new images

analysis/visualisation_geopandas.py

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+import pandas as pd
+import geopandas
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
+fig, ax = plt.subplots(1, 1)
+world.plot(column= 'gdp_per_cap', ax=ax, legend=True, cmap='OrRd')
+plt.savefig('pic1.jpg')
+
+
+""" import os
+import jsonlines
+from pyecharts import Map 
+from pyecharts import Geo
+
+basic_path = os.getcwd()
+file_path_pos_en = basic_path + "/locations/pos_location_en.jsonl"
+file_path_neg_en = basic_path + "/locations/neg_location_en.jsonl"
+file_path_pos_zh = basic_path + "/locations/pos_location_zh.jsonl"
+file_path_neg_zh = basic_path + "/locations/neg_location_zh.jsonl"
+file_path_pos_zhs = basic_path + "/locations/pos_location_zhs.jsonl"
+file_path_neg_zhs = basic_path + "/locations/neg_location_zhs.jsonl"
+file_path_pos_zht = basic_path + "/locations/pos_location_zht.jsonl"
+file_path_neg_zht = basic_path + "/locations/neg_location_zht.jsonl"
+map_path_pos_en = basic_path + "/locations/map_pos_en.html"
+map_path_neg_en = basic_path + "/locations/map_neg_en.html"
+map_path_reletive_en = basic_path + "/locations/map_reletive_en.html"
+map_path_pos_zh = basic_path + "/locations/map_pos_zh.html"
+map_path_neg_zh = basic_path + "/locations/map_neg_zh.html"
+map_path_reletive_zh = basic_path + "/locations/map_reletive_zh.html"
+map_path_pos_zhs = basic_path + "/locations/map_pos_zhs.html"
+map_path_neg_zhs = basic_path + "/locations/map_neg_zhs.html"
+map_path_reletive_zhs = basic_path + "/locations/map_reletive_zhs.html"
+map_path_pos_zht = basic_path + "/locations/map_pos_zht.html"
+map_path_neg_zht = basic_path + "/locations/map_neg_zht.html"
+map_path_reletive_zht = basic_path + "/locations/map_reletive_zht.html"
+min_valid_number_zh = 3
+min_valid_number_zhst = 2
+min_valid_number_en = 10
+
+def drawmap(inputfile, outputfile, name, name_minor = ''):
+    with open(inputfile, 'r') as fi:
+        for line in jsonlines.Reader(fi):
+            # print (line)
+            countries = list(line.keys())
+            numbers = list(line.values())
+            # print ("numbers: ", numbers)
+            MAX = max(numbers)
+            MIN = min(numbers)
+            numbers_modified = []
+            for i in range(len(numbers)):
+                numbers_modified.append(round((numbers[i] - MIN) * 100 / (MAX - MIN)))
+            # print ("numbers: ", numbers)
+            # print ("numbers_modified: ", numbers_modified)
+            map0 = Map(name, name_minor, width=1400, height=700)
+            map0.add(name, attr = countries, value = numbers, maptype="world", is_roam=True, is_map_symbol_show=False, is_visualmap=True, visual_text_color='#000')
+            map0.add('modified to a 0-100 scale', attr = countries, value = numbers_modified, maptype="world", is_roam=True, is_map_symbol_show=False, is_visualmap=True, visual_text_color='#000')
+            map0.render(path=outputfile)
+
+def drawreletivemap(inputfile_pos, inputfile_neg, outputfile, min_valid_number, name, name_minor = ''):
+    country_names = []
+    reletive = {}
+    divide = {}
+    with open(inputfile_pos, 'r') as fp:
+        for lp in jsonlines.Reader(fp):
+            pos = lp
+            for itemsp in lp:
+                country_names.append(str(itemsp))
+    with open(inputfile_neg, 'r') as fn:
+        for ln in jsonlines.Reader(fn):
+            neg = ln
+            for itemsn in ln:
+                if str(itemsn) not in country_names:
+                    country_names.append(str(itemsn))
+    for country_name in country_names:
+        try:
+            reletive[country_name] = pos[country_name] - neg[country_name]
+            divide[country_name] = pos[country_name] / neg[country_name]
+        except:
+            try:
+                reletive[country_name] = pos[country_name]
+                if pos[country_name] > min_valid_number:
+                    # divide[country_name] = 2
+                    divide[country_name] = 0.99999995577455632789      # 需要绘制出归一化到0-100的图，和直接*50的图，为了程序简洁，后者在只有pos没有neg推文的情况下，
+            except:                                                    # 可以与前者共享一个很奇怪的数，后面再直接赋值为100（原本也是赋值为2，后面和别的数一起*50）
+                reletive[country_name] = - neg[country_name]
+                if neg[country_name] > min_valid_number:
+                    divide[country_name] = 0
+    countries = list(reletive.keys())
+    numbers = list(reletive.values())
+    MAX = max(numbers)
+    MIN = min(numbers)
+    for i in range(len(numbers)):
+        numbers[i] = round((numbers[i] - MIN) * 100 / (MAX - MIN))
+    ZERO = round((0 - MIN) * 100 / (MAX - MIN))
+    countries_division = list(divide.keys())
+    numbers_division = list(divide.values())
+    numbers_division_scaleto100 = list(divide.values())
+    # for i in range(len(numbers_division)):
+    #     numbers_division[i] = numbers_division[i] * 50
+    MAX_d = max(numbers_division_scaleto100)
+    MIN_d = min(numbers_division_scaleto100)
+    assert numbers_division == numbers_division_scaleto100
+    for i in range(len(numbers_division)):
+        if numbers_division[i] == 0.99999995577455632789:
+            numbers_division[i] = 100
+            numbers_division_scaleto100[i] = 100
+        else:
+            numbers_division[i] = numbers_division[i] * 50
+            numbers_division_scaleto100[i] = round((numbers_division_scaleto100[i] - MIN_d) * 100 / (MAX_d - MIN_d))
+    ONE = round((1 - MIN_d) * 100 / (MAX_d - MIN_d))
+    plan = Map(name, name_minor, width=1400, height=700)
+    plan.add('minus，{}等效为中性情感'.format(ZERO), countries, numbers, maptype="world", is_roam=True, is_map_symbol_show=False, is_visualmap=True, visual_text_color='#000')
+    plan.add('division，并x50，即50等效为中性情感', countries_division, numbers_division, maptype="world", is_roam=True, is_map_symbol_show=False, is_visualmap=True, visual_text_color='#000')
+    plan.add('division，并归一化到0-100，{}等效为中性情感'.format(ONE), countries_division, numbers_division_scaleto100, maptype="world", is_roam=True, is_map_symbol_show=False, is_visualmap=True, visual_text_color='#000')
+    plan.render(path=outputfile)
+
+# drawmap(file_path_pos_en, map_path_pos_en, '英文推文积极情感分布地图')
+# drawmap(file_path_neg_en, map_path_neg_en, '英文推文消极情感分布地图')
+# drawreletivemap(file_path_pos_en, file_path_neg_en, map_path_reletive_en, '英文推文绝对情感分布地图', '积极情感值与消极情感值之差或之比，并转为0-100尺度')
+
+drawmap(file_path_pos_en, map_path_pos_en, '英文推文积极情感分布地图')
+drawmap(file_path_neg_en, map_path_neg_en, '英文推文消极情感分布地图')
+drawreletivemap(file_path_pos_en, file_path_neg_en, map_path_reletive_en, min_valid_number_en, '英文推文绝对情感分布地图', '积极情感值与消极情感值之差或之比，并转为0-100尺度')
+
+drawmap(file_path_pos_zh, map_path_pos_zh, '中文推文积极情感分布地图')
+drawmap(file_path_neg_zh, map_path_neg_zh, '中文推文消极情感分布地图')
+drawreletivemap(file_path_pos_zh, file_path_neg_zh, map_path_reletive_zh, min_valid_number_zh, '中文推文绝对情感分布地图', '积极情感值与消极情感值之差或之比，并转为0-100尺度')
+
+drawmap(file_path_pos_zhs, map_path_pos_zhs, '简体中文推文积极情感分布地图')
+drawmap(file_path_neg_zhs, map_path_neg_zhs, '简体中文推文消极情感分布地图')
+drawreletivemap(file_path_pos_zhs, file_path_neg_zhs, map_path_reletive_zhs, min_valid_number_zhst, '简体中文推文绝对情感分布地图', '积极情感值与消极情感值之差或之比，并转为0-100尺度')
+
+drawmap(file_path_pos_zht, map_path_pos_zht, '繁体中文推文积极情感分布地图')
+drawmap(file_path_neg_zht, map_path_neg_zht, '繁体中文推文消极情感分布地图')
+drawreletivemap(file_path_pos_zht, file_path_neg_zht, map_path_reletive_zht, min_valid_number_zhst, '繁体中文推文绝对情感分布地图', '积极情感值与消极情感值之差或之比，并转为0-100尺度')
+ """

analysis/visualisation_pygal.py

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+import os
+import jsonlines
+import pygal
+import pygal_maps_world.maps
+from pygal.maps.world import COUNTRIES
+# import json
+
+basic_path = os.getcwd()
+file_path_pos_en = basic_path + "/locations/pos_location_en.jsonl"
+file_path_neg_en = basic_path + "/locations/neg_location_en.jsonl"
+file_path_pos_zh = basic_path + "/locations/pos_location_zh.jsonl"
+file_path_neg_zh = basic_path + "/locations/neg_location_zh.jsonl"
+file_path_pos_zhs = basic_path + "/locations/pos_location_zhs.jsonl"
+file_path_neg_zhs = basic_path + "/locations/neg_location_zhs.jsonl"
+file_path_pos_zht = basic_path + "/locations/pos_location_zht.jsonl"
+file_path_neg_zht = basic_path + "/locations/neg_location_zht.jsonl"
+jpgmap_path_pos_en = basic_path + "/locations/map_pos_en.svg"
+jpgmap_path_neg_en = basic_path + "/locations/map_neg_en.svg"
+jpgmap_path_reletive_en = basic_path + "/locations/map_reletive_en.svg"
+jpgmap_path_pos_zh = basic_path + "/locations/map_pos_zh.svg"
+jpgmap_path_neg_zh = basic_path + "/locations/map_neg_zh.svg"
+jpgmap_path_reletive_zh = basic_path + "/locations/map_reletive_zh.svg"
+jpgmap_path_pos_zhs = basic_path + "/locations/map_pos_zhs.svg"
+jpgmap_path_neg_zhs = basic_path + "/locations/map_neg_zhs.svg"
+jpgmap_path_reletive_zhs = basic_path + "/locations/map_reletive_zhs.svg"
+jpgmap_path_pos_zht = basic_path + "/locations/map_pos_zht.svg"
+jpgmap_path_neg_zht = basic_path + "/locations/map_neg_zht.svg"
+jpgmap_path_reletive_zht = basic_path + "/locations/map_reletive_zht.svg"
+min_valid_number_zh = 3
+min_valid_number_zhst = 2
+min_valid_number_en = 10
+country_codes = {"Russia": "ru", "Côte d'Ivoire": "ci", "Dominican Rep.": "do", "Venezuela": "ve", "Tanzania": "tz", "Vietnam": "vn", "Macedonia": "mk", "Korea": "kr",
+                "Bosnia and Herz.": "ba", "Palestine": "ps", "New Caledonia": "fr", "Czech Rep.": "cz", "Iran": "ir", "Hrvatska": "hr"}
+
+def get_country_code(country_name):
+    #根据指定的国家，返回Pygal使用的两个字母的国别码
+    for code, name in COUNTRIES.items():
+        if name == country_name:
+            return code
+    for name in country_codes:
+        if name == country_name:
+            return country_codes[name]
+    print (country_name)
+    return None
+
+def TransformCountryCode(inputjson):
+    outputjson = {}
+    for items in inputjson:
+        countryCode = get_country_code(items)
+        outputjson[countryCode] = inputjson[items]
+    outputjson["tw"] = outputjson["cn"]
+    return outputjson
+
+def classification(inputjson, isDivide):
+    numbers = list(inputjson.values())
+    if isDivide == True:
+        n9 = 2
+        n7 = 1.5
+        n5 = 1
+        n3 = 0.667
+        n1 = 0.333
+    else:
+        MAX = max(numbers)
+        MIN = min(numbers)
+        n9 = round((MAX - MIN) * 0.9) + MIN
+        n7 = round((MAX - MIN) * 0.7) + MIN
+        n5 = round((MAX - MIN) * 0.5) + MIN
+        n3 = round((MAX - MIN) * 0.3) + MIN
+        n1 = round((MAX - MIN) * 0.1) + MIN
+    out90, out79, out57, out35, out13, out01 = {}, {}, {}, {}, {}, {}
+    for councode,num in inputjson.items():
+        if num >= n9:
+            out90[councode] = num
+        elif num >= n7:
+            out79[councode] = num
+        elif num >= n5:
+            out57[councode] = num
+        elif num >= n3:
+            out35[councode] = num
+        elif num >= n1:
+            out13[councode] = num
+        else:
+            out01[councode] = num
+    return out90, out79, out57, out35, out13, out01, n9, n7, n5, n3, n1
+
+
+def drawmap(inputfile, outputfile, name):
+    with open(inputfile, 'r') as fi:
+        for line in jsonlines.Reader(fi):
+            # print (line)
+            """ countries = list(line.keys())
+            numbers = list(line.values()) """
+            # print ("numbers: ", numbers)
+            newline = TransformCountryCode(line)
+            country90, country79, country57, country35, country13, country01, n9, n7, n5, n3, n1 = classification(newline, False)
+            wm_style=pygal.style.RotateStyle('#D35400',base_style=pygal.style.LightColorizedStyle)
+            wm = pygal_maps_world.maps.World(style = wm_style)
+            wm.title = name
+            wm.add('>' + str(n9), country90)
+            wm.add(str(n7) + '-' + str(n9), country79)
+            wm.add(str(n5) + '-' + str(n7), country57)
+            wm.add(str(n3) + '-' + str(n5), country35)
+            wm.add(str(n1) + '-' + str(n3), country13)
+            wm.add('<' + str(n1), country01)
+            wm.render_to_file(outputfile)
+
+def drawreletivemap(inputfile_pos, inputfile_neg, outputfile, min_valid_number, name):
+    country_names = []
+    # reletive = {}
+    divide = {}
+    with open(inputfile_pos, 'r') as fp:
+        for lp in jsonlines.Reader(fp):
+            pos = lp
+            for itemsp in lp:
+                country_names.append(str(itemsp))
+    with open(inputfile_neg, 'r') as fn:
+        for ln in jsonlines.Reader(fn):
+            neg = ln
+            for itemsn in ln:
+                if str(itemsn) not in country_names:
+                    country_names.append(str(itemsn))
+    for country_name in country_names:
+        try:
+            # reletive[country_name] = pos[country_name] - neg[country_name]
+            divide[country_name] = pos[country_name] / neg[country_name]
+        except:
+            try:
+                # reletive[country_name] = pos[country_name]
+                if pos[country_name] > min_valid_number:
+                    # divide[country_name] = 2
+                    divide[country_name] = pos[country_name]
+            except:                                            
+                # reletive[country_name] = - neg[country_name]
+                if neg[country_name] > min_valid_number:
+                    divide[country_name] = 0
+    # countries = list(reletive.keys())
+    # numbers = list(reletive.values())
+    # MAX = max(numbers)
+    # MIN = min(numbers)
+    # for i in range(len(numbers)):
+    #     numbers[i] = round((numbers[i] - MIN) * 100 / (MAX - MIN))
+    # ZERO = round((0 - MIN) * 100 / (MAX - MIN))
+    # countries_division = list(divide.keys())
+    # numbers_division = list(divide.values())
+    newline = TransformCountryCode(divide)
+    country90, country79, country57, country35, country13, country01, n9, n7, n5, n3, n1 = classification(newline, True)
+    wm_style=pygal.style.RotateStyle('#D35400',base_style=pygal.style.LightColorizedStyle)
+    wm = pygal_maps_world.maps.World(style = wm_style)
+    wm.title = name
+    wm.add('>' + str(n9), country90)
+    wm.add(str(n7) + '-' + str(n9), country79)
+    wm.add(str(n5) + '-' + str(n7), country57)
+    wm.add(str(n3) + '-' + str(n5), country35)
+    wm.add(str(n1) + '-' + str(n3), country13)
+    wm.add('<' + str(n1), country01)
+    wm.render_to_file(outputfile)
+    # numbers_division_scaleto100 = list(divide.values())
+    # for i in range(len(numbers_division)):
+    #     numbers_division[i] = numbers_division[i] * 50
+    # MAX_d = max(numbers_division_scaleto100)
+    # MIN_d = min(numbers_division_scaleto100)
+    # assert numbers_division == numbers_division_scaleto100
+    # for i in range(len(numbers_division)):
+    #     if numbers_division[i] == 0.99999995577455632789:
+    #         numbers_division[i] = 100
+    #         numbers_division_scaleto100[i] = 100
+    #     else:
+    #         numbers_division[i] = numbers_division[i] * 50
+    #         numbers_division_scaleto100[i] = round((numbers_division_scaleto100[i] - MIN_d) * 100 / (MAX_d - MIN_d))
+    # ONE = round((1 - MIN_d) * 100 / (MAX_d - MIN_d))
+    # plan = Map(name, name_minor, width=1400, height=700)
+    # plan.add('minus，{}等效为中性情感'.format(ZERO), countries, numbers, maptype="world", is_roam=True, is_map_symbol_show=False, is_visualmap=True, visual_text_color='#000')
+    # plan.add('division，并x50，即50等效为中性情感', countries_division, numbers_division, maptype="world", is_roam=True, is_map_symbol_show=False, is_visualmap=True, visual_text_color='#000')
+    # plan.add('division，并归一化到0-100，{}等效为中性情感'.format(ONE), countries_division, numbers_division_scaleto100, maptype="world", is_roam=True, is_map_symbol_show=False, is_visualmap=True, visual_text_color='#000')
+    # plan.render(path=outputfile)
+
+drawmap(file_path_pos_en, jpgmap_path_pos_en, '英文推文积极情感分布地图')
+drawmap(file_path_neg_en, jpgmap_path_neg_en, '英文推文消极情感分布地图')
+drawreletivemap(file_path_pos_en, file_path_neg_en, jpgmap_path_reletive_en, min_valid_number_en, '英文推文绝对情感分布地图')
+
+drawmap(file_path_pos_zh, jpgmap_path_pos_zh, '中文推文积极情感分布地图')
+drawmap(file_path_neg_zh, jpgmap_path_neg_zh, '中文推文消极情感分布地图')
+drawreletivemap(file_path_pos_zh, file_path_neg_zh, jpgmap_path_reletive_zh, min_valid_number_zh, '中文推文绝对情感分布地图')
+
+drawmap(file_path_pos_zhs, jpgmap_path_pos_zhs, '简体中文推文积极情感分布地图')
+drawmap(file_path_neg_zhs, jpgmap_path_neg_zhs, '简体中文推文消极情感分布地图')
+drawreletivemap(file_path_pos_zhs, file_path_neg_zhs, jpgmap_path_reletive_zhs, min_valid_number_zhst, '简体中文推文绝对情感分布地图')
+
+drawmap(file_path_pos_zht, jpgmap_path_pos_zht, '繁体中文推文积极情感分布地图')
+drawmap(file_path_neg_zht, jpgmap_path_neg_zht, '繁体中文推文消极情感分布地图')
+drawreletivemap(file_path_pos_zht, file_path_neg_zht, jpgmap_path_reletive_zht, min_valid_number_zhst, '繁体中文推文绝对情感分布地图')