geeViz

https://geeviz.org
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geeViz is a powerful, open source Python toolkit for working with geospatial data in Google Earth...
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### Setup for geeViz outputLib.thumbs Examples

Source: https://geeviz.org/notebooks/thumbLib_examples

Imports necessary libraries and sets up the output directory for geeViz examples. Ensure the output directory exists before running.

```python
import geeViz.geeView as gv
import geeViz.getImagesLib as gil
from geeViz.outputLib import thumbs as tl
import geeViz.getSummaryAreasLib as sal
from IPython.display import Image, display, HTML
import os

ee = gv.ee

# Output directory
output_dir = os.path.join(os.path.dirname(os.path.abspath("__file__")), "outputs", "thumbLib_examples")
os.makedirs(output_dir, exist_ok=True)
print(f"Output dir: {output_dir}")


```

--------------------------------

### List and Get geeViz Examples

Source: https://geeviz.org/mcp

Access geeViz examples using `examples`. `action="list"` displays descriptions for all available examples, while `action="get"` retrieves the full source code for a specified example name.

```python
examples(action="list")
```

```python
examples(action="get", name="...")
```

--------------------------------

### Setup and Import geeViz Modules

Source: https://geeviz.org/notebooks/lcmsViewerExampleNotebook

This snippet handles the necessary setup for using the geeViz library, including installing it if not already present and importing core modules. Ensure this runs before utilizing other geeViz functionalities.

```python
#Boiler plate
#Import modules
import os,sys
sys.path.append(os.getcwd())


try:
    from  geeViz.geeView import *
except:
    !python -m pip install geeViz
    from  geeViz.geeView import *


print('Done')
```

--------------------------------

### Import Libraries and Setup

Source: https://geeviz.org/notebooks/LANDTRENDRWrapperNotebook

Imports necessary libraries for Earth Engine operations and sets up the environment. Includes a fallback to install 'geeViz' if it's not already present.

```python
#Example of how to get Landsat data using the getImagesLib, create median composites, run LandTrendr and then filter 
#LandTrendr output into usable data depicting where, when, and the magnitude of loss and gain
####################################################################################################
import os,sys
sys.path.append(os.getcwd())

#Module imports
try:
    import geeViz.getImagesLib as gil
except:
    !python -m pip install geeViz
    import geeViz.getImagesLib as gil


import geeViz.changeDetectionLib as cdl
ee = gil.ee

#Set up to mapper objects to use
#Can use the default one first
Map = gil.Map


print('done')

```

--------------------------------

### Explicit Multi-Credential Workflow Setup

Source: https://geeviz.org/_modules/geeViz/eeAuth/eeCreds

Register multiple credentials by path or string, start the proxy, and switch between them using eeCreds.addCreds() and eeCreds.use().

```python
from geeViz.eeAuth import eeCreds
import ee

eeCreds.addCreds("path/to/sa-prod.json", name="prod")
eeCreds.addCreds(b64_sa_string,          name="acme")
eeCreds.addCreds("~/.config/earthengine/credentials", name="ian")  # OAuth
eeCreds.start()                # initializes ee + spawns local proxy
```

--------------------------------

### List Example Files

Source: https://geeviz.org/_modules/geeViz/mcp/server

Returns a sorted list of Python (.py) and Jupyter Notebook (.ipynb) files from the examples directory, excluding __init__.py. Handles cases where the examples directory does not exist.

```python
def _list_example_files():
    """Return sorted list of example filenames."""
    if not os.path.isdir(_EXAMPLES_DIR):
        return []
    return sorted(f for f in os.listdir(_EXAMPLES_DIR)
                  if (f.endswith(".py") or f.endswith(".ipynb")) and f != "__init__.py")
```

--------------------------------

### Setup and Import geeViz

Source: https://geeviz.org/notebooks/Annual_NLCD_Viewer_Notebook

Imports necessary libraries and installs geeViz if not already present. Ensures the environment is ready for Earth Engine operations.

```python
import os,sys
sys.path.append(os.getcwd())

#Module imports
try:
    import geeViz.geeView as gv
except:
    !python -m pip install geeViz
    import geeViz.geeView as gv



ee = gv.ee
Map = gv.Map

print('Done')

```

--------------------------------

### GEE Data Loading and Directory Setup (Python)

Source: https://geeviz.org/_modules/geeViz/gee2Pandas

Example of setting up an output directory and loading Earth Engine ImageCollections for testing purposes. This code is typically run within an `if __name__ == "__main__":` block.

```python
import os
output_dir = r"C:\\tmp\\geeToPandasTest"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
pt = ee.Geometry.Point([-65.8491, 18.2233])
comps = ee.ImageCollection("projects/rcr-gee/assets/lcms-training/lcms-training_module-2_composites")
lt = ee.ImageCollection("projects/rcr-gee/assets/lcms-training/lcms-training_module-3_landTrendr")
```

--------------------------------

### Install geeViz MCP SDK

Source: https://geeviz.org/mcp

Install the geeViz package, which includes the MCP SDK as a dependency. This is the initial step for setting up the MCP server.

```bash
$ pip install geeViz

```

--------------------------------

### List geeViz Example Scripts via MCP

Source: https://geeviz.org/mcp

Example of an AI query to the MCP server to list geeViz example scripts filtered by a keyword. This demonstrates the `list_examples` tool usage.

```text
"List the geeViz example scripts that involve LANDTRENDR"

```

--------------------------------

### Quick Start: Search, Inspect, and Query EDW Services

Source: https://geeviz.org/_modules/geeViz/edwLib

Demonstrates the basic usage of the edwLib for searching services, getting service information, and querying features. Loaded into Earth Engine.

```python
import geeViz.edwLib as edw

# Search for fire-related services
services = edw.search_services("fire")

# Get layer info
info = edw.get_service_info("EDW_MTBS_01")

# Query features as GeoJSON
geojson = edw.query_features("EDW_MTBS_01", 15,
    where="FIRE_NAME LIKE '%CAMERON PEAK%'",
    out_fields="FIRE_NAME,ACRES,YEAR")

# Load into Earth Engine
import ee
fc = ee.FeatureCollection(geojson)
```

--------------------------------

### Import and Setup geeViz Modules

Source: https://geeviz.org/_modules/geeViz/foliumView

This snippet shows the necessary imports and setup for using the geeViz package, including Earth Engine initialization and defining file paths for visualization outputs.

```python
# Script to allow GEE objects to be viewed in folium
# Adapted from: https://colab.research.google.com/github/giswqs/qgis-earthengine-examples/blob/master/Folium/ee-api-folium-setup.ipynb
# Intended to work within the geeViz package
######################################################################
# Import modules
import geeViz.geeView as geeView
import os, sys, folium, json, numpy
from folium import plugins
from IPython.display import display, HTML

# Set up GEE and paths
ee = geeView.ee
```

--------------------------------

### Main Function and Example Call

Source: https://geeviz.org/_modules/geeViz/mcp/server

The main entry point for the script, which includes a commented-out example of inspecting an asset and then calls the main application logic.

```python
if __name__ == "__main__":
    # print(inspect_asset("COPERNICUS/S2_SR_HARMONIZED"))
    main()
# %%

```

--------------------------------

### foliumMapper Example

Source: https://geeviz.org/info/geeViz.foliumView.html

Example demonstrating how to initialize foliumMapper, set the map center, add a Landsat layer, and view the map.

```APIDOC
from geeViz.foliumView import foliumMapper

# Initialize the mapper
mapper = foliumMapper()

# Set the map center
mapper.setCenter(-122.4194, 37.7749, 10)

# Add a layer (example assumes you have an ee.Image object)
mapper.addLayer(ee.Image("LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318"),
                {"bands": ["B4", "B3", "B2"], "min": 0, "max": 3000},
                "Landsat Image")

# View the map
mapper.view()
```

--------------------------------

### Install and Import geeViz

Source: https://geeviz.org/notebooks/Aboveground_Biomass_Viewer_Notebook

Installs the geeViz library if not already present and imports necessary modules. This is a prerequisite for using the viewer functionalities.

```python
try:
    import  geeViz.geeView as geeView
except:
    !python -m pip install geeViz
    import  geeViz.geeView as geeView

import geeViz.geePalettes as palettes
ee = geeView.ee
Map = geeView.Map

print('done')
```

--------------------------------

### Install geeViz with Optional Extras

Source: https://geeviz.org/installation

Use pip to install geeViz with specific optional features. Use '[gemini]' for AI, '[segmentation]' for image segmentation, or '[all]' for all available extras.

```bash
$ pip install geeViz[gemini]         # Adds google-genai for AI features

```

```bash
$ pip install geeViz[segmentation]   # Adds torch + transformers for SegFormer

```

```bash
$ pip install geeViz[all]            # Everything

```

--------------------------------

### Start Earth Engine Credentials Service

Source: https://geeviz.org/_modules/geeViz/eeAuth/eeCreds

Starts the Earth Engine credentials service. It handles proxy launching and Earth Engine initialization based on provided flags. Calling start() multiple times is safe and returns the current state.

```python
with self._lock:
            if self._started:
                return self._status()

            if not self._entries:
                raise RuntimeError(
                    "eeCreds.start(): no credentials registered — "
                    "call addCreds(...) first"
                )

            if launch_proxy:
                self._launch_proxy(proxy_host, proxy_port)

            if ee_init:
                proxy_url = self._proxy_url or ""
                if proxy_url:
                    # Pass the first registered credential's project so EE
                    # builds API URLs like ``projects/<real>/value:compute``
                    # instead of the placeholder. The proxy then doesn't have
                    # to rewrite paths — all tenants in a single eeCreds
                    # instance share a process-wide ee.Initialize, and EE
                    # rejects the placeholder string at the path level.
                    first = next(iter(self._entries.values()))
                    initialize_via_proxy(
                        proxy_url, project=first.project_id or None,
                    )
                else:
                    # No proxy → direct ee.Initialize with the FIRST creds
                    # (single-tenant mode; .use() will then raise unless
                    # the user re-initializes).
                    first = next(iter(self._entries.values()))
                    self._direct_init(first)

            self._started = True
            return self._status()
```

--------------------------------

### geeViz.examples module

Source: https://geeviz.org/genindex

Module containing examples for geeViz.

```APIDOC
## geeViz.examples

### Description
Module containing examples for geeViz.
```

--------------------------------

### Install geeViz Package

Source: https://geeviz.org/installation

Install the geeViz package using pip after the Earth Engine API is set up.

```bash
$ pip install geeViz
```

--------------------------------

### Index geeViz Modules and Examples

Source: https://geeviz.org/_modules/geeViz/mcp/server

Walks through the geeViz package to index modules and examples using AST parsing and JSON parsing for notebooks. It populates a module tree and map, and prints a summary of indexed items. Modules are imported on-demand.

```python
import pkgutil
import geeViz

tree = {}
fq_map = {}

for importer, modname, ispkg in pkgutil.walk_packages(
    geeViz.__path__, prefix="geeViz."
):
    # Skip excluded packages
    if any(modname == skip or modname.startswith(skip + ".") for skip in _SKIP_PACKAGES):
        continue
    # Skip private modules
    leaf = modname.rsplit(".", 1)[-1]
    if leaf.startswith("_"):
        continue

    # Find the source file without importing
    try:
        spec = importlib.util.find_spec(modname)
        if spec is None or spec.origin is None:
            continue
    except (ModuleNotFoundError, ValueError):
        continue

    # Parse with AST
    members, module_doc = _ast_extract_members(spec.origin)
    first_line = module_doc.split("\n")[0].strip()[:100] if module_doc else ""

    short = leaf
    entry = {"fq": modname, "mod": None, "file": spec.origin,
             "members": members, "doc": first_line}
    tree[modname] = entry
    if short not in tree:
        tree[short] = entry
    fq_map[short] = modname
    fq_map[modname] = modname

# --- Index examples (AST parse .py, JSON parse .ipynb) ---
example_members = []
for fname in _list_example_files():
    fpath = os.path.join(_EXAMPLES_DIR, fname)
    base = fname.rsplit(".", 1)[0]
    desc = ""
    if fname.endswith(".py"):
        try:
            with open(fpath, "r", encoding="utf-8") as f:
                source = f.read()
            try:
                ex_tree = ast.parse(source)
                doc = ast.get_docstring(ex_tree) or ""
                desc = doc.split("\n")[0].strip()[:100] if doc else ""
            except SyntaxError:
                pass
            if not desc:
                for line in source.split("\n")[:20]:
                    s = line.strip()
                    if s.startswith("#") and len(s) > 2:
                        desc = s.lstrip("#").strip()
                        break
        except Exception:
            pass
    elif fname.endswith(".ipynb"):
        try:
            with open(fpath, "r", encoding="utf-8") as f:
                nb = json.load(f)
            for cell in nb.get("cells", []):
                if cell.get("cell_type") == "markdown":
                    src = "".join(cell.get("source", [])).strip()
                    if src:
                        desc = src.split("\n")[0].lstrip("#").strip()[:100]
                        break
        except Exception:
            pass
    example_members.append({"name": base, "type": "example", "description": desc or fname, "file": fname})

tree["examples"] = {
    "fq": "geeViz.examples", "mod": None, "file": _EXAMPLES_DIR,
    "members": example_members, "doc": "geeViz example scripts and notebooks",
}
fq_map["examples"] = "geeViz.examples"

_MODULE_TREE = tree
_MODULE_MAP = fq_map
n_mods = len(set(e["fq"] for e in tree.values()))
n_examples = len(example_members)
print(f"[geeViz MCP] Module tree: {n_mods} modules, {n_examples} examples indexed (zero imports)")
```

--------------------------------

### Full example: Single-section land cover report

Source: https://geeviz.org/notebooks/report_generation_examples

This comprehensive example demonstrates creating a single-section report for land cover in Salt Lake County. It includes data filtering, report initialization, section addition, and report generation.

```python
# Example 1: Single-section report — LCMS Land Cover in Salt Lake County
study_area_1 = sal.getUSCounties(ee.Geometry.Point([-111.89, 40.76]))
print("County:", study_area_1.aggregate_array("NAMELSAD").getInfo())

lcms = ee.ImageCollection("USFS/GTAC/LCMS/v2024-10")
lcms_2024 = lcms.filter(ee.Filter.calendarRange(2024, 2024, "year")) \
    .filterBounds(study_area_1).mosaic().copyProperties(lcms.first())

report1 = rl.Report(
    title="Salt Lake County Land Cover",
    theme="dark",
    tone="informative",
    header_text="Current land cover from USFS LCMS v2024-10.",
)
report1.add_section(
    ee_obj=lcms_2024,
    geometry=study_area_1,
    title="LCMS Land Cover (2024)",
    band_names=["Land_ Cover"],
    scale=60,
    basemap="esri-satellite",
    burn_in_geometry=True,
    geometry_outline_color="white",
)

out_path = os.path.join(output_dir, "ex1_salt_lake_county.html")
report1.generate(format="html", output_path=out_path)
print(f"\nReport saved to: {out_path}")
print(report1.metadata().to_markdown(index=False))
show_report(out_path)
```

--------------------------------

### Example: Land Cover Comparison with Grouped Bar Chart

Source: https://geeviz.org/_modules/geeViz/outputLib/charts

Demonstrates using `summarize_and_chart` to compare land cover across multiple fire perimeters. This example showcases setting a title, stacking bars, and specifying chart dimensions.

```python
import geeViz.geeView as gv
from geeViz.outputLib import charts as cl
ee = gv.ee
lcms = ee.ImageCollection("USFS/GTAC/LCMS/v2024-10")
fires = ee.FeatureCollection(
    "USFS/GTAC/MTBS/burned_area_boundaries/v1"
).sort("BurnBndAc", False).limit(5)
lc_mode = lcms.select(["Land_Cover"]).mode().set(
    lcms.first().toDictionary()
)
# summarize_and_chart handles reduceRegions + grouped bar:
df, fig = cl.summarize_and_chart(
    lc_mode, fires,
    feature_label="Incid_Name",
    title="Land Cover — 5 Largest Fires",
    stacked=True, width=800,
)
fig.show()
```

--------------------------------

### Get Precomputed Cloud Score Offsets

Source: https://geeviz.org/_modules/geeViz/getImagesLib

Retrieves precomputed cloud score offsets for Landsat and Sentinel-2. Use this to get ee.Image objects representing cloud score offsets for specific percentile values.

```python
offsets = getPrecomputedCloudScoreOffsets(10)
landsat_offset = offsets["landsat"]
sentinel2_offset = offsets["sentinel2"]
```

--------------------------------

### Start Export Thread

Source: https://geeviz.org/_modules/geeViz/phEEnoViz

Starts an export process in a new thread to allow for parallel processing. Includes optional visualization setup for Earth Engine.

```python
tt = threading.Thread(target=getTableWrapper, args=(stack, randomSample, output_table_nameT))
tt.start()
time.sleep(0.1)

# Set thread limit depending on whether visualization is shown
threadLimit = 1
if showGEEViz:
    # Visualize the study area and samples in geeViz
    Map.addLayer(studyArea, {"strokeColor": "00F"}, "Study Area")
    Map.centerObject(studyArea)
    Map.view()
    threadLimit = 2
limitThreads(threadLimit)
```

--------------------------------

### Verify geeViz Installation

Source: https://geeviz.org/installation

Run this Python code in a shell to confirm that Earth Engine is initialized and geeViz is installed correctly. No errors indicate a successful setup.

```python
import ee
import geeViz

# Initialize Earth Engine
ee.Initialize(project='your-project-id')

# Test basic functionality
print(f"geeViz version: {geeViz.__version__}")
print("Earth Engine initialized successfully!")

```

--------------------------------

### Running geeViz MCP Server Examples

Source: https://geeviz.org/_modules/geeViz/mcp/server

Demonstrates different ways to run the geeViz MCP server using command-line arguments and environment variables to control transport and sandbox behavior.

```bash
python -m geeViz.mcp.server                   # stdio, no sandbox (default)
python -m geeViz.mcp.server --sandbox          # stdio with sandbox
MCP_TRANSPORT=streamable-http python -m geeViz.mcp.server  # HTTP, auto-sandbox
python -m geeViz.mcp --help
```

--------------------------------

### timeTaskList

Source: https://geeviz.org/info/geeViz.taskManagerLib.html

Get list of tasks started within a specified time interval.

```APIDOC
## timeTaskList

### Description
Get list of tasks started within a specified time interval.

### Method
APIDOC

### Endpoint
APIDOC

### Parameters
#### Path Parameters
- **starttime** (string) - Required - The start time of the interval.
- **endtime** (string) - Required - The end time of the interval.

### Request Example
APIDOC

### Response
APIDOC
```

--------------------------------

### Get Sentinel-2 Image Collection

Source: https://geeviz.org/_modules/geeViz/getImagesLib

Filters a Sentinel-2 image collection by date, Julian day, and bounds, then maps a function to it and selects specific bands. Imports are assumed to be handled elsewhere.

```python
print("Using S2 Collection:", s2CollectionDict[toaOrSR])
s2s = (
    ee.ImageCollection(s2CollectionDict[toaOrSR])
    .filterDate(startDate, endDate.advance(1, "day"))
    .filter(ee.Filter.calendarRange(startJulian, endJulian))
    .filterBounds(studyArea)
    .map(multS2)
    .select(["QA60"] + sensorBandDict[toaOrSR], ["QA60"] + sensorBandNameDict[toaOrSR])
)
```

--------------------------------

### AI-assisted LANDTRENDR Change Detection Workflow

Source: https://geeviz.org/mcp

This example shows the sequence of AI calls to perform LANDTRENDR change detection. It includes listing examples, getting a specific example wrapper, searching for a geeViz function, and running Python code for analysis and map display.

```text
User: "Do LANDTRENDR change detection near Bozeman and show me the results"

AI calls: list_examples(filter="LANDTRENDR")
AI calls: get_example("LANDTRENDRWrapper")
AI calls: search_geeviz(name="simpleLANDTRENDR")
AI calls: run_code("""
    import geeViz.changeDetectionLib as cdl
    studyArea = ee.Geometry.Point([-111.04, 45.68]).buffer(20000)
    ...
""")
AI calls: run_code("Map.centerObject(studyArea)")
AI calls: map_control(action="view")

AI responds: "Here's your LANDTRENDR analysis. The map has been rendered
and opened in the browser, and the script has been saved to
geeViz/mcp/generated_scripts/session_20260226_143022.py"
```

--------------------------------

### Get UTM EPSG Code for Location and Datum

Source: https://geeviz.org/_modules/geeViz/getImagesLib

Returns the EPSG code string for a UTM zone based on geographic coordinates and a specified datum. Handles both list/tuple coordinates and ee.Geometry.Point objects. Raises ValueError for unsupported datums or location types.

```python
def getUTMEpsg(location, datum: str = "WGS84") -> str:
    """Return the EPSG code string for a UTM zone given a location and datum.

    Combines :func:`getUTMZone` with a datum lookup to produce the full
    EPSG code (e.g. ``"EPSG:32612"`` for WGS84 UTM Zone 12N).

    Args:
        location: One of:
            - ``[longitude, latitude]`` list/tuple (GEE convention: lon first).
            - ``ee.Geometry.Point`` — coordinates are extracted via ``.getInfo()``.
        datum: Datum name. One of ``"WGS84"`` (default), ``"NAD83"``, ``"NAD27"``,
            ``"WGS72"``, ``"ETRS89"``, ``"GDA94"``, ``"GDA2020"``, ``"SIRGAS2000"``.
            Case-insensitive.

    Returns:
        EPSG code string, e.g. ``"EPSG:32612"``.

    Raises:
        ValueError: If the datum is not recognized or ``location`` is not a
            supported type.

    Examples:
        >>> getUTMEpsg([-113.15, 47.15])
        'EPSG:32612'
        >>> getUTMEpsg([-113.15, 47.15], datum="NAD83")
        'EPSG:26912'
        >>> getUTMEpsg([151.21, -33.86])
        'EPSG:32756'
        >>> getUTMEpsg(ee.Geometry.Point([-113.15, 47.15]))
        'EPSG:32612'
    """
    # Parse location into (longitude, latitude)
    if isinstance(location, ee.Geometry):
        coords = location.coordinates().getInfo()
        longitude, latitude = coords[0], coords[1]
    elif isinstance(location, (list, tuple)) and len(location) >= 2:
        longitude, latitude = location[0], location[1]
    else:
        raise ValueError(
            f"location must be a [lon, lat] list/tuple or ee.Geometry.Point, "
            f"got {type(location).__name__}"
        )

    datum_upper = datum.upper().replace(" ", "").replace("-", "")
    if datum_upper not in _UTM_DATUM_EPSG:
        raise ValueError(
            f"Unknown datum '{datum}'. Supported: {', '.join(sorted(_UTM_DATUM_EPSG.keys()))}"
        )
    north_prefix, south_prefix = _UTM_DATUM_EPSG[datum_upper]
    zone = getUTMZone(longitude)
    prefix = north_prefix if latitude >= 0 else south_prefix
    return f"EPSG:{prefix}{zone:02d}"

```

--------------------------------

### Get US Counties - All US Counties

Source: https://geeviz.org/_modules/geeViz/getSummaryAreasLib

Example of retrieving all US counties by calling the function without any filters.

```python
getUSCounties()
```

--------------------------------

### Setup and API Key Check

Source: https://geeviz.org/notebooks/googleMapsLib_examples

Initializes the output directory and checks for the presence of the Google Maps API key.

```python
import geeViz.googleMapsLib as gm
from IPython.display import display, HTML, Image as IPImage, Markdown
import json, os, time

output_dir = os.path.join(os.path.dirname(os.getcwd()), "examples", "outputs", "google_maps")
os.makedirs(output_dir, exist_ok=True)
print(f"Output: {output_dir}")
print(f"Maps key: {'set' if gm._get_api_key() else 'MISSING'}")
```

--------------------------------

### Get Precomputed TDOM Statistics

Source: https://geeviz.org/_modules/geeViz/getImagesLib

Retrieves precomputed TDOM statistics (mean and standard deviation) for Landsat and Sentinel-2. These statistics are used in the TDOM cloud shadow masking algorithm.

```python
stats = getPrecomputedTDOMStats()
landsat_mean = stats["landsat"]["mean"]
sentinel2_stddev = stats["sentinel2"]["stdDev"]
```

--------------------------------

### Example: Get Yellowstone National Park

Source: https://geeviz.org/_modules/geeViz/getSummaryAreasLib

Demonstrates how to retrieve a specific protected area by its name using the getProtectedAreas function.

```python
yellowstone = sal.getProtectedAreas(name="Yellowstone")
```

--------------------------------

### Setup for geeViz Report Generation

Source: https://geeviz.org/notebooks/report_generation_examples

Imports necessary libraries and sets up the output directory. Includes a helper function to display generated reports inline.

```python
# Setup — shared by all examples
import os, time
import geeViz.geeView as gv
import geeViz.getImagesLib as gil
import geeViz.getSummaryAreasLib as sal
import geeViz.edwLib as edw
from geeViz.outputLib import reports as rl
from IPython.display import display, IFrame, HTML
ee = gv.ee

output_dir = os.path.join(os.path.dirname(os.getcwd()), "examples", "outputs", "reports")
os.makedirs(output_dir, exist_ok=True)

def show_report(path, height=800):
    """Display a generated HTML/PDF report inline in the notebook."""
    if path.endswith(".pdf"):
        rel = os.path.relpath(path, os.getcwd())
        display(HTML(f'<object data="{rel}" type="application/pdf" width="100%" height="{height}px">
                     <p>PDF cannot be displayed inline. <a href="{rel}">Download here</a>.</p></object>'))
    else:
        rel = os.path.relpath(path, os.getcwd())
        display(IFrame(src=rel, width="100%", height=height))

print(f"Reports will be saved to: {output_dir}")
print("Use show_report(path) after generate() to display the report inline.")

```

--------------------------------

### Get US Counties - All Montana Counties

Source: https://geeviz.org/_modules/geeViz/getSummaryAreasLib

Example of retrieving all counties within a specific state using the state abbreviation filter.

```python
getUSCounties(state_abbr='MT')
```

--------------------------------

### Get US Counties - Intersecting Area and State Filter

Source: https://geeviz.org/_modules/geeViz/getSummaryAreasLib

Example of filtering counties that intersect a given area and are within a specific state.

```python
getUSCounties(area=my_point, state_abbr='CO')
```

--------------------------------

### Initialize and Configure a Report

Source: https://geeviz.org/_modules/geeViz/outputLib/reports

Demonstrates how to create a Report object, setting the title, theme, and layout. This is the starting point for generating any report.

```Python
import geeViz.geeView as gv
from geeViz.outputLib import reports as rl

ee = gv.ee
report = rl.Report(
    title="Wasatch Front Assessment",
    theme="dark",
    layout="poster",       # landscape multi-column
)
report.header_text = "An analysis of land cover and fire trends."

report.add_section(
    ee_obj=lcms.select(['Land_Cover']),
    geometry=counties,
    title="LCMS Land Cover",
    stacked=True,
    scale=60,
)

# PDF with static chart images
report.generate(format="pdf", output_path="report.pdf")

# HTML (interactive charts)
report.generate(format="html", output_path="report.html")
```

--------------------------------

### Get US Counties - Specific Counties in a State

Source: https://geeviz.org/_modules/geeViz/getSummaryAreasLib

Example of filtering counties by name within a specific state, using both state abbreviation and county name filters.

```python
getUSCounties(state_abbr='MT', county_names='Missoula,Ravalli')
```

--------------------------------

### Instantiate EECreds and Add Credentials

Source: https://geeviz.org/info/geeViz.eeAuth.eeCreds.html

Demonstrates how to create an instance of EECreds, add credentials, and initiate the Earth Engine startup process. This is useful for managing multiple independent credential sets.

```python
from geeViz.eeAuth.eeCreds import EECreds
creds = EECreds()
creds.addCreds(...)
creds.start()
```

--------------------------------

### annualizeCCDC

Source: https://geeviz.org/_modules/geeViz/changeDetectionLib

Function to get yearly CCDC coefficients. It calculates CCDC coefficients for each year, allowing for customization of the year start month/day and the use of composite dates for annualization.

```APIDOC
## annualizeCCDC

### Description
Functions to get yearly ccdc coefficients. Get CCDC coefficients for each year. yearStartMonth and yearStartDay are the date that you want the CCDC "year" to start at. This is mostly important for Annualized CCDC. For CONUS & COASTAL AK LCMS, this is Sept. 1. So any change that occurs before Sept 1 in that year will be counted in that year, and Sept. 1 and after will be counted in the following year. 10/21 LSC Added Capability to use pixel-wise Composite Dates instead of set dates. If used, set annualizeWithCompositeDates to True and provide imported/prepped composite image collection with 'year' and 'julianDay' bands. Optionally, if there are holes in the composite dates, they can be interpolated using linear interpolation across time.

### Parameters
- **ccdcImg** (ee.Image) - The CCDC image.
- **startYear** (integer) - The starting year for annualization.
- **endYear** (integer) - The ending year for annualization.
- **startJulian** (integer) - The Julian day to start the annualization period.
- **endJulian** (integer) - The Julian day to end the annualization period.
- **tEndExtrapolationPeriod** (float) - A period to extrapolate the last tEnd segment by (fraction of a year).
- **yearStartMonth** (integer, optional) - The month to start the CCDC year (default is 9).
- **yearStartDay** (integer, optional) - The day to start the CCDC year (default is 1).
- **annualizeWithCompositeDates** (boolean, optional) - If true, use composite dates from a provided collection (default is False).
- **compositeCollection** (ee.ImageCollection, optional) - An image collection of composites with 'year' and 'julianDay' bands, used if `annualizeWithCompositeDates` is True.
- **interpolateCompositeDates** (boolean, optional) - If true, interpolate missing composite dates (default is True).

### Returns
ee.ImageCollection - An image collection of annual CCDC segment coefficients.
```

--------------------------------

### run_local_server

Source: https://geeviz.org/genindex

Starts a local server using geeViz.geeView.

```APIDOC
## run_local_server()

### Description
Starts a local server for development or testing.

### Method
N/A (Function call)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A
```

--------------------------------

### Example: Get US National Parks in an Area

Source: https://geeviz.org/_modules/geeViz/getSummaryAreasLib

Shows how to filter protected areas to find US national parks (IUCN category II) that intersect with a defined area of interest.

```python
parks = sal.getProtectedAreas(area=my_aoi, iucn_cat="II")
```

--------------------------------

### Get US Counties - Counties by Name Across States

Source: https://geeviz.org/_modules/geeViz/getSummaryAreasLib

Example of retrieving counties by name, without specifying a state. Note that county names may exist in multiple states.

```python
getUSCounties(county_names='Washington')
```

--------------------------------

### Get USFS Forests and Land Cover Data

Source: https://geeviz.org/notebooks/report_generation_examples

Retrieves USFS forest boundaries within a specified region and selects the Land Cover band from the LCMS collection. This is a setup step for generating reports on forest land cover.

```python
# In run_code:
forests = sal.getUSFSForests(ee.Geometry.Point([-114, 46.5]).buffer(300000), region='01')
lcms_lc = ee.ImageCollection('USFS/GTAC/LCMS/v2024-10').select(['Land_Cover'])
```

--------------------------------

### Get GeeViz Theme Presets and Custom Themes

Source: https://geeviz.org/_modules/geeViz/outputLib/themes

Demonstrates how to obtain predefined themes like 'dark', 'light', and 'teal', or generate custom themes by specifying background and font colors. It also shows how to access color values in different formats (hex, RGB, RGBA) and check if a theme is dark.

```python
from geeViz.outputLib.themes import get_theme

# Named presets
dark  = get_theme("dark")
light = get_theme("light")
teal  = get_theme("teal")

# Auto-generate from a single color
red_bg = get_theme(bg_color="#F00")           # dark text auto-picked
custom = get_theme(bg_color="#1a1a2e", font_color="#eee")

# Access colors in different formats
dark.bg_hex          # '#272822'
dark.bg_rgb          # (39, 40, 34)
dark.text_hex        # '#f8f8f2'
dark.is_dark         # True
dark.grid_rgba       # 'rgba(248,248,242,0.15)'
```

--------------------------------

### Explicitly Register Service Account Credential

Source: https://geeviz.org/notebooks/eeAuthExamples

Resets the credential singleton, adds a specific service account JSON file, and starts the proxy. This is useful for explicitly controlling which credentials are used, especially in multi-tenant scenarios. This example is skipped if DEMO_SA_PATH is not configured.

```python
if not HAS_SA:
    print("Skipped (DEMO_SA_PATH not set).")
else:
    from geeViz.eeAuth import eeCreds
    import ee

    # Reset the singleton so we register from a clean slate.
    eeCreds.stop()
    eeCreds._entries.clear()

    eeCreds.addCreds(DEMO_SA_PATH, name="prod")
    eeCreds.start()

    # With only one credential, no `.use()` needed — it's the default.
    print(eeCreds.current(), "→", ee.Number(2).getInfo())
```

```text
[geeViz.eeAuth] EE initialized via proxy: http://127.0.0.1:8890/ee-api (tenant_header=X-geeViz-Creds)


```

```text
prod → 2


```

--------------------------------

### Get Tasks by Time Interval

Source: https://geeviz.org/_modules/geeViz/taskManagerLib

Retrieves a list of Earth Engine tasks that were created within a specified time interval. Requires 'datetime' objects for start and end times. The function converts the datetime objects to milliseconds since the epoch for comparison.

```python
def timeTaskList(starttime, endtime):
    """Get list of tasks started within a specified time interval.

    Args:
        starttime (datetime): UTC datetime object for interval start.
        endtime (datetime): UTC datetime object for interval end.

    Returns:
        list: List of task dicts started within the interval.

    Example:
        >>> from datetime import datetime, timedelta
        >>> start = datetime.utcnow() - timedelta(hours=1)
        >>> end = datetime.utcnow()
        >>> timeTaskList(start, end)
        [{'id': 'ABCD', ...}, ...]
    """
    tasks = ee.data.getTaskList()
    epoch = datetime.utcfromtimestamp(0)
    starttime = (starttime - epoch).total_seconds() * 1000.0  # Convert to ms since epoch
    endtime = (endtime - epoch).total_seconds() * 1000.0
    thisList = [i for i in tasks if (i["creation_timestamp_ms"] >= starttime and i["creation_timestamp_ms"] <= endtime)]
    return thisList
```

--------------------------------

### Initialize and View Folium Map with GEE Layers

Source: https://geeviz.org/_modules/geeViz/foliumView

This example demonstrates how to initialize a Folium map, set its center, add a Landsat image layer with specific visualization parameters, and finally view the map. Ensure you have an ee.Image object available.

```python
from geeViz.foliumView import foliumMapper

# Initialize the mapper
mapper = foliumMapper()

# Set the map center
mapper.setCenter(-122.4194, 37.7749, 10)

# Add a layer (example assumes you have an ee.Image object)
mapper.addLayer(ee.Image("LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318"), 
                    {"bands": ["B4", "B3", "B2"], "min": 0, "max": 3000}, 
                    "Landsat Image")

# View the map
mapper.view()
```

--------------------------------

### Handle GET Request

Source: https://geeviz.org/_modules/geeViz/geeView

Handles GET requests. If the request is for the Earth Engine API, it proxies the request; otherwise, it falls back to the default GET behavior of the base handler (likely serving static files).

```python
    # Override each HTTP verb so reverse-proxy fires for /ee-api/*; everything
    # else falls through to ``SimpleHTTPRequestHandler``'s static-file behavior.
    def do_GET(self):  # noqa: N802 - stdlib API
        if self._is_ee_api():
            return self._proxy_ee_api()
        return super().do_GET()
```

--------------------------------

### Instantiate and Add Credentials to EECreds

Source: https://geeviz.org/_modules/geeViz/eeAuth/eeCreds

Demonstrates how to create an instance of EECreds, add credentials using different methods, and start the credential management process. Useful for setting up independent credential registries.

```python
from geeViz.eeAuth.eeCreds import EECreds
creds = EECreds()
creds.addCreds(...) 
creds.start()
```

--------------------------------

### Run geeViz.eeAuth Server Standalone

Source: https://geeviz.org/info/geeViz.eeAuth.server

Starts the geeViz Earth Engine authentication proxy server as a standalone application. Use this for direct execution from the command line.

```python
python -m geeViz.eeAuth --port 8888

```

--------------------------------

### Import geeViz and Initialize Earth Engine

Source: https://geeviz.org/notebooks/getLandsatWrapperNotebook

Imports the necessary geeViz modules and initializes the Earth Engine library. Includes a fallback to install the library if it's not found.

```python
#Example of how to get Landsat data using the getImagesLib and view outputs using the Python visualization tools
#Acquires Landsat data and then adds them to the viewer
####################################################################################################
import os,sys
sys.path.append(os.getcwd())

#Module imports
try:
    import geeViz.getImagesLib as getImagesLib
except:
    !python -m pip install geeViz
    import geeViz.getImagesLib as getImagesLib


ee = getImagesLib.ee
Map = getImagesLib.Map

print('done')

```

--------------------------------

### Get and Use geeViz Themes

Source: https://geeviz.org/info/geeViz.outputLib.themes.html

Demonstrates how to retrieve predefined themes like 'dark', 'light', and 'teal', or generate custom themes from specific background and font colors. It also shows how to access color values in different formats (hex, RGB, RGBA) and check if a theme is dark.

```python
from geeViz.outputLib.themes import get_theme

# Named presets
dark  = get_theme("dark")
light = get_theme("light")
teal  = get_theme("teal")

# Auto-generate from a single color
red_bg = get_theme(bg_color="#F00")           # dark text auto-picked
custom = get_theme(bg_color="#1a1a2e", font_color="#eee")

# Access colors in different formats
dark.bg_hex          # '#272822'
dark.bg_rgb          # (39, 40, 34)
dark.text_hex        # '#f8f8f2'
dark.is_dark         # True
dark.grid_rgba       # 'rgba(248,248,242,0.15)'

```
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