,
),
dbc.Col(
children=[
dbc.Tabs(children=[
dbc.Tab(label="Seismic Record",
children=[dbc.Card(dbc.CardBody(children=[
dbc.Row(
children=[
html.Div([
html.H6(children="Current File:",
style={"display": "inline", "margin-top": "1em", "margin-bottom": "1em"}),
html.P(children="No file has been uploaded.",
id="filename-display",
style={"display": "inline", "padding": "0.25em", "margin-left": "0.5em"}),
]),
],
),
html.Div(id="plot-seismic-record", className="p-4")], style=default_cardbody_style), className="mt-3"),
],