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Merge pull request #7 from rak183/master
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Added option to select Filters and Methods of Keplerian determination for determining the orbit.
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@aryadas98
aryadas98 authored Jun 5, 2019
2 parents 68b5dba + b4715c6 commit 9d6ef7a
Showing 1 changed file with 163 additions and 12 deletions.
175 changes: 163 additions & 12 deletions app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,20 @@
import orbitdeterminator.kep_determination.ellipse_fit as e_fit
import orbitdeterminator.util.anom_conv as anom_conv
import orbitdeterminator.util.teme_to_ecef as teme_to_ecef
import orbitdeterminator.util.read_data
import orbitdeterminator.util.kep_state
import orbitdeterminator.util.rkf78
import orbitdeterminator.util.golay_window as golay_window
import orbitdeterminator.filters.sav_golay as sav_golay
import orbitdeterminator.filters.triple_moving_average as triple_moving_average
import orbitdeterminator.kep_determination.lamberts_kalman as lamberts_kalman
import orbitdeterminator.kep_determination.interpolation as interpolation
import orbitdeterminator.kep_determination.gibbsMethod as gibbsMethod
import argparse
import matplotlib as mpl
import matplotlib.pylab as plt



app = dash.Dash()
app.title = 'Orbit Determinator'
Expand All @@ -44,22 +58,88 @@ def serve_static(resource):
html.Div('''
Orbit Determinator: A python package to predict satellite orbits.''',id='subtitle'),
html.Div(['''
Choose appropriate unit of length according to the unit used in your .csv file-'''], style={'fontSize': 18,'marginBottom':7},id='unit-selection'),
Choose appropriate unit according to .csv file you are going to upload:'''], style={'fontSize': 18,'marginBottom':7},id='unit-selection'),

dcc.Dropdown(
html.Div(
dcc.Dropdown(
id='my-dropdown',
options=[
{'label': 'Kilometre(km)', 'value': '(km)'},
{'label': 'Metre(m)', 'value': '(m)'},
{'label': 'Kilometers(km)', 'value': '(km)'},
{'label': 'Metres(m)', 'value': '(m)'},

],
value='(km)'
),
)

,style={'width': 250, 'display': 'inline-block'}),




html.Div([''' ''']),
html.Div([''' '''],style={'display': 'inline-block'}),
html.Div([''' ''']),








html.Div(['''
Select filter(for pre-processing):'''], style={'fontSize': 22,'marginBottom':7,'display': 'inline-block'},id='filter-selection'),
html.Div(['''
Select method for keplerian determination:'''], style={'fontSize': 22,'marginBottom':7,'display': 'inline-block','marginLeft':97},id='filter-selection'),
html.Div([''' ''']),

html.Div(
dcc.Dropdown(
id='my-dropdown2',
options=[
{'label': 'Savintzky Golay', 'value': '(sg)'},
{'label': 'Triple moving average', 'value': '(tma)'},
{'label': 'Both', 'value': '(bo)'},
{'label': 'None', 'value': '(nf)'},

],
value='(sg)'
)
,style={'width': 250, 'display': 'inline-block'}),


html.Div(
dcc.Dropdown(
id='my-dropdown3',
options=[
{'label': 'Ellipse Fit', 'value': '(ef)'},
{'label': 'Gibbs Method', 'value': '(gm)'},
{'label': 'Cubic Spline Interpolation', 'value': '(in)'},
{'label': 'Lamberts Kalman', 'value': '(lk)'}


],
value='(ef)'
)

,style={'width': 350, 'display': 'inline-block','marginLeft': 132}),

html.Div([''' ''']),
html.Div([''' '''],style={'display': 'inline-block'}),
html.Div([''' ''']),
html.Div([''' '''],style={'display': 'inline-block'}),
html.Div([''' ''']),

#html.Div(['''
# Input data-- Filter-- Method for keplerian determination-- Orbit of the satellite'''], style={'fontSize': 26,'marginBottom':7,'display': 'inline-block','marginLeft':470},id='filter-selection'),

#html.Div([''' ''']),

html.Div(id='output-container'),

html.Div(id='output-container',style={'fontSize': 0,'marginBottom':15}),

html.Div(['''Upload a csv file below:'''], style={'fontSize': 20,'marginBottom': 10, 'marginTop': 30}),

html.Div(['''Upload a csv file below:'''], style={'fontSize': 22,'marginBottom': 10, 'marginTop': 30}),

html.Div(id='upload-box',
children=dcc.Upload(id='file-upload',
Expand All @@ -69,11 +149,15 @@ def serve_static(resource):
]),
multiple=False)),



html.Div(id='output-div',children=[dt.DataTable(rows=[{}])])
])



])


@app.callback(
dash.dependencies.Output('output-container', 'children'),
[dash.dependencies.Input('my-dropdown', 'value')])
Expand Down Expand Up @@ -142,17 +226,84 @@ def ground_track(kep,time0):
@app.callback(Output('output-div','children'),
[Input('file-upload','contents'),
Input('file-upload','filename'),
Input('my-dropdown', 'value')])
Input('my-dropdown', 'value'),
Input('my-dropdown2','value'),
Input('my-dropdown3','value')])

def display_file(file_content, file_name, dropdown_value):
def display_file(file_content, file_name, dropdown_value,dropdown_value_2,dropdown_value_3):

if file_content is None:
return html.Div(['Choose a file to process it.'])

data = parse_file(file_content)
data_ini = parse_file(file_content)

if('{}'.format(dropdown_value_2)=='(sg)'):
window = golay_window.window(10.0, data_ini)
# Apply the Savintzky - Golay filter with window = 31 and polynomial parameter = 6
data = sav_golay.golay(data_ini, 31, 6)
res = data[:, 1:4] - data_ini[:, 1:4]

elif('{}'.format(dropdown_value_2)=='(tma)'):
data = triple_moving_average.generate_filtered_data(data_ini, 3)
res = data[:, 1:4] - data_ini[:, 1:4]

elif('{}'.format(dropdown_value_2)=='(bo)'):
data= sav_golay.golay(data_ini, 31, 6)
data = triple_moving_average.generate_filtered_data(data, 3)
res = data[:, 1:4] - data_ini[:, 1:4]


elif('{}'.format(dropdown_value_2)=='(nf)'):
data=data_ini
res = data[:, 1:4] - data_ini[:, 1:4]


#else:
#data=data_ini






if (data.shape[0] > 0):
data_dict = [{'No.':'{:03d}'.format(i+1), 't (s)':data[i][0], 'x '+'{}'.format(dropdown_value):data[i][1], 'y '+'{}'.format(dropdown_value):data[i][2], 'z '+'{}'.format(dropdown_value):data[i][3]} for i in range(data.shape[0])]
kep, res = e_fit.determine_kep(data[:,1:])


if('{}'.format(dropdown_value_3)=='(ef)'):
kep, res = e_fit.determine_kep(data[:,1:])


elif('{}'.format(dropdown_value_3)=='(gm)'):
# Apply gibbs method
kep_gibbs = gibbsMethod.gibbs_get_kep(data[:,1:])
kep_final_gibbs = lamberts_kalman.kalman(kep_gibbs, 0.01 ** 2)
kep_final_gibbs = np.transpose(kep_final_gibbs)
kep = np.resize(kep_final_gibbs, ((7, 1)))



elif('{}'.format(dropdown_value_3)=='(in)'):
# Apply the interpolation method
kep_inter = interpolation.main(data)
# Apply Kalman filters, estimate of measurement variance R = 0.01 ** 2
kep_final_inter = lamberts_kalman.kalman(kep_inter, 0.01 ** 2)
kep = np.transpose(kep_final_inter)
kep = np.resize(kep, ((7, 1)))





elif('{}'.format(dropdown_value_3)=='(lk)'):
# Apply Lambert's solution for the filtered data set
kep_lamb = lamberts_kalman.create_kep(data)
# Apply Kalman filters, estimate of measurement variance R = 0.01 ** 2
kep_final_lamb = lamberts_kalman.kalman(kep_lamb, 0.01 ** 2)
kep = np.transpose(kep_final_lamb)
kep = np.resize(kep, ((7, 1)))



# Visuals
orbit_points = data[:,1:]+res
Expand Down

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