Py.Cafe
Euclid OU-EXT TileViewer
- app.py
- requirements.txt
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
from urllib import request
import requests
import io
from typing import Optional, cast
import pandas as pd
import plotly.express as px
import plotly.io as pio
import solara
import solara.express as solara_px
import solara.lab
from solara.components.columns import Columns
import json
theme = pio.templates["plotly_dark"]
theme["layout"]["plot_bgcolor"] = "rgb(17,17,17)"
theme["layout"]["paper_bgcolor"] = "rgb(27,27,27)"
theme["layout"]["geo"]["bgcolor"] = "rgb(30,30,30)"
theme["layout"]["shapedefaults"]["line"]["width"] = 0.1
## Get sample data
dataurl = "https://www.astro.rug.nl/~balbinot/files/Euclid/lightDEARs_noextra.csv"
dataurl = "https://github.com/balbinot/OU-EXT-QA/raw/main/sample.csv.gz"
df_sample = pd.read_csv(dataurl)
#response = requests.get(dataurl)
#content = response.content
#df_sample = pd.read_csv(
# io.BytesIO(content), sep=",", compression="gzip", index_col=0, quotechar='"',
#)
geojsonurl = "https://www.astro.rug.nl/~balbinot/files/Euclid/tiles.json"
with request.urlopen(geojsonurl) as url:
regions = json.load(url)
# Local stuff
# df_sample = pd.read_csv("../protoTV/lightDEARs_noextra.csv.gz")
# regions = json.load(open("../protoTV/tiles.json", "r"))
class State:
dataset = solara.reactive(cast(Optional[str], None))
metric = solara.reactive(cast(Optional[str], 'ap_image_depth10_MEGACAM_r'))
band = solara.reactive(cast(Optional[str], None))
df = solara.reactive(cast(Optional[pd.DataFrame], None))
filter = solara.reactive(None)
@staticmethod
def load_sample():
State.filter.value = None
State.df.value = df_sample
@solara.component
def Page():
solara.Title("Euclid OU-EXT TileViewer")
def makeHist():
fig2 = px.histogram(
df, x=metric, nbins=30, height=600, width=600, template=theme
)
return fig2
def makeFigure():
_fig = px.choropleth(
df,
geojson=regions,
locations="tileid",
color=metric,
color_continuous_scale="Cividis",
template=theme,
custom_data=["tileid", "DataSetRelease"],
hover_data=["tileid", "DataSetRelease"],
)
_fig.update_geos(
projection_type="orthographic",
lonaxis={"showgrid": True, "tick0": 0, "dtick": 15, "gridwidth": 0.3},
lataxis={"showgrid": True, "tick0": 90, "dtick": 30, "gridwidth": 0.3},
showland=False,
showocean=False,
visible=False,
showframe=True,
framewidth=2,
projection_rotation={'lon':270, 'lat': 70},
projection_scale=2
)
## This is not passed along by solara_px!
_fig.update_traces(
marker_line_width=0.1,
selector=dict(type="choropleth"),
)
_fig.update_layout(width=900, height=900)
return _fig
def selection_fn(a):
State.filter.value = a["points"]["point_indexes"]
def filter_df():
print("filter updated")
print(filter)
if filter is not None and df is not None:
return df.loc[filter]
else:
return df
solara.use_memo(lambda: State.load_sample())
df = State.df.value
filter, _set_filter = solara.use_cross_filter(id(df))
dff = solara.use_memo(filter_df, dependencies=[df, filter])
# dark_effective = solara.lab.use_dark_effective()
with solara.AppBar():
solara.lab.ThemeToggle()
with solara.Sidebar():
with solara.Card("Controls", margin=0, elevation=0):
with solara.Column(style={"max-width": "600px"}):
if df is not None:
solara.Select(
"DataSetRelease",
values=df["DataSetRelease"].unique().tolist(),
value=State.dataset,
)
nice_cols = [col for col in df.columns if "depth" in col]
solara.Select("Metric", values=nice_cols, value=State.metric)
if filter is None:
solara.Info(
"If you select points in the scatter plot, you can download the points here."
)
else:
def get_data():
return dff.to_csv(index=False)
solara.FileDownload(
get_data,
label=f"Download {len(dff):,} selected points",
filename="selected.csv",
)
with Columns():
solara.CrossFilterDataFrame(
dff.dropna(axis=1, how="all").round(2), items_per_page=15
)
with Columns():
if State.metric.value is None:
metric = "ap_image_depth10_MEGACAM_r"
else:
metric = State.metric.value
fig = solara.use_memo(
makeFigure,
dependencies=[metric],
)
fig2 = solara.use_memo(makeHist, dependencies=[df, metric])
solara_px.CrossFilteredFigurePlotly(fig)
## When using this all properties for fig are properly shown
solara.FigurePlotly(
fig,
on_selection=selection_fn,
)
#solara_px.CrossFilteredFigurePlotly(fig2)
# solara.FigurePlotly(fig2)
with solara.Column(style={"max-width": "400px"}):
solara.Button(
label="View source",
icon_name="mdi-github-circle",
attributes={"href": "http://balbinot.github.io", "target": "_blank"},
text=True,
outlined=True,
)
@solara.component
def Layout(children):
route, routes = solara.use_route()
dark_effective = solara.lab.use_dark_effective()
return solara.AppLayout(
children=children, toolbar_dark=dark_effective, color=None
) # if dark_effective else "primary")