Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

remove hardcoding from std_ndvi_vals() #50

Merged
merged 2 commits into from
Oct 18, 2023
Merged

remove hardcoding from std_ndvi_vals() #50

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
a265e47
remove hardcoding from std_ndvi_vals()
ericgitonga Oct 16, 2023
99b4a1f
remove hardcoding from std_ndvi_vals()
ericgitonga Oct 16, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
31 changes: 20 additions & 11 deletions ecoscope/analysis/seasons.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,23 +20,32 @@ def _min_max_scaler(x):
return x_std


def std_ndvi_vals(aoi=None, start=None, end=None):
coll = ee.ImageCollection("MODIS/MCD43A4_006_NDVI").select("NDVI").filterDate(start, end)

ndvi_vals = (
coll.toBands()
.reduceRegion("mean", ee.Feature(shapely.geometry.mapping(aoi)).geometry(), bestEffort=True)
.values()
.getInfo()
def std_ndvi_vals(aoi=None, img_coll=None, band=None, img_scale=1, start=None, end=None):

coll = (
ee.ImageCollection(img_coll)
.select(band)
.filterDate(start, end)
.map(lambda x: x.multiply(ee.Image(img_scale)).set("system:time_start", x.get("system:time_start")))
)
ndvi_vals_std = _min_max_scaler(ndvi_vals)
return pd.DataFrame(

if aoi:
geo = ee.Feature(shapely.geometry.mapping(aoi)).geometry()
else:
geo = None

ndvi_vals = coll.toBands().reduceRegion("mean", geo, bestEffort=True).values().getInfo()

df = pd.DataFrame(
{
"img_date": pd.to_datetime(coll.aggregate_array("system:time_start").getInfo(), unit="ms", utc=True),
"NDVI": ndvi_vals_std,
"NDVI": ndvi_vals,
}
).dropna(axis=0)

df["NDVI"] = _min_max_scaler(df["NDVI"])
return df


def val_cuts(vals, num_seasons=2):
distr = sklearn.mixture.GaussianMixture(n_components=num_seasons, max_iter=500)
Expand Down
2 changes: 1 addition & 1 deletion ecoscope/version.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1 +1 @@
__version__ = "1.4.4"
__version__ = "1.4.5"
26 changes: 22 additions & 4 deletions notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@
"source": [
"ECOSCOPE_RAW = \"https://raw.githubusercontent.com/wildlife-dynamics/ecoscope/master\"\n",
"\n",
"# !pip install ecoscope"
"%pip install ecoscope &> /dev/null"
]
},
{
Expand Down Expand Up @@ -154,8 +154,26 @@
"metadata": {},
"outputs": [],
"source": [
"# Extract the standardized NDVI ndvi_vals within the AOI\n",
"ndvi_vals = seasons.std_ndvi_vals(aoi, start=\"2010-01-01\", end=\"2022-06-18\")\n",
"img_coll = \"MODIS/061/MOD13A1\"\n",
"band = \"NDVI\"\n",
"img_scale = 0.0001\n",
"since_filter = \"2010-01-01\"\n",
"until_filter = \"2022-06-18\"\n",
"\n",
"ndvi_vals = seasons.std_ndvi_vals(\n",
" img_coll=img_coll,\n",
" band=band,\n",
" img_scale=img_scale,\n",
" aoi=aoi,\n",
" start=since_filter,\n",
" end=until_filter,\n",
")\n",
"\n",
"# Calculate the seasonal transition point\n",
"cuts = seasons.val_cuts(ndvi_vals, 2)\n",
"\n",
"# Determine the seasonal time windows\n",
"windows = seasons.seasonal_windows(ndvi_vals, cuts, season_labels=[\"dry\", \"wet\"])\n",
"\n",
"# Calculate the seasonal transition point\n",
"cuts = seasons.val_cuts(ndvi_vals, 2)\n",
Expand Down Expand Up @@ -247,7 +265,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.8.10"
}
},
"nbformat": 4,
Expand Down
Loading