import sys,unittest,importlib
import numpy as np
import geopandas as gpd
from shapely.geometry import Point,box
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# =====================================================================
# CORE LOGIC
# =====================================================================
def create_grid(data,grid_size):
    l_minx,l_miny,l_maxx,l_maxy=data.total_bounds
    x_coords=np.arange(l_minx-0.1,l_maxx+grid_size+0.1,grid_size)
    y_coords=np.arange(l_miny-0.1,l_maxy+grid_size+0.1,grid_size)
    grid_cells=[]
    for x in x_coords[:-1]:
        for y in y_coords[:-1]:
            grid_cells.append(box(x,y,x+grid_size,y+grid_size))
    grid=gpd.GeoDataFrame({"geometry":grid_cells},crs=data.crs)
    grid["grid_id"]=grid.index
    sj=gpd.sjoin(data,grid,how="left",predicate="intersects")
    return grid,sj

def aggregate_in_grid(sj):
    return sj.groupby("grid_id").size().reset_index(name="count")

def plot_grid(grid,agg,bmap):
    m=grid.merge(agg,on="grid_id",how="left").fillna({"count":0})
    fig,ax=plt.subplots(figsize=(6,6))
    bmap.plot(ax=ax,color="whitesmoke",edgecolor="darkgray")
    m.plot(column="count",ax=ax,cmap="YlGnBu",edgecolor="k")
    plt.close(fig) 

# =====================================================================
# SAFE DYNAMIC ENGINE (Ultra-compact)
# =====================================================================
def _dyn(tc,cond,s_msg,f_msg):
    c_suc=str(s_msg).replace(chr(10),chr(32)).strip()[:65]
    c_fail=str(f_msg).replace(chr(10),chr(32)).strip()[:65]
    if cond:
        tc._testMethodName=c_suc
        sys.stdout.write(" -> PASS: "+c_suc+chr(10))
        sys.stdout.flush()
        tc.assertTrue(True)
    else:
        tc._testMethodName=c_fail
        sys.stdout.write(" -> FAIL: "+c_fail+chr(10))
        sys.stdout.flush()
        tc.fail(c_fail)

class DynRes(unittest.TextTestResult):
    def startTest(self,test):
        super().startTest(test)
        msg=chr(10)+"[ RUN ] -> "+str(test._testMethodName)+chr(10)
        sys.stdout.write(msg)
        sys.stdout.flush()
    def addSuccess(self,test):pass
    def addFailure(self,test,err):pass 

class DynRun(unittest.TextTestRunner):
    def _makeResult(self):
        return DynRes(self.stream,self.descriptions,self.verbosity)

# =====================================================================
# STANDARD UNITTEST SUITE
# =====================================================================
class TestTask(unittest.TestCase):
    def setUp(self):
        px=[2,8,13,17,22,28,33,38]
        py=[3,7,12,18,21,27,32,39]
        self.points=[Point(x,y) for x,y in zip(px,py)]
        self.gdf=gpd.GeoDataFrame(geometry=self.points,crs="EPSG:4326")
        self.grid_size=10
        self.uc=sys.modules[__name__]

    def test_successfully_maps_coords_to_grid(self):
        uc=self.uc
        grid,sj=uc.create_grid(self.gdf,self.grid_size)
        _dyn(self,isinstance(grid,gpd.GeoDataFrame),"Grid is GeoDataFrame.","Grid error.")
        _dyn(self,isinstance(sj,gpd.GeoDataFrame),"Join is GeoDataFrame.","Join error.")
        _dyn(self,sj["grid_id"].isna().sum()==0,"All points assigned.","Unassigned pts.")

    def test_accurately_counts_incidents(self):
        uc=self.uc
        grid,sj=uc.create_grid(self.gdf,self.grid_size)
        agg=uc.aggregate_in_grid(sj)
        _dyn(self,hasattr(agg,'columns'),"Agg returns structure.","Agg failed.")
        _dyn(self,agg["count"].sum()==len(self.gdf),"Count matches points.","Count mismatch.")

    def test_correctly_groups_duplicate_pts(self):
        uc=self.uc
        pts=[Point(0,0)]*5
        gdf=gpd.GeoDataFrame(geometry=pts,crs="EPSG:4326")
        grid,sj=uc.create_grid(gdf,10)
        agg=uc.aggregate_in_grid(sj)
        _dyn(self,len(agg)==1,"Grouped into one cell.","Failed to group.")
        _dyn(self,agg["count"].iloc[0]==5,"Counted exactly 5.","Count is wrong.")

    def test_executes_drawing_loop_safely(self):
        uc=self.uc
        gc=[box(0,0,10,10),box(10,0,20,10),box(0,10,10,20)]
        grid=gpd.GeoDataFrame({"geometry":gc,"grid_id":[0,1,2]},crs="EPSG:4326")
        agg=gpd.GeoDataFrame({"grid_id":[0,1,2],"count":[2,0,6]})
        bmap=gpd.GeoDataFrame({"geometry":[box(-5,-5,25,25)]},crs="EPSG:4326")
        try:
            uc.plot_grid(grid,agg,bmap)
            _dyn(self,True,"Map rendered successfully.","")
        except Exception as e:
            _dyn(self,False,"","Map crash: "+str(e))

    def test_renders_missing_empty_cells(self):
        uc=self.uc
        gc=[box(0,0,10,10),box(10,0,20,10),box(0,10,10,20)]
        grid=gpd.GeoDataFrame({"geometry":gc,"grid_id":[0,1,2]},crs="EPSG:4326")
        agg=gpd.GeoDataFrame({"grid_id":[0,2],"count":[5,7]})
        bmap=gpd.GeoDataFrame({"geometry":[box(-5,-5,25,25)]},crs="EPSG:4326")
        try:
            uc.plot_grid(grid,agg,bmap)
            _dyn(self,True,"Handled missing data.","")
        except Exception as e:
            _dyn(self,False,"","Crash missing data: "+str(e))

if __name__=="__main__":
    suite=unittest.TestLoader().loadTestsFromTestCase(TestTask)
    DynRun(stream=sys.stdout,verbosity=0).run(suite)

test_main.py

Dive into the fundamentals of geospatial data analysis using Python. This intermediate course introduces you to the core concepts and practical applications of geospatial analysis, focusing on real-world datasets and hands-on projects. You'll learn to manipulate, visualize, and analyze spatial data using Python and popular libraries, with a special emphasis on geopandas. The course is designed to be engaging, featuring interactive grids, videos, and quizzes to reinforce your learning.

Explore the essentials of geospatial data, coordinate systems, and the essential tools for spatial analysis in Python. This section sets the foundation for working with spatial datasets and introduces geopandas in detail, including its core data structures, reading/writing capabilities, and integration with pandas and matplotlib.

Delve into core spatial analysis techniques, including spatial joins, overlays, and aggregations, using geopandas and visualization libraries. This section covers advanced geopandas methods for combining, comparing, and summarizing spatial datasets, as well as best practices for spatial data visualization.

Gain a solid understanding of real-world geospatial datasets and scenarios, focusing on practical analysis and visualization challenges. This section emphasizes project-based learning and synthesis of geospatial analysis techniques.

Challenge: Geospatial Insights

Solution