fix typo

.github/workflows/draft-pdf.yml

@@ -21,3 +21,7 @@ jobs:
           # PDF. Note, this should be the same directory as the input
           # paper.md
           path: paper/md_paper/paper.pdf
+      - name: save pdf to repo
+        uses: stefanzweifel/git-auto-commit-action@v4
+        with:
+          commit_message: Saved new PDF of paper

.gitignore

@@ -14,4 +14,4 @@ stemflow/*/__pycache__/*
 stemflow/__pycache__/*
 *.DS_Store
 *.docx
-./paper/references
+*/references/*

paper/md_paper/paper.bib

@@ -1,5 +1,3 @@
-
-
 @article{fink_adaptive_2013,
 	title = {Adaptive {Spatio}-{Temporal} {Exploratory} {Models}: {Hemisphere}-wide species distributions from massively crowdsourced {eBird} data},
 	volume = {27},

paper/md_paper/paper.md

@@ -16,7 +16,6 @@ authors:
    surname: Gu
    orchid: 
    affiliation: "1, 3"
-
  - given-names: Xiangjiang
    surname: Zhan
    corresponding: true
@@ -26,10 +25,13 @@ authors:
 affiliations:
  - name: Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    index: 1
+
  - name: University of Chinese Academy of Sciences, Beijing, China
    index: 2
+
  - name: Cardiff University-Institute of Zoology Joint Laboratory for Biocomplexity Research, Chinese Academy of Sciences, Beijing, China
    index: 3
+
  - name: Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
    index: 4
 

stemflow/utils/plot_gif.py

@@ -53,15 +53,15 @@ def make_sample_gif(data: pd.core.frame.DataFrame,
         grid: 
             Whether to add grids.
         lng_size: 
-            pixel count to aggregate at longitudinal direction. Larger means finner resolution.
+            pixel count to aggregate at longitudinal direction. Larger means finer resolution.
         lat_size: 
-            pixel count to aggregate at latitudinal direction. Larger means finner resolution.
+            pixel count to aggregate at latitudinal direction. Larger means finer resolution.
         xtick_interval: 
             the size of x tick interval.
         ytick_interval: 
             the size of y tick interval.
         log_scale: 
-            log transfrom the target value or not.
+            log transform the target value or not.
         dpi: 
             dpi of the GIF.
         fps: 
@@ -185,7 +185,7 @@ def make_sample_gif_scatter(data: pandas.core.frame.DataFrame,
         grid: 
             Whether to add grids.
         log_scale: 
-            log transfrom the target value or not.
+            log transform the target value or not.
         s: 
             size of the scatter. 
         dpi: 

stemflow/utils/quadtree.py

@@ -63,7 +63,7 @@ def recursive_subdivide(node: Node,
                         grid_len_lat_upper_threshold: Union[float, int],
                         grid_len_lat_lower_threshold: Union[float, int],
                         points_lower_threshold: Union[float, int]):
-    """recursively subdevide the grids
+    """recursively subdivide the grids
     
     Args:
         node: 
@@ -126,7 +126,7 @@ def contains(x, y, w, h, points):
 
 
 def find_children(node):
-    """return children nodeds of this node"""
+    """return children nodes of this node"""
     if not node.children:
         return [node]
     else:
@@ -161,15 +161,15 @@ def __init__(self,
             grid_len_lat_lower_threshold: 
                 stop divide if grid latitude **will** be below than the threshold
             points_lower_threshold: 
-                stop devide if points count is less than this threshold.
+                stop divide if points count is less than this threshold.
             lon_lat_equal_grid: 
-                whether to split the longtiude and latitude equally.
+                whether to split the longitude and latitude equally.
             rotation_angle: 
-                rangles to rotate the gridding.
+                angles to rotate the gridding.
             calibration_point_x_jitter: 
-                jitting the gridding on longitude.
+                jittering the gridding on longitude.
             calibration_point_y_jitter: 
-                jitting the gridding on latitude.
+                jittering the gridding on latitude.
             
         Example:
             ```py
@@ -183,7 +183,7 @@ def __init__(self,
                             calibration_point_x_jitter = 10,
                             calibration_point_y_jitter = 10)
             >> QT_obj.add_lon_lat_data(sub_data.index, sub_data['longitude'].values, sub_data['latitude'].values)
-            >> QT_obj.generate_griding_params()
+            >> QT_obj.generate_gridding_params()
             >> QT_obj.subdivide() ## Call subdivide to process
             >> gridding_info = QT_obj.get_final_result()  # gridding_info is a dataframe
             ```
@@ -216,7 +216,7 @@ def add_lon_lat_data(self,
             
         """
         if not len(x_array) == len(y_array) or not len(x_array) == len(indexes):
-            raise ValueError("input longitude and latitute and indexes not in same length!")
+            raise ValueError("input longitude and latitude and indexes not in same length!")
 
         data = np.array([x_array, y_array]).T
         angle = self.rotation_angle
@@ -235,8 +235,8 @@ def add_lon_lat_data(self,
 
 
 
-    def generate_griding_params(self):
-        """generate the griding params after data are added
+    def generate_gridding_params(self):
+        """generate the gridding params after data are added
 
         Raises:
             ValueError: self.lon_lat_equal_grid is not a bool
@@ -268,7 +268,7 @@ def get_points(self):
         return self.points
 
     def subdivide(self):
-        """start recursively subdevide"""
+        """start recursively subdivide"""
         recursive_subdivide(self.root, self.grid_len_lon_upper_threshold, self.grid_len_lon_lower_threshold,
                             self.grid_len_lat_upper_threshold, self.grid_len_lat_lower_threshold,
                             self.points_lower_threshold)
@@ -419,7 +419,7 @@ def get_ensemble_quadtree(data: pandas.core.frame.DataFrame,
                             temporal_start: Union[float, int] = 1, temporal_end: Union[float, int]=366, 
                             temporal_step: Union[float, int]=20, temporal_bin_interval: Union[float, int]=50,
                             temporal_bin_start_jitter: Union[float, int, str]= 'random',
-                            spatio_bin_jitter_maginitude: Union[float, int] = 10,
+                            spatio_bin_jitter_magnitude: Union[float, int] = 10,
                             save_gridding_plot: bool=True,
                             njobs: int=1,
                             verbosity: int=1,
@@ -463,16 +463,16 @@ def get_ensemble_quadtree(data: pandas.core.frame.DataFrame,
             jitter of the start of the sliding window. 
             If 'random', a random jitter of range (-bin_interval, 0) will be generated
             for the start.
-        spatio_bin_jitter_maginitude:
+        spatio_bin_jitter_magnitude:
             jitter of the spatial gridding.
         save_gridding_plot:
             Whether ot save gridding plots
         njobs:
             Multi-processes count.
         plot_xlims:
-            If save_gridding_plot=Ture, what is the xlims of the plot
+            If save_gridding_plot=True, what is the xlims of the plot
         plot_ylims:
-            If save_gridding_plot=Ture, what is the ylims of the plot
+            If save_gridding_plot=True, what is the ylims of the plot
         save_path:
             If not '', save the ensemble dataframe to this path
             
@@ -495,10 +495,10 @@ def get_ensemble_quadtree(data: pandas.core.frame.DataFrame,
 
     for ensemble_count in iter_func_:
         rotation_angle = np.random.uniform(0,360)
-        calibration_point_x_jitter = np.random.uniform(-spatio_bin_jitter_maginitude, spatio_bin_jitter_maginitude)
-        calibration_point_y_jitter = np.random.uniform(-spatio_bin_jitter_maginitude, spatio_bin_jitter_maginitude)
+        calibration_point_x_jitter = np.random.uniform(-spatio_bin_jitter_magnitude, spatio_bin_jitter_magnitude)
+        calibration_point_y_jitter = np.random.uniform(-spatio_bin_jitter_magnitude, spatio_bin_jitter_magnitude)
 
-        # print(f'ensembel_count: {ensemble_count}')
+        # print(f'ensemble_count: {ensemble_count}')
 
         temporal_bins = generate_temporal_bins(start = temporal_start, 
                                                 end=temporal_end, 
@@ -528,7 +528,7 @@ def get_ensemble_quadtree(data: pandas.core.frame.DataFrame,
             ## You need to generate the splitting parameters once giving the data. Like the calibration point and min,max.
 
             QT_obj.add_lon_lat_data(sub_data.index, sub_data[Spatio1].values, sub_data[Spatio2].values)
-            QT_obj.generate_griding_params()
+            QT_obj.generate_gridding_params()
 
             ## Call subdivide to precess
             QT_obj.subdivide()

stemflow/version.py

@@ -1 +1 @@
-__version__ = '0.0.24'
+__version__ = '0.0.25'