Merge pull request #7 from ni1o1/0.3.1

0.3.1

docs/source/analysis.rst

@@ -9,6 +9,33 @@ Shadow coverage
 Shadow coverage
 --------------------------------------
 
+.. function:: pybdshadow.cal_sunshine(buildings, day='2022-01-01', roof=False,grids =  gpd.GeoDataFrame(), accuracy=1, precision=3600, padding=0)
+
+Calculate the sunshine time in given date.
+
+**Parameters**
+
+buildings : GeoDataFrame
+    Buildings. coordinate system should be WGS84
+day : str
+    the day to calculate the sunshine
+roof : bool
+    whether to calculate roof shadow.
+grids : GeoDataFrame
+    grids generated by TransBigData in study area
+precision : number
+    time precision(s)
+padding : number
+    padding time before and after sunrise and sunset
+accuracy : number
+    size of grids.
+
+**Return**
+
+grids : GeoDataFrame
+    grids generated by TransBigData in study area, each grids have a `time` column store the sunshine time
+
+
 .. function:: pybdshadow.cal_sunshadows(buildings, cityname='somecity', dates=['2022-01-01'], gap=3600,roof=True, include_building=True,save_shadows=False,printlog=False)
 
 Calculate the sunlight shadow in different date with given time gap.

docs/source/conf.py

@@ -22,8 +22,8 @@
 author = 'Qing Yu'
 
 # The full version, including alpha/beta/rc tags
-release = '0.3.0'
-version = '0.3.0'
+release = '0.3.1'
+version = '0.3.1'
 html_logo = "_static/logo-wordmark-light.png"
 html_favicon = '_static/logo.ico'
 # -- General configuration ---------------------------------------------------

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="pybdshadow",
-    version="0.3.0",
+    version="0.3.1",
     author="Qing Yu",
     author_email="[email protected]",
     description="Python package to generate building shadow geometry",

src/pybdshadow/__init__.py

@@ -32,7 +32,7 @@
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 """
 
-__version__ = '0.3.0'
+__version__ = '0.3.1'
 __author__ = 'Qing Yu <[email protected]>'
 
 # module level doc-string
@@ -51,6 +51,7 @@
     show_bdshadow,
 )
 from .analysis import (
+    cal_sunshine,
     cal_sunshadows,
     cal_shadowcoverage
 )
@@ -59,6 +60,7 @@
            'bdshadow_pointlight',
            'bd_preprocess',
            'show_bdshadow',
+           'cal_sunshine',
            'cal_sunshadows',
            'cal_shadowcoverage',
            ]

src/pybdshadow/analysis.py

@@ -9,9 +9,9 @@
 from .preprocess import bd_preprocess
 
 
-def get_timetable(lon, lat, dates=['2022-01-01'], gap=3600, padding=1800):
+def get_timetable(lon, lat, dates=['2022-01-01'], precision=3600, padding=1800):
     # generate timetable
-    def get_timeSeries(day, lon, lat, gap=3600, padding=1800):
+    def get_timeSeries(day, lon, lat, precision=3600, padding=1800):
         date = pd.to_datetime(day+' 12:45:33.959797119')
         times = get_times(date, lon, lat)
         date_sunrise = times['sunrise']
@@ -21,18 +21,66 @@ def get_timeSeries(day, lon, lat, gap=3600, padding=1800):
         times = pd.to_datetime(pd.Series(range(
             timestamp_sunrise.iloc[0]+padding*1000000000,
             timestamp_sunset.iloc[0]-padding*1000000000,
-            gap*1000000000)))
+            precision*1000000000)))
         return times
     dates = pd.DataFrame(pd.concat(
-        [get_timeSeries(date, lon, lat, gap,padding) for date in dates]), columns=['datetime'])
+        [get_timeSeries(date, lon, lat, precision,padding) for date in dates]), columns=['datetime'])
     dates['date'] = dates['datetime'].apply(lambda r: str(r)[:19])
     return dates
 
 
-def cal_sunshadows(buildings, cityname='somecity', dates=['2022-01-01'], gap=3600,
-              roof=True, include_building=True,save_shadows=False,printlog=False):
+def cal_sunshine(buildings, day='2022-01-01', roof=False,grids =  gpd.GeoDataFrame(), accuracy=1, precision=3600, padding=0):
     '''
-    Calculate the sunlight shadow in different date with given time gap.
+    Calculate the sunshine time in given date.
+
+    **Parameters**
+
+    buildings : GeoDataFrame
+        Buildings. coordinate system should be WGS84
+    day : str
+        the day to calculate the sunshine
+    roof : bool
+        whether to calculate roof shadow.
+    grids : GeoDataFrame
+        grids generated by TransBigData in study area
+    precision : number
+        time precision(s)
+    padding : number
+        padding time before and after sunrise and sunset
+    accuracy : number
+        size of grids.
+        
+    **Return**
+
+    grids : GeoDataFrame
+        grids generated by TransBigData in study area, each grids have a `time` column store the sunshine time
+
+    '''
+    # 计算白天时间
+    lon, lat = buildings['geometry'].iloc[0].bounds[:2]
+    date = pd.to_datetime(day+' 12:45:33.959797119')
+    times = get_times(date, lon, lat)
+    date_sunrise = times['sunrise']
+    data_sunset = times['sunset']
+    timestamp_sunrise = pd.Series(date_sunrise).astype('int')
+    timestamp_sunset = pd.Series(data_sunset).astype('int')
+    sunlighthour = (
+        timestamp_sunset.iloc[0]-timestamp_sunrise.iloc[0])/(1000000000*3600)
+
+    # Generate shadow every 1800 s
+    shadows = cal_sunshadows(buildings, dates=[day], precision=precision, padding=padding)
+    # Grid analysis of shadow cover duration(ground).
+    grids = cal_shadowcoverage(
+        shadows, buildings, grids = grids,roof=roof, precision=precision, accuracy=accuracy)
+
+    grids['Hour'] = sunlighthour-grids['time']/3600
+    return grids
+
+
+def cal_sunshadows(buildings, cityname='somecity', dates=['2022-01-01'], precision=3600, padding=0,
+                   roof=True, include_building=True, save_shadows=False, printlog=False):
+    '''
+    Calculate the sunlight shadow in different date with given time precision.
 
     **Parameters**
 
@@ -42,8 +90,10 @@ def cal_sunshadows(buildings, cityname='somecity', dates=['2022-01-01'], gap=360
         Cityname. If save_shadows, this function will create `result/cityname` folder to save the shadows
     dates : list
         list of dates
-    gap : number
-        time gap(s)
+    precision : number
+        time precision(s)
+    padding : number
+        padding time before and after sunrise and sunset
     roof : bool
         whether to calculate roof shadow.
     include_building : bool
@@ -60,7 +110,7 @@ def cal_sunshadows(buildings, cityname='somecity', dates=['2022-01-01'], gap=360
     '''
     # 获取城市位置
     lon, lat = buildings['geometry'].iloc[0].bounds[:2]
-    timetable = get_timetable(lon, lat, dates, gap)
+    timetable = get_timetable(lon, lat, dates, precision, padding)
     import os
     if save_shadows:
         if not os.path.exists('result'):
@@ -82,18 +132,18 @@ def cal_sunshadows(buildings, cityname='somecity', dates=['2022-01-01'], gap=360
             ground_shaodws = shadows[shadows['type'] == 'ground']
 
             if save_shadows:
-                if len(roof_shaodws)>0:
+                if len(roof_shaodws) > 0:
                     roof_shaodws.to_file(
                         'result/'+cityname+'/roof_'+name+'.json', driver='GeoJSON')
-                if len(ground_shaodws)>0:
+                if len(ground_shaodws) > 0:
                     ground_shaodws.to_file(
-                    'result/'+cityname+'/ground_'+name+'.json', driver='GeoJSON')
+                        'result/'+cityname+'/ground_'+name+'.json', driver='GeoJSON')
             allshadow.append(shadows)
     allshadow = pd.concat(allshadow)
     return allshadow
 
 
-def cal_shadowcoverage(shadows_input,buildings,roof=True,gap = 3600,accuracy=1):
+def cal_shadowcoverage(shadows_input, buildings, grids = gpd.GeoDataFrame(),roof=True, precision=3600, accuracy=1):
     '''
     Calculate the sunlight shadow coverage time for given area.
 
@@ -103,44 +153,46 @@ def cal_shadowcoverage(shadows_input,buildings,roof=True,gap = 3600,accuracy=1):
         All building shadows calculated
     buildings : GeoDataFrame
         Buildings. coordinate system should be WGS84
+    grids : GeoDataFrame
+        grids generated by TransBigData in study area
     roof : bool
         If true roof shadow, false then ground shadow
-    gap : number
-        time gap(s), which is for calculation of coverage time
+    precision : number
+        time precision(s), which is for calculation of coverage time
     accuracy : number
         size of grids.
 
     **Return**
-    
-    bdgrids : GeoDataFrame
+
+    grids : GeoDataFrame
         grids generated by TransBigData in study area, each grids have a `time` column store the shadow coverage time
 
     '''
     shadows = bd_preprocess(shadows_input)
+
+    # 研究区域
+    bounds = buildings.unary_union.bounds
+    if len(grids)==0:
+        grids, params = tbd.area_to_grid(bounds, accuracy)
+
     if roof:
-        ground_shadows = shadows[shadows['type']=='roof'].groupby(['date'])['geometry'].apply(
-                    lambda df: MultiPolygon(list(df)).buffer(0)).reset_index()
-        #研究区域
-        bounds = buildings.unary_union.bounds
-
-        bdgrids,params = tbd.area_to_grid(bounds,accuracy)
+        ground_shadows = shadows[shadows['type'] == 'roof'].groupby(['date'])['geometry'].apply(
+            lambda df: MultiPolygon(list(df)).buffer(0)).reset_index()
+
         buildings.crs = None
-        bdgrids = gpd.sjoin(bdgrids,buildings)
+        grids = gpd.sjoin(grids, buildings)
     else:
-        ground_shadows = shadows[shadows['type']=='ground'].groupby(['date'])['geometry'].apply(
-                    lambda df: MultiPolygon(list(df)).buffer(0)).reset_index()
-
-        #研究区域
-        bounds = buildings.unary_union.bounds
-        bdgrids,params = tbd.area_to_grid(bounds,accuracy)
+        ground_shadows = shadows[shadows['type'] == 'ground'].groupby(['date'])['geometry'].apply(
+            lambda df: MultiPolygon(list(df)).buffer(0)).reset_index()
 
         buildings.crs = None
-        bdgrids = gpd.sjoin(bdgrids,buildings,how='left')
-        bdgrids = bdgrids[bdgrids['index_right'].isnull()]
+        grids = gpd.sjoin(grids, buildings, how='left')
+        grids = grids[grids['index_right'].isnull()]
 
-    gridcount = gpd.sjoin(bdgrids[['LONCOL','LATCOL','geometry']],ground_shadows[['geometry']]).\
-        groupby(['LONCOL','LATCOL'])['geometry'].count().rename('count').reset_index()
-    bdgrids = pd.merge(bdgrids,gridcount,how='left')
-    bdgrids['time'] = bdgrids['count'].fillna(0)*gap
+    gridcount = gpd.sjoin(grids[['LONCOL', 'LATCOL', 'geometry']], ground_shadows[['geometry','date']]).\
+        drop_duplicates(subset=['LONCOL', 'LATCOL','date']).groupby(['LONCOL', 'LATCOL'])['geometry'].\
+            count().rename('count').reset_index()
+    grids = pd.merge(grids, gridcount, how='left')
+    grids['time'] = grids['count'].fillna(0)*precision
 
-    return bdgrids
+    return grids

src/pybdshadow/tests/test_analysis.py

@@ -29,6 +29,9 @@ def test_analysis(self):
         buildings = pybdshadow.bd_preprocess(buildings)
         #分析
         date = '2022-01-01'
-        shadows = pybdshadow.cal_sunshadows(buildings,dates = [date],gap=3600)
-        bdgrids = pybdshadow.cal_shadowcoverage(shadows,buildings,gap = 3600,accuracy=2)
-        assert len(bdgrids)==1185
+        shadows = pybdshadow.cal_sunshadows(buildings,dates = [date],precision=3600)
+        bdgrids = pybdshadow.cal_shadowcoverage(shadows,buildings,precision = 3600,accuracy=2)
+        assert len(bdgrids)==1185
+
+        grids = pybdshadow.cal_sunshine(buildings)
+        assert len(grids)==1882