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Tech 182 python sdk users want an example with historic monthly data #37

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alex-solcast merged 10 commits into from
Jul 4, 2024
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Tech 182 python sdk users want an example with historic monthly data #37

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cfcd1d6
TECH-182 - Added historic_multi_month initially to allow a csv to be …
billy-solcast Jun 26, 2024
3aee978
TECH-182 - Updated to include startdate within the location data file.
billy-solcast Jun 26, 2024
8f0a223
TECH-182 - Added the location data file within the folder. This is on…
billy-solcast Jun 26, 2024
2b6c745
TECH-182 - Updated the example page for historic.md, but without exam…
Jun 28, 2024
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Delete solcast/historic_multi_month.py
billy-solcast Jul 1, 2024
967c73d
TECH-182 - Delete solcast/location_data.csv
billy-solcast Jul 1, 2024
45ffb9e
Updated the historic examples based on the comments from Michael and …
Jul 3, 2024
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Merge branch 'TECH-182-python-sdk-users-want-an-example-with-historic…
Jul 3, 2024
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TECH-182 - Updated the comment headers so that it reflects github's h…
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TECH-182
Jul 3, 2024
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32 changes: 31 additions & 1 deletion docs/historic.md
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Original file line number Diff line number Diff line change
Expand Up @@ -30,4 +30,34 @@ res.to_pandas().head()
| 2022-06-01 07:00:00+00:00 | 13 | 62 | 12 |
| 2022-06-01 07:30:00+00:00 | 13 | 0 | 0 |
| 2022-06-01 08:00:00+00:00 | 12 | 0 | 0 |
| 2022-06-01 08:30:00+00:00 | 12 | 0 | 0 |
| 2022-06-01 08:30:00+00:00 | 12 | 0 | 0 |


## Example of multi period request for the year of 2023 from Jan 01
### The below code is using an unmetered location. If using a metered location, it will consume 12 request.

```python
from solcast import historic
import pandas as pd
from solcast.unmetered_locations import UNMETERED_LOCATIONS
from solcast import historic

site = UNMETERED_LOCATIONS["Stonehenge"]
latitude, longitude = site["latitude"], site["longitude"]

data = []
start_date = '2023-01-01'
start_dates = pd.date_range(start=start_date, periods=12, freq='MS')

for start in start_dates:
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@michaelf-solcast michaelf-solcast Jul 4, 2024

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What I liked about your previous implementation with while loop, is that it did not require the user to think up front about how the queries should be chunked.

The following version of the former while code will only get the exact number of days required (including getting all of the final day).

step = 31
while days < days_required:
    chunk_start = start + pd.DateOffset(days=days)
    duration_days = min(step, days_required - days + 1)
    res = historic.radiation_and_weather(latitude=-33.856784, longitude=151.215297, start=chunk_start, duration=f'P{duration_days}D')
    responses.append(res)
    days += step

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@billy-solcast billy-solcast Jul 25, 2024

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This was updated as a solution to @Zempire's comment:
#37 (review)

I think it's a cleaner way to pull the data, if the final objective is to retrieve only one years worth of data.

start_str = start.strftime('%Y-%m-%dT00:00:00.000Z')
end_date = (start + pd.offsets.MonthEnd(1)).strftime('%Y-%m-%dT23:59:59.000Z')

res = historic.radiation_and_weather(latitude=latitude, longitude=longitude, start=start_str, end=end_date)
if res.success:
data.append(res.to_pandas())
else:
print(res.exception)

output = pd.concat(data)
```