In [13]:
# imports
import numpy as np
import pandas as pd
#import geopandas as gpd
import plotly.io as pio
import plotly.graph_objects as go
import plotly.express as px
pio.templates.default = "plotly_white"
In [29]:
s1617 = pd.read_csv(r'./data/norwegian_avalanche_warnings_season_16_17.csv', sep=";", header=0)
s1617['season'] = '2016/2017'
s1718 = pd.read_csv(r'./data/norwegian_avalanche_warnings_season_17_18.csv', sep=";", header=0)
s1819 = pd.read_csv(r'./data/norwegian_avalanche_warnings_season_18_19.csv', sep=";", header=0)
s1819
Out[29]:
index
reg_id
region_id
region_name
date_valid
region_type_id
region_type_name
utm_east
utm_north
utm_zone
...
avalanche_problem_3_probability_name
avalanche_problem_3_trigger_simple_id
avalanche_problem_3_trigger_simple_name
avalanche_problem_3_distribution_id
avalanche_problem_3_distribution_name
avalanche_problem_3_exposed_height_fill
avalanche_problem_3_exposed_height_1
avalanche_problem_3_exposed_height_2
avalanche_problem_3_valid_expositions
avalanche_problem_3_advice
0
0
169485
3003
Nordenskiöld Land
2018-11-30
10
A
520332
8663904
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
1
1
169531
3003
Nordenskiöld Land
2018-12-01
10
A
520332
8663904
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
2
2
169419
3007
Vest-Finnmark
2018-12-01
10
A
802123
7794717
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
3
3
169439
3009
Nord-Troms
2018-12-01
10
A
750984
7742562
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
4
4
169620
3010
Lyngen
2018-12-01
10
A
692056
7719872
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
3952
3952
195949
3031
Voss
2019-05-31
10
A
28607
6779054
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
3953
3953
195950
3032
Hallingdal
2019-05-31
10
A
150188
6763814
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
3954
3954
195951
3034
Hardanger
2019-05-31
10
A
62473
6692016
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
3955
3955
195952
3035
Vest-Telemark
2019-05-31
10
A
131223
6642571
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
3956
3956
195953
3037
Heiane
2019-05-31
10
A
46852
6517179
33
...
Not given
0
Not given
0
Not given
0
0
0
0
Not given
3957 rows × 103 columns
In [37]:
ap_count1617 = s1617.groupby('avalanche_problem_1_cause_name').count()['index']
print(ap_count1617)
ap_count1718 = (s1718.groupby('avalanche_problem_1_cause_name').count())['index']
print(ap_count1718)
ap_count1819 = (s1819.groupby('avalanche_problem_1_cause_name').count())['index']
print(ap_count1819)
avalanche_problem_1_cause_name
Dårlig binding mellom glatt skare og overliggende snø 157
Dårlig binding mellom lag i fokksnøen 528
Kantkornet snø over skarelag 213
Kantkornet snø under skarelag 26
Kantkornet snø ved bakken 1
Nedføyket svakt lag med nysnø 2026
Nedsnødd eller nedføyket kantkornet snø 128
Nedsnødd eller nedføyket overflaterim 48
Not given 21
Opphopning av vann i/over lag i snødekket 206
Ubunden snø 392
Vann ved bakken/smelting fra bakken 97
Name: index, dtype: int64
avalanche_problem_1_cause_name
Dårlig binding mellom glatt skare og overliggende snø 104
Dårlig binding mellom lag i fokksnøen 534
Kantkornet snø over skarelag 73
Kantkornet snø under skarelag 379
Kantkornet snø ved bakken 16
Nedføyket svakt lag med nysnø 1531
Nedsnødd eller nedføyket kantkornet snø 334
Nedsnødd eller nedføyket overflaterim 117
Not given 29
Opphopning av vann i/over lag i snødekket 87
Ubunden snø 664
Vann ved bakken/smelting fra bakken 14
Name: index, dtype: int64
avalanche_problem_1_cause_name
Dårlig binding mellom glatt skare og overliggende snø 59
Dårlig binding mellom lag i fokksnøen 576
Kantkornet snø over skarelag 165
Kantkornet snø under skarelag 44
Nedføyket svakt lag med nysnø 1875
Nedsnødd eller nedføyket kantkornet snø 179
Nedsnødd eller nedføyket overflaterim 36
Not given 29
Opphopning av vann i/over lag i snødekket 170
Ubunden snø 816
Vann ved bakken/smelting fra bakken 8
Name: index, dtype: int64
In [30]:
c_filter = ['avalanche_danger', 'avalanche_problem_1_cause_id', 'avalanche_problem_1_cause_name', 'avalanche_problem_2_cause_id', 'avalanche_problem_2_cause_name', 'avalanche_problem_3_cause_id', 'avalanche_problem_3_cause_name', 'region_id', 'region_name']
ap_df = pd.concat([s1617.filter(c_filter), s1718.filter(c_filter), s1819.filter(c_filter)], ignore_index=True)
Out[30]:
avalanche_danger
avalanche_problem_1_cause_id
avalanche_problem_1_cause_name
avalanche_problem_2_cause_id
avalanche_problem_2_cause_name
avalanche_problem_3_cause_id
avalanche_problem_3_cause_name
region_id
region_name
0
Natt til fredag ventes intense snøfall over 60...
10
Nedføyket svakt lag med nysnø
24
Ubunden snø
0
Not given
3022
Trollheimen
1
Natt til fredag ventes intense snøfall over 60...
10
Nedføyket svakt lag med nysnø
24
Ubunden snø
0
Not given
3023
Romsdal
2
Natt til fredag ventes intense snøfall over 60...
10
Nedføyket svakt lag med nysnø
24
Ubunden snø
0
Not given
3024
Sunnmøre
3
Natt til fredag ventes intense snøfall over 60...
10
Nedføyket svakt lag med nysnø
24
Ubunden snø
0
Not given
3027
Indre Fjordane
4
Natt til fredag ventes intense snøfall over 60...
10
Nedføyket svakt lag med nysnø
24
Ubunden snø
0
Not given
3029
Indre Sogn
...
...
...
...
...
...
...
...
...
...
3838
Det er framleis mogelegheiter for at det kan l...
20
Vann ved bakken/smelting fra bakken
0
Not given
0
Not given
3029
Indre Sogn
3839
Varme og sol vil gi fortsatt god snøsmelting. ...
24
Ubunden snø
20
Vann ved bakken/smelting fra bakken
0
Not given
3031
Voss
3840
Generelt stabile forhold. Smeltevatn nede i sn...
20
Vann ved bakken/smelting fra bakken
0
Not given
0
Not given
3032
Hallingdal
3841
Det er framleis mogelegheiter for at det kan l...
20
Vann ved bakken/smelting fra bakken
24
Ubunden snø
0
Not given
3034
Hardanger
3842
Generelt stabile forhold. Smeltevatn nede i sn...
20
Vann ved bakken/smelting fra bakken
0
Not given
0
Not given
3035
Vest-Telemark
3843 rows × 9 columns
In [ ]:
In [38]:
# setting up a plot.ly figure
fig = go.Figure()
# avalprob_1 = {
# "type:": "bar",
# # "mode": "lines",
# "name": "Avalanche problem frequency",
# # "line": {"color": c_topo},
# # "fill": "tozeroy",
# "x": df['M'],
# "y": df['Z']
# }
fig.add_trace(go.Histogram(histfunc="count", x=ap_df['avalanche_problem_1_cause_name'], name="Main"))
fig.add_trace(go.Histogram(histfunc="count", x=ap_df['avalanche_problem_2_cause_name'], name="Secondary"))
# fig.add_trace(avalprob_1)
p_layout = {"title": {"text": "Avalanche problems 2016-2019"},
"showlegend": True, "legend": {"orientation": "v"},
"barmode": "stack"
# "xaxis": {"rangemode": "nonnegative"},
# "yaxis": {"rangemode": "nonnegative"}
}
fig.update_layout(p_layout)
# fig.write_image("ap_16_19.png")
pio.show(fig)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-95f4c3f07bf0> in <module>
24
25 fig.update_layout(p_layout)
---> 26 fig.write_image("ap_16_19.png")
27 pio.show(fig)
28
C:\ProgramData\Anaconda3\envs\scientific\lib\site-packages\plotly\basedatatypes.py in write_image(self, *args, **kwargs)
2822 import plotly.io as pio
2823
-> 2824 return pio.write_image(self, *args, **kwargs)
2825
2826 # Static helpers
C:\ProgramData\Anaconda3\envs\scientific\lib\site-packages\plotly\io\_orca.py in write_image(fig, file, format, scale, width, height, validate)
1764 # -------------
1765 # Do this first so we don't create a file if image conversion fails
-> 1766 img_data = to_image(
1767 fig, format=format, scale=scale, width=width, height=height, validate=validate
1768 )
C:\ProgramData\Anaconda3\envs\scientific\lib\site-packages\plotly\io\_orca.py in to_image(fig, format, width, height, scale, validate)
1530 # Make sure orca sever is running
1531 # -------------------------------
-> 1532 ensure_server()
1533
1534 # Handle defaults
C:\ProgramData\Anaconda3\envs\scientific\lib\site-packages\plotly\io\_orca.py in ensure_server()
1359 # Validate psutil
1360 if psutil is None:
-> 1361 raise ValueError(
1362 """\
1363 Image generation requires the psutil package.
ValueError: Image generation requires the psutil package.
Install using pip:
$ pip install psutil
Install using conda:
$ conda install psutil
In [54]:
# s1819[s1819['avalanche_problem_1_cause_name']=='Nedsnødd eller nedføyket overflaterim']['avalanche_problem_1_cause_id']
sh_df1617 = s1617[s1617['avalanche_problem_1_cause_id']==11].copy()
sh_df1617['occurrence'] = 1
sh_df1617
Out[54]:
index
reg_id
region_id
region_name
date_valid
region_type_id
region_type_name
utm_east
utm_north
utm_zone
...
avalanche_problem_3_trigger_simple_id
avalanche_problem_3_trigger_simple_name
avalanche_problem_3_distribution_id
avalanche_problem_3_distribution_name
avalanche_problem_3_exposed_height_fill
avalanche_problem_3_exposed_height_1
avalanche_problem_3_exposed_height_2
avalanche_problem_3_valid_expositions
avalanche_problem_3_advice
occurrence
999
999
110640
3003
Nordenskiöld Land
2017-01-17
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1015
1015
110656
3029
Indre Sogn
2017-01-17
10
A
96001
6816985
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1016
1016
110657
3031
Voss
2017-01-17
10
A
28607
6779054
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1018
1018
110659
3034
Hardanger
2017-01-17
10
A
62473
6692016
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1020
1020
110797
3003
Nordenskiöld Land
2017-01-18
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1036
1036
110813
3029
Indre Sogn
2017-01-18
10
A
96001
6816985
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1037
1037
110843
3031
Voss
2017-01-18
10
A
28607
6779054
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1039
1039
110869
3034
Hardanger
2017-01-18
10
A
62473
6692016
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1041
1041
110946
3003
Nordenskiöld Land
2017-01-19
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1057
1057
110962
3029
Indre Sogn
2017-01-19
10
A
96001
6816985
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1534
1534
114209
3014
Lofoten og Vesterålen
2017-02-11
10
A
527125
7620981
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1535
1535
114210
3015
Ofoten
2017-02-11
10
A
602309
7578309
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1536
1536
114360
3016
Salten
2017-02-11
10
A
533221
7497029
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1537
1537
114212
3017
Svartisen
2017-02-11
10
A
464133
7381882
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1549
1549
114379
3007
Vest-Finnmark
2017-02-12
10
A
802123
7794717
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1550
1550
114333
3009
Nord-Troms
2017-02-12
10
A
750984
7742562
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1551
1551
114394
3010
Lyngen
2017-02-12
10
A
692056
7719872
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1552
1552
114400
3011
Tromsø
2017-02-12
10
A
656496
7764237
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1570
1570
114524
3007
Vest-Finnmark
2017-02-13
10
A
802123
7794717
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1571
1571
114475
3009
Nord-Troms
2017-02-13
10
A
750984
7742562
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1572
1572
114527
3010
Lyngen
2017-02-13
10
A
692056
7719872
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1573
1573
114529
3011
Tromsø
2017-02-13
10
A
656496
7764237
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1576
1576
114480
3014
Lofoten og Vesterålen
2017-02-13
10
A
527125
7620981
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1597
1597
114576
3014
Lofoten og Vesterålen
2017-02-14
10
A
527125
7620981
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2085
2085
118065
3015
Ofoten
2017-03-09
10
A
602309
7578309
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2197
2197
119018
3028
Jotunheimen
2017-03-14
10
A
155607
6844417
33
...
10
Stor tilleggsbelastning
2
Noen bratte heng
1
1000
0
1110000
Unngå ferdsel i skredterreng og i utløpsområde...
1
2218
2218
119047
3028
Jotunheimen
2017-03-15
10
A
155607
6844417
33
...
10
Stor tilleggsbelastning
2
Noen bratte heng
1
900
0
1110000
Unngå ferdsel i skredterreng og i utløpsområde...
1
2239
2239
119214
3028
Jotunheimen
2017-03-16
10
A
155607
6844417
33
...
21
Liten tilleggsbelastning
3
Mange bratte heng
1
900
0
1110000
Unngå bratte heng og terrengfeller under og et...
1
2260
2260
119531
3028
Jotunheimen
2017-03-17
10
A
155607
6844417
33
...
21
Liten tilleggsbelastning
2
Noen bratte heng
1
1200
0
1110000
Unngå ferdsel i skredterreng og i utløpsområde...
1
2302
2302
119964
3028
Jotunheimen
2017-03-19
10
A
155607
6844417
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2323
2323
119974
3028
Jotunheimen
2017-03-20
10
A
155607
6844417
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2344
2344
120079
3028
Jotunheimen
2017-03-21
10
A
155607
6844417
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2366
2366
120242
3028
Jotunheimen
2017-03-22
10
A
155607
6844417
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3150
3150
126069
3003
Nordenskiöld Land
2017-04-29
10
A
520332
8663904
33
...
10
Stor tilleggsbelastning
2
Noen bratte heng
1
0
0
11111111
Unngå ferdsel i skredterreng og i utløpsområde...
1
3171
3171
126067
3003
Nordenskiöld Land
2017-04-30
10
A
520332
8663904
33
...
10
Stor tilleggsbelastning
2
Noen bratte heng
1
0
0
11111111
Unngå ferdsel i skredterreng og i utløpsområde...
1
3192
3192
126100
3003
Nordenskiöld Land
2017-05-01
10
A
520332
8663904
33
...
10
Stor tilleggsbelastning
2
Noen bratte heng
1
0
0
11111111
Unngå ferdsel i skredterreng og i utløpsområde...
1
3213
3213
126223
3003
Nordenskiöld Land
2017-05-02
10
A
520332
8663904
33
...
10
Stor tilleggsbelastning
2
Noen bratte heng
1
0
0
11111111
Hold god avstand til hverandre ved ferdsel i s...
1
3234
3234
126314
3003
Nordenskiöld Land
2017-05-03
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3255
3255
126404
3003
Nordenskiöld Land
2017-05-04
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3276
3276
126499
3003
Nordenskiöld Land
2017-05-05
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3297
3297
126602
3003
Nordenskiöld Land
2017-05-06
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3318
3318
126806
3003
Nordenskiöld Land
2017-05-07
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3339
3339
126887
3003
Nordenskiöld Land
2017-05-08
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3360
3360
126973
3003
Nordenskiöld Land
2017-05-09
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3381
3381
127062
3003
Nordenskiöld Land
2017-05-10
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3528
3528
127800
3003
Nordenskiöld Land
2017-05-17
10
A
520332
8663904
33
...
21
Liten tilleggsbelastning
2
Noen bratte heng
1
200
0
11111000
Vær forsiktig i områder brattere enn 30 grader...
1
3549
3549
127832
3003
Nordenskiöld Land
2017-05-18
10
A
520332
8663904
33
...
21
Liten tilleggsbelastning
2
Noen bratte heng
1
200
0
11111000
Vær forsiktig i områder brattere enn 30 grader...
1
3570
3570
127845
3003
Nordenskiöld Land
2017-05-19
10
A
520332
8663904
33
...
21
Liten tilleggsbelastning
2
Noen bratte heng
1
200
0
11111000
Vær forsiktig i områder brattere enn 30 grader...
1
48 rows × 104 columns
In [55]:
# setting up a plot.ly figure
fig = go.Figure()
avalprob_1 = {
"type": "bar",
"name": "Avalanche problem frequency",
# "line": {"color": c_topo},
# "fill": "tozeroy",
"x": sh_df1617['date_valid'],
"y": sh_df1617['occurrence']
}
fig.add_trace(avalprob_1)
p_layout = {"title": {"text": "Surface hoar 2016-2017"},
"showlegend": False, "legend": {"orientation": "h"},
# "xaxis": {"rangemode": "nonnegative"},
"yaxis": {"title": "#Regions with SH problem"}
}
fig.update_layout(p_layout)
# fig.write_image("ap_16_19.png")
pio.show(fig)
In [ ]:
In [56]:
# s1718[s1718['avalanche_problem_1_cause_name']=='Nedsnødd eller nedføyket overflaterim']['avalanche_problem_1_cause_id']
sh_df1718 = s1718[s1718['avalanche_problem_1_cause_id']==11].copy()
sh_df1718['occurrence'] = 1
sh_df1718
Out[56]:
index
reg_id
region_id
region_name
date_valid
region_type_id
region_type_name
utm_east
utm_north
utm_zone
...
avalanche_problem_3_trigger_simple_id
avalanche_problem_3_trigger_simple_name
avalanche_problem_3_distribution_id
avalanche_problem_3_distribution_name
avalanche_problem_3_exposed_height_fill
avalanche_problem_3_exposed_height_1
avalanche_problem_3_exposed_height_2
avalanche_problem_3_valid_expositions
avalanche_problem_3_advice
occurrence
45
45
133407
3022
Trollheimen
2017-12-02
10
A
210810
6991060
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
46
46
133408
3023
Romsdal
2017-12-02
10
A
123434
6960580
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
47
47
133409
3024
Sunnmøre
2017-12-02
10
A
62473
6916553
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
48
48
133410
3027
Indre Fjordane
2017-12-02
10
A
34025
6868801
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
51
51
133413
3031
Voss
2017-12-02
10
A
28607
6779054
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2242
2242
150321
3032
Hallingdal
2018-03-15
10
A
150188
6763814
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2249
2249
150520
3011
Tromsø
2018-03-16
10
A
656496
7764237
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2255
2255
150526
3017
Svartisen
2018-03-16
10
A
464133
7381882
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2263
2263
150534
3032
Hallingdal
2018-03-16
10
A
150188
6763814
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
3016
3016
160403
3003
Nordenskiöld Land
2018-04-21
10
A
520332
8663904
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
117 rows × 104 columns
In [57]:
# setting up a plot.ly figure
fig = go.Figure()
avalprob_1 = {
"type": "bar",
"name": "Avalanche problem frequency",
# "line": {"color": c_topo},
# "fill": "tozeroy",
"x": sh_df1718['date_valid'],
"y": sh_df1718['occurrence']
}
fig.add_trace(avalprob_1)
p_layout = {"title": {"text": "Surface hoar 2017-2018"},
"showlegend": False, "legend": {"orientation": "h"},
# "xaxis": {"rangemode": "nonnegative"},
"yaxis": {"title": "#Regions with SH problem"}
}
fig.update_layout(p_layout)
# fig.write_image("ap_16_19.png")
pio.show(fig)
In [ ]:
In [52]:
# s1819[s1819['avalanche_problem_1_cause_name']=='Nedsnødd eller nedføyket overflaterim']['avalanche_problem_1_cause_id']
sh_df1819 = s1819[s1819['avalanche_problem_1_cause_id']==11].copy()
sh_df1819['occurrence'] = 1
sh_df1819
Out[52]:
index
reg_id
region_id
region_name
date_valid
region_type_id
region_type_name
utm_east
utm_north
utm_zone
...
avalanche_problem_3_trigger_simple_id
avalanche_problem_3_trigger_simple_name
avalanche_problem_3_distribution_id
avalanche_problem_3_distribution_name
avalanche_problem_3_exposed_height_fill
avalanche_problem_3_exposed_height_1
avalanche_problem_3_exposed_height_2
avalanche_problem_3_valid_expositions
avalanche_problem_3_advice
occurrence
58
58
169768
3028
Jotunheimen
2018-12-03
10
A
155607
6844417
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
79
79
169893
3028
Jotunheimen
2018-12-04
10
A
155607
6844417
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
311
311
171335
3029
Indre Sogn
2018-12-15
10
A
96001
6816985
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
332
332
171434
3029
Indre Sogn
2018-12-16
10
A
96001
6816985
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
353
353
171558
3029
Indre Sogn
2018-12-17
10
A
96001
6816985
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
374
374
171662
3029
Indre Sogn
2018-12-18
10
A
96001
6816985
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
395
395
171780
3029
Indre Sogn
2018-12-19
10
A
96001
6816985
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
418
418
171924
3032
Hallingdal
2018-12-20
10
A
150188
6763814
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
460
460
172166
3032
Hallingdal
2018-12-22
10
A
150188
6763814
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
480
480
172270
3031
Voss
2018-12-23
10
A
28607
6779054
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
481
481
172271
3032
Hallingdal
2018-12-23
10
A
150188
6763814
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1011
1011
175853
3017
Svartisen
2019-01-17
10
A
464133
7381882
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1032
1032
176049
3017
Svartisen
2019-01-18
10
A
464133
7381882
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1053
1053
176252
3017
Svartisen
2019-01-19
10
A
464133
7381882
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1308
1308
178300
3024
Sunnmøre
2019-01-31
10
A
62473
6916553
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1327
1327
178469
3022
Trollheimen
2019-02-01
10
A
210810
6991060
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1328
1328
178470
3023
Romsdal
2019-02-01
10
A
123434
6960580
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1329
1329
178471
3024
Sunnmøre
2019-02-01
10
A
62473
6916553
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1348
1348
178631
3022
Trollheimen
2019-02-02
10
A
210810
6991060
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1349
1349
178632
3023
Romsdal
2019-02-02
10
A
123434
6960580
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1350
1350
178633
3024
Sunnmøre
2019-02-02
10
A
62473
6916553
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1370
1370
178767
3023
Romsdal
2019-02-03
10
A
123434
6960580
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1371
1371
178768
3024
Sunnmøre
2019-02-03
10
A
62473
6916553
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1670
1670
181299
3022
Trollheimen
2019-02-17
10
A
210810
6991060
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
1692
1692
181457
3022
Trollheimen
2019-02-18
10
A
210810
6991060
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2113
2113
185033
3023
Romsdal
2019-03-09
10
A
123434
6960580
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2222
2222
185871
3022
Trollheimen
2019-03-14
10
A
210810
6991060
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2838
2838
190548
3010
Lyngen
2019-04-11
10
A
692056
7719872
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2839
2839
190549
3011
Tromsø
2019-04-11
10
A
656496
7764237
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2840
2840
190550
3012
Sør-Troms
2019-04-11
10
A
594858
7642656
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2860
2860
190702
3010
Lyngen
2019-04-12
10
A
692056
7719872
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2861
2861
190703
3011
Tromsø
2019-04-12
10
A
656496
7764237
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2862
2862
190704
3012
Sør-Troms
2019-04-12
10
A
594858
7642656
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2882
2882
190863
3010
Lyngen
2019-04-13
10
A
692056
7719872
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2884
2884
190865
3012
Sør-Troms
2019-04-13
10
A
594858
7642656
33
...
0
Not given
0
Not given
0
0
0
0
Not given
1
2904
2904
191004
3010
Lyngen
2019-04-14
10
A
692056
7719872
33
...
22
Naturlig utløst
1
Få bratte heng
1
400
400
11111111
Hold god avstand til hverandre ved ferdsel i s...
1
36 rows × 104 columns
In [53]:
# setting up a plot.ly figure
fig = go.Figure()
avalprob_1 = {
"type": "bar",
"name": "Avalanche problem frequency",
# "line": {"color": c_topo},
# "fill": "tozeroy",
"x": sh_df1819['date_valid'],
"y": sh_df1819['occurrence']
}
fig.add_trace(avalprob_1)
p_layout = {"title": {"text": "Surface hoar 2018-2019"},
"showlegend": False, "legend": {"orientation": "h"},
# "xaxis": {"rangemode": "nonnegative"},
"yaxis": {"title": "#Regions with SH problem"}
}
fig.update_layout(p_layout)
# fig.write_image("ap_16_19.png")
pio.show(fig)
In [ ]:
Content source: kmunve/APS
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