能源系统建模：python读取GCAM的xml数据文件

Posted 2021-09-04 Gendan

from pandas import ExcelWriter
import numpy as np
import os
import pandas as pd
import xml.etree.ElementTree as et

UCD_trn xml file path

xml_path = r"..\\gcam-v5.2\\input\\extra" # xml file folder
dir_extra = os.listdir(xml_path) # SSP3 uses trn_UCD_SSP3
trn_xml_ls = [file for file in dir_extra if "transportation_UCD_SSP" in file]
trn_xml_path = dict(

zip(
    ["ssp" + str(i) for i in [1, 2, 4, 5]],
    [os.path.join(xml_path, file) for file in trn_xml_ls],
)

)

Car type

tranSubsector_name_ls = [

"Compact Car",
"Large Car and SUV",
"Mini Car",
"Multipurpose Vehicle",
"Subcompact Car",

]
def get_ene_intensity_xml(path,

                      tranSubsector_name, 
                      supplysector_name="trn_pass_road_LDV_4W"):
xtree = et.parse(path)
xroot = xtree.getroot()
rows = []
for child in xroot:
    for region in child:
        if region.attrib.get("name") == "China":
            for supplysector in region:
                if supplysector.attrib.get("name") == supplysector_name:
                    for tranSubsector in supplysector:
                        if tranSubsector.attrib.get("name") == tranSubsector_name:
                            for stubtechnology in tranSubsector:
                                if stubtechnology.attrib.get("name") == "FCEV":
                                    for period in stubtechnology:
                                        for node in period:
                                            if node.tag == "minicam-energy-input":
                                                for sub_node in node:
                                                    if (
                                                        sub_node.tag
                                                        == "coefficient"
                                                    ):
                                                        s_region = (                                                                region.attrib.get(
                                                                "name"
                                                            )
                                                        )
                                                        s_supplysector = (                                                               supplysector.attrib.get(
                                                                "name"
                                                            )
                                                        )
                                                        s_tranSubsector = tranSubsector.attrib.get(
                                                            "name"
                                                        )
                                                        s_stubtechnology = stubtechnology.attrib.get(
                                                            "name"
                                                        )
                                                        s_period = (                                                               period.attrib.get(
                                                                "year"
                                                            )
                                                        )
                                                        s_tag = sub_node.tag
                                                        s_coefficient = (                                                                float(sub_node.text)
                                                            / 1055  # btu/vkm to J/vkm from https://jgcri.github.io/gcam-doc/en_technologies.html
                                                        )
                                                        rows.append(
                                                            dict(                                                                   region=s_region,

supplysector=s_supplysector,
tranSubsector=s_tranSubsector,
stubtechnology=s_stubtechnology,
period=s_period, tag=s_tag,
value=s_coefficient,

                                                            )
                                                        )
df = pd.DataFrame(rows)
return df

def ene_intensity(scenario):

ene_intensity_ls = []
for car_type in tranSubsector_name_ls:
    ene_intensity = np.array(
        get_ene_intensity_xml(scenario, car_type).loc[:, "value"]
    )[1:]
    ene_intensity_ls.append(ene_intensity)
return ene_intensity_ls

columns = range(2000, 2101, 5) #贝宝读取2000到2100年能源强度，步长为五年
df = (

    pd.DataFrame(
        ene_intensity(trn_xml_path[key]),
        columns=columns,
        index=tranSubsector_name_ls,
    )
)

主要是搞清楚xml文档的数据结构，GCAM交通部门中的结构为
<region>

<supplysector>
    <tranSubsector>
        <stubtechnology>
            <period>
                <minicam-energy-input> # 能源强度