IPython GGtracker Hello, world! plot (hackiness warning)

plot.py

%pylab inline

import urllib2
import json
json_api_test = urllib2.urlopen("http://api.ggtracker.com/api/v1/matches?game_type=1v1&map_name=Derelict%20Watcher%20TE&race=terran&replay=true&vs_race=zerg&paginate=true&average_league=4&limit=100").read()

json_api_result = json.loads(json_api_test)
json_api_results = json_api_result['collection']
json_result_ids = [res['id'] for res in json_api_results]

json_match_data = [json.loads(urllib2.urlopen("http://gg2-matchblobs-prod.s3.amazonaws.com/" + str(id)).read()) for id in json_result_ids]

import matplotlib.pyplot as plt
import matplotlib.cm as cm

active_workers = []
active_mules = []
mineral_incomes = []
gas_incomes = []
match_times = []
base_counts = []

for match in json_match_data:
    for pair in match['WorkersActiveCount']:
        workers = match['WorkersActiveCount'][pair]
        min_incomes = np.array(match['MineralsCollectionRate'][pair])
        ves_incomes = np.array(match['VespeneCollectionRate'][pair])
        active_workers.extend(workers)
        mules = np.zeros_like(workers)
        tmacro_json = match['tmacro'][pair]
        if len(tmacro_json) > 0:
            tmacro = np.array(tmacro_json)
            mule_ticks = np.hstack(tmacro[:,0])
            ticks = 0
            for i in range(len(mules)):
                mules[i] = len([t for t in mule_ticks if t < ticks - 16*1 and t > ticks - 16*90])
                active_workers[i] -= mules[i]
                ticks += 16*10
        base_ticks = [p[1] for p in match['num_bases'] if p[0] == int(pair)][0]
        base_nums = np.zeros_like(workers)
        for i in range(len(base_nums)):
            base_nums[i] = len([1 for t in base_ticks if t[0] < 16*10*i and t[1] > 16*10*i])
        active_mules.extend(mules)
        mineral_incomes.extend(min_incomes)
        gas_incomes.extend(ves_incomes)
        match_times.extend([16*10*n for n in range(len(workers))]) # unused
        base_counts.extend(base_nums)
        
figure(figsize=(32,18))
axes(axisbg='0.1')
xlim(0, 100)
ylim(0, 3500)
xlabel("Worker count")
ylabel("Resources / min")
scatter(np.array(active_workers) + 3 * np.array(active_mules), np.array(mineral_incomes) + np.array(gas_incomes),
        alpha=0.25, s=75, c=base_counts, cmap=cm.get_cmap('Set1'), edgecolors='none')