diff --git a/src/simmate/apps/ethereum/templates/ethereum/transactions/table.html b/src/simmate/apps/ethereum/templates/ethereum/transactions/table.html
index 8c2d1f472..bb573415b 100644
--- a/src/simmate/apps/ethereum/templates/ethereum/transactions/table.html
+++ b/src/simmate/apps/ethereum/templates/ethereum/transactions/table.html
@@ -1,27 +1,27 @@
{% extends "data_explorer/table_entries_base.html" %}
{% block table_headers %}
- {% table_header "id" "Hash:Type:ID" min_width=700 %}
- {% table_header "from_address" "From" min_width=400 %}
- {% table_header "from_address" "To" min_width=400 %}
+ {% table_header "id" "Hash:Type:ID" min_width=150 %}
+ {% table_header "from_address" "From" min_width=150 %}
+ {% table_header "from_address" "To" min_width=150 %}
{% table_header "chain" "Chain" min_width=150 %}
{% table_header "asset" "Asset" min_width=150 %}
{% table_header "amount" "Amount" min_width=150 %}
{% table_header "created_at_original" "Date" min_width=250 %}
{% endblock %}
{% block table_rows %}
- | {% foreign_key_link entry %} |
+ {% foreign_key_link entry truncate_chars=10 %} |
{% if entry.from_address.ens_name %}
{% foreign_key_link entry.from_address "ens_name" %}
{% else %}
- {% foreign_key_link entry.from_address %}
+ {% foreign_key_link entry.from_address truncate_chars=10 %}
{% endif %}
|
{% if entry.to_address.ens_name %}
{% foreign_key_link entry.to_address "ens_name" %}
{% else %}
- {% foreign_key_link entry.to_address %}
+ {% foreign_key_link entry.to_address truncate_chars=10 %}
{% endif %}
|
{{ entry.chain }} |
diff --git a/src/simmate/apps/ethereum/templates/ethereum/wallets/table.html b/src/simmate/apps/ethereum/templates/ethereum/wallets/table.html
index 3eaa13597..050044c1a 100644
--- a/src/simmate/apps/ethereum/templates/ethereum/wallets/table.html
+++ b/src/simmate/apps/ethereum/templates/ethereum/wallets/table.html
@@ -1,15 +1,15 @@
{% extends "data_explorer/table_entries_base.html" %}
{% block table_headers %}
- {% table_header "id" "ETH Address" min_width=400 %}
+ {% table_header "id" "ETH Address" min_width=150 %}
{% table_header "ens_name" "ENS" min_width=200 %}
- {% table_header "ethereum_balance" "ETH" min_width=200 %}
- {% table_header "usdc_balance" "USDC" min_width=200 %}
+ {% table_header "ethereum_balance" "ETH" min_width=150 %}
+ {% table_header "usdc_balance" "USDC" min_width=150 %}
{% table_header "stablecoin_total_balance" "Total Stablecoins" min_width=200 %}
- {% table_header "assets_total_value_usd" "Total Assets" min_width=200 %}
+ {% table_header "assets_total_value_usd" "Total Assets" min_width=150 %}
{% table_header "updated_at" "Updated" min_width=250 %}
{% endblock %}
{% block table_rows %}
- {% foreign_key_link entry %} |
+ {% foreign_key_link entry truncate_chars=10 %} |
{% if entry.ens_name %}
{{ entry.ens_name }}
diff --git a/src/simmate/apps/price_catalog/clients/fred.py b/src/simmate/apps/price_catalog/clients/fred.py
index 74bc33437..08caa5f06 100644
--- a/src/simmate/apps/price_catalog/clients/fred.py
+++ b/src/simmate/apps/price_catalog/clients/fred.py
@@ -18,6 +18,27 @@ class FredClient:
"Electricity": "CUSR0000SEHF01", # or APU000072610
"Housing": "CSUSHPINSA",
"Consumer Price Index": "CPIAUCSL", # use for inflation metrics
+ #
+ # Producer Price Index (PPI):
+ # sections for "Industrial Chemicals" or "Chemicals and Allied Products"
+ # Make sure you look at this link to see the full tree of these tickers
+ # and how they relate to one another (some are subcategories of others)
+ # https://fred.stlouisfed.org/release/tables?rid=46&eid=142872#snid=142874
+ "Chemicals": "WPU06", # & Allied Products
+ "Industrial Chemicals": "WPU061",
+ #
+ "Inorganic Chemicals": "WPU0613",
+ "Organic Chemicals": "WPU0614",
+ "Industrial Gases": "WPU067903",
+ #
+ "Carbon": "WPU06790918", # Carbon Black
+ # "Carbon Dioxide": "WPU06790302",
+ "Nitrogen": "WPU06790303",
+ "Oxygen": "WPU06790304",
+ "Aluminum": "WPU06130209", # Aluminum containing compounds
+ # "Sulfuric Acid": "WPU0613020T1",
+ # "NaCl": "WPU06130271", # Rock Salt
+ # "Ethanol": "WPU06140341",
}
@classmethod
@@ -41,7 +62,7 @@ def get_data(cls, name: str):
@staticmethod
def get_ticker_data(ticker: str):
"""
- Uses the Yahoo ticker value to grab data. Can be any ticker in the live site
+ Uses the FRED ticker value to grab data. Can be any ticker in the live site
such as `SPY` or `GOOGL`
"""
url = f"https://fred.stlouisfed.org/graph/fredgraph.csv?id={ticker}"
diff --git a/src/simmate/apps/price_catalog/components/__init__.py b/src/simmate/apps/price_catalog/components/__init__.py
new file mode 100644
index 000000000..08a41676a
--- /dev/null
+++ b/src/simmate/apps/price_catalog/components/__init__.py
@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from .priced_items_report import PricedItemsReport
diff --git a/src/simmate/apps/price_catalog/components/priced_items_report.py b/src/simmate/apps/price_catalog/components/priced_items_report.py
new file mode 100644
index 000000000..5f1aa3231
--- /dev/null
+++ b/src/simmate/apps/price_catalog/components/priced_items_report.py
@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+
+from simmate.website.htmx.components import HtmxComponent
+
+from ..models import PricedItem
+
+
+class PricedItemsReport(HtmxComponent):
+
+ template_name = "price_catalog/priced_items/report.html"
+
+ item_names_options: list[str] = None # set in mount
+ # years_ago_norm_options: list[int] = PricedItem.years_ago_options
+
+ years_ago_norm_options = [
+ (1, "Δ1y"),
+ (5, "Δ5y"),
+ (10, "Δ10y"),
+ (25, "Δ25y"),
+ ("max", "Max"),
+ ]
+
+ def mount(self):
+
+ self.priced_item_id = self.request.resolver_match.kwargs["table_entry_id"]
+ self.priced_item = PricedItem.objects.get(id=self.priced_item_id)
+
+ self.item_names_options = list(
+ PricedItem.objects.values_list("name", flat=True).order_by("name").all()
+ )
+ self.form_data = {
+ "item_names": [], # ["S&P 500", "Gold"]
+ "use_percent": False,
+ "use_inflation_adj": False,
+ "years_ago_norm": "max",
+ }
+ self.process() # to create figure
+
+ def post_parse(self):
+ # BUG: checkbox field does not show in POST data when =False. This is
+ # normal HTML behavior to save on bandwidth, but causes a bug here.
+ if "use_percent" not in self.post_data.keys():
+ self.post_data["use_percent"] = False
+ if "use_inflation_adj" not in self.post_data.keys():
+ self.post_data["use_inflation_adj"] = False
+
+ def process(self):
+
+ if self.form_data["item_names"]:
+ self.figure = PricedItem.get_report_figure(
+ names=[self.priced_item.name] + self.form_data["item_names"],
+ years_ago_norm=(
+ self.form_data["years_ago_norm"]
+ if self.form_data["years_ago_norm"] != "max"
+ else None
+ ),
+ percent=(
+ self.form_data["use_percent"]
+ if self.form_data["years_ago_norm"] != "max"
+ else None
+ ),
+ inflation_adj=self.form_data["use_inflation_adj"],
+ )
+
+ elif self.form_data["years_ago_norm"] == "max":
+ self.figure = self.priced_item.get_price_figure(
+ inflation_adj=self.form_data["use_inflation_adj"],
+ )
+
+ else:
+ self.figure = self.priced_item.get_delta_figure(
+ years_ago=self.form_data["years_ago_norm"],
+ percent=self.form_data["use_percent"],
+ inflation_adj=self.form_data["use_inflation_adj"],
+ )
+
+ # -------------------------------------------------------------------------
+
+ def on_change_hook__item_names(self):
+ # bug-fix: when only one is selected, the value isn't put in a list
+ if isinstance(self.form_data["item_names"], str):
+ self.form_data["item_names"] = [self.form_data["item_names"]]
+
+ def on_change_hook__years_ago_norm(self):
+ if self.form_data["years_ago_norm"] != "max":
+ self.form_data["item_names"] = []
+ # self.form_data["use_percent"] = True
+ # self.form_data["use_inflation_adj"] = True
diff --git a/src/simmate/apps/price_catalog/data/README.md b/src/simmate/apps/price_catalog/data/README.md
new file mode 100644
index 000000000..9bbbfc7c4
--- /dev/null
+++ b/src/simmate/apps/price_catalog/data/README.md
@@ -0,0 +1,5 @@
+
+To quickly look up common chemicals & their prices, for "the big 3":
+- Sigma-Aldrich (MilliporeSigma): https://www.sigmaaldrich.com/US/en/products/chemistry-and-biochemicals/lab-chemicals
+- Fisher Scientific (Thermo Fisher Scientific): https://www.thermofisher.com/us/en/home/chemicals.html
+- VWR International (Avantor): https://www.avantorsciences.com/us/en/products/chemicals
diff --git a/src/simmate/apps/price_catalog/data/__init__.py b/src/simmate/apps/price_catalog/data/__init__.py
new file mode 100644
index 000000000..25963639b
--- /dev/null
+++ b/src/simmate/apps/price_catalog/data/__init__.py
@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from .utilities import WIKIPEDIA_PRICES_OF_ELEMENTS_DATA
diff --git a/src/simmate/apps/price_catalog/data/utilities.py b/src/simmate/apps/price_catalog/data/utilities.py
new file mode 100644
index 000000000..c0e9dd717
--- /dev/null
+++ b/src/simmate/apps/price_catalog/data/utilities.py
@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+
+from pathlib import Path
+
+import pandas
+
+
+def load_wikipedia_data() -> pandas.DataFrame:
+ """
+ Loads a snapshot dataset from the "Prices of chemical elements" page
+
+ This dataset was pulled from:
+ https://en.wikipedia.org/wiki/Prices_of_chemical_elements
+
+ Note, I used Gemini to web scrape bc I'm lazy. The following prompt was used
+ to generate the CSV file, and I spot checked only a little to confirm:
+
+ '''
+ Grab the table from this link and convert it to a CSV:
+ https://en.wikipedia.org/wiki/Prices_of_chemical_elements
+
+ The following should be done too:
+ - split the "abundance" column into two columns, one for mg/kg and one for total mass
+ - use "e" notation instead of "x10^" so that values are floats
+ - standardize numerical columns (e.g. convert ranges to single value and exclude any >, <, ~, etc symbols)
+ - leave a cell empty instead of using text like "Not traded"
+ - use the columns names of...
+ z, symbol, name, density_kg_l, abundance_mg_kg, total_mass_kg,
+ price_per_kg, price_per_l, year, source, notes
+ '''
+
+ """
+ datafile = Path(__file__).parent / "wikipedia_prices_of_elements.csv"
+ data = pandas.read_csv(datafile)
+ return data
+
+
+WIKIPEDIA_PRICES_OF_ELEMENTS_DATA = load_wikipedia_data()
diff --git a/src/simmate/apps/price_catalog/data/wikipedia_prices_of_elements.csv b/src/simmate/apps/price_catalog/data/wikipedia_prices_of_elements.csv
new file mode 100644
index 000000000..bb0611b23
--- /dev/null
+++ b/src/simmate/apps/price_catalog/data/wikipedia_prices_of_elements.csv
@@ -0,0 +1,124 @@
+z,symbol,name,density_kg_l,abundance_mg_kg,total_mass_kg,price_per_kg,price_per_l,year,source,notes
+1,H,Hydrogen,0.00008988,1400.0,3.878e19,1.39,0.000125,2012,DOE Hydrogen,
+1,"2H (D)",Deuterium,0.0001667,1.5e-5,4.155e11,13400.0,2.23,2020,CIL,
+2,He,Helium,0.0001785,0.008,2.216e14,24.0,0.00429,2018,USGS MCS,
+3,Li,Lithium,0.534,20.0,5.54e17,83.5,44.55,2020,SMM,
+4,Be,Beryllium,1.85,2.8,7.756e16,857.0,1590.0,2020,ISE 2020,
+5,B,Boron,2.34,10.0,2.77e17,3.68,8.62,2019,CEIC Data,
+6,C,Carbon,2.267,200.0,5.54e18,0.122,0.28,2018,EIA Coal,
+7,N,Nitrogen,0.0012506,19.0,5.263e17,0.14,0.000175,2001,Hypertextbook,
+8,O,Oxygen,0.001429,461000.0,1.277e22,0.154,0.00022,2001,Hypertextbook,
+9,F,Fluorine,0.001696,585.0,1.62e19,2.0,0.00338,2017,Echemi,
+10,Ne,Neon,0.0008999,0.005,1.385e14,240.0,0.21,1999,Ullmann,
+11,Na,Sodium,0.971,23600.0,6.537e20,3.0,2.91,2020,SMM,
+12,Mg,Magnesium,1.738,23300.0,6.454e20,2.32,4.03,2019,Preismonitor,
+13,Al,Aluminium,2.698,82300.0,2.28e21,1.79,4.84,2019,Preismonitor,
+14,Si,Silicon,2.3296,282000.0,7.811e21,1.7,3.97,2019,Preismonitor,
+15,P,Phosphorus,1.82,1050.0,2.909e19,2.69,4.9,2019,CEIC Data,
+16,S,Sulfur,2.067,350.0,9.695e18,0.0926,0.191,2019,CEIC Data,
+17,Cl,Chlorine,0.003214,145.0,4.075e18,0.082,0.00026,2013,CnAgri,
+18,Ar,Argon,0.0017837,3.5,9.695e16,0.931,0.00166,2019,UNLV,
+19,K,Potassium,0.862,20900.0,5.789e20,12.85,11.1,2020,SMM,
+20,Ca,Calcium,1.54,41500.0,1.15e21,2.28,3.52,2020,SMM,
+21,Sc,Scandium,2.989,22.0,6.094e17,3460.0,10300.0,2020,ISE 2020,
+22,Ti,Titanium,4.54,5650.0,1.565e20,11.4,51.8,2020,SMM,
+23,V,Vanadium,6.11,120.0,3.324e18,371.0,2265.0,2020,SMM,
+24,Cr,Chromium,7.15,102.0,2.825e18,9.4,67.2,2019,Preismonitor,
+25,Mn,Manganese,7.44,950.0,2.632e19,1.82,13.6,2019,Preismonitor,
+26,Fe,Iron,7.874,56300.0,1.565e21,0.424,3.34,2020,SMM,
+27,Co,Cobalt,8.86,25.0,6.925e17,32.8,291.0,2019,Preismonitor,
+28,Ni,Nickel,8.912,84.0,2.327e18,13.9,124.0,2019,Preismonitor,
+29,Cu,Copper,8.96,60.0,1.662e18,6.0,53.8,2019,Preismonitor,
+30,Zn,Zinc,7.134,70.0,1.939e18,2.55,18.2,2019,Preismonitor,
+31,Ga,Gallium,5.907,19.0,5.263e17,148.0,872.0,2019,Preismonitor,
+32,Ge,Germanium,5.323,1.5,4.155e16,962.0,5125.0,2020,SMM,
+33,As,Arsenic,5.776,1.8,4.986e16,1.155,6.675,2020,SMM,
+34,Se,Selenium,4.809,0.05,1.385e15,21.4,103.0,2019,Preismonitor,
+35,Br,Bromine,3.122,2.4,6.648e16,4.39,13.7,2019,CEIC Data,
+36,Kr,Krypton,0.003733,1.0e-4,2.77e12,290.0,1.1,1999,Ullmann,
+37,Rb,Rubidium,1.532,90.0,2.493e18,15500.0,23700.0,2018,USGS MCS,
+38,Sr,Strontium,2.64,370.0,1.025e19,6.605,17.4,2019,ISE 2019,
+39,Y,Yttrium,4.469,33.0,9.141e17,31.0,139.0,2019,Preismonitor,
+40,Zr,Zirconium,6.506,165.0,4.571e18,36.4,236.5,2020,SMM,
+41,Nb,Niobium,8.57,20.0,5.54e17,73.5,630.0,2020,SMM,
+42,Mo,Molybdenum,10.22,1.2,3.324e16,40.1,410.0,2019,Preismonitor,
+43,Tc,Technetium,11.5,3.0e-9,8.31e7,100000.0,1200000.0,2004,CRC Handbook,
+43,"99mTc","Technetium-99m",11.5,3.0e-9,8.31e7,1.9e12,2.2e13,2008,NRC,
+44,Ru,Ruthenium,12.37,0.001,2.77e13,10500.0,130000.0,2020,SMM,
+45,Rh,Rhodium,12.41,0.001,2.77e13,147000.0,1820000.0,2019,Preismonitor,
+46,Pd,Palladium,12.02,0.015,4.155e14,49500.0,595000.0,2019,Preismonitor,
+47,Ag,Silver,10.501,0.075,2.0775e15,521.0,5470.0,2019,Preismonitor,
+48,Cd,Cadmium,8.69,0.159,4.4043e15,2.73,23.8,2019,Preismonitor,
+49,In,Indium,7.31,0.25,6.925e15,167.0,1220.0,2019,Preismonitor,
+50,Sn,Tin,7.287,2.3,6.371e16,18.7,136.0,2019,Preismonitor,
+51,Sb,Antimony,6.685,0.2,5.54e15,5.79,38.7,2019,Preismonitor,
+52,Te,Tellurium,6.232,0.001,2.77e13,63.5,396.0,2019,Preismonitor,
+53,I,Iodine,4.93,0.45,1.2465e16,35.0,173.0,2019,Industrial Minerals,
+54,Xe,Xenon,0.005887,3.0e-5,8.31e11,1800.0,11.0,1999,Ullmann,
+55,Cs,Caesium,1.873,3.0,8.31e16,61800.0,116000.0,2018,USGS MCS,
+56,Ba,Barium,3.594,425.0,1.177e19,0.2605,0.938,2016,USGS MYB 2016,
+57,La,Lanthanum,6.145,39.0,1.08e18,4.85,29.85,2020,SMM,
+58,Ce,Cerium,6.77,66.5,1.84205e18,4.64,31.4,2020,SMM,
+59,Pr,Praseodymium,6.773,9.2,2.5484e17,103.0,695.0,2019,Preismonitor,
+60,Nd,Neodymium,7.007,41.5,1.14955e18,57.5,403.0,2019,Preismonitor,
+61,"147Pm","Promethium-147",7.26,5.0e-14,1.385e3,460000.0,3400000.0,2003,Radiochemistry Society,
+62,Sm,Samarium,7.52,7.05,1.95285e17,13.9,104.0,2019,Preismonitor,
+63,Eu,Europium,5.243,2.0,5.54e16,31.4,165.0,2020,ISE 2020,
+64,Gd,Gadolinium,7.895,6.2,1.7174e17,28.6,226.0,2020,ISE 2020,
+65,Tb,Terbium,8.229,1.2,3.324e16,658.0,5410.0,2019,Preismonitor,
+66,Dy,Dysprosium,8.55,5.2,1.4404e17,307.0,2630.0,2019,Preismonitor,
+67,Ho,Holmium,8.795,1.3,3.601e16,57.1,503.0,2020,ISE 2020,
+68,Er,Erbium,9.066,3.5,9.695e16,26.4,240.0,2020,ISE 2020,
+69,Tm,Thulium,9.321,0.52,1.4404e16,3000.0,28000.0,2003,IMAR,
+70,Yb,Ytterbium,6.965,3.2,8.864e16,17.1,119.0,2020,ISE 2020,
+71,Lu,Lutetium,9.84,0.8,2.216e16,643.0,6330.0,2020,ISE 2020,
+72,Hf,Hafnium,13.31,3.0,8.31e16,900.0,12000.0,2017,USGS MCS,
+73,Ta,Tantalum,16.654,2.0,5.54e16,305.0,5080.0,2019,ISE 2019,
+74,W,Tungsten,19.25,1.3,3.601e16,35.3,679.0,2019,Preismonitor,
+75,Re,Rhenium,21.02,7.0e-4,1.939e13,3580.0,75300.0,2020,SMM,
+76,Os,Osmium,22.61,0.002,5.54e13,30000.0,678000.0,2025,elementsales.com,
+77,Ir,Iridium,22.56,0.001,2.77e13,144000.0,3276000.0,2025,Umicore,
+78,Pt,Platinum,21.46,0.005,1.385e14,27800.0,596000.0,2019,Preismonitor,
+79,Au,Gold,19.282,0.004,1.108e14,75430.0,1454441.0,2024,London gold fix,
+80,Hg,Mercury,13.5336,0.085,2.3545e15,30.2,409.0,2017,USGS MCS,
+81,Tl,Thallium,11.85,0.85,2.3545e16,4200.0,49800.0,2017,USGS MCS,
+82,Pb,Lead,11.342,14.0,3.878e17,2.0,22.6,2019,Preismonitor,
+83,Bi,Bismuth,9.807,0.009,2.493e14,6.36,62.4,2019,Preismonitor,
+84,"209Po","Polonium-209",9.32,3.0e-16,8.31e0,4.92e13,4.58e14,2004,CRC Handbook (ORNL),
+85,At,Astatine,7.0,3.0e-20,8.31e-4,,,,
+86,Rn,Radon,0.00973,4.0e-13,1.108e4,,,,
+87,Fr,Francium,1.87,1.0e-18,2.77e-2,,,,
+88,Ra,Radium,5.5,9.0e-7,2.493e10,,,,
+89,"225Ac","Actinium-225",10.07,1.0e-11,2.77e5,2.9e13,2.9e14,2004,CRC Handbook (ORNL),
+90,Th,Thorium,11.72,9.6,2.6592e17,287.0,3360.0,2010,USGS MYB 2012,
+91,Pa,Protactinium,15.37,1.4e-6,3.878e10,,,,
+92,U,Uranium,18.95,2.7,7.479e16,101.0,1910.0,2018,EIA Uranium Marketing,
+93,Np,Neptunium,20.45,3.0e-12,8.31e4,660000.0,13500000.0,2003,Pomona,
+94,"239Pu","Plutonium-239",19.84,5.0e-14,1.385e3,6490000.0,129000000.0,2019,DOE OSTI,
+95,"241Am","Americium-241",13.69,,,728000.0,9970000.0,1998,NWA,
+95,"243Am","Americium-243",13.69,,,750000.0,10300000.0,2004,CRC Handbook (ORNL),
+96,"244Cm","Curium-244",13.51,,,185000000.0,2.5e9,2004,CRC Handbook (ORNL),
+96,"248Cm","Curium-248",13.51,,,1.6e11,2.16e12,2004,CRC Handbook (ORNL),
+97,"249Bk","Berkelium-249",14.79,,,1.85e11,2.74e12,2004,CRC Handbook (ORNL),
+98,"249Cf","Californium-249",15.1,,,1.85e11,2.79e12,2004,CRC Handbook (ORNL),
+98,"252Cf","Californium-252",15.1,,,6.0e10,9.06e11,2004,CRC Handbook (ORNL),
+99,Es,Einsteinium,8.84,,,,,,
+100,Fm,Fermium,9.7,,,,,,
+101,Md,Mendelevium,10.3,,,,,,
+102,No,Nobelium,9.9,,,,,,
+103,Lr,Lawrencium,15.6,,,,,,
+104,Rf,Rutherfordium,23.2,,,,,,
+105,Db,Dubnium,29.3,,,,,,
+106,Sg,Seaborgium,35.0,,,,,,
+107,Bh,Bohrium,37.1,,,,,,
+108,Hs,Hassium,40.7,,,,,,
+109,Mt,Meitnerium,37.4,,,,,,
+110,Ds,Darmstadtium,34.8,,,,,,
+111,Rg,Roentgenium,28.7,,,,,,
+112,Cn,Copernicium,14.0,,,,,,
+113,Nh,Nihonium,16.0,,,,,,
+114,Fl,Flerovium,9.928,,,,,,
+115,Mc,Moscovium,13.5,,,,,,
+116,Lv,Livermorium,12.9,,,,,,
+117,Ts,Tennessine,7.2,,,,,,
+118,Og,Oganesson,7.0,,,,,,
\ No newline at end of file
diff --git a/src/simmate/apps/price_catalog/migrations/0001_initial.py b/src/simmate/apps/price_catalog/migrations/0001_initial.py
index 1ae253782..93edd6df1 100644
--- a/src/simmate/apps/price_catalog/migrations/0001_initial.py
+++ b/src/simmate/apps/price_catalog/migrations/0001_initial.py
@@ -1,4 +1,4 @@
-# Generated by Django 4.2.7 on 2025-09-13 22:47
+# Generated by Django 4.2.7 on 2025-12-18 21:44
import django.db.models.deletion
from django.db import migrations, models
@@ -12,7 +12,7 @@ class Migration(migrations.Migration):
operations = [
migrations.CreateModel(
- name="MarketItem",
+ name="PricedItem",
fields=[
("id", models.AutoField(primary_key=True, serialize=False)),
(
@@ -26,25 +26,30 @@ class Migration(migrations.Migration):
("source", models.JSONField(blank=True, null=True)),
("category", models.TextField(blank=True, null=True)),
("name", models.TextField(blank=True, null=True)),
- ("ticker", models.TextField(blank=True, null=True)),
- ("ticker_source", models.TextField(blank=True, null=True)),
+ ("preferred_source", models.TextField(blank=True, null=True)),
("comments", models.TextField(blank=True, null=True)),
- ("global_abundance", models.FloatField(blank=True, null=True)),
("price", models.FloatField(blank=True, null=True)),
- ("price_10y_stats", models.JSONField(blank=True, null=True)),
+ ("price_unit", models.TextField(blank=True, null=True)),
+ ("global_abundance", models.FloatField(blank=True, null=True)),
+ ("global_abundance_unit", models.TextField(blank=True, null=True)),
+ ("market_cap", models.FloatField(blank=True, null=True)),
("global_abundance_kg", models.FloatField(blank=True, null=True)),
(
"global_abundance_mg_per_kg_crust",
models.FloatField(blank=True, null=True),
),
("price_per_kg", models.FloatField(blank=True, null=True)),
+ ("price_1y_stats", models.JSONField(blank=True, null=True)),
+ ("price_5y_stats", models.JSONField(blank=True, null=True)),
+ ("price_10y_stats", models.JSONField(blank=True, null=True)),
+ ("price_25y_stats", models.JSONField(blank=True, null=True)),
],
options={
- "db_table": "price_catalog__market_items",
+ "db_table": "price_catalog__priced_items",
},
),
migrations.CreateModel(
- name="PriceHistory",
+ name="PricePoint",
fields=[
("id", models.AutoField(primary_key=True, serialize=False)),
(
@@ -56,31 +61,54 @@ class Migration(migrations.Migration):
models.DateTimeField(auto_now=True, db_index=True, null=True),
),
("source", models.JSONField(blank=True, null=True)),
+ ("ticker_source", models.TextField(blank=True, null=True)),
+ ("ticker", models.TextField(blank=True, null=True)),
("date", models.DateTimeField(blank=True, null=True)),
("price", models.FloatField(blank=True, null=True)),
- ("price_normalized", models.FloatField(blank=True, null=True)),
+ ("price_inflation_adj", models.FloatField(blank=True, null=True)),
("comments", models.TextField(blank=True, null=True)),
+ ("delta_1y", models.FloatField(blank=True, null=True)),
+ ("delta_1y_inflation_adj", models.FloatField(blank=True, null=True)),
+ ("delta_1y_percent", models.FloatField(blank=True, null=True)),
+ (
+ "delta_1y_percent_inflation_adj",
+ models.FloatField(blank=True, null=True),
+ ),
+ ("delta_5y", models.FloatField(blank=True, null=True)),
+ ("delta_5y_inflation_adj", models.FloatField(blank=True, null=True)),
+ ("delta_5y_percent", models.FloatField(blank=True, null=True)),
+ (
+ "delta_5y_percent_inflation_adj",
+ models.FloatField(blank=True, null=True),
+ ),
("delta_10y", models.FloatField(blank=True, null=True)),
- ("delta_10y_normalized", models.FloatField(blank=True, null=True)),
+ ("delta_10y_inflation_adj", models.FloatField(blank=True, null=True)),
("delta_10y_percent", models.FloatField(blank=True, null=True)),
(
- "delta_10y_percent_normalized",
+ "delta_10y_percent_inflation_adj",
+ models.FloatField(blank=True, null=True),
+ ),
+ ("delta_25y", models.FloatField(blank=True, null=True)),
+ ("delta_25y_inflation_adj", models.FloatField(blank=True, null=True)),
+ ("delta_25y_percent", models.FloatField(blank=True, null=True)),
+ (
+ "delta_25y_percent_inflation_adj",
models.FloatField(blank=True, null=True),
),
(
- "market_item",
+ "priced_item",
models.ForeignKey(
blank=True,
null=True,
on_delete=django.db.models.deletion.PROTECT,
- related_name="price_history",
- to="price_catalog.marketitem",
+ related_name="price_points",
+ to="price_catalog.priceditem",
),
),
],
options={
- "db_table": "price_catalog__price_history",
- "unique_together": {("market_item", "date")},
+ "db_table": "price_catalog__price_points",
+ "unique_together": {("priced_item", "ticker", "date")},
},
),
]
diff --git a/src/simmate/apps/price_catalog/models/__init__.py b/src/simmate/apps/price_catalog/models/__init__.py
index 0f0e427a0..fbd6a42f7 100644
--- a/src/simmate/apps/price_catalog/models/__init__.py
+++ b/src/simmate/apps/price_catalog/models/__init__.py
@@ -1,4 +1,4 @@
# -*- coding: utf-8 -*-
-from .market_items import MarketItem
-from .price_history import PriceHistory
+from .price_points import PricePoint
+from .priced_items import PricedItem
diff --git a/src/simmate/apps/price_catalog/models/market_items.py b/src/simmate/apps/price_catalog/models/market_items.py
deleted file mode 100644
index c1338d32a..000000000
--- a/src/simmate/apps/price_catalog/models/market_items.py
+++ /dev/null
@@ -1,400 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from datetime import datetime
-
-import numpy
-import pandas
-import plotly.express as plotly_express
-import plotly.graph_objects as plotly_go
-from django.utils.timezone import make_aware
-from rich.progress import track
-
-from simmate.database.base_data_types import DatabaseTable, table_column
-
-
-class MarketItem(DatabaseTable):
-
- class Meta:
- db_table = "price_catalog__market_items"
-
- category_options = [
- "Chemical Elements",
- "Fuels & Energy",
- "Crops & Livestock",
- "Cryptocurrency",
- "Market Index",
- "Other", # e.g. housing
- ]
- category = table_column.TextField(blank=True, null=True)
-
- name = table_column.TextField(blank=True, null=True)
-
- ticker = table_column.TextField(blank=True, null=True)
-
- ticker_source_options = [
- "Yahoo", # Yahoo Finance
- "FRED", # Federal Reserve Bank of St. Louis
- "Wikipedia", # https://en.wikipedia.org/wiki/Prices_of_chemical_elements
- "Vendor(s)", # Sigma Aldrich, Fischer, eMolecules, ACD, etc.
- "Other",
- ]
- ticker_source = table_column.TextField(blank=True, null=True)
-
- comments = table_column.TextField(blank=True, null=True)
-
- # -------------------------------------------------------------------------
-
- global_abundance = table_column.FloatField(blank=True, null=True)
- # count
- # kg
- # infinite (for indexes/housing)
-
- price = table_column.FloatField(blank=True, null=True)
-
- price_10y_stats = table_column.JSONField(blank=True, null=True)
- # Start
- # Current
- # Min
- # Max
- # % change
- # % change normalized for inflation
-
- # -------------------------------------------------------------------------
-
- # Normalized values
-
- global_abundance_kg = table_column.FloatField(blank=True, null=True)
-
- global_abundance_mg_per_kg_crust = table_column.FloatField(blank=True, null=True)
-
- price_per_kg = table_column.FloatField(blank=True, null=True)
-
- # -------------------------------------------------------------------------
-
- @classmethod
- def get_figure(cls):
-
- from .price_history import PriceHistory
-
- data = PriceHistory.objects.order_by("market_item__name", "date").to_dataframe(
- [
- "market_item__name",
- "date",
- # "price",
- # "price_normalized",
- # "delta_10y",
- # "delta_10y_normalized",
- # "delta_10y_percent",
- "delta_10y_percent_normalized",
- ]
- )
- fig = plotly_express.line(
- data,
- x="date",
- y="delta_10y_percent_normalized",
- color="market_item__name",
- )
- fig.show(renderer="browser")
-
- def get_price_figure(self, normalized: bool = False):
-
- y_col = "price" if not normalized else "price_normalized"
- columns = ["date", y_col]
- data = self.price_history.order_by("date").to_dataframe(columns)
-
- fig = plotly_express.area(
- data,
- x="date",
- y=y_col,
- )
-
- fig.update_layout(yaxis_tickprefix="$")
-
- fig.show(renderer="browser")
-
- def get_delta_10y_figure(self, normalized: bool = False):
-
- ten_years_ago = self.get_10y_datetime()
- y_col = (
- "delta_10y_percent" if not normalized else "delta_10y_percent_normalized"
- )
- columns = ["date", y_col]
- data = self.price_history.filter(date__gte=ten_years_ago).to_dataframe(columns)
-
- # Separate positive and negative parts
- rel_change = data[y_col]
- pos = numpy.where(rel_change > 0, rel_change, 0) / 100
- neg = numpy.where(rel_change < 0, rel_change, 0) / 100
-
- fig = plotly_go.Figure()
-
- fig.add_trace(
- plotly_go.Scatter(
- x=data.date,
- y=pos,
- fill="tozeroy",
- name="Positive",
- line=dict(color="green"),
- showlegend=False,
- )
- )
-
- fig.add_trace(
- plotly_go.Scatter(
- x=data.date,
- y=neg,
- fill="tozeroy",
- name="Negative",
- line=dict(color="red"),
- showlegend=False,
- )
- )
-
- # Black line at Zero
- fig.add_trace(
- plotly_go.Scatter(
- x=data.date,
- y=[0] * len(data),
- mode="lines",
- line=dict(color="black", width=3),
- name="Zero",
- showlegend=False,
- )
- )
-
- fig.update_layout(yaxis_tickformat=".0%")
-
- fig.show(renderer="browser")
-
- # -------------------------------------------------------------------------
-
- @classmethod
- def _load_fred_data(cls):
-
- from ..clients.fred import FredClient
- from .price_history import PriceHistory
-
- all_data = FredClient.get_all_data()
-
- category_lookup = {
- "Chemical Elements": [],
- "Fuels & Energy": ["Electricity"],
- "Crops & Livestock": [],
- "Cryptocurrency": [],
- "Market Index": ["Consumer Price Index"],
- "Other": ["Housing"],
- }
-
- for name, data in track(all_data.items()):
-
- category_found = False
- for category, cat_names in category_lookup.items():
- if name in cat_names:
- category_found = True
- break
-
- market_item, _ = cls.objects.update_or_create(
- name=name,
- defaults=dict(
- category=category if category_found else None,
- ticker_source="FRED",
- ticker=FredClient.ticker_map[name],
- ),
- )
-
- price_objs = [
- PriceHistory(
- market_item_id=market_item.id,
- date=make_aware(row.observation_date),
- price=row.price, # we use the day's closing price
- )
- for i, row in data.iterrows()
- ]
-
- PriceHistory.objects.bulk_create(
- price_objs,
- batch_size=1_000,
- ignore_conflicts=True,
- )
-
- @classmethod
- def _load_yfinance_data(cls):
- from ..clients.yfinance import YahooFinanceClient
- from .price_history import PriceHistory
-
- all_data = YahooFinanceClient.get_all_data()
-
- category_lookup = {
- "Chemical Elements": [
- "Gold",
- "Silver",
- "Platinum",
- "Palladium",
- "Copper",
- ],
- "Fuels & Energy": [
- "Crude Oil",
- "Natural Gas",
- ],
- "Crops & Livestock": [
- "Lumber",
- "Soybeans",
- "Corn",
- "Wheat",
- "Coffee",
- "Sugar",
- "Cocoa",
- "Cotton",
- "Cattle",
- ],
- "Cryptocurrency": [
- "Bitcoin",
- "Ethereum",
- "Solana",
- ],
- "Market Index": [
- "S&P GSCI",
- "S&P 500",
- "Total Market Index",
- ],
- "Other": [],
- }
-
- for name, data in track(all_data.items()):
-
- category_found = False
- for category, cat_names in category_lookup.items():
- if name in cat_names:
- category_found = True
- break
-
- market_item, _ = cls.objects.update_or_create(
- name=name,
- defaults=dict(
- category=category if category_found else None,
- ticker_source="Yahoo",
- ticker=YahooFinanceClient.ticker_map[name],
- ),
- )
-
- price_objs = [
- PriceHistory(
- market_item_id=market_item.id,
- date=row.Date,
- price=row.Close, # we use the day's closing price
- )
- for i, row in data.iterrows()
- ]
-
- PriceHistory.objects.bulk_create(
- price_objs,
- batch_size=1_000,
- ignore_conflicts=True,
- )
-
- @classmethod
- def _load_wikipedia_data(cls):
- pass # https://en.wikipedia.org/wiki/Prices_of_chemical_elements
-
- # -------------------------------------------------------------------------
-
- @classmethod
- def get_buying_power_series(cls):
-
- cpi = cls.objects.get(name="Consumer Price Index")
- cpi_data = cpi.price_history.order_by("date").to_dataframe(["date", "price"])
-
- # will be set to $1 and used to normalize others
- todays_buying_power = (
- cpi_data.sort_values("date", ascending=False).iloc[0].price
- )
-
- # This plot decreases over time because of inflation. It will always have
- # a value of 1 today, but for example, 10 years ago might have a value
- # of 1.25 (i.e., $1 had 25% more buying power 10yrs ago relative to today).
- # When this plot is multiplied by another price plot, you can see an
- # inflation-adjusted plot
- cpi_data["relative_buying_power"] = 1 / (cpi_data.price / todays_buying_power)
-
- # return only two cols to avoid confusion
- return cpi_data[["date", "relative_buying_power"]]
-
- @classmethod
- def update_price_history_calcs(cls):
-
- from .price_history import PriceHistory
-
- # grab inflation data up front. We use the Consumer Price Index
- # and normalize it to buying power so that we can scale other data
- buying_power_data = cls.get_buying_power_series()
-
- ten_years_ago = cls.get_10y_datetime()
-
- for entry in track(cls.objects.all()):
-
- entry_10y = (
- entry.price_history.filter(date__gte=ten_years_ago)
- .order_by("date")
- .first()
- )
- price_10y = entry_10y.price
- bp_factor_10y = cls.interp_w_datetime(
- original_x=buying_power_data.date,
- original_y=buying_power_data.relative_buying_power,
- new_x=entry_10y.date,
- )
- price_normalized_10y = price_10y * bp_factor_10y
-
- price_objs = entry.price_history.order_by("date").all()
- for i in price_objs:
- # slow bc I call function one new_x value at time. I can speed
- # up by calling the full series, but code it uglier
- bp_factor = cls.interp_w_datetime(
- original_x=buying_power_data.date,
- original_y=buying_power_data.relative_buying_power,
- new_x=i.date,
- )
-
- i.price_normalized = i.price * bp_factor
- i.delta_10y = i.price - price_10y
- i.delta_10y_normalized = i.price_normalized - price_normalized_10y
- i.delta_10y_percent = (i.delta_10y / price_10y) * 100
- i.delta_10y_percent_normalized = (
- i.delta_10y_normalized / price_normalized_10y
- ) * 100
-
- PriceHistory.objects.bulk_update(
- objs=price_objs,
- fields=[
- "price_normalized",
- "delta_10y",
- "delta_10y_normalized",
- "delta_10y_percent",
- "delta_10y_percent_normalized",
- ],
- )
-
- @staticmethod
- def interp_w_datetime(original_x, original_y, new_x):
- # DEPREC: from scipy.interpolate import interp1d
- # For other options look at...
- # https://docs.scipy.org/doc/scipy/tutorial/interpolate/1D.html
- original_x = original_x.astype("int64") // 1e9 # to seconds
- new_x = pandas.Series(new_x).astype("int64") // 1e9 # to seconds
- new_y = numpy.interp(new_x, original_x, original_y)[0]
- return new_y
-
- @staticmethod
- def get_10y_datetime():
- today = datetime.now()
- # BUG: this might break on leap days
- ten_years_ago = make_aware(
- datetime(
- year=today.year - 10,
- month=today.month,
- day=today.day,
- ),
- )
- return ten_years_ago
-
- # -------------------------------------------------------------------------
diff --git a/src/simmate/apps/price_catalog/models/price_history.py b/src/simmate/apps/price_catalog/models/price_history.py
deleted file mode 100644
index 8042b29fd..000000000
--- a/src/simmate/apps/price_catalog/models/price_history.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from simmate.database.base_data_types import DatabaseTable, table_column
-
-from .market_items import MarketItem
-
-
-class PriceHistory(DatabaseTable):
-
- class Meta:
- db_table = "price_catalog__price_history"
- unique_together = (("market_item", "date"),)
-
- market_item = table_column.ForeignKey(
- MarketItem,
- on_delete=table_column.PROTECT,
- blank=True,
- null=True,
- related_name="price_history",
- )
-
- date = table_column.DateTimeField(blank=True, null=True)
-
- price = table_column.FloatField(blank=True, null=True)
-
- price_normalized = table_column.FloatField(blank=True, null=True)
- # for inflation
-
- comments = table_column.TextField(blank=True, null=True)
-
- # -------------------------------------------------------------------------
-
- # 10 years *from the current date*, not the date of this entry
-
- delta_10y = table_column.FloatField(blank=True, null=True)
- delta_10y_normalized = table_column.FloatField(blank=True, null=True)
-
- delta_10y_percent = table_column.FloatField(blank=True, null=True)
- delta_10y_percent_normalized = table_column.FloatField(blank=True, null=True)
-
- # -------------------------------------------------------------------------
diff --git a/src/simmate/apps/price_catalog/models/price_points.py b/src/simmate/apps/price_catalog/models/price_points.py
new file mode 100644
index 000000000..5bfe37d52
--- /dev/null
+++ b/src/simmate/apps/price_catalog/models/price_points.py
@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+
+from simmate.database.base_data_types import DatabaseTable, table_column
+
+from .priced_items import PricedItem
+
+
+class PricePoint(DatabaseTable):
+
+ class Meta:
+ db_table = "price_catalog__price_points"
+ unique_together = (("priced_item", "ticker", "date"),)
+
+ # -------------------------------------------------------------------------
+
+ # This is the the "PriceSource" -- which I keep in the same table for
+ # simplicity, even though it results in more disk space in the db.
+
+ priced_item = table_column.ForeignKey(
+ PricedItem,
+ on_delete=table_column.PROTECT,
+ blank=True,
+ null=True,
+ related_name="price_points",
+ )
+
+ ticker_source_options = [
+ "Yahoo", # Yahoo Finance
+ "FRED", # Federal Reserve Bank of St. Louis
+ "Wikipedia", # https://en.wikipedia.org/wiki/Prices_of_chemical_elements
+ "Chemical Vendors", # Sigma Aldrich, Fischer, VWR, etc.
+ "Other",
+ ]
+ ticker_source = table_column.TextField(blank=True, null=True)
+
+ ticker = table_column.TextField(blank=True, null=True)
+
+ # -------------------------------------------------------------------------
+
+ date = table_column.DateTimeField(blank=True, null=True)
+
+ price = table_column.FloatField(blank=True, null=True)
+
+ price_inflation_adj = table_column.FloatField(blank=True, null=True)
+
+ comments = table_column.TextField(blank=True, null=True)
+
+ # -------------------------------------------------------------------------
+
+ # Cached calculations
+
+ # N years *from today's current date*, not the date of this entry
+
+ delta_1y = table_column.FloatField(blank=True, null=True)
+ delta_1y_inflation_adj = table_column.FloatField(blank=True, null=True)
+ delta_1y_percent = table_column.FloatField(blank=True, null=True)
+ delta_1y_percent_inflation_adj = table_column.FloatField(blank=True, null=True)
+
+ delta_5y = table_column.FloatField(blank=True, null=True)
+ delta_5y_inflation_adj = table_column.FloatField(blank=True, null=True)
+ delta_5y_percent = table_column.FloatField(blank=True, null=True)
+ delta_5y_percent_inflation_adj = table_column.FloatField(blank=True, null=True)
+
+ delta_10y = table_column.FloatField(blank=True, null=True)
+ delta_10y_inflation_adj = table_column.FloatField(blank=True, null=True)
+ delta_10y_percent = table_column.FloatField(blank=True, null=True)
+ delta_10y_percent_inflation_adj = table_column.FloatField(blank=True, null=True)
+
+ delta_25y = table_column.FloatField(blank=True, null=True)
+ delta_25y_inflation_adj = table_column.FloatField(blank=True, null=True)
+ delta_25y_percent = table_column.FloatField(blank=True, null=True)
+ delta_25y_percent_inflation_adj = table_column.FloatField(blank=True, null=True)
+
+ # -------------------------------------------------------------------------
diff --git a/src/simmate/apps/price_catalog/models/priced_items.py b/src/simmate/apps/price_catalog/models/priced_items.py
new file mode 100644
index 000000000..2078fdbe7
--- /dev/null
+++ b/src/simmate/apps/price_catalog/models/priced_items.py
@@ -0,0 +1,577 @@
+# -*- coding: utf-8 -*-
+
+from datetime import datetime
+
+import numpy
+import pandas
+import plotly.express as plotly_express
+import plotly.graph_objects as plotly_go
+from django.utils.timezone import make_aware
+from rich.progress import track
+
+from simmate.database.base_data_types import DatabaseTable, table_column
+
+
+class PricedItem(DatabaseTable):
+ # This page is intended for evaluating long-term trends, not live trading -
+ # so prices are only updated & sync'd at the start of each month.
+
+ class Meta:
+ db_table = "price_catalog__priced_items"
+
+ html_display_name = "Market Data & Price Catalog"
+ html_description_short = (
+ "Prices and economic indicators spanning common chemicals, stocks, "
+ "commodities, cryptocurrencies, and macroeconomic metrics."
+ )
+
+ html_entries_template = "price_catalog/priced_items/table.html"
+ html_entry_template = "price_catalog/priced_items/entry.html"
+
+ # html_form_component = "priced-item-form"
+ # html_enabled_forms = ["search"]
+
+ # -------------------------------------------------------------------------
+
+ category_options = [
+ "Chemical Index",
+ "Chemical Elements",
+ # "Chemical Solvents",
+ # "Chemical Reagents",
+ "Fuels & Energy",
+ "Crops & Livestock",
+ "Cryptocurrency",
+ "Market Index",
+ "Other", # e.g. housing
+ ]
+ category = table_column.TextField(blank=True, null=True)
+
+ name = table_column.TextField(blank=True, null=True)
+
+ preferred_source_options = [
+ "Yahoo", # Yahoo Finance
+ "FRED", # Federal Reserve Bank of St. Louis
+ "Wikipedia", # https://en.wikipedia.org/wiki/Prices_of_chemical_elements
+ "Chemical Vendors", # Sigma Aldrich, Fischer, VWR, etc.
+ "Other",
+ ]
+ preferred_source = table_column.TextField(blank=True, null=True)
+
+ comments = table_column.TextField(blank=True, null=True)
+
+ # -------------------------------------------------------------------------
+
+ price = table_column.FloatField(blank=True, null=True)
+ # filled using the most recent price point of the preferred source
+
+ # per kg
+ # per share
+ # per kW/hr
+ price_unit = table_column.TextField(blank=True, null=True)
+
+ global_abundance = table_column.FloatField(blank=True, null=True)
+
+ # count
+ # kg
+ # infinite (for indexes/housing)
+ global_abundance_unit = table_column.TextField(blank=True, null=True)
+
+ market_cap = table_column.FloatField(blank=True, null=True)
+ # price * global_abundance
+
+ # -------------------------------------------------------------------------
+
+ # Standardized values
+
+ global_abundance_kg = table_column.FloatField(blank=True, null=True)
+
+ global_abundance_mg_per_kg_crust = table_column.FloatField(blank=True, null=True)
+
+ price_per_kg = table_column.FloatField(blank=True, null=True)
+
+ # -------------------------------------------------------------------------
+
+ # Price history stats
+
+ # start ($)
+ # min ($)
+ # max ($)
+ # change (%)
+ # change_inflation_adj (%) for inflation
+
+ years_ago_options = [1, 5, 10, 25]
+
+ price_1y_stats = table_column.JSONField(blank=True, null=True)
+
+ price_5y_stats = table_column.JSONField(blank=True, null=True)
+
+ price_10y_stats = table_column.JSONField(blank=True, null=True)
+
+ price_25y_stats = table_column.JSONField(blank=True, null=True)
+
+ # -------------------------------------------------------------------------
+
+ enable_html_report = True
+ report_df_columns = ["id"]
+
+ @classmethod
+ def get_report_from_df(cls, df: pandas.DataFrame):
+ return {"test": 123}
+
+ # -------------------------------------------------------------------------
+
+ @classmethod
+ def get_report_figure(
+ cls,
+ names: list[str],
+ percent: bool = False,
+ inflation_adj: bool = False,
+ years_ago_norm: int = None,
+ ):
+
+ from .price_points import PricePoint
+
+ if years_ago_norm:
+ price_mode = f"delta_{years_ago_norm}y"
+ if percent:
+ price_mode += "_percent"
+ if inflation_adj:
+ price_mode += "_inflation_adj"
+ date_cutoff = cls.get_years_ago_datetime(years_ago_norm)
+
+ else:
+ price_mode = "price"
+ date_cutoff = cls.get_years_ago_datetime(100)
+
+ data = (
+ PricePoint.objects.filter(
+ priced_item__name__in=names,
+ date__gte=date_cutoff,
+ )
+ .order_by("priced_item__name", "date")
+ .to_dataframe(["priced_item__name", price_mode, "date"])
+ )
+
+ figure = plotly_express.line(
+ data,
+ x="date",
+ y=price_mode,
+ color="priced_item__name",
+ )
+ return figure
+
+ def get_price_figure(self, inflation_adj: bool = False):
+
+ y_col = "price" if not inflation_adj else "price_inflation_adj"
+ columns = ["date", y_col]
+ data = self.price_points.order_by("date").to_dataframe(columns)
+
+ figure = plotly_express.area(
+ data,
+ x="date",
+ y=y_col,
+ )
+
+ figure.update_layout(yaxis_tickprefix="$")
+ return figure
+
+ def get_delta_figure(
+ self,
+ years_ago: int,
+ percent: bool = False,
+ inflation_adj: bool = False,
+ ):
+
+ if years_ago not in self.years_ago_options:
+ raise Exception(
+ f"`years_ago` must be set to one of {self.years_ago_options}"
+ )
+
+ date_cutoff = self.get_years_ago_datetime(years_ago)
+
+ y_col = f"delta_{years_ago}y"
+ if percent:
+ y_col += "_percent"
+ if inflation_adj:
+ y_col += "_inflation_adj"
+ columns = ["date", y_col]
+
+ data = (
+ self.price_points.filter(date__gte=date_cutoff)
+ .order_by("date")
+ .to_dataframe(columns)
+ )
+
+ # Separate positive and negative parts
+ rel_change = data[y_col]
+ pos = numpy.where(rel_change > 0, rel_change, 0)
+ neg = numpy.where(rel_change < 0, rel_change, 0)
+
+ if percent:
+ pos = pos / 100
+ neg = neg / 100
+
+ figure = plotly_go.Figure()
+
+ figure.add_trace(
+ plotly_go.Scatter(
+ x=data.date,
+ y=pos,
+ fill="tozeroy",
+ name="Positive",
+ line=dict(color="green"),
+ showlegend=False,
+ )
+ )
+
+ figure.add_trace(
+ plotly_go.Scatter(
+ x=data.date,
+ y=neg,
+ fill="tozeroy",
+ name="Negative",
+ line=dict(color="red"),
+ showlegend=False,
+ )
+ )
+
+ # Black line at Zero
+ figure.add_trace(
+ plotly_go.Scatter(
+ x=data.date,
+ y=[0] * len(data),
+ mode="lines",
+ line=dict(color="black", width=3),
+ name="Zero",
+ showlegend=False,
+ )
+ )
+
+ if percent:
+ figure.update_layout(yaxis_tickformat=".0%")
+ else:
+ figure.update_layout(yaxis_tickprefix="$")
+
+ return figure
+
+ # -------------------------------------------------------------------------
+
+ @classmethod
+ def get_buying_power_series(cls, reference: str = "Consumer Price Index"):
+
+ cpi = cls.objects.get(name=reference)
+ cpi_data = cpi.price_points.order_by("date").to_dataframe(["date", "price"])
+
+ # will be set to $1 and used to normalize others
+ todays_buying_power = (
+ cpi_data.sort_values("date", ascending=False).iloc[0].price
+ )
+
+ # This plot decreases over time because of inflation. It will always have
+ # a value of 1 today, but for example, 10 years ago might have a value
+ # of 1.25 (i.e., $1 had 25% more buying power 10yrs ago relative to today).
+ # When this plot is multiplied by another price plot, you can see an
+ # inflation-adjusted plot
+ cpi_data["relative_buying_power"] = 1 / (cpi_data.price / todays_buying_power)
+
+ # return only two cols to avoid confusion
+ return cpi_data[["date", "relative_buying_power"]]
+
+ @classmethod
+ def update_price_points_calcs(cls):
+
+ from .price_points import PricePoint
+
+ # grab inflation data up front. We use the Consumer Price Index
+ # and normalize it to buying power so that we can scale other data
+ buying_power_data = cls.get_buying_power_series()
+
+ for entry in track(cls.objects.all()):
+
+ for years_ago in cls.years_ago_options:
+
+ year_ago_dt = cls.get_years_ago_datetime(years_ago)
+
+ entry_closest = (
+ entry.price_points.filter(date__gte=year_ago_dt)
+ .order_by("date")
+ .first()
+ )
+
+ # check that the date is within at least 6months
+ if (
+ entry_closest.date - year_ago_dt
+ ).total_seconds() >= 60 * 60 * 24 * 30 * 6:
+ continue
+
+ price_closest = entry_closest.price
+ bp_factor_closest = cls.interp_w_datetime(
+ original_x=buying_power_data.date,
+ original_y=buying_power_data.relative_buying_power,
+ new_x=entry_closest.date,
+ )
+ price_inflation_adj_closest = price_closest * bp_factor_closest
+
+ price_objs = entry.price_points.order_by("date").all()
+ for i in price_objs:
+
+ # This is the bottleneck + slow bc I call function one
+ # new_x value at time. I can speed up by calling the full
+ # series, but code would be uglier. I cache this data and
+ # run once per day, so I'm okay with the slowdown
+ bp_factor = cls.interp_w_datetime(
+ original_x=buying_power_data.date,
+ original_y=buying_power_data.relative_buying_power,
+ new_x=i.date,
+ )
+
+ price_inflation_adj = i.price * bp_factor
+
+ delta = i.price - price_closest
+
+ delta_inflation_adj = (
+ price_inflation_adj - price_inflation_adj_closest
+ )
+
+ delta_percent = (delta / price_closest) * 100
+
+ delta_percent_inflation_adj = (
+ delta_inflation_adj / price_inflation_adj_closest
+ ) * 100
+
+ update_map = {
+ "price_inflation_adj": price_inflation_adj,
+ f"delta_{years_ago}y": delta,
+ f"delta_{years_ago}y_inflation_adj": delta_inflation_adj,
+ f"delta_{years_ago}y_percent": delta_percent,
+ f"delta_{years_ago}y_percent_inflation_adj": delta_percent_inflation_adj,
+ }
+ for k, v in update_map.items():
+ setattr(i, k, v)
+
+ PricePoint.objects.bulk_update(
+ objs=price_objs,
+ fields=[
+ "price_inflation_adj",
+ f"delta_{years_ago}y",
+ f"delta_{years_ago}y_inflation_adj",
+ f"delta_{years_ago}y_percent",
+ f"delta_{years_ago}y_percent_inflation_adj",
+ ],
+ )
+
+ # update parent object
+ entry.price = entry.price_points.order_by("-date").first().price
+ all_prices = [p.price for p in price_objs]
+ stats = {
+ "start": price_closest,
+ "min": min(all_prices),
+ "max": max(all_prices),
+ "change": ((entry.price - price_closest) / price_closest) * 100,
+ "change_inflation_adj": (
+ (entry.price - price_inflation_adj_closest) / price_closest
+ )
+ * 100,
+ }
+ setattr(entry, f"price_{years_ago}y_stats", stats)
+ entry.save()
+
+ @staticmethod
+ def interp_w_datetime(original_x, original_y, new_x):
+ # DEPREC: from scipy.interpolate import interp1d
+ # For other options look at...
+ # https://docs.scipy.org/doc/scipy/tutorial/interpolate/1D.html
+ original_x = original_x.astype("int64") // 1e9 # to seconds
+ new_x = pandas.Series(new_x).astype("int64") // 1e9 # to seconds
+ new_y = numpy.interp(new_x, original_x, original_y)[0]
+ return new_y
+
+ @staticmethod
+ def get_years_ago_datetime(years: int):
+ today = datetime.now()
+ ten_years_ago = make_aware(
+ datetime(
+ year=today.year - years,
+ month=today.month,
+ day=today.day, # BUG: this might break on leap days
+ ),
+ )
+ return ten_years_ago
+
+ # -------------------------------------------------------------------------
+
+ @classmethod
+ def _load_data(cls):
+ # cls._load_wikipedia_data()
+ cls._load_yfinance_data()
+ cls._load_fred_data()
+ cls.update_price_points_calcs()
+
+ @classmethod
+ def _load_fred_data(cls):
+
+ from ..clients.fred import FredClient
+ from .price_points import PricePoint
+
+ all_data = FredClient.get_all_data()
+
+ category_lookup = {
+ "Chemical Index": [
+ "Chemicals",
+ "Industrial Chemicals",
+ "Inorganic Chemicals",
+ "Organic Chemicals",
+ "Industrial Gases",
+ ],
+ "Chemical Elements": [
+ "Carbon",
+ "Nitrogen",
+ "Oxygen",
+ "Aluminum",
+ ],
+ "Fuels & Energy": ["Electricity"],
+ "Crops & Livestock": [],
+ "Cryptocurrency": [],
+ "Market Index": ["Consumer Price Index"],
+ "Other": ["Housing"],
+ }
+
+ for name, data in track(all_data.items()):
+
+ # sometimes a row is missing the price and/or timestamp
+ data.dropna(inplace=True)
+
+ category_found = False
+ for category, cat_names in category_lookup.items():
+ if name in cat_names:
+ category_found = True
+ break
+
+ priced_item, _ = cls.objects.update_or_create(
+ name=name,
+ defaults=dict(
+ category=category if category_found else None,
+ ),
+ )
+
+ price_objs = [
+ PricePoint(
+ priced_item_id=priced_item.id,
+ ticker_source="FRED",
+ ticker=FredClient.ticker_map[name],
+ #
+ date=make_aware(row.observation_date),
+ price=row.price, # we use the day's closing price
+ )
+ for i, row in data.iterrows()
+ ]
+
+ # TODO: ensure I don't create duplicate entries on update
+ PricePoint.objects.bulk_create(
+ price_objs,
+ batch_size=1_000,
+ ignore_conflicts=True,
+ )
+
+ @classmethod
+ def _load_yfinance_data(cls):
+ from ..clients.yfinance import YahooFinanceClient
+ from .price_points import PricePoint
+
+ all_data = YahooFinanceClient.get_all_data()
+
+ category_lookup = {
+ "Chemical Index": [],
+ "Chemical Elements": [
+ "Gold",
+ "Silver",
+ "Platinum",
+ "Palladium",
+ "Copper",
+ ],
+ "Fuels & Energy": [
+ "Crude Oil",
+ "Natural Gas",
+ ],
+ "Crops & Livestock": [
+ "Lumber",
+ "Soybeans",
+ "Corn",
+ "Wheat",
+ "Coffee",
+ "Sugar",
+ "Cocoa",
+ "Cotton",
+ "Cattle",
+ ],
+ "Cryptocurrency": [
+ "Bitcoin",
+ "Ethereum",
+ "Solana",
+ ],
+ "Market Index": [
+ "S&P GSCI",
+ "S&P 500",
+ "Total Market Index",
+ ],
+ "Other": [],
+ }
+
+ for name, data in track(all_data.items()):
+
+ category_found = False
+ for category, cat_names in category_lookup.items():
+ if name in cat_names:
+ category_found = True
+ break
+
+ priced_item, _ = cls.objects.update_or_create(
+ name=name,
+ defaults=dict(
+ category=category if category_found else None,
+ ),
+ )
+
+ price_objs = [
+ PricePoint(
+ priced_item_id=priced_item.id,
+ ticker_source="Yahoo",
+ ticker=YahooFinanceClient.ticker_map[name],
+ #
+ date=row.Date,
+ price=row.Close, # we use the day's closing price
+ )
+ for i, row in data.iterrows()
+ ]
+
+ PricePoint.objects.bulk_create(
+ price_objs,
+ batch_size=1_000,
+ ignore_conflicts=True,
+ )
+
+ @classmethod
+ def _load_wikipedia_data(cls):
+
+ raise NotImplementedError()
+
+ from ..data import WIKIPEDIA_PRICES_OF_ELEMENTS_DATA
+
+ for row in WIKIPEDIA_PRICES_OF_ELEMENTS_DATA.itertuples():
+ priced_item, _ = cls.objects.update_or_create(
+ name=row.name,
+ defaults=dict(
+ category="Chemical Elements",
+ ticker_source="Wikipedia",
+ ticker=row.symbol,
+ price=row.price_per_kg,
+ price_unit="per kg",
+ price_per_kg=row.price_per_kg,
+ global_abundance=row.total_mass_kg,
+ global_abundance_unit="kg",
+ market_cap=row.price_per_kg * row.total_mass_kg,
+ global_abundance_kg=row.total_mass_kg,
+ global_abundance_mg_per_kg_crust=row.abundance_mg_kg,
+ # TODO: store metadata of year + source + price_per_l + tec
+ ),
+ )
diff --git a/src/simmate/apps/price_catalog/templates/price_catalog/priced_items/entry.html b/src/simmate/apps/price_catalog/templates/price_catalog/priced_items/entry.html
new file mode 100644
index 000000000..b93f4b0df
--- /dev/null
+++ b/src/simmate/apps/price_catalog/templates/price_catalog/priced_items/entry.html
@@ -0,0 +1,122 @@
+{% extends "data_explorer/table_entry.html" %}
+{% block entrycontent %}
+
+
+ {{ table_entry.name }}
+
+
+
+ | Category |
+ {{ table_entry.category }} |
+
+
+ | Price |
+
+ {% if table_entry.price_per_kg %}
+ ${{ table_entry.price_per_kg|floatformat:2 }} per kg
+ {% elif table_entry.price %}
+ ${{ table_entry.price|floatformat:2|intcomma }} {# entry.price_unit #}
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+
+ | 1y |
+
+ {% if table_entry.price_1y_stats.change_inflation_adj > 0 %}
+
+ {{ table_entry.price_1y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif table_entry.price_1y_stats.change_inflation_adj < 0 %}
+
+ {{ table_entry.price_1y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif table_entry.price_1y_stats.change_inflation_adj == 0 %}
+ 0
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+
+ | 5y |
+
+ {% if table_entry.price_5y_stats.change_inflation_adj > 0 %}
+
+ {{ table_entry.price_5y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif table_entry.price_5y_stats.change_inflation_adj < 0.0 %}
+
+ {{ table_entry.price_5y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif table_entry.price_5y_stats.change_inflation_adj == 0 %}
+ 0
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+
+ | 10y |
+
+ {% if table_entry.price_10y_stats.change_inflation_adj > 0 %}
+
+ {{ table_entry.price_10y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif table_entry.price_10y_stats.change_inflation_adj < 0 %}
+
+ {{ table_entry.price_10y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif table_entry.price_10y_stats.change_inflation_adj == 0 %}
+ 0
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+
+ | 25y |
+
+ {% if table_entry.price_25y_stats.change_inflation_adj > 0 %}
+
+ {{ table_entry.price_25y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif table_entry.price_25y_stats.change_inflation_adj < 0 %}
+
+ {{ table_entry.price_25y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif table_entry.price_25y_stats.change_inflation_adj == 0 %}
+ 0
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+
+ | Global Abundance |
+
+ {% if table_entry.global_abundance_mg_per_kg_crust %}
+ ${{ table_entry.price_per_kg }} mg per kg Earth's crust
+ {% elif table_entry.global_abundance_kg %}
+ ${{ table_entry.price_per_kg }} kg
+ {% elif table_entry.global_abundance %}
+ {{ table_entry.global_abundance }}
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+
+ | Market Cap |
+ --- |
+
+
+
+
+ * percents are adjusted for inflation
+
+
+ {% htmx_component "priced-items-report" %}
+
+ {% htmx_button_select name="years_ago_norm" show_label=False defer=False %}
+ {% if component.form_data.years_ago_norm != "max" %}
+ {% htmx_checkbox name="use_percent" show_label=False side_text="Percent (%)" defer=False %}
+ {% endif %}
+
+ {% htmx_checkbox name="use_inflation_adj" show_label=False side_text="Adjust for Inflation (CPI)" defer=False %}
+
+ {% htmx_selectbox name="item_names" show_label=False multiselect=True defer=False %}
+
+
+
+ Percentages are adjusted for inflation using the Consumer Price Index
+
+ | {{ entry.category }} |
+ {% foreign_key_link entry "name" %} |
+
+ {% if entry.price_per_kg %}
+ ${{ entry.price_per_kg|floatformat:2 }} per kg
+ {% elif entry.price %}
+ ${{ entry.price|floatformat:2|intcomma }} {# entry.price_unit #}
+ {% else %}
+ ---
+ {% endif %}
+ |
+ {% if entry.name == "Consumer Price Index" %}
+ 0 |
+ 0 |
+ 0 |
+ 0 |
+ {% else %}
+
+ {% if entry.price_1y_stats.change_inflation_adj > 0 %}
+
+ {{ entry.price_1y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif entry.price_1y_stats.change_inflation_adj < 0 %}
+
+ {{ entry.price_1y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif entry.price_1y_stats.change_inflation_adj == 0 %}
+ 0
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+ {% if entry.price_5y_stats.change_inflation_adj > 0 %}
+
+ {{ entry.price_5y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif entry.price_5y_stats.change_inflation_adj < 0.0 %}
+
+ {{ entry.price_5y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif entry.price_5y_stats.change_inflation_adj == 0 %}
+ 0
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+ {% if entry.price_10y_stats.change_inflation_adj > 0 %}
+
+ {{ entry.price_10y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif entry.price_10y_stats.change_inflation_adj < 0 %}
+
+ {{ entry.price_10y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif entry.price_10y_stats.change_inflation_adj == 0 %}
+ 0
+ {% else %}
+ ---
+ {% endif %}
+ |
+
+ {% if entry.price_25y_stats.change_inflation_adj > 0 %}
+
+ {{ entry.price_25y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif entry.price_25y_stats.change_inflation_adj < 0 %}
+
+ {{ entry.price_25y_stats.change_inflation_adj|floatformat:1|intcomma }}%
+
+ {% elif entry.price_25y_stats.change_inflation_adj == 0 %}
+ 0
+ {% else %}
+ ---
+ {% endif %}
+ |
+ {% endif %}
+
+ {% if entry.global_abundance_mg_per_kg_crust %}
+ ${{ entry.price_per_kg }} mg per kg Earth's crust
+ {% elif entry.global_abundance_kg %}
+ ${{ entry.price_per_kg }} kg
+ {% elif entry.global_abundance %}
+ {{ entry.global_abundance }}
+ {% else %}
+ ---
+ {% endif %}
+ |
+ --- |
+{% endblock %}
diff --git a/src/simmate/configuration/load_settings.py b/src/simmate/configuration/load_settings.py
index 16a02326d..3bb993410 100644
--- a/src/simmate/configuration/load_settings.py
+++ b/src/simmate/configuration/load_settings.py
@@ -263,9 +263,9 @@ def default_settings(self) -> dict:
"simmate.apps.evolution.models.ChemicalSystemSearch",
],
"Business and Finance": [
+ "simmate.apps.price_catalog.models.PricedItem",
"simmate.apps.ethereum.models.EthereumWallet",
"simmate.apps.ethereum.models.EthereumTransaction",
- "simmate.apps.price_catalog.models.MarketItem",
],
"Other": [
"simmate.apps.eppo_gd.models.EppoCode",
@@ -277,7 +277,7 @@ def default_settings(self) -> dict:
"simmate.apps.chembl.models.ChemblDocument",
"simmate.apps.emolecules.models.EmoleculesSupplierOffer",
"simmate.apps.evolution.models.SteadystateSource",
- "simmate.apps.price_catalog.models.PriceHistory",
+ "simmate.apps.price_catalog.models.PricePoint",
],
# "Chemical Inventory & Tracking": [],
# "Computational Assay Tracking": [],
diff --git a/src/simmate/website/core_components/templates/core_components/apps.html b/src/simmate/website/core_components/templates/core_components/apps.html
index 29667661f..e202a1ee4 100644
--- a/src/simmate/website/core_components/templates/core_components/apps.html
+++ b/src/simmate/website/core_components/templates/core_components/apps.html
@@ -18,7 +18,7 @@ {{ extra_app.verbose_name }}
{{ extra_app.description_short }}
Explore
+ href="{{ extra_app.short_name }}/">Explore
diff --git a/src/simmate/website/core_components/templates/core_components/basic_elements/foreign_key_link.html b/src/simmate/website/core_components/templates/core_components/basic_elements/foreign_key_link.html
index b0049c468..307b3d0bd 100644
--- a/src/simmate/website/core_components/templates/core_components/basic_elements/foreign_key_link.html
+++ b/src/simmate/website/core_components/templates/core_components/basic_elements/foreign_key_link.html
@@ -3,9 +3,9 @@
{% if mode == "pill" %} class="badge bg-primary rounded-pill" {% elif mode == "block" %} type="button" class="btn btn-secondary btn-sm col-10 m-0" {% endif %}
{% if open_in_new %}target="_blank"{% endif %}>
{% if display_column %}
- {{ entry|getattribute:display_column }}
+ {{ entry|getattribute:display_column|truncatechars:truncate_chars }}
{% else %}
- {{ entry.id }}
+ {{ entry.id|truncatechars:truncate_chars }}
{% endif %}
diff --git a/src/simmate/website/core_components/templatetags/simmate_input_forms.py b/src/simmate/website/core_components/templatetags/simmate_input_forms.py
index e6f5c8b9a..4870764e4 100644
--- a/src/simmate/website/core_components/templatetags/simmate_input_forms.py
+++ b/src/simmate/website/core_components/templatetags/simmate_input_forms.py
@@ -102,6 +102,7 @@ def foreign_key_link(
entry, # db_object
display_column: str = None,
mode: str = "text", # other options are "pill" and "block"
+ truncate_chars: int = "inf",
):
return locals()
diff --git a/src/simmate/website/data_explorer/templates/data_explorer/table.html b/src/simmate/website/data_explorer/templates/data_explorer/table.html
index 2e9d48500..abe39f635 100644
--- a/src/simmate/website/data_explorer/templates/data_explorer/table.html
+++ b/src/simmate/website/data_explorer/templates/data_explorer/table.html
@@ -86,27 +86,6 @@