Exercise: Visualize a Manual Depth Spiral

Exercise: Visualize a Manual Depth Spiral#

During the SnowEx campaigns a TON of manual snow depths were collected, past surveys for hackweek showed an overhelming interest in the manual snow depths dataset. This tutorial shows how easy it is to get at that data in the database while learning how to build queries

Goal: Visualize a small subset of snow depth, ideally a full spiral (mostly cause they are cool!)

Approach:

  1. Determine the necessary details for isolating manual depths

  2. Find a pit where many spirals were done.

  3. Buffer on the pit location and grab all manual snow depths

Process#

from snowexsql.api import LayerMeasurements
data_type = 'depth'

Step 1: Find a pit of interest#

# Pick the first one we find
site_id = LayerMeasurements().all_site_ids[0]

# Query the database, we only need one point to get a site id and its geometry
site_df = LayerMeasurements.from_filter(site_id=site_id, limit=1)

# Print it out 
site_df
depth site_id pit_id bottom_depth comments sample_a sample_b sample_c value flags ... date time_created time_updated id doi date_accessed instrument type units observers
0 66.0 1N1 COGM1N1_20200208 None None None None None 47.94 None ... 2020-02-08 2024-08-15 20:03:40.149956+00:00 None 2413819 https://doi.org/10.5067/SNMM6NGGKWIT 2022-06-30 IS3-SP-15-01US reflectance None Kehan Yang

1 rows × 29 columns

Step 2: Collect Snow Depths#

# We import the points measurements because snow depths is a single value at single location and date
from snowexsql.api import PointMeasurements 

# Filter the results to within 100m within the point from our pit
df = PointMeasurements.from_area(pt=site_df.geometry[0], type=data_type, buffer=200)
df
version_number equipment value latitude longitude northing easting elevation utm_zone geom ... date time_created time_updated id doi date_accessed instrument type units observers
0 1 CRREL_C 86.0 39.03632 -108.22103 4.324482e+06 740532.506729 3028.7 12 POINT (740532.507 4324482.477) ... 2020-01-28 2022-06-30 22:56:52.635035+00:00 None 5597 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
1 1 CRREL_C 78.0 39.03631 -108.22100 4.324481e+06 740535.137724 3027.6 12 POINT (740535.138 4324481.446) ... 2020-01-28 2022-06-30 22:56:52.635035+00:00 None 5598 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
2 1 CRREL_C 71.0 39.03631 -108.22096 4.324482e+06 740538.600473 3028.8 12 POINT (740538.600 4324481.552) ... 2020-01-28 2022-06-30 22:56:52.635035+00:00 None 5599 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
3 1 CRREL_C 78.0 39.03631 -108.22093 4.324482e+06 740541.197535 3028.4 12 POINT (740541.198 4324481.631) ... 2020-01-28 2022-06-30 22:56:52.635035+00:00 None 5600 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
4 1 CRREL_C 95.0 39.03631 -108.22091 4.324482e+06 740542.928909 3028.4 12 POINT (740542.929 4324481.684) ... 2020-01-28 2022-06-30 22:56:52.635035+00:00 None 5601 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
362 1 CRREL_A 98.0 39.03458 -108.21989 4.324292e+06 740637.101899 3032.6 12 POINT (740637.102 4324292.356) ... 2020-02-11 2022-06-30 22:56:52.635035+00:00 None 33920 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
363 1 CRREL_A 92.0 39.03455 -108.21990 4.324289e+06 740636.338030 3034.3 12 POINT (740636.338 4324289.000) ... 2020-02-11 2022-06-30 22:56:52.635035+00:00 None 33921 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
364 1 CRREL_A 80.0 39.03454 -108.21992 4.324288e+06 740634.640558 3034.6 12 POINT (740634.641 4324287.837) ... 2020-02-11 2022-06-30 22:56:52.635035+00:00 None 33922 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
365 1 CRREL_A 93.0 39.03452 -108.21995 4.324286e+06 740632.111323 3034.2 12 POINT (740632.111 4324285.537) ... 2020-02-11 2022-06-30 22:56:52.635035+00:00 None 33923 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None
366 1 CRREL_A 98.0 39.03448 -108.21995 4.324281e+06 740632.247106 3034.9 12 POINT (740632.247 4324281.097) ... 2020-02-11 2022-06-30 22:56:52.635035+00:00 None 33924 https://doi.org/10.5067/9IA978JIACAR 2022-06-30 magnaprobe depth cm None

367 rows × 23 columns

Step 3: Plot it!#

# Get the Matplotlib Axes object from the dataframe object, color the points by snow depth value
ax = df.plot(column='value', legend=True, cmap='PuBu')
site_df.plot(ax=ax, marker='^', color='m')

# Use non-scientific notation for x and y ticks
ax.ticklabel_format(style='plain', useOffset=False)

# Set the various plots x/y labels and title.
ax.set_title(f'{len(df.index)} Manual Snow depths collected at {site_id}')
ax.set_xlabel('Easting [m]')
ax.set_ylabel('Northing [m]')

Text(121.05281821225769, 0.5, 'Northing [m]')
../../_images/190c817b8122e2982d3d59a35b54bc3fa9a78006de30461467feb19fdfb3c92e.png

Try This:

A. Go back and add a filter to reduce to just one spiral. What would you change to reduce this?

B. Try to filtering to add more spirals. What happens?

Recap#

You just plotted snow depths and reduce the scope of the data by using from_area on it

You should know:

  • Manual depths are neat.

  • filter using from area is pretty slick.

  • We can use LayerMeasurements to get site details easily.

If you don’t feel comfortable with these, you are probably not alone, let’s discuss it!

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