From a9b97910ae26b984251f5b7e8b93cfa3989eaf5d Mon Sep 17 00:00:00 2001
From: Trevor Bedford <trevor@bedford.io>
Date: Mon, 9 Sep 2019 13:58:27 -0700
Subject: [PATCH] Include coloring scripts and update colors.tsv mapping

---
 config/colors.tsv                          | 2127 ++++++--------------
 scripts/generate_coloring_census_tracts.py |   83 +
 scripts/generate_coloring_tsne.py          |   73 +
 3 files changed, 825 insertions(+), 1458 deletions(-)
 create mode 100644 scripts/generate_coloring_census_tracts.py
 create mode 100644 scripts/generate_coloring_tsne.py

diff --git a/config/colors.tsv b/config/colors.tsv
index 8a5db41b..2fd64b89 100644
--- a/config/colors.tsv
+++ b/config/colors.tsv
@@ -33,1461 +33,672 @@ location	south_usa	#AAAAAA
 location	west_usa	#AAAAAA
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 location	seattle	#AAAAAA
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diff --git a/scripts/generate_coloring_census_tracts.py b/scripts/generate_coloring_census_tracts.py
new file mode 100644
index 00000000..902887dd
--- /dev/null
+++ b/scripts/generate_coloring_census_tracts.py
@@ -0,0 +1,83 @@
+#done specifically for Seattle Flu
+import argparse
+import colorsys
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+
+def adjust_color_shading(r, g, b, factor):
+    #converts rgb to hls in order to affect luminance by an inputed factor. returns new rgb
+    h, l, s = colorsys.rgb_to_hls(r , g , b)
+    l = max(min(l * factor, 1.0), 0.0)
+    rgb = colorsys.hls_to_rgb(h, l, s)
+    return rgb
+
+
+def mapping(coor, label):
+    #creates dictionary mapping of lat/long based on a label (i.e region, country, etc.)
+    cfile = pd.read_csv(coor, sep = '\t')
+    location_dict = {}
+    for row in cfile.index:
+        if cfile.iloc[row,0] == label:
+            location_dict[cfile.iloc[row,1]] = {'latitude': cfile.iloc[row,2], 'longitude': cfile.iloc[row,3]}
+
+    location_df = pd.DataFrame.from_dict(location_dict, orient = 'index')
+    location_df.index.name = label
+    location_df = location_df[(location_df['latitude'] >= 47.1619235) & (location_df['latitude'] <= 48.106453)]
+    location_df = location_df[(location_df['longitude'] >= -122.676089) & (location_df['longitude'] <= -121.910404)]
+    location_df = location_df.reset_index()
+    location_df['lat_rank'] = location_df['latitude'].rank()
+    location_df['long_rank'] = location_df['longitude'].rank()
+    location_lat_sorted = location_df.sort_values(by ='lat_rank')
+    location_long_sorted = location_df.sort_values(by = 'long_rank')
+    return location_lat_sorted, location_long_sorted
+
+def color_assignment(lat_df, long_df, output_fname):
+
+    #Evenly assigns a color on the Nextstrain 36 color scale
+    color_scale = ["#511EA8", "#4928B4", "#4334BF", "#4041C7", "#3F50CC", "#3F5ED0", "#416CCE", "#4379CD", "#4784C7", "#4B8FC1", "#5098B9", "#56A0AF", "#5CA7A4", "#63AC99", "#6BB18E", "#73B583", "#7CB878", "#86BB6E", "#90BC65", "#9ABD5C", "#A4BE56", "#AFBD4F", "#B9BC4A", "#C2BA46", "#CCB742", "#D3B240", "#DAAC3D", "#DFA43B", "#E39B39", "#E68F36", "#E68234", "#E67431", "#E4632E", "#E1512A", "#DF4027", "#DC2F24"]
+    counter = 0
+    color_boundary = lat_df['lat_rank'].max()/36
+    for index, row in lat_df.iterrows():
+        if round(row['lat_rank']%color_boundary) != 0:
+            lat_df.loc[index,'color'] = color_scale[counter]
+        elif round(row['lat_rank']%color_boundary) == 0:
+            lat_df.loc[index,'color'] = color_scale[counter]
+            counter += 1
+
+    #the luminance will vary based on a factor ranging from 0.5 to 1.5 depending on the longitude ranking
+    lat_df = lat_df.set_index('location')
+    long_df = long_df.set_index('location')
+    for index, row in long_df.iterrows():
+        factor = (row['long_rank']*(1/long_df['long_rank'].max()))+.25
+        lat_df.loc[index,'shade_factor'] = factor
+
+    #outputs new hex color after appropriate shading
+    for index, row in lat_df.iterrows():
+        r, g, b = colors.hex2color(lat_df.loc[index,'color'])
+        new_rgb = adjust_color_shading(r, g, b, lat_df.loc[index,'shade_factor'])
+        new_hex = colors.rgb2hex(new_rgb)
+        lat_df.loc[index, 'new_color'] = new_hex
+
+    #subsets dataframe to only include location and color and prints to tsv
+    final_colors_df = lat_df['new_color']
+    final_colors_df.to_csv(output_fname, sep='\t', index = True)
+
+    return lat_df
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description="Assigns coloring for census tract",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument('--coordinates', type=str, default="config/lat_longs.tsv", help="tsv file containing lat/long coordinates and cooresponding region")
+    parser.add_argument('--label', type=str, default="location", help="geographic entity of interest like region, country or location")
+    parser.add_argument('--output', type=str, default="config/colors_location.tsv", help="name of output file for colors.tsv")
+    args = parser.parse_args()
+
+#makes Dataframe mapping of lat_longs file
+lat_df, long_df = mapping(args.coordinates, args.label)
+
+#equally distributes colors among the census tracks and outputs a color TSV file
+color_assignment(lat_df, long_df, args.output)
diff --git a/scripts/generate_coloring_tsne.py b/scripts/generate_coloring_tsne.py
new file mode 100644
index 00000000..404ab4e9
--- /dev/null
+++ b/scripts/generate_coloring_tsne.py
@@ -0,0 +1,73 @@
+import argparse
+import pandas as pd
+from sklearn.manifold import TSNE
+
+def mapping(coor, label, city = None):
+    #creates dictionary mapping of lat/long based on a label (i.e region, country, etc.)
+    cfile = pd.read_csv(coor, header = None, sep = '\t')
+    print(cfile)
+    location_dict = {}
+    for row in cfile.index:
+        if cfile.iloc[row,0] == label:
+            location_dict[cfile.iloc[row,1]] = {'latitude': cfile.iloc[row,2], 'longitude': cfile.iloc[row,3]}
+
+    location_df = pd.DataFrame.from_dict(location_dict, orient = 'index')
+    location_df.index.name = label
+    print(location_df)
+    if city != None:
+        location_df = location_df[(location_df['latitude'] >= 47.1619235) & (location_df['latitude'] <= 48.106453)]
+        location_df = location_df[(location_df['longitude'] >= -122.676089) & (location_df['longitude'] <= -121.910404)]
+    lat_long_df = location_df.values[:,0:2]
+    return lat_long_df , location_df
+
+def tsne_ranking(lat_long_df, location_df, label):
+    #creates a ranking of t-SNE
+    lat_long_transformed = TSNE(n_components=1).fit_transform(lat_long_df)
+    lat_long_transformed_df = pd.DataFrame(lat_long_transformed, columns=['1d_loc'])
+    lat_long_transformed_df[label] = location_df.index
+    lat_long_transformed_df["rank"] = lat_long_transformed_df['1d_loc'].rank()
+    lat_long_sorted = lat_long_transformed_df.sort_values(by ="rank")
+    return lat_long_sorted
+
+def color_assignment(lat_long_sorted, output_fname, label):
+    #Evenly assigns a color on the Nextstrain 36 color scale
+    color_scale = ["#511EA8", "#4928B4", "#4334BF", "#4041C7", "#3F50CC", "#3F5ED0", "#416CCE", "#4379CD", "#4784C7", "#4B8FC1", "#5098B9", "#56A0AF", "#5CA7A4", "#63AC99", "#6BB18E", "#73B583", "#7CB878", "#86BB6E", "#90BC65", "#9ABD5C", "#A4BE56", "#AFBD4F", "#B9BC4A", "#C2BA46", "#CCB742", "#D3B240", "#DAAC3D", "#DFA43B", "#E39B39", "#E68F36", "#E68234", "#E67431", "#E4632E", "#E1512A", "#DF4027", "#DC2F24"]
+    counter = 0
+    if len(lat_long_sorted['rank']) >= 36:
+        jump = 1
+    else:
+        jump = 36/len(lat_long_sorted['rank'])
+    color_boundary = lat_long_sorted['rank'].max()/36
+    for index, row in lat_long_sorted.iterrows():
+        if int(row['rank']%color_boundary) != 0:
+            lat_long_sorted.loc[index,'color'] = color_scale[int(counter)]
+        elif int(row['rank']%color_boundary) == 0:
+            lat_long_sorted.loc[index,'color'] = color_scale[int(counter)]
+            counter = counter + jump
+            print(counter)
+
+    #subsets dataframe to only include location and color and prints to tsv
+    final_colors_df = lat_long_sorted[[label, 'color']]
+    final_colors_df.to_csv(output_fname, sep='\t', index = False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description="Assigns coloring for census tract using 1D- t-SNE",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument('--coordinates', type=str, required=True, help="tsv file containing lat/long coordinates and cooresponding region")
+    parser.add_argument('--label', type=str, required=True, help= "geographic entity of interest like region, country or location")
+    parser.add_argument('--city', type=str, help="if 'Seattle' is specified, it limits lat/long to city limits -- not required to run script")
+    parser.add_argument('--output', type=str, required=True, help="name of output file for colors.tsv")
+
+    args = parser.parse_args()
+
+#makes Dataframe mapping of lat_longs file
+coor_mapping, original_df = mapping(args.coordinates, args.label, city = args.city)
+
+#runs PCA on lat/long and order ranks PC1
+tsne_rank = tsne_ranking(coor_mapping, original_df, args.label)
+
+#equally distributes colors among the census tracks and outputs a color TSV file
+color_assignment(tsne_rank, args.output, args.label)