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

Dyk ned i grundlæggende geospatiel dataanalyse med Python. Dette mellemniveaukursus introducerer dig til kernekoncepter og praktiske anvendelser af geospatiel analyse med fokus på virkelige datasæt og praktiske projekter. Du lærer at manipulere, visualisere og analysere spatiale data ved hjælp af Python og populære biblioteker, med særlig vægt på geopandas. Kurset er designet til at være engagerende og indeholder interaktive gittere, videoer og quizzer for at styrke din læring.

Udforsk det grundlæggende inden for geospatiale data, koordinatsystemer og de væsentlige værktøjer til rumlig analyse i Python. Dette afsnit danner grundlaget for at arbejde med spatiale datasæt og introducerer geopandas i detaljer, herunder dets kerne-datastrukturer, læse-/skrivefunktioner og integration med pandas og matplotlib.

Dyk ned i centrale rumlige analyseteknikker, herunder rumlige joins, overlays og aggregeringer, ved brug af geopandas og visualiseringsbiblioteker. Dette afsnit dækker avancerede geopandas-metoder til at kombinere, sammenligne og opsummere rumlige datasæt samt bedste praksis for visualisering af rumlige data.

Opnå en solid forståelse af geospatiale datasæt og scenarier fra den virkelige verden med fokus på praktiske analyse- og visualiseringsudfordringer. Dette afsnit lægger vægt på projektbaseret læring og syntese af geospatiale analyseteknikker.

Challenge: Geospatiale Indsigter

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